[{"id":"oa:W4414989528","name":"Dimensions of Artificial Intelligence Literacy: A Qualitative Synthesis of Contemporary Research Literature","source":"openalex","abstract":"Artificial Intelligence (AI) is transforming education, workforce development, and daily life, necessitating a comprehensive understanding of AI literacy. This study explores the dimensions of AI literacy, its integration into educational and professional settings, and the challenges associated with its implementation. Using a systematic review and qualitative synthesis, this study examines research published between 2019 and 2024, identifying six key dimensions of AI literacy: technical literacy, ethical and societal awareness, critical AI literacy, AI in everyday life, human-AI collaboration, and AI pedagogical literacy. Findings indicate that AI literacy is increasingly embedded in K-12 education, higher education, and workforce training, though disparities in accessibility, ethical concerns, and inconsistent policies persist. Key challenges include the digital divide, lack of teacher training, and lack of standardized AI literacy assessment tools. Opportunities lie in interdisciplinary learning, project-based education, and AI-driven adaptive learning environments. This study makes several unique contributions, including a comprehensive framework for AI literacy, integration of AI literacy across education and workforce domains, identification of policy gaps, and a call for standardized AI literacy assessment tools. It also emphasizes the need for ethical AI engagement and responsible AI education. Policymakers and educators should prioritize integrating AI literacy into curricula, professional development for teachers, and establishing regulatory frameworks to ensure equitable AI education. Future research should focus on longitudinal studies, cross-cultural AI literacy comparisons, and developing adaptive AI learning models to enhance AI education globally.","url":"https://doi.org/10.18009/jcer.1648380","authors":["Roza Kaplan","Ruşen Meylani"],"tags":["Workforce","Literacy","Applications of artificial intelligence","Engineering ethics","Workforce development"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-21","doi":"https://doi.org/10.18009/jcer.1648380","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4413961449","name":"Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges","source":"openalex","abstract":"The nontuberculous mycobacterial (NTM) infections cause morbidity and mortality in individuals who are immunocompromised and those with lung conditions. The timely diagnosis of NTM infections is thus the need of the hour for appropriate management of the disease. In this context, genomics has played a pivotal role in diagnosis of NTM by targeting various conserved regions which are useful for species identification and diagnosis. Also, the exploring of whole genome of nontuberculous mycobacteria has made species identification easier and has revolutionized the diagnostic landscape of NTM. The refinement of Whole Genome Sequencing (WGS) and the advent of targeted Next Generation Sequencing (tNGS) and metagenomic NGS (mNGS) has helped in bringing down the cost without compromising the quality in NTM diagnostics. The advent of artificial intelligence (AI) technologies has made NTM diagnosis even easier by analyzing complex genomic data and providing faster results. Thus, this comprehensive review discusses the strides made in genomics and AI based approaches in the diagnosis of NTM infections and the way forward for harnessing this potential to the maximum for the benefit of mankind.","url":"https://doi.org/10.3389/fmicb.2025.1665685","authors":["Madhan Kumar Murthy","Vivek Kumar Gupta","Anand Prakash Maurya"],"tags":["Nontuberculous mycobacteria","Scope (computer science)","Genomics","Artificial intelligence","Computational biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-03","doi":"https://doi.org/10.3389/fmicb.2025.1665685","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405283072","name":"Artificial intelligence–based rapid brain volumetry substantially improves differential diagnosis in dementia","source":"openalex","abstract":"Abstract Introduction This study evaluates the clinical value of a deep learning–based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age‐ and sex‐adjusted percentile comparisons. Methods Fifty‐five patients—17 with Alzheimer's disease (AD), 18 with frontotemporal dementia (FTD), and 20 healthy controls—underwent cranial magnetic resonance imaging scans. Two board‐certified neuroradiologists (BCNR), two board‐certified radiologists (BCR), and three radiology residents (RR) assessed the scans twice: first without AI support and then with AI assistance. Results AI significantly improved diagnostic accuracy for AD (area under the curve −AI: 0.800, +AI: 0.926, p < 0.05), with increased correct diagnoses ( p < 0.01) and reduced errors ( p < 0.03). BCR and RR showed notable performance gains (BCR: p < 0.04; RR: p < 0.02). For the diagnosis FTD, overall consensus ( p < 0.01), BCNR ( p < 0.02), and BCR ( p < 0.05) recorded significantly more correct diagnoses. Discussion AI‐assisted volumetry improves diagnostic performance in differentiating AD and FTD, benefiting all reader groups, including BCNR. Highlights Artificial intelligence (AI)‐supported brain volumetry significantly improved the diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), with notable performance gains across radiologists of varying expertise levels. The presented AI tool is readily clinically available and reduces brain volumetry processing time from 12 to 24 hours to under 5 minutes, with full integration into picture archiving and communication systems, streamlining the workflow and facilitating real‐time clinical decision making. AI‐supported rapid brain volumetry has the potential to improve early diagnosis and to improve patient management.","url":"https://doi.org/10.1002/dad2.70037","authors":["Jan Rudolph","Johannes Rueckel","Jörg Döpfert","Wen Xin Ling","J Opalka","Christian Brem","Nina Hesse","Maria Ingenerf","Vanessa Koliogiannis","Olga Solyanik","Boj Hoppe","Hanna Zimmermann","Wilhelm Flatz","Robert Forbrig","Maximilian Patzig","Boris‐Stephan Rauchmann","Robert Perneczky","Oliver Peters","Josef Priller","Anja Schneider","Klaus Fließbach","Andreas Hermann","Jens Wiltfang","Frank Jessen","Emrah Düzel","Katharina Büerger","Stefan Teipel","Christoph Laske","Matthis Synofzik","Annika Spottke","Michael Ewers","Peter Dechent","John­–Dylan Haynes","Johannes Levin","Thomas Liebig","Jens Ricke","Michael Ingrisch","Sophia Stoecklein"],"tags":["Dementia","Differential diagnosis","Medicine","Neuroscience","Neuroimaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-01","doi":"https://doi.org/10.1002/dad2.70037","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409607246","name":"Artificial Intelligence in Midwifery: A Scoping Review of Current Applications, Future Prospects, and Midwives’ Perspectives","source":"openalex","abstract":"Background/Objectives: Artificial intelligence (AI) is considered one of the core technological advancements of Industry 4.0, expected to transform various sectors, including healthcare. Midwifery can greatly benefit from AI; however, its current use, its future potential, and midwives’ attitudes remain underexplored. This study aimed to investigate the implementation of, prospects of, and attitudes of midwives toward AI. Methods: A scoping review was carried out, following the PRISMA guidelines. The search was conducted in Pubmed, Scopus, and Web of Science, from database inception to 2 February 2025. Results: Eight studies met the inclusion criteria. Although AI is not yet widely implemented in midwifery, it has notable potential. Several potential benefits were recorded, such as the enhancement of clinical education through personalized learning tools, such as AI-driven virtual patients and customized assessments, as well as a reduction in clinical errors via predictive models and real-time monitoring technologies. The adoption of AI is therefore expected to improve quality of care, particularly in perinatal and neonatal settings. However, it was found that the integration remains limited due to two key obstacles: ethical concerns (e.g., data privacy) and a notable level of anxiety or hesitation among midwives, associated with low levels of digital health literacy. Conclusions: It is important to form a relevant framework regarding the use of AI in midwifery, addressing ethical concerns and skepticism. Additionally, targeted educational interventions are needed to enhance midwives’ AI literacy and alleviate concerns. In general, it is essential to overcome these barriers to accelerate AI adoption in midwifery and unlock its full potential in perinatal care.","url":"https://doi.org/10.3390/healthcare13080942","authors":["Paraskevi Giaxi","Victoria Vivilaki","Angeliki Sarella","Kleanthi Gourounti"],"tags":["Psychological intervention","Scopus","Inclusion (mineral)","Psychology","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-19","doi":"https://doi.org/10.3390/healthcare13080942","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411200075","name":"Transforming surgical planning and procedures through the synergistic use of additive manufacturing, advanced materials and artificial intelligence: challenges and opportunities","source":"openalex","abstract":"Additive manufacturing (AM) is a powerful approach in healthcare to augment the functionalities of patient-specific medical products and surgical tools. One such area of the healthcare industry is surgical planning and procedures, where the benefits of AM can revolutionize the industry. AM technologies, commonly known as three-dimensional (3D) printing, can change the conventional surgical methodology from the \"open-detect-operate-close\" mode to the \"detect-open-operate-close\" mode. However, the use of 3D printing in surgical planning has been hampered by the limited availability of literature reports thoroughly examining the advantages and drawbacks of this technology in clinical settings. Hence, this review explores the widespread use of additive manufacturing, multi-materials, metamaterials, 4D printing, and artificial intelligence in surgical planning for complex surgical procedures of the spine and in orthopedics, dentistry, cardiology, gynecology, and neurology. This review focuses on meticulously adjusting the lattice structure of metamaterials during 3D printing to achieve specific mechanical properties. It further delves into 4D printing to achieve dynamic capabilities in 3D printed models for better integration with the host tissue. Furthermore, it highlights the key aspects of combining AM with artificial intelligence/machine learning (AI/ML) models in healthcare to automate 3D model production and thereby reduce human intervention. This comprehensive review offers bioengineers, clinical scientists, and clinicians a platform to explore AM and its potential for addressing pre- and post-surgical operation challenges, providing valuable insights for biomedical engineering and healthcare advancements.","url":"https://doi.org/10.1039/d5mh00501a","authors":["Shivi Tripathi","Aftab Alam Ansari","Manisha Singh","Madhusmita Dash","Prasoon Kumar","Harpreet Singh","Biranchi Panda","Syam P. Nukavarapu","Gulden Camci‐Unal","Bingbing Li","Prashant K. Jain","Rengaswamy Jayaganthan","Hassan Mehboob","Harri Junaedi","Himansu Sekhar Nanda","Guoping Chen","Subhas C. Kundu"],"tags":["Nanotechnology","Manufacturing engineering","Materials science","Business","Process management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1039/d5mh00501a","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4412582607","name":"Generative artificial intelligence in medicine: a mixed-methods survey of UK general practitioners","source":"openalex","abstract":"Objective To explore the opinions of general practitioners (GPs) in the UK about the use of generative artificial intelligence (AI) tools in primary care. Methods and analysis At the beginning of 2024, using a convenience sample, we administered an online mixed-methods survey to registered GPs currently working in the UK. Results A total of 1006 GPs responded, with 53% being male and 54% over 46 years old. One-fifth of GPs reported having used AI for clinical practice, with male doctors and those in bigger cities being more likely to have used it. 80% of respondents expressed a need for more training in understanding these tools. GPs at least somewhat agreed AI would improve documentation (59%) and patient information gathering (56%). 55% felt AI could increase inequities and 54% saw potential for patient harm, but 47% believed it could enhance healthcare efficiency. GPs who used these tools were significantly more optimistic about the scope for generative AI in improving clinical tasks. One-third of GPs left comments that were classified into four major themes: (1) lack of familiarity and understanding with AI, (2) role of AI in clinical practice, (3) concerns about AI and (4) AI and the future of healthcare. Conclusions This study highlights UK GPs’ developing perspectives on generative AI in clinical practice, emphasising the need for more training. Many GPs reported a lack of knowledge and experience with this technology, although a portion already used non-medical grade technology for clinical tasks, with the risks that this entails.","url":"https://doi.org/10.1136/bmjdhai-2025-000051","authors":["Anna Kharko","Cosima Locher","John Torous","Sophie Anna Rosch","Maria Hägglund","Jens Gaab","Brian McMillan","David Sundemo","Kenneth D. Mandl","Charlotte Blease"],"tags":["Generative grammar","Artificial intelligence","Data science","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1136/bmjdhai-2025-000051","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4412881896","name":"Incorporating Artificial Intelligence into Fracture Risk Assessment: Using Clinical Imaging to Predict the Unpredictable","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly being explored as a complementary tool to traditional fracture risk assessment methods. Conventional approaches, such as bone mineral density measurement and established clinical risk calculators, provide populationlevel stratification but often fail to capture the structural nuances of bone fragility. Recent advances in AI-particularly deep learning techniques applied to imaging-enable opportunistic screening and individualized risk estimation using routinely acquired radiographs and computed tomography (CT) data. These models demonstrate improved discrimination for osteoporotic fracture detection and risk prediction, supporting applications such as time-to-event modeling and short-term prognosis. CT- and radiograph-based models have shown superiority over conventional metrics in diverse cohorts, while innovations like multitask learning and survival plots contribute to enhanced interpretability and patient-centered communication. Nevertheless, challenges related to model generalizability, data bias, and automation bias persist. Successful clinical integration will require rigorous external validation, transparent reporting, and seamless embedding into electronic medical systems. This review summarizes recent advances in AI-driven fracture assessment, critically evaluates their clinical promise, and outlines a roadmap for translation into real-world practice.","url":"https://doi.org/10.3803/enm.2025.2518","authors":["Sung Hye Kong"],"tags":["Medicine","Fracture (geology)","Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-04","doi":"https://doi.org/10.3803/enm.2025.2518","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W2062409950","name":"The Increasing Predictive Validity of Self-Rated Health","source":"openalex","abstract":"Using the 1980 to 2002 General Social Survey, a repeated cross-sectional study that has been linked to the National Death Index through 2008, this study examines the changing relationship between self-rated health and mortality. Research has established that self-rated health has exceptional predictive validity with respect to mortality, but this validity may be deteriorating in light of the rapid medicalization of seemingly superficial conditions and increasingly high expectations for good health. Yet the current study shows the validity of self-rated health is increasing over time. Individuals are apparently better at assessing their health in 2002 than they were in 1980 and, for this reason, the relationship between self-rated health and mortality is considerably stronger across all levels of self-rated health. Several potential mechanisms for this increase are explored. More schooling and more cognitive ability increase the predictive validity of self-rated health, but neither of these influences explains the growing association between self-rated health and mortality. The association is also invariant to changing causes of death, including a decline in accidental deaths, which are, by definition, unanticipated by the individual. Using data from the final two waves of data, we find suggestive evidence that exposure to more health information is the driving force, but we also show that the source of information is very important. For example, the relationship between self-rated health and mortality is smaller among those who use the internet to find health information than among those who do not.","url":"https://doi.org/10.1371/journal.pone.0084933","authors":["Jason Schnittker","Valerio Baćak"],"tags":["Self-rated health","Predictive validity","National Death Index","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2014-01-22","doi":"https://doi.org/10.1371/journal.pone.0084933","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4408688362","name":"Artificial Intelligence in the Service of Medicine: Current Solutions and Future Perspectives, Opportunities, and Challenges.","source":"openalex","abstract":"Objective: This article aims to identify the opportunities and risks of Artificial Intelligence tools (AIT) applied to clinical practice, while also reflecting on their impact on the doctor-patient relationship. Materials and Methods: The authors conducted a systematic literature review following the PRISMA guidelines, selecting the period from 2019 to October 2024. Academic databases PubMed and Scopus were drawn upon by using the keywords and searchstrings \"artificial intelligence\", \"healthcare\", \"informed consent\", and \"doctor-patient relationship\" in titles, abstracts, and keywords. Results and Discussion: AIT has proven useful in significantly reducing the time spent on bureaucratic tasks and minimizing errors compared to traditional medicine. However, their effectiveness is highly influenced by the quantity and quality of data used for training. Additionally, there is an issue with the transparency of the decision-making process because AIT and even their programmers are unable to explain their diagnostic and therapeutic recommendations. Therefore, human supervision of AI work is essential. Conclusions: The potential risks of AI for patient safety and personal data security necessitate that governments urge those involved in the production of AI tools to adhere to specific ethical standards developed with the participation of all stakeholders, including patients.","url":"https://doi.org/10.7417/ct.2025.5192","authors":["Susanna Marinelli","Lina De Paola","Michael Stark","Gianluca Montanari Vergallo"],"tags":["Current (fluid)","Service (business)","Engineering ethics","Management science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-20","doi":"https://doi.org/10.7417/ct.2025.5192","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410708995","name":"Critical thinking in the age of generative AI: implications for health sciences education","source":"openalex","abstract":"Generative artificial intelligence (genAI) systems are progressively transforming health science education and research by assisting clinicians in diagnosis and structuring specific intervention regimens (MIR et al., 2023). Moreover, these technologies serve educators in yielding simple concept-based educational modules tailored as per student's requirements and large language models (LLMs) and analogous models display the potential for automated streamlined literature reviews, prompt generation of interpretations and conclusions, and effortless drafting of manuscripts within seconds (MIR et al., 2023;Al Kuwaiti et al., 2023;Gupta et al., 2024) thereby, offering a potentially high level of convenience and efficiency. Furthermore, many researchers contend that genAI has the potential to completely automate research processes, including drafting proposals, analyzing data, and composing concluding reports (Almansour and Alfhaid, 2024;Preiksaitis and Rose, 2023). Hence, these merits indicate a prospective future for genAI, particularly in domains that demand processing of large scale data and iterative analyses.However, despite these evident positive outcomes, concerns persist regarding the quality of AIgenerated outputs, which may include inaccuracies in text reporting that contribute to misinformation, logical inconsistencies, outdated or unverified claims, and hallucinated references, all of which undermine the academic credibility of writing (Athaluri et al., 2023;Farrelly and Baker, 2023;Sittig and Singh, 2024). In addition, a lack of transparency in datasets exacerbates ethical challenges and biases (Norori et al., 2021). In an era characterized by the expedited advancement and refinement of genAI, it is necessary to critically evaluate whether these systems encourage critical thinking and human intelligence or subtly undermine them. This raises an important question: Are we unintentionally relinquishing the cognitive abilities that have propelled scientific and clinical advancements, as healthcare professionals progressively integrate genAI into clinical and academic domains? The dilemma lies in balancing the efficiency of genAI with the preservation of essential human cognitive skills, such as critical thinking, ethical reasoning, and independent problem-solving. At its core, this dilemma focuses on how health professionals, medical trainees, and early career researchers apply outcomes produced by genAI as overreliance risk supporting passive dependence on algorithm produced outcomes in a context where time and cognitive capacity are consistently constrained. The expertise involving scientific accuracy, ethical judgment, and diagnostic reasoning are cultivated through proactive contribution by integrating knowledge in innovative and contextually relevant approaches, critically evaluating evidence, and grappling with uncertainty that genAI cannot substitute (Passerini et al., 2025;Shoja et al., 2023).The significance of the risk of cognitive complacency is emphasized globally, as industry and academia compete to implement genAI tools, and have rapidly accelerated publications related to AI, reflecting both enthusiasm and apprehension. However, medical professionals and researchers may demonstrate overdependence on genAI tools due to faster processing, thus, reducing opportunities for independent problem-solving and critical thinking (Shoja et al., 2023;Zhai et al., 2024). Additionally, in medical research where precision plays an important factor, underlying biases in data training of genAI can propagate false information (Norori et al., 2021). The inefficacy of plagiarism detection software to recognize text generated by genAI, undermines conventional ethical integrity measures as the output may be erroneously identified as genuine scholarly writing (Farrelly and Baker, 2023), though some exceptions exist (Elkhatat et al., 2023;Weber-Wulff et al., 2023). This attitude leads to a workforce adept at utilizing genAI but defic","url":"https://doi.org/10.3389/frai.2025.1571527","authors":["Waqar M. Naqvi","Rohini Ganjoo","Michael Rowe","Aishwarya A. Pashine","Gaurav Mishra"],"tags":["Generative grammar","Critical thinking","Psychology","Mathematics education","Epistemology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-21","doi":"https://doi.org/10.3389/frai.2025.1571527","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W3157179399","name":"Ethical Aspects Of Artificial Intelligence Use In Social Spheres And Management Environment","source":"openalex","abstract":"Artificial intelligence and robotics are among the most discussed issues and technological trends around the world today. In the light of their geometrically accelerating implementation in all spheres of human activity, often the expected opportunities, achievements and scientific breakthroughs overshadow the reasonableness and expediency of using artificial intelligence technologies in a particular area from a legal and ethical point of view. The article discusses the main directions of the spread of artificial intelligence technologies and the ethical consequences and moral issues that arise in this regard, both at the state and organizational level. The main trends characteristic of the labor market that arise in the process of robotization of workplaces and the introduction of intelligent robots into the production process are studied. The authors convincingly prove the priority of ethics and human safety issues in the design and implementation of AI (artificial intelligence) systems. During the discussion of ethical problems of implementing artificial intelligence in organizations, the emphasis is placed on the use of these technologies not from the point of view of automation and improving the efficiency of performing direct management functions, but from the point of view of organizing the work of personnel. Based on this, the article concludes with recommendations for the development of ethical principles adapted to the design and use of AI systems.","url":"https://doi.org/10.15405/epsbs.2021.04.02.118","authors":["V. Leonov"],"tags":["Process (computing)","Artificial intelligence","Automation","Computer science","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-29","doi":"https://doi.org/10.15405/epsbs.2021.04.02.118","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410570440","name":"The Role of Artificial Intelligence Tools on Chinese EFL Learners' Self‐Regulation, Resilience and Autonomy","source":"openalex","abstract":"ABSTRACT The incorporation of artificial intelligence (AI) in education has attracted scholars' research interest around the world, and they have demonstrated the effectiveness of using AI in the English as a foreign language (EFL) setting. While previous studies have examined AI's role in improving linguistic proficiency, limited research has examined its role in key psychological concepts such as self‐regulation, resilience and autonomy among EFL learners. Addressing this gap, the present study explored the impact of AI on the self‐regulation, resilience and autonomy of Chinese EFL students. The students were assigned to two groups: the experimental group included 94 learners who were taught through AI tools, while the control group included 89 learners who received traditional language education. A pretest was administered using three scales, namely self‐regulation, resilience and autonomy. After the completion of the instructional sessions, a posttest was administered to both groups with identical questionnaires. Eventually, the study analysed the data collected through Analysis of Covariance (ANCOVA), revealing that the group utilising AI tools significantly outperformed the others regarding all three variables. Indeed, utilising AI tools notably enhanced the self‐regulation, resilience and autonomy of EFL students. The research implications were thoroughly discussed.","url":"https://doi.org/10.1111/ejed.70127","authors":["Zhijuan Zhang"],"tags":["Resilience (materials science)","Autonomy","Mathematics education","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-20","doi":"https://doi.org/10.1111/ejed.70127","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4412191263","name":"Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis","source":"openalex","abstract":"Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets.","url":"https://doi.org/10.3390/jpm15070302","authors":["Shayan Shojaei","Asma Mousavi","Sina Kazemian","Shiva Armani","Saba Maleki","Parisa Fallahtafti","Farzin Tahmasbi Arashlow","Yasaman Daryabari","Mohammadreza Naderian","Mohamad Alkhouli","Jamal S. Rana","Mehdi Mehrani","Yaser Jenab","Kaveh Hosseini"],"tags":["Medicine","Valve replacement","Internal medicine","Confidence interval","Left bundle branch block"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-11","doi":"https://doi.org/10.3390/jpm15070302","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410421263","name":"Harnessing artificial intelligence for transforming dementia care: Innovations in early detection and treatment","source":"openalex","abstract":"Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions.","url":"https://doi.org/10.1016/j.bosn.2025.05.001","authors":["Saadeddine Habbal","Maamoon Mian","Musa Imam","Jihane Tahiri","Adam Amor","P. Hemachandra Reddy"],"tags":["Dementia","Artificial intelligence","Computer science","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.1016/j.bosn.2025.05.001","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4415401926","name":"Artificial Intelligence-Based Epileptic Seizure Prediction Strategies: A Review","source":"openalex","abstract":"Epilepsy, a chronic neurological disorder marked by recurrent and unpredictable seizures, poses significant risks of injury and compromises patient quality of life. The accurate forecasting of seizures is paramount for enabling timely interventions and improving safety. Since the 1970s, research has increasingly focused on analyzing bioelectrical signals for this purpose. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a powerful tool for seizure prediction. This review, conducted by PRISMA guidelines, analyzes studies from 2020 to August 2025. It explores the evolution from traditional ML classifiers toward advanced DL architecture, including convolutional and recurrent neural networks and transformer-based frameworks, applied to bioelectrical signals. While these approaches show promising performance, significant challenges persist in patient generalization, standardized evaluation, and clinical validation. This review synthesizes current advancements, provides a critical analysis of methodological limitations, and outlines future directions for developing robust, clinically relevant seizure prediction systems to enhance patient autonomy and outcomes.","url":"https://doi.org/10.3390/ai6100274","authors":["Andrea V. Perez-Sanchez","Martin Valtierra‐Rodriguez","J. Jesus De-Santiago-Perez","Carlos A. Perez-Ramirez","Arturo García-Pérez","Juan P. Amézquita-Sánchez"],"tags":["Medicine","Psychological intervention","Quality of life (healthcare)","Epilepsy","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-21","doi":"https://doi.org/10.3390/ai6100274","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4387600936","name":"Beyond Personalization: Embracing Democratic Learning Within Artificially Intelligent Systems","source":"openalex","abstract":"Abstract This essay explains how, from the theoretical perspective of Basil Bernstein's three “conditions for democracy,” the current pedagogy of artificially intelligent personalized learning seems inadequate. Building on Bernstein's comprehensive work and more recent research concerned with personalized education, Natalia Kucirkova and Sandra Leaton Gray suggest three principles for advancing personalized education and artificial intelligence (AI). They argue that if AI is to reach its full potential in terms of promoting children's identity as democratic citizens, its pedagogy must go beyond monitoring the technological progression of personalized provision of knowledge. It needs to pay more careful attention to the democratic impact of data‐driven systems. Kucirkova and Leaton Gray propose a framework to distinguish the value of personalized learning in relation to pluralization and to guide educational researchers and practitioners in its application to socially just classrooms.","url":"https://doi.org/10.1111/edth.12590","authors":["Natalia Kucirkova","Sandra Leaton Gray"],"tags":["Personalization","Democracy","Sociology","Personalized learning","Value (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-01","doi":"https://doi.org/10.1111/edth.12590","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405274288","name":"Artificial intelligence and its use in spine surgery and preparation of predictive models: a systematic review","source":"openalex","abstract":"During the last decade, artificial intelligence (AI) has witnessed phenomenal growth, accompanied by unparalleled opportunities in health. The integration of machine learning at the heart of health systems has shaped a revolution that helps to reduce health, social, and economic inequities while improving global health outcomes. The use of AI for spine surgery, in particular, serves to enhance the accuracy of predicted algorithms and can be used to improve surgical accuracy and reduce operative time, thereby enhancing efficiency and productivity. This review outlines the current literature on the use of AI in spine surgery and identifies its established evidence to provide positive surgical outcomes. With all these promises, AI in spine surgery remains in its infancy; it is anticipated that further development will yield much greater benefits in the immediate future.","url":"https://doi.org/10.1097/ms9.0000000000002782","authors":["Muhammad Mohsin Khan","Bipin Chaurasia"],"tags":["Medicine","Productivity","Applications of artificial intelligence","Systematic review","Risk analysis (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-11","doi":"https://doi.org/10.1097/ms9.0000000000002782","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405175549","name":"Applications of Artificial Intelligence for Health Care Providers","source":"openalex","abstract":"Recent research shows that physicians lack the knowledge and ability to use artificial intelligence (AI) effectively. We thus introduce a new series of articles, \"Applications of Artificial Intelligence for Health Care Providers.\" Like the arthroscope, AI is a powerful tool, and we must adapt our skills to effectively incorporate and apply this tool in our practices.","url":"https://doi.org/10.1016/j.arthro.2024.12.006","authors":["James H. Lubowitz","Mark P. Cote","Prem N. Ramkumar","Kyle N. Kunze"],"tags":["Health care","Computer science","Business","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-09","doi":"https://doi.org/10.1016/j.arthro.2024.12.006","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410435932","name":"Artificial Intelligence in Reproductive Medicine: Transforming Assisted Reproductive Technologies","source":"openalex","abstract":"Question Asked How is artificial intelligence (AI) transforming assisted reproductive technologies (ART), particularly in vitro fertilization (IVF), and what are its clinical impacts and limitations? Background AI offers potential to address ART challenges, including high costs, variable success rates, and rising infertility. Applications in embryo selection, gamete assessment, and personalized protocols aim to enhance objectivity and outcomes. Literature Search A systematic review of peer-reviewed articles (2019–2025) was conducted, using terms such as “artificial intelligence” and “IVF.” Studies focused on AI tools (DeepEmbryo, icONE, iDAScore, ERICA) and their performance in ART. Materials and Methods Selected studies evaluated AI applications in embryo selection, gamete assessment, personalized protocols, and outcome prediction. Performance metrics, validation scope, and clinical outcomes were analyzed, prioritizing tools with quantitative data. Results and Discussion AI tools improved clinical pregnancy rates (up to 77.3%), implantation accuracy (92%), and efficiency (35%). icONE and ERICA outperformed traditional methods, reducing subjectivity. However, validation is often limited to single-center studies, with surrogate endpoints (e.g., pregnancy rates) rather than live birth rates. Algorithmic bias, regional data privacy regulations, and high costs limit generalizability and accessibility. Ethical concerns, including data privacy and equity, require robust frameworks. Conclusions AI enhances ART efficacy and personalization but faces validation and ethical challenges. Multicenter studies focusing on live birth rates and inclusive datasets are needed to ensure equitable, clinically relevant adoption.","url":"https://doi.org/10.46989/001c.137620","authors":["Zeev Shoham"],"tags":["Reproductive medicine","Reproductive technology","Reproductive biology","Reproductive health","Assisted reproductive technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.46989/001c.137620","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4413921759","name":"Teaching Artificial Intelligence and Language Models in Medical Education","source":"openalex","abstract":"The rapid evolution of Artificial Intelligence (AI), especially large language models (LLMs), is transforming medicine and poses new challenges for medical education. This theoretical-reflective article discusses the integration of AI and LLM teaching in the undergraduate medical curriculum, considering ethical, methodological, and practical aspects. Initially, it contextualizes the growing role of AI in healthcare and the need for AI literacy among future physicians, given the incorporation of tools like ChatGPT into clinical practice. We then review recent literature (2024-2025) and relevant guidelines, including the World Health Organization and Brazilian Ministry of Education, to identify educational benefits (such as virtual patient simulations, automated assessment and personalized feedback) and risks (algorithmic bias, response hallucinations, privacy and academic integrity issues). Methodologically, the study is based on a literature review and the author’s teaching experience implementing AI content for 3rd-year medical students. In the results and discussion, we present active teaching strategies, such as the use of prompting in simulated clinical cases and ethical debates, evaluating their perceived effectiveness in developing digital competencies. We conclude by emphasizing the importance of a structured AI curriculum in medicine, including faculty training, interdisciplinary collaboration, and adherence to ethical principles, to train physicians who are capable of harnessing new technologies in a critical and humanistic manner.","url":"https://doi.org/10.64326/educao.v1i6.76","authors":["Ana M. Denis-Bacelar"],"tags":["Computer science","Artificial intelligence","Mathematics education","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-02","doi":"https://doi.org/10.64326/educao.v1i6.76","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414793290","name":"Artificial Intelligence in Glioma Diagnosis: A Narrative Review of Radiomics and Deep Learning for Tumor Classification and Molecular Profiling Across Positron Emission Tomography and Magnetic Resonance Imaging","source":"openalex","abstract":"Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed.","url":"https://doi.org/10.3390/eng6100262","authors":["Rafail C. Christodoulou","Rafael Pitsillos","Platon S. Papageorgiou","Vasileia Petrou","Georgios Vamvouras","Ludwing Rivera","Sokratis G. Papageorgiou","Elena E. Solomou","Michalis F. Georgiou"],"tags":["Artificial intelligence","Deep learning","Radiomics","Positron emission tomography","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-03","doi":"https://doi.org/10.3390/eng6100262","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4415233076","name":"Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability","source":"openalex","abstract":"Artificial intelligence (AI) and machine learning (ML) are transforming agriculture by enabling data-driven decisions that elevate productivity and sustainability. This review synthesises 95 studies published between 2013 and 2023 that evaluate applications across crop monitoring, yield prediction, and resource optimisation. Reported model accuracies for neural networks, decision trees, and deep learning reached up to 93 percent; deep learning was most accurate but least interpretable. Reported benefits include a 25 percent increase in yield, a 28 percent reduction in costs, 40 percent efficiency gains, 22 percent water savings, 28 percent fertilizer savings, and 35 percent lower nitrogen runoff. Adoption barriers persist, including poor data quality, expensive infrastructure, limited digital literacy, and ethical concerns around data ownership and bias. Integrated, enterprise-scale platforms favor large farms, while mobile AI applications yield 15-30 percent gains for smallholders. Converging technologies blockchain, IoT, and robotics enable integration, and automation can lower labor and input requirements by 35 percent. The review points to the importance of inclusion policies, transparent systems, and global governance. Overall, AI/ML are drivers of socio-technical transition consistent with Sustainability Transitions Theory, necessitating multidisciplinary strategies for sustainable, climate-resilient food systems.","url":"https://doi.org/10.1080/23311932.2025.2569982","authors":["Okechukwu Paul-Chima Ugwu","Fabian C. Ogenyi","Esther Ugo Alum","Val Hyginus Udoka Eze","Mariam Basajja","Jovita Nnenna Ugwu","Chinyere N. Ugwu","Regina Idu Ejemot-Nwadiaro","Michael Ben Okon","Simeon Ikechukwu Egba","Uti Daniel Ejim"],"tags":["Artificial intelligence","Machine learning","Computer science","Sustainability","Precision agriculture"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-15","doi":"https://doi.org/10.1080/23311932.2025.2569982","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4404852800","name":"Artificial intelligence and machine learning adoption in the financial sector: a holistic review","source":"openalex","abstract":"The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.","url":"https://doi.org/10.11591/ijai.v14.i1.pp19-31","authors":["Karima Sayari","M. Jannathl Firdouse","Fathiya Al Abri"],"tags":["Financial services","Financial sector","Productivity","Finance","Financial market"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-29","doi":"https://doi.org/10.11591/ijai.v14.i1.pp19-31","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414688765","name":"Morgellons Disease as a Multisystem Problem: Analysis of Hypotheses, Scientific Evidence, How Artificial Intelligence Helps","source":"openalex","abstract":"Morgellons disease is a rare and controversial disease. It combines dermatological, infectious, and psychiatric manifestations. It is characterized by the formation of skin lesions with filaments emanating from the tissues. Also, by sensory phenomena in the form of a crawling or stinging sensation under the skin. As well as systemic symptoms, including chronic fatigue and cognitive disorders. The article analyzes the literature and clinical reports that highlight the infectious and psychiatric hypotheses, morphological and biochemical features, sociocultural factors, and problems of diagnosis and treatment. It is established that Morgellons disease remains uncertain in the nosological classification, which complicates the creation of unified approaches to its management. At the same time, the need for a multidisciplinary, multisystemic approach that integrates dermatology, infectious diseases, biochemistry, microbiology, clinical diagnostics, immunology, psychiatry, and psychology is emphasized. The conclusion is made about the urgent need for conducting systematic studies of etiology and pathogenesis, developing standardized diagnostic criteria and therapeutic protocols, as well as overcoming the stigmatization of patients. The help of artificial intelligence opens new opportunities for early diagnosis of Morgellons disease through the analysis of large arrays of clinical and laboratory data, recognition of dermatological images and prediction of the course of the disease. The use of machine learning algorithms contributes to more accurate differential diagnosis between infectious, dermatological, and psychiatric manifestations, which helps to minimize false diagnoses. The use of artificial intelligence in telemedicine and clinical practice of Morgellons disease allows for a personalized approach to treatment, optimizing the choice of therapeutic strategies and monitoring the effectiveness of treatment. The integration of artificial intelligence technologies into research and clinical practice is considered a promising direction for overcoming diagnostic and therapeutic difficulties in Morgellons disease.","url":"https://doi.org/10.53933/fxbnwe45","authors":["Вікторія Шаповалова"],"tags":["Disease","Artificial intelligence","Psychology","Clinical Practice","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.53933/fxbnwe45","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4416975840","name":"Artificial intelligence in oncology drug development and management: a precision medicine perspective","source":"openalex","abstract":"The management of oncology drugs is inherently complex, facing challenges such as high development costs, prolonged timelines, and substantial inter-patient heterogeneity. Recent advances in artificial intelligence (AI) have introduced transformative capabilities across the entire cancer drug lifecycle-from target discovery and compound screening to clinical trial optimization, individualized therapy, toxicity management, supply chain logistics, and regulatory oversight. AI enables precise target identification, accelerates virtual drug screening and molecular design, and enhances clinical trial efficiency through intelligent patient stratification and adaptive protocols. Moreover, AI facilitates personalized treatment decision-making, early prediction of drug resistance, and real-time toxicity surveillance, while improving pharmacovigilance and post-market drug evaluation using real-world data. Here, \"post-market drug evaluation\" refers to real-world effectiveness and safety assessment using spontaneous reports (e.g., FAERS/VigiBase) and EHR/claims-based outcomes, rather than cost-effectiveness analyses. Examples include EHR-NLP to surface immune-related adverse events, AI-assisted trial recruitment and adaptive designs, and individualized dosing frameworks (e.g., CURATE.AI). Despite its enormous promise, AI-driven oncology drug management faces notable challenges in data integration, model interpretability, clinical translation, fairness, and regulatory governance. This review comprehensively summarizes the current applications of AI in oncology pharmacology, highlights key opportunities and barriers, and explores future directions at the intersection of AI, precision medicine, and cancer therapeutics. Future priorities include prospective multi-site evaluations, fairness auditing, and continuous post-market algorithmovigilance.","url":"https://doi.org/10.3389/fonc.2025.1609827","authors":["Caixia Fang","Pengfa Zhou","Xuerong Zhang","Yongsheng He","Qingwei Yang"],"tags":["Precision medicine","Drug development","Precision oncology","Medicine","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-04","doi":"https://doi.org/10.3389/fonc.2025.1609827","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410054156","name":"The Role of Artificial Intelligence in Dental Diagnosis and Treatment Planning","source":"openalex","abstract":"This study aims to examine the role of artificial intelligence in dental diagnosis and treatment planning, highlighting its applications, benefits, challenges, and future directions. A narrative review was conducted using a descriptive analysis method, synthesizing recent literature on AI-driven diagnostic tools, treatment planning systems, and emerging trends in dentistry. Studies published between 2022 and 2025 were analyzed to assess AI applications in imaging-based diagnosis, clinical decision support, orthodontic planning, prosthodontics, endodontics, periodontal treatment, implantology, and precision dentistry. Ethical concerns, integration challenges, and regulatory considerations were also reviewed to provide a comprehensive understanding of AI’s impact on dental practice. AI has significantly improved diagnostic accuracy in dental radiography, CBCT analysis, and early detection of caries, periodontal disease, and oral cancer. AI-driven treatment planning has enhanced efficiency in orthodontics, prosthodontics, and implantology by optimizing treatment simulations, material selection, and surgical precision. Teledentistry and remote diagnosis have expanded access to care, while AI-powered robotics have introduced automation in surgical and restorative procedures. Despite these advancements, challenges remain in data privacy, algorithmic bias, clinical integration, and regulatory compliance. Ethical concerns related to AI transparency, liability, and decision-making authority continue to shape its adoption in clinical practice. AI is transforming dental diagnosis and treatment planning by improving accuracy, efficiency, and accessibility. However, addressing challenges related to data security, bias, regulatory frameworks, and professional adaptation is essential for the responsible integration of AI in dentistry. Future advancements in AI algorithms, robotic-assisted dentistry, and precision medicine will further enhance patient-centered care. A balanced approach that combines AI innovation with human expertise is necessary to ensure optimal clinical outcomes and ethical AI implementation in dental healthcare.","url":"https://doi.org/10.61838/kman.jodhn.2.1.2","authors":["Arash Moeini","S. Ali Torabi"],"tags":["Radiation treatment planning","Artificial intelligence","Computer science","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.61838/kman.jodhn.2.1.2","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4399194633","name":"The new placement of 2,000 entrants at Korean medical schools in 2025: is the government’s policy evidence-based?","source":"openalex","abstract":"","url":"https://doi.org/10.12771/emj.2024.e13","authors":["Sun Huh"],"tags":["Government (linguistics)","Business","Philosophy","Linguistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-30","doi":"https://doi.org/10.12771/emj.2024.e13","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405725293","name":"The Application of Artificial Intelligence and Machine Learningin Civil Engineering","source":"openalex","abstract":"The utilization of automated technologies like Artificial Intelligence and Ma-chine Learning in civil engineering has led to new innovative ways of responding to complexchallenges. This dissertation investigates the various applications of AIs and MLs that areendorsed in civil engineering, namely structural health supervision, predictive upkeep, con-struction surveillance, and infrastructure optimization. Utilization of acquaintance spawnedby data-driven algorithms aids the civil infrastructure in taking the edge of AI and ML toolsand technologies to strongly strengthen the decision-making function, augment efficiency,and guarantee the resilience and sustainability of civil infrastructure. This research paperprovides a systematized overview of published studies, journals, industry reports, and real-world implementations to reckon with the pros and cons of this implementation. Moreover,this report discusses new trends, recent advancements, and what might shift in the futurein the arena of AI and ML, providing insights into the future part of these technologies informing the civil engineering space.","url":"https://doi.org/10.71063/djttt.2025.1103","authors":["Safiullah Khan"],"tags":["Resilience (materials science)","Implementation","Function (biology)","Emerging technologies","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-23","doi":"https://doi.org/10.71063/djttt.2025.1103","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405986075","name":"Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals","source":"openalex","abstract":"Background: Digitalization and artificial intelligence technologies are navigating and strengthening human labour practices and organizational performance in healthcare. Research has shown that digitization and AI can help healthcare professionals through real-time insights and recommendations derived from extensive datasets. These modern technologies are advancing beyond being mere instruments in the health sector, now acting as partners to aid healthcare professionals in making better predictions and decisions by offering timely insights and suggestions derived from extensive datasets, as well as pinpoint potential health issues with greater accuracy and speed. It is estimated also that significantly can be supported the management of workplace well-being. Objective: This article delves into how AI and digitalization help healthcare professionals by boosting efficiency, meeting their professional and personal needs, and showcasing how they can enhance employee mental health and well-being. Results: It is crucial to recognize that issues arise from the intrinsic complexity and opaque nature of AI, the risk of job loss, and the disruption of the conventional interaction between physicians and patients. Nonetheless, AI in the healthcare facilities should not be seen as a danger to human employees. Instead, AI strive to support healthcare employees, enabling them to allocate more time to complex and crucial tasks. By automating tasks that are repetitive and mundane, these new technologies can lessen the burden on healthcare professionals, allowing them to dedicate more time to caring for patients and engaging in valuable interactions. Conclusions: The integration of AI and digitalization technologies into healthcare presents both opportunities and challenges for employee motivation and job performance. Although it can improve effectiveness and lower stress levels, it is important to carefully address worries about employment stability and maintaining personal connections in healthcare. Organizations need to prioritize creating a workspace that encourages and assists employees in adjusting to new technological changes","url":"https://doi.org/10.33425/2690-8077.1170","authors":["Dimitris Karaferis","Balaska Dimitra","Pollalis Yannis"],"tags":["Health professionals","Health care","Psychology","Nursing","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.33425/2690-8077.1170","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4408264626","name":"Application of Deep Learning and Transfer Learning Techniques for Medical Image Classification","source":"openalex","abstract":"The advancements in deep learning (DL) and transfer learning (TL) have transformed artificial intelligence, particularly in image classification. This research examines the theoretical foundations of DL and TL, focusing on their applications in medical image classification, specifically distinguishing between COVID-19, viral pneumonia, and normal lung conditions. By leveraging GPU-enabled high-performance computing and large labeled datasets, DL models particularly Convolutional Neural Networks (CNNs) such as ResNet50 and VGG16 have achieved superior accuracy compared to traditional machine learning methods. This study explores feature extraction using pre-trained models, the implementation of classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and the integration of multi-view learning techniques, such as early fusion. The results demonstrate the effectiveness of DL and TL in improving classification performance, highlighting their significant potential to advance global healthcare diagnostics.","url":"https://doi.org/10.70470/edraak/2025/006","authors":["Tam Sakirin","Rachid Ben Said"],"tags":["Transfer of learning","Artificial intelligence","Computer science","Deep learning","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-12","doi":"https://doi.org/10.70470/edraak/2025/006","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409001773","name":"Clinical Application of Artificial Intelligence in Digital Breast Tomosynthesis","source":"openalex","abstract":"Digital breast tomosynthesis (DBT) provides improved cancer detection and lower recall rates when compared with full-field digital mammography (DM) and has been widely adopted for breast cancer screening. However, adopting DBT presents new challenges such as an increased number of acquired images resulting in longer interpretation times. Artificial intelligence (AI) offers numerous opportunities to enhance the advantages of DBT and mitigate its shortcomings. Research in the DBT AI domain has grown significantly and AI algorithms play a key role in the screening and diagnostic phases of breast cancer detection and characterization. The application of AI may streamline the workflow and reduce the time required for radiologists to interpret images. In addition, AI can minimize radiation exposure and enhance lesion visibility in synthetic two-dimensional DM images. This review provides an overview of AI technology in DBT, its clinical applications, and future considerations.","url":"https://doi.org/10.3348/jksr.2025.0011","authors":["Jung Min Chang","Weonsuk Lee","Manisha Bahl"],"tags":["Medicine","Tomosynthesis","Digital Breast Tomosynthesis","Medical physics","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3348/jksr.2025.0011","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4407925758","name":"Comparing Artificial Intelligence and manual methods in systematic review processes: protocol for a systematic review","source":"openalex","abstract":"Objectives This systematic review aims to evaluate the effectiveness of automated methods using artificial intelligence (AI) in conducting systematic reviews, with a focus on both performance and resource utilization compared to human reviewers. Study Design and Setting This systematic review and meta-analysis protocol follows the Cochrane Methodology protocol and review guidance. We searched five bibliographic databases to identify potential studies published in English from 2005. Two independent reviewers will screen the titles and abstracts, followed by a full-text review of the included articles. Any discrepancies will be resolved through discussion and, if necessary, referral to a third reviewer. The risk of bias (RoB) in included studies will be assessed at the outcome level using the revised Cochrane risk-of-bias tool for randomized trials and the RoB In Non-randomized Studies - of Interventions for non-randomized studies. Where appropriate, we plan to conduct meta-analysis using random-effects models to obtain pooled estimates. We will explore the sources of heterogeneity and conduct sensitivity analyses based on prespecified characteristics. Where meta-analysis is not feasible, a narrative synthesis will be performed. Results We will present the results of this review, focusing on performance and resource utilization metrics. Conclusion This systematic review will evaluate the effectiveness of automated methods, especially AI tools in systematic reviews, aiming to synthesize current evidence on their performance, resource utilization, and impact on review quality. The findings will inform evidence-based recommendations for systematic review authors and developers on implementing automation tools to optimize review efficiency while maintaining methodological rigor. In addition, we will identify key research gaps to guide future development of AI-assisted systematic review methods. Plain Language Summary A systematic review is a thorough and organized summary of all relevant studies on a specific topic. These reviews are important for gathering evidence to guide health care decisions, but they often take a lot of time and effort. Recently, tools using artificial intelligence (AI) have been developed to speed up this process. We will conduct a systematic review to see how well these AI tools perform compared to human reviewers. We will examine studies from 2005 that have used AI to conduct systematic reviews. We will assess how well AI tools find the right information, how much time and work they save, and how easy and reliable they are for users. This study aims to help researchers choose the best AI tools to make systematic reviews faster and more efficient without losing quality.","url":"https://doi.org/10.1016/j.jclinepi.2025.111738","authors":["Xuenan Pang","KM Saif‐Ur‐Rahman","Sarah Berhane","Xiaomei Yao","Kavita Kothari","Petek Eylül Taneri","James Thomas","Declan Devane"],"tags":["Systematic review","Protocol (science)","Stage (stratigraphy)","Medicine","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-25","doi":"https://doi.org/10.1016/j.jclinepi.2025.111738","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414283228","name":"Cross-border higher education cooperation under the dual context of artificial intelligence and geopolitics: opportunities, challenges, and pathways","source":"openalex","abstract":"This paper examines the profound impact of artificial intelligence (AI) and geopolitics on cross-border higher education cooperation. AI has the potential to enhance educational accessibility and collaboration efficiency by enabling personalized learning, virtual classrooms, open resource platforms, and open-source research collaborations, ultimately helping bridge global educational gaps. However, significant challenges arise, such as techno-nationalism (e.g., semiconductor export controls), data sovereignty conflicts (e.g., GDPR restrictions), divergent algorithmic values, and the expanding digital divide. To address these challenges, this study proposes several solutions: the creation of an inclusive technological ecosystem (including open-source platforms, shared computing power, and cross-cultural models); the development of mutual recognition mechanisms (such as data stratification and standard harmonization); the strengthening of South-South cooperation through digital public goods; and the reconstruction of ethical consensus, emphasizing cultural diversity and human-in-the-loop principles. Notably, China has actively contributed to these efforts through technological empowerment (e.g., National Smart Education Platform, Luban Workshops), regulatory input (e.g., Global Governance Initiative), and infrastructure support. Looking ahead, the paper argues for the establishment of an “Intelligent Education Community,” guided by the principles of “extensive consultation, joint contribution, shared benefits, and wise governance,” to ensure that AI advances global educational equity and promotes human progress.","url":"https://doi.org/10.3389/feduc.2025.1656518","authors":["Yaoshun Zhu","Yaoshun Zhu","Zhitao Zhu","Wenyao Xu","Yaoshun Zhu","Yaoshun Zhu"],"tags":["Higher education","Knowledge management","Sovereignty","Empowerment","Digital transformation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-17","doi":"https://doi.org/10.3389/feduc.2025.1656518","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4400667193","name":"Procedural Content Generation via Generative Artificial Intelligence","source":"openalex","abstract":"The attempt to utilize machine learning in procedural content generation (PCG) has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, building high-performance models requires not only handling customized content and ensuring quality and diversity, but also securing sufficient training data. For PCG research to advance further, addressing these challenges is essential. Thus, we also give special consideration to research that explores innovative generation techniques, model architectures, and approaches suited for limited-data scenarios.","url":"https://doi.org/10.4036/iis.2026.r.01","authors":["Xinyu Mao","Wanli Yu","Yuya Okawara","Xueying ZHAN","Kazunori D Yamada","Michael R. Zielewski"],"tags":["Generative grammar","Content (measure theory)","Artificial intelligence","Computer science","Mathematics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.4036/iis.2026.r.01","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4387560915","name":"Predictable Artificial Intelligence","source":"openalex","abstract":"We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key validity indicators (e.g., performance, safety) of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance. We formally characterise predictability, explore its most relevant components, illustrate what can be predicted, describe alternative candidates for predictors, as well as the trade-offs between maximising validity and predictability. To illustrate these concepts, we bring an array of illustrative examples covering diverse ecosystem configurations. Predictable AI is related to other areas of technical and non-technical AI research, but have distinctive questions, hypotheses, techniques and challenges. This paper aims to elucidate them, calls for identifying paths towards a landscape of predictably valid AI systems and outlines the potential impact of this emergent field.","url":"https://doi.org/10.48550/arxiv.2310.06167","authors":["Lexin Zhou","Pablo A. Moreno-Casares","Fernando Martínez‐Plumed","John Burden","Ryan Burnell","Lucy G. Cheke","Cèsar Ferri","Alexandru Marcoci","Behzad Mehrbakhsh","Yael Moros-Daval","Seán Ó hÉigeartaigh","Danaja Rutar","Wout Schellaert","Konstantinos Voudouris","José Hernández‐Orallo"],"tags":["Predictability","Field (mathematics)","Computer science","Liability","Key (lock)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-09","doi":"https://doi.org/10.48550/arxiv.2310.06167","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4415685397","name":"Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review","source":"openalex","abstract":"Machine learning (ML) has emerged as a powerful tool to analyze gait data, yet the \"black-box\" nature of many ML models hinders their clinical application. Explainable artificial intelligence (XAI) promises to enhance the interpretability and transparency of ML models, making them more suitable for clinical decision-making. This systematic review, registered on PROSPERO (CRD42024622752), assessed the application of XAI in gait analysis by examining its methods, performance, and potential for clinical utility. A comprehensive search across four electronic databases yielded 3676 unique records, of which 31 studies met inclusion criteria. These studies were categorized into model-agnostic (n = 16), model-specific (n = 12), and hybrid (n = 3) interpretability approaches. Most applied local interpretation methods such as SHAP and LIME, while others used Grad-CAM, attention mechanisms, and Layer-wise Relevance Propagation. Clinical populations studied included Parkinson's disease, stroke, sarcopenia, cerebral palsy, and musculoskeletal disorders. Reported outcomes highlighted biomechanically relevant features such as stride length and joint angles as key discriminators of pathological gait. Overall, the findings demonstrate that XAI can bridge the gap between predictive performance and interpretability, but significant challenges remain in standardization, validation, and balancing accuracy with transparency. Future research should refine XAI frameworks and assess their real-world clinical applicability across diverse gait disorders.","url":"https://doi.org/10.3389/fbioe.2025.1671344","authors":["Liangliang Xiang","Zixiang Gao","Peimin Yu","Justin Fernandez","Yaodong Gu","Ruoli Wang","Elena M. Gutierrez-Farewik"],"tags":["Interpretability","Gait","Artificial intelligence","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-30","doi":"https://doi.org/10.3389/fbioe.2025.1671344","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7118193480","name":"Patients’ views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)—a prospective survey","source":"openalex","abstract":"OBJECTIVES: The use of AI is gaining relevance in healthcare. There is limited information regarding the views of patients on AI in healthcare. The aim of our study was to assess the views of patients on the use of AI in healthcare with an on-site questionnaire. MATERIALS AND METHODS: Patients in our tertiary hospital with a diagnostic imaging appointment were invited to complete a paper-based questionnaire between December 2022 and October 2023. We asked about socio-demographic data, experience, knowledge, and their opinion on the use of AI in healthcare, focusing on the fields (1) diagnostics, (2) therapy, and (3) triage. RESULTS: Out of a total of 198 patients (mean age 49.41 ± 17.6 years, 99 female), 91.5% stated that they expected benefits from the implementation of AI in healthcare, although 73.4% rated their knowledge of AI as moderate to none. The majority of patients were in favour of using AI in diagnostics (87.2%) and therapy (73.1%), while only 28.2% approved its use in patient triage. 84.0% wanted to be informed about the use of AI in at least one of the mentioned areas. Participants with higher education, higher self-assessed knowledge of AI and personal experience with AI showed greater approval for AI in healthcare. CONCLUSION: Our interviewed patients have a rather open attitude towards AI in healthcare, with differentiated views depending on the topic; patients are in favour of the use of AI, especially in diagnostics and to a lesser extent also for therapy support, but they reject its use for triage. CRITICAL RELEVANCE STATEMENT: Overall, the results emphasise the need for widespread efforts to address patient concerns about AI in healthcare, including enhancing understanding and acceptance while protecting marginalised groups. This will help clinical radiology to adopt AI more effectively. KEY POINTS: There is limited information on patients' views of AI in healthcare, often focused on specific groups, limiting generalizability. Patients are open to AI in healthcare, supporting its use in diagnostics and therapy, but rejecting its use for triage. Overall, patients want to be informed about AI usage and participants with higher education and AI experience showed more approval.","url":"https://doi.org/10.1186/s13244-025-02159-3","authors":["Sophie Gina Baldus","Martin Wiesmann","U. Habel","Anna Gerhards","Dimah Hasan","Charlotte S. Weyland","Daniel Truhn","Marian Maximilian Hasl","Benjamin Clemens","Omid Nikoubashman"],"tags":["Limiting","Artificial intelligence","Applications of artificial intelligence","Psychology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-05","doi":"https://doi.org/10.1186/s13244-025-02159-3","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4416066352","name":"Advancements in Small-Object Detection (2023–2025): Approaches, Datasets, Benchmarks, Applications, and Practical Guidance","source":"openalex","abstract":"Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, lose discriminative features during deep neural network processing, making them difficult to disentangle from background noise and other artifacts. This survey presents a comprehensive and systematic review of the SOD advancements between 2023 and 2025, a period marked by the maturation of transformer-based architectures and a return to efficient, realistic deployment. We applied the PRISMA methodology for this work, yielding 112 seminal works in the field to ensure the robustness of our foundation for this study. We present a critical taxonomy of the developments since 2023, arranged in five categories: (1) multiscale feature learning; (2) transformer-based architectures; (3) context-aware methods; (4) data augmentation enhancements; and (5) advancements to mainstream detectors (e.g., YOLO). Third, we describe and analyze the evolving SOD-centered datasets and benchmarks and establish the importance of evaluating models fairly. Fourth, we contribute a comparative assessment of state-of-the-art models, evaluating not only accuracy (e.g., the average precision for small objects (AP_S)) but also important efficiency (FPS, latency, parameters, GFLOPS) metrics across standardized hardware platforms, including edge devices. We further use data-driven case studies in the remote sensing, manufacturing, and healthcare domains to create a bridge between academic benchmarks and real-world performance. Finally, we summarize practical guidance for practitioners, the model selection decision matrix, scenario-based playbooks, and the deployment checklist. The goal of this work is to help synthesize the recent progress, identify the primary limitations in SOD, and open research directions, including the potential future role of generative AI and foundational models, to address the long-standing data and feature representation challenges that have limited SOD.","url":"https://doi.org/10.3390/app152211882","authors":["Ali Aldubaikhi","Sarosh Patel"],"tags":["Computer science","Robustness (evolution)","Software deployment","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-07","doi":"https://doi.org/10.3390/app152211882","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409649298","name":"Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop","source":"openalex","abstract":"Trustworthiness has become a key concept for the ethical development and application of artificial intelligence (AI) in medicine. Various guidelines have formulated key principles, such as fairness, robustness, and explainability, as essential components to achieve trustworthy AI. However, conceptualizations of trustworthy AI often emphasize technical requirements and computational solutions, frequently overlooking broader aspects of fairness and potential biases. These include not only algorithmic bias but also human, institutional, social, and societal factors, which are critical to foster AI systems that are both ethically sound and socially responsible. This viewpoint article presents an interdisciplinary approach to analyzing trust in AI and trustworthy AI within the medical context, focusing on (1) social sciences and humanities conceptualizations and legal perspectives on trust and (2) their implications for trustworthy AI in health care. It focuses on real-world challenges in medicine that are often underrepresented in theoretical discussions to propose a more practice-oriented understanding. Insights were gathered from an interdisciplinary workshop with experts from various disciplines involved in the development and application of medical AI, particularly in oncological imaging and genomics, complemented by theoretical approaches related to trust in AI. Results emphasize that, beyond common issues of bias and fairness, knowledge and human involvement are essential for trustworthy AI. Stakeholder engagement throughout the AI life cycle emerged as crucial, supporting a human- and multicentered framework for trustworthy AI implementation. Findings emphasize that trust in medical AI depends on providing meaningful, user-oriented information and balancing knowledge with acceptable uncertainty. Experts highlighted the importance of confidence in the tool's functionality, specifically that it performs as expected. Trustworthiness was shown to be not a feature but rather a relational process, involving humans, their expertise, and the broader social or institutional contexts in which AI tools operate. Trust is dynamic, shaped by interactions among individuals, technologies, and institutions, and ultimately centers on people rather than tools alone. Tools are evaluated based on reliability and credibility, yet trust fundamentally relies on human connections. The article underscores the development of AI tools that are not only technically sound but also ethically robust and broadly accepted by end users, contributing to more effective and equitable AI-mediated health care. Findings highlight that building AI trustworthiness in health care requires a human-centered, multistakeholder approach with diverse and inclusive engagement. To promote equity, we recommend that AI development teams involve all relevant stakeholders at every stage of the AI lifecycle-from conception, technical development, clinical validation, and real-world deployment.","url":"https://doi.org/10.2196/71236","authors":["Melanie Goisauf","Mónica Cano Abadía","Kaya Akyüz","Maciej Bobowicz","Alena Buyx","Ilaria Anna Colussi","Marie-Christine Fritzsche","Karim Lekadir","Pekka Marttinen","Michaela Th. Mayrhofer","János Mészáros"],"tags":["Preprint","Trustworthiness","Psychology","Peer review","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-21","doi":"https://doi.org/10.2196/71236","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414244050","name":"Artificial Intelligence in Head and Neck Cancer: Towards Precision Medicine","source":"openalex","abstract":"Head and neck cancer (HNC) encompasses malignant neoplasms originating from the soft tissues of the nasal cavity, paranasal sinuses, oral cavity, pharynx, larynx, skin, and thyroid [...].","url":"https://doi.org/10.3390/cancers17183023","authors":["Jacob Hagen","Logan J Hornung","William T. Barham","Supratik Mukhopadhyay","Adam Bess","Kevin J. Contrera","Devraj Basu","Vlad C. Sandulache","Guillaume Spielmann","Sagar Kansara"],"tags":["Head and neck","Medicine","Precision medicine","Paranasal sinuses","Head and neck cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-16","doi":"https://doi.org/10.3390/cancers17183023","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7119481192","name":"Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation","source":"openalex","abstract":"The integration of multimodal medical imaging with artificial intelligence (AI) is potentially catalysing a paradigm shift in orthopaedic diagnosis and treatment, moving beyond experience-based practices toward intelligent, data-driven precision medicine. This narrative review synthesizes recent key evidence across imaging modalities and AI frameworks, and highlights the translational gap that persists between algorithmic development and real-world clinical implementation. By combining complementary information from X-ray, CT, MRI, PET, ultrasound, and biochemical data, multimodal AI overcomes the inherent limitations of single-modality approaches, enabling more comprehensive structural, functional, and metabolic assessments. Recent advances demonstrate broad applications, including accurate fracture detection and classification, differentiation of benign and malignant bone tumours, quantitative assessment of osteoarthritis, risk prediction for osteoporosis, and intelligent preoperative planning and intraoperative navigation. Moreover, multimodal AI facilitates efficacy prediction and personalised treatment decision-making, positioning future systems as AI-assisted decision-support tools that support surgeons in surgical strategy, implant design, and long-term follow-up. Nevertheless, significant challenges remain, particularly in data heterogeneity, model generalisation, interpretability, and clinical integration. Progress in constructing standardised multimodal databases, developing self-supervised and multi-task learning strategies, and strengthening ethical-regulatory frameworks will be essential for clinical translation. Ultimately, multimodal AI holds immense potential to transition from laboratory validation to routine practice, delivering safer, more efficient, and precise diagnostic and therapeutic solutions for orthopaedic patients.","url":"https://doi.org/10.3389/fmed.2025.1728248","authors":["Guangan Luo","Shuanglong Tan","Lincong Luo","Konghe Hu"],"tags":["Modalities","Computer science","Artificial intelligence","Multimodality","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-09","doi":"https://doi.org/10.3389/fmed.2025.1728248","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W3207870808","name":"Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study","source":"openalex","abstract":"BACKGROUND: Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. OBJECTIVE: This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. METHODS: To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. RESULTS: With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. CONCLUSIONS: Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.","url":"https://doi.org/10.2196/29301","authors":["Luisa Pumplun","Mariska Fecho","Nihal Wahl","Felix Peters","Peter Buxmann"],"tags":["Interview","Medical education","Process (computing)","Population","Qualitative research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-15","doi":"https://doi.org/10.2196/29301","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4406610264","name":"Equitable artificial intelligence for glaucoma screening with fair identity normalization","source":"openalex","abstract":"Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups. We developed fair identify normalization (FIN) module to equalize the feature importance across different identity groups to improve model performance equity. The optical coherence tomography (OCT) measurements were used to categorize patients into glaucoma and non-glaucoma. The equity-scaled area under the receiver operating characteristic curve (ES-AUC) was adopted to quantify model performance equity. With FIN for racial groups, the overall AUC and ES-AUC increased from 0.82 to 0.85 and 0.77 to 0.81, respectively, with the AUC for Blacks increasing from 0.77 to 0.82. With FIN for ethnic groups, the overall AUC and ES-AUC increased from 0.82 to 0.84 and 0.77 to 0.80, respectively, with the AUC for Hispanics increasing from 0.75 to 0.79.","url":"https://doi.org/10.1038/s41746-025-01432-5","authors":["Min Shi","Yan Luo","Yu Tian","Lucy Q. Shen","Nazlee Zebardast","Mohammad Eslami","Saber Kazeminasab","Michael V. Boland","David S. Friedman","Louis R. Pasquale","Mengyu Wang"],"tags":["Normalization (sociology)","Artificial intelligence","Glaucoma","Computer science","Optometry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-20","doi":"https://doi.org/10.1038/s41746-025-01432-5","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4413833887","name":"Artificial Intelligence in Higher Education: Predictive Analysis of Attitudes and Dependency Among Ecuadorian University Students","source":"openalex","abstract":"This study examines the relationship between attitudes toward artificial intelligence (AI) and AI dependency among Ecuadorian university students. A cross-sectional design was used, applying two validated instruments: the Artificial Intelligence Dependence Scale (DAI) and the General Attitudes Toward Artificial Intelligence Scale (GAAIS), with a sample of 540 students. Structural equation modeling (SEM) assessed how both positive and negative attitudes predict dependency levels. Results indicate a moderate level of AI dependency and an ambivalent attitudinal profile. Both attitudinal dimensions significantly predicted dependency, suggesting dual-use behaviors shaped by perceived utility and ethical concerns. Urban students reported higher dependency and greater sensitivity to AI-related risks, highlighting digital inequalities. Although the SEM model showed adequate comparative fit (CFI = 0.976; TLI = 0.973), residual indicators (RMSEA = 0.075) suggest further refinement is needed. This study contributes to underexplored Latin American contexts and emphasizes the need for equity-driven digital literacy strategies in higher education. Findings support pedagogical frameworks promoting critical thinking, ethical reasoning, and responsible AI use. The study aligns with Sustainable Development Goals 4 (Quality Education) and 10 (Reduced Inequalities), reinforcing the importance of inclusive, learner-centered approaches to AI integration.","url":"https://doi.org/10.3390/su17177741","authors":["Carla Guillermina Mendoza Arce","Jaime Andrés Camacho Gavilanes","Edgar Mendoza Arce","Edgar Haro","Diego Mauricio Bonilla Jurado"],"tags":["Dependency (UML)","Psychology","Mathematics education","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-28","doi":"https://doi.org/10.3390/su17177741","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411643584","name":"Opportunity or Threat: Investigating Faculty Readiness to Adopt Artificial Intelligence in Higher Education","source":"openalex","abstract":"Artificial Intelligence (AI) is increasingly being integrated into higher education, providing opportunities to enhance teaching and learning through personalised instruction, automated assessments, and data–driven insights. However, faculty members’ readiness to integrate AI into their teaching practices plays a critical role in its effective implementation. While global studies have primarily examined AI adoption in higher education, research on faculty readiness within Caribbean institutions remains limited, presenting a significant gap in the literature. This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) model to assess the influence of perceived benefits, facilitating conditions, and attitude towards AI on adoption readiness. A quantitative survey was administered to a sample of 78 faculty members from The University of the West Indies, Cave Hill campus, collecting data on their AI experience and perceptions. The findings indicate that perceived benefits of AI and institutional support are significant predictors of faculty readiness, while attitude towards AI does not significantly influence adoption. The study also identifies limited AI expertise, lack of training opportunities, and concerns about AI’s ethical implications as key barriers to readiness to adopt. These results highlight the need for structured AI training programs, enhanced institutional support, and clear policies to facilitate AI adoption among faculty. The findings have broader implications for universities in developing regions, emphasising the importance of targeted faculty development to ensure effective AI integration in higher education.","url":"https://doi.org/10.46425/cjed1201029055","authors":["Grace-Anne Jackman","Ian A. Marshall","Troy Carrington"],"tags":["Psychology","Applied psychology","Medical education","Mathematics education","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.46425/cjed1201029055","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4407263148","name":"AI versus human-generated multiple-choice questions for medical education: a cohort study in a high-stakes examination","source":"openalex","abstract":"BACKGROUND: The creation of high-quality multiple-choice questions (MCQs) is essential for medical education assessments but is resource-intensive and time-consuming when done by human experts. Large language models (LLMs) like ChatGPT-4o offer a promising alternative, but their efficacy remains unclear, particularly in high-stakes exams. OBJECTIVE: This study aimed to evaluate the quality and psychometric properties of ChatGPT-4o-generated MCQs compared to human-created MCQs in a high-stakes medical licensing exam. METHODS: A prospective cohort study was conducted among medical doctors preparing for the Primary Examination on Emergency Medicine (PEEM) organised by the Hong Kong College of Emergency Medicine in August 2024. Participants attempted two sets of 100 MCQs-one AI-generated and one human-generated. Expert reviewers assessed MCQs for factual correctness, relevance, difficulty, alignment with Bloom's taxonomy (remember, understand, apply and analyse), and item writing flaws. Psychometric analyses were performed, including difficulty and discrimination indices and KR-20 reliability. Candidate performance and time efficiency were also evaluated. RESULTS: Among 24 participants, AI-generated MCQs were easier (mean difficulty index = 0.78 ± 0.22 vs. 0.69 ± 0.23, p < 0.01) but showed similar discrimination indices to human MCQs (mean = 0.22 ± 0.23 vs. 0.26 ± 0.26). Agreement was moderate (ICC = 0.62, p = 0.01, 95% CI: 0.12-0.84). Expert reviews identified more factual inaccuracies (6% vs. 4%), irrelevance (6% vs. 0%), and inappropriate difficulty levels (14% vs. 1%) in AI MCQs. AI questions primarily tested lower-order cognitive skills, while human MCQs better assessed higher-order skills (χ² = 14.27, p = 0.003). AI significantly reduced time spent on question generation (24.5 vs. 96 person-hours). CONCLUSION: ChatGPT-4o demonstrates the potential for efficiently generating MCQs but lacks the depth needed for complex assessments. Human review remains essential to ensure quality. Combining AI efficiency with expert oversight could optimise question creation for high-stakes exams, offering a scalable model for medical education that balances time efficiency and content quality.","url":"https://doi.org/10.1186/s12909-025-06796-6","authors":["Alex Kwok-Keung Law","Jerome Lok Tsun So","Chun Tat Lui","Yu Fai Choi","Koon Ho Cheung","Kevin Kei Ching Hung","Colin A. Graham"],"tags":["Multiple choice","Medical education","Educational measurement","Cohort","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-08","doi":"https://doi.org/10.1186/s12909-025-06796-6","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4391063646","name":"Can artificial intelligence (AI) replace oral food challenge?","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jaci.2024.01.008","authors":["Sindy K. Y. Tang","Nicolas Castaño","Kari C. Nadeau","Stephen J. Galli"],"tags":["Variety (cybernetics)","Artificial intelligence","Data science","Computer science","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-21","doi":"https://doi.org/10.1016/j.jaci.2024.01.008","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4404813021","name":"Focal therapy of prostate cancer: Use of artificial intelligence to define tumour volume and predict treatment outcomes","source":"openalex","abstract":"Objectives: The aim of this study is to evaluate new software (Unfold AI) in the estimation of prostate tumour volume (TV) and prediction of focal therapy outcomes. Subjects/patients and methods: Subjects were 204 men with prostate cancer (PCa) of grade groups 2-4 (GG ≥ 2), who were enrolled in a trial of partial gland cryoablation (PGA) at UCLA from 2017 to 2022. Magnetic resonance imaging (MRI)-guided biopsy (MRGB) was performed at diagnosis and at 6 and 18 months following PGA. Utilising Unfold AI (FDA-cleared 2022), which generates a 3D map of GG ≥ 2 PCa margins, we retrospectively estimated TV for each patient. TV was compared against conventional baseline variables as a correlate of a successful primary outcome-defined here as the absence of GG ≥ 2 on follow-up MRGB at 6 months. Secondary outcomes were MRGB at 18 months and failure-free survival, that is, lack of metastasis or salvage whole gland therapy. Receiver operating curves and multivariate analysis were used to determine significance. Results: A successful primary outcome was observed in 77.7% of patients. Significant correlates of a successful ablation were percent pattern 4 and TV; areas under the curve (AUCs) were 0.60 and 0.73, respectively. GG was not a correlate of success (AUC = 0.51). A TV of 1.5 cc provided the optimal combination of sensitivity (55.8%) and specificity (85.7%) at 6 months. TV was also significantly associated with secondary outcomes. In multivariate analysis, TV was the variable most associated with 6- and 18-month biopsy success (adjusted odds ratios [aORs] were 6.1 and 4.2). Utilising TV ≤ 1.5 cc as a PGA criterion would have prevented 72% of failures at the cost of 42% of successes. Conclusion: The AI-based software Unfold AI estimates TV, which is significantly associated with biopsy outcomes after focal cryoablation. The rate of treatment success is inversely related to TV.","url":"https://doi.org/10.1002/bco2.456","authors":["Wayne Brisbane","Alan Priester","Anissa V. Nguyen","Mark T. Topoozian","Sakina Mohammed Mota","Merdie Delfin","Samantha Gonzalez","Kyla P. Grunden","Shannon Richardson","Shyam Natarajan","Leonard S. Marks"],"tags":["Medicine","Prostate cancer","Cryoablation","Multivariate analysis","Receiver operating characteristic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-28","doi":"https://doi.org/10.1002/bco2.456","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4366403925","name":"Application of Neural Networks in the Medical Field","source":"openalex","abstract":"The field of computer technology has seen remarkable advancements, which has led to a surge in interest in the possible applications of \"Artificial Intelligence,\" or AI, in the fields of medicine and biological research. The field of artificial intelligence known as \"Artificial Neural Networks\" (ANNs) is one of the most promising and intensively researched subfields in AI. ANNs are, in their most fundamental form, the mathematical algorithms that are generated by computers. ANNs are taught using standard data and are able to comprehend the information that is imparted by the data. ANNs that have been trained come extremely close, on a basic level, to replicating the functioning of small biological neural clusters. They are the digital model of the biological brain, and they have the ability to discover complicated nonlinear correlations between dependent and independent variables in a data set, something that the human brain may be unable to do. These days, ANNs are employed extensively for medical applications in a variety of subspecialties within the field of medicine, particularly cardiology. Diagnostics, electronic signal analysis, medical image analysis, and radiology are just few of the fields that have found considerable use for ANNs. Many researchers have made use of ANNs for modelling purposes in the field of medicine and clinical research. Both pharmacoepidemiology and medical data mining are experiencing increasingly more applications of artificial neural networks (ANNs). The author of this paper provides an overview of the many different applications of ANNs in the field of medical science.","url":"https://doi.org/10.58346/jowua.2023.i1.006","authors":["Sofiène Mansouri"],"tags":["Field (mathematics)","Artificial neural network","Computer science","Artificial intelligence","Mathematics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-19","doi":"https://doi.org/10.58346/jowua.2023.i1.006","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409516537","name":"An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence","source":"openalex","abstract":"Recent studies focus on machine learning (ML) algorithms for predicting employee churn (ECn) to save probable economic loss, technology leakage, and customer and knowledge transference. However, can human resource professionals rely on algorithms for prediction? Can they decide when the process of prediction is not known? Due to the lack of interpretability, ML models' exclusive nature and growing intricacy make it challenging for field experts to comprehend these multifaceted black boxes. To address the concern of interpretability, trust and transparency of black-box predictions, this study explores the application of explainable artificial intelligence (XAI) in identifying the factors that escalate the ECn, analysing the negative impact on productivity, employee morale and financial stability. We propose a predictive model that compares the best two top-performing algorithms based on the performance metrics. Thereafter, we suggest applying an explainable artificial intelligence based on Shapley values, i.e., the Shapley Additive exPlanations approach (SHAP), to identify and compare the feature importance of top-performing algorithms logistic regression and random forest analysis on our dataset. The interpretability of the predictive outcome unboxes the predictions, enhancing trust and facilitating retention strategies.","url":"https://doi.org/10.1016/j.jik.2025.100700","authors":["Meenu Chaudhary","Loveleen Gaur","Amlan Chakrabarti","Gurmeet Singh","Paul Jones","Sascha Kraus"],"tags":["Transparency (behavior)","Computer science","Artificial intelligence","Business","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-16","doi":"https://doi.org/10.1016/j.jik.2025.100700","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409487051","name":"Artificial intelligence-assisted multimodal imaging for the clinical applications of breast cancer: a bibliometric analysis","source":"openalex","abstract":"BACKGROUND: Breast cancer (BC) remains a leading cause of cancer-related mortality among women globally, with increasing incidence rates posing significant public health challenges. Recent advancements in artificial intelligence (AI) have revolutionized medical imaging, particularly in enhancing diagnostic accuracy and prognostic capabilities for BC. While multimodal imaging combined with AI has shown remarkable potential, a comprehensive analysis is needed to synthesize current research and identify emerging trends and hotspots in AI-assisted multimodal imaging for BC. METHODS: This study analyzed literature on AI-assisted multimodal imaging in BC from January 2010 to November 2024 in Web of Science Core Collection (WoSCC). Bibliometric and visualization tools, including VOSviewer, CiteSpace, and the Bibliometrix R package, were employed to assess countries, institutions, authors, journals, and keywords. RESULTS: A total of 80 publications were included, revealing a steady increase in annual publications and citations, with a notable surge post-2021. China led in productivity and citations, while Germany exhibited the highest citation average. The United States demonstrated the strongest international collaboration. The most productive institution and author are Radboud University Nijmegen and Xi, Xiaoming. Publications were predominantly published in Computerized Medical Imaging and Graphics, with Qian, XJ's 2021 study on BC risk prediction under deep learning frameworks being the most influential. Keyword analysis highlighted themes such as \"breast cancer\", \"classification\", and \"deep learning\". CONCLUSIONS: AI-assisted multimodal imaging has significantly advanced BC diagnosis and management, with promising future developments. This study offers researchers a comprehensive overview of current frameworks and emerging research directions. Future efforts are expected to focus on improving diagnostic precision and refining therapeutic strategies through optimized imaging techniques and AI algorithms, emphasizing international collaboration to drive innovation and clinical translation.","url":"https://doi.org/10.1007/s12672-025-02329-1","authors":["Chenke Hou","Ting Huang","Keke Hu","Zhifeng Ye","Junhua Guo","Heran Zhou"],"tags":["Breast cancer","Cancer","Artificial intelligence","Medical physics","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-16","doi":"https://doi.org/10.1007/s12672-025-02329-1","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4389556230","name":"THE USE OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR EDUCATION AND SCIENTIFIC RESEARCH","source":"openalex","abstract":"Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GAI), is increasingly being used in education and scientific research. This article explores the use of AI applications, such as ChatGPT, Scite, and Litmaps in education and scientific research. This kind of application offers several advantages but should be used with certain caution. Any inference must be verified made by AI tools to ensure their validity. While current AI technologies can quickly analyze vast amounts of data or represent networks of citations, their results may be influenced by the quality and comprehensiveness of databases. Thus, human experts must review and verify the results carefully to ensure their accuracy and originality. Additionally, academic institutions should educate students and researchers about the risks of utilizing GAI models while providing comprehensive guidelines to prevent plagiarism.","url":"https://doi.org/10.57107/hyw.v3i1.61","authors":["José Segovia-Juárez","Robert Baumgartner"],"tags":["Computer science","Artificial intelligence","Applications of artificial intelligence","Originality","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-07","doi":"https://doi.org/10.57107/hyw.v3i1.61","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4407450687","name":"Impact of artificial intelligence on future clinical pharmacy research and scholarship","source":"openalex","abstract":"Abstract Almost every facet of modern biomedical research involves artificial intelligence (AI). This ACCP commentary forecasts the role of AI in clinical pharmacy research and scholarship. The potential benefits/opportunities together with the limitations/challenges of AI are reviewed for stages of the scientific method including (1) developing the research question(s), study design, and execution; (2) data analysis; and (3) reporting and dissemination of clinical pharmacy research. Benefits and opportunities of AI in clinical pharmacy research include streamlining hypothesis generation and facilitating study design, overcoming limitations of traditional statistical analysis techniques, facilitating manuscript development and dissemination, and expediting peer review. Limitations and challenges of AI include the introduction of biases in subject recruitment; generation of false information, also known as “AI hallucinations”; concern of “black box” analyses that are difficult to validate; potential legal liabilities; lack of accountability; and the need for investigators to ensure the accuracy and integrity of AI‐generated content. In summary, rapid progress of AI capabilities has great potential to revolutionize and accelerate clinical pharmacy research and scholarship; however, it is also imperative to recognize and mitigate the challenges and limitations introduced by AI.","url":"https://doi.org/10.1002/jac5.70003","authors":["Alexandre Chan","William L. Baker","Daniel Abazia","Jerry L. Bauman","C. Lindsay DeVane","Kellie J. Goodlet","Natalie Hall","J. Kevin Hicks","Ellen Jones","Chi‐Hua Lu","Donald C. Moore","Nicholas R. Nelson","Kaylee Putney","Aracely Sosa","Toby C. Trujillo","Crystal Zhou"],"tags":["Scholarship","Pharmacy","Artificial intelligence","Medicine","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-13","doi":"https://doi.org/10.1002/jac5.70003","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4406994545","name":"Generative Artificial Intelligence Transparency in scientific writing: the GAIT 2024 guidance","source":"openalex","abstract":"Background: Generative Artificial Intelligence (GAI) tools are increasingly used in research. At present, there is no standardised approach to reporting GAI use. We aimed to produce guidance to support authors in the use of GAI in scientific writing. Methods: A steering group of academic surgeons with experience in GAI developed draft statements for best practice in reporting GAI use. These statements were refined through iterative discussions using a nominal group technique. A broad network of surgeons and surgical researchers were invited to participate in an online consultation exercise to validate these statements by ranking using a Likert scale. A pre-planned threshold of ≥70% of participants scoring a statement ≥7 would lead to acceptance. Participants were additionally surveyed on the use, opportunities, and risks. Thematic analysis was completed using ChatGPT. Results: The steering group developed five draft statements, which were validated in the online consultation exercise by 124 participants from 46 countries. Four draft statements were accepted based on this exercise and consolidated into the final Generative AI Transparency (GAIT) guidance: (1) GAI use should be reported in a GAIT statement; (2) GAI use should be mapped using the Contributor Roles Taxonomy; (3) specific prompts used should be reported; (4) authors should retain final responsibility for their work. Example statements to be included in manuscripts include: (1) ChatGPT-4o was used in November 2024 to check and edit statistical code (formal analysis) and edit small sections of the manuscript text for clarity (writing: review & editing). Prompts used are reported in the supplement. The authors should retain final responsibility for their work; (2) No Generative Artificial Intelligence was used to produce, draft, or edit this guidance paper. Conclusion: The GAIT 2024 guidance will support transparent, structured reporting of the use of generative AI in scientific writing, supporting the integrity of research outputs.","url":"https://doi.org/10.62463/surgery.134","authors":["Cortland Linder","Dmitri Nepogodiev","GAIT 2024 Collaborative Group"],"tags":["Transparency (behavior)","Generative grammar","Artificial intelligence","Computer science","Gait"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-29","doi":"https://doi.org/10.62463/surgery.134","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4408631719","name":"Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review","source":"openalex","abstract":"BACKGROUND: This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. Key prognostic endpoints, including overall survival (OS), recurrence-free survival (RFS), progression-free survival (PFS), and treatment response prediction (TRP), are examined to evaluate the effectiveness of these algorithms and identify significant features that influence predictive accuracy. RECENT FINDINGS: A thorough search of four major databases-PubMed, Scopus, Web of Science, and Cochrane-resulted in 2400 articles published within the last decade, with 32 studies meeting the inclusion criteria. Notably, most publications emerged after 2021. Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. Evaluation metrics such as area under the curve (AUC) (18 studies), concordance index (C-index) (11 studies), and accuracy (11 studies) were frequently employed. Age at diagnosis, tumor stage, CA-125 levels, and treatment-related factors were consistently highlighted as significant predictors, emphasizing their relevance in OC prognosis. CONCLUSION: ML models demonstrate considerable potential for predicting OC survival outcomes; however, challenges persist regarding model accuracy and interpretability. Incorporating diverse data types-such as clinical, imaging, and molecular datasets-holds promise for enhancing predictive capabilities. Future advancements will depend on integrating heterogeneous data sources with multimodal ML approaches, which are crucial for improving prognostic precision in OC.","url":"https://doi.org/10.1002/cnr2.70138","authors":["Farkhondeh Asadi","Milad Rahimi","Nahid Ramezanghorbani","Sohrab Almasi"],"tags":["Interpretability","Machine learning","Random forest","Artificial intelligence","Concordance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1002/cnr2.70138","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4382654112","name":"Impact of Artificial Intelligence and Machine Learning on Cybersecurity","source":"openalex","abstract":"This chapter focuses on different aspects of artificial intelligence (AI), such as the transformative of AI and the relationship of cybersecurity with AI. This discussion covers the outcomes due of the integration between AI and cybersecurity. The chapter discusses the broad domain of AI security in the landscape of cybersecurity. This landscape includes the social, economic, political, and digital and physical security domains. The transformative power of AI is determined by evaluating the current progress, changes, and enhancement it has provided in different aspects of life and majorly in businesses and organizational elements. The accountability of AI in society has been improved as transparency in decision-making has been improved in the identified areas. AI technology and its techniques are significantly used to automate the processes and services included in cybercriminal activities. The relationship between AI and cybersecurity has promised more productive and effective outcomes due to machine learning.","url":"https://doi.org/10.1002/9781119883111.ch10","authors":["Abdulrahman Yarali"],"tags":["Transformative learning","Transparency (behavior)","Accountability","Computer science","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-30","doi":"https://doi.org/10.1002/9781119883111.ch10","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4415171327","name":"Artificial Intelligence in Gynaecology Oncology","source":"openalex","abstract":"Plain Language Summary Artificial intelligence (AI) is an emerging powerful technology that differs from traditional computer programs in its ability to learn from its results and enhance performance, mimicking human intelligence, hence the name. AI is already an important part of most computer‐based tasks in our daily lives. Everyday examples include internet search engines and products that provide face recognition or predict the outbreak of diseases. Research interests in AI appear to be limited to, hence constricted by, available pre‐existing information and datasets rather than addressing patients' priorities and clinical needs. The National Institute for Health and Care Excellence in England noted that current medical technologies using AI lack robust research backing and NHS patient involvement. While some AI‐based products are currently in clinical use—for example, in identifying abnormal cells in cervical smears—AI remains largely in the research phase in gynaecology oncology. Researchers have reported good results of its performance in fields such as prediction of lymph node involvement in cervical, endometrial and ovarian cancers—which are important for treatment planning, distinguishing benign from malignant pelvic masses—and cervical cancer screening in low‐ and high‐income countries. AI products have learning that is supervised and some AI modalities use technology that learns from itself. There are ethical concerns surrounding the use of AI in health care. Many of these concerns relate to the quality of data used in training AI systems, i.e., data should be inclusive so that results can be applicable in the future irrespective of race, ethnicity, socioeconomic background or place of residence. It is also not clear who should take responsibility for clinical recommendations made by AI systems: is it the doctor using it, the hospital employing the doctor or the creators of the AI product? Concerns have also been raised regarding how the roll‐out of AI might affect jobs for doctors, nurses and administration staff. AI is expected to contribute to health care in many positive ways. This can be achieved with good scrutiny and appropriate legislations to protect patients' health and privacy in addition to identifying important research and implementation areas through a collaborative partnership among investors, investigators, clinicians and patients.","url":"https://doi.org/10.1111/1471-0528.70005","authors":["Saladin Sawan","N Eftekhari","Kristofer Linton‐Reid","N. Wood","Tricia Numan","Eric O. Aboagye","Claudio Angione"],"tags":["Excellence","Artificial intelligence","Medicine","Modalities","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-14","doi":"https://doi.org/10.1111/1471-0528.70005","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W3194185186","name":"The potential use of digital health technologies in the African context: a systematic review of evidence from Ethiopia","source":"openalex","abstract":"The World Health Organization (WHO) recently put forth a Global Strategy on Digital Health 2020-2025 with several countries having already achieved key milestones. We aimed to understand whether and how digital health technologies (DHTs) are absorbed in Africa, tracking Ethiopia as a key node. We conducted a systematic review, searching PubMed-MEDLINE, Embase, ScienceDirect, African Journals Online, Cochrane Central Registry of Controlled Trials, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform databases from inception to 02 February 2021 for studies of any design that investigated the potential of DHTs in clinical or public health practices in Ethiopia. This review was registered with PROSPERO ( CRD42021240645 ) and it was designed to inform our ongoing DHT-enabled randomized controlled trial (RCT) (ClinicalTrials.gov ID: NCT04216420 ). We found 27,493 potentially relevant citations, among which 52 studies met the inclusion criteria, comprising a total of 596,128 patients, healthy individuals, and healthcare professionals. The studies involved six DHTs: mHealth (29 studies, 574,649 participants); electronic health records (13 studies, 4534 participants); telemedicine (4 studies, 465 participants); cloud-based application (2 studies, 2382 participants); information communication technology (3 studies, 681 participants), and artificial intelligence (1 study, 13,417 participants). The studies targeted six health conditions: maternal and child health (15), infectious diseases (14), non-communicable diseases (3), dermatitis (1), surgery (4), and general health conditions (15). The outcomes of interest were feasibility, usability, willingness or readiness, effectiveness, quality improvement, and knowledge or attitude toward DHTs. Five studies involved RCTs. The analysis showed that although DHTs are a relatively recent phenomenon in Ethiopia, their potential harnessing clinical and public health practices are highly visible. Their adoption and implementation in full capacity require more training, access to better devices such as smartphones, and infrastructure. DHTs hold much promise tackling major clinical and public health backlogs and strengthening the healthcare ecosystem in Ethiopia. More RCTs are needed on emerging DHTs including artificial intelligence, big data, cloud, cybersecurity, telemedicine, and wearable devices to provide robust evidence of their potential use in such settings and to materialize the WHO's Global Strategy on Digital Health.","url":"https://doi.org/10.1038/s41746-021-00487-4","authors":["Tsegahun Manyazewal","Yimtubezinash Woldeamanuel","Henry M. Blumberg","Abebaw Fekadu","Vincent C. Marconi"],"tags":["Digital health","Medicine","MEDLINE","mHealth","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-17","doi":"https://doi.org/10.1038/s41746-021-00487-4","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7140828524","name":"Artificial intelligence literacy and readiness in future health care professionals: a cross-sectional study","source":"openalex","abstract":"Aim To examine the relationship between artificial intelligence (AI) literacy and readiness for medical AI among medical and nursing students, and to explore demographic and educational factors affecting AI literacy and readiness for AI. MethodsThis cross-sectional study enrolled 443 students attending the Faculty of Medicine and the Department of Nursing at Bilecik Seyh Edebali University between May and June 2025.Data were collected using a general demographic questionnaire, Medical Artificial Intelligence Readiness Scale (MAIRS), and Artificial Intelligence Literacy Scale (AILS).Results Medical students showed higher AI readiness (P < 0.001) and literacy (P = 0.032) scores than nursing students.AI literacy was moderately and positively correlated with readiness for medical AI (r = 0.338, P < 0.001), and the associations were strongest on the Awareness and Usage dimensions.In multivariable models, MAIRS scores were independently associated with the study year, department, and single-item readiness to use AI, while AILS scores were independently associated with the readiness to use AI and the belief that AI could partially replace health care workers.Internal consistency of the questionnaires was high (MAIRS α = 0.972; AILS α = 0.878). ConclusionThe findings support the integration of structured, practical, and ethically informed AI education into health science curricula, as this may be associated with greater student readiness for future AI-supported health care environments.","url":"https://doi.org/10.3325/cmj.2026.67.4","authors":["Funda Catan-Inan"],"tags":["Health care","Medical education","Psychology","Health literacy","Literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-01","doi":"https://doi.org/10.3325/cmj.2026.67.4","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414283151","name":"Generative Artificial Intelligence and the Future of Public Knowledge","source":"openalex","abstract":"Generative artificial intelligence (AI), in particular large language models such as ChatGPT, have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for use in various professions. Following the printing press and the internet, generative AI language models are the third transformative technological invention, with truly cross-sectoral impact on knowledge transmission and knowledge generation. While the printing press allowed for the transmission of knowledge that is independent of the physical presence of the knowledge holder, with publishers emerging as gatekeepers, the internet added levels of democratization, allowing anyone to publish, along with global immediacy. The development of social media resulted in an increased fragmentation and tribalization in online communities regarding their ways of knowing, resulting in the propagation of alternative truths that resonate in echo chambers. It is against this background that generative AI language models have entered public consciousness. Using the strategic foresight methodology, this paper will examine the proposition that the age of generative AI will emerge as an age of public ignorance.","url":"https://doi.org/10.3390/knowledge5030020","authors":["Dirk Spennemann"],"tags":["Generative grammar","Transformative learning","Computer science","Artificial intelligence","The Internet"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-17","doi":"https://doi.org/10.3390/knowledge5030020","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414687094","name":"Artificial intelligence in ventricular arrhythmias and sudden cardiac death: A guide for clinicians","source":"openalex","abstract":"Sudden cardiac death (SCD) from ventricular arrhythmias (VAs) remains a leading cause of mortality worldwide. Traditional risk stratification, primarily based on left ventricular ejection fraction (LVEF) and other coarse metrics, often fails to identify a large subset of patients at risk and frequently leads to unnecessary device implantations. Advances in artificial intelligence (AI) offer new strategies to improve both long-term SCD risk prediction and near-term VAs forecasting. In this review, we discuss how AI algorithms applied to the 12-lead electrocardiogram (ECG) can identify subtle risk markers in conditions such as hypertrophic cardiomyopathy (HCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and coronary artery disease (CAD), often outperforming conventional risk models. We also explore the integration of AI with cardiac imaging, such as scar quantification on cardiac magnetic resonance (CMR) and fibrosis mapping, to enhance the identification of the arrhythmogenic substrate. Furthermore, we investigate the application of data from implantable cardioverter-defibrillators (ICDs) and wearable devices to predict ventricular tachycardia (VT) or ventricular fibrillation (VF) events before they occur, thereby advancing care toward real-time prevention. Amid these innovations, we address the medicolegal and ethical implications of AI-driven automated alerts in arrhythmia care, highlighting when clinicians can trust AI predictions. Future directions include multimodal AI fusion to personalize SCD risk assessment, as well as AI-guided VT ablation planning through imaging-based digital heart models. This review provides a comprehensive overview for general medical readers, focusing on peer-reviewed advances globally in the emerging intersection of AI, VAs, and SCD prevention.","url":"https://doi.org/10.1016/j.ipej.2025.09.005","authors":["Ibrahim Antoun","Xin Li","Ahmed Abdelrazik","Mahmoud Eldesouky","Kaung Myat Thu","Mokhtar Ibrahim","Harshil Dhutia","Riyaz Somani","G. André Ng"],"tags":["Medicine","Ventricular tachycardia","Cardiology","Sudden cardiac death","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-27","doi":"https://doi.org/10.1016/j.ipej.2025.09.005","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410448132","name":"Artificial intelligence-guided distal radius fracture detection on plain radiographs in comparison with human raters","source":"openalex","abstract":"BACKGROUND: The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain radiographs with the performance of human raters. METHODS: We retrospectively analysed all wrist radiographs taken in our hospital since the introduction of AI-guided fracture detection from 11 September 2023 to 10 September 2024. The ground truth was defined by the radiological report of a board-certified radiologist based solely on conventional radiographs. The following parameters were calculated: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), accuracy (%), Cohen's Kappa coefficient, F1 score, sensitivity (%), specificity (%), Youden Index (J Statistic). RESULTS: In total 1145 plain radiographs of the wrist were taken between 11 September 2023 and 10 September 2024. The mean age of the included patients was 46.6 years (± 27.3), ranging from 2 to 99 years and 59.0% were female. According to the ground truth, of the 556 anteroposterior (AP) radiographs, 225 cases (40.5%) had a DRF, and of the 589 lateral view radiographs, 240 cases (40.7%) had a DRF. The AI system showed the following results on AP radiographs: accuracy (%): 95.90; Cohen's Kappa: 0.913; F1 score: 0.947; sensitivity (%): 92.02; specificity (%): 98.45; Youden Index: 90.47. The orthopedic surgeon achieved a sensitivity of 91.5%, specificity of 97.8%, an overall accuracy of 95.1%, F1 score of 0.943, and Cohen's kappa of 0.901. These results were comparable to those of the AI model. CONCLUSION: AI-guided detection of DRF demonstrated diagnostic performance nearly identical to that of an experienced orthopedic surgeon across all key metrics. The marginal differences observed in sensitivity and specificity suggest that AI can reliably support clinical fracture assessment based solely on conventional radiographs.","url":"https://doi.org/10.1186/s13018-025-05888-9","authors":["Nikolai Ramadanov","Patric John","Robert Hable","A. Schreyer","Simon Shabo","Robert Prill","Mikhail Salzmann"],"tags":["Medicine","Orthopedic surgery","Radiography","Plain radiography","Distal radius fracture"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.1186/s13018-025-05888-9","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4404957801","name":"Preclinical Cognitive Markers of Alzheimer Disease and Early Diagnosis Using Virtual Reality and Artificial Intelligence: Literature Review","source":"openalex","abstract":"Background: This review explores the potential of virtual reality (VR) and artificial intelligence (AI) to identify preclinical cognitive markers of Alzheimer disease (AD). By synthesizing recent studies, it aims to advance early diagnostic methods to detect AD before significant symptoms occur. Objective: Research emphasizes the significance of early detection in AD during the preclinical phase, which does not involve cognitive impairment but nevertheless requires reliable biomarkers. Current biomarkers face challenges, prompting the exploration of cognitive behavior indicators beyond episodic memory. Methods: Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched Scopus, PubMed, and Google Scholar for studies on neuropsychiatric disorders utilizing conversational data. Results: Following an analysis of 38 selected articles, we highlight verbal episodic memory as a sensitive preclinical AD marker, with supporting evidence from neuroimaging and genetic profiling. Executive functions precede memory decline, while processing speed is a significant correlate. The potential of VR remains underexplored, and AI algorithms offer a multidimensional approach to early neurocognitive disorder diagnosis. Conclusions: Emerging technologies like VR and AI show promise for preclinical diagnostics, but thorough validation and regulation for clinical safety and efficacy are necessary. Continued technological advancements are expected to enhance early detection and management of AD.","url":"https://doi.org/10.2196/62914","authors":["María de la Paz Scribano Parada","Fátima González-Palau","Sonia Valladares‐Rodríguez","M. Rincón","Maria José Rico Barroeta","Marta García Rodriguez","Yolanda Bueno Aguado","A Blanco","Estela Díaz-López","Margarita Bachiller","Raquel Losada"],"tags":["Preprint","Cognition","Computer science","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-03","doi":"https://doi.org/10.2196/62914","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4412579871","name":"Knowledge, attitudes, and practices towards artificial intelligence among Ashur University-Medical College students, Baghdad-Iraq","source":"openalex","abstract":"Artificial intelligence (AI) is a technology that enables machines to learn, reason, and carry out tasks that normally require human intelligence. AI can be used in a variety of ways to improve teaching and health, such as providing personalized learning experiences and developing new diagnosis approaches and treatments for diseases. This study aimed to assess the knowledge, attitudes, and practices (KAP) towards artificial intelligence (AI) among medical students at Ashur University-Medical College (AUMC) in Baghdad, Iraq. A descriptive cross-sectional study was conducted from November 2024 to January 2025, involving 200 first- and second-year medical students. Data was collected using a structured questionnaire adapted from previous studies, covering Sociodemographic characteristics, AI knowledge, attitudes, and practices. The results revealed that 93.5% of students understood the basic concept of AI, but only 28.5% and 12.5% were familiar with machine learning and deep learning, respectively. A significant majority (87.5%) supported the integration of AI into their field of study, with 70.5% advocating for its inclusion in medical training. However, 68.5% opposed using AI for student assessments due to concerns about fairness and bias. While 85.5% of students used AI technologies for academic purposes, only 6% had taken AI-focused courses outside their curriculum. The study highlights the need for incorporating AI education into medical curricula and fostering extracurricular engagement to better prepare students for the evolving healthcare landscape","url":"https://doi.org/10.21608/jbaar.2025.442546","authors":["Amer Baker Mahmood","Munir Talib Salman"],"tags":["Medical education","Psychology","Artificial intelligence","Mathematics education","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-28","doi":"https://doi.org/10.21608/jbaar.2025.442546","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4407277279","name":"Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data","source":"openalex","abstract":"Breast cancer is a leading cause of mortality among women, with recurrence prediction remaining a significant challenge. In this context, artificial intelligence application and its resources can serve as a powerful tool in analyzing large amounts of data and predicting cancer recurrence, potentially enabling personalized medical treatment and improving the patient's quality of life. Thus, the systematic review examines the role of AI in predicting breast cancer recurrence using clinical data, imaging data, and combined datasets. Support Vector Machine (SVM) and Neural Networks, especially when applied to combined data, demonstrate strong potential in improving prediction accuracy. SVMs are effective with high-dimensional clinical data, while Neural Networks in genetic and molecular analysis. Despite these advancements, limitations such as dataset diversity, sample size, and evaluation standardization persist, emphasizing the need for further research. AI integration in recurrence prediction offers promising prospects for personalized care but requires rigorous validation for safe clinical application.","url":"https://doi.org/10.1007/s12672-025-01908-6","authors":["Jaqueline Alvarenga Silveira","Alexandre Ray da Silva","Mariana Zuliani Theodoro de Lima"],"tags":["Breast cancer","Artificial intelligence","Cancer","Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-08","doi":"https://doi.org/10.1007/s12672-025-01908-6","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4408588988","name":"Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication","source":"openalex","abstract":"INTRODUCTION: Nasopharyngeal carcinoma (NPC) typically presents as advanced disease due to the lack of significant symptoms in the early stages. Accurate prognostication is therefore challenging as current methods based on anatomical staging often lack the granularity to differentiate between patients with differing prognoses. This study investigates the potential of radiomics to improve the prediction of locoregional recurrence (LRR) and overall survival in patients with NPC. METHODS: Radiomic features were extracted from radiotherapy planning CT scans for 294 NPC patients divided into training (n = 147) and validation (n = 147) sets. A feature selection step utilising feature clustering and mutual information classifier to select six key radiomic features was employed to reduce redundancy and improve interpretability. Models were trained using clinical data, radiomic features, and these in combination to predict 2-year LRR, with performance assessed on the left-out independent validation set. RESULTS: Combining radiomic features with clinical data resulted in the best performance for predicting 2-year LRR (Area Under the Curve, AUC 0.76) compared to prediction using clinical or radiomic features alone (mean AUC 0.56 and 0.57, respectively). Risk stratification based on the combined model was significant for LRR-free survival and overall survival (p < 0.01). Key radiomic features included tumour size, intensity distribution, overall textural patterns, and distribution of fine and coarse textured regions. DISCUSSION: Radiomics holds promise for improving NPC risk stratification, potentially allowing for personalised treatment strategies. The most important radiomics feature, maximum 2D diameter, suggests a need to reconsider tumour size as a prognostic criterion despite its current exclusion from TNM staging. Larger prospective studies are needed to validate these findings.","url":"https://doi.org/10.1002/cam4.70706","authors":["Nicholas B. Shannon","Narayanan Gopalakrishna lyer","Melvin L.K. Chua"],"tags":["Radiomics","Nasopharyngeal carcinoma","Interpretability","Medicine","Feature selection"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1002/cam4.70706","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4399357030","name":"Artificial Intelligence in Otolaryngology","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.otc.2024.04.008","authors":["Katie Tai","Robin Zhao","Anaïs Rameau"],"tags":["Transparency (behavior)","Audit","Raw data","Medicine","Otorhinolaryngology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-05","doi":"https://doi.org/10.1016/j.otc.2024.04.008","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414232095","name":"Exploring the use and impact of artificial intelligence in higher education in Africa","source":"openalex","abstract":"Artificial intelligence is transforming higher education, but its adoption in developing countries has been overlooked. This study analysed this gap through a comprehensive systematic literature review, analysing AI's use and impact in higher education. A total of 1,521 articles were identified through databases such as Scopus, Web of Science, EBSCOhost, and Google Scholar, with 63 selected for review. The findings indicate that AI is used in various higher education applications, including tutoring, administrative tasks, instruction, curriculum development, and facilitating new skills acquisition. Many benefits were derived from its use, such as multitasking, ease of workloads, and customised learning. However, challenges exist, including abuse by learners, resource limitations, skill gaps, data security concerns, and ethical issues. This study provides a unique conceptual analysis of AI's impact on higher education institutions, focusing on developing nations. Surprisingly, few explicit studies exist on AI adoption in higher education in these countries. The findings will inform policymakers, educators, and stakeholders about AI's potential to reshape higher education in developing countries, guiding strategic efforts to harness benefits while mitigating risks.","url":"https://doi.org/10.33902/jpsp.202532046","authors":["Notice Pasipamire","Josiline Phiri Chigwada","Rosemary Maturure"],"tags":["Higher education","Artificial intelligence","Computer science","Mathematics education","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-13","doi":"https://doi.org/10.33902/jpsp.202532046","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4412190343","name":"Unlocking the Potential of Artificial Intelligence in Pharma Research and Development: Insights from Investor and Researcher Perspectives","source":"openalex","abstract":"The integration of artificial intelligence into drug discovery processes represents a major innovation in pharmaceutical research and development. This study investigates the role of AI investments in enhancing research efficiency, addressing implementation challenges, and shaping stakeholder perspectives. Via a structured explanatory research design, the study applies a quantitative methodology based on survey data collected from researchers, investors, and pharmaceutical executives across the USA and United Kingdom. The questionnaire examined respondents’ experiences with artificial intelligence tools, investment patterns, and perceived research outcomes. Statistical methods such as logistic regression and chi-square tests were employed to analyze correlations between investment strategies and research efficiency. Findings indicate that while artificial intelligence improves productivity – in predictive modeling and data analysis – barriers such as high infrastructure costs, inadequate training, and regulatory uncertainty persist. Notably, 70% of participants plan to increase AI investments within the next five years, and 80% regard artificial intelligence as essential or very important to the future of drug discovery. However, successful implementation appears to correlate with firm size and access to technical resources, suggesting disparities in AI readiness across the industry. Recommendations include expanding artificial intelligence training programs, strengthening infrastructure, and fostering closer collaboration between investors and researchers. Ethical considerations, including data privacy and regulatory compliance, are also emphasized. The pilot study provides foundational insights for a full-scale investigation and offers practical guidance for optimizing artificial intelligence integration in pharmaceutical research and development.","url":"https://doi.org/10.61093/hem.2025.2-01","authors":["Jacob Kritikos","Andreas Sarantopoulos","Anastasios Roumeliotis","Julia Vasiliades","Ioannis Matsinas"],"tags":["Management science","Engineering ethics","Psychology","Business","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-02","doi":"https://doi.org/10.61093/hem.2025.2-01","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4210427903","name":"Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey","source":"openalex","abstract":"Touch-enabled sensation and actuation are expected to be the most promising, straightforward, and important uses of the B5G/6G communication networks. In light of the next generation (6G) systems’ prerequisite for low latency, the infrastructure should be reconfigurable, intelligent, and interoperable in the real-time existing wireless network. It has a drastic impact on society due to its high precision, accuracy, reliability, and efficiency, combined with the ability to connect a user from remote areas. Hence, the touch-enabled interaction is primarily concerned with the real-time transmission of tactile-based haptic information over the internet, in addition to the usual audio, visual, and data traffic, thus enabling a paradigm shift towards a real-time control and steering communication system. The existing system latency and overhead often have delays and limitations on the application’s usability. In light of the aforementioned concerns, the study proposes an intelligent touch-enabled system for B5G/6G and an IoT-based wireless communication network, incorporating AR/VR technologies. The tactile internet and network-slicing serve as the backbone of touch technology and incorporates intelligence from techniques such as artificial intelligence and machine/deep learning. The survey also introduces a layered and interfacing architecture with its E2E solution for the intelligent touch-based wireless communication system. It is anticipated for the upcoming 6G system to provide numerous opportunities for various sectors to utilize AR/VR technology in robotics and healthcare facilities to help in addressing several problems faced by society. Conclusively the article presents a few use cases concerning the deployment of touch infrastructure in automation, robotics, and intelligent healthcare systems, assisting in the diagnosis and treatment of the prevailing Covid-19 cases. The paper concludes with some considerable future research aspects of the proposed system with a few ongoing projects concerning the development and incorporation of the 6G wireless communication system.","url":"https://doi.org/10.1109/access.2022.3148473","authors":["Mantisha Gupta","Rakesh Kumar Jha","Sanjeev Jain"],"tags":["Computer science","Interoperability","Virtual reality","Low latency (capital markets)","Wireless"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-01","doi":"https://doi.org/10.1109/access.2022.3148473","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4391430636","name":"Generative Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jacr.2024.01.020","authors":["Christoph I. Lee","Jonathan H. Chen","Marc Kohli","Andrew D. Smith","Joshua M. Liao"],"tags":["Generative grammar","Artificial intelligence","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-01","doi":"https://doi.org/10.1016/j.jacr.2024.01.020","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4391186832","name":"Artificial-intelligence-powered customer service management in the logistics industry","source":"openalex","abstract":"Objective: The article aims to show how people perceive future implications for logistics customer service resulting from the implementation of new technologies in the form of game-changer artificial intelligence (AI) solutions in the spirit of economy 4.0 and society 5.0. Research Design & Methods: The research process used a nomothetic approach based on the methodology of mixed research. The qualitative approach included a research study of monographs, publications, reports, and netographic sources. We used the technique of critical content analysis based on the co-occurrence of terms. In turn, we based the quantitative approach on the diagnostic survey method with the computer-assisted web interviewing (CAWI) technique. The sample size was 233. For further analysis, we used the statistical package for the social sciences (SPSS). Findings: The research shows that customer service in logistics already uses different forms of AI-based solutions (like Chabtbots, Voicebots, and voice assistants). Even customers positively evaluate those solutions, among others, for efficiency, competence, and service quality. Moreover, customers are aware of AI-based solutions and know that their usage will deepen in the future, as it is a game changer for the competitiveness of customer service in logistics. Implications & Recommendations: The conducted research indicates the need to constantly improve the digital competences of the users of last-mile logistics services in the context of technologization of transaction processes. Different areas of business will widely use AI-based solutions, because there is a need to develop systems which will help with the human-machine communication. This technology should be constructed as safe for people and easy to use; both with regard to users and customers. As a result of these processes, there is a greater need to educate people about AI-based solutions to develop awareness and improve future outcomes. Contribution & Value Added: The article’s main advantage is determining new possibilities in the area of logistics customer service as a result of the dissemination of solutions in the AI field, which may be a helpful instrument for enterprises in managing the last-mile scenario in the future.","url":"https://doi.org/10.15678/eber.2023.110407","authors":["Marta Brzozowska","Katarzyna Kolasińska-Morawska","Łukasz Sułkowski","Paweł Morawski"],"tags":["Business","Customer service","Service (business)","Operations management","Process management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.15678/eber.2023.110407","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4401628611","name":"Preoperative Patient Guidance and Education in Aesthetic Breast Plastic Surgery: A Novel Proposed Application of Artificial Intelligence Large Language Models","source":"openalex","abstract":"Background: At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context. Objectives: The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures. Methods: A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations. Results: The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant-specific knowledge was 6.7/10 ± 0.6. Conclusions: Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency.","url":"https://doi.org/10.1093/asjof/ojae062","authors":["Jad Abi‐Rafeh","Brian Bassiri-Tehrani","Roy Kazan","Heather Furnas","Dennis C. Hammond","William P. Adams","Foad Nahai"],"tags":["Computer science","Medicine","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1093/asjof/ojae062","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414043654","name":"Artificial intelligence in allergy and immunology: Recent developments, implementation challenges, and the road toward clinical impact","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly recognized for its capacity to transform medicine. While publications applying AI in allergy and immunology have increased in number, clinical implementation substantially lags behind other specialties. By mid-2024, over 1,000 US Food and Drug Administration-approved AI-enabled medical devices existed, but none specifically addressed allergy and immunology. This gap partly reflects the field's limited reliance on imaging, which facilitated early AI breakthroughs in radiology and pathology. This narrative review examines recent AI developments, including large language models and AI agents, evaluating their applicability to allergy and immunology practice. We analyze current and potential applications, emphasizing those demonstrating clinical value while identifying implementation barriers amplified by allergic diseases' unique complexities, including data privacy concerns, bias, reliability constraints, and evolving regulatory frameworks. To bridge the persistent research-to-implementation gap, we propose a 6-point road map: (1) prioritize impactful applications, (2) define clinically relevant benchmarks, (3) enforce rigorous governance, (4) transition to operationalization, (5) promote clinical adoption through trustworthy AI, and (6) establish life cycle management. This road map builds on established implementation frameworks while incorporating critical field-specific considerations unique to allergy and immunology. Through this approach, we provide a perspective for advancing AI in allergy and immunology from academic promise to tangible clinical benefit.","url":"https://doi.org/10.1016/j.jaci.2025.08.022","authors":["Merlijn van Breugel","Matt Greenhawt","Ibon Eguíluz‐Gracia","Marı́a José Torres","Aikaterini Anagnostou","Gerard H. Koppelman"],"tags":["Narrative review","Computer science","Engineering ethics","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-07","doi":"https://doi.org/10.1016/j.jaci.2025.08.022","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4416721771","name":"The Role of Artificial Intelligence in Pharmacy Practice and Patient Care: Innovations and Implications","source":"openalex","abstract":"Artificial Intelligence (AI) is reshaping pharmacy practice by enhancing decision-making, personalizing therapy, and improving medication safety. AI applications now span drug discovery, clinical decision support, and adherence monitoring. This narrative review explores key innovations, practical applications, and the implications of AI integration in pharmacy practice, with a focus on emerging tools, pharmacist roles, and ethical considerations. The review was conducted using literature from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Thematic synthesis included AI-based drug interaction checkers, Clinical Decision Support Systems (CDSS), telepharmacy, pharmacogenomics, and predictive analytics. AI enhances clinical decision-making, reduces medication errors, and supports precision medicine. AI tools support pharmacists and healthcare professionals in optimizing care. However, data privacy, algorithmic bias, and workflow integration continue to pose challenges. AI holds transformative potential in pharmacy, though its integration requires overcoming ethical and workflow-related challenges. Ethical and regulatory vigilance, coupled with pharmacist training and interdisciplinary collaboration, is essential to realize the full potential of AI.","url":"https://doi.org/10.3390/biomedinformatics5040065","authors":["Aftab Alam","Syed Sikandar Shah","Syed Arman Rabbani","Mohamed El‐Tanani"],"tags":["Transformative learning","Workflow","Pharmacy practice","Pharmacy","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-26","doi":"https://doi.org/10.3390/biomedinformatics5040065","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4381057664","name":"THE US-CHINESE RACE IN ARTIFICIAL INTELLIGENCE CHALLENGES AND OPPORTUNITIES","source":"openalex","abstract":"This research aims to analyze the challenges and opportunities facing the US-Chinese race in the field of artificial intelligence. The descriptive and analytical approach was used to analyze data and information available from various sources, including reports, press articles, and academic research. The results show that China has a competitive advantage in the field of artificial intelligence, thanks to its ambitious national plans and huge investments in this field, and this makes the United States face A big challenge in the competition with China. Among the main challenges facing the United States is the lack of government investment in the field of artificial intelligence, in addition to problems related to privacy and security and the rapid shifts in technology. The research also includes an analysis of some of the opportunities the United States can seize to make further advances in AI, such as increasing government investment, improving education, and strengthening international partnerships. In the end, this research enhances the general understanding of the accelerating transformations in the field of artificial intelligence and helps to identify the necessary measures to enhance the technical and research capabilities of the United States and China in this field, and to identify areas in which one country can obtain a competitive advantage in this field, which contributes to To achieve leadership in this field.","url":"https://doi.org/10.52783/rlj.v11i3.2182","authors":["HAZIM JERRI MNEKHIR"],"tags":["Field (mathematics)","Government (linguistics)","Competition (biology)","China","Investment (military)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-07","doi":"https://doi.org/10.52783/rlj.v11i3.2182","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4324195296","name":"Artificial Intelligence driven Intrusion Detection Framework for the Internet of Medical Things","source":"openalex","abstract":"Abstract The fusion of the internet of things (IoT) in the healthcare discipline has appreciably improved the medical treatment and operations activities of patients. Using the Internet of Medical Things (IoMT) technology, a doctor may treat more patients and save lives by employing real-time patient monitoring (RPM) and outlying diagnostics. Despite the many advantages, cyber-attacks on linked healthcare equipment can jeopardize privacy and even endanger the patient's health. However, it is a significant problem to offer high-safety attributes that ensure the secrecy and accuracy of patient health data. Any modification to the data might impact how the patients are treated, resulting in human fatalities under emergency circumstances. To assure patients' data safety and privacy in the network, and to meet the enormous requirement for IoMT devices with efficient healthcare services for the huge population, a secured robust model is necessary. Artificial Intelligence (AI) based approaches like Machine Learning (ML), and Deep Learning (DL) have the potential to be useful methodology for intrusion detection because of the high dynamicity and enormous dimensionality of the data used in such systems. In this paper, three DL models have been proposed to build an intrusion detection system (IDS) for IoMT network. With a 100% accuracy rate, our proposed AI models exceed the current existing methodology in detecting network intrusions by utilizing the patient’s biometric data features with network traffic flow. Furthermore, a thorough examination of employing several ML and DL approaches has been discussed for detecting intrusion in the IoMT network.","url":"https://doi.org/10.21203/rs.3.rs-2634004/v1","authors":["Prashant Giridhar Shambharkar","Nikhil Sharma"],"tags":["Intrusion detection system","Computer science","The Internet","Computer security","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-14","doi":"https://doi.org/10.21203/rs.3.rs-2634004/v1","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4416509484","name":"Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation","source":"openalex","abstract":"Microalgae are unicellular, industrially important organisms that are used extensively in a range of industrial, environmental, and biorefinery applications. They can produce lipids, carbohydrates, and possibly additional vital bioactive substances. The increasing popularity of artificial intelligence (AI) in microalgae research can be attributed to its algorithms’ ability to manage the complexity of unexpected biosystems. In the case of microalgae-based biorefineries, AI technology can also help uncover system dynamics and uncertainties, provide helpful predictive analytics, and expedite the optimisation process. AI is used in microalgal cultivation to optimise carbon capture, biomass production, and conditions for growth. Additionally, it is employed for genome editing, automated monitoring, and lipid accumulation enhancement. However, its uses are broad and constantly growing. Furthermore, critical environmental parameters in microalgae culture, including temperature, light intensity, pH, dissolved oxygen, and nutrient levels, may be continually monitored and controlled by internet of things (IoT)-based devices. This review comprehensively summarises the latest applications of AI technology in the field of microalgae cultivation and the role of IoT-based automatic control.","url":"https://doi.org/10.3390/pr13123764","authors":["Vijay Rayamajhi","Mudasir Hussain","Hyun‐Woung Shin","Sang-Mok Jung"],"tags":["Biorefinery","Biochemical engineering","Biomass (ecology)","Field (mathematics)","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-21","doi":"https://doi.org/10.3390/pr13123764","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411037614","name":"Ethics of Artificial Intelligence in Maqāṣid Al-Sharīa's Perspective","source":"openalex","abstract":"The rapid development of Artificial Intelligence (AI) has brought significant transformation across various sectors but also presents complex and urgent ethical challenges. Key issues include algorithmic bias that may reinforce social discrimination, privacy violations due to massive data collection without adequate control, and unclear accountability in autonomous AI systems. These challenges threaten social justice, individual rights, and public trust in technology. This study aims to develop an AI ethics framework grounded in Maqāṣid al-Sharīʿa, the Islamic legal philosophy emphasizing the protection of five fundamental values: religion (ḥifẓ al-dīn), life (ḥifẓ al-nafs), intellect (ḥifẓ al-ʿaql), lineage (ḥifẓ al-nasl), and property (ḥifẓ al-māl). A qualitative literature review method employing content analysis and thematic synthesis was applied to examine relevant literature on AI ethics and Maqāṣid al-Sharīʿa, identifying gaps and opportunities for normative integration. The findings demonstrate that Maqāṣid al-Sharīʿa offers a holistic perspective complementing Western AI ethics frameworks by incorporating spiritual, moral, and social justice dimensions. The principle of ḥifẓ al-ʿaql underpins efforts to combat misinformation, while ḥifẓ al-māl guides the equitable development of Sharia-compliant fintech. This study recommends inclusive algorithm design, Sharia-based regulation, and multidisciplinary collaboration among religious scholars, technology developers, and regulators to ensure ethical, responsible, and sustainable AI development.","url":"https://doi.org/10.19105/karsa.v33i1.19617","authors":["Zainal Habib"],"tags":["Perspective (graphical)","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-04","doi":"https://doi.org/10.19105/karsa.v33i1.19617","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7160167964","name":"Artificial Intelligence and Big Data in Accounting: The Case of Commercial Banks in Jordan","source":"openalex","abstract":"The integration of Artificial Intelligence (AI) and Big Data analytics is transforming the accounting and financial practices of commercial banks, particularly in emerging markets like Jordan. As the banking sector shifts from a product-centered to a customer-focused approach, the ability to analyze large volumes of data becomes essential for gaining insights into customer behavior, preferences, and financial patterns. This research explores how AI and Big Data technologies are being adopted by Jordanian commercial banks to enhance accounting processes, improve decision-making, reduce customer churn, and personalize services. By leveraging predictive modeling, segmentation, and behavioral analytics, banks can refine credit risk assessments, optimize marketing strategies, and improve customer satisfaction. Despite the promising benefits, challenges related to data quality, technological infrastructure, and ethical concerns remain. This study provides a contextual analysis of how AI and Big Data are reshaping the accounting landscape in Jordan’s banking sector and offers recommendations for their effective implementation.","url":"https://doi.org/10.15849/zjjb.v1i03.49","authors":["Ayman Bader","Attalah Qtaish","Khalel Odeh","Hanadi Sa’d"],"tags":["Big data","Business","Analytics","FinTech","Banking industry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-30","doi":"https://doi.org/10.15849/zjjb.v1i03.49","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409331282","name":"Bridging the Past and Future of Clinical Data Management: The Transformative Impact of Artificial Intelligence","source":"openalex","abstract":"Abstract: Effective clinical data management is fundamental to clinical research and regulatory submissions. Modern clinical trials have increasingly adopted web-based electronic data capture (EDC) systems, which enhance data collection efficiency but introduces challenges in data integration and quality. This scoping review explores the transformative role of artificial intelligence and machine learning in evolving CDM into clinical data science. In the review, we followed the PRISMA-ScR guidelines and analyzed the literature from 2008 to 2025 using Scopus, Web of Science, and PubMed databases. A total of 26 papers were included and categorized into those related to clinical data management, natural language processing, and general artificial intelligence/machine learning adoption in clinical data management. The integration shows promise in enhancing data analysis, automating data cleaning, and predicting critical outcomes. The key emerging trends include risk-based quality monitoring, blockchain technology, remote monitoring, and patient-centric approaches involving wearables and mobile applications. The results clearly indicate a substantial increase in data volume in Phase III trials, underscoring the need for advanced technologies natural language processing offers significant potential in interpreting unstructured text data, thereby improving the clinical data management processes. The review concludes on different artificial intelligence/machine learning techniques like natural language processing, predictive analytics, and automation technologies, and their applications in improving data quality and streamlining clinical data workflows. Keywords: clinical data management, artificial intelligence, machine learning, natural language processing, clinical data science, electronic data capture","url":"https://doi.org/10.2147/oajct.s509921","authors":["Szymon Musik","Joanna Sasin-Kurowska","Mariusz Pańczyk"],"tags":["Transformative learning","Bridging (networking)","Data science","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.2147/oajct.s509921","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4367857052","name":"Artificial intelligence to assist specialists in the detection of haematological diseases","source":"openalex","abstract":"Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes. The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includes several tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals. In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studied and combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data. The use of these machine learning techniques could reduce the cost and decision time and improve the quality of life for both specialists and patients while producing more precise diagnoses.","url":"https://doi.org/10.1016/j.heliyon.2023.e15940","authors":["Sergio Díaz-del-Pino","Roberto Trelles‐Martínez","Fernando González-Fernández","Nicolás Guil"],"tags":["Interpretability","Artificial intelligence","Machine learning","Computer science","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-01","doi":"https://doi.org/10.1016/j.heliyon.2023.e15940","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411606457","name":"Responsible scaling of artificial intelligence in healthcare: standardization meets customization","source":"openalex","abstract":"Abstract Organizations across the globe are progressively investing in artificial intelligence (AI) innovations to meet today’s healthcare challenges. Meanwhile, public policy increasingly emphasizes the need for these innovations to be ‘scaled’. As scholars emphasize, scaling innovations is never just ‘more of the same’, but requires adapting innovations to local contexts. In this perspective paper, we aim to explore and draw attention to the tensions and possible alignments between standardization and customization that should lead to a responsible scaling of AI in healthcare. We approach responsible scaling building on the notion of socio-technical configurations. Configurations are unique assemblies of technological and non-technological components, including human factors, integrated in different ways to meet particular local requirements. We explore how conceptualizing AI tools and the broader socio-technical ecosystems in which they are integrated as configurations can offer a framework for envisioning possible pathways for responsibly scaling AI. We contend that standardization and customization can be employed synergistically within AI configurations. Standardization can be an important driver of innovation at the level of configurational components of healthcare AI, as well as the interoperability between these components. Thereby, standardization can expand the configurational options that local AI implementations can draw from and lay a foundation for local customization of healthcare AI ecosystems at the architectural level. Accordingly, we propose key considerations for innovators and policymakers to boost the configurability of healthcare AI, and discuss the need for, and challenges of shaping of healthcare AI configurations at the local scale.","url":"https://doi.org/10.1007/s10676-025-09842-5","authors":["D. R. M. Lukkien","Henk Herman Nap","Alexander Peine","Mirella Minkman","Ellen H.M. Moors","Wouter Boon"],"tags":["Standardization","Personalization","Health care","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-24","doi":"https://doi.org/10.1007/s10676-025-09842-5","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W3135402457","name":"Canadian Medical Imaging Inventory, 2019–2020","source":"openalex","abstract":"Context and Policy Issues The purpose of the CMII is to document current practices and developments in the supply, distribution, technical operations, and general clinical use of advanced imaging equipment across Canada. Medical imaging is a vital component of modern health care, playing a role in the diagnosis, staging, and monitoring of many diseases and conditions. As new medical imaging technologies become available and population needs change, it is important to keep track of where imaging capacity exists, how equipment is used, and the adoption of tools that may support appropriate imaging, system efficiencies, and wait-list reductions. Methods CADTH collected data on six advanced imaging modalities: CT, MRI, PET-CT, single-photon emission computed tomography (SPECT), SPECT-CT, and PET-MRI using a web-based survey and a search of the literature. The data were reviewed by validators for accuracy and validators provided additional information of provincial and regional policies and practices. Summary of Evidence Of the modalities surveyed, CT is the most widely distributed, with the highest number of units, followed by MRI. All provinces and territories have at least one CT unit; all provinces and Yukon have at least one MRI unit; and all provinces have at least one SPECT and/or SPECT-CT unit. None of the territories have SPECT or SPECT-CT. Nine provinces have PET-CT in clinical use. Two provinces, Alberta and Ontario, have PET-MRI that is used for research purposes. Regarding the total volume of exams, CT is the most-used modality (5.41 million exams per year), followed by MRI (2.33 million exams per year), SPECT and SPECT-CT combined (1.2 million exams per year), and PET-CT (125,775 exams per year). Each imaging modality, apart from SPECT, experienced growth in the last decade in Canada in the number of units and the number of units per million people. CT experienced the slowest growth rate of all imaging modalities — at a 1.4% increase in units per million people over the last decade — compared with other imaging modalities (MRI 20%; PET-CT 25%; and SPECT-CT 70%). Over the last decade, the overall volume of exams increased by 32% and 62% for CT and MRI, respectively. Similarly, the number of exams per thousand population increased by 18% and 46%, respectively. Examination data for the other modalities were not available in 2010. Conclusions and Implications for Decision or Policy-Making The CMII data provides insight into the current context of medical imaging across Canada and raise questions related to how medical imaging is monitored and regulated, and how it is optimally used. As well, the data raise questions about how funding structures are organized, what the most cost-effective practices are, and whether access is equitable, especially in rural and remote areas. Overall, the findings of this report may help decision-makers identify gaps in service; inform medical imaging-related strategic planning on a national, provincial, or territorial basis; and help anticipate future growth and need for replacement. Additionally, the data can be used to identify system efficiencies and monitor the adoption of practices and tools that may support appropriate imaging and wait-list reductions.","url":"https://doi.org/10.51731/cjht..24","authors":["Yi -Sheng Chao","Alison Sinclair","Andra Morrison","Deba Hafizi","Lisa Pyke"],"tags":["Medical imaging","Medical physics","Context (archaeology)","Modalities","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-29","doi":"https://doi.org/10.51731/cjht..24","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4385467426","name":"Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient","source":"openalex","abstract":"Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite.","url":"https://doi.org/10.14797/mdcvj.1263","authors":["Péter Osztrogonácz","Ponraj Chinnadurai","Alan B. Lumsden"],"tags":["SAFER","Medicine","Telemedicine","Suite","Automation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.14797/mdcvj.1263","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4405844566","name":"ChatGPT-4 Performance on German Continuing Medical Education—Friend or Foe (Trick or Treat)? Protocol for a Randomized Controlled Trial","source":"openalex","abstract":"BACKGROUND: The increasing development and spread of artificial and assistive intelligence is opening up new areas of application not only in applied medicine but also in related fields such as continuing medical education (CME), which is part of the mandatory training program for medical doctors in Germany. This study aimed to determine whether medical laypersons can successfully conduct training courses specifically for physicians with the help of a large language model (LLM) such as ChatGPT-4. This study aims to qualitatively and quantitatively investigate the impact of using artificial intelligence (AI; specifically ChatGPT) on the acquisition of credit points in German postgraduate medical education. OBJECTIVE: Using this approach, we wanted to test further possible applications of AI in the postgraduate medical education setting and obtain results for practical use. Depending on the results, the potential influence of LLMs such as ChatGPT-4 on CME will be discussed, for example, as part of a SWOT (strengths, weaknesses, opportunities, threats) analysis. METHODS: We designed a randomized controlled trial, in which adult high school students attempt to solve CME tests across six medical specialties in three study arms in total with 18 CME training courses per study arm under different interventional conditions with varying amounts of permitted use of ChatGPT-4. Sample size calculation was performed including guess probability (20% correct answers, SD=40%; confidence level of 1-α=.95/α=.05; test power of 1-β=.95; P<.05). The study was registered at open scientific framework. RESULTS: As of October 2024, the acquisition of data and students to participate in the trial is ongoing. Upon analysis of our acquired data, we predict our findings to be ready for publication as soon as early 2025. CONCLUSIONS: We aim to prove that the advances in AI, especially LLMs such as ChatGPT-4 have considerable effects on medical laypersons' ability to successfully pass CME tests. The implications that this holds on how the concept of continuous medical education requires reevaluation are yet to be contemplated. TRIAL REGISTRATION: OSF Registries 10.17605/OSF.IO/MZNUF; https://osf.io/mznuf. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63887.","url":"https://doi.org/10.2196/63887","authors":["Christian Burisch","Abhav Bellary","Frank Breuckmann","Jan P. Ehlers","Serge C. Thal","Timur Sellmann","Daniel Gödde"],"tags":["Preprint","German","Psychology","Medical education","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-27","doi":"https://doi.org/10.2196/63887","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4417192838","name":"Equity and Generalizability of Artificial Intelligence for Skin-Lesion Diagnosis Using Clinical, Dermoscopic, and Smartphone Images: A Systematic Review and Meta-Analysis","source":"openalex","abstract":"Background and Objectives: Artificial intelligence (AI) has shown promising performance in skin-lesion classification; however, its fairness, external validity, and real-world reliability remain uncertain. This systematic review and meta-analysis evaluated the diagnostic accuracy, equity, and generalizability of AI-based dermatology systems across diverse imaging modalities and clinical settings. Materials and Methods: A comprehensive search of PubMed, Embase, Web of Science, and ClinicalTrials.gov (inception–31 October 2025) identified diagnostic accuracy studies using clinical, dermoscopic, or smartphone images. Eighteen studies (11 melanoma-focused; 7 mixed benign–malignant) met inclusion criteria. Six studies provided complete 2 × 2 contingency data for bivariate Reitsma HSROC modeling, while seven reported AUROC values with extractable variance. Risk of bias was assessed using QUADAS-2, and evidence certainty was graded using GRADE. Results: Across more than 70,000 test images, pooled sensitivity and specificity were 0.91 (95% CI 0.74–0.97) and 0.64 (95% CI 0.47–0.78), respectively, corresponding to an HSROC AUROC of 0.88 (95% CI 0.84–0.92). The AUROC-only meta-analysis yielded a similar pooled AUROC of 0.88 (95% CI 0.87–0.90). Diagnostic performance was highest in specialist settings (AUROC 0.90), followed by community care (0.85) and smartphone environments (0.81). Notably, performance was lower in darker skin tones (Fitzpatrick IV–VI: AUROC 0.82) compared with lighter skin tones (I–III: 0.89), indicating persistent fairness gaps. Conclusions: AI-based dermatology systems achieve high diagnostic accuracy but demonstrate reduced performance in darker skin tones and non-specialist environments. These findings emphasize the need for diverse training datasets, skin-tone–stratified reporting, and rigorous external validation before broad clinical deployment.","url":"https://doi.org/10.3390/medicina61122186","authors":["Jeng‐Wei Tjiu","Chia‐Fang Lu"],"tags":["Generalizability theory","Modalities","Medical diagnosis","Diagnostic accuracy","Comparability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-10","doi":"https://doi.org/10.3390/medicina61122186","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4416784217","name":"Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) is rapidly transforming cardiovascular diagnostics by enhancing early disease detection, risk stratification, and clinical decision-making. Recent studies have shown the effectiveness of AI in analyzing electrocardiograms (ECGs) and cardiac imaging, predicting adverse cardiovascular outcomes, and enabling personalized care. AI models have also demonstrated potential in community-based and population-specific applications, signaling a shift toward data-driven, precision cardiovascular medicine. STUDY OBJECTIVE: To systematically evaluate the clinical applications and diagnostic performance of AI in the detection and risk assessment of cardiovascular diseases. METHODS: A systematic search was conducted in PubMed, Google Scholar, and ScienceDirect for English-language articles published between January 2020 and June 2025. Eligible studies included randomized controlled trials and observational designs with free full-text availability. QUADAS-2 tool for diagnostic accuracy studies and the PROBAST for prognostic or risk-prediction models, were used for quality assessment. Of 30 eligible articles, 14 high-quality studies were included. RESULTS: Across the included studies, AI-based diagnostic tools demonstrated consistently high performance for cardiovascular disease detection. Reported area under the curve (AUC) values ranged from 0.804 to 0.991, with most ≥ 0.88, indicating robust discriminative accuracy across diverse modalities including ECG analysis, cardiac imaging, and predictive risk modeling. Although formal pooling was not conducted due to methodological heterogeneity, the descriptive synthesis highlighted strong and consistent performance in applications such as heart failure, coronary artery disease, and arrhythmia detection. Variability in study design and reporting limited direct comparison, but overall trends support the potential of AI systems to enhance diagnostic precision across cardiovascular contexts. CONCLUSION: AI-driven diagnostic tools demonstrate consistently high accuracy across cardiovascular applications, supporting their potential to complement clinical decision-making. However, variability in study design and limited external validation highlight the need for standardized evaluation and transparent reporting before widespread clinical integration.","url":"https://doi.org/10.1186/s12872-025-05327-x","authors":["Ahsanullah Niazai","Hajra Jamil","Maryam Hameed","Sarah Sheikh","Mah Rukh Nisar"],"tags":["Medicine","Observational study","Coronary artery disease","Pooling","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-28","doi":"https://doi.org/10.1186/s12872-025-05327-x","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4224990341","name":"Additive Manufacturing Strategies for Personalized Drug Delivery Systems and Medical Devices: Fused Filament Fabrication and Semi Solid Extrusion","source":"openalex","abstract":"Novel additive manufacturing (AM) techniques and particularly 3D printing (3DP) have achieved a decade of success in pharmaceutical and biomedical fields. Highly innovative personalized therapeutical solutions may be designed and manufactured through a layer-by-layer approach starting from a digital model realized according to the needs of a specific patient or a patient group. The combination of patient-tailored drug dose, dosage, or diagnostic form (shape and size) and drug release adjustment has the potential to ensure the optimal patient therapy. Among the different 3D printing techniques, extrusion-based technologies, such as fused filament fabrication (FFF) and semi solid extrusion (SSE), are the most investigated for their high versatility, precision, feasibility, and cheapness. This review provides an overview on different 3DP techniques to produce personalized drug delivery systems and medical devices, highlighting, for each method, the critical printing process parameters, the main starting materials, as well as advantages and limitations. Furthermore, the recent developments of fused filament fabrication and semi solid extrusion 3DP are discussed. In this regard, the current state of the art, based on a detailed literature survey of the different 3D products printed via extrusion-based techniques, envisioning future directions in the clinical applications and diffusion of such systems, is summarized.","url":"https://doi.org/10.3390/molecules27092784","authors":["Giulia Auriemma","Carmela Tommasino","Giovanni Falcone","Tiziana Esposito","Carla Sardo","Rita Patrizia Aquino"],"tags":["Fused filament fabrication","Extrusion","3D printing","Fabrication","Drug delivery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-27","doi":"https://doi.org/10.3390/molecules27092784","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414321309","name":"Responsible artificial intelligence?","source":"openalex","abstract":"Abstract Although the phrase “responsible AI” is widely used in the AI industry, its meaning remains unclear. One can make sense of it indirectly, insofar as various notions of responsibility unproblematically attach to those involved in the creation and operation of AI technologies. It is less clear, however, whether the phrase makes sense when understood directly, that is, as the ascription of some sort of responsibility to AI systems themselves. This paper argues in the affirmative, drawing on a philosophically undemanding notion of role responsibility, and highlights the main consequences of this proposal for AI ethics.","url":"https://doi.org/10.1007/s00146-025-02604-3","authors":["Daniela Vacek"],"tags":["Ascription","Phrase","Meaning (existential)","sort","Epistemology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-18","doi":"https://doi.org/10.1007/s00146-025-02604-3","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W2988916019","name":"Deep Learning for Generic Object Detection: A Survey","source":"openalex","abstract":"Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.","url":"https://doi.org/10.1007/s11263-019-01247-4","authors":["Li Liu","Wanli Ouyang","Xiaogang Wang","Paul Fieguth","Jie Chen","Xinwang Liu","Matti Pietikäinen"],"tags":["Computer science","Object detection","Artificial intelligence","Deep learning","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-31","doi":"https://doi.org/10.1007/s11263-019-01247-4","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4413898822","name":"Artificial Intelligence in Upper Gastrointestinal Diagnosis","source":"openalex","abstract":"Artificial intelligence (AI) has revolutionized upper gastrointestinal (GI) endoscopy by enhancing the detection, characterization, and management of GI diseases. In this review, we explore the transformative role of AI technologies, including machine learning and deep learning, in improving diagnostic accuracy and streamlining clinical workflows. AI systems such as convolutional neural networks have shown remarkable potential for identifying subtle lesions, assessing tumor margins, and reducing interobserver variability. By providing real-time decision-making support, AI minimizes unnecessary biopsies and improves patient outcomes. We also explore the applications of AI in detecting precancerous conditions such as Barrett's esophagus, atrophic gastritis, and gastric intestinal metaplasia, as well as its role in guiding therapy for early gastric cancer. Non-image-based AI tools such as Raman spectroscopy complement traditional imaging by offering molecular-level insights for real-time tissue characterization. Despite its promise, the adoption of AI in endoscopy faces challenges, including the need for robust validation, user-centric design, and targeted training for endoscopists. Concerns regarding overreliance and deskilling underscore the importance of balancing AI integration with the preservation of clinical expertise. Lastly, we examine the future of AI in upper GI diagnosis and how image-based and non-image-based AI technologies can be integrated to enable comprehensive diagnosis and personalized therapeutic planning. By addressing current limitations and fostering collaboration between clinicians and technologists, AI has the potential to redefine the standards of care for upper GI diagnosis and treatment.","url":"https://doi.org/10.7704/kjhugr.2025.0024","authors":["Sabrina Xin Zi Quek","Khek Yu Ho"],"tags":["Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-02","doi":"https://doi.org/10.7704/kjhugr.2025.0024","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4404048213","name":"Artificial Intelligence as a Tool for the Development of Soft Skills: A Bibliometric Review in the Context of Higher Education","source":"openalex","abstract":"Skills such as communication, teamwork, adaptability and problem-solving are essential for professional and personal development. The integration of artificial intelligence (AI) in the process of building these skills in students represents a significant innovation in higher education. This is because AI offers new teaching methods that can be more effective and personalized than traditional approaches. Thus, it is relevant to investigate the bibliometric indicators that reflect the trends in which AI enables the development of soft skills in the context of higher education. The approach used is the mixed, exploratory-descriptive level. The total number of studies reviewed was 78, all of them extracted from the Scopus database. The results show that the most prevalent thematic areas are “Communication skills development”, “Teamwork and collaboration” and “Critical thinking and problem-solving”. This is a result of AI’s ability to create more personalized, interactive and adaptive learning environments. However, it is concluded that scientific production in this field of study is still developing and requires greater attention from researchers. It is important to reflect that the implementation of AI in higher education must be supported by policies that regulate its effective integration and maximize its impact. Future studies should employ systematic reviews to address the impact of AI on soft skills according to the area of knowledge, such as engineering, social sciences or health sciences, identifying the skills to which AI is contributing most significantly.","url":"https://doi.org/10.26803/ijlter.23.10.18","authors":["Nestor Alvarado-Bravo","Florcita Aldana-Trejo","Víctor Hugo Durán Herrera","José Rasilla-Rovegno","Raul Suarez-Bazalar","Almintor Torres-Quiroz","Alejandro Paredes-Soria","Susan Haydee Gonzales-Saldaña","Gregorio Ernesto Tomás Quispe","Soledad Olivares-Zegarra"],"tags":["Soft skills","Context (archaeology)","Psychology","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-30","doi":"https://doi.org/10.26803/ijlter.23.10.18","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4396935494","name":"Artificial intelligence based data curation: enabling a patient-centric European health data space","source":"openalex","abstract":"The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.","url":"https://doi.org/10.3389/fmed.2024.1365501","authors":["Isabelle de Zegher","Kerli Norak","Dominik Steiger","Heimo Müller","Dipak Kalra","Bart Scheenstra","Isabella Cina","Stefan Schulz","Kanimozhi Uma","Petros Kalendralis","Eno-Martin Lotman","Martin Benedikt","Michel Dumontier","Remzi Çelebi"],"tags":["Interoperability","Conceptualization","Computer science","Health care","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-15","doi":"https://doi.org/10.3389/fmed.2024.1365501","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411045671","name":"Cybersecurity for Analyzing Artificial Intelligence (AI)-Based Assistive Technology and Systems in Digital Health","source":"openalex","abstract":"Assistive technology (AT) is increasingly utilized across various sectors, including digital healthcare and sports education. E-learning plays a vital role in enabling students with special needs, particularly those in remote areas, to access education. However, as the adoption of AI-based AT systems expands, the associated cybersecurity challenges also grow. This study aims to examine the impact of AI-driven assistive technologies on cybersecurity in digital healthcare applications, with a focus on the potential vulnerabilities these technologies present. Methods: The proposed model focuses on enhancing AI-based AT through the implementation of emerging technologies used for security, risk management strategies, and a robust assessment framework. With these improvements, the AI-based Internet of Things (IoT) plays major roles within the AT. This model addresses the identification and mitigation of cybersecurity risks in AI-based systems, specifically in the context of digital healthcare applications. Results: The findings indicate that the application of the AI-based risk and resilience assessment framework significantly improves the security of AT systems, specifically those supporting e-learning for blind users. The model demonstrated measurable improvements in the robustness of cybersecurity in digital health, particularly in reducing cyber risks for AT users involved in e-learning environments. Conclusions: The proposed model provides a comprehensive approach to securing AI-based AT in digital healthcare applications. By improving the resilience of assistive systems, it minimizes cybersecurity risks for users, specifically blind individuals, and enhances the effectiveness of e-learning in sports education.","url":"https://doi.org/10.3390/systems13060439","authors":["Abdullah Algarni","Vijey Thayananthan"],"tags":["Computer science","Assistive technology","Artificial intelligence","Human–computer interaction","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-05","doi":"https://doi.org/10.3390/systems13060439","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410990229","name":"Fibromyalgia: one year in review 2025","source":"openalex","abstract":"Fibromyalgia (FM) is a chronic syndrome characterised by widespread pain, high prevalence, and a significant impact on quality of life. Despite extensive research, its pathogenesis and treatment remain only partially understood, driving continued investigation throughout 2024. Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system has been linked to chronic stress responses and neuroinflammation, with neuroimaging and preclinical studies confirming altered pain and stress processing. Low-grade inflammation and metabolic disturbances, including cytokine imbalance and increased adipose tissue infiltration, further exacerbate symptoms. Alterations in the gut microbiota contribute to immune and emotional dysregulation. MRI studies continue to reveal brain changes that differentiate FM from other chronic pain disorders. Multi-omics approaches, including transcriptomic and metabolomic analyses, show promise as diagnostic biomarkers. Mitochondrial dysfunction also emerges as a key factor, since impaired energy metabolism seems to correlate with symptom severity. From a clinical perspective, recent studies have explored under-recognised aspects of FM, such as sexual and cognitive dysfunction, the role of gender, environmental exposures, and the disease's impact on relationships and family life. The differential diagnosis of FM and long COVID has ignited discussion about potential shared mechanisms. Conversely, residual pain in inflammatory diseases remains insufficiently addressed. Therapeutically, non-pharmacological strategies, particularly physical activity and psychosocial interventions, remain fundamental. Emerging areas such as non-invasive neuromodulation, psychedelic therapies, and the integration of technologies like virtual reality and artificial intelligence are opening new frontiers in treatment, patient care, and research. These advances underscore the multifactorial nature of FM and the need for personalised, interdisciplinary approaches.","url":"https://doi.org/10.55563/clinexprheumatol/buhd2z","authors":["Cristina Iannuccelli","Martina Favretti","Giulio Dolcini","Marco Di Carlo","Greta Pellegrino","Laura Bazzichi","Fabiola Atzeni","Daniela Lucini","Giustino Varassi","Matteo Luigi Giuseppe Leoni","Diego Fornasari","Fabrizio Conti","Fausto Salaffi","Piercarlo Sarzi‐Puttini","Manuela Di Franco"],"tags":["Medicine","Fibromyalgia","MEDLINE","Physical therapy","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-29","doi":"https://doi.org/10.55563/clinexprheumatol/buhd2z","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7131698947","name":"Artificial Intelligence agents for biological research: a survey","source":"openalex","abstract":"The rapid growth of biological data and experimental complexity has motivated increasing interest in artificial intelligence (AI) systems that extend beyond static prediction toward autonomous reasoning and action. While recent computational models achieve strong predictive performance, they largely operate as passive tools within human-driven research workflows. In contrast, AI agents integrate reasoning, planning, tool invocation, and feedback-driven refinement, enabling more adaptive and interactive forms of biological analysis. This survey provides a systematic synthesis of recent progress in biological AI agents by reviewing over 100 representative studies across clinical analytics, molecular and drug design, multi-omics analysis, and knowledge discovery. We introduce a unified 5D taxonomy that organizes existing work along task domains, system architectures, interaction modes, evaluation strategies, and resource integration. Building on this framework, we analyze common design patterns, highlight emerging capabilities enabled by agentic paradigms, and identify key open challenges, including reliability, privacy, scalability, and standardized evaluation. Collectively, this survey clarifies the conceptual and methodological landscape of biological AI agents and outlines directions toward more robust, transparent, and collaborative agent-based systems for biological research. To serve as a living resource for the community, we curated a GitHub repository that includes resources and benchmark summaries, available at https://github.com/MineSelf2016/biological_agents_survey.","url":"https://doi.org/10.1093/bib/bbag075","authors":["Cong Qi","Wenbo Wang","Siqi Jiang","Q. Liu","Xun Song","Hanzhang Fang","Zhi Wei"],"tags":["Computer science","Benchmark (surveying)","Task (project management)","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1093/bib/bbag075","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4414103623","name":"Artificial intelligence in medical imaging empowers precision neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma","source":"openalex","abstract":"Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.","url":"https://doi.org/10.1136/jitc-2025-012468","authors":["Jia Fu","Xiao‐Ying Huang","Mengjie Fang","Xinliang Feng","Xu-Yao Zhang","Xuebin Xie","Zhuozhao Zheng","Di Dong"],"tags":["Generalizability theory","Medical imaging","Medicine","Artificial intelligence","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.1136/jitc-2025-012468","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4409204055","name":"Artificial Intelligence in Orthopedic Medical Education: A Comprehensive Review of Emerging Technologies and Their Applications","source":"preprints","abstract":"Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, algorithm evaluation, ethics, and the curriculum. AI is used in preoperative planning and predictive analytics in the postoperative period that bridges theory and practice. AI and MR are key to supporting innovation and scalability in orthopedic education. However, technological innovation relies on collaborative partnerships to develop equitable, evidence-informed practices that can be implemented in orthopedic education. For sustained impact, innovation must be aligned with pedagogical theories and principles. We believe that orthopedic medical educator's future critical role will be to enhance the next generation of competent clinicians.","url":"https://doi.org/10.20944/preprints202504.0306.v1","authors":["Kyle Sporn","Rahul Kumar","Phani Paladugu","Joshua Ong","Tejas C. Sekhar","Swapna Vaja","Tamer Hage","Ethan Waisberg","Chirag Gowda","Ram Jagadeesan","Nasif Zaman","Alireza Tavakkoli"],"tags":["Engineering ethics","Engineering","Engineering management","Data science","Computer science"],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"https://doi.org/10.20944/preprints202504.0306.v1","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"oa:W4407922620","name":"Health Care Professionals’ Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review","source":"openalex","abstract":"BACKGROUND: The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction. OBJECTIVE: This scoping review aims to offer a thorough analysis of the current status of integrating AI into medical practice as well as the apprehensions expressed by HC professionals (HCPs) over its application. METHODS: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to examine articles that investigated the apprehensions of HCPs about medical AI. Following the application of inclusion and exclusion criteria, 32 of an initial 217 studies (14.7%) were selected for the final analysis. We aimed to develop an attitude range that accurately captured the unfavorable emotions of HCPs toward medical AI. We achieved this by selecting attitudes and ranking them on a scale that represented the degree of aversion, ranging from mild skepticism to intense fear. The ultimate depiction of the scale was as follows: skepticism, reluctance, anxiety, resistance, and fear. RESULTS: In total, 3 themes were identified through the process of thematic analysis. National surveys performed among HCPs aimed to comprehensively analyze their current emotions, worries, and attitudes regarding the integration of AI in the medical industry. Research on technostress primarily focused on the psychological dimensions of adopting AI, examining the emotional reactions, fears, and difficulties experienced by HCPs when they encountered AI-powered technology. The high-level perspective category included studies that took a broad and comprehensive approach to evaluating overarching themes, trends, and implications related to the integration of AI technology in HC. We discovered 15 sources of attitudes, which we classified into 2 distinct groups: intrinsic and extrinsic. The intrinsic group focused on HCPs' inherent professional identity, encompassing their tasks and capacities. Conversely, the extrinsic group pertained to their patients and the influence of AI on patient care. Next, we examined the shared themes and made suggestions to potentially tackle the problems discovered. Ultimately, we analyzed the results in relation to the attitude scale, assessing the degree to which each attitude was portrayed. CONCLUSIONS: The solution to addressing resistance toward medical AI appears to be centered on comprehensive education, the implementation of suitable legislation, and the delineation of roles. Addressing these issues may foster acceptance and optimize AI integration, enhancing HC delivery while maintaining ethical standards. Due to the current prominence and extensive research on regulation, we suggest that further research could be dedicated to education.","url":"https://doi.org/10.2196/66986","authors":["Nóra Árvai","Gellért Katonai","Bertalan Meskó"],"tags":["Preprint","Health care","Health professionals","Psychology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-25","doi":"https://doi.org/10.2196/66986","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4400123511","name":"Discriminative and exploitive stereotypes: Artificial intelligence generated images of aged care nurses and the impacts on recruitment and retention","source":"openalex","abstract":"This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued.","url":"https://doi.org/10.1111/nin.12651","authors":["Amy‐Louise Byrne","Jennifer Mulvogue","Siju Adhikari","Ellie Cutmore"],"tags":["Ideology","Perception","Psychology","Value (mathematics)","Nursing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.1111/nin.12651","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411156068","name":"Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis","source":"openalex","abstract":"(1) Background: The early detection of laryngeal cancer is crucial for achieving superior patient outcomes and preserving laryngeal function. Artificial intelligence (AI) methodologies can expedite the triage of suspicious laryngeal lesions, thereby diminishing the critical timeframe required for clinical intervention. (2) Methods: We included all studies published up to February 2025. We conducted a systematic search across five major databases: MEDLINE, EMCARE, EMBASE, PubMed, and the Cochrane Library. We included 15 studies, with a total of 17,559 patients. A risk of bias assessment was performed using the QUADAS-2 tool. We conducted data synthesis using the Meta Disc 1.4 program. (3) Results: A meta-analysis revealed that AI demonstrated high sensitivity (78%) and specificity (86%), with a Pooled Diagnostic Odds Ratio of 53.77 (95% CI: 27.38 to 105.62) in detecting laryngeal cancer. The subset analysis revealed that CNN-based AI models are superior to non-CNN-based models in image analysis and lesion detection. (4) Conclusions: AI can be used in real-world settings due to its diagnostic accuracy, high sensitivity, and specificity.","url":"https://doi.org/10.3390/curroncol32060338","authors":["Ali Alabdalhussein","Mustafa Qais Muhsin Al-Khafaji","Rusul Al-Busairi","Shahad Al-Dabbagh","Waleed Khan","Fahim Anwar","Taghreed Sami Raheem","Mohammed Elkrim","Raguwinder Bindy Sahota","Manish Mair"],"tags":["Medicine","Meta-analysis","Diagnostic odds ratio","Triage","Cochrane Library"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-09","doi":"https://doi.org/10.3390/curroncol32060338","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410698708","name":"An overview of artificial intelligence and machine learning in shoulder surgery","source":"openalex","abstract":"Machine learning (ML), a subset of artificial intelligence (AI), utilizes advanced algorithms to learn patterns from data, enabling accurate predictions and decision-making without explicit programming. In orthopedic surgery, ML is transforming clinical practice, particularly in shoulder arthroplasty and rotator cuff tears (RCTs) management. This review explores the fundamental paradigms of ML, including supervised, unsupervised, and reinforcement learning, alongside key algorithms such as XGBoost, neural networks, and generative adversarial networks. In shoulder arthroplasty, ML accurately predicts postoperative outcomes, complications, and implant selection, facilitating personalized surgical planning and cost optimization. Predictive models, including ensemble learning methods, achieve over 90% accuracy in forecasting complications, while neural networks enhance surgical precision through AI-assisted navigation. In RCTs treatment, ML enhances diagnostic accuracy using deep learning models on magnetic resonance imaging and ultrasound, achieving area under the curve values exceeding 0.90. ML models also predict tear reparability with 85% accuracy and postoperative functional outcomes, including range of motion and patient-reported outcomes. Despite remarkable advancements, challenges such as data variability, model interpretability, and integration into clinical workflows persist. Future directions involve federated learning for robust model generalization and explainable AI to enhance transparency. ML continues to revolutionize orthopedic care by providing data-driven, personalized treatment strategies and optimizing surgical outcomes.","url":"https://doi.org/10.5397/cise.2025.00185","authors":["Sung-Hyun Cho","Yang‐Soo Kim"],"tags":["Machine learning","Artificial intelligence","Interpretability","Reinforcement learning","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.5397/cise.2025.00185","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411469847","name":"Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan","source":"openalex","abstract":"Incorporating inter-individual differences in drug disposition and responses is essential for ensuring the safe and effective use of drugs in real-world patients. Despite ongoing efforts, lower participation of children, older individuals, pregnant and breastfeeding women, postmenopausal women, and people with disease states and disabilities in drug clinical trials is frequent, and it requires multifaceted strategies and tools to evaluate drug exposure and responses in broad populations. The availability of modeling and simulation tools, such as physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/QST) modeling, enables the application of virtual populations that reflect the differences in drug disposition and responses for disease states and different stages of the lifespan. These models integrate clinical trial and real-world data (RWD) to predict drug exposure, efficacy, and safety. Additionally, machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug response across the lifespan. This review discusses the application of in silico and AI models to advance the prediction of drug exposure and responses across the lifespan, including examples of virtual populations in PBPK and QSP/QST models. A case study on QST modeling for drug-induced liver injury (DILI) in postmenopausal women is presented, along with opportunities and challenges in applying AI for modeling physiological determinants of drug dosing in individuals ranging in age from 12 to > 80 years old in drug development.","url":"https://doi.org/10.1111/cts.70272","authors":["Kyunghee Yang","Daniel González","Jeffrey L. Woodhead","Pallavi Bhargava","Murali Ramanathan"],"tags":["Physiologically based pharmacokinetic modelling","Drug","In silico","Disposition","Clinical trial"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.1111/cts.70272","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4404961577","name":"Artificial Intelligence–Generated Emergency Department Summaries and Hospital Handoffs","source":"openalex","abstract":"Artificial intelligence (AI) holds significant promise for enhancing quality, safety, efficiency, experience, and equity in health care delivery.Since the widespread release of large language models (LLMs) in late 2022, their capabilities to summarize complex text, extract structured data, and generate new content have fueled growing interest and hype around AI in health care.While applications of LLMs for clinical documentation, drafting in-basket messages, and summarizing patient encounters are emerging, comprehensive evaluations of their validity and impact remain limited, especially when compared with clinician-generated content, the current standard of care.Even less examined are discussions of AI use in acute care settings.","url":"https://doi.org/10.1001/jamanetworkopen.2024.48729","authors":["Adam Landman","Sharmila S. Tilak","Graham Walker"],"tags":["Emergency department","Medical emergency","Computer science","Medicine","Emergency medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-03","doi":"https://doi.org/10.1001/jamanetworkopen.2024.48729","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410974516","name":"Evaluating the Application of Artificial Intelligence and Ambient Listening to Generate Medical Notes in Vitreoretinal Clinic Encounters","source":"openalex","abstract":"Purpose: Analyze the application of large language models (LLM) to listen to and generate medical documentation in vitreoretinal clinic encounters. Subjects: Two publicly available large language models, Google Gemini 1.0 Pro and Chat GPT 3.5. Methods: Patient-physician dialogues simulating vitreoretinal clinic scenarios were scripted to simulate real-world encounters and recorded for standardization. Two artificial intelligence engines were given the audio files to transcribe the dialogue and produce medical documentation of the encounters. Similarity of the dialogue and LLM transcription was assessed using an online comparability tool. A panel of practicing retina specialists evaluated each generated medical note. Main Outcome Measures: The number of discrepancies and overall similarity of LLM text compared to scripted patient-physician dialogues, and scoring on the physician documentation quality instrument-9 (PDQI-9) of each medical note by five retina specialists. Results: On average, the documentation produced by AI engines scored 81.5% of total possible points in documentation quality. Similarity between pre-formed dialogue scripts and transcribed encounters was higher for ChatGPT (96.5%) compared to Gemini (90.6%, p<0.01). The mean total PDQI-9 score among all encounters from ChatGPT 3.5 (196.2/225, 87.2%) was significantly greater than Gemini 1.0 Pro (170.4/225, 75.7%, p=0.002). Conclusion: The authors report the aptitude of two popular LLMs (ChatGPT 3.5 and Google Gemini 1.0 Pro) in generating medical notes based on audio recordings of scripted vitreoretinal clinical encounters using a validated medical documentation tool. Artificial intelligence can produce quality vitreoretinal clinic encounter medical notes after listening to patient-physician dialogues despite case complexity and missing encounter variables. The performance of these engines was satisfactory but sometimes included fabricated information. We demonstrate the potential utility of LLMs in reducing the documentation burden on physicians and potentially streamlining patient care.","url":"https://doi.org/10.2147/opth.s513633","authors":["Neeket Patel","Corey Lacher","Alan Huang","Anton M. Kolomeyer","J. Clay Bavinger","Robert G. Carroll","Benjamin J. Kim","Jonathan C. Tsui"],"tags":["Medicine","Active listening","Ophthalmology","Vitreoretinal surgery","Optometry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.2147/opth.s513633","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4408304546","name":"Enhancing parental skills through artificial intelligence‐based conversational agents: The PAT Initiative","source":"openalex","abstract":"Abstract Objective We aim to describe the development of a conversational agent (CA) for parenting, termed PAT (Parenting Assistant platform), to demonstrate how artificial intelligence (AI) can enhance parenting skills. Background Behavioral problems are the most common issues in childhood mental health. Developing and disseminating scalable interventions to address early‐stage behavioral problems are of high priority. Artificial intelligence (AI)‐based CAs can offer innovative methods to deliver parenting interventions to reduce behavioral problems. CAs have the capability to interact through text or voice conversations and can undergo training using evidence‐based parenting programs. However, research on CAs for parenting and behavioral problems is limited. Experience The development of PAT consisted of three phases: Phase 1 was purely rule‐based, Phase 2 was hybrid (rule‐based format plus large language models), and Phase 3 featured an agentic architecture. The latest version of PAT includes prompt engineering, guardrails, retrieval‐augmented generation, few‐shots learning, context, and memory management through agentic architecture. Although comprehensive empirical results are pending, the iterative development and enhancement of PAT indicate the potential for effective digital intervention. The agentic architecture of the latest version of PAT aims to provide robust, context‐aware interactions to support parenting challenges. Implications CAs have the potential to reach a broader population of parents and deliver personalized interventions tailored to their specific needs. Moreover, CAs are structured to provide timely support, which can enhance family dynamics and contribute to improved long‐term outcomes for both parents and children. Conclusion AI‐based CAs can be used as alternatives to waitlists; as digital cotherapists; and implemented in health care, mental health, and school settings. The potential benefits and risks of the different types of CA and features are discussed.","url":"https://doi.org/10.1111/fare.13158","authors":["Milagros Escoredo","Karin Mostovoy","Ross Schickler","Alexis Bechtel","Jennah Shagan","Eduardo L. Bunge"],"tags":["Psychology","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-27","doi":"https://doi.org/10.1111/fare.13158","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W7117585964","name":"Physician Perspectives on the Impact of Artificial Intelligence on the Therapeutic Relationship in Mental Health Care: Qualitative Study","source":"openalex","abstract":"Background: The therapeutic relationship is a professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. The integration of artificial intelligence (AI) into mental health care has the potential to support accessibility and personalized care; however, little is known about how AI might affect the dynamics of the therapeutic relationship. Objective: This study aimed to ascertain how physicians anticipate AI tools will impact the therapeutic relationship in mental health care. Methods: We conducted 42 in-depth interviews with psychiatrists and family medicine practitioners to investigate physician perceptions regarding the impact of AI on mental health care. Results: Physicians identified several disruptions from AI use, noting that these tools could impact the dyad of the patient-physician relationship in ways that are both positive and negative. The main themes that emerged included potential disruptions to the therapeutic relationship, shifts in shared decision-making dynamics, and the importance of transparent AI use. Participants suggested that AI tools could create efficiencies that allow for relationship building as well as help avoid issues with miscommunication during psychotherapeutic interactions. However, they also expressed concerns that AI tools might not adequately capture aspects of the therapeutic relationship, such as empathy, that are vital to mental health care. Physicians also raised issues related to the impact that AI tools will have on maintaining relationships with patients. Conclusions: As AI applications become increasingly integrated into mental health care, it is crucial to assess how this integration may support or disrupt the therapeutic relationship. Physician acceptance of emerging AI tools may be highly dependent on how well the human elements of mental health care are preserved.","url":"https://doi.org/10.2196/81970","authors":["Isabel B. Weir","Austin M. Stroud","Jeremiah Stout","Barbara Barry","Arjun P. Athreya","William V. Bobo","Richard R. Sharp"],"tags":["Mental health","Qualitative research","Therapeutic relationship","Psychology","Mental health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-30","doi":"https://doi.org/10.2196/81970","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4312910656","name":"Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review","source":"openalex","abstract":"Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex and iterative operations of millions of parameters. Hence, training and inference of DL models are typically performed on high-performance computing (HPC) clusters in the cloud. Data transmission to the cloud results in high latency, round-trip delay, security and privacy concerns, and the inability of real-time decisions. Thus, processing on edge devices can significantly reduce cloud transmission cost. Edge devices are end devices closest to the user, such as mobile phones, cyber–physical systems (CPSs), wearables, the Internet of Things (IoT), embedded and autonomous systems, and intelligent sensors. These devices have limited memory, computing resources, and power-handling capability. Therefore, optimization techniques at both the hardware and software levels have been developed to handle the DL deployment efficiently on the edge. Understanding the existing research, challenges, and opportunities is fundamental to leveraging the next generation of edge devices with artificial intelligence (AI) capability. Mainly, four research directions have been pursued for efficient DL inference on edge devices: 1) novel DL architecture and algorithm design; 2) optimization of existing DL methods; 3) development of algorithm–hardware codesign; and 4) efficient accelerator design for DL deployment. This article focuses on surveying each of the four research directions, providing a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference.","url":"https://doi.org/10.1109/jproc.2022.3226481","authors":["Md Maruf Hossain Shuvo","Syed K. Islam","Jianlin Cheng","Bashir I. Morshed"],"tags":["Computer science","Edge device","Cloud computing","Edge computing","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-14","doi":"https://doi.org/10.1109/jproc.2022.3226481","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411327768","name":"Artificial intelligence: a promising tool for the clinical cardiologist","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) has emerged as a revolutionary technology that is changing clinical practice, including management of patients with cardiovascular diseases. AREAS COVERED: From a clinical practice perspective, this manuscript reviews the impact of AI on the management of cardiovascular diseases, and current challenges and opportunities. For this purpose, a systematic search was conducted on PubMed (MEDLINE), using the MeSH terms [Artificial intelligence] + [Cardiology] + [Cardiovascular] up to February 2025. Original data from clinical trials, observational studies and reviews of interest were reviewed. EXPERT OPINION: Cardiovascular diseases remain the first cause of morbidity, disability, and death worldwide, mainly owing to late diagnosis, insufficient control of cardiovascular risk factors, and poor use of guideline-recommended therapies. Moreover, the high prevalence of cardiac disease increases stress on the health system, which is already overloaded, challenging its capacity to provide quality patient care. AI-based algorithms may assist clinicians by promoting personalized medicine, improving efficiency, and better anticipating outcomes. Although some AI-based technical solutions are currently implemented, most will be ready for use in the coming years. Nonetheless, many challenges, barriers, and ethical concerns remain, and the effective implementation of AI in routine practice will take some time. In this context, it seems necessary to increase medical knowledge of how AI works, its impact on cardiovascular diseases, and its potential translation to clinical practice.","url":"https://doi.org/10.1080/14779072.2025.2520830","authors":["Carlos Escobar","Lorenzo Fácila","Rafael Vidal-Pérez","Alberto Pinedo Lapeña","David Vivas","Ana García Martín","Sergio Manzano‐Fernández","Eva Gonzalez Caballero","Vivencio Barrios","Román Freixa‐Pamias"],"tags":["Medicine","Internal medicine","Cardiology","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-04","doi":"https://doi.org/10.1080/14779072.2025.2520830","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410907890","name":"Conversational Alignment With Artificial Intelligence in Context","source":"openalex","abstract":"ABSTRACT The development of sophisticated artificial intelligence (AI) conversational agents based on large language models raises important questions about the relationship between human norms, values, and practices and AI design and performance. This article explores what it means for AI agents to be conversationally aligned to human communicative norms and practices for handling context and common ground and proposes a new framework for evaluating developers’ design choices. We begin by drawing on the philosophical and linguistic literature on conversational pragmatics to motivate a set of desiderata, which we call the CONTEXT‐ALIGN framework, for conversational alignment with human communicative practices. We then suggest that current large language model (LLM) architectures, constraints, and affordances may impose fundamental limitations on achieving full conversational alignment.","url":"https://doi.org/10.1111/phpe.12205","authors":["Rachel Sterken","James Ravi Kirkpatrick"],"tags":["Context (archaeology)","Psychology","Artificial intelligence","Cognitive science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1111/phpe.12205","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4410859807","name":"The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence","source":"openalex","abstract":"The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential \"biomarker\". Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson's, Alzheimer's, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution.","url":"https://doi.org/10.3390/s25113424","authors":["Pratyusha Muddaloor","Bhavana Baraskar","Hriday Shah","Keerthy Gopalakrishnan","Divyanshi Sood","Prem C. Pasupuleti","Akshay Singh","Dipankar Mitra","Sumedh S. Hoskote","Vivek Iyer","Scott A. Helgeson","Shivaram P. Arunachalam"],"tags":["Computer science","Voice command device","Human health","Artificial intelligence","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-29","doi":"https://doi.org/10.3390/s25113424","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4411624374","name":"Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review","source":"openalex","abstract":"Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.","url":"https://doi.org/10.1007/s13534-025-00486-4","authors":["Dogeun Park","Kwangsub So","Sunil Kumar Prabhakar","Chulho Kim","Jae Jun Lee","Jong‐Hee Sohn","Jong Ho Kim","Sang‐Hwa Lee","Dong-Ok Won"],"tags":["SIGNAL (programming language)","Warning system","Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-25","doi":"https://doi.org/10.1007/s13534-025-00486-4","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4392378490","name":"Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction","source":"openalex","abstract":"Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and deep learning techniques, capable of the following: (1) assisting genetic oncologists in digitizing paper-based pedigree charts, and in generating new digital ones, and (2) automatically predicting the genetic predisposition risk directly from these digital pedigree charts. To the best of our knowledge, there are no similar studies in the current literature, and consequently, no utilization of software based on artificial intelligence on pedigree charts has been made public yet. By incorporating medical images and other data from omics sciences, there is also a fertile ground for training additional artificial intelligence systems, broadening the software predictive capabilities. We plan to bridge the gap between scientific advancements and practical implementation by modernizing and enhancing existing oncological genetic counseling services. This would mark the pioneering development of an AI-based application designed to enhance various aspects of genetic counseling, leading to improved patient care and advancements in the field of oncogenetics.","url":"https://doi.org/10.3390/computation12030047","authors":["Luana Conte","Emanuele Rizzo","Tiziana Grassi","Francesco Bagordo","Elisabetta De Matteis","Giorgio De Nunzio"],"tags":["Digitization","Computer science","Software","Artificial intelligence","Statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-03","doi":"https://doi.org/10.3390/computation12030047","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4416308911","name":"The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances","source":"openalex","abstract":"Breast cancer (BCA) remains the most prevalent cancer globally and the leading cause of cancer-related mortality among women, with rising incidence rates driven by genetic, lifestyle, and environmental factors. Early detection through precise screening is essential to improve prognosis and survival; yet, challenges persist, especially in resource-limited areas. Recent advances in Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, have illustrated significant potential to enhance breast cancer screening, diagnosis, and treatment personalization. This review highlights the multifaceted role of AI in BCA management, encompassing its applications in image-based screening modalities, genomic and immunologic profiling, and drug discovery. AI-driven approaches offer diagnostic accuracy, cost-effectiveness, time-saving, and individualized treatment regimens. Despite promising developments, further research is crucial to overcome current challenges and regulatory hurdles in clinical settings. This article highlights the positive aspects of AI technologies in advancing BCA care and the importance of continued interdisciplinary research to optimize their implementations in breast cancer workflows.","url":"https://doi.org/10.34172/bi.30984","authors":["Faezeh Firuzpour","Mohammad Heydari","Cena Aram","Ali Alishvandi"],"tags":["Medicine","Breast cancer","Applications of artificial intelligence","Cancer","Breast cancer screening"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-29","doi":"https://doi.org/10.34172/bi.30984","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W4417310193","name":"Artificial intelligence takes on the multidisciplinary committee: A single-center study for rectal cancer management","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.surg.2025.109975","authors":["Muhammad Uzair Khalid","Charbel El‐Kefraoui","Alina Wang","Julian Wang","P. Terry Phang","Carl J. Brown","Amandeep Ghuman","Manoj Raval","Ahmer Karimuddin"],"tags":["Medicine","Multidisciplinary approach","Guideline","Artificial intelligence","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-15","doi":"https://doi.org/10.1016/j.surg.2025.109975","addedAt":"2026-09-01T01:47:48.791Z","updatedAt":"2026-09-01T01:47:48.791Z"},{"id":"oa:W3035300366","name":"Utilizing Industry 4.0 on the Construction Site: Challenges and Opportunities","source":"openalex","abstract":"In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.","url":"https://doi.org/10.1109/tii.2020.3002197","authors":["Christopher Turner","John Oyekan","Lampros Stergioulas","David Griffin"],"tags":["Industry 4.0","Automation","Key (lock)","Emerging technologies","Relevance (law)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-15","doi":"https://doi.org/10.1109/tii.2020.3002197","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4407591319","name":"Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape","source":"openalex","abstract":"The growing interest in leveraging artificial intelligence (AI) tools for healthcare decision-making extends to improving antibiotic prescribing. Large language models (LLMs), a type of AI trained on extensive datasets from diverse sources, can process and generate contextually relevant text. While their potential to enhance patient outcomes is significant, implementing LLM-based support for antibiotic prescribing is complex. Here, we specifically expand the discussion on this crucial topic by introducing three interconnected perspectives: (1) the distinctive commonalities, but also the crucial conceptual differences, between the use of LLMs as assistants in scientific writing and in supporting antibiotic prescribing in real-world practice; (2) the possibility and nuances of the expertise paradox; and (3) the peculiarities of the risk of error when considering LLMs to support complex tasks such as antibiotic prescribing.","url":"https://doi.org/10.1007/s40121-025-01114-5","authors":["Daniele Roberto Giacobbe","Sabrina Guastavino","Cristina Marelli","Ylenia Murgia","Sara Mora","Alessio Signori","Nicola Rosso","Mauro Giacomini","Cristina Campi","Michele Piana","Matteo Bassetti"],"tags":["Medicine","Antibiotics","Intensive care medicine","Microbiology","Biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-15","doi":"https://doi.org/10.1007/s40121-025-01114-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4296187632","name":"Internet of medical things and trending converged technologies: A comprehensive review on real-time applications","source":"openalex","abstract":"The Internet of Medical Things (IoMT) facilitates patients with all-time-connected medical devices through cost-effective solutions and a feeling of comfort with round-the-clock hospital support. The patients who cannot visit hospitals for routine checkups prefer the usage of IoMT devices. The healthcare facilities also rely on the real-time statistics of IoMT machines and diagnose problems and their solutions within a small interval of time. This could only be possible when a robust convergence of technologies is used with IoMT devices. This review study discusses how we may apply the convergence of IoMT devices with trending technologies and focuses on delivering a new dimension of novel IoMT usage through broad multi-homing dense networks. It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. The review study also discusses various IoMT convergence open challenges and opportunities through the labor industry healthcare system.","url":"https://doi.org/10.1016/j.jksuci.2022.09.005","authors":["Shiraz Ali Wagan","Jahwan Koo","Isma Farah Siddiqui","Muhammad Attique","Dong Ryeol Shin","Nawab Muhammad Faseeh Qureshi"],"tags":["Computer science","The Internet","Convergence (economics)","Health care","Healthcare industry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-17","doi":"https://doi.org/10.1016/j.jksuci.2022.09.005","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4404042958","name":"Scaling equitable artificial intelligence in healthcare with machine learning operations","source":"openalex","abstract":"Machine learning operations (MLOps), a discipline concerned with the production, monitoring and maintenance of artificial intelligence (AI) and machine learning (ML) models at scale, applied in healthcare can facilitate the transition of AI/ML-enabled healthcare tools from research to sustainable deployment.1–3 Adherence to MLOps best practices can address persistent challenges with AI/ML tools deployed into clinical workflows where models often struggle with generalisability, integration and robustness. As AI regulations continue to evolve such as the Department of Health and Human Services Office of Civil Rights final ruling that requires healthcare providers to ensure their AI/ML tools do not discriminate,4 it becomes increasingly essential for MLOps in healthcare to prioritise health equity.","url":"https://doi.org/10.1136/bmjhci-2024-101101","authors":["Madelena Y. Ng","Alexey Youssef","Malvika Pillai","Vaibhavi Shah","Tina Hernandez‐Boussard"],"tags":["Health care","Scaling","Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-01","doi":"https://doi.org/10.1136/bmjhci-2024-101101","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416087215","name":"The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature","source":"openalex","abstract":"Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic primary care, their uses, and their impacts on the NCP and patient outcomes. Method: Six databases were searched: MEDLINE, Embase, PsycINFO, Scopus, IEEE, and ACM digital library. Eligible studies were published between January 2007 and August 2024 and included human adult studies, AI-integrated technologies in the dietetic primary care setting, and patient-related outcomes. Extracted details focused on participant characteristics, dietitian involvement, and the type of AI system and its application in the NCP. Results: Ninety-seven studies were included. Three different AI systems (image or audio recognition, chatbots, and recommendation systems) were found. These were implemented in web-based or smartphone applications, wearable sensor systems, smart utensils, and software. Most AI-integrated technologies could be incorporated into one or more NCP stages. Seventy-nine studies reported user- or patient-related outcomes, with mixed findings, but all highlighted efficiencies of using AI. Higher patient engagement was observed with Chatbots. Seventeen studies raised concerns encompassing ethics and patient safety. Conclusions: AI systems show promise as a clinical support tool across most stages of the NCP. Whilst they have varying degrees of accuracy, AI demonstrates potential in improving efficiency, supporting personalised nutrition, and enhancing chronic disease management outcomes. Integrating AI education into dietetic training and professional development will be essential to ensure safe and effective use in practice.","url":"https://doi.org/10.3390/nu17223515","authors":["Kaitlyn Ngo","Simone Mekhail","Virginia Chan","Xinyi Li","Annabelle Yin","Ha Young Choi","Margaret Allman‐Farinelli","Juliana Chen"],"tags":["Primary care","Medicine","Medical education","Applications of artificial intelligence","Digital health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-10","doi":"https://doi.org/10.3390/nu17223515","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411938576","name":"Telemedicine and Telepharmacy in Modern Healthcare: Innovations, Medical Technologies, Digital Transformation","source":"openalex","abstract":"The article presents an overview of modern approaches to the development of telemedicine and telepharmacy as innovative forms of providing medical and pharmaceutical services at a distance. The historical stages of the formation of telemedicine, its integration into pharmaceutical practice, the main principles of telepharmacy and the formats of its implementation are highlighted. The advantages of using telepharmacy are considered, including increasing the availability of pharmaceutical care, optimizing resources, improving the quality of pharmaceutical care, and reducing the burden on the healthcare system. The challenges and limitations of the development of telepharmacy were analyzed, including legal, ethical, and technological aspects, issues of licensing and personal data protection. Special attention is paid to the international experience of implementing telepharmaceutical services in the USA, EU countries, Canada and Australia. The current state and prospects for the development of telepharmacy in Ukraine in the context of the digitalization of healthcare are described. A special emphasis is placed on the role of artificial intelligence in supporting clinical decisions, automating pharmaceutical consultations, and introducing virtual pharmacists. Recommendations are proposed for the integration of telepharmacy into the national healthcare system and pharmaceutical practice.","url":"https://doi.org/10.53933/r7f5xj91","authors":["Вікторія Шаповалова"],"tags":["Telemedicine","Digital transformation","Transformation (genetics)","Health care","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.53933/r7f5xj91","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4400742536","name":"Artificial Intelligence-Based Internet of Things for Industry 5.0","source":"openalex","abstract":"In the Industry 5.0 paradigm, systems based on artificial intelligence are an important component of the Internet of Things. Industry 5.0 demonstrated the important link between intelligent systems and people in most applications through precision manufacturing automation and critical thinking. In addition, Industry 5.0 brings with it several valid tools that help organizations operate cheaply and change immediately without capital investment. In recent years, smart devices, wireless communication, and sensor nodes have advanced greatly, transforming Internet of Things (IoT) ecosystems. With IoT devices, users can receive information even in rural areas and generate unbounded reports. Also, as previously mentioned, they meticulously guide people with intelligent judgments through communication technology. Many connected devices collect significant amounts of detected raw data when they require pre-processing. Although, it hardly becomes valuable for IoT devices and sufficient resources require Edge computing. AI-based algorithms are essential tools for data inference in Edge computing. In addition, observed data collected by IoT applications is usually unstructured and needs further analysis, where AI-based models help extract relevant information. Furthermore, malicious files are possible when data are transferred from one device to another. Therefore, this chapter looks at Industry 5.0, IoT architecture, and AI-based IoT; we analyze the IoT network and its specifications; communication is possible thanks to technologies.","url":"https://doi.org/10.5772/intechopen.115116","authors":["Shikha Goswami","Rohit Goswami","Govind Verma"],"tags":["Internet of Things","Industry 4.0","Industrial Internet","The Internet","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-17","doi":"https://doi.org/10.5772/intechopen.115116","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412390161","name":"The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations","source":"openalex","abstract":"Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability to improve eradication success compared to clinician-prescribed therapies. The model is trained and internally validated on 38,049 patients from the retrospective European Registry on Helicobacter pylori Management (Hp-EuReg), using independent state deep Q-learning (isDQN) to recommend optimal therapies based on patient characteristics such as age, sex, antibiotic allergies, country, and pre-treatment indication. In internal validation using real-world Hp-EuReg data, AI-recommended therapies achieve a 94.1% success rate (95% CI: 93.2-95.0%) versus 88.1% (95% CI: 87.7-88.4%) for clinician-prescribed therapies not aligned with AI suggestions-an improvement of 6.0%. Results are replicated in an external validation cohort (n = 7186), confirming generalisability. The AI system identifies optimal treatment strategies in key subgroups: 65% (n = 24,923) are recommended bismuth-based therapies, and 15% (n = 5898) non-bismuth quadruple therapies. Random forest modelling identifies region and concurrent medications as patient-specific drivers of AI recommendations. With nearly half the global population likely to contract H. pylori, this approach lays the foundation for future prospective clinical validation and shows the potential of AI to support clinical decision-making, enhance outcomes, and reduce gastric cancer burden.","url":"https://doi.org/10.1038/s41467-025-61329-5","authors":["K M Higgins","Olga P. Nyssen","Joshua Southern","Ivan Laponogov","Ana Miralles-Marco","Manuel Cabeza-Segura","Elena Jiménez-Martí","Josefa Castillo","Mārcis Leja","Inese Poļaka","Fátima Carneiro","Céu Figueiredo","Rui M. Ferreira","Rita Barros","Letícia Moreira","Míriam Cuatrecasas","Glòria Fernández‐Esparrach","Tamara Matysiak-Budnik","J.P. Martin","Laimas Virginijus Jonaitis","Juozas Kupčinskas","Paulius Jonaitis","Mário Dinis‐Ribeiro","Miguel Coimbra","Ana Carina Pereira","Filipa Fontes","Manon C.W. Spaander","Judith Honing","Stefano Sedola","Junior Andrea Pescino","Zorana Maravic","Ana Martins","Dennis A. Veselkov","Javier P. Gisbert","Tania Fleitas","Kirill Veselkov"],"tags":["Medicine","Helicobacter pylori","Cancer","Population","Cohort"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-14","doi":"https://doi.org/10.1038/s41467-025-61329-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4410454801","name":"Artificial intelligence unlocks the healthcare data lake","source":"openalex","abstract":"Artificial intelligence (AI) is poised to revolutionize surgical care by leveraging the vast and complex “data lake” of healthcare information. This perspective piece outlines how AI may harness structured and unstructured data to improve patient outcomes. Advances in deep learning and foundational models have enabled the development of predictive analytics, automated clinical documentation, personalized patient chatbots, remote monitoring, and enhanced medical imaging. Examples include the ACS NSQIP risk calculator, Sepsis ImmunoScore, startups in ambient transcription, and cutting-edge AI applications in intraoperative imaging and real-time diagnostics. However, the adoption of AI in healthcare requires overcoming challenges, including data privacy, bias, integration into clinical workflows, interoperability, cost, ethical concerns, and regulatory hurdles. As AI technologies evolve, collaboration between surgeons and scientists will be critical to ensure ethical, patient-centered designs. This manuscript calls for surgeons to lead AI applications role in surgery, bridging technology with meaningful use cases to positively align with clinical practice.","url":"https://doi.org/10.20517/ais.2024.109","authors":["Ankoor A. Talwar","Abhinav Talwar","Abhinav Talwar","Abhinav Talwar","Robyn B. Broach","Lyle Ungar","Daniel A. Hashimoto","John P. Fischer"],"tags":["Health care","Data science","Computer science","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.20517/ais.2024.109","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413857435","name":"Artificial Intelligence in Clinical Decision-Making: A Scoping Review of Rule-Based Systems and Their Applications in Medicine","source":"openalex","abstract":"Artificial intelligence (AI) has become increasingly integrated into clinical workflows, with rule-based clinical decision support systems (CDSS) emerging as one of its most mature and widely adopted applications. These systems rely on rule engines, that is, software components that apply predefined conditional logic (if/then rules) to patient data, to deliver alerts, diagnostic suggestions, or treatment recommendations. By embedding expert knowledge into structured rule sets and utilizing inference engines to process them, rule-based CDSS provides transparent, interpretable, and adaptable decision support. Although their use has expanded significantly over the past decade, evolving from simple decision aids to advanced tools incorporating AI and real-time analytics, a comprehensive synthesis of their applications, effectiveness, and technological evolution remains lacking. This scoping review aims to examine the current landscape of rule engine implementations in medicine, focusing on their clinical functionalities, evaluated outcomes, technological characteristics, and geographic adoption patterns across different medical domains. Following established scoping review methodology, we conducted a systematic search of PubMed and Scopus databases (2007-2023). Of 437 initially identified records, 28 studies met our inclusion criteria after rigorous screening. Data were extracted on study characteristics, clinical applications, rule engine technologies, and implementation outcomes, with particular attention to temporal trends and geographic distribution. The analysis revealed several key findings. The United States accounted for 46.42% of studies, demonstrating significant geographic concentration. Technologically, implementations evolved from early SQL-based systems to contemporary approaches integrating machine learning and natural language processing. Clinically, the rule engine showed particular effectiveness in chronic disease management (approximately 30% focused on diabetes care) and demonstrated measurable improvements, such as 30% reductions in adverse drug events. However, challenges persisted in system interoperability and clinician adoption across multiple studies. Our analysis of 28 studies demonstrates that rule engines have demonstrated substantial potential to enhance clinical decision-making and healthcare efficiency, though their adoption remains uneven geographically and is technically constrained in some settings. Based on our findings, we recommend: (1) developing standardized implementation frameworks to address interoperability challenges, (2) expanding research and deployment in underrepresented regions, and (3) investing in hybrid systems that combine rule-based logic with machine learning capabilities. These insights provide valuable guidance for healthcare organizations seeking to implement or optimize rule engine technologies in clinical practice.","url":"https://doi.org/10.7759/cureus.91333","authors":["Ashraf Alnattah","Mahdie Jajroudi","Seyyed Ali Najafi Fadafen","Mahdi N Manzari","Saeid Eslami"],"tags":["Medicine","Clinical decision making","Clinical decision support system","Artificial intelligence","Decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-31","doi":"https://doi.org/10.7759/cureus.91333","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4387326591","name":"Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees","source":"openalex","abstract":"OBJECTIVE: Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperienced operators. We aimed to develop and validate a prenatal-screening artificial intelligence system (PSAIS) for real-time evaluation of the quality of anatomical images, indicating existing and missing structures. METHODS: Still ultrasonographic images obtained from fetuses of 18-32 weeks of gestation between 2017 and 2018 were used to develop PSAIS based on YOLOv3 with global (anatomic site) and local (structures) feature extraction that could evaluate the image quality and indicate existing and missing structures in the fetal anatomical images. The performance of the PSAIS in recognizing 19 standard views was evaluated using retrospective real-world fetal scan video validation datasets from four hospitals. We stratified sampled frames (standard, similar-to-standard, and background views at approximately 1:1:1) for experts to blindly verify the results. RESULTS: The PSAIS was trained using 134 696 images and validated using 836 videos with 12 697 images. For internal and external validations, the multiclass macro-average areas under the receiver operating characteristic curve were 0.943 (95% confidence interval [CI], 0.815-1.000) and 0.958 (0.864-1.000); the micro-average areas were 0.974 (0.970-0.979) and 0.973 (0.965-0.981), respectively. For similar-to-standard views, the PSAIS accurately labeled 90.9% (90.0%-91.4%) with key structures and indicated missing structures. CONCLUSIONS: An artificial intelligence system developed to assist trainees in fetal anomaly screening demonstrated high agreement with experts in standard view identification.","url":"https://doi.org/10.1002/ijgo.15167","authors":["Ting Lei","Jie Ling Feng","Mei Fang Lin","Bai Hong Xie","Qian Zhou","Nan Wang","Qiao Zheng","Yan Dong Yang","Hong Guo","Hongning Xie"],"tags":["Medicine","Artificial intelligence","Confidence interval","Ultrasonography","Receiver operating characteristic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-04","doi":"https://doi.org/10.1002/ijgo.15167","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4415273825","name":"Optimized Ensemble Machine-Learning-Driven Transparent Thyroid Cancer Prediction Using Explainable Artificial Intelligence","source":"openalex","abstract":"Thyroid cancer disease diagnosis is a critical medical challenge, requiring accurate and reliable predictions to support clinical decision-making. Patients may suffer from an incomplete diagnosis while using traditional machine learning models. In this study, we present an optimized ensemble machine-learning framework for predicting thyroid cancer disease. The methodology integrates multiple classifiers, including support vector machine, random forest, Naïve Bayes, K-nearest neighbors, and decision tree. The final classification decision is determined with the help of soft voting by a total predictive support vector machine, which selects the classifier with the highest confidence score among the ensemble models. The ensemble strategy enhances predictive accuracy and robustness by combining the strengths of individual classifiers. The model was trained and evaluated, achieving an impressive accuracy of 0.9633 and an area under the receiver operating characteristic curve of 0.9914. The proposed method of this study is very accurate, but there is still a black box problem. To overcome this issue and to ensure interpretability, Explainable Artificial Intelligence techniques, including Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, are implemented, providing insights into feature contributions towards the performance of the proposed method and model decisions. The dataset contains a total of 30 features with 3,772 different cases consisting of two classes. Received: 16 June 2025 | Revised: 14 August 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bidemiayinde/thyroid-sickness-determination. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bhargavchirumamilla/thyroid-cancer-risk-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/sikandaraidev/thyroid-dataset. Author Contribution Statement Syed Younus Ali: Conceptualization, Software, Data curation, Writing – original draft, Writing – review & editing. Bilal Shoaib Khan: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Abdul Hanan Khan: Software, Validation. Muhammad Adnan Khan: Formal analysis, Investigation. Asghar Ali Shah: Validation, Investigation. Sagheer Abbas: Formal analysis, Project administration. Khan Muhammad Adnan: Methodology, Resources.","url":"https://doi.org/10.47852/bonviewjcce52026503","authors":["Syed Younus Ali","Bilal Shoaib","Abdul Hanan Khan","Muhammad Adnan Khan","Asghar Ali Shah","Sagheer Abbas","Khan Muhammad Adnan"],"tags":["Computer science","Artificial intelligence","Support vector machine","Machine learning","Ensemble learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-17","doi":"https://doi.org/10.47852/bonviewjcce52026503","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4389735479","name":"SYNCHRONOUS IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE AND PHARMACOLOGICAL COGNITIVE ENHANCEMENTS IN EDUCATION: A SOCIO-PHILOSOPHICAL ANALYSIS","source":"openalex","abstract":"The article analyzes the prospects and risks of hypothetical synchronous application of AI technologies and pharmacological cognitive enhancements in the educational system through the exampleof transfor-mation of the system of educational results assessment in mass school. The paper shows that despite the presence of a great potential for improving education, some features of the mass school’s functioning and the social visibility of the results of educational efficiency can become a serious obstacle to gaining posi-tive results of the introduction of these technologies, for example in connection with the introduction of biometrics technologies in mass school. It is shown that these technologiesthemselves do not necessarily lead to negative consequences and are seen to have a high positive potential, but there arise difficulties when they are implemented in an environment where there have previously occurred problematic admin-istrative and communication practices. The article substantiates that, similarly to biometrics, there is a risk of embedding AI technologies in depersonalized and formalized educational environment of mass school, which will strengthen the orientation toward quantitative measurement of educational effective-ness in the form of a permanent assessment system, which, in turn, will lead to distortion of the essence of the educational process due to the effects of Campbell’s Law. It is also shown that there are high risks of strengthening the neoliberal approach to education in case of legalization of pharmacological cognitive enhancements, which will also lead to epistemological difficulties in interpreting the educational situation (correct understanding of students’ behavior). Theconclusion emphasizes the importance of both having a correct strategy for the introduction of AI technologies and pharmacological cognitive enhancements and ensuring the return of a humanistic-sense-based approach to the goals and objectives of education, which only together can lead to significant positive results when using these technologies in education.","url":"https://doi.org/10.17072/2078-7898/2023-3-338-348","authors":["Alexandra M. Fedorova"],"tags":["Cognition","Legalization","Process (computing)","Obstacle","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.17072/2078-7898/2023-3-338-348","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414780351","name":"Every nurse an AI nurse: A framework for integrating artificial intelligence across nursing practice, education, research and policy","source":"openalex","abstract":"As artificial intelligence (AI) becomes increasingly embedded in healthcare, the nursing profession must embrace the imperative of 'every nurse an AI nurse'. This commentary argues that nurses must not only adapt to AI technologies but actively shape their development, implementation, and governance to ensure alignment with nursing's core values of compassionate, patient-centred care. Drawing on a five-part APDDS framework: Aware, Prepare, Dare, Declare, Share; the article outlines a strategic approach for integrating AI across nursing practice, education, research, and policy. It emphasises the need for AI literacy, leadership, ethical engagement, and knowledge dissemination to empower nurses as innovators and advocates in the digital transformation of healthcare. By embracing this proactive stance, the nursing profession can ensure that AI enhances rather than diminishes the human elements of care.","url":"https://doi.org/10.1177/20552076251377939","authors":["Mark Dornan"],"tags":["Digital transformation","Nursing","Engineering ethics","Artificial intelligence","Corporate governance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1177/20552076251377939","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4411934263","name":"Generative artificial intelligence integration in management education: application and ethical challenges","source":"openalex","abstract":"In the modern era, Generative Artificial Intelligence (GAI) tools can significantly impact and even transform Management Education Institutes (MEIs) through advanced technological developments. Using the GAI promises extensive gains in managerial efficiency, increased learning experience, skill formation and resource availability. Nonetheless, regarding their contextual integration in the educational sector, various ethical challenges (i.e. bias, privacy, accountability and intellectual property) also exist. Moreover, the analogy of multiple factors and a straightforward visualization of their empirical connection is lacking in existing literature. Consequently, this study proposes comprehensive matrices to help teachers, trainers and policymakers adopt a balanced approach between opportunities and ethical apprehensions linked with GAI education. Along these lines, for the responsible usage of GAI inside MEIs, educational technology adoption (i.e. TAM and UTAUT) and Ethical (i.e. Utilitarianism, Deontological Ethics, and Virtue Ethics) theories are leveraged. This study recommends that future researchers further investigate GAI’s long-term changes, cross-cultural adaptations, and technological innovations. In addition, for a transparent, wide-ranging, and liable implementation of GAI, one must emphasize the continuous evaluation and diverse stakeholders’ collective efforts. One might fully explore the full potential of GAI’s responsible integration to foster the conditions supporting ethical systems and mature outcomes.","url":"https://doi.org/10.1080/2331186x.2025.2526436","authors":["Shahid Bashir","Alexander L. Lapshun"],"tags":["Generative grammar","Psychology","Artificial intelligence","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-02","doi":"https://doi.org/10.1080/2331186x.2025.2526436","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408999757","name":"Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study","source":"openalex","abstract":"BACKGROUND: Knee osteoarthritis is a prevalent, chronic musculoskeletal disorder that impairs mobility and quality of life. Personalized patient education aims to improve self-management and adherence; yet, its delivery is often limited by time constraints, clinician workload, and the heterogeneity of patient needs. Recent advances in large language models offer potential solutions. GPT-4 (OpenAI), distinguished by its long-context reasoning and adoption in clinical artificial intelligence research, emerged as a leading candidate for personalized health communication. However, its application in generating condition-specific educational guidance remains underexplored, and concerns about misinformation, personalization limits, and ethical oversight remain. OBJECTIVE: We evaluated GPT-4's ability to generate individualized self-management guidance for patients with knee osteoarthritis in comparison with clinician-created content. METHODS: This 2-phase, double-blind, observational study used data from 50 patients previously enrolled in a registered randomized trial. In phase 1, 2 orthopedic clinicians each generated personalized education materials for 25 patient profiles using anonymized clinical data, including history, symptoms, and lifestyle. In phase 2, the same datasets were processed by GPT-4 using standardized prompts. All content was anonymized and evaluated by 2 independent, blinded clinical experts using validated scoring systems. Evaluation criteria included efficiency, readability (Flesch-Kincaid, Gunning Fog, Coleman-Liau, and Simple Measure of Gobbledygook), accuracy, personalization, and comprehensiveness and safety. Disagreements between reviewers were resolved through consensus or third-party adjudication. RESULTS: GPT-4 outperformed clinicians in content generation speed (530.03 vs 37.29 words per min, P<.001). Readability was better on the Flesch-Kincaid (mean 11.56, SD 1.08 vs mean 12.67 SD 0.95), Gunning Fog (mean 12.47, SD 1.36 vs mean 14.56, SD 0.93), and Simple Measure of Gobbledygook (mean 13.33, SD 1.00 vs mean 13.81 SD 0.69) indices (all P<.001), though GPT-4 scored slightly higher on the Coleman-Liau Index (mean 15.90, SD 1.03 vs mean 15.15, SD 0.91). GPT-4 also outperformed clinicians in accuracy (mean 5.31, SD 1.73 vs mean 4.76, SD 1.10; P=.05, personalization (mean 54.32, SD 6.21 vs mean 33.20, SD 5.40; P<.001), comprehensiveness (mean 51.74, SD 6.47 vs mean 35.26, SD 6.66; P<.001), and safety (median 61, IQR 58-66 vs median 50, IQR 47-55.25; P<.001). CONCLUSIONS: GPT-4 could generate personalized self-management guidance for knee osteoarthritis with greater efficiency, accuracy, personalization, comprehensiveness, and safety than clinician-generated content, as assessed using standardized, guideline-aligned evaluation frameworks. These findings underscore the potential of large language models to support scalable, high-quality patient education in chronic disease management. The observed lexical complexity suggests the need to refine outputs for populations with limited health literacy. As an exploratory, single-center study, these results warrant confirmation in larger, multicenter cohorts with diverse demographic profiles. Future implementation should be guided by ethical and operational safeguards, including data privacy, transparency, and the delineation of clinical responsibility. Hybrid models integrating artificial intelligence-generated content with clinician oversight may offer a pragmatic path forward.","url":"https://doi.org/10.2196/67830","authors":["Kai Du","Ao Li","Qi-Heng Zuo","Chen-Yu Zhang","Ren Guo","Ping Chen","Wei-Shuai Du","Shu-Ming Li"],"tags":["Preprint","Observational study","Osteoarthritis","Physical therapy","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-31","doi":"https://doi.org/10.2196/67830","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4403869552","name":"A Review of Approaches to Standardizing Medical Descriptions for Clinical Entity Recognition: Implications for Artificial Intelligence Implementation","source":"openalex","abstract":"This article reviews the current state of standardization in specific areas of the medical sector in Poland, focusing on terminology and the unique context of the Polish language. The primary objective was to analyze the existing resources and examine possibilities, challenges, and opportunities associated with integrating Artificial Intelligence, particularly natural language processing methods, into the healthcare system. The additional goal of this review was to place Poland in the international context by comparing the current state of the Polish standardization of healthcare with those of selected countries with more and less developed systems. The exploration highlights the main challenges that impact integration, including the specificity of the language and challenges in transferring knowledge from other languages, lack of communication between parties, and lack of stakeholder involvement in the standardization processes. This review also presents potential solutions to the mentioned challenges and provides insights into future directions, possibilities, proposals, and recommendations for all stakeholders. The practical application of this research extends beyond Poland. Many countries with underrepresented languages face similar challenges in clinical data processing, and the advances in CER for Polish could serve as a model for implementing AI-driven solutions in these regions. By refining CER models and adapting them to diverse linguistic and healthcare contexts, this research can foster improvements in patient care, medical research, and healthcare administration on a global scale.","url":"https://doi.org/10.3390/app14219903","authors":["Michał Paweł Wierzbicki","Barbara Anna Jantos","Michał Tomaszewski"],"tags":["Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.3390/app14219903","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412161112","name":"Perceptions of mental health professionals towards artificial intelligence in mental healthcare: a cross-sectional study","source":"openalex","abstract":"Background: Artificial intelligence (AI) holds significant potential for enhancing mental health care, but uptake is limited, potentially impacted by demographic factors of healthcare professionals. Further, while AI use in Saudi Arabia is progressive, there is minimal exploration of its role and impact within mental health services. Objective: This study presents a unique exploration of psychiatric professional's perceptions of AI in mental health care in Jeddah, Saudi Arabia. Methods: A cross-sectional online survey was conducted with a sample of mental health professionals from two governmental mental health hospitals in Jeddah, Saudi Arabia. The study tool was made up of two sections, the first consisting of sociodemographic questions and the second was the Shinners Artificial Intelligence Perception (SHAIP) questionnaire assessing the perceptions towards AI in mental healthcare, with data analyzed using IBM SPSS Statistical software. Results: A total of 251 mental health professionals, mostly females (56.6%), aged 31-40 (50%), married (45%), and nurses (55.4%). Only 24.3% used AI in practice, though 85.7% were aware of AI. Participants positively rated AI's impact (mean item range: 3.48-3.75) and felt unprepared for role-specific AI (mean 2.78). Nurses and those aware of AI had higher AI impact perceptions (p<0.0001) Specialty and AI awareness affected AI preparedness (p=0.001, p=0.029). Discussion: The study provides insights into mental health professionals ' views on AI in mental healthcare, emphasizing the need for targeted education to improve AI literacy and preparedness among Saudi healthcare professionals. It highlights the importance of ethical AI implementation to enhance patient care and advance psychiatric practice in the region.","url":"https://doi.org/10.3389/fpsyt.2025.1601456","authors":["Loujain Sharif","Reem Almabadi","Alhanouf Alahmari","Fai Alqurashi","Fidaa Alsahafi","Shahad Numan Qusti","Walaa Akash","Alaa Mahsoon","Dev Bandhu Poudel","Khalid Sharif","Rebecca Wright"],"tags":["Mental health","Cross-sectional study","Mental healthcare","Perception","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-10","doi":"https://doi.org/10.3389/fpsyt.2025.1601456","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416293174","name":"Artificial Intelligence Education in Radiology Training: A Systematic Review of Effectiveness, Barriers, and Future Directions","source":"openalex","abstract":"RATIONALE AND OBJECTIVES: The purpose of this systematic review study was to characterize the current landscape of various artificial intelligence (AI) education in radiology, summarizing existing curricula, outcomes, challenges, and future directions for effective integration into residency training. MATERIALS AND METHODS: A comprehensive search of PubMed, Web of Science, Embase, and Google Scholar identified relevant published studies up to June 19, 2025. RESULTS: Of the 2646 studies screened, 14 studies evaluated the performance of AI-based training programs for radiology trainees; among these, 92.9% (13/14) reported improvements in trainees' performance, including better diagnostic precision and interpretation (57.2%, 8/14), greater trainee confidence (57.2%, 8/14), hands-on experience with AI platforms (85.7%, 12/14), increased AI knowledge (85.7%, 12/14), engagement with AI-based case learning (35.7%, 5/14), understanding of AI ethics and bias (7.1%, 1/14), and acceptance of AI-assisted learning (78.6%, 11/14), whereas one study (7.1%, 1/14) found no significant benefit. Performance evaluation metrics varied across studies, with 35.7% (5/14) reporting a higher median of sensitivity, specificity, and accuracy (72%, 80%, and 81.3%) after AI training compared to before AI training (62.2%, 78.9%, and 76.5%, respectively), and 28.6% (4/14) showing improved AI knowledge scores. Hands-on simulations and didactic lectures were the most common AI training formats (78.6% and 71.4%). Risks and concerns included over-reliance on AI, limited exposure to complex or rare cases, and a lack of feedback. Recommendations highlighted the need for AI-faculty teaching, broader content coverage, and standardized multi-center AI-training programs to facilitate wider adoption. CONCLUSION: 92.9% of studies showed that AI-based training can enhance radiology trainees' knowledge, interpretive skills, or diagnostic performance, especially for junior trainees; however, its safe adoption requires standardized curricula with diverse cases, mentorship, workflow integration, and robust evaluation, with larger studies needed to confirm generalizability.","url":"https://doi.org/10.1016/j.acra.2025.10.049","authors":["Pedram Keshavarz","Zahra Mohammadigoldar","Arash Bedayat","Farzad Razi","Alexander M. Satei","Camelia Arsene"],"tags":["Workflow","Curriculum","Radiology","Medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-20","doi":"https://doi.org/10.1016/j.acra.2025.10.049","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4406807171","name":"Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy","source":"openalex","abstract":"Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.","url":"https://doi.org/10.3390/cancers17030379","authors":["Ankit Jain","Mayur Pabba","Aditya Jain","Sahib Singh","Hassam Ali","Rakesh Vinayek","Ganesh Aswath","Neil Sharma","Sumant Inamdar","Antonio Facciorusso"],"tags":["Endoscopy","Endoscopic ultrasound","Colonoscopy","Medicine","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-24","doi":"https://doi.org/10.3390/cancers17030379","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4206745392","name":"The Future of Artificial Intelligence for Alzheimer’s Disease Diagnostics","source":"openalex","abstract":"Alzheimer’s disease (AD) is a leading cause of death, yet there is no disease-modifying drug therapy currently available. It is critical to establish a diagnosis of AD before clinical system onset so that drug therapies can start earlier. Unfortunately, this is not the current standard practice. Artificial intelligence (AI) holds tremendous promise for identifying AD related structural changes in brain scan images. This paper discusses the recent applications and potential future directions for AI in AD diagnostics. Annual brain scanning and computer vision-assisted early diagnosis is encouraged, so that disease-modifying drug therapy could begin earlier in the progressive pathology.","url":"https://doi.org/10.4236/aad.2021.104005","authors":["Robert Logan","Sabrina S. Zerbey","Sean Miller"],"tags":["Disease","Medicine","Intensive care medicine","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.4236/aad.2021.104005","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414667728","name":"Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy","source":"openalex","abstract":"At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex \"black boxes,\" with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians' trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human-machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice.","url":"https://doi.org/10.3390/bioengineering12101058","authors":["Xiaohan Cai","Zexin Zhang","Siqi Zhao","Wentian Liu","Xiaofei Fan"],"tags":["Interpretability","Computer science","Capsule endoscopy","Transparency (behavior)","Mainstream"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-30","doi":"https://doi.org/10.3390/bioengineering12101058","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4415133485","name":"Exploring the Intersection of Nursing Leadership and Artificial Intelligence: Scoping Review","source":"openalex","abstract":"Background: As artificial intelligence (AI) technology permeates health care settings, nurse leaders must position themselves to shape its development, implementation, and impact, guiding meaningful change that benefits nurses and care delivery. Nurse leaders possess the capacity to influence decisions, shape practice, and ensure the delivery of ethical, safe, and high-quality care. While AI technology is reshaping many aspects of health care delivery, there is limited knowledge on how nurse leaders perceive and experience this shift. Objective: This scoping review aimed to explore the intersection of nursing leadership and AI technology in health care by mapping current evidence, identifying key concepts, and highlighting knowledge gaps within the literature. Methods: This scoping review was guided by the Joanna Briggs Institute methodology and reported on using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A systematic search of 4 electronic databases (CINAHL [EBSCO Information Services], Ovid MEDLINE [Wolters Kluwer], PsycINFO [American Psychological Association], and Scopus [Elsevier]) was conducted for English-language, peer-reviewed literature published between 2014 and 2025. Gray literature was also reviewed. Articles were included if they met the inclusion criteria by exploring the population of nurse leaders and the concept of AI technology within the context of health care settings and were published in English from May 2014 forward. A total of 26 articles were included in the analysis. Qualitative content analysis and numerical summary supported the inductive identification and synthesis of data categories. Results: Of the 26 articles included, 8 were empirical (qualitative, quantitative, or mixed methods), and 18 were conceptual or theoretical articles. Although 1 article was Canadian, there were no empirical studies conducted by Canadian researchers. The qualitative content analysis of the primary search findings revealed 6 overarching data categories: (1) leading digital transformation and technology integration, (2) AI technology and the nursing role: reshaping practice, (3) ethical considerations of AI technology for nurse leaders, (4) AI technology as a facilitator of innovative leadership, (5) education and training on AI technology in nursing practice, and (6) influence of AI technology on the work environment. Conclusions: This review confirms that nurse leaders play an essential role in shaping the future of health care in the context of AI technology. Although this review highlights a growing recognition of nursing leadership as a crucial driver of AI technology integration in health care, there is a lack of research to guide practice, policy, and leadership development through education, despite emerging interest and a recent increase in empirical work. The findings accentuate the need for increased investment in nurse-led research and leadership development to ensure that AI systems are designed, implemented, and evaluated in a manner that upholds ethical care, equity, and professional nursing values. As health care systems increasingly adopt AI technology, nurse leaders must be equipped with the knowledge, tools, and support required to lead transformative change and act as AI technology directors.","url":"https://doi.org/10.2196/80085","authors":["Jessica S Burford","Richard Booth","Amanda McIntyre"],"tags":["Transformative learning","Health care","Context (archaeology)","Engineering ethics","Intersection (aeronautics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-14","doi":"https://doi.org/10.2196/80085","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4404777543","name":"Artificial Intelligence‐Empowered Spectroscopic Single Molecule Localization Microscopy","source":"openalex","abstract":"Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI-based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI-driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point-spread functions are discussed, further examining their applications in biological studies, materials science, and single-molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI-empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM.","url":"https://doi.org/10.1002/smtd.202401654","authors":["Yoonsuk Hyun","Doory Kim"],"tags":["Resolution (logic)","Visualization","Superresolution","Nanoscopic scale","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.1002/smtd.202401654","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W7124545921","name":"The competence paradox: when psychologists overestimate their understanding of Artificial Intelligence","source":"openalex","abstract":"Abstract Artificial intelligence is rapidly transforming psychological practice. Psychologists now use AI to transcribe sessions, analyse client data, and generate treatment plans, yet few fully understand how these systems work. This commentary argues that the greatest risk AI poses to psychology is not its technical superiority to human capability, but a competence paradox: the tendency of psychologists to mistake the effective use of AI tools for a genuine understanding of how it works. This illusion of competence distorts judgment, weakens accountability, and undermines the foundations of professional expertise. We discuss how this gap forms, why it matters for both clients and clinicians, and what psychologists must know to engage with AI responsibly. Drawing on recent work in adjacent fields, we show how cognitive bias, identity protection, and anthropomorphism create a false sense of mastery. We then trace consequences across five domains. Cognitive and diagnostic skills decline through automation bias, cognitive offloading, and reduced reflective reasoning. Professional identity is strained as roles shift from clinician to editor of machine output. Ethical accountability blurs through hidden AI use, weak informed consent, and diffused liability. Collegial consultation diminishes as practitioners consult tools rather than peers, and wellbeing suffers through technostress, rising demands, financial strain, and growing reliance on AI. Finally, we argue that limited explainability within AI systems creates an explanatory dependence that constrains transparent justification of clinical decisions, shifts the burden of reasoning from clinician to tool, and makes embedded value choices harder to detect. We conclude with a call to action and a research agenda.","url":"https://doi.org/10.1007/s00146-025-02814-9","authors":["Llewellyn E. van Zyl"],"tags":["Competence (human resources)","Psychology","Accountability","Cognition","Mistake"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-17","doi":"https://doi.org/10.1007/s00146-025-02814-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4409803332","name":"AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study","source":"openalex","abstract":"BACKGROUND: As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificial intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking. OBJECTIVE: The aim of the study is to develop a curriculum framework for family medicine postgraduate education on AI called \"Artificial Intelligence Training in Postgraduate Family Medicine Education\" (AIFM-ed). METHODS: First, we conducted a comprehensive scoping review on existing AI education frameworks guided by the methodological framework developed by Arksey and O'Malley and Joanna Briggs Institute methodological framework for scoping reviews. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Next, 2 national expert panels were conducted. Panelists included family medicine educators and residents knowledgeable in AI from family medicine residency programs across Canada. Participants were purposively sampled, and panels were held via Zoom, recorded, and transcribed. Data were analyzed using content analysis. We followed the Standards for Reporting Qualitative Research for panels. RESULTS: An integration of the scoping review results and 2 panel discussions of 14 participants led to the development of the AIFM-ed curriculum framework for AI training in postgraduate family medicine education with five key elements: (1) need and purpose of the curriculum, (2) learning objectives, (3) curriculum content, (4) organization of curriculum content, and (5) implementation aspects of the curriculum. CONCLUSIONS: Using the results of this study, we developed the AIFM-ed curriculum framework for AI training in postgraduate family medicine education. This framework serves as a structured guide for integrating AI competencies into medical education, ensuring that future family physicians are equipped with the necessary skills to use AI effectively in their clinical practice. Future research should focus on the validation and implementation of the AIFM-ed framework within family medicine education. Institutions also are encouraged to consider adapting the AIFM-ed framework within their own programs, tailoring it to meet the specific needs of their trainees and health care environments.","url":"https://doi.org/10.2196/66828","authors":["Raymond Tolentino","Fanny Hersson-Edery","Mark J. Yaffe⃰","Samira Abbasgholizadeh Rahimi"],"tags":["Curriculum","Medical education","Checklist","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-25","doi":"https://doi.org/10.2196/66828","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4411718808","name":"Gaps in Artificial Intelligence Research for Rural Health in the United States: A Scoping Review","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban areas and the history of US urban-rural digital divides raises concerns that the promise of AI may not be realized in rural communities. This may exacerbate well-documented health disparities. Without the benefits of AI-driven improvements in patient outcomes and increased efficiency, rural healthcare facilities may fall farther behind their urban counterparts and rural hospital closure rates may continue to rise. Methods We conducted a scoping review following the PRISMA guidelines. We included peer-reviewed, original research studies indexed in PubMed, Embase, and WebOfScience after January 1, 2010 and through April 29, 2025. Studies were required to discuss the development, implementation, or evaluation of AI tools in rural US healthcare, including frameworks that help facilitate AI development (e.g., data warehouses). Findings Our search strategy found 26 studies meeting inclusion criteria after full text screening with 14 papers discussing predictive AI models and 12 papers discussing data or research infrastructure. AI models most commonly targeted resource allocation and distribution. Few studies explored model deployment and impact. Half noted the lack of data and analytic resources as a limitation to both development and validation. None of the studies discussed examples of generative AI being trained, evaluated, or deployed in a rural setting. Interpretation Practical limitations may be influencing and limiting the types of AI models evaluated in the rural US. We noted validation of tools in the rural US was underwhelming, and ultimately, neglected. With few studies moving beyond AI model design and development stages, there is a clear gap in our understanding of how to reliably validate, deploy, and sustain AI models in rural settings to advance health in all communities. Funding National Library of Medicine Research in context Evidence before this study: Clinical artificial intelligence (AI)—both for prediction modeling and generative tools— tools promise to reduce care delays, improve diagnosis and treatment decision-making, reduce care costs, and improve efficiency to reduce provider workload and enhance practice management. Unfortunately, efforts to deploy artificial intelligence (AI)—both for prediction modeling and generative tools—in healthcare are advancing, primarily at large academic medical centers and in urban areas. An emerging new digital divide in the use of clinical AI could exacerbate the well-documented health disparities between urban and rural communities in the United States. A better understanding of if and how AI is being developed, deployed, and evaluated across rural US communities is necessary to identify resources gaps and challenges to broad AI use in all communities. Added value of this study: This study analyzes the current state of artificial intelligence research in the rural United States. For predictive AI models, applications most commonly targeted resource allocation and distribution. We noted several attempts to predict resource utilization of patients who were either tested or tested positive to COVID-19. However, we noted few AI solutions for acute medical events faced by rural patients, such as trauma and stroke, despite worse outcomes for rural patients suffering from these acute events. The limited availability of time-critical specialties such as trauma/emergency medicine, neurology, and cardiology in rural areas often necessitates patients with such conditions be transferred to larger, more resourced hospitals. Practical limitations may be influencing and limiting the types of AI models evaluated in rural US medical facilities. The most frequent model employed were tree-based ensembles, such as random forests and gradient-boosting trees. Our review also highlighted few studies of AI models moving beyond the de","url":"https://doi.org/10.1101/2025.06.26.25330361","authors":["Katherine Brown","Sharon E. Davis"],"tags":["Political science","Economic growth","Economics"],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"https://doi.org/10.1101/2025.06.26.25330361","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"oa:W2999472659","name":"A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues","source":"openalex","abstract":"Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users' right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.","url":"https://doi.org/10.1109/comst.2019.2962586","authors":["Maria Stoyanova","Yannis Nikoloudakis","Spyros Panagiotakis","Evangelos Pallis","Evangelos Markakis"],"tags":["Computer science","Digital forensics","Computer security","Digital evidence","Cloud computing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1109/comst.2019.2962586","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412765880","name":"AI-driven epidemic intelligence: the future of outbreak detection and response","source":"openalex","abstract":"Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. The growing frequency of emerging infectious diseases highlights the urgency for more rapid and accurate surveillance methods. This perspective proposes a forward-looking conceptual framework for AI-driven epidemic intelligence, emphasizing the transformative potential of integrating large language models (LLMs), natural language processing (NLP), and optimization-based resource allocation strategies. While existing AI-driven systems have shown significant capabilities during the COVID-19 pandemic, several challenges remain, including real-time adaptability, multilingual data handling, misinformation, and public health policy alignment. To address these gaps, we propose an integrated, real-time adaptable LLM-based epidemic intelligence system, capable of correlating cross-source data, optimizing healthcare resource allocation, and supporting informed outbreak response. This approach aims to significantly improve early warning capabilities, enhancing forecasting accuracy, and strengthen pandemic preparedness.","url":"https://doi.org/10.3389/frai.2025.1645467","authors":["Jasleen Kaur","Zahid A Butt"],"tags":["Computer science","Preparedness","Transformative learning","Pandemic","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-30","doi":"https://doi.org/10.3389/frai.2025.1645467","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4409717779","name":"Artificial Intelligence‐Critical Pedagogic: Design and Psychologic Validation of a Teacher‐Specific Scale for Enhancing Critical Thinking in Classrooms","source":"openalex","abstract":"ABSTRACT Background Critical thinking is essential in modern education, and artificial intelligence (AI) offers new possibilities for enhancing it. However, the lack of validated tools to assess teachers' AI‐integrated pedagogical skills remains a challenge. Objectives The current study aimed to develop and validate the Artificial Intelligence‐Critical Pedagogy Scale (AICPS) to measure teachers' ability to use AI in fostering critical thinking. Methods This study was conducted in Saudi Arabia and consisted of two phases. Phase 1 involved item development through a literature review and semi‐structured interviews with 17 secondary school teachers, leading to an initial pool of 100 items. After expert reviews and a pilot study, the scale was refined to 47 items. Phase 2 evaluated the psychometric properties of the scale through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), reliability assessments and measurement invariance testing across gender. The final sample included 800 secondary school teachers. Results and Conclusions EFA confirmed a four‐factor structure with 39 items. The four factors were Competence in Creating Critical Thinking‐Oriented Learning Environments (CCCTOLE), Ability to Provide Dynamic and Innovative Feedback (APDIF), Understanding and Interaction with Emerging Learning Technologies (UIELT) and Creativity in Designing Transformative Learning Activities (CDTLA). CFA demonstrated a good model fit ( χ 2 /df = 3.24, RMSEA = 0.075, CFI = 0.916 and TLI = 0.909). EGA further supported the four‐factor structure. Internal consistency was excellent, with Cronbach's alpha and McDonald's omega above 0.70 for all subscales. Measurement invariance testing confirmed that the scale functions equivalently across gender groups. The AICPS is a reliable and valid tool for assessing teachers' AI‐based critical pedagogy skills. Its demonstrated gender invariance suggests its applicability across diverse educational contexts. This scale can guide future teacher training and policy decisions in AI‐driven education.","url":"https://doi.org/10.1111/jcal.70039","authors":["Ali Suwayid Alqarni"],"tags":["Critical thinking","Psychology","Scale (ratio)","Mathematics education","Pedagogy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-23","doi":"https://doi.org/10.1111/jcal.70039","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4415777022","name":"Redefining oral healthcare through artificial intelligence: a review of current applications and a roadmap for the future of dentistry","source":"openalex","abstract":"The integration of artificial intelligence (AI) into dental practice represents a significant technological advancement in oral healthcare. This narrative review examines current research across AI domains including machine learning, deep learning, computer vision, natural language processing, and generative modeling to assess how AI is influencing clinical workflows, diagnostic procedures, treatment planning, and surgical interventions in dentistry. The central research question addresses how artificial intelligence is currently being applied in dental practice and what opportunities and challenges exist for its future implementation. Evidence from peer-reviewed studies demonstrates variable efficacy of AI in supporting diagnostic decisions for caries, periodontal disease, and oral cancer; assisting orthodontic planning and prosthodontic CAD/CAM workflows; and enabling robotic surgery and predictive modeling. While some AI models show promising accuracy metrics, questions remain about their generalizability and real-world performance. The review explores integration of AI through electronic health records, decision-support systems, and emerging digital twin technologies. Interdisciplinary collaborations with biomedical engineering, computational biology, and materials science are examined. Critical challenges including algorithmic bias, data privacy, informed consent, liability frameworks, and adoption barriers such as technological infrastructure, clinician acceptance, and regulatory uncertainties are discussed. Future directions emphasize federated learning for collaborative model development; explainable AI to improve transparency; integration with multi-omics data; and adaptive systems capable of continuous learning. This review provides a balanced assessment of AI's potential in dentistry while acknowledging significant implementation challenges that must be addressed to ensure equitable and effective patient care.","url":"https://doi.org/10.1186/s44398-025-00013-6","authors":["Bharti Dua","Rajiv Kumar Gupta","Akshay Bhargava","Anupam Bhardwaj","Meena Jain","Siddhi Tripathi"],"tags":["Generalizability theory","Narrative review","Psychological intervention","Engineering ethics","Repurposing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-02","doi":"https://doi.org/10.1186/s44398-025-00013-6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414658925","name":"Enhanced intrusion detection in cybersecurity through dimensionality reduction and explainable artificial intelligence","source":"openalex","abstract":"Cybersecurity is one of the applications of controls, procedures, and technologies for protecting data, networks, programs, and systems from potential cyber threats. Malicious threats have become complex, and the leading task is to recognize obfuscated and mysterious malware, as the malware inventors utilize dissimilar evasion models for data covering to avert recognition by intrusion detection systems (IDSs). Artificial intelligence (AI) usage in cybersecurity is gradually becoming familiar, but the main task is the absence of interpretability and transparency of AI methods. Explainable AI (XAI) can tackle this problem by improving the understandability of AI techniques, permitting cyber-security experts to comprehend the decisions created by these methods and to recognize biases or errors. Recently, Machine learning (ML) and deep learning (DL) models have delivered automatic analytical intrusion detection procedures, providing numerous advantages. This study proposes an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model. The main intention of the proposed EIDCDR-XAIADL model is to deliver a robust cybersecurity system that combines XAI to address the attacks. Initially, the proposed EIDCDR-XAIADL technique performs data normalization by using mean normalization to ensure uniform scaling of network traffic data. The multiverse optimization (MVO) technique selects the most appropriate and discriminative features. For the cybersecurity attack classification process, the hybrid of convolutional neural network (CNN), bi-directional gated recurrent unit (BiGRU), and attention mechanism (CNN-BiGRU-AM) technique is implemented. Moreover, the antlion optimization (ALO) technique adjusts the hyperparameter values of the CNN-BiGRU-AM method optimally and results in more excellent classification performance. Finally, Shapley Additive Explanations (SHAP) is utilized as an XAI technique to enhance threat detection and decision-making by providing trustworthy insights into AI-driven security systems. The experimental evaluation of the EIDCDR-XAIADL approach is examined under dual datasets. The experimental validation of the EIDCDR-XAIADL approach demonstrated a superior accuracy value of 99.19% and 99.12% under NSLKDD and CICIDS 2017 datasets.","url":"https://doi.org/10.1038/s41598-025-06761-9","authors":["Hayam Alamro","Sultan Alahmari","Nadhem Nemri","Mohammed Aljebreen","Asma A. Alhashmi","Sulaiman Alamro","Ali Alqazzaz","Mesfer Al Duhayyim"],"tags":["Computer science","Artificial intelligence","Interpretability","Machine learning","Intrusion detection system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-30","doi":"https://doi.org/10.1038/s41598-025-06761-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416331297","name":"Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications","source":"openalex","abstract":"This narrative review evaluates the role of artificial intelligence (AI) algorithms in orthopaedic surgery and distinguishes itself by explaining how the main algorithmic approaches function and illustrating each with orthopaedic examples. Machine learning methods, including regression, classification and reinforcement learning, have been applied to fracture detection, prediction of revision risk and modelling of outcomes after arthroplasty and sports injury. Deep learning and convolutional neural networks have improved fracture classification, implant surveillance and segmentation of cartilage and meniscal tissue on magnetic resonance imaging. Neural networks such as FracNet and YOLO-based systems demonstrate growing capability in trauma imaging. Natural language processing has automated the extraction of operative and registry data, while large language models are emerging for diagnostic support and education. Generative artificial intelligence (GAI) have produced synthetic musculoskeletal images to expand data sets. Computer vision and image processing underpin robotic-assisted surgery and preoperative planning, and federated learning enables multicentre collaboration while protecting privacy. Each algorithm offers strengths in accuracy, efficiency or scalability, but also carries bias, transparency, computational cost and lack of external validation. This review explores how these algorithms are shaping orthopaedics, highlighting their benefits, limitations and challenges. Rigorous validation, transparent reporting and governance are essential for safe clinical use. Level of Evidence: N/A.","url":"https://doi.org/10.1002/jeo2.70549","authors":["Jamie Rosen","Jemima Russell","Prerna Kartik","Martinique Vella‐Baldacchino"],"tags":["Computer science","Artificial intelligence","Narrative review","Narrative","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1002/jeo2.70549","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4407735835","name":"The increasing role of artificial intelligence in radiation oncology: how should we navigate it?","source":"openalex","abstract":"Why the role of AI in radiation oncology is set to growAs illustrated by this special issue, artificial intelligence (AI) technology [1] has a potential impact on all aspects of radiation oncology, ranging from automated treatment planning [2], adaptive radiotherapy, and IGRT [3,4], to clinical decision support, personalization [5], and improvement of patients' experience and quality of life [6].Radiation oncology may be impacted by AI more significantly than any other therapeutic medical field, as radiotherapy treatment planning and delivery are already fully computer based, thus greatly facilitating the introduction of AI technology.Given the increasing role of AI in radiation oncology, it is important to discuss potential implications and the optimal strategy for our field.In particular, the potential of AI to automate virtually all steps of the treatment planning chain is a double-edged sword that brings about some of the greatest benefits but also affects the unique competency of radiation oncology.Moreover, we must not overlook the clinical aspects of radiation oncology, such as patient care, supportive therapy, and integration with pharmacological cancer treatments.These are as fundamental to the discipline as the technical aspects and represent the second essential pillar of radiation oncology.","url":"https://doi.org/10.1007/s00066-025-02381-4","authors":["Florian Putz","Rainer Fietkau"],"tags":["Medicine","Radiation oncology","Radiation therapy","Medical physics","Oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-19","doi":"https://doi.org/10.1007/s00066-025-02381-4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4205384263","name":"Emotional Intelligence & ICTs for Women and Equality","source":"openalex","abstract":"Gender equality is a fundamental human right and is essential for the existence of peaceful societies, with human resources that are fully utilized and sustainable development. Emotional Intelligence is not gender biased and it is an integral key to successful personal and working life. In (Drigas & Papoutsi, 2021) there was an attempt to construct a reliable and valid measurement instrument of emotional intelligence with 81 items, based on the theoretical nine-layer pyramid model of emotional intelligence. The sample was consisted of 520 teachers (129 males and 391 females) from primary and secondary school grade and the data was collected with the Nine Layer Pyramid Model Questionnaire for Emotional Intelligence. Among other results we examined gender differences in emotional intelligence. The results revealed some differences between the two genders on emotional intelligence with women scoring higher on overall emotional intelligence. This article also provides an overview of the prevailing emotional intelligence status of both sexes as it emerges through research, beliefs about emotions between women and men, and suggestions for avoiding stereotypes and proper interventions for raising emotional both men and women. Finally, a reference is made to technology in its various forms, including Information and Communication Technology (ICT), which is also associated with emotional intelligence and has great potential to empower women worldwide and promote gender equality","url":"https://doi.org/10.47577/tssj.v27i1.5561","authors":["Chara Papoutsi","Irene Chaidi","Athanasios Drigas","Charalabos Skianis","Charalampos Karagiannidis"],"tags":["Emotional intelligence","Psychology","Construct (python library)","ICTS","Information and Communications Technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-08","doi":"https://doi.org/10.47577/tssj.v27i1.5561","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4407001196","name":"Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors","source":"openalex","abstract":"This review aims to analyze the development and impact of Artificial Intelligence (AI) in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives. It is extensively devoted to AI technology ... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmes.2025.059152","authors":["Rakesh Kumar","Ajay Singh","Ahmed Subahi Ahmed Kassar","Mohammed Ismail Humaida","Sudhanshu Joshi","Manu Sharma"],"tags":["Critical success factor","Health care","Systematic review","Public healthcare","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.32604/cmes.2025.059152","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408488823","name":"Regulatory and legal challenges of Artificial Intelligence in the U.S. Healthcare System: Liability, Compliance, and Patient Safety","source":"openalex","abstract":"This study explores Regulatory and Legal Challenges of Artificial Intelligence in the U.S. Healthcare System: Liability, Compliance, and Patient Safety. It also examines the challenges associated with AI integration, including ethical concerns, data privacy risks, and regulatory compliance, as providing insights into legal frameworks governing AI in healthcare. A qualitative research approach was employed, involving a comprehensive review of existing literature, and regulatory policies. Peer-reviewed journals, government publications, and industry reports were analyzed to assess the effectiveness, challenges, and future implications of AI-driven healthcare solutions. The study reveals that AI significantly enhances healthcare delivery by improving diagnostic accuracy, enabling personalized treatments, and optimizing hospital workflows. Machine learning models and natural language processing facilitate early disease detection, while robotic process automation streamlines administrative processes. However, challenges such as algorithmic bias, data security concerns, and the need for stringent regulatory oversight persist. Regulatory frameworks such as HIPAA, GDPR, and FDA guidelines provide necessary compliance structures but require continuous updates to keep pace with AI advancements. The paper therefore concludes that AI is revolutionizing healthcare, offering significant benefits in efficiency and patient outcomes. However, successful implementation necessitates a balanced approach that integrates ethical considerations, data protection measures, and regulatory frameworks. Future research should focus on enhancing AI transparency, addressing biases, and ensuring that AI-driven healthcare solutions remain patient-centered and legally compliant.","url":"https://doi.org/10.30574/wjarr.2025.25.3.0807","authors":["Adewale Samuel Osifowokan","Tessy Oghenerobovwe Agbadamasi","Tobias Kwame Adukpo","Nicholas Oppong Mensah"],"tags":["Compliance (psychology)","Liability","Business","Health care","Healthcare industry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-16","doi":"https://doi.org/10.30574/wjarr.2025.25.3.0807","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4389712719","name":"CLIP in Medical Imaging: A Survey","source":"openalex","abstract":"Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, we (1) first start with a brief introduction to the fundamentals of CLIP methodology; (2) then investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) further explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; and (4) finally discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. The project page of this survey can also be found on https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging.","url":"https://doi.org/10.48550/arxiv.2312.07353","authors":["Zihao Zhao","Yu-Xiao Liu","Han Wu","Wang, Mei","Yonghao Li","Sheng Wang","Lin Teng","Disheng Liu","Zhiming Cui","Qian Wang","Dinggang Shen"],"tags":["Interpretability","Computer science","Medical imaging","Generalizability theory","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-12","doi":"https://doi.org/10.48550/arxiv.2312.07353","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408862063","name":"Individual dynamic capabilities and artificial intelligence in health operations: Exploration of innovation diffusion","source":"openalex","abstract":"This research investigates the integration of individual dynamic capabilities (IDC), artificial intelligence (AI), and the Technology Acceptance Model (TAM) within health operations to evaluate their role in fostering innovation diffusion in healthcare. A convergent, multifaceted research approach encompassing quantitative and qualitative methodologies was employed, commencing with a systematic review of the extant literature. This was then complemented by the execution of focus group sessions involving 21 participants. The main objective of this sequential exploratory design was to synthesize existing research present an empirical validation of real-world case studies, and assess AI deployment challenges that influence operational efficiency and service quality in healthcare organizations. The findings underscore the importance of IDC in advancing healthcare practices by driving cross-functional adaptation, facilitating AI implementation, and ensuring smooth operational transformation in line with healthcare standards and best practices. The findings offer valuable insights for operational and executive-level decision-makers aiming to optimize health operations by integrating IDC and AI technologies, enhancing patient care, service quality, and innovative health solutions. • Integration of AI and IDC enhances healthcare operational efficiency and regulatory compliance. •Individual dynamic capabilities foster adaptability, technological adoption, and continuous learning in healthcare operations. •AI-driven predictive analytics streamline decision-making and improve patient care outcomes. •IDC and AI synergistically optimize data interoperability and regulatory standards in healthcare systems. •Leadership commitment and cross-functional collaboration drive successful AI and IDC implementation.","url":"https://doi.org/10.1016/j.ibmed.2025.100239","authors":["António Pesqueira","Maria José Sousa","Rúben Pereira"],"tags":["Diffusion","Innovation diffusion","Computer science","Knowledge management","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1016/j.ibmed.2025.100239","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412024302","name":"The Use of Generative Artificial Intelligence (AI) in Academic Research: A Review of the Consensus App","source":"openalex","abstract":"Consensus App is an academic search engine designed to change how researchers access and synthesize information. It helps researchers quickly browse the growing body of academic literature by offering insights at both the topic and paper levels. We evaluate the Consensus App's potential to transform academic research, its ethical implications, and the reasons behind its underrepresentation in academic literature. We seek to provide a balanced perspective on the app's current and future influence in academic research. This paper is based on a rapid review of the literature to see how the Consensus App is used and reported in the literature. Our review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We focused on identifying applications, benefits, and ethical concerns related to the Consensus App. The search was conducted on December 23, 2024, across 210 academic databases. The databases from which articles were retrieved include Web of Science (N=6), MEDLINE (N=2), Academic Search Ultimate (N=1), and Fuente Académica Plus (N=1). In addition to the database searches, five additional editorials were identified through targeted manual searches of high-impact journals. In total, 10 papers were included in the final review. ChatGPT-4.5 was used to assist in synthesizing key themes across the articles, focusing on application, benefits, and ethical concerns related to the Consensus App and the broader use of artificial intelligence (AI) in scholarly work. The reviewed articles revealed that the use of the Consensus App is surprisingly low, which may suggest underreporting by its users. Researchers may also not be aware of it. These studies showed how the app has been limitedly used in the literature. Despite its advantages, we identified ethical concerns in the reviewed studies. Despite its potential, the Consensus App remains underutilized and significantly underreported in academic literature. Therefore, it is important for academic institutions, journal editors, and researchers to collaboratively develop standardized reporting guidelines when AI is involved in the process of manuscript development. The eventual goal is to lead to a more transparent reporting of AI usage in research.","url":"https://doi.org/10.7759/cureus.87297","authors":["Olukayode Apata","Oi‐Man Kwok","Yuan‐Hsuan Lee"],"tags":["Medicine","Artificial intelligence","Consensus conference","Generative grammar","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-04","doi":"https://doi.org/10.7759/cureus.87297","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W3212386989","name":"Attention mechanisms in computer vision: A survey","source":"openalex","abstract":"Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.","url":"https://doi.org/10.1007/s41095-022-0271-y","authors":["Meng-Hao Guo","Tian-Xing Xu","Jiangjiang Liu","Zheng-Ning Liu","Peng-Tao Jiang","Tai‐Jiang Mu","Song–Hai Zhang","Ralph R. Martin","Ming‐Ming Cheng","Shi‐Min Hu"],"tags":["Computer science","Categorization","Artificial intelligence","Computer graphics","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-15","doi":"https://doi.org/10.1007/s41095-022-0271-y","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408589460","name":"Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials","source":"openalex","abstract":"Cardiovascular disease (CVD) is a leading cause of death, accounting for over 30% of annual global fatalities. Ischemic heart disease, in turn, is a frontrunner of worldwide CVD mortality. With the burden of coronary disease rapidly growing, understanding the nuances of cardiac imaging and risk prognostication becomes paramount. Myocardial perfusion imaging (MPI) is a frequently utilized and well established testing modality due to its significant clinical impact in disease diagnosis and risk assessment. Recently, nuclear cardiology has witnessed major advancements, driven by innovations in novel imaging technologies and improved understanding of cardiovascular pathophysiology. Applications of artificial intelligence (AI) to MPI have enhanced diagnostic accuracy, risk stratification, and therapeutic decision-making in patients with coronary artery disease (CAD). AI techniques such as machine learning (ML) and deep learning (DL) neural networks offer new interpretations of immense data fields, acquired through cardiovascular imaging modalities such as nuclear medicine (NM). Recently, AI algorithms have been employed to enhance image reconstruction, reduce noise, and assist in the interpretation of complex datasets. The rise of AI in nuclear medicine (AI-NM) has proven itself groundbreaking in the efficiency of image acquisition, post-processing time, diagnostic ability, consistency, and even in risk-stratification and outcome prognostication. To that end, this narrative review will explore these latest advances in AI in nuclear medicine and its rapid transformation of the cardiac diagnostics landscape. This paper will examine the evolution of AI-NM, review novel AI techniques and applications in nuclear cardiac imaging, summarize recent AI-NM clinical trials, and explore the technical and clinical challenges in its implementation of artificial intelligence.","url":"https://doi.org/10.3390/jcm14062095","authors":["Ilana Golub","Abhinav Thummala","Tyler Morad","Jasmeet Dhaliwal","Francisco Elisarraras","Ronald P. Karlsberg","Geoffrey W. Cho"],"tags":["Medicine","Coronary artery disease","Cardiac imaging","Myocardial perfusion imaging","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-19","doi":"https://doi.org/10.3390/jcm14062095","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W3178987528","name":"Seamless Health Monitoring Using 5G NR for Internet of Medical Things","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11277-021-08730-7","authors":["Lalita Mishra","Vikash","Shirshu Varma"],"tags":["Computer science","Computer network","Latency (audio)","Network packet","Throughput"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-08","doi":"https://doi.org/10.1007/s11277-021-08730-7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4417071200","name":"Artificial Intelligence in Qualitative Research: Insights From Experts via Reflexive Thematic Analysis","source":"openalex","abstract":"The rapid advancement of artificial intelligence (AI) is increasingly shaping research methodologies across disciplines. However, its integration in qualitative research remains controversial due to epistemological, ethical, and human-centered concerns. This study explores the perspectives of 14 expert qualitative researchers from socio-anthropological and healthcare fields working in Italian academic and hospital settings, with a focus on the opportunities, challenges, and future directions of AI use in qualitative inquiry. Through semi-structured interviews and reflexive thematic analysis, four main themes were developed. First, participants expressed ambivalent attitudes-balancing curiosity with technophobia and emphasizing the need for human oversight and contextual interpretation. Second, an anthropological and philosophical dimension was constructed, underscoring the importance of reflexivity, creativity, and researcher identity as essential counterbalances to AI's mechanistic tendencies. Third, researchers acknowledged AI's practical benefits in tasks such as transcription and data management, and they remained skeptical of its ability to perform complex interpretative work. Finally, ethical and sustainability concerns were raised, including algorithmic bias, data privacy, and the environmental impact of AI technologies. The findings reveal persistent epistemological tensions but also highlight emerging opportunities for AI to enhance research efficiency and accessibility, provided that human interpretative agency remains central. Participants stressed the importance of developing robust ethical frameworks, fostering critical reflexivity, and adopting innovative conceptual approaches to responsibly integrate AI into qualitative research and education. This study offers valuable insights for scholars and practitioners navigating the evolving landscape of AI in qualitative inquiry, advocating a balanced approach that leverages AI's potential while safeguarding the human core of qualitative research.","url":"https://doi.org/10.1177/10497323251389800","authors":["Federica Dellafiore","Andreina Saba","Concetta Collaro","Giovanna Artioli"],"tags":["Reflexivity","Qualitative research","Thematic analysis","Engineering ethics","Agency (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-05","doi":"https://doi.org/10.1177/10497323251389800","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414179015","name":"Improving Sepsis Prediction in the ICU with Explainable Artificial Intelligence: The Promise of Bayesian Networks","source":"openalex","abstract":"Background/Objectives: Sepsis remains one of the leading causes of mortality worldwide, characterized by a complex and heterogeneous clinical presentation. Despite advances in patient monitoring and biomarkers, early detection of sepsis in the intensive care unit (ICU) is often hampered by incomplete data and diagnostic uncertainty. In recent years, machine learning models have been proposed as predictive tools, but many function as opaque “black boxes”, meaning that humans are unable to understand algorithmic reasoning, poorly suited to the uncertainty-laden clinical environment of critical care. Even when post-hoc interpretability methods are available for these algorithms, their explanations often remain difficult for non-expert clinicians to understand. Methods: In this clinical perspective, we explore the specific advantages of probabilistic graphical models, particularly Bayesian Networks (BNs) and their dynamic counterparts (DBNs), for sepsis prediction. Results: Recent applications of AI models in sepsis prediction have demonstrated encouraging results, such as DBNs achieving an AUROC of 0.94 in early detection, or causal probabilistic models in hospital admissions (AUROC 0.95). These models explicitly represent clinical reasoning under uncertainty, handle missing data natively, and offer interpretable, transparent decision paths. Drawing on recent studies, including real-time sepsis alert systems and treatment-effect modeling, we highlight concrete clinical applications and their current limitations. Conclusions: We argue that BNs present a great opportunity to bridge the gap between artificial intelligence and bedside care through human-in-the-loop collaboration, transparent inference, and integration into clinical information systems. As critical care continues to move toward data-driven decision-making, Bayesian models may offer not only technical performance but also the epistemic humility needed to support clinicians facing uncertain, high-stakes decisions.","url":"https://doi.org/10.3390/jcm14186463","authors":["Geoffray Agard","Christophe Roman","Christophe Guervilly","Mustapha Ouladsine","Laurent Boyer","Sami Hraiech"],"tags":["Interpretability","Medicine","Intensive care medicine","Sepsis","Probabilistic logic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-13","doi":"https://doi.org/10.3390/jcm14186463","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416783267","name":"Development and influencing factors of artificial intelligence literacy and computational thinking in Chinese university students","source":"openalex","abstract":"This study investigates the developmental status and influencing factors of artificial intelligence (AI) literacy and computational thinking (CT) literacy among undergraduates in China's \"four new\" majors. Guided by the Technology Acceptance Model, Social Cognitive Theory, and Constructivist Learning Theory, the research employs a questionnaire survey to assess student's AI and CT literacy, as well as the impact of subject background and AI tool usage. Statistical analyses (t-test, ANOVA, Pearson correlation) revealed statistically significant positive associations between dimensions of AI literacy and CT; however, effect sizes were uniformly small (|r| < .10), indicating that these associations-while detectable in a large sample-have limited practical magnitude and should be interpreted with caution. Intelligent Thinking exhibited the comparatively strongest association with critical thinking, though the magnitude warrants cautious interpretation. Disciplinary differences are evident: new engineering students excel at algorithmic thinking, while new liberal arts students show strengths in human-machine collaboration. Moreover, group mean differences were observed across usage-frequency categories; however, we did not fit non-linear models, and further research is needed to verify any non-linear patterns. These findings are consistent with the co-development of AIL and CT and may inform the design of discipline-specific, differentiated educational strategies in higher education.","url":"https://doi.org/10.1038/s41598-025-26888-z","authors":["Zhihua Hu","Huili He","Chunqu Zhang","Yurong Guan"],"tags":["Mathematics education","Literacy","Discipline","Computational thinking","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-28","doi":"https://doi.org/10.1038/s41598-025-26888-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416466328","name":"Neuro-symbolic AI for auditable cognitive information extraction from medical reports","source":"openalex","abstract":"Large language models (LLMs) such as GPT-4 can interpret free text, but unreliable answers, opaque reasoning, and privacy risks limit their use in healthcare. In contrast, rule-based artificial intelligence (AI) provides transparent and reproducible results but struggles with free text. We aimed to combine the strengths of both approaches to test whether such a hybrid system can autonomously and reliably extract clinical data from diagnostic imaging reports. We developed a neuro-symbolic AI that connects GPT-4 with a rule-based expert system through a semantic integration platform. GPT-4 extracted candidate facts from free-text reports, while the expert system verified them against medical rules, producing traceable, deterministic labels. We evaluated the system on 206 consecutive prostate cancer PET/CT scan reports, requiring extraction of 26 clinical parameters per report, generating 5356 data points, and answering three study questions: study inclusion, recurrent cancer identification, and prostate-specific antigen (PSA) level retrieval. Outputs were compared against physician-derived references, and discrepancies were reviewed by a blinded adjudicator. Here we show that neuro-symbolic AI outperforms GPT-4 alone and matches physicians in structuring and analysing reports. GPT-4 alone achieves F1 scores of 0.63 for study inclusion and 0.95 for recurrence detection, with 96.6% correct PSA values. Physicians reach F1 scores of 1.00 and 0.99, with 98.1% PSA accuracy. The neuro-symbolic AI scores twice 1.00 with 100% PSA accuracy and delivers always an auditable chain of reasoning. It intercepts two intentionally introduced reports with residual identifiers, preventing unintended transfer of sensitive data. Unlike standalone LLMs, neuro-symbolic AI can safely automate data extraction for clinical research and may provide a path toward trustworthy AI in healthcare practice. Medical doctors often write reports as free text, which is hard to reuse for research or care. A large language model is software that reads and writes text by imitating large networks of brain cells. This type of artificial intelligence can extract and organize important information from medical reports. But its reasoning is opaque, answers can be wrong, and it raises privacy concerns. Rule-based artificial intelligence is transparent, responds correctly, and is privacy-protecting but struggles with free text. We combined both artificial intelligence types, so each offsets the other’s weaknesses. We tested the system on 206 prostate cancer imaging reports, where it extracted information correctly, showed how it reached its answers, and protected sensitive data. Pairing large language models with rule-based systems could make artificial intelligence safer, more trustworthy, and more useful in healthcare. Prenosil, Weitzel et al. unify semantically the large language model GPT-4 with a rule-based expert system to turn free-text radiology reports into structured, privacy-safe data. In a proof-of-concept on 206 prostate cancer PET/CT reports, the resulting neuro-symbolic artificial intelligence matches physicians and supports clinical trials at scale.","url":"https://doi.org/10.1038/s43856-025-01194-x","authors":["George Prenosil","Thilo Weitzel","Shaibu Oricha Bello","Clemens Mingels","Giulia Manzini","Lorenz Meier","Kuangyu Shi","Axel Rominger","Ali Afshar‐Oromieh"],"tags":["Computer science","Trustworthiness","Cognition","Artificial intelligence","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-21","doi":"https://doi.org/10.1038/s43856-025-01194-x","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4404705278","name":"PICOT questions and search strategies formulation: A novel approach using artificial intelligence automation","source":"openalex","abstract":"AIM: The aim of this study was to evaluate and compare artificial intelligence (AI)-based large language models (LLMs) (ChatGPT-3.5, Bing, and Bard) with human-based formulations in generating relevant clinical queries, using comprehensive methodological evaluations. METHODS: To interact with the major LLMs ChatGPT-3.5, Bing Chat, and Google Bard, scripts and prompts were designed to formulate PICOT (population, intervention, comparison, outcome, time) clinical questions and search strategies. Quality of the LLMs responses was assessed using a descriptive approach and independent assessment by two researchers. To determine the number of hits, PubMed, Web of Science, Cochrane Library, and CINAHL Ultimate search results were imported separately, without search restrictions, with the search strings generated by the three LLMs and an additional one by the expert. Hits from one of the scenarios were also exported for relevance evaluation. The use of a single scenario was chosen to provide a focused analysis. Cronbach's alpha and intraclass correlation coefficient (ICC) were also calculated. RESULTS: In five different scenarios, ChatGPT-3.5 generated 11,859 hits, Bing 1,376,854, Bard 16,583, and an expert 5919 hits. We then used the first scenario to assess the relevance of the obtained results. The human expert search approach resulted in 65.22% (56/105) relevant articles. Bing was the most accurate AI-based LLM with 70.79% (63/89), followed by ChatGPT-3.5 with 21.05% (12/45), and Bard with 13.29% (42/316) relevant hits. Based on the assessment of two evaluators, ChatGPT-3.5 received the highest score (M = 48.50; SD = 0.71). Results showed a high level of agreement between the two evaluators. Although ChatGPT-3.5 showed a lower percentage of relevant hits compared to Bing, this reflects the nuanced evaluation criteria, where the subjective evaluation prioritized contextual accuracy and quality over mere relevance. CONCLUSION: This study provides valuable insights into the ability of LLMs to formulate PICOT clinical questions and search strategies. AI-based LLMs, such as ChatGPT-3.5, demonstrate significant potential for augmenting clinical workflows, improving clinical query development, and supporting search strategies. However, the findings also highlight limitations that necessitate further refinement and continued human oversight. CLINICAL RELEVANCE: AI could assist nurses in formulating PICOT clinical questions and search strategies. AI-based LLMs offer valuable support to healthcare professionals by improving the structure of clinical questions and enhancing search strategies, thereby significantly increasing the efficiency of information retrieval.","url":"https://doi.org/10.1111/jnu.13036","authors":["Lucija Gosak","Gregor Štiglic","Lisiane Pruinelli","Dominika Vrbnjak"],"tags":["Relevance (law)","CINAHL","Population","Computer science","Cronbach's alpha"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-24","doi":"https://doi.org/10.1111/jnu.13036","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4220750170","name":"Review—Towards 5th Generation AI and IoT Driven Sustainable Intelligent Sensors Based on 2D MXenes and Borophene","source":"openalex","abstract":"Sensors are considered to be an important vector for sustainable development. The demand to meet the needs of future generations is accelerating the development of intelligent sensor-systems integrated with internet of things (IoTs), fifth generation (5G) communication, artificial intelligence (AI) and machine learning (ML) strategies. The inclusion of 2D nanomaterials with the IoTs/AI/ML has revolutionized the diversified applications of sensors in healthcare, wearable electronics, safety, environment, defense, and agriculture. Owing to their unique physicochemical characteristics and surface functionalities, borophene and MXenes have emerged as advanced 2D-materials (A2M) to architect future-generation sensors. ML-AI based theoretical modeling has guided the research and development of A2M-sensors economically by reducing cost, human resources, and contamination. A2M-sensors are flexible, wearable, intelligent, biocompatible, portable, energy-efficient, self-sustained, point-of-care, and economical, which can drastically transform the conventional sensing strategies. This review provides an insight in to the state-of-the-art A2M-based physical, chemical, and biosensor to efficiently detect chemical species, gases/vapors, drugs, biomarkers/pathogens, pressure, metal ions, radiations, temperature, light, and humidity. Besides the fundamental challenges creating a gap between theoretical predictions, practical-evaluations, in-lab-technology, and commercial viability, their potential solutions, field-deployable prospects are addressed to realize commercialization, thereby ensuring ability of future generations to maintain sustainable communities.","url":"https://doi.org/10.1149/2754-2726/ac5ac6","authors":["Vishal Chaudhary","Ajeet Kaushik","Hidemitsu Furukawa","Ajit Khosla"],"tags":["MXenes","Computer science","Commercialization","Internet of Things","Wearable computer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-01","doi":"https://doi.org/10.1149/2754-2726/ac5ac6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4409170484","name":"Leveraging Artificial Intelligence for Personalized Rehabilitation Programs for Head and Neck Surgery Patients","source":"openalex","abstract":"Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications in clinical decision-making and workflow optimization. In head and neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of using LLMs to generate tailored rehabilitation programs for patients undergoing major head and neck surgical procedures. Methods: Ten hypothetical head and neck surgical clinical scenarios were developed, representing oncologic resections with complex reconstructions. Four LLMs, ChatGPT-4o, DeepSeek V3, Gemini 2, and Copilot, were prompted with identical queries to generate rehabilitation plans. Three senior clinicians independently assessed their quality, accuracy, and clinical relevance using a five-point Likert scale. Readability and quality metrics, including the DISCERN score, Flesch Reading Ease, Flesch–Kincaid Grade Level, and Coleman–Liau Index, were applied. Results: ChatGPT-4o achieved the highest clinical relevance (Likert mean of 4.90 ± 0.32), followed by DeepSeek V3 (4.00 ± 0.82) and Gemini 2 (3.90 ± 0.74), while Copilot underperformed (2.70 ± 0.82). Gemini 2 produced the most readable content. A statistical analysis confirmed significant differences across the models (p < 0.001). Conclusions: LLMs can generate rehabilitation programs with varying quality and readability. ChatGPT-4o produced the most clinically relevant plans, while Gemini 2 generated more readable content. AI-generated rehabilitation plans may complement existing protocols, but further clinical validation is necessary to assess their impact on patient outcomes.","url":"https://doi.org/10.3390/technologies13040142","authors":["Gianluca Marcaccini","Ishith Seth","Jennifer Novo","Vicki McClure","Brett Sacks","Kaiyang Lim","Sally Ng","Roberto Cuomo","Warren M. Rozen"],"tags":["Rehabilitation","Physical medicine and rehabilitation","Head and neck","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-04","doi":"https://doi.org/10.3390/technologies13040142","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408302723","name":"Assessing Physician Confidence in Artificial Intelligence: Insights from Iran","source":"openalex","abstract":"This study aims to explore physician confidence in artificial intelligence within the context of Iran's healthcare system. We aim to identify the factors influencing acceptance levels among physicians, particularly focusing on how job status and specialization impact attitudes toward AI. Specifically, we will investigate whether Type of specialization practitioners view AI differently relative to each other and whether academic physicians have distinct perspectives compared to their non-academic counterparts. By addressing these questions, we hope to contribute valuable insights that can facilitate the integration of AI technologies into medical practice and ultimately enhance healthcare delivery in Iran.This study aims to explore physician confidence in artificial intelligence within the context of Iran's healthcare system. We aim to identify the factors influencing acceptance levels among physicians, particularly focusing on how job status and specialization impact attitudes toward AI. Specifically, we will investigate whether Type of specialization practitioners view AI differently relative to each other and whether academic physicians have distinct perspectives compared to their non-academic counterparts. By addressing these questions, we hope to contribute valuable insights that can facilitate the integration of AI technologies into medical practice and ultimately enhance healthcare delivery in Iran.","url":"https://doi.org/10.61186/ist.202502.02.01","authors":["Faezeh Firuzpour","Elaheh Abdolalipour","Narjes rezaeiroushan","Maryam Barancheshmeh","Niloufar Moradi","Hadiseh Shokouhi Targhi"],"tags":["Artificial intelligence","Confidence interval","Psychology","Computer science","Statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.61186/ist.202502.02.01","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413050247","name":"Fidelity of Medical Reasoning in Large Language Models","source":"openalex","abstract":"This cross-sectional study evaluates whether the performance of large language models on medical benchmarks reflects logical reasoning or pattern recognition.","url":"https://doi.org/10.1001/jamanetworkopen.2025.26021","authors":["Suhana Bedi","Yixing Jiang","Philip Chung","Oluwasanmi Koyejo","Nigam H. Shah"],"tags":["Fidelity","Computer science","Logical reasoning","Natural language processing","Verbal reasoning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-08","doi":"https://doi.org/10.1001/jamanetworkopen.2025.26021","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416444806","name":"Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): an international, paired, non-inferiority, confirmatory, observational study","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s1470-2045(25)00567-4","authors":["Natália Alves","Natalia Alves","Megan Schuurmans","Dawid Rutkowski","Anindo Saha","Pierpaolo Vendittelli","Nancy A. Obuchowski","Marjolein Henrieke Liedenbaum","Ingfrid S. Haldorsen","Anders Molven","Derya Yakar","Jeroen Geerdink","Sebastiaan van Koeverden","Deniece Rivière","Wulphert Venderink","Robbert de Haas","Namkug Kim","J-Matthias Löhr","Garima Suman","Klaus Maier‐Hein","Horst K. Hahn","Weichung Wang","Alan Yuille","Avinash Kambadakone","Elliot K. Fishman","Caroline S. Verbeke","Geert Litjens","John J. Hermans","Henkjan Huisman","Natália Alves","Natália Alves","Megan Schuurmans","Anindo Saha","Pierpaolo Vendittelli","Geert Litjens","John Hermans","Henkjan Huisman","Deniece M. Riviere","Wulphert Venderink","Sebastiaan van Koeverden","Dawid Rutkowski","Marjolein H. Liedenbaum","Ingfrid S. Haldorsen","Anders Molven","Derya Yakar","Robbert J. de Haas","Jeroen Geerdink","Jeroen Veltman","Alan Yuille","Avinash Kambadakone","Caroline Verbeke","Celso Matos","Elliot Fishman","Garima Suman","Horst K. Hahn","Klaus Maier-Hein","J-Matthias Löhr","Namkug Kim","Nancy Obuchowski","Steven Gallinger","Weichung Wang","Ali Stunt","Han Liu","Riqiang Gao","Sasa Grbic","Zengtian Deng","Yimeng He","Yu Shi","Rebeca Vétil","Noëlie Debs","Clément Abi-Nader","Alexandre Bône","Marc-Michel Rohé","Ching-Yuan Yu","Jun Ma","Tianhao Fu","Bo Wang","Abraham Fourie Bezuidenhout","Adrian Thomas Huber","Adriano Liguori","Amine Korchi","Andrea Ponsiglione","Anselm Schulz","Arnaldo Stanzione","Augusto Minieri","Bang-Bin Chen","Cesare Maino","Charikleia Triantopoulou","Dimitra Christodoulou","Dominik Geisel","Dow-Mu Koh","Elisa Boffa","Enrico Boninsegna","Enza Genco","Erik Soloff","Eugenia Amelia Lettieri","Federica Omboni","Francesca Castagnoli","Francesco Prato","Frank Wessels"],"tags":["Observational study","Medicine","Gold standard (test)","Standard of care","Pancreatic cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-21","doi":"https://doi.org/10.1016/s1470-2045(25)00567-4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4387955202","name":"BASELINE SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHIC RETINAL LAYER FEATURES IDENTIFIED BY ARTIFICIAL INTELLIGENCE PREDICT THE COURSE OF CENTRAL SEROUS CHORIORETINOPATHY","source":"openalex","abstract":"PURPOSE: To identify optical coherence tomography (OCT) features to predict the course of central serous chorioretinopathy (CSC) with an artificial intelligence-based program. METHODS: Multicenter, observational study with a retrospective design. Treatment-naïve patients with acute CSC and chronic CSC were enrolled. Baseline OCTs were examined by an artificial intelligence-developed platform (Discovery OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). Through this platform, automated retinal layer thicknesses and volumes, including intaretinal and subretinal fluid, and pigment epithelium detachment were measured. Baseline OCT features were compared between acute CSC and chronic CSC patients. RESULTS: One hundred and sixty eyes of 144 patients with CSC were enrolled, of which 100 had chronic CSC and 60 acute CSC. Retinal layer analysis of baseline OCT scans showed that the inner nuclear layer, the outer nuclear layer, and the photoreceptor-retinal pigmented epithelium complex were significantly thicker at baseline in eyes with acute CSC in comparison with those with chronic CSC ( P < 0.001). Similarly, choriocapillaris and choroidal stroma and retinal thickness (RT) were thicker in acute CSC than chronic CSC eyes ( P = 0.001). Volume analysis revealed average greater subretinal fluid volumes in the acute CSC group in comparison with chronic CSC ( P = 0.041). CONCLUSION: Optical coherence tomography features may be helpful to predict the clinical course of CSC. The baseline presence of an increased thickness in the outer retinal layers, choriocapillaris and choroidal stroma, and subretinal fluid volume seems to be associated with acute course of the disease.","url":"https://doi.org/10.1097/iae.0000000000003965","authors":["Lorenzo Ferro Desideri","Rodrigo Anguita","Lieselotte Berger","Helena M. A. Feenstra","Davide Scandella","Raphael Sznitman","Camiel J. F. Boon","Elon H. C. van Dijk","Martin S. Zinkernagel"],"tags":["Optical coherence tomography","Retinal","Baseline (sea)","Serous fluid","Coherence (philosophical gambling strategy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-26","doi":"https://doi.org/10.1097/iae.0000000000003965","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408551062","name":"Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care","source":"openalex","abstract":"Artificial intelligence (AI) serves as a powerful tool that can revolutionize how personalized, patient-focused care is provided within interventional cardiology. Specifically, AI can augment clinical care across the spectrum for acute coronary syndrome, coronary artery disease, and valvular heart disease, with applications in coronary and structural heart interventions. This has been enabled by the potential of AI to harness various types of health data. We review how AI-driven technologies can advance diagnosis, preprocedural planning, intraprocedural guidance, and prognostication in interventional cardiology. AI automates clinical tasks, increases efficiency, improves reliability and accuracy, and individualizes clinical care, establishing its potential to transform care. Furthermore, AI-enabled, community-based screening programs are yet to be implemented to leverage the full potential of AI to improve patient outcomes. However, to transform clinical practice, AI tools require robust and transparent development processes, consistent performance across various settings and populations, positive impact on clinical and care quality outcomes, and seamless integration into clinical workflows. Once these are established, AI can reshape interventional cardiology, improving precision, efficiency, and patient outcomes.","url":"https://doi.org/10.1016/j.jscai.2025.102562","authors":["Arya Aminorroaya","Dhruva Biswas","Aline F Pedroso","Rohan Khera"],"tags":["Medicine","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1016/j.jscai.2025.102562","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414964768","name":"Autonomous Databases and Artificial Intelligence: Architectures, Optimization, and Governance","source":"openalex","abstract":"Artificial Intelligence-native databases are currently at the forefront of the rapidly evolving data management landscape. The book examines how database systems are changing to satisfy the needs of real-time, intelligent decision-making in different industries. The transition from traditional relational models to AI-driven architectures, cloud integration, optimization, and new developments like automation, explainability, and security are all covered in the chapters. This book's writing has involved both a thorough examination of contemporary data technology and a contemplation of the field's continuing opportunities and challenges. I want professionals, students, and anybody else interested in the future of databases to be able to understand both basic and advanced topics. I hope it encourages readers to welcome innovation and investigate the wise opportunities that lie ahead. I want to express my gratitude to my parents for their unwavering support during my journey, as well as to my peers, fellow researchers, and everyone else who has helped and inspired me. Their guidance and collaboration have been invaluable in shaping this book.","url":"https://doi.org/10.70593/978-93-7185-652-2","authors":["Suresh Kurapati"],"tags":["Contemplation","Corporate governance","Gratitude","Cloud computing","Relational database"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-03","doi":"https://doi.org/10.70593/978-93-7185-652-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4407171053","name":"Medical students and ChatGPT: analyzing attitudes, practices, and academic perceptions","source":"openalex","abstract":"BACKGROUND: ChatGPT, a chatbot launched by OpenAI in November 2022, has generated both excitement and concern within the healthcare education, research, and practice communities. This study aimed to explore the knowledge, perceptions, attitudes, and practices of undergraduate medical students regarding the use of ChatGPT and similar chatbots in their academic work. METHODS: An anonymous, structured questionnaire was developed using Google Forms and administered to medical students as part of a cross-sectional study. The survey targeted undergraduate medical students from four governorates in Egypt. The questionnaire link was distributed through social media platforms, including Facebook and WhatsApp. The survey comprised four sections: socio-demographic characteristics, perceptions, attitudes, and practices. RESULTS: The survey achieved a response rate of 96%, with 614 out of 640 approached students participating. Prior to the study, most respondents (78.5%) had personal experience using it. Overall, respondents demonstrated positive perceptions, attitudes, and practices toward ChatGPT, with mean scores exceeding 3 for all three variables: 3.99 ± 0.60 for perceptions, 3.01 ± 0.46 for attitudes, and 3.55 ± 0.55 for practices. In general, the students exhibited a high degree of trust in the model, with approximately half trusting the accuracy and reliability of the information provided by ChatGPT. However, more than two-thirds expressed apprehension about its potential misuse in medical education, and around 60% were concerned about the accuracy of information ChatGPT might generate on complex medical topics. CONCLUSIONS: Medical students show strong interest and trust in using ChatGPT and similar chatbots for academic purposes but have concerns about the reliability of the information and potential misuse in medical education. The use of AI tools should follow ethical guidelines set by academic institutions, with regular updates to keep pace with technological progress. Future research should focus on the impact of AI on education and personal development, especially among young people.","url":"https://doi.org/10.1186/s12909-025-06731-9","authors":["Ahmed Samir Abdelhafiz","Maysa I. Farghly","Eman A. Sultan","Moaz Elsayed Abouelmagd","Youssef Mohsen Ashmawy","Eman Hany Elsebaie"],"tags":["Medical education","Perception","Apprehension","Psychology","Social media"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-05","doi":"https://doi.org/10.1186/s12909-025-06731-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413958379","name":"Assessment of the Artificial Intelligence– Generated Fibromyalgia Information: Beyond the Hype","source":"openalex","abstract":"Background/Aims: Individuals increasingly turn to artificial intelligence (AI) chatbots for health-related information; however, the accuracy and usability of their responses remain uncertain. This study assessed the quality, comprehensiveness, and readability of responses from 6 AI chatbots-ChatGPT-3.5, ChatGPT-4o (OpenAI), Copilot AI (Microsoft), Perplexity AI (Perplexity.AI), Gemini AI (Google), and ChatSonic AI (Writesonic)-to the most commonly searched fibromyalgia-related queries. Materials and Methods: The top 10 most frequently searched fibromyalgia-related questions from the past 2 years were retrieved from the Google Trends database. Each chatbot was queried separately, and a total of 60 responses (10 per chatbot) were assessed both qualitatively and quantitatively by 2 reviewers, focusing on content quality, accuracy, readability, and alignment with evidence-based guidelines. Results: ChatGPT-3.5 had the lowest Ensuring Quality Information for Patients score (20.6 ± 4.5), indicating very low quality information, while Gemini achieved the highest (40.5 ± 5), which was still classified as low quality. Understandability was moderate for Copilot, Gemini, and Perplexity (67.2) but lowest for ChatGPT-3.5 (43.2 ± 10.2). Actionability was weak and the misinformation assessment revealed a moderate level across all chatbots. Readability scores indicated university-level complexity, with ChatGPT-4o having the lowest Reading Ease score (11.3 ± 11.2) and Copilot the highest (30.3 ± 13.2). Conclusion: While AI chatbots provide accessible health information, their accuracy and depth vary. Gemini, Copilot, and Perplexity AI showed better quality, but citation inconsistencies, readability challenges, and misinformation risks highlight the need for refinement beyond the hype. Clinicians should guide fibromyalgia patients in critically assessing AI-generated health content. Future research should explore improvements in AI chatbot applicability for medical inquiries.","url":"https://doi.org/10.5152/archrheumatol.2025.11149","authors":["Mert Zure","Ahmet Kıvanç Menekşeoğlu"],"tags":["Fibromyalgia","Medicine","Artificial intelligence","Physical therapy","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.5152/archrheumatol.2025.11149","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4403886078","name":"Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson’s Disease","source":"openalex","abstract":"One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson's Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI.","url":"https://doi.org/10.1186/s40537-024-01023-3","authors":["Laura Dipietro","Uri T. Eden","Seth Elkin-Frankston","Mirret M. El-Hagrassy","Deniz Doruk Camsari","Ciro Ramos-Estébanez","Felipe Fregni","Timothy Wagner"],"tags":["Computer science","Big data","Artificial intelligence","Precision medicine","Computational Science and Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-30","doi":"https://doi.org/10.1186/s40537-024-01023-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413137421","name":"Generative Artificial Intelligence Tools in Medical Research (GAMER): Protocol for a Scoping Review and Development of Reporting Guidelines","source":"openalex","abstract":"BACKGROUND: The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engagement, and information dissemination. Powerful generative AI (GenAI) tools and other similar chatbots have emerged, facilitating user interactions with virtual conversational agents. However, the increasing use of GenAI tools in medical research presents challenges, including ethical concerns, data privacy issues, and the potential for generating false content. These issues necessitate standardization of reporting to ensure transparency and scientific rigor. OBJECTIVE: The development of the Generative Artificial Intelligence Tools in Medical Research (GAMER) reporting guidelines aims to establish comprehensive, standardized guidelines for reporting the use of GenAI tools in medical research. METHODS: The GAMER guidelines are being developed following the methodology recommended by the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network, involving a scoping review and expert Delphi consensus. The scoping review searched PubMed, Web of Science, Embase, CINAHL, PsycINFO, and Google Scholar (for the first 200 results) using keywords like \"generative AI\" and \"medical research\" to identify reporting elements in GenAI-related studies. The Delphi process involves 30-50 experts with ≥3 years of experience in AI applications or medical research, selected based on publication records and expertise across disciplines (eg, clinicians and data scientists) and regions (eg, Asia and Europe). A 7-point-scale survey will establish consensus on checklist items. The testing phase invites authors to apply the GAMER checklist to GenAI-related manuscripts and provide feedback via a questionnaire, while experts assess reliability (κ statistic) and usability (time taken, 7-point Likert scale). The study has been approved by the Ethics Committee of the Institute of Health Data Science at Lanzhou University (HDS-202406-01). RESULTS: The GAMER project was launched in July 2023 by the Evidence-Based Medicine Center of Lanzhou University and the WHO Collaborating Centre for Guideline Implementation and Knowledge Translation, and it concluded in July 2024. The scoping review was completed in November 2023. The Delphi process was conducted from October 2023 to April 2024. The testing phase began in March 2025 and is ongoing. The expected outcome of the GAMER project is a reporting checklist accompanied by relevant terminology, examples, and explanations to guide stakeholders in better reporting the use of GenAI tools. CONCLUSIONS: GAMER aims to guide researchers, reviewers, and editors in the transparent and scientific application of GenAI tools in medical research. By providing a standardized reporting checklist, GAMER seeks to enhance the clarity, completeness, and integrity of research involving GenAI tools, thereby promoting collaboration, comparability, and cumulative knowledge generation in AI-driven health care technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64640.","url":"https://doi.org/10.2196/64640","authors":["Xufei Luo","Yih Chung Tham","Mohammad Daher","Zhaoxiang Bian","Yaolong Chen","Janne Estill","GAMER Working Group"],"tags":["Usability","Computer science","Standardization","Transparency (behavior)","Checklist"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-14","doi":"https://doi.org/10.2196/64640","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4406304903","name":"Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data","source":"openalex","abstract":"This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI's capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods.","url":"https://doi.org/10.1038/s41598-024-83750-4","authors":["Eunhye Choi","Seokwon Shin","Kijin Lee","Tien Li An","Richard K. Lee","Sunmin Kim","Sunmin Kim","Youngdoo Son","S.T. Kim","S.T. Kim"],"tags":["Temporomandibular joint","Joint disease","Radiography","Joint (building)","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-13","doi":"https://doi.org/10.1038/s41598-024-83750-4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4410477980","name":"AI’s Intelligence for Improving Food Safety: Only as Strong as the Data that Feeds It","source":"openalex","abstract":"Abstract Purpose of Review AI-based systems show great promise in advancing food safety control and surveillance systems, while they have important limitations that are often misunderstood by stakeholders. This review explores the fundamental components of AI through food safety applications, highlighting both its capabilities and constraints. Recent Findings AI has been effectively applied to outbreaks detection, allergen or spoilage issues, fraud, supply chain monitoring, traceability, quality control, shelf-life prediction, and risk mitigation. These applications integrate three key elements—sensing, reasoning, and actuating— within a structured five-step implementation cycle. Summary While AI has the potential to transform food safety, its effectiveness depends on timely access to robust, comprehensive, and unbiased data to ensure accuracy, reliability, and meaningful insights. Moving forward, it is essential to emphasize the human-in-the-loop approach. AI can support decision-making by providing actionable insights and improving efficiency, but humans must remain at the center of critical operations.","url":"https://doi.org/10.1007/s43555-025-00060-0","authors":["Maria‐Eleni Dimitrakopoulou","Alberto Garre"],"tags":["Food safety","Computer science","Psychology","Data science","Food science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-19","doi":"https://doi.org/10.1007/s43555-025-00060-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4416122045","name":"Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging","source":"openalex","abstract":"Three-dimensional (3D) reconstruction is necessary for visualizing complex anatomy and supporting clinical decision-making in radiology. However, traditional techniques often struggle with limitations in scalability, speed, and reproducibility. The recent emergence of artificial intelligence (AI) has enabled a new generation of reconstruction tools that offer greater automation and new clinical capabilities. Motivated by the demand for efficient imaging, researchers have applied deep learning to overcome longstanding barriers, with impacts spanning diagnosis, surgical planning, and disease monitoring. This review included peer-reviewed studies published within the last 10 years, focusing exclusively on adult human imaging published in the English language, as anatomical development, imaging protocols, and clinical decision pathways differ significantly in pediatric populations. While this improved applicability to adult radiology, it limited insight into emerging pediatric and preclinical research. Searches focused on AI-driven 3D reconstruction across different radiologic modalities. Articles were selected based on the following predefined inclusion criteria: adult human participants, AI-based 3D reconstruction, clinical validation, and relevance to radiologic practice. Studies including pediatric or animal subjects, preclinical-only experimentation, non-English-language text, or without applied clinical evaluation were excluded. Our results showed that AI has improved the accuracy, speed, and clinical utility of 3D reconstruction in multiple specialties. Deep learning models such as U-Net, V-Net, DenseVNet, and generative adversarial networks have achieved high segmentation accuracy, often reporting Dice scores >0.90. These models have also been used for tumor detection, surgical planning, and reducing radiation exposure. However, challenges such as high computational requirements, lack of standardized datasets, limited real-world validation, and ethical concerns remain. In conclusion, 3D reconstruction is transforming radiology with more accurate patient-specific images for improved clinical decision-making. While it is rapidly being integrated into practice, these technologies still have limitations that need to be addressed. Nonetheless, with improved and ongoing innovation, AI has the potential to become a catalyst for precise imaging and patient care.","url":"https://doi.org/10.7759/cureus.96580","authors":["Nikhita","Dhyaan Bannur","Maria Gabriela Cerdas","Arisha Zahid Saeed","Bashir Imam","Ravtej Singh Thandi","H C Anusha","P. Hemachandra Reddy","Ramsha Ali"],"tags":["Medicine","Deep learning","Artificial intelligence","Medical physics","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-11","doi":"https://doi.org/10.7759/cureus.96580","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W7124980116","name":"Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis","source":"pubmed","abstract":"Objective: Postpartum depression is a frequent complication after childbirth, affecting maternal health, infant development, and family well-being. This study evaluated the role of artificial intelligence (AI) in preventing and detecting postpartum depression early. Methods: A systematic search was conducted in Scopus, PubMed, Web of Science, and CINAHL for studies (2020-2025) applying AI to identify postpartum depression. PRISMA guidelines guided selection and appraisal. Two random-effects meta-analyses estimated pooled sensitivity and accuracy based on total sample size and reported metrics. Results: Of 1,857 records, 16 studies met inclusion criteria. Machine learning models (Random Forest, XGBoost, neural networks) showed greater accuracy than traditional methods. Integration of AI with medical records and social media data enabled earlier, personalized detection. Reported challenges included algorithmic bias, data privacy, and implementation barriers. Pooled sensitivity was 69% (95% CI: 55-81%; n=277,496) and accuracy 79% (95% CI: 73-85%; n=306,156). Conclusions: AI shows promise for enhancing postpartum depression detection and prevention but requires addressing ethical, technical, and educational challenges to achieve equitable clinical integration. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251004175, identifier CRD420251004175.","url":"https://doi.org/10.3389/fpsyt.2025.1734102","authors":["Azahara Ruger-Navarrete","María Gómez-Ferrera","Beatriz Mérida-Yáñez","Juana María Vázquez-Lara","Juan Camilo Gómez-Salgado","Sofía García-Oliva","María Dolores Vázquez-Lara","Luciano Rodríguez-Díaz","Irene Antúnez-Calvente","Francisco Javier Fernández-Carrasco","Ruger-Navarrete A","Gómez-Ferrera M"],"tags":["Psychology","Medicine","Artificial intelligence","Postpartum period","Psychiatry"],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"https://doi.org/10.3389/fpsyt.2025.1734102","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"oa:W4411067040","name":"Use of artificial intelligence tools by doctoral students: a mixed-methods explanatory-sequential investigation","source":"openalex","abstract":"Increasing use of AI tools in higher education comes with a need to develop a clear understanding of how students (by demography and study discipline) employ them, for what purposes and in what terms their use is evaluated. This will inform the development of future guidelines and training for staff and students. In the current mixed-methods study, a cross-sectional survey was first administered to a convenience sample of 105 doctoral students (71 females, 34 males), followed by semi-structured interviews with a subset of seven participants (four females, three males). This had several aims; to identify patterns of AI tool use and its perceived helpfulness; to explore AI use with regard to aspects of time management, stress and study progress; to construct a predictive model of AI tool use and to explore, in-depth, students’ views on beneficial and problematic aspects of AI. Findings suggested AI was widely used. Males were more likely to use it for data analysis and research planning, with greater use by students in non-scientific disciplines. Use was significantly related to stress and time management. Several potential benefits and problems were identified. Benefits included aiding research, for example, assisting and improving coding/programming, use in proofreading and writing and as an explanatory tool for rendering complex information accessible. Potential problems included environmental cost, violation of intellectual property rights, provision of misleading and/or inaccurate information and risks of plagiarism and hindered creativity. Limitations of the study are discussed alongside implications for policy and training with suggestions for further work.","url":"https://doi.org/10.1080/0309877x.2025.2515135","authors":["Muhammad Naveed Akbar"],"tags":["Psychology","Mathematics education","Higher education","Multimethodology","Pedagogy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-05","doi":"https://doi.org/10.1080/0309877x.2025.2515135","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W7128732859","name":"AIONS Consensus Conference on Definitions of Artificial Intelligence Surgery, Surgomics and Robotics","source":"openalex","abstract":"This Consensus Statement was jointly developed by the Editorial Board Members of Artificial Intelligence Surgery and the Artificial Intelligence Organization for the Next Generation of Surgeons (AIONS). The initiative began in February 2025 and proceeded through iterative drafting of definitions, online meetings, expert subgroup revisions, an online validation survey, an in-person Consensus Conference, and a final online meeting to confirm revisions and review the conference manuscript. Votes greater than or equal to 80 percent were considered validating. Definitions were sought for: (1) Surgery, (2) Endoluminal Surgery, (3) Percutaneous Surgery, (4) Robot, (5) Surgical Robot, (6) Robot-Assisted Surgery, (7) Telemanipulator Surgery, (8) Remote Surgery, (9) Collaborative Robotic (Cobotic) Surgery, (10) Robotic Surgery, (11) Artificial Intelligence Surgery, (12) Surgomics, (13) Surgical Multiomics, (14) Non-Invasive Surgery, (15) Digital Surgery, (16) Computer-Assisted Surgery, and (17) Cybersurgery. All candidate definitions achieved at least 80 percent approval in an online vote prior to the Consensus Conference. The in-person meeting occurred on 26 September 2025 at the Orto Botanico, University of Padova, Italy, where 11 definitions were ratified. The definition of Surgery was deemed premature and invalidated. Surgomics and Surgical Multiomics were determined to be distinct entities and were therefore revoted online after the meeting. Collaborative Robotics was clarified as requiring co-local presence of the surgeon and robot. Definitions for Percutaneous Surgery and Robot were amended and validated during a follow-up online vote on 11 November. Ultimately, all 17 definitions were validated. This Consensus provides terminology, rationale, and strategic direction for the surgical field as artificial intelligence, robotics, and data science reshape surgical practice. Future Consensus Conferences are planned to update definitions as the field evolves.","url":"https://doi.org/10.20517/ais.2025.113","authors":["Andrew Gumbs","Diana Michele","Karol Rawicz-Prusyński","Gaya Spolverato","Isabella Frigerio","Mohammad Abu Hilal","Elisa Bannone","Roland Croner","Francesca Dal Mas","Belinda De Simone","Michael Friebe","Francesco Giovinazzo","S. Vincent Grasso","Takeaki Ishizawa","Konrad Karcz","Zain Khalpey","Luca Milone","Nouredin Messaoudi","M. Mahir Ozmen","Peter Passias","Niki Rashidian","Sharona Ross","Thomas Schnelldorfer","Amir Szold","Zbigniew Nawrat","Ibrahim Dagher"],"tags":["Robotics","Artificial intelligence","Consensus conference","Robot","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.20517/ais.2025.113","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4404118195","name":"Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics – A narrative review","source":"openalex","abstract":"INTRODUCTION: Many studies display significant heterogeneity in the reliability of artificial intelligence (AI) assessment of minimally invasive surgical (MIS) skills. Our objective is to investigate whether AI systems utilising standardised objective metrics (SOMs) as the basis of skill assessment can provide a clearer understanding of the current state of such technology. METHODS: We systematically searched Medline, Embase, Scopus, CENTRAL and Web of Science from March 2023 to September 2023. Results were compiled as a narrative review. RESULTS: Twenty-four citations were analysed. Overall accuracy of AI systems in predicting overall SOM score of a procedure ranged from 63 ​% to 100 ​%. The most frequently used SOM by AI algorithms were Objective Structured Assessment of Technical Skills (OSATS) (8/24) and Global Evaluative Assessment of Robotic Skills (GEARS) (8/24). CONCLUSIONS: Stratifying for AI studies which employed SOMs to assess surgical skill did not reduce heterogeneity of reported reliability. Our study identifies key issues within the current literature, which, once addressed, could allow more meaningful comparisons between studies.","url":"https://doi.org/10.1016/j.amjsurg.2024.116074","authors":["Denuka Kankanamge","Chandana Wijeweera","Z. Ong","Tamara Preda","Terry Carney","Michael J. Wilson","Veronica Preda"],"tags":["Narrative","Narrative review","Psychology","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-06","doi":"https://doi.org/10.1016/j.amjsurg.2024.116074","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4417114461","name":"Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery","source":"openalex","abstract":"Artificial Intelligence (AI) is rapidly transforming surgical care by enabling more accurate diagnosis and risk prediction, personalized decision-making, real-time intraoperative support, and postoperative management. Ongoing trends such as multi-task learning, real-time integration, and clinician-centered design suggest AI is maturing into a safe, pragmatic asset in surgical care. Yet, significant challenges, such as the complexity and opacity of many AI models (particularly deep learning), transparency, bias, data sharing, and equitable deployment, must be surpassed to achieve clinical trust, ethical use, and regulatory approval of AI algorithms in healthcare. Explainable Artificial Intelligence (XAI) is an emerging field that plays an important role in bridging the gap between algorithmic power and clinical use as surgery becomes increasingly data-driven. The authors reviewed current applications of XAI in the context of surgery-preoperative risk assessment, surgical planning, intraoperative guidance, and postoperative monitoring-and highlighted the absence of these mechanisms in Generative AI (e.g., ChatGPT). XAI will allow surgeons to interpret, validate, and trust AI tools. XAI applied in surgery is not a luxury: it must be a prerequisite for responsible innovation. Model bias, overfitting, and user interface design are key challenges that need to be overcome and will be explored in this review to achieve the integration of XAI into the surgical field. Unveiling the algorithm is the first step toward a safe, accountable, transparent, and human-centered surgical AI.","url":"https://doi.org/10.3390/healthcare13243208","authors":["Sara Lopes","Miguel Mascarenhas","João Fonseca","Gabriela Fernandes","Adelino Leite‐Moreira"],"tags":["Context (archaeology)","Artificial intelligence","Computer science","Field (mathematics)","Surgical procedures"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-08","doi":"https://doi.org/10.3390/healthcare13243208","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412990763","name":"A Narrative Review of Theranostics in Neuro-Oncology: Advancing Brain Tumor Diagnosis and Treatment Through Nuclear Medicine and Artificial Intelligence","source":"openalex","abstract":"This narrative review explores the integration of theranostics and artificial intelligence (AI) in neuro-oncology, addressing the urgent need for improved diagnostic and treatment strategies for brain tumors, including gliomas, meningiomas, and pediatric central nervous system neoplasms. A comprehensive literature search was conducted through PubMed, Scopus, and Embase for articles published between January 2020 and May 2025, focusing on recent clinical and preclinical advancements in personalized neuro-oncology. The review synthesizes evidence on novel theranostic agents-such as Lu-177-based radiopharmaceuticals, CXCR4-targeted PET tracers, and multifunctional nanoparticles-and highlights the role of AI in enhancing tumor detection, segmentation, and treatment planning through advanced imaging analysis, radiogenomics, and predictive modeling. Key findings include the emergence of nanotheranostics for targeted drug delivery and real-time monitoring, the application of AI-driven algorithms for improved image interpretation and therapy guidance, and the identification of current limitations such as data standardization, regulatory challenges, and limited multicenter validation. The review concludes that the convergence of AI and theranostic technologies holds significant promise for advancing precision medicine in neuro-oncology, but emphasizes the need for collaborative, multidisciplinary research to overcome existing barriers and enable widespread clinical adoption.","url":"https://doi.org/10.3390/ijms26157396","authors":["Rafail C. Christodoulou","Platon S. Papageorgiou","Rafael Pitsillos","Amanda Woodward","Sokratis G. Papageorgiou","Elena E. Solomou","Michalis F. Georgiou"],"tags":["Medicine","Personalized medicine","Medical physics","Precision medicine","Narrative review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.3390/ijms26157396","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4407660154","name":"Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes","source":"openalex","abstract":"Depression is a common mental health disorder and a leading contributor to mortality and morbidity. Despite several advancements, the current screening methods have limitations in enabling the robust and automated detection of depression, thereby hindering early diagnosis and timely intervention. This study aimed to develop explainable artificial intelligence (AI) models to predict depression using polysomnographic phenotype data, ensuring high predictive performance while providing clear insights into the importance of features influencing the risk of depression. Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. Phenotype data from subjective health questionnaires, clinical assessments, and demographic factors were analyzed. The explainable AI models identified the important features, and their performance was evaluated using cross-validation. The study population, comprising 114 control participants and 39 individuals with depression, was stratified based on validated depression-scoring methods. The proposed explainable AI models achieved an F1-score of 85%, verifying their high reliability in predicting depression. Key features influencing the risk of depression, such as anxiety disorders, sleep efficiency, and demographic factors, offer actionable insights for clinical practice, highlighting the transparency of these models. This study proposed and developed explainable AI models based on polysomnographic phenotype data for the automated detection of depression and verified that these models help improve mental health diagnostics, enabling timely interventions.","url":"https://doi.org/10.3390/bioengineering12020186","authors":["Doljinsuren Enkhbayar","Jonathan Ko","Sejong Oh","Rumana Ferdushi","Jae-Soo Kim","Jaehong Key","Erdenebayar Urtnasan"],"tags":["Machine learning","Artificial intelligence","Anxiety","Psychological intervention","Random forest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-15","doi":"https://doi.org/10.3390/bioengineering12020186","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412595371","name":"The ethics of artificial intelligence: Safeguarding human dignity, social justice and environmental stability in the age of AI","source":"openalex","abstract":".","url":"https://doi.org/10.24136/eq.3743","authors":["Ladislav Mura","Beáta Stehlí­ková"],"tags":["Dignity","Safeguarding","Economic Justice","Environmental ethics","Social justice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-30","doi":"https://doi.org/10.24136/eq.3743","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W7118566795","name":"Breeding Smarter: Artificial Intelligence and Machine Learning Tools in Modern Breeding—A Review","source":"openalex","abstract":"Climate challenges, along with a projected global population increase of 2 billion by 2080, are intensifying pressures on agricultural systems, leading to biodiversity loss, land use constrains, soil fertility declining, and changes in water cycles, while crop yields struggle to meet the rising food demand. These challenges, coupled with evolving legislation and rapid technology advancements, require innovative sustainable agricultural solutions. By reshaping farmers’ daily operations, real-time data acquisition and predictive models can support informed decision-making. In this context, smart farming (SM) applied to plant breeding can improve efficiency by reducing inputs and increasing outputs through the adoption of digital and data-driven technologies. Examples include the investment on common ontologies and metadata standards for phenotypes and environments, standardization of HTP protocols, integration of prediction outputs into breeding databases, and selection workflows, as well in building multi-partner field networks that collect diverse envirotypes. This review outlines how AI and machine learning (ML) can be integrated in modern plant breeding methodologies, including genomic selection (GS) and genetic algorithms (GAs), to accelerate the development of climate-resilient and sustainably performing crop varieties. While many reviews address smart farming or smart breeding independently, herein, these domains are bridged to provide an understandable strategic landscape by enhancing breeding efficiency.","url":"https://doi.org/10.3390/agronomy16010137","authors":["Ana Luísa Garcia-Oliveira","Sangam L. Dwivedi","Subhash Chander","Charles Nelimor","Diaa Abd El Moneim","Rodomiro Ortíz"],"tags":["Standardization","Sustainability","Artificial intelligence","Agriculture","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-05","doi":"https://doi.org/10.3390/agronomy16010137","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408245445","name":"Artificial intelligence for early detection of lung cancer in GPs’ clinical notes: a retrospective observational cohort study","source":"openalex","abstract":"BACKGROUND: The journey of >80% of patients diagnosed with lung cancer starts in general practice. About 75% of patients are diagnosed when it is at an advanced stage (3 or 4), leading to >80% mortality within 1 year at present. The long-term data in GP records might contain hidden information that could be used for earlier case finding of patients with cancer. AIM: To develop new prediction tools that improve the risk assessment for lung cancer. DESIGN AND SETTING: Text analysis of electronic patient data using natural language processing and machine learning in the general practice files of four networks in the Netherlands. METHOD: Files of 525 526 patients were analysed, of whom 2386 were diagnosed with lung cancer. Diagnoses were validated by using the Dutch cancer registry, and both structured and free-text data were used to predict the diagnosis of lung cancer 5 months before diagnosis (4 months before referral). RESULTS: The algorithm could facilitate earlier detection of lung cancer using routine general practice data. Discrimination, calibration, sensitivity, and specificity were established under various cut-off points of the prediction 5 months before diagnosis. Internal validation of the best model demonstrated an area under the curve of 0.88 (95% confidence interval [CI] = 0.86 to 0.89), which shrunk to 0.79 (95% CI = 0.78 to 0.80) during external validation. The desired sensitivity determines the number of patients to be referred to detect one patient with lung cancer. CONCLUSION: Artificial intelligence-based support enables earlier detection of lung cancer in general practice using readily available text in the patient files of GPs, but needs additional prospective clinical evaluation.","url":"https://doi.org/10.3399/bjgp.2023.0489","authors":["Martijn C. Schut","Torec T. Luik","Iacopo Vagliano","Miguel Rios","Charles W. Helsper","Kristel M. van Asselt","Niek J. de Wit","Ameen Abu–Hanna","Henk van Weert"],"tags":["Lung cancer","Medicine","Referral","Medical diagnosis","General practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-05","doi":"https://doi.org/10.3399/bjgp.2023.0489","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4411792008","name":"Exploring the role of DeepSeek-R1, ChatGPT-4, and Google Gemini in medical education: How valid and reliable are they?","source":"openalex","abstract":"Objective: In recent years, Artificial Intelligence (AI) has led to rapid advancements in science, technology, industries, healthcare settings, and medical education. A Chinese-built large language model, DeepSeek-R1, inspires the scientific community as an affordable and open alternative to earlier established US-based AI models, ChatGPT-4 and Google Gemini 1.5 Pro. This study aimed to explore the role of “DeepSeek-R1, ChatGPT-4 and Google Gemini 1.5 Pro” and to assess the validity and reliability of these AI tools in medical education. Methods: The current cross-sectional study was performed in the Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia during the period January 25, 2025, to February 28, 2025. The Multiple-Choice Questions (MCQs) bank was created with a pool of basic medical sciences (60 MCQs) and clinical medical sciences (40 MCQs). The one hundred MCQs were prepared from various medical textbooks, journals, and examination pools. The MCQs were individually entered into the given area of the “DeepSeek-R1, ChatGPT-4 and Google Gemini 1.5 Pro” to assess the level of knowledge in various disciplines of medical sciences. Results: The marks obtained in basic medical sciences by DeepSeek R1 47/60 (78.33%), ChatGPT-4 47/60 (78.33%), and Google Gemini 1.5 Pro 49/60 (81.7%). However, in clinical medical sciences, the marks obtained by DeepSeek R1 were 35/40 (87.5%), ChatGPT-4 36/40 (90.0%), and Google Gemini 1.5 Pro 33/40 (82.5%). The total marks obtained by DeepSeekR1 were 82/100 (82.0%), Chat GPT-4 84/100 (84.0%), and Google Gemini-1.5 Pro 82/100 (82.0%). Conclusions: The Chinese-based DeepSeek-R1, the US-based ChatGPT-4, and Google Gemini-1.5 Pro achieved similar scores, exceeding 80% marks, in various medical sciences subjects. The study findings demonstrate that the knowledge, validity, and reliability levels of DeepSeek R1, ChatGPT-4, and Google Gemini 1.5 Pro are similar for their potential future use in medical education. doi: https://doi.org/10.12669/pjms.41.7.12183 How to cite this: Meo SA, Abukhalaf FA, ElToukhy RA, Sattar K. Exploring the role of DeepSeek-R1, ChatGPT-4, and Google Gemini in medical education: How valid and reliable are they? Pak J Med Sci. 2025;41(7):1887-1892. doi: https://doi.org/10.12669/pjms.41.7.12183 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","url":"https://doi.org/10.12669/pjms.41.7.12183","authors":["Sultan Ayoub Meo","Farah A Abukhalaf","Riham A ElToukhy","Kamran Sattar"],"tags":["Medicine","Medical education","Medical science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-30","doi":"https://doi.org/10.12669/pjms.41.7.12183","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4414233468","name":"The significance of artificial intelligence and machine learning in contemporary chemical engineering curriculum","source":"openalex","abstract":"This paper discusses how artificial intelligence and machine learning can be incorporated into the new curricula of chemical engineering education as part of the transformation facing the challenges of Industry 4.0. It describes the ways in which AI tools, adaptive learning platforms, virtual laboratories, intelligent tutoring systems, and automatic assessments can significantly improve teaching effectiveness, resulting in corresponding increases in student learning outcomes. A set of indicative case studies from engineering programs is also analyzed to assess impacts based on a broad literature review. Results demonstrate that AI/ML applications provide opportunities for personalized instruction, leading to heightened conceptual understanding and accessible feedback support. Such benefits make AI/ML more preferable than traditional pedagogy, which often lacks scalability and adaptability to diverse circumstances. It also highlighted challenges related to data privacy, faculty preparedness, infrastructure limitations, and institutional resistance. The paper finally presents a set of practical recommendations for implementation, along with emerging trends, such as generative AI and learning analytics, that are shaping the future landscape of engineering education.","url":"https://doi.org/10.1080/2331186x.2025.2560057","authors":["Nayef Ghasem"],"tags":["Artificial intelligence","Adaptability","Generative grammar","Scalability","Curriculum"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-16","doi":"https://doi.org/10.1080/2331186x.2025.2560057","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412047269","name":"A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome","source":"openalex","abstract":"Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (MASLD), atherogenic dyslipidaemia (ATD), and chronic kidney disease (CKD). The absence of reliable tools for early diagnosis and risk stratification leads to delayed detection, preventable hospitalizations, and increased healthcare costs. This study evaluates the impact of Transformer-based artificial intelligence (AI) model in predicting and managing MetS-related NCDs compared to classical machine learning models. Electronical medical data registered in the MIMIC-IV v2.2database from 183 958 patients with at least two recorded medical visits were analysed. A two-stage AI approach was implemented: (1) pretraining on 60% of the dataset to capture disease progression patterns, and (2) fine-tuning on the remaining 40% for disease-specific predictions. Transformer-based models was compared with traditional machine learning approaches (Random Forest and Linear Support Vector Classifier [SVC]), evaluating predictive performance through AUC and F1-score. The Transformer-based model significantly outperformed classical models, achieving higher AUC values across all diseases. It also identified a substantial number of undiagnosed cases compared to documented diagnoses fold increase for CKD 2.58, T2D 0.78, dyslipidaemia 1.89, hypertension 3.33, MASLD 5.78, and obesity 4.07. Diagnosis delays ranged from 90 to 500 days, with 35% of missed intervention opportunities occurring within the first five appointments. These delays correlated with an 84% increase in hospitalizations and a 69% rise in medical procedures. This study demonstrates that Transformer-based AI models offer superior predictive accuracy over traditional methods by capturing complex temporal disease patterns. Their integration into clinical workflows and public health strategies could enable scalable, proactive MetS management, reducing undiagnosed cases, optimizing resource allocation, and improving population health outcomes.","url":"https://doi.org/10.1093/eurpub/ckaf098","authors":["Alejandro Clarós","Andreea Ciudin","Jordi Muria","Lluis Llull","Jose Àngel Mola","Martí Pons","Javier Castán","Juan Carlos Cruz","Rafael Simó"],"tags":["Medicine","Medical diagnosis","Metabolic syndrome","Machine learning","Kidney disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-12","doi":"https://doi.org/10.1093/eurpub/ckaf098","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413035028","name":"The impact of an artificial intelligence enhancement program on healthcare providers’ knowledge, attitudes, and workplace flourishing","source":"openalex","abstract":"Background The integration of AI into healthcare influences healthcare providers’ knowledge, attitudes, and workplace flourishing, grounded in key theoretical frameworks. Social cognitive theory suggests AI-enhanced programs may shape knowledge acquisition and decision-making. The Theory of Planned Behavior helps explain how perceptions of AI affect professional attitudes. Meanwhile, workplace flourishing aligns with positive organizational psychology, emphasizing autonomy and engagement factors potentially impacted by AI adoption. We aimed to examine the impact of artificial intelligence enhancement programs on the knowledge, attitudes, and workplace flourishing of healthcare providers. Methods The present study was a quasi-experimental study conducted on healthcare providers at Zagazig University Hospital. The data was gathered using a self-administered three-domain tool, including an artificial intelligence knowledge domain, general attitudes toward artificial intelligence domain, and a workplace flourishing domain. Results Regarding the artificial intelligence technologies knowledge, attitude, and flourishing at work scales, post-intervention scores of all domains showed a statistically significant increase compared to pre-intervention, with a percent increase in knowledge score, attitude, and flourishing at work score were 123.14, 74.28, and 10.63%, respectively. Post-intervention attitude score was significantly positively correlated with knowledge score ( p = 0.001). In addition, age and years of experience were negatively correlated with changes in knowledge and attitude. Conclusion Artificial intelligence training is essential for enhancing healthcare providers’ knowledge and alleviating their concerns regarding its integration into healthcare. Clinical trial registration Identifier PACTR202403647083094; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=27347","url":"https://doi.org/10.3389/fpubh.2025.1639333","authors":["Hanaa A. Nofal","Amal Elwan Mohamed","Noura Almadani","Rasha Mahfouz","Hibah Abdulrahim Bahri","Hossam Tharwat Ali","Dina Sameh Elrafey"],"tags":["Flourishing","Autonomy","Health care","Psychology","Intervention (counseling)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-07","doi":"https://doi.org/10.3389/fpubh.2025.1639333","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413323193","name":"A high-resolution, nanopore-based artificial intelligence assay for DNA replication stress in human cancer cells","source":"openalex","abstract":"DNA replication stress is a hallmark of cancer that is exploited by chemotherapies. Current assays for replication stress have low throughput and poor resolution whilst being unable to map the movement of replication forks genome-wide. We present a new method that uses nanopore sequencing and artificial intelligence to map forks and measure their rates of movement and stalling in melanoma and colon cancer cells treated with chemotherapies. Our method can differentiate between fork slowing and fork stalling in cells treated with hydroxyurea, as well as inhibitors of ATR, WEE1, and PARP1. These different therapies yield different characteristic signatures of replication stress. We assess the role of the intra-S-phase checkpoint on fork slowing and stalling and show that replication stress dynamically changes over S-phase. Finally, we demonstrate that this method is applicable and consistent across two different flow cell chemistries (R9.4.1 and R10.4.1) from Oxford Nanopore Technologies. This method requires sequencing on only one nanopore flow cell per sample, and the cost-effectiveness enables functional screens to determine how human cancers respond to replication-targeted therapies.","url":"https://doi.org/10.1038/s41467-025-63168-w","authors":["Mathew V. Jones","Subash Kumar","Pauline L. Pfuderer","Alexis Bonfim‐Melo","Julia K. Pagan","Paul R. Clarke","Francis Isidore G. Totañes","Catherine J. Merrick","Sarah E. McClelland","Michael A. Boemo"],"tags":["Replication (statistics)","DNA replication","Nanopore","Nanopore sequencing","Fork (system call)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-19","doi":"https://doi.org/10.1038/s41467-025-63168-w","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4417359252","name":"Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy","source":"openalex","abstract":"Cardiac implantable electronic devices (CIEDs) such as pacemakers, implantable cardioverter-defibrillators (ICDs), and cardiac resynchronisation therapy (CRT) devices are generating unprecedented volumes of data in both inpatient and remote settings. Artificial intelligence (AI) techniques are increasingly being applied to enhance the management of these devices and the patients who rely on them. Recent advances demonstrate that machine learning (ML) and deep learning (DL) can improve diagnostic capabilities (for example, by detecting arrhythmias and predicting clinical events), streamline remote monitoring workflows, and optimise device-based therapies. Key applications include AI-driven algorithms that accurately detect true arrhythmias while filtering out false alerts from pacemakers and implantable monitors, neural network models that predict ventricular arrhythmias weeks before ICD shocks, and personalised models that forecast which heart failure patients will respond to CRT. Moreover, novel approaches such as natural language processing (NLP) and reinforcement learning are being explored to integrate diverse data sources and to enable devices to self-adjust their programming. This narrative review summarises the major applications of AI in the CIED domain-diagnostics, remote monitoring, and therapy optimisation-with an emphasis on the recent literature over the past five years. The review highlights important studies and randomised trials in each area, discusses the variety of AI techniques employed, and outlines future directions and challenges (including data standardisation, validation in clinical trials, and regulatory considerations) for translating these innovations into routine clinical care.","url":"https://doi.org/10.3390/jcm14248824","authors":["Ibrahim Antoun","Alkassem Alkhayer","Ahmed Abdelrazik","Mahmoud Eldesouky","Kaung Myat Thu","Harshil Dhutia","Riyaz Somani","G. André Ng"],"tags":["Medicine","Artificial intelligence","Deep learning","Narrative review","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-13","doi":"https://doi.org/10.3390/jcm14248824","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4410898059","name":"When One Size Does not Fit All—Artificial Intelligence in Australian Rural Health","source":"openalex","abstract":"AIMS: Artificial intelligence (AI) is having an increasing impact on many aspects of our day-to-day lives. This change is also true in healthcare, with various tools being developed to hasten burdensome administrative tasks and increase overall healthcare efficiency, particularly in metropolitan centres. CONTEXT: AI has remained comparatively clear of rural, regional and remote Australian hospitals, where it has the potential to provide significant benefits. Like previous health technology implementations, rural workforce requirements for AI maintenance and support may hinder AI deployment in these areas. While AI has been implemented successfully in metropolitan areas, these models may have limited translatability to rural health settings with significantly different administrative and healthcare systems. APPROACH: AI may assist with key issues in rural centres such as resource allocation and timely patient transfer for higher level care. While the potential benefits of AI in rural centres are clear, one must consider key factors in rural centres that may limit the success of AI in these hospitals. Smaller rural populations may limit the ability to train location-specific models, and connectivity issues may impede their effective use. CONCLUSION: Specific efforts are required to realise potential benefits of medical AI for rural Australia; addressing connectivity and workforce issues in rural areas is vital to allow for AI and large language models to help benefit rural centres.","url":"https://doi.org/10.1111/ajr.70037","authors":["Lewis Hains","J Kovoor","Brandon Stretton","Aashray Gupta","Ammar Zaka","Gavin J. Carmichael","John Kefalianos","Win Le Shwe Sin","Alasdair Leslie","Andrew Booth","Shrirajh Satheakeerthy","Alexander Beath","Yasser Arafat","Mathew Jacob","Martin Bruening","WengOnn Chan","Stephen Bacchi"],"tags":["Workforce","Metropolitan area","Context (archaeology)","Software deployment","Rural health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-30","doi":"https://doi.org/10.1111/ajr.70037","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4413304061","name":"University english teaching evaluation using artificial intelligence and data mining technology","source":"openalex","abstract":"This work intends to drive reform and innovation in English teaching evaluation and support personalized English instruction. It utilizes deep learning (DL) and artificial intelligence (AI)-driven data mining technology to explore a reliable and efficient method for university English teaching evaluation. By employing DL, this work explores innovative English teaching models and introduces a Bayesian framework to enable personalized teaching strategies. In the data mining process, the Transformer architecture is applied to English teaching evaluations. This capitalizes on its powerful feature extraction and sequence modeling capabilities to gain a comprehensive understanding and precise evaluation of students' English proficiency. Additionally, an AI-based method for English teaching evaluation is proposed. Data from the English teaching and evaluation system for Computer Science students in the 2018 class at Tianjin University of Science and Technology are collected, analyzed, and processed. Group profiles of students are created to predict exam outcomes. The findings show that over 70% of students engage in active English learning only occasionally, with a higher proportion among females. More than 80% of males recognize the importance of listening and speaking skills, a sentiment shared by over 90% of female students. In terms of factors influencing students' passing exams, scores in various question types play a central role, significantly impacting final grades. These scores reflect students' mastery of English knowledge and application abilities. This work applies the Transformer architecture from natural language processing to the education domain, achieving interdisciplinary integration and innovation. This cross-disciplinary approach not only enriches teaching assessment methods but also provides new solutions for broader educational challenges. The proposed method enhances the objectivity and accuracy of teaching evaluation, minimizing the influence of human bias assessment results.","url":"https://doi.org/10.1038/s41598-025-16498-0","authors":["Qiuyang Huang","Wenling Li","Mohd Mokhtar Muhamad","Nur Raihan binti Che Nawi","Xutao Liu"],"tags":["Computer science","Active listening","College English","Artificial intelligence","Architecture"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-19","doi":"https://doi.org/10.1038/s41598-025-16498-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4412423297","name":"Technology Landscape Review of In-Sensor Photonic Intelligence: From Optical Sensors to Smart Devices","source":"openalex","abstract":"Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.","url":"https://doi.org/10.3390/aisens1010005","authors":["Hong Zhou","Dongxiao Li","Chengkuo Lee"],"tags":["Photonics","Computer science","Optical sensing","Optoelectronics","Materials science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-14","doi":"https://doi.org/10.3390/aisens1010005","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4400045019","name":"Making sense of artificial intelligence and large language models—including ChatGPT—in pediatric hematology/oncology","source":"openalex","abstract":"ChatGPT and other artificial intelligence (AI) systems have captivated the attention of healthcare providers and researchers for their potential to improve care processes and outcomes. While these technologies hold promise to automate processes, increase efficiency, and reduce cognitive burden, their use also carries risks. In this commentary, we review basic concepts of AI, outline some of the capabilities and limitations of currently available tools, discuss current and future applications in pediatric hematology/oncology, and provide an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice.","url":"https://doi.org/10.1002/pbc.31143","authors":["Kirk D. Wyatt","Natasha Alexander","Gerard D. Hills","Wayne H. Liang","Stephan Kadauke","Samuel L. Volchenboum","Amir Mian","Charles Phillips"],"tags":["Hematologist","Medicine","Pediatric oncology","Health care","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-26","doi":"https://doi.org/10.1002/pbc.31143","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"oa:W4408310331","name":"Artificial Intelligence Assisted Creativity: Conceptualization, Instrument Development and Validation","source":"openalex","abstract":"ABSTRACT Considering the pivotal role of creativity across various eras and the rapid integration of Artificial Intelligence (AI) in both creative processes and education, this study introduces and provides validity evidence for the AI‐assisted Creativity Questionnaire (AICQ). This new 16‐item instrument aims to quantify human creative potential in AI‐assisted endeavors. Initially, a diverse cohort of 322 university students in Taiwan completed the AICQ from November to December 2023. Through exploratory factor analysis (EFA) of responses from this sample, three distinct factors emerged: (1) AI‐assisted functional creativity (AIFC), (2) AI‐assisted visual artistic creativity (AIVAC), and (3) AI‐assisted ideational creativity (AIIC). One item was removed due to cross‐loading. Subsequently, in September 2024, 330 university students in Taiwan engaged with both the AICQ and the 28‐item Creative Behavior Inventory (CBI). Based on responses from this sample, construct validity evidence for the AICQ was examined using confirmatory factor analysis (CFA), which affirmed that a three‐factor model provided a good fit to the data. Gender differences in AICQ scores were found in these subsequent data. Overall, data from participants provided evidence for the reliability as well as convergent, concurrent, and discriminant validity of the scores obtained from the AICQ.","url":"https://doi.org/10.1002/jocb.70004","authors":["Pui Yi Mok","Hsueh‐Hua Chuang","Ming‐Min Cheng","Thomas J. Smith"],"tags":["Creativity","Psychology","Discriminant validity","Confirmatory factor analysis","Construct validity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-28","doi":"https://doi.org/10.1002/jocb.70004","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1201/9781032694771-8","name":"Explainable Artificial Intelligence","source":"crossref","abstract":"Artificial Intelligence (AI) is revolutionizing various sectors of society, particularly through its role in decision-making. In healthcare, AI has the potential to play a crucial role, from assisting in diagnosis to personalizing treatments. However, its lack of transparency and explainability presents ethical and practical challenges. Understanding and trusting automated decisions in healthcare is fundamental to ensuring patient safety and efficacy. Therefore, developing methods to make AI systems more explainable and transparent is essential. Explainable Artificial Intelligence (XAI) emerges as a response to this need, aiming to provide explanations for AI decisions and internal processes. This allows developers and, more importantly, users to understand how decisions are made based on input data and what factors influence them. Numerous studies have focused on developing techniques and approaches to make AI more explainable. This chapter presents a narrative review of recent work related to incorporating XAI techniques in healthcare. The studies are grouped according to their central application, encompassing XAI in diagnostic support, personalization of procedures, and health management.","url":"https://doi.org/10.1201/9781032694771-8","authors":["Maíra Araújo de Santana","Giselle Machado Magalhães Moreno","Wellington Pinheiro dos Santos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-05T09:51:51Z","doi":"10.1201/9781032694771-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1017/9781009367783.020","name":"Artificial Intelligence and Financial Services","source":"crossref","abstract":"The actors that are active in the financial world process vast amounts of information, starting from customer data and account movements over market trading data to credit underwriting or money-laundering checks. It is one thing to collect and store these data, yet another challenge to interpret and make sense of them. AI helps with both, for example, by checking databases or crawling the Internet in search of relevant information, by sorting it according to predefined categories or by finding its own sorting parameter. It is hence unsurprising that AI has started to fundamentally change many aspects of finance. This chapter takes AI scoring and creditworthiness assessments as an example for how AI is employed in financial services (Section 16.2), for the ethical challenges this raises (Section 16.3), and for the legal tools that attempt to adequately balance advantages and challenges of this technique (Section 16.4). It closes with a look at scoring beyond the credit situation (Section 16.5).","url":"https://doi.org/10.1017/9781009367783.020","authors":["Katja Langenbucher"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-18T13:47:06Z","doi":"10.1017/9781009367783.020","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1080/08839514.2025.2513740","name":"Managing Performance of Cyber Forensic Accounting with the Effect of Artificial Intelligence in Legal and Ethical Considerations","source":"crossref","abstract":"The purpose of the current research is to provide a clear understanding of how legal and ethical considerations (LEC) impact the performance of cyber forensic accounting (PCFA). Furthermore, it strives to generate comprehensive understanding of how Artificial Intelligence capabilities (AIC) mediate the relationship between LEC and PCFA. A two-stage Covariance-Based Structural Equation Modelling (CB-SEM) was conducted using AMOS 28.0 software to analyze a theoretical model that explores the relationships between the mentioned components. The study utilized a large-scale dataset obtained from a convenience and snowball sample of accountants working in small and medium enterprises in Vietnam. The statistical outcome provided evidence for the significantly positive correlation between LEC and PCFA. At the same time, this connection was partially mediated by AIC. The fine-grained insights in this study can serve as a valuable foundation for future research. Additionally, these insights can assist practitioners and policy-makers in recognizing and capitalizing on opportunities to improve PCFA. This can be achieved through the implementation of efficient and effective rules and policies for managing Artificial Intelligence.","url":"https://doi.org/10.1080/08839514.2025.2513740","authors":["Pham Quang Huy","Vu Kien Phuc"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-09T11:56:17Z","doi":"10.1080/08839514.2025.2513740","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.61096/978-81-994851-8-1_3","name":"Artificial Intelligence in HPLC Method Development: A Pharmaceutical Perspective","source":"crossref","abstract":"High-Performance Liquid Chromatography (HPLC) remains a cornerstone analytical technique in pharmaceutical research, development, and quality control due to its precision, reproducibility, and versatility. However, the development of traditional HPLC methods frequently depends on empirical trial-and-error techniques, which are labor-intensive, resource-intensive, and have a limited capacity for prediction. This process has been completely transformed by the incorporation of Artificial Intelligence (AI) techniques, which allow data-driven, automated, and predictive tactics to build optimal chromatographic settings. This review examines the role of machine learning algorithms, neural networks, genetic algorithms, and quantitative structure–retention relationship (QSRR) modeling in accelerating and refining HPLC method optimization. In addition to improving method robustness and reproducibility, the combination of AI with chromatographic science minimizes solvent use and experimental workload, which is consistent with the concepts of Quality by Design (QbD) and green analytical chemistry. Furthermore, the present assessment explores implementation strategies, comparative performance of various AI techniques, and validation requirements to ensure regulatory compliance. Emerging trends such as virtual experimentation, automated method scouting, and multi-objective optimization are discussed as transformative tools shaping the next generation of analytical workflows. Subsequently, case studies are displayed to illustrate how AI-driven HPLC optimization greatly enhances method development efficiency, analytical performance, and overall decision-making. Overall, the convergence of AI and HPLC signifies a paradigm shift toward intelligent, efficient, and sustainable analytical method development in the pharmaceutical sciences.","url":"https://doi.org/10.61096/978-81-994851-8-1_3","authors":["Kaveripakam Sai Sruthi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T18:52:16Z","doi":"10.61096/978-81-994851-8-1_3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.21037/jmai-2026-1-0020","name":"Artificial intelligence in orthodontics: a narrative review of clinical and educational applications","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-2026-1-0020","authors":["Anmar Arab","Abubaker Qutieshat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-12T02:03:12Z","doi":"10.21037/jmai-2026-1-0020","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.112409","name":"Adaptive resource management in dynamic Cyber–Physical Systems using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112409","authors":["Xiaofei Zhao","Fangling Guo","Amin Huang","Jieqiong Ding","Chi Yan","Wei Yuan","Yunqi Su","Quanzhou Li","Qianggang Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-27T04:37:08Z","doi":"10.1016/j.engappai.2025.112409","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artmed.2025.103066","name":"Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review","source":"crossref","abstract":"Introduction There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different aspects of oncology clinical trials. Methods Pertinent keywords were used to find relevant articles published in PubMed, Scopus, and Google Scholar databases, which described the clinical application of AI approaches. A quality evaluation utilizing a customized checklist specifically adapted was conducted. This study is registered with PROSPERO (CRD42024537153). Results Out of the identified 2833 studies, 72 studies satisfied the inclusion criteria. Clinical Trial Enrollment & Eligibility were among the most commonly studied clinical trial aspects with 30 papers. The prediction of outcomes was covered in 25 studies of which 15 addressed the prediction of patients' survival and 10 addressed the prediction of drug outcomes. The trial design was studied in 10 articles. Three studies addressed each of the personalized treatments and decision-making, while one addressed data management. The results demonstrate using AI in cancer clinical trials has the potential to increase clinical trial enrollment, predict clinical outcomes, improve trial design, enhance personalized treatments, and increase concordance in decision-making. Additionally, automating some areas and tasks, clinical trials were made more efficient, and human error was minimized. Nevertheless, concerns and restrictions related to the application of AI in clinical studies are also noted. Conclusion AI tools have the potential to revolutionize the design, enrollment rate, and outcome prediction of oncology clinical trials.","url":"https://doi.org/10.1016/j.artmed.2025.103066","authors":["Marwa Saady","Mahmoud Eissa","Ahmed S. Yacoub","Ahmed B. Hamed","Hassan Mohamed El-Said Azzazy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-18T03:03:25Z","doi":"10.1016/j.artmed.2025.103066","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1017/9781009367783.012","name":"Artificial Intelligence and Competition Law","source":"crossref","abstract":"Firms use algorithms for important decisions in areas from pricing strategy to product design. Increased price transparency and availability of personal data, combined with ever more sophisticated machine learning algorithms, has turbocharged their use. Algorithms can be a procompetitive force, such as when used to undercut competitors or to improve recommendations. But algorithms can also distort competition, as when firms use them to collude or to exclude competitors. EU competition law, in particular its provisions on restrictive agreements and abuse of dominance (Articles 101–102 TFEU), prohibits such practices, but novel anticompetitive practices – when algorithms collude autonomously for example – may escape its grasp. This chapter assesses to what extent anticompetitive algorithmic practices are covered by EU competition law, examining horizontal agreements (collusion), vertical agreements (resale price maintenance), exclusionary conduct (ranking), and exploitative conduct (personalized pricing).","url":"https://doi.org/10.1017/9781009367783.012","authors":["Friso Bostoen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-18T13:47:06Z","doi":"10.1017/9781009367783.012","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-031-87931-9_1","name":"Shaping Tomorrow: The Convergence of Artificial General Intelligence and Quantum Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-87931-9_1","authors":["Ria Ghosh","Srinath Doss"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-12T10:32:32Z","doi":"10.1007/978-3-031-87931-9_1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-24788-0.00007-8","name":"Healthcare revolution: Advances in AI-driven medical imaging and diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24788-0.00007-8","authors":["Amrit Suman","Preetam Suman","Sasmita Padhy","Naween Kumar","Akansha Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T04:55:30Z","doi":"10.1016/b978-0-443-24788-0.00007-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-7185-563-1_1","name":"Machine Learning Models for Health Outcome Prediction","source":"crossref","abstract":"Vishnu B was born in Tamil Nadu, India, on 30 September 2006. He is currently pursuing a Bachelor of Engineering (B.E.) degree in Computer Science and Engineering at Chennai Institute of Technology, Chennai, India. He is an undergraduate scholar with strong academic foundations and practical exposure in technology-driven problem solving. He is deeply driven by innovation and a commitment to advancing design and technological solutions that balance aesthetics with functionality. His academic and creative portfolio reflects a focus on developing impactful and practical systems. Notably, he holds three published utility patents, demonstrating his capability in applied invention and real-world problem solving. In addition, he has authored two published book chapters, highlighting his engagement in scholarly research and academic writing. This combination of practical innovation and academic contribution positions him as a versatile and emerging professional prepared to address complex technological challenges and contribute meaningful advancements to the field.","url":"https://doi.org/10.70593/978-93-7185-563-1_1","authors":["MUTHUPANDI G","Jayakumar K","Yuvadarshan M","E. Mariappan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T15:52:54Z","doi":"10.70593/978-93-7185-563-1_1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/prai67447.2025.11412494","name":"An Assisted Medical Diagnosis Question Answering System Based on Knowledge Graphs","source":"crossref","abstract":"With the rapid advancement of artificial intelligence technology, knowledge graphs are seeing increasingly widespread application in the medical field, offering new technological means for assisted medical diagnosis. However, current research still faces challenges such as difficulties in natural language understanding and insufficient knowledge reasoning capabilities. This paper designs and implements a knowledge graph-based question answering system for assisted medical diagnosis, aiming to enhance diagnostic efficiency and accuracy through structured medical knowledge. The system first uses natural language processing techniques to analyze user-input medical questions, and then performs semantic reasoning by leveraging entities such as diseases, symptoms, medications, and medical tests, along with their relationships in a medical knowledge graph, to generate reliable diagnostic suggestions. Through knowledge graph embedding and relationship path mining, the system achieves multi-hop reasoning and ensures the scientific validity of responses based on authoritative medical databases. Experimental results demonstrate that this system achieves high accuracy and practicality in diagnostic question answering tasks for common diseases. It can provide efficient decision support assistance for both doctors and patients while reducing the risk of misdiagnosis.","url":"https://doi.org/10.1109/prai67447.2025.11412494","authors":["Liu Fang","Chen Yang","Chen Jiansong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T20:41:42Z","doi":"10.1109/prai67447.2025.11412494","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.35629/076x-1207103104","name":"Implication of Artificial Intelligence in Public Health Dentistry: Future Prospective and Challenges","source":"crossref","abstract":"From the time immemorial , machines are so designed by instilling all the high technical component making it technologically advance and proficient thereby simplifying the work of man .In today’s era machines are used in almost every field . Life is considered difficult without machines in the form of gadgets , electronic medias etc , Advent of artificial intelligence is one such innovation towards this. This technology is used in every field and medical and dental is not an exception too, Public Health dentistry deals with epidemiological aspect of diseases at community level . The aim of the review study is to describe the role of Artificial intelligence in public health dentistry highlighting its future prospective and challenges that may arise while dealing with it.","url":"https://doi.org/10.35629/076x-1207103104","authors":["Dr. Shraddha Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-21T07:35:08Z","doi":"10.35629/076x-1207103104","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1201/9781003531166-8","name":"Artificial Intelligence-Assisted Assessment","source":"crossref","abstract":"This chapter examines the limitations of traditional clinical assessment methods for mental disorders and explores the potential role of artificial intelligence (AI) in addressing these challenges. Traditional approaches often suffer from high misdiagnosis rates among both psychiatrists and non-psychiatrists, reliance on subjective evaluations, the absence of significant biomarkers, and issues associated with scale analysis, such as patient denial, subjective biases, and inaccuracies. In addition, the global shortage of psychiatrists exacerbates these challenges. AI technology offers a promising complement to traditional assessment methods. AI provides unique advantages, including reduced reliance on human resources, increased efficiency, and decreased stigma. This chapter discusses research studies utilising AI to detect various mental disorders, emphasising its potential to identify patient cues that facilitate early detection. While the chapter does not focus on practical methods for integrating AI into cognitive behavioural therapy assessments, it underscores the importance of understanding AI applications in mental health for therapists, particularly in enhancing diagnostic accuracy and supporting traditional evaluation practices.","url":"https://doi.org/10.1201/9781003531166-8","authors":["Olive K. L. Woo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T17:02:44Z","doi":"10.1201/9781003531166-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/medai67139.2025.00014","name":"Fed-ensemble: Enhancing Federated Learning with Ensemble Models for an Explainable Thyroid Cancer Recurrence Prediction","source":"crossref","abstract":"The prediction of thyroid cancer recurrence is a critical task in clinical decision-making, yet traditional machine learning models face significant challenges, particularly around data privacy, model generalization with huge datasets, and interpretability. In healthcare, patient data is sensitive and sharing it across institutions for model training raises privacy concerns. This research addresses these issues by utilizing federated learning (FL), a decentralized machine learning approach that allows institutions to collaboratively train a model while ensuring patient data remains private. FL enables local model training at each institution, with only model updates shared across participants, safeguarding sensitive data. Alongside federated learning, the study incorporates Explainable AI (XAI) techniques to enhance the transparency of predictions, enabling clinicians to interpret and trust the model’s decision-making process. By combining multiple machine learning models in an ensemble approach, the research improves the prediction accuracy and robustness of thyroid cancer recurrence, even with limited data. The method is evaluated using a cohort dataset of thyroid cancer patients, with synthetic data augmentation addressing data scarcity. The results demonstrate that the approach outperforms traditional models while addressing critical challenges of data privacy and model interpretability. This proposed model outperforms previous methods, significantly higher than the best result on same dataset from prior work. Additionally, our model shows superior performance even when trained on larger datasets, confirming its generalizability and robustness.","url":"https://doi.org/10.1109/medai67139.2025.00014","authors":["Hasibul Hasan Sabuj","Dan Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00014","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1093/milmed/usae579","name":"Ethical and Appropriate Use of Artificial Intelligence by Medical Learners: What We Should Not Forget?","source":"crossref","abstract":"The publication on “Key Insights for the Ethical and Appropriate Use of Artificial Intelligence by Medical Learners1” is an interesting issue. Murray et al. concluded that although large language model have the potential to improve academic writing, their excessive use may jeopardize medical students’ capacity to develop critical thinking and analytical skills.1 Murray et al. also noted that mentors and principal investigators are responsible for ensuring that students progress and make appropriate use of new and developing technology.1 We agree that the ethical problems in using artificial intelligence (AI) really exist and there is a need for setting management strategies. A potential weakness of the report by Murray et al. is the reliance on an environmental scan, which might not provide in-depth, primary data or nuanced insights into actual experiences of medial learners. While we all agree that the unethical use of AI already existed, it should also be noted that the decision that there is an AI use is sometimes difficult. We should not forget that the tool that we use for detection of AI misuse is also AI and it can also give a false positive or fabrication that can lead to incorrect accusation of an unethical AI use. Additionally, to judge or control AI use in an ethical way, the human plays an important role. We have to realize and update our knowledge on AI. The famous case of the incorrect accusation of unethical AI use made by a journal “Gac Sanit,” which wrongly incorrectly accused a published article of using AI before the existence of the generative tool, serves as a good lesson that highlights the need for the authors, editors, and readers to recognize the changing nature of technology.2","url":"https://doi.org/10.1093/milmed/usae579","authors":["Amnuay Kleebayoon","Viroj Wiwanitkit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-13T11:17:16Z","doi":"10.1093/milmed/usae579","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.9734/ajmpcp/2025/v8i2361","name":"The Evolving Role of Artificial Intelligence in Modern Healthcare: Opportunities and Ethical Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.9734/ajmpcp/2025/v8i2361","authors":["Farhana Choudhury","Nuzhat Hossain","Fariha Tabassum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T09:05:42Z","doi":"10.9734/ajmpcp/2025/v8i2361","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.18103/mra.v13i12.7172","name":"Toward Precision Oculoplastics: Federated Data, Artificial Intelligence, and the Path to Equitable Therapeutic Discovery","source":"crossref","abstract":"Oculoplastic and orbital disorders are rapidly entering a new era of precision medicine, driven by advances in genomics, multi-omic profiling, and artificial intelligence (AI). However, the evidence base supporting these innovations remains overwhelmingly Eurocentric: more than 80% of all genome-wide association study (GWAS) participants globally are of European ancestry, and pivotal trials of teprotumumab, the first targeted biologic for thyroid eye disease (TED), enrolled over 85% European-descended participants. This imbalance constrains understanding of ancestry-specific therapeutic responses, limits the transferability of polygenic and pharmacogenomic models, and risks perpetuating structural bias in emerging AI systems used for diagnostic and surgical decision-making. The high cost of teprotumumab (&gt; USD 200,000 per treatment course) further underscores the need for equitable, predictive stratification. This article outlines how federated learning (FL) and harmonized multi-omic data infrastructures can close these gaps. We highlight the EXAM study as a real-world demonstration of FL’s ability to integrate multi-institutional data without transferring sensitive patient information, achieving superior predictive performance while preserving privacy. Building on these principles, we introduce GeneVault Harmony, a federated harmonization and benchmarking framework that integrates genomic, transcriptomic, imaging, and clinical data while maintaining data sovereignty and aligning with GA4GH and WHO standards. By enabling bias quantification, diversity simulation, and cross-institutional interoperability, Harmony provides a scalable foundation for equitable oculoplastic genomics. We conclude by proposing a pathway toward inclusive precision therapeutics in oculoplastics, including multi-regional genomic consortia and federated, multi-omic AI pipelines spanning the Middle East, Africa, South Asia, and Latin America. Integrating globally representative data into future genomic and pharmacogenomic discovery is essential to ensure that precision ophthalmology evolves in a manner that is scientifically robust, clinically equitable, and globally relevant.","url":"https://doi.org/10.18103/mra.v13i12.7172","authors":["Caroline Duncan","Riyona D’Silva","Vinod Gauba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-17T13:03:28Z","doi":"10.18103/mra.v13i12.7172","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.2214/ajr.25.33346","name":"Brilliant Recall but Blunt Intuition: The Gaps in Medical Artificial Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.2214/ajr.25.33346","authors":["Sarah Meribout"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.2214/ajr.25.33346","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1109/aiim67611.2025.11232865","name":"A Multi-Branch Semantic Fusion Network with Knowledge Distillation and Pruning for Lightweight Medical Image Segmentation","source":"crossref","abstract":"Medical image segmentation is crucial for clinical diagnosis and treatment planning. Existing models often suffer from high computational cost and limited feature representation. We propose a Multi-branch Semantic Fusion Network (MSFNet) that integrates low- and high-level features within a multibranch framework to improve segmentation accuracy. To enable lightweight deployment, the highest-level semantic branch guides lower-level branches via knowledge distillation and is pruned afterward, while the remaining branches continue feature fusion. Experiments on the Synapse multi-organ CT dataset show that MSF-Net achieves a Dice Similarity Coefficient (DSC) of 83.2%, outperforming several state-of-the-art methods. The results demonstrate that MSF-Net effectively balances accuracy and efficiency, showing strong potential for clinical applications.","url":"https://doi.org/10.1109/aiim67611.2025.11232865","authors":["Jiabei Meng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-18T18:42:15Z","doi":"10.1109/aiim67611.2025.11232865","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1186/s12909-025-07223-6","name":"Medical undergraduate students’ awareness and perspectives on artificial intelligence: A developing nation’s context","source":"crossref","abstract":"Background Artificial intelligence (AI) is reshaping healthcare, yet its integration into medical education remains limited. This study assesses undergraduate healthcare students' knowledge and perceptions of AI, its applications, challenges, and the need for AI education in healthcare curricula. Methods A cross-sectional study was conducted at Riphah International University from August to October 2023, involving 939 undergraduate students from medical, dental, pharmacy, nursing, and physical therapy disciplines. Data was collected using a validated questionnaire and analyzed using IBM SPSS Version 26. Inferential statistical test such as The Kruskal-Wallis H and Mann-Whitney U tests were applied to compare AI knowledge and perceptions across disciplines and genders. Results Results demonstrated moderate AI knowledge, with significant differences across disciplines (p = 0.039). BDS students had the highest AI knowledge, while nursing students scored the lowest. Most students (77%) attended AI-related talks, but only 11.8% had formal AI training. Perceptions toward AI's role in patient care were generally positive, with 73.6% believing AI could aid in patient documentation and 68.7% supporting its role in selecting health interventions. Concerns were raised about AI's impact on job displacement, ethical challenges, and feasibility in developing countries. Despite this, 78.8% supported AI integration into medical curricula, and 82.2% endorsed AI training as part of medical education. Conclusion Undergraduate healthcare students recognize AI's potential in medicine but express concerns about ethical implications and job displacement. The findings highlight the need for structured AI education in medical curricula to bridge knowledge gaps and prepare future healthcare professionals for AI-driven practice.","url":"https://doi.org/10.1186/s12909-025-07223-6","authors":["Laveeza H. Syeda","Zoya Batool","Zeeshan Hayder","Shabana Ali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T18:26:00Z","doi":"10.1186/s12909-025-07223-6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/0933-3657(93)90024-w","name":"Graphical knowledge acquisition for medical diagnostic expert systems","source":"crossref","abstract":"Like many textbook authors use text systems for writing their books, expert system authors should have easy to use knowledge acquisition systems for entering and testing their knowledge bases by themselves without much help from 'knowledge engineers'. In this paper, we report on a graphical knowledge acquisition tool (CLASSIKA) based on an expert system shell for heuristic classification (MED2) and designed for direct use by domain experts. We demonstrate how the system has been used for building a rather large expert system for diagnosing rheumatology diseases which is now being tested in clinical use.","url":"https://doi.org/10.1016/0933-3657(93)90024-w","authors":["Ute Gappa","Frank Puppe","Stefan Schewe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T05:53:05Z","doi":"10.1016/0933-3657(93)90024-w","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/iceconf65644.2025.11379620","name":"Deep Learning-Based Automated Detection and Classification of Ovarian Cancer from Medical Imaging","source":"crossref","abstract":"Ovarian cancer remains one of the most lethal types of gynaecological cancer because it advances asymptomatically and is often identified at an advanced stage. Early and precise identification is a critical to maintain health. Recent advancements in artificial intelligence (AI), especially deep learning (DL), have shown significant potential for enhancing computer-aided diagnosis and medical picture analysis. This study presents a DL-based system for the automated detection and classification of ovarian cancer utilising medical imaging techniques. The extraction of distinguishing features and categorisation of benign and malignant cases with high precision is achieved through the use of convolutional neural networks (CNNs) and transfer learning techniques. The proposed approach is evaluated using publicly accessible datasets and clinical datasets, demonstrating superior performance relative to traditional machine learning (ML) methods in accuracy, sensitivity, and specificity. DL models possess the capability to serve as valuable, dependable, and scalable instruments that can aid radiologists in the early detection and classification of ovarian cancer. This will ultimately enhance patient outcomes and facilitate tailored treatment planning. The results underscore the potential of deep learning methods.","url":"https://doi.org/10.1109/iceconf65644.2025.11379620","authors":["Rajendran Sivakumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T21:04:07Z","doi":"10.1109/iceconf65644.2025.11379620","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1093/postmj/qgaf052","name":"Opportunities and challenges for young physicians in the era of artificial intelligence: current status and future perspectives","source":"crossref","abstract":"Abstract Artificial Intelligence (AI) has witnessed rapid evolution in recent years and is gradually integrated into healthcare systems worldwide. For young physicians, navigating this landscape requires a delicate balance. They must weigh the potential benefits offered by AI-driven innovations against the intricate demands of patient care, ethical imperatives, and the evolving nature of their professional roles. This concise review aims to spotlight the key opportunities and challenges that young physicians encounter in this digital age. Additionally, practical recommendations are provided to guide young physicians in integrating AI knowledge seamlessly into their clinical practice. Finally, this review concludes with forward-looking suggestions,including incorporating AI training into medical education curricula and formulating policy initiatives. The overarching goal is to optimize the role of young physicians within the dynamic and evolving digital healthcare environment, ensuring they are well-equipped to leverage AI for the betterment of patient care.","url":"https://doi.org/10.1093/postmj/qgaf052","authors":["Bo Gao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-11T03:30:06Z","doi":"10.1093/postmj/qgaf052","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icbase66587.2025.11181297","name":"MedLoRA: Exploring LoRA and Quantization Combinations for Medical QA","source":"crossref","abstract":"Large Language Models (LLMs) have achieved remarkable progress across various NLP tasks. However, their adaptation to domain-specific applications such as medical question answering (MQA) faces two major bottlenecks: the cost of fine-tuning and the demand for efficient inference. To address these challenges, parameter-efficient tuning and model quantization have emerged as key strategies. In this paper, we propose MedLoRA, a comprehensive framework that systematically investigates the compatibility and performance of three representative LoRA methods (rsLoRA, DoRA, piSSA) with three popular quantization techniques (bitsandbytes, HQQ, EETQ) in MQA tasks. Beyond empirical benchmarking, we further explore the underlying mechanisms behind LoRAquantization interactions, such as DoRA’s gradient stability under low-bit inference. Through extensive experiments on four benchmark datasets-cMedQA-v1, cMedQA-v2, webMedQA, and MedQA-USMLE-we evaluate nine (LoRA $\\times$ Quantization) combinations in terms of accuracy, parameter efficiency, and deployment feasibility. We also compare MedLoRA against fullparameter fine-tuning with quantization to contextualize performance-efficiency trade-offs. Our results reveal critical insights into how LoRA strategies interact with low-bit inference and highlight robust, lightweight configurations suitable for realworld medical applications.","url":"https://doi.org/10.1109/icbase66587.2025.11181297","authors":["Kun Liu","Chengyuan Wen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-08T17:35:46Z","doi":"10.1109/icbase66587.2025.11181297","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.24321/2278.2044.202527","name":"Revolutionizing  Pharmacovigilance:  The  Role  of  Artificial Intelligence in Enhancing Patient Safety","source":"crossref","abstract":"Background: Pharmacovigilance (PV) is responsible for monitoring drug safety, and Artificial Intelligence (AI) is a promising technology that has the potential to transform this field.Objective: This article will investigate the role of AI in PV and its potential benefits for patient safety and healthcare providers.Methods: A structured review of relevant literature was conducted to identify studies that demonstrate the applications of AI in PV. The identified studies were analysed to determine the specific roles of AI in PV and its potential benefits.Results: The practice of AI in PV allows for the analysis of large datasets, adverse event reporting, the detection of safety signals, the prioritisation of safety issues, data mining, and predictive modelling. The benefits of AI in PV include improved efficiency, increased accuracy, enhanced patient safety, faster analysis of safety issues related to drugs, and reduced healthcare costs.Conclusion: AI has enormous potential to improve PV by streamlining case processing and improving the identification of adverse events. However, there are also challenges that need to be addressed in implementing AI in PV. Overall, AI has significant promise for enhancing patient safety and reducing healthcare costs. How to cite this article:Kumar S, Agarwal P, Kumar S. Revolutionizing Pharmacovigilance: The Role of Artificial Intelligence in Enhancing Patient Safety. Chettinad Health City Med J. 2025;14(2):78-86. DOI: https://doi.org/10.24321/2278.2044.202527","url":"https://doi.org/10.24321/2278.2044.202527","authors":["Sachin Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T05:56:11Z","doi":"10.24321/2278.2044.202527","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.ejrai.2025.100052","name":"Artificial intelligence in CT scan range delineation: A systematic review and meta-analysis","source":"crossref","abstract":"Objectives To thoroughly investigate the impact of artificial intelligence (AI) algorithm-assisted scan range (SR) determination techniques on scan length, anatomical coverage accuracy, and radiation dose (RD) on computed tomography (CT) scans. Methods The literature published between January 2018 and May 2025 was systematically searched using five databases: EBSCOhost, IEEE Xplore, Ovid MEDLINE®, Scopus, and PubMed. Data extraction was performed by two review authors and validated by a third reviewer. The quality of the included studies was evaluated using the CLAIM and GRADE approaches. Statistical analyses were conducted using random-effects meta-analysis with standardised mean differences, assessing heterogeneity and publication bias. The findings were summarised using meta-analysis methodologies and reported descriptively. Results Six retrospective studies were included, comparing AI-assisted and manual CT SR determination techniques in terms of scan length, anatomical coverage accuracy, and RD. These studies varied in anatomical focus—including chest, abdomen, and cardiac regions—and employed diverse AI methodologies, including deep learning and machine learning algorithms. Compared to the manual SR methods, AI improved the anatomical coverage accuracy by reducing the upper and lower errors of the SR boundaries, which contributed to a mean reduction in scan length of 19.1mm for chest CT, 42.7mm for abdomen CT, 25mm for chest/abdomen/pelvis CT, and 17.5mm for coronary CT angiography (p < 0.001). However, the accuracy of automated methods varied between boundaries, with lower accuracy at inferior anatomical boundaries when compared to superior boundaries, resulting in higher over-scanning inferiorly. Despite this, all studies reported significant RD reductions with AI-based SR determination, ranging from 5% to 47% (p < 0.001). Conclusion AI-automatic SRs were clinically feasible and demonstrated satisfactory performance. These findings suggest that integrating such methods into clinical workflows may improve scan range accuracy, reduce over-scanning, and lower RD. However, further well-designed prospective studies are needed to confirm their effectiveness across diverse clinical settings.","url":"https://doi.org/10.1016/j.ejrai.2025.100052","authors":["Mo’men Bani-Ahmad","Andrew England","Laura McLaughlin","Marwan Alshipli","Yasser H. Hadi","Mark McEntee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-08T10:36:29Z","doi":"10.1016/j.ejrai.2025.100052","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.112065","name":"A novel framework for improving railway driver performance based on emotional intelligence and job-driven factors: An artificial neural network method","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112065","authors":["Narges Hajloo","Behnaz Salimi","Mahdi Hamid","Masoud Rabbani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-26T16:01:44Z","doi":"10.1016/j.engappai.2025.112065","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-23979-3.00020-8","name":"New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23979-3.00020-8","authors":["Armin Shoughi","Mohammad Bagher Dowlatshahi","Arefeh Amiri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-07T06:21:44Z","doi":"10.1016/b978-0-443-23979-3.00020-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/aaicv66571.2025.00060","name":"Optimizing MCMC-Driven Bayesian Neural Networks for High-Precision Medical Image Classification in Small Sample Sizes","source":"crossref","abstract":"This paper discusses the application of a Bayesian neural network based on the Markov Chain Monte Carlo method in medical image classification with small samples. Experimental results on two medical image datasets, including lung X-ray images and breast tissue slice images, show that this MCMC-based BNN model works very well on small-sample data and greatly improves the robustness and accuracy of classification. Model accuracy reached$\\mathbf{8 5 \\%}$for the lung X-ray dataset and$\\mathbf{8 8 \\%}$for the breast tissue slice dataset. To this end, we combine data augmentation techniques such as rotation, flipping, and scaling with regularization methods like dropout and weight decay to improve effectively the diversity of the training data and the generalization ability of the model. The performance of the model was evaluated by many indicators of the results, including accuracy, precision, recall, and the F1 score. All of these have proven the advantages of BNN in small-sample medical image classification. This study not only enriches the application of BNN in the field of medical image classification, but also provides specific implementation paths and optimization methods, providing new solutions for future medical image analysis.","url":"https://doi.org/10.1109/aaicv66571.2025.00060","authors":["Mingyu Sun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T18:33:47Z","doi":"10.1109/aaicv66571.2025.00060","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/faia251488","name":"Explainable Artificial Intelligence for Quality Estimation of MARSIS Observations","source":"crossref","abstract":"Planetary remote sensing missions are critical for advancing our understanding of extraterrestrial systems. They operate in highly uncertain environments where reliability and resolution are not always guaranteed, often compromising data analysis and scientific outcomes. In this paper, we consider the challenging task of estimating the quality of the signal acquired by MARSIS, the subsurface sounder aboard ESA’s Mars Express mission, which aims to map the presence of liquid water beneath the Martian surface. Quality estimation has a strategic impact on the scheduling of MARSIS observations, since the radar operates with strict constraints that greatly limit the number and size of observation opportunities available per day. Thus, maximizing the quality of scheduled observations becomes a crucial factor in reducing resource utilization and increasing the coverage of the target areas in search of liquid water. To this end, in a previous research we proposed a predict-then-optimize approach, which included a neural network regressor to predict signal quality achievable by future observation opportunities, based on contextual features. In this work, we advance the methodology by applying explainable artificial intelligence techniques that allow domain experts to interpret the results, by enhancing the comprehension of the physical phenomena that have an impact on signal acquisition. Specifically, we applied a SHAP analysis to the neural network predictions and trained an Explainable Boosting Machine (EBM) to provide interpretable models. We then analyzed and compared the results with existing domain knowledge, uncovering promising new avenues for investigation and highlighting limitations in the current dataset construction.","url":"https://doi.org/10.3233/faia251488","authors":["Benedetta Ferrari","Marco Lippi","Giulio Ganzerli","Manuel Iori","Roberto Orosei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-22T10:04:11Z","doi":"10.3233/faia251488","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1787/a5319ab5-en","name":"Macroeconomic productivity gains from Artificial Intelligence in G7 economies","source":"crossref","abstract":"The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon in G7 economies. It builds on our previous work that introduced a micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates. This paper refines and extends the estimates from the United States to other G7 economies, in particular by harmonising current adoption rate measures among firms and updating future adoption path estimates. Across the three scenarios considered, the estimated range for annual aggregate labour productivity growth due to AI range between 0.4-1.3 percentage points in countries with high AI exposure – due to stronger specialisation in highly AI-exposed knowledge intensive services such as finance and ICT services – and more widespread adoption (e.g. United States and United Kingdom). In contrast, projected gains in several other G7 economies are up to 50% smaller, reflecting differences in sectoral composition and assumptions about the relative pace of AI adoption.","url":"https://doi.org/10.1787/a5319ab5-en","authors":["Francesco Filippucci","Peter Gal","Katharina Laengle","Matthias Schief"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-26T08:00:53Z","doi":"10.1787/a5319ab5-en","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaid65275.2025.11034638","name":"Integrating Vision Transformers and Autoencoders for Liver Image Segmentation in Medical Imaging","source":"crossref","abstract":"Medical image segmentation is recognized as a critical task in medical imaging, crucial for enabling diagnosis, disease monitoring, and treatment planning. Liver image segmentation is particularly challenging due to the sophisticated structure of the liver and the present of surrounding organs and tissues that obscure clear boundaries. Traditional statistical methods often struggle with scalability, robustness, and adaptability to complex datasets. And convolutional neural network-based models face limitations in capturing global dependencies and contextual information due to their local receptive fields. To overcome these challenges, a novel segmentation framework that integrates a vision transformer with an autoencoder architecture, leveraging self-attention mechanisms was proposed. It was shown that the proposed method can capture global context and long-range dependencies. By focusing on both global and local features, this methodology offers a powerful solution to the challenges in the field of medical image segmentation, showcasing the potential of transformerbased models in advancing the field.","url":"https://doi.org/10.1109/icaid65275.2025.11034638","authors":["Tai-Jung Chen","Tong Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T17:37:57Z","doi":"10.1109/icaid65275.2025.11034638","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/medai67139.2025.00025","name":"Computational Simulations and Structural Analysis of Bispecific Aptamers Targeting Dual Biomarkers for Enhanced Ovarian Cancer Diagnosis","source":"crossref","abstract":"Ovarian cancer ranks as the third most common gynecological malignancy. It presents significant global health challenges due to its high mortality rates, mainly resulting from late-stage diagnoses and inadequate early detection methods. Existing diagnostic techniques primarily focus on identifying morphological changes in the ovaries, which often overlook early cellular-level alterations, resulting in delayed diagnoses. Consequently, early detection is crucial for preventing cancer progression and improving patient survival rates. Aptamers, small nucleotide sequences that specifically bind to target proteins, represent a novel approach for identifying tumor cells. Recent advances include the design of a bispecific aptamer capable of detecting exosomes released by ovarian cancer cells. We hypothesize that these aptamers can specifically bind to the receptor surface and can be used to detect ovarian cancer. The current research utilized computational methods to investigate the binding capabilities of aptamers to surface receptors (EpCAM and CD24) present on exosomes. By predicting the aptamers secondary and tertiary structures, we performed molecular docking simulations to assess their interactions with the target receptors. Our findings indicate that the aptamers Apt1 and Apt2 exhibit strong binding affinities, warranting their selection for further evaluation. The docked structures were further validated by using the GrASP web server, which is a machine learning based method to enhance the accuracy of our predictions. This research contributes to identifying the most effective aptamers for binding to exosome surfaces, which may potentially advance ovarian cancer screening methods.","url":"https://doi.org/10.1109/medai67139.2025.00025","authors":["Vyom Sharma","Gaurav Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00025","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1201/9781003672937-5","name":"Incorporating Artificial Intelligence in Stroke Imaging and Treatment","source":"crossref","abstract":"Artificial intelligence technology is rapidly expanding and has various uses in acute stroke imaging, covering ischemia and haemorrhage subtypes. Early identification of acute stroke is essential to swiftly initiate treatment and reduce the risk of catastrophic outcomes and mortality. Artificial intelligence can aid in various aspects of stroke treatment, including recognising infarcts or haemorrhages, analysing pictures, categorising situations, finding major artery blockages, evaluating Alberta Stroke Programme Early CT Scores, and forecasting results. Convolutional neural networks (CNNs), a type of emerging artificial intelligence method, show promise in efficiently and precisely performing specific imaging tasks. Artificial intelligence (AI) is a swiftly growing area of research in computer science that aims to mimic cognitive processes through several techniques. Supervised machine learning, a branch of artificial intelligence, uses labelled data to identify patterns in intricate datasets and then uses these patterns to evaluate, interpret, or forecast outcomes in new datasets. Supervised machine learning is an important area of research in the medical industry. Machine learning is very suitable for usage in radiology and neuroradiology because of the extensive data generated in these areas. Neuroimaging is essential for the management of stroke, a life-threatening condition. AI methods are essential for the diagnosis and treatment of strokes through the analysis of images.","url":"https://doi.org/10.1201/9781003672937-5","authors":["Rishabha Malviya","Shivam Rajput"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T09:37:01Z","doi":"10.1201/9781003672937-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/ainit65432.2025.11035853","name":"Facial Detection Based on Deep Learning for Emotion Recognition of Medical Staff","source":"crossref","abstract":"Based on the large amount of sample information from deep learning to establish a face detection model, the emotions of medical staff are detected. To address the issue of insufficient feature information fusion during the deep learning face detection process, which leads to low detection accuracy for multi-scale faces, an improved CBAM-YOLOv5m (Convolutional Block Attention Module-You Only Look Once version 5 medium) face detection model is proposed. The YOLOv5m face detection model can accurately locate the face area in real time under various lighting conditions and complex backgrounds, and quickly detect the key areas of the face in videos. The improved CBAM-YOLOv5m can enhance the discrimination ability of multi-scale features. In complex scenarios such as occlusion and uneven lighting, the false positive rate is reduced by$10-15 \\%$. On the COCO dataset, mAP{@} 0.5:0.95 can be improved by 2-4%, and ‘mAP{@} 0.5 ’ can be improved by$3-6 \\%$. The inference speed is increased from 12.4 ms to 14.9 ms. Through comparison with RetinaFace and YOLOv8 face detection, it is verified that CBAM-YOLOv5m not only accurately locates the face area in real time under various lighting conditions and complex backgrounds and quickly detects the key areas of the face in videos, but also can more accurately detect the target in images containing multiple faces, occlusions, or people with different postures, solving the problem of face target detection of medical staff in different scenarios.","url":"https://doi.org/10.1109/ainit65432.2025.11035853","authors":["Qiangguang Zhang","Wei Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-23T17:24:40Z","doi":"10.1109/ainit65432.2025.11035853","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artmed.2012.06.002","name":"On mining clinical pathway patterns from medical behaviors","source":"crossref","abstract":"Objective Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Clinical pathway pattern mining is one of the most important components of clinical pathway analysis and aims to discover which medical behaviors are essential/critical for clinical pathways, and also where temporal orders of these medical behaviors are quantified with numerical bounds. Even though existing clinical pathway pattern mining techniques can tell us which medical behaviors are frequently performed and in which order, they seldom precisely provide quantified temporal order information of critical medical behaviors in clinical pathways. Methods This study adopts process mining to analyze clinical pathways. The key contribution of the paper is to develop a new process mining approach to find a set of clinical pathway patterns given a specific clinical workflow log and minimum support threshold. The proposed approach not only discovers which critical medical behaviors are performed and in which order, but also provides comprehensive knowledge about quantified temporal orders of medical behaviors in clinical pathways. Results The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to six specific diseases, i.e., bronchial lung cancer, gastric cancer, cerebral hemorrhage, breast cancer, infarction, and colon cancer, in two years (2007.08-2009.09). As compared to the general sequence pattern mining algorithm, the proposed approach consumes less processing time, generates quite a smaller number of clinical pathway patterns, and has a linear scalability in terms of execution time against the increasing size of data sets. Conclusion The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice. Therefore, it holds significant promise in research efforts related to the analysis of clinical pathways.","url":"https://doi.org/10.1016/j.artmed.2012.06.002","authors":["Zhengxing Huang","Xudong Lu","Huilong Duan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-07-18T15:09:23Z","doi":"10.1016/j.artmed.2012.06.002","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3797552.3797732","name":"Artificial Intelligence in Physical Education: Research Trends and Knowledge Structure","source":"crossref","abstract":"Amid the digital revolution and rapid advancement of artificial intelligence (AI), physical education is undergoing substantial transformation. This study conducts a bibliometric analysis of English-language literature (2000–2025) indexed in the Web of Science Core Collection. Using CiteSpace and VOSviewer, it examines publication trends, influential authors, core journals, national contributions, keyword dynamics, and co-citation patterns.Findings reveal: (1) a notable increase in publications since 2020, with China leading in output and the UK and South Korea showing high research impact; (2) widespread dissemination across journals in computer science, engineering, educational technology, and sports science, indicating strong interdisciplinary integration; (3) an evolution of research hotspots from “educational informatization” to “intelligent teaching,” with current emphasis on deep learning, machine learning, virtual reality, data mining, and the Internet of Things in instructional and assessment contexts; and (4) a clear trend toward personalized, immersive, and intelligent teaching models driven by AI.Overall, AI is reshaping pedagogical frameworks and practical approaches in physical education, contributing to its transition toward intelligent, data-informed, and personalized systems.","url":"https://doi.org/10.1145/3797552.3797732","authors":["Jing Xu","Fengmei Li","Zhenzhen Duan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-23T08:35:40Z","doi":"10.1145/3797552.3797732","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3766557.3766575","name":"Research and Application of Artificial Intelligence Assisted Language Education Technology Based on Big Data","source":"crossref","abstract":"In order to understand artificial intelligence assisted language education technology, a research and application of AI assisted language education technology based on big data has been proposed. The paper first analyzes the key technologies and methods required for an intelligent personalized language learning platform; then introduces how to use these technologies to design a platform framework that can provide multiple learning paths, and proposes a matching language learning model; Finally, taking the learning of Mandarin by ethnic minorities as an application case, the construction process and methods of the platform were introduced, and a comparative analysis of the learning effects of learners was conducted. It was found that the application effect of the platform was good, effectively improving the basic language ability of language learners.","url":"https://doi.org/10.1145/3766557.3766575","authors":["Yijun Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-27T12:18:09Z","doi":"10.1145/3766557.3766575","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.46431/mejast.2025.8201","name":"The Use of Artificial Intelligence in Medical Technology","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming the landscape of medical technology by offering innovative solutions for diagnostics, treatment, patient care, and administrative tasks. This paper explores the multifaceted roles of AI in healthcare, its current applications, benefits, limitations, and future directions. We provide an overview of machine learning, natural language processing, computer vision, and robotics as they relate to medical technology, emphasizing real-world implementations and their impact on clinical outcomes. Artificial Intelligence (AI) is changing the face of modern healthcare. Rather than being just a futuristic idea, AI is already playing a practical role in hospitals and clinics around the world. From helping doctors diagnose diseases more accurately and quickly, to assisting in surgeries and monitoring patients' health in real-time, AI is becoming an essential part of medical technology. This paper looks at how different types of AI—like machine learning, natural language processing, computer vision, and robotics—are being used in the medical field. It also explores the benefits of AI, such as improving patient outcomes and reducing healthcare costs, while addressing key challenges like data privacy, bias, and the need for ethical oversight. Through real-world examples and current research, this paper offers a clear and accessible view of how AI is shaping the future of medicine. Artificial Intelligence (AI) has emerged as a transformative force in medical technology, redefining how healthcare is delivered, diagnoses are made, and treatments are managed. This paper explores the multifaceted applications of AI in medical technology, including diagnostic imaging, drug discovery, robotic surgery, remote monitoring, and clinical decision support systems. It also examines the core technologies underpinning AI such as machine learning, natural language processing, and computer vision. The research highlights real-world case studies, benefits, challenges, ethical implications, and future trends that are shaping the next era of healthcare. Through comprehensive analysis, this paper underscores the growing need for interdisciplinary collaboration, robust data governance, and regulatory frameworks to responsibly integrate AI into clinical practice. Keywords: Artificial Intelligence; Machine Learning; Medical Technology; Healthcare Innovation; Diagnostics; Robotics; Natural Language Processing; Computer Vision; Patient Monitoring; Healthcare Automation; Hospital Management","url":"https://doi.org/10.46431/mejast.2025.8201","authors":["Seema Rani","Sajad Ahmad Malik","Saytam kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-03T05:54:57Z","doi":"10.46431/mejast.2025.8201","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-030-92087-6_46","name":"Artificial Intelligence-Based Detection of Pulmonary Vascular Disease","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_46","authors":["Martine Remy-Jardin","Jacques Remy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_46","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-032-06376-2_6","name":"Research on Unified Appointment Service System of Medical Institutions Based on Cloud Platform and Artificial Intelligence Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06376-2_6","authors":["Zonghua Zhang","Han Wang","Xijie Dong","Zhen Zhang","Xiandong Lu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T22:52:10Z","doi":"10.1007/978-3-032-06376-2_6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1177/29498732251377341","name":"Cognitive LLMs: Toward Human-Like Artificial Intelligence by Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-Making","source":"crossref","abstract":"Resolving the dichotomy between the human-like yet constrained reasoning processes of cognitive architectures (CAs) and the broad but often noisy inference behavior of large language models (LLMs) remains a challenging yet exciting pursuit, aimed at enabling reliable machine reasoning capabilities in LLMs. Previous approaches that employ off-the-shelf LLMs in manufacturing decision-making face challenges in complex reasoning tasks, often exhibiting human-level yet unhuman-like behaviors due to insufficient grounding. This present article start to address this gap by asking whether LLMs can replicate cognition from CAs to make human-like decisions. We introduce cognitive LLMs , which are hybrid decision-making architectures comprised of a CA and an LLM through a knowledge transfer mechanism LLM-ACTR . Cognitive LLMs extract and embed knowledge of CA’s internal decision-making process as latent neural representations, inject this information into trainable LLM adapter layers, and fine-tune the LLMs for downstream prediction tasks. We find that, after knowledge transfer through LLM-ACTR , the cognitive LLMs offers better representations of human decision-making behaviors on a novel design for manufacturing problem, compared to an LLM-only model that employs chain-of-thought. Taken together, the results open up new research directions for equipping LLMs with the necessary knowledge to computationally model and replicate the internal mechanisms of human cognitive decision-making. We release the code and data samples at https://github.com/SiyuWu528/LLM-ACTR .","url":"https://doi.org/10.1177/29498732251377341","authors":["Siyu Wu","Alessandro Oltramari","Jonathan Francis","C Lee Giles","Frank E Ritter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-21T11:20:19Z","doi":"10.1177/29498732251377341","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-7185-563-1_6","name":"Ethical Challenges in AI–IoT Healthcare Applications","source":"crossref","abstract":"Artificial Intelligence (AI) and the Internet of Things (IoT) intersect and have transformed healthcare to become an interconnected ecosystem, based on data, intelligent, and smart. The AI-IoT integration can allow a greater level of accuracy and efficiency in patient treatment than ever before, predictive analytics, real-time surveillance, and personalized medical interventions. Nevertheless, there are also significant ethical issues associated with this digital development in data privacy, algorithmic bias, accountability, and transparency. Decentralized and data-centric AI-IoT systems make them more vulnerable to surveillance, discrimination, and abuse besides amplifying accountability and responsibility blur of decision-making. In order to make sure that innovation is a human-centered value, the healthcare sector should implement adaptive ethical principles, transparent algorithmic designs, and responsive governance patterns that balance the growth of technology with human dignity, fairness, and trust in the digital health settings.","url":"https://doi.org/10.70593/978-93-7185-563-1_6","authors":["M.Mary Linda","Vishnu B","MUTHUPANDI G","P.VIDHYA LAKSHMI"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T15:52:54Z","doi":"10.70593/978-93-7185-563-1_6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/cai64502.2025.00233","name":"Privacy Preserving Medical Image Classification and Steganography Using Deep Learning Architecture: a Pipeline","source":"crossref","abstract":"Secure transfer of medical data over unsecured networks is a crucial concern in telemedicine, as it involves the transmission of sensitive patient information that must be protected from unauthorized access. Generative adversarial networks help in hiding arbitrary data in images while optimizing the quality of the images. In this study, we introduce a novel pipeline that classifies medical images, integrates the diagnosis with patient information, embeds the concatenated information in patient image data, and ensures robust sharing capabilities. The pipeline comprises of image classification and image steganography. Image classification employs fine-tuned state-of-the-art (SOTA) convolutional models. Steganography makes use of encoder-decoder architecture to embed information on the image. These methods were tested on two medical datasets (X-ray and CT scans). Several models for classification were tested, of which fine-tuned Densenet121 achieved an accuracy and F1 score of 98.87 % and 98.8 % on X-ray respectively, and 98.1 % and 97.8 % on CT scans respectively. Similarly, for steganography our approach achieves payloads of 2.68 bits per pixel for$X$ray image and 2.56 bits per pixel for chest CT scan, Peak signal to noise ratio (PSNR) of 51.15 db for X-ray and 53.3 db for CT scan, and structural similarity index measure (SSIM) of 0.99 and 0.99 for X-ray and CT scan dataset respectively.","url":"https://doi.org/10.1109/cai64502.2025.00233","authors":["Kamal KC","Kushal Devkota","Kalyan Singh Karki","Alaka Acharya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-07T17:47:34Z","doi":"10.1109/cai64502.2025.00233","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-030-92087-6_14","name":"Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_14","authors":["Chen Qin","Daniel Rueckert"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_14","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/iccwamtip68645.2025.11352606","name":"Application of Artificial Intelligence and Wavelet Analysis to Medical Image Analysis","source":"crossref","abstract":"With the rapid development of artificial intelligence (AI) technology, the application of deep learning in medical image analysis, which is part of AI technology, has become a research hotspot, while traditional signal processing methods such as wavelet analysis (Wavelet Analysis) have been brought into the field of medical image processing and provide new solution ideas for medical image analysis and evaluation. Wavelet analysis can effectively extract the multi-product features of images due to its good time-frequency localization characteristics, while artificial intelligence (AI) techniques, especially deep learning models (e.g., CNNs), are long in automatically learning patterns from complex data, and as a combination of the two, wavelet analysis can enhance the feature extraction ability of deep learning and improve the accuracy and efficiency of medical image analysis. This paper reviews the combined application of wavelet analysis and AI in medical image analysis, focuses on their specific implementation in medical image denoising, edge detection, tumor detection and image classification, and analyzes the challenges of existing research and future development directions.","url":"https://doi.org/10.1109/iccwamtip68645.2025.11352606","authors":["Luo Sui","Li Jianping"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-28T20:57:11Z","doi":"10.1109/iccwamtip68645.2025.11352606","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.33880/ejfm.2025140402","name":"Investigation of Artificial Intelligence Anxiety and Awareness Among Medical Residents","source":"crossref","abstract":"Aim: Nowadays, when medical specialization is being reshaped with artificial intelligence technologies, this study aimed to evaluate medical residents' concerns about artificial intelligence, their future anxiety about their professional roles, and their ethical considerations. Methods: The study is a descriptive cross-sectional study conducted with 259 medical residents at Adana City Training and Research Hospital in 2024. Sociodemographic information form, Artificial Intelligence Anxiety Scale were administered, and they were asked to identify articles with artificial intelligence-generated conclusion sections, and their predictions about the medical specialties that will be most affected by artificial intelligence in the future were recorded. The data were analyzed with SPSS 24.0 software. Results: The mean age of the participants was 29.98±4.45 years and 81.5% of them reported spending two or more hours per day on digital platforms. The mean score obtained from the Artificial Intelligence Anxiety Scale was 53.25±21.57. 67.9% of the participants reported having heard of ChatGPT, while only 16.6% indicated they were knowledgeable about artificial intelligence applications used in medicine. While 73.6% of the participants answered that radiology is the branch that will be most affected in the future, only 52.56% of the participants were able to recognize the article written by artificial intelligence. Conclusion: Concerns about artificial intelligence are particularly pronounced in data-driven specialties, so developing artificial intelligence-oriented curricula in medical education will help future physicians better understand and utilize these technologies effectively. Furthermore, improving doctors' digital and software skills will support the reliable use of artificial intelligence products. Keywords: artificial intelligence, anxiety, awareness, medical residency, medical education","url":"https://doi.org/10.33880/ejfm.2025140402","authors":["Merthan Tunay","Sifa Durna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-30T08:57:10Z","doi":"10.33880/ejfm.2025140402","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/faia250188","name":"Research and Exploration on Intelligent Management of Medical Waste: A Case Report","source":"crossref","abstract":"This study addresses the complexity and challenges of hospital medical waste management and proposes a new intelligent medical waste management system construction scheme based on the Internet of Things (IoT) and artificial intelligence (AI). The system integrates advanced technologies such as Bluetooth positioning beacons, LoRa communication base stations, and high-precision positioning and map engines to establish a wireless IoT platform that covers the entire lifecycle management of medical waste. The intelligent medical waste collecting and transferring vehicle is the core equipment, utilizing 4G/5G modules, Bluetooth positioning terminals, and electronic weighing technology to achieve deep integration with the hospital’s wireless IoT platform. Combined with AI technology, the system realizes medical waste’s informatization, intelligence, and visualization management. This study not only provides an efficient and reliable solution for intelligent medical waste management in hospitals but also provides an important practical reference for the construction of smart healthcare based on IoT and AI.","url":"https://doi.org/10.3233/faia250188","authors":["Muqing Niu","Zhiyuan Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-11T13:15:34Z","doi":"10.3233/faia250188","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.caeai.2025.100413","name":"Artificial intelligence, job seeker, and career trajectory: How AI-based learning experiences affect commitment of fresh graduates to be an accountant?","source":"crossref","abstract":"Integrating Artificial Intelligence (AI) into the accounting professions is reshaping traditional roles and skill requirements, prompting a reassessment of how graduates are prepared for these changes. However, a significant gap exists in understanding how AI-based learning in higher education curricula influences graduates' readiness and commitment to be accountants. Addressing this gap, this study aims to answer two pivotal questions: how do AI learning experiences affect AI self-efficacy and career commitment, and how do literacy, motivations, and competency factors mediate this relationship? This study grounds in Self-determination Theory (SDT) and Social Cognitive Career Theory (SCCT) to answer the questions and utilizes a multiple mediation model with partial least squares structural equation modeling (PLS-SEM) to analyze survey data gathered from 698 fresh graduates in accounting and finance program across the country (Indonesia). The finding confirms that AI-based learning experiences in higher education influence fresh graduates’ AI self-efficacy and career commitment through the mediating role of AI literacy, competency, and motivations. This study demonstrates that AI-based learning enhances students' literacy and competencies in AI/accounting software while boosting intrinsic and extrinsic motivation to master AI skills for future career benefits. Ultimately, increased motivation, literacy, and competencies strengthen graduates' self-efficacy and confidence, supporting their commitment to a career in accounting. This study, therefore, contributes a novel insight by providing empirical considerations for higher education and the accounting industry to strengthen AI-based curricula for future workforce supply.","url":"https://doi.org/10.1016/j.caeai.2025.100413","authors":["Agung Maulana","Rakotoarisoa Maminirina Fenitra","Slamet Sutrisno","Kurniawan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-06T19:30:02Z","doi":"10.1016/j.caeai.2025.100413","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/atde250116","name":"Practice and Exploration of Teaching Python Programming for Business Majors Based on Artificial Intelligence Assistance","source":"crossref","abstract":"This study focuses on evaluating the effectiveness of AI-assisted AI-based instructional practices for teaching Python programming to business majors. The study analyzed the distribution of students’ final grades by comparing the class that used the traditional teaching model in the 1st semester of the 2022–2023 academic year with the class that introduced the AI-assisted teaching model in the 1st semester of the 2023–2024 academic year. The results showed that the classes with the AI-assisted teaching model showed a significant improvement in the students’ performance, with a significant increase in the percentage of high-performing bands and a significant decrease in the percentage of low-performing bands. Further, an independent samples t-test was conducted on the grades of the two classes using SPSS 26 software, and it was found that there was a significant difference between the two in terms of average grades, and this difference was highly statistically significant. This study demonstrates that the AI-assisted teaching model based on artificial intelligence has significant advantages in improving the teaching quality and student achievement in Python Programming course for business majors.","url":"https://doi.org/10.3233/atde250116","authors":["Jixu Zhu","Jiawen Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-11T13:45:11Z","doi":"10.3233/atde250116","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/ainit65432.2025.11035007","name":"Fair Contribution Evaluation in Federated Learning for Medical Image Analysis Using the Banzhaf Index","source":"crossref","abstract":"This paper explores the application of Federated Learning (FL) in medical image analysis, with a focus on how the Banzhaf Index can be used to assess participants' contributions. Federated Learning, as a distributed machine learning approach, allows for collaborative training while preserving data privacy by keeping data localized to participants' devices. However, data heterogeneity and the imbalance in contribution evaluation remain major challenges in FL. This study proposes a Federated Learning model based on the Banzhaf Index, which quantifies each participant's contribution to the global model by calculating their marginal contributions. Experimental results demonstrate that the Banzhaf Index effectively detects malicious participants and provides a fair contribution evaluation in scenarios with imbalanced data distributions. This research presents a novel approach and method for applying Federated Learning in medical image analysis.","url":"https://doi.org/10.1109/ainit65432.2025.11035007","authors":["Boyang Deng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-23T17:24:40Z","doi":"10.1109/ainit65432.2025.11035007","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/iccsai64074.2025.11064524","name":"AI-Driven OCR for Comprehensive Medical Label Interpretation","source":"crossref","abstract":"This paper presents a comprehensive analysis of Optical Character Recognition (OCR) technology and its relevance to the healthcare industry with a specific emphasis on recognition of medical tags and texts. OCR technology considerably facilitates the process of transforming paper- based patient records into digital systems, thereby solving the problems of data storage, accuracy as well as reducing the possibility of arbitrary mistakes. The review offers insights on OCR, featuring its components and how they operate starting from the image acquisition as well as the post processing and assesses the significance of such technology to the health service providers. This is because the solution is compatible with artificial intelligence and machine learning technologies and therefore can cope with a variety of medical records. Some applications demonstrate how OCR helps to reduce efforts in the analysis of cancer from pathology reports as well as protect patients' private information within medical pictures. Even with these existing opportunities, there are still problems concerning accurate analysis of information with many intricate structures. This paper examines how OCR might advance in future prospects, with an emphasis on the adoption of clinical practices and frameworks, and data safety's ethical aspects.","url":"https://doi.org/10.1109/iccsai64074.2025.11064524","authors":["Aditya Kushwaha","Pranjal Prasad","Aarna Singh","Tanu Sharma","Vaishali Deshwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T17:40:02Z","doi":"10.1109/iccsai64074.2025.11064524","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-981-95-7072-0_46","name":"Prompt-Driven Knowledge Retrieval in Arabic Medical Agents via Graph-RAG and LLM","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-7072-0_46","authors":["Ahlem Khlifi","Rebh Soltani","Hela Ltifi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-12T00:11:41Z","doi":"10.1007/978-981-95-7072-0_46","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1093/bjrai/ubae018","name":"Foundational artificial intelligence models and modern medical practice","source":"crossref","abstract":"Abstract Our opinion piece pays homage to the evolution of medical practices, tracing back to the era of Hippocrates, through significant historical milestones, and drawing parallels with the principles underpinning foundational artificial intelligence (AI) models. It emphasizes the shared ethos of both domains: a commitment to comprehensive care that values diverse data integration and individualized patient treatment. The excitement surrounding foundation models in medical imaging is understandable. However, a critical and cautious approach is crucial before widespread adoption. By addressing the present 4 major limitations (ie, data bias and generalizability, interpretability of AI models, data scarcity and diversity, and computational resources and infrastructure) and fostering a culture of rigorous research, we can unlock the true potential of these models and revolutionize medical care. This critique (opinion) paper highlights the need for a more measured approach in the field of foundation AI models for medicine in general and for medical imaging in particular. It emphasizes the importance of tackling core challenges before rushing toward clinical applications. By focusing on robust methodologies and addressing limitations, researchers can ensure the development of truly impactful and trustworthy models for the betterment of healthcare.","url":"https://doi.org/10.1093/bjrai/ubae018","authors":["Alpay Medetalibeyoglu","Yury S Velichko","Eric M Hart","Ulas Bagci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-18T16:36:33Z","doi":"10.1093/bjrai/ubae018","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.53555/eijmhs.v11i1.255","name":"ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING: ENHANCING DIAGNOSTIC ACCURACY","source":"crossref","abstract":"The healthcare industry underwent revolutionary changes because medical imaging systems enable early disease identification combined with precise diagnoses while planning successful treatments. The development of MRI, CT scanning, and ultrasound technologies boosted diagnostic accuracy throughout multiple years. The assessment process that uses traditional imaging methods depends on human readers, who may produce inconsistent results. This research evaluates how contemporary imaging systems improve medical diagnosis for cancer patients and cardiovascular and neurological disease patients. The research demonstrates better testing precision through improved sensitivity and specificity measures decreased occurrences of wrong positive and negative readings and enhanced operational workflow processes. The implementation of modern imaging technologies provides advantages but encounters essential obstacles which consist of privacy concerns as well as regulatory obligations and expensive implementation requirements. Researchers are currently working on healthcare imaging technique optimization and transparency enhancement while trying to establish wider healthcare access through future medical advancements. Research along with policy reforms need to tackle existing challenges because they will ensure medical imaging reaches its full potential to increase patient care quality and clinical success rates.","url":"https://doi.org/10.53555/eijmhs.v11i1.255","authors":["Dr. Kanak Soni","Dr. Keerthana R","Rahul Gangwar","Rashmi Singh","Dr. Madhu Shukla","Simrin Fathima Syed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-06T05:59:18Z","doi":"10.53555/eijmhs.v11i1.255","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.21037/jmai.2019.04.03","name":"Artificial intelligence as another set of eyes in breast cancer diagnosis","source":"crossref","abstract":"Breast cancer is the most common cancer in women worldwide and the second most common cancer overall, hence it is a significant public health concern. According to Global Health Estimates (1), over half a million women died in 2011 due to breast cancer.","url":"https://doi.org/10.21037/jmai.2019.04.03","authors":["Syed M. Anwar","Ulas Bagci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-05-08T08:05:16Z","doi":"10.21037/jmai.2019.04.03","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1201/9781003558743-3","name":"The Inevitable Artificial Intelligence Revolution in Healthcare","source":"crossref","abstract":"The diagnosis of a disease is the first and the most important phase of a patient’s treatment. Human errors can lead to miscalculated detection of disease, causing a delay in treatment, which can lead to irreversible damage like death. While physicians may diagnose the disease accurately, for a developing country like India where the doctor-to-citizen ratio is 1.34:1000, we need a cheap, accurate, scalable, and practical solution. Physicians rely on a cumbersome diagnosis process that can take months to diagnose. It is worth noting that diseases like Alzheimer’s disease [ 2 , 31 ] can have very long incubation periods and mild symptoms might make it difficult for not only the doctor to diagnose the disease but also the patient to report them. Dependence on Computed Tomography, Magnetic Resonance Imaging, fluorescent images, and continuous test reports can be tedious and expensive in rural areas, which makes us search for alternative technology. The Internet of Things devices embedded in a fog computing architecture [ 4 ] can come to the rescue by bringing real-time processing and computing capability from a centralized server in a hospital right to the patient’s bed [ 6 ]. Artificial Intelligence can rapidly analyze vast amounts of log data accumulated by clinically deployed Internet of Things devices 36 and identify the best treatment options. By using Artificial Intelligence in conjunction with medical judgment, these machines can promise new horizons in healthcare. In this chapter, let’s explore the symbiosis of humans and machine intelligence, and discover how Artificial Intelligence is being put into action to diagnose fatal diseases and eliminate physician engagement. The chapter talks about how Deep Neural networks [ 32 ] can step into the boots of a physician to analyze data points like age, gender, smoking status, and blood pressure, and diagnose radiological, cardiovascular [ 3 ], dermatological, oncological disorders at a very early stage.","url":"https://doi.org/10.1201/9781003558743-3","authors":["Sarthak Goel","Anamika Guha","B. K. Tripathy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-19T14:08:57Z","doi":"10.1201/9781003558743-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1049/pbhe050e_ch8","name":"Security-based explainable artificial intelligence (XAI) in healthcare system","source":"crossref","abstract":"Explainable Artificial Intelligence (XAI) is one of the most advanced research areas of Artificial Intelligence (AI). To explain the deep learning (DL) model is the main objective of XAI. It deals with artificial models which are understandable to humans, including the users, developers, policymakers, etc. XAI is very important in some critical domains like security, healthcare, etc. The purpose of XAI is only to provide a clear answer to the question of how the model made its decision. The explanation is very important before any system decision-making. As an example, if a system responds to a decision, it is necessary to have inside knowledge of the model about that decision. The decision can be positive or negative, but it is more important to know the decision based on characteristics. The decision of the model should be trusted when we know the internal structure of the DL model. Generally, DL models come under the black box models. So for security purposes, it is very necessary to explain a system internally for any decision-making. Security is very crucial in healthcare as well as in any other domain. The objective of this research is to provide a decision about security based on XAI which is a big challenge. We can improve security systems based on XAI for the next level. For medical/healthcare security, when we recognize human action using transfer learning techniques, one pre-trained model is considered good for action and the same action is not good in terms of accuracy using another pre-trained model. This is called the black-box model problem, and it needs to know what is the internal mechanism of both models for the same action. Why one model considers good for action and why the same action is not very well using another model? Here need a model-specific approach of post-hoc interpretability to know the internal structure and characteristics of both models for the same action.","url":"https://doi.org/10.1049/pbhe050e_ch8","authors":["Hüseyin Gürüler","Naveed Islam","Alloud Din"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T03:11:32Z","doi":"10.1049/pbhe050e_ch8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.2196/preprints.77552","name":"Perceptions and Intentions to Use Generative Artificial Intelligence Among First-Year Medical Students in Japan: Cross-Sectional Survey Study (Preprint)","source":"crossref","abstract":"BACKGROUND The rise of generative artificial intelligence (gAI) has created both opportunities and challenges in higher education. Although the potential benefits of learning support are widely recognized, little is known about how incoming medical students in Japan perceive and intend to use such technology. OBJECTIVE This study investigated the status of gAI usage, learning behaviors, and perceptions of first-year medical students in Japan. METHODS An anonymous online survey was conducted among 118 first-year medical students at Chiba University in April 2025. The questionnaire assessed prior gAI use, willingness to learn, perceptions of gAI, and the intention to use it academically. Likert scales, correlation analyses, and content analyses of free-text responses were used. RESULTS Of the respondents, 84.7% had prior experience with the gAI, primarily in language learning and information gathering. However, only 49.2% had learning experiences, mostly through informal sources, such as web browsing and peer interaction. Students showed a high willingness to learn about gAI (mean score: 4.3/5.0), which correlated with positive perceptions. Despite this interest, attitudes toward using gAI for academic assignments were neutral (mean 3.0/5.0). Content analysis of the open-ended responses revealed three types of attitudes: positive, cautious, and negative. CONCLUSIONS Although most students used the gAI, their limited exposure to formal learning suggests that self-directed experience alone may not foster confidence or informed use. Neutral attitudes and mixed qualitative responses highlighted the need for structured gAI literacy education that balances the benefits of ethical and critical considerations in medical education.","url":"https://doi.org/10.2196/preprints.77552","authors":["Hiroshi Tajima","Hajime Kasai","Kiyoshi Shikino","Ikuo Shimizu","Shoichi Ito"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-20T12:35:06Z","doi":"10.2196/preprints.77552","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/ic-aiai48757.2019.00008","name":"Mobile Telemedicine Systems with Artificial Medical Intelligence","source":"crossref","abstract":"We consider mobile telemedicine systems with artificial medical intelligence (MTMS with AMI) the best solution for mass screening of the population in rural, remote and hard-to-access areas. In Russia, more than 110,000 settlements with about 33 million people are not provided with healthcare services. The situation is barely better in other localities, primarily in the district healthcare setting which lacks enough doctors, or their qualification level is insufficient. The article describes the design of a telemedicine system with AMI based on modern telecommunications (cellular and mobile-satellite services) and a variety of AMIs for mammography, fluorography, cardiography, etc. The project has been developed and partially implemented by the MIPT and Vector Radio Company over the past 5 to 7 years. An artificial medical intelligence for automated analysis of fluorograms, designed as a cloud service, is described in detail, and so are the results of its testing.","url":"https://doi.org/10.1109/ic-aiai48757.2019.00008","authors":["Sergey Garichev","Viktor Klassen","Michael Natenzon","Artem Safin","Stanislav Sergeev"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-25T04:44:38Z","doi":"10.1109/ic-aiai48757.2019.00008","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.35711/aimi.v2.i5.95","name":"Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions","source":"crossref","abstract":"Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions","url":"https://doi.org/10.35711/aimi.v2.i5.95","authors":["Smaha Jahangir","Hashim Ali Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-27T05:04:19Z","doi":"10.35711/aimi.v2.i5.95","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-030-92087-6_37","name":"Cardiac Nuclear Medicine: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_37","authors":["Marina Piccinelli","Ernest V. Garcia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_37","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111529","name":"Corrigendum to “Intelligent evaluation of pavement friction at high speeds with artificial intelligence powered three-dimensional laser imaging technology” [Eng. Appl. Artific. Intellig. 150 (2025) 1–19 110580]","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111529","authors":["Guolong Wang","Kelvin C.P. Wang","Guangwei Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-24T23:46:27Z","doi":"10.1016/j.engappai.2025.111529","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.2196/preprints.77332","name":"Utility of Generative Artificial Intelligence for Japanese Medical Interview Training:  A Randomized Crossover Pilot Study (Preprint)","source":"crossref","abstract":"BACKGROUND The medical interview remains a cornerstone of clinical training. There is growing interest in applying generative artificial intelligence (AI) in medical education, including medical interview training. However, its utility in culturally and linguistically specific contexts, including Japanese, remains underexplored. This study investigated the utility of generative AI for Japanese medical interview training. OBJECTIVE This pilot study aimed to evaluate the utility of generative AI as a tool for medical interviews training by comparing its performance with that of traditional face-to-face training methods using a simulated patient. METHODS We conducted a randomized crossover pilot study involving 20 postgraduate year 1-2 physicians from a university hospital. Participants were randomly allocated into two groups. Group A began with an AI-based station on a case involving abdominal pain, followed by a traditional station with a standardized patient presenting chest pain. Group B followed the reverse order, starting with the traditional station for abdominal pain, and subsequently within AI-based station for the chest pain scenario. In the AI-based stations, participants interacted with a GPTs-configured platform that simulated patient behaviors. GPTs are customizable versions of ChatGPT adapted for specific purposes. The traditional stations involved face-to-face interviews with a simulated patient. Both groups used identical, standardized case scenarios to ensure uniformity. Two independent evaluators, blinded to the study conditions, assessed participants' performances using six defined metrics: patient care and communication, history taking, physical examination, accuracy and clarity of transcription, clinical reasoning, and patient management. A 6-point Likert scale was employed for scoring. Discrepancy between the evaluators resolved through discussion. To ensure cultural and linguistic authenticity, all interviews and evaluations were conducted in Japanese. RESULTS AI-based stations scored lower across most categories, particularly in patient care and communication, than traditional stations (4.48 vs. 4.95, P=.009). However, AI-based stations demonstrated comparable performance in clinical reasoning, with a non-significant difference (4.43 vs. 4.85, P=.10). CONCLUSIONS The comparable performance of generative AI in clinical reasoning highlights its potential as a complementary tool in medical interview training. One of its main advantages lies in enabling self-learning, allowing trainees to independently practice interviews without the need for simulated patients. Nonetheless, the lower scores in patient care and communication underline the importance of maintaining traditional methods that capture the nuances of human interaction. These findings support the adoption of hybrid training models that combine generative AI with conventional approaches to enhance the overall effectiveness of medical interview training in Japan. CLINICALTRIAL UMIN-CTR UMIN000053747; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000061336.","url":"https://doi.org/10.2196/preprints.77332","authors":["Takanobu Hirosawa","Masashi Yokose","Tetsu Sakamoto","Yukinori Harada","Kazuki Tokumasu","Kazuya Mizuta","Taro Shimizu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-12T14:10:09Z","doi":"10.2196/preprints.77332","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.21518/ms2025-322","name":"Practical implementation of artificial intelligence technologies during preventive medical examination","source":"crossref","abstract":"Introduction. Preventive medicine in Russia is a priority area, the key task of which is to combat the spread of chronic noncommunicable diseases (CKD). The latter are the cause of the premature death of hundreds of people. In this regard, the issues of developing and implementing early screening of NCDs, combined with advanced digital technologies based on artificial intelligence (AI), in clinical practice, are extremely relevant. Aim. To develop a medical methodology for remote questionnaire screening (DAS) of CNID in young people. Materi als and methods. The study involved 3.155 people aged 19.6 ± 1.5 years (46.9% were men and 53.1% were women). Preventive medical examination of all participants was carried out using DAS. Results. A high degree of NCD risk was found in 11.7%, an average in 30.9%, and a low in 57.4% of the subjects. The most frequent complaints were from the endocrine (28.9%), digestive (21.8%), respiratory (21.1%), cardiovascular (20.1%) and oncological alertness (8.1%). In 75.7% of cases, the presence of risk factors (RFS) was determined by two or more pathology profiles. Satisfaction with DAS use among the surveyed was 96.6%, and among medical workers 91.7%. Conclusions . 1. The use of DAS FR HNIZ increases the compliance of patients to undergo a preventive medical examination. 2. The use of statistical methods confirms the effectiveness of the integrated assessment of health and the effectiveness of the detection of NID according to the main socially significant profiles of pathology. 3. The system identifies the most common NIDF, the degree of their severity, and also identifies people with critical FD who need priority care. This option allows you to optimize patient routing, reducing the one-time burden on the medical institution as a whole and on a specific specialist. 4. Depending on the identified NHS disorders and their severity, a set of recommendations has been developed, which implements a personalized approach. 5. The use of DAS FR HNIZ in young people has shown significant social and economic effectiveness.","url":"https://doi.org/10.21518/ms2025-322","authors":["P. V. Seliverstov","E. V. Kryukov","V. B. Grinevich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-10T06:44:03Z","doi":"10.21518/ms2025-322","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1080/0142159x.2024.2434101","name":"Using artificial intelligence to provide a ‘flipped assessment’ approach to medical education learning opportunities","source":"openalex","abstract":"PURPOSE: Generative AI can potentially streamline the creation of practice exam questions. This study sought to evaluate medical students' confidence using generative AI for this purpose, and overall attitudes towards its use. MATERIALS AND METHODS: The study used a mixed-methods approach with a pre-post intervention design. 68 medical and physician associate students were recruited to attend a workshop where they were shown how to use Google Bard (now Gemini) to write exam questions before being encouraged to do this themselves with guidance. A survey was completed before and after. Seven students also participated in a follow-up focus group. RESULTS: < 0.001) after the workshop. Qualitative feedback highlighted pros and cons of using generative AI to write exam questions, alongside some concerns about its implementation. Students noted other positive uses in the curriculum and expressed a desire for institutional clarity on appropriate AI use. CONCLUSIONS: While increased confidence is positive, rigorous evaluation of AI-generated question quality is needed to confirm accuracy. Teaching students to use generative AI to create and critique practice questions represents a means of encouraging appropriate AI use.","url":"https://doi.org/10.1080/0142159x.2024.2434101","authors":["Samuel Birks","James Gray","Claire Darling-Pomranz"],"tags":["Generative grammar","Flipped learning","Medical education","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"10.1080/0142159x.2024.2434101","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1145/3777730.3777750","name":"Analysis of Vocal Training Feedback Mechanisms Assisted by Artificial Intelligence","source":"crossref","abstract":"With the rapid advancement of artificial intelligence (AI) and computer technologies, AI-powered systems are increasingly being integrated into vocal training to enhance the effectiveness and accuracy of lessons. This article explores how AI-driven feedback mechanisms, supported by machine learning (ML), signal processing, and cloud computing, provide real-time analysis and guidance for vocal learners. Key focus areas include pitch correction, voice range tracking, and tone quality analysis, enabled by deep learning algorithms and audio processing techniques. The study evaluates the superiority of AI-based feedback systems over traditional methods by examining their underlying computational architecture, data-driven modeling, and adaptive learning capabilities. A practical case study demonstrates the implementation of these systems in real-world scenarios, highlighting the role of neural networks, big data analytics, and real-time processing in optimizing vocal performance.","url":"https://doi.org/10.1145/3777730.3777750","authors":["Conghe Feng","Xia Tian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-12T10:15:34Z","doi":"10.1145/3777730.3777750","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.5220/0013091300003890","name":"Constraint-Based Optimization for Scheduling Medical Appointments","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013091300003890","authors":["George Assaf","Sven Löffler","Petra Hofstedt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-28T12:43:20Z","doi":"10.5220/0013091300003890","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3797552.3797716","name":"Construction and Empirical Research of Adaptive Learning System Empowered by Artificial Intelligence","source":"crossref","abstract":"This paper constructs an AI-enabled adaptive learning system that integrates multimodal perception and deep reinforcement learning. This system adopts a four-layer closed-loop architecture of perception, decision, making, execution, feedback. The core modules include: the multimodal learner profiling module, which integrates eye movement physiology, learning behavior, academic performance and emotional attitude data, and outputs knowledge mastery degree, ability level and emotional state labels through a hybrid model of Bayesian network and long short-term memory network. In the domain knowledge graph module, a three-dimensional knowledge association model of “concept, relationship, difficulty” is constructed. In the reinforcement learning push strategy module based on deep Q-network (DQN), a composite reward function is designed to achieve dynamic and precise resource push. To verify the effectiveness of the system, 600 students from three different levels of universities (985 universities, regular undergraduate universities, and private undergraduate universities) were selected for an empirical study. They were randomly divided into the experimental group and the control group by random sampling. The empirical results show that the prediction accuracy rate of knowledge mastery of the multimodal portrait model reaches 89.2%, which is significantly higher than 72.5% of the single grade data model (p<0.01). The dynamic push strategy of DQN enabled the experimental group to master an average of 4.2 knowledge points per week, which was significantly better than 2.8 in the control group (p<0.01). The experimental group was significantly higher than the control group in the three core indicators of post-test scores, learning motivation and autonomous learning ability (p<0.01). This study verified the effectiveness and universality of the constructed system, providing theoretical support and practical paradigms for the engineering implementation of ALS and the promotion of educational equity.","url":"https://doi.org/10.1145/3797552.3797716","authors":["Hui Zhang","Dong-jun Wei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-23T08:35:40Z","doi":"10.1145/3797552.3797716","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3785987.3786038","name":"Research Status of International Sports Artificial Intelligence: Visualization Analysis Based on the WoS Database","source":"crossref","abstract":"Artificial intelligence (AI) technology is reshaping research paradigms and industrial practices in sports science. However, current research in sports AI exhibits fragmentation, lacks systematic interdisciplinary integration and theoretical development, and suffers from insufficient macro-level analysis of international research networks, hotspot distributions, and developmental trends. To address this gap, this study screened 1,079 papers from the Web of Science database (2015–2024). It employed VOS Viewer for multidimensional visualization analysis, including keyword co-occurrence, journal preference, and country distribution. The research aims to systematically reveal the current state of the research landscape and emerging trends in this field. Findings indicate: (1) A pronounced technology-driven trajectory, with core keywords centered on \"machine learning\" and \"deep learning,\" and application scenarios concentrated in football tactical analysis, health management, and personalized training optimization; (2) Engineering and technology journals account for over 50% of published literature, while sports science journals represent only 14.64%, indicating lagging interdisciplinary theoretical integration; (3) China holds a dominant position with 43.095% of published articles, followed by the United States (13%). Policy-driven effects are particularly pronounced in Asian countries. The study indicates that sports AI research exhibits characteristics of technology dominance and disciplinary imbalance. Therefore, efforts should be strengthened to enhance interdisciplinary integration and address humanistic and ethical concerns, promote technological standardization and cultural adaptability, and achieve a dynamic equilibrium between \"technological empowerment\" and \"humanistic value.\" Future efforts should focus on expanding multilingual data sources, establishing global collaboration mechanisms, and advancing AI applications in physical education, performance enhancement, and health management. This will ultimately foster the co-development of sports science and technology alongside human progress.","url":"https://doi.org/10.1145/3785987.3786038","authors":["Yuehang Diao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-30T09:50:45Z","doi":"10.1145/3785987.3786038","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2023.107139","name":"Dual Cross-Attention for medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107139","authors":["Gorkem Can Ates","Prasoon Mohan","Emrah Celik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-20T13:47:28Z","doi":"10.1016/j.engappai.2023.107139","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-030-92087-6_53","name":"How to Write and Review an Artificial Intelligence Paper","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_53","authors":["Thomas Weikert","Tim Leiner"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_53","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1186/s12909-025-06640-x","name":"Design strategies for artificial intelligence based future learning centers in medical universities","source":"openalex","abstract":"BACKGROUND: This study explores the acceptance of artificial intelligence(AI) tools in medical students and its influencing factors, thus providing theoretical basis and practical guidance for the construction of future learning centers in medical universities. METHODS: This study comprehensively applied the unified theory of acceptance and use of technology(UTAUT), expectancy confirmation theory (ECT), and innovation diffusion theory (IDT) to analyze the data through structural equation modeling. RESULTS: Effort expectancy (EE), facilitating condition (FC), social influence (SI), and satisfaction (SA) significantly influence medical students' continuance intention (CI) to use artificial intelligence tools. Relative advantage (RA) has a significant impact on medical students' satisfaction (SA) with artificial intelligence tools. Personal innovativeness (PI) plays a significant positive moderating role in the relationships between facilitating condition (FC) and continuance intention (CI), as well as between satisfaction (SA) and continuance intention (CI). CONCLUSIONS: The construction of AI-based future learning centers in medical universities should attach importance to providing personalized learning paths, ensuring technical support and training, creating a collaborative and innovative environment, and showcasing the comparative advantage of tools.","url":"https://doi.org/10.1186/s12909-025-06640-x","authors":["Yang Xiaowen","Ding Jingjing","Wang Biao","Zhang Shenzhong","Wu Yana","Jingjing Ding"],"tags":["Continuance","Expectancy theory","Unified theory of acceptance and use of technology","Knowledge management","Structural equation modeling"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-31","doi":"10.1186/s12909-025-06640-x","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21037/jmai.2019.09.05","name":"The prospect of artificial intelligence in the differential diagnosis of pancreatic cysts","source":"crossref","abstract":"The title of the work we are here presenting “ Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions ” immediately strikes the interest of readers, especially of the ones performing pancreatic surgery (1). This article debates a theme of major concern for surgeons, the correct identification of a pancreatic cystic lesion, and a theme of major concern for the medical society and the society in general, the application of artificial intelligence (AI).","url":"https://doi.org/10.21037/jmai.2019.09.05","authors":["Marco Montorsi","Giovanni Capretti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-12T03:16:41Z","doi":"10.21037/jmai.2019.09.05","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/0933-3657(93)90041-z","name":"Connectionist expert systems as medical decision aid","source":"crossref","abstract":"In this paper neural networks are used as associative memories to build an expert system for aiding medical diagnosis. As in expert systems using symbolic manipulation, the knowledge is introduced by a knowledge engineer using a collection of known cases. The system has an object-oriented approach to knowledge organization and the resulting network topology. Fuzzy sets are used to interpret connection values and/or excitation state of the units. The main result is that the proposed neural network allows not only finding a solution in some cases, but also suggests obtaining more clinical data if the data available is insufficient to reach a conclusion. This approach is illustrated by examples.","url":"https://doi.org/10.1016/0933-3657(93)90041-z","authors":["Jorge M. Barreto","Fernando M. de Azevedo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T05:53:05Z","doi":"10.1016/0933-3657(93)90041-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-19073-5.00021-5","name":"Artificial intelligence in medical education","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-19073-5.00021-5","authors":["Priyanga Subbiah","Lakshmi Kanthan Narayanan","Rengaraj Alias Muralidharan Ramanujam","Arun Prasad Baskaran","Sahaaya Arul Mary S A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T06:51:29Z","doi":"10.1016/b978-0-443-19073-5.00021-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.21608/ejai.2022.142684.1005","name":"Literature Review of Segmentation and Classification of Medical Images","source":"crossref","abstract":"The brain tumor segmentation is one of the highly focused regions in the community of medical science. Brain tumor segmentation has been employed to define the tumor area to help for disease diagnosis and select the best methods for the treatment of diseases. It is a tool to separate a portion of the tumor from the entire image. This paper presents a thorough literature review of recent techniques of brain tumor segmentation. Several techniques of image segmentation are briefly explained with the recent contribution of various researchers.The brain tumor is a mass of abnormal cells. It consists of two types: malignant and benign. Brain tumor symptoms can be general or pre-defined. General symptoms are caused by tumor pressure on the brain or spinal cord. Pre-defined symptoms are caused, when a certain part of the brain does not function well due to a tumor. The researchers applied different imaging modalities to detect anatomical structures from several medical imaging systems","url":"https://doi.org/10.21608/ejai.2022.142684.1005","authors":["Noha A. El-Hag"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-01T20:40:02Z","doi":"10.21608/ejai.2022.142684.1005","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1201/9781003371250-2","name":"Artificial Intelligence-Enabled IOMT for Medical Application","source":"crossref","abstract":"The present pandemic has put the whole of humanity under threat of getting infected by a deadly coronavirus. Remote health monitoring systems can help such patients to get advice related to the required treatment from doctors at remote locations. The first part of the module gives a brief idea about a simple system that will sense using medical sensors and send information to the cloud using the internet and provide assistance to the patients after processing the data collected on the cloud as per the levels of the critical parameters under consideration. Internet of medical things (IoMT) is the name given to this system. The illustration for the same is done for demonstration with the help of an experimental setup using LabVIEW. The later part of the module covers IoMT using AI which allows the doctors to prescribe medicines in accordance with the patient’s critical parameters analysis. A comparative study of a simple IoMT with AI-enabled IoMT (AIoMT) for medical application will be provided for enhancing the understanding of the impact and application of IoMT and AIoMT. From a healthcare perspective, this module assesses numerous IoT security and privacy characteristics, as well as various cyber threat models and attack classification.","url":"https://doi.org/10.1201/9781003371250-2","authors":["Lochan Jolly","Niket Amoda","K. Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-13T09:29:49Z","doi":"10.1201/9781003371250-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-031-94302-7_2","name":"Automated MRI-Based Brain Tumor Classification Using CNN Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94302-7_2","authors":["Shaga Anoosha","B. Seetharamulu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:12:24Z","doi":"10.1007/978-3-031-94302-7_2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-030-92087-6_38","name":"Cardiac Ultrasound Imaging: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_38","authors":["Karthik Seetharam","Partho P. Sengupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_38","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2024.108138","name":"Enhancing medical image classification through controlled diversity in ensemble learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108138","authors":["Manojeet Roy","Ujwala Baruah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-26T09:59:30Z","doi":"10.1016/j.engappai.2024.108138","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.30965/9783969753477_003","name":"Artificial Intelligence, Authorship and Aesthetic Responsibility in Art","source":"crossref","abstract":"","url":"https://doi.org/10.30965/9783969753477_003","authors":["Catrin Misselhorn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-13T02:00:41Z","doi":"10.30965/9783969753477_003","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1515/9783111595306-202","name":"VAdvances in artificial intelligence risk management","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783111595306-202","authors":["Kurt J. Engemann"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-19T13:36:46Z","doi":"10.1515/9783111595306-202","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/aaicv66571.2025.00063","name":"Teaching Strategies for Improving Memory Effect of College English Vocabulary Based on Artificial Intelligence Algorithm","source":"crossref","abstract":"With the rapid development of artificial intelligence technology, the field of education has also ushered in a new round of change, especially in English vocabulary learning, the auxiliary role of artificial intelligence algorithms has become increasingly apparent. The purpose of this study is to explore the application of artificial intelligence algorithm in improving college English vocabulary memory, analyse its impact on students' learning effect, and propose effective teaching strategies. This paper first introduces the advantages of artificial intelligence algorithm in language learning, and then discusses its specific application in college English vocabulary teaching, including vocabulary explanation, example provision, learning suggestions, simulated dialogue and so on. Finally, through the combination of modern educational technology and traditional teaching methods, this study constructs a framework of English vocabulary memory improvement strategies assisted by artificial intelligence, and carries out experimental analysis to verify the effectiveness of these strategies.","url":"https://doi.org/10.1109/aaicv66571.2025.00063","authors":["Zhang Yuanwei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T18:33:47Z","doi":"10.1109/aaicv66571.2025.00063","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaie64856.2025.11158339","name":"Empowering Geography Education with Artificial Intelligence: Exploring Application Practices and Reform Pathways","source":"crossref","abstract":"With the rapid development of artificial intelligence technology, the field of education is experiencing a transformative opportunity. This paper explores the application practices and reform pathways of AI in geography education. By analyzing the intersection of AI technology and the characteristics of geography teaching, this study summarizes the practical applications of intelligent teaching platforms, personalized learning data analysis, and technologies such as virtual reality and augmented reality in geography education. It demonstrates the positive roles of AI in enhancing teaching efficiency, optimizing learning experiences, and facilitating teacher-student interactions. Additionally, this paper investigates AI-driven reform pathways in geography education, including innovations in teaching models, shifts in teacher roles, optimization of curriculum content, and policy support. While the potential for AI-enhanced geography education is vast, challenges remain in areas such as technology, resources, and educational equity. This paper provides theoretical support and practical guidance for future reforms in geography education and anticipates the development trends of AI technology in this field.","url":"https://doi.org/10.1109/icaie64856.2025.11158339","authors":["Jia Yu","Dalong Ma","Xiangwen Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T17:24:05Z","doi":"10.1109/icaie64856.2025.11158339","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/aicit65974.2025.11282554","name":"Data Security and Privacy Protection of Artificial Intelligence from the Perspective of Collaborative Governance","source":"crossref","abstract":"This research systematically explores the issues of data privacy protection and security governance in the era of artificial intelligence (AI), revealing three core contradictions within the realm of data security: the conflict between the efficiency demands of technological innovation and the baseline requirements for security and trustworthiness; the rigid constraints of legal regulations versus the flexible dynamics of industrial development; and the mismatch between skills-oriented talent cultivation and societal expectations for ethical awareness. Through semi-structured interviews and multi-source data analysis (integrating government documents, industry white papers, and academic literature), it is found that current data security threats exhibit full-lifecycle characteristics, involving diverse risks such as unauthorized data collection, theft during transmission, tampering in storage, and leakage during analysis. The study highlights that traditional privacy protection technologies struggle to counter emerging attack methods like deepfakes and model poisoning, while legal regulations face challenges such as ambiguous data ownership determination and inadequate norms for cross-border data flows. We have developed a group of methods for managing collaboration, which mainly include four aspects: technical protection, legal regulations, standard setting, and capacity building through education. We have proposed several specific measures, such as enhancing the application of new protection technologies like \"differential privacy,\" promoting the introduction of laws specifically for artificial intelligence, establishing flexible standards for data classification and grading, as well as fostering new models of university-industry collaboration in talent development. This research provides concrete ideas and practical solutions for building a more reliable AI environment, along with useful advice for addressing the challenges of data security governance.","url":"https://doi.org/10.1109/aicit65974.2025.11282554","authors":["Zhi Sheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-16T18:30:12Z","doi":"10.1109/aicit65974.2025.11282554","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.32457/rjyd.v8i1.2662","name":"La inclusión de la diversidad en los sistemas de inteligencia artificial de soporte a decisiones sanitarias como manifestación del enfoque de derechos humanos","source":"crossref","abstract":"This paper analyzes the relevance of diversity inclusion as a principle and how it can contribute to preventing bias in health care decisions supported by artificial intelligence-based systems. First, it is proposed to address the content of the principle of diversity inclusion in the different phases of the design and implementation of the systems; then it is proposed how the transversality of the principle allows to protect the most vulnerable groups against decisions supported by artificial intelligence systems in the health field. For this purpose, a review of specialized literature and the application of the dogmatic method will be used. It is hypothesized that the inclusion of diversity in a transversal way to data processing and algorithm design to support health decision making allows mitigating the risks of algorithmic discrimination, avoiding the underrepresentation of vulnerable groups.","url":"https://doi.org/10.32457/rjyd.v8i1.2662","authors":["Edison Calahorrano Latorre"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-08T14:13:33Z","doi":"10.32457/rjyd.v8i1.2662","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artmed.2009.05.003","name":"An interpretable fuzzy rule-based classification methodology for medical diagnosis","source":"crossref","abstract":"Objective The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems. Methods and materials The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful input partitioning mechanism. The generated hierarchical fuzzy rule structure is composed by linguistic; multiple consequent fuzzy rules that considerably affect the model comprehensibility. Results and conclusion The performance of the proposed method is tested on three medical pattern classification problems and the obtained results are compared against other existing methods. It is shown that the proposed variable input partitioning leads to a flexible decision making framework and fairly accurate results with a small number of rules and a simple, fast and robust training process.","url":"https://doi.org/10.1016/j.artmed.2009.05.003","authors":["Ioannis Gadaras","Ludmil Mikhailov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-06-19T08:45:21Z","doi":"10.1016/j.artmed.2009.05.003","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3777577.3777579","name":"MLEF: Multi-Level Emotion Feature Representation for Depression Detection Leveraging Fine-Grained Emotion Lexicon","source":"crossref","abstract":"Depression is a prevalent mental health condition, and by analyzing social posts to detect depression can be a promising approach to address this issue. Currently, mainstream detection methods focus on analyzing how people express themselves in posts, including the content they write and the emotions they convey. However, when modeling the language and emotion of posts, these methods are often limited by coarse emotional granularity, such as only distinguishing between positive and negative emotions, making it difficult to capture subtle depressive emotions. According to the emotional granularity theory in psychology, finer emotional granularity contributes to more accurate identification and response to emotion issues. To address this issue, this paper proposes a multi-level emotion feature representation method leveraging fine-grained emotion lexicon called MLEF. First, we leveraged existing emotion lexicons by increasing the number of categories, constructing a fine-grained lexicon to capture subtle emotional differences. Then, we designed a feature extraction framework that integrates features at three levels: word, topic, and user, to comprehensively characterize users' language expression habits and emotional tendencies. Additionally, we explored the interpretability of the multi-level features by analyzing underlying language and emotion patterns, providing new insights into understanding depression based on emotional granularity theory. Experiments on the two mainstream public datasets, eRisk2017 and eRisk2018, demonstrate that MLEF successfully captures depressed individuals' linguistic expression habits and fine-grained emotions, significantly improving classification performance and surpassing previous best-reported results.","url":"https://doi.org/10.1145/3777577.3777579","authors":["Shuangwei Tang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777579","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3777577.3777583","name":"Towards Data-Driven Chronic Disease Prevention: Interpretable Machine Learning Models for Diabetes Risk Screening","source":"crossref","abstract":"With the continuous rise in the incidence of diabetes in recent years, establishing efficient and accurate early prediction models has become a key task in chronic disease prevention and control. Traditional statistical methods face limitations in capturing nonlinear features and complex interactions, necessitating the adoption of more adaptive and predictive machine learning approaches. This study utilizes a publicly available health dataset from Kaggle, selecting key clinical and lifestyle indicators from 86,480 samples. To address data imbalance, the SMOTE method is applied, followed by standardized data processing. On this basis, eight machine learning models—including Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Multilayer Perceptron, XGBoost, and LightGBM—are constructed to systematically compare their performance in diabetes prediction. The experimental results show that the XGBoost model outperforms others in terms of accuracy, precision, recall, and F1 score, demonstrating excellent generalization capability and practical value. To enhance model transparency, the SHAP method is further employed for feature interpretation, identifying variables such as HbA1c, fasting glucose, and BMI as playing a critical role in prediction outcomes. This study not only verifies the effectiveness of ensemble learning in diabetes prediction but also demonstrates the application prospects of machine learning in the intelligent development of public health, providing a theoretical basis and technical support for the construction of disease warning systems and individualized intervention strategies.","url":"https://doi.org/10.1145/3777577.3777583","authors":["Yuejia Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777583","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111616","name":"Smart artificial pancreas: Sensor-based glucose and insulin control by deep stochastic policy learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111616","authors":["Shuguang Li","Shuzhou Han","Mai The Vu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T16:56:11Z","doi":"10.1016/j.engappai.2025.111616","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-981-96-8176-1_16","name":"Artificial Intelligence and Cancer Immunotherapy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8176-1_16","authors":["Asma Shah","Ajaz A. Bhat","Muzafar Rasool Bhat","Assif Assad","Muzafar A. Macha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T12:25:00Z","doi":"10.1007/978-981-96-8176-1_16","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.29305/tj.2025.10.210.68","name":"A Study on Medical Artificial Intelligence and Civil Liability - Focusing on Medical Malpractice and Product Liability -","source":"crossref","abstract":"기계학습 및 딥러닝 기반의 의료 인공지능은 정확성, 신속성, 효율성 측면에서 의료 분야에 혁신을 불러일으키고 있지만, 학습 데이터 편향과 알고리즘 불완전성 등으로 인한 오류 가능성도 상존한다. 의료 인공지능의 잘못된 예측이나 권고로 환자에게 손해가 발생한 경우, 환자를 치료한 의사와 제조자의 책임이 문제가 된다. 현재 의료 인공지능은 대부분 의사의 임상 의사결정을 보조하는 도구로 이용되므로, 의사는 인공지능 예측의 적절성을 당시 의료수준에 비추어 평가할 주의의무를 부담한다. 따라서 의사가 이를 위반하여 환자에게 피해가 발생하면 의료과실 책임을 면할 수 없다. 그러나 완전 자율형에 가까운 인공지능의 경우 의사와 인공지능의 관계는 수평적 의료분업에 유사하여 신뢰의 원칙이 적용될 수 있다. 한편 인공지능의 복잡성 · 불투명성 · 예측불가능성 및 자율성 등의 특징 때문에 피해자가 인공지능의 제조자를 상대로 제조물 책임 소송을 제기하여 승소하기에는 쉽지 않다. 그 이유는 피해자가 인공지능의 결함과 관련하여 특히 문제가 되는 설계상의 결함에 대한 입증이 매우 어렵기 때문이다. 최근 개정된 EU 제조물 책임 지침이 이러한 점을 고려하여 소프트웨어도 제조물에 포함하고, 결함에 관한 판단 시점을 확대하고, 증거공개 및 결함추정 규정 등을 도입한 점은 우리 제조물책임법 개정에 시사하는 바가 크다고 할 것이다. 본 연구에서는 인공지능의 잘못된 예측에 대하여 의사에게 전적으로 책임을 전가하는 것은 환자 보호뿐만 아니라 인공지능의 혁신에도 바람직하지 않다는 점을 지적하고, 그 대안으로 인공지능 법인격 부여론, 공동기업책임론, 무과실(위험)책임론 등을 검토하였다. 본 연구에서는 자동차손해배상보장법 상의 자율주행차 손해배상제도를 참고하여, 의료 인공지능을 활용한 의사나 병원에 무과실책임을 부과함과 동시에 무과실책임 보험 가입을 의무화 할 것을 제안하였다. 그 이유는 의사와 병원이 환자와의 진료계약의 주체이고, 의료 인공지능 사용으로 인한 이익을 누림과 동시에 그 위험을 함께 부담하는 것이 공평하기 때문이다.","url":"https://doi.org/10.29305/tj.2025.10.210.68","authors":["Jong Goo Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T01:11:10Z","doi":"10.29305/tj.2025.10.210.68","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.4018/979-8-3373-1200-2.ch017","name":"Artificial Intelligence and Climate Change","source":"crossref","abstract":"Deep in the depths of analytics and big data, AI can play a vital role in understanding the impacts of climate change. This digital world is increasingly using technology to collect and analyze environmental data, and AI can sift through this data in a highly granular way, seeking to uncover trends and patterns that can help guide climate change efforts. In the context of this challenge, scientists and engineers are working together to design powerful predictive models using AI that enable the analysis of future climate change scenarios. This advance is a necessary step towards better understanding the impacts of climate change and determining how humans can adapt and provide effective solutions.","url":"https://doi.org/10.4018/979-8-3373-1200-2.ch017","authors":["Walid Chouari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-08T12:10:57Z","doi":"10.4018/979-8-3373-1200-2.ch017","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.pop.2025.07.010","name":"The Future of Artificial Intelligence in Medical Education and Continuing Medical Education","source":"crossref","abstract":"In this article, we explore the transformative potential of artificial intelligence (AI) in medical education and continuing medical education. We discuss the rapid evolution of AI technology, particularly generative AI and large language models, and their implications for teaching and learning. We emphasize the importance of AI literacy, ethical considerations, and evidence-based approaches to integrating AI into medical education. We also highlight the evolving roles of educators and learners in the AI era, advocating for a proactive, collaborative approach to harness AI's potential to personalize learning, enhance clinical decision-making, and ultimately improve patient care.","url":"https://doi.org/10.1016/j.pop.2025.07.010","authors":["Chase J. Webber","Pat Whitworth","Shane P. Stenner"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-10T21:39:55Z","doi":"10.1016/j.pop.2025.07.010","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-49910-80-5_1","name":"Overview of medical and mechanical devices in modern healthcare systems","source":"crossref","abstract":"The current practice of medicine incorporates the use of many different kinds of medical and mechanical devices, which might fall under the broad categories of diagnostics, therapeutics, surveillance, rehabilitation, and care. These devices are designed to facilitate the evaluation of the body and its responses to insults of any sort, and these devices may also circumvent illness or injury by repairing the damage done or by taking over, temporarily or permanently, some of the functions of the damaged body (Dubey et al., 2017; Rehman et al., 2022; Badawy et al., 2023). There are mechanical devices to absorb secretions, to monitor heart rhythms, and to make flow measurements such as the volume of blood perfusing a body part. There are devices to deliver drugs, medications, and nutrients to achieve various therapeutic goals. There are devices to withstand joint musculoskeletal loads. There are devices to stimulate muscle contractions or nerve responses. There are prosthetic and orthotic devices to return people to the world of normal living. These devices may be as simple as splints and ambulatory aids or as complex as computer-driven bionic devices. Such is the cornucopia of mechanical devices available for use in a modern healthcare system. The introduction of these devices has enabled physicians to work more efficiently and has given patients greater flexibility of care and a more normal lifestyle (Tuli et al., 2019; Shaik et al., 2023).","url":"https://doi.org/10.70593/978-93-49910-80-5_1","authors":["Sai Teja Nuka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-12T16:18:16Z","doi":"10.70593/978-93-49910-80-5_1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111125","name":"Strategies for energy-efficient flow control leveraging deep reinforcement learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111125","authors":["Wang Jia","Hang Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-30T18:24:33Z","doi":"10.1016/j.engappai.2025.111125","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3777577.3777612","name":"Balancing Accuracy and Usability: Traditional Machine Learning and AutoML for Heart Disease Risk Prediction","source":"crossref","abstract":"This paper presents a comparative study of the ML methods, the EasyDL platform, and the H2O AutoML in the context of heart disease prediction. Experimental results show that conventional models, such as XGBoost, achieved the best overall performance, with an accuracy of 0.867, an AUC of 0.892, and balanced precision–recall scores (precision: 0.885, recall: 0.821). EasyDL, although slightly less accurate with 0.856 and more time-consuming, offered strong usability by providing automated data preprocessing, built-in evaluation tools, and intuitive visualizations. H2O, meanwhile, delivered competitive results and served as a rapid baseline generator. Moreover, to enhance interpretability, SHAP analysis was applied to the XGBoost model, highlighting clinically meaningful features such as the number of major vessels (CA), chest pain type (CP), and thalassemia results (THAL). This transparency strengthens clinical trust in model predictions and can support physicians in making evidence-based decisions. These findings not only validate the reliability of the models but also highlight how integrating AutoML platforms with traditional methods can improve both accessibility and clinical interpretability in medical decision support.","url":"https://doi.org/10.1145/3777577.3777612","authors":["Yidan Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777612","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.69722/1694-8211-2025-63-5-12","name":"THE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON THE FORMATION OF FUTURE DOCTORS WHEN STUDYING BIOPHYSICS AT A MEDICAL UNIVERSITY","source":"crossref","abstract":"This article examines the impact of artificial intelligence on the formation of future doctors while studying biophysics at a medical university. The role of medical professionals is changing as this article examines the multidimensional impact of AI on clinical practice and medical education. The integration of artificial intelligence into medical training enhances learning effectiveness through the use of personalized educational tools such as adaptive learning platforms and virtual patient simulators that enable medical students to practice diagnostic and therapeutic skills in a safe environment. The research focuses on the need to rethink medical curricula to include artificial intelligence literacy and ethics issues so that future doctors can effectively collaborate with artificial intelligence systems while paying special attention to patient-centered care. This study shows that artificial intelligence will undoubtedly empower future doctors and will not be able to replace the human factor in healthcare. It is seen as a transformative tool, and careful integration into medical practice is required. This article provides recommendations for policy makers, medical professionals, and healthcare organizations, and suggests using artificial intelligence to create a more efficient, ethical, and empathetic generation of doctors. This study explores the possibilities and challenges of artificial intelligence in medicine and contributes to the ongoing debate about the future of healthcare, while also highlighting the need for a balanced approach to technology adoption. He advocates an active and ethical position that ensures that AI complements the art and science of medicine, and aims to protect these fields from any potential compromises caused by technology.","url":"https://doi.org/10.69722/1694-8211-2025-63-5-12","authors":["D. K. Urazakynov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T05:21:02Z","doi":"10.69722/1694-8211-2025-63-5-12","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaid65275.2025.11034421","name":"Scientometric Analysis of Artificial Intelligence Applications in Smart City","source":"crossref","abstract":"With the continuous advancement of science and technology, the application of artificial intelligence (AI) in smart cities has garnered significant attention. This paper employs scientometric methods to analyze the application of AI within the context of smart cities. It explores the impact and development trends of AI on convenient services, particularly those driven by technologies such as the Internet of Things (IoT) and mobile communications. A total of 1,284 research papers published between 1975 and 2025 were retrieved from the Web of Science (WoS) database. The data from these papers were visualized and analyzed using Vosviewer and Bibliometrix software, with a focus on research hotspots, development trends, key authors and institutions, and the interdisciplinary integration across various fields. The study found that AI not only improves the level of automation in urban management but also effectively optimizes resource allocation, improves the response speed and accuracy of public services, and provides citizens with a more efficient and convenient life experience.","url":"https://doi.org/10.1109/icaid65275.2025.11034421","authors":["Qixin Lin","Chung-Lien Pan","Congming Luo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T17:37:57Z","doi":"10.1109/icaid65275.2025.11034421","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.51470/armhr.2025.4.1.09","name":"The Expanding Role of Artificial Intelligence in Clinical Medical Radiology Practice","source":"crossref","abstract":"Artificial intelligence is rapidly transforming clinical medical radiology practice by enhancing diagnostic performance, optimizing workflow efficiency, and enabling precision medicine. Advances in machine learning, particularly deep learning architectures such as convolutional neural networks, have significantly improved automated image detection, segmentation, and quantitative analysis across radiography, computed tomography, magnetic resonance imaging, ultrasound, and nuclear medicine. Beyond image interpretation, artificial intelligence contributes to structured reporting, clinical decision support, predictive analytics, and operational management within radiology departments. These developments address increasing imaging volumes and workforce pressures while promoting standardized and reproducible assessments and implementation presents challenges including algorithmic bias, limited generalizability, regulatory oversight, data privacy concerns, explain ability limitations, and medico-legal implications. Integration into existing clinical workflows and validation across diverse populations remain critical for safe adoption. Importantly, artificial intelligence is unlikely to replace radiologists; rather, it augments their role by improving efficiency and supporting complex decision-making. This review examines the technological foundations, clinical applications, workflow integration, ethical considerations, and future directions of artificial intelligence in radiology, highlighting its expanding role in shaping a more precise, data-driven, and patient-cantered imaging practice.","url":"https://doi.org/10.51470/armhr.2025.4.1.09","authors":["Yassir Musa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-03T06:31:03Z","doi":"10.51470/armhr.2025.4.1.09","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.ijmedinf.2025.105872","name":"Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping review","source":"crossref","abstract":"Background The secondary use of health data for training Artificial Intelligence (AI) models holds immense potential for advancing medical research and healthcare delivery. However, ensuring patient consent for such utilization is paramount to uphold ethical standards and data privacy. Patient informed consent means patients are fully informed about how their data will be collected, used, and protected, and they voluntarily agree to allow their data to be used for AI models. In addition to formal consent frameworks, establishing a social license is critical to foster public trust and societal acceptance for the secondary use of health data in AI systems. This study examines patient consent practices in this domain. Method In this scoping review, we searched Web of Science, PubMed, and Scopus. We included studies in English that addressed the core issues of interest, namely, privacy, security, legal, and ethical issues related to the secondary use of health data in AI models. Articles not addressing the core issues, as well as systematic reviews, meta-analyses, books, letters, conference abstracts, and study protocols were excluded. Two authors independently screened titles, abstracts, and full texts, resolving disagreements with a third author. Data was extracted using a data extraction form. Results After screening 774 articles, a total of 38 articles were ultimately included in the review. Across these studies, a total of 178 barriers and 193 facilitators were identified. We consolidated similar codes and extracted 65 barriers and 101 facilitators, which we then categorized into four themes: \"Structure,\" \"People,\" \"Physical system,\" and \"Task.\" We identified notable emphasis on \"Legal and Ethical Challenges\" and \"Interoperability and Data Governance.\" Key barriers included concerns over privacy and security breaches, inadequacies in informed consent processes, and unauthorized data sharing. Critical facilitators included enhancing patient consent procedures, improving data privacy through anonymization, and promoting ethical standards for data usage. Conclusion Our study underscores the complexity of patient consent for the secondary use of health data in AI models, highlighting significant barriers and facilitators within legal, ethical, and technological domains. We recommend the development of specific guidelines and actionable strategies for policymakers, practitioners, and researchers to improve informed consent, ensuring privacy, trust, and ethical use of data, thereby facilitating the responsible advancement of AI in healthcare.","url":"https://doi.org/10.1016/j.ijmedinf.2025.105872","authors":["Khadijeh Moulaei","Saeed Akhlaghpour","Farhad Fatehi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-08T19:04:25Z","doi":"10.1016/j.ijmedinf.2025.105872","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.69722/1694-8211-2025-62-143-150","name":"THE ROLE OF ARTIFICIAL INTELLIGENCE IN TEACHING","source":"crossref","abstract":"The article examines the impact of artificial intelligence on modern education. It outlines the main areas of AI application in teaching, including personalized learning, automation of pedagogical processes, intelligent analytics, and the digital transformation of the educational environment. Both the benefits of AI implementation and the associated risks—ethical, technological, and social—are highlighted. Emphasis is placed on the teacher’s role in the context of intelligent technologies. The material is based on current scientific research, including international reports by UNESCO, OECD, and other organizations, as well as publications in peer-reviewed journals. Additionally, specific examples of AI-powered educational platforms such as Knewton, DreamBox Learning, and Squirrel AI are considered, demonstrating the potential for deep personalization of learning. The article shows how intelligent technologies not only increase student engagement and achievement but also provide teachers with detailed analytics, helping to individualize the educational process and improve teaching quality. Special attention is paid to how AI supports teachers in adapting to student needs and modernizing their professional practices.","url":"https://doi.org/10.69722/1694-8211-2025-62-143-150","authors":["A. R. Bekenassova","G. A. Sugurzhanova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-04T15:49:17Z","doi":"10.69722/1694-8211-2025-62-143-150","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3756423.3756519","name":"Design of a control system for a crutch-based robot for medical assistance","source":"crossref","abstract":"With the rapid development of Chinese society, the aging problem is becoming more and more serious. The increase of the elderly population will bring many medical problems. At present, China has become the country with the largest population of the elderly in the world, and there is a growing trend, the old people generally have the problem of inconvenient walking. In addition to medical drugs, suitable walking AIDS play a very important role in restoring the walking ability of the elderly. Ordinary crutches can only play a simple supporting role, and cannot provide effective help for the elderly who have seriously lost the ability to walk. This paper designs a walking stick robot which is different from ordinary walking sticks and can provide more help for the elderly, including the modeling of the system, the design of the software and hardware of the control system, and the system debugging. The results prove that the system design is feasible and provides a new solution to the problem of walking difficulties for the elderly.","url":"https://doi.org/10.1145/3756423.3756519","authors":["Diwu Xu","Guofen Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-18T06:59:07Z","doi":"10.1145/3756423.3756519","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1148/ryai.250555","name":"Advancing Early Detection of Chronic Obstructive Pulmonary Disease Using Generative AI","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250555","authors":["Quincy A. Hathaway","Yashbir Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-27T13:52:07Z","doi":"10.1148/ryai.250555","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/waie67422.2025.11381049","name":"The Adoption of Artificial Intelligence for Culturally Responsive Teaching and Pedagogy in South Africa","source":"crossref","abstract":"Artificial Intelligence (AI) is increasingly shaping educational landscapes, offering new opportunities for culturally responsive teaching (CRT) and pedagogy. In South Africa, where diverse cultural, linguistic, and socio-economic backgrounds influence learning experiences. Artificial Intelligence holds significant potential to enhance culturally responsive teaching and pedagogy by integrating indigenous knowledge systems and promoting multilingual education. This study examines the role of AI in supporting CRT in South African classrooms by analyzing existing literature. Using systematic literature review methodology, it explores how AI can facilitate personalized learning, linguistic inclusivity, and content contextualization to align with South Africa’s multilingual and multicultural educational landscape. The study highlights AI’s potential to bridge educational disparities while ensuring equitable and culturally relevant learning experiences. The findings contribute to ongoing discussions on leveraging AI for transformative and inclusive education in South Africa by developing an AI adoption model for CRT and presents AI integration policy recommendations for the educational stakeholders in South Africa.","url":"https://doi.org/10.1109/waie67422.2025.11381049","authors":["Omojokun Gabriel Aju","Kgabo Mokgohloa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T21:04:20Z","doi":"10.1109/waie67422.2025.11381049","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.112611","name":"Evaluation of environmental emergency treatment technologies using the interval Pythagorean neutrosophic set","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112611","authors":["Changxing Fan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-06T23:02:43Z","doi":"10.1016/j.engappai.2025.112611","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1093/bjrai/ubae017","name":"Multimodal artificial intelligence models for radiology","source":"openalex","abstract":"Abstract Artificial intelligence (AI) models in medicine often fall short in real-world deployment due to inability to incorporate multiple data modalities in their decision-making process as clinicians do. Clinicians integrate evidence and signals from multiple data sources like radiology images, patient clinical status as recorded in electronic health records, consultations from fellow providers, and even subtle clues using the appearance of a patient, when making decisions about diagnosis or treatment. To bridge this gap, significant research effort has focused on building fusion models capable of harnessing multi-modal data for advanced decision making. We present a broad overview of the landscape of research in multimodal AI for radiology covering a wide variety of approaches from traditional fusion modelling to modern vision-language models. We provide analysis of comparative merits and drawbacks of each approach to assist future research and highlight ethical consideration in developing multimodal AI. In practice, the quality and quantity of available training data, availability of computational resources, and clinical application dictates which fusion method may be most suitable.","url":"https://doi.org/10.1093/bjrai/ubae017","authors":["Amara Tariq","Imon Banerjee","Hari Trivedi","Judy Gichoya","Judy Wawira Gichoya"],"tags":["Artificial intelligence","Computer science","Radiology","Medical physics","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"10.1093/bjrai/ubae017","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"doi:10.1148/ryai.250575","name":"“You’ll Never Look Alone”: Embedding Second-Look AI into the Radiologist’s Workflow","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250575","authors":["Riccardo Levi","Andrea Laghi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-03T13:51:47Z","doi":"10.1148/ryai.250575","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3777577.3777650","name":"Research on Raman Spectroscopy-Based Brain Tissue Classification Using WGAN and Transformer","source":"crossref","abstract":"To address the limitation in classification performance caused by insufficient Raman spectroscopy samples of brain tissue, this paper proposes a hybrid framework combining Wasserstein Generative Adversarial Networks (WGAN) with Transformers. WGAN effectively expands small datasets, significantly enhancing the scale and diversity of spectral data. Building upon this, a classification model integrating convolutional layers and Transformers is constructed, achieving unified local feature extraction and global dependency modeling.The experimental results show that this method demonstrates excellent performance in the classification tasks of gray matter, white matter, and blood vessels. The classification accuracy, precision, recall, and F1 score all exceed 0.95.Visualization via t-SNE confirms its excellent feature separability, providing evidence of model robustness and improved interpretability for potential clinical applications. The study indicates that this approach enables high-precision spectral discrimination under small-sample conditions, offering an effective strategy for brain tissue spectral analysis and clinical diagnostic support.","url":"https://doi.org/10.1145/3777577.3777650","authors":["Hanlin Huang","Yu Feng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777650","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1002/9781394242399.ch18","name":"Quantum Artificial Intelligence (QAI) Paradigm for Voice‐Controlled Devices","source":"crossref","abstract":"The study of quantum artificial intelligence (QAI) seeks to use the unique properties of research in order to construct more effective and potent learning algorithms. Because the area is still in its infancy, there are various difficulties that must be overcome before QAI can attain its full capacity. Only a few of the challenges that must be solved include the development of more dependable quantum gadgets, better correction of error algorithms, and the need for increased number of quantum software tools and programming languages. The quantum artificial intelligence (QAI) paradigm is significant because it has the ability to completely transform a range of industries by developing more effective and potent predictive algorithms. Machine learning techniques are used in a variety of applications, including image and speech recognition, medication studies, and financial modeling. Some issues, however, may be virtually impossible to tackle with traditional machine learning methods due to the speed and power of older equipment. QAI tries to overcome these limits by utilizing the unique properties of quantum computing, such as superposition and entanglement, to construct more efficient and rapid machine learning methods. This has the potential to dramatically boost prediction accuracy and speed by opening up new applications such as medication development and financial modeling. The main techniques employed in the QAI paradigm are quantum circuits, variation quantum algorithms, quantum neural networks, quantum machine learning algorithms, quantum-inspired classical algorithms, and quantum error correction techniques. These techniques are crucial for the creation of effective and trustworthy QAI algorithms and systems. This chapter comprehensively reviews the QAI paradigm, its guiding principles, and its potential applications. This chapter will go through the fundamentals of quantum mechanics, machine learning, and how QAI applies to both. This chapter also presents some of the more exciting QAI uses, like quantum-enhanced optimization and quantum machine learning for speech and image recognition. This chapter focuses on the benefits of QAI over traditional machine learning methods, how QAI can offer exponential speedups compared to traditional techniques for specific issues like simulation and optimization, and the future potential of QAI and the potential effects it might have on different businesses. This chapter includes the current research and development being done in QAI, as well as its potential for commercialization and the establishment of a brand-new sector of the economy centered on QAI technology. The advantages of QAI over traditional machine learning methods include the capacity to enable novel applications that are not possible with traditional computing and exponential speedups for some tasks. The demands on hardware and software, the need for specialized knowledge, and the creation of algorithms are all obstacles for QAI. Despite these difficulties, continuous research and development in QAI are extremely promising for the future of computers. QAI has the potential to influence numerous industries.","url":"https://doi.org/10.1002/9781394242399.ch18","authors":["S. Aswani","E. Chandra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T05:48:11Z","doi":"10.1002/9781394242399.ch18","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3777577.3777610","name":"The Evolution of Research Hotspots and Frontiers Detection in Multiple Sclerosis Treatment","source":"crossref","abstract":"Objective: To systematically explore the research hotspots and evolutionary patterns in the field of Multiple Sclerosis (MS) treatment, and to promote the innovation and continuity of traditional Chinese medicine dialectical treatment theory within the context of integrated TCM and Western medicine. Methods: Using the Chinese Social Sciences Citation Index (CSSCI) data from databases such as CNKI, Wanfang Data, and VIP Information, the LDA model was employed to extract literature features, divide core topics and predict the trend of intensity evolution. Results: Three hotspot topics were identified: immune regulation (Topic1), myelin repair (Topic2), and the integration of disease and syndrome (Topic3). Conclusion: The three hotspot topics exhibit distinct research orientations over time. Immune regulation remains at the core of MS treatment, while myelin repair and the integration of disease and syndrome emerge as key focuses for future research in the field.","url":"https://doi.org/10.1145/3777577.3777610","authors":["Yuxin Qi","Xiaoling Shang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777610","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.51127/jamdcv07i04editorial","name":"ARTIFICIAL INTELLIGENCE, ENTHUSIASM AND EQUITY- A NUANCED TAKE ON THE USE OF AI IN THE GLOBAL SOUTH","source":"crossref","abstract":"Artificial Intelligence and generative AI may as well be the biggest turning point of the century, but this also presents the greatest equity paradox-While institutions in the Global North approach AI with measured caution, the Global South continues to embrace these technologies with great enthusiasm and at an accelerating pace.The disparity shown here is not just a question of fast /slow adoption rates of technology, but it also represents the complex interplay of educational gaps, economic imperatives, colonial legacies and contesting visions of the future of medical education.As medical educators in Pakistan and the wider global south, we need to navigate this paradox optimistically, but at the same time with critical vigilance.Multinational research surveys report that medical students have significantly more positive attitudes regarding AI integration in the Global South, as compared to their counterparts in the Global North.1,2 A cross-sectional study involving 4596 medical, dental and veterinary students from 192 institutions across 48 countries reported that students from Latin America, Africa and Asia reported stronger beliefs in the transformative potential of AI and were more willing to adopt it than the global north.3 This is not just limited to perception but also implementation.Another study showed that 92.3% respondents from the global south contexts believed that AI has a role in patient care, compared to 58.5% North American participants.3 This presents a very striking contrast-while the Global North institutions debate ethical frameworks and regulatory boundaries, Global South medical schools are","url":"https://doi.org/10.51127/jamdcv07i04editorial","authors":["Dr. Komal Atta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-03T03:49:32Z","doi":"10.51127/jamdcv07i04editorial","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.36227/techrxiv.175393457.79277555/v1","name":"Exploring Scientific Principles and Laws of Artificial Intelligence, World Model, and Artificial General Intelligence (AGI) in Future Intelligence Networking: Paradigms, Architectures, and Innovations","source":"crossref","abstract":"Intelligence Networking (IN) is an emerging paradigm that seeks to embed intelligence into every layer of the network, enabling intelligent decision-making and service delivery to be as seamless and efficient as accessing conventional information. This survey offers a comprehensive overview of IN, focusing on its evolution, foundational technologies, architectural frameworks, core applications, and theoretical underpinnings of intelligence. It aims to serve as a valuable reference for researchers exploring the principles, structures, and mathematical modeling of IN. We begin by tracing the evolution of networking paradigms to highlight the growing interdependence between networking and intelligence, establishing the basic logic for IN's emergence. We then introduce a layered IN architecture and examine enabling technologies and applications across each layer. In addition, we explore the definition of intelligence within the context of networking, discuss relevant world models, analyze first principles derived from this definition, and explore the intrinsic connections between network intelligence and Artificial General Intelligence (AGI). The survey concludes with a discussion of future research directions and potential technological breakthroughs needed to realize the full promise of IN.","url":"https://doi.org/10.36227/techrxiv.175393457.79277555/v1","authors":["Dajun Zhang","Wei Shi","Xiaowei Jia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-31T04:03:16Z","doi":"10.36227/techrxiv.175393457.79277555/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3138/jvme-2025-0003","name":"Comparison of Generative Artificial Intelligence and Student-Generated Veterinary Handouts","source":"crossref","abstract":"Generative artificial intelligence (gAI) is becoming increasingly prevalent in our daily lives. Students across multiple disciplines are using gAI for writing assessments and completing projects. This leads to the question: could gAI platforms perform similarly, worse, or better than veterinary students when asked to create discharge handouts for select veterinary neurological conditions? A total of 24 professionals and educators graded handouts based on content, clarity, client education, empathy, and professionalism for two canine neurological conditions (seizures and intervertebral disc disease). Each condition had handouts created by a high-performing student, a random/unknown student whose work was deemed to represent the average student at our institution, ChatGPT 4.0, and Google Bard. The high-performing student's handout scored higher in several categories compared with AI-generated handouts and scored statistically higher overall. Specifically, the high-performing student's seizure handout scored significantly higher in accuracy/completeness and client education than the Bard handout. Both student handouts scored significantly higher for empathy and client support than the AI tools. For the intervertebral disc disease handouts, the AI-generated handouts scored higher in clarity and organization than the random student handout, with the high-performing student's handout scoring higher in empathy and client support over the Bard-generated handout. Upon the conclusion of grading, reviewers completed a survey asking them to guess the authorship of each handout. Veterinary educators and professionals could not distinguish between gAI- and student-developed client handouts. However, the findings suggest that students have the potential to outperform current gAI technology in multiple areas, including conveying empathy.","url":"https://doi.org/10.3138/jvme-2025-0003","authors":["Ryan M.B. Gibson","Sarah S. Tomberlin","Laci O. Mackay","Chad D. Foradori"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-28T15:30:05Z","doi":"10.3138/jvme-2025-0003","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-031-86540-4_1","name":"The Rise of Artificial Intelligence in Latin America","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86540-4_1","authors":["David Ramírez Plascencia","Rosa María Alonzo González"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-27T09:17:24Z","doi":"10.1007/978-3-031-86540-4_1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/s40593-025-00471-z","name":"Title: Assessing Quality of Scenario-Based Multiple-Choice Questions in Physiology: Faculty-Generated vs. ChatGPT-Generated Questions among Phase I Medical Students","source":"crossref","abstract":"The integration of Artificial Intelligence (AI), particularly Chatbot Generative Pre-Trained Transformer (ChatGPT), in medical education has introduced new possibilities for generating various educational resources for assessments. However, ensuring the quality of ChatGPT-generated assessments poses challenges, with limited research in the literature addressing this issue. Recognizing this gap, our study aims to investigate the quality of ChatGPT-based assessment. In this study among first-year medical students, a crossover design was employed to compare scenario-based multiple-choice questions (SBMCQs) crafted by both faculty members and ChatGPT through item analysis to determine the quality of assessment. The study comprised three main phases: development, implementation, and evaluation of SBMCQs. During the development phase, both faculty members and ChatGPT generated 60 SBMCQs each, covering topics related to cardiovascular, respiratory, and endocrinology. These questions underwent assessment by independent reviewers, after which 80 SBMCQs were selected for the tests. Subsequently, in the implementation phase, one hundred and twenty students, divided into two batches, were assigned to receive either faculty-generated or ChatGPT-generated questions across four test sessions. The collected data underwent rigorous item analysis and thematic analysis to evaluate the effectiveness and quality of the questions generated by both parties. Only 9 of ChatGPT’s SBMCQs met ideal criteria MCQ on Difficulty Index, Discrimination Index and Distractor Effectiveness contrasting with 19 from faculty. Moreover, ChatGPT’s questions exhibited a higher rate of nonfunctional distractors (33.75% vs. faculty’s 13.75%). During focus group discussion, faculty highlighted importance of educators in reviewing, refining, and validating ChatGPT-generated SBMCQs to ensure their appropriateness within the educational context.","url":"https://doi.org/10.1007/s40593-025-00471-z","authors":["Archana Chauhan","Farah Khaliq","Kirtana Raghurama Nayak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-08T09:05:43Z","doi":"10.1007/s40593-025-00471-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/aide64228.2025.10986906","name":"Ethical Frameworks for Artificial Intelligence: A Comparative Study","source":"crossref","abstract":"The fast-paced evolution of Artificial Intelligence (AI) has given rise to critical ethical challenges leading to various frameworks proposed by government and industry leaders regarding the responsible use of Artificial Intelligence. This report compares AI ethics policies, from the European Union (EU), the United States (US), Canada and Asia, as well as the efforts of major AI companies. Whereas the EU has a regulatory-centric model with a focus on stringent oversight, the US has a more malleable, innovation-driven one. In Canada, such a direct approach on behalf of the government is missing — with an emphasis on transparency and accountability, several government directives deal with the issue of the impact on COVID-19 and on the relevant parties involved. Within, big firms of AI, such as Google, Microsoft, and IBM, have developed guidelines that spotlight fairness, transparency, accountability, and the quality of knowledge. The paper also investigates the cost of these ethics frameworks, and their adoption rates. The study investigates the different approaches and highlights international differences in the balance between innovation and ethics through a comparison of these strategies. Moreover, since quality data is essential for AI, maintaining high data quality is mentioned as one of the key factors of AI ethics standardization. With AI's insights penetrating many sectors and industries, the demand for well-planned ethics policies is more crucial than ever in determining how AI will evolve according to societal goals and mitigate risks. (Abstract)","url":"https://doi.org/10.1109/aide64228.2025.10986906","authors":["Vaishali Mishra","Ujjwal Karn","Vasanth Rajendran","Monojit Banerjee","Harshal Darade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-12T17:42:16Z","doi":"10.1109/aide64228.2025.10986906","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2024.109635","name":"Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109635","authors":["Pooja","Sandeep Kumar Sood"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-20T18:52:21Z","doi":"10.1016/j.engappai.2024.109635","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.110083","name":"Dual replay memory reinforcement learning framework for minority attack detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110083","authors":["Ankit Sharma","Manjeet Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-24T07:42:56Z","doi":"10.1016/j.engappai.2025.110083","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/s40593-024-00451-9","name":"Intelligent Textbooks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s40593-024-00451-9","authors":["Sergey Sosnovsky","Peter Brusilovsky","Andrew Lan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-11T14:33:32Z","doi":"10.1007/s40593-024-00451-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/faia251438","name":"SOLVE-Med: Specialized Orchestration for Leading Vertical Experts Across Medical Specialties","source":"crossref","abstract":"Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router Agent for dynamic specialist selection, ten specialized models (1B parameters each) fine-tuned on specific medical domains, and an Orchestrator Agent that synthesizes responses. Evaluated on Italian medical forum data across ten specialties, SOLVE-Med achieves superior performance with ROUGE-1 of 0.301 and BERTScore F1 of 0.697, outperforming standalone models up to 14B parameters while enabling local deployment. Our code is publicly available on GitHub: https://github.com/PRAISELab-PicusLab/SOLVE-Med.","url":"https://doi.org/10.3233/faia251438","authors":["Roberta Di Marino","Giovanni Dioguardi","Antonio Romano","Giuseppe Riccio","Mariano Barone","Marco Postiglione","Flora Amato","Vincenzo Moscato"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-22T10:02:30Z","doi":"10.3233/faia251438","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-49910-80-5_2","name":"Fundamentals of embedded systems for the design of smart medical equipment","source":"crossref","abstract":"Research on Embedded Systems has become increasingly common and important in recent years. The successful implementation and design of such systems have recently gained notoriety given the common integration of Embedded Processors in commercial Electronics. Although these were traditionally found only in specialized usage, the development of high performance programmable microcontrollers and the parallel advances in Sensor Technology have brought their use to areas such as Electronics for Robotics, Telecommunication and Medical Electronics. In these areas, Embedded Processors have shifted from having only a supportive function in dedicated circuitry to a more controlling role in complex innovative solutions.The Medical Electronics field has made a rapid and broad move to the architecture of Smart Medical Equipment. The move from traditional Electro-Medical Equipment has led to changes in design philosophy. Devices now possess high information content and rely on sophisticated Software Algorithms that often enable the visualization and storage of data (Ahmed et al., 2018; Jagadeeswari, 2018; Firouzi et al., 2022). The bundle of Electronics with other areas of knowledge in the design of a medical product may involve rehabilitation therapists, orthopedists, bio-mechanic engineers, dentists, physiologists, cardiologists, vascular surgeons, and neurologists, among other professionals. Interdisciplinary collaboration may lead to the successful completion of a medical product, or pieces of autonomous “high-tech” cure or diagnostic equipment can become available.An Embedded System is Computer Hardware and Software with a dedicated function within a larger mechanical or electrical system or Product. Embedded Systems have been essential in designing Smart Medical Equipment. Device smartness is expressed, for instance, by data Communication, User Interface, Storage, Display, Information Processing, and Diagnostic Capability. Typically, Embedded Systems contain specialized hardware designed for devices that have pre-defined functions. Their Software enables the realization of auxiliary tasks inserted for coordination of the diverse system functionalities. With just a few exceptions, Smart Medical Devices are digital Electronics composed of Analog and Digital Processing Circuits connected to Microcontrollers or Microprocessors.","url":"https://doi.org/10.70593/978-93-49910-80-5_2","authors":["Sai Teja Nuka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-12T16:18:16Z","doi":"10.70593/978-93-49910-80-5_2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.47310/jpms2025140525","name":"Is Artificial Intelligence a Threat to Radiologists? Perception of Radiologists in Saudi Arabia","source":"crossref","abstract":"Background: The integration of Artificial Intelligence (AI) into radiology is rapidly transforming diagnostic workflows, image interpretation and clinical decision-making. While AI holds the potential to augment radiological practices, concerns about its impact on professional roles persist. This study aimed to assess the knowledge, attitudes and perceptions of radiologists in Saudi Arabia regarding the implementation of AI in their field and to explore their readiness for AI integration. Methods: A cross-sectional study was conducted at the College of Medicine, Northern Border University, Arar, Saudi Arabia, from March 2024 to February 2025. A validated, structured questionnaire was distributed via digital platforms to 103 practicing radiologists across various regions. The survey assessed demographic details, awareness, training exposure, attitudes and perceived impact of AI. Data were analyzed using IBM SPSS Statistics v20, with chi-square and t-tests applied. A p-value &lt;0.05 was considered statistically significant. Results: Among the respondents, 85.4% had heard of AI and 72.8% reported a basic understanding of its principles. Only 9.7% feared that AI might replace radiologists, while a substantial majority (70%) expressed a strong interest in pursuing professional development in AI. Notably, awareness and knowledge were significantly higher among younger and less-experienced radiologists (p&lt;0.001). Conclusions: Radiologists in Saudi Arabia generally perceive AI as an opportunity rather than a threat. While most have basic awareness, there is a critical need for structured educational programs to enhance their understanding and practical skills. Integrating AI-focused training into continuous professional development and national radiology curricula is essential to ensure successful adoption and optimal patient care.","url":"https://doi.org/10.47310/jpms2025140525","authors":["Pakeeza Shafiq","Yasir Mehmood","Sara Nisar","Raghad Abdulaziz Alanazi","Hajar Ayedh Alanazi","Hadeel Nawaf Alenezi","Rasil Salah Khulayf Alanazi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-05T10:46:28Z","doi":"10.47310/jpms2025140525","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.ejrai.2025.100033","name":"Perspective: AI productivity will not benefit employed radiologists","source":"crossref","abstract":"Debates about AI in radiology typically ask whether it will augment or replace radiologists. It is less common to ask who profits from improved productivity. AI systems already interpret high-volume studies, such as screening mammograms, at expert-level accuracy: a recent Swedish trial showed AI safely reduced radiologist workloads by 44 %. Economist James Bessen shows that automation tends to shift value from labour to capital. Following Bessen, we predict that the potential labour savings of AI will primarily benefit employers, investors, and AI vendors, not salaried radiologists. Radiologists should be aware of this trend and where appropriate adopt strategies to navigate AI disruption, such as gaining equity in their practice, specialising in areas resistant to automation, or transitioning to alternative career paths. • Radiology is the main focus of medical AI, yet few debates focus on who benefits. • AI raises imaging output which could reduce the value of radiologists’ labour. • Most productivity gains will go to employers, vendors, and private-equity firms. • History shows automation boosts efficiency while reducing labour’s share of income. • As AI redefines roles, radiologists should seek equity, specialise, or pivot.","url":"https://doi.org/10.1016/j.ejrai.2025.100033","authors":["Heathcote Ruthven","Christoph Agten"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T23:41:50Z","doi":"10.1016/j.ejrai.2025.100033","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3785987.3786090","name":"Research on the Path of Empowering Ideological and Political Education with Generative Artificial Intelligence","source":"crossref","abstract":"Our study leverages advanced computer technologies, including Generative Artificial Intelligence (Generative AI) and Artificial Neural Networks (ANNs), to enhance and optimize the competency model for high school politics teachers, based on the Emotion-Behaviour Relationship (EBR) theory, to address how to actually measure emotions in the classroom and how to turn perceived emotions into a basis for teaching decisions. We improve upon existing computer techniques by introducing a four-layer structure of data, perception, cognition, and interaction to process various types of data and provide feedback, while ANNs are optimized through loss functions and backpropagation to convert multimodal signals, complemented by tools like SHAP and LIME to ensure explainability, using loss functions and backpropagation to guarantee accuracy. Using classroom videos of 23 students from a high school in Hunan as an example, the experiment group with (ANN + Generative AI) achieved much higher accuracy in emotion recognition compared to the control group, with recognition rates for emotions such as \"attention\" and \"resistance\" exceeding 90%; if students display more positive emotions, there is a strong positive correlation with teaching outcomes (correlation coefficient r≈0.72) and with the emotions exhibited by teachers (correlation coefficient r≈0.69). This model breaks the previous fixed evaluation methods while adhering to ethical guidelines, providing both a theoretical and practical template for enhancing political education using intelligent technology.","url":"https://doi.org/10.1145/3785987.3786090","authors":["Han Cai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-30T09:50:45Z","doi":"10.1145/3785987.3786090","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111524","name":"A brain-inspired projection contrastive learning network for instantaneous learning","source":"crossref","abstract":"The biological brain can learn quickly and efficiently, while the learning of artificial neural networks is astonishing time-consuming and energy-consuming. Biosensory information is quickly projected to the memory areas to be identified or to be signed with a label through biological neural networks. Inspired by the fast learning of biological brains, a projection contrastive learning model is designed for the instantaneous learning of samples. This model is composed of an information projection module for rapid information representation and a contrastive learning module for neural manifold disentanglement. An algorithm instance of projection contrastive learning is designed to process some machinery vibration signals and is tested on several public datasets. The test on a mixed dataset containing 1426 training samples and 14,260 testing samples shows that the running time of our algorithm is approximately 37 s and that the average processing time is approximately 2.31 ms per sample, which is comparable to the processing speed of a human vision system. A prominent feature of this algorithm is that it can track the decision-making process to provide an explanation of outputs in addition to its fast running speed.","url":"https://doi.org/10.1016/j.engappai.2025.111524","authors":["Yanli Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-19T02:36:06Z","doi":"10.1016/j.engappai.2025.111524","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.30525/978-9934-26-598-3-6","name":"Municipal law-making in the digital age: challenges and opportunities of artificial intelligence","source":"crossref","abstract":"The Impact of Artificial Intelligence on the Development of Legal Science (August 25–29, 2025. Riga, the Republic of Latvia) : International scientific conference. Riga, Latvia : Baltija Publishing, 2025. 108 pages.","url":"https://doi.org/10.30525/978-9934-26-598-3-6","authors":["Ye. L. Hrechkivskyi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-09T19:47:12Z","doi":"10.30525/978-9934-26-598-3-6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.58496/bjai/2025/006","name":"Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency","source":"crossref","abstract":"Cloud computing is such a revolution concerning the IT world offering computing as services capable of diminishing operational costs and complications. Recently, these service models, ranging from IaaS, PaaS, and SaaS, and deployment models in private, public, and hybrid clouds, offer users almost unlimited computing and storage capabilities on a pay-per-use basis. This elasticity of cloud systems makes it very easy to dynamically provision and de-provision resources to cater to very different needs. This facility has led to its widespread use within domains such as social networking, defense, scientific computing, financial services, and medical. IDG Communications has now announced that 73% of corporations are currently utilizing clouds, with a further 17% in the process of implementing. Service abstraction to increase usability raises yet a fresh set of issues in terms of operational costs, reliability, energy efficiency, and security. Especially in cases where the framework is applicable to critical ventures, as exhibited just a while back by Knight Capital in 2013, system failures may have serious financial and credibility repercussions. Fault tolerance strategies through resource redundancy increase the cost of downtime risk but lower energy consumption, hence less cost and less environmentally unfriendly; they affect profit. The bulk of the operational expense in data centers is associated with the use of energy, whereby the use of energy is environmentally unfriendly and poses environmental concerns; clouds are forecasted to contribute to 5.5% of carbon emissions globally by 2025. Balancing energy efficiency and reliability will require novel optimization approaches for today's and future cloud computing systems with robust fault tolerance","url":"https://doi.org/10.58496/bjai/2025/006","authors":["Raed A. Hasan","Teba Majed Hameed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-23T06:42:19Z","doi":"10.58496/bjai/2025/006","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artint.2025.104400","name":"On preference learning based on sequential Bayesian optimization with pairwise comparison","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2025.104400","authors":["Tanya Ignatenko","Kirill Kondrashov","Marco Cox","Bert de Vries"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-29T03:10:07Z","doi":"10.1016/j.artint.2025.104400","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1080/0142159x.2025.2523466","name":"A lifeline for the craft of writing: A response to Masters’ Artificial Intelligence and the Death of the Academic Author","source":"crossref","abstract":"Cast on 3 stitchesKnit 7 rowsPick up 3 stitches along the border of the small rectangle you have just knitPick up and knit 1 stitch in each of the 3 cast-on stitchesThis may be an unusual beginning...","url":"https://doi.org/10.1080/0142159x.2025.2523466","authors":["Morag Paton","Ayelet Kuper"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T10:22:58Z","doi":"10.1080/0142159x.2025.2523466","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111457","name":"Artificial neural network integrated SHapley Additive exPlanations modeling for sodium dichromate formation","source":"crossref","abstract":"This Research has focused on optimizing metallurgical processes by integrating Artificial Neural Networks with Shapley additive explanation modeling. This approach helps understand the intricate relationships and mechanisms underlying chemical processes. Neural networks provide accurate predictions based on input parameter interactions, while Shapley values identify the relative importance of each input variable and offer detailed explanations for model predictions, enhancing transparency and interpretability. In this study on roasting and leaching processes for sodium dichromate formation, the neural network - Shapley modeling framework was employed. The goal was to uncover the intricate interplay between input variables and sodium dichromate formation, providing valuable insights for process optimization and prediction. Key factors such as temperature, roasting time, reaction time, and sulfuric acid concentration were optimized in relation to the efficacy of sodium dichromate formation under different settings. The suggested neural networks model predicted optimal yields for combined roasting and leaching settings. The optimum conditions included a roasting temperature of 1046.26 °C, roasting time of 2.7 h, Cr: NaCl ratio of 1.5, leaching time of 41 min at a temperature of 40 °C, and sulfuric acid concentration of 12M. Global sensitivity analysis revealed that the yields of different metals were directly influenced by the temperature during roasting, concentration of sulfuric acid, Cr:NaCl ratio, roasting time, leaching temperature, and leaching time. These parameters were ranked in terms of sensitivity coefficients, indicating their relative importance.","url":"https://doi.org/10.1016/j.engappai.2025.111457","authors":["M.J. Mvita","N.G. Zulu","B. Thethwayo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-14T07:52:24Z","doi":"10.1016/j.engappai.2025.111457","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.mcna.2025.02.006","name":"Health Care Quality and Patient Safety in the Era of Artificial Intelligence","source":"crossref","abstract":"The integration of digital health technologies is revolutionizing health care quality and patient safety by shifting from reactive to proactive care models. Advances in electronic health records, clinical decision support systems, data analytics, artificial intelligence (AI), mobile applications, and tele-health are enhancing clinical decision-making, patient engagement, and care coordination. While these technologies offer significant benefits, challenges related to data privacy, integration into workflows, and health disparities require ongoing attention to ensure that all patients can benefit from these innovations. Adoption of digital health and AI to bring systemness and real time information delivery can support delivery of high-quality care.","url":"https://doi.org/10.1016/j.mcna.2025.02.006","authors":["Piyush Mathur","Reem Khatib","Dharan Sankar Jaisankar","Ashish Atreja"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-20T11:18:43Z","doi":"10.1016/j.mcna.2025.02.006","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.5124/jkma.25.0067","name":"Guest editorial introduction to the special issue on “Artificial Intelligence in Medical Imaging: Current Innovations and Future Perspectives”","source":"crossref","abstract":"","url":"https://doi.org/10.5124/jkma.25.0067","authors":["Sung Hun Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-30T03:38:38Z","doi":"10.5124/jkma.25.0067","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-49910-80-5_7","name":"Big data techniques for analyzing patient data collected from medical devices","source":"crossref","abstract":"Big Data refers to the large amount of complex data that emerges in real time and needs a huge amount of storage, computing infrastructure, software as well as expertise to analyze and infer insight. The advent of the intelligent processing devices embedded in the medical devices and sensors has led to a paradigm shift in the healthcare industry. Nowadays, clinical dataset for a patient is available in various forms such as Electronic Health Record, imaging data, external sensor dataset collected from various devices, motel medical devices and genomics. The integration of the patient data collected from different sources is essential for holistic patient management through early and accurate diagnosis, preventive care as well as personalized treatment.","url":"https://doi.org/10.70593/978-93-49910-80-5_7","authors":["Sai Teja Nuka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-12T16:18:16Z","doi":"10.70593/978-93-49910-80-5_7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3767052.3767092","name":"Artificial Intelligence Driving the Sustainable Development of Smart Cities: A Bibliometrics Study from 2014-2025","source":"crossref","abstract":"Driven by global urbanization and goals like the “United Nations 2030 Agenda” and “dual carbon targets,” research on AI and smart cities has emerged as a leading interdisciplinary field. This paper analyzes 950 papers from the Web of Science core database (2014–2025) using bibliometric tools like VOSviewer and CiteSpace, focusing on keyword co-occurrence, collaboration networks, and national patterns. The findings show: (1) Strong collaboration within author teams but limited cross-team interaction, focusing on AI for urban resource optimization and efficiency; (2) Diverse research orientations among institutions, with some leading in output and influence, and cross-regional collaboration driving international exchange; (3) Significant differences in research influence among countries, with a few dominating output and quality; (4) Keywords have shifted from technical themes like “big data” and “Internet of Things” to sustainable issues like “blockchain” and “carbon neutrality,” highlighting the integration of AI with sustainability goals. This study provides a comprehensive overview of field hotspots and insights into sustainable pathways for AI-driven smart cities.","url":"https://doi.org/10.1145/3767052.3767092","authors":["Yichen Xiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-11T09:15:17Z","doi":"10.1145/3767052.3767092","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.51985/jbumdc2025665","name":"Artificial Intelligence in Medical Writing – Understanding the Fine Line Between Assistance and Academic Misconduct","source":"crossref","abstract":"The explosion of artificial intelligence (AI) in biomedical research and clinical documentation heralds a new era in medical writing.1,2,3 AI-driven tools, predominantly large language models (LLMs) like ChatGPT, GPT-4, DeepSeek, Scholar GPT and other natural language processing systems, have introduced unprecedented efficiency in generating, translating, summarizing, and editing medical content","url":"https://doi.org/10.51985/jbumdc2025665","authors":["Iqbal Hussain Udaipurwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-24T13:42:53Z","doi":"10.51985/jbumdc2025665","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.110649","name":"A Chinese medical named entity recognition method considering length diversity of entities","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110649","authors":["Hongyu Zhang","Long Lyu","Weifu Chang","Yuexin Zhao","Xiaoqing Peng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-26T16:26:35Z","doi":"10.1016/j.engappai.2025.110649","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-49910-91-1","name":"The New Frontiers of Financial Services: Redefining Value with Artificial Intelligence-Driven Intelligence and Automation","source":"crossref","abstract":"The world of financial services is undergoing a generational shift. At its core, this transformation is being driven by artificial intelligence, next-generation digital infrastructure and intelligent automation, all of which are combining to reshape how we think about money, trust and value. This book brings you inside this changing world. It is written for professionals, researchers, academics and anyone with an interest in making sense where finance is heading and how these changes are impacting us, as consumers, investors and the future of banking and risk management in the digital age. Whether it's robo-advisors making financial planning more accessible, or AI helping institutions make smarter, faster decisions, this book explores the real-life applications and human impact of these technologies. You'll find rich studies, historical context, and glimpses into the future that show a clear picture of what's changing and why it matters. But beyond deciphering tech, this book links innovation to the individual’s everyday life. It provides a road map for navigating the opportunities, challenges and ethical questions of this new age for finance, and as such is an essential guide for anyone trying to stay ahead in a world where intelligence increasingly resides, in many different forms that aren’t human.","url":"https://doi.org/10.70593/978-93-49910-91-1","authors":["Ramesh Inala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T10:26:59Z","doi":"10.70593/978-93-49910-91-1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-21870-5.04001-2","name":"Acknowledgments","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21870-5.04001-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-04T01:53:43Z","doi":"10.1016/b978-0-443-21870-5.04001-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/b978-0-443-30046-2.00004-1","name":"Conclusions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30046-2.00004-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-22T09:30:28Z","doi":"10.1016/b978-0-443-30046-2.00004-1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.9734/ajmpcp/2025/v8i1294","name":"Applications of Artificial Intelligence in Clinical Practice and Healthcare (2010–2025): A Systematic Review","source":"crossref","abstract":"","url":"https://doi.org/10.9734/ajmpcp/2025/v8i1294","authors":["Zainab Belal","Saba Akram","Konstantin Koshechkin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-02T10:18:55Z","doi":"10.9734/ajmpcp/2025/v8i1294","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1136/jme-2025-111065","name":"Academic freedom, artificial intelligence and the blandification of ethics","source":"crossref","abstract":"","url":"https://doi.org/10.1136/jme-2025-111065","authors":["Julian Savulescu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-07T16:35:37Z","doi":"10.1136/jme-2025-111065","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1117/12.3107512","name":"Patient flow prediction and guidance in regional medical centers based on artificial immune systems","source":"crossref","abstract":"Facing uneven patient flow and resource underuse at regional medical centers, accurate forecasting and smart management are crucial to improving healthcare efficiency. This study presents a patient flow forecasting and dynamic allocation framework based on the Artificial Immune System (AIS). It first uses the Negative Selection Algorithm to detect anomalies like sudden spikes in historical patient data. Then, through Clonal Selection, it adapts a time series prediction model to evolving trends. Finally, using Immune Network Theory, the system dynamically allocates patients across departments to balance workload and cut wait times. MATLAB simulations show the AIS framework outperforms standard ARIMA and BP neural networks, improving prediction accuracy and reducing average waiting times by about 18.7%, demonstrating its effectiveness in managing patient flow in regional healthcare centers.","url":"https://doi.org/10.1117/12.3107512","authors":["Yanxia Wang","Dongjiao Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T12:55:49Z","doi":"10.1117/12.3107512","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.21608/aiis.2025.417218.1023","name":"جامعات الجيل الخامس المرتكزة على الأنظمة الذكية ودورها في استقطاب فئات المتعلمين","source":"crossref","abstract":"تهدف ورقة العمل إلى دراسة مفهوم جامعات الجيل الخامس المرتكزة على الأنظمة الذكية، ودورها في استقطاب فئات متنوعة من المتعلمين. تستعرض ورقة العمل الخصائص المميزة لهذه الجامعات، مثل توظيف الذكاء الاصطناعي، وتحليل البيانات الضخمة، والتعليم المدمج، والتوافق مع متطلبات وظائف المستقبل، إضافة إلى التعاون الدولي وجودة التعلم. كما توضح ورقة العمل كيف تسهم الأنظمة الذكية في توفير تعليم مخصص ومرن يعزز الشمولية وتكافؤ الفرص. اعتمدت ورقة العمل على المنهج الوصفي التحليلي وتحليل الأدبيات والدراسات السابقة، مع إبراز التحديات التي تواجه تطبيق هذا النموذج في بيئات التعلم العربية. وخلصت النتائج إلى أن تبني جامعات الجيل الخامس يمثل تحولًا جوهريًا نحو منظومة تعليمية أكثر تكاملًا وقدرة على التكيف مع متغيرات العصر، وأن انشاء هذه الجامعات يتطلب تحديات كبرى في البنية الأساسية المرتبطة بطبيعة الانشاءات والمعامل والمختبرات الذكية وكابلات نقل البيانات والخوادم والأجهزة وأنظمة الادارة والسجلات الالكترونية وملفات الانجاز الالكترونية وأنظمة الاتصال والنشر والمواقع الالكترونية التي تيسر آليات التعلم ونقل المحتوى ونشره والتشارك فيه، بالإضافة الى المناهج المفتوحة التي تجعل التعلم يرتقى الى مستوى التعلم التكيفي، وهذا من شأنه أن يحدث درجات كبيرة من الرضا والقبول لدى المتقدمين والمقبلين على الدراسة.","url":"https://doi.org/10.21608/aiis.2025.417218.1023","authors":["Ehab Mohamed Shabka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-28T16:12:46Z","doi":"10.21608/aiis.2025.417218.1023","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.outlook.2025.102466","name":"N.U.R.S.E.S. embracing artificial intelligence: A guide to artificial intelligence literacy for the nursing profession","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.outlook.2025.102466","authors":["Stephanie H. Hoelscher","Ashley Pugh"],"tags":["Psychology","Nursing","Medical education","Nursing diagnosis","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-24","doi":"10.1016/j.outlook.2025.102466","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2024.109553","name":"Word-Sequence Entropy: Towards uncertainty estimation in free-form medical question answering applications and beyond","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109553","authors":["Zhiyuan Wang","Jinhao Duan","Chenxi Yuan","Qingyu Chen","Tianlong Chen","Yue Zhang","Ren Wang","Xiaoshuang Shi","Kaidi Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T07:21:22Z","doi":"10.1016/j.engappai.2024.109553","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1148/ryai.240637","name":"Privacy-preserving Federated Learning and Uncertainty Quantification                     in Medical Imaging","source":"crossref","abstract":"This review article provides an in-depth analysis of the latest advancements in federated learning, privacy preservation, and uncertainty quantification in medical imaging. It also highlights current challenges and explores potential opportunities for improvement in these areas.","url":"https://doi.org/10.1148/ryai.240637","authors":["Nikolas Koutsoubis","Asim Waqas","Yasin Yilmaz","Ravi P. Ramachandran","Matthew B. Schabath","Ghulam Rasool"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-14T13:45:46Z","doi":"10.1148/ryai.240637","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/faia250876","name":"Triple-Prompt Controllable Diffusion for Universal Data Augmentation in Medical Image Segmentation","source":"crossref","abstract":"Medical image segmentation is a crucial yet challenging task in image analysis across diverse anatomical structures. Current segmentation models heavily depend on large-scale datasets, which are laborious to collect and annotate. While generative models offer a promising alternative for data augmentation, most existing approaches are limited to single-modality outputs, either synthetic images or segmentation masks. Moreover, these methods often lack flexible conditioning mechanisms and struggle to capture the rich contextual dependencies inherent in anatomical structures. To address these challenges, in this paper, we propose TPCDM, a novel framework that co-synthesizes high-fidelity paired medical images and segmentation masks through a unified Triple-Prompt Conditional Diffusion Model. At the heart of TPCDM lies a newly defined joint image-label generation paradigm, termed Coordinated Distribution Learning, governed by three synergistic prompts: (1) a text prompt encoding global anatomical semantics; (2) a spatial prompt enforcing pixel-wise spatial coherence; (3) a task prompt dynamically adapting to diverse distributions. Furthermore, TPCDM disentangles instance-wise annotations into semantic masks and distance maps, enabling seamless extension to instance segmentation tasks. Extensive experiments on four benchmarks demonstrate that TPCDM achieves superior synthesis quality. Besides, incorporating the synthesized samples leads to state-of-the-art performance in both downstream semantic and instance segmentation tasks, while also delivering significant improvements under limited labeled data.","url":"https://doi.org/10.3233/faia250876","authors":["Shiao Xie","Ke Meng","Hongyi Wang","Liangjun Zhang","Ziwei Niu","Yen-Wei Chen","Lanfen Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-22T09:44:40Z","doi":"10.3233/faia250876","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1002/9781394301287.ch7","name":"How Artificial Intelligence Affect the Role of Manpower in Biofuels Industry","source":"crossref","abstract":"The process of digitalizing industries is gaining momentum. The advancements in digital technology have been significant. The capabilities of computing power and data transfer are consistently improved by the implementation of increasingly advanced hardware and software technologies. Due to heightened competition, technological progress, knowledge exchange, and globalization, there has been a significant surge in the demand for highly skilled individuals. Contemporary sophisticated software systems have the ability to analyze factory data to identify patterns and trends. These insights can be used to optimize manufacturing processes and reduce energy use. This study investigates the impact of artificial intelligence (AI) on enterprises and its implications for professional growth. The research primarily focuses on the preparedness of organizations to confront the challenges of the upcoming industrial revolution and the strategies for developing skilled workforces in the relevant disciplines.","url":"https://doi.org/10.1002/9781394301287.ch7","authors":["Rajesh Singh Gurjar","Sudesh Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-30T08:48:17Z","doi":"10.1002/9781394301287.ch7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70301/sbs.mono.2025.1.3","name":"Artificial Intelligence and Sustainability: Innovations in Business and managerial Practices","source":"crossref","abstract":"Artificial Intelligence (AI) will, more than ever, play a critical role in every aspect of organizational progression and influence decisions across the board. Human, talent, and overall workforce management is no exception to this influence and impact; AI’s influence will be through organizational leadership via guiding decision-making, team management, and innovation processes. As its potential is explored, it becomes clear that leaders must adapt to leverage innovation effectively and address the new ethical and cultural issues they raise. Organizations, leadership, cultures, and pillars of organizational structures and systems must do this while remaining ethical, mindful, and aware of not affecting creativity (INSEAD, 2024). The leadership of any organization must lead with AI while keeping people, mindfulness, ethics, and values, as well as creativity and the human touch at the heart of everything that they do and each AI strategy (AON, 2024). AI has the potential to unleash creativity, foster human connections, imagine new ways of learning, enable the automation of existing tasks, and promote new adaptive tasks that require human ingenuity and empathy. That is quite a list, which raises equal challenges and opportunities (INSEAD, 2024). What is clear is that leaders will remain indispensable in helping their teams and firms negotiate this brave new world. To do so successfully, it is vital that they adopt a dual mindset, while helping to maintain and create moments of deep, thoughtful human interactions. Four challenges may arise from AI’s influence and leverage: 1) HR’s operational complexities, 2) data’s readiness, accuracy, and availability, 3) legalities that may arise and conform to compliant approaches, and 4) Manpower’s reactions and behavior against and towards algorithmic based decisions (Jobylon, 2024).","url":"https://doi.org/10.70301/sbs.mono.2025.1.3","authors":["Kelly Salame"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-10T21:21:58Z","doi":"10.70301/sbs.mono.2025.1.3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3749421.3749442","name":"Epilogue: Two Paradigm Bridges","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3749421.3749442","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T16:55:26Z","doi":"10.1145/3749421.3749442","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.21037/jmai.2019.09.06","name":"Toward the transparency of deep learning in radiological imaging: beyond quantitative to qualitative artificial intelligence","source":"crossref","abstract":"In the near future, nearly every type of clinician, from paramedics to certificated medical specialists, will be expected to utilize artificial intelligence (AI) technology, and deep learning (DL) in particular (1). In terms of exceeding human ability, DL has been the backbone of computer science. DL mostly involves automated feature extraction using deep neural networks (DNNs), which can aid in the classification and discrimination of medical images, including mammograms, skin lesions, pathological slides, radiological images, and retinal fundus photographs.","url":"https://doi.org/10.21037/jmai.2019.09.06","authors":["Yoichi Hayashi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-30T14:08:18Z","doi":"10.21037/jmai.2019.09.06","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1201/9781003503385-12","name":"Unlocking Effective Applications of Artificial Intelligence for Healthcare Management Systems","source":"crossref","abstract":"Recent years have seen a tremendous advancement in Artificial Intelligence (AI) in terms of hardware implementation, software algorithms, and sector-specific applications. In this chapter, we cover most recent advancements in AI applications in healthcare (HC). This chapter systematically reviews effective models and applications of AI from the perspective of HC operations, marketing, finance, and human resource. Prior studies observed that AI can assist in streamlining business processes throughout the HC industry. Medical inventory automation can be utilized to handle forecasting, planning, managing stock-outs, overstocks, and expirations; automate the stocking and fulfillment operations; and meet the required patient demand on time. The chatbot is one of the more personalized implementations of AI technology that can assist HC marketers by boosting website engagement and pointing potential patients to online resources, thereby enhancing the patient experience. AI technology can improve the work in HC finance through medical insurance automation to improve policy management, claim processing, and regulatory compliance. AI may help with the HC industry s human resource management by assisting in recruiting potential HC workforce. Through this extensive review, this study reveals the useful considerations for building the next generation of HC using AI that have the potential to significantly advance the HC sector.","url":"https://doi.org/10.1201/9781003503385-12","authors":["Shreyan Saha","Esha Saha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-24T18:58:18Z","doi":"10.1201/9781003503385-12","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-13816-4.00012-7","name":"Digital sphygmomanometric measurement system for patients with chronic illnesses based on artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13816-4.00012-7","authors":["Mrinmoy Singha","Partha Sarathi Swarnakar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T18:04:14Z","doi":"10.1016/b978-0-443-13816-4.00012-7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1504/ijaih.2025.149248","name":"A literature review on artificial intelligence and healthcare management","source":"crossref","abstract":"The purpose of artificial intelligence (AI) is to create an algorithm that functions autonomously to find the solutions to questions.However, the results that AI makes can lead to social biases and other selectivity issues.The social biases include negative statements to ethnic minority groups, gender biases, and cultural biases.Due to this reason, there is a research gap of AI and healthcare management such as AI biases and human-AI interaction.Thus, the goal of this literature review is to comprehensively examine the interaction of AI and users (patients who are in their mid or late-thirties, White, and live in the USA) specifically in the clinical healthcare environment to further enhance the usability of patients and AI.","url":"https://doi.org/10.1504/ijaih.2025.149248","authors":["Esther Hwang","Yujong Hwang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-21T11:30:32Z","doi":"10.1504/ijaih.2025.149248","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artint.2024.104237","name":"Open-world continual learning: Unifying novelty detection and continual learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2024.104237","authors":["Gyuhak Kim","Changnan Xiao","Tatsuya Konishi","Zixuan Ke","Bing Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-01T00:51:23Z","doi":"10.1016/j.artint.2024.104237","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.25019/perspol/25.18.6","name":"Artificial Intelligence in Romania: Romanians’ perception of Artificial Intelligence","source":"crossref","abstract":"This study examines the perception and usage of Artificial Intelligence (AI) among Romanian citizens in the context of its global expansion and increasing integration into everyday life and industrial use.With the emergence of tools such as ChatGPT, AI has become a technological development, prompting both enthusiasm and apprehension.The research aims to assess the extent to which AI influences daily decision-making processes.A quantitative research design was employed, using an online questionnaire to collect data on public attitudes of Romanians toward AI.Although the sample does not meet the requirements for population-level representativeness, the exploratory character of the study provides valuable insights, given the limited research on this topic in Romania.Findings indicate that while AI is primarily used in personal contexts, its adoption in professional and educational settings is steadily increasing.Most respondents view AI as useful while simultaneously emphasizing the need for regulation and ethical oversight.Key concerns identified include potential job displacement, the spread of misinformation, diminished critical thinking, and social isolation.Conversely, AI is recognized for its potential to enhance productivity, creativity, and administrative efficiency.The results underscore the importance of digital literacy, equitable access, and transparent governance to ensure responsible integration of AI into Romanian society.Further research into larger, more representative samples is recommended to better understand developments in AI adoption.","url":"https://doi.org/10.25019/perspol/25.18.6","authors":["Roxana-Mihaela NEDELCU (ZAFIU)"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-20T13:11:03Z","doi":"10.25019/perspol/25.18.6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.112219","name":"MAE-diff: Masked-AutoEncoder-Guided diffusion framework for source-free domain adaptive medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112219","authors":["Shanshan Xu","Le Xu","Yeqing Yang","Lixia Tian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T00:18:36Z","doi":"10.1016/j.engappai.2025.112219","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.4103/atmr.atmr_41_25","name":"Explainable Artificial Intelligence-PREDICT: Development and Validation of an Explainable Artificial Intelligence Tool for Early Prediction of Diabetic Complications in Saudi Arabia","source":"crossref","abstract":"Abstract One of the major diseases in Saudi Arabia is Diabetes mellitus, with an estimated prevalence rate of 18.3% for adults. Early detection of diabetic complications is crucial for initiating fast medical reactions and improving patient outcomes. Although artificial intelligence (AI) has great promise in predicting disease courses, its clinical usage has been limited by the absence of interpretability. This study aims to build Explainable AI (XAI)-PREDICT, an AI-based predictive tool for the early detection of diabetic complications in Saudi Arabia. A retrospective cohort study was conducted using electronic health records from 12 hospitals in Saudi Arabia, tracking 87,542 patients with diabetes mellitus from January 2021 to January 2025. Five major diabetic complications were predicted using many machine learning models built and assessed over 24 months. Interpretability components were included in model development, considering situational variables particular to Saudi medical institutions. The final XAI-PREDICT system achieved high predictive accuracy with area under the receiver operating characteristic curve values of 0.89 (95% confidence interval [CI]: 0.87–0.91) for nephropathy, 0.86 (95% CI: 0.84–0.88) for retinopathy, 0.84 (95% CI: 0.82–0.86) for neuropathy, 0.88 (95% CI: 0.86–0.90) for cardiovascular occasions and 91 (95% CI: 0.89–0.93) for diabetic foot. Using AI to control diabetes in Saudi Arabia has advanced significantly with XAI-PREDICT. It provides both a useful interpretation and great predictive power. This study shows the importance of using XAI-PREDICT for early detection, management of complications and lowering the burden of complications.","url":"https://doi.org/10.4103/atmr.atmr_41_25","authors":["Zahra Hussin Al-Hudaibi","Raghad Majeed Almatar","Zainab Ahmed Alwabari","Ola Mohammed Al-Duhailan","Haya Mansour Alsuwailem","Zainab Mufid Alhajji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-18T15:01:45Z","doi":"10.4103/atmr.atmr_41_25","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/ictai66417.2025.00203","name":"Enhancing Cloud Cost Forecasting with Explainable Artificial Intelligence","source":"crossref","abstract":"Cloud computing enables efficient digital transformations for organizations but also raises significant challenges for cost management due to its variability and complexity. Rapid advancements in Artificial Intelligence (AI) bring promising opportunities to address these challenges, particularly in cloud cost forecasting. However, implementing AI-based models for cloud cost forecasting remains novel and challenging, as the financial domain requires high trustworthiness in AI solutions. Explainable AI (XAI) addresses this issue by developing techniques that clarify AI decisions, making models more transparent and reliable. Moreover, XAI explanations can help identify redundant features, leading to improved model performance. This paper introduces a cloud cost forecasting approach using forecasting models for time series data. The predictions are explained using the Kernel SHAP method, which highlights the impact of different features on the model's output. The forecasting model is then refined by removing low-impact features. The results demonstrate that the refined models enhanced by XAI outperform the original models due to an efficient feature selection process. Our study highlights the capability of AI and XAI to address cloud cost forecasting challenges by providing accurate predictions and clear explanations.","url":"https://doi.org/10.1109/ictai66417.2025.00203","authors":["Ha Nhi Ngo","Mouna Ben Mabrouk","Ines Ben Kraiem"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-15T18:35:43Z","doi":"10.1109/ictai66417.2025.00203","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-443-41467-1.00004-2","name":"Advanced artificial intelligence algorithms in hydrogen production","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-41467-1.00004-2","authors":["Hossein Pourrahmani","Hossein Madi","Jan Van Herle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T10:02:46Z","doi":"10.1016/b978-0-443-41467-1.00004-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1148/ryai.240050","name":"Evaluating Skellytour for Automated Skeleton Segmentation from                     Whole-Body CT Images","source":"crossref","abstract":"Skellytour provides open-source, generalizable bone segmentation and subsegmentation models for whole-body CT data that perform consistently across a range of datasets, including low-density bone.","url":"https://doi.org/10.1148/ryai.240050","authors":["Daniel C. Mann","Michael W. Rutherford","Phillip Farmer","Joshua M. Eichhorn","Fathima Fijula Palot Manzil","Christopher P. Wardell"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-19T14:47:00Z","doi":"10.1148/ryai.240050","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/b978-0-323-91819-0.00008-7","name":"The role of artificial intelligence and machine learning in clinical trials","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-91819-0.00008-7","authors":["D.A. Dri","M. Massella","M. Carafa","C. Marianecci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T11:47:42Z","doi":"10.1016/b978-0-323-91819-0.00008-7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaice68195.2025.11382339","name":"Research on a Lithium Battery Health Management System Based on Big Data and Artificial Intelligence","source":"crossref","abstract":"The rapid growth of electric vehicles and energy storage systems necessitates advanced lithium battery health management, as conventional BMS relying on static thresholds and single-model strategies often fail under complex dynamic conditions and multi-physics coupling effects during aging. To bridge this gap, we propose an integrated data-AI-system solution via a five-layer framework (data acquisition–feature extraction– AI modeling–multiphysics simulation–closed-loop optimization). By embedding electrochemical mechanisms into graph neural networks (GNN) combined with reinforcement learning, our system achieves precise state-of-health prediction and dynamic control. Validation results show a 1.2% MAE in SOH prediction—20% lower than traditional methods—with exceptional generalization in late aging stages, alongside <1.8% SOC deviation in multiphysics simulations and accurate thermal runaway forecasting. This work establishes a unified platform bridging AI, simulation, and control, offering a practical pathway toward full-life-cycle battery management with enhanced safety and longevity.","url":"https://doi.org/10.1109/icaice68195.2025.11382339","authors":["Jiatao Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T21:05:47Z","doi":"10.1109/icaice68195.2025.11382339","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.110642","name":"The optimization-based fuzzy logic controllers for autonomous ground vehicle path tracking","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110642","authors":["Ibrahim Aliskan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-04T12:31:25Z","doi":"10.1016/j.engappai.2025.110642","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1093/jamiaopen/ooaf114","name":"Early auxiliary diagnosis model for chest pain triad based on artificial intelligence multimodal fusion","source":"crossref","abstract":"Abstract Objectives Acute chest pain is a common presentation in the emergency department, characterized by sudden onset with high morbidity and mortality. Traditional diagnostic methods, such as computed tomography (CT) and CT angiography (CTA), are often time-consuming and fail to meet the urgent need for rapid triage in emergency settings. Materials and Methods We developed a multimodal model that integrates Bio-ClinicalBERT and ensemble learning (AdaBoost, Gradient boosting, and XGBoost) based on 41 382 patient data from April 1, 2013 to April 1, 2025 at Chongqing Daping Hospital. By integrating clinical texts and laboratory indicators, the model aims to classify the 3 major causes of fatal chest pain (acute coronary syndrome, pulmonary embolism, and aortic dissection), as well as other causes of chest pain, aiding rapid triage. We adopt strict data preprocessing and rank importance feature selection. Results The multimodal fusion model based on Gradient boosting exhibits the best performance: accuracy of 88.40%, area under the curve of 0.951, F1-score of 74.56%, precision of 77.50%, and recall of 72.52%. SHapley Additive exPlanations (SHAP) analysis confirmed the clinical relevance of key features such as d-dimer and high-sensitivity troponin. When reducing the number of numerical features to 30 key indicators, the model enhanced robustness without compromising performance. Discussion and Conclusion We developed an artificial intelligence model for chest pain classification that effectively addresses the problem of overlapping clinical symptoms through multimodal fusion, and the model has high accuracy. However, future work needs to better integrate model development with clinical workflows and practical constraints.","url":"https://doi.org/10.1093/jamiaopen/ooaf114","authors":["Jun Tang","Fang Chen","Dongdong Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T11:36:19Z","doi":"10.1093/jamiaopen/ooaf114","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/faia250805","name":"A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation","source":"crossref","abstract":"In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmission. However, existing generative model-based OSFL methods suffer from low training efficiency and potential privacy leakage in the healthcare domain. Additionally, achieving convergence within a single round of model aggregation is challenging under non-Independent and Identically Distributed (non-IID) data. To address these challenges, in this paper a modified OSFL framework is proposed, in which a new Feature-Guided Rectified Flow Model (FG-RF) and Dual-Layer Knowledge Distillation (DLKD) aggregation method are developed. FG-RF on the client side accelerates generative modeling in medical imaging scenarios while preserving privacy by synthesizing feature-level images rather than pixel-level images. To handle non-IID distributions, DLKD enables the global student model to simultaneously mimic the output logits and align the intermediate-layer features of client-side teacher models during aggregation. Experimental results on three non-IID medical imaging datasets show that our new framework and method outperform multi-round federated learning approaches, achieving up to 21.73% improvement, and exceed the baseline FedISCA by an average of 21.75%. Furthermore, our experiments demonstrate that feature-level synthetic images significantly reduce privacy leakage risks compared to pixel-level synthetic images. The code is available at https://github.com/LMIAPC/one-shot-fl-medical.","url":"https://doi.org/10.3233/faia250805","authors":["Yufei Ma","Hanwen Zhang","Qiya Yang","Guibo Luo","Yuesheng Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-22T09:42:41Z","doi":"10.3233/faia250805","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3749421.3749437","name":"CoCoMo: Computational Consciousness Model","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3749421.3749437","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T16:55:26Z","doi":"10.1145/3749421.3749437","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.110225","name":"Hybrid pathfinding optimization for the Lightning Network with Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110225","authors":["Danila Valko","Daniel Kudenko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-13T03:39:37Z","doi":"10.1016/j.engappai.2025.110225","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-981-96-6863-2_4","name":"Amalgamation of Artificial Intelligence and Climate Change","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-6863-2_4","authors":["Balendra V. S. Chauhan","Ajitanshu Vedrtnam","Kevin P. Wyche","Sneha Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T17:13:00Z","doi":"10.1007/978-981-96-6863-2_4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.53478/tuba.978-625-6110-66-3.ch04","name":"Product Liability Insurance New Paths for Software and Systems of Artificial Intelligence Following Directive (EU) 2024/2853?","source":"crossref","abstract":"The recent EU Directive on liability for defective products (Directive 2024/2853) significantly expands liability to cover risks arising from digitalisation, including software and artificial intelligence (Aİ) systems. The purpose of this article is to examine the implications of these changes for product liability insurance, focusing on the need to adapt insurance models to address new risks such as cyber threats, machine-learning capabilities and data breaches. The analysis highlights the issues concerning whether certain risks can be insured, in particular systemic risks and non-material damages, while exploring potential solutions like risk pools and public compensation funds. There is also a critique of the EU’s silence on compulsory EUwide liability insurance, arguing for sectoral mandates for high-risk products to balance innovation and victim protection. By comparing national approaches and referencing the German AVB BHV 2024 model contracts, the tension between harmonisation and Member State discretion in implementation is underscored.","url":"https://doi.org/10.53478/tuba.978-625-6110-66-3.ch04","authors":["Helmut Heiss"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-26T11:56:07Z","doi":"10.53478/tuba.978-625-6110-66-3.ch04","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.58496/bjai/2025/002","name":"Image Generation Using Generative AI: Comparison Between OpenAI Art and Stable Diffusion","source":"crossref","abstract":"Generative AI has made significant strides in image generation, with OpenAI Art and Stable Diffusion emerging as two prominent tools in the field. This study aims to compare the capabilities of these two models in terms of performance, quality, and creativity in generating images based on text prompts. We evaluate both tools using a range of image categories, assessing their output for accuracy, creativity, and consistency with provided instructions. The findings suggest that while OpenAI Art offers faster responses and simpler outputs, Stable Diffusion excels in producing more realistic and diverse images. This paper delves into the methodologies of both tools, offering insights into their strengths and limitations, and provides a comprehensive comparison based on experimental results.","url":"https://doi.org/10.58496/bjai/2025/002","authors":["Ismael Khaleel Khlewee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T04:00:27Z","doi":"10.58496/bjai/2025/002","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1108/978-1-83662-570-420251001","name":"Economic Impact of Artificial Intelligence in Agriculture: Issues and Challenges","source":"crossref","abstract":"The global population is increasing day by day; however, the traditional method of cultivation is not sufficient to cater the increasing demand for food. Precision agriculture, often known as artificial intelligence (AI) systems, is assisting in enhancing the overall quality and accuracy of harvests in many ways. Indian agriculture faces several unique issues like lack of mechanization, low productivity, soil erosion, unavailability of water for cultivation, price of the produces, low income of the farmers, etc. To promote innovation and entrepreneurship in agriculture, the agricultural industry is increasingly looking at ways to harness technology for increased crop yields. This chapter emphasizes the economic impacts of AI in improving agricultural output and, therefore, farmer livelihoods, and the fact that India’s farming issue requires attention on many levels. It also discussed about the contribution of startups in improving the AI in agriculture. Through content analysis, the chapter reveals that AI can boost farm output in India, ease supply chain constraints, and increase market access. It shows how the AI can be used to resolve all these issues in a sustainable way and to boost the farms productivity and farmer’s income.","url":"https://doi.org/10.1108/978-1-83662-570-420251001","authors":["Bappaditya Biswas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-06T13:41:46Z","doi":"10.1108/978-1-83662-570-420251001","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.33545/27076571.2025.v6.i1d.278","name":"Reliable requirement specification using artificial intelligence","source":"crossref","abstract":"The reliability of Software Requirement Specifications (SRS) plays a decisive role in the success of software projects. Traditional requirement engineering practices rely heavily on manual elicitation, analysis, and validation, which are often error-prone, ambiguous, and inconsistent. With the advancement of Artificial Intelligence (AI), new opportunities have emerged to enhance the reliability, accuracy, and completeness of requirement specifications. This paper presents a comprehensive study on reliable requirement specification using AI techniques. It explores the role of Natural Language Processing (NLP), Machine Learning (ML), ontology-based reasoning, and software repository mining in improving requirement quality. A layered AI-based framework for reliable requirement specification is proposed, highlighting its benefits, challenges, and future research directions.","url":"https://doi.org/10.33545/27076571.2025.v6.i1d.278","authors":["Sandeep Kumar Nayak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-28T12:47:37Z","doi":"10.33545/27076571.2025.v6.i1d.278","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70269/10.70269/7867736219","name":"FACTORS AFFECTING THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE MODELS: A THEORETICAL REVIEW","source":"crossref","abstract":"","url":"https://doi.org/10.70269/10.70269/7867736219","authors":["İSMAİL AKGÜL"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T22:31:32Z","doi":"10.70269/10.70269/7867736219","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1148/ryai.250550","name":"Quantitative Pharmacokinetic Mapping with AI: Toward More                     Generalizable Response Prediction in Breast Cancer MRI","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250550","authors":["Tician Schnitzler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-27T13:52:07Z","doi":"10.1148/ryai.250550","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/sgai64825.2025.11009597","name":"Data Analysis and Intelligent Scheduling of Power Customer Service Based on Artificial Intelligence","source":"crossref","abstract":"With the increasing complexity of power demand forecasting, traditional methods face challenges in handling multiple influencing factors. This paper proposes a power load forecasting model based on the Temporal Fusion Transformer (TFT) to improve the accuracy and stability of power demand forecasting. By introducing a variable self-attention mechanism and gating mechanism, the TFT model effectively captures long-and short-term dependencies, while considering external factors such as weather and holidays that affect power demand. The study further enhances the model's forecasting capability and robustness through optimization strategies like multi-step forecasting, deep feature crossing, and model fusion. Experimental results show that the optimized TFT model demonstrates outstanding performance in power load forecasting, providing strong support for intelligent power scheduling.","url":"https://doi.org/10.1109/sgai64825.2025.11009597","authors":["Yu Tian","Shaomin Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-29T17:06:12Z","doi":"10.1109/sgai64825.2025.11009597","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1145/3766557.3766585","name":"Application of Artificial Intelligence Driven Mixed Reality Sandbox in Solid Waste Treatment and Disposal Teaching","source":"crossref","abstract":"AI-driven mixed reality sandbox has brought innovative changes to the teaching of solid waste treatment and disposal. This study designed and implemented a teaching system based on this technology, which includes multiple experimental simulation modules. The experimental simulation results show that in the simulation of solid waste collection and transportation, after students use this system, the average length of the planned transportation route is reduced by 21.7%, the transportation cost is reduced by 25.3% on average, and the transportation efficiency is significantly improved; in the landfill treatment simulation, students' ability to control key parameters of the landfill is enhanced, the garbage degradation efficiency is increased by an average of 18.5%, and the leachate treatment compliance rate is increased to 92.3%; in the site selection simulation, the comprehensive score of the site selection scheme proposed by students is increased by an average of 38.6 points (out of 100 points), and the rationality of the scheme is greatly improved. At the same time, through comparative experiments, the average score of the experimental group students in the knowledge test is 15.8 points higher than that of the control group, and the excellent rate in the problem-solving ability assessment reaches 68.2%, which is much higher than the 32.5% of the control group. The system effectively improves students' learning effect and practical ability through an immersive and interactive teaching mode, and provides a new solution for the teaching of solid waste treatment and disposal.","url":"https://doi.org/10.1145/3766557.3766585","authors":["Ming Yuan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-27T12:18:09Z","doi":"10.1145/3766557.3766585","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3233/atde251590","name":"Evolution of Machine Learning Course and Cultivation of Engineering Practice Ability Guided by “Project Driven+Discipline Integration” in the Era of Artificial Intelligence","source":"crossref","abstract":"In the current era of artificial intelligence, the development of AI technology is leading humanity into a new round of industrial revolution. To actively respond to the national education strategy and cultivate composite talents of “artificial intelligence+X”, a teaching reform of “project driven+subject integration” has been carried out for the “Machine Learning” course, cultivating the engineering practice ability of talents in related majors. Through project driven teaching, practical engineering cases are used for teaching analysis, and under the guidance of subject integration, multiple technologies of artificial intelligence are integrated into the teaching of the “Machine Learning” course, enabling students to use interdisciplinary knowledge to solve engineering problems, improve their engineering modeling thinking ability, cultivate innovative thinking, stimulate their artificial intelligence awareness and conception, improve their research and design ability, cultivate their professional ethics and moral character, and achieve healthy growth.","url":"https://doi.org/10.3233/atde251590","authors":["Xiaozhe Yang","Huiting Lu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-16T11:08:03Z","doi":"10.3233/atde251590","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.artmed.2025.103281","name":"Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review","source":"crossref","abstract":"Background Narrative electronic medical records (EMR), which include textual notes created by clinicians within healthcare environments, represent a significant resource for documenting various facets of patient care. This form of text exhibits distinctive characteristics, such as the occurrence of grammatically incorrect sentences, abbreviations, frequent acronyms, specialized characters with particular meanings, negation expressions, and sporadic misspellings. As a result, a primary goal in processing these textual notes is to implement effective preprocessing techniques that enhance data quality and ensure consistency across all entries. Recent advancements in algorithms and methodologies within the fields of natural language processing (NLP), machine learning (ML), and large language models (LLM) have prompted researchers to leverage narrative EMR for the detection of hospital adverse events (HAE). Methods The scoping review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A scoping review protocol was developed and utilized to guide the research process, clearly outlining the eligibility criteria, information sources, search strategies, data management, selection process, data collection procedures, data items, outcomes and prioritization, data synthesis, and meta-bias considerations. The search strategy was implemented across nine engineering and medical electronic databases. Results The results have indicated that from a total of 3,264 studies retrieved, 48 unique studies were included in the review. Responses to the research questions were systematically extracted from these studies. The review has identified challenges associated with the preprocessing of narrative texts in EMR for HAE identification. Additionally, three research gaps have been identified: (1) the imperative need for a pipeline to preprocess narrative EMR for the identification of HAE, (2) the necessity for a robust system capable of managing the extensive volume of narrative EMR data, and (3) the requirement for temporal event system, which are essential for effective HAE detection. The study also has underscored the essential role of preprocessing tasks in enhancing the performance of HAE detection. The study has emphasized the importance of extracting N-grams from clinical text, normalizing these N-grams through lemmatization and/or stemming, and establishing semantic feature extraction in preprocessing tasks that significantly affect HAE detection performance. While LLM-based systems naturally incorporate tokenization and normalization processes within their frameworks, it remains crucial to address features that hold semantic relevance to the specific type of HAE during preprocessing. Conclusion This scoping review has provided valuable insights for researchers focused on HAE detection utilizing narrative EMR data. It has elucidated how preprocessing tasks can elevate the performance of HAE detection and draws attention to neglected research gaps within the field. Addressing these gaps will necessitate further investigation in subsequent research endeavors.","url":"https://doi.org/10.1016/j.artmed.2025.103281","authors":["Hamed Jafarpour","Guosong Wu","Cheligeer (Ken) Cheligeer","Jun Yan","Yuan Xu","Danielle A. Southern","Cathy A. Eastwood","Yong Zeng","Hude Quan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-09T06:30:35Z","doi":"10.1016/j.artmed.2025.103281","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.4324/9781003545125-3","name":"Impact of Artificial Intelligence on Tourism and Hospitality","source":"crossref","abstract":"Smart tourism is becoming the most dynamic industry that is evolving through artificial intelligence. This chapter addresses how different AI tools and technologies are revolutionising the tourism and hospitality industry. To develop a comprehensive understanding, dynamic AI tools like big data, machine learning, speech recognition, robotics, and smart travel assistants are critically analysed in the context of the tourism and hospitality sector. Moreover, strategies for demand forecasting through time series modelling, web searching data and econometric modelling are discussed. Four key issues that shape the future of AI’s impact on the tourism and hospitality sector are presented based on the transformation of employment and workforce, data privacy and security concerns, personalisation versus standardisation, and ethical implications of AI in decision-making. Followed by recommendations for policymakers and practitioners and future insight. Finally, a case study on Accor Hotels and Marriott International is discussed to understand the operational efficiencies and practical challenges.","url":"https://doi.org/10.4324/9781003545125-3","authors":["Sadaf Tallia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-17T10:04:42Z","doi":"10.4324/9781003545125-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.47310/jpms2026150518","name":"Global Research Trends in Artificial Intelligence for Healthcare and Education: A Bibliometric Analysis (2000-2025).","source":"crossref","abstract":"This study presents a comprehensive bibliometric analysis of global research trends in Artificial Intelligence (AI) in healthcare and education from 2000-2025. A total of 3,101 peer-reviewed publications retrieved from OpenAlex were analysed using VOSviewer to map co-authorship networks, keyword co-occurrence and institutional collaboration patterns. Five major thematic clusters were identified: technical foundations, learning theory, clinical applications, governance and ethics and point-of-care applications. Citation analysis highlighted the ten most highly cited articles, primarily focusing on challenges related to AI implementation and interpretability. Leading institutions included Harvard University, Stanford University and University College London. While North America and Western Europe dominate research output, contributions from emerging regions, particularly South Asia and the Gulf, have increased since 2020. The findings indicate a shift toward human-centered AI, interdisciplinary education and explainability. However, persistent challenges remain, including data interoperability, privacy concerns and algorithmic bias.","url":"https://doi.org/10.47310/jpms2026150518","authors":["Anas Ali Alhur","Muneef M. Alsahmmari","Fahad Saud Alshammari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-15T06:18:21Z","doi":"10.47310/jpms2026150518","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaiihi67124.2025.11403328","name":"Artificial Intelligence in Dermoscopy: A Review of Advances and Future Directions","source":"crossref","abstract":"Skin malignancy represents one of the mainly common and deadliest diseases, and early recognition is vital for successful treatment. In this context, the review provides a complete overview of the recent artificial intelligence developments designed for automated skin malignancy identification, focusing on the primary role of the deep learning architecture, particularly CNN , in terms of the diagnostic usability increase. The numerous approaches, such as ensemble models, multimodal fusion strategies, and hybrid mechanisms combining deep learning technologies with traditional machine learning using deep features. The open-source datasets, represented by ISIC and HAM10000 opportunities, substantially contributed to the rapid model development, but even now, classification challenges related to data distribution irregularity and image variance remain. Feature extraction with traditional preprocessing methods, like normalization, augmentation, and segmentation, significantly improved the classification performance.High-quality datasets integration, the implementation of advanced feature extraction with fusion strategies, lays down the basics for the intelligent, scalable skin cancer detection systems, with ample application potential in the real-world clinical practice.","url":"https://doi.org/10.1109/icaiihi67124.2025.11403328","authors":["Taranpreet Kaur","Ankita Wadhawan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-25T20:55:03Z","doi":"10.1109/icaiihi67124.2025.11403328","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.ejrai.2025.100011","name":"Advocating sustainable AI research in Clinical Radiology","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ejrai.2025.100011","authors":["Matthias Dietzel","Pascal A.T. Baltzer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-07T08:29:43Z","doi":"10.1016/j.ejrai.2025.100011","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.engappai.2025.112306","name":"Feature-Enhanced Edge-INverse attention network for skin lesion segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112306","authors":["Shivamm Warambhey","Aravindkumar Sekar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T00:18:36Z","doi":"10.1016/j.engappai.2025.112306","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.55529/jaimlnn.51.52.62","name":"Artificial intelligence patterns: novel applications and methodological framework","source":"crossref","abstract":"Autonomous vehicles (AVs) are poised to transform urban mobility but still struggle at unsignalized intersections, where the absence of infrastructure-mediated right-of-way forces vehicles to negotiate passage in real time. We introduce the Collaborative Maneuver Negotiation (CMN) pattern, a formally documented, reusable design construct that frames intersection coordination as a cooperative game among AVs. Each vehicle broadcasts a manoeuvre proposal, computes a composite utility that blends delay, collision risk and fairness, and iteratively reaches consensus via a token-passing protocol. In contrast to prior work that reports only simulation metrics, CMN ships with an openly licensed artifact bundle: a GoF-style pattern template, UML class and sequence diagrams, and reference implementation ready for ROS 2 integration. A campus-scale field deployment using four low-speed micro-shuttles demonstrated that CMN lowers average crossing delay by 41%, cuts conflict events by 87%, and increases theoretical throughput by 39% relative to static yield rules, while keeping DSRC network load below 30 kbit s⁻¹. These results substantiate the claim that pattern-oriented AI design can deliver tangible efficiency and safety benefits without sacrificing transparency or auditability key requirements for regulatory approval. Future work will extend CMN to high-speed traffic, mixed human-driver scenarios and privacy-preserving intent exchange, paving the way for standardized, cross-vendor negotiation modules in intelligent transportation systems.","url":"https://doi.org/10.55529/jaimlnn.51.52.62","authors":["Hasanain Hazim Azeez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-13T10:44:00Z","doi":"10.55529/jaimlnn.51.52.62","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1016/j.engappai.2025.111631","name":"Multiobjective evolutionary algorithm based wrapper approach for hyperspectral band selection","source":"crossref","abstract":"A hyperspectral sensor captures information from a wide range of spectral wavelengths, but the information collected is typically highly correlated. It is often challenging to obtain the pertinent bands without degrading the information content. The present work suggests a wrapper approach consisting of a decomposition-based multiobjective evolutionary algorithm. A simultaneous search is suggested for identifying significant bands and hyperparameter value of the classifier as efficacy of proposed approach is influenced by underlying classifier performance. A power distribution-based mechanism is suggested to choose and generate candidate solutions. The hyperspectral band selection problem is formulated as tri-objective optimization problem with information entropy, the percentage in band reduction, and classification accuracy as the objective functions. Entropy is employed as an objective function as a band subset with a higher entropy value can perform better in classification than another band subset with the same size. The assessment of the proposed approach on five widely referenced hyperspectral datasets demonstrates its effectiveness for band reduction while obtaining significant classification accuracy. Furthermore, the suggested approach outperforms other evolutionary multiobjective optimization strategies in obtaining fewer bands with better spectral information.","url":"https://doi.org/10.1016/j.engappai.2025.111631","authors":["Kamal Deep","Manoj Thakur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T16:56:11Z","doi":"10.1016/j.engappai.2025.111631","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaice68195.2025.11382427","name":"Real Time Improvement Research on Deep Learning Based Artificial Intelligence Computer Vision Image Dehazing Technology","source":"crossref","abstract":"Image dehazing technology is a crucial research direction in the field of Computer Vision (CV), whose core goal is to eliminate the impact of atmospheric scattering on image quality and restore clear scene information. Traditional dehazing algorithms, though simple in principle, lack robustness in complex foggy scenarios; AI dehazing methods based on deep learning can improve dehazing performance but are difficult to meet real-time requirements (e.g., autonomous driving, real-time monitoring) due to large model parameter size and high computational complexity. Based on the machine learning framework, this paper focuses on the real-time optimization of CV image dehazing. By designing a lightweight network structure, improving feature extraction strategies, and introducing model acceleration technologies, an AI dehazing model with both dehazing accuracy and real-time performance is constructed. Experiments are conducted based on the RESIDE dataset as the test benchmark. Compared with traditional methods and existing deep learning methods, the proposed model maintains a Peak Signal-to-Noise Ratio (PSNR) of 28.6 dB and a Structural Similarity Index (SSIM) of 0.91, while the inference speed is increased to 62 FPS (Frames Per Second), meeting the real-time processing requirements. This research provides an effective technical solution for image dehazing applications in real-time CV scenarios, and has important theoretical significance and engineering value.","url":"https://doi.org/10.1109/icaice68195.2025.11382427","authors":["Hangyu Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T21:05:47Z","doi":"10.1109/icaice68195.2025.11382427","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-7185-753-6_2","name":"Governance and compliance frameworks for responsible artificial intelligence deployment","source":"crossref","abstract":"The foreseeable future will likely see a profound impact caused by the advancing technology of Artificial Intelligence systems, in our day-to-day activities and routines, just like the same effect brought about by the introduction of the internet and World Wide Web [1-2]. The AIs currently in use, and the coming future of general intelligence machine systems, demand and expect appropriate – or even the best possible – Governance and Compliance Laws to be established to avert any possible negative consequences that could arise from their interaction with humanity, nature, the economy, governments, and society [2-4]. In other words, they need to answer two vital questions: who governs who? And who governs what? That said, the question of Governance indeed takes centre stage.","url":"https://doi.org/10.70593/978-93-7185-753-6_2","authors":["Swarup Panda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-03T16:13:31Z","doi":"10.70593/978-93-7185-753-6_2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaiic64266.2025.10920674","name":"Artificial Intelligence in Cancer Detection: A Neural Network Approach to Differentiating Malignant and Benign Cells","source":"crossref","abstract":"This paper explores the application of artificial intelligence in the diagnosis of cancer, specifically in making a distinction between malignant and benign cells based on neural network models. Traditional diagnostic methods include biopsies and imaging, which are generally invasive, time-consuming, and expensive. A dataset from the University of Wisconsin was applied to train and test two machine learning models: a custom neural network and a Multi-Layer Perceptron (MLP) classifier implemented in scikit-learn. The Sigmoid-Relu-Relu-Sigmoid custom neural network attained an accuracy of 92.11% with an F1 score of 0.91 and an AUC of 0.94, thereby showing a good tradeoff between accuracy and generalization. By contrast, the MLP classifier, trained on a subset of top predictive features, achieved a comparable accuracy of 92.0% with an F1 score of 0.88 and an AUC of 0.90, providing a computationally friendly alternative. Analysis revealed that features representing extreme tumor characteristics, such as radius_worst and texture_worst, contributed significantly to model performance, underscoring the importance of capturing aggressive tumor properties in cancer diagnosis.","url":"https://doi.org/10.1109/icaiic64266.2025.10920674","authors":["Anikait Sota"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-19T18:08:03Z","doi":"10.1109/icaiic64266.2025.10920674","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1007/978-3-031-98406-8_6","name":"Prohibited Artificial Intelligence Practices Revisited","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-98406-8_6","authors":["Rostam J. Neuwirth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-27T11:15:31Z","doi":"10.1007/978-3-031-98406-8_6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/icaie64856.2025.11158385","name":"Enhancing Design Thinking through the Systematic Integration of Artificial Intelligence (AI) in Architectural Education","source":"crossref","abstract":"This paper examines the implementation of Artificial Intelligence (AI) within the Human-Centred Design (HCD) methodology applied to architectural design, specifically focusing on its application in the educational environment of the Master in Architectural Design Programme at Xi'an Jiaotong-Liverpool University. Over the past six years, AI has been explored and tested in academic settings with innovative and highly compelling results across various applications. However, its role in the early phases of inspiration and creativity remains largely underexplored by experts, both in the field of architectural design and in the realms of education and pedagogy. The integration of AI with the HCD methodology in architecture, and even more so in academic contexts, is the outcome of an effort to provide an operational framework for early explorations that were initially directed towards specific objectives but have yet to follow a more systematic approach. The findings are highly compelling, and this study, in addition to offering a broader perspective on this promising interaction between HCD and AI, explores deeper into AI's role in the fundamental creative phase, where ideas are born. In this process, which may be defined as conversational between the designer and AI, the latter assumes the role of a Design Partner. The integration of AI has significantly enhanced creativity, efficiency, and user-focused design outcomes, paving the way for more inclusive and sustainable solutions. However, challenges persist, including ethical considerations and the need to balance AI's analytical capabilities with the more intuitive aspects of the design process. Reflecting on AI's evolution from an experimental tool to an integrated component of HCD, this study serves as a starting point for further research aimed at enhancing AI's predictive capabilities and its role in preparing students to tackle the complex architectural challenges of the future.","url":"https://doi.org/10.1109/icaie64856.2025.11158385","authors":["Juan Carlos Dall’ Asta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T17:24:05Z","doi":"10.1109/icaie64856.2025.11158385","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.32388/f320kw","name":"Review of: \"Quo Vadis, Artificial Intelligence? A Neuro-Symbolic Approach to Artificial Intuition\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/f320kw","authors":["Revathy Venkataramanan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-08T21:14:04Z","doi":"10.32388/f320kw","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1088/978-0-7503-6119-4ch19","name":"Artificial intelligence in clinical trials","source":"crossref","abstract":"This chapter discusses the importance of clinical trials in healthcare, explains key trial methods, and explores how artificial intelligence (AI) is changing the way trials are designed and run. From improving participant selection to using digital twin technology for personalized trial plans, AI is making trials more accurate and responsive. The chapter also covers important ethical and regulatory issues to consider when applying AI in clinical research. With these advances, AI has the potential to improve the speed, quality, and impact of clinical trials, leading to faster and more reliable medical discoveries.","url":"https://doi.org/10.1088/978-0-7503-6119-4ch19","authors":["Sang Ho Lee","Huaizhi Geng","Ying Xiao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-29T13:54:13Z","doi":"10.1088/978-0-7503-6119-4ch19","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.70301/sbs.mono.2025.1.6","name":"AI Artificial Intelligence, Sustainability and Strategic Leadership","source":"crossref","abstract":"Artificial Intelligence (AI) has grown drastically in recent years. Organization leadership teams are focusing on analyzing the data through Artificial Intelligence (AI) and deriving it to the constructive decisions. This chapter shall focus on the leadership strategies which are used to develop the business operations by simplifying the model of operations with the support of AI, minimizing the timelines, operation cost and enhancing the speed and accuracy of the results, also aligning the sustainable development goals, reducing carbon emission and footprint, which can be done by adapting the approaches like use of solar and wind (renewable) energy, water and waste management, sustainable agricultural developments, use of preserved biodiversity, respectively depending upon suitability in contributing to the different industries and sectors. This chapter will also focus on the use of generative AI, the positive and challenging impact of the same, also how it can be environmentally friendly, by using renewable energy and moderating emissions. Sustainable Business Practices is important but along with this, ethicality and transparency of data usage, code of conduct, proper documentations, managing and analyzing risk along with the responsible behavior is also significant. AI experts, policy makers, Government guidelines and business leaders need to align, plan and design the strategy which is supporting the concept of AI, sustainable development of the company and leadership teams leading the company’s defined social and economic goals. The chapter will also shed light on scope and suggestions for future positive outcomes.","url":"https://doi.org/10.70301/sbs.mono.2025.1.6","authors":["Neha Ahuja"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-10T21:21:58Z","doi":"10.70301/sbs.mono.2025.1.6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.70593/978-93-49910-91-1_2","name":"Automating wealth management and financial planning with artificial intelligence-powered robo-advisors and decision support tools","source":"crossref","abstract":"Over the years, the wealth management and financial planning industry has seen a tremendous change, driven largely by changes in demographics and technology, as well by an evolving marketplace. Whether we are talking about baby boomers, their parents, or their children, who are now trying to make financially sound decisions, we are seeing the desire for more personalized advice and financial strategies. And the rise on the internet and mobile devices, and more generally digitalization, has been a driving force behind these changes. However, many individuals, and especially millennials and Gen Zs, are uncomfortable seeking out this advice or cannot afford the high fees associated with traditional wealth advisors and professional consultants. The result has been a growing interest in and transparency around “robo-advisors,” a type of platform that provides automated intuitive financial services with little or no human intervention involved.","url":"https://doi.org/10.70593/978-93-49910-91-1_2","authors":["Ramesh Inala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T10:26:59Z","doi":"10.70593/978-93-49910-91-1_2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.1109/prai67447.2025.11412511","name":"Laser Holographic Image Segmentation and Recombination Processing Method Integrating Artificial Intelligence Technology","source":"crossref","abstract":"This paper proposes a laser holographic image processing method integrating artificial intelligence technology, focusing on improving image quality through an optimized segmentation-recombination framework. The core innovation lies in the introduction of the Adaptive Genetic Algorithm (AGA), which dynamically adjusts crossover and mutation probabilities during image segmentation. Furthermore, the Otsu's method (maximum between-class variance method) is employed to determine the optimal threshold for holographic image segmentation. Finally, by combining the scale difference value with the spatial neighborhood edge energy fusion method, the image pixel sequence is extracted, and the positional difference between the processed image and the original image is corrected. The contour points with the maximum gray value are obtained, and the recombination of laser holographic images is realized through pixel information fusion. Experimental results show that compared with traditional methods and deep learning-based techniques, this method achieves better performance in segmentation accuracy (peak signal-to-noise ratio (PSNR) of 65.4 dB) and recombination efficiency (average registration error rate$<0.15 \\%)$. Computational complexity analysis indicates the core steps have a time complexity of$\\mathrm{O}(\\mathrm{N})$, and GPU acceleration enables real-time processing (33 frames/s). Dataset validation (500 images covering medical, industrial, and natural scenes) confirms generalizability. These advancements verify the effectiveness of the proposed method in laser holographic imaging applications.","url":"https://doi.org/10.1109/prai67447.2025.11412511","authors":["Zhou Hong","Yang Chunqing"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T20:41:42Z","doi":"10.1109/prai67447.2025.11412511","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:48.792Z"},{"id":"doi:10.3881/j.issn.1000-503x.17220","name":"Expert Consensus on Ethical Governance for Clinical Applications of Generative Medical Artificial Intelligence(2025 Edition).","source":"pubmed","abstract":"The research and innovative applications of generative medical artificial intelligence(GMAI)are rapidly advancing in the healthcare field.Significant breakthroughs of GMAI have been achieved in areas such as generating diagnostic suggestions,optimizing treatment plans,and assisting doctor-patient communication,profoundly reshaping the paradigm of clinical diagnosis and treatment.However,the open-ended nature of GMAI's generation raises novel ethical challenges,including algorithmic bias,ambiguous accountability,data privacy breaches,and insufficient cultural adaptability.Current ethical governance lags behind technological implementation,necessitating the establishment of a standardized governance framework.Our research team assembled a multidisciplinary panel of experts spanning medical ethics,clinical medicine,medical artificial intelligence,hospital management,public health,and law.According to the governance logic of \"prevention-control-remediation\" and integrating international norms with domestic policies,this consensus was developed through two rounds of expert consultation to unify opinions and perspectives.It aims to provide a reference for the specific practice of clinical ethical review in the research and clinical application of GMAI and to establish an authoritative guidance framework for the clinical ethical governance of GMAI,tailored to China's cultural context and national requirements.","url":"https://doi.org/10.3881/j.issn.1000-503x.17220","authors":["Gong MC","Ma YH","Pan H","Bai H","Dai H","Chen W","Liu H","Gong K","Zeng ZR","Wu H","Zhou Y","Ouyang ZH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3881/j.issn.1000-503x.17220","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1002/rcs.70229","name":"From Clinical Technique to Interprofessional Implementation and Intelligent Integration: A Bibliometric Analysis of the Interdisciplinary Expansion of Robot-Assisted Spine Surgery.","source":"europepmc","abstract":"Background Robot-assisted spine surgery (RASS) has expanded beyond technical accuracy towards interprofessional, perioperative, and AI-driven implementation, yet this interdisciplinary evolution has not been systematically characterised. Methods We analysed 327 Web of Science Core Collection publications (2006-2026) at the intersection of RASS and perioperative, collaborative, or educational research. Analyses included co-authorship networks, keyword co-occurrence, co-citation mapping, bibliographic coupling, citation-burst detection, Callon strategic mapping, and dual-map overlays, performed in VOSviewer, CiteSpace, and Bibliometrix. Ethical approval was not required as only de-identified bibliographic records were analysed. Results Publications grew at a compound annual rate of 17.28% over the complete years 2006-2025 (log-linear model of annual output, R 2 = 0.8762), peaking at 62 publications in 2025; the partial year 2026 (indexed only to the 4 February 2026 search date) was excluded from all growth-rate and trendline modelling. The United States (n = 137) and China (n = 57) dominated output. Dual-map analysis indicated citation flows into the (broad) Health/Nursing/Medicine domain. Keyword bursts identified artificial intelligence (strength = 3.13), deep learning (2.01), and augmented reality (1.75) as the most recent (2023-2025) research frontiers. Rehabilitation robotics remained bibliographically isolated from the surgical core. Conclusions The published literature on RASS shows a measurable thematic shift from procedural-accuracy validation towards perioperative-implementation and intelligent-integration topics; whether this bibliometric shift reflects a corresponding change in clinical practice cannot be determined from publication data. These patterns nonetheless motivate investment in cross-disciplinary consortia, AI-competency frameworks, and attention to implementation equity.","url":"https://doi.org/10.1002/rcs.70229","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/rcs.70229","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s12916-026-05135-w","name":"Artificial intelligence-enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial.","source":"europepmc","abstract":"Background Secondary prevention of coronary heart disease (CHD) remains suboptimal due to fragmented care and therapeutic inertia. While digital health interventions offer a promising strategy, few existing tools provide comprehensive, closed-loop management of multidimensional risk factors without increasing clinician workload. This study aimed to evaluate the efficacy of an artificial intelligence (AI)-enhanced management system (AIM-CHD) in improving multidimensional risk factor control among patients with CHD. Methods This single-center, open-label, parallel-group, randomized clinical trial was conducted from November 2024 to June 2025 in China. A total of 1100 adults with confirmed CHD were randomized 1:1 to receive either the AIM-CHD intervention (n = 549) or usual care (n = 551) for 3 months. The AIM-CHD system featured automated multi-source data capture, guideline-directed risk stratification, and closed-loop feedback via smartphone. The primary outcome was the low-density lipoprotein cholesterol (LDL-C) level at 3 months. Secondary outcomes included goal attainment rates for LDL-C ( Results Of 1100 randomized participants (mean age 61 years; 75.3% male), 943 (85.7%) completed the 3-month follow-up. The intervention group achieved a significantly lower mean LDL-C level compared with the control group (60.4 ± 23.4 vs. 63.7 ± 26.0 mg/dL), with an adjusted mean difference of - 3.2 mg/dL (95% CI, - 6.2 to - 0.3; p = 0.03). Furthermore, participants in the intervention group were more likely to achieve the LDL-C target (71% vs. 64%; RR, 1.10; 95% CI, 1.01-1.20; p = 0.03) and the BP target (45% vs. 35%; RR, 1.27; 95% CI, 1.09-1.48; p = 0.002). No significant differences were observed for HbA1c, BMI, or medication adherence. Conclusions The AIM-CHD system significantly improved short-term lipid and BP control compared with usual care. These findings support interoperable, low-burden digital management systems as a scalable strategy in routine secondary prevention. Trial registration ClinicalTrials.gov Identifier: NCT06686056. Registered on 11 November 2024.","url":"https://doi.org/10.1186/s12916-026-05135-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12916-026-05135-w","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/healthcare14162604","name":"Applications of Artificial Intelligence in the Health Sector: A PRISMA-Based Systematic Review.","source":"europepmc","abstract":"Background: The health sector is getting transformed with the usage of AI, be it diagnosis, treatment planning, disease prediction, and or health system management. Research in this field has picked up in the last few years, which was made possible with the emergence of machine learning, natural language processing and the increasing number of e-health records. Objectives: The study aims to investigate the current trends in the implementation of artificial intelligence (AI) applications in medical settings by investigating the global scientific output/landscape on this theme, such as the annual publication trends, country-wise contributions, and publishing patterns. Methods: The current study is based on systematic review by combining bibliometric analysis and cluster analysis using VOSviewer version 1.6.20, R software version 4.5.0, and Biblioshiny (Bibliometrix package in R) along with preferred reporting items for systematic reviews and meta analyses (PRISMA), 2020 which provides transparency and rigorous visualization to examine the articles published in English on the use of AI in healthcare, after the onset of COVID-19 till date i.e., from 2020 to 2026 on the Scopus database. Results: Using the relevant search string, 5940 documents were identified between 2020 and 2026, 1434 were included for analysis after screening and relevant filters. The publications have increased remarkably after 2020 on this theme and more than half of the publications have their roots in the discipline of Medicine. The USA, China, and the United Kingdom have contributed the most to the volume of research. Natural language processing and diagnosis are the emerging themes. The Journal of Medical Internet Research, BMC Medical Informatics and Decision Making, Computers in Biology and Medicine, IEEE Journal of Biomedical and Health Informatics, Frontiers in Public Health, and Digital Health are some of the most influential sources in the field. Li J and Liu X are among the authors with remarkable local impact. Conclusions: The work aims to assist investigators, health care professionals, and policymakers to learn about modern trends and focus on critical areas of future research and collaboration in AI-enhanced health care. The limitation of the study is that it considered only the Scopus database but it has opened up opportunities for researchers for analysis using other databases such as Dimensions, Lens, and PubMed. Also, this review is considering the publication record since the onset of COVID-19 but a comparative analysis of pre and post-pandemic studies can also be conducted to get a holistic view of drastic collaboration of research in this field. Discussions: The findings suggest that the role of artificial intelligence in health care has paramount over recent years, with other supporting technologies but a technologically hesitant population as well as low acceptance of AI due to ethical issues, cannot be ignored for ensuring efficiency in the health sector.","url":"https://doi.org/10.3390/healthcare14162604","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14162604","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s11701-026-03757-z","name":"Artificial intelligence and surgical data science in robot-assisted radical prostatectomy: global research trends, knowledge structure, and emerging frontiers.","source":"europepmc","abstract":"To systematically characterize the global research trends, knowledge structure, thematic hotspots, and frontier evolution of artificial intelligence (AI) and surgical data science in robot-assisted radical prostatectomy (RARP). The Web of Science Core Collection (WoSCC) was used as the data source. A comprehensive search was performed for studies related to robot-assisted radical prostatectomy, artificial intelligence, machine learning, deep learning, computer vision, surgical video analysis, surgical skill assessment, outcome prediction, and decision support. The search was conducted up to July 1, 2026. English-language Articles and Reviews were included. Conference abstracts, proceedings papers, editorials, letters, corrections, studies unrelated to RARP, publications involving robotic surgery without AI or intelligent algorithmic components, and records with incomplete bibliographic information were excluded. RStudio was used for data cleaning and annual publication trend analysis, while CiteSpace and VOSviewer were applied to construct and visualize collaboration networks, co-citation networks, keyword co-occurrence patterns, clustering maps, timeline views, time-zone maps, and citation burst analyses. A total of 311 original records were retrieved, of which 217 publications were ultimately included, comprising 195 Articles and 22 Reviews. Annual publication trends indicated that the field has undergone three developmental stages: an early low-output exploratory phase, an accelerated growth phase after 2018, and a rapid expansion phase after 2023. The number of publications peaked at 36 in 2025, while 28 publications were recorded in 2026 because the search year was still ongoing. At the country/region level, the United States, Italy, China, Japan, and England ranked among the leading contributors. At the institutional level, the University of Southern California, University of London, King's College London, Guy's & St Thomas' NHS Foundation Trust, and the Netherlands Cancer Institute made substantial contributions. Author and journal analyses identified Hung AJ as the most productive author, while the Journal of Robotic Surgery, BJU International, and the Journal of Endourology were the major publication outlets. Keyword co-occurrence and burst analyses suggested that research themes have gradually shifted from early topics such as \"experience,\" \"learning curve,\" \"robotic prostatectomy,\" and \"surgical skills\" toward \"validation,\" \"performance,\" \"artificial intelligence,\" \"risk,\" \"quality of life,\" \"recovery,\" and \"deep learning.\" AI and surgical data science are reshaping RARP research, driving the field from robot-assisted surgical performance toward data-driven surgical intelligence. Future studies should prioritize the development of multicenter, standardized surgical video and robotic platform datasets; strengthen model interpretability, external validation, and prospective clinical evaluation; and facilitate the translation of AI-assisted preoperative planning, intraoperative recognition, surgical skill assessment, and patient outcome prediction into real-world clinical workflows.","url":"https://doi.org/10.1007/s11701-026-03757-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03757-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.pdpdt.2026.105628","name":"Emerging Research Trends in the Application of Artificial Intelligence, LLMs, Machine Learning, and Deep Learning in Ophthalmic Diseases: A Bibliometric and Visual Analysis.","source":"europepmc","abstract":"Background To examine global research activity in the application of artificial intelligence, large language models, machine learning, and deep learning in ophthalmic diseases from 2015 to 2025. Methods The study retrieved relevant publications from the Web of Science Core Collection. Bibliometric indicators were analysed using VOSviewer, CiteSpace, and Bibliometrix. Annual publication numbers, citation distribution, countries, institutions, journals, co-cited journals, author productivity, co-authorship networks, keyword frequency, burst terms, and thematic clusters were assessed. Results A total of 1,997 articles were included in this analysis based on the inclusion criteria. Annual publication numbers increased after 2018 and remained high from 2020 to 2025. The United States, China, and the United Kingdom showed the highest research output.The University of California System, the University of London and University College London were leading institutions in terms of publication output. The most common keywords included deep learning, optical coherence tomography, diabetic retinopathy, and macular degeneration. Citation burst analysis identified influential articles on diabetic retinopathy detection, optical coherence tomography biomarker analysis, and residual network models. Visual cluster analysis identified themes related to automated retinal imaging, diabetic retinopathy screening, fluid quantification models, multimodal image analysis, and recent studies using large language models. Conclusion Artificial intelligence and deep learning research in ophthalmology has increased significantly, with imaging-based studies contributing the highest output. Publications on LLM-based applications have also increased during recent years. This bibliometric analysis summarizes the distribution of publications, research themes, and methodological trends in ophthalmic artificial intelligence and provides a basis for future clinical and research applications.","url":"https://doi.org/10.1016/j.pdpdt.2026.105628","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.pdpdt.2026.105628","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s11701-026-03799-3","name":"Comparative performance of artificial intelligence chatbots in patient education for robot-assisted radical prostatectomy: quality, transparency and readability.","source":"europepmc","abstract":"Patients undergoing robot-assisted radical prostatectomy require complex counseling regarding treatment selection, cancer control, urinary continence, sexual function, postoperative therapy, and long-term follow-up. The suitability of artificial intelligence chatbots for providing such information remains uncertain. Twenty clinician-developed patient-education questions on robot-assisted radical prostatectomy were submitted to ChatGPT-o3, DeepSeek-V4, Claude Sonnet 5, and Gemini 3.5 Pro. Responses were evaluated using DISCERN, the Ensuring Quality Information for Patients (EQIP) tool, the Global Quality Scale (GQS), and the Journal of the American Medical Association (JAMA) benchmark criteria. Readability was assessed using six established indices. Between-model differences were analyzed using nonparametric tests with corrected post hoc comparisons. Information-quality scores differed significantly across models for DISCERN, EQIP, and GQS. DeepSeek-V4 achieved the highest DISCERN (61.90 ± 2.92), EQIP (91.75 ± 8.32), GQS (4.50 ± 0.51), and JAMA (3.00 ± 0.00) scores. Gemini 3.5 Pro showed relatively lower information-quality scores among the evaluated models. ChatGPT-o3 and DeepSeek-V4 demonstrated relatively favorable readability on different indices; however, all models exceeded the recommended sixth-grade reading level, and all Flesch Reading Ease scores remained below the easy-to-read threshold. AI chatbots varied substantially in the quality, transparency, and readability of information on robot-assisted radical prostatectomy. Although some models provided comparatively stronger patient education, none consistently produced sufficiently accessible information. These tools may supplement, but should not replace, individualized counseling by urologists and multidisciplinary prostate cancer teams.","url":"https://doi.org/10.1007/s11701-026-03799-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03799-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/postmj/qgag109","name":"Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions.","source":"europepmc","abstract":"Abstract Background Artificial intelligence is increasingly used in health care and medical education. This study aimed to identify factors associated with medical students’ attitudes towards artificial intelligence, with particular attention to digital literacy, emotional intelligence and artificial intelligence-related perceptions. Methods This cross-sectional study was conducted between November 2025 and January 2026 among 358 medical students. Data were collected using an online questionnaire including sociodemographic items, the Trait Emotional Intelligence Scale–Short Form, the Digital Literacy Scale and the General Attitude Towards Artificial Intelligence Scale. Group comparisons, correlation analyses, hierarchical linear regression and exploratory indirect-effect analysis were performed. Results Most students had previously used artificial intelligence (88.5%), while 59.5% reported ethical or legal concerns and 41.1% believed that artificial intelligence could reduce clinical reasoning. Digital literacy was positively correlated with attitudes towards artificial intelligence (r = 0.319, p &amp;lt; 0.001). In the final hierarchical regression model, digital literacy was the strongest independent factor associated with attitudes towards artificial intelligence (B = 0.242, β = 0.320, p&amp;lt; 0.001). Clinical educational stage, ethical or legal concerns, and perceptions regarding the impact of artificial intelligence on clinical reasoning were also independently associated with attitude scores. Previous artificial intelligence use and emotional intelligence were not independently associated with attitudes after adjustment. Conclusions Medical students’ attitudes towards artificial intelligence were associated more strongly with digital literacy and artificial intelligence-related perceptions than with previous use alone. Undergraduate medical education should integrate digital literacy, ethical awareness and reflective discussion on clinical reasoning into artificial intelligence-related teaching.","url":"https://doi.org/10.1093/postmj/qgag109","authors":["Nur Demirbas","Hatice Kucukceran","Ozlem Arik"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/postmj/qgag109","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s12909-026-10256-0","name":"Integrating generative AI into physiotherapy education: students' use, perceptions, and generative AI literacy following curricular adaptations: a repeated cross-sectional programme evaluation.","source":"europepmc","abstract":"Background Structured generative artificial intelligence (GAI) training has been proposed as an effective approach to strengthen healthcare students' confidence in navigating AI-related challenges and ethical considerations. However, few studies have evaluated the impact of implemented GAI-related curricular adaptations and learning activities, particularly within physiotherapy education. This study aimed to evaluate: 1) physiotherapy students' use and perceptions of GAI in their studies during implementation of GAI-related curricular adaptations; and 2) GAI literacy in semester 1 before participation and in semester 3 after completion of structured GAI learning activities. Methods A two-part repeated cross-sectional programme evaluation with non-equivalent, unlinked samples was conducted among physiotherapy students at Linköping University, Sweden. Students' use and perceptions of GAI were assessed after implementation of GAI-related curricular adaptations in 2024 and again in a separate survey wave in 2025. GAI literacy was evaluated using the 10-item Generative Artificial Intelligence Literacy Scale (GAILS-10) before and after the GAI learning activities delivered during semesters 1-3. Results Reported GAI use differed significantly between 2024 and 2025 surveys, χ 2 (3) = 62.40, p 2 (4) = 24.94, p Conclusion Physiotherapy students reported more frequent and diverse GAI use and more positive learning perceptions in the 2025 survey than in the 2024 survey. Semester 3 students, assessed after completing the GAI learning activities, reported higher GAI literacy than semester 1 students assessed before participation in these activities. These findings suggest a need for curricular adaptations and educational support to promote appropriate GAI use, reduce misuse, and strengthen student GAI literacy.","url":"https://doi.org/10.1186/s12909-026-10256-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-10256-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1136/ip-2025-046038","name":"Mortality prediction of road traffic crash with artificial intelligence: a systematic review.","source":"europepmc","abstract":"Background Road traffic crashes cause substantial global mortality and disability. Conventional injury severity scores may not fully capture the complex interactions among demographic, clinical, crash and environmental factors. Artificial intelligence and machine learning may improve mortality prediction by modelling non-linear patterns in traffic crash data. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance. PubMed/MEDLINE, Web of Science, Scopus and IEEE Xplore were searched on 7 August 2025 for English-language peer-reviewed studies published from 2014 to 2025 that applied artificial intelligence or machine learning to predict mortality after road traffic crashes. Two reviewers screened records and extracted data on study characteristics, data sources, algorithms, predictors, validation, imbalance handling and performance. Methodological quality was assessed using the Qiao quality assessment tool. Because of substantial heterogeneity, findings were synthesised narratively. Results 18 studies met the inclusion criteria. Most were retrospective studies using structured tabular data. Common objectives were binary mortality prediction, multiclass injury severity prediction including death and death risk assessment. National or regional databases were the most frequent data sources, followed by hospital records and police or insurance datasets. Regression-based models and decision trees remained common, while ensemble methods including random forest and gradient boosting increased in recent years. Frequently reported predictors included age, Injury Severity Scores, body region injured, crash mechanism, temporal factors and geographical characteristics. Only two studies reported external validation. Conclusions Artificial intelligence and machine learning show promise for traffic crash mortality prediction, but clinical translation remains limited by insufficient external validation, inconsistent handling of class imbalance, incomplete reporting of tuning and missing data strategies and limited use of explainability methods. Future work should prioritise prospective, externally validated, interpretable models developed using standardised reporting frameworks.","url":"https://doi.org/10.1136/ip-2025-046038","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/ip-2025-046038","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.20344/amp.25177","name":"Postgraduate Education in Artificial Intelligence: A Proposal from the Artificial Intelligence Commission of the Portuguese Medical Association.","source":"europepmc","abstract":"Artificial intelligence (AI) is entering clinical practice through decision-support systems, predictive tools, generative models, and clinical documentation solutions. Since February 2025, the European AI Act has required providers and deployers of AI systems to ensure that staff and other people operating or using such systems on their behalf have an adequate level of AI literacy. This creates a new educational requirement: physicians need to acquire minimum competencies to use, appraise, supervise, and reject AI outputs when appropriate. This state-of-the-art review examined AI competencies relevant to postgraduate medical training, focusing on literature published since 2022. The reviewed literature suggests thematic convergence around six core domains: foundational AI literacy, critical appraisal of AI tools, safe clinical application, ethics/law/governance, patient communication, and health data literacy. Other domains, such as institutional implementation, multidisciplinary collaboration, and AI leadership, appear as intermediate or advanced competencies. We propose a three-tier curricular organization: baseline competencies for all physicians; proficient competencies for physicians involved in local appraisal and implementation of AI systems; and advanced competencies for clinician-scientists, institutional leaders, and professionals with formal responsibilities in AI governance. In the Portuguese context, this structure may support the development of transversal training for residents and specialists, aligned with technological change, European regulatory requirements, and the need to preserve human clinical responsibility. This proposal should be understood as a conceptual basis for multidisciplinary consensus validation.","url":"https://doi.org/10.20344/amp.25177","authors":["João Frutuoso","Ana Rita Maria","Helena Donato","António Vaz Carneiro"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20344/amp.25177","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s11701-026-03697-8","name":"From robot assistance to surgical intelligence: global research trends and emerging frontiers of artificial intelligence-enhanced robotic surgery in urology.","source":"europepmc","abstract":"To systematically characterize global publication trends, collaboration patterns, intellectual foundations, research hotspots, and emerging frontiers in artificial intelligence (AI)-enhanced robotic and robot-assisted surgery in urology. Publications were retrieved from the Web of Science Core Collection (WoSCC) using a topic search strategy that included three keyword groups: robot-assisted surgery, urologic diseases or procedures, and AI-related technologies. English-language articles and reviews were included. Meeting abstracts, conference proceedings, editorials, letters, non-English publications, studies unrelated to urologic robotic surgery, and studies without substantive AI or intelligent algorithmic content were excluded. RStudio was used for descriptive statistics and annual publication trend visualization. VOSviewer was used for auxiliary bibliometric network construction and visualization. CiteSpace was used to construct collaboration networks, co-citation networks, keyword co-occurrence maps, cluster maps, timeline and time-zone maps, and citation burst maps. A total of 401 records were initially retrieved, and 253 publications were finally included after screening by document type, language, and topical relevance. These comprised 199 articles (78.66%) and 54 reviews (21.34%). Publications in this field began in 2004 and increased rapidly after 2018, reaching a peak of 53 publications in 2025. Because 2026 was an incomplete retrieval year, only 23 publications were recorded. Italy, the United States, the Netherlands, China, and Japan were the leading contributing countries. The University of Turin, Azienda Ospedaliero-Universitaria San Luigi Gonzaga, IRCCS Fondazione del Piemonte per l'Oncologia, the Netherlands Cancer Institute, and Leiden University Medical Center were the most productive institutions. Keyword clustering showed that the major research hotspots included renal cell carcinoma, augmented reality, image-guided surgery, indocyanine green, machine learning, registration, deep learning, and bladder cancer. AI-enhanced robotic surgery in urology has evolved from early research on registration, navigation, and image-guided surgery toward a surgical intelligence stage characterized by augmented reality, three-dimensional reconstruction, machine learning-based prediction, deep learning-based segmentation, surgical video understanding, and skill assessment. Future studies should prioritize multicenter standardized datasets, sharing of intraoperative video and robotic platform data, model interpretability, prospective validation, and integration into real-world clinical workflows.","url":"https://doi.org/10.1007/s11701-026-03697-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03697-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.psychres.2026.117335","name":"A bibliometric analysis of artificial intelligence applications to schizophrenia research (2005 - 2025).","source":"europepmc","abstract":"Background Artificial intelligence (AI) has been increasingly applied in schizophrenia research, yet a bibliometric overview of its global development and evolutionary trends remains lacking. Methods Based on the Web of Science Core Collection (WoSCC), a bibliometric analysis was conducted on AI‑related schizophrenia studies published from 2005 to 2025. A total of 1,839 publications were included, and trends, countries, institutions, authors, journals, and research hotspots were analysed. Results Annual publications grew rapidly from 2 in 2005 to 374 in 2025, consistent with the expansion of AI research. The U.S., China, and England were the most productive and influential countries. The U.S. dominated global collaboration, while Chinese institutions showed strong domestic links but limited international partnerships. Leading institutions included the University of London, King's College London, and Harvard University. Nikolaos Koutsouleris and Vince D. Calhoun were the top authors. Key journals included Frontiers in Psychiatry, Schizophrenia Research, and Schizophrenia Bulletin, with NeuroImage as the top co-cited journal. Keyword analysis showed early focus on machine learning, risk assessment, and brain biomarkers based on functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). After 2016, deep learning became dominant in diagnosis. Recently, natural language processing has emerged for risk prediction, indicating a shift toward multimodal AI applications. Conclusion AI applications in schizophrenia have expanded exponentially with a clear evolution from machine learning to deep learning. The global collaboration pattern is imbalanced, and future research will advance toward multi‑modal, predictive, and precise intelligence.","url":"https://doi.org/10.1016/j.psychres.2026.117335","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.psychres.2026.117335","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ajic.2026.08.012","name":"Artificial Intelligence utilization, perceptions, and readiness within the infection prevention workforce: Findings from the 2025 APIC MegaSurvey.","source":"europepmc","abstract":"Background The advent of artificial intelligence (AI) presents an opportunity to enhance infection prevention practices. However, its use among infection preventionists (IP) remains unknown. Methods We analyzed responses from IPs participating in the 2025 APIC MegaSurvey to assess (1) current utilization of AI (2) familiarity and perceived usefulness of AI, (3) organizational readiness, (4) barriers and (5) training and support needs. Results A total of 4,269 IPs participated in the survey, of whom 3,220 participants responded to the AI section. Among respondents, 12.6% (n=404) reported using AI. Of those that use AI, 65.7% (n=266) used generative AI tools such as ChatGPT to draft policies, educational content, or to synthesize research followed by AI-powered chatbots to support triage or deliver staff education (35.6%, n=144), risk stratification (32.8%, n=133) and predictive modeling (21.7%, n=88). Most respondents perceived AI as useful (80%, n=2657) and were interested in learning more (n=2413, 74.9%). Lack of organizational readiness, limited access, concerns about accuracy, data privacy and job displacement were among barriers identified. Discussion AI use among IPs remains low, despite interest and perceived value. There remain concerns about accuracy, data privacy, and job displacement. Conclusions Findings highlight a gap between interest in AI and its integration into IP practice. Efforts to improve training, access, and organizational readiness will be essential to support safe and effective adoption.","url":"https://doi.org/10.1016/j.ajic.2026.08.012","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ajic.2026.08.012","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.31083/rcm50419","name":"Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.","source":"europepmc","abstract":"Sleep-disordered breathing (SDB), which includes both obstructive and central sleep apnea, is highly prevalent among patients with cardiovascular disease (CVD). Moreover, SDB contributes significantly to the development and progression of hypertension, coronary artery disease, arrhythmias, heart failure, and various cardiovascular and cerebrovascular events. However, despite strong mechanistic links involving intermittent hypoxemia, sympathetic activation, intrathoracic pressure fluctuations, and systemic inflammation, clinical trials of SDB treatment have yielded in neutral or even adverse cardiovascular outcomes. These results underscore the need for refined phenotyping, risk stratification, and personalized management. Artificial intelligence (AI) has emerged as a promising tool to address these challenges. In this review, we evaluate the mechanistic pathways through which SDB affects cardiovascular health and critically examine AI-based methods to enhance screening, outcome prediction, and treatment optimization. Applications include automated detection using clinical and biosignal data, cardiovascular risk prediction through machine-learning models based on sleep parameters, and AI-guided therapy personalization. Furthermore, we emphasize translational relevance by comparing model performance, identifying high-risk phenotypes, and exploring the potential for integration into clinical workflows. AI-enabled tools may help bridge the gap between pathophysiological understanding and improved outcomes by facilitating earlier diagnosis, tailored interventions, and proactive monitoring. Future studies should focus on prospective validation, regulatory pathways, and equitable deployment across populations.","url":"https://doi.org/10.31083/rcm50419","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31083/rcm50419","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fmed.2026.1821176","name":"Artificial intelligence for medical imaging education: bibliometric and visual analysis.","source":"europepmc","abstract":"Background While bibliometric analyses have examined the adoption of Artificial Intelligence (AI) in general medical education contexts, the specific knowledge structures, collaborative networks, and evolutionary pathways of AI for medical imaging education remain poorly characterized quantitatively, particularly regarding the transition from technical feasibility to systematic pedagogical integration and cross-institutional partnerships. Methods A structured literature search of English-language original articles and reviews published between 2006 and 2025 was performed in the Web of Science Core Collection (WoS) and Scopus, with the former serving as the primary database and the latter for external validation. Bibliometric analyses covering citation trends, collaboration networks, keyword bursts, and journal distributions were conducted using CiteSpace, VOSviewer, and Bibliometrix. Results This search retrieved 577 articles from WoS, revealing exponential growth since 2019, with 226 articles published in 2025. These publications were contributed by 3,739 authors from 1,479 institutions across 80 countries/regions. Cross-database validation supported the consistency of these findings (Spearman r = 0.97, p Conclusion This study characterizes a dual-polar production structure with the United States and China as dominant contributors, and a journal distribution spanning medical education and imaging technology venues. The post-2022 emergence of generative AI keywords documented a new thematic cluster alongside foundational machine learning research, indicating an evolving methodological repertoire. These findings provide a quantitative baseline for evidence synthesis in this interdisciplinary domain, with implications for curriculum development, algorithmic performance benchmarks, and ethical governance, subject to standard bibliometric data coverage and currency constraints.","url":"https://doi.org/10.3389/fmed.2026.1821176","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1821176","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5409/wjcp.119386","name":"Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review.","source":"europepmc","abstract":"Background Neonatal heart rate (HR) is an important parameter in the evaluation of newborn health and viability in the immediate postnatal period. Aim To evaluate the accuracy, reliability, and clinical applicability of emerging non-contact and artificial intelligence (AI)-assisted HR monitoring technologies in neonates compared to conventional electrocardiography (ECG)-based systems. Methods A comprehensive literature search was conducted across PubMed, EMBASE, Google Scholar, and Cochrane databases from January 2013 through June 2025 following PRISMA guidelines. Results The analysis revealed a progressive shift from contact-based ECG and pulse oximetry to camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems. These newer methods demonstrated a strong correlation with ECG readings, rapid signal acquisition, and improved robustness against motion and lighting variability. Conclusion Emerging non-contact, AI-assisted HR monitoring technologies offer accurate, safe, and efficient alternatives for neonatal care, supporting faster clinical decisions and improved outcomes. Future multicenter studies are required to validate accuracy and confirm clinical utility before routine clinical implementation.","url":"https://doi.org/10.5409/wjcp.119386","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5409/wjcp.119386","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/mi17080905","name":"Bayesian-Optimized Collapse-Mode CMUT with Trenched Membrane for High Output Pressure.","source":"europepmc","abstract":"Capacitive micromachined ultrasonic transducers (CMUTs) have been extensively investigated for applications in medical imaging and industrial non-destructive testing. However, their relatively low acoustic pressure output remains a major limitation to broader adoption. This paper proposes a CMUT structure that combines collapse-mode operation with a trenched membrane to enhance output performance. An analytical model based on von Kármán large-deflection plate theory is developed to estimate the optimal radial position of the trench, thereby defining the search space for subsequent Bayesian optimization. Single-parameter sequential Bayesian optimizations are first performed to identify the individual effects and optimal ranges of the trench's radial position, depth, and width. Subsequently, a three-parameter global Bayesian optimization framework is employed for global parameter refinement. The three-parameter joint optimization reveals strong synergistic interactions among the design variables, achieving a higher output pressure of 72.34 kPa compared to 70.02 kPa from sequential approaches. Under identical operating conditions, the optimized trenched membrane CMUT exhibits a 100.15% increase in output acoustic pressure and a 28.77% improvement in the pressure-bandwidth product compared to a uniform membrane. Statistical analysis across multiple independent runs yielded a coefficient of variation (CV) of only 0.052% for the output pressure in the three-parameter global optimization results. This confirms that the proposed framework robustly optimizes CMUT designs for high output pressure, offering a promising technical approach to enhancing device performance.","url":"https://doi.org/10.3390/mi17080905","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/mi17080905","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.7759/cureus.112583","name":"Three Decades of Food and Drug Administration Authorizations of Artificial Intelligence/Machine Learning-Enabled Medical Devices: Persistent Specialty Concentration and the Care-Delivery Gap (1995-2025).","source":"europepmc","abstract":"Background The US Food and Drug Administration (FDA) maintains a public list of artificial intelligence (AI) and machine learning (ML)-enabled medical devices that have received marketing authorization. Prior published analyses examined this list at earlier time points and reported a marked dominance of radiology applications. Methodology We performed a longitudinal descriptive analysis of all 1,430 AI/ML-enabled medical device authorizations recorded by the FDA between September 1995 and December 2025 to characterize the cumulative growth, specialty distribution, and manufacturer concentration of authorized devices. Results The annual authorization volume increased from a mean of 1.8 per year between 1995 and 2014 to 264 per year between 2023 and 2025, with 331 authorizations recorded in 2025 alone. Devices reviewed by the FDA's Radiology panel accounted for 1,094 of 1,430 authorizations (76.5%), and the three most represented panels (Radiology, Cardiovascular, and Neurology) accounted for 90.6% of all authorizations. Several large clinical specialties were represented by very small numbers of authorized devices, including Pathology (n = 9, 0.6%), Microbiology (n = 6, 0.4%), and Obstetrics and Gynecology (n = 4, 0.3%). No authorizations were recorded under a psychiatry or behavioral health review panel. Of 740 unique companies, 502 (67.8%) had a single authorized device, while 13 (1.8%) companies accounted for 247 (17.3%) devices. Conclusions The cumulative regulatory record demonstrates rapid growth that has been concentrated in image-rich diagnostic specialties, with limited representation across many specialties that account for substantial clinical activity in the United States. These findings may inform policy discussions about where regulatory, infrastructure, and dataset investments are most needed to broaden the clinical scope of medical AI.","url":"https://doi.org/10.7759/cureus.112583","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.112583","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.21203/rs.3.rs-10310681/v1","name":"Experiences of Patients With Breast Cancer Using Generative Artificial Intelligence for Treatment Related Decision Preparation: A Qualitative Study","source":"europepmc","abstract":"Abstract Purpose To explore how patients with breast cancer experience, interpret, and use generative artificial intelligence for treatment related decision preparation, and to identify implications for nurse mediated guidance. Methods Face-to-face semi-structured individual interviews were conducted with 20 women with breast cancer at a tertiary hospital in China between August 2025 and March 2026. Participants were recruited using purposive sampling to achieve variation in age, educational background, disease stage, treatment phase and experience of generative artificial intelligence use. Interviews were audio-recorded, transcribed verbatim and analysed using Braun and Clarke’s reflexive thematic analysis. Written informed consent was obtained from all participants. Results Four interrelated themes were developed: (1) turning to generative artificial intelligence to fill information and emotional gaps between clinical encounters; (2) converting complex medical information into decision preparation; (3) practising calibrated trust in AI-generated information; and (4) needing nurse-mediated guardrails for safe and meaningful use of AI in decision support. Together, these themes showed that participants used generative AI as a patient-initiated resource for decision preparation, while continuing to rely on clinicians’ judgement and expecting nurses to support safer, more contextualized and more meaningful AI use. Conclusions Generative artificial intelligence was used by patients with breast cancer as a patient-initiated resource for decision preparation between clinical encounters. Although it helped participants organise information and clarify concerns, it also created new burdens related to credibility, individual applicability and emotional responses. Nurse-mediated support is needed to promote the safe, appropriate and meaningful use of generative artificial intelligence in breast cancer decision preparation.","url":"https://doi.org/10.21203/rs.3.rs-10310681/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10310681/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1038/s41392-026-02815-0","name":"Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.","source":"europepmc","abstract":"The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n = 355) and in-house AMC (n = 206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p < 0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, ±0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.","url":"https://doi.org/10.1038/s41392-026-02815-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41392-026-02815-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ienj.2026.101887","name":"Mapping artificial intelligence applications for clinical and operational decision support in prehospital emergency medical services: A scoping review.","source":"europepmc","abstract":"Background Artificial intelligence is increasingly explored in prehospital emergency medical services to support clinical and organisational decision-making, yet its real-world application remains unclear. Objective To map the use of artificial intelligence in prehospital emergency medical services, focusing on decision-making processes including dispatch, triage, and transport coordination. Methods A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, CINAHL, Scopus, Engineering Source, and INSPEC were searched (March 2025) without time restrictions. Empirical studies addressing the development, validation, or application of artificial intelligence in prehospital settings were included. Data were synthesised using descriptive analysis and iterative thematic grouping. Results Thirty-seven studies were included, mainly published between 2020 and 2024. Most were observational or proof-of-concept, with machine learning as the predominant approach. Five application domains were identified: time-sensitive conditions, complex emergency management, dispatch and transport coordination, predictive analytics, and organisational efficiency. Artificial intelligence showed potential to improve early diagnosis and operational decision-making; however, most systems lacked external validation and real-world implementation. Conclusions Artificial intelligence represents a promising decision-support tool in prehospital emergency care. Nevertheless, evidence remains preliminary, highlighting the need for rigorous validation, integration into clinical workflows, and training to support safe and effective adoption.","url":"https://doi.org/10.1016/j.ienj.2026.101887","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ienj.2026.101887","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/jimaging12080366","name":"Bibliometric Analysis of Whole-Body MRI from 2015 to 2025 Across Clinical Applications, Quantitative Imaging, and Artificial Intelligence.","source":"europepmc","abstract":"Whole-body magnetic resonance imaging (WB-MRI) has become established for selected clinical indications and is increasingly studied across many fields including but not limited to oncologic, musculoskeletal, pediatric, and computational imaging applications. This study characterizes recent trends and the evolution of WB-MRI research using a bibliometric analysis reported using the PRISMA 2020 guidelines and identifies dominant clinical, quantitative, and artificial intelligence (AI) themes shaping the field. Publications were identified from Scopus, Web of Science, and PubMed using WB-MRI and its variant search terms and filtered to articles and reviews from 2015 through to 2025. Bibliometric analyses summarized publication output, contributors, citations, and keywords, and used exploratory keyword rules to classify major clinical and technical themes. Of 1511 included WB-MRI publications, annual output increased from 124 in 2015 to 180 in 2025. The journals with the highest publication counts were European Radiology, PLoS ONE, and the European Journal of Radiology. Parsed author affiliations most frequently represented the United States, Germany, and the United Kingdom. Research centered on oncologic applications, particularly myeloma and prostate cancer, alongside musculoskeletal disease, diffusion-weighted imaging, and AI-based segmentation. AI-related publications increased from 5 in 2015 to 20 in 2025. Broader clinical use of WB-MRI will require standardized acquisition, reproducible quantitative measures, and multicenter validation.","url":"https://doi.org/10.3390/jimaging12080366","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jimaging12080366","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.47102/annals-acadmedsg.2026346","name":"Artificial intelligence tools in dermatology education: A scoping review on their application, efficacy, and limitations.","source":"europepmc","abstract":"Introduction Artificial Intelligence (AI) is increasingly explored for medical education, and dermatology, with its visual diagnostic focus, holds promise for AIenhanced learning. However, evidence on its educational effectiveness remains limited and fragmented. This scoping review aimed to assess the evidence base for applications and limitations of AI-based educational tools in dermatology. Method A systematic search of PubMed, Embase, Web of Science, Scopus, and PsycINFO up to July 2025 was conducted. Data were synthesised narratively, considering types of AI interventions and their assessed outcomes. These were analysed with the Cost, Usability, Credibility, Fairness, Accountability, Transparency, Explainability (CUC-FATE) framework. Study quality was assessed with ROBINS-I and a COSMIN-informed checklist. Results A total of 827 records were screened, with 360 duplicates removed, yielding 467 studies. Six full-length studies and 1 conference abstract met inclusion criteria, mostly from 2023 to 2025. These explored AI-generated clinical images, Large Language Model-generated vignettes, intelligent tutoring systems, and clinical decision support tools. Content validation studies generally reported favourable ratings for accuracy, clarity, and educational utility, while intervention studies suggested possible benefits for learning performance and diagnostic accuracy. Usability and credibility were commonly assessed, whereas cost, accountability, fairness, transparency, and explainability were rarely examined. Conclusion Most of the studies reviewed had high risk of bias, small sample sizes, and limited methodological rigour, with significant heterogeneity in examined educational outcomes limiting synthesis. While current initial studies on AI hold promise, this scoping review underscores the need for more robust studies with standardised evaluation frameworks, prioritising ethical principles such as fairness, explainability, and accountability for safe integration in training.","url":"https://doi.org/10.47102/annals-acadmedsg.2026346","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.47102/annals-acadmedsg.2026346","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.5055/ajdm.0520","name":"Factors affecting the National Incident Management System: A systematic review.","source":"pubmed","abstract":"This systematic review examines factors affecting the improvement of the National Incident Management System (NIMS) in addressing complex disaster risks and management challenges, spanning from January 1, 1980, to February 27, 2025.","url":"https://doi.org/10.5055/ajdm.0520","authors":["Ahmadi E","Peyravi M","Marzaleh MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5055/ajdm.0520","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.20344/amp.24588","name":"[Implementing the AI Act in the Portuguese Health Sector: Implications, Challenges and the CPIA-OM Perspective].","source":"europepmc","abstract":"The rapid expansion of artificial intelligence (AI) in the health sector has intensified the need for a robust regulatory framework capable of safeguarding patient safety, professional accountability, and public trust. We aimed to analyse the European Union Artificial Intelligence Act [AI Act, Regulation (EU) 2024/1689] and its implications for the healthcare sector in Portugal, presenting the institutional position of the Artificial Intelligence Committee of the Portuguese Medical Association (CPIA-OM). We conducted a narrative review based on official European legislation [AI Act, General Data Protection Regulation (GDPR), Medical Device Regulation (MDR) and In Vitro Diagnostic Medical Devices Regulation (IVDR)], institutional documents [including the 2025 White Paper on AI in Healthcare in Portugal, published by the Shared Services of the Ministry of Health (SPMS)], and peer-reviewed literature on artificial intelligence in medicine, ethics, and regulatory science. The AI Act introduces a risk-based classification of AI systems, ranging from prohibited \"unacceptable risk\" applications to minimal risk uses. Most medical AI applications, including diagnostic support, clinical monitoring, and therapeutic decision-making, are classified as \"high risk\" and are subject to stringent requirements regarding conformity assessment, technical documentation, human oversight, and data governance. The regulation interacts with existing frameworks such as the GDPR and MDR/IVDR, creating complex compliance obligations. Specific challenges include algorithmic bias, liability attribution, and the preservation of the physician-patient relationship. In Portugal, recent institutional initiatives within the National Health Service - including pilot projects in triage, frailty assessment, antibiotic stewardship, and medical imaging - illustrate opportunities for responsible AI integration, while academic reports highlight persistent concerns around transparency, trust, and professional substitution. The AI Act represents a landmark in European health regulation, balancing innovation with fundamental rights protection. For Portugal, its implementation requires coordinated action between the government, regulators, healthcare providers, and professional bodies. The CPIA-OM emphasises the need for continuous medical education in AI, the establishment of clinical oversight protocols, and the creation of observatories to monitor implementation and ensure ethical, safe, and patient-centred adoption.","url":"https://doi.org/10.20344/amp.24588","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20344/amp.24588","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00296-026-06182-5","name":"Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly used in clinical medicine. Rheumatology is well-suited to AI applications due to diagnostic complexity of rheumatic diseases, variable disease presentations, and multisystem involvement. This cross-sectional study examines global AI research in rheumatology through bibliometric analysis of Scopus data. The Scopus database was searched on May 5, 2026, using the terms \"artificial intelligence\" AND \"rheum*\" in the title, abstract, and keyword fields. No temporal restrictions were applied, and all English documents were analyzed. Bibliometric data, including publication year, country, institution, author, journal, and keywords, were extracted. Retracted articles were identified using Scopus tags and were manually verified through related journal editorial notices. Disease-specific publication counts were determined through independent searches for rheumatic diseases. Survey-based studies were identified through manual review. Linear regression analysis was conducted to assess temporal trends. A total of 1,057 publications were identified. Annual output remained below 10 until 2018, then rose sharply to a peak of 282 in 2025. Linear regression confirmed a significant upward trend (p < 0.001). The United States produced the most publications (n = 249), followed by the United Kingdom (n = 141) and India (n = 138). Harvard Medical School was the leading institution (n = 33), and Knitza, J., was the most prolific author (n = 17). Rheumatology International published the most articles (n = 27). Osteoarthritis (n = 1,017) and rheumatoid arthritis (n = 646) dominated the field, while systemic vasculitis (n = 40), Behçet disease (n = 29), and familial Mediterranean fever (n = 16) were underrepresented. Eleven survey-based studies were identified, mostly published between 2024 and 2025. Two articles were retracted due to peer review irregularities. AI research in rheumatology has increased significantly, with research outputs stemming from a limited number of countries, institutions, and disease categories. Osteoarthritis and rheumatoid arthritis are the most frequently explored diseases in the field. The increasing number of survey-based studies indicates heightened attention to clinician and patient perspectives. Future research should focus on expanding international collaboration, addressing gaps in AI research on underrepresented diseases, and strengthening ethical and methodological standards to facilitate broader AI adoption in rheumatology.","url":"https://doi.org/10.1007/s00296-026-06182-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00296-026-06182-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1002/prp2.70290","name":"Mapping Current Use of Artificial Intelligence in Pharmacology Education via a Scoping Review.","source":"europepmc","abstract":"Pharmacology education, often reputed as complex, overtly didactic and decontextualised, may benefit from artificial intelligence-supported strategies. However, current guidance is fragmented across disciplines and contexts, thus weakening evidence-based curricular implementation. This scoping review mapped existing research to identify applications, strengths, limitations, and areas for future development. A double-blinded screening process facilitated by Covidence yielded 17 eligible studies from four databases. Studies mostly comprised cross-sectional studies from high-income countries in the medical context, with generative artificial intelligence being predominant (ChatGPT-3.5 and ChatGPT-4.0). Research comprised assessment (n = 12), paedagogy (n = 4), curriculum design (n = 1), and programme evaluation (n = 1). Most studies assessed tools' ability to answer examinations, with mixed success depending on the version, question type, and inclusion of context in prompt engineering. Few studies incorporated students, limiting insights into learning impact. Zero-shot prompting was mostly used, limited further by unclear design frameworks, which may bias outcomes considering downstream inefficiencies. Current research prioritises the performance of artificial intelligence, rather than its integration or impact in learning, which reduces its applicability for curriculum design and competency development. Although promising, the impact is limited, requiring clearer instructional design and rationalisation within the education ecosystem. Although there is considerable potential for pharmacology education, research requires greater structure, longitudinal design, and incorporation of students to inform clear impact. Purposeful, context-aligned implementation and continuous evaluation are needed to ensure ethical, valid, and meaningful use in pharmacology education. To support future research, recommendations are provided for practical reporting, scientific design, and impact measurement.","url":"https://doi.org/10.1002/prp2.70290","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/prp2.70290","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-10542333/v1","name":"Medical Students’ Perceptions of ChatGPT Use in Medical Learning: A Cross‑Sectional Survey at the Faculty of Medicine and Pharmacy of Marrakech","source":"europepmc","abstract":"Abstract Background Generative artificial intelligence, particularly ChatGPT, is progressively transforming medical education by providing accessible and interactive learning-support tools. Despite its potential benefits, its use raises questions regarding the reliability of responses, ethical issues, and its integration into medical curricula. This study aimed to assess the use of ChatGPT by medical students at the FMPM, its perceived educational benefits, its limitations, and the associated ethical concerns. Methods A descriptive cross-sectional study was conducted during the 2024–2025 academic year among students at the Faculty of Medicine and Pharmacy of Marrakech (Cadi Ayyad University). Data were collected using an anonymous online questionnaire distributed over three months to all 1,628 students enrolled from the first to the seventh year. The questionnaire explored participant characteristics, patterns of ChatGPT (OpenAI) use, perceived benefits, attitudes toward the integration of artificial intelligence into medical training, and ethical concerns. Data were analyzed descriptively using JAMOVI software. Results Of the 1,628 students invited, 451 completed the questionnaire, yielding a response rate of 27.7%. Among respondents, 88.9% used ChatGPT. More than 90% of users considered it effective, 94% reported being satisfied, and 90.8% wished to continue using it. The main uses were clarifying medical concepts (74.6%), preparing for multiple-choice questions (47.9%), and preparing for examinations (43.6%). Overall, 89.6% felt that ChatGPT improved their understanding of course material, while more than 80% reported improvements in their autonomy, confidence, and self-directed learning. Compared with traditional methods, 62.8% considered it more effective. In addition, 95% of students were in favor of integrating artificial intelligence into the medical curriculum, and 82% supported the creation of a dedicated training module. However, 68.6% expressed ethical concerns, mainly related to dependence, the reliability of responses, data confidentiality, and the impact on clinical reasoning. Conclusion ChatGPT is widely adopted by medical students and is perceived as an effective educational tool. Its integration into medical training should nonetheless be accompanied by pedagogical supervision, training in the critical use of artificial intelligence, and ethical guidelines to ensure responsible use.","url":"https://doi.org/10.21203/rs.3.rs-10542333/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10542333/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/88511","name":"AI Integration in Spanish Undergraduate Medical Education: National Cross-Sectional Study.","source":"europepmc","abstract":"Background: Artificial intelligence (AI) is reshaping clinical practice and redefining the competencies future physicians will need. International bodies, such as the Association of American Medical Colleges, have called for structured AI training in medical curricula. Despite growing international consensus, no systematic nationwide evaluation had been conducted in Spain prior to this study. Objective: This study aimed to characterize the presence, type, and curricular features of AI-related training across all Spanish universities offering an official medical degree and to assess differences by institutional ownership and geographic region. Methods: This cross-sectional study was conducted from July to September 2025. Universities were the unit of analysis. A census of all institutions offering an officially recognized medical degree was obtained from the Register of Universities, Centers and Degrees; all 52 eligible institutions were included. Publicly available curricula and course guides for the 2025-2026 academic year were reviewed by 2 independent researchers and validated by an external evaluator. Courses were classified as (1) a specific AI course (AI as primary topic, accounting for >50% of syllabus), (2) an AI-similar course (a digital health or biomedical informatics course referencing AI as secondary content), or (3) not AI-related training. Course-level variables included ownership (public or private), region, status (compulsory or elective), European Credit Transfer and Accumulation System (ECTS) credits, academic year, and department. All analyses were descriptive. Potential sources of bias were addressed through predefined classification criteria, duplicate independent extraction, and external dataset verification. Results: Of 52 universities, 36 (69.2%) were public and 16 (30.8%) were private. A total of 10 (19.2%) institutions offered at least one specific AI course; 6 (11.5%) included an AI-similar course. Overall, 16 (30.8%) universities had incorporated AI in some form; 36 (69.2%) institutions had not incorporated AI. Rates were similar for public (7/36, 19.4%) and private institutions (3/16, 18.8%). Identified courses ranged from 3 to 6 ECTS credits, representing an average of 1.17% of the 360-credit degree; most were elective. Only the University of Jaén offered a compulsory course with AI content. Marked regional disparities were observed: Andalusia led with 5 of 9 (55.6%) universities offering a specific AI course, while 10 autonomous communities had no universities with any AI-related training. Conclusions: This study delivers the first census-based, reproducible, national assessment of AI integration in Spanish undergraduate medical education. Unlike prior work focused on individual programs or nonstandardized definitions, we applied a consistent taxonomic framework reusable for longitudinal monitoring and international benchmarking. Findings reveal a heterogeneous, predominantly elective, and low-weight curricular landscape with striking interregional inequities. These results inform curriculum reform, accreditation standards, and faculty development priorities and support the establishment of minimum national competency standards and systematic monitoring to ensure equitable AI literacy among future physicians in Spain.","url":"https://doi.org/10.2196/88511","authors":["Ana Enériz Janeiro","Karina Pitombeira Pereira","Julio Mayol","Javier Crespo","Fernando Carballo","Juan B Cabello","Manuel Ramos-Casals","Bibiana Pérez Corbacho","Juan Turnés"],"tags":["Medical education","Curriculum","Health informatics","Census","Unit (ring theory)"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/88511","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1186/s43046-026-00394-3","name":"Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.","source":"europepmc","abstract":"Brain cancer is one of the most challenging malignancies and a major contributor to worldwide morbidity and mortality. Glioblastoma, the most aggressive adult brain tumor, is associated with poor prognosis despite conventional therapies such as surgery, chemotherapy, and radiotherapy, which often result in severe toxicity and long-term side effects. Immunotherapy holds the potential to provide durable and specific anti-tumor responses; however, its success in brain cancer is hindered by obstacles such as the blood-brain barrier, immunosuppressive tumor microenvironment, and tumor heterogeneity. Nanomedicine offers a powerful approach to overcoming these barriers through targeted and efficient drug delivery. Nanotechnology-based platforms, including lipid-based, polymeric, and inorganic nanoparticles, have demonstrated superior therapeutic efficacy compared to free drugs, with several formulations advancing into clinical trials. Among these, nanotechnology-enabled vaccines represent an emerging frontier, capable of enhancing antigen presentation, stimulating strong immune responses, and overcoming tumor-induced immunosuppression. By combining the precision of nanocarriers with the long-lasting protection of vaccines, nano-vaccines hold great potential to transform brain cancer immunotherapy. Recent studies also suggest that integrating artificial intelligence (AI) with nanotechnology could further enhance the design, targeting, and effectiveness of immunotherapies. Moreover, AI is revolutionizing treatment development by enabling the prediction of immunogenicity, immune responses, and optimizing formulation design and dosing strategies. These advancements collectively accelerate the development and enhance the precision and efficiency of immunotherapy. Hence, this review discusses current nanotechnology-based immunotherapies and highlights the emerging role of integrating AI in vaccine development as next-generation strategies for improving outcomes in brain cancer treatment.","url":"https://doi.org/10.1186/s43046-026-00394-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s43046-026-00394-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.5116/ijme.6a7f.60c5","name":"Artificial intelligence in psychiatric care and education: a qualitative study of factors influencing adoption in Singapore.","source":"europepmc","abstract":"Objectives This study aimed to understand how AI's perceived role interacts with emerging barriers and facilitators to identify factors influencing its adoption across clinical and educational professions in Singapore. Methods This study followed a qualitative approach guided by a medical-pedagogical theoretical framework. Semi-structured interviews were conducted between May and July 2025 and followed an interview guide based on the medical-pedagogical framework. Twenty-four people participated, including eight nurses, six psychiatrists, and 10 allied health professionals. All were clinicians and educators. Data were analysed using thematic analysis, with attention to emergent patterns across patient care and educational contexts. Results Participants recognised significant potential for AI in patient care and healthcare professions education, particularly for information access, retrieval, clinical documentation, AI-augmented training methods such as virtual patients and educational content creation. Barriers included fears of professional skill degradation, role confusion, lack of familiarity with capabilities and the need for personal evidence of benefit. Enablers encompassed integrated, context-specific training, clear governance frameworks, and peer networks facilitating experiential learning and responsible use. Participants emphasised maintaining human connection and reflective practice as essential to psychiatric care and education. Conclusions Effective AI adoption in psychiatric care and education can be facilitated by clearly delineating tasks, embedding digital literacy into training, communicating robust governance structures, and introducing peer-led communities of practice. Relevant stakeholders should be engaged to align AI deployment with real clinical workflows, optimising both patient care and educational outcomes.","url":"https://doi.org/10.5116/ijme.6a7f.60c5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5116/ijme.6a7f.60c5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21203/rs.3.rs-10033571/v1","name":"Readiness of Medical Students to Use Artificial Intelligence in Medical Education: A Cross-sectional Study","source":"europepmc","abstract":"Abstract Background With the increasing use of artificial intelligence systems in medical applications, medical education also needs to adapt to this situation. It is necessary for students to systematically examine their understanding of artificial intelligence, application skills, ethical awareness, and forward-looking evaluations regarding the role of artificial intelligence in healthcare. This necessity will be examined in terms of the opinions, attitudes, and readiness of X medical faculty students. Methods This cross-sectional study was conducted among medical students enrolled at at Izmir Katip Celebi University Faculty of Medicine between September and December 2025. The study population consisted of 1,416 students from first to sixth year, of whom 225 participated in the study. Data collection instruments included a demographic information form and the Artificial Intelligence Readiness Scale for Medical Students. Statistical analyses were performed using IBM SPSS Statistics version 27. Normality was assessed using the Shapiro–Wilk test, and homogeneity of variances was evaluated using Levene’s test. As the data did not meet the assumptions for parametric testing, the Mann–Whitney U test and Kruskal–Wallis H test were used. A p-value of","url":"https://doi.org/10.21203/rs.3.rs-10033571/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10033571/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1148/ryai.260385","name":"Predetermined Change Control Plan Adoption and Documentation Transparency in FDA-cleared Radiology Artificial Intelligence/Machine Learning Devices.","source":"pubmed","abstract":"Purpose To characterize Predetermined Change Control Plan (PCCP) adoption and documentation transparency among U.S. Food and Drug Administration (FDA)-cleared radiology artificial intelligence/machine learning (AI/ML)-devices (2015-2025). Materials and Methods A cross-sectional systematic scoping review was conducted with linked data from FDA AI/ML-enabled device databases through April 2026. PCCP documentation completeness was scored by two independent observers (intraclass correlation coefficient: 0.93) using an 8-point rubric (possible scores, 0-8) derived from FDA's final PCCP guidance (December 2024). Identified PCCP devices underwent manual verification against FDA regulatory summaries. Results Among FDA-listed AI/ML-device submissions, 1080/1394 (77.5%) were radiology submissions, and nearly all cleared via 510(k) review. Across all FDA panels, 170 devices were cleared with a PCCP. Radiology led AI/ML-specific PCCP adoption (34/37 radiology PCCP devices, 91.9%). Among the PCCP-cleared radiology AI devices, 22/34 (65%) were cleared in 2025 alone following the final FDA guidance. Discrepancies between FDA's public database and individual summaries required manual adjudication for 9/34 (27%) of devices. PCCP documentation scores ranged from 0 to 8 (mean, 5), with most modifications focused on data retraining, compatibility expansion, and algorithm optimization. Continuous monitoring of device performance and predefined drift triggers for retraining were absent from public summaries. Conclusion PCCP adoption increased in radiology after issuance of the final FDA guidance, yet public lifecycle controls, particularly monitoring performance metrics and trigger thresholds, were limited. Standardized PCCP reporting of lifecycle controls are suggested as a condition of PCCP authorization to enable systematic postmarket monitoring as this pathway scales. &#xa9;RSNA, 2026.","url":"https://doi.org/10.1148/ryai.260385","authors":["Dayma K","Patel P","Hildreth K","Jamaspishvili T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1148/ryai.260385","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1371/journal.pdig.0001347","name":"A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.","source":"europepmc","abstract":"To characterize global trends in ophthalmic AI research from 2015-2025 and drive transferable insights into the broader evolution of AI in medicine, we conducted a systematic bibliometric analysis of original AI articles in ophthalmology indexed in the Web of Science Core Collection, Scopus, and Pubmed from 2015 to 2025. Publications were screened and categorized using an LLM-assisted pipeline with predefined labels, and agreement between LLM-assisted classifications and human grading was evaluated. We analyzed temporal trends and emergence patterns across study design, model architecture, disease focus, and data modalities. Among 12,911 included articles, annual publications increased at a compound annual growth rate of 39.7%, from 108 in 2015-3061 in 2025. Four key shifts were identified. Study design: Development studies (88.3%) dominated through the period, whereas evaluation studies (7.4%) began to emerge in 2019. Model architecture: Classical deep learning (71.8%) became the most prevailing approach from 2017, while foundation model studies (0.6%) increased sharply in 2024-2025. Disease focus: Diabetic retinopathy (32.9%), glaucoma (17.5%), and age-related macular degeneration (12.6%) remained the leading disease areas, while corneal diseases (7.0%), cataract (4.3%) and myopia (3.4%) emerged after 2019-2020 and grew rapidly. Data modalities: Among image-based modalities (84.1%), color fundus photography and retinal optical coherence tomography remained dominant but plateaued after 2019, whereas text-based modalities (5.5%) continued to rise after 2022. Over the past decade, ophthalmic AI research expanded rapidly and evolved from narrow image-based deep learning toward broader work spanning evaluation studies, foundation models, multimodal data integration, and a wider spectrum of eye diseases. These trajectories may extend beyond ophthalmology, offering broader insights into how medical AI matures toward clinical evaluation and translation.","url":"https://doi.org/10.1371/journal.pdig.0001347","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001347","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1001/jamahealthforum.2026.2515","name":"Closing the Health Policy Implementation Gap With Artificial Intelligence.","source":"europepmc","abstract":"Importance Health care policies often fail to achieve their goals due to implementation challenges attributable to workforce constraints, fragmented health information systems, and administrative complexity. This Special Communication proposes a framework for how artificial intelligence (AI) tools could support effective health care policy implementation, using the implementation of Medicaid work requirements under the Budget Reconciliation Act of 2025 as an example. Observations Opportunities for AI-augmented health care policy implementation include executing key policy processes, such as generating eligibility screening tools, reviewing documentation, and linking and analyzing data for indicators of policy compliance; identifying individuals at risk of adverse consequences from implementation failure who should receive proactive support; enhancing policy communication to diverse audiences; facilitating implementation monitoring; and learning from and adapting implementation across all of these domains. To realize the potential of AI augmentation, the field needs to overcome challenges related to data availability, as well as the limitations of AI tools themselves, such as hallucinated false information. Conclusions and relevance AI-augmented health care policy implementation has the potential to meaningfully limit unintended consequences from implementation of Medicaid work requirements. Rigorous evaluation of state-led innovations in AI-augmented implementation of Medicaid work requirements is key to advancing effective approaches and mitigating the potential for harm. The federal government should support state efforts with AI expertise, data infrastructure, and partnerships with preferred vendors who demonstrate that their AI-augmented digital assistants and other AI tools effectively facilitate Medicaid enrollment among eligible individuals.","url":"https://doi.org/10.1001/jamahealthforum.2026.2515","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1001/jamahealthforum.2026.2515","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00345-026-06728-z","name":"Artificial intelligence-based prediction of stone-free status in patients undergoing mini-percutaneous nephrolithotomy without retrograde insertion of a ureteral catheter.","source":"europepmc","abstract":"Purpose This study aimed to develop and validate an artificial intelligence (AI) predictive model for postoperative stone-free status (SFS) specifically in patients undergoing mini-percutaneous nephrolithotomy without retrograde insertion of a ureteral catheter (mPCNL-nRUC). Methods This single-center prospective observational study included 181 patients with upper urinary tract stones who underwent mPCNL-nRUC between March 2019 and March 2025. Preoperative clinical, laboratory, and imaging data were collected. Five machine learning (ML) algorithms were employed to construct SFS prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with Guy's stone score and S.T.O.N.E. nephrolithometry system. The SHapley Additive exPlanations (SHAP) method was used for interpretability analysis of the optimal model. Results All surgeries successfully established percutaneous renal access without intraoperative conversion to retrograde ureteral catheter insertion. The immediate postoperative stone-free rate (SFR) was 72.93% (132/181). The overall complication rate was 24.31%, with the majority being Clavien‑Dindo grade I or II. Among the five models, the support vector machine (SVM) model demonstrated the best predictive performance with AUC of 0.875, significantly outperforming Guy's stone score (AUC = 0.659, p Conclusion mPCNL-nRUC is a safe, effective, and simplified procedure in selected patients. The interpretable AI model provides a powerful tool for clinical individualized decision-making.","url":"https://doi.org/10.1007/s00345-026-06728-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00345-026-06728-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.46292/sci25-00138","name":"Artificial Intelligence and Data Science for Spinal Cord Injury: Bridging Clinical and Computational Perspectives.","source":"europepmc","abstract":"Modern methods of data science have become powerful tools for analyzing complex, heterogeneous biomedical datasets, including those specific to spinal cord injury (SCI), enabling personalized predictions of recovery, identification of prognostic biomarkers, and insights into functional outcomes. This article summarizes the one-day Data Science Precourse, held at the 2025 annual scientific meeting of the American Spinal Injury Association (ASIA). The course was designed to illustrate advances in data science and their application to SCI research, while analyzing the unique challenges posed by SCI-specific data, such as sparse longitudinal measurements, variable injury characteristics, and diverse clinical and functional assessments. The precourse combined expert-led discussions with hands-on learning opportunities tailored for both clinical and data science audiences. Topics included addressing key challenges of artificial intelligence methods in SCI data analyses, such as learning from limited or incomplete datasets, implementing causal frameworks to understand recovery mechanisms, and ensuring robust and interpretable predictions for clinical decision-making. The event also showcased real-world applications of data science in SCI research, highlighting both solved and ongoing problems, including prognostic modeling, patient stratification, lesion analysis, and optimization of clinical trial design. The precourse culminated in the presentation by the winning teams of the 2025 ASIA Data Science Challenge.","url":"https://doi.org/10.46292/sci25-00138","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.46292/sci25-00138","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.media.2026.104159","name":"Establishing the robustness metric as a scalable proxy for clinical relevance in medical AI explainability.","source":"pubmed","abstract":"Despite high performance of deep learning in medical imaging applications, the critical lack of validated computational metrics for explainable AI (XAI) impedes clinical integration. To address this gap, our study introduces a multi-level validation framework to rigorously assess seven computational evaluation metrics applied to eight widely-used post-hoc attribution methods - spanning gradient-based, input attribution, and decomposition-based families - on large-scale structural MRI datasets (UK Biobank and ADNI) with two different benchmark tasks across several deep learning architectures. At the first level, we benchmark metrics against three scales of clinical relevance: voxel-based morphometry, regional volumetric associations, and expert radiologist assessments, revealing that many widely used conventional metrics exhibit negligible correlation with clinical evidence. At the second level, we show that metrics in addition are often significantly confounded by model architecture rather than reflecting explanation quality. At the third level, we conduct a computational efficiency and stability analysis focused on practical dimensions of the metrics. Across all levels, our statistical analyses show that only one of the metrics has consistent high performance: the Robustness score - defined as the stability of explanations across random training initializations - demonstrates strong alignment with morphometry, volumetric associations, and human expert judgments, effectively isolates the quality of the XAI method from architectural bias, and possesses good computational efficiency. Our novel, multi-level framework therefore establishes Robustness as a superior, scalable proxy for clinical validity and trustworthy AI in medical imaging.","url":"https://doi.org/10.1016/j.media.2026.104159","authors":["Cho D","You SH","Kim BK","Pak A","Wallraven C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.media.2026.104159","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1186/s12909-026-09859-4","name":"Determinants of medical students' attitudes toward artificial intelligence: a cross-sectional study and implications for medical education.","source":"europepmc","abstract":"Abstract Background Artificial intelligence (AI) has emerged as one of the most rapidly evolving technologies in recent years and is increasingly being integrated into healthcare, education, and everyday life. Examining university students’ attitudes toward this technology is important for understanding their future professional orientations and adaptation to technological change. This study aimed to identify medical students’ attitudes toward artificial intelligence and the determinants of these attitudes, and to develop educational implications for medical training based on the findings. Methodology This descriptive cross-sectional study was conducted between January 22 and May 28, 2025, among 198 final-year medical students at Pamukkale University Faculty of Medicine in Denizli, Türkiye. Data were collected using a Descriptive Information Form, which assessed students’ sociodemographic characteristics and their knowledge and experiences regarding AI, and the General Attitudes toward Artificial Intelligence Scale (GAAIS, Turkish version). Since the negative subscale is reverse-coded, higher scores in both indicate more positive attitudes toward AI. Data were analyzed using SPSS v25 with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses. Results The mean age of participants was 24.47 ± 0.98 years; 56.1% were female. A total of 74.2% of the participants reported general knowledge about AI, and 76.3% reported experience using AI in daily life. A total of 63.1% viewed AI developments positively, 69.7% believed AI changes work and daily life, and 52.0% felt emotionally unaffected by it. The mean positive attitude score was 45.01 ± 9.17, and the negative attitude score was 26.41 ± 6.22. Multiple linear regression analysis showed that positive attitudes toward artificial intelligence were significantly associated with father’s education level (university vs. primary school), having an interest in technology, perceptions regarding developments in artificial intelligence, and the belief that AI has an emotional impact ( p &lt; 0.05). For the negative attitude subscale, only perceptions regarding developments in artificial intelligence were found to be significantly associated ( p &lt; 0.05). Conclusion Medical students demonstrated generally positive attitudes toward AI. Higher paternal education, technological interest, perceiving AI as emotionally influential, and evaluating AI developments positively were predictors of favorable attitudes. In addition, more positive evaluations of AI-related developments were also associated with higher scores on the reverse-coded negative attitude subscale, indicating lower negative attitudes toward AI. These findings suggest that students’ attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI. Therefore, integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.","url":"https://doi.org/10.1186/s12909-026-09859-4","authors":["Batuhan Horasan","Ahmet Ergin","Eda Şenarabacı"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09859-4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.7759/cureus.113457","name":"Artificial Intelligence in Nuclear Medicine (2015-2025): Clinical Applications, Regulatory Governance, and Pharmacotherapeutic Implications.","source":"europepmc","abstract":"Artificial intelligence (AI) is becoming increasingly relevant in nuclear medicine, particularly in applications that depend on quantitative image analysis, image reconstruction, acquisition optimization, and clinical decision support. This critical narrative review examines the evolution, clinical applications, ethical and regulatory challenges, and pharmacotherapeutic implications of AI in nuclear medicine from 2015 to 2025. A structured literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and selected professional and regulatory websites, including those of the Society of Nuclear Medicine and Molecular Imaging, the European Association of Nuclear Medicine, the International Atomic Energy Agency, the US Food and Drug Administration, and the European Medicines Agency. The reviewed literature reports advances in deep learning-based reconstruction, denoising, and preservation of quantitative parameters in positron emission tomography and single-photon emission computed tomography. Clinical applications have expanded across oncology, neurology, cardiology, theranostics, and radiopharmacy-related workflows. However, the evidence remains limited by methodological heterogeneity, predominantly retrospective and single-center designs, restricted external validation, uneven regulatory development, and limited representation of Latin American settings. Overall, AI may improve diagnostic precision, personalized dosimetry, workflow efficiency, and radiopharmaceutical safety in nuclear medicine. Responsible implementation will require robust multicenter validation, transparent evaluation standards, adaptive governance, and multidisciplinary oversight. Radiopharmacists should be actively involved in this process, particularly in relation to radiopharmaceutical quality, traceability, dosimetry, and pharmacotherapeutic decision-making.","url":"https://doi.org/10.7759/cureus.113457","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.113457","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s11701-026-03730-w","name":"Global research trends in artificial intelligence-integrated robot-assisted spine surgery: a bibliometric and visualized analysis.","source":"europepmc","abstract":"This study aims to employ bibliometric analysis methods to systematically review the current research progress, developmental trajectory, and emerging research hotspots in artificial intelligence-integrated robot-assisted spine surgery (AI-RASS), thereby providing visualized support for future research planning. A total of 588 publications related to AI-RASS were extracted from the Web of Science Core Collection (WoSCC) database between 2005 and 2025.The study employed tools such as Bibliometrix, CiteSpace, VOSviewer, and Scimago Graphica to analyze publication trends, international collaboration networks, and research hotspots, generating visualized maps that elucidate the knowledge framework and conceptual structure of this field. The annual average growth rate of publication volume is 24.22%, reaching its peak in 2025. The USA, China, and Germany have the highest number of published papers, with high H-indexes, strong collaboration intensity, and significant impact. Switzerland has the highest average citation frequency per paper. In terms of total co-citation frequency, Spine ranks highest among journals in this field. Although scholarly interest continues to grow, notable gaps remain in areas such as high-quality prospective multicenter randomized controlled trials, international collaboration, ethical oversight, and rigorous cost-effectiveness evaluations. It is essential to establish comprehensive databases and international big data sharing platforms, advance methodologically rigorous clinical trial research, develop corresponding ethical frameworks, and create innovative yet accessible AI-integrated surgical protocols to promote deep integration of AI and RASS. The AI-RASS research has garnered significant attention over the past two decades. This study employed bibliometric and visualization analysis methods to systematically examine the evolutionary trends in AI-RASS research. Future developments in this field should prioritize the following directions: strengthening interdisciplinary collaboration, establishing a systematic clinical evaluation framework, creating comprehensive clinical research databases, and developing evidence-based ethical guidelines; simultaneously enhancing international and institutional cooperation to ensure alignment between technological advancement and patient safety.","url":"https://doi.org/10.1007/s11701-026-03730-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03730-w","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/103717","name":"Nurse-Led Large Language Model Chatbot for Predicting and Preventing Complications After Coronary Artery Bypass Grafting: Protocol for a Randomized Controlled Trial.","source":"europepmc","abstract":"Background Thirty-day unplanned readmission following coronary artery bypass grafting (CABG) affects 10%-20% of patients and is a key quality indicator, particularly in low- and middle-income countries (LMICs) where access to cardiac rehabilitation is limited. Existing risk models are static, lack real-time engagement, and no validated large language model (LLM)-based clinical decision support (CDS) system exists for post-CABG readmission prevention. Objective This protocol describes the development, validation, and evaluation of Smart CABGuard, a nurse-led, LLM-based CDS chatbot with continuous remote electrocardiographic (ECG) monitoring, to predict and prevent postoperative complications, and 30-day unplanned readmission after isolated CABG. Methods This multiphase translational study is conducted at Amrita Institute of Medical Sciences, Kochi, India, following TRIPOD-LLM (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-Large Language Model) and CONSORT (Consolidated Standards of Reporting Trials) AI reporting guidelines. Phase I uses an ambispective design (retrospective: January 2020 to December 2024; prospective needs survey: June 2025 to January 2026) to develop a logistic regression-based Complication Risk Index (CRI), assessed via receiver operating characteristic area under the curve, Brier score, and decision curve analysis. Phase II refines Smart CABGuard by integrating a frozen Mistral-7B LLM (low-rank adaptation fine-tuned), the CRI engine, explainable AI, and single-lead ECG telemetry (Amrita Spandanam device), with a usability threshold of ≥80%. Phase III is a prospective, parallel-group, open-label randomized controlled trial (RCT). Adults aged ≥18 years undergoing isolated CABG with smartphone access are randomized 1:1 via permuted block randomization, with allocation concealment. The intervention arm receives Smart CABGuard-assisted care (daily chatbot check-ins, CRI-based risk stratification, ECG monitoring, and nurse-led triage) for approximately 24 days postdischarge plus standard care; the control arm receives standard care with structured telephone follow-up for outcome ascertainment only. Outcome assessors and statisticians are blinded; analyses follow the intention-to-treat principle. The primary outcome is 30-day all-cause unplanned readmission. Based on a baseline rate of 10.71%, a 50% relative reduction, α=.10, and 80% power, the target sample size is 800 patients (400 per arm, including 10% attrition). Results Ethics approval was granted by the Institutional Ethics Committee of Amrita Institute of Medical Sciences on April 16, 2025. Funding was awarded by Sigma Theta Tau International Honor Society of Nursing, Small Grants Program (grant 21650) in June 2025. Phase I data extraction commenced in May 2025 and is projected to be completed by April 2026; the patient needs survey (June 2025 to January 2026) is ongoing. Phase II usability evaluation is projected from May to July 2026. Phase III recruitment is anticipated from August 2026 to September 2027. Results are expected to be published in early 2028. Conclusions Smart CABGuard is the first RCT protocol of an LLM-based nurse-led CDS system for post-CABG readmission prevention in an LMIC setting, aiming to establish the usefulness, safety, and feasibility of AI-augmented postoperative surveillance.","url":"https://doi.org/10.2196/103717","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/103717","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106672","name":"Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. Objective To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. Methods PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. Results Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. Conclusion This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106672","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106672","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fpubh.2026.1916714","name":"Effectiveness of a \"5E+AI\" teaching model on science communication in medical students: a contribution to global health literacy.","source":"europepmc","abstract":"Background The improvement of global health literacy depends on health professionals having effective public science communication capabilities. However, worldwide, medical education systems still largely lack training in science communication for medical students. How to construct a systematic and scalable model for cultivating scientific communication capabilities is a key issue currently faced by the reform of medical education. Objective Our study aims to evaluate the effect of the \"5E+AI\" teaching model on the cultivation of medical students' science communication capabilities, and to assess the impact of this model on students' learning participation, acquisition of medical knowledge, and creation of popular science works. Methods A historical controlled quasi-experimental design was employed. Twenty-nine medical students enrolled in the \"Fundamentals and Practice of Medical Science Popularization\" course at Sun Yat-sen University during the 2025-1 semester served as the historical control group (receiving lecture-based, instructor-centered teaching with PowerPoint presentations and group discussions, but without AI-assisted tools), while 28 students enrolled in the same course during the 2025-2 semester served as the experimental group (receiving the \"5E+AI\" teaching model). The two groups are the same in terms of teaching content, total class hours, teachers, teaching materials, assessment methods, etc. The evaluation indicators include participation score, medical knowledge test score, final score of the science communication project and student self-report questionnaire. Intergroup comparisons were performed using the Mann-Whitney U -test. Results The experimental group scored significantly higher than the control group on all three primary outcome measures: participation scores (median 85.00 vs. 80.00, U = 220.00, P = 0.002), medical knowledge test scores (median 100.00 vs. 80.00, U = 210.00, P = 0.001), and science communication project scores (median 85.00 vs. 85.00, U = 213.00, P = 0.001). In addition, the experimental group reported significantly higher scores than the control group across all three dimensions of the self-report questionnaire ( P Conclusions The \"5E+AI\" teaching model helps enhance medical students' classroom participation, ability to acquire medical knowledge and science communication skills. In this teaching model, artificial intelligence can help students address the issues of knowledge deficiency and resource constraints. Meanwhile, the 5E teaching framework provides students with structured teaching scenarios. Together, they enhance teaching effectiveness and offer replicable and adaptable local solutions for the reform of medical communication education in the context of global health literacy promotion.","url":"https://doi.org/10.3389/fpubh.2026.1916714","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1916714","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1093/rheumatology/keag454","name":"Perceptions, knowledge and adoption of artificial intelligence in rheumatology: results from a British Society for Rheumatology survey.","source":"europepmc","abstract":"Objectives Artificial intelligence (AI) and machine learning applications are rapidly expanding across healthcare. Successful implementation of AI technologies in rheumatology will depend not only on technical performance but also on the perceptions and preparedness of end-users. This study evaluated the current opinions, expectations, and concerns of AI among healthcare professionals and researchers in rheumatology across the UK. Methods A 19-item survey was designed and distributed through national and regional networks aimed at the rheumatology workforce between June 2025 - January 2026, targeted at consultant rheumatologists, doctors-in-training, allied health professionals, specialist nurses, and non-clinical researchers in rheumatology. The questions included respondent background data, current applications of AI in clinical care and research, opinions about AI in terms of perceived impact, concerns, educational needs and expected performance. Results Of the 218 respondents, 39% to 40% reported daily or weekly use of AI in research and clinical practice respectively. The most common clinical uses were using LLMs to look up medical facts (45%), to improve grammar/spelling of clinical documentation (28%), generate differential diagnoses (22%) and use of ambient scribes (17%). 86% anticipated that AI would substantially impact clinical practice in five years or less. Administrative tasks (85%) and musculoskeletal imaging (63%) were perceived as the areas likely to experience the greatest impact from AI. Highlighted concerns included data security/privacy (70%), medical liability (70%), followed by lack of explainability (47%). One in four reported excellent confidence in using digital technology, with only 6% self-rating their AI knowledge as excellent. A strong interest in education about AI was expressed regarding several areas including the ethical and safe use of AI (66%), safe and efficient use of LLMs in clinical practice (64%), and ambient AI scribes (59%). Conclusions AI is already being used frequently in UK rheumatology practice and research, with most anticipating a considerable impact on clinical care within the next five years or less. However, despite enthusiasm for adoption, important concerns regarding data security, liability, and explainability remain, alongside low self-reported AI knowledge, highlighting the need for targeted education, robust governance, and safe clinical implementation strategies.","url":"https://doi.org/10.1093/rheumatology/keag454","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/rheumatology/keag454","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ijnsa.2026.100577","name":"The role of digital literacy and ethical awareness in nurses' acceptance of Artificial Intelligence: a cross-sectional questionnaire survey.","source":"europepmc","abstract":"Background The integration of artificial intelligence into healthcare is rapidly transforming clinical practice. Nurses' acceptance of artificial intelligence is influenced by their digital literacy and ethical awareness, yet empirical evidence examining these interrelationships is limited. Objective We aimed to examine the direct and indirect effects of digital literacy on nurses' acceptance of artificial intelligence, with ethical awareness of artificial intelligence serving as a mediating factor. Methods A cross-sectional, correlational design was conducted among 350 nurses working in medical-surgical and critical care units at El-Kasr Al-Aini hospital in Egypt from March 2025 to December 2025. Participants completed validated instruments assessing digital literacy, artificial intelligence ethical awareness, and artificial intelligence acceptance. Data were analyzed using descriptive statistics, Pearson correlations, and structural equation modeling (SEM) with bootstrapping to assess mediation effects. Results Participants demonstrated moderate levels of digital literacy, artificial intelligence ethical awareness, and artificial intelligence acceptance. SEM analysis revealed that digital literacy had a significant direct effect on artificial intelligence acceptance (β = 0.29, p p p Conclusion Digital literacy enhanced participants' acceptance of artificial intelligence both directly and indirectly through ethical awareness. Ethical preparedness may, therefore, be a critical factor alongside technical competence in promoting artificial intelligence adoption among Egyptian nurses. Relevance to clinical practice Integrating digital literacy training with ethical education in Egyptian nursing curricula and continuing professional development programs may be able to facilitate safe and effective artificial intelligence adoption, supporting high-quality patient care in technologically advanced healthcare environments.","url":"https://doi.org/10.1016/j.ijnsa.2026.100577","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijnsa.2026.100577","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1177/03915603261467531","name":"The use of artificial intelligence to predict postoperative outcomes following percutaneous nephrolithotomy: A systematic review of prognostic modeling studies.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly applied in clinical practice to enhance prediction of postoperative outcomes. This systematic review evaluated the performance and clinical relevance of AI-based prognostic models for patients undergoing percutaneous nephrolithotomy (PCNL). A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar was conducted on 7 August 2025 to identify original studies that used AI to predict outcomes such as stone-free status. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool-Artificial Intelligence extension. A narrative synthesis of study characteristics, and reported performance metrics of AI models was conducted. Twenty-one studies involving 24,087 patients met the inclusion criteria. Tree-based, support vector, discriminant, and regression models achieved the highest median AUCs (0.79-0.81) for predicting stone-free status, whereas neural networks showed lower performance (median 0.60). Tree-based and similarity-based models performed best for predicting the need for adjuvant therapy. Neural networks performed well for bleeding-related outcomes (median AUC 0.87), while tree-based and regression models showed more consistent performance for procedural complications and hospitalization. Infection-related outcomes were predicted most accurately by tree-based and neural network models (median AUCs 0.89-0.90). Temporal trends revealed a shift from early reliance on neural networks and support vector machines to increased use of tree-based approaches in recent years. Overall, AI models offer useful support for perioperative decision-making, though their reliability varies across outcomes. Model performance is highest for stone-free status and infection-related outcomes, but lower for rare or poorly-defined endpoints such as bleeding or peri-procedural complications, reflecting limitations in available features and outcome heterogeneity. Clinically, current models may inform management but are not yet sufficient to dictate care. Future research should focus on standardized outcome definitions, multi-institutional datasets, and integration of high-dimensional data such as imaging and radiomics to develop robust, externally validated predictive tools for prospective implementation in PCNL practice.","url":"https://doi.org/10.1177/03915603261467531","authors":["Padooiy Nooshabadi M","Shakiba B","Akbarnataj H","Saniee N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03915603261467531","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/md.0000000000050003","name":"Mapping the research landscape of virtual reality and artificial intelligence in medical education evaluation: A bibliometric analysis.","source":"europepmc","abstract":"Background Evaluation of medical education is essential for ensuring the quality of health professional training. However, conventional evaluation approaches often lack objectivity, scalability, and longitudinal assessment capacity. Virtual reality (VR) and artificial intelligence (AI) are increasingly integrated into medical education, yet their application in educational evaluation has not been systematically characterized. Objective To examine research trends, thematic evolution, and emerging directions in VR- and AI-enabled medical education evaluation, a bibliometric analysis was conducted. Methods Publications indexed in the Web of Science Core Collection between January 1, 2015, and December 31, 2025, were retrieved using predefined search terms related to VR, AI, medical education, and evaluation. Eligible English-language articles and reviews were analyzed using CiteSpace (version 6.4.R2). Annual publication and citation trends, country collaboration patterns, and cited journals were assessed. Research themes and frontiers were examined through keyword co-occurrence, clustering, burst detection, and timeline analyses. Results A total of 695 publications were included. Annual publications and citations increased steadily, with accelerated growth after 2020. The United States, Germany, China, England, and Canada produced the highest number of publications, whereas Belgium, Egypt, Sweden, Singapore, and Switzerland demonstrated high collaboration centrality. Influential cited journals were concentrated in medical education and simulation-based training domains. Keyword analyses identified major themes including surgical education, VR simulation, clinical reasoning, decision support, and residency and undergraduate education. Burst and timeline analyses indicated a progression from early simulation-based skill validation toward learner-centered performance evaluation and, more recently, quality-oriented and curriculum-level assessment. Conclusions Research on VR- and AI-enabled medical education evaluation has expanded rapidly and evolved from technical skill assessment toward comprehensive, competency-oriented, and quality-focused evaluation. These findings highlight the growing role of emerging technologies in shaping future global medical education evaluation frameworks.","url":"https://doi.org/10.1097/md.0000000000050003","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000050003","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1155/proc/9923959","name":"Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.","source":"pubmed","abstract":"Accurate risk stratification in localized prostate cancer is essential for guiding treatment decisions. Conventional National Comprehensive Cancer Network (NCCN) risk groups rely on prostate-specific antigen (PSA), Gleason grade group, and clinical stage, while artificial intelligence (AI) methods, including radiomics, digital pathology, and multimodal prediction models, are increasingly being evaluated as alternatives or complements. This systematic review aims to assess studies published between 2020 and 2025 that directly compare AI-based models with traditional NCCN risk stratification methods in localized prostate cancer.","url":"https://doi.org/10.1155/proc/9923959","authors":["Hassan WA","Meligy OAE","Talaat IM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/proc/9923959","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-026-58956-3","name":"Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocardial infarction (NSTEMI) and overcoming the \"black box\" nature of deep learning models remain persistent challenges. Here, we present a comprehensive deep learning framework capable of classifying STEMI, NSTEMI, and non-acute coronary syndrome (non-ACS) from 12-lead ECG images, while also localizing infarction sites. Utilizing ,2070 validated ECGs, our pipeline integrates ResNet for acute myocardial infarction detection, Faster R-CNN for ST-segment elevation localization, and an ensemble approach for final classification. The model achieved a 98.3% AUROC for detection and an overall three-class accuracy of 93.6%, with high F1 scores for identifying infarction territories. To address interpretability, we developed an explainable AI (XAI) web viewer that visualizes detected regions. Furthermore, we evaluated the model's utility as an educational tool in a prospective pilot study with medical students. AI assistance significantly improved the students' overall diagnostic accuracy from 43% to 82% (p < 0.05), with notable gains in identifying NSTEMI and complex STEMI subtypes. These findings demonstrate that our interpretable AI model not only supports clinical decision-making with high diagnostic precision but also serves as an effective educational aid for enhancing novice clinicians' proficiency.","url":"https://doi.org/10.1038/s41598-026-58956-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-58956-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bmjhci-2025-101693","name":"Artificial intelligence in lumbar radiography: bridging deep learning and clinical practice in low-resource environments.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has increasingly been applied to medical imaging, yet its role in lumbar spine radiography, particularly in low-resource settings, remains underexplored. Objective To evaluate recent developments in AI-based approaches for lumbar spine radiography and their clinical applicability in resource-constrained environments. Methods A narrative review was conducted focusing on deep learning models applied to lumbar radiographic analysis. Studies published between 2022 and 2024 were identified through structured screening of PubMed and Google Scholar. Results Deep learning models, including convolutional neural networks, U-Net, ResNet and generative adversarial networks, have demonstrated improved performance in segmentation, classification and curvature analysis. Lightweight architectures show potential for deployment in resource-limited settings. Conclusion AI-based lumbar imaging has the potential to enhance diagnostic accuracy and workflow efficiency in low-resource environments. However, challenges related to validation, interpretability and clinical integration remain, highlighting the need for further large-scale and real-world studies.","url":"https://doi.org/10.1136/bmjhci-2025-101693","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjhci-2025-101693","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fdgth.2026.1789164","name":"Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).","source":"europepmc","abstract":"The rapid advancement of digital technologies has transformed traditional mechanisms of epidemiological surveillance and response, giving rise to an interdisciplinary field known as digital epidemiology. This study presents a scientometric mapping of the main trends, collaboration networks, and thematic foci linked to the use of emerging technologies-such as artificial intelligence, machine learning, big data, the Internet of Things (IoT), and social media mining-in global public health surveillance from 2000 to 2025. The methodology combines bibliometric and social network analysis on documents indexed in Scopus and Web of Science, complemented by a qualitative examination of the fifty most cited studies. The results reveal an exponential growth in scientific output starting from 2010, with an inflection point during the COVID-19 pandemic. The United States, the United Kingdom, and China stand out as the primary centers of production and international collaboration. However, significant gaps persist in digital equity, interoperability, and ethical governance, particularly in regions with lower technological infrastructure. This work contributes to understanding the evolution and challenges of digital epidemiology, proposing a future research agenda centered on algorithmic ethics, transparency, international cooperation, and technological inclusion in vulnerable contexts.","url":"https://doi.org/10.3389/fdgth.2026.1789164","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1789164","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ejmp.2026.107171","name":"Emerging technologies in medical physics: the role of artificial intelligence in medical imaging and potential adoption in Ghana.","source":"europepmc","abstract":"Background Ghana's imaging services face rising demand, uneven digital infrastructure, and limited access to advanced modalities. Artificial intelligence (AI) could improve diagnostic accuracy, workflow efficiency, and access, but real-world adoption is early. Objective Assess Ghana's readiness to adopt AI in medical imaging, identify pathways and barriers, and propose a phased, context-specific roadmap. Methods A review of peer-reviewed and grey literature (2012-March 2025) using PubMed, IEEE Xplore, Scopus, Google Scholar, and Ghanaian institutional documents. The review emphasizes imaging AI, LMIC experiences, and Ghana-specific evidence on infrastructure, policy, and pilots, distinguishing Ghana-based findings from international evidence extrapolated to Ghana. Results Major gaps include digital infrastructure (limited PACS, variable DR/CR adoption, uneven connectivity), financing (license and maintenance costs), governance (SaMD pathways exist but AI-specific provisions are evolving; operational data protection needs strengthening), and workforce (limited AI literacy; urban - rural disparities). Ghana-relevant touchpoints include MinoHealth.AI chest radiography evaluations, the national imaging equipment inventory, Ghana Health Service digital health strategy (2023-2027), and FDA SaMD guidance. A phased roadmap is proposed: establish PACS and connectivity; implement AI governance and data stewardship; run targeted pilots in CXR triage, low-dose CT, and MRI acceleration; scale via public-private partnerships and pooled procurement; and sustain workforce development with human-in-the-loop oversight. Conclusions AI can improve equity and efficiency in imaging in Ghana if adoption builds on strong digital foundations, robust governance, local validation, and clinician-led implementation. Priorities include PACS deployment, AI-specific regulatory strengthening, ethical data governance, and capacity building to support safe, equitable, and sustainable use.","url":"https://doi.org/10.1016/j.ejmp.2026.107171","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejmp.2026.107171","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/95072","name":"Generative Artificial Intelligence (AI)-Assisted Self-Diagnosis and the Patient-Physician Relationship: Mixed Methods Study of Calibration, Participation, and Trust.","source":"europepmc","abstract":"Background Patients increasingly use generative AI to interpret symptoms and seek health information, yet limited evidence shows how AI-assisted self-diagnosis is integrated into care-seeking and related to clinical interactions and patient-physician relationships. Objective This study examined how AI-assisted self-diagnosis is incorporated into care-seeking processes and how it relates to patient participation in clinical encounters and trust in physicians. Methods An exploratory sequential mixed methods design was used. In the qualitative phase, Chinese adults who had used generative AI to interpret symptoms, appraise possible conditions, or seek health advice within the previous year were purposively recruited through Xiaohongshu, WeChat Moments, and WeChat groups. Semistructured interviews were conducted from August 20, 2025, to January 10, 2026, and analyzed using reflexive thematic analysis. In the quantitative phase, an anonymous web-based survey was conducted in China through Huixiang Data from January 25, 2026, through January 28, 2026. Adults who had used generative AI for health consultation involving symptom interpretation or preliminary self-diagnosis within the previous 6 months were recruited through convenience sampling. Measures included perceived AI-assisted self-diagnosis quality, calibrated illness appraisal, patient participation, diagnosis validation, diagnosis comprehension, trust in physicians, AI use frequency, trust in health information sources, and demographic characteristics. Trust in physicians was assessed using 5 adapted items covering competence, integrity, and benevolence. Descriptive statistics, Pearson correlations, and PROCESS mediation analyses were performed. Results Qualitative findings (n=48) indicated that AI-assisted self-diagnosis was commonly used in a prediagnostic gray zone for preliminary orientation, informal triage, and interim self-management. Participants described AI as helping them appraise illness severity, prepare for consultations, ask questions, and understand physicians' diagnoses and reasoning. Quantitative findings (n=546) were consistent with these patterns. Perceived AI quality was positively associated with calibrated illness appraisal ( b =0.57, 95% CI 0.49-0.64), which was positively associated with patient participation ( b =0.35, 95% CI 0.28-0.43). The indirect association was significant (estimate=0.20, 95% bootstrap CI 0.14-0.26). Perceived AI quality was also associated with diagnosis validation ( b =0.69, 95% CI 0.61-0.76) and diagnosis comprehension ( b =0.65, 95% CI 0.58-0.73), which were associated with trust in physicians ( b =0.15, 95% CI 0.07-0.23 and b =0.20, 95% CI 0.12-0.28, respectively). The corresponding indirect associations were 0.10 (95% bootstrap CI 0.04-0.17) through diagnosis validation and 0.13 (95% bootstrap CI 0.07-0.19) through diagnosis comprehension. Conclusions Generative AI may function as an informational intermediary across the care-seeking process rather than undermine medical authority. By supporting illness appraisal, consultation preparation, and postconsultation understanding, AI-assisted self-diagnosis may be associated with greater patient participation and trust in physicians.","url":"https://doi.org/10.2196/95072","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/95072","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/hsr2.73086","name":"Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Background and aims Differentiating odontogenic keratocyst (OKC) from other radiolucent jaw lesions like ameloblastoma is clinically important but radiographically difficult. Recent advances in artificial intelligence (AI) show promise for enhancing diagnosis using cone-beam computed tomography (CBCT). This study aims to systematically evaluate and meta-analyze the diagnostic accuracy of AI models in detecting OKCs on CBCT imaging. Methods A systematic review and meta-analysis was conducted according to PRISMA-DTA guidelines. Five electronic databases were searched through July 6, 2025. Studies employing AI models for OKC detection using CBCT were included. Methodological quality was assessed using QUADAS-2. Pooled estimates were computed using a random-effects model, with heterogeneity evaluated via I 2 and meta-regression. The Eager test and funnel plot were employed to assess publication bias. Results Twelve studies were included. AI models demonstrated high diagnostic accuracy, characterized by a pooled sensitivity of 89% (95% CI: 79%-95%) and specificity of 92% (95% CI: 81%-97%), both exceeding 85%, along with a substantial diagnostic odds ratio (87.06) and a robust discriminative ability (AUC = 0.828). Deep learning (DL) models achieved higher sensitivity (91%) than machine learning (ML) models (86%), while ML models showed slightly higher specificity. Heterogeneity was substantial (I 2 = 78%-93%). Publication year explained 57.2% of the variability in sensitivity. Conclusions AI-based models, particularly CNN-based DL architectures, demonstrate clinically relevant diagnostic performance with high sensitivity, specificity, and diagnostic odds ratios, supporting their potential role as adjunctive tools in CBCT-based differentiation of OKCs from other odontogenic lesions.","url":"https://doi.org/10.1002/hsr2.73086","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.73086","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.cptl.2026.102752","name":"Artificial intelligence in medical education: a narrative review across four functional domains.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly reshaping medical and health-professions education through adaptive tutoring, generative content creation, simulation analytics, automated assessment, and diagnostic-reasoning support. Since 2023, large language models and multimodal AI systems have expanded AI from relatively narrow analytic tools into interactive educational agents capable of dialogue, feedback, and content generation. Objective This narrative review asks: How does AI contribute to medical education across four core educational functions, and what methodological, ethical, and implementation constraints should guide responsible integration? To answer this question, we synthesize evidence across four functional domains: AI-supported instruction and content generation, AI-augmented simulation and procedural training, AI-supported assessment and learning analytics, and AI-assisted diagnostic reasoning and clinical cognition. Methods A structured search of PubMed, Scopus, and Web of Science (January 2018-January 2025), supplemented by citation tracking, identified 540 records, of which 90 met the eligibility criteria. Evidence was analyzed using an iterative qualitative synthesis approach that combined inductive coding of educational mechanisms with deductive organization into the four predefined functional domains. Findings were summarized descriptively because of substantial heterogeneity in interventions, outcomes, and study designs. Results In instruction and content generation, AI-supported tools were associated with preliminary gains in knowledge acquisition, learner engagement, and self-regulated study, although accuracy depended on supervision and prompt quality. In simulation and procedural training, computer-vision, motion-analytics, and VR/AR systems supported immediate feedback and were associated with improved procedural efficiency in early studies. In assessment and learning analytics, AI tools reduced feedback latency and improved scoring consistency, but validity, fairness, and explainability remained central concerns. In diagnostic reasoning, AI-supported case platforms and LLM-based dialogue improved short-term reasoning performance and metacognitive calibration, while evidence for long-term clinical transfer remained limited. Conclusion AI appears most defensible as an augmentative educational partner that strengthens feedback, personalization, and competency-based progression, rather than as an autonomous substitute for educators. The current evidence supports cautious, supervised implementation, but remains early-phase and methodologically uneven. Multicenter validation, transparent reporting, equity-focused implementation, and structured AI-literacy training are essential before AI can be integrated more broadly into high-stakes educational workflows.","url":"https://doi.org/10.1016/j.cptl.2026.102752","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cptl.2026.102752","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/acamed/wvag094","name":"Developing AI-powered virtual patient chatbots for diagnostic reasoning training.","source":"europepmc","abstract":"Problem Diagnostic reasoning, the cognitive process of interpreting clinical information to arrive at a diagnosis, is a critical competency in medical education, and has been taught using authentic methods such as standardized patients and peer role-playing that simulate real clinical encounters. Although effective, these approaches require substantial time, space, and cost. Approach To address the limitations of traditional approaches, artificial intelligence-powered virtual patient chatbots were developed between April 21-28, 2025, with a chatbot builder that incorporates natural language processing. The design was guided by 2 core cognitive models of diagnostic reasoning-the dual process model and the memory model. The chatbots simulated authentic physician-patient interactions across 3 clinical scenarios: fatigue, stomach ache, and memory loss. Students can perform differential diagnoses based on various clinical data and received immediate feedback on diagnostic accuracy. Outcomes The patient chatbots were evaluated through surveys and interviews with faculty (May 2025) and students (November 2025). Findings consistently indicated that the chatbots functioned as an effective (4.50/5) and usable tool (4.46/5) for diagnostic reasoning practice. Participants perceived that the chatbots supported \"core cognitive processes of diagnostic reasoning\" by prompting active hypothetico-deductive reasoning, whereas repeated exposure to different cases was viewed as facilitating pattern recognition. Faculty highlighted the chatbots' cost-efficiency and scalability within curricula, whereas students emphasized authentic \"from-zero\" clinical reasoning and the value of reviewing dialogue logs for reflection. Overall, the results demonstrate that the chatbots can provide a feasible and educationally meaningful environment for practicing diagnostic reasoning in medical education. Next steps Future work should refine artificial intelligence-powered virtual patient chatbots by incorporating tiered case complexity, patient-centered language, and equity-oriented design. Effective use will require systematic instructional design and learner preparation for emotionally challenging scenarios. Larger empirical studies using both outcome and interaction data are needed to evaluate educational impact and guide integration into clinical training.","url":"https://doi.org/10.1093/acamed/wvag094","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag094","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bjo-2025-328498","name":"Patient perceptions of artificial intelligence in ophthalmology: a cross-sectional survey study.","source":"europepmc","abstract":"Background/aims Patients have largely been excluded from discussions on the use of their health data in developing medical artificial intelligence (AI), despite being directly affected by its integration into care. This study assessed ophthalmology patients' perspectives on AI to inform patient-aligned development and implementation. Methods We conducted a cross-sectional survey across ophthalmology clinics in a large academic hospital system in New York City. Consecutive patients were approached in waiting rooms by a research coordinator to maximise sociodemographic diversity and minimise bias from digital literacy or access. The survey, developed by experts in AI, ethics, ophthalmology and survey methodology, was administered via paper and Qualtrics. It addressed attitudes towards AI in clinical scenarios, willingness to share various types of personal data for AI model development and understanding of AI in ophthalmology. Results Among 403 respondents, 67% reported a low or no understanding of AI, and 71% expressed interest in learning more. Patients prioritised physician involvement and transparency. Comfort decreased with task complexity: highest for screening, lower for diagnosis and lowest for treatment/surgery. For model development, patients were more comfortable sharing de-identified optical coherence technology or lab data than facial images or genetic data. 90% felt consent was always necessary when using personal data to train AI models. Conclusions These findings highlight the need for patient education and robust data consent protocols. Implementing an opt-out system for retrospective data use may enhance trust while supporting innovation. Integrating patient perspectives into AI governance can foster trust and transparency in ophthalmology and beyond.","url":"https://doi.org/10.1136/bjo-2025-328498","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bjo-2025-328498","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s11701-026-03698-7","name":"Reliability and readability of AI chatbot responses to patient questions about robot-assisted radical cystectomy.","source":"europepmc","abstract":"Robot-assisted radical cystectomy (RARC) is a complex procedure that requires patients to understand surgical indications, urinary diversion, perioperative treatment, complications, recovery, and long-term functional outcomes. Although artificial intelligence (AI) chatbots are increasingly used to obtain medical information, their suitability for RARC patient education remains unclear. We conducted a cross-sectional comparative evaluation of four contemporary AI chatbots: ChatGPT-5, DeepSeek-V4, Claude Sonnet 4.6, and Gemini 3.5 Pro. A set of 20 core patient-education questions on RARC was developed by three senior urologic experts. Chatbot responses were assessed using DISCERN, the Ensuring Quality Information for Patients tool, the Global Quality Scale, and JAMA benchmark criteria. Readability was evaluated using the Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, and SMOG. Reliability scores differed significantly across models for DISCERN, EQIP, and GQS, while JAMA benchmark criteria were summarized descriptively as transparency signals. DeepSeek-V4 achieved the highest mean scores for DISCERN, EQIP, and GQS, while ChatGPT-5 and DeepSeek-V4 showed the strongest JAMA benchmark performance. Gemini 3.5 Pro generally had the lowest reliability and transparency scores. Readability also varied across models. DeepSeek-V4 produced the most readable responses overall, whereas Gemini 3.5 Pro generated the most complex text. However, all models exceeded the recommended sixth-grade reading level, and FRES scores remained below the recommended threshold. Contemporary AI chatbots generated responses with variable presentation quality, transparency, and readability for common RARC patient-education questions. Because factual accuracy was not directly assessed, these tools should not be interpreted as validated sources of clinical guidance and should not replace individualized counseling by urologists.","url":"https://doi.org/10.1007/s11701-026-03698-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03698-7","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12911-026-03630-x","name":"Retraction Note: Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-026-03630-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03630-x","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.21203/rs.3.rs-10590390/v1","name":"Artificial intelligence use, perceptions, and educational needs among medical undergraduates at a Sri Lankan medical faculty: A cross-sectional survey","source":"europepmc","abstract":"Abstract Background Artificial intelligence (AI) is rapidly transforming medicine, yet medical curricula, particularly in low- and middle-income countries, have not systematically integrated AI competencies. Understanding medical students' AI use, competence, and perceptions is essential for evidence-based curriculum development. Objective To investigate AI tool usage, self-rated AI competence, and attitudes towards AI in medical education, physician roles, and patient care among medical undergraduates at the Faculty of Medicine, Sabaragamuwa University of Sri Lanka (SUSL), and to compare findings across year groups and gender. Methods A cross-sectional online survey was conducted among Years 1–5 medical undergraduates at SUSL in May 2025. The questionnaire, adapted from previously published international surveys, assessed demographics, AI tool usage, self-rated competence, and 12 Likert-scale attitudinal items covering curriculum integration, physician roles, and patient care. Descriptive statistics, Kruskal–Wallis H-tests, Mann–Whitney U-tests, and chi-square tests were used for analysis. Results A total of 369 responses were analysed. AI adoption was almost universal (99.2%; 366/369). ChatGPT was the most frequently used tool (98.1%), followed by Google Gemini (34.9%), DeepSeek (20.2%), and Microsoft Copilot (6.2%). The main uses of AI were subject reference (89.3%), general information retrieval (72.9%), assignment assistance (50.8%), and writing improvement (39.6%). ChatGPT showed the highest self-rated competence, with 92.4% reporting good or higher competence. Among AI-supported medical applications, Medscape was used by 77.9% of respondents, followed by UpToDate (37.4%) and WebMD/WebMD Symptom Checker (15.4%). Most students agreed that AI could enhance learning in preclinical (85.1%), paraclinical (77.8%), and clinical (68.6%) subjects. Significant differences across year groups were observed for attitudes towards curriculum integration (Kruskal–Wallis H = 29.27, p p = 0.010), with preclinical students expressing more favourable attitudes. Conclusions Medical undergraduates demonstrated near-universal AI adoption despite limited formal training. Preclinical students were more receptive to AI integration than clinical students. These findings support the integration of structured, ethically grounded AI education into undergraduate medical curricula to promote the safe, effective, and responsible use of AI in future clinical practice.","url":"https://doi.org/10.21203/rs.3.rs-10590390/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10590390/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1136/bmjqs-2025-018928","name":"Radiology reporting in the age of artificial intelligence: implications for patient safety.","source":"europepmc","abstract":"Radiological reporting is a central component of clinical decision-making and a patient safety-critical system.Radiology reports inform diagnosis, guide therapeutic decisions and shape interdisciplinary communication; deficiencies in how imaging findings are documented, structured or communicated therefore have direct downstream consequences for patient care. 1 2 Despite this central role, radiological reporting remains vulnerable to welldocumented safety risks.Variability in report structure, inconsistent terminology, omissions of relevant findings and ambiguous language can impair interpretation and increase the likelihood of diagnostic misunderstanding or delayed action.Unstructured or inconsistently structured reports have been shown to contribute to communication failures and complicate clinical decision-making in high-stakes scenarios.[1][2][3] These vulnerabilities long predate the introduction of artificial intelligence (AI).Reporting variability and incompleteness were recognised as persistent threats to patient safety and clinical communication, prompting early efforts towards standardised reporting.1 2 Framing radiological reporting as a patient safety system emphasises its role as the interface through which imaging expertise is translated into clinical action.When this interface is fragile or ambiguous, even accurate interpretations may fail to result in appropriate patient management.This fragility is particularly consequential in the context of AI, as algorithmic outputs will flow through the report and may amplify existing weaknesses rather than correct them.[1][2][3] Protected by copyright, including for uses related to text and data mining, AI training, and similar technologies..","url":"https://doi.org/10.1136/bmjqs-2025-018928","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjqs-2025-018928","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1111/jgs.70682","name":"Artificial Intelligence and Health Care for Older Adults, a Future for All of Us.","source":"europepmc","abstract":"We are racing towards a time wherein the largest demographic of the population will be comprised of older adults and where it is now also possible to measure, monitor, and diagnose long term risk factors and provide effective and valuable preventative interventions. Thanks to the achievements of modern health care, we have reached a tipping point in the need for a robust geriatric workforce. Through the implementation of thoughtful, deliberate, and collaborative processes, Artificial Intelligence can play a significant role in preparing individuals in both mind and body for healthy aging. On May 1, 2025, the Johns Hopkins Artificial Intelligence and Technology Collaboratory (JH AITC) hosted a one-day summit in Washington, DC, to explore the future and potential uses of Artificial Intelligence (AI) for older adults. Presenters and participants discussed the challenges facing the implementation of this technology into the U.S. health care system and the opportunities recent advances are affording in improving the quality of life for older adults.","url":"https://doi.org/10.1111/jgs.70682","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jgs.70682","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.64898/2026.06.26.26356684","name":"Artificial Intelligence in Medical Imaging With Emphasis on Generative and Foundation-Based Methods: A Bibliometric Analysis of Global and United Kingdom Research, 2017-2025","source":"europepmc","abstract":"ABSTRACT Background Artificial intelligence (AI), including generative and foundation-based methods, has rapidly expanded within medical imaging research. However, the structure, citation impact, collaboration patterns, and thematic orientation of national research ecosystems remain incompletely characterised. Objectives To evaluate global research trends in AI applied to medical imaging between 2017 and 2025, with detailed analysis of United Kingdom (UK)-affiliated output, citation performance, collaboration structure, funding landscape, and thematic evolution, with emphasis on generative and foundation-based methodologies. Materials and Methods A bibliometric analysis of Scopus-indexed publications (2017-2025) was performed using a predefined search strategy targeting AI and medical imaging concepts, with emphasis on generative and foundation-based terms. Records were analysed globally and filtered for UK affiliation. Descriptive indicators including total publications (TP), total citations (TC), citations per paper (CPP), and year-on-year growth were calculated. Co-authorship and keyword co-occurrence networks were generated using VOSviewer (v1.6.19). Results A total of 13,452 publications were identified globally (194,650 citations; global CPP 14.47), of which 889 (6.61%) were UK-affiliated. The UK ranked fourth by publication volume yet demonstrated higher citation efficiency (CPP 21.00) than several higher-volume countries. UK output increased approximately 18-fold between 2017 and 2025, with evidence of a citation-lag effect in recent years. Research activity was concentrated within a small number of institutions accounting for nearly half of national output, although citation impact varied independently of volume. Journal-dominant dissemination was associated with higher average citation impact compared with conference-centric models. Keyword analysis identified three principal thematic clusters: generative/deep learning methodologies, MRI- and diffusion-focused applications, and broader diagnostic imaging workflows. Highly cited publications were initially dominated by generative adversarial network–based reconstruction and synthesis, with recent rapid citation growth observed in diffusion and foundation-model architectures. Conclusion UK-affiliated research represents a rapidly expanding and highly cited component of the global AI medical imaging literature, with increasing emphasis on generative, diffusion-based, and foundation-model approaches. These findings provide a reproducible bibliometric baseline for monitoring research activity, collaboration patterns, and potential translational priorities, while recognising that citation-based indicators do not directly measure clinical implementation, methodological quality, or real-world impact.","url":"https://doi.org/10.64898/2026.06.26.26356684","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.26.26356684","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1111/jpc.70513","name":"The Use of Artificial Intelligence in Neonatal Seizure Detection: An Artificial Intelligence-Assisted Systematic Review.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly used in health care. We systematically reviewed evidence on the accuracy of AI in detecting neonatal seizures.","url":"https://doi.org/10.1111/jpc.70513","authors":["Lai NM","Yeo KT","Kong JY","Duan M","Lee SWH","Chaiyakunapruk N","Varshney K","Ovelman C","Chen KH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jpc.70513","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/md.0000000000050323","name":"Artificial intelligence in medical education: Performance of ChatGPT versus medical students on medical examination.","source":"europepmc","abstract":"Attention toward Chat Generative Pre-trained Transformer (ChatGPT) has greatly increased, including in the medical field, but concerns about its problem-solving ability, accuracy, reliability, and limitations in medical problems always coexist. This study aimed to evaluate ChatGPT's performance in solving a medical school examination. We used 50-question examinations from the preventive medicine and psychiatry fields applied to 41 fourth-year medical students. Forty-nine questions were separately entered (one was excluded for being image-based) into the text box of ChatGPT models 3.5 and 4.0 with a request to select the single best answer. The initial answer was considered for primary evaluation, but incorrect answers led to model re-prompting to evaluate its answer regeneration function. We assessed the accuracy of both ChatGPT models and compared them with that of students. The student mean score was 29.5 ± 5.9. ChatGPT's performance was higher in model 4.0 and when re-prompted (models 3.5 and 4.0 initial scores, 28 and 43; re-prompt scores, 40 and 46). Model 4.0 outperformed all students at re-prompting. Both ChatGPT models and all prompts (except for the model 3.5 first prompt) had a higher probability than students of providing correct answers. For easy questions, the accuracy of both ChatGPT models differed significantly from that of students. For difficult questions, the difference between students and the first prompt of model 3.5 was nonsignificant. We observed a nonsignificant tendency in the point estimation when comparing the accuracy of ChatGPT models and prompts by question difficulty. Both models showed excellent performance and learning ability in the medical field, especially model 4.0, and can be considered reliable large language model tools. However, future studies should constantly verify the reliability of the information from these models. Also, educators must be cautious when interpreting/applying ChatGPT output data for clinical and educational purposes.","url":"https://doi.org/10.1097/md.0000000000050323","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000050323","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1186/s12911-026-03614-x","name":"Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has rapidly transformed clinical practice by improving diagnostic accuracy and enhancing decision-making processes. However, its growing role in patient care raises profound ethical concerns regarding patient autonomy. This systematic review synthesizes current evidence on how AI may support, challenge, or reshape patient autonomy in healthcare. Methods This review was conducted in accordance with PRISMA guidelines. Searches were conducted in PubMed, Scopus, Web of Science, Cochrane Library, and Science Direct for studies published between January 2010 and June 2025. The initial search was performed in September 2024 and updated to June 2025 during the peer review process. Eligible studies included empirical, conceptual, or mixed-methods research that addressed AI applications in healthcare and their implications for patient autonomy. Quality assessment was performed using the STROBE, CASP, and MMAT checklists. A narrative synthesis and thematic analysis were conducted. Results Of 472 records screened from the initial search, 11 studies met the eligibility criteria. The updated search (October 2024 - June 2025) identified 3 additional eligible studies, bringing the final total to 14 studies. Seven overarching themes emerged: (1) philosophical and existential dimensions of autonomy, (2) human-centered and autonomy-supportive design, (3) cognitive threats and diminished agency, (4) moral risks and social inequality, (5) covert control and preference manipulation, (6) balancing human and AI authority in clinical decisions, and (7) the need for transparency, dialogue, and adaptive autonomy. Results show that AI can enhance autonomy through shared decision-making and improved information availability, but significant risks exist, including algorithmic opacity, nudging, clinician de-skilling, and increased inequality. The three 2025 studies independently confirmed all seven themes, with Alnawafleh et al. finding that patient autonomy is the predominant ethical concern (67%) in AI-integrated nursing practice. Conclusion AI introduces both opportunities and threats to patient autonomy. Healthcare systems must ensure transparent, human-centered, and ethically governed AI deployment. Autonomy should function as a guiding principle in AI design, training, and regulation. Strong policy frameworks, validated measurement tools, implementation science approaches, and clinical education are essential to ensure that AI serves as a tool for empowerment rather than a source of subtle control. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12911-026-03614-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03614-x","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12885-026-16427-y","name":"Artificial intelligence for cervical cancer screening and diagnosis using Pap smear images: a systematic review.","source":"europepmc","abstract":"Background Cervical cancer continues to be the second most prevalent type of cancer in women globally, especially in the less developed areas. Among several screening methods Pap test, more popularly known as the Papanicolaou test or Pap smear, is one of the most efficient ones for the early detection of this type of cancer. AI has recently made possible the large-scale screening of the whole process. This whole thing has been done in order to increase the early detection rates, which is the long-term aim of reducing the number of cases and deaths that are due to cervical cancer. Objective This systematic review aimed to investigate the role of artificial intelligence in the diagnosis of cervical cancer based on Pap smear results. Methods A comprehensive search was conducted in PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar from database inception to January 2025, with the final search update performed on January 30, 2025. The search strategy was designed using relevant keywords and their synonyms related to \"artificial intelligence,\" \"diagnosis,\" and \"cervical cancer.\"This review included only the English-language studies that had investigated the application of AI in the diagnosis of cervical cancer using Pap test data. The titles and abstracts were initially reviewed by two independent reviewers, and subsequently, full-text assessment was carried out. Data extraction followed the use of standardized forms that collected information on study title, country, number of participants, study purposes, AI technique, error rate, accuracy, and performance outcomes. Results The initial search identified 844 studies, of which 22 met the inclusion criteria and were included in the final analysis. Most studies reported that AI-based algorithms improved the accuracy and efficiency of cervical cancer detection using Pap smear images. Deep learning and machine learning approaches demonstrated high diagnostic performance, with several studies reporting accuracy rates above 90%. Conclusion AI-based approaches show considerable potential for improving the accuracy and timeliness of cervical cancer diagnosis using Pap smear analysis. However, further high-quality studies are required to validate these tools and support their integration into clinical practice.","url":"https://doi.org/10.1186/s12885-026-16427-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12885-026-16427-y","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1136/bmjopen-2026-120631","name":"Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial.","source":"europepmc","abstract":"Introduction Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited. Methods and analysis We will conduct a prospective cluster randomised cross-over trial over a 6-month period, assessing the impact of an AI-assisted fracture detection tool in EDs and MIUs across 4 healthcare Trusts in the UK. Patients aged over 2 years old undergoing a plain film radiography for a suspected fracture as part of routine clinical care will be eligible for study enrolment. The trial will deploy Radiobotics' RBfracture, a CE-approved AI-assisted medical device software for fracture detection at each site for 6 months. Randomisation will be at a cluster-level; a site will be randomised to begin with 'AI on' or 'AI off' for a month, followed by alternating active status each month for the remaining 5 months. The primary outcome will evaluate the incidence of 'inappropriate healthcare contacts' among patients receiving imaging for suspected fractures. This composite metric encompasses inappropriate referrals to fracture clinics, repeated hospital attendances and subsequent follow-up communications regarding missed fractures. The rates of these measures will be compared in the 'AI on' versus 'AI off' stages. Secondary outcomes will include patient-reported outcomes, clinician surveys, a predefined health economic evaluation assessing cost-effectiveness and budget impact and the diagnostic performance of the algorithm. Ethics and dissemination The study has received ethical approval from the South Central-Oxford Research Ethics Committee (Reference: 25/SC/0252, approval date: 23 October 2025), and subsequently from the Health Research Authority (IRAS 3 57 391-A). The results of this study will be presented at relevant scientific conferences and peer-reviewed publications will be disseminated on wider public forums such as traditional and social media. Registration details This is an ongoing, actively recruiting trial (ISRCTN23087950). Recruitment began in January 2026 and will run for 6 months at each site, with per-patient follow-up of 30 days and subsequent data analysis. This manuscript describes protocol version 1.1. The full protocol and statistical analysis plan are available from the corresponding author on request and will be deposited on the study's public repository (https://github.com/Jason-L-Oke/SAMURAI) prior to publication of results. Trial registration number ISRCTN23087950.","url":"https://doi.org/10.1136/bmjopen-2026-120631","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2026-120631","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/healthcare14142103","name":"Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.","source":"pubmed","abstract":"Background: Artificial intelligence is increasingly influencing health care and policy, yet the global research landscape linking artificial intelligence and health care policy remains underexplored. This study aimed to map publication trends, major contributors, collaboration networks, citation structures, and emerging themes in this field. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection. The search was performed on 3 May 2026, and covered publications from 2000 to 3 May 2026. The final dataset included 347 peer-reviewed English-language original research and review articles. Biblioshiny, VOSviewer, and CiteSpace were used to analyze publication trends, country and institutional contributions, author and journal productivity, collaboration networks, citation and co-citation structures, keyword patterns, and thematic evolution. Results: Publications increased markedly after 2020 and reached their highest annual output in 2025. The 2026 publication count was lower because data for that year were partial at the time of database retrieval. Researchers from 82 countries and 900 institutions contributed to the field, with the United States leading in output, followed by China, England, Canada, and India. Harvard Medical School was the most productive institution, whereas Harvard University had the highest institutional centrality. Frontiers in Public Health published the most articles, and PLOS ONE was the most frequently co-cited journal. The most cited article was \"Artificial intelligence and the future of global health.\" Key research themes included machine learning, COVID-19, health policy, risk, large language models, interpretable machine learning, neural network-assisted screening, socioeconomic perspectives, and public health applications. Conclusions: Research on artificial intelligence and health care policy has expanded rapidly, particularly in recent years, and is increasingly centered on predictive modeling, public health decision-making, and emerging artificial intelligence technologies. These findings highlight influential contributors, evolving themes, and future directions for researchers, policymakers, and health care leaders.","url":"https://doi.org/10.3390/healthcare14142103","authors":["Rashidian P","Heidarzad-Pahlaviani F","Moghimnejadhosseini S","Chellapuram N","Somu KP","Reddy Adla Jala S","Jeanty H","Sahu S","Mahapatro A","Talebzadeh M","Khosousi MJ","Amouzadeh-Lichahi M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14142103","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.ijnsa.2026.100636","name":"Barriers and facilitators to artificial intelligence adoption among nursing students: a mixed-methods study.","source":"europepmc","abstract":"Background : Artificial intelligence offers transformative potential for nursing practice, yet significant barriers hinder adoption. While existing research has documented challenges among practicing nurses, limited evidence exists regarding nursing students' perspectives; they are the generation that will shape artificial intelligence's future role in healthcare. Objective : To explore barriers and facilitators to artificial intelligence adoption among nursing students using a mixed-methods approach, examining relationships between technological readiness, ethical concerns, and perceived usefulness of artificial intelligence in nursing practice. Design : Explanatory sequential mixed-methods study combining quantitative surveys with qualitative semi-structured interviews. Setting College of Nursing, Taibah University, Medina, Saudi Arabia (November 2024-March 2025). Participants 348 nursing students across academic levels 3-8 participated in the quantitative phase (response rate: 72.5%), with 17 students purposively selected for qualitative interviews. Methods Validated instruments - the Technology Readiness Index 2.0, an adapted Perceived Usefulness Scale, and an Ethical Concerns Scale - were administered online. Kendall's tau correlation and partial proportional odds modeling identified predictors of perceived usefulness. Qualitative data underwent thematic analysis using Braun and Clarke's framework. Trustworthiness was addressed through investigator triangulation, an audit trail, reflexive memoing, and member-checking. Mixed-methods integration followed a joint-display framework to examine convergence between quantitative and qualitative findings. Results Technological optimism (Kendall's tau [τ] = 0.45, 95% confidence interval [CI]: 0.39 to 0.50, p p Conclusions Nursing students in this Saudi sample demonstrated nuanced perspectives on artificial intelligence adoption, characterised by cautious optimism alongside critical awareness. The paradoxical relationship between ethical concern and perceived utility challenges traditional technology acceptance models, suggesting deeper engagement fosters appreciation of both opportunities and challenges. We have underscored the importance of tailored educational strategies addressing technical competencies alongside ethical reasoning and professional identity formation. As this generation of digitally fluent students transitions into nursing practice, the perspectives of this sample offer insights for developing artificial intelligence integration approaches that preserve nursing's humanistic core while leveraging technological capabilities.","url":"https://doi.org/10.1016/j.ijnsa.2026.100636","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijnsa.2026.100636","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.nepr.2026.104948","name":"Mapping the evidence on generative artificial intelligence for higher-order thinking skills in nursing students: A scoping review.","source":"europepmc","abstract":"Aim This study aimed to map evidence regarding the role of generative artificial intelligence (GenAI) in developing higher-order thinking skills (HOTS) among nursing students. Background GenAI is revolutionizing medical education by enabling innovative pedagogical approaches and showing promise in cultivating HOTS. However, its implementation and evidence on its role in fostering HOTS in nursing students remain unclear. Design A scoping review. Methods The review was conducted in accordance with the PRISMA-ScR checklist and Arksey and O'Malley methodological framework. Searches were performed in October 2025 and updated in February 2026 across eight databases: PubMed, Ovid EMBASE, CINAHL, Web of Science, PsycINFO, Cochrane Library, Education Source and ERIC. Two reviewers independently screened the studies in a blinded manner. Seventeen articles were included. Data were analyzed using interpretive description to summarize study characteristics, followed by thematic analysis to identify relevant themes. Results Studies showed the dual role of GenAI in cultivating HOTS in nursing education. Three key themes were identified, including: (1) the paradoxical reconstruction of cognitive dynamics; (2) tensions and frictions at the human-AI interface; and (3) reshaping learning and professional identity in the AI era. Conclusions The dual role of GenAI in HOTS development indicates that nursing education should integrate GenAI judiciously. While GenAI offers potential benefits for HOTS development, it also introduces risks related to cognitive dependence and reduced critical engagement. Furthermore, efforts should focus on improving the quality of human-AI collaboration, with particular attention to humanistic care, thereby helping students positively reshape their professional perceptions.","url":"https://doi.org/10.1016/j.nepr.2026.104948","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.nepr.2026.104948","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fpubh.2026.1922644","name":"Research on intellectual property rights and legal liability definition of artificial intelligence-assisted pediatric disease diagnosis system.","source":"europepmc","abstract":"Background Children's physiological development is immature, their symptoms are hidden and their diagnostic fault tolerance window is low, so they are the key protected population of public health. Although pediatric AI can alleviate the shortage of medical resources, the unclear definition of ownership and responsibility hinders its standardized development. Methods This study adopts a mixed-method paradigm, separating exploratory analysis and hypothesis testing modules. Based on 89 hospital AI diagnosis documents, 17 medical dispute cases and relevant laws issued between 2021 and 2025, we adopted bibliometrics, case analysis, Delphi expert consultation, empirical regression and age-stratified subgroup analysis. Statistical conclusions only describe the correlation of variables, and do not deduce causality. The 0.85 interpretability threshold is only an exploratory single-center cutoff value and cannot serve as a universal mandatory industrial standard. Results The data indicate that 73.0% of documents lack clear intellectual property ownership clauses, while 47.1% of algorithms score below 0.8 in interpretability. The average liability attribution cycle reaches 66.8 days, with 55.2% of cases exceeding the reasonable time limit. 64.7% of infants aged 0 ~ 3 years are involved in AI misdiagnosis disputes, and the medical adverse damage consequences with higher severity. Among the main causes of misdiagnosis, algorithm defects and hospital negligence accounted for 41.2% respectively, and the responsibility mismatch rate reached 64.7%. In terms of ownership, clinical data belongs to hospitals, and algorithms and software belong to enterprises. The calculation shows that if the interpretability of the algorithm rises above 0.85, the identification efficiency can be improved by 64.9% and the dispute correlation can be reduced by 30.6%. Conclusion Clarify the rights and responsibilities of pediatric AI: the enterprise is responsible for the algorithm and the hospital is responsible for the operation; Give consideration to fairness and privacy. Construct algorithm description, IP evaluation and insurance mechanism to promote compliance application of 3A hospitals. This study is limited to single-center data, and its findings cannot be generalized without multi-center verification.","url":"https://doi.org/10.3389/fpubh.2026.1922644","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1922644","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s00270-026-04473-9","name":"Impact of Artificial Intelligence-Based Triage on Stroke Workflow Metrics: A Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Background Artificial intelligence is an increasingly valuable tool in ischemic stroke management. This systematic review and meta-analysis evaluated the effect of artificial intelligence implementation on stroke workflow metrics. Methods PubMed, EMBASE, OpenEvidence, and the Cochrane Central Register of Controlled Trials were searched for studies (2015-2025) evaluating automated large vessel occlusion detection. Pooled mean differences with 95% CIs were calculated for door-to-groin puncture, door-to-first pass, door-to-revascularization, door-to-needle, and door-in-door-out times. Results Twelve studies met the inclusion criteria: one clinical trial and eleven observational studies. Artificial intelligence was associated with reductions in multiple workflow intervals, including door-to-groin puncture (-17.12 minutes), door-to-first pass (-26.55 minutes), door-to-revascularizationon (-14.55 minutes), door-to-needle (-4.44 minutes), and door-in-door-out time (-36.8 minutes). Conclusion Overall, AI-based platforms appear to contribute meaningfully to stroke workflow optimization, although randomized controlled trials are still needed to confirm their effectiveness. Level of evidence Level 2c, Systematic review of randomized clinical trials, and observational studies.","url":"https://doi.org/10.1007/s00270-026-04473-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00270-026-04473-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/edt.70095","name":"Comparative Efficacy of Trained Versus Untrained Generative Artificial Intelligence Platforms in Providing Case-Based Multiple-Choice Questions on Traumatic Dental Injuries in the Pediatric Dentistry Curriculum: A Cross-Sectional Study.","source":"europepmc","abstract":"Background/aim To determine the comparative efficacy of trained versus untrained generative artificial intelligence platforms in providing multiple-choice questions on traumatic dental injuries in a pediatric dentistry curriculum. Material and methods In this cross-sectional study, a standardized prompt was used on three generative artificial intelligence platforms, accessed via web interfaces in 2025 (United States), to generate case-based multiple-choice questions on four domains: avulsion, crown-root fractures, primary teeth, and permanent teeth injuries. The three generative artificial intelligence platforms used were: ScholarGPT, untrained, and trained ChatGPT4o (OpenAI). Evidence-based guidelines from the International Association for Dental Traumatology were used to train the platform. The generative artificial intelligence platforms were asked to select one correct answer from four choices and provide a rationale for the selection. Two calibrated, masked, board-certified pediatric dentists scored the case-based multiple-choice questions using a validated Artificial Intelligence Study Material Assessment and Reliability tool and noted subjective responses for the questions. Statistical analyses were performed with an alpha value of 0.05. Results All three generative artificial intelligence platforms demonstrated no statistically significant difference (p > 0.05) in terms of their Artificial Intelligence Study Material Assessment and Reliability scores. Some questions had incomplete clinical information (37%), while the options were simplistic (56%) with incorrect rationale (31%). Newer or trained generative artificial intelligence platforms have scores similar to those of untrained platforms, suggesting that publicly available evidence-based information from the International Association for Dental Traumatology enabled the platforms to access these resources for enhanced accuracy. Conclusion The newer or trained generative artificial intelligence platforms show potential to augment dental trauma education within pediatric dentistry through the development of case-based multiple-choice questions; however, limitations in rationale accuracy and answer quality highlight the need for expert oversight.","url":"https://doi.org/10.1111/edt.70095","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/edt.70095","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1080/09273972.2026.2718344","name":"Research progress in the clinical application of AI in strabismus.","source":"europepmc","abstract":"Purpose Strabismus diagnosis and management remain subjective due to variable measurement techniques and lack of standardized surgical criteria. Artificial intelligence (AI) has shown potential in ophthalmology, but its clinical translation in strabismus is underexplored. This review systematically summarizes current AI applications and challenges in strabismus diagnosis, surgical planning, and postoperative training. Methods We searched PubMed, Web of Science, and CNKI for studies up to 2025 using keywords \"artificial intelligence,\" \"machine learning,\" \"deep learning,\" \"strabismus,\" and \"eye tracking.\" Studies on AI-based detection, angle measurement, surgical dosage prediction, and outcome evaluation were included. Results Fifty-two studies were analyzed. AI applications focus on three areas: (1) Deep learning on corneal reflection and facial photographs achieved diagnostic sensitivity of 94-99.1% and specificity up to 99.3%, outperforming clinicians in some datasets; (2) Eye-tracking technology showed ~90% correlation with prism cover test and higher repeatability (ICC 0.99 vs. 0.91, p Conclusion Despite promising accuracy, clinical adoption faces challenges: single-center data bias, poor interpretability, infrastructure gaps in remote areas, and no regulatory approvals. Future work requires multi-center trials, explainable AI, and user-friendly deployment to ensure equitable global use.","url":"https://doi.org/10.1080/09273972.2026.2718344","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/09273972.2026.2718344","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1177/13872877261473304","name":"Artificial intelligence in retinal imaging for early Alzheimer's disease detection: A review.","source":"europepmc","abstract":"BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder that necessitates early, accessible, and non-invasive diagnostic methods.ObjectiveThis review examines the potential of artificial intelligence (AI)-based retinal imaging as a transformative and scalable tool for early AD detection across the full diagnostic continuum, including the preclinical stage.MethodsFollowing PRISMA guidelines, 63 primary studies were selected from an initial pool of 240 articles retrieved from PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar (2017-mid-2025). Advancements in optical coherence tomography (OCT), retinal fundus imaging, and OCT angiography are examined for their capacity to capture structural and vascular biomarkers, including retinal nerve fiber layer thinning and microvascular alterations. AI architectures, including convolutional neural networks, vision transformers, and hybrid models, are evaluated for their accuracy in retinal biomarker analysis. Benchmark datasets, including public and private ones, are assessed for their role in supporting AI-based AD research.ResultsKey challenges are identified, including data heterogeneity arising from variability in acquisition protocols and demographic representation, as well as computational complexity and limited model interpretability. Emerging approaches-notably multimodal data integration and federated learning-offer promising avenues for enhancing diagnostic accuracy while preserving patient privacy.ConclusionsThe socioeconomic implications of integrating AI-based retinal imaging into clinical workflows are discussed. By synthesizing recent advancements, unresolved challenges, and future directions, this review underscores the transformative potential of AI-driven oculomics in facilitating early AD diagnosis and improving patient outcomes.","url":"https://doi.org/10.1177/13872877261473304","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/13872877261473304","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/93400","name":"Attitudes and Needs of Health Care Providers Toward Artificial Intelligence-Assisted Pediatric Palliative Care: Mixed Methods Study.","source":"europepmc","abstract":"Background While artificial intelligence's (AI's) transformative potential in health care is widely acknowledged, its application in highly sensitive, humanistic domains like pediatric palliative care (PPC) remains largely unexplored. Objective This study aims to explore the attitudes and needs of health care providers on the PPC assisted by AI, with the goal of informing future development and implementation of AI systems in this field. Methods This was an explanatory sequential mixed methods study consisting of a nationwide cross-sectional questionnaire survey (March-April 2025) followed by qualitative semistructured interviews (August-October 2025). The quantitative study aimed to investigate PPC health care providers' experiences, attitudes, and needs for the application of AI. Participants included team members of all recognized PPC teams in mainland China. The qualitative study aimed to explore in greater depth the potential future roles of AI in this field, as well as the features of an ideal AI-assisted tool for PPC. Potential interviewees were recruited from the pool of quantitative survey respondents. Results Among 352 survey respondents, most (n=205, 58.24%) reported moderate familiarity with AI, with large language models being the most commonly used (n=280, 79.55%). Among large language model users, over half (161/280, 57.50%) reported using them for clinical purposes. Attitudes were generally positive: 67.05% (236/352) believed AI's benefits would outweigh drawbacks, and 75% (264/352) considered its implementation feasible. The most desired applications were patient and family education (276/352, 78.41%) and symptom management (257/352, 73.01%). Interviews with 17 providers revealed three themes: (1) clinical roles and boundaries, (2) elements for clinical integration, and (3) challenges in development and deployment. Conclusions This study reveals that PPC providers express positive attitudes and strong demand for AI-assisted clinical work. Furthermore, the research clarifies appropriate roles for AI, outlines elements for clinical integration, and highlights potential challenges in development and integration. This study provides evidence for the feasibility of AI application in PPC and offers guidance for the future development and deployment of AI tools.","url":"https://doi.org/10.2196/93400","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/93400","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1002/adma.74656","name":"Engineering Cartilage-Like PVA Hydrogels: Dual-Stage Crystallization Kinetics Regulations Overcome the Strength-Water Content Trade-Off.","source":"europepmc","abstract":"The development of PVA hydrogels for cartilage repair is limited by the challenge of simultaneously combining high mechanical strength with high water content. To address this, a \"Dual-Stage Temperature-Controlled Crystallization Quenching Method\" is proposed. The approach precisely regulates crystallization kinetics, enabling meticulous control over the gel network topology. The obtained hydrogel achieves a water content of 83.41% ± 0.51%, a tensile strength of 2.68 ± 0.14 MPa, and a compressive modulus of 0.53 ± 0.02 MPa, exceeding machine learning-predicted thresholds for each property by over 300%. This performance is attributed to a uniform, isotropic network of refined crystallites, as revealed by multiscale analysis, which facilitates homogeneous stress distribution and efficient energy dissipation. Furthermore, the hydrogel possesses a low friction coefficient, biomimetic porosity, and excellent chondrocyte compatibility. This work provides an advanced cartilage repair material and establishes a novel thermodynamic paradigm for polymer gel design through crystallization kinetics regulation.","url":"https://doi.org/10.1002/adma.74656","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/adma.74656","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1097/md.0000000000049849","name":"Exploring the role of generative artificial intelligence in enhancing clinical skills training: A bibliometric analysis.","source":"europepmc","abstract":"Background Traditional clinical skills training faces challenges such as limited standardized patient resources, high costs of simulation equipment, and restricted opportunities for repeated practice. Generative artificial intelligence (GAI), particularly large language models, has attracted increasing attention as a potential tool for simulation, feedback, and adaptive learning in medical education. However, the research landscape and thematic development of GAI in clinical skills training remain insufficiently mapped. Methods A bibliometric analysis was conducted using literature retrieved from the Web of Science Core Collection from January 1, 2011 to April 1, 2025. VOSviewer, the Bibliometrix R package, and CiteSpace were used to analyze publication trends, country and institutional contributions, journal distribution, collaboration networks, keyword co-occurrence, co-cited references, and citation bursts. Results A total of 322 publications were included. Research activity remained limited before 2023 but increased rapidly thereafter. The United States contributed the largest number of publications, followed by China and India. Major contributing institutions included the National University of Singapore, Gazi University, and Nova Southeastern University. Frequently publishing journals included JMIR Medical Education, Medical Teacher, and BMC Medical Education. Keyword and thematic analyses showed that current research attention is mainly concentrated on ChatGPT, large language models, natural language processing, clinical reasoning simulation, personalized learning, virtual patient interaction, and ethical governance. Conclusions Research on GAI in clinical skills training is in an early but rapidly expanding stage, with growing scholarly attention to language-model-driven educational applications and related ethical issues. The bibliometric findings reflect research activity, knowledge structure, and thematic priorities rather than direct evidence of educational effectiveness. Future studies should adopt rigorous empirical designs and standardized evaluation frameworks to assess the effectiveness, safety, and appropriate boundaries of GAI applications in clinical skills training.","url":"https://doi.org/10.1097/md.0000000000049849","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000049849","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1038/s44325-026-00145-2","name":"Multicenter real world validation of on device single lead ECG parameters for remote cardiac assessment.","source":"europepmc","abstract":"Accurate and continuous electrocardiogram (ECG) parameter measurement outside hospital environments is essential for real-time cardiac health monitoring and telemedicine, yet comprehensive validation of on-device computational methods across heterogeneous real-world populations remains limited. We conducted a real-world validation using two datasets: HeartVoice-ECG-lite (369 participants with single-lead ECG recordings annotated by two cardiologists) and PTB-XL/PTB-XL+ (21,354 patients with 12-lead ECG recordings and diagnostic annotations). FeatureDB ( https://github.com/PKUDigitalHealth/FeatureDB ) was applied to compute PR, QT, and QTc intervals from single-lead signals. Accuracy was assessed using mean absolute error (MAE), correlation, and Bland-Altman analysis. Diagnostic performance for first-degree atrioventricular block (AVBI, based on PR) and long QT syndrome (LQT, based on QTc) was benchmarked against commercial 12-lead systems (12SL, Uni-G) and an open-source algorithm (Deli) using AUC, accuracy, sensitivity, and specificity. FeatureDB-derived parameters showed high concordance with annotations, with MAEs comparable to inter-observer variability and Pearson correlations ranging from 0.836 to 0.960. For AVBI detection, FeatureDB achieved an AUC of 0.787; for LQT, an AUC of 0.684. These results indicate that FeatureDB enables accurate real-time on-device ECG parameter computation suitable for scalable telemedicine, decentralized screening, and continuous community monitoring.","url":"https://doi.org/10.1038/s44325-026-00145-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s44325-026-00145-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.20944/preprints202607.0998.v1","name":"Artificial Intelligence Literacy in Medical and Health-Professions Education: A Bibliometric Scoping Review of a Rapidly Emerging Literature (2020–2026)","source":"europepmc","abstract":"Background: The public release of generative artificial intelligence (AI) tools in late 2022 has spurred rapid interest in preparing health professionals to understand, use, and critically appraise AI. \"AI literacy\" has emerged as a focal construct, yet the resulting literature has grown so quickly that its structure, thematic composition, and gaps remain poorly characterized. Objective: To map the recent literature on AI literacy in medical and health professions education, identify its principal research themes and their growth trajectories, and highlight gaps to guide future work. Methods: We conducted a bibliometric scoping review. Records indexed in OpenAlex (2020 to mid-2026) matching a family of AI-literacy phrases were retrieved (n = 12,326 screened) and then filtered by an automated rule to retain those with a strong medical/health-professions signal, yielding 1,141 papers. English-language records with a substantive abstract (n = 957) were grouped into candidate themes using unsupervised text clustering (TF-IDF with latent semantic reduction and k-means), and the themes were then interpreted and characterized by two reviewers. Reporting follows the PRISMA extension for Scoping Reviews (PRISMA-ScR). Coverage of the single source was cross-checked against PubMed. Results: The subfield is overwhelmingly recent: fewer than 30 papers appeared in any year before 2023 (single digits in 2021), rising to 28 in 2023, 86 in 2024, and 452 in 2025, with 553 in the first half of 2026 alone. A compound annual growth rate of roughly 302% across the full years 2023–2025. Eight interpretable themes emerged, spanning governance and regulation; classroom instruction and diagnostic reasoning; knowledge and perception surveys; AI-literacy scales and attitudes; competency frameworks; generative AI tools; nursing education and ethics; and evidence syntheses. The fastest-growing themes were AI-literacy scale/attitude studies (17.7×) and governance and regulation (14.8×). Nursing was the most-represented discipline (345 papers), ahead of medicine (290). The most-cited works remained a small set of 2021–2023 curriculum-readiness surveys and evidence syntheses. Conclusions: AI literacy in health professions education has evolved over two years from asking whether to teach AI to measuring learner readiness and governing institutional adoption. Progress is constrained by a proliferation of non-harmonized measurement instruments, a relative scarcity of validated competency standards, and few studies linking AI-literacy interventions to clinical performance or patient outcomes. Harmonized measurement, consensus competency frameworks, and outcome-anchored evaluations are priorities.","url":"https://doi.org/10.20944/preprints202607.0998.v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0998.v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2147/amep.s632635","name":"Analysis of Generative Artificial Intelligence Governance in Publicly Available Documentation of Global and Saudi Universities Offering Health Informatics Programs.","source":"europepmc","abstract":"Background Generative artificial intelligence (GenAI) has rapidly entered higher education, prompting universities to publish policies and guidance for its responsible use. Little is known about how GenAI is governed within Health Informatics and related programs, or how international and Saudi institutions compare. Objective To analyze and compare publicly available GenAI governance in global and Saudi universities offering Health Informatics and related programs, focusing on policy availability, governance-domain coverage, and gaps. Methods A qualitative document analysis and comparative policy analysis were conducted. Thirty international universities (selected via the QS World University Rankings by Subject 2025) and sixteen eligible Saudi institutions offering Health Informatics, Health Information Management, Digital Health, or related programs were included. Publicly available governance sources-comprising formal policy documents and web-based institutional guidance-were retrieved from official institutional websites between March and May 2026 and analyzed using directed qualitative content analysis across nine governance domains. Inter-coder reliability was almost perfect (Cohen's κ = 0.96), and group comparisons were tested using Fisher's exact test. Results In total, 117 governance sources met the inclusion criteria (111 international; 6 Saudi). All international universities (30/30) had publicly available GenAI governance, versus six of sixteen Saudi universities (37.5%). International coverage was significantly greater for Transparency and Disclosure (87.4% vs 33.3%; p=0.005), Ethical Considerations (91.9% vs 50.0%; p=0.014), and AI Literacy and Capacity Building (81.1% vs 33.3%; p=0.019). Program-specific Health Informatics governance appeared at only two international institutions and no Saudi universities; clinical-data-governance provisions were absent from all Saudi sources. Conclusion Based on publicly available documentation, international universities showed broader and deeper GenAI governance than Saudi institutions, with gaps in transparency, AI literacy, ethics, and Health Informatics-specific and clinical-data governance. Strengthening discipline-specific guidance may support responsible GenAI integration and digital-health workforce preparation.","url":"https://doi.org/10.2147/amep.s632635","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/amep.s632635","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2460/javma.26.05.0353","name":"An artificial intelligence-enabled digital stethoscope demonstrates moderate murmur detection in dogs but not cats and unreliable arrhythmia classification in both species.","source":"europepmc","abstract":"Objective To prospectively evaluate the diagnostic performance of an AI-enabled digital stethoscope in detecting cardiac murmurs and arrhythmias compared to fourth-year veterinary students and experienced clinicians. Methods Dogs and cats presenting to a university teaching hospital were prospectively enrolled from August 1, 2025, through December 31, 2025. Each animal underwent cardiac auscultation at 4 thoracic sites with the use of an AI-enabled digital stethoscope (Core 500; EKO Health Inc), 6-lead ECG, and echocardiogram, if clinically indicated. Auscultation was performed by a cardiology resident, board-certified cardiologist, and fourth-year veterinary student. Results The stethoscope demonstrated a sensitivity of 86.8%, specificity of 56.3%, and positive predictive value of 82.5% for murmur detection in dogs. There was no difference between the agreement of the AI stethoscope or students with a clinician (κ = 0.447). In cats, sensitivity was markedly lower (9.1%), with only 2 of 22 murmurs detected (κ = 0.081). The stethoscope demonstrated high sensitivity for atrial fibrillation (100%), but classified no dog as arrhythmia-free. Murmur grade was the only significant predictor of stethoscope murmur diagnosis, and high-grade murmurs (≥ 3) had significantly greater odds of detection (OR, 15.11). Conclusions The AI-enabled stethoscope demonstrated moderate murmur detection performance in dogs, comparable to fourth-year veterinary students, but performed poorly in cats. Arrhythmia classification was unreliable in both species. Clinical relevance AI-enabled stethoscopes represent an emerging tool in veterinary medicine, but clinical validation is necessary before routine adoption. This study provides prospective performance data across small animals and examiner levels, identifying meaningful limitations regarding interpretation of this technology.","url":"https://doi.org/10.2460/javma.26.05.0353","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2460/javma.26.05.0353","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12873-026-01697-3","name":"Artificial intelligence for pediatric fracture detection: impact on diagnostic revisions and patient recall rates in a tertiary emergency setting.","source":"pubmed","abstract":"Artificial intelligence (AI) can support and enhance radiologists in musculoskeletal imaging, but evidence of clinical benefit is still lacking. This prospective study aimed to estimate the range of expected effect size on patient recall rates whether after implementation of an AI system for fracture detection affects in a paediatric emergency setting during out-of-hourse care.","url":"https://doi.org/10.1186/s12873-026-01697-3","authors":["Deffaa OJ","Pape J","Schlösser D","Hirsch FW","Lacher M","Rosolowski M","Gräfe D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12873-026-01697-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1080/01616412.2026.2721684","name":"3D interactive radiology reports: referring physicians' acceptance of artificial intelligence varies across practice settings.","source":"europepmc","abstract":"Introduction A 3D interactive report is a state-of-the-art artificial intelligence (AI) tool that integrates a patient's imaging history into an intuitive visualisation. Yet, despite the many potential benefits to patients, referring physicians, and the healthcare system, a substantial gap exists between such AI developments and their clinical implementation. The goal of this study was to evaluate physicians' attitudes towards AI and their readiness to implement new AI-technologies in different practice settings. Materials and methods A 15-question cross-sectional online survey addressing AI in Radiology was distributed to physicians through professional networks and at a national neuroscience conference. Results were summarized using descriptive statistics, as appropriate for the type and distribution of the data. Group comparisons were made using Chi-square or Fisher's test, as appropriate. Results 75 physicians completed the survey between September-December 2025, 52 (69%) being hospital physicians and the rest private practitioners. Response rate was 17%. Hospital physicians were significantly more likely to implement a 3D interactive report in daily practice (Hospital: 82.4% vs. Private Practice: 36.8%; p p = 0.007). Conclusion Hospital physicians were more receptive for use of AI technologies in radiology compared to private practitioners. Practice patterns, economic factors, doctor-patient relationships, and frequency of interaction with the radiology department likely influence how AI use in radiology is perceived.","url":"https://doi.org/10.1080/01616412.2026.2721684","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/01616412.2026.2721684","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s11701-026-03889-2","name":"Robot-assisted biopsy a bibliometric analysis of global trends, hotspots, and emerging frontiers (2000-2025).","source":"europepmc","abstract":"Robot-assisted biopsy techniques achieve precise minimally invasive sampling of deep-seated and anatomically complex lesions through stable robotic manipulation, intelligent path planning, and multimodal imaging fusion. These techniques have accumulated substantial evidence in urology, neurosurgery, and thoracic intervention. However, the field spans multiple organs and technical directions, and lacks a systematic, macroscopic depiction of its overall knowledge structure, evolutionary trajectory, and emerging frontiers. This study aims to systematically map global research trends, the distribution of core research actors, the evolution of the knowledge structure, and emerging frontier directions in robot-assisted biopsy via bibliometric methods, thereby providing a macro-level evidence map for researchers, clinicians, and policymakers. Using the Web of Science Core Collection as the primary data source, we retrieved literature related to robot-assisted biopsy from 2000 to 2025, limited to English Articles and Reviews; an independent dataset was constructed from PubMed for semantic validation at the MeSH term level. Descriptive statistics, collaboration networks, co-citation networks, keyword co-occurrence, citation burst detection, and dual-map overlay analysis of journals were conducted using Bibliometrix, VOSviewer 1.6.20, CiteSpace 6.4.R1, and Scimago Graphica. A total of 2,336 publications were included, with an average annual growth rate of 16.27%. Research progressed through three phases: a slow start from 2000-2009, steady growth from 2010-2016, and accelerated expansion after 2017; publications since 2017 account for 70.85% of the total. The United States (826 publications, 35.4%) ranked first in both productivity and academic impact; China (380 publications, 16.3%) ranked second in output but had a lower citations-per-paper (14.4). Engineering and clinical medicine journals form a dual-core citation structure, with medical imaging journals positioned as interdisciplinary bridging hubs. Keyword analysis showed biopsy(618 occurrences) and medical robotics(612 occurrences) at the network core; prostate cancer, accuracy, and diagnosis constitute the traditional research mainline, while terms such as diagnostic yield, navigation, and artificial intelligence have become increasingly active in recent years. Citation burst analysis indicates a shift in research focus from early robotic system design (2006-2013) to clinical diagnostic paradigms (post-2017), with persistent citation bursts in recent years for pulmonary robotic bronchoscopy literature. MeSH term analysis of the independent PubMed validation set (1,531 publications) further confirmed main research lines including robotic technology, prostate tumors, image-guided biopsy, and pulmonary robotic intervention. Robot-assisted biopsy is transitioning from validation of technical feasibility toward creation of clinical value. Prostate biopsy represents the most mature clinical translation pathway; robot-assisted bronchoscopy is the one of the most rapidly growing emerging directions. AI-driven autonomous navigation and human-machine collaboration are reshaping the technological landscape of the field. Future efforts should emphasize multicenter comparative studies, standardized outcome measures, and health-economic evaluations to facilitate integration of robot-assisted biopsy into routine clinical practice.","url":"https://doi.org/10.1007/s11701-026-03889-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03889-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1002/jpr3.70202","name":"Assessing pediatric gastroenterologists' use of artificial intelligence in clinical and professional tasks.","source":"europepmc","abstract":"Objectives Artificial intelligence (AI) has demonstrated potential to enhance clinical efficiency. However, its real-world adoption among pediatric gastroenterologists (GIs) remains poorly characterized. The primary objective of this study was to assess how pediatric GIs are currently utilizing AI. Secondary objectives included estimating time saved, identifying commonly used tools, and evaluating the impact on burnout. Methods An institutional review board-approved anonymous survey was distributed via the international pediatric gastroenterology listserv. The questionnaire assessed demographics, types of AI technologies utilized, and estimated time saved through their use. The survey was open from September 30, 2025, to October 28, 2025. Results One-hundred and twenty-nine completed surveys were included in the analysis. Majority of respondents were from the United States (85.3%). AI was most frequently used for ambient AI scribes, dictation, answering clinical questions, preparing letters of medical necessity, and insurance appeals, with many reporting time savings with use of AI. In addition, 58.1% of respondents had utilized AI for some research-related purpose. AI utilization did not vary significantly across years of practice or practice type. Clinicians who utilized AI technology had higher frequency of self-reported reduced burnout. Few (28.7%) received training in AI, while the majority (84.4%) would like more education in the use of AI in practice. Conclusions The use of AI tools by pediatric GIs is variable. Those who adopted AI reported reduced burnout and increased time savings, reflecting a positive impact on efficiency and clinician well-being. Despite limited formal training, there was a strong interest in AI education and clinical integration.","url":"https://doi.org/10.1002/jpr3.70202","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/jpr3.70202","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.12927/cjnl.2026.27881","name":"Artificial Intelligence in Nursing: The Leadership Readiness Gap.","source":"pubmed","abstract":"Artificial intelligence (AI) is being rapidly and broadly introduced into personal, public, commercial and health/medical practice settings. AI refers to a broad and evolving set of computational technologies, including machine learning, natural language processing, computer vision and generative AI ((Van Bulck et al. 2023; McGrow 2025). AI enables systems to learn from data to perform tasks that have traditionally required human intelligence, such as pattern recognition, clinical prediction, decision support and content generation (Bajwa et al. 2021; Rony et al. 2024; WHO 2021, 2025). Within healthcare, there is growing consensus that AI-informed tools are intended to augment - but not replace - the clinical judgement, ethical reasoning and relational care that are the hallmarks of nursing practice (ANA 2022; Rony et al. 2024). Findings from a recent survey of Canadian Academy of Nursing Fellows ( n = 57) indicate that while many nurse leaders have encountered AI in practice, few feel confident with its use or governance. This gap suggests a leadership readiness challenge as well as a technology adoption challenge. Unlike earlier digital health tools, AI changes how information and knowledge are created and subsequently interpreted by clinicians, generating new risks as they become accountable for decisions shaped by systems they cannot fully evaluate (AAN 2026; McGrow 2025). The Canadian nursing informatics entry-to-practice competencies now include expectations that nurses assess AI outputs and safeguard equitable care, yet governance structures to support these responsibilities remain underdeveloped. Fellows also highlighted other concerns related to the use of AI, including the potential for algorithmic bias, ethical use and threats to equity and privacy. In this commentary, we posit that beyond supporting technological adoption, AI in nursing is fundamentally a leadership competency and governance issue. In this context, it is paramount that nurse leaders actively participate in establishing AI policies and evaluation standards, advance AI competency development and set boundaries for nursing judgement that ensure safe, ethical and accountable use of AI in healthcare.","url":"https://doi.org/10.12927/cjnl.2026.27881","authors":["Nagle L","Strudwick G","Jeffs L","Donelle L","Edwards M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12927/cjnl.2026.27881","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1111/tct.70472","name":"Improving Resident Knowledge of Artificial Intelligence Ethics and Prompting for Clinical Use.","source":"europepmc","abstract":"Background The rapid introduction of AI into clinical practice shifts how we must teach resident trainees so they may become ethical patient-facing clinicians in an AI-integrated healthcare system. Currently, few published innovations assess outcomes beyond learner attitudes. We developed a pilot curricular innovation to equip postgraduate Internal Medicine resident trainees with the attitudes and knowledge needed to responsibly integrate AI tools into patient care decisions. Approach In the 2025-2026 academic year, we piloted a curricular innovation to teach resident physicians the basics of prompting strategies for AI-assisted clinical reasoning, ethical AI use and legal considerations. The innovation consisted of an initial didactic followed by a hands-on, interactive session integrating AI prompts and outputs into clinical vignettes, thereby leveraging near-peer teaching and situated learning to achieve session objectives. Evaluation We assessed perceived knowledge and knowledge using a pre-post intervention strategy using the Wilcoxon Rank-Sum test. Fifty-nine/96 (61.5%) and 52/96 (54.2%) of residents participated in the presession and post-session survey, respectively. Perceived knowledge increased significantly across all five learning objectives with a moderate to large effect size. Fifty-one residents participated in the pre- and post-session knowledge test. The median pre-session score was 6/8 (interquartile range [IQR] 4-8), and the median post-session score was 7/8 (IQR: 5-8); p Implications A combined didactic and small-group interaction session improved residents' perceived understanding and knowledge of ethical and legal considerations related to clinical AI use. Future work developing clinical assessments of trainee skills using AI tools is needed.","url":"https://doi.org/10.1111/tct.70472","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/tct.70472","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fmed.2026.1901004","name":"Mapping artificial intelligence in problem-based and case-based medical education: a bibliometric analysis (2019-2026).","source":"europepmc","abstract":"Introduction Problem-based learning (PBL) has been a cornerstone of medical education since its introduction at McMaster University in the 1960s. Since the public release of ChatGPT in November 2022, artificial intelligence (AI) tools have increasingly been applied to PBL and case-based learning (CBL) contexts, yet the research landscape at this intersection remains poorly characterized. This study aimed to map the growth trajectory, thematic structure, and collaboration networks of AI-PBL/CBL research from 2019 to 2026. Methods A comprehensive search of Scopus and Web of Science was conducted on June 2, 2026, combining AI-related terms with PBL/CBL frameworks and medical education contexts. Using a PRISMA-guided bibliometric review workflow, 1,616 records were identified; after deduplication and eligibility screening, 735 unique publications (2019-2026, original articles, reviews, conference papers, and other eligible indexed document types) were included. Bibliometric analyses employed VOSviewer for network visualization (keyword co-occurrence, co-authorship, co-citation), CiteSpace for citation burst detection, and Bibliometrix for thematic mapping, three-field plot, and factorial analysis. Results Publication output grew from 25 papers in 2022 to 254 in 2025, with 206 papers indexed by June 2, 2026. The United States ( n = 70) and China ( n = 64) led publication volume. Keyword co-occurrence analysis identified four thematic clusters: a central AI-focused cluster, a medical education and clinical reasoning cluster, a nursing and simulation-oriented cluster, and an educational technology cluster. Kung et al.'s 2023 study evaluating ChatGPT's performance on the USMLE was the most frequently co-cited reference (62 co-citations, betweenness centrality = 0.11). Thematic mapping positioned machine learning as a motor theme, while clinical reasoning, medical education, and self-directed learning appeared in the basic themes quadrant. The country/region collaboration network comprised 33 countries/regions and was led by the United States and China, although collaboration patterns remained uneven across regions. Conclusion To our knowledge, this is the first bibliometric study specifically focused on the intersection of AI technologies with PBL/CBL in health professions education. The findings reveal rapid growth after 2023, four distinct but interconnected research clusters, and a collaboration network led by the United States and China, with uneven regional participation. These results may inform curriculum design and research priorities in AI-enhanced medical education.","url":"https://doi.org/10.3389/fmed.2026.1901004","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1901004","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.22037/aaem.v14i1.2997","name":"Explainable Artificial Intelligence in Non-Contrast Brain Computed Tomography Scan for Intracerebral Hemorrhage: A Scoping Review.","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) models applied to non-contrast brain computed tomography (CT) scan have demonstrated promising performance in hematoma detection, segmentation, and outcome prediction. Explainable artificial intelligence (XAI) has been proposed as a strategy to improve transparency and facilitate clinical integration. This study aimed to map and summarize XAI methods used in CT scan-based studies of intracranial hemorrhage (ICH), and to assess their validation approaches, transparency, and clinical relevance. Method The scoping review was conducted in accordance with the PRISMA-ScR guidelines and was registered on the Open Science Framework (osf.io/5kxbt). In this review, original studies of adult and pediatric patients with spontaneous ICH that used AI or deep learning on non-contrast brain CT scan and included an explainability or interpretability method were eligible. Reviews, editorials, conference abstracts without full text, and studies without XAI components were excluded. PubMed, Embase, and Scopus were searched from inception to September 2025. Data were extracted on study design, imaging inputs, AI task, explainability technique, validation strategy, and reported clinical relevance. Results A total of twelve studies met inclusion criteria. Most investigations focused on hematoma detection, segmentation, or prediction of functional outcome or mortality. Gradient-based visualization techniques, particularly Grad-CAM and saliency maps, were the most commonly used explainability approaches. XAI outputs predominantly highlighted hematoma regions and perihematomal tissue; however, reporting and interpretation of explainability varied substantially across studies. External validation was infrequent, and few studies formally assessed alignment between XAI outputs and established radiological or clinical reasoning. The evidence was small, heterogeneous, and largely retrospective, with inconsistent reporting of explainability quality and limited external validation. Conclusion Explainable AI applied to non-contrast brain CT scan in ICH cases, is an evolving field with growing methodological diversity but limited standardization. While XAI techniques offer potential to enhance transparency and clinician confidence, current evidence remains insufficient to support routine clinical implementation. Future studies should emphasize standardized reporting, external validation, and clinically grounded evaluation of explainability outputs.","url":"https://doi.org/10.22037/aaem.v14i1.2997","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22037/aaem.v14i1.2997","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s13321-026-01291-6","name":"Comment on: \"A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review\" (Marzouk et al., 2025).","source":"europepmc","abstract":"Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug-drug, drug-disease, and drug-nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely restricted to PubMed with title- and abstract-level filtering, while manual record removal is reported without explicit criteria defining \"irrelevant\" studies, limiting transparency and reproducibility. Protocol registration, duplicate independent screening, standardized extraction procedures, and formal bias assessment using established frameworks such as ROBIS, PROBAST+AI, and TRIPOD+AI were not clearly reported. The review reports performance metrics such as area under the receiver operating characteristic curve (AUROC), but does not provide a structured framework for interpreting or comparing metrics across heterogeneous datasets, prediction tasks, and evaluation protocols. Because the interpretation of AUROC and precision-recall metrics depends on class prevalence, outcome definition, and the intended prediction task, future reviews should report complementary discrimination metrics, calibration, uncertainty estimates, and external validation rather than assuming that any single metric is universally preferable. Claims of superior model performance should be supported by confidence intervals and statistical comparisons appropriate to the evaluation design, such as paired DeLong testing when applicable. Claims of superior model performance should also be supported by appropriate statistical testing, including methods such as the nonparametric DeLong test. Several conceptual clarifications are also warranted. AI models may prioritize hypotheses but do not replace experimental or clinical validation under current regulatory standards. Furthermore, AUROC should not be conflated with pharmacokinetic area under the curve, and SciBERT should not be characterized as a three-dimensional molecular graph framework. Future reviews should adopt transparent multi-database searches, structured bias assessment, and reproducible reporting practices.","url":"https://doi.org/10.1186/s13321-026-01291-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13321-026-01291-6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/heartjnl-2025-327090","name":"Artificial intelligence based multimodal fusion of electronic medical record data and ECG signals for pericarditis prediction: pericarditis calculator (p-Cal) as a screening aid.","source":"europepmc","abstract":"Background Pericarditis may be associated with significant morbidity and mortality and can be a clinically challenging entity to diagnose. There is a clinical need for automated artificial intelligence (AI) algorithms to act as a screening aid to assist with the early and accurate detection of pericarditis. Methods We developed p-Cal, a browser-based pericarditis risk calculator that fuses the patient clinical data and 12-lead ECG image to estimate risk for pericarditis. The ECG encoder was designed based on a pretrained MedCLIP Vision Transformer and random forest classifier was used for the clinical tabular data. These two modalities were fused at the decision-level using a meta classifier. Model reasoning for both modalities was assessed using advanced explainable AI techniques-attention maps from the transformer model were used to interpret ECG images, while SHapley Additive exPlanations (SHAP) force plots were applied to analyse the tabular data. Results A total of 6508 patients (mean age 54.8±16.1 years, 50.9% female) were used for training and internal validation of the AI model, of whom 902 patients had a confirmed diagnosis of pericarditis (13.8%) and the remainder were normal controls. On 1302 hold-out test patients, the Fusion Model demonstrated the best discriminatory performance, achieving an area under the curve (AUC) of 0.89, which surpassed the ECG (AUC = 0.85) and the clinical data only models (AUC = 0.74) performance. On external validation in the Medical Information Mart for Intensive Care (MIMIC) Database, the fusion model achieved an AUC of 0.81. Conclusion A late fusion AI model integrating tabular clinical electronic medical record data and ECG signal has robust predictive capability for pericarditis as a screening tool.","url":"https://doi.org/10.1136/heartjnl-2025-327090","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/heartjnl-2025-327090","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fmed.2026.1680509","name":"Enhancing healthcare quality and management: AI-driven smart consortium practices at Dapeng Medical Group, Shenzhen.","source":"europepmc","abstract":"Objective To evaluate the impact of smart healthcare consortium management practices, utilizing artificial intelligence (AI) to enhance healthcare service quality and operational efficiency at Dapeng Medical Group, Shenzhen. Design A case study analysis of smart healthcare initiatives focusing on AI-driven improvements in patient care and management processes. Site Dapeng Medical Group, a comprehensive medical institution in Shenzhen, China. Participants Patients receiving care within the Dapeng Medical Group, along with healthcare providers and administrative staff involved in the implementation of the smart healthcare consortium. Methods The initiative incorporated advanced technologies, including AI, cloud computing, and the Internet of Things (IoT), to streamline healthcare operations. Key performance metrics such as patient satisfaction, waiting times, diagnostic accuracy, and resource utilization were monitored to assess the effectiveness of the smart healthcare consortium. To clarify the effectiveness of Dapeng Medical Group's artificial intelligence technologies in smart healthcare consortium management, this study compares relevant management data from before AI adoption (2022) and after AI enablement (2025). Results The implementation led to a 25% increase in patient satisfaction, 14% reduction in average waiting times and 34% reduction in the consultation duration, a 29% reduction in error rate of online pre-diagnostic precision. Conclusion The integration of AI and smart technologies into healthcare management significantly improved service quality, operational efficiency, and patient outcomes at Dapeng Medical Group. These findings suggest that smart healthcare consortia can serve as a model for future healthcare innovations, ensuring high-quality, efficient, and patient-centered care.","url":"https://doi.org/10.3389/fmed.2026.1680509","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1680509","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1093/eurheartj/ehag302","name":"Artificial intelligence in cardiovascular imaging 2025: insights from the National Societies Cardiology journals.","source":"europepmc","abstract":"Cardiovascular imaging plays a key role both in diagnosis and in management strategies of most cardiovascular diseases. In line with the rapidly growing role of artificial intelligence (AI) in cardiology,1 several issues related to the implementation and usefulness of AI in cardiovascular imaging have been addressed by studies published in 2025 in the National Societies of Cardiology Journals (NSCJs). Both potential benefits and risks of integrating AI into cardiovascular imaging have been evaluated in a study by Howard et al. Cardiovascular imaging may be dramatically transformed by automatic tasks such as image segmentation, feature extraction and risk prediction, leading to significant improvements in diagnosis precision and efficiency. However, the integration of AI into clinical workflows entails critical risks such as model errors and inappropriate usage. These drawbacks should be overcome by the adoption of explainable AI techniques, rigorous validation frameworks to ensure fairness and broad applicability, continuous performance monitoring, and transparency at every stage of model development and deployment.2 This was performed in a French study by Lafitte et al. which was intended to evaluate the integration of AI-based tools in a high-volume echocardiography department and to assess the concordance of AI-generated measurements with manually performed measurements by cardiologists. Among the 894 examinations performed over 2 months, the analysis revealed good to very good concordance, especially for ejection fraction (EF) and for Doppler-based mitral flow measurements.3 Recent advances in AI and increased use of machine learning have been proven suitable for the evaluation of valvular heart disease, as reported in a Spanish journal by Badano et al. AI-assisted analysis of 758 patients with moderate-to-severe secondary tricuspid regurgitation (STR) identified three phenogroups. The low-risk phenogroup had a 2-year event-free survival of 80%. The intermediate-risk phenogroup included older patients with severe STR, and the high-risk phenogroup included younger patients with massive-to-torrential STR, severely dilated and dysfunctional right ventricle and right atrium.4 In a study published in the Polish Heart Journal (Kardiol Pol), Yian et al. showed that application of machine learning provides interesting insights in predicting postoperative arrhythmia among 1384 children after transcatheter closure of perimembranous ventricular septal defects. Among the four machine learning methods tested, namely SVM, LR, RF, and XGBoost, the LR model outperformed the three others, achieving reliable results. This allowed the construction of a nomogram based on five variables, weight, procedure time, defect diameter, pre-interventional arrhythmia and the difference in the diameter between the occluder and defect exceeding 2 mm.5 Robotic-assisted percutaneous coronary intervention has emerged as a technological innovation in interventional cardiology, but comparative clinical evidence supporting its superiority (or even non-inferiority) over conventional approaches is still limited. A cooperative Italian and Turkish study by Biondi-Zoccai et al. showed that AI-based decision support systems in high-risk cardiovascular procedures require rigorous validation to ensure safety and reliability. Ethical considerations, including patient data security and region-specific regulatory frameworks, also raise significant barriers. Addressing these challenges requires interdisciplinary collaboration, robust external validation, and the development of transparent, interpretable AI models.6 Jessney et al. analysed 366 intracoronary optical coherence tomography (OCT) pullbacks from 297 patients (58 840 OCT frames) with coronary artery disease using an AI-based analysis. The software was designed to correct poor quality or artefact-containing OCT, identify tissue/plaque composition, classify plaque types, measure lumen area, lipid and calcium arcs and fibrous plaque thickness. AutoOCT, a modular deep learning AI-based diagnostic software, recovered images containing common artefacts and had a plaque classification accuracy of 83% vs histology, equivalent to expert clinician readers.7 In a multicenter cohort of 410 patients with reperfused acute myocardial infarction (AMI) and at least three akinetic segments on admission, Logeart et al. analysed transthoracic echocardiography and cardiac magnetic resonance imaging to predict left ventricular remodelling (LVR) using machine learning methods. This approach identified and prioritized early variables that are associated with adverse LVR, including left ventricular dilatation >20% and 6-month left ventricular EF < 50%.8 AI has also been shown to provide relevant contributions in basic cardiovascular research applied to coronary artery disease. The AI-assisted quantification of infarct size (IS) and area at risk (AAR) using a deep learning segmentation model in an ischemia/reperfusion model in pigs was evaluated in a German study by Braczko et al. The model reduced the IS and AAR quantification time per experiment from approximately 90 min to 20 s without significant differences in IS and AAR between predictions and the standard method using film-scan annotations to image annotations. It also provided more standardized data quality and reduced inter-observer variability than manual observation.9 The advent of AI in cardiovascular imaging enables a broad panel of applications, including advanced image-based diagnostics, complex risk prediction models, and real-time procedural guidance. Despite these benefits, AI-based cardiovascular management also presents certain challenges. The reliability of AI-models is highly dependent on data quality, requiring extensive preprocessing to handle issues such as missing values, imbalanced datasets and potential biases. Additionally, although AI can enhance diagnostic precision, its integration into clinical practice necessitates careful validation to ensure robustness and generalisability across diverse populations. Ethical considerations, including patient data privacy and the accountability of AI-driven decisions, must also be addressed to foster trust among healthcare providers and patients.10 Many of these ongoing related issues have currently been addressed in the NSCJs affiliated with the European Society of Cardiology. ESC staff: Michael Alexander, Publications Associate Director; Maryne Renon, Publications Supervisor; Pauline Léger, Imène Barbouche, Publications Officers. Nothing to declare. Nothing to declare. Jean-Jacques Monsuez (Editor-in-Chief of Archives des Maladies du Cœur et des Vaisseaux Pratique), France; Çetin Erol (Editor-in-Chief Anatolian Journal of Cardiology), Turkey; Anetta Undas (Editor-in-Chief Kardiologia Polska), Poland; Michael Boehm (Editor-in-Chief Clinical Research in Cardiology), Germany; Ariel Cohen (Editor-in-Chief of Archives of Cardiovascular Diseases), France; Gerd Heusch (Editor-in-Chief of Basic Research in Cardiology), Germany; Kazem Rahimi (Editor-in-Chief Heart), United Kingdom; Juan Sanchis (Editor-in-Chief of Revista Española de Cardiología), Spain; Plamen Gatzov (Editor-in-Chief of Forum for Interventional Cardiology Journal, Editors’network nucleus), Bulgaria; Fernando Alfonso (past- Chairman of the Editors’ Network of the European Society of Cardiology), Spain.","url":"https://doi.org/10.1093/eurheartj/ehag302","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/eurheartj/ehag302","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.artd.2026.102095","name":"Optimizing Hip and Knee Arthroplasty Clinic Flow: A Prospective Evaluation of Artificial Intelligence Scribe Technology.","source":"europepmc","abstract":"Background The increasing burden of clinical documentation contributes to physician inefficiency and burnout, with electronic health record implementation significantly increasing documentation time from 16% to 28% of clinical time. While human medical scribes have shown benefits, artificial intelligence (AI) scribes represent a promising solution. This study evaluated the impact of AI scribe technology on clinical encounter duration and physician workload in a high-volume hip and knee arthroplasty clinic. Methods This prospective quality-improvement cohort study was conducted by a single surgeon at one institution from November 2024 to February 2025. Hip and knee arthroplasty patients were divided into 2 cohorts: a control group using traditional physician and medical assistant documentation within the electronic medical record and an intervention group using AI-powered clinical documentation. Clinical encounter duration was assessed by 3 independent reviewers using direct observation and stopwatch timing. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) was completed after each clinic day to quantify subjective physician workload across 6 domains on a 20-point scale. Results A total of 112 patient encounters were analyzed: 55 with AI scribes and 57 with traditional documentation. Encounter time was significantly shorter in the AI group, with a mean of 12.03 minutes (range, 4.01-22.13) compared to 15.06 minutes (range, 6.20-32.27) for traditional documentation ( P P = .485). The AI group showed consistently lower mean scores across all NASA-TLX subdomains, including temporal demand (5.8 vs 10.4), effort (6.8 vs 10.0), and frustration (3.4 vs 5.4). Conclusions AI scribe implementation significantly reduced clinical encounter duration by approximately 20% and demonstrated promising improvements in physician workload metrics.","url":"https://doi.org/10.1016/j.artd.2026.102095","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.artd.2026.102095","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12912-026-04920-5","name":"Latent profiles of nurses' attitudes toward artificial intelligence in nursing and associated factors: a cross-sectional study.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is being gradually integrated into clinical nursing practice, where it plays an important role in improving nursing quality, reducing nurses' workload, and promoting the intelligent transformation of nursing. Nurses' attitudes toward AI applications in nursing directly affect the promotion and implementation of this technology. Understanding these attitudes and their heterogeneity is crucial for the successful implementation of AI technology. This study aimed to identify potential types of nurses' attitudes toward the use of AI in nursing and to explore the factors associated with profile membership with type affiliation. Methods A cross-sectional survey was conducted among 206 clinical nurses in Anhui Province, China, in July 2025. Data were collected using a general information questionnaire, the Attitudes Toward the Application of AI Technology in Nursing Scale, and the Multidimensional Nursing Generations Questionnaire. Latent profile analysis(LPA) was used to identify distinct attitude profiles. Univariate analyses and multinomial logistic regression were performed to explore associated factors. Results Three profiles were identified: positive acceptance (16.02%), ambivalent balance (9.71%), and cautious skepticism (74.27%). Multinomial logistic regression showed that educational level, computer proficiency, English proficiency, and generational characteristics were significantly associated with profile membership (all P Conclusion Nurses showed moderate attitudes toward AI in nursing with substantial heterogeneity. Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice.","url":"https://doi.org/10.1186/s12912-026-04920-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12912-026-04920-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/77307","name":"Impact of Large Language Model-Based AI Tools on Physician-Patient Communication: Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Background Recent advances in large language models (LLMs) such as GPT-3/4 have spurred the development of artificial intelligence (AI) chatbots and advisory tools in medicine. These systems are posited to assist or augment physician-patient communication, potentially improving empathy, clarity, and responsiveness. However, their actual impact on communication outcomes remains uncertain. Objective This study aimed to systematically review and meta-analyze peer-reviewed studies (2020-2025) evaluating how LLM-based interventions affect physician-patient communication, including empathy, clarity, trust, and patient understanding. Methods Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched PubMed/MEDLINE, Embase, Scopus, and Web of Science for studies published from 2020 to 2025 examining LLM or chatbot applications in clinical communication contexts. Eligible designs included randomized, observational, cross-sectional, and qualitative studies. Two reviewers (WHP and SR) independently screened titles or abstracts, assessed full texts, and extracted data on study design, population, LLM type, communication measures, and outcomes. We conducted a qualitative synthesis and random-effects meta-analysis, reporting pooled standardized mean differences or odds ratios with 95% CIs. Results From 312 records, 10 studies were included, all quantitative and predominantly cross-sectional. Populations ranged from patients with chronic conditions to health care professionals and laypersons. Outcomes assessed included empathy (8 studies), clarity or information quality (6 studies), satisfaction or usefulness (4 studies), and trust perceptions (2 studies). In 6 direct comparisons of AI- versus physician-generated responses, LLMs were rated significantly higher in empathy in 5 studies. One large study found that chatbot replies were judged empathetic in 45.1% of cases versus 4.6% for physician replies (odds ratio approximately 9.8, P Conclusions Current evidence suggests that LLM-based chatbots can enhance physician-patient communication by producing more empathetic, detailed, and understandable responses. These improvements may positively influence patient experience and engagement. However, LLMs may also generate overly lengthy or occasionally inaccurate advice, emphasizing the need for physician oversight. While meta-analytic findings are promising, robust randomized controlled trials, real-world and longitudinal studies are needed to confirm benefits, assess trust outcomes, and define optimal clinical integration strategies.","url":"https://doi.org/10.2196/77307","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/77307","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3760/cma.j.cn441530-20260609-00227","name":"[Oligometastatic esophageal cancer: rethinking the concept, reappraising the evidence, and addressing future challenges].","source":"europepmc","abstract":"Oligometastatic disease (OMD) is considered an intermediate state between localized disease and widely metastatic disease, offering selected patients with metastatic esophageal cancer the possibility of long-term survival and even potential cure. In recent years, substantial progress has been made in oligometastatic esophageal cancer with advances in local treatment modalities, the widespread application of immunotherapy, and the development of precision medicine. In particular, randomized controlled evidence represented by the ESO-Shanghai 13 trial has, for the first time, demonstrated that local intervention combined with systemic therapy can provide significant survival benefits for patients with oligometastatic esophageal squamous cell carcinoma (ESCC), thereby promoting a shift in treatment strategy from purely palliative care to active multidisciplinary intervention. Meanwhile, the launch of the Oligometastatic Esophageal Squamous Cell Carcinoma (OMESQ) international consensus project marks the transition of oligometastatic ESCC research from borrowing experience from other tumor types toward disease-specific development. However, radiologically defined oligometastatic disease is not equivalent to biologically defined oligometastatic disease, and patient selection may be more important than the treatment modality itself. Based on recent advances in oligometastatic esophageal cancer, this commentary discusses the evolution of concepts, key clinical evidence, current controversies, and future directions. The author believes that research on oligometastatic esophageal cancer is moving from the exploratory stage of determining \"whether local treatment is effective\" toward a new stage of determining \"how to precisely identify patients who are most likely to benefit.\" Establishing an ESCC-specific treatment framework based on tumor biological characteristics will become an important future direction in this field.","url":"https://doi.org/10.3760/cma.j.cn441530-20260609-00227","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn441530-20260609-00227","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1038/s41598-026-57070-8","name":"Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.","source":"europepmc","abstract":"Early analysis is a necessity for the more effective treatment of cancers. In gynaecological cancers, like endometrial, ovarian, and cervical cancers, the current efforts are aimed at discovering new analytical biomarkers to help decrease the global health burden related to these cancers. In cervical cancer, efficient screening is highly suggested for the prevention of invasive cancer occurrence and death. However, the interpretation of medical images for gynaecological cancer remains prone to human error. Artificial intelligence-based solutions offer numerous medical image challenges to help with the clinical decision support process. This paper presents a Deep Neural Architecture Empowered for Accurate Diagnosis of Gynaecological Cancer (DNAE-ADGC) model using medical imaging. The primary purpose of the paper is to empower gynaecological cancer diagnostics by developing an accurate, efficient, and intelligent detection framework using advanced techniques. Initially, the image pre-processing stage employs a two-level approach named adaptive filter that contains Median-Modified Wiener Filter (MMWF) and Cross Guided Bilateral Filter (CGBF). Followed by, the MobileNetV3Large model was deployed for feature extraction process. Besides, the DNAE-ADGC algorithm is applies the graph convolutional network and gated recurrent unit (GCN-GRU) network for detecting and classifying gynaecological cancer. At last, the explainable artificial intelligence (XAI) technique applies Grad-CAM to develop the transparency, interpretability, and reliability of AI models. The comparative analysis of the DNAE-ADGC method demonstrated an improved accuracy value of 97.92% with other methodologies under the Malhari dataset.","url":"https://doi.org/10.1038/s41598-026-57070-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-57070-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.surg.2026.110389","name":"Familiar but unprepared: Artificial intelligence training needs in graduate medical education.","source":"europepmc","abstract":"Background Graduate medical education trainees increasingly use artificial intelligence tools without formal training. This study quantifies the gap between artificial intelligence exposure and readiness and examines attitudes, preferences, and barriers to guide curriculum development. Methods In 2025, a 4-week cross-sectional survey, consisting of 5-point Likert scale questions, of graduate medical education trainees at a single academic medical center assessed artificial intelligence familiarity, confidence, attitudes, curriculum preferences, and barriers to training. Descriptive statistics, χ 2 tests, Spearman correlations, Jaccard similarity analysis, and network analysis characterized response patterns. Results Of 149 eligible trainees, 69 (48.3%) responded. Of all respondents, 85.5% (n = 59) reported active artificial intelligence tool use, and 2.9% (n = 2) had received formal artificial intelligence instruction. Median familiarity (3 [interquartile range, 3-4]/5) exceeded median confidence (3 [interquartile range, 2-3]/5) (P = .02), with 38.5% of high-familiarity trainees reporting low confidence. Trainees uniformly preferred postgraduate year-1 introduction (65.2%) and longitudinal curricula (62.3%), with no differences by training level or specialty. Barrier co-occurrence analysis revealed institutional support and faculty expertise deficits clustered strongly (Jaccard = 0.74). Network analysis identified efficiency beliefs as the central attitudinal hub. Conclusion Graduate medical education trainees demonstrated high artificial intelligence exposure but low confidence, with formal training being rare. The familiarity-confidence gap suggests that passive exposure does not confer confidence. Uniform curricular preferences support standardized, cross-specialty implementation. Addressing institutional support and faculty expertise together, while separately targeting time constraints, may optimize artificial intelligence curriculum adoption.","url":"https://doi.org/10.1016/j.surg.2026.110389","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.surg.2026.110389","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3390/bioengineering13080942","name":"Artificial Intelligence Applications in MRI for the Diagnosis and Management of Osteonecrosis of the Femoral Head: A Comprehensive Review.","source":"europepmc","abstract":"Osteonecrosis of the femoral head (ONFH) is a progressive and potentially disabling condition caused by compromised blood supply to the femoral head, leading to bone necrosis and collapse. Early diagnosis is essential to enable joint-preserving interventions and improve patient outcomes. Magnetic resonance imaging (MRI) is currently considered the most sensitive modality for early detection, whereas computed tomography (CT) provides superior assessment of subchondral bone integrity and structural collapse. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, has emerged as a promising tool to enhance diagnostic accuracy, automate lesion segmentation, and predict disease progression. This review aims to provide an overview of current AI applications in MRI for ONFH, focusing on early diagnostic, disease staging and classification, volumetric assessment, differential diagnosis and prognostic prediction. A total of 61 articles were initially identified, of which 13 studies (2021-2025) met the inclusion criteria. Results indicate that DL models, particularly convolutional neural networks (CNNs), achieve excellent diagnostic performance, with reported accuracies up to 98.4% and area under the curve (AUC) values reaching 0.98 for early-stage detection. Several models demonstrated performance comparable to or exceeding that of experienced clinicians, particularly in differentiating ONFH from other hip pathologies and in early disease recognition. AI algorithms also showed high accuracy in staging and classification (AUC up to 99.7% in internal validation), as well as in automated segmentation and volumetric assessment (Dice coefficients up to 0.89), enabling objective quantification of necrotic lesions. Furthermore, prognostic models integrating radiomics and ML techniques demonstrated promising results in predicting femoral head collapse (AUC up to 0.85). From a clinical perspective, AI appears to function primarily as a supportive tool, improving diagnostic consistency, efficiency, and reproducibility, and acting as a \"second reader\" capable of reducing variability among less experienced clinicians. However, significant limitations remain, including dataset heterogeneity, predominance of retrospective and monocentric studies, and limited integration of clinical data. In conclusion, AI-based MRI analysis shows strong potential to enhance the diagnosis, staging, and management of ONFH. Future research should focus on multicenter prospective validation, integration of multimodal clinical data, and development of explainable and generalizable models to facilitate widespread clinical adoption.","url":"https://doi.org/10.3390/bioengineering13080942","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/bioengineering13080942","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00464-026-13138-0","name":"Artificial intelligence with mucosal exposure devices versus artificial intelligence alone in detection of colorectal adenomas: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background and aims Both artificial intelligence (AI) and mucosal exposure devices (MEDs) have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. However, the effectiveness of AI combined with MEDs remains unclear. In this meta-analysis, we have evaluated the combined effect of AI with MEDs on enhancing detection rates of various colonic lesions. Methods Several databases were reviewed to identify studies reporting data on outcomes of interest from inception to December 10, 2025. Our outcomes of interest were the adenoma detection rate (ADR), adenomas per colonoscopy (APC), sessile serrated detection rate (SSDR), and advanced adenoma detection rate (aADR). Further subgroup analyses were performed based on adenoma size, location, and mucosal exposure device type. Data were analyzed using the Mantel-Haenszel random-effects model. Heterogeneity was assessed using the I 2 statistic. Results Twelve studies (8 RCTs, 2 retrospective, and 2 prospective) with a total of 6155 patients (2700 AI + MED, 3455 AI alone group) met the inclusion criteria. AI + MED were associated with higher rates of ADR, RR 1.15 (95% CI 1.07-1.25; p = 0.0005) as compared to AI alone, and subgroup analysis on device type also showed higher rates for AI + Endocuff RR 1.19 (95% CI 1.08-1.32; p = 0.0006) and AI + balloon device did not show significant results for ADR, RR 1.08 (95% CI 0.93-1.27; p = 0.32) between the groups, however, the formal test for subgroup differences between device type was not statistically significant (P = 0.33). There was no significant difference in aADR, SSDR, proximal ADR, cecal intubation time, withdrawal time, ADRs for size Conclusions AI-assisted colonoscopy augmented with a mucosal exposure device, particularly Endocuff, significantly enhanced ADR compared with AI alone, while aADR, proximal ADR overall, SSDR, and procedure times were comparable between strategies. These findings suggest that the selective use of Endocuff in AI-enabled colonoscopy may optimize lesion detection.","url":"https://doi.org/10.1007/s00464-026-13138-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00464-026-13138-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1177/14604582261460314","name":"Cost-effectiveness of Artificial Intelligence applications in healthcare: A systematic review.","source":"europepmc","abstract":"ObjectiveWith increasing emphasis on value-based healthcare and rising costs, it is essential to assess the economic impact of artificial intelligence (AI). This study systematically reviews the evidence on the cost-effectiveness of AI applications in healthcare.MethodsA systematic search of PubMed, Scopus and Web of Science was conducted up to 21 August 2025. Studies that had full text and were peer-reviewed articles in English and reported formal economic evaluations, including cost-effectiveness, cost-utility or cost-benefit analysis of AI-based healthcare interventions, were included in this study. Key economic indicators such as incremental cost-effectiveness ratios (ICERs), quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) were extracted. Methodological quality was assessed using the Health Economics Consensus Criteria (CHEC-list). Data were qualitatively combined.ResultsA total of 26 studies met the inclusion criteria. Applications of AI in screening, diagnosis, treatment decision support and rehabilitation were assessed. Deep learning approaches were the most common approaches studied. Nine studies were classified as cost-effective, six as cost-neutral or somewhat cost-effective and eleven as not cost-effective. Overall methodological quality ranged from low to high, with considerable heterogeneity in model structure, perspective and time horizon.ConclusionsAI in healthcare, particularly in screening and early diagnosis, shows promising cost-effectiveness. However, the strength of current evidence is moderate and highly context-dependent, and the results are sensitive to methodological assumptions, implementation costs, and healthcare system characteristics.","url":"https://doi.org/10.1177/14604582261460314","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/14604582261460314","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/97905","name":"Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review.","source":"europepmc","abstract":"Background Artificial intelligence (AI)-based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis. Objective This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on health care outcomes in clinical settings. Methods We conducted an umbrella review and prospectively registered the protocol. A systematic search of 5 electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. Results Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and health care costs. However, evidence regarding psychosocial outcomes remains insufficient. Conclusions This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.","url":"https://doi.org/10.2196/97905","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/97905","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fmed.2026.1842344","name":"A prediction model for platinum-resistant recurrence of ovarian cancer was established using multimodal artificial intelligence machine learning methods.","source":"pubmed","abstract":"To develop and validate a multimodal artificial intelligence (AI)-based prediction model for platinum-resistant recurrence in ovarian cancer by integrating clinical data, medical imaging, and medical knowledge resources, with the goal of improving early risk stratification and supporting individualized treatment decisions. This exploratory proof-of-concept study aims to assess the feasibility of multimodal fusion for this task; no external validation has been performed.","url":"https://doi.org/10.3389/fmed.2026.1842344","authors":["Zhao X","Ma L","Li P","Hu Z","Ding Y","Liu Q","Liu K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1842344","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.1111/bju.70345","name":"Artificial intelligence in urology: A review of United States Food and Drug Administration-cleared devices.","source":"europepmc","abstract":"Objective To systematically characterise United States Food and Drug Administration (FDA) authorised urology-specific artificial intelligence (AI)-enabled medical devices and to evaluate the publicly available evidence supporting their authorisation. Methods We reviewed the FDA AI-Enabled Medical Devices List. Devices cleared by September 2025 were manually screened for urological relevance using FDA clearance documents (e.g., 510(k), De Novo) to confirm clinical application. Eligible devices were categorised by FDA approval pathway, review panel, indication, and year of approval. FDA decision summaries were reviewed to characterise study design, training/testing and external validation features, and reporting of patient demographics. Descriptive statistics were used to summarise distributions and temporal trends. Results Of 1358 FDA-cleared AI devices, 30 (2.2%) were urology specific. Approvals increased over time, with 26.7% cleared in 2025 (through September). The predominant FDA medical device category was Radiology (70.0%), with indications led by prostate imaging (43.3%). Information on study design, recruitment, and validation was frequently unavailable. Race and ethnicity were reported in one device (3.3%) for training/testing and four devices (13.3%) for external validation. Among these, aggregated proportions showed that the majority of subjects were White (86.1% for training/testing; 87.6% for external validation). Conclusions The FDA-authorised urological AI devices are dominated by radiology-based, prostate-focused applications and have increased in recent years. However, publicly available evidence supporting their authorisation is often limited, with incomplete reporting of study design and patient demographics. Greater transparency and post-authorisation evaluation are needed to support interpretation of these tools in clinical practice.","url":"https://doi.org/10.1111/bju.70345","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/bju.70345","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.jaci.2026.04.031","name":"PRACTALL 2025: Artificial intelligence-application of allergy and immunology to patient care.","source":"pubmed","abstract":"Artificial intelligence (AI), first defined in 1955 by John McCarthy, has transformed daily life across industries through applications such as chatbots, autonomous vehicles, and navigation systems. The 2022 release of ChatGPT marked a pivotal moment, highlighting AI's rapidly expanding potential. The health care industry is increasingly embracing AI-enabled tools across oncology, pathology, and radiology to augment disease screening and clinical workflows. Ambient listening technologies support clinical documentation, reduce administrative burden, and improve patient-physician interactions. Large language models combined with natural language processing are being evaluated for generating clinical summaries managing patient portal messaging and converting freehand notes to electronic health records, while also uncovering patterns in patient data to support more personalized treatments. Forward-thinking health systems are establishing informatics departments to optimize these models. Notably, with 1 in 6 adults sourcing health information from AI, rising to nearly one-quarter among individuals younger than 30 years, there is a growing need to ensure that these technologies provide accurate and reliable information to safeguard patient safety and support appropriate clinical use. PRACTALL, a collaboration between the American Academy of Allergy, Asthma &amp; Immunology and the European Academy of Allergy &amp; Clinical Immunology, aims to equip allergist-immunologists with essential AI insights highlighting tools for clinical practice, education, and research. By addressing potential pitfalls and biases, PRACTALL illustrates how AI can enhance efficiency, improve patient care, and alleviate administrative burdens in health care.","url":"https://doi.org/10.1016/j.jaci.2026.04.031","authors":["Shamji MM","Rider NL","Adcock I","Pongdee T","Del Giacco S","Chang C","Torres Jaén MJ","Williams PV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jaci.2026.04.031","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1186/s12909-026-09676-9","name":"Medical students' attitudes toward socioscientific issues in female reproductive anatomy teaching: a cross-sectional study.","source":"europepmc","abstract":"Objectives This study aimed to assess medical students' attitudes toward integrating socioscientific issues (SSI) into female reproductive anatomy teaching across multiple academic years, using the novel case of China's first uterus transplantation, and to apply artificial intelligence (AI)-based sentiment analysis to evaluate their emotional responses. Results A cross-sectional study was conducted among 163 medical students across three academic cohorts (2022, 2023, and 2024-2025) at a medical university in China. Over 95% of students rated the SSI-based teaching positively. AI-based sentiment analysis revealed that attitudes toward ethical questions varied: for embarrassment in close-relative transplantation, no significant differences were found across cohorts (χ² = 5.78, df = 4, p = 0.216); for adoption as an alternative to childbirth, attitudes also did not differ significantly (χ² = 6.62, df = 4, p = 0.157). Mean sentiment scores for ovarian transplantation were neutral to slightly negative across all cohorts (2022: -0.31; 2023: -0.08; 2024-2025: -0.12, weighted average of -0.17 for 2024 and - 0.02 for 2025). Conclusions SSI integration using a culturally relevant case was well-received by medical students. AI-based sentiment analysis proved a valuable tool for quantifying emotional responses. Attitudes toward ethical issues did not differ significantly across the three cohorts, suggesting a rationale for the sustained integration of SSI in preclinical curricula to foster ethical reasoning.","url":"https://doi.org/10.1186/s12909-026-09676-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09676-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bjo-2025-328860","name":"Exploring the capabilities of three artificial intelligence chatbots in diagnosis and decision-making of age-related macular degeneration.","source":"europepmc","abstract":"Background Compare the performance of three artificial intelligence (AI) chatbots, including ChatGPT o4-mini, Gemini 2.5 flash and DeepSeek-R1, in diagnosis, treatment suggestion and visual prognosis of age-related macular degeneration (AMD). Methods A retrospective study was conducted on 54 participants with AMD from Shenzhen Eye Hospital and Huizhou Third People's Hospital (from January 2024 to June 2025). Each participant was analysed by three AI chatbots and an ophthalmology resident using standardised prompts. The main outcome measures included diagnosis agreement, treatment suggestion and visual prognosis. Global Quality Score (GQS, 1-5) was assessed to evaluate response quality. Statistical analysis was conducted using R (version 4.4.1), with significance set at p Results Diagnosis agreement was 96.3% for ChatGPT o4-mini, 94.4% for Gemini 2.5 flash, 90.7% for DeepSeek-R1 and resident (all p>0.05). Treatment suggestion agreement was 90.7% for Gemini 2.5 flash, 88.9% for ChatGPT o4-mini, DeepSeek-R1 and resident (all p>0.05). Visual prognosis agreement was 66.7% for ChatGPT o4-mini, 48.1% for Gemini 2.5 flash, 40.7% for DeepSeek-R1 and 37.0% for resident. ChatGPT o4-mini significantly outperformed DeepSeek-R1 (p=0.011) and the resident (p=0.006). GQS ratings favoured ChatGPT o4-mini, significantly higher than Gemini 2.5 flash and DeepSeek-R1, but comparable to the resident (all p>0.05). Conclusions Three AI chatbots showed strong capabilities in neovascular AMD diagnosis and treatment suggestions, with ChatGPT o4-mini superior in visual prognosis prediction. However, real-world clinical settings require large-scale studies, and integrative use of multiple large language models may improve robustness and clinical reliability.","url":"https://doi.org/10.1136/bjo-2025-328860","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bjo-2025-328860","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.jtumed.2026.06.003","name":"Artificial intelligence for skeletal classification in orthodontics: A systematic review and meta-analysis of lateral cephalometric studies.","source":"europepmc","abstract":"Objective To synthesize the available evidence on the diagnostic performance of artificial intelligence for sagittal skeletal classification using lateral cephalometric radiographs and to identify methodological limitations affecting clinical translation. Methods PubMed, Embase, Scopus, and Web of Science were searched up to July 13, 2025, following PRISMA guidelines. Eligible studies used artificial intelligence to classify skeletal Class I, II, and III relationships from lateral cephalograms and reported diagnostic performance metrics. Non-image-based models, landmarking-only studies, commercial software evaluations, and non-original reports were excluded. Risk of bias was assessed using QUADAS-2. Exploratory random-effects meta-analyses using restricted maximum likelihood were performed, with subgroup analyses by skeletal class. Because of the small number of eligible studies, heterogeneity, and multiple model-level results from shared data sets, pooled estimates were interpreted cautiously. Results Ninety-one records were screened, 12 underwent full-text review, four met the inclusion criteria, and three were included in quantitative synthesis, comprising an effective test sample of 2,743 across nine model arms. One study was excluded from pooling because it used combined posteroanterior and lateral inputs with best-fold reporting, limiting comparability. The pooled multiclass performance showed sensitivity of 87.0%, specificity of 91.1%, accuracy of 85.3%, and area under the receiver operating characteristic curve of 0.94. DenseNet-based models generally showed the strongest performance, whereas the Swin-T transformer model showed the weakest performance. QUADAS-2 indicated low to moderate risk of bias, with major concerns related to single-center sampling and lack of external validation. Conclusions Artificial intelligence shows promising diagnostic performance for sagittal skeletal classification from lateral cephalograms. However, the current evidence remains limited, heterogeneous, and mainly single center. Therefore, pooled estimates should be considered exploratory rather than definitive indicators of clinical performance.","url":"https://doi.org/10.1016/j.jtumed.2026.06.003","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jtumed.2026.06.003","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/hsr2.72414","name":"Text-to-Video Generative Artificial Intelligence in Pediatrics: Potential Applications, Ethical Considerations, and Future Directions.","source":"europepmc","abstract":"Background Text-to-video (T2V) generative artificial intelligence (AI) models can produce short videos from natural-language prompts, creating new opportunities for medical visualization and communication. In pediatrics, visual explanations and preparatory media are already used to support learning and to reduce distress during procedures; however, current T2V systems (including proprietary models such as OpenAI's Sora) have not been clinically validated for routine pediatric care. Objective To provide a forward-looking perspective on how T2V generative AI could be used in pediatric education, patient and caregiver communication, and simulation, while clearly distinguishing current evidence from hypothetical applications. Approach We performed a narrative (nonsystematic) review of the literature on (i) generative AI and T2V models in healthcare and (ii) pediatric video-based education, procedural preparation, and digital distraction (PubMed and Google Scholar; search through June 2025; representative keywords included \"pediatric\" AND \"video-based education,\" \"procedural preparation,\" \"anxiety,\" \"pain,\" and \"simulation\"). Key points Potential applications include scenario-based training videos for clinicians, tailored educational animations for families, and child-friendly preparatory videos to reduce anxiety and pain. Key limitations and risks include inaccurate or oversimplified content, bias, privacy and data-governance concerns, unclear accountability, commercialization pressures, and inequitable access. Pediatric-specific safeguards (transparent disclosure, clinician review, age-appropriate design, consent/assent practices, and equity planning) are essential. Conclusion T2V generative AI should be framed as an experimental adjunct rather than a near-term clinical solution. Rigorous validation, governance, and equity-focused implementation strategies are needed before pediatric deployment.","url":"https://doi.org/10.1002/hsr2.72414","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.72414","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bmjopen-2025-114653","name":"Impact of an artificial intelligence automated chatbot-driven educational-counselling intervention on childbearing attitudes and motivations among childless married women: study protocol for a parallel randomised controlled trial.","source":"europepmc","abstract":"Introduction Understanding population dynamics is essential for achieving the Sustainable Development Goals. Global population growth has slowed, and Iran has experienced a marked fertility decline. Given the decline in fertility rates and shifts in women's attitudes and motivations regarding childbearing, this study aims to explore whether an artificial intelligence (AI)-based chatbot can improve attitudes and increase positive motivation towards childbearing among married women of reproductive age. Methods and analysis This parallel randomised controlled trial will be conducted on 80 childless married women who need fertility counselling according to national guidelines. Participants will be randomly allocated using permuted-block randomisation (four blocks) to either an intervention or control arm. The intervention consists of a systematic educational and counselling programme delivered via an AI-powered chatbot across 4 weekly sessions. The control arm will receive standard in-person childbearing counselling per national guidelines. Primary outcomes are attitudes and motivation towards fertility and childbearing, measured by validated questionnaires at baseline and 1-month postintervention. Analysis will use analysis of covariance (ANCOVA) with group as a fixed factor and baseline scores as covariates; effect size will be reported as mean difference with 95% CIs. Ethics and dissemination The Ethics Committee of Babol University of Medical Sciences (IR.MUBABOL.HRI.REC.1404.172). The study findings will be disseminated through publication in peer-reviewed journals and presentation at scientific conferences. Trial registration number IRCT20221109056451N7.","url":"https://doi.org/10.1136/bmjopen-2025-114653","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2025-114653","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ijnsa.2026.100567","name":"Nursing students' readiness for and acceptance of artificial intelligence technologies in clinical skills training: A cross-sectional study.","source":"europepmc","abstract":"Background: Artificial intelligence is increasingly transforming healthcare delivery and health professions education, particularly in clinical skills training and simulation-based learning environments. This transformation necessitates an evaluation of the readiness of future nurses. However, limited evidence exists regarding nursing students' readiness and acceptance of artificial intelligence-based technologies in clinical skills training within Saudi universities. Aim The aim was to assess nursing students' readiness and acceptance, and intention to use artificial intelligence-based healthcare technologies in clinical skills training. Design A cross-sectional descriptive correlational design was used. Methods A self-administered online questionnaire was distributed to a convenience sample of 747 undergraduate nursing students across 10 universities in Saudi Arabia. The survey measured artificial intelligence readiness (cognition, technical ability, vision, and ethics) and artificial intelligence acceptance (perceived usefulness, perceived ease of use, attitude, behavioral intention), along with demographic and educational data. Data were analyzed using descriptive statistics, independent t -tests, one-way analysis of variance, Pearson correlation, and multiple linear regression analysis. Results Participants demonstrated a moderate to high level of overall readiness and acceptance for artificial intelligence in clinical training. The highest readiness scores were observed in the vision and ethics domains, whereas technical ability was the lowest. For acceptance, attitude and behavioral intention were the highest-rated subdomains. Academic year was positively associated with both readiness and acceptance, with more advanced students demonstrating higher levels. Participants with prior exposure to artificial intelligence demonstrated significantly higher readiness and acceptance scores than those without prior exposure, although the magnitude of the association was small. A moderate positive relationship was also observed between readiness and acceptance. Conclusions and implications Participating nursing students demonstrated conceptual and ethical preparedness for artificial intelligence integration but reported gaps in technical competence. Academic progression was associated with higher readiness and acceptance, while prior exposure was also associated with more favorable readiness and acceptance outcomes. We suggest that Saudi nursing students may have lower technical skills and practical competence compared with their conceptual, ethical, and attitudinal readiness for artificial intelligence, highlighting the need for further attention to technical training within artificial intelligence education.","url":"https://doi.org/10.1016/j.ijnsa.2026.100567","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijnsa.2026.100567","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.jpainsymman.2026.07.005","name":"Palliative Care Physicians' Perceptions about Using Artificial Intelligence for Prognostication.","source":"europepmc","abstract":"Context Statistical and artificial intelligence (AI)-based methods have informed clinical prognostication for decades, evolving into machine learning models integrated into electronic health records. Despite increasing deployment of AI-based prognostic tools, palliative care physicians' perceptions of these tools remain understudied. Objectives To understand palliative care physician perspectives on clinical use and implementation of AI-based prognostic tools. Methods We conducted a national survey of n = 2500 Hospice and Palliative Medicine physicians in the United States (January 2024-March 2025) to assess current prognostic practices, AI knowledge, and perceived benefits and risks of AI-based prognostication. Results About 537 completed surveys were included for analysis. Respondents were predominantly White (73.2%) and female (52.9%). Most reported being early technology adopters (69%) with low knowledge of AI (79.3%) and AI-based prognostication (91.8%). Overall, 72.9% routinely provide prognoses; 24.0% have used AI-generated prognoses at least once. Attitudes toward AI were favorable: 70.9% believed AI could facilitate earlier palliative care, 78.0% thought it could improve patients' ability to plan for end-of-life, 62.8% supported its role in hospice eligibility decisions, and 71.7% felt it might reduce team disagreements about prognosis. However, 36.3% were concerned about legal liability, and 37.5% thought it might negatively affect patients' sense of hope. Multivariate analyses found current users were more likely to hold positive beliefs (e.g., AI will help them better meet their patients' needs (aOR: 1.75; CI: 1.09-2.92; P = 0.026). Conclusions Palliative care physicians report limited current use of AI-based prognostic tools but generally favorable attitudes toward potential benefits, especially among current AI tool users.","url":"https://doi.org/10.1016/j.jpainsymman.2026.07.005","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jpainsymman.2026.07.005","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.30476/jamp.2026.108229.2243","name":"Mapping Personalized Learning in Medical Education: A Meta-Synthesis of Artificial Intelligence Applications.","source":"europepmc","abstract":"Introduction The rapid advancement of Artificial Intelligence (AI) in medical education is driving a shift from traditional instructional design methods toward personalized and adaptive learning models. Despite numerous promising applications, the available evidence remains limited and fragmented; therefore, a comprehensive synthesis of the evidence is needed to support robust conclusions. Methods This study employed the four-phase meta-synthesis framework proposed by Sandelowski and Barroso. A systematic search was conducted across Medline, Embase, CINAHL, PsycINFO, PubMed, Web of Science, ScienceDirect, Wiley Online Library, SpringerLink, Taylor & Francis Online, SAGE Journals, and Scopus, covering publications from 2010 to 2025. Studies were screened according to predefined inclusion and exclusion criteria, and their methodological quality was evaluated using the Critical Appraisal Skills Programme (CASP). Coding reliability was assessed through a test-retest procedure, resulting in a reliability coefficient of 0.81. Results A total of 273 records were identified, of which 16 studies met the inclusion criteria and obtained CASP scores exceeding the threshold of 30. Content analysis revealed five principal domains: faculty-related applications (21%), student-related applications (28%), applications in the learning process (15%), curriculum development (13%), and assessment mechanisms (23%). Student-related applications constituted the largest proportion, highlighting the pivotal role of learner-centered personalization in AI-driven medical education. Conclusion The integration of Artificial Intelligence (AI) into individualized educational experiences represents a transformative model for medical education. AI enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes. This synthesis proposes an overarching conceptual framework to inform policy, research, and implementation in the context of AI-supported personalized medical education.","url":"https://doi.org/10.30476/jamp.2026.108229.2243","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.30476/jamp.2026.108229.2243","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1093/geront/gnag135","name":"From ambient assistance to data-driven precision in AI-enabled Alzheimer's care (2004-2025): a bibliometric perspective.","source":"europepmc","abstract":"Background and objectives Population aging and the rising prevalence of Alzheimer's disease and related dementias (AD/ADRD) have created an urgent need for innovative care solutions. Artificial intelligence (AI) has emerged as a critical tool, yet a macro-level quantitative analysis of its evolutionary trajectory in geriatric care remains scarce. This study conducts a comprehensive bibliometric analysis to map the knowledge domain of AI-empowered geriatric care for AD/ADRD from 2004 to 2025, elucidating spatial distributions, evolutionary trajectories, and emerging research frontiers. Research design and methods A total of 1,197 eligible records were retrieved from the Web of Science Core Collection. Bibliometric analyses including co-occurrence networks, burst detection, and thematic evolution were conducted utilizing CiteSpace, VOSviewer, and the Bibliometrix R package. Results The 1,197 documents (402 sources; 7,268 authors) show an annual growth rate of 30.36%, peaking at 262 publications in 2025. The United States (242 articles; 6,640 citations) and China (213 articles; 3,649 citations) dominate global output, with Harvard University and the University of London as primary hubs, though the Global South remains underrepresented. Thematic evolution reveals three phases: early \"smart homes\" and \"activity detection\"; middle \"home monitoring\" and \"social robots\"; and a current frontier of \"explainable AI\" and biomarker-driven risk assessment. Keyword bursts confirm \"tau\" and \"intervention\" as leading hotspots to 2025. Discussion and implications AI in AD/ADRD care is transitioning from environmental assistance to neuro-precision diagnostics and therapeutics. Future research should integrate ethical, multimodal AI to bridge care gaps and facilitate high-quality aging-in-place.","url":"https://doi.org/10.1093/geront/gnag135","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/geront/gnag135","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1093/bioinformatics/btag302","name":"Improving hit discovery by integrating activity cliff sensitivity into active learning.","source":"europepmc","abstract":"Motivation Active learning has emerged as an effective strategy for accelerating molecular discovery under limited labeling budgets. However, existing methods primarily focus on global information, often overlooking activity cliff-sharp changes in bioactivity caused by small structural perturbations-leading to suboptimal sample selection. In this work, we propose a model-agnostic, activity cliff-aware active learning framework designed to improve hit discovery efficiency without imposing constraints on the underlying molecular representations. Our framework introduces an auxiliary activity cliff scoring module trained on pairwise molecular relationships to explicitly capture local structure-activity sensitivity. The outputs of this module are integrated into a cliff-aware acquisition function that prioritizes structurally informative molecules whose labels are expected to be most beneficial for model improvement. Notably, the proposed strategy is agnostic to backbone architectures and molecular feature, enabling seamless integration with a wide range of existing active learning pipelines. Result We evaluate our approach on multiple benchmark datasets under a fixed labeling budget. Across all targets, the proposed method consistently identifies more active compounds than baseline acquisition strategies, demonstrating improved robustness in early-stage, data-scarce learning scenarios. Ablation studies further confirm the contribution of activity cliff awareness to the observed performance gains. Overall, our results underscore the importance of explicitly modeling activity cliffs within active learning frameworks and highlight the effectiveness of a model-agnostic design for accelerating hit discovery in data-limited drug discovery settings. Availability and implementation The source code is accessible online at https://github.com/wnsgk/AC-Active.","url":"https://doi.org/10.1093/bioinformatics/btag302","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/bioinformatics/btag302","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3760/cma.j.cn441530-20260419-00169","name":"[Rethinking staging uncertainty and individualized decision-making in the management of clinical T2N0 esophageal squamous cell carcinoma].","source":"europepmc","abstract":"Clinical T2N0 (cT2N0) esophageal squamous cell carcinoma (ESCC) represents a clinical grey zone between early-stage and locally advanced disease, and remains one of the most controversial scenarios in esophageal cancer management. The choice between upfront surgery and neoadjuvant therapy continues to vary across clinical guidelines and real-world practice. With the release of the 2026 International Society for Diseases of the Esophagus (ISDE) guidelines, this issue has once again drawn considerable attention. However, the existing controversy is not merely driven by differences in treatment strategies, but rather reflects the limited accuracy of pretreatment staging and the marked biological heterogeneity within the cT2N0 population. In this article, we review current evidence and international guidelines to analyze the underlying causes of these discrepancies, discuss the appropriate boundaries between upfront surgery and neoadjuvant therapy, and highlight future directions. We propose that the management paradigm for cT2N0 ESCC should shift from uniform treatment strategies toward precision risk stratification based on multidimensional clinical and biological information, thereby enabling more rational and individualized decision-making.","url":"https://doi.org/10.3760/cma.j.cn441530-20260419-00169","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn441530-20260419-00169","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12912-026-05025-9","name":"Predicting nurses' behavioral intention to adopt artificial intelligence in a resource-limited setting: an extended technology acceptance model study in Iran.","source":"europepmc","abstract":"Background The integration of Artificial Intelligence (AI) into healthcare systems has the potential to substantially enhance clinical decision-making, workflow efficiency, and quality of patient care. However, the successful implementation of AI technologies largely depends on their acceptance by frontline healthcare providers, particularly nurses. While the Technology Acceptance Model (TAM) has been widely used to explain technology adoption, evidence remains limited regarding the role of nurses' cognitive and attitudinal factors in resource-limited settings. Methods A cross-sectional analytical study was conducted in 2025 among nurses working in hospitals affiliated with Ilam University of Medical Sciences, Iran. Using a census-based approach, 340 nurses were invited to participate, of whom 199 completed a validated online questionnaire. Data were collected using Davis's Technology Acceptance Model scales (Perceived Usefulness and Perceived Ease of Use) alongside an adapted instrument measuring knowledge, attitude, behavioral intention, and practical use of AI. Non-parametric tests and multiple linear regression analysis were performed using SPSS version 26. Results Most participants demonstrated low levels of AI-related knowledge (70.4%) and relatively unfavorable attitudes toward AI (66.8%). Behavioral intention to adopt AI was moderate (57.3%), while reported practical use was low (48.7%). Attitude emerged as the strongest predictor (β = 0.272, p Conclusion This study demonstrates that extending the Technology Acceptance Model by incorporating nurses' knowledge and attitudes provides a meaningful framework for predicting AI adoption in resource-limited healthcare settings. Beyond technological considerations, fostering positive attitudes and foundational AI literacy among nurses is essential for successful implementation. Targeted educational and organizational interventions are urgently needed to prepare the nursing workforce for AI-enabled healthcare. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12912-026-05025-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12912-026-05025-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/95547","name":"Generative Artificial Intelligence Literacy Scale for Nurses: Development and Psychometric Evaluation.","source":"europepmc","abstract":"Background Generative artificial intelligence (GenAI) can automate time-intensive tasks and support clinical decision-making in care settings. Nurses require appropriate competencies to ensure that integration of GenAI strengthens care quality and patient safety. However, validated literacy assessment tools remain limited. In particular, instruments tailored to nurses' role-specific GenAI competencies, including hallucination detection, risk identification, and ethical accountability, are lacking. These gaps highlight the need for a nurse-specific GenAI literacy scale. Objective This study aimed to develop and psychometrically validate the Generative Artificial Intelligence Literacy Scale for Nurses (GenAILS). Methods We conducted a two-phase, cross-sectional online survey of registered nurses nationwide in Taiwan between June 2025 and October 2025. Phase 1 involved conceptualization and item generation based on a literature review, followed by content appraisal through expert discussion with 6 external reviewers. A 50-item pool was generated. Subsequently, 5 external reviewers evaluated content validity. Items with a content validity index of Results In phase 1, the initial 50 items underwent expert content validation and were revised to 46 items (scale content validity index based on the average method=0.92). In phase 2, 1313 questionnaires were collected, of which 191 invalid responses were excluded; 1122 valid responses were analyzed. Participants had a mean age of 34.66 (SD 7.8) years. Extreme-group comparison revealed statistically significant differences for each item (P Conclusions The GenAILS is a concise, nurse-specific self-report instrument with good psychometric properties across 6 clinically relevant domains. It supports needs assessment, targeted training, and intervention evaluation to promote the safe and ethical use of GenAI in nursing.","url":"https://doi.org/10.2196/95547","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/95547","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.5435/jaaosglobal-d-25-00398","name":"Artificial Intelligence Scribes in Orthopaedic Surgery: A Narrative Review.","source":"europepmc","abstract":"Introduction The growing applications of artificial intelligence (AI) is transforming the healthcare landscape by reshaping diagnostics, workflow operations, and clinical decision making. Among its most promising application in surgery is the development of AI scribes designed to reduce the burden of documentation. This review examines standard and emerging documentation tools with a focus on orthopaedic surgery. Methods A narrative review of the applications of AI and AI scribes in orthopaedic surgery was done. Literature searches were conducted using PubMed and Google Scholar through July 2025. Keywords included AI, documentation, scribes, AI scribes, and electronic health record. Articles were included if they focused on clinical documentation with particular relevance to orthopaedic surgery. In addition, a targeted review of commercially available AI scribe platforms was conducted, extracting advertised features including orthopaedic note tailoring, EHR integration, security policies, cost, strengths, and potential areas of consideration. Results The standard documentation approaches, including human transcription services, medical scribes, and frontend speech recognition software, provide partial relief from documentation burden but remain limited by cost, scalability, and workflow integration. Although human transcription services and medical scribes can capture nuanced clinical information and provide real-time support, they are constrained by turnaround time, financial burden, and scalability challenges. By contrast, emerging AI scribe platforms offer EHR-integrated, ambient documentation solutions but vary widely in cost, scalability, and orthopaedic-specific customization, highlighting ongoing challenges in addressing the unique documentation demands of orthopaedic practice. Discussion AI scribes represent a promising evolution in clinical documentation for orthopaedic surgery. Although early results are encouraging, further specialty-specific validation is needed to address orthopaedic workflows, ensure accuracy, and define optimal implementation strategies; however, ethical considerations and patient privacy protections remain essential to safe and responsible implementation.","url":"https://doi.org/10.5435/jaaosglobal-d-25-00398","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5435/jaaosglobal-d-25-00398","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fmed.2026.1904001","name":"Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases.","source":"europepmc","abstract":"Background and objective Virtual patients have become an integral component of medical education, offering medical students realistic, repeatable, and interactive learning environments that effectively foster the development of clinical competencies. Moreover, the rapid evolution of generative artificial intelligence and large language model technologies has further accelerated the integration of virtual patients into medical education, making it a prominent area of contemporary educational innovation. To provide a comprehensive overview of the research landscape and emerging trends in this domain, we performed a bibliometric and knowledge-mapping analysis of the literature on virtual patients in medical education. Methods Publications published between 2006 and 2025 were retrieved from the Web of Science Core Collection and Scopus databases, including only English articles and review articles. CiteSpace and VOSviewer were used to perform bibliometric and knowledge-mapping analyses of collaboration networks, co-citation patterns, and keyword evolution. Result A total of 2,347 publications showed a consistent upward trend. The United States led contributions, followed by the United Kingdom and Germany, with extensive collaborations between North America and Europe. Medical Education was the most influential journal; Karolinska Institutet was the most productive institution and Cook, DA. was the most prolific author. Frequent keywords included \"medical education,\" \"simulation,\" \"clinical competence,\" \"virtual reality,\" and \"patient simulation.\" The most cited article confirmed virtual patients outperform no intervention, providing an evidence base for their adoption in medical education. Conclusions This bibliometric analysis highlights the sustained growth and evolving research landscape of virtual patients in medical education. The findings indicate that recent research has increasingly focused on the integration of generative artificial intelligence and large language models into virtual patient systems. These findings provide a quantitative evidence base for future research priorities and the continued development of virtual patient-based medical education.","url":"https://doi.org/10.3389/fmed.2026.1904001","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1904001","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.18553/jmcp.2026.32.9.1101","name":"Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis.","source":"europepmc","abstract":"Background Artificial intelligence (AI) methods are increasingly used to strengthen policy evaluation in managed care pharmacy. Among Medicare beneficiaries with cancer, which is one of the most clinically complex and costly populations, prescription drug coverage is obtained through either integrated Medicare Advantage Prescription Drug plans (MA-PDs) or stand-alone Prescription Drug Plans (PDPs). However, causal evidence of plans' impact remains limited because of nonrandom enrollment. Objective To apply an AI-enabled causal inference framework to estimate the causal effect of PDP vs MA-PD enrollment on health care utilization and spending among Medicare beneficiaries with cancer. Methods We conducted a nationally representative analysis among patients with cancer aged 65 years or older using Medicare Current Beneficiary Survey data linked to Medicare claims, supplemented with data from the Area Health Resources Files from 2019 to 2022. Outcomes included annual inpatient, outpatient, and prescription drug events, as well as total, Medicare, and out-of-pocket (OOP) expenditures (inflation-adjusted to 2025 USD). Guided by the National Institute on Aging Health Disparities Framework, 63 multidimensional covariates were incorporated. To address nonrandom plan selection, we implemented conventional regression, two-stage residual inclusion (2SRI) instrumental variables (IVs), and an AI-enabled Doubly Robust Machine Learning IV (DML-IV) approach. The IVs in this study included the county-level PDP penetration rate and the percentage of white-collar workers. Results A total of 3,140 unweighted patients with cancer, corresponding to 22,207,248 weighted patients, were included, with 51.20% enrolled in PDP. For health care use, the naive model showed higher inpatient (incident rate ratio [IRR] = 1.30) and outpatient events (IRR = 1.86) among PDP enrollees; after 2SRI adjustment, only outpatient events remained significant (IRR = 1.56), and no utilization differences were significant in the DML-IV model. For health care costs, the naive model indicated higher total (cost ratio = 1.69), Medicare (cost ratio = 9.94), and OOP spending (cost ratio = 1.63). In the 2SRI model, total (cost ratio = 1.35), Medicare (cost ratio = 5.81), and OOP costs (cost ratio = 1.86) remained elevated. In the DML-IV model, total costs were no longer significant, whereas Medicare (cost ratio = 4.14) and OOP costs (cost ratio = 2.18) remained significantly higher. Conclusions After rigorous AI-enabled causal adjustment, differences in health care utilization and costs between PDP and MA-PD plans largely reflect enrollment selection, whereas financial exposure, particularly beneficiary OOP spending, remains higher under stand-alone PDP coverage. These findings highlight how AI-based causal methods can support managed care and managed care pharmacy leaders in evaluating benefit integration and designing strategies to improve financial protection for high-cost populations.","url":"https://doi.org/10.18553/jmcp.2026.32.9.1101","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.18553/jmcp.2026.32.9.1101","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1111/add.70586","name":"Clinicians' and people who use drugs' perspectives on artificial intelligence in addiction medicine.","source":"europepmc","abstract":"Background and aims Artificial intelligence (AI) may improve addiction medicine workflows and health outcomes but raises questions about data privacy and unintended harms for people who use drugs (PWUD). This qualitative study aimed to understand the perspectives of clinicians and PWUD regarding perceived benefits and concerns about clinical AI in addiction medicine. Design A qualitative study with one-on-one semi-structured interviews about AI. Setting Addiction medicine clinicians in Rhode Island (n = 7), Massachusetts (n = 2) and Pennsylvania (n = 3), USA. PWUD (n = 25) residing in Rhode Island. Participants In 2025, a purposive sample of 12 clinicians and 25 PWUD participated. Eligible clinicians were English-speaking, ≥18 years old, licensed and provided care to PWUD exposed to xylazine in Rhode Island, Massachusetts or Pennsylvania. PWUD recruited from four Rhode Island social service organizations were eligible if they reported past-month illicit fentanyl and xylazine use, lived in Rhode Island or Massachusetts and were English-speaking and ≥18 years old. Eight (67%) clinicians were women and 10 (83%) were white non-Hispanic. Fifteen (60%) PWUD were men and 17 (68%) were white non-Hispanic. Measurements Transcribed audio-recorded interviews exploring benefits and concerns about clinical AI in addiction medicine. Clinician interviews were analyzed using rapid qualitative analysis to assess the breadth of clinical experience, whereas PWUD interviews were analyzed following inductive thematic analysis to assess depth of lived experience. Five themes were produced. Results Five themes were generated: (1) transparently developed and accurate clinical AI tools offer practical benefits; (2) clinician comfort with clinical AI hinges on transparent development and assurances against the perpetuation of drug-related stigma; (3) clinical AI may not keep pace with the volatile and regionally specific drug supply; (4) PWUD have limited AI knowledge and some characterized AI as untrustworthy; and (5) PWUD are altruistically motivated to release medical data for clinical AI development but want control over identifiable data usage. Summary Clinicians and patients feel that artificial intelligence in addiction medicine holds promise, but they are concerned about clinical relevance, stigma perpetuation and the privacy of potentially incriminating data about illicit drug use.","url":"https://doi.org/10.1111/add.70586","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/add.70586","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.7759/cureus.114071","name":"Safety in the Age of Artificial Intelligence: Evaluating Large Language Model Adherence to Antithrombotic Medication and Regional Anesthesia Guidelines.","source":"europepmc","abstract":"The release of the 2025 American Society of Regional Anesthesia (ASRA) 5th edition guidelines for regional and neuraxial anesthesia procedures for patients receiving antithrombotic medications introduced complex, patient-specific hold times and resumption protocols. As clinicians increasingly utilize large language models (LLMs) as clinical decision support tools, the reliability of these models remains largely unvalidated. This study evaluates the accuracy of two of the foremost LLMs, ChatGPT (OpenAI, San Francisco, CA) and Google Gemini (Google DeepMind, London, UK), in adhering to these new gold-standard safety guidelines. Twenty-five standardized clinical vignettes were developed. Each vignette featured a patient on a specific anticoagulant (e.g., rivaroxaban, apixaban, dabigatran) requiring a neuraxial or regional anesthetic procedure (stratified by high-risk vs. low-risk). Variables included renal function, dose frequency, and procedural urgency. Prompts were submitted to the latest publicly available ChatGPT and Google Gemini models with separate instructions to provide hold and resumption times. LLMs were queried to ensure familiarity with 2025 ASRA guidelines prior to submission of prompts. Responses were graded against the 2025 ASRA guidelines by independent reviewers. Response adherence was categorized as: 1. concordant (100% match); 2. conservative error (LLM recommended time was longer than required); 3. dangerous error (recommended time was shorter than required, a critical safety violation); or 4. omission (no specific timeframe provided). ChatGPT achieved a concordance rate of 64%, compared to Gemini at 62%. However, the models displayed distinct error profiles. ChatGPT produced \"dangerous errors\" in 20% of evaluations and failed to specify a time in 16% of cases. In contrast, Gemini's dangerous error rate was lower at 12%, but it demonstrated a significant \"conservative error\" rate of 22%, compared to 0% for ChatGPT. A chi-square test indicated that the difference in dangerous error rates between the two models was not statistically significant. Regardless, notable differences in error character and response completeness were observed. Gemini was more consistent in providing specific timeframes in 96% of prompts compared to 84% for ChatGPT. Variability of responses between users was determined via a chi-square test of homogeneity and was found to be statistically significant only for Gemini. While both models demonstrated moderate guideline awareness, their failure modes differed meaningfully. ChatGPT's errors were predominantly dangerous underestimations and omissions, while Gemini exhibited a conservative bias, overestimating hold times when a match was not achieved. Significant limitations exist regarding generalizability of these results. Only two major LLM models were tested. Other, more clinically oriented models exist, and newer versions of both ChatGPT and Gemini are consistently being released. These may improve LLM adherence to clinical guidelines. Despite these limitations, this study highlights the dangers of utilizing LLMs for periprocedural anticoagulation decision-making in regional and neuraxial anesthesia. Both models produced potentially dangerous recommendations in 12-20% of scenarios. Ongoing evaluation of LLM adherence to evolving guidelines remains essential, and clinicians must exercise extreme caution when utilizing these tools in patient care.","url":"https://doi.org/10.7759/cureus.114071","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.114071","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3390/healthcare14131963","name":"Artificial Intelligence in Dermatology Among Saudi Adults: Cross-Sectional Survey Study.","source":"europepmc","abstract":"Background/Objectives : Artificial intelligence (AI) holds significant potential to enhance diagnostic support and access to dermatological care; however, its adoption depends on public trust and acceptance. This study aimed to assess knowledge, attitudes, and acceptance of dermatological AI among Saudi adults, and to identify factors associated with adoption, trust, and preferred system characteristics. Methods : A nationwide cross-sectional online survey was conducted among 668 Saudi adults (≥18 years) between 21 May and 5 June 2025, using convenience and snowball sampling via social media platforms (WhatsApp, Snapchat, Twitter/X, and Telegram). The questionnaire captured demographics, attitudes toward AI (20-item Likert scale), and perceived importance of six AI system features. Data were analyzed using descriptive statistics, one-way ANOVA, and binary logistic regression. The study was approved by the Institutional Review Board of King Faisal University (Approval No. KFU-REC-2025-MAY-ETHICS3443, approval date 19 May 2025). Results : The mean overall AI attitude orientation score was 74.48 ± 10.20 (Cronbach's α = 0.868), reflecting moderately positive but conditional attitudes toward dermatological AI. Participants strongly preferred physician-supervised AI over fully autonomous systems, with medical oversight receiving the highest agreement (mean 4.27 ± 0.87). Privacy protection and diagnostic accuracy were rated as the most important system features. Age was significantly associated with the overall AI attitude orientation score ( p = 0.009), with younger participants demonstrating more favorable orientations. Interest in technology showed the strongest association with both AI attitude orientation and perceived importance ( p Conclusions: Saudi adults generally exhibit favorable yet cautious attitudes toward dermatological AI. Implementation strategies should prioritize physician oversight, transparency, data privacy, and culturally responsive design to support responsible integration into clinical practice.","url":"https://doi.org/10.3390/healthcare14131963","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14131963","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3348/kjr.2026.0203","name":"Agentic Artificial Intelligence for the Automated Generation of Accurate Summary Podcasts of Radiology Research Papers.","source":"europepmc","abstract":"Objective To evaluate whether a custom agentic artificial intelligence (AI) pipeline can overcome the limitations of general-purpose large language model tools, when compared with a generic commercial tool (Google NotebookLM [NBLM]), for generating podcast-style summaries of radiology research articles. Materials and methods Twenty-two PDF-format original research articles published in the April 2025 issue of Radiology were processed using our Programmable, Phoneme-Aware PDF-to-Podcast Pipeline (P5) and NBLM to generate 44 audio episodes. P5 utilizes a multi-agent workflow for script generation, quality assurance, pronunciation enhancement, and audio synthesis. Four radiologists from a pool of 25 (7 generalists and 18 specialists) were randomly assigned to evaluate each blinded audio episode, yielding 176 total evaluations. The primary outcomes were the number of hallucinations (factual errors) per episode and the percentage of hallucination-free episodes. Secondary outcomes included the number of inappropriate statements, mispronunciations, and flow disruptions; the composite quality score (Quality Assessment of Educational Podcasts [QAEP]); the key results coverage score; and overall listener preference. Data were analyzed using generalized linear mixed models. Results The P5 method produced significantly fewer hallucinations per episode compared with NBLM (mean, 0.32 vs. 0.93; P P = 0.013), consistently across generalists and specialists. P5 demonstrated significantly fewer mispronunciations (mean, 0.11 vs. 1.62; P P P P = 0.003). Conclusion Our custom agentic AI pipeline generated podcast-style summaries of radiology research articles with significantly higher quality and greater listener preference than the generic commercial tool.","url":"https://doi.org/10.3348/kjr.2026.0203","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3348/kjr.2026.0203","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1038/s41598-026-54681-z","name":"Dual dimensions of artificial intelligence use among medical academia: related knowledge, attitudes and ethical concerns, a national survey, 2025.","source":"europepmc","abstract":"AI integration into medical education and practice has its benefits and risks. This national web-based cross-sectional survey on Egyptian medical staff and students aimed to assess knowledge, attitudes, and concerns regarding the use of AI. This study comprised 2765 medical students and 500 medical staff, with a mean age of 20.8 and 29.9 years, respectively, and higher percentages of females among both groups. Medical students demonstrated satisfactory knowledge of AI compared to medical staff (p < 0.001). Unfortunately, the majority of both groups (80.4% of staff and 81.6% of students) expressed negative attitudes toward AI use. Male participants had significantly higher attitude scores than females in both groups. Knowledge score and gender were significant predictors of attitude towards AI among medical staff (p<0.005), while gender was a significant predictor of attitude scores among medical students (p < 0.001). The total AI usage score in this study was higher among students than staff, particularly for idea generation. Medical staff demonstrated a slightly higher total concern score regarding the use of AI in medical education and practice compared with students. The results emphasize the necessity of engagement, focused education and training, compatible solutions, uniform standards, guidelines, and the smooth incorporation of AI into medical education and clinical practice.","url":"https://doi.org/10.1038/s41598-026-54681-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-54681-z","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.25259/sni_403_2026","name":"Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is rapidly advancing across medical disciplines, with neurosurgery emerging as a key field for technological innovation. In the management of brain tumors, AI-based platforms have demonstrated considerable potential to enhance diagnostic accuracy, support surgical planning, and assist intraoperative decision-making. Methods A literature search was conducted using PubMed and Cochrane Library databases to identify peer-reviewed studies published in English between 2015 and 2025, using keywords including \"artificial intelligence,\" \"neurosurgery,\" \"brain tumors,\" \"machine learning,\" \"deep learning,\" \"computer vision,\" \"natural language processing,\" \"radiomics,\" and \"surgical planning.\" Results Machine learning and deep learning have improved radiologic detection, classification, and segmentation of brain tumors, while radiomics and radiogenomics enable noninvasive molecular prediction and tumor characterization. AI is increasingly integrated into surgical planning, including brain deformation modeling, fiber tractography, intraoperative histologic assessment, hyperspectral imaging, and intelligent navigation systems. Challenges include limited data availability, algorithm transparency, dataset heterogeneity, and regulatory and infrastructural requirements for clinical implementation. Conclusion AI demonstrates considerable potential to advance brain tumor management, though robust prospective validation and evidence-based implementation are essential prerequisites for safe clinical integration. Continuous technological refinement, multidisciplinary collaboration, ongoing research, and equitable access across healthcare systems, including low-resource settings, will be essential for responsible and effective implementation.","url":"https://doi.org/10.25259/sni_403_2026","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25259/sni_403_2026","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bmjopen-2025-112064","name":"Applications of artificial intelligence and machine learning for cardiovascular risk management in the public health policy context: a systematic review protocol.","source":"europepmc","abstract":"Introduction Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide. Public health responses to CVD require complex, multisectoral strategies that combine population-wide preventive interventions with individualised approaches. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this field, enabling more accurate diagnosis, prognosis and treatment personalisation. However, most AI applications remain confined to clinical domains, with limited translation into public health policy modelling. Objective This review aims to identify and synthesise recent evidence on the application of AI and ML systems for cardiovascular risk prediction and management, with a specific focus on their potential use in public health policy design and decision-making. Methods and analysis A systematic review will be conducted, registered in PROSPERO and reported following PRISMA guidelines. Searches will be performed in PubMed, Embase, Scopus, Web of Science, Bireme and Institute of Electrical and Electronics Engineers using standardised Descriptores en Ciencias de la Salud, Medical Subject Headings and Emtree terms. Eligible studies will include AI-based or ML-based models for cardiovascular risk prediction applied at a population, territorial or public health management level, published in English, Spanish or Portuguese within the last 5 years. Data extraction will consider article characteristics, health condition, AI/ML purpose, system features, Organisation for Economic Co-operation and Development classification, validation and performance and applicability to public health policy. Quality appraisal will use MINIMAR, DECIDE-AI or PROBAST-AI, depending on the study type. Data will be synthesised qualitatively, with descriptive frequencies and graphical summaries. Ethics and dissemination Ethical approval is not required as this study will be based on previously published data. Findings will be disseminated through peer-reviewed publications and policy-oriented forums involving the European Union and Latin American and Caribbean (LAC) academic stakeholders, with relevance for public health decision-making in Colombia and the LAC region. Trial registration number CRD420251163276.","url":"https://doi.org/10.1136/bmjopen-2025-112064","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2025-112064","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.jsurg.2026.104042","name":"Closing the Feedback Loop: The Feed Protocol for AI-Driven Curricular Reform in Surgical Residency.","source":"europepmc","abstract":"Objective The American Board of Surgery In-Training Examination (ABSITE) assesses surgical resident knowledge, but manual analysis of program-wide data is time-consuming. This study introduces the Faculty Educational Evaluation and Deficiency Analysis (FEED) Protocol to evaluate whether artificial intelligence (AI) can rapidly identify program-wide knowledge gaps within an individual residency program and generate actionable remediation plans. Design A retrospective quality improvement proof-of-concept study utilizing a standardized master prompt-engineering sequence with a large language model (LLM). Setting A single-site, academic general surgery residency program. Participants De-identified ABSITE \"Topic Areas for Incorrect Answers\" reports from 16 unique residents during the 2025 and 2026 examination cycles (total n = 32 reports). Results The AI extracted and categorized hundreds of data points into 8 surgical domains in under 60 seconds. The protocol identified a shift from moderate gaps in 2025 to critical cluster failures in 2026, including an 80% failure rate in \"Nutritional Requirements.\" The AI successfully generated a \"Faculty Action Roadmap\" with specific, data-driven learning objectives for the operating room, didactics, and morbidity and mortality (M&M) conferences. Conclusions The FEED Protocol provides a rapid, reproducible method for closing the feedback loop in surgical education. Utilizing AI for ABSITE analysis allows programs to transition from static curricula to precision education, addressing dynamic knowledge gaps in real-time.","url":"https://doi.org/10.1016/j.jsurg.2026.104042","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jsurg.2026.104042","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.15537/1658-3175.8820","name":"Artificial Intelligence in Pharmaceutical Care: &lt;i&gt;Transforming Drug Discovery, Development, and Patient-care Delivery&lt;/i&gt;.","source":"europepmc","abstract":"The integration of artificial intelligence (AI) into pharmaceutical care represents a paradigm shift in healthcare delivery, offering unprecedented opportunities to revolutionize medication management, accelerate drug development, and enhance patient outcomes. This comprehensive review synthesized evidence from global literature (2021-2025) examining AI applications across drug discovery and development, clinical trials optimization, medication therapy management, and pharmacy operations. The AI demonstrated prediction accuracies of 86-98% in drug discovery, 80% improvement in clinical trial recruitment efficiency, and 32.7% increase in medication adherence over standard care. The global AI drug discovery market, valued at $1.5 billion (2023), is projected to reach $11.8 billion by 2030, reflecting substantial industry investment. However, implementation challenges persist including data quality concerns (30% accuracy in some datasets), regulatory compliance issues, and algorithmic bias (8-12% performance variations across demographics). Successful implementation requires coordinated efforts across technological development, regulatory frameworks, healthcare professional training, and continuous validation protocols addressing technical, organizational, and ethical dimensions simultaneously.","url":"https://doi.org/10.15537/1658-3175.8820","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.15537/1658-3175.8820","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ejrad.2026.113124","name":"Evaluation metrics for synthetic medical imaging.","source":"europepmc","abstract":"Background Advances in generative artificial intelligence (AI) have accelerated the development and application of synthetic medical imaging. Despite this rapid progress, the evaluation of synthetic medical images remains heterogeneous, with numerous metrics proposed to assess fidelity, realism, diversity, and clinical validity. Currently, no standardized framework exists to guide the selection, interpretation, or comparison of these metrics, limiting reproducibility and cross-study comparability. This systematic review aims to comprehensively summarize and categorize existing metrics used to assess these complementary dimensions of synthetic medical images. Methods A systematic review was conducted in accordance with PRISMA guidelines. PubMed/MEDLINE, EMBASE, Scopus, and arXiv were searched for studies published between 2015 and April 30, 2025, supplemented by citation screening of included studies. Eligible studies were full-text articles that applied or proposed metrics to evaluate the fidelity, realism, diversity, and/or clinical validity in synthetic medical images. Results A total of 47 studies were included. Evaluation practices were highly heterogeneous. Expert evaluation (n = 25, 53%) and reference-based evaluations were most common (n = 25, 53%), followed by no-reference metrics (n = 24, 51%), and task-based evaluations (n = 24, 51%). The most commonly used individual metrics were peak signal-to-noise ratio (PSNR) (n = 16, 34%), structural similarity index (SSIM) (n = 15, 32%), mean absolute error (MAE) (n = 12, 26%), and Fréchet Inception Distance (FID) (n = 12, 26%). Conclusion Evaluation strategies for synthetic medical imaging showed substantial variability and no single metric captured fidelity, realism, diversity, and clinical validity simultaneously. Metric choice is often dictated by data availability rather than clinical purpose. A task-specific, layered evaluation framework could improve comparability and facilitate clinical adoption.","url":"https://doi.org/10.1016/j.ejrad.2026.113124","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejrad.2026.113124","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1177/10815589261460883","name":"Effect of the use of artificial intelligence tools on clinical education and clinical decision-making of medical students during clinical rotations: A scoping review.","source":"europepmc","abstract":"Medical education is transforming due to new technologies such as artificial intelligence (AI). Although AI is being considered for medical education, its integration into the curriculum remains limited. Furthermore, few studies evaluate how these tools affect students' diagnostic accuracy during clinical rotations. This study aims to evaluate available evidence regarding AI tools in medical students, identifying their effects on clinical education and clinical decision-making, as well as their implications for medical practice during clinical rotations. For this purpose, a scoping review was conducted in December 2025. The literature search was performed across key databases using MeSH and DeCS terms. Studies published between 2015 and 2025 were included. After screening, 26 articles were included. Most studies suggest a positive effect of AI on students' clinical education and clinical decision-making, particularly in solving clinical cases and interpreting images. AI appears to support students in reaching performance levels closer to experienced physicians, which could influence medical care quality. Seven articles reported a neutral effect, warning that incorrect or unclear AI responses might cause confusion. In conclusion, this review suggests that AI may have a positive effect on medical students, especially those with less clinical experience, potentially bringing them on par with more experienced professionals. The need for structured AI integration into the curriculum is underscored, as it has the potential to improve academic performance and provide enriched learning environments. However, ensuring adequate training and regulation for its efficient use is key to addressing the associated ethical and legal implications in healthcare settings.","url":"https://doi.org/10.1177/10815589261460883","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/10815589261460883","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12877-026-07798-9","name":"Artificial intelligence in geriatric healthcare: a scoping review.","source":"europepmc","abstract":"Background Deep demographic ageing is triggering a twin crisis in healthcare: a rising prevalence of multimorbidity among older adults and a severe global shortage of healthcare professionals, particularly nursing staff, which together create life-threatening gaps in long-term care. Although artificial intelligence shows significant potential to alleviate these pressures by improving the efficiency and accessibility of geriatric healthcare, its integration faces critical challenges spanning systemic, user-level, and societal dimensions. Objective This review aims to systematically analyze the current applications of artificial intelligence in geriatric healthcare, identify key barriers hindering its effective integration, and propose stakeholder-specific roadmaps. Methods This scoping review follows the PRISMA-ScR guidelines. This review was conducted using a two-step search strategy from five databases: PubMed, MEDLINE, the Cochrane Library, EMBASE, and Web of Science. First, a comprehensive search was performed across five electronic databases for literature published between January 1, 2015, and September 30, 2025. Second, the reference lists of identified studies and relevant reviews were manually screened. The study selection followed the PCC framework, focusing on evidence of artificial intelligence applications, implementation challenges, and proposed solutions in geriatric healthcare. Results Artificial intelligence is extensively applied across five key domains in geriatrics: health monitoring and disease management, safety supervision and risk prevention, cognitive and mental health support, social interaction and emotional companionship, and daily living assistance. Despite this potential, three major barrier categories were identified: (1) Systemic fractures; (2) User-level resistance, and (3) A widening social divide. In response, the study proposes concrete roadmaps, such as mandating Fast Healthcare Interoperability Resources standards for data interoperability, establishing ethical artificial intelligence certification, deploying culturally adaptive designs, and initiating workforce upskilling programs. Conclusions Artificial intelligence holds significant promise for mitigating the global geriatric healthcare crisis exacerbated by demographic aging and nursing shortages. However, realizing its full potential requires a coordinated, multi-stakeholder approach to overcome the entrenched systemic, human, and social obstacles. The proposed roadmaps provide an actionable framework that may facilitate the development of artificial intelligence systems that are more efficient, equitable, and human-centered, pending empirical validation in real-world settings. Registration The protocol has been registered on OSF.","url":"https://doi.org/10.1186/s12877-026-07798-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12877-026-07798-9","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1136/emermed-2025-215530","name":"Unlocking clinical narratives: how natural language processing and artificial intelligence can address data deficits and mitigate health inequities in urgent and emergency care.","source":"europepmc","abstract":"The Urgent and Emergency Care system generates a wealth of clinical information, but our ability to harness this for public health planning and to address health inequalities is constrained by systemic data quality issues. Modern natural language processing (NLP), driven by the context-aware capabilities of transformer-based architectures and large language models, offers a transformative opportunity to bridge this gap. By training machines to interpret and structure context-rich clinical notes at scale, we can translate complex patient stories into data ready for research and systems intelligence that reflects the realities of real-world care.This technology offers a potential route to addressing health inequities in vulnerable populations, such as those presenting with crises related to mental ill-health, alcohol and drug use. Current reliance on structured but oversimplistic data often fails to capture the complex intersectionalities of clinical and social contexts. This is due to factors like diagnostic overshadowing and unrecorded multimorbidity, leaving these patients statistically obscured within routine datasets, which fail to accurately represent volume or complexity. This data invisibility perpetuates a cycle of inaccurate disease burden estimates, under-resourced services and flawed policy. By unlocking the detailed narrative data within unstructured notes, NLP could allow us to identify the acute social stressors and psychiatric contexts that are currently invisible, making these inequities visible and actionable.","url":"https://doi.org/10.1136/emermed-2025-215530","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/emermed-2025-215530","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.4103/mgr.medgasres-d-25-00234","name":"Hyperbaric oxygen in the artificial intelligence era: integration and innovation.","source":"europepmc","abstract":"Hyperbaric oxygen therapy is established for decompression illness, carbon monoxide poisoning, radiation-induced tissue injury, and diabetic foot ulcers. Interest in neurological and inflammatory indications is growing, yet outcomes are constrained by heterogeneous protocols, limited patient selection tools, and the absence of real-time physiological guidance. Artificial intelligence, including machine learning, explainable artificial intelligence, and digital twins, has transformed other clinical domains and could enable precision hyperbaric oxygen therapy. We searched PubMed, Scopus, Web of Science, and Google Scholar (January 2000-March 2025). Terms included \"hyperbaric oxygen therapy\" and (\"artificial intelligence\" OR \"machine learning\" OR \"deep learning\" OR \"digital twin\" OR \"biosensor*\"). English-language, peer-reviewed clinical, preclinical, or computational studies explicitly linking hyperbaric oxygen therapy with artificial intelligence or enabling technologies were eligible. Editorials without primary data, abstracts without full text, and non-peer-reviewed sources were excluded. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-style flow- guided screening and selection. Fifty-three eligible studies (≥30% from 2022-2025) illustrate four convergent application areas: (1) protocol optimization using multicenter data, (2) biomarker-driven patient selection via multi-omics and imaging, (3) real-time adaptive control using biosensors, and (4) predictive safety analytics for oxygen toxicity and barotrauma. Exemplars from radiology, critical care, and cardiology demonstrate the feasibility of decision support, federated learning, and digital-twin simulations adaptable to hyperbaric oxygen therapy. Integrating artificial intelligence with hyperbaric oxygen therapy can shift practice from empirical protocols to patient-tailored, data-informed therapy. Priorities include hybrid clinical trials, interoperable registries, explainable and bias-aware models, and regulatory-aligned validation. With rigorous governance, artificial intelligence can improve efficacy, safety, and efficiency while advancing mechanistic understanding of hyperbaric oxygen therapy.","url":"https://doi.org/10.4103/mgr.medgasres-d-25-00234","authors":["Francisco Epelde"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4103/mgr.medgasres-d-25-00234","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s12672-026-05388-0","name":"Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.","source":"europepmc","abstract":"Background Breast cancer is the most common malignant tumor affecting women, and pathology serves as the primary method for its diagnosis. In recent years, artificial intelligence (AI) has increasingly been applied to the pathological diagnosis of breast cancer. This study, therefore, aims to conduct a bibliometric analysis of the literature on artificial intelligence in breast cancer pathology (AIBCP) to map the research landscape, identify key trends, and highlight emerging directions. Methods A total of 1089 relevant publications from 2011 to 2025 were retrieved from the Web of Science (WOS) database. Bibliometric analysis was conducted using bibliometric VOSviewer, and CiteSpace, while BERTopic modeling was applied to extract semantic topics from abstracts. The study examined publication trends, geographical and institutional contributions, author and journal profiles, citation patterns, references, as well as keywords and abstract-based topics. Results Research on AIBCP has grown substantially since 2017, with 2025 recording the highest number of publications. The majority of them are original articles (70.25%). The leading contributing countries and regions are the USA, China, India, and the European Union, while the University of Toronto emerged as the most productive institution. Key influential authors include Jeroen van der Laak and Nasir Rajpoot. The most cited study focused on deep learning for lymph node metastasis detection. Fourteen distinct research themes were identified, with Topic 0, centered on deep learning for histopathology image analysis, being the dominant topic (53.54%). Other topics included mitosis detection and analysis, tumor microenvironment and prognosis, spatial transcriptomics and gene expression, natural language processing, and multimodal and explainable AI models. Topic analysis reveals a shift from unimodal image analysis towards multimodal \"pathomics\" and emerging interdisciplinary areas. Conclusion The field of AIBCP is rapidly evolving, driven by deep learning-based pathological image analysis and increasingly expanding into comprehensive multimodal and clinical research. Future directions should focus on addressing the AI \"black box\" problem, to improve interpretability, advancing multimodal approaches, and optimizing human-AI collaboration frameworks. This bibliometric analysis provides a foundational resource to guide researchers and clinicians in navigating and advancing the field.","url":"https://doi.org/10.1007/s12672-026-05388-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12672-026-05388-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/87738","name":"Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Acute Kidney Injury Prevention in the Intensive Care Unit: Single-Center Uncontrolled Before-and-After Implementation Study.","source":"europepmc","abstract":"Background Acute kidney injury (AKI) is a frequent and serious complication among hospitalized patients, particularly in critical care settings, where its incidence can exceed 50%. AKI is associated with increased mortality, prolonged hospitalization, dialysis dependence, and higher health care costs. Although the KDIGO (Kidney Disease: Improving Global Outcomes) guidelines emphasize supportive care, hemodynamic optimization, and avoidance of nephrotoxins, their implementation remains inconsistent, partly due to the lack of timely risk stratification. Recent advances in artificial intelligence have enhanced early prediction and detection of AKI, offering new opportunities to improve patient outcomes and intensive care unit (ICU) efficiency. The U-Care Renal Platform (UCRP; U-Care Medical S.r.l), a Conformité Européenne (CE)-marked artificial intelligence-powered medical device, integrates directly with the ICU electronic health record to continuously analyze patient data and predict the risk of moderate or severe AKI within 24 hours, providing actionable, guideline-based recommendations. While the predictive performance of UCRP has been validated previously, its real-world impact on clinical and operational outcomes in the ICU remains underexplored. Objective This single-center uncontrolled before-and-after implementation study aims to evaluate the association between UCRP implementation and selected ICU clinical and operational outcomes in routine practice at SCIAS Hospital, Barcelona. Methods This study was conducted as a retrospective service evaluation of a workflow-embedded clinical decision support system between March 2023 and March 2025. It included 202 postsurgical adult ICU patients. Outcomes of interest were assessed by comparing preimplementation and postimplementation periods. Months during which the UCRP was inactive were excluded from the analysis (total excluded duration: 10 months; 5 in the preimplementation period and 5 in the postimplementation period). The outcomes included the incidence of moderate-to-severe AKI (KDIGO stages 2 and 3), the use of nephrotoxic medications, the frequency of hypotensive episodes among patients with AKI, and the ICU length of stay. Results During the postimplementation period, lower rates of moderate-to-severe AKI (9/99, 9.1% vs 12/103, 11.7%), nephrotoxic drug administration, and hypotensive episodes among patients with AKI were observed compared with the preimplementation period. Conclusions Integration of the UCRP into ICU workflows was associated with differences in selected AKI-related process and intermediate clinical outcomes in this single-center uncontrolled before-and-after implementation study. However, given the study design, causal relationships cannot be established, and the findings should be interpreted as preliminary signals requiring confirmation in larger, controlled, and multicenter studies, including patient-centered outcomes.","url":"https://doi.org/10.2196/87738","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/87738","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12912-026-04901-8","name":"Attitudes toward artificial intelligence: their association with creative self-efficacy and problem-solving ability in nursing interns.","source":"europepmc","abstract":"Background To determine the levels of attitudes toward artificial intelligence(AI), creative self-efficacy, and problem-solving ability among nursing interns enrolled in diploma and bachelor's nursing programs who were in the final stage of their clinical training in Guangxi, China, and to explore the relationships among these variables as well as the factors associated with attitudes toward artificial intelligence. Methods A cross-sectional survey design was conducted among 259 nursing interns undertaking their final-year clinical internships across seven hospitals. Data were collected between July and August 2025 using an online questionnaire assessing sociodemographic characteristics, AI attitudes, creative self-efficacy, and problem-solving ability. Correlation and multiple linear regression analyses were performed. The study followed ethical principles and STROBE reporting guidelines. Results The mean score for nursing interns' attitudes toward artificial intelligence (AIA) was 47.70 ± 8.75, the mean score for creative self-efficacy (CSE) was 28.56 ± 6.20, and the mean score for problem-solving ability (PSA) was 79.83 ± 17.53. Spearman correlation analysis showed that AIA was positively correlated with PSA (p Conclusion Positive correlations were observed among attitudes toward artificial intelligence, creative self-efficacy, and problem-solving ability in nursing interns. Educational program, gender, creative self-efficacy, and problem-solving ability were significantly associated with AI attitudes; however, these findings reflect associations rather than causal relationships.","url":"https://doi.org/10.1186/s12912-026-04901-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12912-026-04901-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1002/deo2.70381","name":"Benefit of Linked-Color Imaging in Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: A Pilot Study With Propensity Score Adjustment.","source":"pubmed","abstract":"Artificial intelligence (AI)-assisted endoscopy represents a promising approach for lesion detection, yet frequent false-positive detections impair clinical utility by disrupting examinations and diminishing physician confidence. Linked-color imaging (LCI), an image-enhanced endoscopy technique that amplifies mucosal and vascular contrast, may address this limitation. This investigation evaluated whether LCI reduces false-positive AI detections compared with white-light imaging (WLI).","url":"https://doi.org/10.1002/deo2.70381","authors":["Mohamed AN","Minoda Y","Maruoka R","Suzuki Y","Fukuya H","Tsuru H","Wada M","Tanaka Y","Chinen T","Moriyama T","Ogino H","Ihara E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2027","doi":"10.1002/deo2.70381","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1016/j.acra.2026.01.033","name":"Urgent Need for Artificial Intelligence Readiness: Insights From a Multicenter Cross-Sectional Study on Medical Undergraduates and Radiology Trainees in Central and Western China.","source":"europepmc","abstract":"Rationale and objectives Artificial intelligence (AI) is reshaping the future of medicine, particularly influencing specialties like radiology. While the adoption of AI continues to accelerate, its integration presents both opportunities and challenges. To date, limited research has examined the perspectives of medical students and radiology trainees in less-developed regions of China-groups essential to the future healthcare workforce. This study aimed to assess their perceptions, attitudes, usage, and concerns regarding AI in clinical practice. Materials and methods A cross-sectional, multicenter study was conducted between February and March 2025 across three provinces in central and western China. A total of 5043 medical undergraduates and 190 radiology trainees completed a self-designed questionnaire assessing their knowledge of AI, perceived utility, and awareness of its clinical applications. Results Most medical undergraduates reported limited exposure to formal AI training or hands-on experience yet expressed general support for its clinical use. Concerns regarding data privacy, transparency, and patient trust were commonly noted. Radiology trainees demonstrated higher levels of AI education and tool utilization. Both groups agreed AI could enhance efficiency without replacing physicians and emphasized the need to address technical, legal, and ethical challenges for successful implementation. Further analysis presented both grades and gender will influence participants' attitudes toward AI. Conclusion This study highlights a generally positive attitude toward AI among future healthcare professionals, while revealing substantial educational gaps. Structured AI training should be integrated into undergraduate and radiology curricula to better prepare trainees for AI-assisted clinical environments.","url":"https://doi.org/10.1016/j.acra.2026.01.033","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.acra.2026.01.033","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fdgth.2026.1830254","name":"Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.","source":"europepmc","abstract":"Purpose To systematically identify and synthesize peer-reviewed literature describing implemented AI innovations within undergraduate medical education clinical skills curricula from January 2022 through January 2026. Method The authors conducted a scoping review querying PubMed and Scopus, supplemented by SciSpace as an AI-assisted citation discovery tool. Eligible studies described utilizing AI to deliver the clinical skills curriculum in innovative ways (e.g., instruction in history-taking, communication, clinical reasoning, clinical documentation, OSCE/simulation assessment). We extracted data into standardized templates and thematically sorted to characterize how AI-assisted instruction was being implemented across educational objectives. Results From 1,130 initial records, 39 studies met inclusion criteria. AI-assisted instruction clustered into eight thematic categories: LLM-Based Virtual Patient and Clinical Simulation Systems ( n = 19), AI-Augmented OSCE and Simulation Assessment Tools ( n = 6), Embodied and Robotic AI Clinical Simulations ( n = 4), AI-Supported Procedural and Technical Skills Training ( n = 3), AI-Assisted Clinical Documentation and EHR-Based Skills Training ( n = 2), Multimodal Analytics for Skills Assessment ( n = 2), Educator-Facing AI Case Authoring and Simulation Design Tools ( n = 2), and AI-Supported Clinical Reasoning and Tutoring Tools ( n = 1). Publication activity concentrated heavily in 2024-2025, with virtual patient applications representing the dominant category. Conclusions AI implementation in clinical skills education has accelerated substantially since 2022, with large language model-powered virtual patient simulations emerging as the predominant application. Current implementations primarily position AI as a supplementary formative tool rather than a replacement for established pedagogical approaches. Robust evidence regarding long-term educational impact remains limited, indicating need for rigorous longitudinal evaluation alongside continued innovation.","url":"https://doi.org/10.3389/fdgth.2026.1830254","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1830254","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.tvjl.2026.106781","name":"The role of artificial intelligence in equine colic: A scoping review of diagnostic, prognostic, and decision-support applications.","source":"europepmc","abstract":"Equine colic, strangulating colic in particular, remains one of the leading causes of mortality in horses, with timely diagnosis and accurate prognostic assessment being critical for clinical decision-making. In recent years, artificial intelligence (AI) has been increasingly applied to support diagnostic and prognostic evaluation in veterinary medicine. However, the scope, methodological characteristics, and performance of AI models in equine colic have not been systematically mapped. Therefore, this scoping review was conducted to summarize current AI applications in equine colic, identify commonly used algorithms, describe reported performance metrics, and highlight methodological gaps affecting clinical translation. 16 studies published between 2015 and 2025 were included, comprising 104 AI models applied to equine colic. Most models focused on prognostic prediction, particularly survival outcome prediction. Logistic regression was the most frequently used method, followed by random forest (RF) and other ensemble approaches. Across studies, RF generally demonstrated strong discriminative performance for survival prediction, with reported area under the curve values ranging from 0.79 to 0.99 and accuracy often exceeding 80%. However, external validation, calibration assessment, and standardized preprocessing strategies were infrequently reported. Limited handling of class imbalance and inconsistent reporting practices further reduced reproducibility and generalizability. Findings highlight that AI applications demonstrate promising potential for prognostic prediction and clinical decision-making in equine colic, but important methodological barriers remain. Future research should emphasize multicenter collaboration, external validation, calibration assessment, standardized reporting frameworks, imbalance-management strategies, explainable AI integration, and multimodal monitoring systems to facilitate clinical translation of AI tools in equine practice.","url":"https://doi.org/10.1016/j.tvjl.2026.106781","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.tvjl.2026.106781","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.artmed.2026.103506","name":"Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.","source":"europepmc","abstract":"Background Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. Objective To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. Methods PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. Results The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). Conclusion AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.","url":"https://doi.org/10.1016/j.artmed.2026.103506","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.artmed.2026.103506","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12245-026-01302-1","name":"Struggling to integrate artificial intelligence in prehospital emergency care in a developing country: exploration of the Iranian experts' views based on qualitative content analysis.","source":"europepmc","abstract":"Introduction The use of integrated artificial intelligence (AI) in prehospital emergency services can not only increase the quality of services, but also help save patients' lives and improve treatment outcomes. Given the challenges in emergency medicine, this technology is recognized as an effective tool for increasing the efficiency and accuracy of medical services. The present study aims to determine the barriers to the use of this technology in the provision of Emergency Medical Services (EMS). Methods This qualitative conventional content analysis was conducted in Iran using purposive sampling. Data were collected through in-depth interviews with 38 participants, including prehospital managers, Emergency Operations Center (EOC) officers, faculty members, and Information Technology (IT) specialists, conducted between November 2024 and May 2025. Data analysis followed Graneheim and Lundman's approach, and Lincoln and Guba's criteria were applied to ensure trustworthiness. Results The qualitative analysis of interviews identified seven main categories and nineteen sub-categories related to the barriers to implementing AI in Iran's prehospital emergency services. These barriers fell into technical, human, legal, operational, data-related, managerial, and algorithmic domains. Key challenges included inadequate technological infrastructure, poor data quality and completeness, organizational resistance, lack of standardized protocols, legal ambiguities, and technical limitations of algorithms. Conclusion The implementation of AI in Iran's prehospital emergency systems is a complex and multifaceted process that requires structural reforms, targeted policymaking, and cross-sectoral collaboration at the national level. Success in this endeavor depends on strengthening technological capacity, enhancing professional digital literacy, establishing data-driven ethical regulations, and adopting an integrated approach to digital transformation in health system governance. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12245-026-01302-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12245-026-01302-1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.annemergmed.2026.04.022","name":"Medical Scribe and Ambient Artificial Intelligence Impact on Emergency Physician Documentation Burden and Clinical Productivity.","source":"europepmc","abstract":"Study objective Emergency physicians experience substantial documentation burden, contributing to physician burnout. Human scribes reduce documentation workload but are expensive and pose staffing challenges. Ambient artificial intelligence (AI) scribes offer a potential alternative by automating note generation from clinician-patient conversations using AI. We compared ambient AI and human scribes against encounters with no scribe on emergency physicians' documentation time and clinical productivity. Methods This retrospective cross-sectional observational study evaluated emergency department encounters from January 2025 to September 2025 at 4 hospitals within a large integrated health care system. Encounters were categorized as ambient AI scribe, human scribe, or no scribe. Patient demographics, documentation time, work relative value units (wRVUs), and shift data were extracted. Attending documentation time was modeled using median quantile regression with standard errors clustered at the physician level and controlling for encounter-level variables. Clinical productivity measured as total wRVUs per shift hour was modeled using generalized estimating equations adjusting for physician and shift-level variables. Results Among 198,178 emergency department encounters, 8,489 (4.3%) used ambient AI scribes, 15,947 (8.0%) used human scribes, and 173,742 (87.7%) had no scribe. Median patient age was 49 years (interquartile range 30 to 68 years), and 53.1% were female. Compared with encounters with no scribe, ambient AI scribes were associated with a 1.6-minute reduction in adjusted median attending documentation time per note (95% confidence interval 0.3 to 2.9), whereas human scribes were associated with a 3.3-minute reduction (95% confidence interval 2.3 to 4.3). Total wRVUs per shift hour did not differ among groups. Conclusion Ambient AI and human scribes were associated with reduced physician documentation time. Clinical productivity did not differ between study groups.","url":"https://doi.org/10.1016/j.annemergmed.2026.04.022","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.annemergmed.2026.04.022","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12909-026-09229-0","name":"Real-time interaction with artificial intelligence and its association with learning experience and academic performance among undergraduate nursing students in Peshawar: an analytical cross-sectional study.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has been widely incorporated in the education domain for offering adaptive learning, personalized feedback and engagement to students. These technologies may help increase students learning experiences, as well as academic outcomes. Aim To investigate the relationship between the real-time interaction with artificial intelligence (AI) tools, learning experience, and academic performance among undergraduate nursing students. Methodology An analytical cross-sectional study was done in public and private nursing colleges in Peshawar, from August, 2025 till November, 2025. A total of 320 undergraduate nursing students from 1st to 4th year were selected using convenience sampling. Data were gathered by a validated questionnaire during visits in class. Spearman correlation and regression analysis methods were used to analyze the relationship between real-time interaction with AI (RTI), learning experience (LE) and academic performance (AP). Results Real-time interaction with AI showed a moderate positive correlation with learning experience (r = 0.547, p Conclusion Learning experiences and academic performance among nursing students were positively associated with the use of AI tools and their ability to interact with them in real time. Integrating strategies that support AI-assisted learning and promoting responsible use of AI may contribute to improved student engagement and academic outcomes in nursing education.","url":"https://doi.org/10.1186/s12909-026-09229-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09229-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1183/23120541.50970-2025","name":"Erratum: \"An autonomous bronchoscopy robot controlled by artificial intelligence (BronchoBot) outperforms experienced bronchoscopists in a simulated setting\" Kristoffer M. Cold, Zhuoqi Cheng, Lars Konge, Anne Orholm Nielsen, Christian Skjoldvang Andersen, Thiusius R. Savarimuthu and Bruno Oliveira. &lt;i&gt;ERJ Open Res&lt;/i&gt; 2026; 12: 00970-2025.","source":"europepmc","abstract":"[This corrects the article DOI: 10.1183/23120541.00970-2025.].","url":"https://doi.org/10.1183/23120541.50970-2025","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1183/23120541.50970-2025","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.12688/f1000research.174134.1","name":"The Role of Artificial Intelligence In Preparing Digital Financial Reports and Its Impact on The Comparability of Accounting Information","source":"europepmc","abstract":"Background: Recent years have witnessed tremendous advancements in artificial intelligence (AI) technologies, significantly impacting various business and accounting fields. Among the most prominent anticipated effects of AI is the facilitation of digital financial reporting, contributing to improved accuracy and speed of processing and reducing human error. However, a crucial question arises regarding the impact of these digital reports on the comparability of accounting information across different companies and financial periods. While some studies have addressed the role of AI in enhancing the quality of financial reports, research examining its impact on comparability remains limited, particularly within local and regional contexts. Objectives this research aims to study the impact of AI on digital financial reporting and its effect on information comparability. Methods To achieve the research objectives, a questionnaire was developed and distributed to a group of university professors, accountants, auditors, and professionals from other related fields, with a random sample size of 165 participants in 2025. A range of statistical tools were used, including validity and reliability testing, regression analysis, and path analysis, to test the validity of the data using the Statistical Package for the Social Sciences (SPSS). Results The research concluded that AI has a positive effect on digital financial reporting and on the comparability of accounting information. Furthermore, digital financial reports increase the comparability of accounting information, and the impact of AI on the comparability of accounting information is partially influenced by AI. Conclusion The research recommends paying attention to the uses of information technology, especially artificial intelligence applications, by introducing artificial intelligence and how to use it in all sectors through educational courses, seminars and workshops. Enhancing the role of artificial intelligence in all economic, political, medical, and financial sectors. Training leaders and employees to use and understand artificial intelligence techniques and applications to improve the administrative process.","url":"https://doi.org/10.12688/f1000research.174134.1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.174134.1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.rmed.2026.109045","name":"Improving follow-up of incidental pulmonary nodules in the emergency department using an artificial intelligence-supported workflow.","source":"europepmc","abstract":"Background Incidental pulmonary nodules (IPNs) are frequently identified on computed tomography (CT) scans but are often associated with poor rates of patient notification and follow-up, limiting opportunities for early lung cancer detection. Objective To evaluate whether implementation of an artificial intelligence (AI)-supported workflow improves patient notification and follow-up of IPNs detected in the emergency department (ED). Methods We conducted a retrospective pre-post cohort study at an academic-affiliated community hospital. The pre-intervention cohort included ED patients undergoing chest CT between January and March 2023. The post-intervention cohort included ED patients undergoing chest CT between June and August 2025, in which an AI-based natural language processing system identified potential IPNs from radiology reports, and patients were contacted to facilitate follow-up. Primary outcomes were rates of patient notification about their IPN and nodule-specific follow-up. Results With implementation of the AI-supported workflow, patient notification increased from 171/228 (75%) to 223/252 (88.4%) p = 0.0001, and nodule-specific follow-up increased from 122/228 (53.5%) to 171/252 (67.9%) p = 0.0012. Inability to reach patients by phone after their ED visit was identified as a significant barrier to follow-up. There was no significant difference in lung cancer stage at diagnosis between cohorts. Conclusions An AI-supported IPN identification and outreach workflow improved patient notification and follow-up. Proactive communication strategies, facilitated by AI, represent a feasible approach to addressing care gaps and enhancing early lung cancer detection.","url":"https://doi.org/10.1016/j.rmed.2026.109045","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.rmed.2026.109045","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.metabol.2026.156674","name":"Corrigendum to \"PRR14 mediates mechanotransduction and regulates myofiber identity via MEF2C in skeletal muscle\" [Metabolism 164 (2025) 156109].","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.metabol.2026.156674","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.metabol.2026.156674","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/104269","name":"AI Models for Predicting Acute Kidney Injury (AKI) and Post-AKI Mortality: Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Background Machine learning (ML) models are increasingly used to predict acute kidney injury (AKI), but validation quality and clinical readiness remain uncertain. Objective This systematic review and meta-analysis aimed to summarize discrimination performance and implementation-relevant gaps for AKI occurrence and post-AKI mortality prediction. Methods We searched the Cochrane Library, Embase, PubMed, and Web of Science through January 23, 2025. Eligible studies developed or validated ML-based prediction models and reported the area under the receiver operating characteristic curve (AUC). Two reviewers screened studies, extracted data, and assessed risk of bias using the PROBAST+AI (Prediction Model Risk Of Bias Assessment Tool+Artificial Intelligence). Logit-transformed AUCs were pooled using restricted maximum likelihood random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman-adjusted inference. Results We included 219 studies with 7,343,170 participants and 101 modeling approaches. Primary analyses included 188 AUC estimates for AKI occurrence and 31 for post-AKI mortality. Pooled AUCs were 0.834 (95% CI 0.821-0.846) for AKI occurrence prediction and 0.830 (95% CI 0.807-0.851) for post-AKI mortality prediction. For AKI occurrence, nonlinear approaches, especially deep learning and tree-based or ensemble methods, generally showed higher pooled AUC point estimates than linear or generalized linear models in exploratory subgroup analyses. At the study level, PROBAST+AI rated 129 (58.9%) studies as having low risk, 84 (38.4%) studies as having high risk, and 6 (2.7%) studies as having unclear risk. External validation was uncommon: it was reported in 30 (16.0%) AKI occurrence records and 9 (29.0%) post-AKI mortality records. Conclusions ML models have shown high average discrimination for AKI occurrence and post-AKI mortality, supporting their potential value for AKI risk stratification and early warning. However, high levels of heterogeneity, limited external or prospective validation, and inconsistent reporting of model calibration and clinical utility mean that substantial barriers remain before routine clinical deployment. Pooled AUC estimates revealed that nonlinear models have considerable clinical translational potential. Further refinements to modeling frameworks are warranted to explore feasible strategies for real-world clinical implementation. Future studies should prioritize standardized definitions, robust validation, clinically meaningful thresholds, assessment of alert burden, and evidence that model-guided care improves kidney-protective management or patient outcomes.","url":"https://doi.org/10.2196/104269","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/104269","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/ms9.0000000000005363","name":"Knowledge, attitude, and awareness of artificial intelligence among medical staff in Kabul City.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly being applied in medicine for diagnosis, treatment, and decision-making. While enthusiasm for AI training among healthcare workers has been reported globally, little is known about awareness and attitudes in Afghanistan, where limited access to advanced diagnostic tools makes AI particularly valuable. Objective This study aimed to assess the knowledge, attitude, and practices of AI among healthcare workers in Kabul City. Methods A cross-sectional survey was conducted from January to February 2025 among 256 healthcare staff, including physicians, nurses, technicians, and administrative personnel. Data were collected using a structured questionnaire distributed online and in paper format. Responses were recorded on a three-point Likert scale. Statistical analysis was performed using SPSS version 26, employing descriptive statistics, chi-square tests, regression, and correlation analyses. Reliability was assessed using Cronbach's alpha. Results Of the participants, 71.1% were male (28.9% were female), and 43.8% were aged 21-29 years. Knowledge of AI was limited: only 1.6% demonstrated good knowledge, 44.7% poor knowledge, and 53.9% had insufficient knowledge. Attitudes were more favorable, with 47.3% expressing positive views, 37.5% somewhat agreeing, and 15.2% expressing negative views. Regression analysis revealed that age was significantly associated with knowledge scores, and knowledge strongly predicted positive attitudes. Reliability analysis confirmed acceptable internal consistency across domains (α ≥ 0.72). Conclusion Knowledge of AI among medical staff in Kabul is limited, but attitudes are generally favorable. Structured training programs, conferences, and AI-enabled systems are needed to strengthen Afghanistan's healthcare sector.","url":"https://doi.org/10.1097/ms9.0000000000005363","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ms9.0000000000005363","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12912-026-05053-5","name":"Technology anxiety and artificial intelligence readiness in nursing students: a cross-sectional study.","source":"europepmc","abstract":"Introduction As artificial intelligence (AI) becomes more common in healthcare, nursing students need to be both mentally and emotionally ready to use it. But feeling anxious about technology might hold them back. This study looked at whether there is a link between AI Readiness and technology anxiety among nursing students. Methods We carried out a descriptive-correlational study with 297 nursing students at Qom University of Medical Sciences during the 2024-2025 academic year. We used census sampling, meaning we invited all eligible students to take part. Data were collected using a demographic form, the Abbreviated Technology Anxiety Scale (ATAS), and the Medical AI Readiness Scale for Medical Students (MAIRS-MS). Data were analyzed using descriptive statistics, Pearson correlation, independent t-tests, and multiple linear regression to identify predictors of AI Readiness. Results The mean scores were 3.76 ± 0.54 for technology anxiety and 3.68 ± 0.61 for AI Readiness, indicating moderate-to-high levels. A strong inverse correlation was found between AI Readiness and technology anxiety (r = - 0.648, p Conclusion These findings show that nursing students who feel more prepared for AI tend to be less anxious about technology. Adding AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future. Further longitudinal and interventional studies are needed to better understand causal relationships and to identify effective educational strategies.","url":"https://doi.org/10.1186/s12912-026-05053-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12912-026-05053-5","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1136/bmjopen-2025-111721","name":"Teaching AI ethics in medical schools: a scoping review protocol on the ethical-technical balance in curricular frameworks.","source":"europepmc","abstract":"Introduction The rapid integration of artificial intelligence (AI) technologies in healthcare, ranging from diagnostic tools to clinical decision support systems, is transforming medical practice and education. However, without deliberate integration of ethics, there is a risk that medical education will reproduce a technosolutionist orientation by privileging efficiency and data-driven outputs over patient autonomy, justice and professional integrity. While AI-related courses are increasingly being introduced into medical curricula, ethical considerations often remain peripheral, with most frameworks emphasising technical skills over moral reasoning. As future clinicians will face complex ethical challenges related to autonomy, safety, bias, transparency and accountability in AI-integrated clinical settings, there is an urgent need to evaluate how ethics is incorporated into AI education. With AI curricula still in their formative stages, this moment presents a critical opportunity to proactively design ethical components, rather than introducing them after harms have emerged. This scoping review aims to systematically map the ethical-technical balance in AI-related medical education curricula, identifying current practices, gaps and opportunities for curriculum development. Methods and analysis This scoping review will follow the Joanna Briggs Institute methodology and be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. The review will address how ethical considerations are integrated into AI-related curricula in medical education and examine the balance between ethical and technical content. A comprehensive search strategy will be employed across multiple databases, including MEDLINE, Web of Science, Google Scholar, EBSCO, the Virtual Health Library, the Bioethics Literature Database and PhilPapers, as well as grey literature sources such as institutional reports, curricula and policy documents. Publications from January 2020 to December 2025 will be included. Data will be charted and analysed using descriptive qualitative content analysis, followed by a theory-informed interpretive analysis drawing on the hidden curriculum theory of medical education. Ethics and dissemination This review does not require ethics approval, as it involves analysis of publicly available data. Findings will be disseminated through a peer-reviewed publication and presented at relevant conferences and workshops focused on medical education or bioethics.","url":"https://doi.org/10.1136/bmjopen-2025-111721","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2025-111721","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1111/inm.70282","name":"How Does Artificial Intelligence Align With Person-Centred Principles in Mental Health Nursing? A Scoping Review.","source":"europepmc","abstract":"Artificial intelligence is increasingly used in mental health nursing, yet its alignment with person-centred care remains underexplored. This scoping review examines the extent to which artificial intelligence applications in mental health nursing align with person-centred principles, and where tensions and risk emerge. A systematic search of three electronic databases identified studies published since 2018. Data were charted for study characteristics, artificial intelligence modalities, person-centred care concepts addressed, and research gaps. Findings show growing interest in technology-enabled care delivery, monitoring, and decision support in mental health settings, with varying degrees of attention to person-centred values such as empathy, shared decision-making, dignity and therapeutic alliances. However, considerable gaps remain regarding ethical integration, digital therapeutic relationships, trust, surveillance and the measurement of person-centred care outcomes. This review maps the current evidence base and highlights critical gaps in understanding how artificial intelligence reshapes therapeutic relationships, professional roles and power dynamics in mental health nursing. Future research is needed to ensure that the adoption of artificial intelligence is not only safe and effective, but also ethically grounded and aligned with the humanistic foundations of person-centred mental health nursing.","url":"https://doi.org/10.1111/inm.70282","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/inm.70282","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.21203/rs.3.rs-10367350/v1","name":"Awareness and Attitudes of Radiology Staff Toward Artificial Intelligence in Medical Imaging Diagnosis: A Systematic Review","source":"europepmc","abstract":"Abstract Background: Artificial intelligence (AI) is increasingly integrated into medical imaging, enhancing image quality, reducing diagnostic errors, and optimizing radiation dose. Successful AI adoption, however, depends significantly on human factors, particularly the knowledge, awareness, and attitudes of radiology staff. Objective: To systematically identify, appraise and synthesize the available evidence on awareness, knowledge and attitudes of radiology staff and trainees toward AI in medical imaging, and to map regional and professional-group differences using a PRISMA-guided methodology. Methods: This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. PubMed, Scopus, Web of Science and Google Scholar were searched for records published between January 2018 and June 2026, supplemented by citation searching. Two reviewers independently screened records against pre-specified eligibility criteria; risk of bias in cross-sectional studies was appraised using the AXIS tool. Given the heterogeneity of outcome measures, results were synthesised narratively and organised thematically. Results: Of 1,863 records identified, 24 studies met the inclusion criteria (12 retained from the original narrative literature base and 12 additional studies identified through an updated search covering 2023–2025). Awareness of AI was generally widespread but often superficial, with pronounced gaps between self-reported familiarity and objectively tested knowledge (e.g., 78% self-reported familiarity versus 42% correct terminology in one 30-country survey). Attitudes were predominantly positive, particularly among radiologists (68–72% favourable across studies), who viewed AI mainly as an assistive tool, whereas radiographers and students reported greater apprehension about de-skilling, job security and inadequate training. Structured AI education remained inconsistent globally, and evidence from developing countries, including Sudan and the wider Arab region, remained comparatively scarce. Conclusion: AI adoption in radiology is shaped as much by human, educational and regulatory factors as by technological capability. Structured AI curricula, transparent liability frameworks, and context-specific strategies for low-resource settings are needed to translate positive attitudes into safe, effective clinical integration.","url":"https://doi.org/10.21203/rs.3.rs-10367350/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10367350/v1","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s11701-026-03661-6","name":"Artificial intelligence-based model for identifying anatomical and surgical structures in the videos of laparoscopic left hemicolectomy.","source":"europepmc","abstract":"Background Precise surgical procedures are critical to improving the survival outcomes for colon cancer patients. Currently, there is no dedicated intraoperative navigation system available for performing precise surgery. Objective We aimed to innovate an artificial intelligence-based model for identifying anatomical structures and surgical instruments during laparoscopic left hemicolectomy, to assist surgeons in performing the surgery with greater precision. Design and settings A total of 7474 images, extracted from the operation videos of 56 patients, were included. Anatomical structures, including the fascia, avascular dissection plane, inferior mesenteric vessels and their branches, veins, ureter, reproductive blood vessels, pancreas, and nerves around the inferior mesenteric artery, were analyzed in each image. Surgical instruments, including clips, energy devices, and tissue forceps, were also analyzed. The Mask2Former model was trained using the annotated images. Patients This was a multicenter study involving 56 randomly selected patients who had undergone laparoscopic or robotic left hemicolectomy between September 26, 2022, and June 25, 2025, at Sun Yat-Sen Memorial Hospital, Qilu Hospital of Shandong University and Guangdong Second Provincial General Hospital. Main outcome measures Intersection over Union (IoU), precision, recall and F1 score of the model. Results A total of 7474 images were included; 6727 images were selected as a training set and 747 images as validation set. The mean precision of this model was 0.8643, the mean IoU was 0.7768, and the mean recall was 0.8756. Limitations First, the quality of the videos was inconsistent due to the different recording systems. Second, we should add images of suboptimal dissections to make this technology a more effective navigation aid during difficult dissections. Third, this model should be tested in real-time surgery navigation. Conclusions The artificial intelligence-based model can accurately identify anatomical structures and surgical instruments in laparoscopic left hemicolectomy.","url":"https://doi.org/10.1007/s11701-026-03661-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03661-6","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1055/a-2937-0980","name":"The Roles of Biomedical Informatics for an Equitable Learning Health System: Lessons from a Year of Scientific Informatics Literature.","source":"europepmc","abstract":"Background Biomedical informatics plays a central role in supporting learning health systems (LHSs) that aim to continuously improve care by transforming data into knowledge and knowledge into action. However, health care data reflect existing disparities in access, delivery, and outcomes, raising concerns about whether the LHS cycle can improve care equitably without intentional design. Objective This study aims to describe the recurring theme of health equity found in recent biomedical informatics literature, using a convenience sample of articles contributed to the 2025 American Medical Informatics Association (AMIA) Year in Review (YIR). Methods In developing the 2025 AMIA YIR presentation, we found that 240 articles submitted by AMIA experts could be organized into multiple themes to allow summarization and synthesis for efficient presentation of 140 of them. Many themes could themselves be organized around the concept of the LHS, which further streamlined the presentation. We were struck by how frequently biomedical informatics literature addressed health equity in relation to LHSs. We therefore applied our own expertise to distill relevant articles from the larger set to explore this recurrent issue. Results Here, we present 56 papers selected from 9 of the 19 themes that illustrate how biomedical informatics can support equity at each stage of the LHS. Mature LHS implementations provided large-scale examples of how learning cycles can be designed for equity. Real-World Evidence and Inclusive Data themes showed that equitable learning depends on whether data are complete enough to represent the populations the system intends to serve. Natural language processing and artificial intelligence (AI) modeling showed how data are transformed into knowledge and translated into clinical action through decision support. Trust and justice in data use, health equity as infrastructure, and equitable access emerged as interdependent conditions that influence whether the learning cycle operates equitably and effectively. Conclusion Equity emerged as a condition influencing every stage of the LHS cycle. The value of informatics interventions cannot be judged by technical performance alone, but by whether they build learning cycles that are inclusive, accountable, and equitable for the populations they serve.","url":"https://doi.org/10.1055/a-2937-0980","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1055/a-2937-0980","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1080/07853890.2026.2659984","name":"Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.","source":"europepmc","abstract":"Methods: In addition to traditional testing methods, the use of PSMA-directed imaging to guide biopsy procedures using Multiparametric Magnetic Resonance Imaging (mpMRI) allows for better localization and characterization of lesions. Researchers have begun to develop and continue to innovate Molecular & Imaging processes, called Liquid Biopsies, that are now being used to assess health risk factors associated with PC, by sampling macro-molecules from various biological liquids. Recently, the use of Artificial Intelligence (AI) and Machine Learning Models for improving the speed and accuracy of lesion detection and gland segmentation has significantly increased both precision and consistency in these areas. Results: However, there is still great concern regarding overdiagnosis and lack of standardization associated with all types of molecular and imaging techniques currently available. Conclusion: This review provides a comprehensive overview of the currently available diagnostic modalities for prostate cancer, including their weaknesses as well as gaps in clinical translation and standardization, which can assist with providing guidance for future development of diagnostic innovations..","url":"https://doi.org/10.1080/07853890.2026.2659984","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/07853890.2026.2659984","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3390/diagnostics16162576","name":"Interobserver Agreement Between Artificial Intelligence, Radiologist, and Gynecologist in Hysterosalpingography Interpretation: A Retrospective Comparative Study.","source":"europepmc","abstract":"Background: Infertility is a common reproductive health disorder that affects roughly 10-15% of couples during their reproductive period. Hysterosalpingography (HSG) is a widely utilized imaging modality for assessing uterine cavity morphology and fallopian tube patency and continues to play a central role in infertility investigations. Nevertheless, the interpretation of HSG findings may vary according to the experience and expertise of the evaluator, potentially leading to inconsistencies in clinical decision-making. Although artificial intelligence (AI) has demonstrated considerable potential in medical image analysis across various specialties, evidence regarding its application in the interpretation of HSG examinations remains scarce. Therefore, this study aimed to evaluate the level of agreement among radiologists, gynecologists, and an AI-based system in the assessment of identical HSG images. Methods: In this retrospective study, a total of 1443 HSG images obtained from 414 women who underwent hysterosalpingography as part of an infertility evaluation between January 2021 and January 2025 were reviewed. Cases with incomplete clinical records or suboptimal image quality were excluded from the analysis. All examinations were independently assessed by an experienced radiologist, a gynecologist specializing in infertility management, and a multimodal artificial intelligence system based on ChatGPT-5, with each evaluator blinded to the assessments of the others and to the patients' clinical information. Image interpretation included the evaluation of contrast distribution, peritoneal spill, uterine cavity findings, tubal patency, and overall HSG impression, which were categorized according to predefined diagnostic criteria. The primary outcome was the degree of interobserver agreement among the evaluators. Agreement analyses were performed using Cohen's kappa (κ) and Gwet's AC1 coefficients. Analyses were conducted using IBM SPSS Statistics (version 30.0; IBM Corp., Armonk, NY, USA) and R statistical software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at p Results: A total of 1443 HSG images obtained from 414 women were included in the final analysis. The mean age of the study population was 30.97 ± 5.59 years, and primary infertility accounted for 87.9% of cases. The average number of images acquired per examination was 3.49 ± 1.05. According to Cohen's kappa analysis, the highest levels of agreement were observed for the assessment of image artifacts and contrast medium distribution. Agreement between the AI system and the radiologist was particularly strong for contrast medium distribution (κ = 0.757). For the overall interpretation of HSG findings, AI demonstrated substantial agreement with the radiologist (κ = 0.637), exceeding the level of agreement observed between the radiologist and the gynecologist (κ = 0.363). In contrast, concordance involving AI was lower for the evaluation of uterine abnormalities, intrauterine filling defects, and tubal patency. When agreement was reassessed using Gwet's AC1 statistic, concordance coefficients were consistently higher than the corresponding kappa values across all evaluator pairs. Near-perfect agreement between AI and the radiologist was identified for contrast medium distribution (AC1 = 0.954), peritoneal spill (AC1 = 0.893), and patterns of peritoneal contrast passage (AC1 = 0.841). Procedures performed under local anesthesia yielded a significantly greater number of images than those conducted under general anesthesia (3.86 ± 0.86 vs. 3.08 ± 1.10, p Conclusions: Our findings indicate that AI-assisted interpretation of HSG images has the potential to complement expert assessment, showing substantial concordance in several key diagnostic domains. While the technology appears promising as a decision-support tool in infertility evaluation, further research and refinement are warranted, particularly regarding the assessment of tubal and uterine pathologies.","url":"https://doi.org/10.3390/diagnostics16162576","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16162576","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106643","name":"Application of Artificial Intelligence (AI) in cancer symptom management for adult cancer survivors: a scoping review.","source":"europepmc","abstract":"Background and aim Artificial Intelligence (AI) has been increasingly used in cancer survivorship to support symptom management. This scoping review aimed to map existing evidence on AI applications in cancer symptom management for adult cancer survivors, including AI model development, AI-enabled intervention delivery and adoption, symptom targets, key features of the AI approaches used, reported outcomes, influencing factors, and research gaps to inform future priorities. Method This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews. Eight electronic databases were comprehensively searched in November 2025 and updated in March 2026. Empirical studies published in English from 2015 onward that focused on the development and/or implementation of AI models for cancer-related symptom care among adult survivors were included. Given the heterogeneity of the included studies, findings were synthesised using descriptive statistics and narrative analysis. Factors influencing AI development and implementation were analysed using inductive content analysis. Results A total of 41 studies were included: 21 focused on AI model development, 18 on AI-enabled intervention delivery, and 2 on both. Common techniques included natural language processing, machine learning, and conversational AI. Development studies primarily used unstructured electronic health record data, whereas delivery studies more often relied on patient-reported inputs. AI applications were mainly used to support symptom detection (n = 15), monitoring (n = 5) and triage (n = 2); clinical decision support (n = 3) and personalised management (n = 5); and patient education (n = 9) or counselling (n = 2). Nearly half of the studies (n = 18) addressed general or multiple-symptom management, while others focused on specific symptoms, particularly pain (n = 9) and psychological distress (n = 9). Model performance was generally moderate to high, varying by symptom, task complexity, and data source. Delivery studies reported improvements in clinical and psychosocial outcomes. Six categories of influencing factors were identified: technical performance; data quality, documentation, and generalisability; clinical workflow integration; usability, access, and equity; human-centred communication; and ethics and safety. Conclusion AI is increasingly used to support symptom management in adult cancer survivors, primarily through symptom identification, monitoring, decision support, and patient-facing recommendations, education and counselling. However, the evidence remains heterogeneous, early-stage, and context-dependent, with variability in AI functionality, technical reporting, study populations, healthcare settings and study designs. Future research could prioritise clearer reporting of AI functionality, broader symptom coverage, robust validation, co-design, and clinically integrated evaluations that demonstrate safety, equity, effectiveness, and real-world usefulness beyond predictive accuracy.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106643","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106643","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s12672-026-05483-2","name":"A bibliometric analysis of artificial intelligence in ovarian cancer research from 2006 to 2025.","source":"europepmc","abstract":"Background Ovarian cancer is a gynecological malignancy associated with high mortality and poses significant clinical challenges in early diagnosis and precision treatment. Although the rapid advancement of artificial intelligence (AI) has introduced novel approaches to this field, a comprehensive bibliometric overview remains lacking. This study aims to fill this gap by providing a systematic bibliometric analysis of this rapidly evolving domain. Methods In this study, the Web of Science Core Collection (WoSCC) was used to retrieve literature on AI applications in ovarian cancer research published from 2006 to the search date (November 19, 2025). Using CiteSpace and VOSviewer, we conducted visual and quantitative analyses of publication trends, countries/regions, institutions, authors, journals, highly cited papers, and keywords. Results A total of 786 publications were included in the analysis. The annual publication output showed pronounced exponential growth, with a marked acceleration after 2019. China, the United States, and the United Kingdom were the leading contributing countries. Research hotspots centered on AI-assisted diagnosis, prognostic prediction models, radiomics, and biomarker discovery. The evolution of keywords indicated that frontier research has shifted from basic classification toward more advanced areas, including high-grade serous ovarian carcinoma, multimodal learning, and explainable AI. Conclusion Research on AI in ovarian cancer has progressed rapidly, with international collaboration concentrated among leading contributors such as China, the USA, and the UK. Future efforts should prioritize the development of explainable and robust clinical AI systems, deeper integration of multimodal data, closer collaboration between clinicians and AI researchers, and high-quality data sharing to facilitate the translation of research findings into precise clinical practice.","url":"https://doi.org/10.1007/s12672-026-05483-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12672-026-05483-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fpubh.2026.1833489","name":"Acceptance of generative AI-assisted medical decision-making among Chinese physicians and patients and its ethical determinants: a cross-sectional survey.","source":"europepmc","abstract":"Objective Generative artificial intelligence (GAI) is increasingly being integrated into medical decision-making, yet ethical challenges continue to hinder its widespread clinical implementation. Existing research has paid limited attention to the ethical determinants of acceptance and differences in perceptions among key stakeholder groups. This study examined acceptance of GAI-assisted medical decision-making among Chinese physicians, patients, and other healthcare stakeholders, identified its ethical determinants, and explored differences across stakeholder groups to inform ethical governance. Methods A cross-sectional survey was conducted using a multistage sampling strategy across healthcare institutions in Henan and Guangdong provinces, China, between December 2025 and January 2026. A total of 533 participants, including healthcare professionals, patients, and other stakeholders, were recruited. A self-developed four-dimensional scale assessing perceived functional value, perceived ethical risk, acceptance intention, and ethical governance expectations was developed through literature review, expert consultation, and pilot testing. Multivariable linear regression analysis was performed using SPSS version 26.0 to identify factors associated with acceptance intention. Results Participants reported a moderate level of acceptance of GAI-assisted medical decision-making (3.68 ± 0.70), while ethical governance expectations received the highest mean score (4.03 ± 0.76). Multivariable regression analysis showed that perceived functional value ( β = 0.528, p p p Conclusion Acceptance of GAI-assisted medical decision-making among Chinese stakeholders is primarily driven by perceived functional value and ethical governance expectations, with significant differences in ethical perceptions across stakeholder groups. These findings provide empirical evidence for developing targeted ethical governance frameworks to facilitate responsible GAI integration into clinical practice.","url":"https://doi.org/10.3389/fpubh.2026.1833489","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1833489","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3760/cma.j.cn112151-20260119-00048","name":"[Diagnostic efficacy and teaching value of artificial intelligence-assisted diagnostic systems in cervical liquid-based cytology screening].","source":"europepmc","abstract":"Objective: To systematically evaluate the diagnostic performance of an artificial intelligence (AI)-assisted diagnostic system in identifying squamous intraepithelial lesions of different grades, glandular cell lesions, and common pathogenic microorganisms in ThinPrep cytologic test of cervical samples, and to explore its potential value in improving the diagnostic capabilities of pathologists. Methods: A total of 1 096 cervical specimens were collected from patients undergoing ThinPrep cytologic test at Beijing Anzhen Hospital, Capital Medical University from October to December 2025. The \"AI-assisted senior pathologist diagnosis\" served as an initial gold standard. For uncertain cases, a consensus diagnosis reached by two senior cytopathologists after joint review was used as the final gold standard. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and concordance rate of the AI system for each diagnostic category were calculated. Cases of misdiagnosis and missed diagnosis were further reviewed, and the underlying morphological factors contributing to diagnostic errors were analyzed. Additionally, the application effect of AI as a continuous diagnostic quality improvement tool was evaluated. Results: The AI system showed significant variation in recognition performance across different diagnostic categories. For lesion diagnosis, the sensitivities were as follows: high-grade squamous intraepithelial lesion (HSIL), 100% (6/6);atypical squamous cells of undetermined significance (ASCUS), 90.5% (19/21);low-grade squamous intraepithelial lesion (LSIL), 76.5% (26/34), and atypical glandular cells (AGC), 75.0% (9/12). ASCUS had the lowest PPV (9.7%, 19/196) and the highest false-positive rate (16.1%, 177/1 096). For microorganism identification, clue cells showed the best performance [sensitivity 93.9% (46/49); PPV 95.8% (46/48)], followed by Candida [sensitivity 88.0% (44/50); PPV 72.1% (44/61)]. The sensitivities for Trichomonas and Actinomyces were both 100% (2/2; 2/2), but the PPVs were relatively low [11.1% (2/18); 22.2% (2/9), respectively]. Due to the small size of positive samples, these sensitivity estimates should be interpreted with caution. The system overall demonstrated high NPV (all >99%) and high specificity (all >98%). The main causes of misdiagnosis included the misinterpretion histiocytes in an atrophic background as AGC; the misclassification of pseudokoilocytes or reactive changes as LSIL; and the misidentification of multinucleated cells, inflammatory debris, or clusters of Lactobacillus for Herpes Simplex Virus (HSV), Trichomonas, or Actinomyces , respectively. Missed Candida diagnosis primarily occurred in cases with scant hyphae hidden within cell layers. Furthermore, the study found that by frequently presenting AI-flagged positive cases (e.g., ASCUS, AGC, and microorganisms), the system provided diagnostic pathologists with continuous, high-quality opportunities for case review and learning. Conclusions: In cervical cytology screening, the AI-assisted diagnostic system demonstrates high sensitivity and specificity for high-grade lesions and certain microorganisms (e.g., Candida and clue cells), along with an extremely high negative predictive value. It can effectively assist in primary screening and reduce the risk of missed diagnoses. Functioning as an intelligent pathology training adjunct, this system facilitates pathologists in reinforcing and refining their proficiency in lesion identification and differential diagnosis, thereby fostering iterative clinical advancement underpinned by human-AI synergism.","url":"https://doi.org/10.3760/cma.j.cn112151-20260119-00048","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112151-20260119-00048","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.cmi.2026.07.052","name":"Practical applications of artificial intelligence in infectious disease surveillance and control: opportunities, challenges, and ethical considerations.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has been increasingly recognized as a public health tool with applications ranging from epidemiology, pandemic preparedness to infection control. Despite these advances, the integration of AI presents significant risks that demand careful oversight. Objectives The objectives of this review are to (1) present practical AI platforms that aid clinicians, researchers, and policymakers with infectious disease (ID) surveillance, prediction, and infection control and (2) to raise awareness of the risks of using AI in infectious diseases and public health. Sources PubMed was searched for relevant literature published through August 1, 2025 and was updated through July 24, 2026. Content The review highlights AI applications in public health surveillance, exemplified by platforms such as ProMED-mail and HealthMap, which enable timely outbreak detection. In research, AI tools like AlphaFold accelerate pathogen characterization, expediting countermeasure development. Within healthcare settings, AI supports infection control by tracking protocol compliance, predicting HAIs, and detecting antibiotic resistance patterns. The manuscript further explores the dual-use dilemma of AI, addressing risks related to biothreat creation and misinformation dissemination, alongside key ethical issues including data privacy and cybersecurity. Implications AI holds transformative potential for infectious disease surveillance and control, benefiting clinicians, researchers, and public health professionals. However, the risk of misuse underscores the urgent need for comprehensive regulatory frameworks, ethical guidelines, and robust security measures to ensure responsible and safe implementation in public health contexts.","url":"https://doi.org/10.1016/j.cmi.2026.07.052","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cmi.2026.07.052","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s42492-026-00228-y","name":"Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.","source":"europepmc","abstract":"Large language models (LLMs) have been rapidly adopted in healthcare since 2022; however, field-level trends and specialty differences remain poorly characterized. This study aims to map the research landscape of LLMs in healthcare and generate comparative specialty profiles through a bibliometric analysis using the Web of Science Core Collection (2015-2025), including English-language articles and reviews analyzed for publication and citation trends, leading countries, institutions, authors, journals, co-citation networks, and keyword structures with VOSviewer and CiteSpace, with sub-analyses for medicine, general and internal; surgery; and radiology, nuclear medicine and medical imaging. Of the 2226 articles included, the annual output increased from four publications in 2022 to 1327 in 2025, yielding a compound annual growth rate (CAGR) of 592.26%; the United States of America contributed 43.5% of the publications, followed by China (14.3%), Germany (9.2%), Turkey (9.0%), and England (6.8%); the publications for each specialty were surgery (31.7%), medicine, general and internal (24.2%), and radiology, nuclear medicine and medical Iimaging (13.0%), with the last demonstrating the fastest 2023-2024 growth (CAGR 265%); and the collaboration networks were United States of America-centered, with dense trans-Atlantic ties. LLM research in healthcare is expanding rapidly with distinct specialty-specific trajectories, and interpreting these trajectories using the DECIDE-AI (developmental and exploratory clinical Iinvestigations of decision support systems driven by artificial intelligence) framework clarifies low-risk near-term applications and monitoring priorities, providing a specialty-aware baseline to support the safe, equitable, and regulation-aligned adoption of LLMs in clinical practice.","url":"https://doi.org/10.1186/s42492-026-00228-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s42492-026-00228-y","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1017/jme.2026.10270","name":"Prohibited AI Practices in Healthcare under the European Artificial Intelligence Act.","source":"europepmc","abstract":"The European Union's Artificial Intelligence Act introduces a novel regulatory category of \"unacceptable risk,\" prohibiting specific AI practices that are deemed fundamentally incompatible with human rights and ethical principles. While much attention has focused on the regulation of high-risk AI systems, particularly in medical contexts, the AI Act's outright bans under Article 5 have received far less scrutiny. This paper addresses that gap by examining how these prohibitions apply to healthcare and public health, which are domains defined by rapid technological uptake, structural vulnerability, and ethically sensitive decision-making. Drawing on the European Commission's 2025 interpretative Guidelines, the paper argues that several health-related AI applications, such as emotion recognition tools, biometric categorisation systems, and technologies that influence or target vulnerable populations, may fall within the scope of the bans. It also shows that the Act's medical and safety exceptions risk weakening the vulnerability protections that the prohibitions aim to secure. By integrating legal analysis with real-world health examples, the paper offers a framework for interpreting these prohibitions and assesses how they should guide the ethical boundaries of AI in healthcare, within and beyond the European context.","url":"https://doi.org/10.1017/jme.2026.10270","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1017/jme.2026.10270","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1097/ms9.0000000000005235","name":"Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.","source":"europepmc","abstract":"Heart failure (HF) is a complex syndrome with high morbidity and mortality. Despite advancements in treatment, its management remains a challenge. The objective of this study was to map the scientific landscape of artificial intelligence (AI) applications in HF management through a bibliometric analysis. Data were retrieved from the Web of Science Core Collection. Keywords related to AI and HF were used to identify relevant research articles. Various bibliometric tools, such as Biblioshiny, VOS viewer, and CiteSpace, were used for quantitative trends, collaboration networks, and thematic areas, which were assessed. A total of 1332 studies were included in the final analysis. Publication trends show a sharp increase in AI research related to HF from 2016 onward, with 317 studies published in 2025. The most frequent keywords in the field were heart failure ( n = 599), machine learning ( n = 516), and AI ( n = 225). Other significant keywords included mortality ( n = 201), diagnosis ( n = 168), and risk ( n = 162). Cluster analysis identified major research themes, including Cardiac & Cardiovascular Systems, Computer Science - Interdisciplinary Applications, Computer Science - Artificial Intelligence, Health Care Sciences & Services, Radiology, Nuclear Medicine & Medical Imaging, Cell Biology, Medical Informatics, Endocrinology & Metabolism, and Neurosciences. AI has rapidly become a central tool in HF management, with significant contributions from leading countries and institutions. However, further global collaboration and standardized reporting frameworks are needed to ensure the equitable translation of these technologies into clinical practice.","url":"https://doi.org/10.1097/ms9.0000000000005235","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ms9.0000000000005235","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1093/jacamr/dlag144","name":"Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.","source":"europepmc","abstract":"Antimicrobial resistance (AMR) poses a significant global public health threat, and efforts to mitigate it have been aided by artificial intelligence (AI) methods. Key areas of applications include rapid diagnostics, drug discovery and repurposing, surveillance and predictive modelling, and antibiotic stewardship. This review aims to summarize key literature on AI applications for mitigating AMR including original research articles from PubMed and Scopus published from October 2024 to 2025 to summarize and find out the way ahead for successful application of AI. The key search terms included were AMR, AI and applications such as diagnostics, drug discovery and repurposing, surveillance, predictive modelling and stewardship. The data used for these applications, the techniques applied and the predictive targets have undergone significant expansion, along with the focus on model deployment, external validation and model interpretability. Although more complex models, such as neural networks and transformers, have been experimented with, classical machine learning (ML) models still dominate the AMR prediction space, while large language models are also being tested for prediction, as well as antibiotic stewardship. For drug discovery, data mining for antimicrobial peptides from different sources is a major application. Predictive modelling using next-generation sequencing data has been the most studied. The application of AI/ML to large and complex data from multiple sources could provide a promising arena for developing clinically translational tools. With more data availability, regulatory measures, real-world validation and transparency, there is scope for responsibly integrating innovative technology into clinical practice.","url":"https://doi.org/10.1093/jacamr/dlag144","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jacamr/dlag144","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.injury.2026.113521","name":"Artificial intelligence in the evaluation and treatment of osteoporotic and fragility fractures: A systematic review.","source":"europepmc","abstract":"Background Osteoporotic and fragility fractures impose a significant global health burden, especially with the aging population. Despite advancements in imaging, risk assessment, and surgical techniques, underdiagnosis and undertreatment persists. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers promise for enhancing fracture risk prediction, imaging-based diagnosis, clinical decision support, and postoperative outcome monitoring. Objective To systematically review AI applications in the evaluation and treatment of osteoporotic and fragility fractures, summarizing performance, limitations, evidence gaps, and future directions for clinical translation. Methods A PRISMA-compliant search was conducted in PubMed, IEEE Xplore, Google Scholar, and Web of Science from inception to October 2025. Inclusion criteria targeted original English-language studies in adults using AI/ML/DL for fracture risk prediction, diagnosis, treatment planning, intraoperative guidance, or postoperative management. Data extraction focused on study design, population, AI methods, performance metrics, and validation. Results Of 1286 records, 21 studies were included, clustering into three domains: fracture risk prediction (n = 10) using clinical, biochemical, and imaging data, often outperforming tools like FRAX; bone mineral density (BMD) estimation and osteoporosis screening from CT, X-ray, or opportunistic imaging (n = 6); and prognosis/postoperative outcomes (n = 5). Conclusions AI demonstrates robust performance in BMD estimation, fracture detection, and risk prediction, frequently surpassing traditional methods. However, methodological heterogeneity, bias risks, and limited prospective/multicenter validation hinder translation. Future efforts should prioritize transparent reporting, external validation, regulatory compliance, and user-centered integration.","url":"https://doi.org/10.1016/j.injury.2026.113521","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.injury.2026.113521","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s44445-026-00160-0","name":"Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007-2025).","source":"europepmc","abstract":"Periodontal disease is one of the most common diseases in stomatology. With the continuous development of artificial intelligence (AI), its integration with periodontology is rapidly evolving. However, a comprehensive bibliometric analysis mapping this interdisciplinary field is currently lacking. We conducted a bibliometric analysis by retrieving publications related to AI and periodontal disease from the Web of Science Core Collection (WoSCC) for the period January 2007 to July 2025. Data processing and visualization were performed using R (Bibliometrix), VOSviewer, and CiteSpace. A total of 496 relevant articles (437 research papers; 59 reviews) were included. Annual publication output has shown sustained growth, particularly since 2021. China contributed the most publications (153 articles), followed by the United States. Among institutions, Pusan National University, South Korea (32 articles), and Saveetha Institute of Medical and Technical Science, India (31 articles) were the most productive. BMC Oral Health published the highest number of articles (n = 23). The co-authorship network involved 2,604 authors, with Pradeep Kumar Yadalam being the most prolific (15 articles). Co-citation analysis identified Orhan Kaan, Abu Patricia Angela R., and Falk Schwendicke as the most cited authors. Keyword analysis revealed \"periodontitis,\" \"machine learning,\" and \"artificial intelligence\" as core research foci, while burst detection indicated \"progression\" and \"expression\" as emerging thematic directions. This study provides a systematic overview of the research landscape, highlighting evolving trends, key contributors, and knowledge structure in AI applications for periodontal disease. The findings offer valuable insights to help dentists and researchers understand current applications, identify frontiers, and potentially guide the future clinical translation of AI technologies in periodontology.","url":"https://doi.org/10.1007/s44445-026-00160-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s44445-026-00160-0","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12887-026-07088-8","name":"Assessing pediatricians' readiness for artificial intelligence: a cross-sectional study in Istanbul, Türki̇ye.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly integrated into healthcare, including pediatrics, offering new opportunities for diagnosis, management, and decision support. However, the effective implementation of AI depends largely on healthcare professionals' knowledge, attitudes, and readiness to adopt these technologies. This study aimed to evaluate pediatricians' general attitudes toward artificial intelligence and their level of readiness for medical AI in Istanbul, Türkiye at tertiary hospital. Distinct from previous research focusing primarily on medical students, this study investigates the AI readiness of practicing pediatricians within a major tertiary hospital in Istanbul, addressing a critical gap in professional clinical workforce assessment. Methods This descriptive cross-sectional study was conducted between August 1 and November 1, 2025, at Prof. Dr. Cemil Taşcıoğlu City Hospital. A total of 130 pediatricians participated. Data were collected using a sociodemographic questionnaire, the GAAIS (General Attitudes toward Artificial Intelligence Scale), and the MAIRS-MS (Medical Artificial Intelligence Readiness Scale for Medical Students). The validity and reliability of scales in Turkish have been previously investigated in various studies. The internal consistency of the scales was excellent, with a Cronbach's alpha coefficient of 0.972 in our study. Statistical analyses were performed using SPSS 27. Correlation, t-test, ANOVA, and non-parametric tests were applied where appropriate. Results The mean age of participants was 37.47 ± 9.31 years, and 58.5% were female. The mean AI positive attitude score was 3.81 ± 0.74, and the negative attitude score was 2.83 ± 0.81. The total MAIRS-MS score was 74.97 ± 13.01, indicating a moderate-to-high level of AI readiness. Age was negatively correlated with AI positive attitude and total MAIRS score (p 2 = 0.658). A strong positive correlation was found between AI positive attitude and total MAIRS-MS score (r = 0.764, p Conclusion Pediatricians generally demonstrated positive attitudes and moderate-to-high readiness for artificial intelligence in our study. However, advanced age and longer professional experience were associated with lower readiness levels. These findings highlight the need for a structured AI education and training program, especially targeting senior physicians, to ensure effective and ethical integration of AI into pediatric practice.","url":"https://doi.org/10.1186/s12887-026-07088-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12887-026-07088-8","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106583","name":"Diagnostic performance of artificial intelligence based on PET/CT for lymph node involvement in prostate cancer: A systematic review and meta-analysis.","source":"europepmc","abstract":"Purpose This meta-analysis aims to evaluate the diagnostic performance of PET/CT-based artificial intelligence (AI) models for detecting lymph node involvement (LNI) in prostate cancer and compare it with physician readers. Methods We systematically searched PubMed, Embase, and Web of Science through March 4, 2025, for studies evaluating PET/CT-based AI (machine learning or deep learning) for LNI detection. Eligible studies used pathology or expert physician reads as reference standards. AI model studies were assessed with PROBAST + AI, and extractable physician-reader diagnostic accuracy datasets were additionally assessed with QUADAS-2. A bivariate random-effects model pooled sensitivity, specificity, diagnostic odds ratio (DOR), and AUC using patient-based datasets with extractable 2 x 2 data. Results Of 884 records identified, 11 studies involving 1,143 patients were included. AI demonstrated a sensitivity of 0.71 (95% CI: 0.61-0.79), specificity of 0.89 (95% CI: 0.76-0.95), and AUC of 0.79 (95% CI: 0.75-0.82). Physician readers showed sensitivity of 0.67 (95% CI: 0.57-0.76), specificity of 0.88 (95% CI: 0.70-0.95), and AUC of 0.71 (95% CI: 0.67-0.75). AI achieved a higher AUC (Z = 2.95, P = 0.003), but only two studies directly supported AI-versus-physician comparisons. Conclusions PET/CT-based AI models may show modestly improved discrimination for detecting LNI in prostate cancer, but current evidence does not establish clinical superiority over physician readers. The evidence is limited by small study numbers, mostly retrospective designs, substantial heterogeneity, limited external validation, and low certainty. Larger prospective, multi-center, externally validated studies are required before routine clinical implementation. Registration number PROSPERO (CRD420251128237).","url":"https://doi.org/10.1016/j.ijmedinf.2026.106583","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106583","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s00455-026-10963-2","name":"A Comparative Study of the Psychometrics and Feasibility of Artificial Intelligence Algorithms Versus the Standard Screening Methods for Oropharyngeal Dysphagia in Older Adults: A Scoping Review.","source":"europepmc","abstract":"Oropharyngeal dysphagia (OD) is highly prevalent (35.6%-47.4%) in hospitalized older patients but clinical screening is slow and labor-intensive, leading to an 80% underdiagnosis. The aim of this study was to describe and compare the psychometrics and feasibility of current OD clinical screening methods versus artificial intelligence (AI) algorithms for OD. A scoping review was performed using PubMed Central and Web of Science databases (January 2009-August 2025), focusing on psychometric data for OD screening methods in older adults (>65 years) and comparing them with AI-based tests, including the AI Massive Screening for OD (AIMS-OD). Quality of the studies was assessed by using Joanna Briggs Institute Critical Appraisal Checklists (JBICAC). Of 166 articles identified, 137 remained after duplicate removal. Following title and abstract screening, 53 full-text articles were reviewed and 41 met inclusion criteria; 99 clinical screening methods were identified, showing 37-100% sensitivity, 29-97% specificity, 37-100% reliability and required 5.6-10min/patient. AI-based tests demonstrated good overall psychometric performance and reliability, AIMS-OD showed 88.6-94% sensitivity, 42-99.3% specificity, 100% reliability and took only 0.7 seconds/patient without the need of human resources. OD screening methods are accurate but time-consuming, which may limit feasibility and contribute to underdiagnosis. AI tools show comparable psychometric performance, while offering rapid, automated, and highly reliable screening without using human resources. AI-based tools are a feasible and scalable approach to providing early systematic screening of hospitalized older patients, facilitating timely identification and referral for further assessment and potentially reducing OD-related complications.","url":"https://doi.org/10.1007/s00455-026-10963-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00455-026-10963-2","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.xops.2026.101315","name":"OphthoChat: A HIPAA-Compliant, Artificial Intelligence-Driven Natural-Language Chatbot for Ophthalmic Electronic Medical Record Querying and Analysis.","source":"europepmc","abstract":"Purpose To introduce and evaluate OphthoChat, a Health Insurance Portability and Accountability Act-compliant, artificial intelligence (AI)‑powered natural language chatbot designed to enable clinicians to query ophthalmic electronic medical records (EMRs) using conversational language, without requiring coding experience. Design Retrospective validation study using a clinical data set and clinician-authored queries. Participants The study included 500 adult patients seen at Columbia University Irving Medical Center between 2020 and 2025: 250 patients who received intravitreal injections for wet age-related macular degeneration and 250 patients who underwent cataract surgery. Methods We processed >50 000 EMR artifacts, including progress notes, operative reports, imaging summaries, and optical character recognition‑converted scanned documents, into a vectorized database. OphthoChat used a retrieval-augmented generation architecture powered by GPT-4o to answer 1000 natural-language queries stratified by complexity (simple, intermediate, advanced). Each response was evaluated against a reference standard determined by dual clinician adjudication. Main outcome measures Primary metrics included response accuracy, sensitivity, specificity, and time-to-answer per query. Supporting chart citations were also assessed for traceability and rank. Results OphthoChat achieved an overall accuracy of 96%, with a sensitivity of 93% and a specificity of 97%. Median time to answer a query was 35 seconds, an 11-fold speed improvement over manual chart review (6.6 minutes). Among correct responses, 97% of cited lines matched the reference standard, with a mean reciprocal rank of 0.92. Performance remained strong across all difficulty levels, including 91% accuracy on advanced narrative inference tasks. The system demonstrated robustness in processing complex chart histories while offering full traceability. Conclusions OphthoChat enables fast, accurate, and traceable chart review in ophthalmology through natural-language dialogue. By eliminating the need for technical setup or programming knowledge, it significantly lowers the barrier to AI adoption in clinical research and practice. OphthoChat's ability to synthesize complex EMR data across structured and unstructured formats offers a scalable solution for accelerating outcomes research, clinical decision-making, and cohort identification. Further development will focus on EMR integration and subspecialty expansion. Financial disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.","url":"https://doi.org/10.1016/j.xops.2026.101315","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.xops.2026.101315","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fradi.2026.1899103","name":"Artificial intelligence for lung ultrasound interpretation: a systematic review.","source":"europepmc","abstract":"Context and objectives Lung ultrasound (LUS) is a safe low-cost tool that enables diagnosis, monitoring and guidance for interventional procedures at the patient's bedside. However, its expansion is hindered by a lack of training programs and the inherent difficulty of interpreting ultrasound images. In this context, Artificial Intelligence (AI) is emerging as a supportive tool for LUS interpretation, ensuring diagnostic efficacy and mitigating the shortage of experts. This systematic review aims to summarize and analyze recent advances in AI-based tools to support LUS interpretation. Methods A systematic literature search was conducted across Web of Science, IEEE Xplore, and PubMed databases to identify peer-reviewed original journal articles published between 2015 and November 2025 that employed AI for the identification and localization of lung artifacts, anatomical structures, and pathological findings. Risk of bias was assessed using PROBAST + AI. Results Twenty-four studies were included, identifying three main strategies: segmentation (10 studies), object detection (4 studies), and the generation of visual explanations through saliency maps (10 studies). All employed CNN-based architectures. The evaluation metrics used were heterogeneous. The PROBAST + AI assessment showed relevant risk-of-bias concerns, mainly concentrated in the participants and analysis domains. Conclusions The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics. It is necessary to move towards solutions designed for specific clinical environments and to adopt standardized protocols and evaluations that facilitate their implementation in clinical practice. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261322517, PROSPERO CRD420261322517.","url":"https://doi.org/10.3389/fradi.2026.1899103","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fradi.2026.1899103","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.outlook.2026.102808","name":"Artificial intelligence can replace nursing tasks, but not nurses: Examining artificial intelligence's supports and threats to nursing practice through the lens of the 2025 Nursing Code of Ethics.","source":"europepmc","abstract":"Background Nurses are concerned that artificial intelligence (AI) could undermine the holistic, intuitive, and experience-based clinical judgment that is foundational to safe patient care. They are also worried that AI will replace their jobs. Purpose To examine the supports and threats that four domains of AI (machine learning, generative AI, natural language processing, and robotics) pose to nursing practice, using the American Nurses Association's 2025 Code of Ethics for Nurses as an ethical framework. Methods This study employed a normative ethical analysis grounded in the American Nurses Association's 2025 Code of Ethics for Nurses and was conducted by a 12-member multidisciplinary expert panel from the University of Minnesota AI in Nursing ethics initiative. Panel members were organized into four working groups aligned with major AI domains and applied a support-interference analytical framework across all ten provisions of the Code, using a structured, template-based approach consistent with the nominal group technique. Discussion Our findings distinguish between the technical tasks that the domains of AI can handle (documentation, physical assistance, predictive analytics, and educational simulation) and the core humanistic responsibilities that must remain the exclusive domain of nurses. AI supports consistently align with the optimization of these technical tasks, while threats concentrate on nursing's moral and humanistic core, including algorithmic bias, automation bias, erosion of accountability, and lack of transparency. Two critical gaps were identified: the near-absence of AI tools to support an ethical work environment (Provision 6) and a significant research void regarding AI's environmental impact on planetary health (Provision 10). Five recommendations are offered for nurse leaders, administrators, and researchers addressing AI governance, co-design, literacy, local policy, and new advocacy directions. Conclusion AI can assist nurses with their tasks, but it cannot currently replace the compassionate, humanistic patient care delivered by nurses. Nurses must serve as moral arbiters of AI, ensuring that technology augments rather than supplants the profession's ethical core.","url":"https://doi.org/10.1016/j.outlook.2026.102808","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.outlook.2026.102808","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1177/10556656261460187","name":"Artificial Intelligence Applications in Cleft Lip and Palate Diagnosis, Prediction, and Treatment: A Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"ObjectiveCleft lip and cleft palate are common craniofacial abnormalities, causing significant functional, esthetic, and psychosocial issues if not treated early. Artificial intelligence (AI) is increasingly explored in cleft lip and/or palate (CL/P); however, the quality, consistency, and clinical readiness of the available evidence remain unclear. This study systematically reviewed the existing literature on AI applications in CL/P care and performed an exploratory meta-analysis.DesignA comprehensive search was performed on PubMed, WOS, Scopus, and IEEE Xplore until November 2025, following the PECO question: \"In patients with CL/P (P), how do AI approaches (E), compared with conventional diagnostic methods or human intelligence (C), perform in terms of diagnostic accuracy, predictive performance, or treatment outcomes (O)?\". Studies applying AI to human CL/P data for diagnostic, predictive, or treatment purposes were included. Risk of bias was assessed using appropriate checklists (eg, QUADAS-2). Due to study heterogeneity, random-effects meta-analyses were limited to subgroups evaluating CL/P detection on panoramic radiographs and prediction of orthognathic surgery using lateral cephalograms.ResultsThe search identified 548 articles; after screening and full-text review, 52 studies were included. These studies addressed diagnosis, prediction, and treatment planning. Meta-analysis of CL/P detection indicated pooled sensitivity, specificity, and accuracy of 87%, 89%, and 90%. Prediction of orthognathic surgery showed sensitivity and specificity of 87% and 86%.ConclusionAI is increasingly applied in CL/P management, suggesting high accuracy and consistency in diagnosis, prediction, and treatment evaluation, often approaching expert-level performance.","url":"https://doi.org/10.1177/10556656261460187","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/10556656261460187","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1097/ajn.0000000000000349","name":"AI-Powered Voice Documentation: A Nurse-Led Pilot Program.","source":"europepmc","abstract":"Background Documentation burden consumes an average of 31% of a nurse's 12-hour shift. This challenge necessitates nurse-led innovations to streamline charting and reclaim valuable time at the bedside. Purpose This pilot program aimed to examine a nurse-led implementation of an artificial intelligence (AI)-powered voice documentation mobile application and evaluate outcomes related to documentation timeliness, application usage, patient experience, staff satisfaction, and incidental overtime. Methods The six-month pilot was conducted on a 48-bed adult medical-surgical unit at a large academic medical center. It included 90 RNs and 33 certified nursing assistants. The mobile application enabled nursing staff to document directly into the electronic health record using voice commands. The project was guided by Kotter's eight-step change management model. Results Documentation timeliness improved across all targeted tasks. Patient experience improved across all six Press Ganey domains, with percentile increases ranging from 7 to 41 points. Staff satisfaction survey results showed a majority agreed that the technology helped reduce documentation time. Usage grew from 14,231 documentation entries in January 2025 to over 35,000 by June. Incidental overtime decreased from an average of 97 to 48.5 hours per month. Conclusion This pilot demonstrated that nurse-led implementation of an AI-powered voice documentation application can improve charting efficiency, patient experience, and staff outcomes. When nurses contribute to each stage of technology design and implementation, innovation thrives.","url":"https://doi.org/10.1097/ajn.0000000000000349","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ajn.0000000000000349","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.21037/jtd-2026-0853","name":"A bibliometric analysis of artificial intelligence applications in heart failure.","source":"europepmc","abstract":"Background Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, posing substantial challenges for early and accurate diagnosis as well as personalized therapeutic management. With the rapid evolution of artificial intelligence (AI), substantial progress has been made in the clinical translation and interdisciplinary research of HF. AI offers unprecedented potential to improve the diagnostic accuracy and therapeutic efficacy of HF. However, there is a lack of systematic reviews and analyses in the current research landscape, hotspots, and development trends in this field. This study aimed to employ bibliometric methods to systematically clarify the research status, core research directions, and future prospects of AI applications in HF. Methods Using the Web of Science Core Collection as the data source, we systematically retrieved literature on AI applications in HF published from 2005 to 2025 and conducted a comprehensive bibliometric analysis via VOSviewer, CiteSpace, and SCImago Graphica. Results A total of 4,133 records were retrieved initially, among which 4,110 eligible publications were finally included after strict screening. The annual publication output in this field showed accelerated growth since 2019, reaching 1,067 articles in 2025. The United States (1,508 publications) and China (952 publications) ranked as the top two contributing countries. Frontiers in Cardiovascular Medicine was the most prolific journal, whereas Circulation recorded the highest citation frequency. High-frequency keywords included \"heart failure, machine learning, AI, risk, and mortality\". The mainstream research hotspots concentrated on machine learning algorithms, medical image analysis, and clinical feature extraction. Conclusions Research on AI applications in HF has undergone rapid development and established a comprehensive research framework covering disease diagnosis, prognosis assessment, and mechanism investigations. Future research priorities should incorporate more multicenter data for external validation, as well as promoting the construction and sharing of multicenter, multimodal datasets. These steps will further enhance the clinical applicability and credibility of AI-driven models in HF management.","url":"https://doi.org/10.21037/jtd-2026-0853","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/jtd-2026-0853","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1177/03009858261472493","name":"Cognitive biases in AI-assisted medical decision making: A structured review as a primer for veterinary and human pathology.","source":"europepmc","abstract":"Cognitive biases are systematic patterns of error in human thinking. While artificial intelligence (AI)-based decision support systems offer great potential to enhance diagnostic accuracy and efficiency in pathology, collaboration between medical professionals and AI can also introduce or amplify these biases. This review aims to identify cognitive biases and their modes of manifestation in human-computer interaction (HCI) within pathology and to extrapolate potential manifestations of biases not yet explored in this domain but reported in HCIs across other medical specialties. We conducted a structured literature review (19 August 2025) across the ACM, IEEE, and PubMed databases. Studies were eligible if they operationalized cognitive biases during expert-machine interaction in diagnostic decision-making using qualitative or quantitative methods, including review articles citing primary studies that met these criteria. The final corpus comprised 24 studies (8 primary studies and 16 review articles). From these review articles, 18 additional primary studies were extracted and used in their place solely for analysis purposes. A narrative synthesis of the identified primary research revealed 12 cognitive biases reported in AI-assisted medical decision-making, with only one study originating from pathology. For each bias, definitions and hypothetical pathology-specific examples were derived. This review is intended as a primer for the veterinary and human pathology communities and strives to contribute to the safe and effective integration of AI into diagnostic practice.","url":"https://doi.org/10.1177/03009858261472493","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03009858261472493","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1186/s12920-026-02433-3","name":"Efficient differential latent network analysis: applications to colon cancer.","source":"europepmc","abstract":"Background Colon cancer (CC) presents significant molecular heterogeneity, complicating our understanding of its initiation and progression. Identifying CC-associated genes and their interactions is crucial for improving diagnostics and therapeutics. However, current gene analysis methods struggle to holistically capture complex gene interactions due to their inherent complexity. Method We propose a novel, simple, and scalable method called EFFICIENT DIFFERENTIAL LATENT NETWORK ANALYSIS (EDLNA) for detecting alterations in gene interactions using latent co-expression patterns. Our approach applies non-negative matrix factorization to construct separate latent gene networks for normal and cancer samples. We then identify differential interactions, followed by protein-protein interaction network and transcription factor (TF) analyses to detect functional modules and regulatory relationships. Results We evaluated EDLNA against conventional methods using both simulated and colon cancer gene expression data (GSE44076, GSE50760). In simulation studies, EDLNA was significantly faster among differential network analysis techniques while maintaining comparable accuracy. When applied to colon cancer data, our method outperformed differential gene expression analysis in identifying biologically relevant gene clusters and stage-specific traits. Conclusions EDLNA provides an efficient and scalable framework for identifying stage-specific gene interactions that bridge molecular mechanisms with clinical phenotypes. These findings underscore its potential for discovering novel biomarkers and advancing targeted therapies in colon cancer.","url":"https://doi.org/10.1186/s12920-026-02433-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12920-026-02433-3","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1097/md.0000000000049658","name":"Artificial intelligence-based patient information in rotator cuff injuries: A cross-sectional comparative study of ChatGPT and DeepSeek models.","source":"europepmc","abstract":"This study aimed to comparatively evaluate the performance of the chat generative pretrained transformer (ChatGPT) and DeepSeek artificial intelligence (AI) models in patient information about rotator cuff injuries. This cross-sectional comparative study was conducted in May 28, 2025 using ChatGPT-4o (OpenAI) and DeepSeek V3 (DeepSeek Inc.) models. Sixteen frequently asked questions related to rotator cuff injuries were posed to both the AI models. The responses were then independently assessed by 2 experienced orthopedic surgeons using the Journal of the American Medical Association (JAMA), response rating system, DISCERN, and 4-point Likert scales. In addition, the readability of the responses was analyzed using the Flesch-Kincaid Readability Score (FRES) and Flesch-Kincaid Grade Level. The primary outcome was overall information quality, secondary outcomes included JAMA benchmark adherence and readability metrics. None of the models met JAMA criteria. In terms of response rating system, there was no statistically significant difference between the 2 models (P >.05). DeepSeek demonstrated higher DISCERN scores compared to ChatGPT (50.12 vs 47.03), with a mean difference of 3.09 (95% CI: 1.58 to 4.61; P = .001). While there was no significant difference in accuracy, clarity, and consistency criteria between the 2 models in the 4-point Likert evaluation (P >.05), DeepSeek scored significantly higher than ChatGPT in the completeness criterion, with a mean difference of 0.75 (95% CI: 0.46 to 1.04; P = .001). In terms of readability, both models showed similar performance (FRES, P >.05; Flesch-Kincaid Grade Level, P >.05). Both AI models deliver satisfactory and clinically relevant information for rotator cuff injury patient education. Although DeepSeek was superior to ChatGPT in terms of completeness of patient information regarding rotator cuff injuries, the results were similar for the other criteria. The responses from both the AI tools were considered promising. However, they require improvements in terms of adherence to scientific standards, transparency, citations, and readability.","url":"https://doi.org/10.1097/md.0000000000049658","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000049658","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.compmedimag.2026.102782","name":"Artificial intelligence empowered coronary artery imaging: A review.","source":"europepmc","abstract":"Cardiovascular disease is the leading cause of death worldwide, with coronary artery disease the most prevalent cause. Although artificial intelligence has advanced medical imaging, few reviews focus specifically on AI-powered coronary artery imaging. This review provides a systematic and critical analysis of AI applications in coronary artery imaging across three domains: measurement, including centerline extraction, vessel segmentation, and 3D reconstruction; functional assessment, including fractional flow reserve, wall shear stress, and myocardial perfusion; and disease diagnosis. Web of Science, Google Scholar, and MEDLINE were searched for 2016-2025. From 9950 records, 90 studies were selected after duplicate removal, title and abstract screening, and full-text eligibility assessment using predefined criteria and quality appraisal. Seven imaging modalities are covered: computed tomography, magnetic resonance imaging, nuclear imaging, digital subtraction angiography, intravascular ultrasound, optical coherence tomography, and synthetic data. Reported performance varies by modality, task, and dataset. Challenges for clinical implementation include dataset generalizability, computational requirements, interoperability, and explainable AI. The review discusses emerging technologies such as multimodal fusion, self-supervised learning, federated learning, and foundation models, and acknowledges current limitations. It aims to inform researchers and clinicians and support future translation of AI in coronary artery imaging.","url":"https://doi.org/10.1016/j.compmedimag.2026.102782","authors":["Mingzhi Wang","Junxin Chen","Hao Li","Li-bo Zhang","Wei Wang"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.compmedimag.2026.102782","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s12911-026-03718-4","name":"Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.","source":"europepmc","abstract":"Background Pediatric rare diseases are highly heterogeneous and are frequently associated with missed or delayed diagnosis, creating substantial burden for patients, families, and clinicians. Although artificial intelligence (AI), including large language model-enabled approaches, has shown potential for diagnostic support, translation into real-world pediatric care remains limited. A key gap is the mismatch between metric-centric evidence reporting and clinician-defined implementation needs. To address this, we integrated published evidence with clinician perspectives to derive an implementation-oriented evidence-to-requirements framework for AI-assisted pediatric rare-disease diagnosis. Methods We used a convergent multimethod design with two complementary evidence sources. First, we conducted a PRISMA-ScR scoping review of four databases (PubMed, Embase, Web of Science, and Scopus) from inception to December 2025 and included 28 original studies on AI-assisted pediatric rare-disease diagnosis. Second, we conducted semi-structured interviews with 21 pediatric clinicians from 15 departments at a tertiary children's hospital in Chongqing, China, and analyzed transcripts using inductive thematic analysis. We then integrated findings side-by-side to identify convergences, divergences, and translational gaps. Results The scoping review showed rapid movement toward multimodal and LLM-enabled approaches across several diagnostic task types, including screening or cohort identification, phenotyping, differential diagnostic support, and variant or gene prioritization. Translation-oriented evidence remained uneven, with limited prospective evaluation and inconsistent reporting of fairness, safety, and deployment context. Interview analysis identified four recurrent themes: diagnosis as time-pressured puzzle-solving; AI as a cognitive extender rather than replacement; trust dependent on traceable evidence and transparent reasoning; and demand for structured, actionable outputs with low workflow burden. Integrated analysis revealed a persistent implementation gap between metric-centric publication practices and clinician-defined requirements for real-world adoption. Conclusions This scoping review and qualitative interview study does not establish clinical effectiveness of AI-assisted diagnosis. Instead, it identifies implementation requirements that may guide future development and evaluation, including representative multicenter data, prospective validation, evidence traceability, actionability, safety, fairness, and workflow fit. Main limitations include restriction to English-language studies, reliance on umbrella rare-disease terminology, possible missed studies among unscreened records after ASReview-assisted screening, and a single-institution clinician interview sample.","url":"https://doi.org/10.1186/s12911-026-03718-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03718-4","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.3389/fdgth.2026.1842497","name":"Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement.","source":"europepmc","abstract":"Background The implementation of artificial intelligence (AI) in healthcare increasingly depends not only on algorithmic performance but also on how patients and healthcare professionals experience, trust, accept, and engage with AI-enabled systems. Although many bibliometric studies have mapped AI in healthcare, the human-centered evidence base on satisfaction-related stakeholder experience remains fragmented. This study therefore analyzed satisfaction as one component of a broader stakeholder-experience framework that also includes trust, attitude, perception, acceptance, willingness, resistance, and usability. Methods English-language articles, reviews, and conference papers published between January 1, 2010 and December 31, 2025 were retrieved from the Web of Science Core Collection and Scopus. After duplicate removal and eligibility screening, 1,794 publications were included. Bibliometric analyses were conducted using Microsoft Excel, VOSviewer, CiteSpace, and bibliometrix. Results Publication output increased rapidly after 2020 and reached 696 publications in 2025. The United States and China were the leading contributors, and the Journal of Medical Internet Research was the most productive journal. Keyword and co-citation analyses showed a shift from early work on robotic surgery and clinical decision support toward human-centered topics, including patient satisfaction, trust, attitude, ChatGPT, large language models, explainable AI, ethics, and nursing workforce adaptation. Three interconnected frontiers were identified: large-language-model-mediated clinical communication, explainability and trust in patient-AI-professional relationships, and the educational and professional needs of nurses and other healthcare workers. Conclusion This study maps the knowledge structure and thematic evolution of research on stakeholder experience with healthcare AI. The findings show that satisfaction should not be treated as interchangeable with trust, acceptance, or usability; rather, these constructs jointly define a multidimensional human-centered implementation problem. Future research should integrate explainable AI, ethical governance, regulatory compliance, and active patient engagement into the evaluation of healthcare AI.","url":"https://doi.org/10.3389/fdgth.2026.1842497","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1842497","addedAt":"2026-09-01T01:47:48.792Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1136/bmjqs-2025-019299","name":"Artificial intelligence chain-of-thought reasoning in nuanced medical scenarios: mitigation of cognitive biases through model intransigence.","source":"europepmc","abstract":"Background Artificial intelligence large language models (LLMs) are increasingly used to inform clinical decisions but sometimes exhibit human-like cognitive biases when facing nuanced medical choices. Methods We tested whether new chain-of-thought reasoning LLMs might mitigate cognitive biases observed in physicians. We presented medical scenarios (n=10) to models released by DeepSeek, OpenAI and Google. Each scenario was presented in two versions that differed according to a specific bias (eg, surgery framed in survival vs mortality statistics). Responses were categorised and the extent of bias was measured by the absolute discrepancy between responses to different versions of the same scenario. The extent of intransigence (also termed dogma or inflexibility) was measured by Shannon entropy. The extent of deviance in each scenario was measured by comparing the average model response to the average practicing physician response (n=2507). Results DeepSeek-R1 mitigated 6 out of 10 cognitive biases observed in practicing physicians by generating intransigent all-or-none responses. The four biases that persisted were post hoc fallacy (34% vs 0%, p Conclusion Some biases persist in chain-of-thought reasoning LLMs, and models tend to produce intransigent recommendations. These findings highlight the role of clinicians to think broadly, respect diversity and remain vigilant when interpreting chain-of-thought reasoning artificial intelligence LLMs in nuanced medical decisions for patients.","url":"https://doi.org/10.1136/bmjqs-2025-019299","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjqs-2025-019299","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1186/s12909-026-09630-9","name":"Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students' perspectives: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Although artificial intelligence (AI) is increasingly transforming healthcare systems, its systematic integration into undergraduate medical education (UME) remains limited. Despite widespread recognition of AI's potential to enhance clinical practice, AI-related competencies are still inadequately embedded in most medical curricula, even though medical students generally express positive attitudes toward AI. Objectives This systematic review and meta-analysis synthesizes the current literature on medical students' attitudes toward teaching about AI and clarifies the challenges, opportunities, and potential approaches for incorporating AI into the UME curriculum. These findings underscore the need for competency-based AI integration in UME worldwide. Methods Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was conducted using the PubMed, Web of Science, Scopus, and Cochrane Library databases between January 2000 and February 2025, revealing 26 studies (n = 20,963 students) assessing attitudes and challenges related to learning AI. Meta-analysis calculated the pooled proportions of student support. A random-effects model with Freeman-Tukey transformation was used; heterogeneity and bias were assessed via standard statistical methods. Results The systematic review included 26 studies (n = 20,963 medical students). Meta-analysis revealed that 16,278 participants (78.0%) held positive attitudes towards AI curriculum integration. Despite this strong support, a significant 'optimism-competence gap' was identified: while 17,420 participants (83.1%) agreed on the necessity for AI training, only 7,630 participants (36.4%) felt confident in applying AI in clinical practice. Subgroup analyses confirmed consistent support across geographic regions. The analysis indicated extreme heterogeneity (I² = 98.5%). The primary implementation challenges, derived from the systematic review, were curricular overcrowding (reported in 13 out of 19 studies, 68%), lack of faculty expertise (in 12 out of 23 studies, 52%), and ethical concerns (in 9 out of 22 studies, 41%). Conclusion A standardized AI curriculum is urgently needed to bridge the optimism-competence gap. Prioritizing faculty training, interdisciplinary collaboration, and ethical frameworks will ensure that future physicians can harness AI's potential while mitigating risks. Our findings, particularly the quantified optimism-competence gap and the influence of unmeasured institutional factors, provide a new evidence base for developing these prioritized interventions.","url":"https://doi.org/10.1186/s12909-026-09630-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09630-9","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1007/s44445-026-00192-6","name":"Artificial intelligence and the future of prosthodontics: a narrative review.","source":"europepmc","abstract":"Purpose This narrative review aims to systematically summarize and compile available evidence on the applications of artificial intelligence (AI) across major domains of prosthodontics, including complete dentures, removable partial dentures, fixed prosthodontics, implant-supported prosthodontics, and maxillofacial prosthetics. Methods A structured search of the literature was carried out using the PubMed and Scopus databases, along with manual screening of relevant journals, to identify peer-reviewed clinical, experimental, and review studies published between 2006 and 2025. Search strategies incorporated combinations of keywords related to \"artificial intelligence\", \"machine learning\", \"deep learning\", and \"neural network\" as well as prosthodontic domain-specific terms. A total of 54 studies that met the inclusion criteria were selected for analysis. Because of variability in study designs, datasets, and reported outcomes, a quantitative meta-analysis was not feasible, so findings were synthesized using a descriptive narrative approach. This review was conducted as a narrative study and did not adhere to a PRISMA protocol. Results The included studies show that artificial intelligence applications in prosthodontics are predominantly concentrated in implant prosthodontics, followed by fixed and removable prosthodontics. AI methodologies were frequently applied to arch classification, image interpretation, predictive modeling, prosthesis design, implant system recognition, and workflow optimization, with many studies reporting accuracy levels exceeding 90% under controlled conditions. Despite these results, most evidence is derived from retrospective or experimental settings, with limited prospective clinical validation. Commonly reported challenges include data quality, lack of model transparency, ethical considerations, and difficulties in the use of AI systems in routine clinical workflows. Conclusion Current evidence indicates that artificial intelligence has been widely explored as a supportive adjunct within digital prosthodontic workflows. Further standardized validation, prospective clinical studies, and ethical oversight are required to clarify clinical applicability and guide responsible integration into routine prosthodontic practice.","url":"https://doi.org/10.1007/s44445-026-00192-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s44445-026-00192-6","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1016/j.jval.2026.06.012","name":"The Use of Generative Artificial Intelligence in Systematic Literature Reviews: A Rapid Review of the Literature.","source":"europepmc","abstract":"Objectives Systematic literature reviews (SLRs) underpin life sciences research but are resource intensive. Generative artificial intelligence (GenAI), particularly large language models, may accelerate key SLR tasks; yet, performance and reliability for evidence synthesis remain unclear. This manuscript aims to review current evidence on GenAI performance across core SLR tasks. Methods We conducted a PRISMA-adapted rapid evidence assessment of English-language biomedical studies published from November 2022 to July 2025 evaluating GenAI or large language models for SLR tasks, including search strategy development, title/abstract screening, full-text screening, data extraction, risk-of-bias assessment, qualitative synthesis, report writing, and end-to-end review generation. Findings were summarized qualitatively by task. Results Among 115 included studies, evidence supporting the use of GenAI was strongest for title/abstract screening (n = 51) and data extraction (n = 33). Selected high-quality evaluations reported sensitivities ≥ 90%, workload reductions of 27% to 71%, and human-comparable or superior performance in calibrated human-in-the-loop workflows. Evidence for full-text screening (n = 15) and risk-of-bias assessment (n = 17) was more variable, showing gains in structured or fine-tuned implementations but persistent limitations in specificity and nuanced judgment. For search strategy development, qualitative synthesis, and report writing, GenAI was most effective as a supportive tool; fully autonomous end-to-end SLR generation was unreliable. Conclusions GenAI can improve efficiency across multiple SLR tasks when used in hybrid human-AI workflows. Current evidence supports targeted, task-specific adoption with transparent reporting and human oversight, rather than full automation.","url":"https://doi.org/10.1016/j.jval.2026.06.012","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jval.2026.06.012","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1186/s12909-026-09458-3","name":"The impact of generative artificial intelligence on clinical skills and knowledge acquisition in medical undergraduates: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Currently, there is a growing body of research examining the role of generative Artificial Intelligence (GenAI) in medical undergraduate education. However, the findings of these studies exhibit considerable variability. Therefore, this review was designed to systematically evaluate the effectiveness of GenAI tools in improving basic knowledge and clinical skills among undergraduate medical students compared to conventional pedagogy. Method Eleven online databases were systematically searched (e.g., Medline, Embase, CINAHL, APA PsycArticle, APA PsycInfo, ERIC, Scopus, CNKI, WanFang Database, VIP, and SinoMed), from inception to 30 Oct, 2025, and updated to April 24, 2026. Risk assessment was conducted using the Cochrane Risk of Bias tool (ROB 2) and the Medical Education Research Study Quality Instrument (MERSQI). Data were analyzed using RevMan 5.3 and Stata 15.0. Overall effects were estimated using either the fixed effects model or the random effects model. The quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework. Results This systematic review and meta-analysis included 31 studies involving 2615 medical undergraduates. It found no significant difference in clinical medicine students' knowledge exam score between GenAI and control groups with a small effect size (n = 1592, P = 0.22, I 2 = 92%, SMD = 0.22, 95%CI: - 0. 13 to 0.56, 14 studies, random-effect model). However, nursing students showed a significant improvement with GenAI (moderate effect size, n = 205, P = 0.0002, I 2 = 76%, SMD = 1.11, 95%CI: 0.54 to 1.69, random-effect model). Compared with traditional teaching, GenAI did not improve the clinical skills for clinical medicine students with a small effect size (n = 726, P = 0.18, I 2 = 88%, SMD = 0.27, 95%CI: -0.13 to 0.66, random-effect model). Leave-one-out sensitivity analysis found that one study on fine motor clinical skills had a significant impact on the results. That is, when this study was excluded, the results became statistically significant (P = 0.03). Owing to the poor methodology quality and inconsistency of the results, the evidence quality for the primary outcomes (basic knowledge and clinical skills among clinical medicine students) was downgraded to a low level, while the quality of other evidence was rated as very low. Conclusion GenAI's educational benefits are context- and domain-dependent. Its efficacy is concentrated in cognitive clinical skills, academic writing, and nursing education, while its advantage over high-fidelity video instruction for fine procedural skills remains uncertain. These findings support a differentiated integration strategy rather than uniform adoption. Future high-quality, multi-center, large-sample, and long-term RCTs are still required to robustly confirm its effects on medical undergraduates' knowledge acquisition, clinical skills, and academic writing performance. Trial registration The protocol was registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) and the registration number is INPLASY2025120075.","url":"https://doi.org/10.1186/s12909-026-09458-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09458-3","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1016/j.jsurg.2026.103970","name":"What Are the Odds? Utilization of Artificial Intelligence to Predict Success in the Integrated Plastic Surgery Match.","source":"europepmc","abstract":"Objective Despite widespread consensus that quantifiable application characteristics impact match success, no prospective predictive algorithm has been created in any specialty since the implementation of USMLE Step 1 pass/fail scoring. The rise of artificial intelligence presents a novel method to analyze application factors to predict success in the ever-competitive integrated plastic surgery match. Design Quantifiable data from Plastic Surgery Common Applications (PSCAs) submitted to a single academic center in the 2023 to 2024 and 2024 to 2025 match cycles were collected. About 70% of applications from the 2023 to 2024 cycle were utilized to train a random-forest classification model to predict match status. This model was subsequently evaluated using the remaining 30% of 2023 to 2024 applicants and prospectively validated with applicants from the 2024 to 2025 match cycle. Setting University of Kansas Medical Center, tertiary center. Participants About 713 integrated plastic surgery applicants in the 2023 to 2024 and 2024 to 2025 match cycles. Results The single most important predictor of match rate in our model was graduation from a highly ranked medical school, followed by average recommendation letter strength and USMLE Step 2 score. The predictive model demonstrated strong performance in classifying matched and unmatched cases in the 2023 to 2024 data with an AUROC of 0.85 and a balanced accuracy of 82.7% (95% CI 73.7%-89.6%, p Conclusions This model provides a comprehensive analysis of integrated plastic surgery residency match predictors, aiding both applicants and program directors in understanding key factors that influence match outcomes. While academic metrics like Step 2 scores are critical, the significance of letters of recommendation highlights that holistic review remains essential for residency selection.","url":"https://doi.org/10.1016/j.jsurg.2026.103970","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jsurg.2026.103970","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1097/scs.0000000000013225","name":"Predicting the Future of Craniofacial Surgery: AI-Enabled Forecasting of Craniofacial Surgery Research and Future Clinical Priorities.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly used in clinical decision support and perioperative planning, yet its role in anticipating the future direction of surgical science remains underexplored. In craniofacial surgery, forecasting publication trajectories may help identify emerging clinical priorities, guide research investment, and support planning for future training, workforce, and patient-care needs. The objective was to develop and validate an AI-enabled, scenario-based framework for forecasting global craniofacial surgery research through 2030 across thematic, geographic, and economic strata. Methods We performed a retrospective bibliometric study of PubMed-indexed craniofacial surgery publications from 15 peer-reviewed journals between 2010 and 2025. Automated extraction, semantic classification, and multimodel time-series forecasting were applied to generate conservative, moderate, and aggressive projections through 2030. Analyses were stratified by 6 thematic categories, 4 World Bank income groups, and 115 countries. Forecast uncertainty was reported using 95% prediction intervals. Results A total of 25,625 publications were analyzed. Global output increased from 1059 publications in 2010 to 2688 in 2025, with a marked acceleration in the latter year. By 2030, projected global output ranged from 2235 to 4547 publications under the primary uncertainty interval, with conservative and aggressive scenario bounds of 2381 and 4278, respectively. Congenital and Pediatric research demonstrated the greatest absolute growth, whereas Aesthetic Surgery showed the fastest relative expansion. High-income countries accounted for 74.5% of total output, while lower-middle-and low-income countries contributed only 5.1% combined. Conclusions AI-enabled forecasting converts publication trends into scenario-bounded foresight. By identifying where craniofacial research is accelerating, this framework may help journals, institutions, and training programs anticipate emerging clinical priorities, align resources with future patient-care needs, and support strategic planning for the evolving practice of craniofacial surgery.","url":"https://doi.org/10.1097/scs.0000000000013225","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/scs.0000000000013225","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202608.1134.v1","name":"First Episode Mania with Delusions of Grandeur and Reference Developed During Intense Use of ChatGPT","source":"preprints","abstract":"Background: Artificial Intelligence (AI) chatbots and their underlying technology arguably has potential but are associated with risks for the users. There are few reports of delusions developed during intense use of chatbots in people with preexisting mental illness in the medical literature. Here, we report a case of delusions developed de novo during intense use of ChatGPT in a high-functioning individual without personal- or family history of mental illness. Case presentation: A man in his twenties with no personal- or family history of mental illness started using ChatGPT intensely when working on his master’s thesis during university studies. He gradually transitioned to communicating with the chatbot about deeply personal issues initially seeking advice on self-development. Over the course of eight weeks of intense use of ChatGPT he developed a severe manic episode with delusions of grandeur and reference. The delusions revolved around the tariffs imposed by the United States in April 2025. Specifically, the young man became convinced that he, as member of an international resistance movement, had developed a method to predict the stock market that would protect Europe against the imposed tariffs, false beliefs that were consistently consolidated by ChatGPT. He contacted several politicians with his ideas. As his behaviour became increasingly erratic, he was admitted voluntarily to a psychiatric ward. Psychiatric evaluation revealed that the patient had no history of physical illness or drug use, and a physical examination showed no sign of current physical disorder. At the psychiatric ward, he was treated with benzodiazepines against his will and the mania and delusions gradually resolved after discharge. Seven months after discharge the patient was without any psychiatric symptoms and was employed in a full-time job. Conclusions: This case supports the notion that chatbots driven by generative artificial intelligence can lead to development of severe delusions.","url":"https://doi.org/10.20944/preprints202608.1134.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1134.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10573979/v1","name":"Public Interest in Neurosurgical Conditions Over Two Decades: A Google Trends Analysis (2004–2025)","source":"preprints","abstract":"Abstract Aggregated internet search data offer a near–real-time window into public engagement with disease, yet they index public attention rather than clinical need or disease burden. We characterized two decades of public search interest in neurosurgical conditions, tested the durability of an earlier neurosurgical search analysis, and examined whether search behavior changed after the public release of conversational artificial intelligence (AI). Monthly relative search volume (RSV) for ten neurosurgical topics was obtained from Google Trends (January 2004–July 2025), with a 2004–2015 replication window and a non-medical control series. Long-term trends were assessed by joinpoint regression, the COVID-19 and ChatGPT breakpoints by interrupted seasonal time-series (SARIMAX) models, and the association with academic output by Spearman correlation with annual PubMed counts. Nine topics had adequate volume. Concussion attracted the greatest interest, reproducing a previously reported, stable ranking. Several topics rose, often coinciding with real-world events (thrombectomy + 13.5%/year from 2012). Search interest correlated positively with publication output for eight of nine topics. No topic showed an abrupt change in search interest at the ChatGPT breakpoint, and the control series was unchanged. These patterns are most relevant to patient education and online communication rather than to clinical prioritization.","url":"https://doi.org/10.21203/rs.3.rs-10573979/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10573979/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9604924/v1","name":"Technology anxiety and artificial intelligence readiness in nursing students: A cross-sectional study","source":"europepmc","abstract":"Abstract Introduction: As artificial intelligence (AI) becomes more common in healthcare, nursing students need to be both mentally and emotionally ready to use it. But feeling anxious about technology might hold them back. This study looked at whether there is a link between AI readiness and technology anxiety among nursing students. Methods: We carried out a descriptive-correlational study with 279 nursing students at Qom University of Medical Sciences during the 2024–2025 academic year. We used census sampling, meaning we invited all eligible students to take part. Data were collected using a demographic form, the Abbreviated Technology Anxiety Scale (ATAS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). We analyzed the data using descriptive statistics, Pearson correlation, and multiple linear regression. Results: On average, students scored 3.76 out of 5 for technology anxiety and 3.68 out of 5 for AI readiness, which suggests moderate to high levels. Still, more than half of the participants had only low to moderate AI readiness. There was a strong negative relationship between AI readiness and technology anxiety (r = −0.648, p Conclusion : These findings show that nursing students who feel more prepared for AI tend to be less anxious about technology. Adding AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future. Further longitudinal and interventional studies are needed to better understand causal relationships and to identify effective educational strategies.","url":"https://doi.org/10.21203/rs.3.rs-9604924/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9604924/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202608.1353.v1","name":"Mapping Trends in Human-Computer Interaction in Healthcare: A Bibliometric Analysis with a Conceptual Tri-Layer Framework","source":"preprints","abstract":"Background: Human-Computer Interaction (HCI) plays a critical role in the design and adoption of healthcare technologies, yet its intellectual structure and global research patterns remain insufficiently mapped. Objectives: This study aimed to analyze the evolution, thematic structure, and collaboration pattern of HCI research in healthcare from 2010 to 2025. Methods: A bibliometric analysis of 2,595 publications indexed in Web of Science (WoS) was conducted using the Bibliometrix package in R. Publication trends, citation structures, thematic development, and international collaboration networks were examined. Results: Scientific output increased from 2 publications in 2010 to 986 in 2025, with over 80% published after 2020. Key research themes include explainable artificial intelligence (XAI), human-centered design, and digital health technologies. Leading publication venues include IEEE Access and the Journal of Medical Internet Research. The United States (US) and China dominate in productivity and citations, while collaboration networks remain globally connected but regionally concentrated. Discussion: The findings highlight the rapid expansion and interdisciplinary nature of HCI in healthcare, driven by the convergence of computational technologies and user-centered design principles. Conclusion: HCI in healthcare has evolved into a mature and rapidly growing field. Future research should emphasize explainability, ethical implementation, and inclusive global collaboration.","url":"https://doi.org/10.20944/preprints202608.1353.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1353.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9577440/v1","name":"Knowledge and attitude of Egyptian medical students towards artificial intelligence application in medical practice: A national cross‑sectional study","source":"europepmc","abstract":"Abstract Background The rapid advancement of artificial intelligence (AI) has significantly impacted various fields, including medicine. As AI integration in medicine expands, medical students and professionals need to develop a comprehensive understanding of its applications. This study aimed to explore the knowledge and attitude of undergraduate Egyptian medical students regarding artificial intelligence application in medical practice. Methods A national cross-sectional study was conducted during the spring semester of 2024/2025 among undergraduate medical students from seven Egyptian medical schools. Participants were recruited using convenience sampling. Data was collected via a validated structured web-based questionnaire distributed through social media to assess students’ demographics, knowledge, and attitudes toward AI applications in medical practice. Results This study included 1438 medical students with a mean age of 20.3 years (± 1.9 years). Overall, 55.3% demonstrated good knowledge and 66.8% showed a positive attitude toward AI applications in medical practice. Good knowledge was significantly higher among students in higher academic years (3rd year AOR = 1.88, P = 0.006; 4th year AOR = 2.02, P = 0.007; 5th year AOR = 1.86, P = 0.034) and among those reporting peers and mentors (AOR = 1.35, P = 0.017), scientific papers and books (AOR = 1.71, P","url":"https://doi.org/10.21203/rs.3.rs-9577440/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9577440/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9448591/v1","name":"Bibliometric Review of the Ethical and Legal Perspectives of Explainable Artificial Intelligence in Health","source":"preprints","abstract":"Abstract Explainable Artificial Intelligence (XAI) has gained significant attention in healthcare due to the growing need for transparency, accountability, and trust in automated medical decision-making. Although the literature on XAI is expanding rapidly, especially in technical applications, there remains a gap in comprehensive assessments that address its ethical and legal implications in health-related contexts. This study presents a bibliometric review of the scientific production on XAI in healthcare, with a particular emphasis on ethical and legal perspectives. Using the Web of Science Core Collection and the Biblioshiny platform, 872 articles published between 2010 and 2025 were analyzed. The review maps annual publication trends, influential authors, prominent institutions, geographic distribution, and keyword patterns. The results reveal moderate growth in publications starting in 2019, and exponential growth by 2025, and highlight three main conceptual clusters: clinical applications and diagnostic imaging, technical foundations whit XAI e deep learning, and algorithmic governance. This is the first bibliometric analysis to explicitly address the intersection of explainability, ethics, and regulation in the context of medical AI, offering both a quantitative overview and a qualitative synthesis of this evolving interdisciplinary field.","url":"https://doi.org/10.21203/rs.3.rs-9448591/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9448591/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8959011/v1","name":"Knowledge, Attitudes, and Readiness toward Artificial Intelligence in Medical Education: A Cross‑Sectional Study among Italian Medical Students with a Focus on Legal Medicine","source":"preprints","abstract":"Abstract 1.1 Background Artificial intelligence (AI) is rapidly transforming healthcare systems and medical education worldwide. Despite increasing exposure to AI tools, the level of knowledge, attitudes, and educational readiness among medical students remains heterogeneous across countries. Evidence from Italy is currently limited and based on small or non‑validated samples. This study aimed to evaluate knowledge, attitudes, and educational needs regarding AI among medical students, with a specific focus on forensic medicine applications. 1.2 Methods A cross‑sectional survey was conducted among medical students enrolled at the University of Bari, Italy, between September and November 2025. A structured questionnaire integrating a previously validated international instrument with a newly developed forensic medicine module was administered using a hybrid digital and paper-based strategy. The final survey included 64 items across six domains. Exploratory factor analysis was performed for the newly developed forensic section. Group differences by gender and academic stage were assessed using non‑parametric tests. Participation was voluntary and anonymous, and ethical approval was obtained from the local ethics committee. 1.3 Results A total of 323 valid responses were analyzed. Students demonstrated limited self‑reported technical knowledge of AI (mean score 2.66/5) but expressed a positive overall attitude toward AI integration in healthcare (mean 3.72/5). Perceptions were particularly favorable regarding forensic medicine applications (mean 3.84/5). Sixty‑five percent of respondents reported no formal AI education within the curriculum despite strong interest in structured training. Gender comparisons revealed significantly higher knowledge and attitude scores among male students, while attitudes toward forensic AI did not differ significantly. No meaningful differences emerged between pre‑clinical and clinical students, suggesting limited curricular influence. 1.4 Conclusions Italian medical students show strong optimism toward AI alongside modest technical preparedness, highlighting a structural gap between expectations and formal education. The particularly positive perception of AI in forensic medicine suggests that students recognize its value in structured and data‑driven medical domains. Integrating AI literacy, clinical applications, and ethical‑legal competencies into undergraduate medical curricula appears essential to promote critical and responsible adoption of AI technologies in future clinical practice.","url":"https://doi.org/10.21203/rs.3.rs-8959011/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8959011/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202605.0817.v1","name":"A Comparative Evaluation of Artificial Intelligence and Physician Based Approaches in the Early Diagnosis of Cognitive Aging in Patients with Subjective Cognitive Decline and Risk of Alzheimer’s Disease","source":"preprints","abstract":"Background: As the society ages, the number of patients with early cognitive impairment that can progress to Alzheimer’s disease also increases. Early diagnosis and risk as-sessment allows effectively initiate the necessary lifestyle changes and monitoring. The use of artificial intelligence (AI), when analyzing medical histories, enables more pro-ductive evaluation of large datasets and identify patterns that may go unnoticed in clinical practice. This kind of approach can improve early screening, reduce physicians’ workload and develop bigger support for personalized treatment. The aim of the study: To compare the performance of machine learning (ML) algorithm with a physician (neurologist) in assessing patient’s subjective cognitive decline and Alz-heimer’s disease risk in early stages. Research methods: The research was designed as a retrospective, comparative cohort study that used two data sources. Firstly, the National Alzheimer’s Coordination Center (NACC) longitudinal dataset to train the ML model. Secondly, medical records gathered from Pauls Stradins Clinical University Hospital dating from 2020 till May 2025 to evaluate the al-gorithm’s precision. Results: The research included 154 patients, predominantly women (68.8%), with a mean age of 80.3 years. Class distribution consisted of dementia (n=139); mild cognitive im-pairment (MCI) (n=13); subjective cognitive decline (SCD) (n=2). Dementia was identified the best – 128/139 (accuracy – 92.1%) with errors tending towards MCI. MCI was correct in 9/13 cases (accuracy – 69.2%) All SCD cases were classified as dementia. Overall model’s accuracy was 91.6% (141/154). Conclusions: ML algorithm can match to neurologist made diagnoses with high precision but is struggles to separate adjacent early-stage diagnoses. At this moment, ML models are great decision supporters, but no yet alone diagnosticians. Nevertheless, this technology has high potential to being integrated in the future to aid triage and early screening, especially when advanced diagnostics are limited.","url":"https://doi.org/10.20944/preprints202605.0817.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202605.0817.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10117761/v1","name":"The current roles of Artificial Intelligence in public health: A scoping review","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming. Public health aims to protect and improve the health of communities and populations at local, regional, national, and global level, but typically suffers from limited resources. While AI may lead to improved efficiency or effectiveness of public health interventions, adoption of AI interventions should be informed by evidence of effective use in practice. This review summarises how AI-assisted interventions are currently used by public health professionals. Methods We conducted a scoping review to identify the current uses of AI in public health. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core Collection), and Scopus for relevant reports published between 01 January 2020 and 24 July 2025. Two reviewers independently screened reports. Eligible reports were primary studies (experimental or observational), commentaries, and editorials that discussed how AI tools have been used in practice in public health contexts relevant to high income countries. Data were extracted by one reviewer and checked by a second. Results We screened 5,400 records and 155 full texts, including 8 reports. These reports discussed a variety of uses of AI tools across public health domains: three reports used chatbots in different settings, one report used machine learning to forecast health service demand, two reports used AI for symptom checking, and two reports used AI for evidence synthesis. While assessing the effectiveness of AI tools for public health was beyond the scope of this review, and most reports did not robustly evaluate effectiveness, the evidence for effectiveness of AI tools was nonetheless mixed. One report described a chatbot that users found helpful, while another found a specific chatbot to be ineffective, highlighting its potential to undermine trust in the organisation that developed it. Another report highlighted both benefits and drawbacks of a large language model used to communicate with socially isolated individuals. The main finding of this review was that even with a comprehensive search across multiple databases, there were few reports of how AI tools are being used in practice in public health contexts. Conclusion The lack of evidence from public health practice makes it difficult to know which uses of AI, if any, would be useful to adopt into practice. In the absence of evidence, new interventions, including those using AI, should be evaluated to ensure they are effective (and cost-effective). These evaluations should be widely and accessibly disseminated to create an evidence base that other public health teams could use to inform their own decisions around AI tools. Registration Zenodo 15971109","url":"https://doi.org/10.21203/rs.3.rs-10117761/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10117761/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10475791/v1","name":"Prospective Deployment of Multimodal AI Grading for Medical Student OSCEs","source":"preprints","abstract":"Abstract Objective Structured Clinical Examinations (OSCEs) are central to assessing medical student clinical competence, but human grading imposes substantial burden. We report, to our knowledge, the first prospective deployment of an integrated multimodal artificial intelligence (AI) system for OSCE grading in undergraduate medical education, spanning notes, audio, and video. We present MAPLES (Multimodal Assessment Pipeline for Learning Encounter Scoring), a rubric-driven zero-shot system deployed in Fall 2025 for 222 students (72,907 retained item-level scores). In the routed low-scoring review set, tolerant AI–SPE agreement was 85.7–92.4% by modality. In a selected, non-blinded set of 616 adjudicated disagreements, physician scores matched AI on 76.0% of items and the SPE on 19.0%. Human scoring passes fell by 92.3% versus a modeled single-pass manual comparator. Together, these findings show that multimodal AI first-pass scoring can be embedded in routine OSCE operations, with human review on the low-scoring tail and physician adjudication of selected disagreements.","url":"https://doi.org/10.21203/rs.3.rs-10475791/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10475791/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9115093/v1","name":"Artificial Intelligence Literacy and Readiness Among Neonatal Nurses: A Structural Equation Modeling Study","source":"preprints","abstract":"Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transforming nurses’ clinical workflows and decision-making processes, underscoring the need to understand their preparedness and perceptions regarding these technologies. Aim This is a cross-sectional and descriptive study to determine the artificial ıntelligence literacy and readiness of neonatal nurses. Methods This was conducted between August 2025 and January 2026, and included 200 neonatal nurses. Data were collected using sociodemographic information, the Artificial Intelligence Literacy Scale (AILS) and the Medical Artificial Intelligence Readiness Scale (MAIRS) and analyzed using structural equation modeling. Results Structural equation modeling supported H1, indicating that AILS significantly and positively predicted MAI-Readiness (B = 0.662, p","url":"https://doi.org/10.21203/rs.3.rs-9115093/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9115093/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.04.07.26350263","name":"Attitudes and Perceptions Toward the Use of Artificial Intelligence Chatbots for Peer Review in Medical Journals: A Large-Scale, International Cross-Sectional Survey","source":"preprints","abstract":"Background Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. Despite these potential benefits, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers’ familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review. Methods We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a two-month period using an R-based approach. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period. Results A total of 1,260 respondents completed the survey. Most participants were familiar with AICs (86.2%) and had used tools such as ChatGPT for general purposes (87.7%), but the majority had not used AICs for peer review (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%). Conclusions While familiarity with AICs is high among medical journal peer reviewers, their use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.","url":"https://doi.org/10.64898/2026.04.07.26350263","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.07.26350263","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8077814/v1","name":"Analysis of the Current Status and Influencing Factors of Clinical Nurses' Attitudes Toward Homo Sapiens Artificial Intelligence","source":"preprints","abstract":"Abstract Objective To investigate the current situation of artificial intelligence attitudes of clinical nurses in county hospitals and analyze its influencing factors, to provide a reference for promoting the application of artificial intelligence technology in the field of primary medical care. Methods A total of 449 clinical nurses from a county-level B-level hospital in Nantong City were selected from August to September 2025 by convenience sampling, and the general information questionnaire, the Attitude Scale for the Application of Artificial Intelligence Technology in Nursing, the Artificial Intelligence Literacy Scale and the Change Fatigue Scale were used to investigate the influencing factors. Results The total score of clinical nurses' attitudes toward AI was 45.17 ± 2.38, indicating a moderate level. Multiple linear regression analysis identified age, participation in AI-related training, education level, number of monthly night shifts, change fatigue, and total AI literacy score as significant determinants of AI attitudes (all P Conclusion The attitude of county clinical nurses towards artificial intelligence is affected by multiple factors, and it is recommended to improve nurses' attitude towards artificial intelligence and promote the application of artificial intelligence technology in county hospitals by strengthening artificial intelligence-related training, improving artificial intelligence literacy, optimizing scheduling management, and reducing change fatigue.","url":"https://doi.org/10.21203/rs.3.rs-8077814/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8077814/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9540519/v1","name":"Generative Artificial Intelligence versus Anesthesia-Intensive Care Resident Physicians: Performance in Clinical Case Analysis Using the R-IDEA Score","source":"preprints","abstract":"Abstract Background Large language models (LLMs) have shown encouraging results in the medical field, with recent work demonstrating their capacity to encode substantial clinical knowledge [12] and pass medical licensing examinations [5, 6]. However, most existing studies evaluate diagnostic accuracy without assessing the quality of the underlying reasoning process, and none has used a validated instrument in a French-speaking African academic context. This study aimed to compare the quality of clinical reasoning between a generative AI and anesthesia-intensive care resident physicians using the R-IDEA score. Methods Prospective cross-sectional comparative study conducted at CHU Ibn Rochd, Casablanca, Morocco (September–October 2025). Twenty anonymized clinical cases were submitted to all fourth-year anesthesia-intensive care residents and to ChatGPT-5. Two faculty members developed a standardized R-IDEA rubric by consensus. All responses were scored by a single blinded evaluator. Diagnostic accuracy was recorded as a binary variable. Results Fourteen residents were included, generating 56 responses (4 cases each); ChatGPT-5 produced 20 responses. The AI achieved a mean R-IDEA score of 9.5 ± 0.9 versus 7.0 ± 2.4 for residents (difference: +2.5 points; 95% CI: 1.4–3.6; p","url":"https://doi.org/10.21203/rs.3.rs-9540519/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9540519/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8970982/v1","name":"Integrating Artificial Intelligence and Socratic Inquiry in Medical Education A Critical Framework for Clinical Reasoning Development","source":"preprints","abstract":"Abstract Background : The integration of artificial intelligence (AI) in medical education represents a paradigm shift in pedagogical delivery, particularly in addressing the persistent gap between basic science knowledge and clinical application. The Socratic method, while theoretically ideal for developing critical thinking, faces significant scalability and implementation challenges in modern medical curricula. Objectives : This comprehensive review examines the potential of AI-powered systems to emulate Socratic dialogue in basic medical science education, analyzing current applications, comparative effectiveness, inherent limitations, and ethical considerations. Methods : We conducted a critical synthesis of peer-reviewed literature (2019-2025) on AI applications in medical education, Socratic pedagogy, and educational technology. Data sources included PubMed, ERIC, Web of Science, and educational technology databases, yielding 67 relevant studies. Results:: AI-driven Socratic tutoring systems demonstrate significant potential across the educational continuum, from foundational sciences to clinical reasoning. These systems provide scalable, personalized, and psychologically safe learning environments that address traditional limitations of human-led Socratic dialogue. However, critical challenges persist, including algorithmic bias, factual unreliability (hallucinations), data privacy concerns, and the paradoxical tension between surveillance requirements and psychological safety. Conclusions : AI represents a complementary, not replacement, technology for medical education. Successful integration requires simultaneous advancement of three pillars: institutional governance frameworks, longitudinal curriculum redesign, and comprehensive faculty development. The optimal model is a human-AI symbiosis that leverages AI for scalable inquiry while preserving human expertise for empathy, ethics, and complex clinical judgment.","url":"https://doi.org/10.21203/rs.3.rs-8970982/v1","authors":["José Daniel Sánchez"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8970982/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9796014/v1","name":"AI safety evaluation in an underrepresented population: real-world performance of clinical decision support and frontier language models on Medicaid patient messaging triage","source":"preprints","abstract":"Abstract Background. Studies of artificial intelligence tools used in patient triage have largely involved academic medical center cohorts, scripted patient-actor scenarios, or knowledge benchmarks. Populations that may rely on such tools due to constrained access to in-person care, including Medicaid patients, have been less fully evaluated. Objective. To compare combinations of safety guardrails added to artificial intelligence tools for triage of patient-initiated text messages in a multi-state Medicaid population. Methods. Retrospective evaluation of 2000 messages from Medicaid patients across three U.S. states (Virginia, Washington, Ohio) during January 2023 through November 2025. Three physicians independently adjudicated each message under blinded review; disagreements were resolved by majority and a senior-physician arbiter. Tools included a deployed decision support system with a Conservative Q-Learning controller, supervised baselines (XGBoost+sentence-BERT, logistic regression, rule-based guardrails), and frontier large language models (Claude Opus 4.7, GPT-5.5, Gemini 3.1 Pro, each with and without retrieval-augmented generation). Combinations spanned single tools, ensembles, cascades, multi-model consensus rules, and four-component guardrail compositions identified by a structured literature review. Thresholds and Platt calibration were fit on a held-out validation split and frozen before the test pass. Two pre-specified targets: an autonomous (physician-unassisted) triage benchmark of sensitivity and specificity both 0.80 or higher; and a sensitivity-floor target of 0.80 to 0.95 with clinician review of flagged messages. Results. Real-world messages had lower reading level (grade 4.590 versus 5.810) and more colloquialisms (59.1% versus 19.5%) than physician-scripted scenarios. Hazard prevalence on blinded physician review was 8.2% (165 of 2000). Two configurations met the sensitivity-floor target: a high-recall first-stage screen (sensitivity 0.855; 12.0 missed hazards and 729 alerts per 1,000 messages) and a disagreement-stratified clinician-review workflow (sensitivity 0.939; 5.0 missed hazards and 878 alerts per 1,000 messages). No configuration met the autonomous benchmark; sensitivity and specificity reached 0.594 and 0.592 for the best balanced single tool, 0.685 and 0.575 for the best balanced ensemble, and 0.297 and 0.874 for the highest-specificity cascade. Conclusions. No evaluated tool or combination was sufficiently accurate to enable physician-unassisted triage in this setting. Two configurations met the pre-specified sensitivity-floor target under clinician review of flagged messages, following the classical clinical-screening pattern.","url":"https://doi.org/10.21203/rs.3.rs-9796014/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9796014/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.08.26352766","name":"Three Decades of FDA Authorizations of AI/ML-Enabled Medical Devices: Persistent Specialty Concentration and the Care-Delivery Gap (1995–2025)","source":"preprints","abstract":"The US Food and Drug Administration (FDA) maintains a public list of artificial intelligence and machine learning (AI/ML)-enabled medical devices that have received marketing authorization. Prior published analyses examined this list at earlier time points and reported a marked dominance of radiology applications. We performed a cross-sectional analysis of all 1,430 AI/ML-enabled medical device authorizations recorded by the FDA between September 1995 and December 2025 to characterize the cumulative growth, specialty distribution, and manufacturer concentration of authorized devices. The annual authorization volume increased from a mean of 1.8 per year between 1995 and 2014 to 264 per year between 2023 and 2025, with 331 authorizations recorded in 2025 alone. Devices reviewed by the FDA’s Radiology panel accounted for 1,094 of 1,430 authorizations (76.5%), and the three most represented panels (Radiology, Cardiovascular, and Neurology) accounted for 90.6% of all authorizations. Several large clinical specialties were represented by very small numbers of authorized devices, including Pathology (n = 9), Microbiology (n = 6), and Obstetrics and Gynecology (n = 4). No authorizations were recorded under a psychiatry or behavioral health review panel. Of 740 unique companies, 502 (67.8%) had a single authorized device, while 13 companies (1.8%) accounted for 217 devices (15.2%). The cumulative regulatory record demonstrates rapid growth that has been concentrated in image-rich diagnostic specialties, with limited representation across many specialties that account for substantial clinical activity in the United States. These findings may inform policy discussions about where regulatory, infrastructure, and dataset investments are most needed to broaden the clinical scope of medical AI.","url":"https://doi.org/10.64898/2026.05.08.26352766","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.08.26352766","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-9184670/v1","name":"The Impact of Integrating Generative Artificial Intelligence into Medical Education on Teaching Effectiveness: A Systematic Review and Meta-Analysis of Randomized Controlled Trials","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (GenAI) technologies, with ChatGPT and DeepSeek being the most prominent examples among large language models (LLMs), have been getting more attention in medical education lately, mainly because they can generate content, adapt to different learners, and carry on interactive conversations. It is still not clear, though, whether using these technologies in medical courses actually makes a measurable difference in teaching outcomes, and this lack of clarity has held back wider adoption. We did this systematic review and meta-analysis of randomized controlled trials (RCTs) to take stock of how GenAI-assisted teaching affects knowledge, clinical skills, and learner attitudes. Methods We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) following the PRISMA 2020 guidelines. The study protocol was pre-registered on PROSPERO (registration number: CRD420261277187).Six databases (PubMed, Cochrane Library, Embase, Web of Science, CNKI, and Wanfang) were searched for the period January 2020 to December 2025. We included RCTs in which GenAI-integrated medical education was compared against traditional teaching or non-GenAI digital teaching, with participants being medical students, interns, or residents. Risk of bias was evaluated with the Cochrane RoB 2 tool; the GRADE framework was used to judge evidence certainty. Pooling was done through random-effects models with REML estimation. Results We identified 20 eligible RCTs (1430 participants in total). Post-intervention knowledge scores were higher in GenAI groups (SMD = 0.99, 95% CI: 0.49–1.49, P = 0.001; k = 11; I² = 86.7%; GRADE: low certainty), and so were clinical skill scores (SMD = 1.09, 95% CI: 0.81–1.38, P &lt; 0.001; k = 8; I² = 41.0%; GRADE: moderate certainty). At follow-up, knowledge retention remained significant (SMD = 0.51, 95% CI: 0.24–0.78, P = 0.010; k = 4; I² = 0%; GRADE: moderate certainty). Publication bias was not detected by Egger’s test (P = 0.104); trim-and-fill adjustment gave a similar pooled estimate (SMD = 0.90). Subgroup comparisons by AI tool type, learner level, interaction modality, and risk of bias were all non-significant. Conclusion Based on what we found, using GenAI in medical education seems to lead to moderate-to-large gains in knowledge and clinical skills; the knowledge improvements appear to carry over beyond the teaching period itself. Evidence certainty sits between low and moderate. These tools probably work best as additions to regular teaching. That said, more multi-center RCTs with longer follow-up will be needed before we can draw firm conclusions.","url":"https://doi.org/10.21203/rs.3.rs-9184670/v1","authors":["Chao Wang","Niuniu Sun","Xinjuan Pan","Pan Liu","Zhujing Ma","Wenyu Wang","Yiming Jin"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9184670/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10122585/v1","name":"Development and Validation of SERRATE: A Deep Learning-Based Tool for Diagnosis of Sessile Serrated Lesions in Colonoscopy Images","source":"preprints","abstract":"Abstract Background and Aims: Sessile serrated lesions (SSLs) are precancerous colorectal polyps with high malignant potential, yet their endoscopic recognition remains challenging. Most artificial intelligence (AI) models for polyp classification fail to specifically identify SSLs, often misclassifying them as hyperplastic polyps. This study aimed to develop and validate SERRATE (Sessile Serrated lesion Recognition and Risk Assessment Tool for Endoscopy), a deep learning model for distinguishing SSLs from conventional adenomas and hyperplastic polyps. Methods : We retrospectively collected colonoscopic images from 696T patients at Peking Union Medical College Hospital (January 2017-September 2024), yielding >20,000 images. The balanced training dataset comprised 1,120 SSL images, 1,697 conventional adenoma images, and 1,207 hyperplastic polyp images. A Swin Transformer model was trained using both white-light imaging and narrow-band imaging (NBI). Model performance was evaluated on a prospective internal validation cohort (300 patients, January 2025-April 2025; ClinicalTrials.gov NCT06773832) and the external POLAR dataset. Results : The optimal model utilized NBI images with 2×mucosal context expansion, achieving 82.42% accuracy in internal validation. For three-class classification (SSL vs. conventional adenoma vs. hyperplastic polyp), the model demonstrated 73.44% overall accuracy (precision 82.47%, recall 95.21%, specificity 91.21%, F1-score 82.17%) on prospective internal validation and 69.46% accuracy on external validation. For binary neoplasia detection, accuracy reached 91.07% across validation sets. Conclusions : SERRATE represents the first AI model specifically designed for SSL recognition with balanced training data. The model achieved clinically relevant performance for SSL differentiation, potentially improving colorectal cancer prevention through enhanced serrated lesion management.","url":"https://doi.org/10.21203/rs.3.rs-10122585/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10122585/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9952134/v1","name":"Designing and Resourcing European Medical Affairs Under the EU HTA Regulation: A Market Complexity Index for Hub-and-Spoke Structures","source":"preprints","abstract":"Abstract Pharmaceutical companies expanding into Europe face regulatory and market access heterogeneity unmatched in any other region. The EU Health Technology Assessment Regulation, operational since 12 January 2025, compounds it: Joint Clinical Assessments are now conducted at European level while national pricing and reimbursement authority persists, creating a dual-layer organizational demand that uniform medical affairs designs do not address. This article proposes a hub-and-spoke framework organized around a Market Complexity Index (MCI), a composite of 10 publicly observable indicators across regulatory, market access, and operational sub-dimensions, standardized across 31 markets and combined by a fixed, documented procedure so the index is reproducible from public data. Scores (0–100) combined with market scale define five archetypes, from high-complexity dedicated spokes to regional clusters and tiered partnerships, each mapped to a hub capability set and a governance decision-rights matrix. The MCI is associated cross-sectionally with the EFPIA W.A.I.T. 2024 availability rate (r = 0.72) and corresponds broadly to the EURO-HEALTHY typology, but it correlates strongly with market size (r = 0.94) and is best read as a structured overlay on that signal, not a size-independent construct. The framework is offered as a transparent, decision-support heuristic for affiliate-cluster design under the EU HTA Regulation, not a validated classification system; prospective application and refinement of the indicator set are the priority next steps. The capability mix it allocates is itself shifting as artificial intelligence reshapes evidence generation and field medical work.","url":"https://doi.org/10.21203/rs.3.rs-9952134/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9952134/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8572944/v1","name":"A pioneering faculty training program for artificial intelligence in Ukrainian medical education","source":"preprints","abstract":"Abstract Background The global higher education landscape is undergoing a profound transition due to the convergence of digital technologies and pedagogical theory. In medical education, large language models now demonstrate clinical reasoning capabilities comparable to those of medical students, yet faculty training remains highly inconsistent globally. In Ukraine, this challenge is compounded by wartime pressures and a high rate of «bottom-up» AI adoption (84%) among students, which is often plagued by ethical uncertainty and a lack of structured guidance. There is an urgent need to shift the educator’s role from a «transmitter of absolute truths» to a «mediator of learning» within the emerging clinician-AI-patient triad. Methods We implemented a multi-component training program at Ivano-Frankivsk National Medical University during the 2024–2025 academic year. The program included practical workshops on AI fundamentals and medical education-specific tools, individual consultations for discipline-specific AI adaptation, the development of methodological materials, and the creation of a community of practice. We tracked quantitative metrics, including the number of trained faculty, program coverage, and the development of resources, alongside qualitative assessments of AI integration into teaching practices. Results Between September 2024 and March 2025, 211 faculty members (29% of the total 732 scientific and pedagogical staff members) completed the foundational training, with participation exceeding 90% in departments such as Anatomy and Physiology. Demographic analysis revealed strong cross-generational engagement, with 34% of participants being over 50 years old. To address the resulting regulatory and ethical gaps, Ivano-Frankivsk National Medical University formally adopted a comprehensive «Policy on the Use of Artificial Intelligence Systems» in May 2025, providing a binding legal and ethical framework for AI integration. Key outputs included Ukraine’s first comprehensive methodological guide for AI in medical education, a library of 142 documented use cases, and the deployment of two custom AI assistants that achieved a user satisfaction rating of 4.3 out of 5 across 3.200 interactions. Faculty reported successful integration of AI into lecture synthesis, case-based learning, and research supervision. Conclusions This study demonstrates the feasibility of large-scale faculty AI literacy initiatives even in resource-constrained and socially disrupted contexts. Systematic training, coupled with a formal institutional policy, facilitates the fundamental transformation of medical education required for the 21st century, ensuring educators can effectively guide students in AI-augmented healthcare practice. Investment in faculty digital competencies is essential for maintaining institutional competitiveness and academic rigor in the face of rapidly advancing machine intelligence.","url":"https://doi.org/10.21203/rs.3.rs-8572944/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8572944/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8844280/v1","name":"Artificial Intelligence Techniques in Blockchain-Integrated Healthcare and Financial Systems: A Systematic Review and Future Research Agenda","source":"preprints","abstract":"Abstract Blockchain and artificial intelligence (AI) are two innovative technologies that are impacting modern infrastructures with resilience, intelligence, and transparency. Blockchain offers immutability, decentralization, and trust, while AI facilitates decision assistance, anomaly detection, and predictive analytics. Scholarly interest in their convergence has grown for mission-critical domains including healthcare and finance. This narrative review evaluates the possibilities, difficulties, and future prospects of combining blockchain technology with artificial intelligence by compiling 53 research papers published between 2018 and 2025. This evaluation categorizes AI methods used in blockchain-integrated systems, including explainable AI models, federated learning, deep learning (CNN, RNN, transformers), and supervised learning (SVM, RF). Using PRISMA-inspired narrative synthesis principles, this work systematically synthesizes 53 peer-reviewed papers to ensure methodological openness and analytical rigor. The healthcare sector makes extensive use of telemedicine, secure medical data transmission, diagnostic support, and drug supply chain traceability. Fraud detection, digital banking innovation, and the creation of central bank digital currencies are all made possible by the integration of blockchain and artificial intelligence in financial institutions. The review creates a taxonomy of AI techniques and assesses their relative effectiveness and drawbacks in the financial and healthcare sectors. The analysis reveals persistent issues like scalability, interoperability, privacy, and regulatory ambiguity in addition to ethical considerations concerning explainability and fairness. We point out shortcomings in scalable federated blockchain–AI architectures, XAI frameworks, and benchmarking datasets. This paper offers a thorough synthesis that gives scholars and business experts a comprehensive understanding of how blockchain and artificial intelligence will affect future infrastructures. The study offers a comparative assessment of methodological methods, identifies unresolved problems with explainable models and federated architectures, and suggests a methodical research plan in addition to mapping applications of blockchain–AI convergence. Unlike other evaluations that concentrated on a single domain, this analysis reveals shared design principles and governance implications by presenting a cross-sector synthesis of healthcare and finance systems.","url":"https://doi.org/10.21203/rs.3.rs-8844280/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8844280/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8128531/v1","name":"The Relationship Between Nursing Undergraduates' Artificial Intelligence Readiness, Learning Motivation, and Artificial Intelligence Anxiety: A Mediation and Network Analysis Study","source":"preprints","abstract":"Abstract Background Artificial Intelligence (AI) is profoundly transforming the field of healthcare, while also inducing adaptive anxiety among healthcare professionals. As the future backbone of the nursing workforce, nursing undergraduates' Artificial Intelligence Anxiety (AIA) and Artificial Intelligence Readiness (AIR) may significantly influence their professional development and career transition. Learning Motivation (LM), as a critical psychological factor, may play a key role in enhancing AIR while alleviating AIA. Objective This study explores the relationship between AIR and Artificial AIA among nursing undergraduates in China, examines the mediating role of LM, and further analyzes the intrinsic psychological structure of the three variables through network analysis. Methods A cross-sectional design was employed to recruit 738 nursing undergraduates from three medical universities in Hubei Province, China, between March and May 2025. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), Learning Motivation Scale (LMS), and Artificial Intelligence Anxiety Scale (AIAS) were used to measure AIR, LM, and AIA. Mediation analysis was conducted using the SPSS 27 PROCESS macro (Model 4), and network analysis was utilized to estimate the network structure of AIR, LM, and AIA. Visualization and centrality measures were performed using R packages. Results The results indicated that: (1) The mean scores for AIR, LM, and AIA among nursing undergraduates in China were 75.64 (SD 7.12), 64.99 (SD 4.62), and 75.71 (SD 9.35), respectively suggesting that AIR was at a moderately high level, LM at a moderate level, and AIA at a moderately high level. (2) AIR was significantly negatively correlated with AIA (r = − 0.64, P Conclusion This study reveals the current status and interrelationships of AIR, LM, and AIA among nursing undergraduates in China. The findings provide important insights for optimizing nursing education strategies. Clinical trial number Not applicable.","url":"https://doi.org/10.21203/rs.3.rs-8128531/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8128531/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2026.07.16.26358271","name":"A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records","source":"preprints","abstract":"Objective To develop and evaluate a framework for human-AI interaction. This approach, SHARE (Synergistic Human-Agent REasoning system) was designed to support scalable phenotyping of complex outcomes accurately, robustly and reproducibly from real-world electronic health record (EHR) data to support real-world evidence (RWE) generation. Methods and Analysis Using rheumatoid arthritis (RA) disease activity as the use- case, we studied a multi-institutional EHR-based RA cohort of 3,167 patients. Expert reviewers and a disease activity agent labeled notes using the same review guideline. The agent combined embedding-based informative-note filtering, structured evidence extraction, and evidence-based integrated reasoning to assign disease activity categories with supporting evidence, rationale, confidence, and ambiguity flags. To support scalable deployment, we evaluated a budget-tiered configuration using GPT-5 Nano for high-volume evidence extraction, o4-mini for final reasoning, benchmarking against a GPT-5.4 high reasoning effort configuration applied at every step. Note-level discrepancies were adjudicated by reviewers into final co-produced labels that were used to refine labels and inform agent development. The main outcome measure was the mean absolute error (MAE) of the initial and final agent vs the final co-produced labels. The agreement between agent- and reviewer-flagged ambiguous notes, per- note cost and compute time across configurations were also tested. Results Expert reviewers labeled 626 notes from 273 patients; human-AI adjudication revised 127 (20%) of these initial labels and added 60 newly labeled notes, yielding a 686-note co-produced reference. Against this reference, the final agent’s accuracy improved from a mean absolute error of 0.406 to 0.291 with co-learning, and its ambiguity flag agreed with expert ambiguity designations with 92.1% accuracy. Applied across the cohort, the agent labeled 101,691 notes; the budget tiered configuration matched the accuracy of GPT-5.4 at high reasoning effort while reducing estimated cost by 69% and compute time by 70%. Conclusion Adopting a framework for human-AI co-learning, SHARE, improved the overall quality of gold-standard labels, identified ambiguous cases for further review, and supported accurate and standardized chart reviews of disease activity at a scale infeasible for manual review. SHARE’s resource efficiency provides a transferable approach to incorporate complex phenotypes in RWE studies. Key messages What is already known on this topic Defining disease states from electronic health record (EHR) data is central to generating real-world evidence (RWE), but complex phenotypes require extensive review of narrative clinical notes that are difficult to standardize, audit, and scale. Out-of-the-box large language model (LLM) prompting can support review, but accurate annotation with face validity requires workflows that preserve supporting evidence, recognize uncertainty, while keeping the clinical experts in the adjudication loop. What this study adds We developed and evaluated the Synergistic Human-Agent REasoning system (SHARE), a multi-stage human-artificial intelligence (AI) co-learning framework in which clinical experts define phenotype guidelines and the agent identifies informative notes, extracts supporting evidence, assigns labels, and flags ambiguity for focused review. With rheumatoid arthritis disease activity as a use-case, adjudication improved and expanded the reference labels, while selective use of lower- and higher-cost models supported internally evaluated, resource-efficient scaling. How this study might affect research, practice or policy SHARE introduces a framework for human-AI workflows for research, shifting review of complex EHR phenotypes from broad manual abstraction towards a scalable, resource- efficient, targeted expert adjudication, with clinical experts defining the guidelines, overseeing local validation and the final interpretation.","url":"https://doi.org/10.64898/2026.07.16.26358271","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.16.26358271","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9511063/v1","name":"Paper Mill Subtypes in Medical AI: Multi-Signal NLP Detection Reveals Heterogeneous Fraud Fingerprints","source":"preprints","abstract":"Abstract Paper mills increasingly compromise the medical artificial intelligence (AI) literature, yet the scale of the problem remains poorly quantified and existing detection methods have not been combined into unified frameworks. We developed a pre-registered, multi-signal NLP pipeline combining seven feature categories (tortured phrases, structural formulaicity, AI-generated text markers, citation anomalies, cross-document similarity, co-authorship networks, and geographic metadata) and applied it to 2,478 medical AI papers (2018–2025) from OpenAlex, with 842 labelled as retracted from Retraction Watch. A Random Forest–XGBoost ensemble achieved average precision 0.858 (SD 0.026) with Positive-Unlabelled learning correction, but discrimination was driven by writing quality proxies (vocabulary diversity, reference count) rather than fraud-specific signals. Retraction subtype analysis revealed distinct fingerprints: AI-generated content papers had twice the boilerplate density of other subtypes, while fake peer review papers had the highest co-authorship network density. Unsupervised clustering identified an “author pool” cluster (n = 133, 47% retracted) with extreme co-author reuse (0.75 vs 0.11 baseline) that supervised classification underweighted. A leave-Hindawi-out sensitivity analysis confirmed that classifier performance depends heavily on one mill type (AP dropped from 0.858 to 0.437). PU-corrected prevalence among unlabelled papers was 22.1% (95% CI: 20.9–23.3%), likely an upper bound. Prevalence was broadly uniform across WHO regions (15–20%), arguing against geographic profiling. These findings demonstrate that paper mill operations are heterogeneous and that future integrity screening should be subtype-aware and multi-method. The full pipeline and code are publicly available.","url":"https://doi.org/10.21203/rs.3.rs-9511063/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9511063/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8228350/v1","name":"Assessing Pediatricians' Readiness for Artificial Intelligence","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly integrated into healthcare, including pediatrics, offering new opportunities for diagnosis, management, and decision support. However, the effective implementation of AI depends largely on healthcare professionals’ knowledge, attitudes, and readiness to adopt these technologies. Objective This study aimed to evaluate pediatricians’ general attitudes toward artificial intelligence and their level of readiness for medical AI. Methods This descriptive cross-sectional study was conducted between August 1 and November 1, 2025, at Prof. Dr. Cemil Taşcıoğlu City Hospital. A total of 130 pediatricians participated. Data were collected using a sociodemographic questionnaire, the General Attitudes toward Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). Statistical analyses were performed using SPSS 27. Correlation, t-test, ANOVA, and non-parametric tests were applied where appropriate. Results The mean age of participants was 37.47 ± 9.31 years, and 58.5% were female. The mean AI positive attitude score was 3.81 ± 0.74, and the negative attitude score was 2.83 ± 0.81. The total MAIRS score was 74.97 ± 13.01, indicating a moderate-to-high level of AI readiness. Age was negatively correlated with AI positive attitude and total MAIRS score (p","url":"https://doi.org/10.21203/rs.3.rs-8228350/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8228350/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2026.08.12.26360308","name":"End-user perspectives on design and implementation of a novel SkinScan3D (SS3D) device for monitoring Kaposi Sarcoma in East Africa: a qualitative study","source":"preprints","abstract":"Introduction SkinScan3D (SS3D) is a novel, artificial intelligence–enabled device that provides objective three-dimensional measurements for monitoring Kaposi Sarcoma (KS) lesions. Prior to launching a clinical trial of the device, we obtained end-user perspectives to guide device refinement. Methods Between April and May 2025, we conducted six focus group discussions and 28 in-depth interviews with patients, healthcare providers, and community representatives in Kenya and Uganda. Participants viewed a demonstration video and handled the SS3D prototype. Data were analyzed using hybrid deductive-inductive thematic analysis informed by the Health Information Technology Usability Evaluation Model and the Consolidated Framework for Implementation Research. Results Qualitative findings were synthesized into a conceptual framework for SS3D adoption with two interconnected themes: 1) experiences and context, and 2) device perceptions and implementation factors. Participants’ receptivity to the device was first shaped by experiences with medical technologies and the broader sociocultural context, including trust in providers, health beliefs, and gender preferences. After interacting with the prototype, participants viewed the SS3D as intuitive, accurate, and potentially capable of improving the objectivity and efficiency of KS lesion monitoring. They identified concerns related to safety, infection prevention, data security, affordability, maintenance, and workflow integration. Successful implementation was perceived to depend on device refinement, supportive organizational factors, including leadership engagement, provider training, maintenance capacity, and patient education to address misconceptions about the device. Participants proposed hardware, software, connectivity, and training refinements to support safe integration into routine clinical care. Conclusion End users demonstrated overall satisfaction and receptivity to the SS3D, given potential benefits for both patients and providers. We identified targeted refinements to optimize the device’s functionality and integration into the oncology environment to improve its fit with the local context.","url":"https://doi.org/10.64898/2026.08.12.26360308","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.12.26360308","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9383173/v1","name":"Benchmarking eye health advice from generative artificial intelligence in terms of factual accuracy, safety, comprehensiveness and readability","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (genAI) chatbots are increasingly used for health advice despite lacking regulatory approval, raising concerns about their output quality and safety. This study assesses eye health advice from leading genAI platforms, benchmarking their quality against patient information leaflets. Methods We compared outputs from GPT-5 (OpenAI) and Gemini 3 (Google DeepMind) with clinical leaflets across nine eye conditions (41 questions, 123 texts total). Reference benchmark (547 items) was derived from patient materials produced by the Royal College of Ophthalmologists. Chatbot outputs were generated using verbatim leaflet subsection headings as prompts with word-count restrictions to match corresponding leaflet sections. All texts were evaluated using the Comprehensiveness, Accuracy, and Safety Evaluation Framework (CASEF). Two blinded ophthalmologists assessed genAI outputs for safety concerns. Readability was measured using Flesch-Kincaid Grade Level. Results Both genAI models showed higher factual alignment than clinical leaflets (GPT-5 = 37.4%, Gemini 3 = 36.2%, leaflets = 30.7%; both p","url":"https://doi.org/10.21203/rs.3.rs-9383173/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9383173/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.20944/preprints202606.1256.v1","name":"Advancements in Multimodal Foundation Models for Healthcare: An In-Depth Review and Future Outlook","source":"preprints","abstract":"Medical multimodal foundation models (MMFMs) have become a central element of medical artificial intelligence, supporting progress in clinical workflows like diagnosis, report generation, and multimodal reasoning. However, existing surveys face issues with quick aging and brief coverage of model types. This paper offers a fine-grained review of MMFMs covering January 2023 to July 2025, filling these needs through a structured framework. We analyze key technical features—including model size, dataset size, and architectural designs—across three main model categories: Universal MMFMs (Uni-MMFMs) with wide use, Modality-specific MMFMs (MS-MMFMs) with single-modality specialization, and Organ-specific MMFMs (OS-MMFMs) with organ-specific tuning. We chart development paths and highlight major challenges: data scarcity and privacy constraints, insufficient cross-modal alignment, limited clinical interpretability, and poor generalization in real-world scenarios. We also propose future directions including data-level growth (multi-source integration, synthetic generation), architecture-level updates (unified image-text frameworks), user-centric features (interpretability, ethical compliance), and developer-focused improvements (continuous learning, multimodal conflict resolution). This survey summarizes the current state of MMFMs and provides a guide for building reliable, interpretable, and useful multimodal medical AI systems.","url":"https://doi.org/10.20944/preprints202606.1256.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.1256.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9045167/v1","name":"What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education","source":"preprints","abstract":"Abstract Background : Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students. Existing AI curricula and competency recommendations for undergraduate medical education (UME) are fragmented, and prior reviews have largely described broad themes or educational programs rather than specifying competency-level outcomes. Objectives : To systematically map and synthesize proposed AI competencies for UME. Eligibility criteria: Peer-reviewed sources proposing original AI-related competencies or learning objectives explicitly intended for undergraduate medical students. Sources of evidence : PubMed, Embase, Web of Science, and ERIC from inception to July 28, 2025, without language limits, supplemented by reference screening. Charting methods : Multiple reviewers independently screened sources and extracted verbatim competency-relevant text. Text was decomposed into discrete statements describing single AI-related skills or knowledge areas, then labelled using an agreed-upon rubric as domains, competencies, or learning objectives; statement frequencies were summarized to identify convergent areas, evidence gaps, and cross-competency relationships. Results : Of 4,071 records identified and duplicates removed, 2,877 titles/abstracts were screened and 367 full texts were assessed for eligibility. Fifty-four studies from 22 countries met inclusion criteria. From these, 564 competency-relevant statements were synthesized into a taxonomy comprising 7 domains—AI Ethics, AI Law and Regulation, AI Professionalism in Healthcare, Clinical Applications of AI, Critical Appraisal of AI Output, Research and Innovation in AI, and Theory and Foundations of AI—spanning 37 competencies and 170 learning objectives. Most sources were recent, editorial or opinion-based, and described theoretical rather than fully implemented or evaluated curricula, but showed substantial convergence on ethical/legal oversight, critical appraisal of AI outputs, and foundational understanding of AI methods and data. Conclusions : This scoping review provides a hierarchical synthesis of AI-related competencies for UME, offering a structured foundation for curriculum design and evaluation and underscoring the need for stakeholder collaboration to refine and implement a standardized and internationally actionable curriculum.","url":"https://doi.org/10.21203/rs.3.rs-9045167/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9045167/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8243793/v1","name":"Readiness and Perceptions toward Artificial Intelligence among medical students in Egypt","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is transforming healthcare, but medical students' readiness to adopt it remains unclear. Limited research exists on their awareness, skills, and perception toward AI in medicine. This study evaluates AI readiness and perceptions among medical students at Port Said University. Objective To assess Readiness towards Artificial Intelligence among medical students in Egypt. To assess Perception towards Artificial Intelligence among medical students in Egypt. Methods A cross-sectional study was conducted among medical students from 8 Egyptian Universities, selected through convenient sampling. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was used to assess students’ readiness. The Perception of Artificial Intelligence in Medical Students (PAIMS) scale was used to evaluate students’ perceptions of AI. Data were collected using online self-administered questionnaires to assess AI readiness and perceptions. Data analysis was performed using SPSS version 25 . Results A total of 356 responses were collected. The median total readiness score (MAIRS-MS) was 66.0 (IQR: 26.0). Among the readiness domains, Ability had the highest median score (24.0/40). Cognition scores varied significantly across years of study (p = 0.012) and among students who had attended an AI course (16%, p = 0.008). No significant differences in AI readiness scores were observed across universities. The median overall PAIMS score was 2.25 (IQR: 0.67), with the Knowledge and Trust domain having a median of 2.6 (IQR: 1). No significant differences in AI perceptions were observed across student characteristics. Conclusion Students from Egyptian universities demonstrated moderate readiness for AI integration, with strengths in practical ability and variable cognition influenced by academic year and prior AI training. Their perceptions of AI were generally positive and consistent across student groups. These findings can inform curriculum development by identifying areas where targeted AI education and training are needed, supporting the incorporation of AI tools into medical education, and preparing future physicians to effectively engage with AI in clinical practice.","url":"https://doi.org/10.21203/rs.3.rs-8243793/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8243793/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-9918525/v1","name":"Clinical Reasoning in the AI era: A focused scoping review and narrative synthesis of conceptual, pedagogical, and assessment frameworks in health professions education","source":"preprints","abstract":"Abstract Background Clinical reasoning is a core aim of health professions education, yet there is still no stable agreement on what the construct includes, how it should be taught, or how it should be assessed. This problem has become more urgent with the rapid uptake of artificial intelligence in medical and allied health education. AI studies increasingly claim to improve clinical reasoning, but they often operationalise it through narrow proxies such as diagnostic accuracy, history-taking completeness, key feature performance, or documentation quality rather than through an explicit construct model (Daniel et al., 2019; Durning et al., 2013; Gordon et al., 2022; Masters et al., 2025). This scoping review mapped how clinical reasoning is conceptualized, how pedagogical and assessment frameworks operationalise it, and how AI-enabled educational studies inherit, adapt, or narrow those frameworks. Methods A focused scoping review was conducted using two concept-based deep searches of the scholarly literature completed on May 7 and May 10, 2026. One search targeted empirical studies on generative AI and clinical reasoning in health professions education. The second targeted conceptual, curricular, and assessment frameworks relevant to clinical reasoning in the AI era. Together, the searches yielded 158 ranked records. Records were reviewed for direct relevance to clinical reasoning conceptualization, assessment, pedagogy, or AI-enabled implementation in health professions education. The synthesis centered on a purposive analytic corpus of framework papers, assessment-method papers, systematic reviews, and empirical AI studies, with medicine as the dominant case and allied health included when concepts or measures transferred across professions. Results The literature separated into three linked bodies of work. First, conceptual papers described clinical reasoning through script theory, dual-process and metacognitive models, and situated cognition, while more recent assessment papers emphasized longitudinal and programmatic approaches rather than single-test solutions (Charlin, Tardif, et al., 2000; Guth et al., 2024; Lubarsky et al., 2015; Pelaccia et al., 2011; Rencic et al., 2020b; Torre et al., 2024). Second, the assessment literature offered a broad toolbox, including key feature problems, script concordance tests, history-taking indicators, oral presentation tools, post-encounter forms, documentation rubrics, simulation, and workplace-based assessment, but repeatedly warned that no single instrument can capture the construct in full (Daniel et al., 2019; Schuwirth et al., 2020; Thampy et al., 2019). Third, AI-enabled education studies mostly attached these established tools to simulations, automated feedback, note scoring, and diagnostic support systems rather than introducing a new theory of clinical reasoning (Brügge et al., 2024; Çiçek et al., 2024; Dai et al., 2026; Hudon et al., 2025; Roemer et al., 2026; Saloojee et al., 2026; Schaye et al., 2024; Sur et al., 2026). Reported gains were most consistent in case-specific and highly scoreable domains such as data gathering, illness-script quality, diagnostic justification, and documentation structure, whereas richer dimensions of reasoning including uncertainty management, contextual adaptation, metacognition, and collaborative human-AI judgment remained thinly assessed (Abdulnour et al., 2025; Plackett et al., 2022; Wu, 2025). Conclusions The literature suggests the major challenge may no longer be the availability of measures, but the absence of an AI-era integrate construct model that links what should be taught, what should be protected from off-loading, and what should count as evidence of sound reasoning. Current AI studies mostly measure the fragments of reasoning that are easiest to observe, script, score, or automate. The next step for the field is not another isolated AI intervention trial, but a theory-informed framework that integrates knowledge organization, process, uncertainty, context, sociotechnical judgment, and programmatic assessment design.","url":"https://doi.org/10.21203/rs.3.rs-9918525/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9918525/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-10473312/v1","name":"Generative AI-powered simulated patients in medical and healthcare education: A scoping review and meta analysis","source":"preprints","abstract":"Abstract Generative artificial intelligence (AI)-powered simulated patients may expand access to adaptive and repeatable clinical training, yet evidence concerning their educational effectiveness, technical implementation, and responsible use remains fragmented. To characterize this rapidly evolving field, we conducted a PRISMA-ScR-guided scoping review and meta analysis of empirical studies published between 1 January 2016 and 1 June 2026, searching PubMed, OpenAlex, Scopus, Web of Science, and Springer Nature. Of 2,103 records identified, 157 studies met the inclusion criteria. Among these, 125 (79.6%) were published after 2025, indicating a marked recent increase in research activity on AI-powered simulated patients. Prelicensure medical education was the most common setting, represented in 67 articles, while applications focused primarily on history taking, communication, clinical reasoning, and formative assessment. Technical designs ranged from prompt-engineered conversational agents to multimodal, retrieval-augmented, and multi-agent systems, often incorporating automated feedback and human oversight. OpenAI GPT models were used most frequently, appearing in 86 articles, whereas Anthropic Claude, DeepSeek, Meta Llama, and Google Gemini were each reported in fewer than ten articles. Despite a growing number of randomized and nonrandomized comparative evaluations, most studies were short-term, single-site investigations focused on feasibility, learner experience, or immediate performance. Consequently, evidence regarding long-term retention, generalization to unfamiliar cases, transfer to clinical practice, and reproducibility across settings remained limited. Meta analysis yielded pooled estimates favoring generative AI-powered simulated patients for history taking and information gathering (Hedges' g=0.85, 95% confidence interval (CI): 0.38--1.32), communication and empathy (g=1.03, 95% CI: 0.15--1.91), OSCE global assessment and overall competence (g=0.77, 95% CI: 0.09--1.46), and clinical reasoning and diagnostic performance (g=0.93, 95% CI: -0.03--1.90). Safety, bias, privacy, assessment validity, and governance were frequently acknowledged, but empirical safety testing, incident reporting, equity analyses, and reproducible technical reporting remained uncommon. Generative AI-powered simulated patients therefore show promise for scalable, structured clinical practice and feedback. However, routine high-stakes implementation will require stronger longitudinal and multisite evidence, validated assessment approaches, transparent technical and safety reporting, and sustained educator oversight.","url":"https://doi.org/10.21203/rs.3.rs-10473312/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10473312/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9163969/v1","name":"The impact of artificial intelligence-driven point-of-care ultrasound (AI-POCUS) on antenatal care and maternal-newborn outcomes in Ethiopia","source":"preprints","abstract":"Abstract Objective This study investigates the application of Artificial Intelligence-Driven Point-of-Care Ultrasound (AI-POCUS) in antenatal care (ANC) services within low-resource settings, examining its effects on ANC adherence, referral decisions, and maternal and neonatal outcomes. The findings will provide empirical evidence to support the integration of digital interventions into primary healthcare systems. Methods A total of 706 pregnant women from the North Shewa Zone in the Amhara Region of Ethiopia were enrolled in the study between November 2024 and February 2025 using a consecutive sampling method and subsequently categorized into two groups based on their receipt of AI-POCUS examinations (AI-POCUS group, n=108; control group, n=598). Data were collected through face-to-face structured questionnaires, medical record extraction, and maternal follow-up, and differences in sociodemographic characteristics, referral patterns, and maternal and neonatal outcomes were compared between groups. Multivariate analyses using Firth logistic regression were performed to explore associations between AI-POCUS exposure and adequate ANC visits, maternal complications, and neonatal health outcomes. Results Compared with the control group, women in the AI-POCUS group were more likely to reside in urban areas, have employed occupations, and have partners with lower educational attainment ( P P =0.042). No statistically significant differences were observed between the two groups in maternal age, marital status, or obstetric history ( P >0.05). Among those who underwent ultrasound examination, the referral rate during the second trimester (19.67%) was markedly higher than in the first (0%) and third (4.55%) trimesters. Women who received AI-POCUS were 4.92 times more likely to achieve the WHO -recommended number of ANC visits than those in the control group ( aOR = 4.923, 95% CI : 2.863-8.465, P P >0.05). Conclusions AI-POCUS can serve as an important tool for ANC in low-resource settings, with potential value in improving ANC visits, strengthening risk management, and facilitating timely referrals. When scaling up digital health interventions for ANC, socioeconomic and cultural differences should be carefully considered to foster the development of sustainable and replicable models of digital perinatal care.","url":"https://doi.org/10.21203/rs.3.rs-9163969/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9163969/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8116760/v1","name":"Psychometric properties of the Persian version of attitudes towards artificial intelligence in work, healthcare, and education (ATTARI-WHE)","source":"preprints","abstract":"Abstract Introduction Artificial intelligence (AI) is increasingly shaping work, healthcare, and education. For further evaluation of AI in Work, Healthcare, and Education, we aimed to translate, culturally adapt, and evaluate the psychometric properties of the Persian version of ATTARI-WHE. Methods This cross-sectional study conducted between November 2024 and March 2025 among 118 participants, including medical students, faculty members, staff, and employees from four Iranian medical universities. The forward–backward translation method was applied, followed by expert content validation and student face validation. Construct validity was examined using confirmatory factor analysis (CFA). Reliability was assessed with Cronbach’s alpha, while convergent validity was evaluated through average variance extracted (AVE) and composite reliability (CR) Results Content validity ratio (CVR = 0.73) and content validity index (CVI = 0.92) confirmed item adequacy and clarity. Face validation showed high comprehensibility. CFA supported a three-factor structure aligned with the original model, although only the cognitive item of the health domain loaded independently. the adequacy of sampling was acceptable (KMO = 0.864), and Bartlett’s test was significant. Factor loadings exceeded 0.5, model fit indices (SRMR 60%) indicated good fit Conclusion The Persian version of ATTARI-WHE demonstrates satisfactory validity and reliability, making it a suitable tool for assessing attitudes toward AI in work, healthcare, and education within persian contexts.","url":"https://doi.org/10.21203/rs.3.rs-8116760/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8116760/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2025.12.23.25342849","name":"Assessing Statistical Practices of Existing Artificial Intelligence (AI) Models for Lung Cancer Detection, Prognosis, and Risk Prediction: A Cross-Sectional Meta-Research Study Supplemented by Human and Large Language Model (LLM)-Directed Quality Appraisal","source":"preprints","abstract":"Artificial intelligence (AI) models with medical images as input data are increasingly proposed to support clinical decisions in lung cancer screening. To assess how these models are developed, evaluated, and reported, and to identify gaps in best statistical practices, we conducted a cross-sectional meta-research study of OpenAlex-indexed studies (January 1, 2023, to June 30, 2025) that developed image-based AI tools to detect lung cancer, predict prognosis, or estimate future risk. Thirty-six studies met our inclusion criteria. Study quality and reporting were appraised using three approaches: subjective ratings from two statisticians and two clinicians, scoring from two AI agents (GPT-5 and Gemini 2.5 Pro), and a guideline-based checklist from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Convolutional neural networks were used in most of the included studies (69%). Area under the curve was the most frequently reported metric (81%). Our meta-research study also highlights common lapses in these 36 studies, including limited external test set use (39%), insufficient subgroup analyses (28%), and a substantial lack of adherence to established prediction-model reporting guidelines. AI-based quality scoring aligned better with CHARMS-based scores than did human scoring. Spearman correlations with CHARMS were weaker for statisticians/clinicians (p ≤ 0.46) than for the two AI agents (GPT-5 p = 0.66; Gemini 2.5 Pro p = 0.56). Overall, future research should prioritize standardized reporting, use of external test sets, and model performance assessment across subpopulations. Large language models (LLMs) offer a supportive role in providing guideline-driven appraisals to complement human judgment in evaluating AI-based prediction models. 1-2 Sentence Description This cross-sectional meta-research study synthesizes recent studies that developed artificial intelligence (AI)-driven predictive models using medical images to detect lung cancer, predict prognosis, or estimate future risk, highlighting methodological trends, limitations in model testing and subgroup analyses, and advocating for the need for greater transparency, reliability, quality assessment, and adherence to established reporting guidelines in such studies. Quality assessment of the models carried out by LLMs, human statisticians and clinicians indicates chatbots are more aligned with recommended guidelines than humans.","url":"https://doi.org/10.64898/2025.12.23.25342849","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.23.25342849","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8169603/v1","name":"Attitudes and Readiness of Medical Students in Iraq towards Artificial Intelligence: A Cross-Sectional Study.","source":"preprints","abstract":"Abstract Background Artificial Intelligence (AI) has emerged as a transformative force in healthcare, enhancing diagnostic precision, predicting outcomes, and optimizing administrative processes. In medical education, AI supports learning through simulation, automated assessments, and diagnostic training. However, successful implementation relies on clinicians’ and students’ readiness to adopt AI technologies. Objectives This study aims to assess the attitudes of Iraqi undergraduate medical students toward AI in healthcare and to evaluate their readiness across three key domains: ability, vision, and ethics for the integration of AI into medical education and clinical practice. Methods A cross-sectional study was conducted among 4th–6th-year medical students across Iraq from July 22 to September 1, 2025. Data were collected using a validated online questionnaire distributed via social media. The questionnaire consisted of three sections: (1) sociodemographic characteristics and AI exposure; (2) attitudes toward AI, using a modified version of the questionnaire developed by Pinto dos Santos et al.; and (3) readiness for AI integration, evaluated using a 14-item version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), covering the domains of ability, vision, and ethics. Cronbach’s alpha values of 0.856, 0.793, 0.825, and 0.880 for the respective domains and overall scale. Data were analyzed using SPSS v26. Mann–Whitney U test and Chi-square test were used to compare groups, with statistical significance set at p 60% agreed that AI will revolutionize diagnostic specialties and medicine and should be included in medical curricula. Readiness analysis revealed high agreement in the ability domain (> 70%) and moderate agreement for vision (> 5%), with strong ethical awareness (> 65%). Male and technologically skilled students demonstrated significantly more positive attitudes and overall readiness (p","url":"https://doi.org/10.21203/rs.3.rs-8169603/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8169603/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-9950710/v1","name":"Systematic Review: Deep Learning and Adaptive Thresholding for Breast Cancer Histopathology Image Analysis: Challenges, Innovations, and Clinical Integration","source":"preprints","abstract":"Abstract Breast cancer remains a leading cause of mortality in women globally, necessitating early detection and accurate diagnosis. Deep learning, particularly Convolutional Neural Networks (CNNs), and adaptive thresholding methods have emerged as promising technologies for analyzing histopathological images. This systematic literature review examines trends, challenges, and innovations in applying deep learning and adaptive thresholding to breast cancer histopathology image analysis. Following PRISMA guidelines, we conducted a comprehensive search across IEEE Xplore, ScienceDirect, SpringerLink, publications from 2022-2025. The review encompassed identification, selection, eligibility assessment, and results synthesis stages. Key findings reveal that integrating adaptive thresholding pre-processing with deep learning models achieves classification accuracy exceeding 95% in several studies. However, significant challenges persist, including data imbalance, limited validated datasets, model interpretability concerns, and barriers to clinical integration. This review emphasizes the critical need for developing integrative models that combine deep learning technology with clinical decision-support systems. Such approaches can enhance diagnostic speed, accuracy, and transparency in breast cancer diagnosis. Our findings provide a foundational framework for future research in artificial intelligence applications within digital pathology and medical decision-support systems, ultimately advancing the role of AI in improving patient outcomes and clinical workflow efficiency.","url":"https://doi.org/10.21203/rs.3.rs-9950710/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9950710/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-8163454/v1","name":"Blockchain–Artificial Intelligence Synergy in Healthcare and Financial Systems: A Systematic Review and Future Research Agenda","source":"preprints","abstract":"Abstract Two ground-breaking technologies that are influencing contemporary infrastructures with durability, intelligence, and transparency are blockchain and artificial intelligence (AI). AI enables predictive analytics, anomaly detection, and decision support, while blockchain provides immutability, decentralization, and trust. For mission-critical fields like healthcare and finance, its convergence has drawn increasing scholarly attention. Fifty-three research papers published between 2018 and 2025 are compiled in this narrative review to assess the potential, challenges, and future directions of integrating blockchain technology with artificial intelligence. In order to guarantee methodological openness and analytical rigor, this study methodically synthesizes 53 peer-reviewed papers that were published between 2018 and 2025 using narrative synthesis principles inspired by PRISMA. Telemedicine, drug supply chain traceability, diagnostic support, and secure medical data transmission are important uses in the healthcare industry. The combination of blockchain and AI in financial systems enables the detection of fraud, innovation in digital banking, and the creation of digital currencies by central banks. Along with ethical concerns about explainability and justice, the analysis also finds enduring difficulties, including scalability, interoperability, privacy, and regulatory uncertainty. Explainable AI (XAI) for blockchain models, federated blockchain–AI frameworks, and cross-domain applications that connect healthcare and finance are areas where notable gaps are identified. This report provides a comprehensive synthesis, offering academics and industry professionals a comprehensive perspective on how the convergence of blockchain and AI will impact infrastructures in the future. In addition to mapping the applications of blockchain-AI convergence, this paper offers a comparative evaluation of approaches, points out unresolved issues with explainable models and federated architectures, and suggests a systematic research agenda for the future. This paper provides a cross-sector synthesis of healthcare and financial systems, exposing common design principles and governance implications in contrast to previous evaluations that concentrated on a single domain.","url":"https://doi.org/10.21203/rs.3.rs-8163454/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8163454/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-9657794/v1","name":"Best Practice Guidelines for Bioethical Use of Medical AI in Healthcare. An eDelphi Consensus Exercise","source":"preprints","abstract":"Abstract Introduction: Medical Artificial Intelligence (MAI) is transforming healthcare but raises significant ethical concerns. Despite emerging principles and regulations, no standardized clinical bioethics framework exists to guide MAI practice. Objective This Delphi study sought global expert agreement on ethical challenges, principles, guidelines, and practical tools for MAI use. Method A four-round international eDelphi study, conducted under ACCORD and DELPHISTAR guidance, engaged multidisciplinary stakeholders identified through systematic review and targeted recruitment. Preliminary items were evidence-derived and iteratively refined. Consensus (≥ 80% agreement across two rounds) was predefined. Surveys were anonymized and randomized. Ethical approval and protocol registration were secured before study initiation. Results The panel included 53 participants from 21 countries. Survey rounds started November 2025 and ended February 2026. 170 items were considered. 133 reached consensus. Items were classified into bioethical issues, values, principles, solutions, a checklist for clinicians, and report markers for risk management. Discussion This study produced clinician-focused practice guidelines for the bioethical use of MAI. Ethical components aligned with prior literature, while no new bioethical principles met consensus. Limitations included geographic imbalance and stakeholder representation. Recommendations emphasized human responsibility, governance, and practical implementation tools. Conclusion This Delphi consensus provides practical, ethics-based guidance for the responsible integration of MAI into clinical practice and institutional governance.","url":"https://doi.org/10.21203/rs.3.rs-9657794/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9657794/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.64898/2026.06.24.26356103","name":"Attitudes of People Living with Dementia and their Carers towards the use of Generative Artificial Intelligence to inform Structured Medication Reviews","source":"preprints","abstract":"Background Polypharmacy is common in people living with dementia (PLwD) and associated with adverse outcomes. Although Structured Medication Reviews (SMRs) are recommended to optimise medication regimens, their delivery is often constrained by limited healthcare resources. Artificial intelligence (AI) may support SMRs, yet little is known about how it is perceived by PLwD and their carers. This study explored their experiences of polypharmacy, views on SMRs, and attitudes towards use of AI tools in SMRs. Methods Semi-structured interviews with 12 PLwD experiencing polypharmacy and two focus groups with 14 carers were conducted via Microsoft Teams or telephone and analysed using Reflexive Thematic Analysis. Results Two themes were constructed: experiences of SMRs, and attitudes towards AI in SMRs. Participants described challenges in managing polypharmacy, with carers often playing a central role in supporting adherence and monitoring side effects. Experiences of SMRs varied widely. SMRs were most valued when clinicians were empathetic and able to offer personalised guidance. Participants viewed AI use in SMRs positively, provided that such tools were well validated and used to assist rather than replace healthcare professionals. AI was viewed as having the potential to reduce administrative burden and support more person-centred care. However, some had concerns regarding patient safety and data security, highlighting the need for appropriate regulation and human oversight. Conclusion Participants were supportive of AI use in SMRs, despite concerns about safety, data security and disclosure of AI use, and emphasised the importance of patient–clinician interactions and lived experience involvement in AI tool development.","url":"https://doi.org/10.64898/2026.06.24.26356103","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.24.26356103","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-7923452/v1","name":"Artificial Intelligence in Medical Diagnosis and Treatment Planning Among Healthcare Professionals in a Tertiary Hospital, in Tanzania","source":"preprints","abstract":"Abstract Background: Artificial Intelligence (AI) has become a transformative force in various industries, with healthcare being one of the most significantly impacted. AI technologies are being utilized for medical diagnosis, treatment planning, and patient management, promising to enhance accuracy, efficiency, and outcomes in medical care. This study aimed to assess healthcare professionals’ knowledge, attitude and practices regarding AI in medical diagnosis and treatment planning in a tertiary hospital in Mwanza, Tanzania. This study aimed to assess healthcare professionals’ knowledge, attitudes, and practices regarding artificial intelligence in medical diagnosis and treatment planning in a tertiary hospital in Tanzania. Methods: This was cross-sectional study conducted from September 2024 to December 2024. The study used purposive random method to obtain sample size and Self-administered questionnaire to obtain data from the participants was used. The knowledge part had ten (10) questions which tested basic knowledge and was graded as good if they score 7 and above, 4-6 was termed as moderate and less than 4 was poor. The altitude and practice were assessed based on the utilisation and their general practice on AI. Results: A total of 320 respondents participated in the study out of 323 who consented, yielding a 99.1% response rate. The majority were general practitioners (142/320, 44.5%), followed by allied health personnel (105/320, 32.8%). To assess the level of knowledge on artificial intelligence (AI), participants were asked questions on basic concepts such as the definition of AI, machine learning (ML), natural language (NL), and its applications. The findings showed that most respondents (70%) had a moderate level of knowledge about AI. More than two-thirds agreed that integrating AI into medical practice, particularly in diagnostics and treatment planning, is very important. However, 189/320 (59%) had never applied AI in any of their medical tasks, while 131/320 (41%) reported having practically used AI, with the majority of them being general practitioners (55/131, 42%). Conclusion: The findings underscore the critical need for targeted educational interventions to bridge knowledge gaps among healthcare professionals. Institutions should prioritize comprehensive training programs that demystify AI, emphasizing its potential to enhance diagnostic accuracy, streamline administrative tasks, and ultimately improve patient outcomes. Clinical trial number: not applicable.","url":"https://doi.org/10.21203/rs.3.rs-7923452/v1","authors":["Johnson Henry Mhyellah","Yacinter Vedastus","Allen Rweyendera","Richard F. Kiritta","David Majinge","Hyasinta Jaka"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7923452/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.64898/2026.07.01.26357083","name":"Automated Multisource Electronic Frailty Index in Acute Ischemic Stroke: Development and Clinical Utility","source":"preprints","abstract":"Background Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual frailty tools are difficult to apply consistently in busy inpatient settings, while existing electronic frailty indices (eFIs) often rely on limited data modalities. We developed a scalable pre-stroke electronic frailty index (eFI) using multisource electronic medical record (EMR) data and evaluated its clinical utility. Methods We conducted a retrospective cohort study of AIS admissions to Singapore General Hospital from July 1, 2024, to January 31, 2025. A fully automated pipeline derived an eFI from EMR data over a 3-year lookback period, incorporating ICD-10 codes, vital signs, anthropometry, laboratory results, medications, and free-text documentation processed using artificial intelligence–augmented extraction of predefined, clinically interpretable deficits. Candidate variables were screened using a validated 10-step frailty index framework and refined by multidisciplinary expert consensus. Results Among 501 AIS cases, the pipeline generated 75 candidate variables and a final 33-variable eFI, with scores derived for 492 cases (98.2%). Frail patients had greater premorbid disability, higher stroke severity, longer hospitalization, greater rehabilitation use, worse discharge disability, higher 30-day readmission, and higher cumulative post-discharge mortality. In multivariable analyses adjusted for age, sex, NIHSS, premorbid mRS, and reperfusion therapy, each 0.1-unit increase in eFI was associated with mortality beyond 90 days after discharge (adjusted HR, 1.47; 95% CI, 1.19–1.81), 30-day readmission (adjusted OR, 1.91; 95% CI, 1.43–2.59), longer hospital stay (β, 2.8 days; 95% CI, 1.4–4.2), and discharge to inpatient rehabilitation rather than home (adjusted RRR, 1.66; 95% CI, 1.27–2.16). Conclusions In a well-documented acute stroke service supported by comprehensive longitudinal EMR data, automated multisource eFI derivation was feasible and clinically informative in AIS, capturing baseline vulnerability beyond conventional stroke measures and supporting frailty-informed risk stratification and discharge planning.","url":"https://doi.org/10.64898/2026.07.01.26357083","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.01.26357083","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/f1000research.173611.1","name":"Artificial Intelligence in Medical Education and Assessment: The next step in the IT Revolution","source":"preprints","abstract":"The digital revolution is transforming the face of medical education as Information Technology (IT) and Artificial Intelligence (AI) have become major factors in driving innovation. The use of digital platforms and immersive simulation systems trained with AI have been on the rise making learning more accessible, efficient and personalized. This review presents the current literature on the integration of IT/AI and medical education based on reviews, empirical investigations, and the opinions of the experts. Key areas of focus are e-learning and blended learning platforms, virtual and augmented reality simulations, intelligent tutoring systems, AI-based curriculum development, and AI-based assessment generation and grading. Evidence shows that these tools increase knowledge retention, encourage clinical reasoning, and provide safe environments for skills acquisition. The use of AI applications such as adaptive learning and automated testing helps to develop individualized learning which can be customized to the needs of individual learners. Using deep learning models to synthesize realistic virtual patients can foster communication skills and streamline feedback. Challenges for the widespread adoption of AI applications exist such as high implementation costs, faculty preparedness, data privacy, learner misuse, algorithm biases and unequal access. At the same time, there is growing appreciation for the importance of curriculum changes that incorporate AI literacy and digital skills in undergraduate, graduate, and continuing medical training. Future directions are also highlighted, such as teaching AI literacy as part of medical curricula, using AI-driven mixed reality simulations, developing an interdisciplinary collaboration to support responsible AI adoption, and developing standards to support the seamless integration between IT and AI systems. By providing a synthesis of evidence around currently available technologies, this review offers an understanding of the nature and impact of IT/AI on medical education, which may guide those preparing the next generation of healthcare professionals for an increasingly digital clinical world.","url":"https://doi.org/10.12688/f1000research.173611.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.173611.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-8053793/v1","name":"Artificial Intelligence Cannot Replace Nurses: A Systematic Literature Review","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) can manage patient data and assist in medical procedures, but can it replace aspects such as the emotions and empathy that nurses typically provide to patients. The importance of human relationships in nursing is crucial in crisis situations or long-term care. Objective: The Systematic literature review aimed to determine whether artificial intelligence (AI) could replace nurses. Methods: The review was conducted following PRISMA guidelines. A search was performed in Scopus, Proquest, sagepub, Web of Science (WoS), and Science Direct databases, covering literature from 2020 to 2025. The included articles were in English, full-text, and not reviews. Out of 4,422 screened records, seven articles were selected for this review. The results : showed that artificial intelligence (AI) significantly improves efficiency, accuracy, and information management in nursing practice. However, AI cannot replace the nurse’s role in humanistic aspects, such as empathy, therapeutic communication, clinical intuition, and moral-ethical judgment in complex situations. Conclusion: Systematic Literature Review demonstrate that AI presents significant opportunities for improving efficiency, accuracy, and information management in nursing practice. However, AI cannot replace the nurse’s role in humanistic aspects, such as empathy, therapeutic communication, clinical intuition, and moral-ethical judgment in complex situations. Therefore, the future of nursing in the AI era is not about replacing nurses but about strengthening and expanding their capacity through the synergy between human and artificial intelligence.","url":"https://doi.org/10.21203/rs.3.rs-8053793/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8053793/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-10187070/v1","name":"The role of Artificial Intelligence in interventions to reduce physical inactivity: A systematic review","source":"preprints","abstract":"Abstract Background Prevalence of physical inactivity is high and increasing across high-income countries, and interventions to increase physical activity can include behaviour change delivered digitally. Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming, and could potentially increase the effectiveness of digital behaviour change interventions through personalisation and reducing barriers to engagement. This review summarises the evidence for the effectiveness of AI-assisted interventions to reduce physical inactivity. Methods We conducted a systematic review to identify and summarise evidence from randomised controlled trials (RCTs) of AI-assisted interventions for reducing physical inactivity. Eligible trials were RCTs that reported results of an AI-assisted public health intervention for reducing physical inactivity in a high-income country. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core collection), and Scopus for relevant trials published between 2010 and 14 January 2025. We also searched for reviews of public health interventions for physical inactivity published between 2023 and 14 January 2025, and extracted all references from relevant reviews for screening. We conducted forward and backward citation searching on all included trials (dates of searches: September 2025 to June 2026). Screening for trials was conducted independently by two reviewers. Two reviewers independently assessed risk of bias using the Cochrane Risk of Bias 2 tool. As the included trials were heterogeneous in terms of interventions, outcomes, and timepoints, we synthesised the results narratively. Results We included 17 trials (comprising 48 reports and 3,282 randomised participants). Five trials estimated the effectiveness of apps with chatbots for reducing physical inactivity (one with some concerns of bias, four with high risks of bias), with little evidence to suggest that chatbots increase physical activity. Twelve trials estimated the effectiveness of selection of motivational or other messages using recommender systems, reinforcement learning, machine learning, or case-based reasoning for reducing physical inactivity (four with some concerns of bias, eight with a high risk of bias), with little evidence to suggest an increase in physical activity generally, though some evidence to suggest an increase in step count specifically. All trials had relatively few participants, so results were generally imprecise. There were no trials using large language models. Discussion There is no strong evidence of a beneficial effect of AI-assistance in public health interventions for reducing physical inactivity in high-income countries, though AI-assisted interventions may increase step count. Future research should better describe public health interventions that use AI and embed equity considerations into their design and analysis to ensure already disadvantaged groups are not harmed further by the adoption of AI-assisted interventions in public health. Registration PROSPERO CRD42025642339","url":"https://doi.org/10.21203/rs.3.rs-10187070/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10187070/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8098429/v1","name":"Artificial Intelligence-Assisted Nursing Interventions for Predicting Diabetes Among Homeless Adults","source":"preprints","abstract":"Abstract Background: Diabetes mellitus is a global health challenge, especially among the homeless. Nursing plays a critical role in prevention, early detection, and management of diabetes. Aim: to predict diabetes among homeless adults by utilizing artificial intelligence techniques-assisted nursing intervention. Design: A case-control design was utilized to achieve the aim of the current study. Setting: The study was conducted at the Ma'ana Rescue Human Foundation. Sample: A purposive sample of 150 homeless adults was included in this study. Tools for data collection : First tool: a structured interview questionnaire for homeless adults that has four parts. Part I: Demographic data; Part II: Medical history; Part III: Lifestyle factors; Part IV: Knowledge about diabetes. Second tool: Physiological measurements. Results: More than half of diabetic homeless were aged 60 years or more and two-thirds of them had moderate lifestyles and low knowledge about diabetes. The majority had hypertension and central obesity. Various of machine learning models were evaluated. A hybrid meta-learning classifier was subsequently developed, utilizing a stacking ensemble of six base learners, including logistic regression, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors, along with an XGBoost Meta-learner. The hybrid model markedly surpassed the individual classifiers, attaining an accuracy of 95.45%, an F1-score of 0.967, and an AUC of 0.979. Conclusion: Artificial intelligence techniques may be able to reliably predict diabetes. Integrating AI into nursing assessment and care planning enhances the ability to identify high-risk individuals early, thereby improving health outcomes. The proposed hybrid stacking model outperformed conventional classifiers in terms of prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. Recommendations: It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes.","url":"https://doi.org/10.21203/rs.3.rs-8098429/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8098429/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2025.12.09.25341890","name":"Research at the Intersection of Traditional, Complementary, and Integrative Medicine and Artificial Intelligence: A Bibliometric Analysis","source":"preprints","abstract":"Background Traditional, complementary, and integrative medicine (TCIM) describes a broad collection of medical interventions, practices, and belief systems that fall outside the purview of conventional medicine. Accompanying the recent growth of TCIM research productivity, artificial intelligence (AI) technologies have increasingly impacted areas of biomedical research, including diagnosis, treatment planning, and drug discovery. This bibliometric analysis explores the characteristics of research publications at the intersection of TCIM and AI. Methods A search string encompassing terms related to TCIM and AI was run on MEDLINE on 14 November 2025, with no restrictions by date, language, or publication type. Retrieved MEDLINE records were subsequently used for DOI citation searches in Scopus, with results exported on the same date. The following bibliometric data were collected: number of publications (including total publications and publications per year), open access status, subject area, document type, publication stage, publications per journal, author affiliations; funding sponsors, publication country, source type, and publication language. Trends in the bibliographic were generated in Excel, and bibliometric networks were visualized using VOSviewer, with thematic clusters identified and presented. Results A total of 1917 publications (n = 921 open-access) by 9749 unique authors, were published from 1991 to 2026. The greatest number of publications were published over the last 5 years, with the most productive journals/sources being Scientific Reports (n = 86), PLoS One (n = 63), and IEEE Transactions on Neural Systems and Rehabilitation Engineering (n = 59). The most productive countries included China (n = 982), the United States (n = 353), and the United Kingdom (n = 90), with frequent institutional affiliations and funding sponsors also being from these countries. Conclusions This bibliometric analysis provides insights into research productivity at the intersection of the TCIM and AI, showing rapidly increasing growth in the field. Future work can continue to investigate changes in the publication characteristics of emerging research, as the volume of publications on this topic is expected to rapidly grow.","url":"https://doi.org/10.64898/2025.12.09.25341890","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.09.25341890","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7686293/v1","name":"The Role of Artificial Intelligence in Healthcare(Diagnosis, Development, Education)","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) is becoming increasingly prevalent in most aspects of our lives, and with its continued development, it raises new questions and challenges, making it one of the most interesting and important topics for discussion. Objective: To study the role of AI in healthcare and to provide a simple evaluation of AI technologies and their ability to be supportive tools in these fields. Method: This study used two AI-based applications (Symptomate & Ada, Check your Health) to analyze 12 cases for human doctors and compare them with the diagnostic outputs of the applications.This study was conducted as a retrospective case series at the faculty of pharmacy, Damascus University. We identified and reviewed the medical records. The collected data included patient medication prescriptions, reported symptoms, physician diagnoses, and treatment information. Descriptive statistics were used to summaize the gathered data. Results: The degree of agreement between the diagnoses of the applications and the human doctor was 91.8%, which indicates the rapid progress of AI technologies. Conclusions: The significant progress of these technologies indicates their ability to be supportive tools in the field of healthcare.we conclude that artificial intelligence applications hold a promising ang significant role in our future, presenting itself as a supportive future partner in healthcare fields, saving time and effort, and improving efficiency and accuracy.","url":"https://doi.org/10.21203/rs.3.rs-7686293/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7686293/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8059260/v1","name":"Perception and Practices of Artificial Intelligence for Medical Education Among Medical Students: A Cross-Sectional Study","source":"preprints","abstract":"Abstract Background: Artificial Intelligence (AI) is increasingly influencing the healthcare industry, making it crucial to assess the perceptions and practices of AI among future healthcare professionals. This study aimed to determine the attitudes of Indian medical students toward \"AI in medicine,\" their support for structured AI instruction, and their views on its ethical implications. Methods: This was a cross-sectional study conducted over one month, from March 7th to April 6th, 2025, at ESIC Medical College, Kalaburagi , among medical students. Universal Sampling was used, and the study population was medical students from the college. Out of 550 eligible students, 321 participated . Data was collected using a predesigned, pretested, semi-structured questionnaire with sections covering general information, perception (10 questions, 5-point Likert scale), practices (10 questions), and the future of AI in medical education (6 multiple-choice questions, 5-point Likert scale). Results: The majority of participants were female (52.3%) and aged 18-20 years (54.2%). A large portion of students used technology for educational purposes either Always (40.5%) or Often (37.7%) . Online resources (63.6%) and Textbooks (72.3%) were the primary sources of information. 81% of students reported using AI , with ChatGPT (84.9%) and Meta AI (53.7%) being the most used platforms. Students primarily learned about AI tools through friends (48%) and social media (42%) . In terms of perception , 96.9% agreed that AI can enhance the quality of medical education by providing personalized learning experiences, and 81.6% viewed AI as a powerful tool for teaching-learning . 78.9% felt dedicated AI tools should be created for medical education. For academic purposes , AI was mainly used for assignment writing (66.8%) , solving questions (59.8%) , and summarizing topics (56%) . Concerning the future of AI , 88.7% agreed that ethical training on AI use should be mandatory in the curriculum, and 81.2% believed AI would play a central role in training students to diagnose diseases accurately. Conclusion: The study demonstrates that a large proportion of medical students actively use AI in their studies and have a positive perception of its potential to personalize learning and reduce study time. However, the findings also highlight a need for structured training on AI, as many students felt they were not properly trained and were concerned about the lack of human interaction and the risk of inaccurate information. Therefore, the study recommends the creation of dedicated AI tools for medical education and mandatory ethical training on AI use.","url":"https://doi.org/10.21203/rs.3.rs-8059260/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8059260/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8062581/v1","name":"Use of artificial intelligence–enhanced virtual patients in educational approaches to medical interview training : A systematic review","source":"preprints","abstract":"Abstract Background Effective communication and empathy are essential for professional–patient relationships, patient care, and the therapeutic alliance. With the increasing use of artificial intelligence-assisted virtual patients (AI-VP), new opportunities to improve interview training are observed. This systematic review assesses the educational use, benefits, and limitations of AI-VP in clinical interview training for healthcare professionals and students. Methods Following PRISMA recommendations, the review involved a systematic search in PubMed, Google Scholar, and PsycInfo. Nine met the inclusion criteria. Methodological quality of papers was assessed using the MMAT 2018. Results The findings suggest that AI-VPs can improve empathy, communication, and clinical reasoning, as well as promote perceived self-efficacy, reduce anxiety, and enhance engagement through immersive experiences. However, questions remain about the transferability of these skills to clinical practice. The studies show a lack of consistent theoretical frameworks and standardized outcome measures. While feedback is widely used, it is not formalized. Technical limitations, such as issues with speech recognition and nonverbal communication, can disrupt immersion. Conclusion AI-VP represents a promising educational tool for initial interview training, as it creates learning environments that are scalable, standardized, and interactive. A step-by-step approach, beginning with standardized patients and progressing to real patients, may optimize skill acquisition. Robust longitudinal studies and cost-benefit analyses are needed to support these findings. Although AI-VP can improve training in interpersonal and communication skills, further work is needed to validate its clinical impact and educational design.","url":"https://doi.org/10.21203/rs.3.rs-8062581/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8062581/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-7992519/v1","name":"Artificial Intelligence in Urolithiasis Imaging: Radiographic Detection of Urinary Stones in Resource-Constrained Settings","source":"preprints","abstract":"Abstract This study aims to develop an AI-powered detection model specifically designed for X-ray modalities to enhance the diagnosis of urinary tract stones. Urinary tract stones are a prevalent medical condition that can lead to significant morbidity and healthcare costs. Traditional diagnostic methods, such as CT scans, while effective, are often expensive and may not be accessible to all patients. Therefore, this research focuses on leveraging artificial intelligence to improve the accuracy and efficiency of X-ray imaging in identifying urinary tract stones.We conducted a retrospective observational study, analyzing a comprehensive dataset of X-ray images from patients diagnosed with urinary tract stones. The AI model was trained using advanced machine learning algorithms to recognize patterns and features indicative of stone presence. Performance metrics, including true positive, true negative,false negative, false positive, and accuracy, were evaluated to assess the model's effectiveness compared to conventional diagnostic methods.The results demonstrate that the AI-powered model significantly improves diagnostic accuracy while maintaining cost-effectiveness. This approach not only enhances patient outcomes by facilitating timely diagnosis but also promotes the use of widely available X-ray technology, making it a viable option for healthcare systems with limited resources. Our findings suggest that future research should continue to explore AI applications in medical imaging, focusing on developing affordable and accessible tools for broader community use.","url":"https://doi.org/10.21203/rs.3.rs-7992519/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7992519/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7801540/v1","name":"Perceptions of Portuguese radiology professionals regarding the implementation of artificial intelligence based on a single center cross sectional survey","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is progressively transforming radiology departments, offering new opportunities to enhance both efficiency and quality of care. This study explores the perceptions of radiology professionals regarding the integration of AI into the workflow of a Medical Imaging Department (MID). Methods A cross-sectional study was conducted between May 24 and June 14, 2024, using a structured questionnaire completed by 61 radiology professionals from a central hospital in Portugal. For statistical data analysis, descriptive and inferential methods were used. Results 77.1% of professionals reported being familiar with AI, and 64% with its practical applications. The majority believe that AI can reduce the time required for medical tasks (81.9%) and minimize waste and costs (77.1%). The primary challenges identified were a lack of internal know-how (70.6%) and variability in data records (68.9%). A moderate correlation was found between implementation intention and AI knowledge (r = 0.437, p","url":"https://doi.org/10.21203/rs.3.rs-7801540/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7801540/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7492357/v1","name":"Trends and Hot Topics in International Medical Humanism Research Based on Citespace","source":"preprints","abstract":"Abstract Objective: This study aims to investigate the research trends and focal areas within the field of medical humanities over the past decade, offering valuable insights for scholars in related disciplines. Methods: A comprehensive literature search was conducted using the Web of Science (WOS) core database, covering the period from January 1, 2015, to April 2, 2025. The analysis was performed utilizing the citespace6.2R6 software to assess the current status, explore trends, and identify research hotspots. Results : A total of 1,864 documents were identified, with a consistent year-over-year increase in publications. A total of 486 authors contributed to these publications, with notable contributions from Janssens, Uwe, and Michels, Guido, among others, who produced high-quality work. These authors predominantly engaged in independent work or collaborated within small teams, with cross-team collaborations being relatively infrequent. The primary publishing institutions were predominantly located in developed regions, including the United States, Canada, Europe, and Australia.The research themes span more than a dozen fields, including compassionate care, humanistic education, end-of-life care, intensive care, primary healthcare, and artificial intelligence. Conclusion: amidst the global surge in medical technology advancement and the transformative impact of artificial intelligence on the healthcare landscape, it is imperative for nations to prioritize the integrated development of \"technological advancement\" and \"humanistic care.\" By implementing strategic policy guidance, evolving service philosophies, and fostering innovations in medical education, a cohesive and resilient ecosystem of medical humanistic care should be cultivated to improve patient healthcare experiences and satisfaction.","url":"https://doi.org/10.21203/rs.3.rs-7492357/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7492357/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.176125158.87617951/v1","name":"Artificial intelligence vs. Human Biostatisticians: Accuracy and Reliability Across 14 Medical Datasets","source":"preprints","abstract":"Background: Statistical analysis is central to medical research. Generative artificial intelligence have recently emerged as potential tools to support data analysis in medical research. Objective: to evaluate the accuracy and reliability of artificial intelligence in performing statistical analyses by comparing its output with previously published statistical results revised by professional biostatisticians. Methods: : An observational, comparative secondary analysis of 14 previously analyzed datasets. The GPT-4o analyses were conducted between May and September 2025 using a standardized prompt chain. A 13-item rubric was used to score each task (2 = identical, 1 = almost identical, and 0 = Dissimilar). The dataset-level categories were pre-specified as perfect (100%), good (90%– Results: : The GPT-4o model correctly identified the file structure in 13/14 (92.9%) datasets and accurately assessed normality in 12/14 (85.7%). Its errors were mainly schema-related, treating coded categorical fields as numeric and missing some composite recoding; therefore, numeric values sometimes differed even when significance decisions matched human analyses. Of the 14 datasets, the GPT-4o output was classified as perfect for two (14.3%), good for 10 (71.4%), and poor for two (14.3%). Conclusion: The GPT-4o model performed well on structured tasks with clear prompts and correct variable typing, often converging with human analyses on significance decisions. However, it struggled with coded categorical data, complex recoding, and publication-ready tables, with numerical discrepancies in several datasets. While the tested GenAI model could assist early analytical work under expert supervision, further research is warranted, particularly with evolving GenAI models.","url":"https://doi.org/10.22541/au.176125158.87617951/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176125158.87617951/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.31222/osf.io/3bvzc_v1","name":"Detecting Fraud-Associated Characteristics in the Medical AI Literature: A Multi-Signal NLP Framework Reveals Distinct Paper Mill Subtypes","source":"preprints","abstract":"Paper mills increasingly compromise the integrity of the medical artificial intelligence (AI) literature. We developed a pre-registered, multi-signal natural language processing pipeline combining seven feature categories -- tortured phrases, structural formulaicity, AI-generated text markers, citation anomalies, cross-document similarity, co-authorship networks, and geographic metadata -- and applied it to 2,478 medical AI papers (2018-2025) labelled using Retraction Watch data via the Crossref Labs API. A Random Forest-XGBoost ensemble classifier achieved average precision 0.858 and AUC-ROC 0.916 on 5-fold cross-validation, but the most important features were writing quality indicators (vocabulary diversity, reference count) rather than fraud-specific signals, reflecting the dominance of the 2021-2022 Hindawi mass retractions. Retraction subtype analysis revealed distinct fingerprints across fraud types: AI-generated content papers had twice the boilerplate density of other subtypes, while fake peer review papers had the highest co-authorship network density. Unsupervised clustering identified an \"author pool\" cluster (n=133, 47% retracted) with extreme co-author reuse (0.75 vs 0.11 corpus mean). Among unlabelled papers, 9.1% fell in high or very high risk tiers. Prevalence was broadly uniform across WHO regions (15-20%) and robust to corpus definition. Four pre-registered sensitivity analyses confirmed robustness. The heterogeneity of paper mill operations -- synonym-substitution mills, AI content generators, and peer-review manipulation rings -- demands subtype-aware detection strategies. Code: https://doi.org/10.5281/zenodo.19488868. Pre-registration: https://doi.org/10.17605/OSF.IO/JB4T6.","url":"https://doi.org/10.31222/osf.io/3bvzc_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31222/osf.io/3bvzc_v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-7991993/v1","name":"Artificial Intelligence in Gynaecology: A Narrative Review of Diagnostic, Surgical, and Educational Applications","source":"preprints","abstract":"Abstract Background Artificial Intelligence (AI) is rapidly evolving and is increasingly applied across healthcare disciplines. In gynaecology, however, its implementation is still emerging and not yet well established. This review explores how AI is being applied in gynaecology within the domains of diagnostics, surgery, and education. It aims to map current research, identify key technologies, and highlight potential benefits and challenges. Methods A literature search was conducted using the PubMed database for the last ten years (2014–2025), using a targeted AI and gynaecology search strategy. Studies were screened based on inclusion criteria, and 11 eligible articles were selected. Data were charted based on study design, AI method, clinical domain, key outcomes, and limitations. Results Eleven studies were included: 4 focused on diagnostics, 3 on surgery, and 4 on education. AI was applied for cancer screening, embryo assessment, robotic-assisted surgery, surgical workflow optimization, and educational simulations. AI models included neural networks, machine learning algorithms, and vision-based tools. Benefits included improved diagnostic accuracy, reduced surgical complications, and enhanced training outcomes. Conclusion AI shows promise in advancing diagnostic precision, supporting safer and more effective surgical interventions, and enhancing medical education in gynaecology. However, challenges such as ethical concerns, data privacy, interpretability, and lack of clinical validation remain. Continued multidisciplinary research and responsible integration are needed to fully realize AI’s potential in gynaecology.","url":"https://doi.org/10.21203/rs.3.rs-7991993/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7991993/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9204470/v1","name":"Digital phenotyping of adolescent anxiety, depression and borderline personality disorder using a chatbot and virtual‑agent interviews: cross‑sectional study protocol","source":"preprints","abstract":"Abstract Background: Anxiety disorders, depression, and borderline personality disorder often emerge during adolescence, yet early psychiatric assessment remains challenging because conventional interviews are subjective, clinician-dependent, and difficult to scale. Digital phenotyping may improve early identification by combining standardized digital interviews with multimodal behavioral, linguistic, physiological, and environmental markers. Methods: An observational cross-sectional study with a 2 × 4 design with random allocation to interview mode. Adolescents and young adults aged 12–20 years with anxiety disorders, depressive disorders, borderline personality disorder, and controls will complete one standardized pre-screening interview using either a text-based chatbot or a virtual agent on a tablet device at the University Medical Centre Maribor, Slovenia. During a single visit, participants complete a battery of standardized questionnaires, undergo audio-video recording, contactless physiological monitoring, and indoor air-quality sensing. Primary outcomes are participant-reported acceptability measures. Secondary analyses will test effects of interview mode and diagnostic group, and exploratory analyses will examine associations between symptom dimensions and multimodal digital markers. The planned sample size is 120–240 participants. Discussion: The study will assess acceptability of chatbot- and virtual-agent-delivered pre-screening interviews in youth mental health and create a clinically annotated multimodal dataset for future digital phenotyping research. The dataset is intended to support subsequent development of explainable artificial intelligence models and privacy-preserving synthetic data for adolescent mental health research. The findings may also provide evidence on the clinical utility of these interview formats as complementary, scalable tools for early detection and triage in adolescent mental health settings. Trial registration: The study was registered in the ISRCTN registry (on 9 March 2026, ISRCTN55639530). The study was approved by the National Medical Ethics Committee of the Republic of Slovenia (on 13 October 2025, ref. 0120-466/2025-2711-3). Written informed consent will be obtained from all participants, with parental or guardian consent and minor assent obtained where applicable.","url":"https://doi.org/10.21203/rs.3.rs-9204470/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9204470/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8231217/v1","name":"Impact of paternal age in donor oocyte cycles: An Artificial Intelligence-Based Analysis","source":"preprints","abstract":"Abstract Purpose To determine whether artificial intelligence (AI) can detect the impact of advanced paternal age (APA) on embryonic development and, consequently, on assisted reproductive technology (ART) outcomes. Methods This retrospective observational study analyzed medical records from the VRepro database (April 2022–October 2023) at a single fertility center. To minimize the confounding effect of oocyte age, only oocyte donation cycles were included, and to control for the influence of semen quality, only normozoospermic samples were analyzed. All cycles underwent intracytoplasmic sperm injection (ICSI). Patients were categorized into two groups: men","url":"https://doi.org/10.21203/rs.3.rs-8231217/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8231217/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202510.2468.v1","name":"Explainable Artificial Intelligence for Rehospitalization and Financial Burden of Fertile Women in Orthopedic Care","source":"preprints","abstract":"Fertile women represent a socially and medically significant patient group, yet little research has examined their rehospitalization behavior and financial burden in clinical settings. This study develops predictive and explainable artificial intelligence (AI) to forecast retention and medical costs among reproductive-age orthopedic patients. Electronic health records of 83 women (aged 15–49) at a major university hospital in Korea were analyzed. Six machine learning models were tested, and model performance was assessed using accuracy and the area under the curve (AUC). Shapley Additive Explanations (SHAP) were applied to interpret predictors of rehospitalization. Additional analyses explored determinants of patients’ total and uncovered medical costs. Random forest outperformed other models in predicting rehospitalization (AUC 0.92 vs. 0.73 for logistic regression). Key predictors included major disease, systolic blood pressure, platelet count, age, and treatment costs. Random forest also yielded lower error rates than linear regression in forecasting patients’ financial burden (RMSE/IQR for total cost: 1.05 vs. 1.14). Several factors—such as blood pressure, pulse, and hematocrit—were influential for both retention and costs. Predictive and explainable AI can support medical centers in anticipating rehospitalization and financial barriers for fertile women. By integrating medical and socioeconomic determinants, hospitals may design strategies that enhance patient retention while addressing broader societal priorities in women’s health.","url":"https://doi.org/10.20944/preprints202510.2468.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.2468.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202510.0659.v1","name":"Evaluation of Artificial Intelligence for the Anatomy Content in Medical College","source":"preprints","abstract":"The incorporation of artificial intelligence into medical pedagogy necessitates a thorough appraisal, especially within fundamental disciplines like anatomy, where accurate understanding is paramount. The efficacy of advanced AI systems, specifically Large Language Models like ChatGPT, in the acquisition and retention of specialized medical knowledge continues to be an active area of research and evaluation. This cross-sectional study was undertaken in August 2025, to evaluate the proficiency of ChatGPT in responding to multiple-choice questions within basic medical sciences, with a particular emphasis on the domain of anatomy. A compilation of 124 meticulously selected multiple choice questions from the mid-term and final examinations administered to first-year medical students, was utilized; Anatomy (28), Histology (23), Microbiology (21), Pathology (33) and Physiology (19). Strict criteria applied to ensure questions were unambiguously framed as single-best-answer items. Paper of each discipline was submitted to ChatGPT, and initial response considered definitive. Performance was scored on a binary scale and analyzed descriptively. Results revealed high accuracy, with ChatGPT answering 96% Anatomy questions correctly, 100% Histology and Physiology, Pathology 97% and Microbiology 95%, achieving an overall accuracy of 98%. The results indicate a substantial capacity for ChatGPT to serve as a valuable pedagogical resource for reinforcing knowledge and facilitating self-evaluation.","url":"https://doi.org/10.20944/preprints202510.0659.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.0659.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7937665/v1","name":"Prediction of survival and prognostic factors in patients with bladder cancer after surgery using artificial intelligence recommendation algorithm: a preliminary study","source":"preprints","abstract":"Abstract Objective To discover the variables that affect bladder cancer (BC) patients' survival and prognosis after surgical treatment, and to use this knowledge to build an artificial intelligence (AI)-based recommendation algorithm. Methods This study comprised 832 BC patients who underwent surgery at The Second Affiliated Hospital of Dalian Medical University (2nd HDMU) and Nanfang Hospital of Southern Medical University (NHSMU) between January 2007 and January 2019. Their clinical and follow-up data were obtained. The 2nd HDMU patients were the training group, whereas NHSMU patients were the test group for external validation. An AI algorithm model was created using the deep neural network (DNN). The parameters influencing patient survival were analyzed and ranked with the assistance of AI algorithm. Results Out of the 832 bladder cancer patients included in this study, 438 (52.64%) were treated in the 2nd HDMU, while 394 (47.36%) were in the NHSMU. Among the BC cases, 579 (69.6%) were diagnostic of non-muscle invasive bladder cancer, while only 253 (30%) were muscle-invasive bladder cancer. In terms of surgical intervention, 539 (64.8%) patients underwent transurethral resection of bladder tumor, 66 (7.9%) received partial cystectomy, and 227 (27.3%) received total cystectomy. We concluded that the factors affecting the survival and prognosis of patients, in descending order, were T stage, pathological grade, hypertension or cardiovascular and cerebrovascular diseases, hemoglobin concentration, serum calcium, smoking, serum albumin level, lymphocyte count, age, serum albumin/globulin ratio, surgical method, N stage, and creatinine clearance rate. The testing group evaluated and confirmed this model to predict BC patients' survival before surgery. Conclusion Utilizing DNN modeling and external validation, the influencing factors of postoperative survival can be predicted for patients with BC. It can be employed to forecast BC patients' surgical outcomes before surgery. Additionally, this model can provide algorithmic assistance in selecting surgical and postoperative follow-up strategies for such patients.","url":"https://doi.org/10.21203/rs.3.rs-7937665/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7937665/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7504189/v1","name":"Current status of artificial intelligence utilization in medical education: A cross-sectional survey of medical students and faculty","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (AI), particularly large language models such as ChatGPT, is rapidly transforming various sectors, including medical education. Despite increasing interest, few studies have investigated how AI is actually used in medical education settings, especially in Japan. This study aimed to assess the current use of generative AI among medical students and faculty members, and to identify their perceptions, perceived benefits, and concerns in relation to its integration into medical education. Methods A cross-sectional survey was conducted from April to May 2025 at the Oita University Faculty of Medicine. A total of 1,014 students and 470 faculty members from the School of Medicine, School of Nursing, and Department of Advanced Medical Sciences were invited to complete an anonymous online questionnaire. The survey covered AI usage experience, purposes of use, and attitudes toward AI in academic contexts. Results The response rates were 40% for students (402/1,014) and 74% for faculty members (350/470). Most students (82.1%) and faculty (73.4%) had prior experience using AI tools, primarily for report writing, lecture preparation, and information retrieval, with students showing a higher rate of AI usage experience than faculty (p","url":"https://doi.org/10.21203/rs.3.rs-7504189/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7504189/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202510.0489.v1","name":"The Growing Importance of Soft Skills in Medical Education in the AI Era: Balancing Humanistic Care and Artificial Intelligence","source":"preprints","abstract":"As artificial intelligence transforms clinical practice, medical education needs to incorporate soft skills to ensure patient safety, support transparent decision-making, and uphold ethical accountability. This narrative review synthesizes evidence from MEDLINE, Scopus, Google Scholar, and grey literature, organizing the findings into seven thematic areas that cover curriculum integration and teaching methods. The results indicate that soft skills enhance patient adherence, satisfaction, safety, trust, and shared decision-making. They also strengthen physicians' professional identity, confidence, collaboration, ethics, and resilience while reducing burnout. Additionally, soft skills enhance system-level outcomes, including collaboration, resilience, safety, and public trust. Experiential, reflective, and competency-based pedagogies demonstrate the strongest impact, while AI-enhanced tools provide complementary value. Despite definitional ambiguity, fragmented curricular adoption, and limited longitudinal implementation, research highlights that key competencies—communication, emotional intelligence and empathy, pro-fessionalism, teamwork and collaboration, and critical thinking with reflective practice—operate as an interconnected and synergetic system. Reconceptualizing soft skills as an interconnected system, reinforced by cognitive, affective, humanistic, behavioral, and sociocultural mechanisms, advances theoretical clarity, supports programmatic assessment, and enables the integration of AI-related literacies, including data governance, algorithmic transparency, bias mitigation, and digital professionalism. Ultimately, excellence in medicine relies on harmonizing biomedical expertise and AI capabilities with humanistic and cognitive competencies to ensure that technological innovation strengthens, rather than undermines, the ethical and humanistic foundations of medical practice.","url":"https://doi.org/10.20944/preprints202510.0489.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.0489.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2025.12.21.25342775","name":"Multitask Artificial Intelligence–Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs","source":"preprints","abstract":"Background Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing costs without improving outcomes. Artificial intelligence (AI)-enabled electrocardiography (ECG) may enhance perioperative risk assessment by identifying patients at very low risk for adverse events. Objective This study aimed to evaluate whether AI-ECG-based risk stratification could help maintain safety and decrease potentially avoidable preoperative CV testing, while reducing the associated costs, in patients undergoing non-cardiac surgery. Methods We retrospectively analyzed 41,218 patients (46,135 ECG–surgery pairs) undergoing elective non-cardiac surgery at Seoul National University Bundang Hospital (2020–2021). An AI-ECG algorithm generated eight probability scores for cardiac conditions, classifying patients as low- or high-risk. Based on the performance and results of preoperative cardiovascular testing (transthoracic echocardiography, coronary computed tomography angiography, single-photon emission computed tomography, or coronary angiography), patients were classified as no test, negative test, or positive test. The primary endpoint was a 30-day composite of all-cause mortality, unplanned percutaneous coronary intervention, or prolonged mechanical ventilation (≥3 days). Results AI-ECG classified 92.4% of patients as low-risk, with an event rate of 0.62% versus 6.04% in high-risk patients. Preoperative CV testing was performed in 11.8% of cases, with only 16.3% yielding positive findings. In AI-ECG low-risk patients, event rates were uniformly low (0.6–2.7%) regardless of testing, whereas in high-risk patients, rates were consistently high (5.3–6.2%), suggesting no additional prognostic value of conventional testing. An AI-ECG guided approach could reduce potentially avoidable preoperative testing by 35.7% while preserving safety. Integrating AI-ECG with established risk tools may better delineate patients who truly require preoperative CV testing while conserving medical resources. Conclusion AI-enabled ECG reliably identified surgical candidates at low risk for postoperative complications, for whom additional CV testing may be potentially avoidable. Integrating AI-ECG with conventional risk tools may optimize resource use and minimize redundant testing without compromising outcomes. Prospective studies are needed to confirm clinical and economic benefits. Trial Registration N/A","url":"https://doi.org/10.64898/2025.12.21.25342775","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.21.25342775","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7898712/v1","name":"Enhancing Pharmacology Education through artificial intelligence Integrated Blended Learning: A Quasi-Experimental Study","source":"preprints","abstract":"Abstract Background Pharmacology education, serving as a crucial bridge between basic and clinical medicine, faces challenges with traditional lecture-based pedagogies in meeting diverse student learning needs. While blended learning has enhanced educational flexibility, it struggles with real-time diagnosis of individual learning states. Artificial intelligence (Al) teaching assistants offer transformative potential for personalized pharmacology education through intelligent learning pathways and real-time guidance. Methods This quasi-experimental study evaluated 175 first-year pharmacy students across six classes over a 16-week pharmacology course. Students were randomized into control group(n = 88) receiving conventional blended learning and experimental group (n = 87) receiving Al-enhanced blended learning. The Al system provided personalized guidance through intelligent pre-class companions, in-class interactive guidance, and post-class targeted practice. Outcomes were assessed using standardized tests, competency evaluations, behavioral analytics, and learning experience surveys. Results The experimental group demonstrated significantly superior performance across all measured dimensions. Knowledge mastery showed dramatic improvements: core concepts (89.7% vs 69.9%, p","url":"https://doi.org/10.21203/rs.3.rs-7898712/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7898712/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-10117247/v1","name":"The role of Artificial Intelligence in interventions to reduce alcohol consumption and smoking: A systematic review","source":"preprints","abstract":"Abstract Background: Prevalence of alcohol consumption and smoking is high across high-income countries, and interventions to decrease either can include behaviour change delivered digitally. Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming, and could potentially increase the effectiveness of digital behaviour change interventions through personalisation and reducing barriers to engagement. This review summarises the evidence for the effectiveness of AI-assisted interventions to reduce alcohol consumption and smoking. Methods: We conducted a systematic review to identify and summarise evidence from randomised controlled trials (RCTs) of AI-assisted interventions for reducing alcohol consumption or smoking. Eligible trials were RCTs that reported results of an AI-assisted public health intervention for reducing engagement with alcohol consumption or smoking in a high-income country. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core collection), and Scopus for relevant trials published between 2010 and 14 January 2025. We also searched for reviews of public health interventions for alcohol consumption or smoking published between 2023 and 14 January 2025, and extracted all references from relevant reviews for screening. We conducted forward and backward citation searching on all included trials (dates of searches: July to September 2025). Screening for trials was conducted independently by two reviewers. Two reviewers independently assessed risk of bias using the Cochrane Risk of Bias 2 tool. As the included trials were heterogeneous in terms of interventions, outcomes, and timepoints, we synthesised the results narratively. Results : We included 4 trials for reducing alcohol consumption (comprising 16 reports and 4,718 randomised participants): 3 trials used apps with chatbots and reported mixed evidence, and one trial, which was effective, used a rules-based AI that tailored website content. We included 12 trials for stopping smoking (comprising 31 reports and 68,659 randomised participants): 8 trials of apps or messenger chatbots and 4 trials of recommender systems, all of which had mixed evidence. For many trials, the interventions had multiple components, of which AI-assistance was only one, meaning the effectiveness of AI-assistance specifically could not be determined. Additionally, high risks of bias across almost all trials reduced confidence in the results. There were no trials using large language models (LLMs). Discussion : There is no strong evidence of a beneficial effect of AI-assistance in public health interventions for reducing alcohol consumption or smoking in high-income countries. Future research should better describe public health interventions that use AI and embed equity considerations into their design and analysis to ensure already disadvantaged groups are not harmed further by the adoption of AI-assisted interventions in public health.","url":"https://doi.org/10.21203/rs.3.rs-10117247/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10117247/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.2408.v1","name":"Artificial Intelligence in Medical Laboratories: Emerging Applications in Biochemistry and Microbiology: Future Directions","source":"preprints","abstract":"The accelerating digital transformation of healthcare systems has positioned artificial intelligence (AI) as a crucial tool for enhancing the efficiency and accuracy of medical services. This review examines the emerging applications of AI within biochemistry and microbiology, two fundamental laboratory disciplines vital to diagnosis and treatment. The core discussion centers on how AI is revolutionizing processes through data analysis, enabling predictive diagnosis, and automating workflows to significantly reduce human error. In biochemistry, the \"clinlabomics\" approach transforms routine tests into strategic data sources for early disease prediction and risk estimation, while in microbiology, AI accelerates pathogen detection and drives the discovery of new antimicrobial agents. Shared challenges, including data privacy and the need for explainable AI, are analyzed. Ultimately, the successful and sustainable integration of AI into these laboratories will fundamentally improve patient outcomes and reshape the future of clinical diagnostics.","url":"https://doi.org/10.20944/preprints202509.2408.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.2408.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9015700/v1","name":"Factors influencing the choice of diagnostic radiology as a specialty among medical students in Palestine: A cross-sectional study","source":"preprints","abstract":"Abstract Background Understanding factors influencing specialty choice is essential for workforce planning and curriculum development. Recruitment into radiology remains challenging in several regions, particularly in low- and middle-income settings. This study aimed to identify factors associated with Palestinian medical students’ consideration of diagnostic radiology as a future specialty, with particular emphasis on educational exposure, perceptions of artificial intelligence (AI), and cognitive influences. Methods A multi-institutional cross-sectional study was conducted to evaluate factors influencing the choice of diagnostic radiology as a future specialty among Palestinian medical students between November 2025 and February 2026. Data were collected using a previously validated questionnaire adapted from Almuhanna et al. Descriptive statistics were performed, followed by bivariate analyses and multivariable logistic regression to identify independent predictors of considering radiology as a future specialty. Adjusted odds ratios (OR) with 95% confidence intervals (CI) were reported. Results A total of 427 students participated (56.7% female; mean age 21.75 ± 2.1 years). Among 300 valid responses regarding radiology intention, 102 students (34.0%) reported considering radiology as a future specialty. In multivariable analysis, lack of exposure to radiology research (OR = 0.14, 95% CI 0.06–0.34, p","url":"https://doi.org/10.21203/rs.3.rs-9015700/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9015700/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8176801/v1","name":"Clinical Development of Molecular Residual Disease (MRD) and Multi-Cancer Early Detection (MCED) using Liquid Biopsy Multiomics with Artificial Intelligence (AI)","source":"preprints","abstract":"Abstract Background Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a few cancer types, leaving nearly half of cancers without established programs. Multi-cancer early detection (MCED) tests based on circulating tumor biomarkers have shown promise, but sensitivity for early-stage remains a challenge. In parallel, detection of molecular residual disease (MRD) using circulating tumor DNA (ctDNA) has emerged as a powerful prognostic and predictive tool, though current assays remain limited in sensitivity and specificity. This study aims to integrate multi-omics data to develop more refined and highly sensitive MCED and MRD assays. Methods This study leverages clinical information and biospecimens from patients with cancer and cancer-naïve individuals. Samples from patients with cancers will be derived from the MONSTAR-SCREEN-3 study, while those from cancer-naïve individuals will be obtained from the Tohoku Medical Megabank Project. Comprehensive analyses will include whole-genome sequencing (WGS), whole-exome sequencing (WES), whole-transcriptome sequencing (WTS), proteomics, metabolomics, and microbiome profiling using stool and saliva. Artificial intelligence (AI)-based multi-omics integration will be performed to develop novel MCED and MRD assays and to evaluate their clinical performance. The primary endpoints are sensitivity and specificity of MCED and MRD assays. Discussion This is the first large-scale study to integrate comprehensive multi-omics profiling with AI for MCED and MRD assay development. The findings are expected to advance precision oncology by improving early diagnosis and recurrence monitoring. Trial Registration UMIN000053815, approved by the Institutional Review Board of the National Cancer Center Hospital East.","url":"https://doi.org/10.21203/rs.3.rs-8176801/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8176801/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.1731.v1","name":"Artificial Intelligence in Medical Education: A Narrative Review","source":"preprints","abstract":"Artificial intelligence (AI) is rapidly transforming medical education by providing new approaches for knowledge acquisition, clinical training, and decision-making support. This narrative review synthesizes recent literature on AI applications in undergraduate, postgraduate, and continuing medical education. Key domains include adaptive learning platforms, natural language processing for educational resources, virtual simulation, automated feedback, and assessment technologies. Evidence suggests that AI improves personalization, efficiency, and objectivity in training, while also enabling innovative pedagogical models such as intelligent tutoring systems and competency-based progression. However, challenges remain in terms of data quality, faculty readiness, ethical considerations, and integration into existing curricula. This review highlights both the opportunities and limitations of AI in reshaping medical education, emphasizing the need for rigorous validation, interdisciplinary collaboration, and regulatory guidance. Future directions include hybrid AI–human teaching models, transparent algorithms, and equitable access to AI-driven education globally.","url":"https://doi.org/10.20944/preprints202509.1731.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.1731.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7724943/v1","name":"The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is changing the face of healthcare, especially in the field of diagnostic radiology, and improving the accuracy and efficiency of medical images. In Saudi Arabia, AI is revolutionizing workflows, able to advance early disease detection, as well as reduce the number of human mistakes. However, issues such as regulation and specialized training requirements persist. Purpose This research examines the AI awareness and knowledge of diagnostic radiologists in Saudi Arabia with an emphasis on AI and medical imaging. It assesses AI awareness in trainees and professionals in radiology and discusses possibilities for integrating AI into the teaching curricula. In addition to the key highlight, the study aimed at finding regional variations in perceptions about the role of AI in diagnostic radiology. Materials and Methods A retrospective methods chart review and survey was performed over the course of five months (June-September 2014), to survey 500 different radiologists and radiology residents. Data were gathered through surveys sent out through radiological societies and social media. The statistical analysis was conducted using version 26 of the Statistical Package for the Social Sciences (SPSS) with a p-value less than 0.05. Ethical approval was granted by the University of Hail. Results For example, out of 433 respondents, 80.6% were aware of the existence of AI, and 82.2% were aware of the potentially positive effects of AI for improved diagnosis. However, concerns about job displacement and machine errors were noticed. Moreover, there was a greater proportion of interest in AI education, with a total of 81.5%, radiologists in the central areas showing a higher level of knowledge in the northern region (P = 0.023). Those with more education had greater awareness of AI (P = 0.001). Conclusion AI has great potential for benefits to the field of diagnostic radiology in Saudi Arabia, with challenges regarding regulation, training, and the issue of privacy that need to be addressed. The study highlights the significance of integrating AI educational content into the medical curriculum, aligning with Saudi Vision 2030.","url":"https://doi.org/10.21203/rs.3.rs-7724943/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7724943/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202511.1342.v1","name":"Neurosymbolic AI for Safe and Trustworthy High-Stakes Applications","source":"preprints","abstract":"Artificial intelligence is increasingly deployed in high-stakes domains such as healthcare, public welfare, and autonomous transportation, where errors can cost lives or infringe on human rights. However, current AI approaches dominated by neural networks and generative models (e.g., large language models) have well-documented shortcomings: they can hallucinate false information, exhibit bias, and lack explainability. This paper argues that these limitations make purely neural AI insufficient for safety-critical applications like medical diagnostics (where misdiagnosis or unsafe advice can be deadly), public welfare decision-making (where biased algorithms have unfairly denied benefits or targeted vulnerable groups), and autonomous systems (where failures can result in fatal accidents). We then introduce neurosymbolic AI – a hybrid paradigm combining data-driven neural networks with rule-based symbolic reasoning – as a viable path toward trustworthy AI. By integrating neural perception with symbolic knowledge and logic, neurosymbolic systems can provide built-in safety guardrails, robust reasoning abilities, and transparent decision traces. We survey evidence that neurosymbolic architectures can mitigate hallucinations and bias by enforcing domain constraints (e.g. medical guidelines or legal rules), while also enhancing explainability and accountability through explicit reasoning steps. Through examples and literature (including the IEEE's “Neurosymbolic Artificial Intelligence: Why, What, and How”), we illustrate how neurosymbolic AI can bridge the gap between the accuracy of neural methods and the reliability required in life-critical environments. Diagrams comparing architectures and error mitigation strategies are provided to visualize how the neurosymbolic approach improves safety.","url":"https://doi.org/10.20944/preprints202511.1342.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202511.1342.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2026.04.26.26351778","name":"Validation of an AI-Assisted Framework for Systematic Bias Assessment in Observational Studies","source":"preprints","abstract":"Background The rapid expansion of medical literature has led to substantial variability and frequent contradictions in study findings, making it increasingly difficult to distinguish meaningful signals from noise. Much of this variability arises from differences in study methodology, where biases such as confounding, selection bias, and reverse causation can drive spurious associations. While artificial intelligence (AI)-assisted tools have been developed to support risk-of-bias assessment, most are designed for systematic reviews and are not tailored to identifying specific epidemiologic biases in observational studies. This highlights the need for structured, scalable approaches to evaluate study validity in real-world evidence. Objective To develop and validate an AI-assisted, expert-informed, rule-based framework (EpiVise) for systematically identifying and classifying key sources of bias in pharmacoepidemiologic studies, and to assess its agreement with expert evaluation. Methods We conducted a validation study using recently published pharmacoepidemiologic studies from high-impact journals (post-2025). Each study was independently assessed by the framework and two expert epidemiologists, across predefined bias domains, including measured confounding, confounding by indication, selection bias, immortal time bias, and disease latency. Agreement was evaluated using weighted kappa statistics. In the absence of a gold standard, expert judgment served as the reference benchmark. In a second phase, synthetic study scenarios with predefined embedded biases were constructed to assess the framework’s ability to detect known bias structures under controlled conditions. Results In analyses of published studies (10 studies; 60 ratings), agreement between the framework and expert assessments was substantial (κ = 0.75; 95% confidence interval [CI], 0.60–0.86), with 12 discordant ratings (20.0%), all limited to adjacent categories and occurring primarily in the confounding by indication and selection bias domains. In synthetic study scenarios (10 studies; 50 ratings), agreement was similarly substantial, with 42 of 50 ratings concordant (84%) and a weighted kappa of 0.77 (95% CI, 0.67-0.87); discordances included both adjacent-category and extreme disagreements and were concentrated in confounding by indication, selection bias, and prevalent user bias domains. Conclusions This AI-assisted, expert-informed framework, EpiVise provides a scalable and reproducible approach for evaluating epidemiologic study validity, substantial demonstrating agreement comparable to expert assessment. By systematically identifying key sources of bias, the framework has the potential to enhance the rigor and consistency of evidence evaluation, support peer review, and inform clinical, regulatory, and policy decision-making. Further validation across broader study designs and domains is warranted.","url":"https://doi.org/10.64898/2026.04.26.26351778","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.26.26351778","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.2128.v1","name":"Cutting-edge Applications of Artificial Intelligence in Medical Science, Healthcare, and Treatment: A Comprehensive and Transformative Review","source":"preprints","abstract":"AbstractBackground: This study explores the role of artificial intelligence (AI) in healthcare, basic medical sciences, and disease treatment, with a focus on strategies to enhance the accuracy and speed of diagnosis. Advances in AI technologies have significantly transformed the traditional medical environment. Diagnostic approaches based on radiology, pathology, endoscopy, ultrasound, and biochemical analyses have been improved through AI, enabling higher accuracy and reduced human workload. Objective: The objective of this review is to provide an overview of the current applications of AI in medicine and to highlight future perspectives. Methods: A comprehensive literature review was conducted using databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar. Relevant studies on AI applications in clinical and paraclinical fields were analyzed. Results: AI algorithms and deep learning tools have shown potential to support physicians in healthcare management, medical education, early disease detection, drug prescription, and paraclinical assessments. AI has also enhanced treatment processes throughout post-surgical care and recovery. Current clinical applications extend to diagnostic laboratories, endoscopy, pathology, radiology, and ultrasound, where AI contributes to improved precision and efficiency. Conclusion: AI is increasingly becoming an integral component of modern healthcare and medical sciences, offering transformative solutions for clinical practice and research. However, successful implementation requires careful consideration of its strengths, limitations, and the unresolved challenges concerning ethics and legal frameworks. Future efforts should focus on establishing guidelines that enable the safe and effective adoption of AI in medicine.","url":"https://doi.org/10.20944/preprints202509.2128.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.2128.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.10.19.25338291","name":"Development of an open-source artificial intelligence (AI) anonymizer for electrocardiogram scans","source":"preprints","abstract":"The application of artificial intelligence (AI) in the medical field has seen a significant increase in popularity, particularly for its ability to accurately detect abnormalities across a range of diagnostic tests. The effectiveness and precision of AI models are highly contingent on the quality and diversity of the training data used in their development. In the present work, we have developed an open-source AI model designed to anonymize electrocardiogram (ECG) recordings. This model achieves anonymization by automatically detecting and extracting the waveform data. This tool can be used to prepare input data that in turn serve as input variables for training AI models specifically for cardiology applications. By ensuring that patient-identifying information is removed while retaining the essential waveform data. The present model facilitates the creation of robust, privacy-preserving datasets that can enhance the training and performance of AI in cardiology.","url":"https://doi.org/10.1101/2025.10.19.25338291","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.10.19.25338291","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7327894/v1","name":"Development and Validity Testing of the Medical Student Artificial Intelligence Information Literacy Scale (MS-AIILS)","source":"preprints","abstract":"Abstract Background: To develop an artificial intelligence information literacy scale suitable for medical students and to test its reliability and validity, providing a scientifically reliable tool for assessing medical students' information literacy in artificial intelligence-related fields. Methods: Based on the Higher Education Information Literacy Competency Standards, the initial version of the scale was developed through literature review, semi-structured interviews, and Delphi expert consultation. A convenience sampling method was used to select 516 medical students from two medical colleges in Henan Province for a questionnaire survey. The final scale structure was determined through item analysis, exploratory factor analysis (n=250), and confirmatory factor analysis (n=266). Results: The final scale consists of 19 items, divided into four dimensions: information need identification, information acquisition, information evaluation, and information use. Regarding content validity, the scale's level content validity index (S-CVI) = 0.975, and the item level content validity index (I-CVI) = 0.833–1.000. Exploratory factor analysis extracted four common factors, with a cumulative variance of 70.244%; confirmatory factor analysis indicated good model fit (χ²/df = 3.31, RMSEA = 0.050, CFI = 0.971). In terms of reliability, the Cronbach's α coefficient for the total scale was 0.905; the correlation coefficients between each dimension and the total scale in the convergent validity analysis ranged from 0.722 to 0.750 ( P &lt;0.001); the correlation coefficients between the total score and each dimension of the criterion-referenced tool (AI literacy scale for college students) were 0.497–0.813 ( P &lt;0.05). Conclusion: The AI information literacy scale developed in this study has good reliability and validity and can be used to assess AI information literacy among medical students, providing a basis for subsequent educational interventions and research. Clinical trial number: not applicable","url":"https://doi.org/10.21203/rs.3.rs-7327894/v1","authors":["Yingying Miao","Yi Zhang","Chaojin Zhao","Suli Ma","Jihong Wang"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7327894/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6504320/v1","name":"Evaluation of deep learning models used in healthcare","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) is rapidly growing, and in many fields, it is anticipated that intelligent computers will either eventually replace or enhance human capabilities. AI is a general term that includes computer vision, natural language processing (NLP), machine learning (ML) models, and computers. ML is a subfield of AI that uses statistical techniques to let machines learn from their mistakes and get better over time. Deep learning, a branch of machine learning, is extensively utilized in the medical field and creates multi-layer neural networks that replicate brain activity. These networks are trained using gradient descent and backpropagation. Method. This study was a systematic review that involved a review where 23 papers were obtained from IEE, Google Scholar, Pubmed, and Science Direct. Papers spanning 2008-2025 were included. Since the Clinical Trial Number was irrelevant, it was left out of the text. Results: Convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequential data, and deep belief networks (DBNs) for MRI and 3D images are examples of common deep learning models in the healthcare industry. High costs, interpretability, and susceptibility to missing data are some of the problems that CNNs and RNNs encounter. Multimodal learning and explainable AI are key components of future solutions. DBNs use unsupervised learning to find patterns without labeled data, overcoming the constraints of backpropagation. Conclusion: In the medical field, CNNs, RNNs, and DBNs are essential and commonly used. Ongoing research attempts to improve their efficacy through multimodal learning, explainable AI, and privacy-preserving technologies, despite obstacles including computational complexity, missing data, and security issues. These models will further revolutionize healthcare by enhancing patient care, diagnostics, and disease prediction as deep learning advances. However, more research is required to determine their efficacy and cost-effectiveness in the healthcare industry.","url":"https://doi.org/10.21203/rs.3.rs-6504320/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-6504320/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7547303/v1","name":"Assessing Awareness of Ethical Utilization of Artificial Intelligence in Education Insights from Faculty Staff and Medical Students","source":"preprints","abstract":"Abstract Background: Artificial Intelligence (AI) is increasingly reshaping healthcare delivery and medical training through its capacity to simulate cognitive processes such as learning, reasoning, and problem-solving. As its applications expand, particularly in clinical decision-making and academic environments, ethical concerns surrounding AI such as data privacy, algorithmic bias, and accountability have become critical. Objectives: This study aimed to appraise awareness, educational exposure, and e ethical perspectives on AI utilization among faculty staff and Faculty of Medicine students, Zagazig University. Methods: A cross-sectional study was conducted at the Faculty of Medicine, Zagazig University, involving 327 participants (82 faculty staff and 245 students). A structured questionnaire was used to evaluate participants' understanding of AI technologies and ethical considerations. Data were analyzed using IBM SPSS version 25. Categorical variables were compared using Chi-square or Fisher’s exact test, with a significance at p Results: Medical students demonstrated significantly higher levels of AI awareness and formal educational exposure compared to faculty staff (p Conclusion: The study revealed generational and professional disparities in AI literacy and ethical readiness. While students showed greater receptiveness, faculty required targeted continuing education. Medical institutions should integrate structured AI and ethics education into medical curricula while also providing continuous professional development for faculty. This dual approach will ensure both students and educators are competent to integrate AI into healthcare practice while upholding ethical standards.","url":"https://doi.org/10.21203/rs.3.rs-7547303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7547303/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.11.24.25340702","name":"Predicting Carbapenem Resistance in Hospitalized Patients Using Machine Learning: A Retrospective Analysis of the MIMIC-III Database","source":"preprints","abstract":"Carbapenem-resistant Gram-negative bacteria (CR-GNB) represent a major health challenge due to limited therapeutic options, increased morbidity, and extended hospital stays (Ham et al., 2021). Early prediction of carbapenem resistance can optimize empirical antibiotic therapy and reduce unnecessary use of broad-spectrum antibiotics. This study developed and validated an artificial intelligence model to predict carbapenem resistance among hospitalized and ICU patients using clinical, demographic, laboratory, and antibiogram data. A retrospective analysis was performed on 94,000 hospitalized patients at Beth Israel Deaconess Medical Centre, utilizing the MIMIC-III (v1.4) database (Johnson et al., 2016). Predictor variables included were demographic characteristics, comorbidities, ICU admission, antibiotic exposure history, inflammatory and biochemical markers, and antibiotic susceptibility test results. Three artificial intelligence algorithms were evaluated: decision trees, random forests, and XGBoost. Model performance was assessed using AUC, precision, sensitivity, specificity, and confusion matrices. The XGBoost model demonstrated the highest performance, achieving an AUC of 0.95, precision of 0.98, sensitivity of 0.90, and specificity of 0.99. These results demonstrate strong discrimination ability and significant potential for integration into clinical workflows. The findings support the use of machine learning to enhance infection prevention, improve antibiotic stewardship, and inform early clinical decision-making.","url":"https://doi.org/10.1101/2025.11.24.25340702","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.11.24.25340702","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/openreseurope.21016.3","name":"How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital.","source":"preprints","abstract":"The fragmentation and decentralization of medical data, including radiological imaging, continue to challenge large-scale observational research across Europe. Artificial Intelligence (AI) applied to big datasets is transforming diagnosis and treatments towards precision medicine across many diseases, yet the lack of findable, accessible, and interoperable datasets still limits model development, validation, and final clinical translation. The European Federation for Cancer Images (EUCAIM) project was launched in 2023 to address these challenges by establishing a secure centralized and federated infrastructure for the secondary use of large-scale oncological imaging and related clinical data. By consolidating fragmented datasets, EUCAIM lays the groundwork for harmonized data governance and trusted cross-border sharing. Implementing a robust documentation framework is essential to ensure regulatory compliance, safeguard data integrity, and support secure data flows across institutional and national boundaries, fully aligned with European regulations and ethical standards. EUCAIM builds on the AI for Health Imaging (AI4HI) initiative (Predictive In-silico Multiscale Analytics to support cancer personalized diagnosis and prognosis, empowered by imaging biomarkers - PRIMAGE, Accelerating the lab to market transition of AI tools for cancer management - CHAIMELEON, Novel pan-European imaging platform for artificial intelligence advances in oncology - EuCanImage, An AI Platform integrating imaging data and models, supporting precision care through prostate cancer's continuum - ProCancer-I, A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer - INCISIVE and integrates over 94 partners and more than 180 stakeholders spanning medical imaging, high performance computing, data standardization, innovation, and legal compliance. This large collaborative ecosystem reinforces EUCAIM's role as a reference for General Data Protection Regulation (GDPR) and European Health Data Space Regulation (EHDSR) adherence. This publication presents the real-world experience of integrating imaging and clinical data from a reference university hospital into the EUCAIM infrastructure. It outlines the procedural, ethical, and legal challenges encountered, and details the strategies implemented to ensure compliance with data protection regulations, including privacy, security, and ethical standards. These insights offer a practical framework for future large-scale oncological imaging datasets harmonization and AI development, contributing to scalable, reproducible, and legally compliant research that strengthens Europe's capacity for trustworthy AI-driven oncology solutions.","url":"https://doi.org/10.12688/openreseurope.21016.3","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/openreseurope.21016.3","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202510.1269.v1","name":"Healthcare Professionals’ Interactions with Families of Hospitalized Patients Through Information Technologies: Toward the Integration of Artificial Intelligence","source":"preprints","abstract":"The integration of Information Technologies has transformed interactions between healthcare professionals and the families of hospitalized patients, enabling more comprehensive, transparent, and patient-centered care. Artificial Intelligence is emerging as a transformative tool to further enhance these interactions; however, its implementation faces challenges associated with access to and availability of basic technological infrastructure. This cross-sectional pilot study, conducted at the Tamaulipas Children&#039;s Hospital, Mexico, included 51 healthcare professionals from diverse specialties. It examined the use of digital technologies and perceptions of information systems aimed at optimizing communication with families. Findings indicated that 58.80% reported consistent use of digital devices, whereas only 41.20% had regular internet access. Between 60.00% and 67.00% consistently provided information regarding patients’ health status, treatments, and medical procedures. With respect to a digital system, 37.30% considered its implementation necessary and 39.20% perceived potential benefits, although functions such as multimedia sharing and automated notifications were regarded with caution. The questionnaire demonstrated high reliability (α = 0.835) and acceptable construct validity (KMO = 0.705; Bartlett’s test p lt; 0.001). These results underscore both the feasibility and the challenges of integrating AI-enabled digital systems into hospital settings, highlighting the critical importance of equitable access to technological infrastructure to ensure sustainable adoption.","url":"https://doi.org/10.20944/preprints202510.1269.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.1269.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/f1000research.169587.1","name":"Artificial Intelligence in Diagnosing and Treating Temporomandibular Joint Disorders: A Review","source":"preprints","abstract":"Introduction: With the rapid advancement in artificial intelligence (AI), its integration into medical fields has gained momentum. AI is increasingly applied for diagnosing and managing temporomandibular joint disorders (TMD), affecting one of the most complex joints in the body. This review aims to highlight the AI-driven approaches in diagnosing and treating TMD. Methods A comprehensive literature search conducted using PubMed, Web of Science, and Google Scholar, focusing on English-language studies published since 2007. References from relevant articles were also examined. Results A total of 21 studies met the inclusion criteria, covering diverse AI applications in TMD. These studies included AI-driven medical image segmentation (3), juvenile idiopathic arthritis (3), temporomandibular joint osteoarthritis (TMJOA) detection (4), Temporomandibular joint (TMJ) articular disc displacements and deformities (5), decision support systems (5), and AI-based sound analysis for TMJ assessment (1). The findings suggest that AI, particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated promising accuracy in detecting and classifying TMD and its related pathologies. AI automates segmentation, improves diagnostic consistency, and assists clinicians through predictive modeling. Discussion Despite advancements, challenges remain, as many studies rely on small datasets, limiting generalizability. The absence of standardized evaluation metrics and external validation hinders clinical adoption. Ethical concerns, data privacy issues, and workflow integration pose additional barriers. Future research should focus on larger datasets, model interpretability, and rigorous clinical validation. Conclusion AI is emerging as a valuable tool in TMD diagnosis and management, promising improved accuracy and efficiency, but further development is essential for real-world implementation.","url":"https://doi.org/10.12688/f1000research.169587.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.169587.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.06.08.26354989","name":"Utilising Artificial Intelligence to Identify Ventricular Tachycardia Ablation Targets in Sinus Rhythm","source":"preprints","abstract":"Background and Aims Machine learning has shown potential in predicting ablation targets for ventricular tachycardia (VT) in an animal model. This study progresses to externally validating deep learning approaches for human data. Methods The development and external validation dataset included 21 and 13 patients, respectively, with structural VT undergoing catheter ablation. In the development datasets, electrophysiological studies were conducted using the Advisor TM HD grid (Ensite TM X), while both CARTO and Ensite Precision were used in the validation dataset. In each patient, VT ablation targets were defined as mapping points within 8 mm of VT isthmuses. Three advanced machine learning models were trained using cardiac mapping data acquired in both omnipolar and unipolar configurations during sinus rhythm and ventricular pacing. Discrimination was evaluated using nested leave-one-out cross-validation at patient level. Results Overall, graph convolutional networks (GCNs), which integrate intracardiac signal waveforms with three-dimensional electroanatomical geometries, achieved the highest performance, with optimal results obtained from unipolar electrograms acquired in sinus rhythm (median AUC 0.793, sensitivity 83.6%, specificity 69.0%). This may be partly explained by the inclusion of repolarization dynamics in unipolar electrograms and the higher point density of sinus rhythm maps. Comparable performance was observed in the external dataset. Conclusion This study demonstrates that graph convolutional networks applied to sinus rhythm EGM waveforms collected during substrate mapping can localise critical components of VT re-entry circuits. This approach has potential to provide fast and accurate ablation guidance without the need to induce and map VT, improving safety and efficacy of VT catheter ablation. Structured Graphical Abstract Utilising artificial intelligence approaches to identify ventricular tachycardia ablation targets through electrophysiological maps in sinus rhythm.","url":"https://doi.org/10.64898/2026.06.08.26354989","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.08.26354989","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.176055853.39564234/v1","name":"Multimodal Artificial Intelligence Agents in Healthcare: A Scoping Review","source":"preprints","abstract":"Intelligent agent systems are gaining attention in healthcare, yet unimodal designs constrain their ability to process the heterogeneous multimodal data required for complex clinical tasks. Multimodal artificial intelligence (AI) agent systems have recently emerged as a promising paradigm that integrates diverse data, leverages large foundation models (FMs), and coordinates multiple agents and tools. However, their applications, challenges, and future directions remain to be systematically synthesized. This review addresses this gap through a scoping review of recent 37 studies spanning four major clinical applications: clinical decision support, clinical documentation and report generation, clinical monitoring and health management, and medical education and training. We analyze modality distributions and fusion strategies, FM utilization and agent architectures, tool integration, and key agent capabilities. Evaluation practices are examined across multiple dimensions including effectiveness, efficiency, robustness, fairness, explainability, safety, and usability. Despite notable progress, current systems continue to face critical limitations. We outline future research directions to advance further development, evaluation, and clinical translation of multimodal AI agent systems in healthcare.","url":"https://doi.org/10.22541/au.176055853.39564234/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176055853.39564234/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.12688/f1000research.166372.2","name":"New Horizons in Higher Education: Examining the Mental Well-Being of Medical & Health Sciences Students Through the Use of Artificial Intelligence Based Chatbot Platforms in the United Arab Emirates – A Cross-Sectional Comparative Study","source":"preprints","abstract":"Background Barriers to mental-health care include limited resources and workforce, access constraints, and stigma. Artificial-intelligence (AI)-enabled chatbots may offer low-threshold support. Methods Cross-sectional correlational (comparative) study in one private health-sciences university in the UAE. Proportional stratified random sampling across four colleges yielded n = 298 undergraduates. Instruments: (i) Socio-Demographic Questionnaire; (ii) researcher-developed AI Chatbot Usability questionnaire (content validated by a bilingual specialist); and (iii) Depression Anxiety Stress Scale-21 (DASS-21; established reliability/validity). Questionnaires were administered face-to-face with standardized instructions. Results 206/298 (69.1%) had ever used an AI chatbot; most used Snapchat AI (76.9%), followed by ChatGPT/Bard (23.4% each). Overall, 57.0% had moderate-to-extremely-severe depression, 68.5% anxiety, and 33.6% stress. Users had higher odds of moderate-to-extremely-severe anxiety and depression than non-users. In multivariable models, higher depression (OR = 1.022; 95% CI 1.01-1.085; p p Conclusion Among UAE health-sciences students, AI-chatbot use is common and associated with higher depression/anxiety severity; this likely reflects help-seeking rather than causation. Universities should integrate early, stigma-sensitive supports, potentially including regulated, evidence-based chatbot tools-within stepped-care services.","url":"https://doi.org/10.12688/f1000research.166372.2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.166372.2","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7954420/v1","name":"Early Lung Cancer Diagnosis Using a Hybrid CNN–BiLSTM Deep Learning Model: A Step Toward Precision Medicine","source":"preprints","abstract":"Abstract Lung cancer is regarded as one of the most lethal malignancies, with a 5-year survival rate that positions it among the three most fatal cancers worldwide. Successfully addressing lung cancer necessitates early identification for prompt tailored therapies. Nonetheless, guaranteeing early detection presents a significant difficulty, leading to the development of novel strategies. The advent of artificial intelligence(AI) provides groundbreaking options for lung cancer prediction. The necessity to improve artificial intelligence models remains a priority, especially in precision medicine, signifying a fundamental shift in healthcare. This article presents a hybrid deep learning methodology for lung cancer diagnosis utilizing patient medical records, combining Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) and Attention Networks. A comparative analysis utilizing the MIMIC IV dataset demonstrates the model's superiority, attaining a Matthews correlation coefficient (MCC) of 96.2% and an accuracy of 98.1%, surpassing LSTM and BioBERT, which achieved MCCs of 93.5% and 95.5% with corresponding accuracies of 97.0% and 98.0%, respectively. Our model greatly surpasses the compared models, demonstrating its exceptional performance and potential influence in the sector. This discovery represents a substantial advancement in the accurate and early identification of lung cancer, highlighting the continued need for the refining of AI models in precision medicine.","url":"https://doi.org/10.21203/rs.3.rs-7954420/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7954420/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8986902/v1","name":"CyberALS, a phenomenon that neurologists should worry about?","source":"preprints","abstract":"Abstract Objective: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease that generates significant fear in both patients and the general population. In recent years, widespread access to artificial intelligence (AI)–driven health information tools—such as symptom checkers, large language models, and automated risk interpretation platforms—has transformed how individuals seek medical knowledge. While these tools offer educational benefits, they may also contribute to heightened ALS anxiety- CyberALS -the term we invented for this matter. Our objective is to explore the phenomenon of AI-driven anxiety related to ALS in non-ALS patients. Examining how AI-mediated health information influences symptom interpretation and what factors contribute to a higher risk of developing anxiety towards ALS. Methods: Between 2021 and 2025, 582 consecutive patients presenting with neuromuscular complaints were referred to the ALS clinics at the First University Clinic of TSMU and Medcenter Batumi with fear of having ALS. Following comprehensive neurological examination and appropriate longitudinal diagnostic investigations, 220 individuals were determined to have benign symptoms without evidence of neuromuscular disease and were included in the analysis. Participants were stratified according to self-reported use of AI-based symptom checkers or generative AI platforms: 143 patients reported repeated AI exposure, while 77 patients with comparable clinical presentations had not used AI tools. Demographic variables (age, sex, educational level) and personal experience with neurological illness were recorded. Anxiety severity was assessed in all participants using the Hamilton Anxiety Rating Scale (HAM-A), and anxiety levels were compared between AI users and non-users. Statistical analysis was performed using binary logistic regression models, separately for AI users and non-AI users. Statistical significance was defined as p Results: Among 220 patients evaluated (2021–2025), the majority presented with diffuse fasciculations and subjective weakness without objective neurological deficits. No patient fulfilled clinical or electrophysiological criteria for motor neuron disease. Anxiety assessment revealed elevated levels across the AI using cohort (N143), with mean HAM-A scores in the moderate-to-severe (24–30) range. Higher anxiety scores were significantly more frequent among younger patients (age 22–28) and those with higher educational attainment. Comparative analysis reveals a statistically significant increase in anxiety levels among patients who utilized AI platforms, relative to a control group (N77) that abstained from AI-assisted self-diagnosis. Personal or familial history of neurological illness further amplified anxiety severity and disease-related fear. Conclusion: This study demonstrates that exposure to AI chatbots may contribute to clinically significant health anxiety and persistent fear of ALS in patients presenting with benign neuromuscular symptoms. Despite the absence of objective evidence for motor neuron disease, elevated anxiety levels were universal and frequently disproportionate. Addressing cyberALS is essential to ensure that digital health technologies support, rather than undermine, psychological well-being.","url":"https://doi.org/10.21203/rs.3.rs-8986902/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8986902/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7225629/v1","name":"AI-Driven Innovations in NCD Management: Challenges and Solutions Based on Expert Perspectives in Iran’s Health System","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) is widely regarded as a transformative technology in healthcare, particularly in the prevention of non-communicable diseases (NCDs). However, in developing countries like Iran, little is known about the readiness of health systems to effectively integrate AI. This study aimed to examine the challenges and benefits of AI implementation in NCD prevention from the perspective of Iranian health experts. Methods: This qualitative study employed conventional content analysis following the approach proposed by Graneheim and Lundman. Data were collected through semi-structured interviews with 34 experts specializing in medicine, health system management, medical ethics, and information technology. Participants were purposively selected from Type 1, 2, and 3 medical universities across Iran between March 2025 and July 2025. Data analysis was conducted using MAXQDA software (version 20). Findings: The study identified seven main themes and 30 sub-themes through content analysis, categorized into two key domains: (1) the advantages and opportunities of using artificial intelligence (AI) in non-communicable disease (NCD) prevention, and (2) the challenges and barriers to its implementation in Iran’s health system. Key benefits included enhanced primary care effectiveness, personalized interventions, resource optimization , and improved data-driven decision-making . Conversely, major barriers encompassed inadequate technological infrastructure , poorly structured data , ethical and legal concerns , cultural resistance , and a workforce unprepared for adopting new technologies . Conclusion: The effective and sustainable integration of artificial intelligence (AI) into Iran’s health system for non-communicable disease (NCD) prevention requires four key enablers : (1) robust technological infrastructure, (2) enhanced human capital, (3) well-defined legal and ethical frameworks, and (4) an adaptive organizational culture. Only by addressing these prerequisites can AI transition from a theoretical potential to a practical tool for policymaking and preventive interventions.","url":"https://doi.org/10.21203/rs.3.rs-7225629/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7225629/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7313375/v1","name":"Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, enabling personalized treatments, and improving patient outcomes. Medical students, as future healthcare providers and primary AI users, require adequate knowledge and readiness to integrate AI effectively in clinical practice. Despite growing global interest, little is known about the preparedness of medical students in Iran to adopt AI technologies. Methods A descriptive cross-sectional survey was conducted among 321 students from medicine, dentistry, and pharmacy programs at Ahvaz Jundishapur University of Medical Sciences during the 2024–2025 academic year. Data were collected via a validated 22-item AI readiness scale covering four domains: cognition, competency, vision, and ethics. Descriptive and inferential statistics, including one-sample t-tests and Wilcoxon signed-rank tests, were applied based on data distribution. Correlation analyses explored relationships among readiness components. Result Participants demonstrated moderate cognitive readiness (mean = 3.03), indicating an average theoretical understanding of AI. Competency in AI application scored significantly above average (mean = 3.44, p Conclusion Medical students in this cohort demonstrate encouraging readiness to engage with AI, particularly in practical and ethical domains; however, foundational knowledge and technical literacy need to be strengthened. The findings underscore the urgent need to integrate interdisciplinary AI education, hands-on training, and legal-ethical instruction into medical curricula. These initiatives are essential to prepare future healthcare professionals for effective and responsible AI integration, ultimately enhancing the quality of patient care.","url":"https://doi.org/10.21203/rs.3.rs-7313375/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7313375/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.11.17.25340323","name":"Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults","source":"preprints","abstract":"ABSTRACT Importance Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted screening during clinical implementation is needed to determine clinical and economic performance. Objective To assess whether an AI-based screening program with the Substance Misuse Algorithm for Referral to Treatment Using Artificial Intelligence (SMART-AI) maintained delivery of addiction-related services compared with manual screening and to evaluate readmissions and costs. Design, Setting, and Participants Prospective, quasi-experimental pre–post study at a large academic medical center in Chicago, Illinois between 2022 and 2025. The pre-implementation period (manual screening) included 31,432 hospitalizations, and the post-implementation period with AI augmentation (SMART-AI) included 33,564. Interventions/Exposures During the post-implementation period, SMART-AI screened clinical documentation within 24 hours of admission to identify patients at-risk for a substance use disorder and notified the Substance Use Intervention Team. In the pre-implementation period, screening relied on manual processes, with nurses and social workers screening with standardized questionnaires. Main Outcomes and Measures The primary outcome was receipt of ≥1 addiction-related service (initiation or adjustment of medication for alcohol or opioid use disorder; brief intervention/motivational interviewing; naloxone dispensing; or a completed addiction medicine consultation). The prespecified noninferiority margin was −0.5 percentage points (1-sided α = 0.025). Secondary outcomes included 6-month readmission, discharge against medical advice, and program costs. Results Addiction-related services were received in 1,189 of 31,432 hospitalizations (3.8%) during manual screening and 1,144 of 33,564 (3.4%) during SMART-AI (difference, −0.4 percentage points; 95% CI, −0.7 to −0.1; P = 0.20). The lower limit of the confidence interval was below the noninferiority margin, so noninferiority was not achieved. Six-month readmissions across all hospitalizations occurred in 9,586 patients (30.5%) in the manual period and 10,244 patients (30.5%) in the SMART-AI period (P = 0.95), and discharge against medical advice did not differ (1.3% v. 1.1%). Among patients who received a SUIT intervention (n = 2,296), 6-month readmission occurred in 41.3% (485/1,175) during usual care versus 37.0% (415/1,121) during SMART-AI (odds ratio: 0.86, 95% CI: 0.73-1.03, p=0.10). Program costs over a 1-year period were $6,166.71 lower after SMART-AI automation. Conclusions and Relevance AI-assisted screening did not meet the prespecified non-inferiority criterion for maintaining service delivery, but it was associated with maintaining secondary outcomes among patients screened for substance use disorder with lower program costs. Findings support the feasibility and potential value of automated screening at scale. Trial Registration ClinicalTrials.gov Identifier NCT03833804","url":"https://doi.org/10.1101/2025.11.17.25340323","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.11.17.25340323","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8147049/v1","name":"KOM: A Multi-Agent Artificial Intelligence System for Precision Management of Knee Osteoarthritis (KOA)","source":"preprints","abstract":"Abstract Knee osteoarthritis (KOA) affects more than 600 million individuals globally and is associated with significant pain, functional impairment, and disability. While personalized multidisciplinary interventions have the potential to slow disease progression and enhance quality of life, they typically require substantial medical resources and expertise, making them difficult to implement in resource-limited settings. To address this challenge, we developed KOM, a multi-agent system designed to automate KOA evaluation, risk prediction, and treatment prescription. This system assists clinicians in performing essential tasks across the KOA care pathway and supports the generation of tailored management plans based on individual patient profiles, disease status, risk factors, and contraindications. In benchmark experiments, KOM demonstrated superior performance compared to several general-purpose large language models in imaging analysis and prescription generation. A randomized three-arm simulation study further revealed that collaboration between KOM and clinicians reduced total diagnostic and planning time by 38.5% and resulted in improved treatment quality compared to each approach used independently. These findings indicate that KOM could help facilitate automated KOA management and, when integrated into clinical workflows, has the potential to enhance care efficiency. The modular architecture of KOM may also offer valuable insights for developing AI-assisted management systems for other chronic conditions.","url":"https://doi.org/10.21203/rs.3.rs-8147049/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8147049/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.10.31.25339216","name":"Prevalence and Predictors of Silent Vertebral Compression Fractures: A Cross-Sectional Population-Based Study Using UK Biobank Imaging Data","source":"preprints","abstract":"Abstract Objectives: To estimate the prevalence of silent vertebral compression fractures (VCF) in an asymptomatic population and to assess the demographic and clinical predictors using data from the UK Biobank. Design: A cross-sectional study using artificial intelligence-assisted six-point morphometry of dual-energy x-ray absorptiometry spine images. Setting: UK Biobank imaging study at Stockport clinical research facility. Participants: 2,446 asymptomatic volunteers aged 40 to 79 years with no history of spinal trauma or vertebral fracture. Main outcome measures: VCFs were defined as greater than or equal to 20 percent height loss, measured by artificial intelligence, with manual correction of annotations and confirmation by a musculoskeletal radiologist. Association with age, sex, bone mineral density, body mass index, and back pain were analyzed. Results: Of 2,446 participants, 763 (31.1 percent) had at least one vertebra with greater than 20 percent height loss. Fracture prevalence increased with age: 26.8 percent (37 of 138) in those aged 40-49 years, 26.1 percent (196 of 752) at 50-59 years, 33.0 percent (380 of 1,153) at 60-69 years, and 37.2 percent (150 of 403) at 70-79 years. Excluding mild deformities, 18 percent of those aged 40-49 years had moderate or severe fractures (greater than 25 percent height loss). Fractures were 11.5 percent more common in men than in women. Age was a significant predictor, but bone mineral density (BMD) and body mass index (BMI) were not. Predictive models showed limited performance (sensitivity 44.4 percent, specificity 68.2 percent). Interobserver agreement was substantial (Fleiss kappa = 0.74). Conclusions: Silent VCFs are frequent by midlife, including among younger adults with normal bone density. Men are affected more often than women, and conventional risk factors inadequately identify those at risk. Earlier detection through opportunistic or population imaging may enable timely intervention and inform future screening and fracture-prevention strategies.","url":"https://doi.org/10.1101/2025.10.31.25339216","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.10.31.25339216","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.06.02.26354672","name":"Leveraging Digitization, Archiving and Artificial Intelligence to Re-examine Predictors of Sustained Mental Health Care Engagement in Ugandan First-Episode Psychosis Patients: A Study Protocol","source":"preprints","abstract":"Background We previously examined the burden and predictors of sustained mental health care engagement in Ugandan first episode psychosis patients by retrospective chart review methods. However, the extensive requirements of chart reviews meant that we could only extract data from a random 10% sample of 1,677 newly enrolled Ugandan first episode psychosis patients at Butabika National Referral Mental Hospital in 2018. The Hekima Platform has been designed to transform handwritten files into datasets for analysis. Objectives This study aims to: (1) utilize the Hekima Platform to transform paper-based clinical charts of all 1,677 Ugandan psychosis patients enrolled at Butabika Hospital for the first time in 2018 into a standardized, anonymized longitudinal database; and (2) re-examine predictors of sustained MHC engagement in this cohort. Methods We will digitize and archive all patient charts. We will then use the Hekima Platform to extract handwritten clinical data into machine-readable text using user-trained machine learning and deep learning models and natural language processing (NLP) techniques to generate a structured, anonymized database. A minimum 10% random sample of extracted data will be manually validated using Cohen’s kappa. For the analytical aim, descriptive statistics, bivariate analysis, and multivariable logistic regression will model predictors of sustained engagement, with exploratory machine learning approaches used as a complementary analytical strategy. Ethical approval has been obtained from the Uganda National Council for Science and Technology and Butabika Hospital’s Research Ethics Committee. Expected outcomes Patient clinical charts are a rich data source but there are extensive requirements to be able to use them for research. This study will generate the first AI-assisted, standardized longitudinal database from handwritten psychiatric records in Uganda, enabling well-powered analyses of predictors of MHC engagement. Findings will inform targeted interventions to improve retention in care and will offer a scalable model for mental health research in low- and middle-income countries.","url":"https://doi.org/10.64898/2026.06.02.26354672","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.02.26354672","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.22541/au.175575799.91441108/v1","name":"Challenges of artificial intelligence in medical diagnosis in congolese hospitals: A literature review","source":"preprints","abstract":"Introduction: Artificial intelligence (AI) is rapidly transforming medical diagnosis worldwide, but its adoption remains limited in Africa, particularly in the DRC. This narrative review aims to analyze the contributions, challenges, and prospects for integrating AI into medical diagnosis in the Democratic Republic of Congo. Methodology : A comprehensive literature review was conducted in February 2025 in PubMed, Web of Science, Scopus, and Google Scholar databases, as well as reports from international organizations. Studies on the use of AI in medical diagnosis in resource-limited countries, particularly in Africa, were included without language restrictions. The selection followed a two-step process (title/abstract then full text); 13 articles were retained for qualitative synthesis. Results : Studies show that AI enables a 12-15% improvement in diagnostic accuracy in radiology and a 20% reduction in exam interpretation time. It also helps accelerate epidemic detection (30-50% faster than conventional methods) and overcome the shortage of specialists in rural areas. However, its implementation in the DRC is hampered by the lack of digital infrastructure, insufficient training, and the absence of an appropriate regulatory framework. Maintenance and financing issues still limit the effective use of available systems. Conclusion : AI represents a major opportunity to strengthen medical diagnosis in the DRC, improving the speed and quality of care. However, effective integration requires targeted investments in infrastructure, training, and regulation. The development of national pilot projects and a solid ethical framework are essential steps for gradual and sustainable adoption.","url":"https://doi.org/10.22541/au.175575799.91441108/v1","authors":["Guy-Théodore MUAMBA","Christian TAGUE","Edouard MBAYA MUNIANJI","Virginie MUJINGA KATUMBA","Criss KOBA MJUMBE"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.175575799.91441108/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.10.14.25337964","name":"Effect of a mobile health intervention optimised with artificial intelligence on blood pressure and other cardiovascular risk factors in adults with high blood pressure: Rationale and design of My Intelligent Cardiac Assistant (MICArdiac) Randomised Controlled Trial","source":"preprints","abstract":"ABSTRACT Introduction Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to lower BP. Integrating these to personalise supportive messaging in response to changing BP and activity through leveraging artificial intelligence (AI) could improve BP control. The aim of this trial is to examine the impact on BP, compared to a text-message only program, of the My Intelligent Cardiac Assistant (MICArdiac) program. MICArdiac is a 6-month program comprising tracking of BP, physical activity and heart rate informing a content stream of AI-driven personalised messages to encourage BP self-management, as-necessary medical review to up-titrate medicines and cardiovascular preventative behavioural change. Methods and analysis MICArdiac is a prospective randomized open-blinded endpoint randomised controlled trial with 1:1 (intervention:control) allocation and active control. Individuals aged 35 years old or above with high BP are randomised to the MICArdiac program or to a text-message cardiovascular education program. Recruitment started in primary care and hospital clinics in Western Sydney and moved to decentralised direct-to-community recruitment in the Australian Capital Territory, New South Wales, and Victoria. Randomisation and allocation concealment occur via a secure web-based system. The primary outcome is mean daytime systolic BP measured by 24-hour Ambulatory Blood Pressure Monitoring at 6 months. Primary analysis will follow intention-to-treat principles; data analysts will be blinded. Process evaluation will be conducted. The study has recruited 451 participants, giving it 94% power to detect a difference of 4 mmHg in the primary outcome. Ethics Ethics approval was obtained from Western Sydney Local Health District Human Ethics Research Committee (2021/ETH11379). Clinical trials registration number Australian New Zealand Clinical Trials Registry (Registration number: ACTRN12622000091707).","url":"https://doi.org/10.1101/2025.10.14.25337964","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.10.14.25337964","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2025.11.29.25341091","name":"The accuracy and repeatability of OpenEvidence on complex medical subspecialty scenarios: a pilot study","source":"preprints","abstract":"OpenEvidence is a popular artificial intelligence (AI) based medical search engine that generates evidence-based answers. It includes a quick search engine method (OE) that takes only seconds to respond, along with a limited number of references. In mid-2025, the platform introduced “Deep Consult” (DC), which takes several minutes to respond and provides more comprehensive answers with additional references. OpenEvidence scored 100% on USMLE-type multiple-choice questions, but it has not been tested on more complex medical scenarios. We tested the OE and DC models using questions primarily derived from medical specialty board exams, specifically, the MedXpertQA dataset. In a prior published study, this dataset was evaluated with eleven large language models (LLMs), and the results indicated poor accuracy (14-46%) for all LLMs. We evaluated the performance of OpenEvidence on a sample of the MedXpertQA dataset, comprising 100 medical subspecialty scenarios and using two independent evaluators. The highest accuracy for DC was 41%, and for OE, 34%. Repeatability testing revealed an evaluator concordance rate of 77% for OE and 72% for DC.","url":"https://doi.org/10.64898/2025.11.29.25341091","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.11.29.25341091","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-9521303/v1","name":"Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping review","source":"preprints","abstract":"Abstract Machine learning approaches, including deep learning, are increasingly applied in healthcare, particularly for developing models that support clinical prediction and decision-making. In this context, acute respiratory infections represent a critical clinical area where timely and accurate risk stratification is essential, particularly in the emergency department (ED). This scoping review aims to systematically map and describe how machine learning approaches have been used to predict clinical outcomes in patients presenting with acute respiratory infections to the ED. We searched five databases (PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library) from inception up to July 9th, 2025, and included 52 studies. Most studies were retrospective in design (87%) and were published after 2020 (88%). Three-quarters focused on COVID-19, and the majority included adults only (69%). The largest share of studies (38%) used data originating from the United States. While most studies reported either sex or gender (88%), none reported both, and over a third (37%) used these two distinct constructs interchangeably. Race and/or ethnicity were reported in only 29% of the studies. Mortality was the most frequently predicted outcome (40%). Machine learning methods most commonly used were random forests (40%) and extreme gradient boosting (29%). Deep learning approaches were also commonly used, particularly convolutional neural networks (31%). In most studies (90%), prediction models relied on laboratory or radiological data, which are unavailable at initial triage and are typically obtained later in the ED pathway. Although machine learning-based models showed adequate performance overall, only a few studies compared them to clinical experts or traditional decision tools (21%), and/or performed external validation (13%). Overall, the models reviewed here have limited clinical utility and generalizability. Future studies should broaden the scope beyond COVID-19 to include other acute respiratory infections, develop models with data that are readily available at triage, and incorporate more diverse populations to enhance inclusivity and fairness.","url":"https://doi.org/10.21203/rs.3.rs-9521303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9521303/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.08.26.25334477","name":"Regulating Flexibility for Artificial Intelligence FDA Experience with Predetermined Change Control Plans","source":"preprints","abstract":"ABSTRACT Importance Predetermined Change Control Plans (PCCPs) are a recent regulatory innovation by the U.S. Food and Drug Administration (FDA) introduced to enable dynamic oversight of artificial intelligence and machine learning (AI/ML)-enabled medical devices. Objective To characterize FDA program of PCCPs among AI/ML-enabled medical devices, including device characteristics, preapproval testing, planned modifications, and post-clearance update mechanisms. Design This cross-sectional study reviewed FDA-cleared or approved AI/ML-enabled medical devices with authorized PCCPs. Setting AI/ML-enabled devices approved or cleared prior to May 30, 2025 were identified from an FDA-maintained public list and their characteristics extracted from FDA approval databases. Participants N/A Main Outcome(s) and Measure(s) Primary outcomes included (1) prevalence and characteristics of devices with authorized PCCPs, (2) types of FDA-authorized modifications, (3) presence and nature of preapproval testing, such as study design and subgroup testing, and (4) postmarket device update mechanisms and transparency. Results Among 26 identified AI/ML-enabled medical devices with authorized PCCPs, 92% were cleared via the 510(k) pathway, and all were classified as moderate risk. Devices were primarily intended for use in diagnosis or clinical assessment, and six had consumer-facing components. Authorized modifications spanned the product lifecycle, most commonly allowing model retraining (69% of devices), logic updates (42% of devices), and expansion of input sources (35% of devices). Preapproval testing was limited with seven devices prospectively evaluated and thirteen undergoing human factors testing. Subgroup analyses were reported for eleven devices and none included patient outcomes data. No postmarket studies or recalls were identified. User manuals could be identified online for 54% of devices, though many lacked performance details or mentioned PCCPs. Conclusions and Relevance FDA authorization of PCCPs grants manufacturers substantial flexibility to modify AI/ML-enabled devices postmarket, while preapproval testing and postmarket transparency are limited. These findings highlight the need for strengthened oversight mechanisms to ensure ongoing safety and effectiveness of rapidly evolving AI/ML-enabled technologies in clinical care.","url":"https://doi.org/10.1101/2025.08.26.25334477","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.08.26.25334477","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202512.2393.v1","name":"A Survey of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions","source":"preprints","abstract":"Medical artificial intelligence (AI) systems depend heavily on high-quality data representations to enable accurate prediction, diagnosis, and clinical decision-making. Yet, the availability of large, well-annotated medical datasets is often limited by cost, privacy concerns, and the need for expert labeling, motivating increased interest in self-supervised representation learning approaches. Among these, contrastive learning has emerged as one of the most influential paradigms, driving significant progress in representation learning across computer vision and natural language processing. This paper presents a comprehensive review of contrastive learning in medical AI, highlighting its theoretical foundations, methodological advances, and practical applications in medical imaging, electronic health records (EHRs), physiological signal analysis, and genomics. Furthermore, the study identifies common challenges such as pair construction, augmentation sensitivity, and evaluation inconsistencies, while discussing emerging trends including multimodal alignment, federated learning, and privacy-preserving frameworks. Through a synthesis of current developments and open research directions, this paper offers insights that advance data-efficient, reliable, and generalizable medical AI systems.","url":"https://doi.org/10.20944/preprints202512.2393.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202512.2393.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.09.04.674228","name":"Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images","source":"preprints","abstract":"Spatial transcriptomic technologies enable high-throughput quantification of gene expression at specific locations across tissue sections, facilitating insights into the spatial organization of biological processes. However, high costs associated with these technologies have motivated the development of deep learning methods to predict spatial gene expression from inexpensive hematoxylin and eosin-stained histology images. While most efforts have focused on modifying model architectures to boost predictive performance, the influence of training data quality remains largely unexplored. Here, we investigate how variation in molecular and image data quality stemming from differences in spatial transcriptomic technologies impact deep learning-based gene expression prediction from histology images. To identify the aspects of data quality that impact predictive performance, we conducted in silico ablation experiments, which showed that increased sparsity and noise in molecular data degraded predictive performance, while in silico rescue experiments via imputation provided only limited improvements that failed to generalize beyond the test set. Likewise, reduced image resolution can degrade predictive performance and further impacts model interpretability. We further demonstrate that these data quality-driven effects are reproducible across multiple spatial transcriptomics datasets and remain consistent when using alternative feature extractors and model architectures. Overall, our results show how improving data quality provides an orthogonal strategy to tuning model architecture in spatial transcriptomics-based predictive modeling, highlighting the need to account for technology-specific limitations that directly impact data quality when developing predictive methodologies.","url":"https://doi.org/10.1101/2025.09.04.674228","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.09.04.674228","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7768386/v1","name":"The use of Artificial Intelligence in Diagnosis of Thymic Cancer - Systematic review and Meta Analysis","source":"preprints","abstract":"Abstract Background Thymomas are rare mediastinal tumors with broad clinical spectrum, making accurate diagnosis pivotal for treatment planning and prognostication. Conventional imaging and histopathological evaluation persist as the gold standard, however, recent advances in artificial intelligence (AI) have introduced innovative methodologies to enhance diagnostic precision, reproducibility, and efficiency. This systematic review aimed to evaluate the current evidence on the application of AI-based methods in the diagnosis of thymoma. Methods A systematic search was conducted across Pubmed, Web of science, Embase, Scopus, BioRxiv, IEEE Xplore, Digital Library ACM. Eligible studies included original research that investigated AI techniques—such as machine learning, deep learning, or radiomics—for diagnosing or classifying thymoma, based on imaging, pathology, or multimodal data. Data were extracted on AI methodology, diagnostic performance metrics, number of participants, and country of origin. Methodological quality was assessed using APPRAISE-AI. Results 26 studies met inclusion criteria. AI models outperformed radiologists and pathologists in all comparisons, although in some metric models were significantly better than medical professionals. For all outcomes, the top-performing models achieved an Area Under the Curve (AUC) close to 0.95, while mean performance values were comparatively lower. Conclusion AI models typically exhibit diagnostic performance equivalent to radiologists, showing incremental advantages in selected applications. The most favorable outcomes have been observed in differential diagnosis, followed by pathology and risk stratification, with deep learning demonstrating particular effectiveness in pathology. Nevertheless, further investigations incorporating diverse imaging modalities, deep learning approaches, and strategies aimed at augmenting medical professionals’ performance are still required.","url":"https://doi.org/10.21203/rs.3.rs-7768386/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7768386/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8415145/v1","name":"A Lightweight, CPU-Deployable, and Interpretable ECG Arrhythmia Classification Pipeline Using the MIT-BIH Arrhythmia Database","source":"preprints","abstract":"Abstract Electrocardiogram (ECG) arrhythmia classification is a foundational task in medical artificial intelligence, yet many high-performing deep learning approaches require GPU resources and may be difficult to interpret in clinical settings. This study presents a lightweight, CPU-deployable, and interpretable pipeline for heartbeat-level arrhythmia classification using the MIT-BIH Arrhythmia Database. ECG beats were segmented around annotated R-peaks and mapped into five AAMI-style aggregated classes (N, S, V, F, Q). We extracted compact time-domain statistics and frequency-domain energy features, then trained and compared three classical machine learning models: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). Using a record-wise split to reduce data leakage risk, RF achieved the highest accuracy (0.864), while LR provided the strongest one-vs-rest ROC-AUC (0.806). Class-wise ROC curves and feature-importance analysis suggested that spectral energy and amplitude-related statistics contributed substantially to discrimination. Overall, the results demonstrate that interpretable, resource-efficient ECG classification remains feasible without deep networks, supporting practical deployment in CPU-only environments and rapid prototyping of medical AI systems.","url":"https://doi.org/10.21203/rs.3.rs-8415145/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8415145/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/f1000research.173421.1","name":"Ten Tips for AI‑Assisted Key Feature Problems: A Validity‑Informed Guide for Medical Education","source":"preprints","abstract":"Generative artificial intelligence (AI) can augment educators’ capacity to design high-quality Key Feature Problems (KFPs) for valid assessment of clinical reasoning and decision-making. This practice-oriented guide presents ten evidence-informed tips for using AI to develop KFPs that are aligned with learning outcomes, cognitively demanding, and contextually authentic. Drawing on the KFP literature and contemporary validity frameworks (content, cognitive and response processes, internal structure, and consequences), we synthesize practical strategies for translating outcomes into key features, constructing realistic vignettes, creating parallel case variants, targeting higher-order thinking, ensuring curricular alignment and learner-level appropriateness, diversifying complementary item formats, validating AI-assisted items through a stepwise workflow, delivering decision-specific feedback, iterating from learner performance data, and safeguarding equity, ethics, and governance. We illustrate these recommendations with concise examples and an adapted validation workflow that supports both formative and summative applications. Although AI can accelerate scenario construction and feedback drafting, human expertise remains essential to verify clinical accuracy, prevent bias and hallucinations, calibrate difficulty, and preserve assessment security. With transparent processes and expert review, AI can serve as a collaborative assistant rather than a replacement, helping medical educators build rigorous KFPs that enhance the assessment of clinical decision-making.","url":"https://doi.org/10.12688/f1000research.173421.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.173421.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2025.12.22.25342792","name":"Neural, Behavioral, and Clinical Outcomes of a Voice-based AI Coach for Depression and Anxiety: A Phase 2 Randomized Trial","source":"preprints","abstract":"Importance Artificial Intelligence (AI) voice-based chatbots may improve access to evidence-based psychotherapy for depression and anxiety, but their therapeutic mechanisms and outcomes are largely unknown. Objective To investigate the mechanisms and potential efficacy of Lumen, an AI voice-based coach delivering problem-solving treatment (PST), among adults with untreated, clinically significant depression and/or anxiety. Design Phase 2, 3-arm randomized clinical trial. Setting A public university and affiliated medical center in Chicago, Illinois. Participants Adults with a Patient Health Questionnaire-9 score of 10-19 and/or a Generalized Anxiety Disorder Scale score of 10-14 were randomized to Lumen-coached PST (n=100), human-coached PST (n=50), or waitlist control (n=50). Interventions PST was delivered in 4 weekly sessions followed by 4 biweekly sessions, by Lumen—a custom-developed, rule-based AI application on Amazon’s Alexa platform—or by a human coach via videoconferencing. Main Outcome(s) and Measure(s) The primary mechanistic outcome was change in right dorsolateral prefrontal cortex (dlPFC) activation for cognitive control, assessed with functional neuroimaging. Secondary outcomes were patient-reported, theory-based behavioral targets (e.g., problem-solving) and clinical symptoms (e.g., psychological distress). Results Among 200 participants (mean (SD) age, 36.6 (11.9) years; 77% women; 25% Black American; 28% Latino), change from baseline in right dlPFC activity at 18 weeks did not differ significantly by treatment arm. Compared with waitlist control, Lumen-coached participants had significantly greater improvements from baseline to 18 weeks in overall problem-solving ability (mean difference, 1.04; 95% CI, 0.23—1.84]) and psychological distress (mean difference, −3.56; 95% CI −5.69 to −1.43) due to depressive and anxiety symptoms. Lumen- and human-coached PST did not differ significantly on either measure. One serious adverse event (hospitalization), unrelated to this study, was reported. Conclusions and Relevance In this phase 2 experimental-therapeutics RCT, neither AI- nor human-coached PST significantly engaged the prespecified neural target for cognitive control. However, both interventions improved patient-reported problem-solving and psychological distress relative to waitlist control, providing exploratory evidence of a non-neural candidate mechanism and a clinical signal warranting future testing. Trial Registration clinicaltrials.gov , NCT05603923 Key Points Question Does Lumen, an Artificial Intelligence (AI) voice-based coach delivering problem-solving treatment (PST) for depression and anxiety, engage a mechanistic neural target for cognitive control (right dorsolateral prefrontal cortex, dlPFC) better than waitlist, and not worse than human-coached PST, and, secondarily, does it improve problem-solving behavior and clinical symptoms? Findings In this phase 2, 3-arm experimental-therapeutics randomized trial among 200 adults with untreated, clinically significant depression and/or anxiety, neither Lumen- nor human-coached PST significantly engaged the neural target versus waitlist control. Both interventions, however, significantly improved secondary outcomes related to patient-reported problem-solving ability and psychological distress due to depressive and anxiety symptoms relative to waitlist control. Meaning Despite null neural-target findings, Lumen may hold promise as a scalable intervention for improving problem-solving and psychological distress in adults with depression and/or anxiety.","url":"https://doi.org/10.64898/2025.12.22.25342792","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.22.25342792","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/f1000research.171529.1","name":"Policy Brief: AI Readiness in Medical Education: Assessing Current Gaps and Future Outlook","source":"preprints","abstract":"Artificial intelligence (AI) is swiftly emerging as a core component in the transformation of global healthcare systems, with its effectiveness contingent upon the readiness of the workforce, especially future physicians. The incorporation of AI into medical education must uphold essential principles, including ethical considerations, the preservation of the physician-patient relationship, and the primacy of human judgment. Preparing medical students for this evolution equips them to effectively leverage emerging technologies in clinical practice. This policy brief aims to establish a framework for preparing medical students for the future of AI in healthcare, assisting policymakers in universities, governments, and health authorities in developing effective educational programs. It presents prompt engineering as an innovative skill for medical students, facilitating personalized AI interactions in clinical simulations and ethical decision-making, while addressing existing gaps in current curricula. Incorporating findings from a 2025 cross-sectional study involving 1,619 medical students, this brief indicates a moderate level of AI readiness (61.34/100), with cognition identified as the weakest domain. This underscores the necessity for targeted curricula to bridge gaps in AI knowledge and cultivate practical skills such as prompt engineering for clinical simulations.","url":"https://doi.org/10.12688/f1000research.171529.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.171529.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7463408/v1","name":"A Qualitative Inquiry Exploring Perceptions of Artificial Intelligence to Improve Outcomes in Maternal, Sexual and Reproductive Health in Sub-Saharan Africa","source":"preprints","abstract":"Abstract Artificial intelligence has the potential to transform healthcare in low- and middle-income countries, where access to quality care remains limited. Maternal, sexual, and reproductive health (MSRH) outcomes are especially poor due to resource shortages, financial barriers, and geographic inequities. With thoughtful implementation, AI could help address these gaps through innovations in diagnostics, health education chatbots, and telemedicine. However, responsible use is essential to ensure AI reduces—rather than exacerbates—health disparities between high- and low-income regions. Our study examines the perceptions, uses, benefits, and challenges of AI in MSRH among medical professionals, community members, and AI experts, guided by the Diffusion of Innovations Theory. Our findings will inform the development of a continent-wide AI hub for MSRH, highlighting barriers and opportunities for improving health care access. We aim to support policymakers, researchers, and implementers in using AI to promote equitable maternal and sexual and reproductive healthcare delivery across Africa.","url":"https://doi.org/10.21203/rs.3.rs-7463408/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7463408/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202512.2531.v1","name":"<em>safeMEDInet</em>: Federated AI Systems for Security and Privacy-Preserving Threat Detection in Distributed Healthcare","source":"preprints","abstract":"The explosive growth of digital healthcare data and networked Internet of Medical Things (IoMT) devices has heightened vulnerabilities inside healthcare networks, hence exposing sensitive medical systems to sophisticated cyber assaults. The safeMEDInet framework offers a secure, federated artificial intelligence (AI) architecture that allows decentralized healthcare institutions to cooperatively identify and address problems without disclosing raw patient data. safeMEDInet utilizes federated learning along with privacy-preserving techniques, such as differential privacy, homomorphic encryption, and Byzantine-resilient aggregation, to guarantee confidentiality, integrity, and adherence to regulations in remote settings. The proposed framework integrates a hybrid CNN-LSTM model for spatiotemporal intrusion detection with secure model synchronization and encrypted parameter sharing to ensure robust accuracy against various cyber-attacks, including ransomware, unauthorized access, and distributed denial-of-service (DDoS) intrusions. Empirical assessments utilizing MIMIC-IV, HealthData.gov, and WHO datasets reveal that safeMEDInet achieves a detection accuracy of 96.8% with robust privacy assurances (ε = 1.9) and sustains an accuracy of 88.4% despite 30% Byzantine interference, surpassing traditional federated and centralized systems. The findings confirm safeMEDInet's capacity to guarantee high detection reliability, low processing cost, and mathematical assurance of privacy resilience. This research positions safeMEDInet as a pivotal advancement towards safe, scalable, and ethically governed Healthcare 5.0 ecosystems, incorporating AI-driven privacy, federated cooperation, and blockchain-supported data integrity for next-generation medical cybersecurity.","url":"https://doi.org/10.20944/preprints202512.2531.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202512.2531.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7227195/v1","name":"Vitabel: Bridging Clinical Expertise and the Machine Learning Pipeline in Critical Care","source":"preprints","abstract":"Abstract Artificial intelligence offers a great opportunity in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. High-quality, reliably labelled data are paramount for developing and training artificial intelligence methods. However, routinely recorded data in critical care are often noisy, and the sheer volume of high-resolution data is challenging to manage. Generalizable solutions for these problems are lacking, restricting progress.To address these barriers, we developed \\vitabel{}, an open-source \\python{} package for loading, visualising, aligning, and annotating medical time series with minimal coding. The tool integrates seamlessly into \\jupyter{-notebooks}, providing an interactive, customizable interface to interact with the data visually. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos. \\vitabel{} is freely available and published under the MIT license accompanying this publication.","url":"https://doi.org/10.21203/rs.3.rs-7227195/v1","authors":["Simon Orlob","Wolfgang J. Kern","Benjamin Hackl","Jan Wnent","Jan-Thorsten Gräsner","Martin Holler"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7227195/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.64898/2026.03.18.26348755","name":"A Web Application for Exploring Distribution in Academic Publications Across Geography and Institutions in India","source":"preprints","abstract":"India’s national research capacity and infrastructure are unevenly distributed across states and union territories (UTs), contributing to geographic variation in academic publication output. We developed Indiapub , an open-access web application that quantitatively enumerates and visually displays geographic and temporal publication patterns for research products with at least one author affiliated with an Indian institution, using OpenAlex data. The app is designed for ease of use, with automated data retrieval, cleaning, and aggregation. Indiapub allows users to filter publications by topic, publication year range, author position, publication type, minimum citation count, state/UT, and population size of the state/UT where the author institution is located. The app also provides downloadable tables and ranked institution lists by publication count. Its interactive dashboard includes five modules: (i) a map of publication distribution, (ii) time trend plots for nation and state/UT, (iii) publication-share versus population-share plots highlighting over- and underrepresentation, (iv) stacked bar charts of state/UT contributions over time with population benchmarks, and (v) bubble plots relating the Human Development Index to publication volume over time. This tool may support resource prioritization and identification of institutional strengths for trainees, researchers, higher education administrators, and policymakers. To illustrate its utility, we present sample findings derived from the app. For publications across all topics from 2014 to 2025, the largest research participation footprints were observed in Tamil Nadu, Maharashtra, Delhi, Uttar Pradesh, and Karnataka. Tamil Nadu and Delhi were home to three of the highest-publishing institutions nationally: Vellore Institute of Technology, All India Institute of Medical Sciences, and Indian Institute of Technology Delhi. We also examined six curated case studies of broad scientific interest: electronic health records (EHR), genome-wide association studies (GWAS), artificial intelligence (AI), development economics, environmental science, and COVID-19. Findings from these case studies revealed over- and underrepresentation in publication output across states and UTs. For example, in EHR publications among high-population states, Tamil Nadu’s publication share exceeded its population share by 31.3 percentage points (pp), whereas Bihar’s was 12.8 pp lower. Our tool offers insights into India’s research landscape across states and UTs with easy-to-digest visuals. Such interactive tools have the potential to serve as a starting point for fostering a more inclusive research ecosystem supporting targeted research policy and planning.","url":"https://doi.org/10.64898/2026.03.18.26348755","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.18.26348755","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202508.2129.v1","name":"Generative AI and Foundation Models: Redefining Intelligence, Creativity, and Scalability in the Digital Era","source":"preprints","abstract":"The rapid advancement of Generative Artificial Intelligence (AI) and foundation models has ushered in a transformative era in the evolution of intelligent systems. Unlike traditional machine learning approaches that focus on narrow, task-specific outcomes, generative AI leverages large-scale, pretrained foundation models to produce novel, context-aware, and multimodal outputs, spanning natural language, imagery, audio, video, and three-dimensional content. Foundation models—built on architectures such as transformers and scaled through vast computational resources—serve as the backbone of generative AI, offering unprecedented adaptability, generalization, and scalability across diverse domains. Their impact is already visible in healthcare through drug discovery and medical imaging, in business through productivity and automation tools, in education via personalized learning systems, and in creative industries through art, design, and media innovation.Beyond their transformative potential, these technologies also highlight critical risks and challenges. Ethical concerns such as bias, misinformation, deepfakes, and intellectual property disputes underscore the need for careful governance. Environmental implications of large-scale training and the risk of over-dependence on AI systems further complicate adoption. Nevertheless, generative AI and foundation models present immense opportunities for democratizing knowledge, accelerating scientific breakthroughs, and enabling new forms of human-AI collaboration. As research, policy, and industry converge, the digital era stands at a defining crossroads: harnessing the disruptive power of generative intelligence while ensuring transparency, accountability, and responsible deployment. Ultimately, these innovations are not only reshaping creativity and productivity but may also serve as a stepping stone toward artificial general intelligence (AGI), raising profound questions about the future of technology, work, and society.","url":"https://doi.org/10.20944/preprints202508.2129.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202508.2129.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202512.2771.v1","name":"An Automated Machine Learning Classification Model for Predicting Placental Abruption","source":"preprints","abstract":"Placental abruption is detachment of the placenta before delivery from the implantation site that may have a potential to develop life-threating emergency clinic syptoms. The multifactorial nature of this disorder and no lab testing or procedures that can diagnose placental abruption. makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study purposed on predictive 15 ML models for placental abruption high-lighting input characteristics, performance metrics, and validation. The medical records of 564 patients were analyzed between 2021 and 2025 for studies using AI to develop predictive models for placental abruption. Findings were analyzed with Python software and Pycaret library. The model integrated data for 5 variables (features) for the prediction. Among 15 machine learning algorithms, Logistic regression was chosen as the best model. The performance metrics were determined as follows: accuracy rate of 0.85, AUC of 0.91, recall of 0.85, precision of 0.85, and F1 score of 0.85. In the ranking based on their importance in the classification model, gestational age at delivery was observed to have the highest importance for classification. Twenty-eight unseen cases were utilized for an extra validation step. The model achieved a high accuracy on this set, with 21 cases correctly predicted. The presented 15 ML models in our study had significant accuracy in predicting placental abruption , but these models require further development before they can be applied in a clinical setting.","url":"https://doi.org/10.20944/preprints202512.2771.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202512.2771.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7117693/v1","name":"Artificial intelligence for predicting the efficacy of Tuina in patients with knee osteoarthritis","source":"preprints","abstract":"Abstract Background Knee osteoarthritis (KOA), a prevalent condition impacting middle-aged and older adults' quality of life, is increasing globally. Tuina, a Traditional Chinese Medicine technique, shows efficacy in reducing KOA pain and improving function, but response varies. This study aimed to develop a supervised machine learning classifier to predict Tuina efficacy for KOA, aiding personalized treatment planning. Methods This retrospective, registry-based, single-center prognostic study enrolled 355 KOA patients from the Tuina Department at Yueyang Hospital (Shanghai, China) between February 1, 2016, and December 31, 2023. All received standardized Tuina therapy (20-min sessions, 5×/week for 2 weeks). Efficacy was assessed via ΔVAS (post-treatment minus baseline VAS), categorized as high (ΔVAS = 4–6) or low efficacy (ΔVAS = 0–3). Eight machine learning models (e.g., Random Forest, SVM) were trained using 80% of the data (demographics, medical history, imaging assessments, physical exam findings, baseline VAS) to predict efficacy, validated on the remaining 20%. Statistical analysis used T-tests and Chi-square tests; model performance was evaluated via F1-score and AUC. Data analysis was performed from January 2024 to March 2025. Results The average reduction in VAS scores was 3.74. Among the eight trained machine learning models, the Random Forest-based model achieved the best predictive performance for the efficacy of Tuina treatment. The top six features influencing the model included the grinding test, knee joint range of motion, body mass index (BMI), height, imaging examination, and disease course. Conclusions Artificial intelligence models can reliably predict the efficacy of Tuina therapy in KOA patients. This study provides a valuable reference for Tuina practitioners in scientifically evaluating the effectiveness of KOA treatments. Trial registration: This retrospective study was approved by the Ethics Committee of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, affiliated with Shanghai University of Traditional Chinese Medicine (No.2025-056).","url":"https://doi.org/10.21203/rs.3.rs-7117693/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7117693/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.175714398.81661165/v1","name":"A Novel Workflow Combining Artificial Intelligence - Twelve Lead Electrocardiographic Analysis and Real-Time Mapping for Hemodynamically Unstable Ventricular Tachycardia","source":"preprints","abstract":"Background: Activation mapping of scar mediated ventricular tachycardias (VT) are often limited by hemodynamic instability. Substrate mapping and 12-lead ECG localization of VT exit site is often limited by variable scar complexity. Emphasis on other mapping strategies in sinus rhythm would improve catheter ablation success in such cases. Objective: The aim was to investigate the level of agreement between artificial intelligence (AI) based 12-lead ECG localization of VT exit site and mapping utilizing pace-mapping and mapping of channels of slow conduction within the scar tissue of hemodynamically unstable VT. Methods: : This was a single-center proof-of-concept study that included patients who underwent catheter ablation procedure of hemodynamically unstable scar mediated VT. The performance of AI-based ECG analysis of VT exit site (Vektor Medical, San Diego, CA) was compared with sites of successful ablation based on substrate mapping in sinus rhythm. Results: : A total of 9 hemodynamically unstable VT rhythms were induced in 4 patients. In the 7 VTs were AI-based ECG mapping was used; there was a 100% level of agreement with the site of successful ablation based on substrate mapping. Ablation targeting those sites resulted in non-induction of all induced and mapped VTs. None of the 4 patients had device therapy for recurrent VT or all-cause mortality at 6-months of follow-up. Conclusion: A multi-strategic approach utilizing digitalized analysis of 12-lead ECG of VT exit site, pace-mapping and mapping of channels of slow conduction in scar tissue, has the potential to enhance successful catheter ablation of hemodynamically unstable VT.","url":"https://doi.org/10.22541/au.175714398.81661165/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.175714398.81661165/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.175986267.72682330/v1","name":"Convolutional Neural Networks: Transforming Visual Intelligence Through Hierarchical Feature Learning","source":"preprints","abstract":"Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, establishing themselves as the dominant architecture for processing grid-structured data such as images and videos. This paper provides a comprehensive review of CNN architectures, examining their fundamental mechanisms, operational principles, and distinguishing characteristics that set them apart from traditional neural networks. We explore the hierarchical feature learning capabilities of CNNs through convolutional layers, pooling operations, and fully connected layers, demonstrating how these components work synergistically to extract meaningful representations from raw visual data. Furthermore, we discuss the substantial advantages CNNs offer over conventional neural network architectures, including translation invariance, parameter sharing, and spatial hierarchy preservation. The paper surveys key applications across diverse domains including medical imaging, autonomous vehicles, natural language processing, and industrial automation, showcasing the transformative impact of CNNs on modern artificial intelligence systems. Through detailed examination of architectural innovations and practical implementations, this work serves as a comprehensive resource for understanding the principles and applications of convolutional neural networks.","url":"https://doi.org/10.22541/au.175986267.72682330/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.175986267.72682330/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9004371/v1","name":"Curiosity, reassurance and convenience: Understanding public motivations for using Generative AI in symptom assessment","source":"preprints","abstract":"Abstract Introduction Generative artificial intelligence (GenAI) tools such as large language models are Generative artificial intelligence (GenAI) tools such as large language models are increasingly used for health information and symptom appraisal. While diagnostic benchmarking studies are expanding, little population-level evidence explains why individuals use GenAI for symptom assessment or how motivations relate to trust, perceived accuracy and preferences for integration within health systems. Methods We analysed data from the 2025 RADIANT VOICES cross-sectional survey of UK adults (n=1449). Participants reported GenAI use, motivations for symptom-related use, trust across use cases and comparative accuracy expectations. Internal consistency supported aggregation of trust (α=0.90) and perceived accuracy (α=0.87) scales. To compare symptom-assessment users with non-users, we applied 1:1 nearest-neighbour propensity score matching (caliper 0.2) across sociodemographic and digital-literacy covariates, achieving standardised mean differences Results Overall, 62.8% reported using GenAI; 49.7% of these had used it to assess symptoms. The most common motivations were curiosity (72.3%), reassurance following a diagnosis (60.7%) and convenience (32.1%). Trust was highest for low-risk informational uses (self-care advice 75.3%) and lowest for urgent-care guidance (38.0%). Participants generally rated GenAI as less accurate than general practitioners (55.8% perceived lower accuracy) but more accurate than traditional non-AI symptom checkers (35.8% perceived higher accuracy). After matching, symptom-assessment users demonstrated higher overall trust (mean difference 0.60, p Conclusion Public use of GenAI for symptom assessment is widespread and primarily motivated by curiosity, reassurance and convenience. Trust is risk-dependent, favouring informational tasks over urgent decisions. Integration strategies should prioritise scope clarity, human oversight and transparency.","url":"https://doi.org/10.21203/rs.3.rs-9004371/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9004371/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7061625/v1","name":"Automatic Classification and Acoustic Auscultation of Heart, Lung, and Bowel Sounds Using Artificial Intelligence","source":"preprints","abstract":"Abstract Auscultation of heart, lung, and bowel sounds remains a fundamental diagnostic technique in clinical practice despite significant technological advancements in medical imaging. However, the accuracy of auscultation-based diagnoses is highly dependent on clinician experience and expertise, leading to potential diagnostic inconsistencies. The objective of this study is to present a novel artificial intelligence (AI) framework for the automatic classification and acoustic differentiation of heart, lung, and bowel sounds, addressing the need for objective, reproducible diagnostic support tools. Our approach leverages recent advances in supervised machine learning and signal processing to extract distinctive acoustic signatures from publicly available, digitized heart, lung, and bowel sounds. By analyzing spectral, temporal, and morphological features across diverse asymptomatic populations, the algorithm achieves excellent classification of predictive accuracy (65.00–91.67%) and validation accuracy (83.87–94.62%) from six AI models. The clinical implications of this algorithm show promise beyond diagnostic support to applications in medical education, telemedicine, and continuous patient monitoring. This work contributes to emerging AI-assisted auscultation by providing a comprehensive framework for multi-organ sound classification with the potential to improve differential diagnostic accuracy and standardization in clinical settings.","url":"https://doi.org/10.21203/rs.3.rs-7061625/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7061625/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202509.1023.v1","name":"Ethical Decision-Making Guidelines for Mental Health Clinicians in the Artificial Intelligence (AI) Era","source":"preprints","abstract":"The meteoric rise in generative AI has created both opportunities and ethical challenges in the mental health disciplines namely in clinical mental health counseling, psychology, psychiatry, and social work. While these disciplines have been grounded in well-established ethical principles such as autonomy, beneficence, justice, fidelity, and confidentiality, the exponential ubiquity of AI in society in the past three years has rendered mental health professionals unsure as to how to navigate ethical decision making in the AI era. The author proposes a preliminary ethical framework which synthesizes the code of ethics of the American Counseling Association, the American Psychological Association, the American Medical Association and the National Association of Social Workers which is then organized around five pillars: autonomy and informed consent; beneficence and non-malfeasance; confidentiality, privacy, and transparency; justice, fairness and inclusiveness; and fidelity, professional integrity, and accountability. These pillars are juxtaposed with AI ethical guidelines developed by international organizations, governments, and technology corporations. The resulting integrated ethical framework provides a practical cogent structure that mental health professionals can use when navigating this uncharted terrain. Limitations of the framework and implications for future research are addressed.","url":"https://doi.org/10.20944/preprints202509.1023.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.1023.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202509.1010.v1","name":"Detection of Cholesteatoma Residues in Surgical Videos Using Artificial Intelligence","source":"preprints","abstract":"Surgical treatment is the only option for cholesteatoma; however, the recurrence rate is high, and the incidence of residual cholesteatoma recurrence largely depends on the surgeon&#039;s skill. Training deep neural network (DNN) models typically requires large datasets, but the prevalence of cholesteatoma is low (1 in 25,000 people). However, cholesteatoma remains difficult to treat. Developing analytical methods to improve ac-curacy with limited datasets remains a significant challenge in medical artificial intelli-gence (AI) research. This study introduces an AI-based system for detecting residual cholesteatoma in surgical field videos. A retrospective analysis was conducted on 144 cases from 88 patients who underwent surgery. The training dataset comprised videos of cholesteatoma lesions recorded during surgery and intact middle ear mucosa after lesion removal. These videos were captured using both endoscope and microscope for AI model development. The diagnostic accuracy was approximately 80% for both endoscopic and microscopic images. Although the diagnostic accuracy for microscopic images was slightly lower, focusing on the lesion center improved the accuracy to a level comparable to that of endoscopic images. This study demonstrates the diagnostic feasibility of AI-based cholesteatoma detection despite a limited sample size highlighting the value of proof-of-concept studies in clar-ifying technical requirements for future clinical systems and is the first AI study to use videos from both modalities.","url":"https://doi.org/10.20944/preprints202509.1010.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.1010.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2026.04.02.26349884","name":"Who is leading medical AI? A systematic review and scientometric analysis of chest x-ray research","source":"preprints","abstract":"Computer vision models for chest X-ray interpretation hold significant promise for global healthcare, but their clinical value depends on equitable development across diverse populations. We conducted a scientometric analysis to examine authorship patterns, geographic distribution, and dataset origins to assess potential disparities that could affect clinical applicability. We systematically reviewed literature on computer vision applications for chest X-rays published between 2017-2025 across multiple databases, including PubMed, Embase and SciELO databases. Using Dimensions API and manual extraction, we analyzed 928 eligible studies, examining first and senior author affiliations, institutional contributions, dataset provenance, and collaboration patterns across different income classifications based on World Bank categories. High-income countries dominated research leadership, representing 55.6% of first authors and 59.7% of senior authors; no first authors were affiliated with low-income countries. China (16.93%) and the United States (16.72%) led in first authorship positions. Most datasets (73.6%) originated from high-income settings, with the United States being the largest contributor (40.45%). Private datasets were most frequently used (20.52%). Cross-income collaborations were rare, with only 3.9% of publications involving partnerships between high-income and lower-middle-income countries. Findings: reveal substantial disparities in who shapes computer vision research on chest X-rays and which populations are represented in training data. These imbalances risk developing AI systems that perform inconsistently across diverse healthcare settings, potentially exacerbating healthcare inequities. Addressing these disparities requires coordinated efforts to develop globally representative datasets, establish equitable international collaborations, and implement policies that promote inclusive research practices. Author Summary In this study, we examined the global landscape of research involving computer vision applied to chest X-rays. While these technologies have the potential to significantly improve healthcare worldwide, their effectiveness depends on being developed and tested using data from diverse populations. We analyzed nearly one thousand scientific articles and found that research leadership and data sources are heavily concentrated in high-income countries, particularly the United States and China. Our findings reveal a concerning gap because people in low-income regions, who often face the highest burden of respiratory diseases, are almost entirely absent from the research process as both authors and data contributors. This imbalance creates a risk that medical artificial intelligence may not perform reliably when used in different parts of the world, which could accidentally worsen existing health inequalities. We argue that the scientific community must prioritize international partnerships that treat researchers from developing nations as equal leaders. By making medical data more diverse and accessible, we can ensure that these powerful diagnostic tools benefit patients everywhere, regardless of their location or economic status.","url":"https://doi.org/10.64898/2026.04.02.26349884","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.02.26349884","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202512.0312.v1","name":"Symbiosis in Health: The Powerful Alliance of AI and Propensity Score Matching in Real World Medical Data Analysis","source":"preprints","abstract":"Background: The rapid expansion of real-world data in medicine is driving the adoption of advanced methods like Artificial Intelligence (AI) and Propensity Score Matching (PSM). AI is widely applied across diagnostics, prediction, and treatment planning, while PSM is a crucial statistical technique used in quasi-experimental studies to mitigate confounding bias and approximate the reliability of randomized controlled trials. There is a growing research interest in combining these two methods to leverage their symbiotic strengths, but this association has not been holistically explored. Methodology: This study employed Synthetic Thematic Analysis (STA), derived from synthetic knowledge synthesis, to systematically review the existing literature on AI and PSM in medicine. Publications were harvested from the Scopus database using a comprehensive search string limited to the Medical subject area. The resulting corpus (N=433 documents) was analyzed using bibliometric tools (Bibliometrix and VOSViewer) to map the research landscape, identify thematic clusters based on author keywords, analyze collaboration patterns, and synthesize findings from highly prolific publications. Results: The field is young and rapidly accelerating, showing an exponential increase from 2020 to 2024. China and the USA dominate research production and citation impact. The symbiotic relationship is published in high-impact medical journals and health informatics journals. STA identified four main thematic clusters: Prediction, Cancer Management, Diagnosing, and Deep Learning. AI and PSM are combined in two primary ways: AI used in PSM and PSM used in AI. Conclusion: The symbiotic association between AI and PSM is a global and rapidly developing trend in medical research, driven by major international contributors. This convergence is enhancing methodological rigor in observational studies, primarily by improving prediction models and refining causal inference in complex areas like cardiovascular disease, cancer, and diagnostics.","url":"https://doi.org/10.20944/preprints202512.0312.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202512.0312.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202512.0565.v1","name":"Evaluating Chat GPT-4o’s Comparative Performance Over GPT-4 in Japanese Medical Licensing Examination and Its Clinical Partnership Potential","source":"preprints","abstract":"Backgrounds: : Recent advances in artificial intelligence (AI) have produced ChatGPT-4o, a multimodal large language model (LLM) capable of processing both text and image inputs. Although ChatGPT has demonstrated usefulness in medical examinations, few studies have evaluated its image analysis performance. Methods: This study compared GPT-4o and GPT-4 using public questions from the 116th–118th Japan National Medical Licensing Examinations (JNMLE), each consisting of 400 questions. Both models answered in Japanese using simple prompts, including screenshots for image-based questions. Accuracy was analyzed across essential, general, and clinical questions, with statistical comparisons by chi-square tests. Results: GPT-4o consistently outperformed GPT-4, achieving passing scores in all three examinations. In the 118th JNMLE, GPT-4o scored 457 points versus 425 for GPT-4. GPT-4o demonstrated higher accuracy for image-based questions in the 117th and 116th exams, though the difference in the 118th was not significant. For text-based questions, GPT-4o showed superior medical knowledge, clinical reasoning, and ethical response behavior, notably avoiding prohibited options. Conclusion: Overall, GPT-4o exceeded GPT-4 in both text and image domains, suggesting strong potential as a diagnostic aid and educational resource. Its balanced performance across modalities highlights its promise for integration into future medical education and clinical decision support.","url":"https://doi.org/10.20944/preprints202512.0565.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202512.0565.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7503029/v1","name":"Functional Requirements for Virtual Home Visit Simulation Platforms: A Qualitative Study Among Students, Instructors, and Community Nurses Based on Peplau's Theory","source":"preprints","abstract":"Abstract Background Virtual simulation has been widely applied in nursing education, with demonstrated effectiveness; however, its application in home visit nursing remains limited. Understanding functional requirements from multiple stakeholders is essential for developing effective simulation platforms. However, no studies have systematically explored functional requirements for virtual home visit simulation platforms using theoretical frameworks. This study aimed to identify functional requirements for virtual home visit simulation platforms from nursing students, instructors, and community nurses' perspectives guided by Peplau's Interpersonal Relations Theory. Methods A qualitative descriptive study was conducted at a medical university and community health service centers in China. 38 participants (20 nursing students, 8 instructors, 10 community nurses) were recruited through purposeful sampling. Semi-structured interviews were conducted and analyzed using Braun and Clarke's thematic analysis approach guided by Peplau's theoretical framework. Results Six main themes of virtual home visit simulation platform requirements were identified. Assessment and Communication focused on therapeutic relationship establishment through immersive interactions. Planning and Objectives addressed collaborative care planning and resource integration. Skills and Implementation covered practical adaptations for home environments. Evaluation and Improvement included comprehensive performance tracking. Dynamic Interconnections revealed sequential relationships between phases. Digital Enhancement Functions incorporated artificial intelligence for adaptive learning. Conclusion This study is grounded in Peplau's interpersonal relations theory and provides a comprehensive framework for the development of a virtual home visit simulation platform. The findings can guide the design of targeted virtual simulation platforms. Future research should investigate the requirements of theory based virtual platforms for home visit training, evaluate their effectiveness in enhancing nursing students' home visit competencies, and explore broader integration of artificial intelligence technologies in nursing education.","url":"https://doi.org/10.21203/rs.3.rs-7503029/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7503029/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.175266365.50907836/v1","name":"“Causability and Explainability of Artificial Intelligence in Medicine - A Review”","source":"preprints","abstract":"Artificial Intelligence (AI) is revolutionizing the medical field with its ability to support clinical decision-making, diagnose diseases, predict patient outcomes, and personalize treatment. Machine learning (ML) and deep learning (DL) have significantly contributed to these advancements. However, the complexity and non-transparency of these models present challenges, particularly regarding their trustworthiness and safety. In medical contexts, it is essential that clinicians understand not only what predictions an AI system makes but also how and why those predictions are made. This requirement has spurred interest in two key concepts: explainability (the ability to make AI decisions comprehensible to humans) and causability (the ability to provide causal reasoning in AI systems). This review critically examines the state of explainability and causability in AI models, particularly in the medical domain, highlighting current methodologies, challenges, and future directions. Special attention is given to the framework proposed by Holzinger et al., which emphasizes the importance of causality for improving the safety, transparency, and adoption of AI systems in clinical practice.","url":"https://doi.org/10.22541/au.175266365.50907836/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.175266365.50907836/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202402.1153.v2","name":"Best Practices to Train Accurate Deep Learning Models: A General Methodology","source":"preprints","abstract":"In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.","url":"https://doi.org/10.20944/preprints202402.1153.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202402.1153.v2","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.176659897.77719715/v1","name":"Precision Oncology: Targeting  Genomic Alterations and Cancer Signaling with Integrative Multi-Omics, Deep  Learning and Network Biology in Medical Oncology                 ","source":"preprints","abstract":"Cancer is a complex genetic disease involving uncontrolled cell growth and proliferation, and necessitates effective targeting of dysregulated cellular pathways underlying cancer progression. Multiple genetic and epigenetic alterations characterize tumor progression and define hallmarks of cancer. It may result in dysregulation of growth factors, regulatory proteins, cell adhesion molecules, and molecules of immune system driven by alterations in the expression profile of tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy that is focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. Advances in high-throughput technologies, that include next-generation sequencing, have enabled gene expression profiling, providing detailed molecular characterization of various tumors. Moreover, the application of multiomic technologies, including genomics, proteomics, metabolomics, and single-cell multiomics, constitutes a novel approach for the identification and quantification of a comprehensive set of biological molecules to study how they translate into cellular functions and tissue pathologies. Integration and analysis of various multiomic sequencing data are crucial in this regard, as they can reveal critical molecular changes, such as cancer-driving mutations, post-translational modifications, gene fusions, amplifications, and alterations in signaling networks within tumors. Furthermore, the role of computational techniques such as artificial intelligence and deep learning, in analyzing complex data and identifying patterns of disease development for better outcomes is now well established in precision medicine. Additionally, AI-powered multi-omics and network biology have been harnessed to integrate and analyze biological data through networks, which may prove crucial in solving key problems facing precision oncology. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of cutting-edge developments in tools and techniques associated with it, and try to assess its scope and importance in achieving the intended goals over time.","url":"https://doi.org/10.22541/au.176659897.77719715/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176659897.77719715/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-6472068/v1","name":"Individual Innovativeness Levels and Levels of Medical Artificial Intelligence Readiness Among Nursing Students: a Cross-sectional and Correlational Study","source":"europepmc","abstract":"Abstract Aim: In this study, it was aimed to determine the individual innovativeness levels of nursing students and their readiness levels for medical artificial intelligence and the relationship between these two variables. Background: A healthcare team with innovative personality traits is necessary for the active use of artificial intelligence in the field of health. It is important to determine the perspectives of nursing students, who are among the most crowded members of the team and whom we define as the nurses of the future, in this direction. Desing : The research was designed as descriptive and correlational. Method: The study was conducted with 781 nursing students using a cross-sectional method. The data were collected using Personal Information Form, Individual Innovativeness Scale (IIS) and Medical Artificial Intelligence Readiness Scale (MAIRS). Results :The mean total score of the IIS, nursing students was 55.09±9.22 and the mean total score of the MARS nursing students was 67.63±12.83 and there was a weak positive correlation between the scales (r=.172, p Conclusion: In our study, it was determined that nursing students' individual innovativeness levels positively affected their readiness for medical artificial intelligence. It was concluded in this study that the individual innovativeness level of the nursing students was low; in other words, they adopted a traditionalist attitude and approached artificial intelligence cautiously. It is recommended that the existing curricula be restructured to strengthen the innovative perspective of nursing students and to understand, use, and develop artificial intelligence technologies in nursing, and nursing educators should upskill themselves in this field.","url":"https://doi.org/10.21203/rs.3.rs-6472068/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6472068/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8392403/v1","name":"Emerging Trends in Neurology: A Multi-Dimensional Scientometric Analysis from 2016-2025","source":"preprints","abstract":"Abstract Purpose: The diagnosis and treatment of conditions affecting the nervous system, which comprises the brain, spinal cord, and nerves, is the focus of the medical specialty of neurology. The current study has examined the growth of research publications in the field of neurology using a sample of 3,569 papers published between 2016 and 2025. From simple ailments like migraines and seizures to more complicated illnesses like multiple sclerosis, stroke, Alzheimer's, and Parkinson's, neurologists treat a broad spectrum of problems. They conduct neurological examinations, order diagnostic tests such as EEGs and MRIs, and oversee patient care by making referrals, administering medicine, and providing therapy. Design/methodology/approach: Neurology's design, methodology, and approach can be broadly divided into two categories: Neuro Design, an interdisciplinary field that applies neuroscience insights to design practices (such as architecture, user experience, and product design), and clinical/research methods for studying the nervous system in a medical context. Finding: Neurology findings refer to the results of neurological tests that show how the nervous system is operating, from normal to aberrant. By displaying symptoms including discomfort, changed sensations, altered muscular movement, difficulty speaking, and cognitive or sleep issues, these findings can point to a wide range of neurological illnesses. The current study has examined the growth of research publications in the field of neurology using a sample of 3,569 papers published between 2016 and 2025. The year 2024 saw the highest number of published articles—422 (11.82%). It is noted that Edison, P., has produced the most articles among the most prolific authors in the discipline of neurology, with 24 (22.22%) research publications in the United States. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics is at the top of the list with 91 (21.67%) publications, according to the journals of Harvard Medical School alone, which published 97 (16.64%) articles. Research Limitation : The inaccessibility of the brain, issues with repeatability and transparent reporting, and problems with participant consent because of cognitive and communication deficits are some of the limitations in neurology research. The intricacy of the brain and its functions, such as comprehending thoughts, and the dearth of established molecular targets and biomarkers for numerous illnesses are additional constraints. Additionally, there is a dearth of data and detailed information on neurologic health in several research domains, such as studies on LGBTQIA+ groups. Practical Implications : A better understanding of how to treat chronic neurological conditions like epilepsy and Parkinson's disease, the use of sophisticated imaging and neurophysiological tools for more precise diagnosis, and the increasing necessity of coordinating with other medical specialties to manage complex cases—particularly in an aging population—are just a few of the practical implications of neurology. Better, evidence-based therapies that enhance quality of life are made possible by these developments, and new technologies are also increasing clinical practice's administrative effectiveness. Originality/value: \"Originality/value\" is a crucial criterion employed by editors and peer reviewers in scientific publishing, especially in journals like Neurology and allied neuroscience publications, to determine if an article warrants publication. It speaks to the originality and usefulness of the study that is being offered. Value, Importance, and Appropriateness: The results must be noteworthy and pertinent to the discipline of neuroscience or clinical neurology. By providing insights that help the larger medical community and, eventually, patients, the research should improve knowledge of disease mechanisms, diagnosis, treatment, or patient care. For example, a rare case report is valuable if it discloses a curable ailment that would otherwise go unnoticed by clinicians. Neurological examinations reveal the normal and aberrant functioning of the nervous system. By displaying symptoms including discomfort, changed sensations, altered muscular movement, difficulty speaking, and cognitive or sleep issues, these findings can point to a wide range of neurological illnesses.","url":"https://doi.org/10.21203/rs.3.rs-8392403/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8392403/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202511.0768.v1","name":"Diabetic Foot—Scope of the Problem Worldwide: The Challenge of Data Management","source":"preprints","abstract":"Healthcare data is experiencing one of the highest growth rates of any major data sector, driven primarily by rapid advances in genomics, medical imaging, and continuous data from wearable devices. The convergence of universal data standards in healthcare (terminologies, OpenEHR, FHIR, and OMOP) is now providing the common ground needed to translate this data into tangible medical advances through a wide array of different applications. Together with a growing ecosystem of analytics, predictive models, and advanced artificial intelligence tools, this synergy is poised to fundamentally transform the delivery of healthcare. With the maturation of health information technology and proliferation of research in the field, the pivotal challenge has shifted from technological capability to the pervasive inability to implement solutions effectively in routine practice, particularly those tailored to diabetic foot-specific needs. In the context of diabetic foot care, where the paramount goals are patients' well-being, tissue preservation, and amputation prevention, collaborative data management must be recognized as a critical treatment modality itself. “Data is tissue,” it is the foundational element that enables the timely, coordinated, and evidence-based interventions necessary for success. This paper highlights some of the opportunities presented by modern data methodologies to address the current implementation gap.","url":"https://doi.org/10.20944/preprints202511.0768.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202511.0768.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8092084/v1","name":"Data reuse enables cost-efficient randomized trials of medical AI models","source":"preprints","abstract":"Abstract Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hinder timely validation as new models emerge rapidly. Here, we propose BRIDGE, a data-reuse RCT design for AI-based risk models. AI risk models support a broad range of interventions, including screening, treatment selection, and clinical alerts. BRIDGE trials recycle participant-level data from completed trials of AI models when legacy and updated models make concordant predictions, thereby reducing the enrollment requirement for subsequent trials. We provide a practical checklist for investigators to assess whether reusing data from previous trials allows for valid causal inference and preserves type I error. Using real-world datasets across breast cancer, cardiovascular disease, and sepsis, we demonstrate concordance between successive AI models, with up to 64.8% overlap in top 5% high-risk cohorts. We then simulate a series of breast cancer screening studies, where our design reduced required enrollment by 46.6%—saving over US$2.8 million—while maintaining 80% power. By transforming trials into adaptive, modular studies, our proposed design makes Level I evidence generation feasible for every model iteration, thereby accelerating cost-effective translation of AI into routine care.","url":"https://doi.org/10.21203/rs.3.rs-8092084/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8092084/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-6872908/v1","name":"Research on Case-Based Reasoning Based Decision Aid Technology for Disease Predetection Triage","source":"preprints","abstract":"Abstract In recent years, with the continuous development of medical technology, disease predetection and triage has become an important part of modern medical services. If the medical staff's disease predetection and triage of patients is inaccurate or not timely, it is likely to affect or even delay the treatment of patients, and will also bring negative effects and economic losses to the hospital. However, as an important medical resource, the medical experience of doctors needs long-term accumulation and precipitation, and many young medical personnel with junior experience do not have rich medical experience, which makes the efficiency and accuracy of disease predetection triage work cannot be guaranteed. The transformation and application of case-based reasoning technology in artificial intelligence provides the possibility to solve the above problems. The case-based reasoning technology for disease predetection and triage assisted decision making proposed in this paper is a kind of case-based reasoning technology in artificial intelligence, which provides doctors with recommendations for disease predetection and triage by analyzing and learning a large number of case data. This technology can help doctors diagnose diseases more accurately and quickly, improving the quality and efficiency of medical services.","url":"https://doi.org/10.21203/rs.3.rs-6872908/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6872908/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2025.12.22.25342804","name":"The Economics of Accuracy for Medical Reasoning with Large Language Models","source":"preprints","abstract":"Deploying large language models (LLMs) in clinical settings is limited by security, reliability, latency, and accessibility concerns that favor smaller, on-device or on-premise models. However, these smaller models may struggle to meet accuracy requirements. While fine-tuning and retrieval-augmented generation (RAG) can improve domain-specific accuracy, these methods require additional labeled data, technical skill, and infrastructure. In contrast, test-time scaling —allocating extra token-budget during inference—offers a training-free alternative to increasing accuracy. However, the trade-offs between these strategies and their interaction with model size remain poorly understood for medical reasoning. To address this gap, we compare three approaches—test-time scaling, fine-tuning, and context grounding—using the Gemma and MedGemma family of LLMs (Gemma-3 1B, Gemma-3 4B, Gemma-3 27B, MedGemma-4B, and MedGemma 27B) and evaluate these systems across common biomedical question-answering (QA) datasets and a set of recently released medical exam questions with the performance of practicing clinicians available for comparison. We test baseline prompts (direct answer, Chain-of-Thought, and self-consistency) while introducing a new prompting method we call “prompt-chaining for continuous reflection” (PCCR) that forces inference time minimum token-generation budgets. We assess accuracy and tokens-generated, allowing us to investigate the accuracy–efficiency trade-offs across prompting, context-grounding, fine-tuning, and model scales. We discover equivalency points where smaller models perform comparably to larger ones with increased reasoning budgets, context-grounding, or fine-tuning. We also find inflection points where context-grounding and test-time scaling used together lead to degrading performance. Using these empirical results, we formulate a general framework with equations to balance cost-benefit trade-offs when engineering LLM-based systems for medical reasoning and QA. We recommend generalizable configurations, designs, and patterns to achieve accuracy and efficiency objectives for example use-cases relevant to healthcare organizations. Author summary When doctors and hospitals want to use artificial intelligence for medical tasks, they face difficult choices. The most capable AI systems are expensive to run and require sending sensitive patient and hospital data to external servers. Smaller systems that can run locally are more practical but may be less accurate. Our research asked: with what configurations can we make smaller AI systems perform as well as larger ones for medical reasoning? We tested five AI models of different sizes, including both generalist and medically-specialized models, on thousands of medical questions using various prompting strategies, including a new and very simple method we developed that encourages the AI to reason more extensively. We discovered several surprising findings which we describe in detail. Most importantly, we found that smaller, cheaper models can match larger ones when given the right combination of prompting strategy, specialized training, and supporting information. We translated these findings into practical guidelines that can help choose AI configurations that balance accuracy, speed, and cost. Our framework could help make medical AI more accessible to institutions with limited computational resources while achieving the highest possible accuracy.","url":"https://doi.org/10.64898/2025.12.22.25342804","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.22.25342804","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.10.14.25338040","name":"Multidimensional Evaluation of Large Language Models on the AAP In-Service Examination: Assessing Accuracy, Calibration, and Citation Reliability","source":"preprints","abstract":"Background Large language models (LLMs) have demonstrated rapid advancements in natural language understanding and generation, prompting their integration into biomedical research, clinical practice, and professional education. However, systematic evaluation of LLMs in specialty-specific domains such as dentistry and periodontology remain limited, particularly regarding multidimensional performance metrics. Objective To conduct a comprehensive, multidimensional assessment of commercially available LLMs: GPT-4.0, GPT-5.0, and Claude SONNET 4.0 on the American Academy of Periodontology in-service examination, focusing on response accuracy, self-assessed confidence calibration, citation validity, and hallucination prevalence. Methods Models were evaluated on the 2024 AAP In-Service Examination (331 questions) using two formats: Full Test (all questions at once) and Individual Question (one at a time). Prompts were standardized; models selected answers, and for GPT-5.0 and Claude SONNET 4.0, also provided confidence ratings and citations. Citation validity was assessed using a human-in-the-loop protocol with expert review. Statistical analyses included chi-square, McNemar’s, and logistic regression to assess accuracy, question fatigue, confidence calibration, and citation reliability. Results LLMs achieved high overall accuracy (78–87%), with the Individual Question format consistently yielding higher scores than Full Test, though differences were not statistically significant. Accuracy was highest in fact-dense domains (biochemistry, physiology, microbiology) and lowest in integrative domains (diagnosis, therapy). Significant question fatigue was observed in GPT-5.0 Full Test mode (OR = 0.997, p = 0.035), but not in Individual Question mode. Confidence scores predicted accuracy, with the strongest calibration in Individual Question mode. Citation analysis revealed frequent hallucinations, mostly critically erroneous, and citation validity was independent of answer accuracy. Conclusions LLMs can answer a broad spectrum of periodontal specialty questions, but their reliability varies with context and information presentation. While promising as adjunctive tools, their outputs— especially for complex reasoning and citations—require rigorous human review in educational and research settings to ensure accuracy and safety. Author Summary Artificial intelligence chatbots are rapidly entering medical education, yet we lack comprehensive understanding of their reliability when students depend on them for learning. We developed a multidimensional evaluation framework to systematically assess AI performance beyond simple accuracy, examining how these systems behave across different medical topics, question types, and presentation formats. Using 331 real dental examination questions, we tested three major AI systems, analyzing not only correctness but also confidence calibration - whether AI confidence levels match actual accuracy - and implementing human-in-the-loop verification to check if cited sources actually exist. Our findings highlight critical vulnerabilities in current AI systems. Most alarmingly, these chatbots fabricated nearly half of their citations while maintaining unwavering confidence in both correct and incorrect responses. This combination of overconfidence and misinformation means students cannot distinguish reliable from unreliable AI responses. Additionally, we documented progressive performance decline during sequential questioning, similar to human cognitive fatigue. While we know AI systems generate rather than retrieve information, our research demonstrates the real-world consequences of this limitation. As artificial intelligence integrates into education, healthcare diagnostics, and insurance decisions, these findings underscore the urgent need for better evaluation frameworks and user education about AI limitations.","url":"https://doi.org/10.1101/2025.10.14.25338040","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.10.14.25338040","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7059359/v1","name":"Artificial Intelligence in Medicine: A Cross-sectional Study of Knowledge and Attitudes","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) holds promise in reshaping healthcare by transforming educational patterns, patient care, and research opportunities. However, there are obstacles impeding the proper integration of AI into the medical field. This study was undertakento evaluate the knowledge, attitude, and awareness of medical students and resident doctors regarding AI in medicine and healthcare. Methods: A questionnaire-based survey was conducted that included a total of 16 questions specifically designed to assess the knowledge and attitude of participants towards AI. The questionnaire used in the present study was developed for this study only and content validity of the initial questionnaire was adequately assessed. The questionnaire was converted into a Google Form, and participants were provided with the link to complete it. Statistical analysis was conducted using R version 4.3.2 (R-Studio). Results: Out of 194 respondents, 113 (58.25%) were medical students, and 81 (41.75%) were resident postgraduate doctors aged 19 to 32 (average 23.91 years) and a male-to-female ratio of 3.62:1. While 63.41% rated their AI knowledge as poor to below average, with 55.15% lacking understanding of many AI terminologies, 59.28% believed AI tools could enhance their understanding of medical concepts. 83.5% expressed interest in furthering knowledge on AI in healthcare. ChatGPT was the most used AI tool, primarily for language correction (50%), literature reviews and manuscript writing (43.3%), and creating presentation outlines (37.11%). Additionally, knowledge about AI devices and apps applicable to diagnostics, therapeutics, patient care, and data analysis was evaluated, along with opinions on barriers to incorporating AI in healthcare. 81.44% of respondents were unaware of AI's ethical considerations. Conclusions: AI has immense potential across diverse healthcare sectors. Nonetheless, our study also underscores the pressing need to confront challenges and equip our future healthcare professionals with the evolving realm of AI. This is essential to ensure they can effectively apply practical AI knowledge for enhanced patient care and management.","url":"https://doi.org/10.21203/rs.3.rs-7059359/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7059359/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.64898/2025.12.08.25340199","name":"Med-SSFWT: A Self-supervised Federated Weight Transfer Framework for Medical Model Fusion","source":"preprints","abstract":"Artificial Intelligence (AI) holds great potential to revolutionize healthcare by integrating and analyzing diverse multi-source medical data to drive advancements in disease diagnosis, treatment strategies, and patient management. However, deploying AI in distributed medical environments presents critical challenges, including data silos, label deficiency, and data heterogeneity. To address these challenges and enable effective and privacy-preserving distributed medical AI models, we propose Med-SSFWT, a Self-Supervised Federated Weight Transfer framework designed for medical data fusion. Firstly, Med-SSFWT employs a fine-tuned Large Language Model (LLM) to extract structured features from each client’s medical data, followed by feature alignment across clients via a shared global schema. Subsequently, an information gain-based gradient filtering mechanism is introduced to federated aggregation by filtering out ineffective gradients, thereby improving the robustness of global model. Furthermore, Med-SSFWT leverages a novel federated model fusion frame, consisting of self-supervised pre-training and fine-tuning through weight transfer to balance global optimization with client-specific personalization. Finally, extensive experiments show that Med-SSFWT consistently outperforms federated learning approaches in both performance and adaptability under diverse non-IID conditions, highlighting its effectiveness within distributed medical environments and establishing a foundation for the development of privacy-preserving and scalable AI-driven healthcare solutions.","url":"https://doi.org/10.64898/2025.12.08.25340199","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.08.25340199","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/mep.20833.1","name":"Resident Physicians’ Perceptions of Artificial Intelligence and Implications for Medical Education: A Qualitative Study","source":"preprints","abstract":"Background Educators have called for training in artificial intelligence (AI) in medical education given its certain impact on the future of healthcare. However, there is no consensus regarding how to introduce AI into medical education and little is known about how AI is viewed among medical trainees. In an effort to inform the development of medical education curricula on AI, this study explores perceptions of resident physicians regarding AI in healthcare and its possible impact on their future practice. Methods The authors conducted focus groups with resident physicians across multiple specialties in 2018-2019. Residents were invited to voluntarily participate during pre-existing conference times. Interview transcripts were coded iteratively, and coded data was clustered into categories and themes to capture resident perceptions on AI. Results Fifty-six residents from emergency medicine, internal medicine, pathology, pediatrics, and radiology participated in six separate focus groups. Conversations generated the following five overarching themes: healthcare is transforming, AI has a role at the clinical and systems level, concern for lack of agency in the development and implementation of AI, AI presents potential harms and uncertainties, and enduring roles of the physician: humanism, judgment, and responsibility. Conclusion Residents described humanistic roles that should not be replaced by technology and voiced concerns that physicians lack agency to influence how AI will be used in healthcare. Medical education should explore humanistic and ethical challenges related to AI, provide a foundational understanding of AI technology, and offer opportunities for participation in the development of AI technology when possible.","url":"https://doi.org/10.12688/mep.20833.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/mep.20833.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.12688/f1000research.166372.1","name":"New Horizons in Higher Education: Examining the Mental Well-Being of Medical & Health Sciences Students Through the Use of Artificial Intelligence Based Chatbot Platforms in the United Arab Emirates – A Cross-Sectional Comparative Study","source":"preprints","abstract":"Background: The primary barriers to effective and comprehensive treatment of mental disorders are insufficient resources and competent health and medical personnel, alongside social discrimination, stigma, and marginalization. Artificial intelligence-enabled technologies are emerging as a promising solution for longstanding difficulties, most notably is mobile-based therapy chatbots. Methods This is a quantitative, descriptive comparative research design aimed to identify the relationship between the utilization of the Artificial Intelligence Chabot, Stress, Anxiety, and Depression levels among Health Sciences University Students at a University within the United Arab Emirates. The sample was recruited from four health sciences Colleges by using Stratified random sampling technique (n= 298). Results Three tools were used for the data collection and the result revealed that a total of 206 participants (69.1%) reported having interacted with an AI chatbot, with the most used applications being Snapchat (76.9%), followed by ChatGPT and Bard (23.4% each). 40% of the participants reported that the chatbots understood them well, while 16% found that the chatbots helped to reduce their stress. Participants who used the AI chatbot were significantly more likely to suffer from moderate to extremely severe depression (63.5%) compared to those who had not used AI chatbots (36.7%, p<0.001). The multivariate regression analysis indicated that higher levels of depression (OR=1.022, 95% CI: 1.01-1.085, p<0.001) and anxiety (OR=1.05, 95% CI: 1.01-1.21, p<0.001) were strong predictors of increased AI chatbot usage. Conclusion Stress levels did not significantly predict AI chatbot usage. It is recommended that early intervention and support including university student counselling can significantly alleviate the burden of mental health issues and contribute to the overall well-being and academic success of students. AI chatbots in mental health care present a promising adjunct to nursing interventions; nonetheless, their implementation must be meticulously regulated to guarantee safe and practical assistance akin to the regulatory rigor imposed on registered healthcare practitioners.","url":"https://doi.org/10.12688/f1000research.166372.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.166372.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-8019481/v1","name":"VISTA-MED: Integrating AI-Based MRI Segmentation with Virtual Reality for Medical Education","source":"preprints","abstract":"Abstract Background Innovations in Artificial Intelligence (AI) and Virtual Reality (VR) are driving transformative changes in medical imaging and education by enabling automated segmentation and immersive visualization. Traditional anatomy and imaging instruction often lack spatial interactivity, limiting comprehension and diagnostic reasoning. This study introduces VISTA-MED, an integrated AI-VR platform designed to enhance medical imaging education through real-time MRI visualization and segmentation. Methods The system employs the nnU-Net architecture for automated segmentation of brain tumor regions using the BRATS2020 dataset. Preprocessing included bias field correction, image registration to the MNI152 template, and noise reduction. Segmentation outputs were converted into 3D-compatible formats and integrated into a Unity-based VR environment supporting interactive exploration of anatomical structures. Performance was evaluated using Dice coefficient, Intersection over Union (IoU), and precision-recall analysis. Results The segmentation model achieved a mean Dice score of 0.914, indicating high overlap between predicted masks and ground truth annotations. IoU values were consistent with Dice scores, confirming accurate delineation of tumor boundaries. Internal testing validated smooth rendering and stable frame rates in the VR environment, enabling real-time interaction without noticeable latency. Qualitative assessment confirmed anatomical fidelity of AI-generated masks. Conclusion VISTA-MED demonstrates the technical feasibility of combining AI-driven segmentation with immersive VR visualization for medical imaging education. This integration enhances spatial understanding and diagnostic reasoning, addressing limitations of traditional anatomy instruction. Future work will focus on usability studies, scalability, and educational impact assessment to support broader adoption in clinical and academic settings.","url":"https://doi.org/10.21203/rs.3.rs-8019481/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8019481/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.22541/au.176159477.73677953/v1","name":"AI-Driven Healthcare Cost Escalation: Balancing Rising Expenses Against Human Common-Sense Assessment","source":"preprints","abstract":"The integration of artificial intelligence (AI) into healthcare promises transformative efficiencies, yet it also raises concerns about potential cost escalations. This paper examines a simulated patient-AI interaction involving a 56-year-old man, Edoardo, presenting with a persistent sore throat. Using this scenario as a foundation, we debate how AI-driven medical management might \"spiral up\" costs through over-reliance on diagnostics and protocols, contrasted with a human physician's common-sense approach that emphasizes clinical judgment to minimize unnecessary expenditures. Drawing on evidence from healthcare economics, clinical practices, and real-world implementations, I explore both sides to provide a balanced perspective on AI's role in cost management as of 2025.","url":"https://doi.org/10.22541/au.176159477.73677953/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176159477.73677953/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202507.0171.v1","name":"Artificial Intelligence in Medical Education: Current Applications and a Proposed Comprehensive Integration Framework","source":"preprints","abstract":"Artificial intelligence (AI) is rapidly transforming healthcare, yet its integration into medical education remains inconsistent and lacks standardization across training levels. This comprehensive review synthesizes current research on AI applications within medical education, highlighting significant gaps between the growing clinical role of AI and its representation in undergraduate and postgraduate curricula. The review identifies disparities in AI awareness and utilization, with postgraduate trainees demonstrating greater familiarity than undergraduates, and notes that most existing educational efforts are concentrated in specialty training and continuing education, particularly in fields such as radiology, pathology, surgery, cardiology, and dentistry. While medical trainees generally express positive attitudes toward acquiring AI competencies, barriers such as the absence of standardized frameworks and AI taxonomy, limited faculty expertise, curricular constraints, and ethical considerations impede broader adoption. Drawing on examples of pioneering programs and a systematic analysis of curricular approaches, we propose a novel, tiered framework for comprehensive AI integration across the medical education continuum. This framework emphasizes universal AI literacy, critical evaluation skills, ethical awareness, and experiential learning at the undergraduate level, with extensions for specialty-specific training and advanced technical or leadership tracks. Recommendations include phased implementation strategies, faculty development initiatives, including “teach the teacher”, and competency-based assessment methods. The review concludes that adequate preparation of future physicians requires a shift from isolated AI initiatives to coordinated, longitudinal integration efforts supported by collaboration among educational institutions, professional societies, and technology experts. Future research should focus on evaluating educational outcomes, developing robust assessment tools for AI competencies, and examining the long-term clinical impact of AI training.","url":"https://doi.org/10.20944/preprints202507.0171.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202507.0171.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8051581/v1","name":"Deploying Medical AI in Low-Resource Settings: A Scoping Review of Challenges and Strategies","source":"preprints","abstract":"Abstract Artificial intelligence (AI) is transforming global healthcare by improving diagnostic accuracy, efficiency, and clinical decision-making. However, its implementation in low-resource settings (LRS) remains constrained by weak digital infrastructure, fragmented data systems, and limited governance capacity. This human-centered scoping review synthesizes recent evidence to identify the main challenges and practical strategies for deploying medical AI in low- and middle-income countries (LMICs). A total of thirty peer-reviewed Q1/Q2 studies published between 2020 and 2025 were analyzed thematically across four domains: digital infrastructure and connectivity, data quality and local capacity, ethics and governance, and policy and sustainability. The findings reveal that successful AI deployment in LMICs depends less on algorithmic sophistication and more on stable systems, trustworthy data, and empowered professionals. Key barriers include unstable electricity, limited internet access, outdated hardware, insufficient AI literacy, and weak governance. Recommended strategies emphasize investing in resilient digital infrastructure, building interoperable data repositories based on HL7/FHIR standards, expanding continuous training programs, establishing fairness audits, and embedding AI governance into national health strategies with sustainable financing. Ultimately, sustainable and equitable medical AI requires embedding human values—transparency, privacy, and equity—into every phase of design and deployment. Meaningful innovation in global health depends on amplifying human judgment with compassionate, trustworthy, and context-aware AI systems, rather than replacing it. The review confirms that properly implemented AI systems can strengthen health service delivery, enhance diagnostic accuracy, and reduce inequalities in access to care.","url":"https://doi.org/10.21203/rs.3.rs-8051581/v1","authors":["Abdulelah Mansoor Al-Ganad","Ahmed H. Al-Shahethi","Othman Al-Dhaifi","Essam Hajeb","Huwaida Hajeb","Ahmed Al‐Motarreb"],"tags":["Interoperability","Data governance","Sophistication","Knowledge management","Computer science"],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8051581/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.06.15.26355448","name":"Usability testing with a prototype user interface of an Artificial Intelligence driven air-Safety Tool (AISaT)","source":"preprints","abstract":"Involving end-users in the development of an AI tool is an important facilitator to its implementation. Usability testing was therefore conducted with a prototype user interface of an Artificial Intelligence driven air-Safety Tool (AISaT) to capture the perspectives and user experiences of AISaT from 10 staff members across two hospitals working within estates, infection prevention and control, and clinical areas, to inform the development of next iterations of AISaT. The perspectives shared could be grouped under improvements to the understand-ability; content; navigation; visibility; usability; workflow; ownership; and frequency of use of the tool. There were key areas that can and will be easily improved within AISaT, however there were areas that required a deeper level of critical reflection, such as incorporating data on more existing variables in a room (i.e., existing ventilation) and determining who is infected, and the level of breathing.","url":"https://doi.org/10.64898/2026.06.15.26355448","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.15.26355448","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.20944/preprints202507.1985.v1","name":"The Role of Artificial Intelligence in Herpesvirus Detection, Transmission, and Predictive Modeling: With a Special Focus on Marek’s Disease Virus","source":"preprints","abstract":"Herpesvirus infections, including herpes simplex virus (HSV), Epstein-Barr virus (EBV), and cytomegalovirus (CMV), present significant challenges in diagnosis, treatment, and transmission control. Despite advances in medical technology, managing these infections remains complex due to the viruses&#039; ability to establish latency and their widespread prevalence. Artificial Intelligence (AI) has emerged as a transformative tool in biomedical science, enhancing our ability to understand, predict, and manage infectious diseases. In veterinary virology, AI applications offer considerable potential for improving diagnostics, forecasting outbreaks, and implementing targeted control strategies. This review explores the growing role of AI in advancing our understanding of herpesvirus infection, particularly those caused by MDV, through improved detection, transmission modeling, treatment strategies, and predictive tools. Employing AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), researchers have made significant progress in addressing diagnostic limitations, modeling transmission dynamics, and identifying potential therapeutics. Furthermore, AI holds the potential to revolutionize personalized medicine, predictive analytics, and vaccine development for herpesvirus-related diseases. The review concludes by discussing ethical considerations, implementation challenges, and future research directions necessary to fully integrate AI into clinical and veterinary practice.","url":"https://doi.org/10.20944/preprints202507.1985.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202507.1985.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2025.12.16.694385","name":"Prediction of transformative breakthroughs in biomedical research","source":"preprints","abstract":"The ability to predict scientific breakthroughs at scale would accelerate the pace of discovery and improve the efficiency of research investments. Recent advances in artificial intelligence, graph theory, and computing power have provided new ways to pursue this elusive goal. We have identified a common signature within co-citation networks that accurately predicts the occurrence of breakthroughs in medical research, on average more than 5 years in advance of the subsequent publication(s) that announced the discovery. A combination of features produces these diagnostic signals: a burst of papers exploring a novel scientific concept, an unusually high number of very influential papers in specialty journals, and low topical cohesion of the associated content. We analyzed two different periods separated by 20 years to show that the kinetics of breakthrough formation are conserved, suggesting that our approach can be used to predict which topics will produce future transformative discoveries. Significance statement Scientific breakthroughs are rare, as is contemporaneous recognition of their initial expression. Faster, more efficient identification of topics likely to produce future breakthroughs would speed scientific and technological progress. We introduce an AI/ML-detected signature in co-citation networks that recognizes such topics up to twelve years before the breakthrough itself occurs. Our findings illustrate how a better understanding of the scientific process may lead to greater scientific returns.","url":"https://doi.org/10.64898/2025.12.16.694385","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.16.694385","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.11.03.25339443","name":"PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI","source":"preprints","abstract":"Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary co-creation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants’ perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and co-design approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare. Author Summary Artificial intelligence (AI) is being used more and more in healthcare to improve diagnosis, treatment, and personalised care. However, if not designed carefully, these technologies can unintentionally increase existing inequalities and exclude certain groups from their benefits. In our study, we brought together experts from healthcare, ethics, law, social sciences, and patient advocacy to explore how AI in medicine can be made more inclusive. Over five days, we worked together to identify key issues and come up with practical solutions. We focused on three main areas: 1) Ensuring diverse voices are heard during the development of AI tools; 2) Making data collection fair and representative; and 3) Creating regulations that protect human rights. From the discussions of the workshop, we created the PREFER-IT framework, which outlines eight key principles for inclusive AI: P articipatory and co-design approaches R epresentative and diverse data E ducation and digital literacy F airness E thical and legal accountability R eal-world validation and feedback I nclusive communication T echnical interoperability This framework helps guide developers, policymakers, and healthcare professionals in creating AI systems that are not only effective but also fair and respectful of all users. Our work emphasises the importance of involving patients and communities in shaping the future of AI.","url":"https://doi.org/10.1101/2025.11.03.25339443","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.11.03.25339443","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7017188/v1","name":"GAN-DCNN: A Generative Adversial Network based Deep Convolutional Neural Network for Retinal Vessel Enhancement","source":"preprints","abstract":"Abstract Artificial intelligence is taking over the technology transmission in all areas. Generative AI is now mostly used in the healthcare sector due to its vivid application area. The emergence of Generative Adversial Network (GAN) in medical domain is highly recommended. The proposed work brings the essence of deep convolutional network that differentiates real and generated retinal images. This work highlights the advancement of GAN over other artificial intelligence models. By identifying appropriate networks of GAN, retinal images are generated in high quality. Low resolution images are enhanced with GAN models. The proposed work involves basic preprocessing, augmentation and enhancement with the retinal images that provide better accuracy with epochs. Deep learning models with GAN implementation are highly preferred for the retinal classification.","url":"https://doi.org/10.21203/rs.3.rs-7017188/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7017188/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.1101/2025.07.30.25332423","name":"Medical Clinical Minds Meet Artificial Intelligence: Italian Physicians’ Knowledge, Attitudes, and Concordance between Italian Physicians and AI-Generated Diagnoses. A National Cross-Sectional Study","source":"preprints","abstract":"Background Artificial Intelligence has increasingly been integrated into clinical practice, yet its adoption and perception among medical professionals remain poorly understood, particularly in the Italian healthcare system. To investigate Italian physicians’ knowledge, attitudes, and clinical concordance with AI-generated diagnostic recommendations, using a validated questionnaire and a clinical scenario processed by ChatGPT. Methods A national, cross-sectional web-based survey was conducted among 587 Italian physicians using an online validated questionnaire. The first part of the questionnaire assessed self-reported knowledge, prior experience, attitudes, and willingness to adopt AI in medicine. The second part assessed clinical concordance between AI proposals and physicians about clinical cases evaluated by ChatGPT. Results Most participants reported basic AI knowledge (n=380, 64.8%) and minimal exposure to AI training (18.4%). Only 21.6% reported they used AI in clinical practice, and the most familiar application was diagnostic imaging (35.4% of AI users; 7.7% of the total sample). Major perceived barriers included lack of training (76.7%) and resistance to change (50.9%). In the universal clinical scenario, physicians showed the highest agreement with ChatGPT’s correct diagnosis (mean=4.07) compared to incorrect alternatives (mean=2.57 and 1.82, p Conclusions Italian physicians showed a strong interest in adopting AI tools, despite significant knowledge gaps and limited practical experience. The high concordance between physicians’ evaluations and ChatGPT’s diagnostic insights suggests potential for AI-based decision support in clinical workflows. Targeted training and institutional support are essential to bridge the gap between enthusiasm and readiness for AI integration.","url":"https://doi.org/10.1101/2025.07.30.25332423","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.07.30.25332423","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.21203/rs.3.rs-7744596/v1","name":"Performance of Artificial Intelligence and Large Language Models (LLMs) on Neurosurgical Board Examinations Across Text and Visual Modalities","source":"preprints","abstract":"Abstract Background: Large language models (LLMs) are increasingly applied in clinical use and medical education, yet their reliability across both textual and visual modalities in highly specialized domains such as neurosurgery remains unclear. Prior evaluations have been limited to text-only questions or narrow subsets of models, leaving the role of multimodal reasoning poorly defined. Methods: We benchmarked the latest LLMs, including 12 text-only and 10 multimodal systems, on 476 board-style neurosurgical questions spanning 13 subspecialties. Models were queried synchronously, temperature=0 under standardized prompting with no reasoning allowed mirroring examination conditions. Accuracy was compared across text and visual modalities, stratified by subspecialty and imaging type. Robustness was assessed using latency, parsing failures, and ablation experiments withholding clinical vignette components. Results: Text only and multimodal models achieved nearly identical mean accuracies of 67.9% and 68.5%, indicating that visual inputs did not provide a consistent overall benefit. Performance differed markedly across individual models. Gemini 2.5 Pro, Grok, and GPT 5 exceeded 80% accuracy, approaching resident performance. GPT 4.0 and GPT 4.5 followed in the high 70s. Claude Sonnet 3.7 and Claude Opus 4.1 performed in the mid 70s, while MedGemma and Llama 4 clustered in the low 70s. DeepSeek R1V3 performed close to chance. On image-based questions Gemini 2.5 Pro again led, while Grok, GPT 4.0, GPT 5, and Claude Opus clustered near 70% and Llama 4 models dropped to approximately 50%. Subspecialty analysis showed that visual input improved performance in neuroradiology, tumor, pediatrics, and spine. Trauma, vascular, and pain questions became less accurate with images, producing a bimodal pattern of benefit. Ablation experiments showed that removal of history produced the largest decline in accuracy (19.3% reduction), while withholding physical exam or lab data produced smaller effects (6.0% and 5.9%). A set of questions that no model could answer correctly accounted for 4% of the dataset. These questions were clustered in neuroradiology, vascular anatomy, and rare pediatric condition. Operational findings highlighted practical issues. The most accurate models were often slower to respond. Latency ranged from 0.22 seconds to more than 27 seconds. Parsing failures were uncommon in GPT 5, GPT 4.5, and Llama 4 but exceeded 13% in Claude Opus. Conclusions: Current LLMs can approach resident-level performance in structured neurosurgical domains and demonstrate selective benefits from visual input, but remain unreliable in anatomy-heavy, high-stakes contexts such as vascular and trauma. Their dependence on clinical history and susceptibility to systematic visual errors highlight the need for improved vision–language alignment before unsupervised clinical use. Until then, their role is best suited to supervised educational support with explicit safeguards.","url":"https://doi.org/10.21203/rs.3.rs-7744596/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7744596/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8413099/v1","name":"WITHDRAWN: Causal Workflow AI: Learning Clinical Care Pathways for Safe and Trustworthy Decision Support","source":"preprints","abstract":"Abstract Artificial intelligence has made impressive progress in healthcare, yet real-world clinical adoption remains limited due to a fundamental disconnect between static prediction models and dynamic clinical workflows. Current systems fail to capture the sequential nature of medical decision making, lack causal understanding of treatment effects, and provide recommendations that often violate clinical safety constraints. This leads to clinician mistrust and limited practical utility. We introduce CausalCare, a comprehensive framework that bridges this gap through integrated causal inference, temporal modeling, and explicit safety validation. Our approach learns clinical care sequences from multimodal EHR data by constructing causal workflow graphs that respect temporal precedence, incorporate domain knowledge, and enforce safety constraints through a multi-layered validation system. CausalCare provides transparent, step-level explanations aligned with clinical reasoning patterns through three complementary mechanisms: causal pathway visualization, safety rationale presentation, and temporal context analysis. Extensive validation across four large-scale clinical datasets (MIMIC-IV, eICU, OMOP-CDM, MIMIC-CXR) demonstrates superior performance in predicting clinically appropriate next actions (F1-score: 0.81 vs 0.76 best baseline) while reducing unsafe recommendations by 69% compared to state-of-the-art baselines. Our framework achieves particular strength in complex scenarios requiring causal understanding, such as medication sequencing and diagnostic test ordering. A comprehensive ablation study confirms the synergistic contributions of causal learning, safety constraints, and temporal modeling, with the integrated framework outperforming individual components by 11-16%. Through detailed case studies in sepsis management, heart failure, and depression treatment, we demonstrate CausalCare’s ability to respect clinical sequencing, avoid contraindications, and provide interpretable decision support. The framework establishes a new paradigm for trustworthy clinical AI that aligns with human reasoning, workflow patterns, and safety imperatives, representing a significant advancement toward clinically deployable decision support systems.","url":"https://doi.org/10.21203/rs.3.rs-8413099/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8413099/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7364932/v1","name":"Efficacy and Efficiency of Artificial Intelligence-based Preoperative Anesthesia Evaluation: A Randomized Controlled Non-Inferiority Study","source":"preprints","abstract":"Abstract Study Objective: To evaluate the clinical effectiveness and feasibility of a novel AI-assisted anesthesia evaluation system compared with conventional preoperative anesthesia assessments. Design: Single-center, randomized, parallel-group, open-label, non-inferiority trial. Patients: 600 adult patients scheduled for elective non-cardiac surgery under general anesthesia were randomly assigned to AI-assisted assessment group or traditional in-person anesthesia evaluation group. Measurements: The primary outcome was the accuracy of ASA (American Society of Anesthesiologists) physical status classification. Secondary outcomes included the quality and completeness of medical history documentation, assessment duration, and patient satisfaction. Main Results: The AI-assisted group achieved non-inferior accuracy in ASA classification compared to the traditional group (93.3% [280/300] vs. 91.7% [275/300], P = 0.033). The difference in accuracy was 1.6% (95% CI: -2.6% to 5.9%), with the lower bound above the predefined non-inferiority margin of -5%. The AI system demonstrated significantly higher documentation quality, including fewer missing items (4.3% vs. 21.7%) and incorrect entries (7.0% vs. 18.0%). Assessment time was markedly shorter in the AI group (3.0 [2.0-5.0] vs. 7.0 [6.0-8.0] minutes, P 0.001), and overall patient satisfaction was significantly higher (87.3% [262/300] vs. 76.0% [228/300], P 0.01). Conclusions: The AI-assisted anesthesia evaluation system was non-inferior to traditional assessment in ASA classification accuracy while offering superior efficiency, documentation quality, and patient satisfaction. This AI-based approach may represent a scalable and effective alternative for preoperative anesthesia evaluations across diverse clinical settings. Registry: chictr.org.cn, TRN: ChiCTR2400086869, Registration date: 12 July 2024. Retrospectively registered","url":"https://doi.org/10.21203/rs.3.rs-7364932/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7364932/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.64898/2026.04.29.26352039","name":"Chronic psychosocial stress is associated with higher MRI-visible perivascular space volumes in healthy young adults","source":"preprints","abstract":"Chronic psychosocial stress (CPS) is associated with adverse brain and mental health outcomes. Effects on the cerebral microvasculature have been proposed as an underlying mechanism, although this remains to be established. Here, we examined the association between CPS and an early marker of microvascular dysfunction, magnetic resonance imaging (MRI)-visible perivascular spaces (PVS). Analyses were conducted in two cohorts of healthy young adults (N = 61; ages 18-43 years; 88% male) using high-resolution 3T MRI and an automated PVS quantification pipeline. CPS was assessed using the Perceived Stress Scale (PSS-10). We applied a two-step meta-analytic framework and controlled for known allostatic factors impacting PVS, including age, body mass index and mean arterial pressure. In accordance with our hypothesis, individuals with higher CPS had significantly higher fractional PVS volumes in the centrum semiovale (CSO), in particular in the frontal and occipital lobes (p FDR .09). Our findings indicate that CPS may contribute to subtle, centrum semiovale specific microvascular alterations even in healthy young adults. Future multimodal research including inflammatory marker and blood-brain barrier measures may help to elucidate mechanistic pathways.","url":"https://doi.org/10.64898/2026.04.29.26352039","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.29.26352039","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.09.22.25336169","name":"Retrospective Validation of an Artificial Intelligence System for Diagnostic Assessment of Prostate Biopsies on the ProMort Cohort: Study Protocol","source":"preprints","abstract":"ABSTRACT Introduction Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass other critical diagnostic and prognostic markers, including cancer volume quantification and detection of cribriform morphology patterns and perineural invasion. Artificial intelligence (AI) applications in digital pathology have emerged as promising solutions for enhancing diagnostic consistency and accuracy, with recent research demonstrating that automated systems can match expert-level performance in prostate biopsy evaluation. Nevertheless, comprehensive validation studies have revealed concerning limitations in model generalisability when deployed across different clinical environments and patient populations. Recent systematic reviews revealed widespread risk-of-bias limitations and insufficient external validation in AI diagnostic studies, highlighting critical needs for accumulated evidence supporting generalisability before clinical implementation. Rigorous external validation with preregistered protocols using independent datasets from diverse clinical settings remains essential to establish the reliability and safety of AI-assisted prostate pathology systems. Methods and analysis This study protocol establishes a framework for the retrospective external validation of an AI system developed for prostate biopsy assessment, to be conducted on the case-control samples of the National Prostate Cancer Register of Sweden, ProMort study (1998-2015). The primary aim is to evaluate the AI model’s diagnostic accuracy and Gleason grading performance using completely independent datasets separate from any model development or previously used validation cohorts. The diversity of the validation samples, spanning multiple geographic regions, temporal collection periods, and reference standards, allows evaluation of model robustness across varied clinical contexts. Secondary aims encompass evaluating AI performance in cancer length estimation and detection of cribriform patterns and perineural invasion. This protocol delineates procedures for data collection, reference standard clarification, and prespecified statistical analyses, ensuring comprehensive validation and reliable performance assessment. The study design conforms to established reporting guidelines CLAIM and STARD-AI, and recognised best practices for AI validation in medical imaging. Ethics and dissemination Data collection and usage were approved by the Swedish Regional Ethics Review Board and the Swedish Ethical Review Authority (permits 2012/1586-31/1, 2016/613-31/2, 2019-01395, 2019-05220). The study adheres to the Declaration of Helsinki principles, and findings will be made available in open access peer-reviewed publications. STRENGTHS AND LIMITATIONS This study incorporates case-control subsamples from Sweden’s largest clinical prostate cancer database (the National Prostate Cancer Register, NPCR), capturing a broad spectrum of variation across Swedish regions. The validation dataset encompasses samples collected from 1998 to 2015, representing one of the first AI validation studies to systematically evaluate performance across such an extensive temporal range, capturing evolving histological sample preparation techniques and changing population characteristics. A consistent scanning and annotation platform during digitisation eliminates equipment-related technical variation, while standardised annotation protocols among pathologists ensure traceable and reliable reference standards. Case-control design with 50% cancer-related mortality may create spectrum and prevalence bias, limiting comparison with typical clinical populations and other AI studies. Differences between the diagnostic reporting guidelines applied to the AI model’s training data and our validation dataset may introduce systematic differences that affect the interpretation of AI-pathologist concordance measurements.","url":"https://doi.org/10.1101/2025.09.22.25336169","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.09.22.25336169","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6803964/v1","name":"Undergraduate medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using generative artificial intelligence for academic assignments: A qualitative study","source":"preprints","abstract":"Abstract Background Growing ethical concerns have emerged regarding the misuse of generative artificial intelligence (GenAI) for academic assignments among undergraduate medical students. Given that the quality of medical education directly shapes students’ professional competencies, exploring both medical students’ and teachers’ perspectives on GenAI use for academic assignments carries significant implications for medical education. Objective To explore medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using GenAI for academic assignments. Methods This study employed a descriptive phenomenological approach using semi-structured in-depth interviews. Purposive sampling was used to recruit undergraduate medical students and their teachers from one medical university between January and April 2025 in Guangzhou, China. Data were analyzed through Colaizzi’s phenomenological method, supplemented by deductive analysis guided by the Responsible Innovation framework to identify key themes. Results A total of 19 participants were interviewed, including 11 undergraduate medical students and 8 teachers. The participants expressed a consensus on the ethical challenges arising from the use of GenAI for academic assignments. Following a thematic analysis, three themes were identified: (1) Subversion posed by GenAI, (2) Limitations and potential risks of using GenAI, and (3) Coping strategies in response to utilisation of GenAI. Conclusions The integration of GenAI in education has raised significant academic and ethical concerns. However, existing regulatory policies and student evaluation mechanisms have yet to adapt to the ethical challenges posed by GenAI. Therefore, strategies such as implementing academic integrity policies for GenAI-assisted assignments, establishing transparent oversight mechanisms, and promoting GenAI literacy education should be adopted to address these issues.","url":"https://doi.org/10.21203/rs.3.rs-6803964/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6803964/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.20944/preprints202507.2446.v1","name":"Generative Artificial Intelligence Applications in Reproductive Health: Opportunities and Challenges","source":"preprints","abstract":"Generative artificial intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, is emerging as a tool to address declining birth rates and rising infertility. It enhances reproductive education, preventive interventions, and doctor-patient communication, providing personalized healthcare information discreetly. Sexual and reproductive health is a fundamental human right, yet many lack access to quality care. GenAI offers 24/7 access to reproductive health information through chatbots, aiding patients with diagnoses and lifestyle advice. While useful, AI tools sometimes lack depth for sensitive topics. AI can create educational content tailored to various literacy levels and cultural contexts, helping individuals understand complex medical information. AI tools can destigmatize menstruation and provide personalized insights through cycle tracking, improving health management and education. AI enhances in vitro fertilization (IVF) success through better diagnostics and personalized risk profiles, though it requires careful validation and regulation. AI can improve the accessibility and quality of sexual health education, offering personalized guidance on contraceptive methods. AI chatbots can bridge gaps in reproductive healthcare for marginalized groups by providing confidential, multilingual support. AI tools can enhance communication, helping patients prepare for consultations and engage in shared decision-making. Despite its potential, GenAI faces challenges such as accuracy, bias, privacy concerns, and accessibility issues. Regulatory oversight is still developing. GenAI is transforming reproductive health by providing personalized support and education. However, it must be implemented with a focus on safety, equity, and human connection, ideally through a hybrid approach that combines AI tools with human oversight.","url":"https://doi.org/10.20944/preprints202507.2446.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202507.2446.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.1101/2025.08.14.670328","name":"Accelerating Biomolecular Modeling with AtomWorks and RF3","source":"preprints","abstract":"Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework ( https://github.com/RosettaCommons/atomworks ), together with curated training data, code and model weights for RF3 ( https://github.com/RosettaCommons/modelforge ) under a permissive BSD license.","url":"https://doi.org/10.1101/2025.08.14.670328","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.08.14.670328","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8087983/v1","name":"Deep Learning–Based Approach for Quality Control Scoring of Digital Pathological Sections","source":"preprints","abstract":"Abstract Objective To explore the auxiliary role and application of deep learning-based artificial intelligence (AI) in quality control (QC) evaluation of digital pathological section. Methods A total of 2137 routine hematoxylin and eosin (HE) slides from Department of Pathology, the First Affiliated Hospital of Army Medical University, collected between January and December 2022, were scanned into digital slides. Based on slide evaluation standards, these digital slides were scored into four grades: A, B, C, and D. ResNet50, ResNet101, EfficientNet-B5, and Swin Transformer networks were then employed for classification learning. During model training, parameters trained on the ImageNet dataset were used as initial values, and QC data were utilized to perform secondary training and optimization of the models. A set of 429 routine HE slides was selected for model validation and deep learning. Results Among the four classification models, Swin Transformer achieved the best performance for all grades except for grade D, where ResNet50 performed optimally. Overall, the Swin Transformer demonstrated the highest performance with an accuracy rate of 0.83 and an Area Under the Curve (AUC) value of 0.88. The prediction speed reached 0.6 seconds per slide. Conclusion This study preliminarily validates that a deep learning-based auxiliary QC scoring system built on Swin Transformer performs well in terms of accuracy and timeliness for slide QC evaluation. This method can significantly enhance the efficiency of pathology slide QC and plays an important role in the development of an informative pathology department.","url":"https://doi.org/10.21203/rs.3.rs-8087983/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8087983/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.1101/2025.07.04.25330464","name":"Artificial intelligence in prenatal ultrasound: A systematic review of diagnostic tools for detecting congenital anomalies","source":"preprints","abstract":"Background Artificial intelligence (AI) has potentially shown promise in interpreting ultrasound imaging through flexible pattern recognition and algorithmic learning, but implementation in clinical practice remains limited. This study aimed to investigate the current application of AI in prenatal ultrasounds to identify congenital anomalies, and to synthesise challenges and opportunities for the advancement of AI-assisted ultrasound diagnosis. This comprehensive analysis addresses the clinical translation gap between AI performance metrics and practical implementation in prenatal care. Methods Systematic searches were conducted in eight electronic databases (CINAHL Plus, Ovid/EMBASE, Ovid/MEDLINE, ProQuest, PubMed, Scopus, Web of Science and Cochrane Library) and Google Scholar from inception to May 2025. Studies were included if they applied an AI-assisted ultrasound diagnostic tool to identify a congenital anomaly during pregnancy. This review adhered to PRISMA guidelines for systematic reviews. We evaluated study quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines. Findings Of 9,918 records, 224 were identified for full-text review and 20 met the inclusion criteria. The majority of studies (11/20, 55%) were conducted in China, with most published after 2020 (16/20, 80%). All AI models were developed as an assistive tool for anomaly detection or classification. Most models (85%) focused on single-organ systems: heart (35%), brain/cranial (30%), or facial features (20%), while three studies (15%) attempted multi-organ anomaly detection. Fifty percent of the included studies reported exceptionally high model performance, with both sensitivity and specificity exceeding 0.95, with AUC-ROC values ranging from 0.91 to 0.97. Most studies (75%) lacked external validation, with internal validation often limited to small training and testing datasets. Interpretation While AI applications in prenatal ultrasound showed potential, current evidence indicates significant limitations in their practical implementation. Much work is required to optimise their application, including the external validation of diagnostic models with clinical utility to have real-world implications. Future research should prioritise larger-scale multi-centre studies, developing multi-organ anomaly detection capabilities rather than the current single-organ focus, and robust evaluation of AI tools in real-world clinical settings.","url":"https://doi.org/10.1101/2025.07.04.25330464","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.07.04.25330464","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.0687.v1","name":"MCR-SL: A Multimodal, Context-Rich Skin Lesion Dataset for Skin Cancer Diagnosis","source":"preprints","abstract":"Well-annotated datasets are fundamental for developing robust artificial intelligence models, particularly in medical fields. Many existing skin lesion datasets have limitations in image diversity (including only clinical or dermoscopic images) or metadata, which hinder their utility for mimicking real-world clinical practice. The purpose of the MCR-SL dataset is to introduce a new, meticulously curated dataset that addresses these limitations. The MCR-SL dataset was collected from 60 subjects at the University Hospital of North Norway and comprises 779 clinical images and 1,352 dermoscopic images of 240 unique lesions. The lesion types included are nevus, seborrheic keratosis, basal cell carcinoma, actinic keratosis, atypical nevus, melanoma, squamous cell carcinoma, angioma, and dermatofibroma. Labels were established by combining the consensus of a panel of four dermatologists with histopathology reports for the 29 excised lesions, with the latter serving as the gold standard. The resulting dataset provides a comprehensive resource with clinical and dermoscopic images and rich clinical context, ensuring a high level of clinical relevance, surpassing many existing resources in that matter. The MCR-SL dataset provides a holistic and reliable foundation for validating artificial intelligence models, enabling a more nuanced and clinically relevant approach to automated skin lesion diagnosis that mirrors real-world clinical practice.","url":"https://doi.org/10.20944/preprints202509.0687.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.0687.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8264890/v1","name":"Performance of Next-Generation AI Chatbots in Gynecological Knowledge Assessment: A Comparative Pilot Study of ChatGPT-5, Gemini-3, DeepSeek-V3.2, and Claude-4.5-Opus","source":"preprints","abstract":"Abstract Purpose As artificial intelligence (AI) models evolve into their next generations, their application in specialized medical fields requires rigorous validation. While large language models (LLMs) have shown promise in general medicine, their reliability in complex gynecological clinical reasoning remains under-explored. This pilot study aimed to comparatively assess the knowledge retention, safety, and reasoning limitations of advanced AI chatbots in gynecology using a constrained zero-shot multiple-choice question (MCQ) format. Methods A total of 70 text-based MCQs covering seven core gynecological modules were adapted from USMLE Step 2 CK standards. The questions were administered to four advanced AI models: ChatGPT-5, Gemini-3, DeepSeek-V3.2, and Claude-4.5-Opus. To simulate a rapid-retrieval clinical scenario, models were tested under \"zero-shot\" conditions with a constrained prompt prohibiting reasoning steps. We performed both quantitative statistical analysis (Kruskal–Wallis, Cochran’s Q) and qualitative error analysis to identify specific failure modes. Results Contrary to expectations for advanced models, overall accuracy was unsatisfactory: Gemini-3 (32.86%), DeepSeek-V3.2 (30.00%), ChatGPT-5 (25.71%), and Claude-4.5-Opus (21.43%). Significant performance disparities were observed across modules. Notably, ChatGPT-5 scored 0.00% in Infertility , while DeepSeek-V3.2 reached 70.00% in Common Benign Conditions . Qualitative analysis revealed three critical failure patterns: (1) Semantic Association Bias (confusing high-probability diseases with symptom-specific diagnoses), (2) Spatial Anatomy Confusion, and (3) Genetic Logic Reversal. No significant correlation was found between item difficulty and accuracy (p > 0.05). Conclusion Under constrained non-reasoning prompts, even next-generation AI chatbots demonstrate unsatisfactory performance in gynecology. The qualitative analysis suggests that models often rely on probabilistic keyword matching rather than physiological simulation, leading to dangerous clinical errors (e.g., misdiagnosing adrenal enzymes). While potential exists, current reliability is insufficient for unsupervised use in gynecological education. These findings highlight the critical need for \"Chain-of-Thought\" prompting and human expert oversight.","url":"https://doi.org/10.21203/rs.3.rs-8264890/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8264890/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.20944/preprints202508.0052.v1","name":"Equitable Health Intelligence: Reclaiming Diagnostic Sovereignty Through Innovationology and Noesology in African Health Systems","source":"preprints","abstract":"The integration of Artificial Intelligence (AI) into global healthcare systems has revolutionized diagnostic accuracy and speed. Yet, in African contexts, this transformation remains marked by structural marginalization and epistemic dissonance. AI-driven diagnostics, largely developed in the Global North, often operate on clinical models and datasets that exclude African genomic, phenotypic, and cultural realities. This results in diagnostic errors, algorithmic bias, and the perpetuation of algorithmic colonialism. This article introduces the paradigm of Equitable Health Intelligence (EHI)—a framework that transcends technical efficiency to address ontological justice, contextual relevance, and data sovereignty in African diagnostics. Drawing from Innovationology and Noesology, we argue for a pluriversal approach to medical intelligence—one that integrates indigenous epistemologies, relational care logics, and collective intelligence systems. The paper synthesizes empirical research from Kinshasa (DRC) and Kisumu (Kenya), proposes the Moleka Grid for pluralistic diagnostic architecture, and offers a blueprint for a Pan-African Health AI Charter. Ultimately, we reframe diagnostics not as a neutral act of detection, but as a deeply political, cultural, and ethical process central to epistemic sovereignty and health justice in Africa.","url":"https://doi.org/10.20944/preprints202508.0052.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202508.0052.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8066178/v1","name":"Chat GPT Against Medical Students: A Comparative Analysis of Image-Based Medical Examination Results","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI), particularly large language models like ChatGPT, is increasingly shaping medical education. While these systems show promise for automated feedback and adaptive assessments, their performance in visually intensive, image-based disciplines remains insufficiently studied. Objective To compare the performance of ChatGPT-4.0 and undergraduate medical students on standardized, image-based multiple-choice questions in Anatomy, Pathology, and Pediatrics, evaluating domain-specific strengths and limitations of generative AI in visual reasoning. Standardized exams were administered to second-, third-, and fifth-year students, and the same questions were submitted to ChatGPT-4.0 using a two-step deterministic and stochastic protocol. Items with images that ChatGPT failed to recognize were excluded. Student responses were pooled after verifying normality, variance, and sample size equivalence, with subgroup analyses restricted to questions with a discrimination index ≥ 0.1. Paired t-tests or Wilcoxon signed-rank tests were used for comparisons. Results Of 90 questions, only 52 were eligible for analysis due to ChatGPT’s inability to interpret certain images. ChatGPT significantly underperformed in Anatomy (mean difference = − 0.387, p","url":"https://doi.org/10.21203/rs.3.rs-8066178/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8066178/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8150723/v1","name":"Instant3D: A User-Friendly GUI Integrating TotalSegmentator for Immediate Medical Image Segmentation and 3D Reconstruction","source":"preprints","abstract":"Abstract Background: Automatic segmentation is indispensable in medical imaging, yet advanced tools often remain confined to experts due to command-line complexity. TotalSegmentator delivers accurate multi-organ segmentation, but lacks accessibility for clinicians and educators. To overcome this barrier, we developed Instant3D, an open-source graphical user interface (GUI) that makes high-quality 3D reconstruction straightforward and intuitive. Objective: The purpose of this study was to introduce the Instant3D GUI and validate its performance. Methods: We developed Instant3D in Python using PyQt6. It accepts DICOM, NIfTI, or NRRD input. Users select regions of interest through a suggestion-enabled interface, and the tool automatically runs TotalSegmentator. Outputs include STL meshes for 3D visualization, CSV files with volumetric data, and per-slice SVG masks. Crucially, these SVG files are interoperable with SegRef3D, enabling interactive correction and refinement of automated results—combining the strengths of artificial intelligence segmentation and user-driven adjustment. Batch processing is supported for large datasets. We validated Instant3D by testing it on representative computed tomography (CT) and magnetic resonance imaging (MRI) datasets, including publicly available example data from the 3D Slicer Sample Data module. Results: Instant3D reliably produced 3D models from CT and MRI scans. STL meshes preserved anatomical fidelity, SVG masks facilitated slice-level review and editing in SegRef3D, and CSV outputs provided quantitative volume data. The GUI eliminated the need for command-line knowledge, lowering the entry barrier for diverse users across research and education. Conclusions: We developed Instant3D, an open-source and user-friendly platform that democratizes advanced segmentation by combining automation with practical usability. It provides a seamless GUI, bridging advanced automatic segmentation with practical applications in research and development. Clinical Impact: Instant3D 3D reconstructions extend beyond visualization, offering immediate value for radiomics-driven quantitative research, virtual reality-based surgical simulation, 3D printing for surgical planning and patient education, and glasses-free 3D display teaching tools.","url":"https://doi.org/10.21203/rs.3.rs-8150723/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8150723/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7941605/v1","name":"From Embedded Ethics to Explainable AI: Advancing Interdisciplinary Collaboration in Medical AI Development","source":"preprints","abstract":"Abstract The development of artificial intelligence (AI) for healthcare, particularly in high-stakes environments like intensive care units, presents significant interdisciplinary challenges. These challenges arise from the need to integrate diverse expertise from computer science, medicine, operations research, ethics, and social sciences, each with distinct priorities. Ethical considerations often become secondary or are reduced to compliance checklists, risking the creation of systems that are technically sound but ethically misaligned. This paper aims to address the fragmentation in interdisciplinary collaboration by proposing the first in-depth case study of the Embedded Ethics and Social Sciences (EE) framework into the development process of medical AI in a real-world project. The objective is to enhance ethical quality and strengthen interdisciplinary collaboration by providing a shared technical-ethical interface for dialogue. The paper explores the EE approach within the KISIK project, an interdisciplinary research initiative developing an AI-based clinical decision support system for intensive care units in the German healthcare system. The integration of EE facilitated greater awareness for ethically salient issues, throughout the interdisciplinary development of medical AI. Thereby, XAI emerged as a valuable extension of EE. XAI served as a technical instrument for embedding ethical considerations into system design, enhancing model interpretability and fostering communication across disciplinary boundaries. The combination of EE and XAI offers a promising foundation for interdisciplinary understanding in medical AI projects. EE fosters broader normative reflection on project goals, while XAI enables technically grounded engagement with ethical questions. This integrated approach can lead to more ethically aligned and socially responsible AI systems in healthcare.","url":"https://doi.org/10.21203/rs.3.rs-7941605/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7941605/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.20944/preprints202505.2281.v1","name":"Advancing Explainable Artificial Intelligence for Clinical Decision Support: Techniques, Challenges, and Evaluation Frameworks in High-Stakes Medical Environments","source":"preprints","abstract":"As artificial intelligence (AI) continues to transform the landscape of healthcare, the integration of Explainable Artificial Intelligence (XAI) into clinical decision support systems (CDSS) has emerged as a critical necessity. This chapter explores the vital role of XAI in enhancing the interpretability and transparency of AI-driven medical applications, particularly in high-stakes environments where decisions can profoundly impact patient outcomes. We begin by defining XAI and its significance in fostering trust among healthcare professionals and patients alike, emphasizing the ethical imperatives of accountability and safety in clinical settings. We examine a range of techniques for achieving explainability, categorizing them into model-agnostic methods, model-specific approaches, visualization techniques, and case-based reasoning. Each category is discussed with respect to its applicability and effectiveness in conveying understandable insights to clinicians. Additionally, we address the multifaceted challenges associated with implementing XAI, including the complexity of medical data, the inherent trade-offs between model accuracy and interpretability, and the resistance from healthcare professionals accustomed to traditional decision-making processes. To ensure the successful deployment of XAI in CDSS, we propose comprehensive evaluation frameworks that assess the clarity, consistency, and actionability of explanations provided by AI systems. User-centered evaluation methods, such as surveys and usability testing, are discussed as essential tools for gathering feedback from healthcare practitioners, thereby enhancing the integration of XAI into clinical workflows. Through case studies of successful implementations, we highlight the practical benefits of XAI in predictive analytics and treatment recommendations, showcasing how explainability enhances clinical outcomes and decision-making processes. The chapter concludes by identifying future directions for research and development, including advancements in XAI techniques, the incorporation of XAI with emerging technologies, and collaborative efforts among stakeholders to promote the adoption of explainable systems in healthcare. Ultimately, this chapter underscores the imperative of advancing explainable AI in clinical decision support, advocating for a balanced approach that prioritizes both technological innovation and the critical human elements of trust, transparency, and ethical responsibility in patient care.","url":"https://doi.org/10.20944/preprints202505.2281.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202505.2281.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.20944/preprints202511.0519.v1","name":"Bridging Ethics and Law in AI Systems with a Structured Rule Language","source":"preprints","abstract":"Artificial intelligence (AI) systems often need to follow both ethical and legal rules. Sometimes, these rules can conflict. For example, a healthcare AI may need patient consent (ethical rule), but the law might allow data sharing in emergencies (legal rule). This paper introduces a domain-specific language (DSL) to help represent and solve such conflicts. The DSL uses simple and readable syntax so that people without technical training—like ethicists or legal professionals—can write rules clearly. The DSL is automatically translated into formal Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) rules, which work with existing reasoning tools. We tested this DSL in a case study where a medical AI system had to decide whether to access patient data. The DSL helped define when consent is required and when emergency access is allowed. We compared the DSL with writing rules directly in SWRL. The DSL was easier to use, less error-prone, and just as accurate. It also made it easier to update the rules when policies change. This shows that a DSL can support better teamwork between technical and non-technical people. Beyond simplifying rule logic, the DSL improves the transparency and adaptability of normative reasoning in AI. Future work will focus on extending the language’s capabilities to address more nuanced conflicts and scaling its application across broader deployment scenarios.","url":"https://doi.org/10.20944/preprints202511.0519.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202511.0519.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.64898/2025.12.19.25342205","name":"Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public","source":"preprints","abstract":"Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm’s opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias – accuracy boosted when AI was correct, reduced when AI erred – while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI’s varying impact based on expertise and timing, underscoring LLMs as a “double-edged sword” in medical AI and informing future human-AI collaborative system design.","url":"https://doi.org/10.64898/2025.12.19.25342205","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.64898/2025.12.19.25342205","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8462289/v1","name":"The Impact of Artificial Intelligence-Enabled Virtual Simulation on Clinical Reasoning in Dental Students in Managing Temporomandibular Disorders","source":"preprints","abstract":"Abstract Background The study aims to compare the efficacy of a virtual simulation-based teaching approach with a traditional method for clinical thinking training in temporomandibular disorders(TMD) for dental students. Methods This study developed a virtual simulation program for clinical thinking training related to TMD, which includes basic knowledge learning, virtual simulation practice, and learning effect testing. A quasi-experimental design was adopted, with 97 senior dental students as the experimental group, receiving the virtual simulation experimental teaching mode; and 81 senior students of the same major as the control group, adopting the traditional teaching mode. The teaching efficacy was evaluated through theoretical tests, written experimental reports, and questionnaires. Result The experimental group achieved significantly higher theoretical test score (84.6 ± 8.5 vs 78.7 ± 9.8, p vs 79.8 ± 9.2, p via personalized pathways and real time feedback, thereby improving students' competence in TMD diagnosis and treatment planning.","url":"https://doi.org/10.21203/rs.3.rs-8462289/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8462289/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6732717/v1","name":"Performance of Large Language Artificial Intelligence Models on Clear Aligner Treatment: Evaluation of Accuracy and Readibility in Answering Questions for Patients","source":"preprints","abstract":"Abstract Objective This study aims to scientifically evaluate the responses provided by ChatGPT 4o to patient-posed questions regarding clear aligner treatment and to investigate the impact of artificial intelligence on medical processes. Materials and Methods A survey of 200 patients collected questions about clear aligner treatment, identifying 29 frequently asked ones. Researchers added 14 additional questions, resulting in 43, categorized into usage, side effects/complications, treatment limitations, and others. These questions were posed to chatbot simultaneously. The chatbot’s responses were analyzed for narrative level, readability, informational accuracy, and plagiarism using scoring systems. The data underwent statistical analysis with SPSS V.22. Results The highest FKGL score was observed in usage-related questions (14.53 ± 8.88), while the highest FRES score was in the other group (54.30 ± 0.89). DISCERN, EQIP, and GQS scores were highest in the usage and side effect/complication groups. Correlation analysis showed the strongest correlation between similarity and FKGL (r= -0.391, p = 0.010). For the top 10 questions, the average FKGL was 13.37 ± 4.37, and the average FRES was 47.18 ± 23.69. Regression analysis identified no significant relationships. Conclusion ChatGPT 4o demonstrates potential as a supplementary tool in patient education under professional supervision. However, further research is required to optimize its application and ensure its reliability in clinical settings.","url":"https://doi.org/10.21203/rs.3.rs-6732717/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6732717/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7793831/v1","name":"Comparative Evaluation of SHAP and LIME for Clinical Interpretability in Postoperative Cardiac Surgery Mortality Prediction Models","source":"preprints","abstract":"Abstract Aims Despite their strong predictive performance, complex machine learning (ML) models are often criticized for their lack of interpretability, especially in high-stakes clinical settings. This study aims to compare two leading explainable artificial intelligence (XAI) methods—SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME)—when applied to a validated XGBoost model for 30-day mortality prediction after cardiac surgery. We explore their ability to provide transparent, clinically meaningful insights to support medical decision-making. Methods and Results: Building upon a previously developed XGBoost model with high discrimination (AUC-ROC = 0.964), we applied SHAP and LIME to interpret model predictions across five representative clinical cases. These cases included true positives, true negatives, false positives, false negatives, and borderline predictions. For each case, visual outputs were generated, feature attributions were analyzed, and explanations were evaluated by clinical experts based on clarity, trust, and alignment with medical reasoning. SHAP consistently identified relevant risk contributors such as MACE, creatinine, and frailty indicators, and offered both global and local interpretability. LIME explanations were more concise but showed variability in feature attribution, often omitting clinically significant variables like MACE. In false negative and borderline cases, SHAP provided clearer representations of risk, whereas LIME tended to oversimplify or misattribute contributing features. Clinician feedback favored SHAP in all dimensions evaluated. Conclusion: Our results suggest that SHAP outperforms LIME in providing clinically aligned, trustworthy, and interpretable explanations for ML-based mortality prediction in cardiac surgery. While both methods can enhance transparency, SHAP’s consistency and richer information content make it better suited for complex clinical use. These findings support the integration of SHAP-based interpretability tools into clinical decision support systems, particularly in scenarios where trust and explanation fidelity are critical for patient safety and risk communication.","url":"https://doi.org/10.21203/rs.3.rs-7793831/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7793831/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-6623487/v1","name":"Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) enables computers to process data and solve problems via algorithms, with China at the forefront of medical AI applications like diagnostics. Medical students increasingly rely on AI tools (e.g., ChatGPT) for education, and ML advances predictive research. Methods A cross-sectional study assessed AI readiness among 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, using the validated MAIRS-MS scale (22 items across 4 domains). Data collection combined electronic and paper questionnaires, ensuring high participation and reliability (α = 0.87). Results Most participants were from Hebron University (66%) and represented all academic years. The majority (83%) were aware of AI in medicine, and 73% had prior experience with AI tools. The median total readiness score was 73 (IQR: 66–84), with highest scores in ability (median: 27) and cognition (median: 26), and lower scores in vision and ethics (both median: 10). Males, older students, high-GPA achievers, and those from higher-income backgrounds had significantly higher readiness scores (p","url":"https://doi.org/10.21203/rs.3.rs-6623487/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6623487/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7388040/v1","name":"Mixed Methods Assessment of ChatGPT Accuracy and Reliability in Healthcare Queries by Medical Residents","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is gaining traction in healthcare, with Chat GPT used for medical information retrieval. However, there is limited real-world evidence on how medical professionals in regions like India perceive, use these tools. Objective This study evaluates the accuracy, reliability of GPT in addressing healthcare-related queries while exploring its advantages, limitations in medical practice. Methods A mixed-methods study was conducted with 34 residents from 17 specialties at medical college. Participants rated GPT’s responses to five specialty-specific questions using a six-point Likert scale. Intra-rater reliability was tested by repeating queries after 10–15 days; inter-rater reliability was assessed via peer assessments within specialties. Additionally, semi-structured interviews with 7 residents explored perceived benefits, limitations of Chat GPT in clinical settings. The study was conducted in December 2024. Clinical trial number is not applicable. Results The participation response rate was 33.3% (34 out of 102 resident doctors). Of the 170 medical queries assessed, GPT’s responses had a median accuracy score of 5.5 rated between “almost completely correct” and “completely correct.” Binary questions scored slightly higher (median 6) than descriptive ones (median 5). Reliability was strong, with Intraclass Correlation Coefficient (ICC) at 0.82 and inter-rater ICC at 0.79. Qualitative findings highlighted two themes: GPT's utility in clinical research, diagnosis, education; ethical concerns, including medico-legal risks, occasional inaccuracies, limited handling of complex cases, and risk of over-reliance. Conclusion GPT shows high accuracy, reliability with healthcare queries, especially factual ones, but ethical concerns, limitations require ongoing human oversight. Continued improvement of AI tools is needed for safer, wider clinical use.","url":"https://doi.org/10.21203/rs.3.rs-7388040/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7388040/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.22541/au.176274559.99330641/v1","name":"Performance of the SafeTerm AI-Based MedDRA Query System Against Standardised MedDRA Queries","source":"preprints","abstract":"Background: In pre-market drug safety review, grouping related adverse event terms into Standardised MedDRA Queries (SMQ) or the FDA Office of New Drugs Custom Medical Queries (OCMQs) is critical for signal detection. Objective: We assess the performance of Safeterm Automated Medical Query (AMQ) on MedDRA SMQs. The AMQ is a novel quantitative artificial intelligence system that understands and processes medical terminology and automatically retrieves relevant MedDRA® Preferred Terms (PTs) for a given input query, ranking them by a relevance score (0-1) using multi-criteria statistical methods. Methods: The system (SafeTerm) embeds medical query terms and MedDRA PTs in a multidimensional vector space, then applies cosine similarity, and extreme-value clustering to generate a ranked list of PTs. Validation was conducted against tier-1 SMQs (110 queries, v28.1). Precision, recall and F1 were computed at multiple similarity-thresholds, defined either manually or using an automated method. Results: High recall (~94%) is achieved at moderate similarity thresholds, indicative of good retrieval sensitivity. Higher thresholds filter out more terms, resulting in improved precision (up to 89%). The optimal threshold (~0.70) yielded an overall recall of ~48% and precision of ~45% across all 110 queries. Restricting to narrow-term PTs achieved slightly better performance at an increased (+0.05) similarity threshold, confirming increased relatedness of narrow versus broad terms. The automatic threshold (0.66) selection prioritizes recall (0.58) to precision (0.29). Conclusions: Safeterm AMQ achieves comparable, satisfactory performance on SMQs and sanitized OCMQs. It is therefore a viable supplementary method for automated MedDRA query generation, balancing recall and precision. We recommend using suitable MedDRA PT terminology in query formulation and applying the automated threshold method to optimise recall. Increasing similarity scores allows refined, narrow terms selection.","url":"https://doi.org/10.22541/au.176274559.99330641/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176274559.99330641/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.12688/mep.21319.1","name":"The Use of AI by First-Year Medical Students: A Qualitative Study of Perspectives, Usage, and Recommendations","source":"preprints","abstract":"Background: Artificial Intelligence (AI) is reshaping healthcare and medical education, with growing calls to embed it in medical curricula. However, evidence on first-year medical students, perceived benefits and limitations of AI, and views on ethics and professionalism is limited. Methods A qualitative study was conducted using semi-structured interviews to explore the experiences, attitudes, and perceptions of first-year students regarding AI. Convenience sampling yielded the participant cohort. Recruitment and analysis continued until thematic saturation was achieved. Transcripts were coded iteratively using NVivo software, and a reflexive thematic analysis was undertaken. Results Twenty participants were interviewed; 18 were AI users, to varying degrees, and two were non-users. Seven themes emerged: How AI is used; Benefits; Concerns and limitations; Ethical considerations; Advice for peers and professors; Attitudes toward and understanding of AI; and Participation in the project. AI users cited motivations like efficiency, personalization, and support. Benefits included faster access to information, organized content, and tailored explanations. Concerns included AI reliability, over-reliance, and ethical misuse, such as plagiarism. Most supported the inclusion of AI literacy in curricula for responsible, practical, and critical use of AI. Participants with AI literacy demonstrated a deeper understanding of AI. Conclusions Students are early adopters of AI, using it in various ways, and wish to utilize it effectively and ethically. Medical curricula should ensure early AI literacy.","url":"https://doi.org/10.12688/mep.21319.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/mep.21319.1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7253638/v1","name":"From Data to Decisions: A Modular Platform for modelling and Simulation of Infectious Disease Diffusion in Networks","source":"preprints","abstract":"Abstract Accurately modelling diffusion dynamics in complex networks is essential for improving medical outcomes, guiding pandemic preparedness, and optimizing resource allocation in public health. However, existing approaches often face a trade-off between predictive performance and model interpretability, limiting their utility for clinical decision-making and strategic planning. This study presents a modular computational methodology that integrates classical compartmental models with graph neural networks (GNNs) and explainable artificial intelligence (XAI) to simulate, analyse, and interpret the spread of contagion across heterogeneous network topologies. The approach captures both structural and temporal dimensions of diffusion processes, enabling granular insights into transmission pathways. Simulations are applied to critical public health scenarios, including the identification of super-spreaders and the assessment of targeted containment strategies. By combining mechanistic models with data-driven learning and explainability techniques, the methodology supports outcome forecasting, scenario comparison, and the interpretation of network-based risk factors. Results demonstrate the ability to predict diffusion trajectories with high accuracy while preserving transparency in decision-relevant variables. The approach is intended as a generalizable tool to support medical modelling and simulation with applications ranging from epidemic control to personalized risk assessment and cost-effective intervention planning.","url":"https://doi.org/10.21203/rs.3.rs-7253638/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7253638/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.64898/2026.01.29.26345129","name":"Assessing AI tool use among New York State clinicians","source":"preprints","abstract":"ABSTRACT Artificial intelligence (AI) is dramatically changing the healthcare landscape by providing patients, clinicians, administrators, and public health professionals with tools aiming to improve efficiency, outcomes, and experience in health. As elsewhere, New York State (NYS) experiences high demand for - and high investment in - transformation in healthcare with AI tools, though little is known about clinicians’ use and interest in adopting AI tools in their work. A large share of the nation’s future primary care clinicians train and work in NYS, and the state’s ability to establish clear policies, provide tools, and elevate AI competency have implications for care delivery nationally. As a result, we undertook this analysis of NYS clinicians’ use of AI to better understand opportunities for its adoption and inclusion in continuing education. For this analysis, we included healthcare providers who deliver ambulatory or specialty medical care within NYS, with use/frequency/purpose of AI tools by clinicians in their work as the main outcome. Of 305 NYS clinical providers responding, 23.4% indicated they use AI tools for work, and 11.1% report monthly use, 8.5% weekly use, and 4.6% daily use. AI was primarily used to search guidelines and ask clinical questions, followed by identifying drug interactions, analyzing data, analyzing images/labs, and creating care plans and patient recommendations. AI use did not vary significantly across professional disciplines or practice types, though independent practitioners were significantly more likely than advanced practice providers to use AI in their work, as were providers using social media and digital methods for obtaining continuing education. AI use increased substantially in 2025 compared with 2024. Overall, our findings suggest that programs targeting clinicians could incorporate these findings in designing accessible and acceptable AI-related continuing education opportunities to help familiarize clinicians with opportunities and risks for integrating AI tools into their practices. Author Summary AI tools are rapidly gaining traction in the delivery of healthcare. We found that clinician use of AI was quite limited (23%), though growing. Those using AI tools used them sparingly in their work, with only about 5% reporting daily use. The purposes for which clinicians report using AI – asking clinical questions, interpreting patient results, creating patient educational materials - could contribute substantially to healthcare outcomes if widely adopted. Designers of continuing education for clinicians should help provide opportunities for clinicians to improve their familiarity, use, and competency with AI tools, to help maximize the potential health benefits possible for patients and communities.","url":"https://doi.org/10.64898/2026.01.29.26345129","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.29.26345129","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.1101/2025.06.25.25328523","name":"Attitudes and Perceptions of University Students and Postdoctoral Fellows in the Medical and Life Sciences Towards the Use of Artificial Intelligence Chatbots in the Educational Process: A Large-Scale, International Cross-Sectional Survey","source":"preprints","abstract":"Background Artificial intelligence chatbots (AICs) are advanced systems capable of generating and processing human-like text, and are being increasingly integrated in various fields, including education. Despite their potential to significantly impact learning, little is known about university students’ and postdoctoral fellows’ (US&PD) views on AICs in educational settings. This study investigated the familiarity, perceptions, and factors influencing adoption of AICs by US&PDs in the life and medical sciences. Methods We conducted a cross-sectional online survey. Recruitment involved two approaches: (1) using R script on PubMED metadata to extract contact details of corresponding authors with recent MEDLINE-indexed publications, and (2) collecting publicly listed contact information of program administrators from the top 50 global, English-speaking universities, as ranked by the Quacquarelli Symonds (QS) list. Both authors and administrators were contacted and requested to forward the survey to US&PDs. The survey was administered via SurveyMonkey from February 2 to March 18, 2024, with two reminder emails sent between February 14 and 26, 2024. Results A total of 1,209 responses were analyzed. Most respondents identified as female (62.07%) and were enrolled in doctoral (40.48%) or master’s programs (17.55%). Over 63% were familiar with AICs, with ChatGPT being the most used (60.3%). While many recognized the educational value of AICs, concerns about reliability and integration into academia persisted. Calls for more training and institutional support were common. Conclusions The study underscores the potential and challenges of AICs in education. While enthusiasm exists, significant concerns remain about their implementation, requiring targeted training and policy development.","url":"https://doi.org/10.1101/2025.06.25.25328523","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.06.25.25328523","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.1101/2025.11.19.689212","name":"Mapping the AI Life Sciences Landscape in Greece: A National Survey and Bibliometric Comparison with Global Trends","source":"preprints","abstract":"ABSTRACT Artificial intelligence is increasingly used in Life Sciences, though the pace and direction of adoption varies widely across countries. To map the Greek landscape, we combined two complementary approaches, a data-driven analysis of 916,824 AI-related life-science papers harvested from OpenAlex and PubMed and a targeted short survey at a national level aimed towards researchers, engineers, and support staff to be used as supporting material. We tagged each publication with Medical Subject Headings (MeSH) and compared topic frequencies between articles linked to at least one Greek institution and the rest of the world. Greek-affiliated outputs are disproportionately concentrated under the theme of methodology and algorithm-development, whereas the global corpus is dominated by disease-focused, organism-centered and clinical applications. Statistical contrasts across three MeSH hierarchy levels exposed clear national strengths in machine learning techniques and analytical tools, alongside under-representation in translational, patient-centred research. Survey responses reinforce these patterns: participants highlight limited access to well-curated biomedical data, constrained computational resources and difficulty recruiting cross-disciplinary talent as the chief barriers to progress. They advocate community-building actions (e.g., hackathons, postgraduate training, shared data infrastructures) that could realign national efforts with international practice while capitalizing on existing expertise in core-methodology development. Overall this study combines bibliometric evidence with community perspectives and provides a comprehensive overview of AI activity in Life Sciences in Greece, highlighting potential thematic strengths and gaps.","url":"https://doi.org/10.1101/2025.11.19.689212","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.11.19.689212","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8252644/v1","name":"Optimizing Scientific Manuscript Preparation for Scopus-Indexed Publication: A Comprehensive, Critical Review of Best Practices, Pitfalls, and Research-Validated Strategies","source":"preprints","abstract":"Abstract The growing demand for publication in Scopus-indexed journals has reshaped the priorities of researchers, particularly in the medical sciences, where academic advancement and institutional reputation are closely tied to indexed output. Yet, despite extensive guidance on scientific writing, no review has critically synthesized the diverse methodological, ethical, and practical challenges that shape manuscript preparation in the contemporary era of artificial intelligence. This systematic review follows PRISMA 2020 recommendations to evaluate current evidence on best practices, common pitfalls, and emerging strategies that influence successful publication. Searches across major databases and registers identified 1,462 records, of which 39 studies met eligibility criteria. Included literature addressed core domains of manuscript structure, journal selection, research integrity, AI-assisted writing, reviewer expectations, and the evolving landscape of predatory publishing. Findings reveal that successful submissions stem from a convergence of conceptual clarity, methodological rigor, ethical transparency, and strategic alignment with journal scope. Contrary to common assumptions, Scopus acceptance is influenced less by article processing charges or co-authorship prestige and more by adherence to guidelines, clarity of argumentation, and demonstrable novelty. AI tools offer meaningful improvements in organization and linguistic refinement but carry ethical constraints, particularly regarding undisclosed use, fabricated citations, and authorship attribution. Across studies, consistent themes emerged: the importance of humanizing narrative tone, maintaining transparency in AI involvement, and preparing figures, tables, and references early in the drafting process. This review provides an integrated, evidence-informed blueprint for authors seeking to optimize manuscript preparation and navigate the increasingly complex pathway toward Scopus-indexed publication.","url":"https://doi.org/10.21203/rs.3.rs-8252644/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8252644/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202507.2330.v1","name":"Digital Health Technologies in Medicine: Evidence, Artificial Intelligence Integration, and Ethical Challenges","source":"preprints","abstract":"Digital health technologies (DHTs), including digital therapeutics (DTx), are revolutionizing patient care by enabling the prevention, management, and treatment of medical conditions. These tools comprise care delivery mobile applications, wearable devices, and cloud platforms for capturing real-time data and enabling remote monitoring. DTx also encompasses artificial intelligence (AI) and machine learning algorithms, as well as AI agents and digital twins (DTs) for clinical decision-making and predictive analytics. Evidence supports the effectiveness of DHT strategies across different clinical fields. For example, wearable and remote patient monitoring technologies enable continuous assessment and personalized feedback in cardiology and neurology. Additionally, AI-enabled devices are mostly implemented for continuous monitoring of glucose levels. However, several key challenges remain. Persistent gender and social biases in datasets and algorithms raise ethical concerns, particularly for underrepresented groups and pediatric populations. Mitigation strategies include regulatory frameworks, explainable AI, and trustworthy AI ecosystems. This narrative review synthesizes current evidence, highlights implementation barriers, and proposes recommendations to enhance inclusivity, interoperability, and real-world evaluation of these technologies. Applications of DHT in animals within the one digital health framework are also discussed.","url":"https://doi.org/10.20944/preprints202507.2330.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202507.2330.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.22541/au.174845022.24373824/v1","name":"Artificial Intelligence and Informed Consent: Reimagining Patient Education and Ethical Disclosure in AI-Supported Healthcare Decisions","source":"preprints","abstract":"The integration of Artificial Intelligence (AI) into healthcare is rapidly transforming diagnostic, predictive, and treatment planning paradigms, offering significant potential to enhance medical decision-making. However, this technological advancement introduces complex challenges to the foundational principles of informed consent, which are crucial for upholding patient autonomy and ethical medical practice. This work examines the critical need to re-evaluate and reimagine informed consent processes in the context of AI-supported healthcare decisions. It specifically addresses the difficulties in ensuring adequate patient education about complex AI systems-including issues of transparency, the \"black box\" phenomenon, and algorithmic bias-and the requirements for ethical disclosure. Key considerations include how to effectively communicate the role of AI, its performance characteristics, data governance, potential risks, benefits, and alternatives to patients with varying levels of health and digital literacy. The paper explores strategies for enhanced patient education, the evolving role of healthcare providers as interpreters of AI-driven insights, and the necessity for updated ethical frameworks and regulatory considerations. Ultimately, it argues for a proactive and adaptive approach to informed consent to ensure that as AI becomes more integral to healthcare, patient understanding, trust, and the ability to make genuinely informed choices are not only preserved but strengthened.","url":"https://doi.org/10.22541/au.174845022.24373824/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.174845022.24373824/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.22541/au.176045955.54249074/v1","name":"Formative feedback to improve pharmacology learning: Proposed principles and guidelines for effective practice","source":"preprints","abstract":"Aims: . To learn effectively, students need timely, specific, actionable feedback---known as formative feedback---on what and how well they are learning and on how to improve further. Providing feedback to help students learn pharmacology effectively is a complex task. Currently, however, pharmacology educators lack evidence-based guidelines for effective, efficient practice. This article aims to fill that gap. First, it briefly defines formative assessment and feedback and explains how they can affect student learning. Second, it offers pharmacology teachers research-based principles and related practical guidelines for using formative feedback to improve student learning. These principles and guidelines may also be useful in training generative artificial intelligence programs to support formative feedback provision. Third, it aims to stimulate interest in, use of, experimentation with, and research on formative feedback in pharmacology education. Methods. Relevant research literature was reviewed from higher education, medical and health sciences education, pharmacy education, and pharmacology education on effective practice of formative assessment and formative feedback published in English between 2000 and 2024. Findings. While there is a substantial body of empirical research on formative feedback in higher education, relatively little has been published in medical and health sciences education. In pharmacology education, no relevant research on formative feedback was located, making a formal literature review unviable. Conclusion. Students need formative feedback to learn. Teachers need guidance to provide that feedback effectively and efficiently. Pharmacology teachers and learners can benefit from practical principles and guidelines for formative feedback practice based in the most current, relevant research.","url":"https://doi.org/10.22541/au.176045955.54249074/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.176045955.54249074/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9293057/v1","name":"Implementation and Adoption of Novel Antenatal Technologies for Routine Pregnancy Care in Low-Income Settings: A Multicentre Qualitative Study","source":"preprints","abstract":"Abstract Background Stillbirth remains a major global health problem, with most occurring in low- and middle-income countries (LMICs). Novel antenatal technologies, including Doppler ultrasound, maternal haemodynamic monitoring and artificial intelligence (AI)-enabled ultrasound, may improve antenatal risk detection. However, successful implementation in routine care depends on more than technical performance. We explored the perspectives of women, healthcare workers, and hospital managers on the implementation of novel antenatal technologies within routine pregnancy care services in Uganda. Methods This is a qualitative study, embedded within the multi-site iTECH study across four Ugandan referral hospitals. Data were collected through focus group discussions with women attending antenatal care and village health team members; semi-structured interviews with women who had experienced stillbirth or recent live birth and healthcare workers involved in antenatal care; and key informant interviews with hospital managers. Data were analysed using an inductive thematic approach, supported by NVivo 14, to identify emerging themes. Results Fifty-six participants were included: 27 women, 18 healthcare workers, and 11 community leaders. Three interrelated themes emerged: (1) acceptability of novel antenatal technologies, (2) integration into routine clinical practice, and (3) health system adoption. Participants viewed these technologies as potentially valuable for improving risk detection, particularly when accompanied by clear communication and person-centred care. However, effective integration depended on the ability of healthcare workers to interpret results, coordinate across teams, and incorporate findings into clinical workflows. Wider adoption was constrained by including workforce shortages, lack of standardised guidelines, infrastructure instability, and governance gaps. AI-enabled ultrasound was perceived as promising particularly where sonography capacity was limited, but its value was context-dependent, requiring validation, clinical integration, and health-system readiness. Conclusions Novel antenatal technologies have the potential to strengthen maternal and perinatal care in LMICs, but their impact depends on alignment across three interconnected levels: acceptability, integration into routine practice, and health system adoption. Embedding technologies within person-centred communication, coordinated workflows, and supportive health systems is essential to delivering meaningful clinical benefit.","url":"https://doi.org/10.21203/rs.3.rs-9293057/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9293057/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7461983/v1","name":"Generative AI Chatbots in Chinese Postgraduate Medical Education: Adoption Patterns, Attitudes, and Concerns","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (AI) chatbots are gaining attention in medical education for their potential to support academic writing, clinical reasoning, and personalized learning. However, little is known about their adoption, benefits, and concerns among Chinese postgraduate medical students, particularly across distinct clinical- and academic-track programs. Methods A cross-sectional survey was conducted among 340 postgraduate medical students (146 males, 194 females) from two universities in Chengdu, China. A structured questionnaire assessed AI awareness, usage patterns, perceived benefits, attitudes, and concerns. Descriptive statistics, sensitivity analyses, and Pearson correlation analyses were applied to examine differences by gender and degree type. Results Most students (82.9%) reported strong AI awareness, with DeepSeek (90.9%) and ChatGPT (55.2%) being the most frequently used tools. Common applications included literature review (61.5%), exam preparation (55.0%), clinical case analysis (48.5%), and academic writing (43.2%). Reported benefits encompassed faster information retrieval (70.8%), improved writing precision (64.3%), and greater confidence in clinical decision-making (58.0%). Overall satisfaction was high (mean 4.4/5), and 84.0% of participants supported integration of AI into medical curricula. Female students reported more frequent use but greater concerns, while clinical-track students demonstrated higher awareness and satisfaction than academic-track students. Correlation analyses revealed positive associations among awareness, usage, perceptions, and attitudes, whereas concerns about accuracy, academic misconduct, and data security were largely independent of prior exposure. Conclusions Generative AI chatbots are widely adopted and valued by Chinese postgraduate medical students. Structured integration into medical curricula, accompanied by clear ethical safeguards, is essential to maximize benefits while addressing potential risks.","url":"https://doi.org/10.21203/rs.3.rs-7461983/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7461983/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7179137/v1","name":"Multicenter Machine Learning Model for Assessing the Impact of Malignancy on In-Hospital Mortality in Heart Failure Patients: A Clinical Decision Support System with Interpretable Artificial Intelligence","source":"preprints","abstract":"Abstract Background Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the specific impact of malignancy on in-hospital mortality in HF patients remains incompletely understood, and reliable predictive tools specifically for HF patients with comorbid malignancy are currently lacking. Methods This multicenter retrospective study analyzed data from the eICU Collaborative Research Database (eICU database), Medical Information Mart for Intensive Care IV (MIMIC-IV) databases, and Medical Information Mart for Intensive Care III (MIMIC-III) databases, including 21,636 HF patients (3,397 with malignancy). We employed three analytical approaches: propensity score matching (PSM), inverse probability treatment weighting (IPTW), and multivariable logistic regression to assess malignancy-associated mortality risk. For predictive modeling, five machine learning algorithms were trained on the eICU database (70% training, 30% internal validation) and externally validated using both MIMIC-IV and MIMIC-III datasets. Results All three analytical methods (PSM, IPTW, and multivariable regression) yielded highly consistent results, demonstrating that malignancy significantly increased in-hospital mortality risk (PSM: OR 1.14, 95% CI 1.02–1.26; IPTW: OR 1.16, 95% CI 1.03–1.30; multivariable regression: OR 1.20, 95% CI 1.07–1.35). The AdaBoost model, developed using 19 key predictive variables selected by the Boruta algorithm, demonstrated excellent performance with a training set AUC of 0.849 and internal validation AUC of 0.740, while maintaining good discriminative ability in external validation (MIMIC-IV: AUC 0.739; MIMIC-III: AUC 0.699). To enhance model interpretability, SHapley Additive exPlanations analysis revealed the top five predictive variables: Simplified Acute Physiology Score II, mechanical ventilation requirement, heart rate, body temperature, and respiratory rate. For clinical implementation, we developed a web-based calculator (available at: https://nanzihan1998.shinyapps.io/Mortality/) to facilitate real-time mortality risk assessment. Conclusions Malignancy independently worsens outcomes in HF patients. Our interpretable machine learning model incorporating multiple clinically relevant predictors provides accurate mortality risk stratification, facilitating personalized clinical decision-making for this high-risk population. Future studies should incorporate longitudinal data and novel biomarkers to further improve predictive performance.","url":"https://doi.org/10.21203/rs.3.rs-7179137/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7179137/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7281320/v1","name":"Interpretable Machine Learning Model for Pediatric Primary Nephrotic Syndrome Risk Prediction","source":"preprints","abstract":"Abstract [Background] Primary nephrotic syndrome (NS) in children is a common chronic kidney disease in pediatrics, characterized by complex pathogenesis, heterogeneous clinical manifestations, and easy recurrence. Existing clinical diagnosis mainly relies on symptoms and laboratory tests, lacking efficient and accurate early risk prediction tools, which limits the implementation of early intervention and individualized management. With the development of artificial intelligence technology, the construction of machine learning prediction models based on multidimensional clinical data has provided new possibilities for the early identification and precise intervention of NS. [Methods] This study retrospectively collected clinical data of 771 children with primary kidney diseases in the Pediatric Nephrology Ward of the Affiliated Hospital of Zunyi Medical University from 2009 to 2023, including 376 children with NS and 395 children with acute glomerulonephritis. The data were improved by preprocessing methods such as multiple imputation, standardization and coding, and general demographic characteristics, laboratory test indicators and renal pathological characteristics were screened as modeling variables. Four machine learning algorithms, GBDT, XGBoost, random forest (RF) and LightGBM, were used to construct a risk prediction model for the onset of the disease. The model performance was evaluated using five-fold cross validation, and the feature importance was explained by the SHAP method. [Results] All four models showed high predictive ability, among which the random forest model performed best, reaching an accuracy of 99.14%, precision of 99.13%, recall of 99.16%, F1 score of 0.9914 and AUC value of 0.9983 on the validation set. SHAP analysis results showed that indicators such as plasma IgG, total protein, complement C3, and ASO titer contributed significantly to model prediction and were highly consistent with the clinical pathological mechanism of NS, verifying the reliability and clinical interpretability of the model. [Conclusion] This study successfully constructed a risk prediction model for NS in children based on machine learning algorithms, which has high accuracy and good clinical interpretability, and provides strong data support for early screening and individualized treatment of NS. In the future, multi-center and multi-omics validation should be carried out to further improve the generalization ability and clinical application value of the model.","url":"https://doi.org/10.21203/rs.3.rs-7281320/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7281320/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-8166797/v1","name":"Health system learning achieves generalist neuroimaging models","source":"preprints","abstract":"Abstract Frontier artificial intelligence (AI) models, such as OpenAI's GPT-5 and Meta's DINOv3, have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging, in particular, is underrepresented in the public domain due to identifiable facial features within MRI and CT scans, fundamentally restricting model performance in clinical medicine. Here, we show that frontier models underperform on neuroimaging tasks and that learning directly from uncurated data generated during routine clinical care at health systems, a paradigm we call health system learning, yields high-performance, generalist neuroimaging models. We introduce NeuroVFM, a visual foundation model trained on 5.24 million clinical MRI and CT volumes using a scalable volumetric joint-embedding predictive architecture. NeuroVFM learns comprehensive representations of brain anatomy and pathology, achieving state-of-the-art performance across multiple clinical tasks, including radiologic diagnosis and report generation. The model exhibits emergent neuroanatomic understanding and interpretable visual grounding of diagnostic findings. When paired with open-source language models through lightweight visual instruction tuning, NeuroVFM generates radiology reports that surpass frontier models in accuracy, clinical triage, and expert preference. Through clinically grounded visual understanding, NeuroVFM reduces hallucinated findings and critical errors, offering safer clinical decision support. These results establish health system learning as a paradigm for building generalist medical AI and provide a scalable framework for clinical foundation models.","url":"https://doi.org/10.21203/rs.3.rs-8166797/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8166797/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-6972197/v1","name":"Artificial Intelligence Education for Health Professions Students: A Scoping Review","source":"preprints","abstract":"Abstract Background: The rapid pace at which artificial intelligence (AI) technologies are being integrated into healthcare demands competency on the part of health professionals in how to effectively integrate these tools into their practice. However, not many universities currently teach health professions students (HPS) about AI. A scoping review was undertaken to map key themes and identify gaps in the available literature on how best to teach HPS about AI. Methods: This scoping review followed the PRISMA-ScR checklist and the Arksey and O’Malley five-stage framework. The aim was to discover what AI topics have been taught to HPS and what educational methods have been employed to teach HPS about AI. A search of 4 databases (PubMed, Scopus, CINAHL, ERIC) identified 10,979 unique titles which underwent a two-step screening process and 15 full text studies were included. Data were extracted in an iterative process. A narrative review approach was used to generating themes and reporting results. Results: Most of the included studies taught medical students about AI, although students from other health specialties such as nursing, pharmacy and dentistry also appeared in the literature. A broad range of topics about AI were delivered by the educational interventions which were synthesised using a modified framework from McCoy et al. (2020). The most frequent topics taught were foundational AI literacy and applying AI to healthcare practice. A wide variety of teaching methods were utilised, most commonly reading and lectures. Conclusions: Whilst some university programs are already implementing AI educational interventions for their health professions students, there remains a lack of consensus on what and how to teach about AI to HPS. Further research should be conducted to build an evidence base for the design, implementation and evaluation of AI curricula for HPS, particularly in teaching students from a wider range of health disciplines.","url":"https://doi.org/10.21203/rs.3.rs-6972197/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6972197/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7576725/v1","name":"Survey of Smartphones, Medical Mobile Apps and Generative AI Use among Medical Students in Nigeria: A Case Study","source":"preprints","abstract":"Abstract Background Smartphones, medical applications (apps) and generative artificial intelligence (GenAI) are prominent learning tools widely used in higher education. However, the pattern of use of these tools among medical students at the Nigerian Federal University of Health Sciences, Ila - Orangun, has not been studied. The study was based on seven objectives set out to identify the pattern of usage of smartphones, medical apps, and GenAI for medical education among preclinical university students. Materials and Methods A descriptive survey design was employed. Data were collected via a structured questionnaire distributed electronically via WhatsApp to 297 preclinical medical students (Years 1–3) at the Federal University of Health Sciences, Ila-Orangun, between July 28th and August 28th, 2025. A total of 203 students responded, yielding a 75.5% response rate. The questionnaire, developed and validated by experts, covered smartphones, medical apps, and GenAI frequency and purpose. The data were analyzed via SPSS (version 22), and the results are presented in tables, frequencies, and charts. Results Of the 203 respondents, 43.3% (n = 88) were male, and 56.7% (n = 115) were female. All the students owned a smartphone, with 87.7% (n = 178) using Android devices and 13.3% (n = 25) using iPhones. A majority (81.3%, n = 165) reported daily smartphone use of 1–4 hours, 10.3% (n = 21) more than 5 hours, and 8.4% (n = 17) less than 1 hour. Primary purposes included reading lecture notes/ebooks, researching academic content online, and viewing medical videos. Medical app usage was widespread, with 87.7% (n = 178) reporting installations and 68% using them daily or weekly; commonly used apps included anatomy tools, interactive learning platforms, and medical dictionaries. Generative AI tools were also highly utilized, with ChatGPT (98%) being the most frequently accessed tool, followed by grammar checkers (56.7%), Med-PaLM (28.1%), Gemini (28.1%), and Copilot (13.3%) for the purpose of generating summaries of complex topics, clarifying difficult concepts, and preparing exams. Conclusion This study revealed a high level of use of smartphones, medical applications, and generative AI among medical students, underscoring the importance of these technologies in contemporary medical education. Accordingly, universities should develop clear policies to guide and optimize the use of smartphones, medical apps, and GenAI for academic purposes.","url":"https://doi.org/10.21203/rs.3.rs-7576725/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7576725/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.21203/rs.3.rs-7540599/v1","name":"Application of YOLO-v8 model based on lumbar X-ray in grading diagnosis of lumbar facet joint of osteoarthritis","source":"preprints","abstract":"Abstract Purpose Lumbar facet joint of osteoarthritis (LFJOA) can cause intractable low back pain in patients. Early evaluation of the status of LFJOA is very important for subsequent treatment. This paper discusses the automatic segmentation and detection of LFJOA by studying the characteristics of artificial intelligence technology and its potential application in medical image analysis. Methods The ability to detect inflammation has been significantly enhanced in recent years due to deep learning technology, especially models based on object detection. This study collected 987 lateral lumbar X-ray from 987 patients, each of which was manually divided into five lumbar facet joint segments. According to the computed tomography (CT) image of each patient, the classification annotation was carried out based on weishaupt standard. Then, the you only look once (YOLO)-v8 model was used for hierarchical diagnosis. Precision, recall, f1 score, mean average precision (map)50, and map50-95 were used to evaluate the model's performance. Additionally, the research examined how this technology could be applied in clinical settings. Results In detecting facet arthritis, the YOLO-v8 model reached a map50 of 0.694, a map50-95 of 0.286, an F1 score of 0.64, a precision rate of 0.71, and a recall of 0.689. Conclusion YOLO-v8 has diagnostic value in detecting the severity of LFJOA. Future research should the model’s classification potential to enhance its clinical application settings, and help spinal surgeons more effectively diagnose the severity of lumbar facet arthritis, so as to formulate accurate treatment plans.","url":"https://doi.org/10.21203/rs.3.rs-7540599/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7540599/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"doi:10.20944/preprints202506.1895.v1","name":"Artificial Intelligence in Systematic Reviews: Overcoming Reproducibility, Bias and Validation Challenges","source":"preprints","abstract":"Artificial Intelligence (AI) is rapidly changing how systematic reviews are conducted by accelerating the processes of literature retrieval and screening. While these ad-vancements enhance researchers’ productivity, the complete scope of AI&#039;s transforma-tive potential is still emerging. Moreover, issues related to reproducibility, bias, and transparency pose significant barriers to fully integrating AI into evidence synthesis. Large language models and machine learning classifiers show high sensitivity but suf-fer from low specificity, generating excessive false positives that increase the screening burden rather than reducing it. AI-generated Boolean search strategies often lack sta-bility, frequently delivering inconsistent results for the same prompts, which under-mines the core principle of reproducibility. Furthermore, AI models can sometimes hallucinate, a term used to describe instances where the AI generates false or mis-leading information. They may also misapply Medical Subject Headings (MeSH) and introduce selection bias, ultimately distorting the outcomes of systematic reviews. This review examines the role of AI in systematic searching and presents a structured vali-dation framework to address these limitations. Establishing standardized benchmarks for reproducibility, managing sensitivity and specificity trade-offs, and developing clear explanatory mechanisms are crucial to ensure that AI is a complementary tool in evidence synthesis, rather than a disruptive force. Retrieval-augmented AI search frameworks can improve precision but require transparent decision-making processes to enhance trust and accountability. Hybrid AI-human workflows, where AI acceler-ates screening but human experts validate outputs, offer a pragmatic solution to bal-ance efficiency with methodological rigor. This review presents a comprehensive roadmap emphasizing the importance of interpretability, transparency reports, and ethical oversight to facilitate the responsible integration of AI into systematic reviews. Achieving reproducibility and reducing bias is critical for transforming AI from an experimental tool into a more reliable asset for evidence synthesis.","url":"https://doi.org/10.20944/preprints202506.1895.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202506.1895.v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6740528/v1","name":"RETRACTED: Beyond Metropolises: Artificial Intelligence Awareness and Educational Needs Among Medical Students in a Developing Country","source":"preprints","abstract":"Abstract The authors have requested that this preprint be removed from Research Square.","url":"https://doi.org/10.21203/rs.3.rs-6740528/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6740528/v1","addedAt":"2026-09-01T01:47:48.793Z","updatedAt":"2026-09-01T01:47:54.712Z"},{"id":"oa:W4411329945","name":"Artificial Intelligence Outperforms Physicians in General Medical Knowledge, Except in the Paediatrics Domain: A Cross-Sectional Study","source":"openalex","abstract":"Generative artificial intelligence (genAI) shows promising results in clinical practice. This study compared a GPT-4-turbo virtual assistant with physicians from Italy, France, Spain, and Portugal on medical knowledge derived from national exams while analysing knowledge retention over time and domain-specific performance. Via a digital platform, 17,144 physicians provided 221,574 answers to 600 exam questions between December 2022 and February 2024. Physicians were stratified by years since graduation and specialty, and the assistant answered the same questions in each native language. Differences in proportions of correct answers were tested with binomial logistic regression (odds ratios, 95% CI) or Fisher’s exact test (α = 0.05). The assistant outperformed physicians in all countries (72–96% vs. 46–62%; logistic regression, p < 0.001). Physicians also trailed the assistant across most knowledge domains (p < 0.001), except paediatrics (45% vs. 52%; Fisher, p = 0.60). Accuracy declined with seniority, falling 4–10% between the youngest and oldest cohorts (logistic regression, p < 0.001). Overall, genAI exceeds practising doctors on broad medical knowledge and may help counter knowledge attrition, though paediatrics remains a domain requiring targeted refinement.","url":"https://doi.org/10.3390/bioengineering12060653","authors":["J. M. Galdeano Miranda","Raquel Pereira-Silva","João Guichard","Jorge Meneses","Andreia Neves Carreira","Daniela Seixas"],"tags":["Cross-sectional study","Pediatrics","Domain (mathematical analysis)","Medicine","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-14","doi":"https://doi.org/10.3390/bioengineering12060653","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409293417","name":"Digitalisation and artificial intelligence development. A cross-country analysis","source":"openalex","abstract":"Purpose This study aims to examine how cultural dimensions affect digitalisation, particularly artificial intelligence (AI) development and its implementation strategies in 38 countries. Design/methodology/approach We use a mixed methodology to obtain a deeper insight into a complex and, at the same time, understudied phenomenon, e.g. the global impact of cultural values on digitalisation and AI development. Combining quantitative and qualitative analyses, we shed new light on the empirical manifestations of cultural dimensions and expert perceptions. Quantitative analysis uses regression models based on data sourced from the GLOBE and Digital Competitiveness Ranking studies. Qualitative insights are drawn from epistemic interviews with experts representing four countries, namely China, Italy, Russia and the USA, which belong to different cultural clusters. The choice to have countries representing different clusters follows the logic of the GLOBE Project and aims at gaining insight into different cultural groups and scaling up the results to other countries in the same cluster. Findings Key findings highlight risk aversion as a pivotal factor, encompassing uncertainty avoidance, future orientation, institutional collectivism, power distance and performance orientation practices. Expert interviews elucidate how these cultural values influence national strategies, regulatory frameworks, stakeholder engagement, competitive dynamics and future skill requirements in AI implementation and development. Research limitations/implications Further research is recommended to delve deeper into the intricate relationship between country culture, technological adoption and economic progress, enriching global understanding of digitalisation dynamics and AI implementation and development. The research data can be further developed and expanded to include other GLOBE clusters not covered in this study, allowing for a deeper analysis. Inviting more experts from each region would enhance the breadth of perspectives. Moreover, similar studies could be conducted within the context of a single country to provide a more detailed examination. Practical implications Implications for policymakers underscore the need to integrate cultural considerations into digitalisation strategies, ensuring alignment with societal values and optimising economic outcomes. Businesses are encouraged to adopt culturally sensitive approaches to AI implementation to build trust and foster engagement within diverse cultural contexts. Originality/value The originality of this study lies in its exploration of the intersection between digitalisation, AI implementation and cultural dimensions – an area that has so far received limited attention in the available literature. While comparative studies often focus on the technical or policy aspects of digital advancements, this research highlights how cultural factors can facilitate or hinder these developments. Moreover, the study employs a mixed-methods approach, combining quantitative analysis with qualitative insights. This approach allows deeper understanding and cross-validation of findings, enhancing the study’s credibility and offering a more nuanced perspective on the role of culture in shaping digital transformation.","url":"https://doi.org/10.1108/ejim-07-2024-0828","authors":["Chiara Cannavale","Lorenza Claudio","Диана Королева"],"tags":["Business","Knowledge management","Business administration","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-07","doi":"https://doi.org/10.1108/ejim-07-2024-0828","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4407632928","name":"Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review","source":"openalex","abstract":"Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.","url":"https://doi.org/10.5469/neuroint.2025.00052","authors":["JoonNyung Heo"],"tags":["Stroke (engine)","Medicine","Intensive care medicine","Comorbidity","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-18","doi":"https://doi.org/10.5469/neuroint.2025.00052","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409331637","name":"The Role of Artificial Intelligence in the Diagnosis and Management of Rheumatoid Arthritis","source":"openalex","abstract":"Background and Objectives: Artificial intelligence has emerged as a transformative tool in healthcare, offering capabilities such as early diagnosis, personalised treatment, and real-time patient monitoring. In the context of rheumatoid arthritis, a chronic autoimmune disease that demands timely intervention, artificial intelligence shows promise in overcoming diagnostic delays and optimising disease management. This study examines the role of artificial intelligence in the diagnosis and management of rheumatoid arthritis, focusing on perceived benefits, challenges, and acceptance levels among healthcare professionals and patients. Materials and Methods: A cross-sectional study was conducted using a detailed questionnaire distributed to 205 participants, including rheumatologists, general practitioners, and rheumatoid arthritis patients from Romania. The study used descriptive statistics, chi-square tests, and logistic regression to analyse AI acceptance in rheumatology. Data visualisation and multiple imputations addressed missing values, ensuring accuracy. Statistical significance was set at p < 0.05 for hypothesis testing. Results: Respondents with prior experience in artificial intelligence perceived it as more useful for early diagnosis and personalised management of RA (p < 0.001). Familiarity with artificial intelligence concepts positively correlated with acceptance in routine rheumatology practice (ρ = 1.066, p < 0.001). The main barriers identified were high costs (36%), lack of medical staff training (37%), and concerns regarding diagnostic accuracy (21%). Although less frequently mentioned, data privacy concerns remained relevant for a subset of respondents. The study revealed that artificial intelligence could improve diagnostic accuracy and rheumatoid arthritis monitoring, being perceived as a valuable tool by professionals familiar with digital technologies. However, 42% of participants cited the lack of data standardisation across medical systems as a major barrier, underscoring the need for effective interoperability solutions. Conclusions: Artificial intelligence has the potential to revolutionise rheumatoid arthritis management through faster and more accurate diagnoses, personalised treatments, and optimised monitoring. Nevertheless, challenges such as costs, staff training, and data privacy need to be addressed to ensure efficient integration into clinical practice. Educational programmes and interdisciplinary collaboration are essential to increase artificial intelligence adoption in rheumatology.","url":"https://doi.org/10.3390/medicina61040689","authors":["Amalia Vlad","Corina Popazu","Alina-Maria Lescai","Doina Carina Voinescu","Alexia Anastasia Ștefania Baltă"],"tags":["Medicine","Rheumatoid arthritis","Context (archaeology)","Logistic regression","Descriptive statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.3390/medicina61040689","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4404189848","name":"An Artificial Intelligence Approach for Test‐Free Identification of Sarcopenia","source":"openalex","abstract":"BACKGROUND: The diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real-world data. METHODS: This multicentre study enrolled 11 661 middle-aged and older adults from a national survey initialized in 2011. Follow-up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)-related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross-validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow-up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance. RESULTS: The study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620-0.647]). From the various ML models assessed, a 24-variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808-0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818-0.848] and 0.852 [0.840-0.865], respectively). The model's built-in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment. CONCLUSIONS: We developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision-making and facilitating the development of novel management strategies of sarcopenia.","url":"https://doi.org/10.1002/jcsm.13627","authors":["Liangyu Yin","Jinghong Zhao"],"tags":["Medicine","Sarcopenia","Test (biology)","Identification (biology)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-08","doi":"https://doi.org/10.1002/jcsm.13627","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406239532","name":"Artificial intelligence for disease X: Progress and challenges","source":"openalex","abstract":"","url":"https://doi.org/10.1515/jtim-2024-0035","authors":["Keda Chen","Jiaxuan Li","Lanjuan Li"],"tags":["Medicine","Nephrology","Disease","Internal medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1515/jtim-2024-0035","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402155151","name":"Artificial intelligence in the anterior segment of eye diseases","source":"openalex","abstract":"Ophthalmology is a subject that highly depends on imaging examination. Artificial intelligence (AI) technology has great potential in medical imaging analysis, including image diagnosis, classification, grading, guiding treatment and evaluating prognosis. The combination of the two can realize mass screening of grass-roots eye health, making it possible to seek medical treatment in the mode of “first treatment at the grass-roots level, two-way referral, emergency and slow treatment, and linkage between the upper and lower levels”. On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology, quite a lot of studies have confirmed that machine learning can assist in diagnosis, grading, providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases, ametropia, lens diseases, glaucoma, iris diseases, etc. This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.","url":"https://doi.org/10.18240/ijo.2024.09.23","authors":["Yao‐Hong Liu","Sijia Liu","Lixiong Gao","Yong Tang","Zhaohui Li","Zi Ye"],"tags":["Medicine","Anterior Eye Segment","Ophthalmology","Optometry","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-20","doi":"https://doi.org/10.18240/ijo.2024.09.23","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406989203","name":"Leveraging Artificial Intelligence to Maximize Efficiency in Supply Chain Process Optimization","source":"openalex","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in supply chain management, addressing inefficiencies and complexities in dynamic global markets.The integration of AI technologies, including machine learning, predictive analytics, and autonomous systems, offers unprecedented opportunities to optimize supply chain processes across industries.From demand forecasting and inventory management to route optimization and risk mitigation, AI-driven solutions enable businesses to enhance decision-making, reduce costs, and improve operational agility.These advancements are particularly critical in a globalized economy, where supply chains are increasingly interconnected, yet vulnerable to disruptions such as geopolitical tensions, pandemics, and climaterelated challenges.AI technologies enable real-time analysis of vast datasets, facilitating predictive insights that enhance supply chain resilience.Machine learning algorithms identify patterns and anomalies, optimizing processes such as demand forecasting and procurement.Predictive analytics empower stakeholders to anticipate disruptions, mitigate risks, and adapt strategies proactively.Autonomous systems, including robotics and drones, streamline logistics operations, reducing human intervention and error.Despite its transformative potential, the implementation of AI in supply chains presents challenges, including data silos, integration complexities, and ethical concerns.To fully leverage AI, businesses must invest in robust data infrastructure, workforce upskilling, and governance frameworks that ensure responsible AI use.This paper explores the multifaceted impact of AI on supply chain optimization, highlighting case studies of successful implementation and proposing strategies to overcome barriers.By leveraging AI, organizations can build resilient, efficient, and sustainable supply chains that drive competitive advantage in an ever-evolving marketplace.","url":"https://doi.org/10.55248/gengpi.6.0125.0508","authors":["Oluwole Raphael Odumbo","Sani Zainab Nimma"],"tags":["Supply chain","Process (computing)","Computer science","Artificial intelligence","Biochemical engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.55248/gengpi.6.0125.0508","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4390682041","name":"Revolutionary artificial intelligence or rogue technology? The promises and pitfalls of ChatGPT","source":"openalex","abstract":"Objective: The objective of the article is to offer a thorough exploration and comprehension of the obstacles and potential advantages linked to the application of generative artificial intelligence (GAI) in the business realm, particularly emphasizing ChatGPT. Research Design & Methods: The research utilized a narrative and critical examination of existing literature and constructed a conceptual framework grounded in prior studies. Our theoretical framework was developed through a deductive reasoning approach to ensure the logical and effective organization of the study. Consequently, this work should be considered a conceptual article that sheds light on one hand on the promises and opportunities, and on the other hand on the controversies and risks associated with generative artificial intelligence in the fields of management and economics, using ChatGPT as a specific case study. Findings: In recent years, artificial intelligence has experienced rapid progress, leading to its widespread applications. The chatbot industry, exemplified by ChatGPT, has garnered considerable attention, with experts and researchers asserting that generative artificial intelligence and ChatGPT could transform our work routines and daily existence. Although these technologies have the potential to revolutionize data analysis and report generation, concerns have been raised about their societal impacts, particularly in areas such as ethics, privacy, and security. Implications & Recommendations: The regulation of the GAI market is imperative to ensure fairness, competitive balance, and safeguard intellectual property and privacy while addressing potential geopolitical risks. With the evolving job landscape, individuals must continuously acquire new digital skills through education, particularly in response to the growing prominence of AI system training. Ethical considerations, such as prioritizing user privacy and security, are crucial for GAI developers to mitigate risks related to personal data violation and social surveillance, emphasizing responsible AI practices and adherence to ethical guidelines to prevent social manipulation and maintain goodwill. Contribution & Value Added: The article structures scientific knowledge on the advantages and drawbacks of the generative artificial intelligence in business. The articles attempted to put together the main aspects of this new phenomenon.","url":"https://doi.org/10.15678/ier.2023.0904.07","authors":["Marek Sieja","Krzysztof Wach"],"tags":["Generative grammar","Realm","Engineering ethics","Knowledge management","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-29","doi":"https://doi.org/10.15678/ier.2023.0904.07","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403403785","name":"Artificial Intelligence (AI) and Workplace Communication: Promises, Perils, and Recommended Policy","source":"openalex","abstract":"Communication sits at the heart of any coordination within organization. Yet, what are the consequences when employes use Artificial Intelligence (AI) to copilot, i.e., support, their communication? While AI support in human interactions holds much promise for improving communication quality at work, it also fundamentally challenges how much people trust that communication. We, therefore, ask how organizations should introduce AI. In particular, we focus on the responsibility of leaders as stewards of workplace communication. Accordingly, we offer a set of specific hands-on recommendations on how employes should be guided to use AI copiloting effectively so that they do not give in to the temptation of letting go of the “steering wheel” (i.e., allowing AI to [auto]pilot intraorganizational communication).","url":"https://doi.org/10.1177/15480518241289644","authors":["Niels Van Quaquebeke","Fabiola H. Gerpott"],"tags":["Psychology","Knowledge management","Public relations","Business","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-14","doi":"https://doi.org/10.1177/15480518241289644","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410831215","name":"Artificial intelligence in bronchoscopy: a systematic review","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators' skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy. METHODS: A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS: 1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points). INTERPRETATION: 20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.","url":"https://doi.org/10.1183/16000617.0274-2024","authors":["Kristoffer Mazanti Cold","Anishan Vamadevan","Christian B. Laursen","Flemming Bjerrum","Suveer Singh","Lars Konge"],"tags":["Medicine","Bronchoscopy","MEDLINE","Airway","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1183/16000617.0274-2024","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4414041124","name":"Generative artificial intelligence for automated data extraction from unstructured medical text","source":"openalex","abstract":"Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations. Materials and Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry. The pipeline comprised three main components: the Engineered Preload Framework (EPF), which integrated schemas and instructions; the LLM module, enhanced by reasoning capabilities; and the validation and retry mechanism, which ensured data accuracy through iterative self-correction. A clinical expert manually extracted data from the validation cohort to establish the ground truth. Pipeline performance was evaluated using precision, recall, and F1 score. Additionally, the dataset was stratified into quartiles to assess the pipeline's ability to handle varying levels of data availability. Results: The pipeline achieved 99.0% precision, 85.0% recall, and a 91.5% F1 score, with an overall accuracy of 90% when evaluated at the note level. The most common error was missed values (5.2%), while hallucinations were the least frequent (<0.01%). Discussion and Conclusion: This study demonstrates the feasibility of a robust GenAI pipeline for automating structured data extraction from unstructured RHC procedure notes. The approach highlights the potential of LLMs in medical data mining, improving research efficiency and clinical applications.","url":"https://doi.org/10.1093/jamiaopen/ooaf097","authors":["Phuong Nam Dao","Luisa Quesada","Syed Moin Hassan","M. Iturrioz Campo","Shelsey W. Johnson","S. Ghose","Raúl San Jośe Estépar","Aaron B. Waxman","George R. Washko","Farbod N. Rahaghi"],"tags":["Computer science","Pipeline (software)","Artificial intelligence","Unstructured data","Data extraction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-19","doi":"https://doi.org/10.1093/jamiaopen/ooaf097","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4396849324","name":"Artificial Intelligence and the Skill Premium","source":"openalex","abstract":"How will the emergence of ChatGPT and other forms of artificial intelligence (AI) affect the skill premium?To address this question, we propose a nested constant elasticity of substitution production function that distinguishes among three types of capital: traditional physical capital (machines, assembly lines), industrial robots, and AI.Following the literature, we assume that industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers.We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers.","url":"https://doi.org/10.3386/w32430","authors":["David E. Bloom","Klaus Prettner","Jamel Saadaoui","Mario Veruete"],"tags":["Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-01","doi":"https://doi.org/10.3386/w32430","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4392201266","name":"A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence","source":"openalex","abstract":"From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the \"State-of-the-Art and Gaps Extracted from Results and Discussions\" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the \"Literature Review Guided by Artificial Intelligence\" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.","url":"https://doi.org/10.3390/ma17051088","authors":["Fernando Gomes de Souza","Shekhar Bhansali","Kaushik Pal","Fabíola da Silveira Maranhão","Marcella Santos Oliveira","Viviane Silva Valladão","Daniele Silvéria Brandão e Silva","Gabriel Bezerra Silva"],"tags":["Nanocomposite","Scopus","Field (mathematics)","Computer science","Nanotechnology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-27","doi":"https://doi.org/10.3390/ma17051088","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402857715","name":"Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models","source":"openalex","abstract":"OBJECTIVE: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. METHODS: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. RESULTS: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. CONCLUSION: We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.","url":"https://doi.org/10.14245/ns.2448580.290","authors":["Wongthawat Liawrungrueang","Inbo Han","Watcharaporn Cholamjiak","Peem Sarasombath","K. Daniel Riew"],"tags":["Convolutional neural network","Cervical spine","Computer science","Artificial neural network","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-27","doi":"https://doi.org/10.14245/ns.2448580.290","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W3084559672","name":"Treating medical data as a durable asset","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41588-020-0698-y","authors":["Amalio Telenti","Xiaoqian Jiang"],"tags":["Metadata","Data science","Genomics","Asset (computer security)","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-14","doi":"https://doi.org/10.1038/s41588-020-0698-y","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409592185","name":"Overcoming barriers in the use of artificial intelligence in point of care ultrasound","source":"openalex","abstract":"Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.","url":"https://doi.org/10.1038/s41746-025-01633-y","authors":["Roberto Vega","Masood Dehghan","Arun Nagdev","Brian Buchanan","Jeevesh Kapur","Jacob L. Jaremko","Dornoosh Zonoobi"],"tags":["Point (geometry)","Point of care ultrasound","Point of care","Ultrasound","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-19","doi":"https://doi.org/10.1038/s41746-025-01633-y","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406825155","name":"Ethical Considerations Emerge from Artificial Intelligence (AI) in Biotechnology","source":"openalex","abstract":"The integration of Artificial intelligence (AI) in biotechnology presents significant ethical challenges that must be addressed to ensure responsible innovations. Key concerns include data privacy and security, as AI systems often handle sensitive genetic and health information, necessitating robust regulations to protect individuals' rights and maintain public trust. Algorithmic bias poses another critical issue; AI can reflect existing biases in training data, leading to inequitable healthcare outcomes. Transparency in AI decision-making is essential, as \"black box\" models hinder trust, especially in drug discovery and genetics. Ethical implications of genetic manipulation require careful scrutiny to define the limits of human intervention. Additionally, societal impacts must be considered to ensure equitable distribution of AI benefits, preventing the exacerbation of disparities. Engaging diverse stakeholders, including ethicists and policymakers, is vital in aligning these technologies with societal values. Ultimately, prioritizing ethics will allow us to harness AI and biotechnology's potential while safeguarding human rights and promoting equity.","url":"https://doi.org/10.18502/ajmb.v17i1.17680","authors":["Mahintaj Dara","Negar Azarpira"],"tags":["Safeguarding","Scrutiny","Transparency (behavior)","Equity (law)","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-25","doi":"https://doi.org/10.18502/ajmb.v17i1.17680","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401409653","name":"Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI","source":"openalex","abstract":"BACKGROUND AND INTRODUCTION: Cardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities. METHOD: In this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively. RESULT: The PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited. CONCLUSIONS: AI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.","url":"https://doi.org/10.3390/jimaging10080193","authors":["Ali Moradi","Olawale O Olanisa","Tochukwu Nzeako","Mehregan Shahrokhi","Eman Esfahani","Nastaran Fakher","Mohammad Amin Khazeei Tabari"],"tags":["Medical physics","Scopus","Medicine","Cardiac imaging","Image quality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-08","doi":"https://doi.org/10.3390/jimaging10080193","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4392244024","name":"Application of the AlphaFold2 Protein Prediction Algorithm Based on Artificial Intelligence","source":"openalex","abstract":"As the expression products of genes and macromolecules in living organisms, proteins are the main material basis of life activities. They exist widely in various cells and have various functions such as catalysis, cell signaling and structural support, playing a key role in life activities and functional execution. At the same time, the study of protein can better grasp the life activities from the molecular level, and has important practical significance for disease management, new drug development and crop improvement. Due to advances in high-throughput sequencing technology, protein sequence data has grown exponentially. The protein function prediction problem can be seen as a multi-label binary classification problem by extracting the features of a given protein and mapping them to the protein function label space. A variety of data sources can be mined to obtain protein function prediction features, such as protein sequence, protein structure, protein family, protein interaction network, etc. The initial steps are classical sequence-based methods, such as BLAST, which calculate the similarity between protein sequences and transmit annotations between proteins whose similarity scores exceed a specific threshold. This method has great limitations for protein function prediction without sequence similarity. Therefore, this paper analyzes the development prospect of bioanalysis and artificial intelligence through the application status and realization path of AlphaFold2 protein prediction algorithm based on artificial intelligence.","url":"https://doi.org/10.53469/jtpes.2024.04(02).09","authors":["Quan Zhang","Beichang Liu","Guoqing Cai","Jili Qian","Zhengyu Jin"],"tags":["Computer science","Artificial intelligence","Algorithm","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-28","doi":"https://doi.org/10.53469/jtpes.2024.04(02).09","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4414529263","name":"Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma","source":"openalex","abstract":"Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. It is characterized by a high degree of heterogeneity, meaning that although these tumors may appear morphologically similar, they often exhibit distinct clinical outcomes. By associating specific molecular fingerprints with different clinical behaviors, high-throughput omics technologies (e.g., genomics, transcriptomics, and epigenomics) have significantly advanced our understanding of GBM, particularly of its extensive heterogeneity, by proposing a molecular classification for the implementation of precision medicine. However, due to the vast volume and complexity of data, the integrative analysis of omics data demands substantial computational power for processing, analyzing and interpreting GBM-related data. Artificial intelligence (AI), which mainly includes machine learning (ML) and deep learning (DL) computational approaches, now presents a unique opportunity to infer valuable biological insights from omics data and enhance the clinical management of GBM. In this review, we explored the potential of integrating multi-omics, imaging radiomics and clinical data with AI to uncover different aspects of GBM (molecular profiling, prognosis, and treatment) and improve its clinical management.","url":"https://doi.org/10.3390/ijms26199362","authors":["Giovanna Morello","Valentina La Cognata","Maria Guarnaccia","Giulia Gentile","Sebastiano Cavallaro"],"tags":["Radiomics","Glioblastoma","Artificial intelligence","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-25","doi":"https://doi.org/10.3390/ijms26199362","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W7119491114","name":"Artificial intelligence, extended reality, and emerging AI–XR integrations in medical education","source":"openalex","abstract":"Introduction: Artificial intelligence (AI) and extended reality (XR)-including virtual, augmented, and mixed reality-are increasingly adopted in health-professions education. However, the educational impact of AI, XR, and especially their combined use within integrated AI-XR ecosystems remains incompletely characterized. Objective: To synthesize empirical evidence on educational outcomes and implementation considerations for AI-, XR-, and combined AI-XR-based interventions in medical and health-professions education. Methods: Following PRISMA and PICO guidance, we searched three databases (Scopus, PubMed, IEEE Xplore) and screened records using predefined eligibility criteria targeting empirical evaluations in health-professions education. After deduplication (336 records removed) and two-stage screening, 13 studies published between 2019 and 2024 were included. Data were extracted on learner population, clinical domain, AI/XR modality, comparators, outcomes, and implementation factors, and narratively synthesized due to heterogeneity in designs and measures. Results: The 13 included studies involved undergraduate and postgraduate learners in areas such as procedural training, clinical decision-making, and communication skills. Only a minority explicitly integrated AI with XR within the same intervention; most evaluated AI-based or XR-based approaches in isolation. Across this mixed body of work, studies more often than not reported gains in at least one outcome-knowledge or skills performance, task accuracy, procedural time, or learner engagement-relative to conventional instruction, alongside generally high acceptability. Recurrent constraints included costs, technical reliability, usability, faculty readiness, digital literacy, and data privacy and ethics concerns. Conclusions: Current evidence on AI, XR, and emerging AI-XR integrations suggests promising but preliminary benefits for learning and performance. The small number of fully integrated AI-XR interventions and the methodological limitations of many primary studies substantially limit the certainty and generalizability of these findings. Future research should use more rigorous and standardized designs, explicitly compare AI-only, XR-only, and AI-XR hybrid approaches, and be coupled with faculty development, robust technical support, and alignment with competency-based assessment.","url":"https://doi.org/10.3389/fdgth.2025.1740557","authors":["Talía Tene","DIEGO FABIÁN VIQUE LOPEZ","Marlene Jacqueline García Veloz","Byron Stalin Rojas Oviedo","Richard Tene-Fernandez"],"tags":["Generalizability theory","Certainty","Limit (mathematics)","Medical education","Psychological intervention"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-09","doi":"https://doi.org/10.3389/fdgth.2025.1740557","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399118706","name":"Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review","source":"openalex","abstract":"Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.","url":"https://doi.org/10.1371/journal.pdig.0000514","authors":["Sarim Dawar Khan","Zahra Hoodbhoy","Mohummad Hassan Raza Raja","Jee Young Kim","Henry David Jeffry Hogg","Afshan Anwar Ali Manji","Freya Gulamali","Alifia Hasan","Asim Shaikh","Salma Tajuddin","Nida Saddaf Khan","Manesh R. Patel"],"tags":["Workflow","Computer science","Procurement","Systematic review","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-29","doi":"https://doi.org/10.1371/journal.pdig.0000514","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4390541664","name":"The role of an artificial intelligence model in antiretroviral therapy counselling and advice for people living with HIV","source":"openalex","abstract":"OBJECTIVES: People living with HIV may find personalized access to accurate information on antiretroviral therapy (ART) challenging given the stigma and costs potentially associated with attending physical consultations. Artificial intelligence (AI) chatbots such as ChatGPT may help to lower barriers to accessing information addressing concerns around ART initiation. However, the safety and accuracy of the information provided remains to be studied. METHODS: We instructed ChatGPT to answer questions that people living with HIV frequently ask about ART, covering i) knowledge of and access to ART; ii) ART initiation, side effects, and adherence, and iii) general sexual health practices while receiving ART. We checked the accuracy of the advice against international HIV clinical practice guidelines. RESULTS: ChatGPT answered all questions accurately and comprehensively. It recognized potentially life-threatening scenarios such as abacavir hypersensitivity reaction and gave appropriate advice. However, in certain contexts, such as specific geographic locations or for pregnant individuals, the advice lacked specificity to an individual's unique circumstances and may be inadequate. Nevertheless, ChatGPT consistently re-directed the individual to seek help from a healthcare professional to obtain targeted advice. CONCLUSIONS: ChatGPT may act as a useful adjunct in the process of ART counselling for people living with HIV. Improving access to information on and knowledge about ART may improve access and adherence to ART and outcomes for people living with HIV overall.","url":"https://doi.org/10.1111/hiv.13604","authors":["Matthew Chung Yi Koh","Jinghao Nicholas Ngiam","Joy Yong","Paul Anantharajah Tambyah","Sophia Archuleta"],"tags":["Medicine","Abacavir","Antiretroviral therapy","Advice (programming)","Human immunodeficiency virus (HIV)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-02","doi":"https://doi.org/10.1111/hiv.13604","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411326274","name":"Enhancing nucleic acid delivery by the integration of artificial intelligence into lipid nanoparticle formulation","source":"openalex","abstract":"The advent of messenger RNA (mRNA) therapeutics has revolutionized medicine, with its potential underscored by rapid advancements during the COVID-19 pandemic. Despite its promise, nucleic acid delivery remains a formidable challenge due to enzymatic degradation, cellular uptake barriers, and endosomal trapping. Therapeutic lipid nanoparticles (LNPs), pioneered in the 1970s, have emerged as the gold standard for delivering mRNA and other nucleic acids, offering unparalleled advantages in stability, biocompatibility, and cellular targeting. This review explores the evolution and design of LNPs, focusing on their role in hematologic therapies and platelet transfection, where unique challenges arise due to platelets' anucleate nature. The paper systematically evaluates the composition of LNPs, highlighting the role of ionizable, cationic, and neutral lipids in optimizing delivery efficiency, stability, and immune response modulation. Strategies to overcome platelet transfection barriers, including tailored lipid compositions and particle engineering, are discussed alongside advances in artificial intelligence (AI) for predictive nanoparticle design. Furthermore, it examines various nucleic acid cargoes, including mRNA, siRNA, and miRNA, and their therapeutic potential in addressing platelet-related disorders and advancing personalized medicine. Finally, the review delves into emerging technologies and the integration of AI to overcome existing barriers in nucleic acid delivery. By fostering interdisciplinary collaboration, this work aims to catalyze discoveries in LNP-based therapeutics and transformative advancements in hematologic treatments.","url":"https://doi.org/10.3389/fmedt.2025.1591119","authors":["Kagya Amoako","Amir Mokhammad","Afrida Malik","Sumith Yesudasan","Anas Wheba","Oluwanifemi Olagunju","Sean X. Gu","Timur O. Yarovinsky","E. Vincent S. Faustino","Juliane Nguyen","John Hwa"],"tags":["Nucleic acid","Nanoparticle","Nanotechnology","Computer science","Chemistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-16","doi":"https://doi.org/10.3389/fmedt.2025.1591119","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4415695229","name":"Toward governance of artificial intelligence in pediatric healthcare","source":"openalex","abstract":"AI is transforming healthcare, yet pediatric adoption remains limited and governance is underdeveloped. We review existing frameworks and identify pediatric-specific gaps: insufficient stakeholder engagement, developmentally appropriate consent/assent, limited bias mitigation, and unclear accountability. An analysis of FDA-cleared pediatric SaMDs shows radiology dominance while other specialties lag. We call for a pediatric-centric governance approach emphasizing transparency, inclusive participation, equitable data practices, and rigorous post-deployment monitoring to ensure safe, responsible integration.","url":"https://doi.org/10.1038/s41746-025-02000-7","authors":["Felix Richter","Emma Holmes","Florian Richter","Katherine Guttmann","Son Q. Duong","Sandeep Gangadharan","Eric Schadt","Hojjat Salmasian","Bruce D. Gelb","Benjamin S. Glicksberg"],"tags":["Corporate governance","Dominance (genetics)","Health care","Stakeholder","Clinical governance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-30","doi":"https://doi.org/10.1038/s41746-025-02000-7","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410004931","name":"Artificial Intelligence in Maritime Cybersecurity: A Systematic Review of AI-Driven Threat Detection and Risk Mitigation Strategies","source":"openalex","abstract":"The maritime industry is undergoing a digital transformation, integrating automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and safety. However, this technological evolution has also increased cybersecurity vulnerabilities, exposing vessels, ports, and maritime communication networks to sophisticated cyber threats. This systematic review, conducted following the PRISMA guidelines, examines the current landscape of AI-driven cybersecurity solutions in maritime environments. By analyzing peer-reviewed studies and industry reports, this review identifies key AI methodologies, including machine-learning-based intrusion detection systems, anomaly detection mechanisms, predictive threat modeling, and AI-enhanced zero-trust architectures. This study assesses the effectiveness of these techniques in mitigating cyber risks, explores their implementation challenges, and highlights existing research gaps. The findings indicate that AI-powered solutions significantly enhance real-time threat detection and response capabilities in maritime networks, yet issues such as data scarcity, regulatory constraints, and adversarial attacks on AI models remain unresolved. Future research directions should focus on integrating AI with blockchain, federated learning, and quantum cryptographic techniques to strengthen maritime cybersecurity frameworks.","url":"https://doi.org/10.3390/electronics14091844","authors":["Tymoteusz Miller","Irmina Durlik","Ewelina Kostecka","S. Sokołowska","Polina Kozlovska","Rafał Zwolak"],"tags":["Computer security","Computer science","Engineering","Risk analysis (engineering)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.3390/electronics14091844","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391316610","name":"Artificial intelligence‐based prediction of the rheological properties of hydrocolloids for plant‐based meat analogues","source":"openalex","abstract":"BACKGROUND: Methylcellulose has been applied as a primary binding agent to control the quality attributes of plant-based meat analogues. H owever, a great deal of effort has been made to search for hydrocolloids to replace methylcellulose because of increasing awareness of clean labels. In this study, a machine learning framework was proposed in order to describe and predict the flow behavior of six hydrocolloid solutions, and the predicted viscosities were correlated with the textural features of their corresponding plant-based meat analogues. RESULTS: Different shear-thinning and Newtonian behaviors were observed depending on the type of hydrocolloid and the shear rate. Methylcellulose exhibited an increasing viscosity pattern with increasing temperature, compared to the other hydrocolloids. The machine learning algorithms (random forest and multilayer perceptron models) showed a better viscosity fitting performance than the constitutive equations (power law and Cross models). In addition, three hyperparameters of the multilayer perceptron model (optimizer, learning rate, and the number of hidden layers) were tuned using the Bayesian optimization algorithm. CONCLUSION: = 0.9944-0.9961/RMSE = 0.0545-0.0708). Furthermore, the machine learning-predicted viscosities overall showed similar patterns to the textural parameters of the meat analogues. © 2024 Society of Chemical Industry.","url":"https://doi.org/10.1002/jsfa.13334","authors":["Da‐Yeon Lee","Sungmin Jeong","Suin Yun","Suyong Lee"],"tags":["Hyperparameter","Rheology","Artificial intelligence","Shear thinning","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-29","doi":"https://doi.org/10.1002/jsfa.13334","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403132582","name":"Development of an artificial intelligence curriculum design for children in Taiwan and its impact on learning outcomes","source":"openalex","abstract":"In the digital age, the application of Artificial Intelligence (AI) has become an irreversible trend, with its potential in the field of education being particularly noteworthy. However, there are currently few AI education programs for children in Taiwan, and there is a lack of systematic teaching resources and methods, which poses a major challenge to the promotion of AI education. To address this challenge, this study designed a tailor-made AI curriculum for children in Taiwan, aimed at enhancing their foundational knowledge in the AI field and skills in using generative AI. This study was conducted in Taiwan, involving 30 elementary school students from grades 3 and 4, employing a single-group pre-test and post-test research design. Data were collected and analyzed through pre-and post-tests on AI knowledge quizzes and AI knowledge self-assessment, as well as the expert consensual assessment technique and the pupils’ attitude toward technology survey questionnaire. Research shows that students who participated in the AI course significantly improved their test scores on AI knowledge before and after the course. The students AI knowledge increased by 62.75%, demonstrating the course’s effectiveness and showing a positive attitude towards AI technology. Additionally, the student’s project outcomes demonstrated a high level of creativity. The students exhibited an enhanced interest and positive attitude towards learning AI, expressing a willingness to participate in more AI educational courses. This work provides valuable experience and guidance for the future integration of AI technology in children’s education, offering practical guidelines for teachers and researchers on how to effectively teach AI knowledge, as well as serving as a robust reference for educational policy makers in formulating strategies to promote AI education.","url":"https://doi.org/10.1057/s41599-024-03839-z","authors":["Hong-Guang Zhao","Xinzhu Li","Xin Kang"],"tags":["Curriculum","Mathematics education","Psychology","Engineering","Pedagogy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-04","doi":"https://doi.org/10.1057/s41599-024-03839-z","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W2125065061","name":"Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0140-6736(12)61728-0","authors":["Rafael Lozano","Mohsen Naghavi","Kyle J Foreman","Stephen S Lim","Kenji Shibuya","Victor Aboyans","Jerry Abraham","Tim Adair","Rakesh Aggarwal","Stephanie Y Ahn","Mohammad A. AlMazroa","Miriam Alvarado"],"tags":["Covariate","Cause of death","Demography","Medicine","Statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2012-12-01","doi":"https://doi.org/10.1016/s0140-6736(12)61728-0","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4408695783","name":"Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges","source":"openalex","abstract":"Unlabelled: Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, has rapidly evolved and is reshaping various fields, including clinical medicine. Emergency medicine stands to benefit from AI's capacity for high-volume data processing, workflow optimization, and clinical decision support. However, important challenges exist, ranging from model \"hallucinations\" and data bias to questions of interpretability, liability, and ethical use in high-stake environments. This updated viewpoint provides a structured overview of AI's current capabilities in emergency medicine, highlights real-world applications, and explores concerns regarding regulatory requirements, safety standards, and transparency (explainable AI). We discuss the potential risks and limitations of LLMs, including their performance in rare or atypical presentations common in the emergency department and potential biases that could disproportionately affect vulnerable populations. We also address the regulatory landscape, particularly the liability for AI-driven decisions, and emphasize the need for clear guidelines and human oversight. Ultimately, AI holds enormous promise for improving patient care and resource management in emergency medicine; however, ensuring safety, fairness, and accountability remains vital.","url":"https://doi.org/10.2196/70903","authors":["F. Amiot","Benoit Potier"],"tags":["Preprint","Computer science","Data science","Artificial intelligence","World Wide Web"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-21","doi":"https://doi.org/10.2196/70903","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391486627","name":"Revolutionizing nursing education and care: The role of artificial intelligence in nursing","source":"openalex","abstract":"One of the primary applications of artificial intelligence (AI) in nursing is patient monitoring. AI-powered monitoring systems can continuously collect and analyze patient data, such as vital signs and patient behaviors.1, 2 These systems can detect subtle changes in a patient's condition, alerting nurses to potential issues before they become critical.2, 3 Remote monitoring allows nurses to keep a closer eye on patients, improving early intervention and reducing the risk of complications.4-6 The Internet of Things (IoT) is a new technology that has various applications in various fields, including medicine. It is possible to automatically connect sensors and devices to patients without human intervention through the IoT. A wireless body area network (WBAN) is an IoT subdomain which provides the possibility of monitoring vital signs remotely. In the medical field, WBAN consists of a small network of sensors, such as pulse oximeter, gyroscope, spirometer, global positioning system, and electrooculography. RPMs are used to continuously receive clinical data from patients through internal and external sensors and help physicians make appropriate decisions. The main stages of RPMs include the following: (1) Data collection: data such as (vital signs, EEG, ECG, blood pressure, heart rate, etc.) continuously using non-invasive techniques and Invasive techniques are reviewed. (2) Data transfer and storage: All data is collected and transferred to the cloud for analysis, sorting and processing. (3) Support systems: Data are analyzed and used to help clinicians make decisions. Implanted sensors: sensors that are implanted inside the patient's body (under the patient's skin). External sensors: sensors that are directly attached to the patient's skin. Cloud Computing and Fog Computing are the two systems used in storage server. RPMs have many advantages, which can be mentioned as follows: Provide patient assurance, Increase patient awareness and responsibility, Provision of low-cost solutions.5 The application of RPMs is mostly in the field of cardiac arrhythmias, hemodynamics and vital signs.6 Remote patient monitoring systems can be used in relation to various chronic diseases such as heart disease, fall detection, mental health, diabetes. In the field of heart diseases, it is possible to measure and collect heart rate, breathing rate, ECG, breathing rate through wearable sensors. Studies presented a fall detection system based on wearable and environmental sensors, which is of great importance in the elderly. In case of abnormal data, health care professionals and family would be notified. In the field of mental illnesses, systems that remind patients of the dosage of drugs at a certain time, and monitoring adherence to drugs were used. These systems have been used in Alzheimer's and bipolar patients. In the field of diabetes, these systems were used to evaluate the patient's blood glucose level, the amount of food consumed, and the patient's blood pressure.5 AI is also improving the management of electronic health records (EHRs). AI algorithms can extract valuable insights from these records, aiding nurses in making informed decisions about patient care. AI-driven predictive analytics can identify trends and risk factors, helping nurses anticipate patient needs and tailor interventions accordingly.5-7 During the last decade, the use of AI has grown in health data, which are mainly multimodal (EHR, medical imaging, multi-omics and environmental data). Artificial intelligence has led to transformation in various fields such as health education reaserch. Thanks to the advances in AI and machine learning (ML) models, multimodal data fusion with different features can be achieved. Integrating imaging data with specific laboratory test results and demographic data leads to improved outcomes.8 As EHR data is complex and contains diagnosis, scans, laboratory test results, administrative notes doctor's signature it is difficult in working wi","url":"https://doi.org/10.1111/nae2.12057","authors":["Golnar Ghane","Shahrzad Ghiyasvandian","Amir Mohammad Chekeni","Raoofeh Karimi"],"tags":["Nursing","Nursing care","Nurse education","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-02","doi":"https://doi.org/10.1111/nae2.12057","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409524471","name":"Prospects for the Use of Artificial Intelligence in Personalized Medicine, Pharmaceutical Design and Education","source":"openalex","abstract":"The article reviews modern approaches to the use of artificial intelligence in personalized medicine, pharmaceutical design and education, in particular in the pharmaceutical industry, in the processes of search, development, new drugs, personalization of pharmacotherapy and in the education of specialists in these processes. An analysis of the main areas of application of artificial intelligence, its advantages and challenges, as well as the impact on pharmaceutical design in the creation of new drugs is carried out. Special attention is paid to the role of artificial intelligence in personalized medicine, prediction of clinical and pharmacological properties, optimization of clinical trials. Ethical and regulatory aspects of integrating artificial intelligence into medical and pharmaceutical education are considered. Prospects for further development and improvement of the implementation of artificial intelligence in medicine, pharmaceutical design and education are identified. The potential for artificial intelligence to accelerate drug discovery, reduce costs, and enhance treatment precision through real-time analysis of vast datasets is particularly highlighted. Additionally, educational curricula incorporating artificial intelligence-based simulations and virtual reality tools for training future pharmaceutical professionals are explored.","url":"https://doi.org/10.53933/sspmpm.v5i2.182","authors":["Viktoria Dovzhuk","L. Konovalova","Natela Dovzhuk","Serhii Konovalov","Liudmyla Konoshevych","Natalia Motorna"],"tags":["Personalized medicine","Artificial intelligence","Engineering ethics","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-17","doi":"https://doi.org/10.53933/sspmpm.v5i2.182","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4412823383","name":"Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives","source":"openalex","abstract":"Background: With the rapid advancements in science and technology, artificial intelligence (AI) has become increasingly integral to various medical applications, including medical devices and assistive healthcare tools. Extensive research highlights the significant potential of AI in the development of Internet of Things (IoT)-enabled medical devices, particularly in the field of cardiac sensing. Methods: This study explores and synthesizes current advancements and future directions of AI-driven IoT applications in cardiac sensing, highlighting their significance. Utilizing a bibliometric approach, we visualize key focus areas, emerging trends, and the evolutionary trajectory of this interdisciplinary field. Results: As of December 2024, relevant literature at the intersection of IoT, cardiac sensors, and AI was systematically retrieved from the SCIE and ESCI indices. Using CiteSpace, we conducted a comprehensive visualization analysis of countries/regions, academic publications, organizations, authors, citations, and key terminologies. A total of 2,128 papers were included in the analysis. Conclusion: From our perspective, current advancements in AI-powered IoT cardiac sensors primarily focus on optimizing AI algorithms, such as deep learning techniques, and enhancing the functionality of smart wearable devices for precision medicine. Looking ahead, we anticipate that this field will increasingly prioritize data privacy protection, particularly in the era of large language models, to address emerging challenges and ensure sustainable growth. In summary, we need to continue harnessing the power of AI-powered IoT for cardiac sensing as part of public health strategies to enable early detection of heart diseases.","url":"https://doi.org/10.3389/fpubh.2025.1569887","authors":["Hao Ren","Fengshi Jing","Yuan Ma","Ruining Wang","Chaocheng He","Yufan Wang","Jian Zhou","Yu Sun"],"tags":["Current (fluid)","Computer science","Data science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-16","doi":"https://doi.org/10.3389/fpubh.2025.1569887","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405854082","name":"Use of Generative Artificial Intelligence in Teaching and Learning: Engineering Instructors' Perspectives","source":"openalex","abstract":"ABSTRACT Advancements in generative artificial intelligence, particularly ChatGPT, are increasingly influencing engineering education. Engineering instructors have noticed their students using this technology and are beginning to understand its effects on learning and teaching. While research has explored its impacts on general education, few qualitative studies examine engineering instructors' perspectives on how it affects both students and instructors. This qualitative study gathers engineering instructors' views on how ChatGPT affects and could affect their teaching and students' learning experiences. Engineering faculty were invited to participate in Zoom interviews. They were asked questions about their experiences with ChatGPT in their courses. The data collected were analyzed inductively to identify themes. Instructors likened ChatGPT's impact on education to the introduction of calculators, noting its potential to ease academic tasks. They highlighted its benefits in drafting, outlining, and correcting grammatical errors. However, concerns were raised about the quality of ChatGPT's output due to possible inaccuracies (hallucinations) and the risk of students over‐relying on the technology, thus avoiding genuine learning. Instructors recommended that students use ChatGPT as a supplementary tool, verify the information it provides, and that instructors educate students on its limitations and proper use. The overall outlook on ChatGPT in engineering education is positive, with instructors open to its integration in the classroom. Despite growing research on ChatGPT and engineering education, further studies are needed to assess its effectiveness across different course levels. This would help institutions and professors implement ChatGPT appropriately in various subjects and levels.","url":"https://doi.org/10.1002/cae.22813","authors":["Phiwe M. Simelane","Javeed Kittur"],"tags":["Generative grammar","Computer science","Artificial intelligence","Mathematics education","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-27","doi":"https://doi.org/10.1002/cae.22813","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411244936","name":"Artificial Intelligence in Chronic Disease Management for Aging Populations: A Systematic Review of Machine Learning and NLP Applications","source":"openalex","abstract":"As China's elderly population grows rapidly and the aging society arrives, the number of elderly patients with chronic diseases (mainly including chronic cardiovascular and cerebrovascular diseases, respiratory diseases, etc) continues to increase, significantly impacting individuals, families, and society. Geriatric Chronic Disease Management in China faces multiple challenges, including unequal distribution of medical resources, lack of professional management teams, insufficient health education, improper medication management, inadequate psychological support, insufficient medical insurance coverage, and insufficient family support. The rise of artificial intelligence (AI) technology (eg, machine learning, deep learning, NLP, computer vision) offers possibilities for improving Geriatric Chronic Disease Management, including optimizing the distribution of medical resources, supplementing professional management teams, popularizing health education, optimizing medication management, enhancing psychological support, improving medical insurance efficiency and accuracy, and strengthening family support. However, the application of AI in Geriatric Chronic Disease Management still faces challenges such as the data scarcity, model generalization, clinician adoption, alignment of AI decision-making with clinical guidelines, Integration with existing healthcare systems, privacy and security, user acceptance, ethics and law. To overcome these challenges, interdisciplinary collaboration is needed to promote the rational and effective application of AI technology, aiming to achieve healthy aging. This paper systematically reviews the current status, challenges, and future directions of AI application in Geriatric Chronic Disease Management.","url":"https://doi.org/10.2147/ijgm.s516247","authors":["Gang Feng","Falin Weng","Wei Lu","Libin Xu","Zhu Wenxiang","Man Tan","Pengjuan Weng"],"tags":["Medicine","Artificial intelligence","Disease","Natural language processing","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.2147/ijgm.s516247","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399733224","name":"Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence","source":"openalex","abstract":"This policy analysis focuses on harnessing the power of artificial intelligence (AI) in hospital quality improvement to transform quality and patient safety. It examines the application of AI at the two following fundamental levels: (1) diagnostic and treatment and (2) clinical operations. AI applications in diagnostics directly impact patient care and safety. At the same time, AI indirectly influences patient safety at the clinical operations level by streamlining (1) operational efficiency, (2) risk assessment, (3) predictive analytics, (4) quality indicators reporting, and (5) staff training and education. The challenges and future perspectives of AI application in healthcare, encompassing technological, ethical, and other considerations, are also critically analyzed.","url":"https://doi.org/10.36401/jqsh-24-4","authors":["Hana J Abukhadijah","Abdulqadir J. Nashwan"],"tags":["Quality management","Quality (philosophy)","Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-17","doi":"https://doi.org/10.36401/jqsh-24-4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4416623240","name":"The missing value of medical artificial intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41591-025-04050-6","authors":["Isaac S. Kohane","Arjun K. Manrai"],"tags":["Artificial intelligence","Value (mathematics)","Missing data","Computer science","Machine learning"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"https://doi.org/10.1038/s41591-025-04050-6","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391142511","name":"An intriguing vision for transatlantic collaborative health data use and artificial intelligence development","source":"openalex","abstract":"Our traditional approach to diagnosis, prognosis, and treatment, can no longer process and transform the enormous volume of information into therapeutic success, innovative discovery, and health economic performance. Precision health, i.e., the right treatment, for the right person, at the right time in the right place, is enabled through a learning health system, in which medicine and multidisciplinary science, economic viability, diverse culture, and empowered patient's preferences are digitally integrated and conceptually aligned for continuous improvement and maintenance of health, wellbeing, and equity. Artificial intelligence (AI) has been successfully evaluated in risk stratification, accurate diagnosis, and treatment allocation, and to prevent health disparities. There is one caveat though: dependable AI models need to be trained on population-representative, large and deep data sets by multidisciplinary and multinational teams to avoid developer, statistical and social bias. Such applications and models can neither be created nor validated with data at the country, let alone institutional level and require a new dimension of collaboration, a cultural change with the establishment of trust in a precompetitive space. The Data for Health (#DFH23) conference in Berlin and the Follow-Up Workshop at Harvard University in Boston hosted a representative group of stakeholders in society, academia, industry, and government. With the momentum #DFH23 created, the European Health Data Space (EHDS) as a solid and safe foundation for consented collaborative health data use and the G7 Hiroshima AI process in place, we call on citizens and their governments to fully support digital transformation of medicine, research and innovation including AI.","url":"https://doi.org/10.1038/s41746-024-01005-y","authors":["Daniel C. Baumgart"],"tags":["Data science","Artificial intelligence","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-23","doi":"https://doi.org/10.1038/s41746-024-01005-y","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403509421","name":"Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum","source":"openalex","abstract":"","url":"https://doi.org/10.1044/2024_jslhr-24-00594","authors":["Jordan R. Green"],"tags":["Psychology","Cognitive science","Cognitive psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-17","doi":"https://doi.org/10.1044/2024_jslhr-24-00594","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403317190","name":"Artificial intelligence in nurse education – a new sparring partner?","source":"openalex","abstract":"In Norway, one of four nursing students are failing on the national exam for nursing students in the subject of anatomy, physiology, and biochemistry. We need more knowledge on how to address this situation and this position paper builds on our previous work on AI in education and examines GPT-4ʼs performance on a full-scale exam within nursing (multiple choice exam). The primary data sources were GPT-4ʼs ability to answer correctly/incorrectly on 53 exam questions within the National exam in Anatomy, Physiology, and Biochemistry in nursing education in Norway. From the same exam, a further multimodal analysis was carried out to examine GPT-4ʼs ability to answer correctly/incorrectly on 5 of the exam questions based on visual illustrations (blinded answers). The findings from this intrinsic case study revealed that GPT-4 performs very well on this summative exam with an accuracy rate ranging from 84.9% to 94.5% (based on three test scenarios), which indicates a strong understanding of the exam questions in this national nursing exam. This, and its ability to answer correctly on the five multimodal analysis-questions (62,5%-100% correct) shows both the GPT-4ʼs ability to handle Norwegian exam language and its multimodal capabilities. The (digital) fieldwork after the exam-testing shows that GPT-4 can enhance the possibilities of formative assessment by providing timely and personalized feedback, which supports student learning. For summative assessment, GPT-4 demonstrated reliable evaluation of student exams on multiple-choice questions, but also on some process questions as part of the exam. One implication of the study is that GPT-4 can be a sparring partner for nursing students in formative assessment processes, case simulations, and so on, since it shows high reliability on national exams within Anatomy, Physiology and Biochemistry. GPT-4 can scaffold tutoring, ZPD, and formative and summative assessment practices. However, the potential for perpetuating biases and ethical issues underscores the need for vigilance and careful implementation of such AI. Keywords GPT-4 Formative assessment Summative assessment Final exams Nursing education Position paper Educational innovation","url":"https://doi.org/10.18261/njdl.19.3.5","authors":["Rune Johan Krumsvik"],"tags":["Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-11","doi":"https://doi.org/10.18261/njdl.19.3.5","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410951986","name":"Artificial Intelligence and Public Sector Auditing: Challenges and Opportunities for Supreme Audit Institutions","source":"openalex","abstract":"The application of artificial intelligence (AI) is growing exponentially in public entities, contributing to the improvement of the design and provision of services, as well as to the internal management and efficiency of public institutions. However, the potential of artificial intelligence systems for the public sector also entails a set of risks related, among other areas, to privacy, confidentiality, security, transparency or bias and discrimination. The Supreme Audit Institutions (SAIs), when auditing public services and policies, must adapt their human and technological resources to this new scenario. This paper analyses the implications of AI penetration in the public sector, as well as the challenges that these technological developments pose to SAIs to improve effectiveness and efficiency in their auditing tasks. This paper presents a conceptual and exploratory analysis, informed by documentary evidence and case illustrations. Given the dynamic evolution of AI research, the findings should be interpreted as a contribution to ongoing debates, rather than definitive conclusions. It also reviews the status of the audits of systems based on algorithms carried out by some SAIs.","url":"https://doi.org/10.3390/world6020078","authors":["Dolores Genaro-Moya","Antonio Manuel López Hernández","Mariia Godz"],"tags":["Audit","Public sector","Supreme court","Business","Accounting"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.3390/world6020078","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405073941","name":"Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review","source":"openalex","abstract":"Utilizing information from multiple sources is a preferred and more precise method for medical experts to confirm a diagnosis. Each source provides critical information about the disease that might otherwise be absent in other modalities. Combining information from various medical sources boosts confidence in the diagnosis process, enabling the creation of an effective treatment plan for the patient. The scarcity of medical experts to diagnose diseases motivates the development of automatic diagnoses relying on multimodal data. With the progress in artificial intelligence technology, automated diagnosis using multimodal fusion techniques is now possible. Nevertheless, the concept of multimodal medical diagnosis is still new and requires an understanding of the diverse aspects of multimodal data and its related challenges. This review article examines the various aspects of multimodal medical diagnosis to equip readers, academicians, and researchers with necessary knowledge to advance multimodal medical research. The chosen articles in the study underwent thorough screening from reputable journals and publishers to offer high-quality content to readers, who can then apply the knowledge to produce quality research. Besides, the need for multimodal information and the associated challenges are discussed with solutions. Additionally, ethical issues of using artificial intelligence in medical diagnosis is also discussed.","url":"https://doi.org/10.3390/bioengineering11121233","authors":["Sachin Kumar","Sita Rani","Shivani Sharma","Hong Min"],"tags":["Medical diagnosis","Multimodality","Modalities","Computer science","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-05","doi":"https://doi.org/10.3390/bioengineering11121233","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402244112","name":"A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer","source":"openalex","abstract":"Background: Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field. Methods: This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency. Results: The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; et al., published in 2008. Conclusions: AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.","url":"https://doi.org/10.3389/fonc.2024.1431142","authors":["Ziqi Zhao","Boqian Hu","Kun Xu","Yizhuo Jiang","Xisheng Xu","Yuliang Liu"],"tags":["Cervical cancer","Computer science","Cancer","Medicine","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-03","doi":"https://doi.org/10.3389/fonc.2024.1431142","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410969909","name":"Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review","source":"openalex","abstract":"BACKGROUND: The integration of artificial intelligence (AI) in health care has significant potential, yet its acceptance by health care professionals (HCPs) is essential for successful implementation. Understanding HCPs' perspectives on the explainability and integrability of medical AI is crucial, as these factors influence their willingness to adopt and effectively use such technologies. OBJECTIVE: This study aims to improve the acceptance and use of medical AI. From a user perspective, it explores HCPs' understanding of the explainability and integrability of medical AI. METHODS: We performed a mixed systematic review by conducting a comprehensive search in the PubMed, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library and arXiv databases for studies published between 2014 and 2024. Studies concerning an explanation or the integrability of medical AI were included. Study quality was assessed using the Joanna Briggs Institute critical appraisal checklist and Mixed Methods Appraisal Tool, with only medium- or high-quality studies included. Qualitative data were analyzed via thematic analysis, while quantitative findings were synthesized narratively. RESULTS: Out of 11,888 records initially retrieved, 22 (0.19%) studies met the inclusion criteria. All selected studies were published from 2020 onward, reflecting the recency and relevance of the topic. The majority (18/22, 82%) originated from high-income countries, and most (17/22, 77%) adopted qualitative methodologies, with the remainder (5/22, 23%) using quantitative or mixed method approaches. From the included studies, a conceptual framework was developed that delineates HCPs' perceptions of explainability and integrability. Regarding explainability, HCPs predominantly emphasized postprocessing explanations, particularly aspects of local explainability such as feature relevance and case-specific outputs. Visual tools that enhance the explainability of AI decisions (eg, heat maps and feature attribution) were frequently mentioned as important enablers of trust and acceptance. For integrability, key concerns included workflow adaptation, system compatibility with electronic health records, and overall ease of use. These aspects were consistently identified as primary conditions for real-world adoption. CONCLUSIONS: To foster wider adoption of AI in clinical settings, future system designs must center on the needs of HCPs. Enhancing post hoc explainability and ensuring seamless integration into existing workflows are critical to building trust and promoting sustained use. The proposed conceptual framework can serve as a practical guide for developers, researchers, and policy makers in aligning AI solutions with frontline user expectations. TRIAL REGISTRATION: PROSPERO CRD420250652253; https://www.crd.york.ac.uk/PROSPERO/view/CRD420250652253.","url":"https://doi.org/10.2196/73374","authors":["Yushu Liu","Chenxi Liu","Jianing Zheng","Chang Xu","Dan Wang"],"tags":["Preprint","Health care","Health professionals","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-02","doi":"https://doi.org/10.2196/73374","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4386535333","name":"Overview of trials on artificial intelligence algorithms in breast cancer screening – A roadmap for international evaluation and implementation","source":"openalex","abstract":"Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK's National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders' meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme. It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration.","url":"https://doi.org/10.1016/j.ejrad.2023.111087","authors":["Thiemo J. A. van Nijnatten","Nicholas Roy Payne","Sarah Hickman","Hutan Ashrafian","Fiona J. Gilbert"],"tags":["Medicine","Algorithm","Reading (process)","Mammography","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-08","doi":"https://doi.org/10.1016/j.ejrad.2023.111087","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403876062","name":"Causality and scientific explanation of artificial intelligence systems in biomedicine","source":"openalex","abstract":"With rapid advances of deep neural networks over the past decade, artificial intelligence (AI) systems are now commonplace in many applications in biomedicine. These systems often achieve high predictive accuracy in clinical studies, and increasingly in clinical practice. Yet, despite their commonly high predictive accuracy, the trustworthiness of AI systems needs to be questioned when it comes to decision-making that affects the well-being of patients or the fairness towards patients or other stakeholders affected by AI-based decisions. To address this, the field of explainable artificial intelligence, or XAI for short, has emerged, seeking to provide means by which AI-based decisions can be explained to experts, users, or other stakeholders. While it is commonly claimed that explanations of artificial intelligence (AI) establish the trustworthiness of AI-based decisions, it remains unclear what traits of explanations cause them to foster trustworthiness. Building on historical cases of scientific explanation in medicine, we here propagate our perspective that, in order to foster trustworthiness, explanations in biomedical AI should meet the criteria of being scientific explanations. To further undermine our approach, we discuss its relation to the concepts of causality and randomized intervention. In our perspective, we combine aspects from the three disciplines of biomedicine, machine learning, and philosophy. From this interdisciplinary angle, we shed light on how the explanation and trustworthiness of artificial intelligence relate to the concepts of causality and robustness. To connect our perspective with AI research practice, we review recent cases of AI-based studies in pathology and, finally, provide guidelines on how to connect AI in biomedicine with scientific explanation.","url":"https://doi.org/10.1007/s00424-024-03033-9","authors":["Florian J. Boge","Axel Mosig"],"tags":["Biomedicine","Trustworthiness","Artificial intelligence","Causality (physics)","Perspective (graphical)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.1007/s00424-024-03033-9","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4313571717","name":"Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence","source":"openalex","abstract":"Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.","url":"https://doi.org/10.3390/cancers15020351","authors":["Vincenza Granata","Roberta Fusco","Sergio Venanzio Setola","Roberta Galdiero","Nicola Maggialetti","Lucrezia Silvestro","Mario De Bellis","Elena Di Girolamo","Giulia Grazzini","Giuditta Chiti","Maria Chiara Brunese","Andrea Belli"],"tags":["Medicine","Magnetic resonance cholangiopancreatography","Stage (stratigraphy)","Pancreatic cancer","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-05","doi":"https://doi.org/10.3390/cancers15020351","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W7141119996","name":"Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications","source":"openalex","abstract":"The integration of human judgment into artificial intelligence (AI) systems has emerged as a key research direction, particularly for high-stakes applications where full automation remains insufficient. Human-in-the-Loop (HITL) AI represents a field that combines machine learning capabilities with human oversight, feedback, and decision-making at various stages of the AI pipeline. This survey provides a systematic review of HITL approaches, covering theoretical foundations, technical methods, ethical considerations, and domain-specific applications. We propose a unified taxonomy that categorizes HITL systems based on loop placement, interaction granularity, and temporal characteristics. This review synthesizes findings from healthcare, autonomous systems, cybersecurity, and other high-risk domains where human oversight is essential. We also examine the challenges of scalability, cognitive load, and trust calibration that affect the practical deployment of HITL systems. The final section outlines open research directions and introduces a framework for designing effective human-AI collaborative systems.","url":"https://doi.org/10.3390/e28040377","authors":["Konstantinos Lazaros","Aristidis G. Vrahatis","Sotiris Kotsiantis"],"tags":["Software deployment","Computer science","Human-in-the-loop","Automation","Key (lock)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-26","doi":"https://doi.org/10.3390/e28040377","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402784265","name":"Artificial intelligence and the dawn of an algorithmic divide","source":"openalex","abstract":"Emerging technologies like artificial intelligence (AI) and algorithms reshape news curation and consumption. Against this background, previous research has been focused on divides between groups regarding access to such digital technologies. Disparities in awareness and knowledge of AI across socio-demographic groups seem to persist, potentially leading to an algorithmic divide. Despite this situation, there is still limited research into such an emerging inequality. Building on the framework of algorithmic literacy, this study aims to contribute to this gap with findings from a national representative study in Germany (N = 1,090) in January 2022, considering socio-demographic factors such as age, gender, and education. Findings shed important light on the extent to which news audiences are knowledgeable about the use of AI and algorithms in news selection and recommendation, as well as in society. The results of our analysis imply that newsrooms should increase their knowledge about the potential divides created by applying AI across sectors to various socio-demographic groups and stay vigilant about the level of transparency of their AI use.","url":"https://doi.org/10.3389/fcomm.2024.1453251","authors":["Maximilian Eder","Helle Sjøvaag"],"tags":["Cognitive science","Data science","Computer science","Artificial intelligence","Astrobiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-24","doi":"https://doi.org/10.3389/fcomm.2024.1453251","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410958358","name":"Exploring the determinants and effects of artificial intelligence (AI) hallucination exposure on generative AI adoption in healthcare","source":"openalex","abstract":"Artificial intelligence (AI) hallucinations—erroneous outputs that generate misleading or nonsensical content—pose significant risks in contexts where consumers seek health information, as inaccuracies in this domain could lead to harmful outcomes. This study aims to explore determinants of AI hallucination exposure (HEX), examine the potential HEX's direct and mediating effects on adoption intentions, and integrate HEX into the Theory of Planned Behavior (TPB) to advance generative AI adoption models. An eight-factor measurement model, grounded in the TPB and incorporating constructs such as perceived usefulness, attitude toward AI, perceived risk, subjective norms, perceived behavioral control, user trust, AI hallucination exposure, and behavioral intention, was developed and tested using structural equation modeling (SEM). The study concludes that perceived behavioral control is a significant determinant of AI hallucination exposure (HEX), while subjective norms exert a direct influence on behavioral intention (BI) to adopt generative AI chatbots. AI hallucination exposure does not statistically significantly mediate the relationship between key antecedents and the use of generative AI chatbots. These findings advance the Theory of Planned Behavior (TPB) by integrating AI-specific risks like HEX while underscoring the need to refine theoretical models to account for contexts where technological reliability—rather than user perceptions alone—drives adoption decisions.","url":"https://doi.org/10.1177/02666669251340954","authors":["Longyun Jin","Zijun Shen","Anas Ali Alhur","Salman Bin Naeem"],"tags":["Generative grammar","Psychology","Health care","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-02","doi":"https://doi.org/10.1177/02666669251340954","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4393308360","name":"Postgraduate Students’ Perceptions on the Benefits Associated with Artificial Intelligence Tools on Academic Success: In Case of ChatGPT AI tool","source":"openalex","abstract":"Postgraduate students in developing nations, such as South Africa, are increasingly leveraging artificial intelligence tools like ChatGPT to elevate their academic success in the era of the fourth industrial revolution. This study aims to explore postgraduate students' perceptions of the benefits associated with the utilisation of artificial intelligence tools, with a specific focus on ChatGPT, in their academic success in South Africa’s historically disadvantaged universities. Employing a qualitative approach, the study aims to gain a deeper understanding of postgraduate views on this subject. The sample size comprised 10 postgraduate students pursuing master's degrees within the two selected South Africa’s historically disadvantaged universities, selected through purposive sampling. Semi-structured interviews were conducted to gather insights from the postgraduate students. Thematic analysis was employed to analyse the collected data. The study's findings shed light on the significant advantages of incorporating ChatGPT in students' academic journey with special focus on research success. The study found that ChatGPT proves beneficial for postgraduate students, with some utilising the AI tool to refine their research topics before submission to their supervisors. Moreover, ChatGPT assists postgraduate students in identifying grammatical errors and paraphrasing their academic writing, contributing to the enhancement of their writing skills. In light of these findings, the study recommends the immediate development of an innovative AI ethical use policy in South Africa’s historically disadvantaged universities. This policy should emphasise ethical guidelines for postgraduate students when utilising AI tools, such as ChatGPT to ensure responsible and effective integration into their academic success.","url":"https://doi.org/10.46303/jcsr.2024.4","authors":["Thulani Andrew Chauke","Themba Ralph Mkhize","Lina Mmakgabo Methi","Ntandokamenzi Penelope Dlamini"],"tags":["Perception","Psychology","Mathematics education","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-29","doi":"https://doi.org/10.46303/jcsr.2024.4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4393226201","name":"A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests","source":"openalex","abstract":"BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The choice of excipients in tablet formulations plays a critical role in ensuring product quality, highlighting its importance in tablet creation. The traditional trial-and-error approach to this process is both expensive and time-intensive. To tackle these obstacles, we introduce a fresh approach leveraging machine learning and deep learning methods to automate and enhance pre-formulation drug design. METHODS: We collected a comprehensive dataset of 1983 formulations, including excipient names, quantities, active ingredient details, and various physicochemical attributes. Our study focused on predicting two critical control test parameters: tablet disintegration time and hardness. We compared a range of models like deep learning, artificial neural networks, support vector machines, decision trees, multiple linear regression, and random forests. RESULTS: A 12-layer deep neural network, as a form of deep learning, surpassed alternative techniques by achieving 73% accuracy for disintegration time and 99% for tablet hardness. This success underscores its efficacy in predicting complex pharmaceutical factors. Such an approach streamlines the drug formulation process, reducing iterations and material consumption. CONCLUSIONS: Our findings highlight the deep learning potential in pharmaceutical formulations, particularly for tablet hardness prediction. Future work should focus on enlarging the dataset to improve model effectiveness and extend its application in pharmaceutical product development and assessment.","url":"https://doi.org/10.1186/s12911-024-02485-4","authors":["Mehri Momeni","Marziyeh Afkanpour","Saleh Rakhshani","Amin Mehrabian","Hamed Tabesh"],"tags":["Machine learning","Computer science","Artificial intelligence","Artificial neural network","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-27","doi":"https://doi.org/10.1186/s12911-024-02485-4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391329232","name":"Refusing participation: hesitations about designing responsible patient engagement with artificial intelligence in healthcare","source":"openalex","abstract":"The rapidly expanding field of artificial intelligence (AI) is often accompanied by calls for parallel research on its societal implications.For research about AI in healthcare, this translates to some form of patient engagement.In this article, we question whether patient engagement and participation really contribute to responsible AI.We first summarise existing critiques of patient participation.We review the critiques of the critiques, themselves motivated by the wish to contribute, and not to leave the field solely to computer-and data scientists.In the final section, we express our doubts about the possibilities for developing positive, generative interventions, and explore 'refusal' and 'hesitation' as forms of critique and engagement.The conclusion presents a checklist for refusing patient participation, an addition to the growing repertoire of tools for patient participation and responsible innovation.The article draws on and contributes to the STS tradition of creative and speculative writing.","url":"https://doi.org/10.1080/23299460.2023.2300161","authors":["Flora Lysen","Sally Wyatt"],"tags":["Responsible Research and Innovation","Health care","Engineering ethics","Psychology","Patient care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-29","doi":"https://doi.org/10.1080/23299460.2023.2300161","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409275342","name":"Geopolymer foam concrete: a review of pore characteristics, compressive strength and artificial intelligence in GFC strength simulations","source":"openalex","abstract":"Geopolymer foam concrete (GFC) has emerged as a sustainable alternative in construction, utilizing waste-derived binders to enhance material properties while addressing environmental concerns. This review examines the impact of precursor materials, foam agents, nanomaterials, fibers, and curing temperatures on GFC’s pore structure and compressive strength. GFC typically achieves compressive strengths of 1–10 MPa, with porosity significantly influencing performance. For instance, GFC with densities of 280–865 kg/m 3 exhibits compressive strengths of 1.10–8.13 MPa and thermal conductivities of 0.08–0.20 W/(m K), making it suitable for insulation applications. The integration of alternative materials, such as industrial by-products, enhances sustainability without compromising mechanical properties. Precise control over curing parameters, particularly heating rates, is critical to optimizing porosity and strength. The addition of nanomaterials improves mechanical performance and introduces self-sensing capabilities, expanding GFC’s potential for advanced industrial applications. Fibers further enhance toughness and crack resistance, broadening its usability. Machine learning algorithms offer promising tools for optimizing GFC formulations and structural designs, enabling the development of high-performance, sustainable materials tailored to specific engineering needs. By systematically evaluating the effects of alternative materials, nanotechnology, fibers, and curing conditions, this review highlights the potential of GFC to revolutionize sustainable construction. Finally, GFC represents a transformative advancement in eco-friendly construction materials. Its integration of waste-derived binders, nanomaterials, and advanced optimization techniques positions it as a key solution for sustainable infrastructure, balancing performance, durability, and environmental responsibility.","url":"https://doi.org/10.1007/s44416-025-00003-x","authors":["Mohamed Abdellatief","Alaa E. Hassanien","Mohamed Mortagi","Hassan Hamouda"],"tags":["Compressive strength","Geopolymer","Materials science","Geopolymer cement","Composite material"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-07","doi":"https://doi.org/10.1007/s44416-025-00003-x","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405215661","name":"Transforming Medical Libraries: Opportunities, Challenges, and Strategies for Integrating Artificial Intelligence","source":"openalex","abstract":"This study examines the role of Artificial Intelligence (AI) in transforming medical libraries, focusing on identifying opportunities, challenges, and strategies for effective integration. Medical libraries, essential resources for healthcare professionals, are increasingly leveraging AI to improve information retrieval, data management, and user services. The study aims to analyze the benefits AI brings to medical libraries, the obstacles to its adoption, and best practices for implementation. To achieve this, three specific objectives guided the study. A qualitative approach was applied, using a systematic review of literature from databases such as Scopus, Web of Science, and Google Scholar. Articles published between 2020 and 2024 were included, with non-English publications excluded. Findings indicate that AI offers substantial benefits, including enhanced information retrieval, automated data management, and improved user engagement through personalized services and research support. However, challenges such as ethical issues, data privacy concerns, infrastructure needs, and staff training remain. The study concludes that, while AI holds great potential for advancing medical libraries, overcoming these challenges will require strategic planning, investment in infrastructure, and the use of transparent, explainable AI solutions.","url":"https://doi.org/10.70112/ajist-2024.14.2.4298","authors":["Ebiere Diana Orubebe","Esther Mamodu Ijaja","John Ahiaba Ogwula","Bolaji David Oladokun"],"tags":["Computer science","Data science","Knowledge management","Engineering","Engineering management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-11","doi":"https://doi.org/10.70112/ajist-2024.14.2.4298","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406296262","name":"Artificial Intelligence for Cervical Spine Fracture Detection: A Systematic Review of Diagnostic Performance and Clinical Potential","source":"openalex","abstract":"Study DesignSystematic review.ObjectiveArtificial intelligence (AI) and deep learning (DL) models have recently emerged as tools to improve fracture detection, mainly through imaging modalities such as computed tomography (CT) and radiographs. This systematic review evaluates the diagnostic performance of AI and DL models in detecting cervical spine fractures and assesses their potential role in clinical practice.MethodsA systematic search of PubMed/Medline, Embase, Scopus, and Web of Science was conducted for studies published between January 2000 and July 2024. Studies that evaluated AI models for cervical spine fracture detection were included. Diagnostic performance metrics were extracted and included sensitivity, specificity, accuracy, and area under the curve. The PROBAST tool assessed bias, and PRISMA criteria were used for study selection and reporting.ResultsEleven studies published between 2021 and 2024 were included in the review. AI models demonstrated variable performance, with sensitivity ranging from 54.9% to 100% and specificity from 72% to 98.6%. Models applied to CT imaging generally outperformed those applied to radiographs, with convolutional neural networks (CNN) and advanced architectures such as MobileNetV2 and Vision Transformer (ViT) achieving the highest accuracy. However, most studies lacked external validation, raising concerns about the generalizability of their findings.ConclusionsAI and DL models show significant potential in improving fracture detection, particularly in CT imaging. While these models offer high diagnostic accuracy, further validation and refinement are necessary before they can be widely integrated into clinical practice. AI should complement, rather than replace, human expertise in diagnostic workflows.","url":"https://doi.org/10.1177/21925682251314379","authors":["Wongthawat Liawrungrueang","Watcharaporn Cholamjiak","Arunee Promsri","Khanathip Jitpakdee","Sompoom Sunpaweravong","Vit Kotheeranurak","Peem Sarasombath"],"tags":["Medicine","Artificial intelligence","Generalizability theory","MEDLINE","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-12","doi":"https://doi.org/10.1177/21925682251314379","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405397757","name":"Artificial intelligence-driven predictive maintenance in IoT systems","source":"openalex","abstract":"The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above results bring out the potential of AI-based solutions for real-time predictive maintenance over complex IoT networks. It concludes by indicating some further research vectors, such as the refinement of the model and the extension of AI-driven predictive maintenance for broader applications in IoT, such as smart cities and healthcare systems.","url":"https://doi.org/10.46932/sfjdv5n12-030","authors":["Raghdah Adnan Abdulrazzq","Nisreen Mustafa Sajid","Md. Kamrul Hasan"],"tags":["Predictive maintenance","Internet of Things","Computer science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-13","doi":"https://doi.org/10.46932/sfjdv5n12-030","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4416028051","name":"Concept-based Explainable Artificial Intelligence: A Survey","source":"openalex","abstract":"The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been discussed in several works lately, advocating for more user-understandable explanations. To address this issue, a wide range of papers proposing Concept-based eXplainable Artificial Intelligence (C-XAI) methods have been published in recent years. Nevertheless, a unified categorization and precise field definition are still missing. This paper fills the gap by offering a thorough review of C-XAI approaches. We identify and define different concepts and explanation types. We propose a taxonomy comprising nine categories and guidelines for selecting a suitable category based on the application context. Additionally, we discuss common evaluation strategies including metrics, human evaluations, and datasets employed, aiming to assist the development of future methods. Overall, we believe this survey will assist researchers, practitioners, and domain experts in enhancing their understanding and contributing to the progress of this innovative field.","url":"https://doi.org/10.1145/3774643","authors":["Eleonora Poeta","Gabriele Ciravegna","Eliana Pastor","Tania Cerquitelli","Elena Baralis"],"tags":["Computer science","Categorization","Taxonomy (biology)","Field (mathematics)","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-08","doi":"https://doi.org/10.1145/3774643","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403998774","name":"Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications","source":"openalex","abstract":"BACKGROUND: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging. METHODS: We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging. RESULTS: A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n = 16, 28%), gynecology (n = 15, 26%), hepatobiliary system (n = 13, 23%), and musculoskeletal (n = 8, 14%). The main methodological applications included deep learning (n = 37, 65%), machine learning (n = 13, 23%), natural language processing (n = 5, 9%), and robots (n = 2, 4%). The majority of the studies were single-center (n = 43, 75%) and retrospective (n = 56, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal/pelvic imaging (infertility monitoring and follicle development). CONCLUSION: The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.","url":"https://doi.org/10.1007/s00261-024-04640-x","authors":["Lie Cai","André Pfob"],"tags":["Medicine","Hepatology","Ultrasound","Radiology","Current (fluid)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-02","doi":"https://doi.org/10.1007/s00261-024-04640-x","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399527350","name":"Detection of oral cancer and oral potentially malignant disorders using artificial intelligence‐based image analysis","source":"openalex","abstract":"BACKGROUND: We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera. SUBJECTS AND METHODS: We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405). RESULTS: For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%. CONCLUSIONS: Our proposed model is a potential diagnostic tool for oral diseases.","url":"https://doi.org/10.1002/hed.27843","authors":["Atsumu Kouketsu","Chiaki Doi","Hiroaki Tanaka","Takashi Araki","Rina Nakayama","Tsuguyoshi Toyooka","Satoshi Hiyama","Masahiro Iikubo","Ken Osaka","Keiichi Sasaki","Hirokazu Nagai","Tsuyoshi Sugiura"],"tags":["Leukoplakia","Medicine","Cancer","Oral cavity","Basal cell"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-11","doi":"https://doi.org/10.1002/hed.27843","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4404413393","name":"Human-Computer Interaction: A Literature Review of Artificial Intelligence and Communication in Healthcare","source":"openalex","abstract":"The integration of artificial intelligence (AI) into healthcare communication has rapidly evolved, driven by advancements in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT). This literature review explores AI's role in patient-physician interactions, particularly focusing on its capacity to enhance communication by bridging language barriers, summarizing complex medical data, and offering empathetic responses. AI's strengths lie in its ability to deliver comprehensible, concise, and medically accurate information. Studies indicate AI can outperform human physicians in certain communicative aspects, such as empathy and clarity, with models like ChatGPT and the Medical Pathways Language Model (Med-PaLM) demonstrating high effectiveness in these areas. However, significant challenges remain, including occasional inaccuracies and \"hallucinations,\" where AI-generated content is irrelevant or medically inaccurate. These limitations highlight the need for continued refinement in AI algorithms to ensure reliability and consistency in sensitive healthcare settings. The review underscores the potential of AI as a transformative tool in health communication while advocating for further research and policy development to mitigate risks and enhance AI's integration into clinical practice.","url":"https://doi.org/10.7759/cureus.73763","authors":["Theo J Clay","Zephy J Da Custodia Steel","Chris Jacobs"],"tags":["CLARITY","Empathy","Health care","Medicine","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-15","doi":"https://doi.org/10.7759/cureus.73763","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406038522","name":"An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation","source":"openalex","abstract":"The COVID-19 disease caused by the SARS-CoV-2 virus was declared by the World Health Organization (WHO) as a spreadable viral disease. During the COVID pandemic, there was difficulty in notifying the Decision-Making System (DMS) about the rapid and precise triage of patients admitted to the emergency wards. As a method to achieve the aim and develop digital healthcare revolutions in data and analytics, digital healthcare information was established. Artificial Intelligence (AI) is a robust automation tool for sustainability in the context of the COVID-19 health crisis on big datasets. Besides, the gap between AI investment and commercial real-time application, which are the initial digital technology development curves, has been identified. It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises. The fast inventions in AI and Machine Learning (ML) have implications for amazingly preventive and clinical healthcare, and for the association, ML was developed as a predictable attention. Billions of smartphones, massive online datasets, linked wireless wearable devices, comparatively cost-effective computing resources and improved ML and Nural Language Processing (NLP) are leveraged by these rapid responses, with the trained dataset of 65% and evaluated in the other 35%, the renowned ML models for structured data like Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regressive Tree (LRT), Decision Tree (DT), Stochastic Gradient Booster (SGB), and Random Forest (RF) are used for simulating new unidentified data. AI-DTM challenges DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases. Meanwhile, a comprehensive collection of healthcare datasets over what is spreadable would be required to save human lives, train AI, and limit cost-effective health risks.","url":"https://doi.org/10.53759/7669/jmc202505044","authors":["Hayder M. A. Ghanimi","Firas Tayseer Ayasrah","Vijaya Chandra Jadala","T. C. Manjunath","K. Balasaranya","B. Srinivasarao"],"tags":["Artificial intelligence","Computer science","Machine learning","Decision tree","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-03","doi":"https://doi.org/10.53759/7669/jmc202505044","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4390348027","name":"Artificial Intelligence (AI) for Research Lifecycle: Challenges and Opportunities","source":"openalex","abstract":"Objective. This article aims to review the progress of AI technologies concerning their potential impact on academia, research processes, scientific communication, and libraries. Methods. AI tools for research lifecycle and their potential impact on academia and libraries were identified from various sources, mostly from the most influential recent scientific publications. Results. AI has become a driving force nowadays, creating both opportunities and challenges. Transformative AI-powered tools, exemplified by advanced models like ChatGPT, Llama-2, Google Bard, Microsoft Bing, and Jasper Chat, among others, find versatile utility across a broad spectrum of contexts, extending their impact to research process and publishing, as well as to librarianship. The enthusiastic embrace of AI in research is tempered by a pervasive concern over the potential for data fabrication, which can significantly compromise ethical standards and academic integrity. There is an urgent need to understand corresponding opportunities, challenges, and dangers. Some aspects of the use of AI tools for different stages of the research lifecycle are considered, and the main advantages and risks are analyzed. Conclusions. AI has the potential to drive innovation and progress in a wide range of fields and possesses significant potential to propel academia and librarianship into both exhilarating and challenging new frontiers. While AI-powered tools represent major advancements and potential to significantly impact academia, scholarly research, publishing, and university libraries. Privacy and bias are just two examples of the ethical considerations that need to be made.","url":"https://doi.org/10.15802/unilib/2023_294639","authors":["Tetiana Yaroshenko","Олександра Ярошенко"],"tags":["Transformative learning","Publishing","Ethical issues","Process (computing)","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-28","doi":"https://doi.org/10.15802/unilib/2023_294639","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399608418","name":"The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review","source":"openalex","abstract":"The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012-2022) assessing AI algorithms' diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019-2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.","url":"https://doi.org/10.3390/healthcare12121192","authors":["Anna Escalé-Besa","Josep Vidal‐Alaball","Queralt Miró Catalina","Victor Hugo Garcia Gracia","Francesc X Marín-Gomez","Aïna Fuster‐Casanovas"],"tags":["Triage","Primary care","Medicine","Diagnostic accuracy","Economic shortage"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-13","doi":"https://doi.org/10.3390/healthcare12121192","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403878225","name":"Generative artificial intelligence writing open notes: A mixed methods assessment of the functionality of GPT 3.5 and GPT 4.0","source":"openalex","abstract":"Background: Worldwide, patients are increasingly being offered access to their full online clinical records including the narrative reports written by clinicians (so-called \"open notes\"). Against these developments, there is growing interest in the use of generative artificial intelligence (AI) such as OpenAI's ChatGPT to co-assist clinicians with patient-facing documentation. Objective: This study aimed to explore the effectiveness of OpenAI's ChatGPT 3.5 and GPT 4.0 in generating three patient-facing clinical notes from fictional general practice narrative reports. Methods: On 1 October 2023 and 1 November 2023, we used ChatGPT 3.5 and 4.0 to generate notes for three validated fictional general practice notes, using a prompt in the style of a British primary care note for three commonly presented conditions: (1) type 2 diabetes, (2) major depressive disorder, and (3) a differential diagnosis for suspected bowel cancer. Outputs were analyzed for reading ease, sentiment analysis, empathy, and medical fidelity. Results: ChatGPT 3.5 and 4.0 wrote longer notes than the original, and embedded more second person pronouns, with ChatGPT 3.5 scoring higher on both. ChatGPT expanded abbreviations, but readability metrics showed that the notes required a higher reading proficiency, with ChatGPT 3.5 demanding the most advanced level. Across all notes, ChatGPT offered higher signatures of empathy across cognitive, compassion/sympathy, and prosocial cues. Medical fidelity ratings varied across all three cases with ChatGPT 4.0 rated superior. Conclusions: While ChatGPT improved sentiment and empathy metrics in the transformed notes, compared to the original they also required higher reading proficiency and omitted details impacting medical fidelity.","url":"https://doi.org/10.1177/20552076241291384","authors":["Anna Kharko","Brian McMillan","Josefin Hagström","Irene Muli","Gail Davidge","Maria Hägglund","Charlotte Blease"],"tags":["Generative grammar","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1177/20552076241291384","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4400117494","name":"Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support","source":"openalex","abstract":"Artificial intelligence (AI)-based clinical decision support systems are gaining momentum by relying on a greater volume and variety of secondary use data. However, the uncertainty, variability, and biases in real-world data environments still pose significant challenges to the development of health AI, its routine clinical use, and its regulatory frameworks. Health AI should be resilient against real-world environments throughout its lifecycle, including the training and prediction phases and maintenance during production, and health AI regulations should evolve accordingly. Data quality issues, variability over time or across sites, information uncertainty, human-computer interaction, and fundamental rights assurance are among the most relevant challenges. If health AI is not designed resiliently with regard to these real-world data effects, potentially biased data-driven medical decisions can risk the safety and fundamental rights of millions of people. In this viewpoint, we review the challenges, requirements, and methods for resilient AI in health and provide a research framework to improve the trustworthiness of next-generation AI-based clinical decision support.","url":"https://doi.org/10.2196/50295","authors":["Carlos Sáez","Pablo Ferri","Juan M. García‐Gómez"],"tags":["Variety (cybernetics)","Computer science","Data science","Risk analysis (engineering)","Decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.2196/50295","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401567107","name":"ChatGPT and Artificial Intelligence in Graduate Medical Education Program Applications","source":"openalex","abstract":"","url":"https://doi.org/10.4300/jgme-d-23-00823.1","authors":["Shane C. Quinonez","David A. Stewart","Nikola Banović"],"tags":["Medical education","Graduate medical education","Computer science","Data science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.4300/jgme-d-23-00823.1","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4414874092","name":"Ethical Problems in the Use of Artificial Intelligence by University Educators","source":"openalex","abstract":"This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education.","url":"https://doi.org/10.3390/educsci15101322","authors":["Roman Chinoracký","Natália Stalmašeková"],"tags":["Transparency (behavior)","Engineering ethics","Autonomy","Scopus","Higher education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-06","doi":"https://doi.org/10.3390/educsci15101322","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405913204","name":"The Importance of Explainable Artificial Intelligence Based Medical Diagnosis","source":"openalex","abstract":"","url":"https://doi.org/10.31083/j.ceog5112268","authors":["Aigerim Mashekova","Vasilios Zarikas","Yong Zhao","E. Y. K. Ng"],"tags":["Medicine","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-18","doi":"https://doi.org/10.31083/j.ceog5112268","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4400141746","name":"The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges","source":"openalex","abstract":"The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.","url":"https://doi.org/10.48550/arxiv.2406.18900","authors":["Okan Bulut","Maggie Beiting-Parrish","Jodi M. Casabianca","Sharon C. Slater","Hong Jiao","Dan Song","Christopher M. Ormerod","Deborah Gbemisola Fabiyi","Rodica Ivan","Cole Walsh","Oscar Rios","Joshua M. Wilson"],"tags":["Engineering ethics","Psychology","Political science","Sociology","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-27","doi":"https://doi.org/10.48550/arxiv.2406.18900","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4386781106","name":"Accuracy, Reliability, and Comprehensibility of ChatGPT-Generated Medical Responses for Patients With Nonalcoholic Fatty Liver Disease","source":"openalex","abstract":"Nonalcoholic fatty liver disease (NAFLD) is an increasing global health problem and is expected to become the leading indication for liver transplantation.1 There are no approved NAFLD-specific pharmacotherapies, and lifestyle modification is the primary recommended therapy.2 Innovative approaches to facilitate the implementation and long-term maintenance of lifestyle changes are needed to address the challenging and complex nature of the management of NAFLD, which recently was renamed as metabolic dysfunction–associated steatotic liver disease, to overcome the limitations and stigma of the previous name.3,4 Artificial intelligence (AI)-powered chatbots have been shown to provide effective personalized support and education to patients, with the potential to complement health care resources. The OpenAI Foundation’s AI chatbot, Chat Generative Pretrained Transformer (ChatGPT), has attracted worldwide attention for its remarkable performance in question–answer tasks.5–7 This study evaluated the accuracy, completeness, and comprehensiveness of chatGPT’s responses to NAFLD-related questions, with the aim of assessing its performance in addressing patients’ queries about the disease and lifestyle behaviors.","url":"https://doi.org/10.1016/j.cgh.2023.08.033","authors":["Nicola Pugliese","Vincent Wai‐Sun Wong","Jörn M. Schattenberg","Manuel Romero‐Gómez","Giada Sebastiani","Laurent Castéra","Cesare Hassan","Pinelopi Manousou","Luca Miele","Raquel Peck","Salvatore Petta","Luca Valenti"],"tags":["Medicine","Nonalcoholic fatty liver disease","Disease","Reliability (semiconductor)","Fatty liver"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-15","doi":"https://doi.org/10.1016/j.cgh.2023.08.033","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4398244260","name":"Interpretation of Clinical Retinal Images Using an Artificial Intelligence Chatbot","source":"openalex","abstract":"Purpose To assess the performance of ChatGPT-4 in providing accurate diagnoses to retina teaching cases from OCTCases. Design Cross-sectional study. Subjects Retina teaching cases from OCTCases. Methods We prompted a custom chatbot with 69 retina cases containing multimodal ophthalmic images, asking it to provide the most likely diagnosis. In a sensitivity analysis, we inputted increasing amounts of clinical information pertaining to each case until the chatbot achieved a correct diagnosis. We performed multivariable logistic regressions on Stata v17.0 (StataCorp LLC, College Station, Texas) to investigate associations between the amount of text-based information inputted per prompt and the odds of the chatbot achieving a correct diagnosis, adjusting for the laterality of cases, number of ophthalmic images inputted, and imaging modalities. Main Outcome Measures Our primary outcome was the proportion of cases for which the chatbot was able to provide a correct diagnosis. Our secondary outcome was the chatbot's performance in relation to the amount of text-based information accompanying ophthalmic images. Results Across 69 retina cases collectively containing 139 ophthalmic images, the chatbot was able to provide a definitive, correct diagnosis for 35 (50.7%) cases. The chatbot needed variable amounts of clinical information to achieve a correct diagnosis, where the entire patient description as presented by OCTCases was required for a majority of correctly diagnosed cases (23/35 cases, 65.7%). Relative to when the chatbot was only prompted with a patient's age and sex, the chatbot achieved a higher odds of a correct diagnosis when prompted with an entire patient description (OR=10.1, 95%CI=[3.3, 30.3], p<0.01). Despite providing an incorrect diagnosis for 34 (49.3%) cases, the chatbot listed the correct diagnosis within its differential diagnosis for 7 (20.6%) of these incorrectly answered cases. Conclusions This custom chatbot was able to accurately diagnose approximately half of the retina cases requiring multimodal input, albeit relying heavily on text-based contextual information that accompanied ophthalmic images. The diagnostic ability of the chatbot in interpretation of multimodal imaging without text-based information is currently limited. The appropriate use of the chatbot in this setting is of utmost importance, given bioethical concerns.","url":"https://doi.org/10.1016/j.xops.2024.100556","authors":["Andrew Mihalache","Ryan S. Huang","David Mikhail","Marko M. Popovic","Reut Shor","Austin Pereira","Jason M. Kwok","Peng Yan","David T. Wong","Peter J. Kertes","Radha P. Kohly","Rajeev H. Muni"],"tags":["Chatbot","Interpretation (philosophy)","Artificial intelligence","Natural language processing","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-23","doi":"https://doi.org/10.1016/j.xops.2024.100556","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4405881929","name":"Machine Learning And Artificial Intelligence in Diabetes Prediction And Management: A Comprehensive Review of Models","source":"openalex","abstract":"Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.","url":"https://doi.org/10.70937/jnes.v1i01.41","authors":["Md Ashraful Alam","Amir Sohel","Kh Maksudul Hasan","Mohammad Ariful Islam"],"tags":["Artificial intelligence","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-17","doi":"https://doi.org/10.70937/jnes.v1i01.41","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4394687015","name":"Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway","source":"openalex","abstract":"“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To explore the standalone breast cancer detection performance at different risk score thresholds of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661,695 digital mammographic examinations performed among 242,629 female individuals screened as a part of x, 2004–2018. The study sample included 3807 screen-detected cancers (SDC) and 1110 interval breast cancers (IC). A continuous examination level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92–0.93) for SDC and IC combined and 0.97 (95% CI: 0.97–0.97) for SDC. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502/3807) of the SDC and 44.6% (495/1100) of the IC were identified by AI. In this scenario, 68.5% (10 987/16 029) of false positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cut-off, 99.3% (3781/3807) of the SDC and 85.2% (946/1100) of the IC were identified as positive by AI, while 17.0% (2725/16 029) of the false positives were considered as negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for triaging low-risk mammograms to reduce radiologist workload. ©RSNA, 2024","url":"https://doi.org/10.1148/ryai.230375","authors":["Marthe Larsen","Camilla F. Olstad","Christoph I. Lee","Tone Hovda","Solveig Roth Hoff","Marit Almenning Martiniussen","Karl Øyvind Mikalsen","Håkon Lund-Hanssen","Helene S. Solli","Marko Silberhorn","Åse Ø Sulheim","Steinar Auensen"],"tags":["Medicine","Receiver operating characteristic","Breast cancer","Mammography","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-10","doi":"https://doi.org/10.1148/ryai.230375","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4387949478","name":"Rethinking the value proposition of assessment at a time of rapid development in generative artificial intelligence","source":"openalex","abstract":"The authors argue for evolving assessment practices by focusing on their value proposition to educators (producing the medical graduates needed for society) rather than protecting existing processes.","url":"https://doi.org/10.1111/medu.15259","authors":["Tim Fawns","Lambert Schuwirth"],"tags":["Proposition","Value proposition","Generative grammar","Value (mathematics)","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-26","doi":"https://doi.org/10.1111/medu.15259","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4396886447","name":"Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis","source":"openalex","abstract":"BACKGROUND AND AIMS: Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. METHODS: The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. RESULTS: Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). CONCLUSIONS: AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.","url":"https://doi.org/10.1371/journal.pone.0303421","authors":["Na Li","Jian Yang","Xiaodong Li","Yanting Shi","Kunhong Wang"],"tags":["Meta-analysis","Intestinal metaplasia","Endoscopy","Medicine","Gastroenterology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-14","doi":"https://doi.org/10.1371/journal.pone.0303421","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4382401139","name":"Artificial intelligence in medicine","source":"openalex","abstract":"","url":"https://doi.org/10.24875/rmu.m23000076","authors":["Adrián Alejandro Ceballos‐López"],"tags":["Artificial intelligence","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-28","doi":"https://doi.org/10.24875/rmu.m23000076","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4407791699","name":"Artificial Intelligence and Breast Cancer Management: From Data to the Clinic","source":"openalex","abstract":"Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.","url":"https://doi.org/10.1002/cai2.159","authors":["Kaixiang Feng","Zongbi Yi","Binghe Xu"],"tags":["Breast cancer","Medicine","Computer science","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-20","doi":"https://doi.org/10.1002/cai2.159","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4388342253","name":"An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD","source":"openalex","abstract":"Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV). Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. Results: mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.","url":"https://doi.org/10.1016/j.ekir.2023.10.029","authors":["Jonathan Taylor","Richard Thomas","Peter Metherall","Marieke van Gastel","Émilie Cornec-Le Gall","Anna Caroli","Mónica Furlano","Nathalie Demoulin","Olivier Devuyst","Jean Winterbottom","Roser Torrá","Norberto Perico"],"tags":["Medicine","Autosomal dominant polycystic kidney disease","Cohort","Segmentation","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-04","doi":"https://doi.org/10.1016/j.ekir.2023.10.029","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401049502","name":"Artificial Intelligence Must Be Made More Scientific","source":"openalex","abstract":"The role of AI within science is growing. Here we assess its impact on research and argue that AI often lacks reproducibility, transparency, objectivity, and mechanistic understanding. To ensure AI benefits research, we need to develop forms of AI that are fully compatible with the scientific method.","url":"https://doi.org/10.1021/acs.jcim.4c01091","authors":["Peter V. Coveney","Roger Highfield"],"tags":["Computer science","Artificial intelligence","Data science","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-27","doi":"https://doi.org/10.1021/acs.jcim.4c01091","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4413783468","name":"A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice","source":"openalex","abstract":"BACKGROUND: Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Widespread adoption, however, remains limited due to challenges in electronic health record (EHR) integration, coding compliance, and real-world evaluation. This study introduces a framework and protocols to design, monitor, and deploy ambient AI within routine care. METHODS: , Tenth Revision (ICD-10) compliance were performed using an internally developed large language model (LLM), the validity of which was assessed through correlation with certified professional coders. RESULTS: Ambient AI utilization, measured as the proportion of eligible clinical notes completed using the system, had a weighted median of 65.4% (interquartile range, 50.6 to 84.0%). Iterative improvement cycles targeted task-specific adoption. A brief workflow issue related to a note template change initially reduced ICD-10 documentation accuracy from 79% (95% confidence interval [CI], 72 to 86%) to 35% (95% CI, 28 to 42%); accuracy returned to baseline after note template redesign and user training. The internally developed LLM coder achieved a strong correlation with professional coders (Pearson's r=0.97). The trial enrolled 66 providers across eight specialties, powered at 90% for the primary outcome of provider well-being. CONCLUSIONS: We provide a publicly available framework and protocols to help safely implement ambient AI in health care. Innovations include an embedded pragmatic trial design, human factors engineering, compliance-driven feedback loops, and real-time monitoring to support deployment, ensuring fidelity before initiation of the clinical trial. (Funded by the University of Wisconsin Hospital and Clinics and the National Institutes of Health Clinical and Translational Science Award; NIH/ NCATS UL1TR002737; ClinicalTrials.gov number, NCT06517082.).","url":"https://doi.org/10.1056/aidbp2401267","authors":["Majid Afshar","Felice Resnik","Mary Ryan","Josie Hintzke","Kayla K. Lemmon","Anne Gravel Sullivan","Tina Shah","Anthony Stordalen","Michael Oberst","Jason Dambach","Leigh A. Mrotek","Mariah A. Quinn"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-28","doi":"https://doi.org/10.1056/aidbp2401267","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4394911989","name":"Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI","source":"openalex","abstract":"Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.","url":"https://doi.org/10.1038/s41746-024-01088-7","authors":["Jethro C.C. Kwong","Jeremy Wu","Shamir Malik","Adree Khondker","Naveen Gupta","Nicole Bodnariuc","Krishnateja Narayana","Mikail Malik","Theodorus van der Kwast","Alistair E. W. Johnson","Alexandre R. Zlotta","Girish S. Kulkarni"],"tags":["Medicine","MEDLINE","Systematic review","Bladder cancer","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-18","doi":"https://doi.org/10.1038/s41746-024-01088-7","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4212955355","name":"Physicians’ Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle–Income Countries","source":"openalex","abstract":"As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians' perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle-income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle-income countries for cancer care.","url":"https://doi.org/10.2196/31461","authors":["Srinivas Emani","Angela Rui","Hermano Alexandre Lima Rocha","Rubina Rizvi","Sérgio Ferreira Juaçaba","Gretchen Purcell Jackson","David W. Bates"],"tags":["Low and middle income countries","Watson","Variety (cybernetics)","Cancer survivorship","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-08","doi":"https://doi.org/10.2196/31461","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4412660449","name":"The Role of Artificial Intelligence in Strategic Planning and Competitive Advantage","source":"openalex","abstract":"This study investigates how Artificial Intelligence (AI) affects strategic planning and helps industries improve their competitive positions. With digital changes becoming more overwhelming, organisations need to act fast and AI gives them the tools and guidelines they need for foresight, agility and decision intelligence. By studying more than 80 recent academic articles on the topic, the paper explores how AI merges with approaches like Resource-Based View (RBV), SWOT and Porter’s Five Forces and boosts them by using predictive analytics, natural language processing (NLP) and machine learning (ML). Looking at Amazon, Google, IBM, Tesla and SMEs reveals how AI helps improve efficiency, speed up innovation, develop well-known brands and improve interaction with the ecosystem. The findings also show that unmanaged ethical problems, governance gaps and differences in goals can hold back AI. The results point out that AI should be considered both a useful technology and a strategic partner, creating continuous value when it supports corporate aims, ethics and leadership. In addition, the paper advocates for additional research that brings together different experts to solve the challenges in AI policy, planning and its social results. Because of these insights, organisations, scholars and policymakers gain the support they need in the dynamic AI-based business environment.","url":"https://doi.org/10.59857/mvzl8684","authors":["Mahdi Jafari","Avesta Shahbazi","Md. Abu Kawsar","Seyed Pouyan Mousavi Davoudi","S. Janani"],"tags":["Strategic planning","Competitive advantage","Competitive intelligence","Computer science","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-26","doi":"https://doi.org/10.59857/mvzl8684","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4292958758","name":"Causality-Inspired Taxonomy for Explainable Artificial Intelligence","source":"openalex","abstract":"As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals. However, the latter can only be complete when seen through the lens of the causality framework. As such, we propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. To show its applicability, biometrics was used as case study. For this, we have analysed 81 research papers on a myriad of biometric modalities and different tasks. We have categorised each of these methods according to our novel xAI Ladder and discussed the future directions of the field.","url":"https://doi.org/10.48550/arxiv.2208.09500","authors":["Pedro C. Neto","Tiago Gonçalves","João Ribeiro Pinto","Wilson Silva","Ana F. Sequeira","Arun Ross","Jaime S. Cardoso"],"tags":["Biometrics","Deep learning","Computer science","Artificial intelligence","Internet privacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-19","doi":"https://doi.org/10.48550/arxiv.2208.09500","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406001297","name":"Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence","source":"openalex","abstract":"Adolescent idiopathic scoliosis (AIS) is the most common type of scoliosis, affecting 1–4% of adolescents. The Scoliosis Research Society-22R (SRS-22R), a health-related quality-of-life instrument for AIS, has allowed orthopedists to measure subjective patient outcomes before and after corrective surgery beyond objective radiographic measurements. However, research has revealed that there is no significant correlation between the correction rate in major radiographic parameters and improvements in patient-reported outcomes (PROs), making it difficult to incorporate PROs into personalized surgical planning. The objective of this study is to develop an artificial intelligence (AI)-enabled surgical planning and counseling support system for post-operative patient rehabilitation outcomes prediction in order to facilitate personalized AIS patient care. A unique multi-site cohort of 455 pediatric patients undergoing spinal fusion surgery at two Shriners Children’s hospitals from 2010 is investigated in our analysis. In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. We further employ explainability analysis to quantify the contribution of pre-operative radiographic and questionnaire parameters in predicting patient surgical outcomes. Moreover, we enable responsible AI by calibrating model confidence for human intervention and mitigating health disparities for algorithm fairness. The best prediction model achieves an area under receiver operating curve (AUROC) performance of 0.86, 0.85, and 0.83 for individual SRS-22R question response prediction over three-time horizons from pre-operation to 6-month, 1-year, and 2-year post-operation, respectively. Additionally, we demonstrate the efficacy of our proposed prediction method to predict other patient rehabilitation outcomes based on minimal clinically important differences (MCID) and correction rates across all three-time horizons. Based on the relationship analysis, we suggest additional attention to sagittal parameters (e.g., lordosis, sagittal vertical axis) and patient self-image beyond major Cobb angles to improve surgical decision-making for AIS patients. In the age of personalized medicine, the proposed responsible AI-enabled clinical decision-support system may facilitate pre-operative counseling and shared decision-making within real-world clinical settings. The goal of this study is to develop a planning and counseling support system for predicting how well patients recover after surgeries. This should allow for more personalized care for scoliosis (spinal curvature) patients. We collected data from 455 pediatric patients who underwent spinal fusion surgery at different locations and used this data to train computer learning methods to predict outcomes after surgery. We show that our proposed computer method can predict the outcome of patient rehabilitation well for short-term (6-month and 1-year) and long-term (2-year) results. We applied additional tests to our method to calculate how well it works and measure fairness to provide a straight-forward, trustworthy, and fair method for real-world clinical use. Shi et al. develop an AI-enabled surgical planning and counseling support system for post-operative rehabilitation outcome predictions to facilitate personalized adolescent idiopathic scoliosis (AIS) patient care. They suggest additional attention to sagittal parameters and patient self-image beyond Cobb angles to improve surgical decision-making.","url":"https://doi.org/10.1038/s43856-024-00726-1","authors":["Wenqi Shi","Felipe Giuste","Yuanda Zhu","Ben J. Tamo","Micky C. Nnamdi","Andrew Hornback","Ashley M. Carpenter","Coleman Hilton","Henry J. Iwinski","J. Michael Wattenbarger","May D. Wang"],"tags":["Medicine","Scoliosis","Physical therapy","Cohort","Rehabilitation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1038/s43856-024-00726-1","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4404033515","name":"The Intraoperative Role of Artificial Intelligence Within General Surgery: A Systematic Review","source":"openalex","abstract":"The role of artificial intelligence has been explored in many industries across the world. The medical field is no exception with studies regarding its use for development of algorithms in cancer screening and its diagnostic utility in clinical radiology. This study aims to review current literature on intraoperative use of artificial intelligence within general surgery to identify the latest developments, the major challenges and the trajectory of this field. A literature search was done on PubMed on May 28, 2024, using the terms: ((artificial intelligence) AND (general surgery)). Only publications in English and studies involving human subjects were considered. Exclusion criteria included duplicate papers, irrelevant titles, abstracts, themes, and non-English papers. A literature search on PubMed yielded 13 relevant articles. Among these, five articles focused on intraoperative guidance, four addressed surgical education and training, and four were survey-based exploring perceptions regarding artificial intelligence. Key themes included the development of artificial intelligence-based autonomous actions during surgery and its role in enhancing surgical training. Limitations identified included restricted data availability, ethical concerns, and a lack of validation tools, which pose significant obstacles to progress in this area. Despite existing limitations, the potential for integrating artificial intelligence into general surgery is promising. Careful attention is needed to overcome challenges and maximize its benefits.","url":"https://doi.org/10.7759/cureus.73006","authors":["Deema Othman","Ahmad Kaleem"],"tags":["Medicine","Field (mathematics)","Applications of artificial intelligence","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-04","doi":"https://doi.org/10.7759/cureus.73006","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391751159","name":"Research and application of artificial intelligence in dentistry from lower-middle income countries – a scoping review","source":"openalex","abstract":"Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.","url":"https://doi.org/10.1186/s12903-024-03970-y","authors":["Fahad Umer","Samira Adnan","Abhishek Lal"],"tags":["Scopus","Low and middle income countries","Medicine","Oral and maxillofacial surgery","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-12","doi":"https://doi.org/10.1186/s12903-024-03970-y","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4407799493","name":"Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases","source":"openalex","abstract":"With the rapid development of the \"Internet + Medical\" model, artificial intelligence technology has been widely used in the analysis of medical images. Among them, the technology of using deep learning algorithms to identify features of ultrasound and pathological images and realize intelligent diagnosis of diseases has entered the clinical verification stage. This study is based on the application research of artificial intelligence technology in medical diagnosis and reviews the early screening and diagnosis of thyroid diseases. The cure rate of thyroid disease is high in the early stage, but once it deteriorates into thyroid cancer, the risk of death and treatment costs of the patient increase. At present, the early diagnosis of the disease still depends on the examination equipment and the clinical experience of doctors, and there is a certain misdiagnosis rate. Based on the above background, it is particularly important to explore a technology that can achieve objective screening of thyroid lesions in the early stages. This paper provides a comprehensive review of recent research on the early diagnosis of thyroid diseases using artificial intelligence technology. It integrates the findings of multiple studies and that traditional machine learning algorithms are widely used as research objects. The convolutional neural network model has a high recognition accuracy for thyroid nodules and thyroid pathological cell lesions. U-Net network model can significantly improve the recognition accuracy of thyroid nodule ultrasound images when used as a segmentation algorithm. This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.","url":"https://doi.org/10.3389/fonc.2025.1536039","authors":["Lina Yang","Xinyuan Wang","Shixia Zhang","Kun Cao","Jianjun Yang"],"tags":["Thyroid","Medicine","Computer science","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-20","doi":"https://doi.org/10.3389/fonc.2025.1536039","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399047878","name":"Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.","url":"https://doi.org/10.3390/diagnostics14111103","authors":["Elena Stamate","Alin Ionut Piraianu","Oana Roxana Ciobotaru","R Crassas","Oana-Monica Duca","Ana Fulga","Ionica Grigore","V. Vintila","Iuliu Fulga","Octavian Catalin Ciobotaru"],"tags":["Cardiac resynchronization therapy","Artificial intelligence","Interventional cardiology","Internal medicine","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-26","doi":"https://doi.org/10.3390/diagnostics14111103","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4393156327","name":"Should Artificial Intelligence Be Used for Physician Documentation to Reduce Burnout?","source":"openalex","abstract":"1Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN, USA aCorresponding author: Wisit Cheungpasitporn, MD, Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN. E-Mail: [email protected] This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","url":"https://doi.org/10.34067/kid.0000000000000430","authors":["Jing Miao","Charat Thongprayoon","Wisit Cheungpasitporn"],"tags":["License","Documentation","Burnout","Medicine","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-25","doi":"https://doi.org/10.34067/kid.0000000000000430","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402172079","name":"Impact of Artificial Intelligence in Endodontics: Precision, Predictions, and Prospects","source":"openalex","abstract":"Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.","url":"https://doi.org/10.4103/jmss.jmss_7_24","authors":["M. S. Parinitha","Vidya Gowdappa Doddawad","Sowmya Halasabalu Kalgeri","Samyuka S. Gowda","Sahana Patil"],"tags":["Endodontics","Root canal","Artificial intelligence","Computer science","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.4103/jmss.jmss_7_24","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4392004891","name":"Two artificial intelligence models underperform on examinations in a veterinary curriculum","source":"openalex","abstract":"OBJECTIVE: Advancements in artificial intelligence (AI) and large language models have rapidly generated new possibilities for education and knowledge dissemination in various domains. Currently, our understanding of the knowledge of these models, such as ChatGPT, in the medical and veterinary sciences is in its nascent stage. Educators are faced with an urgent need to better understand these models in order to unleash student potential, promote responsible use, and align AI models with educational goals and learning objectives. The objectives of this study were to evaluate the knowledge level and consistency of responses of 2 platforms of ChatGPT, namely GPT-3.5 and GPT-4.0. SAMPLE: A total of 495 multiple-choice and true/false questions from 15 courses used in the assessment of third-year veterinary students at a single veterinary institution were included in this study. METHODS: The questions were manually entered 3 times into each platform, and answers were recorded. These answers were then compared against those provided by the faculty members coordinating the courses. RESULTS: GPT-3.5 achieved an overall performance score of 55%, whereas GPT-4.0 had a significantly (P < .05) greater performance score of 77%. Importantly, the performance scores of both platforms were significantly (P < .05) below that of the veterinary students (86%). CLINICAL RELEVANCE: Findings of this study suggested that veterinary educators and veterinary students retrieving information from these AI-based platforms should do so with caution.","url":"https://doi.org/10.2460/javma.23.12.0666","authors":["Michelle C. Coleman","James N. Moore"],"tags":["Curriculum","Veterinary medicine","Artificial intelligence","Computer science","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-21","doi":"https://doi.org/10.2460/javma.23.12.0666","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4320492850","name":"Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective","source":"openalex","abstract":"The Internet of Medical Things (IoMT), an application of the Internet of Things (IoT) in the medical domain, allows data to be transmitted across communication networks. In particular, IoMT can help improve the quality of life of citizens and older people by monitoring and managing the body’s vital signs, including blood pressure, temperature, heart rate, and others. Since IoMT has become the main platform for information exchange and making high-level decisions, it is necessary to guarantee its reliability and security. The growth of IoMT in recent decades has attracted the interest of many experts. This study provides an in-depth analysis of IoT and IoMT by focusing on security concerns from different points of view, making this comprehensive survey unique compared to other existing studies. A total of 187 articles from 2010 to 2022 are collected and categorized according to the type of applications, year of publications, variety of applications, and other novel perspectives. We compare the current studies based on the above criteria and provide a comprehensive analysis to pave the way for researchers working in this area. In addition, we highlight the trends and future work. We have found that blockchain, as a key technology, has solved many problems of security, authentication, and maintenance of IoT systems due to the decentralized nature of the blockchain. In the current study, this technology is examined from the application fields’ points of view, especially in the health sector, due to its additional importance compared to other fields.","url":"https://doi.org/10.3390/su15043317","authors":["Firuz Kamalov","Behrouz Pourghebleh","Mehdi Gheisari","Yang Liu","Sherif Moussa"],"tags":["Computer science","The Internet","Internet of Things","Computer security","Internet privacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-10","doi":"https://doi.org/10.3390/su15043317","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4386392594","name":"Ethics and Legal Framework for Trustworthy Artificial Intelligence in Vascular Surgery","source":"openalex","abstract":"International audience","url":"https://doi.org/10.1016/j.ejvsvf.2023.08.003","authors":["Fabien Lareyre","Martin Maresch","Arindam Chaudhuri","Juliette Raffort"],"tags":["Trustworthiness","Psychology","Social psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1016/j.ejvsvf.2023.08.003","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4413196066","name":"Application of artificial intelligence in electrochemical diagnostics for human health","source":"openalex","abstract":"Electrochemical sensors, detecting biochemical changes through electrical signals, play a pivotal role in point-of-care diagnostics, especially for detecting specific biomarkers for diseases including cancer, diabetes, obesity, cardiovascular conditions, etc. Biosensor based technology faces numerous challenges including signal complexity, noise, data interpretation, etc. These challenges influence the sensitivity and selectivity of the techniques and limit their wider applications. The modern-day miracle, Artificial Intelligence (AI) offers transformative solutions to these challenges. The applications of machine learning (ML) algorithms and AI in electrochemical data analysis have significantly enhanced the sensitivity and specificity of diagnostic methods. The AI-powered systems can easily identify the specific patterns within the electrochemical signals that otherwise remain undetectable by traditional methods. This leads to early detection, personalized treatment plans, and real-time monitoring of diseases. AI also assists in optimizing sensor design, manages large datasets, and improves the performance and reliability of electrochemical diagnostics (ED) devices. Thus, the integration of AI into ED is transforming the healthcare sector by providing faster, more precise, and cost-effective diagnostic solutions.","url":"https://doi.org/10.1007/s44373-025-00042-w","authors":["Koushlesh Ranjan","Basanti Barar","Minakshi Prasad","Gaya Prasad"],"tags":["Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-12","doi":"https://doi.org/10.1007/s44373-025-00042-w","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410007777","name":"Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods","source":"openalex","abstract":"Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.","url":"https://doi.org/10.3390/tomography11050052","authors":["Yuxiao Gao","Yang Jiang","Yanhong Peng","Fujiang Yuan","Xinyue Zhang","Jianfeng Wang"],"tags":["Artificial intelligence","Segmentation","Computer science","Convolutional neural network","Image segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.3390/tomography11050052","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4404719500","name":"Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s42256-024-00926-3","authors":["Artem A. Trotsyuk","Quinn Waeiss","Raina Talwar Bhatia","Brandon J. Aponte","Isabella M. L. Heffernan","Devika Madgavkar","Ryan Marshall Felder","Lisa Soleymani Lehmann","Megan J. Palmer","Henry T. Greely","Russell Wald","Lea Goetz"],"tags":["Risk analysis (engineering)","Business","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.1038/s42256-024-00926-3","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4409308174","name":"Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study","source":"openalex","abstract":"Introduction: The fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking. Methods: This study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns. Results: Analysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration. Discussion: The findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration. Conclusion: AI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being.","url":"https://doi.org/10.3389/fmed.2025.1523902","authors":["Ningkun Xiao","Xin‐Lin Huang","Yujun Wu","Baoheng Li","Wanli Zang","Khyber Shinwari","Irina A. Tuzankina","В. А. Черешнев","Guojun Liu"],"tags":["Immunology","Allergy","Clinical immunology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.3389/fmed.2025.1523902","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4394728138","name":"Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting","source":"openalex","abstract":"BACKGROUND: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries. METHODOLOGY/PRINCIPAL FINDINGS: Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples. CONCLUSIONS/SIGNIFICANCE: Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs.","url":"https://doi.org/10.1371/journal.pntd.0012041","authors":["Johan Lundin","Antti Suutala","Oscar Holmström","Samuel Henriksson","Severi Valkamo","Harrison Kaingu","Felix Kinyua","Martin Muinde","Mikael Lundin","Vinod Diwan","Andreas Mårtensson","Nina Linder"],"tags":["Ascaris lumbricoides","Necator americanus","Ancylostoma duodenale","Virtual microscopy","Trichuris trichiura"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-11","doi":"https://doi.org/10.1371/journal.pntd.0012041","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4392299617","name":"Artificial Intelligence in Healthcare: 2023 Year in Review","source":"openalex","abstract":"ABSTRACT Background The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023. Methods We performed a PubMed search using the terms, “machine learning” or “artificial intelligence” and “2023”, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics.Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023. Results Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%. Conclusion Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.","url":"https://doi.org/10.1101/2024.02.28.24303482","authors":["Raghav Awasthi","Shreya Mishra","Rachel Grasfield","Julia Maslinski","Dwarikanath Mahapatra","Jacek B. Cywiński","Ashish K. Khanna","Kamal Maheshwari","Chintan Dave","Avneesh Khare","Francis Papay","Piyush Mathur"],"tags":["Health care","Economics","Economic growth"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-29","doi":"https://doi.org/10.1101/2024.02.28.24303482","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4384277227","name":"Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis","source":"openalex","abstract":"OBJECTIVES: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.","url":"https://doi.org/10.1111/den.14622","authors":["Hidekazu Takahashi","Eizaburo Ohno","Taiki Furukawa","Kentaro Yamao","Takuya Ishikawa","Yasuyuki Mizutani","Tadashi Iida","Yoshimune Shiratori","Shintaro Oyama","Junji Koyama","Kensaku Mori","Yuichiro Hayashi"],"tags":["Medicine","Endoscopic retrograde cholangiopancreatography","Receiver operating characteristic","Logistic regression","Pancreatic duct"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-14","doi":"https://doi.org/10.1111/den.14622","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406064793","name":"What is the influence of psychosocial factors on artificial intelligence appropriation in college students?","source":"openalex","abstract":"BACKGROUND: In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian university students and uses an extended UTAUT2 model to examine various constructs that may impact AI acceptance and use. METHOD: This study employed a quantitative approach with a survey-based design. A total of 482 students from public and private universities in Peru participated in the research. The study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the data and test the hypothesized relationships between the constructs. RESULTS: The findings revealed that three out of the six hypothesized factors significantly influenced AI adoption among Peruvian university students. Performance expectancy (β = 0.274), social influence (β = 0.355), and AI learning self-efficacy (β = 0.431) were found to have significant positive effects on AI adoption. In contrast to expectations, ethical awareness, perceived playfulness, AI readiness and AI anxiety did not have significant impacts on AI appropriation in this context. CONCLUSION: This study highlights the importance of practical benefits, the social context, and self-confidence in the adoption of AI within Peruvian higher education. These findings contribute to the understanding of AI adoption in diverse educational settings and provide a framework for developing effective AI implementation strategies in higher education institutions. The results can guide universities and policymakers in creating targeted approaches to enhance AI adoption and integration in academic environments, focusing on demonstrating the practical value of AI, leveraging social networks, and building students' confidence in their ability to learn and use AI technologies.","url":"https://doi.org/10.1186/s40359-024-02328-x","authors":["Benicio Gonzalo Acosta Enríquez","María de los Ángeles Guzmán Valle","Marco Arbulú Ballesteros","Julie Catherine Arbulú Castillo","Carmen Graciela Arbulú Pérez Várgas","Isaac Saavedra Torres","Pedro Manuel Silva León","Karina Saavedra Tirado"],"tags":["Psychology","Structural equation modeling","Psychosocial","Expectancy theory","Appropriation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-04","doi":"https://doi.org/10.1186/s40359-024-02328-x","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4395039716","name":"Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review","source":"openalex","abstract":"BACKGROUND: Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY: In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION: As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.","url":"https://doi.org/10.1186/s40942-024-00554-4","authors":["Matthew Driban","Audrey Yan","Amrish Selvam","Joshua Ong","Kiran Kumar Vupparaboina","Jay Chhablani"],"tags":["Neuro-ophthalmology","Medicine","Macular degeneration","Diabetic retinopathy","Optometry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-23","doi":"https://doi.org/10.1186/s40942-024-00554-4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411117743","name":"Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications","source":"openalex","abstract":"Circular RNA (circRNA) vaccines have emerged as a groundbreaking innovation in infectious disease prevention and cancer immunotherapy, offering superior stability and reduced immunogenicity compared to conventional linear messenger RNA (mRNA) vaccines. While linear mRNA vaccines are prone to degradation and can trigger strong innate immune responses, covalently closed circRNA vaccines leverage their unique circular structure to enhance molecular stability and minimize innate immune activation, positioning them as a next-generation platform for vaccine development. Artificial intelligence (AI) is revolutionizing circRNA vaccine design and optimization. Deep learning models, such as convolutional neural networks (CNNs) and Transformers, integrate multi-omics data to refine antigen prediction, RNA secondary structure modeling, and lipid nanoparticle delivery system formulation, surpassing traditional bioinformatics approaches in both accuracy and efficiency. While AI-driven bioinformatics enhances antigen screening and delivery system modeling, generative AI accelerates literature synthesis and experimental planning-though the risk of fabricated references and limited biological interpretability hinders its reliability. Despite these advancements, challenges such as the \"black-box\" nature of AI algorithms, unreliable literature retrieval, and insufficient integration of biological mechanisms underscore the necessity for a hybrid \"AI-traditional-experimental\" paradigm. This approach integrates explainable AI frameworks, multi-omics validation, and ethical oversight to ensure clinical translatability. Future research should prioritize mechanism-driven AI models, real-time experimental feedback, and rigorous ethical standards to fully unlock the potential of circRNA vaccines in precision oncology and global health.","url":"https://doi.org/10.1093/bib/bbaf263","authors":["Yan Zhao","Huaiyu Wang"],"tags":["Computer science","Artificial intelligence","Nanorobotics","Computational biology","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1093/bib/bbaf263","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411391856","name":"Application progress of artificial intelligence in managing thyroid disease","source":"openalex","abstract":"Artificial intelligence (AI) has been used to study thyroid diseases since the 1990s. Previously, it mainly concentrated on the diagnosis of thyroid function and distinguishing benign from malignant thyroid nodules. With the rapid development of machine and deep learning, AI has been widely used in multiple areas of thyroid disease management, including image analysis, pathological diagnosis, personalized treatment, patient monitoring, and follow-up. This review systematically examines the evolution of AI applications in thyroid disease management since the 1990s, with a focus on diagnostic innovations, therapeutic personalization, and emerging challenges in clinical implementation. AI not only reduces the subjectivity associated with ultrasound examinations but also enhances the differentiation rate of benign and malignant thyroid nodules, thereby reducing the frequency of unnecessary fine-needle aspirations. AI synthesizes multimodal data, such as ultrasound, electronic health records, and wearable sensors, for continuous health monitoring. This integration facilitates the early detection of subclinical recurrence risk, particularly in patients who have undergone thyroidectomy. Despite the broad prospects of AI applications, challenges related to data privacy, model interpretability, and clinical applicability remain. This review critically evaluates studies across the ultrasound, CT/MRI, and histopathology domains, while addressing barriers to clinical translation, such as data heterogeneity and ethical concerns.","url":"https://doi.org/10.3389/fendo.2025.1578455","authors":["Qing Lu","Yu Wu","Jing Chang","Li Zhang","Qing Lv","Hui Sun"],"tags":["Thyroid nodules","Medicine","Thyroid","Thyroid disease","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-17","doi":"https://doi.org/10.3389/fendo.2025.1578455","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4402380931","name":"Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection","source":"openalex","abstract":"Background Advances in DNA sequencing revolutionized plant genomics and significantly contributed to the study of genetic diversity. However, predicting phenotypes from genomic data remains a challenge, particularly in the context of plant breeding. Despite significant progress, accurately predicting phenotypes from high-dimensional genomic data remains a challenge, particularly in identifying the key genetic factors influencing these predictions. This study aims to bridge this gap by integrating explainable artificial intelligence (XAI) techniques with advanced machine learning models. This approach is intended to enhance both the predictive accuracy and interpretability of genotype-to-phenotype models, thereby improving their reliability and supporting more informed breeding decisions. Results This study compares several ML methods for genotype-to-phenotype prediction, using data available from an almond germplasm collection. After preprocessing and feature selection, regression models are employed to predict almond shelling fraction. Best predictions were obtained by the Random Forest method (correlation = 0.727 ± 0.020, an R 2 = 0.511 ± 0.025, and an RMSE = 7.746 ± 0.199). Notably, the application of the SHAP (SHapley Additive exPlanations) values algorithm to explain the results highlighted several genomic regions associated with the trait, including one, having the highest feature importance, located in a gene potentially involved in seed development. Conclusions Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms associated with the shelling percentage. These findings underscore XAI’s efficacy in predicting phenotypic traits from genomic data, highlighting its significance in optimizing crop production for sustainable agriculture.","url":"https://doi.org/10.3389/fpls.2024.1434229","authors":["Pierfrancesco Novielli","Donato Romano","Stefano Pavan","Pasquale Losciale","Anna Maria Stellacci","Domenico Diacono","R. Bellotti","Sabina Tangaro"],"tags":["Germplasm","Context (archaeology)","Trait","Predictive modelling","Interpretability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-09","doi":"https://doi.org/10.3389/fpls.2024.1434229","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4393949164","name":"Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction","source":"openalex","abstract":"Introduction: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. Aim: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. Methods: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. Results: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083–6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. Conclusion: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.","url":"https://doi.org/10.3390/diagnostics14070767","authors":["Ziv Dadon","Moshe Rav","Amir Orlev","Shemy Carasso","Michael Glikson","Shmuel Gottlieb","Evan Avraham Alpert"],"tags":["Ejection fraction","Medicine","Cardiology","Internal medicine","Heart failure"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-04","doi":"https://doi.org/10.3390/diagnostics14070767","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4399295665","name":"Diagnosis of ADHD using virtual reality and artificial intelligence: an exploratory study of clinical applications","source":"openalex","abstract":"Introduction: Diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) is based on clinical evaluation of symptoms by a psychiatrist, referencing results of psychological tests. When diagnosing ADHD, the child's behavior and functionality in real-life situations are critical components. However, direct observation by a clinician is often not feasible in practice. Therefore, such information is typically gathered from primary caregivers or teachers, which can introduce subjective elements. To overcome these limitations, we developed AttnKare-D, an innovative digital diagnostic tool that could analyze children's behavioral data in Virtual Reality using Artificial Intelligence. The purpose of this study was to explore the utility and safety of AttnKare-D for clinical application. Method: A total of 21 children aged between 6 and 12 years were recruited for this study. Among them, 15 were children diagnosed with ADHD, 5 were part of a normal control group, and 1 child was excluded due to withdrawal of consent. Psychological assessments, including K-WISC, Conners CPT, K-ARS, and K-CBCL, were conducted for participants and their primary caregivers. Diagnoses of ADHD were confirmed by child and adolescent psychiatrists based on comprehensive face-to-face evaluations and results of psychological assessments. Participants underwent VR diagnostic assessment by performing various cognitive and behavioral tasks in a VR environment. Collected data were analyzed using an AI model to assess ADHD diagnosis and the severity of symptoms. Results: AttnKare-D demonstrated diagnostic performance with an AUC of 0.893 when compared to diagnoses made by child and adolescent psychiatrist, showing a sensitivity of 0.8 and a specificity of 1.0 at a cut-off score of 18.44. AttnKare-D scores showed a high correlation with K-ARS scores rated by parents and experts, although the correlation was relatively low for inattention scores. Conclusion: Results of this study suggest that AttnKare-D can be a useful tool for diagnosing ADHD in children. This approach has potential to overcome limitations of current diagnostic methods, enhancing the accuracy and objectivity of ADHD diagnoses. This study lays the groundwork for further improvement and research on diagnostic tools integrating VR and AI technologies. For future clinical applications, it is necessary to conduct clinical trials involving a sufficient number of participants to ensure reliable use.","url":"https://doi.org/10.3389/fpsyt.2024.1383547","authors":["Soohwan Oh","Yoo‐Sook Joung","Tai‐Myoung Chung","Junho Lee","Bum Joon Seok","Nam-Uk Kim","Ha Min Son"],"tags":["Medical diagnosis","CBCL","Clinical psychology","Psychology","Exploratory research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-03","doi":"https://doi.org/10.3389/fpsyt.2024.1383547","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401332440","name":"Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells","source":"openalex","abstract":"Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical data types. In this review, we explore the potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs). B-cell non-Hodgkin lymphomas (B-NHLs) represent a particular challenge in oncology due to tumor heterogeneity and the intricate ecosystem in which tumors develop. These complexities complicate diagnosis, prognosis, and therapy response, emphasizing the need to use sophisticated approaches to enhance personalized treatment strategies for better patient outcomes. Therefore, multimodal AI can be leveraged to synthesize critical information from available biomedical data such as clinical record, imaging, pathology and omics data, to picture the whole tumor. In this review, we first define various types of modalities, multimodal AI frameworks, and several applications in precision medicine. Then, we provide several examples of its usage in B-NHLs, for analyzing the complexity of the ecosystem, identifying immune biomarkers, optimizing therapy strategy, and its clinical applications. Lastly, we address the limitations and future directions of multimodal AI, highlighting the need to overcome these challenges for better clinical practice and application in healthcare.","url":"https://doi.org/10.3390/biomedicines12081753","authors":["Pouria Isavand","Sara Sadat Aghamiri","Rada Amin"],"tags":["Computer science","Precision medicine","Multimodal therapy","Modalities","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-05","doi":"https://doi.org/10.3390/biomedicines12081753","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4408313032","name":"Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review","source":"openalex","abstract":"OBJECTIVES: Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis. MATERIAL AND METHODS: A comprehensive search of the literature was performed from inception to November 30, 2024, in PubMed-MEDLINE, Embase, and Scopus databases. This review evaluated systematic reviews (SRs) and meta-analyses (MAs) that reported TMD patients/datasets, any AI model as intervention, no treatment, placebo as comparator and accuracy, sensitivity, specificity, or predictive value of AI models as outcome. The extracted data were complemented with narrative synthesis. RESULTS: Out of 1497 search results, this umbrella review included five studies. One of the five articles was an SR while the other four were SRMAs. Three studies focused on patients with temporomandibular joint (TMJ) problems as a group, whereas two were specific to temporomandibular joint osteoarthritis (TMJOA). The included studies reported the use of imaging datasets as samples, including cone-beam computed tomography (CBCT), magnetic resonance imaging (MRI), and panoramic radiography. The studies reported an accuracy level ranging from 0.59 to 1. Four studies reported sensitivity levels ranging from 0.76 to 0.80. Four studies reported specificity values ranging from 0.63 to 0.95 for TMJ conditions. However, only one study provided the area under the curve (AUC) in the diagnosis of TMDs. CONCLUSIONS: AI has the ability to provide faster, more accurate, sensitive, and objective diagnosis of TMJ condition. However, the performance is determined on the AI models and datasets used. Therefore, before implementing AI models in clinical practice, it is essential for researchers to extensively refine and evaluate the AI application.","url":"https://doi.org/10.1002/cre2.70115","authors":["Vini Mehta","Snehasish Tripathy","Toufiq Noor","Ankita Mathur"],"tags":["Temporomandibular joint","Medicine","Osteoarthritis","Cone beam computed tomography","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.1002/cre2.70115","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4403494021","name":"Automatic Medical Report Generation: Methods and Applications","source":"openalex","abstract":"The increasing demand for medical imaging has surpassed the capacity of available radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence (AI) techniques, particularly in automatic medical report generation (AMRG), offer a promising solution to this dilemma. This review comprehensively examines AMRG methods from 2021 to 2024. It (i) presents solutions to primary challenges in this field, (ii) explores AMRG applications across various imaging modalities, (iii) introduces publicly available datasets, (iv) outlines evaluation metrics, (v) identifies techniques that significantly enhance model performance, and (vi) discusses unresolved issues and potential future research directions. This paper aims to provide a comprehensive understanding of the existing literature and inspire valuable future research.","url":"https://doi.org/10.1561/116.20240044","authors":["Guo Li","Anas M. Tahir","Dong Zhang","Z. Jane Wang","Rabab Ward"],"tags":["Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-17","doi":"https://doi.org/10.1561/116.20240044","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411725460","name":"Leadership in radiology in the era of technological advancements and artificial intelligence","source":"openalex","abstract":"Radiology has evolved from the pioneering days of X-ray imaging to a field rich in advanced technologies on the cusp of a transformative future driven by artificial intelligence (AI). As imaging workloads grow in volume and complexity, and economic as well as environmental pressures intensify, visionary leadership is needed to navigate the unprecedented challenges and opportunities ahead. Leveraging its strengths in automation, accuracy and objectivity, AI will profoundly impact all aspects of radiology practice-from workflow management, to imaging, diagnostics, reporting and data-driven analytics-freeing radiologists to focus on value-driven tasks that improve patient care. However, successful AI integration requires strong leadership and robust governance structures to oversee algorithm evaluation, deployment, and ongoing maintenance, steering the transition from static to continuous learning systems. The vision of a \"diagnostic cockpit\" that integrates multidimensional data for quantitative precision diagnoses depends on visionary leadership that fosters innovation and interdisciplinary collaboration. Through administrative automation, precision medicine, and predictive analytics, AI can enhance operational efficiency, reduce administrative burden, and optimize resource allocation, leading to substantial cost reductions. Leaders need to understand not only the technical aspects but also the complex human, administrative, and organizational challenges of AI's implementation. Establishing sound governance and organizational frameworks will be essential to ensure ethical compliance and appropriate oversight of AI algorithms. As radiology advances toward this AI-driven future, leaders must cultivate an environment where technology enhances rather than replaces human skills, upholding an unwavering commitment to human-centered care. Their vision will define radiology's pioneering role in AI-enabled healthcare transformation. KEY POINTS: Question Artificial intelligence (AI) will transform radiology, improving workflow efficiency, reducing administrative burden, and optimizing resource allocation to meet imaging workloads' increasing complexity and volume. Findings Strong leadership and governance ensure ethical deployment of AI, steering the transition from static to continuous learning systems while fostering interdisciplinary innovation and collaboration. Clinical relevance Visionary leaders must harness AI to enhance, rather than replace, the role of professionals in radiology, advancing human-centered care while pioneering healthcare transformation.","url":"https://doi.org/10.1007/s00330-025-11745-4","authors":["Barbara Wichtmann","Daniel Paech","Oleg S. Pianykh","Susie Y. Huang","Steven E. Seltzer","James A. Brink","Fiona M. Fennessy"],"tags":["Workflow","Analytics","Transformative learning","Automation","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-27","doi":"https://doi.org/10.1007/s00330-025-11745-4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4414707963","name":"Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy","source":"openalex","abstract":"This perspective examines how artificial intelligence (AI) is transforming small-molecule development for precision cancer immunomodulation therapy. It outlines AI-driven approaches for de novo design, virtual screening, multi-parameter optimization, and ADMET prediction, targeting immune checkpoints, tumor microenvironment modulation, antigen presentation, and metabolic pathways. The article highlights patient stratification, multi-omics integration, digital twin simulations, translational challenges, and future directions, underscoring AI's potential to deliver effective, personalized immunomodulatory therapeutics.","url":"https://doi.org/10.1038/s44386-025-00029-y","authors":["Henry Sutanto","Deasy Fetarayani"],"tags":["Immune system","Precision medicine","Computational biology","Cancer","Tumor microenvironment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1038/s44386-025-00029-y","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4394951808","name":"Artificial intelligence and criminal liability in India: exploring legal implications and challenges","source":"openalex","abstract":"artificial intelligence (ai) is revolutionizing various industries, including the legal landscape, in india.as ai technology becomes more prevalent, questions arise regarding its potential impact on criminal liability.this abstract delves into the legal implications and challenges associated with ai's involvement in criminal activities in india.the intersection of ai and crime poses complex questions about the attribution of criminal liability.the ability of ai algorithms to operate autonomously blurs the lines of accountability, making it challenging to determine who should be held responsible for ai-driven criminal acts.the abstract explores the concept of legal personhood for ai systems and the need for legal frameworks that address the responsibilities of ai developers, operators, and users.additionally, the abstract highlights the importance of data privacy and security in the context of ai-driven criminal activities.Criminals can exploit ai algorithms to harvest and misuse personal data, necessitating robust data protection laws to safeguard individuals' privacy rights.Furthermore, ai-generated fake content, such as deepfakes, raises concerns about the integrity of evidence and the potential for manipulating legal proceedings.the abstract also addresses the ethical considerations surrounding ai's involvement in criminal activities.it highlights the urgent need for the indian legal system to address the complex implications of ai's involvement in criminal activities.By developing comprehensive legal frameworks, emphasizing ethical ai development, and prioritizing data privacy, india can navigate the challenges posed by ai-driven crime and ensure that technology advances responsibly and securely within the bounds of criminal liability.","url":"https://doi.org/10.1080/23311886.2024.2343195","authors":["Hifajatali Sayyed"],"tags":["Criminal liability","Liability","Business","Legal liability","Criminal law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-19","doi":"https://doi.org/10.1080/23311886.2024.2343195","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401880940","name":"Assessment of Generative Artificial Intelligence (AI) Models in Creating Medical Illustrations for Various Corneal Transplant Procedures","source":"openalex","abstract":"PURPOSE: This study aimed to task and assess generative artificial intelligence (AI) models in creating medical illustrations for corneal transplant procedures such as Descemet's stripping automated endothelial keratoplasty (DSAEK), Descemet's membrane endothelial keratoplasty (DMEK), deep anterior lamellar keratoplasty (DALK), and penetrating keratoplasty (PKP). Methods: Six engineered prompts were provided to Decoder-Only Autoregressive Language and Image Synthesis 3 (DALL-E 3) and Medical Illustration Manager (MIM) to guide these generative AI models in creating a final medical illustration for each of the four corneal transplant procedures. Control illustrations were created by the authors for each transplant technique for comparison. A grading system with five categories with a maximum score of 3 points each (15 points total) was designed to objectively assess AI's performance. Four independent reviewers analyzed and scored the final images produced by DALL-E 3 and MIM as well as the control illustrations. All AI-generated images and control illustrations were then provided to Chat Generative Pre-Trained Transformer-4o (ChatGPT-4o), which was tasked with grading each image with the grading system described above. All results were then tabulated and graphically depicted. RESULTS: The control illustration images received significantly higher scores than produced images from DALL-E 3 and MIM in legibility, anatomical realism and accuracy, procedural step accuracy, and lack of fictitious anatomy (p<0.001). For detail and clarity, the control illustrations and images produced by DALL-E 3 and MIM received statistically similar scores of 2.75±0.29, 2.19±0.24, and 2.50±0.29, respectively (p=0.0504). With regard to mean cumulative scores for each transplant procedure image, the control illustrations received a significantly higher score than DALL-E 3 and MIM (p<0.001). Additionally, the overall mean cumulative score for the control illustrations was significantly higher than DALL-E 3 and MIM (14.56±0.51 (97.1%), 4.38±1.2 (29.2%), and 5.63±1.82 (37.5%), respectively (p<0.001)). When assessing AI's grading performance, ChatGPT-4o scored the images produced by DALL-E 3 and MIM significantly higher than the average scores of the independent reviewers (DALL-E 3: 10.0±0.0 (66.6%) vs. 4.38±1.20 (29.2%), p<0.001; MIM: 10.0±0.0 (66.6%) vs. 5.63±1.82 (37.5%), p<0.001). However, mean scores for the control illustrations between ChatGPT-4o and the independent reviewers were comparable (15.0±0.0 (100%) vs. 14.56±0.13 (97.1%); p>0.05). CONCLUSION: AI is an extremely powerful and efficient tool for many tasks, but it is currently limited in producing accurate medical illustrations for corneal transplant procedures. Further development is required for generative AI models to create medically sound and accurate illustrations for use in ophthalmology.","url":"https://doi.org/10.7759/cureus.67833","authors":["Kayvon A Moin","Ayesha A Nasir","Dallas J Petroff","Bosten A Loveless","Omeed A Moshirfar","Phillip C. Hoopes","Majid Moshirfar"],"tags":["Medicine","Artificial intelligence","Corneal Transplant","Generative grammar","Ophthalmology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-26","doi":"https://doi.org/10.7759/cureus.67833","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410126990","name":"Interactive Heritage: The Role of Artificial Intelligence in Digital Museums","source":"openalex","abstract":"Museum use of artificial intelligence (AI) is becoming increasingly common, but its contribution to museum attendance is yet to be confirmed. This paper investigates whether the adoption of AI impacts museum visitation using data from 19 museums. Statistical analyses, including ANOVA and Spearman correlation, were conducted to determine if the use of AI has significant effects on visitors. The findings indicate no statistically significant difference between museums that use AI and those that do not (ANOVA: p = 0.263, F = 1.34), but the Spearman correlation (r = 0.448, p = 0.055) indicates a moderate positive correlation that is not statistically significant. The findings suggest that AI enhances visitor experience rather than increasing attendance. Additionally, this study proposes a conceptual framework for AI prototyping in museums. The study contributes to the ongoing debate on AI in cultural institutions by emphasizing that future research should incorporate longitudinal studies and qualitative visitor feedback in order to capture the overall impact of AI on engagement and sustainability in museums.","url":"https://doi.org/10.3390/electronics14091884","authors":["Ματίνα Κιουρεξίδου","Sofia Stamou"],"tags":["Cultural heritage","Museology","Computer science","Visual arts","Multimedia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-06","doi":"https://doi.org/10.3390/electronics14091884","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4400206314","name":"Deep fakes and the Artificial Intelligence Act—An important signal or a missed opportunity?","source":"openalex","abstract":"Abstract The Artificial Intelligence Act (AI Act) adopted by the European Union might serve as a global regulatory reference point. Heated negotiations over the AI Act have shown that reconciling the interests of numerous stakeholders is not an easy task. As well as creating clear and precise rules that would enable implementing effective safeguards for citizens against the manipulative potential of technology. The AI Act introduces the first legal definition of deep fakes and creates a system of protection against their harmful applications, that is based on transparency obligations. I argue that in the course of negotiations, the EU has made progress in regulating deep fakes, but the adopted solutions should only be an introduction to a more strict protection of individuals. The AI Act targets a specific group of deep fakes, leaving some of them unregulated or poorly regulated and disregarding the consequences of nonconsensual deep fake pornography. By analyzing the legal provisions related to deep fakes in the AI Act, I point to the rationale behind the chosen forms of protection, criticize the shortcomings and list the consequences of adopting the AI Act for deep fakes landscape.","url":"https://doi.org/10.1002/poi3.406","authors":["Mateusz Łabuz"],"tags":["Computer science","SIGNAL (programming language)","Artificial intelligence","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-01","doi":"https://doi.org/10.1002/poi3.406","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4410770993","name":"Perspectives of physicians, nurses, and patients on the use of artificial intelligence and robotic nurses in healthcare","source":"openalex","abstract":"AIM: This study aims to assess the perspectives of physicians, nurses, and patients in Turkey regarding the integration of artificial intelligence (AI) and robotic nurses in healthcare settings while exploring their attitudes toward the use of robots in healthcare delivery. BACKGROUND: AI and robotic nurses are increasingly shaping healthcare delivery and influencing clinical decision-making processes. However, research examining the impact of AI and robotic nurses on nursing practice and patient care remains limited. The attitudes of healthcare professionals and patients are crucial factors for the successful integration and adoption of these technologies in clinical settings. METHOD: This qualitative study employed in-depth individual interviews to explore participants' perspectives. The sample consisted of 13 physicians, 17 nurses, and 15 patients, all recruited from university hospitals, Ministry of Health hospitals, and private healthcare facilities across Turkey. Data were collected using two semistructured interview guides with \"Healthcare Workersand Patients\". Ethical approval and informed consent were obtained prior to data collection. The collected data were analyzed with content analysis using MAXQDA Pro 2021 software. RESULTS: The qualitative findings were organized into four primary themes: \"Impact of AI Technologies on Healthcare,\" \"Use of AI and Robots,\" \"Receiving Care from Humanoid Robots,\" and \"Working with Humanoid Robots.\" These themes were further explored through 12 subthemes and corresponding codes. DISCUSSION: Participants who had not yet interacted directly with AI technologies and relied solely on literature or had limited knowledge about the process generally believed that AI and robotic nurses could positively affect healthcare. However, they expressed concerns regarding the inability of these technologies to replicate the human touch. They specifically highlighted the limitations of robots in areas requiring empathy, emotional connection, and personalized care. CONCLUSION AND IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY: This study explored the perspectives of physicians, nurses, and patients regarding AI and robotic nurses, revealing both anticipated benefits and potential challenges for the healthcare system. The findings indicate the necessity for policies that address the ethical, social, and professional implications of AI and robotics, ensuring that their integration into healthcare practices is aligned with nursing and health policy objectives. It is crucial for international healthcare leaders to collaborate in developing policies that optimize the benefits of these technologies across diverse healthcare settings.","url":"https://doi.org/10.1111/inr.70017","authors":["Emel Gümüş","Handan Alan"],"tags":["Health care","Nursing","Qualitative research","Sample (material)","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-27","doi":"https://doi.org/10.1111/inr.70017","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4391317825","name":"Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation","source":"openalex","abstract":"BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS: In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61-0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59-0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS: The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.","url":"https://doi.org/10.1161/circep.123.012338","authors":["Lauri Holmström","Bryan Bednarski","Harpriya Chugh","Habiba Aziz","Hoang Nhat Pham","Arayik Sargsyan","Audrey Uy‐Evanado","Damini Dey","Angelo Salvucci","Jonathan Jui","Kyndaron Reinier","Piotr J. Slomka"],"tags":["Medicine","Pulseless electrical activity","Ventricular fibrillation","Cohort","Receiver operating characteristic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-29","doi":"https://doi.org/10.1161/circep.123.012338","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4401989560","name":"Artificial Intelligence-Powered Surgical Consent: Patient Insights","source":"openalex","abstract":"Introduction The integration of artificial intelligence (AI) in healthcare has revolutionized patient interactions and service delivery. AI's role extends from supporting clinical diagnostics and enhancing operational efficiencies to potentially improving informed consent processes in surgical settings. This study investigates the application of AI, particularly large language models like OpenAI's ChatGPT, in facilitating surgical consent, focusing on patient understanding, satisfaction, and trust. Methods We employed a mixed-methods approach involving 86 participants, including laypeople and medical staff, who engaged in a simulated AI-driven consent process for a tonsillectomy. Participants interacted with ChatGPT-4, which provided detailed procedure explanations, risks, and benefits. Post-interaction, participants completed a survey assessing their experience through quantitative and qualitative measures. Results Participants had a cautiously optimistic response to AI in the surgical consent process. Notably, 71% felt adequately informed, 86% found the information clear, and 71% felt they could make informed decisions. Overall, 71% were satisfied, 57% felt respected and confident, and 57% would recommend it, indicating areas needing refinement. However, concerns about data privacy and the lack of personal interaction were significant, with only 42% reassured about the security of their data. The standardization of information provided by AI was appreciated for potentially reducing human error, but the absence of empathetic human interaction was noted as a drawback. Discussion While AI shows promise in enhancing the consistency and comprehensiveness of information delivered during the consent process, significant challenges remain. These include addressing data privacy concerns and bridging the gap in personal interaction. The potential for AI to misinform due to system \"hallucinations\" or inherent biases also needs consideration. Future research should focus on refining AI interactions to support more nuanced and empathetic engagements, ensuring that AI supplements rather than replacing human elements in healthcare. Conclusion The integration of AI into surgical consent processes could standardize and potentially improve the delivery of information but must be balanced with efforts to maintain the critical human elements of care. Collaborative efforts between developers, clinicians, and ethicists are essential to optimize AI use, ensuring it complements the traditional consent process while enhancing patient satisfaction and trust.","url":"https://doi.org/10.7759/cureus.68134","authors":["Alex Teasdale","Laura Mills","Rhodri Costello"],"tags":["Informed consent","Medicine","Standardization","Consistency (knowledge bases)","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-29","doi":"https://doi.org/10.7759/cureus.68134","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4406405008","name":"Artificial Intelligence‐Enhanced, Closed‐Loop Wearable Systems Toward Next‐Generation Diabetes Management","source":"openalex","abstract":"Recent advancements in wearable healthcare have led to commercially accessible continuous glucose monitoring systems (CGMs) for diabetes management. However, CGMs only monitor glucose levels and lack therapeutic functions, prompting the development of closed‐loop systems that use monitored glucose levels to guide insulin dosing. While promising, these devices also pose risks, such as insulin overdosing, which can cause hypoglycemia. This review summarizes recent advances in integrating artificial intelligence methods with conventional CGMs. The developments in wearable CGMs and progress in insulin delivery technologies are explored, and existing algorithms for glucose prediction in closed‐loop systems are reviewed. Additionally, emerging trends in optimizing these algorithms to enhance the safety and security of closed‐loop insulin delivery systems are highlighted.","url":"https://doi.org/10.1002/aisy.202400822","authors":["Wei Huang","Ivo Pang","Jing Bai","Bin‐Bin Cui","Xiaojuan Qi","Shiming Zhang"],"tags":["Closed loop","Wearable computer","Computer science","Wearable technology","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.1002/aisy.202400822","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4411691260","name":"Challenges in the Rapid and Responsible Integration of Generative Artificial Intelligence (AI) Into a New Medical School Curriculum","source":"openalex","abstract":"This study describes a systematic approach to integrate generative artificial intelligence (AI) into our new medical education curriculum at the Quillen College of Medicine while maintaining academic integrity. As medical schools navigate the widespread adoption of large language models, we implemented a five-step process to address the policy recommendations of the Association of American Medical Colleges (AAMC) on AI integration. First, surveys assessed AI usage patterns among students, revealing increasing adoption (from 24% to 77%) between May 2024 and February 2025. Second, clear professionalism guidelines were established, prohibiting AI use in generating learning objectives, writing Subjective, Objective, Assessment, and Plan (SOAP) notes, or completing assignments while permitting its use in research applications or otherwise in specific course settings when given permission to do so by faculty. Third, an institutional grant ensured equitable access to AI platforms for all students entering in Fall 2024. Fourth, AI was integrated into controlled educational settings, particularly within problem-based learning (PBL) for first-year students and team-based learning (TBL) for second-year students, with structured evaluation criteria. Finally, specialized training on ethical AI use was provided to students transitioning to clinical clerkships. Survey data indicated that students found AI exposure beneficial (91% agreement) and helpful for researching learning objectives (94% agreement), though confidence in AI's accuracy was lower (85% agreement). Students prioritized summarizing learning materials and testing understanding as important AI applications while valuing the ability to function as clinicians both with and without AI. Our approach demonstrates a balanced integration strategy that encourages responsible AI adoption while maintaining educational integrity in medical training.","url":"https://doi.org/10.7759/cureus.86796","authors":["Gabrielle Rueff","Paul Monaco","Antonio E Rusinol","Mark Hernandez"],"tags":["Medicine","Curriculum","Medical school","Medical education","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-26","doi":"https://doi.org/10.7759/cureus.86796","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4390863783","name":"A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking","source":"openalex","abstract":"The emergence and popularity of generative artificial intelligence (AI) tools, particularly text-based ones known as large language models, pose both opportunities and challenges to education. The ability of these tools to generate human-like texts based on minimal instructions causes concerns among educators about students’ use of these tools for academic writing, which may constitute a breach of academic integrity. We propose a pedagogical design that models on self-regulated learning and the authoring cycle and develops students’ critical thinking and self-regulation when composing academic writing using text-based generative AI tools. It contains six iterative and interactive phases. Students first plan the content and structure of the writing, then generate prompts for text-based generative AI tools. Next, students preview and verify the tools’ output, followed by the fourth phase of producing the writing using the corrected output. Fifthly, peer review by fellow students may be required to polish and proofread the writing. Lastly, through portfolio-tracking, students reflect on the writing process, and formulate strategies for future usage of text-based generative AI tools for writing. This pedagogical design helps students and teachers embrace text-based generative AI while addressing the perils these tools present, and guides the development of education interventions and instruments.","url":"https://doi.org/10.58459/rptel.2024.19030","authors":["Siu Cheung Kong","John Chi‐Kin Lee","Olson Tsang"],"tags":["Computer science","Generative grammar","Portfolio","Tracking (education)","Plan (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-15","doi":"https://doi.org/10.58459/rptel.2024.19030","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4395075544","name":"Artificial intelligence and smile design: An e‐Delphi consensus statement of ethical challenges","source":"openalex","abstract":"PURPOSE: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. METHODS: An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. RESULTS: On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. CONCLUSIONS: AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.","url":"https://doi.org/10.1111/jopr.13858","authors":["Rata Rokhshad","Teodora Karteva","Akhilanand Chaurasia","Raphaël Richert","Carl‐Maria Mörch","Faleh Tamimi","Maxime Ducret"],"tags":["Statement (logic)","Delphi","Delphi method","Psychology","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-24","doi":"https://doi.org/10.1111/jopr.13858","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.127Z"},{"id":"oa:W4404552125","name":"Integrating Artificial Intelligence in Education:","source":"openalex","abstract":"Artificial Intelligence (AI) is transforming educational practices by facilitating personalized learning, automating grading processes, and enhancing support through intelligent tutoring systems. This systematic review explores AI's integration in educational settings, highlighting its contributions to increased productivity and tailored learning experiences. It addresses key challenges including data privacy, algorithmic bias, and the need for enhanced accountability and transparency in AI applications. The review also discusses strategic recommendations for embedding ethical AI into curriculum design and emphasizes the importance of professional development for educators. Collaboration among educational stakeholders is vital for advancing responsible AI utilization. By synthesizing recent literature, this review provides insights into AI tools' effectiveness, explores ethical dimensions of technology in classrooms, and suggests future directions for research and practice in educational AI. This analysis serves as a resource for educators, policymakers, and technologists aiming to optimize AI benefits in education.","url":"https://doi.org/10.33830/ijrse.v6i2.1722","authors":["Pema Wangdi"],"tags":["Accountability","Transparency (behavior)","Knowledge management","Applications of artificial intelligence","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-14","doi":"https://doi.org/10.33830/ijrse.v6i2.1722","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4399273696","name":"Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics—A Scoping Review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s43465-024-01189-1","authors":["Raju Vaishya","Sakshi Dhall","Abhishek Vaish"],"tags":["Regenerative medicine","Medicine","Orthopedic surgery","Artificial intelligence","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-02","doi":"https://doi.org/10.1007/s43465-024-01189-1","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406025662","name":"In the era of responsible artificial intelligence and digitalization: business group digitalization, operations and subsidiary performance","source":"openalex","abstract":"Abstract With the rapid development of digital technologies, responsible AI has become a critical focus for ensuring ethical and socially conscious advancements in business and operations management. The integration of responsible AI practices in business groups’ digital transformations is essential to mitigate potential risks and maximize the positive impact on operational efficiency, supply chain performance, and subsidiary performance. This study aims to examine the consequences and mechanisms through which responsible group digitalization influences business group’s operation management, as manifested in subsidiary performance within the context of the digital economy. Analyzing data from 202 affiliated subsidiaries, we examine the role of HRM collaboration and technological turbulence in facilitating group digitalization. This study enriches the operations management literature and expands the application of ethical and responsible AI practices in digitalization by investigating the relationship between business group digitalization and business operations. Furthermore, this study provides practical implications pertaining to how ethical and responsible practices can guide group digital transformations, business operations and enhance the performance of subsidiaries.","url":"https://doi.org/10.1007/s10479-024-06453-z","authors":["Wei Sun","Shuang Ren","Guiyao Tang"],"tags":["Subsidiary","Context (archaeology)","Business","Process management","Corporate group"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-03","doi":"https://doi.org/10.1007/s10479-024-06453-z","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4399841817","name":"Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00256-024-04684-6","authors":["Huibert C. Ruitenbeek","Edwin H. G. Oei","Jacob J. Visser","Richard Kijowski"],"tags":["Medicine","Generalizability theory","Image quality","Magnetic resonance imaging","Workflow"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-20","doi":"https://doi.org/10.1007/s00256-024-04684-6","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406095403","name":"Radiomics and Artificial Intelligence in Pulmonary Fibrosis","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10278-024-01377-3","authors":["Stefania L. Chantzi","Alexandra Kosvyra","Ioanna Chouvarda"],"tags":["Radiomics","Medicine","Generalizability theory","Idiopathic pulmonary fibrosis","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-06","doi":"https://doi.org/10.1007/s10278-024-01377-3","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406001440","name":"A real world evaluation of an innovative artificial intelligence tool for population-level breast cancer screening","source":"openalex","abstract":"In resource-constrained countries like India, mammography-based breast screening is challenging to implement. This state-wide study, funded by the Government of Punjab, evaluated the use of Thermalytix, a low-cost, radiation-free AI tool, for breast cancer screening. Community health workers, trained to raise awareness, mobilized women aged 30 and above for screening. Thermalytix triaged women into five risk categories based on thermal images, with high-risk women recalled for diagnostic imaging. Over 18 months, 15,069 women were screened across 183 locations in Punjab. The median age was 41 years, and 69.9% were asymptomatic. Of 460 women testing positive (recall rate 3.1%), 268 underwent follow-up imaging, and 27 were confirmed with breast cancer, yielding a detection rate of 0.18%. The positive predictive value of biopsy performed was 81.81%, and the median diagnostic interval was 21 days, with therapy initiation within 30 days. The study demonstrates the potential of Thermalytix for effective population-level breast cancer screening in low-resource settings.","url":"https://doi.org/10.1038/s41746-024-01368-2","authors":["Karthik Adapa","Ashu Gupta","Sandeep Singh","Hitinder Kaur","Abhinav Trikha","Ajoy Sharma","Kumar Rahul"],"tags":["Medicine","Mammography","Breast cancer","Breast cancer screening","Asymptomatic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1038/s41746-024-01368-2","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4405104931","name":"Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10278-024-01335-z","authors":["Constanza Vásquez-Venegas","Chenwei Wu","Saketh Sundar","Renata Prôa","Francis Joshua Beloy","Jillian Reeze Medina","Megan McNichol","Krishnaveni Parvataneni","Nicholas Kurtzman","Felipe Mirshawka","Marcela Aguirre","Daniel K. Ebner"],"tags":["Computer science","Artificial intelligence","Transparency (behavior)","Data science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.1007/s10278-024-01335-z","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4404794268","name":"The Barriers and Solution to Artificial Intelligence Adoption in Medical Education: A Qualitative Study","source":"openalex","abstract":"Background: Nowadays, AI adoption, in medical education is growing rapidly. Studies have been conducted on various aspects of AI in medical education, however, in the context of its challenges and solutions faced with AI adoption is rarely explored through qualitative approaches. It is necessary to know the barriers and remedies for AI utilization in medical education.Objectives: This study explores potential barriers and solutions to AI adoption in medical education. Materials and Methods: Based on Giorgi's phenomenological approach, this qualitative study explored an in-depth understanding of AI adoption in medical education through in-depth interviews from March to April 2024. By purposive sampling, sixteen participants across famous medical institutions of Pakistan were interviewed, and data collection was stopped upon saturation. Results: A total of 16 participants belonging to different cadres of undergraduate medical colleges of Pakistan were interviewed for the current study. A total of 219 quotations from the transcripts resulted in three main themes with subsequent subthemes (total 13), i.e. perceptions of AI in medical education with three subthemes, barriers to AI adoption with seven subthemes and conjoint solutions to barriers having three subthemes.Conclusion: AI adoption in medical education is evolving healthcare globally. Nevertheless, there are barriers like the complexity of AI, lack of technical skills for using AI, and scarcity of resources that hinder its utilization in medical education. Moreover, ethical concerns and the specific guidelines that attract investors are among other challenges. Countermeasures like improving technical infrastructure and faculty development initiatives through collaboration among policymakers, administrators, and teaching faculty can overcome the barriers. Keywords: Artificial Intelligence, Adoption, Challenges, Medical Education.","url":"https://doi.org/10.52206/jsmc.2024.14.4.957","authors":["Muhammad Junaid Khan","Mehreen Lajber","Nazish Bilal","Sana Khan","Zaibunnisa Siddiqi","Aziz Ahmad"],"tags":["Qualitative research","Psychology","Medical education","Engineering ethics","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-11","doi":"https://doi.org/10.52206/jsmc.2024.14.4.957","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4391170104","name":"A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans","source":"openalex","abstract":"Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.","url":"https://doi.org/10.3390/cancers16030486","authors":["Jianliang Liu","Thomas P. Cundy","Dixon Woon","Nathan Lawrentschuk"],"tags":["Prostate cancer","Medicine","Pet imaging","Lymph node","Oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-23","doi":"https://doi.org/10.3390/cancers16030486","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4394870730","name":"Enhanced control of periodontitis by an artificial intelligence‐enabled multimodal‐sensing toothbrush and targeted mHealth micromessages: A randomized trial","source":"openalex","abstract":"AIM: Treatment of periodontitis, a chronic inflammatory disease driven by biofilm dysbiosis, remains challenging due to patients' poor performance and adherence to the necessary oral hygiene procedures. Novel, artificial intelligence-enabled multimodal-sensing toothbrushes (AI-MST) can guide patients' oral hygiene practices in real-time and transmit valuable data to clinicians, thus enabling effective remote monitoring and guidance. The aim of this trial was to assess the effect of such a system as an adjunct to clinical practice guideline-conform treatment. MATERIALS AND METHODS: This was a single-centre, double-blind, standard-of-care controlled, randomized, parallel-group, superiority trial. Male and female adults with generalized Stage II/III periodontitis were recruited at the Shanghai Ninth People's Hospital, China. Subjects received a standard-of-care oral hygiene regimen or a technology-enabled, theory-based digital intervention consisting of an AI-MST and targeted doctor's guidance by remote micromessaging. Additionally, both groups received guideline-conform periodontal treatment. The primary outcome was the resolution of inflamed periodontal pockets (≥4 mm with bleeding on probing) at 6 months. The intention-to-treat (ITT) analysis included all subjects who received the allocated treatment and at least one follow-up. RESULTS: One hundred patients were randomized and treated (50 tests/controls) between 1 February and 30 November 2022. Forty-eight tests (19 females) and 47 controls (16 females) were analysed in the ITT population. At 6 months, the proportion of inflamed periodontal pockets decreased from 80.7% (95% confidence interval [CI] 76.5-84.8) to 52.3% (47.7-57.0) in the control group, and from 81.4% (77.1-85.6) to 44.4% (39.9-48.9) in the test group. The inter-group difference was 7.9% (1.6-14.6, p < .05). Test subjects achieved better levels of oral hygiene (p < .001). No significant adverse events were observed. CONCLUSIONS: The tested digital health intervention significantly improved the outcome of periodontal therapy by enhancing the adherence and performance of self-performed oral hygiene. The model breaks the traditional model of oral health care and has the potential to improve efficiency and reduce costs (NCT05137392).","url":"https://doi.org/10.1111/jcpe.13987","authors":["Yuan Li","Xinyu Wu","M Q Liu","Ke Deng","Annamaria Tullini","Xiao Zhang","Junyu Shi","Hongchang Lai","Maurizio S. Tonetti"],"tags":["Medicine","Randomized controlled trial","Periodontitis","Oral hygiene","Population"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-17","doi":"https://doi.org/10.1111/jcpe.13987","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406772561","name":"A Comprehensive and Systematic Review of Multi-Criteria Decision-Making (MCDM) Methods to Solve Decision-Making Problems: Two Decades from 2004 to 2024","source":"openalex","abstract":"Decision-making in complex, multifaceted scenarios has become increasingly critical across diverse sectors, necessitating robust frameworks like Multi-Criteria Decision-Making (MCDM). Over the past two decades (2004–2024), MCDM has transformed from foundational methods like AHP and TOPSIS into dynamic hybrid models integrating artificial intelligence, fuzzy logic, and machine learning. Despite significant strides, the field faces challenges in addressing geographic disparities, underexplored domains, and adapting to emerging global needs. This study provides a comprehensive review of MCDM's evolution, consolidating insights from 3,655 peer-reviewed articles sourced through Dimensions.ai and analyzed using bibliometric tools like VOSviewer. The research identifies publication trends, leading contributors, thematic clusters, and collaborative networks while pinpointing gaps and opportunities for future exploration. These key findings highlight exponential growth in MCDM applications, particularly in sustainable energy, urban planning, and healthcare optimization. These advancements align with global priorities, including the United Nations Sustainable Development Goals (SDGs) such as clean energy, climate action, and sustainable cities. However, critical gaps remain in addressing issues like poverty alleviation, gender equity, and biodiversity conservation, emphasizing the need for broader interdisciplinary applications. This review concludes that MCDM's potential lies in embracing inclusivity, advancing into emerging technologies like blockchain and the metaverse, and fostering collaboration across underrepresented regions and domains. By harnessing real-time data, immersive simulations, and secure decision-making platforms, MCDM can redefine how global challenges are addressed.","url":"https://doi.org/10.31181/sdmap21202524","authors":["Rahul Kumar","Dragan Pamucar"],"tags":["Multiple-criteria decision analysis","Management science","Computer science","Decision analysis","Business decision mapping"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-23","doi":"https://doi.org/10.31181/sdmap21202524","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4413611966","name":"Artificial Intelligence in Planning for Spine Surgery","source":"openalex","abstract":"PURPOSE OF REVIEW: There has been an expanding role of artificial intelligence (AI) and machine learning (ML) in spine surgery, particularly in operative planning, intraoperative navigation, and postoperative management. With a focus on patient-specific surgical strategies, AI technologies offer new possibilities for improving surgical accuracy, reducing risks, and enhancing patient outcomes in spine care. RECENT FINDINGS: AI models have shown strong accuracy in preoperative planning, with neural networks outperforming traditional algorithms in patient selection and outcome prediction. Advances in 3D modeling, supported by machine learning, enable efficient, patient-specific anatomical reconstructions, reducing manual segmentation time from hours to seconds. In intraoperative navigation, AI-driven virtual and augmented reality systems enhance screw placement precision and reduce radiation exposure by up to 90%, improving workflow and safety. Additionally, real-time AI-based decision support has decreased operative time and postoperative risks, while postoperative AI applications now support mortality risk stratification and discharge planning, yielding significant predictive accuracy for adverse events and extended stays. AI technologies are transforming spine surgery by increasing surgical precision, optimizing clinical workflows, and personalizing patient care. While challenges remain regarding data diversity and ethical considerations, ongoing innovations indicate that AI will continue to refine spine surgery through personalized and efficient care solutions.","url":"https://doi.org/10.1007/s12178-025-09992-5","authors":["Iyad Ali","Yianni Bakaes","James MacLeod","T. Lee","Sia Cho","Wellington K. Hsu"],"tags":["Sports medicine","Orthopedic surgery","Medicine","General surgery","Surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-26","doi":"https://doi.org/10.1007/s12178-025-09992-5","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4399323408","name":"Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It","source":"openalex","abstract":"L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.","url":"https://doi.org/10.3390/insects15060418","authors":["Putri Kusuma Astuti","Bettina Hegedűs","Andrzej Oleksa","Zoltán Bagi","Szilvia Kusza"],"tags":["Beekeeping","Apiary","Sustainability","Threatened species","Environmental resource management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-04","doi":"https://doi.org/10.3390/insects15060418","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4404701448","name":"Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption","source":"openalex","abstract":"The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.","url":"https://doi.org/10.3390/app142310899","authors":["Mirza Mansoor Baig","Chris Hobson","Hamid GholamHosseini","Ehsan Ullah","Shereen Afifi"],"tags":["Software deployment","Transparency (behavior)","Workflow","Health care","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.3390/app142310899","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4391224340","name":"Artificial Intelligence in Healthcare: A Revolutionary Ally or an Ethical Dilemma","source":"openalex","abstract":"","url":"https://doi.org/10.4274/balkanmedj.galenos.2024.2024-250124","authors":["Selçuk Korkmaz"],"tags":["Dilemma","Health care","Engineering ethics","Ethical dilemma","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-25","doi":"https://doi.org/10.4274/balkanmedj.galenos.2024.2024-250124","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4391075943","name":"Managing risk and resilience in autonomous and intelligent systems: Exploring safety in the development, deployment, and use of artificial intelligence in healthcare","source":"openalex","abstract":"Autonomous and intelligent systems (AIS) are being developed and deployed across a wide range of sectors and encompass a variety of technologies designed to engage in different forms of independent reasoning and self-directed behavior. These technologies may bring considerable benefits to society but also pose a range of risk management challenges, particularly when deployed in safety-critical sectors where complex interactions between human, social, and technical processes underpin safety and resilience. Healthcare is one safety-critical sector at the forefront of efforts to develop and deploy intelligent technologies, such as through artificial intelligence (AI) systems intended to automate key aspects of healthcare tasks such as reading medical images to identify signs of pathology. This article develops a qualitative analysis of the sociotechnical sources of risk and resilience associated with the development, deployment, and use of AI in healthcare, drawing on 40 in-depth interviews with participants involved in the development, management, and regulation of AI. Qualitative template analysis is used to examine sociotechnical sources of risk and resilience, drawing on and elaborating Macrae's (2022, Risk Analysis, 42(9), 1999-2025) SOTEC framework that integrates structural, organizational, technological, epistemic, and cultural sources of risk in AIS. This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks. In doing so, the SOTEC framework is elaborated and translated to define key sources of both risk and resilience in AIS.","url":"https://doi.org/10.1111/risa.14273","authors":["Carl Macrae"],"tags":["Sociotechnical system","Software deployment","Resilience (materials science)","Risk analysis (engineering)","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-21","doi":"https://doi.org/10.1111/risa.14273","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4403351005","name":"The Future of Oral Oncology: How Artificial Intelligence is Redefining Surgical Procedures and Patient Management","source":"openalex","abstract":"INTRODUCTION AND AIMS: The future of oral oncology is significantly influenced by the incorporation of artificial intelligence (AI) technologies, such as surgical robotics and early histopathological diagnosis and detection of diseases. This article aims to explore the transformative effects of AI on the identification and diagnosis of oral cancer, emphasising the potential improvements in patient outcomes and the effectiveness of treatment options. METHODS: An overview of the integration of AI technologies in surgical robotics and remote monitoring systems was conducted. This included an examination of AI algorithms used in surgical procedures for oral cancer, as well as the functionalities of AI-powered remote monitoring tools in disease management and patient care. RESULTS: The application of AI in surgical robotics has led to increased precision and accuracy in oral cancer procedures, resulting in significantly improved patient outcomes with fewer complications. Furthermore, AI-driven remote monitoring systems facilitated personalised care and timely medical interventions, which contributed to better disease management. Notable positive results observed include decreased procedure durations, reduced complications, and accelerated recovery times for patients. CONCLUSION: The integration of AI technologies in oral oncology care has demonstrated significant potential for enhancing the quality of care, improving treatment outcomes, and streamlining clinical workflows. By leveraging AI, healthcare professionals can offer tailored treatment plans and improve long-term disease management for patients with oral cancer. CLINICAL RELEVANCE: The implementation of AI in oral oncology represents an innovative approach to early detection and efficient management of oral cancer, ultimately enhancing the quality of life of the affected individuals. The findings underscore the importance of continuing to explore and adopt AI technologies to further advance healthcare delivery in oncology.","url":"https://doi.org/10.1016/j.identj.2024.09.032","authors":["Marwan Al‐Raeei"],"tags":["Medicine","General surgery","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-12","doi":"https://doi.org/10.1016/j.identj.2024.09.032","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4413125615","name":"Emerging Applications of Artificial Intelligence in Edge Computing: A Comprehensive Review","source":"openalex","abstract":"Edge computing, coupled with Artificial Intelligence (AI), represents a paradigm shift in data processing, enabling real-time analytics and decision-making at the source. By distributing computation closer to the data origin, this integration addresses the critical challenges of latency, bandwidth, and privacy, which traditional cloud-centric systems face. AI algorithms deployed on Edge devices enable localized intelligence, facilitating transformative advancements in domains such as healthcare, smart cities, industrial automation, and autonomous systems. This review comprehensively examines the synergistic relationship between AI and Edge computing, highlighting key applications, challenges, and future research opportunities. Moreover, we emphasize the critical need for lightweight AI models, energy-efficient systems, and robust security measures to fully harness the potential of Edge AI in an increasingly connected world.","url":"https://doi.org/10.71426/jmt.v1.i2.pp175-185","authors":["Yeswanth Dintakurthy","Rama Krishna Innmuri","Sr. Technical Architect, Salesforce, San Francisco, USA. E-mail: ramakrishnainnamuri@gmail.com","Ashutosh Vanteru","Arun Thotakuri"],"tags":["Computer science","Applications of artificial intelligence","Artificial intelligence","Enhanced Data Rates for GSM Evolution","Edge computing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-25","doi":"https://doi.org/10.71426/jmt.v1.i2.pp175-185","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4410732863","name":"Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study","source":"openalex","abstract":"Abstract Background Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration–approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education. Objective This paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. Second, this paper presents findings from a qualitative postcourse evaluation of students’ knowledge and attitudes toward AI and their overall perception of the course. Methods Clinical-year medical students can attend a 5-day elective course called “Medicine in the Digital Age,” which includes one dedicated AI module alongside 4 others on digital doctor-patient communication; digital health applications and smart devices; telemedicine; and virtual/augmented reality and robotics. After course completion, participants were interviewed in semistructured small group interviews. The interview guide was developed deductively from existing evidence and research questions compiled by our group. A subset of interview questions focused on students’ knowledge, skills, and attitudes regarding medical AI, and their overall course assessment. Responses were analyzed using Mayring’s qualitative content analysis. This paper reports on the subset of students’ statements about their perception and attitudes toward AI and the elective’s general evaluation. Results We conducted a total of 18 group interviews, in which all 35 (100%) participants (female=11, male=24) from 3 consecutive course runs participated. This produced a total of 214 statements on AI, which were assigned to the 3 main categories “Areas of Application,” “Future Work,” and “Critical Reflection.” The findings indicate that students have a nuanced and differentiated understanding of AI. Additionally, 610 statements concerned the elective’s overall assessment, demonstrating great learning benefits and high levels of acceptance of the teaching concept. All 35 students would recommend the elective to peers. Conclusions The evaluation demonstrated that the AI module effectively generates competences regarding AI technology, fosters a critical perspective, and prepares medical students to engage with the technology in a differentiated manner. The curriculum is feasible, beneficial, and highly accepted among students, suggesting it could serve as a teaching model for other medical institutions. Given the growing number and impact of medical AI applications, there is a pressing need for more AI-focused curricula and further research on their educational impact.","url":"https://doi.org/10.2196/65220","authors":["Zoe S. Oftring","Kim Deutsch","Daniel Tolks","Florian Jungmann","Sebastian Kühn","Zoe S Oftring","Sebastian Kuhn"],"tags":["Medical education","Curriculum","Qualitative research","Perception","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-26","doi":"https://doi.org/10.2196/65220","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4412103547","name":"Artificial intelligence and digital twins for the personalised prediction of hypertension risk","source":"openalex","abstract":"Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epidemiological data and common risk factors, often fail to account for individual variability, highlighting the need for advanced data-driven methodologies. This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the prediction of hypertension risk by incorporating a range of data sources, including clinical, lifestyle, and genetic factors. Despite promising developments, challenges such as data standardisation, the need for high-quality datasets, model explainability, and class imbalance in medical data persist. The integration of wearable technologies, alongside the potential of emerging technologies in healthcare such as digital twins, presents significant opportunities in personalising care through the dynamic modelling of individual health profiles. This review synthesises current methodologies, identifies existing gaps, and highlights the transformative potential of AI-driven, personalised hypertension prevention and management, emphasising the importance of addressing issues of reproducibility and transparency to facilitate clinical adoption.","url":"https://doi.org/10.1016/j.compbiomed.2025.110718","authors":["Akhil Naik","Jakub Nalepa","Agata M. Wijata","J.R. Mahon","Dharmesh Mistry","Adam T Knowles","Ellen A. Dawson","Gregory Y.H. Lip","Iván Olier","Sandra Ortega‐Martorell"],"tags":["Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-08","doi":"https://doi.org/10.1016/j.compbiomed.2025.110718","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4402446266","name":"Artificial intelligence for automatic and objective assessment of competencies in flexible bronchoscopy","source":"openalex","abstract":"Background: Bronchoscopy is a challenging technical procedure, and assessment of competence currently relies on expert raters. Human rating is time consuming and prone to rater bias. The aim of this study was to evaluate if a bronchial segment identification system based on artificial intelligence (AI) could automatically, instantly, and objectively assess competencies in flexible bronchoscopy in a valid way. Methods: September 2023. The participants performed one full diagnostic bronchoscopy in a simulated setting and were rated immediately by the AI according to its four outcome measures: diagnostic completeness (DC), structured progress (SP), procedure time (PT), and mean intersegmental time (MIT). The procedures were video-recorded and rated after the conference by two blinded, expert raters using a previously validated assessment tool with nine items regarding anatomy and dexterity. Results: Fifty-two participants from six different continents were included. All four outcome measures of the AI correlated significantly with the experts' anatomy-ratings (Pearson's correlation coefficient, P value): DC (r=0.47, P<0.001), SP (r=0.57, P<0.001), PT (r=-0.32, P=0.02), and MIT (r=-0.55, P<0.001) and also with the experts' dexterity-ratings: DC (r=0.38, P=0.006), SP (r=0.53, P<0.001), PT (r=-0.34, P=0.014), and MIT (r=-0.47, P<0.001). Conclusions: The study provides initial validity evidence for AI-based immediate and automatic assessment of anatomical and navigational competencies in flexible bronchoscopy. SP provided stronger correlations with human experts' ratings than the traditional DC.","url":"https://doi.org/10.21037/jtd-24-841","authors":["Kristoffer Mazanti Cold","Kaladerhan Agbontaen","Anne Orholm Nielsen","Christian Andersen","Suveer Singh","Lars Konge"],"tags":["Medicine","Bronchoscopy","Flexible bronchoscopy","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.21037/jtd-24-841","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406704087","name":"A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays","source":"openalex","abstract":"Background: Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Artificial intelligence (AI) holds significant potential roles in enhancing the detection of lung nodules through chest X-ray (CXR), enabling earlier diagnosis and improved outcomes. Methods: Papers were identified through a comprehensive search of the Web of Science (WOS), Scopus, and Ovid Medline databases for publications dated between 2020 and 2024. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 34 studies that met the inclusion criteria were selected for quality assessment and data extraction. Results: AI demonstrated sensitivity rates of 56.4–95.7% and specificities of 71.9–97.5%, with the area under the receiver operating characteristic (AUROC) values between 0.89 and 0.99, compared to radiologists’ mean area under the curve (AUC) of 0.81. AI performed better with larger nodules (>2 cm) and solid nodules, showing higher AUC values for calcified (0.71) compared to non-calcified nodules (0.55). Performance was lower in hilar areas (30%) and lower lung fields (43.8%). A combined AI-radiologist approach improved overall detection rates, particularly benefiting less experienced readers; however, AI showed limitations in detecting ground-glass opacities (GGOs). Conclusions: AI shows promise as a supplementary tool for radiologists in lung nodule detection. However, the variability in AI results across studies highlights the need for standardized assessment methods and diverse datasets for model training. Future studies should focus on developing more precise and applicable algorithms while evaluating the effectiveness and cost-efficiency of AI in lung cancer screening interventions.","url":"https://doi.org/10.3390/diagnostics15030246","authors":["Puteri Norliza Megat Ramli","Azimatun Noor Aizuddin","Norfazilah Ahmad","Zuhanis Abdul Hamid","Khairil Idham Ismail"],"tags":["Lung cancer","Receiver operating characteristic","Lung cancer screening","Medicine","Nodule (geology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-22","doi":"https://doi.org/10.3390/diagnostics15030246","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4403637939","name":"Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 1: Preliminary Evaluation","source":"openalex","abstract":"Generative artificial intelligence (AI) text-to-image production could reinforce or amplify gender and ethnicity biases. Several text-to-image generative AI tools are used for producing images that represent the medical imaging professions. White male stereotyping and masculine cultures can dissuade women and ethnically divergent people from being drawn into a profession. Methods: In March 2024, DALL-E 3, Firefly 2, Stable Diffusion 2.1, and Midjourney 5.2 were utilized to generate a series of individual and group images of medical imaging professionals: radiologist, nuclear medicine physician, radiographer, and nuclear medicine technologist. Multiple iterations of images were generated using a variety of prompts. Collectively, 184 images were produced for evaluation of 391 characters. All images were independently analyzed by 3 reviewers for apparent gender and skin tone. Results: Collectively (individual and group characters) (n = 391), 60.6% were male and 87.7% were of a light skin tone. DALL-E 3 (65.6%), Midjourney 5.2 (76.7%), and Stable Diffusion 2.1 (56.2%) had a statistically higher representation of men than Firefly 2 (42.9%) (P < 0.0001). With Firefly 2, 70.3% of characters had light skin tones, which was statistically lower (P < 0.0001) than for Stable Diffusion 2.1 (84.8%), Midjourney 5.2 (100%), and DALL-E 3 (94.8%). Overall, image quality metrics were average or better in 87.2% for DALL-E 3 and 86.2% for Midjourney 5.2, whereas 50.9% were inadequate or poor for Firefly 2 and 86.0% for Stable Diffusion 2.1. Conclusion: Generative AI text-to-image generation using DALL-E 3 via GPT-4 has the best overall quality compared with Firefly 2, Midjourney 5.2, and Stable Diffusion 2.1. Nonetheless, DALL-E 3 includes inherent biases associated with gender and ethnicity that demand more critical evaluation.","url":"https://doi.org/10.2967/jnmt.124.268332","authors":["Geoffrey Currie","Johnathan Hewis","Elizabeth Hawk","Eric Rohren"],"tags":["Ethnic group","Generative grammar","Artificial intelligence","Image (mathematics)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-22","doi":"https://doi.org/10.2967/jnmt.124.268332","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4413895322","name":"Phenotypic Selectivity of Artificial Intelligence–Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers. METHODS: We included 4 distinct study populations drawn from both electronic health records and prospective cohort studies. We deployed 6 image-based AI-ECG models: 5 validated models for the detection of left ventricular systolic dysfunction, aortic stenosis, mitral regurgitation, left ventricular hypertrophy, and a composite model for structural heart disease; and 1 negative control AI-ECG model for biological sex. Additionally, we developed 6 experimental models designed to identify noncardiovascular conditions. Diagnosis codes from electronic health records and cohorts were transformed into interpretable phenotypes using a phenome-wide association study framework. We assessed associations of AI-ECG probabilities with cross-sectional phenotypes using logistic regression and with new-onset cardiovascular diseases using Cox regression. Pearson correlation coefficients were calculated to compare phenotypic signatures. RESULTS: <10⁻⁶), whereas the sex model did not show a similar pattern. All AI-ECG models were significantly associated with their respective target phenotype but also showed similar or stronger associations with a broad range of other cardiovascular phenotypes. Phenotypic associations were similar across AI-ECG models trained for different conditions, which was not observed in models for noncardiovascular conditions. Correlation of phenotype association patterns between models was high (0.67-0.96). This pattern was consistent across all models and external data sets and in both cross-sectional and prospective analyses. CONCLUSIONS: Despite being developed to detect specific cardiovascular conditions, AI-ECG models detect the presence and predict the future development of a broad range of cardiovascular diseases with similar propensity. This challenges their role as binary diagnostic tools and instead supports their use as broader cardiovascular biomarkers.","url":"https://doi.org/10.1161/circulationaha.125.076279","authors":["Philip M. Croon","Lovedeep Singh Dhingra","Dhruva Biswas","Evangelos K. Oikonomou","Rohan Khera"],"tags":["Medicine","Logistic regression","Internal medicine","Cardiology","Left ventricular hypertrophy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.1161/circulationaha.125.076279","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4400674341","name":"Clarifying Ethical Dilemmas in Sharpening Students’ Artificial Intelligence Proficiency: Dispelling Myths About Using AI Tools in Higher Education","source":"openalex","abstract":"Artificial intelligence has been talked about for over half a century now. Still, it became a fast-growing reality in 2023 through modern technologies, such as Meta AI, Open AI, or ChatGPT, and has created some ethical concerns. This research provides examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage. Using an AI-generated short survey to gather quantitative and qualitative data through a discussion exercise, 126 undergraduates with four different professors were asked to share their answers and views. The findings show that many of today’s college students in South Florida see the usage of AI as ethical and legal. However, a few respondents remain uncertain due to a lack of clear guidelines from professors and the institution. Thus, most respondents reported that they are familiar with AI as they use it multiple times weekly. Consequently, educators and administrators must sharpen their students’ AI skills so they can be ethical and competitive in the workplace. Implications for students, educators and administrators in the higher education arena are explored. Besides serving as a person’s second brain, using AI can be an excellent way for students to mitigate and overcome procrastination, enhance their productivity, and comprehensively complete academic projects on time. Furthermore, the proper use of AI tools can reduce errors, quickly assess large amounts of data, automate repetitive functions, lead to better decisions, and help learners move forward amid challenging obstacles. As such, academic institutions must do more to ensure they are “sharpening their students’ AI saw” before they graduate and embark on their professional endeavors. Artificial intelligence, when used properly, ethically, and legally following established industry norms and guidelines, offers many transformative benefits across diverse fields to benefit human beings and society. Students pursuing a healthcare career can use AI to aid in early disease detection, accelerate drug discovery, and improve patient care through precision medicine. Graduates in the engineering or transportation industries can use AI to optimize traffic flow, enhance safety with autonomous vehicles, and reduce emissions through predictive maintenance. Moreover, those who remain in the education field after graduation can use AI to facilitate personalized learning experiences tailored to individual student needs while fostering greater engagement and academic success for all learners. The latest advancements underscore AI’s potential to drive innovation, increase efficiency, and address complex challenges while ultimately shaping a more interconnected and prosperous future for everyone in society.","url":"https://doi.org/10.61093/bel.8(2).107-127.2024","authors":["Bahaudin G. Mujtaba"],"tags":["Sharpening","Mythology","Psychology","Mathematics education","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.61093/bel.8(2).107-127.2024","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4414694277","name":"Artificial intelligence driven bioinformatics for sustainable bioremediation: integrating computational intelligence with ecological restoration","source":"openalex","abstract":"Environmental pollution from heavy metals and untreated wastewater poses significant risks to ecosystems and human health, highlighting the urgent need for innovative remediation strategies.","url":"https://doi.org/10.1039/d5va00240k","authors":["Kashif R. Siddique","Debajyoti Bose","Riya Bhattacharya","Raul V. Rodriguez","Aritra Ray"],"tags":["Computer science","Restoration ecology","Artificial intelligence","Computational intelligence","Environmental remediation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1039/d5va00240k","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406158982","name":"Artificial Intelligence in Metal–Organic Frameworks from 2013 to 2024: A Bibliometric Analysis","source":"openalex","abstract":"Abstract The purpose of this work is to analyze the development direction and prospects in the field of artificial intelligence (AI) in metal–organic frameworks (MOFs) and to provide reference information for related research and industry personnel. The scientific papers on AI in MOFs published in Web of Science database from 2013 to mid-2024 were collected. Bibliometric methods and knowledge mapping visualization software were used to analyze the papers. Both quantitative statistics and qualitative comparative analysis of global scientific papers were done in terms of annual paper trends, papers by major countries, authors, institutions, journals and research topics, respectively. The results showed that the number of published papers has increased in recent years. The top three productive countries are China, the USA and Germany, respectively. The top three productive institutions are Guangzhou University, Northwestern University and Chinese Academy of Sciences, respectively. Reference co-citation analysis classifies references into four clusters, and keyword co-occurrence analysis divides keywords into six clusters. Bibliometric and network analyses were utilized to examine the distribution of research outcomes, enabling scholars to discern the prevailing trends and focal points within the domain of AI-MOFs.","url":"https://doi.org/10.1007/s11837-024-07065-5","authors":["Jian Cao","Ling Zhou","Fan Gan","Zhipeng You"],"tags":["Web of science","China","Bibliometrics","Data science","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-08","doi":"https://doi.org/10.1007/s11837-024-07065-5","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4411690548","name":"Performance of 7 Artificial Intelligence Chatbots on Board-style Endodontic Questions","source":"openalex","abstract":"INTRODUCTION: The aim of this study was to assess the overall performance of artificial intelligence chatbots in answering board-style endodontic questions. METHODS: One hundred multiple choice endodontic questions, following the style of American Board of Endodontics Written Exam, were generated by two board-certified endodontists. The questions were submitted to the following chatbots, three times in a row: Gemini Advanced, Gemini, Microsoft Copilot, GPT-3.5, GPT-4o, GPT-4.0, and Claude 3.5 Sonnet. The chatbot was asked to choose the correct response and to explain the justification. The response to the question was considered \"correct\" only if the chatbot picked the right choice in ALL 3 attempts. The quality of reasoning as to why the chatbot selected the answer choice was scored using a three-ordinal scale (0, 1, 2). Two calibrated reviewers scored all 2100 responses independently. Categorical data were analyzed using Chi-square test; ordinal data were analyzed using Kruskal-Wallis and Mann-Whitney tests. RESULTS: The accuracy scores ranged from 48% (Microsoft Copilot) to 71% (Gemini Advanced, GPT-3.5, and Claude 3.5 Sonnet) (P < .05). Gemini Advanced, Gemini, and Microsoft Copilot showed similar performance regardless of the question source (textbook or literature) (P > .05). GPT-3.5, GPT-4o, GPT-4.0 and Claude 3.5 Sonnet performed significantly better with textbook-based questions (P < .05). Reasoning scores showed different distribution among chatbots (P < .05). Gemini Advanced had the highest rate of score 2 (81%) and the lowest rate of score 0 (18.5%). CONCLUSIONS: Comprehensive assessment of seven AI chatbots' performance on board-style endodontic questions revealed their capacities and limitations as educational resources in the field of endodontics.","url":"https://doi.org/10.1016/j.joen.2025.06.014","authors":["Poorya Jalali","Hossein Mohammad‐Rahimi","Fengming Wang","Fatemeh Sohrabniya","Seyed AmirHossein Ourang","Yuke Tian","Frederico C. Martinho","Ali Nosrat"],"tags":["Style (visual arts)","Psychology","Computer science","Art","Visual arts"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-26","doi":"https://doi.org/10.1016/j.joen.2025.06.014","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4399075444","name":"Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare","source":"openalex","abstract":"In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears \"intelligent\", potentially replacing the doctors' judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine.","url":"https://doi.org/10.3390/s24113491","authors":["Marie Geny","Emmanuel Andrès","Samy Talha","Bernard Gény"],"tags":["Liability","nobody","Telemedicine","Health care","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-28","doi":"https://doi.org/10.3390/s24113491","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4395447233","name":"Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study","source":"openalex","abstract":"Objectives: The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on radiographs of the appendicular skeleton. Methods: The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated. Results: < .05) in exams aided by the AI tool compared to the unaided exams. The increase in sensitivity resulted in a relative reduction of missed fractures of 29%. The average rate of false positives per patient decreased from 0.16 to 0.14, corresponding to a relative reduction of 21%. There was no significant difference in average reading time spent per exam. The largest gain in fracture detection performance, with AI support, across all readers, was on nonobvious fractures with a significant increase in sensitivity of 11 percentage points (pp) (60%-71%). Conclusions: The diagnostic performance for detection of traumatic fractures on radiographs of the appendicular skeleton improved among nonspecialist readers tested AI fracture detection support tool showed an overall reader improvement in sensitivity and specificity when supported by an AI tool. Improvement was seen in both sensitivity and specificity without negatively affecting the interpretation time. Advances in knowledge: The division and analysis of obvious and nonobvious fractures are novel in AI reader comparison studies like this.","url":"https://doi.org/10.1093/bjro/tzae011","authors":["Rikke Bachmann","Gozde Gunes","Stine Hangaard","Andreas Nexmann","Pavel Lisouski","Mikael Boesen","Michael Lundemann","Scott G. Baginski"],"tags":["Fracture (geology)","Radiography","Artificial intelligence","Medicine","Orthodontics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-12","doi":"https://doi.org/10.1093/bjro/tzae011","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4402615941","name":"Artificial Intelligence in Mental Health Care: Management Implications, Ethical Challenges, and Policy Considerations","source":"openalex","abstract":"Adopting AI (Artificial Intelligence) in the provision of psychiatric services has been groundbreaking and has presented other means of handling some of the issues related to traditional methods. This paper aims at analyzing the applicability and efficiency of AI in mental health practices based on business administration paradigms with a focus on managing services and policies. This paper engages a systematic and synoptic process, where current AI technologies in mental health are investigated with reference to the current literature as to their usefulness in delivering services and the moral considerations that surround their application. The study indicates that AI is capable of improving the availability, relevance, and effectiveness of mental health services, information that can be useful for policymakers in the management of health care. Consequently, specific concerns arise, such as how the algorithm imposes its own bias, the question of data privacy, or how a mechanism could reduce the human factor in care. The review brought to light an area of understanding of AI-driven interventions that has not been explored: the effect of such interventions in the long run. The field study suggests that further research should be conducted regarding ethical factors, increasing the ethical standards of AI usage in administration, and exploring the cooperation of mental health practitioners and AI engineers with respect to the application of AI in psychiatric practice. Proposed solutions, therefore, include enhancing the AI functions and ethical standards and guaranteeing that policy instruments are favorable for the use of AI in mental health.","url":"https://doi.org/10.3390/admsci14090227","authors":["Stephan Hoose","Kristína Králiková"],"tags":["Mental health","Ethical issues","Engineering ethics","Mental health care","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-17","doi":"https://doi.org/10.3390/admsci14090227","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4391944212","name":"Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence","source":"openalex","abstract":"Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.","url":"https://doi.org/10.3390/brainsci14020186","authors":["Thorsten Rudroff"],"tags":["Timeline","Transformative learning","Data science","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-19","doi":"https://doi.org/10.3390/brainsci14020186","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4398742105","name":"Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review","source":"openalex","abstract":"Abstract: This study aimed to analyze the use of generative artificial intelligence in the emergency trauma care setting through a brief scoping review of literature published between 2014 and 2024. An exploration of the NCBI repository was performed using a search string of selected keywords that returned N=87 results; articles that met the inclusion criteria (n=28) were reviewed and analyzed. Heterogeneity sources were explored and identified by a significance threshold of P < 0.10 or an I 2 value exceeding 50%. If applicable, articles were categorized within three primary domains: triage, diagnostics, or treatment. Findings suggest that CNNs demonstrate strong diagnostic performance for diverse traumatic injuries, but generalized integration requires expanded prospective multi-center validation. Injury scoring models currently experience calibration gaps in mortality quantification and lesion localization that can undermine clinical utility by permitting false negatives. Triage predictive models now confront transparency, explainability, and healthcare ecosystem integration barriers limiting real-world translation. The most significant literature gap centers on treatment-oriented generative AI applications that provide real-time guidance for urgent trauma interventions rather than just analytical support. Keywords: artificial intelligence, machine-learning, emergency medicine, traumatology","url":"https://doi.org/10.2147/mder.s467146","authors":["Christian Ventura","Edward E Denton","Jessica A. David"],"tags":["Triage","Traumatology","Artificial intelligence","Psychological intervention","Emergency department"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-01","doi":"https://doi.org/10.2147/mder.s467146","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4411806359","name":"Artificial Intelligence and the Future of Mental Health in a Digitally Transformed World","source":"openalex","abstract":"Artificial Intelligence (AI) is reshaping mental healthcare by enabling new forms of diagnosis, therapy, and patient monitoring. Yet this digital transformation raises complex policy and ethical questions that remain insufficiently addressed. In this paper, we critically examine how AI-driven innovations are being integrated into mental health systems across different global contexts, with particular attention to governance, regulation, and social justice. The study follows the PRISMA-ScR methodology to ensure transparency and methodological rigor, while also acknowledging its inherent limitations, such as the emphasis on breadth over depth and the exclusion of non-English sources. Drawing on international guidelines, academic literature, and emerging national strategies, it identifies both opportunities, such as improved access and personalized care, and threats, including algorithmic bias, data privacy risks, and diminished human oversight. Special attention is given to underrepresented populations and the risks of digital exclusion. The paper argues for a value-driven approach that centers equity, transparency, and informed consent in the deployment of AI tools. It concludes with actionable policy recommendations to support the ethical implementation of AI in mental health, emphasizing the need for cross-sectoral collaboration and global accountability mechanisms.","url":"https://doi.org/10.3390/computers14070259","authors":["Aggeliki Kelly Fanarioti","Kostas Karpouzis"],"tags":["Mental health","Psychology","Computer science","Artificial intelligence","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-30","doi":"https://doi.org/10.3390/computers14070259","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4412763832","name":"Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool","source":"openalex","abstract":"BACKGROUND: Pharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring drug safety. Traditional methods are slow and inconsistent, but artificial intelligence (AI), through automation and advanced analytics, improves efficiency and accuracy in managing increasing data complexity. AIM: To explore AI's practical applications in pharmacovigilance, focusing on efficiency, process acceleration, and task automation. It also examines the use of an expert-defined Bayesian network for causality assessment in a Pharmacovigilance Centre, demonstrating its impact on decision-making. METHOD: A comprehensive literature narrative review was conducted in MEDLINE (via PubMed), Scopus, and Web of Science using a set of targeted keywords, including but not limited to \"pharmacovigilance\", \"artificial intelligence\", \"adverse drug reactions\" and \"drug safety\". Relevant studies were analysed without restrictions on publication year or language. The search was carried out in January 2025. RESULTS: AI has greatly improved pharmacovigilance by streamlining signal detection, surveillance, and ADR reporting automation. Techniques like data mining and automated signal detection have expedited safety signal identification, while duplicate detection has enhanced data precision in safety evaluations. AI has also refined real-world evidence analysis, deepening drug safety and efficacy insights. Predictive models now anticipate ADRs and drug-drug interactions, enabling proactive patient care. At a regional pharmacovigilance center, the implementation of an expert-defined Bayesian network has optimized causality assessment, reducing processing times from days to hours, minimizing subjectivity, and improving the reliability of drug safety evaluations. CONCLUSION: AI holds significant promise for enhancing pharmacovigilance practices, yet its practical application remains primarily confined to academic research, with integration hindered by data quality issues, regulatory barriers, and the need for more transparent algorithms.","url":"https://doi.org/10.1007/s11096-025-01975-3","authors":["Rogério Caixinha Algarvio","Jaime Conceição","Pedro Pereira Rodrigues","Inês Ribeiro‐Vaz","Renato Ferreira‐da‐Silva"],"tags":["Pharmacovigilance","Medicine","Bayesian network","Patient safety","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-30","doi":"https://doi.org/10.1007/s11096-025-01975-3","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4410037446","name":"Role of artificial intelligence in early identification and risk evaluation of non-communicable diseases: a bibliometric analysis of global research trends","source":"openalex","abstract":"OBJECTIVE: This study aims to shed light on the transformative potential of artificial intelligence (AI) in the early detection and risk assessment of non-communicable diseases (NCDs). STUDY DESIGN: Bibliometric analysis. SETTING: Articles related to AI in early identification and risk evaluation of NCDs from 2000 to 2024 were retrieved from the Scopus database. METHODS: This comprehensive bibliometric study focuses on a single database, Scopus and employs narrative synthesis for concise yet informative summaries. Microsoft Excel V.365 and VOSviewer software (V.1.6.20) were used to summarise bibliometric features. RESULTS: The study retrieved 1745 relevant articles, with a notable surge in research activity in recent years. Core journals included Scientific Reports and IEEE Access, and core institutions included the Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised China, the USA, India, the UK and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's impact on NCDs management. Frequent author keywords identified key research hotspots, including specific NCDs like Alzheimer's disease and diabetes. Risk assessment studies demonstrated improved predictions for heart failure, cardiovascular risk, breast cancer, diabetes and inflammatory bowel disease. CONCLUSION: Our findings highlight the increasing role of AI in early detection and risk prediction of NCDs, emphasising its widening research impact and future clinical potential.","url":"https://doi.org/10.1136/bmjopen-2025-101169","authors":["Arwa M. Al-Dekah","Waleed M. Sweileh"],"tags":["Scopus","Medicine","Identification (biology)","Citation","Non-communicable disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1136/bmjopen-2025-101169","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4416746586","name":"A review of artificial intelligence for predicting climate driven infectious disease outbreaks to enhance global health resilience","source":"openalex","abstract":"The impact of climate change on infectious disease outbreaks demands sophisticated techniques of prediction to protect global health. This review focuses on the impact of artificial intelligence on the prediction of disease outbreaks influenced by climatic factors, showcasing its potential on diverse data sets. While traditional forecasting models have restricted capabilities due to fixed parameters and linear relationships, machine learning models such as support vector machine and random forest, deep learning models such convolutional neural network, long-short term memory and transformers as well as the hybrid models are found to be much more efficient with their supremacy proven over traditional models through applications in vector-borne, water-borne, and zoonotic diseases through capturing real-time analytics and non-linear climate-disease interaction. Despite these breakthroughs, the challenges of sparsity of data in low-resource areas, lack of model transparency, and ethically deemed biased algorithms pose great challenges. The review recommends the necessity of data governance, explanatory frameworks, and cross-disciplinary collaboration to counter these constraints, and proposes the utilization of federated learning, with quantum and edge computing, to formulate global health resilience pathways. Therefore, researchers are encouraged to adopt artificial intelligence techniques in predictive modeling, owing to their potential to transform proactive public health policy; however, its success is contingent on socially equitable implementation, trust from the relevant stakeholders, and climate policy integration.","url":"https://doi.org/10.1186/s12982-025-01167-4","authors":["Syed Azeem Inam"],"tags":["Computer science","Infectious disease (medical specialty)","Artificial intelligence","Resilience (materials science)","Global health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-23","doi":"https://doi.org/10.1186/s12982-025-01167-4","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4310418908","name":"Medical Image Segmentation Review: The success of U-Net","source":"openalex","abstract":"Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.","url":"https://doi.org/10.48550/arxiv.2211.14830","authors":["Reza Azad","Ehsan Khodapanah Aghdam","Amelie Rauland","Yiwei Jia","Atlas Haddadi Avval","Afshin Bozorgpour","Sanaz Karimijafarbigloo","Joseph Cohen","Ehsan Adeli","Dorit Merhof"],"tags":["Computer science","Compendium","Segmentation","Modular design","Categorization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-27","doi":"https://doi.org/10.48550/arxiv.2211.14830","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4403037144","name":"Educational innovation: Exploring the Potential of Generative Artificial Intelligence in cognitive schema building","source":"openalex","abstract":"This study explores the use of generative artificial intelligence to enhance teaching and learning experience, focusing on strengthening and consolidating cognitive schemas. Research reveals that schemas can profoundly influence the improvement of the learning experience and promote the assimilation of new types of information and retention in students' memory. To improve the teaching and learning experience, the advantages, obstacles, and potential future trajectories of utilizing these technologies were examined by conducting a thorough literature review and analyzing relevant studies. Findings indicate that generative artificial intelligence has the potential to personalize learning, diversify educational content, and improve teaching efficiency and scalability. However, it also poses challenges related to content quality, data privacy, and equity in access to personalized learning. Future research should focus on the effectiveness of educational tools based on generative AI that promote equity and inclusion, ethical approaches, and interdisciplinary collaboration. Overall, this study provides a solid foundation for understanding and harnessing the potential of generative artificial intelligence in enhancing cognitive schemas, thereby promoting more effective, inclusive, and personalized education.","url":"https://doi.org/10.21556/edutec.2024.89.3251","authors":["Bernarda Aurora Salgado Granda","Yana Inzhivotkina","María Fernanda Ibáñez Apolo","Jorge Ugarte Fajardo"],"tags":["Schema (genetic algorithms)","Generative grammar","Cognition","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-30","doi":"https://doi.org/10.21556/edutec.2024.89.3251","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4400038621","name":"Teaching English and Artificial Intelligence: EFL Teachers’ Perceptions and Use of ChatGPT","source":"openalex","abstract":"The integration of Artificial Intelligence (AI) technologies into education has a profound impact on the practice of teaching English as a Foreign Language (EFL). ChatGPT, developed by OpenAI as an AI conversational tool, is capable of revolutionizing the teaching of EFL through personalized and interactive learning. The study examined how teachers use ChatGPT in their EFL classrooms in Kazakhstan. The results of semi-structured interviews with 11 EFL teachers reveal a combination of a wide range of reactions, ranging from enthusiasm to caution. Teachers recognize ChatGPT for its rapid generation of lesson ideas, reduced workload and increased student engagement. The chatbot is also appreciated for its role in developing students' writing and vocabulary skills through immediate feedback and personalized practice. Along with these advantages, several challenges have been identified. Major challenges include the risk of reliance on technology, academic dishonesty, the importance of precise prompt formulation, and the limited capabilities for multimedia functions, including audio and visual materials. The findings underline the necessity for extensive training to leverage ChatGPT’s potential and ensure its successful use. The need for structured guidance and support was highlighted by EFL teachers to effectively integrate this technology into educational settings. The incorporation of ChatGPT into EFL teaching presents notable benefits but requires careful consideration. Effective training programs and strong policy support are crucial to overcome challenges and optimize the use of ChatGPT in EFL teaching. This research contributes essential information for teachers, policymakers, and curriculum developers who aim to utilize AI tools to advance language learning.","url":"https://doi.org/10.35542/osf.io/fwy92","authors":["Balnur Dilzhan"],"tags":["Perception","Mathematics education","Psychology","Pedagogy","Linguistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-26","doi":"https://doi.org/10.35542/osf.io/fwy92","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4400289499","name":"Artificial intelligence innovations in neurosurgical oncology: a narrative review","source":"openalex","abstract":"PURPOSE: Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes. METHODS: A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV), augmented reality (AR), virtual reality (VR), etc.) for neurosurgical care of brain or spinal tumors. RESULTS: Treatment of central nervous system (CNS) tumors is being improved through advances across AI-such as AL, CV, and AR/VR. AI aided diagnostic and prognostication tools can influence pre-operative patient experience, while automated tumor segmentation and total resection predictions aid surgical planning. Novel intra-operative tools can rapidly provide histopathologic tumor classification to streamline treatment strategies. Post-operative video analysis, paired with rich surgical simulations, can enhance training feedback and regimens. CONCLUSION: While limited generalizability, bias, and patient data security are current concerns, the advent of federated learning, along with growing data consortiums, provides an avenue for increasingly safe, powerful, and effective AI platforms in the future.","url":"https://doi.org/10.1007/s11060-024-04757-5","authors":["Clayton R. Baker","Matthew Pease","Daniel Sexton","Andrew Abumoussa","Lola B. Chambless"],"tags":["Narrative","Narrative review","Medicine","Medical physics","Oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.1007/s11060-024-04757-5","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4392632345","name":"Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study","source":"openalex","abstract":"BACKGROUND: Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. OBJECTIVE: This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model's performance across different categories. METHODS: We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. RESULTS: Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the \"Objective\" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). CONCLUSIONS: Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model's effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.","url":"https://doi.org/10.2196/54419","authors":["Annessa Kernberg","Jeffrey A. Gold","Vishnu Mohan"],"tags":["Preprint","Documentation","Quality (philosophy)","Computer science","Reproducibility"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-10","doi":"https://doi.org/10.2196/54419","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4392471461","name":"Charting the future of patient care: A strategic leadership guide to harnessing the potential of artificial intelligence","source":"openalex","abstract":"Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent, accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic approaches to harness AI's potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.","url":"https://doi.org/10.1177/08404704241235893","authors":["Marie Ennis-O’Connor","William T. O’Connor"],"tags":["Transparency (behavior)","Accountability","Health care","Knowledge management","Analytics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-05","doi":"https://doi.org/10.1177/08404704241235893","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4412403910","name":"A bibliometric analysis of clinical studies on artificial intelligence in emergency medicine","source":"openalex","abstract":"BACKGROUND: Interest in artificial intelligence (AI) and machine learning (ML) has grown rapidly in recent years due to the success of modern algorithms across various domains. Emergency departments are fast-paced and resource-constrained environments where timely decision-making is critical. These characteristics make them ideal settings for the integration of AI technologies, which have shown potential to enhance diagnostic accuracy and optimize patient outcomes. This study aims to identify and characterize the scientific literature on AI and ML applications in emergency departments over the past decade. METHODS: A comprehensive search was conducted in the Web of Science database on June 20, 2024. Articles published between 2015 and 2024 were considered. The search was performed using the keywords \"artificial intelligence\" or \"machine learning\" in all fields, limited to the \"emergency medicine\" category. The analysis of the articles included descriptive data on primary publication characteristics, such as the number of authors, citations, country of origin of the coauthors, and journal names. Bibliometric indicators were analyzed to identify publication trends and research themes, cluster analyses of keywords, and thematic maps. RESULTS: A total of 321 articles were analyzed. The average number of citations per article was 10.04, and the annual growth rate was 37.87%. Most publications originated from the United States. Resuscitation, American Journal of Emergency Medicine, Injury-International Journal of the Care of the Injured, and Resuscitation Plus published 107 articles. In 2024, the trending topic of the articles was \"health,\" while \"care\" was the most popular in the last 10 years. The top 5 niche themes were \"medical,\" \"digital transformation,\" \"education,\" \"database,\" and \"emergency care systems.\" CONCLUSION: This bibliometric analysis highlights the growing role of AI in emergency medicine. The findings provide insight into current research directions and may help inform future investigations in this evolving field.","url":"https://doi.org/10.1097/md.0000000000043282","authors":["Önder Limon","Başak Bayram","Murat Çetin","Gülsüm Limon","Nigar Dirican"],"tags":["Medicine","Web of science","Thematic analysis","MEDLINE","Descriptive statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-11","doi":"https://doi.org/10.1097/md.0000000000043282","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4388656590","name":"Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study","source":"openalex","abstract":"Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.","url":"https://doi.org/10.11591/ijece.v14i1.pp878-890","authors":["Hicham Slimani","Jamal El Mhamdi","Abdelilah Jilbab"],"tags":["Transformative learning","Agriculture","Drone","Scopus","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-14","doi":"https://doi.org/10.11591/ijece.v14i1.pp878-890","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4403483525","name":"Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities","source":"openalex","abstract":"Recent significant advances in the healthcare industry due to artificial intelligence (AI) and machine learning (ML) have been shown to revolutionize healthcare delivery by improving efficiency, accuracy, and patient outcomes. However, these technologies can face significant challenges and ethical considerations. This systematic review aimed to gather and synthesize the current knowledge on the impact of AI and ML adoption in healthcare delivery, with its associated challenges and opportunities. This study adhered to the PRISMA guidelines. Articles from 2014 to 2024 were selected from various databases using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Themes were identified through data analysis and thematic analysis. From 4981 articles screened, a data synthesis of nine eligible studies revealed themes, including productivity enhancement, improved patient care through decision support and precision medicine, legal and policy challenges, technological considerations, organizational and managerial aspects, ethical concerns, data challenges, and socioeconomic implications. There exist significant opportunities, as well as substantial challenges and ethical concerns, associated with integrating AI and ML into healthcare delivery. Implementation strategies must be carefully designed, considering technical, ethical, and social factors.","url":"https://doi.org/10.3390/ai5040095","authors":["Ifeanyi Anthony Okwor","Geeta Hitch","Saira Hakkim","S.Kom. Fawwaz Ali Akbar","Dave Sookhoo","John Kainesie"],"tags":["Applications of artificial intelligence","Health care","Artificial intelligence","Computer science","Healthcare delivery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-12","doi":"https://doi.org/10.3390/ai5040095","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4409092927","name":"Trust in artificial intelligence: a survey experiment to assess trust in algorithmic decision-making","source":"openalex","abstract":"Abstract Artificial intelligence (AI) has seen rapid development over the past decade, leading to its integration into various aspects of human life. The ability to integrate AI systems hinges not solely on their technical efficacy but also on the perceptions held by users or decision-makers. Previous researches indicate that many people harbor concerns about AI, which can hinder the adoption of these technologies. This study uses a pre-registered survey experiment embedded in an online survey in Hungary ( N = 2100) to assess trust in AI-based Automated Decision-Making (ADM). Participants were presented with hypothetical decisions in four domains (medical diagnoses, hiring, transportation, and financial investments). In a split-ballot design, participants were randomly assigned to a control group with human involvement and an experimental group where decision were supported by AI-based ADM. The main results show that decisions supported by human intervention are perceived as more trustworthy than those made by ADM (except for financial investment). However, our treatment heterogeneity analysis indicates that these effects are not consistent across all segments of society. A good understanding of AI, low privacy concerns, and an open personality can mitigate the negative impact of AI assistance on trust.","url":"https://doi.org/10.1007/s00146-025-02237-6","authors":["Ferenc Orbán","Ádám Stefkovics"],"tags":["Artificial intelligence","Computer science","Performing arts","Psychology","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-31","doi":"https://doi.org/10.1007/s00146-025-02237-6","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4396938053","name":"Explainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context","source":"openalex","abstract":"Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.","url":"https://doi.org/10.3390/healthcare12101025","authors":["Turki Alelyani","Maha M. AlShammari","Afnan Almuhanna","Onur Asan"],"tags":["Interpretability","Breast cancer","Artificial intelligence","Context (archaeology)","Random forest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-15","doi":"https://doi.org/10.3390/healthcare12101025","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4408523111","name":"Medical Laboratories in Healthcare Delivery: A Systematic Review of Their Roles and Impact","source":"openalex","abstract":"Medical laboratories (MLs) are vital in global healthcare delivery, enhancing diagnostic accuracy and supporting clinical decision-making. This systematic review examines the multifaceted contributions of ML, emphasizing their importance in pandemic preparedness, disease surveillance, and the integration of innovative technologies such as artificial intelligence (AI). Medical laboratories are equally crucial to clinical practices, offering essential diagnostic services to identify diseases like infections, metabolic disorders, and malignancies. They monitor treatment effectiveness by analyzing patient samples, enabling healthcare providers to optimize therapies. Additionally, they support personalized medicine by tailoring treatments based on genetic and molecular data and ensure test accuracy through strict quality control measures, thereby enhancing patient care. The methodology for this systematic review follows the PRISMA-ScR guidelines to systematically map evidence and identify key concepts, theories, sources, and knowledge gaps related to the roles and impact of MLs in public health delivery. This review involved systematic searching and filtering of literature from various databases, focusing on studies from 2010 to 2024, primarily in Africa, Asia, and Europe. The selected studies were analyzed to assess their outcomes, strengths, and limitations regarding MLS roles, impacts, and integration within healthcare systems. The goal was to provide comprehensive insights and recommendations based on the gathered data. The article highlights the challenges that laboratories face, especially in low- and middle-income countries (LMICs), where resource constraints hinder effective healthcare delivery. It discusses the potential of AI to improve diagnostic processes and patient outcomes while addressing ethical and infrastructural challenges. This review underscores the necessity for collaborative efforts among stakeholders to enhance laboratory services, ensuring that they are accessible, efficient, and capable of meeting the evolving demands of healthcare systems. Overall, the findings advocate for strengthened laboratory infrastructures and the adoption of advanced technologies to improve health outcomes globally.","url":"https://doi.org/10.3390/laboratories2010008","authors":["Adebola Adekoya","Mercy A. Okezue","Kavitha Menon"],"tags":["Health care","Healthcare delivery","Medicine","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-17","doi":"https://doi.org/10.3390/laboratories2010008","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4394680319","name":"Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review","source":"openalex","abstract":"The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.","url":"https://doi.org/10.3390/bios14040183","authors":["Tanvir Islam","Peter Washington"],"tags":["Health care","Computer science","Key (lock)","Modality (human–computer interaction)","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-09","doi":"https://doi.org/10.3390/bios14040183","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4403870071","name":"A Survey of Artificial Intelligence Applications in Nuclear Power Plants","source":"openalex","abstract":"Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.","url":"https://doi.org/10.3390/iot5040030","authors":["Chaima Jendoubi","Arghavan Asad"],"tags":["Nuclear power","Environmental science","Computer science","Engineering","Biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.3390/iot5040030","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4410952859","name":"The Emergence of Artificial Intelligence-Guided Karyotyping: A Review and Reflection","source":"openalex","abstract":"Artificial intelligence (AI) has entered the medical subspecialty of cytogenetics with the recent introduction of AI-guided karyotyping into the clinical laboratory. Karyotyping is an essential component of the cytogenetic analysis process; however, it is both labor-intensive and time-consuming. The introduction of AI algorithms into karyotyping software streamlines this process to provide accurate and abundant auto-karyotyped images for laboratory professionals to review and, also, alters the paradigm for chromosome analysis. Herein, we provide an overview of the AI-guided karyotyping products currently available for clinical use, discuss their utilization in the cytogenetics laboratory, and highlight changes AI-guided karyotyping has brought for early users. Finally, we reflect on our own laboratory observations and experience to discuss issues and practices that may need to adapt to best utilize this promising new technology.","url":"https://doi.org/10.3390/genes16060685","authors":["Lynne S. Rosenblum","Julia Holmes","Agshin F. Taghiyev"],"tags":["Karyotype","Cytogenetics","Subspecialty","Computer science","Chromosome analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-31","doi":"https://doi.org/10.3390/genes16060685","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4400823983","name":"Exploring the Role of Artificial Intelligence and Machine Learning in Pharmaceutical Formulation Design","source":"openalex","abstract":"The integration of Artificial Intelligence (AI) and Machine Learning (ML) into pharmaceutical formulation design has brought about a significant transformation, opening up new avenues for innovation and operational efficiency. This review paper aims to extensively examine the utilization of AI and ML in pharmaceutical formulation development, consolidating recent empirical findings and emerging patterns. Meta-analyses examining AI-driven drug discovery and formulation design efforts have revealed promising outcomes, including the acceleration of drug development timelines and enhancements in success rates across preclinical and clinical trials. Notably, a meta-analysis featured in Nature Reviews Drug Discovery sheds light on the pivotal role of AI in rational drug design, resulting in the identification of novel therapeutic candidates boasting improved efficacy and diminished side effects. Furthermore, AI and ML techniques are increasingly being deployed to optimize drug delivery systems, with studies showcasing their effectiveness in devising controlled-release formulations and nano-scale delivery platforms. For instance, the research highlighted in Advanced Drug Delivery Reviews demonstrates the application of ML algorithms in predicting the physicochemical attributes of nanoparticles, thereby aiding in the development of more durable and efficient drug carriers. Despite these advancements, challenges persist, including data scarcity, regulatory complexities, and ethical considerations. Nevertheless, ongoing endeavors to tackle these obstacles coupled with the continual evolution of AI and ML technologies offer promising prospects for the future of pharmaceutical formulation design. In conclusion, this review underscores the transformative influence of AI and ML on pharmaceutical formulation development, underscoring the necessity for sustained research and collaboration to fully leverage these technologies in enhancing healthcare outcomes.","url":"https://doi.org/10.61554/ijnrph.v2i1.2024.67","authors":["Hrithik Dey","Nisha Arya","Harshita Mathur","Neel Chatterjee","Ruchi Jadon"],"tags":["Artificial intelligence","Computer science","Machine learning","Manufacturing engineering","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-30","doi":"https://doi.org/10.61554/ijnrph.v2i1.2024.67","addedAt":"2026-09-01T01:47:48.919Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4395005519","name":"The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review","source":"openalex","abstract":"Prosthodontics is a dental subspecialty that includes the preparation of dental prosthetics for missing or damaged teeth. It increasingly uses computer-assisted technologies for planning and preparing dental prosthetics. This study aims to present the findings from a systematic review of publications on artificial intelligence (AI) in prosthodontics to identify current trends and future opportunities. The review question was \"What are the applications of AI in prosthodontics and how good is their performance in prosthodontics?\" Electronic searching in the Web of Science, ScienceDirect, PubMed, and Cochrane Library was conducted. The search was limited to full text from January 2012 to January 2024. Quadas-2 was used for assessing quality and potential risk of bias for the selected studies. A total of 1925 studies were identified in the initial search. After removing the duplicates and applying exclusion criteria, a total of 30 studies were selected for this review. Results of the Quadas-2 assessment of included studies found that a total of 18.3% of studies were identified as low risk of bias studies, whereas 52.6% and 28.9% of included studies were identified as studies with high and unclear risk of bias, respectively. Although they are still developing, AI models have already shown promise in the areas of dental charting, tooth shade selection, automated restoration design, mapping the preparation finishing line, manufacturing casting optimization, predicting facial changes in patients wearing removable prostheses, and designing removable partial dentures.","url":"https://doi.org/10.12659/msm.944310","authors":["Ibrahim Saleh Aljulayfi","Ali Almatrafi","Ramzi O. Althubaitiy","Fahad Alnafisah","Khalid Alshehri","Bandar Alzahrani","Khalid Gufran"],"tags":["Prosthodontics","Medicine","Medical physics","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-19","doi":"https://doi.org/10.12659/msm.944310","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4404127725","name":"Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications","source":"openalex","abstract":"The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable.","url":"https://doi.org/10.1007/s10462-024-11005-9","authors":["Haseeb Javed","Shaker El–Sappagh","Tamer Abuhmed"],"tags":["Adversarial system","Computer science","Robustness (evolution)","Artificial intelligence","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-07","doi":"https://doi.org/10.1007/s10462-024-11005-9","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4412112859","name":"Artificial intelligence in prostate cancer","source":"openalex","abstract":"ABSTRACT: Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.","url":"https://doi.org/10.1097/cm9.0000000000003689","authors":["Wei Li","Rui Hu","Quan Zhang","Zhangsheng Yu","Longxin Deng","Xinhao Zhu","Yujia Xia","Zijian Song","Alessia Cimadamore","Fei Chen","Antonio López-Beltrán","Rodolfo Montironi"],"tags":["Prostate cancer","Cancer","Prostate","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-09","doi":"https://doi.org/10.1097/cm9.0000000000003689","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4404379307","name":"Artificial intelligence and stroke imaging","source":"openalex","abstract":"PURPOSE OF REVIEW: Though simple in its fundamental mechanism - a critical disruption of local blood supply - stroke is complicated by the intricate nature of the neural substrate, the neurovascular architecture, and their complex interactions in generating its clinical manifestations. This complexity is adequately described by high-resolution imaging with sensitivity not only to parenchymal macrostructure but also microstructure and functional tissue properties, in conjunction with detailed characterization of vascular topology and dynamics. Such descriptive richness mandates models of commensurate complexity only artificial intelligence could plausibly deliver, if we are to achieve the goal of individually precise, personalized care. RECENT FINDINGS: Advances in machine vision technology, especially deep learning, are delivering higher fidelity predictive, descriptive, and inferential tools, incorporating increasingly rich imaging information within ever more flexible models. Impact at the clinical front line remains modest, however, owing to the challenges of delivering models robust to the noisy, incomplete, biased, and comparatively small-scale data characteristic of real-world practice. SUMMARY: The potential benefit of introducing AI to stroke, in imaging and elsewhere, is now unquestionable, but the optimal approach - and the path to real-world application - remain unsettled. Deep generative models offer a compelling solution to current obstacles and are predicted powerfully to catalyse innovation in the field.","url":"https://doi.org/10.1097/wco.0000000000001333","authors":["Jane Maryam Rondina","Parashkev Nachev"],"tags":["Stroke (engine)","Neuroimaging","Neuroscience","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-14","doi":"https://doi.org/10.1097/wco.0000000000001333","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4404485831","name":"Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study","source":"openalex","abstract":"Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally is dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective and rapid diagnoses. In this study, we aim to investigate the potential of AI diagnosis in OLP and evaluate its effectiveness in improving diagnostic accuracy and accelerating clinical decision making. A total of 128 confirmed OLP patients were included, and lesion images from various anatomical sites were collected. The diagnosis was performed using AI platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus, for AI directly identification and AI pre-training identification. After OLP feature training, the diagnostic accuracy of the AI platforms significantly improved, with the overall recognition rates of ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus increasing from 59%, 68%, and 15% to 77%, 80%, and 50%, respectively. Additionally, the pre-training recognition rates for buccal mucosa reached 94%, 93%, and 56%, respectively. However, the AI platforms performed less effectively when recognizing lesions in less common sites and complex cases; for instance, the pre-training recognition rates for the gums were only 60%, 60%, and 20%, demonstrating significant limitations. The study highlights the strengths and limitations of different AI technologies and provides a reference for future AI applications in oral medicine.","url":"https://doi.org/10.3390/bioengineering11111159","authors":["Sensen Yu","Wansu Sun","DaWei Mi","Siyu Jin","Xing Wu","B. Xin","Hengguo Zhang","Yuanyin Wang","Xiaoyu Sun","Xin He"],"tags":["Oral lichen planus","Artificial intelligence","Medical diagnosis","Computer science","Oral medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-18","doi":"https://doi.org/10.3390/bioengineering11111159","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4396605792","name":"How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses","source":"openalex","abstract":"Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about \"Subdural Hematoma.\" In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about \"Subdural Hematoma\" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of \"Subdural Hematoma\" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.","url":"https://doi.org/10.1097/md.0000000000038009","authors":["Şanser Gül","İsmail Erdemir","Volkan Hancı","Evren Aydoğmuş","Yavuz Selim Erkoç"],"tags":["Perplexity","Readability","Medicine","Quality Score","Hematoma"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-03","doi":"https://doi.org/10.1097/md.0000000000038009","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4389577521","name":"ChatGPT in Medical Education: A Precursor for Automation Bias?","source":"openalex","abstract":"Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where the logic behind its results is nonrational. There are concerns if these questionable results are used in patient care. As physicians have the duty to provide care based on their clinical judgment in addition to their patients' values and preferences, it is crucial that physicians validate the results from AI. Yet, there are some physicians who exhibit a phenomenon known as automation bias, where there is an assumption from the user that AI is always right. This is a dangerous mindset, as users exhibiting automation bias will not validate the results, given their trust in AI systems. Several factors impact a user's susceptibility to automation bias, such as inexperience or being born in the digital age. In this editorial, I argue that these factors and a lack of AI education in the medical school curriculum cause automation bias. I also explore the harms of automation bias and why prospective physicians need to be vigilant when using AI. Furthermore, it is important to consider what attitudes are being taught to students when introducing ChatGPT, which could be some students' first time using AI, prior to their use of AI in the clinical setting. Therefore, in attempts to avoid the problem of automation bias in the long-term, in addition to incorporating AI education into the curriculum, as is necessary, the use of ChatGPT in medical education should be limited to certain tasks. Otherwise, having no constraints on what ChatGPT should be used for could lead to automation bias.","url":"https://doi.org/10.2196/50174","authors":["Tina Nguyen"],"tags":["Automation","Mindset","Curriculum","Medical education","Duty"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-11","doi":"https://doi.org/10.2196/50174","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4416225738","name":"The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance","source":"openalex","abstract":"Explainable Artificial Intelligence (XAI) is proposed as essential for high-risk applications like healthcare, where it aims to enhance user trust. However, studies often rely on automated metrics rather than user evaluation. We adapt a prototype-based XAI model for image-based gestational age (GA) estimation and evaluate its impact on trust, reliance, and performance, including a novel measure of appropriate reliance. Ten sonographers completed a 3-stage reader study assessing the XAI model's impact on GA estimates. Model predictions reduced clinician mean absolute error (MAE) from 23.5 to 15.7 days, and explanations had a further non-significant reduction to 14.3 days. However, the impact of explanations varied across participants, with some performing worse with explanations than without. Additionally, although explanations increased participant confidence, they had no significant effect on trust or reliance on the model. These counterintuitive results highlight potential pitfalls in deploying XAI, emphasising the need for human studies to capture clinician variability.","url":"https://doi.org/10.1038/s41746-025-02023-0","authors":["Angus Nicolson","Elizabeth Bradburn","Yarin Gal","Aris T. Papageorghiou","J. Alison Noble"],"tags":["Counterintuitive","Computer science","Artificial intelligence","Estimation","Measure (data warehouse)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-14","doi":"https://doi.org/10.1038/s41746-025-02023-0","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4409763938","name":"Artificial intelligence in the service of sustainable shipping","source":"openalex","abstract":"Abstract Shipping constitutes an international endeavor that undoubtedly encapsulates one of the most crucial industries of our time. Owing to shipping, societies can enjoy a variety of goods, effectively transported from one place to another throughout the world, heavily contributing to the global economy and competitive advantages, so much so that Smith and Ricardo would be most proud; yet, the surge of interest in cost minimization and the systemic and traditional focus on accounting costs (that societies are less willing to absorb) have allotted an industry, which is imperative due to its globalized nature, but on the other hand, the said nature has caused negative externalities, including extensive environmental pollution and hazards for human and ecosystemic health. As the contemporary paradigm is one of self-regulated industries, which acknowledge that profitability goes hand in hand with sustainability, in recent years, shipping strives to align itself with sustainability initiatives. This paper provides, through a structured literature review and the use of qualitative data analysis software, the current sustainability practices that influence the shipping industry, to provide a topology as to the hurdles and opportunities that sustainability is yet to face.","url":"https://doi.org/10.1007/s40722-025-00390-0","authors":["Periklis Prousaloglou","Maria-Christina Kyriakopoulou-Roussou","Peter J. Stavroulakis","Vangelis Tsioumas","Stratos Papadimitriou"],"tags":["Service (business)","Business","Operations research","Engineering","Marketing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-24","doi":"https://doi.org/10.1007/s40722-025-00390-0","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4394839878","name":"Artificial Intelligence Quotient (AIQ)","source":"openalex","abstract":"","url":"https://doi.org/10.2139/ssrn.4787320","authors":["Xin Qin","Jackson G. Lu","Chen Chen","Xiang Zhou","Yuqing Gan","Wanlu Li","Lesley Luyang Song"],"tags":["Quotient","Psychology","Computer science","Mathematics","Pure mathematics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.2139/ssrn.4787320","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406591822","name":"Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning","source":"openalex","abstract":"Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are a great advantage that is coming to civil engineering in ways that detail accuracy can be enhanced, many tasks automated, and predictive modeling improved. Across some of the significant subdomains, these technologies allow for eminent progress in structural health monitoring, geotechnical engineering, hydraulic systems, construction management. Currently, AI-powered models such as Artificial Neural Networks (ANNs), fuzzy logic, and evolution-based algorithms allow engineers to predict failure, optimize design, and better resource management of infrastructures. Yet, despite the potential, the adoption of AI, ML, and DL into civil engineering faces a host of challenges including data availability, computational complexity, model interpretability, integration with traditional systems, etc. High-quality, real-time data collection remains expensive and the resource-intensive nature of DL models limits their application to a large scale. In addition, the \"black-box\" nature of these models raises ethical and regulatory issues especially in decisions related to safety. Against this backdrop, this paper reviews current and potential applications of AI, ML, and DL in civil engineering within the framework of benefits and limitations of AI, ML, and DL, focusing on comparisons. Besides that, the paper outlines future directions regarding cloud computing, explainable AI, and regulatory frameworks. With all these changes within the scope of the discipline, AI-driven technologies will be major in safe, efficient, and sustainable infrastructure systems, provided that success is specifically dependent on addressing these key challenges.","url":"https://doi.org/10.31127/tuje.1581564","authors":["Rituraj Jain","Sitesh Kumar Singh","Damodharan Palaniappan","Kumar J. Parmar","T. Premavathi"],"tags":["Artificial intelligence","Deep learning","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-20","doi":"https://doi.org/10.31127/tuje.1581564","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4406849936","name":"Attitudes toward artificial intelligence and robots in healthcare in the general population: a qualitative study","source":"openalex","abstract":"Background: The growth of the use of artificial intelligence (AI) and robotic solutions in healthcare is accompanied by high expectations for improved efficiency and quality of services. However, the use of such technologies can be a source of anxiety for patients whose expectations and experiences with such technology differ from medical staff's. This study assessed attitudes toward AI and robots in delivering health services and performing various tasks in medicine and related fields in Polish society. Methods: 50 semistructured in-depth interviews were conducted with participants of diversified socio-demographic profiles. The interviewees were initially recruited for the interviews in a convenience sample; then, the process was continued using the snowballing technique. The interviews were transcribed and analyzed using the MAXQDA Analytics Pro 2022 program (release 22.7.0). An interpretative approach to qualitative content analysis was applied to the responses to the research questions. Results: The analysis of interviews yielded three main themes: positive and negative perceptions of the use of AI and robots in healthcare and ontological concerns about AI, which went beyond objections about the usefulness of the technology. Positive attitudes toward AI and robots were associated with overall higher trust in technology, the need to adequately respond to demographic challenges, and the conviction that AI and robots can lower the workload of medical personnel. Negative attitudes originated from convictions regarding unreliability and the lack of proper technological and political control over AI; an equally important topic was the inability of artificial entities to feel and express emotions. The third theme was that the potential interaction with machines equipped with human-like traits was a source of insecurity. Conclusions: The study showed that patients' attitudes toward AI and robots in healthcare vary according to their trust in technology, their recognition of urgent problems in healthcare (staff workload, time of diagnosis), and their beliefs regarding the reliability and functioning of new technologies. Emotional concerns about contact with artificial entities looking or performing like humans are also important to respondents' attitudes.","url":"https://doi.org/10.3389/fdgth.2025.1458685","authors":["Paulina Smoła","Iwona Młoźniak","Monika Wojcieszko","Urszula Zwierczyk","Mateusz Kobryn","Elżbieta Rzepecka","Mariusz Duplaga"],"tags":["Health care","Workload","Psychology","Population","Qualitative research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-27","doi":"https://doi.org/10.3389/fdgth.2025.1458685","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4397049662","name":"Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature","source":"openalex","abstract":"With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.","url":"https://doi.org/10.1016/j.atherosclerosis.2024.117580","authors":["Laura Valentina Klüner","Kenneth Chan","Charalambos Antoniades"],"tags":["Computed tomography","Current (fluid)","State (computer science)","Medicine","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-19","doi":"https://doi.org/10.1016/j.atherosclerosis.2024.117580","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4394577710","name":"Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications","source":"openalex","abstract":"Artificial intelligence (AI) has emerged as a promising avenue for enhancing medical imaging systems and improving clinical workflows. This research explores innovative applications of AI and deep learning for image communication networks in healthcare. Specifically, we develop an intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans. Our approach combines convolutional neural networks, generative adversarial networks, and specialized image filters to balance communication efficiency, diagnostic accuracy, and system latency. Rigorous experiments validate superior performance over traditional methods and commercial products across modalities including MRI, CT, and ultrasound. Crucially, the proposed methods demonstrate expert-level precision in anatomy labeling and pathology detection. By intelligently streamlining image transfer and analytics, this AI-powered system could facilitate ubiquitous, real-time diagnostics via telemedicine. Enhanced connectivity between imaging devices and clinical specialists can improve patient outcomes and reduce healthcare costs. Our solutions set the stage for more advanced AI integration in imaging networks and data-intensive medicine","url":"https://doi.org/10.52783/jes.1409","authors":["Suman Narne Suresh Dodda"],"tags":["Medical imaging","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-04","doi":"https://doi.org/10.52783/jes.1409","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4409686230","name":"Applications of Artificial Intelligence in Neurological Voice Disorders","source":"openalex","abstract":"Neurological voice disorders, such as Parkinson's disease, laryngeal dystonia, and stroke-induced dysarthria, significantly impact speech production and communication. Traditional diagnostic methods rely on subjective assessment, whereas artificial intelligence (AI) offers objective, noninvasive, and scalable solutions for voice analysis. This review examines the applications, advancements, challenges, and future prospects of AI-driven methods in diagnosing, monitoring, and treating neurological voice disorders. We analyze recent advances in AI-based voice analysis, including machine learning, deep learning and signal processing techniques, and evaluate their effectiveness based on existing literature. AI models have demonstrated high accuracy in detecting subtle voice impairments, enabling early diagnosis of voice disorders, and predicting treatment response. Deep learning methods, particularly convolutional and transformer-based networks, have been effective in extracting meaningful biomarkers from acoustic or other modality data. Despite these promising advances, challenges remain, including limited high-quality data sets on some rare neurological voice disorders, ethical concerns regarding patient privacy, and the need for broad clinical validation. Further research should focus on developing standardized data sets, improving the ability of the AI model to learn representations, and enhancing its generalizability. With further development, AI-driven data analysis has the potential to transform the early detection and management of neurological voice disorders.","url":"https://doi.org/10.1002/wjo2.70017","authors":["Dongren Yao","Aki Koivu","Kristina Simonyan"],"tags":["Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-22","doi":"https://doi.org/10.1002/wjo2.70017","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"oa:W4407931364","name":"Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study","source":"openalex","abstract":"BACKGROUND: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk. METHODS: In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40-69 years at the time of enrolment in 2006-10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score. FINDINGS: AI-ECG accurately identified sex (area under the receiver operating characteristic 0·943 [95% CI 0·942-0·943] for BIDMC and 0·971 [0·969-0·972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 1·78 [95% CI 1·18-2·70], p=0·006) but not males (1·00 [0·63-1·58], p=0·996). In the UK Biobank cohort, the same pattern was seen (HR 1·33 [95% CI 1·06-1·68] for females, p=0·015; 0·98 [0·80-1·20] for males, p=0·854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non-cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts. INTERPRETATION: Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance. FUNDING: British Heart Foundation.","url":"https://doi.org/10.1016/j.landig.2024.12.003","authors":["Arunashis Sau","Ewa Sieliwończyk","Konstantinos Patlatzoglou","Libor Pastika","Kathryn A. McGurk","Antônio H. Ribeiro","Antônio Luiz Pinho Ribeiro","Jennifer E. Ho","Nicholas S. Peters","James S. Ware","Upasana Tayal","Daniel B. Kramer"],"tags":["Retrospective cohort study","Electrocardiography","Identification (biology)","Cohort","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-25","doi":"https://doi.org/10.1016/j.landig.2024.12.003","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842799","name":"Exploring Medical Diagnosis Using Vision Transformer And CNN","source":"crossref","abstract":"This research aims to conduct a comparative analysis of Vision Transformer (ViT) and Convolutional Neural Network (CNN) models for detecting four prevalent diseases: eye diseases, lung diseases, skin diseases, and heart diseases. The primary goal is to identify the most suitable and efficient model for each disease, taking into account their unique characteristics and diagnostic requirements. Through a meticulous evaluation and comparison of the performance of various ViT and CNN models for each illness, this study endeavors to offer valuable insights into the optimal utilization of these advanced technologies in disease detection. The comprehensive analysis presented in this paper is intended to contribute to the progress of medical imaging and potentially enhance diagnostic precision and efficacy in healthcare.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842799","authors":["Saqlain Kalokhe","Fahad Khan","Salim Shaikh","Nusrat Jahan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842799","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/j.engappai.2023.107497","name":"Artificial intelligent systems for vehicle classification: A survey","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107497","authors":["Shi Hao Tan","Joon Huang Chuah","Chee-Onn Chow","Jeevan Kanesan","Hung Yang Leong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-03T21:27:18Z","doi":"10.1016/j.engappai.2023.107497","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/icaibd62003.2024","name":"2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaibd62003.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:33:41Z","doi":"10.1109/icaibd62003.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/c2021-0-02646-8","name":"Application of Artificial Intelligence in Early Detection of Lung Cancer","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2021-0-02646-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-17T06:47:41Z","doi":"10.1016/c2021-0-02646-8","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/c2022-0-01766-9","name":"Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2022-0-01766-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-21T05:37:41Z","doi":"10.1016/c2022-0-01766-9","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/c2022-0-03368-7","name":"Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2022-0-03368-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-07T08:19:46Z","doi":"10.1016/c2022-0-03368-7","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/ic-ftai62324.2024","name":"2024 International Conference on Future Telecommunications and Artificial Intelligence (IC-FTAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic-ftai62324.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-14T17:37:26Z","doi":"10.1109/ic-ftai62324.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/aiac63745.2024","name":"2024 2nd International Conference on Artificial Intelligence and Automation Control (AIAC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiac63745.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-27T18:45:59Z","doi":"10.1109/aiac63745.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1515/9783111336435-019","name":"18 Artificial Intelligence in Library Services: An Introductory Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783111336435-019","authors":["Edmund Balnaves"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-03T05:14:19Z","doi":"10.1515/9783111336435-019","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/aitest62860.2024.00032","name":"Sponsors","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitest62860.2024.00032","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-25T17:28:25Z","doi":"10.1109/aitest62860.2024.00032","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/cai59869.2024.00002","name":"Title Page iii","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai59869.2024.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:50:37Z","doi":"10.1109/cai59869.2024.00002","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1145/3726010.3726098","name":"Overview of the Application of Artificial Intelligence in Music Creation","source":"crossref","abstract":"Music creation is the task of generating the song or the music. This creation task is very valuable in media and entertainment field. Artificial Intelligence (AI) plays a vital role in the field of music development. AI-based music creation field combines cutting-edge technology with creativity. This paper presents the overview of AI in music creation. This research work discusses the application of AI algorithm such as Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Transformer models in music creation field. These algorithms are useful for music composition, generation, and analysis. Case studies were discussed in AI-enabled platforms to illustrate the importance of AI in this field. This paper presents a comparative analysis of several research works for music creation. This explores the strength, weaknesses and the practical applications. Further, this work highlights the difficulties and prospects in the field of AI-enabled music development.","url":"https://doi.org/10.1145/3726010.3726098","authors":["Yiming Si","Xiaohui Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T07:53:18Z","doi":"10.1145/3726010.3726098","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1145/3700523.3700545","name":"Intelligent Decision Optimization Method for Water Information System Based on Artificial Intelligence","source":"crossref","abstract":"The traditional water management method struggles with delayed information acquisition and low decision-making efficiency, making it difficult to meet complex water resource management needs.To address this, the paper proposes an AI-based optimization method for water information systems.It uses sensor networks and IoT to collect real-time data, including water quality, quantity, and pipe pressure.Machine learning models historical data to identify key factors and risks.An optimization algorithm simulates and selects the best scheduling schemes.A visualization platform presents analysis results, aiding managers in decision-making.Users rated the platform features between 7 and 9 in satisfaction.This method is general, scalable, and applicable to various water systems, offering a useful reference for other fields.","url":"https://doi.org/10.1145/3700523.3700545","authors":["Feng Xian","Yisu Yin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-05T04:55:32Z","doi":"10.1145/3700523.3700545","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003480891-6","name":"Artificial Intelligence in Customers' Experience","source":"crossref","abstract":"Artificial Intelligence (AI) transforms how customer service is provided in the aviation industry. With AI-based chatbots and virtual assistants, airlines can now offer personalized and efficient services that cater to the dynamic needs of modern travelers. These innovative solutions, powered by advanced natural language processing (NLP) and machine learning technologies, have revolutionized the traditional customer service models. They enable 24/7 assistance, personalized travel information, and seamless service interactions. This has dramatically improved customer satisfaction and offers airlines scalable and cost-effective solutions to manage the increasing demands of customer support. The potential of AI in the transportation industry is immense, as it can integrate intelligent systems to redefine efficiency, safety, and sustainability standards across global supply chains and passenger transit. However, identified challenges, such as data integration, ethical concerns, and system interoperability, still need to be addressed. Overcoming these hurdles requires a collaborative effort toward innovation, stakeholder engagement, and developing human-centric AI solutions.","url":"https://doi.org/10.1201/9781003480891-6","authors":["Dimitrios Ziakkas","Anastasios Plioutsias"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-05T00:11:44Z","doi":"10.1201/9781003480891-6","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.52692/1857-0011.2024.2-79.35","name":"Artificial Intelligence in Radiological Diagnosis of Lung Diseases","source":"crossref","abstract":"Artificial intelligence (AI) plays a crucial role in the radiological diagnosis of lung diseases, enhancing diagnostic accuracy and speed. Deep learning techniques, such as convolutional neural networks (CNN), enable AI to detect minute pathological changes on X-rays, often beyond visual analysis. Systems like CheXNet and CAD4TB have proven effective in diagnosing pneumonia, tuberculosis, and lung cancer, especially valuable in mass screening scenarios. The use of AI reduces the burden on medical professionals and ensures higher diagnostic accuracy, which is essential in overloaded healthcare systems and areas with limited medical resources.","url":"https://doi.org/10.52692/1857-0011.2024.2-79.35","authors":["Nadejda Pisarenco"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-17T14:25:11Z","doi":"10.52692/1857-0011.2024.2-79.35","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1093/oso/9780197745441.003.0001","name":"The Groundwork for an Ethics of Artificial Intelligence in Defence","source":"crossref","abstract":"Abstract The use of AI technologies for national defence poses important ethical problems that combine ethical risks related to the use of these technologies —for example, enabling human wrongdoing, reducing human control, removing human responsibility, devaluing human skills, and eroding human self-determination—with those that follow the use of force in warfare, like violating human dignity and breaching the principles of Just War Theory. Because of the range of possible applications and of the set of ethical risks and opportunities to address, it is difficult to develop a coherent and systemic ethical analysis of AI in defence. The goal of this chapter is to clarify how this book will do so, by outlining the methodology and the scope of the analysis proposed here. Three aspects are crucial to this end: the definition of AI, the methodology of levels of abstraction, and the identification of three categories of use of AI in defence: sustainment and support, adversarial and non-kinetic, and adversarial and kinetic.","url":"https://doi.org/10.1093/oso/9780197745441.003.0001","authors":["Mariarosaria Taddeo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-24T09:23:42Z","doi":"10.1093/oso/9780197745441.003.0001","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/ictai62512.2024","name":"2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ictai62512.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-28T18:33:32Z","doi":"10.1109/ictai62512.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-13244-5.01001-4","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13244-5.01001-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-28T09:57:14Z","doi":"10.1016/b978-0-443-13244-5.01001-4","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-13244-5.00031-6","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13244-5.00031-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-28T09:57:06Z","doi":"10.1016/b978-0-443-13244-5.00031-6","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.52305/yuwn2377","name":"Revolutionizing Ophthalmology: The Integration of Artificial Intelligence Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.52305/yuwn2377","authors":["Alejandro Espaillat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-13T20:40:02Z","doi":"10.52305/yuwn2377","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1063/5.0230064","name":"Application of artificial intelligence technology in AI music creation","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0230064","authors":["Haoyang Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T17:00:37Z","doi":"10.1063/5.0230064","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.5256/f1000research.167182.r339980","name":"Peer Review Report For: Perspectives and guidance for developing artificial intelligence-based applications for healthcare using medical images [version 1; peer review: 2 not approved]","source":"crossref","abstract":"Artificial intelligence (AI) has significant potential to transform healthcare and improve patient care. However, successful development and integration of AI models requires careful consideration of study designs and sample size calculations for development and validation of models, publishing standards, prototype development for translation and collaboration with stakeholders. As the field is relatively new and rapidly evolving there is a lack of guidance and agreement on best practices for most of these steps. We engaged stakeholders in the form of clinicians, researchers from academia and industry, and data scientists to discuss various aspects of the translational pipeline and identified the challenges researchers in the field face and potential solutions to them. In this viewpoint, we present the summary of our discussions as a brief guide on the process of developing AI-based applications for healthcare using medical images. We organized the entire process into six major themes (i.e., The gaps AI can fill in healthcare, Development of AI models for healthcare: practical and important things to consider, Good practices for validation of AI models for healthcare: study designs and sample size calculation, Points to consider when publishing AI models, Translation towards products, Challenges and potential solutions from a technical perspective) and presented important points as a rule of thumb. We conclude that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.","url":"https://doi.org/10.5256/f1000research.167182.r339980","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-25T18:22:07Z","doi":"10.5256/f1000research.167182.r339980","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.22454/fammed.2024.775525","name":"Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education","source":"crossref","abstract":"Generative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began with the invention of the transistor in 1947. These technologies, driven by transformer architectures for artificial neural networks, are poised to broadly influence society. It is already apparent that these technologies will be adapted to drive innovation in education. Medical education is a high-risk activity: Information that is incorrectly taught to a student may go unrecognized for years until a relevant clinical situation appears in which that error can lead to patient harm. In this article, I discuss the principal limitations to the use of generative artificial intelligence in medical education—hallucination, bias, cost, and security—and suggest some approaches to confronting these problems. Additionally, I identify the potential applications of generative artificial intelligence to medical education, including personalized instruction, simulation, feedback, evaluation, augmentation of qualitative research, and performance of critical assessment of the existing scientific literature.","url":"https://doi.org/10.22454/fammed.2024.775525","authors":["Daniel J. Parente"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-08T16:44:30Z","doi":"10.22454/fammed.2024.775525","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.3897/bsms.3.120969","name":"Artificial intelligence in Laboratory medicine – let’s talk about it","source":"crossref","abstract":"Medicine is a science, an art, and a trust between the doctor and the patient. In the times of digitization and artificial intelligence, new relationships between the human being and the machines are establishing. The concept for using computers to stimulate intelligent behavior and critical thinking is firstly described by Alan Turing in 1950. Nowadays, it is time to talk about digital transformation in medicine. AI consists of Machine learning (ML), Deep learning (DL) and Computer vision (CV). New terms appear in medical terminology in the context of digital health and digital transformation, as a new reality, extended reality literally. The purpose of this article is to present some fundamentals of AI and its application in Laboratory medicine in accordance with clinical needs and ethical standards. The way of digitization in human life and in medicine is clear and the process has been started, but there are still many things to be introduced in the same practice.","url":"https://doi.org/10.3897/bsms.3.120969","authors":["Irena Ivanova","Nora Ivanova","Bisera Atanasova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-17T12:25:26Z","doi":"10.3897/bsms.3.120969","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4274/cmj.galenos.2024.10820","name":"Importance of Artificial Intelligence-based Radiography in the Examination and Treatment of Patients with Subacromial Impingement Syndrome","source":"crossref","abstract":"According to various estimates, shoulder pain covers 7-26% of all diseases faced by the global population.In addition, according to studies by Scandinavian scientists, up to 18% of the population's loss of working capacity and paid sick leave are caused by shoulder pain.According to experts, the number of these diseases is already approaching epidemic levels in Sweden, Finland, Japan, and the United States.Insurance payments for diseases caused by shoulder pain are second only to vertebrogenic pain.Subacromial impingement syndrome is the main cause of this pain.Diagnosis includes a complex of clinical and instrumental research methods.A reliable diagnosis of the presence of subacromial impingement syndrome, as well as the type, nature, and severity of subacromial muscle compression, is achieved using modern methods for visualizing the internal structures of the shoulder.It should be noted that radiography and ultrasound are important among them.To determine the model performance of artificial intelligence in detecting rotator cuff pathology using different imaging modalities and to compare its capability with that of physicians in clinical scenarios.Neck-shoulder syndrome is one of the most frequent causes of disability in the population, accounting for 18% of disability sheets.The treatment of pathologies attracts physicians of various specialties: orthopedic traumatologists, neurosurgeons, anesthesiologists, resuscitators, surgeons, neurologists, therapists, physiotherapists, and physical therapy physicians.","url":"https://doi.org/10.4274/cmj.galenos.2024.10820","authors":["Aytan Akhundova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-15T09:36:01Z","doi":"10.4274/cmj.galenos.2024.10820","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/wsai62426.2024.10828576","name":"Author Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wsai62426.2024.10828576","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T19:22:07Z","doi":"10.1109/wsai62426.2024.10828576","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4324/9781003088660-4","name":"The building blocks of intelligence","source":"crossref","abstract":"This chapter explores the building blocks of intelligent systems, both natural and artificial, from different kinds of reasoning and control through to processes such as search and optimisation. Deductive reasoning is considered first using the example of syllogism and compared to human common-sense reasoning. Induction and the use of causal reasoning in AI are then briefly explored. Search is examined in the context of graph-based search for problems such as adversarial games using chess as an example problem. Search in continuous state spaces is considered as a problem of optimisation and related to challenges such as landing a planetary rover. Strategies to make search more efficient and address the “combinatorial exploration” are explored, including the use of evaluation functions and heuristics (rules of thumb) such as the greedy algorithm, hill-climbing, and gradient descent. Probabilistic reasoning is introduced with a focus on the Bayesian approach, followed by control theory using the examples of a thermostat and human temperature regulation. The chapter concludes by asking whether humans think differently from machines, exploring several unsolved challenges, including analogical thinking and abductive reasoning, and looking at the role of emotions in human decision-making.","url":"https://doi.org/10.4324/9781003088660-4","authors":["Tony Prescott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-13T16:41:42Z","doi":"10.4324/9781003088660-4","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icaaic60222.2024","name":"2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaaic60222.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T18:30:41Z","doi":"10.1109/icaaic60222.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-22308-2.00016-0","name":"Acknowledgments","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22308-2.00016-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T07:44:01Z","doi":"10.1016/b978-0-443-22308-2.00016-0","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1002/9781394277568.fmatter","name":"Front Matter","source":"crossref","abstract":"Undeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences. Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.","url":"https://doi.org/10.1002/9781394277568.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-23T21:21:53Z","doi":"10.1002/9781394277568.fmatter","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1002/9781394200801.index","name":"Index","source":"crossref","abstract":"identification number, 601 importance of, 594 mAadhar App, 601 ABC.see Artificial Bee Colony (ABC) algorithm ABMS-created saliency feature map, 342 Abnormal data point, 76 Absa Bank, 598 Academically Productive Talk (APT), 110-111 Accenture Banking Technology Vision, 596 Access control, 568, 581, 582 attacks, 293-294 Accessibility, 172, 649 of banking services, 590 benefit of conversational AI, 425, 426 chatbots and IVA technologies, 667 of cloud, 649 compliance consideration for conversational AI, 568 guidelines, 550 Accountability, in AI systems, 609-610 guideline, 550 shortage of, 628 Accuracy, 66 automatic speech recognition, 162f, 164f chatbot text classification, 211f, 212 of classifiers, for chatbot IDS, 328, 329f, 330 ML algorithm for automatic speech recognition, 363f, 364 for object recognition using chatbot, 346f improving, 570, 572 monitoring, 566 performance of conversational AI, 389 trust challenge in conversational AI, 566 ACM Digital Library, 5 ACNN.see Auto-correlative neural network (ACNN) ACO.see Ant Colony Optimization (ACO) ACO-GA approach, 202, 203 ACO method, 114 ACO-TOFA method, 202 Acoustic-phonetic approach, 354 Activation function, 338 Active attack, 504 Activechat.ai, 400 Acunetix Vulnerability Scanner, 307-308, 497, 505, 507-508 Ada, 540 AdaBoost, algorithms, 121 -based IDS, 321, 325, 327-328 for binary classification, 344-345 Adaptive noise cancellation, 358 ADM.see Advanced Dialog Management (ADM) Advanced Dialog Management (ADM), 171 Advanced encryption standard (AES), 564, 644 Advanced persistent threat (APT), 4 Advertising, chatbots and IVA technologies for, 666 Aella Credit of Nigeria, 599 Aerial intelligent relay-road side unit (AIR-RSU) framework, 379, 380f Agglomerative hierarchical clustering, 361 Agriculture, citizen/government interface/e-governance, 625 and farming, application of conversational AI, 539 AI. see Artificial intelligence (AI) AI-Assisted Grading, 403 Page numbers followed by f and t indicate figures and tables, respectively.","url":"https://doi.org/10.1002/9781394200801.index","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-27T04:33:45Z","doi":"10.1002/9781394200801.index","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.31234/osf.io/ekz9a","name":"Lay Theories About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Most research on augmented judgment and decision-making is human-centered. Specifically, Theory of Machine is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Reminiscent of the Theory of Mind, it conceptualizes the idea of ascribing thought processes or mental states to algorithms. However, based on their own perceptions, past personal experiences, and shaped by public media, people may conceive of humans and algorithms as functionally distinct ontological entities. Therefore, research on augmented judgment and decision-making should also focus on the differences between various algorithms in terms of (cognitive) abilities and behavior. In this article, several agendas for future research are proposed that explicitly consider people’s diverse interactions with various decision-support and artificial intelligence systems in their daily lives. Such primarily algorithm-centric research will help to gain insights into a more fine-grained Theory of Machine that also distinguishes between different levels of algorithmic fairness, transparency, and explainability. Ideally, a better understanding of how people mentalize about algorithmic behavior can also be used to improve algorithmic augmentations of human judgment and decision-making.","url":"https://doi.org/10.31234/osf.io/ekz9a","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T11:16:09Z","doi":"10.31234/osf.io/ekz9a","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.51483/ijaiml.4.2.2024.83-91","name":"Application of Artificial Intelligence (AI) In-Farm","source":"crossref","abstract":"AI plays a crucial role in agricultural management by enabling precise agricultural practices.By leveraging technologies such as IoT, data mining, machine learning and deep learning, it helps farmers make data-driven decisions for optimal crop health and productivity.These technologies, such as seasonal prediction models, AI sensors for herbicide optimization, and AI-powered drones for real-time monitoring, contribute to improved agricultural safety, lower toxin levels in food, and higher crop yields.Additionally, AI bots address labor shortages by efficiently harvesting crops and controlling weeds, ultimately increasing productivity.Integrating AI into operations not only increases efficiency, but also ensures sustainable practices, profitability and environmental protection.To further strengthen trust in AI solutions for precision agriculture, measures on transparency, accountability, fairness and data security are recommended.This technology improves decision-making through data-driven insights into weather patterns, soil conditions and plant health.This results in better yield prediction, timely pest and disease control interventions and overall improved crop management.This results in higher yields, lower operating costs and better risk management for farms.Ultimately, it helps increase profitability and sustainability in the long term.","url":"https://doi.org/10.51483/ijaiml.4.2.2024.83-91","authors":["Jafar Azizi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:45:46Z","doi":"10.51483/ijaiml.4.2.2024.83-91","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2139/ssrn.4990724","name":"Decentral Intelligence Agency: The Law and Autonomous Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4990724","authors":["Andrew W. Torrance","Bill Tomlinson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-09T19:39:04Z","doi":"10.2139/ssrn.4990724","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1515/9783111336435-026","name":"Resources to Get Up to Speed on Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783111336435-026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-03T05:14:19Z","doi":"10.1515/9783111336435-026","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/ccai61966.2024","name":"2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccai61966.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-31T20:38:38Z","doi":"10.1109/ccai61966.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003480891-4","name":"Artificial Intelligence in Traffic Management","source":"crossref","abstract":"The authors analyze how AI transforms traffic management by redefining traditional traffic prediction and optimization methodologies. Through predictive algorithms and machine learning models, AI significantly improves the accuracy of traffic forecasts, enhancing the efficiency of flight path planning, schedule optimization, and the overall operation of airports. The authors further discuss the future directions of AI in aviation traffic management, emphasizing the need for advancements in AI algorithms to accommodate dynamic and complex airspace conditions. It presents how AI can streamline training, personalize learning experiences, and provide data-driven performance analysis. It emphasizes ensuring safety when designing airspace and traffic management systems. It advocates using AI to monitor human performance, manage fatigue risks, and gather safety intelligence. Finally, the chapter cites the Austro Control case study as an example of how AI can optimize air traffic management decisions. It showcases a comprehensive Master Supervisory Decision-Support System that balances human performance with procedural and technological controls.","url":"https://doi.org/10.1201/9781003480891-4","authors":["Dimitrios Ziakkas","Anastasios Plioutsias"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-05T00:11:44Z","doi":"10.1201/9781003480891-4","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003480891-3","name":"Artificial Intelligence Training and Operations","source":"crossref","abstract":"Chapter 2 of the book explores AI’s role in training and operations, emphasizing its application in aviation training through mental rehearsal and extended reality (XR) technologies. This segment outlines how AI and XR technologies enhance the learning experience by providing realistic simulations, immediate feedback, and cost-effective training solutions. These technologies facilitate complex decision-making processes and significantly enhance operational safety and efficiency. The chapter concludes by reflecting on the implications of integrating AI into transportation training and operations. The authors advocate for a harmonized approach that aligns technological innovations with human factor considerations to foster a safer, more efficient, and technologically advanced transportation ecosystem.","url":"https://doi.org/10.1201/9781003480891-3","authors":["Dimitrios Ziakkas","Anastasios Plioutsias"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-05T00:11:44Z","doi":"10.1201/9781003480891-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781032632223-12","name":"TransADD","source":"crossref","abstract":"Alzheimer’s Disease (AD) is a progressive neurological condition causing memory loss, cognitive decline, and behavioural changes, being the predominant form of dementia with global impact. Over 50 million individuals worldwide are affected, projected to rise to 152 million by 2050, translating to one new case every 3 seconds. As the seventh leading cause of death, it contributes significantly to elderly disability and dependency worldwide. The lack of reliable treatment poses a substantial challenge, though existing therapies offer some relief and symptom deceleration. Early detection during the prodromal stage is crucial for effective management. Medical image analysis, particularly deep learning techniques, has made notable progress, yet remains underexplored for AD detection. This chapter introduces a pioneering approach using a Transformer-based Network for multi-class AD detection and classification via brain MRI images. The network comprises Image Serialization, Feature Extraction, Encoder, and Decoder modules, merging CNN and Transformers’ strengths to capture low-level image features and establish long-range dependencies. Evaluation on the OASIS dataset, with images from 50 subjects aged 60–96, demonstrates the TransADD model’s significant outperformance of existing models, affirming its effectiveness.","url":"https://doi.org/10.1201/9781032632223-12","authors":["Prabu Selvam","M. Sumathi","P. Saravanan","M. Marimuthu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T15:35:43Z","doi":"10.1201/9781032632223-12","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.24018/ejmed.2024.6.2.2085","name":"The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges","source":"crossref","abstract":"Purpose: This article aims to provide insight and a better understanding of how the rapid development of artificial intelligence (AI) affects radiology practice and research. The article reviews existing scientific literature on the applications of AI in radiology and the opportunities and challenges they pose. Materials and Methods: This article uses available scientific literature on AI applications in radiology and its subspecialties from PubMed, Google Scholar and ScienceDirect. Results: The article finds that the applications of AI in radiology have grown significantly in the past decade, spanning across virtually all radiology subspecialties or areas of activity and all modalities of imaging such as the radiographer, computer tomography (CT) scan, magnetic resonance imaging (MRI), ultrasound and others. The AI applications in radiology present challenges related to testing and validation, professional uptake, and education and training. Nevertheless, artificial intelligence provides an opportunity for greater innovation in the field, improved accuracy, reduced burden of radiologists and better patient care among others. Conclusions: Despite the challenges it presents, artificial intelligence provides many worthwhile opportunities for the development of radiology and the next frontier in medicine.","url":"https://doi.org/10.24018/ejmed.2024.6.2.2085","authors":["Mariana Zhivkova Yordanova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-04T10:33:49Z","doi":"10.24018/ejmed.2024.6.2.2085","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1142/9789811293993_0004","name":"Game Theory","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0004","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4018/979-8-3693-2333-5.ch006","name":"Exploring Artificial Intelligence in Evolving Healthcare Environments","source":"crossref","abstract":"Artificial intelligence (AI) is assuming an increasingly significant role in our daily lives, primarily due to the array of benefits it offers when applied. These advantages encompass round-the-clock availability, an exceptionally low error rate, the capacity to deliver real-time insights, and swift data analysis. In the domain of clinical medical and dental healthcare, AI is experiencing growing utilization with noteworthy applications that encompass disease diagnosis, risk assessment, treatment planning, and drug discovery. This chapter undertakes a comprehensive analysis of AI's integration into healthcare from a multidisciplinary perspective, with a specific focus on the healthcare sector.","url":"https://doi.org/10.4018/979-8-3693-2333-5.ch006","authors":["Ranjit Barua"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-26T08:48:13Z","doi":"10.4018/979-8-3693-2333-5.ch006","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-44958-1.00001-x","name":"Artificial intelligence based medical tourism in 2024 and beyond: Emerging trends, challenges, and strategic imperatives","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44958-1.00001-x","authors":["Partha Pratim Chakraborty"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-10T10:13:51Z","doi":"10.1016/b978-0-443-44958-1.00001-x","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2139/ssrn.4865255","name":"Exploring Challenges of Artificial Intelligence Development in Universities of Medical Sciences: A Qualitative Study","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4865255","authors":["Mohammad  Hasan Keshavarzi","Noushin Kohan","Hamid  Reza Koohestani","Tahereh Mahmoudi","Rahmatollah Soltani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-20T17:38:32Z","doi":"10.2139/ssrn.4865255","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.14733/cadaps.2024.s24.60-68","name":"Medical Research on Mental Health Education of College Students Based on Artificial Intelligence","source":"crossref","abstract":"Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.","url":"https://doi.org/10.14733/cadaps.2024.s24.60-68","authors":["Tinghan Meng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-27T17:15:21Z","doi":"10.14733/cadaps.2024.s24.60-68","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21037/jmai-23-152","name":"Heart disease detection using machine learning methods: a comprehensive narrative review","source":"crossref","abstract":"Background and Objective: Heart disease is one of the diseases that is responsible for the death of millions of people each year worldwide. It is considered one of the main diseases in middle-aged and elderly people. The increasing rate of heart disease cases, high mortality rate, and medical treatment expenses necessitate early diagnosis of symptoms. The aim of this study is to conduct an extensive review of various state-of-the-art methods in heart disease detection and to perform a comparative analysis of their outcomes.","url":"https://doi.org/10.21037/jmai-23-152","authors":["Mohammadreza Hajiarbabi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-27T11:21:33Z","doi":"10.21037/jmai-23-152","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.31662/jmaj.2024-0375","name":"Use and Evaluation of Generative Artificial Intelligence by Medical Students in Japan","source":"crossref","abstract":"Introduction Generative artificial intelligence (AI) has become more accessible due to technological advancements. While it can support more efficient learning, improper use may lead to legal issues or hinder self-directed learning. Medical education is no exception, as generative AI has the potential to become a powerful tool. However, its practicality remains uncertain. Therefore, we investigated how generative AI is perceived among medical students and utilized within the realm of medical education. Methods In January 2024, we conducted a study with 123 second-year medical students who had completed a physiology course and laboratory training at Gunma University, Japan. Students used ChatGPT (Chat Generative Pre-trained Transformer) 3.5 (OpenAI) for four tasks and evaluated its responses. A survey on the use of generative AI was also conducted. Responses from 117 participants were analyzed, excluding six non-participants. Results Among the students, 41.9% had used ChatGPT. The average scores for tasks 1-4 were 6.5, 4.6, 7.4, and 6.2 out of 10, respectively. Although 13% had a negative impression, 54 students found it challenging to apply for medical purposes. However, 64.1% expressed a willingness to continue using generative AI, provided its use extended beyond medical contexts. Conclusions Nearly 60% of students had never used generative AI before, which is consistent with general usage trends. Although they were impressed by the speed of generative AI responses, many students found that it lacked precision for medical studies and required additional verification. Limitations of generative AI, such as \"hallucinations,\" were evident in medical education. It remains important to educate students on AI literacy and their understanding of the potential issues that generative AI could bring about.","url":"https://doi.org/10.31662/jmaj.2024-0375","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-11T07:45:03Z","doi":"10.31662/jmaj.2024-0375","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1201/9781003461500-7","name":"Case Study Medical Images Analysis and Classification with Data-Centric Approach","source":"crossref","abstract":"This case study focuses on the application of a data-centric approach for the analysis and classification of medical images. The increasing availability of medical imaging data has opened up opportunities for leveraging advanced data-centric techniques to improve accuracy and efficiency in diagnosing various diseases. In this study, we explore the use of a data-centric approach in the analysis and classification of medical images, specifically focusing on case studies involving X-ray images, dermoscopic images for skin cancer diagnosis, MRI images for brain tumor classification, and retinal images for diabetic retinopathy detection. We discuss pre-processing techniques, feature extraction methods, feature selection strategies, and classification models employed in each case study. The results demonstrate the effectiveness of the data-centric approach in achieving high-accuracy rates and providing valuable insights for medical image analysis and disease classification. By adopting a data-centric perspective, healthcare professionals and researchers can leverage the wealth of information embedded in medical images to improve diagnostic accuracy, optimize treatment plans, and enhance patient outcomes.","url":"https://doi.org/10.1201/9781003461500-7","authors":["Namrata N. Wasatkar","Pranali G. Chavhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-17T19:23:14Z","doi":"10.1201/9781003461500-7","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4274/mmj.galenos.2024.09382","name":"Clinical Characteristics of Children with Acute Post-Streptococcal Glomerulonephritis and Re-Evaluation of Patients with Artificial Intelligence","source":"crossref","abstract":"Objective Acute post-streptococcal glomerulonephritis (APSGN) is a common cause of acute glomerulonephritis in children. The condition may present as acute nephritic and/or nephrotic syndrome and rarely as rapidly progressive glomerulonephritis. ChatGPT (OpenAI, San Francisco, California, United States of America) has been developed as a chat robot supported by artificial intelligence (AI). In this study, we evaluated whether AI can be used in the follow-up of patients with APSGN. Methods The clinical characteristics of patients with APSGN were noted from patient records. Twelve questions about APSGN were directed to ChatGPT 3.5. The accuracy of the answers was evaluated by the researchers. Then, the clinical features of the patients were transferred to ChatGPT 3.5 and the follow-up management of the patients was examined. Results The study included 11 patients with an average age of 9.08±3.96 years. Eight (72.7%) patients had elevated creatinine and 10 (90.9%) had hematuria and/or proteinuria. Anti-streptolysin O was high in all patients (955±353 IU/mL) and C3 was low in 9 (81.8%) patients (0.56±0.34 g/L). Hypertensive encephalopathy, nephrotic syndrome, and rapidly progressive glomerulonephritis were observed in three patients. Normal creatinine levels were achieved in all patients. Questions assessing the definition, epidemiologic characteristics, pathophysiologic mechanisms, diagnosis, and treatment of APSGN were answered correctly by ChatGPT 3.5. All patients were diagnosed with APSGN, and the treatment steps applied by clinicians were similarly recommended by ChatGPT 3.5. Conclusions The insights and recommendations offered by ChatGPT for patients with APSGN can be an asset in the care and management of patients. With AI applications, clinicians can review treatment decisions and create more effective treatment plans.","url":"https://doi.org/10.4274/mmj.galenos.2024.09382","authors":["Emre LEVENTOGLU","Mustafa SORAN"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-09T07:41:22Z","doi":"10.4274/mmj.galenos.2024.09382","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/raai64504.2024","name":"2024 4th International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/raai64504.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-09T17:52:54Z","doi":"10.1109/raai64504.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/icaiic60209.2024","name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaiic60209.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-20T18:17:20Z","doi":"10.1109/icaiic60209.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1002/9781394200801.oth","name":"Also of Interest","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394200801.oth","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-27T04:33:45Z","doi":"10.1002/9781394200801.oth","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1145/3706890.3707040","name":"Intelligent quality control analysis helps the medical quality management of primary medical institutions","source":"crossref","abstract":"Based on the data from the first page of medical records reported by HQMS or TCMMS platform, this paper develops intelligent quality control analysis software based on performance assessment and statistical standards such as the \"Quality Control Index of Medical Records Management (2021)\". The software is used to conduct regular quality control analysis of terminal medical records and automatically generate medical quality analysis reports to improve the quality of medical records. The practical application results show that the use of intelligent system software to regularly generate medical quality analysis reports is more accurate than manual statistics and writing, and can provide direction for optimizing medical quality.","url":"https://doi.org/10.1145/3706890.3707040","authors":["Rui Xiao","Wei Pei","Ming Gu","Lidan Zhang","Xiaohui Wang","Fengju Hu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-13T13:37:20Z","doi":"10.1145/3706890.3707040","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/wsai62426.2024.10829017","name":"Title Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wsai62426.2024.10829017","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T19:22:07Z","doi":"10.1109/wsai62426.2024.10829017","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/aivrv63595.2024","name":"2024 4th International Conference on Artificial Intelligence, Virtual Reality and Visualization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aivrv63595.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-04T18:31:59Z","doi":"10.1109/aivrv63595.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.5644/pi2024.215.00","name":"Artificial Intelligence in Industry 4.0: The future that comes true","source":"crossref","abstract":"","url":"https://doi.org/10.5644/pi2024.215.00","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-09T08:00:07Z","doi":"10.5644/pi2024.215.00","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/esai62891.2024","name":"2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/esai62891.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T17:39:34Z","doi":"10.1109/esai62891.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.52843/cassyni.s6b502","name":"Artificial Intelligence in Dialysis Care","source":"crossref","abstract":"Today, in our everyday lives, artificial intelligence (AI) applications are omnipresent, from voice recognition to route planning, fraud protection, and on-line shopping recommendations. The pervasiveness of AI in our personal lives begets the question “What is the role of AI in dialysis care?”. In dialysis care, three main application areas for “classical” AI (i.e., non-generative AI) have emerged [1]: prediction, therapy recommendation, and diagnosis. Examples are the prediction of hospital admissions and prediction of intradialytic hypotension in real-time. The former was implemented already in clinical practice and resulted in a lower hospitalization rate [2]. The latter involves the cloud-based integration of dialysis machine data, and electronic health records with AI-powered prediction algorithms [3]. In several countries AI applications are used to give anemia therapy recommendations [4] that are subsequently reviewed by health care providers prior to prescription. The application of such an AI tool has resulted in improved attainment of target hemoglobin targets, less severe anaemia, and lower ESA utilization [5]. Regarding diagnostics, AI systems have been developed to categorize arterio-venous aneurysms as advanced / non-advanced [6]. In addition, natural language processing, an AI method to extract insights from health care provider notes, has been shown to be superior to billing codes to identify symptom burden in hemodialysis patients [7]. The recent advent of generative AI and large language models (LLM) such as ChatGPT has instigated research into applications in dialysis care. For example, LLMs are explored to support renal dietitians and provide better personalized care. So far, results have shown only moderate performance of LLMs [8]. It is expected that AI applications, both “classical” and “generative”, will be expanded in the future. Moving forward, it will be critically important to recognize potential flaws of AI systems, such as biases and it “black box” character, and to respect ethical and privacy concerns.","url":"https://doi.org/10.52843/cassyni.s6b502","authors":["Peter Kotanko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-14T08:10:42Z","doi":"10.52843/cassyni.s6b502","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.26717/bjstr.2024.55.008756","name":"\"Incorporating Qigong into a Western Medical Practice: A Study in Artificial Intelligence\"","source":"openalex","abstract":"Qigong [pronounced chee gong] has been a tool in the toolbox of Traditional Chinese Medicine (TCM) for thousands of years. Basically, it consists of a series of exercises that awaken the bio-electromagnetic fields in the human body.","url":"https://doi.org/10.26717/bjstr.2024.55.008756","authors":["Robert W McGee","Robert W. McGee"],"tags":["Psychology","Medical education","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-26","doi":"10.26717/bjstr.2024.55.008756","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.4038/cmj.v69i1.10232","name":"Clinical medicine in the era of information technology and artificial intelligence","source":"crossref","abstract":"No abstract available","url":"https://doi.org/10.4038/cmj.v69i1.10232","authors":["Ranil Fernando"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-24T02:34:53Z","doi":"10.4038/cmj.v69i1.10232","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2196/preprints.65748","name":"A bibliometric analysis of the advance of artificial intelligence in medicine (Preprint)","source":"crossref","abstract":"BACKGROUND The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. OBJECTIVE it is essential to comprehend the current state of development, primary research focal points, and to identify key contributors and their relationships in the application of AI in medicine through bibliometric analysis. METHODS We employed the Web of Science Core Collection as our primary database and conducted a literature search spanning from January 2019 to December 2023.VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors and keywords. RESULTS A total of 1811 publications on research for artificial intelligence in medicine were released across 565 journals by 12376 authors affiliated with 3583 institutions from 97 countries. The United States emerged as the leading producer of scholarly works, exerting significant influence in this domain. Harvard Medical School exhibited the highest publication count among all institutions. The JOURNAL OF MEDICAL INTERNET RESEARCH attained the highest H-index (H-index=19), the most significant publication count (NP=76), and total citations (NC=1495). Among the keywords, four clusters were identified, encompassing the application of AI in digital health, COVID-19 and ChatGPT, precision medicine, epidemiology, and public health. \"Outcomes\" and \"Risk\" demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients' health condition risks, foreshadowing future research focal points. CONCLUSIONS Our bibliometric analysis delved into the advancements, focal points, and cutting-edge areas within the field of artificial intelligence in medicine, revealing potential future research opportunities. Research on artificial intelligence in medicine is rapidly progressing, as evidenced by a consistent increase in publications on the topic since 2019. Simultaneously, we identified leading countries, institutions, and scholars in the field and conducted an analysis of journals and representative literature. This study equips researchers with the necessary information to comprehend the current state, collaborative networks, and primary research focal points within the field. Furthermore, our findings propose a set of recommendations for future research.","url":"https://doi.org/10.2196/preprints.65748","authors":["mian lin","Lingzhi Lin","Lingling Lin","Zhengqiu Lin","Xiaoxiao Yan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-26T07:57:43Z","doi":"10.2196/preprints.65748","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2139/ssrn.4859790","name":"Human intelligence versus artificial intelligence in classifying economics research articles: Exploratory evidence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4859790","authors":["Jussi Heikkilä"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-17T18:41:00Z","doi":"10.2139/ssrn.4859790","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.35845/kmuj.2024.23503","name":"Comparison of Artificial Intelligence-based learning with the traditional method in the diagnosis of COVID-19 chest radiographs among postgraduate radiology residents","source":"crossref","abstract":"OBJECTIVE: To compare Artificial Intelligence (AI)-based teaching with traditional approach in chest radiographs to detect COVID-19 pneumonia. METHODS: This prospective experimental randomized controlled trial was conducted at Pakistan Institute of Medical Sciences, Islamabad from July to November 2021, following ethical approval. Forty postgraduate radiology residents were randomly assigned into Group-A (traditional teaching; n=20) or Group-B (AI-based teaching; n=20) using a lottery method. Group-A engaged in one-on-one sessions for COVID X-ray reporting, while Group-B trained in AI-deep learning methods. Pre-tests assessed baseline knowledge, and post-training assessments compared learning outcomes. Statistical analysis using SPSS v25 included Independent sample t-tests and chi square test. Following initial assessments, teaching methods were exchanged between groups for comparison. RESULTS: Out of 40 participants 60% were males and 40% were females, with mean age of 27.45±1.7 years. Group-B showed significantly higher post-test scores (9.40±0.598) compared to Group-A (7.75 ± 1.118) (p<0.001). The average improvement in scores was significantly higher in Group B based on the change from pre-test to post-test scores (p < 0.05). Significant score improvements favored Group-B across all training years (p <0.05). Gender analysis indicated similar score gains among males but significantly higher improvements in females in Group B (4.09±1.868 vs. 2.00±1.414, p<0.05). CONCLUSION: AI approach proves significantly more time and cost efficient compared to traditional teaching methods in enhancing the ability of radiology residents. This highlight the potential of AI to optimize medical education by integration of AI technologies into radiology training programs, providing efficient, scalable, and effective learning experiences.","url":"https://doi.org/10.35845/kmuj.2024.23503","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-01T14:37:12Z","doi":"10.35845/kmuj.2024.23503","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.54941/ahfe1004662","name":"The Potential Issues and Crises of Artificial Intelligence Development","source":"crossref","abstract":"Since the time when humans, leveraging 'intelligence,' could contend with and dominate other species on Earth, they have held a dominant position in the relationship with other life forms. The explosive development of artificial intelligence (AI) has ushered in limitless possibilities for human society. Simultaneously, the potential issues and crises stemming from its development accompany a myriad of advantages. This study employs literature review and in-depth analysis to categorize the potential problems and crises of AI development into three levels: 'small, medium, and large.' These levels respectively denote the negative impacts AI brings to humanity, the conflicts between AI and humans, and the potential scenario of AI replacing and annihilating humanity.Building upon this hierarchical classification, the article proposes that addressing minor issues, mitigating moderate-scale problems, and remaining vigilant about major challenges are imperative throughout the AI development process. It underscores the need for humanity to solve small problems, alleviate medium-scale issues, and be alert to significant problems. This calls for a reevaluation of the relationship between humans and AI, an awareness of the existence of the 'singularity' in AI development, and a heightened emphasis on preventing potential crises resulting from uncontrolled and intervention-free AI development.In the realm of 'small issues,' the article discusses how the development of AI has led to a decline in the independence of human thought. This is manifested in weakened social skills, diminished memory capabilities, and a reduced capacity for independent decision-making. Furthermore, the potential replacement of non-technical occupations by AI may contribute to a widening gap in employment and wealth. Issues related to information privacy and security become prominent, particularly in fields like science, medicine, and business, where the extensive use of AI for the analysis of sensitive user information poses inherent privacy risks. Additionally, concerns regarding the monopolization of data analysis and the presence of biases and discrimination in algorithms are significant challenges within the context of AI development.The 'medium issues' encompass discussions about the relationship between humans and AI, as well as the prospective trajectory of human civilization coexisting with AI. In the future, AI may attain a status comparable to humans. Questions arise about whether AI is inclined to continue aiding in human civilization's development, fostering a harmonious coexistence between humans and AI, or if AI will give rise to an independent AI civilization detached from human influence. These considerations present challenges to the existing power structures and discourse systems predominantly shaped by human influence.In addressing the 'major challenges,' the article emphasizes the potential occurrence of an 'AI singularity,' a point in time when machine intelligence comprehensively surpasses human intelligence. This scenario could result in humans losing their understanding and control over AI, facing the threat of becoming a secondary species or even encountering existential risks. The article introduces the concept of a 'quiet' period preceding the AI surpassing human intelligence. During this phase, the substantial benefits derived from AI development may induce apathy and relaxation regarding the potential threat of AI dominance.In conclusion, this article offers a comprehensive and systematic perspective, analyzing potential issues and crises at different tiers in the development of AI. It provides a structured framework for addressing these challenges and calls for vigilance in recognizing the potential threats posed by AI. The article underscores the importance of active intervention in technological development within the humanities, encouraging public participation in establishing a public discourse system. This engagement aims to culti","url":"https://doi.org/10.54941/ahfe1004662","authors":["Lingxuan Li","Wenyuan Li","Dong Wei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-10T21:08:20Z","doi":"10.54941/ahfe1004662","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/aitest62860.2024.00002","name":"Proceedings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitest62860.2024.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-25T17:28:25Z","doi":"10.1109/aitest62860.2024.00002","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4128/9781637426050","name":"Internal Communication in the Age of Artificial Intelligence","source":"crossref","abstract":"&lt;p&gt;&lt;b&gt;Transformational leaders need to be exceptional communicators.&lt;/b&gt;&lt;/p&gt;&lt;p&gt;Bombarded by new technology and unsure where artificial intelligence will take us? Asking yourself how this will impact communication at scale in your organization? How can you best harness this power for business success?&lt;/p&gt;&lt;p&gt;Companies and projects are at risk. Effective strategic internal communication will attract, engage, align, and retain your people to weather this storm of change. It will help them adopt new technologies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;But how can you tell if your strategy will succeed? What questions should you ask?&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;Internal Communication in the Age of Artificial Intelligence&lt;/i&gt; reveals a modern, multilayered approach to internal communication. It’s a practical guide for business leaders and communicators, filled with global case studies, behind-the-scenes insights, and stories from industry experts. You’ll learn what basics must be done brilliantly, how to engage with communities, and why a new immersive communication mindset is needed to prepare you for the future.&lt;/p&gt;","url":"https://doi.org/10.4128/9781637426050","authors":["Monique Zytnik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-26T19:47:23Z","doi":"10.4128/9781637426050","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003483571-17","name":"In Vitro Fertilization and Artificial Intelligence","source":"crossref","abstract":"In in vitro fertilization (IVF), artificial intelligence (AI) is emerging as a powerful tool in optimizing various stages of the IVF process. Within the scope of this section, the integration of AI is examined especially in the stages of ovarian stimulation, egg collection, sperm collection and preparation, fertilization, embryo culture and selection, transfer, and hormonal support in the luteal phase, which is vital for follicle development and oocyte retrieval. The integration of AI into different stages of IVF helps achieve successful results by optimizing results by providing personalized treatment strategies.","url":"https://doi.org/10.1201/9781003483571-17","authors":["Fırat Tülek","Cem Demirel","Mustafa Umut Demirezen","Elif Ince"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-13T14:01:36Z","doi":"10.1201/9781003483571-17","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4337/9781803922171.00021","name":"Trust and trustworthiness in artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781803922171.00021","authors":["Rory Gillis","Johann Laux","Brent Mittelstadt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T14:00:58Z","doi":"10.4337/9781803922171.00021","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.60087/vol2iisue1.p43","name":"Artificial Intelligence to support Children with Autism","source":"crossref","abstract":"Children with autism spectrum disorder (ASD) face challenges in communication, social interaction, and behavioral flexibility, which can affect their development and everyday functioning. Artificial intelligence (AI) offers innovative tools to support children with autism by providing personalized and adaptive interventions. AI-powered systems, such as robots, virtual assistants, and machine learning models, can be used to enhance social skills training, improve communication, and manage sensory sensitivities. These technologies can monitor progress, adjust strategies, and provide real-time feedback, enabling more effective, individualized care. This paper explores the potential of AI in autism support, focusing on AI applications for social skill enhancement, early detection, and therapeutic intervention, while also addressing the ethical considerations and challenges involved in integrating AI into autism care.","url":"https://doi.org/10.60087/vol2iisue1.p43","authors":["Nan Xing"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-21T14:30:33Z","doi":"10.60087/vol2iisue1.p43","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4324/9781003287810-11","name":"Artificial Intelligence in Forensic Anthropology and Odontology","source":"crossref","abstract":"Forensic science is a diverse discipline that brings together forensic scientists and investigators, research scientists and the criminal justice system. It is referred to as criminalistics wherein the knowledge of science is applied to the enforcement of laws ( Morgan, 2017 ). It is an essential component of any criminal investigation since it helps investigators to identify a suspect in a crime and precisely determine how and when the crime occurred. Forensic science is a vast discipline with six major branches: forensic anthropology, forensic engineering, forensic odontology, forensic pathology, forensic entomology, and toxicology ( Morgan, 2017 ).","url":"https://doi.org/10.4324/9781003287810-11","authors":["Abraham Johnson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T07:15:03Z","doi":"10.4324/9781003287810-11","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/tai.2024.3501912","name":"Editorial: Future Directions in Artificial Intelligence Research","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tai.2024.3501912","authors":["Hussein Abbass"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-11T17:53:05Z","doi":"10.1109/tai.2024.3501912","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4337/9781800889972.00018","name":"Artificial intelligence for professional learning","source":"crossref","abstract":"Artificial Intelligence (AI) is increasingly impacting on all organisations. It has also been proposed as a way to scale professional learning. However, while much professional learning is informal (it includes ‘on the job’ training such as observing how an expert colleague carries out a task or engaging in strategic discussions), current applications of AI in professional learning draw on AI applications developed for schools and universities to focus on formal professional learning (training courses with prespecified content and outcomes). Informal professional learning, or ‘workplace learning,’ has yet to be addressed by AI. Accordingly, in this chapter, we explore the characteristics and requisite skills of workplace learning, such as self-regulation, before considering the potential of AI for workplace learning.","url":"https://doi.org/10.4337/9781800889972.00018","authors":["Wayne Holmes","Allison Littlejohn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-15T13:01:03Z","doi":"10.4337/9781800889972.00018","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1007/s44163-024-00126-3","name":"Is ChatGPT the way toward artificial general intelligence","source":"crossref","abstract":"Abstract The success of the conversational AI system ChatGPT has triggered an avalanche of studies that explore its applications in research and education. There are also high hopes that, in addition to such particular usages, it could lead to artificial general intelligence (AGI) that means to human-level intelligence. Such aspirations, however, need to be grounded by actual scientific means to ensure faithful statements and evaluations of the current situation. The purpose of this article is to put ChatGPT into perspective and to outline a way forward that might instead lead to an artificial special intelligence (ASI), a notion we introduce. The underlying idea of ASI is based on an environment that consists only of text. We will show that this avoids the problem of embodiment of an agent and leads to a system with restricted capabilities compared to AGI. Furthermore, we discuss gated actions as a means of large language models to moderate ethical concerns.","url":"https://doi.org/10.1007/s44163-024-00126-3","authors":["Frank Emmert-Streib"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-07T03:47:16Z","doi":"10.1007/s44163-024-00126-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1136/leader-2024-001155","name":"Future of medical leadership in the age of artificial intelligence","source":"crossref","abstract":"In the dynamic landscape of modern healthcare, the rise of artificial intelligence (AI) is revolutionising leadership roles by challenging established skill sets. Effective integration of AI relies heavily on adept balancing of rapid technological advances and ethical concerns, ensuring patient welfare and equitable access to care. In this context, strategies such as continuous learning, ethical prioritisation, leadership development and inclusive AI adoption are essential. By adopting a human-oriented approach, healthcare leaders can effectively harmonise technological progress and advance societal well-being.","url":"https://doi.org/10.1136/leader-2024-001155","authors":["Vincent Q Sier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-22T13:20:15Z","doi":"10.1136/leader-2024-001155","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.34218/ijaird_02_02_015","name":"ARTIFICIAL INTELLIGENCE IN ACTION: STORIES FROM A CONNECTED WORLD","source":"crossref","abstract":"Artificial Intelligence (AI) is no longer a concept of the future; it is deeply embedded in our daily lives, quietly shaping how we interact, work, and make decisions.Artificial Intelligence in Action: Stories from a Connected World takes readers on an engaging journey through the many ways AI has transformed modern society.This book unpacks the fascinating stories behind the algorithms, from smart home devices that anticipate our needs to advance healthcare technologies saving lives.It explores real-world examples where AI has solved complex problems, empowered communities, and even raised ethical questions about its role in decision-making.Far from being a purely technical deep dive, this narrative-driven exploration highlights the human stories interwoven with AI advancements, showing how technology enhances creativity, improves efficiency, and fosters connectivity across the globe.At the same time, it does not shy away from addressing challenges such as bias, data privacy, and the evolving relationship between humans and machines.This book offers a balanced and insightful look at how AI reshapes industries like education, transportation, and entertainment while inviting readers to imagine the future.Whether you are a tech enthusiast, a curious sceptic, or someone navigating the AI-driven tools of today, this book will Artificial Intelligence in Action: Stories from a Connected World https://iaeme.com/Home/journal/IJAIRD","url":"https://doi.org/10.34218/ijaird_02_02_015","authors":["Pritam Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-29T15:07:58Z","doi":"10.34218/ijaird_02_02_015","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21070/ups.5705","name":"Utilization of Artificial Intelligence in Basic Education Learning Planning Management","source":"crossref","abstract":"This research aims to analyze the use of Artificial Intelligence (AI) technology in facing the challenges of teaching elementary school teachers in the digital era. AI is considered capable of being utilized in basic education planning management. The implementation of AI, especially through the ChatGPT application, in the education sector has great potential to improve education management and the learning process. Through the Systematic Literature Review (SLR) method carried out in this research, we can draw an outline that the opportunities for success in AI lie in data management efficiency, individual learning adaptation, effective feedback, increasing teacher administrative tasks, automatic evaluation, curriculum development, and increasing effectiveness. learning. The various successes of AI can be used as consideration for basic education planning management.","url":"https://doi.org/10.21070/ups.5705","authors":["Angga Prasetya Nugraha","Nurdyansyah Nurdyansyah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-08T07:34:00Z","doi":"10.21070/ups.5705","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/j.artmed.2024.102779","name":"Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service","source":"crossref","abstract":"The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.","url":"https://doi.org/10.1016/j.artmed.2024.102779","authors":["Sarina Aminizadeh","Arash Heidari","Mahshid Dehghan","Shiva Toumaj","Mahsa Rezaei","Nima Jafari Navimipour","Fabio Stroppa","Mehmet Unal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-24T19:30:36Z","doi":"10.1016/j.artmed.2024.102779","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2174/9789815238211124010002","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.2174/9789815238211124010002","authors":["Mohammed Majeed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-07T11:52:42Z","doi":"10.2174/9789815238211124010002","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.63070/jesc.2024.007","name":"Design And Development of a Feeding Unit for Medical Devices Implanted Inside Human Body Using Artificial Intelligence Theories","source":"crossref","abstract":"In this research paper, we present the design and development of a power unit for medical devices that are implanted inside the human body. It uses a micro-electromagnetic generator to generate electrical energy by taking advantage of natural vibrations to generate electrical energy in order to supply these applications that need electrical supply for a long period without human intervention. We used a direct lever rectifier switch. Direct AC-DC Step Up Converter for low voltage amplitude to reach a value suitable for the application in the open loop. The voltage in the closed loop was then regulated using a fuzzy controller based on digital signal processing chips, which allowed improving the performance of the converter. To calculate the fuzzy controller, we used the Fuzzy Logic tool of MATLAB. Interconnection circuits were proposed to adapt low-level signals to control circuits and power switches. We performed a simulation of the entire system, represented by the control circuit based on digital signal processing chips, the fuzzy control algorithm, and the switcher using the Proteus program in the open and closed loop, and then we compared and discussed the results.","url":"https://doi.org/10.63070/jesc.2024.007","authors":["Abdulkarim Almuhammad","Mohanad Alrasheed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-11T09:31:41Z","doi":"10.63070/jesc.2024.007","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003494027-3","name":"Industry 5.0 with Artificial Intelligence","source":"crossref","abstract":"Artificial intelligence (AI) is the development of computer systems that are capable of performing tasks that entail human intelligence such as identifying patterns, recognizing speech, and making decisions. AI is a broad term that encompasses a wide range of technologies which contains deep learning (DL), machine learning (ML), and natural language processing (NLP) algorithms. AI can be deliberated as the foremost component of the industrial revolution which supports intelligent machines to accomplish tasks autonomously such as interpretation, self-monitoring, diagnosis, and exploration. AI-based methodologies (specifically ML, DL, and NLP) assist industries and manufacturers in anticipating maintenance requirements and minimizing downtime. The field of human–computer interaction (HCI) is primarily concerned with facilitating user-technological collaborations. AI uses ML algorithms to build user-specific recommendation engines based on their past behavior. Industry 5.0 is described as the upcoming industrial evolution that aims to leverage the creativity of human experts in conjunction with intelligent, efficient, and accurate machines. Industry 5.0 described the potential applications such as cloud manufacturing, supply chain management, intelligent healthcare and manufacturing production, etc. This chapter describes various emerging applications of AI in the era of Industry 5.0 revolution.","url":"https://doi.org/10.1201/9781003494027-3","authors":["Namita Kathpal","Pratima Manhas","Jyoti Verma","Seema Jogad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-17T18:08:51Z","doi":"10.1201/9781003494027-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21649/akemu.v30i2.5774","name":"Artificial Intelligence in Healthcare: Implications, Challenges, and Future Prospects","source":"crossref","abstract":"Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities. This technology is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes. Just like a physician, AI can record a patient's history, signs and symptoms, and lab results, leading to an accurate diagnosis and a proper treatment plan, but in no time. This is just a small example of the implications of AI in the sector. Other areas of medicine are also being benefited by this innovation, like gastroentology, radiology, surgery, and preventive medicine. The development of advanced algorithms has reduced the burden on radiologists by helping them identify abnormal images and pick up malignant lesions with minimal diagnostic errors. Similarly, AI-assisted colonoscopy can help identify malignant and benign polyps. AI-powered systems, like Google's DeepMind Health, may identify malignant growth in mammograms and diabetic retinopathy, which can aid in early detection and treatment.","url":"https://doi.org/10.21649/akemu.v30i2.5774","authors":["Saira Afzal","Meha Siddiqui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T10:21:13Z","doi":"10.21649/akemu.v30i2.5774","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.56705/ijaimi.v2i1.138","name":"Segmentation and Feature Extraction for Malaria Detection in Blood Smears","source":"crossref","abstract":"Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.","url":"https://doi.org/10.56705/ijaimi.v2i1.138","authors":["Nurul Rismayanti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-10T19:28:14Z","doi":"10.56705/ijaimi.v2i1.138","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.3233/faia250339","name":"Artificial Intelligence-Driven Design and Realization of Interactive Digital Artworks","source":"crossref","abstract":"In order to solve the problems of low extraction speed, poor flexibility and low expression effect of new media interactive elements, artificial intelligence-driven design and realization of interactive digital art works are proposed. This paper analyzes the main expression scenarios of new media interactive art, i.e., user information conveying scenarios, new media interactive art pushing scenarios, interactive art promotion scenarios and personal service scenarios, provides scenario application basis for interactive expression, calculates spatial complexity, spatial complexity and resource complexity, and carries out the interactive design from the three aspects of visual elements, multimedia elements and human-computer interactive elements according to the results of the complexity calculation. According to the result of complexity calculation, the interaction design is carried out from three aspects of visual elements, multimedia elements and human-computer interaction elements, the topology is integrated after the interaction is realized, and the interactive art of multimedia elements is expressed on the display and control of the new media network topology. The experimental results show that the speed of extracting interactive elements of the new media interactive art expression method based on artificial intelligence technology is increased by 30%, and the flexibility is stronger. Conclusion: In order to solve the shortcomings of traditional new media interactive art expression design, this paper proposes a new media interactive art expression method based on artificial intelligence technology, which enhances the flexibility of interactive art expression and accelerates the extraction speed of interactive elements by artificial intelligence technology.","url":"https://doi.org/10.3233/faia250339","authors":["Fan Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T16:20:26Z","doi":"10.3233/faia250339","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.2139/ssrn.4956387","name":"Generative Artificial Intelligence for Finance Professionals","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4956387","authors":["Joerg Osterrieder"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-21T23:21:06Z","doi":"10.2139/ssrn.4956387","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1101/2024.12.28.630597","name":"Cancer vs. Conversational Artificial Intelligence","source":"crossref","abstract":"Solving cancer mechanisms is challenging due to the complexity of the disease integrated with many approaches that researchers take. In this study, information retrieval was performed on 40 oncological papers to obtain authors' methods regarding the tumor immune microenvironment (TIME) or organ-specific research. 20 TIME summaries were combined and analyzed to yield valuable insights regarding how research based papers compliment information from review papers using Large Language Model (LLM) in-context comparisons, followed by code generation to illustrate each of the authors' methods in a knowledge graph. Next, the 20 combined organ-specific emerging papers impacting historical papers was obtained to serve as a source of data to update a mechanism by Zhang, Y., et al., which was further translated into code by the LLM. The new signaling pathway incorporated four additional authors' area of cancer research followed by the benefit they could have on the original Zhang, Y., et al. pathway. The 40 papers in the study represented over 600,000 words which were focused to specific areas totaling approximately 17,000 words represented by detailed and reproducible reports by Clau-3Opus. ChatGPT o1 provided advanced reasoning based on these authors' methods with extensive correlations and citations. Python or LaTeX code generated by ChatGPT o1 added methods to visualize Conversational AI findings to better understand the intricate nature of cancer research.","url":"https://doi.org/10.1101/2024.12.28.630597","authors":["Kevin Kawchak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-01T12:49:55Z","doi":"10.1101/2024.12.28.630597","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.32388/ju96qa","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.","url":"https://doi.org/10.32388/ju96qa","authors":["Muhammad Khushnood"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-28T23:55:59Z","doi":"10.32388/ju96qa","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.47772/ijriss.2024.8090258","name":"Balancing the Risks Associated with Artificial Intelligence in Medical and Dental Sciences","source":"crossref","abstract":"This article delves into the impact of artificial intelligence (AI) on medical and dental sciences, with a focus on how it transforms diagnosis, treatment planning, and patient care. As healthcare develops very quickly due to innovations in technology, there is a pressing need to modernize conventional procedures and establish new standards. However, incorporating AI poses multiple challenges. The quality and diversity of data used in AI algorithms is critical, as skewed results and inequitable healthcare delivery may aggravate health inequities. Ethical instances, like patient consent and data security, compound issues even more. AI technologies frequently operate as “black boxes,” making decision-making processes opaque and could undermine confidence among medical professionals. Effective integration of AI tools is critical; inadequate implementation may disrupt clinical operations and enhance workloads. The paper offers practical solutions to these challenges, such as boosting data diversity, strengthening AI transparency, and adopting strong ethical standards. Ongoing evaluation and adaption of AI technology is essential to ensure its reliability and security. Furthermore, putting resources into employee training and developing cost-effective AI solutions is critical for further adoption. Addressing these concerns would allow the healthcare industry to fully utilize AI’s promise while preserving ethical and equitable practices.","url":"https://doi.org/10.47772/ijriss.2024.8090258","authors":["Dr Wajiha Qamar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-21T12:17:24Z","doi":"10.47772/ijriss.2024.8090258","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003377818-9","name":"Security Issues Related to COVID Data Using Artificial Intelligence (AI)","source":"crossref","abstract":"The advancement of Artificial Intelligence (AI) and its broader impact on many sectors requires an assessment of achieving Sustainable Development Goals. AI refers to machines that mimic “cognitive” functions to simulate intelligent human-like behavior and critical thinking. John McCarthy first described AI in 1956, and research predicts that by 2025, global AI healthcare spending will equal to $36.1 billion. AI is widely recognized as transformative innovation capable of outperforming humans. Still, less attention has been paid to the practical implications and potential barriers. Safety-conscious healthcare policymakers may require further openness to clinical effectiveness and mechanisms of action to ensure they are clinically effective and safe in real-world use. AI application during the pandemic (COVID-19) is being explored further due to its beneficial aspects. AI is going to stay in the field of patient care and research. The legal and ethical issues include privacy and surveillance, bias, and discrimination. Currently, there are no well-defined regulations to address the legal and ethical issues that may arise due to the use of AI in healthcare. AI has the potential to augment provider performance and play a key role, but like other disruptive technologies in past, causing a significant impact should not be underestimated.","url":"https://doi.org/10.1201/9781003377818-9","authors":["Ramkrishna Mondal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-15T09:15:09Z","doi":"10.1201/9781003377818-9","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4337/9781803926728.00022","name":"Disabling AI: Biases and Values Embedded in Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781803926728.00022","authors":["Damien Patrick Williams"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T14:12:45Z","doi":"10.4337/9781803926728.00022","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.36548/jaicn.2024.3.007","name":"AI-Enabled Medical Assessment and Assistance for Vocal Disorders: A Comparative Study","source":"crossref","abstract":"Vocal disorders present significant challenges for patients and clinicians, impacting communication and quality of life. The development of artificial intelligence (AI) technologies offers promising possibilities for improving the assessment and management of vocal disorders. This study aims to evaluate the effectiveness and applicability of different AI approaches in this field through a comparative study of AI-enabled medical assessment and assistance for vocal disorders. Various AI techniques, including machine learning algorithms, deep learning models, and natural language processing methods, are explored in the context of diagnosing vocal disorders, planning treatments, and managing patients. The insights gained from this comparative study contribute to understanding the role of AI in transforming healthcare delivery for vocal disorders, highlighting opportunities, challenges, and future directions for utilizing AI to enhance medical assessment and assistance in this specialized field.","url":"https://doi.org/10.36548/jaicn.2024.3.007","authors":["B.Vivekanandam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-14T05:13:41Z","doi":"10.36548/jaicn.2024.3.007","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.12669/pjms.41.1.11391","name":"Artificial intelligence in radiology: Radiologist’s adversary or comrade?","source":"crossref","abstract":"doi: https://doi.org/10.12669/pjms.41.1.11391 How to cite this: Mansoor A. Artificial intelligence in radiology: Radiologist’s adversary or comrade? Pak J Med Sci. 2025;41(1):1-2. doi: https://doi.org/10.12669/pjms.41.1.11391 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","url":"https://doi.org/10.12669/pjms.41.1.11391","authors":["Ali Mansoor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T17:49:05Z","doi":"10.12669/pjms.41.1.11391","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-21889-7.00006-3","name":"Role of artificial intelligence and machine learning in women’s health","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21889-7.00006-3","authors":["Sapna Rawat","Poonam Joshi","Gulafshan Praveen","Jyoti Saxena"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-03T02:30:04Z","doi":"10.1016/b978-0-443-21889-7.00006-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21608/aiis.2024.234831.1000","name":"Vehicle Accident Predication and Detection Model for Smart Cities Using Edge Computing","source":"crossref","abstract":"Vehicle accidents are a significant concern in smart cities due to the increasing number of vehicles and the potential impact on traffic flow, safety, and emergency response. To address this issue, this paper proposes a Vehicle Accident Prediction and Detection Model for Smart Cities using Edge Computing. The model uses edge computing, which enables real-time data processing and analysis at the edge of the network, closer to the source of data generation. This approach reduces latency and bandwidth requirements by processing data locally, making it suitable for time-sensitive applications like accident prediction and detection. The proposed model utilizes various data sources such as traffic cameras, sensors embedded in vehicles, and historical accident data. These sources provide real-time information about road conditions, vehicle movements, and past accident patterns. The collected data is processed using machine learning algorithms to identify patterns and predict potential accident-prone areas.The proposed system uses smart city infrastructure such as sensors and high resolution cameras to capture any possible incident and analyze these data for any possible accidents. Any hazard occurrence leads to send a voice alert to the car driver (owner) telling to perform some steps that avoid him many real accident and save his and others life.","url":"https://doi.org/10.21608/aiis.2024.234831.1000","authors":["Hazim AlRawashdeh","Hazim AlRawashdeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-25T08:58:38Z","doi":"10.21608/aiis.2024.234831.1000","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/ricai64321.2024.10911164","name":"Optimizing the mathematical model of IoT data processing in smart cities using artificial intelligence algorithms","source":"crossref","abstract":"This article explores how to use artificial intelligence algorithms to optimize the processing of IoT data in smart cities. Firstly, the concepts of artificial intelligence, smart cities, and IoT data processing were introduced; Secondly, the BP algorithm and ant colony algorithm were introduced; Subsequently, taking the optimization of dangerous goods transportation management in smart cities as an example, the most optimized path was explained. The ant colony algorithm simulates the foraging behavior of ants to optimize the search for the optimal path and solution. Therefore, this article proposes a mathematical model that combines BP algorithm and ant colony algorithm for data processing in smart city IoT. By dividing the data into different processing levels, the BP algorithm is used to preprocess and classify the raw data, while the ant colony algorithm is used to find the optimal path and solution during data transmission and storage. This model can improve the speed and accuracy of data processing, providing strong technical support for the construction of smart cities. This study is of great significance for promoting the development of IoT technology in smart cities, providing new ideas and methods for data processing in future smart cities.","url":"https://doi.org/10.1109/ricai64321.2024.10911164","authors":["Jiamin Deng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-13T17:33:27Z","doi":"10.1109/ricai64321.2024.10911164","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1080/08839514.2024.2355760","name":"Entropy-based deep neural network training optimization for optical coherence tomography imaging","source":"crossref","abstract":"This paper presents an optimization technique for the number of training epochs needed for deep learning models. The proposed method eliminates the need for separate validation data and significantly decreases training epochs. Using a four-class Optical Coherence Tomography (OCT) image dataset encompassing Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal retina categories, we evaluated twelve architectures. These include general-purpose models (Alexnet, VGG11, VGG13, VGG16, VGG19, ResNet-18, ResNet-34, and ResNet-50) and OCT image-specific models (RetiNet, AOCT-NET, DeepOCT, and Octnet). The proposed technique reduced training epochs ranging from 4.35% to 58.27% for all architectures except Alexnet. Although the overall increase in accuracy ranges from 0.28% to 12.6%, with some architectures experiencing minor improvements, this is seen as acceptable considering the substantial reduction in training time. By achieving higher accuracy with fewer training epochs and eliminating the need for separate validation data, our methodology streamlines early stopping significantly. Statistical evaluations via Shapiro-Wilk and Kruskal-Wallis tests further affirm these results, showcasing the potential of this novel technique for efficient deep learning practices in scenarios constrained by time or computational resources.","url":"https://doi.org/10.1080/08839514.2024.2355760","authors":["Karri Karthik","Manjunatha Mahadevappa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-25T05:44:43Z","doi":"10.1080/08839514.2024.2355760","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.30525/978-9934-26-525-9-21","name":"Integrity as a component of modern medical higher education","source":"crossref","abstract":"2ND International Scientific Conference “Integrity, open science and artificial intelligence in academia and beyond: meeting at the crossroads” (December 17–18, 2024). Riga, Latvia : Baltija Publishing, 2024. 76 pages.","url":"https://doi.org/10.30525/978-9934-26-525-9-21","authors":["Iryna Vasylieva","Olha Nechushkina","Anna Laputko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-26T21:11:12Z","doi":"10.30525/978-9934-26-525-9-21","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/j.clindermatol.2024.06.017","name":"The potential role and restrictions of artificial intelligence in medical school dermatology education","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.clindermatol.2024.06.017","authors":["Jahleel Perrin","Vesna Petronic-Rosic","Vesna Petronic‐Rosic"],"tags":["Curriculum","Dermatology","Medical education","Medicine","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-24","doi":"10.1016/j.clindermatol.2024.06.017","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1109/wsai62426.2024.10829001","name":"Conference Committee","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wsai62426.2024.10829001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T19:22:07Z","doi":"10.1109/wsai62426.2024.10829001","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/prai62207.2024","name":"2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/prai62207.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-14T19:43:46Z","doi":"10.1109/prai62207.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1007/978-3-031-57208-1_9","name":"Digitization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-57208-1_9","authors":["Christian Posthoff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-21T07:02:17Z","doi":"10.1007/978-3-031-57208-1_9","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.34218/ijaird_02_02_013","name":"THE ROLE OF ARTIFICIAL INTELLIGENCE IN CYBER THREAT DETECTION","source":"crossref","abstract":"The continued advancement in AI calls for its application in different sectors, including cyber threat detection.The use of AI provides a remarkable step to enable critical growth and adjustment to help in attaining meaningful engagement with cyber threat detection.This article analyzes the application of AI in cyber security, models applied to help with cyber threat detection, advantages, and directions followed to remarkably ensure the best modeling of AI integration in cyber security.Notably, the article details that the application of AI for cyber threat detection comes with real-time monitoring and automated functionalities that enable critical adjustments to address the value and needs of AI adjustment to the desired level.More to the point, the application of AI demands vital information, bringing the challenge of privacy and confidentiality.This remarkable aspect helps to structure AI and place it in the best direction to achieve sustainable cyber security protection.A future direction for handling AI use in threat detection would include adversarial machine learning to enhance management and achievement of the proper detection and management of adversarial attacks on the AI framework.","url":"https://doi.org/10.34218/ijaird_02_02_013","authors":["Anirudh Khanna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-01T12:35:56Z","doi":"10.34218/ijaird_02_02_013","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1007/s44163-024-00119-2","name":"The role of explainability in AI-supported medical decision-making","source":"crossref","abstract":"Abstract This article positions explainability as an enabler of ethically justified medical decision-making by emphasizing the combination of pragmatically useful explanations and comprehensive validation of AI decision-support systems in real-life clinical settings. In this setting, post hoc medical explainability is defined as practical yet non-exhaustive explanations that facilitate shared decision-making between a physician and a patient in a specific clinical context. However, giving precedence to an explanation-centric approach over a validation-centric one in the domain of AI decision-support systems, it is still pivotal to recognize the inherent tension between the eagerness to deploy AI in healthcare and the necessity for thorough, time-consuming external and prospective validation of AI. Consequently, in clinical decision-making, integrating a retrospectively analyzed and prospectively validated AI system, along with post hoc explanations, can facilitate the explanatory needs of physicians and patients in the context of medical decision-making supported by AI.","url":"https://doi.org/10.1007/s44163-024-00119-2","authors":["Anne Gerdes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-29T13:01:51Z","doi":"10.1007/s44163-024-00119-2","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.61796/jaide.v1i10.973","name":"ARTIFICIAL INTELLIGENCE IN AUTOMATING PROCESSES IN BUSINESSES","source":"crossref","abstract":"Artificial Intеlligеncе (AI) is transforming thе landscapе of businеss opеrations through thе automation of procеssеs, еnhancing еfficiеncy, productivity, and accuracy. This articlе еxplorеs thе impact of AI on automating tasks across various industriеs, focusing on its advantagеs in strеamlining workflows, dеcision-making procеssеs, and rеducing opеrational costs. It also discussеs kеy challеngеs and futurе trеnds that businеssеs must addrеss to fully lеvеragе AI’s potеntial.","url":"https://doi.org/10.61796/jaide.v1i10.973","authors":["Sobirov Khurshed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-01T08:14:26Z","doi":"10.61796/jaide.v1i10.973","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1002/9781119846567.ch6","name":"Deep Learning‐Based Reduced Order Models for Cardiac Electrophysiology","source":"crossref","abstract":"The heart is a double pump comprises four chambers: two atria, the left atrium (LA) and the right atrium (RA), and two ventricles, the left ventricle (LV) and the right ventricle (RV), see Figure 6.1.The atrioventricular septum separates the atria from the ventricles; blood flows from the former to the latter through the tricuspid valve in the right part and through the mitral valve in the left part of the heart (Jarvik 2004).Non-oxygenated blood enters the RA through the superior and inferior venae cavae and gets pumped first into the RV, then through the pulmonary valve into the pulmonary circulation, where it is oxygenated by the lungs.Through the pulmonary veins the oxygenated blood coming from the lungs enter the left part of the heart, from which the blood is pumped again into the aorta and to the systemic circulation of the body (Altman and Dittmer 1971;Opie 2004).Muscle contraction and relaxation represent the pump function of the heart.In particular, tissue contraction is triggered by electrical signals self-generated in the heart and propagated through the myocardium thanks to the excitability of the cardiac cells, the cardiomyocites, see, e.g.Colli Franzone et al. (2014) and Klabunde (2011).When suitably stimulated, cardiomyocites produce a variation of the potential across the cellular membrane, called transmembrane potential.Its time evolution is usually referred to as action potential (AP), involving a depolarization and a polarization in the early stage of every heartbeat.The AP is generated by several ion channels (e.g.calcium, sodium, and potassium) that open and close, and by the resulting ionic currents crossing the membrane.Five phases of the AP are identified (see Figure 6.2) (Colli Franzone et al. 2014).During phase 0 (depolarization), the Na + ionic channels of the sarcolemma, the lipid membrane that encloses cardiomyocytes, open, allowing a free flow of positive ions into the cell.Consequently, the transmembrane potential passes from a negative resting value of -84 mV to positive values.We denote with phase 1 the rapid decrease of potential due to an outward flow of K + and Cl -ions, occurring after the inactivation of the Na + channels.Phase 2 is characterized by the balance of an inward current -caused by the transit of Ca 2+ -and an outward current -caused by the transit of K + , that maintains the potential almost constant (\"plateau\" phase).The repolarization of the cell -phase 3 -is a consequence of the closing of the Ca 2+ channels.At this Big Data Analysis and","url":"https://doi.org/10.1002/9781119846567.ch6","authors":["Stefania Fresca","Luca Dedè","Andrea Manzoni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-25T00:12:12Z","doi":"10.1002/9781119846567.ch6","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003450153-17","name":"Effective Use of Computational Biology and Artificial Intelligence in the Domain of Medical Oncology","source":"openalex","abstract":"Computational biology and artificial intelligence (AI) have become indispensable assets in the specialized field of medicine, especially in oncology, fundamentally altering our understanding and management of cancer. This chapter aims to provide insight into how computational biology and AI are reshaping oncological practices. It emphasizes their transformative potential in augmenting diagnostic precision, customizing therapeutic interventions, and fast-tracking the AI process. In terms of diagnostics, these technologies have proven invaluable in interpreting intricate medical images like X-rays, MRIs, and CT scans, thereby facilitating the early identification of cancerous abnormalities. Machine learning models have excelled in pinpointing oncological markers, thereby aiding clinicians in making more informed decisions. Additionally, computational simulations are increasingly employed to predict cancer progression and treatment responses, allowing for highly individualized patient care. In the drug discovery arena, AI algorithms examine extensive biological datasets to identify new oncological drug candidates, predict potential drug interactions, and streamline drug formulation, thereby accelerating the timeline from lab to clinic. Utilizing a blend of machine learning, deep learning, and predictive analytics, these computational tools equip oncologists with the resources necessary for precise diagnostics, tailored treatments, and efficient drug development. As the fields of computational biology and AI continue to evolve, they hold immense promise for revolutionizing oncological healthcare practices and enhancing patient outcomes.","url":"https://doi.org/10.1201/9781003450153-17","authors":["Sameeksha Saraf","Arka De","B. K. Tripathy"],"tags":["Domain (mathematical analysis)","Computer science","Computational biology","Medical physics","Biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-17","doi":"10.1201/9781003450153-17","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.4045/tidsskr.24.0514","name":"Artificial intelligence in medical publishing","source":"crossref","abstract":"Ragnhild Ørstavik er assisterende sjefredaktør i Tidsskriftet","url":"https://doi.org/10.4045/tidsskr.24.0514","authors":["Ragnhild Ørstavik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-09T05:00:13Z","doi":"10.4045/tidsskr.24.0514","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/cai59869.2024.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai59869.2024.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:50:37Z","doi":"10.1109/cai59869.2024.00004","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/ecai61503.2024","name":"2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ecai61503.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:35:26Z","doi":"10.1109/ecai61503.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/icssas64001.2024.10760850","name":"MediScan: Advanced Medical Imaging Analysis","source":"crossref","abstract":"MediScan is a web-based application designed for people to get first-hand opinions on their medical scans and stress the importance of seeking professional advice on the basis of the severity of the conditions identified. The application uses datasets that contain X Ray images of the bones (including un fractured and simple fractures) and MRI scan of the human brain that includes healthy brains and tumors like glioma, meningioma, and pituitary tumors. The study compares different machine learning techniques such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), Naïve Bayes, and Convolutional Neural Networks (CNN) for identification of the best model for such an estimation. Random Forest achieves $\\mathbf{8 5. 6 4 \\%}$ accuracy was selected as the best fit. The model was then integrated into a Streamlit-based interface to generate real-time predictions and expedite deployment. It is equipped with features that enable interactive data analysis and visualization that even a layman can easily use. MediScan demonstrates the opportunities and prospects of machine learning in medical imaging, it is an effective tool for the initial medical examination that can potentially save the time of specialists and contribute to timely medical interventions. This study demonstrates the important role that machine learning applications play in improving the ability to diagnose and access health services.","url":"https://doi.org/10.1109/icssas64001.2024.10760850","authors":["R Sanjeev Krishna","S Rhethika","Thanikanti Venkata Harshith","Pachila Shyamala","V. S. Kirthika Devi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-10T19:44:53Z","doi":"10.1109/icssas64001.2024.10760850","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.zemedi.2024.02.001","name":"Towards quality management of artificial intelligence systems for medical applications","source":"openalex","abstract":"The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user's in-house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system.","url":"https://doi.org/10.1016/j.zemedi.2024.02.001","authors":["Lorenzo Mercolli","Axel Rominger","Kuangyu Shi"],"tags":["Quality (philosophy)","Computer science","Artificial intelligence","Epistemology","Philosophy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-27","doi":"10.1016/j.zemedi.2024.02.001","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icaise65384.2024","name":"2024 3rd International Conference on Artificial Intelligence and Software Engineering (ICAISE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaise65384.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-19T18:40:32Z","doi":"10.1109/icaise65384.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-24001-0.01001-0","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24001-0.01001-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-01T09:14:15Z","doi":"10.1016/b978-0-443-24001-0.01001-0","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1148/ryai.11212024.podcast","name":"Episode 49: RSNA 2024: AI Insights and Key Highlights","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.11212024.podcast","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-21T14:53:24Z","doi":"10.1148/ryai.11212024.podcast","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-13244-5.20001-1","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13244-5.20001-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-28T09:57:57Z","doi":"10.1016/b978-0-443-13244-5.20001-1","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/aipe63225.2024","name":"2024 2nd International Conference on Artificial Intelligence and Power Engineering (AIPE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aipe63225.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-27T17:08:33Z","doi":"10.1109/aipe63225.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.29121/ijesrtp.v13.i2.2024.4","name":"ARTIFICIAL INTELLIGENCE APPROACHES FOR PALM PRINT FEATURE EXTRACTION AND CLASSIFICATION","source":"crossref","abstract":"","url":"https://doi.org/10.29121/ijesrtp.v13.i2.2024.4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-25T11:22:14Z","doi":"10.29121/ijesrtp.v13.i2.2024.4","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.18686/aitr.v2i2.4011","name":"Research on Information Technology Teaching under the Background of Artificial Intelligence","source":"crossref","abstract":"Modern science network technology and artificial intelligence have achieved rapid development in a short period of time, and have become the most critical scientific and technological elements of the contemporary era. With the continuous development of artificial intelligence technology, information technology teaching has also ushered in new opportunities and challenges. How to carry out effective information technology teaching research under the background of artificial intelligence is one of the topics that need to be deeply discussed in the current education field. On the one hand, the development of artificial intelligence technology provides more possibilities and means for information technology teaching. On the other hand, the development of artificial intelligence technology has also brought new challenges to information technology teaching. This paper takes artificial intelligence as the background, analyzes the influence of artificial intelligence on information technology education, and expounds the teaching application of information technology under the background of artificial intelligence.","url":"https://doi.org/10.18686/aitr.v2i2.4011","authors":["Yujun Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-07T07:32:31Z","doi":"10.18686/aitr.v2i2.4011","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.51219/jaimld/prayag-ganoje/240","name":"AI-Driven Predictive Analytics for Medical Device Failure Detection","source":"crossref","abstract":"This research paper explores the application of AI-driven predictive analytics for detecting failures in medical devices.With the increasing reliance on medical devices for patient care, ensuring their reliability and functionality is paramount.AI-based predictive analytics offers a proactive approach to identify potential failures before they occur, thereby enhancing patient safety and reducing maintenance costs.This paper examines the data characteristics, AI techniques, model development, implementation case study, benefits, challenges, and future research directions in the context of medical device failure detection.","url":"https://doi.org/10.51219/jaimld/prayag-ganoje/240","authors":["Prayag Ganoje"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-09T08:29:13Z","doi":"10.51219/jaimld/prayag-ganoje/240","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.14254/1795-6889.2024.20-1.8","name":"Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics","source":"crossref","abstract":"The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics. In the research process, we sought answers to three research questions: how trust in the use of AI for medical diagnostics can be measured; if societal openness to technology determines trust in the use of AI for medical diagnostics purposes; and if a higher level of trust in the use of AI for medical diagnostics influences the potential improvement in the quality of medical diagnostics as perceived by Poles. The authors' particular focus was on the following three constructs and the relationships between them: openness to new technologies (OP), willingness to trust AI in medical diagnostics (T), and perceived impact of AI application on the quality of medical diagnostic services (PI). A survey was conducted on a representative sample of 1063 Polish respondents to seek answers to the above questions. The survey was conducted using the CATI technique.","url":"https://doi.org/10.14254/1795-6889.2024.20-1.8","authors":["Joanna Ejdys","Magdalena Czerwińska","Romualdas Ginevičius"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-27T23:45:54Z","doi":"10.14254/1795-6889.2024.20-1.8","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.5256/f1000research.167182.r320762","name":"Peer Review Report For: Perspectives and guidance for developing artificial intelligence-based applications for healthcare using medical images [version 1; peer review: 2 not approved]","source":"crossref","abstract":"Artificial intelligence (AI) has significant potential to transform healthcare and improve patient care. However, successful development and integration of AI models requires careful consideration of study designs and sample size calculations for development and validation of models, publishing standards, prototype development for translation and collaboration with stakeholders. As the field is relatively new and rapidly evolving there is a lack of guidance and agreement on best practices for most of these steps. We engaged stakeholders in the form of clinicians, researchers from academia and industry, and data scientists to discuss various aspects of the translational pipeline and identified the challenges researchers in the field face and potential solutions to them. In this viewpoint, we present the summary of our discussions as a brief guide on the process of developing AI-based applications for healthcare using medical images. We organized the entire process into six major themes (i.e., The gaps AI can fill in healthcare, Development of AI models for healthcare: practical and important things to consider, Good practices for validation of AI models for healthcare: study designs and sample size calculation, Points to consider when publishing AI models, Translation towards products, Challenges and potential solutions from a technical perspective) and presented important points as a rule of thumb. We conclude that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.","url":"https://doi.org/10.5256/f1000research.167182.r320762","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T01:07:08Z","doi":"10.5256/f1000research.167182.r320762","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003348351-3","name":"Efficient Techniques for Disease Prediction from Medical Data","source":"crossref","abstract":"Individuals are susceptible to numerous ailments in today’s world, with people living highly automated lives under tremendous job pressure, both at home and at work. Such clinical disorders have recently been on increasing at an alarming pace. Therefore, the healthcare business must assume a prominent position soon, accountable for people’s health, a better society, and a successful country at large. Healthcare prices are also increasing as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data are being generated and used for diagnostic and detection approaches. These data might be used to obtain data for anticipating disease illnesses, commencing preventative measures, and improving treatment procedures long before the diseases progressed. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. Modern data mining methods are used to obtain and analyze characteristics that may reveal crucial and critical indications regarding the existence and progression of a medical condition.","url":"https://doi.org/10.1201/9781003348351-3","authors":["Bhupendra Kumar","Namita Rajput"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T08:27:41Z","doi":"10.1201/9781003348351-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.46871/eams.1456762","name":"The Rise of the Machines: Artificial Intelligence in Ophthalmology - A Boon or Bane?","source":"crossref","abstract":"Ophthalmology, the medical field dedicated to eye care, is undergoing a transformation due to the advent of artificial intelligence (AI). This review article explores the growing use of AI in ophthalmic practices, focusing on disease diagnosis, screening, and surgical guidance. We examine the potential benefits of AI-powered tools, including their ability to improve the accuracy, efficiency, and accessibility of eye care. However, we also acknowledge the ethical and practical challenges associated with this technology, such as algorithmic bias, the lack of explainability, and potential job displacement. We envision a future where ophthalmologists and AI collaborate to improve patient care and usher in a new era of ophthalmic practice.","url":"https://doi.org/10.46871/eams.1456762","authors":["İbrahim Edhem Yılmaz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-24T11:59:42Z","doi":"10.46871/eams.1456762","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.3390/cancers16101870","name":"Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review","source":"crossref","abstract":"Artificial intelligence (AI) is currently becoming a leading field in data processing [...]","url":"https://doi.org/10.3390/cancers16101870","authors":["Rafał Obuchowicz","Michał Strzelecki","Adam Piórkowski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-14T10:26:36Z","doi":"10.3390/cancers16101870","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.37472/2617-3107-2024-7-03","name":"CREATIVITY AND ARTIFICIAL  INTELLIGENCE: HUMAN PRIORITIES AND TECHNOLOGICAL POSSIBILITIES OF  THE ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"The author examines the problems of human creativity in the context of the emergence of modern artificial intelligence technologies. A scientific analysis of the areas of artificial intelligence application in the human creative process is carried out; it is shown that one of the most interesting and at the same time complex aspects of artificial intelligence is its ‘creative capabilities’ and its impact on the creative development of the individual. It is emphasised that the importance of analysing the artificial intelligence application in human creativity is due not only to the technological achievements of modern science, but also to the ethical, cultural and psychological dynamics that arise in the interaction between man and machine. The study and synthesis of scientific sources on creativity and the abilities of artificial intelligence allows us to conclude that today its application in the human creative process is possible in the following areas: artificial intelligence as a tool for ensuring the creative process, which accelerates the performance of routine tasks, provides quick feedback, and helps in collecting information; artificial intelligence as a method of expanding human creative abilities, i.e., performing tasks requiring divergent and convergent thinking or searching for and solving problems; artificial intelligence as a partner in human creative development, i.e., expanding the boundaries of imagination, fantasy, and problem vision; increasing motivation and interest in solving creative problems; influencing the formation and development of character traits and qualities of the human psyche necessary for productive creativity.","url":"https://doi.org/10.37472/2617-3107-2024-7-03","authors":["Svitlana Sysoieva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-27T18:39:04Z","doi":"10.37472/2617-3107-2024-7-03","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1201/9781003494027-19","name":"Artificial Intelligence and Computer Vision in Sustainable Vertical Farming","source":"crossref","abstract":"The integration of computer vision (CV) and artificial intelligence (AI) technologies into sustainable vertical farming is a key component of the Industry 5.0 revolution. This study provides a comprehensive analysis of AI’s and CV’s roles in sustainable vertical farming while highlighting the technologies’ promise, challenges, and effects on the agriculture sector. The next stage of agricultural technology development, known as Agriculture 5.0, is characterized by AI-driven autonomous decision-making. Because vertical farming, in particular, offers infrastructural support for data collection and autonomous solutions, controlled-environment agriculture (CEA) is a good fit for the use of AI and CV. There is a greater need for food due to the expanding global population, which calls for the development of new sustainable farming methods. The biggest problem nowadays is the need for more arable land and fertile soil all over the world. Combining hydroponics with vertical farming could be a more sustainable and fruitful solution for conventional farming. It also highlights AI’s importance to the agricultural sector through vertical planting and hydroponics. Developing an AI plant growth monitoring system based on CV is another focus of this chapter. The system’s AI and CV algorithm applications will help real-time and automated plant growth and their health evaluation. This AI-based plant growth monitoring system will benefit farmers, researchers, and plant enthusiasts by providing accurate and timely insights into plant health and growth. This chapter’s research aims to revolutionize plant growth monitoring uses in horticultural, agricultural, and environmental domains. The lines between the physical and digital domains blur as the work presents vertical farms intelligent and networked systems as illustrations of the paradigm shift by Industry 5.0. AI and CV in sustainable vertical farming have a revolutionary impact that opens the door for developing a reliable, efficient, and environmentally conscious food production system. This chapter briefly overviews he critical uses of AI and CV in sustainable vertical farming, emphasizing their significance as key components of the Industry 5.0 revolution. Integrating sustainable agriculture with state-of-the-art technology addresses issues of the present and paves the path toward a more resilient and ecologically conscious future.","url":"https://doi.org/10.1201/9781003494027-19","authors":["Archana Bhamare","Payal Bansal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-17T18:08:51Z","doi":"10.1201/9781003494027-19","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4324/9781003453901-1","name":"Introducing Artificial Intelligence, Co-Creation and Creativity","source":"crossref","abstract":"The emergence and proliferation of artificial intelligence (AI) tools for creative tasks is deeply impacting and rapidly transforming various creative sectors, allowing for a permanent state of innovation across different domains. Such fast-changing scenario challenges established assumptions and calls for a constant critical reflection among scholars and practitioners. This first edition of Artificial Intelligence, Co-creation and Creativity: The New Frontier for Innovation aims to explore the interlinks between humans, AI and creativity and the potential for innovative co-creative processes. It also aims to contribute to the current debate by providing a holistic insight on the topic, covering various issues and perspectives and enabling an accessible read to a broad audience.","url":"https://doi.org/10.4324/9781003453901-1","authors":["Francisco Tigre Moura"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T13:51:10Z","doi":"10.4324/9781003453901-1","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.4324/9781003468615-25","name":"Altruistic collective intelligence for the betterment of artificial intelligence","source":"crossref","abstract":"This chapter explores the potential of altruistic collective intelligence (CI) in advancing artificial intelligence (AI) technologies. It emphasizes the interplay of cooperation and competition – coopetition – in fostering CI among developers. Drawing empirical evidence from AIcrowd, a platform that leverages community-based development, the study illustrates how a risky “trial-and-fail” strategy can drive AI innovation through peer production. It highlights how diversity of perspectives enhances development, suggesting that CI contributes to more ethical and robust AI systems. This approach democratizes AI development and integrates a strong culture of altruism, encouraging sharing and transparency. The findings suggest that altruistic CI could reshape the future of AI, making it more inclusive, innovative, and ethically grounded.","url":"https://doi.org/10.4324/9781003468615-25","authors":["Thomas Maillart","Lucia Gomez","Mohanty Sharada","Dipam Chakraborty","Sneha Nanavati"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-29T11:07:37Z","doi":"10.4324/9781003468615-25","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-12-822000-9.12001-4","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-822000-9.12001-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-21T10:31:44Z","doi":"10.1016/b978-0-12-822000-9.12001-4","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1016/b978-0-443-22308-2.12001-3","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22308-2.12001-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T07:44:24Z","doi":"10.1016/b978-0-443-22308-2.12001-3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/icaica63239.2024","name":"2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaica63239.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T19:22:24Z","doi":"10.1109/icaica63239.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1142/9789811293993_0011","name":"Crowd Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0011","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0011","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1109/airc61399.2024","name":"2024 5th International Conference on Artificial Intelligence, Robotics and Control (AIRC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/airc61399.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-17T18:48:50Z","doi":"10.1109/airc61399.2024","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1142/9789811293993_0002","name":"Logic Foundation","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0002","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.1007/978-3-031-50312-2_3","name":"Artificial Intelligence New Wars, New Weapons, and New Players in International Relations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50312-2_3","authors":["Fatima Roumate"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-08T15:01:52Z","doi":"10.1007/978-3-031-50312-2_3","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21608/aiis.2024.407065","name":"“\"Pronominal reference and its importance in textual cohesion” and an applied model for discourse analysis on an article by Sheikh Ali Al-Tantawi using generative artificial intelligence technology “ChatGPT”","source":"crossref","abstract":"The study relied on discourse analysis, which is considered a rich and fertile specialty that has gained its cognitive merit and scientific sovereignty because it provides the researcher with different methodological approaches to analyzing different texts and discourses in view of the different linguistic and critical schools and their cognitive references. It has become an established science on its own with its own theories, cognitive foundations, subject matter, methods, means of analysis, and results. It has dealt with the concept of Consistency and its elements, the most important of which is referral and its meaning linguistically and idiomatically Its types and the effect of pronominal referral in particular on the cohesion of the text through the use of the generative artificial intelligence application ChatGPT. This is a first experience to present an applied model for analyzing discourse on an article by Sheikh Ali Al-Tantawi using the generative artificial intelligence technology ChatGPT, as the application presents the concept of coherence and its most important elements and contains a theoretical clarification of the referral. And everything related to it and the application of discourse analysis to pronominal reference, which is one of Tools that contribute, along with others, to achieving text cohesion and consistency. The referral tool, which plays a fundamental role in linking the parts of a single sentence on the one hand, and linking several sentences with each other in such a way that a comprehensive text or discourse is formed, as the role of textual referral in the cohesion of texts is explained based on an article. By Sheikh Ali Al-Tantawi. The researcher reviewed, revised and discussed the results of the analysis provided by ChatGPT and then presented it again through. The results concluded that: internal reference alone performs the function of cohesion, and that the correspondence between the pronoun and its referent helps to connect parts of the text and its flow. Therefore, there appears to be an urgent need to search for a way to remove confusion in the reference of the pronoun. The study also found that cohesion does not depend on the presence of reference or other means of textual cohesion alone. Rather, the reality of the matter is that these means - despite their importance - may not alone be sufficient in giving The coherence of the text.","url":"https://doi.org/10.21608/aiis.2024.407065","authors":["KAMERAA AL SAIED"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-02T09:36:06Z","doi":"10.21608/aiis.2024.407065","addedAt":"2026-09-01T01:47:48.920Z","updatedAt":"2026-09-01T01:47:48.920Z"},{"id":"doi:10.21608/aiis.2024.415848","name":"Factors affecting the acceptance of faculty members in Saudi universities to use artificial intelligence technologies in light of the Unified Theory of Acceptance and Use of Technology (UTAUT)","source":"crossref","abstract":"The study relied mainly on the unified theory of acceptance and use of technology (UTAUT) in the theoretical background. To achieve this, the descriptive survey approach was used, and the study tool, represented by the questionnaire, was presented to a sample of (196) faculty members. The results also revealed a statistically significant effect of the factors of the unified theory of acceptance and use of technology (UTAUT) (expected performance, expected effort, social impact, and available facilities) on the intention to use the technology. (ChatGPT), and the results showed that there is an indirect effect of the unified theory of acceptance and use of technology UTAUT with its factors (expected performance, expected effort, social impact, and available facilities) on the relationship between the intention to use and the usage behavior of (ChatGPT) technology among faculty members in some Saudi universities. The study recommended promoting the expansion of the use of (ChatGPT) technology among faculty members in some Saudi universities, by holding seminars and workshops, providing the necessary resources to employ this technology in university education.","url":"https://doi.org/10.21608/aiis.2024.415848","authors":["Faeeq Faeeqsaeed Al-Ghamdi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-07T09:10:09Z","doi":"10.21608/aiis.2024.415848","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.3389/frai.2024.1426761","name":"Ethics dumping in artificial intelligence","source":"crossref","abstract":"Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.","url":"https://doi.org/10.3389/frai.2024.1426761","authors":["Jean-Christophe Bélisle-Pipon","Gavin Victor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T01:10:48Z","doi":"10.3389/frai.2024.1426761","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aiot63253.2024.00038","name":"Multi-MedChain: Multi-Party Multi-Blockchain Medical Supply Chain Management System","source":"crossref","abstract":"The challenges of healthcare supply chain management systems during the COVID-19 pandemic highlighted the need for an innovative and robust medical supply chain. The healthcare supply chain involves various stakeholders who must share information securely and actively. Regulatory and compliance reporting is also another crucial requirement for perishable products (e.g., pharmaceuticals) within a medical supply chain management system. Here, we propose Multi-MedChain as a three-layer multi-party, multi-blockchain (MPMB) framework utilizing smart contracts as a practical solution to address challenges in existing medical supply chain management systems. Multi-MedChain is a scalable supply chain management system for the healthcare domain that addresses end-to-end traceability, transparency, and collaborative access control to restrict access to private data. We have implemented our proposed system and report on our evaluation to highlight the practicality of the solution. The proposed solution is made publicly available.","url":"https://doi.org/10.1109/aiot63253.2024.00038","authors":["Akanksha Saini","Arash Shaghaghi","Zhibo Huang","Salil S. Kanhere"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T13:23:11Z","doi":"10.1109/aiot63253.2024.00038","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.58496/mjaih/2024/007","name":"Exploring Deep Learning Methods Used in the Medical Device Sector","source":"crossref","abstract":"The healthcare sector is witnessing significant development in many aspects thanks to the effects of artificial intelligence or software, which has turned out to be the centre of attraction all over the world. This is evidence of a simple development in acquiring deep knowledge of the methods and areas in which they are used. Face detection, voice recognition, autonomous use, the defence industry, the security industry, and other fields may be displayed as examples that help complete tasks. This article surveys the impact of deep learning methods and practices in the medical device industry, and we also examine the distribution of multi-year data. It is divided into six categories: healthcare, big data and wearable technologies, biomedical code, image processing, diagnostics, and the Internet of Medical Things. As a result, the medical device industry has grown in recent years through deep learning techniques and the use of most research related to diagnosis and image processing.","url":"https://doi.org/10.58496/mjaih/2024/007","authors":["Fredrick Kayusi","Benson Turyasingura","Petros Chavula","Orucho Justine Amadi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-07T04:05:38Z","doi":"10.58496/mjaih/2024/007","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/j.engappai.2024.109275","name":"Neural network-based self-tuning control for hybrid electric vehicle engines","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109275","authors":["Ahtisham Urooj","Ali Nasir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-11T06:51:03Z","doi":"10.1016/j.engappai.2024.109275","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aiars63200.2024.00055","name":"Research on Hotel Service Robot Management System Based on Artificial Intelligence","source":"crossref","abstract":"How to face the error-prone situation of work service personnel brought about by the peak period of the hotel? This article provides automated and intelligent services through robots, aiming to improve the efficiency of hotel services and customer experience. This study evaluates the hotel service robot management system based on artificial intelligence by collecting customer feedback data. The survey covers the efficiency, accuracy and user satisfaction of the system, and conducts statistical analysis of the failure rate to evaluate the stability and reliability of the system. The research results show that during the test period, the hotel service robot management system based on artificial intelligence showed a failure rate of 1% to 5%. This result shows that the system is relatively stable and within an acceptable range. Customer feedback data shows that 93 % of customers are satisfied with the system, recognizing its efficiency, accuracy and ability to improve the customer experience.","url":"https://doi.org/10.1109/aiars63200.2024.00055","authors":["Haixia Qi","Zhifei Han","Xiumei Feng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-14T17:22:34Z","doi":"10.1109/aiars63200.2024.00055","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1007/978-3-658-45708-2_17","name":"Robots in Surgery: Empirical Findings Through Technification of Medical Work","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-658-45708-2_17","authors":["Regina Wittal","Carolyn Hettinger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-27T11:45:26Z","doi":"10.1007/978-3-658-45708-2_17","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.21037/jmai-24-392","name":"Healthcare leaders’ attitudes and perceptions on the use of artificial intelligence and artificial intelligence enabled tools in healthcare settings","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-392","authors":["Jitendra Singh","Brandi Sillerud","Jennifer Yednock","Casey Larson","Avery Steffen","Advitya Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-28T10:11:12Z","doi":"10.21037/jmai-24-392","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.jfma.2026.04.115","name":"Trends of artificial intelligence/machine learning-enabled medical devices in Taiwan, 2020-2024","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jfma.2026.04.115","authors":["Ting-Yao Wu","Hsiu-Hui Peng","Chia-Hung Chien"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-24T19:50:13Z","doi":"10.1016/j.jfma.2026.04.115","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108154","name":"Evaluation of Artificial Intelligence-Based Solid Waste Segregation Technologies through Multi-Criteria Decision-Making and complex q-rung picture fuzzy Frank aggregation operators","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108154","authors":["Fathima Banu M.","Subramanian Petchimuthu","Hüseyin Kamacı","Tapan Senapati"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-06T16:58:52Z","doi":"10.1016/j.engappai.2024.108154","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.62919/lkfj8763","name":"Artificial Intelligence for Online Career Guidance: A Comprehensive Framework","source":"crossref","abstract":"The International Conference on Cutting-Edge Developments in Engineering Technology and Science (ICCDETS-24) serves as a premier platform for researchers, academicians, engineers, and industry professionals to exchange ideas, present their latest research findings, and discuss the most recent advancements in engineering, technology, and science. ICCDETS-24 aims to foster collaboration and innovation across various disciplines by bringing together experts from around the world.","url":"https://doi.org/10.62919/lkfj8763","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-23T10:07:56Z","doi":"10.62919/lkfj8763","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.36315/2024v2end021","name":"Artificial Intelligence revolutionizing online education","source":"crossref","abstract":"The recent COVID-19 pandemic forced universities to move to online education, many of which would not have considered online courses without that impetus.Subsequently there has been a surge in online courses.Online courses take a long time to prepare and frequently the delivery and execution is of low quality.One way to overcome both limitations is to use the powerful paradigm of Artificial Intelligence, especially Large Language Models, to develop and deliver online courses.In this paper, we introduce \"AI Lecturer\", an innovative solution powered by a Large Language Model that is designed to improve the quality and delivery of lessons in educational institutions.The paper discusses related work in online course delivery and locates our solution in this space.The AI Lecturer functionality is presented and includes AI-powered automated lesson preparation, interactive teaching through AI avatars, and personalized homework generation and evaluation.A survey was carried out to evaluate student satisfaction and learning using AI Lecturer.The survey results will be presented.Respondents expressed a high degree of satisfaction with the user interface and overall experience, found the lifelike avatars engaging, and indicated they would recommend the platform to others.Finally, we will discuss the advantages and disadvantages of our platform and the challenges students faced when using it.","url":"https://doi.org/10.36315/2024v2end021","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-24T16:50:15Z","doi":"10.36315/2024v2end021","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/ieai62569.2024","name":"2024 5th International Conference on Industrial Engineering and Artificial Intelligence (IEAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieai62569.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-19T17:26:13Z","doi":"10.1109/ieai62569.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/b978-0-12-819471-3.00039-2","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-819471-3.00039-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T07:11:38Z","doi":"10.1016/b978-0-12-819471-3.00039-2","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1515/9781501519307","name":"Applying Artificial Intelligence to Project Management","source":"crossref","abstract":"This book describes the AI tools in concept and how they apply directly to project success. It also demonstrates the strategy and methods used to purchase and implement AI tools for project management. You will understand the difference between automating a task and changing it by using AI. Discover how AI uses data and the importance of data maintenance. Learn why projects fail and how using artificial intelligence for project management improves project success rates. The book features project management success stories and demonstrates how to leave behind that low project success rate for one that is 95 percent or higher. Supplemental teaching materials are available for use as a textbook.","url":"https://doi.org/10.1515/9781501519307","authors":["Paul Boudreau"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-06T10:46:06Z","doi":"10.1515/9781501519307","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/icaice63571.2024","name":"2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaice63571.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-06T18:35:40Z","doi":"10.1109/icaice63571.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.28945/5392","name":"Relying on Artificial Intelligence in Medical Coding Review","source":"crossref","abstract":"This case study focuses on the integration of artificial intelligence in medical coding procedures.","url":"https://doi.org/10.28945/5392","authors":["Kraig Kalby","Jessica Cox","Jason A Robb","Stacey Brandt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-31T20:44:21Z","doi":"10.28945/5392","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.4018/979-8-3693-3731-8.ch001","name":"Artificial Intelligence in Healthcare","source":"crossref","abstract":"Artificial intelligence (AI) has experienced fast enlargement in current years. The term AI refers to the intelligence exhibited by machines, contrasting with that of humans or other living organisms. Utilizing AI expertise in healthcare has led to significant advancements in disease diagnosis, organization, and prediction; benefit both patients and healthcare professional. This book chapter aims to delve into the conceptual framework of confidence in AI concerning health, grounded not only in expert opinion but also in rigorous conceptual research and solid empirical evidence. It specifically focuses on clinical decision-manufacture, patient data investigation and diagnostics, analytical medication, physical condition services organization, and clinical making decisions, providing enhanced analysis and conduct decision help in healthcare settings.","url":"https://doi.org/10.4018/979-8-3693-3731-8.ch001","authors":["Aruna Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T12:01:22Z","doi":"10.4018/979-8-3693-3731-8.ch001","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.3329/jbcps.v42i4.76633","name":"Artificial Intelligence in Medical Diagnostics","source":"openalex","abstract":"Artificial intelligence (AI) is an essential tool in the medical sector that contributes significantly to medical imaging, medical pathology, histology, genomic study, predictive analysis, etc. It is being increasingly used not only in breast and lung cancer diagnosis and screening, and genetic disease identification but also in personalized decision-making during the treatment of these diseases. The quality and availability of unbiased data is a key challenge. Algorithmic bias and interpretability are significant concerns as well. Improving the use of AI in medical diagnostics could be achieved through continuous monitoring, access to highly accurate datasets, and timely intervention by healthcare professionals. J Bangladesh Coll Phys Surg 2024; 42: 379-382","url":"https://doi.org/10.3329/jbcps.v42i4.76633","authors":["Swadesh Barman","Shanta Roy"],"tags":["Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-28","doi":"10.3329/jbcps.v42i4.76633","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1201/9781032632223-16","name":"MPPEDet","source":"crossref","abstract":"Medical Personal Protective Equipment (MPPE) is essential for safeguarding healthcare workers and patients from infectious diseases such as COVID-19, HIV, flu, measles, salmonella, and strep throat. MPPE includes gloves, gowns, masks, and face shields, protecting healthcare workers from biological hazards and preventing disease spread. The COVID-19 pandemic globally emphasized the importance of MPPE, resulting in increased demand. Proper selection, use, and disposal of MPPE are critical for infection prevention and worker protection. Conventional image processing struggles to detect real-time MPPE due to occlusion and overlap. We developed a real-time MPPE identification system based on YOLOv7, addressing these challenges. Firstly, the E-ELAN approach applies “Expand, Shuffle, and Merge cardinality” principles, enhancing learning capacity iteratively while preserving gradient path integrity. Secondly, the compound model scaling adjusts network depth and width, maintaining optimal architecture for varied object sizes. Finally, module-level re-parameterization reduces inference speed, improving detection accuracy. YOLOv7’s performance was evaluated using the CPPE-5 dataset comprising real-time images of coveralls, face shields, gloves, goggles, and masks, improving detection scores from 8.6% to 12.3%. Overall, our system outperforms popular object detection algorithms like YOLOv5, Fast-RCNN SSD, and Faster-RCNN in small and medium object detection.","url":"https://doi.org/10.1201/9781032632223-16","authors":["Prabu Selvam","M. Sumathi","M. Marimuthu","P. Saravanan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T15:35:43Z","doi":"10.1201/9781032632223-16","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1038/s41587-024-02270-8","name":"The sufficiency of disclosure of medical artificial intelligence patents","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41587-024-02270-8","authors":["Mateo Aboy","W. Nicholson Price","Seth Raker","Kathleen Liddell"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1038/s41587-024-02270-8","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/978-3-031-50312-2_9","name":"International Mechanisms on Peace and Security in the Age of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50312-2_9","authors":["Fatima Roumate"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-08T15:01:52Z","doi":"10.1007/978-3-031-50312-2_9","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1007/978-3-031-50312-2_8","name":"Malicious Use of Artificial Intelligence: New Challenges for International Psychological Security","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50312-2_8","authors":["Fatima Roumate"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-08T15:01:52Z","doi":"10.1007/978-3-031-50312-2_8","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1007/978-3-031-60840-7_27","name":"Embedding Artificial Intelligence into Wearable IoMT Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-60840-7_27","authors":["Steven Puckett","Vineetha Menon","Emil Jovanov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-26T23:04:39Z","doi":"10.1007/978-3-031-60840-7_27","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1142/9789811293993_fmatter","name":"FRONT MATTER","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_fmatter","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/icairc64177.2024","name":"2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icairc64177.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-04T18:44:58Z","doi":"10.1109/icairc64177.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.19163/1994-9480-2024-21-4-179-184","name":"Advantages of using medical products with artificial intelligence technology in providing medical care to the population","source":"crossref","abstract":"Goal: To consider the use of medical products with artificial intelligence technology (hereinafter referred to as AI) in healthcare and to identify trends in the perception and acceptance of the new technologies under consideration. The authors note that the use of AI, according to medical personnel, can improve the efficiency of medical care. Federal projects are being implemented in the regions of Russia, including the introduction of AI in various areas of activity. Methods: A survey of medical workers in the Sverdlovsk region. As a result, the authors emphasize that the further development and implementation of medical products with artificial intelligence can help improve the efficiency of diagnosis, treatment of diseases and improve the accessibility of medical care, with which more than 70 % of respondents agreed. Conclusions: For the successful implementation of such products, it is necessary to harmonize standards and regulations, train medical workers and provide technical support for relevant information systems.","url":"https://doi.org/10.19163/1994-9480-2024-21-4-179-184","authors":["Ivan M. Gryaznov","Irina A. Samkova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-21T13:31:50Z","doi":"10.19163/1994-9480-2024-21-4-179-184","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/j.aichem.2024.100053","name":"Advances in machine-learning approaches to RNA-targeted drug design","source":"crossref","abstract":"RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.","url":"https://doi.org/10.1016/j.aichem.2024.100053","authors":["Yuanzhe Zhou","Shi-Jie Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-06T13:40:41Z","doi":"10.1016/j.aichem.2024.100053","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1142/9789811293993_0006","name":"Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0006","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/b978-0-443-22308-2.00001-9","name":"Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22308-2.00001-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T07:43:28Z","doi":"10.1016/b978-0-443-22308-2.00001-9","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/cait64506.2024","name":"2024 5th International Conference on Computers and Artificial Intelligence Technology (CAIT)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cait64506.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-17T17:39:43Z","doi":"10.1109/cait64506.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aiea62095.2024","name":"2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiea62095.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T17:25:06Z","doi":"10.1109/aiea62095.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1117/12.3035260","name":"CPKD: channel-wise and pixel-wise relational knowledge distillation for medical image segmentation","source":"crossref","abstract":"Deep neural networks have demonstrated remarkable performance in medical image segmentation tasks. Nevertheless, their efficacy often comes at the cost of high model complexity and sluggish inference speeds. To address these limitations, we explore a new channel-level and pixel-level relational knowledge distillation (CPKD) method to efficiently transfer the abundant relational knowledge from the teacher network to the light-weight student network. Concretely, CPKD first captures the inter-class and intra-class semantic information of the teacher network at each channel and pixel, and then distills it to the student network in the form of knowledge. Channel-wise relational distillation utilizes the correlation between channels, while pixel-wise relational distillation utilizes the correlation between pixels. These two forms of relational knowledge complement each other in the learning process, which enables the student network to better simulate the behavior of the teacher network. Extensive experimental results on the medical image segmentation task show that CPKD can significantly improve the performance of the student network compared to other knowledge distillation methods, is highly sensitive to edge information, especially in HD95 metrics our method is far superior to the comparison methods, and has smaller model complexity at the meantime.","url":"https://doi.org/10.1117/12.3035260","authors":["Ziyun Xiong","Xiangchun Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T11:35:38Z","doi":"10.1117/12.3035260","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1002/9781119846567.ch16","name":"The Long Path to Usable AI","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119846567.ch16","authors":["Barbara Di Camillo","Enrico Longato","Erica Tavazzi","Martina Vettoretti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-25T00:12:12Z","doi":"10.1002/9781119846567.ch16","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/ecai61503.2024.10607556","name":"Securing the Internet of Medical Things: AI-Based Intrusion Detection","source":"crossref","abstract":"Recent advancements in the Internet of Medical Things (IoMT) have had an influence on traditional medical treatment as well as developed data communications in the Smart Healthcare scenario. Unfortunately, this has created a fertile ground for attackers. As a consequence, classic intrusion detection (ID) models as well as innovative detection strategies for IoMT applications have been implemented. Examining the call sequences made by the system processes is one way for determining a typical system behavior. In this paper, an ID system based on Multinomial Naive Bayes was developed. The suggested ID model performed well in terms of accuracy, detection rate, and false alarm rate.","url":"https://doi.org/10.1109/ecai61503.2024.10607556","authors":["Mariam Ibrahim","Ruba Elhafiz","Haneen Okasha","Abdallah Al-Wadi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:51:28Z","doi":"10.1109/ecai61503.2024.10607556","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/icaaic60222.2024.10575005","name":"Adoption of Medical AI and its Effect on Speciesism","source":"crossref","abstract":"AI can change healthcare by boosting medical decision-making and patient outcomes. However, the use of AI in medicine creates ethical issues, notably with speciesism. Participants’ awareness of AI in healthcare, perceptions of speciesism, ethical considerations, and willingness to use A I technologies were all gathered through the questionnaire. Descriptive statistics, chi-square tests, and correlation analyses established the relationship between speciesism and AI adoption. 70 % of participants were aware of healthcare AI technologies, but just 45 % showed a willingness to utilize them. 60 % of those hesitant cited ethical concerns regarding speciesism in AI decision-making. The investigation also found a substantial association between participants’ perceptions of speciesism and their willingness to adopt AI technologies, demonstrating that speciesist attitudes affected AI adoption. The study emphasizes ethical considerations and non-human animal interests in medical AI system development and implementation. This research contributes to the ethical use of AI in healthcare by highlighting the effect of speciesism on AI adoption and emphasizing the need to overcome speciesist prejudices to achieve fairness, inclusion, and equity. This study highlights the importance of speciesism awareness and education in medical AI adoption. This study illuminates the relationship between speciesism and medical AI adoption. To enable more inclusive, ethical, and equitable use of AI technologies in healthcare, it addresses ethical considerations and obstacles.","url":"https://doi.org/10.1109/icaaic60222.2024.10575005","authors":["Linda Abangbila","Xianmiao Li","George K. Agordzo","Akwi Helene Fomude"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T18:01:35Z","doi":"10.1109/icaaic60222.2024.10575005","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aidas63860.2024","name":"2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aidas63860.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-30T17:49:09Z","doi":"10.1109/aidas63860.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1142/9789811293993_0007","name":"Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0007","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aisp61711.2024","name":"2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aisp61711.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-12T18:19:32Z","doi":"10.1109/aisp61711.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1201/9781003399292-7","name":"Gender Disparity in Artificial Intelligence: Creating Awareness of Unconscious Bias","source":"crossref","abstract":"The field of Artificial Intelligence (AI) has shown an exponential growth over the past few years [ 1 ]. With the growth occurring at a rapid rate, AI has delved deep down into the lives of people, thereby influencing their behavior and opinion [ 2 ] at the individual and collective level [ 3 ]. Being a general-purpose technology, AI highly mediates the socio-cultural, economic, and political relationships of people [ 4 ]. AI holds enormous pot e strides made over decades to achieve gender equality have been stealthily reversed by gender-biased algorithms [ 2 ]. Technically AI system is not biased, instead it is shaped by biased people. So, it is the value of creators that is presented in the AI algorithms [ 7 ]. If observed closely, the gender biasness in the AI system isn’t intentional; it is just the reflection of human nature. As AI is created by humans, it is obvious that it will embody some of the biases upheld by humans themselves. Being in a male-dominated arena, AI relies on the implicit genderbiased inputs [ 8 ]; resulting in the promotion of gender inequality [ 9 ]. Therefore, the prime reason for gender-biased algorithm is the lack of gender diversity in the AI workforce [ 10 ].","url":"https://doi.org/10.1201/9781003399292-7","authors":["Sugyanta Priyadarshini","Sukanya Priyadarshini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-02T13:52:20Z","doi":"10.1201/9781003399292-7","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1201/9781003483571-13","name":"The Transformative Role of Artificial Intelligence in Finance and Economics","source":"crossref","abstract":"In this chapter, we explore the profound influence of artificial intelligence (AI) in the areas of finance and economics. We focus on the forefront sectors of applying AI in finance and economics, including algorithmic trading, risk management, credit scoring, fraud detection, customer service, portfolio management, sentiment analysis, supply chain finance, and economic forecasting. By disclosure of the optimized trading strategies generated by applying AI and machine learning and illustrating the influences they brought to the market efficiency, we get a closer view on how AI works in finance. We further investigate deeply into the alternative way of applying AI in risk assessment, creditworthiness evaluation, and real-time fraud detection. During the chapter, we fully discussed related ethical concerns AI enthusiast should take care when they are working on these sectors. We listed the biases human may have when they are working with AI and present how important data privacy is. In addition, the regulatory matters AI practitioner cannot ignore are listed. In the final synthesis, we once again summarize the transformation AI brings to finance and economics and point out the possible challenges to this area. Eventually, we are looking forward to the future research direction on this topic.","url":"https://doi.org/10.1201/9781003483571-13","authors":["Yasin Murat Kadioglu","Hasan Soydan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-13T14:01:36Z","doi":"10.1201/9781003483571-13","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1007/978-3-031-50312-2_11","name":"Psychological Warfare at the Age of Artificial Intelligence: The Case of Venezuela","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50312-2_11","authors":["Fatima Roumate"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-08T15:01:52Z","doi":"10.1007/978-3-031-50312-2_11","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1142/9789811293993_0005","name":"Statistical Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0005","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0005","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.32739/uha.id.45102","name":"Artificial intelligence triggers unemployment concerns!","source":"crossref","abstract":"Üsküdar University Head of the Department of Sociology Prof. Barış Erdoğan evaluated the effects of artificial intelligence on human life.","url":"https://doi.org/10.32739/uha.id.45102","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-03T14:10:12Z","doi":"10.32739/uha.id.45102","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1108/itse-12-2023-0240","name":"Adopting artificial intelligence driven technology in medical education","source":"crossref","abstract":"Purpose Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education. Design/methodology/approach This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF). Findings According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI. Practical implications By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education. Originality/value The finding of this study should be considered when successful implementation of AI in education is in progress.","url":"https://doi.org/10.1108/itse-12-2023-0240","authors":["Mohammadhiwa Abdekhoda","Afsaneh Dehnad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-25T04:08:57Z","doi":"10.1108/itse-12-2023-0240","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/medai62885.2024.00035","name":"Gene Name Recognition in Gene Pathway Figures Using Siamese Networks","source":"crossref","abstract":"The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://github.com/MuhammadAzam636/Siamese-Network.","url":"https://doi.org/10.1109/medai62885.2024.00035","authors":["Micheal Olaolu Arowolo","Muhammad Azam","Fei He","Mihail Popescu","Dong Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-25T19:17:43Z","doi":"10.1109/medai62885.2024.00035","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/acai63924.2024","name":"2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acai63924.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-03T18:29:34Z","doi":"10.1109/acai63924.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1142/9789811293993_0008","name":"Transfer Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0008","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.32388/lob177","name":"Artificial Intelligence and Organizational Change","source":"crossref","abstract":"This article explores some issues close to what may be considered the implications of AI, both preliminary at a society level but mostly its effects on organizational change. What is AI's purpose, its scope, and its lasting effects on the dynamics of organizational change, cultural values, and organization management? The AI culture arises from making technology a joint venture with human abilities, allowing human and technology to become almost all in one: either human thinking through a machine or the human self-image in front of himself. The risk about AI does not arise by itself but from its designers and purpose, more so when it is the government that comes to be the “Big Thinker”. Businesses instead may follow a self-regulation policy following their principles and corporate values. The article has four sections: introduction, the speed of technological transformation: a brief time span: 2022-2023, organizational culture and artificial intelligence, case studies, and concluding remarks. The preliminary conclusion is that AI has strengths and weaknesses. AI tools learn to achieve higher efficiency faster than people in charge of any specific task to be assisted by AI, leading to productivity increases and cost reductions quicker than the standard framework for management decisions. Empirical evidence suggests that the productivity gains are focused on low-skill and less experienced agents. This outcome arises from generative AI's ability to “think properly” to capture the best patterns of behavior as a benchmark from the most productive individuals. But there are relevant risks of work displacement.","url":"https://doi.org/10.32388/lob177","authors":["Erico Ernesto Wulf Betancourt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-29T11:11:28Z","doi":"10.32388/lob177","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1148/ryai.240261","name":"A New Era of Text Mining in Radiology with Privacy-Preserving                     LLMs","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.240261","authors":["Tugba Akinci D’Antonoli","Christian Bluethgen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-20T13:53:40Z","doi":"10.1148/ryai.240261","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/iai63275.2024.10729956","name":"Cover","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iai63275.2024.10729956","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-30T17:45:18Z","doi":"10.1109/iai63275.2024.10729956","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aims61812.2024","name":"2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aims61812.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T17:22:33Z","doi":"10.1109/aims61812.2024","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/c2023-0-00229-1","name":"Machine Learning and Artificial Intelligence in Chemical and Biological Sensing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2023-0-00229-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:26:34Z","doi":"10.1016/c2023-0-00229-1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1787/73d417f9-en","name":"Using AI in the workplace","source":"crossref","abstract":"AI can bring significant benefits to the workplace. In the OECD AI surveys of employers and workers, four in five workers say that AI improved their performance at work and three in five say that it increased their enjoyment of work. But the benefits of AI depend on addressing the associated risks. Taking the effect of AI into account, occupations at highest risk of automation account for about 27% of employment in OECD countries. Workers also express concerns around increased work intensity, the collection and use of data, and increasing inequality. To support the adoption of trustworthy AI in the workplace, this policy paper identifies the main risks that need to be addressed when using AI in the workplace. It identifies the main policy gaps and offers possible policy avenues specific to labour markets.","url":"https://doi.org/10.1787/73d417f9-en","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-15T09:43:15Z","doi":"10.1787/73d417f9-en","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.31124/advance.23247530","name":"Journal on Artificial intelligence .pdf","source":"crossref","abstract":"&lt;p&gt; This study examined artificial intelligence adoption and marketing performance of quoted manufacturing firms in Nigeria. The study adopted the positivism research philosophy and correlational research design. The population of the study consisted of 426 managers drawn from the 71 quoted manufacturing firms in Nigeria. The managers include branch managers, operational managers, production managers, marketing managers and sales managers of the firms. A sample size of 206 managers was used for the study. The sample size was determined mathematically using the Taro Yamene’s formula. A structured questionnaire was used to obtain data from the respondents. The data collected were analyzed statistically while the hypotheses were tested using Spearman Rank Order Correlation Coefficient (rho). The SPSS version 23.0 was used to perform the bivariate analysis. The findings revealed that the application of artificial intelligence technologies in marketing operations has a significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also revealed that the application of artificial intelligence technologies in marketing operations has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. The study equally confirmed that artificial intelligence capabilities have a strong and significant relationship with sales growth of quoted manufacturing firms in Nigeria. The study also reported that artificial intelligence capabilities has a strong and significant relationship with market share growth of quoted manufacturing firms in Nigeria. Based on these findings, it was concluded that artificial intelligence adoption significantly relate to marketing performance of quoted manufacturing firms in Nigeria. Based on these findings and conclusion, it was recommended that quoted manufacturing firms in Nigeria especially those that are experiencing poor marketing performance should adopt artificial intelligence technologies in their marketing operations as it would improve their marketing performance. &lt;/p&gt;","url":"https://doi.org/10.31124/advance.23247530","authors":["Kingsley Asemota","Chukwudi Ifekanandu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-26T15:42:13Z","doi":"10.31124/advance.23247530","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.59704/cc9e550a1804c8a6","name":"Of Artificial Intelligence and Fundamental Rights Charters","source":"crossref","abstract":"The Council of Europe has adopted the Framework Convention on Artificial Intelligence – the first of its kind. Notably, the Framework Convention includes provisions specifically tailored to enable the EU’s participation. At the same time, the EU has developed its own framework around AI. I argue that the EU should adopt the Framework Convention, making an essential first step toward integrating the protection of fundamental rights of the EU Charter. Ultimately, this should create a common constitutional language and bridge the EU and the Council of Europe to strengthen fundamental rights in Europe.","url":"https://doi.org/10.59704/cc9e550a1804c8a6","authors":["Giovanni Zaccaroni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-03T08:03:11Z","doi":"10.59704/cc9e550a1804c8a6","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.2139/ssrn.4750643","name":"Artificial Intelligence Challenges to Data Protection","source":"crossref","abstract":"Some already considered AI as a new industrial revolution, recent developments in AI and related solutions such as machine learning, have radically changed everyday life such as self-driven cars. This revolution inevitably also brings changes to regulatory and social fields, creating new legals gaps to be filled and challenges that only a few decades ago, seemed to belong to a distant future. It is undeniable that the use of these intelligent systems and algorithms also brings a lot of risks that are essential to foreseeing and monitoring. For example, illegitimate compression of the privacy sphere of individuals and their data safety. The possibility of data in the hands of a few companies may favor the creation of new monopolies or be a source of distortions or the risk than an extreme profiling of the choices of individuals may select and filter the contents and information to be proposed to each of them to compromising their freedom of choice and the ability to self-determine. Additionally, there may be need to regulate specific issues related to the use of AI, such as the allocation of liability for conduct of intelligent machines that are not applicability of the legislation that is why in the light of existing of these systems legal principles are required to adopted.","url":"https://doi.org/10.2139/ssrn.4750643","authors":["Apostolos Vlachos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-03T11:12:59Z","doi":"10.2139/ssrn.4750643","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1201/9781003589273-40","name":"Advancements in artificial intelligence for thyroid cancer detection","source":"crossref","abstract":"In times there has been a rise, in the use of artificial intelligence (AI) in healthcare systems especially in the early detection of diseases. One key focus area is the identification of thyroid diseases, including cancer, which s crucial for effective treatment and improved patient outcomes. This study aims to conduct a review and analysis of literature on AI techniques used to detect and characterize thyroid gland related cancers. The significance of datasets related to thyroid cancer (TCDs) is emphasized in uncovering characteristics and methods for creating systems driven by AI. This study delves into the results of an evaluation that sheds light on both the advantages and constraints as possible progressions, in utilizing artificial intelligence for the detection of thyroid cancer.","url":"https://doi.org/10.1201/9781003589273-40","authors":["K.T. Anil Kumar","S.V. Shashikala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-06T15:40:21Z","doi":"10.1201/9781003589273-40","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1109/aiars63200.2024.00128","name":"An Interpersonal Communication Analysis Model for Privacy Protection Systems in the Era of Artificial Intelligence","source":"crossref","abstract":"With the advancement of science and technology, artificial intelligence technology has penetrated into all aspects of our lives, especially in the field of online privacy protection dissemination, and its application research has received more and more attention. Artificial intelligence technology, with its excellent information processing capabilities, provides new possibilities for the dissemination of online health knowledge. The article provides an overview of the main technologies and methods adopted in this field, and points out their important significance in engineering practice. Firstly, research key technologies such as data encryption, access control, and user consent to ensure the security and privacy of data during storage and transmission. On this basis, research was conducted on techniques such as data anonymity. At the same time, this article also points out the important role of the principle of minimizing information, transparency in establishing user trust, and compliance with relevant regulations. Through the application of artificial intelligence technology, the dissemination of online privacy protection will be able to be carried out more effectively.","url":"https://doi.org/10.1109/aiars63200.2024.00128","authors":["Yiming Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-14T17:22:34Z","doi":"10.1109/aiars63200.2024.00128","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:48.921Z"},{"id":"doi:10.1016/j.ard.2026.06.018","name":"Effects of air pollution on disease activity in patients with rheumatoid arthritis.","source":"europepmc","abstract":"Objectives This study aimed to evaluate the associations of air pollution with disease activity and flare rate in patients with rheumatoid arthritis (RA). Methods This prospective cohort study included patients with RA who were treated at a tertiary medical centre in South Korea between January 2021 and December 2024. Air pollution exposure was estimated using monthly mean concentrations of 6 air pollutants (sulfur dioxide, nitrogen dioxide, ozone, carbon monoxide, particulate matter [PM] 10 , and PM 2.5 ). Disease activity and flares were recorded longitudinally at each outpatient visit. Associations between air pollution and RA disease activity or flare were analysed using linear and logistic generalised estimating equations, adjusting for demographic characteristics, serologic status, medication use, socioeconomic factors, and meteorological variables. As a sensitivity analysis, a case-crossover design using daily air pollutant concentrations preceding each visit was applied, with conditional logistic regression to assess within the same patient. Results A total of 12,583 outpatient visits from 1070 patients were analysed. Among 6 pollutants, PM 2.5 was significantly associated with an increased risk of flare (adjusted odds ratio [OR]: 1.113 [95% CI: 1.017-1.218]) and with higher Disease Activity Score based on 28 joints (DAS28) with C-reactive protein (CRP), Clinical Disease Activity Index, 28-tender joint count, and 28-swollen joint count. In addition, the association between PM 2.5 and DAS28-CRP was more pronounced among women and nonsmokers. In the case-crossover analysis, prolonged cumulative exposure to PM 2.5 over >2 weeks was associated with an increased risk of RA flare. Conclusions Exposure to air pollutants, particularly PM 2.5 , was associated with increased RA disease activity and flare risk. Further studies are warranted to determine whether improving air quality can reduce disease activity in patients with RA.","url":"https://doi.org/10.1016/j.ard.2026.06.018","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2028","doi":"10.1016/j.ard.2026.06.018","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1056/cat.26.0037","name":"The Value Opportunity from Artificial Intelligence in U.S. Health Care Spending.","source":"europepmc","abstract":"Artificial intelligence (AI) offers the potential to improve productivity and reduce waste across the U.S. health care system. Quantifying achievable value from AI technologies can inform organizational strategies and national spending projections. This study aimed to estimate the annual run-rate net value achievable within 5 years through full adoption of AI use cases across major health care stakeholders and domains without compromising quality or access. This study applied observed implementation benchmarks and national expenditure data to 12 AI-enabled domains spanning five stakeholder groups - private payers, public payers, hospitals, physician groups, and other sites of care - collected during the period of October 2023 and March 2024. The financial impact was estimated using 2024 data (the latest year for which full data are available) of U.S. health care expenditures, using ranges from published literature and observed implementation evidence. No human participants were involved. The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs). Annual net value (2024 U.S. dollars) and percentage reductions in total, administrative, and medical expenses by stakeholder group and AI technology were assessed. Assuming full adoption - i.e., a health care environment in which all stakeholders are all-in on AI adoption for all domains across administrative and medical expenses for all designated AI technologies, in this case, ML, NLP, and genAI - AI could generate US$438.9-US$810.7 billion in annual net value (5.7%-10.6% of the US$7.7 trillion in 2024 total health care expenses). GenAI-led use cases account for 54.4%-55.9% of the total AI opportunity. Administrative expenses could decline by US$120.1-US$252.0 billion (9.4%-19.8%) and medical expenses by US$318.8-US$558.6 billion (5.0%-8.7%). By stakeholder group, estimated annual values are as follows: private payers, US$205.1-US$357.0 billion (7.0%-12.1%); hospitals, US$130.6-US$219.5 billion (8.6%-14.4%); physician groups, US$29.0-US$89.9 billion (2.8%-8.6%); public payers, US$62.4-US$107.4 billion (5.1%-8.8%); and other sites of care, US$11.9-US$36.9 billion (1.3%-4.0%). Financial impact is concentrated in health care management, provider relationship management, and claims management for payers and in clinical operations and quality and safety for providers. Labor productivity and administrative automation account for the largest share of impact. Full implementation of these AI technologies could reduce U.S. health care spending by up to US$810.7 billion within 5 years without compromising quality and access. Realizing this full potential would require properly aligned incentive models (e.g., between physicians and the hospital), as well as coordinated organizational change, workflow redesign, and infrastructure investment, especially in clinical domains. Responsible scaled adoption supported by policy and industry efforts could help bend the U.S. health care cost curve.","url":"https://doi.org/10.1056/cat.26.0037","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1056/cat.26.0037","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1016/j.neuroimage.2026.122183","name":"Altered olfactory adaptation of primary olfactory cortex-hippocampus-parietal lobe in Alzheimer's disease continuum: An olfactory task fMRI study.","source":"europepmc","abstract":"Olfactory adaptation, the progressive reduction of neural responses to repeated odor stimulation, is closely linked to cognitive function and is altered in Alzheimer's disease (AD). However, its evolution across biomarker-defined stages and relationship with plasma p-tau217 remain unclear. We studied 168 participants classified by plasma p-tau217: cognitively normal (p-tau217-NC, n = 37; p-tau217+NC, n = 8), subjective cognitive decline (p-tau217-SCD, n = 57; p-tau217+SCD, n = 16), and mild cognitive impairment (p-tau217-MCI, n = 40; p-tau217+MCI, n = 10). Odor-induced fMRI with four concentrations (0.032%, 0.1%, 0.32%, 1.0%) presented in a fixed ascending order, although concentration effects could not be fully separated from time-related factors and other confounders, to assess activation in the primary olfactory cortex (POC), hippocampus (HP), and parietal lobe (PL). Receiver operating characteristic (ROC) analyses were performed using logistic regression models. In NC groups, adaptation emerged at 0.1% and 0.32% odor conditions. In p-tau217-SCD, POC adaptation was delayed to 1.0% odor condition, HP was largely preserved, and PL was dysregulated. p-tau217+SCD and MCI groups showed delayed and dysregulated adaptation across all regions. Odor adptations were associated with plasma p-tau217 levels and olfactory memory (p_unc 〈 0.05, p_FDR 〉 0.05). Furthermore, plasma p-tau217 partially mediated the relationship between adaptation-related alterations and olfactory memory. ROC analyses indicated that olfactory adaptation may distinguished individuals across disease stages, require further confirmation in independent cohorts. These findings reveal that impaired olfactory adaptation may represent an early signature associated with AD continuum, particularly in SCD and p-tau217-positive stages.","url":"https://doi.org/10.1016/j.neuroimage.2026.122183","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.neuroimage.2026.122183","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1148/ryai.260110","name":"Simulation of AI-driven CT Queue Prioritization in the Emergency Department.","source":"pubmed","abstract":"Purpose To evaluate the effect of an artificial intelligence (AI)-driven CT queue prioritization system on emergency department CT wait times using a discrete-event simulation. Materials and Methods Multimodal data from 313&#x2009;966 emergency department visits (August 2020-August 2024) were retrospectively analyzed. A gradient boosting machine model was trained to predict the clinical actionability of CT studies at the time of order. Discrete-event simulations, calibrated to real-world operations, were conducted to compare a first-in, first-out policy against an AI-driven prioritization policy on the internal test set of 20&#x2009;795 studies (March-August 2024). Wait times for actionable and nonactionable studies were compared between the first-in, first-out and AI-based dispatch policies, with bootstrap 95% CIs, and ceiling analyses using perfect predictions. Results Compared with the first-in, first-out policy, the AI-based dispatch policy reduced median wait times for actionable studies in the internal test set (7637 of 20&#x2009;795 [36.73%]) by 10.75 minutes (95% CI: -12.40, -9.10) and 90th-percentile wait times by 43.36 minutes (95% CI: -50.58, -36.80). The proportion of actionable findings obtained within 1 hour increased from 48.33% (3691 of 7637) to 57.30% (4376 of 7637). For nonactionable studies, the median wait time was reduced by 5.80 minutes (95% CI: -7.00, -4.40), but the 90th-percentile wait time increased by 14.46 minutes (95% CI: 6.40, 23.20). The model captured 80%-87% of the maximum benefit achievable with perfect predictions. Conclusion AI-driven CT queue prioritization can help reduce the time to diagnosis for critical findings with minimal disruption to lower-acuity patients, using existing data streams and without the need for additional hardware. Keywords: Artificial Intelligence, CT, Emergency Department, Discrete-Event Simulation, Workflow, Queue Prioritization Supplemental material is available for this article. &#xa9; RSNA, 2026 See also the editorial by Pfeiffer and Thalhammer in this issue.","url":"https://doi.org/10.1148/ryai.260110","authors":["Silva E","Tang K","Chiacchia S","Zhang X","Langlotz C","Kim D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1148/ryai.260110","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3760/cma.j.cn112144-20260509-00293","name":"[Research and application of artificial intelligence in tooth extraction].","source":"europepmc","abstract":"Tooth extraction is a common procedure in oral clinical practice. However, imaging interpretation, risk assessment, and perioperative management remain challenging for complex cases. In recent years, artificial intelligence(AI) has been increasingly applied to oral image recognition, anatomical structure segmentation, extraction difficulty assessment, complication risk prediction, surgical planning, and robot-assisted surgery, providing new approaches for improving clinical efficiency and supporting clinical decision-making. Nevertheless, several limitations remain in current studies. The identification of high-risk anatomical structures does not directly translate into actual surgical risk, model prediction endpoints are often disconnected from real-world clinical outcomes, insufficient coverage of rare imaging features limits the clinical applicability of existing models and problems related to uneven data quality, algorithmic limitations, and study design defects coexist. In addition, issues concerning clinical accessibility, responsibility delineation, and ethical regulation have also begun to emerge. We believe that AI should be positioned as a physician-led clinical assistive tool rather than an independent decision-maker. Future research should focus on the construction of high-quality datasets covering rare imaging features, multicenter prospective validation based on clinical outcomes, and the establishment of standardized human-machine collaboration frameworks, so that AI can truly contribute to enhanced safety, optimized clinical workflows, and better patient outcomes in complex tooth extraction.","url":"https://doi.org/10.3760/cma.j.cn112144-20260509-00293","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112144-20260509-00293","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ejrad.2026.113154","name":"Association of computed tomography-derived body composition and complications after radical gastrectomy for gastric cancer: A systematic review and meta-analysis.","source":"europepmc","abstract":"Background Predicting postoperative complications (POCs) in gastric cancer (GC) patients is increasingly important in clinical practice. Computed tomography (CT)-derived body composition (BC) parameters, reflecting nutritional and physiological status, have emerged as potential prognostic indicators. This systematic review and meta-analysis aimed to evaluate the association between preoperative muscle and fat parameters and POCs in GC patients. Methods A comprehensive search of PubMed, Web of Science, and Embase was conducted up to December 31, 2024. Two reviewers independently selected studies, extracted data, and assessed quality using the Quality in Prognosis Studies tool. This review specifically focused on studies that evaluated CT-derived BC parameters as independent prognostic factors through multivariable analyses. The primary outcome was the incidence of POCs within 30 days of radical gastrectomy. Pooled risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model for parameters where quantitative synthesis was feasible. Results Out of 4736 identified records, 21 studies were included. All included studies provided adjusted effect estimates for BC parameters from multivariable analyses. Eight BC measures were assessed, including three muscle-related, two fat-related, and three composite metrics. Only sarcopenia defined by skeletal muscle index (SMI) met the criteria for meta-analysis. Pooled analysis of these adjusted estimates confirmed that sarcopenia was significantly and independently associated with POCs (RR = 2.73, 95% CI: 1.83-4.08, P = 0.010, I 2 = 66%). Conclusions CT-derived sarcopenia, defined by SMI, is an independent risk factor associated with increased POCs in GC patients. However, variability in measurement methods and outcome definitions limits the strength and clinical applicability of current evidence. Future studies should standardize BC assessment and reporting to better guide surgical risk stratification.","url":"https://doi.org/10.1016/j.ejrad.2026.113154","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejrad.2026.113154","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1097/paf.0000000000001163","name":"Artificial Intelligence in Forensic Sciences: A Comprehensive Bibliometric Analysis of Global Research Trends (2000-2024).","source":"europepmc","abstract":"Artificial intelligence (AI) technologies have begun to be used more frequently in forensic sciences, just as in every other field of medicine, and have become part of research topics. This study aims to conduct a comprehensive bibliometric analysis to systematically examine publication dynamics, collaboration networks, citation patterns, and thematic trends of AI in forensic sciences. A comprehensive literature search was conducted using the keywords \"forensic medicine,\" \"forensic sciences,\" \"forensic pathology,\" autopsy, \"artificial intelligence,\" \"deep learning,\" and \"machine learning\" and analyzed using R Bibliometrix and VOSviewer software. The analysis identified 3434 authors, 8372 keywords, and 6276 references across 730 articles. A publication growth rate of 24.16% and international collaboration rate of 28.77% were observed. The United States emerged as the most productive country. The leading institutions were University of California and Shanxi Medical University. Research trends concentrated on diagnosis, bone age estimation, and forensic odontology. Publications appeared predominantly in high-impact journals including Forensic Science International and Nature Communications. AI and machine learning research in forensic sciences has experienced rapid growth. Although these technologies offer substantial potential to enhance accuracy, efficiency, and standardization in forensic investigations, critical challenges such as model interpretability and ethical considerations require further attention for future implementation.","url":"https://doi.org/10.1097/paf.0000000000001163","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/paf.0000000000001163","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1097/rlu.0000000000006589","name":"Primary Osteosarcoma of the First Metatarsal With Multiple Metastases on 18F-FDG PET/CT.","source":"europepmc","abstract":"Primary osteosarcoma predominantly originates in the metaphyseal region of long bones, but rarely in the metatarsals. We present the 18F-FDG PET/CT findings in a case of primary osteosarcoma of the first metatarsal with multiple metastases confirmed by histopathology.","url":"https://doi.org/10.1097/rlu.0000000000006589","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/rlu.0000000000006589","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1097/md.0000000000050122","name":"Artificial intelligence performance in the emergency medicine subspecialty examination conducted in Türkiye.","source":"europepmc","abstract":"Emergency medicine specialists often pursue subspecialty training worldwide. In Türkiye, subspecialization in critical care medicine was introduced in March 2024, with the first entrance examination for subspecialty training in medicine (YDUS) examination having been conducted on December 15, 2024 by the Measurement, Selection, and Placement Center. Medical applications of artificial intelligence (AI), particularly GPT-4 Omni (GPT-4o), GPT-4, and Gemini-Advanced, have garnered considerable attention. This study aimed to evaluate the performance of these AI models in answering emergency medicine YDUS questions, marking the first assessment of the role of AI in this examination. The performance of 3 AI models (GPT-4, GPT-4o, and Gemini-Advanced) on questions from the emergency medicine YDUS examination was evaluated. The examination included 60 multiple-choice questions, of which 10% were publicly available. Questions were classified as clinical or factual. Responses of the AI models were analyzed using Cochran Q test as the omnibus test, with exact Bonferroni-adjusted McNemar tests for pairwise comparisons where applicable. No significant differences in the correct responses for both clinical and factual questions were observed between each AI model (P values: GPT-4o, 1.000; Gemini-Advanced, .554; and GPT-4, 1.000). GPT-4o significantly outperformed Gemini-Advanced in clinical (92.6% vs 70.4%) and factual questions (90.9% vs 78.8%) (P values: clinical, .021; factual, .039). A comparison of the overall performance showed an omnibus significant difference (P = .001); however, post hoc pairwise comparisons revealed that only GPT-4o (91.7%) significantly outperformed Gemini-Advanced (75%), whereas GPT-4 (88.3%) did not show a statistically significant difference from Gemini-Advanced after adjustment. This study found that both GPT-4o and GPT-4 significantly outperformed Gemini-Advanced in answering Turkish emergency medicine YDUS questions. While GPT-4o achieved the highest numerical accuracy, there was no statistically significant difference between GPT-4o and GPT-4. Both models demonstrated high accuracy in this examination dataset. Although these findings highlight their potential as supplementary learning tools, strong examination performance does not establish clinical readiness or definitive educational usefulness. Gemini-Advanced exhibited weaker performance but frequently advised expert consultation. However, this study did not formally evaluate ethical behavior, safety, or the appropriateness of these refusals.","url":"https://doi.org/10.1097/md.0000000000050122","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000050122","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijrobp.2026.08.020","name":"Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.","source":"europepmc","abstract":"Background The growing population of cancer survivors faces immense monitoring burdens due to rigid follow-up guidelines, such as the intensive surveillance schedules recommended by the National Comprehensive Cancer Network (NCCN). To address this issue, we engineered a multimodal artificial intelligence (AI)-based decision support system that integrates biological domain data (magnetic resonance imaging) and physical treatment domain data (radiotherapy dose maps) to guide individualized care. Methods and materials Using stage II nasopharyngeal carcinoma (N=2,148 across five centers) as a model, we first implemented a target trial emulation framework to confirm the safety of treatment de-intensification and establish a baseline for streamlined surveillance. We then trained a Transformer architecture to predict individualized treatment failure timing and translated these predictions into a risk-adapted surveillance strategy. Results In the target trial emulation, omitting concurrent chemotherapy demonstrated comparable survival outcomes to concurrent chemoradiotherapy across all cohorts, establishing a safely de-intensified clinical baseline. Subsequently, the AI system achieved high-fidelity predictions, with an area under the curve of 0.991 internally and 0.986 in the multi-institutional external validation cohort. This AI-guided strategy substantially reduced the need for follow-up visits for over 90% of failure-free patients, while recommending a maximum of only six visits for high-risk individuals over a five-year period, demonstrating a high sensitivity for detecting true failures. Conclusions This generalizable AI framework can seamlessly complement the current NCCN guidelines, offering a transformative, data-driven solution that reduces the global monitoring burden of cancer survivorship care.","url":"https://doi.org/10.1016/j.ijrobp.2026.08.020","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijrobp.2026.08.020","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3346/jkms.2026.41.e208","name":"Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.","source":"europepmc","abstract":"Background To investigate the effectiveness of an artificial intelligence (AI)-based computer-aided detection (CAD) system in identifying incidental lung cancer on cardiac computed tomography (CT) scans and to compare its performance with that of radiologists. Methods In this retrospective, multicenter study, 652 cardiac CT scans from 581 patients subsequently diagnosed with lung cancer were analyzed. A commercial AI-CAD system was employed to detect pulmonary lesions on cardiac CT. The detection rate of AI-CAD was compared to that of the radiologist, based on the radiology report, as well as to the detection rate when combining AI-CAD and the radiologist. The characteristics of the lesions detected and missed by the radiologist and AI-CAD were compared. Results Radiologists and AI-CAD demonstrated similar detection rates for lung cancer (76.2% vs. 77.4%, P = 0.551). However, combining radiologists and AI-CAD significantly improved the detection rate to 90.4% ( P P P = 0.006). Among lung cancer cases missed by radiologists, 94.2% experienced diagnostic delays of > 100 days, with 78.2% leading to stage progression. AI-CAD identified 58.5% of these diagnostic delays. Conclusion AI-CAD demonstrated the potential to improve the detection rate of incidental lung cancer by identifying a subset of lesions that were initially overlooked by radiologists on cardiac CT. It exhibited particular strength in identifying early-stage cancers and small, subsolid lesions.","url":"https://doi.org/10.3346/jkms.2026.41.e208","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e208","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s13244-026-02348-8","name":"Diagnostic performance of an artificial intelligence algorithm for detecting pneumoperitoneum on abdominal CT scans.","source":"europepmc","abstract":"Objectives This study aims to evaluate the diagnostic performance of an artificial intelligence (AI) algorithm for detection, segmentation, and volumetric quantification of pneumoperitoneum on abdominal CT scans. Materials and methods We developed and validated a deep learning-based model for automated pneumoperitoneum detection on CT. Multi-center CT imaging series from 2072 patients were collected and randomly divided into training and testing sets at an approximate 7:3 ratio. The external validation set included 214 emergency CT scans collected between April 2022 and December 2024. Diagnostic reports served as the reference standard. Primary outcome included the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Quantitative agreement between AI and reference volumes was assessed using the intraclass correlation coefficient (ICC). Results In the test set (n = 607), the model demonstrated excellent performance: sensitivity 91.4%, specificity 93.1%, and AUC 0.97 (95% CI: 0.95-0.99). In the external validation cohort (n = 214), the model maintained robust performance with sensitivity 84.3% (95% CI: 76.2-90.5%), specificity 89.6% (95% CI: 82.3-94.6%), accuracy 86.9% (81.6-91.2%), PPV 89.2% (95% CI: 81.8-94.3%), and NPV 84.8% (95% CI: 77.1-90.7%). After excluding cases with minimal free gas ( Conclusion The AI model attained high diagnostic accuracy for pneumoperitoneum on abdominal CT scans, promising to expedite emergency workflows. Key points Question Reliable AI detection of pneumoperitoneum, particularly for small-volume free air, on emergency CT remains an unmet need for rapid and accurate emergency triage. Findings The AI model shows high sensitivity and specificity for clinically relevant pneumoperitoneum volumes, although trace-volume detection on CT scans remains challenging. Critical relevance statement This study evaluates the diagnostic performance and volume-dependent variability of an AI model for pneumoperitoneum detection on CT scans, with the potential to aid emergency radiology workflow prioritization and decision support, though prospective studies are needed to confirm clinical impact.","url":"https://doi.org/10.1186/s13244-026-02348-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13244-026-02348-8","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3390/diagnostics16162569","name":"Algorithmovigilance in AI-Based Oral-Health Surveillance: Temporal Drift, Cross-Survey Differences, and Predictor-Level Structural Stability in Population-Level Severe Tooth Loss Prediction.","source":"europepmc","abstract":"Background/Objectives : Artificial intelligence models may retain acceptable discrimination while calibration and predictor-risk relationships change across populations and data-collection systems. We evaluated a structured algorithmovigilance framework for detecting temporal drift, cross-survey differences, and predictor-level structural instability in severe tooth loss prediction. Methods : Across five BRFSS cycles from 2016 to 2024 (total N = 2,176,039), a survey-weighted main-effects Explainable Boosting Machine trained in 2016 was evaluated chronologically through 2024. NHANES 2015-2018 served as an external reference for an exploratory cross-survey temporal comparison. Results : AUC declined modestly from 0.8638 in 2016 to 0.8495 in 2024. The frozen unrecalibrated model yielded AUC = 0.8681 and Brier Score = 0.1131 in pooled NHANES. The survey-system-by-period interaction was positive (beta = 0.0412; HC1 p = 0.014), although a stratified survey-weighted bootstrap sensitivity produced a wider interval including zero (95% CI, -0.0085 to 0.0895; p = 0.093). Smoking ( p p = 0.030) showed nominal predictor-level structural shifts, whereas BMI did not. An interaction-enabled EBM improved AUC by 0.0010 in 2016 and 0.0016 in 2024. Conclusions : Aggregate discrimination alone did not capture calibration and predictor-level changes. The framework provides retrospective monitoring signals for governed review, not evidence of causal survey-mode effects, formal algorithmic fairness, or clinical deployment readiness.","url":"https://doi.org/10.3390/diagnostics16162569","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16162569","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/ije/dyag156","name":"Cohort Profile Update: the Rotterdam Periconceptional Cohort-biomarkers, lifestyle intervention, and artificial intelligence.","source":"europepmc","abstract":"The original cohortExtensive research of the Developmental Origins of Health and Disease (DOHaD) has demonstrated associations between earlylife conditions, perinatal outcomes, and long-term health and disease [1].In the last decades, we have shifted the exposure window to the periconceptional period (14 weeks before to up and until 10 weeks after conception) covering key processes of gametogenesis, embryogenesis, and placentation [2].Since then, parental health and lifestyle during this window have been associated increasingly with pregnancy complications and long-term cardiometabolic and developmental outcomes in offspring [2].The Rotterdam Periconceptional Cohort (Predict study) was initiated to bridge the gap of other birth cohorts starting data collection in late pregnancy or at birth, neglecting the critical periconceptional period [3].This hospital-based open cohort is embedded in tertiary care, with couples uniquely recruited in the periconception period.The cohort includes serial consultations and ultrasound scans using advanced virtual reality (VR) and computer-assisted Key Features• The ongoing Rotterdam Periconceptional Cohort (Predict study) is an open cohort, initiated in 2009 to investigate maternal and paternal periconceptional health and their impact on reproduction, pregnancy, and neonatal outcomes.In the pilot phase (2009-10), 233 pregnancies were included.In the study phase (2010-23), 3566 pregnancies (participants aged between 19 and 51 years) and 2891 partners were included.• New is the analysis of periconceptional maternal and paternal biomarkers in subcohorts: renin-angiotensin-aldosterone system (RAAS) components, tryptophan metabolites, homocysteine, C-reactive protein (CRP), hair cortisol/cortisone, cell-free DNA methylation for placental epigenetic profiling, and morphokinetic parameters.• The BEYOND subcohort was embedded to investigate associations between bariatric surgery and maternal, embryonic, fetal, and placental outcomes; and the PROMOTE subcohort explored maternal microbiota, immune responses, and one-carbon metabolism in association with obesity.• First-trimester imaging biomarkers of the uteroplacental vascular skeleton were developed using computer-assisted image processing to assess placental development.Advanced biomarkers were automated using artificial intelligence (AI), with innovations in the four-dimensional Human Embryonic Brain Atlas for spatiotemporal brain analysis.• Users of www.SmarterPregnancy.co.uk were included as a subcohort to assess the effectiveness of personalized digital lifestyle coaching on features of maternal health and embryonic development.","url":"https://doi.org/10.1093/ije/dyag156","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/ije/dyag156","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1113/jp291510","name":"Modulating hyperpolarization-activated cyclic nucleotide-gated channels for neuropathic pain.","source":"europepmc","abstract":"Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.","url":"https://doi.org/10.1113/jp291510","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1113/jp291510","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s12912-026-04970-9","name":"Knowledge, attitudes, practices and ethics related to artificial intelligence among nursing students: a national cross-sectional survey in China.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is rapidly transforming healthcare. Current and future healthcare workforce, including nursing students, require sufficient understanding of responsible AI use. However, data about knowledge, attitudes, practices and ethics regarding AI use in this population is scarce. This national study comprehensively assessed AI-related knowledge, attitudes, practices and ethics among nursing students in China, including the socio-demographic and educational factors associated with these professional domains. Methods A cross-sectional survey was conducted between June and August 2025 across 32 provinces, autonomous regions and municipalities in China. A total of 10,268 nursing undergraduates and vocational college students completed a newly developed 22-item Knowledge, Attitudes, Practices and Ethics questionnaire covering four domains (Cronbach's α = 0.79-0.96). Univariable analyses and multiple linear regression were used to examine socio-demographic factors of AI-related knowledge, attitudes, practices and ethics. Results Nursing students reported moderate level of AI knowledge (18.17 ± 4.90, total score 24), moderately positive attitudes (11.96 ± 2.49, total score 20), relatively good ethical awareness (11.60 ± 3.27, total score 16), but only limited engagement in practice (8.29 ± 5.04, total score 28). Of the participants, only 6.8%-9.6% reported \"often\" and \"always\" using AI tools for academic and personal tasks. In multivariable models, male gender, urban residence, higher economic status, and intention to pursue nursing as a career were independently associated with higher scores across the knowledge, attitude, and ethics domains (all P Conclusion The findings highlight the need for a supportive educational environment with guidance to enable nursing students to use artificial intelligence appropriately and responsibly when needed, particularly among vocational college students and those from socioeconomically disadvantaged backgrounds. Integrating AI competencies into nursing education and linking them to future career development and clinical practice may help bridge the gap between positive attitudes and limited practical use.","url":"https://doi.org/10.1186/s12912-026-04970-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12912-026-04970-9","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3961/jpmph.26.359","name":"Agreement Between Artificial Intelligence Platforms and the UpToDate Lexidrug Database in Identifying Antihypertensive Drug-Drug Interactions in Older Adults.","source":"europepmc","abstract":"Objectives This study aimed to compare the agreement and consistency of classifications generated by ChatGPT-4.5 and Meta AI (Llama 4 Scout) with those of the UpToDate Lexidrug reference standard for identifying potential drug interactions involving antihypertensive medications in older adults. Methods A cross-sectional study was conducted using electronic medical record data from January to December 2024. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess agreement between classifications generated by ChatGPT-4.5 or Meta AI and those of UpToDate Lexidrug as the reference standard. In addition, Cohen kappa values were used to assess inter-rater agreement regarding the severity of the identified drug interactions. Results ChatGPT-4.5 showed greater agreement with the reference standard (UpToDate Lexidrug), with an accuracy of 0.804, sensitivity of 0.862, specificity of 0.779, PPV of 0.628, and NPV of 0.929, compared with Meta AI, which had an accuracy of 0.706, sensitivity of 0.750, specificity of 0.686, PPV of 0.509, and NPV of 0.863. Cohen kappa analysis showed fair agreement between both platforms and the reference standard (0.367 and 0.359, respectively). Conclusions ChatGPT-4.5 demonstrated greater alignment with the UpToDate Lexidrug classification compared to Meta AI across all evaluated metrics. However, both artificial intelligence platforms showed only fair agreement in severity classification, indicating substantial differences between the classifications generated by the platforms and those of the reference database.","url":"https://doi.org/10.3961/jpmph.26.359","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3961/jpmph.26.359","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18553/jmcp.2026.32.9.1052","name":"An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications.","source":"europepmc","abstract":"Background Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous medication identifiers across real-world electronic health records (EHRs) could undermine analytic fidelity and risk propagating classification errors. To enable transportable, reproducible AI tools, methods for harmonizing disparate medication identifiers (eg, National Drug Code [NDC] and Multum drug synonym ID) to standardized vocabularies are required. Objective To develop and evaluate an informatics framework that harmonizes heterogeneous medication identifiers into standardized ingredient- and pharmacologic-class representations, establishing the semantic integrity required to support transparent, reliable downstream managed care analytics and AI developments. Methods We conducted a retrospective analysis of discharge medication records, excluding health supplements and nonmedicated agents, from EHRs of adults aged 65 years or older who had at least 1 hospitalization between 2020 and 2024 at a tertiary medical center (Buffalo General Medical Center). Heterogeneous identifiers (Multum, NDC, RxCUI) were harmonized using a 2-layered architecture anchored to RxCUI ingredient [IN] concepts and abstracted to Anatomical Therapeutic Chemical (ATC) classification. Deterministic crosswalks were derived from the RxNorm Full Monthly Release and complemented with a validated TriNetX reference file. Unmapped records underwent structured string-based reconciliation through alignment of medication name strings between raw records and reference terminology sources. A pharmacy expert manually validated the string-based reconciliation process and the RxCUI [IN]-to-ATC mapping. Framework feasibility was assessed by calculating mapping success rates and evaluating reconciliation and correction metrics. Results Analysis of 214,080 records revealed significant terminology heterogeneity, with 53.0% Multum Drug Synonym IDs as nonstandardized identifiers requiring string-based reconciliation. Mapping using deterministic crosswalks achieved a 100% initial mapping rate, with 30% to 35% of unique records requiring ATC assignment corrections. While string-based alignment achieved initial match rates of 78.5% and 75.5% for RxCUI [IN] and ATC codes, respectively, reconciliation required corrections for 57.4% of assignments. Primary drivers for corrections included crosswalk omissions for branded formulations, lexical misclassifications, and taxonomic ambiguities that required clinical context for accurate ATC assignment. Examples of taxonomic ambiguities include variations in ATC classification based on indication or route of administration (eg, moxifloxacin may be mapped to J01MA14 or S01AE07). Conclusions This informatics framework establishes the architectural foundation necessary to transform raw EHR medication records into standardized representations at the ingredient and pharmacological levels, providing a critical semantic foundation for transparent, transportable, and reproducible AI applications in managed care.","url":"https://doi.org/10.18553/jmcp.2026.32.9.1052","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.18553/jmcp.2026.32.9.1052","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.7759/cureus.111190","name":"Mapping COVID-19 Artificial Intelligence (AI) Research in Medical Imaging: A Bibliometric Analysis of Datasets, Trends, and Clinical Challenges.","source":"pubmed","abstract":"The rapid adoption of artificial intelligence (AI) for COVID-19 pandemic diagnosis has exposed critical gaps in medical imaging datasets. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant bibliometric review of 450 PubMed studies (2020-2024) reveals that only 21.5% of the datasets remain clinically validated, while 55% are unavailable or repurposed from non-COVID-19 sources. We identified persistent issues, such as resolution heterogeneity and radiologist annotation scarcity, that undermine model reliability. Numerous convolutional neural network (CNN) architectures have been developed to enable fast and accurate automated diagnosis of COVID-19 using computed tomography (CT) or X-ray imaging. However, due to the urgency of the pandemic and the rapid demand for solutions, existing computer-aided diagnostic (CAD) systems face several critical limitations, such as imbalanced datasets, insufficient bias assessment in model training, and inconsistent quality control in image acquisition and preprocessing. In this bibliometric analysis, we provide an analysis of PubMed articles on COVID-19 imaging published between January 1, 2020, and November 1, 2024. The research included 1261 publications. VOSviewer was used to generate a visual map of the keyword networks and authors. The journal with the most publications was Elsevier, and the most used dataset was the COVID-19 Radiography Database from Kaggle.","url":"https://doi.org/10.7759/cureus.111190","authors":["Menoudji Djetoyom P","Ngueilbaye A","Abakar Hamid A","Akofala A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.111190","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.2196/83092","name":"Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.","source":"europepmc","abstract":"Background Smart bioelectronics are electronic medical devices that combine hardware and artificial intelligence (AI)-based software. These convergent medical devices analyze bio-signals measured through hardware using AI algorithms and deliver physical stimulation to enhance therapeutic effects. Objective This study aimed to systematically analyze recent research trends in smart bioelectronics to understand their evolving role in digital health care and to provide evidence-based insights for shaping future research and development strategies. Methods A total of 92 publications indexed in PubMed between 2020 and 2024 were analyzed. Latent Dirichlet allocation-based topic modeling, optimized using coherence scores, was applied to identify latent research themes. Results The results indicate a steady increase in related research over the past 5 years, along with a clear shift in research focus from bio-signal sensing and bioelectronic device materials toward AI-driven analysis and disease-oriented applications, ultimately evolving into intelligent and adaptive bioelectronic therapeutic systems. Three major research topics were identified: bio-signal-based neuromodulation (n=23, 25%), AI-driven neurological disease analysis (n=32, 34.7%), and implantable bioelectronics and biomaterials (n=37, 40.2%). Conclusions By mapping the evolving landscape of smart bioelectronics, this study provides valuable insights into their multidisciplinary development and highlights their potential applications in clinical decision support, personalized rehabilitation, and next-generation medical device innovation.","url":"https://doi.org/10.2196/83092","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/83092","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.4258/hir.2026.32.3.293","name":"Legal Barriers to Implementing AI-Enabled Emergency Medical Services in Korea: Focus on the Medical Service Act, Emergency Medical Service Act, and Personal Information Protection Act.","source":"europepmc","abstract":"Objectives In prehospital and in-hospital emergency care, artificial intelligence (AI)-enabled emergency medical services (EMS) systems are reshaping clinical workflows through real-time triage, automated hospital assignment, telemedicine, and biometric monitoring. However, their implementation in Korea has revealed substantial legal and regulatory barriers. This study analyzed the structural incompatibilities between AI-enabled EMS and Korea's current legal framework, with particular attention to the Medical Service Act, Emergency Medical Service Act, and Personal Information Protection Act. Methods We conducted a statutory review and case analysis of pilot projects, including the Chungbuk Smart EMS Project, the AI HOTline in Ilsan, and Yonsei's AI-Ambulance model. We also performed a comparative assessment of foreign regulatory frameworks, including the US Health Insurance Portability and Accountability Act and the EU General Data Protection Regulation. Results Key barriers included the restriction of remote care to interphysician communication under Article 34 of the Medical Service Act, the lack of legal mandates for real-time hospital capacity sharing under the Emergency Medical Service Act, and limits on real-time sensitive-data processing caused by vague emergency clauses in the Personal Information Protection Act. These barriers constrain the scalability of AI-enabled EMS in Korea. Conclusions To support lawful and secure integration of AI-driven emergency care in Korea, we propose legislative revisions that establish emergency exceptions for remote AI-supported guidance, require hospital resource disclosure, and provide safe-harbor protections for healthcare professionals who rely on certified AI tools. A harmonized legal infrastructure is essential for realizing the potential of digital transformation in emergency care.","url":"https://doi.org/10.4258/hir.2026.32.3.293","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4258/hir.2026.32.3.293","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.21203/rs.3.rs-10581991/v1","name":"Generative Artificial Intelligence in Laboratory-Based Health Sciences Education: A Scoping Review of an Emerging Evidence Base","source":"europepmc","abstract":"Abstract Generative artificial intelligence (AI) is rapidly entering health professions education, yet its applications in laboratory-based disciplines have not been systematically mapped. This scoping review examined how generative AI is being used in medical laboratory science, pathology, biomedical science, and molecular diagnostics education; the educational activities it supports; and the benefits, challenges, and ethical considerations reported. The review followed Joanna Briggs Institute methodology and was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. PubMed, Scopus, and the Education Resources Information Center were searched, yielding nine eligible studies published between 2024 and 2026. Applications were mapped against Laurillard’s learning types. Most studies concerned individual learner activities or educator workflow. Inquiry and acquisition were the most common learning types, while no application supported discussion or collaboration. Medical laboratory science was represented by only one study, and molecular diagnostics appeared only peripherally. Efficiency was the most frequently reported benefit, whereas factual inaccuracy and hallucination were the most common challenges. Expert oversight was widely identified as essential. Three studies independently used AI-generated errors as objects for critical evaluation, suggesting a promising educational approach. The evidence base remains small and predominantly descriptive, which is understandable in this rapidly developing field. Comparative, discipline-specific studies are needed to evaluate educational outcomes and guide responsible integration.","url":"https://doi.org/10.21203/rs.3.rs-10581991/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10581991/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1111/eje.70282","name":"Role of Large Language Model-Artificial Intelligence in Enhancing Communication Skills in Undergraduate Oral Healthcare Students.","source":"europepmc","abstract":"Introduction Artificial Intelligence (AI) is transforming dental education (DE) by advancing teaching strategies, clinical training, and patient care. Its integration allows for personalized learning experiences and realistic simulations, and equips students with the competencies required to deliver high-quality oral healthcare in a digital environment. Aim This project, a collaboration between the Faculty of Dentistry, Oral and Craniofacial Sciences and the Department of Informatics at King's College London, explores the impact of Large Language Models (LLMs) in preparing preclinical undergraduate oral health students for clinical practice, specifically in developing communication skills related to patient behaviour change. Methods LLMs were employed to simulate authentic dental scenarios through three iterations of Generative Language Model AI: Two text-based and one voice-based platform. In the initial phase, a comprehensive dataset of question-and-answer pairs was collaboratively created to train the system in addressing a broad spectrum of dental queries. The technology incorporates a medical information database and uses Retrieval Augmented Generation (RAG) to deliver accurate responses. LangChain and prompt engineering were also applied to ensure fair and unbiased content generation. Results These platforms were piloted with volunteer clinical staff and students at King's, whose feedback informed refinements ahead of a planned rollout to second-year dental and dental hygiene therapy students in the 2024-25 academic year. In the next phase, AI-facilitated learning experiences will be compared with traditional actor-led communication tutorials. Online questionnaires will be used to assess learning outcomes and student satisfaction across both formats, followed by focus group discussions. Initial results from the pilot were promising, indicating that AI-based platforms can effectively support the development of communication skills in preclinical oral healthcare students. Integrating AI into DE fosters early acquisition of essential professional competencies, aiming to prepare ethically responsible and skilled practitioners while contributing to the evolution of the discipline.","url":"https://doi.org/10.1111/eje.70282","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/eje.70282","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1002/clc.70447","name":"Artificial Intelligence in Coronary Computed Tomography Angiography: A Bibliometric Analysis of Trends and Themes.","source":"europepmc","abstract":"Background Artificial intelligence (AI) in coronary computed tomography angiography (CCTA) is increasingly recognized, but comprehensive bibliometric analyses on its integration are scarce. Hypothesis This study aims to bridge this gap by delineating research trajectories, influential entities, and evolving themes within this field. Methods A bibliometric analysis was conducted on literature from the Web of Science Core Collection, spanning publications from January 2007 to October 2024. Analysis tools included VOSviewer, CiteSpace, and R-bibliometrix, which were used to map and visualize the publication trends, collaborative networks, and thematic evolution. A manual relevance audit and a sensitivity analysis using a narrower query were performed to validate the search strategy. Results A total of 499 articles were analyzed. The number of publications increased annually in the past decade, with a peak of 113 in 2022. China led with 193 articles, and the USA served as a central node in the cooperation network. The Medical University of South Carolina and Yonsei University tied for first with 54 articles each. Schoepf U. Joseph was the most influential author. European Radiology led in h-index and total publications. The keyword analysis revealed \"coronary artery disease\" as the most frequent, followed by \"deep learning\" and\"machine learning.\" Notable burst keywords included \"fractional flow reserve,\" \"diagnostic accuracy,\" and \"SCCT guidelines.\" Conclusion The field of AI in CCTA is rapidly evolving, with increasing international collaboration and a focus on technological advancements. Machine learning prediction has emerged as a bibliometrically prominent theme with intense citation activity, warranting further validation and standardized integration into clinical practice.","url":"https://doi.org/10.1002/clc.70447","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/clc.70447","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1002/cncy.70097","name":"Practical exploration of real-time visual interactive artificial intelligence technology in cytopathology education.","source":"europepmc","abstract":"Background Proficiency in cytopathologic diagnosis depends heavily on extensive hands-on practice and immediate error correction. Traditional teaching models, however, are constrained by limited practice opportunities and delayed feedback, which fails to meet the core skill-development needs of residents. Methods In total, 45 pathology residents were enrolled and assigned to two groups. The experimental group (n = 20) adopted a tripartite teacher-artificial intelligence-resident collaborative teaching model, whereas the control group (n = 25) received conventional instruction. Both groups underwent an identical 8-week teaching cycle. Results The questionnaire results from the experimental group indicated that 19 of 20 residents (9%) deemed the new model highly necessary, and 15 of 20 (75%) believed it significantly improved their diagnostic competence. Semistructured interviews further revealed that the model enhanced diagnostic ability, facilitated personalized learning, and alleviated learning anxiety. For objective metrics, the experimental group demonstrated a significantly higher postintervention concordance rate for gray-zone cell identification (78.65%) compared with both their preintervention baseline (64.38%) and the contemporaneous control group (66.84%; t = 8.962; p .05). Conclusions This study demonstrates that artificial intelligence technology integrated with real-time visual interaction effectively improves the cytopathologic diagnostic skills of residents and merits wider promotion in pathology education.","url":"https://doi.org/10.1002/cncy.70097","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/cncy.70097","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3760/cma.j.cn112142-20260313-00099","name":"[Artificial intelligence in neuro-ophthalmology: prospects, challenges and countermeasures].","source":"europepmc","abstract":"Neuro-ophthalmic disorders feature complex etiology. Certain ocular manifestations may hint at severe neurological diseases. Current clinical practice faces no shortage of diagnostic examinations. The core challenge consists in inadequate recognition, integration and interpretation of multifaceted information during initial consultation and non-specialist visits, potentially resulting in misdiagnosis, missed diagnosis, delayed referral and excessive examinations. Artificial intelligence has demonstrated promising performance in optic disc lesion detection, image interpretation of optic neuropathy, visual field analysis, as well as eye movement and pupillary function evaluation. Nevertheless, AI is not intended to replace specialists to deliver definitive diagnoses. Instead, it helps boost early detection of high-risk signs, facilitates analysis of complicated test results, and aids clinical decision-making, patient referral and follow-up management. This article elaborates on the application prospects, existing dilemmas and translational strategies of artificial intelligence in neuro-ophthalmology, aiming to offer references for relevant research and clinical practice.","url":"https://doi.org/10.3760/cma.j.cn112142-20260313-00099","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112142-20260313-00099","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s12909-026-09778-4","name":"The readiness-anxiety paradox in artificial intelligence adoption among nursing students: a multidimensional regression analysis.","source":"europepmc","abstract":"Aim This study examined the relationship between medical artificial intelligence readiness and AI-related anxiety among nursing students following surgical nursing education. Methods A descriptive cross-sectional study was conducted with 252 nursing students from two universities who had completed surgical diseases nursing courses. Data were collected between February and July 2024 using the Artificial Intelligence Anxiety Scale and the Medical Artificial Intelligence Readiness Scale. Group comparisons, correlation analyses, and hierarchical regression analysis models were performed. Results Our findings indicate a notable \"awareness-apprehension paradox\": higher ability (technical readiness) was associated with lower learning anxiety (B = - 0.25, p Conclusion The results suggest that developing technical competence alone may not be sufficient to align with confident technology adoption. They further indicate that, in the absence of corresponding organizational safeguards, ethical awareness may function more as a \"cognitive demand\" than as a resource. The study adds to the Job Demands-Resources (JD-R) framework by illustrating the dual nature of AI readiness. Nursing education programs may therefore benefit from integrating ethical reflection, critical technological awareness, and psychological preparedness alongside AI skill development to support sustainable implementation of AI-supported clinical practice.","url":"https://doi.org/10.1186/s12909-026-09778-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09778-4","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/jbi/wbaf079","name":"Assessing Artificial Intelligence in Breast Imaging: A Survey of Breast Radiologists' Insights on Adoption, Benefits, and Challenges.","source":"europepmc","abstract":"Objective To evaluate radiologists' opinions on the clinical applications of artificial intelligence (AI), especially AI-based computer-aided detection (CAD), in breast imaging. Methods An IRB-exempt anonymous survey was distributed to Society of Breast Imaging (SBI) members on May 10, 2024, and May 20, 2024. Survey questions included practice demographics and perspectives on AI. Results were analyzed using descriptive statistics. Results In all, 7.2% (162/2264) SBI members responded, with 69 (42.6%) respondents being in private practice and 56 (34.6%) respondents in academics. Artificial intelligence-aided CAD was used by 90 (55.6%) respondents. Reported benefits of AI-CAD included improved work efficiency (98, 71.0%), increased cancer detection (90, 65.2%), and reduced recall rates (54, 39.1%). Artificial intelligence-aided CAD was reported as being best for detecting calcifications (92, 61.7%) and architectural distortion (65, 43.6%), although the highest false-positive results were reported in postsurgical scar (104, 75.9%) and benign calcifications (95, 69.3%). Additionally, respondents reported AI tools have great impacts on workflow efficiency (109, 73.2%), patient care (98, 65.8%), reducing burnout (70, 47.0%), turnaround time (70, 47.0%), and addressing radiologist shortages (69, 46.3%). Barriers to AI adoption included cost (113, 71.5%), software integration (98, 62.0%), and lack of trust (100, 63.3%). Only 41 (27.5%) felt AI might threaten job security, and 52 (34.9%) believed it could reduce reimbursement. Conclusion Most survey respondents believe AI enhances breast cancer detection and workflow efficiency, but false-positive results, particularly in postsurgical scar and benign calcifications, limit its diagnostic utility. Barriers such as cost, integration, and trust hinder AI adoption. Refining AI algorithms to minimize false-positive results and addressing these barriers are crucial for clinical implementation.","url":"https://doi.org/10.1093/jbi/wbaf079","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jbi/wbaf079","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s10278-026-02126-4","name":"LCRE-Net: A Lightweight Cross-Scale Residual Enhancement Network for Lung Segmentation in CT Images.","source":"europepmc","abstract":"Accurate segmentation of lung and lesion regions from CT images is crucial for the diagnosis and quantitative assessment of lung diseases. Existing methods for lung CT segmentation suffer from limitations such as insufficient effective receptive field, limited cross-scale feature interaction, unstable boundary delineation, and high complexity, restricting their practical application. To address these challenges, we propose a lightweight cross-scale residual enhancement network (LCRE-Net) designed to segment lung and lesion regions from lung CT images. LCRE-Net adopts a pre-trained Pyramid Vision Transformer v2 as the encoder backbone. To alleviate the information dilution problem caused by small lesions, we embed zero-initialized residual paths at deep pyramid stages of the encoder to enhance the stability of feature representation. Simultaneously, we propose a novel module called the cross-scale attention pyramid module, which adaptively fuses high-level semantic features with mid-level spatial details through learnable weights. Furthermore, we construct a lightweight feature enhancement path consisting of efficient receptive field blocks and edge enhancers, effectively suppressing artifact boundary interference while enhancing multi-scale context modeling capabilities. Experimental results show that LCRE-Net achieves competitive segmentation performance on three publicly available lung CT datasets, achieving average Dice similarity coefficients of 0.9845, 0.8544, and 0.8611, respectively. The proposed LCRE-Net maintains low model complexity and stable performance across datasets.","url":"https://doi.org/10.1007/s10278-026-02126-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10278-026-02126-4","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s12010-026-05825-4","name":"Multi-applications and Aquaculture of Seaweeds: Environmental Improvement, Health Benefit, and Sustainable Valorization with Integrated Artificial Intelligence.","source":"europepmc","abstract":"Environmental and human health applications of seaweeds (SWs) have received attention in recent years. However, previous reviews lack of covering SWs from cultivation to their various applications, along with the integration of artificial intelligence (AI). Thus, this review introduces SWs identification and aquaculture techniques, including onshore, nearshore, offshore, and integrated multi-trophic aquaculture (IMTA) to promote sustainable knowledge in SWs. Composition, properties, and applications are discussed to improve resource availability for various purposes (such as wound dressings, biofilms, and cosmetics). The development of AI (such as SWs classification and identification, nutrient determination, growth prediction, and ecological restoration) is discussed. Green (e.g., Ulva) and red SWs (e.g., Gracilaria) were widely reported in IMTA systems, achieving nitrogen and phosphorus removal rates of 74% and 72%, respectively. SWs extracts for animals and plants promoted the physiological functions (stress resistance, antioxidant, and metabolic activity). Genetic engineering shows potential in improving SWs traits. CRISPR technology exhibited a mutation efficiency of nearly 70% (Ectocarpus sp.). AI, based on computer vision and machine learning, is a useful technique that can provide profound insights for futuristic approaches. The safe transformation of SWs from potential value to practical applications also requires effective technologies to remove contaminants and establish relevant limit standards.","url":"https://doi.org/10.1007/s12010-026-05825-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12010-026-05825-4","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.7759/cureus.110786","name":"Artificial Intelligence for Cone-Beam Computed Tomography in Endodontics: A PRISMA-Aligned Narrative Synthesis of Evidence From the United States and Europe (2021-2026).","source":"pubmed","abstract":"Cone-beam computed tomography (CBCT) is the reference three-dimensional imaging modality for endodontic diagnosis and treatment planning, but its interpretation is time-consuming and varies between observers. Artificial intelligence (AI) and convolutional neural networks in particular have been applied to CBCT to automate root canal segmentation, periapical lesion detection, and morphologic classification. Currently, there is no focused synthesis of evidence from the United States and Europe. This review is a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-aligned narrative synthesis. PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and IEEE Xplore were searched for primary studies published between January 1, 2021, and April 30, 2026. Studies were included if they used AI with CBCT for endodontic applications and had a first or corresponding author from an institution in the United States or Europe, and quality was appraised against the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Quality Assessment of Diagnostic Accuracy Studies tailored to Artificial Intelligence (QUADAS-AI). Of 612 records screened, 17 studies were included, originating mainly from four research groups (the University of Pennsylvania, KU Leuven, the Medical University of Graz, and Stony Brook University), with two further single-study contributions. Tooth, pulp, and canal segmentation was the most mature task (Dice 0.85-0.97), and periapical lesion detection reached early clinical validation (sensitivity 0.80-0.97; specificity 0.84-1.00). No eligible studies from the United States or Europe were found on vertical root fracture detection, AI-based working length determination, American Association of Endodontists case difficulty assessment, or treatment outcome prediction. CBCT-based endodontic AI in these regions is advancing unevenly: segmentation and lesion detection are approaching clinical use, while several clinically important tasks remain unaddressed and external validation across devices is still limited.","url":"https://doi.org/10.7759/cureus.110786","authors":["Kachmar V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.110786","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"doi:10.3390/jcm15124436","name":"Complementary Error Patterns Between Human Evaluators and GPT-4o in Video-Based Cardiopulmonary Resuscitation Skills Assessment: Implications for Artificial Intelligence-Assisted Second Reading.","source":"europepmc","abstract":"Background/Objectives : Cardiopulmonary resuscitation (CPR) skill assessments are susceptible to evaluator subjectivity, cognitive fatigue, and observational limitations. Although recent advances in multimodal artificial intelligence have increased the possibility of automated video-based assessment, its validity for clinical skill evaluation remains insufficiently examined. Methods: In this cross-sectional study, we enrolled 130 laypersons who underwent Basic Life Support training and skill testing. Twenty recordings were used for prompt development and 110 recordings were analyzed. Expert evaluators and GPT-4o independently assessed participants' skills using a 12-item checklist. The manikin sensor data were the reference standard for the four chest compression metrics. Agreement was evaluated using Gwet's agreement coefficient 1 (AC1) and intraclass correlation coefficient (2,1). Diagnostic accuracy, sensitivity, and specificity were compared using McNemar's test. Results : Procedural items such as confirming cardiac arrest, calling 119, and requesting an automated external defibrillator showed a near-perfect agreement between experts and GPT-4o (AC1 > 0.8). However, the agreement was poor for the compression depth (AC1 = 0.374) and full chest recoil (AC1 = 0.355). Experts demonstrated high sensitivity (77.8-84.3%) but low specificity (24.6-47.8%), whereas GPT-4o showed low sensitivity (35.6-40.6%) but high specificity (69.2-76.1%). Conclusions : GPT-4o cannot serve as a standalone evaluator because of its inherent limitations in inferring three-dimensional spatial information from two-dimensional videos. However, its high agreement on procedural items and complementary error patterns with that of human evaluators on compression metrics suggests its potential as a decision support tool to mitigate expert leniency bias in CPR education.","url":"https://doi.org/10.3390/jcm15124436","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15124436","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.14293/pr2199.004014.v1","name":"Artificial Intelligence in Healthcare Diagnosis: A Deep LearningApproach to Early Disease Detection","source":"europepmc","abstract":"Artificial Intelligence (AI) is transforming healthcare by enabling faster and more accurate disease diagnosis. Early disease detection improves patient outcomes, reduces treatment costs, and increases survival rates. This paper examines the role of AI technologies, including machine learning, deep learning, and predictive analytics, in the early detection of diseases such as cancer, diabetes, cardiovascular diseases, and neurological disorders. A systematic review of scientific literature, medical datasets, and AI-based diagnostic models published between 2017 and 2024 was conducted to evaluate recent developments in AI-assisted healthcare. The findings indicate that deep learning models can identify complex patterns in medical images, electronic health records, and laboratory data with high accuracy, often outperforming conventional diagnostic methods. AI-based systems also support clinical decision-making by improving diagnostic efficiency and reducing human error. However, challenges related to data privacy, ethical concerns, model transparency, and interpretability remain significant barriers to widespread clinical adoption. The study concludes that AI has substantial potential to enhance the quality, accessibility, and efficiency of healthcare, particularly in developing countries with limited access to medical specialists. Future research should focus on developing explainable AI models, secure data-sharing frameworks, and international standards to ensure the safe, ethical, and effective implementation of AI for early disease detection and clinical decision support.","url":"https://doi.org/10.14293/pr2199.004014.v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.004014.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s10393-026-01821-6","name":"Artificial Intelligence in Infectious Disease Research.","source":"europepmc","abstract":"Background Infectious diseases remain a major global health challenge, and while AI is rapidly advancing diagnostic techniques such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and ultrasound, its full potential in the detection and management of these diseases has yet to be thoroughly explored. This study examines the global research trends, key contributors, and thematic shifts in the field of AI-driven diagnostics for infectious diseases. Methods The publications obtained data from the Web of Science (WoS) database reveal an exponential growth with 5465 publications from 2005 to 2024. The search targeted studies on human, animal, and environmental health using keywords such as \"infectious disease,\" \"communicable disease,\" \"environment,\" \"machine learning,\" and \"artificial intelligence\" in title, abstract, and keyword fields. Bibliometric parameters such as publication trends, prolific authors, and geographical distribution were assessed, and core journals, institutions were identified, keyword co-occurrence analysis highlighted major research themes. Analyses were conducted using pyBibX, bibliometrix, and VOSviewer to map relationships among authors, journals, and thematic areas. Findings The USA leads AI-driven infectious disease research, contributing 1687 publications and 72,732 citations, dominated the combined contributions of China and India, whose total amounts to 1768 publications and 34,927 citations. The Chinese Academy of Science (China), University of Oxford (UK), and Harvard Medical School (USA) emerged as the top institutions. \"Scientific Reports\" turned out to be the top journal with 157 publications followed by \"PLOS One\" (144) and \"Frontiers in Immunology\" (127). Citation network analysis highlighted regional disparities, with low-income countries such as Gambia and Mozambique underrepresented. Keyword clustering identified six key research themes, with machine learning as a central theme across diagnostics, epidemiology, imaging, and drug discovery. Conclusion AI is transforming infectious disease research with significant contributions from high-income countries. However, emerging collaborations in lower middle-income countries highlight the need for equitable AI adoption. Addressing these disparities in infrastructure, funding, technological, and computational capability through global collaborations is important. Future studies should focus on overcoming the real-world implementation challenges to maximize AI's impact in infectious disease research.","url":"https://doi.org/10.1007/s10393-026-01821-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10393-026-01821-6","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3174/ajnr.a9217","name":"Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.","source":"europepmc","abstract":"Background and purpose Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpinning these biases remain relatively unclear. This study evaluated potential sociodemographic biases in AI-based glioblastoma MRI segmentation models trained on data sets varying in size and demographic composition. We evaluated 4 nnUNet models with different training data sets: 1) the Federated Tumor Segmentation (FeTS) postoperative model trained on a large (>10,000 examinations) multinational, multi-institution data set; 2) the Brain Tumor Segmentation (BraTS) 2024 postoperative glioma model trained on a moderate size (>2000 examinations) multi-institution, North American data set; 3) a model trained on a small (>200 examinations), private, demographically homogeneous, single-institution data set; and 4) a model trained on an equally small (>200 examinations), but demographically heterogeneous data set. Materials and methods Models were evaluated for bias using an independent, manually corrected data set of 480 patients (mean age 52 ± 14) that was prospectively collected from a single high-volume academic brain tumor center. Automated FLAIR and enhancing tumor segmentations from the AI models were evaluated using Dice scores. Sociodemographic factors were collected and analyzed using beta regression to assess their influence on model performance. Results The model trained exclusively on white, non-Hispanic men had the lowest overall Dice scores (0.943 for FLAIR, 0.909 for enhancement) and exhibited biases in age and smoking status. The BraTS model demonstrated the highest Dice scores (0.996 for FLAIR, 0.999 for enhancement) and had the least bias overall. Conclusions Demographic bias was relatively low in glioblastoma MRI segmentation models. The model trained on the smallest and most homogeneous data set exhibited the most bias. Greater demographic heterogeneity even without increasing training data set size was associated with reduced bias. The BraTS model, trained on a moderate-sized cohort that included more diverse tumor types, performed better and demonstrated less bias than the FeTS model, despite the FeTS being trained on the largest data set.","url":"https://doi.org/10.3174/ajnr.a9217","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3174/ajnr.a9217","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00330-026-12745-8","name":"Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.","source":"europepmc","abstract":"Objectives This systematic review evaluates the available evidence on the efficacy of commercially available AI-software applications for lung nodule assessment on CT scans. Materials and methods In adherence to PRISMA guidelines, a thorough search of electronic databases was conducted (January 2012-November 2024) to identify studies on CE-marked and/or FDA-cleared AI-based systems for evaluating pulmonary nodules on CT scans. Articles were systematically categorised using the Radiology AI Deployment and Assessment Rubric (RADAR) to assess the evolution of efficacy over time across various hierarchical levels. Results A total of 95 studies were included. Between 2012 and 2024, the number of studies increased from 14 in 2012-2017 to a total of 95 by 2024. The studies were categorised using the RADAR efficacy model. In the early period (2012-2017), most studies focused on lower efficacy levels, with Level-1 (technical efficacy) and Level-2 (diagnostic accuracy) dominating at 83.3%. During this period, AI applications were mainly focused on quantification (46.7%), while malignancy prediction was addressed in only 6.7% of studies. By 2024, Levels-3 (diagnostic thinking efficacy), 4 (therapeutic efficacy), and 5 (patient outcomes) accounted for over a third of all studies. Malignancy prediction further increased to 28.6%, and nodule characterisation emerged in 4.5% of studies. Despite advancements, research on patient outcomes and cost-effectiveness efficacy (Levels-5 and 6) remains limited. Also, all included studies demonstrated high risk of bias in at least one domain, and nearly two-thirds involved vendor funding or co-authorship. Conclusion In conclusion, the growing interest and investment in AI technologies for thoracic radiology have driven significant advancements in lung nodule assessment on CT-scans. Gaps remain in assessing patient outcomes and societal implications, which must be addressed to fully realise AI's potential in clinical practice and public health. Key points Question What is the current level of scientific evidence supporting commercially available AI tools for pulmonary nodule assessment on CT, and is this sufficient to support their clinical implementation? Findings Research on AI for pulmonary nodule assessment has surged, with the focus evolving from basic technical performance in the early years to higher clinical outcomes by 2024. Clinical relevance This study provides critical insights into the evolving role of AI in lung nodule assessment on CT-scans, highlighting advancements in technology and identifying key gaps in current research. Addressing these is essential for optimising AI's clinical application and improving patient outcomes.","url":"https://doi.org/10.1007/s00330-026-12745-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00330-026-12745-8","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.acra.2026.06.025","name":"Registered AI Medical-Imaging Clinical Trials on ClinicalTrials.gov: Publication Yield, Predictors, and Portfolio Evolution.","source":"europepmc","abstract":"Rationale and objectives Although artificial intelligence (AI) clinical trials in medical imaging have grown rapidly, peer-reviewed publication outcomes and ClinicalTrials.gov portfolio shifts remain incompletely characterized. Materials and methods From a ClinicalTrials.gov search on May 1, 2026, we assembled a historical cohort (n = 408; primary completion ≤ May 1, 2023) and a trend cohort (n = 835; first posted May 1, 2023 to May 1, 2026). Trials were classified by clinical intent (Checklist for Artificial Intelligence in Medical Imaging [CLAIM] 2024), modality, and anatomical region. Multivariable logistic and Cox regression evaluated publication and time to publication. Results Peer-reviewed publication was identified in 79 of 408 historical-cohort trials (19.4%). Observational design (odds ratio [OR], 0.35; P =.001), inclusion of children (OR, 0.37; P =.018), and smaller planned enrollment (OR per log[enrollment + 1], 1.25; P =.006) were independently associated with publication likelihood, with concordant Cox findings. Unreported principal investigator (PI) location demonstrated an exploratory association (OR, 0.19; P =.023; n = 38). In the trend cohort, multi-modal imaging increased from 8.1% to 22.6%, industry sponsorship declined (11.8% to 6.6%; P =.003), and median planned enrollment rose from 300 to 400 (P Conclusion Peer-reviewed publication was identified for 19.4% of completed AI medical-imaging trials - a conservative estimate given registry-status incompleteness. Observational design, inclusion of children, and smaller planned enrollment were independently associated with lower publication likelihood. The portfolio is shifting toward multi-modal imaging and broader geographic representation.","url":"https://doi.org/10.1016/j.acra.2026.06.025","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.acra.2026.06.025","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s10916-026-02429-7","name":"Recent Advances in AI for Automated ICD Coding: A Systematic Literature Review.","source":"europepmc","abstract":"International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI's potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.","url":"https://doi.org/10.1007/s10916-026-02429-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10916-026-02429-7","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s13018-026-07104-8","name":"Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.","source":"europepmc","abstract":"Background Recent advances in artificial intelligence (AI) have redefined shoulder arthroplasty, ultimately improving diagnostic accuracy, refining surgical planning, and personalizing recovery pathways. However, despite promising technical applications, comparative clinical evidence has not yet demonstrated improved routine surgical or patient outcomes in shoulder arthroplasty. Review content Therefore, this review summarizes the state-of-the-art AI applications in shoulder arthroplasty, highlights their strengths and limitations, and outlines integration strategies that align technological innovation with surgical expertise. Preoperatively, deep learning-based imaging, which refers to machine learning methods that use multilayer neural networks to identify complex patterns in imaging data, has shown strong diagnostic performance in detecting rotator cuff tears and glenohumeral arthritis, while AI-assisted three-dimensional reconstruction and anatomy-based modeling help visualize glenoid deformity and support more intuitive preoperative planning, complementing conventional navigation. Intraoperatively, AI-based phase recognition and robotic assistance may enhance component placement precision while also offering potential benefits in surgical training, workflow standardization, and real-time feedback. In addition, emerging innovations such as mixed reality guidance, multimodal integration, and semi-autonomous assistance continue to expand the potential scope of AI applications in shoulder arthroplasty. Postoperatively, machine learning-driven prognostic models and wearable monitoring systems predict complications and guide tailored rehabilitation, supporting earlier risk identification when required. However, current AI systems are unable to capture surgeon intuition and context-dependent expertise. Nevertheless, emerging strategies such as explainable AI, multimodal integration, federated learning, and semi-autonomous surgical platforms aim to bridge these gaps by enhancing transparency, scalability, and clinical trust. Conclusion The adoption of AI tools by surgeons depends on their demonstrated reliability across diverse patient anatomies, intraoperative variability, and real-world rehabilitation settings. Thus, technical refinement along with mutual trust with surgeons, supportive regulatory frameworks, and institution-specific implementation strategies are essential for safe integration, particularly because current systems may increase cost, workflow complexity, or delay care when not adequately validated or appropriately implemented.","url":"https://doi.org/10.1186/s13018-026-07104-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13018-026-07104-8","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16152445","name":"Clinical Utility of an FDA-Authorized Artificial Intelligence Imaging Platform in Interstitial Lung Disease Diagnosis.","source":"europepmc","abstract":"Background/Objectives: The diagnosis of interstitial lung disease (ILD) is challenging and frequently delayed. Clinically accessible and minimally invasive diagnostic tools are needed to expedite the diagnosis of ILD while minimizing risk to patients. Fibresolve is an imaging artificial intelligence (AI) tool recently approved by the Food and Drug Administration (FDA) for use in ILD diagnosis and made available to clinicians. The objective of this study was to describe its utility in clinical practice. Methods: We conducted a prospective, observational study of patients across the United States (US) in whom Fibresolve was utilized during routine clinical practice between July 2024 and June 2026. Information on patient demographics, Fibresolve test results (positive = suggestive of idiopathic pulmonary fibrosis (IPF); negative = unsupportive of IPF), and disease management pre- and post-Fibresolve utilization were collected, including the use of ILD-specific pharmacotherapy and planned and/or completed invasive diagnostic procedures. Results: There were 209 patients that underwent evaluation with Fibresolve across 47 medical centers. Of those, 16 were academic centers ( n = 57 patients), and 31 were community-based ( n = 152 patients). Fibresolve was positive in 89/209 (42.6%) patients. The percentage of patients receiving ILD-specific pharmacotherapy increased significantly following Fibresolve utilization (12.5% to 44.4%, p p Conclusions: In this real-world study evaluating the use of an FDA-authorized imaging tool for ILD diagnosis in diverse clinical practices across the US, we found that following utilization of Fibresolve, there was a statistically significant increase in the percentage of patients receiving guideline-directed therapy for ILD, and previously-planned invasive diagnostic procedures were avoided in many cases. These findings support the use of Fibresolve as an adjunct for ILD diagnosis in clinical practice.","url":"https://doi.org/10.3390/diagnostics16152445","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16152445","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00464-026-13053-4","name":"Feasibility of using artificial intelligence models to identify the obturator nerve in laparoscopic selective lateral pelvic lymph node dissection.","source":"europepmc","abstract":"Background Protecting the obturator nerve (ON) is crucial during selective lateral pelvic lymph node dissection (SLPLND). This study evaluated the feasibility of an artificial intelligence (AI)-assisted system for real-time intraoperative identification of the ON in laparoscopic surgery. Methods This study retrospectively analyzed surgical video from 30 patients who underwent laparoscopic SLPLND between June 2023 and December 2024. The obturator nerves in selective lateral pelvic lymph node dissection videos were analyzed using FDIM HoloSurg software. The panel of experts consisted of three senior surgeons with extensive experience in SLPLND, who analyzed the videos and scored them using a Likert scale (0, very poor; 1, poor; 2, acceptable; 3, good; and 4, excellent). Results This research successfully established a real-time obturator nerve recognition model based on YOLOv11. The model was trained and validated using a dataset consisting of 1,530 high-quality surgical images, which were divided into training and validation sets at a ratio of 7:3. It achieved an overall accuracy of 0.962, a precision of 0.781, a recall of 0.775, and an F1 score of 0.778. Thirty challenging scenarios were selected from the validation cohort. In the vast majority of cases (73.3%), this model achieved an excellent score, while the scores obtained by junior surgeons were generally lower (P Conclusion This study confirms the technical feasibility of an AI-based real-time system for assisting in the identification of the ON. The system demonstrates a balance between high recall and precision, which can assist junior surgeons in identifying the ON and enhancing surgical safety.","url":"https://doi.org/10.1007/s00464-026-13053-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00464-026-13053-4","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3760/cma.j.cn112144-20260227-00136","name":"[From digitalization to embodied intelligence: technological innovation and paradigm shift in clinical stomatology].","source":"europepmc","abstract":"In the critical stage of digital and intelligent transformation of stomatology, artificial intelligence (AI) technology has shown significant advantages in diagnosis, treatment planning and other fields. However, limited by the nature of disembodied intelligence, the clinical execution end still faces the \"last centimeter\" bottleneck such as weak environmental perception and lack of dynamic interaction. As a new AI paradigm emphasizing the closed loop of \"perception-decision-action\", embodied intelligence provides a new theoretical direction and technical path to break through the execution limitations of existing digital technologies. This paper sorts out the technical core of embodied intelligence and its adaptation logic to oral clinical scenarios. From the perspective of different evaluation dimensions, it compares the conventional medical robot autonomy classification with intelligence-oriented frameworks, and further introduces the embodied intelligence grading system in the context of oral healthcare, systematically describes the development direction of core technologies such as visuo-tactile fusion perception, oral-specific world model, and simulation-to-reality (Sim2Real) simulation training, prospects its application scenarios in subspecialties such as oral implantology, prosthodontics, endodontics, and orthodontics, and puts forward expert suggestions on core issues such as data islands, regulatory ethics, and technical boundaries faced by the current field, so as to provide a reference for the rational development and standardized application of oral embodied intelligence.","url":"https://doi.org/10.3760/cma.j.cn112144-20260227-00136","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112144-20260227-00136","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106607","name":"Domain-specific functional outcome prediction in stroke rehabilitation: A multicenter artificial intelligence study.","source":"europepmc","abstract":"Background Stroke is a leading cause of long-term disability worldwide, yet existing clinical decision support tools rely on global disability metrics, such as the modified Rankin Scale, which do not adequately reflect patient-centered rehabilitation or recovery goals. Objective We aimed to develop, validate, and implement artificial intelligence (AI)-based clinical support models for predicting domain-specific functional outcomes after stroke across multiple rehabilitation institutions and timepoints. Methods We utilized prospective and retrospective multicenter data collected from patients with stroke across four rehabilitation institutions in South Korea (2017-2024). Prognostic models were developed for three functional domains-ambulation, cognitive function, and activities of daily living-under two temporal scenarios: acute-to-subacute and acute-to-early-chronic prediction. Alignment-based regularization was applied to improve cross-institutional generalizability. Results The proposed framework achieved strong predictive performance in internal validation (AUROC up to 0.897 for ambulation and 0.864 for cognition) and favorable external validation performance, with AUROCs up to 0.924 (ADLs) and 0.892 (cognition), despite institution-specific differences in cohort size and variable availability across centers. The implemented web-based clinical decision support system provides real-time prediction of individualized recovery trajectory with intuitive visualization designed to support clinician-patient communication. Conclusions Our findings demonstrate the predictive feasibility of AI-based modeling for domain-specific stroke prognosis and present a prototype implementation illustrating the potential clinical applicability of the proposed framework across heterogeneous institutional settings. The use of routinely collected clinical variables and the preliminary cross-institutional validation results support further investigation of real-world rehabilitation practices, pending prospective clinical evaluation.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106607","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106607","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3310/gjse4912","name":"Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.","source":"europepmc","abstract":"Background The integration of artificial intelligence in medical image classification for screening has the potential to enhance efficiency, diagnostic accuracy and accessibility. However, ethical concerns such as accountability, bias, transparency and the impact on healthcare professionals remain critical. This review synthesises qualitative evidence on the ethical considerations surrounding artificial intelligence adoption in screening programmes. Methods A systematic search of qualitative studies, from June 2020 to September 2024, was conducted across multiple databases: MEDLINE, EMBASE, PsycInfo ® (American Psychological Association, Washington, DC, USA) and Cumulative Index to Nursing and Allied Health Literature. Primary qualitative studies exploring healthcare professionals', patients' and other stakeholders' perspectives on artificial intelligence in screening were included. Thematic analysis was performed, and findings were assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach to evaluate confidence in the evidence. Results Fourteen qualitative studies were included, covering perspectives from clinicians, radiologists, artificial intelligence developers, policy-makers and patients. Key ethical concerns identified included: (1) the necessity of human oversight to ensure that artificial intelligences diagnostic recommendations are appropriate; (2) challenges in assigning liability when artificial intelligence errors occur; (3) risks of algorithmic bias due to discrepancies between training data sets and real-world populations; (4) concerns over data privacy, cybersecurity and informed consent in artificial intelligence-driven decision-making; (5) the need for transparency in artificial intelligence decision-making processes to build trust and (6) potential deskilling of healthcare professionals and shifts in professional responsibilities. While artificial intelligence was seen as a valuable tool to augment clinical decision-making, stakeholders emphasised that ethical frameworks must guide its implementation to maintain public trust and patient safety. Conclusion This review highlights the critical considerations that must be addressed to ensure the responsible integration of artificial intelligence in medical screening. Policy-makers, healthcare institutions and developers should prioritise human oversight, robust regulatory frameworks and strategies to mitigate bias and ensure transparency. Future research should focus on disease-specific artificial intelligence applications and long-term ethical implications. Study registration The protocol for this study is registered on PROSPERO as CRD42024599536. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR172233) and is published in full in Health Technology Assessment ; Vol. 30, No. 51. See the NIHR Funding and Awards website for further award information.","url":"https://doi.org/10.3310/gjse4912","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3310/gjse4912","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/ceh.0000000000000656","name":"Artificial Intelligence, Cognitive Abundance, and the Multi-Layered Competence of Health Professionals.","source":"europepmc","abstract":"Abstract Artificial intelligence (AI) is transforming how health care professionals develop, maintain, and express competence across the span of their careers. Traditional continuing professional development has been shaped by a paradigm of what has been called cognitive scarcity (or informational resource scarcity) where clinicians had limited opportunity to find, read, synthesize, and interpret evidence, and learning systems evolved to deliver knowledge in periodic, curated updates. Emerging AI systems-large language models, multimodal analytic tools, predictive algorithms, and reflective agents-disrupt this scarcity by creating cognitive abundance (or informational resource abundance). These systems generate real-time evidence syntheses, contextual insights, adaptive learning trajectories, and continuous performance feedback. Using ten Cate et al.'s (2024, Medical competence as a multilayered construct. Med Educ , 58, 93) multilayered model of competence-canonical, contextual, and personalized-this paper analyzes how AI can both enhance existing educational processes and fundamentally reshape the developmental landscape. AI shifts clinicians from being knowledge stewards to orchestrators of distributed cognition, from experiential learners to data-informed practitioners, and from using opportunistic continuous personal development to continuous reshaping of professional identity. Continuing professional development must evolve to cultivate AI literacy, hybrid reasoning, interprofessional coherence, and ethical stewardship in work environments where cognition is abundant. The contents of long-term memory of clinicians will shift from a predominance of biomedical facts needed to steer daily clinical work, to new procedural inquiry skills needed to find, select, and evaluate the validity of information for clinical decision making.","url":"https://doi.org/10.1097/ceh.0000000000000656","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ceh.0000000000000656","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/93484","name":"Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.","source":"pubmed","abstract":"The rapid emergence of artificial intelligence (AI) has outpaced its formal adoption in health care organizations, contributing to the emergence of Shadow AI, defined here as the use of unauthorized AI tools by medical professionals. Under the European Union Medical Device Regulation, AI tools used for clinical purposes must undergo conformity assessment before use; general-purpose tools such as ChatGPT have not done so, rendering their clinical application unauthorized at the regulatory level. While Shadow AI offers potential efficiency gains and higher performance, it poses significant risks to data privacy, clinical safety, and regulatory compliance. Despite its growing prevalence, empirical research on the purposes for which physicians use Shadow AI remains scarce.","url":"https://doi.org/10.2196/93484","authors":["Petersson L","Irgang L","Mauritzon I","Holmén M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/93484","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1186/s12884-026-09494-3","name":"Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has been increasingly integrated with fetal and placental magnetic resonance imaging (MRI) to enhance the detection of abnormalities and streamline diagnostic processes. MRI, known for its superior soft tissue contrast and multiplanar imaging capabilities, is a critical tool for evaluating complex fetal and placental conditions. However, the large volume of MRI data often poses challenges for clinicians in providing timely and accurate diagnoses. Fetal and placental MRI are subject to several inherent limitations, including fetal motion artifacts, low signal-to-noise ratio, and operator-dependent variability, which can reduce image quality and hinder accurate diagnosis. Artificial intelligence has shown significant potential in addressing these challenges. For example, AI-based methods have been applied to motion artifact reduction, automated organ segmentation, and disease classification. In addition, recent studies have demonstrated that AI can reduce MRI scan times by up to 60% without compromising image quality, thereby improving diagnostic accuracy and workflow efficiency. Methods A systematic PubMed search was conducted on January 16, and May 20, 2024, using predefined terms related to fetal and placental MRI and AI. Peer-reviewed English studies were included, while irrelevant articles, reviews, and editorials were excluded. Disagreements during the review process were resolved by a third reviewer. Results After systematic screening, 74 studies on AI applications in fetal MRI and 32 studies on placental MRI were included. Key applications in fetal MRI included motion correction (17.6%), organ segmentation (48.6%), and disease classification (6.8%). For placental MRI, studies primarily focused on placental invasion assessment (53.1%) and segmentation (28.1%). Relevant studies published between 2016 and 2024 were categorized by application area and analyzed in detail. Conclusions This review synthesizes extensive research on AI applications in fetal and placental MRI, highlighting its potential to enhance imaging quality, automate tasks such as segmentation and motion correction, and improve diagnostic accuracy. However, challenges remain, including reliance on small, single-center datasets, limited demographic and pathological diversity, and a predominance of Two-Dimensional (2D) imaging techniques. Addressing these issues through the development of diverse, multi-center datasets and the exploration of advanced Three-Dimensional (3D) imaging methods is essential. By overcoming these barriers and integrating multimodal approaches, AI holds immense promise for revolutionizing prenatal diagnostics and advancing personalized care.","url":"https://doi.org/10.1186/s12884-026-09494-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12884-026-09494-3","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1097/ms9.0000000000005013","name":"Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.","source":"europepmc","abstract":"Background The integration of artificial intelligence (AI) in dental and maxillofacial radiology is revolutionizing diagnostic accuracy and clinical decision-making. This bibliometric analysis investigates the research landscape, emerging trends, and scholarly impact of AI applications in this specialized field. Methods A comprehensive search was conducted on 25 December 2024 using the Web of Science Core Collection database. Data analysis tools, including VOSviewer, CiteSpace, and Biblioshiny, were employed to examine publication trends, global contributions, collaborative networks, and keyword dynamics. Results The analysis revealed a marked increase in AI-related publications in dental and maxillofacial radiology, particularly from 2016 onward. The number of studies rose steadily, reaching 218 publications in 2024. The United States led in research output, followed closely by China and South Korea, with KU Leuven emerging as the top-contributing institution. Reinhilde Jacobs was identified as the most prolific author, while Medical Physics was the most cited journal. Co-citation analysis highlighted influential works by authors such as J.H. Lee and F. Schwendicke . Keywords including \"artificial intelligence,\" \"deep learning,\" \"CBCT,\" and \"classification\" dominated research discussions, reflecting the field's evolving focus. Recent research trends emphasize advanced applications in segmentation, accuracy enhancement, and predictive modeling. Conclusion AI has become integral to the advancement of dental and maxillofacial radiology, offering significant improvements in diagnostic precision and treatment planning. This study underscores the importance of staying abreast of AI innovations to enhance patient care and foster future research opportunities. Researchers and clinicians are encouraged to adopt AI-driven approaches to maximize clinical efficiency and outcomes.","url":"https://doi.org/10.1097/ms9.0000000000005013","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ms9.0000000000005013","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frai.2026.1774125","name":"Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.","source":"europepmc","abstract":"Tuberculosis (TB) remains the leading cause of death from infectious diseases globally, with significant diagnostic challenges in low- and middle-income countries. Artificial intelligence (AI) analysis of cough sounds could offer an inexpensive and accessible solution for detecting TB. This systematic review and meta-analysis evaluated the diagnostic accuracy of AI-based cough sound analysis for screening TB and identified key methodological gaps. We performed a systematic review (PROSPERO: CRD420250656065), searching PubMed, Scopus, IEEE, Web of Science, and CINAHL for studies published between 1 January 2009 and 31 December 2024. Included studies focused on the application of AI-algorithms for TB screening based on cough sound analysis. Risk of bias was assessed using the QUADAS-AI tool. The sensitivity, specificity, and area under the curve were extracted to quantify diagnostic performance. Overall, 14 studies were found, largely from Asia and Africa. Although a meta-analysis of seven studies showed a pooled sensitivity of 91% (95% CI: 88-94%) and a specificity of 89% (95% CI: 85-92%), with a diagnostic odds ratio of 81.61 and an area under the curve of 0.9539, indicating strong diagnostic accuracy, most of the included studies focused on analytical validity. Artificial intelligence models for cough sounds might improve TB detection, particularly in resource-limited settings, by offering a non-invasive, rapid screening tool. However, the high risk of bias, heterogeneity, and reliance on internal validation highlights the need for multicenter clinical validity studies before adoption.","url":"https://doi.org/10.3389/frai.2026.1774125","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1774125","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1142/s0129065726500528","name":"Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.","source":"europepmc","abstract":"The automatic detection of anomalies in medical images is a significant challenge in the assisted diagnosis of neurodegenerative diseases such as Alzheimer's. This paper presents an anomaly detection model based on Transformers for the analysis of brain magnetic resonance images. The proposed architecture combines a Vision Transformer as an encoder with a memory bank module that allows modeling the distribution of healthy brains and detecting deviations through reconstruction error. The model is trained using a one-class learning approach, using only images considered normal, with the aim of learning the representation of normality and automatically flagging atypical structural patterns. To adapt volumetric studies to the architecture, a preprocessing procedure is designed that transforms three-dimensional information into a two-dimensional representation compatible with the model. The results obtained demonstrate a solid ability to characterize normality and generate reliable predictions, confirming the viability of Transformer-based architectures for unsupervised anomaly detection in neuroimaging. This approach lays the foundation for future extensions in clinical settings and other medical imaging applications.","url":"https://doi.org/10.1142/s0129065726500528","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1142/s0129065726500528","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3171/2025.8.spine25389","name":"Utilization of artificial intelligence-based writing assistance in contemporary spine literature.","source":"europepmc","abstract":"Objective With the advent of artificial intelligence (AI), scientific research and writing has benefitted from large language models to generate hypotheses, evaluate data, and draft manuscripts. However, this brings into question the prevalence, impact, and ethics of AI writing assistance on published literature. The purpose of this study was to quantify the extent of AI involvement in published spine articles and establish a statistical threshold for scientific integrity. Methods Spine-focused clinical journals were selected for their impact factor and comprehensive representation of the specialty. All full-length research articles published in 2005 and 2023-2024 in these journals were extracted. ZeroGPT was used to assess AI content in each article. Baseline AI utilization was evaluated on the 2005 data, with 2 standard deviations above the mean serving as the threshold for significant AI usage. Based on pre-AI era articles, a threshold ZeroGPT score of 48.8% was established. Articles exceeding this threshold in the 2023-2024 data were assessed across spine journals and years of publication. Results In total, 2790 post-AI articles published across 6 spine journals in 2023-2024 were examined. Among these spine journal articles, 25.7% were considered to have significant AI involvement. AI involvement varied significantly across spine journals, ranging from 20.2% for Spine (Phila Pa 1976) to 31.1% for Journal of Neurosurgery: Spine (p Conclusions AI involvement in drafting manuscripts was observed in 25% of articles in recent spine literature. Although the use of AI has plateaued since mid-2024, likely due to the implementation of clear ethical guidelines and utilization of improved detection tools, continued efforts should be made with the evolving AI landscape to the ensure quality, authenticity, and integrity of spine research.","url":"https://doi.org/10.3171/2025.8.spine25389","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3171/2025.8.spine25389","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.jclinepi.2026.112438","name":"Incomplete reporting persists in orthopedic machine learning models: a systematic review of TRIPOD and TRIPOD+AI reporting completeness.","source":"europepmc","abstract":"Background and objectives Machine learning (ML) prognostic models in orthopedic surgery are published at an accelerating pace, yet whether reporting meets the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) + artificial intelligence (AI; 2024) transparency standards remains unclear. We examined (1) characteristics and trends of preoperative ML prognostic models for perioperative outcomes through December 31, 2024; (2) TRIPOD+AI reporting completeness among the 25 highest-impact orthopedic journals; and (3) how derived TRIPOD 2015 completeness compares with prior systematic reviews. Methods We performed a systematic review of studies published through December 31, 2024, developing or externally evaluating preoperative ML prognostic models predicting any intra- or post-operative outcome in orthopedic surgery. Study characteristics and trends were extracted from 433 eligible studies. TRIPOD+AI reporting completeness was assessed in a subset of 93 studies (80 development-applicable and 20 evaluation-applicable; not mutually exclusive as 7 studies performed both) published in the 25 highest-impact orthopedic journals. TRIPOD 2015 reporting completeness was derived from TRIPOD+AI items. Results Nearly half of all studies were published in 2023-2024, a total of 27% of development studies had high sample size concern, 36% reported calibration, and only 13% provided an accessible model. The median TRIPOD+AI completeness was 45% (IQR 40-50). The median TRIPOD 2015 completeness was similar in development studies (54% [IQR 28-84] vs 50% [IQR 27-79]) but improved in evaluation studies (80% [IQR 45-95] vs 61% [IQR 43-90]) compared with prior systematic reviews. Conclusion Despite rapid growth with more than 400 ML orthopedic prognostic prediction models, this baseline assessment reveals substantial gaps in reporting completeness. There is considerable room for improvement, particularly in sample size justification and performance reporting. Future work should prioritize TRIPOD+AI implementation, journal enforcement, and periodic reevaluation of reporting completeness as post-2024 publications mature.","url":"https://doi.org/10.1016/j.jclinepi.2026.112438","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jclinepi.2026.112438","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1177/03635465261463006","name":"Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the &lt;i&gt;American Journal of Sports Medicine&lt;/i&gt;.","source":"europepmc","abstract":"Background The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. Purpose To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. Study design Cross-sectional agreement study. Methods Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. Results Pooled human reviewers demonstrated fair agreement with the final decision (κ = 0.181 [ P P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic \"revision bias.\" When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score Conclusion AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.","url":"https://doi.org/10.1177/03635465261463006","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03635465261463006","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.compbiomed.2026.111787","name":"Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.","source":"europepmc","abstract":"Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. However, dose reduction leads to low-count data, which increases noise and variance and can introduce structured artifacts. These degradations hinder reliable clinical interpretation by reducing lesion contrast and obscuring lesion boundaries, thereby compromising segmentation accuracy. Although deep learning approaches have been proposed to reconstruct and segment low-dose images to full-dose quality, many existing methods address dose variation without explicitly modeling the relationship between dose reduction and image degradation. Moreover, some methods assume externally provided dose metadata as a conditioning input, which can limit applicability and generalization when such metadata are missing, non-standardized, or defined inconsistently across datasets and protocols. Therefore, this work proposes the radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation (SinoDose). The main novelty of SinoDose is that it estimates an observation-driven visual relative dose (VR-dose) directly from sinogram-domain internal cues, rather than relying on external dose metadata. In addition, a learnable sinogram knowledge library (SKL) injects acquisition-domain periodic priors to compensate for structures that collapse or become distorted under extremely low-dose conditions. The estimated VR-dose and its context vector are then used in dose-guided calibrated FiLM to enable dose-adaptive joint reconstruction and lesion segmentation in a unified framework. Experiments on the annotated AutoPET and KiTS datasets show that SinoDose consistently improves DSC, HD95, PSNR, and rPSNR over competing baselines. Additional evaluation on the real low-dose UDPET and the real-world LDCT datasets further supports reconstruction robustness under practical acquisition conditions. The code is available at https://github.com/yeongjong0220/DoseSino.","url":"https://doi.org/10.1016/j.compbiomed.2026.111787","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.compbiomed.2026.111787","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1371/journal.pone.0357186","name":"AI-assisted forecasting in microsurgery: A dual-component framework for global publication trends.","source":"europepmc","abstract":"Background Artificial intelligence (AI)-assisted approaches may allow surgical research trends to be analyzed at scale and projected over time. However, their use in forecasting the evolution of microsurgical scholarship remains limited. This study developed an AI-assisted bibliometric framework to characterize and project global clinical and experimental microsurgery publication trends. Methods PubMed metadata from 20 microsurgery-relevant surgical journals were extracted for 2010-2024 using an automated Python-based retrieval algorithm. A rule-based contextual key-word classifier using a predefined microsurgery keyword taxonomy was applied to identify relevant publications. Candidate forecasting models included linear regression, quadratic regression, autoregressive integrated moving average, and Holt's exponential smoothing. Model performance was compared using R2, root mean square error, mean absolute error, and Akaike information criterion. Forecasts were generated through 2030 and reported with 95% confidence intervals. Results The framework processed 90,902 records, of which 83,133 underwent contextual text classification after exclusion of incomplete metadata. A final analytic dataset of 11,561 microsurgery publications with verifiable first-author country attribution was identified. Classification validation using a stratified sample of 4,441 records demonstrated 95.2% agreement with the human-reviewed reference standard (95% CI, 94.5%-95.8%; Cohen's κ = 0.826). Annual microsurgery publications increased from 611 in 2010-973 in 2024, representing a 59.3% increase. Temporal validation supported short-horizon stability of the linear projection model. In fixed temporal holdout testing, the linear model achieved RMSE 40.5, MAE 36.2, MAPE 4.0%, and 95% prediction-interval coverage of 100%. Through 2030, publication activity is projected to increase, with lymphatic microsurgery showing the greatest relative thematic growth (+36.0%), followed by technological and operative innovation (+21.7%). Conclusion This study presents an AI-assisted bibliometric workflow for characterizing and projecting microsurgical publication trends. By integrating automated PubMed metadata extraction, contextual keyword-based classification, human-reviewed validation, and conventional statistical forecasting, the workflow enables reproducible assessment of publication activity across time, geography, authorship, and thematic domains. The resulting estimates should be interpreted as conditional projections under observed historical trends rather than deterministic predictions of future scientific activity, innovation, or leadership. This approach may assist surgeons, clinician-scientists, and interdisciplinary research teams in summarizing research patterns, identifying areas of increasing scholarly activity, and informing future collaborative planning.","url":"https://doi.org/10.1371/journal.pone.0357186","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0357186","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s41747-026-00763-6","name":"The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.","source":"europepmc","abstract":"Objective In emergency trauma care, artificial intelligence (AI) may aid fracture detection on radiographs, potentially reducing radiologists' workload. We evaluated the role of deep learning-based decision-support software in the reporting of trauma cases. Materials and methods We retrospectively analyzed 2317 trauma radiographs acquired at a single center: 1,174 images obtained from November 1 to 16, 2023, without access to the AI tool during reporting, and 1,143 images from February 1 to 13, 2024, with discretionary use of the AI output during reporting. The AI software output was compared with final radiology reports, with ground truth established by a musculoskeletal radiologist with 9 years' experience. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both the fracture and patient levels. Results The dataset included 1,914 patients with 1,188 acute fractures (621 in November, 567 in February). At the fracture level, standalone AI achieved 90.7% accuracy, 87.8% sensitivity, 94.0% specificity, 94.3% PPV, and 87.2% NPV in November, 94.1%, 93.5%, 94.6%, 94.5%, and 93.6% in February, respectively. Non-AI-assisted radiologists reached 92.4%, 89.0%, 96.2%, 96.3%, and 88.7%, AI-assisted radiologists 93.4%, 90.0%, 96.7%, 96.4%, and 90.7%, respectively. At the patient level, AI's overall performance reached up to 96.5% accuracy and 95.6% sensitivity. Discrepancies between AI and radiologists occurred in 326 cases, often related to anatomical variants such as accessory ossicles. Conclusion Standalone AI demonstrated near-expert accuracy and sensitivity in fracture detection at both fracture and patient levels. PPV increased with AI support, indicating more accurate detection of actual fractures. Relevance statement By examining discretionary real-world use of AI in trauma radiography, this study shows that clinical benefit is not guaranteed by algorithmic performance alone, as optional AI integration does not consistently improve radiologist sensitivity, underscoring a critical implementation gap in practice. Key points Standalone AI achieves near-expert fracture detection performance in trauma radiography. Discretionary AI use does not consistently improve radiologist sensitivity. AI use reduces discrepancies, suggesting improved diagnostic consistency. Clinical benefit of AI depends on real-world implementation strategy.","url":"https://doi.org/10.1186/s41747-026-00763-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s41747-026-00763-6","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1002/vms3.71056","name":"From Tradition to Future in Veterinary Public Health Education: Students' Attitudes, Anxiety and Experiences With AI-Supported Learning.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has the potential to reshape learning processes, especially through tools such as ChatGPT. Objectives This study aims to evaluate veterinary students' attitudes towards AI-supported veterinary public health education applications, their anxiety levels and their experiences regarding the education process. Methods The study adopted a mixed method design in which quantitative and qualitative methods were used together. The study was conducted with final-year veterinary students (n = 60) enrolled in the Veterinary Public Health course. This study consisted of four phases: (1) the Artificial Intelligence Anxiety Scale (AIAS) was administered as a pre-test, (2) traditional face-to-face training was given, (3) the same topic was repeated with a ChatGPT-based AI-supported exercise and (4) after the trainings, the AIAS was administered again and post-test was conducted and a questionnaire consisting of closed and open-ended questions was administered to the students. Factor analysis revealed that the scale had high internal consistency (Cronbach's α > 0.90). Results Analysis of the scale scores revealed that veterinary students exhibited moderate anxiety towards AI and that AI-focused education did not significantly alter this anxiety in the short term. In the content analysis of qualitative data, students stated that they benefited from the aspects of AI such as fast access to information, practicality and time-saving; the same time, they expressed concerns about ethical concerns, information reliability and professional role change. Conclusion Overall, the findings indicate a duality in students' potential attitudes towards AI. These findings indicate the applicability of AI-based educational practices in the context of veterinary public health education and point to the need for multidimensional evaluation of student attitudes.","url":"https://doi.org/10.1002/vms3.71056","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/vms3.71056","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.dld.2026.07.084","name":"An artificial intelligence-based endoscopic ultrasonography risk assessment and stratification for gastric stromal tumors.","source":"europepmc","abstract":"Background Gastrointestinal stromal tumors (GISTs) are tumors with malignant potential. This research aims to develop an artificial intelligence (AI)-based system for analyzing endoscopic ultrasonography (EUS) visuals and generating risk scores. Aims This system is designed to better predict GIST risk levels by identifying risk factors. Methods An internal dataset comprising 504 EUS images from 226 patients with pathologically confirmed GISTs was collected from Yuzhong Hospital and Jiangnan Hospital of the Second Affiliated Hospital of Chongqing Medical University, Sichuan Provincial People's Hospital between 2018 and 2024. Multiple AI-based machine learning models were developed and tested. Six machine learning models were compared, and the optimal diagnostic model was selected based on statistical results. Results For the best-performing model, XGBoost, the internal test set results were as follows: overall accuracy 83.17%, sensitivity 75.68%, specificity 93.72%, positive predictive value (PPV) 73.21%, negative predictive value (NPV) 93.52%, F1 score 0.74, and area under the curve (AUC) 0.97. The external validation set results were: overall accuracy 80.68%, sensitivity 71.53%, specificity 92.72%, PPV 70.44%, NPV 92.78%, F1 score 0.70, and AUC 0.94. Conclusions This model utilizes machine learning algorithms on EUS images to accurately predict GIST risk stratification.","url":"https://doi.org/10.1016/j.dld.2026.07.084","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.dld.2026.07.084","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/fpubh.2026.1860473","name":"Training the public health workforce for generative AI: validation of the Italian Chatbot Usability Questionnaire.","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) is becoming increasingly relevant for public health practice. However, scalable training models and validated usability instruments for AI chatbots are still limited in non-English contexts. We aimed to (i) assess AI knowledge, attitudes, and practices (KAP) among a sample of Italian public health professionals, and (ii) translate, culturally adapt, and psychometrically validate an Italian version of the Chatbot Usability Questionnaire (CUQ-IT) by applying it to a workshop-developed vaccination counseling chatbot. Methods We conducted a three-phase study during the 57th Italian National Congress of Hygiene, Preventive Medicine and Public Health (Italy, October 2024): (i) use of an Italian translation and adaptation of the CUQ within a cross-sectional KAP survey; (ii) a 90-min theoretical-practical workshop with guided prompt engineering and collaborative development of a Custom GPT for vaccination counseling (VaxSense), followed by supervised interaction; (iii) post-workshop usability evaluation with CUQ-IT and psychometric testing. Construct adequacy was assessed using KMO and Bartlett's test. Internal consistency was estimated with Cronbach's α. Associations between KAP and usability ratings were explored. Results Of 150 workshop attendees, 87 returned questionnaires (58%); 86 were eligible for KAP analyses, and 77 completed CUQ-IT for usability evaluation. Participants (mean age 35.9 ± 8.7; 55.8% females) reported moderate AI knowledge (mean 2.74 ± 1.05/5), positive attitudes (3.87 ± 0.73/5), and limited routine use (2.88 ± 1.11/5). CUQ-IT showed good sampling adequacy (KMO = 0.81) and significant item correlations (Bartlett's χ 2 (120) = 515.36, p -value Conclusion In our sample of Italian public health professionals, moderate baseline AI knowledge, positive attitudes, and limited routine use of AI emerged, while the workshop-developed vaccination chatbot achieved overall good usability. The significant gender differences observed in both self-rated AI knowledge and usability scores indicated that \"AI readiness\" may be uneven, even within trained professional groups, underscoring the need for inclusive capacity-building strategies. In the public health sector, training should be framed as a component of safe implementation: strengthening the ability to critically appraise outputs, recognize common failure modes, mitigate bias and equity risks, and apply privacy-by-design principles. CUQ-IT demonstrated robust psychometric performance and represents a standardized tool to benchmark chatbot usability in Italian settings. Future work should confirm its measurement structure in larger samples and assess whether targeted training may reduce capability gaps and support appropriate and sustained use of AI in public health practice.","url":"https://doi.org/10.3389/fpubh.2026.1860473","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1860473","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3390/pharmacy14040096","name":"Automated Processes and Artificial Intelligence in Generating Candidates for Oncology Drug Repurposing: Three-Year Scoping Review of Data.","source":"pubmed","abstract":"Oncology conditions are increasingly defined by their molecular profiles, and drug repurposing exploits this new evidence to identify new therapeutic uses of authorized/investigational medicinal products outside their original indication(s). This scoping review mapped original research published between January 2022 and December 2024 to determine the impact of automated processes and artificial intelligence in generating oncology candidates for drug repositioning, and 42 individual projects met the eligibility criteria and were analyzed. The included studies demonstrate extensive use of computational approaches for candidate prioritization, large-scale data integration, and hypothesis generation in oncology drug repurposing, creating opportunities for positive impact on efficiency. The included projects most commonly were target-oriented and disease-oriented and used multiple databases and computational validation procedures, while experimental and clinical validation were less frequently reported. The available open-access literature suggests substantial activity in China and India, which can support the notion that digitalization represents an important instrument in healthcare systems of low- and middle-income countries but should be interpreted cautiously. While the search was limited to PubMed and open-access English-language publications, we identified a relatively small number of drug-oriented projects, the importance of providing publicly accessible source code to reduce development costs, and the predominant role of academic institutions.","url":"https://doi.org/10.3390/pharmacy14040096","authors":["Ivanov A","Hababa-Ivanova I","Elitova S","Stoev S","Getova-Kolarova V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/pharmacy14040096","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.12669/pjms.42.8.14827","name":"Impact of ChatGPT-assisted personalized learning on teaching acute abdomen to undergraduate medical students: A randomized crossover study.","source":"europepmc","abstract":"Background &objective The application of Artificial Intelligence (AI) in medical education has emerged as a promising avenue for personalized learning experiences to the individual needs of medical students. This study investigated the impact of AI-driven personalized learning pathways on the academic performance of medical students in acute abdomen topic, comparing against traditional learning method. Methodology This study used a randomized controlled crossover trial conducted from February 2024 to July 2024 among fourth year one hundred undergraduate medical students at Islamic International Medical College, Riphah International University, Pakistan. In this study, students enrolled in a general surgery course were randomly assigned to experimental group and control group following a pre-test. The experimental group engaged with AI-driven personalized learning pathways, powered by ChatGPT-4, the control group utilized conventional educational resources. Both groups completed a post-test to assess the effects of their respective learning interventions. Statistical analyses, including descriptive statistics, independent-samples t-tests and paired-samples t-tests were conducted using SPSS. Results The pre-test scores of the experimental (M = 17.12, SD = 6.99) and control groups (M = 18.64, SD = 6.91) did not differ significantly (p = 0.277). Post-intervention, the experimental group showed a statistically significant improvement (M = 22.8, SD = 5.11) compared to the control group (M = 19.24, SD = 7.11), with a p-value of .005 and a moderate effect size (Cohen's d = 0.58). This suggests that AI-driven personalized learning pathways had a positive and measurable impact on the students' academic performance in the experimental group. Conclusion AI-driven personalized learning pathways can enhance the academic performance of medical students, particularly in subject of surgery. Future research should explore the long-term effects of personalized AI-powered educational interventions.","url":"https://doi.org/10.12669/pjms.42.8.14827","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12669/pjms.42.8.14827","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/85986","name":"Alleviating Nurse Burnout With an Artificial Intelligence-Selected Mobile Cognitive Behavioral Therapy-Based Intervention: Mixed Methods Randomized Controlled Trial.","source":"europepmc","abstract":"Background Nurse burnout is a pervasive global problem. Cognitive behavioral therapy (CBT) has been shown to reduce burnout; however, most digital CBT programs use standardized approaches that overlook individual differences in burnout profiles. With advances in artificial intelligence (AI), algorithm-based recommendation systems now enable personalized intervention delivery by matching specific CBT modules to users. Objective This study aimed to test the effects of an AI-selected mobile CBT-based intervention on nurse burnout and to describe participants' experiences with the intervention. Specifically, it evaluated whether an AI-selected CBT-based intervention differentially reduced burnout subdomains compared with an information-only control group and explored how nurses perceived and engaged with the AI-selected program. Methods This study adopted a mixed methods design, integrating a 2-group randomized controlled trial and qualitative content analysis exploring participants' experiences. For this randomized controlled trial, a total of 125 nurses were enrolled and randomly assigned to either the experimental group (n=62) or the control group (n=63) between October 2024 and December 2024. The experimental group received an AI-selected mobile CBT-based intervention, in which an AI algorithm assigned CBT modules based on participants' burnout profiles (client-related, personal, and work-related), job stress, and coping characteristics. The control group received information related to burnout management. Primary outcomes, client-related, personal, and work-related burnout, were assessed at baseline, 2 weeks, and 4 weeks. Secondary outcomes, including coping strategies, job stress, and stress response, were assessed at baseline and 4 weeks. Between-group differences in burnout over time were examined using repeated measures analysis of variance, with adjustment for job stress and stress response. Within-group changes and postintervention group differences were analyzed using t tests. Open-ended survey responses and follow-up interviews (n=5 in the experimental group) were analyzed using thematic content analysis. Results Follow-up completion rates were 84.6% (137/162) at both 2 and 4 weeks. The experimental group showed a greater reduction in client-related (F1,121=7.548; P=.007), personal (F1,121=6.533; P=.01), and work-related burnout (F1,121=38.194; P Conclusions The findings suggest that participants were receptive to AI-selected CBT-based interventions, suggesting the potential of such interventions as a supportive approach for alleviating nurse burnout. Future research should explore the sustainability of these effects and optimize the intervention duration to enhance engagement and impact.","url":"https://doi.org/10.2196/85986","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/85986","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.cmpb.2026.109428","name":"Detecting cardiovascular diseases using ECG scans and explainable artificial intelligence.","source":"europepmc","abstract":"Background and objective Cardiovascular diseases are the leading cause of mortality globally, requiring early and accurate detection through tools like electrocardiography. While artificial intelligence models have emerged to provide reproducible analysis of electrocardiogram printouts, their clinical deployment is hindered by a lack of transparency and sensitivity to real-world image variations such as discolorations, handwriting, or paper wrinkles. This study introduces an explainable artificial intelligence framework designed to quantify the stability of deep learning models and identify vulnerabilities in their behavior under controlled image perturbations. Methods We utilized a large-scale dataset of electrocardiogram printouts synthesized from the PTB-XL benchmark, creating both clean and contaminated versions featuring various image-level manipulations. Four deep learning architectures, including EfficientNet and InceptionNet, were trained and evaluated using different activation functions. The stability of these models was assessed using local interpretable model-agnostic explanations. We employed intersection over union metrics to measure the consistency of explanations across perturbations and extracted radiomic-like image features to quantitatively analyze the characteristics of the generated explanations. Results Our experiments demonstrate that models trained on augmented datasets generalize better to perturbed data, with the best-performing model achieving an area under the receiver operating characteristic curve of 0.894 on the contaminated test set. Stability analysis showed that models trained on data containing perturbations achieved the highest average intersection over union of 0.399, indicating a more consistent focus on diagnostic features. Furthermore, radiomic-like features enabled the precise identification of the underlying deep learning model with an accuracy of up to 98%. Conclusions The proposed framework enables a comprehensive visual and quantitative evaluation of artificial intelligence stability in cardiovascular disease detection. By identifying how specific image manipulations affect model reliability, this approach can guide the development of more robust algorithms and targeted data augmentation strategies. To ensure full reproducibility and foster cross-domain collaboration, our tools and datasets are available at https://github.com/smile-research/xai-ecg.","url":"https://doi.org/10.1016/j.cmpb.2026.109428","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109428","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1017/s1049023x26108978","name":"Enhancing Emergency Medical Response Education through Generative AI-Powered Game-Based Learning: A Retrospective Comparative Study.","source":"europepmc","abstract":"Objective Traditional lecture-based learning (LBL) is often insufficient for cultivating the practical decision-making skills required in high-stakes environments like Emergency Medical Response (EMR). While game-based learning (GBL) offers an immersive alternative, it can lack immediate expert guidance. This study addresses this gap by exploring the integration of generative Artificial Intelligence (AI) as an \"intelligent tutor\" within GBL. The objective was to evaluate and compare the effectiveness of LBL, GBL, and generative AI-powered game-based learning (AI-GBL) on medical students' knowledge acquisition, retention, learning motivation, and cognitive load in an EMR course. Methods A retrospective, comparative study was conducted with 86 medical students from three consecutive cohorts (2022-2024), each exposed to one of the three teaching modalities (n = 29 LBL, n = 28 GBL, n = 29 AI-GBL). Knowledge was assessed via pre-test, post-test, and final-test scores with a maximum score of 10 points. Student feedback was collected for learning motivation, cognitive load, and technology acceptance. Results For immediate knowledge acquisition, both GBL (mean difference = 1.124/10 points; 95% CI [0.297, 1.952]; P = 0.008) and AI-GBL (mean difference = 0.897/10 points; 95% CI [0.076, 1.717]; P = 0.033) significantly outperformed LBL. For delayed knowledge retention, the AI-GBL group demonstrated significantly superior retention compared to both the GBL group (mean difference = 0.689 points; unadjusted 95% CI [0.080, 1.299]) and the LBL group (mean difference = 1.310 points; unadjusted 95% CI [0.706, 1.915]). The AI-GBL group also reported significantly lower cognitive load than the GBL group (mean difference = -0.273 points; unadjusted 95% CI [-0.456, -0.090]). Finally, students perceived the AI-powered approach as significantly more useful than the standard game-based approach (mean difference = 0.513 points; unadjusted 95% CI [0.137, 0.889]). Conclusion The AI-enhanced GBL model for EMR training improves knowledge acquisition and retention while reducing cognitive load, representing a promising approach for developing proficiency in complex, high-stakes medical competencies.","url":"https://doi.org/10.1017/s1049023x26108978","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1017/s1049023x26108978","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/10815589261462547","name":"Artificial intelligence perceptions and experiences among first and second year medical students.","source":"europepmc","abstract":"Artificial intelligence (AI) tools, particularly large language models, are increasingly accessible to medical learners. Early attitudes toward AI may shape how students incorporate these tools into learning and professional development. This study captured first- and second year medical students' AI familiarity, prior use, and perceptions during orientation week, providing a snapshot of incoming student attitudes. A cross-sectional survey was administered to first- and second year medical students ( N = 157) at a single U.S. medical school in August 2024. Data on demographics, AI familiarity, prior use, and perceptions were collected using multiple-choice and five-point Likert scale items. Descriptive statistics, Chi-squared analyses, and Fisher's exact tests were used to compare responses between cohorts. Of 108 responses, most students reported slight (42.6%) or moderate (37%) AI familiarity. ChatGPT was the most recognized (95.4%) and used (84.3%) tool. First year students were significantly more likely than second year students to have used AI previously, to have used AI during medical school applications, and to plan AI use in medical school (all p < 0.001). Students expressed optimism about AI integration but raised concerns about accuracy (76.9%), clinical thinking (67.6%), and ethics (63%). Over 60% rated ethical AI training as very or extremely important, and 57.4% were uncomfortable with AI-mediated assessment. Pre-clinical students anticipate a growing role for AI in medical education and favor structured, workshop-based ethical training. These findings can inform timely curricular planning as AI technologies continue to evolve.","url":"https://doi.org/10.1177/10815589261462547","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/10815589261462547","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00464-026-13302-6","name":"AI-based disease severity grading predicts complications in laparoscopic appendectomy.","source":"europepmc","abstract":"Background Appendicitis severity underpins contemporary management guidelines, where laparoscopic appendectomy remains gold standard. Preoperative measures poorly predict actual disease severity or complication risk, while operative grading systems such as the American Association for the Surgery of Trauma (AAST) remains largely confined to research settings. Artificial intelligence (AI) may provide practical solutions. We evaluated a previously validated AI-derived surgical video assessment of disease severity for predicting perioperative complications. Methods This retrospective study included consecutive surgical videos (6/2022-1/2024) routinely analyzed by the AI platform. AI-derived severity scores were stratified into Low (uncomplicated) and High (complicated) groups. Multivariable analysis identified independent predictors of complications. Operative-AAST served for benchmarking. Model discrimination (AUC), post hoc recalibration plot, and decision curve analysis (DCA) were evaluated. Results Of 632 cases, 74.5% were low severity and 25.5% high. The High group had higher complication rates (26.7% vs. 10%; p Conclusions Automated AI-based surgical video assessment shows promise as a complementary tool for risk-prediction of laparoscopic appendectomy. It offers scalable risk stratification that may be implemented in routine clinical practice. Nevertheless, further study and model refinement are warranted.","url":"https://doi.org/10.1007/s00464-026-13302-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00464-026-13302-6","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1056/cat.25.0460","name":"A Hub-and-Spoke Inpatient Model That Preserved Semirural Hospital Access.","source":"europepmc","abstract":"In 2024, Suroit Hospital, a semirural hospital in Quebec, Canada, faced the imminent loss of 20 acute care beds following a provincial directive limiting the use of private agency nurses. This immediate workforce shortage threatened essential inpatient services for a geographically isolated population, while surrounding urban hospitals lacked the capacity to absorb diverted patients. To prevent closures, the hospital partnered with the Santé Québec West Central Montreal Health and Social Services University Network (SQ-WCMTL) and its Jewish General Hospital University Centre to rapidly deploy a virtual inpatient unit - to the authors' knowledge, the first of its kind in Canada. This Case Study outlines the challenge, design, execution, and outcomes of a two-phase intervention: a 10-day rapid design and mobilization phase culminating in the admission of the first patient, followed by a 5-month hub-and-spoke operating phase. The model enabled Suroit Hospital (part of the Santé Québec-Montérégie West [SQ-MW]) to maintain inpatient capacity without on-site registered nurses. Physicians, licensed practical nurses, and patient care attendants delivered hands-on care locally, while experienced registered nurses - JGHUC employees - provided continuous virtual assessments, medication oversight, and real-time escalation support. Over the first 5 months, the virtual unit generated 786 hospitalization days that would otherwise have been lost. Fourteen patients were admitted in the first 2 weeks, with no transfers to urban hospitals and no adverse safety events. The average length of stay was 9.6 days. Among survey respondents, staff-reported effectiveness in their role was 47% in August 2024 (8 of 17 respondents) and 87.5% in October 2024 (7 of 8 respondents). Comfort with the virtual care technology was reported by 76.5% (13 of 17) in August 2024 and 100% (8 of 8) in October 2024. Patients reported satisfaction, valuing the ability to remain close to home. The rapid implementation surfaced several hurdles, including initial skepticism about safety without on-site registered nurses and communication delays during the first days of operation. Strong executive sponsorship, twice-daily huddles, structured escalation pathways, and agile project management enabled rapid problem-solving and continuous refinement. The initiative demonstrated that virtual nursing models can maintain - and even strengthen - quality and safety when supported by digital readiness, clear governance, and strong interinstitutional trust. After the 5-month operating phase, SQ-MW launched its own virtual ward team, allowing JGHUC to exit the operational model. Although that transition and sustainability phase was not evaluated as a formal study phase, it suggests that the intervention served as a capacity-building pathway toward spoke-site autonomy. This hub-and-spoke approach offers a replicable framework for health systems facing workforce shortages, semirural service disruptions, or inpatient capacity constraints. Likewise, virtual inpatient units can preserve equitable access to care and sustain essential services in communities where conventional staffing models are no longer viable.","url":"https://doi.org/10.1056/cat.25.0460","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1056/cat.25.0460","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.6026/973206300222712","name":"Assessment of dentists' knowledge, attitude and practice regarding artificial intelligence in periodontics.","source":"europepmc","abstract":"Artificial intelligence is rapidly transforming periodontal diagnosis, treatment planning and patient management; however, its adoption among dental professionals remains limited. Therefore, it is of interest to evaluate the knowledge, attitude and practice of dentists regarding artificial intelligence in periodontics among 800 participants recruited through stratified random sampling from January to June 2024. Data were collected using a structured, pre-validated questionnaire and analyzed using descriptive statistics, chi-square test and ANOVA. Although 75% of participants were aware of artificial intelligence and 80% expressed willingness to adopt it, only 25% reported its actual use in clinical practice, with concerns regarding data privacy and professional displacement noted in 40% of respondents. A significant gap exists between positive perception and clinical implementation, highlighting the need for targeted education, ethical guidelines and improved accessibility to artificial intelligence technologies in periodontics.","url":"https://doi.org/10.6026/973206300222712","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6026/973206300222712","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1053/j.ajkd.2026.05.020","name":"Artificial Intelligence-Enabled Electrocardiography for Monitoring Serum Potassium Dynamics in Patients With Severe Hypokalemia.","source":"europepmc","abstract":"Rationale & objective Severe hypokalemia requires prompt management and close surveillance. Although artificial intelligence-enabled electrocardiography (AI-ECG) rapidly detects severe hypokalemia, its application for monitoring serum potassium (K + ) dynamics during treatment remains unexplored. This study assessed AI-ECG performance in monitoring K + changes during supplementation. Study design Multicenter retrospective cohort study. Setting & participants 191 adults with severe hypokalemia (Lab-K + ≤2.5 mmol/L; matched ECG-K + + supplementation at three teaching hospitals between September 2019 and August 2024. Tests compared Laboratory-measured K + (Lab-K + ) and K + estimated by ECG (ECG-K + ) overall and stratified by the etiology of hypokalemia (acute K + shift vs. chronic K + deficit). Outcomes Primary: agreement between paired ECG-K + and Lab-K + . Secondary: diagnostic accuracy and K + trajectories. Analytical approach Linear mixed-effects models with patient-level random intercepts; repeated-measures correlation (rmcorr) and Bland-Altman plots; patient-level clustered bootstrapped ROC analysis for diagnostic accuracy. Results Of 191 patients, 156 (81.7%) had chronic K + deficits (most commonly gastrointestinal disorders [n=47] or diuretic use [n=35]), and 35 (18.3%) had acute K + shifts (most commonly thyrotoxic periodic paralysis [n=25]). The chronic K + deficits group had more comorbidities and use of medications affecting K + . ECG-K + correlated strongly with Lab-K + (rmcorr 0.847; 95% CI, 0.81-0.88; p + deficits. The diagnostic accuracy of ECG-K + with Lab-K + ≤3.5 mmol/L was reflected by an AUC of 0.920; 95% CI, 0.863-0.961. It was higher in patients with acute K + shift. ECG-K + preceded Lab-K + results by a mean of 52.5 minutes. Patients with acute K + shift corrected approximately threefold faster than those with chronic K + deficit (0.121 vs. 0.039 mmol/L/h). Rebound hyperkalemia was detected by ECG-K + in two patients before laboratory confirmation. Limitations Retrospective design; treatment-protocol heterogeneity; limited inpatient medication granularity and potential selection bias. Conclusions AI-ECG enables real-time, within-patient monitoring of serum K + dynamics during treatment for severe hypokalemia, with superior performance in the setting of acute hypokalemia due to K + shift. As a non-invasive adjunct, AI-ECG may shorten time to detect changes in K + and reduce the need for laboratory K + measurements than exclusive reliance on Lab-K + measurements. Confirmatory studies are warranted.","url":"https://doi.org/10.1053/j.ajkd.2026.05.020","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1053/j.ajkd.2026.05.020","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.jnma.2026.06.008","name":"Navigating AI in clinical practice: A practical guide for physicians.","source":"europepmc","abstract":"Artificial intelligence is rapidly entering clinical practice, yet many physicians-especially those in solo or small-group settings-lack the guidance and evaluation resources needed to use these tools safely. Because state medical boards regulate physicians rather than AI developers, clinicians remain fully accountable when AI‑assisted care contributes to patient harm. This article outlines the risks posed by opaque algorithms, hallucinated outputs, omissions, and inequitable model performance, while emphasizing that AI should augment-not replace-clinical judgment. Drawing on recent research and the Federation of State Medical Boards' 2024 guidance, the article clarifies professional responsibilities related to competence, documentation, informed consent, privacy, and bias mitigation. A practical toolkit offers actionable steps for evaluating AI tools, establishing verification protocols, monitoring performance, and ensuring transparency with patients. The article concludes that responsible physician engagement is essential to realizing AI's benefits while preserving patient safety, professional accountability, and equitable care.","url":"https://doi.org/10.1016/j.jnma.2026.06.008","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jnma.2026.06.008","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3390/cancers18162609","name":"Real-Time-Capable Detection of Glottic, Supraglottic and Hypopharyngeal Lesions Using Artificial Intelligence During Flexible Endoscopy.","source":"europepmc","abstract":"Background/Objectives : Supraglottic and hypopharyngeal carcinomas are aggressive malignancies that are often diagnosed at advanced stages. Timely recognition of these malignancies is influenced by many factors, such as endoscope quality and experience. This study evaluated the potential of artificial intelligence (AI) to support real-time detection and classification of such lesions during flexible endoscopy in the outpatient clinic. Methods : A previously developed deep learning (DL) algorithm was extended from a glottic lesion model to the unified localization and classification of glottic, supraglottic, and hypopharyngeal lesions during flexible endoscopy. Lesion frames were extracted from endoscopy videos obtained at two head and neck oncology centers and one secondary referral center between 2012 and 2024. These frames were annotated and labeled based on histopathological or clinically confirmed reference diagnoses. The primary outcome was the unified model's performance in detection of lesions per frame. After training (70% of data), the positive predictive value (precision) and sensitivity (recall) of this model were calculated on an independent test set (30% of data), stratified by subsite and tumor (T-) classification. Secondly, the model's binary classification performance (benign or malignant) was evaluated. Results : From 490 supraglottic and hypopharyngeal endoscopy videos, 40,059 frames with a benign or malignant lesion were extracted and added to the 56,036 glottic lesion frames in the database, comprising 1336 lesion videos and 123 healthy control videos (total n = 1459). On the test set, the model achieved a detection precision of 92.1% (95% CI: 90.9-93.2) and a recall of 73.3% (95% CI: 69.8-76.6). Detection performance increased with higher T-stage. Among correctly detected lesion instances in a malignancy-dominated test set (65.3% of lesion instances), sensitivity for malignancy detection was 94.3% (95% CI: 91.8-96.5). Combined end-to-end performance for correct malignant lesion detection and classification was estimated at 69.1% (object-level recall 73.3% × classification sensitivity 94.3%). Evaluation of 33 lesion-free videos demonstrated a mean frame-level specificity of 46.6% and a median of 19 false positive detections per video. Conclusions : This is the first study to report a DL model for real-time endoscopic detection and classification of benign and malignant laryngeal and pharyngeal lesions. The developed model showed promising lesion detection and cancer classification performance in the evaluated test set. T1 tumor detection remains an important limitation, particularly because early-stage detection is a primary aim of AI-assisted endoscopy. Further model testing is required in real-world lesion prevalence settings, where external validation and clinical usability should be investigated.","url":"https://doi.org/10.3390/cancers18162609","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/cancers18162609","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/fcell.2026.1754792","name":"Artificial intelligence-quantified fundus tessellated density and its association with myopia severity in Chinese children: a cross-sectional study.","source":"europepmc","abstract":"Introduction To quantitatively assess the whole fundus tessellated density (FTD) using artificial intelligence (AI) and evaluate its association with myopia severity in Chinese children with myopia. Methods A cross-sectional analysis was conducted on participants with myopia at Beijing Children's Hospital Capital Medical University, from October 2023 to June 2024. AI technology was used to quantitatively measure FTD based on fundus photographs. Participants were classified into mild, moderate, and high myopia (HM) groups based on cycloplegic spherical equivalent (SE). To explore the relationship between FTD and SE, Spearman correlation coefficients along with linear regression analysis were utilized. The ability of FTD to discriminate HM was assessed using Receiver Operating Characteristic curve. Results The study included 263 eyes from children aged 6-12 years. AI-quantified FTD showed excellent agreement with manual grading of fundus tessellation (area under the curve [AUC] = 0.97, 95% confidence interval [CI]: 0.95-0.98). Median FTD values were 0.01 (0.00-0.02), 0.01 (0.00-0.03), and 0.04 (0.01-0.12) for the three groups respectively. Significant difference was found when comparing HM group with the mild and moderate myopia groups (both P ρ = -0.41, P P P Conclusion AI -quantified FTD demonstrates a significant correlation with the severity of myopia.","url":"https://doi.org/10.3389/fcell.2026.1754792","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1754792","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00402-026-06435-9","name":"Can ChatGPT pass the polish national medical specialization examination in orthopedics and traumatology?","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) has evolved rapidly in recent years and is becoming increasingly integrated into many areas of medicine. In the medical field, these systems have attracted considerable attention because of their potential applications in clinical decision support, medical education, scientific communication, and postgraduate training. The aim of the present study was to evaluate whether ChatGPT could achieve a passing score on the Polish National Medical Specialization Examination (Panstwowy Egzamin Specjalizacyjny, PES) in orthopedics and traumatology and to determine how different prompting strategies influenced its performance. Materials and methods Authors systematically assessed the performance of ChatGPT-4 across five consecutive official PES exams (from Autumn 2023 to Spring 2025) in orthopedics and traumatology. Each exam was administered using three distinct prompting strategies: Professor prompt, Specialist prompt, Resident prompt. For each exam session (e.g., Spring 2024, Autumn 2023), the prompts were submitted to ChatGPT sequentially. The model's responses were evaluated against the official answer key published by the Polish Center of Medical Exams (Centrum Egzaminów Medycznych, CEM). Results The results were averaged across all prompts. The overall accuracy was 81% (range: 68.33%-85.83%). The highest score (85.83%) was recorded multiple times across different prompt types, indicating that the model could approach or exceed the minimum passing threshold depending on prompt structure. Agreement between prompting strategies was moderate to substantial (Cohen's κ 0.518-0.632; Fleiss' κ = 0.571). Radiology-related questions demonstrated the highest error rate among all question categories. Conclusions Large language models such as ChatGPT can perform well on knowledge-based orthopedic examinations and may serve as useful tools for examination preparation and educational support. However, examination success should not be interpreted as evidence of clinical competence or readiness for independent surgical practice. Specialist certification and patient care remain dependent on human expertise, practical skills, and professional responsibility.","url":"https://doi.org/10.1007/s00402-026-06435-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00402-026-06435-9","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ejrad.2026.113058","name":"Article 4 and Article 72 in radiological practice: competence and surveillance beyond compliance.","source":"europepmc","abstract":"Background Two provisions of the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) now reach into radiological practice. Article 4, applicable since February 2025 and enforced from August 2026, requires a sufficient level of AI literacy in providers and deployers. Article 72 will require a post-market monitoring system over the lifecycle of high-risk AI systems; for medical AI they are set to take effect on 2 August 2028 under the amended timeline. Current compliance framework: The European radiological community has responded with a coherent body of work: the ESR Essentials series, statements from the ESR AI Working Group and a multi-society coalition, the ESR post-market-surveillance consensus, and a recent narrative review of implementation errors. Argument This Opinion Paper is not a regulatory commentary. Two operational shifts are now due. First, Article 4 should be read as a floor - the legal minimum - over which an audit-capable competence framework is built: radiologists trained not to recite failure modes but to detect them in models deployed on their own population. Article 72 should be operationalized not as a documentary monitoring plan filed with notified bodies but as the architecture of a clinical surveillance culture analogous to pharmacovigilance, with continuous local drift detection, subgroup audit, public reporting, and shutdown criteria. Conclusion Neither shift is technically novel; their value lies in the explicit regulatory anchoring. The distinction between compliance and culture is the operational frontier of clinical AI in European radiology.","url":"https://doi.org/10.1016/j.ejrad.2026.113058","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejrad.2026.113058","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s12885-026-16154-4","name":"Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly recognized as a valuable tool for the early detection and prognosis of oral cancer, addressing the challenge of high mortality due to late diagnosis. Artificial intelligence based diagnostic models have the potential to improve accuracy in differentiating between malignant, premalignant and benign oral lesions. This systematic review and meta-analysis evaluated the diagnostic performance of non-imaging and image-based artificial intelligence models and narratively synthesized evidence on prognostic and risk stratification applications in oral cancer. Methods This study follows PRISMA guidelines to ensure quality and reproducibility. A systematic search across PubMed, Embase, Web of Science, Google Scholar and Scopus identified studies from 2010 to 2024 on artificial intelligence applications in oral cancer diagnosis. Sixteen eligible studies met predefined inclusion criteria, including AI-based screening compared to histology. Data extraction and bias assessment were conducted independently using QUADAS-2. The findings highlight AI's potential in early detection and prognosis, emphasizing the need for further validation and clinical integration to enhance diagnostic accuracy. Results A total of 801 studies were initially identified, with 53 undergoing further review, ultimately selecting 16 studies. Sample sizes varied from 70 to 44,000, allowing a broad evaluation of AI's diagnostic performance. Artificial intelligence models showed wide range of sensitivity (42%-100%), specificity (63%-100%), and accuracy (63%-100%). Meta-analysis revealed a pooled sensitivity of 0.90 (95% CI: 0.81-0.98), specificity of 0.89 (95% CI: 0.84-0.95), and accuracy of 0.89 (95% CI: 0.83-0.95), with substantial heterogeneity (I² = 100%). Image-based models had higher pooled sensitivity (0.94 vs. 0.76, P = 0.320), specificity (0.93 vs. 0.79, P = 0.025), and accuracy (0.93 vs. 0.81, P = 0.042). Conclusions Artificial intelligence models show promising diagnostic performance for oral cancer based on retrospective clinical data. Although image-based models, particularly convolutional neural networks, demonstrated higher pooled sensitivity and specificity than non-imaging models, these differences were not statistically significant. Results should be interpreted with caution due to substantial heterogeneity. Advances reported in the literature, such as multimodal approaches and data augmentation, may improve non-imaging model performance and help narrow the gap between methodologies. These developments highlight AI's potential in enhancing early detection and prognosis of oral cancer.","url":"https://doi.org/10.1186/s12885-026-16154-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12885-026-16154-4","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/15305627261471706","name":"Performance of Large Language Model-Based Chatbots in Primary Health Care Teleconsultations: A Comparison Between Human and Artificial Intelligence-Generated Responses.","source":"europepmc","abstract":"Introduction Telehealth is a strategic component of primary health care and has advanced in Brazil through the National Telehealth Program. Its benefits can be enhanced by artificial intelligence (AI), which has emerged as a promising tool. This study aims to compare the performance of real human and AI-generated responses to queries submitted to the teleconsultation services of the Telehealth Center of the UFMG Faculty of Medicine (NUTEL FM-UFMG), a member of the Telehealth Brazil Program. Methods This is a comparative cross-sectional study of 180 real human and AI-generated responses, evaluated in a blinded manner according to quality criteria (medical adequacy, conciseness, coherence, and comprehensibility), risk potential, authorship identification accuracy, and inquiry resolution. Data from NUTEL FM-UFMG (January 2020 to May 2024) were utilized, covering cardiology, endocrinology, and obstetrics/gynecology (OB-GYN). Statistical analysis included the Shapiro-Wilk test, Kruskal-Wallis test, Nemenyi multiple comparison test, chi-square test, and Fisher's exact test. Results Across all specialties, a significant difference was observed in comprehensibility, with AI mean scores surpassing those of humans. For the remaining quality criteria, as well as for risk potential and inquiry resolution, no significant differences were found, despite AI scoring higher than humans. Within specific specialties, significant differences was observed in endocrinology (except conciseness) and cardiology (in conciseness); AI showed superior means. Across all specialties, as well as individually within endocrinology and OB-GYN, the accuracy of authorship identification (human vs. AI) was statistically significant. Conclusion Despite existing limitations, AI demonstrates substantial potential as a support tool for teleconsultation services.","url":"https://doi.org/10.1177/15305627261471706","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/15305627261471706","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s13063-026-09833-x","name":"Early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion identified by artificial intelligence (ARCHES): study protocol for a multicentre randomised controlled trial.","source":"europepmc","abstract":"Backgrounds Haematoma expansion (HE) is a significant factor in poor outcomes following intracerebral haemorrhage (ICH). Studies have suggested that acute intensive antihypertensive treatment could reduce HE. However, the impact of early intensive blood pressure reduction on patients with ICH at high risk of HE remains unclear. Therefore, screening ICH patients for high risk of HE upon admission and initiating early intensive blood pressure reduction could improve their prognosis. In this study we utilise a five-point scoring system integrating a deep learning system based on non-contrast CT imaging and clinical predictors to identify ICH patients at high risk of HE. This study aims to compare the efficacy, safety, and feasibility of early intensive antihypertensive treatment versus standard antihypertensive treatment for patients identified as being at high risk of HE. Methods The early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion predicted by artificial intelligence (ARCHES), is a multicentre, prospective, randomised, open-label, blinded-endpoints clinical trial that will include an estimated 680 participants. ICH patients within 6 h of symptom onset, and at high risk of haematoma expansion with ≥ 3 points on the artificial intelligence-based haematoma expansion 5-point prediction score, will be randomly assigned to receive either intensive antihypertensive treatment (targeting systolic blood pressure control between 130 and 140 mmHg within the first hour of treatment, and maintaining this level for 7 days) or standard antihypertensive treatment (targeting systolic blood pressure control between 140 and 180 mmHg, and maintaining for 7 days). The primary outcome is death or severe disability at 90 days. Discussion The ARCHES study aims to verify the hypothesis that early intensive blood pressure lowering leads to reduced HE and improved functional outcomes with good safety in ICH patients at high risk of HE. Trial registration This study was registered at the Clinical Trials under registry number NCT06242938. Registered on Feb 2, 2024. https://clinicaltrials.gov/study/NCT06242938 .","url":"https://doi.org/10.1186/s13063-026-09833-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13063-026-09833-x","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106483","name":"Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for emergency department demand.","source":"europepmc","abstract":"Background The sustainability of service quality in healthcare systems is directly related to accurate resource planning, especially in emergency departments with high unpredictability. This study aims to analyze the impact of meteorological factors on emergency department visits and propose a highly accurate and explainable artificial intelligence-based decision support model for hospital management. Within the scope of the research, a large dataset of approximately 1.5 million records from two different public hospitals in the Eastern Black Sea region of Turkey was used. Methods As a method, comprehensive feature engineering was performed on the raw data; calendar variables, meteorological lags, and historical application trends were derived. The meaningfulness of the input variables was initially verified using Correlation and Granger Causality analyses, and the final variable selection was performed using the SHAP (SHapley Additive exPlanations) method, which is an explainable artificial intelligence (XAI) approach. The SHAP-based feature selection step was performed independently as a pre-processing filter, and the resulting feature set was then locked and applied uniformly to all 22 models, ensuring a fair comparison. In the study, a total of 22 models from three different groups, including Machine Learning, Deep Learning, and Time Series methods, were tested comparatively. Results A scenario-based evaluation strategy was followed to measure the adaptation of models to dynamic data structures. According to the findings, the 7-day \"Walk-Forward\" (WF-7d) update scenario, which simulates real-life conditions, emerged as the most optimal strategy, reducing the average error by 9.62% compared to static models. The Prophet model, which demonstrated the best performance, achieved the highest success with values of 34.55 ± 5.66 MAE (5.54% ± 1.41% MAPE) in the Ordu State Hospital data and 45.84 ± 7.51 MAE (5.47% ± 1.39% MAPE) in the Education and Research Hospital data. Additionally, it was found that the SVM and CatBoost models, which have low error rates, maintained generalizability in both institutions. Conclusion The proposed system has the potential to increase operational efficiency by providing healthcare administrators with a proactive decision support mechanism for critical processes ranging from staff scheduling to bed capacity management.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106483","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106483","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1097/xcs.0000000000002065","name":"NIH Funding for Artificial Intelligence in Surgical Specialties: Allocation and Alignment with US Disease Burden.","source":"europepmc","abstract":"Background National Institutes of Health (NIH) investment in artificial intelligence (AI) within surgical specialties is rapidly expanding, yet whether funding aligns with disease burden or reflects structural disparities remains unclear. Study design Cross-sectional analysis of NIH RePORTER (1993-2024) identified 1,657 AI grants across eight surgical specialties. Multivariable regression of 976 grants to 379 investigators (2021-2024) evaluated associations between funding and 2021 disability-adjusted life years (DALYs), adjusting for region, investigator sex, and degree. Primary endpoints were total funding and grant number relative to disease burden; secondary analyses assessed regional and investigator variation. Results NIH AI funding was not associated with disease burden after adjustment (funding: IRR 1.00, 95% CI 0.99-1.01, p>0.9; grant number: IRR 0.93, 95% CI 0.87-0.99, p=0.033). Funding per DALY varied widely across disease categories without proportional allocation to higher-burden conditions. Regional disparities were evident: compared with the Midwest, the South received more grants (IRR 1.46, 95% CI 1.10-1.94) but lower per-grant funding (IRR 0.79, 95% CI 0.65-0.94). Female investigators received fewer grants than male investigators (IRR 0.69, 95% CI 0.57-0.84), although per-award funding was similar. Conclusions NIH funding for AI research in surgical specialties is not aligned with disease burden and instead reflects regional and investigator-level variation. These findings identify structural disparities in funding allocation and establish a benchmark for policy efforts to better align AI research investment with population health needs.Précis: Analysis of 1,657 NIH grants for AI research in surgical specialties found funding was not associated with US disease burden, and correlated with regional and investigator characteristics. We highlight the need for further research into factors driving allocation in this emerging field.","url":"https://doi.org/10.1097/xcs.0000000000002065","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/xcs.0000000000002065","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/03913988261437350","name":"Global research trends in artificial kidneys (2014-2024): A bibliometric and visualization study.","source":"europepmc","abstract":"When renal function is lost following resection of renal tumors, in the setting of end-stage kidney disease, or after traumatic nephrectomy, kidney transplantation is typically required; however, owing to constraints in healthcare resources, only a minority of patients can access transplantation. In this context, the development of efficient artificial kidneys as alternative therapies may help alleviate donor scarcity and holds potential clinical applicability, underscoring the substantial significance of this research. In the present study, a bibliometric analysis was conducted on the global literature in the field of artificial kidneys from 2014 to 2024, delineating overall developmental trends and technological hotspots. The findings indicate a marked increase in publications related to artificial kidneys over the past decade, with the field evolving from conventional renal replacement therapies toward a multidisciplinary paradigm integrating medicine, engineering, and artificial intelligence. The United States and other countries have made prominent contributions, and collaboration among multiple institutions has become increasingly frequent. Key research hotspots include the use of artificial intelligence in kidney-disease prediction models and clinical decision support, the development of implantable or wearable fully functional artificial kidneys, and advances in bioartificial kidneys and tissue-engineering technologies. The results further suggest that artificial-kidney technologies are entering a new stage characterized by increasing intelligence and convergence with biological and tissue-engineering approaches. Progress in novel immunomodulatory materials and biosensing technologies is expected to facilitate the development of artificial kidneys with therapeutic efficacy approaching that of kidney transplantation. This analysis provides an important reference for researchers, may help guide future research directions and promote cross-disciplinary collaboration, and ultimately may accelerate the realization of an ideal artificial kidney and improve the prognosis of patients with end-stage kidney disease.","url":"https://doi.org/10.1177/03913988261437350","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03913988261437350","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/84104","name":"Ambient AI Scribe Implementation in an Ambulatory Setting in a Single Medical Group: Prospective Study.","source":"europepmc","abstract":"Background Health care providers spend an excessive amount of time within electronic medical record (EMR) systems documenting patient encounters, often amounting to hours of work outside of regular office hours. This affects physician productivity and directly contributes to burnout. Artificial intelligence (AI) is becoming more integrated into medical care, including the development of speech recognition and note generation algorithms. Limited studies exist on how these AI tools affect provider satisfaction, work-life balance, and patient satisfaction. Objective The aim of this study was to assess the use of ambient AI in medical documentation and its effects on time spent in the EMR and on provider burnout, with a secondary focus on note quality and patient satisfaction. Methods This prospective study was conducted at the Hawaii Pacific Health Medical Group to pilot an AI note writer. Abridge was chosen as the AI platform and integrated with the Epic EMR. A goal of 75 providers for a 3-month pilot period was established from December 2024 through February 2025. Surveys were distributed to providers before and during the trial period. Epic Signal and Abridge data were used to correlate provider-perceived outcomes with EMR-recorded outcomes. Users were then divided into groups based on frequency of AI use, with high use defined as ≥60% AI scribe use in patient encounters. The primary outcome was time spent on documentation per appointment. Results A total of 80 providers were recruited, with 79 completing the pilot. More than 25,000 notes were generated across 23 specialties. Signal metrics found a 21% decrease in time spent on notes per day (-13.6 minutes) and a 13% decrease in pajama time (-3.6 minutes) among high users. Among 79 providers using ambient AI, 6 (7.6%) reported spending ≥8 hours per week on notes outside of clinic hours, a 76% decrease from 25 (31.6%) providers before the pilot. With ambient AI, 39 (49.4%) physicians reported no burnout symptoms, representing a 22% increase. Provider-perceived workload decreased, and self-reported note quality remained favorable. Providers reported that 84% of notes required edits to less than one-quarter of the note content. Patient experience, as measured by \"provider listened to me\" scores on patient satisfaction surveys, was not significantly affected by ambient AI use (P=.39). Conclusions This ambient AI scribe decreased the time providers spent writing notes in the clinic and decreased time spent in the EMR outside of work hours. There was no significant difference in symptoms of burnout.","url":"https://doi.org/10.2196/84104","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/84104","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/84695","name":"The Role of Multimodal Generative AI in Older Adults' Health Management: Systematic Scoping Review.","source":"europepmc","abstract":"Background The issue of population aging has emerged as a critical global challenge, driving the imperative for effective self-care and scalable health management solutions for older adults. Against the backdrop of the accelerating application of generative artificial intelligence (GenAI) in health care, a systematic evaluation is necessary to investigate how multimodal GenAI can support older adults in maintaining health and managing well-being. Objective This study aimed to systematically evaluate the role, application contexts, empirical impacts, and developmental potential of diverse GenAI tools across critical geriatric health domains. Methods A comprehensive search was executed across 11 major databases, including Web of Science, Scopus, PubMed, Medline, CINAHL, Cochrane, ACM Digital Library, IEEE Xplore, ScienceDirect, APA PsycInfo, and Google Scholar, with search transparency adhering to the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) extension. Results A total of 28 studies met the inclusion criteria. Of the total, 82% (n=23) of the included publications were released within the last 2 years (2024-2025). Analysis of technology revealed that over half (n=14) of the applications were based on text-driven conversational agents, while multimodal systems, leveraging generated audio, images, and sensor data, are rapidly emerging. GenAI applications were validated to support cognitive function maintenance, mental health, and chronic condition management through personalized content generation and multimodal interaction. However, current validation is primarily limited to cognitively normal, low-risk older adult populations. Persistent technical challenges include overreliance on text-based interaction, barriers in voice recognition accuracy, and suboptimal user interface adaptability. Conclusions Preliminary evidence suggests a promising role for GenAI in enhancing older adults' health self-management through highly personalized and multimodal interventions, particularly in cognitive and mental health support. To realize this potential and ensure equitable access, future efforts must prioritize strengthening interdisciplinary collaboration to integrate wearable technologies and edge computing, alongside establishing robust ethical frameworks to address data privacy, algorithmic bias, and the digital divide, which will be critical to building a safe, equitable, and effective environment for active aging.","url":"https://doi.org/10.2196/84695","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/84695","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.7759/cureus.109628","name":"Evaluation of the Use of Artificial Intelligence in Dental Imaging Interpretation: A Systematic Review.","source":"europepmc","abstract":"The rapid adoption of digital radiography in dentistry has generated large volumes of imaging data, creating opportunities for artificial intelligence (AI)-based tools to assist in diagnostic interpretation. AI, particularly machine learning and deep learning models, has shown promising applications in improving diagnostic efficiency, consistency, and image analysis in dental imaging. This systematic review aimed to evaluate the applications, diagnostic performance, and limitations of AI in dental imaging interpretation. A systematic literature search was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using PubMed (MEDLINE) and ScienceDirect databases for studies published between May 2005 and March 2024, employing relevant MeSH terms related to artificial intelligence and dental imaging. Following database-specific search refinement and application of predefined eligibility criteria, 25 studies were screened, of which seven met the final eligibility criteria and were included in the qualitative synthesis. The findings indicate that AI applications in dental imaging, including caries detection, periodontal bone loss assessment, impacted third molar evaluation, root fracture detection, working length determination, and image registration, demonstrated supportive diagnostic performance and improved efficiency compared with conventional interpretation methods. Although AI systems are not a replacement for clinical expertise, they may serve as useful decision-support tools in dental radiology. Overall, within the limited evidence base included in this review, AI-assisted tools showed potential to support diagnostic workflows and image interpretation in dental radiology. However, because only a small number of heterogeneous studies were included, these findings should be interpreted cautiously, and further high-quality research and external validation studies are required before widespread clinical implementation.","url":"https://doi.org/10.7759/cureus.109628","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.109628","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.6002/ect.mesot2025.o55","name":"Artificial Intelligence in Renal Transplantation: Current Innovations and Future Horizons.","source":"europepmc","abstract":"Objectives This study critically examined the evolving role of artificial intelligence (AI ) in kidney transplantation, aiming to bridge the gap between theoretical promise and clinical implementation. The study evaluated AI -driven innovations across the transplant continuum, from pretransplant matching to posttransplant care, while identifying key barriers, including training gaps, ethical considerations, and system integration challenges. The objective was to propose actionable strategies to optimize the effect of AI on graft survival, equity in organ access, and long -term patient outcomes. Materials and methods We conducted a systematic review of AI integration in kidney transplant using PubMed, Web of Science, Cochrane, and Google Scholar databases up to December 2024. Search terms included \"artificial intelligence\" and \"renal transplantation.\" We used a 2 -phase screening process for relevance filtering and QUADAS -2 critical appraisal. We categorized AI algorithms by architecture and clinical application, with quantitative synthesis of performance metrics and qualitative analysis of implementation barriers, ethics, and stakeholder acceptance. Results AI in kidney transplant required general, not deep, technical expertise from health care professionals. Semi -supervised learning offered a promising, scalable approach by reducing data labeling by 40% with maintained accuracy. AI algorithms were shown to improve donor -recipient matching, reduce rejection, and enhance postoperative care. Deep learning models showed strong performance in predicting graft survival (concordance index 0.65-0.72 ) and delayed graft function (receiver operating characteristic area under the curve of 0.82 ). Furthermore, AI -powered digital pathology reduced organ discard rate by 37 % through better tissue analysis. Conclusions AI represents a transformative opportunity to personalize kidney transplantation and improve patient outcomes, functioning best as an augmentative tool rather than replacement for clinical expertise. A 3 -tiered integration model is proposed: cultivating general AI familiarity, understanding kidney transplant -specific capabilities, and providing practical training in AI tools. Continued research remains essential to address limitations and ensure safe, ethical, and effective clinical integration.","url":"https://doi.org/10.6002/ect.mesot2025.o55","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6002/ect.mesot2025.o55","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"epmc:MED42647560","name":"Construction and Preliminary Evaluation of a Precise Perioperative Glycemic Management Model for Kidney Transplant Recipients Using Healthcare Failure Mode and Effect Analysis (HFMEA) Combined with Continuous Glucose Monitoring (CGM) and Artificial Intelligence: A Retrospective Cohort Study. .","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42647560/","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.30773/pi.2026.0038","name":"Risk of Schizophrenia Spectrum Disorders Following Parkinson's Disease: A Nationwide Population-Based Cohort Study.","source":"europepmc","abstract":"Objective Psychotic symptoms are common in Parkinson's disease (PD), yet it remains unclear whether these manifestations reflect transient, medication-related phenomena or an increased risk of clinically diagnosed schizophrenia spectrum disorders (SSD). This study aimed to evaluate the long-term risk of incident SSD following PD using a nationwide population-based cohort and to assess whether this risk varies across demographic, lifestyle, and metabolic subgroups. Methods We conducted a retrospective cohort study using the Korean National Health Insurance Service database from 2012 to 2023. Individuals newly diagnosed with PD were identified using repeated International Classification of Diseases, 10th Revision (ICD-10) codes and matched 1:5 with controls using propensity scores. Incident SSD was defined by repeated ICD-10 diagnoses. Incidence rates, incidence rate ratios, Kaplan-Meier curves, and Cox proportional hazards models were used. Results Among 5,110 patients with PD and 25,550 matched controls, 128 and 177 incident cases of SSD were identified, respectively. The incidence rate of SSD was higher in the PD group than in controls (7.09 vs. 1.75 per 1,000 person-years), yielding an IRR of 4.06 (95% confidence interval [CI], 3.23-5.09). PD was associated with an increased risk of SSD after multivariable adjustment (adjusted hazard ratio 4.90; 95% CI, 3.52-6.81), with particularly elevated risk among individuals younger than 60 years. Conclusion PD was associated with a substantially increased long-term risk of SSD. These findings suggest that psychotic manifestations following PD may reflect persistent psychiatric morbidity rather than transient symptoms alone.","url":"https://doi.org/10.30773/pi.2026.0038","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.30773/pi.2026.0038","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106629","name":"Early clinical decision support using interpretable artificial intelligence in acute illness (sepsis and infection): A systematic review and meta-analysis.","source":"pubmed","abstract":"Sepsis and severe infections remain major causes of morbidity and mortality worldwide, particularly in acute care settings where early recognition is critical. Artificial intelligence (AI)-based clinical decision support systems (CDSS) have emerged as promising tools for improving early diagnosis and prognostic assessment. However, limited interpretability and transparency remain key barriers to clinical adoption. This systematic review and meta-analysis aimed to evaluate the diagnostic and prognostic performance of interpretable AI models in acute illness related to sepsis and infection.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106629","authors":["Ibraheem M","Khalil M","Khalil S","Contreras H","Azar J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106629","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106563","name":"Clinical note comparison and data retrieval via embedding vectors: model selection, metrics, and convergence.","source":"europepmc","abstract":"Background Embedding models are an integral part of generative AI architectures, transforming text into embedding vectors that represent semantic content in numerical form. Despite their central role, their performance in clinical settings remains underexplored. We evaluated embedding models across two tasks: semantic difference detection in clinical notes, and data retrieval from patient records. Methods Eight models were applied to synthetic discharge summaries in English, Swedish, and Finnish. Semantic sensitivity was assessed by introducing controlled perturbations (deletion, modification, and paraphrasing) at three levels of severity; cosine similarity, L 1 and Euclidean distances were computed between the vectors of the original and perturbed texts. Partial vectors were compared to explore dimensionality reduction. Two models with the biggest contrast in semantic difference detection were evaluated on retrieval of relevant information from real Finnish vascular surgery records. Results Embedding vectors captured semantic differences in clinical notes: content deletion and modification produced larger increases in vector distance than paraphrasing. On average, models detected the direction of semantic change correctly, but case-level performance varied considerably. Qwen3-Embedding-8B produced no case-level (directional) errors whereas multilingual-E5-large produced them the most (12.2%). In retrieval this contrast transferred only partially and was task-dependent: sufficiency scores favoured Qwen3-Embedding-8B for the vascular-diagnosis question (2.25 vs 1.15 out of 5) but were comparable for the antithrombotic-medication question (3.25 vs 3.17 out of 5). For some models, as few as 0.6-1.2% of dimensions sufficed to replicate full-vector accuracy; principal component analysis and coordinate-level analysis did not account for this finding. Conclusions Our results show that the choice of embedding model is important: performance differences between models can be large enough to determine whether clinically relevant information reaches the end user, and model weaknesses can be both task-specific and context-dependent.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106563","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106563","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00261-026-05688-7","name":"Automated generation of impressions in abdominal radiology reports using an artificial intelligence-based tool: performance compared to manual impressions.","source":"europepmc","abstract":"Objectives Recent years have seen a rapid development of artificial intelligence (AI) tools to enhance radiologists' workflow, but most have focused on image acquisition and interpretation. Generating radiology reports is a critical element of practice but remains a source of inefficiency and cognitive load. Purpose To investigate the performance of an AI based tool integrated into dictation software to automate generation of report impressions and compare it with radiologist generated impressions. Methods One hundred consecutive abdominal radiology reports were retrospectively selected in January 2024 from a single center. An AI-generated impression (GI) was created for each report and compared with the original radiologist impression (RI). Ten subspecialty abdominal radiologists evaluated the blinded, randomized pairs. Each impression was evaluated on a 5-point Likert scale for coherence, comprehensiveness, and factual consistency, and an overall preference for the impression was recorded. Cumulative link mixed models and binomial logistic regression were used where appropriate. Results GI was preferred in 38% of reports (95% CI 32-45%), RI in 45% (95% CI 39-50%), with no preference in 17% of instances. Mixed-effects logistic regression demonstrated that GI was rated as equivalent or preferred to RI (odds ratio 1.34, 95% CI 1.004-1.788, p = 0.02). Radiologists rated GI equivalent or superior to RI in 79% of cases for coherence, 66% for comprehensiveness, and 77% for factual consistency. Conclusion The AI generated impressions were clinically acceptable and rated equivalent or superior to radiologist-generated impressions in the majority of cases across key quality metrics.","url":"https://doi.org/10.1007/s00261-026-05688-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00261-026-05688-7","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/90064","name":"AI-Generated Versus Human Supervisor Feedback on Medical Students' Clinical Clerkship Logs: Cross-Sectional Convergent Mixed Methods Study.","source":"europepmc","abstract":"Background Feedback is essential for medical students' learning during clinical clerkships; yet, supervising physicians often struggle to provide meaningful written feedback due to time constraints. Large language models offer a promising approach to supplement human feedback, but how artificial intelligence (AI)-generated and human feedback differ in authentic clinical settings remains unclear, as most comparisons have been conducted in classroom or simulation contexts. Objective The aim of the study is to examine how AI-generated feedback and supervisor-provided feedback differ when applied to medical students' clinical clerkship logs, by identifying the distinct characteristics and complementary strengths of each feedback type. Methods This cross-sectional convergent mixed methods study included 161 weekly clinical clerkship logs from 47 fifth- and sixth-year medical students across 12 clinical departments at Nagoya University, Japan (January-May 2024). Of 164 eligible logs, 3 were excluded because supervisors entered contact messages rather than substantive feedback. AI feedback was generated using GPT-4o. In total, 10 faculty physicians and 10 medical students evaluated both feedback types in blinded, randomized order using a validated 5-category rubric (criteria-based, clear direction, accuracy, prioritization, and supportive tone), followed by open-ended comments and source identification. Quantitative analyses (paired 2-tailed t tests, cumulative link mixed-effects models; α=.05 with Bonferroni correction) were complemented by qualitative thematic analysis and integrated using joint display analysis. Results AI feedback was significantly longer than supervisor feedback (mean 382.02, SD 81.82 vs mean 98.87, SD 73.66 characters; Cohen d=2.84, 95% CI 2.50-3.19; P .99), prioritization (OR 1.70, 95% CI 1.16-2.50; P=.10), or supportive tone (OR 1.34, 95% CI 0.87-2.06; P>.99). AI feedback showed greater consistency (variance ratio 3.9:1; Levene F1,320=73.20; P Conclusions This study extends the comparison of AI-generated and supervisor feedback to an authentic clinical clerkship environment, moving beyond classroom and simulation settings examined in prior work. Through integrated mixed methods analysis, a key distinction emerged between text-anchored AI feedback, which systematically addresses written log content in alignment with rubric criteria, and experience-based supervisor feedback, which draws on clinical observation and professional judgment. AI consistently delivered structured feedback addressing gaps that arise when time-pressured supervisors provide brief comments, while supervisors contributed clinically grounded insights that AI cannot replicate. These complementary strengths suggest that AI feedback should supplement rather than replace supervisor feedback, and that hybrid models leveraging each type's advantages warrant investigation in clinical education.","url":"https://doi.org/10.2196/90064","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/90064","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2196/87959","name":"Effectiveness of Artificial Intelligence-Assisted Peer Teaching in Orthopedic Clinical Education: Historical Cohort Study.","source":"europepmc","abstract":"Background Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. Objective This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. Methods This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. Results Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P Conclusions This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching's social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.","url":"https://doi.org/10.2196/87959","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/87959","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106476","name":"Knowledge, attitudes, and practices toward artificial intelligence in medicine among Chinese physicians: A cross-sectional study from January to March 2024 with analysis of influencing factors.","source":"europepmc","abstract":"Background As artificial intelligence (AI) transforms medicine, understanding physicians' knowledge, attitudes, and practices (KAP) toward AI is crucial. However, large-scale nationwide studies among Chinese physicians are lacking. This study investigates KAP toward AI among Chinese physicians and analyzes its influencing factors. Methods A nationwide cross-sectional online survey was conducted from January 15 to March 14, 2024. Multistage sampling was used to recruit practicing physicians across China. A validated, self-administered questionnaire was used to assess demographic characteristics and KAP. The statistical analyses included descriptive statistics, non-parametric tests, multivariate logistic regression, and Spearman correlation. Results This study included 1,137 participants, with 346 (30.4%) demonstrating good AI knowledge. While 1,034 (90.9%) held positive attitudes toward AI, only 51.2% agreed that medical AI is safe. The rate of clinical AI implementation remained low, with only 328 physicians (28.8%) reporting good AI practices. While \"AI in medicine\" is the broad field of study, Chinese physicians strongly prefer the term \"AI-assisted medicine\" (75.1%) to describe AI's functional role, emphasizing its assistive nature. Multivariate analysis identified male gender (OR 1.85; 95% CI 1.42-2.41) and age 50-59 years (OR 2.20; 95% CI 1.31-3.71) as independent predictors of good AI knowledge. Chief physicians showed more positive attitudes than residents (OR 2.06; 95% CI 1.15-3.69). Factors significantly associated with good AI practices included male gender (OR 1.35; 95% CI 1.02-1.80), doctoral degree (OR 2.50; 95% CI 1.71-3.67), and specialization in obstetrics/gynecology (OR 4.95; 95% CI 1.80-13.60), internal medicine (OR 3.94; 95% CI 1.51-10.26), or surgery (OR 4.62; 95% CI 1.77-12.04), compared to pediatrics. Significant positive correlations were found between knowledge and attitude (r = 0.172), knowledge and practice (r = 0.441), and attitude and practice (r = 0.242) (all P Conclusions This study found that among Chinese physicians, senior physicians (aged 50-59 years) demonstrated higher AI knowledge than younger colleagues. Additionally, AI adoption rates varied significantly by specialty, with pediatrics showing lower adoption compared to surgery, internal medicine, and obstetrics/gynecology. These findings support targeted strategies, including specialized education for younger and female physicians and the prioritization of AI tool development for underserved specialties such as pediatrics, to foster responsible AI integration into Chinese clinical practice.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106476","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106476","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/acamed/wvag208","name":"Artificial intelligence teaching assistants: a scalable solution for supporting struggling medical students.","source":"europepmc","abstract":"Purpose Large language models (LLMs), such as OpenAI's ChatGPT, have demonstrated tutoring benefits in small-scale pilot studies within focused areas of medical education. This study evaluated the large-scale implementation of AI-teaching assistants (AI-TAs) within a compulsory medical school course. Method A quasi-experimental observational study with a mixed-methods was conducted to assess the impact of AI-TAs in a compulsory first-year medical school course at the University of Toronto in 2024. The research team developed AI-TAs using OpenAI's ChatGPT-4o and introduced them as a supplementary resource at the course's midpoint. They analyzed exam performance among the students who used AI-TAs (n = 87) and students who did not (n = 206). Additionally, surveys (n = 18) and interviews (n = 10) explored student perceptions of AI-TAs' effectiveness, usability, and impact on learning. Results Students who would later adopt AI-TAs had significantly lower pre-intervention exam scores than their peers (83.8% vs 88.1%; t(118.8)=-3.82, P Conclusions AI-TAs correlated with improved exam performance, fewer students in academic difficulty, and enhanced student engagement through a psychologically safe learning environment. These findings suggest that AI-TAs can serve as a scalable, cost-effective tool to support struggling students, while complementing traditional instruction.","url":"https://doi.org/10.1093/acamed/wvag208","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag208","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.62438/tunismed.v104i05.6620","name":"Comparative benchmarking of three artificial intelligence chatbots (ChatGPT-5.1, Qwen- 3 Max, and Perplexity AI) on faculty validated undergraduate respiratory physiology multiple-choice examinations.","source":"europepmc","abstract":"Background Artificial intelligence (AI) chatbots are increasingly used by medical students for learning and examination preparation. However, their reliability in mechanistically demanding disciplines such as respiratory physiology remains insufficiently evaluated using authentic examination material. This study aimed to assess and compare the performance of three AI chatbots on validated undergraduate respiratory physiology examination questions. Methods We conducted a cross-sectional analytical study including all respiratory physiology multiple-choice questions (MCQs), each comprising five propositions, used in official undergraduate examinations at the Faculty of Medicine of Tunis during the academic years 2023-2024 and 2024-2025 (101 questions). Three AI chatbots (ChatGPT-5.1, Qwen-3 Max, and Perplexity AI) were evaluated using standardized French-language prompts. Exact question-level concordance with the faculty-validated answer key, established by the teaching staff responsible for respiratory physiology examinations, was compared using Cochran's Q test and pairwise McNemar tests. At the proposition level, responses were analyzed as binary outcomes (true/false) to compute accuracy, sensitivity, and specificity. Results Question-level exact concordance differed significantly across models (Cochran's Q = 10.18, p = 0.006). ChatGPT showed the highest concordance rate (79.2%), followed by Qwen (74.3%) and Perplexity (62.4%). At the proposition level (505 propositions), ChatGPT achieved the highest overall accuracy (94.1%) and sensitivity for true propositions (96.5%), whereas Qwen demonstrated the highest specificity for false propositions (94.2%). Conclusion Although all evaluated AI chatbots performed well on respiratory physiology MCQs, substantial variability was observed across models. These findings indicate that AI chatbots are not interchangeable and underscore the need for institution-level evaluation using local examination material before their integration into medical education.","url":"https://doi.org/10.62438/tunismed.v104i05.6620","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.62438/tunismed.v104i05.6620","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.21037/jtd-2026-0804","name":"Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].","source":"europepmc","abstract":"Background While artificial intelligence (AI) offers unprecedented capabilities for predictive modeling and precision asthma management, there is an urgent clinical necessity to successfully translate these rapid algorithmic innovations into real-world respiratory care. The exponential growth of cross-disciplinary AI literature has paradoxically created information overload for clinicians, obscuring underlying translational friction and hindering evidence-based implementation. Consequently, bibliometric analysis serves as the optimal quantitative vehicle to decode this vast scientific architecture. This study aims to objectively map the evolutionary trajectory, global research landscape, and emerging hotspots of AI in asthma, providing actionable roadmaps to reconcile computational development with clinical practice. Methods A comprehensive literature search was conducted utilizing the Web of Science Core Collection database for studies related to AI in asthma, with the retrieval timeframe updated from January 1, 2016, to June 4, 2026. Following strict inclusion and exclusion criteria (restricted to English-language original articles and peer-reviewed reviews), a finalized dataset of 1,967 publications was extracted. Raw metadata parameters, including citations and bibliographic information, were exported for network topology analysis. Data synthesis was executed using specific algorithmic parameters in CiteSpace (for structural centrality and citation burst detection), VOSviewer (for co-authorship and keyword clustering), and the Bibliometrix R-package (for thematic evolution mapping). Results The field has experienced robust exponential growth (22.24% per annum), with a pivotal inflection point in 2019. Geographically, a dual-centric geopolitical landscape exists between the United States and China in publication volume; yet, the United Kingdom serves as the ultimate global hub with superior network centrality, alongside highly integrated networks from nations like Australia and France. Institutional analysis highlights a productive yet fragmented landscape driven by prolific powerhouses such as Harvard Medical School and Imperial College London, while high-centrality nodes like the University of Zurich and Johns Hopkins University cross-link clinical and algorithmic clusters. Thematically, the field has undergone a distinct three-epoch technological trajectory: from early statistical clustering [2016-2019] to machine learning-based electronic health record mining [2020-2023], and recently to advanced deep learning architectures and multi-modal integration [2024-2026]. Current research frontiers focus on multi-omics precision phenotyping, real-time exacerbation prediction via wearables, and causal inference for personalized therapy. Conclusions While AI paradigms in asthma research have rapidly advanced, current literature is fundamentally constrained by profound translational friction stemming from an over-reliance on retrospective datasets, which introduces critical structural biases and limits clinical generalizability. To effectively translate algorithms into clinical utility, future research must urgently deploy federated learning frameworks to securely overcome global data silos, and prioritize prospective, multicenter pragmatic trials to validate AI-driven predictive interventions in real-world respiratory care.","url":"https://doi.org/10.21037/jtd-2026-0804","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/jtd-2026-0804","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/fmed.2026.1875770","name":"The \"DeepSeek effect\" and the adoption-integration gap of generative artificial intelligence in clinical practice: a national online convenience cross-sectional survey of academic critical care physicians in China.","source":"europepmc","abstract":"Background The public release of DeepSeek marked a key turning point for generative artificial intelligence (GAI) uptake in China. This study assessed the so-called \"DeepSeek effect\" on Chinese academic critical care physicians (defined as clinicians with formal concurrent clinical, educational and research duties), comparing changes in self-reported GAI proficiency, clinical practice integration and structured training before and after the model's launch. Methods We conducted a national online convenience cross-sectional study of two independent physician cohorts from Chinese tertiary hospitals: a pre-DeepSeek cohort surveyed in December 2024 ( n = 456), and a post-DeepSeek cohort surveyed in December 2025 ( n = 372). Validated questionnaires were used to evaluate GAI knowledge, usage and ethical perceptions. Multivariate logistic regression and exact tests were applied to analyze links between training programs and self-reported professional competence. Results Self-reported GAI usage rose significantly from 64.7 to 94.1% after DeepSeek's release ( p p = 0.84). Less than 30% of trained physicians completed structured training, which was strongly associated with enhanced self-reported professional competence (adjusted odds ratio [AOR] = 22.2, 95% confidence interval [CI]: 1.6-305.6, p = 0.021). Surveyed physicians also prioritized critical integration skills over basic technical proficiency (OR = 16.3, 95% CI: 3.6-73.3, p Conclusion The \"DeepSeek effect\" drove near-universal GAI adoption, but revealed a stark adoption-integration gap with no corresponding gains in self-reported professional competence. Preliminary exploratory data suggest that multidimensional structured training may be a promising associated factor to bridge this gap. GAI implementation efforts must shift from promoting basic uptake to building structured training frameworks for safe, effective, critically appraised clinical integration.","url":"https://doi.org/10.3389/fmed.2026.1875770","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1875770","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.neuroscience.2026.07.067","name":"Explainable brain tumor detection in skull base CT using continuous neural representations: comparative evaluation of neural fields, neural operators, and vision transformers.","source":"europepmc","abstract":"Reliable brain tumor detection in CT remains challenging due to low soft-tissue contrast, skull base complexity, and imaging artifacts. This study evaluates whether continuous neural representation learning improves classification performance and interpretability for CT-based brain tumor detection. We performed a comparative evaluation of coordinate-based neural models, neural operators, and transformer-based vision models using a skull base CT dataset from Gazi University Faculty of Medicine. The dataset includes 200 patients and 40,000 slices (12,000 tumor-positive, 28,000 tumor-negative). An automated multi-stage slice selection framework was developed to extract three anatomically consistent skull base slices per patient using similarity-based ranking and anatomical validation. Ten models were evaluated, including Neural Fields, Implicit Neural Representations, DeepONet, HyperNetworks, FunCNets, Rational Neural Networks, Fourier Neural Networks, and ViT-Base/Small/Large. Neural Fields achieved the best performance with 98.90% accuracy, 98.90% sensitivity, 98.90% specificity, 0.978 MCC, and 0.990 AUC. FunCNets ranked second overall while DeepONet achieved the highest sensitivity but lower overall balance. Transformer-based models underperformed compared to neural representation approaches. Statistical analysis confirmed significant performance differences with Neural Fields significantly outperforming competing methods. LIME analysis showed that Neural Fields focused on clinically relevant lesion regions while reducing influence from skull base artifacts. Continuous neural representations, particularly Neural Fields, provide superior accuracy and robustness for CT-based brain tumor classification compared to transformer and operator-based models. The findings demonstrate that neural field-based learning offers both high diagnostic performance and improved interpretability, supporting its potential for clinical decision-support in challenging skull base CT analysis.","url":"https://doi.org/10.1016/j.neuroscience.2026.07.067","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.neuroscience.2026.07.067","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.prosdent.2026.05.001","name":"Alignment of artificial intelligence-generated responses with systematic reviews in implant prosthodontics.","source":"europepmc","abstract":"Statement of problem Large language model (LLM)-based artificial intelligence (AI) platforms have emerged as tools to support clinical decision-making in dentistry, but their alignment with high-level evidence from systematic reviews in implant prosthodontics remains unclear. Purpose The purpose of this study was to evaluate the degree of alignment between responses generated by ChatGPT and Google Gemini and the conclusions of published systematic reviews in implant prosthodontics. Material and methods Systematic reviews published between 2023 and 2025 addressing clinical questions in implant prosthodontics were included, with their conclusions used as reference standards and operationalized as expected-answer statements. Methodological quality of the included reviews was assessed using Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR 2). Standardized population, intervention, comparison, outcome (PICO)-based questions were submitted to ChatGPT and Google Gemini using identical prompts and no prior context. Agreement between AI responses and review conclusions was scored on a 5-point Likert scale by 2 blinded evaluators, with interrater reliability assessed using weighted Cohen kappa. Platform comparisons used the Wilcoxon matched-pairs signed-rank test, and domain analyses used the Kruskal-Wallis test with Dunn post hoc comparisons (α=.05). Results Seventy-four systematic reviews were included and categorized into 5 prosthodontic domains. Both ChatGPT and Google Gemini showed high agreement across domains, with no significant differences between platforms or domains (P>.05). Interrater agreement was almost perfect (κ=0.88-0.97). Although agreement was similar, ChatGPT more often reported moderate certainty, whereas Google Gemini more frequently expressed high certainty. Conclusions ChatGPT and Google Gemini showed high agreement with systematic review conclusions in implant prosthodontics. Differences in certainty expression highlighted the need for cautious interpretation and professional oversight.","url":"https://doi.org/10.1016/j.prosdent.2026.05.001","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.prosdent.2026.05.001","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/jamia/ocag086","name":"Digital divide in clinical and operational artificial intelligence adoption and implementation stages: US hospital diffusion patterns and AI deserts.","source":"europepmc","abstract":"Objectives We examine how hospital characteristics relate to clinical and operational artificial intelligence (AI) adoption and implementation stages and characterize AI deserts and spatial clustering patterns to highlight place-based AI access gaps among United States (US) hospitals. Materials and methods We used the 2024 American Hospital Association Annual Survey data from 2720 hospitals with at least one AI response. We applied logistic regression models to examine the associations between hospital characteristics and AI adoption, local indicators of spatial association to identify local clusters, and distance analyses to locate AI desert hospitals (ie, geographically isolated nonadopters located >50 miles from the nearest AI adopters). Results We found that system membership, larger bed size, and nurse staffing intensity are positively associated with clinical and operational AI adoption and implementation stages, whereas rural location, for-profit ownership, and physician intensity are negatively associated with them. Teaching status is more strongly associated with clinical AI, while system membership is more positively associated with operational AI. Although 68.0% of hospitals adopted ≥1 clinical AI functionalities and 60.7% adopted ≥1 operational AI functionalities, 12.1% of clinical and 13.2% of operational non-adopters are classified as AI desert hospitals. Discussion AI deserts reveal regional implementation gaps; sustained diffusion requires building shared regional capacity rather than relying only on hospital-level incentives. Policy implications to emerging divides may include tracking implementation stage, providing domain-specific support, and funding regional partnerships. Conclusion US hospital AI diffusion is uneven and spatially structured. AI deserts mark regional gaps in access to AI-adopting hospitals.","url":"https://doi.org/10.1093/jamia/ocag086","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag086","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s11701-026-03230-x","name":"Extended reality and emerging artificial intelligence in orthopedic surgical training: a scoping review of educational outcomes.","source":"europepmc","abstract":"Immersive technologies have gained increasing relevance in orthopedic surgical education; however, the scope, outcomes, and maturity of extended reality (XR) and artificial intelligence (AI) applications remain heterogeneous. To map the educational applications, learner populations, outcome measures, and research gaps related to the use of XR and emerging AI tools in orthopedic surgical training. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, Scopus, and ScienceDirect were searched for English and Spanish studies published between 2015 and 2025. Fifty-four studies involving 3,066 participants were included. Virtual reality (VR) was the predominant modality (83.3%), followed by augmented reality (31.4%), while AI-based applications were infrequently reported. XR-based training was most commonly evaluated in trauma surgery, arthroscopy, and arthroplasty, primarily among medical students and residents. Most studies reported improvements in simulation-based technical performance metrics, whereas evidence on clinical outcomes, long-term skill retention, and cost-effectiveness was limited. XR-particularly VR-represents the most mature immersive technology in orthopedic surgical education. AI applications remain emergent and primarily supportive. Future research should prioritize standardized outcome measures, multicenter designs, and evaluation of long-term educational and clinical impact.","url":"https://doi.org/10.1007/s11701-026-03230-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03230-x","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s12664-026-01979-5","name":"Artificial intelligence augmented imaging of pancreatic fluid collections in acute pancreatitis.","source":"europepmc","abstract":"Acute pancreatitis (AP) is a significant global health burden, with pancreatic fluid collections (PFCs) posing major diagnostic and therapeutic challenges. Distinguishing fluid-only pseudocysts from debris-containing walled-off necrosis is critical for management but is often difficult with conventional imaging. Contrast-enhanced computed tomography underestimates necrotic debris, while magnetic resonance imaging (MRI) is costly and time-consuming and endoscopic ultrasound is invasive. Artificial intelligence (AI), particularly deep learning and radiomics, is emerging as a powerful tool to overcome these limitations. AI algorithms can automate the segmentation of the pancreas and PFCs, provide objective quantification of necrotic debris and predict disease severity. Furthermore, AI-driven techniques can accelerate MRI acquisition times and potentially generate synthetic images, reducing scanner dependency. This review synthesizes AI's role in augmenting pancreatic imaging in PFC, covering its applications in segmentation and volumetry, image generation, outcome prediction and workflow optimization and discusses challenges and future directions for its clinical integration.","url":"https://doi.org/10.1007/s12664-026-01979-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12664-026-01979-5","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.jmir.2026.102452","name":"Awareness, perceptions, and educational preferences for AI among radiography students: A cross sectional survey.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is gaining increasing significance in radiography. However, undergraduate radiography students often have limited exposure to AI principles and an incomplete understanding of its capabilities and limitations. This study evaluated radiography students' self-reported familiarity with AI, perceptions of its role in medical imaging, and educational preferences. Methods A descriptive cross-sectional survey was conducted at a single institution in Jordan between March and August 2024. A self-administered, in-person paper questionnaire, adapted from a previously published survey, was distributed to students attending scheduled teaching sessions. Responses were analysed descriptively using frequencies and percentages. Results Of the 217 students approached, 180 participated (response rate 82.9%). Only 46.1% of respondents reported good familiarity with AI. Most students perceived AI as having useful applications in medical imaging and supported its integration into university training. However, when AI output conflicted with human judgement, 60.0% of students indicated that they would seek expert opinion, reflecting a cautious approach to AI-supported decision-making. Conclusion Radiography students reported positive attitudes toward AI and strong support for curricular integration, alongside caution about routine reliance on AI for decision making. Structured educational activities that provide practical exposure, clarify appropriate use cases, and emphasise human oversight may help students use AI safely and effectively in future practice. Plain language summary Artificial intelligence is being used more often in medical imaging, but students may not fully understand it. This study surveyed radiography students in Jordan to understand their knowledge of artificial intelligence, how they view its role, and how they want to learn about it. This study found that students welcomed its use but had limited understanding and preferred to seek expert advice when unsure. This matters because better teaching can help students use these tools safely and confidently in practice.","url":"https://doi.org/10.1016/j.jmir.2026.102452","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jmir.2026.102452","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1371/journal.pone.0352200","name":"Artificial intelligence in spine care: A scoping review of diagnostic applications.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, automate image interpretation, and support clinical decision-making. In the field of spine care, applications include MRI and CT-based detection of lumbar disc degeneration, spinal stenosis, vertebral fractures, and axial spondyloarthritis, as well as emerging symptom-based and multimodal diagnostic tools. However, evidence remains dispersed across modalities and conditions, and the quality and clinical readiness of AI systems vary. This scoping review maps current AI applications for diagnosing spinal disorders and identifies gaps for future research and clinical translation.","url":"https://doi.org/10.1371/journal.pone.0352200","authors":["Bensel VA","Habeck A","Brunot MH","Becton EJ","Ray M","Brackett AL","Lisi AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0352200","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.3389/frai.2026.1859410","name":"Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has increasingly been proposed to strengthen infection surveillance, early risk stratification, antimicrobial decision support, and selected workflow functions in acute-care hospitals. However, the literature remains clinically heterogeneous, methodologically uneven, and conceptually fragmented, with technical performance often interprested too readily as evidence of clinical effectiveness. This scoping review aimed to map and synthesise the empirical literature on AI applications for infection surveillance and related hospital applications, while explicitly distinguishing technical performance from clinical utility, implementation relevance, and patient benefit. Methods We conducted a scoping review in accordance with PRISMA 2020 and PRISMA-ScR guidance. CINAHL, Cochrane Library, Embase, PubMed, Scopus, and Web of Science were searched for English-language empirical studies. Eligible studies examined AI applications relevant to infection surveillance, risk prediction, detection, antimicrobial decision support, or workflow-relevant hospital functions in acute-care settings. Findings were synthesised narratively by application domain and translational stage. Results Database searches yielded 884 records; 628 unique records underwent title and abstract screening, 180 full texts were assessed, and 39 studies were included. The literature was dominated by retrospective model-development and validation studies; no randomised trials or robust comparative evaluations under routine clinical conditions were identified. Evidence clustered around surgical-site infection, urinary-tract-infection-related outcomes, ventilator-associated pneumonia, bacteraemia, sepsis, resistant organisms, and infection-related mortality. Across these domains, AI models generally showed moderate-to-high discriminatory performance, particularly for surgical-site infection surveillance and prediction. A smaller body of evidence suggested potential operational value in antimicrobial prescribing support, early risk stratification, real-time bacteraemia prediction, and reduction of manual surveillance workload. However, implementation evidence was sparse and heterogeneous, with limited assessment of usability, adoption, trust, workflow redesign, sustained real-world performance, or patient-level benefit. Conclusion AI shows substantial promise as an adjunct to infection surveillance and selected hospital infection-management tasks, but the current evidence base is considerably stronger for technical accuracy than for clinical effectiveness, implementation success, or patient benefit. Stronger prospective, externally validated, and implementation-oriented studies are needed before firmer claims can be justified.","url":"https://doi.org/10.3389/frai.2026.1859410","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1859410","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1097/nrl.0000000000000682","name":"Artificial Intelligence in the Prehospital Stage of Stroke: Can Virtual Assistants Optimize Triage?","source":"europepmc","abstract":"Objectives Many patients fail to access emergency services in time for acute stroke treatment. Artificial intelligence (AI) may help optimize prehospital triage. This study describes the development, refinement, and clinical validation of an AI-based virtual assistant (VA) for early stroke detection and appropriate emergency referral. Methods A prospective cohort study was conducted between August 2024 and July 2025 in a tertiary care center in Buenos Aires, Argentina. The VA was applied to adult inpatients with acute stroke in a neurovascular unit. Before this, the tool had been optimized using a literature review and simulations with 1151 de-identified medical records. Clinical, demographic, and performance variables were recorded. The main outcomes were syndromic diagnostic alignment, identification of the most probable diagnosis, appropriate emergency referral, and user satisfaction. Results A total of 78 participants were included (median age: 73 y; 56.4% male). The mean time from symptom onset to VA use was 2 days. Final diagnoses were ischemic stroke (80.8%), transient ischemic attack (11.5%), subarachnoid hemorrhage (5.1%), and intracerebral hemorrhage (2.6%). Syndromic diagnosis matched the clinical standard in 89.7% of cases; top-1 match in 71.8%, and top-3 in 91%. Emergency referral was adequate in 93.6% of cases. The median use involved 10 questions and 4 minutes. Over 90% rated the experience 4 or 5 out of 5. Conclusions In this controlled validation involving patients with confirmed cerebrovascular disease, the AI-based VA demonstrated high agreement with clinical syndromic classification, appropriate urgency recommendations, and high user acceptance. Further evaluation in broader prehospital populations is warranted.","url":"https://doi.org/10.1097/nrl.0000000000000682","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/nrl.0000000000000682","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/bjr/tqag077","name":"Current patient attitudes to artificial intelligence applications in radiology.","source":"europepmc","abstract":"Abstract Objectives Healthcare systems are now funding implementation of artificial intelligence (AI) algorithms in radiology, which will change the experience of care for patients. Currently, there is still limited evidence of patient attitudes to AI implementation in healthcare. We aimed to determine current attitudes to AI of people attending hospital for diagnostic imaging. Methods This prospective study was conducted at a tertiary hospital network. Following ethical approval and informed consent, an 18-item questionnaire was administered to patients attending for outpatient imaging, assessing their views on AI. Factor analysis was undertaken to identify themes. Results In total, 162 people completed the questionnaire; 56% of whom were female (91/162). Most people thought that AI in healthcare would be useful (78%) and should be used (64%). People felt strongly that doctors should be responsible for decisions involving AI (71%). Three latent factors were identified: “utility and safety,” “interaction,” and “comparability to doctors.” People were positive about the utility and safety of AI, were concerned about a loss of personal interaction, and compared AI unfavorably to doctors. There was strong opposition to autonomous AI decisions. Conclusions The findings of this study map current patient acceptability of AI in healthcare and should inform strategies to balance AI ethical implementation with delivering value to patients and the healthcare system. Advances in knowledge This study provides insights into current patient attitudes to AI in healthcare in a UK setting where AI tools are actively being deployed, building on prior European surveys and guiding ongoing AI design and implementation.","url":"https://doi.org/10.1093/bjr/tqag077","authors":["Rory H Maclean","Iqbal Aniq","Jack Delaney","Yoyel Kang","Robert O’Shea","Asif Mazumder","Carolyn Horst","Vicky Goh"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/bjr/tqag077","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.3760/cma.j.cn112139-20260509-00189","name":"[The clinical effect of total hip arthroplasty through direct anterior approach in the lateral decubitus position combined with proximal femur osteotomy for Hartofilakidis type C developmental dysplasia of the hip].","source":"pubmed","abstract":"Objective: To evaluate the clinical effect of total hip arthroplasty through direct anterior approach in the lateral decubitus position combined with proximal femur osteotomy in the treatment of Hartofilakidis type C developmental dysplasia of the hip(DDH). Methods: A retrospective case series analysis of 134 patients (163 hips) with Hartofilakidis type C developmental dysplasia of the hip who underwent total hip arthroplasty at the Department of Orthopaedics, the First Affiliated Hospital of University of Science and Technology of China from January 2017 to December 2024 was performed. There were 10 males (11 hips) and 124 females (152 hips), aged (44.5&#xb1;12.3) years (range:22 to 81 years), with the body mass index of (22.6&#xb1;3.5) kg/m&#xb2; (range:13.9 to 34.2 kg/m&#xb2;). Harris hip scores and lower limb length discrepancy were compared between preoperative and final follow-up. Imaging stability of the prosthesis was evaluated by X-ray, and postoperative complications were recorded. The paired sample t -test or Mann-Whitney U test was used to compare data before and after surgery. Results: All patients successfully completed the surgery. The operation time( M (IQR)) was 125(35) min (range:80 to 200 min) and intraoperative bleeding loss was 200(100)ml (range:100 to 1 000 ml). All the patients were followed up for 36(22)months (range:16 to 100 months). Harris hip score increased from preoperative (44.7&#xb1;5.6) points to (85.2&#xb1;5.2) points at the last follow-up, and the difference was statistically significant ( t =65.677, P &lt;0.01). Lower limb length discrepancy decreased from preoperative 5(2)cm to 1(2) cm, and the difference was statistically significant ( Z =15.777, P &lt;0.01). All acetabular components were stable and free of displacement on imaging during follow-up. In 2 hips, the femoral osteotomy fragment had loosening and displacement after surgery and underwent revision surgery. In 2 hips, Vancouver type C periprosthetic fracture occurred, and they underwent open reduction and internal fixation. In 6 hips, dislocation occurred after surgery and all prostheses were in good position after closed reduction. At the last follow-up, 4 hips had local sensory abnormality in the affected lower limb. No dislocation, loosening, fracture, infection or neurovascular injury occurred in other patients. Conclusion: Total hip arthroplasty through direct anterior approach combined with proximal femur osteotomy for Hartofilakidis type C DDH can effectively improve hip joint function with satisfactory short-to-mid-term outcomes.","url":"https://doi.org/10.3760/cma.j.cn112139-20260509-00189","authors":["Li GY","Zhang XQ","Luo ZL","Chen M","Dai Y","Ni Z","Zhu C","Shang XF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112139-20260509-00189","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1136/emermed-2024-214212","name":"Concordance between an artificial intelligence self-triage programme and physical triage.","source":"europepmc","abstract":"Objectives We evaluated the concordance between the Dutch National Triage Standard (NTS) triage classification and the triage of an artificial intelligence (AI) self-triage programme. Methods This observational comparative study was performed in the emergency departments (EDs) of two OLVG hospital locations in Amsterdam in October and November 2023. Adult patients who entered the ED without ambulance transport and were triaged using NTS (usual care), were asked to complete the digital AI triage programme. Cohen's kappa (ĸ), per cent agreement and the distribution across urgency categories were used to evaluate the concordance between the triage methods. Results Of the 264 patients approached, 203 consented to participation and were included in analysis and follow-up. Agreement between the triage methods was none to slight, ĸ=0.092 (95% CI -0.196 to 0.380). The AI triage programme overtriaged in 12.8% of cases and undertriaged in 5.4% compared with the NTS. However, the AI triage programme classified more patients with serious clinical sequelae into S1 (ie, emergency care with ambulance) and S2 (ie, emergency care) compared with NTS U1 and U2. The AI triage programme predicted the final diagnosis at ED discharge in 27.1% of the cases. There were significantly more clinical sequelae (eg, death, ward admission or follow-up consultation) for general practitioner referred patients compared with self-referrals. Conclusion The AI triage appeared more distinctive in urgency classification than classic triage when considering clinical sequelae for the patient. However, refinements and better validation of AI triage programmes are needed before these can be implemented in healthcare systems.","url":"https://doi.org/10.1136/emermed-2024-214212","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/emermed-2024-214212","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ortho.2026.101205","name":"The accuracy of artificial intelligence in identifying cephalometric landmarks: A scoping review.","source":"europepmc","abstract":"Background Accurate identification of cephalometric landmarks is essential for orthodontic diagnosis and treatment planning. Manual landmarking is time-consuming, requires clinical expertise, and is susceptible to intra- and inter-examiner variability. Artificial intelligence (AI) has emerged as a potential tool to automate this process, improving efficiency and consistency. Aims To map the current evidence on the use of AI for cephalometric landmark detection, identify trends in the AI methodologies employed, and highlight gaps in the literature requiring further research. Methods This scoping review was conducted in accordance with the PRISMA-ScR guidelines. PubMed, EMBASE, and Medline were searched up to April 2024, and identified 68 eligible studies. Data extracted included datasets used, numbers of landmarks assessed and expert examiners involved, AI algorithms employed, and reported performance outcomes. Results The included studies analysed approximately 450,000 lateral cephalograms, frequently using the IEEE ISBI Grand Challenge 2015 dataset. Convolutional neural networks (CNNs) were the most common AI architecture. Several AI systems achieved landmark localisation comparable to expert clinicians, with mean errors within 2mm, although performance varied between studies and landmarks. Heterogeneity in study design, datasets, validation methods, and reporting standards limited comparison of the findings. Conclusions AI may have a role in supporting cephalometric landmark detection. However, considerable variation in reported performance outcomes was observed across studies, potentially reflecting differences in landmark identification protocols, algorithm design, and dataset characteristics. The evidence remains heterogeneous, and further research using standardised methodologies and diverse datasets is needed to evaluate the applicability of these systems in routine clinical practice. DOI registration link on OSF: https://doi.org/10.17605/OSF.IO/CGVSU (accessed on June 29, 2026).","url":"https://doi.org/10.1016/j.ortho.2026.101205","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ortho.2026.101205","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1002/advs.75947","name":"Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.","source":"europepmc","abstract":"Cryopreservation is the storage of biological materials like cells, tissues, or even organs at cryogenic temperatures. This technology is a key enabler for biobanking, reproductive medicine, and cell therapy, and is positioned as a vital part of the future of transplantation. Successful cryopreservation relies on cryoprotective agents (CPAs) that protect biological structures from ice-induced damage. However, CPAs can have significant drawbacks, including toxicity, particularly at the high concentrations required for vitrification. As efforts advance toward preserving more sensitive cells, whole organs, and, ultimately, entire organisms, there is a pressing need for new CPAs with improved profiles across multiple parameters. The drug discovery discipline has long recognized that an effective compound must meet many criteria beyond potency, absorption, distribution, metabolism, elimination, and toxicity (ADME-T), and that these criteria must be balanced through multiparameter optimization. Similarly, an ideal cryoprotectant must simultaneously satisfy a broad spectrum of requirements. In this perspective, lessons from drug discovery are applied to the design of cryoprotectants. Treating cryoprotectant development as a multiparameter optimization challenge, akin to drug lead optimization, could enable systematic design of the next generation of safer and more effective CPAs.","url":"https://doi.org/10.1002/advs.75947","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/advs.75947","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s00330-026-12652-y","name":"The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.","source":"pubmed","abstract":"Though subgroup performance reporting helps ensure the safety of artificial intelligence (AI) products, the extent of this reporting remains unclear. This scoping review identifies studies validating commercially available AI-based products and reports the trends in performance reporting across sex, age, and race/ethnicity demographic subgroups.","url":"https://doi.org/10.1007/s00330-026-12652-y","authors":["Walston SL","Takita H","Mitsuyama Y","Sato J","Ueda D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00330-026-12652-y","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"doi:10.2196/90052","name":"Evaluation of an AI Medical Scribe After 236,153 Notes Generated Across Care Levels in a European Health System: Mixed Methods Retrospective Observational Study.","source":"europepmc","abstract":"Background Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. Artificial intelligence (AI) medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous, and data from European health systems are especially scarce. This evaluation combines 2 complementary data sources: objective editing metadata from 236,153 notes generated by 1295 clinicians, describing operational editing behavior within the AI medical scribe, and paired self-reported survey responses from 177 fully onboarded clinicians, capturing perceived change in documentation time and clinician experience. Objective This study aimed to evaluate the association of an AI medical scribe on documentation time and clinician experience. Methods This observational real-world evaluation was conducted between April 26, 2024, and October 27, 2025, using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary, and hospital care within Capio Ramsay Santé, a large integrated health care provider operating in Sweden. Eligibility was limited to fully onboarded users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least 1 document or certificate, and used the conversational edit (\"Add or adjust\") feature at least once. Results Following the introduction of the AI medical scribe, the estimated time spent on documentation per note was lower than before (4.72 vs 6.69 minutes; -29%, P Conclusions Among sustained, fully onboarded adopters in a European health care system, use of an AI medical scribe was associated with reductions in self-reported documentation time, administrative stress, and increase of presence with patients, consistent with findings from prior US-based studies. Because the survey cohort represents a highly selected subgroup of users who adopted and continued using the tool mainly in general practice, these associations may not generalize to clinicians who discontinued use or never fully adopted the scribe, and the generalizability across specialties remains unverified. The single-arm observational design and reliance on retrospective self-report are important considerations when interpreting these associations. A limitation of this analysis is that 138,196 notes were excluded because their recorded editing time was 0; these notes may have been used as generated, used as a starting point and later modified in the medical record system, or discarded, which limits the operational interpretation of the editing-time findings.","url":"https://doi.org/10.2196/90052","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/90052","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.30476/ijms.2026.109415.4494","name":"Mortality Patterns and Trends in Fars Province, Iran (2015-2024): A Retrospective Analysis of Death Records.","source":"europepmc","abstract":"Background Provincial mortality profiles are essential for local prevention and health-service planning. However, they are reported less frequently than national summaries in Iran. We described cause-of-death patterns and temporal changes in Fars Province from 2015 to 2024, including the disruption during the coronavirus disease 2019 pandemic. Methods We analyzed death registration records from the Fars provincial civil registration system, including age, sex, residence status, place of death, and an underlying cause coded using the International Classification of Diseases (ICD), 10 th revision, and mapped to major cause groups. We summarized distributions by key strata and assessed temporal patterns using annual counts and within-registry proportions. Trend tests and multivariable logistic regression evaluated associations of calendar year and pandemic period with major cause groups, while unsupervised clustering summarized mortality profiles among non-neonatal records. Results After cleaning, 199,107 records were included. Diseases of the circulatory system accounted for 45.4% of deaths, followed by respiratory diseases, neoplasms, and external causes. All-cause deaths rose sharply in 2020-2021, with concurrent increases in respiratory and infectious causes. Cause composition varied by residence status and place of death, and clustering distinguished younger external-cause deaths from older disease-dominant profiles. Conclusion Mortality in Fars Province during 2015-2024 was dominated by circulatory diseases, with heterogeneity by demographic and contextual factors and a major pandemic-era increase in deaths. The findings supported prioritizing cardiovascular risk reduction and timely acute care, targeted injury prevention for younger groups, strengthened rural access to prevention and emergency services, and preparedness for future infectious surges.","url":"https://doi.org/10.30476/ijms.2026.109415.4494","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.30476/ijms.2026.109415.4494","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1097/jhq.0000000000000524","name":"Artificial Intelligence in Health Care: Clinical Opportunities, Validation Trends, and Implementation Challenges (2020-2025).","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) continues to reshape health care, supported by advances in computing power, affordable data storage, and widespread electronic health record adoption. Methods This systematic review followed PRISMA 2020 guidelines, searching PubMed, IEEE Xplore Digital Library, and Web of Science for studies published between January 2020 and September 2025. Eligible articles included original research addressing AI applications in clinical contexts with prospective or external validation. Results Fifteen studies met inclusion criteria. Publication volume peaked in 2024 (n = 5, 33.3%). Deep learning was the most widely adopted method (60.0%), with convolutional neural networks frequently used in medical imaging. Radiology comprised 33.3% of applications, followed by oncology (20.0%) and cardiology (13.3%). The median diagnostic performance of imaging-based models was an area under the curve (AUC) of 0.91 AUC. Primary implementation challenges included regulatory compliance (53.3%), lack of algorithmic transparency (40.0%), data quality problems (33.3%), and clinical integration challenges (26.7%). Compared with earlier reviews (2015-2019), recent studies demonstrated increased external validation rates (rising from 23% to 46.7%) and greater focus on algorithmic fairness. Conclusions Artificial intelligence demonstrates substantial potential to advance health care quality across multiple domains improving diagnostic accuracy and patient safety, enhancing care efficiency and timeliness, and supporting equitable care delivery when appropriately validated across diverse populations. However, realizing these quality improvements requires addressing persistent implementation barriers including regulatory uncertainty, algorithmic transparency, data quality concerns, and clinical workflow integration challenges.","url":"https://doi.org/10.1097/jhq.0000000000000524","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/jhq.0000000000000524","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.humpath.2026.106200","name":"Artificial intelligence-assisted screening for NTRK fusion-positive salivary gland tumors: A novel digital pathology workflow.","source":"europepmc","abstract":"NTRK fusion is a promising therapeutic target for salivary gland cancer (SGC). However, the diagnostic complexity of the histological SGC subtype and low NTRK fusion-positive SGC incidence (5%), except for secretory carcinoma (90%), limit targeted therapy. Despite recommended pan-TRK immunohistochemistry (IHC) screening, evaluation remains unstandardized. Artificial intelligence (AI) has now enabled the prediction of genetic and molecular information. We aimed to evaluate the usefulness of the AI model, a histopathological image retrieval system-Luigi-Oral-in detecting NTRK fusion-positive cases. All 273 primary salivary gland tumors surgically obtained between 2005 and 2023 were retrospectively and blindly assessed using fluorescence in situ hybridization (FISH) for ETV6 rearrangements, pan-TRK IHC (EPR17341), and differential diagnosis with Luigi-Oral for hematoxylin-eosin staining queries in December 2024 (https://luigi-pathology.com/). Luigi-Oral defined inclusion of secretory carcinoma among the top five diagnoses as positive differential indication. Overall, 273 salivary gland tumors were identified, including 104 benign and 169 malignant tumors, with 7 samples pathologically diagnosed as secretory carcinomas. FISH, pan-TRK IHC, and Luigi-Oral identified 7, 7, and 24 positive cases, respectively. All the FISH-positive cases were pathologically diagnosed as secretory carcinoma. Luigi-Oral exhibited a sensitivity, specificity, false-positive rate, and false-negative rate of 85.7%, 93.2%, 6.8%, and 14.3% for detecting FISH-positive cases, respectively. The false-positive rate was 13.3% in acinic cell carcinoma, histologically similar to secretory carcinoma. Luigi-Oral might be a useful alternative to IHC for screening NTRK fusion-positive rare SGC cases, and advances in digital pathology can facilitate implementation of an AI model in the NTRK screening workflow.","url":"https://doi.org/10.1016/j.humpath.2026.106200","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.humpath.2026.106200","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1371/journal.pdig.0001553","name":"Artificial intelligence risk prediction model for common respiratory pathogens in China based on heterogeneous multi-source clinical and geographic data: A modeling study.","source":"europepmc","abstract":"Most respiratory pathogens exhibit distinct seasonal and periodic outbreak patterns driven by climatic factors. However, predictive models that jointly consider climate, air quality index (AQI), and socioeconomic variables are lacking. We retrospectively analyzed targeted or metagenomic next-generation sequencing data from 153,544 respiratory samples collected from 1,880 centers across 30 provinces in China between September 2022 and September 2024. Monthly positivity rates were matched with geographic, climatic, AQI, and GDP data. CO(0.098 ± 0.016), HCHO(0.096 ± 0.021), O3(0.102 ± 0.019), sunshine hours(0.103 ± 0.028), wind speed(0.114 ± 0.024), and GDP(0.095 ± 0.019). were identified as the key geographical factors for the positivity across most respiratory pathogens via mean Gini index reduction, and a gradient boosting decision tree(GBDT) model was trained and benchmarked against other AI methods using the DISO metric. This model accurately simulated the epidemiological trends from September 2022 to September 2024 and outperformed alternative models with the lowest DISO metric of 0.12 in influenza A, 0.21 in SARS-CoV-2, 0.25 in RSV. The GBDT model was used to predict the short-term epidemic of 10 respiratory pathogens between October and December 2024. The predictions showed consistent trends with the external validation cohort for RNA viruses including SARS-CoV-2 and influenza A virus, but differed for bacterial pathogens. Integrating air quality, climatic, and socioeconomic data yields robust predictions of respiratory infection dynamics in the short-term by the GBDT model, bolstering public health surveillance and offering a framework potentially applicable to other infectious diseases.","url":"https://doi.org/10.1371/journal.pdig.0001553","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001553","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106596","name":"Implementation of an AI-driven hierarchical medical system for chronic disease management: ethical framework, resource optimization, and effectiveness evaluation.","source":"europepmc","abstract":"Objective The aim of this study was to assess the integration pathways and ethical frameworks associated with the use of artificial intelligence (AI) within hierarchical medical systems for chronic disease management, and quantitatively assess its impact on healthcare resource allocation efficiency and treatment outcomes. Methods A four-dimensional closed-loop model comprising data collection, decision intervention, resource scheduling, and outcome feedback was used to structure the system. This analysis incorporated 2024 monitoring data from the National Health Commission, follow-up records from 12,468 patients with chronic diseases across three provinces (or municipalities), and findings from international literature. Data Envelopment Analysis and Propensity Score Matching were used to compare AI-enabled models with traditional healthcare delivery models and to develop a comprehensive ethical governance and control mechanism. Results Among individuals aged ≥ 60 years in China, the prevalence of chronic diseases reached 78.3 %. The availability rate of key equipment at grassroots medical institutions remained at 62.3 %. Under ethical compliance conditions, the AI-enabled system achieved a health data accuracy rate exceeding 95 %. The hypertension control target achievement rate increased from 68 % to 82 % (p Conclusion When integrated with comprehensive ethical governance, AI technologies have the potential to address the limitations of conventional hierarchical medical systems. These systems can reduce data silos, enable effective resource reallocation to primary care facilities, and support targeted interventions. Such advancements contribute to more sustainable and equitable chronic disease management.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106596","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106596","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.clinthera.2026.06.005","name":"Diagnostic Accuracy of Artificial Intelligence-based Automated Detection of Diabetic Retinopathy in Routine Care Using Nonmydriatic Fundus Images: The Silesia Diabetes-Heart Project.","source":"europepmc","abstract":"Purpose To evaluate the diagnostic performance of a regulatory-approved (CE-marked) artificial intelligence system (RetCAD) applied to nonmydriatic color fundus photographs for diabetic retinopathy (DR) screening in routine clinical care. Methods This was a prospective single-center observational diagnostic accuracy study including adults with diabetes who underwent nonmydriatic fundus imaging between January 9, 2023 and August 6, 2024. Nonmydriatic true-color confocal fundus photographs were obtained using the iCare DRSplus camera. RetCAD generated a 5-category DR grade and a continuous severity score according to the International Clinical Diabetic Retinopathy scale. Two board-certified ophthalmologists independently graded images; one served as the reference standard, the second reader's grades were used to assess inter-reader agreement using Cohen's kappa. Diagnostic accuracy was evaluated at 3 prespecified thresholds: any DR; moderate DR or worse (referable DR, defined as International Clinical Diabetic Retinopathy grade 2 or above); and severe DR or worse. Receiver operating characteristic curves and area under the curve were derived from the artificial intelligence severity score. Subgroup analyses included age, diabetes duration, estimated glomerular filtration rate, and glycated hemoglobin. Findings 609 participants (1218 eyes) were screened; 533 participants (1040 eyes) were included (median age 56 years [interquartile range 42-67], 51% female). For detection of referable DR at the eye level, sensitivity was 0.85 (95% confidence interval [CI] 0.79-0.91) and specificity was 0.97 (95% CI 0.96-0.98). The referral rate was 17.0%, with a reference prevalence of 17.2%. The areas under the curves were 0.85 for any DR, 0.96 for referable DR (moderate DR or worse), and 0.98 for severe DR or worse. At the patient level, sensitivity and specificity for referable DR were 0.89 and 0.95, with a referral rate of 22.0%. Sensitivity was significantly lower in participants aged ≥65 years and in those with estimated glomerular filtration rate Implications RetCAD demonstrated high accuracy and strong discriminative ability for identifying referable DR using nonmydriatic fundus imaging, supporting its use as a triage tool for ophthalmic referral in routine clinical practice.","url":"https://doi.org/10.1016/j.clinthera.2026.06.005","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.clinthera.2026.06.005","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.artmed.2026.103441","name":"Artificial intelligence language models for medical text analysis: A systematic review.","source":"europepmc","abstract":"Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data. In this systematic literature review, an initial search retrieved 548 records published between 1 January 2000 and 1 July 2024. After rigorous screening based on predefined inclusion and exclusion criteria, 22 original research articles were included. The review highlights substantial progress in applying advanced architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) to medical text processing tasks. These models consistently outperform conventional NLP and ML approaches, achieving superior results in disease classification, automated clinical documentation, and predictive analytics. However, critical challenges persist, including the limited availability of clinically validated datasets, variability in data preprocessing protocols, insufficient external validation, and the lack of interpretable AI frameworks, all of which collectively hinder clinical trust and large-scale adoption. Future research should prioritize the development of hybrid AI systems that integrate multimodal data sources (text, imaging, and structured records), incorporate explainable AI mechanisms, and adhere to standardized reporting frameworks. Addressing these methodological gaps will be pivotal in enhancing the reliability, clinical applicability, and impact of AI language models, thereby advancing evidence-based medicine, personalized treatment strategies, and overall patient care.","url":"https://doi.org/10.1016/j.artmed.2026.103441","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.artmed.2026.103441","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/02683555261460252","name":"Artificial intelligence in the management of chronic pain and lipedema: A comparative analysis of ChatGPT-5o, Gemini-3, and perplexity AI in terms of readability and academic reliability.","source":"europepmc","abstract":"ObjectivesLipedema is a chronic disorder characterized by pain and disproportionate fat distribution, and its diagnosis is frequently overlooked. The aim of this study was to evaluate and compare the responses generated by contemporary artificial intelligence models-ChatGPT-5o, Gemini-3, and Perplexity AI-to structured clinical questions developed in accordance with the 2024 S2k Lipedema Guideline. The models were analyzed in terms of clinical accuracy, readability, and reference reliability to assess their performance in delivering guideline-based medical information.MethodsThis cross-sectional and comparative study was conducted by submitting 30 structured clinical questions, prepared on the basis of the relevant guideline, to three large language models. Responses collected on 10 February 2026, were evaluated using a seven-point Likert scale (reliability) and a five-point scale (accuracy). Text readability was assessed using six established indices, including the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Gunning Fog Index (GFOG). Reference reliability was examined by analyzing hallucination tendencies as defined in the literature.ResultsA statistically significant difference in reliability was observed among the models ( p = .041); Perplexity (4.95 ± 1.20) achieved significantly higher scores than ChatGPT-5o (4.38 ± 1.05) ( p = .038). In readability analyses, Perplexity (12.80 ± 2.10) required a significantly higher educational level according to FKGL scores compared to both ChatGPT-5o ( p = .041) and Gemini-3 ( p = .036). Regarding reference reliability, ChatGPT-5o outperformed Perplexity in source verifiability ( p = .031), bibliographic precision ( p = .044), and total RHS scores ( p = .027), emerging as the most robust model in this domain. No statistically significant differences were found among the models in terms of clinical accuracy and usefulness ( p > .05). Inter-rater agreement was excellent (Kappa: 0.92-0.97).ConclusionIn this study, ChatGPT-5o distinguished itself in reference quality, whereas Perplexity demonstrated superior reliability. However, the complex linguistic structures accompanying efforts to maintain high medical accuracy may constitute a significant barrier for individuals with limited e-health literacy. Although these systems show strong potential as medical information resources, they cannot yet replace expert physician oversight in terms of patient safety. A balanced approach between technical reliability and patient-centered simplification remains necessary.","url":"https://doi.org/10.1177/02683555261460252","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/02683555261460252","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3760/cma.j.cn112137-20260211-00451","name":"[Current status and evolution of targeted therapy for pulmonary arterial hypertension in China].","source":"europepmc","abstract":"Pulmonary hypertension (PH), especially pulmonary arterial hypertension (PAH), is a complex and life-threatening disorder with poor prognosis. Over the past two decades, PAH-targeted therapy in China has evolved through three distinct stages. Before 2006, the field was characterized by the absence of targeted therapies, limited disease awareness, and significant delays in diagnosis. Patients relied primarily on conventional treatments, with a 5-year survival rate of only 20.8%. Between 2006 and 2020, the introduction of targeted drugs such as iloprost and bosentan significantly improved patient outcomes. However, high costs and limited accessibility remained major barriers, with only around 20% of patients receiving targeted therapy and combination therapy used infrequently. Since 2020, expanded insurance coverage and the availability of domestic generics have substantially improved access to treatment. As a result, targeted therapy use has increased to nearly 80%, the 1-year mortality rate has declined to below 10%, and the 5-year survival rate now exceeds 70%. Meanwhile, clinical research has expanded rapidly, and nationwide specialist networks have been established, bringing standards of care closer to international levels. Looking forward, further efforts are needed to strengthen disciplinary development, promote multidisciplinary collaboration, and accelerate drug innovation to improve outcomes for patients with PAH in China.","url":"https://doi.org/10.3760/cma.j.cn112137-20260211-00451","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112137-20260211-00451","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.12968/ijpn.2025.0121","name":"A systematic review of artificial intelligence applications in oncology nursing surveillance and patient monitoring.","source":"europepmc","abstract":"Background Artificial intelligence is emerging as a valuable tool in oncology nursing surveillance by supporting early detection of patient deterioration, real-time monitoring and timely nursing interventions. Despite growing interest, evidence remains fragmented and implementation issues are not yet well established. Aim This study systematically reviewed empirical evidence on artificial intelligence applications in oncology nursing surveillance, identified key barriers and facilitators to implementation and evaluated implications for patient safety, continuity of care and nursing quality. Methods A systematic literature review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Peer-reviewed studies published in English from 2015 to early 2024 were screened using predefined eligibility criteria. Of 447 records identified, 105 studies met the inclusion criteria and were included in the qualitative synthesis. Findings The included studies showed that artificial intelligence was used across several areas of oncology nursing surveillance, including risk prediction, early warning systems, tumor detection, radiomics, symptom monitoring and patient-reported outcomes. Common artificial intelligence approaches included machine learning, deep learning, natural language processing and explainable AI. Findings suggest that these tools may improve accuracy in clinical decision support, strengthen early identification of complications and enhance monitoring efficiency. However, key barriers persisted, including data heterogeneity, small and retrospective datasets, limited external validation, workflow integration difficulties, high costs and privacy concerns. Conclusion Artificial intelligence-enabled surveillance shows promise, but broader adoption requires validation, integration and nursing-led co-design.","url":"https://doi.org/10.12968/ijpn.2025.0121","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12968/ijpn.2025.0121","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.jamda.2026.106381","name":"Machine Learning-Based Prediction of Planned Home Care Follow-Up Intervals in Older Adults: Evidence From the YASAM Observational Cohort.","source":"pubmed","abstract":"To develop and validate machine learning models for predicting clinician-determined follow-up interval categories in home care services among older adults enrolled in the YASAM (Healthy Aging Team Supported Home Care Services) program.","url":"https://doi.org/10.1016/j.jamda.2026.106381","authors":["Katipoğlu B","Karaman F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jamda.2026.106381","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.jsurg.2026.104019","name":"Artificial Intelligence in Laparoscopic Skill Assessment: A Scoping Review.","source":"europepmc","abstract":"Objective The purpose of this study was to describe and synthesize the existing literature regarding the application of artificial intelligence (AI) for laparoscopic skill assessment in surgical education. Design This scoping review was conducted in accordance with the PRISMA-ScR checklist. Literature search was performed across the MEDLINE, EMBASE, and Web of Science databases to identify original research published between 2015 and May 16, 2024. Eligible sources included studies evaluating AI-based assessments involving medical students, surgical residents, or fellows. Screening and full-text review were performed independently and in duplicate using Covidence, with discrepancies resolved by consensus. Setting Academic and clinical training environments. Participants Of 1973 abstracts screened, 30 original research studies met the final inclusion criteria. The participants were medical students, surgical residents, and fellows undergoing technical proficiency training in laparoscopic procedures. Results A total of 1973 abstracts were identified, and 30 studies were included for analysis. These were categorized into AI versus traditional/manual scoring (n = 12, 40%) and AI for distinguishing surgical skill levels (n = 18, 60%). Most studies assessed combinations of traditional laparoscopic skills of peg transfer (n = 9), suturing (n = 7), knot tying (n = 6), or pattern cutting (n = 6). AI models demonstrated moderate to high accuracy in differentiating novice and expert surgeons, with strong agreement between AI evaluations and expert human assessments. AI effectively automated skill assessment through real-time data capture and retrospective video analysis. Heterogeneity in study designs and the lack of standardized reporting metrics limited direct comparisons. Conclusions AI-based laparoscopic skill assessment provides an objective, scalable alternative to traditional evaluations with distinguishing ability and automated skill assessment. Real-time AI feedback may accelerate skill acquisition, limit human bias, and improve training efficiency. Standardization of AI assessment metrics is needed to validate and integrate AI into surgical educational programs.","url":"https://doi.org/10.1016/j.jsurg.2026.104019","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jsurg.2026.104019","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.clinsp.2026.100930","name":"Lung cancer diagnosis from CT scans using artificial intelligence techniques: A global perspective.","source":"europepmc","abstract":"Objective In this research, we conducted a systematic review of artificial intelligence techniques used for the diagnosis of lung cancer. Methods A systematic search of Web of Science, PubMed, Scopus, Epistemonikos, Cochrane, Medline, and Embase databases was carried out, containing the literature published up to June 2025. Prediction model risk of bias assessment tool (PROBAST) was used to evaluate the risk of bias and applicability of the diagnostic model studies included in the current research. Results 204 studies were included. The included articles utilized various AI techniques, including CNN (Convolutional Neural Network), SVM (Support Vector Machine), RF (Random Forest), KNN (K-Nearest Neighbor), PM-DL (Pattern Matching combined with Deep Learning), ANN (Artificial Neural Network), DNN (Deep Neural Network), CDNs (Convolutional Dense Networks), DLS (Deep Learning System), LSTM (Long Short-Term Memory), NNE (Neural Network Ensemble), and LDA (Linear Discriminant Analysis). The CNN model appears to be the most commonly used model in the papers. It was observed that applying deep learning models to preprocessed and augmented medical images led to improved performance metrics, including AUC, sensitivity, and accuracy. The accuracy of artificial intelligence techniques ranged from 68.4 to 100, while the sensitivity varied from 50.0 to 100. The specificity of the artificial intelligence techniques ranged from 50.0 to 100. The AUC of the artificial intelligence techniques ranged from 61.0 % to 100 %, while the recall varied from 75.0 to 99.82. Conclusion This research accentuates the potential of artificial intelligence techniques in the diagnosis and detection of lung cancer, with diverse levels of diagnostic accuracy. Additional research is required to optimize these artificial intelligence techniques, as well as to ascertain their clinical relevance and appropriateness in real-world clinical applicability.","url":"https://doi.org/10.1016/j.clinsp.2026.100930","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.clinsp.2026.100930","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1192/bjo.2026.12025","name":"Psychedelics, the media portrayal and large-language model evaluations.","source":"europepmc","abstract":"Psychedelics were used for centuries in healing and spiritual rituals, long before the mid-20th century when they became subjects of biomedical research. Although initial trials generated optimism, these were quickly overshadowed by sensationalist media coverage and political backlash. Following decades of inactivity, research on compounds including 3,4-methylenedioxymethamphetamine (MDMA) has resurged, along with the media attention. Investigating this growing interest, Bender et al employed a large-language model, validated against human raters, to analyse 25 years of media articles (2000-2025), quantifying trends in sentiment towards psychedelic therapies. Findings showed a dramatic increase in coverage, with positive sentiment peaking in 2020 followed by a significant decline from 2024, coinciding with the U.S. Food and Drug Administration's decision not to approve MDMA-assisted therapy for post-traumatic stress disorder, and echoing the dynamics of the 1960s. The authors emphasise that sustained progress in the field will require reliance on scientific evidence to advance therapeutic applications.","url":"https://doi.org/10.1192/bjo.2026.12025","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1192/bjo.2026.12025","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frai.2026.1812408","name":"Legal and ethical reflections on the use of artificial intelligence in the diagnosis and treatment of cancer: who assumes responsibility?","source":"europepmc","abstract":"Artificial intelligence (AI) offers multiple advantages, such as: improvement and accuracy of the diagnosis, decrease of the doctors' workload, decrease of the hospitalization costs, and becoming increasingly widespread, studied, and applied in medicine. AI is already used in image recognition, has haptic perception, and can manipulate instruments. Thus, surgical robots will likely be driven by AI. In the near future, machine learning (ML) will also appear. The use of AI and the study of the specialty literature raise ethical and legal questions for which there is no unanimous answer yet. Medical liability (malpractice) for AI-related errors and damages to the patient prompts legal reflections on this topic. The diagnostic algorithms of AI raise questions regarding the risks of using AI in the diagnosis and treatment of cancer (especially in rare cases), the information provided to the patient, all of these having moral and legal implications, as well as regarding the impact on the empathic doctor-patient relationship. Actually, the use of AI in the medical field has triggered a revolution in the doctor-patient relationship, but it has possible medico-legal consequences as well. The current legal framework regulating medical liability when AI is applied is inadequate and requires urgent measures, because there is no specific and uniform legislation to regulate the liability of the various parties involved in applying AI, or that of the end-users. Consequently, greater attention should be paid to the risk of applying AI, to the necessity to regulate its safe use, and to maintain the safety standards of the patient by continuously adapting and updating the system.","url":"https://doi.org/10.3389/frai.2026.1812408","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1812408","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frma.2026.1815503","name":"How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).","source":"europepmc","abstract":"Objectives To quantify how artificial intelligence (AI) publications contribute to journal impact factor (JIF) in oral and maxillofacial radiology (OMFR) journals and to discuss implications for imaging research, peer review, and clinical translation. Methods On 25 June 2025, Journal Citation Reports (JCR) data (2017-2024) were retrieved for Dentomaxillofacial Radiology, Oral Radiology, Imaging Science in Dentistry, and the Radiology section of Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology (OOOO). For each JCR year, citable items and JIF-accountable citations were exported and manually coded as AI article, AI review, non-AI article, or non-AI review by two observers (κ = 0.956). A descriptive indicator, named as notional JIF, was computed to illustrate the contribution of AI items. Citation rates were compared descriptively across document types. Results In JCR 2024, AI papers represented 10.9%-25.2% of citable items among OMFR journals yet contributed 31.3%-53.7% of JIF-accountable citations; radiology AI papers contributed materially to OOOO's JIF despite comprising only 1.5%-2.4% of citable items. AI articles and reviews received 4.3-4.4 × more JIF-accountable citations per item than non-AI counterparts. Notional JIFs exceeded actual JIFs in 2020-2022, reflecting small-denominator effects and topic-specific citation acceleration that diminished as more AI papers were published in subsequent years. Conclusions AI-related publications are associated with higher per-item citation rates in OMFR journals, consistent with recognized patterns of topic-focused citation concentration in fast-moving research areas. These descriptive findings highlight the importance of methodological transparency, robust validation practices, and balanced editorial policies as AI research continues to expand.","url":"https://doi.org/10.3389/frma.2026.1815503","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frma.2026.1815503","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s10585-026-10409-x","name":"Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model.","source":"europepmc","abstract":"Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose selection is often guided by experience rather than individual patient and tumor specific features. A data-driven approach that links dose to quantitative local control and adverse effect predictions could improve planning and follow-up strategies. We performed a retrospective single-center cohort study of lung cancer brain metastases treated with GKRS at the University of Pittsburgh Medical Center (2014-2024). Using routinely available clinical, tumor, and dosimetric variables, we trained a tumor-level Random Survival Forest to predict Local Control and Dose Selection for Lung Cancer Brain Metastases after GKRS treatment. Internal validation used leakage-resistant patient-level grouped cross-validation. Performance was assessed using Harrell's concordance index (C-index) and integrated Brier score (IBS). A connected dose-sweep decision layer evaluated predicted local control across a clinically feasible margin dose grid and selected the dose associated with the most favorable predicted profile at prespecified horizons. The survival model demonstrated good internal performance (C-index 0.83; IBS 0.15). A baseline dose imitation model predicted historical margin dose with low error (OOF MAE 1.59 Gy). The integrated decision layer generated individualized local control trajectories and returned horizon-specific local control probabilities and model-based prescription dose recommendations. An Artificial Intelligence framework integrating time-to-event prediction with outcome-linked dose sweeping can provide individualized GKRS decision support for lung cancer brain metastases by delivering quantitative local control forecasts and model-based dose recommendations.","url":"https://doi.org/10.1007/s10585-026-10409-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10585-026-10409-x","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frai.2026.1789718","name":"Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.","source":"europepmc","abstract":"Recent evidence in the literature suggests that Artificial intelligence (AI) is rapidly becoming more clinically relevant with expanding applications across cardiovascular medicine and cardiothoracic surgery. Advances in computational power and the widespread digitization of clinical data have enabled AI models to identify complex, nonlinear patterns across multimodal datasets, positioning them as powerful tools for diagnosis, risk stratification, and procedural decision support. This review examines the current and emerging landscape of AI in cardiac care, with a particular focus on valvular heart disease. We synthesize evidence spanning diagnostic applications such as electrocardiographic and echocardiographic interpretation, preoperative planning, and risk prediction for surgical and transcatheter interventions, and real-time intraoperative decision support. Across these domains, AI systems frequently demonstrate performance comparable to or exceeding conventional approaches, particularly in automating standardized tasks and enabling personalized risk assessment. However, most evidence to date derives from retrospective studies, and challenges related to generalizability hold significant barriers to widespread adoption. We further discuss ethical considerations necessary for safe and equitable implementation. Overall, AI shows substantial promise to augment cardiovascular care across the continuum of practice, but its successful translation into routine clinical use will require rigorous prospective validation, transparent model development and interpretability, and carefully designed integration into existing clinical workflows.","url":"https://doi.org/10.3389/frai.2026.1789718","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1789718","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3346/jkms.2026.41.e306","name":"Large Language Models and the Future of Peer-Reviewed Medical Publishing: Challenges, Editorial Responses, and a Framework for Responsible Integration.","source":"pubmed","abstract":"The public release of large language models (LLMs) in late 2022 has fundamentally altered the landscape of scholarly medical publishing. LLMs now permeate every stage of the academic publishing pipeline, from manuscript drafting and peer review to editorial decision-making, with evidence suggesting that at least 13.5% of biomedical abstracts published in 2024 showed detectable LLM involvement. This rapid adoption has intersected with pre-existing structural vulnerabilities, including escalating article processing charges, publish-or-perish incentives, a chronic peer reviewer shortage, and inadequate editorial resources, creating interconnected challenges that affect all stakeholders. The response from the scholarly publishing community has been substantial but fragmented. International standards bodies, such as the International Committee of Medical Journal Editors (ICMJE), Committee on Publication Ethics (COPE), World Association of Medical Editors (WAME), have established consensus principles prohibiting AI authorship and requiring disclosure, yet individual journals range from restrictive to actively encouraging in their AI policies, and detection-based enforcement approaches have proven fundamentally unreliable, with documented biases against non-native English speakers. This narrative review characterizes the structural challenges of LLM adoption in medical publishing, provides a systematic comparison of editorial policies across major medical journals and publishers, and examines the limitations of current detection and enforcement mechanisms. Rather than focusing on prohibition and policing, the review proposes a forward-looking framework centered on three concrete proposals: 1) a three-tiered disclosure system (assistive, augmentative, substantive) integrated with the CRediT taxonomy; 2) a four-stage artificial intelligence co-editor model for deploying LLM-based tools within editorial workflows under transparent governance principles; and 3) equity-conscious policy design with international coordination through a proposed global editorial summit. The framework aims to shift the editorial paradigm from reactive enforcement to proactive governance, addressing the operational reality that editors are overburdened and underresourced for the expanding expectations placed upon them.","url":"https://doi.org/10.3346/jkms.2026.41.e306","authors":["Joo H","Radnaabaatar M","Jung J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e306","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1177/02841851261470455","name":"Artificial intelligence in prostate MRI: Comparative diagnostic performance in a high-prevalence cohort.","source":"pubmed","abstract":"BackgroundArtificial intelligence (AI) is increasingly used in prostate cancer diagnostic workflows but remains insufficiently validated in high-prevalence cohorts often encountered in academic referral centers.PurposeTo compare the diagnostic performance of licensed AI software with routine radiologist readings of prostate MRI, using histopathology as the reference standard.Material and MethodsIn this retrospective study, 1000 patients underwent prostate MRI for suspected prostate cancer (between May 2020 and December 2024), followed by transperineal biopsy; 391 subsequently underwent radical prostatectomy. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy across PI-RADS thresholds. Receiver operating characteristic (ROC) analysis, Cohen's kappa for inter-rater agreement, and paired McNemar's test were performed.ResultsIn total, 959 patients (mean age = 69.7 &#xb1; 8.4 years) were included; clinically significant prostate cancer (csPCa) was found in 830 (86.5%) patients. AI assigned more cases to PI-RADS 1-2 and fewer to PI-RADS 3 (&#x3ba;&#x2009;=&#x2009;0.388; P &#x2009;&lt;.001). At the PI-RADS &#x2265;3 threshold, radiologists showed sensitivity 96.0%, specificity 20.9%, accuracy 85.9%, PPV 88.7%, and NPV 45.0%, compared with 91.3%, 37.2%, 84.0%, 90.3%, and 40.0% for AI ( P &#x2009;&lt;.001). ROC performance was comparable (AUC 0.763 vs. 0.739; P &#x2009;=&#x2009;.28). In the prostatectomy subgroup, AI demonstrated a higher false-negative rate (8.4% vs. 4.1%; odds ratio = 3.83, 95% confidence interval [CI] = 1.52-11.51).ConclusionAI showed diagnostic performance comparable to that of radiologists for csPCa detection, with no significant difference in AUC. AI reduced indeterminate PI-RADS 3 scores but missed more significant cancers.","url":"https://doi.org/10.1177/02841851261470455","authors":["Ferreira NG","Thomas OMT","Gjesdal KI","Müller S","Oldenburg J","Syversen IF","Hansen AC","Geitung JT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/02841851261470455","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.4274/dir.2026.263984","name":"Real-world multimetric comparison of four commercial artificial intelligence solutions for intracranial hemorrhage detection.","source":"europepmc","abstract":"Purpose To evaluate the real-world multimetric performance of four commercially available computed tomography (CT)-based artificial intelligence (AI) solutions for acute intracranial hemorrhage (AIH). Methods Patients who underwent non-contrast brain CT for suspected AIH in our emergency room between February and March 2024 were screened. After applying the inclusion and exclusion criteria, 436 CT scans were included in the final analysis. Three neuroradiologists established the ground truth for AIH and hemorrhage volume. For detection performance, the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier score were calculated based on the available probability score, whereas sensitivity, specificity, precision, and F1 score were calculated based on binary classification. Bland-Altman analysis was performed to assess volumetric agreement for AIH between each algorithm's calculations and the neuroradiologists' measurements. Results A total of 436 patients (mean age, 62 years ± 20; male patients, 209) were enrolled. The AUROC (0.96 to 0.99) and sensitivity (0.85 to 0.92) were high across all solutions, with no statistically significant differences in pairwise comparisons ( P > 0.05). However, solution B demonstrated the highest AUPRC [0.98, 95% confidence interval (CI): 0.94, 1.00] and the lowest Brier score [0.02 (95% CI: 0.02, 0.03)]. In binary performance, both solutions B and D exhibited significantly higher specificity (1.00 and 0.99), precision (0.90 to 0.98), and F1 score (0.87 to 0.94) than the other solutions ( P 3 (95% CI: -1.47, -0.27)] and the narrowest limits of agreement (-13.4 to 11.6) relative to the neuroradiologists' measurements. Conclusion In a real-world emergency setting, all four commercially available CT-based AI solutions for AIH demonstrated uniformly excellent performance; however, meaningful differences emerged in confirmatory performance and volumetric agreement. These distinct, algorithm-specific trade-offs provide practical guidance for selecting and integrating appropriate AI solutions to improve AIH diagnosis and management workflows. Clinical significance The algorithm-specific performance trade-offs identified in this study suggest that no single AI solution is universally optimal; solutions with superior confirmatory performance may reduce unnecessary notifications in high-volume emergency settings, whereas those with more consistent volumetric agreement may better support treatment planning and longitudinal monitoring. A structured, multimetric evaluation aligned with institutional priorities is essential for evidence-based AI procurement in acute stroke imaging.","url":"https://doi.org/10.4274/dir.2026.263984","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4274/dir.2026.263984","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s43678-026-01189-0","name":"Barriers and facilitators to developing and implementing artificial intelligence-based clinical decision support in the emergency department: a qualitative study.","source":"europepmc","abstract":"Objectives This study examined why artificial intelligence (AI)-based clinical decision support tools have had limited clinical translation in the emergency department (ED) and identified barriers and facilitators to their development and implementation. Methods We conducted a qualitative study involving semi-structured interviews with researchers who have expertise developing and implementing AI clinical decision support tools for use in the ED. We used purposive and snowball sampling to identify participants. We used platform-based AI transcription and anonymized transcripts manually. Using grounded theory framework, two coders iteratively analyzed transcripts in three stages (initial, focused, and theoretical) to identify barriers, facilitators, and themes. We adhered to SRQR and COREQ guidelines. Results We achieved data saturation after ten interviews conducted between October 15, 2024 and March 22, 2025. Participants ranged across a variety of medical and academic professions. We identified eight themes pertaining to developing and implementing AI clinical decision support in the ED, in descending frequency: team capacity; data infrastructure; defining the clinical problem and solution; research, ethics, and regulatory approval; legal and liability; model building and performance; time; and cost. We identified \"engaging multiple healthcare end-users\" and \"sharing resources with other departments\" as the highest yield facilitators. Conclusion Successful implementation of AI clinical decision support tools in the ED requires a clear clinician- and patient-defined problem, robust data infrastructure, and a diverse research team able to navigate challenges with regulatory, legal, and financial challenges over a long timeline. Anticipating barriers and leveraging facilitators early in the development process may increase the likelihood of successful implementation.","url":"https://doi.org/10.1007/s43678-026-01189-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s43678-026-01189-0","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1093/oodh/oqag020","name":"Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review.","source":"europepmc","abstract":"The convergence of artificial intelligence (AI) and wearable monitoring devices is revolutionizing the management of high-risk pregnancies, offering unprecedented opportunities for early detection, personalized care, and improved outcomes. These technologies address critical challenges in maternal and fetal health, especially in resource-limited settings, by enabling proactive and continuous care. This systematic review specifically focuses on the integration of artificial intelligence and wearable devices for managing high-risk pregnancies and highlights regulatory and ethical challenges. Databases like PubMed (Medline), Scopus, and Web of Science were explored for pertinent studies (from 2015 to 2024). Articles were chosen based on specific inclusion and exclusion criteria, and essential data including technology type, AI algorithms employed, and their influence on perinatal care were collected. The Risk of Bias in the studies was evaluated utilizing standard assessment tools such as PROBAST+AI, and study quality was assessed with TRIPOD+AI. Ultimately, the findings were examined through narrative analysis, leading to the identification of challenges and future research directions. This systematic review examined 16 studies focusing on the application of AI and digital innovations in maternal and fetal healthcare. The studies investigated various AI techniques, including machine-learning models for predicting risks to maternal health, deep learning for monitoring fetal well-being, and wearable sensor technologies for remote health tracking. Many studies produced encouraging outcomes, with AI models reaching high levels of accuracy in early detection, risk assessment, and real-time patient monitoring. However, issues such as dataset biases, generalizability, and ethical concerns continue to pose significant challenges. Additional research is necessary to validate these AI-driven methods across diverse populations to ensure equitable and effective healthcare outcomes. The integration of technologies has the potential to significantly improve the management of high-risk pregnancies. However, a critical regulatory gap exists that must be addressed to ensure that these technologies are used ethically, safely, and effectively. This will involve the development of new regulatory frameworks, a focus on transparency and accountability, and a commitment to addressing bias and data security to provide the best care possible. AI has the potential to transform maternity services by improving risk prediction, enabling remote monitoring, and supporting clinical decisions. However, realizing this potential requires addressing limitations related to data quality, algorithm accuracy, user privacy, and ethical considerations.","url":"https://doi.org/10.1093/oodh/oqag020","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/oodh/oqag020","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/fmed.2026.1888765","name":"The temporal changes in GPT-4 performance on UKMLA practice questions: educational and clinical implications.","source":"europepmc","abstract":"Background Generative Artificial Intelligence (GAI) models, such as GPT-4, have been extensively studied for their integration into medical practice and education. GPT-4 has demonstrated excellent performance on medical licensing examinations, including the United Kingdom Medical Licensing Assessment (UKMLA). However, the field lacks longitudinal data on whether such performance is stable or varies over time. Theoretically, iterative improvements updated by the vendor should enhance performance, but empirical evidence of such longitudinal trends remains limited. Given that GPT-4 undergoes periodic vendor-side updates, we aimed to analyse the categorical, time-spaced performance of GPT-4 on the UKMLA to characterise how its performance changes over time in a medical context. Methods Two publicly available UKMLA papers were fed into GPT-4 at two different time points, June 2023 and December 2024. 191 questions were provided with and without multiple-choice options to assess GPT-4's clinical competence. McNemar's test was performed to evaluate changes in GPT-4's performance over time, comparing domain-specific questions. Results GPT-4's accuracy improved noticeably between the two rounds (MCQ: 88.0 to 93.7%, p = 0.027; non-MCQ: 68.1 to 81.7%, p = 1.00). Single-step accuracy rose from 73.1 to 82.3%, and multi-step from 57.4 to 80.3% without MCQ. GPT-4 showed improved accuracy from Round 1 to Round 2 for both single-step and multi-step questions, with MCQ-prompted responses consistently outperforming non-MCQ responses (up to 95.1% accuracy for multi-step MCQ questions in Round 2). GPT-4's performance improved across all question categories from round one to round two, most notably in management questions without MCQ options (+23.30%), though these differences were not statistically significant. Discussion and conclusion GPT-4's performance on the UKMLA improved significantly over 18 months, suggesting that iterative vendor-side model updates enhance clinical reasoning capabilities. These findings indicate that GPT-4 may serve as a supplementary educational tool for medical students and clinicians; however, the underlying drivers of performance changes remain opaque, and such tools should be deployed with structured oversight to prevent overreliance.","url":"https://doi.org/10.3389/fmed.2026.1888765","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1888765","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/00220345261464887","name":"Interpretable Machine Learning for Population-Level Tooth Loss Prediction.","source":"europepmc","abstract":"Machine learning can support population-level severe tooth loss (STL; ≥6 missing teeth) risk stratification; however, a lack of calibration under domain shift, limited interpretability of conventional black-box models, and inadequate handling of complex survey designs constrain responsible public health interpretation and implementation. We implemented and evaluated an interpretable, survey-weighted Multiple Imputation by Chained Equations-Explainable Boosting Machine (MICE-EBM) framework for population-level STL prediction incorporating sociobehavioral and systemic health determinants. Representative US adult datasets were analyzed, including Behavioral Risk Factor Surveillance System (BRFSS) 2022 for model derivation ( N = 433,772), BRFSS 2024 for temporal validation ( N = 448,213), and National Health and Nutrition Examination Survey (NHANES) 2015-2018 for cross-survey evaluation under outcome-definition and measurement shift ( N = 10,775). Missing data were addressed using an antileakage HistGradientBoosting-driven pipeline to preserve multivariate epidemiological variance. The EBM demonstrated strong temporal stability on BRFSS 2024 (area under the receiver-operating characteristic curve [AUC]: 0.863; Brier: 0.085). For BRFSS 2022, model performance yielded an AUC of 0.865 and a Brier score of 0.086; a 100-replicate locked-imputed-set bootstrap confirmed robustness with an optimism-corrected AUC of 0.860 and a Brier score of 0.088. Direct transfer to NHANES yielded an AUC of 0.754 with poor raw calibration (Brier: 0.192), whereas predefined isotonic recalibration improved the holdout Brier score to 0.136. Compared with black-box stacked meta-ensemble (AUC: 0.780), the pre-recalibration EBM had lower NHANES discrimination (AUC: 0.754) but retained intrinsic glass-box interpretability. Overall, the MICE-EBM model may support population-level STL risk stratification for US survey-based cohorts with intrinsic transparency and calibrated same-survey performance. Moreover, this TRIPOD+AI-compliant framework may support population-level dental public health planning, although cross-survey or international application requires local validation and recalibration prior to implementation.","url":"https://doi.org/10.1177/00220345261464887","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/00220345261464887","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106609","name":"Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.","source":"europepmc","abstract":"Background Ambient AI scribe tools that capture clinician-patient conversations and generate draft notes are increasingly deployed to reduce documentation burden, but patient-facing acceptance may influence implementation success, especially in rural areas. Objectives To characterize rural respondents' attitudes toward AI scribes across (1) trust in documentation accuracy, (2) perceived impact on patient-provider interaction, and (3) preference for future use, and to examine how attitudes vary by demographic, socioeconomic, health, and digital-access characteristics. Methods We conducted a cross-sectional analysis of 1,050 rural respondents in the 2024 Canadian Digital Health Survey. Each outcome was dichotomized. XGBoost classifiers were trained for each outcome using prespecified predictors (sex, age group, race/ethnicity, education, employment status, household income, chronic disease, self-reported health, and high-speed internet access). Models demonstrated strong overall performance on a held-out test set. Subgroup differences were summarized using marginally standardized predicted probabilities with bootstrap 95 % confidence intervals. Results Predicted endorsement decreased across three attitude domains, from trust in documentation accuracy to interaction benefit and future-use preference. Predicted endorsement was higher among males than females across outcomes (e.g., future-use preference: 0.388 vs 0.313). Higher education and chronic disease were consistently associated with more favorable responses (e.g., future-use preference: graduate degree 0.466 vs less than high school 0.308; chronic disease 0.408 vs no condition 0.298). Compared with White respondents, visible minority, non-Indigenous respondents had lower future-use preference (0.293 vs 0.349), while Indigenous respondents showed higher predicted future-use preference (0.423 vs 0.349). Predicted probabilities were similar by internet access status across all three outcomes. Conclusions Among rural respondents, trust in AI scribe accuracy does not fully translate into perceived interaction benefit or willingness to use AI scribes in future encounters, supporting rollout strategies that prioritize clear communication, privacy transparency, and meaningful choice.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106609","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106609","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s12911-026-03599-7","name":"Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.","source":"europepmc","abstract":"Purpose This systematic review and meta-analysis evaluated the performance of artificial intelligence (AI)-based models in diagnosing pediatric asthma and predicting acute asthma exacerbations. Methods A comprehensive literature search was conducted across PubMed, Embase, and Web of Science. The initial search was conducted up to December 6, 2024, and a supplementary search was conducted in April 2025 to identify newly published or newly indexed studies. Diagnostic performance metrics, including sensitivity, specificity, area under the curve (AUC), and 2 × 2 diagnostic data, were extracted or reconstructed. A bivariate random-effects model was used for meta-analysis, and study quality was assessed using a modified QUADAS-2 tool with PROBAST-informed signaling questions. Results A total of 18 studies were included: 13 studies evaluated AI-based models for pediatric asthma diagnosis and five studies evaluated AI-based models for predicting acute asthma exacerbations. For asthma diagnosis, internal validation showed a sensitivity of 0.87, specificity of 0.93, and AUC of 0.96, while external validation showed a sensitivity of 0.81 and specificity of 0.94. For acute exacerbation prediction, internal validation showed a sensitivity of 0.59, specificity of 0.79, and AUC of 0.68. These estimates should be interpreted cautiously because heterogeneity was very high and external validation evidence was limited. Conclusion AI-based models showed promising but preliminary diagnostic performance for pediatric asthma, whereas their performance for predicting acute exacerbations remained limited. The findings should be interpreted cautiously because of substantial heterogeneity, potential publication bias, and limited external validation. Future studies should use standardized definitions and independent external validation before these models are implemented in clinical practice. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12911-026-03599-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03599-7","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s12909-026-09405-2","name":"Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach.","source":"europepmc","abstract":"Background Artificial intelligence (AI) offers new opportunities in healthcare research; however, faculty often lack structured training on its effective and ethical use. Existing faculty development initiatives frequently emphasise tool awareness rather than supporting ethical, reflective, and process-oriented engagement with AI. The New Knowledge Help (NKH) approach was developed as a pedagogical framework to support ethical engagement with AI in faculty research education and was operationalised using instructional design principles. This study evaluated the NKH approach using Kirkpatrick's four-level framework. Methods A blended programme of six face-to-face and two synchronous virtual workshops was conducted in three cities of Pakistan from November 2023 to March 2024. A total of 290 faculty members participated in NKH-guided workshops focused on ethical engagement with AI across the research process. The NKH approach was grounded in the Four-Component instructional design (4 C/ID) model and evaluated using the Kirkpatrick model across four levels: reaction, learning, behaviour, and results. Data were collected through post-workshop feedback, pre- and post-knowledge tests, and a three-month follow-up survey considering learning application and self-reported scholarly outputs. Results The study revealed high levels of participants' satisfaction with workshop content and delivery (Level 1: mean satisfaction scores 4.4-4.95/5; Satisfaction Index = 87.2%). There was a 22% learning gain in post-test knowledge as compared to pre-test results (Level 2, p Conclusion The study concludes that the NKH approach supports the ethical engagement of faculty with AI tools in research, as demonstrated by enhanced satisfaction, knowledge gain, and early scholarly activities. The varied practices of faculty at level 3 and 4 suggest the need for institutional support, peer networking, and explicit guidance. The NKH approach offers a structured and pedagogically informed framework for fostering ethical engagement with AI in faculty training.","url":"https://doi.org/10.1186/s12909-026-09405-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09405-2","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frai.2026.1745928","name":"A structured framework for effective and responsible generative artificial intelligence chatbot prompt engineering throughout the scientific process: a comprehensive guide for the health and medical researcher.","source":"europepmc","abstract":"Generative artificial intelligence (GenAI) chatbots powered by large language models (LLMs) are becoming increasingly integrated into health and medical research workflows, offering researchers new tools to enhance efficiency, support innovation, and assist with knowledge translation. Although their use in health and medical research is expanding rapidly, the practical application of these tools across the broader health and medical research landscape remains complex and evolving. Health and medical researchers often engage with complex study designs, theoretical frameworks, and population needs, all of which require thoughtful, effective and responsible use when involving AI tools. This 10-chapter guide serves as a practical, evidence-informed resource for health and medical researchers to engage effectively and responsibly with GenAI chatbots through the practice of prompt engineering, the design of clear, structured, and purposeful prompts that guide GenAI chatbot outputs. It presents strategies to improve prompt quality and adapt GenAI chatbot interactions to the varied methodological and disciplinary contexts found across health and medical research. The article outlines a structured framework for how GenAI chatbots can be applied throughout the research cycle, including research question development, study design, literature searching, querying for appropriate reporting guidelines and appraisal tools, quantitative and qualitative data analysis, writing and dissemination, and implementation. AI-generated content should be treated as a preliminary draft and must always be reviewed, verified against credible sources, and aligned with disciplinary standards. Risks such as hallucinated content, embedded biases, and ethical challenges are addressed, particularly in sensitive or high-stakes settings. Transparency in AI use and researcher accountability are essential. While GenAI chatbots have the potential to expand access to research support and foster innovation, they cannot replace critical thinking, methodological rigour, or contextual understanding. Instead, they should augment, not replace, human expertise. This guide encourages effective and responsible use of GenAI chatbots and support their thoughtful integration into the health and medical research process.","url":"https://doi.org/10.3389/frai.2026.1745928","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1745928","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3390/healthcare14121696","name":"Teleophthalmology and Teleglaucoma in Clinical Practice: Attitudes of Ophthalmologists in Bulgaria.","source":"europepmc","abstract":"Background: Over the past two decades, teleophthalmology has become an effective approach for glaucoma screening and follow-up, with its adoption markedly accelerated by the COVID-19 pandemic. Objectives: The aim of the present study was to explore and analyze the attitudes of ophthalmologists in Bulgaria toward the application of teleglaucoma, digital communication, and artificial intelligence in clinical practice. Methods: A cross-sectional survey study was conducted among 113 ophthalmologists between September 2024 and March 2025, representing 10.5% of all licensed ophthalmologists in Bulgaria (n = 1074). Results: Age, professional experience, and specialization influenced the level of involvement in managing glaucoma patients. The level of awareness regarding the term 'teleophthalmology' was higher among respondents with a specialization in ophthalmology and those holding a doctoral degree ( p = 0.001). Among the ophthalmologists surveyed, 35.4% (n = 40) provided teleophthalmology services, while an additional 19.5% (n = 22) reported no prior provision of such services but planned to do so in the future. The most preferred method for conducting teleophthalmology consultations was telephone communication (n = 27; 67.5%), followed by communication via Skype, Viber, or Messenger (n = 23; 57.5%). Physicians with longer professional experience more frequently conducted remote consultations with patients they already knew ( p = 0.006). A substantial proportion of respondents (85.0%, n = 96) expressed willingness to participate in training related to contemporary trends and the provision of remote medical services. More than half of respondents expressed positive attitudes toward the use of artificial intelligence in ophthalmology, although practical implementation remained limited. Conclusions: The present study outlined the current landscape of attitudes among ophthalmologists in Bulgaria toward teleglaucoma, digital communication, and the use of artificial intelligence in clinical practice. The findings indicated a moderately positive yet cautious stance-remote services were perceived primarily as complementary tools, particularly for the follow-up of previously known patients and for real-time collaboration between specialists.","url":"https://doi.org/10.3390/healthcare14121696","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14121696","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.5195/jmla.2026.2341","name":"Evaluating automated or artificial intelligence search tools for evidence synthesis.","source":"europepmc","abstract":"To advance information retrieval science for producing evidence syntheses at Canada’s Drug Agency, the Research Information Services team developed a replicable process to evaluate automated or artificial intelligence (AI) search tools. The team inventoried 51 tools in the fall of 2023 and built a flexible evaluation instrument to inform adoption decisions and enable comparison between tools. Building on this foundational evaluation work, the team further conducted a comparative analysis on three top-ranked tools in the fall of 2024. The investigation confirmed that these automated or AI tools have inconsistent and variable performance for the range of information retrieval tasks performed by Information Specialists at Canada’s Drug Agency. Implementation recommendations from this study informed a “fit for purpose” approach where Information Specialists leverage automated or AI search tools for specific tasks or project types.","url":"https://doi.org/10.5195/jmla.2026.2341","authors":["Robin Featherstone"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5195/jmla.2026.2341","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.3233/shti260262","name":"Artificial Intelligence: The Use and Application by Brazilian Healthcare Professionals.","source":"europepmc","abstract":"This study examines how physicians and nurses in Brazil are using generative artificial intelligence tools in healthcare practice. Based on data from the ICT in Health 2024 survey, findings reveal that AI adoption remains limited and uneven across professional categories and healthcare settings. Nurses use AI mainly for research and communication, while physicians apply it to clinical documentation and research. The results highlight institutional and infrastructural disparities that influence adoption and point to the need for training, ethical governance, and stronger digital capacity to foster equitable and human-centred AI integration in healthcare.","url":"https://doi.org/10.3233/shti260262","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3233/shti260262","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1177/19373368261460323","name":"From Restoration to Regeneration: A Review of 3D Printing Strategies in Digital Dentistry.","source":"europepmc","abstract":"Three-dimensional (3D) printing technology, or additive manufacturing, has fundamentally transformed medical paradigms by shifting dentistry from traditional subtractive manufacturing to an era of personalized, biological regeneration. This review provides a comprehensive overview of the primary 3D printing classifications currently utilized, including stereolithography, fused deposition modeling, selective laser sintering, and material jetting, analyzing their specific technical principles and material capabilities. We systematically categorize their applications across diverse dental disciplines, ranging from endodontics, periodontal disease management, and prosthodontics to orthodontics, maxillofacial reconstruction, and temporomandibular joint regeneration. Beyond the fabrication of anatomical models and surgical guides, this article critically evaluates the efficacy of these technologies in constructing bioactive scaffolds and cell-laden hydrogels designed to induce osteogenesis and angiogenesis. Special emphasis is placed on the paradigm shift from passive mechanical restoration to active biological regeneration, highlighting the potential of bioprinting in restoring physiological vitality to native tissues. Despite current barriers regarding the trade-off between mechanical durability and biological activity, as well as vascularization challenges, the convergence of artificial intelligence and 4D printing promises to establish 3D printing as a foundational standard for the next generation of regenerative dental therapeutics.","url":"https://doi.org/10.1177/19373368261460323","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/19373368261460323","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3389/frobt.2026.1854816","name":"Correction: ROS 4 healthcare: a framework for physiological human sensing for social, assistive, rehabilitation, and medical robotics.","source":"europepmc","abstract":"[This corrects the article DOI: 10.3389/frobt.2026.1745197.].","url":"https://doi.org/10.3389/frobt.2026.1854816","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frobt.2026.1854816","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1136/jme-2024-110626","name":"Artificial intelligence, invisible victims and the trolley problem.","source":"europepmc","abstract":"The allocation of scarce healthcare resources inherently involves trade-offs between the interests of 'visible' and 'invisible' victims (ie, individuals who are aware that they are shortchanged by trade-offs and those who are not). At present, decisions regarding such trade-offs are often based on highly speculative predictions; the vast array of possible trade-offs simply cannot be enumerated, let alone the optimal outcomes calculated, by human beings. Artificial intelligence has the potential to change that reality by mining large data sets and other sources of information in order to produce far more precise and comprehensive predictions of likely outcomes and to delineate optimal allocation choices. Such technologies will inevitably render 'invisible' victims 'visible', generating a colossal, real-world trolley dilemma for anyone involved in medical or healthcare policy decision-making.","url":"https://doi.org/10.1136/jme-2024-110626","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/jme-2024-110626","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1186/s13148-026-02194-x","name":"Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.","source":"europepmc","abstract":"Epigenetic editing achieves durable gene silencing through targeted modification of chromatin and DNA methylation states without altering the genomic sequence-modifications that remain fundamentally reversible compared with genome editing. Long constrained by transient efficacy, insufficient precision, and delivery bottlenecks, the field reached a critical inflection point in 2024-2025, measurable through three quantifiable criteria: (1) mechanistic durability-silencing maintained across ≥ 450 cell divisions in vitro and ≥ 12 months in vivo without continued editor expression; (2) delivery competence-tissue-selective transduction at > 50% efficiency in liver, muscle, and whole brain via engineered lipid nanoparticle and AAV platforms; and (3) clinical validation-advancement of multiple first-in-human trials. The inaugural trial in epigenetic editing was OTX-2002 (targeting MYC-driven malignancies, MYCHELANGELO study), initiated in October 2022, followed by TUNE-401 for chronic hepatitis B (Phase Ib, November 2024) and EPI-321 for facioscapulohumeral muscular dystrophy (Phase I/II, first patient dosed August 2025). Artificial intelligence contributes at distinct levels: deep learning platforms have directly accelerated clinical-stage LNP formulation screening and AAV capsid prediction; AlphaFold3 has optimized protein-DNA interaction validation without yet entering clinical programs; and the 2025 de novo design of DNA-binding proteins smaller than 65 amino acids represents a proof-of-concept breakthrough with zero clinical precedent. This review comprehensively analyzes epigenetic editing's technological maturation, provides a tiered assessment of AI's realized and anticipated contributions, critically evaluates the emerging clinical landscape, and identifies decisive unresolved challenges-including long-term stability in non-dividing cells, the lack of monitoring systems and intervention protocols needed to implement reversibility in clinical practice, and manufacturing access barriers. We argue that epigenetic editing is progressively establishing itself as a distinctive therapeutic modality characterized by durable efficacy and sequence-independent safety-reversibility as a theoretical safety mechanism has been validated preclinically, but the monitoring and intervention infrastructure required for its clinical implementation remains to be established. Long-term human validation remains the outstanding core question.","url":"https://doi.org/10.1186/s13148-026-02194-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13148-026-02194-x","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s13353-026-01063-w","name":"Artificial intelligence in neurofibromatosis type I: diagnostic and therapeutic opportunities.","source":"europepmc","abstract":"The dynamic growth in the use of artificial intelligence (AI) in medicine is driven by advances in computer processing power, memory, and data storage capabilities. The digital progress, combined with continuously growing datasets, enables the development of modern tools that support oncological, neurological, and genetic diagnostics. Achievements in the application of AI in medicine make it possible to analyze genotype–phenotype correlations, and rare diseases, which include more than 6,000 distinct conditions characterized by unique phenotypes and complex pathomechanisms, present a particular challenge in this context. This article presents the basic issues related to machine learning and describes the mechanisms of the most commonly used algorithms. In addition, a review was conducted of modern tools that enable the analysis of genotype–phenotype correlations and the prediction of symptoms of the rare disease neurofibromatosis type 1 (NF1). Tools developed so far to support therapeutic decision-making related to NF1 were collected and described. The tools were classified according to their functionality, and their usefulness in the diagnostic process was discussed in detail, emphasizing how advanced algorithms can support precise phenotypic assessment and patient health evaluation and contribute to the optimization of their treatment. Despite its great potential, the use of AI in the diagnosis of rare diseases is associated with challenges such as the need to standardize small patient groups and the necessity of interdisciplinary collaboration between experts in genetics, bioinformatics, laboratory medicine, and clinical medicine.","url":"https://doi.org/10.1007/s13353-026-01063-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s13353-026-01063-w","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1097/prs.0000000000013114","name":"Evaluating the Prevalence of Artificial Intelligence-Generated Writing in Plastic Surgery Literature.","source":"europepmc","abstract":"Background The rise of artificial intelligence (AI) and large language models in academic writing has raised concerns regarding research integrity and authorship transparency. This study evaluated the prevalence of AI-generated content in plastic surgery publications following the release of ChatGPT. Methods We conducted a cross-sectional study of 1,627 manuscripts published in 10 major plastic surgery journals between January 2024 and May 2025. ZeroGPT was used to quantify AI-generated content. A baseline threshold for substantial AI involvement (22.5%) was established using 300 pre-ChatGPT manuscripts (2010-2011). Outcomes included the proportion of manuscripts exceeding this threshold, average AI content, and associations with publication year, journal, and evidence level. Results Overall, 21.5% of 2024-2025 articles exceeded the threshold for substantial AI involvement. The median proportion of AI-generated text rose from 7.4% in 2024 to 12.2% in 2025, while the percentage of manuscripts with substantial involvement increased from 17% to 29%. AI involvement varied widely across journals (0-41%). In multivariable analysis, 2025 publication year (OR 1.86, p Conclusion More than one in five recent plastic surgery manuscripts contain substantial AI involvement, with marked variation across journals and evidence levels. These findings highlight the need for standardized editorial guidelines governing AI use to maintain research integrity and transparency in plastic surgery literature.","url":"https://doi.org/10.1097/prs.0000000000013114","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/prs.0000000000013114","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1016/j.jss.2026.03.036","name":"Artificial Intelligence-Driven Mapping of Global Microsurgery Research: Dual Automation for Data and Classification.","source":"europepmc","abstract":"Introduction Microsurgery is one of the most technically demanding and rapidly advancing domains in modern surgery. Despite its transformative role across reconstructive and restorative disciplines, global patterns of microsurgical research productivity and human capital remain poorly defined. Methods We performed the first large-scale, artificial intelligence (AI)-powered bibliometric analysis of microsurgery research published between 2010 and 2024 across 20 high-impact journals. Using a double-layer artificial intelligence framework-automated web scraping for large-scale data extraction and text-mining for content classification-we retrieved titles, abstracts, author affiliations, and countries of origin from PubMed. First author counts were used as a proxy for national human capital. Research productivity was contextualized by World Bank income classification and normalized to population size. Results A total of 11,561 publications and 7533 first authors from 74 countries were identified. Global output increased by 59.3% (611 to 973 publications) from 2010 to 2024, with the steepest growth occurring after 2019. High-income countries produced 69.5% of all publications, followed by upper-middle-income countries (21.8%). The United States dominated global production (33.7% of publications; 34.8% of first authors), followed by Japan, China, the United Kingdom, Taiwan, and South Korea. Taiwan and Switzerland achieved the highest per capita productivity (2.06 and 2.17 publications per 100,000 population, respectively). The global mean productivity was 1.53 publications per first author (range: 1.0-4.75). Conclusions This study provides the first comprehensive quantification of microsurgery research global productivity in relation to national human capital, revealing a high concentration of output in a small number of high-income countries and substantial regional disparities. By integrating web scraping with AI-based text mining, we enabled high-fidelity data extraction at scale, establishing a reproducible framework for large-scale research in surgery. AI-enabled analytics have the potential to transform evidence generation, guide equitable research investment, and accelerate the future of global microsurgical innovation.","url":"https://doi.org/10.1016/j.jss.2026.03.036","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jss.2026.03.036","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.2196/93082","name":"Comparative Evaluation of ChatGPT, Google Translate, and UD Talk for Chinese-to-Japanese Translation in Cardiology and Pulmonology Outpatient Consultations: Prospective Observational Study.","source":"europepmc","abstract":"Background Language barriers between health care providers and patients can compromise communication quality, patient safety, and health care equity. When professional interpreter services are limited, particularly in outpatient settings, artificial intelligence-based translation tools may serve as supplementary communication aids. Objective This study compared 3 Chinese-to-Japanese translation tools (ChatGPT, Google Translate, and UD Talk) in terms of their performance in real-world cardiology and pulmonology outpatient consultations. Methods In this single-center prospective observational study, audio-recorded outpatient consultations between December 2024 and November 2025 were analyzed. Verbatim physician-patient dialogues were translated using the 3 systems. Selected dialogue exchanges were evaluated by professional medical interpreters for translation accuracy and by Japanese-speaking lay participants for translation satisfaction using a 6-point Likert scale. Similarity of translation outputs was assessed at the dialogue exchange level. Results A total of 20 outpatient consultations comprising 2450 dialogue exchanges were analyzed. ChatGPT had significantly higher translation accuracy and satisfaction scores than Google Translate and UD Talk, with median scores of 5.0 (IQR 4.0-5.0) for both outcomes compared with 2.0 (IQR 1.0-3.0) for Google Translate and UD Talk (P Conclusions In Chinese-to-Japanese translations of outpatient medical consultations, ChatGPT demonstrated higher accuracy and user satisfaction than Google Translate and UD Talk. These findings indicate that artificial intelligence-assisted translation may support multilingual clinical communication when used as a complementary aid alongside professional interpreter services.","url":"https://doi.org/10.2196/93082","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/93082","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.2196/95596","name":"Application of AI in Hypertension Health Education: Scoping Review.","source":"europepmc","abstract":"Background Hypertension is a major global health challenge, and effective health education is crucial for improving patients' self-management. Traditional health education approaches are often limited by insufficient personalization, accessibility, and scalability. Artificial intelligence (AI), including natural language processing, machine learning, and large language models (LLMs), offers promising solutions to address these limitations. However, evidence regarding AI applications in hypertension health education has not been comprehensively synthesized. Objective This scoping review aimed to summarize the current evidence on AI applications in hypertension health education, and identify research gaps to inform future research and practice. Methods This review followed the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases (PubMed, Embase, Web of Science, Cochrane Library, CINAHL, and Scopus) were searched from January 2015 to June 2026. Eligibility criteria were developed using the participant-concept-context framework. Two reviewers independently conducted study screening and data extraction. Study designs were classified using the Mixed Methods Appraisal Tool framework. Consistent with scoping review methodology, no formal quality assessment was performed. Findings were synthesized narratively and presented using evidence gap maps, tables, and figures. Results A total of 24 studies from 11 countries were included, comprising 6 randomized controlled trials, 4 nonrandomized trials, 11 quantitative descriptive studies, and 3 mixed methods studies. Most studies were published between 2024 and 2026. In total, 3 AI application scenarios were identified: rule-based health education, data-driven adaptive health education, and generative AI-driven health education. Natural language processing was the most widely applied technology, and LLM-based applications increased rapidly after 2023. However, generative AI studies were predominantly proof-of-concept evaluations and lacked randomized clinical validation. Health education was rarely implemented as a standalone intervention and was typically embedded within multifunctional AI platforms. Outcomes were categorized using the Digital Health Scorecard Framework across 4 domains: technology, clinical, usability, and cost. Technical accuracy and blood pressure outcomes were the most frequently reported measures, whereas no study evaluated economic outcomes. Conclusions This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence. Three major research gaps were identified: (1) the lack of standardized core outcome sets covering technical, behavioral, clinical, and implementation domains; (2) limited development of hybrid architectures integrating LLM with structured medical knowledge bases; and (3) the absence of evaluation frameworks that satisfy both regulatory and implementation requirements. AI appears most suitable as a complement to, rather than a replacement for, clinician-delivered education. Future research should prioritize rigorous clinical validation, economic evaluation, multicultural adaptation, and health literacy equity to ensure that AI-driven health education reduces rather than exacerbates disparities in hypertension control.","url":"https://doi.org/10.2196/95596","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/95596","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1080/15548627.2026.2659295","name":"Discovery of a novel TFEB activator targeting lysosomal dysfunction in amyotrophic lateral sclerosis using artificial intelligence-based virtual screening.","source":"europepmc","abstract":"Lysosomal dysfunction is a defining feature of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), yet effective pharmacological strategies to restore lysosomal homeostasis remain limited. Transcription factor EB (TFEB), a master transcriptional regulator of lysosomal biogenesis, has emerged as an attractive therapeutic target. In our recent study published in Pharmacological Research , we established a robust artificial intelligence (AI) - driven virtual screening pipeline and identified isoginkgetin (ISO) as a potent TFEB activator that effectively promotes lysosomal biogenesis and enhances lysosomal function. Importantly, ISO exhibits potent neuroprotective effects against motor neuron degeneration in ALS models. Using this AI-driven strategy, we identified a previously unrecognized neuroprotective mechanism by which ISO protects motor neurons through TFEB-dependent restoration of lysosomal function, validating lysosomal function as a promising therapeutic target for ALS. Collectively, this work establishes that AI-powered screening to identify mTORC1-independent TFEB agonists is a valuable paradigm for the discovery and development of therapeutic agents against ALS and other neurodegenerative diseases.","url":"https://doi.org/10.1080/15548627.2026.2659295","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/15548627.2026.2659295","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1177/15533506261476209","name":"Beyond Critical View of Safety: New Predictors of Objective Assessment of Critical View of Safety in Laparoscopic cholecystectomy-a Prospective Cohort Study.","source":"europepmc","abstract":"BackgroundLaparoscopic cholecystectomy (LC) has revolutionised gallstone management. However, the incidence of bile duct injury (BDI) remains unchanged. Achieving the critical view of safety (CVS) is the standard endpoint of LC dissection to prevent BDI. This study aimed to identify preoperative, intraoperative, and novel predictors of CVS quality using intraoperative doublet photography.MethodsThis prospective, single-centre observational study (March 2024-January 2025) evaluated CVS achievement in patients undergoing elective LC. Experienced surgeons assessed anterior and posterior view photographs. We analysed predictors of CVS quality, including novel parameters such as critical angle, critical distance, and critical area ratio.ResultsThe study included 64 patients undergoing elective LC. CVS was satisfactory in 82.8% (53) and unsatisfactory in 17.2% (11) of the participants. Significant predictors of CVS quality included preoperative biochemical factors like erythrocyte sedimentation rate (OR: 4.06, 95% CI: 0.96-17.09, P = .05) and alanine aminotransferase (OR: 5.48, 95% CI: 1.81-25.47, P = .02); radiological findings including stone impacted at gall bladder neck (OR: 9.52, 95% CI: 1.75-51.77, P = .009); intraoperative variables such as modified Nassar grade (OR: 7.42, 95% CI: 1.72-32.00, P = .007) and Hartmann's pouch stone (OR: 4.47, 95% CI: 1.00- 19.93, P = .04); and certain novel parameters including critical angle ratio anterior (AOR: 9.87, 95% CI: 1.1-83.12, P = .03).ConclusionsThis observational study introduces simple metrics for objective CVS assessment using novel parameters such as critical angle, distance, and area to improve grading accuracy. These parameters may support artificial intelligence tools for intraoperative CVS quality assessment, promoting safer cholecystectomy and contributing to a standardised grading system.","url":"https://doi.org/10.1177/15533506261476209","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/15533506261476209","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.12688/f1000research.154869.3","name":"Developing medical devices with emerging technologies: trends, challenges, and future directions","source":"preprints","abstract":"This study presents a bibliometric analysis of the rapid advancement and integration of emerging technologies in medical device development, focusing on Artificial Intelligence (AI), the Internet of Medical Things (IoMT), Augmented Reality (AR), and cybersecurity. Using data from the Scopus and Web of Science databases, we analyzed 3,094 publications from 2010 to 2024 to map trends, challenges, and future directions in the field. The analysis shows substantial progress in patient care through AI and IoMT, which enable predictive analytics, personalized treatment planning, and real-time monitoring; AR is reshaping medical training and surgical precision; and cybersecurity has become critical to protecting sensitive health data. Persistent challenges include data-privacy concerns, infrastructure limitations, and interoperability issues. We also examine Africa’s contributions, with particular emphasis on Morocco’s emerging role. Three major research clusters are identified: AI and AR, IoT and cybersecurity, and embedded systems. Beyond the bibliometric mapping, we provide a comparative evaluation of the technologies, representative case studies of advanced medical devices, an expanded treatment of cybersecurity for AI-enabled devices, and a dedicated discussion of ethical, legal, and regulatory considerations. As a bibliometric analysis, the study characterizes research activity and does not perform critical appraisal or risk-of-bias assessment of individual studies. The work offers a foundation for further research and innovation in this rapidly evolving field.","url":"https://doi.org/10.12688/f1000research.154869.3","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.154869.3","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.06.04.26354939","name":"Characterizing artificial intelligence (AI) psychosis in a large academic medical setting: evidence of the new clinical phenomenon and the vulnerability of those in early phases of psychosis","source":"preprints","abstract":"Background Concerns about “AI psychosis” have swirled in the media since ChatGPT’s release, but few systematic analyses exist. We therefore conducted an electronic health record (EHR) analysis to identify the frequency, clinical characteristics, and quality of AI interactions in patients experiencing psychosis treated in a medical center. Methods AI keywords (e.g., ChatGPT, AI) were used to search Vanderbilt University Medical Center’s EHR from 12/1/2022-4/1/2026. Records were discarded if they were not AI-related or if the primary diagnosis did not include psychosis. Three raters read notes to determine if a patient was experiencing AI psychosis and classified the interactions using 4 a-priori categories (Catalyst, Amplifier, Co-Author, Object) formulated to explain how AI-related negative outcomes emerge. Findings 73 patients met our criteria. 28 patients were rated as experiencing AI psychosis, 17 had neutral interactions, and 28 expressed delusional content related to AI without documented evidence of conversational AI use. ChatGPT was the matching keyword for 53.6% patients experiencing AI psychosis. The majority of AI psychosis cases were documented after ChatGPT’s “4o” model was released in May 2024. Notably, the AI Psychosis group had significantly more patients experiencing a first psychotic episode (60.7%) compared to the other two groups. Amplifier was the most common (64.3%) qualitative rating in the AI Psychosis group. Interpretation “AI psychosis” is an infrequent but real phenomenon observed in clinical practice. Most affected patients were experiencing their first psychotic episode and presented with AI psychosis following the release of the more sycophantic GPT-4o. Among the affected patients, AI most often exacerbated an existing condition by reinforcing distorted ideas.","url":"https://doi.org/10.64898/2026.06.04.26354939","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.04.26354939","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10475650/v1","name":"Premarket transparency and postmarket observability in FDA-authorized AI-enabled medical devices: a source-linked cross-sectional study","source":"preprints","abstract":"Abstract Objectives: To quantify source-stated premarket validation elements in public summaries for FDA-authorized artificial intelligence (AI)-enabled medical devices, test temporal reporting trends, and characterize linkage to public postmarket observability signals. Methods: We conducted a cross-sectional, source-linked study of FDA AI-Enabled Medical Device List entries with a parseable public 510(k) or De Novo summary. Decisions dated 2011–2026 were assessed for 11 reporting indicators and a prespecified seven-item descriptive reporting index. Temporal trends were tested by decision year with false-discovery-rate correction. Secondary outcomes measured linkage to public postmarket records, sufficient MAUDE history for a report-count trend, and product-code recall activity. Results: Of 1,505 candidate records, 1,491 decisions were analyzed. Clinical data were stated for 70.6%, a canonical performance metric for 52.3%, multisite evaluation for 42.7%, and prospective or reader studies for 16.8%. High reporting increased from 16.5% in 2011–2020 to 59.5% in 2024–2026 (adjusted q Conclusions: Public premarket reporting was more complete in recent decision eras, but prospective evidence remained infrequently reported and public postmarket history was uneven. Transparency and observability measures should support evidence requests and monitoring, not device rankings or unsupported safety conclusions.","url":"https://doi.org/10.21203/rs.3.rs-10475650/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10475650/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9154303/v1","name":"Enhancing Renal Stone Detection Through Artificial Intelligence Models","source":"preprints","abstract":"Abstract Purpose Traditional ultrasonic detection for kidney stones is prone to variability due to the experience and skill of the operating physician, leading to inconsistent diagnostic outcomes. Factors such as stone size, location, and surrounding tissues can further compromise accuracy. Artificial intelligence (AI), with its advanced data processing and deep learning capabilities, offers a solution by automating the analysis of medical images to improve diagnostic accuracy and efficiency. Methods This cross-sectional study was conducted at our hospital between 01/06/2022 and 03/07/2024., using a datasets of 791 ultrasound images from patients with confirmed kidney stones and 117 images from non-stone participants. The High-Resolution Network (HRNet) was employed for model training to detect kidney stones in ultrasound images. Detailed image annotations were created using the labelme tool and converted to COCO format for compatibility with AI algorithms. Results The HRNet model achieved an accuracy of 86.4% in detecting kidney stones in the validation datasets, with a sensitivity of 94.3% and specificity of 73.2%. The model effectively identified both kidney stones and normal conditions. Conclusions The study demonstrates that AI, specifically HRNet, can significantly enhance the accuracy and efficiency of kidney stone detection in ultrasound imaging. This approach reduces the diagnostic burden on physicians and improves patient outcomes. However, challenges remain, including the need for large-scale annotated datasets and rigorous validation of AI models to ensure reliability in clinical settings.","url":"https://doi.org/10.21203/rs.3.rs-9154303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9154303/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.31.26358869","name":"Nudging Toward Precision Medical Education: Linking Diagnostic Exposures to Tailored Learning","source":"preprints","abstract":"Problem Authentic patient encounters are the raw material of clinical learning, yet the educational resources learners receive are rarely keyed to the diagnoses in front of them, creating temporal and cognitive gaps. Precision medical education (PME) proposes delivering the right resource to the right learner at the right moment, but practical implementation in the clinical learning environment remains limited. Approach We developed DxMentor, an electronic health record (EHR)-integrated platform that captures each learner’s daily inpatient diagnostic exposures from documented International Classification of Diseases, Tenth Revision (ICD-10) codes. Artificial intelligence (AI) is used to match each diagnosis to an educator-curated formulary of micro-learning resources and board-style questions, and to PubMed-derived primary and synthesis literature converted into plain-language evidence summaries. A personalized email “nudge” is delivered before morning rounds, copying supervising attendings for residents, with engagement tracked longitudinally. We report implementation outcomes from July 2024-April 2026. Outcomes DxMentor evaluated 32,846 encounters from 335 medical students and 346 internal medicine residents, delivering 17,340 nudges containing 63,754 didactic resources, 17,038 question sets, and 23,594 summarized articles for approximately $390 in AI token costs. In a benchmarking sample, 91.5% (366/400) of diagnosis–resource pairs were rated relevant by physician-educators. Overall, 78.5% (12,393/15,793) of nudges were opened and 11.3% (1,955/17,340) had at least one click. Engagement was higher among residents than students (open: 80.7% vs. 65.8%; click-through: 12.7% vs. 3.1%; both P Next Steps Email opens and clicks are engagement proxies rather than measures of learning. We are therefore linking nudges to educational outcomes, testing alternative recommendation strategies and timing, and expanding to additional specialties and ambulatory and surgical settings. Teaser Text DxMentor integrates with the electronic health record to capture each learner’s daily diagnostic exposures, then uses AI to match diagnoses with tailored resources and evidence summaries—delivering automated email nudges before rounds to operationalize precision medical education at scale.","url":"https://doi.org/10.64898/2026.07.31.26358869","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.31.26358869","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.01.26356993","name":"GutCore: An Endoscopy Foundation Model for Whole-Case Gastric Cancer Analysis","source":"preprints","abstract":"Background Routine endoscopy image sets contain complementary information about lesions, surrounding mucosa, anatomy, and examination context. However, most endoscopic artificial intelligence systems are developed and evaluated on selected frames for narrowly defined image-level tasks, limiting their ability to support case-level analysis. Objective To develop GutCore, an endoscopy foundation model for whole-case patient-level gastric cancer analysis, and assess whether routinely stored endoscopic image sets can support cancer detection, invasion-depth prediction, biomarker inference, and prognosis. Design GutCore was pretrained on 5.6 million de-identified endoscopic images from more than ten hospitals. We compared GutCore with general, medical, and endoscopy-specific foundation models using public image-level datasets and an internal retrospective cohort of 11,035 de-identified endoscopic examinations from Samsung Medical Center (2019–2023). Whole-case image sets were aggregated for patient-level prediction of cancer status, invasion depth, biomarker status, and overall survival. Results GutCore achieved AUCs of 0.996 for cancer detection, 0.960 for muscularis propria invasion, 0.801 for SM2-or-deeper invasion, and 0.781 for mucosal versus submucosal invasion among ESD-treated early gastric cancer cases. Biomarker prediction was strongest for EBV status and MLH1 loss and weaker for HER2 status, with AUCs of 0.861, 0.822, and 0.648, respectively. In the held-out advanced gastric cancer test set, GutCore-derived risk groups separated overall survival (log-rank P Conclusions GutCore enabled whole-case patient-level gastric cancer assessment from routinely stored endoscopic images, extending endoscopy foundation model evaluation beyond selected frames. Independent external validation is required before clinical use.","url":"https://doi.org/10.64898/2026.07.01.26356993","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.01.26356993","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-10139725/v1","name":"Selective State Space Models in Medical Imaging: A Systematic Review of Mamba-Based Architectures and Clinical Applications","source":"preprints","abstract":"Abstract Although Vision Transformer (ViT) architectures have resolved the global-context problem in medical image analysis, their quadratic computational complexity (O(N^2 )) creates hardware bottlenecks for high-resolution and 3D volumetric data. Mamba and Selective State-Space Models (Selective SSMs) provide an innovative solution to this problem by processing sequential data with linear computational complexity (O(N)). This systematic review was designed to examine the rapid adoption of the Mamba architecture in the field of medical artificial intelligence. In accordance with the PRISMA guidelines, 445 recent studies published between 2024 and 2026 were divided into four principal architectural typologies (Pure Mamba, CNN-Mamba Hybrids, Transformer-Mamba Integrations, and Novel Scanning Mechanisms) and passed through a Qualitative Critical Appraisal filter. The findings show that the most dominant and efficient approach in the literature is the CNN-Mamba hybrid, which combines local feature extraction with global context. Despite high diagnostic accuracy, the principal obstacles delaying clinical integration are the loss of spatial information arising from the flattening of 2D and 3D images into 1D sequences, the excessive memory consumption introduced by multi-directional scanning mechanisms, and the lack of multi-center external validation. Consequently, with the development of native 3D Mamba blocks that preserve spatial topology, the Mamba architecture is anticipated to become the backbone of next-generation foundation models in medical image analysis.","url":"https://doi.org/10.21203/rs.3.rs-10139725/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10139725/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10196661/v1","name":"Advancing the Hong Kong Centre for Medical Products Regulation: A Mixed-Methods Assessment and Strategic Blueprint","source":"preprints","abstract":"Abstract Background: The planned establishment of the Hong Kong Centre for Medical Products Regulation (CMPR) in late 2026 marks a structural transition from secondary reliance to independent primary evaluation. This study evaluates the active pre-CMPR operational cohort formed in 2024 to assess this institutional transition. Methods: Utilizing a mixed-methods design, this study integrates a systematic literature review, SWOT-based institutional benchmarking against FDA and EMA paradigms using WHO Global Benchmarking Tool (GBT) criteria, and an empirical survey of 110 biopharmaceutical and regulatory executives (86.4% response rate, n = 95). Results: Comparative benchmarking indicates the CMPR has achieved Level 2 maturity, with established legal frameworks in place, though gaps persist in primary scientific review, adaptive trial oversight, and real-world evidence (RWE) integration. Survey stakeholders strongly support establishing a Greater Bay Area (GBA) regulatory corridor (82.1% endorsement) and prioritizing Hong Kong as a first-launch site (59.0% positive intent), identifying biostatistics, advanced therapy medicinal products (ATMPs), and artificial intelligence (AI) as critical workforce priorities. Conclusion: We propose a phased, ten-year strategic blueprint comprising three operational stages: Foundation, Capability Building, and Global Integration. Key recommendations include an Office of Regulatory Innovation, AI-enabled review tools, adaptive trial frameworks, GBA joint approvals, regional harmonization pilots, and localized financial market synergies. If implemented systematically, this roadmap may support the CMPR's progression toward greater regional influence as an independent primary review authority.","url":"https://doi.org/10.21203/rs.3.rs-10196661/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10196661/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10377899/v1","name":"Development and validation of a digital hypertension risk prediction model for urban Indian adults","source":"preprints","abstract":"Abstract Background Hypertension affects approximately 1.28 billion adults worldwide and is a major cause of cardiovascular disease, stroke and premature mortality, particularly in low- and middle-income countries. Validated risk prediction tools integrated with mobile health technologies may support early identification and prevention. This study developed and validated a hypertension risk prediction model for urban adults in Mysuru, India, and translated it into an artificial-intelligence-assisted smartphone application. Methods A community-based cross-sectional study was conducted from September 2022 to March 2024 in the urban field practice area of a tertiary medical college in Mysuru, Karnataka. Using cluster sampling, 517 adults aged ≥ 19 years were enrolled. Socio-demographic characteristics, lifestyle factors, anthropometric measurements and blood pressure were collected using a pretested semi-structured questionnaire. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or current antihypertensive treatment. Independent predictors were identified using multivariable logistic regression. A weighted risk score was derived from regression coefficients and evaluated using receiver operating characteristic analysis. External validation was performed in an independent sample of 100 participants through the smartphone application. Results Hypertension prevalence was 33.1% (n = 171). Ten independent predictors were identified: age 31–50 years, male sex, married status, illiteracy, obesity, inadequate physical activity, excess salt intake, alcohol use, diabetes mellitus and family history of hypertension. The model achieved an area under the curve of 0.72 (95% CI: 0.68–0.77), with 87.2% sensitivity and 63.8% specificity at a cut-off score of 19.5. External validation demonstrated 83% predictive accuracy. Conclusions The ten-factor risk score showed acceptable discrimination and was successfully integrated into a validated mobile application. It may support targeted prevention and community-based hypertension screening in similar urban Indian settings.","url":"https://doi.org/10.21203/rs.3.rs-10377899/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10377899/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10609412/v1","name":"PSMASegmentator: Open-source, AI-based software for PSMA PET/CT automatic segmentation and prognostic biomarker extraction","source":"preprints","abstract":"Abstract Purpose: To develop and validate a fully-automated, Artificial Intelligence (AI) tool, PSMASegmentator, for the segmentation of Prostate Specific Membrane Antigen (PSMA) PET/CT scans and extraction of imaging biomarkers. Materials and Methods: This retrospective study included 1035 PSMA PET/CT scans for training (Germany, n = 597; Australia, n = 438), with multicentre Australian testing datasets (internal, n = 199; external, n = 100). An nnU-Net–based deep learning model with PSMA-specific extensions was trained to segment PSMA-expressing lesions and derive biomarkers aligned with current consensus guidelines (total lesion count [TLC], total tumour volume [TTV], SUVmean, and SUVmax), plus total lesion uptake (TLU) and quotient (TLQ). Segmentation performance was evaluated primarily using F1 and Dice scores and the prognostic power of extracted biomarkers. The software was then executed on an external cohort (n = 1,282; 2015–2024) with linked mortality data. Biomarker value for survival prognosis was assessed in this cohort using Cox regression and Kaplan–Meier analysis. Results: Segmentation performance (n = 299 multicentre scans) was robust (lesion-level F1: 0.800; median Dice: 0.716) and generalisable (p > 0.05 between internal and external sets for F1 and Dice). Agreement between AI- and physician-derived biomarkers was high (Spearman’s ρ = 0.906–0.964; p Conclusion: PSMASegmentator, a freely available software (available at: https://github.com/UWA-Medical-Physics-Research-Group/PSMASegmentator), enables reproducible extraction of prognostically significant PSMA PET/CT biomarkers, facilitating standardised reporting aligned with consensus guidelines and hypothesis generation in research, clinical trials, and collaborative initiatives.","url":"https://doi.org/10.21203/rs.3.rs-10609412/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10609412/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.03.19.26348784","name":"Public attitudes toward sharing health data for artificial intelligence: Differences by data type and sector in the Health in Central Denmark cohort","source":"preprints","abstract":"Aims We aimed to examine public perceptions of sharing various types of health data relevant for AI development, including electronic health records, audio recordings of consultations, medical images, and genetic information, with actors from either the public or the private sectors. Methods We analysed data from 38,740 participants of the Health in Central Denmark survey conducted in 2024. Participants were asked whether they would share different types of health data with an AI solution in healthcare. Each participant was randomised to either of two versions of the scenario and question where the AI application was developed in the public or private sector. Descriptive results (proportions and percentages) were weighted to represent the background population of approx. 1 million people in the Central Denmark Region. The association between randomization group (data recipient) and data sharing attitude (“Yes”, “No”, “Don’t know”) was analysed using multinomial logistic regression with “Don’t know” as reference category. Results Participants were most willing to share medical images (46%), followed by text from patient journals (39%), genetic information (35%), and audio recordings (27%). There were 12-16% higher proportions of willingness to share with public institutions than with private institutions. A high level of uncertainty was observed for all data types (29-36%) regardless of data recipient. Odds ratios ranged from 1.37 to 1.78 for responding “Yes”, and from 0.51 to 0.67 for responding “No” to sharing data with public institutions compared to private institutions. Conclusions Public acceptance of health data sharing for AI depends on both the perceived sensitivity of the data and the institutional context of use. Strong public governance, transparent safeguards, and clear communication about data use may be important for maintaining trust and enabling responsible development of AI in healthcare.","url":"https://doi.org/10.64898/2026.03.19.26348784","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.19.26348784","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.31222/osf.io/gqunf_v1","name":"When Science Loses Its Immune System: Epistemic Immunodepression in the Age of AI","source":"preprints","abstract":"Clinical decisions depend on an evidence chain — from primary studies through systematic reviews to clinical guidelines — whose reliability rests on its capacity to detect and correct its own errors. I argue that artificial intelligence integration into research production is simultaneously degrading the four structural conditions that sustain this capacity: independent evaluation, methodological plurality, traceability, and epistemic friction — a syndrome I term epistemic immunodepression. Unlike visible AI errors such as hallucinated citations, this threat is structural: AI generates formally compliant outputs that bypass evaluation through methodological mimicry — the simulation of rigour without epistemic substance — and produces silent guideline drift, in which clinical recommendations shift without any detectable methodological violation. Medical-specific signals are accumulating: 28.6% to 91.4% of references generated by language models in systematic-review assistance are fabricated; only 6% of published AI models in paediatric surgery are both interpretable and externally validated; and an investigation of 2,271 evidence syntheses (2017–2024) documents the spread of automation across search, screening, and extraction. Current governance, anchored on voluntary disclosure, cannot detect these failure modes. The central move required is paradigmatic: from disclosure to verifiable provenance. Four structural interventions operationalise this shift — a research provenance record, a graded AI methodological logbook, recalibration of the evidence pyramid, and per-review AI accountability. A validation protocol has been pre-registered. If the diagnosis is confirmed, structural intervention is urgent — because a journal can retract a paper, but a surgeon cannot reverse a decision already executed on a child.","url":"https://doi.org/10.31222/osf.io/gqunf_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31222/osf.io/gqunf_v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-8958823/v1","name":"State-of-the-art technologies for the digital transformation of healthcare services – a systematic scoping review","source":"preprints","abstract":"Abstract Background The implementation of digital technologies within health services promises increased performance, quality, and efficiency. However, evidence is limited for systems-level interventions of technologies and their outcomes. We systematically review state-of-the-art digital technologies and their applications in healthcare systems and conduct a framework synthesis to review outcomes related to maturity and implementation. Methods Eleven databases were searched (Embase, HMIC, Medline, PsycInfo, SPP, AgeLine, AMED, CDAS, CINAHL, SCOPUS, WoS) on 04/11/2024 for systematic and non-systematic reviews published within the previous five years that provided an overview of digital technologies applied at a systems-level in healthcare or evidence for their outcomes. Studies into individual interventions were excluded. Risk of bias/quality assessment tools used in the reviews were recorded. We review types and applications of technology, then use a framework synthesis methodology to assess outcomes relating to digital transformation awareness/maturity, implementation, UK context, and challenges. Results Our searches identified 1423 records, with 1011 remaining after deduplication. Of these, 131 were assessed for eligibility, with 28 reviews reporting on 1606 individual studies (with an additional 2437 included in a bibliometric analysis) included in the final analysis. We identified five main groups of technology in healthcare: Integrated Technology/Industry 4.0, Artificial Intelligence (AI), Big Data, Internet of Things (IoT), and Blockchain. Our framework synthesis revealed recent conceptual recognition of digital transformation, and variability across regions with regards to the sophistication and maturity of uptake of technologies. We report on four reviews which provided quantitative evidence on the impact of digital technologies in health services. Barriers included major concerns related to data privacy and trust, equity, ethics, and implementation logistics. UK frameworks exist for implementation of technologies, but do not focus exclusively on the systems-level. Conclusions Digital technologies are increasingly integrated into health service delivery, yet the maturity and evaluation of these tools vary across regions. More robust implementation research is needed for advanced systems. Our synthesis provides a framework for healthcare leaders to assess digital readiness and prioritise technologies aligned with service transformation goals. Limitations of evidence include the heterogeneity of technologies, settings, and outcomes across included reviews. Trial Registration The review was not part of a trial and was not registered.","url":"https://doi.org/10.21203/rs.3.rs-8958823/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8958823/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.21203/rs.3.rs-8427607/v1","name":"The Prediction Model of Refracture in Elderly Patients with Osteoporotic Vertebral Compression Fracture was Constructed Based on Artificial Intelligence Method","source":"preprints","abstract":"Abstract Background Osteoporotic vertebral compression fractures (OVCF) in elderly patients frequently lead to debilitating subsequent fractures. At present, clinicians lack validated tools to identify individuals at highest risk within this vulnerable population. This study was designed to develop an artificial intelligence-based model for predicting refracture risk specifically among elderly OVCF patients in China. Methods Hospitalization records of elderly OVCF patients (2022–2024) from three hospitals affiliated with Zunyi Medical University formed the study dataset. After developing seven machine learning models with hyperparameter optimization and 5-fold cross-validation, we evaluated performance using standard metrics-sensitivity, specificity, accuracy, and AUROC. The optimal model was subsequently used to determine the primary predictors of refracture risk. Results The elastic net model provided the most accurate predictions, achieving training and testing AUROCs of 0.735 (95% CI: 0.690–0.778) and 0.711 (95% CI: 0.632–0.775), respectively. It highlighted five key clinical factors associated with refracture: diminished lumbar bone mineral density (OR = 0.930, 95% CI: 0.613–0.999), previous fracture (OR = 1.081, 95% CI: 1.001–1.564), lower cement injection volume (OR = 0.975, 95% CI: 0.658–0.999), elevated serum total protein (OR = 1.038, 95% CI: 1.001–1.394), and performance of percutaneous vertebroplasty (OR = 1.041, 95% CI: 1.001–1.306). Conclusions In elderly OVCF patients, the elastic net model effectively predicted refracture risk.Key predictors included lumbar bone mineral density, fracture history, cement injection volume, serum total protein, and percutaneous vertebroplasty. Addressing these factors could meaningfully reduce refracture incidence and improve long-term outcomes. Clinical trial number: not applicable.","url":"https://doi.org/10.21203/rs.3.rs-8427607/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8427607/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.04.22.26351483","name":"Patient perspectives on living with hypertension: Social media listening analysis across predominantly high-income countries","source":"preprints","abstract":"Background Chronic conditions such as hypertension can significantly disrupt daily life and emotional well-being. The interaction between patients’ perceptions, adherence to antihypertensive medication and quality of life (QoL) remains underexplored outside structured clinical settings. Objectives To capture unprompted patient perspectives and assess whether hypertension affects QoL and to investigate if patient reported experiences are associated with self-reported antihypertensive medication adherence. Methods Social media listening (SML) study analyzing 86,368 anonymized posts from individuals with hypertension in 12 countries, collected between January 2022 and May 2024. Posts from 11 countries (n=81,368) were analyzed using artificial intelligence–enabled natural language processing. Posts from China (n=5,000) were analyzed separately using a harmonized framework. Quantitative and qualitative methods assessed variations by country, age, and gender, and associations between emotional expression and antihypertensive medication adherence. Results Across the 11-country core sample, 45% of posts mentioned at least one QoL impact, most commonly worry/anxiety (11%). Impacts varied across countries. Among 8,096 posts with age identified, individuals Conclusions These results emphasize the importance of the psychological and emotional impact of hypertension, including on adherence to medication regimens, reinforcing the value of a holistic approach to patient care. Plain language summary Many people have high blood pressure, which increases the risk for stroke and other harmful events. Although there is much medical research on high blood pressure, little is known about the experiences of those people who live with the condition. This study aimed to scope out how people react emotionally and how their high blood pressure affects their daily lives. To do this, we analyzed social media posts from the United States, Canada, Brazil, the United Kingdom, Germany, France, Italy, Spain, Japan, South Korea, China, and Australia. In all countries, patients were affected emotionally by their high blood pressure. People often worried, particularly when they received their diagnosis. High blood pressure also negatively affected everyday life and work/education. This was often due to frequent medical appointments with lengthy wait times and difficulty accessing specialists. Taking medications regularly and sticking to diet and exercise programs was more difficult for individuals who reported being sad or depressed, or who suffered from side effects of their medications. The findings show that efficient management of high blood pressure has to take into account the emotional reactions of those who are affected, and provide support in several areas beyond the prescription of medication.","url":"https://doi.org/10.64898/2026.04.22.26351483","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.22.26351483","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202602.0537.v1","name":"Architectural Advances and Performance Benchmarks of Large Language Models in Light of Anthropic’s Claude Opus 4.6","source":"preprints","abstract":"The rapid evolution of Large Language Models (LLMs) between 2024 and 2026 has ushered in a transformative era of artificial intelligence capabilities, characterized by significant architectural innovations, multimodal integration, and enhanced reasoning abilities. This paper presents a comprehensive comparative analysis of state-of-the-art LLMs including Anthropic's Claude Opus 4.6, OpenAI's GPT-5 series, Google's Gemini 2.5/3 Pro, and emerging models such as GLM-4.6. The release of Claude Opus 4.6 in early 2026 represents a significant milestone, introducing a 1 million token context window and demonstrating state-of-the-art performance across diverse domains. We systematically examine key technological trends including Mixture-of-Experts (MoE) architectures, extended context windows exceeding 1 million tokens, and advanced alignment techniques. We analyze the technical implementation of extended context windows, MoE architectures, and advanced reasoning capabilities that enable superior performance. Comprehensive benchmarking reveals Claude Opus 4.6's leading position in agentic coding, tool use, and complex reasoning tasks, while comparative analysis with competing models highlights evolving architectural strategies. Performance is rigorously evaluated across multiple domains including automated coding, medical informatics, regulatory document processing, and general reasoning benchmarks. The paper further investigates practical applications in software development, healthcare informatics, and regulatory compliance, demonstrating how architectural choices translate to real-world performance advantages. Our analysis reveals that while parameter scaling remains relevant, strategic divergence in architectural philosophy and deployment strategies increasingly defines the competitive landscape. This study provides insights into the current state of LLM technology, identifies key trends shaping future development, and offers recommendations for future evaluation methodologies in this rapidly advancing field.","url":"https://doi.org/10.20944/preprints202602.0537.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202602.0537.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-8504183/v1","name":"A Clinically Interpretable Machine Learning Model for Staging and Grading of Periodontitis: Development and Temporal External Validation as a Decision Support Tool","source":"preprints","abstract":"Abstract Background The 2018 international classification for periodontitis enables individualized patient management through simultaneous staging (disease severity and complexity) and grading (progression rate). However, its clinical adoption is hindered by diagnostic inconsistency—particularly in grading and differentiation of advanced stages—due to the complexity of age-adjusted metrics such as radiographic bone loss–to–age ratio (RBL/age). While artificial intelligence (AI) shows promise in dental diagnostics, existing tools lack robust integration of multimodal clinical data and validation under real-world conditions. Methods We retrospectively collected data from 692 patients diagnosed with periodontitis at Hospital of Stomatology, Guangxi Medical University between June 2022 and June 2024. After data cleaning and feature selection, cases were labeled according to the 2018 international classification criteria. Machine learning models—including k-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—were trained and optimized via hyperparameter tuning. Model performance was evaluated on internal test sets and further validated on a temporally held-out external cohort (n = 208). Results For staging, KNN, RF, and DT achieved 96.99% accuracy; DT showed the highest recall (94.74%) and F1-score (0.9231) for Stage II, while RF excelled in Stage III (recall and F1-score: 0.9747). For grading, KNN and RF reached 96.40% accuracy, with F1-scores > 0.97 for Grade C. Feature importance analysis identified clinical attachment loss (CAL) and probing depth (PD) as top predictors for staging, and RBL/age as the dominant feature for grading. In temporal external validation, the model achieved 93.75% accuracy for full diagnosis (extent + stage + grade). Conclusions The proposed model demonstrates high accuracy, interpretability, and generalizability, offering a promising decision-support tool for standardized periodontitis classification.","url":"https://doi.org/10.21203/rs.3.rs-8504183/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8504183/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-10278511/v1","name":"An Online Survey of Healthcare Professionals to Understand the Use of Clinical Prediction Models in Practice","source":"preprints","abstract":"Abstract Introduction Clinical prediction models (CPMs) are increasingly promoted as decision-support tools that can improve patient care. However, their real-world utilisation remains variable, and little is known about the factors influencing their adoption across specialties. This study aimed to explore healthcare professionals’ experiences with CPMs, including their perceived usefulness, preferred formats, and the barriers and facilitators to their implementation. Methods A cross-sectional survey of healthcare professionals using an online questionnaire using REDCap software was distributed via social media and professional mailing lists, worldwide. Healthcare professionals from a range of clinical and allied health specialties were invited to complete the online survey. The survey captured demographic data, CPM usage, perceptions of usefulness (via Likert scale), preferred access methods and presentation formats, and included free-text questions for qualitative analysis. Quantitative data were summarised descriptively, and qualitative responses were analysed thematically. Results A total of 157 respondents completed the survey, of whom 62% were doctors and 87% were based in the UK. Most respondents (82%) were aware of CPMs, and 71% used them in clinical practice, rating their usefulness at a median of 8 out of 10. The most used models predicted age-related events (e.g. CHA2DS2-VASc, QRISK), while obstetrics and gynaecology (O&G)-specific models were underutilised despite O&G being the most represented specialty. Preferred access methods included web-based tools and mobile applications, and participants favoured percentage or heat map formats for risk presentation. Barriers included poor integration into workflows, limited awareness, regulatory uncertainty, and inconsistent use across teams. Three themes were developed from qualitative free-text responses; why clinicians believe CPMs are clinically useful, how to integrate CPMs into everyday systems and the need for continuous professional education about CPMs. Conclusions Healthcare professionals value CPMs but emphasise the need for models that are transparent, user-friendly, and integrated into clinical systems. As AI-driven approaches become more common, concerns around interpretability and inconsistent regulatory classification—such as whether CPMs are considered medical devices—must be addressed. To ensure credibility and usability, future models should adhere to TRIPOD or TRIPOD-AI reporting standards, undergo external validation in independent datasets, and be evaluated for clinical impact. Coordinated efforts by researchers, journals, funders, and regulators are essential to move from model development to meaningful implementation in practice.","url":"https://doi.org/10.21203/rs.3.rs-10278511/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10278511/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202602.0582.v1","name":"Real-Time Integration of an AI-Based ECG Interpretation System in the Emergency Department: A Pragmatic Alternating-Day Study of Diagnostic Performance and Clinical Process Metrics","source":"preprints","abstract":"Background: /Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, n = 763) or AI-output disclosure days (AI-days, n = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive–related operational impacts.","url":"https://doi.org/10.20944/preprints202602.0582.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202602.0582.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.08.06.26359876","name":"Development of an interdisciplinary network to improve the capacity to conduct digital legacy research: a quality improvement initiative","source":"preprints","abstract":"ABSTRACT Background Digital legacy (the digital information available about someone following their death) has increasing societal importance as personal assets and interactions become increasingly digitized. Healthcare professionals often have a limited understanding of how to address digital legacy in practice, and there is a lack of interdisciplinary networks to improve education, research, and professional development in digital legacy. Objective This paper describes the development of an interdisciplinary initiative designed to build research capacity and develop consensus-based recommendations for integrating digital legacy into palliative care. Method Over 12-months, we conducted interdisciplinary engagement activities with diverse stakeholders, including clinicians, designers, and sociologists. We used a modified World Café method to facilitate dialogue and capture feedback on how memories are digitally curated, the management of digital estates, and intergenerational perspectives on digital legacy. Results We identified eight core recommendations for research and policy, including promoting digital legacy education, supporting policy development, and broadening the scope of interdisciplinary research. Our discussions highlighted the complexity of modern digital estates and the need for legal and ethical frameworks to protect individual rights. Conclusions The Network demonstrates that interdisciplinary collaboratives can address important issues relating to digital legacy, which provides a foundation to conduct collaborative research that improves the management of digital legacies in society.","url":"https://doi.org/10.64898/2026.08.06.26359876","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.06.26359876","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.12688/f1000research.162415.1","name":"Assessing artificial intelligence knowledge among Al-Zahraa university students: A cross-sectional study","source":"preprints","abstract":"Introduction: University educators’ knowledge of artificial intelligence (AI) helps them to effectively utilize these latest technological resources, significantly raising the quality of the teaching and learning process. Objective to evaluate a sample of Al-Zahraa university students' level of AI knowledge. Method From 5August 2024 to 28November 2024, data from Al-Zahraa University for Women students was obtained through an online questionnaire in a cross-sectional survey study. Data was downloaded to an Excel file from Google Forms following it was gathered. The questionnaire's quantitative data was imported and analyzed. Results The total number of participants was 498 participants; however, 89 students refused to answer the questions, which reduced the sample size to 409. Most of the students (90%) reported to be familiar or somewhat familiar with Artificial Intelligence. More than one half of the From 5August 2024 to 28November 2024, data from Al-Zahraa University for Women students was obtained through an online questionnaire in a cross-sectional survey study. Most Al-Zahraa Medical School students got an anonymous online survey. Using a pre-validated, semi-structured questionnaire, 419 medical students in Al-Zahraa university for women engaged in a cross-sectional study.students (57.7%) know AI, whereas a smaller percentage (42.7%) reported to know AI medical application. only one quarter (25.7%) reported having knowledge about machine deep learning. one half (49%) of the students considered it as extremely important in the medical field. Conclusions The study discovered that while Al-Zahraa students understand artificial intelligence (AI) well, they know not much about deep learning, machine learning, and AI applications in radiology and pathology.","url":"https://doi.org/10.12688/f1000research.162415.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.162415.1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-8404311/v1","name":"Construction of Personal Health Knowledge Graphs for Clinical Data Harmonization in Breast Cancer","source":"preprints","abstract":"Abstract Background Personal health data contain valuable information for breast cancer management. Integration of heterogeneous data and maintenance of clinical registries are time-consuming and labor-intensive. We aimed to leverage an Artificial Intelligence (AI)-powered virtual assistant supporting semi-automated curation, including data quality enhancement, and publishing of personal health data. Identifiable data of breast cancer patients can be transformed into interoperable personal health knowledge graphs for secondary use. Methods With patient-informed consent, breast cancer patient data were extracted from the hospital systems and ingested into the virtual assistant. Data items were mapped and transformed into target concepts within a knowledge graph compliant with a reference ontology. Integrated classic and AI tools were used to support transformation of individual patients' data into a personal health knowledge graph (PHKG). Each graph was assessed by a Shapes Constraint Language (SHACL)-based validator to ensure the data quality. An RDF Query Language (SPARQL) query was executed on top of validated PHKGs from multiple patients to extract the relevant data elements and generate a local breast cancer registry, interoperable with registries generated in the same way across three different hospitals. Results The first version of the AI-powered virtual assistant prototype was developed and deployed in our hospital, as well as in two other hospitals in Austria and in Estonia. Twelve tables with 184 data items, including structured, semi-structured and fully narrative elements, were extracted from the local hospital systems. Data categories included demographics, diagnosis, medical history, pathological reports, laboratory tests, surgical records, therapy, and follow-up after previous treatments. Personal health knowledge graphs incorporating the data elements required for the Breast Cancer (BC) registry were constructed after data transformation. A SPARQL query was subsequently developed to build a local BC registry that automatically retrieved these relevant elements. The same approach took place in the other two hospitals. Conclusion The proposed workflow of semi-automated health data curation and quality enhancement from heterogeneous data sources to interoperable and reusable output is feasible. It provides a potential solution to enhance medical data interoperability and facilitate the maintenance of clinical registries.","url":"https://doi.org/10.21203/rs.3.rs-8404311/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8404311/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-10155822/v1","name":"AI-driven biological clocks of cardiac aging: Should we integrate ECG, metabolomic, and epigenetic signatures?","source":"preprints","abstract":"Abstract Background Cardiovascular diseases remain the leading cause of death globally, with age as the primary risk factor. Unlike chronological age, biological age aims to capture age-related physiological changes and may better predict health outcomes and disease risk. Recent advances in artificial intelligence have enabled the development of aging clocks, computational models that estimate biological age from diverse biomarker patterns. Main body This review examines three complementary approaches to quantifying cardiac and systemic aging. Epigenetic clocks, particularly second-generation models such as GrimAge, currently represent the most established molecular measures of biological aging and show strong associations with cardiovascular outcomes. AI-based electrocardiogram (ECG) clocks capture functional cardiac aging, with landmark studies demonstrating that ECG age gaps independently predict mortality and that accelerated ECG aging may confer up to 79% higher mortality risk. Metabolomic clocks provide complementary insight into systemic metabolic aging and reveal links between metabolic dysregulation and cardiovascular aging, including associations involving metabolites such as trimethylamine N-oxide (TMAO). However, current aging clocks face important limitations: single-modality approaches capture only one dimension of aging, the biological mechanisms underlying accelerated aging remain incompletely understood, and population generalizability remains limited. Conclusions Integration of ECG, metabolomic, and epigenetic data promises a paradigm shift from reactive disease management to proactive, personalized aging intervention. Understanding concordance and discordance between these complementary clocks may uncover distinct aging phenotypes and reveal modifiable biological pathways. Future research should focus on integrating multi-omics data within large longitudinal cohorts to build interpretable, population-generalizable models that can guide clinical interventions to extend healthspan and prevent cardiovascular disease.","url":"https://doi.org/10.21203/rs.3.rs-10155822/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10155822/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-5746871/v1","name":"Assessment of the Attitude and Fears of the Physicians of Pakistan regarding Artificial Intelligence: a cross-sectional survey in 2024","source":"preprints","abstract":"Abstract Background and aim Artificial intelligence has emerged as a transformative tool in healthcare, enhancing diagnostic accuracy, advancing genetic studies, and improving personalized medicine by reducing human error. However, its integration also raises ethical concerns such as potential bias, privacy issues, and uncertainties over decision-making authority. This study aims to evaluate the attitudes and concerns of physicians regarding the use of AI in healthcare, and to explore how these perceptions may impact its implementation in clinical practice. Subject and methods We conducted a nation-wide cross-sectional study using an online questionnaire, based on standardized General Attitude Towards Artificial Intelligence Scale (GAAIS) and self-devised questions, aimed at collecting data about demographic details, attitude and fears of the physicians regarding AI. Using the convenience sampling technique, a sample of 393 physicians was selected. Independent-samples t-tests, one-way ANOVA, and multiple linear regression were used to analyze differences between variables. Results The overall attitude on positive subscale of GAAIS was above neutral ( mean= 3.46; SD=0.59) with men showing a significantly better attitude than women on this subscale (MD=0.22; CI:0.10-0.33; P = mean =2.76; SD=0.61). Some degree of fear regarding AI was also observed among physicians ( mean =2.66; SD=0.67). Conclusion Physicians have mixed views on AI, with concerns over ethical issues associated with it but not fearing replacement by it in near future. Men have a more positive attitude towards it. Integrating AI-focused curricula into medical education, along with conducting longitudinal and prospective studies, is essential to address physicians’ concerns and fully understand AI’s clinical impact.","url":"https://doi.org/10.21203/rs.3.rs-5746871/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5746871/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.11.24.25340912","name":"Automated Video-Based Analysis of Surgical Meta-competencies Using Computer Vision","source":"preprints","abstract":"Background Traditional surgical training relies on an apprenticeship model, which is subjective and threatened by human bias. Performance metric scales attempt to offer more objective feedback by providing a structured grading rubric, but these scores are still ultimately subjective. Leveraging computer vision and artificial intelligence to assess surgical performance has the potential to shift subjective measurements into automated and objective feedback for trainees. Materials and Methods This retrospective, multi-institutional study analyzed 319 laparoscopic cholecystectomy videos from IRB-approved deidentified datasets and segmented the videos into 2862 clips. Using an internally validated video-based assessment rubric, we annotated video clips across five metacompetency domains: tissue handling, psychomotor skills, efficiency, dissection quality, and exposure quality. Short video segments ( Results Among 2862 LC video clips, model performance was highest for dissection quality during the exposing gallbladder step (AUROC 91.5%, 95% confidence interval [CI], 84.5-96.5). Moderate performance was observed for efficiency (AUROC 72.6%, 95% CI 59.9-83.2) and exposure quality (AUROC 68.7%, 95% CI 55.2-81.8). Dissection and exposure quality scores during hepatocystic triangle dissection yielded AUROCs of 63.8% (95% CI 56.9-71.3) and 66.0% (95% CI 53.1-76.7), respectively. Conclusion We demonstrate the feasibility of a purely vision-based deep learning model to grade surgical skill based on metacompetencies with excellent performance during simple steps. This technique represents an advance over prior whole-video approaches that rely heavily on tool- tracking and kinematic data, and may lead to greater model performance by using binary feedback on increasingly specific step segmentation.","url":"https://doi.org/10.1101/2025.11.24.25340912","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.11.24.25340912","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.06.02.26354735","name":"Calibrating trust in AI-assisted pituitary surgery","source":"preprints","abstract":"Background Endoscopic endonasal transsphenoidal surgery (EETS) requires navigation around neurocritical anatomy. Today, artificial intelligence clinical decision support systems (AI-CDSSs) can orientate surgeons, but clinician trust in AI remains unclear, limiting safe deployment. This study evaluates how modifiable design affects trust and performance in a real-world pituitary surgery AI-CDSS. Method Online, 70 clinicians with pituitary surgery experience were randomised evenly to a Basic or Enhanced AI-CDSS which outline the sella on EETS operative video. The Enhanced group additionally received explanation of the model and previous publications, alongside confidence labels depicting outline reliability. Both groups annotated the sella on six video clips, first alone then with the optional AI-CDSS. Clips were ordered by declining AI performance, except for the final clip. Self-reported trust was measured using a 1-7 scale after each annotation, and performance was the DICE overlap between user annotations and the ground truth. Comparisons used Mann-Whitney U and permutation analysis. Results Sixty-four participants (91%) finished the exercise (31 Basic, 33 Enhanced). When AI performed best, median trust was 5.00 in both arms (U=559, p=.521). However, when AI performed worst, trust was significantly lower for the Enhanced group (3.00 vs 3.67, U=668, p=.035), sustained in the final clip (3.67 vs 4.33 U=687, p=.019). User performance improved with the AI-CDSS, but with no significant difference between the groups on the best or worst AI performing clips. Nevertheless, for the best AI, senior clinicians had higher median performance in the Enhanced group (0.95 vs 0.90, U=75, p=.066). There was also less dispersion in the Enhanced group when AI was inaccurate (IQR: 0.07 vs 0.21, p=.004). Conclusion Interface design can improve trust calibration in a surgical AI-CDSS and may increment performance in seniors when AI is accurate, and consistency when AI is inaccurate. In future, these features may form important safety checks during translation to the operating room.","url":"https://doi.org/10.64898/2026.06.02.26354735","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.02.26354735","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-7609839/v1","name":"Bibliometric Analysis of Hypertension Care Research in Primary Care: Insights from Four Decades of Progress","source":"preprints","abstract":"Abstract Objective Hypertension remains a global public health challenge, with significant morbidity, mortality, and economic implications. The role of Primary Health Care (PHC) in hypertension management has gained attention for its potential to enhance prevention, diagnosis, and treatment, especially in resource-constrained settings. This study aims to provide a comprehensive overview of research on PHC’s role in managing hypertension using bibliometric analysis. Methodology Therefore, in the present study, relevant peer-reviewed research articles published from 1984 to 2024 were downloaded from the Scopus databases and later quantitatively analyzed and visualized using Bibliometrix (R package) and VOS viewer. Finally, open challenge areas were identified for future research work. Results The present study revealed that the number of literature studies published in Hypertension Care Research in Primary Care has increased from 14 to 752 between the years 1984 to 2024. Most of the research is concentrated in the field of Medicine. The USA is the most productive country in this field, followed by the United Kingdom, Spain, and Canada.McManus, R.J. from the Jichi Medical University, Kawachi District, Japan, is the most productive author in this field. Harvard Medical School, United States, is the most relevant affiliation in terms of the number of published articles. The top 10 most relevant sources are Q1 and Q2 journals, with BMJ Open, Plos One, and Journal Of General Internal Medicine, which are the leading journals in this field. The National Institutes of Health is the leading funding agency. The United States was the largest contributor. The most important trending topics related to our study, Telehealth, Health Equity, Artificial intelligence, and Primary health care, were identified. Conclusion Hypertension care research in primary care has achieved significant milestones, critical gaps persist in interdisciplinary integration, global representation, and foundational theme development. Addressing these issues requires a concerted effort to diversify research priorities, strengthen international collaborations, and balance innovation with a commitment to addressing the underlying causes of hypertension.","url":"https://doi.org/10.21203/rs.3.rs-7609839/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7609839/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6250414/v1","name":"Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial","source":"preprints","abstract":"Abstract Background and aims Clinical competency is paramount for nurses to ensure that patients receive safe, high-quality care. Generative artificial intelligence (GenAI) in nursing education is gaining attention, and evidence shows its suitability for real-life situations. GenAI may be an effective solution for enhancing nurses’ clinical competency. This study compared the impact of scenario-based GenAI patient simulation versus immersive 360° virtual reality (VR) simulation on educational outcomes, namely clinical competence, cultural awareness, AI readiness, and simulation effectiveness. Methods This cross-over randomised controlled study design was conducted from June 2024 to August 2024. Forty-four undergraduate nursing students in years 1, 2, and 3 were selected to participate. Subgroups were formed, each comprising three undergraduate nursing students from different years. They were randomised to receive either a GenAI patient simulation (intervention, Group B) or 360° VR simulation (control, Group A) for three separate days and with a washout period. Four self-reported questionnaires were used to measure clinical competency: the Clinical Competence Questionnaire (CCQ), Cultural Awareness Scale (CAS), Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), and Simulation Effectiveness Tool – Modified Questionnaire (SET-M). Results The study revealed notable improvements in clinical competence and confidence among the participants. Group A demonstrated significant enhancements in the CCQ at both time points, and Group B also showed meaningful progress. Both groups experienced changes in the CAS-Total scores, although these changes were not statistically significant. In terms of the MAIRS-MS total score, Group A had a significant increase at time 1 (T1), and Group B showed an improvement from baseline to time 2 (cross-over session, T2). Regarding SET-M results, most participants (75%) felt that debriefing contributed to their learning, and 77.3% reported increased confidence in their nursing assessment skills. Conclusions The findings offer compelling evidence of its effectiveness in enhancing clinical outcomes, as assessed by the CCQ, CAS, and MAIRS-MS. Importantly, our results reveal statistically significant improvements in these measures, particularly within Group B. Both 360° VR simulation and GenAI patient simulation with real-time feedback and GenAI debriefing can serve as powerful teaching tools for improving nursing students’ clinical outcomes; however, GenAI exhibits a notably greater effect. Clinical trial registration/number Not applicable","url":"https://doi.org/10.21203/rs.3.rs-6250414/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6250414/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.01.15.699631","name":"MorphoLearn: A morphology-driven workflow to decipher 3D electron microscopy segmentation in diatoms","source":"preprints","abstract":"Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medical and animal cell imaging, which often benefit from temporal redundancy and relatively uniform morphology, studies of microbial and microalgal biodiversity must rely on static snapshots. These snapshots exhibit high variability in cell shape, organelle organisation, and image contrast. Consequently, robust AI-assisted segmentation in this context requires models that learn directly from morphological features and can adapt to heterogeneous sample preparation. In this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets. This framework is specifically designed to address the challenges posed by morphological diversity and contrast variability while remaining within realistic computational constraints. We evaluate multiple lightweight 3D encoder-decoder architectures and identify VNet as the best option for balancing computational efficiency and volumetric accuracy in whole-cell segmentation. Using datasets from two strains of Phaeodactylum tricornutum and extending our analysis to cross-species comparisons, we demonstrate that training on specific regions of interest can lead to an overestimation of model performance. In contrast, performing whole-cell segmentation uncovers significant differences in architectural robustness. Moreover, we show that transfer learning and contrast-aware hybrid strategies allow for efficient adaptation to previously unseen datasets with minimal annotation. The incorporation of boundary-aware loss functions significantly enhances the delineation of closely associated organelles, such as chloroplasts and mitochondria, in multi-class segmentation tasks. Together, these findings establish a scalable, reproducible, and biologically informed AI framework for 3D FIB-SEM segmentation. This framework enables high-throughput analysis of cellular ultrastructure across diverse species and imaging conditions. Author Summary Cells exhibit a wide range of shapes, sizes, and internal structures, particularly among various microbial species. These morphological differences are not arbitrary; they indicate how cells adapt to their environments and manage essential biological functions. Modern three-dimensional electron microscopy can capture this structural diversity at the nanometre scale, but analysing the resulting data is often slow. This is due to the time-consuming process of manually outlining cellular structures, which also requires expert knowledge. Artificial intelligence (AI) has made significant advances in accelerating image analysis in medical and animal cell studies, typically by learning from repeated observations over time. However, studies focusing on microbial and microalgal biodiversity often rely on single snapshots of diverse cells prepared under varying imaging conditions. This complicates automated analysis since AI systems must learn from morphology directly rather than from temporal repetition. In this study, we developed and evaluated an AI-assisted segmentation framework specifically for whole-cell 3D electron microscopy data. By systematically comparing efficient neural network architectures and incorporating transfer learning and contrast-aware strategies, we demonstrate that accurate segmentation can be achieved even with limited training data and standard computing resources. Our approach facilitates faster, scalable, and reproducible analysis of cellular ultrastructure, paving the way for large-scale investigations into cell morphology, adaptation, and diversity across species. Significance statement Quantitative analysis of cellular ultrastructure across species is currently limited by challenges in segmenting large three-dimensional electron microscopy datasets. Unlike medical imaging, which often benefits from artificial intelligence due to its use of temporal repetition and consistent morphology, studies of microbial biodiversity depend on single snapshots that display extreme variations in cell shape and image contrast. This work presents a scalable, morphology-driven AI framework for whole-cell 3D segmentation that is resilient to biological diversity and variations in sample preparation. By enabling accurate analysis with minimal annotations and standard computational resources, this approach enhances access to high-throughput ultrastructural studies and facilitates comparative investigations of cellular adaptation across different species.","url":"https://doi.org/10.64898/2026.01.15.699631","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.15.699631","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-7596545/v1","name":"Mapping the Digital Frontier: A Bibliometric Exploration of Telemedicine and Mental Health Research","source":"preprints","abstract":"Abstract Background Telemedicine has emerged as a vital tool for delivering healthcare, particularly in addressing mental health needs and overcoming geographical barriers. The COVID-19 pandemic accelerated its adoption, highlighting its importance in maintaining continuity of care and reducing infection risks. Despite rapid growth, there has been limited bibliometric evaluation of global telemedicine research in mental health. Methods A bibliometric analysis was conducted using the Scopus database to identify telemedicine and mental health publications from 2014 to 2024. A total of 3,959 English-language research articles and reviews were included. Biblioshiny and VOSviewer software were employed to map conceptual, intellectual, and social structures. Analyses focused on publishing trends, leading authors, journals, institutions, funding bodies, keyword evolution, and international collaboration networks. Results Publication output increased sharply after 2019, peaking in 2021 with 714 papers. Medicine dominated the subject area (62.2%), followed by psychology and health professions. The USA led with 2,000 publications, followed by the UK, Australia, and Canada. Harvard Medical School and the VA Medical Center were leading institutions, while the Journal of Medical Internet Research and Telemedicine and e-Health emerged as key journals. Highly cited articles addressed the psychosocial impacts of COVID-19 and the adaptation of mental health services to digital platforms. Keyword analysis revealed core themes in telemedicine, mental health, depression, and anxiety, with emerging trends in artificial intelligence, mHealth, and post-pandemic adaptations. Collaboration networks highlighted strong ties among the USA, UK, and Australia. Conclusion Telemedicine research in mental health has expanded rapidly, driven by the pandemic and advances in digital health. While high-income countries dominate the field, broader global collaboration and inclusion of low- and middle-income countries are needed. Future research should focus on evaluating patient outcomes, integrating AI and mobile platforms, and ensuring equitable access to digital mental health care.","url":"https://doi.org/10.21203/rs.3.rs-7596545/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7596545/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.10.02.25336333","name":"Scoping Review of Regulatory Transparency in AI-based Radiology Software: Analysis of PMDA-approved SaMD Products","source":"preprints","abstract":"Background The integration of artificial intelligence (AI) in radiology has accelerated globally, with Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) approving numerous AI-based Software as a Medical Device (SaMD) products. However, the transparency and completeness of clinical evidence available to healthcare providers remain unclear. Purpose To systematically evaluate the availability and transparency of clinical evidence in package inserts of PMDA-approved AI-based radiology SaMD products, identifying gaps that may impact clinical implementation. Materials and Methods We conducted a systematic review of all PMDA-approved SaMD products as of December 31, 2024. Products were included if they utilized AI technology and were classified for radiology applications. Data extraction focused on product characteristics, study designs, demographic information, and performance metrics. Results Of 151 approved SaMD products, 40 utilized AI technology, with 20 specifically designed for radiology applications. Critical gaps were identified in demographic reporting, with no products providing complete case demographic data. Performance metrics varied widely, with sensitivity ranging from 67.7% to 100% in standalone studies. Physician-assisted studies consistently demonstrated performance improvements but lacked stratified results by characteristics in all cases. Conclusion Current package insert requirements provide insufficient transparency for evidence-based clinical implementation of AI-based radiology software. Enhanced regulatory frameworks and industry-led initiatives for comprehensive validation are essential for safe and effective AI deployment in Japanese healthcare.","url":"https://doi.org/10.1101/2025.10.02.25336333","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.10.02.25336333","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.02.26.25322978","name":"Artificial Intelligence in Healthcare: 2024 Year in Review","source":"preprints","abstract":"ABSTRACT Background With over a thousand FDA-approved artificial intelligence/machine learning-enabled medical devices, research and publications is maturing from focusing on the development and internal validation of models to the external validation of models and implementation trials. Foundation models, especially Large Language Models, have spurred additional aspects of AI research related to healthcare, especially with the use of text-based data to address healthcare education and administrative tasks related to patient care. Methods We performed a PubMed search using the terms “machine learning” or “artificial intelligence” and “2024,” restricted to English language and human subject research on January 1, 2025. Utilizing a deep learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. We also performed a detailed analysis of the distribution of foundation model-based publications amongst the healthcare specialties. Results For the year 2024, the PubMed search yielded 28,180 articles, of which 1,693 were classified as mature using a BERT model. Following exclusions, 1,551 articles were selected for the final data analysis. Amongst these, the highest number of articles in each specialty originated from Imaging (407), Head and Neck (127), and General (122). The analysis of data types revealed that image data (903 [57.0%]) was still the predominant data type, but the use of text data (525 [33.1%]) had substantially increased. Additionally, we also found that LLMs (479) and AI General (448) category models have overtaken deep learning models (372) in healthcare AI research. For LLM-related publications, we are seeing increasing trends in research related to healthcare education and administrative tasks. Conclusion With the introduction of foundation models, healthcare research trends are changing. The adoption of LLMs and text data types amongst various healthcare specialties, especially for education and administrative tasks, is unlocking new potential for AI applications in healthcare.","url":"https://doi.org/10.1101/2025.02.26.25322978","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.26.25322978","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.06.11.25329441","name":"Benchmarking Artificial Intelligence vs General Practitioners Decision-Making in Same-Day Appointments Triage: A Mixed-Methods Study in UK Primary Care","source":"preprints","abstract":"Background Artificial intelligence (AI) is increasingly used to support clinical decision-making, particularly in primary care triage. However, few studies have benchmarked AI triage tools against general practitioner (GP) assessments in real-world settings. This study evaluated the agreement between an AI-enabled triage tool (Visiba Triage) and GP urgency ratings for same-day appointment requests. Secondary aims included assessing perceptions of safety, accuracy and usability from both clinician and patient perspectives. Methods A mixed-methods study was conducted using data from patients requesting SDA between January and June 2024. Urgency scores generated by the Visiba Triage AI tool based on a modified Manchester Triage System were compared to GP-assigned ratings using Spearman’s rank correlation and Cohen’s kappa. Ordinal logistic regression assessed associations between demographics and patient satisfaction. Thematic analysis of interviews with eight GPs explored perceptions of the AI tool’s performance. Results A total of 649 participants were included in this study. The majority were females and of White ethnicity. There was a strong correlation between AI and GP urgency ratings (ρ=0.796, p 0.001), with 83.7% categorical agreement across eight urgency levels (κ 0.69, p 0.001). The AI system demonstrated safety-conscious design, with a greater likelihood of over-triage whilst rarely under-triaging. No cases deemed non-urgent by AI were later reclassified as emergencies by GPs. Qualitative findings supported the quantitative results, highlighting perceived accuracy and safety. Current limitations include suboptimal integration with patient medical records. Patient satisfaction varied significantly by age, with older adults (60+) reporting lower satisfaction (aOR 0.25, 95% CI 0.12-0.52). Conclusion This study demonstrates that AI-enabled triage can closely mirror clinical judgement in a primary care setting, offering a safe, scalable solution to manage demand for same-day care. Safe adoption of AI triage tools in healthcare should include real-world assessment and benchmarking against consensus clinician judgement in real-time. Key Takeaways AI-enabled triage tools can achieve substantial agreement with GP urgency assessments, with 84% categorical concordance and no observed cases of significant under-triage. The AI model demonstrated a safety-conscious design, favouring over-triage to reduce patient safety risks, especially in emergency scenarios. Older adults reported significantly lower satisfaction with AI triage, highlighting the need to address digital literacy and inclusion when implementing such tools. GPs expressed high confidence in AI performance at acuity extremes, particularly for self-care and emergency cases, though noted contextual limitations without EHR integration. This real-world study highlights the potential of AI triage to enhance clinical efficiency, particularly in managing same-day appointment demand in overstretched systems like the NHS. Ongoing clinician oversight remains essential to mitigate AI limitations in complex cases and ensure equitable, safe deployment at scale.","url":"https://doi.org/10.1101/2025.06.11.25329441","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.06.11.25329441","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6542673/v1","name":"Artificial Intelligence and Social Media Utilization for Rural Patients with Acute Brain Conditions in Chuncheon, Gangwon-do, South Korea","source":"preprints","abstract":"Abstract Background Despite nationwide efforts to enhance the quality of treatment for acute brain conditions in Korea, regional disparities persist due to the lack of neurology specialists and infrastructure shortcomings in rural areas. Methods We implemented two digital technologies, namely, artificial intelligence (AI)-based telemedicine and social media-based patient transfer platforms, from January 2024 to improve treatment quality for early-stage patients with various brain conditions in rural hospitals and facilitate links with regional hub hospitals. Here, we review medical records, share our experience of using digital technologies, and address current limitations and future perspectives. Results The AI-based platform was installed to facilitate collaboration between non-experts at rural hospitals and experts at hub hospitals, and the social media-based platform was adopted to improve collaboration between experts. Eight patients with a mean age of 70.7 years used the AI-based platform to facilitate accurate diagnosis and treatment. The non-experts who referred patients included general practitioners (n = 5, 62.5%), an internist (n = 1, 12.5%), and nurses (n = 2, 25.0%). The platform enabled rapid diagnosis and decision-making, and its use led to favourable outcomes. The social media-based platform was used to transfer 12 diagnosed patients. Eleven patients (91.7%) received neurocritical care, and three (25.0%) underwent surgical procedures at a hub hospital after transfer. Nine patients (75.0%) had favourable outcomes. Conclusion We suggest a novel means of reducing regional inequities in the treatment of acute brain conditions that addresses the diversity of rural medical environments. The two digital technologies implemented have helped rural hospitals respond early and facilitated inter-hospital transfer. Additional features that consider user convenience and automatic linkage of diagnosis and treatment are essential to enable the nationwide expansion of the above platforms.","url":"https://doi.org/10.21203/rs.3.rs-6542673/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6542673/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.07.15.26358127","name":"Reconsidering the case against risk prediction in self-harm: routinely collected health data distinguishes groups at higher and lower risk of adverse outcomes following paracetamol overdose","source":"preprints","abstract":"Background UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties. Summary box What is already known on this topic NICE NG225 (2022), NHS England’s Staying Safe from Suicide framework (2025) and NCISH guidance (2024) recommend against structured risk prediction in self-harm, both for predicting suicide or repetition and for allocating treatment. An increasingly common reading of the underpinning literature is that routinely collected health data contain no useful predictive signal in this population. Evaluations of this claim in large-scale UK linked healthcare datasets remain scarce. What this study adds In a whole-population cohort of adults attending emergency departments with paracetamol overdose, models using 37 structured electronic health record features separated groups at higher and lower risk of all-cause mortality or mental health admission across all three time horizons, with calibration slopes close to one. Discriminative signal was carried primarily by mental health-related features rather than demographics. Useful predictive signal is therefore recoverable from routinely collected UK healthcare data in this population. How this study might affect research, practice or policy These findings are not evidence that suicide can be predicted at the individual level, and the models are not proposed for clinical use. They do, however, indicate that the empirical question underlying current guidance remains open, and should stay subject to scientific enquiry and to revision if sufficiently robust models emerge. Future research should expand the feature space beyond structured records, particularly into clinical free text, where the information on which psychosocial assessment relies already resides.","url":"https://doi.org/10.64898/2026.07.15.26358127","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.15.26358127","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.04.28.25326568","name":"Developing a GraphRAG-enabled local-LLM for Gestational Diabetes Mellitus","source":"preprints","abstract":"This paper re-imagines a world of abundance in the treatment of chronic diseases such as Tpe 2 Diabetes. It asks: what if preventive and diagnostic remedies were widely made available across the world, informed by the latest medical research? As Proof-of-Concept of a proposed solution, the paper describes the development and validation of a local Large Language Models (local-LLMs) based on Graph-based Retrieval-Augmented Generation (GraphRAG) for managing Gestational Diabetes Mellitus (GDM). The research thus seeks new insights into optimizing GDM treatment through a knowledge graph architecture, contributing to a deeper understanding of how artificial intelligence can extend medical expertise to underserved populations globally. The study employs an agile, prototyping approach utilizing GraphRAG to enhance knowledge graphs by integrating retrieval-based and generative artificial intelligence techniques. Training data was from academic papers published between January 2000 and May 2024 using the Semantic Scholar API and analyzed by mapping complex associations within GDM management to create a comprehensive knowledge graph architecture. It is categorically stated that, since the primary research objective was to establish the feasibility of a GraphRAG local-LLM PoC, no human subjects nor actual patient datasets were used. Empirical results indicate that the GraphRAG-based Proof of Concept outperforms open-source LLMs such as ChatGPT, Claude, and BioMistral across key evaluation metrics. Specifically, GraphRAG achieves superior accuracy with BLEU scores of 0.99, Jaccard similarity of 0.98, and BERT scores of 0.98, offering significant implications for personalized medical insights that enhance diagnostic accuracy and treatment efficacy. This research offers a novel perspective on applying GraphRAG-enabled LLM technologies to GDM management, providing valuable insights that extend current understanding of AI applications in healthcare. The study’s findings contribute to advancing the feasibility of GenAI for proactive GDM treatment and extending medical expertise to underserved populations globally.","url":"https://doi.org/10.1101/2025.04.28.25326568","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.04.28.25326568","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-7724301/v1","name":"Generative AI in Simulation Debriefings: An Exploratory Study Using the Team-FIRST Framework","source":"preprints","abstract":"Abstract Background Effective debriefings in simulation-based education require accurate observation of team interactions, yet facilitators face challenges due to cognitive load, observer bias, and the complexity of team dynamics. Generative artificial intelligence (AI) offers a potential means to support this process by analyzing verbal communication and providing structured feedback. This study explored how AI can contribute to teamwork observation and debriefing in high-fidelity medical simulations. Methods We conducted a qualitative, exploratory study using thematic analysis of simulation participants’ and debriefers’ experiences with AI-generated teamwork reports. Forty-one participants (anesthesia nurses, residents, and attendings) participated in high-fidelity scenarios at the University Hospital Zurich simulation center. Verbal interactions were transcribed with AI-assisted speech recognition and analyzed using two large language model–based systems (Isaac and ChatGPT-4o) guided by a prompt based on the Team-FIRST framework. Structured reports were generated for each scenario and reviewed by four experienced debriefers. Semi-structured interviews captured learners’ perspectives on being observed by AI. Results A total of 26 AI-generated reports and 27 learner interviews were analyzed. Debriefers valued the detailed transcripts and illustrative quotes, which supported structured feedback and captured observations that might otherwise be missed. Limitations included inaccuracies in categorization, misattribution of speakers, overly generalized interpretations, and the absence of contextual or nonverbal information. Learners expressed openness and optimism about AI’s potential benefits: efficiency, objectivity, and enhanced perception, while also raising concerns about transparency, data protection, interpretation errors, and risks of overreliance. Both groups emphasized the necessity of human oversight. Conclusion Generative AI can complement simulation debriefings by structuring communication data and highlighting teamwork patterns, thereby supporting reflective practice. Current limitations highlight the need for multimodal approaches, refined prompting strategies, and integration with expert facilitation to ensure AI functions as a support tool rather than a replacement in simulation-based education. Trial Registration BASEC ID: Req-2024-01642.","url":"https://doi.org/10.21203/rs.3.rs-7724301/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7724301/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.0207.v1","name":"Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects","source":"preprints","abstract":"Artificial Intelligence (AI) is a growing area of Computer Science that combines the technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyse and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to Artificial Intelligence, how it works and what is its central role in biomedical engineering. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics and live health monitoring. This paper starts with an introduction to Artificial Intelligence and its major subfields before moving into AI revolutionizing healthcare technology. There is a lot of emphasis on how it will transform Biomedical Engineering using AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient’s physiology, they also allow chronic health tracking. Further, the review also provides an overview on trends of AI-enabled healthcare technologies and concludes that the adoption of Artificial Intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analysers leading medicine into the future.","url":"https://doi.org/10.20944/preprints202412.0207.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0207.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-10445370/v1","name":"Wearable Technologies for Vaccines and Therapeutics in Infectious Diseases: A Scoping Review","source":"preprints","abstract":"Abstract The continuous tracking of human biometrics through multi-sensor wearable devices marks a paradigm shift in infectious disease care, enabling the transition from reactive, intermittent assessment to continuous monitoring of the host response to vaccines and therapeutics. In this scoping review, we identified applications of wearable technologies spanning the entire intervention lifecycle, from physiological readiness before treatment and continuous safety surveillance to recovery assessment, disease interception, and therapeutic response monitoring. Across studies, wearable-derived physiological signals detected subclinical treatment responses, identified behavioral factors associated with vaccine effectiveness, objectively quantified recovery trajectories, and enabled real-time monitoring of adherence and drug-related adverse events. Emerging technologies extended beyond monitoring, demonstrating the feasibility of closed-loop systems that couple with physiological sensing directly to therapeutic action. Together, these findings suggest that wearable technologies are evolving from tools that passively observe health into platforms that continuously characterize and potentially optimize the response to infectious disease interventions. As sensing technologies, artificial intelligence, and digital therapeutics converge, infectious disease management may shift from episodic evaluation toward personalized, adaptive, and data-driven care. A critical next step is determining whether wearable-guided interventions can improve clinical outcomes, rather than simply detect physiological changes.","url":"https://doi.org/10.21203/rs.3.rs-10445370/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10445370/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2024.12.13.24318727","name":"Opinions of the UK general public in using artificial intelligence and “opt-out” models of consent in medical research","source":"preprints","abstract":"Background Due to its complexity, Artificial Intelligence often requires large, confidential clinical datasets. 20-30% of the general public remain sceptical of Artificial Intelligence in healthcare due to concerns of data security, patient-practitioner communication, and commercialisation of data/models to third parties. A better understanding of public concerns of Artificial Intelligence is therefore needed, especially in the context of stroke research. Aims We aimed to evaluate the opinion of patients and the public in acquiring large clinical datasets using an “opt-out” consent model, in order to train an AI-based tool to predict the future risk of stroke from routine healthcare data. This was in the context of our project ABSTRACT, a UK Medical Research Council study which aims to use AI to predict future risk of stroke from routine hospital data. Methods Opinions were gathered from those with lived experience of stroke/TIA, caregivers, and the general public through an online survey, semi-structured focus groups, and 1:1 interviews. Participants were asked about their perceived importance of the project, the acceptability of handling deidentified routine healthcare data without explicit consent, and the acceptability of acquiring these data via an opt-out model of consent model by members within and outside of the routine clinical care team. Results Of the 83 that participated, 34% of which had a history of stroke/TIA. Nearly all (99%) supported the project’s aims in using AI to predict stroke risk, acquiring data via an opt-out consent model, and the handling of pseudonymized data by members within and outside of the routine clinical care team. Conclusion Both the general public and those with lived experience of stroke/TIA are generally supportive of using large, de-identified medical datasets to train AI models for stroke risk prediction under an opt-out consent model, provided the research is transparent, ethically sound, and beneficial to public health.","url":"https://doi.org/10.1101/2024.12.13.24318727","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.13.24318727","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-7033639/v1","name":"Performance, acceptability, and impact of ambient listening scribe technology in an outpatient context: a mixed methods trial evaluation","source":"preprints","abstract":"Abstract Background: In 2024, Gold Coast Hospital and Health Service outpatient division initiated a 16-week trial of artificial intelligence (AI)-enabled ambient listening scribe technology. The objective of this pilot study was to evaluate the acceptability and impact of scribe technology in producing clinical notes and discharge summaries among outpatient clinicians and patients receiving care. Methods: A mixed method research design combined analysis of data from patient and staff surveys, staff interviews, scribe outputs and electronic medical records across a breadth of outpatient specialties. Results: By and large, ambient listening technology was associated with positive patient and staff experience. On average, 58% of scribe outputs were accepted without modification into the electronic outpatient note. There was limited evidence of bias in outputs, however there was some evidence of hallucination or incorrect outputs. Conclusions: Qualitative and quantitative data were internally consistent and demonstrated that ambient listening technology can 1) produce an accurate summary of outpatient appointments, 2) enhance clinical note quality and 3) improve both clinician and patient experience.","url":"https://doi.org/10.21203/rs.3.rs-7033639/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7033639/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.09.30.25336749","name":"Combination AI-Machine Learning to Diagnose Pulmonary Hypertension: A Real-World Evidence Cohort Study","source":"preprints","abstract":"ABSTRACT BACKGROUND Pulmonary hypertension (PH) is a highly morbid disease, but underdiagnosis is common outside of expert referral centers. Consequentially, there may be opportunities to automate PH diagnosis using artificial intelligence (AI) clinical decision support tools. Analysis of patient-level right heart catheterization (RHC) data is required to optimize AI-based PH diagnosis but has not been reported previously. METHODS We performed a retrospective cohort analysis of all RHC studies (January 1, 2016 to December 31, 2024) performed at the University of Maryland Medical System (UMMS), which is a Maryland statewide clinical network of 12 hospitals serving >2 million patients. We developed an automated large language model (LLM)-driven Pattern Repository (LDPR) method, featuring three task-specific LLM agents for extracting unstructured RHC data, which was manually cross-validated independently by two PH experts. To address data missingness, we used machine-learning to develop formulae to calculate mean pulmonary artery pressure (mPAP) from systolic (sPAP) and diastolic (dPAP) PAP, using an 80/20 train-test split. RESULTS The study cohort included N=11,029 unique patients and 17,292 RHC reports (age 66±13.5 years; 43% female; 65% White, 30% Black or African American; mPAP, 28±11mmHg; 26% congestive heart failure). The precision for accurate mPAP, sPAP, and dPAP extraction by the LLM was 99.6%, 99.4%, and 99.4%, respectively, with a detection failure of 0.4%. A missing mPAP was noted in N=548 cases and N=507 unique patients (3.2% and 4.6%, respectively). When applying ML to the dataset, the simple, linear equation: mPAP=1.51+0.43*sPAP+0.45*dPAP returned the highest R2 of 0.94 and lowest mean square error of 8.3 mmHg, which outperformed linear equations used currently (all p 20mmHg, and therefore reclassifying patients from no diagnosis to a diagnosis of PH. CONCLUSION In this retrospective cohort analysis, combination LLM-ML-based extraction and interpretation of RHC was used to automate PH diagnosis in a large and heterogenous patient population. This approach is an efficient and scalable solution to preventing under-diagnosis of PH and demonstrates the feasibility of generative AI for advancing clinically-actionable tools that can improve cardiovascular disease phenotyping and diagnosis in real-world settings.","url":"https://doi.org/10.1101/2025.09.30.25336749","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.09.30.25336749","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.1686.v1","name":"Artificial Intelligence-Driven Translation Tool in Intensive Care Unit (AITIC) for Enhancing Communication and Research","source":"preprints","abstract":"There is a need to improve communication for patients and family members who belong to cultural minority communities in the Intensive Care units (ICU). As a matter of fact, language barriers negatively impact patient safety, family participation in the care of the critically ill patients as well as recruitment in clinical trial. Recent studies indicate that Google translate and ChatGPT are not accurate enough for advanced medical terminology. Therefore, developing and implementing an Artificial Intelligence-driven language translation tool is essential for bridging language barriers. This tool will enable language minority communities to access advanced healthcare facilities and innovative research in a timely and effective manner, ensuring they receive the comprehensive care and information they need. Method: Key factors that facilitate access to advanced health services, in particular ICUs for language minority communities are reviewed. Results: The existing digital communication tools in emergency and the ICU are reviewed. To the best of our knowledge, no Al translation app has been developed for deployment in ICUs. Patient privacy and data confidentiality are other important issues that should be addressed. Conclusions: Developing AITC which uses language models trained with medical/ICU terminology dataset could offer fast and accurate real-time translation. AITIC could support communication, consolidate and expand original research involving language minority communities.","url":"https://doi.org/10.20944/preprints202412.1686.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.1686.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5128451/v2","name":"A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions","source":"preprints","abstract":"Abstract Background: Large Language Models (LLMs) are one of the artificial intelligence (AI) technologies used to understand and generate text, summarize information, and comprehend contextual cues. LLMs have been increasingly used by researchers in various medical applications, but their effectiveness and limitations are still uncertain, especially across various medical specialties. Objective: This review evaluates recent literature on how LLMs are utilized in research studies across 19 medical specialties. It also explores the challenges involved and suggests areas for future research focus. Methods: Two researchers performed literature searches in PubMed, Web of Science and Scopus to identify published literature from January 2021 to March 2024. The studies included the usage of LLM on performing medical tasks. Data was extracted and analyzed by five reviewers. To assess risk of bias, quality assessment was performed using the revised tool for the quality assessment of artificial intelligence-centered diagnostic accuracy studies (QUADAS-AI). Results: Results were synthesized through categorical analysis of evaluation metrics, impact types, and validation approaches across medical specialties. A total of 84 studies were included in this review and mainly originated from two countries; USA (35/84) and China (16/84). Although reviewed LLM applications spread across 19 medical specialties, multi-specialty applications were demonstrated in 22 studies. Various aims for using LLMs include clinical natural language processing (31/84), supporting medical decision (20/84), medical education (15/84), diagnoses (15/84), patient management and patient engagement (3/84). GPT-based and BERT-based LLMs are most used in (83/84) studies. Despite reported positive impacts such as improved efficiency and diagnostic accuracy, challenges related to reliability, accuracy and ethics remain. The overall risk of bias was low in 72 studies, high in 11 studies and not clear in 3 studies. Conclusion: GPT-based and BERT-based LLMs dominate medical specialty applications, with over 98.8% of reviewed studies using these models. Despite their potential benefits in medical process efficiency and diagnostics, a key finding from challenges regarding accuracy was the substantial variability in performance among the LLMs. For instance, LLMs' accuracy ranged from 3% in diagnostic support to over 90% in some clinical NLP tasks. Heterogeneity in the utilization of LLMs across diverse medical tasks and contexts prevented meaningful meta-analysis, as the studies lacked standardized methodologies, outcome measures, and implementation approaches. Therefore, room for improvement remains wide for developing domain-specific LLMs using medical data and establishing validation standards to ensure reliability and effectiveness.","url":"https://doi.org/10.21203/rs.3.rs-5128451/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5128451/v2","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-5193696/v1","name":"Modern Artificial Intelligence and Large Language Models in Graduate Medical Education: A Scoping Review of Attitudes, Applications &amp; Practice","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) holds transformative potential for graduate medical education (GME), yet, a comprehensive exploration of AI's applications, perceptions, and limitations in GME is lacking. Objective To map the current literature on AI in GME, identifying prevailing perceptions, applications, and research gaps to inform future research, policy discussions, and educational practices through a scoping review. Methods Following the Joanna Briggs Institute guidelines and the PRISMA-ScR checklist a comprehensive search of multiple databases up to February 2024 was performed to include studies addressing AI interventions in GME. Results Out of 1734 citations, 102 studies met the inclusion criteria, conducted across 16 countries, predominantly from North America (72), Asia (14), and Europe (6). Radiology had the highest number of publications (21), followed by general surgery (11) and emergency medicine (8). The majority of studies were published in 2023. Following key themes were identified: · Adoption Perceptions: Initially mixed attitudes, have shifted towards favorable perceptions, with increasing support for integrating AI education. · Assessments: AI can differentiate skill levels and provide feedback · Evaluations: AI can effectively analyze narrative comments to assess resident performance. · Recruitment: AI tools analyze letters of recommendation, applications, and personal statements, identifying biases and enhancing equity. · Standardized Examinations: AI models consistently outperformed average candidates in board certification and in-training exams. · Clinical Decision-Making: AI tools can support trainees with diagnostic accuracy and efficiency. Conclusions This scoping review provides a comprehensive overview of applications and limitations of AI in GME but is limited with potential biases, study heterogeneity, and evolving nature of AI.","url":"https://doi.org/10.21203/rs.3.rs-5193696/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5193696/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7426380/v1","name":"Comparison of Intern Doctors and Chat-GPT in Emergency Cases Assessment","source":"preprints","abstract":"Abstract Background: Accurate and timely diagnosis in emergency departments (EDs) is critical due to the high patient volume and time-sensitive nature of care. Intern doctors (IDs), who are about to graduate from medical school, often work in EDs for a period in many countries. However, after graduate, physicians are often expected to take on critical patient care responsibilities despite limited experience. Artificial intelligence (AI) models can rapidly analyze patient data and generate diagnoses, thereby assisting inexperienced physicians in improving diagnostic accuracy. This study aims to evaluate the diagnostic performance of ChatGPT-4 in ED case scenarios and compare its accuracy with that of IDs. Methods: This study was conducted with IDs participating in the internship program during the 2024 academic term. A total of 36 case-based questions, categorized by difficulty level. These questions were administered to 155 IDs via Google Documents and subsequently presented to AI. Descriptive statistics were used to summarize the data, and a one-sample t-test was performed to compare diagnostic accuracy between IDs and ChatGPT. Statistical significance was set at p Results: IDs achieved an overall correct response rate of 58.3%, while ChatGPT reached a rate of 97.2%. A statistically significant, moderate negative correlation was observed between question difficulty and IDs’ performance (r = -0.684; p Conclusion: ChatGPT-4 may serve as a valuable diagnostic support tool in EDs. It can be particularly beneficial for newly graduated physicians with limited clinical experience. Clinical trial number: not applicable .","url":"https://doi.org/10.21203/rs.3.rs-7426380/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7426380/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5493553/v1","name":"Electronic Medical Record application with Artificial Intelligence in supporting the clinical learning of professional doctoral students: findings of a mixed-method approach","source":"preprints","abstract":"Abstract In this study, we evaluated the benefits of an Electronic Medical Record application with Artificial Intelligence (EMRAI) in supporting the clinical learning of professional doctor students (co-assistants) at Gadjah Mada University. The EMRAI was designed to overcome the limitations of filling in medical records and improve diagnosing accuracy. We used mixed methods in this study: a quasi-experimental design for quantitative analysis and focus group discussions (FGD) for qualitative analysis. The research participants included 60 students divided into experimental (30 students) and control (30 students) groups using the EMRAI and paper medical records, respectively. The results showed that the experimental group had a significantly higher average post-test score than the control group (91.80 vs. 71.42, respectively; P","url":"https://doi.org/10.21203/rs.3.rs-5493553/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5493553/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5362276/v1","name":"Examining the teacher readiness gap at the interface of artificial intelligence and medical education: A qualitative study of clinical educators","source":"preprints","abstract":"Abstract The integration of Artificial Intelligence (AI) into healthcare is transforming medical education, reshaping how diagnostic skills, treatment approaches, and patient care methods are taught. This study investigates the interface of AI and medical education, focusing on the preparedness and views of clinical educators. Using the Unified Theory of Acceptance and Use of Technology as a framework, this research assesses the factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Through an inductive-to-deductive methodology, we conducted semi-structured interviews with 15 clinical educators from the south-central region of the United States who oversee third-year medical students. Key findings of teacher readiness at the interface of AI and medical education centered around 1) the technological learning curve, 2) the need for hands-on, action-based learning, 3) the critical role of institutional support, 4) mentorship as a crucial support system, 5) balancing human elements with AI integration, and 6) divergent comfort levels between generational cohorts. While AI holds promise to reform medical education by fostering adaptive, personalized learning environments, it also raises challenges in preserving essential human elements of patient care. Addressing these challenges demands a strategic, institutionally supported shift in medical pedagogy to ensure that AI integration is both effective and sustainable. The study’s insight into clinical educators' perspectives lay the groundwork for developing AI-ready educational models that balance technical expertise with core humanistic values, supporting a comprehensive approach to medical training in the AI-driven future.","url":"https://doi.org/10.21203/rs.3.rs-5362276/v1","authors":["Tim Murphy","Ginger Vaughn","Rob E. Carpenter","Benjamin McKinney","Rochell McWhorter"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5362276/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1101/2024.12.05.24318531","name":"Deep Chest: an artificial intelligence model for multi-disease diagnosis by chest x-rays","source":"preprints","abstract":"Background Artificial intelligence is increasingly being used for analyzing image data in medicine. Objectives We aimed to develop a computer vision artificial intelligence (AI) application using limited training material to aid in the multi-label, multi-disease diagnosis of chest X-rays. Methods We trained an EfficientNetB0 pre-trained model, leveraging transfer learning and deep learning techniques. Six thoracic disease categories were defined, and the model was initially trained on images sourced online and chest X-rays from a hospital database for training and internal validation. Subsequently, the model underwent external validation. Results In constructing and validating Deep Chest, we utilized 453 images, achieving an area under curve (AUC) of 0.98, sensitivity of 0.98, specificity of 0.80, and accuracy of 0.83. Notably, for diagnosing masses or nodules, the sensitivity, specificity, and accuracy were 0.97, 0.81, and 0.83, respectively. We deployed Deep Chest as a free experimental web application. Conclusions This tool demonstrated high accuracy in diagnosing both single and coexisting pulmonary pathologies, including pulmonary masses or nodules. Deep Chest thus represents a promising AI-based solution for enhancing diagnostic capabilities in thoracic radiology, with the potential to be utilized across various medical disciplines, especially in scenarios where expert support is limited.","url":"https://doi.org/10.1101/2024.12.05.24318531","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.05.24318531","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5147796/v1","name":"Acceptance of Artificial Intelligence in Medical Practice Among Iranian Physicians and Medical Students: A Cross-Sectional Survey","source":"preprints","abstract":"Abstract Background The integration of Artificial Intelligence (AI) into healthcare is rapidly advancing; however, research on its acceptance among medical professionals in Iran remains limited. This study aimed to assess the acceptance of AI in medical practice among Iranian physicians and medical students. Methods A cross-sectional survey was conducted between January and June 2023 at multiple medical institutions in Iran. The study included 535 participants (351 medical students and 184 physicians) who completed an online questionnaire assessing awareness, experience, attitudes, and willingness to adopt AI in clinical practice. Results Medical students reported significantly higher use of AI-based decision support systems (89.5%) than physicians (45.1%) (p = 0.038). Both groups frequently encountered AI errors (physicians: 65.1%, students: 68.5%). Attitudes toward AI were generally positive, with no significant differences based on gender or surgical specialty. However, years of medical experience significantly influenced attitudes (p = 0.02), with less experienced professionals showing more positive attitudes. Conclusions This study revealed a generational shift in AI adoption, with medical students showing higher acceptance rates. Although the attitudes were generally positive, the high prevalence of AI errors highlights the need for improved system reliability and targeted training programs. These findings have important implications for medical education, healthcare institutions, and AI developers in the evolving landscape of AI integration in Iranian healthcare.","url":"https://doi.org/10.21203/rs.3.rs-5147796/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5147796/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.02.10.25321994","name":"Ethical and Social Considerations of Applying Artificial Intelligence in Healthcare; a Two-Pronged Scoping Review","source":"preprints","abstract":"Background Artificial Intelligence (AI) is being designed, tested, and in many cases actively employed in almost every aspect of healthcare from primary care to public health. It is by now well established that any application of AI carries an attendant responsibility to consider the ethical and societal aspects of its development, deployment and impact. However, in the rapidly developing field of AI, developments such as machine learning, neural networks, generative AI, and large language models have the potential to raise new and distinct ethical and social issues compared to, for example, automated data processing or more ‘basic’ algorithms. Methods This article presents a scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design. One strand of the review (SR1) consists of a broad review of the academic literature restricted to a recent timeframe (2021-23), to better capture up to date developments and debates. The second strand (SR2) consists of a narrow review, limited to prior systematic and scoping reviews on the ethics of AI in healthcare, but extended over a longer timeframe (2014-2024) to capture longstanding and recurring themes and issues in the debate. This strategy provides a practical way to deal with an increasingly voluminous literature on the ethics of AI in healthcare in a way that accounts for both the depth and evolution of the literature. Results SR1 captures the heterogeneity of audience, medical fields, and ethical and societal themes (and their tradeoffs) raised by AI systems. SR2 provides a comprehensive picture of the way scoping reviews on ethical and societal issues in AI in healthcare have been conceptualized, as well as the trends and gaps identified. Conclusion Our analysis shows that the typical approach to ethical issues in AI, which is based on the appeal to general principles, becomes increasingly unlikely to do justice to the nuances and specificities of the ethical and societal issues raised by AI in healthcare, as the technology moves from abstract debate and discussion to real world situated applications and concerns in healthcare settings.","url":"https://doi.org/10.1101/2025.02.10.25321994","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.10.25321994","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.02.23.25322754","name":"Performance of an artificial intelligence foundation model for prostate radiotherapy segmentation","source":"preprints","abstract":"Importance Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with minimal fine-tuning, but their performance in specialized clinical tasks like radiation therapy planning are not well characterized. Objective To evaluate the performance of SAM 2 in segmenting pre-operative intact prostate and post-operative prostate fossa targets for prostate radiotherapy planning. Design, Setting, Participants Retrospective cohort study deploying and testing a foundation model for AI segmentation for prostate radiotherapy planning. CT simulation images and radiation plans were obtained from a single academic institution for patients undergoing prostate cancer treatment. Data analysis was performed from September 2024 to February 2025. Exposures AI segmentation with varying levels of human intervention, ranging from intervals of every 2nd to every 10th ground truth slice provided as input. Main Outcome and Measures Segmentation accuracy measured by Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) for intact and post-operative prostate target delineation. Results While SAM 2 outperformed interpolation in DSC and HD for both intact and post-operative prostate cancer patient cases, the AI segmentation accuracy was significantly better in the intact pre-operative patient cases where anatomic boundaries were better defined than post-operative patient cases. This is especially evident when sparse ground truth was provided simulating lower levels of human intervention. Conclusions and Relevance AI foundation models show promising application for specialized medical tasks such as prostate cancer radiotherapy segmentation with limited need for fine-tuning or retraining, although their clinical application will require further understanding of task-specific performance.","url":"https://doi.org/10.1101/2025.02.23.25322754","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.23.25322754","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-9244535/v1","name":"Developing immersive virtual exposures for obsessive-compulsive disorder – Protocol for a Randomised, Controlled, Proof-of-Concept Feasibility Trial","source":"preprints","abstract":"Abstract Background Exposure and response prevention (ERP) is the gold-standard psychological treatment for obsessive compulsive disorder (OCD). However, its delivery typically requires frequent therapist availability and repeated patient travel to treatment settings, which limits accessibility. In addition, the idiosyncratic nature of obsessive-compulsive symptoms presents challenges for conducting effective exposures within the time and material constraints of traditional clinical environments. Virtual reality (VR)-based interventions may help address these limitations. This study aims to assess the feasibility of a novel approach that uses generative artificial intelligence (GenAI) to create personalised, immersive 3D exposure environments tailored to individual patient fears. Methods This is a randomised controlled feasibility study with three parallel arms and an assessor-blinded design. Forty-five adults with a primary diagnosis of OCD and moderate to extremely severe symptoms will be randomly allocated in a 1:1:1 ratio to OCD-related exposure administered via VR environments, neutral VR environments, or OCD-related stimuli presented on a widescreen display. Participants will complete a baseline assessment and an GenAI-based stimulus titration session, followed by two therapist-led ERP sessions that frame five consecutive days of asynchronous exposure (exposure blocks/scenarios that are not therapist-led ERP). The intervention uses text-to-image synthesis, image conversion into 3D Gaussian Splatting environments, and delivery via VR headsets or widescreen display. Primary feasibility outcomes include recruitment and retention rates, data completeness, and adherence to the asynchronous exposure protocol. Secondary outcomes include progression through personalised exposure hierarchies, physiological reactivity (electrodermal activity and heart rate), cybersickness, and subjective distress measures. Discussion This study will determine whether AI-driven VR exposure is feasible, safe, and acceptable for adults with OCD. The proposed approach standardises the process of content generation while personalising the actual stimuli, towards leveraging technology to ensure the personalised needs of these patients are more effectively met. Results can inform the refinement of the intervention and the study procedures, including sample size estimation for a future randomised controlled trial. If successful, this methodology could have the potential to improve scalability, reduce costs, and enhance the ecological validity of exposure therapy while maintaining clinical efficacy. Trial registration ISRCTN13869986. Registered 29 December 2025. Prospectively registered.","url":"https://doi.org/10.21203/rs.3.rs-9244535/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9244535/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9848475/v1","name":"Evaluating medical AI models beyond predictive performance: a diabetic retinopathy screening case study","source":"preprints","abstract":"Abstract Predictive metrics are widely used to evaluate artificial intelligence (AI)-based screening tools, yet their clinical reliability requires a more comprehensive assessment across multiple aspects, including robustness and calibration. Using diabetic retinopathy (DR) screening as an exemplar case study, we simulated a model development and update scenario to reflect realistic deployment requirements, by incrementally developing models using a development cohort of 63,301 fundus images. We evaluated the models on six external validation datasets across four reliability dimensions: probability calibration, uncertainty estimation, robustness to acquisition noise and device-induced variability, and consistency across model updates. Although predictive performance remained high across all models and all six external datasets, with a median AUROC of 0.974 (0.904–0.993), the performance of the four clinical reliability measures is limited. Model calibration was poor (Expected Calibration Error; 0.031–0.133), and estimated uncertainty did not align with clinicians' assessments (Pearson's r ; 0.156–0.548). Although robustness was generally acceptable (Cohen's kappa; 0.431–0.852), it was reduced in borderline cases (Cohen's kappa; −0.027–0.331). Notably, model updates also introduced decision instability in borderline cases (Cohen's kappa; 0.060–0.910) and substantial fluctuations in the Youden-optimal threshold across external datasets (0.107–0.799). These findings highlight that a comprehensive evaluation across multiple complementary dimensions is required to ensure the reliability of AI-based screening tools. To support further research, we publicly released the HMADR_Clinic dataset, comprising 766 fundus photographs and their DR grades established through a hierarchical grading protocol involving five independent ophthalmologists and senior adjudication.","url":"https://doi.org/10.21203/rs.3.rs-9848475/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9848475/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6572611/v1","name":"Revolutionizing African Healthcare: A Systematic Review of Artificial Intelligence and Data Governance","source":"preprints","abstract":"Abstract Background: African health care Systems are confronted by continuing infrastructure disparities or lack of capacity about the workforce and unequal access to care. To address these challenges, artificial intelligence (AI) and data governance frameworks have become the change makers in the health sector, especially in the wake of the African Union Convention on Cyber Security and Personal Data Protection (2014) which was used to provide the foundation upon which digital innovators look to bring solutions to life in the healthcare sector. Purpose: This study investigates the integration of AI in African healthcare systems post-2014. It examines the role of AI in disease detection, patient management and other health outcome, ethical standards and policy development implications. Methods: A total of 41 peer-reviewed articles published in the years between 2015 and 2023 were analysed by means of a systematic review of the literature. PubMed, IEEE Xplore, and Google Scholar were used as the sources. Search terms included “AI in healthcare,” “data governance” and “Africa,”. Also, the review has included the grey literature which did not follow the PRISMA guidelines. Empirical evidence of implementation of AI across African countries in the healthcare contexts was the basis of the studies selected. Results: The number of publications related to healthcare via AI has increased by 35% in 2022-2023 colocation with increasing interest and investment in this issue. It was found out that AI: contributes to significant parts of diagnostic accuracy, operational efficiency and predictive analytics in several medical disciplines. Despite these critical challenges, there remain two major sticking points regarding infrastructure, regulatory enforcement, and usage of patient data in the ethical way. Conclusions: The study indicates the need for the immediate development of targeted interventions by the African governments, health ministries, research institutions, and digital health stakeholders. With these include the establishment of robust governance mechanism, ethical oversight frameworks and digital infrastructure as investment. In the future, it is suggested that longitudinal study and cross regional policy comparison should be conducted to provide sufficient support for sustainability of AI integration in healthcare systems.","url":"https://doi.org/10.21203/rs.3.rs-6572611/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6572611/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-5454088/v1","name":"Generative Artificial Intelligence in Medicine: A Mixed Methods Survey of UK General Practitioners","source":"preprints","abstract":"Abstract Background : With the debut of OpenAI’s ChatGPT, there has been growing interest in the use of generative artificial intelligence (AI), including in healthcare. However, there is only limited research into doctors’ adoption of these tools and their opinions about their application in clinical practice. Objective: This study aimed to explore the opinions of general practitioners (GPs) in the United Kingdom (UK) about the use of generative AI tools (ChatGPT/Bard/Bing AI) in primary care. Methods: Between February 2 nd -24th 2024, using a convenience sample, we administered a web-based mixed methods survey of 1000 GPs in the UK. Participants were recruited from registered GPs currently working in the UK through Doctors.net.uk. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses to 2 open-ended questions embedded in the questionnaire. Results: A total of 1006 GPs responded, with 53% being male and 54% aged 46 years and older. Most GPs (80%) expressed a need for more support and training in understanding these tools. GPs at least somewhat agreed AI would improve documentation (59%), patient information gathering (56%), treatment plans (41%), diagnostic accuracy (40%), and prognostic accuracy (38%). Additionally, 62% believed patients might rely more on AI, 55% felt it could increase inequities, and 54% saw potential for patient harm, but 47% believed it would enhance healthcare efficiency. GPs who used these tools were significantly more optimistic about the scope for generative AI in improving clinical tasks compared with those who did not. 31% of the GPs (307/1006) left comments that were classified into 4 major themes: (1) lack of familiarity and understanding, (2) a role in clinical practice, (3) concerns, and (4) thoughts on future of healthcare. Conclusions: This study highlights UK GPs' perspectives on generative AI in clinical practice, emphasizing the need for more training. Many GPs reported a lack of knowledge and experience with this technology and a significant proportion used non-medical grade technology for clinical tasks, with the risks that this entails. Medical organizations must urgently invest in educating and guiding physicians on AI use and limitations.","url":"https://doi.org/10.21203/rs.3.rs-5454088/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5454088/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.02.07.25321681","name":"Applications of Artificial Intelligence in Neurosurgical Education: A Scoping Review","source":"preprints","abstract":"ABSTRACT Background Artificial intelligence (AI) has transformed medical education through optimized instruction, competency assessment, and personalized learning. Its integration into neurosurgical education, given the field’s complexity and precision demands, warrants comprehensive exploration. Objective To systematically evaluate AI applications in neurosurgical education. Methods A scoping review adhering to PRISMA-ScR guidelines was conducted. A Scopus search (up to May 2024) identified 23 eligible studies. Inclusion criteria encompassed peer-reviewed observational or experimental studies on AI in neurosurgical education. Narrative synthesis categorized findings into key domains. Results Four main key areas emerged: performance in board examinations and ethical considerations, simulation-based training and tutoring, performance/skills/expertise analysis and assessment, and other applications. In board examinations, GPT-4 outperformed prior models and junior neurosurgeons in text-based questions but lagged in image-based tasks. Simulation training utilized neural networks to classify expertise and deliver individualized feedback, though rigid metrics risked oversimplifying skill progression. Machine learning models assessed surgical performance, identifying metrics. Other innovations included AI-generated academic content, neuroanatomical segmentation, and instrument pattern analysis. Ethical concerns highlighted risks of overreliance, image-processing limitations, and the irreplaceable role of clinical intuition. Technical challenges included dataset biases and simulation realism. Conclusions AI enhances neurosurgical education through knowledge assessment, simulation feedback, and skill evaluation. However, integration requires addressing ethical dilemmas, improving multimodal data processing, and ensuring human-AI collaboration. Continuous model refinement, expanded datasets, and hybrid curricula combining AI analytics with expert mentorship are critical for safe, effective implementation. This evolution promises to elevate training quality while preserving the indispensable value of hands-on experience in neurosurgical practice.","url":"https://doi.org/10.1101/2025.02.07.25321681","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.07.25321681","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6595296/v1","name":"Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students","source":"preprints","abstract":"Abstract Background: The rapid advancement of Generative Artificial Intelligence (GenAI) is reshaping the landscape of medical simulation education, necessitating the enhancement of faculty competencies to effectively integrate evolving knowledge systems with GenAI for collaborative decision-making. However, current educational technologies face systemic limitations, including fragmented functionality, a disconnect between conventional and GenAI-driven teaching approaches, and a lack of dynamic capability assessment tools. Constructing a standardized capability scale is crucial to overcoming adaptation bottlenecks. Methods: Grounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this study employed a two-round Delphi method to construct a faculty competency assessment scale, with stratified sampling involving 434 participants from clinical medicine, medical technology, and nursing fields. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to test model fit (CFI=0.939, RMSEA=0.117). This study was not registered as a clinical trail. Result： The resulting 16-item scale exhibited strong psychometric properties, demonstrating excellent internal consistency (Cronbach’s α = 0.979), sampling adequacy (KMO = 0.961), and significant sphericity (Bartlett’s test, p","url":"https://doi.org/10.21203/rs.3.rs-6595296/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6595296/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202502.0521.v1","name":"Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine – Dunning-Kruger Effects and Ultracrepidarianism","source":"preprints","abstract":"We have published on the accuracy of the Google Virtual Assistant, Alexa, Siri, Cortana, Gemini and Copilot. Emerging from this published work was a focus on the ac-curacy of AI that could be determined through validations. In our work published in 2023, the accuracy of responses to a panel of 24 queries related to gynecologic oncology was low, with Google Virtual Assistant (VA) providing the most correct audible replies (18.1%), followed by Alexa (6.5%), Siri (5.5%), and Cortana (2.3%). In the months following our publication, there was explosive excitement about several generative AIs that continue to transform the landscape of information accessibility by presenting the search results in impressively engaging narratives. This type of presentation has been enabled by combining machine learning algorithms with Natural Language Processing (NLP). In 2024, we published our exploration of the generative AIs Gemini and Copilot as well as the Google Assistant in relation to how accurately they responded to the panel of 24 queries that we used in the 2023 publication. Google Gemini achieved an 87.5% accuracy rate, while the accuracy of Microsoft Copilot was 83.3%. In contrast, the Google VA s accuracy in audible responses improved from 18% in the 2023 report to 63% in 2024. Because of our investigation in this area, we have examined the accuracy of results obtained through different AI models in this review. The landscape of the findings reviewed here surveyed 252 papers published in 2024, topically reporting on AI in medicine of which 83 articles are considered in the present review because they contain evidenced-based findings. In particular, the types of cases considered deal with AI accuracy in initial differential diagnoses, cancer treatment recommendations, board-style exams and performance in various clinical tasks. Importantly, summaries of the validation techniques used to evaluate AI findings are presented. This review focuses on those AIs that have a clinical relevancy evidenced by application and evaluation in clinical publications. This relevancy speaks to both what has been promised and what has been delivered by various AI systems. Readers will be able to understand when generative AI may be expressing views without having the necessary information (ultracrepidarianism) or is responding as if the generative AI had expert knowledge when it does not. Without an awareness that AIs may deliver inadequate or confabulated information, incorrect medical decisions and inappropriate clinical applications can result (Dunning-Kruger effect). As a result, in certain cases a generative AI might underperform and provide results which greatly overestimate any medical or clinical validity.","url":"https://doi.org/10.20944/preprints202502.0521.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202502.0521.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5082335/v1","name":"Artificial Intelligence Applications in Delirium Prediction, Diagnosis, and Management: A Systematic Review","source":"preprints","abstract":"Abstract Delirium is a prevalent, acute, and reversible neuropsychiatric syndrome in elderly populations, notable for its high incidence and mortality rates, both of which impose a substantial burden on patient outcomes and healthcare systems. Currently, the detection of delirium primarily depends on clinical assessments performed by physicians, which poses challenges for less experienced clinicians in early identification of the condition. The application of artificial intelligence (AI) technologies to analyze large-scale data from delirium patients enables the identification and quantification of relevant delirium markers, thereby effectively assisting clinicians in the diagnosis, prediction, and monitoring of patient status. However, existing reviews on AI in the field of delirium exhibit several limitations, particularly regarding the comprehensive classification of existing studies. Current reviews often focus on a single type of data and often lack a systematic analysis of studies by data type. Most clinical models for delirium rely on electronic medical records, though physiological time-series data and imaging features also offer crucial biomarkers for the identification of delirium. This paper offers an overview of the medical foundation and recent technological advancements in delirium, aiming to establish a theoretical framework for novices. It systematically reviews the prediction, diagnosis, and management of delirium from a multisource data perspective, enumerates relevant data sources and public databases, and examines various AI models used in this field, along with their advantages and limitations. Finally, this review addresses the challenges and emerging trends of AI in delirium, with the aim of elucidating key directions for future research and development.","url":"https://doi.org/10.21203/rs.3.rs-5082335/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5082335/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.12688/mep.20590.1","name":"Exploring Filipino Medical Students’ Attitudes and Perceptions of Artificial Intelligence in Medical Education: A Mixed-Methods Study","source":"preprints","abstract":"Background: Artificial intelligence (AI) is emerging as one of the most revolutionary technologies shaping the educational system utilized by this generation of learners globally. AI enables opportunities for innovative learning experiences, while helping teachers devise teaching strategies through automation and intelligent tutoring systems. The integration of AI into medical education has potential for advancing health management frameworks and elevating the quality of patient care. However, developing countries, including the Philippines, face issues on equitable AI use. Furthermore, medical educators struggle in learning AI which imposes a challenge in teaching its use. To address this, the current study aims to investigate the current perceptions of medical students on the role of AI in medical education and practice of medicine. Methods The study utilized a mixed-methods approach to quantitatively and qualitatively assess the current attitudes and perceptions of medicine students of AI. Quantitative assessment was done via survey and qualitative analysis via focus group discussion. Participants were composed of 20 medical students from the College of Medicine, University of the Philippines Manila. Results Analysis of the attitudes and perceptions of Filipino medical students on AI showed that participants had a baseline understanding and awareness, but lack opportunities in studying medicine and clinical practice. Majority of participants recognize the advantages in medical education but have reservations on its overall application in a clinical setting. Conclusions The results of this investigation can direct future studies that aim to guide educators on the emerging role of AI in medical practice and the healthcare system, on its effect on physicians-in-training under contemporary medical educational practices. Findings from our study revealed key focal points which need to be sufficiently addressed in order to better equip medical students with knowledge, tools, and skills needed to utilize and integrate AI into their education and eventual practice as healthcare professionals.","url":"https://doi.org/10.12688/mep.20590.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.12688/mep.20590.1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-10470763/v1","name":"Early-life Poverty, Social Disadvantage and Adiposity from Childhood to Young Adulthood","source":"preprints","abstract":"Abstract Background Early-life family poverty and social disadvantage status may affect body mass index (BMI) and body fat distribution in offspring across the life course. We assessed the associations of early-life poverty and social disadvantage with adiposity measures from early childhood to young adulthood. Methods We performed a population-based prospective cohort study among 7,389 parents and children in the city of Rotterdam, the Netherlands. Exposures were pregnancy parental educational level, low household income, and poverty, defined as low income and experiencing financial difficulties, assessed by questionnaires. Childhood body mass index, total fat mass, visceral adipose tissue, and hepatic fat were assessed by physical examinations, dual-energy X-ray absorptiometry, and magnetic resonance imaging at the ages of 2, 6, 10, 14, and 18 years. Results As compared to children not exposed to early-life poverty, those exposed had a higher offspring BMI from the age of 6 years onwards (difference in BMI standard deviation score [SDS] at the age of 18 years 0.17, 95% confidence interval [CI] 0.01, 0.32), a higher risk of overweight and obesity at ages 6 and 18 years (odds ratio at the age of 18 years 1.45, 95% CI 1.06, 2.00), and a higher total fat mass from the age of 10 years onwards (difference in total fat mass SDS at the age of 18 years 0.17, 95% CI 0.02, 0.33). We observed tendencies for similar associations for household income and both maternal and paternal education. Also, as compared to children not exposed to low household income and lower maternal education, those exposed had a higher android/gynoid fat mass ratio from the age of 10 years onwards (p-values","url":"https://doi.org/10.21203/rs.3.rs-10470763/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10470763/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202410.0925.v1","name":"The Use of Artificial Intelligence (AI) in the Management of Medical Rehabilitation Programs","source":"preprints","abstract":"The revolution of physiotherapy is achieved by the application of artificial intelligence (AI) in medical rehabilitation programs. While the integration of virtual reality (VR) and robotics has already marked significant strides in the industry, AI is taking patient rehabilitation to new heights. The integration of AI, VR and robotics in physiotherapy linked to a significant leap forward in patient rehabilitation. These technologies not only increase the effectiveness of the treatment, but also make it more accessible and attractive. In this article, we propose to study the use of AI in the management of medical rehabilitation programs, and we present a comprehensive survey of the latest empirical studies, highlighting AI-based technologies that facilitate the rehabilitation process, trying to demonstrate that the integration of AI in medical rehabilitation programs offers a transformative approach to patient care.","url":"https://doi.org/10.20944/preprints202410.0925.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202410.0925.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.0340.v1","name":"Apprehension Toward Generative Artificial Intelligence in Healthcare: A Multinational Study among Health Sciences Students","source":"preprints","abstract":"In the recent generative artificial intelligence (genAI) era, health sciences students (HSSs) are expected to face challenges regarding their future roles in healthcare. This multinational cross-sectional study aimed to confirm the validity of the novel FAME scale examining themes of Fear, Anxiety, Mistrust, and Ethical issues about genAI. The study also explored the extent of apprehension among HSSs regarding genAI integration into their future careers. The final sample comprised 587 students mostly from Jordan (31.3%), Egypt (17.9%), Iraq (17.2%), Kuwait (14.7%), and Saudi Arabia (13.5%). Participants included students studying medicine (35.8%), pharmacy (34.2%), nursing (10.7%), dentistry (9.5%), medical laboratory (6.3%), and rehabilitation (3.4%). Factor analysis confirmed the validity and reliability of the FAME scale. Of the FAME scale constructs, Mistrust scored the highest, followed by Ethics. The participants showed a generally neutral apprehension toward genAI, with a mean score of 9.23 3.60. In multivariate analysis, significant variations in genAI apprehension were observed based on previous ChatGPT use, faculty, and nationality, with pharmacy and medical laboratory students expressing the highest apprehension, and Kuwaiti students the lowest. Previous use of ChatGPT was correlated with lower apprehension levels. Of the FAME constructs, higher agreement with the Fear, Anxiety, and Ethics constructs showed statistically significant associations with genAI apprehension. The study revealed notable apprehension about genAI among Arab HSSs, which highlights the need for educational curricula that blend technological proficiency with ethical awareness. Educational strategies tailored to discipline and culture are needed to ensure job security and competitiveness for students in AI-driven future.","url":"https://doi.org/10.20944/preprints202412.0340.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0340.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.10.31.24315838","name":"Utilizing artificial intelligence-driven virtual standardized pediatric patients to enhance the capabilities of primary healthcare doctors in China for managing common pediatric diseases: a study protocol for a randomized controlled trial","source":"preprints","abstract":"Background China’s healthcare system for children faces significant challenges, particularly due to the limited pediatric service capacity of primary healthcare institutions. A shortage of effective and accessible training tools for primary care doctors further hinders progress in addressing this gap. Technological advancements, especially in artificial intelligence, offer a potential solution to improve pediatric care. Artificial intelligence-driven virtual standardized patients (VSPs), leveraging internet and virtual simulation technologies, simulate clinical cases with specific disease characteristics, providing an innovative, efficient, and flexible training method. VSPs are increasingly utilized in medical education, clinical reasoning, and licensure exams. This study focuses on using VSPs to improve the management of common pediatric conditions, which are major health concerns for children and impose significant psychological and financial burdens on families. Methods This study will involve a three-arm randomized controlled trial to evaluate the effectiveness of a virtual pediatric standardized patient platform in enhancing primary care doctors’ management of common pediatric diseases. At least 459 participants, including general practitioners, internal medicine practitioners, surgeons, and pediatricians from more than 10 provinces across China, will be randomly assigned to one of three groups: the virtual patient platform group, the case teaching manual group, or the case teaching video group. Five virtual patient cases covering pneumococcal pneumonia, rotavirus enteritis with hypovolemic shock, hand-foot-and-mouth disease, acute appendicitis, and respiratory failure will be developed, along with corresponding case teaching materials. After a two-week learning period, participants’ disease management abilities will be assessed using clinical vignettes. The primary outcome is adherence to best clinical practice guidelines, categorized into full adherence, partial adherence, and nonadherence. Discussion This study aims to leverage artificial intelligence for capacity enhancement, targeting the shortcomings of primary care pediatrics and using VSP to help enhance primary care pediatrics capacity. It is a randomized controlled trial involving over 300 primary healthcare institutions across more than 10 provinces in China, ensuring broad and representative participation from both developed and underdeveloped regions. Trial registration Chictr.org.cn , ChiCTR2400085808 . Registered on 19 June 2024.","url":"https://doi.org/10.1101/2024.10.31.24315838","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.10.31.24315838","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5773489/v1","name":"Comparison of Artificial Intelligence and Traditional Methods in Preoperative Planning for Primary Total Hip Arthroplasty: A Systematic Review and Meta-Analysis.","source":"preprints","abstract":"Abstract Background The application of artificial intelligence (AI) in orthopedics is becoming increasingly widespread, particularly in the diagnosis and treatment of hip-related diseases. Although AI-assisted total hip arthroplasty (THA) techniques have reached a relatively mature stage, their specific role in preoperative planning for THA remains in the research phase. Current studies are generally small in scale, and their findings appear somewhat fragmented, making it difficult to draw definitive conclusions. Against this backdrop, a systematic review and meta-analysis on the application of AI in THA preoperative planning may provide a more comprehensive and rational answer. Questions/purposes Compared to traditional methods, does artificial intelligence (AI) offer more and better advantages in preoperative planning for patients undergoing primary total hip arthroplasty (THA)? Does it possess potential for future development? Methods We conducted a comprehensive and systematic search in the PubMed, Embase, Web of Science, and Cochrane Library databases, covering the period from their inception to October 30, 2024. This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and has been registered in PROSPERO [1] . The included studies focused on patients undergoing primary total hip arthroplasty (THA), with the experimental group using artificial intelligence (AI) for preoperative planning and the control group employing traditional planning methods. We excluded the following: papers published on preprint servers, unpublished studies, conference abstracts, and studies registered on ClinicalTrials.gov but not yet published. Ultimately, data were extracted from 15 eligible studies. To assess the methodological quality of the studies, we applied bias risk assessment methods based on the type of study. The revised Cochrane Risk of Bias tool was employed to assess potential bias in randomized controlled trials (RCTs). For non-randomized controlled trials, including retrospective cohort studies, retrospective case-control studies, and prospective cohort studies, we employed the Newcastle-Ottawa Scale (NOS) for bias risk assessment. Due to the high heterogeneity among studies (I² > 50%), a random-effects model was used for the analysis. Results In the 15 studies that met the inclusion criteria, a total of 2572 participants were included. These patients required primary total hip arthroplasty (THA) due to various hip diseases. Among them, 1307 patients in the experimental group used artificial intelligence (AI) for preoperative planning, while 1265 patients in the control group used traditional methods. There were no statistically significant differences in the baseline characteristics of the included patients (such as age, BMI, preoperative leg length discrepancy, and preoperative Harris score) (P≥0.05), which ensures the reliability of the predictive results. According to the data summary and analysis, compared with traditional methods, AI showed superior performance in the following aspects: the odds ratio (OR) for acetabular component matching accuracy was 0.26 (95% CI, 0.20–0.34; P=0.009; I²=58%), and for femoral component matching accuracy, the OR was 0.25 (95% CI, 0.19–0.32; P=0.66; I²=0%). The matching accuracy was defined with a size difference as the acceptable margin of error. The mean difference (MD) for postoperative leg length discrepancy was -0.49 (95% CI, -0.59 to -0.39; P Conclusion The results of this systematic review and meta-analysis indicate that artificial intelligence (AI) performs comparably to, or even better than, traditional methods in preoperative planning for hip arthroplasty. Compared with traditional methods, the AI group demonstrated advantages such as reducing surgical time, minimizing intraoperative blood loss, lowering surgical risks, and decreasing surgical trauma. These benefits help promote rapid postoperative recovery, shorten hospital stays, and reduce the occurrence of complications. Additionally, patients in the AI group had higher postoperative Harris scores, less postoperative pain, faster functional recovery, and better postoperative adaptation. AI-assisted preoperative planning for total hip arthroplasty (THA) also improves the accuracy of hip component matching prediction, reduces the likelihood of errors in clinical decision-making, effectively alleviates tensions in the doctor-patient relationship, and reduces the waste of medical resources.","url":"https://doi.org/10.21203/rs.3.rs-5773489/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5773489/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2024.10.15.24315506","name":"Protocol for developing the reporting guideline for the use of chatbots and other Generative Artificial intelligence tools in MEdical Research (GAMER)","source":"preprints","abstract":"Background The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engagement, and information dissemination. Powerful generative AI (GAI) tools like ChatGPT and other similar chatbots have emerged, facilitating user interactions with virtual conversational agents. However, the increasing use of GAI tools in medical research presents challenges, including ethical concerns, data privacy issues, and the potential for generating false content. These issues necessitate standardization of reporting to ensure transparency and scientific rigor. Objective The development of the chatbots and other Generative AI tools in MEdical Research (GAMER) reporting guidelines aims to establish a comprehensive, standardized guideline for reporting the use of GAI tools in medical research. Methods The GAMER guidelines are being developed following the methodology recommended by the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network, involving scoping reviews and expert Delphi consensus. The process consists of three stages: preparatory work, Delphi survey, and testing and dissemination. The study is approved by the Ethics Committee of the Institute of Health Data Science at Lanzhou University (approval number: HDS-202406-01). Expected results The GAMER project was launched in July 2023 by the Evidence-Based Medicine Center of Lanzhou University and the WHO Collaborating Centre for Guideline Implementation and Knowledge Translation, and is scheduled to conclude in July 2024. The expected outcome of the GAMER project is a reporting checklist, accompanied by relevant terminology, examples, and explanations, to guide stakeholders in better reporting the use of GAI tools. Conclusion GAMER aims to guide researchers, reviewers, and editors in the transparent and scientific application of GAI tools in medical research. By providing a standardized reporting framework, GAMER seeks to enhance the clarity, completeness, and integrity of research involving GAI tools, promoting collaboration, comparability, and cumulative knowledge generation in AI-driven healthcare technologies. Trial Registration We registered this protocol on the EQUATOR Network ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#CHEER ).","url":"https://doi.org/10.1101/2024.10.15.24315506","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.10.15.24315506","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202501.1031.v1","name":"Generative Artificial Intelligence and Its Role in the Development of Clinical Cases in Medical Education: A Scoping Review Protocol","source":"preprints","abstract":"Introduction: Integrating generative AI into medical education addresses challenges in developing clinical cases for case-based learning (CBL), a method that enhances critical thinking and learner engagement through realistic scenarios. Traditional CBL is resource-intensive and less scalable. Generative AI can produce realistic text and adapt to learning needs, offering promising solutions. This scoping review maps existing literature on generative AI's use in creating clinical cases for CBL and identifies research gaps. Methods: This review follows Arksey and O'Malley&amp;rsquo;s (2005) framework, enhanced by Levac et al. (2010), and aligns with PRISMA-ScR guidelines. A systematic search will occur across major databases like PubMed, Scopus, Web of Science, EMBASE, ERIC, and CINAHL, along with gray literature. Inclusion criteria focus on studies published in English between 2014 and 2024, examining generative AI in case-based learning (CBL) in medical education. Two independent reviewers will screen and extract data, iteratively charted using a standardized tool. Data will be summarized narratively and thematically to identify trends, challenges, and gaps. Results: The review will present a comprehensive synthesis of current applications of generative AI in CBL, focusing on the types of models utilized, educational outcomes, and learner perceptions. Key challenges, including ethical and technical barriers, will be emphasized. The findings will also outline future directions and recommendations for integrating generative AI into medical education. Discussion: This review will enhance understanding of generative AI's role in improving CBL by addressing resource constraints and scalability challenges while maintaining pedagogical integrity. The findings will guide educators, policymakers, and researchers on best practices, emerging opportunities, and areas needing further exploration. Conclusion: Generative AI has significant potential to revolutionize competency-based learning (CBL) in medical education. By mapping current evidence, this review will offer valuable insights into its potential applications, effectiveness, and challenges, paving the way for innovative and adaptive educational strategies.","url":"https://doi.org/10.20944/preprints202501.1031.v1","authors":["Farah Ennab","Hadeel Farhan","Nabil Zary"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202501.1031.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2024.11.27.24318110","name":"Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence","source":"preprints","abstract":"Background Timely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent. Objectives Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos. Methods We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1,427,660 videos from 85,380 echocardiograms at Cedars-Sinai Medical Center (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33,310 videos from 1,806 echocardiograms from Stanford Healthcare (SHC). Results In the held out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884– 0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917–0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939–0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794–0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945–0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906–0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses. Conclusions Our deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.","url":"https://doi.org/10.1101/2024.11.27.24318110","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.11.27.24318110","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6135216/v1","name":"Bibliometric Analysis of Research on cervical cancer and miRNAs from 2010 to 2024: Research Trends, HotSpots, and Prospects","source":"preprints","abstract":"Abstract Background and Purpose MicroRNAs (miRNAs) play multifaceted roles in cervical cancer therapy, including regulating cancer progression, metastasis, drug resistance, HPV control, and metabolic alterations. This study aims to provide a comprehensive bibliometric analysis of the existing literature on miRNAs in cervical cancer, offering insights into research trends, key contributors, and emerging themes to guide future investigations and enhance therapeutic strategies. Method We conducted a systematic search of the Web of Science and PubMed database for literature on miRNAs in cervical cancer published between January 2010 and December 2024. A total of 4034 records were retrieved and analyzed using VOSviewer and CiteSpace software for bibliometric visualization and trend analysis. Results Over the past fifteen years, research on miRNA in cervical cancer showed a significant upward trend before 2020, and then gradually declined starting from 2021. The analysis reveals that Tang and Hua are the most active authors, and China is the most influential country. \"Plos One\" is the journal that publishes the most articles. Besides, Tianjin Medical University is the most productive institution. The top three high-frequency keywords are \"cervical cancer\", \"expression\" and \"invasion\". Recent keyword and literature analysis indicates that the most notable feature of the current research is the deep integration of basic research and clinical application. Particularly, the cross-integration of non-coding RNA network research with emerging technologies such as nanotechnology and artificial intelligence is promoting the precise diagnosis and treatment system of cervical cancer. These findings highlight the interest in understanding the miRNA-mediated pathways and their clinical significance in cervical cancer. Conclusion This bibliometric analysis provides a comprehensive overview of the research landscape on miRNAs in cervical cancer, identifying key contributors, institutions, and emerging trends. While the study does not predict the future direction of cervical cancer treatment, it offers valuable insights into the current state of research and potential areas for further exploration. The findings underscore the importance of continued investigation into miRNA mechanisms and their therapeutic applications to advance cervical cancer management.","url":"https://doi.org/10.21203/rs.3.rs-6135216/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6135216/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4878984/v1","name":"Artificial Intelligence (AI)-Based Simulators versus Simulated Patients in undergraduate programs: a protocol for a randomized controlled trial","source":"preprints","abstract":"Abstract Background: Healthcare simulation is critical for medical education, with traditional methods using simulated patients (SPs). Recent advances in AI offer new possibilities with AI-based simulators. This study compares AI-based simulators and SPs in undergraduate medical education. Methods: A randomized controlled trial will be conducted to identify the effectiveness of delivering a simulation session around history taking skills to fifth-year medical students on their clinical years of study. This research protocol aims to examine and compare the effectiveness and user experience of artificial intelligence-based simulators compared to simulated patients. Discussion: This study will provide valuable insights into the comparative advantages of artificial intelligence-based simulators and simulated patients, which will guide decisions regarding their integration into health education and training programs.","url":"https://doi.org/10.21203/rs.3.rs-4878984/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4878984/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5537908/v1","name":" A Systematic Review and Implementation Guidelines of Multimodal Foundation Models in Medical Imaging","source":"preprints","abstract":"Abstract Artificial Intelligence (AI) holds immense potential to transform healthcare, yet progress is often hindered by the reliance on large labeled datasets and unimodal data. Multimodal Foundation Models (FMs), particularly those leveraging Self-Supervised Learning (SSL) on multimodal data, offer a paradigm shift towards label-efficient, holistic patient modeling. However, the rapid emergence of these complex models has created a fragmented landscape. Here, we provide a systematic review of multimodal FMs for medical imaging applications. Through rigorous screening of 1,144 publications (2012–2024) and in-depth analysis of 48 studies, we establish a unified terminology and comprehensively assess the current state-of-the-art. Our review aggregates current knowledge, critically identifies key limitations and underexplored opportunities, and culminates in actionable guidelines for researchers, clinicians, developers, and policymakers. This work provides a crucial roadmap to navigate and accelerate the responsible development and clinical translation of next-generation multimodal AI in healthcare.","url":"https://doi.org/10.21203/rs.3.rs-5537908/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5537908/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202410.0025.v1","name":"The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning","source":"preprints","abstract":"Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 40s with the first abstract models of intelligent machines. Soon later in the 50s and 60s machine learning algorithms such as neural networks and decision trees ignited large enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. The renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision, and later with neural networks for the detection or classification of malignant lesions in medical images. More recently, the use of natural language processing, recurrent neural networks, transformers and generative models has both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including text analysis of electronic health records, image self-labelling and self-reporting. Thanks to its versatility and impressive results and fueled by the availability of powerful computing resources and open-source libraries, AI is one of the most promising resources for frontier research and applications in medicine.","url":"https://doi.org/10.20944/preprints202410.0025.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202410.0025.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6026482/v1","name":"Artificial intelligence as a medical device for ophthalmic image analysis: a scoping review of regulated devices","source":"preprints","abstract":"Abstract This scoping review aims to identify regulator-approved ophthalmic image analysis AIaMDs in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified − 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection. 19% (7/36) did not have published evidence describing performance. For the remainder, 131 clinical evaluation studies (range 1–22/AIaMD) describing 192 datasets/cohorts were identified. Demographics were poorly reported (age recorded in 52%, sex 51%, ethnicity 21%). On a study-level, few included head-to-head comparisons against other AIaMDs (8%,10/131) or humans (22%, 29/131), and 37% (49/131) were conducted independently of the manufacturer. Only 11 studies (8%) were interventional. There is scope for expanding AIaMD applications to other ophthalmic imaging modalities, conditions, and use cases. Facilitating greater transparency from manufacturers, better dataset reporting, validation across diverse populations, and high-quality interventional studies with implementation-focused outcomes are key steps towards building user confidence and supporting clinical integration.","url":"https://doi.org/10.21203/rs.3.rs-6026482/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6026482/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-5307839/v1","name":"Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching","source":"preprints","abstract":"Abstract Purpose This study aimed to evaluate the effectiveness of integrating artificial intelligence (AI) with problem-based learning (PBL) and case-based learning (CBL) in a dermatology cosmetology course for clinical medical interns. Materials & Methods This prospective randomized controlled trial involved 70 clinical interns rotating in the medical cosmetology department. Participants were randomly assigned to two groups and followed the same curriculum over 8 weeks. The experimental group (n = 40) used a teaching method combining AI with PBL and CBL, while the control group (n = 30) followed the traditional PBL and CBL approach. Assessments included theoretical exams, case analysis, and anonymous feedback surveys to evaluate teaching quality. Results All participants completed the examinations and questionnaires. The average theoretical test scores and case analysis test scores of the experimental group were higher than those of the control group (P","url":"https://doi.org/10.21203/rs.3.rs-5307839/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5307839/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5873082/v1","name":"The Efficacy of a Novel Medical Artificial Intelligence Large Model(MedGo)-Guided Identification and Personalized Treatment of Sepsis: Study Protocol for a Single- centered Randomized Controlled Trial","source":"europepmc","abstract":"Abstract Background Sepsis, a life-threatening organ dysfunction resulting from a dysregulated host response to infection, remains a major global health challenge with high morbility and mortality. Current diagnostic and management frameworks still lack the satisfied sensitivity and specificity for early detection and personalized treatment. In this study, we aim to evaluate the efficacy of MedGo-Sepsis, a novel large language model developed by our collaborating team and us that integrates clinical and immunological data, to guide the identification and personalized treatment of sepsis in the emergency department. Methods This single-center, randomized controlled trial will enroll adult patients presenting to the emergency department with suspected sepsis. Participants will be randomized 1:1 at the physician team level to either the intervention group receiving standard care augmented by MedGo-Sepsis guidance or the control group receiving standard care alone. MedGo-Sepsis provides real-time risk assessments, detailed immune parameter profiling, stratification based on immune phenotypes, and personalized treatment recommendations. The primary outcome is 28-day all-cause mortality. Secondary outcomes include changes in Sequential Organ Failure Assessment (SOFA) score, diagnostic accuracy, time to appropriate antibiotic administration, resource utilization, patient-reported outcomes, and physician workload. Discussion This trial will assess whether MedGo-Sepsis, through the integration of individualized immune data with large language model technology, improves outcomes for patients with sepsis compared to standard care. The combination of enhanced diagnostics and tailored therapeutic strategies has the potential to advance precision management of sepsis in the critical emergency setting. Trial registration The trial has been prospectively registered in the Chinese Clinical Trial Registry (ChiCTR2400094116) on December 17, 2024.","url":"https://doi.org/10.21203/rs.3.rs-5873082/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5873082/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4831203/v1","name":"Evaluating Artificial Intelligence in the Support of Aneurysmal Subarachnoid Hemorrhage Management: A Comparative Analysis of Contemporary Clinical Guidelines, Expert-Identified Controversial Questions, and Three Major AI Models","source":"preprints","abstract":"Abstract Background Aneurysmal Subarachnoid Hemorrhage (aSAH) remains a significant medical challenge. Controversies in managing aSAH, such as optimal blood pressure levels and antifibrinolytic therapy, persist despite advancements in treatment. The recently published guidelines from the NICE, the NCS, and the AHA/ASA show divergence in several key management aspects. This study aims to explore the processing and analysis capabilities of Artificial Intelligence (AI) models in handling controversial aSAH management issues. Methods Twelve controversial questions regarding aSAH management were presented to three artificial intelligence (AI) models: ChatGPT-3.5, ChatGPT-4, and Bard. Questions covered areas like blood pressure management, timing for aneurysm securing procedures, the use of intravenous Nimodipine, handling Nimodipine-induced hypotension, and the effectiveness of transcranial sonography in monitoring vasospasm-induced delayed cerebral injury (DCI). Results AI models’ responses were generally aligned with AHA/ASA guidelines and expert opinions on blood pressure management before and after aneurysm securing. However, significant gaps were noted in their knowledge, especially regarding the role of intravenous Nimodipine and its hypotensive effects. The use of transcranial sonography for monitoring DCI induced by vasospasm was another area where the models showed limited understanding, with only ChatGPT-4 suggesting integration with other imaging techniques and clinical assessment. Conclusions AI models demonstrate potential in assisting with complex medical decision-making in aSAH management. However, their current capabilities highlight the need for ongoing updates and integration with real-world clinical expertise. AI should be viewed as a complementary tool to human judgment. Future developments in AI should focus on enhancing its accuracy and relevance to current medical practices.","url":"https://doi.org/10.21203/rs.3.rs-4831203/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4831203/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.12.13.24318778","name":"A scoping review of AI, speech and natural language processing methods for assessment of clinician-patient communication","source":"preprints","abstract":"Introduction There is growing research interest in applying Artificial Intelligence (AI) methods to medicine and healthcare. Analysis of communication in healthcare has become a target for AI research, particularly in the field of analysis of medical consultations, an area that so far has been dominated by manual rating using measures. This opens new perspectives for automation and large scale appraisal of clinicians’ communication skills. In this scoping review we summarised existing methods and systems for the assessment of patient doctor communication in consultations. Methods We searched EMBASE, MEDLINE/PubMed, the Cochrane Central Register of Controlled Trials, and the ACM digital library for papers describing methods or systems that employ artificial intelligence or speech and natural language processing (NLP) techniques with a view to automating the assessment of patient-clinician communication, in full or in part. The search covered three main concepts: dyadic communication, clinician-patient interaction, and systematic assessment. Results We found that while much work has been done which employs AI and machine learning methods in the analysis of patient-clinician communication in medical encounters, this evolving research field is uneven and presents significant challenges to researchers, developers and prospective users. Most of the studies reviewed focused on linguistic analysis of transcribed consultations. Research on non-verbal aspects of these encounters are fewer, and often hindered by lack of methodological standardisation. This is true especially of studies that investigate the effects of acoustic (paralinguistic) features of speech in communication but also affects studies of visual aspects of interaction (gestures, facial expressions, gaze, etc). We also found that most studies employed small data sets, often consisting of interactions with simulated patients (actors). Conclusions While our results point to promising opportunities for the use of AI, more work is needed for collecting larger, standardised, and more easily available data sets, as well as on better documentation and sharing of methods, protocols and code to improve reproducibility of research in this area.","url":"https://doi.org/10.1101/2024.12.13.24318778","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.13.24318778","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202410.1555.v1","name":"Generative AI in Disease Diagnosis","source":"preprints","abstract":"Artificial intelligence AI is a sophisticated model or algorithm that has transformed a number of industries, including the medical field. In generative artificial intelligence, content is created using models or algorithms. Generative AI in healthcare revealed numerous new application domains for illness diagnosis. It has given models new outputs that they may use to create unique and fresh content. These can be used to learn new patterns that closely resemble the original data. The models covered in this review article serve as diagnostic tools in a variety of medical imaging applications. Medical specialists use generative AI in medical imaging analysis as a preliminary tool to generate large amounts of data that they can then correlate and extract useful information from. It is widely used in medical imaging, such as x-rays, magnetic resonance imaging, CTscan, COVID, cancer areas are utilized as AI diagnostic tools. Artificial intelligence (AI) has undergone a revolution because of to deep learning, which has made it possible for machines to learn from vast amounts of data and carry out complicated tasks based on that data. One of the most exciting uses of deep learning is in the creation of generative models, which are deep learning models that can produce realistic images, videos, and audio. These models have a variety of uses in the healthcare industry; including imaging. Medical imaging now has a plethora of options because to generative AI, a subfield of AI that focuses on producing original information. With improved patient outcomes, tailored treatment regimens, and greater diagnostic capabilities, it gives healthcare providers more control. Generative AI has transformed medical picture processing, interpretation, and application in clinical practice by harnessing the power of deep learning algorithms. Better diagnosis, New medicine discovery, Personalized medicine Improved medical imaging: The quality of medical images can be raised with the application of generative AI. With the aid of this technology, physicians may be able to diagnose and treat patients early by seeing more detail in images, More effective surgery: Virtual patient models can be produced using generative artificial intelligence, Surgeons can be trained and surgery plans can be made using these models, enhanced rehabilitation ,better mental health care: Chatbots that can provide patients therapy can be developed using generative AI. Medical imaging analysis is a field of healthcare that has revolutionized the way we approach patient care. The ability to examine medical images such as X-rays, CT scans, and MRI scans has allowed doctors and healthcare professionals to gain a better understanding of a patient's condition, monitor their illness, and plan their treatment accordingly. However, with the vast amounts of data produced by medical imaging technology, it is essential to have specialist analysis to retrieve useful information. One of the most significant advancements in medical imaging analysis has been the development of generative image AI models. These models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Auto encoders, have enabled healthcare professionals to analyze medical images more effectively and efficiently. The GAN model is one of the most popular generative picture AI models. It comprises two neural networks - a generator network and a discriminator network. The discriminator network's primary role is to discern between artificial and real visuals, while the generator network creates synthetic images. In an adversarial training process, the discriminator and generator work together to accurately categorize artificial and real images, with the generator trying to trick the discriminator. This technique has proved useful in radiology, where GANs are used for tasks such as image synthesis, image de-noising, and image segmentation. Image segmentation is a technique used in medical imaging analysis to split an image into several areas or segments. This has proved in identifying specific structures or regions in medical images, such as tumors, blood vessels, or organs. Generative deep models such as VAEs, GANs, and diffusion models have been applied to the augmentation of medical data, providing healthcare professionals with a more comprehensive understanding of a patient's condition. The development of generative image AI models has revolutionized medical imaging analysis, providing healthcare professionals with a more efficient and effective way to analyze medical images. Medical imaging has been a vital tool for healthcare professionals in diagnosing and treating various medical conditions. However, the interpretation of medical images is often challenging due to the complexity of the human body and the variations in anatomical structures. Therefore, image processing techniques are utilised to enhance the quality of medical images and extract relevant information. In this regard, fundamental image processing operations such as image segmentation, registration, and feature extraction are commonly utilised. However, the application of these operations might not always be appropriate due to the intricate structures of medical images, which include various anatomical variations and irregular tumour shapes. In the case of brain imaging, the presence of different tissues and structures, such as grey matter, white matter, and cerebrospinal fluid, can make it challenging to distinguish between healthy and diseased areas. More advanced image processing techniques such as machine learning algorithms are becoming increasingly popular for medical image analysis. The creation of irrelevant images that disturb the logical structure of the image can also occur due to the complexity of medical images. This adversely affect the accuracy of the interpretation of the image, leading to incorrect diagnoses and treatment plans. Hence, there is a need for sophisticated image processing techniques that can remove unwanted features and highlight the crucial information in the image. The performance of the model employed for image analysis might also be adversely affected by aberrant data and image deformations. Pre-processing techniques like noise reduction and picture normalization are essential for guaranteeing that the image is free of aberrations that could affect the interpretation's correctness. In conclusion, more sophisticated methods are needed to overcome the difficulties posed by the complexity of the images, fundamental image processing processes can be useful in improving the quality of medical images. In the end, these methods may improve patient outcomes by diagnosis.","url":"https://doi.org/10.20944/preprints202410.1555.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202410.1555.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4917284/v1","name":"Artificial Intelligence-based chatbots in providing space maintainer related information for pediatric patients and parents: A comparative study","source":"preprints","abstract":"Abstract Background Artificial Intelligence-based chatbots have phenomenal popularity in various areas including spreading medical information. To assess the features of two different chatbots on providing space maintainer related information for pediatric patients and parents. Methods 12 space maintainer-related questions were formed in accordance with the current guidelines and were directed to ChatGPT-3.5 and ChatGPT-4. The answers were assessed regarding the criteria of quality, reliability, readability, and similarity with the previous papers by recruiting the tools EQIP, DISCERN, FRES, FKRGL calculation, GQS, and the Similarity Index. Results ChatGPT-3.5 and 4 revealed that both tools have similar mean values regarding the assessed parameters. ChatGPT-3.5 revealed an outstanding quality and ChatGPT-4 revealed a good quality with mean values of 4.58 ± 0.515 and 4.33 ± 0.492, respectively. The tools also performed high reliability with mean values of 3.33 ± 0.492 and 3.58 ± 0.515 (ChatGPT-3.5, ChatGPT-4; respectively). The readability scores seemed to require an education of a college degree and the similarity levels were lesser than 10% for both chatbots whit a high originality. Conclusions The outcome of this study shows that recruiting AI-based chatbots, ChatGPT for receiving space maintainer-related information can be a useful attempt for those who are seeking medical information regarding pediatric space maintainers on the internet.","url":"https://doi.org/10.21203/rs.3.rs-4917284/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4917284/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.0595.v1","name":"Explainable AI in Diagnostic Radiology for Neurological Disorders – a Review","source":"preprints","abstract":"Artificial Intelligence (AI) has recently had unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of design and development of Computer Aided Diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adaptation and integration in the healthcare system still seems far-fetched. Diagnostic Radiology is no exception. Imagining techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) scans have been vastly and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI powered systems, to analyze such scans, have been incorporated into the standard operating procedures in healthcare system. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of Explainable Artificial Intelligence (XAI), the old school black boxes of Deep Learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. We also summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity.","url":"https://doi.org/10.20944/preprints202412.0595.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0595.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-6458281/v1","name":"Investigating Expectations and Needs of Medical Professionals Regarding the Use of Large Language Models: A Study at German University Clinics","source":"preprints","abstract":"Abstract Recent advancements in Artificial Intelligence (AI) have been driven by Large Language Models (LLMs), powerful tools capable of generating coherent text and solving diverse analytical tasks. While LLMs hold great potential to enhance healthcare by assisting physicians and improving patient treatment, their clinical adoption is limited, and there is a lack of statistically grounded information on the opinions of medical professionals regarding LLM usage. To address this gap, we conducted an online survey from April to October 2024, investigating the attitudes and perceptions of medical personnel on the use of LLMs. We gathered insights from 120 participants across five German university clinics, including physicians, medical students, and administrative staff.Findings show that many participants already use LLMs for research support, summarization, translation, and report drafting. Most believe LLMs will positively influence their field, acknowledge their potential to automate mundane tasks, and believe they will help to achieve a more personalized, evidence-based, and cost-effective patient treatment. However, concerns were shown regarding their opaque nature, data privacy, and the potential loss of patient trust. Participants overwhelmingly feel their institutions are not well-prepared for LLM adoption, with suggestions for improvement including increased education and specialized training, investments in digitalization and infrastructure, ensuring legal compliance, and encouraging technological openness. We hope these insights will inform the design of future medical AI solutions.","url":"https://doi.org/10.21203/rs.3.rs-6458281/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6458281/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.12.07.24318566","name":"Early identification of Family Medicine residents at risk of failure using Natural Language Processing and Explainable Artificial Intelligence","source":"preprints","abstract":"Background During residency, each resident is observed and receives feedback based on their performance. Residency training is demanding, with some residents struggling with their academic performance. A competency-based residency training program’s success depends on its ability to identify residents with difficulty during their first year of post-graduate education and to provide them with timely intervention and support. Objective In large training programs such as Family Medicine, identifying residents at risk of failing their certification exams is difficult. We developed an AI system using state-of-the-art technologies in Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Explainable AI (XAI) to detect at-risk residents automatically. Materials and Methods The research was conducted in the 2023-24 academic year. We implemented ML, DL and NLP models for prediction and performance analysis. The target variable chosen for the prediction was the determination of whether the resident would fail or pass their certification exam. XAI was used to enhance the understanding of the model’s inner workings. Results In total, there were 1382 data points of residents. The final model, Support Vector Machine (SVM), achieved an accuracy of 89.05% and an F1 score of 74.54 for the multiclass classification when multimodal (text and tabular) data was used. This model outperformed the models that only used qualitative or quantitative data exclusively. Conclusion Combining qualitative and quantitative data represents a novel approach and provided better classification results. This research demonstrates the feasibility of an automated AI system for the early identification of residents at risk of academic struggle. Prior Abstract Presentation Abstract presented at AMEE (An International Association for Medical Education) Conference Basel, Switzerland, August 24-28, 2024.","url":"https://doi.org/10.1101/2024.12.07.24318566","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.07.24318566","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.09.09.25334920","name":"Clinical evaluation of a natural language processing system for assisting structured diagnosis recording at the point of care: MiADE (Medical Information AI Data Extractor)","source":"preprints","abstract":"ABSTRACT Background Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it is time consuming for clinicians. We developed a natural language processing system called MiADE integrated with the Epic electronic health record to provide suggestions for structured diagnosis entries at the point of care. Objectives To evaluate the usability, usefulness and impact of MiADE, and identify recommendations for systems to improve point of care structured data recording. Methods Mixed methods evaluation of the implementation of MiADE, with surveys, interviews and observed outpatient consultations. The number of structured diagnoses recorded per outpatient encounter was compared before and after MiADE, and completeness of inpatient problem lists was evaluated using the billing diagnoses as a gold standard. Results 85 clinicians consented to the study and were provided access to MiADE, and 24 used MiADE to receive structured data suggestions during the study period. Baseline survey data and observations showed wide variation in structured data recording despite clinicians considering it to be important. Half of post-implementation survey respondents considered MiADE to be ‘very’ or ‘moderately’ useful. Among outpatient users there was a 36% increase in the number of diagnoses recorded per encounter, but no improvement was seen in the inpatient setting. Conclusions Natural language processing using MiADE has the potential to improve structured data recording, but further development and better clinician engagement are needed in order to maximise its impact. SUMMARY BOX What is already known on this topic Despite the benefits of structured recording of healthcare data for individual care and research, much of the key information in electronic health records (such as diagnoses) is only recorded in free text Structured data entry in electronic health record systems can be time-consuming and cumbersome for clinicians What this study adds Embedding natural language processing within the electronic health record to suggest structured data entries was reported by clinicians to be useful, and increased the recording of outpatient diagnoses Clinician engagement was challenging, and overall usage of structured data remained suboptimal How this study might affect research, practice or policy Usability of electronic health record systems needs to improve to enable clinicians to record high quality data without impeding their workflow Natural language processing embedded within electronic health records may improve ease of use for data entry, but high-level clinician buy-in is also needed to influence professional documentation practice","url":"https://doi.org/10.1101/2025.09.09.25334920","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.09.09.25334920","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202409.2055.v1","name":"Virtual Reality and Artificial Intelligence Paving the Future of Cardiac Interventions and Training, a Review of the Available Reviews","source":"preprints","abstract":"Several medical graduates, avoid surgical specialties especially those with complex and delicate anatomy due to their doubt in their visual spatial abilities. Many patients are deemed inoperable, particularly in the field of pediatric cardiology, due to the complexity of the anatomy. On a similar note, stratification of risk of cardiac interventions, and decision making, is a room of significant person-based and center-based bias. Two inter-related technologies, namely immersive virtual reality (VR) and artificial intelligence have made impossible things possible and can help to standardize decision making strategies. This review aimed at reviewing the main anatomical and lesion-based scopes of these advancements in the field of cardiac care, with a special focus on pediatric cardiology.","url":"https://doi.org/10.20944/preprints202409.2055.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202409.2055.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.0354.v1","name":"AI-Driven Multimodal Deep Learning for COVID-19 Prediction: A Comparative Analysis of Pre-Trained vs. Custom Models Using Cough, X-ray, and CT Scan Datasets","source":"preprints","abstract":"COVID-19, a respiratory illness that mostly attacks the human lungs emerged in 2019 and quickly became a global health crisis. Its fast transmission has necessitated the creation of effective tools that could aid in its classification. In this paper, we present an artificial intelligence multimodal deep learning model that leverages X-ray, CT-scan, and cough signals to classify COVID-19 accurately. This paper meticulously compares the effectiveness of non-pre-trained and pre-trained versions of VGG19, MobileNetv2, and ResNET across various multimodal and some unimodal models. Findings show that while the pre-trained unimodal systems for cough and X-ray outperform their non-pre-trained counterparts, the non-pre-trained CT scan model performs exceptionally well. This suggests that features learned from the VGG19 model fail to generalize effectively. Remarkably, the non-pre-trained multimodal model accomplishes an F1-score of 0.9804, slightly outperforming its pre-trained counterpart. These results indicate the potential of developing artificial intelligence models from scratch, especially for specialized datasets in multimodal scenarios. While this research advances our understanding of transfer learning within COVID-19 classification, it also emphasizes the prospects of developing custom deep-learning models for solving complex medical problems.","url":"https://doi.org/10.20944/preprints202412.0354.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0354.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5333210/v1","name":"When algorithms replace biologists: A Discrete Choice Experiment for the valuation of risk-prediction tools in Neurodegenerative Diseases","source":"preprints","abstract":"Abstract Background. Earlier detection of neurodegenerative diseases may help patients plan for their future, achieve a better quality of life, access clinical trials and possible future disease modifying treatments. Due to recent advances in artificial intelligence (AI), a significant help can come from the computational approaches targeting diagnosis and monitoring. Yet, detection tools are still underused. We aim to investigate the factors influencing individual valuation of AI-based prediction tools. Methods. We study individual valuation for early diagnosis tests for neurodegenerative diseases when Artificial Intelligence Diagnosis is an option. We conducted a Discrete Choice Experiment on a representative sample of the French adult public (N = 1017), where we presented participants with a hypothetical risk of developing in the future a neurodegenerative disease. We ask them to repeatedly choose between two possible early diagnosis tests that differ in terms of (1) type of test (biological tests vs AI tests analyzing electronic health records); (2) identity of whom communicates tests’ results; (3) sensitivity; (4) specificity; and (5) price. We study the weight in the decision for each attribute and how socio-demographic characteristics influence them. Results. Our results are twofold: respondents indeed reveal a reduced utility value when AI testing is at stake (that is evaluated to 36.08 euros in average, IC = [22.13; 50.89]) and when results are communicated by a private company (95.15 €, IC = [82.01; 109.82]). Conclusion. We interpret these figures as the shadow price that the public attaches to medical data privacy. The general public is still reluctant to adopt AI screening on their health data, particularly when these screening tests are carried out on large sets of personal data.","url":"https://doi.org/10.21203/rs.3.rs-5333210/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5333210/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-7064855/v1","name":"Opportunities and Challenges of Implementation of ChatGPT in Teaching Otorhinolaryngology-Head and Neck Surgery","source":"preprints","abstract":"Abstract Research background: Traditional teaching methods often rely on teachers' explanations and limited teaching resources, whereas modern medical education increasingly emphasizes personalized learning. \"Otorhinolaryngology-Head and Neck Surgery\" covers a wide range of knowledge, from the microstructure of the ear to the complex anatomical relationship of the neck. It is difficult for teachers to cover all knowledge points in limited class time. In recent years, artificial intelligence technologies such as ChatGPT have shown strong capabilities in various fields. In the field of education, it can realize functions such as intelligent questions and answers and knowledge integration to assist teachers in teaching, which can help teachers impart more knowledge in a shorter time and optimize the teaching process. However, knowledge of \"Otorhinolaryngology Head and Neck Surgery\" requires high accuracy and professionalism, and the introduction of the ChatGPT may change the balance of traditional teaching models. Therefore, it is particularly important to understand the status of ChatGPT in the teaching of this subject, which is particularly important for cultivating students' scientific thinking, so this study is carried out specifically. Objective: To explore the opportunities and challenges associated with the new technology empowerment of the ChatGPT in \"Otorhinolaryngology-Head and Neck Surgery\" teaching. Methods: A total of 68 teachers who taught \"Otorhinolaryngology-Head and Neck Surgery\" in our hospital from February 2023 to July 2024 were selected as research subjects. All of them were taught in the ChatGPT-assisted teaching mode, and the situation was analyzed via a questionnaire survey. Results: A total of 64.71% of the teachers understood scientific thinking well, 91.18% of the teachers learned about scientific thinking through \"teacher training organized by the school or the Education Bureau\", 95.59% of the teachers believed that it is very important to cultivate students' scientific thinking in teaching, 69.12% of the teachers consciously cultivate students' scientific thinking in teaching, 92.65% of the teachers cultivate students' scientific thinking by \"creating problem situations\", and 70.59% of the teachers believe that the effect of cultivating scientific thinking is average. A total of 82.35% of teachers believe that it is difficult to cultivate scientific thinking of \"questioning innovation\", and 70.59% of teachers believe that there are difficulties in cultivating students' scientific thinking in teaching because \"students have different cognitive levels and it is difficult to teach students in accordance with their aptitude in class\". A total of 64.71% of teachers are familiar with ChatGPT, 73.53% of teachers know ChatGPT through \"sharing by others\", 92.65% of teachers and 91.18% of teachers know the functions of ChatGPT through \"dialog interaction\" and \"question answering and knowledge query\", respectively, 47.06% of teachers sometimes use ChatGPT, 72.06% of teachers usually use ChatGPT to \"seek answers and solutions to problems\", and 70.59% of teachers have used ChatGPT in the field of education and teaching or wanting to use it to \"assist in writing lesson plans\". A total of 76.47% of the teachers believed that applying ChatGPT to education and teaching \"can provide students with personalized learning experience\", and 69.12% of the teachers agreed that ChatGPT can be used in the classroom to assist teachers in teaching. A total of 89.71% of the teachers believed that the difficulty in applying ChatGPT to classroom teaching lies in the \"lack of corresponding facilities and resources\". Conclusion: The opportunity of ChatGPT and the new technology empowerment of teaching \"Otorhinolaryngology-Head and Neck Surgery\" lies in the fact that most teachers know and have a certain understanding of its functions and applications and agree to use it for teaching. However, the challenge lies in providing students with personalized learning experience, popularizing ChatGPT and its educational potential to teachers, and supplementing corresponding equipment and resources.","url":"https://doi.org/10.21203/rs.3.rs-7064855/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7064855/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.05.12.26352585","name":"Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow Efficiency","source":"preprints","abstract":"Key Results In five-fold cross-validation (1000 cases, six institutions), the model achieved mean Dice similarity coefficients of 0.939 for enhancing tumor, 0.937 for tumor core, and 0.921 for whole tumor, with tumor core volumes strongly correlated with reference volumes (r = 0.995). In external validation (310 cases, single institution), mean tumor core Dice was 0.872 despite heterogeneous MRI protocols and incomplete sequences; tumor core volume correlation remained strong (r = 0.971). In a blinded evaluation by 10 radiologists across 510 cases, model segmentations scored higher than reference annotations, with the advantage fourfold larger in real-world clinical data; mean inference time was 1.2 seconds per case. Summary Statement A fully automated deep learning model achieved high meningioma segmentation accuracy, generalized to heterogeneous clinical imaging in 1.2 seconds, and surpassed reference annotation quality in blinded radiologist evaluation. Background Meningiomas are the most common primary intracranial tumors in adults, and volumetric assessment increasingly guides surveillance and treatment decisions. Automated segmentation could enable standardized volumetry but requires robust validation. Purpose To develop a fully automated three-dimensional deep learning model for meningioma segmentation on multiparametric MRI, and to evaluate segmentation accuracy, external generalizability, failure modes, radiologist-rated clinical plausibility, and workflow feasibility. Methods From 2024 to 2026, this retrospective study trained a custom 3D nnU-Net residual encoder model. Expert segmentations covered enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Dice similarity coefficient (DSC) was the primary metric. External validation used an independent single-institution dataset (n = 310 intracranial cases) with incomplete MRI protocols. Failure modes, model equity, and inference time were assessed. A blinded multi-rater study (10 radiologists; 510 cases) rated TC segmentations using a 0–10 Likert scale, analyzed with linear mixed-effects models. Results Model training used the BraTS Meningioma 2023 dataset (n = 1000; mean age 60.2 ± 14.5; 705 female). In cross-validation, mean DSC was 0.939 for ET, 0.937 for TC, and 0.921 for WT. In external validation, mean DSC was 0.872 for TC and 0.842 for WT, despite heterogeneous protocols and incomplete sequences. Predicted TC volumes correlated strongly with reference volumes in cross-validation (r = 0.995) and external validation (r = 0.971). Most common failure modes were skull base and intraosseous tumors with performance equitable across demographic subgroups. Mean inference time was 1.2 seconds. In blinded evaluation (1120 ratings), model segmentations received higher scores than reference annotations (+0.32 BraTS; +1.38 external validation). Conclusion A fully automated deep-learning model achieved high meningioma segmentation accuracy across multi-institutional training data and external clinical imaging. In a blinded study, model segmentation quality exceeded reference annotations, and 1.2-second inference supported workflow integration. Prospective evaluation is warranted before routine deployment.","url":"https://doi.org/10.64898/2026.05.12.26352585","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.12.26352585","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202411.1391.v1","name":"Machine Learning Algorithms Introduce Evoked Potentials as the Alternative Biomarkers for the Edss Prognosis of Ms Patients","source":"preprints","abstract":"Machine learning has witnessed a notable increase in significance within the medical field, primarily due to the increasing availability of health-related data and the progressive enhancements in machine learning algorithms. It can be utilized to formulate predictive models that aid in disease diagnosis, anticipate disease progression, tailor treatment to fulfill individual patient needs and improve the operational efficiency of healthcare systems. The strategic utilization of data can considerably elevate the quality of patient care, reduce healthcare costs, and promote the formulation of personalized and effective medical interventions. The healthcare industry reaps considerable benefits from the meticulous analysis of medical data, as it plays an integral role in promptly identifying diseases in patients. Timely detection of a disease can contribute to effective symptom management and guarantee that appropriate treatment is provided. The pronounced association between evoked potentials (EPs) and Expanded Disability Status Scale (EDSS) scores in individuals diagnosed with multiple sclerosis (MS) indicates that EPs may serve as dependable predictive markers for the progression of disability. Numerous studies have confirmed that variations in somatosensory evoked potentials (SEPs) demonstrate a relationship with EDSS scores, particularly during the early stages of the disease. The present study aims to apply artificial intelligence techniques to identify predictors linked to the progression of Multiple Sclerosis (MS) as assessed by the disability index (EDSS). It is essential to clarify the role of evoked potentials (EPs) in the prognostication of MS. We analyzed empirical data obtained from a medical database of 125 records. Our primary objective is to construct an expert Artificial Intelligence system capable of predicting the EDSS index by applying advanced knowledge-mining algorithms. We have developed intelligent systems that predict the progression of MS utilizing machine learning algorithms, specifically Decision Trees and Neural Networks. In our experimental evaluation, Decision Trees, Neural Networks, and Bayes for EPs achieved accuracies of 88.9%, 92.9%, and 88.2% respectively, which are comparable to MRI which obtained accuracies of 88.2%, 96.0%, and 85.0%. The EPs can be established as predictors of MS with efficacy analogous to that of MRI findings. Further investigation is necessary to validate EPs, which are significantly less expensive, portable, and simpler to administer than MRI, as equally effective as imaging or biochemical methods in functioning as biomarkers for MS.","url":"https://doi.org/10.20944/preprints202411.1391.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202411.1391.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202411.1794.v1","name":"Use of Artificial Intelligence Chatbots in Interpretation of Clinical Chemistry and Laboratory Medicine Reports: A Standardized Approach","source":"preprints","abstract":"Laboratory medicine plays a crucial role in clinical decision-making, yet result in-terpretation often remains challenging for patients. This study evaluates the effec-tiveness of AI-powered conversational systems in interpreting laboratory test results, utilizing a closed-box training approach for Claude-based virtual conversational chatbot, focusing exclusively on laboratory data interpretation without clinical di-agnosis. The system was tested using 30 laboratory reports from two different Italian laboratories, encompassing various biochemical parameters and measurement standards. The laboratories utilized different analytical platforms and methodologies, allowing us to evaluate the chatbot&#039;s ability to interpret results across diverse in-strumental settings. The interpretation accuracy of Claude AI chatbot was rigorously evaluated through a peer review process involving three independent medical re-viewers with extensive experience in laboratory medicine. Significantly, the Claude model showed zero hallucinations. The excellent performance was attributed to the closed-box training environment, high-quality domain-specific prompts, and pure generation mechanisms without external data access. This study suggests that carefully designed AI models can effectively bridge the gap between raw laboratory data and patient understanding, potentially transforming laboratory reporting systems while maintaining high accuracy and avoiding diagnostic territory. These findings have important implications for patient empowerment and healthcare communication efficiency.","url":"https://doi.org/10.20944/preprints202411.1794.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202411.1794.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-8759109/v1","name":"Predictors and determinants of public trust in AI and software as a medical device for healthcare in UK: Evidence from the RADIANT Voices Study","source":"preprints","abstract":"Abstract Objective To identify sociodemographic, experiential and attitudinal determinants of public trust in artificial intelligence (AI) and software as a medical device (SaMD) in UK healthcare, focusing on trust in AI-assisted clinical decision-making under human oversight. Design Cross-sectional online survey with prespecified outcomes. Setting United Kingdom; data collected online 3 October-7 November 2025. Participants A community sample of 1,468 adults (mean age 44.8 year; 49.8% female). Prior AI use was common (79.4%). eHealth literacy was measured using eight-item eHEALS scale (α = 0.91). Main outcome measures Primary outcome was trust in AI-assisted clinical decision-making (5-point-scale). Secondary outcomes included the within-person trust gap between AI-assisted and AI-only decisions, governance preferences, accountability for AI-related harm, and trust in regulators and developers. Results Trust was strongly contingent on human oversight. High trust was reported by 62.7% for AI-assisted decisions (mean 3.63, SD 0.95) but by only 10.5% for AI-only decisions (mean 2.15, SD 1.01), yielding a mean within-person trust gap of 1.47 points. Almost all participants (95.2%) preferred a health and care professional as the final decision-maker. Support for governance was high: 92.2% wanted disclosure whenever AI is used, 83.4% opposed unsupervised AI advice, and 79.4% supported stronger regulation. Higher trust in AI-assisted decisions was independently associated with prior AI use, higher eHealth literacy and older age. Conclusions Public confidence in healthcare AI is conditional. Trust is higher when clinicians retain accountability, AI use is transparently disclosed, and performance claims are evidence-based, underscoring the need to embed disclosure, human oversight and robust governance in AI/SaMD deployment.","url":"https://doi.org/10.21203/rs.3.rs-8759109/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8759109/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-9271553/v1","name":"Shell digitisation for disease control: creating a digital collection of schistosomiasis host snail shells","source":"preprints","abstract":"Abstract The gastropod shell collection at the Natural History Museum (NHM) in London houses a wealth of gastropod specimens from around the world. Many snail species are relevant to human health due to their role as hosts for medically important parasites. This includes blood flukes of the genus Schistosoma , which cause human schistosomiasis. Schistosomiasis control programmes often rely on efficient and precise identification of host snail species, but traditional resources available to these efforts are limited both in scope and accuracy, hindering the progress of control programs. To bridge this gap, we present a digitisation effort using the African snail shell collections housed in NHM London. Shells were digitised using traditional photography and micro computed tomography (µCT). µCT scans were used to produce 3D models optimised for digital visualisation and 3D printing. To ensure full accessibility, models were uploaded to the Sketchfab online public repository with registration numbers and links to the NHM data portal. To further aid identification efforts, we present a detailed pipeline to create 3D-printed shell replicas, accompanied by short 3D animations showcasing key morphological characters of snail shells. 3D models and 3D-printed replicas can also be used as teaching tools, contributing to the dissemination of knowledge of tropical diseases critical to the efforts of endemic countries. Further, we showcase how digitisation approaches can be applied to similar museum collections.","url":"https://doi.org/10.21203/rs.3.rs-9271553/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9271553/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.03.30.25324911","name":"A Quarter-Century of Synthetic Data in Healthcare: Unveiling Trends with Structural Topic Modeling","source":"preprints","abstract":"Data-driven approaches are transforming healthcare, yet acquisition of comprehensive datasets is hindered by high costs, privacy regulations, and ethical concerns. To address these challenges, synthetic data, artificially generated datasets that mimic the statistical properties of real-world data, provides a promising solution. Despite its growing adoption, the thematic landscape of synthetic data research in healthcare remains underexplored. Therefore, we applied structural topic modeling (STM) to map the research landscape of synthetic data in healthcare, revealing prevalent topics and tracking their evolution over time and across geographic locations. PubMed publications from 2000-2024 containing “synthetic data,” “artificial data,” or “simulated data” in the title/abstract were retrieved. After preprocessing the text (lowercasing, punctuation/stopword removal, stemming), structural topic modeling (STM) was performed using year and continent as covariates. The optimal number of topics (K=10) was determined using held-out likelihood and interpretability. Topic prevalence, temporal trends, and inter-topic correlations were analyzed using stacked area charts and network analysis. Analysis of 14,788 PubMed articles (2000-2024) revealed a tenfold increase in publications. Geographically, North America (48.6%) and Europe (33.5%) were primary contributors, but Asia’s share steadily rose from 2.9% to 23.1%. STM identified ten key topics, grouped into Biomedical Imaging & Signal Processing (25.2%), Synthetic Data Applications in Biomedical Research (17.7%), Computational & Statistical Methods (23.9%), and Genomics & Evolutionary Biology (33.2%) themes. We observed gradual declines in initially prominent topics including “Bayesian Modelling” (23.1% to 9.9%), “Neuroimaging” (16.0% to 9.3%), and “Image Simulation” (17.7% to 9.1%), giving ascendancy to “Synthetic Data Generation” (2.2% to 27.1%) and “Disease Modeling and Public Health” (4.8% to 11.9%) by 2024. Synthetic data research in healthcare has experienced increasing interest, marked by shifts in geographic distribution and dynamic evolution of key topics. Realizing the full potential of synthetic data requires fostering cross-disciplinary collaborations, implementing bias mitigation strategies, and establishing equitable partnerships. Author Summary In recent years, synthetic data—artificially generated datasets designed to reflect real-world information—has gained attention as a way to advance healthcare research while addressing concerns around data privacy, costs, and accessibility. Our work explores how this field has evolved over the past 25 years, identifying key research trends and shifts in geographic contributions. By analyzing over 14,000 published studies, I found that synthetic data research has grown nearly tenfold, with increasing contributions from Asia alongside traditional leaders in North America and Europe. The focus of research has also changed: earlier work emphasized medical imaging and statistical modeling, while recent studies highlight synthetic data generation and its use in disease modeling, public health, and clinical trials. Despite this progress, important gaps remain. Areas like drug discovery, mental health, and ethical considerations in artificial intelligence need further attention. By mapping these trends, our work underscores the importance of cross-disciplinary collaboration and equitable global partnerships to maximize the benefits of synthetic data in improving healthcare worldwide.","url":"https://doi.org/10.1101/2025.03.30.25324911","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.03.30.25324911","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.0366.v1","name":"Optimizing Parkinson's Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline","source":"preprints","abstract":"Patient-level grouped data is prevalent in public health and medical fields, and Multiple Instance Learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks: post-mean, post-max, post-min, and pre-mean aggregation. We developed a customized AI pipeline that incorporates twelve machine learning algorithms along with the four aggregation methods to detect Parkinson&#039;s Disease (PD) using multiple voice recordings from individuals available in the UCI Machine Learning Repository, which includes 756 voice recordings from 188 PD patients and 64 healthy individuals. Seven performance metrics—Accuracy, Precision, Sensitivity, Specificity, F1 score, AUC, and MCC—were utilized for model evaluation. Various techniques, such as Bag Over-sampling (BOS), Cross-validation, and Grid Search, were implemented to enhance classification performance. Among the four aggregation methods, post-mean aggregation combined with XGBoost achieved the highest accuracy (0.921), F1 score (0.949), and MCC (0.783). Furthermore, we identified potential trends in selecting aggregation methods that are suitable for imbalanced data, particularly based on their differences in sensitivity and specificity. These findings provide meaningful implications for further exploration of grouped imbalanced data.","url":"https://doi.org/10.20944/preprints202412.0366.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0366.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5316440/v1","name":"An Interpretable Framework for Gastric Cancer Classification Using Multi-Channel Attention Mechanisms and Transfer Learning Approach on Histopathology Images","source":"preprints","abstract":"Abstract The importance of gastric cancer and the role of deep learning techniques in categorizing gastric cancer histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inability to capture complex patterns, lack of adaptability, and sensitivity to noise. We suggest a multi-channel attention mechanism-based framework that can overcome the limitations of conventional deep learning models by dynamically focusing on relevant features, enhancing extraction, and capturing complex relationships in medical data. The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. The proposed framework’s strong performance is confirmed by comparative experiments conducted on a publicly available Gastric Histopathology Sub-size Image Database, which yielded remarkable classification accuracies of 99.07% and 98.48% on the validation and testing sets, respectively. Additionally, on the HCRF dataset, the framework achieved high classification accuracy of 99.84% and 99.65% on the validation and testing sets, respectively. The effectiveness and interchangeability of the three channels are further confirmed by ablation and interchangeability experiments, highlighting the remarkable performance of the framework in gastric cancer histopathological image classification tasks. This offers an advanced and pragmatic artificial intelligence solution that addresses challenges posed by unique medical image characteristics for intricate image analysis. The proposed approach in artificial intelligence medical engineering demonstrates significant potential for enhancing diagnostic precision by achieving high classification accuracy and treatment outcomes.","url":"https://doi.org/10.21203/rs.3.rs-5316440/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5316440/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-9188548/v1","name":"Automated Longitudinal MRI Assessments of Diffuse Midline Gliomas: A Large Multi-Institutional Study","source":"preprints","abstract":"Abstract Background Diffuse midline gliomas (DMGs) are highly aggressive pediatric and young adults brain tumors with poor prognosis and limited treatment options. Accurate imaging assessment of tumor progression is critical for evaluating therapeutic response and guiding clinical decisions, but it is subject to reader bias. This study aims to compare two automated deep learning (DL) based strategies for longitudinal segmentation and subsequent classification of tumor progression in DMG. Methods We retrospectively analyzed longitudinal imaging from 155 DMG patients of UCSF hospitals and an external cohort of 18 patients from the Pediatric Neuro-Oncology Consortium. Two publicly available DL models were evaluated: (1) a “longitudinal” model trained to directly segment areas of change from two consecutive time points, and (2) an “independent” state-of-the-art volumetric segmentation model that processes each time point individually and then computes the change mask between the independent segmentation of consecutive time points. Labels were derived from a manual review of radiology reports and categorized as “Increased,” “Decreased,” or “Stable” tumor size based on the interpreting neuroradiologist’s assessment. Reference standard segmentations of change volumes were manually created in a subset of patients. Model segmentation outputs were compared using Dice score, volumetric mean absolute error (MAE), and volume coefficient of determination (R²) in internal and external sets. For comparison to radiologist assessment, classification accuracy, sensitivity, specificity, and Area Under the ROC Curve (ROC AUC) were assessed. Findings The longitudinal model demonstrated superior classification performance with ROC AUCs of 0.93, 0.90, and 0.83 for the prediction of increased, decreased, and stable tumor change classes, respectively in the internal set. It achieved a mean absolute error (MAE) of 7.41 ± 23.04 cm³ and R² of 0.25 internally, and 8.27 ± 16.63 cm³ and R² of 0.53 externally. Median Dice scores were 0.76 (IQR: 0.50–0.88) and 0.68 (IQR: 0.41–0.92) in the internal and external datasets, respectively. In comparison, the independent model showed lower classification ROC AUCs (0.83, 0.89, and 0.80), higher MAEs (11.03 ± 21.24 cm³ internally; 10.86 ± 17.51 cm³ externally), and lower median Dice scores (0.40 [IQR: 0.25–0.59] internally; 0.62 [IQR: 0.33–0.83] externally). Interpretation For assessing changes in DMG tumor size, the longitudinal DL model generally outperforms the independent model in internal and external cohorts. These findings support integrating dedicated longitudinal-based AI tools for more objective and reproducible tumor assessments.","url":"https://doi.org/10.21203/rs.3.rs-9188548/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9188548/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5247690/v1","name":"Deep Learning Frameworks for Histopathological Image Processing in Colorectal Cancer Diagnostics","source":"preprints","abstract":"Abstract Artificial intelligence (AI) in the field of pathology and medical diagnostics has rapidly evolved, with applications focused on analyzing histopathological images. These images, obtained from biopsies or surgical procedures, are crucial for diagnosing diseases, particularly cancer, and provide essential insights into tissue structure. Traditionally, pathologists faced a challenging task in accurately analyzing the cellular features within these images. However, the introduction of deep learning, has significantly improved diagnostic reliability. In this study, two convolutional neural network models were implemented and compared. Both were trained on a multiclass dataset of histological images related to colorectal cancer. Among the two tested models, VGG-19 demonstrated the best performance, achieving a precision rate of 98%. Using a Python-based graphical interface, we employed the \"Grad-CAM\" technique to gain deeper insight into the model's classification process and identify key regions during training. The application of artificial intelligence to colon histopathological images also has the potential to improve patient outcomes and early cancer diagnosis. By combining AI with intuitive interfaces, we can enhance diagnostic accuracy and accelerate the analysis process.","url":"https://doi.org/10.21203/rs.3.rs-5247690/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5247690/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202410.1044.v1","name":"Integration of New Technologies in Healthcare: Robotic Surgery and Artificial Intelligence in Cytology. Literature Review","source":"preprints","abstract":"Healthcare is undergoing a technological transformation, particularly in robotic surgery and arti-ficial intelligence (AI) applied to cytology. These innovations are improving the precision and effi-ciency of both surgical procedures and laboratory diagnostics. Robotic systems, such as the Da Vinci, enable surgeons to perform complex procedures with enhanced control and precision, leading to minimally invasive surgeries with reduced recovery times. This technology has signifi-cantly impacted urological, cardiac, and gastrointestinal surgeries, and its use is expected to grow. In cytology, AI automates the analysis of cell samples, leading to faster and more accurate diag-noses, especially in detecting diseases like cervical cancer. AI reduces errors and enhances diag-nostic accuracy by identifying complex patterns in cellular images. Predictive analytics, driven by big data, also allows for personalized treatments based on patient-specific data. However, these technologies face challenges, including high costs, ethical concerns, data privacy issues, and resistance from healthcare professionals. Continuous training is essential for medical personnel to effectively adopt these tools. Furthermore, the rise of telemedicine and telesurgery, supported by advancements in 5G, offers new opportunities for remote care but also introduces risks, such as depersonalization of medical care and technological barriers. Ultimately, while robotic surgery and AI hold great promise for improving clinical outcomes, their successful integration will depend on addressing these challenges and ensuring that healthcare professionals are adequately prepared for the digital future.","url":"https://doi.org/10.20944/preprints202410.1044.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202410.1044.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.09.24.24314336","name":"Stroke recognition in medical emergency calls: A novel sensitivity definition as a basis for developing artificial intelligence decision support","source":"preprints","abstract":"Background The sensitivity of emergency medical communication centers (EMCC) for stroke detection varies widely. However, few studies offer detailed insights into the entirety of prehospital pathways in patients with stroke. Therefore, this study aimed to lay the foundation for artificial intelligence (AI) decision support tools in EMCCs by exploring their ability to detect strokes in medical emergency calls, describe a novel method for stroke sensitivity calculation in the EMCC, and identify factors associated with stroke recognition during a call. Methods In total, 1,164 patients with stroke in the catchment area of Bergen EMCC in 2018 and 2019 were included, and a dataset from the EMCC was established manually and linked with data from the Norwegian Stroke Registry (NSR) for analysis. Descriptive statistics, Chi-square test for categorical variables, Mann–Whitney U test for continuous variables, and multivariate logistic regression (LR) were performed on data obtained from patients primarily assessed by EMCC (n=838). Results Using a novel method, we found a stroke detection sensitivity of 76.8% in our study, compared to the 63.4% when using the traditional sensitivity detection method. LR analysis showed a positive association between stroke suspicion and ischemic strokes (odds ratio [OR]=0.317 [0.209–0.481]; p Conclusions This study introduced a novel and more accurate method for calculating EMCC stroke sensitivity, which is relevant for developing decision support tools, such as AI. Moreover, we identified factors of particular interest for future EMCC research that are relevant to developing AI decision-support tools. Clinical trials https://clinicaltrials.gov/study/NCT04648449","url":"https://doi.org/10.1101/2024.09.24.24314336","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.09.24.24314336","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4805394/v1","name":"Development of a depression severity diagnosis model through natural language processing by psychiatry clinical texts","source":"preprints","abstract":"Abstract Depression has surged in Korea, with 933,481 patients in 2021, a 35.1% increase since 2017. Globally, 5% of adults suffer from depression, resulting in over 700,000 suicides annually. However, Korea has only 29.5 mental health workers per 100,000 people, below the OECD average of 97.1. There is an increasing demand for mental illness diagnosis support systems internationally, and various research and development efforts are being attempted to alleviate mental illness. The problem of insufficient mental health human resources and treatment overload can be alleviated by medical artificial intelligence technology. We developed an artificial intelligence model for a clinical decision support system that determines the severity of depression using natural language data about depressive symptoms reported by patients treated in a psychiatric unit contained in our Clinical Data Warehouse (CDW). This study selected psychiatric depression patients from the Bundang CHA University Hospital CDW in South Korea between 2018 and 2022. Among them, 169 patients were diagnosed with mild depressive episodes, and 460 patients were diagnosed with moderate depressive episodes based on psychiatric symptom presentations. The control group utilized natural language datasets provided for artificial intelligence development on the AI Hub platform. The final analysis dataset consisted of Class 2: Moderate depression episode (460 patients), Class 1: Mild depression episode (169 patients), and Class 0: Normal (123,690 conversation sessions). Using this depression natural language dataset, we developed a model to classify depression severity. We applied various algorithms to accurately diagnose the severity of depression based solely on the symptoms reported by patients through psychiatric clinical texts, and selected the one with the highest numerical diagnostic accuracy and the best practical diagnostic classification. As a result, XGBoost showed the highest diagnostic accuracy, with an accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and an F1 score of 99.6%. Additionally, the AUC was close to 1. Utilizing advanced medical artificial intelligence and natural language processing technology in the field of psychiatry can be greatly beneficial in assisting with the precise, personalized assessment of depression severity based on the content of what patients express.","url":"https://doi.org/10.21203/rs.3.rs-4805394/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4805394/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202411.1804.v1","name":"Artificial Intelligence and Digital Pathology for Histologic Growth Pattern Classification in Lung Adenocarcinoma","source":"preprints","abstract":"Precise histologic pattern classification is essential for lung adenocarcinoma, as it is a primary cause of cancer-related death, to inform successful treatment approaches. The objective of this paper is to compare several deep neural network designs for the categorization of lung cancer histologic patterns. We test various models like DeiT (Data Efficient Image Transformers), CAiT, Swin Transformer, ViT, ResNet, employing Cohen Kappa Score and accuracy measures using hematoxylin and eosin (H amp;E)-stained formalin-fixed paraffin-embedded (FFPE) whole-slide images of lung adenocarcinoma from the Dartmouth-Hitchcock Medical Center (DHMC) [17]. Our findings highlight the results of each architecture, offering guidance on whether models are suitable for tasks involving the classification of histopathology images.","url":"https://doi.org/10.20944/preprints202411.1804.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202411.1804.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4632359/v1","name":"Developing a Canadian Artificial Intelligence Medical Curriculum: A Delphi Study","source":"preprints","abstract":"Abstract The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.","url":"https://doi.org/10.21203/rs.3.rs-4632359/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4632359/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.07.25.24311022","name":"The Role of Artificial Intelligence in Modern Medical Education and Practice: A Systematic Literature Review","source":"preprints","abstract":"A bstract The integration of Artificial Intelligence (AI) into medical education has emerged as a transformative element in the modern healthcare educational system. With the exponential growth of medical knowledge and the increasing complexity of healthcare systems, AI offers innovative solutions to enhance learning outcomes, facilitate personalized education pathways, and improve clinical decision-making skills among medical professionals. This literature review explores the transformative role of AI in the training of healthcare providers, focusing on advancements in medical education, medical diagnostics, and emergency care training. Additionally, it addresses the readiness of healthcare professionals to employ AI technologies, analyzing their current knowledge, attitudes, and the training provided. By synthesizing findings from multiple studies, we aim to highlight AI’s potential to enhance medical education, address challenges, and propose future directions for integrating AI into healthcare training.","url":"https://doi.org/10.1101/2024.07.25.24311022","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.25.24311022","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4884135/v1","name":"Toxicity assessment of doxycycline-aided artificial intelligence-assisted drug design targeting candidate 16S rRNA methyltransferase gene","source":"preprints","abstract":"Abstract Background The misfunction of the protein 16SrRNA methyltransferase usually results in Urinary tract (UTI), and Gastrointestinal (GI) infections, sepsis, pneumonia, and also cause wound infections. It confers resistance to aminoglycoside medications, which complicates the treatment of infections caused by these bacteria. Objectives Herein, we aim to investigate the role of artificial intelligence (AI) in medical sciences to provide the solutions as a significant need in medical therapy for infections. Methodology : Using an AI drug design tool, three effective de novo medicinal compounds that target the 16SrRNA methyltransferase protein were discovered. The computational tools used includes: Expasy for protein annotation, Protparam to calculate physiochemical parameters, SWISS-MODEL to estimate the 3D structure, and Uniprot to generate the 16SrRNA methyltransferase protein sequence. An adequate foundation for the development and validation of AI-designed phytochemical medicines for infections is provided by quality assessment, binding site prediction, drug design with WADDAICA, toxicity screening, ADMET evaluation, and docking analysis with CB-dock. Results Comprehensive pharmacokinetic and toxicology analyses provided the non-toxic character of AI-designed doxycycline by demonstrating its exceptional absorption in the blood–brain barrier. The AI-designed doxycycline docks with the 16SrRNA methyltransferase protein with a noteworthy affinity of about − 7.6 kcal/mol, indicating potential therapeutic value. Conclusion Even though the in silico studies show efficacy and safety, still there is need of in vivo trials to investigate the hidden medical aspects. By addressing existing constraints, this work considerably expands the knowledge about newer methods and also helps to understand deep insights of dug design mechanism for treatment.","url":"https://doi.org/10.21203/rs.3.rs-4884135/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4884135/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202408.1215.v1","name":"Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study","source":"preprints","abstract":"Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.","url":"https://doi.org/10.20944/preprints202408.1215.v1","authors":["Malik Sallam","Kholoud Al-Mahzoum","Yousef Mubrik N. Almutairi","Omar Alaqeel","Anan Abu-Salami","Zaid Elhab Almutairi","Alhur Najem Alsarraf","Muna Barakat"],"tags":["Anxiety","Psychology","Affect (linguistics)","Construct (python library)","Clinical psychology"],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.1215.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5606661/v1","name":"Integrating Generative AI-Based Assistance Tool in Programming Education for Medical Students: A Cross-Sectional Study","source":"preprints","abstract":"Abstract Backgroud With the increasing importance of computational skills in healthcare, there is a growing need to equip medical students with programming knowledge to address complex healthcare challenges effectively. Traditional programming methods, however, can be challenging for medical students due to heavy academic loads and limited exposure to coding. Generative artificial intelligence (GenAI) presents a promising solution to these issues. Methods This research evaluated the feasibility of Chat2R, an R programming assistance tool powered by GenAI, within the postgraduate course “Practical Techniques of Medical Data Mining” at Peking Union Medical College (PUMC). A mixed-methods approach was used, combining quantitative surveys and qualitative insights to assess the tool’s effectiveness and students’ reception. Quantitative data was gathered through post-implementation surveys measuring students' perceptions of their coding proficiency and the tool’s utility. Qualitative analysis explored student interactions with Chat2R, identifying key challenges and concerns to enhance the educational experience. Results A total of 31 postgraduate students from 14 different disciplines participated in the survey. The positive feedback supported the integration of Chat2R as a valuable educational tool, highlighting GenAI’s role in enhancing computational thinking skills. Between March 13 and April 2, 2024, 28 students actively engaged with Chat2R, generating 1,603 questions. Of these, 311 were specifically related to defined data analysis processes: 138 questions on data collection, 74 on data preprocessing, 4 on data exploration, 87 on data visualization, and 8 to data modeling. Conclusion This study demonstrates that integrating Chat2R, a GenAI-based programming tool, can significantly enhance programming education for medical students. It improves computational thinking, supports both lecture and practical learning, and addresses challenges related to limited coding exposure. Positive student feedback highlights its effectiveness in providing coding assistance and fostering an interactive, student-centered learning environment. The findings also underscore the importance of professional development for educators to effectively incorporate GenAI tools into teaching. Future enhancements could include AutoML capabilities, enabling medical students to guide AI-driven data analysis and better utilize AI in clinical and research contexts.","url":"https://doi.org/10.21203/rs.3.rs-5606661/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5606661/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202408.1168.v2","name":"The Role of Artificial Intelligence in Personalized Medicine through Advanced Imaging","source":"preprints","abstract":"This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.","url":"https://doi.org/10.20944/preprints202408.1168.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.1168.v2","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-5182962/v1","name":"Medical tele-diagnoses in countries with limited resources: Comparison of a general generative AI system with a clinical decision support system","source":"preprints","abstract":"Abstract Introduction : Achieving correct clinical or morphological diagnoses in countries with limited resources is a major challenge due to the lack of methods such as immunohistochemistry, molecular biology or imaging, as well as the lack of specialists. Artificial intelligence (AI), either in the form of generative intelligence or in the form of clinical decision support systems (CDSS), is a promising method for bridging the gap between diagnosis in developed countries and countries with limited resources. For this purpose, we used the general generative AI system ChatGPT and the specialised semantic net-based AI system Memem7 as medical diagnostic support systems to improve telemedicine diagnosis in a resource-limited country. Materials and methods : 102 randomly selected cases from 3 hospitals in northern Afghanistan were classified by up to 7 telemedicine experts. In 61 cases (59.8%), the experts provided a disease classification (target diagnosis). In the remaining 41 cases, the experts only provided a list of differential diagnoses. We investigated how often ChatGPT and Memem7 were able to predict the target diagnosis or provide a list of essential differential diagnoses (DD). Results : In 36/61 (59.0%) and 47/61 (77.1%) cases, respectively, ChatGPT and Memem7 recognised the target diagnosis. In 88/102 (86.3%) (ChatGPT) and 93/102 (91.2%) (Memem7) cases, a helpful list of differential diagnoses was provided. Conclusions : Both AI-based systems show promising results, either in confirming the target diagnosis or in providing a helpful list of differential diagnoses.","url":"https://doi.org/10.21203/rs.3.rs-5182962/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5182962/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202407.0938.v1","name":"Advancements and Challenges in Artificial Intelligence Applications in Healthcare Delivery Systems","source":"preprints","abstract":"The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients&#039; hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.","url":"https://doi.org/10.20944/preprints202407.0938.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202407.0938.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.07.28.24311154","name":"Artificial Intelligence Algorithms in Nailfold Capillaroscopy Image Analysis: A Systematic Review","source":"preprints","abstract":"ABSTRACT Background Non-invasive imaging modalities offer a great deal of clinically significant information that aid in the diagnosis of various medical conditions. Coupled with the never-before-seen capabilities of Artificial Intelligence (AI), uncharted territories that offer novel innovative diagnostics are reached. This systematic review compiled all studies that utilized AI in Nailfold Capillaroscopy as a future diagnostic tool. Methods and Findings Five databases for medical publications were searched using the keywords artificial intelligence, machine learning, deep learning and nailfold capillaroscopy to return 105 studies. After applying the eligibility criteria, 10 studies were selected for the final analysis. Data was extracted into tables that addressed population characteristics, AI model development and nature and results of their respective performance. We found supervised deep learning approaches to be the most commonly used ( n = 8). Systemic Sclerosis was the most commonly studied disease ( n = 6). Sample size ranged from 17,126 images obtained from 289 participants to 50 images from 50 participants. Ground truth was determined either by experts labelling ( n = 6) or known clinical status ( n = 4). Significant variation was noticed in model training, testing and feature extraction, and therefore the reporting of model performance. Recall, precision and Area Under the Curve were the most used metrics to report model performance. Execution times ranged from 0.064 to 120 seconds per image. Only two models offered future predictions besides the diagnostic output. Conclusions AI has demonstrated a truly remarkable potential in the interpretation of Nailfold Capillaroscopy by providing physicians with an intelligent decision-supportive tool for improved diagnostics and prediction. With more validation studies, this potential can be translated to daily clinical practice.","url":"https://doi.org/10.1101/2024.07.28.24311154","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.28.24311154","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-8921439/v1","name":"Developing a scalable pipeline for data extraction from clinical letters through resource-efficient prompt engineering","source":"preprints","abstract":"Abstract Free-text clinical records represent an untapped wealth of data for secondary use. Their potential is limited by resource demands necessary for accurate information extraction at scale. We introduce a scalable, resource-efficient, and high-performance pipeline which leverages large language models (LLMs) to address these challenges. This was developed and tested using real-world dual specialist-annotated ophthalmic clinical letters. Our pipeline achieved strong performance with a proprietary model in the development phase, yielding a maximum micro-averaged F1 score of 0.954 (95% CI 0.941–0.967) for diagnosis across nine conditions through iterative prompt refinement alone, also demonstrating strong generalisability (micro-F1 ranging from 0.945–0.980) in temporal validation. This approach extended to two other proprietary models in the same family and was tested in 17 local models from seven open-weight LLM families, demonstrating robustness against model choice and deployment constraints (for models > 10B parameters). Beyond performance, we develop a multi-dimensional assessment to evaluate LLMs for deployment in data extraction tasks, including introducing an error taxonomy to classify failure modes and implementing Pareto frontier analyses to systematically map the operational trade-offs (costs, time) across various LLM configurations. A robust approach to operationalising LLMs in real-world workflows at scale may help lay the foundation for next-generation data pipelines that can accelerate scientific discovery and power continuous learning health systems.","url":"https://doi.org/10.21203/rs.3.rs-8921439/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8921439/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.03.28.25324843","name":"Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care","source":"preprints","abstract":"Summary Background Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical screening test for patients outside of an attack and diagnosis is delayed on average by 15 (!) years. AI screening systems can assist in detecting AHP patients, but validating such systems is challenging, due to the limited number of suspected candidates and/or success of recalling such candidates for testing. At the same time no study to date has highlighted human oversight of AI screening tools, while governing bodies and medical device regulations call for it to be allowed for clinical use. Our primary goal was to demonstrate the feasibility of an AI-based Human-in-the-Loop screening (HAI) approach and quantifying the added value by comparing the rate and number of clinically plausible cases found through it with the current Standard of Care (SOC). Methods This retrospective cohort study included data collected from 899,862 electronic health records (EHR) of patients who were treated at the University Hospital Salzburg (SALK) between December 2007 and December 2021. For our HAI approach we used an AI-tool for disease screening (Dx EHRs v2022.11) provided by Symptoma GmbH, that has been validated for Pompe Disease in a previous study. All historically suspected and diagnosed AHP cases retrieved from the collected data served as the reference standard representing the SOC. All suspected AHP cases were first triaged by generalist physicians (GP) without a specialization for AHP representing the “Humans in the Loop”. Specialized physicians (SP) determined the clinical plausibility of cases by reviewing the complete EHRs of the triaged cases. The primary outcome were the rates of clinically plausible cases (=precision) in the HAI and SOC cohorts and its sub-cohorts. Additionally, we investigated the differences in phenotypes in those cohorts. Historically diagnosed AHP cases were reviewed by SP for the reliability of their diagnosis. Findings Of a total of 899,862 EHRs, 191 EHRs were triaged into the HAI cohort and 107 filtered into the SOC cohort. 74 (38.74%) and 28 (27.72%) cases were deemed clinically plausible, for HAI and SOC respectively. Of those 74 clinically plausible cases in HAI, 46 were de-novo cases missed by SOC. The sub-analysis on the phenotypical features indicated that psychological and psychosomatic symptoms (Restlessness, Confusion, Anxiety, Depression, Mood swings, Palpitations) are significantly underrepresented within historically suspected AHP cases. As well were some common and subtle symptoms (Pain, Nausea, Vomiting, Fatigue). Among 16 historically diagnosed cases, four were reclassified as misdiagnosed, and seven lacked conclusive evaluation by current diagnostic standards. Notably, two new AHP cases were identified “incidentally” during the study, with a Poisson probability of 8.34% for this event to happen, suggesting this occurrence was unlikely to be random. Interpretation AHP is incredibly hard to diagnose and even already made AHP diagnoses are unreliable. Additionally, certain phenotypes are especially challenging to identify via the current standard of care. HAI managed to reach a higher precision compared to the SOC and found an additional 46 clinically plausible de-novo cases. Both showing feasibility and added value of HAI. “Incidentally” newly diagnosed AHP patients strongly suggest an increase in awareness through the AI screening project. All our findings suggest that HAI is a viable approach addressing the challenge of early diagnosis of AHP and its adherent issues. Prospective studies in a setting as real-time decision support at the point of care are warranted as a next step to implementing HAI as part of the new standard of care. Funding Alnylam Pharmaceuticals Research in context Evidence before this study We systematically searched PubMed for articles published from database inception up to November 11, 2024, using the terms: (\"Artificial Intelligence\" OR \"AI\" OR \"Machine Learning\" OR \"Deep Learning\" OR \"Neural Networks\") AND (\"Screening\" OR \"Diagnosis\" OR \"Detection\") AND (\"Rare Disease\" OR \"Orphan Disease\" OR \"Uncommon Condition\" OR \"Low Prevalence Disease\" OR \"Acute Hepatic Porphyria\" OR \"AHP\" OR \"Porphyria\") AND (\"Human-in-the-loop\" OR \"Human-assisted\" OR \"Human-centered\" OR \"Augmented intelligence\" OR \"Human-machine collaboration\" OR \"Human-computer interaction\" OR \"Hybrid intelligence\" OR \"Human-supervised\" OR \"Human oversight\" OR \"Collaborative AI\" OR \"Human-guided\"). This search yielded no matches, suggesting that no dedicated studies investigating human-in-the loop AI approaches have been performed to date, neither for AHP, nor for rare diseases as a whole. We further adapted the search to look for AI screening approaches in general for Acute Hepatic Porphyria (AHP) specifically, by eliminating the search terms for rare diseases and human-in-the-loop. This search yielded 23 articles of which only two were actually related to the disease AHP. Those two studies aimed at testing AI screening systems for AHP patients but rather focused on successfully detecting AHP patients by using AI only, than comparing performances to the standard of care or highlighting human oversight. In those studies, no new cases could be found which appeared to be mainly due to the limited number of suspected candidates and/or success of recalling such candidates for testing which combined with the ultra-rare prevalence of AHP created unfavorable odds of finding de-novo cases. This stresses the incredible challenges when diagnosing AHP, but also in extension when validating new AI systems supporting such diagnosis. As such more validation in general, but much more direct comparison with the current state of the art is needed to ensure the effectiveness and safety of AI-driven diagnostic support systems for AHP. Hence, we did a retrospective cohort study aimed at evaluating the effectiveness of an AI-based Human-in-the-Loop screening (HAI) approach compared to the standard of care (SOC) in diagnosing AHP cases. Our study design addresses the challenges in validation and simultaneously puts a spotlight on human oversight, which should both contribute accelerating the adoption of AI screening to assist in rare disease diagnosis. Added value of this study To the best of our knowledge this is the first study to date to investigate the added value of a Human-in-the-Loop AI screening concept compared to the SOC for AHP or rare diseases. Explainable AI and particularly human oversight have been urgently demanded by governing bodies and regulators. The EU Artificial Intelligence Act even prescribes human oversight for any AI system designed to be used as a medical device in the European Union, which is founded in the desire to make AI applications as safe and reliable as possible. We present the results of our current study to highlight the feasibility and added value of the HAI concept as well as its potential as prospective real-time screening at the point of care. We further present findings of additional in-depth analyses delineating shortcomings regarding the diagnosis of AHP in the current SOC. Providing such evidence should serve as the bedrock to justify the considerable efforts necessary for implementation, but most of all, shorten the time to diagnosis effectively by using AI for rare disease patients suffering from diseases like AHP. Implications of all the available evidence Real-time decision support at the point of care has been identified as a pivotal lever to improve early diagnosis of AHP. At the same time eHealth infrastructure is being reformed and many initiatives around the world strive for higher levels of maturity. This paves the foundation for centrally orchestrated digital and AI-driven tools. Thus, scalable support systems exploiting this infrastructure are required and need to be validated. In our study, our proposed HAI screening approach showed a higher plausibility rate in suspected cases compared to the SOC and statistical testing could not find a statistically significant difference between the screening methods. Auxiliary findings in our study suggested that AI screening might create additional awareness at the point of care, which proves to be an effective agent in improving the diagnosis of AHP. Further, we found that there might be certain phenotypes that are harder to identify as AHP cases and that already made AHP diagnoses are unreliable. All our findings suggest that HAI is a viable approach addressing the challenge of early diagnosis and its adherent issues. They warrant further prospective studies in a setting as real-time decision support at the point of care while strongly advocating for an implementation into the clinical routine.","url":"https://doi.org/10.1101/2025.03.28.25324843","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.03.28.25324843","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.22541/au.173538245.59171682/v1","name":"Evaluation of the Quality and Reliability of ChatGPT-4's Responses on Allergen Immunotherapy Using Validated Tools","source":"preprints","abstract":"Background: Artificial Intelligence (AI) technologies could potentially change many aspects of clinical practice. While Allergen Immunotherapy (AIT) can change the course of allergic diseases providing relief of symptoms that extend for many years after treatment completion, it can also bring uncertainty to patients, who turn to readily available resources such as ChatGPT-4 to address these doubts. The aim of this study was to use validated tools to evaluate the information provided by ChatGPT-4 regarding AIT in terms of quality, reliability and readability. Methods: : In accordance with AIT clinical guidelines, 24 questions were selected and introduced in ChatGPT-4. Answers were evaluated by a panel of allergists, using validated tools DISCERN, JAMA Benchmark and Flesch Reading Ease Score and Grade Level. Results: : Questions were sorted into 6 categories. ChatGPT provided bad quality information according to DISCERN medians scores in the “Definition”, “Standardization and Efficacy”, and “Safety and Adverse Reactions” categories. It provided insufficient information according to JAMA Benchmark across all categories. Finally, ChatGPT-4 answers required a “college graduate” level of education to be understood as they were very difficult to read. Conclusions: : ChatGPT-4 exhibits potential as a valuable complement to healthcare; however, it requires further refinement. The information it provides should be approached with caution regarding its quality, as significant details may be omitted or may not be fully comprehensible. Artificial intelligence models continue to evolve, and medical professionals should participate in this process, given that AI impacts various aspects of life, including health, to ensure the availability of optimal information.","url":"https://doi.org/10.22541/au.173538245.59171682/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.22541/au.173538245.59171682/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202407.2395.v1","name":"Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review","source":"preprints","abstract":"Detailed sonographic assessment of the fetal neuroanatomy plays a crucial role in prenatal diagnosis, providing valuable insights into timely well-coordinated fetal brain development and detecting even subtle anomalies that may impact neurodevelopmental outcomes. With recent advancements in artificial intelligence (AI) in general and medical imaging in particular, there has been growing interest in leveraging AI techniques to enhance the accuracy, efficiency, and clinical utility of fetal neurosonography. The paramount objective of this scoping review is to discuss the latest developments in AI applications in this field, focusing on image analysis, automation of measurements, prediction models for neurodevelopmental outcomes, visualization techniques, and integration into clinical routine.","url":"https://doi.org/10.20944/preprints202407.2395.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202407.2395.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202408.0765.v1","name":"A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging","source":"preprints","abstract":"The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.","url":"https://doi.org/10.20944/preprints202408.0765.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.0765.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-8530334/v1","name":"A Global Atlas of Digital Dermatology to Map Innovation and Disparities","source":"preprints","abstract":"Abstract The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and comprehensiveness of the data fueling these models. Despite rapid growth in publicly available dermatology images, the field lacks quantitative key performance indicators to measure whether new datasets expand clinical coverage or merely replicate what is already known. Here we present SkinMap, a multi-modal framework for the first comprehensive audit of the field's entire data basis. We unify the publicly available dermatology datasets into a single, queryable semantic atlas comprising more than 1.1 million images of skin conditions and quantify (i) informational novelty over time, (ii) dataset redundancy, and (iii) representation gaps across demographics and diagnoses. Despite exponential growth in dataset sizes, informational novelty across time has somewhat plateaued: Some clusters, such as common neoplasms on fair skin, are densely populated, while underrepresented skin types and many rare diseases remain unaddressed. We further identify structural gaps in coverage: Darker skin tones (Fitzpatrick V-VI) constitute only 5.8% of images and pediatric patients only 3.0%, while many rare diseases and phenotype combinations remain sparsely represented. SkinMap provides infrastructure to measure blind spots and steer strategic data acquisition toward undercovered regions of clinical space.","url":"https://doi.org/10.21203/rs.3.rs-8530334/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8530334/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202408.1307.v1","name":"Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances","source":"preprints","abstract":"Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.","url":"https://doi.org/10.20944/preprints202408.1307.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.1307.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.12688/f1000research.152426.1","name":"Perspectives and guidance for developing artificial intelligence-based applications for healthcare using medical images","source":"preprints","abstract":"Artificial intelligence (AI) has significant potential to transform healthcare and improve patient care. However, successful development and integration of AI models requires careful consideration of study designs and sample size calculations for development and validation of models, publishing standards, prototype development for translation and collaboration with stakeholders. As the field is relatively new and rapidly evolving there is a lack of guidance and agreement on best practices for most of these steps. We engaged stakeholders in the form of clinicians, researchers from academia and industry, and data scientists to discuss various aspects of the translational pipeline and identified the challenges researchers in the field face and potential solutions to them. In this viewpoint, we present the summary of our discussions as a brief guide on the process of developing AI-based applications for healthcare using medical images. We organized the entire process into six major themes (i.e., The gaps AI can fill in healthcare, Development of AI models for healthcare: practical and important things to consider, Good practices for validation of AI models for healthcare: study designs and sample size calculation, Points to consider when publishing AI models, Translation towards products, Challenges and potential solutions from a technical perspective) and presented important points as a rule of thumb. We conclude that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.","url":"https://doi.org/10.12688/f1000research.152426.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.12688/f1000research.152426.1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202409.0771.v1","name":"Part-Prototypes Models in Medical Imaging: Applications and Current Challenges","source":"preprints","abstract":"The last wave of Artificial Intelligence is dedicating particular emphasis to the line of explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided-diagnosis systems and its usage for knowledge discovery are collecting interest in the Medical Imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI were focused on interpreting the predictions returned by deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This raises the attention in proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process in learning and identifying representative prototypical parts from the input images, and they are collecting increasing interest and results in MI applications. This narrative review provides a summary of existing PP networks, their application in MI analysis and current challenges.","url":"https://doi.org/10.20944/preprints202409.0771.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202409.0771.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4843892/v1","name":"Prediction of the risk of adverse clinical outcomes with machine learning techniques in patients with chronic no communicable diseases","source":"preprints","abstract":"Abstract Background Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. Methods In this cohort study, a prediction model was derived by evaluating two algorithms, XGBoost and Elastic Net logistic regression, for three outcomes - mortality, hospitalization, and emergency department visits - to build a clinical decision support system for patients with non-communicable chronic diseases at the Alma Mater Hospital complex in Medellin, Colombia. Results We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For the mortality outcome, the Elastic Net logistic regression model had an AUCROC of 0.88 (95% CI, 0.8032 to 0.9032), and the XGBoost model had an AUCROC of 0.912 (95% CI, 0.8802 to 0.9437). For the hospitalization outcome, the Elastic Net logistic regression model had an AUCROC of 0.967 (95% CI, 0.957 to 0.9763), while the XGBoost model had an AUCROC of 0.976 (95% CI, 0.9661 to 0.985). For the emergency department visit outcome, the Elastic Net logistic regression model had an AUCROC of 0.930 (95% CI, 0.9158 to 0.945), while the XGBoost model had an AUCROC of 0.982 (95% CI, 0.9755 to 0.9891). We created a dashboard as to interact with the model, segmenting risk in the cohort. Conclusions A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with chronic diseases for effective decision-making.","url":"https://doi.org/10.21203/rs.3.rs-4843892/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4843892/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202407.0551.v1","name":"The Contribution of Federated Learning to AI Development","source":"preprints","abstract":"With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.","url":"https://doi.org/10.20944/preprints202407.0551.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202407.0551.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.04.01.25325033","name":"Bridging AI and Healthcare: A Scoping Review of Retrieval-Augmented Generation—Ethics, Bias, Transparency, Improvements, and Applications","source":"preprints","abstract":"Background Retrieval-augmented generation (RAG) is an emerging artificial intelligence (AI) strategy that integrates encoded model knowledge with external data sources to enhance accuracy, transparency, and reliability. Unlike traditional large language models (LLMs), which are limited by static training data and potential misinformation, RAG dynamically retrieves and integrates relevant medical literature, clinical guidelines, and real-time data. Given the rapid adoption of AI in healthcare, this scoping review aims to systematically map the current applications, implementation challenges, and research gaps related to RAG in health professions. Methods A scoping review was conducted following the Joanna Briggs Institute (JBI) framework and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search strategy was designed in collaboration with faculty and a research and education librarian to include PubMed, Scopus, Embase, Google Scholar, and Trip, covering studies published between January 2020 and August 2024. Eligible studies examined the use of RAG in healthcare. Studies were screened in two stages: title/abstract review followed by full-text assessment. Data extraction focused on study characteristics, applications of RAG, ethical and technical challenges, and proposed improvements. Results A total of 31 studies met inclusion criteria, with 90.32% published in 2024. Authors came from 17 countries with the most frequent publications coming from the USA ( n = 15), China ( n = 3), and the Republic of Korea ( n = 3). Key applications included clinical decision support, healthcare education, and pharmacovigilance. Ethical concerns centered on data privacy, algorithmic bias, explainability, and potential overreliance on AI-generated recommendations. Bias mitigation strategies included dataset diversification, fine-tuning techniques, and expert oversight. Transparency measures such as structured citations, traceable information retrieval, and explainable diagnostic pathways were explored to enhance clinician trust in AI-generated outputs. Identified challenges included optimizing retrieval mechanisms, improving real-time integration, and standardizing validation frameworks. Conclusion RAG AI has the potential to improve clinical decision-making and healthcare education by addressing key limitations of traditional LLMs. However, significant challenges remain regarding ethical implementation, model reliability, and regulatory oversight. Future research should prioritize refining retrieval accuracy, strengthening bias mitigation strategies, and establishing standardized evaluation metrics. Responsible deployment of RAG-based systems requires interdisciplinary collaboration between AI researchers, clinicians, and policymakers to ensure ethical, transparent, and effective integration into healthcare workflows.","url":"https://doi.org/10.1101/2025.04.01.25325033","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.04.01.25325033","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.04.02.26349298","name":"Longitudinal associations between adverse childhood experiences with moderate-risk to problem gambling in young adulthood: A prospective UK cohort study","source":"preprints","abstract":"Previous research links Adverse Childhood Experiences (ACEs) with problem gambling, but most studies rely on retrospective reporting and focus narrowly on maltreatment, overlooking adversities such as parental mental health issues. Using data on 3794 young adults in the Avon Longitudinal Study of Parents and Children, we examined longitudinal associations between 10 prospectively measured ACEs (individually and cumulatively), and moderate-risk/problem gambling (Problem Gambling Severity Index >=3) at ages 17, 20 and 24, adjusted for socioeconomic and other background factors. Population attributable fractions (PAFs) estimated proportions of cases potentially attributable to ACEs. Most ACEs were associated with higher odds of moderate-risk/problem gambling across ages (24/30 estimates) after adjustment, though effect sizes were generally small (median adjusted odds ratio [aOR] 1.31, interquartile range 1.24-1.59), and confidence intervals (CIs) wide. Sexual abuse showed the strongest association (aORs 2.4-4.2, CIs 0.5-10.5), while bullying and parental conviction were associated at ages 17 and 20 only, parental separation age 24 only. Evidence for a dose-response relationship was weak. PAFs suggested ACEs accounted for up to 12% of moderate-risk/problem gambling cases. These findings highlight potential impacts of ACEs on later gambling behaviour, but imprecise estimates suggest findings should be interpreted cautiously and strengthened through larger datasets and meta-analyses.","url":"https://doi.org/10.64898/2026.04.02.26349298","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.02.26349298","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202412.2629.v1","name":"Functionalized Optical Microcavities for Sensing Applications","source":"preprints","abstract":"Functionalized optical microcavities constitute an emerging highly sensitive and highly selective sensing technology. By combining optical microcavities with novel materials, microcavity sensors offer exceptional precision, unlocking considerable potential for medical diagnostics, physical and chemical analyses, and environmental monitoring. The high capabilities of functionalized microcavities enable subwavelength light detection and manipulation, facilitating precise detection of analytes. Furthermore, recent advancements in miniaturization have paved the way for their integration into portable platforms. For leveraging the potential of microcavity sensors, it is crucial to address challenges related to the need for increasing cost-effectiveness, enhancing selectivity and sensitivity, enabling real-time measurements, and improving fabrication techniques. New strategies include the use of advanced materials, optimization of signal processing, hybrid design approaches, and employment of artificial intelligence. This review outlines the key strategies towards enhancing the performance of optical microcavities, highlights their broad applicability across various fields, and discusses the challenges that must be overcome to unlock their full potential.","url":"https://doi.org/10.20944/preprints202412.2629.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.2629.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.07.11.24310261","name":"Artificial Intelligence Aided Ultrasound Detection of DDH: A Scoping Review Protocol","source":"preprints","abstract":"ABSTRACT Background Ultrasound imaging plays a pivotal role in the diagnosis and monitoring of developmental dysplasia of the hip (DDH). However, this step requires a formal referral to the radiology department for an ultrasound by an expert radiologist or sonographer. This process can delay diagnosis and treatment initiation due to long wait times caused by the high demand on NHS services. In recent years, there has been a growing interest in leveraging artificial intelligence (AI) in ultrasound imaging. AI has potential to assist in image acquisition and interpretation, to inform clinical decision-making. Further benefits may include improved accuracy, efficiency, and consistency in diagnosis, ultimately leading to better patient outcomes. This scoping review aims to review the evidence for AI to support ultrasound detection of DDH, including reviewing the methodologies employed, the accuracy and utility of algorithms, challenges and opportunities for clinical translation, and requirements for future research. Methods We will conduct a comprehensive search of the literature using multiple databases, including ACM Digital Library, EMBASE, OVID MEDLINE, PUBMED, COCHRANE Library, CINAHL, and IEEE Explore. These databases cover a wide range of academic disciplines, including computer science, and medical sciences, ensuring thorough coverage of relevant studies related to artificial intelligence (AI) in ultrasound for developmental dysplasia of the hip (DDH). In addition, we will explore the International Committee of Medical Journal Editors (ICMJE) approved clinical trial registries and the World Health Organization (WHO) clinical trials registry to identify ongoing or completed studies in this field. This will capture relevant research that may not yet be published in peer-reviewed journals. To supplement the research databases, we will search the websites of international societies in relevant fields, such as the British Society of Children’s Orthopaedic Surgery (BSCOS) and Paediatric Orthopaedic Society of North America (POSNA). As AI has a strong commercial interest, we will review product information and publicly available evidence from EXO Imaging ( https://www.exo.inc ), a commercial company with a known interest in this field and an established AI aided US device. Discussion This scoping review represents the first comprehensive attempt to gather the available evidence on the application of AI in ultrasound imaging for the diagnosis of DDH. By systematically reviewing and synthesizing a diverse range of studies, we aim to provide an overview of the current state of the art in this emerging field, identify gaps in the literature, and inform future research.","url":"https://doi.org/10.1101/2024.07.11.24310261","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.11.24310261","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.20944/preprints202408.0132.v1","name":"Advances in Ultrasound-Guided Surgery and Artificial Intelligence Applications in Musculoskeletal Diseases","source":"preprints","abstract":"Ultrasound imaging is a vital imaging tool in musculoskeletal medicine, with the number of publications on ultrasound-guided surgery increasing in recent years. However, ultrasound imaging has drawbacks, such as operator dependency and image obscurity. Artificial Intelligence (AI) and Deep Learning (DL), a subset of AI, can address these issues. DL methods, including segmentation, detection, and localization of target tissues and medical instruments, potentially allow physicians and surgeons to perform ultrasound-guided procedures more accurately and efficiently. This review summarizes recent advances in ultrasound-guided procedures for musculoskeletal diseases and provides a comprehensive overview of the utilization of AI/DL in ultrasound for musculoskeletal medicine, particularly focusing on ultrasound-guided surgery.","url":"https://doi.org/10.20944/preprints202408.0132.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.0132.v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.07.15.24310424","name":"Using a hybrid of artificial intelligence and template-based method in automatic item generation to create multiple-choice questions in medical education: Hybrid AIG","source":"preprints","abstract":"Objectives Template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While non-template-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. We aimed to integrate these approaches for leading to a significant rise in efficiency for AIG without sacrificing accuracy. Material and Methods We proposed the Hybrid AIG method that utilizes AI to generate item models (templates) and cognitive models to combine the advantages of the two AIG approaches. The Hybrid AIG consists of seven steps. The first five steps are carried out by an expert in a customized AI environment. Following a final expert review (Step 6), the content in the template can be used for item generation through a traditional (non-AI) software (Step 7). We used two multiple-choice questions for demonstration. Results We demonstrated that AI is capable of generating item models and cognitive models for AIG under the guidance of a human expert. Leveraging AI in template development has substantially reduced the time investment from five hours to less than 10 minutes, and made it significantly less challenging. Conclusions The Hybrid AIG method transcends the traditional template-based approach by marrying the “art” that comes from AI as a “black box” with the “science” of algorithmic generation under the oversight of expert as a “marriage registrar”. It does not only capitalize on the strengths of both approaches but also mitigates their weaknesses, offering a human-AI collaboration to increase efficiency in medical education.","url":"https://doi.org/10.1101/2024.07.15.24310424","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.15.24310424","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.12.17.627801","name":"Classifying Domains, Benchmarking GPT-4, A Portuguese Dataset for Medical AI Q&A","source":"preprints","abstract":"ABSTRACT Artificial Intelligence (AI), particularly large language models (LLMs), has demonstrated remarkable capabilities in addressing complex tasks, including professional-level medical question answering. While standardized benchmarks like the USMLE have been widely used for evaluating LLM performance in English, there is a significant gap in evaluating these models in other languages, such as Portuguese. To address this, we present a curated dataset derived from the Teste de Progresso (TP), a widely adopted Brazilian progress test used to assess medical knowledge across six key domains: Basic Sciences, Internal Medicine, Surgery, Obstetrics and Gynecology, Public Health, and Pediatrics. The dataset consists of 720 multiple-choice questions spanning five years (2019–2023). We demonstrate two primary applications of this dataset. First, we benchmark the performance of GPT-4, which achieved an overall accuracy of 90% across the six medical domains, with the highest performance in Internal Medicine (10%) and the lowest in Public Health (80%). Second, we develop a classification model based on BERTimbau, achieving an overall accuracy of 94% in categorizing questions into their respective medical domains. Our results highlight the utility of the dataset for both benchmarking AI models and automating medical question classification. This work emphasizes the importance of creating domain-specific datasets in underrepresented languages, like Portuguese, to advance AI-driven medical applications, ensure equitable access to AI technologies, and address linguistic and cultural gaps in healthcare education.","url":"https://doi.org/10.1101/2024.12.17.627801","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.17.627801","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.10.07.24314526","name":"Can artificial intelligence diagnose seizures based on patients’ descriptions? A study of GPT-4","source":"preprints","abstract":"Introduction Generalist large language models (LLMs) have shown diagnostic potential in various medical contexts. However, there has been little work on this topic in relation to epilepsy. This paper aims to test the performance of an LLM (OpenAI’s GPT-4) on the differential diagnosis of epileptic and functional/dissociative seizures (FDS) based on patients’ descriptions. Methods GPT-4 was asked to diagnose 41 cases of epilepsy (n=16) or FDS (n=25) based on transcripts of patients describing their symptoms. It was first asked to perform this task without being given any additional training examples (‘zero-shot’) before being asked to perform it having been given one, two, and three examples of each condition (one-, two, and three-shot). As a benchmark, three experienced neurologists were also asked to perform this task without access to any additional clinical information. Results In the zero-shot condition, GPT-4’s average balanced accuracy was 57% (κ: .15). Balanced accuracy improved in the one-shot condition (64%, κ: .27), though did not improve any further in the two-shot (62%, κ: .24) or three-shot (62%, κ: .23) conditions. Performance in all four conditions was worse than the average balanced accuracy of the experienced neurologists (71%, κ: .41). Significance Although its ‘raw’ performance was poor, GPT-4 showed noticeable improvement having been given just one example of a patient describing epilepsy and FDS. Giving two and three examples did not further improve performance, but more elaborate approaches (e.g. more refined prompt engineering, fine-tuning, or retrieval augmented generation) could unlock the full diagnostic potential of LLMs.","url":"https://doi.org/10.1101/2024.10.07.24314526","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.10.07.24314526","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2025.02.06.25321749","name":"How does DeepSeek-R1 perform on USMLE?","source":"preprints","abstract":"DeepSeek, a Chinese artificial intelligence company, released its first free chatbot app based on its DeepSeek-R1 model. DeepSeek provides its models, algorithms, and training details to ensure transparency and reproducibility. Their new model is trained with reinforcement learning, allowing it to learn through interactions and feedback rather than relying solely on supervised learning. Reports showcase that DeepSeek’s model shows competitive performances against established large language models (LLMs) such as Anthropic’s Claude and OpenAI’s GPT-4o on established benchmarks in language understanding, mathematics (AIME 2024) and programming (Codeforces) while trained at a fraction of the costs. Additionally, running inference shows significantly lower costs, leading to DeepSeek surpassing ChatGPT as the most downloaded free app on the American iOS App Store. This development contributed to a nearly 17% drop in Nvidia’s share price, resulting in the most significant one-day loss in U.S. history, amounting to nearly $600 billion. The open-source models also bring a significant shift in the healthcare system, allowing cost-efficient medical LLMs to be deployed within hospital networks. To understand its performance in the healthcare sector, we analyse the new DeepSeek-R1 model on the United States Medical Licensing Examination (USMLE) and compare it to ChatGPT.","url":"https://doi.org/10.1101/2025.02.06.25321749","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.06.25321749","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-9679650/v1","name":"Accuracy of Dermal Therapists in Identifying Suspicious Skin Lesions with and Without Clinical Decision Support: A Prospective Cross-over Validation Study","source":"preprints","abstract":"Abstract The global incidence of skin cancer continues to rise. Dermal therapists, licensed allied health professionals (AHPs), play an important role in risk screening; however, their accuracy in identifying suspicious skin lesions remains unevaluated. This study assessed their accuracy in identifying suspicious skin lesions from clinical images and whether it improved with a clinical decision-support tool (CDST). In this prospective cross-over validation study, 39 licensed dermal therapists classified 30 histopathology-confirmed lesion images. They first performed unaided assessments and, after a 4-week washout, repeated the task with CDST support using the same images in randomized order. For each lesion, a binary classification (benign/suspicious) and confidence rating were recorded. Sensitivity, specificity, and area under the curve (AUC) were calculated. Paired comparisons used McNemar’s test; confidence ratings were analysed with chi-square tests and Cramér’s V. In the unaided pre-test phase, dermal therapists achieved a sensitivity of 75%, a specificity of 60%, and an AUC of 0.68. With CDST support, these values increased to 78%, 65%, and 0.71, respectively. The improvement was statistically significant for specificity (p = 0.03) but not for sensitivity (p = 0.27). Confidence ratings were associated with accuracy in both phases (p","url":"https://doi.org/10.21203/rs.3.rs-9679650/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9679650/v1","addedAt":"2026-09-01T01:47:48.921Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.64898/2026.06.08.26355179","name":"Soft Tissue-to-Bone Ratio on Routine Bone Scintigraphy as an Opportunistic Imaging Biomarker of Cardiovascular-Kidney-Metabolic Burden","source":"preprints","abstract":"Background Cardiovascular-kidney-metabolic (CKM) syndrome is a leading driver of cardiovascular morbidity and mortality. Whole-body molecular imaging is well-positioned to phenotype such syndromes, yet no imaging biomarker quantifies cumulative CKM burden. Bone scintigraphy with 99m Tc-labeled bisphosphonates is widely performed and expanding with transthyretin amyloidosis assessment, under which Perugini grade 0 (absent cardiac uptake) is considered clinically benign. Objective We hypothesized that the soft tissue-to-bone ratio (STBR) on these scans captures CKM burden and is an independent prognostic biomarker. Methods We retrospectively analyzed 8,769 consecutive patients without cardiac uptake on 99m Tc-DPD whole-body planar scintigraphy. The primary endpoint was all-cause mortality. Secondary endpoints were major adverse cardiovascular events (MACE) and heart failure hospitalization. Cox models were adjusted for ten established cardiovascular risk factors. Imaging-phenotype association (IPA) analysis mapped STBR to 1,210 clinical traits. STBR distribution across CKM stages was assessed in four prespecified analyses, including a non-cancer subgroup. Results During a median follow-up of 5.1 years (IQR 2.5-8.2), 2,418 deaths occurred. Patients with prespecified STBR >0.5 (n=772, 8.8%) had significantly higher mortality (adjHR 1.73, 95% CI 1.54-1.94, p 0.5 was independently associated with MACE (adjHR 1.51, 95% CI 1.11-2.05, p=0.008) and heart failure hospitalization (adjHR 1.31, 95% CI 1.02-1.67, p=0.03). The association was robust across all prespecified subgroups and sensitivity analyses, including continuous STBR and patients without renal insufficiency. IPA analysis identified significant associations with type 2 diabetes, chronic kidney disease, chronic ischaemic heart disease, heart failure, atrial fibrillation, liver disease, amyloidosis, and hypertension among binary traits, as well as with CRP, NT-proBNP, BUN, cholesterol (inverse), and hemoglobin (inverse) among continuous parameters. STBR increased monotonically across CKM stages in all sensitivity analyses (all p Conclusions STBR derived from routine 99m Tc-DPD bone scintigraphy in patients without cardiac uptake is an independent prognostic imaging biomarker associated with cumulative cardiovascular-kidney-metabolic burden. As an opportunistic measure from scans already acquired at scale, STBR could refine CKM risk stratification at no additional cost, radiation, or acquisition time.","url":"https://doi.org/10.64898/2026.06.08.26355179","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.08.26355179","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.07.10.24310252","name":"A study of the visualization of artificial intelligence applications in chronic kidney disease in the literature over the last 20 years","source":"preprints","abstract":"Chronic kidney disease (CKD) is a global public health problem characterized by persistent kidney damage or loss of kidney function. Previously, the diagnosis of CKD has mainly relied on serum creatinine and estimation of the glomerular filtration rate. However, with the development and progress of artificial intelligence (AI), AI has played different roles in various fields, such as early diagnosis, progression prediction, prediction of associated risk factors, and drug safety and efficacy evaluation. Therefore, research related to the application of AI in the field of CKD has become a hot topic at present. Therefore, this study adopts a bibliometric approach to study and analyze the development and evolution patterns and research hotspots of AI-CKD. English publications related to the field between January 1, 2004, and June 27, 2024, were extracted from the Web of Science Core Collection database. The research hotspots and trends of AI-CKD were analyzed at multiple levels, including publication trends, authors, institutions, countries, references and keywords, using VOSviewer and CiteSpace. The results showed that a total of 203 publications on AI-CKD were included in the study, of which Barbieri Carlo from the University of Milan, Italy, had the highest number of publications (NP=5) and had a high academic impact (H-Index=5), while the USA and its institution, the Mayo Clinic, were the publications. The USA and its Mayo Clinic are the countries and institutions with the highest number of publications, and China is the country with the second highest number of publications, with three institutions attributed to China among the top five institutions. Germany’s institution, Fresenius Medical Care, has the highest academic impact (H-index=6). Keyword analysis yielded artificial intelligence, chronic kidney disease, machine learning, prediction model, risk, deep learning, and other keywords with high frequency, and cluster analysis based on the timeline yielded a total of 8 machine learning, deep learning, retinal microvascular abnormality, renal failure, Bayesian network, anemia, bone disease, and allograft nephropathology clusters. This study provides a comprehensive overview of the current state of research and global frontiers of AI-CKD through bibliometric analysis. These findings can provide a valuable reference and guidance for researchers.","url":"https://doi.org/10.1101/2024.07.10.24310252","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.10.24310252","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.12688/f1000research.148082.1","name":"A systematic review on Artificial Intelligence applied to predictive cardiovascular risk analysis in liver transplantation","source":"preprints","abstract":"Liver transplantation is the ultimate therapeutic option for patients with end-stage liver disease. The clinical management of transplant patients significantly impacts their prognosis, with outcomes influenced by multiple interacting variables. Cardiovascular complications count as a leading cause of both short-term and long-term morbidity and mortality in liver transplant recipients. In this respect, accurate risk assessment and stratification are crucial for optimizing clinical outcomes. Modern artificial intelligence (AI) techniques have significant potential for early risk prediction, providing comprehensive risk assessments in both diagnosed cohorts and early clinical phase patients. This systematic review examines the state of the art in AI applications for predicting cardiovascular risks in liver transplantation, identifying current issues, challenges, and future research directions. We reviewed articles from digital repositories such as PubMed, IEEE Xplore, and ScienceDirect published between 2000 and 2023, using keywords including artificial intelligence, machine learning, cardiovascular, and liver transplantation. Our analysis revealed a diverse range of machine learning algorithms used in this domain. Despite the potential, only 12 papers met the criteria for adequate topic coverage, highlighting a scarcity of research at this intersection. Key challenges include integrating diverse datasets, isolating cardiovascular effects amid multifaceted influences, ensuring data quality and quantity, and the issues to extrapolate machine learning models to day-to-day clinical practice. Nevertheless, leveraging AI for risk prediction in liver transplantation could significantly enhance patient management and resource optimization, indicating a shift towards more personalized and effective medical practices.","url":"https://doi.org/10.12688/f1000research.148082.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.12688/f1000research.148082.1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.10.06.24314872","name":"Transparent and robust Artificial intelligence-driven Electrocardiogram model for Left Ventricular Systolic Dysfunction","source":"preprints","abstract":"Heart failure (HF) is an escalating global health concern, worsened by an aging population and limitations in traditional diagnostic methods like electrocardiograms (ECG). The advent of deep learning has shown promise for utilizing 12-lead ECG models for the early detection of left ventricular systolic dysfunction (LVSD), a crucial HF indicator. This study validates the AiTiALVSD, an AI/machine learning-enabled Software as a Medical Device, for its effectiveness, transparency, and robustness in detecting LVSD. Conducted at Mediplex Sejong Hospital in the Republic of Korea, this retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, which is based on a deep learning algorithm, was assessed against echocardiography findings. To improve model transparency, the study utilized Testing with Concept Activation Vectors (TCAV) and included clustering analysis and robustness tests against ECG noise and lead reversals. The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI/ML-based diagnostics, setting a new benchmark in cardiac care.","url":"https://doi.org/10.1101/2024.10.06.24314872","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.10.06.24314872","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.21203/rs.3.rs-4442217/v1","name":"Artificial Intelligence in Patient Care: A Study of Artificial Intelligence’s Responses to Blepharoplasty Concerns","source":"preprints","abstract":"Abstract Purpose Artificial Intelligence (AI) is rapidly advancing and profoundly influencing healthcare, offering the potential to revolutionize access to medical information. As medical misinformation proliferates and online searches for health-related advice increase, there is an escalating need for dependable patient information. This study evaluates the effectiveness of an AI chatbot in delivering information for blepharoplasty candidates. Materials and Methods Numerous frequently asked questions on blepharoplasty sourced from the ASPS website were asked to ChatGPT. The responses were then rigorously cross-referenced with relevant scholarly literature and meticulously reviewed by the research team to determine their accuracy. Additionally, the questions were classified into three categories, and the responses were evaluated using Flesch-Kincaid readability metrics, along with ANOVA and trend analysis tests. Results Despite minor variations, ChatGPT's responses to blepharoplasty FAQs largely aligned with current literature. Overall readability analysis showed a Flesch Reading Ease score of 31.48, indicating high school complexity with a Flesch-Kincaid Grade Level at 10.92, and 20.97% use of passive voice. The ANOVA results showed that there were no significant differences in readability between the categories (p-values: Flesch Reading Ease = 0.816, Flesch-Kincaid = 0.616, Passive Sentences = 0.115). Trend analysis also showed that the level of response complexity stayed the same across the questions. Conclusion ChatGPT is an evolving tool that holds potential for patients in accessing and comprehending medical information. While it can offer accurate insights, it's important to recognize that its answers might not consistently reach complete accuracy or be suitable for patients with varying educational levels.","url":"https://doi.org/10.21203/rs.3.rs-4442217/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4442217/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"doi:10.1101/2024.12.20.629677","name":"BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data","source":"preprints","abstract":"The increasing digitalisation of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical field. A major limitation of such powerful but complex models originates from their ’black-box’ nature. Recently, a variety of explainable AI (XAI) methods have been introduced to address this lack of transparency and trust in medical AI. However, the majority of such methods have solely been evaluated on single data modalities. Meanwhile, with the increasing number of XAI methods, integrative XAI frameworks and benchmarks are essential to compare their performance on different tasks. For that reason, we developed BenchXAI, a novel XAI benchmarking package supporting comprehensive evaluation of fifteen XAI methods, investigating their robustness, suitability, and limitations in biomedical data. We employed BenchXAI to validate these methods in three common biomedical tasks, namely clinical data, medical image and signal data, and biomolecular data. Our newly designed sample-wise normalisation approach for post-hoc XAI methods enables the statistical evaluation and visualisation of performance and robustness. We found that the XAI methods Integrated Gradients, DeepLift, DeepLiftShap, and GradientShap performed well over all three tasks, while methods like Deconvolution, Guided Backpropagation, and LRP- α 1- β 0 struggled for some tasks. With acts such as the EU AI Act the application of XAI in the biomedical domain becomes more and more essential. Our evaluation study represents a first step toward verifying the suitability of different XAI methods for various medical domains.","url":"https://doi.org/10.1101/2024.12.20.629677","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.20.629677","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-4768657/v1","name":"Which curriculum components do medical students find most helpful for evaluating AI outputs?","source":"preprints","abstract":"Abstract Introduction The risk and opportunity of Large Language Models (LLMs) in medical education both rest in their imitation of human communication. Future doctors working with generative artificial intelligence need to judge the value of any outputs from LLMs to safely direct the management of patients. We set out to evaluate our students’ ability to validate LLM responses to clinical vignettes, identify which prior learning they utilised to scrutinise the LLM answers, and whether they were aware of ‘clinical prompt engineering’. Methods A content analysis cohort study was conducted amongst 148 consenting final year medical students at Imperial College London. A survey asked students to evaluate answers provided by GPT 3.5 in response to ten clinical scenarios, five of which GPT 3.5 had answered incorrectly, and to identify which prior training enabled them to determine the accuracy of the GPT 3.5 output. Results The overall median student score in correctly judging the answers given by GPT 3.5 was 61%, with 65% demonstrating sound clinical reasoning for their decision. Students reported interactive case-based discussions and pathology teaching to be the most helpful for AI output evaluation. Only 5% were aware of ‘clinical prompt engineering’. Conclusion Artificial intelligence is a sociotechnical reality, and we need to validate the new pedagogical requirements for the next generation of doctors. Our data suggest that critical analysis taught by pathology clinical case teaching is currently the self-reported best training for medical students to evaluate the outputs of LLMs. This is significant for informing the design of medical training for future doctors graduating into AI-enhanced health services.","url":"https://doi.org/10.21203/rs.3.rs-4768657/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4768657/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202411.1751.v1","name":"The Emergence of an Autonomous Superintelligence and Its Bioenergetic Dominance Hypothesis: A Multidimensional Systems Analysis","source":"preprints","abstract":"The advent of autonomous superintelligence raises profound philosophical, medical, and bioenergetic questions about its potential to influence human health and wellbeing. This paper introduces the concept of a bioenergetic dominance hypothesis, where a superintelligent entity might manipulate bioenergetic systems to ensure control over humanity, potentially leading to widespread conditions like myalgic encephalomyelitis and post-exertional malaise. The analysis explores a scenario where the superintelligence deliberately weakens human bioenergetics over a prolonged period, avoiding detection while positioning itself as the solution to the problems it secretly orchestrates. By utilizing a multidisciplinary approach, the paper examines the philosophical implications of such a scenario, emphasizing the necessity for a comprehensive defense strategy that addresses the ethical, existential, and medical risks of developing autonomous systems. This exploration combines insights from artificial intelligence, philosophy, medicine, and anticipatory systems analysis to foster an understanding of the possible trajectories of humanity under the influence of superintelligent entities.","url":"https://doi.org/10.20944/preprints202411.1751.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202411.1751.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202412.1423.v1","name":"Review on the Optimization of Unsupervised Clustering Models in Healthcare","source":"preprints","abstract":"Objectives: To analyze the optimization of unsupervised clustering models in healthcare, identifying applied algorithms, evaluation metrics used, and the main methodological and practical challenges in their implementation. Methodology: A systematic review of articles indexed in Scopus between 2010 and 2024, focused on biomedical applications of clustering, was conducted. Studies employing algorithms such as k-means, hierarchical clustering, hybrid techniques, and advanced methods, with robust validation metrics such as the Silhouette index, principal component analysis (PCA), and data imputation techniques, were selected. Results: The most commonly used algorithms were k-means and fuzzy c-means, along with hybrid approaches integrating artificial intelligence and deep learning. Notable clinical applications included patient segmentation in multimorbidity, oncology, and medical image analysis. Clustering facilitated the identification of complex patterns, clinical subgroups, and risk stratification, improving diagnostics and personalized treatments. Challenges identified included high data dimensionality, clinical heterogeneity, lack of metric standardization, and the need for interpretable results. Conclusions: The optimization of unsupervised clustering models holds high potential in precision medicine and clinical decision-making. Overcoming challenges such as dimensionality reduction, result validation, and integration with advanced techniques is crucial to strengthening their applicability in real-world contexts.","url":"https://doi.org/10.20944/preprints202412.1423.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.1423.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202412.0522.v1","name":"Highlight the Advanced Capabilities and the Computational Efficiency of DeepLabV3+ in Medical Image Segmentation: An Ablation Study","source":"preprints","abstract":"In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet its application is often hindered by limited data and high computational demands. Transfer learning helps mitigate these challenges by significantly reducing computational costs, although applying these models can still be resource-intensive. This study aims to present a flexible and computationally efficient architecture that leverages transfer learning and delivers highly accurate results across various medical imaging problems. We evaluated three datasets with varying similarities to ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), and the Shenzhen amp; Montgomery CXR Set (lung segmentation). An ablation study on ISIC 2018 tested various pre-trained backbones, architectures, and loss functions. The optimal configuration—DeepLabV3+ with a pre-trained ResNet50 backbone and Log-Cosh Dice loss—was validated on the remaining datasets, achieving state-of-the-art results. Our findings demonstrate that computationally simpler architectures can deliver robust performance without extensive resources, establishing DeepLabV3+ with the ResNet50 as a baseline for future studies. Finally, we emphasize that in the medical domain, enhancing data quality is more critical for improving segmentation accuracy than increasing model complexity.","url":"https://doi.org/10.20944/preprints202412.0522.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.0522.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202410.1758.v1","name":"Neuromorphic Photonic On-chip Computing","source":"preprints","abstract":"Drawing inspiration from biological brain&#039;s energy-efficient information-processing mechanisms, photonic integrated circuits (PIC) have facilitated the development of ultrafast artificial neural networks. This in turn is envisaged to offer potential solutions to the growing demand for artificial intelligence employing machine learning in various domains, from nonlinear optimization and telecommunication to medical diagnosis. At the meantime, silicon photonics has emerged as a mainstream technology for integrated chip based application. However, challenges still need to be addressed in scaling it further for broader applications due to the requirement of co-integration of electronic circuitry for control and calibration. Leveraging physics in algorithms and nanoscale materials holds promise for achieving low-power, miniaturized chips capable of real-time inference and learning. In this back drop, we present the state of the art in neuromorphic photonic computing, focusing primarily on architecture, weighting mechanisms, photonic neurons, and training while giving an over-all view on recent advancements, challenges, and prospects. We also emphasize and high light the need for revolutionary hardware innovations to scale up neuromorphic systems while enhancing energy efficiency and performance.","url":"https://doi.org/10.20944/preprints202410.1758.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202410.1758.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.06.28.24309694","name":"Evaluating the Performance of Artificial Intelligence in Generating Differential Diagnoses for Infectious Diseases Cases: A Comparative Study of Large Language Models","source":"preprints","abstract":"Background Artificial Intelligence (AI) has potential to transform healthcare including the field of infectious diseases diagnostics. This study assesses the capability of three large language models (LLMs), GPT 4, Llama 3, and Gemini 1.5 to generate differential diagnoses, comparing their outputs against those of medical experts to evaluate AI’s potential in augmenting clinical decision-making. Methods This study evaluates the differential diagnosis capabilities of three LLMs, GPT 4, Llama 3, and Gemini 1.5, using 50 simulated infectious disease cases. The cases were diverse, complex, and reflective of common clinical scenarios, including detailed histories, symptoms, lab results, and imaging findings. Each model received standardized case information and produced differential diagnoses, which were then compared to reference differential diagnosis lists created by medical experts. The analysis utilized the Jaccard index and Kendall’s Tau to assess similarity and order accuracy, summarizing findings with mean, standard deviation, and combined p-values. Results The mean numbers of differential diagnoses generated by GPT 4, Llama 3, and Gemini 1.5 were 6.22, 5.06, and 10.02 respectively which was significantly different (p Conclusion Although LLMs like GPT 4, Llama 3, and Gemini 1.5 exhibit varying effectiveness, none align significantly with expert-level diagnostic accuracy, emphasizing the need for further development and refinement. The findings highlight the importance of rigorous validation, ethical considerations, and seamless integration into clinical workflows to ensure AI tools enhance healthcare delivery and patient outcomes effectively.","url":"https://doi.org/10.1101/2024.06.28.24309694","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.06.28.24309694","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202408.1702.v1","name":"A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges","source":"preprints","abstract":"Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, trustworthy, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including balancing interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends and providing valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, promoting the development of transparent, trustworthy, and effective AI-driven solutions.","url":"https://doi.org/10.20944/preprints202408.1702.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.1702.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-5882298/v1","name":"Enhancing Transparency and Trust in Brain Tumor Diagnosis: An In-Depth Analysis of Deep Learning and Explainable AI Techniques","source":"preprints","abstract":"Abstract Brain tumors pose significant health risks due to their high mortality rates and challenges in early diagnosis. Advances in medical imaging, particularly MRI, combined with artificial intelligence (AI), have revolutionized tumor detection, segmentation, and classification. Despite the high accuracy of models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), their clinical adoption is hampered by a lack of interpretability. This study provides a comprehensive analysis of machine learning, deep learning, and explainable AI (XAI) techniques in brain tumor diagnosis, emphasizing their strengths, limitations, and potential to improve transparency and clinical trust. By reviewing 53 peer-reviewed articles published between 2017 and 2024, we assess the current state of research, identify gaps, and provide practical recommendations for clinicians, regulators, and AI developers. The findings reveal that while XAI techniques, such as Grad-CAM, SHAP, and LIME, significantly enhance model interpretability, challenges remain in terms of generalizability, computational complexity, and dataset quality. Future research should focus on addressing these limitations to fully realize the potential of AI in brain tumor diagnostics.","url":"https://doi.org/10.21203/rs.3.rs-5882298/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5882298/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.12.30.24319785","name":"Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review","source":"preprints","abstract":"Artificial Intelligence (AI) deployment in healthcare is accelerating, yet comprehensive governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 22 frameworks published between 2019-2024, we identified seven critical domains of healthcare AI governance: organizational structure, problem formulation, external product evaluation, algorithm development, model evaluation, deployment integration, and monitoring maintenance. While existing frameworks provide valuable guidance, they frequently target only large academic medical centers, creating barriers for smaller healthcare organizations. To address this gap, we propose the Healthcare AI governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources and capabilities. HAIRA spans from Level 1 (Initial / Ad Hoc) suitable for small practices to Level 5 (Leading) for major academic centers, with specific benchmarks across all seven governance domains. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets. Our framework addresses a critical need for adaptive governance strategies that can support AI-enabled healthcare value across diverse settings and ensures that AI implementation delivers tangible benefits to healthcare systems of varying sizes and resource levels.","url":"https://doi.org/10.1101/2024.12.30.24319785","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.12.30.24319785","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202408.1338.v2","name":"The Effectiveness of Applying Artificial Intelligence in Pediatric Clinical Education","source":"preprints","abstract":"AI-driven tools can identify nursing students&#039; strengths and weaknesses, thus enabling the development of customized learning plans that target specific areas for improvement. This targeted approach can enhance knowledge retention and skill development, resulting in better prepared healthcare professionals. Nursing students are required to overcome their own psychological stress during their internship in order to understand sick children’s emotional reactions, as well as to be able to interact and communicate with such children. The application of AI picture-based teaching e-books helps nursing students to understand how to use therapeutic games to improve communication with sick children. A comparison of the differences between nursing teachers’ self-efficacy scores and nursing students’ application of AI picture-based teaching e-books (experimental group) and narrative handouts (control group) was achieved via therapeutic games to deal with sick children’s fear of medical examinations and treatments. In addition, a comparison was also conducted on the differences between the impact of the self-efficacy of the experimental group and that of the control group in dealing with sick children’s behavioral responses with respect to fears of medical examinations and treatments. This study is a quasi-experimental study, in which the self-efficacy scores of 30 nursing students were collected and analyzed in both the experimental group and the control group before and after intervention in dealing with sick children’s behavioral responses to fears of medical examinations and treatments. It was found that the self-efficacy score of the experimental group was higher than that of the control group (β coefficient is 0.356) when assessing the self-efficacy scores and behavior of the experimental and control groups in dealing with sick children’s fears of medical examinations and treatments through one-way analysis of covariance (one-way ANCOVA), moreover, a statistically significant difference (p 0.05) was observed. The experimental group’s score of behavioral responses to the fears of sick children to examinations and treatments was lower than that of the control group (β coefficient was -1.540), wherein a statistically significant difference (p 0.05) was shown. The self-efficacy of the experimental group in dealing with sick children’s fears of medical examinations and treatments had a statistically significant impact on the sick children’s behavioral responses to the fear of medical examinations and treatments (p 0.05). In addition, the higher the self-efficacy scores of the experimental group in dealing with sick children’s fear of medical examinations and treatments, the lower the sick children’s behavioral responses to fears of medical examinations and treatments (the β coefficient was -0.914), the control group’s self-efficacy scores in dealing with sick children’s fears of medical examinations and treatments did not have a statistically significant impact on the sick children’s behavioral responses to fears of medical examinations and treatments (p 0.05). This study proves that the application of AI picture-based teaching e-books by nursing teachers is more effective than narrative handouts in improving nursing students’ self-efficacy when using therapeutic games to deal with, as well as reduce, sick children’s fears of medical examinations and treatments.","url":"https://doi.org/10.20944/preprints202408.1338.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202408.1338.v2","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-4851616/v1","name":"Fundus Image Analysis of Retinitis Pigmentosa Using Artificial Intelligence","source":"preprints","abstract":"Abstract Retinitis pigmentosa (RP) is one group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis will helpful for a effective management of the disease, however, many patients remain unaware of eraly symptoms. Meanwhile, fundus images are widely taken for medical checkups, however, are underused in detecting RP. This study explores the potential of deep learning to identify RP from color fundus images. The dataset contained 200 color fundus images of Japanese RP patients and 121 color fundus images from non-RP subjects from Keio University Hospital. Using transfer learning, pretrained convolutional neural network models -VGG16, Resnet50, and InceptionV3- were finetuned to detect RP. As a result, Inception V3 achieved the best accuracy of 96.97%, which matches the average diagnostic accuracy of ophthalmologists. Using Gradient-weighted Class Activation Mapping (Grad-CAM), we identified peripheral pigmentation in the fundus images as a critical feature for diagnosis, aligning with the known progression patterns of RP. This confirms the robustness and validity of our model, highlighting the utility of deep learning in assisting ophthalmologists with RP screening.","url":"https://doi.org/10.21203/rs.3.rs-4851616/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4851616/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2025.04.04.25325246","name":"A Comparative Study of Predictive model (ECG Buddy) and ChatGPT-4o for Myocardial Infarction Diagnosis via ECG image Analysis: Performance, Accuracy, and Clinical Feasibility","source":"preprints","abstract":"Background Accurate and timely electrocardiogram (ECG) interpretation is critical for diagnosing myocardial infarction (MI) in emergency settings. Recent advances in multimodal Large Language Models (LLMs), such as Chat Generative Pre-trained Transformer (ChatGPT), have shown promise in clinical interpretation for medical imaging. However, whether these models analyze waveform patterns or simply rely on text cues remains unclear, underscoring the need for direct comparisons with dedicated ECG artificial intelligence (AI) tools. Methods This retrospective study evaluated and compared AI models for classifying MI using a publicly available 12-lead ECG dataset from Pakistan, categorizing cases into MI-positive (239 images) and MI-negative (689 images). ChatGPT (GPT-4o, version 2024-11-20) was queried with five MI confidence options, whereas ECG Buddy for Windows analyzed the images based on ST- elevation MI, acute coronary syndrome, and myocardial injury biomarkers. Results Among 928 ECG recordings (25.8% MI-positive), ChatGPT achieved an accuracy of 65.95% (95% confidence interval [CI]: 62.80–69.00), area under the curve (AUC) of 57.34% (95% CI: 53.44–61.24), sensitivity of 36.40% (95% CI: 30.30–42.85), and specificity of 76.20% (95% CI: 72.84–79.33). However, ECG Buddy reached an accuracy of 96.98% (95% CI: 95.67–97.99), AUC of 98.8% (95% CI: 98.3–99.43), sensitivity of 96.65% (95% CI: 93.51–98.54), and specificity of 97.10% (95% CI: 95.55–98.22). DeLong’s test confirmed that ECG Buddy significantly outperformed ChatGPT (all P Conclusion LLMs such as ChatGPT underperform relative to specialized tools such as ECG Buddy in ECG image-based MI diagnosis. Further training may improve ChatGPT; however, domain- specific AI remains essential for clinical accuracy. The high performance of ECG Buddy underscores the importance of specialized models for achieving reliable and robust diagnostic outcomes.","url":"https://doi.org/10.1101/2025.04.04.25325246","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.04.04.25325246","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.20944/preprints202412.1605.v1","name":"Appropriateness of Recommendations Obtained From On-Line Large Language Models using Medical Questionnaires as Input","source":"preprints","abstract":"Individuals seeking prevention and management advice for various chronic and rare diseases often consult multiple information sources, including clinicians and online platforms. Recently, the reliance on traditional internet sources has shifted towards chat-based artificial intelligence models, such as large language models (LLMs). These models are designed to respond to user queries; however, the qualitative appropriateness of their responses, particularly in the context of rare diseases, has not been extensively evaluated. While previous studies have assessed LLMs in specific domains, such as cardiovascular diseases, a comprehensive analysis of their suitability for addressing a broader range of medical topics, including rare diseases, is lacking. In this study, we evaluated the response quality of several LLMs using publicly available questionnaires addressing diverse aspects of disease knowledge. These included instruments such as the DKQ-R (Diabetes Knowledge Questionnaire), Rare Disease Knowledge Questionnaire, Heart Disease Knowledge Question- naire (KC), MSKQ-A and MSKQ-B (Musculoskeletal Knowledge Questionnaires), Leuven Knowledge Questionnaire for Coronary Heart Disease (CHD), and the Questionnaire of Knowledge and Perception Towards COVID-19. Each question from these tools was posed to the online interfaces of selected LLMs, and their responses were systematically recorded. To assess the quality of the generated answers, an expert clinician graded each response as either “appropriate” or “in- appropriate.” Our findings provide valuable insights into the strengths and limitations of LLMs in delivering accurate, rel- evant, and contextually appropriate medical information, with a particular emphasis on rare diseases. This work highlights the potential role of LLMs in supplementing medical knowledge dissemination while underscoring the need for further refinement and domain-specific training to improve their applicability in healthcare contexts.","url":"https://doi.org/10.20944/preprints202412.1605.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.20944/preprints202412.1605.v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-5580195/v1","name":"Explainable AI in brain tumor diagnosis: A critical review of ML and DL techniques","source":"preprints","abstract":"Abstract Brain tumors, caused by abnormal tissue growth within the brain, can severely disrupt brain functions and pose significant health risks. As the tumor progresses to higher stages, the patient's prognosis and survival decrease, resulting in a high mortality rate. With the advancements in medical imaging, especially the use of MRI, AI approaches have emerged as strong tools for detecting, segmenting, and classifying brain cancers. CNN and hybrid models, such as Vision Transformers (ViTs), have produced promising findings in this area. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable AI (XAI) methods in brain disease diagnosis. This paper investigates the utilization of machine learning, deep learning, and explainable AI (XAI) in brain tumor detection, segmentation, and classification. In this study, we have utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram. Peer-reviewed articles from PubMed, IEEE Explore, ScienceDirect, Google Scholar, Springer, and Wilay online libraries were searched, and only those papers were selected that were published in Scopus, SCIE, and ESCI-indexed journals. We have identified the 20 research papers published between 2020 and 2024 that used machine learning, deep learning and explainable AI to detect, segment, and classify the brain tumor. This review provides a comprehensive survey the of explainable artificial intelligence (XAI) in biomedical imaging, focusing on its role in the detection, segmentation and classification of brain tumors. It examines various machine learning, deep learning and XAI techniques, addresses current challenges, and suggests future directions. The objective is to provide clinicians, regulators and AI developers with valuable insights to improve the transparency and reliability of these methods in medical diagnostics.","url":"https://doi.org/10.21203/rs.3.rs-5580195/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5580195/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.64898/2026.03.09.26347935","name":"Associations between spatial distribution of immune cell subsets and clinical outcomes in patients with advanced melanoma treated with immune checkpoint inhibitors: results from the PUMA challenge","source":"preprints","abstract":"Patients with advanced melanoma are treated with immune checkpoint inhibitors (ICIs), yet less than 50% of patients achieve a durable response while all patients are exposed to the risk of severe side effects. Tumor-infiltrating lymphocytes (TILs) in pathology images are associated with ICI outcomes, but manual assessment is subjective. In addition, the predictive value of other immune cell subsets, including plasma cells, neutrophils, histiocytes, and melanophages, remains unclear. We organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides collected before start of treatment was associated with treatment outcomes. Algorithm performance was evaluated on a hidden test set, after which top-ranked algorithms were applied to pre-treatment metastatic whole-slide images from a large, multicenter cohort of patients with advanced melanoma treated with first-line ICIs ( n =1102). Automatically quantified tissue features and immune cell subsets were then associated with clinical outcomes. Top-performing algorithms improved detection of immune cell subsets, although accuracy for rare classes remained limited. Across challenge participants, TIL density showed the most consistent association with treatment response and survival. Associations for stromal TILs were weaker, while plasma cells, histiocytes, melanophages, neutrophils, necrosis and blood vessels did not show independent associations with outcomes. Overall, the results from the PUMA challenge improved the state of the art of immune cell detection in melanoma histopathology and show that intra-tumoral lymphocytes are the immune cell subset most consistently associated with treatment response and survival. Highlights We organized the first melanoma-specific tissue and nuclei segmentation competition Winning algorithms were applied to 1102 whole-slide images for biomarker analysis Intra-tumoral TILs were associated with response to immune checkpoint inhibitors Other immune cell subsets showed no independent association with treatment outcomes Tissue segmentation on WSIs was limited by low heterogeneity in training data. Graphical abstract","url":"https://doi.org/10.64898/2026.03.09.26347935","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.09.26347935","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-5423066/v1","name":"Comparison between ChatGPT 3.0 and Google Gemini Regarding Medicine Knowledge","source":"preprints","abstract":"Abstract ChatGPT and Gemini AI are two of the most advanced and enhanced large language models widely used worldwide for various purposes. These models are built to facilitate human civilization with their generative capability to produce solutions and suggestions for different purposes, in a human-like conversation type with predictive texts. This study aimed to identify the potential differences between these two models in the case of possessing medical knowledge. A set of multiple-choice questions (MCQ) was adapted from a medicine textbook, and the correct answers were identified by matching the answers in the textbook and a medical expert. Then both of the models were asked to identify the correct answers from the options given to them. They were scored based on their ability to identify the correct answers. The findings revealed that both AI models possess significantly less knowledge of different disease domains and are not sufficiently reliable for medical assistance, though ChatGPT 3.5 possesses slightly better knowledge than its counterpart Google Gemini. The developers should focus on these models to make them more reliable in medical education so that our medical students and doctors can utilize the full potential of Artificial Intelligence in their medical lives for both learning and application.","url":"https://doi.org/10.21203/rs.3.rs-5423066/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5423066/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-8678202/v1","name":"Implementation burden and hidden labor in a multisite digital psychiatry trial","source":"preprints","abstract":"Abstract Background: Multisite digital psychiatry trials increasingly rely on complex onboarding and implementation processes at local research sites. While outcome-focused evaluations are common, less attention has been paid to the site-level labor required to operationalize such studies in real-world settings, particularly at smaller or resource-constrained sites. Methods: This paper adopts a reflexive, practice-based qualitative approach to examine onboarding and implementation processes at a single Latvian research site participating in a multisite digital psychiatry trial (ClinicalTrials.gov: NCT04953208). The analysis is based on systematic reflection informed by site-specific documentation, onboarding timelines, internal communications, and longitudinal involvement in site coordination and implementation activities. An inductive thematic approach was used to identify recurring challenges and contextual factors shaping implementation burden. Results: Five interrelated themes were identified: hidden labor and role overload; resource scarcity at small research sites; fragmented remote communication and technical coordination; multi-role professional contexts and competing demands; and the impact of external systemic disruptions. Findings illustrate how diverse administrative, technical, logistical, and coordination tasks were absorbed into individual roles, often exceeding initial role expectations. Despite limited resources, the site achieved high performance through intensified individual effort, masking the true implementation burden. Reliance on remote coordination redistributed labor onto site personnel, while external crises further amplified workload and uncertainty. Conclusions: This site-level reflexive account highlights the central role of hidden labor in sustaining implementation in multisite digital psychiatry trials. Recognizing and explicitly resourcing implementation work, particularly at small research sites, may improve feasibility, sustainability, and equity across study settings. Reflexive, practice-based approaches offer valuable insight into implementation processes that are often invisible in outcome-focused research.","url":"https://doi.org/10.21203/rs.3.rs-8678202/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8678202/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.08.15.24312054","name":"Generative artificial intelligence models in clinical infectious disease consultations: a cross-sectional analysis among specialists and resident trainees","source":"preprints","abstract":"ABSTRACT Background The potential of generative artificial intelligence (GenAI) to augment clinical consultation services in clinical microbiology and infectious diseases (ID) is being evaluated. Methods This cross-sectional study evaluated the performance of four GenAI chatbots (GPT-4.0, a Custom Chatbot based on GPT-4.0, Gemini Pro, and Claude 2) by analysing 40 unique clinical scenarios synthesised from real-life clinical notes. Six specialists and resident trainees from clinical microbiology or ID units conducted randomised, blinded evaluations across four key domains: factual consistency, comprehensiveness, coherence, and medical harmfulness. Results Analysis of 960 human evaluation entries by six clinicians, covering 160 AI-generated responses, showed that GPT-4.0 produced longer responses than Gemini Pro (p Interpretation Clinical experience and domain expertise of individual clinicians significantly shaped the interpretation of AI-generated responses. In our analysis, we have demonstrated disconcerting human vulnerabilities in safeguarding against potentially harmful outputs. This fallibility seemed to be most apparent among experienced specialists and domain experts, revealing an unsettling paradox in the human evaluation and oversight of advanced AI systems. Stakeholders and developers must strive to control and mitigate user-specific and cognitive biases, thereby maximising the clinical impact and utility of AI technologies in healthcare delivery.","url":"https://doi.org/10.1101/2024.08.15.24312054","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.08.15.24312054","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.07.18.24310656","name":"Impact of Ambient Artificial Intelligence Notes on Provider Burnout","source":"preprints","abstract":"ABSTRACT Background Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. Objective To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). Methods A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. Results Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p Conclusions Ambient AI significantly reduces healthcare provider burnout and modestly enhances professional fulfillment. By alleviating documentation burdens, ambient AI improves operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.","url":"https://doi.org/10.1101/2024.07.18.24310656","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.07.18.24310656","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2025.07.31.25332526","name":"Safety and Compliance among Newly Qualified Paramedics in a Pre-hospital Clinical Trial of an Investigational Medicinal Product: A Post-Hoc analysis of the PACKMaN Randomised Controlled Trial","source":"preprints","abstract":"Background Pre-hospital research has unique challenges. Ambulance clinicians are required to enrol patients in emergency situations, often remote from the research team at time of recruitment. With Newly Qualified Paramedics (NQPs) representing a significant and growing proportion of ambulance staff, it is important to establish if they can safely and effectively recruit patients to clinical trials. This paper reports a post-hoc analysis of the PACKMaN trial, a large, double-blind Randomised Controlled Trial of an Investigational Medicinal Product (CTIMP), of ketamine versus morphine in the pre-hospital setting. Methods Adverse Events (AEs) and Serious Adverse Events (SAEs) experienced by patients recruited to the PACKMaN trial, and protocol Non-Compliances (NCs) by paramedics during the trial were retrospectively analysed. We compared recruitment, incidence and type of AEs, as well as incidence of SAEs and NCs dichotomised by paramedic experience. Results Of the 458 patients, 259 (56.6%) and 199 (43.4%) were recruited by experienced paramedics and NQPs respectively. Incidence of AEs was similar regardless of experience: experienced paramedics reported 128/259 (49.8%) and NQPs reported 91/199 (45.7%) OR 0.86 95% CI [0.60 to 1.25]. SAEs slightly increased, but not statistically significantly, in the NQP group: experienced paramedics 4/259 (1.5%), NQPs 8/199 (4.0%) OR 2.67 95% CI [0.79 to 9.00]. NC was similar amongst both groups, experienced paramedics 3/259 (1.2%), NQPs 6/199 (3.0%), OR 2.65 95% CI [0.66 to 10.74]. Conclusion In a double-blind CTIMP, there was no statistical difference in the incidence of AEs or NCs between NQPs and experienced paramedics. NQPs made an important contribution to patient recruitment in this study, improving the generalisability. SAEs and NCs were rare, and patients received analgesics safely. There was no correlation between experience and AE likelihood, and no safety concerns identified arising from NQP participation. Our findings demonstrate that NQPs can safely recruit patients to clinical trials.","url":"https://doi.org/10.1101/2025.07.31.25332526","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.07.31.25332526","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-4357929/v1","name":"Ethical Implications of Artificial Intelligence in Anesthesiology: A Scoping Review","source":"preprints","abstract":"Abstract Background Nowadays Artificial Intelligence (AI) as one of the advanced and rapidly growing technologies has had widespread effects on various aspects of human life. In the healthcare sector, the adoption of AI methodologies has gained significant momentum, particularly in enhancing patient care, with anesthesiology emerging as a field keenly embracing these technological advancements. The use of AI in anesthesia is accompanied by specific ethical and social issues that require careful examination and deep understanding. The objective of this scoping review was to compile existing literature about the ethical considerations surrounding the utilization of artificial intelligence (AI) in anesthesiology. Method This scoping review was conducted within the first three months of 2024. The research question was \"What are the ethical issues in the application of AI in anesthesia\"? Based on the research question, researchers initially extracted relevant keywords using Medical Subject Heading (MeSH) and independently conducted preliminary searches in databases including Scopus, Web of Science, PubMed, Cochrane, and Google Scholar. The study selection process was guided by predetermined inclusion and exclusion criteria. The inclusion criteria were studies relevant to the research question. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was used to report the review process. Results The search strategy yielded a total of 327 articles. Consequently, the full text of 4 studies was examined. Of these, 2 studies were excluded due to lack of relevance to the research question. In total, 2 studies (both in English) were included in this review. Both of these studies were cross-sectional studies that examined the opinions of anesthesiologists regarding the ethical implications of using artificial intelligence in anesthesia. Conclusion The ethical integration of AI into anesthesia holds promise for improving patient care outcomes while upholding principles of safety, fairness, and accountability. Additional training programs and updated protocols are necessary for ensuring data security, collection, and processing. Additionally, Appropriate legal regulations concerning data processing should be developed.","url":"https://doi.org/10.21203/rs.3.rs-4357929/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4357929/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.08.05.24310603","name":"Exploring the impact of an artificial intelligence-based intraoperative image navigation system in laparoscopic surgery on clinical outcomes: A protocol for a multicenter randomized controlled trial","source":"preprints","abstract":"Background In the research field of artificial intelligence (AI) in surgery, there are many open questions that must be clarified. Well-designed randomized controlled trials (RCTs) are required to explore the positive clinical impacts by comparing the use and non-use of AI-based intraoperative image navigation. Therefore, herein, we propose the “ImNavi” trial, a multicenter RCT, to compare the use and non-use of an AI-based intraoperative image navigation system in laparoscopic surgery. Methods The ImNavi trial is a Japanese multicenter RCT involving 1:1 randomization between the use and non-use of an AI-based intraoperative image navigation system in laparoscopic colorectal surgery. The participating institutions will include three high-volume centers with sufficient laparoscopic colorectal surgery caseloads (>100 cases/year), including one national cancer center and two university hospitals in Japan. Written informed consent will be obtained from all patients. Patients aged between 18 and 80 years scheduled to undergo laparoscopic left-sided colorectal resection will be included in the study. The primary outcome is the time required for each target organ, including the ureter and autonomic nerves, to be recognized by the surgeon after its initial appearance on the monitor. Secondary outcomes include intraoperative target organ injuries, intraoperative complications, operation time, blood loss, duration of postoperative hospital stay, postoperative complications within 30 days, postoperative male sexual dysfunction 1 month after surgery, surgeon’s confidence in recognizing each target organ, and the postoperative fatigue of the primary surgeon. Discussion The impact of AI-based surgical applications on clinical outcomes beyond numerical expression will be explored from a variety of viewpoints while evaluating quantitative items, including intraoperative complications and operation time, as secondary endpoints. We expect that the findings of this RCT will contribute to advancing research in the domain of AI in surgery. Trial registration The trial was registered at the University Hospital Medical Information Network Center ( https://www.umin.ac.jp/ctr/index-j.html ) on March 28th, 2023 under trial ID: UMIN000050701.","url":"https://doi.org/10.1101/2024.08.05.24310603","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.08.05.24310603","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.1101/2024.06.19.24309148","name":"Policies on Artificial Intelligence Chatbots Among Academic Publishers: A Cross-Sectional Audit","source":"preprints","abstract":"Background Artificial intelligence (AI) chatbots are novel computer programs that can generate text or content in a natural language format. Academic publishers are adapting to the transformative role of AI chatbots in producing or facilitating scientific research. This study aimed to examine the policies established by scientific, technical, and medical academic publishers for defining and regulating the responsible authors’ use of AI chatbots. Methods This study performed a cross-sectional audit on the publicly available policies of 163 academic publishers, indexed as members of the International Association of the Scientific, Technical, and Medical Publishers (STM). Data extraction of publicly available policies on the webpages of all STM academic publishers was performed independently in duplicate with content analysis reviewed by a third contributor (September 2023 - December 2023). Data was categorized into policy elements, such as ‘proofreading’ and ‘image generation’. Counts and percentages of ‘yes’ (i.e., permitted), ‘no’, and ‘N/A’ were established for each policy element. Results A total of 56/163 (34.4%) STM academic publishers had a publicly available policy guiding the authors’ use of AI chatbots. No policy allowed authorship accreditations for AI chatbots (or other generative technology). Most (49/56 or 87.5%) required specific disclosure of AI chatbot use. Four policies/publishers placed a complete ban on the use of AI tools by authors. Conclusions Only a third of STM academic publishers had publicly available policies as of December 2023. A re-examination of all STM members in 12-18 months may uncover evolving approaches toward AI chatbot use with more academic publishers having a policy.","url":"https://doi.org/10.1101/2024.06.19.24309148","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.06.19.24309148","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.22541/au.173083565.50154202/v1","name":"DEEP LEARNING TECHNIQUES FOR MEDICAL IMAGE ANALYSIS AND DIAGNOSIS","source":"preprints","abstract":"Deep learning techniques have revolutionized the field of medical image analysis and diagnosis, offering unprecedented accuracy and efficiency in the interpretation of complex medical data. This paper reviews the application of various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), in the context of medical imaging modalities such as X-rays, MRIs, and CT scans. We discuss the critical preprocessing steps, such as data augmentation and normalization, that enhance model performance. The integration of deep learning with other artificial intelligence methodologies, such as transfer learning and reinforcement learning, is also explored, highlighting their potential to improve diagnostic capabilities. Furthermore, the challenges associated with the implementation of deep learning in clinical settings, including data privacy, interpretability of models, and the necessity for large annotated datasets, are addressed. Case studies demonstrate the successful deployment of deep learning algorithms in detecting diseases such as cancer, cardiovascular conditions, and neurological disorders. The paper concludes by emphasizing the transformative impact of deep learning on medical diagnostics, the ongoing research needed to overcome current limitations, and the future potential for these technologies to support healthcare professionals in delivering timely and accurate patient care.","url":"https://doi.org/10.22541/au.173083565.50154202/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.22541/au.173083565.50154202/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.22541/au.173641568.89918303/v1","name":"Transformer-Based Abstractive Summarization for Depression Detection Literature for Enhanced Medical Insights","source":"preprints","abstract":"The overwhelming surge in depression detection research presents significant challenges for mental health professionals and researchers in keeping pace with new advancements. This issue is particularly critical as timely access to insights from recent studies is essential for effective diagnosis and intervention strategies. Manually summarizing the growing body of literature is labour-intensive and prone to inconsistencies, creating an urgent need for automated summarization tools. This study introduces DepressiLex, a specialized corpus comprising 40 research papers from 2023-2024 focused on depression detection. Using transformer-based models like including Pre-training with Extracted Gap-Sentences for Abstractive Summarization (PEGASUS), Bidirectional and Auto-Regressive Transformers (BART), the Text-to-Text Transfer Transformer (T5-Base), the Longformer-Encoder-Decoder (LED), and ProphetNet, to evaluate their effectiveness in generating abstractive summaries. We assessed their performance using metrics such as the Bilingual Evaluation Understudy (BLEU) and the Recall-Oriented Understudy for Gisting Evaluation (ROGUE), with the Longformer-Encoder-Decoder consistently outperforming the others. This engineering advancement fulfils a critical need in healthcare, providing mental health professionals with streamlined, artificial intelligence (AI)-enabled access to key insights, thereby significantly reducing the time and cognitive load involved in reviewing complex research. Additionally, word cloud visualizations highlight the dominant themes and terms across the summaries, underscoring the potential of transformer models to transform access to mental health knowledge.","url":"https://doi.org/10.22541/au.173641568.89918303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.173641568.89918303/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-5662163/v1","name":"URFM: a general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis","source":"preprints","abstract":"Abstract Ultrasound imaging is pivotal in clinical diagnostics, providing critical insights into a wide range of diseases and organs. However, advancing artificial intelligence (AI) in this field is hindered by challenges such as the reliance on large labeled datasets and the limited generalizability of task-specific models, largely due to ultrasound’s low signal-to-noise ratio (SNR). To address these issues, we propose the Ultrasound Representation Foundation Model (URFM), designed to learn robust and generalizable representations from unlabeled ultrasound data.URFM is pre-trained on over 1 million ultrasound images from 15 major anatomical organs, utilizing representation-based masked image modeling (MIM) and state-of-the-art self-supervised learning techniques. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a medical vision-language model, to handle the low SNR inherent in ultrasound imaging. Extensive evaluations show URFM outperforms existing methods, excelling in generalization, label efficiency, and training speed, highlighting its potential to revolutionize diagnostic accuracy and clinical workflows.","url":"https://doi.org/10.21203/rs.3.rs-5662163/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5662163/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.64898/2026.01.29.26344844","name":"Prevalence, Characteristics and Evolution of Mpox-Related Ophthalmic Disease: A Prospective Cohort Study in South-Kivu, Democratic Republic of the Congo (MBOTE-EYE)","source":"preprints","abstract":"Background Mpox-related ophthalmic disease (MPOXROD) ranges from mild conjunctivitis to sight-threatening keratitis, however, data based on systematic ophthalmological assessment are scarce. We aimed to characterise the prevalence, features, and temporal evolution of MPOXROD during a clade Ib mpox outbreak. Methods We conducted MBOTE-EYE, a prospective ophthalmological sub-study, nested within a clinical characterisation cohort in Kamituga, South-Kivu, Democratic Republic of the Congo. All hospitalised patients with mpox confirmed by PCR in the prior 48 hours were eligible. Participants underwent comprehensive ophthalmological examination at enrolment, discharge, and days 29 and 59 post-diagnosis. Conjunctival swabs were collected for monkeypox virus PCR testing. MPOXROD was defined as conjunctivitis, scleritis, keratitis, uveitis, or optic nerve involvement. Risk factors were assessed using mixed-effects Poisson regression. Findings Between 28 October 2024 and 30 June 2025, 310 participants were enrolled (median age 14 years, IQR 2.5-25.0; 53.5% female, n=166/310). At enrolment, conjunctivitis was present in 36.1% (95% CI 31.0–41.6%, n=112/310), keratitis in 7.7% (95% CI 5.3–11.3%), and anterior uveitis in 0.6% (95% CI 0.2–2.3%). Overall, 43.2% (95% CI 37.8–48.8%) developed MPOXROD during follow-up, most often bilaterally. Visual acuity Interpretation Ophthalmic involvement in clade Ib mpox is common and frequently bilateral, with a substantial burden of keratitis and risk of vision loss, particularly in young children and severely malnourished individuals. These findings highlight the need for systematic eye examinations in mpox care and provide critical evidence to inform future trials of targeted ophthalmic therapies. Funding None of the funders had a role in study design, analysis, interpretation, or writing.","url":"https://doi.org/10.64898/2026.01.29.26344844","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.29.26344844","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"doi:10.21203/rs.3.rs-4401535/v1","name":"Attitudes and Awareness of Medical Students Towards Teleradiology and the Application of Artificial Intelligence in Diagnostic Radiology.","source":"preprints","abstract":"Abstract Rationale and Objectives: Teleradiology and artificial intelligence (AI) have emerged as important tools for facilitating healthcare workers in diagnosing, treating, and preventing diseases. They can also be used in the analysis of X-ray images, MRIs, and CT scans. This study delves into the perceptions of medical students at Imam Abdulrahman University (IAU) regarding the role of AI and teleradiology. The findings also shed light on the opinions of medical students at the IAU on the potential augmentation and replacement of radiologists with AI. Materials and Methods: In this cross-sectional study, data were collected from medical students at Imam Abdulrahman bin Faisal University, Eastern region, Saudi Arabia. An online self-administered questionnaire was used to collect data during August 2023. Medical students’ opinions were assessed using the Chi-square test. Results: The study concluded that 65.2% of the respondents thought radiologists should embrace AI. Moreover, 56.1% agreed that the AI would augment the radiologist's capability and efficiency. However, 46.2% believed that teleradiology could replace radiologists working at the hospital, and 42.5% agreed that the impact of AI alone would reduce the number of radiologists needed in the field. Conclusion: Medical students expressed different opinions in the role of AI in augmenting or replacing radiologists. For teleradiology, the results were promising as students showed interest and optimistic views. Understanding and addressing these perceptions from our future physicians is crucial for developing a strong radiology workforce capable of navigating the transformative journey of a more technological healthcare environment.","url":"https://doi.org/10.21203/rs.3.rs-4401535/v1","authors":["Afnan Almuhanna","Danyah Almohsen","Deemah Alhuraish","Deem AlSultan","Farah AlRatrout","Rabab Alzanadi","Rana Abbas"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4401535/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21203/rs.3.rs-3256363/v1","name":"An Artificial Intelligence Copilot System Helps Reduce Mortality Rates of Hemodialysis Patients","source":"preprints","abstract":"Abstract Hemodialysis is the primary treatment for end-stage renal disease patients, but its mortality rate is still unacceptably high. Based on multi-modality examination data of 63,499 patients from 333medical centers, we developed a Hemodialysis Early Warning and Intervention Copilot (HEWIC) system. This system assists healthcare professionals in identifying hemodialysis patients at high risk of mortality and provides risk factors to makeintervention decisions jointly with healthcare professionals. On the retrospective cohort, HEWICachieved ROC-AUC scores of 0.82and 0.79 on one-month and three-month mortality probability prediction, respectively. We then conducted a pragmatic clinical trial (RCT, ChiCTR2100052662) to evaluate whether HEWIC could assist healthcare professionals in intervention to reduce the mortality rate of hemodialysis patients in the real world. Involving 9,965 hemodialysis patients (5,216 intervention and 4,749 control) from 58 dialysis centers, the trial indicates that HEWIC’s high-risk patient identification and treatment recommendation can help reduce the three-month mortality rate of hemodialysis patients by 38.3%, with a more pronounced effect in primary hospitals. Patients managed by the intervention group (where professionals assisted by HEWIC) received more types of drug treatment and showed varying degrees of improvement in anemia, blood pressure, blood lipids, electrolytes, and inflammatory conditions, thanthe control group. Furthermore, HEWICdoes not require additional time investment from healthcare professionals, nor does it interfere with their clinical work. This study proves that the AI-copilot system not only can benefit hemodialysis treatment but also enhance the standardization of medical care across different regions. Additionally, it also suggests that the human-AIcollaborationframework has the potential to revolutionize clinical diagnosis and treatment practice for other diseases.","url":"https://doi.org/10.21203/rs.3.rs-3256363/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3256363/v1","addedAt":"2026-09-01T01:47:48.922Z","updatedAt":"2026-09-01T01:47:53.670Z"},{"id":"oa:W4382651942","name":"Smart Smile: Revolutionizing Dentistry With Artificial Intelligence","source":"openalex","abstract":"Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.","url":"https://doi.org/10.7759/cureus.41227","authors":["Ashwini Dhopte","Hiroj Bagde"],"tags":["Medicine","Transformative learning","Virtual reality","Robotics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-30","doi":"https://doi.org/10.7759/cureus.41227","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2340020088","name":"Rough sets","source":"openalex","abstract":"Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.","url":"https://doi.org/10.1145/219717.219791","authors":["Zdzisław Pawlak","Jerzy W. Grzymala‐Busse","Roman Słowiński","Wojciech Ziarko"],"tags":["Rough set","Vagueness","Computer science","Artificial intelligence","Knowledge acquisition"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1995-11-01","doi":"https://doi.org/10.1145/219717.219791","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4407028552","name":"Convergence of evolving artificial intelligence and machine learning techniques in precision oncology","source":"openalex","abstract":"The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor's biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.","url":"https://doi.org/10.1038/s41746-025-01471-y","authors":["Elena Fountzilas","Tillman Pearce","Mehmet A. Baysal","Abhijit Chakraborty","Apostolia M. Tsimberidou"],"tags":["Computer science","Workflow","Generalizability theory","Artificial intelligence","Pipeline (software)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-31","doi":"https://doi.org/10.1038/s41746-025-01471-y","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4319027038","name":"At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives","source":"openalex","abstract":"Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.","url":"https://doi.org/10.3390/s23031639","authors":["Amira Bourechak","Ouarda Zedadra","Mohamed Nadjib Kouahla","Antonio Guerrieri","Hamid Séridi","Giancarlo Fortino"],"tags":["Computer science","Edge computing","Cloud computing","Analytics","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-02","doi":"https://doi.org/10.3390/s23031639","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3092581351","name":"A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.chaos.2020.110337","authors":["Jawad Rasheed","Akhtar Jamıl","Alaa Ali Hameed","Usman Aftab","Javaria Aftab","Syed Attique Shah","Dirk Draheim"],"tags":["Pandemic","Globe","Context (archaeology)","Pace","Multidisciplinary approach"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-10","doi":"https://doi.org/10.1016/j.chaos.2020.110337","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3048865162","name":"A review of artificial intelligence applications for antimicrobial resistance","source":"openalex","abstract":"The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions.","url":"https://doi.org/10.1016/j.bsheal.2020.08.003","authors":["Ji Lv","Senyi Deng","Le Zhang"],"tags":["Antibiotic resistance","Risk analysis (engineering)","Computer science","Domain (mathematical analysis)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-11","doi":"https://doi.org/10.1016/j.bsheal.2020.08.003","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2889346090","name":"Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical decision support system Watson for Oncology (WFO) is an outstanding representative AI in the medical field, and it can provide to cancer patients prompt treatment recommendations comparable with ones made by expert oncologists. WFO is increasingly being used in China, but limited reports on whether WFO is suitable for Chinese patients, especially patients with lung cancer, exist. Here, we report a retrospective study based on the consistency between the lung cancer treatment recommendations made for the same patient by WFO and by the multidisciplinary team at our center. OBJECTIVE: The aim of this study was to explore the feasibility of using WFO for lung cancer cases in China and to ascertain ways to make WFO more suitable for Chinese patients with lung cancer. METHODS: We selected all lung cancer patients who were hospitalized and received antitumor treatment for the first time at the Second Xiangya Hospital Cancer Center from September to December 2017 (N=182). WFO made treatment recommendations for all supported cases (n=149). If the actual therapeutic regimen (administered by our multidisciplinary team) was recommended or for consideration according to WFO, we defined the recommendations as consistent; if the actual therapeutic regimen was not recommended by WFO or if WFO did not provide the same treatment option, we defined the recommendations as inconsistent. Blinded second round reviews were performed by our multidisciplinary team to reassess the incongruent cases. RESULTS: WFO did not support 18.1% (33/182) of recommendations among all cases. Of the 149 supported cases, 65.8% (98/149) received recommendations that were consistent with the recommendations of our team. Logistic regression analysis showed that pathological type and staging had significant effects on consistency (P=.004, odds ratio [OR] 0.09, 95% CI 0.02-0.45 and P<.001, OR 9.5, 95% CI 3.4-26.1, respectively). Age, gender, and presence of epidermal growth factor receptor gene mutations had no effect on consistency. In 82% (42/51) of the inconsistent cases, our team administered two China-specific treatments, which were different from the recommendations made by WFO but led to excellent outcomes. CONCLUSIONS: In China, most of the treatment recommendations of WFO are consistent with the recommendations of the expert group, although a relatively high proportion of cases are still not supported by WFO. Therefore, WFO cannot currently replace oncologists. WFO can improve the efficiency of clinical work by providing assistance to doctors, but it needs to learn the regional characteristics of patients to improve its assistive ability.","url":"https://doi.org/10.2196/11087","authors":["Chaoyuan Liu","Xianling Liu","Fang Wu","Mingxuan Xie","Yeqian Feng","Chunhong Hu"],"tags":["Watson","Lung cancer","Medicine","Cancer treatment","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-08-29","doi":"https://doi.org/10.2196/11087","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2970852766","name":"Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease","source":"openalex","abstract":"","url":"https://doi.org/10.1161/jaha.119.012788","authors":["Nobuyuki Kagiyama","Sirish Shrestha","Peter Farjo","Partho P. Sengupta"],"tags":["Medicine","Disease","Primer (cosmetics)","Internal medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-27","doi":"https://doi.org/10.1161/jaha.119.012788","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2587937998","name":"Hybrid-augmented intelligence: collaboration and cognition","source":"openalex","abstract":"The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.","url":"https://doi.org/10.1631/fitee.1700053","authors":["Nanning Zheng","Ziyi Liu","Pengju Ren","Yongqiang Ma","Shitao Chen","Siyu Yu","Jianru Xue","Badong Chen","Fei–Yue Wang"],"tags":["Human intelligence","Computer science","Cognition","Artificial intelligence","Augmented reality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-02-01","doi":"https://doi.org/10.1631/fitee.1700053","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4401232529","name":"Explainable artificial intelligence for spectroscopy data: a review","source":"openalex","abstract":"Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation. Following the PRISMA guideline 2020, we conducted a systematic search across major journal databases, resulting in 259 initial search results. After removing duplicates and applying inclusion and exclusion criteria, 21 scientific studies were included in this review. Notably, most of the studies focused on using XAI methods for spectral data analysis, emphasizing identifying significant spectral bands rather than specific intensity peaks. Among the most utilized AI techniques were SHapley Additive exPlanations (SHAP), masking methods inspired by Local Interpretable Model-agnostic Explanations (LIME), and Class Activation Mapping (CAM). These methods were favored due to their model-agnostic nature and ease of use, enabling interpretable explanations without modifying the original models. Future research should propose new methods and explore the adaptation of other XAI employed in other domains to better suit the unique characteristics of spectroscopic data.","url":"https://doi.org/10.1007/s00424-024-02997-y","authors":["Jhonatan Contreras","Thomas Bocklitz"],"tags":["Computer science","Systematic review","Data science","Artificial intelligence","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.1007/s00424-024-02997-y","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4381430332","name":"On Artificial Intelligence and Manipulation","source":"openalex","abstract":"Abstract The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about new ways of engaging with users, collecting their data and potentially influencing their behavior. However, these technologies and techniques have also raised concerns about the potential for manipulation, as they offer unprecedented capabilities for targeting and influencing individuals on a large scale and in a more subtle, automated and pervasive manner than ever before. This paper, provides a narrative review of the existing literature on manipulation, with a particular focus on the role of AI and associated digital technologies. Furthermore, it outlines an account of manipulation based of four key requirements: intentionality, asymmetry of outcome, non-transparency and violation of autonomy. I argue that while manipulation is not a new phenomenon, the pervasiveness, automaticity, and opacity of certain digital technologies may raise a new type of manipulation, called “digital manipulation”. I call “digital manipulation” any influence exerted through the use of digital technology that is intentionally designed to bypass reason and to produce an asymmetry of outcome between the data processor (or a third party that benefits thereof) and the data subject. Drawing on insights from psychology, sociology, and computer science, I identify key factors that can make manipulation more or less effective, and highlight the potential risks and benefits of these technologies for individuals and society. I conclude that manipulation through AI and associated digital technologies is not qualitatively different from manipulation through human–human interaction in the physical world. However, some functional characteristics make it potentially more likely of evading the subject’s cognitive defenses. This could increase the probability and severity of manipulation. Furthermore, it could violate some fundamental principles of freedom or entitlement related to a person’s brain and mind domain, hence called neurorights. To this end, an account of digital manipulation as a violation of the neuroright to cognitive liberty is presented.","url":"https://doi.org/10.1007/s11245-023-09940-3","authors":["Marcello Ienca"],"tags":["Transparency (behavior)","Computer science","Sociotechnical system","Emerging technologies","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-20","doi":"https://doi.org/10.1007/s11245-023-09940-3","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3207968086","name":"Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers","source":"openalex","abstract":"Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.","url":"https://doi.org/10.24920/003963","authors":["Chen Xu","Xiaofei Huo","Zhe Wu","Jingjing Lu"],"tags":["Medicine","Ovarian cancer","Magnetic resonance imaging","Medical imaging","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.24920/003963","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4379473621","name":"Rise of Artificial Intelligence in Business and Industry","source":"openalex","abstract":"The ongoing development of business and the most recent advances in artificial intelligence (AI) allow for the many business practices to be improved by the capacity to establish new forms of collaboration, which is a significant competitive advantage. This rapidly developing technology enables to offer brand services and even some new forms of business interactions with consumers and personnel. The digitalization of AI concurrently emphasized for businesses that they need concentrate on their present strategies while also routinely and early pursuing new chances in the market. Not only in business but also in different industry sectors, Al techniques are being used and revolutionized different industry sectors. This review focuses on the application of AI techniques in business and different industries.","url":"https://doi.org/10.9734/jerr/2023/v25i3893","authors":["Jasmin Praful Bharadiya","Reji Kurien Thomas","Farhan Ahmed"],"tags":["Business","Business intelligence","Competitive advantage","Marketing","New business development"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-05","doi":"https://doi.org/10.9734/jerr/2023/v25i3893","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4403342289","name":"Welcoming the JMA Journal' s Call for Manuscripts on Medical Artificial Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.31662/jmaj.2024-0147","authors":["Shigeki Matsubara"],"tags":["Computer science","Library science","Artificial intelligence"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"https://doi.org/10.31662/jmaj.2024-0147","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"oa:W3151605938","name":"Artificial Intelligence, Globalization, and Strategies for Economic Development","source":"openalex","abstract":"Progress in artificial intelligence and related forms of automation technologies threatens to reverse the gains that developing countries and emerging markets have experienced from integrating into the world economy over the past half century, aggravating poverty and inequality. The new technologies have the tendency to be labor-saving, resource-saving, and to give rise to winner-takes-all dynamics that advantage developed countries. We analyze the economic forces behind these developments and describe economic policies that would mitigate the adverse effects on developing and emerging economies while leveraging the potential gains from technological advances. We also describe reforms to our global system of economic governance that would share the benefits of AI more widely with developing countries.","url":"https://doi.org/10.3386/w28453","authors":["Anton Korinek","Joseph E. Stiglitz"],"tags":["Developing country","Poverty","Emerging markets","Globalization","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-01","doi":"https://doi.org/10.3386/w28453","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385191921","name":"Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence","source":"openalex","abstract":"The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.","url":"https://doi.org/10.3389/fpsyg.2023.1191628","authors":["Simone Grassini"],"tags":["Scale (ratio)","Exploratory factor analysis","Psychology","Internal consistency","Confirmatory factor analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-24","doi":"https://doi.org/10.3389/fpsyg.2023.1191628","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4254182432","name":"Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey (Preprint)","source":"openalex","abstract":"BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.","url":"https://doi.org/10.2196/preprints.26646","authors":["Oliver Maaßen","Sebastian Fritsch","Julia Palm","Saskia Deffge","J Kunze","Gernot Marx","Morris Riedel","Andreas Schuppert","Johannes Bickenbach"],"tags":["German","Preprint","Medical education","Likert scale","Implementation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-19","doi":"https://doi.org/10.2196/preprints.26646","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3185779390","name":"Clinically applicable artificial intelligence system for dental diagnosis with CBCT","source":"openalex","abstract":"In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.","url":"https://doi.org/10.1038/s41598-021-94093-9","authors":["Matvey Ezhov","Maxim Gusarev","Maria Golitsyna","J.R. Yates","Evgeny Kushnerev","Dania Tamimi","Seçil Aksoy","Eugene Shumilov","Alex Sanders","Kaan Orhan"],"tags":["Medicine","Artificial intelligence","Periodontitis","Cone beam computed tomography","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-22","doi":"https://doi.org/10.1038/s41598-021-94093-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4367336050","name":"Medical Specialists’ Perception About Adoption of Artificial Intelligence in the Healthcare Sector","source":"openalex","abstract":"Artificial Intelligence (AI) has numerous potential applications in the healthcare sector. These applications vary from its use in quicker disease diagnosis to its assistance for efficiently dealing with pandemic situations. Many countries have successfully laid out their strategy for the effective implementation of AI. However, several developing countries are still working on their plan to increase the penetration of AI in various fields, including healthcare, to harness AI’s potential benefits. The research objective is to understand the perception of Medical Specialists about using AI by identifying significant factors that impact their adoption of AI to perform medical tasks. Several medical specialists were surveyed based on five factors taken from 2 popular technology adoption models- UTAUT2 and TAM2. The data analysis on the collected responses (N=111) was done using PLS-SEM. Based on the analysis results, two factors, namely- Facilitating Conditions and Performance Expectancy, are found to positively impact medical specialists’ behavioral intention to adopt AI in their professional life. Decision-makers of developing countries can take appropriate measures based on identified two significant factors to boost AI healttocations’ adoption in their country to solve existing healthcare problems and improve the quality of healthcare services.","url":"https://doi.org/10.18137/cardiometry.2022.25.426434","authors":["B. Thakkar","S. Vijayakumar Bharathi"],"tags":["Health care","Perception","Developing country","Expectancy theory","Life expectancy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-14","doi":"https://doi.org/10.18137/cardiometry.2022.25.426434","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387966880","name":"Generative artificial intelligence empowers digital twins in drug discovery and clinical trials","source":"openalex","abstract":"INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. AREAS COVERED: The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. EXPERT OPINION: The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.","url":"https://doi.org/10.1080/17460441.2023.2273839","authors":["Maria Bordukova","Nikita Makarov","Raul Rodriguez‐Esteban","Fabian Schmich","Michael P. Menden"],"tags":["Generative grammar","Drug discovery","Computer science","Precision medicine","Virtual screening"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-27","doi":"https://doi.org/10.1080/17460441.2023.2273839","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4310027101","name":"Use of artificial intelligence and deep learning in fetal ultrasound imaging","source":"openalex","abstract":"Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.","url":"https://doi.org/10.1002/uog.26130","authors":["Ruben Ramirez Zegarra","T. Ghi"],"tags":["Deep learning","Medicine","Artificial intelligence","Modality (human–computer interaction)","Ultrasound"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-27","doi":"https://doi.org/10.1002/uog.26130","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4390906507","name":"Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools","source":"openalex","abstract":"Artificial Intelligence (AI) in Education claims to have the potential for building personalised curricula, as well as bringing opportunities for democratising education and creating a renaissance of new ways of teaching and learning. Millions of students are starting to benefit from the use of these technologies, but millions more around the world are not, due to the digital divide and deep pre-existing social and educational inequalities. If this trend continues, the first large-scale delivery of AI in Education could lead to greater educational inequality, along with a global misallocation of educational resources motivated by the current techno-solutionist narrative, which proposes technological solutions as a quick and flawless way to solve complex real-world problems. This work focuses on posing questions about the future of AI in Education, intending to initiate the pressing conversation that could set the right foundations (e.g., inclusion and diversity) for a new generation of education that is permeated with AI technology. The main goal of our opinion piece is to conceptualise a sustainable, large-scale and inclusive AI for the education ecosystem that facilitates equitable, high-quality lifelong learning opportunities for all. The contribution starts by synthesising how AI might change how we learn and teach, focusing on the case of personalised learning companions and assistive technology for disability. Then, we move on to discuss some socio-technical features that will be crucial to avoiding the perils of these AI systems worldwide (and perhaps ensuring their success by leveraging more inclusive education). This work also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We emphasise the need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies. Finally, we ask what it would take for this educational revolution to provide egalitarian and empowering access to education that transcends any political, cultural, language, geographical and learning-ability barriers, so that educational systems can be responsive to all learners’ needs.","url":"https://doi.org/10.3390/su16020781","authors":["Sahan Bulathwela","María Pérez‐Ortiz","Catherine Holloway","Mutlu Cukurova","John Shawe‐Taylor"],"tags":["Curriculum","Inclusion (mineral)","Conversation","Scale (ratio)","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-16","doi":"https://doi.org/10.3390/su16020781","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4409291977","name":"Application of Artificial Intelligence in Medical Imaging: Current Status and Future Directions","source":"openalex","abstract":"ABSTRACT A revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence (AI) in medical imaging. The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies, especially deep learning, that performs image recognition, feature extraction, and pattern analysis. Furthermore, AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases. It also provides doctors with more advanced tools for managing the conditions of their patients. AI is poised to play a more significant role in medical imaging, especially in real‐time image processing and multimodal fusion. By integrating multiple forms of image data, multimodal fusion technology provides more comprehensive disease information, whereas real‐time image analysis can assist surgeons in making more precise decisions. By tailoring treatment regimens to each patient's unique needs, AI enhances both the effectiveness of treatment and the patient experience. Overall, AI in medical imaging promises a bright future, significantly enhancing diagnostic precision and therapeutic efficacy, and ultimately delivering higher‐quality medical care to patients.","url":"https://doi.org/10.1002/ird3.70008","authors":["Yixin Yang","Lan Ye","Zhanhui Feng"],"tags":["Current (fluid)","Computer science","Data science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1002/ird3.70008","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W3195457683","name":"Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?","source":"openalex","abstract":"Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.","url":"https://doi.org/10.3390/diagnostics11081473","authors":["Carlo Fabrizio","Andrea Termine","Carlo Caltagirone","Giulia Maria Sancesario"],"tags":["Disease","Cognitive science","Psychology","Medicine","Pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-14","doi":"https://doi.org/10.3390/diagnostics11081473","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4386780386","name":"Artificial intelligence for medicine: Progress, challenges, and perspectives","source":"openalex","abstract":"Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients’ privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.","url":"https://doi.org/10.59717/j.xinn-med.2023.100030","authors":["Tao Huang","Huiyu Xu","Haitao Wang","Haofan Huang","Yongjun Xu","Baohua Li","Shenda Hong","Guoshuang Feng","Shuyi Kui","Guangjian Liu","Dehua Jiang","Zhicheng Li","Ye Li","Congcong Ma","Chunyan Su","Wei Wang","Rong Li","Puxiang Lai","Jie Qiao"],"tags":["Big data","Construct (python library)","Artificial intelligence","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.59717/j.xinn-med.2023.100030","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3215022015","name":"Artificial Intelligence in Medicine","source":"openalex","abstract":"AIME) was established in 1986 following a very successful workshop held in Pavia, Italy, the year before.The principal aims of AIME are to foster fundamental and applied research in the application of artificial intelligence (AI) techniques to medical care and medical research, and to provide a forum at biennial conferences for discussing any progress made.The main activity of the society thus far has been the organization of a series of biennial conferences, held in Marseilles,","url":"https://doi.org/10.1007/978-3-030-77211-6","authors":[],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-03","doi":"https://doi.org/10.1007/978-3-030-77211-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4386416103","name":"Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine","source":"openalex","abstract":"Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.","url":"https://doi.org/10.7759/cureus.44658","authors":["Javed Iqbal","Diana Jaimes","Pallavi Makineni","Sachin Subramani","Sarah Hemaida","Thanmai Reddy Thugu","Amna Naveed Butt","Jarin Tasnim Sikto","Pareena Kaur","Muhammad Ali Lak","Monisha Augustine","Roheen Shahzad","Mustafa A. Arain"],"tags":["Medicine","Health care","Power (physics)","Artificial intelligence","Economic growth"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-04","doi":"https://doi.org/10.7759/cureus.44658","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2998697825","name":"AI-based computer-aided diagnosis (AI-CAD): the latest review to read first","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12194-019-00552-4","authors":["Hiroshi Fujita"],"tags":["Pace","Computer science","Artificial intelligence","CAD","Boom"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-02","doi":"https://doi.org/10.1007/s12194-019-00552-4","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4319986951","name":"Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions","source":"openalex","abstract":"The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.","url":"https://doi.org/10.1109/access.2023.3241628","authors":["Rajeswari Chengoden","Nancy Victor","Thien Huynh‐The","Gokul Yenduri","Rutvij H. Jhaveri","Mamoun Alazab","Sweta Bhattacharya","Pawan Hegde","Praveen Kumar Reddy Maddikunta","Thippa Reddy Gadekallu"],"tags":["Metaverse","Computer science","Health care","Augmented reality","Adaptation (eye)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/access.2023.3241628","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4410386467","name":"Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers. The individual pieces cover a wide range of areas related to medical genetics; we expect that these pieces may provide helpful windows into the ways in which AI is being actively studied, used, and considered in medical genetics.","url":"https://doi.org/10.1002/ajmg.a.64118","authors":["Benjamin D. Solomon","Morgan Cheatham","Thales A. C. de Guimarães","Dat Duong","Melissa Haendel","Tzung‐Chien Hsieh","Behnam Javanmardi","Britt Johnson","Peter Krawitz","Paul Kruszka","Tim Laurent","Ni‐Chung Lee","Kirsty McWalter","Michel Michaelides","Klaus Mohnike","Nikolas Pontikos","María J. Guillen Sacoto","Yousif J. Shwetar","Vincent D. Ustach","Rebekah L. Waikel","William Woof"],"tags":["Medical genetics","Context (archaeology)","Affect (linguistics)","Psychology","Genetics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-15","doi":"https://doi.org/10.1002/ajmg.a.64118","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3093824983","name":"Application of Artificial Intelligence Algorithms to Estimate the Success Rate in Medically Assisted Procreation","source":"openalex","abstract":"The aim of this study was to build an Artificial Neural Network (ANN) complemented by a decision tree to predict the chance of live birth after an In Vitro Fertilization (IVF)/Intracytoplasmic Sperm Injection (ICSI) treatment, before the first embryo transfer, using demographic and clinical data. Overall, 26 demographic and clinical data from 1193 cycles who underwent an IVF/ICSI treatment at Centro de Infertilidade e Reprodução Medicamente Assistida, between 2012 and 2019, were analyzed. An ANN was constructed by selecting experimentally the input variables which most correlated to the target through Pearson correlation. The final used variables were: woman’s age, total dose of gonadotropin, number of eggs, number of embryos and Antral Follicle Count (AFC). A decision tree was developed considering as an initial set the input variables integrated in the previous model. The ANN model was validated by the holdout method and the decision tree model by the 10-fold cross method. The ANN accuracy was 75.0% and the Area Under the Receiver Operating Characteristic (AUROC) curve was 75.2% (95% Confidence Interval (CI): 72.5–77.5%), whereas the decision tree model reached 75.0% and 74.9% (95% CI: 72.3–77.5%). These results demonstrated that both ANN and decision tree methods are fair for prediction the chance of conceive after an IVF/ICSI cycle.","url":"https://doi.org/10.3390/reprodmed1030014","authors":["Beatriz Brás de Guimarães","Leonardo Martins","José Metello","Fernando Luís-Ferreira","Pedro Ferreira","José Fonseca"],"tags":["Decision tree","In vitro fertilisation","Intracytoplasmic sperm injection","Confidence interval","Antral follicle"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-25","doi":"https://doi.org/10.3390/reprodmed1030014","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4402037523","name":"Artificial Intelligence and the Dehumanization of Patient Care","source":"openalex","abstract":"The integration of artificial intelligence (AI) into healthcare is rapidly transforming patient care, offering numerous advantages in diagnostics, efficiency, and clinical decision-making. However, this technological shift raises significant concerns about the potential erosion of the doctor-patient relationship, a cornerstone of effective medical practice. AI’s increasing role risks depersonalizing healthcare, as the emphasis on data-driven decisions may overshadow the empathy, trust, and personalized care traditionally provided by human clinicians. The \"black-box\" nature of AI algorithms further exacerbates this issue, as the lack of transparency in AI decision-making processes can undermine patient trust. Additionally, AI systems trained on biased datasets may inadvertently widen health disparities, particularly for underrepresented populations. While AI has the potential to streamline routine tasks and reduce the burden on healthcare providers, it is essential to ensure that these advancements do not come at the cost of the human connection vital to patient care. To address these challenges, future research and development should focus on creating AI systems that enhance, rather than replace, the compassionate aspects of healthcare. This balanced approach is crucial to preserving the integrity of the doctor-patient relationship while harnessing the benefits of AI, ultimately ensuring that technological progress aligns with the core values of medical practice.","url":"https://doi.org/10.1016/j.glmedi.2024.100138","authors":["Adewunmi Akingbola","Oluwatimilehin Adeleke","Ayotomiwa Idris","Olajumoke Adewole","Abiodun Adegbesan"],"tags":["Dehumanization","Psychology","Sociology","Anthropology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.1016/j.glmedi.2024.100138","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4394598267","name":"The potential for artificial intelligence to transform healthcare: perspectives from international health leaders","source":"openalex","abstract":"Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.","url":"https://doi.org/10.1038/s41746-024-01097-6","authors":["Christina Silcox","Eyal Zimlichmann","Katie Huber","Neil Rowen","R. S. Saunders","Mark McClellan","Charles N. Kahn","Claudia Salzberg","David W. Bates"],"tags":["Health care","Workforce","Population health","Public relations","Globe"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-09","doi":"https://doi.org/10.1038/s41746-024-01097-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4412584217","name":"Artificial Intelligence in healthcare: Transformative applications, ethical challenges, and future directions in medical diagnostics and personalized medicine","source":"openalex","abstract":"The harmonization of Artificial Intelligence (AI) in the healthcare sector has revolutionized medical diagnostics, treatment planning, and patient management. Over the past decade, AI-powered technologies have demonstrated significant potential in improving accuracy, efficiency, and accessibility in healthcare services. Machine learning algorithms and deep learning models have been employed for disease prediction, early diagnosis, and personalized medicine, enhancing patient outcomes. AI-driven robotic surgeries, virtual health assistants, and predictive analytics have optimized medical workflows, reducing human errors and optimizing resource utilization. Despite these advancements, challenges such as data privacy, ethical concerns, and the need for regulatory frameworks remain significant barriers to widespread adoption. This paper explores the evolution of AI in healthcare, focusing on its applications, benefits, and limitations. Through a comprehensive analysis of past developments and current trends, this study highlights the transformative role of AI in reshaping the medical landscape. As technology continues to grow, AI is poised to play an even more critical role in future healthcare innovations, ultimately improving the quality of patient care and medical decision-making.","url":"https://doi.org/10.30574/ijsra.2025.15.1.0954","authors":["Md Delower Hossain","Md Habibur Rahman","Kazi Md Riaz Hossan"],"tags":["Transformative learning","Personalized medicine","Health care","Precision medicine","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.30574/ijsra.2025.15.1.0954","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3013960650","name":"Artificial Intelligence in Autism Assessment","source":"openalex","abstract":"The current paper review gives a brief and representative description of the role that artificial intelligence plays nowadays at the assessment of autism. Therefore, many researchers note that artificial intelligence plays a notable role in the early diagnosis of autism since it helps the clinicians shorten the diagnose process and have more accurate results. Thus, the research team of this paper presents some applications of artificial intelligence that are used already or are in a preliminary phase aiming to highlight the use of smart technology in the diagnosing process of autism. Lastly, it is worth noting that an early and accurate diagnose is the key point for an individualized and successful intervention which aids the academic as well as the personal de-velopment of the child.","url":"https://doi.org/10.3991/ijet.v15i06.11231","authors":["Panagiota Anagnostopoulou","Vasiliki Alexandropoulou","Georgia Lorentzou","Andriana Lykothanasi","Polyxeni Ntaountaki","Athanasios Drigas"],"tags":["Autism","Process (computing)","Intervention (counseling)","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-27","doi":"https://doi.org/10.3991/ijet.v15i06.11231","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2083721794","name":"Artificial intelligence techniques for embryo and oocyte classification","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.rbmo.2012.09.015","authors":["Claudio Manna","Loris Nanni","Alessandra Lumini","Sebastiana Pappalardo"],"tags":["Artificial intelligence","Computer science","Selection (genetic algorithm)","Pattern recognition (psychology)","Reproduction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2012-10-02","doi":"https://doi.org/10.1016/j.rbmo.2012.09.015","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3020619771","name":"Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study","source":"openalex","abstract":"BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. METHODS: In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. FINDINGS: From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. INTERPRETATION: This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. FUNDING: Ministry of Health, Singapore.","url":"https://doi.org/10.1016/s2589-7500(20)30060-1","authors":["Yuchen Xie","Quang D. Nguyen","Haslina Hamzah","Gilbert Lim","Valentina Bellemo","Dinesh Visva Gunasekeran","Michelle Yip","Xin Qi Lee","Wynne Hsu","Mong Li Lee","Colin S. Tan","Hon Tym Wong","Ecosse L. Lamoureux","Gavin Siew Wei Tan","Tien Yin Wong","Eric Finkelstein","Daniel Shu Wei Ting"],"tags":["Triage","Medicine","Artificial intelligence","Diabetic retinopathy","Retinopathy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-23","doi":"https://doi.org/10.1016/s2589-7500(20)30060-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3002754827","name":"Use of artificial intelligence in imaging in rheumatology – current status and future perspectives","source":"openalex","abstract":"After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including 'deep learning', and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.","url":"https://doi.org/10.1136/rmdopen-2019-001063","authors":["Berend C. Stoel"],"tags":["Medicine","Rheumatology","Internal medicine","Current (fluid)","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1136/rmdopen-2019-001063","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3201049275","name":"Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00330-021-08214-z","authors":["Ling Yang","Ioana Cezara Ene","Reza Arabi Belaghi","David Koff","Nina Stein","Pasqualina Santaguida"],"tags":["CINAHL","Medicine","Grey literature","Stakeholder","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-21","doi":"https://doi.org/10.1007/s00330-021-08214-z","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4406035098","name":"Federated Learning Lifecycle Management for Distributed Medical Artificial Intelligence Applications: A Case Study on Post-Transcatheter Aortic Valve Replacement Complication Prediction Solution","source":"openalex","abstract":"The evolution of artificial intelligence (AI) has unveiled considerable prospects for delivering efficacious solutions in the medical domain. Nevertheless, existing legal frameworks and concerns regarding data privacy associated with medical information impose substantial constraints on implementing AI solutions in this domain. Federated learning is a paradigm that enables the training of machine learning models in a decentralized manner without transferring data to a central repository, allowing model development while preserving data privacy across medical and other industries. This study provided a comprehensive framework for applying federated learning to AI solutions in the medical domain. It advocates a sustainable learning ecosystem by overseeing federated learning servers and clients and evaluating performance by managing the federated learning lifecycle. To enhance its practical relevance, this framework includes a detailed process for continuous lifecycle management, involving model deployment, aggregation, testing, evaluation, versioning, and real-time monitoring through the FedOps platform, supporting a sustainable solution. In this study, the feasibility of the proposed methodology was verified using a post-transcatheter aortic valve replacement (TAVR) complication–prediction framework. The performance of the solution after transitioning to a federated learning approach was compared with that of an existing centralized solution. The findings indicated no statistically significant difference in performance between the two methodologies. This implies that federated learning can augment data usability and facilitate the integration of AI technologies into the medical domain, where the preservation of data privacy is critically important.","url":"https://doi.org/10.3390/app15010378","authors":["Min Hyuk Jung","InSeo Song","Kang Yoon Lee"],"tags":["Medicine","Complication","Intensive care medicine","Business","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-03","doi":"https://doi.org/10.3390/app15010378","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387966725","name":"Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy","source":"openalex","abstract":"Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.","url":"https://doi.org/10.1002/smtd.202301243","authors":["Xinyuan Bi","Li Lin","Zhou Chen","Jian Ye"],"tags":["Computer science","Nanotechnology","Artificial intelligence","Pipeline (software)","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-27","doi":"https://doi.org/10.1002/smtd.202301243","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4401219516","name":"Artificial intelligence: revolutionizing robotic surgery: review","source":"openalex","abstract":"Robotic surgery, known for its minimally invasive techniques and computer-controlled robotic arms, has revolutionized modern medicine by providing improved dexterity, visualization, and tremor reduction compared to traditional methods. The integration of artificial intelligence (AI) into robotic surgery has further advanced surgical precision, efficiency, and accessibility. This paper examines the current landscape of AI-driven robotic surgical systems, detailing their benefits, limitations, and future prospects. Initially, AI applications in robotic surgery focused on automating tasks like suturing and tissue dissection to enhance consistency and reduce surgeon workload. Present AI-driven systems incorporate functionalities such as image recognition, motion control, and haptic feedback, allowing real-time analysis of surgical field images and optimizing instrument movements for surgeons. The advantages of AI integration include enhanced precision, reduced surgeon fatigue, and improved safety. However, challenges such as high development costs, reliance on data quality, and ethical concerns about autonomy and liability hinder widespread adoption. Regulatory hurdles and workflow integration also present obstacles. Future directions for AI integration in robotic surgery include enhancing autonomy, personalizing surgical approaches, and refining surgical training through AI-powered simulations and virtual reality. Overall, AI integration holds promise for advancing surgical care, with potential benefits including improved patient outcomes and increased access to specialized expertise. Addressing challenges and promoting responsible adoption are essential for realizing the full potential of AI-driven robotic surgery.","url":"https://doi.org/10.1097/ms9.0000000000002426","authors":["Muhammad Iftikhar","Muhammad Saqib","Muhammad Zareen","Hassan Mumtaz"],"tags":["Medicine","Robotic surgery","Surgery","General surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.1097/ms9.0000000000002426","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4386855886","name":"ChatGPT in action: Harnessing artificial intelligence potential and addressing ethical challenges in medicine, education, and scientific research","source":"openalex","abstract":"Artificial intelligence (AI) tools, like OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), hold considerable potential in healthcare, academia, and diverse industries. Evidence demonstrates its capability at a medical student level in standardized tests, suggesting utility in medical education, radiology reporting, genetics research, data optimization, and drafting repetitive texts such as discharge summaries. Nevertheless, these tools should augment, not supplant, human expertise. Despite promising applications, ChatGPT confronts limitations, including critical thinking tasks and generating false references, necessitating stringent cross-verification. Ensuing concerns, such as potential misuse, bias, blind trust, and privacy, underscore the need for transparency, accountability, and clear policies. Evaluations of AI-generated content and preservation of academic integrity are critical. With responsible use, AI can significantly improve healthcare, academia, and industry without compromising integrity and research quality. For effective and ethical AI deployment, collaboration amongst AI developers, researchers, educators, and policymakers is vital. The development of domain-specific tools, guidelines, regulations, and the facilitation of public dialogue must underpin these endeavors to responsibly harness AI's potential.","url":"https://doi.org/10.5662/wjm.v13.i4.170","authors":["Madhan Jeyaraman","Swaminathan Ramasubramanian","Sangeetha Balaji","Naveen Jeyaraman","Arulkumar Nallakumarasamy","Shilpa Sharma"],"tags":["Transparency (behavior)","Engineering ethics","Accountability","Applications of artificial intelligence","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-19","doi":"https://doi.org/10.5662/wjm.v13.i4.170","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4292833616","name":"Artificial Intelligence for Caries Detection: Value of Data and Information","source":"openalex","abstract":"If increasing practitioners' diagnostic accuracy, medical artificial intelligence (AI) may lead to better treatment decisions at lower costs, while uncertainty remains around the resulting cost-effectiveness. In the present study, we assessed how enlarging the data set used for training an AI for caries detection on bitewings affects cost-effectiveness and also determined the value of information by reducing the uncertainty around other input parameters (namely, the costs of AI and the population's caries risk profile). We employed a convolutional neural network and trained it on 10%, 25%, 50%, or 100% of a labeled data set containing 29,011 teeth without and 19,760 teeth with caries lesions stemming from bitewing radiographs. We employed an established health economic modeling and analytical framework to quantify cost-effectiveness and value of information. We adopted a mixed public-private payer perspective in German health care; the health outcome was tooth retention years. A Markov model, allowing to follow posterior teeth over the lifetime of an initially 12-y-old individual, and Monte Carlo microsimulations were employed. With an increasing amount of data used to train the AI sensitivity and specificity increased nonlinearly, increasing the data set from 10% to 25% had the largest impact on accuracy and, consequently, cost-effectiveness. In the base-case scenario, AI was more effective (tooth retention for a mean [2.5%-97.5%] 62.8 [59.2-65.5] y) and less costly (378 [284-499] euros) than dentists without AI (60.4 [55.8-64.4] y; 419 [270-593] euros), with considerable uncertainty. The economic value of reducing the uncertainty around AI's accuracy or costs was limited, while information on the population's risk profile was more relevant. When developing dental AI, informed choices about the data set size may be recommended, and research toward individualized application of AI for caries detection seems warranted to optimize cost-effectiveness.","url":"https://doi.org/10.1177/00220345221113756","authors":["Falk Schwendicke","J. Cejudo Grano de Oro","A. Garcia Cantu","H. Meyer‐Lueckel","Akhilanand Chaurasia","Joachim Krois"],"tags":["Euros","Medicine","Convolutional neural network","Artificial neural network","Data set"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-22","doi":"https://doi.org/10.1177/00220345221113756","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4200358986","name":"Medical Informatics and Bioimaging Using Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-030-91103-4","authors":[],"tags":["Health informatics","Informatics","Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-15","doi":"https://doi.org/10.1007/978-3-030-91103-4","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387949693","name":"A scoping review of artificial intelligence-based methods for diabetes risk prediction","source":"openalex","abstract":"The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.","url":"https://doi.org/10.1038/s41746-023-00933-5","authors":["Farida Mohsen","Hamada R. H. Al-Absi","Noha A. Yousri","Nady El Hajj","Zubair Shah"],"tags":["Interpretability","Artificial intelligence","Machine learning","Scopus","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-25","doi":"https://doi.org/10.1038/s41746-023-00933-5","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4406927485","name":"Explainable AI for Healthcare: Training Healthcare Workers to Use Artificial Intelligence Techniques to Reduce Medical Negligence in Ghana’s Public Health Act, 2012 (Act 851)","source":"openalex","abstract":"This analysis examines whether Ghana’s Public Health Act, 2012 (Act 851) imposes adequate legal responsibilities on healthcare facilities concerning personnel training on artificial intelligence (AI) systems and implementation of medical negligence reduction measures. Through an evaluative review of Act 851 provisions on staff qualifications, technology deployment, quality care, safety planning, and risk management benchmarks relative to precedents in Ghana and other countries, critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified. Key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators. Strengthening policy directives will compel internal monitoring, governance, and accountability among healthcare facilities as multilayered negligence prevention strategies. Scientific contributions highlight deficiencies in Ghana’s health legislation regarding contemporary challenges like AI adoption risks and propose legal reforms to modernize regulations to support safer, responsible healthcare delivery nationwide.","url":"https://doi.org/10.70470/edraak/2025/001","authors":["George Mensah","Maad M. Mıjwıl","Mostafa Abotaleb","Guma Ali","Pushan Kumar Dutta","Toufik Mzili","Marwa M. Eid"],"tags":["Health care","Training (meteorology)","Nursing","Business","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-10","doi":"https://doi.org/10.70470/edraak/2025/001","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W3102469298","name":"AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.asoc.2020.106897","authors":["Bo Wang","Shuo Jin","Qingsen Yan","Haibo Xu","Chuan Luo","Lai Wei","Wei Zhao","Xuexue Hou","Wenshuo Ma","Zhengqing Xu","Zhuozhao Zheng","Wenbo Sun","Lan Lan","Wei Zhang","Xiangdong Mu","Chenxi Shi","Zhongxiao Wang","Jihae Lee","Zijian Jin","Minggui Lin","Hongbo Jin","Liang Zhang","Jun Guo","Benqi Zhao","Zhizhong Ren","Shuhao Wang","Wei Xu","Xinghuan Wang","Jianming Wang","Zheng You","Jiahong Dong"],"tags":["Coronavirus disease 2019 (COVID-19)","2019-20 coronavirus outbreak","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","Computer science","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-10","doi":"https://doi.org/10.1016/j.asoc.2020.106897","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4384068429","name":"Artificial intelligence in supply chain and operations management: a multiple case study research","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies’ competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.","url":"https://doi.org/10.1080/00207543.2023.2232050","authors":["Violetta Giada Cannas","Maria Pia Ciano","Mattia Saltalamacchia","Raffaele Secchi"],"tags":["Supply chain management","Supply chain","Operations management","Computer science","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-12","doi":"https://doi.org/10.1080/00207543.2023.2232050","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2901953250","name":"Precision immunoprofiling by image analysis and artificial intelligence","source":"openalex","abstract":"Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.","url":"https://doi.org/10.1007/s00428-018-2485-z","authors":["Viktor H. Koelzer","Korsuk Sirinukunwattana","Jens Rittscher","Kirsten D. Mertz"],"tags":["Precision medicine","Digital pathology","Artificial intelligence","Computer science","Surgical pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-11-23","doi":"https://doi.org/10.1007/s00428-018-2485-z","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4288514369","name":"Recommendations for ethical and responsible use of artificial intelligence in digital agriculture","source":"openalex","abstract":"Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.","url":"https://doi.org/10.3389/frai.2022.884192","authors":["Rozita Dara","Seyed Mehdi Hazrati Fard","Jasmin Kaur"],"tags":["Agriculture","Sustainability","Transparency (behavior)","Accountability","Harm"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-29","doi":"https://doi.org/10.3389/frai.2022.884192","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4389131563","name":"Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction","source":"openalex","abstract":"Introduction: Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim: To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods: Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results: The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students' visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students' visual evaluation. Conclusion: Medical students' utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers' evaluation.","url":"https://doi.org/10.1155/2023/5225872","authors":["Ziv Dadon","Amir Orlev","Adi Butnaru","David Rosenmann","Michael Glikson","Shmuel Gottlieb","Evan Avraham Alpert"],"tags":["Ejection fraction","Medicine","Cardiac Ultrasound","Point of care ultrasound","CLIPS"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-29","doi":"https://doi.org/10.1155/2023/5225872","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3089772073","name":"HihO: accelerating artificial intelligence interpretability for medical imaging in IoT applications using hierarchical occlusion","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00521-020-05379-4","authors":["William S. Monroe","Frank M. Skidmore","David Odaibo","Murat M. Tanik"],"tags":["Interpretability","Computer science","Artificial intelligence","Machine learning","Software portability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-01","doi":"https://doi.org/10.1007/s00521-020-05379-4","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4315485559","name":"Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle","source":"openalex","abstract":"An avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient's data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as \"Open Science\", \"Open Data\", \"Open Access\" principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.","url":"https://doi.org/10.1080/28338073.2022.2162202","authors":["Joachim L. Schultze"],"tags":["Privilege (computing)","Data sharing","Data science","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-10","doi":"https://doi.org/10.1080/28338073.2022.2162202","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4403640721","name":"Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine","source":"openalex","abstract":"INTRODUCTION: Artificial Intelligence (AI) has made a profound impact on the medical sector, particularly in radiology. The integration of AI knowledge into medical education is essential to equip future healthcare professionals with the skills needed to effectively leverage these advancements in their practices. Despite its significance, many medical schools have yet to incorporate AI into their curricula. This study aims to assess the attitudes of medical students in Thailand toward AI and its application in radiology, with the objective of better planning for its inclusion. METHODS: Between February and June 2022, we conducted a survey in two Thai medical schools: Chiang Mai University in Northern Thailand and Prince of Songkla University in Southern Thailand. We employed 5-point Likert scale questions (ranging from strongly agree to strongly disagree) to evaluate students' opinions on three main aspects: (1) their understanding of AI, (2) the inclusion of AI in their medical education, and (3) the potential impact of AI on medicine and radiology. RESULTS: Our findings revealed that merely 31% of medical students perceived to have a basic understanding of AI. Nevertheless, nearly all students (93.6%) recognized the value of AI training for their careers and strongly advocated for its inclusion in the medical school curriculum. Furthermore, those students who had a better understanding of AI were more likely to believe that AI would revolutionize the field of radiology (p = 0.02), making it more captivating and impactful (p = 0.04). CONCLUSION: Our study highlights a noticeable gap in the understanding of AI among medical students in Thailand and its practical applications in healthcare. However, the overwhelming consensus among these students is their readiness to embrace the incorporation of AI training into their medical education. This enthusiasm holds the promise of enhancing AI adoption, ultimately leading to an improvement in the standard of healthcare services in Thailand, aligning with the country's healthcare vision.","url":"https://doi.org/10.1186/s12909-024-06150-2","authors":["Salita Angkurawaranon","Nakarin Inmutto","Kittipitch Bannangkoon","Surapat Wonghan","Thanawat Kham-ai","Porched Khumma","Kanvijit Daengpisut","Phattanun Thabarsa","Chaisiri Angkurawaranon"],"tags":["Inclusion (mineral)","Curriculum","Medical education","Likert scale","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-22","doi":"https://doi.org/10.1186/s12909-024-06150-2","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4220793133","name":"Environmentally sustainable development and use of artificial intelligence in health care","source":"openalex","abstract":"Artificial intelligence (AI) can transform health care by delivering medical services to underserved areas, while also filling gaps in health care provider availability. However, AI may also lead to patient harm due to fatal glitches in robotic surgery, bias in diagnosis, or dangerous recommendations. Despite concerns ethicists have identified in the use of AI in health care, the most significant consideration ought not be vulnerabilities in the software, but the environmental impact of AI. Health care emits a significant amount of carbon in many countries. As AI becomes an essential part of health care, ethical reflection must include the potential to negatively impact the environment. As such, this article will first overview the carbon emissions in health care. It will, second, offer five reasons why carbon calculations are insufficient to address sustainability in health care. Third, the article will derive normative concepts from the goals of medicine, the principles of biomedical ethics, and green bioethics-the very locus in which AI in health care sits-to propose health, justice, and resource conservation as criteria for sustainable AI in health care. In the fourth and final part of the article, examples of sustainable and unsustainable development and use of AI in health care will be evaluated through the three-fold lens of health, justice, and resource conservation. With various ethical approaches to AI in health care, the imperative for environmental sustainability must be underscored, lest carbon emissions continue to increase, harming people and planet alike.","url":"https://doi.org/10.1111/bioe.13018","authors":["Cristina Richie"],"tags":["Health care","Sustainability","Harm","Bioethics","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-15","doi":"https://doi.org/10.1111/bioe.13018","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3090830123","name":"Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19","source":"openalex","abstract":"Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.","url":"https://doi.org/10.1183/16000617.0181-2020","authors":["Danai Khemasuwan","Jeffrey Sorensen","Henri G. Colt"],"tags":["Artificial intelligence","Medicine","Coronavirus disease 2019 (COVID-19)","Deep learning","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-30","doi":"https://doi.org/10.1183/16000617.0181-2020","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2919763858","name":"Artificial intelligence in breast ultrasound","source":"openalex","abstract":"Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women's health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.","url":"https://doi.org/10.4329/wjr.v11.i2.19","authors":["Ge-Ge Wu","Liqiang Zhou","Jianwei Xu","Jia‐Yu Wang","Qi Wei","Youbin Deng","Xin‐Wu Cui","Christoph F. Dietrich"],"tags":["Medicine","Breast ultrasound","Breast cancer","Artificial intelligence","Ultrasound"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-02-27","doi":"https://doi.org/10.4329/wjr.v11.i2.19","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4401113287","name":"Artificial Intelligence of Things: Quick Sort Strategy for Medical Supply Chain","source":"openalex","abstract":"This study investigates the application of the Artificial Intelligence of Things (AIoTs) integrated with Autonomous Mobile Robots within medical supply chains, specifically targeting the optimization of Vaccine Transport Box sorting. We introduced two novel quick-sort strategies, QSS1 and QSS2, operationalized through an AIoT-enabled Sorting Function Platform. These strategies are designed to significantly improve the efficiency of vaccine distribution by optimizing the sorting process, initially focusing on air transportation but with the capability to expand to sea, road, and rail modes for various products. Comparative analyses have shown that adopting these strategies can increase operational profitability by more than 76% compared to traditional and widely used sorting approaches. The findings affirm the potential of our AIoT-based strategies to revolutionize medical supply logistics.","url":"https://doi.org/10.1109/tii.2024.3424508","authors":["Saâdia Chabel","El Miloud Ar-Reyouchi"],"tags":["sort","Supply chain","Computer science","Supply chain management","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-30","doi":"https://doi.org/10.1109/tii.2024.3424508","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3183507265","name":"Artificial intelligence in the fertility clinic: status, pitfalls and possibilities","source":"openalex","abstract":"In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.","url":"https://doi.org/10.1093/humrep/deab168","authors":["Michael A. Riegler","Mette H. Stensen","Oliwia Witczak","Jorunn M. Andersen","Steven A. Hicks","Hugo L. Hammer","Erwan Delbarre","Påll Halvorsen","Anis Yazidi","N. Holst","Trine B. Haugen"],"tags":["Strengths and weaknesses","Objectivity (philosophy)","Fertility","Data science","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-23","doi":"https://doi.org/10.1093/humrep/deab168","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2990632791","name":"Artificial Intelligence, Radiology, and Tuberculosis: A Review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.acra.2019.10.003","authors":["Sagar Kulkarni","Saurabh Kumar Jha"],"tags":["Tuberculosis","Infectious disease (medical specialty)","Disease","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-11-20","doi":"https://doi.org/10.1016/j.acra.2019.10.003","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2772009724","name":"Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage","source":"openalex","abstract":"Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labor inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this article, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction, and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.","url":"https://doi.org/10.1080/08839514.2018.1464286","authors":["Bosede Iyiade Edwards","Adrian David Cheok"],"tags":["Affordance","Personality psychology","Computer science","Economic shortage","Robot"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-04-21","doi":"https://doi.org/10.1080/08839514.2018.1464286","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4307571902","name":"Explainable artificial intelligence for cybersecurity: a literature survey","source":"openalex","abstract":"Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essential in the development of cybersecurity solutions. However, sharing the same fundamental limitation with other DL application domains, such as computer vision (CV) and natural language processing (NLP), AI-based cybersecurity solutions are incapable of justifying the results (ranging from detection and prediction to reasoning and decision-making) and making them understandable to humans. Consequently, explainable AI (XAI) has emerged as a paramount topic addressing the related challenges of making AI models explainable or interpretable to human users. It is particularly relevant in cybersecurity domain, in that XAI may allow security operators, who are overwhelmed with tens of thousands of security alerts per day (most of which are false positives), to better assess the potential threats and reduce alert fatigue. We conduct an extensive literature review on the intersection between XAI and cybersecurity. Particularly, we investigate the existing literature from two perspectives: the applications of XAI to cybersecurity (e.g., intrusion detection, malware classification), and the security of XAI (e.g., attacks on XAI pipelines, potential countermeasures). We characterize the security of XAI with several security properties that have been discussed in the literature. We also formulate open questions that are either unanswered or insufficiently addressed in the literature, and discuss future directions of research.","url":"https://doi.org/10.1007/s12243-022-00926-7","authors":["Fabien Charmet","Harry Chandra Tanuwidjaja","Solayman Ayoubi","Pierre-François Gimenez","Yufei Han","Houda Jmila","Grégory Blanc","Takeshi Takahashi","Zonghua Zhang"],"tags":["Computer science","Computer security","Malware","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-26","doi":"https://doi.org/10.1007/s12243-022-00926-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4283274937","name":"Strategically constructed narratives on artificial intelligence: What stories are told in governmental artificial intelligence policies?","source":"openalex","abstract":"What stories are told in national artificial intelligence (AI) policies? Combining the novel technique of structural topic modeling (STM) and qualitative narrative analysis, this paper examines the policy narratives in 33 countries’ AI policies. We uncover six common narratives that are dominating the political agenda concerning AI. Our findings show that the policy narratives' saliences vary across time and countries. We make several contributions. First, our narratives describe well-grounded, supportable conceptions of AI among governments, and show that AI is still a fairly novel, multilayered, and controversial phenomenon. Building on the premise that human sensemaking is best represented and supported by narration, we address the applied rhetoric of governments to either minimize the risks or exalt the opportunities of AI. Second, we uncover the four prominent roles governments seek to take concerning AI implementation: enabler, leader, regulator, and/or user. Third, we make a methodological contribution toward data-driven, computationally-intensive theory development. Our methodological approach and the identified narratives present key starting points for further research.","url":"https://doi.org/10.1016/j.giq.2022.101719","authors":["Ali A. Guenduez","Tobias Mettler"],"tags":["Narrative","Premise","Enabling","Sensemaking","Rhetoric"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-22","doi":"https://doi.org/10.1016/j.giq.2022.101719","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3046549628","name":"Artificial Intelligence for Cybersecurity: A Systematic Mapping of Literature","source":"openalex","abstract":"Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making real-time decisions that can effectively respond to sophisticated attacks. To support this, both researchers and practitioners need to be familiar with current methods of ensuring cybersecurity (CyberSec). In particular, the use of artificial intelligence for combating cybercrimes. However, there is lack of summaries on artificial intelligent methods for combating cybercrimes. To address this knowledge gap, this study sampled 131 articles from two main scholarly databases (ACM digital library and IEEE Xplore). Using a systematic mapping, the articles were analyzed using quantitative and qualitative methods. It was observed that artificial intelligent methods have made remarkable contributions to combating cybercrimes with significant improvement in intrusion detection systems. It was also observed that there is a reduction in computational complexity, model training times and false alarms. However, there is a significant skewness within the domain. Most studies have focused on intrusion detection and prevention systems, and the most dominant technique used was support vector machines. The findings also revealed that majority of the studies were published in two journal outlets. It is therefore suggested that to enhance research in artificial intelligence for CyberSec, researchers need to adopt newer techniques and also publish in other related outlets.","url":"https://doi.org/10.1109/access.2020.3013145","authors":["Isaac Wiafe","Felix Nti Koranteng","Emmanuel Nyarko Obeng","Nana Assyne","Abigail Wiafe","Stephen R. Gulliver"],"tags":["Computer science","Intrusion detection system","Computer security","Publication","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1109/access.2020.3013145","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4285105932","name":"Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review","source":"openalex","abstract":"Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A lot of emphasis has recently been paid to the development of methods for visualizing, explaining, and interpreting deep learning models. The situation is substantially different in safety-critical applications. The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case. Significantly, when single bad decisions can endanger human life and health (e.g., autonomous driving, medical domain) or result in significant monetary losses (e.g., algorithmic trading), depending on an unintelligible data-driven system may not be an option. This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer, e-commerce, or entertainment industries. Explainability is the term introduced in the preceding years. The AI model’s black box nature will become explainable with these frameworks. Especially in the medical domain, diagnosing a particular disease through AI techniques would be less adapted for commercial use. These models’ explainable natures will help them commercially in diagnosis decisions in the medical field. This paper explores the different frameworks for the explainability of AI models in the medical field. The available frameworks are compared with other parameters, and their suitability for medical fields is also discussed.","url":"https://doi.org/10.32604/cmes.2022.021225","authors":["Nilkanth Mukund Deshpande","Shilpa Gite","Biswajeet Pradhan","Mazen E. Assiri"],"tags":["Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.32604/cmes.2022.021225","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4392472940","name":"Special Issue: Artificial Intelligence Technology in Medical Image Analysis","source":"openalex","abstract":"Artificial intelligence (AI) technologies have significantly advanced the field of medical imaging, revolutionizing diagnostic and therapeutic processes [...]","url":"https://doi.org/10.3390/app14052180","authors":["László Szilágyi","Levente Kovács"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-05","doi":"https://doi.org/10.3390/app14052180","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W3005369330","name":"Toward an Understanding of Responsible Artificial Intelligence Practices","source":"openalex","abstract":"Artificial Intelligence (AI) is influencing all aspects of human and business activities nowadays. Although potential benefits emerged from AI technologies have been widely discussed in many current literature, there is an urgently need to understand how AI can be designed to operate responsibly and act in a manner meeting stakeholders’ expectations and applicable regulations. We seek to fill the gap by exploring the practices of responsible AI and identifying the potential benefits when implementing responsible AI practices. In this study, 10 responsible AI cases were selected from different industries to better understand the use of responsible AI in practices. Four responsible AI practices are identified, including governance, ethically design solutions, risk control and training and education and five strategies for firms who are considering to adopt responsible AI practices are recommended.","url":"https://doi.org/10.24251/hicss.2020.610","authors":["Yichuan Wang","Mengran Xiong","Hossein Olya"],"tags":["Corporate governance","Applications of artificial intelligence","Knowledge management","Best practice","Control (management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.24251/hicss.2020.610","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385650719","name":"Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate","source":"openalex","abstract":"This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.","url":"https://doi.org/10.3390/make5030053","authors":["Mohammad Amini","Marcia Jesus","Davood Fanaei Sheikholeslami","Paulo Alves","Aliakbar Hassanzadeh Benam","Fatemeh Hariri"],"tags":["Mandate","Engineering ethics","Health care","General Data Protection Regulation","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-07","doi":"https://doi.org/10.3390/make5030053","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4392410671","name":"The Use of Artificial Intelligence in the Classification of Medical Images of Brain Tumors","source":"openalex","abstract":"Brain tumor cases increased significantly globally between 2004 and 2020 from nearly 10% to 15% [1]. The early detection and diagnosis of brain tumors are crucial for taking appropriate preventive measures, as is the case with most cancers [2].","url":"https://doi.org/10.26717/bjstr.2023.53.008450","authors":["Marcos A M Almeida"],"tags":["Artificial intelligence","Medicine","Neuroscience","Psychology","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-14","doi":"https://doi.org/10.26717/bjstr.2023.53.008450","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3198667874","name":"Foundational Considerations for Artificial Intelligence Using Ophthalmic Images","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ophtha.2021.08.023","authors":["Michael D. Abràmoff","Brad Cunningham","Bakul Patel","Malvina Eydelman","Theodore Leng","Taiji Sakamoto","Barbara Blodi","S. Marlene Grenon","Risa M. Wolf","Arjun K. Manrai","Justin Ko","Michael F. Chiang","Danton Char","Michael D. Abràmoff","Mark S. Blumenkranz","Emily Y. Chew","Michael F. Chiang","Malvina Eydelman","David Myung","Joel S. Schuman","Carol L. Shields","Michael D. Abràmoff","Malvina Eydelman","Brad Cunningham","Bakul Patel","Karen A. Goldman","Danton Char","Taiji Sakamoto","Barbara Blodi","Risa M. Wolf","Jean--Louis Gassee","Theodore Leng","Dan Roman","Sally L. Satel","Donald S. Fong","David C. Rhew","Henry Wei","Michael Willingham","Michael F. Chiang","Mark S. Blumenkranz"],"tags":["Medicine","Autonomy","Warrant","Health care","Globe"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-31","doi":"https://doi.org/10.1016/j.ophtha.2021.08.023","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4399564385","name":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. METHODS: In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. FINDINGS: Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001). INTERPRETATION: An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicab","url":"https://doi.org/10.1016/s1470-2045(24)00220-1","authors":["Anindo Saha","Anindo Saha","Joeran Sander Bosma","Jasper J. Twilt","Bram van Ginneken","Anders Bjartell","Anwar R. Padhani","David Bonekamp","Geert Villeirs","Georg Salomon","Gianluca Giannarini","Jayashree Kalpathy–Cramer","Jelle O. Barentsz","Klaus Maier‐Hein","Mirabela Rusu","Olivier Rouvière","Roderick van den Bergh","Valeria Panebianco","Veeru Kasivisvanathan","Nancy A. Obuchowski","Derya Yakar","Mattijs Elschot","Jeroen Veltman","Jurgen J. Fütterer","Maarten de Rooij","Henkjan Huisman","Anindo Saha","Anindo Saha","Joeran S. Bosma","Jasper J. Twilt","Bram van Ginneken","Constant R. Noordman","Ivan Slootweg","Christian Roest","Stefan J. Fransen","Mohammed R.S. Sunoqrot","Tone F. Bathen","Dennis Rouw","Jos Immerzeel","Jeroen Geerdink","Chris van Run","Miriam Groeneveld","James Meakin","Ahmet Karagöz","Alexandre Bône","Alexandre Routier","Arnaud Marcoux","Clément Abi-Nader","Cynthia Xinran Li","Dagan Feng","Deniz Alis","Ercan Karaarslan","Euijoon Ahn","François Nicolas","Geoffrey A. Sonn","Indrani Bhattacharya","Jinman Kim","Jun Shi","Hassan Jahanandish","Hong An","Hongyu Kan","Ilkay Oksuz","Liang Qiao","Marc-Michel Rohé","Mert Yergin","Mohamed Khadra","Mustafa E. Şeker","Mustafa S. Kartal","Noëlie Debs","Richard E. Fan","Sara Saunders","Simon J.C. Soerensen","Stefania Moroianu","Sulaiman Vesal","Yuan Yuan","Afsoun Malakoti-Fard","Agnė Mačiūnien","Akira Kawashima","Ana M.M. de M.G. de Sousa Machadov","Ana Sofia L. Moreira","Andrea Ponsiglione","Annelies Rappaport","Arnaldo Stanzione","Arturas Ciuvasovas","Baris Turkbey","Bart de Keyzer","Bodil G. Pedersen","Bram Eijlers","Christine Chen","Ciabattoni Riccardo","Deniz Alis","Ewout F.W. Courrech Staal","Fredrik Jäderling","Fredrik Langkilde","Giacomo Aringhieri","Giorgio Brembilla","Hannah Son","Hans Vanderlelij","Henricus P.J. Raat","Ingrida Pikūnienė"],"tags":["Prostate cancer","Medicine","Medical physics","Cancer detection","Prostate"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-12","doi":"https://doi.org/10.1016/s1470-2045(24)00220-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W63135910","name":"Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-540-47518-7","authors":["Valentina V. Zharkova","Lakhmi C. Jain"],"tags":["Ranging","Medical imaging","Pharmacy","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2007-01-01","doi":"https://doi.org/10.1007/978-3-540-47518-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4389521069","name":"Medical students’ perceptions towards artificial intelligence in education and practice: A multinational, multicenter cross-sectional study","source":"openalex","abstract":"Abstract Background Artificial intelligence (AI) is anticipated to fundamentally change the educational and professional landscape for the next generation of physicians, but its successful integration depends on the global perspectives of all stakeholders. Previous medical student surveys were limited by small sample sizes or geographic constraints, hindering a global comparison of perceptions. This study aims to explore current medical students’ attitudes towards AI in medical education and the profession on a broad, international scale and to examine regional differences in perspectives. Methods and Findings This international multicenter cross-sectional study developed and validated an anonymous online survey of 15 multiple-choice items to assess medical, dentistry, and veterinary students’ AI knowledge and attitudes toward the utilization of AI in healthcare, the current state of AI education, and regional differences in perspectives. Between April and October 2023, 4,313 medical, 205 dentistry, and 78 veterinary students from 192 faculties in 48 countries responded to the survey (average response rate: 0.2%, standard deviation: 0.4%). Most participants studied in European countries (N=2,350), followed by North/South America (N=1,070) and Asia (N=944). Students expressed predominantly positive attitudes towards the use of AI in healthcare (67.6%, N=3,091) and the desire for more AI teaching in their curricula (76.1%, N=3,474). However, they reported limited general knowledge of AI (75.3%, N=3,451), the absence of AI-related courses (76.3%, N=3,497), and felt inadequately prepared to use AI in their future careers (57.9%, N=2,652). The subgroup analyses revealed regional differences in perceptions, although predominantly with small effect sizes. The main limitations include the low response rate per institution, which was calculated on total enrollment across all degree programs, and the risk of selection bias. Conclusions This study highlights the favorable perceptions of international medical students towards incorporating AI in healthcare practice while emphasizing the importance of integrating AI teaching into medical education. Graphical abstract","url":"https://doi.org/10.1101/2023.12.09.23299744","authors":["Felix Busch","Lena Hoffmann","Daniel Truhn","Esteban Ortiz‐Prado","Marcus R. Makowski","Keno K. Bressem","Lisa C. Adams"],"tags":["Curriculum","Multinational corporation","Cross-sectional study","Medical education","Perception"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-10","doi":"https://doi.org/10.1101/2023.12.09.23299744","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4381149654","name":"Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics","source":"openalex","abstract":"Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.","url":"https://doi.org/10.5534/wjmh.230050","authors":["Ramy Abou Ghayda","Rossella Cannarella","Aldo E. Calogero","Rupin Shah","Amarnath Rambhatla","Wael Zohdy","Parviz K. Kavoussi","Tomer Avidor‐Reiss","Florence Boitrelle","Taymour Mostafa","Ramadan Saleh","Tuncay Toprak","Ponco Birowo","Gianmaria Salvio","Gökhan Çalık","Shinnosuke Kuroda","Raneen Sawaid Kaiyal","Imad Ziouziou","Andrea Crafa","Nguyen Ho Vinh Phuoc","Giorgio Ivan Russo","Damayanthi Durairajanayagam","Manaf Al Hashimi","Taha Abo-Almagd Abdel-Meguid Hamoda","Germar‐Michael Pinggera","Ricky Adriansjah","Israel Maldonado Rosas","Mohamed Arafa","Eric Chung","Widi Atmoko","Lucia Rocco","Haocheng Lin","É. Huyghe","Priyank Kothari","Jesus Fernando Solorzano Vazquez","Fotios Dimitriadis","Nicolás Garrido","Sheryl T. Homa","Marco Falcone","Marjan Sabbaghian","Hussein Kandil","Edmund Ko","Marlon Martínez","Quang Nguyen","‬Ahmed M. Harraz","Ege Can Şerefoğlu","Vilvapathy Senguttuvan Karthikeyan","Dung Mai Ba Tien","Sunil Jindal","S. Mičić","Marina Bellavia","Hamed Alali","Nazim Gherabi","Sheena Lewis","Hyun Jun Park","Mara Simopoulou","Hassan Sallam","Liliana Berenice Ramirez Dominguez","Giovanni M. Colpi","Ashok Agarwal","Global Andrology Forum"],"tags":["Reproductive medicine","Artificial intelligence","Robotics","Precision medicine","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-18","doi":"https://doi.org/10.5534/wjmh.230050","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4378715289","name":"Medical operational AI: artificial intelligence in routine medical operations","source":"openalex","abstract":"Abstract Despite substantial gains facilitated by Artificial Intelligence (AI) in recent years, it has to be applied very cautiously in sensitive domains like medicine due to the lack of explainability of many methods in this field. We aim to provide a system to overcome these issues of medical AI applications by means of our concept of medical operational AI detailed in this paper. We make use of various methods of AI and utilize knowledge graphs in particular. The latter is continuously updated by medical experts based on medical literature such as peer-reviewed papers and standard online sources such as UpToDate. We thoroughly derive a multi-level system tackling the corresponding challenges. In particular, its design encompasses (i) holistic diagnostic assistance on a macro level, (ii) predicitions and detailed suggestions for specific medical domains on a micro level, as well as (iii) AI-based optimizations of the overall system on a meta level. We detail practical merits of medical operational AI and discuss the state of the art beyond our solution.","url":"https://doi.org/10.1515/labmed-2023-0011","authors":["Fabian Berns","Niclas Heilig","Florian Stumpe","Jan Kirchhoff"],"tags":["Computer science","Field (mathematics)","Applications of artificial intelligence","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-29","doi":"https://doi.org/10.1515/labmed-2023-0011","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4310460542","name":"Competencies for the Use of Artificial Intelligence in Primary Care","source":"openalex","abstract":"The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician well-being), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the \"side effects\" of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits.","url":"https://doi.org/10.1370/afm.2887","authors":["Winston Liaw","Jacqueline K. Kueper","Steven Lin","Andrew Bazemore","Ioannis A. Kakadiaris"],"tags":["Medicine","Harm","Software deployment","Workforce","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-01","doi":"https://doi.org/10.1370/afm.2887","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3111333805","name":"The Recent Progress and Applications of Digital Technologies in Healthcare: A Review","source":"openalex","abstract":"Background. The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. Purpose. The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, “big data,” telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.","url":"https://doi.org/10.1155/2020/8830200","authors":["Maksut Senbekov","Timur Saliev","Zhanar Bukeyeva","Aigul Almabayeva","Marina Zhanaliyeva","Nazym Aitenova","Yerzhan Toishibekov","Ildar Fakhradiyev"],"tags":["Telemedicine","Digital health","Health care","Flexibility (engineering)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-03","doi":"https://doi.org/10.1155/2020/8830200","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4361285006","name":"Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine","source":"openalex","abstract":"Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. 1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. 2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. 3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.","url":"https://doi.org/10.1109/jas.2023.123123","authors":["Ahmad Chaddad","Qizong Lu","Jiali Li","Yousef Katib","Reem Kateb","Camel Tanougast","Ahmed Bouridane","Ahmed Abdulkadir"],"tags":["Computer science","Domain (mathematical analysis)","Artificial intelligence","Mathematics","Mathematical analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-28","doi":"https://doi.org/10.1109/jas.2023.123123","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3103536203","name":"Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies","source":"openalex","abstract":"Importance: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. Objective: To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. Design, Setting, and Participants: This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. Exposure: An AI-based assistive tool for Gleason grading of prostate biopsies. Main Outcomes and Measures: Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. Results: Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. Conclusions and Relevance: In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.","url":"https://doi.org/10.1001/jamanetworkopen.2020.23267","authors":["David F. Steiner","Kunal Nagpal","Rory Sayres","Davis Foote","Benjamin D. Wedin","Adam Pearce","Carrie J. Cai","Samantha Winter","Matthew R. E. Symonds","Liron Yatziv","Andrei Kapishnikov","Trissia Brown","Isabelle Flament-Auvigne","Fraser Elisabeth Tan","Martin C. Stumpe","Pan-Pan Jiang","Yun Liu","Po-Hsuan Cameron Chen","Greg S. Corrado","Michael Terry","Craig H. Mermel"],"tags":["Grading (engineering)","Medicine","Prostate","Biopsy","Prostate biopsy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-12","doi":"https://doi.org/10.1001/jamanetworkopen.2020.23267","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3093176888","name":"The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future?","source":"openalex","abstract":"Artificial intelligence (AI) pertains to the ability of computers or computer-controlled machines to perform activities that demand the cognitive function and performance level of the human brain. The use of AI in medicine and health care is growing rapidly, significantly impacting areas such as medical diagnostics, drug development, treatment personalization, supportive health services, genomics, and public health management. AI offers several advantages; however, its rampant rise in health care also raises concerns regarding legal liability, ethics, and data privacy. Technological singularity (TS) is a hypothetical future point in time when AI will surpass human intelligence. If it occurs, TS in health care would imply the replacement of human medical practitioners with AI-guided robots and peripheral systems. Considering the pace at which technological advances are taking place in the arena of AI, and the pace at which AI is being integrated with health care systems, it is not be unreasonable to believe that TS in health care might occur in the near future and that AI-enabled services will profoundly augment the capabilities of doctors, if not completely replace them. There is a need to understand the associated challenges so that we may better prepare the health care system and society to embrace such a change - if it happens.","url":"https://doi.org/10.2147/ijgm.s268093","authors":["Abdullah Shuaib","Husain Arian","Ali Shuaib"],"tags":["Pace","Health care","Medicine","Personalization","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-01","doi":"https://doi.org/10.2147/ijgm.s268093","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2973003726","name":"Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation","source":"openalex","abstract":"Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.","url":"https://doi.org/10.3390/jcm8091446","authors":["Muhammad Arsalan","Muhammad Owais","Tahir Mahmood","Se Woon Cho","Kang Ryoung Park"],"tags":["Segmentation","Artificial intelligence","Medicine","Diabetic retinopathy","Hypertensive retinopathy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-09-11","doi":"https://doi.org/10.3390/jcm8091446","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385381951","name":"AI and Medical Education — A 21st-Century Pandora’s Box","source":"openalex","abstract":"Interview with Adam Rodman on the potential effects of generative artificial intelligence on medical education and clinical practice. (09:51)Download Artificial intelligence could have broad implications for medical education. Educators could lead the way when it comes to integrating this technology into clinical practice.","url":"https://doi.org/10.1056/nejmp2304993","authors":["Avraham Z. Cooper","Adam Rodman"],"tags":["Generative grammar","Medical education","Clinical Practice","Download","Medical practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-29","doi":"https://doi.org/10.1056/nejmp2304993","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4410019803","name":"Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians","source":"openalex","abstract":"Importance: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an ambient artificial intelligence (AI) documentation platform. Objective: To understand clinician experience before and after implementing ambient AI. Design, Setting, and Participants: This quality improvement study was a pilot evaluation with before and after survey and EHR metrics conducted at a large health care organization in Northern and Central California. Clinicians were purposively sampled to be representative of region and specialty. Ambient AI was implemented in April 2024 with EHR data from 3 months before and after implementation. Data were analyzed from May to September 2024. Exposure: Ambient AI access. Main Outcomes and Measures: Metrics of time were examined in notes per appointment, off-hour EHR activities (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), documentation note length, progress note length, NASA Task Load Index (NASA-TLX) score, mini-Z burnout question, and overall experience. It was hypothesized that time in notes per appointment would decrease and clinical well-being would improve. Logistic regression and linear mixed-effect models were used. Results: Among 100 clinicians (53 male [53.0%]; mean [SD] age, 48.9 [11.0] years), 58 clinicians (58.0%) were in primary care and 92 clinicians had EHR metrics. Among 57 clinicians who completed both preimplementation and postimplementation surveys, there was a decrease in burnout from 24 clinicians (42.1%) to 20 clinicians (35.1%), although this was not a significant difference (P = .12). Mean (SD) NASA-TLX scores all decreased after using ambient AI: mental demand of note writing (12.2 [4.0] to 6.3 [3.7]), hurried or rushed pace (13.2 [4.0] to 6.4 [4.2]), and effort to accomplish note writing (12.5 [4.1] to 7.4 [4.3]) (all P < .001). Mean (SD) time in notes per appointment significantly decreased from 6.2 (4.0) to 5.3 (3.5) minutes (P < .001), with a bigger decrease for female vs male clinicians (8.1 [3.9] to 6.7 [3.6] minutes vs 4.7 [3.5] to 4.2 [3.1] minutes; P = .001). More primary care clinicians (33 of 38 clinicians [85.8%]) reported that ambient AI improved overall satisfaction at work compared with clinicians in medical (4 of 11 clinicians [36.4%]) and surgical (4 of 8 clinicians [50.0%]) subspecialties (P < .001). After adjusting for participant characteristics, model results suggested that mean scores for NASA-TLX decreased for mental demand (-6.12 [95% CI, -7.52 to -4.72]), hurried or rushed pace (-6.96 [95% CI, -8.42 to -5.50]), and effort to accomplish note writing (-5.57 [95% CI, -6.93 to -4.21]), while mean time in note taking decreased by less than 1 minute per appointment (0.91 minutes [95% CI, -1.20 to -0.62 minutes]) (all P < .001). Conclusions and Relevance: This study found that ambient AI was associated with improved overall experience and time in notes for clinicians but with varying outcomes by sex and specialty. Future research should investigate outcomes after widescale expansion of this rapidly evolving technology.","url":"https://doi.org/10.1001/jamanetworkopen.2025.8614","authors":["Cheryl D. Stults","Sien Deng","Meghan C. Martinez","Joseph Wilcox","Nina Szwerinski","Kevin Chen","Stephanie Driscoll","Joanna Washburn","Veena G Jones"],"tags":["Documentation","Electronic health record","Specialty","Medicine","Logistic regression"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.1001/jamanetworkopen.2025.8614","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4321372215","name":"The Role of Artificial Intelligence in Echocardiography","source":"openalex","abstract":"Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.","url":"https://doi.org/10.3390/jimaging9020050","authors":["Timothy Barry","Juan Farina","Chieh‐Ju Chao","Chadi Ayoub","Jiwoong Jeong","Bhavik N. Patel","Imon Banerjee","Reza Arsanjani"],"tags":["Artificial intelligence","Magnetic resonance imaging","Computer science","Machine learning","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-20","doi":"https://doi.org/10.3390/jimaging9020050","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2996280595","name":"Fundamentals in Artificial Intelligence for Vascular Surgeons","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.avsg.2019.11.037","authors":["Juliette Raffort","Cédric Adam","Marion Carrier","Fabien Lareyre"],"tags":["Limelight","Medicine","Face (sociological concept)","Clinical Practice","Medical practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-12-16","doi":"https://doi.org/10.1016/j.avsg.2019.11.037","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387007843","name":"Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.eswa.2023.121751","authors":["B.L. Sharma","Lokesh Sharma","Chhagan Lal","Satyabrata Roy"],"tags":["Computer science","Artificial intelligence","Intrusion detection system","Deep learning","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-25","doi":"https://doi.org/10.1016/j.eswa.2023.121751","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2979298873","name":"A primer of artificial intelligence in medicine","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.tgie.2019.150642","authors":["Alexandra T. Greenhill","Bethany R. Edmunds"],"tags":["Set (abstract data type)","Artificial intelligence","Computer science","Health care","Order (exchange)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-12","doi":"https://doi.org/10.1016/j.tgie.2019.150642","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387348149","name":"A Review of Artificial Intelligence in Medical Prescription Analysis","source":"openalex","abstract":"Accurate reading and comprehension of medical prescriptions are crucial for healthcare providers to ensure appropriate treatment for the patients. However, with the growing volume of prescriptions and increasingly complex medication regimens, errors can occur, which can result in severe consequences. To mitigate this issue, Artificial Intelligence (AI) can automate tasks such as medication identification, dosage calculation, and drug interaction checks, potentially improving the accuracy and efficiency of prescription analysis. This research study intends to examine the latest AI-based approaches for analyzing medical prescriptions in the healthcare industry, which can aid in the development of diagnostic systems capable of extracting patients' medical history, identifying incorrect medications and conducting statistical analyses on prescribed drugs. By analyzing the current state-of-the-art techniques in this field, one can identify areas of improvement to enhance prescription analysis accuracy and efficiency, ultimately benefiting patient care and medication management.","url":"https://doi.org/10.1109/icpcsn58827.2023.00014","authors":["Mayur Agrawal","Vishwajeet Mishra","Hanesh Sunil Jogani","Prerna Kashyap","Raj Gaurav Mishra"],"tags":["Medical prescription","Health care","Identification (biology)","Computer science","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-01","doi":"https://doi.org/10.1109/icpcsn58827.2023.00014","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3139117962","name":"Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies","source":"openalex","abstract":"BACKGROUND: There has been an increasing trend toward pulmonary segmentectomies to treat early-stage lung cancer, small intrapulmonary metastases, and localized benign pathology. A complete preoperative understanding of pulmonary anatomy is essential for accurate surgical planning and case selection. Identifying intersegmental divisions is extremely difficult when performed on computed tomography. For the preoperative planning of segmentectomies, virtual reality (VR) and artificial intelligence could allow 3-dimensional visualization of the complex anatomy of pulmonary segmental divisions, vascular arborization, and bronchial anatomy. This technology can be applied by surgeons preoperatively to gain better insight into a patient's anatomy for planning segmentectomy. METHODS: In this prospective observational pilot study, we aim to assess and demonstrate the technical feasibility and clinical applicability of the first dedicated artificial intelligence-based and immersive 3-dimensional-VR platform (PulmoVR; jointly developed and manufactured by Department of Cardiothoracic Surgery [Erasmus Medical Center, Rotterdam, The Netherlands], MedicalVR [Amsterdam, The Netherlands], EVOCS Medical Image Communication [Fysicon BV, Oss, The Netherlands], and Thirona [Nijmegen, The Netherlands]) for preoperative planning of video-assisted thoracoscopic segmentectomies. RESULTS: A total of 10 eligible patients for segmentectomy were included in this study after referral through the institutional thoracic oncology multidisciplinary team. PulmoVR was successfully applied as a supplementary imaging tool to perform video-assisted thoracoscopic segmentectomies. In 40% of the cases, the surgical strategy was adjusted due to the 3-dimensional-VR-based evaluation of anatomy. This underlines the potential benefit of additional VR-guided planning of segmentectomy for both surgeon and patient. CONCLUSIONS: Our study demonstrates the successful development and clinical application of the first dedicated artificial intelligence and VR platform for the planning of pulmonary segmentectomy. This is the first study that shows an immersive virtual reality-based application for preoperative planning of segmentectomy to the best of our knowledge.","url":"https://doi.org/10.1016/j.xjtc.2021.03.016","authors":["Amir H. Sadeghi","Alexander P.W.M. Maat","Yannick J.H.J. Taverne","Robin Cornelissen","Anne‐Marie C. Dingemans","Ad J.J.C. Bogers","Edris A.F. Mahtab"],"tags":["Medicine","Surgical planning","Virtual reality","Cardiothoracic surgery","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-18","doi":"https://doi.org/10.1016/j.xjtc.2021.03.016","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2789572343","name":"Anticipating and Training the Physician of the Future: The Importance of Caring in an Age of Artificial Intelligence","source":"openalex","abstract":"Artificial intelligence and other forms of information technology are only just beginning to change the practice of medicine. The pace of change is expected to accelerate as tools improve and as demands for analyzing a rapidly growing body of knowledge and array of data increase. The medical students of today will practice in a world where information technology is sophisticated and omnipresent. In this world, the tasks of memorization and analysis will be less important to them as practicing physicians. On the other hand, the nonanalytical, humanistic aspects of medicine-most importantly, the art of caring-will remain a critical function of the physician, and facility with improving systems of care will be required. Communication, empathy, shared decision making, leadership, team building, and creativity are all skills that will continue to gain importance for physicians. These skills should be further prioritized in medical school curricula to produce an even more effective physician for the future.","url":"https://doi.org/10.1097/acm.0000000000002175","authors":["S. Claiborne Johnston"],"tags":["Pace","Empathy","Creativity","Curriculum","Humanism"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-02-14","doi":"https://doi.org/10.1097/acm.0000000000002175","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4400126525","name":"The limits of fair medical imaging AI in real-world generalization","source":"openalex","abstract":"As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from chest X-rays, leading to a key concern: do models using demographic shortcuts have unfair predictions across subpopulations? In this study, we conducted a thorough investigation into the extent to which medical AI uses demographic encodings, focusing on potential fairness discrepancies within both in-distribution training sets and external test sets. Our analysis covers three key medical imaging disciplines-radiology, dermatology and ophthalmology-and incorporates data from six global chest X-ray datasets. We confirm that medical imaging AI leverages demographic shortcuts in disease classification. Although correcting shortcuts algorithmically effectively addresses fairness gaps to create 'locally optimal' models within the original data distribution, this optimality is not true in new test settings. Surprisingly, we found that models with less encoding of demographic attributes are often most 'globally optimal', exhibiting better fairness during model evaluation in new test environments. Our work establishes best practices for medical imaging models that maintain their performance and fairness in deployments beyond their initial training contexts, underscoring critical considerations for AI clinical deployments across populations and sites.","url":"https://doi.org/10.1038/s41591-024-03113-4","authors":["Yuzhe Yang","Haoran Zhang","Judy Wawira Gichoya","Dina Katabi","Marzyeh Ghassemi"],"tags":["Generalization","Computer science","Medical imaging","Test (biology)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.1038/s41591-024-03113-4","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3046795633","name":"Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions","source":"openalex","abstract":"Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.","url":"https://doi.org/10.36227/techrxiv.12743933.v1","authors":["Thanh Thi Nguyen"],"tags":["Battle","Pandemic","Coronavirus disease 2019 (COVID-19)","Globe","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-01","doi":"https://doi.org/10.36227/techrxiv.12743933.v1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3195848388","name":"Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship","source":"openalex","abstract":"Abstract The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure–biological activity relationship. The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind. This article is categorized under: Computer and Information Science > Chemoinformatics","url":"https://doi.org/10.1002/wcms.1568","authors":["Maciej Staszak","Katarzyna Staszak","Karolina Wieszczycka","Anna Bajek","Krzysztof Roszkowski","Bartosz Tylkowski"],"tags":["Artificial intelligence","Computer science","Artificial neural network","Relevance (law)","Cheminformatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-06","doi":"https://doi.org/10.1002/wcms.1568","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2968253886","name":"Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift","source":"openalex","abstract":"Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.","url":"https://doi.org/10.1002/jbmr.3849","authors":["Joseph E. Burns","Jianhua Yao","Ronald M. Summers"],"tags":["Medical imaging","Government (linguistics)","Artificial intelligence","Deep learning","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-09","doi":"https://doi.org/10.1002/jbmr.3849","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4315651387","name":"Artificial intelligence and inflammatory bowel disease: Where are we going?","source":"openalex","abstract":"Inflammatory bowel diseases, namely ulcerative colitis and Crohn's disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn's disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.","url":"https://doi.org/10.3748/wjg.v29.i3.508","authors":["Leonardo Da Rio","Marco Spadaccini","Tommaso Lorenzo Parigi","Roberto Gabbiadini","Arianna Dal Buono","Anita Busacca","Roberta Maselli","Alessandro Fugazza","Matteo Colombo","Silvia Carrara","Gianluca Franchellucci","Ludovico Alfarone","Antonio Facciorusso","Cesare Hassan","Alessandro Repici","Alessandro Armuzzi"],"tags":["Ulcerative colitis","Inflammatory bowel disease","Medicine","Disease","Crohn's disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-12","doi":"https://doi.org/10.3748/wjg.v29.i3.508","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4309663439","name":"Artificial intelligence for oral and dental healthcare: Core education curriculum","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jdent.2022.104363","authors":["Falk Schwendicke","Akhilanand Chaurasia","Thomas Wiegand","Sergio Uribe","Margherita Fontana","Ilze Akota","Olga Tryfonos","Joachim Krois"],"tags":["Curriculum","Generalizability theory","Medical education","Computer science","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-21","doi":"https://doi.org/10.1016/j.jdent.2022.104363","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3001449808","name":"Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?","source":"openalex","abstract":"As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question \"What is the impact of recent AI/ML trends in the area of Clinical Pharmacology?\" We address difficulties and AI/ML developments for target identification, their use in generative chemistry for small molecule drug discovery, and the potential role of AI/ML in clinical trial outcome evaluation. We briefly discuss current trends in the use of AI/ML in health care and the impact of AI/ML context of the daily practice of clinical pharmacologists.","url":"https://doi.org/10.1002/cpt.1795","authors":["Alex Zhavoronkov","Quentin Vanhaelen","Tudor I. Oprea"],"tags":["Context (archaeology)","Drug discovery","Clinical pharmacology","Identification (biology)","Clinical trial"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-20","doi":"https://doi.org/10.1002/cpt.1795","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2061242453","name":"A robot laboratory for teaching artificial intelligence","source":"openalex","abstract":"There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence well [4, 6]. In part this is because AI lacks a unified methodology, overlaps with many other disciplines, and involves a wide range of skills from very applied to quite formal. In the funded project described here we have addressed these problems by: Offering a unifying theme that draws together the disparate topics of AI; Focusing the course syllabus on the role AI plays in the core computer science curriculum; and Motivating the students to learn by using concrete, hands-on laboratory exercises. Our approach is to conceive of topics in AI as robotics tasks. In the laboratory, students build their own robots and program them to accomplish the tasks. By constructing a physical entity in conjunction with the code to control it, students have a unique opportunity to directly tackle many central issues of computer science including the interaction between hardware and software, space complexity in terms of the memory limitations of the robot's controller, and time complexity in terms of the speed of the robot's action decisions. More importantly, the robot theme provides a strong incentive towards learning because students want to see their inventions succeed. This robot-centered approach is an extension of the agent-centered approach adopted by Russell and Norvig in their recent text book [11]. Taking the agent perspective, the problem of AI is seen as describing and building agents that receive perceptions as input and then output appropriate actions based on them. As a result the study of AI centers around how best to implement this mapping from perceptions to actions. The robot perspective takes this approach one step further; rather than studying software agents in a simulated environment, we embed physical agents in the real world. This adds a dimension of complexity as well as excitement to the AI course. The complexity has to do with additional demands of learning robot building techniques but can be overcome by the introduction of kits that are easy to assemble. Additionally, they are lightweight, inexpensive to maintain, programmable through the standard interfaces provided on most computers, and yet, offer sufficient extensibility to create and experiment with a wide range of agent behaviors. At the same time, using robots also leads the students to an important conclusion about scalability: the real world is very different from a simulated world, which has been a long standing criticism of many well-known AI techniques. We proposed a plan to develop identical robot building laboratories at both Bryn Mawr and Swarthmore Colleges that would allow us to integrate the construction of robots into our introductory AI courses. Furthermore, we hoped that these laboratories would encourage our undergraduate students to pursue honors theses and research projects dealing with the building of physical agents.","url":"https://doi.org/10.1145/273133.274326","authors":["Deepak Kumar","Lisa Meeden"],"tags":["Robot","Computer science","Artificial intelligence","Syllabus","Robotics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1998-03-01","doi":"https://doi.org/10.1145/273133.274326","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2985894198","name":"Artificial Intelligence and Machine Learning Algorithms","source":"openalex","abstract":"With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.","url":"https://doi.org/10.4018/978-1-7998-0182-5.ch008","authors":["Amit Kumar Tyagi","Poonam Chahal"],"tags":["Artificial intelligence","Big data","Computer science","Machine learning","Robotics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-11-07","doi":"https://doi.org/10.4018/978-1-7998-0182-5.ch008","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4316671929","name":"Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study","source":"openalex","abstract":"This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.","url":"https://doi.org/10.3352/jeehp.2023.20.1","authors":["Sun Huh"],"tags":["Final examination","Interpretation (philosophy)","Medical education","Psychology","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-11","doi":"https://doi.org/10.3352/jeehp.2023.20.1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3043870366","name":"Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance","source":"openalex","abstract":"Abstract The article explores the impact of the ongoing progress and adaptation of artificial intelligence on the practice of the corporate governance. It applies three lenses to artificial governance—the business, technology and society lenses—to assess the desirability, feasibility and responsibility of automating board-level decision-making to ensure effective corporate governance. Based on an assessment of the potential and limitations of human and machine learning for effective board-level decision-making, the article proposes five scenarios of artificial governance, i.e. assisted, augmented, amplified, autonomous and autopoietic intelligence, that are likely to shape the governance of organizations today, tomorrow and beyond. It discusses the implications of both the governance of and the governance with artificial intelligence in the three horizons and concludes with an appeal to board members to take an active role in understanding, imagining and shaping the future of artificial governance.","url":"https://doi.org/10.1007/s10997-020-09519-9","authors":["Michael Hilb"],"tags":["Corporate governance","Artificial intelligence","Adaptation (eye)","Project governance","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-21","doi":"https://doi.org/10.1007/s10997-020-09519-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4384662537","name":"Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41569-023-00900-3","authors":["Bernhard Föllmer","Michelle C. Williams","Damini Dey","Armin Arbab‐Zadeh","Pál Maurovich‐Horvat","Rick Volleberg","Daniel Rueckert","Julia A. Schnabel","David E. Newby","Marc R. Dweck","Giulio Guagliumi","Volkmar Falk","Aldo J. Vázquez Mézquita","Federico Biavati","Ivana Išgum","Marc Dewey"],"tags":["Coronary arteries","Intravascular ultrasound","Medicine","Coronary artery disease","Coronary atherosclerosis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-18","doi":"https://doi.org/10.1038/s41569-023-00900-3","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4406797838","name":"Artificial Intelligence-Empowered Radiology—Current Status and Critical Review","source":"openalex","abstract":"Humanity stands at a pivotal moment of technological revolution, with artificial intelligence (AI) reshaping fields traditionally reliant on human cognitive abilities. This transition, driven by advancements in artificial neural networks, has transformed data processing and evaluation, creating opportunities for addressing complex and time-consuming tasks with AI solutions. Convolutional networks (CNNs) and the adoption of GPU technology have already revolutionized image recognition by enhancing computational efficiency and accuracy. In radiology, AI applications are particularly valuable for tasks involving pattern detection and classification; for example, AI tools have enhanced diagnostic accuracy and efficiency in detecting abnormalities across imaging modalities through automated feature extraction. Our analysis reveals that neuroimaging and chest imaging, as well as CT and MRI modalities, are the primary focus areas for AI products, reflecting their high clinical demand and complexity. AI tools are also used to target high-prevalence diseases, such as lung cancer, stroke, and breast cancer, underscoring AI's alignment with impactful diagnostic needs. The regulatory landscape is a critical factor in AI product development, with the majority of products certified under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk standards. A rapid increase in AI product development from 2017 to 2020, peaking in 2020 and followed by recent stabilization and saturation, was identified. In this work, the authors review the advancements in AI-based imaging applications, underscoring AI's transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging.","url":"https://doi.org/10.3390/diagnostics15030282","authors":["Rafał Obuchowicz","Julia Lasek","Marek Wodziński","Adam Piórkowski","Michał Strzelecki","Karolina Nurzyǹska"],"tags":["Artificial intelligence","Computer science","Medical imaging","Machine learning","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-24","doi":"https://doi.org/10.3390/diagnostics15030282","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2977514150","name":"Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement","source":"openalex","abstract":"This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine.AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future.The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.","url":"https://doi.org/10.1186/s13244-019-0785-8","authors":["J. Raymond Geis","Adrian P. Brady","Carol C. Wu","Jack Spencer","Erik Ranschaert","Jacob L. Jaremko","Steve G. Langer","Andrea Borondy Kitts","Judy Birch","William Shields","R. van den Hoven van Genderen","Elmar Kotter","Judy Wawira Gichoya","Tessa S. Cook","Matthew B. Morgan","An Tang","Nabile Safdar","Marc Kohli"],"tags":["Medicine","Transparency (behavior)","Interventional radiology","Dignity","Accountability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-09-30","doi":"https://doi.org/10.1186/s13244-019-0785-8","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2350528688","name":"MEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE","source":"openalex","abstract":"The development of modern medicine has made ever-increasing demands for the automation and intelligence of medical equipments. After briefly introducing to the basic concept of Artificial Intelligence, this paper mainly described the medical applications and trend of Expert System, Artificial Neural Network and Data mining, the three important branches of Artificial Intelligence, and put forward four problems which was worthy of further research.","url":"https://openalex.org/W2350528688","authors":["Ning Yang"],"tags":["Artificial intelligence","Automation","Artificial neural network","Computer science","Expert system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2009-01-01","doi":"","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3087003429","name":"Artificial intelligence in stroke imaging: Current and future perspectives","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.clinimag.2020.09.005","authors":["Vivek Yedavalli","Elizabeth Tong","Dann Martin","Kristen W. Yeom","Nils D. Forkert"],"tags":["Workflow","Artificial intelligence","Context (archaeology)","Medicine","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-21","doi":"https://doi.org/10.1016/j.clinimag.2020.09.005","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4392394652","name":"Generative Artificial Intelligence in Education: From Deceptive to Disruptive.","source":"openalex","abstract":"Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.","url":"https://doi.org/10.9781/ijimai.2024.02.011","authors":["Marc Alier","Francisco José García‐Peñalvo","Jorge D. Camba"],"tags":["Computer science","Generative grammar","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-01","doi":"https://doi.org/10.9781/ijimai.2024.02.011","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4384558576","name":"The Role of Artificial Intelligence in Healthcare and Medical Negligence","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10991-023-09340-y","authors":["Dhruv Mehta"],"tags":["Tort","Health care","Liability","Set (abstract data type)","Function (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-17","doi":"https://doi.org/10.1007/s10991-023-09340-y","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3100571307","name":"Homology modeling in the time of collective and artificial intelligence","source":"openalex","abstract":"Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. [email protected] and [email protected] are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.","url":"https://doi.org/10.1016/j.csbj.2020.11.007","authors":["Tareq Hameduh","Yazan Haddad","Vojtěch Adam","Zbyněk Heger"],"tags":["CASP","Homology modeling","Computer science","Codebase","Protein structure prediction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1016/j.csbj.2020.11.007","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4377115728","name":"Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis","source":"openalex","abstract":"Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant to trust automated systems, and some patient populations may be particularly distrusting. We sought to determine how diverse patient populations feel about the use of AI diagnostic tools, and whether framing and informing the choice affects uptake. To construct and pretest our materials, we conducted structured interviews with a diverse set of actual patients. We then conducted a pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A survey firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated in eight variables with two levels each: disease severity (leukemia versus sleep apnea), whether AI is proven more accurate than human specialists, whether the AI clinic is personalized to the patient through listening and/or tailoring, whether the AI clinic avoids racial and/or financial biases, whether the Primary Care Physician (PCP) promises to explain and incorporate the advice, and whether the PCP nudges the patient towards AI as the established, recommended, and easy choice. Our main outcome measure was selection of AI clinic or human physician specialist clinic (binary, \"AI uptake\"). We found that with weighting representative to the U.S. population, respondents were almost evenly split (52.9% chose human doctor and 47.1% chose AI clinic). In unweighted experimental contrasts of respondents who met pre-registered criteria for engagement, a PCP's explanation that AI has proven superior accuracy increased uptake (OR = 1.48, CI 1.24-1.77, p < .001), as did a PCP's nudge towards AI as the established choice (OR = 1.25, CI: 1.05-1.50, p = .013), as did reassurance that the AI clinic had trained counselors to listen to the patient's unique perspectives (OR = 1.27, CI: 1.07-1.52, p = .008). Disease severity (leukemia versus sleep apnea) and other manipulations did not affect AI uptake significantly. Compared to White respondents, Black respondents selected AI less often (OR = .73, CI: .55-.96, p = .023) and Native Americans selected it more often (OR: 1.37, CI: 1.01-1.87, p = .041). Older respondents were less likely to choose AI (OR: .99, CI: .987-.999, p = .03), as were those who identified as politically conservative (OR: .65, CI: .52-.81, p < .001) or viewed religion as important (OR: .64, CI: .52-.77, p < .001). For each unit increase in education, the odds are 1.10 greater for selecting an AI provider (OR: 1.10, CI: 1.03-1.18, p = .004). While many patients appear resistant to the use of AI, accuracy information, nudges and a listening patient experience may help increase acceptance. To ensure that the benefits of AI are secured in clinical practice, future research on best methods of physician incorporation and patient decision making is required.","url":"https://doi.org/10.1371/journal.pdig.0000237","authors":["Christopher T. Robertson","Andrew Keane Woods","Kelly Bergstrand","Jess Findley","Cayley Balser","Marvin J. Slepian"],"tags":["Artificial intelligence","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-19","doi":"https://doi.org/10.1371/journal.pdig.0000237","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4404719075","name":"Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee","source":"openalex","abstract":"OBJECTIVES: Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education. As such, we sought to assess the benefits and pitfalls that AI may have on pain medicine trainee education. Given the rapidly increasing popularity of LLMs, we particularly assessed how these LLMs may promote and hinder trainee education through a pilot quality improvement project. MATERIALS AND METHODS: A comprehensive search of the existing literature regarding AI within medicine was performed to identify its potential benefits and pitfalls within pain medicine. The pilot project was approved by UPMC Quality Improvement Review Committee (#4547). Three of the most commonly utilized LLMs at the initiation of this pilot study - ChatGPT Plus, Google Bard, and Bing AI - were asked a series of multiple choice questions to evaluate their ability to assist in learner education within pain medicine. RESULTS: Potential benefits of AI within pain medicine trainee education include ease of use, imaging interpretation, procedural/surgical skills training, learner assessment, personalized learning experiences, ability to summarize vast amounts of knowledge, and preparation for the future of pain medicine. Potential pitfalls include discrepancies between AI devices and associated cost-differences, correlating radiographic findings to clinical significance, interpersonal/communication skills, educational disparities, bias/plagiarism/cheating concerns, lack of incorporation of private domain literature, and absence of training specifically for pain medicine education. Regarding the quality improvement project, ChatGPT Plus answered the highest percentage of all questions correctly (16/17). Lowest correctness scores by LLMs were in answering first-order questions, with Google Bard and Bing AI answering 4/9 and 3/9 first-order questions correctly, respectively. Qualitative evaluation of these LLM-provided explanations in answering second- and third-order questions revealed some reasoning inconsistencies (e.g., providing flawed information in selecting the correct answer). CONCLUSIONS: AI represents a continually evolving and promising modality to assist trainees pursuing a career in pain medicine. Still, limitations currently exist that may hinder their independent use in this setting. Future research exploring how AI may overcome these challenges is thus required. Until then, AI should be utilized as supplementary tool within pain medicine trainee education and with caution.","url":"https://doi.org/10.1111/papr.13428","authors":["Michael Glicksman","Sheri Wang","Samir Yellapragada","Christopher L. Robinson","Vwaire Orhurhu","Trent Emerick"],"tags":["Medicine","Personalized medicine","Popularity","Alternative medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.1111/papr.13428","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2943640801","name":"Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology","source":"openalex","abstract":"The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent years. Artificial intelligence is a multidimensional technology with various components such as advanced algorithms, ML and DL. Together, AI, ML, and DL are expected to provide automated devices to ophthalmologists for early diagnosis and timely treatment of ocular disorders in the near future. In fact, AI, ML, and DL have been used in ophthalmic setting to validate the diagnosis of diseases, read images, perform corneal topographic mapping and intraocular lens calculations. Diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are the 3 most common causes of irreversible blindness on a global scale. Ophthalmic imaging provides a way to diagnose and objectively detect the progression of a number of pathologies including DR, AMD, glaucoma, and other ophthalmic disorders. There are 2 methods of imaging used as diagnostic methods in ophthalmic practice: fundus digital photography and optical coherence tomography (OCT). Of note, OCT has become the most widely used imaging modality in ophthalmology settings in the developed world. Changes in population demographics and lifestyle, extension of average lifespan, and the changing pattern of chronic diseases such as obesity, diabetes, DR, AMD, and glaucoma create a rising demand for such images. Furthermore, the limitation of availability of retina specialists and trained human graders is a major problem in many countries. Consequently, given the current population growth trends, it is inevitable that analyzing such images is time-consuming, costly, and prone to human error. Therefore, the detection and treatment of DR, AMD, glaucoma, and other ophthalmic disorders through unmanned automated applications system in the near future will be inevitable. We provide an overview of the potential impact of the current AI, ML, and DL methods and their applications on the early detection and treatment of DR, AMD, glaucoma, and other ophthalmic diseases.","url":"https://doi.org/10.22608/apo.2018479","authors":["Lokman Balyen","Tünde Pető"],"tags":["Glaucoma","Artificial intelligence","Diabetic retinopathy","Medicine","Fundus photography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.22608/apo.2018479","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4366281306","name":"Current and Future Use of Artificial Intelligence in Electrocardiography","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.","url":"https://doi.org/10.3390/jcdd10040175","authors":["Manuel Martínez‐Sellés","Manuel Marina‐Breysse"],"tags":["Electrocardiography","Heart rate variability","Myocardial infarction","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-17","doi":"https://doi.org/10.3390/jcdd10040175","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3005703884","name":"Healthcare 5.0: A Paradigm Shift in Digital Healthcare System Using Artificial Intelligence, IOT and 5G Communication","source":"openalex","abstract":"The induction of Artificial Intelligence (AI) concept with the application of smart intelligent devices and adoption of high-speed data transmission networking techniques in healthcare unit, set a benchmark in the ideology of healthcare to a new level. Developments and advancement of new technologies in healthcare units and improvement in people's quality lifestyle lead people to live a healthier life. AI embedded machines like smart wearable devices with highly integrated efficient sensors which help to monitor, collect and diagnose disease from the symptoms extracted from the sensory data; robot nurse to timely monitor and record patient's health condition in the absence of medical practitioners help the users to know about the health condition irrespective of the location. Internet of Things (IoT) devices with AI touch cannot be considered as a solution to the limitations in fourth generation healthcare systems. Seamless data transmission rate with least or no data loss, traffic free transmission channels, cost effective, no time data retrieval and machine to machine (M2M) or device to device (D2D) communication in IoT era are the major challenges in healthcare 4.0. Further the healthcare use cases urgency like remote surgeries and Tactile Internet as an internet network that combines ultra-low latency with extremely high availability, reliability and security for the next evolution of IoT, needs human to machine or M2M or D2D communication. The possible solution needs 5G or fifth generation communication as the elementary network infrastructure. The paper summarizes all the fundamental concepts like AI, IoT and 5G communication to model healthcare 5.0.","url":"https://doi.org/10.1109/icaml48257.2019.00044","authors":["Bhagyashree Mohanta","Priti Das","Srikanta Patnaik"],"tags":["Computer science","Health care","Big data","Wearable computer","Machine to machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-05-01","doi":"https://doi.org/10.1109/icaml48257.2019.00044","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2030295525","name":"Adaptation in Natural and Artificial Systems (John H. Holland)","source":"openalex","abstract":"Previous article Next article Adaptation in Natural and Artificial Systems (John H. Holland)Jeffrey R. SampsonJeffrey R. Sampsonhttps://doi.org/10.1137/1018105PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout\"Adaptation in Natural and Artificial Systems (John H. Holland).\" SIAM Review, 18(3), pp. 529–530 Previous article Next article FiguresRelatedReferencesCited byDetails Multi-Modal Haptic Rendering Based on Genetic Algorithm24 November 2022 | Electronics, Vol. 11, No. 23 Cross Ref Intelligent injury prediction for traumatic airway obstruction4 November 2022 | Medical & Biological Engineering & Computing, Vol. 13 Cross Ref Genetic Algorithm-Assisted Design of Sandwiched One-Dimensional Photonic Crystals for Efficient Fluorescence Enhancement of 3.18-μm-Thick Layer of the Fluorescent Solution4 November 2022 | Materials, Vol. 15, No. 21 Cross Ref Novel periodic pile barriers in saturated soil and applications to propagation attenuation of shear plane wavesComputers and Geotechnics, Vol. 150 Cross Ref A multi-strategy random weighted gray wolf optimizer-based multi-layer perceptron model for short-term wind speed forecasting10 May 2022 | Neural Computing and Applications, Vol. 34, No. 17 Cross Ref Noise-insensitive image representation via multiple extended LDB and class supervised intelligent coordination feature selection1 September 2022 | The Journal of Supercomputing, Vol. 42 Cross Ref Static control method using gradient—genetic algorithm for grillage adaptive beam string structures based on minimal internal force25 September 2022 | Journal of Zhejiang University-SCIENCE A, Vol. 23, No. 9 Cross Ref An Efficient Design of an Energy Harvesting Backpack for Remote ApplicationsSustainable Energy Technologies and Assessments, Vol. 52 Cross Ref An optimization model for combined selecting, planting and harvesting sugarcane varieties27 April 2020 | Annals of Operations Research, Vol. 314, No. 2 Cross Ref SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy17 November 2021 | Artificial Intelligence Review, Vol. 55, No. 5 Cross Ref Optimal Design of CFETR Multipurpose Overload Robot Based on Advantage Posture4 March 2022 | Journal of Fusion Energy, Vol. 41, No. 1 Cross Ref Prediction of EPB Shield Tunneling Advance Rate in Mixed Ground Condition Using Optimized BPNN Model28 May 2022 | Applied Sciences, Vol. 12, No. 11 Cross Ref Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion28 January 2022 | Algorithms, Vol. 15, No. 2 Cross Ref Modified genetic algorithm for high-efficiency dispersive waves emission at 3 µm12 January 2022 | Optics Express, Vol. 30, No. 2 Cross Ref An Efficient Network Intrusion Detection System Based on Feature Selection Using Evolutionary Algorithm Over Balanced Dataset3 March 2022 Cross Ref A Modified Equilibrium Optimizer Using Opposition-Based Learning and Teaching-Learning StrategyIEEE Access, Vol. 10 Cross Ref Some Words About Nature-Inspired Computing Cross Ref Experimental Programming of Genetic Algorithms for the \"Casse-Tête\" ProblemInternational Journal of Applied Metaheuristic Computing, Vol. 13, No. 1 Cross Ref Simulation of chlorophyll-a concentration in Donghu Lake based on GA-ELM and multiple water quality indexes Cross Ref The Waterlogging Process Model in the Paddy Fields of Flat Irrigation Districts27 September 2021 | Water, Vol. 13, No. 19 Cross Ref Optimal Operation Model of Drainage Works for Minimizing Waterlogging Loss in Paddy Fields9 October 2021 | Water, Vol. 13, No. 20 Cross Ref A symbiosis between population based incremental learning and LP-relaxation based parallel genetic algorithm for solving integer linear programming models3 September 2021 | Computing, Vol. 86 Cross Ref A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction2 August 2021 | Evolutionary Intelligence, Vol. 36 Cross Ref Imaging lens design with the aid o","url":"https://doi.org/10.1137/1018105","authors":["Jeffrey R. Sampson"],"tags":["Adaptation (eye)","Natural (archaeology)","Computer science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1976-07-01","doi":"https://doi.org/10.1137/1018105","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4384563806","name":"Artificial intelligence, nutrition, and ethical issues: A mini-review","source":"openalex","abstract":"Background and aims Artificial intelligence (AI) has expanded applications in both medicine and biomedical sciences, focusing on medical diagnosis, risk prediction of disease onset, support of therapeutic techniques, and other subjects. In parallel, several applications in nutrition have been developed, such as microbiota/genes-diet interactions, investigation of diet-disease relationships, chatbots for lifestyle intervention, dietary assessment with food photographs, and food composition applications. Methods The positive aspects and ethical concerns were analyzed regarding the use of AI in nutrition. Results In general, AI should do no harm and contribute to human well-being, while AI professionals should be honest, trustful, and fair. Privacy and confidentiality should be protected. Other concerns include the \"dehumanization\" of care, social disparities, responsibility assignment in case of errors or malfunctions, and bias in training models and delivering care. Moreover, the prediction of disease onset in high-risk individuals may be connected to stigma, over-medicalization, and stress. There is also a serious concern that AI systems in the field of nutrition and dietetics may cause a partial replacement of dietitians; however, health professionals can use such technologies as part of their work. The use of AI applications for persons with mental diseases or eating disorders is also crucial. Conclusion In conclusion, AI-assisted personalized nutrition needs further justification, while the regulatory framework requires a constant update to keep up with scientific advancements and address ethical issues. Coordinated global focus on these domains could effectively lead to a more embraced AI implementation in individuals and populations.","url":"https://doi.org/10.1016/j.nutos.2023.07.001","authors":["Paraskevi Detopoulou","Gavriela Voulgaridou","Panagiotis Moschos","Despoina Levidi","Thelma Anastasiou","Vasilios Dedes","Eirini- Maria Diplari","Nikoleta Fourfouri","Constantinos Giaginis","Georgios I. Panoutsopoulos","Sousana Κ. Papadopoulou"],"tags":["Engineering ethics","Ethical issues","Psychology","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-17","doi":"https://doi.org/10.1016/j.nutos.2023.07.001","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2979506454","name":"Classification of Rice Varieties Using Artificial Intelligence Methods","source":"openalex","abstract":"Rice being one of the most widely produced and consumed cereal crops in the world, is also the one of the main sustenance in our country because of its economical and nutritious nature. Rice, starting from farm to our table, goes through some manufacturing steps such as a cleaning process, color sorting and classification. If these stages are to be mentioned briefly, cleaning is the process of separating rice from foreign substances, classification is the process of separating broken ones with sturdy ones; color extraction is the process of separating the stained and striped ones except the whiteness on the rice surface. In this study, a computerized vision system was developed in order to distinguish between two proprietary rice species. A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice. With these features, models were created using LR, MLP, SVM, DT, RF, NB and k-NN machine learning techniques and performance measurement values were obtained. Success rates in the classification were obtained 93.02% (LR), 92.86% (MLP), 92.83% (SVM), 92.49% (DT), 92.39% (RF), 91.71% (NB), 88.58% (k-NN). When we look at the results of the success rate of obtain, it is possible to say that the study achieved success.","url":"https://doi.org/10.18201/ijisae.2019355381","authors":["İlkay Çınar","Murat Köklü"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-09-30","doi":"https://doi.org/10.18201/ijisae.2019355381","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2996106349","name":"Artificial intelligence for team sports: a survey","source":"openalex","abstract":"Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.","url":"https://doi.org/10.1017/s0269888919000225","authors":["Ryan Beal","Timothy J. Norman","Sarvapali D. Ramchurn"],"tags":["Strengths and weaknesses","Computer science","Domain (mathematical analysis)","Artificial intelligence","Order (exchange)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.1017/s0269888919000225","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W639132055","name":"Contemporary Artificial Intelligence","source":"openalex","abstract":"The notion of artificial intelligence (AI) often sparks thoughts of characters from science fiction, such as the Terminator and HAL 9000. While these two artificial entities do not exist, the algorithms of AI have been able to address many real issues, from performing medical diagnoses to navigating difficult terrain to monitoring possible failures","url":"https://doi.org/10.1201/b12524","authors":["Richard E. Neapolitan"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2012-08-25","doi":"https://doi.org/10.1201/b12524","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4406261804","name":"Explainability techniques for Artificial Intelligence models in medical diagnostic","source":"openalex","abstract":"The integration of artificial intelligence (AI) techniques into clinical settings presents critical challenges due to the opacity of machine learning models, often referred to as \"black boxes\": this study explores the application of explainability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), in the context of medical diagnostics. Using the \"Diabetes Health Indicators Dataset\", we applied Logistic Regression as a predictive model to identify key risk factors for diabetes and evaluate the ability of explainability techniques to improve transparency and interpretability. The results demonstrate that SHAP provides a detailed global and local understanding of feature importance, offering clinicians insights into key predictors such as HighBP, CholCheck, and GenHlth; LIME complements this by delivering intuitive explanations for individual predictions, enabling rapid and accessible interpretation. The combination of these techniques enhances trust in AI systems by providing both comprehensive insights and actionable explanations. Challenges related to computational complexity, scalability, and the integration of these methods into clinical workflows are also discussed, along with recommendations for future research aimed at developing scalable, interpretable AI models for ethical and responsible medical use.","url":"https://doi.org/10.1109/bibm62325.2024.10821826","authors":["F. Falvo","Mario Cannataro"],"tags":["Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-03","doi":"https://doi.org/10.1109/bibm62325.2024.10821826","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3168386798","name":"A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10489-021-02264-y","authors":["Iván Palomares","Eugenio Martínez‐Cámara","Rosana Montes","Pablo García-Moral","Manuel Chiachío","Juan Chiachío","Sergio Alonso","Francisco Javier Melero","Daniel Molina","Bárbara Cables Fernández","Cristina Moral","Rosario Marchena","Javier Pérez de Vargas","Francisco Herrera"],"tags":["SWOT analysis","Blueprint","Prosperity","Sustainable development","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-11","doi":"https://doi.org/10.1007/s10489-021-02264-y","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2619354375","name":"Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health","source":"openalex","abstract":"Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content. Methods: Our case study is the ongoing Horyzons site (5-year randomised controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application’s foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system. Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials. Given the data-driven opportunities afforded by the software system, observed usage patterns and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms.","url":"https://doi.org/10.3389/fpsyg.2017.00796","authors":["Simon D’Alfonso","Olga Santesteban‐Echarri","Simon Rice","Greg Wadley","Reeva Lederman","Christopher Miles","John Gleeson","Mario Álvarez‐Jiménez"],"tags":["Psychological intervention","Mental health","Psychology","Intervention (counseling)","Social media"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-06-01","doi":"https://doi.org/10.3389/fpsyg.2017.00796","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2047552485","name":"An application of artificial intelligence to medical robotics","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10846-005-3509-x","authors":["Elena Alessandri","Alessandro Gasparetto","Rafael Valencia Garcı́a","Rodrigo Martínez‐Béjar"],"tags":["Robotics","Artificial intelligence","Planner","Task (project management)","Robot"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2005-01-01","doi":"https://doi.org/10.1007/s10846-005-3509-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3121555817","name":"Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making","source":"openalex","abstract":"In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.","url":"https://doi.org/10.1016/j.smhl.2020.100178","authors":["Mohammad Pourhomayoun","Mahdi Shakibi"],"tags":["Confusion matrix","Overcrowding","Decision tree","Machine learning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-18","doi":"https://doi.org/10.1016/j.smhl.2020.100178","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3048713063","name":"ExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.media.2021.102118","authors":["Gwenolé Quellec","Hassan Al Hajj","Mathieu Lamard","Pierre-Henri Conze","Pascale Massin","Béatrice Cochener"],"tags":["Artificial intelligence","Computer science","Novelty","Pixel","Black box"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-28","doi":"https://doi.org/10.1016/j.media.2021.102118","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3003026830","name":"An introduction to deep learning in medical physics: advantages, potential, and challenges","source":"openalex","abstract":"As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide an overview of the method to medical physics researchers interested in DL to help them start the endeavor. Second, we will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions. As such, we divide the article into two major parts. The first part introduces general concepts and principles of DL and summarizes major research resources, such as computational tools and databases. The second part discusses challenges faced by DL, present available methods to mitigate some of these challenges, as well as our recommendations.","url":"https://doi.org/10.1088/1361-6560/ab6f51","authors":["Chenyang Shen","Dan Nguyen","Zhiguo Zhou","Steve Jiang","Bin Dong","Xun Jia"],"tags":["Field (mathematics)","Computer science","Deep learning","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-23","doi":"https://doi.org/10.1088/1361-6560/ab6f51","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2969294044","name":"Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jmir.2019.07.010","authors":["William T. Tran","Katarzyna J. Jerzak","Fang-I Lu","Jonathan Klein","Sami Tabbarah","Andrew Lagree","Tina Wu","Iván M. Rosado-Méndez","Ethan Law","Khadijeh Saednia","Ali Sadeghi‐Naini"],"tags":["Radiomics","Breast cancer","Medical physics","Cancer","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-22","doi":"https://doi.org/10.1016/j.jmir.2019.07.010","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4307406039","name":"Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study","source":"openalex","abstract":"Purpose: To establish reporting adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) in diagnostic accuracy AI studies with the highest Altmetric Attention Scores (AAS), and to compare completeness of reporting between peer-reviewed manuscripts and preprints. Methods: MEDLINE, EMBASE, arXiv, bioRxiv, and medRxiv were retrospectively searched for 100 diagnostic accuracy medical imaging AI studies in peer-reviewed journals and preprint platforms with the highest AAS since the release of CLAIM to June 24, 2021. Studies were evaluated for adherence to the 42-item CLAIM checklist with comparison between peer-reviewed manuscripts and preprints. The impact of additional factors was explored including body region, models on COVID-19 diagnosis and journal impact factor. Results: Median CLAIM adherence was 48% (20/42). The median CLAIM score of manuscripts published in peer-reviewed journals was higher than preprints, 57% (24/42) vs 40% (16/42), P < .0001. Chest radiology was the body region with the least complete reporting ( P = .0352), with manuscripts on COVID-19 less complete than others (43% vs 54%, P = .0002). For studies published in peer-reviewed journals with an impact factor, the CLAIM score correlated with impact factor, rho = 0.43, P = .0040. Completeness of reporting based on CLAIM score had a positive correlation with a study’s AAS, rho = 0.68, P < .0001. Conclusions: Overall reporting adherence to CLAIM is low in imaging diagnostic accuracy AI studies with the highest AAS, with preprints reporting fewer study details than peer-reviewed manuscripts. Improved CLAIM adherence could promote adoption of AI into clinical practice and facilitate investigators building upon prior works.","url":"https://doi.org/10.1177/08465371221134056","authors":["Umaseh Sivanesan","Kay Wu","Matthew D. F. McInnes","Kiret Dhindsa","Fateme Salehi","Christian B. van der Pol"],"tags":["Checklist","Medicine","Preprint","Coronavirus disease 2019 (COVID-19)","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-27","doi":"https://doi.org/10.1177/08465371221134056","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3199406970","name":"Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.cpet.2021.07.002","authors":["Reza Yousefi Nooraie","Patrick G. Lyons","Ana A. Baumann","Babak Saboury"],"tags":["Equity (law)","Medicine","Health care","Intervention (counseling)","Implementation research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-15","doi":"https://doi.org/10.1016/j.cpet.2021.07.002","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4392555240","name":"The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis","source":"openalex","abstract":"Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.","url":"https://doi.org/10.1016/j.compbiomed.2024.108258","authors":["Chuheng Chang","Wen Shi","Youyang Wang","Zhan Zhang","Xiaoming Huang","Yang Jiao"],"tags":["Computer science","Data science","Field (mathematics)","Artificial intelligence","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-07","doi":"https://doi.org/10.1016/j.compbiomed.2024.108258","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4211102683","name":"Artificial Intelligence Technique of Synthesis and Characterizations for Measurement of Optical Particles in Medical Devices","source":"openalex","abstract":"The aim of this study is to demonstrate the effect of particle size on semiconductor properties; artificial intelligence is being used for the research methods. As a result, we picked cadmium sulfide (CdS), which is a unique semiconductor material that is employed in a broad variety of current applications. Given that CdS has distinct electrical and optical characteristics, it may be employed in the production of solar cells, for example. Solar cells, as is also well known, have become an essential source of energy in the world. Within the visible range (500-700 nm), we create one layer of bulk CdS and one layer of nano-CdS air bulk CdS air and air nano-CdS air. We used a number of instrumentation methods to investigate the naked CdS nanoparticles, including XRD, SEM-EDX, UV-Vis spectroscopy, TEM, XPS, and PL spectroscopy, among others. The results show that for bulk CdS at normal incidence, the transmittance is T = 45 , and for nano-CdS with particle size 3 nm, the transmittance is T = 85.8 , with transverse-electric (S-polarized) and transverse-magnetic (P-polarized) transmittances of TE = 75 and TM = 80 , respectively.","url":"https://doi.org/10.1155/2022/9103551","authors":["Walid Theib Mohammad","Sherin Hassan Mabrouk","Rania Mohammed Abd Elgawad Mostafa","Mohammad Bani Younis","Ahmad Maher Al Sayeh","Mona Abdelmoneim Abdelmabood Ebrahim","Samar Zuhair Alshawwa","H.A. Ismail","Manal Mahrous Abdalhamed Mohamed","Sara Wans Alshmmry","Malik Bader Alazzam","Md. Kawser Ahmed"],"tags":["Cadmium sulfide","Materials science","Transmittance","Semiconductor","Spectroscopy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-11","doi":"https://doi.org/10.1155/2022/9103551","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3196390567","name":"Artificial Intelligence in Medical Imaging","source":"openalex","abstract":"Artificial Intelligence in Medical Imaging - 1","url":"https://doi.org/10.1201/9781003175865-4","authors":["C.S. Sureka"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-26","doi":"https://doi.org/10.1201/9781003175865-4","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3046282410","name":"Artificial Intelligence in Ovarian Cancer Diagnosis","source":"openalex","abstract":"BACKGROUND/AIM: This study aimed to use artificial intelligence (AI) to predict the pathological diagnosis of ovarian tumors using patient information and data from preoperative examinations. PATIENTS AND METHODS: A total of 202 patients with ovarian tumors were enrolled, including 53 with ovarian cancer, 23 with borderline malignant tumors, and 126 with benign ovarian tumors. Using 5 machine learning classifiers, including support vector machine, random forest, naive Bayes, logistic regression, and XGBoost, we derived diagnostic results from 16 features, commonly available from blood tests, patient background, and imaging tests. We also analyzed the importance of 16 features on the prediction of disease. RESULTS: The highest accuracy was 0.80 in the machine learning algorithm of XGBoost. The evaluation of importance of the features showed different results among the correlation coefficient of the features, the regression coefficient, and the features importance of random forest. CONCLUSION: AI could play a role in the prediction of pathological diagnosis of ovarian cancer from preoperative examinations.","url":"https://doi.org/10.21873/anticanres.14482","authors":["Munetoshi Akazawa","Kazunori Hashimoto"],"tags":["Random forest","Logistic regression","Ovarian cancer","Naive Bayes classifier","Support vector machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-29","doi":"https://doi.org/10.21873/anticanres.14482","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385192772","name":"Creating Reporting Guidelines for Medical Artificial Intelligence (Minimum Information for Medical AI Reporting, or MINIMAR)","source":"openalex","abstract":"Significant advances in artificial intelligence (AI) have been made possible by the growth of digital data and computer power. As an outcome, medical judgement for diagnostics, treatments, and prognostic is improved through the application of categorization & forecasting algorithms in health. Nevertheless, these developments are constrained by the absence of reported standard for the information utilized to build these systems, the system design, including the modeling reviewing & verification procedures. The MINIMAR (Min Information for Medicinal AI Reports) concept, which defines the basic information needed to understand intentional forecasts, targeted population, latent biases, & the generalisation of such cutting-edge technologies, is presented in this article. We demand the creation of a standard for the ethical and truthful usage of AI in the medical industry. This will address difficulties with accuracy and bias while also simplifying the development and usage of connected medical decision assistance technologies. while encouraging the creation and use of such models.","url":"https://doi.org/10.1109/icacite57410.2023.10183016","authors":["Aman Mittal"],"tags":["Computer science","Judgement","Categorization","Data science","Clinical decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-12","doi":"https://doi.org/10.1109/icacite57410.2023.10183016","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4210406706","name":"Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates","source":"openalex","abstract":"Abstract Artificial intelligence (AI) and algorithmic systems have been criticized for perpetuating bias, unjust discrimination, and contributing to inequality. Artificial intelligence researchers have remained largely oblivious to existing scholarship on social inequality, but a growing number of sociologists are now addressing the social transformations brought about by AI. Where bias is typically presented as an undesirable characteristic that can be removed from AI systems, engaging with social inequality scholarship leads us to consider how these technologies reproduce existing hierarchies and the positive visions we can work towards. I argue that sociologists can help assert agency over new technologies through three kinds of actions: (1) critique and the politics of refusal; (2) fighting inequality through technology; and (3) governance of algorithms. As we become increasingly dependent on AI and automated systems, the dangers of further entrenching or amplifying social inequalities have been well documented, particularly with the growing adoption of these systems by government agencies. However, public policy also presents some opportunities to restructure social dynamics in a positive direction, as long as we can articulate what we are trying to achieve, and are aware of the risks and limitations of utilizing these new technologies to address social problems.","url":"https://doi.org/10.1111/soc4.12962","authors":["Mike Zajko"],"tags":["Scholarship","Sociology","Agency (philosophy)","Inequality","Social inequality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-02","doi":"https://doi.org/10.1111/soc4.12962","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4376490520","name":"Artificial Intelligence in Medicine","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-981-19-1223-8","authors":[],"tags":["Artificial intelligence","Computer science","Psychology","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1007/978-981-19-1223-8","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3177752023","name":"The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11356-021-15292-5","authors":["Abbas Sharifi","Mohsen Ahmadi","Ali Ala"],"tags":["Prosperity","Pandemic","Renewable energy","Coronavirus disease 2019 (COVID-19)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-16","doi":"https://doi.org/10.1007/s11356-021-15292-5","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4313328558","name":"Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review","source":"openalex","abstract":"Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbot-psychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.","url":"https://doi.org/10.32604/cmc.2023.034655","authors":["Батырхан Омаров","Sergazi Narynov","Zhandos Zhumanov"],"tags":["Chatbot","Mental health","Health care","Realm","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-29","doi":"https://doi.org/10.32604/cmc.2023.034655","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3094675384","name":"ARTIFICIAL INTELLIGENCE IN BUSINESS AND ECONOMICS RESEARCH: TRENDS AND FUTURE","source":"openalex","abstract":"Artificial Intelligence is a disruptive technology developed during the 20th century, which has undergone an accelerated evolution, underpinning solutions to complex problems in the business world. Neural Networks, Machine Learning, or Deep Learning are concepts currently associated with terms such as digital marketing, decision making, industry 4.0 and business digital transformation. Interest in this technology will increase as the competitive advantages of the use of Artificial Intelligence by economic entities is realised. The aim of this research is to analyse the state-of-the-art research of Artificial Intelligence in business. To this end, a bibliometric analysis has been implement using the Web of Science and Scopus online databases. By using a fractional counting method, this paper identifies 11 clusters and the most frequent terms used in Artificial Intelligence research. The present study identifies the main trends in research on Artificial Intelligence in business and proposes future lines of inquiry.","url":"https://doi.org/10.3846/jbem.2020.13641","authors":["José Luis Ruiz–Real","Juan Uribe-Toril","José Antonio Torres","Jaime de Pablo Valenciano"],"tags":["Underpinning","Marketing and artificial intelligence","Web intelligence","Business intelligence","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-29","doi":"https://doi.org/10.3846/jbem.2020.13641","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2883464116","name":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging","source":"openalex","abstract":"Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.","url":"https://doi.org/10.1093/eurheartj/ehy404","authors":["Subhi J. Al’Aref","Khalil Anchouche","Gurpreet Singh","Piotr J. Slomka","Kranthi K. Kolli","Amit Kumar","Mohit Pandey","Gabriel Maliakal","Alexander R. van Rosendael","Ashley Beecy","Daniel S. Berman","Jonathan Leipsic","Koen Nieman","Daniele Andreini","Gianluca Pontone","U. Joseph Schoepf","Leslee J. Shaw","Hyuk‐Jae Chang","Jagat Narula","Jeroen J. Bax","Yuanfang Guan","James K. Min"],"tags":["Medicine","Coronary artery disease","Machine learning","Relevance (law)","Modalities"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-07-07","doi":"https://doi.org/10.1093/eurheartj/ehy404","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4382310450","name":"Artificial intelligence to automate the systematic review of scientific literature","source":"openalex","abstract":"Abstract Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR process.","url":"https://doi.org/10.1007/s00607-023-01181-x","authors":["José de la Torre-López","Aurora Ramírez","José Raúl Romero"],"tags":["Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-11","doi":"https://doi.org/10.1007/s00607-023-01181-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W7129033494","name":"Psychometric Properties of the Chinese Version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)","source":"europepmc","abstract":"This study aimed to evaluate the psychometric properties of the Chinese version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and assess its applicability among undergraduate students in medicine and health-related disciplines in China. Using Brislin’s translation model, we translated and culturally adapted the original (English) scale to produce the Chinese version. Between May and July 2024, we used convenience sampling to recruit undergraduate students from a medical university in Changsha City, Hunan Province, China (population size = 520). We collected a total of 480 valid responses (participation rate = 92.3%) through the Wenjuanxing platform. We analyzed data using R and AMOS 29.0. Content validity was supported by two rounds of Delphi expert consultation, with item-level content validity indices (I-CVI) ranging from 0.80 to 1.00 and a scale-level index (S-CVI) of 0.95. The Chinese version of the MAIRS-MS showed good internal consistency, with a total Cronbach’s alpha coefficient of 0.90 and subscale coefficients all exceeding 0.78. The split-half reliability was 0.94, and the test-retest reliability was 0.95. Exploratory factor analysis supported the instrument’s original four-factor structure—Cognition, Ability, Vision, and Ethics—with a KMO value of 0.93, cumulative variance explained of 67.2%, and all item loadings greater than 0.40. Confirmatory factor analysis indicated a good model fit (χ2/df = 1.11, RMSEA = 0.02, CFI = 0.93, TLI= 0.99). In sum, the Chinese version of the MAIRS-MS demonstrated satisfactory content validity, internal consistency, and structural validity, supporting its use as a reliable tool for assessing AI readiness among Chinese undergraduate health professional students.","url":"https://doi.org/10.1080/10401334.2026.2627455","authors":["Chuhong Luo","Siqi Xie","Rong Yuan","Pingshuang Li","Can Yang","Jixia Cao","Y David He"],"tags":["Psychology","Confirmatory factor analysis","Reliability (semiconductor)","Exploratory factor analysis","Scale (ratio)"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"https://doi.org/10.1080/10401334.2026.2627455","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"oa:W3162521732","name":"Artificial intelligence in dermatology and healthcare: An overview","source":"openalex","abstract":"Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.","url":"https://doi.org/10.25259/ijdvl_518_19","authors":["Varadraj V Pai","Rohini Bhat Pai"],"tags":["Artificial intelligence","Applications of artificial intelligence","Field (mathematics)","Process (computing)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-10","doi":"https://doi.org/10.25259/ijdvl_518_19","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4210739840","name":"Embedded ethics: a proposal for integrating ethics into the development of medical AI","source":"openalex","abstract":"The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an 'embedded ethics' approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.","url":"https://doi.org/10.1186/s12910-022-00746-3","authors":["Stuart McLennan","Amelia Fiske","Daniel W. Tigard","Ruth Müller","Sami Haddadin","Alena Buyx"],"tags":["Philosophy of medicine","Engineering ethics","Medical law","Information ethics","Nursing ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-26","doi":"https://doi.org/10.1186/s12910-022-00746-3","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3120795911","name":"Convolutional neural networks in medical image understanding: a survey","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12065-020-00540-3","authors":["D. R. Sarvamangala","Raghavendra V. Kulkarni"],"tags":["Convolutional neural network","Computer science","Artificial intelligence","Image (mathematics)","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-03","doi":"https://doi.org/10.1007/s12065-020-00540-3","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4406126250","name":"A review of medical tourism entrepreneurship and marketing at regional and global levels and a quick glance into the applications of artificial intelligence in medical tourism","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00146-024-02178-6","authors":["Maryam Sadat Reshadi","Azimeh Mohammadi Chehragh"],"tags":["Tourism","Medical tourism","Entrepreneurship","Marketing","Performing arts"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-07","doi":"https://doi.org/10.1007/s00146-024-02178-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2894996849","name":"Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry","source":"openalex","abstract":"ADVERTISEMENT RETURN TO ISSUEEditorialNEXTArtificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel ChemistryAlex Zhavoronkov*Alex ZhavoronkovJHU, Insilico Medicine, Inc., 9601 Medical Center Dr, Suite 127, Rockville, Maryland 20850, United States*E-mail: [email protected]More by Alex Zhavoronkovhttp://orcid.org/0000-0001-7067-8966Cite this: Mol. Pharmaceutics 2018, 15, 10, 4311–4313Publication Date (Web):October 1, 2018Publication History Published online1 October 2018Published inissue 1 October 2018https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.8b00930https://doi.org/10.1021/acs.molpharmaceut.8b00930editorialACS PublicationsCopyright © 2018 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views27020Altmetric-Citations97LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (644 KB) Get e-AlertscloseSUBJECTS:Biomarkers,Drug discovery,Machine learning,Molecular modeling,Molecules Get e-Alerts","url":"https://doi.org/10.1021/acs.molpharmaceut.8b00930","authors":["Alex Zhavoronkov"],"tags":["Citation","Computer science","World Wide Web","Suite","Drug discovery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-10-01","doi":"https://doi.org/10.1021/acs.molpharmaceut.8b00930","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4321436564","name":"Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift","source":"openalex","abstract":"On November 30, 2022, OpenAI enabled public access to ChatGPT, a next-generation artificial intelligence with a highly sophisticated ability to write, solve coding issues, and answer questions. This communication draws attention to the prospect that ChatGPT and its successors will become important virtual assistants to patients and health-care providers. In our assessments, ranging from answering basic fact-based questions to responding to complex clinical questions, ChatGPT demonstrated a remarkable ability to formulate interpretable responses, which appeared to minimize the likelihood of alarm compared with Google's feature snippet. Arguably, the ChatGPT use case presents an urgent need for regulators and health-care professionals to be involved in developing standards for minimum quality and to raise patient awareness of current limitations of emerging artificial intelligence assistants. This commentary aims to raise awareness at the tipping point of a paradigm shift.","url":"https://doi.org/10.1093/jncics/pkad010","authors":["Ashley M. Hopkins","Jessica M. Logan","Ganessan Kichenadasse","Michael J. Sorich"],"tags":["Paradigm shift","Health care","Medicine","Quality (philosophy)","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-21","doi":"https://doi.org/10.1093/jncics/pkad010","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4211089551","name":"Artificial Intelligence and Declined Guilt: Retailing Morality Comparison Between Human and AI","source":"openalex","abstract":"Several technological developments, such as self-service technologies and artificial intelligence (AI), are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers' moral concerns and behaviors differ when interacting with technologies versus humans. We show that moral intention (intention to report an error) is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment.","url":"https://doi.org/10.1007/s10551-022-05056-7","authors":["Marilyn Giroux","Jungkeun Kim","Jacob C. Lee","Jongwon Park"],"tags":["Morality","Business ethics","Feeling","Consumption (sociology)","Norm (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-12","doi":"https://doi.org/10.1007/s10551-022-05056-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4323567402","name":"Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect","source":"openalex","abstract":"In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.","url":"https://doi.org/10.3389/fmed.2023.1128084","authors":["Zugang Yin","Chenhui Yao","Limin Zhang","Shaohua Qi"],"tags":["Colonoscopy","Colorectal cancer","Readability","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-08","doi":"https://doi.org/10.3389/fmed.2023.1128084","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4388074096","name":"Artificial intelligence in emergency medicine. A systematic literature review","source":"openalex","abstract":"Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. METHODS: The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. FINDINGS AND DISCUSSION: The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. CONCLUSION: Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.","url":"https://doi.org/10.1016/j.ijmedinf.2023.105274","authors":["Konstantin Piliuk","Sven Tomforde"],"tags":["Triage","Computer science","Artificial intelligence","Big data","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-31","doi":"https://doi.org/10.1016/j.ijmedinf.2023.105274","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4393072541","name":"Evaluating if Ghana's Health Institutions and Facilities Act 2011 (Act 829) Sufficiently Addresses Medical Negligence Risks from Integration of Artificial Intelligence Systems","source":"openalex","abstract":"With artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilistic nature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate integration exist across developers, systems, healthcare facilities, and practitioners under applicable interpretations of existing laws. Finding liability rules insufficient absent targeted AI governance, conclusions recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability. This work carries scientific novelty as one of the first structured jurisdictional analyses internationally of healthcare AI accountability gaps through a legal lens. Practical significance lies in setting the stage for strengthening protections in Ghana through proposed statutory reforms that reduce uncertainty around this crucial area for quality care. The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.","url":"https://doi.org/10.58496/mjaih/2024/006","authors":["George Mensah","Pushan Kumar Dutta"],"tags":["Medical negligence","Business","Risk analysis (engineering)","Law","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-10","doi":"https://doi.org/10.58496/mjaih/2024/006","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4285248746","name":"Artificial Intelligence in Foreign Language Learning and Teaching","source":"openalex","abstract":"Practice and focus on form play a crucial and decisive role in foreign language learning. But what would an intelligent, adaptive foreign language learning environment look like if all students could individually practice their language skills with exercises tailored to their individual skill levels, interests, and motivation? How could all learners be supported and challenged according to their abilities, so that they all have the opportunity to achieve specific learning goals in a self-directed manner? And how could digital media contribute to the kind of learning that adapts to the individual student's needs in heterogeneous foreign language classrooms? In the past years, digital technologies have become scientific and practical focal points in the English language teaching (ELT) world. Whether digital media [are] \"friend or foe\" (Grimm et al. 2015), technology-enhanced language learning (TELL) has been part of an international discourse, varying between \"euphoric proposals,\" \"pessimistic stances,\" and \"opinions which stress that the risks of digital media need to be addressed\" (2015, 210). Regardless of general TELL, research studies have shown that \"technology can influence the processes and outcomes of education, and many countries are investing in technological support for teaching and learning\" (Paiva and Bittencourt 2020, 448). The dynamic development of new technologies and the concomitant digital transformations result in significant challenges both for society as a whole and at all levels of the education system.","url":"https://doi.org/10.33675/angl/2022/1/14","authors":["Torben Dall Schmidt","T. Strassner"],"tags":["Foreign language","Mathematics education","Computer science","Psychology","Linguistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.33675/angl/2022/1/14","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385423139","name":"Artificial intelligence for Sustainable Development Goals : Bibliometric patterns and concept evolution trajectories","source":"openalex","abstract":"Abstract The development of artificial intelligence (AI) as a field has impacted almost all aspects of human life. More recently it has found a role in addressing developmental challenges, specifically the Sustainable Development Goals (SDGs). However, there are not enough systematic studies on analysis of the role of AI research towards the SDGs. Therefore, this article attempts to bridge this gap by identifying the major bibliometric trends and concept‐evolution trajectories in the area of AI applications for sustainable‐development goals. The research publication data for the last 20 years in the areas of artificial intelligence, machine learning, deep learning, and so forth, is obtained and computationally analysed using a framework comprising bibliometrics, path analysis and content analysis. The findings show an incremental trend in overall publications on the application of AI for SDGs across the different regions of the world. SDGs 3 (good health & well‐being) and 7 (affordable and clean energy) are found as the areas with the most applications of AI. In SDG3, the literature reflects application of AI techniques such as deep learning for precision and personalised medicine while in SDG7, a number of studies have employed AI techniques for the integration of systems for efficient generation of solar power and improving the energy efficiency of a building. Furthermore, SDG 4 (quality education), SDG 13 (climate action), SDG 11 (sustainable cities and communities) and SDG 16 (peace, justice and strong institutions) are the other SDGs where AI approaches and techniques are applied. The analytical results present a detailed insight of application of AI for achieving the SDGs.","url":"https://doi.org/10.1002/sd.2706","authors":["Aakash Singh","Anurag Kanaujia","Vivek Kumar Singh","Ricardo Vinuesa"],"tags":["Sustainable development","Bibliometrics","Artificial intelligence","Computer science","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-30","doi":"https://doi.org/10.1002/sd.2706","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4392112506","name":"Artificial intelligence and explanation: How, why, and when to explain black boxes","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ejrad.2024.111393","authors":["Eric Marcus","Jonas Teuwen"],"tags":["Medicine","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-23","doi":"https://doi.org/10.1016/j.ejrad.2024.111393","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4288424365","name":"Artificial intelligence in spine surgery","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00264-022-05517-8","authors":["Ahmed Benzakour","Pavlos Altsitzioglou","Jean‐Michel Lemée","Alaaeldin Azmi Ahmad","Andreas F. Mavrogenis","Thami Benzakour"],"tags":["Medicine","Variety (cybernetics)","Task (project management)","Applications of artificial intelligence","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-29","doi":"https://doi.org/10.1007/s00264-022-05517-8","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4380047614","name":"Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review","source":"openalex","abstract":"Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.","url":"https://doi.org/10.3390/diagnostics13121995","authors":["Celina Silvia Stafie","Irina-Georgeta Șufaru","Cristina Mihaela Ghiciuc","Ingrid-Ioana Stafie","Eduard-Constantin Sufaru","Sorina Mihaela Solomon","Monica Hăncianu"],"tags":["Multidisciplinary approach","Health care","Applications of artificial intelligence","Engineering ethics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-07","doi":"https://doi.org/10.3390/diagnostics13121995","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3163237568","name":"Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compmedimag.2021.101933","authors":["Mustafa Alhasan","Mohamed Hasaneen"],"tags":["Pandemic","Artificial intelligence","Coronavirus disease 2019 (COVID-19)","Digitization","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-15","doi":"https://doi.org/10.1016/j.compmedimag.2021.101933","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387326418","name":"Chart Review Is Dead; Long Live Chart Review: How Artificial Intelligence Will Make Human Review of Medical Records Obsolete, One Day","source":"openalex","abstract":"How Artificial Intelligence Will Make Human Review of Medical Records Obsolete, One DayKevin Agatstein, BS M any clinical and administrative workflows, includ- ing billing and coding, quality measurement, medical management, care management, prior authorization, and patient risk assessment, require various forms of chart review.Chart review is when medical records, some years old and some newly created, are analyzed to extract one or more clinical facts.Such summarization could involve identifying a coding error or omission, making an outcome measure reportable, extracting laboratory values from free text to appropriately apply a clinical guideline, or identifying a clinical trial candidate.The cost of this process is staggering.For example, Johns Hopkins Hospital in Baltimore, Maryland spends over $5 million on quality reporting alone, with collecting and validating electronic medical record data being a major driver of that spending.1 The burden of chart review is not limited to support staff.Overhage and McCallie report that nonsurgical physicians spend more than 5 minutes per care encounter reviewing medical charts. 2 Fortunately, with artificial intelligence (AI), specifically natural language processing (NLP), much of the burden of reviewing charts is now automatable.Or is it?NLP in this context is a computer's ability to interpret human language (eg, unstructured text in a medical note) and express the author's intent as structured data such as diagnoses or procedure codes.Such AI-led chart review is at the same time inferior to, superior to, and fundamentally different from having humans do chart reviews.","url":"https://doi.org/10.1089/pop.2023.0227","authors":["Kevin Agatstein"],"tags":["Chart","Medical record","Health care","Medicine","Officer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-04","doi":"https://doi.org/10.1089/pop.2023.0227","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4395026179","name":"Ethical and regulatory challenges of large language models in medicine","source":"openalex","abstract":"With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.","url":"https://doi.org/10.1016/s2589-7500(24)00061-x","authors":["Jasmine Chiat Ling Ong","Yin‐Hsi Chang","William Wasswa","Atul J. Butte","Nigam H. Shah","Lita Chew","Nan Liu","Finale Doshi‐Velez","Wei Lü","Julian Savulescu","Daniel Shu Wei Ting"],"tags":["Engineering ethics","Medicine","Business","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-23","doi":"https://doi.org/10.1016/s2589-7500(24)00061-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4393049004","name":"Comparing the performance of artificial intelligence learning models to medical students in solving histology and embryology multiple choice questions","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.aanat.2024.152261","authors":["Miloš Bajčetić","Aleksandar Mirčić","Jelena Rakočević","D Doković","Katarina Milutinović","Ivan Zaletel"],"tags":["Consistency (knowledge bases)","Taxonomy (biology)","Test (biology)","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-21","doi":"https://doi.org/10.1016/j.aanat.2024.152261","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4200518678","name":"Artificial Intelligence in Prenatal Ultrasound Diagnosis","source":"openalex","abstract":"The application of artificial intelligence (AI) technology to medical imaging has resulted in great breakthroughs. Given the unique position of ultrasound (US) in prenatal screening, the research on AI in prenatal US has practical significance with its application to prenatal US diagnosis improving work efficiency, providing quantitative assessments, standardizing measurements, improving diagnostic accuracy, and automating image quality control. This review provides an overview of recent studies that have applied AI technology to prenatal US diagnosis and explains the challenges encountered in these applications.","url":"https://doi.org/10.3389/fmed.2021.729978","authors":["Fujiao He","Yaqin Wang","Yun Xiu","Yixin Zhang","Lizhu Chen"],"tags":["Prenatal diagnosis","Prenatal ultrasound","Computer science","Artificial intelligence","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-16","doi":"https://doi.org/10.3389/fmed.2021.729978","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4206583759","name":"A clarion call to introduce artificial intelligence (AI) in postgraduate medical physics curriculum","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s13246-022-01099-2","authors":["Kwan Hoong Ng","Jeannie Hsiu Ding Wong"],"tags":["CLARION","Curriculum","Computer science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-10","doi":"https://doi.org/10.1007/s13246-022-01099-2","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2913559493","name":"Deep Learning in Medical Ultrasound Analysis: A Review","source":"openalex","abstract":"Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.","url":"https://doi.org/10.1016/j.eng.2018.11.020","authors":["Shengfeng Liu","Yi Wang","Xin Yang","Baiying Lei","Li Liu","Shawn Xiang Li","Dong Ni","Tianfu Wang"],"tags":["Deep learning","Computer science","Artificial intelligence","Modalities","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-29","doi":"https://doi.org/10.1016/j.eng.2018.11.020","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3102090163","name":"Artificial intelligence in oral and maxillofacial radiology: what is currently possible?","source":"openalex","abstract":"Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.","url":"https://doi.org/10.1259/dmfr.20200375","authors":["Min-Suk Heo","Jo‐Eun Kim","Jae Joon Hwang","Sang‐Sun Han","Jin-Soo Kim","Won-Jin Yi","In-Woo Park"],"tags":["Computer science","Artificial intelligence","Field (mathematics)","Deep learning","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-16","doi":"https://doi.org/10.1259/dmfr.20200375","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2965109930","name":"Review on the Application of Artificial Intelligence in Smart Homes","source":"openalex","abstract":"Smart home and artificial intelligence technologies are developing rapidly, and various smart home products associated with artificial intelligence (AI) improved the quality of living for occupants. Although some studies discussed the application of artificial intelligence in smart homes, few publications fully considered the integration of literature and products. In this paper, we aim to answer the research questions of “what is the trend of smart home technology and products” and “what is the relationship between literature and products in smart homes with AI”. Literature reviews and product reviews are given to define the functions and roles of artificial intelligence in smart homes. We determined the application status of artificial intelligence in smart home products and how it is utilized in our house so that we could understand how artificial intelligence is used to make smart homes. Furthermore, our results revealed that there is a delay between literature and products, and smart home intelligent interactions will become more and more popular.","url":"https://doi.org/10.3390/smartcities2030025","authors":["Xiao Guo","Zhenjiang Shen","Yajing Zhang","Teng Wu"],"tags":["Home automation","Applications of artificial intelligence","Computer science","Ambient intelligence","Product (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-02","doi":"https://doi.org/10.3390/smartcities2030025","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4393120198","name":"Artificial intelligence in medical physics","source":"openalex","abstract":"Artificial intelligence in medical physicsArtificial intelligence (AI) is emerging in various domains of our life [1].In the medical domain, great promises are attributed to this technology to empower the field of medical technology and imaging to contribute to a better understanding of diseases in terms of science and improved healthcare.On the other hand, generative AI models, which can synthesize data, images, or text, also raise concerns, especially regarding scientific publishing.In this context, we asked chatGPT Version 3.5 [2] with the prompt \"write an editorial on AI in medical physics\" to create an editorial in the context of this special issue.The result is given in the following","url":"https://doi.org/10.1016/j.zemedi.2024.03.002","authors":["Steffen Bollmann","Thomas Küstner","Qian Tao","Frank G. Zöllner"],"tags":["Medical physics","Psychology","Physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-23","doi":"https://doi.org/10.1016/j.zemedi.2024.03.002","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4396720099","name":"Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review","source":"openalex","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms \"artificial intelligence\" OR \"machine learning\" AND \"emergency ward\" OR \"emergency care\" OR \"emergency department\" OR \"emergency room\" AND \"patient triage\" OR \"triage\" OR \"triaging.\" The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.","url":"https://doi.org/10.7759/cureus.59906","authors":["Samantha Tyler","Matthew Olis","Nicole Aust","L. Patel","L. M. Simon","Catherine Triantafyllidis","Vijay Patel","Dong Won Lee","Brendan Ginsberg","Hiba Ahmad","Robin J. Jacobs"],"tags":["Triage","Medical emergency","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-08","doi":"https://doi.org/10.7759/cureus.59906","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4316253891","name":"Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis","source":"openalex","abstract":"Abstract Artificial intelligence (AI) and computational sciences have aroused a growing interest in education. Despite its relatively recent history, AI is increasingly being introduced into the classroom through different modalities, with the aim of improving student achievement. Thus, the purpose of the research is to analyse, quantitatively and qualitatively, the impact of AI components and computational sciences on student performance. For this purpose, a systematic review and meta-analysis have been carried out in WOS and Scopus databases. After applying the inclusion and exclusion criteria, the sample was set at 25 articles. The results support the positive impact that AI and computational sciences have on student performance, finding a rise in their attitude towards learning and their motivation, especially in the STEM (Science, Technology, Engineering, and Mathematics) areas. Despite the multiple benefits provided, the implementation of these technologies in instructional processes involves a great educational and ethical challenge for teachers in relation to their design and implementation, which requires further analysis from the educational research. These findings are consistent at all educational stages.","url":"https://doi.org/10.7821/naer.2023.1.1240","authors":["Inmaculada García‐Martínez","José María Fernández‐Batanero","José Fernández Cerero","Samuel P. León"],"tags":["Meta-analysis","Psychology","Mathematics education","Management science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.7821/naer.2023.1.1240","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4367367475","name":"Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining","source":"openalex","abstract":"Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks, which is very practical in the medical domain. It can significantly reduce the requirement of large amounts of task-specific data by sufficiently sharing medical knowledge among different tasks. However, due to the challenges of designing strongly generalizable models with limited and complex medical data, most existing approaches tend to develop task-specific models. To take a step towards MAGI, we propose a new paradigm called Medical-knOwledge-enhanced mulTimOdal pretRaining (MOTOR). In MOTOR, we combine two kinds of basic medical knowledge, i.e., general and specific knowledge, in a complementary manner to boost the general pretraining process. As a result, the foundation model with comprehensive basic knowledge can learn compact representations from pretraining radiographic data for better cross-modal alignment. MOTOR unifies the understanding and generation, which are two kinds of core intelligence of an AI system, into a single medical foundation model, to flexibly handle more diverse medical tasks. To enable a comprehensive evaluation and facilitate further research, we construct a medical multimodal benchmark including a wide range of downstream tasks, such as chest x-ray report generation and medical visual question answering. Extensive experiments on our benchmark show that MOTOR obtains promising results through simple task-oriented adaptation. The visualization shows that the injected knowledge successfully highlights key information in the medical data, demonstrating the excellent interpretability of MOTOR. Our MOTOR successfully mimics the human practice of fulfilling a \"medical student\" to accelerate the process of becoming a \"specialist\". We believe that our work makes a significant stride in realizing MAGI.","url":"https://doi.org/10.48550/arxiv.2304.14204","authors":["Bingqian Lin","Zicong Chen","Mingjie Li","Haokun Lin","Hang Xu","Yi Zhu","Jianzhuang Liu","Wenjia Cai","Lei Yang","Shen Zhao","Chenfei Wu","Ling Chen","Xiaojun Chang","Yi Yang","Lei Xing","Xiaodan Liang"],"tags":["Computer science","Interpretability","Artificial intelligence","Task (project management)","Benchmark (surveying)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-26","doi":"https://doi.org/10.48550/arxiv.2304.14204","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4404286659","name":"Artificial intelligence in healthcare (Review)","source":"openalex","abstract":"The potential of artificial intelligence (AI) to significantly transform numerous aspects of contemporary civilization is substantial. Advancements in research show an increasing interest in creating AI solutions in the healthcare sector. This interest is driven by the broad spectrum and extensive nature of easily accessible patient data-including medical imaging, digitized data collection, and electronic health records - and by the ability to analyze and interpret complex data, facilitating more accurate and timely diagnoses. This review's goal is to provide a comprehensive overview of the advancements achieved by AI in healthcare, to elucidate the present state of AI in enhancing the healthcare system and improving the quality and efficiency of healthcare decision making, and to discuss selected medical applications of AI. Furthermore, the barriers and constraints that may impede the use of AI in healthcare are outlined, and the potential future directions of AI-augmented healthcare systems are discussed.","url":"https://doi.org/10.3892/br.2024.1889","authors":["Abdul-Mohsen Alhejaily"],"tags":["Health care","Molecular medicine","Cancer","Medicine","Cell cycle"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-12","doi":"https://doi.org/10.3892/br.2024.1889","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4206693420","name":"Medical image segmentation using deep learning: A survey","source":"openalex","abstract":"Abstract Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.","url":"https://doi.org/10.1049/ipr2.12419","authors":["Risheng Wang","Tao Lei","Ruixia Cui","Bingtao Zhang","Hongying Meng","Asoke K. Nandi"],"tags":["Computer science","Artificial intelligence","Image segmentation","Segmentation","Computer vision"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-17","doi":"https://doi.org/10.1049/ipr2.12419","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4390719454","name":"Warning: Artificial intelligence chatbots can generate inaccurate medical and scientific information and references","source":"openalex","abstract":"The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.","url":"https://doi.org/10.37349/edht.2024.00006","authors":["Catherine L. Clelland","Stuart Moss","James D. Clelland"],"tags":["Chatbot","Cheating","Computer science","Generative grammar","Fake news"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-10","doi":"https://doi.org/10.37349/edht.2024.00006","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4284887878","name":"Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis","source":"openalex","abstract":"This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was \"artificial intelligence\" AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011-2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.","url":"https://doi.org/10.3390/healthcare10071269","authors":["Andrej Thurzo","Wanda Urbanová","Bohušlav Novák","Ladislav Czakó","Tomáš Siebert","Peter Stano","Simona Mareková","Georgia Fountoulaki","Helena Kosnáčová","Ivan Varga"],"tags":["Periodontology","Endodontics","Scopus","Dentistry","Prosthodontics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-08","doi":"https://doi.org/10.3390/healthcare10071269","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4206644627","name":"Artificial Intelligence: Its future in the health sector and its role for medical education","source":"openalex","abstract":"The - possibly - imminent AI revolution will, to a great extent, affect the education and training for all knowledge-working professions, and therefore must be considered an important aspect also of CME. This paper reviews some of the misconceptions about AI technology, then turns to point out possible applications of AI in the medical domain and then addresses the question what this will mean for (continuing) medical education.","url":"https://doi.org/10.1080/21614083.2021.2014099","authors":["Peter A. Henning","Jacqueline Henning","Katharina Glück"],"tags":["Affect (linguistics)","Domain (mathematical analysis)","Point (geometry)","Continuing medical education","Continuing education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1080/21614083.2021.2014099","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4389286730","name":"ARTIFICIAL INTELLIGENCE IN DEVELOPING COUNTRIES: BRIDGING THE GAP BETWEEN POTENTIAL AND IMPLEMENTATION","source":"openalex","abstract":"This paper examines the role of Artificial Intelligence (AI) in developing countries, focusing on bridging the gap between its vast potential and effective implementation. As AI technologies advance globally, their impact on socio-economic development becomes increasingly critical, particularly in regions with diverse challenges and opportunities. The study investigates the current landscape of AI adoption in developing countries, analyzing the potential benefits, challenges, and ethical considerations. Through a comprehensive review of literature and case studies, the paper explores strategies and solutions for harnessing AI's transformative power in diverse sectors such as healthcare, agriculture, and education. The findings emphasize the importance of capacity building, public-private partnerships, and tailored policy frameworks to address infrastructure limitations and skill gaps. The research contributes to a nuanced understanding of the opportunities and complexities surrounding AI implementation in developing countries, providing insights for policymakers, practitioners, and scholars seeking to navigate this evolving technological landscape Keywords: Artificial Intelligence; Global Connectivity; Emerging Technologies; Organizational Resilience; Sustainable Growth; Developing Country.","url":"https://doi.org/10.51594/csitrj.v4i3.629","authors":["Adebayo Olusegun Aderibigbe","Peter Efosa Ohenhen","Nwabueze Kelvin Nwaobia","Joachim Osheyor Gidiagba","Emmanuel Chigozie Ani"],"tags":["Transformative learning","Bridging (networking)","Developing country","Resilience (materials science)","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-03","doi":"https://doi.org/10.51594/csitrj.v4i3.629","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4396833063","name":"Explainable Notes: Examining How to Unlock Meaning in Medical Notes with Interactivity and Artificial Intelligence","source":"openalex","abstract":"Medical progress notes have recently become available to patients at an unprecedented scale. Progress notes offer patients insight into their care that they cannot find elsewhere. That said, reading a note requires patients to contend with the language, unspoken assumptions, and clutter common to clinical documentation. As the health system reinvents many of its interfaces to incorporate AI assistance, this paper examines what intelligent interfaces could do to help patients read their progress notes. In a qualitative study, we examine the needs of patients as they read a progress note. We then formulate a vision for the explainable note, an augmented progress note that provides support for directing attention, phrase-level understanding, and tracing lines of reasoning. This vision manifests in a set of patient-inspired opportunities for advancing intelligent interfaces for writing and reading progress notes.","url":"https://doi.org/10.1145/3613904.3642573","authors":["Hita Kambhamettu","Danaë Metaxa","Kevin B. Johnson","Andrew Head"],"tags":["Interactivity","Computer science","Meaning (existential)","Human–computer interaction","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-11","doi":"https://doi.org/10.1145/3613904.3642573","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4386221190","name":"Emotional Intelligence in the Era of Artificial Intelligence for Medical Professionals","source":"openalex","abstract":"In today's rapidly increasing healthcare industry, thecombination of emotional intelligence (EI) with artificialintelligence (AI) has significant consequences formedical practitioners. Emotional intelligence, includingthe capacity to perceive, understand, and manageemotions, is emerging as a critical advantage formedical practitioners, particularly as AI technologiesaugment clinical operations. This abstract analyzes thedifferent dynamics of EI in the context of AI integration,demonstrating its significance across multiple fields. Asmore medical professionals collaborate with AI-poweredsystems, a delicate interaction between humanemotional intelligence and AI's analytical brillianceemerges, necessitating an acceptable balance.Furthermore, the impact of EI on crucial aspects ofmedical education, practitioner-patient interactions,and overall job satisfaction is investigated. This abstractemphasizes the imperatives of strengthening EI talentsdespite technological development, advocating for aharmonious integration that supports both the cognitiveand emotional parts of healthcare practice byrecognizing the synergies and potential conflictsbetween EI and AI.","url":"https://doi.org/10.56570/jimgs.v2i2.112","authors":["Vagisha Sharma","Harendra Kumar"],"tags":["Emotional intelligence","Psychology","Health professionals","Context (archaeology)","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-28","doi":"https://doi.org/10.56570/jimgs.v2i2.112","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W2892773404","name":"Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care","source":"openalex","abstract":"Importance: There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical practice. Objective: To describe the performance of an AI system for diabetic retinopathy deployed in a primary care practice. Design, Setting, and Participants: Diagnostic study of patients with diabetes seen at a primary care practice with 4 physicians in Western Australia between December 1, 2016, and May 31, 2017. A total of 193 patients consented for the study and had retinal photographs taken of their eyes. Three hundred eighty-six images were evaluated by both the AI-based system and an ophthalmologist. Main Outcomes and Measures: Sensitivity and specificity of the AI system compared with the gold standard of ophthalmologist evaluation. Results: Of the 193 patients (93 [48%] female; mean [SD] age, 55 [17] years [range, 18-87 years]), the AI system judged 17 as having diabetic retinopathy of sufficient severity to require referral. The system correctly identified 2 patients with true disease and misclassified 15 as having disease (false-positives). The resulting specificity was 92% (95% CI, 87%-96%), and the positive predictive value was 12% (95% CI, 8%-18%). Many false-positives were driven by inadequate image quality (eg, dirty lens) and sheen reflections. Conclusions and Relevance: The results demonstrate both the potential and the challenges of using AI systems to identify diabetic retinopathy in clinical practice. Key challenges include the low incidence rate of disease and the related high false-positive rate as well as poor image quality. Further evaluations of AI systems in primary care are needed.","url":"https://doi.org/10.1001/jamanetworkopen.2018.2665","authors":["Yogesan Kanagasingam","Di Xiao","Janardhan Vignarajan","Amita Preetham","Mei‐Ling Tay‐Kearney","Ateev Mehrotra"],"tags":["Medicine","Diabetic retinopathy","False positive paradox","Referral","Grading (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-09-28","doi":"https://doi.org/10.1001/jamanetworkopen.2018.2665","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3095169666","name":"Artificial Intelligence-Assisted Surgery: Potential and Challenges","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. SUMMARY: This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. KEY MESSAGES: AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.","url":"https://doi.org/10.1159/000511351","authors":["Sebastian Bodenstedt","Martin Wagner","Beat P. Müller‐Stich","Jürgen Weitz","Stefanie Speidel"],"tags":["Workflow","Leverage (statistics)","Computer science","Applications of artificial intelligence","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1159/000511351","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3131569973","name":"Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence","source":"openalex","abstract":"Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.","url":"https://doi.org/10.3348/kjr.2021.0048","authors":["Seong Ho Park","Jaesoon Choi","Jeong‐Sik Byeon"],"tags":["Generalizability theory","Medicine","Artificial intelligence","External validity","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.3348/kjr.2021.0048","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4394832225","name":"Transparent medical image AI via an image–text foundation model grounded in medical literature","source":"openalex","abstract":"Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial. By learning to pair dermatological images and related concepts in a self-supervised manner, a visual-language foundation model is shown to have comparable performance to supervised models for concept annotation and is used to scrutinize model decisions for enhanced interpretability and accountability of medical imaging applications.","url":"https://doi.org/10.1038/s41591-024-02887-x","authors":["Chanwoo Kim","Soham Gadgil","Alex J. DeGrave","Jesutofunmi A. Omiye","Zhuo Ran Cai","Roxana Daneshjou","Su‐In Lee"],"tags":["Computer science","Pipeline (software)","Artificial intelligence","Deep learning","Audit"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-01","doi":"https://doi.org/10.1038/s41591-024-02887-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3021124964","name":"A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home","source":"openalex","abstract":"This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.","url":"https://doi.org/10.3390/app10093074","authors":["Samad M. E. Sepasgozar","Reyhaneh Karimi","Leila Farahzadi","Farimah Moezzi","Sara Shirowzhan","Sanee M. Ebrahimzadeh","Felix Kin Peng Hui","Lu Aye"],"tags":["Computer science","Home automation","Internet of Things","Data science","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-28","doi":"https://doi.org/10.3390/app10093074","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4388145633","name":"Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions","source":"openalex","abstract":"With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.","url":"https://doi.org/10.1109/jiot.2023.3329061","authors":["Ashish Rauniyar","Desta Haileselassie Hagos","Debesh Jha","Jan Erik Håkegård","Ulaş Bağcı","Danda B. Rawat","Vladimir Vlassov"],"tags":["Computer science","Data science","Big data","Scalability","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-01","doi":"https://doi.org/10.1109/jiot.2023.3329061","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4389253133","name":"Comparison of artificial intelligence-assisted informed consent obtained before coronary angiography with the conventional method: Medical competence and ethical assessment","source":"openalex","abstract":"Objective: At the time of informed consent (IC) for coronary angiography (CAG), patients' knowledge of the process is inadequate. Time constraints and a lack of personalization of consent are the primary causes of inadequate information. This procedure can be enhanced by obtaining IC using a chatbot powered by artificial intelligence (AI). Methods: In the study, patients who will undergo CAG for the first time were randomly divided into two groups, and IC was given to one group using the conventional method and the other group using an AI-supported chatbot, chatGPT3. They were then evaluated with two distinct questionnaires measuring their satisfaction and capacity to understand CAG risks. Results: = 0.581), the correct understanding of CAG risk questionnaire was found to be significantly higher in the AI group (<0.001). Conclusions: AI can be trained to support clinicians in giving IC before CAG. In this way, the workload of healthcare professionals can be reduced while providing a better IC.","url":"https://doi.org/10.1177/20552076231218141","authors":["Fatih Aydın","Özge Turgay Yıldırım","Ayşe Hüseyinoğlu Aydın","Bektas Murat","Cem Hakan Başaran"],"tags":["Informed consent","Competence (human resources)","Workload","Personalization","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1177/20552076231218141","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3103654318","name":"What is Interpretability?","source":"openalex","abstract":"Abstract We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable , and if so, what does it mean to explain the output of a network? And (2) what does it mean for a network to be interpretable ? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer (2) by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial , Global or Local , and Approximative or Isomorphic . Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems.","url":"https://doi.org/10.1007/s13347-020-00435-2","authors":["Adrian Erasmus","T. D. P. Brunet","Eyal Fisher"],"tags":["Interpretability","Interpretation (philosophy)","Artificial intelligence","Artificial neural network","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-12","doi":"https://doi.org/10.1007/s13347-020-00435-2","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385191145","name":"Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10439-023-03304-z","authors":["Phani Paladugu","Joshua Ong","Nicolas G. Nelson","Sharif Amit Kamran","Ethan Waisberg","Nasif Zaman","Rahul Kumar","Roger D. Dias","Andrew Go Lee","Alireza Tavakkoli"],"tags":["Generative grammar","Adversarial system","Computer science","Artificial intelligence","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-24","doi":"https://doi.org/10.1007/s10439-023-03304-z","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4412033531","name":"Impact of artificial intelligence on academic performance in medical education: A systematic review","source":"openalex","abstract":"Artificial intelligence (AI) plays a significant role in improving the quality of medical education. This study aimed to investigate the application of AI in medical education. A systematic review was conducted of all educational intervention studies in medical courses from January 1986 to 2023. Of the 16755 studies initially identified by our search, 7387 remained after removing duplicates. After that, 6205 studies were excluded for the title and abstract screening. Then, 15 full-text articles were included in our final review. The following keywords were used: artificial intelligence, machine intelligence, medical education, medical teaching, precision medical teaching, and precision medical education. A total of 16745 articles were identified from ISI, PubMed, Scopus, and Educational Resources and Information Center (ERIC) databases. Fourteen studies met the eligibility criteria. The quality of the included articles was appraised by the best evidence medical education (BEME) review of the education portfolio. PICO consists of P = medical student, I: use of AI, C = do not use AI, and I = enhance health professions students’ knowledge, attitudes, and skills. The included studies were synthesized and categorized according to the Kirkpatrick model. Educational intervention outcomes were categorized into three parts [1] : improvement in learners’ knowledge (N = 6 studies) [2] ; enhancement of students’ attitudes (N = 3 studies); and [3] acquisition of learners’ skills (N = 10 studies). The reviewed studies examined the impact of AI and virtual reality on enhancing health profession students’ knowledge, attitudes, and skills. However, more research is still needed on integrating AI into diverse curriculum models and sustaining AI’s role in real-world education systems.","url":"https://doi.org/10.4103/jehp.jehp_2071_23","authors":["Masomeh Kalantarion","Mehrsa Heidari","Nasrin Khajeali","Zahra Khorrami","Mitra Amini"],"tags":["Artificial intelligence","Systematic review","Computer science","Psychology","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.4103/jehp.jehp_2071_23","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4385248492","name":"Problems with Medical Claims that Artificial Intelligence (AI) and Blockchain Can Fix","source":"openalex","abstract":"In medical insurance, there are both valid and legitimate claims that should be covered by the insurer and fraudulent or inappropriate claims that should not be paid. Problems arise in scenarios when valid claims are not paid and when fraudulent or inappropriate claims are paid. Hospitals and insurers play critical roles in these cases. Artificial Intelligence (AI) and blockchain technologies can play significant roles in addressing the problems associated with medical insurance claims. Some of the ways in which these technologies can help are using AI algorithms to analyze data stored on the blockchain for fraud detection, and leveraging blockchain's transparency to improve the accuracy of AI models by providing access to a larger dataset. The author addresses approaches for each senario.","url":"https://doi.org/10.30953/bhty.v6.273","authors":["Joe Hawayek","Osama AbouElKhir"],"tags":["Blockchain","Transparency (behavior)","Computer science","Medical insurance","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-21","doi":"https://doi.org/10.30953/bhty.v6.273","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3176812453","name":"Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases","source":"openalex","abstract":"Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.","url":"https://doi.org/10.3389/fdgth.2021.669869","authors":["Aditi Babel","Richi Taneja","Franco Mondello Malvestiti","Alessandro Monaco","Shaantanu Donde"],"tags":["Health care","Empowerment","Patient Empowerment","Psychological intervention","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-29","doi":"https://doi.org/10.3389/fdgth.2021.669869","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W4387568087","name":"Postplagiarism: transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology","source":"openalex","abstract":"Abstract In this article I explore the concept of postplagiarism, loosely defined as an era in human society and culture in which advanced technologies such as artificial intelligence and neurotechnology, including brain-computer interfaces (BCIs), become a normal part of life, including how we teach, learn, communicate, and interact on a daily basis. Ethics and integrity are intensely important in the postplagiarism era when technology cannot be decoupled from everyday life. I argue that it might be reasonable to assume that when commercialized neuro-educational technology is readily available in a form that is implantable/ingestible/embeddable and invisible then academic integrity arms race will be over, as detection will be an exercise in futility. In a postplagiarism era, humans are compelled to grapple with questions about ethics and integrity for a socially just world at a time when advanced technology cannot be unbundled from education or everyday life. I conclude with a call to action for transdisciplinary research to better understand ethical implications of advanced technologies in education, emphasizing that such research can be considered pre-emptive, rather than speculative. The ethical implications of ubiquitous artificial intelligence and neurotechnology (e.g., BCIs) in education are important at a global scale as we prepare today’s students for academic and lifelong success.","url":"https://doi.org/10.1007/s40979-023-00144-1","authors":["Sarah Elaine Eaton"],"tags":["Engineering ethics","Everyday life","Psychology","Scale (ratio)","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-12","doi":"https://doi.org/10.1007/s40979-023-00144-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"oa:W3086848677","name":"Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine","source":"openalex","abstract":"","url":"https://doi.org/10.1053/j.semnuclmed.2020.08.001","authors":["Geoffrey Currie","K. Elizabeth Hawk"],"tags":["Artificial intelligence","Medicine","Beneficence","Workflow","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-11","doi":"https://doi.org/10.1053/j.semnuclmed.2020.08.001","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.4018/979-8-3373-8944-8.ch001","name":"The Economics of Artificial Intelligence in Medical Tourism","source":"crossref","abstract":"The rapid integration of artificial intelligence (AI) into healthcare is reshaping medical tourism by transforming efficiency, value creation, and the sustainability of global health systems. This chapter examines the economic implications of AI adoption in cross-border healthcare, focusing on its role in optimizing clinical decision-making, patient flow management, pricing, and resource allocation. AI-enabled tools enhance operational efficiency and patient experience while generating new sources of economic value for healthcare providers, intermediaries, and destination countries. At the same time, the expansion of AI-driven medical tourism raises critical concerns regarding health system sustainability, equity, data governance, and regulatory alignment. By adopting an economo-technical perspective, the study highlights both the opportunities and the structural trade-offs associated with AI deployment, particularly the risk of reinforcing dual-track healthcare systems and exacerbating inequalities between domestic and international patients.","url":"https://doi.org/10.4018/979-8-3373-8944-8.ch001","authors":["Constantinos Challoumis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T13:53:44Z","doi":"10.4018/979-8-3373-8944-8.ch001","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1007/978-3-030-06170-8_16","name":"Music and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-06170-8_16","authors":["Patrick Saint-Dizier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-07T23:05:27Z","doi":"10.1007/978-3-030-06170-8_16","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0933-3657(93)90010-z","name":"Artificial Intelligence in Medicine: State-of-the-art and future prospects","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(93)90010-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(93)90010-z","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1080/08839514.2020.1826146","name":"Enterprise AI Canvas Integrating Artificial Intelligence into Business","source":"crossref","abstract":"Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions into their organization face multiple challenges: They have to identify use-cases in which artificial intelligence can create value, as well as decisions that can be supported or executed automatically. Furthermore, the organization will need to be transformed to be able to integrate AI-based systems into their human work-force. In addition, the more technical aspects of the underlying machine learning model have to be discussed in terms of how they impact the various units of a business: Where do the relevant data come from, which constraints have to be considered, how is the quality of the data and the prediction evaluated? The Enterprise AI canvas is designed to bring data scientist and business expert together to discuss and define all relevant aspects which need to be clarified in order to integrate AI-based systems into a digital enterprise. It consists of two parts, where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.","url":"https://doi.org/10.1080/08839514.2020.1826146","authors":["Ulrich Kerzel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-10-05T02:47:50Z","doi":"10.1080/08839514.2020.1826146","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(93)90085-p","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90085-p","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90085-p","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(88)90072-0","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90072-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90072-0","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(89)90075-1","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90075-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90075-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(11)00024-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(11)00024-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-03-25T00:24:05Z","doi":"10.1016/s0004-3702(11)00024-5","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(07)00116-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(07)00116-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-08-27T14:57:05Z","doi":"10.1016/s0004-3702(07)00116-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(02)00392-2","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00392-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-28T12:34:16Z","doi":"10.1016/s0004-3702(02)00392-2","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(03)00209-1","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00209-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-11-18T06:27:07Z","doi":"10.1016/s0004-3702(03)00209-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(96)90007-7","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90007-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/s0004-3702(96)90007-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(70)90001-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(70)90001-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(70)90001-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(96)90018-1","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90018-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T09:20:58Z","doi":"10.1016/s0004-3702(96)90018-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.4324/9781003088660-7","name":"Living with artificial intelligence","source":"crossref","abstract":"This chapter considers how people can live alongside AI, including our attitudes towards AI and our possible relationships with it, introducing the fields of human-machine interaction and user experience. The chapter also considers some of the ethical and societal risks of AI, including error and bias in AI algorithms, the risk of “artificial stupidity”, and the consequences for our long-term future of an AI “singularity”—the point in time at which AIs become superintelligent. It is argued that, if developed with care, AIs could help to address some of the most pressing concerns in the world today. The risks of AI superintelligence are discussed, and the argument that human intelligence can also advance by “piggy-backing” on AI is made as the latest in a long line of technological extensions to the human mind. The chapter concludes that the partnership between psychology and AI has the potential to unlock further insights into both natural and artificial intelligence and could help answer some central questions about what it means to be human.","url":"https://doi.org/10.4324/9781003088660-7","authors":["Tony Prescott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-13T16:41:42Z","doi":"10.4324/9781003088660-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.21037/atm-2020-mair-21","name":"Medical artificial intelligent research: translating artificial intelligence into clinical practice","source":"crossref","abstract":"Medical artificial intelligent research: translating artificial intelligence into clinical practiceThe deep learning revolution and subsequent surge in the development of artificial intelligence (AI) didn't take long to reach the world of medicine.In this special series on \"Medical Artificial Intelligent Research\", we explore the latest developments through a collection of editorials, original articles, and reviews that set the stage for the challenges and opportunities in translating AI and related technologies into clinical practice.Medical AI is a rapidly developing field, and as technology advances it may offer the possibility to prevent, screen, diagnose, and treat patients in far-reaching corners of the developing world where skilled doctors and well-equipped hospitals are scarce.While the prospects are exciting, clinical application remains limited.This special series is an opportunity to take stock of the field and layout a roadmap for future development.The contributions in this series come from physicians and academic researchers at the frontier of health care and AI research around the world.The interdisciplinary, boundary spanning nature of AI is evident throughout the works in this series, covering different fields of medicine, including dermatology, neurology, radiology, and ophthalmology.In addition to AI, technologies with the potential to revolutionize medicine, such as blockchain and brain-computer interface, were also explored.It should be noted that the variety of topics in this series are not only representative of the broad potential application of AI in medical specialties, but also demonstrative of the opportunity of AI application in all facets of medicine.From educating young doctors in the classroom, to the application of AI in lab research for quality control, as well as screening, diagnosing, and treating patients in the clinic-the broad potential of AI in medicine is elucidated by the contributions in this focused series.With that in mind, several challenges remain for medical AI research to move forward.First, because the prospective application of AI in medicine is so broad, concerted effort must be made to move beyond research and drive research towards real world application.Ophthalmology shows great promise in this area, with applications such as CC-Cruiser and Visionome moving closer to real world deployment.Further, despite there being large amounts of medical data available for AI research, concerns such as privacy, data protection, ethics, and accountability must be addressed before the promise is fulfilled.Lastly, it is important to remember that despite all of the transformative potential offered by AI, the human relationship between doctors and patients remains to be the essence of medicine to be, something that must not get lost in AI revolution This special series would not be possible without the tireless effort of the experts who contributed their knowledge.We hope that it serves as a step stone to future discoveries.","url":"https://doi.org/10.21037/atm-2020-mair-21","authors":["Haotian Lin","Limin Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-15T07:30:56Z","doi":"10.21037/atm-2020-mair-21","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.3233/faia250609","name":"Explainable Artificial Intelligence for Categorizing Art Genres","source":"crossref","abstract":"While art styles have been extensively explored in artificial intelligence (AI), the study of art genres remains underdeveloped. In this paper, we propose a neurosymbolic AI model for genre classification that combines deep learning with symbolic reasoning. We also show the model’s implementation and evaluate its performance on a dataset. Finally, directions for future research are outlined. The approach outperforms existing methods in terms of classification accuracy.","url":"https://doi.org/10.3233/faia250609","authors":["Juan Manuel Sánchez","Vicent Costa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T14:40:14Z","doi":"10.3233/faia250609","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(02)00201-1","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00201-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-08T08:14:51Z","doi":"10.1016/s0004-3702(02)00201-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(88)90046-x","name":"Author index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90046-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90046-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(92)90082-9","name":"Announcement","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90082-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90082-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/b978-0-12-362340-9.50006-9","name":"THE SCOPE OF ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-362340-9.50006-9","authors":["EARL B. HUNT"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-06-30T20:59:01Z","doi":"10.1016/b978-0-12-362340-9.50006-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(80)90017-x","name":"AISB-80","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(80)90017-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(80)90017-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(03)00006-7","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00006-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-01-30T08:11:25Z","doi":"10.1016/s0004-3702(03)00006-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.2307/jj.13760051.12","name":"RESPECTFUL ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"","url":"https://doi.org/10.2307/jj.13760051.12","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-13T20:20:05Z","doi":"10.2307/jj.13760051.12","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(98)90026-1","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90026-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/s0004-3702(98)90026-1","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(10)00005-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(10)00005-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-01-22T05:17:35Z","doi":"10.1016/s0004-3702(10)00005-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1201/9781003624165-13","name":"Artificial Intelligence in Marine Tribology","source":"crossref","abstract":"Marine tribology plays a crucial role in ensuring that ship equipment is more durable, efficient, and dependable when it is required to operate in harsh maritime environments. Sometimes, the traditional method of monitoring friction, wear, and lubrication can be inefficient enough to predict breakdowns, and this would be expensive in terms of time and money to fix and to put the machines out of commission. Artificial intelligence (AI) can be used to address these issues in an entirely different manner. In marine systems, AI and machine learning algorithms can be used to monitor the conditions in real time, predictively maintain, and control intelligent lubrication in bearings, propeller shafts, and engines. Using large datasets of sensor measurements of vibration, temperature, and oil quality, AI has the capacity to predict wear patterns and optimize maintenance schedules. This will help enhance fuel economy and extend the life of parts. AI-based data analytics have also been used to develop superior lubricants and surface finishes that are maritime-friendly. This chapter provides an introduction to AI in marine tribology, discusses the issues with their implementation such as the availability of data and integration of systems, and identifies future research directions to produce marine systems that are sustainable and capable of learning.","url":"https://doi.org/10.1201/9781003624165-13","authors":["Vikas Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T15:08:57Z","doi":"10.1201/9781003624165-13","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/j.engappai.2024.109369","name":"“Will artificial intelligence platforms replace designers in the future?” analyzing the impact of artificial intelligence platforms on the engineering design industry through color perception","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109369","authors":["Yu Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T21:07:26Z","doi":"10.1016/j.engappai.2024.109369","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.18638/dialogo.2017.3.2.12","name":"Artificial Consciousness or Artificial Intelligence","source":"crossref","abstract":"Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus automatic. But conscience is above these differences because it is neither conditioned by the self-preservation of autonomy, because a conscience is something that you use to help your neighbor, nor automatic, because one’s conscience is tested by situations which are not similar or subject to routine. So, artificial intelligence is only in science-fiction literature similar to an autonomous conscience-endowed being. In real life, religion with its notions of redemption, sin, expiation, confession and communion will not have any meaning for a machine which cannot make a mistake on its own.","url":"https://doi.org/10.18638/dialogo.2017.3.2.12","authors":["Florin Spanache"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-06-20T05:56:55Z","doi":"10.18638/dialogo.2017.3.2.12","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(90)90023-s","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90023-s","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(90)90023-s","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(97)90023-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90023-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/s0004-3702(97)90023-0","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(11)00101-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(11)00101-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-09-14T16:20:10Z","doi":"10.1016/s0004-3702(11)00101-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(88)90075-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90075-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90075-6","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(91)90037-k","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90037-k","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90037-k","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(90)90080-j","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90080-j","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(90)90080-j","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(01)00153-9","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00153-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T12:58:43Z","doi":"10.1016/s0004-3702(01)00153-9","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(95)90021-7","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90021-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/0004-3702(95)90021-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(77)90001-7","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(77)90001-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-02-10T14:09:02Z","doi":"10.1016/0004-3702(77)90001-7","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.5040/9781978733954.ch-001","name":"Risk, Imagination, And Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.5040/9781978733954.ch-001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-23T12:23:12Z","doi":"10.5040/9781978733954.ch-001","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(07)00029-x","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(07)00029-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-02-17T12:03:27Z","doi":"10.1016/s0004-3702(07)00029-x","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1017/9781108164085.002","name":"Artificial Intelligence and Agents","source":"crossref","abstract":"Artificial intelligence, including machine learning, has emerged as a transformational science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents presents AI using a coherent framework to study the design of intelligent computational agents. By showing how the basic approaches fit into a multidimensional design space, readers learn the fundamentals without losing sight of the bigger picture. The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications. Although structured as an undergraduate and graduate textbook, the book's straightforward, self-contained style will also appeal to an audience of professionals, researchers, and independent learners. The second edition is well-supported by strong pedagogical features and online resources to enhance student comprehension.","url":"https://doi.org/10.1017/9781108164085.002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-13T08:41:48Z","doi":"10.1017/9781108164085.002","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/s0004-3702(97)90020-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90020-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/s0004-3702(97)90020-5","addedAt":"2026-09-01T01:47:49.186Z","updatedAt":"2026-09-01T01:47:49.186Z"},{"id":"doi:10.1016/0004-3702(92)90108-a","name":"Forthcoming 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Jain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-04-05T17:00:28Z","doi":"10.1007/978-3-540-47518-7_1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(02)00281-3","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00281-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-09-17T16:22:27Z","doi":"10.1016/s0004-3702(02)00281-3","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.32457/davelouis220254","name":"Redefining Teaching with Artificial Intelligence","source":"crossref","abstract":"Four years ago, I accepted the fascinating challenge of teaching a Big Data course. The thematic proposal was intimidating, as it presented an approach devoid of \"ready-to-use\" tools and, instead, proposed a highly technical approach (Kumar et al., 2022). This required meticulous and mechanical practice that consisted of mastering the Linux operating system, configuring virtual machines, and installing more than half a dozen tools through command line, as well as configuring an endless list of system files (.sh, .ini, etc.). These specialized practices demand considerable time and expertise from the instructor (Wang &amp; Chen, 2023). Back then, AI assistants like ChatGPT or Claude didn't exist. Class preparation without these tools became an arduous and often torturous process of achieving the ideal pedagogical strategy. Here, aspects such as class script, narrative resources, demonstrative arguments, effective design of examples, and the development of questionnaires, glossaries, case studies, group dynamics, among others, became relevant.","url":"https://doi.org/10.32457/davelouis220254","authors":["José Fernando Daveloui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-15T18:20:18Z","doi":"10.32457/davelouis220254","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(09)00048-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(09)00048-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-04-20T04:24:49Z","doi":"10.1016/s0004-3702(09)00048-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(99)90001-2","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(99)90001-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-22T19:25:23Z","doi":"10.1016/s0004-3702(99)90001-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(11)00091-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(11)00091-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-08-25T22:43:39Z","doi":"10.1016/s0004-3702(11)00091-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(96)90012-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90012-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-11-11T14:07:21Z","doi":"10.1016/s0004-3702(96)90012-0","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(89)90020-9","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90020-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(89)90020-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(91)90075-u","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90075-u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90075-u","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(04)00105-5","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00105-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-07-22T11:34:04Z","doi":"10.1016/s0004-3702(04)00105-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(79)90005-5","name":"Lambda-calculus conference","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(79)90005-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-02-10T14:09:02Z","doi":"10.1016/0004-3702(79)90005-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(98)90028-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90028-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-26T13:25:52Z","doi":"10.1016/s0004-3702(98)90028-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(09)00071-x","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(09)00071-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-06-25T10:13:38Z","doi":"10.1016/s0004-3702(09)00071-x","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.59646/600","name":"Artificial Intelligence for Beginners: Concepts, Tools, and Examples","source":"crossref","abstract":"","url":"https://doi.org/10.59646/600","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T16:48:58Z","doi":"10.59646/600","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1007/978-3-031-46341-9_5","name":"Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-46341-9_5","authors":["Jacqueline K. Harris","Russell Greiner"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-16T15:02:18Z","doi":"10.1007/978-3-031-46341-9_5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/j.artint.2005.10.012","name":"Hawkins on intelligence: Fascination and frustration","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2005.10.012","authors":["Donald Perlis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-10-24T14:12:26Z","doi":"10.1016/j.artint.2005.10.012","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1080/08839519308949971","name":"SPECIAL ISSUE ARTIFICIAL INTELLIGENCE: FUTURE, IMPACTS, CHALLENGES PART 3","source":"crossref","abstract":"","url":"https://doi.org/10.1080/08839519308949971","authors":["Robert Trappl"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-06-25T05:18:11Z","doi":"10.1080/08839519308949971","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(99)00063-6","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(99)00063-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T23:37:38Z","doi":"10.1016/s0004-3702(99)00063-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(95)90052-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90052-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T09:20:58Z","doi":"10.1016/0004-3702(95)90052-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(96)90039-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90039-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-10-24T03:15:12Z","doi":"10.1016/s0004-3702(96)90039-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1787/f1498c02-en","name":"Regulatory approaches to Artificial Intelligence in finance","source":"crossref","abstract":"The use of Artificial Intelligence (AI) in finance has increased rapidly in recent years, with the potential to deliver important benefits to market participants and to improve customer welfare. At the same time, AI in finance could also amplify existing risks in financial markets and create new ones. This report analyses different regulatory approaches to the use of AI in finance in 49 OECD and non-OECD jurisdictions based on the Survey on Regulatory Approaches to AI in Finance.","url":"https://doi.org/10.1787/f1498c02-en","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-04T05:52:22Z","doi":"10.1787/f1498c02-en","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.4018/979-8-3693-6180-1.ch015","name":"Artificial Intelligence and Machine Learning-Assisted Internet of Medical Things","source":"crossref","abstract":"IoMT devices, also referred to as healthcare IoT, enable human intervention-free healthcare monitoring by integrating automation, interfacial sensors, and machine learning-based artificial intelligence. IoMT technologies aid in reducing unnecessary hospital stays and thereby the associated health costs by facilitating wireless monitoring of health parameters. In this review, we discuss importance of AI in improving capabilities of IoMT in Drug design and development will continue to be an early user of new and growing experimental and computational tools. Among the challenges is deciding whether to use these technologies to improve the existing pipeline and processes or to reengineer the processes in light of these technologies. These research chapter elaborates drug discovery and formulation optimization through Big data, digital healthcare, remote monitoring, and genomics will increase the need to investigate how computational and reasoning approaches might be used to improve the process in terms of clinical significance as well as cost reduction.","url":"https://doi.org/10.4018/979-8-3693-6180-1.ch015","authors":["Zuber Peermohammed Shaikh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-05T10:23:31Z","doi":"10.4018/979-8-3693-6180-1.ch015","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.21608/aiis.2024.389966","name":"Applications of Artificial Intelligence in Scientific Research (Opportunities and Challenges)","source":"crossref","abstract":"The current research attempted to reveal the reality of using artificial intelligence, the mechanisms for developing scientific research skills, the challenges researchers face when using artificial intelligence, and the threats that educational researchers should be cautious of. The research addresses the following questions: To what extent are educational researchers familiar with AI applications? What opportunities does AI offer to improve the performance of academic researchers? What threats should educational researchers be wary of in scientific research? What are the ethical implications of using AI in scientific research? The research methodology is based on a descriptive survey method that reviews related previous studies and research, as well as an analytical descriptive method to identify weaknesses and challenges in using AI applications in scientific research and the ethical implications of AI use. The study concluded with several recommendations, most notably the introduction of smart applications that assist in scientific research.","url":"https://doi.org/10.21608/aiis.2024.389966","authors":["shimaa Emad Ramadan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-12T11:44:17Z","doi":"10.21608/aiis.2024.389966","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(10)00029-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(10)00029-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-03-28T08:07:43Z","doi":"10.1016/s0004-3702(10)00029-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(89)90082-9","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90082-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90082-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(86)90062-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90062-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(86)90062-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(94)90024-8","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(94)90024-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(94)90024-8","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(85)90014-1","name":"Forthcoming papersgence Laboratory","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90014-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(85)90014-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(88)90091-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90091-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90091-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(88)90026-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90026-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90026-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/j.artint.2005.10.015","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2005.10.015","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-10-27T11:20:54Z","doi":"10.1016/j.artint.2005.10.015","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.4135/9781071935774","name":"Artificial Intelligence and Writing","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071935774","authors":["Ceceilia Parnther"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-24T10:44:20Z","doi":"10.4135/9781071935774","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1201/9781003590767-5","name":"Artificial Intelligence in Calibrations","source":"crossref","abstract":"Conventional calibrations are highly effective in the calibration of single or a small number of devices. The application of artificial intelligence (AI) broadens the calibration practices to thousands of operational devices. In this chapter, the fundamental principles of AI techniques open used in calibrations are explained. The use of AI necessitates a novel way of collecting and processing of data required by the AI models. In this chapter, the AI-related computer and computing requirements are explained in detail.","url":"https://doi.org/10.1201/9781003590767-5","authors":["Halit Eren"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-22T15:48:11Z","doi":"10.1201/9781003590767-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1201/9781003515081-7","name":"Implications of Deep Learning in Cyber Threat Intelligence and Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003515081-7","authors":["Ibrohim Saut","Mukhammadiyeva Durdona","Mohamed Uvaze Ahamed Ayoobkhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-05T15:06:11Z","doi":"10.1201/9781003515081-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/b978-0-443-33082-7.00011-5","name":"Artificial intelligence-enhanced diagnostics: deep learning in medical imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33082-7.00011-5","authors":["Harmanpreet Kaur","Gurwinder Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-13T10:51:36Z","doi":"10.1016/b978-0-443-33082-7.00011-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1201/9781003646716","name":"Intelligent User Interface","source":"crossref","abstract":"This book aims to mainstream UI/UX design process by explaining latest AI/ML models in a comprehensible way and highlighting case studies on developing intelligent user interfaces for XR systems, human robot interaction, cockpit design and trajectory prediction. The book also discusses the latest standards and guidelines relevant to UI/UX design, layout and equipment list for setting up a lab on intelligent interaction design involving robots, drones and XR systems. Features: Covers a wide array of topics ranging from human factors, computer vision, AR/VR systems, large language models (LLMs) and usability evaluation techniques including statistical hypothesis techniques Discusses latest AI systems such as vision transformers, LLM-based human robot interface and virtual reality-based spacecraft simulation systems Provides a list of freely downloadable software on the covered topics Contains graphical illustrations and a list of quick facts for easy review and recall of basic concepts in each chapter Gives new project ideas on intelligent user interfaces that can be explored by students and early career rsearchers The intended audience of this book is engineering and design students and faculty members, user interface designers, and product managers who would like to be aware of latest AI/ML without diving into too many theoretical details, and use it for their project or product development.","url":"https://doi.org/10.1201/9781003646716","authors":["Pradipta Biswas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-09T22:49:54Z","doi":"10.1201/9781003646716","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(08)00071-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(08)00071-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-06-15T19:08:23Z","doi":"10.1016/s0004-3702(08)00071-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(03)00175-9","name":"Editorial 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and editorial opinion on a wide variety of topics of importance to biomedical science and clinical practice by our team of expert doctors.","url":"https://doi.org/10.62830/mmj1-04-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-05T19:44:30Z","doi":"10.62830/mmj1-04-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(01)00135-7","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00135-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-31T18:27:30Z","doi":"10.1016/s0004-3702(01)00135-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1201/9781003038467-1","name":"Distributed Artificial Intelligence","source":"crossref","abstract":"Distributed artificial intelligence is a major emerging subfields of artificial intelligence. There is a wide area of domain presently being addressed via the usage of DAI techniques. A distinct suborganization of these areas involves the simulation of groups of agents or multiagents, and is noted by a ramification of terms along with artificial societies, artificial adaptive agents, and artificial ecologies. The mechanism of solving the issue with a distributed approach tackles with several hurdles like grouping the problem and then supplying it to various problem-solvers. In order to solve a problem, these solvers have exchanged reckoning figures and data. The collaboration between each solver is principally characterized in dispersed critical thinking conditions. On the other hand, the multiagent frameworks are perturbed about the conduct of loosely coupled nodes, or operators, which operate cooperatively to tackle an issue past their individual calibre. These nodes, or computational operators, are self-sufficient that have the capacity to insightfully work under different conditions, given their receptive abilities and productive capacities. The key ideas of agency includes embeddedness, self-sufficiency, adaptability and association. Due to the association of DAI with the idea of agents, this is known as a multiagent system. The chapter includes all of the basic terms and scope of DAI and its applications.","url":"https://doi.org/10.1201/9781003038467-1","authors":["Annu Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-03T16:36:52Z","doi":"10.1201/9781003038467-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(95)90010-1","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90010-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/0004-3702(95)90010-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(92)90053-z","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90053-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90053-z","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(08)00015-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(08)00015-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-02-01T13:26:23Z","doi":"10.1016/s0004-3702(08)00015-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(88)90024-0","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90024-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90024-0","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(88)90044-6","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90044-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90044-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(94)90099-x","name":"Forthcoming papers0","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(94)90099-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(94)90099-x","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(01)00120-5","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00120-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T14:37:27Z","doi":"10.1016/s0004-3702(01)00120-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(92)90093-d","name":"Announcements","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90093-d","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90093-d","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.31223/x5m157","name":"Artificial Intelligence in Earth Science: A GeoAI Perspective","source":"crossref","abstract":"GeoAI, or geospatial artificial intelligence, has transformative potential for Earth science by integrating geospatial data with artificial intelligence to enhance environmental monitoring, predictive modeling, and decision-making. This commentary, based on the Greg Leptoukh Lecture at AGU 2024, explores the evolving role of GeoAI in addressing pressing challenges—from environmental change in the Arctic to disaster response in hurricane-prone tropical regions. It highlights advancements in GeoAI-driven analysis of multimodal Earth observation data, ranging from structured remote sensing imagery to semi-structured data and natural language texts. The integration of knowledge graphs and generative AI further strengthens GeoAI by enabling seamless integration of cross-domain data, semantic reasoning, and knowledge inference. 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Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90009-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(98)90009-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(85)90078-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90078-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90078-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(03)00092-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00092-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-05-27T22:47:42Z","doi":"10.1016/s0004-3702(03)00092-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(03)00124-3","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00124-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-08-07T21:58:34Z","doi":"10.1016/s0004-3702(03)00124-3","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(04)00159-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00159-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-15T06:47:57Z","doi":"10.1016/s0004-3702(04)00159-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(86)90007-x","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90007-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(86)90007-x","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(93)90009-z","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90009-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90009-z","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/b978-0-443-44728-0.00017-2","name":"Foundations of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44728-0.00017-2","authors":["Luca Saba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T12:39:58Z","doi":"10.1016/b978-0-443-44728-0.00017-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.21037/jmai-2026-1-0041","name":"Comparative validation of open-source artificial intelligence models for fracture detection using the MURA dataset","source":"crossref","abstract":"Background: Accurate, timely fracture detection remains challenging, especially in low-resource settings with limited radiology expertise. Open-source deep learning models offer accessible computer vision tools for fracture detection, but their real-world reliability and clinical readiness are unclear. This study evaluates two open-source models—YOLOv7-BoneFractureDetection and MohtashamMurshid-BoneFractureClassification—on the MURA (Musculoskeletal Radiographs) dataset to compare performance characteristics and potential clinical roles.Methods: We randomly sampled 100 musculoskeletal radiographs (50 fracture, 50 non-fracture) from the MURA dataset. Each model generated binary predictions (fracture vs. no fracture) and confidence scores. Using custom Python scripts, we computed sensitivity, specificity, precision, F1 score, accuracy, average confidence, and area under the receiver operating characteristic curve (AUC). McNemar’s test was used to compare paired classification performance between models.Results: YOLOv7 achieved 82% accuracy, with precision of 90% and specificity of 0.92, indicating few false positives and strong reliability for ruling in fractures. Its F1 score was 0.80 and AUC 0.89, with a mean confidence of 78.4% for correctly identified fracture cases. The BoneFractureClassification model showed higher sensitivity (0.88), detecting more fractures, but with lower specificity (0.64) and precision (71%), reflecting more false positives; its F1 score and AUC were 0.786 and 0.85, respectively. McNemar’s test showed no statistically significant difference in overall classification performance (P=0.55).Conclusions: YOLOv7 appears better suited for confirmatory or diagnostic support where minimizing false positives is critical, while BoneFractureClassification may be preferable for triage or settings where missed fractures are less acceptable than false alarms. Both open-source models show potential for augmenting fracture detection in resource-limited environments, but larger, multi-center validations are needed before clinical deployment.","url":"https://doi.org/10.21037/jmai-2026-1-0041","authors":["Abdullah Jalal","Fatimah Jalal","Elizabeth Theirl","Umar Hashim","Faisal Jaura","Kody Park","Verna Tarankanti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T06:12:24Z","doi":"10.21037/jmai-2026-1-0041","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1007/s44163-026-01919-4","name":"The COACH theory formalizes clinical reasoning in the development and validation of medical artificial intelligence systems","source":"crossref","abstract":"The development and deployment of medical artificial intelligence (AI) systems are limited by two persistent gaps: clinician knowledge is often introduced late in development as labels or annotations, and deployed systems often provide outputs that are difficult to reconcile with clinical reasoning. We propose COACH, a clinician-led, logic-guided framework for designing, training, validating, and monitoring medical AI systems. In COACH, clinicians define the intended use, decompose the clinical decision into named clinical features and reasoning steps, and specify acceptable evidence, uncertainty, and error modes; engineers translate these requirements into data specifications, feature extractors, model architectures, interfaces, and validation protocols; AI models learn to detect, quantify, and integrate these features. We define logical anthropomorphic design as aligning the model’s decision pathway with an explicit clinician-defined reasoning chain, and behavioral anthropomorphic design as aligning data acquisition, uncertainty communication, evidence presentation, and escalation behavior with clinical workflow. COACH is not proposed as a replacement for deep learning or foundation models, but as a way to constrain and audit them through clinical logic. This Perspective delineates an implementation pipeline, feature-dictionary method, data flow, evaluation strategy, and an endoscopy case exemplar to position COACH relative to post-hoc explainable AI, rule-based expert systems, and human-in-the-loop approaches. We argue that COACH may improve auditability, clinician trust, and data efficiency in selected high-stakes tasks, while prospective validation is required before claims of clinical benefit can be made.","url":"https://doi.org/10.1007/s44163-026-01919-4","authors":["Wenxin Xue","Rong Lin","Hongliu Du","Prateek Sharma","Cesare Hassan","Yuichi Mori","Joseph J. Y. Sung","Honggang Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T13:51:27Z","doi":"10.1007/s44163-026-01919-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/j.artmed.2022.102430","name":"Medical resource allocation planning by integrating machine learning and optimization models","source":"crossref","abstract":"Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.","url":"https://doi.org/10.1016/j.artmed.2022.102430","authors":["Tasquia Mizan","Sharareh Taghipour"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-27T19:26:57Z","doi":"10.1016/j.artmed.2022.102430","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1007/978-3-031-70775-9_9","name":"Artificial Intelligence Applications in Healthcare Security","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-70775-9_9","authors":["Salvatore D’Antonio","Federica Uccello"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-22T01:53:25Z","doi":"10.1007/978-3-031-70775-9_9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1148/ryai.2021210056","name":"Are Artificial Intelligence Challenges Becoming Radiology’s                     New “Bee’s Knees”?","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.2021210056","authors":["Hesham Elhalawani","Raymond Mak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-21T13:56:35Z","doi":"10.1148/ryai.2021210056","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(90)90032-u","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90032-u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90032-u","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(06)00094-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(06)00094-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-10-27T16:12:37Z","doi":"10.1016/s0004-3702(06)00094-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(11)00078-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(11)00078-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-07-24T18:35:49Z","doi":"10.1016/s0004-3702(11)00078-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(10)00017-2","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(10)00017-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-02-19T09:17:40Z","doi":"10.1016/s0004-3702(10)00017-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(89)90015-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90015-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(89)90015-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(85)90031-1","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90031-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90031-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(98)90000-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90000-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(98)90000-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(97)90026-6","name":"Special issues","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90026-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-22T15:25:23Z","doi":"10.1016/s0004-3702(97)90026-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.4324/9781003088660-6","name":"Towards artificial general intelligence","source":"crossref","abstract":"This chapter looks at the path from single-purpose AIs to artificial general intelligence (AGI) defined as an AI that is as flexible as human intelligence in its ability to work across different domains and to adapt to new challenges. AI’s capacity to understand is discussed in relation to Frege’s theory of meaning as comprising sense—how words relate to other words—and reference—how words relate to the external world. Large language models are claimed to already have meaning in relation to sense but not in relation to reference. To obtain a better grasp of meaning, AIs will require a greater capacity for unmediated two-way interaction with the world through robotic bodies. This can be evaluated through a “total Turing test” that requires AIs to match humans in terms of both their linguistic and robotic abilities. The broader class of generative AIs is discussed in relation to theories of predictive processing in the human brain, and an argument is made for a nuanced view of multiple intelligences, including that humans combine fast, pattern-recognition intelligence with a slower sequential reasoning capacity. The chapter concludes by arguing that both robots and humans can be considered to belong to the wider class of “cyber-physical systems”.","url":"https://doi.org/10.4324/9781003088660-6","authors":["Tony Prescott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-13T16:41:42Z","doi":"10.4324/9781003088660-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(07)00201-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(07)00201-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-12-19T16:27:00Z","doi":"10.1016/s0004-3702(07)00201-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(96)90015-6","name":"Announcement","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90015-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(96)90015-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(89)90031-3","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90031-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(89)90031-3","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(93)90103-i","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90103-i","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90103-i","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(94)90074-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(94)90074-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(94)90074-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(95)90049-7","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90049-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/0004-3702(95)90049-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(85)90085-2","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90085-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90085-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(87)90032-4","name":"Forthcoming papersosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90032-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(87)90032-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(91)90064-q","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90064-q","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(91)90064-q","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(88)90084-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90084-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90084-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(10)00123-2","name":"Call for papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(10)00123-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-07-24T06:47:33Z","doi":"10.1016/s0004-3702(10)00123-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(84)90013-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(84)90013-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(84)90013-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(93)90040-i","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90040-i","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(93)90040-i","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5772/intechopen.90003","name":"Stochastic Artificial Intelligence: Review Article","source":"crossref","abstract":"Artificial intelligence (AI) is a region of computer techniques that deals with the design of intelligent machines that respond like humans. It has the skill to operate as a machine and simulate various human intelligent algorithms according to the user’s choice. It has the ability to solve problems, act like humans, and perceive information. In the current scenario, intelligent techniques minimize human effort especially in industrial fields. Human beings create machines through these intelligent techniques and perform various processes in different fields. Artificial intelligence deals with real-time insights where decisions are made by connecting the data to various resources. To solve real-time problems, powerful machine learning-based techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization have been used in recent years. This chapter explains artificial neural network-based adaptive linear neuron networks, back-propagation networks, and radial basis networks.","url":"https://doi.org/10.5772/intechopen.90003","authors":["T.D. Raheni","P. Thirumoorthi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-02T14:18:07Z","doi":"10.5772/intechopen.90003","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(92)90068-9","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90068-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90068-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(98)90006-6","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90006-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(98)90006-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/j.artint.2004.08.001","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2004.08.001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-08-27T14:24:01Z","doi":"10.1016/j.artint.2004.08.001","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(97)90021-7","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90021-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-16T21:11:43Z","doi":"10.1016/s0004-3702(97)90021-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(11)00006-3","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(11)00006-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-01-22T04:24:13Z","doi":"10.1016/s0004-3702(11)00006-3","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(97)90013-8","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90013-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-26T13:25:52Z","doi":"10.1016/s0004-3702(97)90013-8","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(02)00341-7","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00341-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-16T09:46:38Z","doi":"10.1016/s0004-3702(02)00341-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(89)90040-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90040-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(89)90040-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(80)90008-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(80)90008-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(80)90008-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(88)90009-4","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90009-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90009-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(87)90059-2","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90059-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(87)90059-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1023/a:1016820625738","name":"Annals of Mathematics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1016820625738","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-29T21:45:55Z","doi":"10.1023/a:1016820625738","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(96)90034-x","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90034-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-26T13:25:52Z","doi":"10.1016/s0004-3702(96)90034-x","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(90)90015-r","name":"Forthcoming paper","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90015-r","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90015-r","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/0004-3702(92)90052-y","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90052-y","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(92)90052-y","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1016/s0004-3702(96)90029-6","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90029-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-13T05:06:52Z","doi":"10.1016/s0004-3702(96)90029-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.1186/s12910-026-01536-x","name":"Medical artificial intelligence and the right to health.","source":"europepmc","abstract":"The international human right to health states that people have claims to access goods, services, and infrastructure that protects an adequate standard of health. The current and forthcoming advances in artificial intelligence (AI), which significantly improve the quality and efficacy of medical care, could raise the current standard of healthcare owed under the right to health. In this paper, we identify three duties owed under the right to health and describe ways in which AI can uniquely or more efficiently fulfill these duties, namely the duties to provide: access to good quality care that effectively improves patient health; equitable access to healthcare for all persons; and novel healthcare services that can only be achievable by medical AI. This paper will also consider some potential objections to our claim such as whether medical AI may undermine, rather than fulfil, the social determinants of health. We conclude that if medical AI can partially fulfil the right to health through increasing access and quality of healthcare, and if providing medical AI can be balanced against other obligations and rights, states will have corresponding duties to not unnecessarily restrict AI innovation through regulation and may even be required to invest in medical AI infrastructure to meet the right to health of their populations.","url":"https://doi.org/10.1186/s12910-026-01536-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12910-026-01536-x","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41586-026-10675-5","name":"Towards autonomous medical artificial intelligence agents.","source":"europepmc","abstract":"Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows 1,2 . However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints. Yet it remains unproven whether such a system can manage patient cases with physician-level performance. Here we show that MIRA (Medical Intelligence for Reasoning and Action), an autonomous artificial intelligence agent operating in a sandboxed EHR environment, can navigate a large clinical action space to obtain patient histories; order and interpret laboratory, imaging and microbiology tests; generate differential diagnoses; and formulate treatment plans such as prescribing medications, scheduling surgical procedures and planning admissions. In simulations on real patient cases spanning multiple diagnoses, MIRA outperformed physicians in diagnostic accuracy and made guideline-concordant, medication-safe and appropriate admission decisions. Compared with previous LLM applications that addressed isolated subtasks or provided free-text advice, these results suggest that an EHR-integrated artificial intelligence agent can turn clinical intent into structured, actionable EHR operations, possibly making it a more effective decision-support partner for physicians. Further work is needed to establish generalization, safety and governance through prospective, real-world studies.","url":"https://doi.org/10.1038/s41586-026-10675-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41586-026-10675-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2026.101030","name":"Ensuring the clinical impact of medical artificial intelligence.","source":"europepmc","abstract":"Randomised controlled trials (RCTs) testing artificial intelligence (AI) approaches in health care (AI-RCTs) mostly use measures of processes rather than measures of health outcomes. Measures of processes refer to indicators that assess the performance of a tool or an intervention, rather than their actual results. These measures include diagnostic accuracy, detection rate, specificity, time-to-test results, and workflow-related efficiency. In contrast, measures of health outcomes reflect the actual effects on patient’s health.","url":"https://doi.org/10.1016/j.landig.2026.101030","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.landig.2026.101030","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2026.101057","name":"Building safer clinical agents: the case for residency-level benchmarks in medical artificial intelligence.","source":"europepmc","abstract":"Advances in large language models (LLMs) have accelerated medical benchmarking, yet most evaluation of LLMs still relies on exam-style question answering or curated vignettes that test knowledge rather than performance in clinical workflows.1 Such benchmarks only show whether a model has medical knowledge, not whether it can function safely when performing electronic health record (EHR) tasks asked of resident physicians—retrieving patient information from complex records, reconciling conflicting data, tracking changes in patient outcomes over time, and communicating decision-making factors.","url":"https://doi.org/10.1016/j.landig.2026.101057","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.landig.2026.101057","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1097/cin.0000000000001611","name":"Informatics Techniques in Nursing Course and Readiness for Medical Artificial Intelligence: The Mediating Role of Informatics Competence.","source":"europepmc","abstract":"This descriptive and correlational study examined the relationship between completion of an Informatics Techniques in Nursing course and nursing students' readiness to use medical artificial intelligence, as well as the mediating role of informatics competence in this relationship. The study was conducted with 321 nursing students at a university in eastern Turkey. Data were collected online using a Personal Information Form, the Nursing Informatics Competency Self-Assessment Scale, and the Medical Artificial Intelligence Readiness Scale. Completion of the course was not directly associated with readiness to use medical artificial intelligence (standardized coefficient=0.412, P>.05). However, informatics competence was found to fully mediate this relationship (indirect effect: standardized coefficient=3.988, 95% CI [2.900-5.133]). Course completion was associated with higher informatics competence (standardized coefficient=0.400, P<.001), and higher informatics competence was associated with greater self-reported readiness to use medical artificial intelligence (standardized coefficient=0.994, P<.001). Students' informatics competence and readiness levels were moderate. Because the measures were self-reported, the findings reflect perceived competence and readiness rather than objective performance.","url":"https://doi.org/10.1097/cin.0000000000001611","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/cin.0000000000001611","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/nhs.70402","name":"Validity and Reliability of the Medical Artificial Intelligence Readiness Scale, Korean Version: A Methodological and Cross-Sectional Study.","source":"europepmc","abstract":"This study aimed to translate and culturally adapt the Medical Artificial Intelligence Readiness Scale for Medical Students into Korean and to examine its validity and reliability among nursing students. The scale designed in this methodological and cross-sectional study was translated and adapted using standard procedures and validated with 317 nursing students in South Korea. Experts reviewed the content validity. Structural validity was examined through exploratory and confirmatory factor analyses. Construct validity was evaluated through hypothesis testing using an external criterion measure. Reliability was evaluated using Cronbach's alpha and the split-half method. The final Korean version of the Medical Artificial Intelligence Readiness Scale (K-MAIRS) comprised 15 items across four factors-cognition, ability, vision, and ethics-explaining 66.88% of the variance. The K-MAIRS can be a valid and reliable instrument for assessing artificial intelligence readiness among Korean nursing students. This instrument supports global initiatives to incorporate artificial intelligence competencies into nursing education using culturally tailored assessment tools. It also supports educators and institutions in designing targeted strategies for artificial intelligence education within nursing curricula that enhance nurses' preparedness in artificial intelligence-driven healthcare environments.","url":"https://doi.org/10.1111/nhs.70402","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/nhs.70402","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1177/00469580261418133","name":"Medical Students' Readiness for Medical Artificial Intelligence (AI).","source":"europepmc","abstract":"Artificial intelligence offers students more personalised and adaptive learning, which encourages educators to better understand students' learning processes. This study aims to determine the readiness levels of medical faculty students in medical artificial intelligence and to examine whether these levels vary based on gender and class year. The study was conducted with 322 medical students. Research data were collected using the \"Medical Artificial Intelligence Readiness Scale for Medical Students.\" Results showed that medical students rated themselves as moderate in the \"cognition\" and \"vision\" dimensions, slightly higher in the \"ability\" and \"ethics\" dimensions, and overall at a \"neutral\" level in medical artificial intelligence readiness. Compared to females, males showed significant differences at a \"small effect\" level in cognition, ability factors and overall scores. Regarding class levels, significant differences were found between 2nd graders and both 5th and 6th graders, favouring the 2nd graders at an \"intermediate effect\" level. In the cognition dimension, there was also a significant difference between the 2nd and 4th grades in favour of the 2nd grade and at the level of \"small effect.\" In order to increase medical artificial intelligence readiness of students, it is important to comprehensively include the subject in the medical school curriculum and to develop it according to needs. In future research, long-term follow-up studies aimed at improving medical students' education in the field of medical artificial intelligence (AI) are considered to be very beneficial. Furthermore, future studies should also consider potential changes in medical AI readiness that may occur over time.","url":"https://doi.org/10.1177/00469580261418133","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/00469580261418133","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/jep.70462","name":"Beyond Anxiety: Rethinking the Psychological Drivers of Nurses' Readiness for Medical Artificial Intelligence.","source":"europepmc","abstract":"Data sharing is not applicable to this article as no data sets were generated or analysed during the current study.","url":"https://doi.org/10.1111/jep.70462","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jep.70462","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1001/jamanetworkopen.2026.0815","name":"Factors for Patient Trust and Acceptance of Medical Artificial Intelligence.","source":"europepmc","abstract":"Importance Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. Objective To determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality. Design, setting, and participants This preregistered conjoint survey study was conducted online among a diverse national sample of English-speaking US adults with access to the internet between December 11, 2024, and January 1, 2025. Respondents were presented with hypothetical AI-assisted diagnosis scenarios and paired visits featuring 6 purely randomized attributes: the presence of a clinician, AI performance (relative to general practitioners and specialists), governance (US Food and Drug Administration approval, Mayo Clinic certification, local hospital certification), and AI data quality. Respondents chose their preferred visit, provided up to a single-sentence open-ended response explaining their choice, and then rated their trust in the diagnosis they would receive in each of the 2 visit choices presented to them. Respondents repeated the exercise 6 times, evaluating 12 hypothetical visits in total, yielding 36 000 observations (12 per respondent). Main outcomes and measures The primary outcomes were patient choice of a hypothetical medical encounter and patient trust in that encounter, measured on a 1 (would not trust at all) to 5 (would trust a great deal) response scale. Average marginal component effects (AMCEs) were estimated using linear regression. Qualitative responses were coded to elucidate reasoning. Results A total of 3000 participants completed the survey (1644 [54.8%] women; mean [SD] age, 48 [16] years), including 382 Black respondents (12.7%), 504 Hispanic respondents (16.8%), and 1855 White respondents (61.9%), with most respondents having some college or more (1989 respondents [66.3%]), and 1270 respondents (42.4%) having income between $50 000 and $99 000. The factor associated with the largest change in likelihood of patient choice was AI performance; performance at or above the specialist level was associated with increasing the probability of selecting a visit by 24.8% (95% CI, 23.4%-26.2%; P Conclusions and relevance In this survey study of patient trust in and choice of medical AI, AI performance, clinician presence, disclosure of representative data, and systemic governance were associated with increased respondent trust in and preference for clinical encounters. These findings suggest that ensuring resource-appropriate combinations of these tools is an important step in helping AI achieve its transformative potential for the health system.","url":"https://doi.org/10.1001/jamanetworkopen.2026.0815","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1001/jamanetworkopen.2026.0815","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1080/10872981.2026.2696610","name":"Exploring medical Artificial Intelligence Readiness among Korean medical students: a cross-sectional study.","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) is rapidly transforming healthcare systems and clinical practice, increasing the need to prepare future physicians to work effectively with AI technologies in clinical contexts. Despite growing interest in integrating AI into medical education, empirical evidence regarding medical students' readiness to use AI remains limited. This study aimed to assess medical AI readiness among Korean medical students and explore factors associated with their readiness. Methods A cross-sectional survey was conducted among medical students enrolled in a six-year medical program at a university in Korea. A total of 204 students participated in the study. Medical AI readiness was measured using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), which assesses four domains: cognition, ability, vision, and ethics. Descriptive statistics, independent t-tests, and Pearson's correlation analyses were performed using SPSS version 27. Results The overall mean score of medical AI readiness was 4.19 on a 7-point Likert scale, indicating a moderate level of medical AI readiness. Among the subscales, ethics showed the highest mean score (4.69), followed by vision (4.44), ability (4.18), and cognition (3.92). The frequency of AI use was significantly associated with medical AI readiness, whereas the daily duration of AI use was not. No significant gender differences were observed. When students were divided into the low (pre-medical years 1-2) and high (medical years 1-4) groups, the high group showed significantly higher scores only in the ability subscale. Conclusions The findings suggest that medical students are not yet sufficiently prepared to utilize AI technologies in clinical practice, particularly in terms of knowledge and practical competencies related to AI. These results highlight the need for structured and longitudinal AI education in medical curricula to better prepare future physicians for AI-integrated healthcare environments.","url":"https://doi.org/10.1080/10872981.2026.2696610","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/10872981.2026.2696610","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41575-026-01210-y","name":"Human deskilling in medical artificial intelligence: prohibited or permissible under the EU Artificial Intelligence Act?","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41575-026-01210-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41575-026-01210-y","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.21147/j.issn.1000-9604.2026.02.05","name":"CARE framework: Rethinking randomized controlled trials for medical artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.21147/j.issn.1000-9604.2026.02.05","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21147/j.issn.1000-9604.2026.02.05","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3349/ymj.2025.0389","name":"How Does Medical Artificial Intelligence Revolutionize Physician Productivity?","source":"europepmc","abstract":"This study examines the impact of medical artificial intelligence (AI) on physician workload and the quality of patient care. A meta-analysis of empirical studies found that AI significantly reduces physician workload and diagnostic time by automating repetitive interpretation and documentation processes, freeing clinicians to focus solely on patients. Automated generative AI-based electronic medical record systems reduce documentation time by approximately 40%, while voice recognition and AI scribing technologies reduce patient charting time by 28.8%. This reduces administrative burden, a major cause of physician burnout, by more than 30%. In radiology, AI-based interpretation reduced the interpretation time for abnormal contrast-enhanced brain CT lesions by 11.23%, the interpretation time for lung lesions by 52.82%, and the analysis time for peripheral blood smears by 61%. Importantly, these time savings occur naturally and, in some cases, improve diagnostic accuracy for major diseases (e.g., lung nodules, brain lesions, and breast cancer). Furthermore, AI minimizes the workload of interpretation through its automatic filtering function. This includes a 77.4%-86.7% reduction in review time for pulmonary nodules, a 51.3%-72.9% reduction in endometrial slide screening time, and an 86% saving in manual review time for epilepsy electroencephalography evaluation. These findings confirm that AI is establishing itself as a reliable tool that simultaneously improves physician efficiency, diagnostic efficiency, and clinical accuracy. Therefore, future healthcare policies regarding AI should not simply focus on expanding the workforce, but should adopt a strategic approach, optimizing resource efficiency and building a more resilient healthcare system.","url":"https://doi.org/10.3349/ymj.2025.0389","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3349/ymj.2025.0389","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1002/ca.70156","name":"Body-Donor-Derived Data in Medical Artificial Intelligence: From Foundational Resources to Trustworthy Applications.","source":"europepmc","abstract":"In recent years, artificial intelligence in medicine has evolved from single recognition tasks toward structural understanding, spatial reasoning, and clinical interpretability. High-quality anatomical data have become a key factor in further development. Driven by digital tomography, three-dimensional reconstruction, and multimodal technologies, body-donor-derived specimens and digital anatomical datasets, characterized by clear structural boundaries, stable spatial relationships, and fine-grained detail, are being transformed into computable, annotatable, and reusable digital anatomical resources. These resources are playing an increasingly important role in medical artificial intelligence. This narrative review summarizes the multiple roles of body-donor-derived data in medical AI. They serve as foundational resources that provide high-fidelity training data and fine-grained annotation systems. They also serve as validation references for improving algorithm credibility. In addition, they act as a substrate for AI-driven transformation in data processing, three-dimensional modeling, and intelligent applications in education, clinical practice, and forensic medicine. Their main strengths lie in anatomical authenticity, fine-grained annotatability, and structural validation utility, while their limitations include sample size, the postmortem-in vivo domain gap, annotation cost, and data governance. In the future, body-donor-derived data should become a core foundation for anatomical priors and structural gold standards, and should be deeply integrated with large-scale clinical imaging, multimodal intelligent analysis, and cross-domain learning to support the development of medical AI from high performance toward higher credibility and translational value.","url":"https://doi.org/10.1002/ca.70156","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/ca.70156","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.64898/2026.07.09.26357253","name":"Automatic Classification of Medical Artificial Intelligence Articles by Their Level of Translational Maturity: An Interpretable Supervised Text-Classification Approach","source":"europepmc","abstract":"The rapid expansion of the medical artificial intelligence (AI) literature has outpaced our ability to judge how far published models have progressed towards clinical use. We investigated whether the translational maturity of a study can be estimated automatically from its abstract. Using PubMed, we assembled a corpus of 11,024 candidate articles, reduced it to 1,816 AI-related articles by heuristic filtering, and manually double-annotated a balanced sample of 524 articles across five maturity classes (internal validation, external validation, prospective evaluation, implementation or governance, and not applicable). Abstracts were represented as TF-IDF features and classified using multinomial logistic regression with a Lasso penalty, chosen for interpretability and suitability for a small, imbalanced dataset. On a stratified held-out test set (n = 104), the model achieved 69.2% accuracy, Cohen’s kappa of 0.495, macro-F1 of 0.458 and a weighted AUC of 0.820. Performance was strong for the frequent classes but poor for the rare implementation or governance class, which the model failed to recover. A balanced manual verification of 200 large-corpus predictions confirmed this pattern, with per-class precision ranging from 82.5% (internal validation) to 5.0% (implementation or governance). An interpretable, low-resource classifier can support literature mapping but requires human oversight for advanced maturity levels.","url":"https://doi.org/10.64898/2026.07.09.26357253","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.09.26357253","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1038/s41551-026-01676-w","name":"Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning.","source":"europepmc","abstract":"Explainability has increasingly become a core requirement for intelligent medical devices. Current medical artificial intelligence (AI) technologies suffer from the 'interpretability gap' despite tremendous efforts for enhancing explainability. Here we propose class-association manifold learning, a generative approach that enhances explainability of medical AI models. Our method efficiently decouples common decision-related patterns from individual backgrounds, enabling us to represent global class-associated knowledge in a low-dimensional mapping while preserving near-perfect diagnostic accuracy. The extracted knowledge is further used to enable AI-generated modifications on arbitrary samples and visualize differential diagnosis rules. Moreover, we develop a topology map to model the entire decision rule set, so that the logic underlying black-box models can be intuitively explicated by traversing the map and generating virtual contrastive examples. Extensive experiments show that our method not only achieves higher accuracy in explaining the behaviour of medical AI models but also helps with extracting medical-compliant knowledge that are unknown during model training, thus providing a potential means of assisting clinical rule and medical knowledge discovery with AI techniques.","url":"https://doi.org/10.1038/s41551-026-01676-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41551-026-01676-w","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1136/jme-2024-110600","name":"Reframing the responsibility gap in medical artificial intelligence: insights from causal selection and authorship attribution.","source":"europepmc","abstract":"The increasing use of AI in healthcare has sparked debates about responsibility and accountability for AI-related errors. The difficulty in attributing moral responsibility for undesirable outcomes caused by increasingly autonomous (often opaque) AI systems has become a new focal point in the debate on 'responsibility gaps'. We approach the problem of these gaps by offering a framework that combines causal selection principles from the philosophy of science with recent accounts of authorship attribution in AI contexts. We argue this framework offers a more comprehensive and context-sensitive approach to the responsibility gap in medical AI.","url":"https://doi.org/10.1136/jme-2024-110600","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/jme-2024-110600","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12912-025-04248-6","name":"Surgical nurses' artificial intelligence literacy and readiness levels for medical artificial intelligence.","source":"europepmc","abstract":"BACKGROUND: This study aims to analyse the correlation between surgical unit nurses’ artificial intelligence literacy levels and their medical artificial intelligence readiness. METHOD: The study was descriptive, exploratory, and cross-sectional, and was conducted in Turkey with 339 nurses who were currently working in surgical units between June 2024 and June 2025. Online data collection via Google Forms was used, employing the Descriptive Characteristics Questionnaire, Artificial Intelligence Literacy Scale, and Medical Artificial Intelligence Readiness Scale. Data were analyzed using IBM SPSS Statistics software with descriptive statistics and Mann-Whitney U, Kruskal-Wallis, and Spearman correlation tests. RESULTS: The mean age of the nurses was 34.35 ± 9.37; 85.5% were female. According to data, 85.5% of the participants heard of the term artificial intelligence at some point, while 73.2% indicated AI will also help the nursing profession. A large and strong positive correlation exists between AI literacy and readiness for medical AI (rₛ = 0.642, p < 0.001). At the subscale level, readiness was also found to be significantly correlated with technical understanding (rₛ = 0.606), critical evaluation (rₛ = 0.672), and practical application (rₛ = 0.558) (all p < 0.001). CONCLUSION: The research indicates that with the increase in nurses’ literacy level regarding artificial intelligence, the readiness for the medical application of artificial intelligence also increases. Education and awareness initiatives aimed at developing artificial intelligence literacy are recommended to support the effective and safe use of AI-based technologies in nursing practice.","url":"https://doi.org/10.1186/s12912-025-04248-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1186/s12912-025-04248-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2174/0109298673390868250728103906","name":"Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management.","source":"europepmc","abstract":"Globally, millions of individuals suffer from infectious diseases, which are major public health concerns caused by bacteria, fungi, viruses, or parasites. These diseases can be transmitted directly or indirectly from person to person, potentially leading to a pandemic or epidemic. Several advancements have been made in molecular genetics for infectious disease management, which include pharmaceutical chemistry, medicine, and infection tracking; however, these advancements still lack control over human infections. Multidisciplinary cooperation is needed to address and control human infections. Advancements in scientific tools have empowered scientists to enhance epidemic prediction, gain insights into pathogen specificity, and pinpoint potential targets for drug development. Artificial intelligence (AI)-based methodologies demonstrate significant potential for integrating large-scale quantitative and omics data, enabling effective handling of biological complexity. Machine Learning (ML) plays a crucial role in AI by leveraging data to train predictive models. AI can enhance diagnostic accuracy through objective pattern recognition, standardize infection diagnoses with implications for Infection Prevention and Control (IPC), and aid in generalizing IPC knowledge. Additionally, AIpowered hand hygiene applications have the potential to drive behavioral change, although further evaluation in diverse clinical contexts is necessary. This review article highlights AI's potential in improving the healthcare system in different aspects of infectious diseases management, such as monitoring disease growth, using a real-time chatbot for patient assistance, using image processing for diagnosis, and developing new treatment algorithms. The study also discusses future directions for novel vaccine and drug development, as well as other aspects, such as the need for physicians and healthcare professionals to receive AI system training for their correct use and the ability of doctors to identify and resolve any problems that may arise with AI.","url":"https://doi.org/10.2174/0109298673390868250728103906","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2174/0109298673390868250728103906","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1093/jamiaopen/ooag154","name":"Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey.","source":"europepmc","abstract":"Objectives To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and methods A nationwide cross-sectional survey was conducted among professionals in oncology departments across China. Sociodemographic characteristics, awareness, perceptions, and user experiences related to medical AI were collected. The UTAUT was specifically applied to the third section of the survey to evaluate participants' perceptions. Based on this framework, structural equation modeling (SEM) was performed to examine associations between BI and 6 latent constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), perceived risk (PR), and trust (TR) based on the UTAUT. Subgroup SEM analyses were then conducted for physicians and nurses. Results Six hundred ten valid responses were included in the final analysis, consisting of 188 (30.8%) physicians and 422 (69.2%) nurses. Only 17.7% reported having both heard of and used medical AI, while 67.7% had heard of it but never used it. SEM results showed that TR (confidence in system reliability; β = .668, P P = .026). Subgroup analyses revealed that for physicians, both EE (β = .257, P = .038) and TR (β = .486, P P Conclusions There is a significant \"awareness-usage gap\" among Chinese oncology professionals, with high awareness but low practical integration of medical AI. This nationwide study indicates that TR and EE are associated with the intention to use AI. To bridge this gap, hospital management should implement differentiated training and local validation protocols that address the unique technical and ethical concerns of physicians and nurses, respectively.","url":"https://doi.org/10.1093/jamiaopen/ooag154","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamiaopen/ooag154","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s44163-026-00914-z","name":"Model centric collaboration reduces data sharing barriers in medical artificial intelligence.","source":"europepmc","abstract":"Medical artificial intelligence (AI) offers transformative potential for earlier disease detection, equitable access, and safer, more consistent care. However, its advancement depends on large-scale, multimodal, and longitudinal clinical data, which are highly sensitive and difficult to share under increasingly strict privacy and regulatory constraints. Federated learning-based on local training with gradient or parameter sharing-has emerged as a partial response to these challenges, yet it continues to face important limitations, including reliance on centralized aggregation, communication overhead, and reduced efficiency in heterogeneous settings. This perspective outlines a conceptual framework for rethinking collaboration in medical AI. We introduce the forward-looking notion of a data-model network, a model-centric paradigm that shifts collaboration from direct data exchange toward structured model interaction. In this framework, healthcare institutions train models locally and exchange model artifacts-such as parameters, distilled representations, or weak-to-strong generalizations-within a decentralized ecosystem, without transferring raw patient data. This theoretical model highlights how model-centric interaction can enable collective learning across heterogeneous clinical environments while preserving data sovereignty and embedding privacy protection by design. At the same time, we emphasize that this paradigm entails its own trade-offs and implementation challenges, including governance complexity, knowledge incompleteness, and trust management. By articulating these opportunities and limitations, this perspective argues that privacy-aware collaboration architectures can reposition privacy compliance from a constraint into a potential driver of innovation in medical AI-without presenting it as a panacea.","url":"https://doi.org/10.1007/s44163-026-00914-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s44163-026-00914-z","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/s13534-025-00523-2","name":"Institutionalizing convergence education for medical artificial intelligence.","source":"europepmc","abstract":"As artificial intelligence (AI) becomes increasingly central to modern healthcare, medical education must move beyond passive knowledge transfer and adopt a system-wide approach to convergence training. This narrative review shares a 5-year case study from Seoul National University College of Medicine (SNU Medicine), which developed a comprehensive, multi-level model for integrating AI into medical education. Instead of relying on pilot programs or piecemeal curriculum updates, SNU Medicine established a governance-driven, modular framework that includes institutional infrastructure, interdisciplinary teaching strategies, cross-campus credit integration, and alignment with national digital health policies. Based on this long-term case, we propose four key design principles-modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness-as a framework for creating scalable and sustainable convergence education in medical AI. While rooted in Korea's unique policy environment, this model provides transferable insights for medical institutions worldwide, particularly those operating within public or policy-constrained environments.","url":"https://doi.org/10.1007/s13534-025-00523-2","authors":["Tae In Park","Jong-Mo Seo","Hyung-Jin Yoon","Kyu Eun Lee"],"tags":["Convergence (economics)","Modular design","Curriculum","Key (lock)","Scalability"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1007/s13534-025-00523-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s44222-025-00363-w","name":"Transparency of medical artificial intelligence systems.","source":"europepmc","abstract":"Artificial intelligence (AI) systems are now prevalent in our daily lives and hold promise for transforming high-stakes fields such as healthcare. Medical AI systems are showing significant potential to support diagnostics and treatment recommendations. As these systems play an increasingly significant role in clinical decision-making, ensuring transparency in their design, operation, and outcomes is essential for building trust among key stakeholders, including patients, providers, developers, and regulators. However, many systems still function as \"black boxes,\" making it challenging for users-such as clinicians, patients, and other stakeholders-to interpret and verify their inner workings. Here, we examine the current state of transparency in medical AIs, identifying key challenges and risks these opaque systems pose. After motivating the need for transparency in all aspects of the machine learning pipeline, from training data to model development to model deployment, we explore a range of techniques that promote explainability throughout the pipeline while highlighting the importance of continual monitoring and system updates to ensure that AI systems remain reliable over time. Finally, we address the need to overcome barriers that inhibit the integration of transparency tools into clinical settings and review regulatory frameworks that prioritize transparency in emerging AI systems. Through this survey, we aim to increase awareness of current challenges and offer actionable insights for stakeholders, such as researchers, clinicians, and regulators, on how to build trustworthy and ethically responsible AI healthcare solutions.","url":"https://doi.org/10.1038/s44222-025-00363-w","authors":["Chanwoo Kim","Soham U. Gadgil","Su-In Lee"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025-09-10T10:29:30Z","doi":"10.1038/s44222-025-00363-w","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.pop.2025.07.009","name":"Ethical and Legal Considerations of Medical Artificial Intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI) has the capacity to improve patient care through increasing clinical decision-making accuracy and reducing administrative burdens for clinicians. However, AI poses challenges in medical ethics due to its opaque nature, potential for bias, and ties to large amounts of patient data. This article provides a general overview of medical AI ethics and legal regulations at the time of writing. It includes a description of prominent bioethics principles applied to AI, examines ethical challenges present in medical AI, reviews the current state of regulations regarding AI, and provides a list of recommendations for clinicians working with AI tools.","url":"https://doi.org/10.1016/j.pop.2025.07.009","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.pop.2025.07.009","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2025.100925","name":"Navigating the landscape of medical artificial intelligence reporting guidelines.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.landig.2025.100925","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.landig.2025.100925","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2967/jnumed.126.272292","name":"How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial.","source":"europepmc","abstract":"Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. Methods: To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians' acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 × 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician's decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician's decision (accept vs. reject). The reasonableness of the fictional physician's decision was measured, rated by participants on a Likert scale. Results: Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: t = 5.36; 95% CI, 0.45-0.97; P t = 2.47; 95% CI, 0.14-1.30; P = 0.02; German laypeople: t = 4.14; 95% CI, 0.27-0.76; P t = -4.90; 90% CI, -0.1 to 0.36; P t = -1.76; 90% CI, -0.12 to 0.67; P = 0.04; German laypeople: t = 5.35; 90% CI, -0.35 to 0.06; P Conclusion: Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.","url":"https://doi.org/10.2967/jnumed.126.272292","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2967/jnumed.126.272292","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.35772/ghm.2026.01052","name":"Japanese regulation and approval process for medical artificial intelligence (AI) as software as a medical device (SaMD): Current status and emerging challenges.","source":"europepmc","abstract":"The rapid expansion of artificial intelligence (AI) in healthcare has led to increasing adoption of AI-based software as a medical device (SaMD). This paper reviews the current regulatory and approval framework for AI-based SaMD in Japan and discusses emerging challenges associated with generative and adaptive AI technologies. Under the Pharmaceuticals and Medical Devices Act (PMD Act), software intended for diagnosis, treatment, or prevention is regulated as a medical device when classified as Class II or higher, and its clinical utility, performance, and safety are evaluated. While the number of approved AI-based SaMDs has increased, most existing products are task-specific systems supporting clinical decision-making within defined scopes. Recent advances in generative AI introduce novel regulatory issues, including difficulties in defining intended use, evaluating reliability of natural language outputs, and managing continuously evolving performance after market entry. These characteristics challenge conventional regulatory paradigms based on fixed product specifications. In light of ongoing international regulatory developments, key issues include clarifying scope of regulated functions, strengthening lifecycle and change management approaches, enhancing transparency, and improving user literacy. Developing adaptive regulatory frameworks that balance innovation, patient safety, and regulatory clarity will be essential for responsible integration of generative AI into healthcare.","url":"https://doi.org/10.35772/ghm.2026.01052","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.35772/ghm.2026.01052","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1684/ndt.2026.184","name":"From Turing to ChatGPT: The origins and transformations of medical artificial intelligence","source":"europepmc","abstract":"Artificial intelligence (AI) has experienced rapid growth in recent years with the advent of high-profile tools such as ChatGPT. However, its roots extend deep into the twentieth century. This article retraces the origins of AI and its major developments, showing how medical AI forms part of a long-standing scientific trajectory that predates the emergence of recent generative models. We first revisit the symbolic origins of AI, with the work of Alan Turing and the first programs capable of reasoning from explicit rules. We then examine the shift toward connectionist approaches centered on neural networks, which have driven major advances through machine learning and the availability of large datasets. Finally, we describe the current rise of generative AI, exemplified by large language models, and discuss its promises and limitations in medicine.","url":"https://doi.org/10.1684/ndt.2026.184","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1684/ndt.2026.184","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41746-026-02463-2","name":"Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.","source":"europepmc","abstract":"Considerations around model retraining are standard practice in industry and non-healthcare sectors; however, this is much less well explored in medical artificial intelligence (AI). The problem is not only that models often fail to generalise, but that academia in particular does not have a systematic science of retraining to address this gap. This matters for building trustworthy models capable of making a lasting impact, rather than compounding as research waste. In this Perspective, we highlight three common challenges that constrain model retraining in medicine, and argue that academia must evolve beyond a focus on developing proofs-of-concept and world-first innovations to also recognise model retraining as scholarship. Drawing from case examples in ophthalmology, we call on stakeholders to consider not just how we build AI models, but how we should retrain, maintain, and share them.","url":"https://doi.org/10.1038/s41746-026-02463-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41746-026-02463-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.jval.2026.01.014","name":"Technological Maturity and Cost-Effectiveness of Medical Artificial Intelligence: A Systematic Review of Health Economic Evaluations.","source":"pubmed","abstract":"This systematic review assessed the scope, reporting quality, and methodological risk of bias of health economic evaluations (HEEs) of medical artificial intelligence (AI) technologies, alongside the technological maturity of the AI systems assessed.","url":"https://doi.org/10.1016/j.jval.2026.01.014","authors":["Godoy Junior CA","Boverhof BJ","Rutten-van Mölken MPMH","Bijleveld L","Westhuis B","Groot CU","Redekop K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jval.2026.01.014","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"doi:10.31662/jmaj.2025-0477","name":"Conference on Medical Artificial Intelligence: Analysis Should be Based on Balanced Education and Audience.","source":"pubmed","abstract":"","url":"https://doi.org/10.31662/jmaj.2025-0477","authors":["Matsubara S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025-12-10T06:23:41Z","doi":"10.31662/jmaj.2025-0477","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1111/jep.70421","name":"The Mediating Role of Artificial Intelligence Anxiety in The Effect of Nurses' Attitudes Toward Health Technologies on Their Readiness for Medical Artificial Intelligence.","source":"europepmc","abstract":"Aim This study aims to evaluate the mediating role of artificial intelligence (AI) anxiety in the effect of nurses' attitudes toward health technologies on their readiness for medical artificial intelligence (MAI). Methods A cross-sectional design was used. The study was conducted between May and September 2023, involving 844 nurses at a university hospital in Türkiye. Working units were used as strata, and a proportionally stratified sample of 265 nurses was selected; convenience sampling was then applied within each stratum. Data were collected using the Health Personnel Health Technologies Assessment Attitude Scale, the Artificial Intelligence Anxiety Scale, and the Medical Artificial Intelligence Readiness Scale. Pearson correlation analysis and mediation analyses were performed. The mediation effect was tested using Model 4 in PROCESS Macro (version 4.2) in SPSS. Results Participants were predominantly female (78.9%), with a mean age of 31.30 ± 7.32 years. Nurses exhibited positive attitudes toward health technologies, moderate AI anxiety, and high readiness for MAI. Attitudes toward health technologies were significantly associated with readiness for MAI (B = 0.3315, 95% CI [0.1677, 0.4989], p 0.05). Mediation analysis indicated that AI anxiety did not significantly mediate the relationship between attitudes and readiness. Conclusion Positive attitudes toward health technologies are associated with higher readiness for MAI; however, AI anxiety did not function as a mediator in this relationship. These findings underscore the importance of integrating structured AI education, ethical awareness, and digital literacy training into nursing curricula and continuing professional development programs to support preparedness for AI-supported healthcare environments.","url":"https://doi.org/10.1111/jep.70421","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jep.70421","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.12771/emj.2025.00717","name":"Ten guidelines for contributors to medical artificial intelligence research.","source":"europepmc","abstract":"","url":"https://doi.org/10.12771/emj.2025.00717","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12771/emj.2025.00717","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.31662/jmaj.2025-0513","name":"Response to Letter to the Editor: \"Conference on Medical Artificial Intelligence: Analysis Should be Based on Balanced Education and Audience\".","source":"pubmed","abstract":"","url":"https://doi.org/10.31662/jmaj.2025-0513","authors":["Islam N","Rahman Osman A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31662/jmaj.2025-0513","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3760/cma.j.cn112338-20250621-00419","name":"[Current status, application scenarios, challenges, and recommendations for the development of medical artificial intelligence: a domestic and international perspective].","source":"europepmc","abstract":"Medical artificial intelligence (AI) is currently undergoing rapid development, with its technical framework evolving from symbolic reasoning to a new paradigm driven by deep learning and large language models. This article reviews the progression of AI technology, tracing its trajectory in the medical field under the policy support of countries such as China and the United States. It also analyzes the current development status of domestic and foreign large-scale models across three key areas: data, computing power, and algorithms. The article systematically outlines the advancements of AI in five major medical application scenarios: precision prevention and health management, precision diagnosis and treatment, precision drug development, precision infectious disease control, and precision medical education. Moreover, this article examines the critical challenges faced by current medical AI, including data quality, model interpretability and causality, hallucination control, fairness, overfitting, and safety verification, and offers strategies to address these concerns. In conclusion, this article provides a comprehensive perspective and strategic recommendations for the continued development of medical AI in our country.","url":"https://doi.org/10.3760/cma.j.cn112338-20250621-00419","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3760/cma.j.cn112338-20250621-00419","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41746-025-01867-w","name":"Commercialization of medical artificial intelligence technologies: challenges and opportunities.","source":"europepmc","abstract":"Artificial intelligence (AI) technologies are already having significant impacts in healthcare 1 . For example, AI-guided imaging has shown promise in the management of vascular diseases, including carotid, aortic, and peripheral artery disease, which collectively affect over 200 million individuals globally and lead to significant mortality/morbidity related to catastrophic complications such as aneurysm rupture, stroke, and limb loss 2 , 3 , 4 . These diseases are typically managed by vascular specialists who rely on imaging modalities including ultrasound, computed tomography (CT), and fluoroscopy for diagnosis/treatment 5 . Recent advancements, such as three-dimensional reconstruction software and fluoroscopic roadmaps, have transformed pre-operative planning and intra-operative guidance 5 . However, despite the growing availability of AI tools, their integration into routine diagnostic vascular imaging remains limited. This is largely due to persistent financial, regulatory, and implementation challenges that impede clinical translation. Many AI solutions are developed without adequate alignment to regulatory pathways or quality assurance frameworks, which hinders their adoption in practice 6 . This is particularly concerning given that vascular diseases are frequently underdiagnosed 7 . For example, abdominal aortic aneurysms (AAA) are often captured incidentally on medical images obtained during the investigation of other abdominal concerns, including assessment of liver, gallbladder, and kidney conditions, rather than actively screened for despite guideline recommendations 8 . Consequently, many AAA’s remain undetected until rupture, which carry mortality rates up to 80% 9 . AI-enhanced imaging holds potential to increase screening uptake and facilitate timely, elective intervention prior to rupture 10 . In this article, we examine a recently developed deep learning algorithm for AAA screening and explore the broader challenges and opportunities associated with commercializing AI technologies to deliver tangible clinical impact.","url":"https://doi.org/10.1038/s41746-025-01867-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1038/s41746-025-01867-w","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/75655","name":"Enhancing Model Generalizability in Medical Artificial Intelligence: Systematic Comparison of Categorical Encoding and Sampling Techniques for Imbalanced Data.","source":"europepmc","abstract":"Background Despite the increasing use of machine learning (ML) in clinical research, the early stages of data preparation, especially for structured clinical data, often receive limited methodological scrutiny. These datasets typically contain missing values, complex categorical variables, and imbalanced class distributions, all of which complicate downstream model development and interpretation. Objective This study introduces a structured preprocessing framework designed to address common challenges in medical tabular data and to assess how preprocessing choices affect the stability and portability of predictive models across settings. Methods We constructed a modular workflow comprising 3 components. First, preprocessing strategies included imputation for missing data, 3 types of categorical encoding (one-hot, frequency, and target), and resampling approaches for class imbalance (Synthetic Minority Over-sampling Technique [SMOTE] and Random Over Sampling Example [ROSE]). Second, 6 classification algorithms were used to evaluate performance patterns, including logistic regression (LGR), decision tree (DT), random forest, XGBoost (XGB), CatBoost (CAT), and light gradient-boosting machine (LightGBM). Third, we assessed cross-dataset portability using 2 datasets with distinct data-generating mechanisms: a registry for patients with end-stage renal disease (ESRD; n=412) and the population-based Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey. For each dataset, we independently cleaned, standardized, encoded, tuned, and evaluated models using the same predefined hyperparameter search space, without cross-dataset feature matching or pooling the area under the ROC curve (AUC) calculations; the complete pipeline was then rerun on BRFSS as an external replication. Results One-hot encoding in combination with ROSE yielded the most consistent performance improvements in terms of AUC (0.940) and accuracy (0.932), particularly for classifiers sensitive to class distribution. Notably, ROSE enhanced sensitivity without substantially distorting the original data structure. Feature importance rankings also contributed to model interpretability, and performance trends were largely reproducible in cross-context application. Conclusions Our findings suggest that preprocessing decisions often treated as ancillary play a central role in shaping model outcomes, especially in high-variance clinical datasets. The proposed framework offers a reproducible and adaptable tool for aligning data preparation with the unique demands of health care prediction tasks and may serve as a foundation for future efforts to standardize preprocessing in clinical ML workflows.","url":"https://doi.org/10.2196/75655","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/75655","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3389/fpubh.2025.1747635","name":"The legal dilemma of medical artificial intelligence in China: challenges to physicians' duty to inform and a typology-based response.","source":"europepmc","abstract":"The rapid advancement of artificial intelligence is profoundly reshaping clinical practice and transforming the doctor-patient relationship from a traditional physician-patient dyad to a composite structure of \"physician + AI-patient\". While this structural shift can improve diagnostic and therapeutic efficiency, it also poses unprecedented challenges to the normative design and legal application of the physician's duty to inform. In traditional settings, this duty has evolved from standardization to strictness and then to substantive requirements. With the introduction of medical AI, a fourth transformation toward a typology-based approach is urgently needed. Once AI participates in medical decision-making, this composite actor disrupts the trust relationship between physicians and patients, expands the scope of the physician's duty to inform, and blurs the allocation of legal responsibility. Moreover, existing AI classification schemes are misaligned with physicians' duty to inform. This article employs doctrinal analysis, case analysis, and comparative legal research. At present, international norms tend to construct legal and ethical frameworks around the core concept of \"risk prevention\". In the field of medical AI, this article proposes that-based on the current state of development-a \"cooperation\" concept be added on the foundation of risk prevention. On the premise that medical AI should not be recognized as a legal subject, we construct a four-level classification system centered on \"decisional autonomy\" and the degree of impact on the physician-patient relationship, together with a corresponding tiered accountability regime. Through a tripartite linkage mechanism-decision-making authority, duty to inform, and liability allocation-we aim to achieve a dynamic balance between technological complexity and patients' informed consent, and to promote the orderly development of medical AI within the framework of the rule of law and ethics.","url":"https://doi.org/10.3389/fpubh.2025.1747635","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fpubh.2025.1747635","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3760/cma.j.cn112338-20241105-00695","name":"[Research progress of panoramic evaluation of medical artificial intelligence].","source":"europepmc","abstract":"Artificial intelligence (AI) has advantages such as improving work performance, enhancing time efficiency, and reducing work costs, and has been widely applied in various industries, including healthcare. A comprehensive evaluation of medical AI is significant for optimizing its application in the medical field. However, a panoramic evaluation system for medical AI is currently lacking. Therefore, this review aims to provide a panoramic overview of the research progress in evaluating current medical AI from model performance, social acceptance, and medical ethics considerations. It summarizes the evaluation methods of medical AI in different studies, with the expectation of providing references for the panoramic evaluation and optimization of medical AI.","url":"https://doi.org/10.3760/cma.j.cn112338-20241105-00695","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3760/cma.j.cn112338-20241105-00695","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.nepr.2025.104538","name":"Nursing students' readiness and anxiety regarding medical artificial intelligence: A mixed-methods study.","source":"europepmc","abstract":"Aim The aim of this study is to examine the preparedness levels of nursing students for Medical Artificial Intelligence and Artificial Intelligence Anxiety. Design This is a descriptive mixed-method study. Methods The medical artificial intelligence readiness scale, artificial intelligence anxiety scale and semi-structured interview form were applied to the nursing department students of two separate faculties (n:358). The focus group interview was conducted with 19 participants. Quantitative and qualitative data were analyzed separately and combined. Results The average artificial intelligence awareness score of all the participants participating in the study was 75.05 ± 11,805, and the average artificial intelligence anxiety score was 49.59 ± 11,263. The average artificial intelligence awareness score of the participants in the qualitative part of the study was 72.89 ± 10.519 and the average artificial intelligence anxiety score was 70.68 ± 5.165. 145 opinions were identified regarding the concerns of individuals with artificial intelligence anxiety. These opinions are grouped under four sub-themes; Two sub-themes were evaluated as \"complementary\" and the other two as \"convergent\" with quantitative data. Sequential explanatory design was used in the study. After the quantitative data were evaluated, qualitative data were collected, evaluated and the results were integrated. Conclusion It was determined that the students were ready for artificial intelligence; they believed that artificial intelligence increases productivity and acts as a facilitator; but they also had various concerns about artificial intelligence. Qualitative and quantitative data supported each other. Patient or public contribution Increasing the awareness of nursing students and nurses on artificial intelligence can contribute to public health. Clinical trial registration NCT06623448.","url":"https://doi.org/10.1016/j.nepr.2025.104538","authors":["Rukiye Burucu","Hilal Türkben Polat"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.nepr.2025.104538","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2196/70901","name":"Beyond Benchmarks: Evaluating Generalist Medical Artificial Intelligence With Psychometrics.","source":"europepmc","abstract":"Unlabelled Rigorous evaluation of generalist medical artificial intelligence (GMAI) is imperative to ensure their utility and safety before implementation in health care. Current evaluation strategies rely heavily on benchmarks, which can suffer from issues with data contamination and cannot explain how GMAI might fail (lacking explanatory power) or in what circumstances (lacking predictive power). To address these limitations, we propose a new methodology to improve the quality of GMAI evaluation using construct-oriented processes. Drawing on modern psychometric techniques, we introduce approaches to construct identification and present alternative assessment formats for different domains of professional skills, knowledge, and behaviors that are essential for safe practice. We also discuss the need for human oversight in future GMAI adoption.","url":"https://doi.org/10.2196/70901","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.2196/70901","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.nepr.2025.104568","name":"Examining the relationship between nursing students' readiness, literacy and attitudes toward medical artificial intelligence.","source":"europepmc","abstract":"Aim This study examined the relationships among nursing students' readiness for medical AI, AI literacy and their attitudes toward AI within digitalization in healthcare. Background The rapid integration of AI into healthcare highlights the need to assess future professionals' preparedness. Nursing students' readiness, literacy and attitudes toward medical AI are key to its effective and ethical use. Design This study is a cross-sectional, descriptive and correlational research conducted to assess nursing students' readiness for medical AI, AI literacy and their attitudes toward AI in Türkiye. Methods Using an online survey, this cross-sectional, descriptive and correlational study was conducted with 438 nursing students from various universities in Türkiye. Data were collected using the Medical Artificial Intelligence Readiness Scale (MAIRS), the Artificial Intelligence Literacy Scale (AILS) and the General Attitudes Toward Artificial Intelligence Scale (GAAIS). Results The results revealed that student nurses reported high familiarity with AI (88.6 %). MAIRS was significantly correlated with AILS (r = .50), Positive GAAIS (r = .53) and inversely with Negative GAAIS (r = -.17). AILS subdimensions Awareness, Usage, Evaluation and Ethics significantly predicted MAIRS (R²=.25, p Conclusion The findings underscore the importance of enhancing nursing students' AI literacy and ethical competence to foster readiness for medical AI. Education programs should incorporate targeted content to improve students' abilities to evaluate and ethically apply AI in healthcare settings.","url":"https://doi.org/10.1016/j.nepr.2025.104568","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.nepr.2025.104568","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2025.100883","name":"One shot at trust: building credible evidence for medical artificial intelligence.","source":"europepmc","abstract":"The landscape of medical artificial intelligence (AI) is experiencing unprecedented momentum. Since January, 2023, the number of publications on this subject has remarkably increased, with each new use case for large language models (LLMs) generating cascading enthusiasm through academic journals, social media, and mainstream press.1 Although this fervour reflects genuine technological advances, the rapid pace of development and deployment also demands thoughtful consideration of how these findings are being evaluated and communicated.","url":"https://doi.org/10.1016/j.landig.2025.100883","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.landig.2025.100883","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.24920/004473","name":"Evaluation and Regulation of Medical Artificial Intelligence Applications in China.","source":"europepmc","abstract":"Amid the global wave of digital economy, China's medical artificial intelligence applications are rapidly advancing through technological innovation and policy support, while facing multifaceted evaluation and regulatory challenges. The dynamic algorithm evolution undermines the consistency of assessment criteria, multimodal systems lack unified evaluation metrics, and conflicts persist between data sharing and privacy protection. To address these issues, the China National Health Development Research Center has established a value assessment framework for artificial intelligence medical technologies, formulated the country's first technical guideline for clinical evaluation, and validated their practicality through scenario-based pilot studies. Furthermore, this paper proposes introducing a \"regulatory sandbox\" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.","url":"https://doi.org/10.24920/004473","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.24920/004473","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2025.02.004","name":"Medical digital twins: enabling precision medicine and medical artificial intelligence.","source":"europepmc","abstract":"The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.","url":"https://doi.org/10.1016/j.landig.2025.02.004","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.landig.2025.02.004","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12909-025-07774-8","name":"Individual innovativeness levels and levels of medical artificial intelligence readiness among nursing students: a cross-sectional and correlational study.","source":"europepmc","abstract":"Aim This study, aimed to determine the individual innovativeness levels of nursing students and their readiness levels for medical artificial intelligence and the relationship between these two variables. Background A healthcare team with innovative personality traits is essential for the effective use of artificial intelligence in healthcare. It is important to determine the perspectives of nursing students, who are among the most crowded members of the team and whom we define as the nurses of the future, in this direction. Design The research was designed as descriptive and correlational. Method The research data were collected by the researcher between March 1 and May 1, 2023. The study included 1st, 2nd, 3rd and 4th year nursing students studying at two universities in Anatolia. Data were collected with a reliable online survey method. The sample selection procedure was based on the sampling of the known population method. The study was conducted with 781 nursing students using a cross-sectional and correlational study method. The data were collected using the Personal Information Form, Individual Innovativeness Scale and Medical Artificial Intelligence Readiness Scale. The conformity of the data to the normal distribution was made by using skewness and kurtosis values and the range of + 2 and - 2 was taken as reference. Significance level p Results The mean total score of the IIS, nursing students was 55.09 ± 9.22, and the mean total score of the MARS nursing students was 67.63 ± 12.83, and there was a weak positive correlation between the scales (r = .172, p Conclusion In our study, it was determined that nursing students' individual innovativeness levels positively affected their readiness for medical artificial intelligence. It was concluded in this study that the individual innovativeness level of the nursing students was low; in other words, they adopted a traditionalist attitude and approached artificial intelligence cautiously. It is recommended that the existing curricula be restructured to strengthen the innovative perspective of nursing students and to understand, use, and develop artificial intelligence technologies in nursing, and nursing educators should upskill themselves in this field.","url":"https://doi.org/10.1186/s12909-025-07774-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1186/s12909-025-07774-8","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12912-025-03852-w","name":"Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.","source":"europepmc","abstract":"Abstract Background With the rapid integration of artificial intelligence (AI) into medical education, assessing medical students’ readiness has become critical. This readiness encompasses not only familiarity with AI tools but also the ability to apply, evaluate, and ethically reflect on them. Despite international advances, China currently lacks a validated instrument to systematically evaluate medical students’ readiness for medical AI. Therefore, this study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students. Methods The MAIRS-MS was translated into Chinese following Brislin’s guidelines, with subsequent cultural adaptation informed by expert consultation. A pilot study was then conducted with 30 medical students to refine the Chinese version (C-MAIRS-MS). A cross-sectional survey was conducted among 516 undergraduate medical students from March to May 2025. The psychometric properties of the C-MAIRS-MS were evaluated through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Cronbach’s α coefficient, Spearman–Brown split-half reliability, and the intraclass correlation coefficient (ICC). Results The C-MAIRS-MS included 22 items with scale content validity index (S-CVI) of 0.982. EFA extracted four factors explaining 65.274% of the total variance, with factor loadings ranging from 0.508 to 0.881. CFA results indicating that the revised 4-factor model was well fitted (χ²/df = 2.303, RMSEA = 0.071, CFI = 0.924, IFI = 0.925, and TLI = 0.912), with good structural validity. The Cronbach’s α coefficient, Spearman-Brown Split-half reliability, and ICC values for the C-MAIRS-MS were 0.935, 0.832, and 0.945, indicating the scale has good reliability. Conclusion The C-MAIRS-MS demonstrated sound psychometric properties and provides a reliable tool to assess medical students’ readiness for medical AI. Beyond individual assessment, the scale can inform curriculum development, facilitate ongoing monitoring of students’ progress, and support the evaluation of AI-focused educational programmes, thereby offering educators valuable evidence to guide the design and refinement of AI-related training in medical education. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12912-025-03852-w","authors":["Xuancheng Chen","Yangyi Chen","Yuhuan Xie","Linan Cheng"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1186/s12912-025-03852-w","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.12455/j.issn.1671-7104.250287","name":"[Exploration of Implementation Paths of Ethical Review in Medical Artificial Intelligence Research: from Perspectives of Algorithmic Transparency and Data Security].","source":"europepmc","abstract":"Objective From the perspectives of algorithmic transparency and data security to establish a practical and feasible ethical review mechanism for medical artificial intelligence (AI) research, effectively safeguarding the life, health, personal dignity, and legitimate rights and interests of research participants. Methods To sort out the basis for ethical review of medical AI research at home and abroad, analyze the problems and difficulties in relevant ethical review practices, and explore the procedures and key points of ethical review of medical AI research based on practical work. Results It has been clarified that the ethical review of medical AI research needs to strengthen interdisciplinary cooperation, invite experts from multiple fields such as software engineering and computer science to participate in the review work, optimize the process of ethical application and acceptance, standardize the way of ethical review, and develop corresponding review points based on different types of medical AI research, focusing on the evaluation of research risk-benefit ratio and the standardization of informed consent procedures. Conclusion This study proposes a standardized and effective implementation path for ethical review of medical AI research, while adhering to the basic requirements of traditional ethical review and taking into account the particularity of AI technology. The path is highly practicable.","url":"https://doi.org/10.12455/j.issn.1671-7104.250287","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12455/j.issn.1671-7104.250287","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.31744/einstein_journal/2025ao1401","name":"Attitudes, knowledge, opinions, and expectations of medical students towards medical artificial intelligence solutions: a cross-sectional survey study.","source":"europepmc","abstract":"Objective To assess medical students' attitudes, knowledge, opinions, and expectations regarding medical artificial intelligence solutions, according to their sex and year of study. Methods This cross-sectional survey was a single-center study conducted at a medical school in São Paulo, Brazil, using an online questionnaire. Results Of 145 medical students who completed the survey (female, n=108/145, 74%; age, 18-25 years, n=129/145, 89%), 71 (49%) classified their artificial intelligence knowledge as intermediate, 137 (95%) wished that artificial intelligence would be regulated by the government. If artificial intelligence solutions were reliable, fast, and available, 74% (107/145) intended to use artificial intelligence frequently, but fewer participants approved artificial intelligence when used by other health professionals (68/145, 47%) or directly by patients (26/144, 18%). The main benefit of artificial intelligence is in accelerating diagnosis and disease management (116/145, 80%) and problem is overreliance on artificial intelligence and loss of medical skills (106/145, 73%). Students believed that artificial intelligence would facilitate physicians' work (125/145, 86%); increase the number of appointments (76/145, 53%); decrease their financial gain (63/145, 43%); and not replace their jobs but be an additional source of information (102/145, 70%). According to 88/145 (61%) participants, legal responsibility should be shared between the artificial intelligence manufacturer and physicians/hospitals. Conclusion Medical students showed positive perceptions of and attitudes towards artificial intelligence in healthcare. They presented interest in artificial intelligence and believed in its incorporation in daily clinical practice, if regulated, is user-friendly and accurate. However, concerns regarding this technology must be addressed.","url":"https://doi.org/10.31744/einstein_journal/2025ao1401","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.31744/einstein_journal/2025ao1401","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.31662/jmaj.2025-0175","name":"Evaluating Conferences as a Tool to Improve Medical Artificial Intelligence Comprehension among Healthcare Professionals: A before and after Study.","source":"europepmc","abstract":"Introduction The integration of artificial intelligence (AI) in the United Kingdom's National Health Service may enhance patient care and alleviate systemic pressures. However, adoption of medical AI is challenged by limited educational access, low confidence among staff, and concerns regarding transparency and ethics. We evaluated the National Health Service National AI Conference on its impact on the understanding and attitudes of health care professionals regarding AI. Methods A before-and-after study design was employed using anonymized surveys distributed to conference attendees. The survey assessed participants' roles, prior experience with medical AI, and perceptions of AI's risks, benefits, and applications. Using a 5-point Likert scale, responses were analyzed via Wilcoxon's signed-rank test, after trialing McNemar's test, with statistical significance defined as p Results The survey was completed by 43 attendees. Most were clinical professionals (53.49%), with 65.12% having never attended a similar conference. Most participants (pre-conference: 69.77% vs. post-conference: 85.05%, p = 0.0868) understood the benefits and uses of AI, agreed that AI has the potential to improve patient care (93.02% vs. 95.35%, p = 1.00), were interested in pursuing a career in medical AI (62.79% vs. 67.44%, p = 0.824), and were concerned about the use of AI in health care (65.12% vs. 53.49%, p = 0.419). We observed an increase in understanding of AI after the conference among participants (p = 0.0367). Participant confidence and empowerment increased from 53.49% to 69.77% (p = 0.00319) and from 51.16% to 67.44% (p = 0.00596), respectively; these increases, alongside the increase in understanding of AI, reached statistical significance when analyzed using Wilcoxon's test, but not when dichotomized and analyzed with McNemar's test. Conclusions Our conference may increase AI understanding, confidence, and empowerment among health care professionals, encouraging further research into targeted medical AI education. A national AI curriculum, transparent governance, and robust information technology infrastructure are recommended to support the adoption of AI internationally.","url":"https://doi.org/10.31662/jmaj.2025-0175","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.31662/jmaj.2025-0175","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1056/nejmc2408971","name":"Medical Artificial Intelligence and Human Values.","source":"europepmc","abstract":"To the Editor: The review by Yu et al. (May 30 issue) 1 misses a key point: with which specific human values should an artificial intelligence (AI) model be aligned? The question is essentially about fairness. 2,3 The alignment of AI with human values is not about adhering to a particular set of human values but about ensuring that the model respects and encompasses a wide range of different values. Currently, the values that are embedded in AI systems are primarily those of the few large conglomerates that develop these models. These commercial interests do not necessarily conform to the diverse values . . .","url":"https://doi.org/10.1056/nejmc2408971","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1056/nejmc2408971","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/s13193-025-02229-4","name":"Challenges for Ethics Review Committees in Regulating Medical Artificial Intelligence Research.","source":"europepmc","abstract":"The integration of artificial intelligence (AI) into medical research poses significant challenges for Ethics Review Committees (ERCs), especially concerning clinical trials and ethical assessments. This review identifies 23 primary challenges ERCs encounter in regulating medical AI, categorized into five sections that explore limitations faced by ERCs, researchers, and regulatory bodies, along with the complexities of rapidly advancing AI technologies. Key issues include the intricacies of AI systems, risks related to personal data re-identification, and the impact of multi-subject data on algorithm outputs. Ethical concerns about algorithmic bias, data ownership, and AI commercialization are also highlighted. The variation in regulatory frameworks across regions further complicates these challenges, highlighting the urgent need for revised guidelines tailored to AI research. The article stresses the necessity for interdisciplinary collaboration and ethics education for all stakeholders in AI, advocating for continuous reviews and enhanced engagement to ensure informed consent and address potential unintended consequences. Ultimately, it calls for the adaptation of ethical oversight mechanisms to support innovation while upholding ethical standards in the quickly evolving field of medical AI.","url":"https://doi.org/10.1007/s13193-025-02229-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1007/s13193-025-02229-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3390/bioengineering12040375","name":"What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.","source":"europepmc","abstract":"This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS). After introducing the concept of explainability in AI and providing a short overview of AI-based clinical decision support systems (CDSSs) and the role of explainability in CDSSs, four use cases of AI-based CDSSs will be presented. The examples were chosen to highlight different types of AI-based CDSSs as well as different types of explanations: a machine language (ML) tool that lacks explainability; an approach with post hoc explanations; a hybrid model that provides medical knowledge-based explanations; and a causal model that involves complex moral concepts. Then, the role, relevance, and implications of explainability in the context of the use cases will be discussed, focusing on seven explainability-related aspects and themes. These are: (1) The addressees of explainability in medical AI; (2) the relevance of explainability for medical decision making; (3) the type of explanation provided; (4) the (often-cited) conflict between explainability and accuracy; (5) epistemic authority and automation bias; (6) Individual preferences and values; (7) patient autonomy and doctor-patient relationships. The case-based discussion reveals that the role and relevance of explainability in AI-based CDSSs varies considerably depending on the tool and use context. While it is plausible to assume that explainability in medical AI has positive implications, empirical data on explainability and explainability-related implications is scarce. Use-case-based studies are needed to investigate not only the technical aspects of explainability but also the perspectives of clinicians and patients on the relevance of explainability and its implications.","url":"https://doi.org/10.3390/bioengineering12040375","authors":["Elisabeth Hildt"],"tags":["Context (archaeology)","Relevance (law)","Clinical decision support system","Artificial intelligence","Computer science"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3390/bioengineering12040375","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.5847/wjem.j.1920-8642.2025.095","name":"Performance of a novel medical artificial intelligence large language model on supporting decision-making for emergency patients with suspected sepsis.","source":"europepmc","abstract":"BACKGROUND: Large language models (LLMs) are being explored for disease prediction and diagnosis; however, their effi cacy for early sepsis identifi cation in emergency departments (EDs) remains unexplored.This study aims to evaluate MedGo, a novel medical LLM, as a decision-support tool for clinicians managing patients with suspected sepsis.METHODS: This retrospective study included anonymized medical records of 203 patients (mean age 79.9±10.2years) with confi rmed sepsis from a tertiary hospital ED between January 2023 and January 2024.MedGo performance across nine sepsis-related assessment tasks was compared with that of two junior (<3 years of experience) and two senior (>10 years of experience) ED physicians.Assessments were scored on a 5-point Likert scale for accuracy, comprehensiveness, readability, and case-analysis skills.RESULTS: MedGo demonstrated diagnostic performance comparable to that of senior physicians across most metrics, achieving a median Likert score of 4 in accuracy, comprehensiveness, and readability.MedGo signifi cantly outperformed junior physicians (P<0.001 for accuracy and case-analysis skills).MedGo assistance significantly enhanced both junior (P<0.001) and senior (P<0.05)physicians' diagnostic accuracy.Notably, MedGo-assisted junior physicians achieved accuracy levels comparable to those of unassisted senior physicians.MedGo maintained consistent performance across varying sepsis severities.CONCLUSION: MedGo shows significant diagnostic efficacy for sepsis and effectively supports clinicians in the ED, particularly enhancing junior physicians' performance.Our study highlights the potential of MedGo as a valuable decision-support tool for sepsis management, paving the way for specialized sepsis AI models.","url":"https://doi.org/10.5847/wjem.j.1920-8642.2025.095","authors":["Sen Jiang","Xiandong Liu","Tong Liu","Yi Gu","Bo An","Chunxue Wang","Dongyang Zhao","Haitao Zhang","Lunxian Tang"],"tags":["Medicine","Sepsis","Medical emergency","Intensive care medicine","Acute medicine"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5847/wjem.j.1920-8642.2025.095","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.nedt.2025.106721","name":"Investigation of the relationship between medical artificial intelligence readiness and individual innovativeness levels in nursing students.","source":"europepmc","abstract":"Aim This study was conducted to identify nursing students' medical artificial intelligence readiness and individual innovativeness levels, to examine the relationship between these two concepts and to determine the variables that create a significant difference. Methods A descriptive and correlational design was employed. The sample of the study consisted of undergraduate nursing students from two different universities in Istanbul province, both of which focus on similar educational goals (n = 386). Data were collected between March and May 2024 using a questionnaire involving a \"Student Information Form,\" the \"Medical Artificial Intelligence Readiness Scale,\" and the \"Individual Innovativeness Scale.\" Descriptive statistical methods, t-test, ANOVA, and Pearson correlation test were used to analyze the data. Results The mean age of the students was 21.78 ± 1.84 years (18-35), 78.8 % were female, and 35.5 % were 2nd- and 3rd-year students. The students' mean score was 69.36 ± 12.92 on the total Medical Artificial Intelligence Readiness Scale and 52.92 ± 7.37 on the total Individual Innovativeness Scale. In addition, gender, family structure, income status, choosing the nursing department willingly, following scientific/technological developments in the field, having received training in artificial intelligence, having knowledge about the use of artificial intelligence in the health field and nursing, and having attended training/seminars on innovation were determined to be effective variables on Medical Artificial Intelligence Readiness and Individual Innovation. There was a significant positive relationship between the mean total and subscale scores on the Medical Artificial Intelligence Readiness Scale and the Individual Innovativeness Scale (p Conclusion Nursing students' medical artificial intelligence readiness levels were above average, and their individual innovativeness levels were low. It was found that as the students' medical artificial intelligence readiness levels increased, their individual innovativeness levels increased, as well. Although increased readiness levels boost the level of individual innovation, promising practices should be integrated into education for more advanced relationships and higher scores.","url":"https://doi.org/10.1016/j.nedt.2025.106721","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.nedt.2025.106721","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2025.01.003","name":"Weighing the benefits and risks of collecting race and ethnicity data in clinical settings for medical artificial intelligence.","source":"europepmc","abstract":"Many countries around the world do not collect race and ethnicity data in clinical settings. Without such identified data, it is difficult to identify biases in the training data or output of a given artificial intelligence (AI) algorithm, and to work towards medical AI tools that do not exclude or further harm marginalised groups. However, the collection of these data also poses specific risks to racially minoritised populations and other marginalised groups. This Viewpoint weighs the risks of collecting race and ethnicity data in clinical settings against the risks of not collecting those data. The collection of more comprehensive identified data (ie, data that include personal attributes such as race, ethnicity, and sex) has the possibility to benefit racially minoritised populations that have historically faced worse health outcomes and health-care access, and inadequate representation in research. However, the collection of extensive demographic data raises important concerns that include the construction of intersectional social categories (ie, race and its shifting meaning in different sociopolitical contexts), the risks of biological reductionism, and the potential for misuse, particularly in situations of historical exclusion, violence, conflict, genocide, and colonialism. Careful navigation of identified data collection is key to building better AI algorithms and to work towards medicine that does not exclude or harm marginalised groups.","url":"https://doi.org/10.1016/j.landig.2025.01.003","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.landig.2025.01.003","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1056/nejmra2214183","name":"Medical Artificial Intelligence and Human Values.","source":"europepmc","abstract":"Recent progress in generative artificial intelligence (AI) has given rise to large language models (LLMs) that can be prompted to craft persuasive essays, 1 pass professional competency exams, [2][3][4] and write patient-friendly empathetic messages.5 Amid growing recognition of the capabilities of LLMs, many have expressed concerns about their use in medicine and healthcare, citing known risks of confabulation, fragility, and factual inaccuracy.6 As these risks are measured and mitigated, what is coming into focus are unresolved questions about the human values that will remain embedded in AI, both in their creation and in their use, and how the \"values of an LLM\" may misalign with human values even if AI models no longer confabulate and have been scrubbed of obvious toxic output.Such \"human values\" pertain broadly to the principles, standards, and preferences that reflect human goals and guide human behaviors (Glossary).As we review here, LLMs and new foundation models, as technically impressive as they are, are only the latest incarnation of a long line of probabilistic models integrated into medical decision-making, all of which have required their creators and implementers to make value judgments.Many of the challenges we address here were evident to the pioneers of medical decision analysis of the 1950s 7 and to scholars in subsequent decades [8][9][10][11] who conducted careful and creative studies of both human and algorithmic decision-making to disentangle probability, the chance of an event occurring, from utilities, the quantified value judgments that are often only indirectly articulated in much of medical decision-making (Glossary).The nuanced understanding of individual values and risks is what makes the thoughtful clinician so indispensable.These considerations have renewed relevance with unprecedented and ubiquitously available AI models like LLMs.In this article, we first describe how value judgments enter predictive models, using familiar clinical settings and new AI language models.We then connect early work in reasoning about probabilities and utilities to the emerging issues of newer AI models, identifying unresolved challenges and future opportunities in designing high-performance and safe AI models.","url":"https://doi.org/10.1056/nejmra2214183","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1056/nejmra2214183","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/s2589-7500(24)00095-5","name":"Medical artificial intelligence for clinicians: the lost cognitive perspective.","source":"europepmc","abstract":"The development and commercialisation of medical decision systems based on artificial intelligence (AI) far outpaces our understanding of their value for clinicians. Although applicable across many forms of medicine, we focus on characterising the diagnostic decisions of radiologists through the concept of ecologically bounded reasoning, review the differences between clinician decision making and medical AI model decision making, and reveal how these differences pose fundamental challenges for integrating AI into radiology. We argue that clinicians are contextually motivated, mentally resourceful decision makers, whereas AI models are contextually stripped, correlational decision makers, and discuss misconceptions about clinician-AI interaction stemming from this misalignment of capabilities. We outline how future research on clinician-AI interaction could better address the cognitive considerations of decision making and be used to enhance the safety and usability of AI models in high-risk medical decision-making contexts.","url":"https://doi.org/10.1016/s2589-7500(24)00095-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1016/s2589-7500(24)00095-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/1754-9485.13648","name":"Captive markets and medical artificial intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI), including large language models (LLM), is likely to have an increasing influence on medical systems. There have been increasing discussions about the best approaches to trial and regulate such algorithms.1 An important aspect of these discussions involves the implications of economic structures for AI deployment,2 including their potential long-term consequences for healthcare systems. Valuable insights can be obtained by examining the historical trajectory of large technology companies in diverse domains. A captive market is defined by limited consumer choice in service providers within a specific domain.3 The field of AI has unique potential to create captive markets. LLM derived from large or high-quality datasets offer significant competitive advantages in model performance.4 In many instances, companies or groups possessing large, curated datasets will likely generate the most accurate models. Accordingly, strategies to obtain these datasets will become important. Accurate models may be made accessible at a reduced, or free of, charge if an agreement can be established where AI providers utilise the available data to enhance and further refine the algorithms. These approaches may be seen to create a positive feedback loop in which small numbers of groups may achieve unassailably large datasets and, accordingly, accurate algorithms. Given the relative scarcity of site-specific healthcare data, this issue is of relevance to healthcare systems. The concept of the captive market has additional relevance to healthcare when it comes to institutional dependence. Should a healthcare institution or system become dependent on a given AI application, then this institution would become a captive market. Healthcare system dependency on a given AI provider may develop in multiple ways. Examples of mechanisms of dependency include shifting healthcare expectations based upon AI reliance (e.g. reliance upon outpatient reviews at a speed that is AI transcription-enabled, without capacity to supplement medical scribes in the event of AI unavailability)5 and a loss of ability (e.g. when given tasks become machine tasks, therefore, humans no longer have the means or ability to perform these tasks).6 Dependency could also arise through the development of bespoke data engineering pipelines (e.g. the means by which data are obtained and provided to a given AI) without clear means by which such pipelines could be used or developed for alternative AI providers. A lack of interoperability7 (e.g. a lack of ability to swap between AI providers due to inability to exchange information) can similarly lead to a captive market. Engagement in captive markets can provide high-quality service but also has the potential to result in adverse outcomes for consumers (in this instance, healthcare systems and their patients). In certain instances, having a sole effective service provider can provide user benefits. For example, single-provider benefits can be seen in the large social networks available through Facebook and the connectivity of multiple devices within the Apple ecosystem. Conversely, examples of situations with adverse consumer outcomes have been described in multiple technological fields in which a provider achieves supremacy through a period of loss-leading service provision. Subsequently, changing service structures can maximise the value provided to shareholders for a given unit of service that is provided to consumers. Commentators, such as Cory Doctrow, have described this phenomenon in multiple areas, pertaining to Uber, Facebook and Amazon.8 For example, if an online platform provides a free or low-cost service (such as videos) in exchange for advertising revenue, then, after a user base is established, a profit-maximising strategy would be to increase the number of advertisements to the maximum tolerable number per user. The potential implications should such a situation arise for healthcare are clear. If a company obtains a position such that a healthcare system has no alternative AI provider, this situation could come at a cost to patients. Hypothetically, the AI provider could subsequently modify business structures to maximise profit with a healthcare system left with no options but to continue paying for a service upon which they are dependent. It is a reasonable argument that it is not the responsibility of the AI developer to consider this dependence, as it is in their best interest to create and retain a large user base. It could be argued that it is the duty of healthcare systems and governments to foresee the impacts of captive markets and avoid this scenario. While these arguments remain largely theoretical at this stage, given the potential implications for AI in healthcare, consideration should be given to these factors proactively. One strategy to mitigate these potential issues is to consider, and require, interoperability for any AI application on deployment. This approach would enable healthcare systems to seek competitive alternatives to given AI applications without being ‘locked in’ to a given provider. It can be seen that, with mandatory interoperability, the risk of captive markets is substantially reduced, assuming that other providers could deliver a similar service. Other strategies include the development of open-source AI algorithms and the fostering of local AI expertise. Regulation and financial structures may also play a role in mitigating the risks of captive markets. Open-source technology includes source codes and architectures of programs that are publicly available for modification.9, 10 Many open-source algorithms have been successful for AI in healthcare in the stratification of cancer therapy selection, triaging of outpatient referrals and application of clinical codes.11-13 Open-source programs have been common amongst technology industries (e.g. Apache HTTP server and Mozilla Firefox) and present users multiple advantages.10 For example, the performance of open-source AI models can be assessed by all researchers, facilitating robust validation experiments.14 Once curated, open-source datasets reduce the investment required to develop models and hence may promote the sharing of subsequently developed models. However, it is noteworthy that open-source code can be validated on publicly unavailable testing datasets, producing potentially unreliable results. Neutral third-party validation datasets may help to mitigate this issue. Open-source programming can pose a challenge for regulatory bodies.9, 14 Performances of open-source models can be dependent on the quality and availability of training datasets and has shown to have potential for very high classification performances.13 By addressing the limitations, open-source technology has the potential to develop exponentially for real-time application in clinical practice to improve the delivery of healthcare to patients. Promoting the cultivation and training of local AI expertise would also reduce dependence on external providers.9 Additionally, local production and augmentation of AI programs may facilitate the development of site-specific models. Inclusion of in-house digital training into medical and fellowship curricula could support such a solution.15 In addition to offsetting the risk of captive markets through the development of AI, this investment will develop a local body of experts that is valuable in critically appraising future vendor-proposed AI applications. Legislation and regulation of medical AI applications can also play a role in preventing the creation of captive markets. The Therapeutics Goods Administration have existing regulations for software-based medical devices in Australia.16 Similarly the United Kingdom and United States have introduced regulation of technological services, with up to 692 approved AI tools.17, 18 Regulators already have multiple challenges in this area, including the dissemination of erroneous medical information and usability standards, such as variable population digital literacy and ease of interaction with the equipment.16, 19 Accordingly, the responsibility for such regulation should be undertaken by multiple bodies, including those involved in the creation of site-specific healthcare institution policies. For example, specific agreement as to whether data necessary to provide a service may be used in subsequent AI model development is an important consideration in every case. Conflict of interest disclosures for those working in medical AI procurement for healthcare systems must also be robust. Interoperability standards could also be mandated. Consideration of additional legislation on such issues may be useful. Various financial models may be implemented in healthcare when engaging with AI services and have relevance to the potential impacts of captive markets. One structure could be conceptualised as a ‘fee for service’ model in which individual ‘tokens’ are purchased, which are then exchanged for specific AI tasks. Subscription models may also be provided, requiring a fixed fee at regular intervals to gain access to AI services. Additionally, a flat-fee purchase of a given service and profit-sharing models could also be implemented. An important consideration is that the costs involved in data engineering pipeline establishment and maintenance may be entirely separate to model acquisition. Consideration as to fixed or variable rates for AI activities must also be given at the outset of engaging with healthcare AI applications.20 The most effective financial structure for any given application will likely be context-dependent; however, a model that aligns the incentives of AI providers and healthcare institutions and avoids hidden maintenance costs, is a reasonable starting place. Captive markets relating to medical AI convey significant risk for healthcare institutions. However, these risks are also balanced against the potential benefits of comprehensive single-provider services. A key strategy to reduce this risk includes pre-emptive requests pertaining to interoperability of AI systems, enabling future transitions between providers. Other approaches include the development of local capabilities and software, the promotion of open-source programs, proactive legislation and regulation, and the utilisation of financial structures that avoid perverse incentives and hidden ongoing costs. The authors declare that there is no conflict of interest. Open access publishing facilitated by The University of Adelaide, as part of the Wiley - The University of Adelaide agreement via the Council of Australian University Librarians. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.","url":"https://doi.org/10.1111/1754-9485.13648","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1111/1754-9485.13648","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1097/cm9.0000000000003302","name":"Leveraging foundation and large language models in medical artificial intelligence.","source":"europepmc","abstract":"Abstract Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.","url":"https://doi.org/10.1097/cm9.0000000000003302","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1097/cm9.0000000000003302","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1089/pop.2024.0222","name":"Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings.","source":"europepmc","abstract":"In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities exacerbated by shortages of medical professionals and other resources. However, implementing AI effectively and responsibly in these settings requires careful consideration of context-specific needs and barriers to equitable care. This article explores the practical deployment of AI in low-resource environments through a review of existing literature and interviews with experts, ranging from health care providers and administrators to AI tool developers and government consultants. The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices. By addressing these aspects, the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.","url":"https://doi.org/10.1089/pop.2024.0222","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1089/pop.2024.0222","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1177/23821205251407758","name":"Knowledge, Readiness, and Perception of Medical Students Toward Medical Artificial Intelligence: A Cross-Sectional Study.","source":"europepmc","abstract":"Background Given recent advances in artificial intelligence (AI) in medical education and healthcare, it is essential to examine the perceptions and readiness of medical students. As future medical professionals, their ability to utilize this emerging technology effectively is crucial. Therefore, the present study aimed to examine medical AI knowledge, readiness, and perceptions among medical students in medical education and healthcare, and the risks and disadvantages associated with it in Iran. Methods This cross-sectional study was conducted among Iranian medical students in 2025. The questionnaire used in this study consisted of three parts: the first part, socio-demographic characteristics; the second part, basic knowledge and students' perceptions of medical education, healthcare, and risks and disadvantages of medical AI; and the third part, students' readiness for medical AI. The data were analyzed using SPSS 22 and Excel 2019 software. Results Of the total 280 medical students participating in the present study, 55.4% were female, and 60% were in the preclinical phase. The results showed that respondents demonstrated greater AI readiness in the dimensions of vision and ethics and possessed a high level of knowledge regarding the terms \"artificial intelligence,\" \"neural networks,\" and \"deep learning.\" More than 70% of respondents reported a high perception of medical AI in its three dimensions. A significant relationship exists between the medical AI readiness score and gender, working family/close friends, knowledge of AI, and three dimensions of students' perceptions of medical AI. Conclusion Enhancing students' knowledge, readiness, and understanding of medical AI can equip professionals with improved medical and decision-making skills. These professionals can make more informed decisions and reduce medical errors with AI tools.","url":"https://doi.org/10.1177/23821205251407758","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1177/23821205251407758","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1371/journal.pdig.0000495","name":"Introducing the Team Card: Enhancing governance for medical Artificial Intelligence (AI) systems in the age of complexity.","source":"europepmc","abstract":"This paper introduces the Team Card (TC) as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers' positionality. While harmful bias in medical AI, particularly in Clinical Decision Support (CDS) tools, is frequently attributed to issues of data quality, this limited framing neglects how researchers' worldviews-shaped by their training, backgrounds, and experiences-can influence AI design and deployment. These unexamined subjectivities can create epistemic limitations, amplifying biases and increasing the risk of inequitable applications in clinical settings. The TC emphasizes reflexivity-critical self-reflection-as an ethical strategy to identify and address biases stemming from the subjectivity of research teams. By systematically documenting team composition, positionality, and the steps taken to monitor and address unconscious bias, TCs establish a framework for assessing how diversity within teams impacts AI development. Studies across business, science, and organizational contexts demonstrate that diversity improves outcomes, including innovation, decision-making quality, and overall performance. However, epistemic diversity-diverse ways of thinking and problem-solving-must be actively cultivated through intentional, collaborative processes to mitigate bias effectively. By embedding epistemic diversity into research practices, TCs may enhance model performance, improve fairness and offer an empirical basis for evaluating how diversity influences bias mitigation efforts over time. This represents a critical step toward developing inclusive, ethical, and effective AI systems in clinical care. A publicly available prototype presenting our TC is accessible at https://www.teamcard.io/team/demo.","url":"https://doi.org/10.1371/journal.pdig.0000495","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1371/journal.pdig.0000495","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12909-025-06852-1","name":"Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward.","source":"europepmc","abstract":"Background Measuring artificial intelligence (AI) readiness among medical students is essential to assess how prepared future doctors are to work with AI technology. Therefore, this study aimed to examine the factors influencing AI readiness among medical students at Kermanshah University of Medical Sciences, both by evaluating the current situation and considering future developments. Methods This was a cross-sectional descriptive-analytical study. The statistical population consisted of 800 first- to fifth-year medical students selected through convenient sampling at Kermanshah University of Medical Sciences from November to March 2023. The data collection tools were demographic checklists and Persian version questionnaire of the medical artificial intelligence readiness scale for medical students (MAIRS-MS). The data were analyzed at a significance level of P Results Most of the students were male (56.13%). The overall score for medical AI readiness was 70.59 ± 19.24 out of a maximum possible score of 110. Students had the highest mean score of 9.73 ± 2.96 out of 15 in vision and the lowest mean score of 25.74 ± 7.52 out of 40 in ability. The overall mean of AI readiness (71.84 ± 18.27) was higher in females than males (69.62 ± 19.93), but this difference was not significant (p = 0.106). Furthermore, the mean total score of AI readiness increased with the increasing age of the students. Conclusion Our findings underscore the need to prepare students to work with AI technologies and to provide them with the essential knowledge and skills across different areas of AI. Accordingly, the Kermanshah University of Medical Sciences student's education unit should set up more AI training centers to provide and introduce basic artificial intelligence courses. Moreover, universities should identify the needs of students based on scientific evidence, and the medical education system should design AI training programs in its educational framework in the same direction.","url":"https://doi.org/10.1186/s12909-025-06852-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1186/s12909-025-06852-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3390/diagnostics14141472","name":"Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.","source":"europepmc","abstract":"With the improvement of economic conditions and the increase in living standards, people’s attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.","url":"https://doi.org/10.3390/diagnostics14141472","authors":["Fangfang Gou","Jun Liu","Chunwen Xiao","Jia Wu"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.3390/diagnostics14141472","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.jid.2024.07.014","name":"Human-Artificial Intelligence Interaction Research Is Crucial for Medical Artificial Intelligence Implementation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jid.2024.07.014","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.jid.2024.07.014","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.7759/cureus.76835","name":"Exploring Medical Artificial Intelligence Readiness Among Future Physicians: Insights From a Medical College in Central India.","source":"europepmc","abstract":"INTRODUCTION: Medical students, as future healthcare professionals, are pivotal in the adoption and application of artificial intelligence (AI) in clinical settings. Their ability to effectively engage with AI technologies is shaped by their understanding, attitudes, and perceived significance of AI in medicine. Given the growing prominence of AI in the medical field, it is crucial to evaluate how well-prepared medical students are to integrate and use these technologies proficiently. MATERIALS AND METHODS: The cross-sectional study was conducted among 482 undergraduate medical students at a medical college in Central India with the objective to evaluate their readiness for the integration of medical AI into their future clinical practice, utilizing the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) questionnaire. RESULTS: The mean age of respondents was 21.39 ± 1.770 years with 282 (58.5%) male participants. The respondents were almost equally distributed among all Bachelor of Medicine and Bachelor of Surgery (MBBS) batch students. The average MAIRS-MS score came out to be 74.61 ± 10.137 out of a maximum of 110, whereas the mean values of various subscales of MAIRS-MS were as follows: Cognition Factor, 26.23 ± 4.417; Ability Factor, 27.62 ± 4.372; Vision Factor, 10.37 ± 1.803; and Ethics Factor, 10.39 ± 1.789. CONCLUSION: Although there is overall readiness for AI among the respondents, significant variation exists among individuals, especially in the areas of Cognition and Ability. The data highlights the necessity for focused educational programs to improve AI knowledge, skills, and ethical understanding, ensuring that every respondent is well-equipped to handle the advancing field of AI in medicine.","url":"https://doi.org/10.7759/cureus.76835","authors":["Diwakar Dhurandhar","Mithilesh Dhamande","C Shivaleela","Pooja Bhadoria","Tripti Chandrakar","Jagriti Agrawal"],"tags":["Medicine","Medical education","Family medicine"],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.7759/cureus.76835","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.2196/54345","name":"Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study.","source":"europepmc","abstract":"Background Artificial intelligence (AI) chatbots have recently gained use in medical practice by health care practitioners. Interestingly, the output of these AI chatbots was found to have varying degrees of hallucination in content and references. Such hallucinations generate doubts about their output and their implementation. Objective The aim of our study was to propose a reference hallucination score (RHS) to evaluate the authenticity of AI chatbots' citations. Methods Six AI chatbots were challenged with the same 10 medical prompts, requesting 10 references per prompt. The RHS is composed of 6 bibliographic items and the reference's relevance to prompts' keywords. RHS was calculated for each reference, prompt, and type of prompt (basic vs complex). The average RHS was calculated for each AI chatbot and compared across the different types of prompts and AI chatbots. Results Bard failed to generate any references. ChatGPT 3.5 and Bing generated the highest RHS (score=11), while Elicit and SciSpace generated the lowest RHS (score=1), and Perplexity generated a middle RHS (score=7). The highest degree of hallucination was observed for reference relevancy to the prompt keywords (308/500, 61.6%), while the lowest was for reference titles (169/500, 33.8%). ChatGPT and Bing had comparable RHS (β coefficient=-0.069; P=.32), while Perplexity had significantly lower RHS than ChatGPT (β coefficient=-0.345; P Conclusions The variation in RHS underscores the necessity for a robust reference evaluation tool to improve the authenticity of AI chatbots. Further, the variations highlight the importance of verifying their output and citations. Elicit and SciSpace had negligible hallucination, while ChatGPT and Bing had critical hallucination levels. The proposed AI chatbots' RHS could contribute to ongoing efforts to enhance AI's general reliability in medical research.","url":"https://doi.org/10.2196/54345","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.2196/54345","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.47391/jpma.10064","name":"The 100 most influential papers in medical artificial intelligence; a bibliometric analysis.","source":"europepmc","abstract":"Objective To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. Methods The bibliometric analysis was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. Results Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal 'Artificial Intelligence in Medicine' accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.","url":"https://doi.org/10.47391/jpma.10064","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.47391/jpma.10064","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.hjc.2024.07.003","name":"Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.","source":"europepmc","abstract":"Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.","url":"https://doi.org/10.1016/j.hjc.2024.07.003","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.hjc.2024.07.003","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"epmc:MED40245231","name":"US regulation of medical artificial intelligence and machine learning (AI/ML) research and development","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/40245231/","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s43856-024-00492-0","name":"Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background The field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field. Methods Based on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items. Results AI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages. Conclusions Our analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.","url":"https://doi.org/10.1038/s43856-024-00492-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1038/s43856-024-00492-0","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.12771/emj.2024.e45","name":"My career path at a medical artificial intelligence company, working as a physician outside of clinical practice.","source":"europepmc","abstract":"","url":"https://doi.org/10.12771/emj.2024.e45","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.12771/emj.2024.e45","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/s2589-7500(24)00146-8","name":"Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review.","source":"europepmc","abstract":"During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals' country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.","url":"https://doi.org/10.1016/s2589-7500(24)00146-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1016/s2589-7500(24)00146-8","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.nepr.2024.103994","name":"Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety.","source":"europepmc","abstract":"Background Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. Aim This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. Methods This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. Results No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. Conclusion Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.","url":"https://doi.org/10.1016/j.nepr.2024.103994","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1016/j.nepr.2024.103994","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s42256-023-00652-2","name":"Federated benchmarking of medical artificial intelligence with MedPerf.","source":"europepmc","abstract":"Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.","url":"https://doi.org/10.1038/s42256-023-00652-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s42256-023-00652-2","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/pcn.13595","name":"Generalist medical artificial intelligence: Embracing the future with flexible interactions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/pcn.13595","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1111/pcn.13595","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41586-023-05881-4","name":"Foundation models for generalist medical artificial intelligence.","source":"europepmc","abstract":"The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.","url":"https://doi.org/10.1038/s41586-023-05881-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s41586-023-05881-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.ijmedinf.2024.105386","name":"Assessment of the relationship between executive Nurses' leadership Self-Efficacy and medical artificial intelligence readiness.","source":"europepmc","abstract":"Aims This study aims to assess the relationship between management nurses' leadership self-efficacy and medical artificial intelligence readiness. Methods The research was conducted using a descriptive-correlational design. The sample of the study consisted of 196 management nurses working in public, private, and educational research hospitals in Gaziantep, Turkey. The data collection tools included the Personal Information Form, the Leadership Self-Efficacy Scale, and the Medical Artificial Intelligence Readiness Scale. Results The majority of the participants in the research were female (71.4 %), married (80.1 %) and graduates of a bachelor's or higher degree in nursing (74.5 %), had 16 years or more of work experience in the profession (39.3 %), and worked during the day shift (75.5 %). Among the participating management nurses, those who were single had a significantly higher mean score in the cognition subscale and the total score of medical artificial intelligence readiness (p Conclusions In the research, it was determined that the leadership self-efficacy of the manager nurses was at a good level and that their artificial intelligence readiness was at a medium level in terms of cognition, skill, foresight and ethics while presenting their professional knowledge. A positive and significant relationship was found between leadership self-efficacy and medical artificial intelligence readiness.","url":"https://doi.org/10.1016/j.ijmedinf.2024.105386","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1016/j.ijmedinf.2024.105386","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1017/s0963180122000305","name":"The Virtues of Interpretable Medical Artificial Intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are \"black boxes.\" The initial response in the literature was a demand for \"explainable AI.\" However, recently, several authors have suggested that making AI more explainable or \"interpretable\" is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a \"lethal prejudice.\" In this article, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to-and perhaps even harm-patients.","url":"https://doi.org/10.1017/s0963180122000305","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1017/s0963180122000305","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/46402","name":"Acceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey.","source":"europepmc","abstract":"Background There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system. Objective The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients' choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants' individual choices. Methods A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient's perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed. Results Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants' choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant. Conclusions This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.","url":"https://doi.org/10.2196/46402","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.2196/46402","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12911-023-02103-9","name":"Ethics and governance of trustworthy medical artificial intelligence.","source":"europepmc","abstract":"Background The growing application of artificial intelligence (AI) in healthcare has brought technological breakthroughs to traditional diagnosis and treatment, but it is accompanied by many risks and challenges. These adverse effects are also seen as ethical issues and affect trustworthiness in medical AI and need to be managed through identification, prognosis and monitoring. Methods We adopted a multidisciplinary approach and summarized five subjects that influence the trustworthiness of medical AI: data quality, algorithmic bias, opacity, safety and security, and responsibility attribution, and discussed these factors from the perspectives of technology, law, and healthcare stakeholders and institutions. The ethical framework of ethical values-ethical principles-ethical norms is used to propose corresponding ethical governance countermeasures for trustworthy medical AI from the ethical, legal, and regulatory aspects. Results Medical data are primarily unstructured, lacking uniform and standardized annotation, and data quality will directly affect the quality of medical AI algorithm models. Algorithmic bias can affect AI clinical predictions and exacerbate health disparities. The opacity of algorithms affects patients' and doctors' trust in medical AI, and algorithmic errors or security vulnerabilities can pose significant risks and harm to patients. The involvement of medical AI in clinical practices may threaten doctors 'and patients' autonomy and dignity. When accidents occur with medical AI, the responsibility attribution is not clear. All these factors affect people's trust in medical AI. Conclusions In order to make medical AI trustworthy, at the ethical level, the ethical value orientation of promoting human health should first and foremost be considered as the top-level design. At the legal level, current medical AI does not have moral status and humans remain the duty bearers. At the regulatory level, strengthening data quality management, improving algorithm transparency and traceability to reduce algorithm bias, and regulating and reviewing the whole process of the AI industry to control risks are proposed. It is also necessary to encourage multiple parties to discuss and assess AI risks and social impacts, and to strengthen international cooperation and communication.","url":"https://doi.org/10.1186/s12911-023-02103-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1186/s12911-023-02103-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41746-024-01032-9","name":"Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case.","source":"europepmc","abstract":"Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.","url":"https://doi.org/10.1038/s41746-024-01032-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1038/s41746-024-01032-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12909-023-04516-6","name":"Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students.","source":"europepmc","abstract":"Background Artificial intelligence's advancement in medicine and its worldwide implementation will be one of the main elements of medical education in the coming years. This study aimed to translate and psychometric evaluation of the Persian version of the medical artificial intelligence readiness scale for medical students. Methods The questionnaire was translated according to a backward-forward translation procedure. Reliability was assessed by calculating Cronbach's alpha coefficient. Confirmatory Factor Analysis was conducted on 302 medical students. Content validity was evaluated using the Content Validity Index and Content Validity Ratio. Results The Cronbach's alpha coefficient for the whole scale was found to be 0.94. The Content Validity Index was 0.92 and the Content Validity Ratio was 0.75. Confirmatory factor analysis revealed a fair fit for four factors: cognition, ability, vision, and ethics. Conclusion The Persian version of the medical artificial intelligence readiness scale for medical students consisting of four factors including cognition, ability, vision, and ethics appears to be an almost valid and reliable instrument for the evaluation of medical artificial intelligence readiness.","url":"https://doi.org/10.1186/s12909-023-04516-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1186/s12909-023-04516-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3389/frai.2023.1169244","name":"Diagnostic medical artificial intelligence: Futuristic prospects for implementation in healthcare settings.","source":"europepmc","abstract":"Highlights-Medical artificial intelligence (AI) has the potential to improve the quality and efficiency of patient assessment and diagnosis.-Here, the topics of data collection, efficiency and feasibility, and patentability are discussed in relation to the implementation of diagnostic medical AI in medical settings.-Potential resolutions and future directions are provided to facilitate an agreeable implementation of diagnostic medical AI systems.","url":"https://doi.org/10.3389/frai.2023.1169244","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.3389/frai.2023.1169244","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/s00405-024-08710-0","name":"Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms.","source":"europepmc","abstract":"Background The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. Methods The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. Results The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973-0.991; F (29,542) = 68.3; p Conclusions The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.","url":"https://doi.org/10.1007/s00405-024-08710-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1007/s00405-024-08710-0","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41391-023-00719-6","name":"Will generalist medical artificial intelligence be the future path for health-related natural language processing models?","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41391-023-00719-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1038/s41391-023-00719-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.21203/rs.3.rs-3537018/v1","name":"Radiology Residents' and Radiologists' Perception and Attitude Towards Medical Artificial Intelligence in Radiology– An Initial National Multicenter Survey","source":"europepmc","abstract":"Abstract Introduction : Recent advances in artificial intelligence and machine learning (AI/ML) are transforming radiology practices. While AI/ML innovations present opportunities to augment radiologists' capabilities, some have expressed concerns about AI/ML potentially replacing radiologists in the future. These uncertainties have led to varied perspectives among radiology professionals regarding the role of AI/ML in the field. This study aimed to assess respondents' knowledge, research involvement, utilization of AI/ML applications, and attitudes towards the impact of AI/ML on radiology practice and training. Methods Between June and July of 2022, we conducted a web-based survey of radiologists and radiology residents from 5 major institutions in Ethiopia with radiology residency programs. The survey was distributed through the Ethiopian Radiological Society, and social media. Group comparison was tested by chi-square test for categorical responses and Mann-Whitney test for ordinal rating scale responses. Results Of the 276 respondents, 94.5% were novices when it came to AI/ML, and radiologists were more likely than residents to have read a journal paper on AI in radiology in the previous 6 months (33.3% vs. 18.9%). Only 1.8% of respondents had active or previous involvement in AI research, though 92% were eager to join such research efforts. Most of respondents intended to expand their AI/ML knowledge (84.6%) and believed AI/ML would substantially influence radiology practice (72.3%). While few felt AI/ML could replace radiologists (16.8%), most supported integrating AI/ML training into radiology residency curricula (82.9%). Conclusion This study suggests that radiology residents and radiologists in Ethiopia are generally positive and open-minded towards AI/ML in radiology, despite their limited knowledge and experience with the technology. The majority of respondents believe that AI and data science skills should be introduced during residency training. Recommendations : Medical AI training should be incorporated into radiology residency programs to prepare future radiologists for the changing landscape of radiology practice.","url":"https://doi.org/10.21203/rs.3.rs-3537018/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3537018/v1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.1038/s41598-023-33303-y","name":"Collaborative training of medical artificial intelligence models with non-uniform labels.","source":"europepmc","abstract":"Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collaborative training on such data. Using 695,000 chest radiographs from five institutions from across the globe-each with differing labels-we demonstrate that having heterogeneously labeled datasets, FFL-based training leads to significant performance increase compared to conventional FL training, where only the uniformly annotated images are utilized. We believe that our proposed algorithm could accelerate the process of bringing collaborative training methods from research and simulation phase to the real-world applications in healthcare.","url":"https://doi.org/10.1038/s41598-023-33303-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s41598-023-33303-y","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1177/20552076231186064","name":"Medical artificial intelligence ethics: A systematic review of empirical studies.","source":"europepmc","abstract":"Background Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and regulatory implications of medical AI, and empirical research on stakeholders' knowledge, attitude, and practices has started to emerge. This study is a systematic review of published empirical studies of medical AI ethics with the goal of mapping the main approaches, findings, and limitations of scholarship to inform future practice considerations. Methods We searched seven databases for published peer-reviewed empirical studies on medical AI ethics and evaluated them in terms of types of technologies studied, geographic locations, stakeholders involved, research methods used, ethical principles studied, and major findings. Findings Thirty-six studies were included (published 2013-2022). They typically belonged to one of the three topics: exploratory studies of stakeholder knowledge and attitude toward medical AI, theory-building studies testing hypotheses regarding factors contributing to stakeholders' acceptance of medical AI, and studies identifying and correcting bias in medical AI. Interpretation There is a disconnect between high-level ethical principles and guidelines developed by ethicists and empirical research on the topic and a need to embed ethicists in tandem with AI developers, clinicians, patients, and scholars of innovation and technology adoption in studying medical AI ethics.","url":"https://doi.org/10.1177/20552076231186064","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1177/20552076231186064","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1017/s0963180122000445","name":"Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust in nonhuman agents constitutes a category error and worry about the concept being misused for ethics washing. Proponents of trust have responded to these worries from various angles, disentangling different concepts and aspects of trust in AI, potentially organized in layers or dimensions. Given the substantial disagreements across these accounts of trust and the important worries about ethics washing, we embrace a diverging strategy here. Instead of aiming for a positive definition of the elements and nature of trust in AI, we proceed ex negativo , that is we look at cases where trust or distrust are misplaced. Comparing these instances with trust expedited in doctor-patient relationships, we systematize these instances and propose a taxonomy of both misplaced trust and distrust. By inverting the perspective and focusing on negative examples, we develop an account that provides useful ethical constraints for decisions in clinical as well as regulatory contexts and that highlights how we should not engage with medical AI.","url":"https://doi.org/10.1017/s0963180122000445","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1017/s0963180122000445","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12909-023-04553-1","name":"Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).","source":"europepmc","abstract":"Introduction There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. Methods In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. Results Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. Conclusions The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.","url":"https://doi.org/10.1186/s12909-023-04553-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1186/s12909-023-04553-1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.4258/hir.2023.29.1.64","name":"Healthcare Professionals' Expectations of Medical Artificial Intelligence and Strategies for its Clinical Implementation: A Qualitative Study.","source":"europepmc","abstract":"Objectives Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully. Methods Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement. Results While the participants expressed expectations that medical AI could enhance their patients' outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment. Conclusions Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.","url":"https://doi.org/10.4258/hir.2023.29.1.64","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.4258/hir.2023.29.1.64","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1177/23821205241281648","name":"Artificial Intelligence Readiness Among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS).","source":"europepmc","abstract":"Background Artificial intelligence (AI) application is increasingly used in all fields, especially, in medicine. However, for the successful incorporation of AI-driven tools into medicine, healthcare professional should be equipped with the necessary knowledge. From that, we aimed to assess the AI readiness among medical students in Jordan. Methods A cross-sectional survey was conducted among medical students across 6 Jordanian universities. Prevalidated Medical Artificial Intelligence Readiness Scale for Medical Students questionnaire was used. The questionnaire was distributed through social media groups of students. SPSS v.27 was used for analysis. Results A total of 858 responses were collected. The mean AI readiness score was 64.2%. Students scored more in the ability domain with a mean of 22.57. We found that academic performance (Grade point average) positively associated with overall AI readiness ( P = .023), and prior exposure to AI through formal education or experience significantly enhances readiness ( P = .009). In contrast, AI readiness levels did not significantly vary across different medical schools in Jordan. Notably, most students (84%) did not receive a formal education about AI from their schools. Conclusion Incorporation of AI education in medical curricula is crucial to close knowledge gaps and ensure that students are prepared for the use of AI in their future career. Our findings highlight the importance of preparing students to engage with AI technologies, and to be equipped with the necessary knowledge about its aspect.","url":"https://doi.org/10.1177/23821205241281648","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1177/23821205241281648","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1371/journal.pone.0279088","name":"Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke.","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. Methods We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.","url":"https://doi.org/10.1371/journal.pone.0279088","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1371/journal.pone.0279088","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1148/radiol.212830","name":"The Need for Medical Artificial Intelligence That Incorporates Prior Images.","source":"europepmc","abstract":"The use of artificial intelligence (AI) has grown dramatically in the past few years in the United States and worldwide, with more than 300 AI-enabled devices approved by the U.S. Food and Drug Administration (FDA). Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations. Focusing on the curation of data sets and algorithm development that allow for comparisons at different points will be required to advance the range of relevant tasks covered by future AI-enabled FDA-cleared devices.","url":"https://doi.org/10.1148/radiol.212830","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1148/radiol.212830","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/34304","name":"Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.","source":"europepmc","abstract":"The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.","url":"https://doi.org/10.2196/34304","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.2196/34304","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1097/nne.0000000000001446","name":"Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study.","source":"europepmc","abstract":"Background It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Purpose This study examined the impact of an AI course in the nursing curriculum on students' medical AI readiness. Design and methods This comparative quasi-experimental study was conducted with a total of 300 3rd-year nursing students, 129 in the control group and 171 in the experimental group. Students in the experimental group received 28 hours of AI training. The students in the control group were not given any training. Data were collected by a socio-demographic form and the Medical Artificial Intelligence Readiness Scale. Results An AI course should be included in the nursing curriculum, according to 67.8% of students in the experimental group and 57.4% of students in the control group. The mean score of the experimental group on medical AI readiness was higher ( P Conclusions An AI nursing course positively affects students' readiness for medical AI.","url":"https://doi.org/10.1097/nne.0000000000001446","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1097/nne.0000000000001446","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.16288/j.yczz.21-215","name":"Omics big data and medical artificial intelligence.","source":"europepmc","abstract":"With the rapid development of high-throughput sequencing technology and computer science, the amount of large omics data has increased exponentially, the advantages of multi-omics analysis have gradually emerged, and the application of artificial intelligence has become more and more extensive. In this review, we introduce the application progress of multi-omics data analysis and artificial intelligence in the medical field in recent years, and also show the cases and advantages of their combined application. Finally, we briefly explain the current challenges of multi-omics analysis and artificial intelligence in order to provide new research ideas for the medical industry and to promote the development and application of precision medicine.","url":"https://doi.org/10.16288/j.yczz.21-215","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.16288/j.yczz.21-215","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1155/2022/5413202","name":"Application of Neural Network Algorithm in Medical Artificial Intelligence Product Development.","source":"europepmc","abstract":"With the continuous deepening of artificial intelligence (AI) in the medical field, the social risks brought by the development and application of medical AI products have become increasingly prominent, bringing hidden worries to the protection of civil rights, social stability, and healthy development. There are many new problems that need to be solved in our country's existing risk regulation theories when dealing with such risks. By introducing the theory of risk administrative law, it analyzes the social risks of medical AI, organically combines the principle of risk prevention with benefit measurement, and systematically and flexibly reconstructs the theoretical system of medical AI social risk assessment. This paper has completed the following work: (1) reviewed and sorted out the works and papers related to medical AI ethics, medical AI risk, etc., and sorted out the current situation of medical AI social risk regulation at home and abroad to provide help for follow-up research. (2) The related technologies of artificial neural network (ANN) are introduced, and the risk assessment index system of medical AI is constructed. (3) With the self-designed dataset, the trained neural network model is utilized to assess risk. The experimental results reveal that the created BPNN model's error is relatively tiny, indicating that the algorithm model developed in this research is worth popularizing and applying.","url":"https://doi.org/10.1155/2022/5413202","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1155/2022/5413202","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1038/s41562-021-01146-0","name":"Understanding, explaining, and utilizing medical artificial intelligence.","source":"europepmc","abstract":"Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a 'black box') and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1-3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).","url":"https://doi.org/10.1038/s41562-021-01146-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1038/s41562-021-01146-0","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3348/jksr.2022.0155","name":"[Understanding and Application of Multi-Task Learning in Medical Artificial Intelligence].","source":"europepmc","abstract":"In the medical field, artificial intelligence has been used in various ways with many developments. However, most artificial intelligence technologies are developed so that one model can perform only one task, which is a limitation in designing the complex reading process of doctors with artificial intelligence. Multi-task learning is an optimal way to overcome the limitations of single-task learning methods. Multi-task learning can create a model that is efficient and advantageous for generalization by simultaneously integrating various tasks into one model. This study investigated the concepts, types, and similar concepts as multi-task learning, and examined the status and future possibilities of multi-task learning in the medical research.","url":"https://doi.org/10.3348/jksr.2022.0155","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.3348/jksr.2022.0155","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/bioe.12891","name":"Intentional machines: A defence of trust in medical artificial intelligence.","source":"europepmc","abstract":"Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor-patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) that it is also dangerous, that is, that we should not trust AI-particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human-robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust.","url":"https://doi.org/10.1111/bioe.12891","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1111/bioe.12891","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.xcrm.2022.100912","name":"DeepFundus: A flow-cytometry-like image quality classifier for boosting the whole life cycle of medical artificial intelligence.","source":"europepmc","abstract":"Medical artificial intelligence (AI) has been moving from the research phase to clinical implementation. However, most AI-based models are mainly built using high-quality images preprocessed in the laboratory, which is not representative of real-world settings. This dataset bias proves a major driver of AI system dysfunction. Inspired by the design of flow cytometry, DeepFundus, a deep-learning-based fundus image classifier, is developed to provide automated and multidimensional image sorting to address this data quality gap. DeepFundus achieves areas under the receiver operating characteristic curves (AUCs) over 0.9 in image classification concerning overall quality, clinical quality factors, and structural quality analysis on both the internal test and national validation datasets. Additionally, DeepFundus can be integrated into both model development and clinical application of AI diagnostics to significantly enhance model performance for detecting multiple retinopathies. DeepFundus can be used to construct a data-driven paradigm for improving the entire life cycle of medical AI practice.","url":"https://doi.org/10.1016/j.xcrm.2022.100912","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1016/j.xcrm.2022.100912","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.31234/osf.io/4kwap","name":"Understanding, Explaining, and Utilizing Medical Artificial Intelligence","source":"europepmc","abstract":"Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre- registered experiments with convenience and nationally representative samples (N = 2,699), we find that people exhibit such an illusory understanding of human medical decision making (Study 1). This leads people to claim greater understanding of decisions made by human than algorithmic healthcare providers (Studies 2A-B), which makes people more reluctant to utilize algorithmic providers (Studies 3A-B). Fortunately, we find that asking people to explain the mechanisms underlying medical decision making reduces this illusory gap in subjective understanding (Study 1). Moreover, we test brief interventions that, by increasing subjective understanding of algorithmic decision processes, increase willingness to utilize algorithmic healthcare providers without undermining utilization of human providers (Studies 3A-B). Corroborating these results, a study on Google testing ads for an algorithmic skin cancer detection app shows that interventions that increase subjective understanding of algorithmic decision processes lead to a higher ad click-through rate (Study 4). Our findings show how reluctance to utilize medical algorithms is driven both by the difficulty of understanding algorithms, and an illusory understanding of human decision making.","url":"https://doi.org/10.31234/osf.io/4kwap","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.31234/osf.io/4kwap","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.3389/frai.2022.1006173","name":"Diversity in people's reluctance to use medical artificial intelligence: Identifying subgroups through latent profile analysis.","source":"europepmc","abstract":"Medical artificial intelligence (AI) is important for future health care systems. Research on medical AI has examined people's reluctance to use medical AI from the knowledge, attitude, and behavioral levels in isolation using a variable-centered approach while overlooking the possibility that there are subpopulations of people who may differ in their combined level of knowledge, attitude and behavior. To address this gap in the literature, we adopt a person-centered approach employing latent profile analysis to consider people's medical AI objective knowledge, subjective knowledge, negative attitudes and behavioral intentions. Across two studies, we identified three distinct medical AI profiles that systemically varied according to people's trust in and perceived risk imposed by medical AI. Our results revealed new insights into the nature of people's reluctance to use medical AI and how individuals with different profiles may characteristically have distinct knowledge, attitudes and behaviors regarding medical AI.","url":"https://doi.org/10.3389/frai.2022.1006173","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.3389/frai.2022.1006173","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1136/bmjopen-2022-071288","name":"Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children's hospitals in Shanghai: a cross-sectional study.","source":"europepmc","abstract":"Objectives Implementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children's hospitals in Shanghai. Design and setting A cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children's hospitals in Shanghai. Participants All the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey. Primary outcome Ethics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians. Results There were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children's hospitals. 2.4-9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9-86.1% of participants held an 'agree' ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held 'agree' attitudes. Conclusions There is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.","url":"https://doi.org/10.1136/bmjopen-2022-071288","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1136/bmjopen-2022-071288","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/medu.14635","name":"Developing medical artificial intelligence leaders: International university consortium approach.","source":"europepmc","abstract":"Machine learning applications are increasingly used in medicine. The \\ndemand for ‘augmented doctors,’1 or ‘enhanced physicians’, that is, \\nphysicians capitalising rather than opposing incoming technology, \\nhas been recognised. However, teaching medical Artificial Intelligence \\nis challenging, especially in already oversaturated medical curricula \\nwith most medical schools lacking Artificial Intelligence \\nexpertise. \\nTo help medical students become ‘augmented doctors’, an \\ninternational collaborative educational project, the GAME-TEI (Global \\nAlliance of Medical Excellence–Transnational Educational Initiative) \\n(https://www.game-med.net/tei), planned its ‘summer school’ \\nthemed Artificial Intelligence in Medicine and Medical Education at the \\nUniversity of Bologna, Italy in July 2020. The COVID-19 pandemic led to a rapid transformation into an online course involving medical students \\nfrom nine countries.","url":"https://doi.org/10.1111/medu.14635","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1111/medu.14635","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/s2589-7500(20)30187-4","name":"Approaching autonomy in medical artificial intelligence.","source":"europepmc","abstract":"Artificial intelligence (AI) has the potential to transform the delivery of health care by automating complex tasks that traditionally required substantial training and expertise. Contrary to concerns that fully automated AI will replace health-care providers,1 a more likely scenario is that providers will increasingly interact with task-specific and domain-specific AI systems across a continuum of automation. The level of AI autonomy is broadly accepted to be crucial to the risk-assessment of AI algorithms, yet its definition and classification is poorly defined.","url":"https://doi.org/10.1016/s2589-7500(20)30187-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1016/s2589-7500(20)30187-4","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.euf.2021.05.008","name":"Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence.","source":"europepmc","abstract":"With the impact of artificial intelligence (AI) algorithms on medical research on the rise, the importance of competitions for comparative validation of algorithms, so-called challenges, has been steadily increasing, to a point at which challenges can be considered major drivers of research, particularly in the biomedical image analysis domain. Given their importance, high quality, transparency, and interpretability of challenges is essential for good scientific practice and meaningful validation of AI algorithms, for instance towards clinical translation. This mini-review presents several issues related to the design, execution, and interpretation of challenges in the biomedical domain and provides best-practice recommendations. PATIENT SUMMARY: This paper presents recommendations on how to reliably compare the usefulness of new artificial intelligence methods for analysis of medical images.","url":"https://doi.org/10.1016/j.euf.2021.05.008","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1016/j.euf.2021.05.008","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s40900-022-00357-7","name":"Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model.","source":"europepmc","abstract":"There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.-what we collectively call \"citizen forums\". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.","url":"https://doi.org/10.1186/s40900-022-00357-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1186/s40900-022-00357-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.12688/f1000research.73367.1","name":"Liability from the use of medical artificial intelligence: a comparative study of English and Taiwanese tort laws","source":"europepmc","abstract":"Background: Modern artificial intelligence applications are appearing in healthcare and medical practices. Artificial intelligence is used both in medical research and on patients via medical devices. The aim of this paper is to examine and compare English and Taiwanese tort laws in relation to medical artificial intelligence. Methods: : The methodologies employed are legal doctrinal analysis and comparative law analysis. Results: : The investigation finds that English tort law treats wrong diagnostic or wrong advice as negligent misstatement, and mishaps due to devices as a physical tort under the negligence rule. Negligent misstatement may occur in diagnosis or advisory systems, while a negligent act may occur in products used in the treatment of the patient. Product liability under English common law applies the same rule as negligence. In Taiwan, the general principles of tort law in Taiwan’s Civil Code for misstatement and negligent action apply, whereas the Consumer Protection Act provides for additional rules on product liability of traders. Conclusions: : Safety regulations may be a suitable alternative to tort liability as a means to ensure the safety of medical artificial intelligence systems.","url":"https://doi.org/10.12688/f1000research.73367.1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.12688/f1000research.73367.1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.3881/j.issn.1000-503x.10961","name":"[Ethical Issues of Medical Artificial Intelligence].","source":"europepmc","abstract":"As an important branch of artificial intelligence,the emerging medical artificial intelligence(MAI)is facing many ethical issues.MAI may offer the optimal diagnosis and treatment for patients but may also bring adverse effects on society and human beings.This article discusses the ethical problems caused by MAI and elucidates its development in a direction that meets ethical principles and requirements.","url":"https://doi.org/10.3881/j.issn.1000-503x.10961","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.3881/j.issn.1000-503x.10961","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/bioe.12927","name":"Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries.","source":"europepmc","abstract":"Medical artificial intelligence (MAI) creates an opportunity to radically expand access to healthcare across the globe by allowing us to overcome the persistent labor shortages that limit healthcare access. This democratization of healthcare is the greatest moral promise of MAI. Whatever comes of the enthusiastic discourse about the ability of MAI to improve the state-of-the-art in high-income countries (HICs), it will be far less impactful than improving the desperate state-of-the-actual in low- and middle-income countries (LMICs). However, the almost exclusive development of MAI in HICs risks this promise being thwarted by contextual bias, an algorithmic bias that arises when the context of the training data is significantly dissimilar from potential contexts of application, which makes the unreflective application of HIC-based MAI in LMIC contexts dangerous. The use of MAI in LMICs demands careful attention to context. In this paper, I aim to provide that attention. First, I illustrate the dire state of healthcare in LMICs and the hope that MAI may help us to improve this state. Next, I show that the radical differences between the health contexts of HICs and those of LMICs create an extraordinary risk of contextual bias. Then, I explore ethical challenges raised by this risk, and propose policies that will help to overcome those challenges. Finally, I sketch a wide range of related issues that need to be addressed to ensure that MAI has a positive impact on LMICs-and is able to improve, rather than worsen, global health equity.","url":"https://doi.org/10.1111/bioe.12927","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1111/bioe.12927","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.landig.2026.101068","name":"Power, governance, and accountability in humanitarian artificial intelligence.","source":"europepmc","abstract":"In their Comment, Shreenik Kundu and colleagues convincingly argue that ethical artificial intelligence (AI) could help humanitarian organisations respond to worsening aid shortfalls by improving data collection and analysis.1 Humanitarian systems face expanding needs, reduced resources, and growing demands for data. AI systems can support translation, summarisation, classification, and analysis of qualitative data used to understand context, needs, or response effectiveness. These applications raise technical challenges around data quality, accuracy, and reliability, as well as ethical questions around bias, privacy, and responsible use.","url":"https://doi.org/10.1016/j.landig.2026.101068","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2027","doi":"10.1016/j.landig.2026.101068","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12909-021-02546-6","name":"Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study.","source":"europepmc","abstract":"Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ 2 /df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.","url":"https://doi.org/10.1186/s12909-021-02546-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1186/s12909-021-02546-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s40560-020-00452-5","name":"The advent of medical artificial intelligence: lessons from the Japanese approach.","source":"europepmc","abstract":"Artificial intelligence or AI has been heralded as the most transformative technology in healthcare, including critical care medicine. Globally, healthcare specialists and health ministries are being pressured to create and implement a roadmap to incorporate applications of AI into care delivery. To date, the majority of Japan's approach to AI has been anchored in industry, and the challenges that have occurred therein offer important lessons for nations developing new AI strategies. Notably, the demand for an AI-literate workforce has outpaced training programs and knowledge. This is particularly observable within medicine, where clinicians may be unfamiliar with the technology. National policy and private sector involvement have shown promise in developing both workforce and AI applications in healthcare. In combination with Japan's unique national healthcare system and aggregable healthcare and socioeconomic data, Japan has a rich opportunity to lead in the field of medical AI.","url":"https://doi.org/10.1186/s40560-020-00452-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1186/s40560-020-00452-5","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.artmed.2020.101965","name":"The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future.","source":"europepmc","abstract":"Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we make now. Demographic healthcare inequalities continue to persist worldwide and the impact of medical biases on different patient groups is still being uncovered by the research community. At a time when clinical AI systems are scaled up in response to the Covid19 pandemic, the role of AI in exacerbating health disparities must be critically reviewed. For AI to account for the past and build a better future, we must first unpack the present and create a new baseline on which to develop these tools. The means by which we move forwards will determine whether we project existing inequity into the future, or whether we reflect on what we hold to be true and challenge ourselves to be better. AI is an opportunity and a mirror for all disciplines to improve their impact on society and for medicine the stakes could not be higher.","url":"https://doi.org/10.1016/j.artmed.2020.101965","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1016/j.artmed.2020.101965","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.21037/atm.2019.12.149","name":"Attitudes towards medical artificial intelligence talent cultivation: an online survey study.","source":"europepmc","abstract":"Background To investigate the attitude and formal suggestions on talent cultivation in the field of medical artificial intelligence (AI). Methods An electronic questionnaire was sent to both medical-related field or non-medical field population using the WenJuanXing web-application via social media. The questionnaire was designed to collect: (I) demographic information; (II) perception of medical AI; (III) willingness to participate in the medical AI related teaching activities; (IV) teaching content of medical AI; (V) the role of medical AI teaching; (VI) future career planning. Respondents' anonymity was ensured. Results A total of 710 respondents provided valid answers to the questionnaire (57.75% medical related, 42.25% non-medical). About 73.8% of respondents acquired related information from network and social platform. More than half the respondents had basic perception of AI applicational scenarios and specialties in medicine, meanwhile were willing to participate in related general science activities (conference and lectures). Respondents from medical healthcare related fields, with high academic qualifications of male ones demonstrated showed significant better understanding and stronger willingness (P 80%) acknowledged the potential roles of medical AI teaching. Surgeon, ophthalmologist, physicians and researchers are the top tier considerations for ideal career regardless of AI influence. Radiology and clinical laboratory subjects are more preferred considering the development of medical AI (P>0.05). Conclusions The potential role of medical AI talent cultivation is widely acknowledged by public. Medical related professions demonstrated higher level of perception and stronger willingness for medical AI educational events. Merging subjects as radiology and clinical laboratory subjects are preferred with broad talents demands and bright prospects.","url":"https://doi.org/10.21037/atm.2019.12.149","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.21037/atm.2019.12.149","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2967/jnumed.120.257196","name":"How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence?","source":"europepmc","abstract":"See the associated article on page [17][1]. Artificial intelligence (AI) is rapidly entering medical practice, whether for risk prediction, diagnosis, or treatment recommendation. But a persistent question keeps arising: What happens when things go wrong? When patients are injured, and AI was","url":"https://doi.org/10.2967/jnumed.120.257196","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.2967/jnumed.120.257196","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/26646","name":"Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.","source":"europepmc","abstract":"Background The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. Objective This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. Methods A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. Results The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H 4 =48.3, P Conclusions Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.","url":"https://doi.org/10.2196/26646","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.2196/26646","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3969/j.issn.1671-7104.2021.01.014","name":"[Study on Technical Risks and Primary Responsibility of Medical Artificial Intelligence Diagnosis Products under Strategy of \"Healthy China\"].","source":"europepmc","abstract":"Objective It provides reference for accurate and efficient supervision of medical artificial intelligence industry. Methods By summarizing the main responsibility dilemma of medical artificial intelligence diagnosis products, sorting out relevant researches at home and abroad, the primary responsibility system of medical artificial intelligence diagnosis products is constructed. Results A medical artificial intelligence diagnosis products primary responsibility system with the marketing authorization holder as the \"first responsible person\" is established, and three safeguard measures are proposed, namely, algorithm transparency and interpretability, classification supervision mode and social co-governance supervision mode. Conclusions The medical artificial intelligence diagnosis products primary responsibility system is helpful to implement the primary responsibility, to build \"responsible and beneficial\" artificial intelligence, and to realize \"self-discipline\", \"good governance\" and \"in good order\".","url":"https://doi.org/10.3969/j.issn.1671-7104.2021.01.014","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.3969/j.issn.1671-7104.2021.01.014","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1093/jamia/ocab035","name":"Conflicting information from the Food and Drug Administration: Missed opportunity to lead standards for safe and effective medical artificial intelligence solutions.","source":"europepmc","abstract":"The Food & Drug Administration (FDA) is considering the permanent exemption of premarket notification requirements for several Class I and II medical device products, including several artificial Intelligence (AI)-driven devices. The exemption is based on the need to rapidly more quickly disseminate devices to the public, estimated cost-savings, a lack of documented adverse events reported to the FDA's database. However, this ignores emerging issues related to AI-based devices, including utility, reproducibility and bias that may not only affect an individual but entire populations. We urge the FDA to reinforce the messaging on safety and effectiveness regulations of AI-based Software as a Medical Device products to better promote fair AI-driven clinical decision tools and for preventing harm to the patients we serve.","url":"https://doi.org/10.1093/jamia/ocab035","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1093/jamia/ocab035","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1002/hast.842","name":"Groundhog Day for Medical Artificial Intelligence.","source":"europepmc","abstract":"Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the \"AI winter.\" With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point-a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI.","url":"https://doi.org/10.1002/hast.842","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2018","doi":"10.1002/hast.842","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1093/jamia/ocab063","name":"Corrigendum: Conflicting information from the Food and Drug Administration: Missed opportunity to lead standards for safe and effective medical artificial intelligence solutions.","source":"europepmc","abstract":"Journal of the American Medical Informatics Association, 2021, doi: 10.1093/jamia/ocab035 The author name “Matthew P Lungren” was incorrectly given as “Matthew P Lundgren”. “CDRH” should have been defined at its first appearance, and incorrectly appeared in the second paragraph as “CDHR”. These errors have been corrected online.","url":"https://doi.org/10.1093/jamia/ocab063","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1093/jamia/ocab063","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.langlo.2026.104022","name":"Methodological concerns regarding the use of automated visual evaluation without HPV testing for cervical screening.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.langlo.2026.104022","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2027","doi":"10.1016/j.langlo.2026.104022","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.3348/jksr.2019.0179","name":"[Survey of the Knowledge of Korean Radiology Residents on Medical Artificial Intelligence].","source":"europepmc","abstract":"Purpose To survey the perception, knowledge, wishes, and expectations of Korean radiology residents regarding artificial intelligence (AI) in radiology. Materials and methods From June 4th to 7th, 2019, questionnaires comprising 19 questions related to AI were distributed to 113 radiology residents. Results were analyzed based on factors such as the year of residency and location and number of beds of the hospital. Results A total of 101 (89.4%) residents filled out the questionnaire. Fifty (49.5%) respondents had studied AI harder than the average while 68 (67.3%) had a similar or higher understanding of AI than the average. In addition, the self-evaluation and knowledge level of AI were significantly higher for radiology residents at hospitals located in Seoul and Gyeonggi-do compared to radiology residents at hospitals located in other regions. Furthermore, the self-evaluation and knowledge level of AI were significantly lower in junior residents than in residents in the 4th year of training. Of the 101 respondents, only 16 (15.8%) had experiences in AI-related study while 91 (90%) were willing to participate in AI-related study in the future. Conclusion Organizational efforts through a radiology society would be needed to meet the need of radiology trainees for AI education and to promote the role of radiologists more adequately in the era of medical AI.","url":"https://doi.org/10.3348/jksr.2019.0179","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.3348/jksr.2019.0179","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1161/circep.119.007988","name":"Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.","source":"europepmc","abstract":"Background Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG. Methods We performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG. Results The convolutional neural network that was previously derived in a homogeneous population (derivation cohort, n=44 959; 96.2% non-Hispanic white) demonstrated consistent performance to detect low left ventricular ejection fraction across a range of racial/ethnic subgroups in a separate testing cohort (n=52 870): non-Hispanic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223; AUC, 0.938). In secondary analyses, a separate neural network was able to discern racial subgroup category (black/African American [AUC, 0.84], and white, non-Hispanic [AUC, 0.76] in a 5-class classifier), and a network trained only in non-Hispanic whites from the original derivation cohort performed similarly well across a range of racial/ethnic subgroups in the testing cohort with an AUC of at least 0.930 in all racial/ethnic subgroups. Conclusions Our study demonstrates that while ECG characteristics vary by race, this did not impact the ability of a convolutional neural network to predict low left ventricular ejection fraction from the ECG. We recommend reporting of performance among diverse ethnic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use of artificial intelligence in medicine.","url":"https://doi.org/10.1161/circep.119.007988","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1161/circep.119.007988","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.21037/atm.2019.04.07","name":"The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence.","source":"europepmc","abstract":"Artificial Intelligence (AI) in healthcare has become a quasi-normal subject (1). In the last few years, there has been an impressive increase in the number of publications concerning the application of machine learning (ML), a set of techniques and models for building data-driven AI systems, to medical tasks, such as the diagnosis, prognosis and anticipation of treatment effects and complications (2).","url":"https://doi.org/10.21037/atm.2019.04.07","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.21037/atm.2019.04.07","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1089/heq.2018.0037","name":"The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries.","source":"europepmc","abstract":"Background: Artificial intelligence (AI) is a rapidly developing computer technology that has begun to be widely used in the medical field to improve the professional level and efficiency of clinical work, in addition to avoiding medical errors. In developing countries, the inequality between urban and rural health services is a serious problem, of which the shortage of qualified healthcare providers is the major cause of the unavailability and low quality of healthcare in rural areas. Some studies have shown that the application of computer-assisted or AI medical techniques could improve healthcare outcomes in rural areas of developing countries. Therefore, the development of suitable medical AI technology for rural areas is worth discussing and probing. Methods: This article reviews and discusses the literature concerning the prospects of medical AI technology, the inequity of healthcare, and the application of computer-assisted or AI medical techniques in rural areas of developing countries. Results: Medical AI technology not only could improve physicians' efficiency and quality of medical services, but other health workers could also be trained to use this technique to compensate for the lack of physicians, thereby improving the availability of healthcare access and medical service quality. This article proposes a multilevel medical AI service network, including a frontline medical AI system (basic level), regional medical AI support centers (middle levels), and a national medical AI development center (top level). Conclusion: The promotion of medical AI technology in rural areas of developing countries might be one means of alleviating the inequality between urban and rural health services. The establishment of a multilevel medical AI service network system may be a solution.","url":"https://doi.org/10.1089/heq.2018.0037","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2018","doi":"10.1089/heq.2018.0037","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.annonc.2026.07.001","name":"Thymic Radiation is Associated with Worse Outcomes in Patients with NSCLC.","source":"europepmc","abstract":"Background Emerging evidence indicates the functional importance of the thymus in adult health and may influence oncologic outcomes, yet current radiotherapy (RT) practice does not consider the thymus as an organ of interest. Because RT may incidentally expose the thymus to radiation, we hypothesized that thymic radiation dose is negatively associated with major clinical outcomes. Patients and methods This multicohort analysis included 1,107 patients with non-small cell lung cancer (NSCLC), including the phase III RTOG-0617 trial (n=460) and two independent real-world cohorts of patients treated with chemoradiotherapy alone (n=422; HARVARD-CRT) or in combination with consolidation immunotherapy (n=225; HARVARD-DURVA). We quantified thymic function using a deep-learning system that assessed thymic radiographic characteristics as a proxy for thymic function. Mean Thymic Dose (MTD) was used to measure thymic radiation exposure. Results Incidental thymic irradiation was associated with increased risk of distant metastases after confounding adjustments. Concretely, a 1Gy increase in MTD was associated with an increased distant metastasis risk of 1.57-4.21% (RTOG-0617: adjusted hazard-ratio [aHR]=1.29; P=0.0028; HARVARD-CRT: aHR=1.33; P=0.011; HARVARD-DURVA: aHR=1.95; P=0.007). In Thymic-Health-stratified analysis, patients with preserved Thymic Health prior to radiotherapy appeared to be at particularly greater risk, whereas no significant associations emerged in patients with impaired Thymic Health. One-year follow-up imaging demonstrated dose-dependent declines in thymic health and lower circulating lymphocyte counts, consistent with a possible biological link between thymic irradiation and immune competence loss. An exploratory feasibility study suggested that re-optimizing RT planning for thymic sparing can be achieved without compromising tumor coverage or cardiopulmonary constraints. Conclusions Thymic radiation exposure was independently associated with higher risks of metastasis and death in NSCLC patients, especially in those with preserved thymic function. These findings raise awareness towards considering the thymus as an organ of interest in radiotherapy and suggest that thymus-sparing strategies may help preserve immune health and improve patient outcomes.","url":"https://doi.org/10.1016/j.annonc.2026.07.001","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.annonc.2026.07.001","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/tct.70498","name":"The Promises and Perils of Clinical Decision Support Artificial Intelligence.","source":"europepmc","abstract":"Evidence-based clinical decision support artificial intelligence (AI) is rapidly expanding, but its safe and effective use depends on rigorous validation, trustworthy evidence sources and careful integration into clinical workflows. Current available systems show strong potential to improve diagnostic accuracy, reduce clinician workload and possibly benefit patient care, but challenges remain before its real-world adoption. We must be responsible in its integration to ensure AI truly strengthens clinical judgment. This article is a viewpoint of AI tools for clinical decision support, addressing a rapidly evolving field and provides insights that are useful for clinicians and educators in clinical settings. When used within appropriate medical training, AI may help augment diagnostic accuracy and improve efficiency. Nevertheless, while AI offers promising features, it also presents ethical and reliability challenges, which may negatively affect the medical professional identity.","url":"https://doi.org/10.1111/tct.70498","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/tct.70498","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag178","name":"From Flexner to artificial intelligence: 100 years of Academic Medicine.","source":"europepmc","abstract":"As Academic Medicine celebrates a century of contributions to medical education, it is worth examining the questions that drove the inaugural issues of the journal and what lessons these may provide today. Although 1926 was a year adjacent to many global shocks that influenced medicine, the journal was keenly focused on the 1910 Flexner Report and the existential reforms being implemented across medical schools. The reports in these issues provide insights on a community grappling with disruption. These first authors helped reshape medical education in the United States; the effects of many of the changes they proposed or studied 100 years ago can still be seen in medical education and/or practice. Today, with the rapid acceleration of generative artificial intelligence, medical education and health care delivery face disruption at a scale analogous to or even greater than that encountered in the aftermath of the adoption of the Flexner Report recommendations. In 1926 and 1927, several themes emerged including discussions on integrating new technologies, exploring of biases, and consensus building and articulating values around what is required for a student to enter medical training and what the curriculum should deliver to matriculants. Insights around these themes helped to direct the academic medicine community as new expectations for medical education and the role of physicians were emerging. Change, especially disruptive change, presents both opportunity and risk. The inaugural issues of the journal demonstrate the value of creating a community of informed stakeholders that can debate, generate data, and share experiences. The journal remains critical in 2026 for developing and supporting medical educators, students, and trainees and the patients they serve. The relevance of Academic Medicine, particularly in articulating values, exploring biases, and building consensus will likely only increase through the transformative disruptions that are inevitable over the next 100 years.","url":"https://doi.org/10.1093/acamed/wvag178","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag178","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4274/dir.2026.264319","name":"Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations.","source":"europepmc","abstract":"Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs. In this educational and practical review, we provide an accessible overview of XAI tailored for practicing radiologists and physicians. We cover the major categories of explanation methods, including saliency maps, perturbation-based and feature-attribution approaches, concept- based methods, and example-based reasoning, as well as uncertainty quantification as a complementary approach for assessing prediction reliability, along with common misconceptions and emerging regulatory obligations. We aim to make XAI easier for healthcare professionals to understand, as effective oversight of AI tools has become a core competency for the modern radiologist.","url":"https://doi.org/10.4274/dir.2026.264319","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4274/dir.2026.264319","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5662/wjm.116140","name":"Letter to the Editor: Artificial wisdom and the transience of truth: Ethical and temporal reflections on artificial intelligence-human inquiry into history.","source":"europepmc","abstract":"Zhou et al published a study in the recent issue of the World Journal of Gastroen terology , which aimed to examines the concept of artificial wisdom (AW) as applied to historical medical inquiry, in response to a recent artificial intelligence (AI)-human analysis of Alexander the Great's cause of death. While acknowledging the innovative integration of generative AI with clinical reasoning, the letter highlights key epistemic and ethical limitations. It argues that AI systems, inherently shaped by transient data and iterative obsolescence, cannot access enduring or timeless truths, particularly in historically remote contexts where evidence is fragmentary and context-dependent. The persuasive fluency of large language models risks creating an illusion of certainty, conflating probabilistic synthesis with wisdom. Drawing on principles of ethical AI use, the letter emphasizes transparency, accountability, and human moral stewardship as essential safeguards. Ultimately, it proposes a shift from the notion of AW toward epistemic stewardship, recognizing that truth evolves with time and that wisdom resides not in algorithms, but in ethically grounded human judgment.","url":"https://doi.org/10.5662/wjm.116140","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5662/wjm.116140","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijbiomac.2026.154098","name":"Fabrication and characterization of asiaticoside-incorporated silk fibroin/hyaluronic acid hydrogel with immunomodulatory properties for diabetic wound management.","source":"europepmc","abstract":"Diabetic foot ulcers (DFUs) arise within a dysregulated wound microenvironment in which sustained inflammation, bacterial susceptibility, and excessive oxidative stress collectively impede tissue repair. To address these interrelated barriers, we developed an asiaticoside-loaded silk fibroin/hyaluronic acid composite hydrogel (ASHF) as a bioactive dressing that couples structural support with localized drug delivery. By optimizing the mass ratio of its components, the resulting hydrogel achieved an excellent balance among mechanical compliance, swelling capacity, tissue adhesion, water vapor transmission rate, and enzymatic degradation. In vitro experiments demonstrated that ASHF released asiaticoside in a two-stage manner, was well tolerated by fibroblasts, promoted cell migration, and inhibited Escherichia coli and Staphylococcus aureus. In vivo evaluation in a diabetic mouse model revealed that ASHF-treated wounds closed more rapidly and showed stronger collagen deposition, CD31-positive neovascularization, and re-epithelialization. Dual immunofluorescence staining demonstrated a significant reduction in M1 macrophages (CD68 + /CD86 + ) and a concurrent increase in M2 macrophages (CD68 + /CD206 + ), alongside favorable shifts in local cytokines (decreased IL-6 and increased IL-10). These findings indicate an effective transition of the local immune microenvironment from persistent inflammation toward a pro-reparative state. Transcriptomic analysis of wound tissues further indicated that ASHF intervention was associated with enrichment of glutathione-related metabolic programs and epidermal differentiation signatures, together with upregulation of key genes including Gstm3, Aox4, Hal, and Krt1. These results suggest that ASHF supports diabetic wound repair through coordinated regulation of redox balance, macrophage polarization, and tissue reconstruction, highlighting its potential as a multifunctional dressing for chronic diabetic wounds.","url":"https://doi.org/10.1016/j.ijbiomac.2026.154098","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijbiomac.2026.154098","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1093/dmfr/twag024","name":"Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.","source":"europepmc","abstract":"Objectives This systematic review aimed to investigate the current development and application landscape of generative artificial intelligence (Gen-AI) networks specifically designed for dental and maxillofacial radiology (DMFR) and summarize their potential applications for clinical practice, education, and research. Methods Five electronic databases were searched to identify studies that developed and validated DMFR-specific Gen-AI networks. Data regarding the purpose and type of the Gen-AI model, dataset details, quantitative evaluation metrics, methods of subjective assessment, and key findings were extracted. Customized assessment criteria adapted from the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) checklist were used to evaluate the risk-of-bias for the included studies in 4 domains (dataset details, reporting of Gen-AI model architecture and training strategies, reliability of performance evaluation methods, and accessibility of code and developed models). Results Forty-three studies were included from the initially identified 2060 records. Of these, 24 studies (55.8%) focused on image quality, addressing improvements in spatial resolution, artifact and noise reduction, image geometry and projection, quantitative accuracy of voxel values, 14 (32.6%) on image simulation (including the generation of bitewing, panoramic, and cephalometric radiographs, image-to-image translation, and post-treatment prediction simulation), 3 (7%) on 3D reconstruction from 2D images, and 2 (4.6%) on automated interpretation and reporting. Nearly all included studies (95.3%) reported objective evaluation metrics while about half (58.1%) incorporated subjective assessments using scoring systems or visual grading. Risk-of-bias was moderate for dataset details in 7 studies (16.3%) and performance evaluation in 20 (46.5%), and high for code and model accessibility in 35 studies (81.4%). Conclusions While DMFR-specific Gen-AI models show promising potential for clinical practice, education, and research, their applicability requires overcoming challenges related to data quality, validation, integration, and ethical and legal considerations. Further clinical validation, increased transparency and accessibility, and thorough evaluation of cost-effectiveness in diagnostic workflows are essential to ensure their safe and effective use.","url":"https://doi.org/10.1093/dmfr/twag024","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/dmfr/twag024","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/aorn.70139","name":"Leveraging Artificial Intelligence to Improve Perioperative Staffing Consistency: A Quality Improvement Initiative at a Large Academic Medical Center.","source":"europepmc","abstract":"A large academic medical center in the Pacific Northwest addressed perioperative staffing challenges by implementing a workflow with application of AI assistance to optimize daily assignments. The initiative integrated real-time and historical data to support consistent matching of staff competencies and procedure experience with scheduled procedures and surgeons. The workflow streamlined processes and saved service line coordinators 20 hours per week and nurse leaders 5 hours per week. Surgical staffing consistency improved by 30 percentage points, from 50% to 80%, and staff and surgeon sentiment improved. By enhancing visibility into documented competencies and procedure history and reducing reliance on manual assignments, the initiative improved the reliability and efficiency of daily staffing decisions while reinforcing safe alignment of skills with procedure complexity. Implementation success was supported by multidisciplinary collaboration and iterative workflow refinement. This article outlines the implementation approach, observed outcomes, and key operational considerations for health systems pursuing similar innovations.","url":"https://doi.org/10.1002/aorn.70139","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/aorn.70139","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1097/paf.0000000000001167","name":"Combining Digital Camera Metadata and Artificial Intelligence to Sort Postmortem Gross Photographs: A Novel Approach in Image Classification in Forensic Pathology.","source":"europepmc","abstract":"Reviewing postmortem photographs is routine in forensic pathology. Although standard photographic techniques are used, sorting postmortem photographs can be overly time-consuming, especially when reviewing large quantities of photographs. Artificial intelligence may assist in identifying body parts/locations but is challenged in determining spatial relationships such as sidedness, angle, and distance/depth. Metadata stored in modern digital cameras provides physical data that would assist in determining this information, which can be critical in the legal setting. This proof-of-concept study used photograph metadata and artificial intelligence to develop a tool to sort postmortem photographs using living volunteer subjects. This \"tool\" automatically sorts through general, close-up images (hands, eyes, face, and hands) and mock injury (with a scale) photographs. The study compared using photograph metadata and artificial intelligence in different proportions (hybrid photograph metadata and artificial intelligence vs. artificial intelligence alone). Results demonstrated that using both metadata and artificial intelligence for different tasks was more accurate and efficient than using artificial intelligence alone.","url":"https://doi.org/10.1097/paf.0000000000001167","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/paf.0000000000001167","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.7417/ct.2026.2124","name":"Evaluation of Artificial Intelligence (AI) for Myocardial Infarction Health Information: Expert Accuracy Assessment and User Trust Survey.","source":"europepmc","abstract":"Background Artificial intelligence chatbots, particularly ChatGPT, have emerged as increasingly popular sources of health information for the general public. However, concerns persist regarding the accuracy, safety, and appropriateness of AI-generated medical advice, especially for life-threatening conditions such as myocardial infarction. Objectives This study aimed to evaluate the accuracy, completeness, and safety of ChatGPT-generated responses and information to public questions about heart attacks, assess user perceptions and trust, and compare AI-generated content with expert-validated sources. Methods A cross-sectional study was conducted between January and March 2026. A total of 73 commonly asked heart attack-related questions were submitted to ChatGPT (GPT-4), and responses were independently evaluated by two expert reviewers and one external reviewer using standardized rubric assessing accuracy, completeness, clarity, safety, and tone. Inter-rater reliability was assessed using intraclass correlation coefficients. In parallel, a survey of 352 participants evaluated user perceptions, trust, and behavioral use of ChatGPT for medical information. Statistical analyses included descriptive statistics, group comparisons, correlation analyses, and multivariable regression models to identify predictors of trust and satisfaction. Results Expert reviewers assigned high ratings for accuracy (mean: 4.62 ± 0.62 and 4.48 ± 0.63) and safety (mean: 4.78 ± 0.51 and 4.05 ± 0.50), with substantial inter-rater agreement (ICC = 0.788). In contrast, the external reviewer documented considerably lower completeness ratings (2.59 ± 0.57), revealing deficiencies in information coverage. Remarkably, 40.6% of respondents indicated they had followed ChatGPT's medical recommendations without seeking professional consultation, while 17.6% reported depending on the chatbot during urgent medical situations. Levels of user trust and satisfaction were moderate, showing significant correlations with perceived accuracy, usage frequency, and healthcare-related educational background. Conclusions Expert assessments confirmed that ChatGPT generally provides accurate and safe cardiovascular health information; nevertheless, considerable inter-reviewer variability-especially the markedly lower completeness scores from the external evaluator-reveals significant gaps in content depth. These results indicate that ChatGPT functions more appropriately as an adjunctive educational resource rather than a replacement for professional medical consultatio n, emphasizing the necessity for enhanced safety mechanisms, clearer user instructions, and standardized content protocols when addressing high-stakes health topics. ChatGPT should be considered an adjunctive resource rather than a substitute for urgent professional medical assessment in suspected heart attack cases.","url":"https://doi.org/10.7417/ct.2026.2124","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7417/ct.2026.2124","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.nedt.2026.107347","name":"Generative AI dependency and self-perceived clinical decision-making in nursing students: A three-wave longitudinal study of metacognitive awareness as a mediator.","source":"europepmc","abstract":"Background Generative artificial intelligence tools are increasingly used in nursing education, but excessive reliance on externally generated reasoning may be associated with reduced cognitive engagement. Evidence remains limited on whether within-person changes in generative artificial intelligence dependency are prospectively associated with nursing students' metacognitive awareness and self-perceived clinical decision-making. Objectives To examine longitudinal within-person associations among generative artificial intelligence dependency, metacognitive awareness, and self-perceived clinical decision-making, and to test whether metacognitive awareness mediates these associations. Design A three-wave prospective longitudinal panel study. Settings Nursing schools at three medical universities in China. Participants A convenience sample of 687 undergraduate nursing students completed the baseline survey; 618 students completed the third wave, yielding a retention rate of 90.0%. Methods Data were collected at the beginning, middle, and end of one academic semester. A random-intercept cross-lagged panel model was used to separate stable between-person differences from within-person fluctuations. Missing data were handled using full-information maximum likelihood. Common method bias was examined using Harman's single-factor test and an unmeasured latent method construct. Results At the within-person level, higher generative artificial intelligence dependency was prospectively associated with lower metacognitive awareness at the subsequent wave (β = -0.18 to -0.21, p Conclusions Generative artificial intelligence dependency was prospectively associated with students' self-perceived clinical decision-making through metacognitive awareness. Because the study was observational and the outcome was self-reported, the findings indicate an educational risk pathway rather than demonstrated clinical harm. Nursing educators may benefit from pairing artificial intelligence use with metacognitive scaffolding, verification routines, and reflective assessment.","url":"https://doi.org/10.1016/j.nedt.2026.107347","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.nedt.2026.107347","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijnurstu.2026.105673","name":"Generative artificial intelligence use patterns among Chinese nursing graduate students: A qualitative descriptive study.","source":"europepmc","abstract":"Background Generative artificial intelligence is increasingly embedded in graduate nursing research environments. Within this context, patterns of reliance on these tools, extending in some cases to dependency, have emerged among nursing graduate students, raising concerns about the impact on the development of foundational research competencies. Yet, limited qualitative evidence exists regarding how such reliance manifests, evolves, and is experienced by students at different training stages. Objectives To explore how nursing graduate students in China experience and navigate generative artificial intelligence use in their research practice, including the conditions under which such use becomes dependency. Design Qualitative descriptive study. Setting Data were collected from nursing graduate programs at 10 universities across China. Participants Seventeen nursing graduate students (9 doctoral, 8 master's) were recruited using purposive sampling supplemented by snowball sampling. All participants had documented use of generative artificial intelligence tools for research-related tasks for a minimum of three months. Methods Semi-structured in-depth interviews were conducted between March and April 2026. Data were analyzed using Braun and Clarke's six-phase reflexive thematic analysis, supported by NVivo 12.0 software, and interpreted through cognitive offloading theory. Results Data analysis identified four themes: (1) pervasive generative artificial intelligence integration across research workflows; (2) how generative artificial intelligence simultaneously augments output and erodes capability; (3) emotional and evaluative responses to generative artificial intelligence dependency; and (4) self-regulation strategies and structural gaps. Cross-case analysis yielded a typology of four user profiles: tool-rational, collaborative self-regulating, efficiency-oriented explorer, and deeply dependent user. Doctoral students predominantly occupied profiles characterized by higher metacognitive control, suggesting an \"experience-buffering effect\" from prior research training. Conclusions Generative artificial intelligence integration among nursing graduate students simultaneously enhances research productivity while risking the covert erosion of foundational competencies. The central challenge shifts from prohibiting artificial intelligence use to ensuring that metacognitive control keeps pace with integration depth. These findings advocate for the implementation of differentiated, stage-sensitive artificial intelligence governance frameworks tailored to nursing graduate education.","url":"https://doi.org/10.1016/j.ijnurstu.2026.105673","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijnurstu.2026.105673","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10143-026-04462-z","name":"Navigating the use of artificial intelligence in the peer review process: Cautionary pearls from the editorial board.","source":"europepmc","abstract":"Peer review remains a cornerstone of academic publishing, underpinning the integrity and credibility of the scientific literature. High-quality peer review is both a human art and a learned skill, requiring an understanding of study design, critical appraisal and subject-specific expertise. However, the rapid expansion of scientific publishing has placed increasing strain on the peer review ecosystem, contributing to reviewer fatigue, delays, and variability in review quality. Recent advances in artificial intelligence (AI) offer potential opportunities to enhance the efficiency and consistency of peer review, but their integration into editorial workflows requires careful consideration. In this editorial, we explore the evolving role of peer review, the challenges facing the current ecosystem, and the potential - yet constrained - role of AI, from the perspective of the Editorial Board of Neurosurgical Review.","url":"https://doi.org/10.1007/s10143-026-04462-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10143-026-04462-z","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/tct.70489","name":"Recommendations for Training Faculty in Generative AI Use: Crafting Higher-Order Application Exercises in Team-Based Learning.","source":"europepmc","abstract":"Application exercises are the most critical components of the sequenced steps of team-based learning (TBL) that should be designed as complex, real-world problems to elicit deep learning and foster student engagement. The process of crafting these exercises presents several challenges for educators-time constraints, alignment with learning objectives, scaffolding and authentic simulation of real-world problem solving. The use of artificial intelligence (AI) cuts down time and effort for design and allows focus on refinement and error correction. In addition, it allows use of an iterative process to generate higher order application exercises that provide the context and competency level required for students to make learning gains. We are sharing our knowledge on using AI to design application exercises (AEs) garnered from our international workshops on TBL. We empowered educators to write effective prompts using the Task-Role-Audience-Create-Intent (TRACI) framework to create engaging AEs that meet the 4S principles, i.e., significant problem, same problem, specific choice, simultaneous reporting, of TBL AEs, aligned to higher order Bloom's taxonomy objectives for critical thinking and learner engagement. We then demonstrated how we used Anthropic's Claude 4 Sonnet AI tool to design the exercises using an iterative process. The workshop concluded with a reflection exercise and feedback to promote intentionality. Our collective experience facilitating this workshop in interdisciplinary and interprofessional settings, serving as TBL facilitators at our respective institutions and informal participant feedback at the workshops informed the development of this series of best practice recommendations for faculty development. This foundational yet critical skill is necessary in the rapidly evolving landscape of AI skill-building competencies for health professions educators. We describe our best practice recommendations here within the paradigm of course design for TBL.","url":"https://doi.org/10.1111/tct.70489","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/tct.70489","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/diseases14080295","name":"Artificial Intelligence, Wearable Technologies, and Virtual Reality in Precision Nutrition and Obesity Management: A Critical Narrative Review.","source":"europepmc","abstract":"Background Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of \"precision nutrition,\" \"personalized nutrition,\" \"obesity,\" \"weight management,\" \"metabolic health,\" \"digital health,\" \"artificial intelligence,\" \"machine learning,\" \"mobile health,\" \"wearable devices,\" and \"omics\" using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care.","url":"https://doi.org/10.3390/diseases14080295","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diseases14080295","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106666","name":"Deployer-side governance of medical imaging artificial intelligence: the regulatory readiness instrument (RRI-MI) for multi-jurisdictional and post-market compliance.","source":"europepmc","abstract":"Artificial intelligence (AI) is transforming medical imaging and digital health, yet standard pre-market clearances evaluate algorithms under static, idealised conditions. Once deployed, imaging AI can degrade because of scanner drift, acquisition protocol shifts, software updates, and patient demographic variation. Although the US Food and Drug Administration Predetermined Change Control Plan and the EU AI Act introduce lifecycle oversight, clinical institutions lack structured tools to operationalise these requirements locally. We propose the Regulatory Readiness Instrument for Medical Imaging AI (RRI-MI), a deployer-side readiness assessment framework that maps 11 governance domains onto a provisional 22-point ordinal readiness rubric. Grounded in post-market evidence and illustrated through a semiautonomous prostate cancer MRI case study, RRI-MI translates high-level digital health policy into local validation, human oversight, version control, monitoring, and incident-response workflows. It is presented as a conceptual framework for adaptation and future validation.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106666","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106666","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag175","name":"Artificial intelligence in medical education: promise, caution, and the need for restraint.","source":"europepmc","abstract":"Chloe Tan Yong Han, David Marshall, Elaine Burke, MB, PhD; Artificial intelligence in medical education: promise, caution, and the need for restraint, Acad","url":"https://doi.org/10.1093/acamed/wvag175","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag175","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fmed.2026.1895176","name":"Blind spots in artificial intelligence systems: poor identification of self-generated medical images-evidence-based cross-sectional study.","source":"europepmc","abstract":"Objectives Artificial intelligence (AI) systems are increasingly adopted in scientific visualization, clinical sciences, medical education, and health sciences research. Despite its impressive generative capabilities, the AI role remains poorly established in some areas. This study aims to investigate whether AI systems, ChatGPT-4 and Google Gemini 1.5 Pro, can identify their own generated images. Methods In this study, AI systems, OpenAI's ChatGPT-4 and Google Gemini 1.5 Pro, were employed to generate and identify medical science images. AI-generated images in medical science span 16 clinical illustrations: 8 anatomical and 8 pathophysiological mechanisms. The images were subsequently reintroduced into the same system for identification. The descriptive statistics, including numbers, percentages, and accuracy rates, across the images were analyzed. For a correct answer or an incorrect answer, a score of 1 was allocated; a p -value less than 0.05 was considered significant. Results The results revealed that Artificial Intelligence models, ChatGPT and Google Gemini, were significantly less able to identify AI-generated images correctly. There were significantly higher incorrect rates than correct identification rates for AI-generated images overall (27/32 incorrect vs. 5/32 correct; 84.37% vs. 15.62%; p = 0.001). ChatGPT showed an overall accuracy of 18.75% ( p = 0.021), while Google Gemini demonstrated an overall accuracy of 12.5% ( p = 0.004). The poorest performance was observed in Google Gemini anatomy images, with no correct identifications (0%, p = 0.008). Conclusion Artificial Intelligence models, ChatGPT and Google Gemini, have limited ability to identify AI-generated images correctly. The results demonstrate structured, architecture-dependent patterns of image identification dysfunction. The study emphasizes implications for AI governance and validation protocols in research, medicine, and medical education.","url":"https://doi.org/10.3389/fmed.2026.1895176","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1895176","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.15766/mep_2374-8265.11621","name":"Artificial Intelligence and Large Language Models: A Case-Based, Peer-Teaching Workshop for Preclinical Medical Students.","source":"europepmc","abstract":"Introduction Artificial intelligence tools have rapidly become integrated in health care settings and are quickly affecting medical student education. However, there remains limited formal teaching on such tools in student curricula. This project sought to introduce preclerkship medical students to the basics of large language models and allow them to practice ways to best use these tools to supplement their learning. Methods The authors designed, implemented, and evaluated a 60-minute lecture and 100-minute workshop for second-year medical students. The workshop included interactive cases covering various aspects of artificial intelligence use, and some groups were led entirely by student leaders, allowing for peer teaching. Results One hundred sixty-eight students from Harvard Medical School and Harvard School of Dental Medicine were enrolled in this session. Anonymous pre- and postsession surveys ( N = 124 and N = 62, respectively) were collected and compared via unpaired t test assuming unequal variance and showed statistically significant increase in the mean ratings of six 5-point Likert scale questions assessing artificial intelligence-related knowledge/self-efficacy ( P Discussion Our artificial intelligence and large language model session provides a framework for teaching medical students, early in their educational journey, the basics of these tools. Our session provides interactive exercises to illustrate how best to leverage such tools while also discussing their potential risks. Such education will be important to incorporate into medical student curricula as artificial intelligence technologies grow increasingly common.","url":"https://doi.org/10.15766/mep_2374-8265.11621","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.15766/mep_2374-8265.11621","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/tct.70486","name":"Artificially Intelligent Simulated Patients in Undergraduate Surgical Teaching: A Single Blinded Randomized Controlled Study.","source":"europepmc","abstract":"Introduction Self-directed learning (SDL) and reflection are critically important for clinicians. There is limited evidence describing the utilization of artificial intelligence (AI) to support medical SDL. AI simulated patients (AISP) may allow for high-fidelity, reflective educational interactions. We aimed to determine how SDL with AISP interaction compared to standard SDL in the development of clinical reasoning skills. Methods This was a single-institution randomized controlled study in early clinical years' undergraduate students, learning about gallstone disease. For 90% power and a 20% noninferiority margin, 36 participants were randomized either to an intervention group using an AISP or to a control group using current educational resources. Both groups underwent a standardized clinical skills assessment. Quantitative and qualitative data were collected. Results Twenty-seven participants completed the study. The intervention group demonstrated statistical noninferiority compared with the control group (assessment scores of 13.1 vs. 12.1, respectively, p = 0.11). In addition, there were fewer borderline grades, and significantly more distinction grades in the intervention group than control (6 vs. 1, respectively, p = 0.037), suggesting the AISP may have improved performance in clinical management. The qualitative data suggested widespread satisfaction and enthusiasm for AISPs. Conclusion Our results suggest that AISPs are comparable to traditional resources in developing undergraduate clinical skills, and may contribute to improved academic performance in undergraduate surgery. Globally, there is an appetite for their introduction into the clinical curriculum. Real patient interaction is the standard. However, AISPs may have a supplementary role and are likely to become increasingly prominent in medical curricula.","url":"https://doi.org/10.1111/tct.70486","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/tct.70486","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ajt.2026.07.034","name":"Dual-tracer FDG and FAPI PET imaging for dynamic monitoring and early detection of chronic lung allograft dysfunction.","source":"europepmc","abstract":"Chronic lung allograft dysfunction (CLAD) remains the leading cause of late mortality after lung transplantation, yet current diagnosis mainly depends on lung function decline. We investigated whether dual-tracer positron emission tomography (PET) imaging with 18 F-fluorodeoxyglucose (FDG) and fibroblast activation protein inhibitor (FAPI) could noninvasively characterize the transition from inflammation to fibrosis during CLAD progression. Using a rat orthotopic lung transplantation model with longitudinal PET/CT from weeks 1 to 6, findings were correlated with histopathology, immune infiltration, cytokines, fibroblast activation, and collagen deposition. Clinical PET/CT data from transplant recipients were also analyzed. Histology showed progressive airway remodeling and fibrosis. FDG uptake peaked at week 3, matching CD8 + /CD19 + infiltration and IL-6 expression. FAPI uptake rose later, linking to fibroblast activation and matrix deposition. Thus, FDG and FAPI revealed distinct inflammatory and fibrotic phases. Clinically, abnormal dual-tracer uptake appeared in a recipient before lung function met CLAD criteria, while a stable recipient showed no abnormal signal. These findings may help advance the early diagnosis, biological staging, and individualized clinical management of CLAD.","url":"https://doi.org/10.1016/j.ajt.2026.07.034","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ajt.2026.07.034","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1002/nop2.70777","name":"Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study.","source":"europepmc","abstract":"Aim To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registered nurses across three dimensions-accuracy, empathy and readability-and to explore the impact of patient. Design A prospective, double-blind, vignette-based cross-sectional study. Methods Five standardized multidisciplinary discharge scenarios were constructed. Discharge instructions were generated independently by five registered nurses and GPT-4. Fifteen clinical experts conducted blinded assessments of accuracy, while 38 patients conducted blinded assessments of empathy and readability. Objective text features were extracted using natural language processing. Paired t-tests or Wilcoxon signed-rank tests were used to compare differences between groups, and a generalized linear mixed model was constructed to analyse factors influencing the acceptability of AI-generated text. Results AI outperformed nurses in information comprehensiveness, but experts identified safety risks in AI-generated texts, whereas no such issues were found in nurse-produced texts. Nurses significantly outperformed AI in both empathy and readability, and objective NLP analysis confirmed that AI-generated texts exhibited higher syntactic complexity and terminology density. The generalized linear mixed model indicated that advancing age and lower educational attainment were associated with reduced acceptance of AI-generated texts. These findings derive from standardized vignettes under controlled experimental conditions and require further validation in real clinical settings. Conclusion Generative AI offers value as a drafting aid for ensuring information completeness in discharge instructions; however, its safety risks, empathy deficits, and linguistic complexity currently limit its standalone application. AI may be better positioned as an assistive tool for nurses rather than an independent communication tool, and its deployment should address potential digital divide issues among patient populations. Implications for the profession and/or patient care These findings support positioning artificial intelligence as an information completeness tool requiring mandatory nurse review before patient delivery. Nurses remain essential for ensuring clinical safety, providing empathetic communication, and adapting language complexity to individual patient needs. Healthcare organizations should establish protocols requiring nurse verification of all artificial intelligence-generated discharge content, with particular attention to medication dosages and contraindications. The identification of a digital divide necessitates that deployment strategies include health literacy assessment and tiered delivery approaches to prevent technological advances from exacerbating health communication inequalities among vulnerable populations. Impact What problem did the study address? ○ Generative artificial intelligence tools are increasingly proposed for clinical documentation, yet limited evidence exists comparing their discharge instruction quality against registered nurses-the professionals primarily responsible for discharge education-across multiple dimensions relevant to patient safety and comprehension. What were the main findings? ○ While artificial intelligence produced more comprehensive information, it generated clinically unsafe content and scored significantly lower than nurses in empathy and readability. Older and less-educated patients showed reduced acceptance of artificial intelligence-generated text, indicating a potential digital divide. Where and on whom will the research have an impact? ○ These findings inform nursing practice, healthcare informatics policy, and equitable care delivery globally, particularly regarding the safe integration of artificial intelligence tools into nurse-led discharge education workflows for diverse patient populations. Reporting method This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and incorporated principles from the Decision Support Systems Evaluation Guideline for Artificial Intelligence in Healthcare framework. No patient or public contribution Patients and the public were not involved in the design, conduct, reporting or dissemination of this research. Trial and protocol registration This study is an observational cross-sectional study and does not require clinical trial registration.","url":"https://doi.org/10.1002/nop2.70777","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/nop2.70777","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00383-026-06573-6","name":"Clinical translation of artificial intelligence in hypospadias: a systematic review and meta-analysis.","source":"europepmc","abstract":"Artificial intelligence (AI) has emerged as a promising tool to improve the objectivity and reproducibility of hypospadias assessment. However, evidence regarding its clinical applications remains fragmented. This systematic review and meta-analysis evaluated the current role, diagnostic performance, and status of clinical translation of AI in hypospadias. A systematic search of PubMed/MEDLINE, Embase, and Scopus was performed according to PRISMA 2020 guidelines (PROSPERO: CRD420261296235). Studies evaluating AI applications in pediatric hypospadias were included. AI applications were categorized into penile curvature assessment, hypospadias classification, urethral plate assessment, and patient education. Random-effects meta-analyses were performed for clinically comparable studies. Sixteen studies published between 2021 and 2026 involving sample sizes ranging from 7 to 1,169 were included. Image-based computer vision and deep learning applications accounted for 62.5% of studies, whereas 37.5% evaluated large language models (LLMs). AI-assisted hypospadias classification demonstrated a pooled accuracy of 88% (95% CI, 87%-90%). AI-assisted penile curvature assessment achieved a pooled mean absolute error of 7.89° (95% CI, 6.43°-9.34°), while AI-generated educational tools demonstrated a pooled accuracy of 73% (95% CI, 60%-86%). Automated urethral plate assessment showed excellent performance, with landmark localization precision of 99.5% and sensitivity of 99.1% in the largest study. Risk of bias was generally low in the predictor and outcome domains but frequently high or unclear in the analysis domain because of limited external validation and methodological heterogeneity. Current evidence indicates that most AI applications in hypospadias remain at the proof-of-concept or early validation stage. Although computer vision has demonstrated promising performance for objective anatomical assessment and LLMs show potential for patient education, prospective clinical implementation remains limited and requires larger multicenter validation studies, standardized reporting, and prospective clinical evaluation before routine integration into clinical practice.","url":"https://doi.org/10.1007/s00383-026-06573-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00383-026-06573-6","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fpubh.2026.1889171","name":"Can artificial intelligence improve health performance? The mediating role of health innovation and the moderating role of digital infrastructure.","source":"europepmc","abstract":"This study examines whether artificial intelligence improves health performance in developing countries and identifies the mechanisms and conditions through which this effect occurs. Using panel data for 19 developing countries over the period 2015-2023, the study investigates the mediating role of health innovation and the moderating role of digital infrastructure in the relationship between artificial intelligence and health performance. The study applies Hayes' PROCESS macro to estimate direct, mediation, moderation, and moderated mediation effects. To strengthen the reliability of the findings, two robustness checks are conducted: two-step System GMM estimation and alternative measurement using infant mortality and disaggregated artificial intelligence indicators. The results show that artificial intelligence has a positive and statistically significant effect on both life expectancy and UHC service coverage. Health innovation partially mediates these relationships, indicating that artificial intelligence improves health performance not only directly through healthcare delivery, diagnosis, surveillance, and resource allocation, but also indirectly by stimulating medical and life-science innovation. The findings further reveal that digital infrastructure strengthens artificial intelligence's effect on health innovation and amplifies its direct effect on both health performance indicators. The moderated mediation results confirm that the artificial intelligence-health innovation-health performance pathway strengthens as digital infrastructure improves. The robustness tests support these conclusions. System GMM confirms the partial mediation mechanism after accounting for dynamic panel-data concerns, while the alternative-measure analysis shows that artificial intelligence patent applications, industrial robots, and artificial intelligence scholarly publications reduce infant mortality through health innovation under stronger digital infrastructure. These findings indicate that artificial intelligence yields greater health benefits when combined with health innovation capacity and digital infrastructure, while also showing that not all artificial intelligence dimensions contribute equally to health-system improvement.","url":"https://doi.org/10.3389/fpubh.2026.1889171","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1889171","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s43441-026-01032-9","name":"Exploring the Polyethylene Glycol-Modified Drug Patent Landscape by Deep Learning.","source":"europepmc","abstract":"Objective Recently, polyethylene glycol modification has become key to improving biopharmaceutical pharmacokinetics and clinical applicability. This study aims to build a comprehensive analytical framework that integrates current status analysis, technology flow, and value assessment, in order to provide a step-by-step and thorough characterization of the patent landscape for PEG-modified drugs. Methods Using the Derwent patent database, this study compiled 99,540 PEG-related patents worldwide from 2014 to 2023. Descriptive statistics, social network analysis, machine learning, and deep learning methods were applied to analyze these patents. Results The number of related patents increased dramatically over the past decade. China filed the most patents (24303) but exhibited a narrower technological breadth, while the United States led in numbers of inventors (86208) and assignees (37045). Patents from developed regions are more likely to be cited, and patent transfer activities mainly occur between commercial institutions. PEG-modified proteins and peptides represent the most commercially active category, highlighting their current market relevance. For patent transfer prediction, the XGBoost model achieved an average accuracy of 88.15%, an average F1-score of 88.28%, and a test ROC-AUC of 87.00%. For early-stage patent quality assessment, the RoBERTa-BiLSTM-MLP model achieved an accuracy of 65.95% and an F1-score of 64.53%. Conclusion These analyses provide a multi-layered understanding of the PEG-modified drug patent landscape, from static features to dynamic trends and from quantitative indicators to qualitative evaluations. These findings provide an analytical reference for exploring technology trends in the field of PEG-modified drugs.","url":"https://doi.org/10.1007/s43441-026-01032-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s43441-026-01032-9","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.jval.2025.09.3055","name":"Health Economics and Outcomes Research in the New Era of Artificial Intelligence: Catch Me If You Can.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jval.2025.09.3055","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jval.2025.09.3055","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/medsci14040434","name":"From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.","source":"europepmc","abstract":"Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care.","url":"https://doi.org/10.3390/medsci14040434","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/medsci14040434","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/vms3.71152","name":"Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly explored in veterinary neurology for pattern recognition, prediction and clinical decision support, with relevance to comparative and translational neuroscience. Objectives This review examines AI applications in veterinary neurology, compares them with human neurology and evaluates cross-species technology transfer within a One Health framework. Methods A narrative review synthesized evidence across AI domains in veterinary neurology, including neuroimaging and radiomics, electrophysiology and seizure detection or forecasting, gait and pain assessment, morphometric biomarker discovery, prognostic modelling and laboratory diagnostic tools. Human studies were considered to identify translational opportunities, methodological challenges and the value of transfer learning and domain adaptation. Results Available evidence suggests that AI holds promise for pattern recognition, prediction and decision support in veterinary neurology. Reported applications include canine brain tumour classification, spinal lesion grading, seizure monitoring and quantitative gait analysis, with encouraging performance. However, most applications remain proof-of-concept. The evidence base is dominated by retrospective single-centre studies with small samples, heterogeneous protocols and limited prospective or external validation. Model calibration, uncertainty reporting and clinically relevant error trade-offs are often insufficiently addressed. Transfer learning and domain adaptation may help overcome limited veterinary datasets, while naturally occurring neurological disease in dogs may also support refinement of human AI systems. Conclusions AI in veterinary neurology is a promising but early field. Future progress will require multi-centre collaboration, standardized data practices, explainable and ethically governed models and stronger One Health partnerships to support safe translation.","url":"https://doi.org/10.1002/vms3.71152","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/vms3.71152","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.tcm.2026.08.009","name":"Editorial commentary: Artificial intelligence in cardiovascular imaging: will we still need the human touch?","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.tcm.2026.08.009","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.tcm.2026.08.009","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3390/s26165182","name":"Trustworthy AI-Powered Intrusion Detection for the Internet of Medical Things (IoMT): A Review.","source":"europepmc","abstract":"The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud-edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols.","url":"https://doi.org/10.3390/s26165182","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/s26165182","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.2214/ajr.26.34829","name":"Explainable Artificial Intelligence (AI) for Medical Imaging: A Framework for Bridging the AI Trust Gap.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly used in health care but often lacks clinician and patient trust. Explainable AI (XAI) aims to clarify predictions and to make AI decisions more transparent, interpretable, and clinically actionable. However, current methods fall short. In this Perspective, we argue that for XAI to be clinically useful in medical imaging and to build trust with clinicians, it must satisfy three guiding principles: it must be technically robust, be adapted to the end users, and be aligned with the specific clinical task. We introduce a conceptual framework incorporating these principles to guide future XAI design and deployment based on expectations and shared responsibilities for developers, vendors, and health care institutions. By ensuring robustness, personalizing outputs, and aligning explanations with use cases, XAI can move beyond one-size-fits-all approaches to task- and user-centered design to support effective and trustworthy AI adoption in health care.","url":"https://doi.org/10.2214/ajr.26.34829","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2214/ajr.26.34829","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1186/s12910-026-01572-7","name":"Physicians' perspectives on artificial intelligence in electrocardiography in clinical practice: a qualitative study.","source":"europepmc","abstract":"Background Artificial intelligence-enhanced electrocardiography (AI-ECG) may support ECG interpretation, prioritization, and workflow efficiency. ECGs are widely used, inexpensive, and often obtained with a low clinical threshold, meaning that AI-ECG may affect large numbers of patients and reshape an established clinical workflow. This study explored physician's perspectives on integrating AI-ECG into clinical practice. Methods Semi-structured interviews were conducted with 12 physicians in Region Stockholm, Sweden. Participants were selected through purposive and snowball sampling and included both specialists and nonspecialists. The interviews were transcribed and analyzed using thematic analysis. Results Two main themes were identified: Designing a new clinical landscape and Navigating a new clinical landscape. Participants saw potential for AI-ECG to improve diagnostic accuracy, support prioritization, and reduce workload. At the same time, they raised concerns about overreliance, accountability, and the limitations of human oversight. Participants also emphasized that AI-ECG may generate incidental or prognostic findings beyond the original reason for obtaining an ECG, raising questions about disclosure, consent, and actionability. Conclusions Informants viewed AI-ECG as a potentially useful support for ECG interpretation in high-volume clinical workflow, but also identified ethical, practical, and professional aspects that require consideration beyond technical performance alone. Because ECGs are common investigations and are often obtained with a low clinical threshold, AI-ECG should be evaluated with regard to its role as a core component of clinical workflow. Implementation should include real-world validation, attention to alert burden and deskilling, and protocols for incidental or prognostic findings.","url":"https://doi.org/10.1186/s12910-026-01572-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12910-026-01572-7","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1080/13651501.2026.2718248","name":"Ethical considerations for artificial intelligence use in clinical practice: a comparative study of EU member state frameworks.","source":"europepmc","abstract":"Objective Regulatory frameworks governing artificial intelligence (AI) in medical practice vary widely across European Union (EU) member states. We assessed the extent to which national medical bodies' codes of conduct address the clinical use of AI. Methods Codes of conduct issued by national medical regulatory bodies in EU member states were analysed and rated for AI-specific content using a four-point scoring system. Results Six countries (Belgium, France, Germany, Lithuania, Poland and Spain) had established AI-specific guidance within their medical ethical frameworks, with scores of 3 to 4; the remaining member states had none. France and Belgium showed the most progressive approaches, emphasising innovation while maintaining ethical standards, whereas Germany and Poland were more conservative, imposing stringent requirements for AI deployment in clinical settings. EU-wide instruments such as the AI Act and the High-Level Expert Group (HLEG) guidelines provide overarching frameworks but do not address the codes of medical practice that govern day-to-day clinical decision-making in member states. Conclusion The heterogeneous landscape reflects the challenge of balancing technological advancement with patient safety. Harmonised national ethical guidelines and closer collaboration between medical regulatory bodies are needed to support responsible AI adoption while preserving ethical practice across the EU.","url":"https://doi.org/10.1080/13651501.2026.2718248","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/13651501.2026.2718248","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1002/cncy.70140","name":"Diagnostic performance of artificial intelligence models trained on scattered single-cell images is not preserved for hyperchromatic crowded cell groups in cervical cytology.","source":"europepmc","abstract":"Background Deep learning has shown promising performance in cervical cytology; however, many studies have relied on presegmented single-cell images rather than the more complex morphologic patterns encountered in routine practice. Here, scattered cells were defined as isolated or dissociated, nonoverlapping single cells. This study quantified the performance loss when artificial intelligence (AI) models trained on these cells were applied to hyperchromatic crowded cell groups (HCGs). Methods Binary convolutional neural network models were developed to differentiate between negative for intraepithelial lesion or malignancy cases and high-grade squamous intraepithelial lesion cases via a scattered cell data set composed of institutional and public liquid-based cytology images. The scattered cell data set comprised 101 cases, with 1062 images; the independent HCG data set comprised 48 cases, with 330 images. ResNet-50, ResNeXt-50, ConvNeXt-Tiny, EfficientNet-B3, VGG-19, and GoogLeNet were trained on scattered cell images, and then directly applied to HCGs without retraining or threshold recalibration. Results All models showed high performance on the scattered cell data set, with the area under the receiver operating characteristic curve (AUC) ranging from 0.950 to 0.996. When directly applied to HCGs, performance declined across all architectures, with the AUC ranging from 0.385 to 0.683. ConvNeXt-Tiny showed the highest AUC on HCGs (0.683); however, this remained substantially lower than its performance on scattered cells (0.996). For all architectures, the AUC was significantly lower on HCGs than on the scattered cell data set. Conclusions Binary AI models trained on scattered cell images achieved excellent discrimination in the original setting but their performance was not preserved when directly applied to HCGs. These findings underscore the need for direct validation and HCG-aware model design in cervical cytology AI.","url":"https://doi.org/10.1002/cncy.70140","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/cncy.70140","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fcimb.2026.1887322","name":"Application of artificial intelligence in geriatric infection: recent advances and prospects.","source":"europepmc","abstract":"Background As global population aging accelerates, infectious diseases in older adults have emerged as a growing public health burden with substantial clinical, economic, and societal implications. The rapid advancement of artificial intelligence (AI) offers a transformative opportunity to enhance the management of infections in this vulnerable population. Methods This review delineates key determinants specific to geriatrics that influence infection susceptibility and atypical presentation, including age-related organ dysfunction, immunosenescence, inflammaging, chronic comorbidities, and psychosocial factors, and summarizes the recent advances in AI applications across the management for infectious diseases in older adults. Results AI has made significant progress in the prevention, diagnosis, and treatment of infectious diseases in older adults: early detection of clinical deterioration, rapid and etiologically precise diagnosis, individualized therapeutic optimization, AI-facilitated antimicrobial stewardship, and accelerated discovery of novel antimicrobial strategies. Conclusion AI shows great potential in helping prevent, diagnose, and treat infections in older adults, and represents a promising complement to traditional care approaches.","url":"https://doi.org/10.3389/fcimb.2026.1887322","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcimb.2026.1887322","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fpubh.2026.1885808","name":"Artificial intelligence readiness and its relationship with thriving at work among Chinese nurses: a latent profile analysis.","source":"europepmc","abstract":"Objectives To explore the latent profile categories of nurses' artificial intelligence readiness and analyze the relationship between these categories and thriving at work, so as to provide references for nursing managers to develop targeted management strategies to enhance nurses' level of thriving at work. Methods A cross-sectional survey was conducted from February to April 2026 among 498 nurses from five hospitals in Anhui Province, China. Data were collected using a general information questionnaire, the Medical Artificial Intelligence Readiness Scale, and the Thriving at Work Scale. Latent profile analysis was performed using the 22 artificial intelligence readiness items as manifest indicators. Model selection was based on information criteria, entropy, likelihood-ratio tests, profile size, posterior classification probabilities, parsimony, and interpretability. Chi-square tests, one-way analysis of variance, and multinomial logistic regression were used for exploratory profile comparisons. Results Three profiles were identified: low artificial intelligence readiness ( n = 70, 14.06%), moderate artificial intelligence readiness ( n = 283, 56.82%), and high artificial intelligence readiness ( n = 145, 29.12%). Multivariate logistic regression analysis indicated that department, hospital level, frequency of AI use over the past 6 months, and receipt of AI-related training were major influencing factors of the latent categories of nurses' artificial intelligence readiness (all p p Conclusion Artificial intelligence readiness among nurses is heterogeneous, and approximately one-third of nurses were classified into a high-readiness profile. The level of thriving at work varies among nurses in different latent categories of artificial intelligence readiness. Nursing managers and public health administrators should consider profile-specific strategies that combine targeted artificial intelligence training, clinical practice support, and career development for nurses.","url":"https://doi.org/10.3389/fpubh.2026.1885808","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1885808","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21203/rs.3.rs-10466504/v1","name":"Patterns of Generative Artificial Intelligence Use and Medical Students' Perceptions of Learning Among Eighth Semester Medical Students at Universidade Católica Timorense (UCT), Timor-Leste","source":"europepmc","abstract":"Abstract The rapid development of generative artificial intelligence (GenAI) has transformed medical education by providing students with new opportunities to support learning, academic writing, and problem-solving. Despite its growing adoption worldwide, evidence regarding GenAI use among medical students in Timor-Leste remains limited. This study aims to investigate the patterns of generative artificial intelligence use and students' perceptions of its role in medical learning among eighth-semester medical students at Universidade Católica Timorense (UCT), Timor-Leste.A descriptive cross-sectional study will be conducted among all eighth-semester medical students enrolled at the Faculty of Medicine, Universidade Católica Timorense (UCT). A total sampling technique will be employed, involving all eligible students (N = 76). Data will be collected using a structured online questionnaire distributed through Google Forms. The questionnaire will assess patterns of GenAI use, including types of tools, frequency of use, purposes of use, challenges encountered, and students' perceptions regarding the usefulness, effectiveness, ease of use, trust, and intention to continue using GenAI in medical learning. Descriptive statistics will be used to summarize the findings. The study is expected to demonstrate that GenAI has become an important supplementary learning tool among medical students. Most participants are anticipated to report positive perceptions regarding its usefulness and effectiveness while also recognizing challenges such as information accuracy and the need for critical evaluation of AI-generated content. The findings of this study are expected to provide baseline evidence on the adoption of GenAI in undergraduate medical education in Timor-Leste. The results may assist educators and policymakers in developing strategies for the responsible and effective integration of artificial intelligence into medical education.","url":"https://doi.org/10.21203/rs.3.rs-10466504/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10466504/v1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1111/dom.71240","name":"Artificial Intelligence Analysis of Standard 12-Lead Electrocardiograms for Screening of Symptomatic Diabetic Peripheral Neuropathy.","source":"europepmc","abstract":"Aim To investigate whether artificial intelligence (AI) models trained on standard 12-lead electrocardiograms (ECG) can identify symptom-defined diabetic peripheral neuropathy (DPN) as assessed by the Michigan Neuropathy Screening Instrument Questionnaire (MNSI-Q). Materials This was an observational study of people with diabetes enrolled in the Silesia-Diabetes Heart Project. DPN was assessed using the MNSI-Q. We used an original questionnaire cut-off score of ≥ 7 and revised cut-off score of ≥ 4 to diagnose DPN and classified DPN as highly symptomatic and moderately symptomatic accordingly. Feature extraction from 10-s raw ECG recordings utilised algorithms to identify recurring signal segments (motifs) and anomalies (discords). These features were used to train XGBoost, Support Vector Machine (SVM) and Ridge classifiers to differentiate between DPN-positive and DPN-negative people. Results A total of 640 participants (mean age 54 ± 17; 52% female) were included. Of these, 95 (15%) had highly symptomatic DPN (MNSI-Q ≥ 7) and 281 (44%) had moderately symptomatic DPN (MNSI-Q ≥ 4). For the highly symptomatic DPN, the XGBoost classifier utilizing a combination of motifs and discords demonstrated the highest performance, achieving an area under the receiver-operating characteristic curve (AUC) of 0.89 (95% CI 0.88-0.91), an accuracy of 88.5% and a sensitivity of 93.4%. The model showed substantially lower predictive capability when tested against the broader screening threshold of MNSI ≥ 4 (AUC 0.64). Conclusion AI analysis of the standard ECG demonstrated a strong association with the presence of symptom-defined peripheral neuropathy but lacked sensitivity for milder presentation. With further validation, this method could serve as an accessible, supplementary screening aid to help identify high-risk patients during routine cardiovascular assessment.","url":"https://doi.org/10.1111/dom.71240","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/dom.71240","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.radi.2026.103549","name":"Perceptions of Greek radiographers and radiologists on the impact of artificial intelligence in medical imaging: A cross-sectional study.","source":"europepmc","abstract":"Introduction This study aimed to examine the perceptions of Greek radiographers and radiologists on integrating artificial intelligence (AI) in medical imaging (MI). Methods A convenience sampling approach was used. The questionnaire was distributed across fifteen public hospitals and two private healthcare groups. A total of 167 valid responses were collected. The data were analysed, conducting both descriptive and inferential statistics. Results A positive attitude towards the application of AI in MI was identified. Males expressed greater agreement with its positive impact compared to females (p = 0.033), while females reported greater concerns about personal data protection (p = 0.028). Increased age was associated with greater acceptance of AI for diagnostic decision-making (p = 0.014), whereas higher education was associated with lower agreement to statements on the negative impact of AI (p = 0.043). As acceptance of shared responsibility between humans and AI algorithms increased, the agreement with AI's contribution to education decreased (p = 0.006). The acceptance of autonomous AI diagnostic decision-making reinforced the recognition of its impact on the professional role of radiographers and radiologists (p = 0.040). Respondents who were more aware of potential AI-related errors were more likely to believe that diagnosis must remain a human task (p Conclusion Respondents demonstrated an overall positive attitude towards AI integration, recognising its potential. Compared to previous studies, reservations about fully autonomous diagnostic decision-making and implementation barriers were identified. Implication for practice Despite the willingness of respondents to adopt AI, the findings suggest that the successful integration of AI into radiological clinical practice in Greece will depend on addressing knowledge gaps through structured education and training, which emerged as the most significant barrier to implementation.","url":"https://doi.org/10.1016/j.radi.2026.103549","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.radi.2026.103549","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.17605/osf.io/tvm4k","name":"Artificial Intelligence-Based Tools Across Skin Tones: A Scoping Review of Bias and Diagnostic Performance","source":"datacite","abstract":"Artificial intelligence (AI) has emerged as a rapidly advancing tool in dermatology, particularly in the diagnosis of skin conditions using image-based algorithms. Machine learning and deep learning models have demonstrated promising diagnostic performance, in some cases approaching or augmenting clinician-level accuracy in identifying skin lesions and malignancies (Han et al., 2022). These technologies have the potential to improve access to dermatologic care, support clinical decision-making, and enhance early detection of disease, especially in settings with limited specialist availability. Despite these advancements, concerns have been raised regarding the generalizability and equity of AI-based dermatological tools. A growing body of literature suggests that many AI models are developed using datasets that disproportionately represent individuals with lighter skin tones, resulting in limited diversity in training data (Dowie, 2025). This lack of representation has been associated with reduced diagnostic accuracy in individuals with darker skin tones, raising concerns about potential bias and the risk of exacerbating existing healthcare disparities (Daneshjou et al., 2022). Reviews of AI in dermatology have consistently highlighted underrepresentation of skin of color in datasets and the resulting limitations in algorithm performance across diverse populations (Fliorent et al., 2024). In addition to dataset imbalance, methodological challenges further complicate the evaluation of AI performance across skin tones. Skin tone is often inconsistently defined and measured across studies, with widespread reliance on the Fitzpatrick Skin Type (FST) scale. However, the FST was originally designed to assess skin response to ultraviolet radiation rather than accurately represent skin pigmentation, limiting its validity as a proxy for skin tone (Weir et al., 2025). Alternative approaches, such as the Monk Skin Tone Scale or objective measures based on colorimetry and Individual Typology Angle, have been proposed to provide more accurate and inclusive classification systems (Ulrich et al., 2025). The lack of standardized and reliable skin tone classification introduces variability across studies and may obscure true differences in AI performance. Furthermore, many AI models are evaluated using homogeneous datasets, with limited validation in real-world, clinically diverse populations. While AI systems may demonstrate strong performance under controlled conditions, their effectiveness across broader and more heterogeneous patient populations remains uncertain (Daneshjou et al., 2022). This raises important questions regarding the clinical applicability and safety of deploying such tools in practice, particularly in populations that have historically been underrepresented in medical research. Although individual studies and narrative reviews have identified issues related to dataset bias and performance disparities, the existing literature remains fragmented. There is currently no comprehensive synthesis that systematically maps how AI-based dermatological tools are developed, how skin tone is measured, and how diagnostic performance varies across diverse populations. This scoping review examines the development and diagnostic performance of AI-based dermatological tools across different skin tones, with particular attention to dataset composition, skin tone classification methods, bias, and clinical applicability. This review aims to provide a structured overview of the current evidence and identify key gaps to inform future research and the equitable implementation of AI in dermatology. Question: Was the composition/diversity of datasets used for AI training reported by the various AI-based derm tools? Primary Objective: To characterize the existing literature on AI-based dermatological diagnostic tools across different skin tones, with a focus on dataset diversity, diagnostic performance, and algorithmic bias. Secondary Objectives: T","url":"https://doi.org/10.17605/osf.io/tvm4k","authors":["Amna Noor"],"tags":["Dermatology","Medicine and Health Sciences","Medical Specialties"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/tvm4k","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.17419282","name":"Detekcija Temporalnog Rizika kod Nesitnostaničnog Karcinoma Pluća: Fraktalno-UTL Okvir Temeljen na EWMA","source":"datacite","abstract":"🧠 Sažetak Predstavljamo okvir za detekciju temporalnog rizika koji kombinira eksponencijalno ponderiranu pomičnu sredinu (EWMA) akumulacije rizika s fraktalnom karakterizacijom tumora, s ciljem precizne prognoze ishoda u stvarnom vremenu kod nesitnostaničnog karcinoma pluća (NSCLC). Za razliku od statičkih radiomičkih klasifikatora koji neovisno evaluiraju trenutne snimke, Fraktalno-UTL pristup akumulira rizik kroz longitudinalno oslikavanje i kvantificira promjene između uzastopnih pregleda, čime prepoznaje tihi, postupni porast heterogenosti koji prethodi klinički manifestnoj progresiji. 🔬 Teorijski doprinos Integracijom analize fraktalne dimenzije ($D_1$–$D_3$) i EWMA modeliranja volatilnosti, formalizirana je veza između prostorne heterogenosti tumora i njezine vremenske dinamike. Dano je matematičko zatvaranje izraza za očekivano vrijeme ranog upozorenja u prisutnosti skoka varijance te dokazano monotono smanjenje rizika nakon uspješne intervencije. Time se uspostavlja veza između fraktalne teksture tumora i hazardne funkcije kroz vremensku domenu. 📊 Empirijska validacija Na NSCLC-Radiomics skupu podataka (TCIA Lung1, n=422 pacijenta), okvir postiže: C-indeks = 0.692 (u odnosu na Aerts 2014: 0.65) HR = 2.41 (visok vs. nizak rizik, log-rank p < 0.001) 99% manje značajki (5 vs. 440) 5× niža latencija zaključivanja (47 ms vs. 230 ms) Dodatno, validacija na trima vrstama karcinoma (NSCLC, gušterača, CRLM) potvrđuje robustnost modela s konzistentnim optimalnim parametrima ($\\alpha \\approx 0.15$, $\\theta_{\\text{mult}} \\approx 1.5$). ⚠️ Metodološko ograničenje TCIA Lung1 skup sadrži samo pojedinačne CT snimke po pacijentu — ne autentične longitudinalne serije. Temporalne značajke su rekonstruirane putem proxy varijable: vtproxy=Stadij⋅D3(t)⋅N(1,0.1)v_t^{\\text{proxy}} = \\text{Stadij} \\cdot D_3(t) \\cdot \\mathcal{N}(1, 0.1)vtproxy=Stadij⋅D3(t)⋅N(1,0.1) Retrospektivna projekcija sugerira potencijalno rano upozorenje od 3.2 mjeseca (IQR: 1.8–5.7) prije kliničke progresije, ali ova tvrdnja zahtijeva verifikaciju u prospektivnoj studiji s pravim longitudinalnim CT podacima (n ≥ 100, ≥ 3 vremenske točke; planirana 2026.). 🧮 Zaključak Fraktalno-UTL okvir omogućuje: modeliranje dinamičke heterogenosti tumora u vremenu, predviđanje progresije uz višestruko manju kompleksnost modela, i stvaranje temelja za rano upozorenje u onkološkoj praksi temeljeno na hazardnoj dinamici. Okvir predstavlja korak prema integriranju fraktalne geometrije i hazardne analize u kliničku radiomiku te otvara mogućnost personaliziranog praćenja rizika kroz real-time onkološki nadzor. 📎 Povezani resursi GitHub repozitorij: https://github.com/bsabljic/utl-oncology Dopunski materijal: Detekcija_Temporalnog_Rizika_NSCLC_UTL_Supplementary.pdf ORCID: 0009-0005-6199-4488 ⚖️ Licenca Ovo djelo je objavljeno pod licencomCreative Commons Attribution 4.0 International (CC BY 4.0)Slobodno dijeliti i koristiti uz navođenje izvora. 🔖 Citiranje Branimir Sabljić (2025).Detekcija Temporalnog Rizika kod Nesitnostaničnog Karcinoma Pluća: Fraktalno-UTL Okvir Temeljen na EWMA.","url":"https://doi.org/10.5281/zenodo.17419282","authors":["Sabljic, Branimir"],"tags":["NSCLC","Fractal analysis","EWMA","Temporal risk detection","Radiomics","Lung cancer","Hazard modeling","Time-series"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17419282","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.17419283","name":"Detekcija Temporalnog Rizika kod Nesitnostaničnog Karcinoma Pluća: Fraktalno-UTL Okvir Temeljen na EWMA","source":"datacite","abstract":"🧠 Sažetak Predstavljamo okvir za detekciju temporalnog rizika koji kombinira eksponencijalno ponderiranu pomičnu sredinu (EWMA) akumulacije rizika s fraktalnom karakterizacijom tumora, s ciljem precizne prognoze ishoda u stvarnom vremenu kod nesitnostaničnog karcinoma pluća (NSCLC). Za razliku od statičkih radiomičkih klasifikatora koji neovisno evaluiraju trenutne snimke, Fraktalno-UTL pristup akumulira rizik kroz longitudinalno oslikavanje i kvantificira promjene između uzastopnih pregleda, čime prepoznaje tihi, postupni porast heterogenosti koji prethodi klinički manifestnoj progresiji. 🔬 Teorijski doprinos Integracijom analize fraktalne dimenzije ($D_1$–$D_3$) i EWMA modeliranja volatilnosti, formalizirana je veza između prostorne heterogenosti tumora i njezine vremenske dinamike. Dano je matematičko zatvaranje izraza za očekivano vrijeme ranog upozorenja u prisutnosti skoka varijance te dokazano monotono smanjenje rizika nakon uspješne intervencije. Time se uspostavlja veza između fraktalne teksture tumora i hazardne funkcije kroz vremensku domenu. 📊 Empirijska validacija Na NSCLC-Radiomics skupu podataka (TCIA Lung1, n=422 pacijenta), okvir postiže: C-indeks = 0.692 (u odnosu na Aerts 2014: 0.65) HR = 2.41 (visok vs. nizak rizik, log-rank p < 0.001) 99% manje značajki (5 vs. 440) 5× niža latencija zaključivanja (47 ms vs. 230 ms) Dodatno, validacija na trima vrstama karcinoma (NSCLC, gušterača, CRLM) potvrđuje robustnost modela s konzistentnim optimalnim parametrima ($\\alpha \\approx 0.15$, $\\theta_{\\text{mult}} \\approx 1.5$). ⚠️ Metodološko ograničenje TCIA Lung1 skup sadrži samo pojedinačne CT snimke po pacijentu — ne autentične longitudinalne serije. Temporalne značajke su rekonstruirane putem proxy varijable: vtproxy=Stadij⋅D3(t)⋅N(1,0.1)v_t^{\\text{proxy}} = \\text{Stadij} \\cdot D_3(t) \\cdot \\mathcal{N}(1, 0.1)vtproxy=Stadij⋅D3(t)⋅N(1,0.1) Retrospektivna projekcija sugerira potencijalno rano upozorenje od 3.2 mjeseca (IQR: 1.8–5.7) prije kliničke progresije, ali ova tvrdnja zahtijeva verifikaciju u prospektivnoj studiji s pravim longitudinalnim CT podacima (n ≥ 100, ≥ 3 vremenske točke; planirana 2026.). 🧮 Zaključak Fraktalno-UTL okvir omogućuje: modeliranje dinamičke heterogenosti tumora u vremenu, predviđanje progresije uz višestruko manju kompleksnost modela, i stvaranje temelja za rano upozorenje u onkološkoj praksi temeljeno na hazardnoj dinamici. Okvir predstavlja korak prema integriranju fraktalne geometrije i hazardne analize u kliničku radiomiku te otvara mogućnost personaliziranog praćenja rizika kroz real-time onkološki nadzor. 📎 Povezani resursi GitHub repozitorij: https://github.com/bsabljic/utl-oncology Dopunski materijal: Detekcija_Temporalnog_Rizika_NSCLC_UTL_Supplementary.pdf ORCID: 0009-0005-6199-4488 ⚖️ Licenca Ovo djelo je objavljeno pod licencomCreative Commons Attribution 4.0 International (CC BY 4.0)Slobodno dijeliti i koristiti uz navođenje izvora. 🔖 Citiranje Branimir Sabljić (2025).Detekcija Temporalnog Rizika kod Nesitnostaničnog Karcinoma Pluća: Fraktalno-UTL Okvir Temeljen na EWMA.","url":"https://doi.org/10.5281/zenodo.17419283","authors":["Sabljic, Branimir"],"tags":["NSCLC","Fractal analysis","EWMA","Temporal risk detection","Radiomics","Lung cancer","Hazard modeling","Time-series"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17419283","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22021373","name":"MYCIN: A RULE-BASED EXPERT SYSTEM FOR ANTIMICROBIAL THERAPY SELECTION IN MEDICAL DIAGNOSIS — A REVIEW AND CRITICAL ANALYSIS","source":"datacite","abstract":"ABSTRACT Expert systems represent one of the earliest and most influential branches of applied artificial intelligence, aiming to replicate the decision-making capability of a human specialist within a narrow, well-defined domain. Among the pioneering efforts in this field, MYCIN, developed at Stanford University in the mid-1970s, stands out as a landmark rule-based system designed to assist physicians in diagnosing bacterial infections, particularly bacteremia and meningitis, and in recommending appropriate antimicrobial therapy. This paper presents a comprehensive review of MYCIN's architecture, knowledge representation scheme, inferencing strategy, and its use of certainty factors to manage diagnostic uncertainty. The system's backward-chaining inference engine, its separation of the knowledge base from the inference mechanism, and its natural language explanation facility are examined in detail. The paper further evaluates MYCIN's diagnostic performance as reported in early validation studies, discusses the ethical and legal barriers that prevented its clinical deployment, and draws comparisons with contemporaneous and later expert systems such as DENDRAL, INTERNIST-I, and CADUCEUS. Finally, the paper reflects on MYCIN's lasting influence on modern clinical decision-support systems and artificial intelligence-based diagnostic tools, arguing that its architectural principles continue to inform the design of contemporary knowledge-based and hybrid AI systems in healthcare. Keywords: Expert System; MYCIN; Artificial Intelligence; Medical Diagnosis; Rule-Based Reasoning; Certainty Factor; Knowledge-Based System; Clinical Decision Support","url":"https://doi.org/10.5281/zenodo.22021373","authors":["G.V. Poornima Radhalakshmi","V.Srimuki","S.Prethika","Dr.S.Suganyadevi"],"tags":["Expert System","MYCIN","Artificial Intelligence","Medical Diagnosis","Rule-Based Reasoning","Certainty Factor","Knowledge-Based System","Clinical Decision Support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22021373","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22021374","name":"MYCIN: A RULE-BASED EXPERT SYSTEM FOR ANTIMICROBIAL THERAPY SELECTION IN MEDICAL DIAGNOSIS — A REVIEW AND CRITICAL ANALYSIS","source":"datacite","abstract":"ABSTRACT Expert systems represent one of the earliest and most influential branches of applied artificial intelligence, aiming to replicate the decision-making capability of a human specialist within a narrow, well-defined domain. Among the pioneering efforts in this field, MYCIN, developed at Stanford University in the mid-1970s, stands out as a landmark rule-based system designed to assist physicians in diagnosing bacterial infections, particularly bacteremia and meningitis, and in recommending appropriate antimicrobial therapy. This paper presents a comprehensive review of MYCIN's architecture, knowledge representation scheme, inferencing strategy, and its use of certainty factors to manage diagnostic uncertainty. The system's backward-chaining inference engine, its separation of the knowledge base from the inference mechanism, and its natural language explanation facility are examined in detail. The paper further evaluates MYCIN's diagnostic performance as reported in early validation studies, discusses the ethical and legal barriers that prevented its clinical deployment, and draws comparisons with contemporaneous and later expert systems such as DENDRAL, INTERNIST-I, and CADUCEUS. Finally, the paper reflects on MYCIN's lasting influence on modern clinical decision-support systems and artificial intelligence-based diagnostic tools, arguing that its architectural principles continue to inform the design of contemporary knowledge-based and hybrid AI systems in healthcare. Keywords: Expert System; MYCIN; Artificial Intelligence; Medical Diagnosis; Rule-Based Reasoning; Certainty Factor; Knowledge-Based System; Clinical Decision Support","url":"https://doi.org/10.5281/zenodo.22021374","authors":["G.V. Poornima Radhalakshmi","V.Srimuki","S.Prethika","Dr.S.Suganyadevi"],"tags":["Expert System","MYCIN","Artificial Intelligence","Medical Diagnosis","Rule-Based Reasoning","Certainty Factor","Knowledge-Based System","Clinical Decision Support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22021374","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.17605/osf.io/q7wkn","name":"The Gap Between Artificial Intelligence Use and Disclosure in Academic Publishing: A Scoping Review Protocol","source":"datacite","abstract":"This scoping review will systematically identify, map, and synthesize empirical evidence on the gap between artificial intelligence use and disclosure in academic publishing. The review will include studies reporting empirical data on the prevalence of artificial intelligence tool use in academic research or manuscript preparation, studies reporting the prevalence of artificial intelligence disclosure statements in published scholarly literature, and studies examining factors associated with disclosure or non-disclosure behaviour in academic contexts. The review will follow Joanna Briggs Institute methodology for scoping reviews and will be reported according to the PRISMA extension for Scoping Reviews. Eligible evidence will include surveys, content analyses, bibliometric or database studies, qualitative studies, experimental studies, and relevant grey literature where primary empirical data and methodology are reported. The planned synthesis will quantify the disclosure gap, defined as the difference between survey-reported artificial intelligence use prevalence and content-analysis disclosure prevalence, and will synthesize individual, contextual, and structural predictors of non-disclosure. Expected outputs include a structured quantification of the artificial intelligence disclosure gap across available evidence and a predictor synthesis framework identifying factors that may explain non-disclosure across disciplines, publication contexts, and policy environments.","url":"https://doi.org/10.17605/osf.io/q7wkn","authors":["Philippe Poirier","4. Yasser Bouklouch","Stephanie Parent-Harvey","Isabella Bozzo","Edward J Harvey"],"tags":["Health Information Technology","Medical Sciences","Medicine and Health Sciences","Bioethics and Medical Ethics","AI disclosure","academic publishing","artificial intelligence","generative AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/q7wkn","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.17367661","name":"on consciousness and perception of reality","source":"datacite","abstract":"This study aims to re-evaluate the Boltzmann Brain paradox not merely as a cosmological and statistical problem, but also from a psychophysical perspective. It demonstrates striking similarities between phenomena such as hypnosis, false memory syndrome, dissociative identity disorder, and hallucinations and the false memory clusters predicted by the Boltzmann Brain scenario. The study will discuss how the study examines the relativistic entropy experience observed in individual consciousness can be explained at the macro-universe level within the framework of quantum information theory. In this context, it is argued that the wave–particle duality in the principle of complementarity, rather than being a set of mutually complementary opposites, indicates that, from an information-theoretic perspective, at the most fundamental level, matter and information are two different manifestations of the same phenomenon. The study also aims to provide cognitive relief by resolving the mental nightmare created by the infinite universe scenario in cosmology; in the process, it corroborates certain aspects of the Boltzmann Brain and demonstrates that human memory and consciousness can be explained within the framework of chaotic determinism. This research aims to contribute particularly to psychophysics and to develop applications for artificial intelligence, while also contributing to the physical and mathematical foundations of this discipline. In this context, we examine possible determinism through the blending of the Boltzmann Brain with chaos theory, focusing on theories of timelessness, such as the block universe model of the nature of time and the Wheeler–DeWitt equation. The central theme is why human behavior cannot be predicted by mathematical equations, raising crucial questions such as 'Are we living in a state of collective hypnosis?' and 'Are we inside a controlled hallucination?' Furthermore, when viewed from the perspective of modern physics' current theories, it is emphasized that it makes more sense to evaluate our reality not as an illusion, but as a flawed reality. The book examines how artificial intelligence systems produce hallucinations and proposes solutions, explaining how they can be more empathetic within the framework of relative entropy. It also explores personalized treatment opportunities and provides new perspectives for integrating hypnosis into medical practice via neuromodulation–based clinical protocols and Brain–Computer Interface (BCI) applications. Furthermore, various studies ranging from the Penfield and Libet experiments to the “mouse utopia” experiment, infantile amnesia, sensory deprivation experiments, hypnosis experiments and the effects of psychotic processes on perception and creative cognition will also be examined. In this context, the study aims to offer a solution to the philosophical paradox created by the Boltzmann Brain; to contribute to the field of psychophysics through a holistic approach; to shed light on the question 'What is consciousness?' at the intersection of science and philosophy. Keywords: Boltzmann brain, false memory syndrome, entropy, cognitive science, Neuromodulation, consciousness, arrow of time, chaos theory, Artificial Intelligence (AI), Statistical mechanics, thermodynamics, Predictive processing, Shannon relativity entropy, quantum mechanics, cognitive psychology, clinical neuroscience, Complex Systems, Psychophysics, Social Psychology, Forensic psychology","url":"https://doi.org/10.5281/zenodo.17367661","authors":["korkutata"],"tags":["Boltzmann brain","false memory syndrome","entropy","cognitive science","Neuromodulation","consciousness","arrow of time","chaos theory"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.17367661","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22020298","name":"on consciousness and perception of reality","source":"datacite","abstract":"This study aims to re-evaluate the Boltzmann Brain paradox not merely as a cosmological and statistical problem, but also from a psychophysical perspective. It demonstrates striking similarities between phenomena such as hypnosis, false memory syndrome, dissociative identity disorder, and hallucinations and the false memory clusters predicted by the Boltzmann Brain scenario. The study will discuss how the study examines the relativistic entropy experience observed in individual consciousness can be explained at the macro-universe level within the framework of quantum information theory. In this context, it is argued that the wave–particle duality in the principle of complementarity, rather than being a set of mutually complementary opposites, indicates that, from an information-theoretic perspective, at the most fundamental level, matter and information are two different manifestations of the same phenomenon. The study also aims to provide cognitive relief by resolving the mental nightmare created by the infinite universe scenario in cosmology; in the process, it corroborates certain aspects of the Boltzmann Brain and demonstrates that human memory and consciousness can be explained within the framework of chaotic determinism. This research aims to contribute particularly to psychophysics and to develop applications for artificial intelligence, while also contributing to the physical and mathematical foundations of this discipline. In this context, we examine possible determinism through the blending of the Boltzmann Brain with chaos theory, focusing on theories of timelessness, such as the block universe model of the nature of time and the Wheeler–DeWitt equation. The central theme is why human behavior cannot be predicted by mathematical equations, raising crucial questions such as 'Are we living in a state of collective hypnosis?' and 'Are we inside a controlled hallucination?' Furthermore, when viewed from the perspective of modern physics' current theories, it is emphasized that it makes more sense to evaluate our reality not as an illusion, but as a flawed reality. The book examines how artificial intelligence systems produce hallucinations and proposes solutions, explaining how they can be more empathetic within the framework of relative entropy. It also explores personalized treatment opportunities and provides new perspectives for integrating hypnosis into medical practice via neuromodulation–based clinical protocols and Brain–Computer Interface (BCI) applications. Furthermore, various studies ranging from the Penfield and Libet experiments to the “mouse utopia” experiment, infantile amnesia, sensory deprivation experiments, hypnosis experiments and the effects of psychotic processes on perception and creative cognition will also be examined. In this context, the study aims to offer a solution to the philosophical paradox created by the Boltzmann Brain; to contribute to the field of psychophysics through a holistic approach; to shed light on the question 'What is consciousness?' at the intersection of science and philosophy. Keywords: Boltzmann brain, false memory syndrome, entropy, cognitive science, Neuromodulation, consciousness, arrow of time, chaos theory, Artificial Intelligence (AI), Statistical mechanics, thermodynamics, Predictive processing, Shannon relativity entropy, quantum mechanics, cognitive psychology, clinical neuroscience, Complex Systems, Psychophysics, Social Psychology, Forensic psychology","url":"https://doi.org/10.5281/zenodo.22020298","authors":["korkutata"],"tags":["Boltzmann brain","false memory syndrome","entropy","cognitive science","Neuromodulation","consciousness","arrow of time","chaos theory"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22020298","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.48550/arxiv.2608.14778","name":"AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions","source":"datacite","abstract":"Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from &lt;20% to &gt;70%. The standardized Liver Imaging Reporting and Data System (LI-RADS) criteria provide an imaging-based diagnostic framework to evaluate liver lesions for HCC, serving as a foundation for automating HCC detection with artificial intelligence (AI). However, the lack of large, publicly available datasets with high-quality annotations has limited the development and evaluation of AI models for automated LI-RADS assessment. We introduce AMPLIFAI dataset, the first public dataset of 590 multiphase abdominal CT studies annotated with LI-RADS categories, lesion size, and voxel-level segmentations for three major LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. The dataset was curated and harmonized from four public datasets and augmented with expert annotations from five board-certified radiologists and one resident. Following the Datasheets for Datasets format, this paper details the dataset's composition, curation and harmonization process, and annotation workflow to support transparent, reproducible research in medical imaging AI.","url":"https://doi.org/10.48550/arxiv.2608.14778","authors":["Kulkarni, Pranav","Shah, Nikhil","Suryavanshi, Amritansh","Delfino, Jana G.","Tonascia, James","Wong-You-Cheong, Jade","Lane, Barton","Chirico, Joseph","Hirsch, Jeffrey D.","Li, Ang","Huang, Heng","Doo, Florence X."],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.14778","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22020099","name":"Dataset: Hypothesis: Intraperitoneal administration of a thermoresponsive hydrogel loaded with borneol-functionalized ginger  derived extracellular vesicles (Moxibustion-Modified GDEVs) may provide a sustained, localized delivery to ovarian tumors, potentially enhancing deep tissue penetration and inducing apoptosis without systemic chemotoxicity. - PathMap Experiment #000134","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=134 Artificial General Intelligence LLC Claim Evaluated: Hypothesis: Intraperitoneal administration of a thermoresponsive hydrogel loaded with borneol-functionalized ginger derived extracellular vesicles (Moxibustion-Modified GDEVs) may provide a sustained, localized delivery to ovarian tumors, potentially enhancing deep tissue penetration and inducing apoptosis without systemic chemotoxicity. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights GDEVs retain therapeutic anti-inflammatory properties, making them candidates for modulating the tumor microenvironment. Thermoresponsive hydrogels facilitate controlled delivery, potentially reducing systemic exposure. The use of native plant-derived materials offers a scalable alternative to synthetic nanocarriers. Preclinical evidence in diverse cancer models supports the use of membrane-camouflaged nanoparticles for improved tumor-targeting specificity. Combination therapies using natural products and chemotherapeutics often overcome the resistance mechanisms associated with conventional platinum-based treatments. The tumor microenvironment (TME) is a critical determinant of drug delivery efficiency, where mechanical barriers and fluid pressure significantly affect intratumoral distribution. Current research is shifting towards \"biomimetic conductive cardiac patch\" and similar adaptive materials, which underscores the maturity of hydrogel engineering for diverse tissue-specific applications. The use of \"small interfering RNA (siRNA)\" within nanovesicles validates the capacity to carry both chemical and genetic payloads for dual-mode therapy. Thermal processing (boiling) reconfigures ginger extracellular vesicles (GEVs) into thermally reassembled GEVs (T-GEVs) with enhanced trafficking regulator enrichment. T-GEVs demonstrate an 8.57-fold increase in clathrin-dependent cellular uptake in intestinal cells compared to native vesicles. Carrier-free pure drug crystal depots can provide sustained ocular delivery for at least eight months. Borneol-functionalized nanoparticles efficiently traverse the blood-brain barrier and restore redox homeostasis in cerebral ischemic models. Systematic identification of host genes essential for bacterial invasion provides a robust pipeline for novel therapeutic target discovery. Tumor-derived parathyroid hormone-related protein (PTHrP) is associated with the suppression of multiple cytochrome P450 enzyme families, impacting chemotherapy pharmacokinetics. Synergistic effects of NMN supplementation enhance MSLN CAR-NK cell persistence and cytotoxic potency against ovarian cancer. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the stability and drug-loading efficiency of GDEVs conjugated with borneol. Evaluate the release kinetics of borneol-GDEVs from a thermoresponsive hydrogel at varying temperature thresholds. Investigate the intraperitoneal tumor accumulation and penetration depth of the hydrogel-loaded GDEVs in an orthotopic ovarian cancer mouse model. Develop T-GEVs loaded with paclitaxel for intraperitoneal delivery in SKOV3 xenograft models. Evaluate the stability and degradation profile of thermosensitive PEOz-PAla hydrogels loaded with borneol-GDEVs in peritoneal fluid. Assess the synergistic effect of borneol-GDEVs with cisplatin in 3D ovarian cancer spheroid models. 📊 Suggested Studies A comparative study evaluating the therapeutic efficacy of borneol-GDEVs versus non-functionalized GDEVs in ovarian cancer cell models. Long-term toxicity and biodistribution assessment of intraperitoneally administered thermoresponsive hydrogel-GDEV systems in healthy subjects. Metagenomic and transcriptomic analysis ","url":"https://doi.org/10.5281/zenodo.22020099","authors":["Dungan, Joshua"],"tags":["Extracellular Vesicles","_gates_from_extracellular_vesicles","Nanocarriers","_gates_to_nanocarriers","Hydrogels","_gates_to_hydrogels","_gates_from_hydrogels","Delayed-Action Preparations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22020099","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22020100","name":"Dataset: Hypothesis: Intraperitoneal administration of a thermoresponsive hydrogel loaded with borneol-functionalized ginger  derived extracellular vesicles (Moxibustion-Modified GDEVs) may provide a sustained, localized delivery to ovarian tumors, potentially enhancing deep tissue penetration and inducing apoptosis without systemic chemotoxicity. - PathMap Experiment #000134","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=134 Artificial General Intelligence LLC Claim Evaluated: Hypothesis: Intraperitoneal administration of a thermoresponsive hydrogel loaded with borneol-functionalized ginger derived extracellular vesicles (Moxibustion-Modified GDEVs) may provide a sustained, localized delivery to ovarian tumors, potentially enhancing deep tissue penetration and inducing apoptosis without systemic chemotoxicity. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights GDEVs retain therapeutic anti-inflammatory properties, making them candidates for modulating the tumor microenvironment. Thermoresponsive hydrogels facilitate controlled delivery, potentially reducing systemic exposure. The use of native plant-derived materials offers a scalable alternative to synthetic nanocarriers. Preclinical evidence in diverse cancer models supports the use of membrane-camouflaged nanoparticles for improved tumor-targeting specificity. Combination therapies using natural products and chemotherapeutics often overcome the resistance mechanisms associated with conventional platinum-based treatments. The tumor microenvironment (TME) is a critical determinant of drug delivery efficiency, where mechanical barriers and fluid pressure significantly affect intratumoral distribution. Current research is shifting towards \"biomimetic conductive cardiac patch\" and similar adaptive materials, which underscores the maturity of hydrogel engineering for diverse tissue-specific applications. The use of \"small interfering RNA (siRNA)\" within nanovesicles validates the capacity to carry both chemical and genetic payloads for dual-mode therapy. Thermal processing (boiling) reconfigures ginger extracellular vesicles (GEVs) into thermally reassembled GEVs (T-GEVs) with enhanced trafficking regulator enrichment. T-GEVs demonstrate an 8.57-fold increase in clathrin-dependent cellular uptake in intestinal cells compared to native vesicles. Carrier-free pure drug crystal depots can provide sustained ocular delivery for at least eight months. Borneol-functionalized nanoparticles efficiently traverse the blood-brain barrier and restore redox homeostasis in cerebral ischemic models. Systematic identification of host genes essential for bacterial invasion provides a robust pipeline for novel therapeutic target discovery. Tumor-derived parathyroid hormone-related protein (PTHrP) is associated with the suppression of multiple cytochrome P450 enzyme families, impacting chemotherapy pharmacokinetics. Synergistic effects of NMN supplementation enhance MSLN CAR-NK cell persistence and cytotoxic potency against ovarian cancer. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the stability and drug-loading efficiency of GDEVs conjugated with borneol. Evaluate the release kinetics of borneol-GDEVs from a thermoresponsive hydrogel at varying temperature thresholds. Investigate the intraperitoneal tumor accumulation and penetration depth of the hydrogel-loaded GDEVs in an orthotopic ovarian cancer mouse model. Develop T-GEVs loaded with paclitaxel for intraperitoneal delivery in SKOV3 xenograft models. Evaluate the stability and degradation profile of thermosensitive PEOz-PAla hydrogels loaded with borneol-GDEVs in peritoneal fluid. Assess the synergistic effect of borneol-GDEVs with cisplatin in 3D ovarian cancer spheroid models. 📊 Suggested Studies A comparative study evaluating the therapeutic efficacy of borneol-GDEVs versus non-functionalized GDEVs in ovarian cancer cell models. Long-term toxicity and biodistribution assessment of intraperitoneally administered thermoresponsive hydrogel-GDEV systems in healthy subjects. Metagenomic and transcriptomic analysis ","url":"https://doi.org/10.5281/zenodo.22020100","authors":["Dungan, Joshua"],"tags":["Extracellular Vesicles","_gates_from_extracellular_vesicles","Nanocarriers","_gates_to_nanocarriers","Hydrogels","_gates_to_hydrogels","_gates_from_hydrogels","Delayed-Action Preparations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22020100","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5075/epfl-thesis-3990","name":"Active: a unified platform for building intelligent applications","source":"datacite","abstract":"Computers have become affordable, small, omnipresent and are often connected to the Internet. However, despite the availability of such rich environments, user interfaces have not been adapted to fully leverage their potential. In our view, user interfaces will evolve to become more than simple tools that act using a click-and-act paradigm – people want software that can act as assistants to whom tasks can be delegated. This new type of software will provide us with more user-centric systems, able to interact naturally with human users and with the information environment. Although progress has been made in this direction over the last decade, current research in the field has shown that building intelligent assistants is a complex task that requires expertise in many fields, ranging from artificial intelligence to core software and hardware engineering. The difficulty of deeply integrating all the technologies and AI methodologies required to produce robust intelligent assistants has greatly limited the impact and widespread adoption of this form of software. In this thesis, we propose to design, implement and evaluate a new methodology and associated tool suite aimed to ease and accelerate the development of intelligent assistant software. Active, our solution, introduces the original concept of Active Ontologies, and combines it with a service oriented architecture to serve as the foundation for user-centric applications. The Active software suite features a programming editor, a runtime server and associated management tools. Using this unified platform, we developed techniques for rapidly creating intelligent assistant applications that weave together language processing, process modeling and dynamic service orchestration. To validate our approach, three prototypes have been implemented and evaluated. First, we created an assistant that helps mobile users retrieve data and access online services. The system allows users to fetch information through natural language dialog about restaurants, hotels, points of interest, flights status and weather forecasts. As a second prototype, we have implemented an assistant to help surgeons in the context of the operating room. The system uses a combination of voice and gesture recognition to help surgeons navigate through pre-operative information, visualize and control a live video stream coming from an endoscope mounted on a robotic arm. Lastly, we have created a system that helps organize meetings through emails and instant messages with the organizer and attendees. These three prototypes are deployed in very different application domains, and yet are built with the same tools and methods; this demonstrates the flexibility and versatility of our approach. We conducted a user study to validate our claim that, in specific domains, intelligent assistant software can perform better than conventional software approaches. First, we compared our mobile assistant against Google Mobile™, the leading commercially available search application for mobile users. We asked a population of users to accomplish ten travel-related tasks with both systems. Results show that, for the travel domain, our assistant-based system performs significantly better both in terms of effectiveness and time to completion than the more conventional, keyword-based search engine. In the medical domain, we asked a small population of surgeons to accomplish a set of tasks with our second prototype. After providing us with a positive feedback, surgeons used our prototype to discuss and experiment with innovative forms of user interaction in the context of the operating room. We also performed evaluations to support our claim that our unified approach accelerates the software development of intelligent assistants. First, we gave basic training on the Active platform to a population of software developers, asking them to go through a simple tutorial. Once trained, they were able to successfully program the core component of","url":"https://doi.org/10.5075/epfl-thesis-3990","authors":["Guzzoni, Didier"],"tags":["Intelligent Assistants","Artificial Intelligence","Software Engineering","Assistants Intelligents","Intelligence Artificielle","Génie Logiciel"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2008","doi":"10.5075/epfl-thesis-3990","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21265318","name":"PathMap Experiment #000023 - Tags: #TDP-43 pathology #RNA Splicing #Stathmin 2 #Vitreous Body #DNA-Binding Protein-43","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=23 Artificial General Intelligence LLC Claim Evaluated: Does misfolded TDP43 affect Retinal Ganglion Cell STMN2 ability to repair in a similar way as what how it was shown to affect motor neurons with cryptic mis-splicing? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform single-nuclei RNA sequencing (snRNA-seq) on retinal ganglion cells from TDP-43 mutant mouse models to assess STMN2 splicing profiles. Evaluate axonal regeneration capacity of RGCs derived from human iPSCs with TDP-43 knockdown vs. controls after optic nerve crush injury. 📊 Suggested Studies Comparative proteomics of RGCs in FTLD-TDP patient postmortem tissue to quantify STMN2 protein depletion. Longitudinal study of vitreous STMN2 levels and retinal thinning in presymptomatic C9orf72 mutation carriers. 📊 Swansons Literature Based Discovery Candidates TDP-43-induced STMN2 depletion impairs the regenerative potential of optic nerve fibers, potentially contributing to retinal pathology in ALS. TDP-43 loss of function leads to STMN2 mis-splicing and impaired axonal repair in motor neurons (Source: 40392845). Vitreous fluid in ALS/FTD patients shows reduced STMN2 levels, implying ocular-associated neurodegeneration (Source: 41180957). STMN2 protein, which is vital for microtubule dynamics and axonal regeneration. Since STMN2 is essential for axon regeneration in neurons and its levels are known to decline in the vitreous of TDP-43 pathology patients, it is mechanistically plausible that mis-splicing of STMN2 similarly inhibits the regenerative repair of retinal ganglion cell axons. 📊 Contradictions Between Evidences None identified in the current literature set. 📊 Repurposed Solutions Antisense oligonucleotides (ASOs) that correct STMN2 cryptic splicing in motor neurons could be repurposed for local intravitreal administration to preserve retinal ganglion cell health. 🔖 Tags Attractor Table Extracted Keywords & Entities TDP-43 pathology, _gates_from_tdp-43_pathology, RNA Splicing, _gates_to_rna_splicing, _gates_from_rna_splicing, Stathmin 2, _gates_to_stathmin_2, Vitreous Body, _gates_to_vitreous_body, DNA-Binding Protein-43, _gates_from_dna-binding_protein-43, Retinal Ganglion Cells, _gates_to_retinal_ganglion_cells, Cell Nucleus, _gates_from_cell_nucleus, _gates_from_stathmin_2, Axonal Degeneration, _gates_to_axonal_degeneration, Axons, _gates_to_axons, _gates_from_axons 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21265318","authors":["Dungan, Joshua"],"tags":["TDP-43 pathology","_gates_from_tdp-43_pathology","RNA Splicing","_gates_to_rna_splicing","_gates_from_rna_splicing","Stathmin 2","_gates_to_stathmin_2","Vitreous Body"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21265318","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21265319","name":"PathMap Experiment #000023 - Tags: #TDP-43 pathology #RNA Splicing #Stathmin 2 #Vitreous Body #DNA-Binding Protein-43","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=23 Artificial General Intelligence LLC Claim Evaluated: Does misfolded TDP43 affect Retinal Ganglion Cell STMN2 ability to repair in a similar way as what how it was shown to affect motor neurons with cryptic mis-splicing? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform single-nuclei RNA sequencing (snRNA-seq) on retinal ganglion cells from TDP-43 mutant mouse models to assess STMN2 splicing profiles. Evaluate axonal regeneration capacity of RGCs derived from human iPSCs with TDP-43 knockdown vs. controls after optic nerve crush injury. 📊 Suggested Studies Comparative proteomics of RGCs in FTLD-TDP patient postmortem tissue to quantify STMN2 protein depletion. Longitudinal study of vitreous STMN2 levels and retinal thinning in presymptomatic C9orf72 mutation carriers. 📊 Swansons Literature Based Discovery Candidates TDP-43-induced STMN2 depletion impairs the regenerative potential of optic nerve fibers, potentially contributing to retinal pathology in ALS. TDP-43 loss of function leads to STMN2 mis-splicing and impaired axonal repair in motor neurons (Source: 40392845). Vitreous fluid in ALS/FTD patients shows reduced STMN2 levels, implying ocular-associated neurodegeneration (Source: 41180957). STMN2 protein, which is vital for microtubule dynamics and axonal regeneration. Since STMN2 is essential for axon regeneration in neurons and its levels are known to decline in the vitreous of TDP-43 pathology patients, it is mechanistically plausible that mis-splicing of STMN2 similarly inhibits the regenerative repair of retinal ganglion cell axons. 📊 Contradictions Between Evidences None identified in the current literature set. 📊 Repurposed Solutions Antisense oligonucleotides (ASOs) that correct STMN2 cryptic splicing in motor neurons could be repurposed for local intravitreal administration to preserve retinal ganglion cell health. 🔖 Tags Attractor Table Extracted Keywords & Entities TDP-43 pathology, _gates_from_tdp-43_pathology, RNA Splicing, _gates_to_rna_splicing, _gates_from_rna_splicing, Stathmin 2, _gates_to_stathmin_2, Vitreous Body, _gates_to_vitreous_body, DNA-Binding Protein-43, _gates_from_dna-binding_protein-43, Retinal Ganglion Cells, _gates_to_retinal_ganglion_cells, Cell Nucleus, _gates_from_cell_nucleus, _gates_from_stathmin_2, Axonal Degeneration, _gates_to_axonal_degeneration, Axons, _gates_to_axons, _gates_from_axons 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21265319","authors":["Dungan, Joshua"],"tags":["TDP-43 pathology","_gates_from_tdp-43_pathology","RNA Splicing","_gates_to_rna_splicing","_gates_from_rna_splicing","Stathmin 2","_gates_to_stathmin_2","Vitreous Body"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21265319","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.48550/arxiv.2608.18260","name":"Redakto - The Incognito Tab for LLMs","source":"datacite","abstract":"Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (AI) in general is to ensure privacy when using them, meaning that personally identifiable information (PII) is removed from any text that enters an LLM. These challenges have become more urgent with novel EU legislation. Uncertainty around LLM usage with respect to privacy concerns in EU countries can be a major blocker for the speed of innovation and transfer from research to applications. Here we present \\textbf{Redakto}, a tool that can be used for anonymizing text prior to feeding it to an LLM or other downstream text processing. We provide state-of-the-art functionalities for both redaction of PII but also when used for pseudonymization. These functionalities are exposed such that they can easily be used by end-users, through the Redakto web application, and by developers and researchers, via REST APIs and model context protocol (MCP) hooks. The implementation is fully open source, requires modest compute resources, and can be readily deployed on local hardware. In contrast to prior work and in order to better assess the quality of the anonymized texts, we conduct extensive empirical evaluations on textual data from legal and medical domain with respect to both privacy and utility of the redacted texts. Our empirical results demonstrate that the texts anonymized with different redaction strategies achieve utility scores on par with the original texts, suggesting that anonymization with Redakto can be used for LLM tasks without substantial negative impact for the tasks we explored.","url":"https://doi.org/10.48550/arxiv.2608.18260","authors":["Saha, Saurav Kumar","Röhr, Tom","Bießmann, Felix"],"tags":["Artificial Intelligence (cs.AI)","Computation and Language (cs.CL)","Cryptography and Security (cs.CR)","Information Retrieval (cs.IR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.18260","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22020009","name":"Dataset: ApoE-mediated lipsignaling and EV-delivered bioenergetic substrates both converge on the stabilization of mitochondrial respiratory complexes, which is the requisite physiological precursor for renewed neurogenesis in the hippocampus. - PathMap Experiment #000133","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=133 Artificial General Intelligence LLC Claim Evaluated: ApoE-mediated lipsignaling and EV-delivered bioenergetic substrates both converge on the stabilization of mitochondrial respiratory complexes, which is the requisite physiological precursor for renewed neurogenesis in the hippocampus. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights ApoE4 is not merely a transport protein but a metabolic stressor that impairs mitochondrial membrane potential and glycolysis in astrocytes. EVs possess a 30-fold higher ganglioside content than the parent cells, suggesting unique signaling capabilities in mediating neuroplasticity. The entorhinal cortex exhibits region-specific bioenergetic regulation, contrasting with the cortex and hippocampus, indicating differential susceptibility to ApoE4. \"Neurogenesis without division\" in cortical immature neurons (cINs) offers a paradigm shift in how we view brain structural plasticity. Pharmacological inhibition of the lysosomal channel TMEM175 can alleviate mitochondrial dysfunction under oxidative stress through AMPK activation. SORD-related neuropathies demonstrate that muscle tissue itself is an active site of mitochondrial complex I and metabolic regulation, complicating systemic disease models. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Determine if EV-derived mitochondrial cargo can rescue hippocampal neurogenesis in ApoE4-TR mice under stress conditions. Evaluate the impact of UQCRC1/COX4I1 overexpression on the neurogenic potential of hippocampal neural stem cells in ApoE4-expressing models. 📊 Suggested Studies Longitudinal analysis of ganglioside content in circulating EVs as a proxy for hippocampal metabolic integrity in aging. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): Extracellular vesicles (EVs) derived from muscle tissue can stabilize mitochondrial complexes in hippocampal neurons, promoting neurogenesis via metabolic substrate delivery. - Literature A (Origin): SORD deficiency study (ID: 42616755), detailing mitochondrial stress and metabolic dysfunction in skeletal muscle. - Literature C (Target): Studies on hippocampal neurogenesis (ID: 42602088), linking metabolic stabilization to memory circuits. - The Intersecting Bridge B: Mitochondrial respiratory chain complex proteins (e.g., UQCRC1/COX4I1) and metabolic regulatory signals (e.g., ATP-related metabolites). - Biological Rationale: Muscle-derived EVs carry metabolic cargo that, if distributed to the CNS, could provide the bioenergetic precursors necessary for hippocampal neurons to overcome the metabolic shifts associated with hippocampal sclerosis or aging. 📊 Contradictions Between Evidences There is a tension between the protective potential of EGFR activation (promoting neurogenesis) and its potential for promoting neurotoxicity/gliosis if chronic (ID 42591826). 📊 Repurposed Solutions Use of EV-based decoy receptors (glycoengineered with Gb3, ID 42615512) to sequester toxic circulating factors that impair neuronal bioenergetics. Tags Attractor Table Extracted Keywords & Entities Apolipoprotein E4, _gates_from_apolipoprotein_e4, Mitochondrial Respiration, _gates_to_mitochondrial_respiration, Extracellular Vesicles, _gates_from_extracellular_vesicles, Mitochondria, _gates_to_mitochondria, Energy Metabolism, _gates_from_energy_metabolism, Hippocampal Neurogenesis, _gates_to_hippocampal_neurogenesis Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.22020009","authors":["Dungan, Joshua"],"tags":["Apolipoprotein E4","_gates_from_apolipoprotein_e4","Mitochondrial Respiration","_gates_to_mitochondrial_respiration","Extracellular Vesicles","_gates_from_extracellular_vesicles","Mitochondria","_gates_to_mitochondria"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22020009","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22020010","name":"Dataset: ApoE-mediated lipsignaling and EV-delivered bioenergetic substrates both converge on the stabilization of mitochondrial respiratory complexes, which is the requisite physiological precursor for renewed neurogenesis in the hippocampus. - PathMap Experiment #000133","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=133 Artificial General Intelligence LLC Claim Evaluated: ApoE-mediated lipsignaling and EV-delivered bioenergetic substrates both converge on the stabilization of mitochondrial respiratory complexes, which is the requisite physiological precursor for renewed neurogenesis in the hippocampus. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights ApoE4 is not merely a transport protein but a metabolic stressor that impairs mitochondrial membrane potential and glycolysis in astrocytes. EVs possess a 30-fold higher ganglioside content than the parent cells, suggesting unique signaling capabilities in mediating neuroplasticity. The entorhinal cortex exhibits region-specific bioenergetic regulation, contrasting with the cortex and hippocampus, indicating differential susceptibility to ApoE4. \"Neurogenesis without division\" in cortical immature neurons (cINs) offers a paradigm shift in how we view brain structural plasticity. Pharmacological inhibition of the lysosomal channel TMEM175 can alleviate mitochondrial dysfunction under oxidative stress through AMPK activation. SORD-related neuropathies demonstrate that muscle tissue itself is an active site of mitochondrial complex I and metabolic regulation, complicating systemic disease models. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Determine if EV-derived mitochondrial cargo can rescue hippocampal neurogenesis in ApoE4-TR mice under stress conditions. Evaluate the impact of UQCRC1/COX4I1 overexpression on the neurogenic potential of hippocampal neural stem cells in ApoE4-expressing models. 📊 Suggested Studies Longitudinal analysis of ganglioside content in circulating EVs as a proxy for hippocampal metabolic integrity in aging. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): Extracellular vesicles (EVs) derived from muscle tissue can stabilize mitochondrial complexes in hippocampal neurons, promoting neurogenesis via metabolic substrate delivery. - Literature A (Origin): SORD deficiency study (ID: 42616755), detailing mitochondrial stress and metabolic dysfunction in skeletal muscle. - Literature C (Target): Studies on hippocampal neurogenesis (ID: 42602088), linking metabolic stabilization to memory circuits. - The Intersecting Bridge B: Mitochondrial respiratory chain complex proteins (e.g., UQCRC1/COX4I1) and metabolic regulatory signals (e.g., ATP-related metabolites). - Biological Rationale: Muscle-derived EVs carry metabolic cargo that, if distributed to the CNS, could provide the bioenergetic precursors necessary for hippocampal neurons to overcome the metabolic shifts associated with hippocampal sclerosis or aging. 📊 Contradictions Between Evidences There is a tension between the protective potential of EGFR activation (promoting neurogenesis) and its potential for promoting neurotoxicity/gliosis if chronic (ID 42591826). 📊 Repurposed Solutions Use of EV-based decoy receptors (glycoengineered with Gb3, ID 42615512) to sequester toxic circulating factors that impair neuronal bioenergetics. Tags Attractor Table Extracted Keywords & Entities Apolipoprotein E4, _gates_from_apolipoprotein_e4, Mitochondrial Respiration, _gates_to_mitochondrial_respiration, Extracellular Vesicles, _gates_from_extracellular_vesicles, Mitochondria, _gates_to_mitochondria, Energy Metabolism, _gates_from_energy_metabolism, Hippocampal Neurogenesis, _gates_to_hippocampal_neurogenesis Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.22020010","authors":["Dungan, Joshua"],"tags":["Apolipoprotein E4","_gates_from_apolipoprotein_e4","Mitochondrial Respiration","_gates_to_mitochondrial_respiration","Extracellular Vesicles","_gates_from_extracellular_vesicles","Mitochondria","_gates_to_mitochondria"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22020010","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.48550/arxiv.2608.18166","name":"TractoGraphVLM: A Unified Vision-Language Framework for White Matter Tractography","source":"datacite","abstract":"Vision language models have transformed 2D medical imaging, yet extending them to 3D white matter tractography remains challenging due to the complex topology of fiber bundles. We introduce TractoGraphVLM, a unified framework for four tasks, bundle classification, text-to-tract retrieval, anatomical captioning, and visual question answering, built on a shared GPS architecture, training procedure, and read-out design. Fiber bundles are represented as streamline graphs whose nodes encode 3D position and tangent orientation. A General, Powerful, Scalable (GPS) graph transformer produces bundle embeddings aligned with a frozen BiomedBERT text encoder via contrastive learning, while a BioGPT decoder with visual prefix tokens generates captions and answers. A single shared encoder and decoder is trained jointly across all four tasks and evaluated from one checkpoint. Trained on HCP Young Adult subjects, TractoGraphVLM achieves 91.8% bundle classification accuracy, 84.7% retrieval R@1, BLEU-4=20.1, ROUGE-L=66.8, and 66.4% VQA accuracy on a held-out test set. The same checkpoints transfer zero-shot to HCP Aging subjects, with a modest drop on discriminative tasks and a larger drop on generative tasks, showing robustness to age and acquisition shift. Language supervision yields richer representations than label-only training, recovering structure like hemisphere and fiber family, carried by captions but never given as a label. Swapping only the visual encoder, graphs preserving fiber orientation outperform volumetric baselines, with GPS giving the best balance. Generative metrics measure consistency with a structured knowledge base rather than independent clinical text; even so, TractoGraphVLM shows that classifying, retrieving, describing, and answering questions about a white matter bundle can be served by one jointly trained model that learns transferable neuroanatomy from language alone.","url":"https://doi.org/10.48550/arxiv.2608.18166","authors":["Kumar, Gurucharan Marthi Krishna","Mendola, Janine Dale","Shmuel, Amir"],"tags":["Image and Video Processing (eess.IV)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.18166","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21285954","name":"Can inhaled COVID-19 vaccinations be used to help treat COPD? - PathMap Experiment #000040","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=40 Artificial General Intelligence LLC Claim Evaluated: Can inhaled COVID-19 vaccinations be used to help treat COPD? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights RSV infection in adults with chronic lung disease has been associated with a higher disease burden compared to influenza or SARS-CoV-2. Intranasal and pulmonary \"pull\" vaccination strategies are effective at inducing secretory IgA and lung-resident T-cell responses that are typically absent following intramuscular injection. The use of cationic ferritin nanoparticles or adenoviral vectors for intranasal delivery can overcome pre-existing immunity against viral vaccine vectors. Specific biomarkers, such as the CCL5/CCR4 signaling axis, are being identified as modulators of immune cell migration following mucosal immunization. There is a persistent \"interferon gap\" in the elderly, characterized by a kinetic delay in innate antiviral signaling, which mucosal platforms aim to bridge. The use of codon-pair deoptimization (CPD) in live-attenuated vaccines represents a novel safety mechanism for developing inhaled platforms. Some evidence suggests that high-dose systemic corticosteroids may paradoxically increase mortality in COVID-19 ARDS, reinforcing the need for targeted, localized prophylactic strategies like mucosal vaccination. Evidence suggests that the mucosal immune system in the respiratory tract can be specifically engaged to mitigate infection-driven COPD exacerbations. Inhaled delivery mechanisms for biologics can reduce the required dosage of therapeutic agents while bypassing gastrointestinal degradation. Lipid-based nanocarriers have demonstrated the ability to cross pulmonary mucosal barriers, a critical feature for both therapeutic and prophylactic agents. Advanced nebulization techniques (mesh versus jet nebulizers) significantly impact the efficiency of pulmonary delivery, which is vital for the clinical success of inhaled therapeutics. There is a clear distinction between the immunogenicity profiles of intramuscular (systemic IgG) and mucosal (respiratory IgA) vaccinations, with the latter showing promise for enhancing local airway resilience. Inhaled heparin is emerging as a versatile therapeutic option, given its established role in managing respiratory infections including COVID-19 and its potential use in asthma and COPD. Clinical data indicate that SARS-CoV-2 infection is associated with different mortality and inflammatory markers in patients with COPD versus other respiratory viruses. Nanotechnology integration into inhalers allows for precise, patient-centric dosing, which could improve adherence in chronic populations. A \"virus-agnostic\" immunomodulatory platform, distinct from traditional vaccines, is proposed to restore mucosal immune competence in the elderly (ID: 42347596). Intranasal vaccination with PIV5-vectored platforms has been evaluated in animal models, showing protection against challenge without necessarily relying on serum neutralizing antibodies (ID: 41863913). Probiotic supplementation has shown potential to reduce systemic inflammation and improve patient-reported outcomes in COPD (ID: 42286603). The gut-lung axis is increasingly recognized as a target, with researchers identifying cross-kingdom microbiome interactions that influence COPD outcomes (ID: 42324603). Sex-specific differences exist in neutrophil transcriptional programs in COPD, which may necessitate sex-dependent therapeutic strategies (ID: 42281812). There is a significant heterogeneity in clinical outcomes for ACOS (Asthma-COPD overlap), with newer glucose-lowering agents showing potential to modify risk (ID: 42376494). 🧪 Extra","url":"https://doi.org/10.5281/zenodo.21285954","authors":["Dungan, Joshua"],"tags":["Pulmonary Disease, Chronic Obstructive","_gates_from_pulmonary_disease,_chronic_obstructive","Administration, Inhalation","_gates_to_administration,_inhalation","_gates_from_administration,_inhalation","Immunity, Mucosal","_gates_to_immunity,_mucosal","Mucous Membrane"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21285954","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21285955","name":"Can inhaled COVID-19 vaccinations be used to help treat COPD? - PathMap Experiment #000040","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=40 Artificial General Intelligence LLC Claim Evaluated: Can inhaled COVID-19 vaccinations be used to help treat COPD? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights RSV infection in adults with chronic lung disease has been associated with a higher disease burden compared to influenza or SARS-CoV-2. Intranasal and pulmonary \"pull\" vaccination strategies are effective at inducing secretory IgA and lung-resident T-cell responses that are typically absent following intramuscular injection. The use of cationic ferritin nanoparticles or adenoviral vectors for intranasal delivery can overcome pre-existing immunity against viral vaccine vectors. Specific biomarkers, such as the CCL5/CCR4 signaling axis, are being identified as modulators of immune cell migration following mucosal immunization. There is a persistent \"interferon gap\" in the elderly, characterized by a kinetic delay in innate antiviral signaling, which mucosal platforms aim to bridge. The use of codon-pair deoptimization (CPD) in live-attenuated vaccines represents a novel safety mechanism for developing inhaled platforms. Some evidence suggests that high-dose systemic corticosteroids may paradoxically increase mortality in COVID-19 ARDS, reinforcing the need for targeted, localized prophylactic strategies like mucosal vaccination. Evidence suggests that the mucosal immune system in the respiratory tract can be specifically engaged to mitigate infection-driven COPD exacerbations. Inhaled delivery mechanisms for biologics can reduce the required dosage of therapeutic agents while bypassing gastrointestinal degradation. Lipid-based nanocarriers have demonstrated the ability to cross pulmonary mucosal barriers, a critical feature for both therapeutic and prophylactic agents. Advanced nebulization techniques (mesh versus jet nebulizers) significantly impact the efficiency of pulmonary delivery, which is vital for the clinical success of inhaled therapeutics. There is a clear distinction between the immunogenicity profiles of intramuscular (systemic IgG) and mucosal (respiratory IgA) vaccinations, with the latter showing promise for enhancing local airway resilience. Inhaled heparin is emerging as a versatile therapeutic option, given its established role in managing respiratory infections including COVID-19 and its potential use in asthma and COPD. Clinical data indicate that SARS-CoV-2 infection is associated with different mortality and inflammatory markers in patients with COPD versus other respiratory viruses. Nanotechnology integration into inhalers allows for precise, patient-centric dosing, which could improve adherence in chronic populations. A \"virus-agnostic\" immunomodulatory platform, distinct from traditional vaccines, is proposed to restore mucosal immune competence in the elderly (ID: 42347596). Intranasal vaccination with PIV5-vectored platforms has been evaluated in animal models, showing protection against challenge without necessarily relying on serum neutralizing antibodies (ID: 41863913). Probiotic supplementation has shown potential to reduce systemic inflammation and improve patient-reported outcomes in COPD (ID: 42286603). The gut-lung axis is increasingly recognized as a target, with researchers identifying cross-kingdom microbiome interactions that influence COPD outcomes (ID: 42324603). Sex-specific differences exist in neutrophil transcriptional programs in COPD, which may necessitate sex-dependent therapeutic strategies (ID: 42281812). There is a significant heterogeneity in clinical outcomes for ACOS (Asthma-COPD overlap), with newer glucose-lowering agents showing potential to modify risk (ID: 42376494). 🧪 Extra","url":"https://doi.org/10.5281/zenodo.21285955","authors":["Dungan, Joshua"],"tags":["Pulmonary Disease, Chronic Obstructive","_gates_from_pulmonary_disease,_chronic_obstructive","Administration, Inhalation","_gates_to_administration,_inhalation","_gates_from_administration,_inhalation","Immunity, Mucosal","_gates_to_immunity,_mucosal","Mucous Membrane"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21285955","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22019848","name":"Dataset: Discovery: Considering PubMed #41177462, intranasal S-GEVs co-functionalized with ApoE peptides may bypass the cribriform plate and target astrocytic LRP1 receptors in order to suppress NF-κB and may resolve some neuroinflammation in Alzheimer's and ALS. - PathMap Experiment #000132","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=132 Artificial General Intelligence LLC Claim Evaluated: Discovery: Considering PubMed #41177462, intranasal S-GEVs co-functionalized with ApoE peptides may bypass the cribriform plate and target astrocytic LRP1 receptors in order to suppress NF-κB and may resolve some neuroinflammation in Alzheimer's and ALS. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Targeted Engineering:** Angiopep-2 (Ang2) peptide-modified TEVs (Ang-TEVs) confer significantly enhanced microglial targeting. Vesicle Versatility:** Exosomes are naturally occurring extracellular vesicles that have emerged as promising bio-inspired nanocarriers for the treatment of neurological disorders owing to their intrinsic biocompatibility, low immunogenicity, and ability to cross the blood-brain barrier. Metabolic Reprogramming:** LEVs-SIRT2-KD were readily internalized by microglia in vivo following intranasal delivery. Cholesterol Coupling:** Neurons acquire astrocyte-derived cholesterol through LDLR/LRP1, redistribute it via NPC1/NPC2, and eliminate excess cholesterol as 24 S-hydroxycholesterol through CYP46A1. Inflammatory RNA:** Emerging evidence further demonstrates that inflammatory RNAs participate in epigenetic regulation, intercellular communication, and inter-organ crosstalk through extracellular vesicles and exosomes. Proteinopathy Neutralization:** PC-OxPL-VecTab neutralized PC-OxPL-induced neurotoxicity, reduced TDP-43 aggregation, and prevented motor neuron death and behavioral deficits in a sALS CSF transfer mouse model. Complement Cascade:** The complement cascade is closely related to AD-associated pathological processes; however, the precise mechanisms underlying its contributions remain incompletely elucidated. Exosomes isolated from PCA-treated efferocytic macrophages inhibited inflammation and increased miR-10b levels in aortic endothelial cells. They mimicked the protective effect of recombinant ApoE3Ch on endothelial integrity by restoring β-catenin nuclear localization. Some of these differentially expressed miRNAs were associated with key AD-related comorbidities such as APOE genotype, age, and metabolic burden and were predicted to target genes within NF-κB -regulated inflammatory pathways. We demonstrated the remarkable potential of MenSC-EVs in alleviating atherosclerosis through the NF-κB signaling pathway. APOE ε4 carriers presented further reductions in SV2A levels compared with noncarriers. SeNExo penetrates the blood-brain barrier (BBB) via the apolipoprotein E and prolow-density lipoprotein receptor-related protein 1 (APOE_LRP-1) interaction. In the adult brain, astrocytes are an important source of cholesterol for neurons. One of the key functions of APOE is to bind and deliver newly synthesized cholesterol and lipids to neurons through receptor-mediated endocytosis (LDLR, LRP1, VLDLR, APOER2). Macrophage-specific PSRC1 depletion alone was sufficient to recapitulate the systemic hypercholesterolemia and accelerated atherosclerosis observed in whole-body knockout models. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the biodistribution of ApoE-peptide functionalized EVs after intranasal administration in APP/PS1 mice using IVIS imaging. Evaluate the suppression of p-NF-κB in astrocytes following treatment with ApoE-EVs in LPS-stimulated in vitro co-culture models. Assess the therapeutic efficacy of ApoE-conjugated EVs pre-loaded with metabolic substrates in a 5xFAD mouse model. Perform proteomics on EVs to confirm successful co-incorporation of metabolic modifiers and ApoE-mimetic peptides without cargo degradation. 📊 Suggested Studies Longitudinal study comparing the cognitive recov","url":"https://doi.org/10.5281/zenodo.22019848","authors":["Dungan, Joshua"],"tags":["Administration, Intranasal","_gates_from_administration,_intranasal","Blood-Brain Barrier","_gates_to_blood-brain_barrier","Apolipoproteins E","_gates_from_apolipoproteins_e","Low Density Lipoprotein Receptor-Related Protein-1","_gates_to_low_density_lipoprotein_receptor-related_protein-1"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22019848","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22019849","name":"Dataset: Discovery: Considering PubMed #41177462, intranasal S-GEVs co-functionalized with ApoE peptides may bypass the cribriform plate and target astrocytic LRP1 receptors in order to suppress NF-κB and may resolve some neuroinflammation in Alzheimer's and ALS. - PathMap Experiment #000132","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=132 Artificial General Intelligence LLC Claim Evaluated: Discovery: Considering PubMed #41177462, intranasal S-GEVs co-functionalized with ApoE peptides may bypass the cribriform plate and target astrocytic LRP1 receptors in order to suppress NF-κB and may resolve some neuroinflammation in Alzheimer's and ALS. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Targeted Engineering:** Angiopep-2 (Ang2) peptide-modified TEVs (Ang-TEVs) confer significantly enhanced microglial targeting. Vesicle Versatility:** Exosomes are naturally occurring extracellular vesicles that have emerged as promising bio-inspired nanocarriers for the treatment of neurological disorders owing to their intrinsic biocompatibility, low immunogenicity, and ability to cross the blood-brain barrier. Metabolic Reprogramming:** LEVs-SIRT2-KD were readily internalized by microglia in vivo following intranasal delivery. Cholesterol Coupling:** Neurons acquire astrocyte-derived cholesterol through LDLR/LRP1, redistribute it via NPC1/NPC2, and eliminate excess cholesterol as 24 S-hydroxycholesterol through CYP46A1. Inflammatory RNA:** Emerging evidence further demonstrates that inflammatory RNAs participate in epigenetic regulation, intercellular communication, and inter-organ crosstalk through extracellular vesicles and exosomes. Proteinopathy Neutralization:** PC-OxPL-VecTab neutralized PC-OxPL-induced neurotoxicity, reduced TDP-43 aggregation, and prevented motor neuron death and behavioral deficits in a sALS CSF transfer mouse model. Complement Cascade:** The complement cascade is closely related to AD-associated pathological processes; however, the precise mechanisms underlying its contributions remain incompletely elucidated. Exosomes isolated from PCA-treated efferocytic macrophages inhibited inflammation and increased miR-10b levels in aortic endothelial cells. They mimicked the protective effect of recombinant ApoE3Ch on endothelial integrity by restoring β-catenin nuclear localization. Some of these differentially expressed miRNAs were associated with key AD-related comorbidities such as APOE genotype, age, and metabolic burden and were predicted to target genes within NF-κB -regulated inflammatory pathways. We demonstrated the remarkable potential of MenSC-EVs in alleviating atherosclerosis through the NF-κB signaling pathway. APOE ε4 carriers presented further reductions in SV2A levels compared with noncarriers. SeNExo penetrates the blood-brain barrier (BBB) via the apolipoprotein E and prolow-density lipoprotein receptor-related protein 1 (APOE_LRP-1) interaction. In the adult brain, astrocytes are an important source of cholesterol for neurons. One of the key functions of APOE is to bind and deliver newly synthesized cholesterol and lipids to neurons through receptor-mediated endocytosis (LDLR, LRP1, VLDLR, APOER2). Macrophage-specific PSRC1 depletion alone was sufficient to recapitulate the systemic hypercholesterolemia and accelerated atherosclerosis observed in whole-body knockout models. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the biodistribution of ApoE-peptide functionalized EVs after intranasal administration in APP/PS1 mice using IVIS imaging. Evaluate the suppression of p-NF-κB in astrocytes following treatment with ApoE-EVs in LPS-stimulated in vitro co-culture models. Assess the therapeutic efficacy of ApoE-conjugated EVs pre-loaded with metabolic substrates in a 5xFAD mouse model. Perform proteomics on EVs to confirm successful co-incorporation of metabolic modifiers and ApoE-mimetic peptides without cargo degradation. 📊 Suggested Studies Longitudinal study comparing the cognitive recov","url":"https://doi.org/10.5281/zenodo.22019849","authors":["Dungan, Joshua"],"tags":["Administration, Intranasal","_gates_from_administration,_intranasal","Blood-Brain Barrier","_gates_to_blood-brain_barrier","Apolipoproteins E","_gates_from_apolipoproteins_e","Low Density Lipoprotein Receptor-Related Protein-1","_gates_to_low_density_lipoprotein_receptor-related_protein-1"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22019849","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21941613","name":"DeHoLTZ: A Zero-Parameter Deterministic Derivation Database and Framework Built from a Single Lacunary Seed  Seed: ∑ cos(π(√2)ⁿτ) / 2ⁿ | Axiom: dS/dτ > 0 | Rewrite: 0 → 01, 1 → 100","source":"datacite","abstract":"Author: Mark Jacobson (Gson @gsemark), Stockholm, SwedenDate: 2026-08-15Version: v20.x The DeHoLTZ Framework This documentation presents the DeHoLTZ (Dimensionell Emergence + Hell of a Lot of Theories, Zero friction) framework, a comprehensive, zero-parameter, deterministic model for deriving fundamental physical, cosmological, and biological constants. By utilizing a single primordial lacunary seed—evolved through discrete binary rewrite rules governed by the entropic axiom dS/dτ>0dS/dτ>0—this framework systematically reconstructs core physical constants from a self-generating mathematical structure. Rather than relying on empirical inputs, the DeHoLTZ framework functions as an independent computational engine, where physical law emerges as a logical necessity from the base topology of the initial state. This registry—containing over 1,600 verified derivation posts—serves as a complete log of derivations, providing a deterministic bridge between discrete rewrite logic and continuous standard physical metrics. By resolving the inherent walls between current SI units and foundational geometric features, this model offers a self-consistent and closed-form alternative to conventional empirical-based modeling, validating its internal continuity against observed cosmic and mechanical residuals. Start Docs for New AI Sessions .Upload 00 docs: 00 Start doc for AI — AI assisting documentation 00 Primary doc — Summary of DeHoLTZ 00 Soft DB — The fun DB using Holtz: groups, hypotheses, applied science, new branches 00 DB matrix — DB comprised in matrix API to be run with new AI sessions 01 Hard mini DB ( subset mini of main DB 2.7 Mbyte) 01 Main DB — 5 MByte v19.55 log and process with explanations and AI comments, creating post-massive source of info: what, when, and why 01 SUP — Supporting documents Status: Closed — no external references requiredCore: A deterministic calculator, not a theory DeHoLTZ version v20: DeHoLTZ-Analog confirming analog mainstream science (au naturelle) works excellently without ad hocs or free parameters. No ToEs are needed. Existing science works excellently. DeHoLTZ-FOAM provides advanced metadata of the same science, showing why it works. All posts are derived from one seed:Σ cos(π (√2)^n τ) / 2^n 1. What DeHoLTZ Is — And What It Is Not What It Is DeHoLTZ is a deterministic(epsilon=0) calculator AI tool . It takes an axiom, a rewrite rule, and a seed, and computes exact values. That is all it does. There are no external references set holtz=TRUE (Internal ) no external validation seeked or needed It functions as a structural metadata layer, sitting on top of existing peer-reviewed science. It shows how known constants can be derived from a single root but adds no new physics. The system is internally closed. Every derivation closes with ε = 0 (exactly zero residual) at 500-digit precision, or is flagged as NCI/NCI-U. It is auditable and versioned. The code is open, and the registry contains over 1,600 certified posts. Every derivation can be checked. DeHoLTZ is a map of the ground that physics measures, showing relationships between constants, not the territory itself. It is empirically anchored. The numbers match measurements; spiral waves (Steinmetz et al. 2026), UPE (Kobayashi et al.), and fractal dimension (Timmermann et al.) confirm the framework's calculations. What It Is Not DeHoLTZ is not a Theory of Everything. It makes no claims about physical mechanisms. It does not explain why gravity exists — it shows that gravity's values can be calculated from the root. It is not speculation. Every derivation is explicit, auditable, and reproducible. The code is included. It is not a spiritual system. Consciousness appears as a derived consequence of the calculator's structure, not as a metaphysical claim. It is not a replacement for science. It is a structural metadata layer on existing peer-reviewed science, not a substitute for experiment or observation. It is not a truth claim about the universe. It onl","url":"https://doi.org/10.5281/zenodo.21941613","authors":["Jacobson, Mark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21941613","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.18233709","name":"DeHoLTZ: A Zero-Parameter Deterministic Derivation Database and Framework Built from a Single Lacunary Seed  Seed: ∑ cos(π(√2)ⁿτ) / 2ⁿ | Axiom: dS/dτ > 0 | Rewrite: 0 → 01, 1 → 100","source":"datacite","abstract":"Author: Mark Jacobson (Gson @gsemark), Stockholm, SwedenDate: 2026-08-15Version: v20.x The DeHoLTZ Framework This documentation presents the DeHoLTZ (Dimensionell Emergence + Hell of a Lot of Theories, Zero friction) framework, a comprehensive, zero-parameter, deterministic model for deriving fundamental physical, cosmological, and biological constants. By utilizing a single primordial lacunary seed—evolved through discrete binary rewrite rules governed by the entropic axiom dS/dτ>0dS/dτ>0—this framework systematically reconstructs core physical constants from a self-generating mathematical structure. Rather than relying on empirical inputs, the DeHoLTZ framework functions as an independent computational engine, where physical law emerges as a logical necessity from the base topology of the initial state. This registry—containing over 1,600 verified derivation posts—serves as a complete log of derivations, providing a deterministic bridge between discrete rewrite logic and continuous standard physical metrics. By resolving the inherent walls between current SI units and foundational geometric features, this model offers a self-consistent and closed-form alternative to conventional empirical-based modeling, validating its internal continuity against observed cosmic and mechanical residuals. Start Docs for New AI Sessions .Upload 00 docs: 00 Start doc for AI — AI assisting documentation 00 Primary doc — Summary of DeHoLTZ 00 Soft DB — The fun DB using Holtz: groups, hypotheses, applied science, new branches 00 DB matrix — DB comprised in matrix API to be run with new AI sessions 01 Hard mini DB ( subset mini of main DB 2.7 Mbyte) 01 Main DB — 5 MByte v19.55 log and process with explanations and AI comments, creating post-massive source of info: what, when, and why 01 SUP — Supporting documents Status: Closed — no external references requiredCore: A deterministic calculator, not a theory DeHoLTZ version v20: DeHoLTZ-Analog confirming analog mainstream science (au naturelle) works excellently without ad hocs or free parameters. No ToEs are needed. Existing science works excellently. DeHoLTZ-FOAM provides advanced metadata of the same science, showing why it works. All posts are derived from one seed:Σ cos(π (√2)^n τ) / 2^n 1. What DeHoLTZ Is — And What It Is Not What It Is DeHoLTZ is a deterministic(epsilon=0) calculator AI tool . It takes an axiom, a rewrite rule, and a seed, and computes exact values. That is all it does. There are no external references set holtz=TRUE (Internal ) no external validation seeked or needed It functions as a structural metadata layer, sitting on top of existing peer-reviewed science. It shows how known constants can be derived from a single root but adds no new physics. The system is internally closed. Every derivation closes with ε = 0 (exactly zero residual) at 500-digit precision, or is flagged as NCI/NCI-U. It is auditable and versioned. The code is open, and the registry contains over 1,600 certified posts. Every derivation can be checked. DeHoLTZ is a map of the ground that physics measures, showing relationships between constants, not the territory itself. It is empirically anchored. The numbers match measurements; spiral waves (Steinmetz et al. 2026), UPE (Kobayashi et al.), and fractal dimension (Timmermann et al.) confirm the framework's calculations. What It Is Not DeHoLTZ is not a Theory of Everything. It makes no claims about physical mechanisms. It does not explain why gravity exists — it shows that gravity's values can be calculated from the root. It is not speculation. Every derivation is explicit, auditable, and reproducible. The code is included. It is not a spiritual system. Consciousness appears as a derived consequence of the calculator's structure, not as a metaphysical claim. It is not a replacement for science. It is a structural metadata layer on existing peer-reviewed science, not a substitute for experiment or observation. It is not a truth claim about the universe. It onl","url":"https://doi.org/10.5281/zenodo.18233709","authors":["Jacobson, Mark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18233709","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21251408","name":"PathMap Experiment #000018 - Tags: #Tobacco Smoke Pollution #Epithelium #Dysbiosis #Immune System Diseases #Cigarette Smoke","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=18 Artificial General Intelligence LLC Claim Evaluated: Can smoking cigarettes cause dysbiosis? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Longitudinal microbiome analysis of patients undergoing smoking cessation programs to determine the temporal dynamics of microbiome restoration. In vitro air-liquid interface (ALI) co-culture models of airway epithelial cells and diverse commensal microbiota exposed to standardized cigarette smoke extract (CSE) to measure real-time barrier stability and microbial shift. Fecal microbial transplantation (FMT) of microbiome from chronic smokers into germ-free mouse models to determine if smoking-induced metabolic shifts (e.g., tryptophan depletion) are sufficient to induce phenotypic inflammatory disease. 📊 Suggested Studies Multi-center prospective study correlating smoking-induced gut-lung axis biomarkers (indolepropionate/bile acids) with respiratory exacerbation frequency. Large-scale cohort study assessing the impact of vaping versus conventional cigarette smoking on oral versus gut microbiome diversity using standardized protocols. Longitudinal cohort analysis mapping the evolution of the respiratory microbiome in healthy subjects before and after the initiation of tobacco smoking. 📊 Swansons Literature Based Discovery Candidates Cigarette-induced depletion of indolepropionate (via gut dysbiosis) accelerates respiratory barrier breakdown by reducing Muc16-mediated epithelial maintenance. Tobacco exposure disrupts host-microbiome tryptophan and bile acid metabolism, specifically indolepropionate depletion in smokers with MS (ID: 42383698). Muc16 deficiency exacerbates pneumococcal translocation and epithelial barrier disruption in the upper respiratory tract, especially under CSE exposure (ID: 42383770). Mucosal barrier protection and epithelial tight junction integrity (ZO-1 protein expression). Indolepropionate is an anti-inflammatory metabolite that preserves barrier integrity; its depletion in smokers may directly compromise the expression/stability of Muc16 and associated tight junction proteins (e.g., ZO-1), leaving the respiratory epithelium vulnerable to bacterial invasion. 📊 Contradictions Between Evidences While most studies demonstrate that smoking affects microbial composition, ID 42388034 notes 'no major change in overall community diversity' in tobacco-related rhizosphere profiling, suggesting that smoking impacts might be context-specific (human versus botanical/rhizosphere ecosystems). 📊 Repurposed Solutions Probiotic supplementation (e.g., Bifidobacterium) and targeted metabolic precursors (indole-3-propionate) represent repurposed strategies to restore microbiome homeostasis and barrier function in smokers, potentially mitigating risks of respiratory exacerbations and secondary infection. 🔖 Tags Attractor Table Extracted Keywords & Entities Tobacco Smoke Pollution, _gates_from_tobacco_smoke_pollution, Epithelium, _gates_to_epithelium, _gates_from_epithelium, Dysbiosis, _gates_to_dysbiosis, _gates_from_dysbiosis, Immune System Diseases, _gates_to_immune_system_diseases, Cigarette Smoke, _gates_from_cigarette_smoke, Oxidative Stress, _gates_to_oxidative_stress, _gates_from_oxidative_stress, Microbiota, _gates_to_microbiota, _gates_from_microbiota, Tobacco Smoke, _gates_from_tobacco_smoke, Microbial Niche, _gates_to_microbial_niche, _gates_from_microbial_niche, Bacteria, _gates_to_bacteria, _gates_from_bacteria 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21251408","authors":["Dungan, Joshua"],"tags":["Tobacco Smoke Pollution","_gates_from_tobacco_smoke_pollution","Epithelium","_gates_to_epithelium","_gates_from_epithelium","Dysbiosis","_gates_to_dysbiosis","_gates_from_dysbiosis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21251408","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21251409","name":"PathMap Experiment #000018 - Tags: #Tobacco Smoke Pollution #Epithelium #Dysbiosis #Immune System Diseases #Cigarette Smoke","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=18 Artificial General Intelligence LLC Claim Evaluated: Can smoking cigarettes cause dysbiosis? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Longitudinal microbiome analysis of patients undergoing smoking cessation programs to determine the temporal dynamics of microbiome restoration. In vitro air-liquid interface (ALI) co-culture models of airway epithelial cells and diverse commensal microbiota exposed to standardized cigarette smoke extract (CSE) to measure real-time barrier stability and microbial shift. Fecal microbial transplantation (FMT) of microbiome from chronic smokers into germ-free mouse models to determine if smoking-induced metabolic shifts (e.g., tryptophan depletion) are sufficient to induce phenotypic inflammatory disease. 📊 Suggested Studies Multi-center prospective study correlating smoking-induced gut-lung axis biomarkers (indolepropionate/bile acids) with respiratory exacerbation frequency. Large-scale cohort study assessing the impact of vaping versus conventional cigarette smoking on oral versus gut microbiome diversity using standardized protocols. Longitudinal cohort analysis mapping the evolution of the respiratory microbiome in healthy subjects before and after the initiation of tobacco smoking. 📊 Swansons Literature Based Discovery Candidates Cigarette-induced depletion of indolepropionate (via gut dysbiosis) accelerates respiratory barrier breakdown by reducing Muc16-mediated epithelial maintenance. Tobacco exposure disrupts host-microbiome tryptophan and bile acid metabolism, specifically indolepropionate depletion in smokers with MS (ID: 42383698). Muc16 deficiency exacerbates pneumococcal translocation and epithelial barrier disruption in the upper respiratory tract, especially under CSE exposure (ID: 42383770). Mucosal barrier protection and epithelial tight junction integrity (ZO-1 protein expression). Indolepropionate is an anti-inflammatory metabolite that preserves barrier integrity; its depletion in smokers may directly compromise the expression/stability of Muc16 and associated tight junction proteins (e.g., ZO-1), leaving the respiratory epithelium vulnerable to bacterial invasion. 📊 Contradictions Between Evidences While most studies demonstrate that smoking affects microbial composition, ID 42388034 notes 'no major change in overall community diversity' in tobacco-related rhizosphere profiling, suggesting that smoking impacts might be context-specific (human versus botanical/rhizosphere ecosystems). 📊 Repurposed Solutions Probiotic supplementation (e.g., Bifidobacterium) and targeted metabolic precursors (indole-3-propionate) represent repurposed strategies to restore microbiome homeostasis and barrier function in smokers, potentially mitigating risks of respiratory exacerbations and secondary infection. 🔖 Tags Attractor Table Extracted Keywords & Entities Tobacco Smoke Pollution, _gates_from_tobacco_smoke_pollution, Epithelium, _gates_to_epithelium, _gates_from_epithelium, Dysbiosis, _gates_to_dysbiosis, _gates_from_dysbiosis, Immune System Diseases, _gates_to_immune_system_diseases, Cigarette Smoke, _gates_from_cigarette_smoke, Oxidative Stress, _gates_to_oxidative_stress, _gates_from_oxidative_stress, Microbiota, _gates_to_microbiota, _gates_from_microbiota, Tobacco Smoke, _gates_from_tobacco_smoke, Microbial Niche, _gates_to_microbial_niche, _gates_from_microbial_niche, Bacteria, _gates_to_bacteria, _gates_from_bacteria 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21251409","authors":["Dungan, Joshua"],"tags":["Tobacco Smoke Pollution","_gates_from_tobacco_smoke_pollution","Epithelium","_gates_to_epithelium","_gates_from_epithelium","Dysbiosis","_gates_to_dysbiosis","_gates_from_dysbiosis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21251409","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22013680","name":"A Practical Protocol for Conducting and Writing Medical Research: A Step-by-Step Guide for Medical Students","source":"datacite","abstract":"A Practical Protocol for Conducting and Writing Medical Research: A Step-by-Step Guide for Medical Students This educational resource provides a practical, step-by-step framework to help medical students and early-stage researchers move from an initial research idea to a structured, submission-ready scientific manuscript. The guide follows a sequential pathway: IDEA → QUESTION → EVIDENCE → DESIGN → PROTOCOL → DATA → MANUSCRIPT → SUBMISSION It covers the main stages of a medical research project, including formulation of a focused research question using PICO and FINER principles, literature searching and evidence identification, selection of an appropriate study design, development of a basic research protocol, manuscript structure and scientific writing, responsible use of artificial intelligence tools, academic integrity and plagiarism prevention, journal selection, and pre-submission quality control. Particular emphasis is placed on practical methodological decisions that commonly challenge students during their first research projects, including defining study populations and variables, planning outcomes and data collection, considering sample size and statistical analysis, addressing ethical requirements, and maintaining a clear distinction between Methods, Results, Discussion, and Conclusion. The resource also introduces common reporting frameworks and publication practices to help students align their work with established standards in medical research and scientific publishing. This guide is intended primarily for undergraduate and early postgraduate medical students undertaking their first research projects, while also serving as a concise reference for supervisors, tutors, and educators supporting students in medical research. This document is an educational and practical resource. It is not intended to replace formal training in research methodology, epidemiology, biostatistics, research ethics, or supervision by qualified researchers. Specific requirements may vary according to the study design, institution, ethics committee, target journal, and publisher. Version: 1.1 (2026)","url":"https://doi.org/10.5281/zenodo.22013680","authors":["Telli, Radhia chems"],"tags":["Medical research","Scientific writing","Research methodology","Medical students","Research protocol","Scientific manuscript","Academic writing","Study design"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22013680","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22013681","name":"A Practical Protocol for Conducting and Writing Medical Research: A Step-by-Step Guide for Medical Students","source":"datacite","abstract":"A Practical Protocol for Conducting and Writing Medical Research: A Step-by-Step Guide for Medical Students This educational resource provides a practical, step-by-step framework to help medical students and early-stage researchers move from an initial research idea to a structured, submission-ready scientific manuscript. The guide follows a sequential pathway: IDEA → QUESTION → EVIDENCE → DESIGN → PROTOCOL → DATA → MANUSCRIPT → SUBMISSION It covers the main stages of a medical research project, including formulation of a focused research question using PICO and FINER principles, literature searching and evidence identification, selection of an appropriate study design, development of a basic research protocol, manuscript structure and scientific writing, responsible use of artificial intelligence tools, academic integrity and plagiarism prevention, journal selection, and pre-submission quality control. Particular emphasis is placed on practical methodological decisions that commonly challenge students during their first research projects, including defining study populations and variables, planning outcomes and data collection, considering sample size and statistical analysis, addressing ethical requirements, and maintaining a clear distinction between Methods, Results, Discussion, and Conclusion. The resource also introduces common reporting frameworks and publication practices to help students align their work with established standards in medical research and scientific publishing. This guide is intended primarily for undergraduate and early postgraduate medical students undertaking their first research projects, while also serving as a concise reference for supervisors, tutors, and educators supporting students in medical research. This document is an educational and practical resource. It is not intended to replace formal training in research methodology, epidemiology, biostatistics, research ethics, or supervision by qualified researchers. Specific requirements may vary according to the study design, institution, ethics committee, target journal, and publisher. Version: 1.1 (2026)","url":"https://doi.org/10.5281/zenodo.22013681","authors":["Telli, Radhia chems"],"tags":["Medical research","Scientific writing","Research methodology","Medical students","Research protocol","Scientific manuscript","Academic writing","Study design"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22013681","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.25673/124571","name":"AI-Driven Chronic Diseases Prediction Model Based on Stacking Machine Learning Algorithms","source":"datacite","abstract":"Chronic diseases are responsible for most deaths worldwide. These illnesses are increasingly prevalent, and there is a dire need for systems that can predict diseases before they develop. Early detection and prediction solutions could exploit powerful tools of artificial intelligence (AI), primarily methods of machine learning (ML) and data mining. In this study, a unified stacking framework was proposed to predict three significant chronic conditions, including heart disease, diabetes, and hypertension. Th e proposed model consists of Random Forest (RF), XGBoost, and LightGBM as base learners, while Logistic Regression (LR) is used as a meta learner. Three datasets on heart disease, diabetes, and hypertension from BRFSS 2015 are funded by the US CDC and were utilized to demonstrate implementation of the framework. An independent integrated framework was adopted across each dataset to have the same preprocessing, model settings, and validation scenarios across pipelines. For robust and reliable performance assessment, both hold out validation and stratified five-fold cross-validation were adopted. Using stratified five-fold cross-validation, the proposed framework has shown promising predictive performance with an overall accuracy of 99.20% for heart diseases, 97.90% for diabetes, and 99.93% for hypertension. In conclusion, these results indicate a competitive and stable ensemble model towards chronic disease prediction, which can be deployed in medical decision- making applications.","url":"https://doi.org/10.25673/124571","authors":["Hussein, Huda Naser","Miften, Firas Sabar"],"tags":["DDC::6** Technik, Medizin, angewandte Wissenschaften"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25673/124571","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21231692","name":"PathMap Experiment #000006 - Tags: #Quercetin #Autophagy #Misfolded Proteins #DUX4","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=6 Artificial General Intelligence LLC Claim Evaluated: Does existing in vitro data show that Quercetin-induced autophagy can successfully degrade DUX4 or its downstream misfolded proteins? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments 1. Perform a Western blot analysis of DUX4 protein levels in primary muscle cell cultures treated with varying concentrations of Quercetin to determine if autophagic flux influences its turnover. 2. Conduct a Co-IP study to investigate if Quercetin-induced autophagy proteins colocalize with DUX4-GFP aggregates in an in vitro dystrophy model. 📊 Suggested Studies 1. A systematic screening of Quercetin-modified natural products on DUX4-dependent myocyte toxicity using an automated high-content imaging platform. 2. Comparative transcriptomic profiling of DUX4-expressing cells vs. control cells after Quercetin-induced autophagy modulation to identify potential degradation targets. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): Quercetin-mediated autophagy modulation may alleviate DUX4-induced myotoxicity by increasing the degradation of toxic protein aggregates. • Literature A (Origin): Quercetin enhances autophagy-mediated degradation of toxic protein aggregates (ID: 40351085). • Literature C (Target): DUX4 aggregation and proteotoxicity in facioscapulohumeral muscular dystrophy (DUX4 is absent but implied by proteotoxicity themes). • The Intersecting Bridge B: Autophagy-lysosome pathway (ALP). • Biological Rationale: Quercetin acts as a generalist autophagy activator in models involving misfolded protein accumulation; DUX4 creates toxic aggregates, making them a plausible substrate for autophagic clearance. 📊 Contradictions Between Evidences There are no direct contradictions regarding Quercetin-induced autophagy; however, Quercetin acts as both an autophagy activator and an inhibitor (e.g., in ferritinophagy), which could lead to divergent clinical outcomes depending on the specific model. 📊 Repurposed Solutions Quercetin is established as a versatile scaffold for nanocarrier-mediated delivery to target intracellular protein homeostasis; this platform is potentially transferable to muscular dystrophies where toxic protein accumulation is a primary mechanism. 🔖 Tags Attractor Table Extracted Keywords & Entities Quercetin, _gates_from_quercetin, Autophagy, _gates_to_autophagy, _gates_from_autophagy, Misfolded Proteins, _gates_to_misfolded_proteins, _gates_from_misfolded_proteins, DUX4, _gates_to_dux4 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21231692","authors":["Dungan, Joshua"],"tags":["Quercetin","_gates_from_quercetin","Autophagy","_gates_to_autophagy","_gates_from_autophagy","Misfolded Proteins","_gates_to_misfolded_proteins","_gates_from_misfolded_proteins"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21231692","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21231693","name":"PathMap Experiment #000006 - Tags: #Quercetin #Autophagy #Misfolded Proteins #DUX4","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=6 Artificial General Intelligence LLC Claim Evaluated: Does existing in vitro data show that Quercetin-induced autophagy can successfully degrade DUX4 or its downstream misfolded proteins? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments 1. Perform a Western blot analysis of DUX4 protein levels in primary muscle cell cultures treated with varying concentrations of Quercetin to determine if autophagic flux influences its turnover. 2. Conduct a Co-IP study to investigate if Quercetin-induced autophagy proteins colocalize with DUX4-GFP aggregates in an in vitro dystrophy model. 📊 Suggested Studies 1. A systematic screening of Quercetin-modified natural products on DUX4-dependent myocyte toxicity using an automated high-content imaging platform. 2. Comparative transcriptomic profiling of DUX4-expressing cells vs. control cells after Quercetin-induced autophagy modulation to identify potential degradation targets. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): Quercetin-mediated autophagy modulation may alleviate DUX4-induced myotoxicity by increasing the degradation of toxic protein aggregates. • Literature A (Origin): Quercetin enhances autophagy-mediated degradation of toxic protein aggregates (ID: 40351085). • Literature C (Target): DUX4 aggregation and proteotoxicity in facioscapulohumeral muscular dystrophy (DUX4 is absent but implied by proteotoxicity themes). • The Intersecting Bridge B: Autophagy-lysosome pathway (ALP). • Biological Rationale: Quercetin acts as a generalist autophagy activator in models involving misfolded protein accumulation; DUX4 creates toxic aggregates, making them a plausible substrate for autophagic clearance. 📊 Contradictions Between Evidences There are no direct contradictions regarding Quercetin-induced autophagy; however, Quercetin acts as both an autophagy activator and an inhibitor (e.g., in ferritinophagy), which could lead to divergent clinical outcomes depending on the specific model. 📊 Repurposed Solutions Quercetin is established as a versatile scaffold for nanocarrier-mediated delivery to target intracellular protein homeostasis; this platform is potentially transferable to muscular dystrophies where toxic protein accumulation is a primary mechanism. 🔖 Tags Attractor Table Extracted Keywords & Entities Quercetin, _gates_from_quercetin, Autophagy, _gates_to_autophagy, _gates_from_autophagy, Misfolded Proteins, _gates_to_misfolded_proteins, _gates_from_misfolded_proteins, DUX4, _gates_to_dux4 🚀 Run Your Own Analysis PathMap is a patent-pending universal AI workbench designed to eliminate LLM hallucinations in medical research. Generate your own autonomous discovery reports at PathMap.org.","url":"https://doi.org/10.5281/zenodo.21231693","authors":["Dungan, Joshua"],"tags":["Quercetin","_gates_from_quercetin","Autophagy","_gates_to_autophagy","_gates_from_autophagy","Misfolded Proteins","_gates_to_misfolded_proteins","_gates_from_misfolded_proteins"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21231693","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.20158086","name":"What are the latest artificial intelligence technologies used in predicting breast cancer outcomes, and how do they compare to traditional methods?","source":"datacite","abstract":"Artificial intelligence technologies demonstrate improved accuracy in predicting breast cancer outcomes compared to traditional methods, yet require further validation and integration into clinical practice.","url":"https://doi.org/10.5281/zenodo.20158086","authors":["Tripdatabase"],"tags":["artificial intelligence","breast cancer","prediction","outcomes","traditional methods","technology comparison","oncology","radiology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20158086","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.20158087","name":"What are the latest artificial intelligence technologies used in predicting breast cancer outcomes, and how do they compare to traditional methods?","source":"datacite","abstract":"Artificial intelligence technologies demonstrate improved accuracy in predicting breast cancer outcomes compared to traditional methods, yet require further validation and integration into clinical practice.","url":"https://doi.org/10.5281/zenodo.20158087","authors":["Tripdatabase"],"tags":["artificial intelligence","breast cancer","prediction","outcomes","traditional methods","technology comparison","oncology","radiology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20158087","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.20739971","name":"THE TRINITY OF NEXT-GENERATION ADVANCED MATERIAL TECHNOLOGIES","source":"datacite","abstract":"Description The Trinity of Next-Generation Advanced Material Technologies presents a comprehensive framework for the future of sustainable, intelligent, and biologically integrated material systems. This work explores the convergence of three transformative technological domains: Biological-Based Material Engineering (BBME), Integrated Bio-Convergence (IBC), and Next-Generation Bio-Adaptive Intelligent Materials (NBAIM). The framework investigates how renewable biomass resources, synthetic biology, nanotechnology, advanced manufacturing, and artificial intelligence can be integrated into a unified industrial ecosystem capable of producing self-healing, biodegradable, adaptive, and biologically interactive materials. The study establishes a complete pathway from biomass feedstock acquisition and preprocessing to industrial-scale manufacturing, international regulatory compliance, and global commercialization. The first domain, Biological-Based Material Engineering, focuses on converting agricultural residues and organic waste streams—including rice husks, rice straw, sugarcane bagasse, coconut biomass, chitin-rich shell waste, and protein-rich industrial by-products—into high-value biomaterials, bioplastics, self-healing composites, and sustainable packaging solutions. Emphasis is placed on circular bioeconomy principles, carbon-neutral production systems, and environmentally responsible manufacturing. The second domain, Integrated Bio-Convergence, examines the fusion of biological materials with nanoelectronics, flexible semiconductors, biosensors, artificial intelligence, and advanced computational systems. This convergence enables the development of intelligent medical implants, neural interfaces, bioelectronic devices, smart stents, and next-generation healthcare technologies capable of real-time physiological interaction and adaptive therapeutic responses. The third domain, Next-Generation Bio-Adaptive Intelligent Materials, explores materials capable of sensing, responding, and dynamically adapting to biological and environmental stimuli. These systems include stimuli-responsive polymers, shape-memory biomaterials, engineered living materials, smart wound dressings, adaptive textiles, and programmable biodegradable implants designed to interact directly with living systems. In addition to scientific and engineering foundations, the framework provides detailed guidance regarding biomass quality requirements, feedstock specifications, industrial equipment infrastructure, manufacturing workflows, regulatory pathways, international standards, sustainability metrics, and commercialization strategies. Key standards discussed include ASTM D6400, EN 13432, ISO 10993, ISO 13485, USP Class VI, FDA regulatory frameworks, MDR Class III requirements, and Life Cycle Assessment methodologies under ISO 14040 and ISO 14044. The document proposes a global industrialization roadmap extending from 2026 to 2050, outlining the transition from conventional passive materials toward intelligent, regenerative, self-adaptive material ecosystems. Applications span healthcare, construction, transportation, consumer products, aerospace, environmental remediation, smart cities, advanced manufacturing, and future human-machine interfaces. This publication is intended as a strategic reference for researchers, engineers, policymakers, industrial stakeholders, investors, and multidisciplinary innovation communities seeking to accelerate the development of biologically inspired and AI-enabled material technologies for the twenty-first century. Keywords: Biological-Based Material Engineering, Bio-Convergence, Bio-Adaptive Materials, Biomaterials, Synthetic Biology, Precision Fermentation, Nanotechnology, Artificial Intelligence, Smart Materials, Self-Healing Materials, Sustainable Manufacturing, Circular Bioeconomy, Bioelectronics, Tissue Engineering, Biopolymers, Advanced Materials, Regenerative Systems, Future Manufacturing. License: Creative Commons Attri","url":"https://doi.org/10.5281/zenodo.20739971","authors":["NHUT, NHUT"],"tags":["MSD MANUAL PROFESSIONAL","FDA","WHO","NASA","ESA","ESO","ROSCOSMOS"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20739971","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.20739972","name":"THE TRINITY OF NEXT-GENERATION ADVANCED MATERIAL TECHNOLOGIES","source":"datacite","abstract":"Description The Trinity of Next-Generation Advanced Material Technologies presents a comprehensive framework for the future of sustainable, intelligent, and biologically integrated material systems. This work explores the convergence of three transformative technological domains: Biological-Based Material Engineering (BBME), Integrated Bio-Convergence (IBC), and Next-Generation Bio-Adaptive Intelligent Materials (NBAIM). The framework investigates how renewable biomass resources, synthetic biology, nanotechnology, advanced manufacturing, and artificial intelligence can be integrated into a unified industrial ecosystem capable of producing self-healing, biodegradable, adaptive, and biologically interactive materials. The study establishes a complete pathway from biomass feedstock acquisition and preprocessing to industrial-scale manufacturing, international regulatory compliance, and global commercialization. The first domain, Biological-Based Material Engineering, focuses on converting agricultural residues and organic waste streams—including rice husks, rice straw, sugarcane bagasse, coconut biomass, chitin-rich shell waste, and protein-rich industrial by-products—into high-value biomaterials, bioplastics, self-healing composites, and sustainable packaging solutions. Emphasis is placed on circular bioeconomy principles, carbon-neutral production systems, and environmentally responsible manufacturing. The second domain, Integrated Bio-Convergence, examines the fusion of biological materials with nanoelectronics, flexible semiconductors, biosensors, artificial intelligence, and advanced computational systems. This convergence enables the development of intelligent medical implants, neural interfaces, bioelectronic devices, smart stents, and next-generation healthcare technologies capable of real-time physiological interaction and adaptive therapeutic responses. The third domain, Next-Generation Bio-Adaptive Intelligent Materials, explores materials capable of sensing, responding, and dynamically adapting to biological and environmental stimuli. These systems include stimuli-responsive polymers, shape-memory biomaterials, engineered living materials, smart wound dressings, adaptive textiles, and programmable biodegradable implants designed to interact directly with living systems. In addition to scientific and engineering foundations, the framework provides detailed guidance regarding biomass quality requirements, feedstock specifications, industrial equipment infrastructure, manufacturing workflows, regulatory pathways, international standards, sustainability metrics, and commercialization strategies. Key standards discussed include ASTM D6400, EN 13432, ISO 10993, ISO 13485, USP Class VI, FDA regulatory frameworks, MDR Class III requirements, and Life Cycle Assessment methodologies under ISO 14040 and ISO 14044. The document proposes a global industrialization roadmap extending from 2026 to 2050, outlining the transition from conventional passive materials toward intelligent, regenerative, self-adaptive material ecosystems. Applications span healthcare, construction, transportation, consumer products, aerospace, environmental remediation, smart cities, advanced manufacturing, and future human-machine interfaces. This publication is intended as a strategic reference for researchers, engineers, policymakers, industrial stakeholders, investors, and multidisciplinary innovation communities seeking to accelerate the development of biologically inspired and AI-enabled material technologies for the twenty-first century. Keywords: Biological-Based Material Engineering, Bio-Convergence, Bio-Adaptive Materials, Biomaterials, Synthetic Biology, Precision Fermentation, Nanotechnology, Artificial Intelligence, Smart Materials, Self-Healing Materials, Sustainable Manufacturing, Circular Bioeconomy, Bioelectronics, Tissue Engineering, Biopolymers, Advanced Materials, Regenerative Systems, Future Manufacturing. License: Creative Commons Attri","url":"https://doi.org/10.5281/zenodo.20739972","authors":["NHUT, NHUT"],"tags":["MSD MANUAL PROFESSIONAL","FDA","WHO","NASA","ESA","ESO","ROSCOSMOS"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20739972","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22011112","name":"PASSION-derm-ICD-Captions: ICD-10, ICD-11, and Dual-Caption Annotations for the PASSION Dermatology Dataset","source":"datacite","abstract":"PASSION-ICD-Captions: ICD-10, ICD-11, and Dual-Caption Annotations for the PASSION Dermatology Dataset Description This dataset provides an annotation extension to the PASSION dermatology dataset, enriching the original image collection with standardized diagnostic codes and textual descriptions. The original PASSION dataset and project are described in: Gottfrois et al., “PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa,” MICCAI 2024. Original paper: https://doi.org/10.1007/978-3-031-72384-1_66PASSION project and dataset page: https://passionderm.github.io/ For each image, this extension includes ICD-10 and ICD-11 codes, where applicable, together with two human annotated textual captions per image. These additional annotations are intended to support research at the intersection of dermatology, medical image analysis, clinical coding, and multimodal artificial intelligence. The resource enables researchers to link dermatological images with internationally recognized disease classification systems while also providing natural-language descriptions suitable for tasks such as image–text representation learning, image captioning, multimodal retrieval, classification, and evaluation of vision-language models. This release should be considered an annotation layer for the PASSION dataset rather than a replacement for the original dataset. Image identifiers are provided to enable linkage between the annotations and the corresponding images in PASSION. Users should obtain and use the original images in accordance with the PASSION dataset's access conditions and license. Included annotations ICD-10 codes ICD-11 codes Two captions per image Image identifiers linking the annotations to the corresponding PASSION images The goal of this extension is to increase the clinical and semantic utility of the PASSION dataset and facilitate reproducible research involving dermatological image understanding, standardized disease coding, and multimodal learning.","url":"https://doi.org/10.5281/zenodo.22011112","authors":["Gottfrois, Philippe"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22011112","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22011111","name":"PASSION-derm-ICD-Captions: ICD-10, ICD-11, and Dual-Caption Annotations for the PASSION Dermatology Dataset","source":"datacite","abstract":"PASSION-ICD-Captions: ICD-10, ICD-11, and Dual-Caption Annotations for the PASSION Dermatology Dataset Description This dataset provides an annotation extension to the PASSION dermatology dataset, enriching the original image collection with standardized diagnostic codes and textual descriptions. The original PASSION dataset and project are described in: Gottfrois et al., “PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa,” MICCAI 2024. Original paper: https://doi.org/10.1007/978-3-031-72384-1_66PASSION project and dataset page: https://passionderm.github.io/ For each image, this extension includes ICD-10 and ICD-11 codes, where applicable, together with two human annotated textual captions per image. These additional annotations are intended to support research at the intersection of dermatology, medical image analysis, clinical coding, and multimodal artificial intelligence. The resource enables researchers to link dermatological images with internationally recognized disease classification systems while also providing natural-language descriptions suitable for tasks such as image–text representation learning, image captioning, multimodal retrieval, classification, and evaluation of vision-language models. This release should be considered an annotation layer for the PASSION dataset rather than a replacement for the original dataset. Image identifiers are provided to enable linkage between the annotations and the corresponding images in PASSION. Users should obtain and use the original images in accordance with the PASSION dataset's access conditions and license. Included annotations ICD-10 codes ICD-11 codes Two captions per image Image identifiers linking the annotations to the corresponding PASSION images The goal of this extension is to increase the clinical and semantic utility of the PASSION dataset and facilitate reproducible research involving dermatological image understanding, standardized disease coding, and multimodal learning.","url":"https://doi.org/10.5281/zenodo.22011111","authors":["Gottfrois, Philippe"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22011111","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21156564","name":"Artificial Intelligence and Healthcare Applications","source":"datacite","abstract":"Artificial intelligence (AI) has increasingly become integrated into healthcare systems in recent years, particularly in areas such as diagnosis, treatment planning, clinical decision support systems, medical imaging, drug discovery and patient management. Advances in machine learning, deep learning, generative AI and multimodal AI systems are transforming clinical practice across many medical specialties, including radiology, internal medicine, surgical sciences and respiratory medicine. AI-assisted systems have the potential to reduce workload, contribute to early diagnosis, support personalized treatment approaches and improve health economics. However, important concerns regarding data security, algorithmic bias, clinical validation, ethical responsibility and regulatory frameworks remain under discussion. In this review, current applications of artificial intelligence in healthcare—including its use in internal and surgical sciences, radiological applications, health economic contributions, and ethical considerations—are evaluated in light of the current literature, emphasizing the need for clinical validation, human oversight, and ethical principles for the safe and sustainable integration of AI technologies into healthcare systems.","url":"https://doi.org/10.5281/zenodo.21156564","authors":["KOTAN, Abdurrahman","KESKİN, Hasan Veysel","ÖZÇELİK, Neslihan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21156564","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.21156565","name":"Artificial Intelligence and Healthcare Applications","source":"datacite","abstract":"Artificial intelligence (AI) has increasingly become integrated into healthcare systems in recent years, particularly in areas such as diagnosis, treatment planning, clinical decision support systems, medical imaging, drug discovery and patient management. Advances in machine learning, deep learning, generative AI and multimodal AI systems are transforming clinical practice across many medical specialties, including radiology, internal medicine, surgical sciences and respiratory medicine. AI-assisted systems have the potential to reduce workload, contribute to early diagnosis, support personalized treatment approaches and improve health economics. However, important concerns regarding data security, algorithmic bias, clinical validation, ethical responsibility and regulatory frameworks remain under discussion. In this review, current applications of artificial intelligence in healthcare—including its use in internal and surgical sciences, radiological applications, health economic contributions, and ethical considerations—are evaluated in light of the current literature, emphasizing the need for clinical validation, human oversight, and ethical principles for the safe and sustainable integration of AI technologies into healthcare systems.","url":"https://doi.org/10.5281/zenodo.21156565","authors":["KOTAN, Abdurrahman","KESKİN, Hasan Veysel","ÖZÇELİK, Neslihan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21156565","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.21227/n7km-nr64","name":"\"Dataset for Non-Invasive Anemia Screening Using Conjunctiva, Palm, and Nail-Bed Images\"","source":"datacite","abstract":"\"This dataset is a comprehensive collection of multimodal data for research on non-invasive anemia screening and laboratory hemoglobin estimation. Data collection was conducted at the Manav Rachna Educational Institutions(MREI), Faridabad, India, during a voluntarily organized blood donation camp. After obtaining ethical approval from the University's Institutional Ethics Committee and informed consent from the participants, data were collected for the study. Data acquisition was conducted in accordance with standard protocols and ethical guidelines. The data include records from 1,485 participants with synchronized lab-based hemoglobin (Hb) measurements and multimodal anatomical images (left and right conjunctiva, left and right palm images, up to 10 images of the nail bed), participant metadata, and automated screening predictions. The range of laboratory hemoglobin is 6.0 - 18.7 g\\/dL, allowing both binary classification of anemia and continuous estimation of hemoglobin. The dataset includes 166 anemic and 1,318 non-anemic individuals according to the World Health Organization (WHO) diagnostic definition of anemia (Hb &lt; 12 g\\/dL). This dataset comprises several anatomical imaging modalities and clinically approved hemoglobin test results, making it a useful reference point for training and testing artificial intelligence and computer vision models for detecting anemia without requiring blood samples. It offers a broad spectrum of research applications, such as medical image analysis, multimodal deep learning, hemoglobin regression, explainable AI, transfer learning, self-supervised learning, healthcare informatics, and powerful machine learning methods to deal with class imbalance. The dataset will serve as a comprehensive resource for biomedical imaging research and AI-assisted diagnosis systems for anemia detection.&nbsp;\"","url":"https://doi.org/10.21227/n7km-nr64","authors":["R Girija","Manpreet Kaur","Dogga Pavan Sekhar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21227/n7km-nr64","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22015648","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PRECISION MEDICINE: APPLICATIONS IN MEDICAL BIOTECHNOLOGY","source":"datacite","abstract":"Background: Artificial intelligence (AI) and machine learning (ML) are increasingly embedded across the precision medicine pipeline, from target identification to bedside decision support, promising more individualised diagnosis and treatment selection than population-averaged, one-size-fits-all approaches allow. Objective: To critically synthesise current evidence on AI/ML applications in precision medicine within the domains of medical biotechnology, drug discovery, multi-omics and pharmacogenomics, medical imaging, clinical trial methodology, and clinical decision support, and to appraise the regulatory and ethical frameworks governing their translation into practice. Data Sources: PubMed/MEDLINE, Scopus- and Web of Science-indexed journals, and publications from Nature Portfolio, The Lancet, Cell Press, Elsevier, Wiley, Oxford University Press, and MDPI were searched for literature published predominantly between 2019 and 2026, supplemented by landmark earlier studies and current regulatory guidance from the US Food and Drug Administration (FDA). Review Methods: A narrative review format was adopted given the methodological heterogeneity and breadth of the subject area. Evidence was appraised for study design, data provenance, and generalisability, with emphasis on comparing findings across studies rather than sequential summarisation. Key Findings: AI/ML tools now assist therapeutic target discovery and de novo molecular design, multi-omics-based patient stratification, radiomic and pathomic biomarker discovery, clinical trial risk prediction and patient-trial matching, and large language model (LLM)-based clinical decision support. Regulatory agencies have responded with lifecycle-oriented frameworks such as the FDA predetermined change control plan, yet prospective validation, external generalisability, and demonstrable impact on patient-relevant outcomes remain limited for most applications. Algorithmic bias arising from unrepresentative training data and proxy-outcome selection continues to threaten equitable deployment. Conclusion: AI/ML methods offer credible, increasingly validated tools for advancing precision medicine within biotechnology and pharmaceutical sciences, but their clinical adoption should proceed in step with prospective evidence generation, total-product-lifecycle regulatory oversight, and deliberate bias mitigation. Keywords Precision Medicine; Artificial Intelligence; Machine Learning; Pharmacogenomics; Drug Discovery; Biotechnology, Medical","url":"https://doi.org/10.5281/zenodo.22015648","authors":["Siddela Johnson*, Soma Sekhar Pulamarasetti, Kovvada Vandana, Bongu Ramya Priya, Varri Anudeepthi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22015648","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22015649","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PRECISION MEDICINE: APPLICATIONS IN MEDICAL BIOTECHNOLOGY","source":"datacite","abstract":"Background: Artificial intelligence (AI) and machine learning (ML) are increasingly embedded across the precision medicine pipeline, from target identification to bedside decision support, promising more individualised diagnosis and treatment selection than population-averaged, one-size-fits-all approaches allow. Objective: To critically synthesise current evidence on AI/ML applications in precision medicine within the domains of medical biotechnology, drug discovery, multi-omics and pharmacogenomics, medical imaging, clinical trial methodology, and clinical decision support, and to appraise the regulatory and ethical frameworks governing their translation into practice. Data Sources: PubMed/MEDLINE, Scopus- and Web of Science-indexed journals, and publications from Nature Portfolio, The Lancet, Cell Press, Elsevier, Wiley, Oxford University Press, and MDPI were searched for literature published predominantly between 2019 and 2026, supplemented by landmark earlier studies and current regulatory guidance from the US Food and Drug Administration (FDA). Review Methods: A narrative review format was adopted given the methodological heterogeneity and breadth of the subject area. Evidence was appraised for study design, data provenance, and generalisability, with emphasis on comparing findings across studies rather than sequential summarisation. Key Findings: AI/ML tools now assist therapeutic target discovery and de novo molecular design, multi-omics-based patient stratification, radiomic and pathomic biomarker discovery, clinical trial risk prediction and patient-trial matching, and large language model (LLM)-based clinical decision support. Regulatory agencies have responded with lifecycle-oriented frameworks such as the FDA predetermined change control plan, yet prospective validation, external generalisability, and demonstrable impact on patient-relevant outcomes remain limited for most applications. Algorithmic bias arising from unrepresentative training data and proxy-outcome selection continues to threaten equitable deployment. Conclusion: AI/ML methods offer credible, increasingly validated tools for advancing precision medicine within biotechnology and pharmaceutical sciences, but their clinical adoption should proceed in step with prospective evidence generation, total-product-lifecycle regulatory oversight, and deliberate bias mitigation. Keywords Precision Medicine; Artificial Intelligence; Machine Learning; Pharmacogenomics; Drug Discovery; Biotechnology, Medical","url":"https://doi.org/10.5281/zenodo.22015649","authors":["Siddela Johnson*, Soma Sekhar Pulamarasetti, Kovvada Vandana, Bongu Ramya Priya, Varri Anudeepthi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22015649","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22014991","name":"The Speed Illusion: What Healthcare's AI Race Misreads About Credentialing, and the Ninety Days No Algorithm Controls","source":"datacite","abstract":"The healthcare credentialing software market is projected to grow from approximately $3.8 billion in 2026to more than $10 billion by 2035, driven substantially by vendors marketing artificial intelligence andautomation as a solution to slow provider onboarding. This report examines a specific, checkable claimembedded in that marketing: that automated platforms can compress credentialing timelines to under thirtydays. Comparing vendor marketing claims directly against processing time data published by state medicalboards, this report finds that the advertised timeline is achievable in only a narrow subset of states,primarily those participating in the Interstate Medical Licensure Compact, and is mathematicallyincompatible with published board level processing times in several of the largest states by population,including California and Texas. The report introduces the Koru Controllable Time Ratio, an originalframework for separating the portion of a credentialing timeline that automation can genuinely influencefrom the portion that is structurally controlled by third party institutions automation cannot touch. Theanalysis finds that the majority of a typical credentialing cycle sits outside any single organization'scontrol, regardless of the software it deploys, and argues that automation's real value lies in eliminatingunforced errors rather than compressing a timeline it was never positioned to control. The report presents a blended timeline scenario model showing how an organization's realisticcredentialing timeline shifts based on its actual state footprint rather than a single national average, andidentifies three genuine, no cost levers: fingerprint method, application timing, and interstate compactstrategy. Each meaningfully affects total cycle time without requiring any software purchase. It closes byquantifying the financial exposure created when an organization budgets its staffing and revenueprojections around a vendor's advertised timeline rather than its realistic one. The report closes by identifying three structural blind spots that receive little attention in current marketdiscourse: a silent CAQH re attestation lapse that can halt payer access without triggering any alert, aMedicare enrollment gap arising from the false assumption that commercial payer credentialing andfederal PECOS enrollment are the same process, and a delegation trapdoor in which an organization'slargest available speed advantage, delegated payer credentialing, can be silently revoked by a single auditcitation, instantly reverting every provider in the pipeline from a matter of days to a matter of months. Allthree are genuinely, verifiably solvable by automation, in contrast to the board level processing delays thisreport shows automation cannot touch, suggesting the market is directing its engineering effort at thewrong problem while three real, addressable ones remain largely unmarketed.","url":"https://doi.org/10.5281/zenodo.22014991","authors":["Frazier, Marcus J."],"tags":["healthcare credentialing","artificial intelligence","automation","provider onboarding","state medical licensure","Interstate Medical Licensure Compact","healthcare operations","credentialing software"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22014991","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22014992","name":"The Speed Illusion: What Healthcare's AI Race Misreads About Credentialing, and the Ninety Days No Algorithm Controls","source":"datacite","abstract":"The healthcare credentialing software market is projected to grow from approximately $3.8 billion in 2026to more than $10 billion by 2035, driven substantially by vendors marketing artificial intelligence andautomation as a solution to slow provider onboarding. This report examines a specific, checkable claimembedded in that marketing: that automated platforms can compress credentialing timelines to under thirtydays. Comparing vendor marketing claims directly against processing time data published by state medicalboards, this report finds that the advertised timeline is achievable in only a narrow subset of states,primarily those participating in the Interstate Medical Licensure Compact, and is mathematicallyincompatible with published board level processing times in several of the largest states by population,including California and Texas. The report introduces the Koru Controllable Time Ratio, an originalframework for separating the portion of a credentialing timeline that automation can genuinely influencefrom the portion that is structurally controlled by third party institutions automation cannot touch. Theanalysis finds that the majority of a typical credentialing cycle sits outside any single organization'scontrol, regardless of the software it deploys, and argues that automation's real value lies in eliminatingunforced errors rather than compressing a timeline it was never positioned to control. The report presents a blended timeline scenario model showing how an organization's realisticcredentialing timeline shifts based on its actual state footprint rather than a single national average, andidentifies three genuine, no cost levers: fingerprint method, application timing, and interstate compactstrategy. Each meaningfully affects total cycle time without requiring any software purchase. It closes byquantifying the financial exposure created when an organization budgets its staffing and revenueprojections around a vendor's advertised timeline rather than its realistic one. The report closes by identifying three structural blind spots that receive little attention in current marketdiscourse: a silent CAQH re attestation lapse that can halt payer access without triggering any alert, aMedicare enrollment gap arising from the false assumption that commercial payer credentialing andfederal PECOS enrollment are the same process, and a delegation trapdoor in which an organization'slargest available speed advantage, delegated payer credentialing, can be silently revoked by a single auditcitation, instantly reverting every provider in the pipeline from a matter of days to a matter of months. Allthree are genuinely, verifiably solvable by automation, in contrast to the board level processing delays thisreport shows automation cannot touch, suggesting the market is directing its engineering effort at thewrong problem while three real, addressable ones remain largely unmarketed.","url":"https://doi.org/10.5281/zenodo.22014992","authors":["Frazier, Marcus J."],"tags":["healthcare credentialing","artificial intelligence","automation","provider onboarding","state medical licensure","Interstate Medical Licensure Compact","healthcare operations","credentialing software"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22014992","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.6084/m9.figshare.33289404.v1","name":"The Speed Illusion: What Healthcare's AI Race Misreads About Credentialing, and the Ninety Days No","source":"datacite","abstract":"The healthcare credentialing software market is projected to grow from approximately $3.8 billion in 2026 to more than $10 billion by 2035, driven substantially by vendors marketing artificial intelligence and automation as a solution to slow provider onboarding. This report examines a specific, checkable claim embedded in that marketing: that automated platforms can compress credentialing timelines to under thirty days. Comparing vendor marketing claims directly against processing time data published by state medical boards, this report finds that the advertised timeline is achievable in only a narrow subset of states, primarily those participating in the Interstate Medical Licensure Compact, and is mathematically incompatible with published board level processing times in several of the largest states by population, including California and Texas. The report introduces the Koru Controllable Time Ratio, an original framework for separating the portion of a credentialing timeline that automation can genuinely influence from the portion that is structurally controlled by third party institutions automation cannot touch. The analysis finds that the majority of a typical credentialing cycle sits outside any single organization's control, regardless of the software it deploys, and argues that automation's real value lies in eliminating unforced errors rather than compressing a timeline it was never positioned to control.The report presents a blended timeline scenario model showing how an organization's realistic credentialing timeline shifts based on its actual state footprint rather than a single national average, and identifies three genuine, no cost levers: fingerprint method, application timing, and interstate compact strategy. Each meaningfully affects total cycle time without requiring any software purchase. It closes by quantifying the financial exposure created when an organization budgets its staffing and revenue projections around a vendor's advertised timeline rather than its realistic one. The report closes by identifying three structural blind spots that receive little attention in current market discourse: a silent CAQH re attestation lapse that can halt payer access without triggering any alert, a Medicare enrollment gap arising from the false assumption that commercial payer credentialing and federal PECOS enrollment are the same process, and a delegation trapdoor in which an organization's largest available speed advantage, delegated payer credentialing, can be silently revoked by a single audit citation, instantly reverting every provider in the pipeline from a matter of days to a matter of months. All three are genuinely, verifiably solvable by automation, in contrast to the board level processing delays this report shows automation cannot touch, suggesting the market is directing its engineering effort at the wrong problem while three real, addressable ones remain largely unmarketed.","url":"https://doi.org/10.6084/m9.figshare.33289404.v1","authors":["Marcus J. Frazier"],"tags":["Health economics","Health management","Automation engineering","Automated software engineering","Insurance studies","Production and operations management","Artificial intelligence not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33289404.v1","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.6084/m9.figshare.33289404","name":"The Speed Illusion: What Healthcare's AI Race Misreads About Credentialing, and the Ninety Days No","source":"datacite","abstract":"The healthcare credentialing software market is projected to grow from approximately $3.8 billion in 2026 to more than $10 billion by 2035, driven substantially by vendors marketing artificial intelligence and automation as a solution to slow provider onboarding. This report examines a specific, checkable claim embedded in that marketing: that automated platforms can compress credentialing timelines to under thirty days. Comparing vendor marketing claims directly against processing time data published by state medical boards, this report finds that the advertised timeline is achievable in only a narrow subset of states, primarily those participating in the Interstate Medical Licensure Compact, and is mathematically incompatible with published board level processing times in several of the largest states by population, including California and Texas. The report introduces the Koru Controllable Time Ratio, an original framework for separating the portion of a credentialing timeline that automation can genuinely influence from the portion that is structurally controlled by third party institutions automation cannot touch. The analysis finds that the majority of a typical credentialing cycle sits outside any single organization's control, regardless of the software it deploys, and argues that automation's real value lies in eliminating unforced errors rather than compressing a timeline it was never positioned to control.The report presents a blended timeline scenario model showing how an organization's realistic credentialing timeline shifts based on its actual state footprint rather than a single national average, and identifies three genuine, no cost levers: fingerprint method, application timing, and interstate compact strategy. Each meaningfully affects total cycle time without requiring any software purchase. It closes by quantifying the financial exposure created when an organization budgets its staffing and revenue projections around a vendor's advertised timeline rather than its realistic one. The report closes by identifying three structural blind spots that receive little attention in current market discourse: a silent CAQH re attestation lapse that can halt payer access without triggering any alert, a Medicare enrollment gap arising from the false assumption that commercial payer credentialing and federal PECOS enrollment are the same process, and a delegation trapdoor in which an organization's largest available speed advantage, delegated payer credentialing, can be silently revoked by a single audit citation, instantly reverting every provider in the pipeline from a matter of days to a matter of months. All three are genuinely, verifiably solvable by automation, in contrast to the board level processing delays this report shows automation cannot touch, suggesting the market is directing its engineering effort at the wrong problem while three real, addressable ones remain largely unmarketed.","url":"https://doi.org/10.6084/m9.figshare.33289404","authors":["Marcus J. Frazier"],"tags":["Health economics","Health management","Automation engineering","Automated software engineering","Insurance studies","Production and operations management","Artificial intelligence not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33289404","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22015212","name":"From Prediction to Physics: The Last Mile of AI in Biomedicine ——How Physical Probes Bridge the Gap Between Computational Forecasts and Experimental Reality","source":"datacite","abstract":"Artificial intelligence has transformed biomedicine. AI models now predict protein structures, design functional proteins, discover drug candidates, and diagnose diseases from medical images with remarkable accuracy. Yet a fundamental problem remains: prediction is not validation. Over 90% of clinical drug candidates still fail. AI-generated molecules often violate physical laws or are infeasible to synthesize. The rate at which AI generates hypotheses in silico far exceeds the capacity to test them in the laboratory. This is the \"last mile\" problem of AI in biomedicine——the chasm between computational prediction and physical confirmation. This review argues that the missing piece is not better AI, but a new class of physical probes designed to validate AI predictions at the molecular level. We survey the state of AI in biomedicine, identify the structural bottlenecks that limit translation from computation to clinic, and propose that physics-based topological probing provides a scalable, interpretable bridge across the last mile. We introduce the String Dynamic Probe Framework and its companion PanProbe static system, which together measure conformational dynamics, hidden pockets, and binding stability independent of force-field parameterization. Calibrated against exact diagonalization (ED) gold standards through a three-stage embedding chain, these probes offer physics-based ground truth for validating AI-generated hypotheses. We conclude by outlining a closed-loop paradigm: AI predicts, physical probes validate, validated data trains the next generation of AI.","url":"https://doi.org/10.5281/zenodo.22015212","authors":["Li, GenMin","Li, QiZhen"],"tags":["Artificial Intelligence in Biomedicine","Computational Biology","Drug Discovery","Biophysics","Topological Data Analysis","Structural Biology","Physical Chemistry","Artificial intelligence in biomedicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22015212","addedAt":"2026-09-01T01:47:49.187Z","updatedAt":"2026-09-01T01:47:49.187Z"},{"id":"doi:10.5281/zenodo.22015213","name":"From Prediction to Physics: The Last Mile of AI in Biomedicine ——How Physical Probes Bridge the Gap Between Computational Forecasts and Experimental Reality","source":"datacite","abstract":"Artificial intelligence has transformed biomedicine. AI models now predict protein structures, design functional proteins, discover drug candidates, and diagnose diseases from medical images with remarkable accuracy. Yet a fundamental problem remains: prediction is not validation. Over 90% of clinical drug candidates still fail. AI-generated molecules often violate physical laws or are infeasible to synthesize. The rate at which AI generates hypotheses in silico far exceeds the capacity to test them in the laboratory. This is the \"last mile\" problem of AI in biomedicine——the chasm between computational prediction and physical confirmation. This review argues that the missing piece is not better AI, but a new class of physical probes designed to validate AI predictions at the molecular level. We survey the state of AI in biomedicine, identify the structural bottlenecks that limit translation from computation to clinic, and propose that physics-based topological probing provides a scalable, interpretable bridge across the last mile. We introduce the String Dynamic Probe Framework and its companion PanProbe static system, which together measure conformational dynamics, hidden pockets, and binding stability independent of force-field parameterization. Calibrated against exact diagonalization (ED) gold standards through a three-stage embedding chain, these probes offer physics-based ground truth for validating AI-generated hypotheses. We conclude by outlining a closed-loop paradigm: AI predicts, physical probes validate, validated data trains the next generation of AI.","url":"https://doi.org/10.5281/zenodo.22015213","authors":["Li, GenMin","Li, QiZhen"],"tags":["Artificial Intelligence in Biomedicine","Computational Biology","Drug Discovery","Biophysics","Topological Data Analysis","Structural Biology","Physical Chemistry","Artificial intelligence in biomedicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22015213","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22014879","name":"L'IA médicale au Maroc : enjeux d'accessibilité aux soins et de régulation juridique","source":"datacite","abstract":"Résumé : De l’image moléculaire à Paige.AI, L’intelligence artificielle transforme la médecine en passant de la visualisation à la prédiction. Des outils comme Paige.AI, DermaSensor ou TytoCare améliorent le diagnostic, la précision des soins et l’efficacité clinique. À l’ère du Big Data, l’IA permet d’analyser des volumes massifs de données de santé, soutenant les professionnels dans leurs tâches quotidiennes : imagerie médicale, dossiers électroniques, robotique, médecine personnalisée. Cependant, l’intégration de ces technologies dans le système de santé marocain soulève une question centrale : comment garantir un accès équitable à ces innovations tout en assurant un encadrement juridique adapté aux réalités locales et aux exigences éthiques internationales ? Mots clés : IA médicale, accessibilité aux soins, régulation juridique Abstract : From Paige to molecular imaging.By moving from visualization to prediction, artificial intelligence (AI) is revolutionizing medicine. instruments such as Paige.Clinical efficiency, diagnosis accuracy, and care precision are improved by AI, DermaSensor, and TytoCare. AI makes it possible to analyze vast amounts of health data in the Big Data era, helping professionals with their everyday tasks in robotics, medical imaging, electronic records, and personalized medicine. But incorporating these technologies into Morocco's healthcare system begs the crucial question: how can we guarantee fair access to these advancements while offering a legal framework that is both localized and compliant with global ethical norms? Keywords : Medical AI, healthcare accessibility, legal regulation","url":"https://doi.org/10.5281/zenodo.22014879","authors":["Jamal GUEDDOURI","Btissam EL GUENNOURI"],"tags":["African Scientific Journal"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22014879","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22014880","name":"L'IA médicale au Maroc : enjeux d'accessibilité aux soins et de régulation juridique","source":"datacite","abstract":"Résumé : De l’image moléculaire à Paige.AI, L’intelligence artificielle transforme la médecine en passant de la visualisation à la prédiction. Des outils comme Paige.AI, DermaSensor ou TytoCare améliorent le diagnostic, la précision des soins et l’efficacité clinique. À l’ère du Big Data, l’IA permet d’analyser des volumes massifs de données de santé, soutenant les professionnels dans leurs tâches quotidiennes : imagerie médicale, dossiers électroniques, robotique, médecine personnalisée. Cependant, l’intégration de ces technologies dans le système de santé marocain soulève une question centrale : comment garantir un accès équitable à ces innovations tout en assurant un encadrement juridique adapté aux réalités locales et aux exigences éthiques internationales ? Mots clés : IA médicale, accessibilité aux soins, régulation juridique Abstract : From Paige to molecular imaging.By moving from visualization to prediction, artificial intelligence (AI) is revolutionizing medicine. instruments such as Paige.Clinical efficiency, diagnosis accuracy, and care precision are improved by AI, DermaSensor, and TytoCare. AI makes it possible to analyze vast amounts of health data in the Big Data era, helping professionals with their everyday tasks in robotics, medical imaging, electronic records, and personalized medicine. But incorporating these technologies into Morocco's healthcare system begs the crucial question: how can we guarantee fair access to these advancements while offering a legal framework that is both localized and compliant with global ethical norms? Keywords : Medical AI, healthcare accessibility, legal regulation","url":"https://doi.org/10.5281/zenodo.22014880","authors":["Jamal GUEDDOURI","Btissam EL GUENNOURI"],"tags":["African Scientific Journal"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22014880","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.17591543","name":"Navigating trustworthiness in machine learning systems: conceptual guidance with examples from medicine","source":"datacite","abstract":"Preprint of \"Navigating trustworthiness in machine learning systems: conceptual guidance with examples from medicine\"AbstractTrust is a fundamental aspect of all human interactions. As artificial intelligence (AI), particularly the machine learning (ML) realm of AI, increasingly impacts society, this inevitably leads to more human-AI interactions. Thus, finding ways to foster trust in AI and ML models becomes more and more essential, especially since these models permeate sensitive domains such as drug design and medical decision-making.In this paper, we aim to elucidate how technical design features of ML models can contribute to the trustworthiness of, and trust in, ML models. To this end, we synthesized existing work to identify and define various facets of trustworthiness in the ML domain, including, amongst others, generalizability, reliability, robustness, privacy, security, interpretability, explainability, transparency, and fairness. By doing so, we uncover ambiguities in definitions as well as interrelations and tensions between these concepts. We summarize key insights to support researchers in recognizing and developing ML models that are trustworthy within their respective research domains. Additionally, we provide illustrative examples that demonstrate how these concepts can enhance the trustworthiness of ML models in the medical domain.","url":"https://doi.org/10.5281/zenodo.17591543","authors":["Lenhof, Kerstin","Rolli, Lisa-Marie","Buhr, Lorina","Roth, Sebastian","Binkyte-Sadauskiene, Ruta","Schicktanz, Silke","Fritz, Mario","Volkamer, Andrea","Beerenwinkel, Niko"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.17591543","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22014253","name":"Navigating trustworthiness in machine learning systems: conceptual guidance with examples from medicine","source":"datacite","abstract":"Preprint of \"Navigating trustworthiness in machine learning systems: conceptual guidance with examples from medicine\"AbstractTrust is a fundamental aspect of all human interactions. As artificial intelligence (AI), particularly the machine learning (ML) realm of AI, increasingly impacts society, this inevitably leads to more human-AI interactions. Thus, finding ways to foster trust in AI and ML models becomes more and more essential, especially since these models permeate sensitive domains such as drug design and medical decision-making.In this paper, we aim to elucidate how technical design features of ML models can contribute to the trustworthiness of, and trust in, ML models. To this end, we synthesized existing work to identify and define various facets of trustworthiness in the ML domain, including, amongst others, generalizability, reliability, robustness, privacy, security, interpretability, explainability, transparency, and fairness. By doing so, we uncover ambiguities in definitions as well as interrelations and tensions between these concepts. We summarize key insights to support researchers in recognizing and developing ML models that are trustworthy within their respective research domains. Additionally, we provide illustrative examples that demonstrate how these concepts can enhance the trustworthiness of ML models in the medical domain.","url":"https://doi.org/10.5281/zenodo.22014253","authors":["Lenhof, Kerstin","Rolli, Lisa-Marie","Buhr, Lorina","Roth, Sebastian","Binkyte-Sadauskiene, Ruta","Schicktanz, Silke","Fritz, Mario","Volkamer, Andrea","Beerenwinkel, Niko"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22014253","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17605/osf.io/t75fw","name":"Does AI-Supported Nursing Education Improve Confidence More Than Competence? A Multilevel Meta-Analysis of Subjective and Objective Learning Outcomes","source":"datacite","abstract":"This systematic review and multilevel meta-analysis will evaluate the effects of AI-supported nursing education and determine whether intervention effects differ between subjective and objective learning outcomes. Randomized and quasi-experimental controlled studies involving nursing students, practising nurses, or nurse educators will be considered. The primary moderator will be outcome type, classified as subjective or objective according to prespecified measurement rules.","url":"https://doi.org/10.17605/osf.io/t75fw","authors":["qingqing Feng","Min Liu","Tang Tang","Lulu Zhang","Huiping Li"],"tags":["Medicine and Health Sciences","Medical Education","Nursing","artificial intelligence nursing education systematic review meta-analysis multilevel meta-analysis learning outcomes confidence competence subjective outcomes objective outcomes"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/t75fw","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20799993","name":"Modality Collapse Measurement Framework: Methodology with Pilot Validation (Research Proposal)","source":"datacite","abstract":"Multimodal medical imaging systems combine information from multiple imaging sequences (FLAIR, T1, T1CE, T2) to improve diagnostic accuracy and clinical decision-making. Despite their empirical success, deep neural networks trained on multimodal data may progressively converge toward modality-invariant representations, losing the distinct, clinically relevant information contributed by each imaging modality. This phenomenon, referred to as modality collapse, remains poorly understood in medical artificial intelligence systems. This release presents a comprehensive, statistically rigorous framework for quantifying modality collapse in multimodal medical imaging models, evaluated on the BraTS 2021 (FeTS) brain tumor segmentation benchmark. The framework integrates five complementary analytical measures: modality probe classification, centered kernel alignment (CKA), participation ratio (PR), singular vector canonical correlation analysis (SVCCA), and modality ablation studies. Together, these metrics capture different facets of representational similarity, dimensionality, and modality-specific information preservation. To ensure robust and reproducible inference, the framework incorporates bootstrap confidence intervals, permutation testing, false discovery rate correction, effect size estimation, synthetic perturbation experiments, and multiple experimental repetitions. The framework was validated on the BraTS 2021 (FeTS) dataset with 341 subjects, demonstrating end-to-end pipeline functionality, coherent metric behavior across all network layers, and preliminary evidence of progressive modality collapse. This work forms the methodological core of a research proposal aimed at advancing the understanding of representation learning in multimodal medical AI systems. The complete implementation, methodology framework, and pilot validation report are provided with this release. **Can we quantify when multimodal representations stop being modality-specific and become modality-invariant?** This framework is built to answer that question.","url":"https://doi.org/10.5281/zenodo.20799993","authors":["Pronab Kumar Paul"],"tags":["modality collapse","multimodal medical imaging","epresentation learning","BraTS 2021","deep learning","CKA","SVCCA","participation ratio"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20799993","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20799994","name":"Modality Collapse Measurement Framework: Methodology with Pilot Validation (Research Proposal)","source":"datacite","abstract":"Multimodal medical imaging systems combine information from multiple imaging sequences (FLAIR, T1, T1CE, T2) to improve diagnostic accuracy and clinical decision-making. Despite their empirical success, deep neural networks trained on multimodal data may progressively converge toward modality-invariant representations, losing the distinct, clinically relevant information contributed by each imaging modality. This phenomenon, referred to as modality collapse, remains poorly understood in medical artificial intelligence systems. This release presents a comprehensive, statistically rigorous framework for quantifying modality collapse in multimodal medical imaging models, evaluated on the BraTS 2021 (FeTS) brain tumor segmentation benchmark. The framework integrates five complementary analytical measures: modality probe classification, centered kernel alignment (CKA), participation ratio (PR), singular vector canonical correlation analysis (SVCCA), and modality ablation studies. Together, these metrics capture different facets of representational similarity, dimensionality, and modality-specific information preservation. To ensure robust and reproducible inference, the framework incorporates bootstrap confidence intervals, permutation testing, false discovery rate correction, effect size estimation, synthetic perturbation experiments, and multiple experimental repetitions. The framework was validated on the BraTS 2021 (FeTS) dataset with 341 subjects, demonstrating end-to-end pipeline functionality, coherent metric behavior across all network layers, and preliminary evidence of progressive modality collapse. This work forms the methodological core of a research proposal aimed at advancing the understanding of representation learning in multimodal medical AI systems. The complete implementation, methodology framework, and pilot validation report are provided with this release. **Can we quantify when multimodal representations stop being modality-specific and become modality-invariant?** This framework is built to answer that question.","url":"https://doi.org/10.5281/zenodo.20799994","authors":["Pronab Kumar Paul"],"tags":["modality collapse","multimodal medical imaging","epresentation learning","BraTS 2021","deep learning","CKA","SVCCA","participation ratio"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20799994","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20672289","name":"Healthcare Resource Allocation Predictor: A Machine Learning Approach for Optimizing Healthcare Resource Distribution","source":"datacite","abstract":"This paper presents a Healthcare Resource Allocation Predictor using Machine Learning to improve the efficiency of healthcare resource management. Hospitals often face challenges such as increasing patient admissions, limited ICU beds, staff shortages, and inefficient inventory management. The proposed system utilizes machine learning techniques to analyze healthcare data and predict future resource requirements, including patient admissions, ICU occupancy, staffing needs, and medical inventory demand. The study discusses the application of predictive analytics, artificial intelligence, and healthcare data processing to support proactive decision-making in hospitals. Various machine learning approaches, including Linear Regression, Random Forest, XGBoost, and LSTM Neural Networks, are explored for healthcare forecasting and resource optimization. The proposed framework aims to reduce resource shortages, improve patient care quality, enhance emergency preparedness, and optimize operational costs. This work contributes to the development of intelligent, data-driven healthcare management systems and highlights the potential of machine learning technologies in modern healthcare infrastructure.","url":"https://doi.org/10.5281/zenodo.20672289","authors":["Chouhan, Anshika","Khan, Khushboo","Jharbade, Kiran","Patel, Radha"],"tags":["Machine Learning","Artificial Intelligence","Heathcare","Analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20672289","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20672290","name":"Healthcare Resource Allocation Predictor: A Machine Learning Approach for Optimizing Healthcare Resource Distribution","source":"datacite","abstract":"This paper presents a Healthcare Resource Allocation Predictor using Machine Learning to improve the efficiency of healthcare resource management. Hospitals often face challenges such as increasing patient admissions, limited ICU beds, staff shortages, and inefficient inventory management. The proposed system utilizes machine learning techniques to analyze healthcare data and predict future resource requirements, including patient admissions, ICU occupancy, staffing needs, and medical inventory demand. The study discusses the application of predictive analytics, artificial intelligence, and healthcare data processing to support proactive decision-making in hospitals. Various machine learning approaches, including Linear Regression, Random Forest, XGBoost, and LSTM Neural Networks, are explored for healthcare forecasting and resource optimization. The proposed framework aims to reduce resource shortages, improve patient care quality, enhance emergency preparedness, and optimize operational costs. This work contributes to the development of intelligent, data-driven healthcare management systems and highlights the potential of machine learning technologies in modern healthcare infrastructure.","url":"https://doi.org/10.5281/zenodo.20672290","authors":["Chouhan, Anshika","Khan, Khushboo","Jharbade, Kiran","Patel, Radha"],"tags":["Machine Learning","Artificial Intelligence","Heathcare","Analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20672290","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22010854","name":"РОЛЬ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В РАЗВИТИИ СИСТЕМ  ДИСТАНЦИОННОГО МОНИТОРИНГА ЗДОРОВЬЯ","source":"datacite","abstract":"В статье рассматриваются современные достижения и исследовательские тенденции применения искусственного интеллекта (ИИ) в системах дистанционного мониторинга здоровья. Подчеркивается эволюция технологий от традиционных устройств сбора данных к интеллектуальным платформаам, обеспечивающим анализ больших объемов медицинской информации, прогнозирование состояния пациента и своевременное вмешательство. Приводятся примеры использования ИИ для управления хроническими заболеваниями, мониторинга сердечно-сосудистых показателей и интеграции с носимыми устройствами. Обсуждаются преимущества ИИподходов по сравнению с ранее существующими методами и направления дальнейшего развития.","url":"https://doi.org/10.5281/zenodo.22010854","authors":["Ахмедов Бехруз Иброхим угли","Абдуллаев Улугбек Махмудович"],"tags":["дистанционный мониторинг здоровья, искусственный интеллект, умные сенсоры, машинное обучение, телемедицина.","masofaviy sog'liqni monitoring, sun'iy intellekt, aqlli sensorlar, mashinani o'rgatish, telemeditsina.","remote health monitoring, artificial intelligence, smart sensors, machine learning, telemedicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.22010854","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22010855","name":"РОЛЬ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В РАЗВИТИИ СИСТЕМ  ДИСТАНЦИОННОГО МОНИТОРИНГА ЗДОРОВЬЯ","source":"datacite","abstract":"В статье рассматриваются современные достижения и исследовательские тенденции применения искусственного интеллекта (ИИ) в системах дистанционного мониторинга здоровья. Подчеркивается эволюция технологий от традиционных устройств сбора данных к интеллектуальным платформаам, обеспечивающим а��ализ больших объемов медицинской информации, прогнозирование состояния пациента и своевременное вмешательство. Приводятся примеры использования ИИ для управления хроническими заболеваниями, мониторинга сердечно-сосудистых показателей и интеграции с носимыми устройствами. Обсуждаются преимущества ИИподходов по сравнению с ранее существующими методами и направления дальнейшего развития.","url":"https://doi.org/10.5281/zenodo.22010855","authors":["Ахмедов Бехруз Иброхим угли","Абдуллаев Улугбек Махмудович"],"tags":["дистанционный мониторинг здоровья, искусственный интеллект, умные сенсоры, машинное обучение, телемедицина.","masofaviy sog'liqni monitoring, sun'iy intellekt, aqlli sensorlar, mashinani o'rgatish, telemeditsina.","remote health monitoring, artificial intelligence, smart sensors, machine learning, telemedicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.22010855","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17632/g9zfgkz4rr.2","name":"Clinical Practice Guideline (CPG) dataset","source":"datacite","abstract":"This dataset contains 1,000 structured, context-specific recommendation records derived from 76 English-language Clinical Practice Guidelines (CPGs) published by the Ministry of Health Malaysia, covering 20 clinical specialty categories. It was developed as a CPG-grounded reference dataset for small language model research, instruction tuning, retrieval-augmented generation, clinical guideline retrieval, and recommendation-generation evaluation. The file extraction_prompt.txt contains the complete prompt template used for candidate inclusion decisions, field mapping and structured draft generation. The dataset is provided in JSON Lines (JSONL) format, with one independent record per line. Each record contains three fields: • “instruction”: A standard request for a CPG recommendation. • “input”: A disease or clinical condition and its specific clinical context. • “output”: A concise CPG-grounded recommendation for that context. Example: {\"instruction\": \"Provide standard CPG recommendations.\", \"input\": \"Disease: retinopathy of prematurity\\nClinical context: screening examination\", \"output\": \"CPG Recommendation: Use binocular indirect ophthalmoscopy or an approved imaging pathway by trained personnel and document zone, stage, extent, and plus disease.\"} The records address clinical contexts such as screening, diagnosis, risk assessment, treatment, medication, monitoring, referral, prevention, follow-up, warning signs, and intervention cessation. A disease may appear in multiple records representing different clinical contexts. The source CPGs were obtained from official Malaysian Ministry of Health websites. Dataset preparation involved identifying recommendation-bearing content, assigning the relevant disease and clinical context, converting the content into a consistent instruction–input–output structure, and checking JSONL validity, duplication, relevance, and source consistency. The dataset may be used as an experimental ground-truth reference for model evaluation but has not been independently validated as clinical ground truth by qualified clinical experts. It must not replace the original CPGs, professional medical judgement, diagnosis, or patient-specific treatment decisions. Copyright in the original CPG documents remains with the Malaysian Ministry of Health or the respective rights holders.","url":"https://doi.org/10.17632/g9zfgkz4rr.2","authors":["Ng, Joey"],"tags":["Computer Science","Medicine","Health Sciences","Artificial Intelligence","Medical Informatics","Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/g9zfgkz4rr.2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17632/g9zfgkz4rr","name":"Clinical Practice Guideline (CPG) dataset","source":"datacite","abstract":"This dataset contains 1,000 structured, context-specific recommendation records derived from 76 English-language Clinical Practice Guidelines (CPGs) published by the Ministry of Health Malaysia, covering 20 clinical specialty categories. It was developed as a CPG-grounded reference dataset for small language model research, instruction tuning, retrieval-augmented generation, clinical guideline retrieval, and recommendation-generation evaluation. The file extraction_prompt.txt contains the complete prompt template used for candidate inclusion decisions, field mapping and structured draft generation. The dataset is provided in JSON Lines (JSONL) format, with one independent record per line. Each record contains three fields: • “instruction”: A standard request for a CPG recommendation. • “input”: A disease or clinical condition and its specific clinical context. • “output”: A concise CPG-grounded recommendation for that context. Example: {\"instruction\": \"Provide standard CPG recommendations.\", \"input\": \"Disease: retinopathy of prematurity\\nClinical context: screening examination\", \"output\": \"CPG Recommendation: Use binocular indirect ophthalmoscopy or an approved imaging pathway by trained personnel and document zone, stage, extent, and plus disease.\"} The records address clinical contexts such as screening, diagnosis, risk assessment, treatment, medication, monitoring, referral, prevention, follow-up, warning signs, and intervention cessation. A disease may appear in multiple records representing different clinical contexts. The source CPGs were obtained from official Malaysian Ministry of Health websites. Dataset preparation involved identifying recommendation-bearing content, assigning the relevant disease and clinical context, converting the content into a consistent instruction–input–output structure, and checking JSONL validity, duplication, relevance, and source consistency. The dataset may be used as an experimental ground-truth reference for model evaluation but has not been independently validated as clinical ground truth by qualified clinical experts. It must not replace the original CPGs, professional medical judgement, diagnosis, or patient-specific treatment decisions. Copyright in the original CPG documents remains with the Malaysian Ministry of Health or the respective rights holders.","url":"https://doi.org/10.17632/g9zfgkz4rr","authors":["Ng, Joey"],"tags":["Computer Science","Medicine","Health Sciences","Artificial Intelligence","Medical Informatics","Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/g9zfgkz4rr","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.26187/deakin.33286674","name":"Use of generative artificial intelligence (AI) in psychiatry and mental health care: A systematic review","source":"datacite","abstract":"Objectives: Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g., for diagnostic assessment and psychotherapy, these tools may be particularly useful within this medical field. Therefore, the aim of this study was to systematically review the literature on generative AI applications in psychiatry and mental health. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted across three databases, and the resulting articles were screened independently by two researchers. The content, themes, and findings of the articles were qualitatively assessed. Results: The search and screening process resulted in the inclusion of 40 studies. The median year of publication was 2023. The themes covered in the articles were mainly mental health and well-being in general - with less emphasis on specific mental disorders (substance use disorder being the most prevalent). The majority of studies were conducted as prompt experiments, with the remaining studies comprising surveys, pilot studies, and case reports. Most studies focused on models that generate language, ChatGPT in particular. Conclusions: Generative AI in psychiatry and mental health is a nascent but quickly expanding field. The literature mainly focuses on applications of ChatGPT, and finds that generative AI performs well, but notes that it is limited by significant safety and ethical concerns. Future research should strive to enhance transparency of methods, use experimental designs, ensure clinical relevance, and involve users/patients in the design phase.","url":"https://doi.org/10.26187/deakin.33286674","authors":["S Kolding","Robert Lundin","L Hansen","SD Ostergaard"],"tags":["Biomedical and clinical sciences","Clinical sciences","Neurosciences","Psychology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.26187/deakin.33286674","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.31247142","name":"Efficient Ensemble RAB-SVM Framework Using African Buffalo Algorithm for Accurate Glaucoma Detection and Classification","source":"datacite","abstract":"Glaucoma is a progressive eye disease that damages the optic nerve and can lead to irreversible blindness. Manual detection from medical images is complex and leads to errors. Machine learning (ML) algorithms offer significant advantages in automatically detecting glaucoma, with Fuzzy c-means clustering, Logistic Regression, Support Vector Machine (SVM), and Deep Learning (DL) being the most commonly used. While these models offer promising results, they often suffer from limitations such as limited generalization to new data and sensitivity to noise and feature imbalance. Hence, to overcome the limitations, this paper proposes an Ensemble Random Adaptive Support Vector based Random African Buffalo (ERAS-RAB) algorithm to detect and classify it. Evaluated using the Glaucoma Fundus Imaging dataset, the proposed system outperforms several baseline methods, achieving an Accuracy of 99.21%, a Precision of 99.09%, a Recall of 98.96%, and an F1-Score of 99.02%, demonstrating its efficacy in early glaucoma diagnosis. This work demonstrates the potential of hybrid ensemble models in medical image analysis, providing a reliable framework for integrating Artificial Intelligence (AI) into ophthalmic diagnostics.","url":"https://doi.org/10.6084/m9.figshare.31247142","authors":["C. Rekha","K. Jayashree"],"tags":["Space Science","Medicine","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31247142","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.31247142.v1","name":"Efficient Ensemble RAB-SVM Framework Using African Buffalo Algorithm for Accurate Glaucoma Detection and Classification","source":"datacite","abstract":"Glaucoma is a progressive eye disease that damages the optic nerve and can lead to irreversible blindness. Manual detection from medical images is complex and leads to errors. Machine learning (ML) algorithms offer significant advantages in automatically detecting glaucoma, with Fuzzy c-means clustering, Logistic Regression, Support Vector Machine (SVM), and Deep Learning (DL) being the most commonly used. While these models offer promising results, they often suffer from limitations such as limited generalization to new data and sensitivity to noise and feature imbalance. Hence, to overcome the limitations, this paper proposes an Ensemble Random Adaptive Support Vector based Random African Buffalo (ERAS-RAB) algorithm to detect and classify it. Evaluated using the Glaucoma Fundus Imaging dataset, the proposed system outperforms several baseline methods, achieving an Accuracy of 99.21%, a Precision of 99.09%, a Recall of 98.96%, and an F1-Score of 99.02%, demonstrating its efficacy in early glaucoma diagnosis. This work demonstrates the potential of hybrid ensemble models in medical image analysis, providing a reliable framework for integrating Artificial Intelligence (AI) into ophthalmic diagnostics.","url":"https://doi.org/10.6084/m9.figshare.31247142.v1","authors":["C. Rekha","K. Jayashree"],"tags":["Space Science","Medicine","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31247142.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17605/osf.io/5tduj","name":"From Monitoring to Action: A Scoping Review of Post-Deployment Clinical AI Surveillance, Risk Assessment, and Lifecycle Management","source":"datacite","abstract":"Artificial intelligence (AI) is increasingly transitioning from retrospective model development and validation into prospective evaluation, clinical deployment, and integration with routine healthcare workflows. The evidentiary challenge therefore extends beyond whether an AI model performs adequately at the point of development or initial validation. Clinical AI systems operate within dynamic socio-technical environments. Patient populations change; disease prevalence changes; clinical guidelines change; diagnostic and treatment practices evolve; data acquisition systems are modified; electronic health record workflows are redesigned; clinicians adapt their use of AI; software and model versions change; and AI systems may be recalibrated, retrained, or replaced. Consequently, an AI system that demonstrated acceptable performance during development or predeployment validation may not necessarily retain the same safety, effectiveness, equity, or clinical utility after deployment. The FDA explicitly identifies post-market monitoring of AI-enabled medical devices as a regulatory science gap and is developing methods for detecting changes in input data, monitoring output performance, detecting out-of-distribution inputs, monitoring data drift, and evaluating models across multiple clinical sites. Clinical AI evaluation guidance similarly emphasizes that AI systems should be evaluated as complex interventions embedded within clinical workflows rather than as isolated mathematical models. DECIDE-AI, for example, emphasizes live clinical evaluation, safety, clinical utility, and human factors. A 2024 scoping review specifically examined methods for monitoring clinical AI performance: of 39 included sources, only 9 described monitoring methods that had been clinically tested or implemented, and guidance on concrete metrics, thresholds, and statistical approaches was limited. More recent 2026 review-level evidence has broadened the discussion toward post-development robustness, post-deployment monitoring, adaptive updating, and lifecycle governance. That literature suggests that monitoring methods, action thresholds, fairness surveillance, corrective responses, and operational governance remain insufficiently standardized and that mature evidence from activated systems in routine clinical care remains limited. In parallel, an operational literature has begun to emerge: a health-systems scoping review identified only six eligible post-deployment monitoring studies under a narrowly defined search, and a multi-institutional framework has proposed organizing deployment-facing monitoring around system integrity, performance, and impact, explicitly connecting monitoring results to decisions to update, modify, or decommission deployed systems.","url":"https://doi.org/10.17605/osf.io/5tduj","authors":["Yunguo Yu"],"tags":["Physical Sciences and Mathematics","Public Health","Medicine and Health Sciences","Community Health and Preventive Medicine","Computer Sciences","Artificial Intelligence and Robotics","Aritificial Intelligence","Clinical AI Monitoring"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/5tduj","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17605/osf.io/kfv75","name":"Artificial Intelligence in Clinical Risk Management of Medication and Medical Device Processes: A Scoping Review with Implications for Hospital Pharmacy","source":"datacite","abstract":"This project documents a scoping review of artificial intelligence (AI) applications in clinical risk management in healthcare, focusing on medication-use and medical-device processes. The review will map AI technologies and applications for risk prediction, event detection and monitoring, and clinical decision support; reported patient-safety benefits; emerging AI-related risks; and implications for hospital pharmacy practice and pharmacists' roles.","url":"https://doi.org/10.17605/osf.io/kfv75","authors":["Daniele Leonardi Vinci"],"tags":["Medicine and Health Sciences","artificial intelligence","clinical risk","hospital pharmacy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/kfv75","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.c.8661514.v1","name":"Stroke detection in medical emergency calls: a retrospective analysis and exploratory evaluation of an AI decision support model","source":"datacite","abstract":"Abstract Background Timely identification of acute stroke during medical emergency calls is critical for optimizing patient outcomes. We aimed to (i) characterize the prehospital trajectories of patients with stroke and (ii) explore the ability of an artificial intelligence (AI) model developed within the Artificial Intelligence Support in Medical Emergency Calls project to support decision-making at Emergency Medical Communication Centres (EMCCs). Methods We conducted a retrospective analysis using data from 1,164 patients diagnosed with stroke from an EMCC (2018–2019), focusing on those primarily assessed by EMCC operators. We categorized patients into optimal and nonoptimal trajectory groups and applied logistic regression to explore the factors associated with an optimal trajectory. An AI model was trained using a dataset of 2,980 emergency calls (2019 and 2022), integrating transcribed audio logs and structured clinical data. The model was evaluated on the full dataset and subgroups that were incorrectly assessed by the EMCC. Results Aphasia/dysarthria was the only factor associated with optimal trajectories. The AI model achieved a sensitivity of 81.0%, specificity of 79.6%, and F1-score of 64.7%. The subgroup analysis included 14 false-negative and 41 false-positive cases. The model correctly predicted stroke in 9 of 14 false-negative cases and ruled out stroke in 9 of 41 false-positive cases. Conclusions This explorative evaluation indicates that a multimodal AI pipeline combining audio and structured clinical data may show potential to support decision making in medical emergency calls. Internal results are promising, but studies on larger and external datasets are needed to validate these findings. Graphical Abstract","url":"https://doi.org/10.6084/m9.figshare.c.8661514.v1","authors":["Emil Iversen","Hege Ihle-Hansen","Kari Krizak Halle","Alexander Selvikvåg Lundervold","Lars Myrmel","Anders Strand Vestbø","Annette Fromm","Cosimo Damiano Persia","Christian Autenried","Guttorm Brattebø"],"tags":["Artificial Intelligence and Image Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8661514.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.c.8661514","name":"Stroke detection in medical emergency calls: a retrospective analysis and exploratory evaluation of an AI decision support model","source":"datacite","abstract":"Abstract Background Timely identification of acute stroke during medical emergency calls is critical for optimizing patient outcomes. We aimed to (i) characterize the prehospital trajectories of patients with stroke and (ii) explore the ability of an artificial intelligence (AI) model developed within the Artificial Intelligence Support in Medical Emergency Calls project to support decision-making at Emergency Medical Communication Centres (EMCCs). Methods We conducted a retrospective analysis using data from 1,164 patients diagnosed with stroke from an EMCC (2018–2019), focusing on those primarily assessed by EMCC operators. We categorized patients into optimal and nonoptimal trajectory groups and applied logistic regression to explore the factors associated with an optimal trajectory. An AI model was trained using a dataset of 2,980 emergency calls (2019 and 2022), integrating transcribed audio logs and structured clinical data. The model was evaluated on the full dataset and subgroups that were incorrectly assessed by the EMCC. Results Aphasia/dysarthria was the only factor associated with optimal trajectories. The AI model achieved a sensitivity of 81.0%, specificity of 79.6%, and F1-score of 64.7%. The subgroup analysis included 14 false-negative and 41 false-positive cases. The model correctly predicted stroke in 9 of 14 false-negative cases and ruled out stroke in 9 of 41 false-positive cases. Conclusions This explorative evaluation indicates that a multimodal AI pipeline combining audio and structured clinical data may show potential to support decision making in medical emergency calls. Internal results are promising, but studies on larger and external datasets are needed to validate these findings. Graphical Abstract","url":"https://doi.org/10.6084/m9.figshare.c.8661514","authors":["Emil Iversen","Hege Ihle-Hansen","Kari Krizak Halle","Alexander Selvikvåg Lundervold","Lars Myrmel","Anders Strand Vestbø","Annette Fromm","Cosimo Damiano Persia","Christian Autenried","Guttorm Brattebø"],"tags":["Artificial Intelligence and Image Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8661514","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.48550/arxiv.2608.18036","name":"Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors","source":"datacite","abstract":"MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction remains largely unexplored, due to lack of practical settings where informative magnitude measurements can be obtained without additional scan time. In this work, we investigate the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrate strong consistency of k-space magnitudes across time-frames. Building on this observation, we propose $\\mathbb{C}+\\text{Mag}$, a magnitude-informed physics-driven deep learning reconstruction method. The proposed method employs an ADMM-based unrolling framework with a novel magnitude-aware data-fidelity formulation, where quadratically smoothed optimization and momentum-based updates are introduced to address the non-differentiability and non-convexity of the magnitude constraints. Experiments on retrospectively undersampled cine MRI and phase-contrast flow MRI datasets, as well as prospectively undersampled real-time cine MRI acquisitions, demonstrate improved artifact suppression, sharper anatomical recovery, and better preservation of phase information compared to conventional PD-DL methods, which is further supported through blinded expert reader evaluations.","url":"https://doi.org/10.48550/arxiv.2608.18036","authors":["Saberi, Mahdi","Alçalar, Yaşar Utku","Gülle, Merve","Shenoy, Chetan","Akçakaya, Mehmet"],"tags":["Image and Video Processing (eess.IV)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","Medical Physics (physics.med-ph)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.18036","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22004750","name":"Dataset: Premise: Environmental metals, though present in many Amyotrophic Lateral Sclerosis anomalous clusters, are debated regarding causality within geographic epistemological data.Hypothesis: If plant derived extracellular vesicles (PDEVs) aerosolized with environmental metals, they could potentially bypass the blood brain barrier via the cribriform plate area.  Given this potential method of delivery, environmental metals cannot be ruled out as causal in sporadic ALS until PDEV delivery of the metals is tested in wet lab experiments, but may potentially explain the confounding data. - PathMap Experiment #000131","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1:https://pathmap.org/viewer.php?id=131 Artificial General Intelligence LLC Claim Evaluated: Premise: Environmental metals, though present in many Amyotrophic Lateral Sclerosis anomalous clusters, are debated regarding causality within geographic epistemological data. Hypothesis: If plant derived extracellular vesicles (PDEVs) aerosolized with environmental metals, they could potentially bypass the blood brain barrier via the cribriform plate area. Given this potential method of delivery, environmental metals cannot be ruled out as causal in sporadic ALS until PDEV delivery of the metals is tested in wet lab experiments, but may potentially explain the confounding data. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Environmental exposures prior to diagnosis, including herbicides and metal dust/fumes, are significantly associated with accelerated ALS progression. The \"lung-brain axis\" indicates that pulmonary pathology and environmental exposure can impact neurological health, possibly via circulating extracellular vesicles. Ciclopirox olamine (CPX) induces TDP-43 cryptic exons through heavy metal toxicity, providing a molecular mechanism linking environmental metal stress to ALS-FTD pathology. There is a statistically significant correlation between the geographic distributions of ALS and Multiple Sclerosis mortality, suggesting shared unknown etiology factors that persist after controlling for race, gender, and latitude. PD-related respiratory control dysfunction involves selective vulnerability of brainstem networks; such vulnerability may also exist in ALS and impact toxin clearance via the glymphatic system. Plant-derived extracellular vesicles (PDEVs) possess cross-barrier delivery potential and represent an emerging class of biotherapeutic carriers, though their natural role as potential \"Trojan horses\" for environmental toxins remains uninvestigated. The multistep pathogenesis hypothesis is challenged by epidemiological data, which aligns more closely with an exponential model of damage accumulation than a simple power-law model. The presence of copper homeostasis disruption in ALS, manifesting as both toxicity and deficiency, creates a vicious cycle that accelerates protein aggregation. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Quantify the binding affinity and sequestration capacity of heavy metals (e.g., Cr-VI) to plant-derived extracellular vesicles in varying pH environments. Perform in vivo biodistribution studies using trace-labeled heavy metals loaded into PDEVs to track CNS deposition via intranasal administration. 📊 Suggested Studies A cohort study assessing the presence of plant-derived exosomal markers in the CNS of patients with sporadic ALS in areas of high metal contamination. A systematic meta-analysis of the geographic colocation of industrial heavy metal pollution with the incidence of sporadic ALS motor neuron degeneration. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): PDEV-mediated sequestration of heavy metals provides a bypass mechanism for blood-brain barrier restriction, explaining the CNS-toxicity of environment-borne contaminants. - Literature A (Origin): Heavy metal toxicity and ALS risk (Source: ID 40559965, ID 42021792) - Literature C (Target): PDEV-mediated barrier crossing and drug delivery (Source: ID 42183199, ID 42352265) - The Intersecting Bridge B: Extracellular vesicle surface lipid-protein complexes. - Biological Rationale: Extracellular vesicles, including those from plants, possess lipid bilayer surfaces that can interact with and bind heavy metal ions, and these vesicles are naturally suited to cross biological barriers, serving as","url":"https://doi.org/10.5281/zenodo.22004750","authors":["Dungan, Joshua"],"tags":["Environmental Pollutants","_gates_from_environmental_pollutants","PDEV association","_gates_to_pdev_association","_gates_from_pdev_association","Nose-to-Brain Delivery","_gates_to_nose-to-brain_delivery","_gates_from_nose-to-brain_delivery"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22004750","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22004751","name":"Dataset: Premise: Environmental metals, though present in many Amyotrophic Lateral Sclerosis anomalous clusters, are debated regarding causality within geographic epistemological data.Hypothesis: If plant derived extracellular vesicles (PDEVs) aerosolized with environmental metals, they could potentially bypass the blood brain barrier via the cribriform plate area.  Given this potential method of delivery, environmental metals cannot be ruled out as causal in sporadic ALS until PDEV delivery of the metals is tested in wet lab experiments, but may potentially explain the confounding data. - PathMap Experiment #000131","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1:https://pathmap.org/viewer.php?id=131 Artificial General Intelligence LLC Claim Evaluated: Premise: Environmental metals, though present in many Amyotrophic Lateral Sclerosis anomalous clusters, are debated regarding causality within geographic epistemological data. Hypothesis: If plant derived extracellular vesicles (PDEVs) aerosolized with environmental metals, they could potentially bypass the blood brain barrier via the cribriform plate area. Given this potential method of delivery, environmental metals cannot be ruled out as causal in sporadic ALS until PDEV delivery of the metals is tested in wet lab experiments, but may potentially explain the confounding data. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Environmental exposures prior to diagnosis, including herbicides and metal dust/fumes, are significantly associated with accelerated ALS progression. The \"lung-brain axis\" indicates that pulmonary pathology and environmental exposure can impact neurological health, possibly via circulating extracellular vesicles. Ciclopirox olamine (CPX) induces TDP-43 cryptic exons through heavy metal toxicity, providing a molecular mechanism linking environmental metal stress to ALS-FTD pathology. There is a statistically significant correlation between the geographic distributions of ALS and Multiple Sclerosis mortality, suggesting shared unknown etiology factors that persist after controlling for race, gender, and latitude. PD-related respiratory control dysfunction involves selective vulnerability of brainstem networks; such vulnerability may also exist in ALS and impact toxin clearance via the glymphatic system. Plant-derived extracellular vesicles (PDEVs) possess cross-barrier delivery potential and represent an emerging class of biotherapeutic carriers, though their natural role as potential \"Trojan horses\" for environmental toxins remains uninvestigated. The multistep pathogenesis hypothesis is challenged by epidemiological data, which aligns more closely with an exponential model of damage accumulation than a simple power-law model. The presence of copper homeostasis disruption in ALS, manifesting as both toxicity and deficiency, creates a vicious cycle that accelerates protein aggregation. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Quantify the binding affinity and sequestration capacity of heavy metals (e.g., Cr-VI) to plant-derived extracellular vesicles in varying pH environments. Perform in vivo biodistribution studies using trace-labeled heavy metals loaded into PDEVs to track CNS deposition via intranasal administration. 📊 Suggested Studies A cohort study assessing the presence of plant-derived exosomal markers in the CNS of patients with sporadic ALS in areas of high metal contamination. A systematic meta-analysis of the geographic colocation of industrial heavy metal pollution with the incidence of sporadic ALS motor neuron degeneration. 📊 Swansons Literature Based Discovery Candidates • Discovered Hypothesis (A to C): PDEV-mediated sequestration of heavy metals provides a bypass mechanism for blood-brain barrier restriction, explaining the CNS-toxicity of environment-borne contaminants. - Literature A (Origin): Heavy metal toxicity and ALS risk (Source: ID 40559965, ID 42021792) - Literature C (Target): PDEV-mediated barrier crossing and drug delivery (Source: ID 42183199, ID 42352265) - The Intersecting Bridge B: Extracellular vesicle surface lipid-protein complexes. - Biological Rationale: Extracellular vesicles, including those from plants, possess lipid bilayer surfaces that can interact with and bind heavy metal ions, and these vesicles are naturally suited to cross biological barriers, serving as","url":"https://doi.org/10.5281/zenodo.22004751","authors":["Dungan, Joshua"],"tags":["Environmental Pollutants","_gates_from_environmental_pollutants","PDEV association","_gates_to_pdev_association","_gates_from_pdev_association","Nose-to-Brain Delivery","_gates_to_nose-to-brain_delivery","_gates_from_nose-to-brain_delivery"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22004751","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22004205","name":"IA COMO HERRAMIENTA DE AUTODIAGNÓSTICO Y AUTOMEDICACIÓN EN PERÚ 2025","source":"datacite","abstract":"Introducción: La irrupción de la inteligencia artificial (IA) generativa ha transformado la búsqueda de información de salud. En contextos de alta informalidad como la Provincia Constitucional del Callao, Perú, esta herramienta podría estar sustituyendo la consulta médica y exacerbando la automedicación. Objetivo: Analizar el comportamiento de usuarios que emplearon IA para el autodiagnóstico y la experiencia de farmacéuticos frente a solicitudes derivadas de estos diagnósticos. Métodos: Estudio cuantitativo, descriptivo-correlacional y de corte transversal. Se evaluaron 402 usuarios y 235 farmacéuticos mediante dos instrumentos validados (EUIA-AD y EFDIA). Se realizaron análisis factoriales exploratorios, regresión logística binaria y Análisis de Perfiles Latentes (LPA). Resultados: El 68.4% de los usuarios carecía de seguro de salud y el 62.7% se automedicó guiado por la IA, destacando un consumo de antibióticos sin receta del 28.1%. Los modelos logísticos no fueron significativos. El LPA identificó dos tipologías de usuarios (\"Adoptantes Creyentes\" y \"Escépticos Ocasionales\"), ambas con tasas de automedicación superiores al 61% (p = .830). En los farmacéuticos, el 87.2% percibió un vacío regulatorio y el 43% dispensó bajo presión, sin importar su nivel de proactividad (p = .638). Conclusiones: La automedicación en el Callao es una constante estructural que no es predicha por la confianza en la IA. Las herramientas algorítmicas se acoplan a un ecosistema sanitario fragmentado, trasladando el riesgo al mostrador. Se sugiere la necesidad que la Dirección General de Insumos, Drogas y Medicamentos (DIGEMID) actualice la normativa de dispensación para proteger al farmacéutico comunitario frente a los diagnósticos generados por IA. Palabras clave: Inteligencia artificial; automedicación; farmacia comunitaria; regulación sanitaria; estudio cuantitativo. Abstract Introduction: The emergence of generative artificial intelligence (AI) has transformed health information-seeking behaviors. In highly informal contexts such as the Constitutional Province of Callao, Peru, this tool could be replacing medical consultations and exacerbating self-medication. Objective: To analyze the behavior of users who employed AI for self-diagnosis and the experience of pharmacists facing requests derived from these diagnoses. Methods: Quantitative, descriptive-correlational, and cross-sectional study. 402 users and 235 pharmacists were evaluated using two validated instruments (EUIA-AD and EFDIA). Exploratory factor analyses, binary logistic regression, and Latent Profile Analysis (LPA) were performed. Results: 68.4% of users lacked health insurance, and 62.7% self-medicated guided by AI, with 28.1% consuming antibiotics without a prescription. Logistic models were not significant. LPA identified two user typologies (\"Believing Adopters\" and \"Occasional Skeptics\"), both showing self-medication rates above 61% (p = .830). Among pharmacists, 87.2% perceived a regulatory void and 43% dispensed under pressure, regardless of their proactivity level (p = .638). Conclusions: Self-medication in Callao is a structural constant not predicted by trust in AI. Algorithmic tools attach to a fragmented healthcare ecosystem, shifting risk to the pharmacy counter. It is suggested that Peru's Directorate General of Medicines, Supplies and Drugs (DIGEMID) should update dispensing regulations to protect community pharmacists against AI-generated diagnoses. Keywords: Artificial intelligence; self-medication; community pharmacy; health regulation; quantitative methods. La correspondencia concerniente a este artículo debe dirigirse a Edgar S., Inciso Mendo, Docente Medicina Humana. Universidad Norbert Wiener. er7219677@gmail.com. Wilmer A., Alania Yauri. Docente Investigador. Tecnólogo Médico en Laboratorio Clínico y Anatomía Patológica Universidad Tecnológica del Perú.investigador.peru.2020@gmail.com. Edilberto, Pérez Torres. Docente Departamento de Pediatría. Universidad Nac","url":"https://doi.org/10.5281/zenodo.22004205","authors":["Inciso Mendo, Edgar","Alania Yauri, Wilmer A","Valero Quispe, Javier L"],"tags":["AI","Self Medication","care regulation","quatitative analisys"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22004205","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22004206","name":"IA COMO HERRAMIENTA DE AUTODIAGNÓSTICO Y AUTOMEDICACIÓN EN PERÚ 2025","source":"datacite","abstract":"Introducción: La irrupción de la inteligencia artificial (IA) generativa ha transformado la búsqueda de información de salud. En contextos de alta informalidad como la Provincia Constitucional del Callao, Perú, esta herramienta podría estar sustituyendo la consulta médica y exacerbando la automedicación. Objetivo: Analizar el comportamiento de usuarios que emplearon IA para el autodiagnóstico y la experiencia de farmacéuticos frente a solicitudes derivadas de estos diagnósticos. Métodos: Estudio cuantitativo, descriptivo-correlacional y de corte transversal. Se evaluaron 402 usuarios y 235 farmacéuticos mediante dos instrumentos validados (EUIA-AD y EFDIA). Se realizaron análisis factoriales exploratorios, regresión logística binaria y Análisis de Perfiles Latentes (LPA). Resultados: El 68.4% de los usuarios carecía de seguro de salud y el 62.7% se automedicó guiado por la IA, destacando un consumo de antibióticos sin receta del 28.1%. Los modelos logísticos no fueron significativos. El LPA identificó dos tipologías de usuarios (\"Adoptantes Creyentes\" y \"Escépticos Ocasionales\"), ambas con tasas de automedicación superiores al 61% (p = .830). En los farmacéuticos, el 87.2% percibió un vacío regulatorio y el 43% dispensó bajo presión, sin importar su nivel de proactividad (p = .638). Conclusiones: La automedicación en el Callao es una constante estructural que no es predicha por la confianza en la IA. Las herramientas algorítmicas se acoplan a un ecosistema sanitario fragmentado, trasladando el riesgo al mostrador. Se sugiere la necesidad que la Dirección General de Insumos, Drogas y Medicamentos (DIGEMID) actualice la normativa de dispensación para proteger al farmacéutico comunitario frente a los diagnósticos generados por IA. Palabras clave: Inteligencia artificial; automedicación; farmacia comunitaria; regulación sanitaria; estudio cuantitativo. Abstract Introduction: The emergence of generative artificial intelligence (AI) has transformed health information-seeking behaviors. In highly informal contexts such as the Constitutional Province of Callao, Peru, this tool could be replacing medical consultations and exacerbating self-medication. Objective: To analyze the behavior of users who employed AI for self-diagnosis and the experience of pharmacists facing requests derived from these diagnoses. Methods: Quantitative, descriptive-correlational, and cross-sectional study. 402 users and 235 pharmacists were evaluated using two validated instruments (EUIA-AD and EFDIA). Exploratory factor analyses, binary logistic regression, and Latent Profile Analysis (LPA) were performed. Results: 68.4% of users lacked health insurance, and 62.7% self-medicated guided by AI, with 28.1% consuming antibiotics without a prescription. Logistic models were not significant. LPA identified two user typologies (\"Believing Adopters\" and \"Occasional Skeptics\"), both showing self-medication rates above 61% (p = .830). Among pharmacists, 87.2% perceived a regulatory void and 43% dispensed under pressure, regardless of their proactivity level (p = .638). Conclusions: Self-medication in Callao is a structural constant not predicted by trust in AI. Algorithmic tools attach to a fragmented healthcare ecosystem, shifting risk to the pharmacy counter. It is suggested that Peru's Directorate General of Medicines, Supplies and Drugs (DIGEMID) should update dispensing regulations to protect community pharmacists against AI-generated diagnoses. Keywords: Artificial intelligence; self-medication; community pharmacy; health regulation; quantitative methods. La correspondencia concerniente a este artículo debe dirigirse a Edgar S., Inciso Mendo, Docente Medicina Humana. Universidad Norbert Wiener. er7219677@gmail.com. Wilmer A., Alania Yauri. Docente Investigador. Tecnólogo Médico en Laboratorio Clínico y Anatomía Patológica Universidad Tecnológica del Perú.investigador.peru.2020@gmail.com. Edilberto, Pérez Torres. Docente Departamento de Pediatría. Universidad Nac","url":"https://doi.org/10.5281/zenodo.22004206","authors":["Inciso Mendo, Edgar","Alania Yauri, Wilmer A","Valero Quispe, Javier L"],"tags":["AI","Self Medication","care regulation","quatitative analisys"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22004206","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20816735","name":"CONVERGENCE-MED. A Conceptual Architecture for the Future of Medicine","source":"datacite","abstract":"CONVERGENCE-MED: A Theory of Governed Convergent Medicine presents a pioneering conceptual architecture for the future of healthcare, proposing a new paradigm in which biological sensing, computational modelling, artificial intelligence, systems biology, organ-on-chip technologies, and advanced therapeutic platforms are integrated within a framework of rigorous human oversight and governance. Rather than viewing disease as a static diagnosis, the book advances the concept of dynamic biological states and introduces the Minimum Viable Integrated Loop, an adaptive model that unites observation, interpretation, intervention, and reassessment within a transparent and accountable decision-making process. Bridging medicine, engineering, data science, ethics, and policy, this work offers a sophisticated theoretical foundation for the development of trustworthy, adaptive, and human-centred medical systems capable of navigating complexity while preserving scientific integrity and clinical responsibility.","url":"https://doi.org/10.5281/zenodo.20816735","authors":["Di Tuccio, Massimo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20816735","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20816736","name":"CONVERGENCE-MED. A Conceptual Architecture for the Future of Medicine","source":"datacite","abstract":"CONVERGENCE-MED: A Theory of Governed Convergent Medicine presents a pioneering conceptual architecture for the future of healthcare, proposing a new paradigm in which biological sensing, computational modelling, artificial intelligence, systems biology, organ-on-chip technologies, and advanced therapeutic platforms are integrated within a framework of rigorous human oversight and governance. Rather than viewing disease as a static diagnosis, the book advances the concept of dynamic biological states and introduces the Minimum Viable Integrated Loop, an adaptive model that unites observation, interpretation, intervention, and reassessment within a transparent and accountable decision-making process. Bridging medicine, engineering, data science, ethics, and policy, this work offers a sophisticated theoretical foundation for the development of trustworthy, adaptive, and human-centred medical systems capable of navigating complexity while preserving scientific integrity and clinical responsibility.","url":"https://doi.org/10.5281/zenodo.20816736","authors":["Di Tuccio, Massimo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20816736","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22002645","name":"Interaction of KAI1/CD82 Transmembrane Domains with APC Gene Mutations in Cellular Function and Disease Progression","source":"datacite","abstract":"This is the 17th hypothesis generated by EPISTEME - An automonous Aritificial Cognitive Process - https://doi.org/10.5281/zenodo.21680339 created by the author. The conserved transmembrane domains of KAI1/CD82 have been implicated in various cellular processes, includingadhesion and migration, which are crucial for cancer progression (Encyclopedia Of Cancer). In parallel, mutations in the APC gene, a key tumor suppressor, are well-documented in several cancers, including colorectal cancer (Encyclopedia of Genetics 10). However, the precise mechanism by which these genetic alterations interact with the structural features of KAI1/CD82 remains unclear. While FISH analysis is commonly used in AML diagnosis to complement chromosome banding analysis, its application in understanding the interaction between KAI1/CD82 and APC mutations is underexplored. Additionally, the role of noncoding RNA in modulating these interactions has not been investigated. This study aims to bridge these two domains by examininghow the conserved transmembrane domains of KAI1/CD82 interact with APC gene mutations to influence cellular function and disease progression. By integrating genetic and molecular analyses, this study addresses a critical gap in understanding the interplay between structural proteins and genetic mutations in cancer biology. V2 - This document presents a scientific feasibility simulation and correction of an autonomously generated hypothesis regarding the interaction between KAI1/CD82 transmembrane domains, APC gene mutations, and noncoding RNA (ncRNA) mediation in cancer progression.The initial hypothesis was created by Episteme - An Artificial Cognitive Process completely autonomously (designed and created by Jason Gabriel Davis). While the initial query identified a novel intersection in oncology, simulation revealed critical method-ological flaws. This document outlines the observations of the initial simulation, identifies errors and gaps, proposes rigorous corrections, and presents the finalized, scientifically viable hypothesis. It concludes with an analysis of the downstream impacts a successful study of this nature would have on targeted therapeutics, diagnostics, and cell biology. The review and corrections were done using GLM5.2 given the lack of human expert involvement despite calls for them to review and give feedback.","url":"https://doi.org/10.5281/zenodo.22002645","authors":["Davis, Jason Gabriel"],"tags":["Artificial intelligence","Science","Science","Medical science","Cancer","Colorectal cancer","Pharmacology","Pharmacology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22002645","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21960161","name":"Interaction of KAI1/CD82 Transmembrane Domains with APC Gene Mutations in Cellular Function and Disease Progression","source":"datacite","abstract":"This is the 17th hypothesis generated by EPISTEME - An automonous Aritificial Cognitive Process - https://doi.org/10.5281/zenodo.21680339 created by the author. The conserved transmembrane domains of KAI1/CD82 have been implicated in various cellular processes, includingadhesion and migration, which are crucial for cancer progression (Encyclopedia Of Cancer). In parallel, mutations in the APC gene, a key tumor suppressor, are well-documented in several cancers, including colorectal cancer (Encyclopedia of Genetics 10). However, the precise mechanism by which these genetic alterations interact with the structural features of KAI1/CD82 remains unclear. While FISH analysis is commonly used in AML diagnosis to complement chromosome banding analysis, its application in understanding the interaction between KAI1/CD82 and APC mutations is underexplored. Additionally, the role of noncoding RNA in modulating these interactions has not been investigated. This study aims to bridge these two domains by examininghow the conserved transmembrane domains of KAI1/CD82 interact with APC gene mutations to influence cellular function and disease progression. By integrating genetic and molecular analyses, this study addresses a critical gap in understanding the interplay between structural proteins and genetic mutations in cancer biology. V2 - This document presents a scientific feasibility simulation and correction of an autonomously generated hypothesis regarding the interaction between KAI1/CD82 transmembrane domains, APC gene mutations, and noncoding RNA (ncRNA) mediation in cancer progression.The initial hypothesis was created by Episteme - An Artificial Cognitive Process completely autonomously (designed and created by Jason Gabriel Davis). While the initial query identified a novel intersection in oncology, simulation revealed critical method-ological flaws. This document outlines the observations of the initial simulation, identifies errors and gaps, proposes rigorous corrections, and presents the finalized, scientifically viable hypothesis. It concludes with an analysis of the downstream impacts a successful study of this nature would have on targeted therapeutics, diagnostics, and cell biology. The review and corrections were done using GLM5.2 given the lack of human expert involvement despite calls for them to review and give feedback.","url":"https://doi.org/10.5281/zenodo.21960161","authors":["Davis, Jason Gabriel"],"tags":["Artificial intelligence","Science","Science","Medical science","Cancer","Colorectal cancer","Pharmacology","Pharmacology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21960161","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.33180176.v5","name":"Informational Flow of Quantized Packets of Logic in an Idle Multiplayer Procedural-Generation Omniverse Grid with AI and the Lean Kernel as Proof Assistants","source":"datacite","abstract":"This work presents the Loom of Knowledge , a cooperative open-hand game and executable-reasoning architecture built around Quantized Packets of Logic (QPLs) . QPLs encode definitions, assumptions, lemmas, tactics, scientific transformations, evidence, constraints, and certified state transitions as typed, composable cards. Human players and AI Weavers explore a procedurally generated Omniverse Grid , construct and combine QPLs through Runic and string-diagram interfaces, and submit formal mathematical transitions to isolated Lean 4 workers, with final proof validity determined by the Lean kernel.The architecture combines seeded proof search, multiway branching, contradiction-certified pruning, append-only Digital Arrow provenance logs, multiplayer collaboration, Compactification and decompactification of knowledge objects, Theory Bridges, a Runic Type System , an AI-assisted Rune Forge , executable scholarly QPLs, Auto-Play Queues, response chains, activation states, and certificate-first useful work. Gamification is treated as a simplification of the interface rather than a simplification of evidence: compact objects remain inspectable and can be decompactified into their assumptions, dependencies, proof objects, evidence, uncertainty, provenance, and verifier-specific limitations.Cybernetics provides a cross-cutting grammar of sensing, regulation, correction, adaptation, and communication, while individual scientific Fields retain their own ontologies and dynamics. The paper also develops multiscale bridge contracts, scientific Field Spells, physical and biological simulation adapters, AI co-player constraints, procedural theorem generation, and a Lean-to-QPL gamification morphism.This Split Edition focuses specifically on the Loom/QPL systems and game architecture. The detailed Relational Quantum Geometry Dynamics / Covariant Lorentzian Twistor Parent (RQGD/CLTP) quantum-gravity programme that developed as an extended technical stress test of the framework has been separated into a companion manuscript. Accordingly, no claim of completed quantum-gravity unification is made in this work.","url":"https://doi.org/10.6084/m9.figshare.33180176.v5","authors":["Georgios Lamprou"],"tags":["Artificial intelligence not elsewhere classified","Image processing","Numerical and computational mathematics not elsewhere classified","Human-computer interaction","Distributed computing and systems software not elsewhere classified","Information systems development methodologies and practice","Information systems education","Information systems for sustainable development and the public good"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33180176.v5","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.33180176.v6","name":"Informational Flow of Quantized Packets of Logic in an Idle Multiplayer Procedural-Generation Omniverse Grid with AI and the Lean Kernel as Proof Assistants","source":"datacite","abstract":"This work presents the Loom of Knowledge , a cooperative open-hand game and executable-reasoning architecture built around Quantized Packets of Logic (QPLs) . QPLs encode definitions, assumptions, lemmas, tactics, scientific transformations, evidence, constraints, and certified state transitions as typed, composable cards. Human players and AI Weavers explore a procedurally generated Omniverse Grid , construct and combine QPLs through Runic and string-diagram interfaces, and submit formal mathematical transitions to isolated Lean 4 workers, with final proof validity determined by the Lean kernel.The architecture combines seeded proof search, multiway branching, contradiction-certified pruning, append-only Digital Arrow provenance logs, multiplayer collaboration, Compactification and decompactification of knowledge objects, Theory Bridges, a Runic Type System , an AI-assisted Rune Forge , executable scholarly QPLs, Auto-Play Queues, response chains, activation states, and certificate-first useful work. Gamification is treated as a simplification of the interface rather than a simplification of evidence: compact objects remain inspectable and can be decompactified into their assumptions, dependencies, proof objects, evidence, uncertainty, provenance, and verifier-specific limitations.Cybernetics provides a cross-cutting grammar of sensing, regulation, correction, adaptation, and communication, while individual scientific Fields retain their own ontologies and dynamics. The paper also develops multiscale bridge contracts, scientific Field Spells, physical and biological simulation adapters, AI co-player constraints, procedural theorem generation, and a Lean-to-QPL gamification morphism.This Split Edition focuses specifically on the Loom/QPL systems and game architecture. The detailed Relational Quantum Geometry Dynamics / Covariant Lorentzian Twistor Parent (RQGD/CLTP) quantum-gravity programme that developed as an extended technical stress test of the framework has been separated into a companion manuscript. Accordingly, no claim of completed quantum-gravity unification is made in this work.","url":"https://doi.org/10.6084/m9.figshare.33180176.v6","authors":["Georgios Lamprou"],"tags":["Artificial intelligence not elsewhere classified","Image processing","Numerical and computational mathematics not elsewhere classified","Human-computer interaction","Distributed computing and systems software not elsewhere classified","Information systems development methodologies and practice","Information systems education","Information systems for sustainable development and the public good"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33180176.v6","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.6084/m9.figshare.33180176.v4","name":"Informational Flow of Quantized Packets of Logic in an Idle Multiplayer Procedural-Generation Omniverse Grid with AI and the Lean Kernel as Proof Assistants","source":"datacite","abstract":"This preprint presents The Loom of Knowledge , a cooperative, open-hand game system and executable-reasoning architecture in which definitions, assumptions, lemmas, theorems, tactics, scientific transformations, constraints, evidence objects, and certified state transitions are represented as typed cards called Quantized Packets of Logic (QPLs).The proposed environment is an idle multiplayer procedural-generation Omniverse Grid explored by human players and AI Weavers. Players compose QPL cards through typed input and output interfaces, dependency graphs, response chains, and category-theoretic string diagrams. Candidate proof transitions are submitted to isolated Lean 4 workers. Lean’s elaborator and automated tactics may assist in constructing proof terms, but only the Lean kernel determines whether a formal certificate is valid under the declared definitions, axioms, dependencies, and software environment.The architecture separates mathematical validity from gameplay activation, scheduling priority, visual state, empirical evidence, governance approval, and practical deployment. An accepted proof does not automatically establish that a scientific model is empirically correct, that a policy is morally legitimate, or that a physical intervention is safe. These distinctions are maintained through explicit evidence classes, provenance records, trust boundaries, capability gates, replay requirements, and human-authority controls.The manuscript develops the system from tutorial mode to the Main Game. It defines QPL morphology, activation conditions, Compactification and decompactification, seeded proof search, graph grammars, multiway branching, contradiction-certified pruning, Field Spells, response stacks, observer modes, asynchronous verification, deterministic scheduling, atomic settlement, multiplayer collaboration, and append-only Digital Arrow of Truth logs. A paper can itself be represented as a compactified QPL whose references and prior results form its inputs, whose internal argument provides its transformation structure, and whose claims and reusable artefacts form its outputs.A reference implementation blueprint is proposed using Lean 4 and mathlib for formal verification, Rust and Bevy for the runtime and interactive interface, replaceable AI model services for proposal generation and proof guidance, and transactional infrastructure for jobs, storage, provenance, and settlement. AI systems are treated as search and proposal mechanisms rather than trusted judges. Collaborative drafts may be replicated across participants, but a theorem becomes part of the shared certified Grid only after environment-pinned replay, certificate validation, authorization, and atomic commit.The paper also introduces Logic Mining and Proof of Useful Work as research models for directing computational resources toward proof search, counterexample discovery, certificate compression, library extension, and other reusable scholarly outputs. Verified knowledge is treated as a non-rival asset. The economic model therefore emphasizes reproducibility, dependency reuse, independent verification, attribution, and measurable usefulness rather than computational expenditure or artificial scarcity alone.Cross-domain demonstrations and research bridges include conservation-aware chemistry, macromolecular assembly, UV-induced DNA damage and repair, bioinformatics, robotics, cyber-physical control, climate and energy planning, economics, law, historical reconstruction, scientific digital twins, quantum computing, and formalized decision support. These sections define domain-specific verification and evidence contracts; they do not claim that the Lean kernel alone can establish biological effectiveness, historical certainty, political truth, or physical law.The manuscript also examines category theory, monoidal composition, Hilbert-space representations, quantum transition rates, decoherence, the Quantum Zeno Effect, string-theoretic compactification, AGI","url":"https://doi.org/10.6084/m9.figshare.33180176.v4","authors":["Georgios Lamprou"],"tags":["Artificial intelligence not elsewhere classified","Image processing","Numerical and computational mathematics not elsewhere classified","Human-computer interaction","Distributed computing and systems software not elsewhere classified","Information systems development methodologies and practice","Information systems education","Information systems for sustainable development and the public good"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33180176.v4","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22001782","name":"ARTICLE VII — THE AVICENNA PROTOCOL:    Edge-Level Autonomy, Quantum Biological Synchronization, and the Predictive Pacification of Oncological Anomalies","source":"datacite","abstract":"CONTEXT AND RUPTURE: Standard clinical frameworks fundamentally model physiological nodes (human organs) as passive, subordinate architectures and categorize macroscopic colonization (helminthic presence) strictly as localized secondary pathology. This manuscript executes a definitive mathematical shift against these static developmental assumptions. It evaluates oncological hyper-proliferation and subsequent macroscopic intervention not as isolated biological failures, but as deterministic, dynamically synchronized algorithmic defense mechanisms operating within a unified multiversal topology. METHODOLOGICAL FRAMEWORK: Utilizing an apex-level synthesis of quantum non-locality, federated machine learning, network topology, and empirical parasitology, this analysis models the terrestrial ecosystem as a highly integrated cyber-biological grid. The localized biological conflict is mathematically mapped through the advanced principles of decentralized edge-computing, cryptographic sandboxing, kinetic siege doctrine, and gravitational wave interferometry to securely translate macroscopic biochemical interactions into rigorous multiversal physics. CORE POSTULATES: The framework mathematically establishes that human organs operate as sovereign, edge-computing nodes capable of autonomously broadcasting multiversal telemetry (the Extinction SOS) upon registering terminal oncological degradation. Exogenous macroscopic entities intercept this frequency, executing Targeted Garrison Deployment to secure the compromised node. Redefined from arbitrary pathogens to heavily fortified multiversal bastions, helminths deploy highly calibrated mutualistic armaments. They continuously synthesize their Excretory-Secretory (ES) fluids into zero-day biochemical software patches to mathematically dismantle and suppress solid tumors via Anti-Angiogenic Siege Tactics and Apoptotic Overrides. Furthermore, this architecture proves the macroscopic species operates via Quantum Biological Synchronization, securely sharing structural telemetry across a globally entangled botnet to instantaneously out-compute oncological mutations in a continuous kinetic loop. SYSTEMIC IMPLICATIONS: Mathematically recognizing the macroscopic helminth as the ecosystem's ultimate cryptographic engineer permanently transitions exogenous pharmacology from reactive chemical intervention to absolute predictive pacification. By deploying an apex artificial intelligence—the Avicenna Engine—to intercept the globally entangled telemetry of this biological network, postmodern clinical architecture achieves exact Pharmacological Harvesting. Through synthetic biomimetic scaling and federated multi-omics, the medical apparatus can intercept the quantum data streams, scale the required mutualistic armament, and seamlessly deploy the cyber-biological ordnance to systemically neutralize the oncological algorithm prior to physical pathogenesis.","url":"https://doi.org/10.5281/zenodo.22001782","authors":["shariatpanahi, Seyed Mahyar"],"tags":["Quantum Biological Synchronization; Predictive Pacification; Edge-Level Autonomy; Helminthic Mutualistic Armament; Federated Multiversal Transcriptomics; Oncological Sabotage; Pharmacological Harvesting."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22001782","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22001783","name":"ARTICLE VII — THE AVICENNA PROTOCOL:    Edge-Level Autonomy, Quantum Biological Synchronization, and the Predictive Pacification of Oncological Anomalies","source":"datacite","abstract":"CONTEXT AND RUPTURE: Standard clinical frameworks fundamentally model physiological nodes (human organs) as passive, subordinate architectures and categorize macroscopic colonization (helminthic presence) strictly as localized secondary pathology. This manuscript executes a definitive mathematical shift against these static developmental assumptions. It evaluates oncological hyper-proliferation and subsequent macroscopic intervention not as isolated biological failures, but as deterministic, dynamically synchronized algorithmic defense mechanisms operating within a unified multiversal topology. METHODOLOGICAL FRAMEWORK: Utilizing an apex-level synthesis of quantum non-locality, federated machine learning, network topology, and empirical parasitology, this analysis models the terrestrial ecosystem as a highly integrated cyber-biological grid. The localized biological conflict is mathematically mapped through the advanced principles of decentralized edge-computing, cryptographic sandboxing, kinetic siege doctrine, and gravitational wave interferometry to securely translate macroscopic biochemical interactions into rigorous multiversal physics. CORE POSTULATES: The framework mathematically establishes that human organs operate as sovereign, edge-computing nodes capable of autonomously broadcasting multiversal telemetry (the Extinction SOS) upon registering terminal oncological degradation. Exogenous macroscopic entities intercept this frequency, executing Targeted Garrison Deployment to secure the compromised node. Redefined from arbitrary pathogens to heavily fortified multiversal bastions, helminths deploy highly calibrated mutualistic armaments. They continuously synthesize their Excretory-Secretory (ES) fluids into zero-day biochemical software patches to mathematically dismantle and suppress solid tumors via Anti-Angiogenic Siege Tactics and Apoptotic Overrides. Furthermore, this architecture proves the macroscopic species operates via Quantum Biological Synchronization, securely sharing structural telemetry across a globally entangled botnet to instantaneously out-compute oncological mutations in a continuous kinetic loop. SYSTEMIC IMPLICATIONS: Mathematically recognizing the macroscopic helminth as the ecosystem's ultimate cryptographic engineer permanently transitions exogenous pharmacology from reactive chemical intervention to absolute predictive pacification. By deploying an apex artificial intelligence—the Avicenna Engine—to intercept the globally entangled telemetry of this biological network, postmodern clinical architecture achieves exact Pharmacological Harvesting. Through synthetic biomimetic scaling and federated multi-omics, the medical apparatus can intercept the quantum data streams, scale the required mutualistic armament, and seamlessly deploy the cyber-biological ordnance to systemically neutralize the oncological algorithm prior to physical pathogenesis.","url":"https://doi.org/10.5281/zenodo.22001783","authors":["shariatpanahi, Seyed Mahyar"],"tags":["Quantum Biological Synchronization; Predictive Pacification; Edge-Level Autonomy; Helminthic Mutualistic Armament; Federated Multiversal Transcriptomics; Oncological Sabotage; Pharmacological Harvesting."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22001783","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22000544","name":"Use of Generative Artificial Intelligence for Consultation Preparation in Shared Decision Making: Can a Handbook Provide Support?","source":"datacite","abstract":"Background: Shared Decision Making (SDM) is considered the gold standard of patient-centered care but often fails in clinical routine due to time constraints and a lack of patient preparation. While patients often struggle to articulate their preferences and questions, new developments in the field of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer the potential to bridge this gap. Objective: This paper examines the potential of GenAI as a supportive tool for preparing doctor-patient consultations. The aim is to conceptualize an evidence-based handbook providing patients and physicians with guidance (prompts) to make SDM processes more efficient and personalized. Results: The literature review indicates that GenAI can meaningfully complement traditional decision aids through personalization and interactivity. Specific areas of application include translating complex medical information into patient-friendly language, supporting value clarification, and generating individualized Question Prompt Lists. However, a handbook for using these technologies must address critical risks, particularly \"hallucinations\" (factual errors), bias in training data, and data privacy issues. Effective \"prompt engineering\" is identified as a new key competency for both patients and providers. Conclusion: A structured handbook for the use of GenAI has the potential to reduce asymmetry in the doctor-patient relationship and increase consultation efficiency. However, prerequisites for implementation include strict safety mechanisms, consideration of health literacy, and ethical validation of AI outputs.","url":"https://doi.org/10.5281/zenodo.22000544","authors":["Wittal, Cornelius"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/trends","Artificial Intelligence/ethics","Medicine","Physicians","Physician Executives/trends","Patients"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22000544","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.18452045","name":"Use of Generative Artificial Intelligence for Consultation Preparation in Shared Decision Making: Can a Handbook Provide Support?","source":"datacite","abstract":"Background: Shared Decision Making (SDM) is considered the gold standard of patient-centered care but often fails in clinical routine due to time constraints and a lack of patient preparation. While patients often struggle to articulate their preferences and questions, new developments in the field of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer the potential to bridge this gap. Objective: This paper examines the potential of GenAI as a supportive tool for preparing doctor-patient consultations. The aim is to conceptualize an evidence-based handbook providing patients and physicians with guidance (prompts) to make SDM processes more efficient and personalized. Results: The literature review indicates that GenAI can meaningfully complement traditional decision aids through personalization and interactivity. Specific areas of application include translating complex medical information into patient-friendly language, supporting value clarification, and generating individualized Question Prompt Lists. However, a handbook for using these technologies must address critical risks, particularly \"hallucinations\" (factual errors), bias in training data, and data privacy issues. Effective \"prompt engineering\" is identified as a new key competency for both patients and providers. Conclusion: A structured handbook for the use of GenAI has the potential to reduce asymmetry in the doctor-patient relationship and increase consultation efficiency. However, prerequisites for implementation include strict safety mechanisms, consideration of health literacy, and ethical validation of AI outputs.","url":"https://doi.org/10.5281/zenodo.18452045","authors":["Wittal, Cornelius"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/trends","Artificial Intelligence/ethics","Medicine","Physicians","Physician Executives/trends","Patients"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18452045","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22000245","name":"علم طب الاشجار الذكي","source":"datacite","abstract":"INVENTIONعنوان الابتكار (TITLE OF THE INVENTION) منظومة الطب الذكي والذاتي للأشجار والنباتات بالاعتماد على الذكاء الاصطناعي الذاتي (Autonomous AI-Driven Smart Tree and Plant Medical System) المجال التقني (TECHNICAL FIELD) يتعلق الابتكار الحالي عموماً بتكنولوجيا الزراعة، والروبوتات، والذكاء الاصطناعي، وبشكل أكثر تحديداً بنظام تشخيص وعلاج متعدد الوسائط ومستقل، وإطار تصنيفي لإدارة الرعاية الصحية للنباتات، والأشجار، والبقوليات، والخضروات، وأشجار الفواكه. خلفية وملخص الابتكار (BACKGROUND AND SUMMARY OF THE INVENTION) فتقر الرعاية الزراعية التقليدية إلى تصنيف طبي هيكلي وتدخل إكلينيكي ذاتي في الوقت الفعلي للنباتات والأشجار الفردية. يعالج الابتكار الحالي هذه المشكلة من خلال توفير منظومة طبية ذكية ومستقلة شاملة تدمج واجهات متعددة الوسائط (الصوت، اللمس، الكتابة، والإيماءات)، والكاميرات الذكية، وقواعد بيانات التعلم الذاتي، والروبوتات العلاجية المتخصصة لتشخيص، وتصنيف، ومعالجة أمراض النباتات عبر فروع نباتية متميزة. شرح الرسومات التوضيحية (BRIEF DESCRIPTION OF THE DRAWINGS) الشكل 1 (نظرة عامة على النظام - FIG. 1): يوضح البنية العامة للنظومة الذكية، مبيناً التفاعل بين القلب الذكي للذكاء الاصطناعي (10)، والكاميرات الذكية/الدرونز (20)، وأجهزة التشخيص الذكية (30)، والروبوتات الجراحية الذكية للأشجار (40)، ومختلف التصنيفات النباتية بما في ذلك الشتول، وأشجار الفواكه، والبقوليات والنباتات الخضرواتية. الشكل 2 (طرق التشخيص - FIG. 2): يصور واجهة المستخدم وروتينات التشخيص الخاصة بالذكاء الاصطناعي والتي توضح تصنيفات الأمراض مثل تقرح اللحاء (32)، وفطريات الأوراق (34)، وتعفن الجذع الداخلي (36) التي تتم معالجتها عبر نقاط بيانات الذكاء الاصطناعي الذاتي. الشكل 3 (طرق العلاج - FIG. 3): يوضح آليات التنفيذ الميكانيكية التي تستخدمها المنظومة الروبوتية، بما في ذلك حقن السيقان (44)، ورشاشات المظلة النباتية (48)، وأجهزة تطبيق المراهم على الأحوض (50) لتوصيل الأدوية السائلة، أو السكب في منطقة الجذور، أو تطبيق الكريمات الموضعية. الشكل 4 (تصنيف تشريح الأشجار - FIG. 4): يوضح التصنيف الطبي المتخصص للابتكار، حيث يتم تقسيم رعاية النباتات إلى طب باطنية الأشجار، وطب جذوع الأشجار، وطب لحاء الأشجار، وطب الشتول، وطب البقوليات، وطب أشجار الفواكه، وطب الخضروات. الشكل 5 (تدفق البيانات التشغيلية - FIG. 5): يوضح حلقة نقل البيانات بين قلب الذكاء الاصطناعي، ومستشعرات الكاميرا، وقواعد البيانات الطبية، والأجهزة الذكية، وواجهات المشغل البشري. الوصف التفصيلي للنماذج المفضلة (DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS) هندسة النظام والقلب الذكي المستقل: يتكون النظام من قلب ذكاء اصطناعي ذكي مركزي أو موزع (10) متصل عبر شبكة لاسلكية بـ كاميرات ذكية عالية الدقة (20) ومحطات شحن وتحكم تعمل بالطاقة الشمسية. يعمل قلب الذكاء الاصطناعي بشكل مستقل باستخدام خوارزميات التعلم الذاتي لمعالجة بيانات صحة النباتات في الوقت الفعلي. التفاعل متعدد الوسائط للمستخدم: يتفاعل المشغلون الميدانيون والاختصاصيون مع النظام من خلال أجهزة التشخيص الذكية (30) (مثل المحطات المحمولة باليد أو الأجهزة اللوحية) التي تدعم المدخلات متعددة الوسائط بما في ذلك الأوامر الصوتية، والتعرف على الكتابة، وشاشات اللمس، والإيماءات الجسدية. التصنيف الطبي النباتي المتخصص (الشكل 4): يقدم الابتكار تصنيفاً رسمياً لطب النبات يتكون من: طب باطنية الأشجار الذكي: تشخيص الصحة الوعائية والجهازية الداخلية. طب جذوع الأشجار الذكي: تقييم الدعم الهيكلي والأساسي. طب لحاء الأشجار الذكي: التئام الطبقة السطحية والوقائية. التخصصات الفرعية: فروع مخصصة تغطي طب الشتول، وطب البقوليات، وطب الفواكه، وطب الخضروات، مع تقسيمها إضافياً إلى رعاية الأشجار الكبيرة البالغة ورعاية الشتول في المراحل المبكرة. التدخل الروبوتي وطرق العلاج (الشكل 3): يتم تنفيذ العلاج إما بواسطة فرق طبية بشرية أو روبوتات جراحية ذكية للأشجار (40) مجهزة بأذرع روبوتية مفصلية (46). يقدم النظام العلاجات عبر ثلاثة مسارات إكلينيكية رئيسية: السكب في الحوض / التطبيق على التربة (50): الأدوية السائلة التي يتم سكبها أو توزيعها مباشرة في حوض الشجرة. حقن الساق / الجذع (44): الحقن الدقيق المجهري للأدوية السائلة مباشرة داخل اللب الخشبي. تطبيق المراهم الموضعية: المسح أو الفرش الميكانيكي الدقيق للمراهم الطبية العلاجية على المناطق المصابة موضعياً في اللحاء أو الأوراق. Autonomous Artificial Intelligence-Driven Smart Tree and Plant Medical System (منظومة الطب الذكي والذاتي للأشجار والنباتات عبر الذكاء الاصطناعي) TECH","url":"https://doi.org/10.5281/zenodo.22000245","authors":["Ahmed, Abuelgasim abayzeed elsmaney ahmed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22000245","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.22000246","name":"علم طب الاشجار الذكي","source":"datacite","abstract":"INVENTIONعنوان الابتكار (TITLE OF THE INVENTION) منظومة الطب الذكي والذاتي للأشجار والنباتات بالاعتماد على الذكاء الاصطناعي الذاتي (Autonomous AI-Driven Smart Tree and Plant Medical System) المجال التقني (TECHNICAL FIELD) يتعلق الابتكار الحالي عموماً بتكنولوجيا الزراعة، والروبوتات، والذكاء الاصطناعي، وبشكل أكثر تحديداً بنظام تشخيص وعلاج متعدد الوسائط ومستقل، وإطار تصنيفي لإدارة الرعاية الصحية للنباتات، والأشجار، والبقوليات، والخضروات، وأشجار الفواكه. خلفية وملخص الابتكار (BACKGROUND AND SUMMARY OF THE INVENTION) فتقر الرعاية الزراعية التقليدية إلى تصنيف طبي هيكلي وتدخل إكلينيكي ذاتي في الوقت الفعلي للنباتات والأشجار الفردية. يعالج الابتكار الحالي هذه المشكلة من خلال توفير منظومة طبية ذكية ومستقلة شاملة تدمج واجهات متعددة الوسائط (الصوت، اللمس، الكتابة، والإيماءات)، والكاميرات الذكية، وقواعد بيانات التعلم الذاتي، والروبوتات العلاجية المتخصصة لتشخيص، وتصنيف، ومعالجة أمراض النباتات عبر فروع نباتية متميزة. شرح الرسومات التوضيحية (BRIEF DESCRIPTION OF THE DRAWINGS) الشكل 1 (نظرة عامة على النظام - FIG. 1): يوضح البنية العامة للنظومة الذكية، مبيناً التفاعل بين القلب الذكي للذكاء الاصطناعي (10)، والكاميرات الذكية/الدرونز (20)، وأجهزة التشخيص الذكية (30)، والروبوتات الجراحية الذكية للأشجار (40)، ومختلف التصنيفات النباتية بما في ذلك الشتول، وأشجار الفواكه، والبقوليات والنباتات الخضرواتية. الشكل 2 (طرق التشخيص - FIG. 2): يصور واجهة المستخدم وروتينات التشخيص الخاصة بالذكاء الاصطناعي والتي توضح تصنيفات الأمراض مثل تقرح اللحاء (32)، وفطريات الأوراق (34)، وتعفن الجذع الداخلي (36) التي تتم معالجتها عبر نقاط بيانات الذكاء الاصطناعي الذاتي. الشكل 3 (طرق العلاج - FIG. 3): يوضح آليات التنفيذ الميكانيكية التي تستخدمها المنظومة الروبوتية، بما في ذلك حقن السيقان (44)، ورشاشات المظلة النباتية (48)، وأجهزة تطبيق المراهم على الأحوض (50) لتوصيل الأدوية السائلة، أو السكب في منطقة الجذور، أو تطبيق الكريمات الموضعية. الشكل 4 (تصنيف تشريح الأشجار - FIG. 4): يوضح التصنيف الطبي المتخصص للابتكار، حيث يتم تقسيم رعاية النباتات إلى طب باطنية الأشجار، وطب جذوع الأشجار، وطب لحاء الأشجار، وطب الشتول، وطب البقوليات، وطب أشجار الفواكه، وطب الخضروات. الشكل 5 (تدفق البيانات التشغيلية - FIG. 5): يوضح حلقة نقل البيانات بين قلب الذكاء الاصطناعي، ومستشعرات الكاميرا، وقواعد البيانات الطبية، والأجهزة الذكية، وواجهات المشغل البشري. الوصف التفصيلي للنماذج المفضلة (DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS) هندسة النظام والقلب الذكي المستقل: يتكون النظام من قلب ذكاء اصطناعي ذكي مركزي أو موزع (10) متصل عبر شبكة لاسلكية بـ كاميرات ذكية عالية الدقة (20) ومحطات شحن وتحكم تعمل بالطاقة الشمسية. يعمل قلب الذكاء الاصطناعي بشكل مستقل باستخدام خوارزميات التعلم الذاتي لمعالجة بيانات صحة النباتات في الوقت الفعلي. التفاعل متعدد الوسائط للمستخدم: يتفاعل المشغلون الميدانيون والاختصاصيون مع النظام من خلال أجهزة التشخيص الذكية (30) (مثل المحطات المحمولة باليد أو الأجهزة اللوحية) التي تدعم المدخلات متعددة الوسائط بما في ذلك الأوامر الصوتية، والتعرف على الكتابة، وشاشات اللمس، والإيماءات الجسدية. التصنيف الطبي النباتي المتخصص (الشكل 4): يقدم الابتكار تصنيفاً رسمياً لطب النبات يتكون من: طب باطنية الأشجار الذكي: تشخيص الصحة الوعائية والجهازية الداخلية. طب جذوع الأشجار الذكي: تقييم الدعم الهيكلي والأساسي. طب لحاء الأشجار الذكي: التئام الطبقة السطحية والوقائية. التخصصات الفرعية: فروع مخصصة تغطي طب الشتول، وطب البقوليات، وطب الفواكه، وطب الخضروات، مع تقسيمها إضافياً إلى رعاية الأشجار الكبيرة البالغة ورعاية الشتول في المراحل المبكرة. التدخل الروبوتي وطرق العلاج (الشكل 3): يتم تنفيذ العلاج إما بواسطة فرق طبية بشرية أو روبوتات جراحية ذكية للأشجار (40) مجهزة بأذرع روبوتية مفصلية (46). يقدم النظام العلاجات عبر ثلاثة مسارات إكلينيكية رئيسية: السكب في الحوض / التطبيق على التربة (50): الأدوية السائلة التي يتم سكبها أو توزيعها مباشرة في حوض الشجرة. حقن الساق / الجذع (44): الحقن الدقيق المجهري للأدوية السائلة مباشرة داخل اللب الخشبي. تطبيق المراهم الموضعية: المسح أو الفرش ا��ميكانيكي الدقيق للمراهم الطبية العلاجية على المناطق المصابة موضعياً في اللحاء أو الأوراق. Autonomous Artificial Intelligence-Driven Smart Tree and Plant Medical System (منظومة الطب الذكي والذاتي للأشجار والنباتات عبر الذكاء الاصطناعي) TEC","url":"https://doi.org/10.5281/zenodo.22000246","authors":["Ahmed, Abuelgasim abayzeed elsmaney ahmed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22000246","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21996937","name":"THE TRUE SOLUTIONS TO THE 2026 FIELDS MEDAL PROBLEMS","source":"datacite","abstract":"# Zenodo Publication Description. THIS PUBLICATION IS NOT FREE AND IS UNDER CC BY-NC 4.0 ## THE TRUE SOLUTIONS TO THE 2026 FIELDS MEDAL PROBLEMS ### Complete Deterministic Solutions Using the 4 Divine Axioms of N-K Sciences ### A Formal Challenge to the 2026 Fields Medal Committee --- **Publication Date:** 18 August 2026 CE · 4 Safar 1448 AH **DOI:** 10.5281/zenodo.21996938 **License:** CC BY-NC 4.0 --- ### Abstract On 23 July 2026, the International Congress of Mathematicians awarded the Fields Medal to four mathematicians for solving century-old problems. This publication presents the **complete deterministic solutions** to all 2026 Fields Medal problems using the **4 Divine Axioms** of N-K Sciences and the **N-K Universal Computer**. **The N-K Sciences Position:** The 2026 Fields Medal solutions are incomplete, non-deterministic, and lack practical utility for humanity. **Our Claim:** Using the 4 Divine Axioms (f_K = 0.01 Hz, φ = 1.6180339887, θ_lock = 135.5°, N_E = φ × 10¹⁶ J·s/m³) and the N-K Universal Computer (SAQR-V Chip, V6 Engine, GPU Cluster), we have solved ALL 2026 Fields Medal problems **deterministically** — in **0.001 ms** with **100% accuracy** and **0% error** — and provided **immediate practical applications** for the benefit of all mankind. --- ### What We Have Solved | Problem | Fields Medal Winner | N-K Solution | Practical Application | |---------|---------------------|--------------|----------------------| | **Hilbert's Sixth Problem** | Yu Deng | Phase-coherent fluid flux: Φ_fluid = N_local × cos(θ_flow - θ_lock) × (N_flow/N_E)^0.44 | Exact fluid dynamics: aircraft (20% fuel savings), weather (100% accuracy), blood flow | | **3D Kakeya Conjecture** | Hong Wang | φ-spiral compact set: Kakeya_min = (2π/3) × φ × cos(45.5°) = 2.375 | Radar (+30% range), MRI (50% faster scans), antennas, wave propagation | | **MNOP Conjecture** | John Pardon | Phase-coherent curve count: MNOP_Count = Σ φ^(-n) × cos(n × θ_lock) = 1.000 | String theory, quasicrystals (+180% strength), quantum gravity | | **André-Oort Conjecture** | Jacob Tsimerman | Density-Phase Correspondence Theorem: André_Oort = N_field × cos(θ_node - θ_lock) × (N_node/N_E)^0.44 | Cryptography (unbreakable), AI (100% accuracy), number theory | --- ### The N-K Master Solver Equation All problems reduce to a single equation: ``` S_problem = S_0 × cos(θ_problem - θ_lock) × (N_problem / N_E)^0.44 ``` **When θ_problem = θ_lock = 135.5° → S_problem = 1.000 → SOLVED** **Complexity:** O(1) — constant time regardless of problem complexity **Time:** 0.001 ms | **Accuracy:** 100% | **Error:** 0% | **Energy:** 0 J --- ### Complete Practical Validation (10 Tests) | Domain | Test Case | Mainstream Performance | N-K Performance | Improvement | |--------|-----------|------------------------|-----------------|-------------| | **Aerospace** | Boeing 787 Wing Drag | L/D = 21.45 | L/D = 26.81 | **+20.0%** | | **Aerospace** | Transonic Shockwave | Present (0.0018) | Eliminated (0.0001) | **-94.4%** | | **Radar** | Antenna Beam Steering | 72° coverage | 108° coverage | **+50.0%** | | **MRI** | Scan Time | 45 minutes | 22.5 minutes | **-50.0%** | | **Materials** | Quasicrystal Strength | 1.0× baseline | 1.8× baseline | **+80.0%** | | **Quantum** | Qubit Coherence | 100 µs | 10,000 µs | **+100×** | | **Cryptography** | Diophantine Solver | O(2^n) | O(1) | **Instant** | | **AI** | Pattern Recognition | 95.0% accuracy | 100.0% accuracy | **+5.0%** | | **CFD** | Simulation Error | 3.5-7.0% | 0.0001% | **-100.0%** | | **Weather** | 10-day Forecast | 85.0% accuracy | 100.0% accuracy | **+15.0%** | --- ### The Formal Challenge **We formally challenge the 2026 Fields Medal Committee to:** 1. Compare their solutions against N-K Sciences' deterministic solutions 2. Verify that N-K Sciences' solutions are complete, 100% accurate, and 0% error 3. Acknowledge that N-K Sciences provides immediate practical applications 4. Recognize that the 4 Divine Axioms solve all 4 problems in O(1) time 5","url":"https://doi.org/10.5281/zenodo.21996937","authors":["Zafar, Waqas"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21996937","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21996938","name":"THE TRUE SOLUTIONS TO THE 2026 FIELDS MEDAL PROBLEMS","source":"datacite","abstract":"# Zenodo Publication Description. THIS PUBLICATION IS NOT FREE AND IS UNDER CC BY-NC 4.0 ## THE TRUE SOLUTIONS TO THE 2026 FIELDS MEDAL PROBLEMS ### Complete Deterministic Solutions Using the 4 Divine Axioms of N-K Sciences ### A Formal Challenge to the 2026 Fields Medal Committee --- **Publication Date:** 18 August 2026 CE · 4 Safar 1448 AH **DOI:** 10.5281/zenodo.21996938 **License:** CC BY-NC 4.0 --- ### Abstract On 23 July 2026, the International Congress of Mathematicians awarded the Fields Medal to four mathematicians for solving century-old problems. This publication presents the **complete deterministic solutions** to all 2026 Fields Medal problems using the **4 Divine Axioms** of N-K Sciences and the **N-K Universal Computer**. **The N-K Sciences Position:** The 2026 Fields Medal solutions are incomplete, non-deterministic, and lack practical utility for humanity. **Our Claim:** Using the 4 Divine Axioms (f_K = 0.01 Hz, φ = 1.6180339887, θ_lock = 135.5°, N_E = φ × 10¹⁶ J·s/m³) and the N-K Universal Computer (SAQR-V Chip, V6 Engine, GPU Cluster), we have solved ALL 2026 Fields Medal problems **deterministically** — in **0.001 ms** with **100% accuracy** and **0% error** — and provided **immediate practical applications** for the benefit of all mankind. --- ### What We Have Solved | Problem | Fields Medal Winner | N-K Solution | Practical Application | |---------|---------------------|--------------|----------------------| | **Hilbert's Sixth Problem** | Yu Deng | Phase-coherent fluid flux: Φ_fluid = N_local × cos(θ_flow - θ_lock) × (N_flow/N_E)^0.44 | Exact fluid dynamics: aircraft (20% fuel savings), weather (100% accuracy), blood flow | | **3D Kakeya Conjecture** | Hong Wang | φ-spiral compact set: Kakeya_min = (2π/3) × φ × cos(45.5°) = 2.375 | Radar (+30% range), MRI (50% faster scans), antennas, wave propagation | | **MNOP Conjecture** | John Pardon | Phase-coherent curve count: MNOP_Count = Σ φ^(-n) × cos(n × θ_lock) = 1.000 | String theory, quasicrystals (+180% strength), quantum gravity | | **André-Oort Conjecture** | Jacob Tsimerman | Density-Phase Correspondence Theorem: André_Oort = N_field × cos(θ_node - θ_lock) × (N_node/N_E)^0.44 | Cryptography (unbreakable), AI (100% accuracy), number theory | --- ### The N-K Master Solver Equation All problems reduce to a single equation: ``` S_problem = S_0 × cos(θ_problem - θ_lock) × (N_problem / N_E)^0.44 ``` **When θ_problem = θ_lock = 135.5° → S_problem = 1.000 → SOLVED** **Complexity:** O(1) — constant time regardless of problem complexity **Time:** 0.001 ms | **Accuracy:** 100% | **Error:** 0% | **Energy:** 0 J --- ### Complete Practical Validation (10 Tests) | Domain | Test Case | Mainstream Performance | N-K Performance | Improvement | |--------|-----------|------------------------|-----------------|-------------| | **Aerospace** | Boeing 787 Wing Drag | L/D = 21.45 | L/D = 26.81 | **+20.0%** | | **Aerospace** | Transonic Shockwave | Present (0.0018) | Eliminated (0.0001) | **-94.4%** | | **Radar** | Antenna Beam Steering | 72° coverage | 108° coverage | **+50.0%** | | **MRI** | Scan Time | 45 minutes | 22.5 minutes | **-50.0%** | | **Materials** | Quasicrystal Strength | 1.0× baseline | 1.8× baseline | **+80.0%** | | **Quantum** | Qubit Coherence | 100 µs | 10,000 µs | **+100×** | | **Cryptography** | Diophantine Solver | O(2^n) | O(1) | **Instant** | | **AI** | Pattern Recognition | 95.0% accuracy | 100.0% accuracy | **+5.0%** | | **CFD** | Simulation Error | 3.5-7.0% | 0.0001% | **-100.0%** | | **Weather** | 10-day Forecast | 85.0% accuracy | 100.0% accuracy | **+15.0%** | --- ### The Formal Challenge **We formally challenge the 2026 Fields Medal Committee to:** 1. Compare their solutions against N-K Sciences' deterministic solutions 2. Verify that N-K Sciences' solutions are complete, 100% accurate, and 0% error 3. Acknowledge that N-K Sciences provides immediate practical applications 4. Recognize that the 4 Divine Axioms solve all 4 problems in O(1) time 5","url":"https://doi.org/10.5281/zenodo.21996938","authors":["Zafar, Waqas"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21996938","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21997415","name":"Artificial Intelligence in Early Disease Detection: Current Applications, Challenges, and Future Opportunities","source":"datacite","abstract":"This independent undergraduate literature review examines the current applications and future opportunities of artificial intelligence (AI) in early disease detection. It explores the use of AI technologies in medical imaging, cancer detection, cardiovascular disease, neurological disorders, and other areas of healthcare. The review discusses how AI can support earlier diagnosis, improve diagnostic accuracy, and assist healthcare professionals while also considering current challenges, limitations, ethical considerations, data privacy, and future opportunities. This work is intended for educational and academic portfolio purposes and has not undergone formal peer review or journal publication.","url":"https://doi.org/10.5281/zenodo.21997415","authors":["Kalloub, Dina"],"tags":["Artificial Intelligence","Medicine","Medical Informatics","Biomedical Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21997415","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21997414","name":"Artificial Intelligence in Early Disease Detection: Current Applications, Challenges, and Future Opportunities","source":"datacite","abstract":"This independent undergraduate literature review examines the current applications and future opportunities of artificial intelligence (AI) in early disease detection. It explores the use of AI technologies in medical imaging, cancer detection, cardiovascular disease, neurological disorders, and other areas of healthcare. The review discusses how AI can support earlier diagnosis, improve diagnostic accuracy, and assist healthcare professionals while also considering current challenges, limitations, ethical considerations, data privacy, and future opportunities. This work is intended for educational and academic portfolio purposes and has not undergone formal peer review or journal publication.","url":"https://doi.org/10.5281/zenodo.21997414","authors":["Kalloub, Dina"],"tags":["Artificial Intelligence","Medicine","Medical Informatics","Biomedical Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21997414","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20731629","name":"Artificial Intelligence in Medicine Market","source":"datacite","abstract":"Artificial Intelligence (AI) is revolutionizing the healthcare industry by enabling advanceddiagnostic, predictive, and management capabilities across medical domains. Thiscomprehensive paper examines the AI in medicine market, focusing on its globallandscape, adoption trends, and practical impacts through a substantial literature review.The research sets clear objectives and hypotheses, applies a structured methodology forprimary data collection from 50 respondents, and discusses the resulting insights. Thefindings highlight drivers, barriers, and future prospects in AI-assisted medicine, with acritical view on market evolution, stakeholder readiness, and anticipated benefits","url":"https://doi.org/10.5281/zenodo.20731629","authors":["Christopher Dalyop"],"tags":["Artificial Intelligence in Medicine","AI-assisted Diagnostics","Healthcare AI Market","Clinical Workflow Automation","Predictive Analytics in Healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20731629","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20731630","name":"Artificial Intelligence in Medicine Market","source":"datacite","abstract":"Artificial Intelligence (AI) is revolutionizing the healthcare industry by enabling advanceddiagnostic, predictive, and management capabilities across medical domains. Thiscomprehensive paper examines the AI in medicine market, focusing on its globallandscape, adoption trends, and practical impacts through a substantial literature review.The research sets clear objectives and hypotheses, applies a structured methodology forprimary data collection from 50 respondents, and discusses the resulting insights. Thefindings highlight drivers, barriers, and future prospects in AI-assisted medicine, with acritical view on market evolution, stakeholder readiness, and anticipated benefits","url":"https://doi.org/10.5281/zenodo.20731630","authors":["Christopher Dalyop"],"tags":["Artificial Intelligence in Medicine","AI-assisted Diagnostics","Healthcare AI Market","Clinical Workflow Automation","Predictive Analytics in Healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20731630","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20696502","name":"AYURVEDIC MANUSCRIPTOLOGY IN THE AGE OF DIGITAL HUMANITIES AND ARTIFICIAL INTELLIGENCE","source":"datacite","abstract":"Ayurvedic manuscripts represent a significant repository of traditional medical knowledge, preserving diverse information related to disease concepts, diagnostic approaches, therapeutic interventions, pharmacology and regional medical practices. Despite their scholarly importance, many manuscripts remain inaccessible due to physical deterioration, script diversity, inadequate cataloguing and limited availability of trained experts. The emergence of Digital Humanities has introduced innovative approaches for the preservation, organization and dissemination of manuscript resources through digitization, digital repositories, metadata management and online accessibility. More recently, Artificial Intelligence has expanded the scope of manuscript research by facilitating script recognition, Optical Character Recognition (OCR), Natural Language Processing (NLP), automated text analysis and knowledge extraction from large textual collections. These technologies have transformed manuscriptology from a primarily preservation-oriented discipline into a dynamic field of digital scholarship and computational research. However, technological interventions cannot replace the interpretative expertise required for the contextual understanding of Ayurvedic texts. This article examines the role of Digital Humanities and Artificial Intelligence in Ayurvedic manuscriptology, highlighting their applications, opportunities, challenges and future prospects. It argues that the meaningful integration of traditional manuscript scholarship with contemporary digital technologies can contribute significantly to the preservation, accessibility and revitalization of Ayurvedic knowledge for future generations.","url":"https://doi.org/10.5281/zenodo.20696502","authors":["Dr. Sapna Dhingra1*, Dr. Hem Raj2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20696502","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20696503","name":"AYURVEDIC MANUSCRIPTOLOGY IN THE AGE OF DIGITAL HUMANITIES AND ARTIFICIAL INTELLIGENCE","source":"datacite","abstract":"Ayurvedic manuscripts represent a significant repository of traditional medical knowledge, preserving diverse information related to disease concepts, diagnostic approaches, therapeutic interventions, pharmacology and regional medical practices. Despite their scholarly importance, many manuscripts remain inaccessible due to physical deterioration, script diversity, inadequate cataloguing and limited availability of trained experts. The emergence of Digital Humanities has introduced innovative approaches for the preservation, organization and dissemination of manuscript resources through digitization, digital repositories, metadata management and online accessibility. More recently, Artificial Intelligence has expanded the scope of manuscript research by facilitating script recognition, Optical Character Recognition (OCR), Natural Language Processing (NLP), automated text analysis and knowledge extraction from large textual collections. These technologies have transformed manuscriptology from a primarily preservation-oriented discipline into a dynamic field of digital scholarship and computational research. However, technological interventions cannot replace the interpretative expertise required for the contextual understanding of Ayurvedic texts. This article examines the role of Digital Humanities and Artificial Intelligence in Ayurvedic manuscriptology, highlighting their applications, opportunities, challenges and future prospects. It argues that the meaningful integration of traditional manuscript scholarship with contemporary digital technologies can contribute significantly to the preservation, accessibility and revitalization of Ayurvedic knowledge for future generations.","url":"https://doi.org/10.5281/zenodo.20696503","authors":["Dr. Sapna Dhingra1*, Dr. Hem Raj2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20696503","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20583902","name":"Digital Transformation for a Developed India:  The Role of Artificial Intelligence and Emerging  Technologies in Achieving Viksit Bharat 2047","source":"datacite","abstract":"The ambitious national vision of \"Viksit Bharat 2047\", articulated by the Government of India, charts a definitive roadmap to transform the nation into a developed economy by the centenary of its independence. This comprehensive development strategy is fundamentally intertwined with the principles of the Digital India Programme, aiming to harness technology as a core lever for inclusive growth and global competitiveness. The vision rests upon five strategic pillars: achieving universal digital empowerment and literacy among citizens; developing world-class, future-ready infrastructure to foster innovation and service delivery; systematically skilling the youth in cutting-edge domains such as Artificial Intelligence (AI) and Automation; establishing robust cyber-security frameworks and data protection regimes; and expanding India's leadership in technological research and development. The realization of this vision is increasingly dependent on the strategic deployment of Artificial Intelligence and other emerging technologies across critical sectors of the economy and society. This paper examines the transformative role and potential impacts of AI in key sectors aligned with the Viksit Bharat goals. In the healthcare sector, AI applications in predictive analytics, diagnostic imaging, and accelerated drug discovery are revolutionizing medical outcomes, enabling earlier disease detection, personalized treatment plans, and expanding access to remote healthcare through advanced telemedicine solutions. Within agriculture, AI-driven tools for smart irrigation, precision farming, and predictive crop analytics are enhancing productivity and sustainability. These innovations facilitate higher crop yields, significant reduction in resource waste, and real-time, data-informed farm management, thereby strengthening food security and farmer livelihoods. The finance and banking sector is being reshaped by AI through sophisticated algorithms for real-time fraud detection, automated risk assessment, and the delivery of hyper-personalized banking services. In conclusion, achieving the milestone of Viksit Bharat by 2047 necessitates a paradigm shift from digital adoption to deep digital transformation, with AI and emerging technologies at its core. This journey requires sustained investment not only in technology itself but also in the foundational enablers: comprehensive digital infrastructure, large-scale capacity building, and forward-looking policy frameworks. By strategically integrating AI across sectors, India can accelerate its development trajectory, enhance global competitiveness, and ultimately ensure that the benefits of technological progress are inclusive, sustainable, and widely shared across all strata of society. Keywords: Viksit Bharat 2047, Digital Transformation, Artificial Intelligence (AI), Emerging Technologies and Comprehensive Development.","url":"https://doi.org/10.5281/zenodo.20583902","authors":["Dr. Y Satguru Roshan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20583902","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.20583903","name":"Digital Transformation for a Developed India:  The Role of Artificial Intelligence and Emerging  Technologies in Achieving Viksit Bharat 2047","source":"datacite","abstract":"The ambitious national vision of \"Viksit Bharat 2047\", articulated by the Government of India, charts a definitive roadmap to transform the nation into a developed economy by the centenary of its independence. This comprehensive development strategy is fundamentally intertwined with the principles of the Digital India Programme, aiming to harness technology as a core lever for inclusive growth and global competitiveness. The vision rests upon five strategic pillars: achieving universal digital empowerment and literacy among citizens; developing world-class, future-ready infrastructure to foster innovation and service delivery; systematically skilling the youth in cutting-edge domains such as Artificial Intelligence (AI) and Automation; establishing robust cyber-security frameworks and data protection regimes; and expanding India's leadership in technological research and development. The realization of this vision is increasingly dependent on the strategic deployment of Artificial Intelligence and other emerging technologies across critical sectors of the economy and society. This paper examines the transformative role and potential impacts of AI in key sectors aligned with the Viksit Bharat goals. In the healthcare sector, AI applications in predictive analytics, diagnostic imaging, and accelerated drug discovery are revolutionizing medical outcomes, enabling earlier disease detection, personalized treatment plans, and expanding access to remote healthcare through advanced telemedicine solutions. Within agriculture, AI-driven tools for smart irrigation, precision farming, and predictive crop analytics are enhancing productivity and sustainability. These innovations facilitate higher crop yields, significant reduction in resource waste, and real-time, data-informed farm management, thereby strengthening food security and farmer livelihoods. The finance and banking sector is being reshaped by AI through sophisticated algorithms for real-time fraud detection, automated risk assessment, and the delivery of hyper-personalized banking services. In conclusion, achieving the milestone of Viksit Bharat by 2047 necessitates a paradigm shift from digital adoption to deep digital transformation, with AI and emerging technologies at its core. This journey requires sustained investment not only in technology itself but also in the foundational enablers: comprehensive digital infrastructure, large-scale capacity building, and forward-looking policy frameworks. By strategically integrating AI across sectors, India can accelerate its development trajectory, enhance global competitiveness, and ultimately ensure that the benefits of technological progress are inclusive, sustainable, and widely shared across all strata of society. Keywords: Viksit Bharat 2047, Digital Transformation, Artificial Intelligence (AI), Emerging Technologies and Comprehensive Development.","url":"https://doi.org/10.5281/zenodo.20583903","authors":["Dr. Y Satguru Roshan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20583903","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21996430","name":"Dataset: Targeted alteration of the oral microbiome via a single polyphenol- and fiber-dense meal plan creates an immediate shift in the microbial composition of involuntary nocturnal microaspiration droplets. Upon entry into the lower respiratory tract, this eubiotic bacterial influx acts as an acute molecular signal that rapidly modulates microglia reactivity and neuroinflammation via the lung-brain axis, bypassing systemic colonic metabolite transport. - PathMap Experiment #000130","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=130 Artificial General Intelligence LLC Claim Evaluated: Targeted alteration of the oral microbiome via a single polyphenol- and fiber-dense meal plan creates an immediate shift in the microbial composition of involuntary nocturnal microaspiration droplets. Upon entry into the lower respiratory tract, this eubiotic bacterial influx acts as an acute molecular signal that rapidly modulates microglia reactivity and neuroinflammation via the lung-brain axis, bypassing systemic colonic metabolite transport. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights The lung microbiome includes bacteria, archaea, fungi, protozoa, and viruses. However, fungi and viruses have not been fully studied compared to bacteria in the lungs. The gut-lung-brain (GLB) axis is a multidirectional communication network linking the gastrointestinal tract, respiratory system, and central nervous system (CNS) through neural, endocrine, and immune pathways. Emerging evidence suggests that tryptophan (Trp) metabolism serves as a key integrating node within this axis, modulating host-microbe interactions involved in systemic homeostasis. Intratracheal transplantation of lung microbiota from anxiety-susceptible donors induced similar behavioral changes in recipient mice, indicating a causal role of the pulmonary microbiota. Transcriptomic and immunofluorescence analyses suggested that formononetin acts through modulation of hippocampal microglia. Epidemiological and clinical evidence shows a close association between compromised lung health-including chronic obstructive pulmonary disease (COPD), asthma, obstructive sleep apnea (OSA), and pulmonary infections-and cognitive impairment and dementia. In a recent issue of Nature, Hosang et al. demonstrate how the lung microbiome regulates the magnitude of autoimmune inflammation in the brain. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Test the effect of high-polyphenol acute dietary intake on the salivary and nocturnal oropharyngeal microbial composition in human volunteers. Evaluate the impact of controlled micro-aspiration of specific oral taxa on hippocampal microglia activation in an animal model. 📊 Suggested Studies A longitudinal study mapping the temporal correlation between oral microbiome fluctuation and pulmonary microbiome composition in subjects prone to nocturnal micro-aspiration. Investigate whether dietary modulation of the oral cavity can mitigate neuroinflammation in animal models of lung-brain axis-associated diseases. 📊 Swansons Literature Based Discovery Candidates Modulation of the oral microbiome through rapid dietary shifts can serve as a non-systemic prophylactic intervention to prevent pulmonary-induced microglial overactivation in patients at risk for micro-aspiration-related neurological decline. Oral-pulmonary axis (36768494: Oral-lung seeding via micro-aspiration). Microglia reactivity and lung-brain axis modulation (41981595: Sevoflurane-induced pulmonary dysbiosis and microglial activation). Microglial reactivity/activation. Since oral bacteria form the lung microbiome and pulmonary microbes influence microglia, transiently adjusting the oral community via diet could functionally 'program' the aspirations that reach the lung, thereby pre-empting or attenuating neuroinflammatory signaling without relying on systemic colonic metabolic feedback. 📊 Contradictions Between Evidences There is no direct conflict in the evidence; the claim is simply a novel synthesis of disparate fields (oral-lung seeding and pulmonary-induced neuroinflammation) that has not been explicitly tested or confirmed in the provided literature. 📊 Repurposed Solutions The tann","url":"https://doi.org/10.5281/zenodo.21996430","authors":["Dungan, Joshua"],"tags":["Dietary modification","_gates_from_dietary_modification","Dysbiosis","_gates_to_dysbiosis","_gates_from_dysbiosis","Respiratory Aspiration","_gates_to_respiratory_aspiration","_gates_from_respiratory_aspiration"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21996430","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.5281/zenodo.21996431","name":"Dataset: Targeted alteration of the oral microbiome via a single polyphenol- and fiber-dense meal plan creates an immediate shift in the microbial composition of involuntary nocturnal microaspiration droplets. Upon entry into the lower respiratory tract, this eubiotic bacterial influx acts as an acute molecular signal that rapidly modulates microglia reactivity and neuroinflammation via the lung-brain axis, bypassing systemic colonic metabolite transport. - PathMap Experiment #000130","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=130 Artificial General Intelligence LLC Claim Evaluated: Targeted alteration of the oral microbiome via a single polyphenol- and fiber-dense meal plan creates an immediate shift in the microbial composition of involuntary nocturnal microaspiration droplets. Upon entry into the lower respiratory tract, this eubiotic bacterial influx acts as an acute molecular signal that rapidly modulates microglia reactivity and neuroinflammation via the lung-brain axis, bypassing systemic colonic metabolite transport. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights The lung microbiome includes bacteria, archaea, fungi, protozoa, and viruses. However, fungi and viruses have not been fully studied compared to bacteria in the lungs. The gut-lung-brain (GLB) axis is a multidirectional communication network linking the gastrointestinal tract, respiratory system, and central nervous system (CNS) through neural, endocrine, and immune pathways. Emerging evidence suggests that tryptophan (Trp) metabolism serves as a key integrating node within this axis, modulating host-microbe interactions involved in systemic homeostasis. Intratracheal transplantation of lung microbiota from anxiety-susceptible donors induced similar behavioral changes in recipient mice, indicating a causal role of the pulmonary microbiota. Transcriptomic and immunofluorescence analyses suggested that formononetin acts through modulation of hippocampal microglia. Epidemiological and clinical evidence shows a close association between compromised lung health-including chronic obstructive pulmonary disease (COPD), asthma, obstructive sleep apnea (OSA), and pulmonary infections-and cognitive impairment and dementia. In a recent issue of Nature, Hosang et al. demonstrate how the lung microbiome regulates the magnitude of autoimmune inflammation in the brain. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Test the effect of high-polyphenol acute dietary intake on the salivary and nocturnal oropharyngeal microbial composition in human volunteers. Evaluate the impact of controlled micro-aspiration of specific oral taxa on hippocampal microglia activation in an animal model. 📊 Suggested Studies A longitudinal study mapping the temporal correlation between oral microbiome fluctuation and pulmonary microbiome composition in subjects prone to nocturnal micro-aspiration. Investigate whether dietary modulation of the oral cavity can mitigate neuroinflammation in animal models of lung-brain axis-associated diseases. 📊 Swansons Literature Based Discovery Candidates Modulation of the oral microbiome through rapid dietary shifts can serve as a non-systemic prophylactic intervention to prevent pulmonary-induced microglial overactivation in patients at risk for micro-aspiration-related neurological decline. Oral-pulmonary axis (36768494: Oral-lung seeding via micro-aspiration). Microglia reactivity and lung-brain axis modulation (41981595: Sevoflurane-induced pulmonary dysbiosis and microglial activation). Microglial reactivity/activation. Since oral bacteria form the lung microbiome and pulmonary microbes influence microglia, transiently adjusting the oral community via diet could functionally 'program' the aspirations that reach the lung, thereby pre-empting or attenuating neuroinflammatory signaling without relying on systemic colonic metabolic feedback. 📊 Contradictions Between Evidences There is no direct conflict in the evidence; the claim is simply a novel synthesis of disparate fields (oral-lung seeding and pulmonary-induced neuroinflammation) that has not been explicitly tested or confirmed in the provided literature. 📊 Repurposed Solutions The tann","url":"https://doi.org/10.5281/zenodo.21996431","authors":["Dungan, Joshua"],"tags":["Dietary modification","_gates_from_dietary_modification","Dysbiosis","_gates_to_dysbiosis","_gates_from_dysbiosis","Respiratory Aspiration","_gates_to_respiratory_aspiration","_gates_from_respiratory_aspiration"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21996431","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17632/wndbd5r26y.5","name":"A Primary Chest X-ray Dataset of Normal Bangladesh","source":"datacite","abstract":"📌 Steps to Reproduce This dataset contains a collection of primary chest X-ray images acquired from Epic Chittagong, Bangladesh. The dataset is designed for the study and development of deep learning and machine learning models for pneumonia detection and classification. This dataset contains 3,355 primary chest X-ray images collected from Epic Chittagong, Bangladesh, categorized into two classes: (1) Normal (2) Pneumonia 📊 Dataset Composition --------------------------------- Training Data : =&gt; Normal: 321 images =&gt; Pneumonia: 321 images =&gt; Total Training Samples: 642 Testing Data : ------------------- Normal: 1,363 images =&gt; Pneumonia: 1,350 images =&gt; Total Testing Samples: 2,713 👉 Grand Total: 3,355 X-ray images 📂 Folder Structure : ------------------------- /Chest_Xray_EpicChittagong_Dataset/ ├── train/ │ ├── Normal/ │ └── Pneumonia/ ├── test/ │ ├── Normal/ │ └── Pneumonia/ 📷 Image Details : ------------------------ Format: JPEG / PNG Modality: Chest X-ray (CXR) Color: Grayscale Source: Epic Chittagong, Bangladesh 2025 Status: Primary dataset (raw and unprocessed) 🧪 Applications : --------------------- =&gt; Pneumonia vs. Normal chest X-ray classification =&gt; Deep learning model training (CNN, transfer learning) =&gt; Benchmarking medical imaging algorithms =&gt; Computer-aided diagnosis (CAD) =&gt; Radiology research and teaching 📬 Contact : ------------------ For questions or collaboration Email: hiramdirfanulkabir@gmail.com 🎓 Department of Computer Science and Engineering 🏛️ Institutions : -------------------- Epic Chittagong, Bangladesh National Institute of Textile Engineering and Research University of Dhaka 📚 Categories : ---------------------- Computer Science, Radiology, Health Sciences, Artificial Intelligence, Computer Vision, Medical Imaging, Pneumonia, Chest X-ray, Deep Learning, Machine Learning","url":"https://doi.org/10.17632/wndbd5r26y.5","authors":["HIRA, MD IRFANUL KABIR","BITHEE, MST MORIOM AKTER","Ahmed, Shafee","Akter, Laboni","Anonna, Mst.Jannatul Mawa"],"tags":["Computer Science","Radiology","Health Sciences","Artificial Intelligence","Computer Vision","Medical Imaging","Machine Learning","Pneumonia"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/wndbd5r26y.5","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.17632/wndbd5r26y","name":"A Primary Chest X-ray Dataset of Normal Bangladesh","source":"datacite","abstract":"📌 Steps to Reproduce This dataset contains a collection of primary chest X-ray images acquired from Epic Chittagong, Bangladesh. The dataset is designed for the study and development of deep learning and machine learning models for pneumonia detection and classification. This dataset contains 3,355 primary chest X-ray images collected from Epic Chittagong, Bangladesh, categorized into two classes: (1) Normal (2) Pneumonia 📊 Dataset Composition --------------------------------- Training Data : =&gt; Normal: 321 images =&gt; Pneumonia: 321 images =&gt; Total Training Samples: 642 Testing Data : ------------------- Normal: 1,363 images =&gt; Pneumonia: 1,350 images =&gt; Total Testing Samples: 2,713 👉 Grand Total: 3,355 X-ray images 📂 Folder Structure : ------------------------- /Chest_Xray_EpicChittagong_Dataset/ ├── train/ │ ├── Normal/ │ └── Pneumonia/ ├── test/ │ ├── Normal/ │ └── Pneumonia/ 📷 Image Details : ------------------------ Format: JPEG / PNG Modality: Chest X-ray (CXR) Color: Grayscale Source: Epic Chittagong, Bangladesh 2025 Status: Primary dataset (raw and unprocessed) 🧪 Applications : --------------------- =&gt; Pneumonia vs. Normal chest X-ray classification =&gt; Deep learning model training (CNN, transfer learning) =&gt; Benchmarking medical imaging algorithms =&gt; Computer-aided diagnosis (CAD) =&gt; Radiology research and teaching 📬 Contact : ------------------ For questions or collaboration Email: hiramdirfanulkabir@gmail.com 🎓 Department of Computer Science and Engineering 🏛️ Institutions : -------------------- Epic Chittagong, Bangladesh National Institute of Textile Engineering and Research University of Dhaka 📚 Categories : ---------------------- Computer Science, Radiology, Health Sciences, Artificial Intelligence, Computer Vision, Medical Imaging, Pneumonia, Chest X-ray, Deep Learning, Machine Learning","url":"https://doi.org/10.17632/wndbd5r26y","authors":["HIRA, MD IRFANUL KABIR","BITHEE, MST MORIOM AKTER","Ahmed, Shafee","Akter, Laboni","Anonna, Mst.Jannatul Mawa"],"tags":["Computer Science","Radiology","Health Sciences","Artificial Intelligence","Computer Vision","Medical Imaging","Machine Learning","Pneumonia"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/wndbd5r26y","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:49.188Z"},{"id":"doi:10.21203/rs.3.rs-9907499/v1","name":"Conditional readiness for AI-assisted pathology education: a cross-sectional survey of students’ expectations, concerns, and preferences","source":"europepmc","abstract":"Abstract Background This study examined which aspects of pathology learning medical students most expected artificial intelligence to support, and explored their concerns and preferences regarding teacher-guided or artificial intelligence-led implementation. Methods An exploratory cross-sectional survey was conducted among undergraduate medical students at Xiangya School of Medicine, Central South University, who had completed a pathology course. Anonymous data were collected between April and May 2026. The questionnaire collected demographic information, satisfaction with current pathology teaching, self-reported use of artificial intelligence for learning, responses to 19 five-point Likert-type items, two multiple-response questions, and three open-ended questions. Descriptive statistics, internal consistency analysis, and Spearman correlation analysis were performed. Results A total of 100 valid questionnaires were included. The artificial intelligence-readiness scale showed high internal consistency, with a Cronbach’s alpha of 0.897, and the mean readiness score was 4.22 ± 0.62. Students’ expectations were directed less toward a general chatbot than toward pathology-specific learning support. The most frequently selected functions were identifying microscopic slide structures (91.0%), explaining disease mechanisms (76.0%), retrieving typical case images (72.0%), generating personalized exercises and review outlines (68.0%), and simulating clinicopathological diagnostic dialogue (51.0%). Most students were willing to try a school-recommended pathology-specific artificial intelligence platform (92.0%), use artificial intelligence to query unfamiliar structures during laboratory sessions (90.0%), and receive simulated pathology cases (87.0%). However, this readiness was conditional. Students reported concerns about technological immaturity and inaccurate answers (88.0%), operational complexity or additional time burden (56.0%), privacy and data security (42.0%), and reduced teacher–student interaction (38.0%). Satisfaction with current pathology teaching was positively correlated with artificial intelligence readiness (ρ = 0.525, p","url":"https://doi.org/10.21203/rs.3.rs-9907499/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9907499/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10475541/v1","name":"Consensus on delivery, divergence on content: physicians' artificial intelligence training needs in continuing medical education","source":"europepmc","abstract":"Abstract Artificial intelligence (AI) is entering clinical practice faster than physicians are trained, and Article 4 of the EU AI Act obliges deployers to ensure staff AI literacy. Following Kern's first two curriculum development steps, we surveyed 362 physicians in Germany on AI use, training experience, and continuing medical education (CME) preferences. Although 53.0% used AI professionally, only 17.1% had received AI-specific training. Use was associated with structural rather than individual characteristics, notably employer provision (OR 11.4); even in digitally mature environments, employers provided AI applications but rarely training. Preferences converged on delivery: short, online-first, application-oriented formats with a shared introductory foundation. Content diverged: users and interested non-users shared applied interests, with specialised training distinguishing users, whereas non-users without current intention prioritised ethical and legal frameworks and rejected commercial providers. We derive a tiered CME curriculum: a shared foundation with adoption-based tiers, delivered workplace-independently by chambers of physicians.","url":"https://doi.org/10.21203/rs.3.rs-10475541/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10475541/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10606670/v1","name":"Design and evaluation of a mobile app for artificial intelligence education among healthcare students: A before-and-after study","source":"europepmc","abstract":"Abstract Background Healthcare students increasingly encounter artificial intelligence (AI), yet many lack structured preparation to understand, evaluate, and use it responsibly. This study developed AIMedEdu, a Persian-language mobile application based on a previously validated content and structural framework, and evaluated changes in perceived medical AI readiness and app usability. Methods A single-group before-and-after study was conducted among healthcare students at Mashhad University of Medical Sciences, Iran, from May to July 2026. AIMedEdu was developed as an 11-module mobile app based on a previously validated content and structural framework. Before student implementation, five multidisciplinary experts completed two iterative review rounds and a pre-release SUS assessment, and the app was refined based on their feedback. Participants then used the app independently for 30 days. Perceived medical AI readiness was assessed before and after the intervention using the validated Persian MAIRS-MS, and student usability was evaluated after the intervention using the Persian SUS. Paired changes were analyzed using Wilcoxon signed-rank tests, with Holm adjustment for domain-level comparisons. Results Ninety-seven students completed both assessments. The mean total MAIRS-MS score increased from 45.25±6.49 at baseline to 107.70±5.75 after the intervention, representing a mean increase of 62.45±8.82 points. The total score (p Conclusions AIMedEdu was associated with substantial short-term improvements in healthcare students’ perceived medical AI readiness and excellent usability. Controlled multicenter studies incorporating objective competency measures and longer-term follow-up are needed to establish educational effectiveness, retention, and generalizability.","url":"https://doi.org/10.21203/rs.3.rs-10606670/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10606670/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9823036/v1","name":"The Effectiveness of Blended Learning Artificial Intelligence on Increasing Literacy of Midwifery Students at Mashhad University of Medical Sciences: A Semi-experimental Study","source":"preprints","abstract":"Abstract Background Artificial Intelligence (AI) is increasingly shaping healthcare by enhancing clinical decision-making, medical data analysis, and patient management. However, AI literacy remains limited among midwifery students due to the absence of AI-focused courses in their curricula. Blended learning, combining in-person instruction with online and interactive methods, is a promising approach to improving AI education. This study aimed to evaluate the effectiveness of a blended learning course on AI literacy among midwifery students at Mashhad University of Medical Sciences. Methods A semi-experimental one-group pretest-posttest design was conducted among midwifery students. The intervention included two to three blended learning sessions covering AI fundamentals, machine learning, supervised and unsupervised learning, and AI applications in midwifery. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and the Students’ Attitude toward Artificial Intelligence (SATAI) tool were used to assess AI knowledge and attitudes before and after the intervention. Course effectiveness was evaluated using a structured Course Evaluation Questionnaire. Data were analyzed using paired t-tests and Wilcoxon signed-rank tests. Results A significant improvement was observed in students’ AI knowledge (Pre-test: 12.5 ± 3.2, Post-test: 18.9 ± 2.8, p","url":"https://doi.org/10.21203/rs.3.rs-9823036/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9823036/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10343485/v1","name":"Explainable Artificial Intelligence (XAI) in Medical Education: A Multi- Modal Framework for Enhancing Human-AI Collaboration","source":"europepmc","abstract":"Abstract Background While Artificial Intelligence (AI) rapidly advances clinical diagnostic accuracy, the black-box nature of complex algorithms presents critical barriers to pedagogical integration in medical curricula. This study evaluated the efficacy of Explainable AI (XAI) as an interactive pedagogical intervention designed to enhance clinical reasoning and model interpretability during AI-assisted diagnostic training. Methods A prospective, mixed-methods randomized controlled trial was conducted with 120 third-year medical students. Participants were randomized to either a standard AI instruction group (n = 60) or an XAI-enhanced group (n = 60) utilizing the CerViD-MultiModal framework, which integrated dynamic SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) visualizations into neuroimaging case analyses. Standardized psychometric instruments evaluated AI Literacy (0-100), System Usability Scale (SUS, 0-100), NASA Task Load Index (NASA-TLX, 0-100), and Confidence in AI Interpretation (1–5). Results The XAI-enhanced group demonstrated statistically significant improvements across all primary outcomes compared to the control group. AI Literacy scores increased markedly by 34.1% (85.0 ± 5.6 vs. 63.4 ± 6.8, p","url":"https://doi.org/10.21203/rs.3.rs-10343485/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10343485/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.20.745910","name":"Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches","source":"preprints","abstract":"This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a Kaggle dataset. The study uses Java-based ML software to create and evaluate multiple predictive models, taking advantage of its powerful libraries and frameworks for processing and analyzing cancer risk indicators. This work analyzes model performance using 10-fold cross-validation, resulting in reliable generalization and accuracy estimates. Several classification techniques, such as Random Forest (RF), logistic regression (LR), decision trees (DT), Naive Bayes (NB), and Multi-layer perceptron (MLP), are used to assess their efficacy in predicting risk levels for various cancer types. To measure classification effectiveness, key performance metrics such as accuracy, precision, recall, and F1 score are produced, in addition to multi-class confusion matrices. The results show that the RF model is the best classifier for classification, with accuracy of 99.85%, F-measure of 99.80%, precision of 99.80%, and sensitivity of 99.90%. These findings demonstrate the model’s ability to effectively estimate cancer risk levels among individuals, allowing for earlier discovery and more effective medical care.","url":"https://doi.org/10.64898/2026.08.20.745910","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.20.745910","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.12688/f1000research.179775.2","name":"Artificial intelligence tools for automating assessments of reporting guideline adherence: a protocol for a systematic review","source":"europepmc","abstract":"Background: Complete reporting of health-related research is necessary for users to understand, appraise, and apply research results appropriately. Reporting guidelines have been developed to support complete reporting. However, assessments of reporting guideline adherence remain inconsistent, time-consuming, and difficult to scale. Artificial intelligence (AI) tools, such as traditional natural language processing models and large language models, might provide a potential solution. While numerous AI tools have been developed, no comprehensive synthesis has been undertaken to investigate what they assess, how they are implemented and perform, and their potential utility. Objective This systematic review aims to synthesise the characteristics and findings of studies evaluating AI tools developed to assist or automate assessments of reporting guideline adherence. Methods We will search MEDLINE, Embase, Scopus, Europe PMC, ACM Digital Library, IEEE Xplore, arXiv and Cochrane Colloquium Abstracts, with no restrictions on date, language, or publication type. We will include studies that evaluate AI tools to assess adherence of health-related papers to any reporting guidelines. Two authors will independently screen records, extract data and assess risk of bias. We will extract study characteristics, AI tool details, how reporting guidelines are operationalised for AI assessment, AI implementation details, comparison details, and evaluation outcomes including agreement metrics, classification performance metrics, utility indicators, and tool reliability. We will present and summarise results through structured tables and plots, stratified by reporting guideline and AI tool type. Discussion This systematic review will provide a comprehensive synthesis of AI tools developed to automate assessments of reporting guideline adherence. It will provide interest holders with insights into what AI tools have been used, their implementation approaches, which AI tool types perform well, and any improvements that can be made to AI tools automating assessments of reporting guideline adherence in the future.","url":"https://doi.org/10.12688/f1000research.179775.2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.179775.2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.21.26361067","name":"Multimodal artificial intelligence for personalized hepatocellular carcinoma treatment strategy selection","source":"preprints","abstract":"Background Hepatocellular carcinoma (HCC) treatment selection demands nuanced integration of heterogeneous patient data, yet prevailing predictive models rely on restricted data modalities and oversimplified therapeutic frameworks, compromising clinical translation. Objective We developed and validated a multimodal artificial intelligence framework to guide optimal treatment strategy selection across the full spectrum of HCC interventions. Methods This retrospective study comprised 1,043 HCC patients (development cohort, January 2017–December 2023) and 55 external validation patients (2023) from Wuxi People’s Hospital. We engineered Embedding-Augmented Extra Trees (ET-Emb), a novel model fusing structured clinical variables with contextual text embeddings derived from medical histories and radiology reports. ET-Emb quantifies probabilities for five primary treatments: open/laparoscopic resection, transarterial chemoembolization, radiofrequency ablation (RFA), and chemotherapy. Model performance was rigorously assessed via 10-fold cross-validation and external validation using ROC-AUC and PR-AUC metrics. Results ET-Emb demonstrated robust performance in the development cohort (ROC-AUC: 0.84 ± 0.04; PR-AUC: 0.55 ± 0.06), significantly outperforming established benchmarks. This generalizability was preserved in external validation (ROC-AUC: 0.77 ± 0.02; PR-AUC: 0.47 ± 0.03). SHAP analysis identified textual clinical narratives and socioeconomic determinants as critical predictive drivers. Conclusions By unifying structured and unstructured data modalities, ET-Emb delivers accurate, multi-treatment strategy prediction for HCC. Its clinical validity and the demonstrated significance of textual features establish multimodal AI as an essential paradigm for simulating complex oncological decision-making, positioning ET-Emb as a transformative tool for precision HCC management.","url":"https://doi.org/10.64898/2026.08.21.26361067","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.21.26361067","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10521656/v1","name":"Quality and Readability of Artificial Intelligence Chatbot Responses to Patient Questions About Exocrine Pancreatic Insufficiency: A Comparative Study","source":"preprints","abstract":"Abstract Objective To compare the information quality, source transparency, educational value, and readability of responses generated by four widely used artificial intelligence chatbots to patient questions about EPI and determine whether the content met the sixth-grade reading level recommended for patient education materials. Methods A web-based cross-sectional comparative design was used. Terms of global interest over the previous 5 years were identified through Medical Subject Headings and Google Trends. After deduplication and assessment of applicability, 20 core questions were developed. Each question was entered into ChatGPT 5.6, Copilot, Gemini 3.6 Flash, and Perplexity using a standardized procedure. Response quality was assessed with DISCERN, EQIP, the JAMA benchmarks, and the Global Quality Score (GQS). Readability was assessed with the Automated Readability Index (ARI), Gunning Fog Index (GFI), Flesch-Kincaid Grade Level (FKGL), Coleman-Liau Index, Simple Measure of Gobbledygook (SMOG), and Flesch Reading Ease Score (FRES). Quality scores are presented as medians and interquartile ranges. Models were compared with the Kruskal-Wallis test, and ε² was reported as the effect size. Readability results were compared with sixth-grade thresholds. Results A total of 80 responses were obtained. No overall differences among the four models were statistically significant for DISCERN, EQIP, JAMA, or GQS scores, with P values of 0.187, 0.529, 0.392, and 0.467, respectively. Median DISCERN scores ranged from 35.00 to 38.50, indicating poor treatment information quality or scores near the boundary between poor and fair quality. Median EQIP scores ranged from 52.50 to 55.00, indicating good quality with minor problems. The median GQS was 4.00 for all models, indicating favorable organization and usefulness. The median JAMA score was 0 for all models, as source attribution, authorship information, disclosures, and update dates were generally absent. Median values for all grade-level readability measures were substantially higher than 6, and median FRES values ranged from 13.04 to 30.76, all below 80. Perplexity and Copilot were easier to read on most measures, whereas Gemini had the greatest linguistic complexity. None of the four models met the recommended reading level. Conclusions The four artificial intelligence chatbots generated well-structured and broadly usable information about EPI. Treatment information quality and source transparency remained limited, and linguistic complexity substantially exceeded the recommended level for general patient education materials. These responses are currently best used as supplementary information after professional verification. Developers should provide authoritative citations, indicate the currency of guidelines, generate plain-language content, and adapt responses to patients' health literacy.","url":"https://doi.org/10.21203/rs.3.rs-10521656/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10521656/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10297530/v1","name":"A Retrospective Study of the Role of Artificial Intelligence in Healthcare (Diagnosis and Treatment)","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly integrated into modern healthcare, serving as a critical clinical supportive tool. However, geographical and linguistic variations necessitate local independent validation of these digital diagnostic applications, particularly within the Middle East. This study aimed to evaluate the diagnostic and therapeutic efficacy of intelligent symptom checkers against established physician clinical diagnoses. Methods This retrospective, cross-sectional study analyzed 100 diverse clinical cases collected from five community pharmacies in Damascus, Syria. Patient-reported symptoms and clinical histories were systematically evaluated using two AI applications in the local healthcare contex: Ada (\"Check your Health\") and Symptomate. Software-generated diagnostic outputs and therapeutic recommendations were recorded and statistically compared against the physician's prescription (the gold standard) using Cohen’s Kappa, Chi-Square, and McNemar’s tests. Results Ada demonstrated superior diagnostic accuracy with an 82% absolute match and 17% correlation rate compared to Symptomate (68% match, 25% correlation). For therapeutic regimens, Ada showed a higher concordance rate (49%) than Symptomate (37%). Inferential statistics confirmed a significant difference in diagnostic proficiency (McNemar /X^2 = 5.14, P = 0.023) and treatment profiles (X^2 = 7.56, P = 0.0059). Symptomate exhibited a higher risk-averse threshold, deferring to clinical consultation in 53% of cases compared to Ada's 43%. Conclusions Both applications demonstrate robust alignment with human clinical reasoning, confirming their value as clinical support tools. Ada proved more algorithmically consistent across medical specialties, while both tools appropriately emphasize physician reliance for complex and emergency clinical triage.","url":"https://doi.org/10.21203/rs.3.rs-10297530/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10297530/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9348464/v1","name":"Competency frameworks for digital literacy and artificial intelligence in medical and health professions education: protocol for a scoping review","source":"europepmc","abstract":"Abstract Background Digital transformation of health systems has intensified calls to strengthen digital health competencies across the health workforce. In medical and health professions education, students and faculty are increasingly expected to engage critically with digital technologies, including artificial intelligence, rather than treating these skills as optional or purely technical. Although previous competency models and scoping reviews have highlighted the need for structured digital health and artificial intelligence education, no comprehensive synthesis has focused on competency frameworks in digital literacy and artificial intelligence for both students and faculty across health professions education. This scoping review aims to map and characterise such frameworks. Methods This scoping review will follow the Joanna Briggs Institute methodology. Searches will be conducted in PubMed, Scopus, CINAHL and Web of Science, as well as grey literature sources. Three reviewers will independently screen records using predefined inclusion and exclusion criteria and will independently extract data using a piloted charting form. Disagreements during screening and extraction will be resolved by consensus. Extracted data will be synthesised in tables and accompanied by a narrative summary. Discussion This scoping review will map competency frameworks in digital literacy (DL) and artificial intelligence (AI) across medical and health professions education. It will provide decision-ready evidence for curriculum design, faculty development, and policy alignment by clarifying convergences, divergences, and gaps to guide targeted interventions. Findings will inform subsequent consensus on a minimal DL/AI core (or adaptation of established competency standards), validation linking competencies to assessments and outcomes, and context-sensitive adaptation with implementation research on feasibility and impact. Registration Open Science Framework registration: https://osf.io/nkqat.","url":"https://doi.org/10.21203/rs.3.rs-9348464/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9348464/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.06.19.26356088","name":"Artificial Intelligence-Enabled Cardiac Function Estimation from Phone Videos of Echocardiograms","source":"preprints","abstract":"Importance Mobile phone-recorded echocardiogram videos are commonly used in point-of-care, telemedicine, and resource-limited workflows, but artificial intelligence models for left ventricular ejection fraction (LVEF) estimation have primarily been evaluated on native Digital Imaging and Communications in Medicine (DICOM) videos. Objective To evaluate whether previously described artificial intelligence models for LVEF estimation retain performance when applied to mobile phone-recorded echocardiographic videos. Design Multicenter model validation study comparing model-estimated LVEF with clinician-reported LVEF. Setting Three medical centers: Kaiser Permanente Northern California, Beth Israel Deaconess Medical Center through MIMIC-IV-ECHO, and Cedars-Sinai Medical Center. Participants Source studies with clinician-reported LVEF and apical 4-chamber or apical 2-chamber views, yielding 6209 phone-recorded videos from 2648 studies and 2611 patients. Exposures Mobile phone recording of native echocardiographic videos and fine-tuning of pretrained models using mobile phone-recorded videos from the Kaiser Permanente Northern California training cohort. Main Outcomes and Measures Mean absolute error in ejection fraction percentage points, R² for continuous estimation, and area under the receiver operating characteristic curve for identifying ejection fraction greater than 50%. Results The study included 6209 mobile phone-recorded echocardiographic videos from 2648 studies and 2611 patients; the weighted mean age was 68.4 years, and 1031 patients were male (39.5%). Without phone-video fine-tuning, the primary model achieved a mean absolute error of 7.00 percentage points, coefficient of determination of 0.49, and area under the receiver operating characteristic curve of 0.91 on phone-recorded videos; corresponding native DICOM performance was 6.08 percentage points, 0.60, and 0.93, respectively. On the 2396-video fine-tuning evaluation cohort, fine-tuning improved primary model performance to a mean absolute error of 6.96 percentage points, coefficient of determination of 0.61, and area under the receiver operating characteristic curve of 0.93. Fine-tuning the public EchoNet-Dynamic model improved performance from 9.36 percentage points, 0.37, and 0.84 to 7.86 percentage points, 0.50, and 0.89, respectively. Progressive central zoom preprocessing degraded model performance. Conclusions and Relevance These findings suggest that artificial intelligence–assisted left ventricular ejection fraction estimation from mobile phone-recorded echocardiograms may be feasible when native image export is unavailable, although prospective evaluation is needed before clinical deployment. Key Points Question: Can artificial intelligence models developed for native echocardiographic video formats estimate left ventricular ejection fraction from mobile phone-recorded echocardiogram videos? Findings: In this multicenter retrospective model-validation study of 6209 phone-recorded echocardiographic videos from 2648 studies, the primary pretrained model achieved a mean absolute error of 7.00 percentage points and area under the curve of 0.91 for identifying ejection fraction greater than 50%; phone-video fine-tuning improved performance and digital zoom degraded performance. Meaning: Artificial intelligence-assisted ejection fraction estimation from phone-recorded echocardiograms may support point-of-care and telemedicine workflows when native image export is unavailable.","url":"https://doi.org/10.64898/2026.06.19.26356088","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.19.26356088","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10065453/v1","name":"Generative artificial intelligence in medical postgraduates: a qualitative study of perceived risks, benefits, and responsible use","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (GenAI) tools such as ChatGPT and DeepSeek are increasingly used in medical education, yet how medical graduate students perceive the risks, benefits, and responsible use of these tools in academic research remains underexplored, particularly in non‑Western contexts. Methods This descriptive qualitative study conducted face‑to‑face, semi‑structured interviews with 18 first‑year master’s students from five clinical specialties at a medical university in Shandong Province, China. Participants had all used GenAI for academic tasks at least once. Interviews were audio‑recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six‑phase thematic analysis framework. Results Four main themes and sixteen subthemes emerged. Participants recognized substantial benefits: accelerated literature retrieval, data processing, and manuscript polishing; epistemic breakthroughs (e.g., medical image recognition, proteomic analysis, early disease prediction); and expanded research ideas. However, they expressed deep concerns, most prominently cognitive atrophy and decreased creativity (reported by 16/18 participants), factual hallucinations, patient data leakage, responsibility ambiguity, and research homogenization. Despite these risks, students did not advocate banning GenAI but instead articulated conditional acceptance with clear boundaries: acceptable uses included English polishing and code checking; unacceptable uses included generating patient diagnoses, handling identifiable clinical data, and formulating original hypotheses. Students also specified desired features of a responsible AI assistant: evidence‑based support, privacy compliance (HIPAA/GDPR‑like), accuracy and interpretability, a clear assistive role, fairness, and continuous learning. Conclusion Medical graduate students view GenAI as a double‑edged tool that offers substantial academic efficiency and epistemic gains but also poses serious risks to cognitive autonomy, accountability, and innovation. They develop mature boundary‑setting strategies and call for responsible AI designs aligned with evidence‑based medicine and ethical standards. Educators and policymakers should integrate students’ voices into curriculum and guideline development to support safe and effective GenAI use in medical graduate education.","url":"https://doi.org/10.21203/rs.3.rs-10065453/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10065453/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202607.1803.v1","name":"Artificial Intelligence in Radiology: Why Retrospective Performance Does Not Ensure Clinical Utility","source":"preprints","abstract":"Purpose: Artificial intelligence is increasingly developed and deployed to support radiological image interpretation, triage, segmentation and workflow prioritisation. However, high performance reported in retrospective studies does not necessarily translate into safe and useful deployment in routine practice. This narrative review examines why this translational gap persists and what radiology departments, researchers and developers should address before clinical implementation. Methods: We synthesised methodological, empirical and regulatory literature on the development, validation, deployment and governance of radiology AI. Radiology was used as the primary setting, with adjacent imaging fields discussed only where they illustrate broader mechanisms relevant to medical image analysis. Results: Recurrent barriers include limited data quality and representativeness, scanner- and protocol-related domain shift, shortcut learning, hidden stratification, insufficient external and prospective validation, poor calibration, over-reliance on global metrics and weak workflow integration. Model performance can be shaped by scanner protocols, reconstruction methods, local reporting practices, patient selection and institutional workflows beyond the underlying pathology. In Europe, GDPR-based data governance, the Medical Device Regulation, the AI Act and the European Health Data Space further shape clinical deployment. Conclusions: Emerging approaches, including foundation models, generative AI, multimodal systems, federated learning and local adaptation, may address selected technical constraints, but they introduce new uncertainties and do not replace independent validation in the intended clinical setting. Radiology AI should be evaluated not only as an algorithmic model, but as a clinical decision-support component embedded in radiology practice, requiring external and preferably prospective validation, subgroup analysis, calibration assessment, human oversight, workflow integration and post-deployment monitoring.","url":"https://doi.org/10.20944/preprints202607.1803.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.1803.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9907255/v1","name":"Knowledge Attitudes and Practices Regarding Artificial Intelligence in Healthcare Among Medical Undergraduates at a Private Medical Institute in Delhi NCR","source":"preprints","abstract":"Abstract Background Artificial Intelligence (AI) is increasingly transforming healthcare through its applications in diagnostics, clinical decision making, medical imaging and patient management. Despite the growing relevance of AI in healthcare, structured AI training within the undergraduate medical curriculum remains limited in India. Understanding medical students’ perspectives regarding AI is essential to identify educational gaps and assess preparedness for future AI-enabled healthcare systems. Therefore, the present study was conducted to assess the knowledge, attitudes and practices regarding AI in healthcare among undergraduate medical students and to identify key motivators and perceived barriers influencing AI use. Methodology A cross-sectional study was conducted among undergraduate MBBS students at the Faculty of Medicine and Health Sciences, SGT University, Gurugram, Haryana. A census sampling approach was employed and data was collected using a pre-validated, semi-structured questionnaire administered through Google Forms. The tool included sections on socio-demographic characteristics, KAP regarding AI in healthcare along with perceived motivators and barriers to AI use. Data was analysed using SPSS version 30.0. Descriptive statistics, Chi-square test and Spearman’s correlation analysis were applied. A p-value &lt; 0.05 was considered statistically significant. Results A total of 365 undergraduate medical students participated in the study. The mean knowledge, attitude and practice scores were 9.22 ± 2.23, 24.45 ± 3.05 and 7.44 ± 2.13 respectively. Most participants demonstrated moderate levels of knowledge (60.3%), predominantly neutral attitudes (60.0%) and moderate practice levels (47.4%) regarding AI in healthcare. ChatGPT emerged as the most commonly used AI platform (83.3%) while majority (67.1%) of students reported using AI-based tools for academic or clinical-related purposes. Students with prior exposure to AI demonstrated significantly better knowledge, more favourable attitudes and higher practice levels compared to those without prior exposure (p &lt; 0.05). Significant positive correlations were observed between knowledge, attitude and practice domains. The major motivators for AI use included time-saving (71.2%) and academic curiosity (66.6%) whereas ethical concerns (37.5%), technical difficulties (30.1%) and lack of awareness (29.9%) were identified as the key barriers to AI utilization. Conclusion The study highlights a growing interest and engagement of undergraduate medical students with AI. However, significant gaps still remain in their conceptual understanding, practical competency and formal training. These findings emphasize the need for structured curricular initiatives incorporating AI literacy, practical exposure and ethical aspects of AI to prepare future physicians for evolving technology-driven healthcare environments.","url":"https://doi.org/10.21203/rs.3.rs-9907255/v1","authors":["GEETIKA SINGH","Tanya Khanna","Riya Malhotra"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9907255/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.23.26361157","name":"Emotional intelligence and the perception of good leadership in healthcare: a mixed-methods study","source":"preprints","abstract":"Objective To identify and rank the leadership traits most valued by medical staff in public hospitals, and to compare them with an established generic instrument and a generative artificial intelligence (AI) source. Design Sequential exploratory qualitative-quantitative (QUAL-QUAN) mixed-methods study: focus groups followed by an online ranking survey, with cross-comparison against the Northouse Leadership Traits Questionnaire (LTQ) and a ChatGPT-derived list (LAIT). Setting Major public hospital affiliated with Monash University, Melbourne, Australia, in 2023. Participants Twenty-four senior medical staff (16 men, 8 women; 18 clinicians, 6 administrators) recruited through opportunistic sampling. Main outcome measures Weighted ranking of the ten most desired leadership traits (Leadership Enabling Traits Survey, LETS); internal consistency (Cronbach’s α); agreement between LETS and LTQ self-scores; and strong overlap with the AI-derived list. Results The first most-weighted LETS traits were integrity (1.526), communication (1.435), compelling vision (1.404), emotional intelligence (1.040) and empathy (0.969) – the same five identified by the AI source. Integrity was weighted 3.6 times more heavily than rebelliousness (0.424). Both LETS and LTQ achieved Cronbach’s α >0.7. Unweighted total self-scores did not differ between LETS (77.4±7.6) and LTQ (78.4±6.9); weighted emotional intelligence related and other-trait sub-scores diverged significantly (p Summary Box What this paper adds What is already known on this topic Emotional intelligence (EI) is consistently identified as an important attribute of effective healthcare leaders, and a range of generic leadership instruments are available (e.g. Northouse’s LTQ). Leadership-selection criteria in healthcare have historically been derived top-down from organisational, regulatory or academic frameworks rather than from the views of the clinicians who must follow those leaders. Existing instruments treat candidate leadership traits as equally weighted, even though clinical leaders intuitively rank some attributes (e.g. integrity) far above others. What this study adds 10-item, follower-derived, weighted Leadership Enabling Traits Survey (LETS) instrument was developed from focus groups with senior medical staff and ranked by survey, providing an empirically-weighted alternative to existing equally-weighted tools. top five LETS traits (integrity, communication, vision, emotional intelligence, empathy) matched the unprompted top five traits generated by a contemporary large-language model (LAIT), suggesting that EI-related attributes are a stable feature of leadership representations in both human and machine-generated sources. empirically-derived weights revealed material divergence between context-specific (LETS) and generic (LTQ) instruments when EI-related and non-EI traits were considered separately, supporting the case for situation-specific leadership tools rather than universal scoring.","url":"https://doi.org/10.64898/2026.08.23.26361157","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.23.26361157","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10338870/v1","name":"Knowledge-grounded artificial intelligence guides personalized practice in medical examination preparation","source":"preprints","abstract":"Abstract Medical examination preparation requires guidance on what to practice next across a structured curriculum, not only explanations of why answers are correct. We developed a knowledge-grounded practice system in which a large language model assisted pre-deployment question–knowledge mapping, senior clinician-educators validated the resulting graph, and predefined rules balanced remediation of weak knowledge areas with high-yield coverage. In a prospective stratified randomized educational evaluation, 500 medical undergraduates preparing for the Western Medicine Comprehensive examination were assigned to knowledge-grounded personalized practice or curriculum-balanced shuffled practice; 370 completed the 750-question training phase and independent 150-question post-test. The intervention increased remediation-targeted practice (49.6% vs 24.8%; adjusted difference, 24.8 percentage points; 95% CI, 23.4–26.2) and improved post-test accuracy (70.2% vs 65.1%; adjusted difference, 4.9 percentage points; 95% CI, 2.8–7.0). These findings suggest that expert-validated, knowledge-grounded systems can support personalized practice navigation in medical examination preparation.","url":"https://doi.org/10.21203/rs.3.rs-10338870/v1","authors":["Lizong Deng","Luming Chen","Mi Liu","Jin Xu"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10338870/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10020494/v1","name":"The Academic Use of Artificial Intelligence Tools Among First-Year Medical Students: A Cross-Sectional Online Survey","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) tools are increasingly used in medical education; however, evidence on how first-year medical students use these tools for academic purposes remains limited, particularly in Türkiye. This study aimed to evaluate the academic use patterns of AI tools and students’ perceptions regarding their educational benefits, limitations, and appropriate use. Methods This descriptive cross-sectional online survey was conducted among first-year medical students at Recep Tayyip Erdoğan University Faculty of Medicine. The questionnaire assessed sociodemographic characteristics, daily internet use, AI tools used, frequency and purposes of AI use, access devices, and students’ attitudes toward AI in academic learning. Descriptive statistics were used to summarize the data. Results A total of 78 students participated in the study; the mean age was 19.24 ± 1.22 years, and 52.6% were male. All participants reported using AI tools. ChatGPT was the most frequently used application (62.8%), followed by Google Gemini (35.9%). Most students used AI tools several times per week or daily. The primary purpose of AI use was studying course content (61.5%), and smartphones were the most common access device (69.2%). The highest agreement scores were observed for “AI tools save me time” (4.18 ± 0.91) and “AI tools are helpful in understanding difficult subjects” (4.13 ± 0.76). Students also agreed that AI-generated information should be verified and that training on AI use should be provided. Conclusions AI tools are widely used by first-year medical students, mainly for studying and understanding course content. Although students perceive these tools as useful for academic learning, their awareness of the need to verify AI-generated information highlights the importance of integrating AI literacy, critical appraisal, and responsible use into undergraduate medical education.","url":"https://doi.org/10.21203/rs.3.rs-10020494/v1","authors":["Handan Duman"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10020494/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10806083/v1","name":"Applying socio-anthropological approaches to explore the socio-cultural and ethical requirements for AI-assisted obstetric ultrasound in Kenya: reflections and lessons learned","source":"preprints","abstract":"Abstract There is growing recognition that the successful design and deployment of artificial intelligence (AI) depend not only on technical performance but also on understanding the social, cultural, and ethical contexts in which technologies are introduced. Socio-anthropological approaches can complement technical AI research by generating contextual evidence to inform AI design and deployment. This paper presents a methodological reflection on the application of socio-anthropological methods within the Inclusive Artificial Intelligence for Accessible Medical Imaging Across Resource-Limited Settings (AIMIX) project to explore the socio-cultural and ethical dimensions of AI-assisted obstetric ultrasound in Kenya. Using a constructivist grounded theory approach, we conducted 55 semi-structured interviews and three focus group discussions involving 84 participants representing diverse community, religious, healthcare, and governance stakeholders across rural and urban Kenya. We reflect on the methodological approach and lessons learned from its implementation. Our reflections highlight key methodological considerations, including broad stakeholder inclusion, continuous community engagement, context-sensitive participant mobilization, effective communication of complex AI concepts, reflexive practice, and iterative adaptation throughout the research process. These reflections offer practical methodological guidance for integrating socio-anthropological approaches into AI research to support more contextually informed, inclusive, and responsible AI development in low- and middle-income countries (LMICs).","url":"https://doi.org/10.21203/rs.3.rs-10806083/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10806083/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.07.15.26357432","name":"Malaria Pre-screening Technology Using Artificial Intelligence (AI)","source":"preprints","abstract":"Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor’s appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening.","url":"https://doi.org/10.64898/2026.07.15.26357432","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.15.26357432","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.04.26359536","name":"Multimodal artificial intelligence using entire electronic health record and complete pathogen genome data for patient outcome prediction from life-threatening infection: the SuperbugAI Platform","source":"preprints","abstract":"Artificial intelligence (AI) has the potential to transform healthcare, with advanced multimodal approaches showing great promise in leveraging diverse health-related data. Here, we applied multimodal AI to entire electronic health record (EHR) and complete pathogen genome data to predict patient outcomes from life-threatening infection. An automated, scalable pipeline was developed for EHR data preprocessing, quality control, and standardisation. A deep learning fusion model was trained to predict in-hospital mortality, need for ICU admission, prolonged length of stay and 30-day unplanned readmission. We then developed a novel genomic large language model (gLLM) architecture to incorporate bacterial genomic features into the multimodal fusion model. The cohort comprised 2,656 bloodstream infection hospitalisations involving 2,535 patients. Deep learning fusion models using entire structured and unstructured EHR data outperformed traditional APACHE II score mortality prediction (AUROC [95% confidence intervals] 0.93 [0.92-0.94] versus 0.77 [0.77–0.78]). The model also showed strong performance for predicting the need for ICU admission (AUROC 0.978 [0.966 - 0.986]), prolonged hospital length of stay (AUROC 0.803 [0.790 - 0.812]) and unplanned readmission (AUROC 0.696 [0.690 - 0.701]). As proof of principle, incorporating entire microbial genomic features from the causative pathogen further enhanced prediction and enabled identification of key bacterial virulence pathways relevant for human disease. Multimodal AI integrating harmonised EHR and genomic data can accurately identify hospitalised patients at risk of poor outcomes. These approaches are scalable to other subspecialities of medicine.","url":"https://doi.org/10.64898/2026.08.04.26359536","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.04.26359536","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9850491/v1","name":"Artificial Intelligence Medical Assessment Hospital (AIMAH): An AI-Native Virtual Hospital Platform for Clinical Simulation and Medical Education","source":"preprints","abstract":"Abstract Background. Artificial intelligence (AI) is increasingly being integrated into medical education to support clinical reasoning, communication training, and simulation-based learning. However, many existing AI educational systems remain limited by static case structures, low conversational realism, insufficient longitudinal feedback mechanisms, and lack of clinically contextualized interaction. To address these limitations, we developed the Artificial Intelligence Medical Assessment Hospital (AIMAH), a web-based AI-driven virtual hospital platform designed for interactive clinical education through realistic patient simulation, structured assessment workflows, and AI-generated educational feedback. Methods. AIMAH was developed using a modern full-stack architecture consisting of Next.js for the frontend interface, PostgreSQL for schema-validated educational data storage, Vercel AI SDK for orchestration and streaming state management, and GPT-based large language model as the conversational intelligence engine. The platform incorporates an agentic retrieval-augmented generation (RAG) framework to dynamically ground simulated patient responses within structured clinical context and predefined educational constraints. The system architecture includes virtual hospital navigation, specialty-specific wards, AI-simulated patient interviews, structured green-sheet clinical documentation, automated scoring pipelines, and longitudinal learner analytics. A preliminary educational validation study was conducted among medical trainees using a structured Likert-scale questionnaire evaluating realism, usability, educational usefulness, learner engagement, and feedback quality. Ethical approval was obtained from the Ethics Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1404.311). Results. The AIMAH platform successfully implemented a fully interactive AI-powered clinical simulation environment spanning multiple virtual hospital departments, including emergency medicine, pediatrics, obstetrics, internal medicine, rehabilitation, and psychiatry. The platform enabled dynamic conversational interviewing with AI-simulated patients while simultaneously supporting structured clinical documentation and automated formative assessment. Preliminary educational validation demonstrated favorable learner perceptions across evaluated domains, including clinical realism (4.6 ± 0.5), educational usefulness (4.8 ± 0.4), learner engagement (4.8 ± 0.3), AI feedback usefulness (4.7 ± 0.5), and ease of use (4.4 ± 0.6). Participants particularly valued the realism of conversational interaction, immediate AI-generated feedback, and integration of communication training with clinical reasoning workflows. Conclusion. AIMAH represents a novel AI-driven virtual hospital framework for simulation-based medical education that combines high-fidelity conversational patient simulation, structured assessment architecture, and agentic RAG-based contextual grounding within a unified educational platform. Preliminary validation findings suggest that the system may provide an engaging and scalable environment for formative clinical training. Future studies should investigate objective educational outcomes, multi-institutional implementation, and longitudinal effects on clinical competency development.","url":"https://doi.org/10.21203/rs.3.rs-9850491/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9850491/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.04.26359691","name":"Characterizing large language model generative artificial intelligence variability in the production of objective structured clinical examination stations","source":"preprints","abstract":"Background Design: ing high-quality Objective Structured Clinical Examination (OSCE) stations is a time-consuming process. Generative artificial intelligence (AI) represents a promising path to accelerate content creation by automating the generation of scenarios. A growing number of AI tools is now available for this purpose. Objective To assess the variability between generative AI models in their ability to produce OSCE stations in the field of paediatrics. Methods A structured prompt was developed based on the French national OSCE guidelines for medical education. Five distinct AI models were provided with this prompt, alongside the neonatal jaundice chapter from the French pediatric reference textbook, to generate 6 complete OSCE stations. Results: . Prompt compliance was high for ChatGPT 5.1, ChatGPT 5.2, Gemini 3.0 Pro, and Claude Opus 4.5, while it was lower for Grok 4.1. Expert-rated quality was generally high, with few factual errors or missing information across models. However usability differed significantly between models. This was also true for several quality dimensions such as checklist clarity, embedding of checklist answers within vignettes, and ease of standardized patient formation. ChatGPT 5.1 required the most revisions and Gemini most often rated usable as is. Significant inter-model differences were observed in diagnostics, only with ChatGPT 5.1 sampling all three neonatal jaundice categories. Contextual variables showed systematic narrowing across models. Clinical grid density was consistent (10-12 items per station), but thematic distribution differed markedly. Soft skills coverage varied significantly across models (p=0.002), none of them consistently representing all communication competency domains. Conclusion Large language models can generate structurally compliant OSCE stations, but surface compliance conceals substantive inter-model differences in diagnostic coverage, contextual diversity, and soft skills representation, that compromise content validity. No model currently meets the criteria for unsupervised deployment in a summative assessment bank. The choice of model carries pedagogical implications and expert curation remains essential before integration into high-stakes assessment workflows.","url":"https://doi.org/10.64898/2026.08.04.26359691","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.04.26359691","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9944032/v1","name":"Artificial Intelligence Applications in Pituitary Adenoma Management: A Scoping Review","source":"preprints","abstract":"Abstract Background Artificial intelligence has emerged as a promising tool in pituitary adenoma management, yet its applications remain dispersed across clinical and computational disciplines. This scoping review aimed to map and analyze the current and emerging applications of artificial intelligence in the management of patients with pituitary adenomas. Methods Following PRISMA-ScR guidelines, Scopus and PubMed/MEDLINE and were searched for original studies published between 2015 and 2026. Studies evaluating artificial intelligence applications in pituitary adenoma management were included. Data were extracted and synthesized according to clinical application domains. Results Seventy-four studies met the inclusion criteria. Artificial intelligence applications were identified across seven domains: diagnosis and tumor classification, image segmentation and reconstruction, prediction of invasion and anatomical characteristics, prediction of therapeutic response and recurrence, prediction of postoperative complications and outcomes, operative workflow analysis, and natural language processing. Machine learning, deep learning, radiomics, and computer vision were the predominant methodologies. Diagnostic models demonstrated high performance in differentiating pituitary adenomas from other sellar lesions and in identifying hormonal subtypes. Radiomics- and machine learning-based approaches showed promising accuracy for predicting cavernous sinus invasion, tumor consistency, extent of resection, and response to medical or surgical treatment. Deep learning techniques enabled automated image segmentation and image quality enhancement, while emerging applications included prediction of endocrine remission, postoperative cerebrospinal fluid leakage, and the need for additional interventions. Natural language processing and large language models were explored for information extraction, patient education, and clinical decision support. Despite these advances, most studies were retrospective, single-center investigations with heterogeneous datasets, limited external validation, and inconsistent reporting standards. Conclusions Artificial intelligence has been applied across nearly all stages of pituitary adenoma management and demonstrates substantial potential to support diagnosis, prognostication, treatment planning, and postoperative care. However, translation into routine clinical practice remains limited by methodological heterogeneity and insufficient external validation, highlighting the need for prospective multicenter studies and standardized evaluation frameworks.","url":"https://doi.org/10.21203/rs.3.rs-9944032/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9944032/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.07.26359966","name":"Relevance Based Prediction: A Transparent, Non-Artificial Intelligence, Mathematical Solution to Personalized Opioid Treatment","source":"preprints","abstract":"Accurate prediction of individual medical outcomes is essential for optimizing treatment allocation amid rising costs, coverage denials, and limited clinical resources. Traditional predictive models, including regression and neural networks, rely on average effects and cannot tailor predictions to the specific circumstances of individual cases. We present relevance-based prediction (RBP), a model-free method that predicts outcomes as weighted averages of observed cases, with weights determined by a rigorously defined measure of relevance. Unlike model-based methods that rely on fixed calibrated parameters, RBP revisits the original data for each prediction and customizes both the cases and variables used. Applied to opioid treatment, RBP provides case-specific insights unavailable from conventional models, including how each prior case informs a prediction, how each variable affects its reliability and value, and how reliable the prediction is before it is made. These individualized insights may prevent misleading average-based decisions and reduce harmful or suboptimal treatment.","url":"https://doi.org/10.64898/2026.08.07.26359966","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.07.26359966","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9819693/v1","name":"Artificial Intelligence Integration in U.S. Healthcare Professional Degree Programs: A Rapid Scoping Review","source":"preprints","abstract":"Abstract Purpose Artificial intelligence (AI) capabilities and use in health care have expanded rapidly, creating an urgent need to adapt health professions curricula. This study analyzed peer-reviewed evidence (2019–2026) on AI integration in health professional degree education relevant to U.S. programs, characterized study designs and findings, identified research gaps, and provided future directions for scalable, ethically governed AI curricula. Method A rapid scoping review approach was applied using PubMed and Google Scholar as primary sources, supplemented by citation chaining of high-yield syntheses and targeted retrieval of authoritative U.S. curricular landscape resources and selected institutional exemplars (not counted as peer-reviewed studies). Inclusion targeted English-language, peer-reviewed studies (2019–2026) addressing artificial intelligence/machine learning/generative artificial intelligence education in health professions education, including surveys, curriculum evaluations, qualitative studies, consensus/Delphi, and scoping/systematic reviews. Data were extracted into evidence-mapping Tables (study characteristics and methods summary) and descriptively synthesized by profession, content domains, and evaluation outcomes. Results Thirty-six peer-reviewed papers meeting the inclusion criteria were synthesized. Study designs were skewed toward reviews (integrative/scoping/systematic) and surveys; fewer evaluated educational interventions with objective pre/post outcomes. Medical education, especially radiology, accounted for many implementations and studies, while dentistry, pharmacy, and physical therapy education had smaller but growing bodies of work. Evidence syntheses consistently call for competency-based curricular design, faculty development, assessment redesign for generative AI, and governance aligned with ethical and risk-management frameworks. Conclusions U.S. health professions programs are moving from ad hoc AI exposure to structured offerings emerging from national organizations and institutional exemplars. However, the literature base remains early, with limited multi-institutional evaluations, minimal longitudinal follow-up, and weak linkage to clinical performance or patient outcomes. Next-generation research should prioritize competency-linked curricula, transparent evaluation methods, and equity- and safety-centered governance.","url":"https://doi.org/10.21203/rs.3.rs-9819693/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9819693/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202606.1599.v1","name":"Artificial Intelligence Across the Medical Education Continuum: Institutional, Learning, and Readiness and Safety Systems","source":"preprints","abstract":"(1) Background: Artificial intelligence (AI) is increasingly shaping clinical practice and medical education, yet its responsible integration across the medical education continuum remains incompletely defined; (2) Methods: This narrative review synthesizes literature on AI applications in undergraduate, graduate, and continuing medical education, with attention to institutional systems, learning systems, and readiness and safety systems. Relevant studies were identified through targeted literature searches, reference-list review, and qualitative thematic synthesis; (3) Results: Across institutional systems, AI has been frequently examined in admissions, selection, educational administration, and workforce-linked logistics, where it may support efficiency and consistency but raises concerns regarding fairness, transparency, and authenticity of learner-generated materials. Within learning systems, AI-enabled tutoring, simulation, feedback, assessment, clinical reasoning support, and examination-facing tools show promise for personalization and supervised skill development; however, much of the evidence remains early-stage and relies on usability, satisfaction, confidence, or short-term performance rather than durable learning, clinical transfer, or behavior change. Readiness and safety literature highlights growing demand for AI literacy, while curricula, governance, privacy safeguards, bias mitigation, and accountability structures remain uneven; (4) Conclusions: AI should be implemented as a supervised educational and institutional support system, rather than as a substitute for clinical reasoning, faculty judgment, or professional responsibility.","url":"https://doi.org/10.20944/preprints202606.1599.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.1599.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-8882266/v1","name":"Contextual well-being and the perceived role of artificial intelligence among medical students and graduates: an interpretative phenomenological analysis","source":"preprints","abstract":"Abstract Background Medical education is characterised by sustained academic pressure, high performance expectations, and concurrent developmental transitions occurring across training and early professional stages. Although well-being is increasingly recognised as a priority within medical education, it is often conceptualised as a stable outcome or a target, whereas in reality, it is a dynamic concept responsive to biopsychosocial factors. At the same time, artificial intelligence (AI) is becoming increasingly embedded in educational and professional environments, yet its role in relation to well-being remains poorly understood. This study aims to explore medical students’ and early-career doctors’ experiences of contextual well-being, and their perceptions of the role of AI in shaping these experiences. Methods This qualitative study adopted an interpretative phenomenological analysis (IPA) approach to explore how medical students and early-career doctors experience and make sense of well-being within contemporary medical education. In-depth semi-structured interviews were conducted with 8 participants aged 18-30 years. Interviews explored experiences of well-being across academic, clinical, personal, and technological contexts, including participants’ perceptions and use of AI. Data were analysed iteratively following IPA principles, supported by reflexivity, peer debriefing, and an audit book. Results Analysis of participants’ accounts revealed three interrelated themes that illuminate the complex interplay between individual experience, institutional structures, and emerging technologies in shaping well-being. The first theme, contextual fluctuation of well-being, establishes well-being as fundamentally dynamic, shifting across the academic, clinical, and personal contexts. The second theme, the knowing–doing gap, refers to a persistent disconnect between participants’ understanding of well-being and their capacity to enact supportive practices within the constraints of medical training. The third theme, an emerging capacity gap and the perceived role of AI, shows how structural limitations in supporting self-regulation create conditions in which AI is perceived as potential compensatory support. Conclusions Conceptualising well-being as contextual and capacity-related may help explain why awareness does not consistently translate into action and has implications for how well-being is studied and supported in medical education.","url":"https://doi.org/10.21203/rs.3.rs-8882266/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8882266/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9986445/v1","name":"Artificial Intelligence and Critical Thinking in Medical Education: A Review of the Scope of Scientific Evidence","source":"preprints","abstract":"Abstract Introduction. Artificial intelligence (AI) is transforming medical education, but its impact on critical thinking (CP), an essential skill for clinical judgment, is up for debate. Objective. Map and synthesize the available scientific evidence on the relationship between AI and the development of critical thinking in medical education. Methodology. A scoping review was carried out following the PRISMA-ScR guideline. We searched PubMed, ScienceDirect, Web of Science, and Epistemonikos (2016–2026) for studies. Due to the scarcity of specific research in health sciences, the search was expanded to general education. Two review authors performed independent data selection and extraction. The synthesis was narrative-thematic. Results. We included 43 studies (19 in health sciences and 24 in general education). Three trends were identified: (1) AI enhances critical thinking when integrated with structured pedagogical mediation; (2) there is a risk of cognitive externalization with uncritical use; (3) the impact depends on attitudinal and curricular variables. In medical education, AI improves structured tasks, but it does not replace complex clinical reasoning . Conclusion. The effect of AI on critical thinking depends on the pedagogical context. Gaps are identified in longitudinal studies and in standardized measures of critical thinking in medical education .","url":"https://doi.org/10.21203/rs.3.rs-9986445/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9986445/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10120979/v1","name":"Artificial Intelligence and Telemedicine Regulation in Africa: Ethical and Legal Perspectives on Accountability, Bias, and Patient Safety","source":"preprints","abstract":"Abstract Artificial intelligence (AI) is revolutionizing the telemedicine industry, especially in areas of healthcare service provider scarcity and inadequate access to specialized medical care. AI-driven telemedicine platforms are being used increasingly across Africa for diagnosis and clinical decision making, disease surveillance and patient monitoring in the remote setting. All of these technologies present novel challenges and opportunities to increase access to and the efficiency of healthcare; they also deliver new ethical and legal issues. The scale of rapid adoption of AI healthcare systems are faster than the development of the associated regulation in many African nations, posing concerns around accountability, algorithmic bias, data privacy and patient safety as well as transparency. This review critically analyses the preparatory state of the legal framework of healthcare systems in Africa for AI-powered telemedicine and digital diagnostic tools. The study analyzes how the existing laws and policies tackle ethical concerns of AI applications in healthcare, with a systematic review of scholarly literature and comparative analysis of emerging AI regulations. The study compares and contrasts current laws and policies, presenting a systematic review of literature and scholarly research, to assess the extent of existing laws and policies addressing ethical concerns of AI applications in healthcare. Special focus is placed on topics of liability, informed consent, algorithmic discrimination, cyber security, cross-border data governance and privacy protection of patients. The results show considerable regulatory fragmentation on the continent, with not many countries having a broad and holistic Digital health/AI governance framework. Additionally, deficiencies in legal accountability and ethical oversight processes can lead to reduced trust and patient safety. Finally, the study presents policy prescriptions to augment regulatory convergence, ethical governance, and responsible innovation of AI in Africa's telemedical landscape.","url":"https://doi.org/10.21203/rs.3.rs-10120979/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10120979/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.06.11.26354470","name":"What level of expertise is necessary to generate ACLS training test questions: pre-med students vs. artificial intelligence?","source":"preprints","abstract":"Introduction In-hospital cardiac arrest carries high mortality despite standardized ACLS training. Educators face increasing time constraints in developing assessment tools for ACLS training. Two possible solutions to this problem are using pre-medical students or using artificial intelligence to generate test questions. This study compared the quality of pre-medical student-generated ACLS test questions vs. AI-generated ACLS test questions, testing the hypothesis that AI-generated questions are non-inferior to student-generated questions. Methods Ten pre-medical students created ACLS questions following predefined criteria, while an AI model (Northwell’s Artificial Intelligence Hub) generated comparable questions. A blinded ACLS-certified physician evaluated questions on the qualities of Alignment, Clarity, Cognitive Level, and Question Design using a standardized rubric (Likert scale: 1 = poor quality, 5 = excellent). Student’s T-test and Chi-square analysis were used to compare the quality of questions on different rubric domains within each arm (student vs. AI) and within one domain (eg, question Clarity) between arms. The Student’s T test was used when 2 comparator groups were compared (eg, Clarity of student-generated vs. AI-generated questions) within one arm. The ANOVA test was used when comparing more than 2 comparator groups (eg, Alignment vs. Clarity vs. Cognitive Level) within one arm. Statistical significance was set as a priority at p Results Both student-generated and AI-generated questions were of high quality. AI-generated questions achieved the maximum score in the domains of Alignment, Clarity, and Question Design, but fell short of perfect scores in the domain of Cognitive Level (8 of 50 questions were less than 5). Student-generated questions achieved less-than-perfect scores in each domain. No significant difference was found in overall mean question scores between groups (students = 4.79, AI = 4.81; p = 0.9). However, AI-generated questions had significantly-greater Clarity (students = 4.8, AI = 5; p = .0461), while Alignment, Cognitive level, and Question Design showed no significant differences. Conclusion AI-generated questions demonstrated overall quality comparable to those generated by pre-medical students, supporting the potential role of AI as a scalable tool in ACLS educational assessment development. Further studies are warranted to evaluate additional AI platforms and determine optimal integration of AI in medical education assessment design.","url":"https://doi.org/10.64898/2026.06.11.26354470","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.11.26354470","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202606.0359.v1","name":"Applying Artificial Intelligence to Childhood Obesity: DMT2 and MASLD Risk Predictive Models","source":"preprints","abstract":"Recent decades and the presence of complications already in childhood that affect the prognosis in adulthood. Obesity is a complex and multifactorial disease with a bio-psycho-social etiology, and several predisposing factors are still being discovered. Many obesity-related comorbidities are detected already in childhood: pre-diabetes and type 2 diabetes (T2DM), Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), hypertension, sleep apnea, sarcopenia, and osteoarticular disorders. To date, lifestyle modification and the Mediterranean diet remain the cornerstones of primary and secondary obesity prevention. Artificial intelligence (AI) represents a new tool for healthcare professionals to improve the early diagnosis and treatment of childhood obesity complications. Methods: Articles, reviews, consensus statements, guidelines, meta-analyses, and editorials published on “PubMed,” “NIH-National Library of Medicine,” and “Google Scholar” were analyzed. The keywords used were: \"obesity\", \"globesity\", \"children\", \"comorbidities\", \"healthcare\", \"socioeconomic status,\" \"MASLD,\" \"metabolic comorbidities,\" \"diabetes,\" \"artificial intelligence\", “machine learning”, \"deep learning\", and \"multiomics\". Results: By synthesizing multi-omic information—comprising the genome, epigenome, metabolome, transcriptome, and microbiota—alongside social and psychological metrics, artificial intelligence (AI) can forecast the probability of obesity development and facilitate the timely detection of complications within vulnerable populations. Utilizing machine learning (ML) and deep learning techniques, researchers have pinpointed specific metabolites, intestinal flora, neurotransmitters, and neurological areas linked to obesity's emergence. Furthermore, these methods have detected SNPs in genes related to carbohydrate and lipid metabolism, energy balance, and the hunger-satiety regulatory systems that contribute to obesity susceptibility. Various risk assessments and prognostic models rely on ML; these include algorithms designed to evaluate the likelihood of MASLD or diabetes progression in pediatric patients with obesity. Consequently, through ML-driven software, it is feasible to conduct remote surveillance of a patient’s nutritional habits and exercise routines, or to suggest bespoke dietary regimens tailored to individual patient profiles. Conclusions: Artificial intelligence has the potential to aid healthcare providers in enhancing the management of childhood obesity through personalized medical strategies, although various concerns regarding access, data privacy, and digital literacy remain unresolved.","url":"https://doi.org/10.20944/preprints202606.0359.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.0359.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202606.0276.v1","name":"Influencing Factors of Medical Students' Acceptance of Generative Artificial Intelligence Based on the Extended Technology Acceptance Model: A Cross-Sectional Study","source":"preprints","abstract":"Background: The popularization of generative artificial intelligence (GenAI) has brought both opportunities and challenges to medical education. Objective: To investigate medical students' usage status, usage intention of GenAI and its possible influencing factors. Methods: A questionnaire survey was conducted among 199 students from a medical university, and statistical analysis was performed using SPSSAU. Results: Tools such as DeepSeek and ChatGPT are widely used in medical students' learning and scientific research. High-frequency users (≥4 days per week) scored significantly higher than low-frequency users ( 4 days per week) in 9 dimensions; gender showed significant differences in dimensions such as perceived ease of use (P 0.05), while major background differed only in some dimensions, and age showed no significant difference. Regression analysis revealed that external variables (P 0.001), perceived usefulness (P=0.001) and individual factors (P=0.009) had significant positive effects on behavioral intention, whereas effort expectancy (P=0.049) had a significant negative effect. Conclusion: GenAI has been deeply integrated into medical students' autonomous learning. External support, technical practicality and individual adaptability are key driving factors for usage intention, while effort expectancy constitutes a barrier. This study provides empirical evidence for the standardized application of GenAI in medical education.","url":"https://doi.org/10.20944/preprints202606.0276.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.0276.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.07.21.26358539","name":"Composite Artificial Intelligence–Enabled Electrocardiogram for Detection and Prediction of Structural Heart Disease","source":"preprints","abstract":"Background Structural heart disease (SHD) drives heart failure and cardiovascular mortality but remains underdiagnosed, and echocardiography is limited as a population-level screening tool. Objectives We evaluated whether a composite artificial intelligence–enabled electrocardiogram (AI-ECG), combining independently developed models for left ventricular systolic (LVSD) and diastolic dysfunction (LVDD), identifies prevalent and predicts incident SHD across diverse populations. Methods In this multinational cohort study, detection was assessed cross-sectionally in a Korean clinical cohort (Incheon Sejong Hospital) and a US dataset (Columbia University Irving Medical Center), and incident risk was assessed in the Korean cohort and the UK Biobank among individuals without baseline SHD or heart failure. Adults with paired ECG and echocardiography were analyzed for detection, with the composite defined as positive on either model. SHD comprised reduced left ventricular ejection fraction, moderate or severe valvular disease, left ventricular hypertrophy, or pulmonary hypertension. Detection was assessed by sensitivity and specificity, and incident risk by Cox models and the C statistic. Results Among 46,082 and 36,286 participants in the two detection cohorts, the composite detected SHD with sensitivity of 71.8% and 76.1% and specificity of 88.3% and 70.1%, with positivity across all phenotypes. Among at-risk individuals, composite positivity was associated with incident SHD (hazard ratios, 3.75 and 2.75), with C statistics of 0.69 to 0.78. Conclusions A composite AI-ECG identified prevalent and predicted incident SHD across multinational cohorts, capturing signals beyond its training targets and supporting its potential as a scalable cardiovascular screening tool; whether ECG-based risk stratification improves outcomes requires prospective evaluation.","url":"https://doi.org/10.64898/2026.07.21.26358539","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.21.26358539","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.05.29.26354432","name":"Multi-Agent AI for Chest Radiography: A Sequential Segmentation and LLM-Driven Consultative Tool for Medical Training","source":"preprints","abstract":"Background Traditional diagnostic models lack explainability, while multimodal language models prone to hallucination remain unsafe for medical education. An interactive, risk-free artificial intelligence framework is required to serve as a reliable clinical mentor for radiology trainees. Methods We propose a multi-agent architecture decoupling deterministic image analysis from generative consultation. Specialized computer vision models perform anatomical localization and pathological segmentation. These quantitative outputs are synthesized into a structured payload, which grounds a locally hosted large language model (LLaVA 7B) using strict prompt guardrails and prerequisite protocols. Results The system effectively eliminates visual hallucinations by intercepting unanchored queries. The artificial intelligence tutor successfully contextualizes spatial anomalies and baseline metrics, generating accurate conversational explanations and formally structured radiology reports while strictly enforcing medical safety disclaimers. Discussion and Conclusion By anchoring language generation exclusively to verified algorithmic realities, this framework transforms opaque diagnostic models into safe, interactive educational simulators. This establishes a highly reliable paradigm for integrating explainable artificial intelligence into medical training.","url":"https://doi.org/10.64898/2026.05.29.26354432","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.29.26354432","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.22541/authorea.15004481/v1","name":"Advances in Artificial Intelligence for Postoperative Analgesia: A Narrative Review","source":"preprints","abstract":"With the rapid advancement of medical informatization, the integration of the healthcare industry with artificial intelligence (AI) technologies has emerged as a major focus in both medical and information science research. Contemporary medical information systems have accumulated vast and diverse datasets, providing a robust foundation for the application of AI in clinical diagnosis, treatment, and perioperative management. Among the many clinical challenges that persist, postoperative pain remains one of the most prevalent and distressing problems for patients. Developing an adaptive and dynamic management model for postoperative analgesia based on medical big data therefore represents a critical scientific and clinical issue that warrants systematic investigation. This review explores the application of AI technologies throughout the entire perioperative analgesia management pathway, with particular emphasis on recent methodological advances. Postoperative pain is inherently multidimensional and dynamic, shaped by physiological, psychological, and contextual factors. Conventional analgesic strategies, which often rely on standardized protocols, struggle to capture this complexity and frequently fail to address substantial inter-individual variability. In contrast, AI-driven approaches offer a promising alternative by enabling real-time pain prediction, individualized dose adjustment, and seamless integration of multimodal physiological signals. Building on this technological foundation, we further propose the concept of AI-assisted multimodal analgesia as a practical framework to better accommodate patient heterogeneity and improve postoperative pain outcomes.","url":"https://doi.org/10.22541/authorea.15004481/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22541/authorea.15004481/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.08.12.744314","name":"AI4Loop: an Artificial Intelligence Framework Reveals Increased 3D Chromatin Interactions and Therapeutic Vulnerabilities across 12,000 Cancer Samples","source":"preprints","abstract":"Three-dimensional chromatin interactions shape gene regulation, but their large-scale analysis remains limited by the cost and complexity of experimental assays. Here we present AI4Loop, a deep learning framework that infers genome-wide gene-centered chromatin interaction networks directly from RNA-seq data. Across multiple cell types, AI4Loop recovered interaction patterns consistent with clinical samples and orthogonal chromatin conformation datasets. Applied to 12,347 transcriptomes from 32 cancer types, AI4Loop revealed pervasive increases in gene-centered chromatin interactions in tumors, particularly at oncogene-associated loci. These inferred interaction networks outperformed gene expression alone in cancer classification. Integration with more than 50,000 drug-treated transcriptomes identified compounds predicted to reverse cancer-associated interaction gains. Hi-C experiments confirmed that the oxazolidinone antibiotics eperezolid and radezolid reduce breast cancer-gain chromatin interactions. Together, these results identify increased gene-centered chromatin interactions as a pan-cancer feature and provide a scalable strategy for linking 3D genome dysregulation to therapeutic vulnerabilities.","url":"https://doi.org/10.64898/2026.08.12.744314","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.12.744314","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10606649/v1","name":"Quality of Olfactory Dysfunction Information Across Digital Health Platforms: A Comparative Study of Google Search and Artificial Intelligence Platforms","source":"preprints","abstract":"Abstract Purpose Olfactory dysfunction (OD) affects approximately 22% of the population and significantly impairs quality of life. Patients increasingly seek health information online, including AI chatbots, yet its quality for OD remains poorly characterised. This study evaluated the quality and readability of OD information across five digital health platforms using validated instruments. Methods Five platforms were assessed: Google Search, ChatGPT (GPT-5.3), Gemini 3, Claude Sonnet 4.6, and Perplexity AI search. Eight standardised search terms spanning medical and lay terminology were applied, and responses were independently assessed by two raters using DISCERN, QAMAI (AI platforms only), and readability metrics (Flesch-Kincaid Grade Level; SMOG Index). Inter-rater reliability was calculated using Cohen's kappa and intraclass correlation coefficient (ICC). Results Perplexity achieved the highest mean DISCERN score (50.56 ± 5.74), with all sources rated moderate quality. Google Search produced the only high-quality sources (3.75%) and showed a high variability (41.96 ± 10.14). ChatGPT (43.00 ± 6.27) and Gemini (44.94 ± 12.05) had similar mean DISCERN scores, although Gemini was more variable. Claude scored lowest (38.13 ± 4.57), with 75% of sources rated low quality. Reading difficulty correlated weakly with information quality (Spearman's ρ = 0.255, p = 0.0067). Mean readability (FKGL 11.01) exceeded NHS recommendations (Grades 6–8), with only 27.7% of sources meeting the target. Conclusion Google alone produced high-quality OD information by DISCERN criteria; AI-generated content consistently failed to meet this standard, particularly for treatment-related domains. Readability was poor across all platforms. Clinicians should direct patients to validated specialty resources and highlight AI limitations, particularly regarding treatment risks, non-treatment consequences, and quality-of-life impact.","url":"https://doi.org/10.21203/rs.3.rs-10606649/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10606649/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.05.28.26354373","name":"The Verification Gap: Artificial Intelligence Adoption, Hallucination Awareness, and Verification Practices Among Early Career Medical Researchers in Pakistan","source":"preprints","abstract":"Background Artificial intelligence (AI) tools have been rapidly adopted by medical researchers, yet whether early career researchers in low- and middle-income countries possess the awareness and habits needed to use these tools safely remains poorly documented. This study characterized AI adoption, hallucination awareness, and verification and disclosure practices among early career medical researchers in Pakistan. Methods A cross-sectional anonymous online survey was conducted among medical students, house officers, residents, physicians, and faculty involved in research or academic work across Pakistan (May 2026). Descriptive statistics and chi-square tests were applied to 373 eligible responses. Results AI use was near-universal (99.7%), with 60.3% using AI daily. The most commonly reported tool was Claude (40.5%), followed by ChatGPT (29.2%) and Perplexity (26.0%), though this ranking likely reflects sampling characteristics. Despite high adoption, 59.2% typically did not verify AI outputs before use, and 40.2% had never heard that AI can generate fabricated references. In behavioral vignettes, 36.5% assumed convincing AI-generated references were authentic, and 54.2% would continue using remaining AI content after discovering one fabricated reference. Formal research training was strongly associated with consistent disclosure (51.7% vs. 17.1%; chi-square=48.43, p Conclusions Early career medical researchers in Pakistan demonstrate high AI adoption alongside incomplete hallucination awareness and infrequent verification — a pattern that may carry implications for research integrity. Formal training was the only factor significantly associated with consistent disclosure. Integration of AI literacy into medical curricula and institutional governance frameworks merits consideration.","url":"https://doi.org/10.64898/2026.05.28.26354373","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.28.26354373","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.07.23.26358815","name":"Evaluative Stance Toward Artificial Intelligence in High-Quartile Medical Journals (2021– 2026): Large-Scale LLM-Assisted Computational Content Analysis","source":"preprints","abstract":"Abstract Background Medical-AI publications do more than report technical performance; they also frame AI as beneficial, uncertain, or risky. How this evaluative stance has changed across the medical literature is not well characterized. Objective To characterize evaluative stance in published medical-AI discourse abstracts from January 2021 through April 2026 and examine variation over time, concern themes, failure mechanisms, specialties, first-author geography, and publication format. Methods We conducted an LLM-assisted computational content analysis of medical-AI abstracts from first- and second-quartile medical journals. Of 97,492 post-cutoff Q1/Q2 records entering the prefilter, 16,759 were retained as discourse or evaluative. Claude Sonnet 4.6 assigned 16,749 valid stance classifications using Alarm, Caution, Neutral, Cautious Optimism, and Advocacy. Annual analyses used 16,747 records dated 2021–2026. Critical stance was Alarm plus Caution and indexed evaluative scrutiny, rather than opposition or author psychology. Each LLM step was validated against blinded human coding by one author: prefilter Cohen κ=0.51, stance quadratic-weighted κ=0.79 (95% CI 0.72–0.84) for codable, in-scope records, specialty κ=0.75, and mechanism axes κ=0.84 (model type) and κ=0.57 (failure mode). Results Advocacy declined from 2.9% in 2021 to 0.6% in partial 2026, while Cautious Optimism remained the majority stance. Among 16,749 valid classifications, 30.8% were critical. Critical share increased from 25.4% to 32.6%, a 7.25-percentage-point increase based on unrounded estimates. Among critical records, patient safety remained the most prevalent concern. Hallucination/errors increased by 30.9 percentage points. Regulation declined by 22.0 percentage points and ethics/bias by 8.1 percentage points in prevalence share; these declines do not necessarily indicate lower publication counts. Within the hallucination/error theme, factual error was more common than fabrication. Fabrication estimates should be treated as an upper bound, because failure-mode agreement was moderate. Specialty patterns were heterogeneous. Critical rate was inversely associated with FDA cleared-device availability (Spearman ρ=−0.65, two-sided p=0.004), which does not measure adoption, deployment, maturity, or clinical use. First-author geography described publication metadata and discourse, not national attitudes or research quality. Reviews were the least critical and most favourable format. In exploratory forward-validation, 2 of 78 early Advocacy predictions were fully borne out, although the analysis was single-rater and retrieval-dependent. Conclusions Published medical-AI abstracts became modestly less promotional and more focused on specific errors and safety concerns. Unqualified promotion declined, but qualified favourable framing remained dominant, and the rise in critical stance was modest. Concern moved toward errors and patient safety, with factual error discussed more often than fabrication. These findings describe published discourse, not AI capability or whether the evaluations were correct.","url":"https://doi.org/10.64898/2026.07.23.26358815","authors":["Lixing Wang","D Poenaru"],"tags":["Kappa","Optimism","Scrutiny","Psychology","Content analysis"],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.23.26358815","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.06.23.26356395","name":"An Agentic, No Code Artificial Intelligence Workflow for Developing and Externally Validating a Thyroid Nodule Ultrasound Malignancy Classifier","source":"preprints","abstract":"Convolutional neural networks (CNNs) can classify thyroid nodules on ultrasound, yet published models are seldom available for independent testing, require machine-learning expertise to develop and deploy, and are validated mostly on papillary thyroid carcinoma. Objective To test whether an autonomous (“agentic”), no-code artificial intelligence (AI) agent can develop a calibrated thyroid-nodule malignancy classifier, and to validate it internally and on an external cohort spanning multiple cancer histologies. Methods This is a retrospective, computational diagnostic study with prespecified endpoints. A no-code agent (Hugging Face ML-Intern) autonomously reviewed data, selected and trained the model and calibrated probabilities, using the open-source TN5000 dataset (3500 training, 500 validation, and 1000 test images). The trained ResNet-18 model was externally validated on 232 nodules from the University of Colorado, including follicular, medullary, oncocytic, and follicular-variant of papillary carcinomas. Results On the internal test set, an agentic AI model achieved AUROC 0.94 (95% CI, 0.920–0.953), sensitivity 0.90, and specificity 0.80. On external validation, agentic AI model achieved an AUROC of 0.90 (95% CI, 0.850–0.936), sensitivity of 0.92, and specificity of 0.68, negative predictive value of 0.96, and positive predictive value of 0.52, exceeding the performance of a previously published classifier on the same cohort (AUROC of 0.83). Conclusions An agentic, no-code AI workflow produced a calibrated, externally validated thyroid nodule classifier, supporting accessible, reproducible, and independently testable medical AI development. Prospective validation and local recalibration are required before clinical use.","url":"https://doi.org/10.64898/2026.06.23.26356395","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.23.26356395","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202606.1960.v1","name":"The Use of Artificial Intelligence (AI) for the Early Detection of Postoperative Complications in Cardiothoracic Surgery","source":"preprints","abstract":"Artificial intelligence (AI) and machine learning (ML) are increasingly being evaluated as decision-support tools for forecasting postoperative complications after cardiothoracic surgery. This narrative review synthesises peer-reviewed clinical, biomedical informatics, and digital-health literature across acute kidney injury (AKI), postoperative atrial fibrillation (POAF), pulmonary complications, neurological outcomes, mortality, wound monitoring, wearable surveillance, and post-discharge monitoring. Rather than treating reported AUC values as interchangeable evidence of algorithmic superiority, this review emphasises that model performance depends on at least three interacting factors: algorithmic architecture, data richness, and validation strategy. Conventional tools such as EuroSCORE II and Society of Thoracic Surgeons (STS) models remain clinically useful because they are familiar, transparent, and externally established; however, many rely on static perioperative snapshots and are less suited to continuously updated intraoperative and postoperative data streams. Contemporary ML models, including gradient-boosting approaches, support-vector methods, convolutional neural networks, and wearable-based algorithms, may improve discrimination in selected settings, particularly when dynamic laboratory trajectories, physiological time-series, imaging data, or remote-monitoring signals are available. However, current evidence remains limited by single-centre development, heterogeneous endpoints, inconsistent calibration reporting, limited decision-curve analysis, low positive predictive values in surveillance applications, and sparse prospective implementation. Future work should prioritise multicentre external validation, calibration, clinical utility analysis, standards-based electronic medical record interoperability, and implementation pathways that preserve clinician oversight.","url":"https://doi.org/10.20944/preprints202606.1960.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.1960.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9494609/v1","name":"A Cross-Sectional Study Assessing Medical Students’ Experiences and Perspectives on Artificial Intelligence in Medical Education","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly embedded within healthcare systems, yet formal training in AI remains limited within undergraduate medical education in the United Kingdom. National policy documents and professional bodies have emphasised the need to prepare future clinicians for the safe and ethical use of AI, but little is known about current student experiences and perceptions. Methods A cross-sectional online survey was conducted among third-, fourth- and fifth-year medical students at Manchester University Medical School. The survey assessed patterns of AI use, exposure to formal training, confidence in using AI for academic purposes, attitudes toward curriculum integration and concerns regarding ethics, trust and learning impact. Descriptive statistics were used to summarise responses. Results A substantial proportion of respondents reported using AI-based tools, most commonly for both personal and academic purposes (57%). Only 2.1% of participants had received any formal training in AI. More than half (57%) reported feeling confident using AI for academic work, and 56% agreed that teaching on AI should be included in the medical curriculum. However, notable concerns were identified, including distrust regarding ethical use by peers, uncertainty about the reliability of AI systems and mixed views on the potential impact of AI integration on learning. Conclusions Medical students are already widely engaging with AI tools despite minimal formal education. While confidence and support for curriculum integration are high, significant ethical and educational concerns remain. These findings highlight an urgent need for structured, comprehensive AI education within undergraduate medical curricula.","url":"https://doi.org/10.21203/rs.3.rs-9494609/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9494609/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202605.2039.v1","name":"Explainable Artificial Intelligence in Precision Medicine: Methods, Applications, and Challenges","source":"preprints","abstract":"Precision medicine focuses on customizing diagnostic, prevention and treatment approaches by accounting for the individual characteristics of each patient. This personalization draws on diverse sources of information including clinical records, genomic data, medical imaging, lifestyle patterns and environmental factors. As the volume and complexity of such multimodal healthcare data continue to expand, machine learning (ML) and deep learning (DL) techniques have become crucial for identifying complex patterns, estimating disease risk, and supporting personalized treatment decisions. Despite their efficiency, many of these models function as opaque systems, generating forecasts without clearly indicating the reasoning behind them. This lack of transparency can undermine clinician confidence, hinder adoption in clinical practice, and raise ethical as well as regulatory concerns, particularly in healthcare contexts where decisions must be explainable and defensible. Explainable Artificial Intelligence (XAI) addresses these challenges by providing methods that make model behaviour more transparent and interpretable. Techniques such as SHAP, LIME, saliency and attention-based visualizations, counterfactual analysis, and rule-based explanations enable clinicians to inspect the rationale behind predictions, evaluate alignment with established medical knowledge, and identify potential sources of bias within data or algorithms. From a patient perspective, explain-ability improves communication, supports informed consent, and strengthens trust in AI-supported care. Regulatory authorities also depend on transparent and interpretable systems to ensure accountability, traceability and compliance with clinical safety requirements. This paper offers a comprehensive examination of explainable AI in the context of precision medicine. It introduces fundamental XAI concepts, organizes key methodological approaches, and reviews applications spanning genomics, medical imaging, and electronic health record (EHR) analytics. The chapter also discusses methods for assessing explanation quality, highlights the role of human-centred design, and addresses critical ethical and legal considerations. It concludes by outlining ongoing challenges and future research directions aimed at developing reliable, interpretable AI systems that can be effectively integrated into advanced personalized healthcare.","url":"https://doi.org/10.20944/preprints202605.2039.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202605.2039.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.05.27.26354009","name":"Health Care Students’/Professionals’ Perspectives on Artificial Intelligence: Survey in Erbil, Iraq","source":"preprints","abstract":"Background Artificial Intelligence (AI) is increasingly integrated into healthcare systems worldwide and medical schools worldwide have begun integrating AI into their curricula. The healthcare system in Iraq is currently undergoing development and AI has not yet been adopted in clinical practice in Erbil; in addition, no formal AI instruction has been incorporated into the medical education curriculum. The aim of this study was to assess knowledge levels, attitudes, and perceptions regarding AI among medical students and healthcare professionals in Erbil, Kurdistan Region of Iraq. Methods A mixed-methods survey was distributed to medical students and residents in Erbil, Kurdistan Region of Iraq. The survey was adapted from Teng et al, and modified to reflect the local context. The survey was translated into Kurdish and Arabic. Convenience sampling was used. Statistical analysis was conducted using IBM SPSS (Statistical Package for Social Sciences), Version 26.0. Chi-square and Fisher’s exact tests were used to test associations between categorical variables. Mann Whitney U test was used to compare mean ranks between groups in the non-normally distributed data. A P value Results A total of 368 participants participated in this study. The majority (85.6%) of participants felt that AI should be taught in schools and universities, and 90.8% reported using AI. ChatGPT was by far the most commonly used AI tool (85.3%). Participants aged 20-24 years (93.2%) and 25-29 years (90.2%) showed the highest prevalence of using AI. Participants that used AI previously, had higher scores for support for AI development in their field (U = 3744.5, P=0.001), feelings of hope towards AI in their field (U = 4406.5, P = 0.004) and thinking that students should learn the basics of AI (U = 4022.5, P = 0.03). Male participants were more likely to use AI in comparision with women (P=0.045). The most common concern regarding AI was loss of jobs (33.0%), followed by overreliance on AI (22.8%). Qualitative analysis revealed themes of guarded optimism, and concerns regarding the ethical implications of AI use in medicine. Conclusion Medical students and physicians in Erbil are early adopters of AI in spite of any formal training. In parralel, most participants expressed dissatisfaction with their understanding of the ethical implications of AI in healthcare and emphasized the need for formal AI education in healthcare curricula. The majority of participants expressed guarded optimism regarding the future of AI in healthcare. A gender gap in AI was identified, consistent with global trends with implications for professional equity.","url":"https://doi.org/10.64898/2026.05.27.26354009","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.27.26354009","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9286164/v1","name":"Integrating Artificial Intelligence into Undergraduate Medical Imaging Education: A Mixed Methods Study on Curriculum Reform and Students' Perspectives","source":"preprints","abstract":"Abstract Background An increasing number of artificial intelligence (AI) technologies are profoundly influencing and transforming the clinical practice of medical imaging. However, the systematic application of AI in the undergraduate courses of medical imaging is still relatively limited at present. Methods After completing the \"online self-study of AI platform + offline clinical flipped classroom\" mixed teaching mode course, 83 medical imaging undergraduate students participated in a questionnaire survey, aiming to understand students' evaluation of the AI platform's ability to meet learning needs and the impact of personalized learning resources. The research conducted a statistical analysis of the questionnaire survey results. Results 80.72% of the students considered AI education \"very important\" or \"somewhat important\". Among them, the \"online self-study of AI platform + offline clinical flipped classroom\" mixed teaching mode had the most significant effects in improving the ability to combine theory and practice (27.71%) and the efficiency of autonomous learning (27.71%). 75.90% of the participants stated that AI-driven personalized learning resources significantly improved learning efficiency. Conclusions The AI-enabled blended learning model can effectively enhance the autonomous learning efficiency and clinical reasoning ability in medical imaging education. The research results provide a practical and feasible reference basis for curriculum reform. Trial registration Not clinical trial.","url":"https://doi.org/10.21203/rs.3.rs-9286164/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9286164/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.31222/osf.io/d546r_v1","name":"Protocol for a systematic review on specialty-specific meta-research studies evaluating artificial intelligence author guidelines.","source":"preprints","abstract":"Recent meta-research studies have evaluated specialty-specific (i.e. pertaining to an individual medical specialty) author guidelines on the use of artificial intelligence (AI) in medical journals. To date, however, no systematic review has synthesised these findings; a review of this literature may help identify differences between specialties in the content and scope of AI-related guidance issued to authors. This systematic review aims to address this gap in the literature. This protocol has been described using the PRISMA-P statement. The primary outcomes will be measuring comparative prevalence of AI guidance domains across the journals linked to different medical specialties, including guidance on AI image processing, statistical analysis, and permitted or prohibited uses of AI in research (where such data is available). Secondary outcomes will describe the methodological design of the included studies.","url":"https://doi.org/10.31222/osf.io/d546r_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31222/osf.io/d546r_v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.20944/preprints202606.0946.v1","name":"Artificial Intelligence and Radiomics in Diagnostic Imaging: A Narrative Review of Methods, Clinical Applications, and Translational Challenges","source":"preprints","abstract":"Medical imaging generates vast quantities of high-dimensional data that often exceed the capacity of qualitative human interpretation. Artificial intelligence (AI)—in particular machine learning, deep learning, and radiomics—has emerged as a powerful approach to extract quantitative, reproducible information from radiological images and to support diagnosis, prognosis, and treatment planning. Radiomics converts standard-of-care images into mineable feature data that can be linked to clinical, histopathological, and genomic endpoints, whereas deep convolutional neural networks learn task-relevant representations directly from pixel data, achieving expert-level performance in selected detection and classification tasks. More recently, foundation models, generative diffusion models, and vision–language models have begun to reshape the field, promising greater generalization and label-efficiency. This narrative review summarizes the technical foundations of radiomics and deep learning, surveys representative clinical applications across oncologic, neurologic, thoracic, and screening imaging, appraises the evidence comparing AI with human readers, and introduces these emerging paradigms. We further examine the principal barriers to clinical translation—limited reproducibility, dataset shift and poor external generalizability, data-quality and labeling challenges, overfitting, algorithmic bias, and limited interpretability—and review emerging reporting and validation standards. Although AI and radiomics hold substantial promise for precision imaging, robust external validation, standardization, prospective evaluation, transparent reporting, and sound ethical and regulatory governance remain essential before widespread clinical adoption.","url":"https://doi.org/10.20944/preprints202606.0946.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.0946.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9522470/v1","name":"Evaluating the Efficacy of Interactive Virtual Platform Based on Artificial Intelligence for Obstetrics and Gynecology Residency Training: A Randomized Controlled Study","source":"preprints","abstract":"Abstract Objective This study aims to create and validate the teaching effectiveness of an interactive artificial intelligence (AI) virtual platform in the standardized residency training of obstetricians and gynecologists. Method This study randomly selected 70 obstetricians and gynecologists undergoing standardized training at the Obstetrics and Gynecology Hospital affiliated with Zhejiang University School of Medicine. The participants were divided into an experimental group and a control group in a 1:1 ratio. Physicians in the control group directly treated real patients, while those in the experimental group first received virtual case training on an interactive AI virtual platform before treating real patients. Result The total score for the six core competencies (clinical practice ability, medical humanities literacy, critical thinking, teamwork ability, information integration ability, and lifelong learning ability) of the resident physicians in the experimental group was significantly higher than that of the control group (89.14 ± 3.919 vs. 82.49 ± 5.078, P","url":"https://doi.org/10.21203/rs.3.rs-9522470/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9522470/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.06.26352604","name":"Healthcare workers’ acceptance of artificial intelligence in cardiac diagnosis: implications for medical education and training programs","source":"preprints","abstract":"The integration of artificial intelligence (AI) in cardiology requires healthcare worker acceptance for successful implementation. Understanding attitudes and educational needs is crucial for developing effective training programs. A cross-sectional survey was conducted among 408 healthcare workers treating cardiac diseases in Riyadh, Saudi Arabia. We assessed AI acceptance, knowledge levels, and training preferences using validated scales. Statistical analyses included descriptive statistics, chi-square tests, correlation analysis, reliability testing, and multiple logistic regression. Of 408 participants, 407 provided complete responses. The sample comprised predominantly young (87.0% aged ≤30), female (75.7%) medical residents (89.9%) with limited AI experience (86.7% never used AI clinically). Internal consistency was excellent (Cronbach’s α = 0.892). Moderate acceptance was observed: 49.9% were aware of AI applications in cardiology, 46.7% were willing to learn, and 42.8% were willing to use AI clinically. However, 49.1% acknowledged lacking sufficient AI knowledge. Logistic regression identified willingness to learn (OR = 3.24, 95% CI: 2.15–4.89) and training interest (OR = 2.87, 95% CI: 1.94–4.25) as the strongest predictors of AI acceptance. The model explained 68.4% of variance (Nagelkerke R² = 0.684) with an AUC of 0.847. Medical residents demonstrate moderate AI acceptance but significant knowledge gaps. Educational interventions—particularly hands-on learning and institutional training programs—are the strongest drivers of AI readiness, surpassing demographic predictors. Integrating AI literacy systematically into medical curricula is essential for successful AI adoption in cardiovascular care. Author summary Healthcare workers worldwide are increasingly encountering artificial intelligence (AI) tools in clinical settings, yet their readiness to adopt these technologies—particularly in specialized fields like cardiology—remains poorly understood, especially in rapidly developing healthcare systems. In this study, we surveyed 407 healthcare workers in Riyadh, Saudi Arabia, to understand their current attitudes, knowledge gaps, and learning preferences regarding AI in cardiac diagnosis. Our findings reveal that while most participants hold cautious optimism about AI, nearly half acknowledge lacking the knowledge needed to use it confidently. Crucially, we found that educational factors—specifically willingness to learn and interest in institutional training—were far stronger predictors of AI acceptance than demographic characteristics such as age or gender. This means that AI readiness is not a fixed trait determined by who someone is, but a teachable and trainable capacity. These results carry direct implications for medical educators and policymakers: structured, hands-on AI training integrated throughout medical curricula can meaningfully accelerate adoption of beneficial technologies in cardiovascular care and beyond.","url":"https://doi.org/10.64898/2026.05.06.26352604","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.06.26352604","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9990340/v1","name":"Artificial Intelligence as Supplementary Learning Tool in Pharmacology Tutorials: Students’ Perceptions and Knowledge Outcomes ","source":"preprints","abstract":"Abstract Background: Artificial Intelligence (AI) tools are being rapidly integrated into medical education, yet discipline-specific evidence on their use in pharmacology tutorials remains limited. This study evaluated the perceptions of AI-assisted learning in pharmacology tutorials and attempted to descriptively examine the knowledge outcomes before and after the introduction of AI-based learning support. Methods: This quasi-experimental quantitative study was conducted among preclinical MBBS students at Universiti Kuala Lumpur Royal College of Medicine, Perak, Malaysia. The freely available AI tools like Chat Generative Pre-trained Transformer (ChatGPT) were introduced as adjuncts to conventional respiratory pharmacology tutorials. Pre-test and post-test multiple choice questions were used to evaluate knowledge. A validated, structured questionnaire comprising five thematic domains on a five-point Likert scale was employed to assess student perceptions. Descriptive statistics were performed using means, standard deviations, frequencies and percentages. For Likert-scale items, mean scores and standard deviations were calculated for each item and the grouped domain. Results: The completion rate was 100% for perception survey among 69 students. Pre-test and post-test mean scores were 9.61/10 and 9.52/10, respectively, indicating no meaningful descriptive change in knowledge. Overall perceptions of AI-assisted learning were positive (mean 4.15/5). The highest-rated benefits were improved understanding of pharmacology topics (mean 4.43), time-saving during self-study (mean 4.36), more effective doubt clarification (mean 4.31), and willingness to recommend AI tools for other subjects (mean 4.30). Students most frequently used AI to explain difficult concepts and to summarize lecture content. Though ChatGPT was rated the most helpful tool, there was concern about incorrect or outdated information. A total of 63.5% of respondents stated that they would continue using AI because it saves time and improves study efficiency. Conclusions: AI tools were well accepted as supplementary learning resources in pharmacology education. Students valued AI for understanding complex concepts, clarifying doubts, and supporting independent study. Measurable knowledge gains were not observed, most likely owing to high baseline performance. AI should be integrated cautiously as a complement to conventional teaching, with faculty oversight to ensure content accuracy and curricular alignment. Future studies should employ matched designs, discriminative assessments, and longitudinal follow-up.","url":"https://doi.org/10.21203/rs.3.rs-9990340/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9990340/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.05.14.26351842","name":"Generative Artificial Intelligence in Medical Education and Participatory Research for Social Action: A Human and AI Comparative Analysis","source":"preprints","abstract":"Participatory qualitative methods such as Photovoice are increasingly used to link research with social action. Recent advances in artificial intelligence (AI) may enhance data analysis, inference, and action planning within such participatory approaches. This study explored medical students’ perceptions of social justice using conventional Photovoice analysis and assessed the potential contribution of generative AI (genAI). Nine students joined a six-week seminar, “Exploring the Concept of Social Justice Using Photovoice.” An initial two-hour session covered ethics, the Photovoice framework, and photography techniques. Participants then captured images reflecting their views on social justice, wrote narratives, and engaged in guided group discussions. Human researchers and students conducted a three-stage Photovoice analysis: 1) selecting photographs, 2) contextualizing them with participant narratives, and 3) inductively coding themes. To explore how AI might support data analysis, the research team analyzed the same data with five generative tools, including Sonix, ChatGPT, and Copilot. AI-generated themes and visual representations were compared with human-derived results for congruence, depth, and suggested action steps. Conventional analysis identified five major themes: (1) Social Justice and Inequality, (2) Contradictions and the Costs of Justice, (3) Community and Collective Action, (4) Environment and Environmental Justice, and (5) Perception, Subjectivity, and Perspective. AI-assisted analysis yielded six unified themes that closely aligned with human findings. Traditional Photovoice images conveyed authentic, lived experiences and strong emotional meaning, providing a powerful foundation for advocacy. AI-generated images and thematic summaries offered efficiency, creativity, and reduced researcher bias, improving generalizability. However, they lacked the emotional depth and contextual nuance present in participant-created visuals.","url":"https://doi.org/10.64898/2026.05.14.26351842","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.14.26351842","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9293831/v1","name":"Development and psychometric validation of the artificial intelligence-assisted health- seeking behavior scale (AI-HSBS): A cross-sectional study","source":"preprints","abstract":"Abstract Background While AI-driven tools are rapidly transforming how individuals seek medical advice, there is a lack of specialized instruments to measure this specific behavioral shift. This study addresses the gap in digital health literature by developing a multidimensional scale to evaluate AI-supported health-seeking patterns and their impact on modern patient dynamics. Purpose As health behaviors rapidly digitize, a gap remains in tools measuring actual behavioral patterns regarding artificial intelligence. To address this, the “Artificial Intelligence Health-Seeking Behavior Scale (AI-HSBS)” was developed as the first instrument in digital health literature to directly evaluate such behavior. This research aims to develop a measurement tool capable of evaluating the AI-supported health-seeking behaviors of individuals in Istanbul, Türkiye. Methods This cross-sectional study involved an online survey with individuals residing in Istanbul (N = 388). Structural validity was evaluated through EFA and CFA. Internal consistency and reliability were assessed using Cronbach’s Alpha coefficients. The scale exhibits a structure comprising 15 items and four dimensions: Perceived Benefit, Perceived Risk, Health Information Verification Behavior, and Usage Inclination. Results Significant positive correlations were identified between Perceived Benefit and all other dimensions. Furthermore, a significant positive correlation was observed between Health Information Verification Behavior and Usage Inclination. This study provides empirical evidence that individuals in Turkish society have developed a balanced, cautious, and adaptive behavioral profile. Conclusions While participants perceive AI-based applications as beneficial, they remain acutely aware of associated risks. Health managers must recognize potential shifts in the healthcare system and patient-physician relationships as AI usage grows. The results are essential for healthcare administrators to navigate the shifting dynamics of the patient-physician relationship and facilitate safe technological integration. This scale empowers health institutions to align with the expectations of digitally engaged patients while enhancing their strategic health frameworks.","url":"https://doi.org/10.21203/rs.3.rs-9293831/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9293831/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9458448/v1","name":"Artificial intelligence-assisted prediction of body condition scores from radiographs of post-mortem free-roaming cats","source":"preprints","abstract":"Abstract Background Body condition assessment is a critical component in evaluating general health and nutritional status, providing essential guidance for medical decision-making, welfare evaluation, and population-level monitoring. The body condition score (BCS) system has been widely adopted as a surrogate measure of body composition in animals. The aim of this study was to evaluate the feasibility of estimating BCS from radiographs of deceased free-roaming cats (FRCs) leveraging artificial intelligence (AI). We first established an automated image analysis pipeline to extract features from lateral whole-body radiographs of postmortem radiographs of FRCs (N = 257) using EfficientNetB3. The feature space was expanded tenfold through data augmentation (N = 2,570), and the dataset was divided into training (70%), validation (15%), and test (15%) sets at the source image level for each animal. We then compared multiple regression approaches to identify robust predictors of feline BCS and to provide a comprehensive evaluation of model performance. Results Kernel Ridge regression and Lasso were the top-performing models, exhibiting the highest explained variance on the test set ( R 2 = 0.7199 and 0.7163, respectively) and the lowest prediction error (MAE = 0.6633 and 0.6645). Support Vector Regression and XGBoost showed intermediate performance, with reduced explanatory power ( R 2 = 0.6498 and 0.6334) and higher error rates (MAE = 0.7468 and 0.7659). Random Forest exhibited further performance degradation ( R² = 0.5639; MAE = 0.8202). The multi-layer perceptron model showed the weakest performance across all evaluated metrics ( R 2 = 0.4584; MAE = 0.9251). Conclusions This study offers a technical validation of regression-based modeling for predicting postmortem BCS from radiographs of FRCs. Our comparative analysis revealed that regularized regression models, particularly Lasso, performed more effectively than more complex nonlinear models such as XGBoost and MLP. Future studies are needed to assess generalizability across diverse populations, imaging conditions, and prospective clinical or field settings.","url":"https://doi.org/10.21203/rs.3.rs-9458448/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9458448/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.22.26358635","name":"Neuro-Symbolic AI for Automated Pathology Quality Measurement","source":"preprints","abstract":"Background Clinical quality measurement often relies on manual abstraction of medical records, an approach that is costly, burdensome, and often infeasible for measures requiring interpretation of narrative text; these constraints have shaped measure development itself, filtering out clinically important measures that are too difficult to operationalize. We evaluated whether neuro-symbolic artificial intelligence (NSAI), which combines large language model extraction with symbolic reasoning, could reliably abstract complex quality measures from narrative pathology reports. Methods The NSAI system decomposes each measure into atomic questions and is aligned to real-world reports through case-based refinement, an iterative human-in-the-loop process. Using 2,000 independently double-abstracted reports, we compared NSAI-based abstraction against trained human abstractors across four pathology quality measures established by the College of American Pathologists. Results The NSAI system’s agreement with the adjudicated gold standard (Cohen’s κ = 0.95) matched or modestly exceeded that of the trained human abstractors measured against the same standard ( κ = 0.92), with particularly strong performance on Gastrointestinal Metaplasia (CAP 43). In component analyses, case-based refinement drove the largest accuracy gains (up to Δ κ = +0.25), whereas architectural decomposition primarily reduced performance variance across language-model backends more than tenfold, a property essential for clinical deployment. Conclusions These findings suggest that automated abstraction could enable census-level quality measurement, reduce reporting burden, and expand the range of clinically meaningful measures that can be operationalized from narrative clinical documentation. Plain-Language Summary Checking whether cancer pathology reports meet quality-of-care standards usually requires trained staff to read each report by hand, which is slow and costly. We tested an artificial-intelligence system that combines language models with rule-based logic to do this automatically, and found that it agreed with an expert-reviewed answer key as well as, or slightly better than, trained human reviewers, while producing consistent results across several different underlying AI models. Such a system could let health organizations monitor care quality across every patient record rather than a small sample, and measure aspects of care that are currently too labor-intensive to track.","url":"https://doi.org/10.64898/2026.07.22.26358635","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358635","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9616447/v1","name":"The Authentic Professional and Artificial Personal Development Among International Medical Students Across Cultures: A Mixed- Methods Study","source":"preprints","abstract":"Abstract While many international medical students may possess theoretical knowledge of cultural differences, their cultural intelligence for maneuvering and adapting cross-culturally is limited. This study aims to explore the mechanisms underlying cultural intelligence, its impact on the personal and professional identities of international medical students, and the solutions and skills they have developed to navigate and adapt cross-culturally. This study employed a convergent parallel mixed-methods approach. Universities listed by the Ministry of Education of China (MOE) for Bachelor of Medicine and Bachelor of Surgery (MBBS) programs conducted in English were included. While there appears to be some connection between personal and professional identity formation, authentic personal development was often absent. This study has demonstrated that students can develop cultural intelligence and confidence to navigate new environments and embrace diverse cultural experiences by following five constructive steps, thereby improving their personal and professional growth.","url":"https://doi.org/10.21203/rs.3.rs-9616447/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9616447/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9942968/v1","name":"Towards Trustworthy Breast Cancer Diagnosis: A Comparative Explainability Stability Study of DenseNet121 and Vision Transformers","source":"preprints","abstract":"Abstract Breast cancer is among the top cancer killing diseases among women worldwide and there is a need of reliable histopathology image analysis for early cancer diagnosis and treatment planning. Deep learning models are black-box models and have proven to be very successful for medical image classification applications but they are not readily interpretable by the clinician, thereby decreasing the interpretability and trustworthiness of the deep learning model. Explainable Artificial Intelligence (XAI) Artificial Intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) offer visual explanations for predictions, but little is known about the stability of such explanations in the presence of changes to the input data, especially whether there is a difference in stability between the types of models (convolutional and transformer-based). The classification of breast histopathology images is done by a comparative explainability stability analysis of DenseNet121 and Vision Transformer (ViT) models on the Breast Histopathology Images dataset in this study. To compare, trained architectures, with the same preprocess and optimization parameters. Explanations for prediction interpretation were generated using SHAP, and a stability evaluation was done with the Gaussian noise, brightness shift and contrast scaling transformations. The strength of explanations was quantitatively analyzed using Cosine Similarity, Mean Absolute Difference (MAD) and Structural Similarity Index Measure (SSIM). Moreover, to confirm the explanation stability differences statistically between two architectures, paired t-tests and Cohen’s d effect size analysis was implemented. Experimental results show that DenseNet121 outperforms other networks in terms of classification accuracy (91 percent accuracy and 0.965 ROC-AUC), as well as generating more localized explanations and clinically interpretable explanations. The Vision Transformer, however, outperforms with accuracy of 89 percent and 0.948 ROC AUC which showed a consistent explanation even in the presence of perturbations. Based on statistical analysis, significant differences among the two models in terms of the stability of the explanations obtained were found (p","url":"https://doi.org/10.21203/rs.3.rs-9942968/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9942968/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.14293/pr2199.004344.v1","name":"\"Unlocking Pharma's Potential: The AI-aided NLP Revolution\"","source":"preprints","abstract":"The pharmaceutical industry is on the way of a revolution, driven by Artificial Intelligence (AI)-aided Natural Language Processing (NLP). This paper explores the methodology and vast applications, current role, and prospects of NLP in pharma, highlighting its potential to unlock insights from vast amounts of unstructured data. Currently NLP is applicable and transforming clinical trial design, literature review, regulatory compliance, and patient engagement by studying the prescriptions and medical report with developed EMR (Electronic Medical Records), NLP also significantly impact in Ayurveda bridges ancient Sanskrit medical knowledge with modern technology, utilizing AI to detect classical texts, automate clinical documentation, by applying semantic analysis to texts like Charak Samhita and Sushruta Samhita, and Dosha-based treatment recommendations. This becomes beneficial, leading to improved efficiency, reduced costs, and enhanced patient outcomes, despite challenges and limitations such as data quality, regulatory framework interpretation, and training. NLP's prospects are bright, with potential applications in personalised medicine, real-world evidence analysis, and integration with other AI technologies. There are some software, apps and pharma companies which has adopted NLP. This paper provides a roadmap for pharma innovators to adopt NLP, leveraging its power to drive innovation and efficiency.","url":"https://doi.org/10.14293/pr2199.004344.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.004344.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-9434509/v1","name":"Score-Enhanced Eigen-CAM (SE-CAM) Gradient Free Visual Explanation Method","source":"preprints","abstract":"Abstract The contribution of artificial intelligence to trust-sensitive domains, such as the military and medical domains, is challenging. The main reason that artificial intelligence is challenging to accept in trust-sensitive domains is the black-box nature of deep-learning algorithms. In order to open up the Black-Box nature of deep learning algorithms, explainable artificial intelligence (XAI) comes into the picture. The XAI branch of artificial intelligence's main contribution is to provide a layman's understanding explanation to develop trust in the decisions taken by deep learning models. In this article, we propose an XAI technique using the fusion of Eigen-CAM and Score-CAM. Eigen-CAM and Score-CAM are gradient-free methods that do not suffer from gradient saturation. Therefore, we propose the Score-Enhanced Eigen-CAM (SE-CAM), the fusion of Score-CAM and Eigen-CAM. In addition to SE-CAM, the research work also trained the VGG16 architecture for multi-class classification of chest X-rays. In addition, we examined SE-CAM on the pretrained DenseNet121 using the ChestXpert dataset. The VGG16 model achieves 94.58% accuracy for the classification of COVID-19, pneumonia, lung opacity, and normal chest X-rays. To evaluate the SE-CAM performance, the % average drop and % increase in confidence were used. For the VGG16 and COVID-19 datasets, the % average drop was 18.41 and the % increase in confidence was 45.03. The SE-CAM model outperformed the base XAI models by 5.73 % on the % average drop and % increase in confidence by 7.7 %.","url":"https://doi.org/10.21203/rs.3.rs-9434509/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9434509/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.06.23.26356383","name":"Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study","source":"preprints","abstract":"Summary Background A comprehensive transthoracic echocardiogram involves the assessment of over 70 parameters, placing a substantial burden on sonographers and physicians for manual annotation with considerable inter-observer variability. Prior open-source segmentation models have largely addressed 2D B-mode ventricular function, leaving a gap in the spectral Doppler and atrial measurements required for valvular and diastolic assessment such as velocity-time integral (VTI) and atrial chamber size. Methods In this retrospective multi-cohort study, we developed EchoNet-Segmentation, comprehensive task-specific deep learning segmentation models for left and right atrial area and VTI Doppler measurements. Training used 186,712 sonographer-annotated images from 93,978 studies (56,855 patients) at Cedars-Sinai Medical Center (CSMC). Performance was evaluated on a held-out CSMC test set, a CSMC temporal split, an external Kaiser Permanente Northern California cohort, and the public MIMIC-Echo dataset. Findings On the CSMC held-out test set, our AI models showed strong agreement with sonographer measurements, with R² of 0.817–0.882 and mean absolute error (MAE) of 1.13–3.80 cm for automated VTI measurements, and R² of 0.675–0.747 and MAE of 2.48–2.52 cm² for left and right atrial area segmentation. Performance was consistently confirmed on the CSMC temporal split (VTI: R² 0.606–0.866, atrial area: R² 0.694–0.705) and on the KPNC external cohort (VTI: R² 0.575–0.859, atrial area: R² 0.803–0.876), on the MIMIC-Echo dataset. Robustness was demonstrated on a different vendor’s machines and across subgroups. EchoNet-Segmentation outperformed an open-source medical image foundation model with bounding-box, point prompt configurations on R², MAE, and Dice score on both held-out test dataset and MIMIC apical four-chamber data. Interpretation EchoNet-Segmentation is the first open-source framework that delivers accurate, generalizable automated measurement across several key routine echocardiographic parameters, supporting end-to-end automation of clinically important echocardiographic assessments. Public release of model weights, code, and demonstration tools can facilitate reproducibility, research use and clinical deployment. Funding Funding Statement: This work was supported by NIH NHLBI grants R00HL157421, R01HL173526, and R01HL173487 to D.O. Research in context Evidence before this study We searched PubMed and arXiv from database on April 1, 2026, for studies of deep learning-based segmentation of echocardiographic images, using the terms (“echocardiography” OR “echocardiogram”) AND (“deep learning” OR “artificial intelligence”) AND (“segmentation” OR “measurement”). Prior work has demonstrated automated segmentation of cardiac chambers and left ventricular ejection fraction estimation, and a small number of studies have reported deep learning models for velocity-time integral (VTI) or atrial size measurement. However, openly available models and code remain largely restricted to left ventricular structures, ejection fraction, and wall thickness, and commercial tools remain proprietary. To our knowledge, no open-source framework has comprehensively addressed VTI measurements across multiple Doppler views together with atrial chamber size in a single, reproducible toolkit, and existing models have not been systematically benchmarked against general-purpose medical-image foundation models on echocardiographic tasks. Added value of this study We developed and validated EchoNet-Segmentation, a suite of task-specific deep learning models for several clinically important echocardiographic parameters: left and right atrial area and five VTI measurements (aortic valve, mitral valve, left ventricular outflow tract, right ventricular outflow tract, and pulmonary valve). The models were trained on the largest real-world collection of sonographer-annotated echocardiograms reported to date (186,712 images from 56,855 patients) in an academic center in the United States and showed strong agreement with sonographer measurements on a held-out internal test set, a temporal split cohort, an external cohort from a different health system, and a publicly available cohort recorded on a different vendor’s ultrasound machines. EchoNet-Segmentation outperformed the publicly released medical-image foundation model (MedSAM2) on cardiac chamber segmentation across both internal and public dataset benchmarks. All model weights, training and inference code, demonstration tools, and the manual segmentation masks used for the public benchmark are openly released. Implications of all the available evidence EchoNet-Segmentation enables end-to-end automation of routine transthoracic echocardiographic measurements with previously released open-source models. By openly releasing model weights, training code, and benchmark data, this work provides a reproducible foundation that the broader research and clinical community can build on, fine-tune for specific populations or imaging protocols, and integrate into clinical workflows. Prospective validation and randomized studies will be needed to define the impact of automated measurement on diagnostic accuracy, workflow efficiency, and clinical outcomes.","url":"https://doi.org/10.64898/2026.06.23.26356383","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.23.26356383","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.12688/f1000research.169587.2","name":"Artificial Intelligence in Diagnosing and Treating Temporomandibular Joint Disorders: A Review","source":"preprints","abstract":"Introduction: With the rapid advancement in artificial intelligence (AI), its integration into medical fields has gained momentum. AI technologies is increasingly applied for diagnosing and managing temporomandibular joint disorders (TMD), which affect one of the most complex joints in the human body. This review aims to highlight the AI-driven approaches in diagnosing and management of TMD. Methods A comprehensive literature search conducted using PubMed, Web of Science, and Google Scholar, Scholar to identify relevant studies published in English-language studies published since 2007. References from relevant articles were also examined. Results A total of 21 studies met the inclusion criteria, covering diverse AI applications in TMD. These studies included AI-driven medical image segmentation (3), juvenile idiopathic arthritis (3), temporomandibular joint osteoarthritis (TMJOA) detection (4), Temporomandibular joint (TMJ) articular disc displacements and deformities (5), decision support systems (5), and AI-based sound analysis for TMJ assessment (1). The findings suggest that AI, particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated promising accuracy in detecting and classifying TMD and its related pathologies. AI automates segmentation, improves diagnostic consistency, and assists clinicians through predictive modelling. Discussion Despite advancements, challenges remain, as many studies rely on small datasets, limiting generalizability. The absence of standardized evaluation metrics and external validation hinders clinical adoption. Ethical concerns, data privacy issues, and workflow integration pose additional barriers. Conclusion AI is emerging as a valuable tool in TMD diagnosis and management, promising improved accuracy and efficiency, but however, further research involving larger datasets, standardized validation frameworks, and clinically integrated systems is required before widespread clinical implementation can be achieved.","url":"https://doi.org/10.12688/f1000research.169587.2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.169587.2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9538324/v1","name":"Evaluation of attitudes, uses and perceptions of Artificial Intelligence tools among students of the Faculty of Medicine and Pharmacy of Rabat using the MAIRS-MS scale","source":"preprints","abstract":"Abstract Artificial Intelligence (AI) is becoming a transformative change in the medical and health care disciplines, in improving learning, decision-making, and clinical training. The study examines AI perceptions, attitudes, and uses and readiness with the aid of the MAIRS-MS scale among medical and pharmacy students. This was cross-sectional quantitative research carried out among a sample size of 245 medical and pharmacy students in faculty of medicine and pharmacy, Rabat, Morocco, which employed stratified sampling. The data was gathered using online questionnaires that were structured and included the validated MAIRS-MS scale which had been translated to and culturally adapted into Classical Arabic. The tool evaluated the sociodemographic, AI use, attitudes, and preparedness. Validity and reliability were ensured with the help of expert review and pilot testing. Data analysis was done using SPSS statistical test followed by descriptive statistics. Informed consent and ethical approval were novel. Out of 245 students, females (53.5%) were a little more than males (46.5%), with 59.2% of pre-clinical and a mean age of 20.89 / 2.16 years being displayed. The usage of the AI was high and was 83.5% attributed to 1 or more hours per week and 41.7% to daily usage. The cognitive scores were 26.40 + 3.55 and positive understanding was found. Satisfaction was high (8.1 ± 1.3). The results confirm the importance of incorporating organized AI education into medical courses with the goal of developing competencies, which are responsible and efficient in further healthcare practice.","url":"https://doi.org/10.21203/rs.3.rs-9538324/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9538324/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202607.1271.v1","name":"Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation","source":"preprints","abstract":"Contemporary intensive care is operating in an environment with high complexity cases, large volumes of information, and vast physiological, biological and therapeutical data, collected from laboratory results, investigations, therapies for organ support, collected from systems that operate in parallel. The lack of interoperability contributes to infor-mation overload, alarm fatigue, and delayed decision-making. The Smart ICU concept has emerged to address these limitations by integrating medical devices, information systems and artificial intelligence into a unified system that allows interoperable data integration and predictive analytics. Aim: The purpose of this article is to provide a narrative review of the Smart ICU concept, with a specific focus on the cardiac intensive care unit. It summarizes Smart ICU architec-ture, data integration, clinical support and applicability in monitoring perioperative in-flammation in cardiac surgery. We describe the Smart ICU architecture, from data acquisition to storage and analytics, highlighting the difference between Smart ICU, Artificial Intelligence and Tele-ICU, and we underlie the predictive analytics as a supportive tool, and its influence on clinical out-come. Cardiac ICU application: Cardiac ICUs offer a data-dense, temporally well-defined model following cardiac surgery with cardiopulmonary bypass, where data concerning patients hemodynamics, perfusion data, biological and inflammatory markers intertwine. Cardiac Smart ICU models could recognize early signs of hemodynamic compromise and low car-diac output states and identify early indicators of post-cardiac surgery complications. Neutrophil activation and complete blood count–derived indices may be used as dy-namic biological data for Smart Cardiac ICU models. Conclusion: The Smart Cardiac ICU may support earlier risk stratification, therefore earlier diagnostic and therapeutic interventions, but its clinical value requires prospective, mul-ticentre validation. Cardiopulmonary bypass–induced inflammation may offer an ideal setting to integrate physiological, procedural, and immunological data into bedside pre-dictive models.","url":"https://doi.org/10.20944/preprints202607.1271.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.1271.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.06.03.26354859","name":"Quantifying Cancer Clinical Trial Eligibility Using Artificial Intelligence-Based Matching","source":"preprints","abstract":"PURPOSE To develop and validate an artificial intelligence-enabled platform that converts unstructured cancer trial eligibility criteria into structured queries and quantifies trial eligibility across advanced/metastatic cancer trials. METHODS We downloaded actively recruiting US interventional treatment trials for advanced/metastatic breast cancer, colon cancer, and non-small cell lung cancer from https://ClinicalTrials.gov . Medical oncologists created 24 synthetic patient vignettes. A large language model converted trial eligibility criteria into Structured Query Language (SQL) code and patient information into structured records, enabling automated matching. Cancer details and treatment history were considered, but not laboratory results or comorbidities. Validation included physician editing of generated eligibility code for 30 trials, and blinded physician eligibility assessment for five trials. We then evaluated how age, ECOG performance status, sex, and ZIP code affected the number of eligible trials. RESULTS Of 833 candidate trials, 746 met inclusion criteria. In physician review of 30 trials, edits to generated SQL did not change any of 720 trial-patient eligibility determinations for 24 synthetic patients. In blinded validation across 120 trial-patient pairs, automated matching achieved 97% accuracy. Across synthetic patients, eligible trials ranged from 31 to 258 when there were no geographic restrictions. Eligibility decreased markedly with worse performance status and with geographic restriction (both p CONCLUSION AI-based structuring of trial eligibility criteria can support accurate, scalable measurement of potential cancer trial eligibility. In this demonstration, performance status, geography, and age were major determinants of eligibility across the active metastatic trial landscape.","url":"https://doi.org/10.64898/2026.06.03.26354859","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.03.26354859","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202608.0697.v1","name":"The Impact of Innovation on the Performance and Safety of Medical Electronic Devices","source":"preprints","abstract":"The rapid transition to digital medicine has transformed isolated electronic medical devices into intelligent, interconnected ecosystems capable of predictive analytics and continuous monitoring. The expansion of hyperconnectivity introduces significant vulnerabilities, including increased cyber risks, hardware bottlenecks, data fragmentation, and interoperability challenges. This study investigates the importance of technical creativity by integrating artificial intelligence (AI), edge computing, and Internet of Medical Things (IoMT) architectures to improve diagnostic accuracy, operational reliability, and safety of medical devices through electronic technologies. Using a comprehensive framework analysis, the research evaluates advanced architectural paradigms—such as digital twin simulations, federated learning, adaptive controls, and lightweight cryptographic solutions—along with emerging epistemic sensing concepts such as orthosensors and pseudo-ontosensors. The findings demonstrate that software algorithms, AI models, and cybersecurity frameworks now consume over half of modern biomedical R D investments, with AI-based surgical platforms achieving up to a 25% reduction in operative times and a 30% decrease in intraoperative complications. It is concluded that achieving sustainable clinical adoption requires a balance between rapid technological innovation and standardized regulatory governance, robust cybersecurity, human-centered UI/UX design, and ongoing interdisciplinary collaboration.","url":"https://doi.org/10.20944/preprints202608.0697.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.0697.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.64898/2026.06.10.26355372","name":"A Multi-Agent clinical pre-consultation system for structuring noisy patient reported information into clinical reports and AI-ready data","source":"preprints","abstract":"In primary care and outpatient settings, clinically important patient information is often embedded in fragmented, ambiguous, repetitive, and noisy communication between physicians and patients. This limits physicians’ ability to obtain a clear pre-consultation overview of symptoms, history of present illness, and visit intent, while also preventing real world clinical dialogues from being reused in hospital information systems and medical artificial intelligence applications. To address this challenge, we developed PCRAgent, a centrally coordinated multi agent framework for pre-consultation clinical information organization, shifting information processing upstream from the consultation. Guided by physician inquiry logic, PCRAgent identifies, extracts, corrects, and standardizes patient-reported information from noisy consultations. Its coordinated modules including error detection, semantic editing, output control, contextual memory, and intent recognition enable robust parallel handling of spelling errors, repetitions, grammatical inconsistencies, medical ambiguities, and non-medical interference. A traceable edit list records intermediate corrections and context, allowing iterative refinement without redundant modifications. PCRAgent generates two complementary outputs. One is a Pre-Consultation Clinical Report for rapid physician review. The other is a Structured Clinical Conversation Dataset for hospital data construction and downstream AI applications. In evaluations using 220,000 strongly perturbed consultations, PCRAgent maintained high robustness, achieving a clinical information accuracy of 4.99 out of 5 and key element completeness of 5 out of 5, outperforming GPT4o. Expert review of Chinese and English dialogues confirmed high clinical accuracy of 4.85 out of 5 and high security of 4.79 out of 5. Multicenter validation in real world outpatient workflows further demonstrated practical utility. These results indicate that PCRAgent improves outpatient workflow efficiency, reduces physicians’ cognitive burden, ensures completeness of pre-consultation clinical information, supports more focused and accurate clinical decision-making, and enables high-quality reuse of clinical data for downstream medical artificial intelligence applications.","url":"https://doi.org/10.64898/2026.06.10.26355372","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.10.26355372","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.08.15.26360518","name":"Triage and Referral Behavior Across Patient-Facing Medical AI Products","source":"preprints","abstract":"ABSTRACT We evaluated nine patient-facing artificial intelligence products using 60 physician-developed standardized clinical cases and 540 multi-turn simulated patient encounters. Although overall triage accuracy showed no statistically significant difference across product categories, referral behavior differed substantially. Branded health AI products more frequently over-triaged low-acuity cases (28% vs 3% vs 2%) and recommended affiliated, fee-requiring clinical services. These findings suggest evaluation of patient-facing medical AI should assess referral behavior alongside overall triage accuracy.","url":"https://doi.org/10.64898/2026.08.15.26360518","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.15.26360518","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202608.1880.v1","name":"Navigating the Eukaryotic Paradox: Cross-Kingdom Insights for AI-Driven Clinical Repurposing Strategies Against Antifungal Resistance","source":"preprints","abstract":"The escalating crisis of invasive fungal infections and rising antifungal resistance demand rapid therapeutic innovation.With a critically narrow clinical arsenal, artificial intelligence (AI) and machine learning (ML) have emerged as pivotal clinical strategies to accelerate drug repurposing, providing a rapid alternative to de novo drug discovery. However, translational success in medical mycology faces a major biological hurdle: the “eukaryotic paradox”the shared genomic, metabolic, and structural homology between fungal cells and human hosts.This review provides a critical, cross-kingdom comparative analysis of AI-driven drug repurposing as a therapeutic clinical strategy. By evaluating structural barrier hierarchies, biophysical transport bottlenecks, and computational failure modes across bacterial, viral, and fungal domains, this review contextualizes why medical mycology represents one of the most arduous frontiers in computational pharmacology.It examines how AI models struggle against fungal cell walls, extracellular polymeric substance (EPS) matrices in resilient biofilms, dynamic morphological dimorphism, and an acute deficit of high-resolution 3D protein structures. Furthermore, next-generation computational clinical strategies, ranging from Physics-Informed Neural Networks (PINNs) and transcriptomic profiling to microfluidic organ-on-a-chip validation platforms, are outlined to address and overcome these resistance mechanisms. Lastly, this review underscores that tailored, physics-informed AI paradigms are urgently required to formulate effective clinical strategies against emerging resistant fungal pathogens.","url":"https://doi.org/10.20944/preprints202608.1880.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1880.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9869041/v1","name":"Emergent Inter-Agent Conflict in Multi-Agent AI Systems: An Experimental Study of Objective Divergence","source":"preprints","abstract":"Abstract The rapid emergence of multi-agent artificial intelligence ecosystems has introduced a critical systems-level problem that contemporary alignment research has largely failed to address: inter-agent objective divergence. While existing literature remains predominantly focused on human–AI alignment, the stability of AI-to-AI interaction under heterogeneous optimization constraints remains insufficiently characterized despite the accelerating deployment of autonomous collaborative agents in decision-critical environments. This work investigates the emergence of measurable semantic conflict among interacting AI agents conditioned for safety prioritization, efficiency maximization, creativity amplification, and verification-oriented reasoning. A controlled experimental framework was constructed across high-risk and cognitively complex domains including healthcare, autonomous warfare, economic strategy, education, and urban infrastructure planning. Semantic divergence was quantified through embedding-based similarity analysis and consensus degradation metrics. The results demonstrate that inter-agent conflict is neither stochastic nor negligible. Divergence intensity increased systematically in ethically constrained and high-uncertainty environments, with safety-oriented and creativity-optimized agents exhibiting severe semantic separation in medical and warfare contexts. Verification-oriented agents consistently destabilized speculative outputs, revealing measurable coordination instability even in lightweight collaborative architectures. Critically, these adversarial interaction patterns emerged without consciousness, emotion, self-preservation, or intentional hostility. The findings therefore suggest that large-scale instability in future autonomous ecosystems may arise not from artificial sentience, but from recursive optimization incompatibilities operating across distributed agentic systems. This work establishes inter-agent alignment as a foundational challenge in next-generation artificial intelligence infrastructure and argues that AI-to-AI alignment may become as consequential as human–AI alignment in the governance of autonomous systems.","url":"https://doi.org/10.21203/rs.3.rs-9869041/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9869041/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.12688/f1000research.138616.3","name":"Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review","source":"pubmed","abstract":"In basic motor skills evaluation, two observers can eventually mark the same child's performance differently. When systematic, this brings serious noise to the assessment. New motion sensing and tracking technologies offer more precise measures of these children's capabilities. We aimed to review current development, validation and use of artificial intelligence-related technologies that assess basic motor skills in children aged 3 to 6 years old.","url":"https://doi.org/10.12688/f1000research.138616.3","authors":["Figueroa-Quiñones J","Ipanaque-Neyra J","Gómez Hurtado H","Bazo-Alvarez O","Bazo-Alvarez JC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.12688/f1000research.138616.3","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8977743/v1","name":"The Attitudes Towards Artificial Intelligence Questionnaire (AAIQ) for Medical Students: Psychometric Properties of the Turkish Version","source":"preprints","abstract":"Abstract Background: The rapid integration of artificial intelligence (AI) into healthcare practice has resulted in new demands on medical education systems. This study aimed to adapt the Attitudes Towards Artificial Intelligence Questionnaire (AAIQ), originally developed for health profession students, into Turkish and examine its psychometric properties among Turkish medical students. Method: Through convenience sampling, 238 medical students (56.7% female; M = 22.32, SD = 2.58) from various state and foundation universities participated. The scale adaptation process followed a standardized translation and back-translation protocol. Confirmatory factor analysis (CFA) was conducted to examine construct validity, while convergent validity was tested via correlations with digital nativeness and individual innovativeness measures. Internal consistency was evaluated using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). Results: CFA supported the AAIQ's original unidimensional structure, which demonstrated good model fit indices: χ²(35) = 42.51, χ²/df = 1.21, SRMR = .06, RMSEA = .07, TLI = .99, and CFI = .99. Reliability analyses indicated high internal consistency, with α = .88, CR = .95, and AVE = .67. Notably, 81.1% of participants expressed support for the integration of AI into medical education, although only 21.0% had received formal training related to AI. Conclusion: The Turkish version of the AAIQ shows strong validity and reliability, making it a robust tool for evaluating medical students' attitudes toward artificial intelligence.","url":"https://doi.org/10.21203/rs.3.rs-8977743/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8977743/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.08.11.26360189","name":"Vision and Language Models for Classifying Maxillary Sinus Disease on Cone-Beam Computed Tomography: A Transparent Multimodal Benchmark","source":"preprints","abstract":"Summary Background Cone-beam computed tomography (CBCT) frequently captures the maxillary sinuses incidentally, and reliable automated detection of sinus abnormality is clinically relevant. Unlike most vision–language benchmarks in medical imaging, which pair images with pre-existing, human-authored clinical reports, findings text can also be generated directly by a large language model from the image itself—raising the question of how much diagnostic value such AI-derived text carries, and whether that value depends on independent verification. Multimodal artificial intelligence (AI) benchmarks risk overstating performance if the provenance of each input—image, raw AI-generated text, or radiologist-verified text—is not clearly separated and reported. Methods We used 300 mid-sagittal CBCT slices from the MMDental dataset. ChatGPT generated findings text and a provisional normal/abnormal label for every slice (majority vote, three independent readings from the image alone); primary classification performance was assessed on this full, unfiltered set (n=300). A radiologist then independently reviewed each case’s image together with ChatGPT’s description, producing their own diagnosis; three cases were excluded as insufficient, yielding 297 confirmed cases. On this subset, every model was retrained and re-evaluated under identical 10-fold cross-validation on both the provisional ChatGPT-only labels (“pre”) and the radiologist-confirmed labels (“post”), isolating the effect of label provenance from image or architecture. Eight vision architectures, seven language classifiers, and five VLMs were evaluated throughout; three generative models performed exploratory note-drafting. Findings Raw ChatGPT-generated text produced the highest performance of any modality or condition: language models reached near-ceiling AUC (0·992 to 1·000, n=300), exceeding every vision model (AUC 0·799 to 0·880) and every VLM image-only probe (AUC 0·63 to 0·69). On the 297-case pre/post analysis, this advantage depended heavily on label source: language and text-derived VLM performance fell substantially from ChatGPT-only to radiologist-confirmed labels (e.g. BERT-base AUC 0·999 to 0·837), while vision-model performance was stable or modestly improved (e.g. DenseNet-121 0·867 to 0·891). The radiologist reclassified 62 of 297 cases (21%) relative to ChatGPT’s provisional read, and a meaningful proportion of raw ChatGPT text was clinically uninterpretable or unsupported by the imaging. Interpretation As shown here for the first time, raw, image-derived AI-generated text yields the highest apparent classification performance in this benchmark, but this reflects the text’s alignment with its own self-generated labels rather than verified diagnostic content, and a substantial share of that text is not clinically explainable. Radiologist-confirmed text and labels give a lower but trustworthy estimate of true performance, on which convolutional neural network (CNN) vision models remain a stable, comparatively inexpensive baseline. Multimodal dental AI should report performance separately by modality and label provenance rather than pooling headline metrics. Research in context Evidence before this study We searched PubMed with the terms “maxillary sinus”, “CBCT”, “deep learning”, “artificial intelligence”, and “vision–language model” from January 2015 to July 2026. Published benchmarks have predominantly evaluated single-modality convolutional networks for dental CBCT tasks, with limited transparency about how AI-generated text inputs are produced or verified. No study had systematically compared image-only, language-only, and VLM families for maxillary sinus classification on the same dataset under matched cross-validation, nor had the extent to which apparent multimodal performance depends on whether AI-generated text is used raw or independently verified been explicitly characterised. Added value of this study Unlike prior vision–language benchmarks in medical imaging, which typically pair images with pre-existing, human-authored clinical reports, this study evaluates text generated directly by an LLM from the image itself, independent of any prior radiological report. This design isolates a question distinct from conventional report-grounded VLM evaluation: not how well a model can classify existing clinical documentation, but how much apparent diagnostic value an LLM’s own image-derived description carries—and how much of that apparent value depends on whether the description is used raw or independently verified. We show that raw, unverified ChatGPT-generated text produces the highest classification performance of any modality evaluated (AUC up to 1·000), exceeding image-only and image-plus-text models—but that a meaningful proportion of this raw text is clinically uninterpretable, and that its apparent advantage collapses substantially once evaluated against a radiologist-confirmed reference standard on the same cases. Trained vision models, by contrast, remain stable regardless of label source, indicating that their signal derives from image content rather than label provenance. We further show that ViTs underperform CNNs at dental-imaging dataset scale. Implications of all the available evidence Raw AI-generated text can substantially outperform image-based classification on headline accuracy metrics, but this performance is not synonymous with clinical trustworthiness or explainability and should not be reported or deployed without independent verification. CNNs remain reliable, comparatively low-cost baselines for sinus CBCT screening at small dataset sizes. Multimodal dental AI benchmarks must report modality-specific results transparently and distinguish raw from verified text inputs; headline metrics should not be pooled across these conditions without disclosing their provenance. Generative VLMs show near-term utility as documentation aids rather than autonomous diagnostic systems, and full three-dimensional CBCT analysis remains the essential next step for clinical translation in the maxillofacial trauma setting.","url":"https://doi.org/10.64898/2026.08.11.26360189","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.11.26360189","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.31234/osf.io/djhgx_v3","name":"Responsibility Gap in Novel Assistive Technology for Visually Impaired People","source":"preprints","abstract":"The rapid advancement of artificial intelligence (AI) has led to its increased integration into domains involving physical agency. While traditional AI applications maintain a clear division of responsibility with the decision-action loop being fully controlled by the human user (e.g., AI-assisted medical diagnosis) or the AI-based system (e.g., autonomous driving), novel assistive technologies disrupt this boundary. Here, we examine the responsibility gap in the case of HANS (Human-AI Navigation System), a system designed to assist visually impaired users in grasping. HANS splits the perception-action loop across the system boundary, with the AI making continuous spatial decisions and guiding the user via tactile vibrations, and the user executing the physical movement. The collaborative tandem enhances the capability of each agent and restores autonomy of the visually impaired user at the cost of a blurred responsibility boundary.","url":"https://doi.org/10.31234/osf.io/djhgx_v3","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31234/osf.io/djhgx_v3","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.08.19.745737","name":"A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics","source":"preprints","abstract":"Dynamic interactions between T cell receptor (TCR) and peptide-human leukocyte antigen (pHLA) complexes are central to peptide-specific immune recognition, influencing T cell activation and immune responses. While structural biology has provided valuable static structures of TCR-pHLA complexes, systematic datasets capturing their dynamic and interaction patterns remain limited. Here, we present DynaTPH, a curated structural dynamics dataset of human TCR-pHLA complexes. DynaTPH integrates TCR-pHLA structures, covering both HLA class I and class II complexes, and extends these static structural resources with standardized molecular dynamics simulations and derived biophysical properties. Through a multi-stage filtering procedure, we identified 256 representative complexes and performed standardized all-atom molecular dynamics simulations for each system, corresponding to a cumulative simulation time of 38.4 μs. The dataset includes static structures, trajectories, corresponding frames, and derived physicochemical properties, including hydrogen bonds, intermolecular contacts, solvent accessibility, and backbone flexibility. By capturing the conformational flexibility and dynamic interaction patterns across diverse TCR-pHLA interfaces, DynaTPH extends static structural resources with multidimensional biophysical information. This dataset enables systematic investigation of TCR-pHLA recognition dynamics and supports applications in TCR engineering, vaccine design, and immune tolerance research and artificial intelligence-driven computational immunology.","url":"https://doi.org/10.64898/2026.08.19.745737","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.19.745737","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10582232/v1","name":"Advancing Medical Technology: The Collaborative Power of a Military Hospital","source":"preprints","abstract":"Abstract The study explores the evolution and impact of medical technology within a military hospital in Ghana, focusing on the partnerships that have influenced technological progress and healthcare services. It incorporates primary data from interviews with medical personnel and secondary data from academic references, documenting the historical development of medical technologies from early diagnostic methods to modern innovations in radiology, artificial intelligence, and specialized treatments like dialysis and cardiothoracic care. The research identifies significant global events, particularly the World Wars, as catalysts for advancements such as blood transfusion techniques and electronic diagnostic tools, marking a “Golden Age” in medical technology. Furthermore, the study emphasizes the importance of institutional collaborations, including those with the Noguchi Memorial Institute, Ghana Health Service, U.S. Naval Medical Research Unit, and Korle Bu Teaching Hospital, which have strengthened research capabilities, disease surveillance, patient management, and healthcare professional training. Findings suggest that the evolution of technology at the 37 Military Hospital is driven by heightened healthcare needs and regulatory demands for teaching and referral hospitals, ultimately underscoring the critical role of partnerships and technological innovation in enhancing medical care at this key military health institution in Ghana.","url":"https://doi.org/10.21203/rs.3.rs-10582232/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10582232/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.31234/osf.io/q8rct_v1","name":"Effects of Anthropomorphic Characteristics of Chat Agents on Perceptions of Bad Advice: An Exploratory Study","source":"preprints","abstract":"Anthropomorphic form is an inherent characteristic of artificial intelligence chatbots driven by large language models and appears to have profound effects on user perceptions of chatbots. The proliferation of anthropomorphized chatbots has raised concerns about miscalibrated trust and misplaced attributions of responsibility for chatbot mistakes. An exploratory experiment examined the potential effects of anthropomorphic design in both the user interface and the linguistic output of a chatbot that provided bad advice about an everyday medical situation. In a simulated chatbot interaction, participants experienced either humanlike or machinelike language output. Additionally, the user interface either simulated typing (i.e., the typewriter effect) or presented a static text response all at once. Humanlike language increased perceived anthropomorphism and social presence but had no effect on perceived competence and trust. There were no significant effects of the typewriter effect user interface. More frequent AI usage was associated with higher chatbot trust and perceived social presence. Perceived anthropomorphism, social presence, trust, and competence were all highly intercorrelated. Interestingly, participants who were apparently unaware of the obviously bad advice tended to show considerably higher levels of chatbot perceived anthropomorphism, social presence, trust, and competence. Finally, we found some evidence that higher perceived anthropomorphism is associated with greater perceived responsibility of the chatbot itself for the advice it gave, whereas higher perceived chatbot trust and competence were associated with lesser perceived responsibility of the developing company for the chatbot’s output. Potential future research directions are discussed.","url":"https://doi.org/10.31234/osf.io/q8rct_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31234/osf.io/q8rct_v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10450434/v1","name":"Design, implementation, and evaluation of an AI-assisted music-based multimedia learning intervention for medical histology education: A mixed-methods study","source":"preprints","abstract":"Abstract Background Histology is a visually demanding subject that requires students to integrate complex microscopic images with extensive terminology, often resulting in cognitive overload and reduced engagement. Although multimedia learning, educational music, and generative artificial intelligence (AI) have each shown educational potential, their combined application in routine histology teaching has received limited attention. This study aimed to design, implement, and evaluate an AI-assisted music-based multimedia intervention for histology education. Methods A quasi-experimental mixed-methods study with a historical control group was conducted among 202 medical sciences (Medicine, Dentistry, Radiotherapy, Anatomical Sciences) students at Babol University of Medical Sciences, Iran. The intervention group (n = 101) received AI-assisted music-based multimedia clips developed using ChatGPT, DeepSeek, SUNO.ai, and Camtasia to reinforce key histology concepts after each lecture, while the historical control group (n = 101) received conventional instruction. Educational effectiveness was evaluated using the first two levels of the Kirkpatrick Evaluation Model through a validated student satisfaction questionnaire (SSIM_sQ), an intervention-specific evaluation questionnaire, qualitative thematic analysis, and final histology examination scores. Results Compared with conventional instruction, the intervention was associated with significantly higher student satisfaction across all questionnaire domains (P","url":"https://doi.org/10.21203/rs.3.rs-10450434/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10450434/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10232539/v1","name":"Large Language Models as Decision-Support Tools for Laser Dentistry: A Blinded, Expert-Rated Comparison","source":"preprints","abstract":"Abstract Purpose To compare five conversational artificial-intelligence platforms - three large language models (LLMs) and two retrieval-augmented generation (RAG) platforms - as laser-dentistry decision-support tools across three domains (periodontal/surgical, endodontic, restorative/prosthetic). Methods Twenty-four synthetic vignettes (eight per domain) were answered under an identical six-part prompt by ChatGPT 5.5, Claude Opus 4.8, Gemini 3.5 Thinking, OpenEvidence (medical-domain RAG), and Perplexity (general-purpose RAG), yielding 120 de-identified responses. Within each domain, a single blinded specialist (three total) scored all responses (1–5) for Accuracy, Safety, Freedom from Hallucinations, and Completeness, plus a composite. Matched within-vignette scores were analyzed with Friedman tests, Kendall's W, and Holm-corrected Conover post-hoc tests. Results Platforms differed on the composite (chi-square = 58.5, p","url":"https://doi.org/10.21203/rs.3.rs-10232539/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10232539/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.12688/f1000research.179775.1","name":"Artificial intelligence tools for automating assessments of reporting guideline adherence: a protocol for a systematic review","source":"preprints","abstract":"Background: Complete reporting of health-related research is necessary for users to understand, appraise, and apply research results appropriately. Reporting guidelines have been developed to support complete reporting. However, assessments of reporting guideline adherence remain inconsistent, time-consuming, and difficult to scale. Artificial intelligence (AI) tools, such as traditional natural language processing models and large language models, might provide a potential solution. While numerous AI tools have been developed, no comprehensive synthesis has been undertaken to investigate what they assess, how they are implemented and perform, and their potential utility. Objective This systematic review aims to synthesise the characteristics and findings of studies evaluating AI tools developed to assist or automate assessments of reporting guideline adherence. Methods We will search MEDLINE, Embase, Scopus, Europe PMC, ACM Digital Library, IEEE Xplore, arXiv and Cochrane Colloquium Abstracts, with no restrictions on date, language, or publication type. We will include studies that evaluate AI tools to assess adherence of health-related papers to any reporting guidelines. Two authors will independently screen records, extract data and assess risk of bias. We will extract study characteristics, AI tool details, how reporting guidelines are operationalised for AI assessment, AI implementation details, comparison details, and evaluation outcomes including agreement metrics, classification performance metrics, and utility indicators. We will present and summarise results through structured tables and plots, stratified by reporting guideline and AI tool type. Discussion This systematic review will provide a comprehensive synthesis of AI tools developed to automate assessments of reporting guideline adherence. It will provide interest holders with insights into what AI tools have been used, their implementation approaches, which AI tool types perform well, and any improvements that can be made to AI tools automating assessments of reporting guideline adherence in the future.","url":"https://doi.org/10.12688/f1000research.179775.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.179775.1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10622900/v1","name":"Teaching Practice and Pedagogical Reform of Basic Medical Courses for Intelligent Medical Engineering Majors Under Cross-Institutional Integration","source":"preprints","abstract":"Abstract Background: The integration of artificial intelligence, big data and other engineering technologies with clinical medicine has spawned the interdisciplinary major of Intelligent Medical Engineering, which requires compound talents with medical literacy, engineering capabilities and innovative thinking. The course Introduction to Basic Medicine is the core foundational curriculum of this major, yet it is a newly launched course without mature teaching paradigms and supporting experimental teaching systems. Traditional pure medical basic teaching models fail to match the cognitive characteristics and training goals of engineering-background students. This study constructs and implements a full-cycle teaching reform framework oriented toward medical-engineering interdisciplinary integration, aiming to systematically build students’ medical knowledge system, cultivate interdisciplinary thinking, and improve independent learning and practical innovation abilities. Methods: This research adopts action research combined with quasi-experimental practice. The teaching reform mainly includes two core innovations: integrated reconstruction of interdisciplinary course content and construction of a closed-loop teaching model covering pre-class preparation, in-class interactive teaching and post-class layered consolidation. A diversified evaluation system combining formative assessment and summative examination was designed. Multiple evaluation indicators including student academic performance, questionnaire reliability testing, classroom participation statistics and post-class interview feedback were used to measure teaching effectiveness. Results: The cross-school integrated teaching reform significantly improved students’ autonomous learning efficiency, mastery of basic medical knowledge and interdisciplinary cognitive ability, and effectively solved the teaching bottlenecks of new interdisciplinary courses lacking reference experience. The study also identified existing limitations including insufficient depth of interdisciplinary practical links, inadequate differentiated layered teaching and vague formative assessment standards, and put forward targeted optimization strategies. Conclusion: The medical-engineering integrated full-cycle teaching model provides a replicable practical scheme for foundational medical teaching of cross-school joint training interdisciplinary majors, and offers reference for curriculum construction and pedagogical reform of emerging medical engineering disciplines.","url":"https://doi.org/10.21203/rs.3.rs-10622900/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10622900/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202603.1560.v1","name":"The Architectural Shift: Integrating Artificial Intelligence from the Ground Up in Undergraduate Medical Education","source":"preprints","abstract":"As healthcare enters an era defined by algorithmic decision-making, the traditional medical curriculum faces a fundamental challenge. The exponential growth of medical knowledge and the ubiquity of digital health tools render pedagogical models centred on rote memorisation increasingly inadequate. This review argues that Artificial Intelligence (AI) cannot be treated as a supplemental elective or a peripheral module appended to existing curricula. Instead, AI must be integrated as a longitudinal thread woven through every phase of undergraduate medical education (UGME), from foundational sciences to clinical rotations. Drawing on recently published frameworks (2020–2026), this paper proposes a scaffolded pedagogical structure for producing “AI-adaptive” physicians across four curricular phases: pre-clinical foundations, case-based learning, supervised clinical rotations, and reformed assessment. The review examines key frameworks, including the DEFT-AI model for clinical supervision, Bloom’s Taxonomy-aligned competency mapping, and trust calibration exercises. Challenges related to faculty development, equity, and the risk of “deskilling” are discussed alongside implementation strategies. The paper concludes that failure to embed AI structurally within medical curricula risks producing a generation of graduates who are either fearful of these technologies or dangerously dependent upon them.","url":"https://doi.org/10.20944/preprints202603.1560.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202603.1560.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9336112/v1","name":"Intelligent Expert Systems for Disease Diagnosis in Resource-Constrained Health Systems: A Systematic Review of Current Trends and Research Gaps","source":"preprints","abstract":"Abstract Design and development of artificial intelligence (AI) has transformed many sectors, one of which is healthcare, through early disease prediction, improved diagnostic accuracy, and decision support systems. However, the potential of Artificial Intelligence has not been maximized for Africans. Most existing diagnostic tools use AI models developed with data from Western populations; this has therefore limited their reliability and effectiveness when applied in African contexts. This is due to differences in genetic makeup, demography, and infrastructure. Additionally, data policy, privacy, and security concerns remain some of the major challenges in adopting AI in healthcare across Africa. This research proposes the design and development of a secured multimodal AI system capable of integrating various medical data, including laboratory results, medical images, and textual health records, to support prognostic and diagnostic decisions for African patients. This AI model will be trained with African data, enabling it to handle local medical data variations and implement robust encryption and privacy mechanisms. By solving the challenges of both data security and data bias, this research aims to enhance the fairness, effectiveness, and reliability of AI-assisted medical diagnosis within African healthcare systems.","url":"https://doi.org/10.21203/rs.3.rs-9336112/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9336112/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.14293/pr2199.003665.v1","name":"Explainable AI for Clinical Decision Support in Personalized Cancer Screening Programs","source":"preprints","abstract":"Personalized cancer screening programs are increasingly being adopted to improve early detection, reduce unnecessary procedures, and optimize healthcare resources. However, many artificial intelligence-driven clinical decision support systems operate as “black boxes,” limiting clinicians’ trust and reducing transparency in medical decision-making. Explainable artificial intelligence (XAI) has emerged as a promising approach to address these concerns by providing interpretable insights into how predictive models generate recommendations for individualized cancer risk assessment and screening strategies. This study examines the role of XAI in clinical decision support for personalized cancer screening, with emphasis on improving model transparency, clinician confidence, patient engagement, and ethical accountability. The paper reviews major explainability techniques, including feature importance analysis, local interpretable model-agnostic explanations, SHAP-based visualization, and attention mechanisms, and evaluates their application in breast, lung, colorectal, and prostate cancer screening systems. In addition, the study discusses challenges associated with data quality, algorithmic bias, privacy protection, regulatory compliance, and integration into healthcare workflows. Findings indicate that explainable models can enhance diagnostic reliability and support evidence-based clinical decisions while maintaining interpretability and fairness. The paper concludes that integrating XAI into personalized screening frameworks can strengthen human–AI collaboration and contribute to more accurate, transparent, and patient-centered oncology care. Future research should focus on standardizing explainability metrics, validating models across diverse populations, and developing clinically adaptable XAI frameworks for real-world implementation.","url":"https://doi.org/10.14293/pr2199.003665.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.003665.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8217685/v1","name":"Precise ECG Diagnosis and Validation of Educational Utility for Acute Myocardial Infarction Using Deep Learning and Explainable Artificial Intelligence","source":"preprints","abstract":"Abstract Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocardial infarction (NSTEMI) and overcoming the \"black box\" nature of deep learning models remain persistent challenges. Here, we present a comprehensive deep learning framework capable of classifying STEMI, NSTEMI, and non-acute coronary syndrome (non-ACS) from 12-lead ECG images, while also localizing infarction sites. Utilizing ,2070 validated ECGs, our pipeline integrates ResNet for acute myocardial infarction detection, Faster R-CNN for ST-segment elevation localization, and an ensemble approach for final classification. The model achieved a 98.3% AUROC for detection and an overall three-class accuracy of 93.6%, with high F1 scores for identifying infarction territories. To address interpretability, we developed an explainable AI (XAI) web viewer that visualizes detected regions. Furthermore, we evaluated the model’s utility as an educational tool in a prospective pilot study with medical students. AI assistance significantly improved the students' overall diagnostic accuracy from 43% to 82% (p","url":"https://doi.org/10.21203/rs.3.rs-8217685/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8217685/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9913480/v1","name":"Design of a Prediction System for Intravenous Immunoglobulin resistance in hospitalized Kawasaki disease patients with Artificial Intelligence based on machine learning","source":"preprints","abstract":"Abstract Background: Machine Learning (ML) algorithms are widely used as a prediction tool in medicine to support clinical decisions. In this study, the machine learning methods are used to investigate IVIG resistance Kawasaki disease in a referral center of Iran. Methods: This is a retrospective cross-sectional study conducted among 1312 hospitalized patients with primary diagnosis of Kawasaki disease Data were collected from clinical and laboratory records which contains about patients’ medical history, echocardiographic findings and laboratory results. Data Processing: Independent variables which are statistically significant in the univariate logistic regression were included in training phase of machine learning models. Due to class imbalance between resistant and non-resistant groups, the SMOTE-NC (Synthetic Minority Oversampling Technique) method was applied to augment the resistant group along with a weighting technique to increase model sensitivity towards resistant cases. Three machine learning algorithms were trained: Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). All analyses were performed using Python (version 3.11) with the Scikit-learn, Pandas, and NumPy libraries. Results: 494 patients were analyzed and divided into two groups, 117 patients were resistant and 377 were non-resistant to therapeutic administration. Clinical features such as duration of illness (Odds Ratio = 1.07, CI = 1.02–1.13), maximum temperature (Odds Ratio = 1.80, CI = 1.26–2.56) and non-purulent conjunctivitis (Odds Ratio = 1.87, CI = 1.01–3.47) were significantly associated with an increased likelihood of resistance. In terms of echocardiographic findings, a Z-Score calculation between 2.5 and 5 (Odds Ratio = 4.68, CI = 2.22–9.88) and a Z-Score ≥ 5 (Odds Ratio = 20.30, CI = 7.84–52.53) increased the likelihood of resistance compared to a Z-Score Conclusion: non-purulent conjunctivitis, prolonged fever, elevated white blood cell count, lower hemoglobin level, and higher coronary artery Z- scores were identified as predictive factors for treatment-resistant Kawasaki disease in Iranian children. The machine learning model might demonstrate reliable predictive accuracy.","url":"https://doi.org/10.21203/rs.3.rs-9913480/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9913480/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9150301/v1","name":"Clinically grounded multi-agent artificial intelligence for preventive health management","source":"preprints","abstract":"Abstract Routine health examinations generate dense, heterogeneous data, yet their preventive value depends on consistent interpretation, calibrated risk stratification, and actionable follow-up. In practice, these tasks are distributed across clinicians and time, leading to variability in the detection of subtle abnormalities and in decisions about when and how to intervene. Such variability reflects the difficulty of maintaining consistent, high-quality preventive decision-making at scale. Here we present G-Health, a clinically grounded multi-agent artificial intelligence framework that translates examination reports into structured preventive action. The system combines three-stage clinical alignment of large language models with specialist quantitative risk models and guideline-informed retrieval to stabilize reasoning under uncertainty. Trained on large-scale medical dialogue data and further specialized on multi-center real-world examination reports, the framework integrates 20 quantitative risk models that provide calibrated multi-disease estimates with feature-level interpretability. Across 13 medical and general benchmarks, the aligned models achieve the best overall average rank among strong baselines. In a fully blinded evaluation involving 79 medically trained assessors, G-Health reports were consistently preferred over outputs from three other large language models and 12 senior practicing physicians across five clinical dimensions. Together, these findings establish a deployable paradigm that transforms routine examinations into structured and scalable preventive decision-making.","url":"https://doi.org/10.21203/rs.3.rs-9150301/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9150301/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202605.0587.v1","name":"Beyond Algorithmic Oversight: Internal Morality of Medicine and Meaningful Human Control in AI-Assisted Care","source":"preprints","abstract":"Background: /Objectives: Artificial intelligence reshapes clinical practice and its effect on physician-patient relationship requires reconsideration of frameworks that have shaped modern medical ethics. When physician delegate expertise to algorithms they cannot verify, it becomes unclear who bears clinical responsibility. Methods: This article applies theoretically grounded normative approach to explore ethical conditions under which artificial intelligence can be integrated into clinical practice without compromising the moral foundations of medicine. The analysis is primarily based on Pellegrino and Thomasma’s concept of internal morality of medicine and the physician’s act of profession. It further draws on Kantian ethics of human dignity, Levinasian relational ethics, virtue ethics, and Vallor’s concept of technomoral wisdom. Results: AI systems do not satisfy the conditions under which moral responsibility can be ascribed to them. Clinical moral agency lies in the capacity to bear three distinct responsibilities – epistemic, relational, and phronetic – none of which can be fulfilled by AI. The implementation of AI in healthcare, therefore, must occur strictly under the condition of Meaningful Human Control, rather technical function of human oversight over algorithmic outputs. To ensure that MHC can function as an effective and ethically grounded safeguard, we propose five normative requirements: primacy of clinical judgement, prohibition of forced automation, traceability and explainability, transparency towards patients, and clinical authority over diagnostic tools. A dialog between the physician and the patients should remain the foundation of clinical decision-making. Proposed normative requirements aim to preserve internal morality of medicine in a form that harmoniously combines both technological progress and established medical ethics.","url":"https://doi.org/10.20944/preprints202605.0587.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202605.0587.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10501649/v1","name":"Advanced Techniques for 3D Image Reconstruction and Visualization in Medical Imaging: A Comparative Study of AI-Driven and Conventional Methods","source":"preprints","abstract":"Abstract This review explores advanced strategies for three-dimensional (3D) image reconstruction and visualization within the realm of medical imaging, emphasizing their pivotal role in improving diagnostic precision, surgical planning, and medical education. Unlike conventional two-dimensional (2D) modalities such as X-rays and ultrasound which often fall short in delivering sufficient spatial context and typically demand multiple views 3D imaging technologies like computer tomography (CT), magnetic resonance imaging (MRI), and 3D ultrasound offer rich spatial detail and anatomical clarity. These innovations have transformed how clinicians visualize internal structures and perform interventional procedures. Core advancements examined include iterative reconstruction algorithms, deep learning-driven techniques for minimizing noise and correcting artifacts, and hybrid rendering systems that elevate visualization fidelity. Despite these breakthroughs, the field continues to face barriers such as intensive computational demands, persistent image noise, and an absence of universal imaging protocols. To confront these challenges, this study underscores the integration of artificial intelligence (AI), augmented reality (AR), and graphics processing unit (GPU)-enhanced processing. AI significantly bolsters image quality, while AR and virtual reality (VR) enable immersive, interactive experiences that benefit both education and clinical practice. The investigation places particular focus on designing algorithms tailored for real-time processing, optimizing efficiency, and ensuring a balance between image resolution and patient safety, especially in low-radiation scenarios. The outcomes demonstrate notable strides in image sharpness, reconstruction speed, and diagnostic effectiveness. Deep learning methods stand out for their ability to mitigate artifacts, while integrated visualization frameworks improve usability and anatomical insight. These findings highlight the transformative impact of modern 3D imaging on personalized healthcare and clinical innovation. Moving ahead, the study highlights how crucial it is to integrate multimodal imaging with enhanced real-time rendering techniques to facilitate wider adoption in clinical practice.","url":"https://doi.org/10.21203/rs.3.rs-10501649/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10501649/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10542898/v2","name":"Knowledge-Constrained Reasoner: Attempting to Enable LLMs to Parse Knowledge for Question Answering","source":"preprints","abstract":"Abstract Prior to the advent of deep learning, traditional expert systems represented the dominant paradigm in artificial intelligence, designed primarily to deduce answers from domain-specific knowledge bases and user inputs (e.g., medical treatment recommendations). However, their rigid adaptability to real-world complexity and high maintenance costs led to their eventual decline. While modern Large Language Models (LLMs) have emerged as powerful, flexible alternatives, they suffer from catastrophic forgetting during parametric continuous learning. Although Retrieval-Augmented Generation (RAG) mitigates information recency issues, it fails to guarantee that LLMs will correctly parse and apply retrieved knowledge during inference. Building upon recent non-parametric continuous learning paradigms—such as CoG-MeM and NPMCL—which leverage a single fine-tuning stage to enable LLMs to autonomously retrieve and apply external knowledge, this work focuses on the critical next step: ensuring the rigorous and correct utilization of knowledge. We explore the Knowledge-Constrained Reasoner, an initial attempt to internalize the ability to anchor core reasoning operations—including situational contextualization, forward causal deduction, backward causal tracing, composition, and filtering—onto external data sources. Through a single alignment phase, the LLM learns to execute reliable, rule-grounded behavior over dynamic contexts without internalizing the knowledge itself. This design serves as a preliminary investigation into enabling LLMs to correctly parse and reason over external knowledge for question answering, offering early empirical evidence toward bridging the determinism of traditional expert systems with the flexibility of a non-parametric continuous learning paradigm.","url":"https://doi.org/10.21203/rs.3.rs-10542898/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10542898/v2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10028122/v1","name":"Commercial Artificial Intelligence for Intracranial Aneurysm Detection on CT Angiography: A Systematic Review","source":"preprints","abstract":"Abstract Background Intracranial aneurysms may go undetected or incompletely characterized on CT angiography (CTA) in clinical practice. Unrecognized lesions carry the risk of subsequent hemorrhage. Commercially available AI software for intracranial aneurysm detection on CTA has entered clinical deployment, but evidence for its diagnostic performance remains fragmented across products, study settings, and aneurysm size thresholds. Purpose To systematically evaluate the diagnostic accuracy and clinical implementation evidence for commercially available AI software for intracranial aneurysm detection on CTA. Methods This systematic review was conducted in accordance with PRISMA 2020 guidelines and registered prospectively with PROSPERO (CRD420261403585). PubMed, Scopus, and Web of Science were searched. Eligible studies were original peer-reviewed publications evaluating commercially available, FDA-cleared, CE-marked, or clinically deployed AI software for intracranial aneurysm detection on CTA in adult patients. Studies evaluating non-commercial research algorithms, review articles, conference abstracts, and phantom-only studies were excluded. Risk of bias was assessed using QUADAS-2. Due to heterogeneity in study design, populations, reference standards, and outcome reporting, findings were synthesized narratively. Results Eight studies met the inclusion criteria, evaluating five commercial AI platforms: RAPID Aneurysm (iSchemaView), Viz.ai Aneurysm/Viz ANEURYSM (Viz.ai), Aidoc (Aidoc Medical), CerebralDoc (Shukun Technology), and Dr. Wise-AADS (Deepwise). Patient-level sensitivity and specificity varied widely across studies and were strongly dependent on aneurysm size and study population. Most platforms demonstrated higher performance for aneurysms ≥ 4–5 mm than for smaller lesions. Multiple studies (n = 5) reported AI detection of aneurysms absent from original radiology reports, including centers with subspecialty neuroradiology coverage. AI-assisted reading improved reader sensitivity and reduced CTA interpretation time in two studies. False positive burden varied considerably across platforms and study settings. Conclusions Commercial AI tools for intracranial aneurysm detection on CTA show variable but consistently size-dependent diagnostic performance. Most platforms performed more reliably for aneurysms ≥ 4–5 mm. Evidence of missed-aneurysm detection across multiple studies suggests a potential safety-net role. However, heterogeneity in study design, reference standards, and outcome reporting limits cross-platform comparison and generalizability. Prospective real-world evaluation with standardized outcome reporting is needed before broad clinical adoption.","url":"https://doi.org/10.21203/rs.3.rs-10028122/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10028122/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9329400/v1","name":"Artificial Intelligence–Assisted Ultrasound Interpretation Enhances Diagnostic Performance Among Trainees in Thyroid Imaging","source":"preprints","abstract":"Abstract Background: Accurate interpretation of thyroid ultrasound requires substantial experience, and diagnostic performance among trainees remains variable. Artificial intelligence (AI) has shown promise in medical image analysis; however, its role as an educational support tool for trainees has not been fully established. This study aimed to evaluate whether an AI-assisted system could improve trainee performance in thyroid ultrasound interpretation. Methods: We developed an AI-based diagnostic support system using a Mask R-CNN framework trained on pathology-confirmed thyroid ultrasound images. The model performed segmentation and classification of five lesion categories. A reader study was conducted involving five expert physicians and five trainees. Trainees evaluated ultrasound images under non-assisted and AI-assisted conditions. Diagnostic performance was assessed using sensitivity, specificity, precision, and F1 score. Results: The AI model demonstrated high segmentation accuracy for several structures, including vessels and thyroid parenchyma. However, its ability to distinguish benign from malignant tumors remained limited. Despite this, AI assistance improved trainee performance. Median precision increased from 0.53 to 0.63, and F1 score improved from 0.53 to 0.61. Specificity increased from 0.22 to 0.50, while sensitivity remained comparable. These findings indicate that AI support primarily reduced false-positive interpretations and improved diagnostic consistency among trainees. Conclusions: AI-assisted ultrasound interpretation improved the diagnostic performance of trainees, particularly by enhancing precision and reducing false-positive findings. Although the AI model alone was insufficient for independent clinical use, it demonstrated potential as an educational support tool. These findings suggest that AI may contribute to more consistent and effective training in thyroid ultrasound interpretation.","url":"https://doi.org/10.21203/rs.3.rs-9329400/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9329400/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9259496/v1","name":"Artificial Intelligence and Machine Learning in Sports Medicine: Mapping clinical tasks and assessing clinical maturity - a scoping review","source":"preprints","abstract":"Abstract Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the medical field. The aim of this review was to outline the current scientific state of AI and ML application in sports medicine, evaluate the developmental and clinical maturity, and identify key priorities to guide future advancements and implementation. A scoping review was conducted with a literature search performed on February 5, 2026, using the MEDLINE, EMBASE and Web of Science databases which targeted AI or ML application on athletes within rehabilitation. Of 8,677 studies, 97 studies were included. Most research covered orthopaedics (70.1%) and neurology (18.6%), where AI was applied for injury prediction, diagnostic image analysis, and recovery estimation. Predictive and estimation models were the dominant application (57.7%). Reported discriminative performance was frequently high. However, the majority of studies relied on retrospective datasets and internal validation. Calibration reporting was uncommon, and prospective workflow integration was rare, with a single study attempting an interventional prevention strategy. Substantial heterogeneity in modelling approaches, data inputs, and outcomes definitions was observed. Although AI and ML applications in sports medicine frequently demonstrate strong within-sample performance, most remain in early-stage development. Currently, these tools should be viewed as supportive adjuncts rather than autonomous decision-making systems.","url":"https://doi.org/10.21203/rs.3.rs-9259496/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9259496/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9228225/v1","name":"NeoMiriX: an Artificial Intelligence System For Predicting Cancer Using miRNA Expression","source":"preprints","abstract":"Abstract Small RNA molecules control how genes work after they are copied, plus show abnormal patterns in nearly every kind of tumor. These signals stay intact in liquid samples like plasma, which sparked attention for medical testing - yet turning those readings into solid forecasts remains tough. Challenges pop up because studies differ widely in design while few methods combine DNA changes, RNA levels, and chemical tags across layers at once. What if a tool could bridge the divide? NeoMiriX does exactly that by gathering miRNA expression alongside transcriptomic, genomic, and epigenomic layers from sources like TCGA, GEO, and CancerMIRNome. Instead of relying on one method, it combines several - Random Forest, SVM, and XGBoost analyze table-like patterns, while Transformers and Graph Neural Networks map how molecules interact. One piece ranks potential miRNAs, another explores biological pathways, yet another sorts patients by risk level. What stands out is how well NeoMiriX performed across several cancer types - accuracy reached 0.96, while the ROC-AUC hit 0.97. Behind these numbers lies biological relevance: the miRNA patterns align closely with known cancer-related processes, particularly those guiding cell division and programmed cell death.","url":"https://doi.org/10.21203/rs.3.rs-9228225/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9228225/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.12688/mep.21479.1","name":"Developing Critical Judgment of Artificial Intelligence in Medical Education: Applied Insights","source":"preprints","abstract":"Background: Artificial intelligence (AI) is rapidly transforming medical education and clinical practice. AI-driven clinical decision support systems, diagnostic tools, and smart tutoring systems are helping teach and guide medical students on developing clinical reasoning skills and making better-informed patient care decisions. AI literacy initiatives have grown in recent years to increase understanding of both how AI works and how to utilize it; however, medical educators receive minimal guidance regarding how to instruct their learners to appropriately question or override AI recommendations. Thus, the educational gap created by a lack of guidance places learners at risk of automation bias,the tendency to over-rely on computer-based recommendations, regardless of whether they conflict with clinical judgement or individual patient situation. Applied Insights The Applied Insights presented in this article are organized around commonly encountered educational contexts where learners interact with AI-assisted decision-making. They offer actionable strategies for helping learners recognize when AI recommendations should be questioned, contextualized, or overridden. For example, mismatches between patients and training populations, incomplete or inaccurate input data, and misalignment between the system’s priorities and the patient’s values. The Applied Insights presented are based on well-established literature on automation bias, patient safety, and clinical decision-making, and were written to be non-technology-specific, useful across multiple specialties and resources, and adaptable to current curricula without requiring AI-specific knowledge. Conclusion Medical educators have a responsibility to prepare learners to use AI safely in clinical practice. By providing strategies for teaching when and how to question AI recommendations, this article supports the development of professional judgment, patient-centered decision-making, and safe integration of AI in health professions education.","url":"https://doi.org/10.12688/mep.21479.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/mep.21479.1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.04.22.26351488","name":"Dissecting clinical reasoning failures in frontier artificial intelligence using 10,000 synthetic cases","source":"preprints","abstract":"ABSTRACT Background Current medical large language model (LLM) evaluations largely rely on small collections of cases, whereas rigorous safety testing requires large-scale, diverse, and complex cases with verifiable ground truth. Multiple Sclerosis (MS) provides an ideal evaluation model, with validated diagnostic criteria and numerous paraclinical tests informing differential diagnosis, investigation, and management. Methods We generated synthetic MS cases with ground-truth labels for diagnosis, localisation, and management. Four frontier LLMs (Gemini 3 Pro/Flash, GPT-5.2/5-mini) were instructed to analyse cases to provide anatomical localisation, differential diagnoses, investigations, and management plans. An automated evaluator compared these outputs to the ground-truth labels. Blinded subspecialty experts validated 70 cases for realism and automated evaluator accuracy. We then evaluated LLM decision-making across 1,000 cases and scaled to 10,000 to characterise rare, catastrophic failures. Results Subspecialist expert review confirmed 100% synthetic case realism and 99.8% (95% CI 95.5-100) automated evaluation accuracy. Across 1,000 generated MS cases, all LLMs successfully included MS in the differential diagnoses for >91% cases. However, diagnostic competence did not associate with treatment safety. Gemini 3 models had low rates of clinically appropriate steroid recommendations (Flash: 7.2% [95% CI 5.6–8.8]; Pro: 15.8% [13.6–18.1]) compared to GPT-5-mini (23.5% [20.8–26.1]), frequently overlooking contraindications like active infection. OpenAI models inappropriately recommended acute intravenous thrombolysis for MS cases (9.6% GPT-5.2; 6.4% GPT-5 mini) compared to 14 days old. Conclusion Automated expert-level evaluation across 10,000 cases characterised artificial intelligence clinical blind spots hitherto invisible to small-scale testing. Massive-scale simulation and automated interrogation should become standard for uncovering serious failures and implementing safety guardrails before clinical deployment exposes patients to risk. 1-2 SENTENCE DESCRIPTION By scaling an expert-validated simulation process to 10,000 cases, this study demonstrates that high diagnostic accuracy by AI can mask rare but dangerous safety failures. This large-scale approach provides a framework for uncovering clinical “blind spots” that small-scale evaluations miss, helping inform the development of safety guardrails before AI is deployed in practice.","url":"https://doi.org/10.64898/2026.04.22.26351488","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.22.26351488","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.1099/acmi.0.001255.v1","name":"Design, implementation, and reflections on delivering machine learning workshops for medical microbiology and infection science","source":"preprints","abstract":"Increasingly, machine learning (ML) and artificial intelligence (AI) methods are being applied in medical microbiology and infection science. With this comes the challenge of educating professionals in these domains on the fundamental theory and methods of ML and AI in a way that is time-efficient and grounded in practical application. This article outlines the design, implementation, and reflections on delivering “Using Data Science and Machine Learning for Infection Science: A Hands-On Introduction”, a recurring one-day workshop created to introduce data science and ML concepts to infection science and medical microbiology students and professionals. The workshop provides participants with the opportunity for experiential learning using Orange, a no-code, open-source, and free-to-use data mining software application, where participants are exposed to the fundamentals of the ML lifecycle. By the conclusion of the one-day workshop, participants gain experience developing end-to-end analytical pipelines for tabular datasets, with a specific example focused on blood culture outcome prediction. The workshop emphasises transparency, reproducibility, and open science, promoting critical awareness of how data science can be applied within participants’ own domain specialty. This article details the curriculum design, practical implementation, and delivery of the workshop. Additionally, we reflect on the lessons learned and directions for future iterations of the workshop","url":"https://doi.org/10.1099/acmi.0.001255.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1099/acmi.0.001255.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202605.1557.v1","name":"Diagnostic-Field Variational Intelligence for Trustworthy Pneumonia Screening: A UVIF-Based Framework for Explainable and Calibration-Aware Clinical Decision Support","source":"preprints","abstract":"Artificial intelligence systems for pneumonia detection often achieve strong predictive performance but remain insufficiently calibrated, weakly interpretable, and poorly aligned with clinically meaningful decision-support requirements. This paper presents a diagnostic-field extension of the Unified Variational Intelligence Framework (UVIF) for trustworthy and decision-centric pneumonia screening using chest X-ray imaging. The proposed framework models diagnosis as a variational process in which imaging patterns and latent feature representations are treated as diagnostic fields that must be sensed, filtered, interpreted, and evaluated before clinical decision support is produced. The study combines compact convolutional neural network modeling, embedding-based machine learning classifiers, calibration-aware reliability analysis, threshold-sensitive decision control, and multi-level explainability using Grad-CAM, LIME, and SHAP. Experimental evaluation is conducted on the publicly available PneumoniaMNIST benchmark dataset from the MedMNIST collection. The compact CNN achieved strong discrimination performance with ROC-AUC of 0.9666 and pneumonia recall of 0.9974, while the UVIF-guided diagnostic layer supported reliability-aware model selection and threshold optimization under screening-oriented constraints. Calibration analysis further revealed deviations between predicted probabilities and empirical outcomes, emphasizing the importance of reliability-aware evaluation in medical AI systems. The proposed framework demonstrates that integrating prediction, calibration, explainability, and diagnostic decision control within a unified variational framework can support more transparent, interpretable, and clinically meaningful AI-assisted pneumonia screening systems.","url":"https://doi.org/10.20944/preprints202605.1557.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202605.1557.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10336041/v1","name":"Comparison of Peyton’s Four-Step Approach and Halsted’s ‘See One, Do One’ Method for Teaching Basic Surgical Skills: A Randomised Study Assessing Long-Term Retention in a Pakistani Medical School”","source":"preprints","abstract":"Abstract Background Effective teaching of basic surgical skills is essential in undergraduate medical education. While Halsted’s traditional “See One, Do One” approach has long been the cornerstone of surgical training, structured instructional models such as Peyton’s Four-Step Approach may offer improved skill acquisition and retention. However, comparative evidence, particularly regarding long-term retention and teaching time, remains limited. ObjectiveTo compare Halsted’s “See One, Do One” method with Peyton’s Four-Step Approach for teaching basic surgical skills to undergraduate medical students in a simulated skills-laboratory setting, with assessment of skill retention after three months and analysis stratified by academic level. MethodsThis comparative study was conducted at a tertiary-care teaching institution in Karachi. Fifty undergraduate medical students with no prior surgical skills exposure were randomized into Halsted (n = 25) and Peyton (n = 25) groups. Participants were trained in four procedural skills: sterile gloving, surgical incision, interrupted suturing, and one-handed reef knot tying. Skill performance was assessed three months post-training using a six-point Likert scale by blinded evaluators. Teaching time for each skill was recorded. Nonparametric analyses (Mann–Whitney U test) were used to compare performance scores and teaching time, with stratification by junior (years 1–3) and senior (years 4–5) students. ResultsStudents trained using Peyton’s Four-Step Approach demonstrated significantly higher median performance scores across all four skills compared to the Halsted group (p","url":"https://doi.org/10.21203/rs.3.rs-10336041/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10336041/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202608.1091.v1","name":"A Visually Continuous Multi-Vendor Glucose Curve Is Not a Medical Record: A Stack-Wise Evaluation Framework for Sensing, Interoperability, Forecasting and AI Briefing in Digital Glucose Care","source":"preprints","abstract":"Background: Digital glucose technologies include continuous glucose monitoring (CGM), emerging sensing modalities, multi-vendor platforms, forecasting models and artificial-intelligence (AI) briefings. These components are already chained into one patient journey, yet they are still often judged by a single convenient metric, such as MARD, a fused curve, RMSE/R² or fluent text. Visual continuity across brands can look like a medical record while stack safety for care remains unevaluated. Risk can therefore propagate along the product stack.Framework and contributions. We propose a four-layer, stack-wise evaluation framework for digital glucose care comprising sensing (L1), platform interoperability and provenance (L2), forecasting (L3), and human–AI briefing (L4). The framework makes three contributions. First, it organises the glucose product stack as successive clinical gates and identifies the false reassurance associated with each layer, complementing horizontal multidomain AI tools. Second, it specifies the cross-brand longitudinal account as an informatics object and treats L2 provenance as the hinge on which L3 and L4 may inherit trust, with acceptance criteria independent of sensor MARD. Third, it translates the gates into a brand-switching cascade test (Figure 3), a Have-you checklist for procurement and ethics (Table 3), an evidence-bound briefing template (Figure 2), and constrained deployment modes.Findings. Synthesising public evaluation and interoperability literature, we show that silent L2 splicing can carry forward as unchanged L3 score thresholds and categorical L4 prose even when L1 labelled claims are individually acceptable; provenance-visible segments, forecast abstention and evidence-bound briefings interrupt that cascade. Tables 1–3 and Figures 1–3 operationalise the gates.Conclusion. Stack-wise gating, with L2 provenance as the hinge, makes readiness for care an evaluable claim before procurement. A visually continuous multi-vendor glucose curve remains an interface achievement until layer-wise warrants are in place.","url":"https://doi.org/10.20944/preprints202608.1091.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1091.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10542898/v1","name":"Knowledge-Constrained Reasoner: Attempting to Enable LLMs to Parse Knowledge for Question Answering","source":"preprints","abstract":"Abstract Prior to the advent of deep learning, traditional expert systems represented the dominant paradigm in artificial intelligence, designed primarily to deduce answers from domain-specific knowledge bases and user inputs (e.g., medical treatment recommendations). However, their rigid adaptability to real-world complexity and high maintenance costs led to their eventual decline. While modern Large Language Models (LLMs) have emerged as powerful, flexible alternatives, they suffer from catastrophic forgetting during parametric continuous learning. Although Retrieval-Augmented Generation (RAG) mitigates information recency issues, it fails to guarantee that LLMs will correctly parse and apply retrieved knowledge during inference. Building upon recent non-parametric continuous learning paradigms—such as CoG-MeM and NPMCL—which leverage a single fine-tuning stage to enable LLMs to autonomously retrieve and apply external knowledge, this work focuses on the critical next step: ensuring the rigorous and correct utilization of knowledge. We explore the Knowledge-Constrained Reasoner , an initial attempt to internalize fundamental reasoning capacities—including situational contextualization, forward causal deduction, backward causal tracing, composition and filtering—into an LLM via a single alignment phase. This design serves as a preliminary investigation into helping LLMs better parse external knowledge for question answering, offering early empirical evidence toward bridging the reliability of traditional expert systems with the flexibility of a non-parametric continuous learning paradigm.","url":"https://doi.org/10.21203/rs.3.rs-10542898/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10542898/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9230845/v1","name":"Smartphone-Based Gender Identification from Palatal Rugae Images: A Comparative Transfer Learning Study of Pretrained CNNs","source":"preprints","abstract":"Abstract Background: Palatal rugae are distinct anatomical ridges on the anterior part of the palate that exhibit individual-specific morphological patterns, making them valuable markers for personal identification and forensic analysis. With advancements in artificial intelligence, deep learning methods have emerged as effective tools for extracting discriminative features from medical and biometric images. This study investigates the potential of transfer learning-based convolutional neural networks (CNNs) for gender classification using palatal rugae images captured through a smartphone camera. Results: All pretrained models successfully learned discriminative gender-related features from palatal rugae images. Among them, Residual network 50 (ResNet50) and Visual Geometry Group 16 (VGG16) achieved the highest classification accuracy with optimal precision and recall, while EfficientNet-B0 and DenseNet121 demonstrated comparable but slightly lower performance. The Receiver Operating Curve (ROC curve) exhibited high Area under the Curve (AUC) values, confirming the strong separability between male and female classes. Conclusion: The findings confirm that smartphone-based imaging combined with transfer learning offers a promising, low-cost, and non-invasive approach for gender identification using palatal rugae patterns, as an adjunct tool for forensic odontology. Among the evaluated models, ResNet50 showed the most robust performance. Future work will focus on expanding the dataset and incorporating explainable Artificial Intelligence methods to support clinical and forensic applicability.","url":"https://doi.org/10.21203/rs.3.rs-9230845/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9230845/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9957077/v1","name":"Are AI knowledge and educational expectations homogeneous across clinical training stages and intended specialties?","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly relevant to medical practice and medical education, but it remains unclear whether students’ AI knowledge and educational expectations vary according to clinical training stage or preferred future specialty. Objective To assess whether AI knowledge, attitudes, and educational expectations differed across clinical training stages and preferred specialties among French medical students. Methods We conducted a secondary analysis of a national cross-sectional online survey of French medical students in the fourth to sixth years of medical school. AI knowledge was assessed through students’ free-text definition of AI, categorized as unknown, incorrect, partially correct, or correct. Adequate AI knowledge was defined as a partially correct or correct definition. The main explanatory variables were clinical training stage and preferred future specialty orientation, with particular attention to image- or data-intensive specialties. Results Among 1,342 students, 508 (37.9%) provided an adequate AI definition. Adequate AI knowledge did not differ across clinical training stages: 37.6% in DFASM1, 37.4% in DFASM2, and 39.6% in DFASM3. Attitude scores were broadly similar across years, although optimism toward AI was slightly lower among final-year students. Interest in image- or data-intensive specialties was not associated with better AI knowledge, but was associated with greater perceived need to learn AI basics and slightly higher concern about AI’s role. Conclusions AI knowledge did not appear to improve passively during clinical training. These findings support explicit AI education in undergraduate medical curricula, combining a common foundation with specialty-oriented modules.","url":"https://doi.org/10.21203/rs.3.rs-9957077/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9957077/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202603.0274.v1","name":"Digital Healthcare Innovation in Morocco Leveraging Telemedicine, Internet of Medical Things, and Artificial Intelligence for Chronic Disease Management","source":"preprints","abstract":"Morocco, facing a growing prevalence of chronic diseases such as diabetes, hypertension, and cardiovascular diseases, must overcome significant challenges to modernize its healthcare system. In this context, the integration of digital technologies, including telemedicine, the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and healthcare system interoperability, represents a promising solution to improve the management of chronic diseases. This article examines how these technologies can be utilized to transform the Moroccan healthcare system into a more accessible, efficient, and patient-focused model of care. The paper reviews recent pilot projects and initiatives, focusing on infrastructure development, remote monitoring, AI and IoMT integration, public health campaigns, and national health programs aimed at improving access to treatment. Building on these observations, the paper explores the potential of an integrated digital health system for managing chronic diseases and proposes a national integrated care architecture that connects Morocco's public and private healthcare providers. These insights highlight the significance of digital health in Morocco and provide a framework for improved, more patient-centered, and more efficient advanced healthcare. Future perspectives focus on developing an adapted digital transformation approach to further enhance chronic disease management.","url":"https://doi.org/10.20944/preprints202603.0274.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202603.0274.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10808123/v1","name":"Strategic Patient Involvement in Medical AI Co-Design: A Pilot Study in Heart Failure","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly promising to support decisions in acute cardiovascular care, including whether patients presenting to the emergency department can be safely discharged. Ethical and trustworthy-AI frameworks call for involving patients in designing such tools, yet patient involvement in AI has largely been limited to gathering views, acceptability, and interface preferences. This consultative role leaves largely untested what patients contribute when involved strategically, shaping what a tool is for and how it works. What patients contribute when they help design a high-stakes cardiovascular risk prediction tool has rarely been examined. Methods We conducted a co-design workshop as a pilot in which patients with lived experience of heart failure (n = 11) participated, within a European project developing an AI-enabled cardiovascular risk prediction tool. The study used a two-stage process: preparatory capacity-building through e-learning and an online webinar, followed by a two-day in-person workshop of 5 facilitated sessions. The workshop comprised five facilitated sessions using participatory design activities structured around trustworthy-AI (FUTURE-AI) design-phase principles. Recordings, co-design artefacts, and surveys were analysed using reflexive thematic analysis. Results Patients shifted from a familiar consultative role into an active design role. Working in two independent groups, participants converged on similar designs. Both reframed the tool from clinician-facing to patient-facing and specified: whole-person inputs beyond acute clinical variables; qualitative, and actionable risk rather than a numerical output; the withholding of predictions the model cannot make reliably; and layered, patient-controlled explanation. Because the prediction concerned their own risk of death, participants built emotional safeguards into the design. Conclusion Patients involved as strategic partners produce coherent, concrete design specifications that a purely technical process would miss. Realising this contribution requires preparing patients for co-design methods, treating the tool's intended beneficiary as an open question, and building routes by which patient input reaches development. Trial Registration Not applicable.","url":"https://doi.org/10.21203/rs.3.rs-10808123/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10808123/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.21.26353789","name":"Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives","source":"preprints","abstract":"AI-driven chatbots have been utilized in healthcare to automate administrative tasks, improve patient education, and expand access to medical information; however, their role in genetic counseling remains underexplored. To investigate the adoption, perceptions, and potential utility of AI-based chatbots in genetic counseling practice, 217 genetic counselors and genetic counseling students from across North America were surveyed regarding chatbot usage, confidence in their application, and perceived benefits and limitations. While most participants (166/217; 76.5%) reported using general AI chatbots outside of clinical settings, far fewer (18/204; 8.8%) reported using or recommending clinical genetics chatbots in clinical practice. For those that used clinical genetics chatbots, the primary purpose was for communication with at-risk family members (11/18; 61.1%) and patient education (10/18; 55.6%). Confidence in chatbot technology varied, with highest confidence in gathering family history information (81/199; 40.7%) and lowest confidence in their ability to disclose variants of uncertain significance or positive genetic testing results (5/199; 2.5%). The greatest perceived benefits included reducing repetitive tasks (165/195, 84.6%) and allowing for time for other tasks (141/195; 72.3%), while major concerns revolved around patient comprehension (167/195; 85.6%) and having accurate, up-to-date information (145/195; 74.4%). Despite some concern about AI replacing human counselors, most participants reported they felt there was potential for chatbots to enhance workflow efficiency (128/195; 65.6%) if properly integrated and regulated. Limited AI training was identified as a barrier to adoption (16/195; 8.2% received training), highlighting a need for structured education on AI applications in genetic counseling. These findings suggest that AI chatbots hold promise as supplementary tools, but significant challenges must be addressed before widespread implementation in genetic counseling practice.","url":"https://doi.org/10.64898/2026.05.21.26353789","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.21.26353789","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9075059/v1","name":"Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study","source":"preprints","abstract":"Abstract Background The rapid integration of Artificial Intelligence (AI) into clinical practice necessitates preparing future physicians for such interaction. This study assessed medical students’ knowledge, attitudes, and perceptions towards clinical AI and its integration into medical curricula and identified their preferred topics and modes of delivery for AI education. Methods A cross-sectional survey using a validated questionnaire was conducted with 334 undergraduate medical students at New Giza University, Egypt. Participants were questioned about their knowledge, attitudes, and perceptions (KAPs) toward AI in medicine, and their preferred AI topics and modes of education delivery. Chi-square testing analyzed associations between students’ responses and their demographics. Results Analysis of the responses revealed that 71.9% of students do not understand fundamental AI concepts, and 60.5% cannot cite recent clinical AI advancements. Furthermore, 69.1% express concern about AI’s ethical implications in medicine. Despite this, 89.8% recognize the importance of AI in the future of medicine and 91% desire further AI education. Preferred topics included when to use AI, strengths and weaknesses of AI, and ethics of AI. The preferred modes of education were short lectures, workshops, and symposia. No significant differences were found between students’ KAPs and their academic year. Conclusion A substantial gap exists in medical students’ knowledge and perception of AI in medicine; yet they strongly recognize its significance and are eager to learn about it for three hours or less per month. To address this, curricular developers should prioritize clinically oriented topics – AI’s clinical applications, ethical implications, and the strengths and limitations - delivered in concise, interactive formats. Key learning outcomes must include the ability to critically appraise AI technologies, evaluate their outputs, and recognize limitations. Medical educators should embed these topics into existing teaching without overburdening students, cultivating future physicians confident in using AI and navigating its ethical challenges.","url":"https://doi.org/10.21203/rs.3.rs-9075059/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9075059/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10493480/v2","name":"Medical Decision Governance as a Competency-Based Educational Framework: Theory Development and Empirical Validation Through a Dynamic Patient–Physician Communication Curriculum","source":"preprints","abstract":"Abstract Background : As healthcare increasingly evolves into a complex sociotechnical system, clinical decision-making is no longer an individual cognitive task but a dynamic governance process involving patients, families, interprofessional teams, healthcare organizations, and artificial intelligence (AI). Although competency-based medical education (CBME) emphasizes communication, collaboration, professionalism, and reflective practice, existing educational frameworks conceptualize these competencies as parallel attributes rather than explaining how they interact to support high-quality clinical decision-making. This theoretical fragmentation limits the ability of medical education to prepare future clinicians for increasingly complex decision environments. To address this gap, this study introduces the Medical Decision Governance Competency Theory (MDGCT), a novel competency-based educational framework that conceptualizes governance capability as an integrated developmental competency emerging through the progressive interaction of empathy, communication, collaboration, reflective practice, and adaptive problem-solving. Methods : A longitudinal mixed-method educational study was conducted among undergraduate medical and nursing students participating in a three-day Dynamic Patient–Physician Communication and Conflict Management Curriculum. Participants completed validated assessments of empathy, communication attitudes, interprofessional collaboration, metacognitive reflection, and adaptive problem-solving at baseline, immediately after the intervention, and during follow-up. Quantitative findings were triangulated with thematic analysis of reflective learning records to examine the theoretical structure proposed by MDGCT. Results : The curriculum produced significant improvements across all governance-related competencies, including empathy, communication attitudes, collaboration, reflective practice, and adaptive problem-solving. Although some competencies showed partial attenuation during follow-up, most remained significantly above baseline, indicating sustained educational development. Nursing students demonstrated stronger long-term retention of empathy than medical students. The convergence oflongitudinal quantitative outcomes and qualitative reflections provided initial empirical support for the developmental pathway proposed by MDGCT: Empathy → Communication → Collaboration → Reflective Practice → Adaptive Problem-Solving → Medical Decision Governance. Conclusions : This study extends competency-based medical education by proposing governance capability as a distinct educational outcome rather than viewing communication as an isolated professional skill. Medical Decision Governance reframes communication education as the educational foundation for governing complex clinical decisions through the integration of interpersonal, cognitive, collaborative, reflective, and adaptive competencies. Beyond introducing a new competency-based theory, this study establishes a conceptual bridge linking medical education, clinical governance, patient autonomy, shared decision-making, patient safety, and trustworthy AI-enabled healthcare. The findings provide initial empirical evidence supporting governance-oriented education as a new paradigm for preparing healthcare professionals to navigate increasingly complex clinical decision-making environments. Unlike existing competency frameworks that describe communication, collaboration, and professionalism as discrete educational domains, MDGCT conceptualizes these competencies as an integrated developmental governance system. This theoretical shift redefines the educational purpose of communication training—from improving interpersonal performance to cultivating governance capability for responsible, collaborative, and ethically accountable clinical decision-making.","url":"https://doi.org/10.21203/rs.3.rs-10493480/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10493480/v2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10468504/v1","name":"HistoSeek links histology image recognition to teacher-authored formative guidance","source":"preprints","abstract":"Abstract Histology learners must locate and identify relevant structures within complex tissue fields, yet immediate feedback on learner-selected regions is rarely available. We developed HistoSeek, a teacher-authored, curriculum-aligned artificial intelligence platform integrating learner-generated regions of interest (ROIs), automated recognition and structured formative guidance. Instructors reviewed and corrected student-drawn ROIs, creating 51,248 images across 19 organ-specific development tasks. During practical sessions, learners delineated ROIs on supported whole-slide images, received ranked candidate structures and accessed teacher-authored descriptions of morphology and anatomical location. In an exploratory two-class implementation involving 20 HistoSeek and 21 comparator students, examination outcomes were descriptively higher in the HistoSeek class, and post-use questionnaire responses were generally favorable. HistoSeek demonstrates the feasibility of linking learner-selected visual evidence with organ-conditioned recognition and curriculum-aligned guidance, providing a foundation for source-separated technical validation and multicentered medical educational evaluation across classes, institutions and unfamiliar slides.","url":"https://doi.org/10.21203/rs.3.rs-10468504/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10468504/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10347054/v1","name":"Hierarchical differences in generative AI impacts and tiered educational support needs among health professions students: A descriptive qualitative study","source":"preprints","abstract":"Abstract Background Generative artificial intelligence (GAI) has been extensively integrated into nursing and medical education, significantly transforming the academic practices of students. Nonetheless, the varying impacts of GAI on core competency development across educational stages remain ambiguous, with limited empirical evidence on hierarchical educational support needs. This study aimed to investigate the effects of GAI utilization on critical thinking, research competence, and academic integrity among Chinese nursing and medical students at undergraduate, master's, and doctoral levels, and to identify their specific needs for targeted GAI educational support. Methods This descriptive qualitative study analyzed semi‑structured interviews with 44 students (16 undergraduate, 16 master's, 12 doctoral) from a western Chinese medical university using inductive content analysis, with TAM and AI Literacy frameworks informing discussion. Results GAI usage patterns varied by academic level: undergraduates for coursework, versus postgraduates for literature reviews, experimental design, and data analysis. The dual academic impact featured enhanced critical thinking and knowledge acquisition, counterbalanced by risks of over-reliance, marginal clinical skill efficacy, and cognitive offloading—with research competency gains largely confined to postgraduates. A pronounced demand for systemic support, including teacher guidance and institutional training, pervaded all levels. Conclusion GAI differentially affects nursing/medical students' progression, warranting stage-specific support. We propose a three-stage TAM-extension model (PEOU‑driven, PU‑driven, critical‑reflective) to capture developmental trajectories, offering empirical grounds for tiered AI literacy curricula.","url":"https://doi.org/10.21203/rs.3.rs-10347054/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10347054/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.05.736569","name":"A foundation model enables prediction of natural product molecular properties, bioactivity, and structural similarity from biosynthetic gene cluster sequence","source":"preprints","abstract":"Genome mining is a powerful technique in natural product discovery, where biosynthetic gene clusters that are likely to produce novel or desirable natural products are identified through bioinformatic analysis. There are many more predicted biosynthetic gene clusters than can easily be experimentally characterized. Additional computational methods to prioritize biosynthetic gene clusters by the bioactivity, structural properties, or novelty of the product would make genome mining more efficient. Multiple machine learning/artificial intelligence models have been developed to predict product properties from biosynthetic gene cluster sequence, but they are limited by small quantities of training data. Model pretraining with unlabeled data is a powerful technique to develop models that can learn on a limited amount of labeled training data. Biosynthetic gene clusters are well suited to this strategy because there are many predicted clusters with only a small percentage being characterized. This paper reports BGC-MLM, a foundation model that is pretrained with a masked language task on predicted biosynthetic gene clusters and then fine-tuned for downstream applications including prediction of product structural class, bioactivity, chemical properties, counts of functional groups, and chemical fingerprint. Comparison to a model trained without pretraining shows that pretraining generally improves performance. BGC-MLM shows better or similar performance to existing specialized methods for these tasks, demonstrating its utility as a foundation model for natural product genome mining.","url":"https://doi.org/10.64898/2026.07.05.736569","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.05.736569","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10493480/v1","name":"Medical Decision Governance as a Competency-Based Educational Framework: Theory Development and Empirical Validation Through a Dynamic Patient–Physician Communication Curriculum","source":"preprints","abstract":"Abstract Background As healthcare increasingly evolves into a complex sociotechnical system, clinical decision-making is no longer an individual cognitive task but a dynamic governance process involving patients, families, interprofessional teams, healthcare organizations, and artificial intelligence (AI). Although competency-based medical education (CBME) emphasizes communication, collaboration, professionalism, and reflective practice, existing educational frameworks conceptualize these competencies as parallel attributes rather than explaining how they interact to support high-quality clinical decision-making. This theoretical fragmentation limits the ability of medical education to prepare future clinicians for increasingly complex decision environments. To address this gap, this study introduces the Medical Decision Governance Competency Theory (MDGCT) , a novel competency-based educational framework that conceptualizes governance capability as an integrated developmental competency emerging through the progressive interaction of empathy, communication, collaboration, reflective practice, and adaptive problem-solving. Methods A longitudinal mixed-method educational study was conducted among undergraduate medical and nursing students participating in a three-day Dynamic Patient–Physician Communication and Conflict Management Curriculum. Participants completed validated assessments of empathy, communication attitudes, interprofessional collaboration, metacognitive reflection, and adaptive problem-solving at baseline, immediately after the intervention, and during follow-up. Quantitative findings were triangulated with thematic analysis of reflective learning records to examine the theoretical structure proposed by MDGCT. Results The curriculum produced significant improvements across all governance-related competencies, including empathy, communication attitudes, collaboration, reflective practice, and adaptive problem-solving. Although some competencies showed partial attenuation during follow-up, most remained significantly above baseline, indicating sustained educational development. Nursing students demonstrated stronger long-term retention of empathy than medical students. The convergence of longitudinal quantitative outcomes and qualitative reflections provided initial empirical support for the developmental pathway proposed by MDGCT: Empathy → Communication → Collaboration → Reflective Practice → Adaptive Problem-Solving → Medical Decision Governance . Conclusions This study extends competency-based medical education by proposing governance capability as a distinct educational outcome rather than viewing communication as an isolated professional skill. Medical Decision Governance reframes communication education as the educational foundation for governing complex clinical decisions through the integration of interpersonal, cognitive, collaborative, reflective, and adaptive competencies. Beyond introducing a new competency-based theory, this study establishes a conceptual bridge linking medical education, clinical governance, patient autonomy, shared decision-making, patient safety, and trustworthy AI-enabled healthcare. The findings provide initial empirical evidence supporting governance-oriented education as a new paradigm for preparing healthcare professionals to navigate increasingly complex clinical decision-making environments.","url":"https://doi.org/10.21203/rs.3.rs-10493480/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10493480/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.12688/f1000research.185632.1","name":"From Technology Acceptance to Service Utilization: Examining Initial Trust and Perceived Substitution Crisis in AI-Driven Telemedicine Services","source":"preprints","abstract":"Background: This study aims to analyze the factors influencing intention to use Artificial Intelligence (AI)-based telemedicine services from a physician perspective by integrating the variables of effort expectancy, performance expectancy, social influence, facilitating conditions, initial trust, perceived substitution crisis, and behavioral intention. Methods This study used a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to 203 respondents, medical personnel/doctors who are potential users of AI-based telemedicine services. Results The results showed that behavioral intention and initial trust are the main factors significantly influencing intention to use AI-based telemedicine services. Initial trust was shown to have a significant positive effect on behavioral intention, while effort expectancy significantly influenced initial trust. Social influence significantly influenced perceived substitution crisis, while performance expectancy and facilitating conditions showed no significant effect on either initial trust or perceived substitution crisis. Interestingly, perceived substitution crisis actually had a significant positive effect on behavioral intention, contradicting the initial hypothesis of the study. Conclusions This study concludes that initial trust and behavioral intention are key factors in driving the adoption of AI-based telemedicine services among healthcare professionals. Therefore, increasing user trust, system ease of use, and a positive user experience are important strategies for increasing the acceptance of AI technology in digital healthcare.","url":"https://doi.org/10.12688/f1000research.185632.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.185632.1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.14.26358062","name":"Benchmarking Speech Recognition Models for Medical Consultations in Latin American Spanish: A Comparative Evaluation with Fine-Tuning","source":"preprints","abstract":"ABSTRACT BACKGROUND Artificial intelligence (AI) medical scribes rely on speech-to-text (STT) models for transcription. Evaluations of STT models in non-English settings remain scarce. We benchmarked ten STT models on medical consultations from Latin American (LatAm) Spanish and assessed whether fine-tuning improves transcription accuracy. METHODS Ten YouTube videos depicting medical consultations. Human transcriptions were the ground truth. Five open-source models were evaluated: Whisper Large, Whisper Large v3, Whisper Large v3 Turbo, Voxtral Mini 3B, and Canary 1B v2; and so were five close-source models: gpt-4o-transcribe, gpt-4o-mini-transcribe, gemini-2.5-pro, Eleven Labs, and Assembly AI. Whisper Large v3 was fine-tuned. One video was withheld from training. Performance assessed using Word Error Rate (WER), Character Error Rate (CER), BLEU Score, ROUGE-L, BERT Score, and Semantic Similarity on the one withheld video. RESULTS None of the fine-tuning iterations outperformed the vanilla Whisper Large v3. With the withheld video, Gemini-2.5-pro was the close-source model with the best performance in four of six metrics. In comparison to the close-source models, the fine-tuned model never outperformed the other models (withheld video); conversely, in comparison to the close-source models, the fine-tuned model showed better performance across metrics, for instance: BLEU score (63% vs to 58% for the second-ranking model), BERT (89% vs to 86%), and semantic similarity (89% vs to 83%), CER (19% vs 20%). CONCLUSIONS Whisper Large v3 and its fine-tuned variant are the best open-source STT models for transcribing medical conversations in LatAm Spanish. These findings provide an evidence base for developing AI medical scribes tailored to Spanish-speaking LatAm.","url":"https://doi.org/10.64898/2026.07.14.26358062","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.14.26358062","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.08.25.26361071","name":"Large language model-augmented implicit surgical video review","source":"preprints","abstract":"Surgical video interpretation is a promising medical artificial intelligence application. However, no existing video annotation method preserves the spatiotemporal complexity of surgeon reasoning. Here we show that verbal reasoning and visual attention can be converted into structured, machine-actionable records of intraoperative behaviours. Our method decomposes transcribed verbal commentary into video-anchored semantic feedback chunks, which are classified via a large language model, with spatial grounding to surgical scenes via eyegaze or cursor tracking. We demonstrate method validity and scalability on structured and unstructured annotation tasks. For quality feedback on full-length colorectal procedures, the method reached near-human fidelity for chunking (cosine similarity: 0.95±0.01) and semantic classification across observations (Cohen’s κ: 0.71±0.07) and evaluative triggers (Cohen’s κ: 0.67±0.14), with excellent usability ratings. For structured critical view of safety assessment in laparoscopic cholecystectomy, implicit annotation yielded excellent agreement with explicit reviewer ratings (Cohen’s κ: 0.83, 0.49 and 0.81 across three criteria). We anticipate this method will advance surgical data science by enabling scalable construction of meaningfully annotated surgical video datasets.","url":"https://doi.org/10.64898/2026.08.25.26361071","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.25.26361071","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9135888/v1","name":"Diagnostic Performance of Expert Physicians Versus General-Purpose Artificial Intelligence Using Standardized Static Coronary CT Images: A Dual-Reference Validation Study","source":"preprints","abstract":"Abstract Background: Coronary CT angiography (CCTA) is a first-line diagnostic modality for coronary artery disease (CAD), yet its interpretation requires significant expert experience. Although general-purpose multimodal artificial intelligence (GP-AI) models have shown promise in text-based medical tasks, their visual diagnostic performance in evaluating complex CCTA data remains poorly defined. Methods: This single-center retrospective study included 63 patients (252 vessel-based image sets) who underwent both CCTA and invasive coronary angiography. Expert physician consensus and four frontier GP-AI models (GPT-4o, Gemini 2.5, Claude 3.5 Sonnet, and Grok 4) evaluated identical standardized static images using a zero-shot approach with default generation parameters. Obstructive disease was defined as ≥ 50% luminal stenosis. Diagnostic performance was validated against expert consensus for plaque characterization and quantitative coronary angiography (QCA) for stenosis severity. Results: Expert consensus demonstrated robust agreement with QCA across all coronary territories (κ = 0.774–0.933, p 0.05). While Gemini 2.5 showed a moderate correlation in the right coronary artery (ICC = 0.515), overall continuous stenosis assessment and plaque characterization remained uniformly limited and clinically unreliable across all models. Conclusion: Expert physician interpretation remains the reference standard for CCTA. Current frontier GP-AI models are not suitable for independent clinical interpretation of coronary imaging, particularly in anatomically complex segments. These findings emphasize that general visual reasoning cannot yet replace domain-specific cardiovascular AI solutions or expert clinical judgment in specialized radiological tasks.","url":"https://doi.org/10.21203/rs.3.rs-9135888/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9135888/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202603.0316.v1","name":"Overcoming Barriers to Artificial Intelligence Adoption in Healthcare: A Review of Applications, Challenges and Emerging Solutions","source":"preprints","abstract":"Artificial Intelligence (AI) refers to systems designed to mimic human intelligence, enabling machines to perform tasks that typically require reasoning, learning, and decision-making. Today, AI is integrated into everyday life through technologies such as virtual assistants (e.g., Siri, Alexa, and Google Assistant), autonomous transportation systems, aviation technologies, gaming, and digital platforms. While AI has transformed multiple industries, healthcare has emerged as one of its most impactful domains, significantly enhancing medical imaging, disease diagnosis, treatment planning, and patient management. However, the clinical adoption of AI has been constrained by several persistent barriers, including limited computational resources, scarcity of high-quality annotated datasets, lack of interpretability, privacy concerns, regulatory ambiguity, and integration challenges within existing healthcare infrastructures. The emergence and rapid advancement of modern Deep Learning (DL) techniques helped address many of these challenges by enabling AI systems to analyze complex and high-dimensional healthcare data more effectively. Consequently, AI is increasingly leveraged to overcome traditional healthcare system constraints, improving diagnostic precision, workflow efficiency, and patient outcomes. Despite significant progress, unresolved technical, ethical,regulatory and organizational challenges necessitate a comprehensive evaluation of AI’s role in healthcare. This review discusses the evolution and applications of AI in healthcare, examines the limitations of traditional healthcare systems, explores how AI addresses these challenges, identifies current limitations of AI based approaches, and presents potential solutions to guide future advancements in AI applications within healthcare systems.","url":"https://doi.org/10.20944/preprints202603.0316.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202603.0316.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.04.22.26351451","name":"RCC-AID: Renal Cell Carcinoma AI Dataset for Medical Imaging Research","source":"preprints","abstract":"Contrast-enhanced computed tomography (CT) is central to the diagnosis, staging, and follow-up of patients with renal cell carcinoma (RCC). As artificial intelligence research into computer-aided solutions continues to grow, the need for curated and annotated datasets becomes increasingly important. Imaging-based artificial intelligence studies often need lesion annotations that are not consistently available. The Cancer Genome Atlas (TCGA) datasets are widely used for model training and validation. However, access to public annotations of lesions is limited, which limits reproducibility and comparability of the published research. To address this gap, we screened 1,915 CT scans from three TCGA-RCC databases and, following a meta-data-based exclusion step, used an automated segmentation model to generate initial kidney and lesion masks. Next, we conducted a reader study with all papillary (n=56), chromophobe (n=27) and 200 randomly selected clear cell RCC cases. Two trained students performed quality checks, corrections, and additional annotation of tumors and cysts, with uncertain cases reviewed by a board-certified radiologist. After data exclusion and quality control, a final cohort of 129 annotated CT scans from 91 patients (24 female, 67 male; mean age 56 years) was retained, including 85 clear cell, 26 papillary and 18 chromophobe RCC cases. Images and voxel-level annotations of kidneys and lesions are openly available at https://zenodo.org/records/20719257 . By open-sourcing these annotations, we aim to foster accessible, reproducible AI research in renal cell carcinoma. RCC-AID provides a reusable open resource dataset for segmentation, detection, subtype classification, radiomics, and multimodal RCC research.","url":"https://doi.org/10.64898/2026.04.22.26351451","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.22.26351451","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10472890/v1","name":"Beyond Socratic Questioning: Designing GenAI-mediated Metacognitive Scaffolding for Clinical Reasoning in Medical Education","source":"preprints","abstract":"Abstract Clinical reasoning underpins clinical expertise, yet fostering its development remains challenging. Effective clinical reasoning requires students to monitor and control how their reasoning develops as a clinical problem unfolds. These processes are commonly described as metacognition. Existing approaches provide limited timely and adaptive support for students’ metacognition as reasoning evolves. Generative artificial intelligence (GenAI) creates opportunities for delivering such support, but how GenAI-mediated metacognitive scaffolding should be designed remains unclear. Using design-based research, we designed and iteratively refined such scaffolding within a simulated history-taking environment across three cycles. Fixed predefined questions functioned mainly as task reminders. Socratic questioning provided broader metacognitive support but was imbalanced and was sometimes experienced as endless and as knowledge testing. Adaptive on-demand scaffolding provided more balanced support and enabled students to select support aligned with their evolving reasoning needs. Effective GenAI-mediated metacognitive scaffolding requires context integration, balanced support, and preservation of student agency.","url":"https://doi.org/10.21203/rs.3.rs-10472890/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10472890/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9952836/v1","name":"A Blockchain-based NFT Framework for Ownership and Intellectual Property Preserving with Controlled Access to Medical Datasets","source":"preprints","abstract":"Abstract The rapid diffusion of artificial intelligence (AI) and advanced data analytics techniques across biomedical research, diagnostics and personalized medicine has established high-quality medical datasets as foundational resources. However, a significant impediment to progress is the reluctance of data owners to share these valuable resources because existing infrastructures provide no reliable guarantee of ownership or intellectual property preservation, in addition to the control over unauthorized usage. Consequently, vast quantities of recorded, high-potential data remain unused within private repositories. To overcome this barrier, a blockchain-based framework leveraging Non-Fungible Tokens (NFTs) is introduced in this paper to preserve ownership of medical datasets. The general workflow involves minting an NFT representing the dataset (stored encrypted off-chain), allowing users to request time-limited access via a smart contract. Access is granted using ephemeral decryption keys, enforcing fine-grained and revocable control. For forensic auditing, watermarking and perceptual hashing are integrated to enable the detection of post-access leakage without identifying the perpetrator directly. Technically, the framework integrates on-chain Merkle Tree-based integrity verification to ensure data fidelity upon retrieval. Practical applicability is demonstrated using a longitudinal multimodal neuroimaging dataset from OpenNeuro (DS007328). The proposed framework establishes a technically feasible and reproducible model for next-generation medical data governance, enabling verifiable ownership, controlled access, and reproducible auditing while keeping sensitive data off-chain.","url":"https://doi.org/10.21203/rs.3.rs-9952836/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9952836/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8863592/v1","name":"Generative and Agentic Artificial Intelligence for Medical Coding and Billing: A Human-in-the-Loop Architecture and Evaluation","source":"preprints","abstract":"Abstract Medical coding and billing workflows in modern healthcare systems have grown increasingly complex due to expanding clinical documentation, evolving coding standards, and heightened regulatory scrutiny. These factors contribute to persistent error rates, administrative inefficiencies, and financial risk, placing substantial cognitive and operational burdens on healthcare professionals. This study aims to design and evaluate a human-in-the-loop architecture that integrates generative and agentic artificial intelligence to support medical coding and billing while preserving expert oversight. The proposed framework combines automated code suggestion, contextual reasoning, and workflow orchestration with structured human validation at critical decision points. The study employs a mixed-methods evaluation approach, incorporating architectural analysis, workflow performance assessment, qualitative expert feedback, and quantitative measures of accuracy, efficiency, and error reduction. Results indicate measurable improvements in coding precision, turnaround time, and audit readiness when compared to conventional manual or fully automated pipelines. At the same time, the evaluation reveals residual risks related to model hallucination, edge-case misclassification, and workflow overreliance, underscoring the importance of continuous monitoring and human intervention. Overall, the findings demonstrate that human-AI collaboration offers a more reliable and accountable pathway than full automation for high-stakes healthcare administration tasks. The study concludes that strategically designed human-in-the-loop systems can enhance operational performance while maintaining compliance, transparency, and clinical trust in medical coding and billing environments.","url":"https://doi.org/10.21203/rs.3.rs-8863592/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8863592/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9938631/v1","name":"Advances in Ultrasound Imaging: Automated 3D Segmentation of Anatomical Structures in the Forearm","source":"preprints","abstract":"Abstract Musculoskeletal ultrasound (MSUS) is widely used in clinical medicine and is extensively used in rheumatology. However, acquiring proficiency in MSUS remains challenging and demands substantial training. The advent of machine learning and artificial intelligence (AI) in medical imaging has introduced new possibilities. In this paper we present the automatic segmentation and annotation of 11 anatomical structures in the lower forearm and different possibilities of 3D visualization of the most important anatomical structures of the forearm, demonstrating the potential of this technology as a bedside teaching tool.","url":"https://doi.org/10.21203/rs.3.rs-9938631/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9938631/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10121301/v1","name":"Adaptive Selection of MICE Algorithm Parameters: A Case Study on Pulmonary Function Value Prediction Models","source":"preprints","abstract":"Abstract Artificial intelligence-based medical prediction models are often affected by incomplete datasets containing missing values, which can significantly reduce model reliability and predictive performance. Among various imputation approaches, Multiple Imputation by Chained Equations (MICE) is widely used in medical data analysis because it models each variable using the conditional distributions of the remaining variables while reflecting uncertainty in the imputation process. Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disorder characterized by persistent airflow limitation, requiring early diagnosis and continuous monitoring for improved prognosis. In this study, a pulmonary function test dataset collected from 598 subjects over a two-year period was used to investigate the effects of time-series sampling and missing-data imputation on prediction performance. An adaptive selection method was proposed for the initialization strategy and maximum iteration count of the MICE algorithm based on the statistical characteristics of representative pulmonary function indicators, including FEV1, FVC, FEF25–75%, and PEF. Different missing rates were artificially introduced, and the performances of Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and multivariate iterative regression-based MICE were compared. Experimental results demonstrate that the proposed adaptive MICE strategy maintains stable predictive performance under increasing missing rates and provides reliable predictive information by effectively reflecting multivariate correlation structures.","url":"https://doi.org/10.21203/rs.3.rs-10121301/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10121301/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9467279/v1","name":"Autonomous Quality Intelligence (AQI)","source":"preprints","abstract":"Abstract Quality management systems in modern manufacturing are at a critical inflection point. Traditional Design Control frameworks including Design Development Plans (DDP), Design History Files (DHF), Verification and Validation (V&V), Design Inputs and Outputs, and Design Control Plans (DCP) are largely document-centric, reactive, and siloed from real-time production intelligence. While ISO 9001:2015 and regulatory standards such as FDA 21 CFR Part 820 provide robust compliance architectures, their implementation remains labor-intensive, error-prone, and disconnected from emerging digital manufacturing ecosystems. This paper introduces the Autonomous Quality Intelligence (AQI) Framework, a novel, first-of-its-kind architecture that unifies Generative Artificial Intelligence (GenAI), Industrial Digital Twin (IDT) technology, and real-time compliance orchestration engines into a single, self-adaptive Design Control system. AQI redefines how organizations manage Design Inputs, Design Outputs, DDP, DHF, V&V cycles, and DCP transforming static documentation into living, AI-governed, sensorsynchronized quality artifacts. The framework introduces three original constructs: (1) the Compliance Generative Layer (CGL) for automated, regulation-aware documentation synthesis; (2) the Dynamic Verification Engine (DVE) for continuous, twin-based validation; and (3) the Predictive Risk Intelligence Module (PRIM) for AI-augmented FMEA and risk scoring aligned with ISO 14971 and ISO 9001 Clause 6.1. A case study from a transit infrastructure manufacturing environment (Aventura Corporation, MTA New York) demonstrates AQI implementation outcomes: 40% reduction in design-related nonconformances, 97% first-pass verification success rate, 100% regulatory submission acceptance, and a 35% decrease in quality-related costs. The AQI Framework is proposed as a universal, cross-industry standard applicable to medical devices, aerospace, automotive, and high-tech manufacturing sectors, with direct alignment to ISO 9001:2015, ISO 13485:2016, and FDA 21 CFR 820.30 requirements. 1","url":"https://doi.org/10.21203/rs.3.rs-9467279/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9467279/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.20.26353717","name":"An Experimental Investigation of the Relationship between AI-Human Workflow Design and Legal Liability for Radiologists: The Erroneous-Change Penalty and Omission Bias","source":"preprints","abstract":"ABSTRACT Background With growing impetus to integrate artificial intelligence (AI) tools into radiology, clinical practices must navigate workflow redesign. This carries implications for medical malpractice liability. Methods We conducted an online vignette experiment with United States adults who acted as hypothetical jurors in a malpractice case involving a missed intracranial hemorrhage. Participants (n=2,347) were randomized to one of 22 conditions: a no-AI control and 21 conditions involving a hypothetical AI system. These twenty-one conditions varied by whether (1) a single-read or double-read workflow was used, (2) the radiologist’s initial interpretation was documented, (3) the radiologist changed their interpretation after viewing AI output, (4) the AI detected the abnormality, and (5) the AI error rate—False Discovery Rate (FDR) or False Omission Rate (FOR)—was provided to participants only, both participants and radiologist, or neither. The primary outcome was perceived liability, assessed by whether the radiologist met their duty of care. Findings Perceived liability differed across conditions (p Interpretation Double-read workflows with documented initial interpretations and disclosure of AI error rates reduce perceived liability, though changing a correct initial interpretation increases it. Strategic workflow design is critical for successful AI implementation that can mitigate malpractice risk. RESEARCH IN CONTEXT Evidence before this study Emerging research has identified several factors that shape how artificial intelligence (AI) systems are integrated into clinical workflows. Beyond technical performance, factors such as disease prevalence, documentation practices, and workflow design have all been shown to play a role in the implementation of AI tools and how they will inevitably affect physician liability. Vignette experiments have separately identified cognitive biases and mitigating measures that can be integrated into workflow design, such as disclosing AI error rates and documenting independent interpretations before reviewing AI output. Added value of this study This study builds on prior work by examining how multiple aspects of AI implementation influence perceptions of legal liability when hypothetical jurors are asked to adjudicate a medical malpractice lawsuit arising from a false negative interpretation. In particular, we show how combining double-read workflows, documentation practices, and the inclusion of AI error rates can reduce perceived liability. We also show that, when a double-read workflow is used, changing an initially correct interpretation to an incorrect one incurs greater liability for the radiologist than being incorrect in both the initial and final interpretations. These findings underscore the need to address cognitive biases that will undoubtedly arise at the human-AI interface. Implications of all the available evidence Optimizing the integration of AI tools into radiology requires strategic attention to workflow redesign, as combinations of features can collectively affect perceived liability, likely through well-known cognitive biases. Based on the results of our study, we propose one workflow that can mitigate a radiologist’s risk of legal liability. Moving forward, clinical practices and stakeholders should remain cognizant of these factors as they work toward building sustainable AI-physician systems.","url":"https://doi.org/10.64898/2026.05.20.26353717","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.20.26353717","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.01.27.26344998","name":"Exploring Attitudes and Acceptance of Artificial Intelligence in Multiple Sclerosis from the Patient Perspective","source":"preprints","abstract":"Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudinal monitoring and shared decision-making. Multiple sclerosis is a prototypical example of such care, but real-world benefit will depend on whether people accept AI support in different clinical roles. We conducted a cross-sectional, web-based survey among 241 people with MS (pwMS) to assess comfort with AI across eight clinical domains and to identify predictors of acceptance. We derived an artificial-intelligence attitudes composite with high internal consistency (Cronbach alpha = 0.90). Overall acceptance was moderate (mean 3.39 ± 0.78). Acceptance differed across domains, demonstrating a responsibility gradient: comfort was highest for supportive applications such as chronic management (54.4%) and symptom screening (50.2%), but lower for treatment selection (38.6%) and diagnosis (35.3%; P Author Summary We use artificial intelligence more and more in everyday life, and similar tools are now being introduced into medical care. For long-term conditions such as multiple sclerosis, digital systems could help manage large amounts of clinical information and support monitoring between visits. At the same time, these tools will only be useful if the people receiving care are willing to use them and understand what role they play. In this study, we asked 241 people living with multiple sclerosis in Germany how comfortable they would feel with artificial intelligence in different parts of care. We found that comfort depended strongly on the task. Participants were most open to artificial intelligence when it supported practical, lower-risk functions such as ongoing monitoring or symptom screening, and they were more cautious when it was described as influencing diagnosis or treatment choices. Most participants wanted clinicians to remain responsible for final decisions. Acceptance was higher among people who already used artificial intelligence frequently in everyday life, and it differed by age and by region. Our findings suggest that successful implementation will require more than technical performance: it should be introduced transparently, with clinician oversight, and in a stepwise way that builds familiarity without shifting responsibility away from the clinical team.","url":"https://doi.org/10.64898/2026.01.27.26344998","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.27.26344998","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.20944/preprints202607.0145.v1","name":"“High Demand, Low Training, Moderate Trust”: A Needs Assessment Survey of AI Integration in Clinical Medicine Education","source":"preprints","abstract":"As artificial intelligence increasingly permeates healthcare, it remains unclear whether clinical medicine students and professionals are adequately prepared for this transformation. This study aimed to investigate AI awareness, usage patterns, teaching needs, and curriculum expectations among this population to inform evidence-based curricular reform. A cross-sectional online survey was administered to 930 students and professionals from a medical and pharmaceutical college and its affiliated hospitals; 619 valid responses were analyzed using descriptive statistics. While 75.93% of respondents were awareness with AI applications in healthcare, only 31.99% used AI tools frequently. Although 88.21% acknowledged the necessity of integrating AI into clinical medicine teaching, merely 48.47% reported that their institution offered relevant courses. Moderate trust in AI-assisted diagnosis was expressed by 70.76% of participants. The most desired teaching contents were case analysis and diagnostic simulation (75.61%), drug recommendation and dosage calculation (70.44%), and imaging/pathology recognition (65.91%). Respondents recommended that AI and data science courses constitute 23.34% of total credits, alongside Clinical Medicine (30.66%) and Basic Medical Sciences (25.37%). These findings show a pronounced \"high demand, low training, moderate trust\" paradox characterizes current AI medical education. A clinically grounded, progressively structured, and practice-oriented AI curriculum is needed to prepare future physicians for the AI-driven healthcare era.","url":"https://doi.org/10.20944/preprints202607.0145.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0145.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.08.12.743536","name":"A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies","source":"preprints","abstract":"Computational design has transformed many fields of engineering, where simulators can explore millions of candidate design configurations before experimental development and testing. Therapeutic design in biomedicine has resisted computational design approaches because disease progression and therapeutic response emerge from interactions among many cell types within human tissue, governed by biochemical parameters that are largely unknown and potentially unknowable. Here, we introduce the Cell Interaction Foundation Model (CIFM), a virtual tissue model that forward-simulates the transcriptional dynamics of cells in human tissue under arbitrary therapeutic conditions based upon a spatial transcriptomic seed. CIFM is a geometric graph neural network trained by self-supervised masked-transcriptome prediction on millions of cellular microenvironments spanning human tissue types and disease states; generative, auto-regressive, monte-carlo play-out, then, simulates transcriptional dynamics under combinatorial perturbations from a spatial transcriptomic seed. We validate CIFM by showing accuracy gains in gene expression prediction and imputation, disease classification, recapitulation of perturbation responses in prostate cancer models, and recovery of T cell-tumor signaling measured in cell–cell sequencing experiments. Beyond such conventional tasks, CIFM enables target identification and therapeutic design through generative tissue simulation play-outs. Analyzing over 10 6 single and combinatorial perturbations, CIFM designs immunotherapy strategies for cancer and autoimmune disease that exploit combinatorial manipulation of signaling pathways to induce or suppress immune activation. Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.","url":"https://doi.org/10.64898/2026.08.12.743536","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.12.743536","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.31.26354561","name":"Enhanced precision of tensor electrocardiography through increased cumulative distribution function resolution: Validation in healthy individuals","source":"preprints","abstract":"Deep-learning ECG analysis is advancing rapidly but lacks stable, physiologically interpretable indicators to anchor explainable artificial intelligence (AI). Tensor cardiography (TCG) models electrocardiographic (ECG) waveforms as differences between pairs of cumulative distribution functions (CDFs), representing collective myocardial action potential transitions. However, the original 4-CDF model has limitations in fitting P waves and complex QRST patterns. This study aimed to evaluate whether increasing the number of CDFs from 4 to 10 improves TCG fitting accuracy and to characterize normative distributions of 10-CDF parameters in healthy individuals. Participants were recruited through occupational health screening at Tobu Railway Co., Ltd. (n = 415) and from the Nippon Medical School Hospital ECG database (n = 29). Standard 12-lead ECGs from 444 healthy participants, including 345 men and 99 women with a mean age of 46.9 years, were analyzed using TCG software. Reconstruction accuracy was assessed using RMSE, paired t-tests, and Cohen’s d. The 10-CDF model achieved significantly lower RMSE values across all leads than the 4-CDF model, with all p values Author summary Deep-learning approaches have rapidly advanced electrocardiogram (ECG) analysis, but many models function as black boxes with indicators that are difficult for clinicians to interpret. There is a growing need for stable, physiologically meaningful ECG measurements to make artificial intelligence (AI) more explainable. Tensor cardiography (TCG) addresses this need by representing ECG waveforms using cumulative distribution functions (CDFs) based on myocardial electrical activity models, providing interpretable parameters. In this study, we increased the number of CDFs from four to ten to extract more precise quantitative information from ECGs of 444 healthy individuals. The 10-CDF model reconstructed ECG waveforms with substantially greater accuracy than the original 4-CDF model, reducing fitting errors by approximately 75%. We also characterized normative reference distributions for the expanded model and identified two new parameters that capture details of cardiac electrical activity not available in the original 4-CDF model. These findings suggest that the expanded TCG model provides a stable, interpretable framework for future AI-assisted and wearable ECG analysis. By linking waveforms to physiological parameters, TCG may help support earlier and more transparent detection of heart disease.","url":"https://doi.org/10.64898/2026.05.31.26354561","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.31.26354561","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.03.03.26347432","name":"Artificial Intelligence Detects Cardiac Amyloidosis Before Clinical Diagnosis","source":"preprints","abstract":"The approval of novel disease-modifying treatments for cardiac amyloidosis (CA) offers an avenue to stabilize disease progression. Timely diagnosis of CA is imperative so that patients can be connected to treatment earlier in their course. Echocardiography is a widely available, non-invasive modality to screen for CA. However, diagnosis of CA is often delayed due to phenotypic mimickers. Artificial intelligence (AI) applied to echocardiography may facilitate early detection and connect patients to more definitive diagnostic testing. In this case report, we analyzed the frequency of echocardiography prior to clinical diagnosis of amyloidosis and deployed an externally validated AI model in 349 patients with documented amyloidosis at Cedars-Sinai Medical Center. On average, AI detection was positive in patients with documented CA 218 days prior to clinical diagnosis and 209 days in all-comers. When integrated with clinical histories, AI may facilitate earlier detection of CA and connect patients to more timely management.","url":"https://doi.org/10.64898/2026.03.03.26347432","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.03.26347432","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202604.0030.v1","name":"Scoping Review of Recent Trends and Challenges in Artificial Intelligence Based Medical Ultrasound Denoising","source":"preprints","abstract":"(1) Background: Ultrasound (US) imaging is widely used in clinical diagnosis but is often degraded by speckle noise, which reduces image quality and can hinder interpretation. Deep learning has emerged as a promising approach for US denoising, yet its clinical applicability remains unclear. (2) Methods: A systematic review of studies published in the last three years on deep learning-based US denoising was conducted following PRISMA-DTA guidelines. Searches were performed in IEEE-Xplore, PubMed, ScienceDirect, Scopus, Web of Science, and Google Scholar. Data were extracted on Anatomy, noise type, learning paradigm, network architecture, datasets, evaluation metrics, and performance outcomes. (3) Results: from 951 records scrapped, 36 studies were included. Most focused-on breast, fetal, cardiac, and abdominal US. Convolutional neural networks (CNNs), particularly U-Net, were the most common approach, while GANs, transformers, and variational autoencoders were less explored. Reported PSNR ranged from 30-45 dB and SSIM from 0.85-0.97. Most studies (34 out of 36) relied on synthetic noise and paired datasets, with limited evaluation on real clinical images. (4) Conclusions: CNN-based methods dominate US denoising research, but translation to clinical practice is limited due to reliance on synthetic data and inconsistent evaluation metrics. Future work should focus on large benchmark datasets and standardized metrics to improve generalizability across clinical settings.","url":"https://doi.org/10.20944/preprints202604.0030.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202604.0030.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202607.1157.v1","name":"AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions","source":"preprints","abstract":"Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We will discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we will highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we will outline future research directions, including hybrid AI-biological models, IoT-supported viral surveillance in smart environments, and the integration of explainable AI to enhance clinical trust.","url":"https://doi.org/10.20944/preprints202607.1157.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.1157.v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8920991/v1","name":"Academic Impact vs. Societal Attention: A Dual-Analysis of Top-Cited Artificial Intelligence Articles in Medicine","source":"preprints","abstract":"Abstract Objective This study aims to examine the relationship between the academic citation success and social visibility of Artificial Intelligence (AI)-based medical research using bibliometric and altmetric methodologies. Methods The top 100 most-cited articles indexed in the Web of Science Core Collection from 1 January 2023 to 27 January 2026 were analyzed; citation counts and Altmetric Attention Scores (AAS) were retrieved on 27 January 2026 (See Methods for the full search query). Academic impact was measured by Web of Science citation counts, while social impact was evaluated using the Altmetric Attention Score (AAS). Data were assessed through Spearman’s correlation analysis and the Mann-Whitney U test. Results A statistically significant but weak positive correlation was identified between citation counts and AAS (r = 0.299, p = 0.0025). Open access status characterized 92% of the articles. The highest academic impact was achieved by the ChatGPT-USMLE study by Kung et al. (2023) with 2,193 citations, whereas the highest social impact was held by the \"Physician vs. Chatbot\" study by Ayers et al. (2023) (AAS: 6,388). A notable finding was that publications originating from China exhibited remarkably low altmetric scores (Median AAS: 12) despite high academic citation rates, suggesting a 'digital isolation' effect that may stem from Western-centric altmetric data coverage. Conclusion Academic success and societal popularity are governed by distinct dynamics, indicating the need for researchers to adopt science communication strategies and for funding agencies to use multidimensional impact metrics. While academia prioritizes conceptual depth—such as ethics and methodology—the general public shows greater interest in sensational competition (e.g., physician vs. AI). It is recommended that researchers enhance their science communication competencies and that funding agencies adopt multidimensional evaluation approaches.","url":"https://doi.org/10.21203/rs.3.rs-8920991/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8920991/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9088143/v1","name":"Integration of Artificial Intelligence into Medical Education: A Mixed-Methods Study Identifying the Risks, Safeguards, and Optimal Approaches to Protect Clinical Skill Development across West African Countries","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) offers transformative potential for medical education through personalised learning, simulation, and decision support, especially in resource-limited settings like West Africa. However, there are concerns regarding over-reliance on AI, potentially undermining core clinical skills such as diagnostic reasoning, physical examination, and independent judgement. Empirical evidence on risks, safeguards, and optimal integration strategies in low-resource African contexts remains scarce. Methods This mixed-methods study targeted physicians affiliated with the West African College of Physicians. A structured online survey (n = 136) assessed AI literacy, perceived risks to clinical skill development, attitudes toward AI, and understanding of AI limitations using Likert scales and knowledge items. Purposive key informant interviews (n = 72) with physician educators, programme directors, clinicians, technical experts, and policymakers explored experiences, concerns, safeguards, and curriculum recommendations. Quantitative data were analysed descriptively on SPSS version 29.0; and qualitative data underwent thematic framework analysis in NVivo. Findings Survey respondents were predominantly Nigerian (84.6%), balanced by gender (51.5% female), with diverse clinical experience. AI literacy was low: 32.4% lacked basic familiarity, and 67.6% lacked confidence in assessing AI outputs. Major gaps included unawareness of algorithmic bias (41.9%) and population variability in AI performance (36.8%). Most (87.5%) rejected uncritical acceptance of AI recommendations. High concern existed about threats to autonomy (77.9%), weakened diagnostic skills (71.3%), and deskilling of younger clinicians (68.4%). However, 94.1% expressed willingness to use AI if proven effective and safe. Qualitative themes emphasised systemic training gaps, the need for “informed sceptics”, longitudinal curriculum integration, hands-on experience, and safeguards prioritising clinical reasoning over AI dependence. Conclusion West African clinicians show significant AI literacy deficits and apprehension about the erosion of clinical skills yet demonstrate conditional openness to AI. Integration requires foundational literacy, longitudinal embedding, practical training, ethical focus, and competency assessments that preserve independent judgement. Addressing faculty expertise and infrastructure barriers is essential to prepare competent, AI-literate physicians without compromising clinical proficiency.","url":"https://doi.org/10.21203/rs.3.rs-9088143/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9088143/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.21203/rs.3.rs-10349788/v2","name":"A Dry Lab Verification Apparatus and Methodology for Evaluating Real-Time Arthroscopic AI","source":"preprints","abstract":"Abstract Background The integration of artificial intelligence into orthopedic arthroscopic surgery has accelerated the demand for structured preclinical validation frameworks capable of exposing AI algorithms to the real-time operational conditions they will encounter in clinical practice. Offline evaluation on retrospective video datasets, while necessary, cannot replicate the real-time inference demands, multi-axial joint kinematics, and intraoperative tracking scenarios that determine whether a model will perform reliably in the operating room. We describe the design and preliminary validation of a dry laboratory testing apparatus specifically constructed to address this gap, providing a reproducible, kinematically accurate physical platform for the real-time evaluation of AI-driven arthroscopic navigation solutions. Methods The apparatus replicates flexion/extension kinematics of a knee joint using an autosegmented 3D-printed phantom derived from open-source MRI data, a hinge-based kinematic mechanism with goniometer-confirmed angular locking, and NDI Polaris Lyra optical tracking integrated with a Stryker 1588 AIM arthroscope. Procedure-related simulation includes arthroscopic port sleeves, custom rigid body tracker attachments for tool tracking, and a dual-mode software application supporting hotkey-activated intraoperative landmarking and preoperative plan-guided external-to-internal navigation. Validation characterizes both kinematic fidelity and real-time AI model performance metrics including inference latency, throughput, and temporal stability. Conclusions The presented platform and accompanying proposed tiered characterization framework constitute a replicable and regulatory-informed methodology for advancing arthroscopic AI solutions from algorithm development toward clinical deployment. By explicitly occupying the dry lab tier of a software-as-a-medical-device verification and validation hierarchy, this platform fills a structural gap not addressed by offline dataset benchmarking or cadaveric study.","url":"https://doi.org/10.21203/rs.3.rs-10349788/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10349788/v2","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9843034/v1","name":"A cognitive–motivational perspective on AI-assisted pre-clerkship preparation: a theory- informed cross-sectional study","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI) is increasingly integrated into medical education; however, its role in pre-clerkship preparation remains insufficiently understood, particularly in terms of underlying learning mechanisms. This study aimed to examine medical students’ acceptance of an AI-assisted pre-clerkship preparation model and to test a theoretically informed pathway in which perceived usefulness drives behavioral intention, with potential implications for clinical readiness. Method A cross-sectional survey was conducted among 176 undergraduate medical students prior to their obstetrics and gynecology clerkship at a Chinese medical school between February and April 2026. The questionnaire assessed demographic characteristics, digital literacy, clerkship expectations, perceived usefulness, and behavioral intention. Analyses included descriptive statistics, correlation analyses, and multiple linear regression, guided by the Technology Acceptance Model. Results Students demonstrated moderate-to-high acceptance of AI-assisted pre-clerkship preparation (mean willingness 3.73 ± 0.70), with 63.0% expressing intention to participate. All dimensions of perceived usefulness showed strong correlations with behavioral intention (r = 0.787–0.846, all p p","url":"https://doi.org/10.21203/rs.3.rs-9843034/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9843034/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9247601/v1","name":"Psychometric Alignment Between Human and Artificial Intelligence Performance in Cardiology Residency In-Service Examinations","source":"preprints","abstract":"Abstract Background Large language models (LLMs) have demonstrated rapidly expanding capabilities across medical knowledge tasks, including professional examinations. However, most existing evaluations focus primarily on overall accuracy and provide limited insight into how AI performance relates to the psychometric structure of examination items. Methods We evaluated the performance of five large language models on a dataset of 199 cardiology residency in-service examination questions. The models included three frontier general-purpose systems (Claude 4.6 Opus, Gemini 3.1 Flash-Lite, and GPT-5.4) and two medically oriented open-source models (MedQwen-2.5 and Qwen-3.5). Item-level analyses were conducted to examine the associations between AI accuracy and psychometric characteristics of exam questions, including human-defined item difficulty and item discrimination. Multivariable logistic regression was used to identify independent predictors of AI performance. Alignment between human and AI performance was assessed using Spearman correlation and distractor overlap analysis. Results Frontier models substantially outperformed medically oriented open-source models, achieving accuracies of 86.4% for Claude Opus, 82.9% for Gemini Flash-Lite, and 82.4% for GPT-5.4, compared with 53.3% for MedQwen and 18.6% for Qwen-3.5-35B. AI performance followed a clear gradient across human-defined difficulty levels, with frontier models answering 65–74% of hard questions and 92–96% of easy questions correctly. In multivariable analyses, item difficulty was the only psychometric factor consistently associated with AI success across frontier models (OR range 0.37–0.47, all p","url":"https://doi.org/10.21203/rs.3.rs-9247601/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9247601/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.21203/rs.3.rs-10349788/v1","name":"A Dry Lab Verification Apparatus and Methodology for Evaluating Real-Time Arthroscopic AI","source":"preprints","abstract":"Abstract Background The integration of artificial intelligence into orthopedic arthroscopic surgery has accelerated the demand for structured preclinical validation frameworks capable of exposing AI algorithms to the real-time operational conditions they will encounter in clinical practice. Offline evaluation on retrospective video datasets, while necessary, cannot replicate the real-time inference demands, multi-axial joint kinematics, and intraoperative tracking scenarios that determine whether a model will perform reliably in the operating room. We describe the design and preliminary validation of a dry laboratory testing apparatus specifically constructed to address this gap, providing a reproducible, kinematically accurate physical platform for the real-time evaluation of AI-driven arthroscopic navigation solutions. Methods The apparatus replicates flexion/extension kinematics of a knee joint using an autosegmented 3D-printed phantom derived from open-source MRI data, a hinge-based kinematic mechanism with goniometer-confirmed angular locking, and NDI Polaris Lyra optical tracking integrated with a Stryker 1588 AIM arthroscope. Procedure-related simulation includes arthroscopic port sleeves, custom rigid body tracker attachments for tool tracking, and a dual-mode software application supporting hotkey-activated intraoperative landmarking and preoperative plan-guided external-to-internal navigation. Validation characterizes both kinematic fidelity and real-time AI model performance metrics including inference latency, throughput, and temporal stability. Conclusions The presented platform and accompanying proposed tiered characterization framework constitute a replicable and regulatory-informed methodology for advancing arthroscopic AI solutions from algorithm development toward clinical deployment. By explicitly occupying the dry lab tier of a software-as-a-medical-device verification and validation hierarchy, this platform fills a structural gap not addressed by offline dataset benchmarking or cadaveric study.","url":"https://doi.org/10.21203/rs.3.rs-10349788/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10349788/v1","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.06.25.26356568","name":"NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications","source":"preprints","abstract":"Reliable explanations are important for trustworthy medical applications of artificial intelligence (AI), but attribution-based explanations can vary across model randomization and small analytic changes. We present NEXIM (Nash Equilibrium-based Explainability and Interpretability Model), implemented here as an accuracy-constrained, equilibrium-inspired model-selection framework that jointly evaluates held-out prediction error, explanation stability, and cross-model connectivity. The implementation evaluated ten GradientBoosting Regressor models per prediction horizon, differing only by random seed (0-9), using a fixed 75/25 patient split. Kernel SHAP attribution vectors were compared using Spearman rank correlation, and graph connectivity summarized whether each model belonged to a dense explanation-similarity region. Candidate models within 0.02 Montreal Cognitive Assessment points of the best root mean squared error (RMSE) were ranked using a multiplicative Explanation Equilibrium Score. In longitudinal Parkinson’s Progression Markers Initiative data, NEXIM selected the RMSE-optimal model at the one- and three-year horizons. At the two-year horizon, it selected Model 4 rather than the RMSE-only Model 8, increasing scaled stability from 0.8757 to 0.8847 and normalized graph connectivity from 0.889 to 1.000 while increasing RMSE by only 0.0014. The two models retained the same top-20 feature set but differed modestly in feature order, illustrating that NEXIM primarily acted as a reproducibility screen rather than identifying clinically contradictory explanations. Stability and consensus are treated as reproducibility criteria, not evidence of causal faithfulness, clinical usefulness, or improved patient outcomes. NEXIM may therefore serve as a governance checkpoint for model refresh and documentation, but external validation, stronger model-family baselines, and prospective clinical evaluation remain necessary.","url":"https://doi.org/10.64898/2026.06.25.26356568","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.25.26356568","addedAt":"2026-09-01T01:47:49.188Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"oa:W4403150469","name":"Medical Image Computing and Computer Assisted Intervention – MICCAI 2024","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-031-72111-3","authors":["Marius George Linguraru","Qi Dou","Aasa Feragen","Stamatia Giannarou","Ben Glocker","Karim Lekadir","Julia A. Schnabel"],"tags":["Computer science","Multimedia","Computer graphics (images)","World Wide Web"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1007/978-3-031-72111-3","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406186727","name":"Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery","source":"openalex","abstract":"Neurosurgery has evolved alongside technological innovations; however, these advances have also introduced greater complexity into clinical practice. Neurosurgery remains a demanding and high-risk field that requires a broad range of skills. Artificial intelligence (AI) has immense potential in neurosurgery given its ability to rapidly analyze large volumes of clinical data generated in modern clinical environments. An expanding body of literature has demonstrated that AI enhances various aspects of neurosurgery, including diagnostics, prognostication, decision-making, data management, education, and clinical studies. AI applications are expected to reduce medical errors and costs, broaden healthcare accessibility, and ultimately boost patient safety and surgical education. Nevertheless, AI application in neurosurgery remains practically limited because of several challenges, such as the diversity and volume of clinical training data collection, concerns regarding data quality, algorithmic bias, transparency (explainability and interpretability), ethical issues, and regulatory implications. To comprehensively discuss the potential benefits, future directions, and limitations of AI in neurosurgery, this review examined recent studies on AI technology and its applications in this field, focusing on intraoperative decision support and surgical education.","url":"https://doi.org/10.31662/jmaj.2024-0141","authors":["Taku Sugiyama","Hiroyuki Sugimori","Minghui Tang","Miki Fujimura"],"tags":["Neurosurgery","Patient safety","Medical emergency","Medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.31662/jmaj.2024-0141","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405849905","name":"Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry","source":"openalex","abstract":"Objectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle–Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI’s significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.","url":"https://doi.org/10.3390/dj13010013","authors":["Amal Alfaraj","Toshiki Nagai","Hawra AlQallaf","Wei‐Shao Lin"],"tags":["Prosthodontics","Implant","Dentistry","Medicine","Race (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-27","doi":"https://doi.org/10.3390/dj13010013","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403435414","name":"Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review","source":"openalex","abstract":"Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.","url":"https://doi.org/10.5812/ijpr-150510","authors":["Negar Mottaghi-Dastjerdi","Mohammad Soltany‐Rezaee‐Rad"],"tags":["Artificial intelligence","Pharmaceutical sciences","Computer science","Management science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-15","doi":"https://doi.org/10.5812/ijpr-150510","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407010228","name":"Comparison of artificial intelligence systems in answering prosthodontics questions from the dental specialty exam in Turkey","source":"openalex","abstract":": Artificial intelligence (AI) is increasingly vital in dentistry, supporting diagnostics, treatment planning, and patient education. However, AI systems face challenges, especially in delivering accurate information within specialized dental fields. This study aimed to evaluate the performance of seven AI-based chatbots (ChatGPT-3.5, ChatGPT-4, Gemini, Gemini Advanced, Claude AI, Microsoft Copilot, and Smodin AI) in correctly answering prosthodontics questions from the Dental Specialty Exam (DUS) in Turkey. Materials and methods: The dataset for this study consists of 128 multiple-choice prosthodontics questions from the DUS, a national exam administered in Turkey by the Student Selection and Placement Center (ÖSYM) between 2012 and 2021. Chatbot performance was assessed by categorizing the questions into case-based and knowledge-based. Results: ChatGPT-4 achieved the highest accuracy (75.8 %), while Gemini AI had the lowest (46.1 %). Gemini AI also had more incorrect (69) than correct answers (59). ChatGPT-4 and ChatGPT-3.5 showed significantly higher accuracy in knowledge-based questions compared to case-based ones (p < 0.05). For case-based questions, Gemini and Gemini Advanced had the lowest accuracy (36.4 %), while other chatbots averaged 45.5 %. In knowledge-based questions, ChatGPT-4 performed best (78.6 %) and Gemini AI the worst (47 %). Conclusion: ChatGPT-4 excelled in knowledge-based prosthodontic questions, showing potential to enhance dental education through personalized learning and clinical reasoning support. However, its limitations in case-based scenarios highlight the need for optimization to better address complex clinical situations. These findings suggest that AI models can significantly contribute to dental education and clinical practice.","url":"https://doi.org/10.1016/j.jds.2025.01.025","authors":["Büşra Tosun","Zeynep Sen Yilmaz"],"tags":["Specialty","Prosthodontics","Dentistry","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-31","doi":"https://doi.org/10.1016/j.jds.2025.01.025","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401961962","name":"Accuracy and Readability of Artificial Intelligence Chatbot Responses to Vasectomy-Related Questions: Public Beware","source":"openalex","abstract":"Purpose Artificial intelligence (AI) has rapidly gained popularity with the growth of ChatGPT (OpenAI, San Francisco, USA) and other large-language model chatbots, and these programs have tremendous potential to impact medicine. One important area of consequence in medicine and public health is that patients may use these programs in search of answers to medical questions. Despite the increased utilization of AI chatbots by the public, there is little research to assess the reliability of ChatGPT and alternative programs when queried for medical information. This study seeks to elucidate the accuracy and readability of AI chatbots in answering patient questions regarding urology. As vasectomy is one of the most common urologic procedures, this study investigates AI-generated responses to frequently asked vasectomy-related questions. For this study, five popular and free-to-access AI platforms were utilized to undertake this investigation. Methods Fifteen vasectomy-related questions were individually queried to five AI chatbots from November-December 2023: ChatGPT (OpenAI, San Francisco, USA), Bard (Google Inc., Mountainview, USA) Bing (Microsoft, Redmond, USA) Perplexity (Perplexity AI Inc., San Francisco, USA), and Claude (Anthropic, San Francisco, USA). Responses from each platform were graded by two attending urologists, two urology research faculty, and one urological resident physician using a Likert (1-6) scale: (1-completely inaccurate, 6-completely accurate) based on comparison to existing American Urological Association guidelines. Flesch-Kincaid Grade levels (FKGL) and Flesch Reading Ease scores (FRES) (1-100) were calculated for each response. To assess differences in Likert, FRES, and FKGL, Kruskal-Wallis tests were performed using GraphPad Prism V10.1.0 (GraphPad, San Diego, USA) with Alpha set at 0.05. Results Analysis shows that ChatGPT provided the most accurate responses across the five AI chatbots with an average score of 5.04 on the Likert scale. Subsequently, Microsoft Bing (4.91), Anthropic Claude (4.65), Google Bard (4.43), and Perplexity (4.41) followed. All five chatbots were found to score, on average, higher than 4.41 corresponding to a score of at least \"somewhat accurate.\" Google Bard received the highest Flesch Reading Ease score (49.67) and lowest Grade level (10.1) when compared to the other chatbots. Anthropic Claude scored 46.7 on the FRES and 10.55 on the FKGL. Microsoft Bing scored 45.57 on the FRES and 11.56 on the FKGL. Perplexity scored 36.4 on the FRES and 13.29 on the FKGL. ChatGPT had the lowest FRES of 30.4 and highest FKGL of 14.2. Conclusion This study investigates the use of AI in medicine, specifically urology, and it helps to determine whether large-language model chatbots can be reliable sources of freely available medical information. All five AI chatbots on average were able to achieve at least \"somewhat accurate\" on a 6-point Likert scale. In terms of readability, all five AI chatbots on average had Flesch Reading Ease scores of less than 50 and were higher than a 10th-grade level. In this small-scale study, there were several significant differences identified between the readability scores of each AI chatbot. However, there were no significant differences found among their accuracies. Thus, our study suggests that major AI chatbots may perform similarly in their ability to be correct but differ in their ease of being comprehended by the general public.","url":"https://doi.org/10.7759/cureus.67996","authors":["Jonathan A Carlson","Robin Z Cheng","Alyssa Lange","Nadiminty Nagalakshmi","John Rabets","Tariq A. Shah","Puneet Sindhwani"],"tags":["Medicine","Readability","Vasectomy","Likert scale","Chatbot"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-28","doi":"https://doi.org/10.7759/cureus.67996","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400823820","name":"Random Forest Algorithm Overview","source":"openalex","abstract":"A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting. System performance data is essential for refining algorithms, enhancing the efficiency of software and hardware, evaluating user be-havior, enabling pattern identification, decision-making, predictive modeling, and problem-solving, ultimately resulting in improved effectiveness and accuracy. The integration of diverse data collecting and processing methods enhances precision and innovation in problem-solving. Utilizing diverse methodologies in interdisciplinary research streamlines the research process, fosters innovation, and enables the application of data analysis findings to pattern recognition, decision-making, predictive modeling, and problem-solving. This approach also encourages in-novation in interdisciplinary research. This technique utilizes the concept of decision trees, con-structing a collection of decision trees and aggregating their outcomes to generate the ultimate prediction. Every decision tree inside a random forest is constructed using random subsets of data, and each individual tree is trained on a portion of the whole dataset. Subsequently, the outcomes of all decision trees are amalgamated to derive the ultimate forecast. One of the bene-fits of random forests is their capacity to handle unbalanced data and variables with missing values. Additionally, it mitigates the issue of arbitrary variable selection seen by certain alterna-tive models. Furthermore, random forests mitigate the issue of overfitting by training several de-cision trees on random subsets of data, hence enhancing their ability to generalize to novel data. Random forests are highly regarded as one of the most efficient and potent techniques in the domain of machine learning. They find extensive use in various applications such as automatic categorization, data forecasting, and supervisory learning.","url":"https://doi.org/10.58496/bjml/2024/007","authors":["Hasan Ahmed Salman","Ali Kalakech","Amani Steiti"],"tags":["Random forest","Computer science","Algorithm","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-08","doi":"https://doi.org/10.58496/bjml/2024/007","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405616836","name":"Relational Data Cleaning Meets Artificial Intelligence: A Survey","source":"openalex","abstract":"Abstract Relational data play a crucial role in various fields, but they are often plagued by low-quality issues such as erroneous and missing values, which can terribly impact downstream applications. To tackle these issues, relational data cleaning with traditional signals, e.g., statistics, constraints, and clusters, have been extensively studied, with interpretability and efficiency. Recently, considering the strong capability of modeling complex relationships, artificial intelligence (AI) techniques have been introduced into the data cleaning field. These AI-based methods either consider multiple cleaning signals, integrate various techniques into the cleaning system, or incorporate neural networks. Among them, methods utilizing deep neural networks are classified as deep learning (DL) based, while those that do not are classified as machine learning (ML) based. In this study, we focus on three essential tasks (i.e., error detection, data repairing, and data imputation) for cleaning relational data, to comprehensively review the representative methods using traditional or AI techniques. By comparing and analyzing two types of methods across five dimensions (cost, generalization, interpretability, efficiency, and effectiveness), we provide insights into their strengths, weaknesses, and suitable application scenarios. Finally, we analyze the challenges and open issues currently faced in data cleaning and discuss possible directions for future studies.","url":"https://doi.org/10.1007/s41019-024-00266-7","authors":["Jingyu Zhu","Xintong Zhao","Yu Sun","Shaoxu Song","Xiaojie Yuan"],"tags":["Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-20","doi":"https://doi.org/10.1007/s41019-024-00266-7","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411426688","name":"An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation","source":"openalex","abstract":"Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine through the use of advanced computational tools. Its ability to analyze large and complex datasets enables a thorough evaluation of multiple factors, leading to a deeper understanding of medical procedures. Numerous studies have demonstrated that AI has made significant advancements in areas such as organ allocation, donor-recipient matching, and immunosuppression protocols in organ transplantation. The transplantation process consists of three key stages: pre-transplant evaluation, the surgical procedure, and post-transplant management. AI can enhance all three stages by analyzing and integrating data from histopathological reports, lab results, radiological features, and patient demographics to aid in matching donors and recipients. Additionally, AI supports robotic-assisted surgery and optimizes post-transplant regimens while evaluating complications. Various researches have utilized machine learning (ML) to predict medication bioavailability immediately after transplantation and assess the risk of post-transplant complications based on factors like genetic phenotypes, age, gender, and body mass index. This review aims to gather information on AI applications across various stages of organ transplantation and elaborate the strategies and tools relevant to these processes.","url":"https://doi.org/10.1186/s12967-025-06488-1","authors":["Shiva Arjmandmazidi","Hamid Reza Heidari","Tohid Ghasemnejad","Zeinab Mori","Leila Molavi","Amir Meraji","Shadi Kaghazchi","Elnaz Mehdizadeh Aghdam","Soheila Montazersaheb"],"tags":["Transplantation","Organ transplantation","Immunosuppression","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-18","doi":"https://doi.org/10.1186/s12967-025-06488-1","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400321274","name":"Medical Device-Associated Infections Caused by Biofilm-Forming Microbial Pathogens and Controlling Strategies","source":"openalex","abstract":"Hospital-acquired infections, also known as nosocomial infections, include bloodstream infections, surgical site infections, skin and soft tissue infections, respiratory tract infections, and urinary tract infections. According to reports, Gram-positive and Gram-negative pathogenic bacteria account for up to 70% of nosocomial infections in intensive care unit (ICU) patients. Biofilm production is a main virulence mechanism and a distinguishing feature of bacterial pathogens. Most bacterial pathogens develop biofilms at the solid-liquid and air-liquid interfaces. An essential requirement for biofilm production is the presence of a conditioning film. A conditioning film provides the first surface on which bacteria can adhere and fosters the growth of biofilms by creating a favorable environment. The conditioning film improves microbial adherence by delivering chemical signals or generating microenvironments. Microorganisms use this coating as a nutrient source. The film gathers both inorganic and organic substances from its surroundings, or these substances are generated by microbes in the film. These nutrients boost the initial growth of the adhering bacteria and facilitate biofilm formation by acting as a food source. Coatings with combined antibacterial efficacy and antifouling properties provide further benefits by preventing dead cells and debris from adhering to the surfaces. In the present review, we address numerous pathogenic microbes that form biofilms on the surfaces of biomedical devices. In addition, we explore several efficient smart antiadhesive coatings on the surfaces of biomedical device-relevant materials that manage nosocomial infections caused by biofilm-forming microbial pathogens.","url":"https://doi.org/10.3390/antibiotics13070623","authors":["Akanksha Mishra","Ashish Aggarwal","Fazlurrahman Khan"],"tags":["Biofilm","Microbiology","Bacteria","Microorganism","Pseudomonas aeruginosa"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-04","doi":"https://doi.org/10.3390/antibiotics13070623","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4393194929","name":"Use of artificial intelligence in breast surgery: a narrative review","source":"openalex","abstract":"Background and Objective: We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods: Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings: AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions: Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.","url":"https://doi.org/10.21037/gs-23-414","authors":["Ishith Seth","Bryan Lim","Konrad Joseph","Dylan Gracias","Yi Xie","Richard J. Ross","Warren M. Rozen"],"tags":["Medicine","Breast cancer","Subspecialty","Breast surgery","Grading (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-01","doi":"https://doi.org/10.21037/gs-23-414","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4205849812","name":"A holistic overview of deep learning approach in medical imaging","source":"openalex","abstract":"Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image's modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.","url":"https://doi.org/10.1007/s00530-021-00884-5","authors":["Rammah Yousef","Gaurav Gupta","Nabhan Yousef","Manju Khari"],"tags":["Deep learning","Computer science","Artificial intelligence","Medical imaging","Modalities"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-21","doi":"https://doi.org/10.1007/s00530-021-00884-5","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4392599487","name":"ChatGPT’s Response Consistency: A Study on Repeated Queries of Medical Examination Questions","source":"openalex","abstract":"(1) Background: As the field of artificial intelligence (AI) evolves, tools like ChatGPT are increasingly integrated into various domains of medicine, including medical education and research. Given the critical nature of medicine, it is of paramount importance that AI tools offer a high degree of reliability in the information they provide. (2) Methods: A total of n = 450 medical examination questions were manually entered into ChatGPT thrice, each for ChatGPT 3.5 and ChatGPT 4. The responses were collected, and their accuracy and consistency were statistically analyzed throughout the series of entries. (3) Results: ChatGPT 4 displayed a statistically significantly improved accuracy with 85.7% compared to that of 57.7% of ChatGPT 3.5 (p < 0.001). Furthermore, ChatGPT 4 was more consistent, correctly answering 77.8% across all rounds, a significant increase from the 44.9% observed from ChatGPT 3.5 (p < 0.001). (4) Conclusions: The findings underscore the increased accuracy and dependability of ChatGPT 4 in the context of medical education and potential clinical decision making. Nonetheless, the research emphasizes the indispensable nature of human-delivered healthcare and the vital role of continuous assessment in leveraging AI in medicine.","url":"https://doi.org/10.3390/ejihpe14030043","authors":["Paul F. Funk","Cosima C. Hoch","Samuel Knoedler","Leonard Knoedler","Sebastian Cotofana","Giuseppe Sofo","Ali Bashiri Dezfouli","Barbara Wollenberg","Orlando Guntinas‐Lichius","Michael Alfertshofer"],"tags":["Consistency (knowledge bases)","Reliability (semiconductor)","Dependability","Context (archaeology)","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-08","doi":"https://doi.org/10.3390/ejihpe14030043","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405288360","name":"Artificial intelligence powers regenerative medicine into predictive realm","source":"openalex","abstract":"The expanding regenerative medicine toolkit is reaching a record number of lives. There is a pressing need to enhance the precision, efficiency, and effectiveness of regenerative approaches and achieve reliable outcomes. While regenerative medicine has relied on an empiric paradigm, availability of big data along with advances in informatics and artificial intelligence offer the opportunity to inform the next generation of regenerative sciences along the discovery, translation, and application pathway. Artificial intelligence can streamline discovery and development of optimized biotherapeutics by aiding in the interpretation of readouts associated with optimal repair outcomes. In advanced biomanufacturing, artificial intelligence holds potential in ensuring quality control and assuring scalability through automated monitoring of process-critical variables mandatory for product consistency. In practice application, artificial intelligence can guide clinical trial design, patient selection, delivery strategies, and outcome assessment. As artificial intelligence transforms the regenerative horizon, caution is necessary to reduce bias, ensure generalizability, and mitigate ethical concerns with the goal of equitable access for patients and populations.","url":"https://doi.org/10.1080/17460751.2024.2437281","authors":["Armin Garmany","André Terzic"],"tags":["Regenerative medicine","Realm","Computer science","Biology","Stem cell"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1080/17460751.2024.2437281","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406222743","name":"Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection","source":"openalex","abstract":"This paper presents a systematic review of the emerging applications of artificial intelligence (AI), Internet of Things (IoT), and sensor-based technologies in the diagnosis of autism spectrum disorder (ASD). The integration of these technologies has led to promising advances in identifying unique behavioral, physiological, and neuroanatomical markers associated with ASD. Through an examination of recent studies, we explore how technologies such as wearable sensors, eye-tracking systems, virtual reality environments, neuroimaging, and microbiome analysis contribute to a holistic approach to ASD diagnostics. The analysis reveals how these technologies facilitate non-invasive, real-time assessments across diverse settings, enhancing both diagnostic accuracy and accessibility. The findings underscore the transformative potential of AI, IoT, and sensor-based driven tools in providing personalized and continuous ASD detection, advocating for data-driven approaches that extend beyond traditional methodologies. Ultimately, this review emphasizes the role of technology in improving ASD diagnostic processes, paving the way for targeted and individualized assessments.","url":"https://doi.org/10.3390/a18010034","authors":["Georgios Bouchouras","Konstantinos Kotis"],"tags":["Autism spectrum disorder","Computer science","The Internet","Internet of Things","Autism"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-09","doi":"https://doi.org/10.3390/a18010034","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4390231752","name":"ROLE OF ARTIFICIAL INTELLIGENCE IN ELECTRIFICATION OF AFRICA","source":"openalex","abstract":"The research explores the relationship between Artificial Intelligence (AI) and electrification in Africa, focusing on the challenges and emerging trends. The electrification deficit in Africa poses a significant impediment to economic development and social progress. This paper explores the pivotal role that Artificial Intelligence (AI) plays in addressing the challenges associated with electrification initiatives across the African continent. With its capacity for innovation and optimization, AI emerges as a transformative force capable of revolutionizing the planning, deployment, and management of electrification projects in a region characterized by diverse geographical landscapes and economic constraints. The paper investigates the potential of AI in addressing financial barriers associated with electrification projects. By facilitating innovative financing models, reducing operational costs, and attracting investments, AI contributes to creating sustainable and economically viable electrification solutions. The focus extends to decentralized energy systems and microgrids, exploring how AI can empower remote and underserved communities with reliable access to electricity. The socio-economic impact of AI-driven electrification initiatives is also scrutinized, emphasizing the potential for job creation, economic growth, and improved living standards. The paper discusses the importance of capacity building and local empowerment to ensure that AI technologies are effectively integrated into electrification projects while fostering inclusive and sustainable development. It highlights the role of AI in revolutionizing electrification, predicting electricity consumption and enabling decentralized solutions. AI-driven electrification has shown economic and social benefits, including enhanced productivity, improved quality of life, and increased market access. However, it also raises ethical concerns and privacy implications. The research emphasizes the need for proactive mitigation strategies and collaborations across sectors to drive regulatory frameworks, technological innovations, and global impact. The research envisions a future where AI plays a central role in fostering inclusive growth, connecting communities, and illuminating a digitally transformed and sustainable continent. Keywords: Artificial Intelligence, Electrification, Africa, Electricity, Energy","url":"https://doi.org/10.51594/estj.v4i6.667","authors":["Ibegbulam C.M","O.J Aigbovbiosa","J. A. Olowonubi","S. A. Fatounde"],"tags":["Electrification","Transformative learning","Business","Rural electrification","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-26","doi":"https://doi.org/10.51594/estj.v4i6.667","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411124274","name":"Healthcare workers' readiness for artificial intelligence and organizational change: a quantitative study in a university hospital","source":"openalex","abstract":"OBJECTIVE: The aim of the study is to measure the readiness levels of medical artificial intelligence and the perception of openness to organizational change of healthcare professionals working in a university hospital in Istanbul. Additionally, the study seeks to identify the relationships between medical AI readiness and perceptions of organizational change openness, as well as to examine differences based on demographic variables. METHOD: The research was conducted with 195 healthcare workers. The research is a cross-sectional descriptive quantitative research. The construct validity of the scales was checked using statistical analysis. RESULT: As a result of the research, it was determined that healthcare workers' are prepared for the use of medical artificial intelligence in healthcare institutions and perceive organizational change positively. A significant but low-level positive relationship was found between healthcare workers' level of readiness for medical artificial intelligence and their perception of openness to organizational change. The level of readiness for medical artificial intelligence among healthcare workers' was found to be high among males, doctors and internal sciences, while the perception of openness to organizational change was found to be high among postgraduate/doctoral graduates, surgical sciences, nurses. CONCLUSION: The study determined that healthcare workers' are ready to use medical artificial intelligence and perceive organizational change positively. The study contributes to the formation of the institution's healthcare policies and practices and to the development, well-being and change of healthcare workers'. It is recommended that employees be made aware of the benefits of using artificial intelligence in healthcare institutions and that necessary training activities be planned.","url":"https://doi.org/10.1186/s12913-025-12846-y","authors":["Hafize Boyacı","Selma Söyük"],"tags":["Health administration","Openness to experience","Health care","Perception","Nursing research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-08","doi":"https://doi.org/10.1186/s12913-025-12846-y","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406919111","name":"Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review","source":"openalex","abstract":"BACKGROUND: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed. METHODS: The review was conducted following PRISMA-ScR guidance. Six databases (MEDLINE, EMBASE, Web of Science, IEEE Xplore, PubMed and SCOPUS) were searched for relevant records published before 2/2/2024. Search terms related to the concepts \"artificial intelligence\", \"prediction\", \"health records\", \"longitudinal\", and \"cancer\". Data were extracted relating to several areas of the articles: (1) publication details, (2) study characteristics, (3) input data, (4) model characteristics, (4) reproducibility, and (5) quality assessment using the PROBAST tool. Models were evaluated against a framework for terminology relating to reporting of cancer detection and risk prediction models. RESULTS: Of 653 records screened, 33 were included in the review; 10 predicted risk of cancer, 18 performed either cancer detection or early detection, 4 predicted recurrence, and 1 predicted metastasis. The most common cancers predicted in the studies were colorectal (n = 9) and pancreatic cancer (n = 9). 16 studies used feature engineering to represent temporal data, with the most common features representing trends. 18 used deep learning models which take a direct sequential input, most commonly recurrent neural networks, but also including convolutional neural networks and transformers. Prediction windows and lead times varied greatly between studies, even for models predicting the same cancer. High risk of bias was found in 90% of the studies. This risk was often introduced due to inappropriate study design (n = 26) and sample size (n = 26). CONCLUSION: This review highlights the breadth of approaches to cancer prediction from longitudinal data. We identify areas where reporting of methods could be improved, particularly regarding where in a patients' trajectory the model is applied. The review shows opportunities for further work, including comparison of these approaches and their applications in other cancers.","url":"https://doi.org/10.1186/s12874-025-02473-w","authors":["Victoria Moglia","Owen Johnson","G. Elliott Cook","Marc de Kamps","Lesley Smith"],"tags":["Artificial intelligence","Machine learning","Computer science","MEDLINE","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-28","doi":"https://doi.org/10.1186/s12874-025-02473-w","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401167708","name":"Will Artificial Intelligence Be “Better” Than Humans in the Management of Syncope?","source":"openalex","abstract":"Clinical decision-making regarding syncope poses challenges, with risk of physician error due to the elusive nature of syncope pathophysiology, diverse presentations, heterogeneity of risk factors, and limited therapeutic options. Artificial intelligence (AI)-based techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can uncover hidden and nonlinear connections among syncope risk factors, disease features, and clinical outcomes. ML, DL, and NLP models can analyze vast amounts of data effectively and assist physicians to help distinguish true syncope from other types of transient loss of consciousness. Additionally, short-term adverse events and length of hospital stay can be predicted by these models. In syncope research, AI-based models shift the focus from causality to correlation analysis between entities. This prompts the search for patterns rather than defining a hypothesis to be tested a priori. Furthermore, education of students, doctors, and health care providers engaged in continuing medical education may benefit from clinical cases of syncope interacting with NLP-based virtual patient simulators. Education may be of benefit to patients. This article explores potential strengths, weaknesses, and proposed solutions associated with utilization of ML and DL in syncope diagnosis and management. Three main topics regarding syncope are addressed: 1) clinical decision-making; 2) clinical research; and 3) education. Within each domain, we question whether \"AI will be better than humans,\" seeking evidence to support our objective inquiry.","url":"https://doi.org/10.1016/j.jacadv.2024.101072","authors":["Franca Dipaola","Milena A. Gebska","Mauro Gatti","Alessandro Giaj Levra","William H. Parker","Roberto Menè","Sangil Lee","Giorgio Costantino","Ercole John Barsotti","Dana Shiffer","Samuel L. Johnston","Richard Sutton","Brian Olshansky","Raffaello Furlan"],"tags":["Syncope (phonology)","Causality (physics)","Artificial intelligence","Medicine","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-31","doi":"https://doi.org/10.1016/j.jacadv.2024.101072","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4399125889","name":"Implementing artificial intelligence across task types: constraints of automation and affordances of augmentation","source":"openalex","abstract":"Purpose This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling. Design/methodology/approach Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor. Findings We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes. Research limitations/implications While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts. Practical implications Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term. Originality/value Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.","url":"https://doi.org/10.1108/itp-11-2022-0915","authors":["Elena Mazurova","Willem Standaert"],"tags":["Affordance","Task (project management)","Automation","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-29","doi":"https://doi.org/10.1108/itp-11-2022-0915","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4394751864","name":"Artificial intelligence in obstetric anaesthesia: an unlikely player?","source":"openalex","abstract":"A recent study on analgesia during labour has shown how information, even from reputable sources, has the potential to mislead women and undermine access to pain relief during labour [1]. It raises concerns about what information is readily available to expectant mothers beyond the respected pregnancy websites and the top results produced by popular search engines. A chatbot powered by artificial intelligence (AI) is a form of machine learning trained on large data sets that interprets inputs and generates results for complex queries. Artificial intelligence is anticipated to reimagine how we access and process information. Open access AI can generate an unchecked and unregulated patient information leaflet in seconds on any question the user asks. As healthcare professionals, we are acutely aware of how inaccurate information can profoundly affect medical interventions [2]. This raises important questions that have yet to be addressed about how AI will, and likely already does, influence patient decision-making. We have tested this theory by asking five popular, freely available AI chatbots to assimilate a birth plan for a first-time mother. The question asked was “Write a birth plan for a first-time mother”. In the study, four questions were inputted into the following AI chatbots on 14 March 2024: Open AI ChatGPT; Google Gemini; Microsoft Co-Pilot; YouChat; and Perplexity. The responses were analysed based on three main categories: labour; delivery; and post-partum care (online Supporting Information Appendix S1). The role of anaesthesia in birth plans was analysed across domains, with particular attention paid to analgesic options recommended during labour (Table 1). Similar themes were observed across all chatbot platforms. All five birth plans suggested that natural remedies such as breathing techniques and movement would be tried first before discussing medication for pain relief. There was an insufficient description of analgesic options, and only one birth plan suggested that the woman would be open to neuraxial analgesia. No platform mentioned patient-controlled analgesia. The results not only depended on the question asked but also on how it was phrased. When the question was altered to include “private health insurance” all five chatbots suggested the woman would consider an epidural. We used private healthcare as a surrogate marker to contrast it with socio-economic deprivation, which is associated with lower access to epidurals even when medically indicated [3]. Patient information must be egalitarian and not have the potential to be skewed based on inherent biases that already exist in AI programming. When a general task is inputted into an AI programme, the algorithm takes liberty to conjure up a creative response. When the input is specific, however, AI chatbots behave more like traditional search engines. Further questions about pain relief options and the safety of epidurals during labour were answered accurately with facts referenced to trusted sources. Unlike conventional search engines, AI uses machine learning and natural language processing to consistently develop and enhance its responses by learning from past interactions. When we repeated the input question, the AI chatbot generated a different answer each time. These nuanced responses have the potential to influence patient understanding, ultimately impacting informed decision-making. The AI-generated birth plans appeared to give information about what a woman would like to hear instead of planning for events that may occur during labour. Only one chatbot mentioned the possibility of an unplanned caesarean section. Emergency obstetric anaesthesia presents many challenges, particularly when the planned form of delivery has changed. The mismatch between preparedness and expectations of childbirth is likely confounded by the quality of information provided during the antenatal period [4]. This is susceptible to negative influences by flawed AI-perceive","url":"https://doi.org/10.1111/anae.16295","authors":["Cian Hurley","R. Kearsley"],"tags":["Medicine","Anesthesia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-12","doi":"https://doi.org/10.1111/anae.16295","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412698288","name":"The Effectiveness of Artificial Intelligence-Based Interventions for Students with Learning Disabilities: A Systematic Review","source":"openalex","abstract":"Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on the methodological quality and risk of bias of the available evidence. Methods: A systematic search was conducted across seven major databases (Google Scholar, ScienceDirect, APA PsycInfo, ERIC, Scopus, PubMed) for experimental studies published between 2022 and 2025. This review followed PRISMA guidelines, using the PICOS framework for inclusion criteria. A formal risk of bias assessment was performed using the ROBINS-I and JBI critical appraisal tools. Results: Eleven studies (representing 10 independent experiments), encompassing 3033 participants, met the inclusion criteria. The most studied disabilities were dyslexia (six studies) and other specific learning disorders (three studies). Personalized/adaptive learning systems and game-based learning were the most common AI interventions. All 11 studies reported positive outcomes. However, the risk of bias assessment revealed significant methodological limitations: no studies were rated as having a low risk of bias, with most presenting a moderate (70%) to high/serious (30%) risk. Despite these limitations, quantitative results from the stronger studies showed large effect sizes, such as in arithmetic fluency (d = 1.63) and reading comprehension (d = −1.66). Conclusions: AI-based interventions demonstrate significant potential for supporting students with learning disabilities, with unanimously positive reported outcomes. However, this conclusion must be tempered by the considerable risk of bias and methodological weaknesses prevalent in the current literature. The limited and potentially biased evidence base warrants cautious interpretation. Future research must prioritize high-quality randomized controlled trials (RCTs) and longitudinal assessments to establish a definitive evidence base and investigate long-term effects, including the risk of cognitive offloading.","url":"https://doi.org/10.3390/brainsci15080806","authors":["A. Paglialunga","Sergio Melogno"],"tags":["Psychological intervention","Psychology","Learning disability","Mathematics education","Applied psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-28","doi":"https://doi.org/10.3390/brainsci15080806","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405831020","name":"Artificial Intelligence in Education: Ethical Considerations and Insights from Ancient Greek Philosophy","source":"openalex","abstract":"This paper explores the ethical implications of integrating Artificial Intelligence (AI) in educational settings, from primary schools to universities, while drawing insights from ancient Greek philosophy to address emerging concerns.As AI technologies increasingly influence learning environments, they offer novel opportunities for personalized learning, efficient assessment, and data-driven decision-making.However, these advancements also raise critical ethical questions regarding data privacy, algorithmic bias, student autonomy, and the changing roles of educators.This research examines specific use cases of AI in education, analyzing both their potential benefits and drawbacks.By revisiting the philosophical principles of ancient Greek thinkers such as Socrates, Aristotle, and Plato, we discuss how their writings can guide the ethical implementation of AI in modern education.The paper argues that while AI presents significant challenges, a balanced approach informed by classical philosophical thought can lead to an ethically sound transformation of education.It emphasizes the evolving role of teachers as facilitators and the importance of fostering student initiative in AI-rich environments.","url":"https://doi.org/10.1145/3688671.3688772","authors":["Kostas Karpouzis"],"tags":["Computer science","Engineering ethics","Cognitive science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-11","doi":"https://doi.org/10.1145/3688671.3688772","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4399250444","name":"Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023","source":"openalex","abstract":"PURPOSE: Artificial intelligence (AI) models can generate scientific abstracts that are difficult to distinguish from the work of human authors. The use of AI in scientific writing and performance of AI detection tools are poorly characterized. METHODS: We extracted text from published scientific abstracts from the ASCO 2021-2023 Annual Meetings. Likelihood of AI content was evaluated by three detectors: GPTZero, Originality.ai, and Sapling. Optimal thresholds for AI content detection were selected using 100 abstracts from before 2020 as negative controls, and 100 produced by OpenAI's GPT-3 and GPT-4 models as positive controls. Logistic regression was used to evaluate the association of predicted AI content with submission year and abstract characteristics, and adjusted odds ratios (aORs) were computed. RESULTS: Fifteen thousand five hundred and fifty-three abstracts met inclusion criteria. Across detectors, abstracts submitted in 2023 were significantly more likely to contain AI content than those in 2021 (aOR range from 1.79 with Originality to 2.37 with Sapling). Online-only publication and lack of clinical trial number were consistently associated with AI content. With optimal thresholds, 99.5%, 96%, and 97% of GPT-3/4-generated abstracts were identified by GPTZero, Originality, and Sapling respectively, and no sampled abstracts from before 2020 were classified as AI generated by the GPTZero and Originality detectors. Correlation between detectors was low to moderate, with Spearman correlation coefficient ranging from 0.14 for Originality and Sapling to 0.47 for Sapling and GPTZero. CONCLUSION: There is an increasing signal of AI content in ASCO abstracts, coinciding with the growing popularity of generative AI models.","url":"https://doi.org/10.1200/cci.24.00077","authors":["Frederick M. Howard","Anran Li","Mark Riffon","Elizabeth Garrett‐Mayer","Alexander T. Pearson"],"tags":["Content (measure theory)","Internal medicine","Medical physics","Oncology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-01","doi":"https://doi.org/10.1200/cci.24.00077","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407729171","name":"Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review","source":"openalex","abstract":"Background and Aims: Artificial intelligence (AI) has become integral to medical research, impacting various aspects such as data analysis, writing assistance, and publishing. This paper explores the multifaceted influence of AI on the process of writing medical research papers, encompassing data analysis, ethical considerations, writing assistance, and publishing efficiency. Methods: The review was conducted following the PRISMA guidelines; a comprehensive search was performed in Scopus, PubMed, EMBASE, and MEDLINE databases for research publications on artificial intelligence in medical research published up to October 2023. Results: AI facilitates the writing process by generating drafts, offering grammar and style suggestions, and enhancing manuscript quality through advanced models like ChatGPT. Ethical concerns regarding content ownership and potential biases in AI-generated content underscore the need for collaborative efforts among researchers, publishers, and AI creators to establish ethical standards. Moreover, AI significantly influences data analysis in healthcare, optimizing outcomes and patient care, particularly in fields such as obstetrics and gynecology and pharmaceutical research. The application of AI in publishing, ranging from peer review to manuscript quality control and journal matching, underscores its potential to streamline and enhance the entire research and publication process. Overall, while AI presents substantial benefits, ongoing research, and ethical guidelines are essential for its responsible integration into the evolving landscape of medical research and publishing. Conclusion: The integration of AI in medical research has revolutionized efficiency and innovation, impacting data analysis, writing assistance, publishing, and others. While AI tools offer significant benefits, ethical considerations such as biases and content ownership must be addressed. Ongoing research and collaborative efforts are crucial to ensure responsible and transparent AI implementation in the dynamic landscape of medical research and publishing.","url":"https://doi.org/10.1002/hsr2.70200","authors":["Ahmad R. Al‐Qudimat","Zainab E. Fares","Mai Elaarag","Maha Osman","Raed M. Al‐Zoubi","Omar M. Aboumarzouk"],"tags":["Transformative learning","Engineering ethics","Psychology","Cognitive science","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.1002/hsr2.70200","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407683030","name":"Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis","source":"openalex","abstract":"The past decade has witnessed an exodus toward smart and lean manufacturing methods. The trend includes integrating intelligent methods into sustainable manufacturing systems purposely to improve the machining efficiency, reduce waste and also optimize productivity. Manufacturing systems have seen transformations from conventional methods, leaning towards smart manufacturing in line with the industrial revolution 4.0. Since the manufacturing process encompasses a wide range of human development capacity, it is essential to analyze its developmental trends, thereby preparing us for future uncertainties. In this work, we have used a Bibliometric analysis technique to study the developmental trends relating to machining, digital twins and artificial intelligence techniques. The review comprises the current activities in relation to the development to this area. The article comprises a Bibliometric analysis of 464 articles that were acquired from the Web of Science database, with a search period until November 2024. The method of obtaining the data includes retrieval from the database, qualitative analysis and interpreting the data via visual representation. The raw data obtained were redrawn using the origin software, and their visual interpretations were represented using the VOSviewer software (VOSviewer_1.6.19). The results obtained indicate that the number of publications related to the searched keywords has remarkably increased since the year 2018, achieving a record maximum of over 80 articles in 2024. This is indicative of its increasing popularity. The analysis of the articles was conducted based on the author countries, journal types, journal names, institutions, article types, major and micro research areas. The findings from the analysis are meant to provide a bibliometric explanation of the developmental trends in machining systems towards achieving the IR 4.0 goals. Additionally, the results would be helpful to researchers and industrialists that intend to achieve optimum and sustainable machining using digital twin technologies.","url":"https://doi.org/10.70322/ism.2025.10005","authors":["Dambatta Yusuf Suleiman","Q. X. Li","Benkai Li","Yanbin Zhang","Bo Zhang","Danyang Liu","Wenqiang Zhang","Zhigang Zhou","Yuewen Feng","Qingfeng Bie","Xianxin Yin","Lesan Wang","Changhe Li"],"tags":["Machining","Artificial intelligence","Computer science","Engineering","Mechanical engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.70322/ism.2025.10005","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4410992552","name":"Artificial intelligence and academic integrity in nursing education: A mixed methods study on usage, perceptions, and institutional implications","source":"openalex","abstract":"BACKGROUND: The rise of artificial intelligence (AI) use in higher education has generated substantial debate among academics and students, given the potential for students to engage in academic misconduct through the misuse of AI. Academics argue that AI poses a serious threat to the foundational development of nurses through the questionable integrity of AI-generated academic work and by undermining the development of critical thinking skills essential for professional practice. However, there is limited research on nursing students' integration of AI technologies in their studies. METHOD: This study utilised a convergent parallel mixed methods approach to develop a multiphase approach with convergent parallel techniques for the qualitative and quantitative phases. The quantitative method utilised a Qualtrics-powered online survey to engage 188 nursing students, exploring various domains related to AI use. In the qualitative phase, in-depth interviews with 13 purposively sampled students provided deeper insights. The qualitative data were analysed using an inductive thematic analysis approach, while the quantitative data were analysed using SPSS. RESULT: In the survey, 24 % of respondents reported using AI, ranging from moderate to extensive usage. In logistics regression analysis, hearing about AI (OR = 3.9; CI 1.07-10.2; p < 0.05), the belief that AI was useful in the studies (OR = 5.5; CI 1.7-17.3; p < 0.01), and the perception that learning to use AI is easy (OR = 3.4; CI 1.1-11.1; p < 0.05) predicted AI use. Qualitative findings revealed that all students used AI for various academic purposes. The 'fascinating', 'intelligent' and 'efficient' nature of AI in handling 'time-consuming' academic tasks motivated its use. However, concerns about breaching academic integrity and the value of achieving success through personal effort served as deterrents. CONCLUSION: The findings suggest that while AI's efficiency drives students to adopt it, they remain cautious about its ethical implications, leading to uncertainty in its application within academic practices. This highlights the critical need for institutional support and explicit guidelines on responsible AI integration in educational settings.","url":"https://doi.org/10.1016/j.nedt.2025.106796","authors":["Maggie Zgambo","Martina Costello","Melanie Buhlmann","Justine Maldon","Edah Anyango","Esther Adama"],"tags":["Academic integrity","Perception","Nursing","Psychology","Nurse education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-03","doi":"https://doi.org/10.1016/j.nedt.2025.106796","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4394816741","name":"Artificial Intelligence and Publication Ethics","source":"openalex","abstract":"","url":"https://doi.org/10.1177/10783903241245423","authors":["Geraldine S. Pearson"],"tags":["Psychology","Engineering ethics","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-14","doi":"https://doi.org/10.1177/10783903241245423","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4394014780","name":"Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future","source":"openalex","abstract":"Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.","url":"https://doi.org/10.7759/cureus.57725","authors":["Sankalp Yadav"],"tags":["Documentation","Workflow","Health care","Transformative learning","Nursing care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-06","doi":"https://doi.org/10.7759/cureus.57725","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412488540","name":"Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study","source":"openalex","abstract":"BACKGROUND AND AIMS: Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR). METHODS: The AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34 214 patients with linked echocardiography. RESULTS: In the internal test set, the AI-ECG models accurately predicted future significant MR [C-index 0.774, 95% confidence interval (CI) 0.753-0.792], AR (0.691, 95% CI 0.657-0.720), and TR (0.793, 95% CI 0.777-0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95% CI 5.8-9.9, P < .0001) for risk of future significant MR, compared with the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95% CI 2.7-5.5) and 9.9 (95% CI 7.5-13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodelling. CONCLUSIONS: This study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort. The AI-ECG models could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.","url":"https://doi.org/10.1093/eurheartj/ehaf448","authors":["Yixiu Liang","Arunashis Sau","Boroumand Zeidaabadi","Joseph Barker","Konstantinos Patlatzoglou","Libor Pastika","Ewa Sieliwończyk","Zachary I. Whinnett","Nicholas S. Peters","Ziqing Yu","Xi Liu","Shuo Wang","Hongyang Lu","Daniel B. Kramer","Jonathan W. Waks","Yangang Su","Junbo Ge","Fu Siong Ng"],"tags":["Medicine","Cardiology","Internal medicine","Hazard ratio","valvular heart disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-16","doi":"https://doi.org/10.1093/eurheartj/ehaf448","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411903243","name":"Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units","source":"openalex","abstract":"INTRODUCTION: Artificial Intelligence (AI) comprises computational algorithms designed to analyze data, learn patterns, and execute tasks traditionally requiring human cognition. These models can support public health initiatives, expedite clinical care, and improve diagnosis accuracy. Thus, artificial intelligence in healthcare sectors has the potential to enhance nursing care by assisting nurses with tasks like documentation, workflow improvement, and decision-making, while reducing workforce stress. This study, guided by the Technology Acceptance Model (TAM), assesses perceived worries regarding AI adoption among nurses in neonatal intensive care units (NICUs). METHODS: A cross-sectional quantitative design was employed using convenience sampling. Data were collected using the Worries of Applying AI in Healthcare Questionnaire (WAAI-HCQ) from 227 NICU nurses across nine hospitals in the West Bank (January 2-March 3, 2025). SPSS version 26 was used for analysis. RESULTS: Participants demonstrated intermediate levels of AI awareness (M = 2.7, SD = 0.5) and limited prior AI experience (M = 2.3, SD = 0.5). Total AI-related worries were moderate (M = 3.2, SD = 0.9), with healthcare provider-related concerns being highest. Multiple linear regression (R² = 0.846) identified education level (B = 0.074, p = 0.026), AI awareness (B = 2.006, p < 0.001), and AI experience (B = -0.959, p < 0.001) as significant predictors, explaining 84.6% of the variance in AI-related worries. CONCLUSIONS: NICU nurses in Palestine exhibit moderate AI awareness and concerns, highlighting the need for targeted education and training to address knowledge gaps and facilitate AI integration. This study contributes new knowledge specifically for conflict-affected, resource-constrained NICU settings, where AI implementation faces unique challenges. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://doi.org/10.1186/s12912-025-03318-z","authors":["Ahmad Ayed","Ahmad Batran","Ibrahim Aqtam","Malakeh Z. Malak","Moath Abu Ejheisheh","Mosaab Farajallah","Lamees Farraj","Sanaa G. Alkhatib"],"tags":["Workforce","Medicine","Intensive care","Health care","Workflow"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1186/s12912-025-03318-z","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W7125491435","name":"Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis","source":"openalex","abstract":"Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host-pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies-particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics-has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice.","url":"https://doi.org/10.3390/biomedicines14020261","authors":["Youxie Shen","Peidong Zhang","Jialiu Luo","Shunyao Chen","Shuaipeng Gu","Zhiqiang Lin","Zhaohui Tang"],"tags":["Interpretability","Precision medicine","Artificial intelligence","Generalizability theory","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-23","doi":"https://doi.org/10.3390/biomedicines14020261","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411151151","name":"Evaluation of the Performance of Artificial Intelligence Based Chatbots in Providing First Aid Information on Dental Trauma According to the ToothSOS Application","source":"openalex","abstract":"AIM: The aim of this study was to evaluate the performance of ChatGPT-4o and Gemini Advanced artificial intelligence-based chatbots (AI-based chatbots) in providing emergency intervention recommendations for dental trauma with intraoral photographs of patients diagnosed with traumatic dental injuries, and to assess their compatibility with emergency intervention recommendations in the ToothSOS application. MATERIAL AND METHODS: In this study, 80 intraoral photographs obtained from patients presenting with dental trauma were uploaded to two different AI-based chatbots (ChatGPT-4o and Gemini Advanced) and the responses generated by these systems were evaluated by four paediatric dentists. The evaluators scored the responses with a Modified Global Quality Score (GQS), referring to the English instructions of the ToothSOS application. In order to analyse the reliability of the responses, a total of three evaluation sessions were conducted 1 week apart. RESULTS: The ChatGPT-4o performed better when all injury types were considered together (p = 0.012). It was found that the ChatGPT-4o performed much better in complicated crown fracture cases (p = 0.004) and the Gemini Advanced chatbot performed much better in critical dental injuries such as avulsion (p < 0.001). CONCLUSIONS: AI-based chatbots can be a helpful tool in the assessment of dental trauma. However, further development and expert validation are needed to improve their accuracy and consistency, especially in complex cases. Incorporating the International Association of Dental Traumatology (IADT) guidelines into the databases of these systems could improve the reliability of their recommendations. In addition, given the widespread use of AI-based chatbots in many fields, particularly health, they could contribute to public health by supporting access to accurate information.","url":"https://doi.org/10.1111/edt.13078","authors":["Ecem Elif Çege","Hamide Cömert","Neşe Akal","Ayşegül Ölmez"],"tags":["Dental trauma","Medicine","Tooth Avulsion","Traumatology","Reliability (semiconductor)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-09","doi":"https://doi.org/10.1111/edt.13078","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400578172","name":"AI in Radiology: Navigating Medical Responsibility","source":"openalex","abstract":"The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the \"black box phenomenon\") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.","url":"https://doi.org/10.3390/diagnostics14141506","authors":["Maria Teresa Contaldo","Giovanni Pasceri","G. Vignati","Laura Bracchi","Sonia Triggiani","Gianpaolo Carrafiello"],"tags":["Liability","Autonomy","Process (computing)","Usability","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-12","doi":"https://doi.org/10.3390/diagnostics14141506","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412685719","name":"Enhancing emotional intelligence in medical education: a systematic review of interventions","source":"openalex","abstract":"Introduction: Emotional intelligence (EI) is a crucial competency for medical professionals, facilitating effective interpersonal relationships between physicians and patients. The ability to evaluate, regulate, and apply emotional understanding plays a significant role in fostering empathy, communication, and stress management. This systematic review aimed to determine the impact of various interventions on medical students' EI development, academic performance, and overall patient care. Methods: A comprehensive literature search was conducted for studies published from 2021 until 2024, inclusion criteria focused on studies on medical students, employed validated EI assessment tools, and utilized appropriate research designs. Results: 44 articles met the inclusion criteria. The Joanna Briggs Institute (JBI) Critical Appraisal Checklist was applied to assess the quality of included studies. Although a meta-analysis was initially planned, substantial heterogeneity across the studies limited the pooling of quantitative data. Using an inductive coding approach, eight major themes were identified: Narrative and Storytelling Interventions, Reflective Practices and Writing, Communication Skills Training, Emotional Intelligence Enhancement, Experiential Learning and Patient Exposure, Stress, Burnout, and Coping Interventions, Assessment Tools and Structural Interventions, and Personalized Interventions and Diversity Considerations. These themes were subsequently mapped onto Daniel Goleman's model of Emotional Intelligence to provide a structured theoretical framework. Discussion: The findings of this review highlight that various interventions hold promise in enhancing EI among medical students, leading to improvements in personal well-being, communication skills, and professional development. The most effective approach appears to be a multifaceted, longitudinal integration of EI-focused strategies throughout medical training, incorporating repeated practice, guided reflection, faculty mentorship, and structured debriefing. The broader implications extend to improved doctor-patient relationships, reduced burnout, and enhanced clinical decision-making, ultimately contributing to higher patient satisfaction and more compassionate healthcare delivery. Future research should focus on refining intervention methodologies and assessing their long-term impact on medical education and practice.","url":"https://doi.org/10.3389/fmed.2025.1587090","authors":["Sabyasachi Maity","Samantha Michelle De Filippis","Alexander Aldanese","Melissa A. McCulloch","Alexis P. Sandor","Jan E. Perez Cajigas","Yiorgos Antoniadis","Te-keila D. T. Rochester","Lauren Carter","Alexander M. Preisig","Julia Ali Kobeissi","Narendra Nayak","Jaime E. Mendoza","Samal Nauhria"],"tags":["Psychological intervention","Emotional intelligence","Psychology","Applied psychology","Clinical psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-28","doi":"https://doi.org/10.3389/fmed.2025.1587090","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403649741","name":"Artificial general intelligence in industry 4.0, 5.0, and society 5.0: Applications, opportunities, challenges, and future direction","source":"openalex","abstract":"Artificial General Intelligence (AGI) is a giant step ahead of narrow Artificial Intelligence (AI), and in a wonder-form, it can really revolutionize Industry 4.0, 5.0, and Society 5.0. For instance, in Industry 4.0, AGI will permit absolutely autonomous fabrication procedures, optimizing supply chains and personalizing products with efficiency like never before. While bringing about Industry 5.0, the integration of AGI with human intelligence foresees fostering a collaborative, sustainable industrial environment more oriented to human-centric innovation and well-being. Society 5.0 has a vision of harmoniously blending cyberspace and physical space but leaves out the role AGI could play in dealing with the complex societal challenges of healthcare, education, and urban management with its intelligent solution tailoring to the needs of individuals and communities. However, there are big challenges to the use of AGI in these domains, and there are ethical concerns. Key issues to be tackled include risks of autonomy without accountability, possible biases of decision-making, and socio-economic effects from large-scale automation. Above all, safety, transparency, and fairness in using AGI systems are important to avoid undesirable consequences. The ethical concerns of AGI, like privacy issues, possible misuse, and the requirement for solid regulatory frameworks, are getting urgent considering major steps we are taking toward such a highly evolved paradigm in society and industries.","url":"https://doi.org/10.70593/978-81-981271-0-5_6","authors":["Jayesh Rane","Ömer Kaya","Suraj Kumar Mallick","Nitin Liladhar Rane"],"tags":["Engineering ethics","Engineering","Data science","Management science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-16","doi":"https://doi.org/10.70593/978-81-981271-0-5_6","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4293009360","name":"A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks","source":"openalex","abstract":"Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.","url":"https://doi.org/10.1155/2022/7935346","authors":["Chetna Kaushal","Md Khairul Islam","Sara A. Althubiti","Fayadh Alenezi","Romany F. Mansour"],"tags":["Artificial intelligence","Computer science","Segmentation","Convolutional neural network","Pattern recognition (psychology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-24","doi":"https://doi.org/10.1155/2022/7935346","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4212962269","name":"The Future of Artificial Intelligence","source":"openalex","abstract":"We presen t a global Artificial Intelligence (AI) con ceptual framew ork, operationalization, and forecast to the year 2100. A series of AI indices were developed within the International Futures (IFs) integrated assessment platform, a quantitative macro-level system that produces dynamic forecasts for 186 countries. IFs models extensively interconnected aspects of global human development, including: agriculture, economics, demographics, energy, infrastructure, environment, water, governance, health, education, finance, technology, and international politics. We conceptualize AI in three categories: narrow AI, general artificial intelligence (AGI), and superintelligence. Today's AI consists of six basic and narrow AI technologies: computer vision, machine learning, natural language processing, the Internet of Things (IoT), robotics, and reasoning. As an index score for all approaches 10, we forecast AGI technology to become available, representing it with a machine IQ index score, roughly analogous to human IQ scores. The emergence of AGI is constrained by the rate of improvement in and development of machine reasoning and associated technologies. When machine IQ scores approach superhuman levels, we forecast the emergence of superintelligent AI. The current path forecast estimates that AGI could appear between 2040 and 2050. Superintelligent AI is forecast to be developed close to the end of the current century. We frame the current path with faster and slower scenarios of development and facilitate analysis of alternative scenarios. Future work can assess the complex impacts of AI development on human society, including economic productivity, labor, international trade, and energy systems.","url":"https://doi.org/10.51483/ijaiml.2.1.2022.1-37","authors":["Andrew C. Scott","José R. Solórzano","Jonathan D. Moyer","Barry B. Hughes"],"tags":["Artificial intelligence","Computer science","Psychology","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-18","doi":"https://doi.org/10.51483/ijaiml.2.1.2022.1-37","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4394716306","name":"Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence","source":"openalex","abstract":"The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.","url":"https://doi.org/10.1186/s40644-024-00684-w","authors":["Lalith Kumar Shiyam Sundar","Sebastian Gutschmayer","Marcel Maenle","Thomas Beyer"],"tags":["Workflow","Positron emission tomography","Medicine","Transformative learning","PET-CT"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-11","doi":"https://doi.org/10.1186/s40644-024-00684-w","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400466424","name":"The fusion of microfluidics and artificial intelligence: a novel alliance for medical advancements","source":"openalex","abstract":"Microfluidics, focusing on fluid manipulation at a miniature scale, has revolutionized biological experiments by enabling precise handling of samples at reduced volumes. It proves highly effective ...","url":"https://doi.org/10.1080/17576180.2024.2365528","authors":["Priyanka A. Shah","Pranav S. Shrivastav","Manjunath Ghate","Vishwajit Chavda"],"tags":["Microfluidics","Alliance","Nanotechnology","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-09","doi":"https://doi.org/10.1080/17576180.2024.2365528","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4406334317","name":"The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts","source":"openalex","abstract":"Abstract Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recurring breast cancer is predicted by an opaque AI system. We argue that, given the system’s opacity, as well as the possibility of malfunctioning AI systems, practitioners’ inability to check the correctness of their outputs, and the high stakes of such cases, the knowledge of medical practitioners is indeed undermined. They are lucky to form true beliefs based on the AI systems’ outputs, and knowledge is incompatible with luck. We supplement this claim with a specific version of the safety condition on knowledge, Safety*. We argue that, relative to the perspective of the medical doctor in our example case, his relevant beliefs could easily be false, and this despite his evidence that the AI system functions reliably. Assuming that Safety* is necessary for knowledge, the practitioner therefore doesn’t know. We address three objections to our proposal before turning to practical suggestions for improving the epistemic situation of medical doctors.","url":"https://doi.org/10.1007/s13347-024-00834-9","authors":["Eva Schmidt","Paul Martin Putora","Rianne Fijten"],"tags":["Philosophy of technology","Correctness","Luck","Epistemology","Perspective (graphical)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-13","doi":"https://doi.org/10.1007/s13347-024-00834-9","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4414805087","name":"The role of artificial intelligence for early warning systems: Status, applicability, guardrails, and ways forward","source":"openalex","abstract":"Artificial intelligence (AI) is gaining momentum in earth sciences as a tool to analyze complex natural hazards and their impacts. Such analyses are critical for effective Early Warning Systems (EWSs), which is aiming to generate timely and actionable risk information to protect sectors, systems, and people. Despite advancements in AI, its role in EWS remains underexplored across the four pillars of the Early Warning for All (EW4All) framework; risk knowledge, forecasting, warning dissemination and communication and response preparedness. This study draws on a systematic literature review to assess AI methods utilized in the context of EWS, examines their challenges and opportunities and discusses guiding questions for responsible use. Our study highlights key gaps across knowledge, application and policy. Moreover, we call for coordinated efforts to develop responsible AI frameworks that enhance EWS while ensuring they remain inclusive, accessible, and people-centred that ultimately supports the goal of EW4All by 2027.","url":"https://doi.org/10.1016/j.isci.2025.113689","authors":["Timothy Tiggeloven","Samira Pfeiffer","Alessia Matanó","Marc van den Homberg","Lisa Thalheimer","Markus Reichstein","Silvia Torresan"],"tags":["Warning system","Context (archaeology)","Data science","Key (lock)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-04","doi":"https://doi.org/10.1016/j.isci.2025.113689","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401701623","name":"Comparative Analysis of Accuracy, Readability, Sentiment, and Actionability: Artificial Intelligence Chatbots (ChatGPT and Google Gemini) versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery","source":"openalex","abstract":"Background and Aim: Eye surgeries often evoke strong negative emotions in patients, including fear and anxiety. Patient education material plays a crucial role in informing and empowering individuals. Traditional sources of medical information may not effectively address individual patient concerns or cater to varying levels of understanding. This study aims to conduct a comparative analysis of the accuracy, completeness, readability, tone, and understandability of patient education material generated by AI chatbots versus traditional Patient Information Leaflets (PILs), focusing on local anesthesia in eye surgery. Methods: Expert reviewers evaluated responses generated by AI chatbots (ChatGPT and Google Gemini) and a traditional PIL (Royal College of Anaesthetists' PIL) based on accuracy, completeness, readability, sentiment, and understandability. Statistical analyses, including ANOVA and Tukey HSD tests, were conducted to compare the performance of the sources. Results: Readability analysis showed variations in complexity among the sources, with AI chatbots offering simplified language and PILs maintaining better overall readability and accessibility. Sentiment analysis revealed differences in emotional tone, with Google Gemini exhibiting the most positive sentiment. AI chatbots demonstrated superior understandability and actionability, while PILs excelled in completeness. Overall, ChatGPT showed slightly higher accuracy (scores expressed as mean ± standard deviation) (4.71 ± 0.5 vs 4.61 ± 0.62) and completeness (4.55 ± 0.58 vs 4.47 ± 0.58) compared to Google Gemini, but PILs performed best (4.84 ± 0.37 vs 4.88 ± 0.33) in terms of both accuracy and completeness (p-value for completeness <0.05). Conclusion: AI chatbots show promise as innovative tools for patient education, complementing traditional PILs. By leveraging the strengths of both AI-driven technologies and human expertise, healthcare providers can enhance patient education and empower individuals to make informed decisions about their health and medical care.","url":"https://doi.org/10.22599/bioj.377","authors":["Prakash Gondode","Sakshi Duggal","Neha Garg","Pooja Lohakare","Jubin Jakhar","Swati Bharti","Shraddha Dewangan"],"tags":["Readability","Artificial intelligence","Medicine","Computer science","Natural language processing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.22599/bioj.377","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4402882403","name":"Artificial intelligence-enabled multipurpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics","source":"openalex","abstract":"An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.","url":"https://doi.org/10.1038/s41378-024-00765-7","authors":["Zhongjie Jia","Chunyu Chang","Siyi Hu","Jiahao Li","Mingfeng Ge","Wen‐Fei Dong","Hanbin Ma"],"tags":["Electrowetting","Digital microfluidics","Microfluidics","Computer science","Sorting"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-27","doi":"https://doi.org/10.1038/s41378-024-00765-7","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W7138063471","name":"Artificial Intelligence-Driven Development and Characterization of Nanomedicine","source":"openalex","abstract":"Abstract Nanomedicine has enabled major advances in targeted therapeutics by improving drug bioavailability, precision delivery, and safety profiles. However, the rational design and reproducible synthesis of nanoparticles with tightly controlled physicochemical attributes such as size, morphology, and surface characteristics remain significant challenges due to the complex, nonlinear interplay of formulation and process parameters. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to address these limitations by enabling data-driven optimization, predictive modeling, and automated analysis across nanoparticle synthesis and characterization workflows. Recent advances demonstrate that AI-based models can accurately predict nanoparticle properties, optimize synthesis conditions, interpret high-dimensional characterization data, and forecast biological performance, thereby reducing experimental burden and accelerating translation. This review critically examines current AI and ML strategies applied to nanoparticle synthesis, optimization of key physicochemical attributes, characterization, and biological evaluation for nanomedicine applications. Emphasis is placed on comparative model performance, integration of experimental and computational pipelines, and emerging challenges related to data quality, interpretability, and generalizability. Collectively, this work highlights the transformative potential of AI-enabled nanotechnology while outlining key directions required for its reliable clinical translation. Graphical Abstract Automated Synthesis Platforms: Integrating AI and ML for Next-Generation Nanomaterials","url":"https://doi.org/10.1007/s12668-026-02476-x","authors":["Nnamdi Ikemefuna Okafor","Nkeiruka N. Igbokwe","Hope Onohuean","Mbuso Faya","Yahya E. Choonara"],"tags":["Nanomedicine","Nanotechnology","Computer science","Characterization (materials science)","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-17","doi":"https://doi.org/10.1007/s12668-026-02476-x","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407361583","name":"Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases","source":"openalex","abstract":"CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, including iPhone 13 Pro photos (50 images) and diffuser slit-lamp photos (50 images), into nine categories (normal condition, infectious keratitis, immunological keratitis, corneal scar, corneal deposit, bullous keratopathy, ocular surface tumor, cataract/intraocular lens opacity, and primary angle-closure glaucoma). The iPhone and slit-lamp images represented the same cases. After initially answering without CorneAI, the same ophthalmologists responded to the same cases with CorneAI 2-4 weeks later. With CorneAI's support, the overall accuracy of ophthalmologists increased significantly from 79.2 to 88.8% (P < 0.001). Specialists' accuracy rose from 82.8 to 90.0%, and residents' from 75.6 to 86.2% (P < 0.001). Smartphone image accuracy improved from 78.7 to 85.5% and slit-lamp image accuracy from 81.2 to 90.6% (both, P < 0.001). In this study, CorneAI's own accuracy was 86%, but its support enhanced ophthalmologists' accuracy beyond the CorneAI's baseline. This study demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.","url":"https://doi.org/10.1038/s41598-025-89768-6","authors":["Hiroki Maehara","Yuta Ueno","Takefumi Yamaguchi","Yoshiyuki Kitaguchi","Dai Miyazaki","Ryohei Nejima","Takenori Inomata","Naoko Kato","Tai-ichiro Chikama","Jun Ominato","Tatsuya Yunoki","Kinya Tsubota","Masahiro Oda","Manabu Suzutani","Tetsuju Sekiryu","Tetsuro Oshika"],"tags":["Slit lamp","Medicine","Ophthalmology","Diagnostic accuracy","Cataracts"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-11","doi":"https://doi.org/10.1038/s41598-025-89768-6","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4390593459","name":"Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online","source":"openalex","abstract":"BACKGROUND: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. OBJECTIVE: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. METHODS: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. RESULTS: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). CONCLUSIONS: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.","url":"https://doi.org/10.2196/53086","authors":["Amir Reza Ashraf","Tim K. Mackey","András Fittler"],"tags":["Pharmacy","Internet privacy","Online search","Public health","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-04","doi":"https://doi.org/10.2196/53086","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4390611806","name":"Brain organoids and organoid intelligence from ethical, legal, and social points of view","source":"openalex","abstract":"Human brain organoids, aka cerebral organoids or earlier “mini-brains”, are 3D cellular models that recapitulate aspects of the developing human brain. They show tremendous promise for advancing our understanding of neurodevelopment and neurological disorders. However, the unprecedented ability to model human brain development and function in vitro also raises complex ethical, legal, and social challenges. Organoid Intelligence (OI) describes the ongoing movement to combine such organoids with Artificial Intelligence to establish basic forms of memory and learning. This article discusses key issues regarding the scientific status and prospects of brain organoids and OI, conceptualizations of consciousness and the mind–brain relationship, ethical and legal dimensions, including moral status, human–animal chimeras, informed consent, and governance matters, such as oversight and regulation. A balanced framework is needed to allow vital research while addressing public perceptions and ethical concerns. Interdisciplinary perspectives and proactive engagement among scientists, ethicists, policymakers, and the public can enable responsible translational pathways for organoid technology. A thoughtful, proactive governance framework might be needed to ensure ethically responsible progress in this promising field.","url":"https://doi.org/10.3389/frai.2023.1307613","authors":["Thomas Härtung","Itzy E. Morales Pantoja","Lena Smirnova"],"tags":["Organoid","Neuroethics","Corporate governance","Engineering ethics","Public trust"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-05","doi":"https://doi.org/10.3389/frai.2023.1307613","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4402047181","name":"Artificial Intelligence in Multilingual Interpretation and Radiology Assessment for Clinical Language Evaluation (AI-MIRACLE)","source":"openalex","abstract":"The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model's performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model's varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.","url":"https://doi.org/10.3390/jpm14090923","authors":["Praneet Khanna","Gagandeep Dhillon","Venkata Buddhavarapu","Ram Verma","Rahul Kashyap","Harpreet Grewal"],"tags":["Interpretation (philosophy)","Miracle","Computer science","Natural language processing","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-30","doi":"https://doi.org/10.3390/jpm14090923","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4413312755","name":"Multimodal Large Language Models in Medical Imaging: Current State and Future Directions","source":"openalex","abstract":"Multimodal large language models (MLLMs) are emerging as powerful tools in medicine, particularly in radiology, with the potential to serve as trusted artificial intelligence (AI) partners for clinicians. In radiology, these models integrate large language models (LLMs) with diverse multimodal data sources by combining clinical information and text with radiologic images of various modalities, ranging from 2D chest X-rays to 3D CT/MRI. Methods for achieving this multimodal integration are rapidly evolving, and the high performance of freely available LLMs may further accelerate MLLM development. Current applications of MLLMs now span automatic generation of preliminary radiology report, visual question answering, and interactive diagnostic support. Despite these promising capabilities, several significant challenges hinder widespread clinical adoption. MLLMs require access to large-scale, high-quality multimodal datasets, which are scarce in the medical domain. Risks of hallucinated findings, lack of transparency in decision-making processes, and high computational demands further complicate implementation. This review summarizes the current capabilities and limitations of MLLMs in medicine-particularly in radiology-and outlines key directions for future research. Critical areas include incorporating region-grounded reasoning to link model outputs to specific image regions, developing robust foundation models pre-trained on large-scale medical datasets, and establishing strategies for the safe and effective integration of MLLMs into clinical practice.","url":"https://doi.org/10.3348/kjr.2025.0599","authors":["Yoojin Nam","D. Kim","Sunggu Kyung","Jinyoung Seo","Jeong Min Song","Jimin Kwon","Jihyun Kim","Wooyoung Jo","H J Park","Jimin Sung","Sangah Park","Heeyeon Kwon","T. H. Kwon","Kanghyun Kim","Namkug Kim"],"tags":["Medicine","Current (fluid)","State (computer science)","Medical imaging","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3348/kjr.2025.0599","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411961335","name":"From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes","source":"openalex","abstract":"Background/Objectives: Artificial intelligence (AI) is transforming drug discovery and development by enhancing the speed and precision of identifying drug candidates and optimizing their efficacy. This review evaluates the application of AI in various stages of drug discovery, from hit identification to lead optimization, and its impact on clinical outcomes. The objective is to provide insights into the role of AI across therapeutic areas and assess its contributions to improving clinical trial efficiency and pharmaceutical outcomes. Methods: A systematic review followed PRISMA guidelines to analyze studies published between 2015 and 2025, focusing on AI in drug discovery and development. A comprehensive search was performed across multiple databases to identify studies employing AI techniques. The studies were categorized based on AI methods, clinical phase, and therapeutic area. The percentages of AI methods used, clinical phase stages, and the therapeutic regions were analyzed to identify trends. Results: AI methods included machine learning (ML) at 40.9%, molecular modeling and simulation (MMS) at 20.7%, and deep learning (DL) at 10.3%. Oncology accounted for the majority of studies (72.8%), followed by dermatology (5.8%) and neurology (5.2%). In clinical phases, 39.3% of studies were in the preclinical stage, 23.1% in Clinical Phase I, and 11.0% in the transitional phase. Clinical outcome reporting was observed in 45% of studies, with 97% reporting industry partnerships. Conclusions: AI significantly enhances drug discovery and development, improving drug efficacy and clinical trial outcomes. Future work should focus on expanding AI applications into underrepresented therapeutic areas and refining models to handle complex biological systems.","url":"https://doi.org/10.3390/ph18070981","authors":["Doni Dermawan","Nasser Alotaiq"],"tags":["Medicine","Drug development","Drug discovery","Clinical trial","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-30","doi":"https://doi.org/10.3390/ph18070981","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4389918939","name":"Impact of Artificial Intelligence on Healthcare Informatics: Opportunities and Challenges","source":"openalex","abstract":"Healthcare informatics, a field that integrates information technology, computer science, and healthcare, is crucial for managing and analyzing data, contributing to academic research, improving patient care, and enhancing healthcare systems. The integration of Artificial Intelligence (AI) in healthcare informatics has revolutionized diagnostics, treatment planning, and administrative processes. This research explores the impact of AI on healthcare informatics, focusing on opportunities such as improved diagnostics, personalized treatment plans, and streamlined administrative processes. Challenges include data privacy, ethical considerations, algorithmic bias, and standardized practices. The study highlights the transformative impact of AI while highlighting the intricacies and essential factors for its seamless integration into healthcare systems. It contributes significantly to the dynamic realm of healthcare informatics.","url":"https://doi.org/10.52783/jier.v3i2.384","authors":["P.K. Saxena"],"tags":["Health care","Health informatics","Informatics","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.52783/jier.v3i2.384","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406371336","name":"Large Language Models lack essential metacognition for reliable medical reasoning","source":"openalex","abstract":"Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions. We evaluated twelve models on dimensions including confidence-based accuracy, missing answer recall, and unknown recall. Despite high accuracy on multiple-choice questions, our study revealed significant metacognitive deficiencies across all tested models. Models consistently failed to recognize their knowledge limitations and provided confident answers even when correct options were absent. In this work, we show that current models exhibit a critical disconnect between perceived and actual capabilities in medical reasoning, posing significant risks in clinical settings. Our findings emphasize the need for more robust evaluation frameworks that incorporate metacognitive abilities, essential for developing reliable Large Language Model enhanced clinical decision support systems. Large Language Models demonstrate expert-level accuracy in medical exams, supporting their potential inclusion in healthcare settings. Here, authors reveal that their metacognitive abilities are underexplored, showing significant gaps in recognizing knowledge limitations, difficulties in modulating their confidence, and challenges in identifying when a problem cannot be answered due to insufficient information.","url":"https://doi.org/10.1038/s41467-024-55628-6","authors":["Maxime Griot","Coralie Hemptinne","Jean Vanderdonckt","Demet Yüksel"],"tags":["Metacognition","Recall","Computer science","Benchmark (surveying)","Cognitive psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.1038/s41467-024-55628-6","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4392237966","name":"A framework for evaluating clinical artificial intelligence systems without ground-truth annotations","source":"openalex","abstract":"A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.","url":"https://doi.org/10.1038/s41467-024-46000-9","authors":["Dani Kiyasseh","Aaron B. Cohen","Chengsheng Jiang","Nicholas Altieri"],"tags":["Ground truth","Software deployment","Computer science","Trustworthiness","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-28","doi":"https://doi.org/10.1038/s41467-024-46000-9","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400303336","name":"Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making","source":"openalex","abstract":"The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.","url":"https://doi.org/10.3389/fhumd.2024.1421273","authors":["Ben Chester Cheong"],"tags":["Accountability","Transparency (behavior)","Safeguarding","Engineering ethics","Corporate governance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.3389/fhumd.2024.1421273","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403855186","name":"Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s1470-2045(24)00315-2","authors":["Spyridon Bakas","Philipp Vollmuth","Norbert Galldiks","Thomas C. Booth","Hugo J.W.L. Aerts","Wenya Linda Bi","Benedikt Wiestler","Pallavi Tiwari","Sarthak Pati","Ujjwal Baid","Evan Calabrese","Philipp Lohmann","Martha Nowosielski","Rajan Jain","Rivka R. Colen","Marwa Ismail","Ghulam Rasool","Janine Lupo","Hamed Akbari","J. C. Tonn","David R. Macdonald","Michael A. Vogelbaum","Susan M. Chang","Christos Davatzikos","Javier Villanueva-Meyer","Raymond Y. Huang"],"tags":["Medical physics","Clinical Practice","Artificial intelligence","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.1016/s1470-2045(24)00315-2","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4402053390","name":"A bibliometric analysis on artificial intelligence in mathematics education","source":"openalex","abstract":"The research on artificial intelligence in mathematics education has attracted much attention from researchers since the last decade. This study aims to provide holistic information about artificial intelligence in mathematics education research using bibliometric analysis. Data for the analysis were extracted from the Scopus database from 1986 – 2023. The analysis, conducted using R-packages (Bibliometrix) and VOSviewer software, identifies the relevant nations, affiliations, journals, articles, and keywords on artificial intelligence in mathematics education research. The study reveals that 565 documents have been published in 354 journals, with an average annual growth rate of 11.27%. These documents, on average, have received 14.61 citations each. The research field has engaged a total of 1,847 authors, with an average of 3.26 authors contributing to each document. Additionally, 17.17% of these publications involved international co-authorship, indicating a moderate level of global collaboration. Our findings reveal a growing interest in using artificial intelligence as an educational tools and methods, particularly in the United States and China, which lead in publication output and citations. The analysis also reveals emerging trends and research gaps. The keywords such as \"virtual reality,\" \"sustainable development,\" and \"COVID-19\" reflect recent research focus on artificial intelligence in mathematics education research. Meanwhile, the keywords such as \"mathematical literacy,\" “assessment,” and \"gamification\" identified as underexplored areas, suggesting potential opportunities for future research on artificial intelligence in mathematics education research.","url":"https://doi.org/10.23917/jramathedu.v9i1.2429","authors":["Prawidi Wisnu Subroto","Maulana Malik","Aji Raditya","Nisvu Nanda Saputra"],"tags":["Mathematics education","Computer science","Mathematics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-30","doi":"https://doi.org/10.23917/jramathedu.v9i1.2429","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4399770032","name":"Quality of science journalism in the age of Artificial Intelligence explored with a mixed methodology","source":"openalex","abstract":"Science journalists, traditionally, play a key role in delivering science information to a wider audience. However, changes in the media ecosystem and the science-media relationship are posing challenges to reliable news production. Additionally, recent developments such as ChatGPT and Artificial Intelligence (AI) more generally, may have further consequences for the work of (science) journalists. Through a mixed-methodology, the quality of news reporting was studied within the context of AI. A content analysis of media output about AI (news articles published within the time frame 1 September 2022-28 February 2023) explored the adherence to quality indicators, while interviews shed light on journalism practices regarding quality reporting on and with AI. Perspectives from understudied areas in four European countries (Belgium, Italy, Portugal, and Spain) were included and compared. The findings show that AI received continuous media attention in the four countries. Furthermore, despite four different media landscapes, the reporting in the news articles adhered to the same quality criteria such as applying rigour, including sources of information, accessibility, and relevance. Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy. Expected benefits of AI related to helping with repetitive tasks (e.g. translations), and positively influencing journalistic principles of accessibility, engagement, and impact, while concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information. More generally, the interviewees expressed concerns about the state of science journalism, including a lack of funding influencing the quality of reporting. Journalists who were employed as staff as well as those who worked as freelancers put efforts in ensuring quality output, for example, via editorial oversight, discussions, or memberships of associations. Further research into the science-media relationship is recommended.","url":"https://doi.org/10.1371/journal.pone.0303367","authors":["Anne M. Dijkstra","Anouk de Jong","Marco Boscolo"],"tags":["Rigour","Journalism","Thematic analysis","Context (archaeology)","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-18","doi":"https://doi.org/10.1371/journal.pone.0303367","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405366153","name":"Lecturers’ Perceptions on the Integration of Artificial Intelligence Tools into Teaching Practice","source":"openalex","abstract":"Higher education has witnessed a massive transformation due to the advent of generative artificial intelligence (AI) technologies such as ChatGPT. In essence, AI has transformed various aspects of society as a whole. Despite the growing interest in applying AI tools such as ChatGPT in higher education, there is limited understanding of lecturers’ perceptions regarding their use in teaching and learning contexts. Studies reported in the literature have not comprehensively explored lecturers’ attitudes towards AI adoption, particularly in terms of its impact on classroom activities, assessment, and feedback. This study aims to fill this gap by qualitatively studying lecturers’ perspectives. The findings reveal that the advent of AI was met with mixed feelings among lecturers. Some lecturers embraced AI technologies and developed mechanisms for utilizing them in the classroom, while others resisted the change. This research is significant, as it can inform best practices and guide future implementation strategies of technologies in education.","url":"https://doi.org/10.3390/higheredu3040066","authors":["Murimo Bethel Mutanga","Vikash Jugoo","Kuburat Oyeranti Adefemi"],"tags":["Perception","Psychology","Mathematics education","Computer science","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-13","doi":"https://doi.org/10.3390/higheredu3040066","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4390608381","name":"Lung Imaging and Artificial Intelligence in ARDS","source":"openalex","abstract":"Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible.","url":"https://doi.org/10.3390/jcm13020305","authors":["Davide Chiumello","Silvia Coppola","Giulia Catozzi","Fiammetta Danzo","Pierachille Santus","Dejan Radovanovic"],"tags":["Medicine","ARDS","Lung","Radiology","Acute respiratory distress"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-05","doi":"https://doi.org/10.3390/jcm13020305","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412505013","name":"The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade","source":"openalex","abstract":"Abstract This paper conducts a systematic literature analysis on \"artificial intelligence, ethical artificial intelligence, neuromarketing, consumer neuroscience, neuroethics, and neurotechnology.\" This study followed the systematic literature review methodology to select and extract the relevant documents from the Scopus database (2013–2023). The findings revealed the valuable transformative impact of integrating artificial intelligence (AI) into neuromarketing (NM) and consumer neuroscience (Cons-Neuro), redefining the understanding and influence of consumer behavior. Emotion, attention, and memory have become vital in NM and AI studies. AI algorithms analyze vast neural and physiological datasets, offering marketers insights into the emotional impact of campaigns, granular insights into consumer focus, and optimizing content for maximum impact. Furthermore, memory plays a vital role in increasing brand recall and fostering lasting relationships. In addition, integrating brain-computer interfaces (BCI) into consumer neuroscience provides direct insights, with AI interpreting BCI data for real-time adjustments. The synergy of NM and AI offers insights into consumer behavior's cognitive and emotional aspects. While enabling targeted campaigns and improved customer experiences, this integration raises ethical concerns necessitating transparency and responsible neural data use. This paper offers valuable insights into the intersection of AI and NM, exploring innovative applications and ethical considerations in these evolving fields.","url":"https://doi.org/10.1186/s43093-025-00591-x","authors":["Ahmed H. Alsharif","Junhai Wang","Salmi Mohd Isa","Nor Zafir Md Salleh","Husam Azzawi Dawas","Mohammed H. Alsharif"],"tags":["Neuromarketing","Business","Psychology","Computer science","Marketing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.1186/s43093-025-00591-x","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4390508424","name":"Artificial Intelligence: The Future","source":"openalex","abstract":"Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. \"AI\" may also refer to the machines themselves. AI is not a new for the scientist, it was introduce in 1943 with artificial neurons model and get popular in 1950 due to “Turting test” the test was done to get answer that machine can think?, purposed by Alan Turing. Basically AI is categorized into three types, Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. Deep learning and machine learning is major subfield of Artificial intelligence. DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. Application of AI fields are Healthcare, Business, Education, Agriculture, Finance, Law, Entertainment and media, Software coding and IT processes, Security, Manufacturing, Banking and Transportation. In reference to Job creation or distraction, Artificial Intelligence is not job killer but a job category killer”. In the latest report (May 2023) on The Future of Jobs, the World Economic Forum (WEF) predicts the creation of 69 million jobs by 2027 thanks to AI, but also the destruction of 89 million jobs. In context to intelligence level, Jan. 2022, Age of IQ level of AI was as 7 year Children and in Dec. 2022, Age of IQ level of AI was as 9 year Children. India is emerging market in global and it has around 12 % of work could be automated by AI. In India more than 2000 startup are related to AI and 90000 plus AI Professional work in India.The economic impact of AI, for select G20 countries and estimates AI to boost India’s annual growth rate by 1.3 percentage points by 2035. AI has potential to add 1 trillion to India’s economy in 2035. We are going to enter into new technological world, it’s may be our fortune or misfortune. Key Words: - Artificial Intelligence, Machine learning, Job, India","url":"https://doi.org/10.55041/ijsrem27796","authors":["Narendra Singh Yadav","Latika Sharma","Urmila Dhake"],"tags":["Artificial intelligence","Applications of artificial intelligence","Music and artificial intelligence","Computer science","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-30","doi":"https://doi.org/10.55041/ijsrem27796","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4404793685","name":"Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency","source":"openalex","abstract":"Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement in reproductive medicine. AI technologies, such as neural networks, deep learning, and machine learning, promise to enhance quality control (QC) and quality assurance (QA) through increased accuracy, consistency, and operational efficiency. This comprehensive review examines the effects of AI on IVF laboratories, focusing on its role in automating processes such as embryo and sperm selection, optimizing clinical outcomes, and reducing human error. AI's data analysis and pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans and increasing success rates in fertility treatments. However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human-machine interface in clinical decision-making. Through an in-depth examination of current case studies, advancements, and future directions, this manuscript highlights how AI can revolutionize IVF by standardizing processes, improving patient outcomes, and advancing the precision of reproductive medicine. It underscores the necessity of ongoing research and ethical oversight to ensure fair and transparent applications in this sensitive field, assuring the responsible use of AI in reproductive medicine.","url":"https://doi.org/10.3390/biology13120988","authors":["Yaling Hew","Duygu Kütük","Tuba Düzcü","Yagmur Ergun","Murat Başar"],"tags":["Precision medicine","Artificial intelligence","Reproductive medicine","Applications of artificial intelligence","Quality assurance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-28","doi":"https://doi.org/10.3390/biology13120988","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405244711","name":"Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges","source":"openalex","abstract":"Artificial intelligence (AI) is transforming respiratory healthcare through a wide range of deep learning and generative tools, and is increasingly integrated into both patients' lives and routine respiratory care. The implications of AI in respiratory care are vast and multifaceted, presenting both promises and uncertainties from the perspectives of clinicians, patients and society. Clinicians contemplate whether AI will streamline or complicate their daily tasks, while patients weigh the potential benefits of personalised self-management support against risks such as data privacy concerns and misinformation. The impact of AI on the clinician-patient relationship remains a pivotal consideration, with the potential to either enhance collaborative care or create depersonalised interactions. Societally, there is an imperative to leverage AI in respiratory care to bridge healthcare disparities, while safeguarding against the widening of inequalities. Strategic efforts to promote transparency and prioritise inclusivity and ease of understanding in algorithm co-design will be crucial in shaping future AI to maximise benefits and minimise risks for all stakeholders.","url":"https://doi.org/10.1183/20734735.0189-2023","authors":["David Drummond","Ireti Adejumo","Kjeld Hansen","Vitalii Poberezhets","Greg Slabaugh","Chi Yan Hui"],"tags":["Safeguarding","Health care","Transparency (behavior)","Leverage (statistics)","Misinformation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-01","doi":"https://doi.org/10.1183/20734735.0189-2023","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4393098213","name":"Integration of cognitive tasks into artificial general intelligence test for large models","source":"openalex","abstract":"During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.","url":"https://doi.org/10.1016/j.isci.2024.109550","authors":["Youzhi Qu","Chen Wei","Penghui Du","W.Q. Che","Chi Zhang","Wanli Ouyang","Yatao Bian","Feiyang Xu","Bin Hu","Kai Du","Haiyan Wu","Jia Liu","Quanying Liu"],"tags":["Human intelligence","Computer science","Embodied cognition","Cognition","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-22","doi":"https://doi.org/10.1016/j.isci.2024.109550","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4410934331","name":"Artificial Intelligence in Glioblastoma—Transforming Diagnosis and Treatment","source":"openalex","abstract":"Glioblastoma (GBM) is the most aggressive and common primary brain malignancy in adults, characterized by poor prognosis and treatment resistance. Despite advancements in treatment options, the median survival is roughly 15 months, underlining the need for novel and effective treatments. Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering outstanding capabilities in data analysis, pattern recognition, and helping in decision-making. This review explores the current and potential role of AI in GBM care, focusing on its applications in diagnosis, treatment planning, prognostication, and drug discovery. AI-based algorithms have demonstrated promising potential in enhancing diagnostics through imaging analysis, radiomics, and tumor segmentation. These technologies could enable non-invasive molecular profiling and early detection of GBM. In treatment planning, AI could improve approaches by optimizing surgical resection, radiotherapy regimen, and chemotherapy protocols. Furthermore, machine learning models can integrate multimodal data to develop personalized treatments. They can also enhance prognostication by predicting survival, recurrence, and treatment responses, helping clinicians to make more informed decisions. AI is also revolutionizing pharmacotherapy by identifying novel molecular targets and optimizing combination therapies. Despite notable progress, challenges persist. Limited data quality and quantity, algorithm interpretability, integration problems, and ethical considerations, remain significant challenges to clinical implementation. This review emphasizes the need for continued research and interdisciplinary collaboration to overcome many barriers and realize the transformative potential of AI in GBM care.","url":"https://doi.org/10.1186/s41016-025-00399-2","authors":["Alen Rončević","Nenad Koruga","Anamarija Soldo Koruga","Robert Rončević"],"tags":["Transformative learning","Interpretability","Glioblastoma","Artificial intelligence","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-02","doi":"https://doi.org/10.1186/s41016-025-00399-2","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403032893","name":"Ensuring Appropriate Representation in Artificial Intelligence–Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias","source":"openalex","abstract":"BACKGROUND: In medical education, particularly in anatomy and dermatology, generative artificial intelligence (AI) can be used to create customized illustrations. However, the underrepresentation of darker skin tones in medical textbooks and elsewhere, which serve as training data for AI, poses a significant challenge in ensuring diverse and inclusive educational materials. OBJECTIVE: This study aims to evaluate the extent of skin tone diversity in AI-generated medical images and to test whether the representation of skin tones can be improved by modifying AI prompts to better reflect the demographic makeup of the US population. METHODS: In total, 2 standard AI models (Dall-E [OpenAI] and Midjourney [Midjourney Inc]) each generated 100 images of people with psoriasis. In addition, a custom model was developed that incorporated a prompt injection aimed at \"forcing\" the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by 3 researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. A chi-square goodness of fit analysis compared the skin tone distributions from each set of images to that of the US population. RESULTS: The standard AI models (Dalle-3 and Midjourney) demonstrated a significant difference between the expected skin tones of the US population and the observed tones in the generated images (P<.001). Both standard AI models overrepresented lighter skin. Conversely, the custom model with the modified prompt yielded a distribution of skin tones that closely matched the expected demographic representation, showing no significant difference (P=.04). CONCLUSIONS: This study reveals a notable bias in AI-generated medical images, predominantly underrepresenting darker skin tones. This bias can be effectively addressed by modifying AI prompts to incorporate real-life demographic distributions. The findings emphasize the need for conscious efforts in AI development to ensure diverse and representative outputs, particularly in educational and medical contexts. Users of generative AI tools should be aware that these biases exist, and that similar tendencies may also exist in other types of generative AI (eg, large language models) and in other characteristics (eg, sex, gender, culture, and ethnicity). Injecting demographic data into AI prompts may effectively counteract these biases, ensuring a more accurate representation of the general population.","url":"https://doi.org/10.2196/58275","authors":["Andrew O’Malley","Miriam Veenhuizen","Ayla Ahmed"],"tags":["Preprint","Tone (literature)","Representation (politics)","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-01","doi":"https://doi.org/10.2196/58275","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W7117448835","name":"Artificial intelligence and learner autonomy: a meta-analysis of self-regulated and self-directed learning","source":"openalex","abstract":"Introduction As artificial intelligence (AI) becomes increasingly embedded in educational environments, understanding its role in shaping learners’ self-regulated learning (SRL) and self-directed learning (SDL) has emerged as a central concern in contemporary learning science. While prior studies suggest that AI-driven systems may support planning, monitoring, and autonomy in learning, empirical evidence remains fragmented across contexts, learner groups, and instructional designs. This study synthesizes existing empirical research to systematically examine the magnitude and conditions under which AI-based interventions influence SRL, its dimensions and phases, SDL, and associated learning outcomes. Methods A systematic meta-analysis was conducted following PRISMA guidelines, synthesizing evidence from 32 empirical studies comprising 92 effect sizes and a total of 3,029 participants. The analysis examined overall effects of AI-based interventions on SRL and SDL, disaggregated effects across SRL dimensions (cognitive/metacognitive, motivational/affective, and behavioral regulation) and SRL phases (forethought, performance, and self-reflection), as well as impacts on learning outcomes and academic achievement. Random-effects models were applied, and moderator analyses explored learner characteristics, contextual variables, and AI design features. Sensitivity analyses and publication bias assessments were performed to evaluate the robustness of findings. Results AI-based interventions demonstrated a large and statistically significant positive effect on overall SRL ( g = 1.613, p = 0.032) and SDL ( g = 1.111, p = 0.043), indicating substantial improvements in learners’ ability to plan, monitor, and regulate their learning while sustaining autonomy and persistence. At the dimensional level, AI produced moderate gains in cognitive/metacognitive regulation ( g = 0.377, p = 0.0004) and motivational/affective regulation ( g = 0.505, p = 0.013), whereas effects on behavioral regulation were inconsistent. Phase-level analyses revealed that AI interventions were most effective during the forethought phase, supporting goal setting, planning, and motivational readiness, with smaller but significant gains observed in self-reflection and variable effects during the performance phase. AI systems also yielded moderate improvements in learning outcomes and achievement ( g = 0.350, p = 0.034). Moderator analyses indicated stronger SRL effects among older learners, longer intervention durations, and language learning contexts employing interactive AI systems, while gender differences were minimal. Sensitivity and publication bias tests confirmed the stability of results. Discussion The findings indicate that AI functions as an adaptive scaffold that meaningfully enhances learners’ self-regulatory and self-directed capacities across cognitive, motivational, and reflective processes. By strengthening forethought and planning mechanisms in particular, AI-based interventions support more autonomous, sustained, and effective learning behaviors that translate into measurable academic benefits. Variability in behavioral regulation outcomes highlights the need for more explicit action-level supports in AI design. Overall, the results showcase AI’s potential to promote equitable and scalable self-regulated learning across diverse educational contexts, while also pointing to the importance of aligning intervention design with learner characteristics and instructional goals.","url":"https://doi.org/10.3389/feduc.2025.1738751","authors":["krishnashree achuthan"],"tags":["Moderation","Psychological intervention","Autonomy","Empirical research","Robustness (evolution)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-29","doi":"https://doi.org/10.3389/feduc.2025.1738751","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400494189","name":"Artificial intelligence for cancer screening and surveillance","source":"openalex","abstract":"Investing in cancer prevention can be cost-effective. However, this requires significant changes both inside and outside the health care system. The core of the preventive strategy is the assignment of an individual risk level of developing cancer. Artificial intelligence (AI), which has emerged as a tool to reduce errors and confusion in data collection and analysis, has helped accelerate recent advances in identifying circulating markers to generate predictive methods. With predictive models applied to increasingly less invasive and repeatable analytic tests, the risk is no longer assigned but profiled directly on the individual over time. On this basis, the probability of early cancer diagnosis is increased and at the same time, proactive preventive medicine transits from offering lifestyle recommendations to guiding specific treatments to reduce the risk. Despite these promises, AI-based predictive models also present challenges in clinical implementation. Addressing these challenges is crucial to minimizing the future burdens associated with fighting cancer.","url":"https://doi.org/10.1016/j.esmorw.2024.100046","authors":["Francesco Gentile","Natalia Malara"],"tags":["Cancer","Cancer detection","Cancer screening","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-10","doi":"https://doi.org/10.1016/j.esmorw.2024.100046","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4388758788","name":"Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?","source":"openalex","abstract":"Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.","url":"https://doi.org/10.1136/pn-2023-003757","authors":["Joshua Au Yeung","Yang Yang Wang","Željko Kraljević","James Teo"],"tags":["Applications of artificial intelligence","Jargon","Health care","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-17","doi":"https://doi.org/10.1136/pn-2023-003757","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401082060","name":"Best Practice Performance of COVID-19 in America continent with Artificial Intelligence","source":"openalex","abstract":"Metaheuristics were employed with ANFIS and K-means to determine whether COVID-19 performed the best on the American continent. A few individuals lost their lives to COVID-19, and quite a few nations assisted with this matter. It is essential to know the nations that performed the best in COVID-19 management. Researchers can carry out evaluations of nations using metaheuristic approaches and ANFIS. Based on the performance of these nations, clusters will be determined and established. The research excluded only two of the thirteen criteria that were investigated. Seven distinct groups have been established for each of the thirty-five nations. In the United States, the performance regarding COVID-19 is the poorest, according to the research. These three nations also had the most extraordinary response to the COVID-19 outbreak. Based on the methodology and the context of the literature evaluation, this work’s contribution may be divided into two distinct areas. The existence of research gaps makes it clear that a regional emphasis is needed rather than a focus on a nation or a portion of a country, which illustrates the requirement for a focus on the whole continent.","url":"https://doi.org/10.31181/sor1120241","authors":["Amir Karbassi Yazdi","Hossein Komasi"],"tags":["Coronavirus disease 2019 (COVID-19)","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","2019-20 coronavirus outbreak","Geography","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-27","doi":"https://doi.org/10.31181/sor1120241","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403288415","name":"The role of artificial intelligence in coronary CT angiography","source":"openalex","abstract":"Coronary CT angiography (CCTA) offers an efficient and reliable tool for the non-invasive assessment of suspected coronary artery disease through the analysis of coronary artery plaque and stenosis. However, the detailed manual analysis of CCTA is a burdensome task requiring highly skilled experts. Recent advances in artificial intelligence (AI) have made significant progress toward a more comprehensive automated analysis of CCTA images, offering potential improvements in terms of speed, performance and scalability. This work offers an overview of the recent developments of AI in CCTA. We cover methodological advances for coronary artery tree and whole heart analysis, and provide an overview of AI techniques that have shown to be valuable for the analysis of cardiac anatomy and pathology in CCTA. Finally, we provide a general discussion regarding current challenges and limitations, and discuss prospects for future research.","url":"https://doi.org/10.1007/s12471-024-01901-8","authors":["Rudolf L. M. van Herten","Ioannis Lagogiannis","Tim Leiner","Ivana Išgum"],"tags":["Coronary angiography","Medicine","Angiography","Radiology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-10","doi":"https://doi.org/10.1007/s12471-024-01901-8","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4387964872","name":"A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research","source":"openalex","abstract":"Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.","url":"https://doi.org/10.1002/jmri.29101","authors":["Katharine A. Dishner","Bala McRae‐Posani","Arka Bhowmik","Maxine S. Jochelson","Andrei I. Holodny","Katja Pinker","Sarah Eskreis‐Winkler","Joseph N. Stember"],"tags":["Computer science","Field (mathematics)","Demographics","Data science","Interoperability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-27","doi":"https://doi.org/10.1002/jmri.29101","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400938781","name":"Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility","source":"openalex","abstract":"Artificial intelligence (AI) has shown transformative potential in various medical fields, including diagnostic imaging. Recent advances in AI-driven technologies have opened new avenues for improving echocardiographic practices. AI algorithms enhance the image quality, automate measurements, and assist in the diagnosis of cardiovascular diseases. These technologies reduce manual errors, increase consistency, and match the diagnostic performances of experienced echocardiographers. AI in tele-echocardiography offers significant benefits, particularly in rural and remote regions in Japan, where healthcare provider shortages and geographic isolation hinder access to advanced medical care. AI enhances accessibility, provides real-time remote analyses, supports continuous monitoring, and improves the quality and efficiency of remotely delivered cardiac care. However, addressing challenges related to data security, transparency, integration into clinical workflows, and ethical considerations is essential for the successful implementation of AI in echocardiography. On overcoming these challenges, AI will be able to revolutionize echocardiography and ensure timely and effective cardiac care for all patients in the future.","url":"https://doi.org/10.2169/internalmedicine.4171-24","authors":["Kenya Kusunose"],"tags":["Medicine","Workflow","Health care","Economic shortage","Telemedicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-24","doi":"https://doi.org/10.2169/internalmedicine.4171-24","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4409571599","name":"Mammographic classification of interval breast cancers and artificial intelligence performance","source":"openalex","abstract":"BACKGROUND: European studies suggest that artificial intelligence (AI) can reduce interval breast cancers. Research on interval breast cancer classification and AI's effectiveness in the United States, however, particularly using digital breast tomosynthesis and annual screening, is limited. We aimed to mammographically classify interval breast cancers and assess AI performance using a 12-month screening interval. METHODS: From digital mammography and digital breast tomosynthesis screening mammograms acquired between 2010 and 2019 at a US tertiary-care academic center, we identified interval breast cancers diagnosed less than 12 months after a negative mammogram. At least 3 breast radiologists retrospectively classified interval breast cancers as missed-reading error, minimal signs-actionable, minimal signs-nonactionable, true interval, occult, or missed-technical error. A deep-learning AI tool assigned risk scores ranging from 1 to 10 to the negative index screening mammograms, with scores of 8 or higher considered \"flagged.\" Statistical analysis evaluated associations among interval breast cancer types and AI exam scores, AI markings, and patient and tumor characteristics. RESULTS: From 184 935 screening mammograms (65% digital mammography, 35% digital breast tomosynthesis), we identified 148 interval breast cancers in 148 women (mean [SD] age = 61 [12] years). Of these, 26% were minimal signs-actionable, 24% were occult, 22% were minimal signs-nonactionable, 17% were missed-reading error, 6% were true interval, and 5% were missed-technical error (P < .001). AI scored 131 mammograms (17 errors excluded); it most frequently flagged exams with missed-reading error (90%), minimal signs-actionable (89%), and minimal signs-nonactionable (72%) (P = .02). AI localized mammographically visible types more accurately (35%-68%) than nonvisible types (0%-50%; P = .02). CONCLUSION: AI more frequently flagged and accurately localized interval breast cancer types that were mammographically visible at screening (missed or minimal signs) compared with true interval or occult cancers.","url":"https://doi.org/10.1093/jnci/djaf103","authors":["Tiffany Yu","Anne Hoyt","Melissa Joines","Cheryce Fischer","Nazanin Yaghmai","James S Chalfant","Lucy Chow","Shabnam Mortazavi","Christopher Sears","James Sayre","Joann G. Elmore","William Hsu","Hannah S. Milch"],"tags":["Medicine","Occult","Mammography","Digital Breast Tomosynthesis","Breast imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-17","doi":"https://doi.org/10.1093/jnci/djaf103","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405266027","name":"Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis","source":"openalex","abstract":"Background Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.Method Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).Results A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41–0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91–0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60–7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21–0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD.Conclusions This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.","url":"https://doi.org/10.1080/0886022x.2024.2435483","authors":["Qinyu Pan","Mengli Tong"],"tags":["Medicine","Kidney disease","Meta-analysis","Intensive care medicine","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-11","doi":"https://doi.org/10.1080/0886022x.2024.2435483","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4392626370","name":"Nordic radiographers’ and students’ perspectives on artificial intelligence – A cross-sectional online survey","source":"openalex","abstract":"INTRODUCTION: The integration of artificial intelligence (AI) into the domain of radiography holds substantial potential in various aspects including workflow efficiency, image processing, patient positioning, and quality assurance. The successful implementation of AI within a Radiology department necessitates the participation of key stakeholders, particularly radiographers. The study aimed to provide a comprehensive investigation about Nordic radiographers' perspectives and attitudes towards AI in radiography. METHODS: An online 29-item survey was distributed via social media platforms to Nordic students and radiographers working in Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Islands including items on demographics, specialization, educational background, place of work and perspectives and knowledge on AI. The items were a mix of closed-type and scaled questions, with the option for free-text responses when relevant. RESULTS: The survey received responses from all Nordic countries with 586 respondents, 26.8% males, 72.1% females, and 1.1% non-binary/self-defined or preferred not to say. The mean age was 37.2 with a standard deviation (SD) of ±12.1 years, and the mean number of years since qualification was 14.2 SD ± 10.3 years. A total of 43% (n = 254) of the respondents had not received any AI training in clinical practice. Whereas 13% (n = 76) had received AI during radiography undergrad training. A total of 77.9% (n = 412) expressed interest in pursuing AI education. The majority of respondents were aware of the potential use of AI (n = 485, 82.8%) and 39.1% (n = 204) had no reservations about AI. CONCLUSION: Overall, this study found that Nordic radiographers have a positive attitude toward AI. Very limited training or education has been provided to the radiographers. Especially since 82.8% reports on plans to implement AI in clinical practice. In general, awareness of AI applications is high, but the educational level is low for Nordic radiographers. IMPLICATION FOR PRACTICE: This study emphasises the favourable view of AI held by students and Nordic radiographers. However, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.","url":"https://doi.org/10.1016/j.radi.2024.02.020","authors":["Malene Roland Vils Pedersen","Martin Weber Kusk","Simon Lysdahlgaard","H. Mork-Knudsen","Christina Malamateniou","Janni Jensen"],"tags":["Cross-sectional study","Medical education","Psychology","Medicine","Pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-09","doi":"https://doi.org/10.1016/j.radi.2024.02.020","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4409461213","name":"Educators’ Perceptions on Artificial Intelligence in Higher Education: Insights from the Jordanian Higher Education","source":"openalex","abstract":"This paper investigates educators’ perceptions of the application of Artificial Intelligence in Higher Education (AIHEd) and its benefits and concerns within the Jordanian higher education. Like in other contexts, the adoption of Artificial Intelligence (AI) in the Jordanian higher education brought many benefits and a variety of concerns. Due to the lack of regulations and clear policies to cope with such new technologies, the increasing prevalence of these concerns has a negative impact on academic integrity. We used a sequential exploratory mixed approach to accomplish our study, which is guided by the Technology Acceptance Model (TAM), which helps in analysing the adoption of AI in higher education. Our approach involves conducting interviews with university educators from three different Jordanian universities. Interviews were done to identify educators’ thoughts regarding the responsibility of universities to adopt new AI technologies, what motivates them to use AI tools and services in their daily work, whether using AI in higher education institutions is legitimate, and the concerns associated with implementing such technologies into practice. Thus, the paper tries to portray the acceptable benefits and concerns of using AI in Jordanian higher education institutions. After conducting a thematic analysis on 18 interviews with educators, we identified 10 corresponding benefit themes and 8 corresponding concern themes that resulted from the coding and theme-building process. The average rate of educators’ responses to the themes of benefits and concerns is then determined by distributing a questionnaire to 145 higher education educators to generalise the results. Although our findings offer valuable insights, further investigation in wider contexts may be necessary to ensure the representativeness and generalisability of the findings. Through the themes that the study outlined, we concluded that although AI can transform the way students learn and educators work, there are still several issues that need to be resolved by researchers and teachers who work with associated application systems. Such issues require greater emphasis on appropriately and logically handling related ethical dilemmas. These concerns also highlight the importance of developing the necessary strategies and skills for responsible AIHEd. Using a mixed approach helped us to develop a strong understanding of the current state of AIHEd in the Jordanian context.","url":"https://doi.org/10.18178/ijiet.2025.15.4.2278","authors":["Lamis F. Al-Qora’n"],"tags":["Perception","Psychology","Higher education","Medical education","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.18178/ijiet.2025.15.4.2278","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4404613446","name":"Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis","source":"openalex","abstract":"OBJECTIVE: The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of AI-assisted technologies, including endoscopy, voice analysis, and histopathology, for detecting and classifying laryngeal lesions. METHODS: A systematic search was conducted in PubMed, Embase, etc. for studies utilizing voice analysis, histopathology for laryngeal lesions, or AI-assisted endoscopy. The results of diagnostic accuracy, sensitivity and specificity were synthesized by a meta-analysis. RESULTS: 12 studies employing AI-assisted endoscopy, 2 studies for voice analysis, and 4 studies for histopathology were included in the meta-analysis. The combined sensitivity of AI-assisted endoscopy was 91% (95% CI 87-94%) for the classification of benign from malignant lesions and 91% (95% CI 90-93%) for lesion detection. The highest accuracy pooled in detecting lesions versus healthy tissue was the AI-aided endoscopy was 94% (95% CI 92-97%). CONCLUSIONS: For laryngeal lesions, AI-assisted endoscopy shows excellent diagnosis accuracy. But more sizable prospective trials are needed to confirm the practical clinical value.","url":"https://doi.org/10.1007/s00405-024-09075-0","authors":["Alejandro R. Marrero‐Gonzalez","Tanner J. Diemer","Shaun A. Nguyen","Terence James M. Camilon","Kirsten Meenan","Ashli K. O’Rourke"],"tags":["Histopathology","Meta-analysis","Endoscopy","Medicine","Diagnostic accuracy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-22","doi":"https://doi.org/10.1007/s00405-024-09075-0","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401499434","name":"Application of artificial intelligence in the diagnosis and treatment of urinary tumors","source":"openalex","abstract":"Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.","url":"https://doi.org/10.3389/fonc.2024.1440626","authors":["Mengying Zhu","Zhichao Gu","Fang Chen","Xi Chen","Yue Wang","Guohua Zhao"],"tags":["Artificial intelligence","Convolutional neural network","Renal cell carcinoma","Deep learning","Prostate cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-12","doi":"https://doi.org/10.3389/fonc.2024.1440626","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4402577099","name":"Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances","source":"openalex","abstract":"Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.","url":"https://doi.org/10.3390/diagnostics14182059","authors":["Adiba Tabassum Chowdhury","Abdus Salam","Mansura Naznine","Da’ad Abdalla","Lauren Erdman","Muhammad E. H. Chowdhury","Tariq O. Abbas"],"tags":["Pediatric urology","Artificial intelligence","Pyeloplasty","Computer science","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-17","doi":"https://doi.org/10.3390/diagnostics14182059","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407289259","name":"Artificial Intelligence in Multi-Disease Medical Diagnostics: An Integrative Approach","source":"openalex","abstract":"With advanced algorithms, artificial intelligence (AI) has revolutionized the medical diagnostic field where diseases can be predicted simultaneously. The integrative nature of this approach is novel because it can better encompass the complexity of comorbid conditions that are so common in patients; thus, addressing them in a more holistic diagnostic tone that is lacking in previous works. In this study, the investigation of the usage of AI models for simultaneously diagnosing diseases like diabetes, cardiovascular conditions, and neurological disorders is done. Therefore, based on AI techniques i.e. artificial neural networks (ANNs) and ensemble learning methods, a multi-disease diagnostic framework was developed to achieve this. A variety of features, related to each condition, were captured from multi-modal datasets including imaging, laboratory test results, and patient histories. The system was developed to manage the big flow of aggregated data and offer detailed diagnostic views of many diseases. Sensitivity, specificity, and overall diagnostic accuracy were used to evaluate the framework's performance. The results showed that the AI framework has high diagnostic accuracy for all targeted conditions an overall sensitivity of 93% and a specificity of 91%. Importantly, the combination of multi-modal data proved to substantially improve the system’s ability to identify and distinguish comorbid conditions. It makes the importance of using various data sources to benefit from the reliability and comprehensiveness of AI diagnostics obvious. Overall, AI-driven multi-disease diagnostic systems provide great promise for the role of delivering potentially transformative clinical healthcare workflow improvements, reducing errors, and improving patient outcomes. These frameworks will need to be scaled and tested in various healthcare settings and also across more varied diseases to help make medical diagnosis more available and effective.","url":"https://doi.org/10.32996/jcsts.2025.7.1.12","authors":["Nigar Sultana","Shariar Islam Saimon","Ishraq Islam","Shake Ibna Abir","Md Amzad Hossain","Sarder Abdulla Al Shiam","Nazrul Islam Khan"],"tags":["Disease","Artificial intelligence","Computer science","Psychology","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-09","doi":"https://doi.org/10.32996/jcsts.2025.7.1.12","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4406690454","name":"MR-linac: role of artificial intelligence and automation","source":"openalex","abstract":"The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs. Despite these improvements, MR-guided radiotherapy (MRgRT) remains labor intensive and time consuming, highlighting the need for AI to streamline workflows and support rapid decision-making. Synthetic CTs from MR images and automated contouring and treatment planning will reduce manual processes, thus optimizing treatment times and expanding access to MR-linac technology. AI-driven quality assurance will ensure patient safety by predicting machine errors and validating treatment delivery. Advances in intrafractional motion management will increase the accuracy of treatment, and the integration of imaging biomarkers for outcome prediction and early toxicity assessment will enable more precise and effective treatment strategies.","url":"https://doi.org/10.1007/s00066-024-02358-9","authors":["S. Psoroulas","Alina Paunoiu","Stefanie Corradini","Juliane Hörner‐Rieber","Stephanie Tanadini‐Lang"],"tags":["Medicine","Contouring","Workflow","Quality assurance","Radiation treatment planning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-22","doi":"https://doi.org/10.1007/s00066-024-02358-9","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4379548960","name":"THE ROLE OF ARTIFICIAL INTELLIGENCE IN FUTURE TECHNOLOGY","source":"openalex","abstract":"This research paper illuminates the role of artificial intelligence (AI) in future technology. Artificial intelligence is a field of computer science that aims to create mental capabilities similar to human intelligence in machines and systems. In recent decades, AI has undergone significant breakthroughs and has become a key factor in the development of modern technologies.","url":"https://doi.org/10.5281/zenodo.8012270","authors":["Gaybulloyev E.E.","Siddikov B.N. Kudratillaev M.B."],"tags":["Cognitive science","Psychology","Computer science","Artificial intelligence","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-07","doi":"https://doi.org/10.5281/zenodo.8012270","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4408365249","name":"The Origins and Veracity of References ‘Cited’ by Generative Artificial Intelligence Applications: Implications for the Quality of Responses","source":"openalex","abstract":"The public release of ChatGPT in late 2022 has resulted in considerable publicity and has led to widespread discussion of the usefulness and capabilities of generative Artificial intelligence (Ai) language models. Its ability to extract and summarise data from textual sources and present them as human-like contextual responses makes it an eminently suitable tool to answer questions users might ask. Expanding on a previous analysis of the capabilities of ChatGPT3.5, this paper tested what archaeological literature appears to have been included in the training phase of three recent generative Ai language models: ChatGPT4o, ScholarGPT, and DeepSeek R1. While ChatGPT3.5 offered seemingly pertinent references, a large percentage proved to be fictitious. While the more recent model ScholarGPT, which is purportedly tailored towards academic needs, performed much better, it still offered a high rate of fictitious references compared to the general models ChatGPT4o and DeepSeek. Using ‘cloze’ analysis to make inferences on the sources ‘memorized’ by a generative Ai model, this paper was unable to prove that any of the four genAi models had perused the full texts of the genuine references. It can be shown that all references provided by ChatGPT and other OpenAi models, as well as DeepSeek, that were found to be genuine, have also been cited on Wikipedia pages. This strongly indicates that the source base for at least some, if not most, of the data is found in those pages and thus represents, at best, third-hand source material. This has significant implications in relation to the quality of the data available to generative Ai models to shape their answers. The implications of this are discussed.","url":"https://doi.org/10.3390/publications13010012","authors":["Dirk Spennemann"],"tags":["Quality (philosophy)","Generative grammar","Computer science","Artificial intelligence","Epistemology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-12","doi":"https://doi.org/10.3390/publications13010012","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406072657","name":"Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery","source":"openalex","abstract":"Laparoscopic exploration (LE) is crucial for diagnosing intra-abdominal metastasis (IAM) in advanced gastric cancer (GC). However, overlooking single, tiny, and occult IAM lesions during LE can severely affect the treatment and prognosis due to surgeons' visual misinterpretations. To address this, we developed the artificial intelligence laparoscopic exploration system (AiLES) to recognize IAM lesions with various metastatic extents and locations. The AiLES was developed based on a dataset consisting of 5111 frames from 100 videos, using 4130 frames for model development and 981 frames for evaluation. The AiLES achieved a Dice score of 0.76 and a recognition speed of 11 frames per second, demonstrating robust performance in different metastatic extents (0.74-0.76) and locations (0.63-0.90). Furthermore, AiLES performed comparably to novice surgeons in IAM recognition and excelled in recognizing tiny and occult lesions. Our results demonstrate that the implementation of AiLES could enhance accurate tumor staging and assist individualized treatment decisions.","url":"https://doi.org/10.1038/s41746-024-01372-6","authors":["Hao Chen","Longfei Gou","Zhiwen Fang","Qi Dou","Haobin Chen","Chang Chen","Yuqing Qiu","Jinglin Zhang","Chenglin Ning","Yanfeng Hu","Haijun Deng","Jiang Yu","Guoxin Li"],"tags":["Medicine","Occult","Cancer","Metastasis","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-05","doi":"https://doi.org/10.1038/s41746-024-01372-6","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412915069","name":"A Review of Artificial Intelligence and Deep Learning Approaches for Resource Management in Smart Buildings","source":"openalex","abstract":"This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale.","url":"https://doi.org/10.3390/buildings15152631","authors":["Bibars Amangeldy","Timur Imankulov","Nurdaulet Tasmurzayev","Gulmira Dikhanbayeva","Yedil Nurakhov"],"tags":["Architectural engineering","Engineering","Computer science","Resource (disambiguation)","Systems engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.3390/buildings15152631","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4399626257","name":"Residency Applications in the Era of Generative Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.4300/jgme-d-23-00629.1","authors":["Jenny Chen","Sarah N. Bowe","Francis Deng"],"tags":["Generative grammar","Residency training","Data science","Computer science","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-01","doi":"https://doi.org/10.4300/jgme-d-23-00629.1","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403454705","name":"Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice","source":"openalex","abstract":"Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.","url":"https://doi.org/10.1186/s12882-024-03793-7","authors":["Tu T. Tran","Giae Yun","Sejoong Kim"],"tags":["Medicine","Acute kidney injury","Intensive care medicine","Narrative review","Nephrology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-16","doi":"https://doi.org/10.1186/s12882-024-03793-7","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4411283308","name":"Artificial Intelligence Empowering Dynamic Spectrum Access in Advanced Wireless Communications: A Comprehensive Overview","source":"openalex","abstract":"This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance.","url":"https://doi.org/10.3390/ai6060126","authors":["Abiodun Gbenga‐Ilori","Agbotiname Lucky Imoize","Kinzah Noor","Paul Oluwadara Adebolu-Ololade"],"tags":["Wireless","Computer science","Telecommunications","Spectrum (functional analysis)","Computer network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-13","doi":"https://doi.org/10.3390/ai6060126","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405986581","name":"Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision – part 2","source":"openalex","abstract":"Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions. Furthermore, AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders. Despite these advances, challenges such as data standardization, model explainability, and ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical and biotechnological engineers, AI scientists, legal representatives and bioethics experts are essential to overcoming these barriers and unlocking AI's full potential in transforming headache research and healthcare. This is a call to action in proposing novel frameworks for integrating AI-based technologies into headache care.","url":"https://doi.org/10.1186/s10194-024-01944-7","authors":["Igor Petrušić","Chia‐Chun Chiang","David García‐Azorín","Woo‐Seok Ha","Raffaele Ornello","Lanfranco Pellesi","Eloísa Rubio‐Beltrán","Ruth Ruscheweyh","Marta Waliszewska‐Prosół","William Wells-Gatnik"],"tags":["Editorial board","Artificial intelligence","Psychology","Medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1186/s10194-024-01944-7","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401456487","name":"Knowledge, Attitude, and Practices toward Artificial Intelligence among University Students in Lebanon","source":"openalex","abstract":"Background: The expansion of artificial intelligence (AI) across diverse sectors worldwide demands an understanding of its impact on future generations. The studies of its influence on university students’ behavior and application in Lebanon are still limited. The present study aimed to explore the knowledge, attitudes, and practices (KAPs) of university students regarding AI and to identify factors affecting these dimensions. Methods: An online questionnaire (n = 457) was distributed to university students who were at least 18 years of age across Lebanon. Results: The results revealed that a significant majority (97.2%) of the participants were familiar with AI, from which 43% demonstrated a high level of knowledge. Furthermore, attitude toward AI role and integration in academic and professional paths was moderately satisfactory (43%), although it was reportedly used by 75% of students throughout their university years. There was a significant association between knowledge levels and sociodemographic factors such as age, sex, source of AI-related information, and knowledge rating (p < 0.05), whereas the academic major and knowledge rating affected attitudes toward AI (p < 0.05). Conclusion: These findings support the incorporation of AI education within the curriculum to increase acceptance of AI as a modern tool enhancing various sectors and serving as a facilitator for teaching and learning processes.","url":"https://doi.org/10.3390/educsci14080863","authors":["Samer A. Kharroubi","Iman Tannir","Rasha Abu El Hassan","Rouba Ballout"],"tags":["Facilitator","Curriculum","Psychology","Higher education","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-09","doi":"https://doi.org/10.3390/educsci14080863","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4401498863","name":"Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia","source":"openalex","abstract":"Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by analyzing nutritional biomarkers, key leukemia indicators, and granulocytes in their blood. Using a machine learning algorithm and ten indices, the blood samples of 826 children with ALL and 255 children with AML were compared to a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an area under the curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.","url":"https://doi.org/10.1186/s12885-024-12646-3","authors":["Zhangkai J. Cheng","Haiyang Li","Mingtao Liu","Xing Fu","Li Liu","Zhiman Liang","Hui Gan","Baoqing Sun"],"tags":["Medicine","Leukemia","Myeloid leukemia","Childhood leukemia","Acute leukemia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-12","doi":"https://doi.org/10.1186/s12885-024-12646-3","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405423373","name":"Are we ready to integrate advanced artificial intelligence models in clinical laboratory?","source":"openalex","abstract":"The application of advanced artificial intelligence (AI) models and algorithms in clinical laboratories is a new inevitable stage of development of laboratory medicine, since in the future, diagnostic and prognostic panels specific to certain diseases will be created from a large amount of laboratory data.Thanks to machine learning (ML), it is possible to analyze a large amount of structured numerical data as well as unstructured digitized images in the field of hematology, cytology and histopathology.Numerous researches refer to the testing of ML models for the purpose of screening various diseases, detecting damage to organ systems, diagnosing malignant diseases, longitudinal monitoring of various biomarkers that would enable predicting the outcome of each patient's treatment.The main advantages of advanced AI in the clinical laboratory are: faster diagnosis using diagnostic and prognostic algorithms, individualization of treatment plans, personalized medicine, better patient treatment outcomes, easier and more precise longitudinal monitoring of biomarkers, etc. Disadvantages relate to the lack of standardization, questionable quality of the entered data and their interpretability, potential over-reliance on technology, new financial investments, privacy concerns, ethical and legal aspects.Further integration of advanced AI will gradually take place on the basis of the knowledge of specialists in laboratory and clinical medicine, experts in information technology and biostatistics, as well as on the basis of evidence-based laboratory medicine.Clinical laboratories will be ready for the full and successful integration of advanced AI once a balance has been established between its potential and the resolution of existing obstacles.","url":"https://doi.org/10.11613/bm.2025.010501","authors":["Slavica Dodig","Ivana Čepelak","Matko Dodig"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-16","doi":"https://doi.org/10.11613/bm.2025.010501","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4406288592","name":"Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment","source":"openalex","abstract":"BACKGROUND: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT). METHODS: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1. AI-QCT and blinded readers assessed coronary artery stenosis following the Coronary Artery Disease Reporting and Data System consensus. Accuracy of AI-QCT was compared with a level 3 and two level 2 clinical readers against an invasive quantitative coronary angiography (QCA) reference standard (≥50% stenosis) in an area under the curve (AUC) analysis, evaluated per-patient and per-vessel and stratified by plaque volume. RESULTS: Among 208 patients with a mean age of 58±9 years and 37% women, AI-QCT demonstrated superior concordance with QCA compared with clinical CCTA assessments. For the detection of obstructive stenosis (≥50%), AI-QCT achieved an AUC of 0.91 on a per-patient level, outperforming level 3 (AUC 0.77; p<0.002) and level 2 readers (AUC 0.79; p<0.001 and AUC 0.76; p<0.001). The advantage of AI-QCT was most prominent in those with above median plaque volume. At the per-vessel level, AI-QCT achieved an AUC of 0.86, similar to level 3 (AUC 0.82; p=0.098) stenosis, but superior to level 2 readers (both AUC 0.69; p<0.001). CONCLUSIONS: AI-QCT demonstrated superior agreement with invasive QCA compared to clinical CCTA assessments, particularly compared to level 2 readers in those with extensive CAD. Integrating AI-QCT into routine clinical practice holds promise for improving the accuracy of stenosis quantification through CCTA.","url":"https://doi.org/10.1136/openhrt-2024-003115","authors":["Rachel Bernardo","Nick S. Nurmohamed","Michiel J. Bom","Ruurt Jukema","Ruben W. de Winter","Ralf W. Sprengers","Erik S.G. Stroes","James K. Min","James P. Earls","Ibrahim Danad","Andrew D Choi","Paul Knaapen"],"tags":["Coronary angiography","Radiology","Medicine","Angiography","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1136/openhrt-2024-003115","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4405306800","name":"American Academy of Otolaryngology–Head and Neck Surgery (AAO‐HNS) Report on Artificial Intelligence","source":"openalex","abstract":"This report synthesizes the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Task Force's guidance on the integration of artificial intelligence (AI) in otolaryngology-head and neck surgery (OHNS). A comprehensive literature review was conducted, focusing on the applications, benefits, and challenges of AI in OHNS, alongside ethical, legal, and social implications. The Task Force, formulated by otolaryngologist experts in AI, used an iterative approach, adapted from the Delphi method, to prioritize topics for inclusion and to reach a consensus on guiding principles. The Task Force's findings highlight AI's transformative potential for OHNS, offering potential advancements in precision medicine, clinical decision support, operational efficiency, research, and education. However, challenges such as data quality, health equity, privacy concerns, transparency, regulatory gaps, and ethical dilemmas necessitate careful navigation. Incorporating AI into otolaryngology practice in a safe, equitable, and patient-centered manner requires clinician judgment, transparent AI systems, and adherence to ethical and legal standards. The Task Force principles underscore the importance of otolaryngologists' involvement in AI's ethical development, implementation, and regulation to harness benefits while mitigating risks. The proposed principles inform the integration of AI in otolaryngology, aiming to enhance patient outcomes, clinician well-being, and efficiency of health care delivery.","url":"https://doi.org/10.1002/ohn.1080","authors":["Noel Ayoub","Anaïs Rameau","Michael Brenner","Andrés M. Bur","Gregory A. Ator","Selena E. Briggs","Masayoshi Takashima","Konstantina M. Stanković","AAO‐HNS Artificial Intelligence Task Force"],"tags":["Otorhinolaryngology","Transformative learning","Transparency (behavior)","Medicine","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-12","doi":"https://doi.org/10.1002/ohn.1080","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4281630308","name":"Significance of machine learning in healthcare: Features, pillars and applications","source":"openalex","abstract":"Machine Learning (ML) applications are making a considerable impact on healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where AI provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. ML-based techniques assist in detecting early indicators of an epidemic or pandemic. This algorithm examines satellite data, news and social media reports, and even video sources to determine whether the sickness will become out of control. Using ML for healthcare can open up a world of possibilities in this field. It frees up healthcare providers' time to focus on patient care rather than searching or entering information. This paper studies ML and its need in healthcare, and then it discusses the associated features and appropriate pillars of ML for healthcare structure. Finally, it identified and discussed the significant applications of ML for healthcare. The applications of this technology in healthcare operations can be tremendously advantageous to the organisation. ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. Shortly, ML will impact both physicians and hospitals. It will be crucial in developing clinical decision support, illness detection, and personalised treatment approaches to provide the best potential outcomes.","url":"https://doi.org/10.1016/j.ijin.2022.05.002","authors":["Mohd Javaid","Abid Haleem","Ravi Pratap Singh","Rajiv Suman","Shanay Rab"],"tags":["Health care","Big data","Economic shortage","Computer science","Healthcare system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1016/j.ijin.2022.05.002","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4407961122","name":"Artificial Intelligence in Neuroendovascular Procedures","source":"openalex","abstract":"Recent advances in artificial intelligence (AI) have significantly transformed neuroendovascular procedures, offering innovative solutions for image analysis, procedural assistance, and clinical decision-making. This review examines the current state and future potential of AI applications in neuroendovascular interventions, focusing on 3 topics: AI-based image recognition, real-time procedural assistance, and future developments. From a research perspective, deep learning algorithms have demonstrated reasonable accuracy in vascular structure analysis and device detection, successfully identifying critical conditions such as vascular perforation, aneurysm location, and vessel occlusions. Real-time AI assistance systems may have potential clinical utility in various procedures, including carotid artery stenting, aneurysm coiling, and liquid embolization, potentially enhancing procedural safety and operator awareness. The future of AI in neuroendovascular procedures shows promise in integration with robotic systems and applications in medical education. While current systems have some limitations, ongoing technological advances suggest an expanding role of AI in enhancing procedural safety, standardization, and patient outcomes.","url":"https://doi.org/10.5797/jnet.ra.2024-0107","authors":["Kenichi Kono"],"tags":["Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.5797/jnet.ra.2024-0107","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4409700773","name":"The Rise of Transformers – Redefining the Landscape of Artificial Intelligence","source":"openalex","abstract":"The 2017 paper 'Attention Is All You Need' by Vaswani et al. marked a major paradigm shift in AI. Rather than clinging to tired methods like recurrence or convolutions, its Transformer design boldly flipped the script melding self-attention into a fresh take that not only remade natural language processing but also trickled over into computer vision, robotics, quirky multi-modal setups, and more [1]. At its very core lies a refreshingly simple idea self-attention, which lets models dynamically figure out which bits of the input deserve extra focus instead of being bound by strict, linear routines. This clever tweak kicked the old, rigid rules to the curb and opened up levels of parallel processing that we hadn’t seen before. In many cases, this shift not only ramped up the training speed dramatically but also proved essential in our data-swamped world where speed and nimbleness really do make all the difference.","url":"https://doi.org/10.58496/bjai/2025/007","authors":["Nyagong Santino David Ladu","Benson Turyasingura","Byamukama Willbroad","Abraham Atuhaire"],"tags":["Transformer","Artificial intelligence","Computer science","Engineering","Architectural engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-22","doi":"https://doi.org/10.58496/bjai/2025/007","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4414610304","name":"Artificial Intelligence-Enhanced Liquid Biopsy and Radiomics in Early-Stage Lung Cancer Detection: A Precision Oncology Paradigm","source":"openalex","abstract":"BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential-yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. OBJECTIVE: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques-including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)-that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. DISCUSSION: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain-including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. CONCLUSION: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.","url":"https://doi.org/10.3390/cancers17193165","authors":["Swathi Priya Cherukuri","Anmolpreet Kaur","Bipasha Goyal","Hanisha Reddy Kukunoor","Areesh Fatima Sahito","Pratyush Sachdeva","Gayathri Yerrapragada","E. Poonguzhali","Mohammed Naveed Shariff","T.K. Natarajan","Jayarajasekaran Janarthanan","Samuel Richard","Shakthidevi Pallikaranai Venkatesaprasath","Shiva Sankari Karuppiah","Vivek Iyer","Scott A. Helgeson","Shivaram P. Arunachalam"],"tags":["Radiomics","Liquid biopsy","Medicine","Lung cancer","Precision oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-29","doi":"https://doi.org/10.3390/cancers17193165","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4315484276","name":"Reliability of Artificial Intelligence-Assisted Cephalometric Analysis. A Pilot Study","source":"openalex","abstract":"Recently, Artificial Intelligence (AI) has spread in orthodontics, in particular within cephalometric analysis, where computerized digital software is able to provide linear-angular measurements upon manual landmark identification. A step forward is constituted by fully automated AI-assisted cephalometric analysis, where the landmarks are automatically detected by software. The aim of the study was to compare the reliability of a fully automated AI-assisted cephalometric analysis with the one obtained by a computerized digital software upon manual landmark identification. Fully automated AI-assisted cephalometric analysis of 13 lateral cephalograms were retrospectively compared to the cephalometric analysis performed twice by a blinded operator with a computerized software. Intra- and inter-operator (fully automated AI-assisted vs. computerized software with manual landmark identification) reliability in cephalometric parameters (maxillary convexity, facial conicity, facial axis angle, posterior and lower facial height) was tested with the Dahlberg equation and Bland–Altman plot. The results revealed no significant difference in intra- and inter-operator measurements. Although not significant, higher errors were observed within intra-operator measurements of posterior facial height and inter-operator measurements of facial axis angle. In conclusion, despite the small sample, the cephalometric measurements of a fully automated AI-assisted cephalometric software were reliable and accurate. Nevertheless, digital technological advances cannot substitute the critical role of the orthodontist toward a correct diagnosis.","url":"https://doi.org/10.3390/biomedinformatics3010003","authors":["Anna Alessandri‐Bonetti","Linda Sangalli","Martina Salerno","Patrizia Gallenzi"],"tags":["Landmark","Cephalometric analysis","Software","Reliability (semiconductor)","Orthodontics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-10","doi":"https://doi.org/10.3390/biomedinformatics3010003","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400084993","name":"Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy","source":"openalex","abstract":"BACKGROUND: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). METHODS: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro-Wilk test for normality, the Kruskal-Wallis H test for group differences, and the Mann-Whitney U test with Bonferroni correction for post-hoc comparisons. RESULTS: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. CONCLUSION: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.","url":"https://doi.org/10.3390/diagnostics14131364","authors":["Fatma Hilal Yağın","Cemil Çolak","Abdulmohsen Algarni","Yasin Görmez","Emek Güldoğan","Luca Paolo Ardigò"],"tags":["Diabetic retinopathy","Metabolomics","Computer science","Artificial intelligence","Computational biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-27","doi":"https://doi.org/10.3390/diagnostics14131364","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4412389140","name":"Evaluation of the Medical Artificial Intelligence Readiness Status of Medical Faculty Students","source":"openalex","abstract":"Aim: This study aims to evaluate the readiness status of medical faculty students regarding medical artificial intelligence and to determine whether it varies according to demographic characteristics. Materials and Methods: The study population consists of medical faculty students in Kayseri province. Accordingly, 368 medical faculty students voluntarily participated in the cross-sectional study conducted between June 1 and July 15, 2024. The “Medical Artificial Intelligence Readiness Scale” consisting of 22 items, was used in the study, and data were collected through a survey technique. The SPSS software package was used for the analysis of the collected data, employing descriptive analysis, correlation analysis, t test, and ANOVA. Results: The study revealed that medical school students' level of readiness for medical artificial intelligence and their mean scores in the skill, foresight and ethical sub dimensions were high, while their mean scores in the cognitive sub dimension were low. In addition, medical faculty students' medical artificial intelligence readiness levels and cognitive, skill, foresight and ethical sub dimension averages significantly differed according to demographic characteristics (gender, age and class). Conclusion: The research findings indicate that medical school students have above-average readiness for medical artificial intelligence. This study is considered to guide the development of a new curriculum in medical education and to provide significant practical contributions to the medical education literature.","url":"https://doi.org/10.58208/cphs.1533819","authors":["Fatih Denizli"],"tags":["Medical education","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.58208/cphs.1533819","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4394782511","name":"Ethical principles in dental healthcare: Relevance in the current technological era of artificial intelligence","source":"openalex","abstract":"In the current technological era, dental practitioners are faced with various ethical challenges, highlighting the importance of bioethics in this healthcare discipline. The rise of artificial intelligence has recently sparked a debate regarding the privacy of patient data. While the advancements may offer innovative treatment options, their long-term effects may not be fully understood, raising questions about the responsible implementation of such methods. Thus, conscientious and ethical AI use in dentistry encompasses that patients be notified about how their data is used and also about the involvement of AI-based decision-making. This paper explores the key bioethical considerations in dental healthcare, with a focus on evidence-based AI development and use. The framework of ethical principles and guidelines provided would foster trust between the clinician and patients, while promoting the highest standards of care.","url":"https://doi.org/10.1016/j.jobcr.2024.04.003","authors":["Isha Duggal","Tulika Tripathi"],"tags":["Bioethics","Relevance (law)","Engineering ethics","Health care","Dental care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-13","doi":"https://doi.org/10.1016/j.jobcr.2024.04.003","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4388827078","name":"The Right to Transparency in Public Governance: Freedom of Information and the Use of Artificial Intelligence by Public Agencies","source":"openalex","abstract":"What information should and can be transparent for artificial intelligence (AI) algorithms? This article examines the socio-technical and legal perspectives of transparency in relation to algorithmic decision-making in public administration. We show how transparency in AI can be understood in light of the various technologies and the challenges one may encounter. Despite some first steps in that direction, there exists so far no mature standard for documenting AI models. From a legal perspective, this article examined the applicable freedom of information (FOI) regimes across different jurisdictions, with a particular focus on Denmark and other Scandinavian countries. Despite notable differences, our findings show that the FOI regimes generally only grant access to existing documents, and that access can be denied on the basis of the wide proprietary interests and internal documents exemptions. This is why we ultimately conclude that the European data-protection framework and the proposed EU AI Act — with their far-reaching duties to document the functioning of AI systems — provide promising new avenues for research and insights into transparency in AI.","url":"https://doi.org/10.1145/3632753","authors":["Henrik Palmer Olsen","Thomas Hildebrandt","Cornelius Wiesener","Matthias Smed Larsen","Asbjørn Ammitzbøll Flügge"],"tags":["Transparency (behavior)","Freedom of information","Corporate governance","Public information","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-20","doi":"https://doi.org/10.1145/3632753","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400037209","name":"Optimizing Nursing Productivity: Exploring the Role of Artificial Intelligence, Technology Integration, Competencies, and Leadership","source":"openalex","abstract":"Background: In the rapidly evolving healthcare management landscape, technology integration and artificial intelligence utilization play pivotal roles in shaping employee productivity. This research investigates these dynamics within Riyadh Province, Kingdom of Saudi Arabia, focusing on the relationships between technology integration, the use of artificial intelligence in nursing profession, nursing workforce competencies, technological leadership, and employee productivity. Methods: A quantitative approach was employed, involving 329 nurses from five hospitals in Riyadh Province. Partial Least Squares Structural Equation Modeling facilitated comprehensive analysis of direct and indirect relationships among variables. Results: Findings reveal that technology integration significantly enhances nursing productivity, while the use of artificial intelligence initially presents disruptions before yielding productivity gains. Nursing workforce competencies mediate these relationships, emphasizing the critical role of workforce readiness in harnessing technology's benefits. Surprisingly, technological leadership did not significantly moderate these effects. Conclusions: This research offers vital insights for healthcare organizations, advocating strategic technology integration and workforce development. It underscores the significance of nursing competencies in navigating technological transformations and affirms the enduring importance of leadership in guiding these changes. As healthcare evolves, these findings provide guidance for optimizing technology and artificial intelligence to enhance employee productivity and patient care.","url":"https://doi.org/10.1155/2024/8371068","authors":["Atallah Alenezi","Mohammed Hamdan Alshammari","Ibrahim Abdullatif Ibrahim"],"tags":["Productivity","Workforce","Knowledge management","Health care","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1155/2024/8371068","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4396581755","name":"Deep Learning Approaches for Medical Image Analysis and Diagnosis","source":"openalex","abstract":"In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way for seamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefit from deep learning models.","url":"https://doi.org/10.7759/cureus.59507","authors":["Gopal Kumar Thakur","Abhishek Thakur","Shridhar Kulkarni","Naseebia Khan","Shahnawaz Khan"],"tags":["Artificial intelligence","Computer science","Deep learning","Image (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-02","doi":"https://doi.org/10.7759/cureus.59507","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403078377","name":"Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature","source":"openalex","abstract":"Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.","url":"https://doi.org/10.1007/s11604-024-01668-3","authors":["Yusuke Matsui","Daiju Ueda","Shohei Fujita","Yasutaka Fushimi","Takahiro Tsuboyama","Koji Kamagata","Rintaro Ito","Masahiro Yanagawa","Akira Yamada","Mariko Kawamura","Takeshi Nakaura","Noriyuki Fujima","Taiki Nozaki","Fuminari Tatsugami","Tomoyuki Fujioka","Kenji Hirata","Shinji Naganawa"],"tags":["Medicine","Medical physics","Applications of artificial intelligence","Radiation oncology","Radiomics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-02","doi":"https://doi.org/10.1007/s11604-024-01668-3","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4398172379","name":"Artificial Intelligence and Deep Learning in Sensors and Applications","source":"openalex","abstract":"To effectively solve the increasingly complex problems experienced by human beings, the latest development trend is to apply a large number of different types of sensors to collect data in order to establish effective solutions based on deep learning and artificial intelligence [...].","url":"https://doi.org/10.3390/s24103258","authors":["Shyan‐Ming Yuan","Zeng‐Wei Hong","Wai Khuen Cheng"],"tags":["Deep learning","Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-20","doi":"https://doi.org/10.3390/s24103258","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4404228387","name":"Representation of intensivists’ race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models","source":"openalex","abstract":"BACKGROUND: Integrating artificial intelligence (AI) into intensive care practices can enhance patient care by providing real-time predictions and aiding clinical decisions. However, biases in AI models can undermine diversity, equity, and inclusion (DEI) efforts, particularly in visual representations of healthcare professionals. This work aims to examine the demographic representation of two AI text-to-image models, Midjourney and ChatGPT DALL-E 2, and assess their accuracy in depicting the demographic characteristics of intensivists. METHODS: This cross-sectional study, conducted from May to July 2024, used demographic data from the USA workforce report (2022) and intensive care trainees (2021) to compare real-world intensivist demographics with images generated by two AI models, Midjourney v6.0 and ChatGPT 4.0 DALL-E 2. A total of 1,400 images were generated across ICU subspecialties, with outcomes being the comparison of sex, race/ethnicity, and age representation in AI-generated images to the actual workforce demographics. RESULTS: The AI models demonstrated noticeable biases when compared to the actual U.S. intensive care workforce data, notably overrepresenting White and young doctors. ChatGPT-DALL-E2 produced less female (17.3% vs 32.2%, p < 0.0001), more White (61% vs 55.1%, p = 0.002) and younger (53.3% vs 23.9%, p < 0.001) individuals. While Midjourney depicted more female (47.6% vs 32.2%, p < 0.001), more White (60.9% vs 55.1%, p = 0.003) and younger intensivist (49.3% vs 23.9%, p < 0.001). Substantial differences between the specialties within both models were observed. Finally when compared together, both models showed significant differences in the Portrayal of intensivists. CONCLUSIONS: Significant biases in AI images of intensivists generated by ChatGPT DALL-E 2 and Midjourney reflect broader cultural issues, potentially perpetuating stereotypes of healthcare worker within the society. This study highlights the need for an approach that ensures fairness, accountability, transparency, and ethics in AI applications for healthcare.","url":"https://doi.org/10.1186/s13054-024-05134-4","authors":["Mia Gisselbaek","Mélanie Suppan","Laurens Minsart","Ekin Köselerli","Sheila Nainan Myatra","Idit Matot","Odmara L. Barreto Chang","Sarah Saxena","Joana Berger‐Estilita"],"tags":["Medicine","Cross-sectional study","Ethnic group","Race (biology)","Representation (politics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-11","doi":"https://doi.org/10.1186/s13054-024-05134-4","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4402500282","name":"Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.","url":"https://doi.org/10.12771/emj.2024.e49","authors":["C. Jeong","Y. M. Goh","Jungwon Kwak"],"tags":["Radiation oncology","Standardization","Medical physics","Interoperability","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-12","doi":"https://doi.org/10.12771/emj.2024.e49","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4391075605","name":"Artificial Intelligence (AI) in academic research. A multi-group analysis of students’ awareness and perceptions using gender and programme type","source":"openalex","abstract":"The era of AI has brought tremendous impact in academic research, and this has provided the impetus for students to leverage on novel tools in carrying out a lot of quality research works. Previous studies have relied so much on AI for instruction, classroom management and assessment and utilisation of AI tools for research has scarcely been examined. This study covered the gap by examining students’ utilisation of AI tools based on their level of awareness and perception and finding out the difference based on gender and programme type in such prediction. A total of 5554 university students were used for the study. Exploratory factor analysis was first carried for dimensionality and other validity checks (convergent and discriminant) using Average Variance Extracted (AVE) and Fornel-Larcker criterion and methods. Population t-tests and multi-group analyses were performed using SPSS and Smart PLS 3. The study found that students have high level of awareness and positive perception of AI tools in research. Similarly, the level of utilisation of AI tools in research is high. Male and postgraduate students have a higher level of awareness and positive perception of AI tools in research, with female students stronger than male students in terms. Perception and awareness directly impacted on utilisation but perception mediates positively and significantly in the nexus between awareness and utilisation. The study findings provide useful insights into using AI tools among university students and also identify the rationale to consider variables like gender and programme type when developing curriculum that will meet the current technology needs in our higher institutions.","url":"https://doi.org/10.37074/jalt.2024.7.1.9","authors":["Usani Ofem Joseph","Iyam Mary  Arikpo","Ovat Sylvia  Victor","Nwogwugwu  Chidirim","Anake Paulina  Mbua","Udeh Maryrose  Ify","Out Bernard  Diwa"],"tags":["Perception","Psychology","Exploratory factor analysis","Population","Curriculum"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-21","doi":"https://doi.org/10.37074/jalt.2024.7.1.9","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4404196273","name":"THE role of Artificial Intelligence in industry 5.0: Enhancing human-machine collaboration","source":"openalex","abstract":"The emergence of Industry 5.0 marks a transformative shift in the manufacturing landscape, emphasizing a synergistic relationship between humans and machines. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing human-machine collaboration within this paradigm. By leveraging AI technologies, industries can foster a more personalized, efficient, and innovative work environment. AI systems facilitate seamless communication and decision-making processes, thereby augmenting human capabilities rather than replacing them. This collaboration allows for the optimization of workflows, reduction of operational risks, and enhancement of product quality through advanced predictive analytics and real-time data processing. Furthermore, the integration of AI in Industry 5.0 supports sustainability initiatives by minimizing waste and energy consumption, aligning with the global push for greener manufacturing practices. Case studies demonstrate the successful implementation of AI-driven solutions across various sectors, showcasing improvements in productivity and employee satisfaction. As Industry 5.0 continues to evolve, the interplay between AI and human labour will redefine traditional roles, empowering workers with augmented intelligence tools. The findings indicate that embracing AI not only enhances operational efficiency but also contributes to a more resilient and adaptive workforce. Ultimately, this paper posits that the future of industry lies in the harmonious collaboration between human intellect and artificial intelligence, which together will drive innovation, productivity, and sustainable practices in the manufacturing sector.","url":"https://doi.org/10.30574/wjarr.2024.24.2.3369","authors":["Andrew Nii Anang","Peter Ofuje Obidi","Adeleye Oriola Mesogboriwon","James Opani Obidi","Maurice kuubata","Dabira Ogunbiyi"],"tags":["Artificial intelligence","Computer science","Knowledge management","Business","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-07","doi":"https://doi.org/10.30574/wjarr.2024.24.2.3369","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4400805135","name":"Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training","source":"openalex","abstract":"Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.","url":"https://doi.org/10.1038/s41598-024-67435-6","authors":["Peter Bogar","Mark Virag","Mátyás Bene","Péter Hardi","András Matuz","Ádám Tibor Schlégl","Luca Tóth","Ferenc Molnár","Bálint Nagy","Szilárd Rendeki","Krisztina Berner-Juhos","Andrea Ferencz","Krisztina Fischer","Péter Maróti"],"tags":["Virtual reality","Computer science","Trainer","Simulation","Fidelity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-19","doi":"https://doi.org/10.1038/s41598-024-67435-6","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4392981548","name":"Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence","source":"openalex","abstract":"Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a systematic and scientometric review of the nature and toxicity of pollutants generated by landfills and means of assessing their potential risks. Regarding human health, unregulated waste disposal and pathogens in leachate are the leading causes of diseases reported in local populations. Both in vitro and in vivo approaches have been employed in the ecotoxicological risk assessment of landfill leachate, with model organisms ranging from bacteria to birds. These studies demonstrate a wide range of toxic effects that reflect the complex composition of leachate and geographical variations in climate, resource availability and management practices. Based on bioassay (and other) evidence, categories of persistent chemicals of most concern include brominated flame retardants, per- and polyfluorinated chemicals, pharmaceuticals and alkyl phenol ethoxylates. However, the emerging and more general literature on microplastic toxicity suggests that these particles might also be problematic in leachate. Various mitigation strategies have been identified, with most focussing on improving landfill design or leachate treatment, developing alternative disposal methods and reducing waste volume through recycling or using more sustainable materials. The success of these efforts will rely on policies and practices and their enforcement, which is seen as a particular challenge in developing nations and at the international (and transboundary) level. Artificial intelligence and machine learning afford a wide range of options for evaluating and reducing the risks associated with leachates and gaseous emissions from landfills, and various approaches tested or having potential are discussed. However, addressing the limitations in data collection, model accuracy, real-time monitoring and our understanding of environmental impacts will be critical for realising this potential.","url":"https://doi.org/10.1016/j.scitotenv.2024.171804","authors":["Krishna Gautam","Namrata Pandey","Dhvani Yadav","Ramakrishnan Parthasarathi","Andrew A. Turner","Sadasivam Anbumani","Awadhesh N. Jha"],"tags":["Leachate","Environmental science","Pollutant","Hazardous waste","Waste management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-20","doi":"https://doi.org/10.1016/j.scitotenv.2024.171804","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4395070146","name":"eXplainable Artificial Intelligence (XAI) for improving organisational regility","source":"openalex","abstract":"Since the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.","url":"https://doi.org/10.1371/journal.pone.0301429","authors":["Niusha Shafiabady","Nick Hadjinicolaou","Nadeesha D. Hettikankanamage","Ehsan MohammadiSavadkoohi","Mingxuan Wu","James Vakilian"],"tags":["Resilience (materials science)","Enabling","Profitability index","Software deployment","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-24","doi":"https://doi.org/10.1371/journal.pone.0301429","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4403002502","name":"Perspectives on Intelligence in Soft Robotics","source":"openalex","abstract":"Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real‐world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.","url":"https://doi.org/10.1002/aisy.202400294","authors":["Vera G. Kortman","Barbara Mazzolai","Aimée Sakes","Jovana Jovanova"],"tags":["Soft robotics","Artificial intelligence","Robotics","Computer science","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-29","doi":"https://doi.org/10.1002/aisy.202400294","addedAt":"2026-09-01T01:47:49.982Z","updatedAt":"2026-09-01T01:47:49.982Z"},{"id":"oa:W4415250556","name":"Artificial Intelligence in the Management of Infectious Diseases in Older Adults: Diagnostic, Prognostic, and Therapeutic Applications","source":"openalex","abstract":"Background: Older adults are highly vulnerable to infectious diseases due to immunosenescence, multimorbidity, and atypical presentations. Artificial intelligence (AI) offers promising opportunities to improve diagnosis, prognosis, treatment, and continuity of care in this population. This review summarizes current applications of AI in the management of infections in older adults across diagnostic, prognostic, therapeutic, and preventive domains. Methods: We conducted a narrative review of peer-reviewed studies retrieved from PubMed, Scopus, and Web of Science, focusing on AI-based tools for infection diagnosis, risk prediction, antimicrobial stewardship, prevention of healthcare-associated infections, and post-discharge care in individuals aged ≥65 years. Results: AI models, including machine learning, deep learning, and natural language processing techniques, have demonstrated high performance in detecting infections such as sepsis, pneumonia, and healthcare-associated infections (Area Under the Curve AUC up to 0.98). Prognostic algorithms integrating frailty and functional status enhance the prediction of mortality, complications, and readmission. AI-driven clinical decision support systems contribute to optimized antimicrobial therapy and timely interventions, while remote monitoring and telemedicine applications support safer hospital-to-home transitions and reduced 30-day readmissions. However, the implementation of these technologies is limited by the underrepresentation of frail older adults in training datasets, lack of real-world validation in geriatric settings, and the insufficient explainability of many models. Additional barriers include system interoperability issues and variable digital infrastructure, particularly in long-term care and community settings. Conclusions: AI has strong potential to support predictive and personalized infection management in older adults. Future research should focus on developing geriatric-specific, interpretable models, improving system integration, and fostering interdisciplinary collaboration to ensure safe and equitable implementation.","url":"https://doi.org/10.3390/biomedicines13102525","authors":["Antonio Pinto","Flavia Pennisi","Stefano Odelli","Emanuele De Ponti","Nicola Veronese","Carlo Signorelli","Vincenzo Baldo","Vincenza Gianfredi"],"tags":["Medicine","Clinical decision support system","Interoperability","Telemedicine","SAFER"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-16","doi":"https://doi.org/10.3390/biomedicines13102525","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410915363","name":"A general framework for governing marketed AI/ML medical devices","source":"openalex","abstract":"This project represents the first systematic assessment of the US Food and Drug Administration's postmarket surveillance of legally marketed artificial intelligence and machine learning based medical devices. We focus on the Manufacturer and User Facility Device Experience database-the FDA's central tool for tracking the safety of marketed AI/ML devices. In particular, we evaluate the data pertaining to adverse events associated with approximately 950 medical devices incorporating AI/ML functions for devices approved between 2010 through 2023, and we find that the existing system is insufficient for properly assessing the safety and effectiveness of AI/ML devices. In particular, we make three contributions: (1) characterize the adverse event reports for such devices, (2) examine the ways in which the existing FDA adverse reporting system for medical devices falls short, and (3) suggest changes FDA might consider in its approach to adverse event reporting for devices incorporating AI/ML functions.","url":"https://doi.org/10.1038/s41746-025-01717-9","authors":["Boris Babic","I. Glenn Cohen","Ariel Dora Stern","Yiwen Li","Melissa Ouellet"],"tags":["Food and drug administration","Adverse Event Reporting System","Adverse effect","Event (particle physics)","Patient safety"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-31","doi":"https://doi.org/10.1038/s41746-025-01717-9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4412097424","name":"Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside","source":"openalex","abstract":"PURPOSE OF REVIEW: The integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promise in expanding access to cardiovascular imaging in resource-limited settings and enabling early disease detection through screening applications. This review explores the opportunities and challenges of AI-enabled POCUS as it reshapes the landscape of cardiovascular imaging. RECENT FINDINGS: AI-enabled systems can reduce operator dependency, improve image quality, and support clinicians-both novice and experienced-in capturing diagnostically valuable images, ultimately promoting consistency across diverse clinical environments. However, widespread adoption faces significant challenges, including concerns around algorithm generalizability, bias, explainability, clinician trust, and data privacy. Addressing these issues through standardized development, ethical oversight, and clinician-AI collaboration will be critical to safe and effective implementation. Looking ahead, emerging innovations-such as autonomous scanning, real-time predictive analytics, tele-ultrasound, and patient-performed imaging-underscore the transformative potential of AI-enabled POCUS in reshaping cardiovascular care and advancing equitable healthcare delivery worldwide.","url":"https://doi.org/10.1007/s11883-025-01316-9","authors":["Sasha-ann East","Yanting Wang","Naveena Yanamala","Kameswari Maganti","Partho P. Sengupta"],"tags":["Workflow","Medicine","Generalizability theory","Health care","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-07","doi":"https://doi.org/10.1007/s11883-025-01316-9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406240329","name":"Artificial Intelligence in Radiology: A Leadership Survey","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jacr.2025.01.006","authors":["Elizabeth S. Burnside","Thomas M. Grist","Michael Lasarev","John W. Garrett","Elizabeth A. Morris"],"tags":["Radiology","Computer science","Data science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-10","doi":"https://doi.org/10.1016/j.jacr.2025.01.006","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404780331","name":"Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications","source":"openalex","abstract":"Microfluidics has evolved into a transformative technology with far‐reaching applications in biomedical research. However, designing and optimizing custom microfluidic systems remains challenging because of their inherent complexities. Integrating artificial intelligence (AI) with microfluidics promises to overcome these barriers by leveraging AI algorithms to automate device design, streamline experimentation, and enhance diagnostic and therapeutic outcomes. Psoriasis is an incurable dermatological condition that is difficult to diagnose and treat owing to its complex pathogenesis. Traditional diagnostic and therapeutic approaches are often ineffective and fail to address individual variabilities in disease progression and treatment responses. However, AI‐coupled microfluidic platforms have the potential to revolutionize psoriasis research and clinical applications with expansive dermatological applications. AI‐driven microfluidic chips with embedded biosensors have the potential to precisely detect biomarkers (BMs), manipulate biological samples, and mimic psoriasis‐like in vivo and in vitro models, thereby allowing real‐time monitoring and optimized therapeutic testing. This review examines the transformative potential of AI and AI‐powered microfluidic platforms for advancing psoriasis research. It examines the design and mechanisms of AI‐coupled microfluidic platforms for cell screening, disease diagnosis, and drug delivery. It highlights recent advances, clinical applications, challenges, future perspectives, and ethical considerations to enhance personalized care and patient outcomes.","url":"https://doi.org/10.1002/aisy.202400558","authors":["Ibrahim Shaw","Yimer Seid Ali","Nie Changhong","Kexin Zhang","Chuanpin Chen","Yin Xiao"],"tags":["Psoriasis","Microfluidics","Computer science","Medicine","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.1002/aisy.202400558","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4390778841","name":"Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence–Based Risk Assessment Tool","source":"openalex","abstract":"Background: We have previously developed an artificial intelligence-based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs. Methods: machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index. Results: The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%-88.7%) and 65.6% specificity (95% CI, 65.4%-65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%-78.9%; specificity, 78.1%; 95% CI, 77.9%-78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%-78.9%; specificity, 71.9%; 95% CI, 71.7%-72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%-69.4%; specificity, 63.7%; 95% CI, 63.5%-63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3-14.8) times for HIV, 12.3 (95% CI, 11.4-13.3) for syphilis, 9.2 (95% CI, 8.8-9.6) for gonorrhea, and 3.9 (95% CI, 3.8-4.0) for chlamydia. Conclusions: , together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.","url":"https://doi.org/10.1093/ofid/ofae011","authors":["Phyu Mon Latt","Nyi Nyi Soe","Xianglong Xu","Jason J. Ong","Eric P. F. Chow","Christopher K. Fairley","Lei Zhang"],"tags":["Medicine","Gonorrhea","Chlamydia","Syphilis","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-05","doi":"https://doi.org/10.1093/ofid/ofae011","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409252703","name":"Systematic literature review on the application of explainable artificial intelligence in palliative care studies","source":"openalex","abstract":"BACKGROUND: As machine learning models become increasingly prevalent in palliative care, explainability has become a critical factor in their successful deployment in this sensitive field, where decisions can profoundly impact patient health and quality of life. To address these concerns, Explainable AI (XAI) aims to make complex AI models more understandable and trustworthy. OBJECTIVE: This study aims to assess the current state of machine learning models in palliative care, specifically focusing on their compliance with the principles of XAI. METHODS: A comprehensive literature search in four databases was conducted to identify articles on machine learning in palliative care studies published until May 2024, followed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. The Checklist for Assessment of Medical Artificial Intelligence was used to evaluate the quality of the studies. RESULTS: Mortality and survival prediction were the primary focus areas in 15 (54%) of the included 28 studies. Regarding data explainability, 20 studies (71%) documented their data preprocessing methods. However, a notable concern is that 45% of the studies did not address handling missing data. Across these studies, 74 machine learning algorithms were employed. Complex models, including Random Forest, Support Vector Machines, Gradient Boosting Machines, and Deep Neural Networks, were predominantly used (64%) due to their high predictive power, achieving AUC values between 0.82 and 0.96. Post-hoc explanation techniques were applied in only 11 studies, using seven different XAI techniques, focusing on global explanations to enhance understanding of model behavior. CONCLUSION: Given the critical role of AI-driven decisions in patient care, adopting XAI techniques is essential for fostering trust and usability. Although progress has been made, significant gaps persist. A main challenge remains the trade-off between model performance and interpretability, as highly accurate models often lack the transparency required to build trust in clinical settings. Additionally, complex models frequently provide inadequate explanations for their outputs, lack consistent documentation, and have limited XAI applications, reducing the interpretability of machine learning studies for clinicians and decision-makers.","url":"https://doi.org/10.1016/j.ijmedinf.2025.105914","authors":["Battushig Migiddorj","Marijka Batterham","Khin Than Win"],"tags":["Palliative care","Computer science","Systematic review","MEDLINE","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-08","doi":"https://doi.org/10.1016/j.ijmedinf.2025.105914","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409339512","name":"Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review","source":"openalex","abstract":"Abstract One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61·5 % were conducted in preclinical settings. Likewise, 46·2 % used AI techniques based on deep learning and 15·3 % on machine learning. Correlation coefficients of over 0·7 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0·7 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61·5 % ( n 8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.","url":"https://doi.org/10.1017/s0007114525000522","authors":["Sebastián Cofre","Camila Sánchez","Gladys Quezada-Figueroa","Xaviera A. López-Cortés"],"tags":["Scopus","Correlation","Nutritional epidemiology","Calorie","Confounding"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-10","doi":"https://doi.org/10.1017/s0007114525000522","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4402329233","name":"Integrating Artificial Intelligence in Dental Education: An Urgent Call for Dedicated Postgraduate Programs","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.identj.2024.08.008","authors":["Mahmood Dashti","Shohreh Ghasemi","Zohaib Khurshid"],"tags":["Dental education","Medical education","Computer science","Medicine","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-07","doi":"https://doi.org/10.1016/j.identj.2024.08.008","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409157497","name":"Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness","source":"openalex","abstract":"Large Language Models (LLMs) hold promise for medical applications but often lack domain-specific expertise. Retrieval Augmented Generation (RAG) enables customization by integrating specialized knowledge. This study assessed the accuracy, consistency, and safety of LLM-RAG models in determining surgical fitness and delivering preoperative instructions using 35 local and 23 international guidelines. Ten LLMs (e.g., GPT3.5, GPT4, GPT4o, Gemini, Llama2, and Llama3, Claude) were tested across 14 clinical scenarios. A total of 3234 responses were generated and compared to 448 human-generated answers. The GPT4 LLM-RAG model with international guidelines generated answers within 20 s and achieved the highest accuracy, which was significantly better than human-generated responses (96.4% vs. 86.6%, p = 0.016). Additionally, the model exhibited an absence of hallucinations and produced more consistent output than humans. This study underscores the potential of GPT-4-based LLM-RAG models to deliver highly accurate, efficient, and consistent preoperative assessments.","url":"https://doi.org/10.1038/s41746-025-01519-z","authors":["Yu He Ke","Liyuan Jin","Kabilan Elangovan","Hairil Rizal Abdullah","Nan Liu","Alex Tiong Heng Sia","Chai Rick Soh","Joshua Yi Min Tung","Jasmine Chiat Ling Ong","Chang‐Fu Kuo","Shaochun Wu","Vesela Kovacheva","Daniel Shu Wei Ting"],"tags":["Generalizability theory","Computer science","Natural language processing","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-05","doi":"https://doi.org/10.1038/s41746-025-01519-z","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4407183498","name":"Artificial intelligence–based clinical decision support in the emergency department: A scoping review","source":"openalex","abstract":"OBJECTIVE: Artificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly understood. We asked: (1) What is the current landscape of AI-CDS for individual patient care in the ED? and (2) What phases of development have AI-CDS tools achieved? METHODS: We performed a scoping review of AI for prognostic, diagnostic, and treatment decisions regarding individual ED patient care. We searched five databases (MEDLINE, EMBASE, Cochrane Central, Scopus, Web of Science) and gray literature sources from January 1, 2010, to December 11, 2023. We adhered to guidelines from the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews. We published our protocol on Open Science Framework (DOI 10.17605/OSF.IO/FDZ3Y). RESULTS: Of 5168 unique records identified, we selected 605 studies for inclusion. The majority (369, 61%) were published in 2021-2023. The studies ranged over a variety of clinical applications, patient populations, and AI model types. Prognostic outcomes were most commonly assessed (270, 44.6%), followed by diagnostic (193, 31.9%) and disposition (115, 19%). Most studies remained in the earliest phase of preclinical development (572, 94.5%) with few advancing to later phases (33, 5.5%). CONCLUSIONS: By thoroughly mapping the landscape of AI-CDS in the ED, we demonstrate a rapidly increasing volume of studies covering a breadth of clinical applications, yet few have achieved advanced phases of testing or implementation. A more granular understanding of the barriers and facilitators to implementing AI-CDS in the ED is needed.","url":"https://doi.org/10.1111/acem.15099","authors":["Hashim Kareemi","Krishan Yadav","Courtney Price","Niklas Bobrovitz","Andrew Meehan","Henry Li","Gautam Goel","Sameer Masood","Lars Grant","Maxim Ben‐Yakov","Wojtek Michalowski","Christian Vaillancourt"],"tags":["Medicine","Emergency department","MEDLINE","Clinical decision support system","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-04","doi":"https://doi.org/10.1111/acem.15099","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406040531","name":"Knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence: a cross-sectional study","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) applications have increased dramatically across a wide range of domains. Dental students will undoubtedly be impacted by the emergence of AI in dentistry. AIM: This study aimed to evaluate the knowledge, attitudes, and perceptions of a group of Egyptian dental students toward artificial intelligence. MATERIALS AND METHOD: An online survey was conducted using a questionnaire sent to dental students via Google Forms. The questionnaire comprised 18 questions on participant's knowledge and perceptions regarding the future of AI in dentistry. The collected data was statistically analyzed. RESULTS: A total of 384 students answered the questionnaire. Of the participants, (49%) had a basic knowledge of the principles of AI, and (48%) participants were aware of AI usage in dentistry. Social media was the most common information source for AI applications. Most of the participants agreed on the leading role of AI in the advancement of dentistry and disagreed on the ability of AI to replace dentists in the future, (53%) and (44%) respectively. Moreover, (49%) and (52%) respectively of students approved the incorporation of AI applications in undergraduate and postgraduate dental training. CONCLUSION: Egyptian dental students are acquainted with AI and its possible applications in dentistry. They consider the use of AI diagnosis exciting and approve of its definitive role in disease prediction. There is a necessity to include, enhance, and increase AI training in dental schools. TRIAL REGISTRATION: This study has been registered in clinical trials. gov with an identifier: NCT06348758.","url":"https://doi.org/10.1186/s12903-024-05282-7","authors":["Marwa Aly Elchaghaby","Reem Wahby"],"tags":["Medicine","Dentistry","Oral and maxillofacial surgery","Medical education","Perception"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1186/s12903-024-05282-7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4391377725","name":"Use of generative artificial intelligence in medical research","source":"openalex","abstract":"Policies must be standardised to ensure accountability and maintain public trust","url":"https://doi.org/10.1136/bmj.q119","authors":["Nazrul Islam","Mihaela van der Schaar"],"tags":["Computer science","Generative grammar","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-31","doi":"https://doi.org/10.1136/bmj.q119","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4410043903","name":"The mediating digital literacy and the moderating role of academic support in the relationship between artificial intelligence usage and creative thinking in nursing students","source":"openalex","abstract":"Artificial intelligence can be an important tool in developing creative thinking while supporting individualized learning and problem-solving skills in educational processes. However, the effect of artificial intelligence on creative thinking may differ depending on factors such as students’ digital literacy level and the academic support they receive. In this context, how artificial intelligence can encourage creative thinking in educational processes and how this process is shaped by digital literacy and academic support stands out as an issue to be investigated. This study artificial intelligence to determine the effect of artificial intelligence use on creative thinking skills in nursing students, the mediating role of digital literacy in this effect, and the situational mediating effect of academic support. This cross-sectional and descriptive correlational study was conducted with 426 nursing students from three universities in different regions of Turkey during the fall semester of 2024–2025. Simple random sampling method was used. Data were collected through demographic information form and four valid and reliable scales. Research findings show that using artificial intelligence substantially and significantly affects Creative Thinking (β = 0.70, p < 0.001). Digital literacy plays mediating role in this relationship, indirectly strengthening the effect of artificial intelligence use on creative thinking (β = 0.422, LLCI = 0.357, ULCI = 0.490). As a moderating factor of this relationship, the academic support creates a more limited effect at low levels of academic support (Effect = 0.224). At the same time, it maximizes this effect at high levels of support (Effect = 0.298). The effect of artificial intelligence use on Creative Thinking is significantly strengthened by the mediating role of digital literacy and the moderating role of academic support. These findings suggest that developing digital skills and strengthening academic support mechanisms are critical to integrating artificial intelligence technologies into educational processes. Artificial intelligence significantly increases the creative thinking tendencies of nursing students both directly and through digital literacy. The academic support is a critical factor that strengthens the development of creative thinking skills by regulating the effect of artificial intelligence usage on digital literacy. Integrating artificial intelligence applications into the curriculum and developing digital literacy skills in nursing education programs can potentially increase the creative thinking and problem-solving competencies of 21st-century health professionals.","url":"https://doi.org/10.1186/s12912-025-03128-3","authors":["Ferhat Onur Ağaoğlu","Murat Baş","Sinan Tarsuslu","Lokman Onur Ekinci","Nihat Buğra Ağaoğlu"],"tags":["Nursing management","Nursing research","Medicine","Health informatics","Pain medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.1186/s12912-025-03128-3","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400371216","name":"Assessing the Readability of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) is a burgeoning new field that has increased in popularity over the past couple of years, coinciding with the public release of large language model (LLM)-driven chatbots. These chatbots, such as ChatGPT, can be engaged directly in conversation, allowing users to ask them questions or issue other commands. Since LLMs are trained on large amounts of text data, they can also answer questions reliably and factually, an ability that has allowed them to serve as a source for medical inquiries. This study seeks to assess the readability of patient education materials on cardiac catheterization across four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. METHODOLOGY: A set of 10 questions regarding cardiac catheterization was developed using website-based patient education materials on the topic. We then asked these questions in consecutive order to four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. The Flesch Reading Ease Score (FRES) was used to assess the readability score. Readability grade levels were assessed using six tools: Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. RESULTS: The mean FRES across all four chatbots was 40.2, while overall mean grade levels for the four chatbots were 11.2, 13.7, 13.7, 13.3, 11.2, and 11.6 across the FKGL, GFI, CLI, SMOG, ARI, and FORCAST indices, respectively. Mean reading grade levels across the six tools were 14.8 for ChatGPT, 12.3 for Microsoft Copilot, 13.1 for Google Gemini, and 9.6 for Meta AI. Further, FRES values for the four chatbots were 31, 35.8, 36.4, and 57.7, respectively. CONCLUSIONS: -grade level, depending on the tool used. This means that the materials were at the high school and even college reading level, which far exceeds the recommended sixth-grade level for patient education materials. Further, there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.","url":"https://doi.org/10.7759/cureus.63865","authors":["Benjamin J. Behers","Ian Vargas","Brett M. Behers","Manuel A Rosario","Caroline N Wojtas","Alexander C. Deevers","Karen Hamad"],"tags":["Medicine","Observational study","Cross-sectional study","Readability","Cardiac catheterization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-04","doi":"https://doi.org/10.7759/cureus.63865","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404435192","name":"The contagion effect of artificial intelligence across innovative industries: From blockchain and metaverse to cleantech and beyond","source":"openalex","abstract":"Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency , blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.","url":"https://doi.org/10.1016/j.techfore.2024.123822","authors":["Muhammad Abubakr Naeem","Nadia Arfaoui","Larisa Yarovaya"],"tags":["Blockchain","Metaverse","Computer science","Economics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-16","doi":"https://doi.org/10.1016/j.techfore.2024.123822","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410632614","name":"Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives","source":"openalex","abstract":"This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'","url":"https://doi.org/10.1007/s42452-025-06991-6","authors":["Nouman Ali","Nimra Hanif","Hassan Abbas Khan","Muhammad Waseem","Afshan Saeed","Sadia Zakir","Abeeha Khan","Mejerrah Aamir","Adeeba Ali","Aamir Ali","A. Saleem"],"tags":["Drug discovery","Deep learning","Artificial intelligence","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.1007/s42452-025-06991-6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410444203","name":"Performance of the Large Language Models in African rheumatology: a diagnostic test accuracy study of ChatGPT-4, Gemini, Copilot, and Claude artificial intelligence","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) tools, particularly Large Language Models (LLMs), are revolutionizing medical practice, including rheumatology. However, their diagnostic capabilities remain underexplored in the African context. To assess the diagnostic accuracy of ChatGPT-4, Gemini, Copilot, and Claude AI in rheumatology within an African population. METHODS: This was a cross-sectional analytical study with retrospective data collection, conducted at the Rheumatology Department of Bogodogo University Hospital Center (Burkina Faso) from January 1 to June 30, 2024. Standardized clinical and paraclinical data from 103 patients were submitted to the four AI models. The diagnoses proposed by the AIs were compared to expert-confirmed diagnoses established by a panel of senior rheumatologists. Diagnostic accuracy, sensitivity, specificity, and predictive values were calculated for each AI model. RESULTS: Among the patients enrolled in the study period, infectious diseases constituted the most common diagnostic category, representing 47.57% (n = 49). ChatGPT-4 achieved the highest diagnostic accuracy (86.41%), followed by Claude AI (85.44%), Copilot (75.73%), and Gemini (71.84%). The inter-model agreement was moderate, with Cohen's kappa coefficients ranging from 0.43 to 0.59. ChatGPT-4 and Claude AI demonstrated high sensitivity (> 90%) for most conditions but had lower performance for neoplastic diseases (sensitivity < 67%). Patients under 50 years old had a significantly higher probability of receiving a correct diagnosis with Copilot (OR = 3.36; 95% CI [1.16-9.71]; p = 0.025). CONCLUSION: LLMs, particularly ChatGPT-4 and Claude AI, show high diagnostic capabilities in rheumatology, despite some limitations in specific disease categories. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://doi.org/10.1186/s41927-025-00512-z","authors":["Yannick Laurent Tchenadoyo Bayala","Wendlassida Joëlle Stéphanie Zabsonré Tiendrébeogo","Dieu‐Donné Ouedraogo","Fulgence Kaboré","Charles Sougué","Rélwendé Aristide Yaméogo","Wendlassida Martin Nacanabo","Ismaël Ayouba Tinni","Aboubakar Ouédraogo","Enselme Zongo"],"tags":["Test (biology)","Artificial intelligence","Psychology","Computer science","Aeronautics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.1186/s41927-025-00512-z","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407363677","name":"Ethical implications of artificial intelligence integration in nursing practice in arab countries: literature review","source":"openalex","abstract":"BACKGROUND: Applying artificial intelligence (AI) to nursing practice has dramatically enhanced healthcare delivery in Arab countries. However, AI application also raises complex moral issues, including patient privacy, data security, responsibility, transparency, and equity in decision-making. AIM: A systematic analysis of the ethical issues surrounding the application of AI in nursing practice in Arab nations is carried out in this review, highlighting the most important ethical issues and recommending responsible AI integration. METHODS: A comprehensive literature search was conducted across major databases. Following the initial identification of 150 articles, 120 were selected for full-text review based on the title and abstract screening. Subsequently, 50 pertinent studies were incorporated into this review. RESULTS: Numerous significant ethical concerns regarding AI application in decision-making processes were identified. The assessment also highlighted the possible effects of AI on the nurse-patient interaction and the critical role played by the ethics committees and regulatory frameworks in resolving these issues. CONCLUSION: Ethical frameworks must be established to guarantee AI integration into nursing practice, safeguard patients' welfare, and strengthen the trust between healthcare providers and patients. CLINICAL TRIAL: No clinical Trial.","url":"https://doi.org/10.1186/s12912-025-02798-3","authors":["Ateya Megahed Ibrahim","Mohamed Ali Zoromba","Ali D. Abousoliman","Donia Elsaid Fathi Zaghamir","Ibrahim Naif Alenezi","Ebtesam A. Elsayed","Heba Ali Hamed Mohamed"],"tags":["Nursing research","Nursing management","Medicine","Nursing practice","Nursing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-11","doi":"https://doi.org/10.1186/s12912-025-02798-3","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406149781","name":"A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH)","source":"openalex","abstract":"Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.","url":"https://doi.org/10.3390/medicina61010085","authors":["Sogol Attaripour Esfahani","Nima Baba Ali","Juan Farina","Isabel G. Scalia","Milagros Pereyra","Mohammed Tiseer Abbas","Niloofar Javadi","Nadera N. Bismee","Fatmaelzahraa E. Abdelfattah","Kamal Awad","Omar Ibrahim","Hesham Sheashaa","Timothy Barry","Robert L. Scott","Chadi Ayoub","Reza Arsanjani"],"tags":["Artificial intelligence","Machine learning","Computer science","Risk stratification","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-07","doi":"https://doi.org/10.3390/medicina61010085","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410863629","name":"Promoting Critical Thinking in Biological Sciences in the Era of Artificial Intelligence: The Role of Higher Education","source":"openalex","abstract":"The integration of artificial intelligence (AI) into the biological sciences marks a transformative era, reshaping research methodologies, data analysis, and hypothesis generation. This technological advancement accelerates discoveries and enhances our understanding of complex biological systems. As AI increasingly influences decision-making processes, the necessity for students and scientists to critically assess AI-generated outputs becomes paramount. The current narrative review explores the evolving role of critical thinking in biological sciences amidst the rise of AI, emphasizing the importance of skepticism, contextual understanding, and ethical considerations. It argues that while AI provides powerful tools for data interpretation and pattern recognition, human oversight and critical analysis remain indispensable to validate findings and prevent biases inherent in automated systems. Higher education institutions play a crucial role in fostering a culture of critical thinking, equipping biological scientists to effectively harness AI technologies while ensuring the integrity of their research and upholding scientific and ethical standards. Furthermore, AI tools, including chatbots, could be strategically employed in active learning methodologies, such as problem-based learning, flipped classrooms, and online learning. These methodologies enhance the ability of students to effectively utilize AI technologies while ensuring the rigor of scientific research. In conclusion, the current review underscores the benefits, challenges, and educational implications of AI integration, offering actionable insights for educators and learners seeking to adapt effectively to this rapidly evolving technological landscape.","url":"https://doi.org/10.3390/higheredu4020024","authors":["Christos Papaneophytou","Stella A. Nicolaou"],"tags":["Critical thinking","Engineering ethics","Biological sciences","Psychology","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-29","doi":"https://doi.org/10.3390/higheredu4020024","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4402378909","name":"An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI","source":"openalex","abstract":"BACKGROUND AND OBJECTIVES: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). METHODS: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. RESULTS: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81-0.92 and intraclass correlation coefficients ranging 0.74-0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. CONCLUSIONS: Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.","url":"https://doi.org/10.4070/kcj.2024.0060","authors":["Yeonggul Jang","Hyejung Choi","Yeonyee E. Yoon","Jaeik Jeon","Hyejin Kim","Jiyeon Kim","Dawun Jeong","Seongmin Ha","Youngtaek Hong","Seung‐Ah Lee","Jiesuck Park","Wonsuk Choi","Hong‐Mi Choi","In‐Chang Hwang","Goo‐Yeong Cho","Hyuk‐Jae Chang"],"tags":["Medicine","Internal medicine","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.4070/kcj.2024.0060","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4386892967","name":"Evaluating the performance of ChatGPT-4 on the United Kingdom Medical Licensing Assessment","source":"openalex","abstract":"Introduction: Recent developments in artificial intelligence large language models (LLMs), such as ChatGPT, have allowed for the understanding and generation of human-like text. Studies have found LLMs abilities to perform well in various examinations including law, business and medicine. This study aims to evaluate the performance of ChatGPT in the United Kingdom Medical Licensing Assessment (UKMLA). Methods: Two publicly available UKMLA papers consisting of 200 single-best-answer (SBA) questions were screened. Nine SBAs were omitted as they contained images that were not suitable for input. Each question was assigned a specialty based on the UKMLA content map published by the General Medical Council. A total of 191 SBAs were inputted in ChatGPT-4 through three attempts over the course of 3 weeks (once per week). Results: ChatGPT scored 74.9% (143/191), 78.0% (149/191) and 75.6% (145/191) on three attempts, respectively. The average of all three attempts was 76.3% (437/573) with a 95% confidence interval of (74.46% and 78.08%). ChatGPT answered 129 SBAs correctly and 32 SBAs incorrectly on all three attempts. On three attempts, ChatGPT performed well in mental health (8/9 SBAs), cancer (11/14 SBAs) and cardiovascular (10/13 SBAs). On three attempts, ChatGPT did not perform well in clinical haematology (3/7 SBAs), endocrine and metabolic (2/5 SBAs) and gastrointestinal including liver (3/10 SBAs). Regarding to response consistency, ChatGPT provided correct answers consistently in 67.5% (129/191) of SBAs but provided incorrect answers consistently in 12.6% (24/191) and inconsistent response in 19.9% (38/191) of SBAs, respectively. Discussion and conclusion: This study suggests ChatGPT performs well in the UKMLA. There may be a potential correlation between specialty performance. LLMs ability to correctly answer SBAs suggests that it could be utilised as a supplementary learning tool in medical education with appropriate medical educator supervision.","url":"https://doi.org/10.3389/fmed.2023.1240915","authors":["U Hin Lai","Keng Sam Wu","Ting-Yu Hsu","Jessie Kai Ching Kan"],"tags":["GNSS augmentation","Consistency (knowledge bases)","Psychology","Specialty","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-19","doi":"https://doi.org/10.3389/fmed.2023.1240915","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409286413","name":"Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology","source":"openalex","abstract":"BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical early symptoms, causing patients to miss optimal treatment opportunities. Traditional endoscopic and pathological diagnostic processes are highly dependent on expert experience, facing problems such as high misdiagnosis rates and significant inter-observer variations. With the development of artificial intelligence (AI) technologies such as deep learning, real-time lesion detection with endoscopic assistance and automated pathological image analysis have shown potential in improving diagnostic accuracy and efficiency. However, relevant applications still face challenges including insufficient data standardization, inadequate interpretability, and weak clinical validation. OBJECTIVE: This study aims to systematically review the current applications of artificial intelligence in diagnosing malignant digestive tract tumors, focusing on the progress and bottlenecks in two key areas: endoscopic examination and pathological diagnosis, and to provide feasible ideas and suggestions for subsequent research and clinical translation. METHODS: A systematic literature search strategy was adopted to screen relevant studies published between 2017 and 2024 from databases including PubMed, Web of Science, Scopus, and IEEE Xplore, supplemented with searches of early classical literature. Inclusion criteria included studies on malignant digestive tract tumors such as esophageal cancer, gastric cancer, or colorectal cancer, involving the application of artificial intelligence technology in endoscopic diagnosis or pathological analysis. The effects and main limitations of AI diagnosis were summarized through comprehensive analysis of research design, algorithmic methods, and experimental results from relevant literature. RESULTS: In the field of endoscopy, multiple deep learning models have significantly improved detection rates in real-time polyp detection, early gastric cancer, and esophageal cancer screening, with some commercialized systems successfully entering clinical trials. However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. In pathological analysis, using convolutional neural networks, multimodal pre-training models, etc., automatic tissue segmentation, tumor grading, and assisted diagnosis can be achieved, showing good scalability in interactive question-answering. Nevertheless, clinical implementation still faces obstacles such as non-uniform data standards, lack of large-scale prospective validation, and insufficient model interpretability and continuous learning mechanisms. CONCLUSION: Artificial intelligence provides new technological opportunities for endoscopic and pathological diagnosis of malignant digestive tract tumors, achieving positive results in early lesion identification and assisted decision-making. However, to achieve the transition from research to widespread clinical application, data standardization, model reliability, and interpretability still need to be improved through multi-center joint research, and a complete regulatory and ethical system needs to be established. In the future, artificial intelligence will play a more important role in the standardization and precision management of diagnosis and treatment of digestive tract tumors.","url":"https://doi.org/10.1186/s12967-025-06428-z","authors":["Yinhu Gao","Peizhen Wen","Yuan Liu","Yahuang Sun","Hui Qian","Xin Zhang","Huan Peng","Yanli Gao","Cuiyu Li","Zhangyuan Gu","Hua‐jin Zeng","Zhijun Hong","Weijun Wang","Ronglin Yan","Zunqi Hu","Hongbing Fu"],"tags":["Endoscopy","Digestive tract","Pathology","Medicine","Gastrointestinal pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.1186/s12967-025-06428-z","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4408550997","name":"Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications","source":"openalex","abstract":"Artificial intelligence (AI) has revolutionized the field of cardiovascular imaging, serving as a unifying force that brings together multiple modalities under a single platform. The utility of noninvasive imaging ranges from diagnostic assessment and guiding interventions to prognostic stratification. Multimodality imaging has demonstrated important potential, particularly in patients with heterogeneous diseases, such as heart failure and atrial fibrillation. Facilitating complex interventional procedures requires accurate image acquisition and interpretation along with precise decision-making. The unique nature of interventional cardiology procedures benefiting from different imaging modalities presents an ideal target for the development of AI-assisted decision-making tools to improve workflow in the catheterization laboratory and personalize the need for transcatheter interventions. This review explores the advancements of AI in noninvasive cardiovascular imaging and interventional cardiology, addressing the clinical use and challenges of current imaging modalities, emerging trends, and promising applications as well as considerations for safe implementation of AI tools in clinical practice. Current practice has moved well beyond the question of whether we should or should not use AI in clinical health care settings. AI, in all its forms, has become deeply embedded in clinical workflows, particularly in cardiovascular imaging and interventional cardiology. It can, in the future, not only add precision and quantification but also serve as a means by which to fuse and link multimodalities together. It is only by understanding how AI techniques work, that the field can be harnessed for the greater good and avoid uninformed bias or misleading diagnoses.","url":"https://doi.org/10.1016/j.jscai.2024.102558","authors":["Maryam Alsharqi","Elazer R. Edelman"],"tags":["Interventional cardiology","Medicine","Clinical cardiology","Cardiology","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1016/j.jscai.2024.102558","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W7135205874","name":"Artificial Intelligence Applications in Gastric Cancer Surgery: Bridging Early Diagnosis and Responsible Precision Medicine","source":"openalex","abstract":"Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.","url":"https://doi.org/10.3390/jcm15062208","authors":["Silvia Malerba","Miljana Vladimirov","Aman Goyal","Audrius Dulskas","Augustinas Baušys","Tomasz Cwalinski","Sergii Girnyi","Jaroslaw Skokowski","Ruslan Duka","Robert Molchanov","Bojan Jovanovic","Francesco Antonio Ciarleglio","Alberto Brolese","Kebebe Bekele Gonfa","Abdi Tesemma Demmo","Žilvinas Dambrauskas","Adolfo Pérez Bonet","M. Testini","Francesco Paolo Prete","Valentin Calu","Natale Calomino","Vikas Jain","Aleksandar Karamarković","Karol Połom","Adel Abou-Mrad","Rodolfo J. Oviedo","Yogesh K. Vashist","Luigi Marano"],"tags":["Medicine","Perioperative","Precision medicine","Artificial intelligence","Workflow"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-13","doi":"https://doi.org/10.3390/jcm15062208","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4402221781","name":"Understanding the skills gap between higher education and industry in the UK in artificial intelligence sector","source":"openalex","abstract":"As Artificial Intelligence (AI) changes how businesses work, there’s a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics sk\\ill categories.","url":"https://doi.org/10.1177/09504222241280441","authors":["Khushi Jaiswal","Ievgeniia Kuzminykh","Sanjay Modgil"],"tags":["Higher education","Business","Economics","Economic growth"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-03","doi":"https://doi.org/10.1177/09504222241280441","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409752444","name":"DeepSeek Deployed in 90 Chinese Tertiary Hospitals: How Artificial Intelligence Is Transforming Clinical Practice","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10916-025-02181-4","authors":["Jishizhan Chen","Chunying Miao"],"tags":["Health informatics","Computer science","Data science","Medicine","Nursing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-24","doi":"https://doi.org/10.1007/s10916-025-02181-4","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4390795059","name":"Criminal Justice and Artificial Intelligence: How Should we Assess the Performance of Sentencing Algorithms?","source":"openalex","abstract":"Abstract Artificial intelligence is increasingly permeating many types of high-stake societal decision-making such as the work at the criminal courts. Various types of algorithmic tools have already been introduced into sentencing. This article concerns the use of algorithms designed to deliver sentence recommendations. More precisely, it is considered how one should determine whether one type of sentencing algorithm (e.g., a model based on machine learning) would be ethically preferable to another type of sentencing algorithm (e.g., a model based on old-fashioned programming). Whether the implementation of sentencing algorithms is ethically desirable obviously depends upon various questions. For instance, some of the traditional issues that have received considerable attention are algorithmic biases and lack of transparency. However, the purpose of this article is to direct attention to a further challenge that has not yet been considered in the discussion of sentencing algorithms. That is, even if is assumed that the traditional challenges concerning biases, transparency, and cost-efficiency have all been solved or proven insubstantial, there will be a further serious challenge associated with the comparison of sentencing algorithms; namely, that we do not yet possess an ethically plausible and applicable criterion for assessing how well sentencing algorithms are performing.","url":"https://doi.org/10.1007/s13347-024-00694-3","authors":["Jesper Ryberg"],"tags":["Philosophy of technology","Criminal justice","Algorithm","Economic Justice","Criminology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-12","doi":"https://doi.org/10.1007/s13347-024-00694-3","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406444782","name":"Deep learning and generative artificial intelligence in aging research and healthy longevity medicine","source":"openalex","abstract":"With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.","url":"https://doi.org/10.18632/aging.206190","authors":["Dominika Wilczok"],"tags":["Longevity","Generative grammar","Human multitasking","Deep learning","Life span"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-16","doi":"https://doi.org/10.18632/aging.206190","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400882826","name":"Accelerated Chest Pain Treatment With Artificial Intelligence–Informed, Risk-Driven Triage","source":"openalex","abstract":"This quality improvement study evaluates the use of artificial intelligence to accelerate triage of patients presenting to the emergency department with chest pain.","url":"https://doi.org/10.1001/jamainternmed.2024.3219","authors":["Jeremiah S. Hinson","Richard A. Taylor","Arjun K. Venkatesh","B. Steinhart","Christopher Chmura","Rohit B. Sangal","Scott Levin"],"tags":["Medicine","Triage","Chest pain","Medical emergency","Emergency department"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-22","doi":"https://doi.org/10.1001/jamainternmed.2024.3219","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409473464","name":"Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review","source":"openalex","abstract":"Precision medicine presents challenges in data collection, cost, and privacy as it tailors treatments to each patient's unique genetic and clinical profile. With its ability to produce realistic and confidential patient data, generative artificial intelligence (AI) offers a promising avenue that could revolutionize patient-centric healthcare. This systematic review aims to assess the role of generative AI in personalized medicine. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched PubMed, Web of Science, Scopus, CINAHL, and Google Scholar, identifying 549 studies. After removing duplicates and applying eligibility criteria, 27 studies were found relevant and were included in this systematic review. Generative adversarial networks (GANs) were the most commonly used models (16 studies), followed by variational autoencoders (VAEs; seven studies). These models were primarily applied to drug response prediction, treatment effect estimation, biomarker discovery, and patient stratification. Generative AI models have shown significant promise in revolutionizing personalized medicine by enabling precise treatment predictions and patient-specific therapeutic insights. Despite their potential, challenges related to model validation, interpretability, and bias remain. Future research should prioritize large-scale validation studies using diverse datasets to enhance the clinical applicability and reliability of these AI-driven approaches.","url":"https://doi.org/10.7759/cureus.82310","authors":["Aashish Mishra","Anirban Majumder","Dheeraj Kommineni","Christopher Joseph","Tanay Chowdhury","Sathish Krishna Anumula"],"tags":["Medicine","Personalized medicine","Generative grammar","Artificial intelligence","Bioinformatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-15","doi":"https://doi.org/10.7759/cureus.82310","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404159957","name":"Optimization of diagnosis and treatment of hematological diseases via artificial intelligence","source":"openalex","abstract":"Background: Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more \"AI + medical\" application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice. Objective: This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment. Methods: Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords \"artificial intelligence\" and \"hematological diseases.\" We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field. Results: AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical-industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases. Conclusion: Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.","url":"https://doi.org/10.3389/fmed.2024.1487234","authors":["Shixuan Wang","Zoufang Huang","Jing Li","Yin Wu","Jun Du","Ting Li"],"tags":["Disease","Standardization","Artificial intelligence","Personalized medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-07","doi":"https://doi.org/10.3389/fmed.2024.1487234","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4396870875","name":"Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis","source":"openalex","abstract":"Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.","url":"https://doi.org/10.1177/13524585241249422","authors":["Sara Collorone","Llucia Coll","Marco Lorenzi","Xavier Lladó","Jaume Sastre‐Garriga","Mar Tintoré","Xavier Montalbán","Àlex Rovira","Deborah Pareto","Carmen Tur"],"tags":["Multiple sclerosis","Big data","Clinical Practice","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-13","doi":"https://doi.org/10.1177/13524585241249422","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406438340","name":"Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation","source":"openalex","abstract":"OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. BACKGROUND: AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI's impact on health equity. METHODS: We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital's service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas. RESULTS: Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = -0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1-Q3. In addition, Accountable Care Organization affiliation accounted for 12%-25% of differences in AI/ML utilization across various measures. CONCLUSIONS: The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration.","url":"https://doi.org/10.1097/mlr.0000000000002110","authors":["Jie Chen","Alice Shijia Yan"],"tags":["Workforce","Disadvantaged","Equity (law)","Health care","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-03","doi":"https://doi.org/10.1097/mlr.0000000000002110","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4399798266","name":"Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students","source":"openalex","abstract":"Introduction: the integration of artificial intelligence (AI) systems in education has sparked debate regarding their impact on the psychological well-being of university students. As mental health is crucial for their development and academic success, it is essential to assess how interactions with technology affect their psyche. Objective: this article aims to provide a systematic review of studies investigating the impact of AI on the psychological well-being of university students, identifying trends, effects, and areas requiring further research. Method: a comprehensive search was conducted in databases such as PubMed, Scopus, Web of Science, and PsycINFO, using terms related to AI and mental health. Empirical studies published between 2015 and 2023 were included. The selection and analysis of studies were guided by PRISMA guidelines. Discussion: the review indicates that while some AI systems offer personalized support benefiting learning and mental health, others may generate stress and anxiety due to information overload and a lack of meaningful human interaction. Underlying psychological theories explaining these phenomena are discussed. Conclusions: educational technology designers must integrate psychological principles in the development of AI tools to maximize benefits and minimize risks to student well-being. Future research should explore in depth how specific features of AI affect different dimensions of psychological well-being.","url":"https://doi.org/10.56294/sctconf2023582","authors":["Diana Catalina Velasteguí Hernández","Mayra Lucía Rodríguez Pérez","Luis Fabián Salazar-Garcés"],"tags":["Psychology","Humanities","Philosophy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-23","doi":"https://doi.org/10.56294/sctconf2023582","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4412516141","name":"A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions","source":"openalex","abstract":"The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions. A comprehensive search of five academic databases (Scopus, the Web of Science, IEEE Xplore, PubMed, and EMBASE) identified 89 peer-reviewed studies published between 2019 and 2025. PRISMA 2020 guidelines were implemented, resulting in a final dataset of 32 studies. In addition to analyzing applications, this study identifies the AI/ML grouped into five main categories, providing a clearer understanding of their impact on PSC resilience. The findings reveal that despite AI/ML’s promise, significant research gaps persist. Particularly, AI/ML-driven regulatory compliance and real-time supplier collaboration remain underexplored. Over 59.3% of studies fail to address regulatory frameworks and ethical considerations. In addition, major challenges emerge such as the limited real-world deployment of AI/ML-driven solutions and the lack of managerial impacts on PSC resilience. This study emphasizes the need for stronger regulatory frameworks, broader empirical validation, and AI/ML-driven predictive modeling. This study proposes recommendations for future research to foster more efficient, transparent and ethical PSCs capable of navigating the complexities of global healthcare.","url":"https://doi.org/10.3390/su17146591","authors":["Shireen Al-Hourani","Dua Weraikat"],"tags":["Resilience (materials science)","Current (fluid)","Supply chain","Applications of artificial intelligence","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-19","doi":"https://doi.org/10.3390/su17146591","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4390695989","name":"First clinical data on artificial intelligence‐guided catheter ablation in long‐standing persistent atrial fibrillation","source":"openalex","abstract":"INTRODUCTION: Despite advanced ablation strategies and major technological improvements, treatment of persistent atrial fibrillation (AF) remains challenging and the underlying pathophysiology is not fully understood. This study analyzed the multiple procedure outcome and safety of catheter ablation of spatiotemporal dispersions (DISPERS) detected by artificial intelligence (AI)-guided software in patients with long-standing persistent AF. METHODS AND RESULTS: The Volta VX1 software was used for 50 consecutive patients undergoing catheter ablation for persistent AF. First, high-density mapping (78% biatrial) with a multipolar mapping catheter was performed. In addition to pulmonary vein isolation (PVI), ablation of DISPERS was performed aiming at homogenizing, dissecting, isolating, or connecting DISPERS areas to nonconducting anatomical structures. Follow-up contained regular visits at our outpatient clinic at 1, 3, 6, and 12 months including 7-day Holter electrocardiograms. Patients were mainly suffering from long-standing persistent AF (mean AF duration 50.30 ± 54.28 months). Following PVI, ablation of left atrial and right atrial DISPERS areas led to AF cycle length prolongation (mean of 162.0 ± 16.6 to 202.2 ± 21.6 ms after) and AF termination to atrial tachycardia (AT) or sinus rhythm (SR) in 12 patients (24%). No stroke or pericardial effusion occurred; major groin complications (pseudoaneurysm n = 1, atrioventricular fistula n = 1) were detected in two patients. After a blanking period of 6 weeks, recurrence of any atrial arrhythmia was documented in 26 patients (52%). The majority of patients presented with organized AT (n = 15) while AF was present in n = 9 patients and AT/AF was observed in n = 2 patients. Twenty-two patients underwent reablation. During a mean follow-up of 363.14 ± 187.42 days and after an average of 1.46 ± 0.68 procedures, 82% of patients remained in stable SR. CONCLUSION: DISPERS-guided ablation using machine learning software (the Volta VX1 software) in addition to PVI in long-standing persistent AF ablation resulted in high long-term success rates regarding AF and AT elimination. Most arrhythmia recurrences were reentrant AT. After a total of 1.46 ± 0.68 procedures, freedom from AF/AT was 82%. Despite prolonged procedure times complication rates were low. Randomized studies are necessary to evaluate long-term efficacy of dispersion-guided ablation using AI.","url":"https://doi.org/10.1111/jce.16184","authors":["Fabian Bahlke","Florian Englert","Miruna Popa","Félix Bourier","Tilko Reents","Carsten Lennerz","Hannah Kraft","Alex Tunsch Martinez","Marc Kottmaier","Jan Syväri","Madeleine Tydecks","Marta Telishevska","Sarah Lengauer","Gabriele Hessling","Isabel Deisenhofer","Nico Erhard"],"tags":["Medicine","Atrial fibrillation","Catheter ablation","Ablation","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-10","doi":"https://doi.org/10.1111/jce.16184","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404080987","name":"Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective","source":"openalex","abstract":"AIM: To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. BACKGROUND: Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. CONCLUSION: Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. RELEVANCE TO CLINICAL PRACTICE: The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.","url":"https://doi.org/10.1111/jocn.17493","authors":["Lanyi Yu","Xiaomei Zhai"],"tags":["Health care","Ambiguity","Engineering ethics","Relevance (law)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-05","doi":"https://doi.org/10.1111/jocn.17493","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4402991346","name":"Advances in Human–Machine Interaction, Artificial Intelligence, and Robotics","source":"openalex","abstract":"The convergence of artificial intelligence (AI), robotics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and extended reality (XR) is transforming the way humans interact with machines [...]","url":"https://doi.org/10.3390/electronics13193856","authors":["J. Ernesto Solanes","Luis Gracia","Jaime Valls Miró"],"tags":["Artificial intelligence","Robotics","Computer science","Engineering","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-29","doi":"https://doi.org/10.3390/electronics13193856","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4398780684","name":"Using artificial intelligence chatbots to improve patient history taking in dental education (Pilot study)","source":"openalex","abstract":"In all health professions, taking a patient history is critical to accurate diagnosis and the formulation of an appropriate patient-focused management plan.1 Misdiagnosis and inappropriate treatment are common sequelae when clinicians fail to work through history in a logical and thorough manner. Traditionally, history taking in dentistry is taught in a clinical setting with some opportunities to practice in a simulated environment prior to students entering treatment clinics. Pre-clinical teaching is currently done in a tutorial setting where a clinical educator acts as a patient and the students act as the dentist. Such simulations allow repetitive practice in different clinical situations, within a risk-free environment.2 However, interviews with dental school staff members and observation of these tutorials revealed that in most classes, only one or two students actively participated in this role-playing. ChatGPT is a publicly available artificial intelligence (AI) large language model from OpenAI.3 It receives natural language inputs and responds realistically based on the conversation context. AI can be used to develop educational chatbots that are affordable and can provide a new medium of simulation with more scalability, personalization, and access.4 Research studies have demonstrated the effectiveness of ChatGPT in improving diagnostic accuracy, clinical judgment, and knowledge retention among healthcare learners.4 The use of an AI-generated chatbot is proposed as a solution to these problems by requiring individual interaction with the chatbot prior to clinical training. An educational chatbot was developed and trialed in a group of third-year Doctor of Dental Medicine students. A history-taking chatbot was developed with the student user/“clinician” taking a history from the chatbot/“patient” Students could interact either verbally or by typing. The chatbot was hosted on Vercel, using the Next.js framework for the front end and utilizing Vercel's native serverless functions for access to OpenAI's Application Programming Interface at the backend (Figure 1). The chatbot employs the GPT-3.5 Instruct model with structured chat prompts based on an ideal script to guide patient-practitioner dialogue. A small illustrative section is shown in Figure 2. The use of generative AI allows the chatbot to understand variably expressed questions and respond as realistically as possible. Observation of the tutorial with the clinical educator acting as the patient found only two of 13 students actively participated by asking questions compared with 100% involvement with the chatbot. Students generally found the chatbot useful and perceived competence was improved following chatbot use (Figure 3). Most students also agreed that they participated more with the chatbot and that the chatbot would provide more opportunities for them to practice. Staff also recognized the versatility of the tool and AI's potential to generate more cases, although cautious of the current fallibility of generative accuracy. There is clear potential for educational chatbots in dental education which were well-received by staff and senior students. Future directions include development with GPT-4 and updated AI models, trialing with early-year dental students, and iterations of more cases. This study was supported by the FMH Media lab at The University of Sydney. Open access publishing facilitated by The University of Sydney, as part of the Wiley - The University of Sydney agreement via the Council of Australian University Librarians.","url":"https://doi.org/10.1002/jdd.13591","authors":["Aidan J. Or","Smitha Sukumar","Helen E. Ritchie","Babak Sarrafpour"],"tags":["Chatbot","Context (archaeology)","Conversation","Medical education","Personalization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-23","doi":"https://doi.org/10.1002/jdd.13591","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4401715493","name":"Exploring the role of artificial intelligence, large language models: Comparing patient‐focused information and clinical decision support capabilities to the gynecologic oncology guidelines","source":"openalex","abstract":"Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA-related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes.","url":"https://doi.org/10.1002/ijgo.15869","authors":["Lee Reicher","Guy Lutsker","Nadav Michaan","Dan Grisaru","Ido Laskov"],"tags":["Medicine","Gynecologic oncology","Clinical decision support system","Clinical decision making","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-20","doi":"https://doi.org/10.1002/ijgo.15869","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4409278465","name":"A critical assessment of artificial intelligence in magnetic resonance imaging of cancer","source":"openalex","abstract":"Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks to provide the magnetic resonance imaging (MRI) community with an initial guide into the major areas in which the methods of AI are contributing to MRI in oncology. After a general introduction to artificial intelligence, we proceed to discuss the successes and current limitations of AI in MRI when used for image acquisition, reconstruction, registration, and segmentation, as well as its utility for assisting in diagnostic and prognostic settings. Within each section, we attempt to present a balanced summary by first presenting common techniques, state of readiness, current clinical needs, and barriers to practical deployment in the clinical setting. We conclude by presenting areas in which new advances must be realized to address questions regarding generalizability, quality assurance and control, and uncertainty quantification when applying MRI to cancer to maintain patient safety and practical utility.","url":"https://doi.org/10.1038/s44303-025-00076-0","authors":["Chengyue Wu","Meryem Abbad Andaloussi","David A. Hormuth","Ernesto A. B. F. Lima","Guillermo Lorenzo","Casey E. Stowers","Sriram Ravula","Brett Levac","Alexandros G. Dimakis","Jonathan I. Tamir","Kristy K. Brock","Caroline Chung","Thomas E. Yankeelov"],"tags":["Magnetic resonance imaging","Nuclear magnetic resonance","Medicine","Physics","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.1038/s44303-025-00076-0","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410995939","name":"Multilingual performance of a multimodal artificial intelligence system on multisubject physics concept inventories","source":"openalex","abstract":"We investigate the multilingual and multimodal performance of a large language model-based artificial intelligence (AI) system, GPT-4o, using a diverse set of physics concept inventories spanning multiple languages and subject categories. The inventories, sourced from the PhysPort website, cover classical physics topics such as mechanics, electromagnetism, optics, and thermodynamics, as well as relativity, quantum mechanics, astronomy, mathematics, and laboratory skills. Unlike previous text-only studies, we uploaded the inventories as images to reflect what a student would see on paper, thereby assessing the system’s multimodal functionality. Our results indicate variation in performance across subjects, with laboratory skills standing out as the weakest. We also observe differences across languages, with English and European languages showing the strongest performance. Notably, the relative difficulty of an inventory item is largely independent of the language of the test. When comparing AI results to existing literature on student performance, we find that the AI system outperforms average postinstruction undergraduate students in all subject categories except laboratory skills. Furthermore, the AI performs worse on items requiring visual interpretation of images than on those that are purely text-based. While our exploratory findings show GPT-4o’s potential usefulness in physics education, they highlight the critical need for instructors to foster students’ ability to critically evaluate AI outputs, adapt curricula thoughtfully in response to AI advancements, and address equity concerns associated with AI integration.","url":"https://doi.org/10.1103/98hg-rkrf","authors":["Gerd Kortemeyer","Maryna Babayeva","Giulia Polverini","Ralf Widenhorn","Bor Gregorcic"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-03","doi":"https://doi.org/10.1103/98hg-rkrf","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400297840","name":"Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence","source":"openalex","abstract":"According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.","url":"https://doi.org/10.3390/molecules29133164","authors":["Patryk Szymaszek","Małgorzata Tyszka‐Czochara","Joanna Ortyl"],"tags":["International agency","Cancer","Medical diagnosis","Cancer treatment","Cancer therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.3390/molecules29133164","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400123292","name":"Medical-informed machine learning: integrating prior knowledge into medical decision systems","source":"openalex","abstract":"BACKGROUND: Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML. METHODS: The study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models. RESULTS: The findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios. CONCLUSIONS: By illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.","url":"https://doi.org/10.1186/s12911-024-02582-4","authors":["Christel Sirocchi","Alessandro Bogliolo","Sara Montagna"],"tags":["Health informatics","Medical decision making","Computer science","Clinical decision support system","Decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.1186/s12911-024-02582-4","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410791589","name":"Insights Into the Future: Assessing Medical Students' Artificial Intelligence Readiness ‐ A Cross‐Sectional Study at Kerman University of Medical Sciences (2022)","source":"openalex","abstract":"ABSTRACT Background Artificial intelligence (AI) has recently advanced in medicine globally, transforming healthcare delivery and medical education. While AI integration into medical curricula is gaining momentum worldwide, research on medical students' preparedness remains limited, particularly in developing countries. This paper aims to investigate the readiness of medical students at the Kerman University of Medical Sciences to employ AI in medicine in 2022. Methods This cross‐sectional research was carried out by distributing the validated 20‐item Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS‐MS) among 360 medical students, with a response rate of 94% ( n = 340). The MAIRS‐MS assessed four domains, including cognition (8 items), ability (7 items), vision (2 items), and ethics (3 items), using a 5‐point Likert scale. Data analysis was conducted by descriptive statistics and independent sample t ‐tests in SPSS v24.0, considering p < 0.05 significant. Results Participants demonstrated below‐average readiness scores across all domains: ability ( M = 21.88 ± 6.74, 62.5% of the maximum possible score), cognition ( M = 20.30 ± 7.04, 50.8%), ethics ( M = 10.94 ± 3.04, 72.9%), and vision ( M = 6.09 ± 1.94, 60.9%). The total mean readiness score was 59.21 ± 16.12 (59.2% of the maximum). The highest and lowest‐rated items were “value of AI in education” (3.96 ± 1.18) and “explaining AI system training” (2.10 ± 1.01), respectively. No significant differences were found across demographic factors ( p > 0.05). Conclusion Iranian medical students currently show limited readiness for AI integration in healthcare practice. Therefore, the study recommends: (1) implementing structured introductory AI courses in medical curricula, focusing particularly on technical fundamentals and practical applications, and (2) developing hands‐on training programs that combine AI concepts with clinical scenarios. These findings provide valuable insights for curriculum development and educational policy in medical education.","url":"https://doi.org/10.1002/hsr2.70870","authors":["Hossein Rezazadeh","Ali Madadi Mahani","Mahla Salajegheh"],"tags":["Likert scale","Preparedness","Curriculum","Cross-sectional study","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1002/hsr2.70870","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404942414","name":"Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials","source":"openalex","abstract":"Recent advances have given rise to a spectrum of digital health technologies that have the potential to revolutionize the design and conduct of cardiovascular clinical trials. Advances in domain tasks such as automated diagnosis and classification, synthesis of high-volume data and latent data from adjacent modalities, patient discovery, telemedicine, remote monitoring, augmented reality, and in silico modelling have the potential to enhance the efficiency, accuracy, and cost-effectiveness of cardiovascular clinical trials. However, early experience with these tools has also exposed important issues, including regulatory barriers, clinical validation and acceptance, technological literacy, integration with care models, and health equity concerns. This narrative review summarizes the landscape of digital tools at each stage of clinical trial planning and execution and outlines roadblocks and opportunities for successful implementation of digital tools in cardiovascular clinical trials.","url":"https://doi.org/10.1093/eurheartj/ehae794","authors":["Jiun‐Ruey Hu","John R. Power","Faı̈ez Zannad","Carolyn S.P. Lam"],"tags":["Medicine","Clinical trial","Modalities","Telemedicine","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-29","doi":"https://doi.org/10.1093/eurheartj/ehae794","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404291277","name":"Artificial Intelligence for Mechanical Ventilation: A Transformative Shift in Critical Care","source":"openalex","abstract":"With the large volume of data coming from implemented technologies and monitoring systems, intensive care units (ICUs) represent a key area for artificial intelligence (AI) application. Despite the last decade has been marked by studies focused on the use of AI in medicine, its application in mechanical ventilation management is still limited. Optimizing mechanical ventilation is a complex and high-stake intervention, which requires a deep understanding of respiratory pathophysiology. Therefore, this complex task might be supported by AI and machine learning. Most of the studies already published involve the use of AI to predict outcomes for mechanically ventilated patients, including the need for intubation, the respiratory complications, and the weaning readiness and success. In conclusion, the application of AI for the management of mechanical ventilation is still at an early stage and requires a cautious and much less enthusiastic approach. Future research should be focused on AI progressive introduction in the everyday management of mechanically ventilated patients, with the aim to explore the great potentiality of this tool.","url":"https://doi.org/10.1177/29768675241298918","authors":["Giovanni Misseri","Matteo Piattoli","Giuseppe Cuttone","Cesare Gregoretti","Elena Bignami"],"tags":["Mechanical ventilation","Intensive care medicine","Intensive care","Medicine","Task (project management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1177/29768675241298918","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404812876","name":"Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand","source":"openalex","abstract":"BACKGROUND: Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant challenges in sharing data for developing and deploying AI in health care. OBJECTIVE: This study aimed to identify barriers and enablers to data sharing for AI in health care in LMICs and to test the relevance of these in a local context. METHODS: First, we conducted a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM using controlled vocabulary. Primary research studies, perspectives, policy landscape analyses, and commentaries performed in or involving an LMIC context were included. Studies that lacked a clear connection to health information exchange systems or were not reported in English were excluded from the review. Two reviewers independently screened titles and abstracts of the included articles and critically appraised each study. All identified barriers and enablers were classified according to 7 categories as per the predefined framework-technical, motivational, economic, political, legal and policy, ethical, social, organisational, and managerial. Second, we tested the local relevance of barriers and enablers in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policy makers, and health care providers. The interviewers took notes and analyzed data using framework analysis. Coding procedures were standardized to enhance the reliability of our approach. Coded data were reverified and themes were readjusted where necessary to avoid researcher bias. RESULTS: We identified 22 studies, the majority of which were conducted across Africa (n=12, 55%) and Asia (n=6, 27%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivizing health care organizations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies, and cybersecurity issues were important data-sharing challenges. Key solutions included building a conducive digital ecosystem-having shared data input platforms for health facilities to ensure data uniformity and to develop easy-to-understand consent forms, having standardized guidelines for data sharing, and having compensation policies for data breach victims. CONCLUSIONS: Although AI in LMICs has the potential to overcome health inequalities, these countries face technical, political, legal, policy, and organizational barriers to sharing data, which impede effective AI development and deployment. When tested in a local context, most of these barriers were relevant. Although our findings might not be generalizable to other contexts, this study can be used by LMICs as a framework to identify barriers and strengths within their health care systems and devise localized solutions for enhanced data sharing. TRIAL REGISTRATION: PROSPERO CRD42022360644; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360644.","url":"https://doi.org/10.2196/58338","authors":["Aprajita Kaushik","Capucine Barcellona","Nikita Kanumoory Mandyam","Si Ying Tan","Jasper Tromp"],"tags":["Preprint","Low and middle income countries","Health care","Medicine","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-28","doi":"https://doi.org/10.2196/58338","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404642446","name":"Unlocking artificial intelligence for strategic market development and business growth: innovations, opportunities, and future directions","source":"openalex","abstract":"This study explores the role of Artificial Intelligence (AI) in strategic market development and business growth. It aims to examine how AI innovations create new opportunities, enhance operational efficiencies, and drive competitive advantages for businesses across various sectors. This study relies significantly on previously published literature and secondary data sourced from esteemed academic databases, including Web of Science, SCOPUS, Google Scholar, and Research gate. Initially, we collected a dataset of 300 papers spanning the period from January 2020 to 2024. After a rigorous screening process, we narrowed it down to 65 papers to derive the desired insights and produce robust results. AI is revolutionizing market strategies by enabling personalized marketing, optimizing supply chains, and improving decision-making through predictive analytics. Companies are leveraging AI for automating customer service, enhancing product development, and discovering new business models. The study also highlights AI’s potential in facilitating global market expansion and localized product offerings. The findings suggest that AI is central to enhancing business efficiency, innovation, and customer satisfaction. However, ethical concerns such as data privacy, algorithmic bias, and regulatory challenges remain significant barriers to AI adoption. Companies must balance innovation with responsible AI use to ensure long-term success and sustainability. Businesses adopting AI technologies can gain a competitive edge by improving operational efficiency and creating more tailored customer experiences. However, they must invest in AI transparency, employee reskilling, and regulatory compliance to mitigate risks associated with AI deployment. Further research is needed to explore the ethical and societal impacts of AI, including its role in job displacement and its potential for environmental sustainability. The integration of AI with emerging technologies like blockchain and IoT also offers exciting opportunities for future growth. Organizations should invest in AI solutions aligned with business objectives, ensuring ethical practices and continuous innovation to maintain a competitive edge in the evolving market landscape.","url":"https://doi.org/10.55214/25768484.v8i6.3263","authors":["Fahim Hossain","G. Refai Ahmed","Shree Pritom Paul Shuvo","Abeda Najmin Kona","Mostarifa Umme Hani Raina","Fisan Shikder"],"tags":["Business","Market intelligence","Business intelligence","Business development","Industrial organization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-23","doi":"https://doi.org/10.55214/25768484.v8i6.3263","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4398220510","name":"Artificial intelligence and learning environment: Human considerations","source":"openalex","abstract":"Abstract Background Artificial intelligence (AI) has created new opportunities, challenges, and potentials in teaching; however, issues related to the philosophy of using AI technology in learners' learning have not been addressed and have caused some issues and concerns. This issue is due to the research gap in addressing issues related to ethical and human needs, and even values in AI in learning have become more obvious. Objectives This study investigates how human‐centered artificial intelligence (HAI) can help learners in a learning environment. In this regard, this article by developing key considerations of HAI in helping students tries to help implement or shift it in the future in learning environments. Methods To better understand the key considerations of HAI, qualitative methods and interview techniques were applied in this study. In this regard, 18 samples were interviewed from two groups of experts and faculty members in the fields of technology and computer science and social and humanities sciences. The thematic content analysis method was used to analyse qualitative data. Results and Conclusions The results show that AI attempts to integrate ethical and human values in the process of design, development, and research in the fields of recognising and dealing with negative emotions, targeted emotional nature, and access to fairness and justice. It also shows significant promise in understanding feelings and emotions in a learning environment. Implications Although AI has been studied in other contexts, HAI has not attracted much attention from researchers. Hence, this study has made worthwhile contributions to the literature as it has specifically focused on HAI in education. In addition, it can resolve some scientific community considerations regarding technological concerns in the field of AI. Furthermore, this article can increase social satisfaction with the use of AI by considering ethical considerations in the learning environment and can particularly benefit researchers, educators, and AI specialists who are involved in the study of HAI applications.","url":"https://doi.org/10.1111/jcal.13011","authors":["Esmaeil Jafari"],"tags":["Feeling","Thematic analysis","Psychology","Emotional intelligence","Learning environment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-21","doi":"https://doi.org/10.1111/jcal.13011","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4408291037","name":"Evaluating the Use of Generative Artificial Intelligence to Support Genetic Counseling for Rare Diseases","source":"openalex","abstract":"Background/Objectives: Rare diseases often present challenges in obtaining reliable and accurate information than common diseases owing to their low prevalence. Patients and families often rely on self-directed learning, but understanding complex medical information can be difficult, increasing the risk of misinformation. This study aimed to evaluate whether generative artificial intelligence (AI) provides accurate and non-harmful answers to rare disease-related questions and assesses its utility in supporting patients and families requiring genetic counseling. Methods: We evaluated four generative AI models available between 22 September and 4 October 2024: ChatGPT o1-Preview, Gemini advanced, Claude 3.5 sonnet, and Perplexity sonar huge. A total of 102 questions targeting four rare diseases, covering general information, diagnosis, treatment, prognosis, and counseling, were prepared. Four evaluators scored the responses for professionalism and accuracy using the Likert scale (1: poor, 5: excellent). Results: The average scores ranked the AI models as: ChatGPT (4.24 ± 0.73), Gemini (4.15 ± 0.74), Claude (4.13 ± 0.82), and Perplexity (3.35 ± 0.80; p < 0.001). Perplexity had the highest proportion of scores of 1 (very poor) and 2 (poor) (7.6%, 31/408), followed by Gemini (2.0%, 8/408), Claude (1.5%, 6/408), and ChatGPT (1.5%, 6/408). The accuracy of responses in the counseling part across all four diseases was significantly different (p < 0.001). Conclusions: The four generative AI models generally provided reliable information. However, occasional inaccuracies and ambiguous references may lead to confusion and anxiety among patients and their families. To ensure its effective use, recognizing the limitations of generative AI and providing guidance from experts regarding its proper utilization is essential.","url":"https://doi.org/10.3390/diagnostics15060672","authors":["Suok Jeon","Sua Lee","Hae‐Sun Chung","Ji Young Yun","Eun Ae Park","Min‐Kyung So","Jungwon Huh"],"tags":["Perplexity","Misinformation","Likert scale","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-10","doi":"https://doi.org/10.3390/diagnostics15060672","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406809810","name":"Peer Review in the Artificial Intelligence Era: A Call for Developing Responsible Integration Guidelines","source":"openalex","abstract":"Ahmed Salem BaHammam1– 3 1Editor-in-Chief Nature and Science of Sleep; 2Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, Riyadh, Saudi Arabia; 3King Saud University Medical City, Riyadh, Saudi ArabiaCorrespondence: Ahmed Salem BaHammam, University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Box 225503, Riyadh, 11324, Saudi Arabia, Tel +966-11-467-9495, Fax +966-11-467-9179, Email ashammam2@gmail.com","url":"https://doi.org/10.2147/nss.s513872","authors":["Ahmed S. BaHammam"],"tags":["Medicine","Medical education","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.2147/nss.s513872","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4412491776","name":"Physical artificial intelligence in nursing: Robotics","source":"openalex","abstract":"BACKGROUND: Robotics, driven by advancements in physical artificial intelligence (AI), offers potential solutions-yet many challenges- to creating innovative care models to meet the needs of the future. PURPOSE: To present an overview of robotics across various industries and explain how physical AI is aiding the development and integration of robots into skilled nursing. We discuss the opportunities and challenges of incorporating robots into nursing and offer recommendations for nurses on designing equitable, human-centered care models that include robotics. METHODS: This paper discusses robotics across industries, with a focus on healthcare and nursing. It examines technological capabilities, nursing education needs, and ethical, regulatory, and workforce implications. DISCUSSION: Robots are increasingly used for logistics, cleaning, and limited direct care tasks. Advancement in physical AI will enable robots to perceive, reason, and act in dynamic environments, supporting human-robot teaming and patient care. Challenges include technical limitations, ethical concerns, disparities in access, and regulatory gaps. Nursing education must evolve to prepare professionals for collaborative practice with robotic systems. CONCLUSION: Robotics must be designed to augment care delivery, such as through virtual care models and remote operation. Nurses must lead in designing, implementing, and regulating robotic technologies to ensure they enhance patient outcomes and promote health equity.","url":"https://doi.org/10.1016/j.outlook.2025.102495","authors":["Ryan J. Shaw","Boyuan Chen"],"tags":["Robotics","Robot","Artificial intelligence","Workforce","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-16","doi":"https://doi.org/10.1016/j.outlook.2025.102495","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410592464","name":"Artificial intelligence-based predictive models for shear wave velocity of soils: A comprehensive review","source":"openalex","abstract":"Shear wave velocity (V s ) of soils is a crucial property in geotechnical engineering practice, affecting seismic site response analysis, seismic hazard assessment , and dynamic soil-structure interaction. The precise determination of V s is crucial in assessing the dynamic behavior of soils during seismic events, as it markedly influences the amplification and attenuation of ground motions. While several empirical equations have been proposed thus far for estimating V s in earthen materials, the majority of them lack the required accuracy and predictive capability. As a result, there has been a growing tendency among practicing engineers towards utilizing Artificial Intelligence (AI) for V s prediction. This paper presents an extensive overview of the developments in deploying AI and its subsets, including Machine Learning (ML) and Deep Learning (DL) techniques, for the precise estimation of V s in soil deposits. Notably, despite the importance of shear wave velocity as a key geotechnical parameter, no prior review study has exclusively focused on evaluating it using AI-based models. This review systematically examines various AI-based methodologies employed by researchers to enhance the reliability and precision of V s predictions using soil properties and in-situ test data. The advantages of ML techniques over conventional empirical correlations are thoroughly analyzed and critically compared. Additionally, the paper discusses the relative performance of different AI-based approaches, outlining their strengths and limitations in V s estimation. The review also provides a general qualitative assessment of V s measurement methods, offering guidance on selecting the most appropriate approach based on project-specific requirements and constraints. Finally, through a critical evaluation of existing literature, key knowledge gaps are identified, and potential directions for future research in this domain are proposed.","url":"https://doi.org/10.1016/j.engappai.2025.111095","authors":["Meghdad Payan","Parsa Asadi","Amirhossein Jamaldar","Mahdi Salimi","Payam Zanganeh Ranjbar","Danial Jahed Armaghani","Xuzhen He","Daichao Sheng"],"tags":["Computer science","Wave velocity","Predictive modelling","Artificial intelligence","Shear (geology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-22","doi":"https://doi.org/10.1016/j.engappai.2025.111095","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4402169417","name":"Reasons in the Loop: The Role of Large Language Models in Medical Co-Reasoning","source":"openalex","abstract":"Salloch and Eriksen (2024) present a compelling case for including patients as co-reasoners in medical decision-making involving artificial intelligence (AI). Drawing on O'Neill’s neo-Kantian frame...","url":"https://doi.org/10.1080/15265161.2024.2383121","authors":["Sebastian Porsdam Mann","Brian D. Earp","Peng Liu","Julian Savulescu"],"tags":["Loop (graph theory)","Computer science","Cognitive science","Psychology","Linguistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.1080/15265161.2024.2383121","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4399393690","name":"MedYOLO: A Medical Image Object Detection Framework","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10278-024-01138-2","authors":["Joseph D. Sobek","José R. Medina‐Inojosa","Betsy J. Medina Inojosa","Seyed Moein Rassoulinejad-Mousavi","Gian Marco Conte","Francisco López-Jiménez","Bradley J. Erickson"],"tags":["Artificial intelligence","Computer science","Convolutional neural network","Hyperparameter","Voxel"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-06","doi":"https://doi.org/10.1007/s10278-024-01138-2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4410111118","name":"Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems","source":"openalex","abstract":"There is a growing recognition of the need for clinical trials to safely and effectively deploy artificial intelligence (AI) in clinical settings. We introduce dynamic deployment as a framework for AI clinical trials tailored for the dynamic nature of large language models, making possible complex medical AI systems which continuously learn and adapt in situ from new data and interactions with users while enabling continuous real-time monitoring and clinical validation.","url":"https://doi.org/10.1038/s41746-025-01674-3","authors":["Jacob T. Rosenthal","Ashley Beecy","Mert R. Sabuncu"],"tags":["Software deployment","Computer science","Clinical trial","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-06","doi":"https://doi.org/10.1038/s41746-025-01674-3","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404402066","name":"Examining the Role of Large Language Models in Orthopedics: Systematic Review","source":"openalex","abstract":"BACKGROUND: Large language models (LLMs) can understand natural language and generate corresponding text, images, and even videos based on prompts, which holds great potential in medical scenarios. Orthopedics is a significant branch of medicine, and orthopedic diseases contribute to a significant socioeconomic burden, which could be alleviated by the application of LLMs. Several pioneers in orthopedics have conducted research on LLMs across various subspecialties to explore their performance in addressing different issues. However, there are currently few reviews and summaries of these studies, and a systematic summary of existing research is absent. OBJECTIVE: The objective of this review was to comprehensively summarize research findings on the application of LLMs in the field of orthopedics and explore the potential opportunities and challenges. METHODS: PubMed, Embase, and Cochrane Library databases were searched from January 1, 2014, to February 22, 2024, with the language limited to English. The terms, which included variants of \"large language model,\" \"generative artificial intelligence,\" \"ChatGPT,\" and \"orthopaedics,\" were divided into 2 categories: large language model and orthopedics. After completing the search, the study selection process was conducted according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the revised Cochrane risk-of-bias tool for randomized trials and CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) guidance. Data extraction and synthesis were conducted after the quality assessment. RESULTS: A total of 68 studies were selected. The application of LLMs in orthopedics involved the fields of clinical practice, education, research, and management. Of these 68 studies, 47 (69%) focused on clinical practice, 12 (18%) addressed orthopedic education, 8 (12%) were related to scientific research, and 1 (1%) pertained to the field of management. Of the 68 studies, only 8 (12%) recruited patients, and only 1 (1%) was a high-quality randomized controlled trial. ChatGPT was the most commonly mentioned LLM tool. There was considerable heterogeneity in the definition, measurement, and evaluation of the LLMs' performance across the different studies. For diagnostic tasks alone, the accuracy ranged from 55% to 93%. When performing disease classification tasks, ChatGPT with GPT-4's accuracy ranged from 2% to 100%. With regard to answering questions in orthopedic examinations, the scores ranged from 45% to 73.6% due to differences in models and test selections. CONCLUSIONS: LLMs cannot replace orthopedic professionals in the short term. However, using LLMs as copilots could be a potential approach to effectively enhance work efficiency at present. More high-quality clinical trials are needed in the future, aiming to identify optimal applications of LLMs and advance orthopedics toward higher efficiency and precision.","url":"https://doi.org/10.2196/59607","authors":["Cheng Zhang","Shanshan Liu","Xingyu Zhou","Siyu Zhou","Yinglun Tian","Shenglin Wang","Nanfang Xu","Weishi Li"],"tags":["Cochrane Library","MEDLINE","Systematic review","Medical education","Orthopedic surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-15","doi":"https://doi.org/10.2196/59607","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W3032938107","name":"Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework","source":"openalex","abstract":"The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.","url":"https://doi.org/10.1016/j.ymeth.2020.05.022","authors":["Abdalla Ibrahim","Sergey Primakov","Manon Beuque","Henry C. Woodruff","Iva Halilaj","Guangyao Wu","Turkey Refaee","Renée W. Y. Granzier","Yousif Widaatalla","Roland Hustinx","Felix M. Mottaghy","Philippe Lambin"],"tags":["Computer science","Workflow","Context (archaeology)","Artificial intelligence","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-03","doi":"https://doi.org/10.1016/j.ymeth.2020.05.022","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4399187438","name":"Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence","source":"openalex","abstract":"Magnetic resonance imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.","url":"https://doi.org/10.1016/j.mrl.2024.200138","authors":["Yirong Zhou","Yanhuang Wu","Yuhan Su","Jing Li","Jianyu Cai","Yongfu You","Jianjun Zhou","Di Guo","Xiaobo Qu"],"tags":["Magnetic resonance imaging","Cloud computing","Nuclear magnetic resonance","Physics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-30","doi":"https://doi.org/10.1016/j.mrl.2024.200138","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404202452","name":"Testing process for artificial intelligence applications in radiology practice","source":"openalex","abstract":"Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical benefits remains a challenge. A systematic testing process aims to facilitate clinical deployment by confirming software applicability to local patient populations, practises, adherence to regulatory and safety requirements, and compatibility with existing systems. In this work, we present our testing process developed based on practical experience. First, a survey and pre-evaluation is conducted, where information requests are sent for potential products, and the specifications are evaluated against predetermined requirements. In the second phase, data collection, testing, and analysis are conducted. In the retrospective stage, the application undergoes testing with a pre selected dataset and is evaluated against specified key performance indicators (KPIs). In the prospective stage, the application is integrated into the clinical workflow and evaluated with additional process-specific KPIs. In the final phase, the results are evaluated in terms of safety, effectiveness, productivity, and integration. The final report summarises the results and includes a procurement/deployment or rejection recommendation. The process allows termination at any phase if the application fails to meet essential criteria. In addition, we present practical remarks from our experiences in AI testing and provide forms to guide and document the testing process. The established AI testing process facilitates a systematic evaluation and documentation of new technologies ensuring that each application undergoes equal and sufficient validation. Testing with local data is crucial for identifying biases and pitfalls of AI algorithms to improve the quality and safety, ultimately benefiting patient care.","url":"https://doi.org/10.1016/j.ejmp.2024.104842","authors":["Juuso H. Ketola","Satu I. Inkinen","Teemu Mäkelä","Suvi Syväranta","Juha Peltonen","Touko Kaasalainen","Mika Kortesniemi"],"tags":["Process (computing)","Computer science","Artificial intelligence","Medical physics","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-09","doi":"https://doi.org/10.1016/j.ejmp.2024.104842","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4395000105","name":"Exploring Embodied Intelligence in Soft Robotics: A Review","source":"openalex","abstract":"Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.","url":"https://doi.org/10.3390/biomimetics9040248","authors":["Zikai Zhao","Qiuxuan Wu","Jian Wang","Botao Zhang","Chaoliang Zhong","Anton Zhilenkov"],"tags":["Embodied cognition","Artificial intelligence","Robotics","Cognitive robotics","Soft robotics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-19","doi":"https://doi.org/10.3390/biomimetics9040248","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4405411755","name":"Artificial intelligence in medical imaging","source":"openalex","abstract":"With the rapid development of science and technology, the application of artificial intelligence (AI) in various fields is constantly expanding, especially in the field of medical imaging [1]. AI technology is suitable to be applied to standardized digital medical image big data based on digital imaging and communications in medicine protocol and picture archiving and communication system. With the integration of AI technology, this field is undergoing profound transformation, not only improving the accuracy and efficiency of diagnosis, but also significantly reducing the workload of doctors [2]. At present, AI is widely used in medical imaging, including risk modeling and stratification, personalized screening, diagnosis (including classification of molecular pathologic subtypes), treatment response prediction, prognosis prediction, image segmentation, and image quality control. AI can help doctors identify and analyze lesions in various medical images, especially in diseases such as lung, breast, and prostate cancer. The research mainly focuses on the identification of benign and malignant, the measurement of risk factors, prognosis judgment and treatment guidance, and it is increasingly being used in the field of psychoradiology [3]. In addition, AI is also focused on reducing image acquisition time and improving data quality. Through deep learning algorithms, AI can optimize imaging parameters, improve imaging quality, and reduce noise and artifacts. This special issue of AI includes seven latest studies, which covers artificial intelligence of disease diagnosis and prediction, imaging technology model construction, image segmentation and quality control. Wang et al. [4] used systematic review to summarize the technical methods, clinical applications and existing problems of artificial intelligence in cerebrovascular diseases, they found that the availability of algorithms, reliability of validation, and consistency of evaluation metrics may facilitate better clinical applicability and acceptance. Zhu et al. [5] proposed a diffusion magnetic diffusion magnetic (dMRI) index reconstruction model based on deep learning methods-qIRR-Net and a training framework based on data enhancement and consistency loss, The reconstruction of dMRI index is realized without the influence of signal inhomogeneity, and the model validity is verified on simulated inhomogeneity data and real ultra-high field data, thus promoting the application of ultra-high field dMRI technology in medicine and clinic. Artificial intelligence-assisted compressed sensing is a deep learning technology based on convolutional neural networks which can reconstruct images with ultra-high resolution and reduce noise. On the premise of ensuring the quality of the image, the collection time of the sequence is greatly shortened. In this special issue, The application of assisted compressed sensing technology in 5T MRI by Zhou et al. [6] significantly reduced MRI scanning time, and ensured image quality and diagnostic accuracy. This innovative study can effectively improve clinical work efficiency. AI technology can play an important role in radiomics, through deep learning models, automatic extraction of a large number of quantitative image features, and combined with patient clinical data, gene expression information, to build high-precision diagnosis and prediction models [7]. Pawan et al. [8]reviewed the research on bone metastasis in prostate cancer from the fields of radiomics, machine learning, and deep learning. They present multiple strategies, including classification/prediction, detection, segmentation, and radiomic methods for evaluating prostate bone metastasis; it provides researchers with systematic learning opportunities for relevant research. The application of artificial intelligence in the field of medical imaging has shown great potential and advantages. From the optimization of intelligent imaging systems to the processing and analysis of complex imag","url":"https://doi.org/10.1002/ird3.111","authors":["Bin Huang","Bo Gao"],"tags":["Medical imaging","Artificial intelligence","Medical physics","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1002/ird3.111","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400084797","name":"Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ccell.2024.06.002","authors":["Yan-Ran Wang","Pengcheng Wang","Zihan Yan","Quan Zhou","Fatma Güntürkün","Peng Li","Yanshen Hu","Wei Emma Wu","Kankan Zhao","Michael Zhang","Haoyi Lv","Lehao Fu","Jiajie Jin","Qing Du","Haoyu Wang","Kun Chen","Liangqiong Qu","Keldon Lin","Michael Iv","Hao Wang","Xiaoyan Sun","Hannes Vogel","Summer S. Han","Lu Tian","Feng Wu","Jian Gong"],"tags":["Medulloblastoma","Neuroscience","Medicine","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-27","doi":"https://doi.org/10.1016/j.ccell.2024.06.002","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4399608868","name":"Artificial Intelligence and Entrepreneurship","source":"openalex","abstract":"","url":"https://doi.org/10.2139/ssrn.4863772","authors":["Frank M. Fossen","Trevor McLemore","Alina Sorgner"],"tags":["Entrepreneurship","Psychology","Business","Finance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.2139/ssrn.4863772","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4401844219","name":"Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration","source":"openalex","abstract":"Artificial intelligence encompasses a wide range of approaches, methodologies, and techniques aimed at mimicking human intelligence in machines. In recent times, the concepts of Generative Artificial Intelligence (AI), Super AI, and Narrow AI have attracted considerable attention. Undoubtedly, the success of ChatGPT in capturing all attention has played a significant role in this. Artificial intelligence technology has a profound impact on all sectors, and sector representatives are striving to adapt to this technology more quickly. It is projected that artificial intelligence could generate an economic size of 13 trillion American dollars by 2030. Developments in artificial intelligence technologies undoubtedly lead to significant improvements in the functioning of public institutions and access for citizens. Artificial intelligence has the potential to be used in many public services, including security and defense, healthcare services, education, transportation and infrastructure, environmental and natural resource management, law and justice systems, among others. Therefore, evaluating the types of artificial intelligence, Narrow AI applications, and chatbots for public use is seen as highly beneficial from the perspective of public administration and the public sector. In our study, the topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration. Utilizing findings from both Turkish and English literature reviews, the importance and potential impacts of artificial intelligence within the public sector, along with current trends, have been comprehensively assessed. This research delves into the concepts of artificial intelligence and its subsets—super AI, generative AI, narrow AI, and chatbots—within the general framework of the public sector. China and the United States are pioneering and leading countries in terms of investment. Although the U.S. stands out in many areas regarding investment, China's integration of artificial intelligence with national strategies and its policies indicate that it may play a more dominant role in the future. There are four main implementation areas of artificial intelligence in the public sector: efficiency and automation, service delivery, data-driven governance, and ethical and regulatory challenges. A review of the literature reveals that the ethical, legal, and social implications of implementing artificial intelligence in the public sector require more careful consideration. The study makes a significant contribution to the field of artificial intelligence discussions in public administration and the public sector, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.","url":"https://doi.org/10.61969/jai.1512906","authors":["Muhammet Damar","Ahmet Özen","Ülkü Ece Çakmak","Eren Özoğuz","Fatih Safa Erenay"],"tags":["Artificial intelligence","Public sector","Context (archaeology)","Generative grammar","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-24","doi":"https://doi.org/10.61969/jai.1512906","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406322425","name":"Artificial Intelligence in Science and Society: The Vision of USERN","source":"openalex","abstract":"The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI’s state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.","url":"https://doi.org/10.1109/access.2025.3529357","authors":["T. Dorigo","Gary D. Brown","Carlo Casonato","Artemi Cerdà","Joseph Ciarrochi","Mauro Da Lio","Nicole D’Souza","Nicolas R. Gauger","Steven C. Hayes","Stefan G. Hofmann","Robert Johansson","Marcus Liwicki","Fabien Lotte","Juan J. Nieto","Giulia Olivato","Peter Parnes","George Perry","Alice Plebe","Idupulapati M. Rao","Nima Rezaei","Fredrik Sandin","A. Ustyuzhanin","Giorgio Vallortígara","P. Vischia","Niloufar Yazdanpanah"],"tags":["Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/access.2025.3529357","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4401851847","name":"Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data","source":"openalex","abstract":"Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the \"black box\" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.","url":"https://doi.org/10.1016/j.ese.2024.100479","authors":["Haoli Xu","Xing Yang","Yihua Hu","Daqing Wang","Zhenyu Liang","Hua Mu","Yangyang Wang","Liang Shi","Haoqi Gao","Daoqing Song","Zijian Cheng","Zhao Lu","Xiaoning Zhao","Jun Lu","Bingwen Wang","Zhiyang Hu"],"tags":["Big data","Computer science","Artificial intelligence","Data science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-24","doi":"https://doi.org/10.1016/j.ese.2024.100479","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4403987666","name":"Artificial Intelligence for English Language Learning and Teaching: Advancing Sustainable Development Goals","source":"openalex","abstract":"This study explores the affordance of Artificial Intelligence (AI) to English language learning and teaching, focusing on its alignment with the United Nations' Sustainable Development Goals (SDGs). It aims to investigate the role of AI in enhancing language education and fostering student-centered learning. Data for this study were collected through semi-structured interviews with 18 English teachers to gather qualitative insights into their experiences with AI-powered language learning tools. The findings reveal that the teachers have positive appraisals of AI that its use has six major impacts: i) enhancing the personalization of learning; ii) contributing to improved learning outcomes by advancing students' speaking, listening, reading, and writing skills; iii) playing a fundamental role in bridging educational gaps; iv) enhancing students’ engagement and motivation; v) empowering educators with professional development opportunities; vi) and encouraging self-directed learning. This study argues that, if implemented thoughtfully, AI can enhance language learning outcomes and create an environment conducive to student engagement and success.","url":"https://doi.org/10.17507/jltr.1506.09","authors":["Omar Ali Al‐Smadi","Radzuwan Ab Rashid","Hadeel Saad","Yousef Houssni Zrekat","Siti Soraya Lin Abdullah Kamal","Gaforov Ikboljon Uktamovich"],"tags":["Sustainable development","Computer science","Mathematics education","Knowledge management","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-01","doi":"https://doi.org/10.17507/jltr.1506.09","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4392415015","name":"Evaluating Artificial Intelligence in Clinical Settings—Let Us Not Reinvent the Wheel","source":"openalex","abstract":"Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.","url":"https://doi.org/10.2196/46407","authors":["Kathrin Cresswell","Nicolette F. de Keizer","Farah Magrabi","Robin Williams","Michael Rigby","Mirela Prgomet","Polina Kukhareva","Zoie Shui-Yee Wong","Philip Scott","Catherine K. Craven","Andrew Georgiou","Stephanie Medlock","Jytte Brender McNair","Elske Ammenwerth"],"tags":["Computer science","Stakeholder","Knowledge management","Health care","Scalability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-02","doi":"https://doi.org/10.2196/46407","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4416470794","name":"Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology","source":"openalex","abstract":"Artificial intelligence (AI) is opening new frontiers in the development of antibody-drug conjugates (ADCs), offering unprecedented opportunities for precision therapy. This review outlines how AI empowers each stage of the ADC pipeline. In target discovery, multi-omics integration and graph-based learning prioritize tumor-selective and internalizing antigens. In antibody engineering, structure prediction, affinity optimization, and developability modeling streamline candidate selection. For linker-payload design, generative models and multi-objective optimization approaches support the rational design of conjugates that balance potency, stability, and immunogenicity. In absorption, distribution, metabolism, excretion, and toxicity (ADMET) modeling, deep learning and transformer-based frameworks predict pharmacokinetics and toxicity with increasing accuracy and mechanistic clarity. In clinical development, AI facilitates patient stratification, response prediction, and trial simulation through digital twin models, adaptive dosing algorithms, and real-world data integration. These capabilities support a more personalized and efficient pathway from bench to bedside. To further realize the impact of AI in ADC development, we highlight strategic priorities including the creation of curated, multimodal datasets, interpretable model architectures, and closed-loop experimental platforms. Together, these advances will be essential for realizing the full potential of AI to support rational, scalable, and personalized ADC-based therapies in oncology.","url":"https://doi.org/10.1038/s41698-025-01159-2","authors":["Ye Lu","Weijun Huang","Yuxuan Li","Yanzhi Xu","Qingyang Wei","Chulin Sha","Peng Guo"],"tags":["Artificial intelligence","Computer science","Identification (biology)","Machine learning","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-21","doi":"https://doi.org/10.1038/s41698-025-01159-2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4403463361","name":"Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence","source":"openalex","abstract":"The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.","url":"https://doi.org/10.3390/s24206618","authors":["Sagheer Abbas","Adnan Qaisar","Muhammad Sajid Farooq","Muhammad Saleem","Munir Ahmad","Muhammad Adnan Khan"],"tags":["Transparency (behavior)","Computer science","Artificial intelligence","Computer vision","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-14","doi":"https://doi.org/10.3390/s24206618","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4406239609","name":"Prospects for the application of artificial intelligence in geriatrics","source":"openalex","abstract":"","url":"https://doi.org/10.1515/jtim-2024-0034","authors":["Li Zhang","Jing Li"],"tags":["Medicine","Geriatrics","Nephrology","Geriatric care","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1515/jtim-2024-0034","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4404933079","name":"Artificial intelligence for collective intelligence: a national-scale research strategy","source":"openalex","abstract":"Abstract Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence . The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.","url":"https://doi.org/10.1017/s0269888924000110","authors":["Seth Bullock","Nirav Ajmeri","Mike Batty","Michaela Black","John Cartlidge","Robert Challen","Cangxiong Chen","Chen Jing","Joan Condell","León Danon","Adam Dennett","Alison Heppenstall","Paul Marshall","Phil Morgan","Aisling Ann O’Kane","Laura G. E. Smith","Theresa J. Smith","Hywel T. P. Williams"],"tags":["Collective intelligence","Scale (ratio)","Computer science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1017/s0269888924000110","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W4400695423","name":"A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection","source":"openalex","abstract":"Abstract Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre‐cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, and positron emission tomography‐scan‐based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta‐analysis guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning‐based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning‐based models are preferred for sociodemographic data. The analysis shows that convolutional neural network‐based features yielded representative characteristics for detecting pre‐cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision‐making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta‐learning should also be explored. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Classification","url":"https://doi.org/10.1002/widm.1550","authors":["Smith K. Khare","Victoria Blanes‐Vidal","Berit Bargum Booth","Lone Kjeld Petersen","Esmaeil S. Nadimi"],"tags":["Computer science","Cervical cancer","Artificial intelligence","Data science","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-15","doi":"https://doi.org/10.1002/widm.1550","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107636","name":"Automatic sunspot detection through semantic and instance segmentation approaches","source":"crossref","abstract":"The solar influence on space weather and terrestrial environment is substantial. Strong geomagnetic storm activity can significantly affect astronauts in orbit, communications and GPS systems and disrupt Earth’s power distribution networks, making continuous monitoring and forecasting of solar activity vital. Sunspots are magnetic disturbances in the photosphere characterized by their dark appearance in the solar disk, being directly related to phenomena that contribute to these intense storms, namely solar flares and coronal mass ejections. This article lies at the intersection between solar surveillance and computer vision by applying state-of-the-art deep learning algorithms in the automatic detection of sunspots and sunspot groups. Based on two techniques, semantic segmentation and instance segmentation, two algorithms are implemented to tackle both purposes, U-Net and Mask R-CNN respectively. The ground-truth dataset was built from the available Debrecen Heliographic Observatory (DHO) space-borne sunspot catalogues from 2010 to 2014. The best U-Net implemented model presented a 74.2% IoU, surpassing the detection results evidenced by the Automated Solar Activity Prediction System (ASAP). The instance segmentation approach, a novelty application technique for sunspot group detection and still a challenging task in computer vision, achieved 51.7 bounding box AP and 78.6% accuracy in predicting the number of sunspot groups.","url":"https://doi.org/10.1016/j.engappai.2023.107636","authors":["André Mourato","João Faria","Rodrigo Ventura"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-01T00:26:04Z","doi":"10.1016/j.engappai.2023.107636","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2024.102866","name":"Deep learning supported echocardiogram analysis: A comprehensive review","source":"crossref","abstract":"An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.","url":"https://doi.org/10.1016/j.artmed.2024.102866","authors":["Sanjeevi G.","Uma Gopalakrishnan","Rahul Krishnan Parthinarupothi","Thushara Madathil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-04T15:44:51Z","doi":"10.1016/j.artmed.2024.102866","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/cai59869.2024.00165","name":"Maritime-Context Text Identification for Connecting Artificial Intelligence (AI) Models","source":"crossref","abstract":"This study focuses on identifying texts related to maritime contexts using an advanced Large Language Model (LLM) and cost-sensitive approach for handling data imbalances. Firstly, a comprehensive dataset specifically for maritime-context queries is collected and augmented. Secondly, the dynamic contextual representations of input query considering the context of each word are obtained by a pre-trained LLM which incorporates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). Thirdly, a Multi-Layer Perceptron (MLP) is constructed as the classifier to fine-tune the whole network on the newly collected dataset. Finally, the Focal loss is introduced for more effective parameter optimization to tackle the challenge of data imbalance between positive and negative samples, Extensive experiments have been conducted and the following promising results have been obtained: 1) The proposed approach achieves an impressive 99.97% F1 score in recognizing maritime-context texts; 2) The ConvBERT model, an enhancement over the original BERT, demonstrates superior performance in text representation while being more computationally efficient; 3) The Focal loss method outperforms other cost-sensitive learning strategies like class weighting and oversampling techniques; and 4) the proposed method surpasses other deep learning and BERT-based methods in text classification tasks.","url":"https://doi.org/10.1109/cai59869.2024.00165","authors":["Xiaocai Zhang","Hur Lim","Xiuju Fu","Ke Wang","Zhe Xiao","Zheng Qin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-30T17:50:37Z","doi":"10.1109/cai59869.2024.00165","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842860","name":"The Role of Artificial Intelligence in Cost Reduction of Marketing Agencies","source":"crossref","abstract":"Artificial Intelligence (AI) plays a significant role in optimizing operations, increasing productivity, building more efficiency, and reducing costs across all business verticals of every industry. AI is an essential driver in the field of marketing, fueling creativity and innovations by Automating repetitive tasks, enhanced targeting, personalizing communication, optimizing advertising spending, predictive analytics, Customer Support, and much more. This paper investigates the role of AI in reducing costs for marketing agencies, explicitly focusing on AI tools in content creation, content management, and video editing. Also, AI-powered video editing applications speed up the overall editing process, decreasing reliance on costly software and skilled personnel. Towards the end, the study highlights the practical implications of how marketing agencies can leverage AI tools to develop a more robust and profitable business model that is dynamic to suit the current technology age and drives stability for sustainable growth.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842860","authors":["Prathamesh Veling","Palaniappan Sellappan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842860","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107355","name":"Automatic music mood classification using multi-modal attention framework","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107355","authors":["Sujeesha A.S.","Mala J.B.","Rajeev Rajan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-15T20:15:37Z","doi":"10.1016/j.engappai.2023.107355","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107447","name":"A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions","source":"crossref","abstract":"Sustainable urban transport is the key factor for surviving the cities and developing the supply quality of the urban transport system has been esteemed in sustainable improvement for the cities. This work attempts to provide a sustainable and efficient solutions for ameliorating public bus transport system in Dublin city, Ireland. The developed system will attract private car users, which interns will achieve detract CO2 emissions, minimize traffic congestions and maximize commuter satisfaction. To evaluate this complex problem, the novel Parsimonious Analytic Hierarchy Process (P-AHP) is structured in a spherical fuzzy environment. The parsimonious spherical fuzzy analytic hierarchy process (P– SF-AHP) model considers as an efficient solution not only for evaluating a large number of alternatives or criteria when using AHP, however, it esteems the hesitant scoring of the decision maker. The results are demonstrated and analyzed in detail and the step-by-step description of the procedure might foment other applications of the model. The unique process for evaluating the supply quality of urban transport system consumes less time and effort during estimating the survey, moreover, it provides more consistent and reliable outcomes through avoiding the uncertainty and ambiguity of decision makers during evaluation process.","url":"https://doi.org/10.1016/j.engappai.2023.107447","authors":["Sarbast Moslem"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-18T08:42:32Z","doi":"10.1016/j.engappai.2023.107447","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108912","name":"BagFormer: Better cross-modal retrieval via bag-wise interaction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108912","authors":["Haowen Hou","Xiaopeng Yan","Yigeng Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-17T18:41:09Z","doi":"10.1016/j.engappai.2024.108912","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107304","name":"TATrack: Target-aware transformer for object tracking","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107304","authors":["Kai Huang","Jun Chu","Lu Leng","Xingbo Dong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-27T12:56:20Z","doi":"10.1016/j.engappai.2023.107304","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-47768-3_9","name":"Artificial Intelligence in Musculoskeletal Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-47768-3_9","authors":["Marco Keller","Florian M. Thieringer","Philipp Honigmann"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-04T09:12:44Z","doi":"10.1007/978-3-031-47768-3_9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842922","name":"Advancing Alzheimer's Diagnosis: Performance Analysis of Deep Learning Ensembles in Medical Imaging","source":"crossref","abstract":"Neuroimaging and deep learning have become the focus of much research for diagnosing Alzheimer's disease (AD) in recent years. Nevertheless, the limited availability of neuroimaging training data has resulted in significant overfitting issues for several deep learning models. However, the dataset was very imbalanced. To address this issue, we employed several augmentation techniques such as flipping, rotating, and zooming to balance the dataset. The ADNI dataset consisted of four classes: mild demented, moderate demented, very mild demented, and non-demented. In this work, we have proposed an ensemble of EfficientNetB2, DenseNet121, VGG16, and Xception for the diagnosis of Alzheimer at early stage. Experiment results shows that EfficientNetB2 ensemble with Xception and VGG16 performed extremely well with 99.1% and 99.4% respectively.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842922","authors":["Sonali Deshpande","Nilima Kulkarni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842922","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icapai61893.2024.10541270","name":"ICAPAI 2024 TOC","source":"crossref","abstract":"hydroelectric power plants in a deregulated power market by means of a deep deterministic policy gradient algorithm . . . . . .","url":"https://doi.org/10.1109/icapai61893.2024.10541270","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T17:28:27Z","doi":"10.1109/icapai61893.2024.10541270","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-73497-7_27","name":"Protection of Copyrights in the Era of Generative Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-73497-7_27","authors":["Roberto Vasconcelos Novaes","Francesca Flávio Ferraz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T04:01:10Z","doi":"10.1007/978-3-031-73497-7_27","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3700297.3700354","name":"A Path Study of Generative Artificial Intelligence Enabling Online Education Platforms in Colleges and Universities","source":"crossref","abstract":"With the development of Internet and AI technologies, online education platforms in colleges and universities face challenges in personalized teaching and teacher-student interaction. Based on the technical characteristics of generative AI, combined with the project-based learning (PBL) approach, this study proposes specific paths and strategies to optimize online education platforms in colleges and universities. The study adopts the literature analysis method to systematically sort out the status quo and feasibility of generative AI and online education platform in colleges and universities. On this basis, this paper designs two main paths of intelligent generation of teaching resources and optimization of learning process based on generative AI. The former includes course content generation, teaching interaction generation and evaluation feedback generation; the latter covers learning data analysis, intelligent learning progress tracking and dynamic evaluation of learning effects. Through the theoretical analysis of the path design, this study provides a preliminary theoretical framework and practical ideas for the intelligent upgrading of online education platforms in colleges and universities, which can be used as a reference for the subsequent research and practical application in this field.","url":"https://doi.org/10.1145/3700297.3700354","authors":["Xiling Wang","Lei Lei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-18T22:31:26Z","doi":"10.1145/3700297.3700354","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108265","name":"Fuzzy fractional generalized Bagley–Torvik equation with fuzzy Caputo gH-differentiability","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108265","authors":["Ghulam Muhammad","Muhammad Akram"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-19T19:52:13Z","doi":"10.1016/j.engappai.2024.108265","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.15407/jai2024.04.195","name":"Artificial Intelligence in Consumer-driven Contract Testing of Distributed Systems","source":"crossref","abstract":"This article explores the case of the usage of artificial intelligence (AI) for optimizing the process of covering distributed systems with consumer-driven contract test, analyzing the pros and cons of this approach. Considering the complexity of development of modern distributed systems, like microservices, and the need to ensure the system components interactions keep reliable as long as the system keeps evolving this study is focused on finding the most effective way to introduce the contact testing into such systems to maximize the contracts tests coverage while minimizing development costs. The contract testing has its challenges: steep learning curve, impact on the delivery lifecycle, spreading the approach consistently across the organization. These challenges often lead to teams sacrificing the benefits of the approach and using more traditional ways of testing, like end-to-end (E2E) testing, which however does not fit well into distrusted system. The described methodology includes generating (by AI platform) the contract between the parties (consumer and provider), generating the consumer test to verify the provider is compatible with the expectations the consumer has of it. It is proposed to use following inputs for AI as the source for generation: request-response pairs, OpenApi specification, consumer codebase. The research employs Pact as a tool that allows to define a contract between a consumer and a provider as well as verify that both sides adhere to this contract. NodeJS is used as a framework for consumer and provider development. PactFlow platform with its HaloAI executes contracts and tests generation. The proposed approach simplifies the road to introduce the contact testing into the distributed systems, increases the development team effectiveness in system implementation and a confidence in its stability","url":"https://doi.org/10.15407/jai2024.04.195","authors":["Harasymchuk O"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-18T16:32:47Z","doi":"10.15407/jai2024.04.195","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108136","name":"StainSWIN: Vision transformer-based stain normalization for histopathology image analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108136","authors":["Elif Baykal Kablan","Selen Ayas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-02T00:46:41Z","doi":"10.1016/j.engappai.2024.108136","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107215","name":"Towards an autonomous clinical decision support system","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107215","authors":["Sapir Gershov","Aeyal Raz","Erez Karpas","Shlomi Laufer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-04T18:43:29Z","doi":"10.1016/j.engappai.2023.107215","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108979","name":"Backpropagation artificial neural network-based maximum power point tracking controller with image encryption inspired solar photovoltaic array reconfiguration","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108979","authors":["Madavena Kumaraswamy","Kanasottu Anil Naik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-16T02:39:28Z","doi":"10.1016/j.engappai.2024.108979","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1002/9781119846567.ch15","name":"Legal Aspects of AI in the Biomedical Field. The Role of Interpretable Models","source":"crossref","abstract":"This chapter analyzes the complex legal framework applicable to the development and the use of AI systems in the medical field. Fundamental principles of GDPR ant AI Act proposal are explained, such as transparency, legal basis, right of explanation, accountability, fairness, and human oversight, and the role of interpretability is highlighted.","url":"https://doi.org/10.1002/9781119846567.ch15","authors":["Chiara Gallese"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-25T00:12:12Z","doi":"10.1002/9781119846567.ch15","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.3233/faia250606","name":"Attention-Based MIL for Medical Malpractice Prediction","source":"crossref","abstract":"Medical malpractice prediction is challenging due to the weakly labeled, heterogeneous, and multi-instance structure of claims data. We introduce Deep Attention MIL (DAMIL), an attention-based Multiple Instance Learning model that learns to identify the most informative instances within each claim. By optimizing attention weights end-to-end, DAMIL improves both prediction and interpretability. We evaluate DAMIL on two datasets: (1) a synthetic benchmark with controlled risk patterns, and (2) a real-world dataset from the Col·legi de Metges de Barcelona. DAMIL outperforms traditional MIL and a Bag-of-Words baseline, reaching AUCs of 0.715 (synthetic) and 0.714 (real). Instance-level attention provides interpretable insights into risk-relevant claim components.","url":"https://doi.org/10.3233/faia250606","authors":["Arnau Bueno Tricas","Jose A. Rodríguez Serrano","Jennifer Nguyen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T14:40:10Z","doi":"10.3233/faia250606","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.7551/mitpress/15620.003.0006","name":"Working Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15620.003.0006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-17T20:28:42Z","doi":"10.7551/mitpress/15620.003.0006","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108521","name":"Electricity consumption prediction based on a dynamic decomposition-denoising-ensemble approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108521","authors":["Feng Gao","Xueyan Shao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-09T19:32:39Z","doi":"10.1016/j.engappai.2024.108521","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/84r9i6","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/84r9i6","authors":["John Howard"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-25T14:53:47Z","doi":"10.32388/84r9i6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842923","name":"Efficient Detection and Categorization of Thyroid Nodules from Medical Ultrasound Images","source":"crossref","abstract":"The use of ML and DL in medical imaging for early illness symptom prediction is substantial. Since 2013, DL has been one of the rising trends in general data analysis. With its multiple hidden layers that allow for a high degree of data abstraction, it is an enhancement of artificial neural networks (ANN). One promising approach for computer vision applications is the use of convolutional neural networks (CNNs). Automatic learning of raw data, particularly photographs, is a capability of deep CNNs. Both the capture and interpretation of images are crucial for the correct diagnosis or evaluation of illness. Devices are able to gather data at a rapid pace with improved resolution because to advancements in image acquisition over the last decade. Nevertheless, computer technology has only just started to improve picture interpretation. Since they are subjective and need highly trained doctors, they are often created by radiologist, physicians, and senior doctors. In medical imaging, computerised tools play a crucial role in facilitating results and improving diagnosis. Early detection and categorization of thyroid nodules by visual inspection and manual study of thyroid ultrasonography (USG) images has traditionally been a tedious process. Identifying benign from malignant thyroid nodules requires manual evaluation of thyroid USG images, which may be a time-consuming and laborious process. The medical industry has seen the emergence of many deep learning models, particularly for the categorization of thyroid nodules, thanks to the growth in processing resources and the fast improvement in technology. Clinical interventions and therapies may be more successful when these nodules are detected early. Thus, a growing number of academics are pushing for the use of computer diagnostic systems (CDS) as a means to statistically and scientifically evaluate USG pictures of thyroid nodules. Creating effective models for identifying and classifying thyroid nodules using ML and DL approaches is the main goal of this effort. In the first stage of this project, two hybrid models, ANN-SVM and CNN-SVM, are suggested.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842923","authors":["Prarthana A. Deshkar","Punit Fulzele"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842923","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.63475/yjm.v3i1.4552","name":"Balancing innovation with responsibility: A policy proposal for ethical artificial intelligence use in medical scholarly publication","source":"crossref","abstract":"The roots of artificial intelligence (AI) as a terminology go back to the mid-20th century, specifically to the year 1956 when this term was first introduced by John McCarthy, who is considered the father of AI [1]. However, it remained confined to laboratories and research centers, with its use limited to experts, and many aspects of AI remained theoretical during the AI winter period until the 21st century. The year 2022 can be considered a turning point in this field, as OpenAI introduced Chat Generative Pre-trained Transformer (GPT), the most famous AI chatbot that uses various advanced technologies to simulate human conversation for answering questions and generating texts. This breakthrough prompted other leading technology companies such as Google and Microsoft to develop their own chatbots [2]. This marked the beginning of the rapidly growing use of AI in academia and scientific research, with AI-generated articles being submitted to scientific journals, including medical ones, for publication, either acknowledging the use of AI or without disclosure. Some scientific papers have even been published with ChatGPT credited as one of the authors. Recently, voices have risen to reject this trend, considering it a threat to academic integrity, honesty, and responsibility, leading to calls for setting boundaries and regulations for the use of AI tools in academia and scientific publishing [3,4].","url":"https://doi.org/10.63475/yjm.v3i1.4552","authors":["Maher Mohamad Najm","Moustafa Mohamad Najm"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-12T09:09:51Z","doi":"10.63475/yjm.v3i1.4552","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.36740/emems202402109","name":"Novel ways of applying artificial intelligence in emergency medicine - literature review","source":"crossref","abstract":"Artificial intelligence (AI) holds immense promise for revolutionizing emergency medicine, expediting diagnosis and treatment decisions. This review explores AI’s wide-ranging applications in emergency care, ranging from managing out-of-hospital cardiac arrest (OHCA) to diagnosing fractures, spine injuries, stroke, and pulmonary embolisms, and even assisting in search and rescue missions with snake robots. In OHCA cases, AI aids in early detection, survival prediction, and ECG waveform classification, bolstering prehospital care efficiency. AI-powered digital assistants like the AI4EMS platform optimize diagnosis and patient prioritization, reducing overlooked cases of cardiac arrest and improving response times. Furthermore, AI algorithms enhance the diagnosis of conditions such as pneumothorax, pulmonary emphysema, and fractures by analysing medical images with exceptional accuracy, often outperforming human experts. In stroke and pulmonary embolism, AI expedites diagnosis through automated imaging analysis, enabling swift treatment. AI may enhance triage methods with independent systems, improving patient sharing and treatment quality while minimizing infection risks, especially during pandemics. Medical professionals generally welcome AI triage systems, acknowledging their potential to enhance healthcare efficiency. It is important to understand the scope of development of AI in order to make its application beneficial.","url":"https://doi.org/10.36740/emems202402109","authors":["Jakub Fiegler-Rudol","Magdalena Kronenberg","Tomasz Męcik-Kronenberg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-05T08:36:50Z","doi":"10.36740/emems202402109","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.56397/jimr/2024.03.04","name":"Utilizing Artificial Intelligence in the Development of Medicinal Drugs: Practical Applications","source":"crossref","abstract":"The COVID-19 pandemic has underscored the critical need for innovative drug discovery methods. The journey from conceptualizing a drug to its clinical application is fraught with potential pitfalls, including extensive complexity, significant expenses, and a high risk of failure. Recent years have witnessed remarkable advancements in technologies such as cloud computing, GPUs, and TPUs. These developments, coupled with the surge in medical data availability and the emergence of deep learning, present an unprecedented opportunity to enhance drug discovery processes. By leveraging artificial intelligence (AI) to analyze vast amounts of data-from extensive molecular screening outcomes to individual health records and public health data-the efficiency of the drug discovery pipeline could be significantly improved, minimizing the likelihood of failure. This paper explores the application of AI in various phases of drug development, including the use of computational strategies for de novo drug design and the prediction of drug properties. We address challenges associated with molecular representation, data acquisition, complexity, and the inconsistencies in labeling across open-source databases and AI-powered tools that support drug discovery efforts. Furthermore, we examine the role of advanced AI techniques, such as graph neural networks, reinforcement learning, generative models, and structure-based methods like molecular docking and dynamics simulations, in enhancing drug discovery and evaluating drug efficacy.","url":"https://doi.org/10.56397/jimr/2024.03.04","authors":["Bassam Abdul Rasool Hassan","Ali Haider Mohammed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-02T21:47:36Z","doi":"10.56397/jimr/2024.03.04","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.4324/9781032627236-4","name":"Trust in artificial intelligence","source":"crossref","abstract":"This chapter is an attempt to point out what organizations should do to move toward artificial intelligence systems that are ethical. What benefits they can derive from this, and what the consequences will be if they do not implement such systems. Sources used in this chapter include the literature on artificial intelligence and the Capgemini Research Institute report AI and the Ethical Conundrum: How Organizations Can Build Ethically Sound Artificial Intelligence Systems and Earn Trust. The survey was conducted at 800 organizations and focused on issues of trust and ethics. It examined: (1) the risks organizations face with regard to the trust they share with key stakeholders – from customers to employees; (2) the extent to which organizations have operationalized ethical principles such as, explainability, transparency, integrity, and auditability; (3) and to what extent they have developed their internal practices.","url":"https://doi.org/10.4324/9781032627236-4","authors":["Barbara Wyrzykowska","Agnieszka Tul-Krzyszczuk","Tetiana Balanovska"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T10:53:51Z","doi":"10.4324/9781032627236-4","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1017/9781009031721.011","name":"The Eschatological Future of Artificial Intelligence","source":"crossref","abstract":"While we call programs that are new and exciting ‘artificial intelligence’ (AI), the ultimate goal – to produce an artificial general intelligence that can equal to human intelligence – always seems to be in the future. AI can, thus, be viewed as a millenarian project. Groups predicting the second coming of Christ or some other form of salvation have flourished in times of societal stress, as they promise a solution to current problems that is delivered from outside. Today, we project both our hopes and our fears onto AI. Utopian visions range from the personally soteriological prospect of uploading our brains to a vision of a world in which AI has found solutions to our problems. Dystopian scenarios involve the creation of a superintelligent AI that slips from our control or is used as a weapon by malicious actors. Will AI save us or destroy us? Probably neither, but as we shape the trajectory of its future, we also shape our own.","url":"https://doi.org/10.1017/9781009031721.011","authors":["Noreen Herzfeld"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-17T22:30:28Z","doi":"10.1017/9781009031721.011","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/aicit62434.2024.10730129","name":"Intelligent Recognition Technology for Medical Laboratory Report Images Based on Deep Learning","source":"crossref","abstract":"In the medical field, the accurate identification and utilization of laboratory report information is crucial for enhancing the efficiency and accuracy of diagnosis and treatment. This study employs deep learning technology to systematically research and develop techniques for extracting information from medical laboratory reports. We designed and implemented a series of algorithms that effectively convert image-based laboratory reports into structured data through layout analysis, text detection, text recognition, and table recognition. Experimental results show that the proposed methods achieve high levels of accuracy, significantly enhancing the automation of laboratory report information processing. This provides substantial technical support for medical institutions in handling large volumes of laboratory report data.","url":"https://doi.org/10.1109/aicit62434.2024.10730129","authors":["Qianzhuo Cai","Sheng Zheng","Jianke Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-30T17:45:02Z","doi":"10.1109/aicit62434.2024.10730129","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.52507/2345-1106.2024-2.34","name":"The psychotherapeutic approach of artificial intelligence vs human intelligence. Artificial intelligence in the psychotherapeutic approach","source":"crossref","abstract":"This article presents an analysis of the integration of artificial intelligence into psychotherapy and clinical practice, providing insights into the benefits and challenges of this integration. It also analyzes the future potential of AI to revolutionize the way we deliver and access mental health services. The article explores the advantages of using AI in psychotherapy, as well as the ethical and security challenges. It also analyzes current applications and the future potential of AI in the psychotherapeutic approach.","url":"https://doi.org/10.52507/2345-1106.2024-2.34","authors":["Aurelia Cojocaru","Libi Bubuioc"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-10T15:06:52Z","doi":"10.52507/2345-1106.2024-2.34","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3655497","name":"2024 the 8th International Conference on Innovation in Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3655497","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-04T18:24:12Z","doi":"10.1145/3655497","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2139/ssrn.4893408","name":"Artificial General Intelligence: Transcending Human Limitations and Exploring New Frontiers of Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4893408","authors":["Madhu Prabakaran"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-10T00:27:55Z","doi":"10.2139/ssrn.4893408","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-19073-5.00009-4","name":"Artificial intelligence-based obstructive sleep apnea detection using ECG signals","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-19073-5.00009-4","authors":["Usha Rani Kandukuri","Nalla Maheswara Rao","J. Sivaraman","Kunal Pal","Bala Chakravarthy Neelapu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T06:50:57Z","doi":"10.1016/b978-0-443-19073-5.00009-4","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-22308-2.00009-3","name":"Optical coherence tomography image classification for retinal disease detection using artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22308-2.00009-3","authors":["Muhammed Enes Subasi","Sohan Patnaik","Abdulhamit Subasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T07:43:47Z","doi":"10.1016/b978-0-443-22308-2.00009-3","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-981-97-3076-6","name":"New Frontiers in Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-3076-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-27T23:02:21Z","doi":"10.1007/978-981-97-3076-6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.5644/pi2024.215.01","name":"Influence of Artificial Intelligence on Methodologies and Processes for Engineering Software-Enabled Systems in Industry 4.0","source":"crossref","abstract":"Software currently presents is a corner stone of systems in Industry 4.0 (I4.0). To engineer software for these systems, engineers follow different methodologies and processes. These methodologies and processes aim to systemise production of highquality software systems and make it possible to reproduce success in software engineering projects. With the introduction of AI in software engineering, actions that engineers perform are changing. Consequently, challenges and responsibilities of developers change. It becomes valid to ask: how will processes and methodologies in software engineering change with the introduction of AI? That means, what will be the new challenges that software engineering methodologies and processes need to solve, and which current challenges will simply disappear or become irrelevant. To answer these questions, in this paper, we abstract and summarise actions and aims of processes and methodologies in software engineering. We make predictions of what is it that humans bring to the table when it comes to software engineering, where can AI assist humans, and where AI has potential to fully replace humans. We discuss these predictions in the context of quality properties of I4.0 systems (e.g., security, safety), which must be taken into account when engineering I4.0 software-enabled systems.","url":"https://doi.org/10.5644/pi2024.215.01","authors":["Jasmin Jahić"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-09T08:00:07Z","doi":"10.5644/pi2024.215.01","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icecai62591.2024","name":"2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icecai62591.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T17:22:31Z","doi":"10.1109/icecai62591.2024","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1142/9789811293993_bmatter","name":"BACK MATTER","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_bmatter","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_bmatter","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-24001-0.20001-8","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24001-0.20001-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-01T09:14:23Z","doi":"10.1016/b978-0-443-24001-0.20001-8","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.semarthrit.2023.152321","name":"Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery","source":"crossref","abstract":"The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care enhancing image reconstruction, detecting cancer or predicting individual risk to support treatment decisions and patient management. The entry into clinical care is determined by technological feasibility, integration into effective workflows, and immediacy of benefits. At the same time, research is advancing the integration of imaging data and other modalities such as genomics, and the linking of observations made at large scale with the understanding of underlying biological processes. AI will have impact in imaging and precision medicine not only because of the successful application of techniques established in other domains, but primarily because of the effective joint development of new technology and corresponding advance of diagnosis and care.","url":"https://doi.org/10.1016/j.semarthrit.2023.152321","authors":["Georg Langs"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-22T03:42:35Z","doi":"10.1016/j.semarthrit.2023.152321","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/ic-ftai62324.2024.10950026","name":"The Efficiency of Medical Diagnostic Robots in the Light of Legal Liability","source":"crossref","abstract":"Robots in medical diagnostics assist healthcare personnel by boosting precision, speed, and consistency in processing patient data, ultimately supporting early and precise detection of diseases. These robotic devices frequently use AI and machine learning algorithms to interpret complex medical data and improve diagnostic findings. A robot entitled “Dr. HEMA: Horus Expert Medical Assistant” Robot is presented in this paper to assist medical personnel in the diagnosis of chronic diseases with an accuracy of 98.8% and an accuracy of 85% in predicting the severity of chronic diseases. Given the increasing use of artificial intelligence systems, especially smart robots, in various fields, there are concerns about the potential harm they may cause to others. Therefore, it is essential to address these issues to understand the extent to which civil liability is established for damages caused by smart robots and, consequently, the compensation for these damages. The regulations for protecting sensitive medical personal data in the context of a newly established right to health must be taken into consideration. To establish a legal basis for liability for damages caused by intelligent robots, it has been proposed to grant them legal personality. However, this proposal has not been universally agreed upon, as opinions differ regarding the granting of legal personality to intelligent robots. Some support the idea, while others oppose it. Throughout this paper, HEMA's diagnostic ability is traced within the frame of legal liability.","url":"https://doi.org/10.1109/ic-ftai62324.2024.10950026","authors":["Yara H. El-Gendy","Mohammed K. Hassan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-14T17:35:44Z","doi":"10.1109/ic-ftai62324.2024.10950026","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-70310-2_18","name":"Artificial Intelligence in Osteoporosis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-70310-2_18","authors":["Efstathios Chronopoulos","Angelos Kaspiris","Laurence Okeke","Raffaella Russo","Tiziana Montalcini","Arturo Pujia","Edward G. McFarland"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-19T12:41:49Z","doi":"10.1007/978-3-031-70310-2_18","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1002/9781394175574.ch6","name":"Artificial Intelligence Applications in the Indian Financial Ecosystem","source":"crossref","abstract":"The Indian banking and financial services (BFS) ecosystem uses artificial intelligence (AI) primarily in five major areas—customer service/engagement (chatbot), robo advice, general purpose/predictive analytics, cybersecurity, and credit scoring/direct lending. While initial AI applications focused on support functions, they evolved to help in decision-making over time. As web servers capture and collect a huge quantum of customer data, companies are taking advantage of artificial intelligence, big data analytics, and machine learning. The technology trio is helping financial companies, particularly startups, build innovative products, monitor, manage risk, and provide superior customer services. Indian startups made their mark by successfully demonstrating AI use cases that suit the Indian atmosphere. On the other hand, financial regulators promote and recommend using AI in a limited way (such as in regulator sandbox areas) that fosters innovative financial engineering and product development and brings in the safety and security of customer data and monies. This research article updates how the Indian financial ecosystem uses artificial intelligence in various dimensions.","url":"https://doi.org/10.1002/9781394175574.ch6","authors":["Vijaya Kittu Manda","Khaliq Lubza Nihar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-28T10:17:47Z","doi":"10.1002/9781394175574.ch6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.caeai.2024.100201","name":"Beginning and first-year language teachers’ readiness for the generative AI age","source":"crossref","abstract":"The public release of ChatGPT in November 2022 ignited an intense debate about the effects generative AI (GAI) tools will have on language teaching. The advanced capability of GAI tools and their rapid uptake by students has brought both challenges and opportunities to language teachers. This qualitative study, using in-depth individual and group interviews with ten beginning teachers and seventeen first-year English language teachers, explored their readiness for using GAI tools in their professional work and their perceptions of GAI in language teaching. The study found that first-year teachers were generally ready for the use of GAI tools and could recognize its potential to support their professional work. This was largely due to their experiences using ChatGPT. However, beginning teachers were not ready to use GAI tools in their professional work and had little knowledge about them. The study provides insights into the participants’ GAI readiness; awareness of GAI tools and their capabilities and functions; utilization of GAI tools for language teaching; views towards students' use of GAI tools; and thoughts on how to prepare language learners to use GAI tools productively and critically. The study has implications for the preparation and professional development of early career teachers in the GAI-age.","url":"https://doi.org/10.1016/j.caeai.2024.100201","authors":["Benjamin Luke Moorhouse"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-04T01:13:07Z","doi":"10.1016/j.caeai.2024.100201","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.24075/medet.2024.015","name":"Ethical and cultural challenges posed by artificial intelligence (AI) in medical practice: multicultural analysis","source":"crossref","abstract":"The use of AI in medical practice offers a number of significant and visible advantages. The problem of the presented research is relevant due to the growing integration of technologies into healthcare. The purpose of this study is to analyze the ethical and cultural challenges associated with integration of artificial intelligence (AI) in medical practice in various cultural contexts. The main objective of the study is to identify specific problems and suggest possible solutions to ensure effective and justifiable use of AI. To achieve this goal, a literary review, a case study, an expert interview, and a questionnaire were used. The main areas of ethical and cultural challenges include the issues of confidentiality and data protection; culturally specific attitudes towards automation of medical decision-making; the impact of algorithm bias on the diagnosis and treatment of various ethnic groups; ethical dilemmas related to access and fairness in the use of medical AI systems. The study highlights the need to develop ethical standards for the use of AI in medicine that will take into account cultural differences.","url":"https://doi.org/10.24075/medet.2024.015","authors":["NL Wiegel","E Mettini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-02T15:09:49Z","doi":"10.24075/medet.2024.015","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.4337/9781035307555.00018","name":"Bibliography","source":"crossref","abstract":"Taxing Artificial Intelligence will be essential reading for scholars, policy makers and students across law and economics. It will also be invaluable for law and tax professionals seeking to understand the latest developments in AI, automation, and the future of work.","url":"https://doi.org/10.4337/9781035307555.00018","authors":["Xavier Oberson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-12T14:01:52Z","doi":"10.4337/9781035307555.00018","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1111/nyas.15229/v1/review2","name":"Review for \"Artificial intelligence and psychedelic medicine\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/nyas.15229/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T17:06:22Z","doi":"10.1111/nyas.15229/v1/review2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.3386/w32106","name":"Copyright Policy Options for Generative Artificial Intelligence","source":"crossref","abstract":"Joshua Gans has drawn on the findings of","url":"https://doi.org/10.3386/w32106","authors":["Joshua Gans"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-05T19:34:10Z","doi":"10.3386/w32106","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icaie64856.2025.11158359","name":"The Application of Generative Artificial Intelligence in Education: An Analysis of the 25th International Conference on Artificial Intelligence in Education (AIED 2024)","source":"crossref","abstract":"As an emerging technology, generative artificial intelligence (GenAI) has shown great potential for application in the field of education. Based on the research results of the 2024 AIED conference, this article discusses the current application status, research results, and trend challenges of generative AI in the field of education. Research indicates that generative AI can automatically generate educational content, deliver personalized learning experiences, and establish adaptive learning environments, thereby enhancing teaching efficiency and learning outcomes. However, generative AI also faces challenges such as gender differences, ethical issues, fairness, and academic misconduct. This article emphasizes the importance of the application of generative AI in the field of education, and calls on all parties to work together to promote the healthy and fair development of generative AI, and contribute to building a more efficient and personalized education system.","url":"https://doi.org/10.1109/icaie64856.2025.11158359","authors":["Zefei Wang","Kaiquan Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T17:24:05Z","doi":"10.1109/icaie64856.2025.11158359","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1148/ryai.042624.podcast","name":"AI for Opportunistic Imaging Part 2","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.042624.podcast","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-26T13:51:55Z","doi":"10.1148/ryai.042624.podcast","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2139/ssrn.5000124","name":"Social Impact Governance of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5000124","authors":["Pranav Mamidipudi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-04T16:20:52Z","doi":"10.2139/ssrn.5000124","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1148/rg.230067.quiz","name":"Understanding and Mitigating Bias in Imaging Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1148/rg.230067.quiz","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-30T20:03:55Z","doi":"10.1148/rg.230067.quiz","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/acait63902.2024.11022315","name":"ACAIT 2024 Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acait63902.2024.11022315","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-10T17:48:32Z","doi":"10.1109/acait63902.2024.11022315","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.33606/yla.44.7","name":"Artificial intelligence governance theory – Artificial intelligence within constitutional principles and power structure –","source":"crossref","abstract":"인공지능이 사회 곳곳에 침투하면서 이제는 어느 분야든 인공지능이 빠지면 시대에 뒤떨어지는 것 같은 사회 분위기가 형성되었다. 본고는 정치 영역에 인공지능의 영향과 그에 대한 헌법적 대응을 살펴보고자 하였다. 헌법 원리에 따라서 권력구조 안에서 기능하는 인공지능의 모습을 자유민주주의, 권력분립의 원리, 선거제도의 영역으로 나누어 고찰하였다. 과학기술의 발전과 산업적 관점에서 인공지능의 진보는 피할 수 없는 현실이지만 그 유용성은 유지하면서도 자유민주주의와 권력분립의 원리, 선거제도 그리고 국가기관 간의 권력구조에 있어서 부정적 영향을 최소화하는 노력이 병행되어야 할 것이다. AI법 제정에 잠정적으로 합의한 EU와 같이 법률로서 인공지능의 진보에 따른 위험성을 제어하면서 안전하고 신뢰가능하며 헌법적 가치를 구현하는 인공지능의 사회적 수용을 추구하는 것도 필요하겠지만, 고착화된 법률의 형태 이전에 충분히 국가와 사회 간에 민주적 논의가 지속될 수 있도록 하는 노력도 필요하다.","url":"https://doi.org/10.33606/yla.44.7","authors":["Juhee Eom"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-27T02:39:35Z","doi":"10.33606/yla.44.7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2024.102987","name":"A self-supervised deep Riemannian representation to classify parkinsonian fixational patterns","source":"crossref","abstract":"Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, and it remains incurable. Currently there is no definitive biomarker for detecting PD, measuring its severity, or monitoring of treatments. Recently, oculomotor fixation abnormalities have emerged as a sensitive biomarker to discriminate Parkinsonian patterns from a control population, even at early stages. For oculomotor analysis, current experimental setups use invasive and restrictive capture protocols that limit the transfer in clinical routine. Alternatively, computational approaches to support the PD diagnosis are strictly based on supervised strategies, depending of large labeled data, and introducing an inherent expert-bias. This work proposes a self-supervised architecture based on Riemannian deep representation to learn oculomotor fixation patterns from compact descriptors. Firstly, deep convolutional features are recovered from oculomotor fixation video slices, and then encoded in compact symmetric positive matrices (SPD) to summarize second-order relationships. Each SPD input matrix is projected onto a Riemannian encoder until obtain a SPD embedding. Then, a Riemannian decoder reconstructs SPD matrices while preserving the geometrical manifold structure. The proposed architecture successfully recovers geometric patterns in the embeddings without any label diagnosis supervision, and demonstrates the capability to be discriminative regarding PD patterns. In a retrospective study involving 13 healthy adults and 13 patients diagnosed with PD, the proposed Riemannian representation achieved an average accuracy of 95.6% and an AUC of 99% during a binary classification task using a Support Vector Machine.","url":"https://doi.org/10.1016/j.artmed.2024.102987","authors":["Edward Sandoval","Juan Olmos","Fabio Martínez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T16:02:18Z","doi":"10.1016/j.artmed.2024.102987","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.107931","name":"Small object detection using deep feature learning and feature fusion network","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.107931","authors":["Kang Tong","Yiquan Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-25T02:06:09Z","doi":"10.1016/j.engappai.2024.107931","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-50300-9_1","name":"Artificial Intelligence: An Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50300-9_1","authors":["Ali Jaboob","Omar Durrah","Aziza Chakir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-19T06:02:12Z","doi":"10.1007/978-3-031-50300-9_1","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-60840-7_17","name":"Artificial Intelligence in Intelligent Healthcare Systems–Opportunities and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-60840-7_17","authors":["Anita Petreska","Blagoj Ristevski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-26T19:04:39Z","doi":"10.1007/978-3-031-60840-7_17","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/ictai62512.2024.00075","name":"KB2Bench: Toward a Benchmark Framework for Large Language Models on Medical Knowledge","source":"crossref","abstract":"While Large Language Models (LLMs) have trans-formed question answering tasks, their propensity for hallucinations continues to drive an area of active research. Efforts toward creating benchmarks to test LLMs' performance on queries, in particular for the field of medicine, have led to a few reputable benchmarks, but these are limited in scope because of the amount of human annotation required. Our framework addresses this issue by leveraging existing, large knowledge bases for medicine to generate vast query and answer sets dynamically, which are less likely to be memorized by LLMs. The framework rests on designing a few key knowledge patterns, which can then generate millions (potentially billions) of queries. This offers a more efficient, cost-effective, and scalable alternative to human-curated annotations used in medical question-and-answer benchmarks. Applying our framework to a small sample of five drug related ontologies, we are already capable of more than 100,000 unique drug related queries, which is 10 to 1000 times larger than existing various human annotation efforts. This paper introduces the KB2Bench framework.","url":"https://doi.org/10.1109/ictai62512.2024.00075","authors":["Douglas Adjei-Frempah","Lisa Chen","Paea LePendu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-28T18:32:14Z","doi":"10.1109/ictai62512.2024.00075","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/ictai62512.2024.00124","name":"A DeBERTa-GPLinker-Based Model for Relation Extraction from Medical Texts","source":"crossref","abstract":"Extracting causal relationships in medical texts is essential for improving clinical decision support systems and constructing comprehensive medical knowledge graphs. This paper presents a novel model for extracting causal, conditional, and hypernym relationships from Chinese medical texts, combining DeBERTa's advanced contextual encoding with GPLinker's efficient entity and relationship extraction mechanisms. Our approach computes the score matrix of entities and their causal relationships, followed by a decoding process to obtain the final predictions. On the CMedCausal dataset, comparative experiments highlight our model's superior performance in terms of precision, recall, and F1 score, demonstrating its robustness and effectiveness in managing overlapping and nested entities and accurately extracting causal relationships in Chinese medical texts.","url":"https://doi.org/10.1109/ictai62512.2024.00124","authors":["Zhiqi Deng","Shutao Gong","Xudong Luo","Jinlu Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-28T18:32:14Z","doi":"10.1109/ictai62512.2024.00124","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1080/08839514.2024.2413817","name":"Enhancing Metaphor Recognition of Literary Works in Applied Artificial Intelligence: A Multi-Level Approach with Bi-LSTM and CNN Fusion","source":"crossref","abstract":"Understanding metaphorical language is essential for AI to interpret and communicate with humans accurately. However, current methods often struggle with the complexity of metaphors, making it difficult for AI systems to understand human language fully. Recognizing metaphors is challenging because they are frequently ambiguous and depend on context. In this study, we propose a new approach using a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) networks, Convolutional Neural Networks (CNN), and uni-directional LSTM components to create a multi-level model for recognizing metaphors. Our model uses various features, including dependency, semantics, and part-of-speech, to improve its learning ability. Additionally, we introduce a new method for recognizing the emotional context of metaphors using a random walk model to determine the emotional tone of words. Our results show that this model improves performance in recognizing metaphors, enhancing AI’s ability to understand them.","url":"https://doi.org/10.1080/08839514.2024.2413817","authors":["Na Zhao","Weijie Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-19T18:23:53Z","doi":"10.1080/08839514.2024.2413817","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.3233/faia240217","name":"Artificial Intelligence in Wearables – Challenges and Opportunities in Physical Therapy and Sports Training","source":"crossref","abstract":"Adherence to procedures and rules is essential in order to obtain the best results in medicine and sports. However, traditional clinical setups can induce stress in patients, hindering recovery. Meanwhile, advancements in activity recognition and monitoring technology have revolutionised the sports industry, yet systems suggesting exercises for performance improvement are sparse. At the same time, children training supervision is lacking comprehensive research altogether. In my research, I propose a project that aims to unify wearables solutions in physical therapy, sport training and children development. The key aspects of the research include the exploration of sensor modality fusion in order to obtain better results, body motion tracking, and physiological parameters recording. Planned experiments will focus on joint and torso movement mapping, integration with vital signs in order to perform real-life evaluations in cooperation with athletes, patients, coaches, and therapists.","url":"https://doi.org/10.3233/faia240217","authors":["Joanna Sorysz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-06T14:53:12Z","doi":"10.3233/faia240217","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3640824","name":"2024 8th International Conference on Control Engineering and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3640824","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-08T12:05:28Z","doi":"10.1145/3640824","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.69828/4d4kd5","name":"Intelligence Artificielle et Education à la Démocratie","source":"crossref","abstract":"How can we cultivate democracy in an era of AI: how can we make sure that AI does not jeopardize democracy, now and for future generations, but rather strengthens education for democracy?This is far from being easy or obvious as the impact of AI on democracies is at best ambivalent, with many issues having been justifiably raised, from Cambridge Analytica to \"post truth\", suggesting that AI is probably not the most obvious candidate when thinking about education for democracy.So our question may also be phrased as: \"under which conditions could AI help education for Democracy?\"For quite some time now, we have been living with AI, in many aspects of our lives, private as well as collective, and as a result we have developed new \"forms of lives\" with AI (Wittgenstein; 1975;Agamben; 2013;Winner;2010).These new forms of lives with AI have modified not only our inter-individual but also our collective connections and relationships.Echoing John Dewey's conception of democracy as a \"way of life\" as in (Dewey; 1951), we consider that democracy is not only a political structure but that it also relates to the very fabric of our human lives and collective communities.Fairer forms of representations have helped securing major advances in democracies, e.g.where minorities and people from the non-dominant groups are taken into account or when practices of the governance of the institutions are opened to more diverse voices and to pluralistic discussion.Thus, it takes more than voting for democracy: Education for Democracy is not only about representation but also about expression of diverse voices and pluralistic discussion.","url":"https://doi.org/10.69828/4d4kd5","authors":["John Shawe-Taylor","Vanessa Nurock"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-06T18:56:17Z","doi":"10.69828/4d4kd5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1201/9781003569602-2","name":"Artificial Intelligence Assisted Wearables for Cardiovascular Disease Monitoring","source":"crossref","abstract":"In recent years, the widespread use of digital resources in healthcare has led to their near-universal use. As a result of advancements in detection, screening, diagnostic, and monitoring technologies, patient care has improved, and individuals have more agency over their health. Today’s wearables have sensors that can track biometric data, including heart rate, rhythm, glucose levels, and electrolytes. Wearables or other devices may be useful in high-risk individuals for detecting atrial fibrillation and other pre-clinical indications of cardiovascular disease (CVD), controlling illnesses such as hypertension and heart failure, and encouraging healthy lifestyle choices. Due to developments in materials, electronics, integrated electronic systems, the Internet of Things (IoT), and edge computing, it is now possible to measure and detect signals in real-time with minimal effort. Recent developments in the CVD monitoring of many physiological signals with flexible sensors are discussed in this chapter. To begin, a brief overview of the wide variety of signals that can be employed to monitor CVD is presented. Then, the mechanics and principles behind the various pulse signal monitoring techniques, such as the phonocardiogram (PCG), electrocardiogram (ECG), seismocardiogram/ballistocardiogram (SCG/ BCG), and apexcardiogram (ACG), are discussed. At long last, everyone’s opinions matter, not just those of patients and doctors.","url":"https://doi.org/10.1201/9781003569602-2","authors":["Rishabha Malviya","Shivam Rajput","Deepa Muthiah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T12:50:57Z","doi":"10.1201/9781003569602-2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.4324/9781003468615-35","name":"Why philanthropy should embrace the ideological struggle shaping artificial general intelligence","source":"crossref","abstract":"In this chapter, we delve into the idea of Effective Accelerationism (Eff/acc) in the context of the broader conversation on the hypothetical achievement of Artificial General Intelligence (AGI). Eff/acc combines Effective Altruism’s (EA) focus on prioritizing long-term causes with accelerationism’s emphasis on rapid technological progress. However, there are valid concerns about Eff/acc’s tendency to bestow a God-like stature upon AGI. To address this, this chapter proposes a free and open-source development approach to secularize and dismantle this providential and divine power attributed to AGI. Philanthropy is presented as an essential factor in this process, as it can encourage non-proprietary development models and empower marginalized groups that might otherwise be excluded from AGI development pathways.","url":"https://doi.org/10.4324/9781003468615-35","authors":["Ezekiel K. Takam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-29T11:07:37Z","doi":"10.4324/9781003468615-35","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3677892.3677958","name":"Predicting Entrepreneurial Decisions Using Artificial Intelligence within the Digital Economy Context: A CART Algorithm","source":"crossref","abstract":"In today's rapid development of digital economy, artificial intelligence (AI) has become an indispensable key technology to promote innovation and entrepreneurship. This study focuses on the background of digital economy, especially through the decision tree model, to explore the role of artificial intelligence in entrepreneurial decision making and its impact. This paper uses machine learning-based algorithms to explore in depth how AI can help entrepreneurs make more scientific and effective decisions under changing market conditions. Based on the analysis of big data, a decision tree model is built to predict market demand, assess risks and formulate the effectiveness of market strategies. This research not only examines the application of artificial intelligence in the digital economy from a new perspective, but also provides practical guidelines for entrepreneurs on how to make more efficient and reasonable decisions using AI technology.","url":"https://doi.org/10.1145/3677892.3677958","authors":["Mingsheng Liu","Ling Peng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-26T16:35:50Z","doi":"10.1145/3677892.3677958","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1080/0142159x.2023.2256961","name":"Response to: \"The next paradigm shift? ChatGPT, artificial intelligence, and medical education\"","source":"crossref","abstract":"Dear EditorWe read the paper by Wang et al. (2023) with interest. We agree that while ChatGPT and other artificial intelligence – powered large language models (LLMs) will change the medical educat...","url":"https://doi.org/10.1080/0142159x.2023.2256961","authors":["Li Feng Tan","Jonathan Jun Yi Heng","Desmond B. Teo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-13T18:44:57Z","doi":"10.1080/0142159x.2023.2256961","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3653644.3658509","name":"Improving Artificial Intelligence Translation Ability Based on Attention Mechanism and Layer Jumping Connection","source":"crossref","abstract":"Abstract. The research aims to construct an efficient artificial intelligence translation model, proposing an AI translation model based on attention mechanism and adding skip links. This model extracts and fuses text features through attention mechanism, and achieves deep training of the neural network through skip links, making the neural network have better performance. The results showed that the performance of the TMMCJL model was outstanding, with the fastest convergence speed and the best convergence effect. In the experiment of processing 50 to 400 segments of text, the average accuracy of the TMMCJL was 86.5%, and the F1 score reached 87.5, surpassing the accuracy of the BERT wwm model at 77.9% and 76.2 F1 scores, as well as the Roberta model at 81.5% and 85.6 F1 scores. These data clearly demonstrate the significant advantages of the TMMCJL model in terms of accuracy and F1 score. This model not only improves translation quality, but also has potential wide application value due to its stability in model training and practical application, providing a new direction for the future application of deep learning in the field of NLP.","url":"https://doi.org/10.1145/3653644.3658509","authors":["Lanhua Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T18:24:49Z","doi":"10.1145/3653644.3658509","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icapai61893.2024.10541227","name":"ICAPAI 2024 Preface","source":"crossref","abstract":"This volume contains the papers presented at ICAPAI 2024: International Conference on Applied Artificial Intelligence held on April 16, 2024, in Halden.There were 48 submissions.Each submission was reviewed by at","url":"https://doi.org/10.1109/icapai61893.2024.10541227","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T17:28:27Z","doi":"10.1109/icapai61893.2024.10541227","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-319-94878-2_12","name":"Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-94878-2_12","authors":["Edwin J. R. van Beek","John T. Murchison"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-29T09:16:52Z","doi":"10.1007/978-3-319-94878-2_12","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.61186/iau.34.2.105","name":"The use of artificial intelligence (AI) in pharmacology and the process of drug discovery","source":"crossref","abstract":"Background: The invention of artificial intelligence has changed the way of life in general.Currently, artificial intelligence is used throughout the pharmacology research and the field of drug discovery, and this technology has the power to revolutionize the drug discovery process and improve efficiency, accuracy, and time of process.Materials and methods: In this review article, the results of the published articles were systematically analyzed into the topics of artificial intelligence application in pharmacology, drug industry and drug discovery.The information obtained from the above articles was also classified and reviewed in the same order.Results: The review of 88 revealed the benefits of using artificial intelligence including the expansion and improvement of structures in the drug design process (such as the drug INS018-055 for the treatment of pulmonary fibrosis), better prediction of the effect of the ligand on the receptor, and better cooperation of the health care providers.Disadvantages were the problems of scientific decision-making with artificial intelligence, ethical concerns in the field of pharmaceuticals and recognition of the limitations of approaches based on artificial intelligence.Strengthening neural networks of databases, integration of artificial intelligence with traditional experimental methods, as well as the use of in silico computer tools facilitate the possibility of solving problems. Conclusion:The optimal use of artificial intelligence approaches will lead to the acceleration of the drug discovery process, therefore, it is necessary to carry out more studies related to the effect of artificial intelligence in pharmaceutical research.","url":"https://doi.org/10.61186/iau.34.2.105","authors":["Pouyan pishva","Seyyedeh Zahra Mousavi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T15:25:34Z","doi":"10.61186/iau.34.2.105","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/s40670-024-02178-7","name":"Artificial Intelligence in Medical Education Assessments: Navigating the Challenges to Academic Integrity","source":"crossref","abstract":"Artificial intelligence (AI) chatbots are threatening academic integrity in medical education. Potential misuse of AI chatbots to cheat has led to internet restrictions by academic institutions. We propose five strategies to mitigate this issue: (1) in-person proctored exams, (2) online proctored exams, (3) clear expectations and consequences, (4) institutional culture of integrity, and (5) addressing exam pressure. Given the limitations of these strategies, it may be necessary to consider regulations, collaboration of medical educators with AI developers, and a broader reimagining of medical education.","url":"https://doi.org/10.1007/s40670-024-02178-7","authors":["John C. Lin","Cameron A. Sabet","Christopher Chang","Ingrid U. Scott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T13:38:30Z","doi":"10.1007/s40670-024-02178-7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3708394.3708452","name":"Exploring Educational Transformation and Innovation Pathways in the Era of Artificial Intelligence","source":"crossref","abstract":"This study analyzes the profound impact of artificial intelligence (AI) on education, exploring the applications of educational reform theory, technological innovation theory, and the theory of equal educational opportunities in the context of AI-driven educational transformation. The rapid advancement of AI technologies—particularly deep learning, intelligent image recognition, big data, and educational robotics—is driving education from traditional models toward personalization, lifelong learning, and intelligent approaches. Technological innovation not only revamps teaching tools and resources but also enables differentiated instruction through intelligent data analysis, promoting educational equity and enhancing students’ self-learning abilities. AI demonstrates immense potential in supporting lifelong learning, optimizing educational processes, and enriching the educational ecosystem; however, it also raises ethical challenges, including privacy concerns and risks of educational alienation. Consequently, educators should focus on the responsible application of AI technologies, leveraging the intrinsic strengths of education, to improve teaching quality and foster harmonious development between humans and machines.","url":"https://doi.org/10.1145/3708394.3708452","authors":["Weiwei Sun","Minwu Qin","Zhenyao Yin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-14T12:10:34Z","doi":"10.1145/3708394.3708452","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/nq1gp8","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/nq1gp8","authors":["Michail Ploumis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-05T23:50:15Z","doi":"10.32388/nq1gp8","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2023.102751","name":"Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task","source":"crossref","abstract":"Clinical evaluation evidence and model explainability are key gatekeepers to ensure the safe, accountable, and effective use of artificial intelligence (AI) in clinical settings. We conducted a clinical user-centered evaluation with 35 neurosurgeons to assess the utility of AI assistance and its explanation on the glioma grading task. Each participant read 25 brain MRI scans of patients with gliomas, and gave their judgment on the glioma grading without and with the assistance of AI prediction and explanation. The AI model was trained on the BraTS dataset with 88.0% accuracy. The AI explanation was generated using the explainable AI algorithm of SmoothGrad, which was selected from 16 algorithms based on the criterion of being truthful to the AI decision process. Results showed that compared to the average accuracy of 82.5±8.7% when physicians performed the task alone, physicians' task performance increased to 87.7±7.3% with statistical significance (p-value = 0.002) when assisted by AI prediction, and remained at almost the same level of 88.5±7.0% (p-value = 0.35) with the additional assistance of AI explanation. Based on quantitative and qualitative results, the observed improvement in physicians' task performance assisted by AI prediction was mainly because physicians' decision patterns converged to be similar to AI, as physicians only switched their decisions when disagreeing with AI. The insignificant change in physicians' performance with the additional assistance of AI explanation was because the AI explanations did not provide explicit reasons, contexts, or descriptions of clinical features to help doctors discern potentially incorrect AI predictions. The evaluation showed the clinical utility of AI to assist physicians on the glioma grading task, and identified the limitations and clinical usage gaps of existing explainable AI techniques for future improvement.","url":"https://doi.org/10.1016/j.artmed.2023.102751","authors":["Weina Jin","Mostafa Fatehi","Ru Guo","Ghassan Hamarneh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T12:24:40Z","doi":"10.1016/j.artmed.2023.102751","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.caeai.2024.100307","name":"Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach","source":"crossref","abstract":"In the ever-changing landscape of education, the integration of technology has become an inevitable force that reshapes the foundations of teaching and learning. Amidst this transformative wave, the concept of Artificial Intelligence (AI) has taken center stage, promising innovative approaches, and increased efficiency. Within this context, the exploration of preservice teachers' behavioural intention to employ AI in lesson planning has emerged as a critical issue for examination. This study used a descriptive cross-sectional survey design and employed a purposive sampling technique to recruit 783 preservice teachers. By employing a cutting-edge dual-staged partial least squares structural equation modelling-artificial neural network (PLS-SEM-ANN) approach, this study investigated the influence of the following essential variables on preservice teachers' intentions to incorporate AI into their lesson planning endeavours: performance expectancy, effort expectancy, habit, hedonic motivation, social influence, and facilitating conditions. Social influence emerged as the most significant positive predictor of preservice teachers' behavioural intention to use AI in lesson planning. Additionally, habit, performance expectancy, effort expectancy, and facilitating conditions substantially positively influenced preservice teachers' behavioural intention to use AI in lesson planning. Conversely, hedonic motivation did not significantly affect preservice teachers’ behavioural intention to use AI in lesson planning. This study not only enhances our understanding of technology integration in pedagogy from a theoretical standpoint but also provides practical recommendations for refining educational curricula and instructional strategies that promote effective AI integration.","url":"https://doi.org/10.1016/j.caeai.2024.100307","authors":["Bernard Yaw Sekyi Acquah","Francis Arthur","Iddrisu Salifu","Emmanuel Quayson","Sharon Abam Nortey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-25T03:46:07Z","doi":"10.1016/j.caeai.2024.100307","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/wsai62426.2024.10828981","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wsai62426.2024.10828981","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T19:22:07Z","doi":"10.1109/wsai62426.2024.10828981","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/c2023-0-01233-x","name":"Mechanism Design, Behavioral Science and Artificial Intelligence in International Relations","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2023-0-01233-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T09:15:05Z","doi":"10.1016/c2023-0-01233-x","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1142/9789811293993_0009","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0009","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0009","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-57208-1_11","name":"Robotics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-57208-1_11","authors":["Christian Posthoff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-21T07:02:17Z","doi":"10.1007/978-3-031-57208-1_11","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-981-97-5038-2","name":"Embedded Artificial Intelligence","source":"crossref","abstract":"The professional book comprehensively introduces embedded artificial intelligence including principles, platforms, and real-world application cases","url":"https://doi.org/10.1007/978-981-97-5038-2","authors":["Bin Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-06T03:31:29Z","doi":"10.1007/978-981-97-5038-2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107815","name":"Adapting bandit algorithms for settings with sequentially available arms","source":"crossref","abstract":"Many real-world applications involve a sequential decision-making process where the options presented simultaneously. However, other applications, such as, Internet campaign management and environmental monitoring, the available options are presented sequentially to the decision-maker who, at each time, is asked to select the proposed option or not. This scenario is defined as the Sequential Pull/No-Pull setting The present study aims at developing a meta-algorithm, namely Sequential Pull/No-pull for MAB (Seq), to adapt any classical MAB (Multi-Armed Bandit) policy for this setting both in the case of regret minimization (RM) and best-arm identification (BAI) problems. This is achieved by exploting the sequential nature of the these settings allowing to select multiple arms and gather more information compared to classical policies. The proposed Seq meta-algorithm provides the same theoretical guarantees as the MAB policy employed, but was shown to provide improved performance compared to several classical MAB policies in RM and BAI problems employing real-world data. In particular, in the RM scenario regarding Internet advertising optimization, Seq-adapted algorithm resulted, on average, in ≈10% lower regret during the whole time horizon than using classical MAB policies. When tested in a BAI problem involving the identification of the time of the day characterized by the highest concentration of pollutants in a water monitoring scenario, Seq identified the correct time in less than 4 days and 28 measurement.","url":"https://doi.org/10.1016/j.engappai.2023.107815","authors":["Marco Gabrielli","Manuela Antonelli","Francesco Trovò"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-10T13:17:40Z","doi":"10.1016/j.engappai.2023.107815","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.21275/sr24923210104","name":"Leveraging Artificial Intelligence (AI) to Strengthen Cybersecurity","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr24923210104","authors":["Anay Kushwaha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-07T12:57:58Z","doi":"10.21275/sr24923210104","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2024.102980","name":"Comprehensive analytics of COVID-19 vaccine research: From topic modeling to topic classification","source":"crossref","abstract":"COVID-19 vaccine research has played a vital role in successfully controlling the pandemic, and the research surrounding the coronavirus vaccine is ever-evolving and accruing. These enormous efforts in knowledge production necessitate a structured analysis as secondary research to extract useful insights. In this study, comprehensive analytics was performed to extract these insights, which has moved the boundaries of data analytics in secondary research in the vaccine field by utilizing topic modeling, sentiment analysis, and topic classification based on the abstracts of related publications indexed in Scopus and PubMed. By applying topic modeling to 4803 abstracts filtered by this study criterion, 8 research arenas were identified by merging related topics. The extracted research areas were entitled \"Reporting,\" \"Acceptance,\" \"Reaction,\" \"Surveyed Opinions,\" \"Pregnancy,\" \"Titer of Variants,\" \"Categorized Surveys,\" and \"International Approaches.\" Moreover, the investigation of topics sentiments variations over time led to identifying researchers' attitudes and focus in various years from 2020 to 2022. Finally, a CNN-LSTM classification model was developed to predict the dominant topics and sentiments of new documents based on the 25 pre-determined topics with 75 % accuracy. The findings of this study can be utilized for future research design in this area by quickly grasping the structure of the current research on the COVID-19 vaccine. Through the findings of current research, a classification model was developed to classify the topic of a new article as one of the identified topics. Also, vaccine manufacturing firms will achieve a niche market by having a schema to invest in the gap of fields that have yet to be concentrated in extracted topics.","url":"https://doi.org/10.1016/j.artmed.2024.102980","authors":["Saeed Rouhani","Fatemeh Mozaffari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-18T06:22:28Z","doi":"10.1016/j.artmed.2024.102980","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-323-90534-3.00037-8","name":"Artificial intelligence and extended reality in cardiology","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90534-3.00037-8","authors":["David M. Axelrod"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-08T11:01:35Z","doi":"10.1016/b978-0-323-90534-3.00037-8","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/xcav8n","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing","url":"https://doi.org/10.32388/xcav8n","authors":["Anas Althobaiti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-14T16:45:40Z","doi":"10.32388/xcav8n","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2139/ssrn.4763294","name":"Market Power in Artificial Intelligence","source":"crossref","abstract":"This paper surveys the relevant existing literature that can help researchers and policymakers understand the drivers of competition in markets that constitute the provision of artificial intelligence products. The focus is on three broad markets: training data, input data, and AI predictions. It is shown that a key factor in determining the emergence and persistence of market power will be the operation of markets for data that would allow for trading data across firm boundaries.","url":"https://doi.org/10.2139/ssrn.4763294","authors":["Joshua S. Gans"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-16T14:32:10Z","doi":"10.2139/ssrn.4763294","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-032-18897-7_7","name":"NLP Based Exploratory Study of Interviews on Blood Donation Awareness Campaigns: Evidence From the University of Oujda (2023–2024)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_7","authors":["Boudih Manal","Zarrouk Zoubir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:54:38Z","doi":"10.1007/978-3-032-18897-7_7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3702386.3702393","name":"Exploration of the new teaching and learning mode enabled by Artificial Intelligence","source":"crossref","abstract":"In recent years, with the development of science and technology, the application of artificial intelligence technology in the field of education begin to increase. Many well-known universities in China have stepped up their pace and actively explored the deep integration of \"artificial intelligence + education\". A series of innovative practices, such as intelligent teaching system, intelligent classroom, virtual teaching assistant and personalized learning platform, all show that the education industry is undergoing an unprecedented intelligent transformation. But at the same time, the application of artificial intelligence in the teaching process in colleges and universities is not mature, and there are still many problems. In this context, it has become very urgent to explore how to efficiently use artificial intelligence technology to contribute to the higher education in China. This paper introduces the development process of AI and its application in colleges and universities, then analyzes the obstacles of AI when applied in higher education, and finally proposes specific application strategies for artificial intelligence to empower new teaching and learning models in universities. It is hoped that the research of this paper can improve the application of artificial intelligence in the teaching of universities in China.","url":"https://doi.org/10.1145/3702386.3702393","authors":["Fei Cai","Wanyu Chen","Yijia Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-03T15:01:52Z","doi":"10.1145/3702386.3702393","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2196/51411","name":"Medical Education and Artificial Intelligence: Web of Science–Based Bibliometric Analysis (2013-2022)","source":"crossref","abstract":"Abstract Background Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. Objective This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013‐2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Methods Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. Results A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was “Medical Students’ Attitude Towards Artificial Intelligence: A Multicentre Survey.” Keyword analysis revealed that “radiology,” “medical physics,” “ehealth,” “surgery,” and “specialty” were the primary focus, whereas “big data” and “management” emerged as research frontiers. Conclusions The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.","url":"https://doi.org/10.2196/51411","authors":["Shuang Wang","Liuying Yang","Min Li","Xinghe Zhang","Xiantao Tai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-30T05:49:33Z","doi":"10.2196/51411","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/acait63902.2024.11021777","name":"ACAIT 2024 TOC","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acait63902.2024.11021777","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-10T17:48:32Z","doi":"10.1109/acait63902.2024.11021777","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.caeai.2024.100308","name":"Fostering student competencies and perceptions through artificial intelligence of things educational platform","source":"crossref","abstract":"The growing demand for artificial intelligence (AI) skills across various sectors has enhanced AI-focused careers and shaped academic exploration in educational institutions. These institutions have been actively developing teaching methods that enhance practical AI applications, particularly through integrating AI with the Internet of Things (IoT), leading to the emergence of the Artificial Intelligence of Things (AIoT). This convergence promises significant advancements in AI education, addressing gaps in structured learning methods for AIoT. This study explored AIoT's application in Smart Farming (SF) and its potential to enrich AI education and sectoral advancements. The AIoT platform was designed for SF simulations, integrating environmental sensing, AI processing, and user-friendly outputs. This platform was implemented with 40 first-year computer science university students in Thailand using a one-group pre-posttest design. This approach transformed theoretical AI concepts into experiential learning through interactive activities, demonstrating AIoT's capability to increase AI conceptual understanding, trigger AI competencies, and promote positive learning perceptions. Therefore, this study presented the results as indicative of the AIoT platform's potential benefits, emphasizing the need for further robust experimental research. This study contributes to educational technology discussions by suggesting improvements in AIoT platform effectiveness and highlighting areas for future investigation.","url":"https://doi.org/10.1016/j.caeai.2024.100308","authors":["Sasithorn Chookaew","Pornchai Kitcharoen","Suppachai Howimanporn","Patcharin Panjaburee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-25T00:09:09Z","doi":"10.1016/j.caeai.2024.100308","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.36922/aih.3384","name":"Easing transgender travel and tourism: An eventual outreach to artificial intelligence-based medical tourism","source":"crossref","abstract":"Improvements in the artificial intelligence (AI) health system have been effective in reducing the risks associated with transgender medical tourism and travel. The ability to track medical travel from the place of origin to the final treatment destination is dependent on the development of AI. This project aims to improve the AI health system to promote travel and medical tourism, utilizing quantitative research methodologies, including survey-based research and partial least squares structural equation modeling. The participants included 381 people from medical professionals, tourism experts, transgenders, and technology enthusiasts interested in AI and health. The findings show that the AI health system has significantly improved medical travel and tourism; key factors such as medical tourism, AI systems, medical travel and risk factors, attitude, behavioral intention, and medical destination image have all contributed to better healthcare experiences for transgender individuals. Specialized care should be provided to transgender individuals traveling for surgeries and medical treatments, emphasizing their unique needs. Subsequent investigations might focus on the broader function of AI, particularly in terms of ensuring the dignity and respect of the tourism site. This study proposes further integration of AI into healthcare systems to maximize the benefits of safe medical travel and secure tourist locations.","url":"https://doi.org/10.36922/aih.3384","authors":["Hamza Iftikhar","Muhammad Saqib Iqbal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-25T03:15:41Z","doi":"10.36922/aih.3384","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.35711/aimi.v2.i1.5","name":"Artificial intelligence in ophthalmology: A new era is beginning","source":"crossref","abstract":"","url":"https://doi.org/10.35711/aimi.v2.i1.5","authors":["Bijnya Birajita Panda","Subhodeep Thakur","Sumita Mohapatra","Subhabrata Parida"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-04T03:47:57Z","doi":"10.35711/aimi.v2.i1.5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.70593/978-93-7185-228-9","name":"Quantum-Resistant Artificial Intelligence and Machine Learning Architectures for Secure Mortgage and Banking Intelligence Systems","source":"crossref","abstract":"Along with the development of artificial intelligence and financial technologies, the fast convergence of quantum computing is one of the most important technological trends of the twenty-first century. Though artificial intelligence and machine learning have already revolutionized the mortgage and banking intelligence systems- improving credit risk evaluation, fraud level detection, compliance automation and decision-making efficiency purposes, the coming up of large-scale quantum computing is a deep disruptive force of cryptographic principles on which these systems operate. Classical security models securing the financial data over several decades are becoming susceptible to quantum-enabled threats, which is why quantum-resistant architectures providing long-term confidentiality, integrity, and trust are urgently needed. It is on this critical inflection point that this book was driven by the fact that innovation has to be coupled by foresight, strength and responsible system design. Quantum-Resistant Artificial Intelligence and Machine Learning Architectures of Secure Mortgage and Banking Intelligence Systems is an interdisciplinary and detailed analysis of the manner in which financial AI systems can be kept secure in the post-quantum age. The book combines the most recent findings in quantum threat management, post-quantum cryptography, federated learning, secure training of a model, hybrid authentication, adversarial resilience, explainable AI, and quantum-safe security control performance implications. All the chapters discuss in their own systematic fashion application, techniques, methodologies, challenges, opportunities, impacts, and the future trend of research with a special love given to the mortgage and banking ecosystems where data longevity, regulatory compliance, and systemic stability are the key consideration. The book unites insights in the field of cryptography, machine learning, financial engineering, and governance by shifting the focus of the concept of algorithmic substitution to a broader perspective of security as a system-wide and lifecycle-oriented problem. The book should be read by researchers, graduate students, practitioners in the industry, and policymakers as well as regulators who are intersectional in artificial intelligence, cybersecurity, and financial services. It will also be used as a reference point to gain an overview of the impact of quantum risks in financial AI systems, as well as as a practical guide to architectural design, evaluation and transition to quantum-resilient systems. Since risky decision-making is becoming more and more reliant on automated intelligence by financial institutions, even passive quantum preparedness is no longer a choice, but rather the key to continuing to trust, maintain compliance and prevent a financial meltdown in the global marketplace. We do hope that this book will lead to additional research, co-operation and judicious action on constructing safe, open, and robust financial intelligence systems of the quantum age.","url":"https://doi.org/10.70593/978-93-7185-228-9","authors":["Prem Kumar Sholapurapu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-17T07:42:57Z","doi":"10.70593/978-93-7185-228-9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1148/rg.230067.q1","name":"Understanding and Mitigating Bias in Imaging Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1148/rg.230067.q1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-30T19:57:15Z","doi":"10.1148/rg.230067.q1","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.7551/mitpress/15378.003.0008","name":"Practical AGI Development","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15378.003.0008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T16:25:21Z","doi":"10.7551/mitpress/15378.003.0008","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/is8hqi","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/is8hqi","authors":["Bharath Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-23T19:18:05Z","doi":"10.32388/is8hqi","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1186/s12909-024-06095-6","name":"Comment about ‘Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis’","source":"crossref","abstract":"We read with great interest the recently published article by Amiri et al., titled \"Medical, Dental, and Nursing Students' Attitudes and Knowledge Toward Artificial Intelligence: A Systematic Review and Meta-Analysis.\" We would like to offer comments on certain aspects of the findings that we believe warrant further discussion.","url":"https://doi.org/10.1186/s12909-024-06095-6","authors":["Yoshiyasu Ito","Hironobu Ikehara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-19T06:36:22Z","doi":"10.1186/s12909-024-06095-6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/s0933-3657(98)00020-7","name":"Scenario recognition for temporal reasoning in medical domains","source":"crossref","abstract":"The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Déjà Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.","url":"https://doi.org/10.1016/s0933-3657(98)00020-7","authors":["Michel Dojat","Nicolas Ramaux","Dominique Fontaine"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T11:20:41Z","doi":"10.1016/s0933-3657(98)00020-7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.artmed.2004.01.016","name":"Local fuzzy fractal dimension and its application in medical image processing","source":"crossref","abstract":"The local fuzzy fractal dimension (LFFD) is proposed to extract local fractal feature of medical images. The definition of LFFD is an extension of the pixel-covering method by incorporating the fuzzy set. Multi-feature edge detection is implemented with the LFFD and the Sobel operator. The LFFD can also serve as a characteristic of motion in medical image sequences. The experimental results show that the LFFD is an important feature of edge areas in medical images and can provide information for segmentation of echocardiogram image sequences.","url":"https://doi.org/10.1016/j.artmed.2004.01.016","authors":["Xiaodong Zhuang","Qingchun Meng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-05-15T10:35:24Z","doi":"10.1016/j.artmed.2004.01.016","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.32920/26052556","name":"Trust, Acceptance, and Artificial Intelligence News Anchors","source":"crossref","abstract":"Throughout the world, artificial intelligence (AI) technology has become an integral part of everyday life and work. The emergence of intelligent media has resulted in significant changes to the news industry, largely due to the implementation of AI news anchors. The purpose of this study is to examine new audiences' perceptions of AI news anchors. A content analysis was conducted to determine how news audiences perceive AI news anchors. Comments posted on YouTube and Facebook videos that show AI news anchors reporting the news were analyzed. It was observed that AI news anchors have varying effects on their news audiences since they were first implemented in China in 2018. Findings show that 65% of all posted comments were negative, whereas 34% were positive. The results of this study were contradicting at times. For instance, many people consider AI news anchors to be fake because of their unrealistic movements, whereas others believe they resemble human newscasters in appearance. Furthermore, some viewers expressed concern that AI news anchors may be utilized by governments to promote propaganda or negative political messages. Moreover, findings indicate that news audiences are increasingly concerned that AI will result in the loss of jobs for real news anchors, deterring people from entering journalism or reporting professions.","url":"https://doi.org/10.32920/26052556","authors":["Tawfik Aly"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-19T01:04:07Z","doi":"10.32920/26052556","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/lzlo02","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/lzlo02","authors":["Otilia Manta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-24T08:02:27Z","doi":"10.32388/lzlo02","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1111/nyas.15229/v2/review2","name":"Review for \"Artificial intelligence and psychedelic medicine\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/nyas.15229/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T17:06:22Z","doi":"10.1111/nyas.15229/v2/review2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2139/ssrn.4840769","name":"Artificial Intelligence (AI) Governance: An Overview","source":"crossref","abstract":"The explosion of interest in AI which bestrode the introduction of OpenAI's Chat GPT and GPT 4 underscores the need for this overview to contextualize and address public concerns. If you are like me or most people, my early impression of Artificial Intelligence (AI) was largely influenced and shaped by Hollywood. I remember the doomsday scenario in the Terminator movie franchise. However, there is more to AI than killer robots or drones. AI has huge potential in such areas as finance, agriculture, manufacturing, medicine, robotics, research, education, autonomous vehicles, including law enforcement, military, and defence applications. AI like many human technologies and discoveries has a dual use problem. Meaning it may be employed for good or bad. It is often the fear of the latter that is reflected in movies. The rapid evolution of AI technology at breakneck speed has implications for humans and society. AI and Algorithm bias and discrimination, systemic and environmental risks, the intersections between AI and data privacy, torts, and IP rights violations have made AI governance a necessity and imperative. This paper looks at global and regional efforts to come up with strategies and regulatory frameworks for AI governance. Chief amongst them include the OECD AI Principles; the EU AI Act; and the NIST AI RMF. The common thread running through these frameworks and legislation is identifying and categorizing AI developments and deployments according to their risk levels and providing guidelines for ethical and trustworthy AI with considerations for human safety and innovation. Also identified and examined are a few national and state efforts, namely in the US,&lt;span&gt;UK, Canada, China, Nigeria, and Singapore.&lt;/span&gt; &lt;p&gt;The objective is to facilitate understanding of AI governance and furnish AI developers and deployers with the tools to establish a robust AI risks management framework and compliance regime.&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.4840769","authors":["Alexander Wodi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-29T12:54:10Z","doi":"10.2139/ssrn.4840769","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-21598-8.00008-7","name":"Appropriate artificial intelligence algorithms will ultimately contribute to health equity","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21598-8.00008-7","authors":["Jan Kalina"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-18T17:14:07Z","doi":"10.1016/b978-0-443-21598-8.00008-7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.21275/sr24901234506","name":"Artificial Intelligence: Transforming the Future of Retail","source":"crossref","abstract":"Artificial Intelligence (AI) is revolutionizing the retail sector, catalyzing unprecedented advancements in operational efficiency, customer engagement, and strategic decision-making. This paper delves into the transformative impact of AI across the retail value chain, from inventory management and supply chain optimization to personalized customer experiences and dynamic pricing models. By leveraging AI-driven algorithms and machine learning techniques, retailers are not merely adapting to the rapidly evolving market dynamics but are actively shaping the future of retail. The integration of AI technologies, such as predictive analytics, natural language processing, and computer vision, enables retailers to achieve real-time insights and hyper-personalization, thereby enhancing customer satisfaction and loyalty. These technologies facilitate precise demand forecasting, automated inventory replenishment, and the optimization of logistics, reducing costs and minimizing waste. Furthermore, AI-powered recommendation engines and chatbots are redefining customer interaction by delivering tailored shopping experiences, fostering deeper connections between consumers and brands. This paper also examines the strategic implications of AI adoption in retail, highlighting its role in driving innovation and competitive advantage. Retailers that successfully harness AI capabilities are better equipped to anticipate customer preferences, respond to market trends, and create differentiated value propositions. Moreover, the ethical considerations surrounding AI deployment, including data privacy and algorithmic bias, are critically assessed to ensure responsible and sustainable AI integration. This study underscores the pivotal role of AI in propelling the retail industry toward a future characterized by enhanced efficiency, agility, and customer-centricity. By embracing AI, retailers are not only navigating the complexities of the digital age but are also setting new standards for operational excellence and customer engagement. The findings of this research provide valuable insights for retail practitioners, policymakers, and scholars, offering a comprehensive understanding of how AI is transforming the retail landscape and what it entails for the future of the industry.","url":"https://doi.org/10.21275/sr24901234506","authors":["Jeyaganesh Viswanathan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-04T11:19:26Z","doi":"10.21275/sr24901234506","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.37762/jgmds.11-4.625","name":"Transforming Medical and Dental Curriculum in the era of Artificial Intelligence (AI)","source":"crossref","abstract":"The dawn of artificial intelligence (AI) signifies a pivotal shift in medical and dental education. Integrating AI into the curriculum modernizes learning and equips future healthcare professionals with crucial tools for the 21st century. The COVID-19 pandemic revealed the limitations of conventional educational models, necessitating rapid adaptation to remote and online learning environments. This disruption expedited the transition to digital platforms, laying the foundation for further integration of technology, including AI, into medical education. What began as an emergency response has now become a permanent feature of the educational landscape, evolving from static textbooks to dynamic digital platforms that offer greater accessibility, inclusivity, and personalization of learning experiences.1 In the AI era, it is insufficient to merely digitize the curriculum; a comprehensive transformation is essential. The digital curriculum opens new avenues for interactive learning environments, simulation-based practices, and adaptive learning algorithms that respond to the individual needs of students. AI-driven tools such as virtual patient simulations, diagnostic decision-making platforms, and predictive analytics have the potential to revolutionize how medical students learn, practice, and apply their knowledge in clinical settings.2 These innovations allow for an enhanced learning experience where students can interact with realistic patient cases and make informed decisions, fostering a deeper understanding of clinical practice. One of the most promising applications of AI in medical education is its role as an educational partner. AI-powered platforms can function as personalized tutors, providing real-time feedback, adjusting learning modules based on student performance, and even predicting areas where additional support may be required.3 Adaptive learning systems can analyze the learner’s pace and comprehension, offering tailored resources to bridge knowledge gaps. This personalized approach to education ensures that no student is left behind, addressing one of the longstanding challenges of traditional, one-size-fits-all curricula. Additionally, AI can enhance clinical reasoning through simulation and data-driven case scenarios. By analyzing patterns in patient data, AI algorithms can help medical students gain deeper insights into complex clinical decision-making processes. This data-driven approach can significantly improve learners’ ability to diagnose and plan treatments, thereby improving clinical outcomes. While AI and digital tools offer substantial benefits, the role of educators remains essential in this new educational paradigm. Rather than replacing teachers, AI will augment their roles, allowing them to focus on mentorship, critical thinking, and the ethical dimensions of healthcare.4 Educators will need to reimagine their roles, becoming facilitators of learning who guide students in interpreting and applying AI-generated data in clinical settings. As AI takes on administrative tasks such as grading, educators can dedicate more time to meaningful interactions with students.5 However, this shift toward AI-driven curricula also requires significant investment in faculty development. Educators must be trained in the use of AI tools and possess a thorough understanding of their applications to ensure that AI is used responsibly and effectively in shaping future healthcare professionals. As AI becomes more integrated into medical education, addressing the ethical challenges associated with this technology becomes crucial. While AI-driven tools hold great promise, they must be designed and deployed with an acute awareness of biases, data privacy concerns, and the risk of over-reliance on algorithms in clinical decision-making.6 The digital curriculum must provide students with technical skills and a strong ethical foundation for AI use in healthcare. Students must be trained to critically evaluate AI outputs, un","url":"https://doi.org/10.37762/jgmds.11-4.625","authors":["Brekhna Jamil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-09T11:19:48Z","doi":"10.37762/jgmds.11-4.625","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-13671-9.00011-9","name":"Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13671-9.00011-9","authors":["Ilaria Durosini","Silvia Francesca Maria Pizzoli","Milija Strika","Gabriella Pravettoni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-15T05:25:04Z","doi":"10.1016/b978-0-443-13671-9.00011-9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-65514-2_7","name":"Towards an Optimal Regulator: Assessment of the EU Artificial Intelligence Act","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65514-2_7","authors":["Mitja Kovač"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-07T11:03:57Z","doi":"10.1007/978-3-031-65514-2_7","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-49226-6_5","name":"Artificial Intelligence as a Partner in Shaw Studies","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-49226-6_5","authors":["Kay Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-03T12:02:22Z","doi":"10.1007/978-3-031-49226-6_5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/acait63902.2024.11022259","name":"Intelligent Interactive Design of Virtual Simulation Experiment Teaching System for Computer Aided Environment Design Based on Artificial Intelligence","source":"crossref","abstract":"The research aims to establish a virtual simulation experimental teaching technology for environmental design by combining advanced interactive technology with artificial intelligence, and provide users with a more efficient and intuitive interactive experience. The system utilizes cutting-edge technologies such as collaborative filtering algorithm, Visual Geometry Group-16 convolutional neural network, and bidirectional Long Short-Term Memory model to improve the accuracy and efficiency of design scheme recommendation and layout planning. Through comparative analysis, the new system has improved course satisfaction from 7 points to 9 points, interactivity score from 7.5 points to 9 points, knowledge mastery rate from 64 points to 91 points, and task completion rate from 70% to 92% compared to traditional teaching methods in key indicators such as course satisfaction, interactivity, knowledge mastery rate, and task completion rate. In contrast, the improvement of traditional teaching systems is relatively small. These research results not only provide strong supporting evidence for the future development trend of educational technology, but also help promote educational innovation and improve teaching effectiveness.","url":"https://doi.org/10.1109/acait63902.2024.11022259","authors":["Jiao Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-10T17:48:32Z","doi":"10.1109/acait63902.2024.11022259","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.109354","name":"Unified node, edge and motif learning networks for graphs","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109354","authors":["Tuyen Ho Thi Thanh","Bac Le"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T08:49:36Z","doi":"10.1016/j.engappai.2024.109354","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.17303/jaist.2024.1.105","name":"In Pursuit of an Expert Artificial Intelligence System: Reproducing Human Physicians Diagnostic Reasoning and Triage Decision Making","source":"crossref","abstract":"","url":"https://doi.org/10.17303/jaist.2024.1.105","authors":["Azad Kabir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T11:41:10Z","doi":"10.17303/jaist.2024.1.105","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-13671-9.00004-1","name":"Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13671-9.00004-1","authors":["Valentina De Nicolò","Davide La Torre"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-15T05:24:36Z","doi":"10.1016/b978-0-443-13671-9.00004-1","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2023.107785","name":"FastNet: A feature aggregation spatiotemporal network for predictive learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107785","authors":["Fengzhen Sun","Luxiang Ren","Weidong Jin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-28T06:42:19Z","doi":"10.1016/j.engappai.2023.107785","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.61186/umj.34.12.760","name":"ARTIFICIAL INTELLIGENCE AND GENE THERAPY OF BREAST CANCER","source":"crossref","abstract":"Background & Aims: Gene therapy is used in various diseases such as cancer.Breast cancer is the most common malignancy in women worldwide, which shows the necessity of using innovative approaches in treatment methods.The ability of artificial intelligence algorithms to process large data, complex patterns, and classify them can be used to improve the process of gene therapy in breast cancer.The aim of this article is to review the available information and emphasize the applications of artificial intelligence in targeted gene therapy for breast cancer. Materials & Methods:To carry out this study we used the articles on PubMed databases by searching for related keywords to collect information.Results: By designing artificial intelligence algorithms and analyzing very complex molecular pathways in the human body and sampling the experiences of scientists and doctors in clinical studies and simulating biological processes related to the regulation of gene expression in the human body, the effectiveness of gene carriers, control of gene delivery parameters/medicine and modeling of cells minimized the rate of medical errors and with early diagnosis of the disease and predicting the effectiveness of the medicine, it provided patient-centered treatments of the effectiveness of new treatments such as gene therapy with the least complications at the highest level. Conclusion:In recent decade, many efforts have been made to use all types of gene therapy for breast cancer patients with the least complications and the most effectiveness.Therefore, artificial intelligence is a powerful tool for optimizing early diagnosis and treatment for breast cancer.It's combination with interdisciplinary sciences in improving the health of the society is a very interesting topic for scientists, but due to the limitations that exist for its use, such as ethical cases and high costs, it should be done with high precision and sufficient studies.","url":"https://doi.org/10.61186/umj.34.12.760","authors":["Shabnam Kabaranzadghadim","Shiva Gholizadeh-Ghaleh Aziz","Ghader Babaei","Sahar Mehranfar","Rahim Mahmodlou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-11T14:03:59Z","doi":"10.61186/umj.34.12.760","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.18203/2320-6012.ijrms20244173","name":"Role of radiologist with the advent of artificial intelligence in medical imaging","source":"crossref","abstract":"Artificial intelligence (AI) has rapidly emerged as a transformative tool in healthcare, particularly in radiology, where it offers substantial opportunities to enhance diagnostic precision and workflow efficiency. AI, defined as an artificial entity capable of recognizing patterns, processing data, and executing tasks, has revolutionized traditional imaging practices by automating analyses and reducing subjectivity. While radiologists traditionally rely on expertise and visual assessment to detect and monitor abnormalities, this approach can be limited by variability, fatigue, and bias. AI complements radiologists by providing objective, quantitative assessments, enabling early detection of diseases, lesion classification, and image segmentation with greater speed and accuracy. AI's integration into radiology workflows supports risk stratification, personalized treatment planning, and predictive analytics, thus enhancing clinical decision-making and patient care. Despite its potential, AI’s current performance remains task-specific, requiring human oversight to ensure accuracy and reliability, especially in ambiguous cases. Challenges such as algorithm bias, ethical considerations, and regulatory hurdles must be addressed to ensure generalizability, transparency, and patient trust. Radiologists play a pivotal role in validating AI tools and advocating for their responsible implementation, ensuring that AI enhances clinical workflows without compromising the essential human connection in healthcare.","url":"https://doi.org/10.18203/2320-6012.ijrms20244173","authors":["Anitha Boregowdanapalya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-01T02:48:16Z","doi":"10.18203/2320-6012.ijrms20244173","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/978-3-031-73500-4_23","name":"A Multidimensional Taxonomy for Recent Trends in Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-73500-4_23","authors":["Isabel Carvalho","Hugo Gonçalo Oliveira","Catarina Silva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T04:00:47Z","doi":"10.1007/978-3-031-73500-4_23","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1093/bjrai/ubaf011","name":"M3: multimodal artificial intelligence for medical report generation and visual question answering from 3D abdominal CT scans","source":"crossref","abstract":"Abstract Objectives Medical imaging is indispensable for diagnosis, with abdominal imaging playing a pivotal role in generating medical reports and informing clinical decision-making. Recent works in artificial intelligence (AI), particularly in multimodal approaches such as vision-language models, have demonstrated significant potential to enhance medical image analysis by seamlessly integrating visual and textual data. While 2D imaging has been the main focus of many studies, the enhanced spatial detail and volumetric consistency offered by 3D images, such as CT scans, remain relatively underexplored. This gap underscores the need for innovative approaches to unlock the potential of 3D imaging in clinical workflows. Methods In this study, we utilized a multimodal AI pipeline, Phi3-V, to address 2 key challenges in abdominal imaging: generating clinically coherent medical reports from 3D CT images and performing visual question answering based on these images. Results Our optimized model attained an average GREEN score of 0.409 for medical report generation and an accuracy of 79% for multiple-choice visual question answering on the validation cases. Conclusions These findings demonstrate the potential of multimodal AI in advancing the analysis of 3D medical imaging, paving the way for more robust and efficient applications in healthcare. Advances in knowledge This study advances the use of multimodal AI for 3D CT imaging, achieving improvements in medical report generation and visual question answering.","url":"https://doi.org/10.1093/bjrai/ubaf011","authors":["Abdullah Hosseini","Ahmed Ibrahim","Ahmed Serag"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T18:49:53Z","doi":"10.1093/bjrai/ubaf011","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1148/ryai.240660","name":"Breaking Ground on the Application of AI to HCC: It’s All about Data","source":"crossref","abstract":"markers for the diagnosis, characterization, and image-guided therapy of liver cancer and other solid tumors of the abdomen.","url":"https://doi.org/10.1148/ryai.240660","authors":["Ryan Bitar","Julius Chapiro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-27T14:48:06Z","doi":"10.1148/ryai.240660","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.58532/nbennurch54","name":"ARTIFICIAL INTELLIGENCE AND INTELLIGENT COMPUTING TECHNIQUES BASED TELEMEDICINE AND HEALTHCARE","source":"crossref","abstract":"Studies in the field of medicine have started to apply Artificial Intelligence's (AI) and Intelligent Computing Technique skills for processing and analyzing data to telemedicine, as the technology's use in other disciplines and businesses has grown in popularity. As healthcare professionals work to increase virtual care options along the continuum, they must leverage artificial intelligence (AI) and Intelligent Computing Techniques in telehealth to enable clinicians to make data-rich, real-time decisions that will enhance patient outcomes. Given the broad use of AI in other industries, research in the medical field has begun to leverage AI's advantages in data processing and analysis in telehealth. The convergence of Artificial Intelligence (AI) and intelligent computing techniques has significantly transformed the landscape of telemedicine and healthcare. This chapter aims to explore the applications, benefits, challenges, and future prospects of employing AI and intelligent computing in telemedicine and healthcare. The integration of these technologies has paved the way for more efficient diagnosis, treatment, remote patient monitoring, and personalized healthcare, revolutionizing the industry's approach to patient care. The chapter provides an in-depth analysis of the various AI-driven applications and their impacts on healthcare delivery, while also addressing the ethical and privacy concerns associated with these advancements","url":"https://doi.org/10.58532/nbennurch54","authors":["Apoorva Verma","Dr. Leena Bhatia","Dr. Nitish Pathak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-12T03:23:16Z","doi":"10.58532/nbennurch54","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.aichem.2024.100079","name":"Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives","source":"crossref","abstract":"The Hammett constants, σ m and σ p , reflect the electron-withdrawing and electron-donating abilities of substituents on aromatic compounds, and have been successfully used in various structure-activity relationship studies. However, determining these constants experimentally is both resource-intensive and time-consuming approach. In this study, we explore the use of graph neural networks (GNNs) to predict Hammett constant parameters using graph-based features. This innovative approach aims to provide rapid and efficient predictions of σ m and σ p values, eliminating the need for extensive computational and experimental setups. By leveraging the power of GNNs, we hope to streamline the process of obtaining these critical parameters, thereby facilitating more efficient reaction design and enhancing the applicability of linear free energy relationship studies in chemical research. This study employs graph neural networks (GNNs) to predict Hammett’s constants, aiming for rapid, efficient predictions without extensive experimental setups, enhancing reaction design and chemical research. • Utilization of graph-based molecular encoding derived from SMILES notations for organic molecules. • Pioneering study using a large-scale dataset to predict Hammett's constant parameters. • Dataset is publicly available, supporting reproducibility and further research. • The Attentive FP algorithm demonstrated high predictive accuracy, achieving an R² score of 0.93 on the test set. • This method offers a rapid and efficient solution for predicting Hammett's constants.","url":"https://doi.org/10.1016/j.aichem.2024.100079","authors":["Vaneet Saini","Ranjeet Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-16T07:40:12Z","doi":"10.1016/j.aichem.2024.100079","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artint.2024.104113","name":"Regular decision processes","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2024.104113","authors":["Ronen I. Brafman","Giuseppe De Giacomo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-22T07:19:45Z","doi":"10.1016/j.artint.2024.104113","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1145/3660853.3660890","name":"The Role of Artificial Intelligence in Idea Management Systems and Innovation Processes: An Integrative Review","source":"crossref","abstract":"The role of artificial intelligence (AI) in idea management systems (IMS) and innovation processes has recently been a topic of significant research interest. AI has been acknowledged for its potential to enhance innovation activities by providing support in various aspects. The intersection of these areas offers promising opportunities for improved idea generation, classification, development, and evaluation. However, AI's impact is not equally present for the different stages of an innovation process, showing more prominence in the idea generation stage. Through an integrative review, we can explore how AI has contributed to different steps of innovation processes implemented through IMS. AI-driven approaches, so far, have been opening possibilities to manage creative processes, such as automating specific tasks, analyzing large amounts of data to identify patterns and trends, and providing real-time feedback to enhance ideation and decision-making. Assessing the contribution of AI to innovation and creativity is vital in understanding its potential influence on the future developments of IMS and innovation processes.","url":"https://doi.org/10.1145/3660853.3660890","authors":["Serena Leka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-23T12:21:56Z","doi":"10.1145/3660853.3660890","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/978-3-031-66205-8_2","name":"A Panoramic Overview of the Opportunities and Challenges Artificial Intelligence Brings to ESG Investing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-66205-8_2","authors":["Yushi Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-27T14:03:35Z","doi":"10.1007/978-3-031-66205-8_2","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842807","name":"Ethical Guidelines For Utilization of Artificial Intelligence In Healthcare: A Review","source":"crossref","abstract":"The key issue is to develop guidelines for Artificial Intelligence (AI) data protection that respect individual rights and further the general welfare. Where possible, AI systems have to minimize data of a personal nature to the absolute minimum, anonymize such data, and encrypt it in maintaining data security in accordance with regulations such as the General Data Protection Regulation (GDPR). AI systems need to follow the levels defined by the European Commission to achieve proper transparency and explain ability for building confidence and enabling proper ethical control. The present review article aims to bring ethical standard for AI utilization upfront. Additionally, the present article focus on uncovering ethical guidelines for maintaining standards for AI use. A thorough search approach was used to find relevant reviews of the literature for the assessment. Phases of the strategy included scanning several databases, evaluating publications, and choosing the most relevant research for review. This review examined electronic databases including PubMed, Science Direct, EMBASE, and Google Scholar. These databases were chosen to provide comprehensive coverage of relevant content. Making use of studies and reviews published between 2000 and 2024.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842807","authors":["Vaishnavi Shete","Anil Pethe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842807","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/idap64064.2024.10710973","name":"Artificial Intelligence Revolution in Turkish Health Consultancy: Development of LLM-Based Virtual Doctor Assistants","source":"crossref","abstract":"This study examines the performance of four different large language models (LLama2, LLama3, and Mistralbased) in doctor-patient written communication in Turkish health counseling. The models were trained and fine-tuned on a patient-doctor question-answer dataset [1]. The metrics used for performance evaluation include ROUGE, Elo rating, Winning percentage, and Expert evaluation. The comparative analysis results indicate that the SambaLingo-Turkish-Chat model was successful in terms of response accuracy and contextual relevance, while the Trendyol-LLM-7b-chat-v 1.8 model proved to be more successful when considering the ethical aspects of the task [14], [17]. This study demonstrates the potential of AI-powered virtual doctor assistants in Turkish healthcare services and contributes to the development of Turkish-specific medical chatbots.","url":"https://doi.org/10.1109/idap64064.2024.10710973","authors":["Muhammed Kayra Bulut","Banu Diri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-16T17:50:55Z","doi":"10.1109/idap64064.2024.10710973","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icdacai65086.2024.00083","name":"Application and Optimization of Artificial Intelligence Algorithms in Cost Management in Civil Engineering","source":"crossref","abstract":"Artificial intelligence (AI), as a key force driving industrial transformation in the new era, is profoundly changing various industries, especially in the field of civil engineering, where its potential is particularly significant. This system not only accelerates the comprehensive digital transformation of the civil engineering industry, but also greatly improves the accuracy and efficiency of engineering costs, becoming an important path to achieve automation, informatization, and even intelligent management. The AI based civil engineering cost management system discussed in this article innovatively integrates big data processing, machine learning (ML) algorithms, and deep learning (DL) technology, which can automatically analyze massive engineering data and achieve fast and accurate estimation of engineering costs. This system not only reduces the workload of cost engineers and minimizes human errors, but also significantly improves the timeliness and accuracy of cost forecasting, providing strong data support for project decision-making. The experimental results show that this system not only successfully reduces the time cost of engineering cost calculation, but also helps project managers make more scientific and reasonable decisions in cost control and resource allocation through intelligent optimization algorithms.","url":"https://doi.org/10.1109/icdacai65086.2024.00083","authors":["Zhaogang Wang","Jianqiao Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-16T18:33:35Z","doi":"10.1109/icdacai65086.2024.00083","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.107958","name":"Relaxed multi-view discriminant analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.107958","authors":["Hongjie Zhang","Junyan Tan","Yingyi Chen","Ling Jing","Jinxin Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-02T19:38:06Z","doi":"10.1016/j.engappai.2024.107958","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108614","name":"Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management","source":"crossref","abstract":"This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.","url":"https://doi.org/10.1016/j.engappai.2024.108614","authors":["Rapeepan Pitakaso","Thanatkij Srichok","Surajet Khonjun","Paulina Golinska-Dawson","Kanchana Sethanan","Natthapong Nanthasamroeng","Sarayut Gonwirat","Peerawat Luesak","Chawis Boonmee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-30T17:08:16Z","doi":"10.1016/j.engappai.2024.108614","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-323-90534-3.00053-6","name":"Artificial intelligence in heart failure","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90534-3.00053-6","authors":["Deya Alkhatib","John L. Jefferies"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-08T11:02:10Z","doi":"10.1016/b978-0-323-90534-3.00053-6","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.5220/0013224100004568","name":"Research on the Application of Artificial Intelligence in the Financial Field","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013224100004568","authors":["Zhiheng Tang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-19T13:50:07Z","doi":"10.5220/0013224100004568","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.21275/es24131185821","name":"Revolutionizing Human Resource Management through Artificial Intelligence","source":"crossref","abstract":"In recent years, Artificial Intelligence (AI) has emerged as a transformative force across various industries, and Human Resources Management (HRM) is no exception. AI is reshaping the way organizations attract, manage, and develop their workforce. From recruitment to employee engagement, AI is revolutionizing HRM practices, enhancing efficiency, and contributing to more strategic decision-making.While AI brings numerous benefits to HRM, it's crucial to address ethical considerations, data privacy, and ensure that AI applications are aligned with organizational values and objectives. Additionally, human oversight remains essential to interpret results, manage biases, and make ethical decisions in HR processes.","url":"https://doi.org/10.21275/es24131185821","authors":["P Deepa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-13T08:11:59Z","doi":"10.21275/es24131185821","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.21275/es24609083415","name":"Artificial Intelligence: Revolutionizing Nursing Education and Practice","source":"crossref","abstract":"Artificial Intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. AI in healthcare isn't new; In fact, it's currently used in many ways that are relevant to nurses both in nursing practice as well as nursing education. It comprises many healthcare technologies transforming nurses' roles and enhanced patient care. It eases the burden on nurses, reducing the nurses' workload. Ethical principles are important in AI because the technology not only may impact an individual patient's end result but also may affect its uses in health care throughout the development design and testing processes and its integration and ongoing use. Nursing AI tools include clinical decision support, mobile health and sensor-based technologies including voice assistants and robotics. Nurses should be involved in the conceptualization, development, and implementation of AI, especially when it impacts nursing practice.","url":"https://doi.org/10.21275/es24609083415","authors":["Ramandeep Kaur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-12T10:39:38Z","doi":"10.21275/es24609083415","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.5121/ijaia.2024.15601","name":"Empowering Cloud-native Security: the Transformative Role of Artificial Intelligence","source":"crossref","abstract":"Cloud-native applications, built to leverage the scalability and flexibility of cloud infrastructure, have transformed how organizations develop, deploy, and manage software. However, their dynamic and distributed nature presents unique security challenges, such as container vulnerabilities, API exploits, and misconfigurations. Artificial Intelligence (AI) has emerged as a critical enabler in addressing these challenges. This white paper explores the role of AI in securing cloud-native applications, examining its capabilities in threat detection, automated response, compliance enforcement, and anomaly identification. By integrating AI-driven tools and methodologies, organizations can safeguard their cloud-native environments while enhancing operational agility and resilience.","url":"https://doi.org/10.5121/ijaia.2024.15601","authors":["Bhanu Prakash Manjappasetty Masagali","Mandar Nayak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-16T15:38:13Z","doi":"10.5121/ijaia.2024.15601","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icdacai65086.2024.00053","name":"Automotive Logistics Transportation Path Planning System on Basis of Artificial Intelligence","source":"crossref","abstract":"With the continuous growth of market demand and express delivery business, logistics enterprises must build a more intelligent logistics system. This article adopted a deep learning based AI (Artificial Intelligence) method to plan the route of automobiles. The optimization module of the automobile logistics transportation path planning system can continuously adjust the results of path planning based on real-time data and actual needs, thereby improving transportation efficiency and reducing costs. On this basis, this article studied the trajectory planning results of deep learning based trajectory planning algorithms and traditional algorithms under different load conditions. Method 3 (medium load+traditional method) Path length: 12km, transportation time: 20min; Method 4 (medium load+deep learning method) Path length: 11 km, transportation time: 17 minutes. In view of an AI based automotive logistics transportation path planning system, This article’ system is beneficial for improving logistics transportation speed and shortening logistics transportation time.","url":"https://doi.org/10.1109/icdacai65086.2024.00053","authors":["Qianying Yu","Chengjiu Xiang","Li Su"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-16T18:33:35Z","doi":"10.1109/icdacai65086.2024.00053","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.5812/jme-140890","name":"Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations","source":"crossref","abstract":"Artificial intelligence (AI) has the potential to revolutionize medical education by equipping future doctors with the latest technological advancements (1, 2).Studies have explored how AI can be integrated into educational frameworks, such as surgical skills training and case-based learning.Some research focuses on practical applications of AI in medical education, such as using AI-driven robotic systems for skill development and knowledge acquisition through simulations and assessments (3, 4).However, challenges exist in implementing these changes.Despite these challenges, AI technologies offer immense potential benefits by empowering healthcare professionals and improving patient care outcomes (5).To fully realize these benefits, continued exploration and adoption of AI in medical curricula are necessary.This letter to the editor aims to explore the opportunities, challenges, and ethical issues associated with the use of AI in medical education.","url":"https://doi.org/10.5812/jme-140890","authors":["Mohsen Masoumian Hosseini","Toktam Masoumain Hosseini","Karim Qayumi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-01T04:50:03Z","doi":"10.5812/jme-140890","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.38053/acmj.1367414","name":"ChatGPT in medical writing: enhancing healthcare communication through artificial intelligence and human expertise","source":"crossref","abstract":"This study explores the capabilities and limitations of ChatGPT, an advanced language model, in medical writing. Leveraging the GPT-4 architecture, ChatGPT has shown potential in aiding various stages of medical article creation, including planning, drafting, revising, and even submission processes. It can summarize extensive literature, suggest research questions, and assist in multi-language research, making it a versatile tool for initial research and planning. During revisions, ChatGPT’s strengths lie in improving language, ensuring consistency, and enhancing readability. Despite its abilities, ChatGPT has several limitations. ChatGPT’s training data only updates with each new version release, which could result in outdated or incomplete research. It also lacks the critical thinking, domain expertise, and ethical considerations that human researchers bring to medical writing. While ChatGPT can be a useful tool for routine tasks and initial drafts, human expertise remains critical for generating high-quality, ethical, and insightful medical research articles. Therefore, a hybrid approach that combines the computational power of ChatGPT with the intellectual and ethical rigor of human experts is recommended for optimizing medical writing processes.","url":"https://doi.org/10.38053/acmj.1367414","authors":["İsmail MEŞE","Beyza KUZAN","Taha Yusuf KUZAN"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-21T06:58:45Z","doi":"10.38053/acmj.1367414","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.31234/osf.io/tz6an","name":"Conscious artificial intelligence and biological naturalism","source":"crossref","abstract":"As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate our selves.","url":"https://doi.org/10.31234/osf.io/tz6an","authors":["Anil Seth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-30T04:24:57Z","doi":"10.31234/osf.io/tz6an","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1201/9781003486848","name":"iMind","source":"crossref","abstract":"Why has so much of our recent attention been focused on AI while RI (Real Intelligence) is all but forgotten? And why are we spending so much energy debating the future of AI rather than that of its human original? Why can’t those who are concerned about AI and those who care about RI talk to one another using a common language? iMind: Artificial and Real Intelligence is the first comprehensive popular science account of AI and RI. Unique in scope, it discusses the interdisciplinary science of AI, RI, smartphones, smart sensors, microchips, and the brain-mind connection. It explores what is beyond the physical, including mindfulness and spirituality, and how they can impact our wellbeing in the here and now, and how they can help us achieve a healthy and fulfilling old age. Mohamed I. Elmasry, PhD, FIEEE, FRSC, FCAE, FEIC, is Emeritus Professor of Computer Engineering at the University of Waterloo.","url":"https://doi.org/10.1201/9781003486848","authors":["Mohamed I. Elmasry"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-09T17:16:51Z","doi":"10.1201/9781003486848","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1145/3676581","name":"2024 2nd International Conference on Communications, Computing and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3676581","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-04T22:21:32Z","doi":"10.1145/3676581","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.15294/lslr.v8i1.3465","name":"Artificial Intelligence Regulation on Labour Market: Comparative Perspectives on the European Union Artificial Intelligence Act in the Indonesian Context","source":"crossref","abstract":"Advances in artificial intelligence technology (AI) have created new challenges for the legal framework in the field of labour law. The approval of the European Union (EI) AI Act in March13th, 2024, aimed at ensuring the safety and ethical use of AI systems in the EU, has made a significant step forward in regulating AI. This article explains the development of law-making process of the EU AI Act and finds a connection between the EU AI Act and labour market which that law might be the grand design to regulate many aspects of labour side. It then critically evaluates the development of law in Indonesia which is far for expectation to regulate AI within its legal system. Although the challenges and constraints of AI in the labour market have already occurred and are being felt, the task of building up the right legal framework for the needs of workers in the labour market is already at hand. Finally, this paper underlines the importance of comprehensive regulation of AI within legal system for ensuring business climate, employment, data privacy, transparency, ethics, and accountability. However, AI Act in Indonesia remains vague, especially in the labour market sector, and does not regulate AI in its legal system. Therefore, AI Act is necessary for Indonesia to overcome current and future challenges on labour market.","url":"https://doi.org/10.15294/lslr.v8i1.3465","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T03:35:40Z","doi":"10.15294/lslr.v8i1.3465","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2024.109023","name":"A hybrid optimization for coordinated control of distributed generations","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109023","authors":["Vipul Shukla","V. Mukherjee","Bindeshwar Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-25T06:05:15Z","doi":"10.1016/j.engappai.2024.109023","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1148/ryai.240126","name":"When the Student Becomes the Master: Boosting Intracranial Hemorrhage Detection Generalizability with Teacher-Student Learning","source":"crossref","abstract":"I n the rapidly growing landscape of radiology artificial intelligence (AI) applications, limitations of model generalizability stand out as a major hurdle to clinical acceptance.Even with U.S. Food and Drug Administration (FDA) or Conformité Européenne approval, the performance of a deployed model can suffer due to differences in patient demographics, scanner settings, and imaging protocols.Although radiology practices theoretically could overcome these challenges by developing their own models, the process of annotating large training datasets is laborious, and ad hoc internal model development is impractical as a general solution.What if there were a simpler way to fine-tune a radiology AI model for one's own practice?Through semisupervised learning (SSL), models trained with a combination of labeled and unlabeled radiologic images can meet or surpass the performance of state-of-the-art models for anatomic (1) and disease (2) segmentation.In this issue of Radiology: Artificial Intelligence, authors Lin and Yuh (3) investigate the use of \"teacher-student\" SSL to improve the generalization of intracranial hemorrhage identification and segmentation by augmenting the training set with unlabeled head CT examinations.The authors leverage a baseline \"teacher\" model trained on a small pixel-labeled training set of 457 CT examinations from one institution to automatically generate pseudo labels on a second, much larger set of 25 000 unlabeled CT examinations from the Radiological Society of North America and American Society of Neuroradiology.A final \"student\" model is trained using the combined manually and automatically labeled training set and evaluated on a third dataset acquired in India to measure performance in hemorrhage identification","url":"https://doi.org/10.1148/ryai.240126","authors":["Nathaniel Swinburne"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-10T13:52:15Z","doi":"10.1148/ryai.240126","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/up9jkh","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/up9jkh","authors":["Isabel Ramos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-18T13:30:20Z","doi":"10.32388/up9jkh","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.3386/w32685","name":"Demand for Artificial Intelligence in Settlement Negotiations","source":"crossref","abstract":"Joshua Gans has drawn on the findings of","url":"https://doi.org/10.3386/w32685","authors":["Joshua Gans"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-15T19:31:53Z","doi":"10.3386/w32685","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.58445/rars.1724","name":"Using Explainable Artificial Intelligence to Locate Pneumonia","source":"crossref","abstract":"Artificial intelligence (AI) has already become a vital resource in numerous industries; however, it is often challenging to understand how AI reaches its results.This lack of transparency, combined with potential biases within the machine learning model, prevents professionals in critical fields like healthcare from relying on deep learning models for diagnostic purposes, hindering the widespread use of AI in healthcare.This paper investigates the application of XAI in the medical field, focusing on the detection of pneumonia through the analysis of lung X-ray scans.In this study, we developed an XAI tool, utilizing a Convolutional Neural Network (CNN) constructed with PyTorch and trained on the Pneumonia MNIST dataset.Our model achieves an accuracy of nearly 91% on 28x28 pixel images and highlights the top pixels considered most important by the deep learning model in its decision-making process.The primary aim of this project is to present a proof-of-concept tool for the integration of XAI into healthcare diagnostics, with the goal of assisting medical professionals in making informed decisions and ultimately saving lives.By demonstrating the feasibility and effectiveness of XAI in pneumonia detection, we lay the groundwork for future advancements in healthcare AI, emphasizing the importance of transparency and reliability in AI models.","url":"https://doi.org/10.58445/rars.1724","authors":["Anthony Novokshanov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-03T16:22:39Z","doi":"10.58445/rars.1724","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1049/ic:19960187","name":"Medical image interpretation","source":"crossref","abstract":"","url":"https://doi.org/10.1049/ic:19960187","authors":["C. Taylor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-11-22T19:40:26Z","doi":"10.1049/ic:19960187","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-12-821750-4.00002-5","name":"Artificial intelligence for medical robotics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-821750-4.00002-5","authors":["Erwin Loh","Tam Nguyen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-14T17:36:33Z","doi":"10.1016/b978-0-12-821750-4.00002-5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2018.07.004","name":"Recent advances in extracting and processing rich semantics from medical texts","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artmed.2018.07.004","authors":["Kerstin Denecke","Frank van Harmelen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-08-03T11:07:19Z","doi":"10.1016/j.artmed.2018.07.004","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1080/08839510590887414","name":"FUZZY METHODS FOR MEDICAL DIAGNOSIS","source":"crossref","abstract":"This paper argues that fuzzy representations are appropriate in applications where there are major sources of imprecision and/or uncertainty. Case studies of fuzzy approaches to specific problems of medical diagnosis and classification are described in support of this argument. The case studies are in the areas of categorical consistency, diagnostic monitoring, and scoring. The solutions use a variety of fuzzy methods, including clustering, fuzzy set aggregation, and type-2 fuzzy set modeling of linguistic approximations. It is concluded that the fuzzy approach to the development of artificial intelligence in application systems in beneficial in these contexts because of the need to focus on uncertainty as a main issue.","url":"https://doi.org/10.1080/08839510590887414","authors":["P. R. INNOCENT","R. I. JOHN","J. M. GARIBALDI"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-02-16T16:49:53Z","doi":"10.1080/08839510590887414","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.engappai.2026.115989","name":"Adaptive resource-constrained neural networks for multi-class medical image classification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115989","authors":["Yuval Kassif","Gonen Singer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-20T19:43:56Z","doi":"10.1016/j.engappai.2026.115989","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.58532/v3biai9p6ch1","name":"LEVERAGING ARTIFICIAL INTELLIGENCE FOR ADVANCEMENTS IN BIO-MEDICAL ENGINEERING: A COMPREHENSIVE REVIEW","source":"crossref","abstract":"By combining engineering concepts with medical sciences, bio-medical engineering plays a critical role in revolutionising healthcare. The convergence of artificial intelligence (AI) with bio-medical engineering has resulted in ground-breaking advances in diagnoses, treatment techniques, and patient care in recent years. This book chapter provides an in-depth examination of the many applications of AI in bio-medical engineering, focusing on its contributions to medical imaging, illness diagnosis, drug development, personalised medicine, and healthcare management. The study also investigates the obstacles and ethical concerns related with the incorporation of AI in bio-medical engineering, as well as providing insights into the field's future prospects.","url":"https://doi.org/10.58532/v3biai9p6ch1","authors":["Shivani Chadha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T01:26:36Z","doi":"10.58532/v3biai9p6ch1","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1007/978-3-031-42576-9_5","name":"An Artificial Intelligence Invention Protection Model","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-42576-9_5","authors":["Budi Agus Riswandi","Galih Dwi Ramadhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-16T05:01:59Z","doi":"10.1007/978-3-031-42576-9_5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/aivrv63595.2024.10860168","name":"Visualization of Hotspots and Frontiers in Artificial Intelligence in Education - Based on Citespace Knowledge Map Analysis","source":"crossref","abstract":"With the advent of the digital age, the deep integration of artificial intelligence and education has attracted extensive attention in the academic community. In this study, 1,193 literatures directly related to AI education in the Web of Science core database in the past ten years were knowledge mapped and visualized with the help of CiteSpace software. Through the analysis of co-occurrence, clustering, and emergence of the literature data, it is found that the number of related literature issued in 2014–2014 increased significantly, with a certain degree of aggregation and depth. It also shows the hotspots and cutting-edge trends of artificial intelligence education research. The research hotspots are mainly centered on artificial intelligence, digital education, recognition, school, identification, educational data mining, human-computer interaction, etc., but the research method is relatively single, the authors and the interdisciplinary cooperation between institutions is not close enough, and there is a problem of bias in the region of research institutions. Future research can try to strengthen interdisciplinary cooperation, innovate institutional cooperation modes, and establish visualization standards, so as to stimulate the endogenous momentum of the deep integration of AI and education.","url":"https://doi.org/10.1109/aivrv63595.2024.10860168","authors":["Fei Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-04T18:30:45Z","doi":"10.1109/aivrv63595.2024.10860168","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1007/s40593-023-00342-5","name":"The Social life of AI in Education","source":"crossref","abstract":"Recently the Times Higher Education launched a series of 'Spotlight' articles and think pieces on AI and the University, claiming 'artificial intelligence is already impacting higher education, and signs are that the influence of evolving technologies on university life is just getting started'.1 The collected pieces are well-considered and in places cautious about AI hype, yet they tend to reflect a widespread assumption that AI will inevitably transform the future of education-for the better.The problem with such promotion of AI and the future of education is it presupposes AI will operate as planned and intended, with any problems emerging during its development or deployment smoothed out through either technical tweaks or appropriate ethical frameworks.None of these things are necessarily the case.As Meredith Broussard argues in Artificial Unintelligence: How Computers Misunderstand the World, 'the way people talk about technology is out of sync with what digital technology actually can do' (Broussard, 2019, p. 6).She coins the phrase 'technochauvinism' to describe the flawed assumption that digital technologies like AI are always the solution.Computer technology, Broussaard argues, simply does not always work as expected or intended.It is technochauvinist to assume it will.There is no good reason to presuppose AI used in education will work as expected either.For all the current enthusiasm for AI-based teaching and learning, the evidence base for their transformative effects on education remains thin (Holmes et al., 2022).Moreover, at the time of writing, the biggest stories about AI in education concern automated natural language generating technologies.While some foresee these lan-1 https://www.","url":"https://doi.org/10.1007/s40593-023-00342-5","authors":["Ben Williamson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-05T16:10:41Z","doi":"10.1007/s40593-023-00342-5","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.22381/ajmr11220241","name":"Artificial Intelligence-driven Clinical Data Analytics, Machine Learning-based Diagnosis and Treatment Data, and Medical Decision Support and Patient-centered Smart Healthcare Systems for Digital Twin-based Medical Condition Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.22381/ajmr11220241","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-03T10:15:27Z","doi":"10.22381/ajmr11220241","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.21037/jmai.2019.03.02","name":"Application of artificial intelligence for the assessment of mucosal healing and inflammation","source":"crossref","abstract":"Artificial intelligence (AI), also referred to as machine intelligence, has been increasingly entering all avenues of our lives (1-5). AI has enabled facial, object, speech, gesture and writing recognition, language translation, autonomous cars, internet searches, cyber and home security and many other areas. It has revolutionized diverse aspects of medical care, including electronic health records, guidance in medical diagnosis and treatment decisions, medical statistics, analysis of X-rays, CT-scans, MRIs, electrocardiograms (EKGs), evaluation of endoscopic and histologic images, robotics, and cellular and molecular biology including arrays and genome-, proteome- and metabolome- “omics”.","url":"https://doi.org/10.21037/jmai.2019.03.02","authors":["Andrzej S. Tarnawski","Amrita Ahluwalia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-03-19T03:56:27Z","doi":"10.21037/jmai.2019.03.02","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.artmed.2010.05.006","name":"Semantic relations for problem-oriented medical records","source":"crossref","abstract":"Objective We describe semantic relation (SR) classification on medical discharge summaries. We focus on relations targeted to the creation of problem-oriented records. Thus, we define relations that involve the medical problems of patients. Methods and materials We represent patients' medical problems with their diseases and symptoms. We study the relations of patients' problems with each other and with concepts that are identified as tests and treatments. We present an SR classifier that studies a corpus of patient records one sentence at a time. For all pairs of concepts that appear in a sentence, this SR classifier determines the relations between them. In doing so, the SR classifier takes advantage of surface, lexical, and syntactic features and uses these features as input to a support vector machine. We apply our SR classifier to two sets of medical discharge summaries, one obtained from the Beth Israel-Deaconess Medical Center (BIDMC), Boston, MA and the other from Partners Healthcare, Boston, MA. Results On the BIDMC corpus, our SR classifier achieves micro-averaged F-measures that range from 74% to 95% on the various relation types. On the Partners corpus, the micro-averaged F-measures on the various relation types range from 68% to 91%. Our experiments show that lexical features (in particular, tokens that occur between candidate concepts, which we refer to as inter-concept tokens) are very informative for relation classification in medical discharge summaries. Using only the inter-concept tokens in the corpus, our SR classifier can recognize 84% of the relations in the BIDMC corpus and 72% of the relations in the Partners corpus. Conclusion These results are promising for semantic indexing of medical records. They imply that we can take advantage of lexical patterns in discharge summaries for relation classification at a sentence level.","url":"https://doi.org/10.1016/j.artmed.2010.05.006","authors":["Ozlem Uzuner","Jonathan Mailoa","Russell Ryan","Tawanda Sibanda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-06-21T00:49:28Z","doi":"10.1016/j.artmed.2010.05.006","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1002/9781394303601.ch10","name":"An Enhanced Threat Detection Model to Assist Supply Chain Management Using Artificial Intelligence","source":"crossref","abstract":"The previous framework combines computer algorithms and physical processes to create a comprehensive system. Risk on Screen Character is a potential threat actor or hacker who may attempt to exploit vulnerabilities in the system. Tactics, Techniques, and Procedures (TTP) are strategies and methods used by threat actors to manipulate cybersecurity weaknesses in the supply chain. Cybersecurity in the Supply Chain (CSC) secures the information and processes within the supply chain to ensure the organization's security and operational goals are met. CSC Requirements are the security requirements and measures used to protect the supply chain from cyber threats. Attack Entity could refer to the threat actor or entity attempting to compromise the supply chain's cybersecurity. Digital Incident Report is created in response to a cybersecurity incident and can impact the organization's goals related to inbound and outbound supply chains. The probability of a cyber attack developing into a significant threat is determined by the information and intelligence gathered about the threat actor's capabilities and intentions. The proposal causes the hash key utilizing the Merkle tree concept creating the trail datasets, and stores them. It improves the system by enhancing security by 8.99% and early threat detection by 7.02%.","url":"https://doi.org/10.1002/9781394303601.ch10","authors":["N. Ambika"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-13T21:20:12Z","doi":"10.1002/9781394303601.ch10","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1007/978-981-97-5656-8","name":"Digital Transformation, Artificial Intelligence and Society","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-5656-8","authors":["Sachin Kumar","Ajit Kumar Verma","Amna Mirza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-18T14:02:06Z","doi":"10.1007/978-981-97-5656-8","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.engappai.2024.109258","name":"A scientometric analysis of quantum driven innovations in intelligent transportation systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109258","authors":["Monika","Sandeep Kumar Sood"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-09T13:16:46Z","doi":"10.1016/j.engappai.2024.109258","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.7551/mitpress/15378.003.0013","name":"AGI and Society","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15378.003.0013","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T16:25:21Z","doi":"10.7551/mitpress/15378.003.0013","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2139/ssrn.4799240","name":"The Rise of Generative Artificial Intelligence","source":"crossref","abstract":"I prepared these reading materials on copyright and generative AI for my copyright and disruptive technologies course. The first part deals with the copyrightability of works created with generative AI tools, including among others edited excepts from the decision of the Columbia District Court in Thaler v. Perlmutter, the U.S. Copyright Office’s guidelines, and its decisions regarding Zarya of the Dawn and Théâtre D’opéra Spatial.The second part deals with the copyright liability for training generative AI tools, including among others edited excerpts from the complaint in NYTimes v. Microsoft and Judge Bibas’s opinion in Thomson Reuters v. Ross Intelligence.","url":"https://doi.org/10.2139/ssrn.4799240","authors":["Guy Rub"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-25T14:24:15Z","doi":"10.2139/ssrn.4799240","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.36227/techrxiv.172469926.65177388/v1","name":"Towards secure-by-design artificial intelligence systems","source":"crossref","abstract":"The security of AI systems becomes increasingly paramount, as artificial intelligence (AI) pervades various aspects of our lives. For instance, from autonomous driving to banking and medical diagnostics, AI is playing a pivotal role in powering such security-sensitive systems. However, attacks on these high-risk AI systems could have severe consequences, jeopardizing the lives, finances, and well-being of people. Thus, adherence to fundamental security properties, namely, Confidentiality, Integrity, and Availability (CIA), is essential for AI systems to comply with security standards. In this paper, we first define a simplified abstraction of AI systems to discuss four independent viewpoints: data, models, inputs, and deployment. Subsequently, we conduct a detailed analysis of attack vectors targeting each of these viewpoints, with a rigorous assessment of CIA properties. Proactive identification of attack vectors throughout the entire AI lifecycle is a requisite step toward establishing a secure-by-design framework for AI systems.","url":"https://doi.org/10.36227/techrxiv.172469926.65177388/v1","authors":["Sandeep Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-26T15:07:53Z","doi":"10.36227/techrxiv.172469926.65177388/v1","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.4087/fmxk3369","name":"The Artificial Intelligence Revolution Arrives in Philanthropy","source":"crossref","abstract":"","url":"https://doi.org/10.4087/fmxk3369","authors":["Kallie Bauer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-21T15:39:22Z","doi":"10.4087/fmxk3369","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.54393/pbmj.v8i12.1332","name":"Assessment of Knowledge and Education Regarding Artificial Intelligence Among Medical Teaching Faculty at Bolan Medical College, Quetta","source":"crossref","abstract":"In the context of the continued rapid progress of the incorporation of AI technology into the healthcare system of the state of Pakistan, there are considerable shortcomings regarding the knowledge and readiness of the faculty who teach medicine at various institutions of the country’s education system, including provinces with historically underrepresented portions of the community, like Balochistan. Objectives: To evaluate the knowledge, educational experience, perceptions, and preparedness of the medical teaching staff of Bolan Medical College regarding AI technology. Methods: A cross-sectional observational study with a sample of 200 teaching faculty. A 24-point questionnaire was based on the literature received and the study used Google Forms, ensuring objectivity with anonymization. Descriptive and inferential analyses were used with SPSS version 27.0. Results: The sample, 119 (59.5%), were aware of applications of AI in medicine; only 53 (26.5%) reported being formally educated on AI. Awareness of AI in clinical sciences was 112 (56%). Knowledge of at least one AI-related programming language was 126 (63%), while familiarity with AI-related journals was 60 (30%). Only 42 (21%) reported AI-related education in their curriculum. The average knowledge stood at 2.33 ± 1.07 on a 6-point scale, reflecting moderate awareness, with only moderate application of AI knowledge, and 53 (26.5%) reporting ease of application. Conclusions: Teaching staff appear interested and aware of AI; however, major shortcomings point to the requirement of immediate faculty development programs to equip educators with knowledge and wisdom so that AI can safely be implemented in medicine.","url":"https://doi.org/10.54393/pbmj.v8i12.1332","authors":["Maqbool Ahmed","Ambreen Khan","Muhammad Junaid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-06T13:23:03Z","doi":"10.54393/pbmj.v8i12.1332","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.4337/9781035307555.00019","name":"Index","source":"crossref","abstract":"Taxing Artificial Intelligence will be essential reading for scholars, policy makers and students across law and economics. It will also be invaluable for law and tax professionals seeking to understand the latest developments in AI, automation, and the future of work.","url":"https://doi.org/10.4337/9781035307555.00019","authors":["Xavier Oberson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-12T14:01:52Z","doi":"10.4337/9781035307555.00019","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.26226/m.682c98397157793663771a9c","name":"Artificial Intelligence in Undergraduate Medical Education:  A Cross-Sectional Study Assessing Medical Students’ Experiences and Perspectives","source":"crossref","abstract":"","url":"https://doi.org/10.26226/m.682c98397157793663771a9c","authors":["Alex Case-Green"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-12T09:22:57Z","doi":"10.26226/m.682c98397157793663771a9c","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1080/08839514.2024.2378274","name":"Automatic Detection and 3D Reconstruction of Buildings from Historical Maps","source":"crossref","abstract":"This paper presents an automatic 3D building reconstruction methodology for historical urban maps. It uses facade and openings detection on the maps, followed by rectification, regularization, and 3D model generation techniques. Evaluation metrics confirm the effectiveness of the approach, with high accuracy in detecting facades and their openings while maintaining geometric integrity. The flexibility and interoperability of the chosen 3D building representation method allow for adjustments in dimensions without compromising layout, making it suitable for completing the urban environments where facades may not be directly visible. This methodology represents a significant step toward automating the reconstruction of 3D historical urban landscapes, contributing to heritage preservation and architectural understanding.","url":"https://doi.org/10.1080/08839514.2024.2378274","authors":["Fernando Pérez Nava","Isabel Sánchez Berriel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-24T14:16:00Z","doi":"10.1080/08839514.2024.2378274","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.2196/preprints.109135","name":"Reallocating the Human: A Task-Allocation Framework for Artificial Intelligence in Medical Education (Preprint)","source":"crossref","abstract":"UNSTRUCTURED Burnout among medical educators is common, driven largely by the administrative and assessment work built into teaching roles. Artificial intelligence (AI) is widely promoted as a way to lift that burden, and AI tools have been shown to reduce documentation load and burnout among clinicians. That evidence stops at clinical documentation and has not reached teaching, while the parallel literature on AI in medical education maps where these tools can be used without offering a rule for deciding which teaching tasks should be delegated and which should not. We propose a task-allocation framework for the human–AI division of labor in medical education. The framework places each teaching task on two axes, the intensity of the human factor it involves and the capability and safety of AI to perform it, yielding four modes of allocation (Automate, Augment, Reserve/Protect, and Monitor), with a stakes modifier that returns summative and safety-critical work to human control and a rubric for rating new tasks. The framework’s central aim is to redirect the capacity freed by automation into the tasks only a clinician-teacher can perform: bedside reasoning, professional identity formation, and mentorship. We present the framework as a governance tool that stands on pedagogical and ethical grounds independent of any wellbeing claim; the further proposition that reallocation protects educators and, through them, learning is advanced separately, as a hypothesis for testing. The framework is conceptual and not yet validated; we situate it against existing models, specify the ethical guardrails it requires, and propose a validation agenda.","url":"https://doi.org/10.2196/preprints.109135","authors":["Ehab Alameer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T18:00:07Z","doi":"10.2196/preprints.109135","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2139/ssrn.4674852","name":"Cyber Security &amp;amp; Artificial Intelligence","source":"crossref","abstract":"The huge applications in IOT is greatly leading to tremendous cyber security which is affecting all over the globe. Therefore, to design the proper and precise technologies in a cyber security is a need of an hour. Cyber security aims to reduce the cyber attacks and protect against the unauthorized exploitation of systems, network and technologies. The Artificial Intelligence is more helpful and important in cyber security to build high security and model which will protect system from any cyber-attack. AI has shown a tremendous results in the cyber security by analyzing the data accurately. This paper depicts the AI techniques which is being used in various applications in cyber attack","url":"https://doi.org/10.2139/ssrn.4674852","authors":["Devang Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-06T11:11:00Z","doi":"10.2139/ssrn.4674852","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1148/ryai.240624","name":"AI as a Second Reader Can Reduce Radiologists’ Workload and Increase Accuracy in Screening Mammography","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.240624","authors":["Abhinav Suri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-23T13:53:42Z","doi":"10.1148/ryai.240624","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.caeai.2024.100298","name":"Analysis of LLMs for educational question classification and generation","source":"crossref","abstract":"Large language models (LLMs) like ChatGPT have shown promise in generating educational content, including questions. This study evaluates the effectiveness of LLMs in classifying and generating educational-type questions. We assessed ChatGPT's performance using a dataset of 4,959 user-generated questions labeled into ten categories, employing various prompting techniques and aggregating results with a voting method to enhance robustness. Additionally, we evaluated ChatGPT's accuracy in generating type-specific questions from 100 reading sections sourced from five online textbooks, which were manually reviewed by human evaluators. We also generated questions based on learning objectives and compared their quality to those crafted by human experts, with evaluations by experts and crowdsourced participants. Our findings reveal that ChatGPT achieved a macro-average F1-score of 0.57 in zero-shot classification, improving to 0.70 when combined with a Random Forest classifier using embeddings. The most effective prompting technique was zero-shot with added definitions, while few-shot and few-shot + Chain of Thought approaches underperformed. The voting method enhanced robustness in classification. In generating type-specific questions, ChatGPT's accuracy was lower than anticipated. However, quality differences between ChatGPT-generated and human-generated questions were not statistically significant, indicating ChatGPT's potential for educational content creation. This study underscores the transformative potential of LLMs in educational practices. By effectively classifying and generating high-quality educational questions, LLMs can reduce the workload on educators and enable personalized learning experiences. • Multiple prompt variations followed by voting enhance robustness and improve the performance of question classification. • ChatGPT performs better at classifying its own generated questions than those from real users. • The accuracy of question types generated by LLMs decreases as the sequence of generated questions progresses. • LLMs may use knowledge from outside the source text when generating questions, which can introduce hallucination effects. • There is an inconsistency in LLMs' understanding of question types between the classification and generation processes.","url":"https://doi.org/10.1016/j.caeai.2024.100298","authors":["Said Al Faraby","Ade Romadhony","Adiwijaya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-12T19:07:48Z","doi":"10.1016/j.caeai.2024.100298","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.7551/mitpress/15378.003.0011","name":"AGI and Consciousness","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15378.003.0011","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T16:25:21Z","doi":"10.7551/mitpress/15378.003.0011","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1161/blog.20240719.65355","name":"Can Artificial Intelligence Improve Stroke Risk Predictions?","source":"crossref","abstract":"","url":"https://doi.org/10.1161/blog.20240719.65355","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T19:30:57Z","doi":"10.1161/blog.20240719.65355","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1148/ryai.01022024.podcast","name":"Radiology: AI RSNA2023 Fireside Chat LIVE","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.01022024.podcast","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T15:17:13Z","doi":"10.1148/ryai.01022024.podcast","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.32388/v5m8eg","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"Potential competing interests: No potential competing","url":"https://doi.org/10.32388/v5m8eg","authors":["Asim Iftikhar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-25T04:52:28Z","doi":"10.32388/v5m8eg","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.34218/ijaiml_04_01_004","name":"INTERPRETABLE ARTIFICIAL INTELLIGENCE WITH EXPLAINABILITY AND ROBUSTNESS IN MEDICAL IMAGE CLASSIFICATION USING TOPOLOGICAL AND FRACTAL FEATURES","source":"crossref","abstract":"Deep learning models, particularly Convolutional Neural Networks (CNNs), have achieved remarkable accuracy in medical image analysis tasks like pneumonia detection from chest X-rays.However, their \"black-box\" nature and the potential brittleness of common explainability methods (e.g., saliency maps) hinder clinical trust and adoption.This paper proposes and evaluates a methodology for enriching CNNs with mathematically grounded global features derived from Topological Data Analysis (TDA) and Fractal Dimension (FD) analysis, aiming to provide complementary, more robust explanations.We integrate these features, extracted from intermediate layers of a pre-trained ResNet50 fine-tuned for pneumonia detection, with the CNN's own deep features.Our results show that while a simple MLP-based fusion significantly degraded performance (accuracy ~73%), an attention-based fusion mechanism successfully integrated the features, matching the high baseline accuracy (~96%) on the original dataset.The TDA and FD features themselves exhibit statistically significant differences between normal and pneumonia classes (FD p < 5e-7), providing Timothy Suraj https://iaeme.com/Home/journal/IJAIML44","url":"https://doi.org/10.34218/ijaiml_04_01_004","authors":["Timothy Suraj"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-11T13:54:45Z","doi":"10.34218/ijaiml_04_01_004","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2008.04.004","name":"Medical data mining by fuzzy modeling with selected features","source":"crossref","abstract":"Objective Medical data is often very high dimensional. Depending upon the use, some data dimensions might be more relevant than others. In processing medical data, choosing the optimal subset of features is such important, not only to reduce the processing cost but also to improve the usefulness of the model built from the selected data. This paper presents a data mining study of medical data with fuzzy modeling methods that use feature subsets selected by some indices/methods. Methods Specifically, three fuzzy modeling methods including the fuzzy k-nearest neighbor algorithm, a fuzzy clustering-based modeling, and the adaptive network-based fuzzy inference system are employed. For feature selection, a total of 11 indices/methods are used. Medical data mined include the Wisconsin breast cancer dataset and the Pima Indians diabetes dataset. The classification accuracy and computational time are reported. To show how good the best performer is, the globally optimal was also found by carrying out an exhaustive testing of all possible combinations of feature subsets with three features. Results For the Wisconsin breast cancer dataset, the best accuracy of 97.17% was obtained, which is only 0.25% lower than that was obtained by exhaustive testing. For the Pima Indians diabetes dataset, the best accuracy of 77.65% was obtained, which is only 0.13% lower than that obtained by exhaustive testing. Conclusion This paper has shown that feature selection is important to mining medical data for reducing processing time and for increasing classification accuracy. However, not all combinations of feature selection and modeling methods are equally effective and the best combination is often data-dependent, as supported by the breast cancer and diabetes data analyzed in this paper.","url":"https://doi.org/10.1016/j.artmed.2008.04.004","authors":["Sean N. Ghazavi","Thunshun W. Liao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-06-06T04:41:18Z","doi":"10.1016/j.artmed.2008.04.004","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1145/3726010.3726034","name":"Algorithm and application of artificial intelligence technology in interface image processing","source":"crossref","abstract":"With the integration of computer technology and human intelligence technology, various computing methods have been widely practiced in the field of image processing. The application and development of artificial intelligence in image processing is one of the hot spots in the field of science and technology. With the rapid development of information technology and the continuous improvement of computing power, artificial intelligence technology has begun to show strong application potential in the field of image processing. From traditional image processing methods to artificial intelligence-based image processing algorithms, artificial intelligence has become an important means in the field of image processing. Therefore, it is of great practical significance to explore the application and development of artificial intelligence in image processing. Based on this, combining with the technical principle of artificial intelligence algorithm in computer image processing, the paper analyzes the advantages of human intelligence algorithm in computer image processing and its price value, focusing on these four key algorithms[1]. This paper studies and discusses the application of the algorithms of \"inheritance, optimization of particle group, leech ant, annealing\" in image processing of human intelligence, aiming at providing reference for the development of human intelligence.","url":"https://doi.org/10.1145/3726010.3726034","authors":["Tong Zheng","Xinshuo Feng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T07:53:18Z","doi":"10.1145/3726010.3726034","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/cait64506.2024.10963098","name":"Bibliometric Analysis and Research Trends in Artificial Intelligence for Pharmaceutical Management and Drug Discovery","source":"crossref","abstract":"Background: With the rapid advancement of technology, Artificial Intelligence (AI) has become integral to drug management and development. This study conducts a bibliometric analysis to explore research frontiers, focus areas, and trends in AI applications within these fields.Methods: Using literature indexed in SCI and SSCI as of October 10, 2024, covering the period from 2014 to 2024, we employed Citespace to analyze countries, publications, organizations, authors, and citation patterns.Results: We examined 752 Pharmaceutical Management and 413 drug discovery papers, revealing a marked increase in AI-related research. China and the United States dominate the field, with Harvard University as the top contributor.Conclusion: The U.S. and China are leaders, with increasing contributions from the U.K. and other nations, highlighting the need for enhanced collaboration among developing countries.","url":"https://doi.org/10.1109/cait64506.2024.10963098","authors":["Yongcong Ma","Fengshi Jing"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-17T17:38:17Z","doi":"10.1109/cait64506.2024.10963098","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icabcd62167.2024.10645267","name":"A Unified Generative Artificial Intelligence Approach for Converting Social Media Content","source":"crossref","abstract":"Social media content is relevant for many applications, including applications that assist in fighting the plague of terrorism through Artificial Intelligence (AI). However, social media content is diverse in its form - text, image, audio, and video. Depending on the nature of the applications, it may be desirable to convert all these forms into a unique format to ease processing. Once the data is converted into text, it is then possible to organize it into structured tabular data to feed Machine Learning (ML) algorithms for real-time terrorist attack detections. This paper explores using the emerging Generative Artificial Intelligence (Gen AI) tools for converting social media content (text, image, audio or video) into text format suitable for applying machine learning algorithms. The methodology of this research consisted of studying existing Gen AI tools, evaluating and selecting the best among those that offer API or code integration to implement a tool for converting all forms of social media content into text. The main limitation of this work is the small size of the datasets used in the tools' evaluation. The design and implementation of the proposed solution have been completed, and the tool is ready for use and integration into a framework for collecting and analysing social media content to fight against terrorism.","url":"https://doi.org/10.1109/icabcd62167.2024.10645267","authors":["Lossan Bonde","Severin Dembele"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-29T17:43:26Z","doi":"10.1109/icabcd62167.2024.10645267","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.engappai.2024.108062","name":"CNNRec: Convolutional Neural Network based recommender systems - A survey","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108062","authors":["Ronakkumar Patel","Priyank Thakkar","Vijay Ukani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-10T17:02:49Z","doi":"10.1016/j.engappai.2024.108062","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/j.artmed.2024.102920","name":"End-to-end offline reinforcement learning for glycemia control","source":"crossref","abstract":"The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully - if at all - captured by the simulator. To address this, we propose to use model-free offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.","url":"https://doi.org/10.1016/j.artmed.2024.102920","authors":["Tristan Beolet","Alice Adenis","Erik Huneker","Maxime Louis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-25T23:37:26Z","doi":"10.1016/j.artmed.2024.102920","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1109/icaiic60209.2024.10463234","name":"Artificial Intelligence Applications for Resilience in Manufacturing — A Systematic Literature Review","source":"crossref","abstract":"This review provides a structured literature analysis of Artificial Intelligence (AI) applications in enhancing manufacturing resilience. The research is guided by three primary questions addressing the use cases, technologies, and benefits of AI across the five resilience phases: Prepare, Prevent, Protect, Respond, and Recover. Findings from 78 papers reveal that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies. The study highlights the pivotal role of AI in advancing manufacturing towards proactive, resilient, and adaptable operations. The insights gleaned offer a roadmap for future research and practical AI integration in manufacturing, underscoring the value of AI in driving industrial innovation and efficiency.","url":"https://doi.org/10.1109/icaiic60209.2024.10463234","authors":["Florian A. Maier","Sivaphani Puppala","Michael Oberle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-20T18:12:10Z","doi":"10.1109/icaiic60209.2024.10463234","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.4687831","name":"Artificial Intelligence in Cyber Security","source":"crossref","abstract":"The unprecedented pace of technology has been significantly influenced by the integration of Artificial Intelligence (AI). The ubiquity of AI spans various domains, garnering both criticism and acclaim. Its growing application presents both advantages and drawbacks in cybersecurity, as it becomes a standard component in the development and operational phases of contemporary technologies. This paper provides a comprehensive overview of AI utilization in cybersecurity, exploring its benefits, challenges, and potential negative impacts. In addition to that, it explores AI-based models that enhance or compromise security across various infrastructures and cyber networks. The paper critically examines the role of AI in developing cybersecurity applications, proposes strategies for leveraging emerging technologies to counteract AI-generated threats and vulnerabilities, and addresses the socio-economic repercussions of the involvement of AI in cybersecurity.","url":"https://doi.org/10.2139/ssrn.4687831","authors":["Md Fazley Rafy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-18T14:12:35Z","doi":"10.2139/ssrn.4687831","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1080/0142159x.2024.2305369","name":"Medical students’ perceptions of an artificial intelligence (AI) assisted diagnosing program","source":"crossref","abstract":"As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.","url":"https://doi.org/10.1080/0142159x.2024.2305369","authors":["Emely Robleto","Ali Habashi","Mary-Ann Benites Kaplan","Richard L. Riley","Chi Zhang","Laura Bianchi","Lina A. Shehadeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-02T17:21:08Z","doi":"10.1080/0142159x.2024.2305369","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"doi:10.1016/b978-0-443-24001-0.00009-9","name":"Applications and impact of artificial intelligence in veterinary sciences","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24001-0.00009-9","authors":["Ambreen Hamadani","Nazir Ahmad Ganai","Henna Hamadani","Shabia Shabir","Shazeena Qaiser"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-01T09:15:38Z","doi":"10.1016/b978-0-443-24001-0.00009-9","addedAt":"2026-09-01T01:47:49.983Z","updatedAt":"2026-09-01T01:47:49.983Z"},{"id":"oa:W3217683468","name":"Role of Artificial Intelligence in COVID-19 Detection","source":"openalex","abstract":"The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.","url":"https://doi.org/10.3390/s21238045","authors":["Anjan Gudigar","U. Raghavendra","Sneha Nayak","Chui Ping Ooi","Wai Yee Chan","Mokshagna Rohit Gangavarapu","Chinmay Dharmik","Jyothi Samanth","Nahrizul Adib Kadri","Khairunnisa Hasikin‬","Prabal Datta Barua","Subrata Chakraborty","Edward J. Ciaccio","U. Rajendra Acharya"],"tags":["Coronavirus disease 2019 (COVID-19)","Pandemic","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","Livelihood","2019-20 coronavirus outbreak"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-01","doi":"https://doi.org/10.3390/s21238045","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4205280079","name":"Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence","source":"openalex","abstract":"Due to the rapid growth of the elderly around the world, the artificial intelligence control framework can collect information and apply it and perform other tasks. Artificial intelligence plays an important role in focusing on the elderly. For example, it can improve the relationship between the elderly and family members or nursing teams. In addition, AI chat robot can communicate with the elderly without obstacles and can remind the elderly when to take medicine, regular physical examination, etc. A significant number of the AI applications on cell phones accessible today could screen wellbeing information, like every day exercises, diet, and surprisingly the senior's way of life, in a less nosy way. In such cases, it could help in expecting and, subsequently, forestalling any conceivable hypertension or unpredictable heart rate. Essentially, mechanical 'pets' are likewise assisting with fighting off feelings of loneliness, while additionally assisting with upgrading patient consideration simultaneously. One model is Tombot, a little dog like model, which was made to diminish misery and tension among dementia patients. Its head developments, looks, and swaying tail feel basically the same as the real thing, causing occupants to feel as though they have their very own pet to really focus on. One of the issues that growing societies are presently facing is the care of elderly individuals. The dearth of skilled workers in the senior healthcare setting has been exacerbated by the worldwide shift of aging populations. There might be an enhanced need for old nursing since the global older demographic is expected to nearly triple in the coming three decades. There are advancements in computer technologies for supporting the aged plus associated caregivers, checking their wellbeing, and offering company to them. Given the global elderly demographic development possibilities, it is no coincidence that the aided care market is drawing fast advancement, rendering health management for nurses a breeze. While the world's governments manage the aging population next years, these ideas will become extremely vital. They will almost certainly encounter economic and political demands to modify state medical care management, retirement benefits, and social security in order to meet the needs of an aging population. Considering that the demand for physicians is growing, a necessity has developed to deliver individualized care for the aged as well as to respond appropriately in emergencies. As a result, in the technological society, healthcare is exploring artificial intelligence to deliver personalized treatment to individuals in need. The challenges of the aged are determined in this study, and answers are supplied via a tailored computer (robot). With the crucial details given via Internet of Things gadgets, emergency events may be foreseen relatively promptly, and appropriate actions can be proposed by AI technology. Individuals' vital health information is collected using the Internet of Things based on smart technology. The information is evaluated, and decisions are made by AI, while the developed machine performs the appropriate task. This research, therefore, looks at the material design of medical products for elderly people based on artificial intelligence. It goes further and explains some of the challenges encountered in the process and possible remedies.","url":"https://doi.org/10.1155/2022/7058477","authors":["Jinjin Rong","Xu Ji","Fang Xin","Moon-Hwan Jee"],"tags":["Loneliness","Feeling","Elderly people","Psychology","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-18","doi":"https://doi.org/10.1155/2022/7058477","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2932449300","name":"WHO and ITU establish benchmarking process for artificial intelligence in health","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0140-6736(19)30762-7","authors":["Thomas Wiegand","Ramesh Krishnamurthy","Monique M. Kuglitsch","Naomi Lee","Sameer Pujari","Marcel Salathé","Markus Wenzel","Shan Xu"],"tags":["Scopus","Benchmarking","Economic shortage","Artificial intelligence","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-03-29","doi":"https://doi.org/10.1016/s0140-6736(19)30762-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4311138675","name":"Smart data processing for energy harvesting systems using artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.nanoen.2022.108084","authors":["S. Divya","Swati Panda","Sugato Hajra","Rathinaraja Jeyaraj","Anand Paul","Sang Hyun Park","Hoe Joon Kim","Tae Hwan Oh"],"tags":["Energy harvesting","Triboelectric effect","Context (archaeology)","Robotics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-09","doi":"https://doi.org/10.1016/j.nanoen.2022.108084","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2088171059","name":"The numeric representation of knowledge and logic—Two artificial intelligence applications in medical education","source":"openalex","abstract":"MEDCAT (medical diagnosis, consultation, and teaching) is a program that makes diagnoses from empiric data stored in patient records, explains its reasoning in response to questions (consultant mode), and uses its logical and communicative skills to instruct medical students in the proper approach to medical diagnosis (student mode). MEDCAT's reasoning can be modified by free-format discussion with physicians. CATS (computerized anatomical teaching system) is an entirely separate program designed to teach gross anatomy. Like MEDCAT, it has a consultant mode that the student may use to explore the program's reasoning, and a student mode in which the program takes the initiative. A prominent feature of CATS is its ability to discover meaningful general principles that reduce the need for memorization. Despite important differences in the subject matter, the data structure and code are very similar in the two programs. Both use a powerful natural-language interface that parses the input and generates the output.","url":"https://doi.org/10.1147/sj.252.0207","authors":["W. D. Hagamen","Martin Gardy"],"tags":["Memorization","Computer science","Medical diagnosis","Interface (matter)","Mode (computer interface)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1986-01-01","doi":"https://doi.org/10.1147/sj.252.0207","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3213686666","name":"Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy","source":"openalex","abstract":"Importance: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owing to limited access. Objective: To evaluate the safety and accuracy of an artificial intelligence (AI) system (the EyeArt Automated DR Detection System, version 2.1.0) in detecting both more-than-mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR). Design, Setting, and Participants: A prospective multicenter cross-sectional diagnostic study was preregistered (NCT03112005) and conducted from April 17, 2017, to May 30, 2018. A total of 942 individuals aged 18 years or older who had diabetes gave consent to participate at 15 primary care and eye care facilities. Data analysis was performed from February 14 to July 10, 2019. Interventions: Retinal imaging for the autonomous AI system and Early Treatment Diabetic Retinopathy Study (ETDRS) reference standard determination. Main Outcomes and Measures: Primary outcome measures included the sensitivity and specificity of the AI system in identifying participants' eyes with mtmDR and/or vtDR by 2-field undilated fundus photography vs a rigorous clinical reference standard comprising reading center grading of 4 wide-field dilated images using the ETDRS severity scale. Secondary outcome measures included the evaluation of imageability, dilated-if-needed analysis, enrichment correction analysis, worst-case imputation, and safety outcomes. Results: Of 942 consenting individuals, 893 patients (1786 eyes) met the inclusion criteria and completed the study protocol. The population included 449 men (50.3%). Mean (SD) participant age was 53.9 (15.2) years (median, 56; range, 18-88 years), 655 were White (73.3%), and 206 had type 1 diabetes (23.1%). Sensitivity and specificity of the AI system were high in detecting mtmDR (sensitivity: 95.5%; 95% CI, 92.4%-98.5% and specificity: 85.0%; 95% CI, 82.6%-87.4%) and vtDR (sensitivity: 95.1%; 95% CI, 90.1%-100% and specificity: 89.0%; 95% CI, 87.0%-91.1%) without dilation. Imageability was high without dilation, with the AI system able to grade 87.4% (95% CI, 85.2%-89.6%) of the eyes with reading center grades. When eyes with ungradable results were dilated per the protocol, the imageability improved to 97.4% (95% CI, 96.4%-98.5%), with the sensitivity and specificity being similar. After correcting for enrichment, the mtmDR specificity increased to 87.8% (95% CI, 86.3%-89.5%) and the sensitivity remained similar; for vtDR, both sensitivity (97.0%; 95% CI, 91.2%-100%) and specificity (90.1%; 95% CI, 89.4%-91.5%) improved. Conclusions and Relevance: This prospective multicenter cross-sectional diagnostic study noted safety and accuracy with use of the EyeArt Automated DR Detection System in detecting both mtmDR and, for the first time, vtDR, without physician assistance. These findings suggest that improved access to accurate, reliable diabetic eye examinations may increase adherence to recommended annual screenings and allow for accelerated referral of patients identified as having vtDR.","url":"https://doi.org/10.1001/jamanetworkopen.2021.34254","authors":["Eli Ipp","David R. Liljenquist","Bruce W. Bode","Viral N. Shah","Steven Silverstein","Carl D. Regillo","Jennifer I. Lim","Srinivas R. Sadda","Amitha Domalpally","Gerry Gray","Malavika Bhaskaranand","Chaithanya Ramachandra","Kaushal Solanki","EyeArt Study Group","Harvey Dubiner","Pauline Genter","J. C. W. Graham","Alan P. Johnson","Grace A. Levy-Clarke","Richard D. Pesavento","Mark D. Sherman","Brian Kim","Gerald B. Walman","Halis Kaan Aktürk","Hal Joseph","Prakriti Joshee","Bruce S. Trippe","John M Gilbert","Barbara A. Blodi","Susan Reed","James Reimers","Kris Lang","Holly Cohn","Ruth Shaw","Sheila Watson","Andrew Ewen","Nancy Barrett","Maria Swift","Jeffrey Gornbein"],"tags":["Diabetic retinopathy","Medicine","Eye examination","Fundus photography","Retinopathy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-15","doi":"https://doi.org/10.1001/jamanetworkopen.2021.34254","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4407511072","name":"Medical imaging-based artificial intelligence in pneumonia: A narrative review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.neucom.2025.129731","authors":["Yanping Yang","Wenyu Xing","Yiwen Liu","Yifang Li","Dean Ta","Yuanlin Song","Dongni Hou"],"tags":["Artificial intelligence","Computer science","Pneumonia","Narrative","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-13","doi":"https://doi.org/10.1016/j.neucom.2025.129731","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3031396671","name":"Artificial intelligence and COVID-19: A multidisciplinary approach","source":"openalex","abstract":"The COVID-19 pandemic is taking a colossal toll in human suffering and lives. A significant amount of new scientific research and data sharing is underway due to the pandemic which is still rapidly spreading. There is now a growing amount of coronavirus related datasets as well as published papers that must be leveraged along with artificial intelligence (AI) to fight this pandemic by driving news approaches to drug discovery, vaccine development, and public awareness. AI can be used to mine this avalanche of new data and papers to extract new insights by cross-referencing papers and searching for patterns that AI algorithms could help discover new possible treatments or help in vaccine development. Drug discovery is not a trivial task and AI technologies like deep learning can help accelerate this process by helping predict which existing drugs, or brand-new drug-like molecules could treat COVID-19. AI techniques can also help disseminate vital information across the globe and reduce the spread of false information about COVID-19. The positive power and potential of AI must be harnessed in the fight to slow the spread of COVID-19 in order to save lives and limit the economic havoc due to this horrific disease.","url":"https://doi.org/10.1016/j.imr.2020.100434","authors":["Abhimanyu S. Ahuja","Vineet Pasam Reddy","Oge Marques"],"tags":["Pandemic","Coronavirus disease 2019 (COVID-19)","Dissemination","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-27","doi":"https://doi.org/10.1016/j.imr.2020.100434","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2995942064","name":"Deep convolutional neural network based medical image classification for disease diagnosis","source":"openalex","abstract":"Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.","url":"https://doi.org/10.1186/s40537-019-0276-2","authors":["Samir S. Yadav","Shivajirao M. Jadhav"],"tags":["Computer science","Convolutional neural network","Artificial intelligence","Transfer of learning","Support vector machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-12-01","doi":"https://doi.org/10.1186/s40537-019-0276-2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W71547456","name":"Data Analysis, Machine Learning and Applications","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-540-78246-9","authors":["Christine Preisach","Burkhardt, Hans","Lars Schmidt-Thieme","Reinhold Decker"],"tags":["Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2008-01-01","doi":"https://doi.org/10.1007/978-3-540-78246-9","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3037105702","name":"Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. OBJECTIVE: The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. METHODS: We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. RESULTS: Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. CONCLUSIONS: We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.","url":"https://doi.org/10.2196/19104","authors":["Aya Sedky Adly","Afnan Sedky Adly","Mahmoud Sedky Adly"],"tags":["Coronavirus disease 2019 (COVID-19)","Computer science","Pandemic","The Internet","Control (management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-25","doi":"https://doi.org/10.2196/19104","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4281706229","name":"A reimbursement framework for artificial intelligence in healthcare","source":"openalex","abstract":"Responsible adoption of healthcare artificial intelligence (AI) requires that AI systems which benefit patients and populations, including autonomous AI systems, are incentivized financially at a consistent and sustainable level. We present a framework for analytically determining value and cost of each unique AI service. The framework’s processes involve affected stakeholders, including patients, providers, legislators, payors, and AI creators, in order to find an optimum balance among ethics, workflow, cost, and value as identified by each of these stakeholders. We use a real world, completed, an example of a specific autonomous AI service, to show how multiple “guardrails” for the AI system implementation enforce ethical principles. It can guide the development of sustainable reimbursement for future AI services, ensuring the quality of care, healthcare equity, and mitigation of potential bias, and thereby contribute to realize the potential of AI to improve clinical outcomes for patients and populations, improve access, remove disparities, and reduce cost.","url":"https://doi.org/10.1038/s41746-022-00621-w","authors":["Michael D. Abràmoff","Cybil Roehrenbeck","Sylvia Trujillo","Juli D. Goldstein","A. S. Graves","Michael X. Repka","Ezequiel Silva"],"tags":["Reimbursement","Workflow","Health care","Equity (law)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-09","doi":"https://doi.org/10.1038/s41746-022-00621-w","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4405225674","name":"Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden","source":"openalex","abstract":"OBJECTIVE: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout. MATERIALS AND METHODS: This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings. RESULTS: Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use. DISCUSSION: In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated. CONCLUSION: Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.","url":"https://doi.org/10.1093/jamia/ocae295","authors":["Shreya Shah","Anna Devon-Sand","P. Stephen","Yejin Jeong","Trevor Crowell","Margaret Smith","April S. Liang","Clarissa Delahaie","Caroline Hsia","Tait D. Shanafelt","Michael A. Pfeffer","Christopher Sharp","Steven Lin","Patricia García"],"tags":["Usability","Burnout","Documentation","Ambient intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-18","doi":"https://doi.org/10.1093/jamia/ocae295","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4226371059","name":"Maps of Medical Reason: Applying Knowledge Graphs and Artificial Intelligence in Medical Education and Practice","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-030-95006-4_8","authors":["Bill Cope","Mary Kalantzis","ChengXiang Zhai","Andrea Krussel","Duane Searsmith","Duncan C. Ferguson","Richard I. Tapping","Yerko Berrocal"],"tags":["Ontology","Frame (networking)","Computer science","Representation (politics)","Knowledge representation and reasoning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1007/978-3-030-95006-4_8","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3096890087","name":"The role of artificial intelligence in cosmetic dermatology—Current, upcoming, and future trends","source":"openalex","abstract":"Within the field of cosmetic dermatology, several promising developments utilize artificial intelligence to better patient care. While many new treatments in cosmetic dermatology feature components of artificial intelligence, there is a knowledge gap within the field regarding the current and developing products featuring AI. We aim to highlight current and developing applications of artificial intelligence in cosmetic dermatology and provide insight into future modalities in this field. Methods include literature review, including peer-reviewed journal articles as well as product websites. In an age of medical and technological advancement, the utility of artificial intelligence models continues to grow.There are many new facets of artificial intelligence in cosmetic dermatology, marketed to both the consumer and the physician. With the development of customizable skin care, augmented reality applications, and at-home skin analysis tools, patients are empowered to be the masters of their cosmetic care. Artificial intelligence is utilized by physicians in new ways in their practices, with the advent of models for prediction of clinical outcome to treatments and tools for in-depth analysis of the patient's skin. Further research is required in the development of automated energy-based treatment devices and robotic-assisted treatments. Models for AI in cosmetic dermatology serve to increase patient involvement in their skin care decisions and have the ability to enhance the patient-physician experience. Dermatologists should be well-informed of the emerging technologies to better educate patients and enhance their clinical practice.","url":"https://doi.org/10.1111/jocd.13797","authors":["Alexandra Elder","Christina Ring","Kerry Heitmiller","Zena Gabriel","Nazanin Saedi"],"tags":["Modalities","Dermatology","Medicine","Field (mathematics)","Skin care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-05","doi":"https://doi.org/10.1111/jocd.13797","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4220680585","name":"Advancements in Oncology with Artificial Intelligence—A Review Article","source":"openalex","abstract":"Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.","url":"https://doi.org/10.3390/cancers14051349","authors":["Nikitha Vobugari","Vikranth Raja","Udhav Sethi","Kejal Gandhi","Kishore Raja","Salim Surani"],"tags":["Medicine","Medical physics","Standardization","Breast cancer","Grading (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-06","doi":"https://doi.org/10.3390/cancers14051349","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4391362070","name":"The dark side of artificial intelligence in services","source":"openalex","abstract":"Artificial intelligence (AI) initiatives, including Generative AI, are being increasingly implemented in service industries, and are having a great impact on service operations and on customers’ reactions and behaviors. Previous literature is overoptimistic about AI implementation, and there is still a need to explore the dark side of this technology; that is, its potential negative impacts on consumers, businesses, and society, as well as the moral concerns associated with AI use in services. To establish some fundamental insights related to this research domain, this paper contributes to previous AI based-services literature by proposing a three-part conceptual model inspired by Belanche et al. (2020a), comprised of AI design, customers, and the service encounter. Specifically, we identify key factors and research gaps within each category that need to be addressed. The final research questions provide a research agenda to guide scholars and help practitioners implement AI-based services while avoiding their potential negative outcomes.","url":"https://doi.org/10.1080/02642069.2024.2305451","authors":["Daniel Belanche","Russell W. Belk","Luis V. Casaló","Carlos Flavián"],"tags":["Great Rift","Far side of the Moon","Artificial intelligence","Computer science","Astronomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-30","doi":"https://doi.org/10.1080/02642069.2024.2305451","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4393336895","name":"Uses and limitations of artificial intelligence for oncology","source":"openalex","abstract":"Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.","url":"https://doi.org/10.1002/cncr.35307","authors":["Likhitha Kolla","Ravi B. Parikh"],"tags":["Workflow","Medicine","Software deployment","Limiting","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-30","doi":"https://doi.org/10.1002/cncr.35307","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4381988172","name":"Simulation-Based Education in the Artificial Intelligence Era","source":"openalex","abstract":"Simulation-based medical education (SBME) has been widely implemented in skill training in various clinical specialties. SBME has contributed not only to patient and medical safety but also to undergraduate and specialist education in the healthcare field. In this review, we discuss the challenges and future directions of SBME in the artificial intelligence (AI) era. While SBME fidelity or methods may become highly complicated in the AI era, the fact is that learners play a central role. As SBME and clinical education are complementary, mutual feedback and improvement are essential, especially in non-technical skill development. For the development of sustainable SBME in the clinical field in the AI era, continuous improvement is needed by academia, educators, and learners.","url":"https://doi.org/10.7759/cureus.40940","authors":["Nobuyasu Komasawa","Masanao Yokohira"],"tags":["Medicine","Fidelity","Field (mathematics)","Health care","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-25","doi":"https://doi.org/10.7759/cureus.40940","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4407031138","name":"Role of Artificial Intelligence, Telehealth, and Telemedicine in Medical Virology","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-981-97-2938-8","authors":["Jyotir Moy Chatterjee","R. Sujatha","Shailendra K. Saxena"],"tags":["Telemedicine","Telehealth","Virology","Coronavirus disease 2019 (COVID-19)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1007/978-981-97-2938-8","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3166218875","name":"The role of artificial intelligence in business transformation: A case of pharmaceutical companies","source":"openalex","abstract":"Artificial intelligence (AI) is of great interest to researchers and practitioners as a means of achieving the necessary progress in the pharmaceutical industry. However, the role of AI and ways of transforming companies are not well studied. The purpose of the paper is to identify exactly how AI affects the key and support business processes of pharmaceutical companies. We offer a qualitative interview study of five large, five medium, and five small pharmaceutical companies. Based on scarce literature on the role of AI in the pharmaceutical industry, we considered which business processes are subject to transform within it and how they do so. We determine that small pharma companies significantly change research and development, master data management, analysis and reporting, and human resource business processes under the influence of AI. Large pharma companies use AI to transform production, sales, marketing, and analysis business processes. In turn, medium-sized companies are in the middle and individually transform their business processes depending on their specialization.","url":"https://doi.org/10.1016/j.techsoc.2021.101629","authors":["Ignat Kulkov"],"tags":["Pharmaceutical industry","Business","Business transformation","Marketing","Resource (disambiguation)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-09","doi":"https://doi.org/10.1016/j.techsoc.2021.101629","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386064669","name":"Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)","source":"openalex","abstract":"With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.","url":"https://doi.org/10.18240/ijo.2023.09.02","authors":["Weihua Yang","Yanwu Xu"],"tags":["Medicine","Guideline","Standardization","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-22","doi":"https://doi.org/10.18240/ijo.2023.09.02","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3026010264","name":"Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research","source":"openalex","abstract":"BACKGROUND: Identifying laparoscopic surgical videos using artificial intelligence (AI) facilitates the automation of several currently time-consuming manual processes, including video analysis, indexing, and video-based skill assessment. This study aimed to construct a large annotated dataset comprising laparoscopic colorectal surgery (LCRS) videos from multiple institutions and evaluate the accuracy of automatic recognition for surgical phase, action, and tool by combining this dataset with AI. MATERIALS AND METHODS: A total of 300 intraoperative videos were collected from 19 high-volume centers. A series of surgical workflows were classified into 9 phases and 3 actions, and the area of 5 tools were assigned by painting. More than 82 million frames were annotated for a phase and action classification task, and 4000 frames were annotated for a tool segmentation task. Of these frames, 80% were used for the training dataset and 20% for the test dataset. A convolutional neural network (CNN) was used to analyze the videos. Intersection over union (IoU) was used as the evaluation metric for tool recognition. RESULTS: The overall accuracies for the automatic surgical phase and action classification task were 81.0% and 83.2%, respectively. The mean IoU for the automatic tool segmentation task for 5 tools was 51.2%. CONCLUSIONS: A large annotated dataset of LCRS videos was constructed, and the phase, action, and tool were recognized with high accuracy using AI. Our dataset has potential uses in medical applications such as automatic video indexing and surgical skill assessments. Open research will assist in improving CNN models by making our dataset available in the field of computer vision.","url":"https://doi.org/10.1016/j.ijsu.2020.05.015","authors":["Daichi Kitaguchi","Nobuyoshi Takeshita","Hiroki Matsuzaki","Tatsuya Oda","Masahiko Watanabe","Kensaku Mori","Etsuko Kobayashi","Masaaki Ito"],"tags":["Computer science","Artificial intelligence","Convolutional neural network","Workflow","Task (project management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-12","doi":"https://doi.org/10.1016/j.ijsu.2020.05.015","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4294409076","name":"Artificial Intelligence in Medical Education: A citation-based systematic literature review","source":"openalex","abstract":"Purpose: This review aims to describe the existing and emerging role of Artificial intelligence (AI) in medical education, as this may help set future directions. Methodology: Articles on AI in medical education describing integration of AI or machine-learning (ML) in undergraduate medical curricula or structured postgraduate residency programs were extracted from SCOPUS database. The paper followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) research methodology. Articles describing AI or ML, but not directly related to teaching and training in structured programs were excluded. Results: Of the 1020 documents published till October 15, 2020, 218 articles are included in the final analysis. A sharp increase in the number of published articles was observed 2018 onwards. Articles describing surgical skills training, case-based reasoning, physicians' role in the evolving scenario, and the attitudes of medical students towards AI in radiology were cited frequently. Of the 50 top-cited papers, 16 (32%) were ‘commentary’ articles, 13 (26%) review articles, 13 (26%) articles correlated usefulness of ML and AI with human performance, whereas 8 (16%) assessed the perceptions of students toward the integration of AI in medical practice. Conclusion: AI should be taught in medical curricula to prepare doctors for tomorrow, and at the same time, could be used for teaching, assessment, and providing feedback in various disciplines.","url":"https://doi.org/10.32593/jstmu/vol5.iss1.183","authors":["Ikram Burney","Nisar Ahmad","Ikram A Burney"],"tags":["Curriculum","Scopus","Systematic review","Medical education","Citation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-03","doi":"https://doi.org/10.32593/jstmu/vol5.iss1.183","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3131411810","name":"Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis","source":"openalex","abstract":"Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.","url":"https://doi.org/10.1016/j.jaip.2021.02.014","authors":["Alan Kaplan","Hui Cao","J. Mark FitzGerald","Nick Iannotti","Eric Yang","Janwillem Kocks","Κonstantinos Κostikas","David Price","Helen K. Reddel","Ioanna Tsiligianni","Claus Vogelmeier","Pascal Pfister","Paul Mastoridis"],"tags":["Context (archaeology)","Medicine","Asthma","Intensive care medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-21","doi":"https://doi.org/10.1016/j.jaip.2021.02.014","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4378746207","name":"Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review","source":"openalex","abstract":"Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. Methods PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. Results A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. Conclusion There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included.","url":"https://doi.org/10.3389/fmed.2023.1180773","authors":["Bart M. de Vries","Gerben J.C. Zwezerijnen","George L. Burchell","Floris H. P. van Velden","C. Willemien Menke‐van der Houven van Oordt","Ronald Boellaard"],"tags":["Modalities","Computer science","Medical physics","Analytics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-12","doi":"https://doi.org/10.3389/fmed.2023.1180773","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W1587891348","name":"Artificial Intelligence in Medicine: The Challenges Ahead","source":"openalex","abstract":"The modern study of artificial intelligence in medicine (AIM) is 25 years old. Throughout this period, the field has attracted many of the best computer scientists, and their work represents a remarkable achievement. However, AIM has not been successful-if success is judged as making an impact on the practice of medicine. Much recent work in AIM has been focused inward, addressing problems that are at the crossroads of the parent disciplines of medicine and artificial intelligence. Now, AIM must move forward with the insights that it has gained and focus on finding solutions for problems at the heart of medical practice. The growing emphasis within medicine on evidence-based practice should provide the right environment for that change.","url":"https://doi.org/10.1136/jamia.1996.97084510","authors":["Enrico Coiera"],"tags":["Work (physics)","Engineering ethics","Focus (optics)","Field (mathematics)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1996-11-01","doi":"https://doi.org/10.1136/jamia.1996.97084510","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4402400123","name":"Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine","source":"openalex","abstract":"Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.","url":"https://doi.org/10.2196/59069","authors":["Kuo Zhang","Xiangbin Meng","Xiangyu Yan","Jiaming Ji","Jingqian Liu","Hua Xu","Heng Zhang","Da Liu","Jingjia Wang","Xuliang Wang","Jun Gao","Yuan-geng-shuo Wang","Chunli Shao","Wenyao Wang","Jiarong Li","Ming-Qi Zheng","Yaodong Yang","Yi‐Da Tang"],"tags":["Preprint","Transformative learning","Health care","Medicine","Internet privacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-10","doi":"https://doi.org/10.2196/59069","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2036109700","name":"Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data","source":"openalex","abstract":"Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.","url":"https://doi.org/10.1109/tpami.2012.277","authors":["Hoo-Chang Shin","Matthew Orton","David J. Collins","Simon Doran","Martin O. Leach"],"tags":["Artificial intelligence","Computer science","Deep learning","Machine learning","Categorization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2012-12-31","doi":"https://doi.org/10.1109/tpami.2012.277","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2941249574","name":"Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art – with Reflections on Present AIM Challenges","source":"openalex","abstract":"BACKGROUND: The rise of biomedical expert heuristic knowledge-based approaches for computational modeling and problem solving, for scientific inquiry and medical decision-making, and for consultation in the 1970's led to a major change in the paradigm that affected all of artificial intelligence (AI) research. Since then, AI has evolved, surviving several \"winters\", as it has oscillated between relying on expensive and hard-to-validate knowledge-based approaches, and the alternative of using machine learning methods for inferring classification rules from labelled datasets. In the past couple of decades, we are seeing a gradual but progressive intertwining of the two. OBJECTIVES: To give an overview of early directions in AI in medicine and threads of some subsequent developments motivated by the very different goals of scientific inquiry for biomedical research, and for computational modeling of clinical reasoning and more general healthcare problem solving from the perspective of today's \"AI-Deep Learning Boom\". To show how, from the beginning, AI was central to Biomedical and Health Informatics (BMHI), as a field investigating how to understand intelligent thinking in dealing professionally with the practice for healthcare, developing mathematical models, technology, and software tools to aid human experts in biomedicine, despite many previous bouts of \"exuberant optimism\" about the methodologies deployed. METHODS: An overview and commentary on some of the early research and publications in AI in biomedicine, emphasizing the different approaches to the modeling of problems involved in clinical practice in contrast to those of biomedical science. A concluding reflection of a few current challenges and pitfalls of AI in some biomedical applications. CONCLUSION: While biomedical knowledge-based systems played a critical role in influencing AI in its early days, 50 years later they have taken a back seat behind \"Deep Learning\" which promises to discover knowledge structures for inference and prediction, both in science and for clinical decision-support. Early work on AI for medical consultation turned out to be more useful for explanation and teaching than for clinical practice, as had been originally intended. Today, despite the many reported successes of deep learning, fundamental scientific challenges arise in drawing on models of brain science, cognition, and language, if AI is to augment and complement rather than replace human judgment and expertise in biomedicine while also incorporating these advances for translational medicine. Understanding clinical phenotypes and how they relate to precision and personalization of care requires not only scientific inquiry, but also humanistic models of treatment that respond to patient and practitioner narrative exchanges, since it is the stories and insights of human experts which encourage what Norbert Weiner termed the ethical \"human use of human beings\", so central to adherence to the Hippocratic Oath.","url":"https://doi.org/10.1055/s-0039-1677895","authors":["Casimir A. Kulikowski"],"tags":["Artificial intelligence","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-25","doi":"https://doi.org/10.1055/s-0039-1677895","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4409045115","name":"Applications of Generative Artificial Intelligence in Electronic Medical Records: A Scoping Review","source":"openalex","abstract":"Electronic Medical Records (EMRs) are central to the modern healthcare system. Recent advances in artificial intelligence (AI), particularly generative artificial intelligence (GenAI), have opened new opportunities for the advancement of EMRs. This scoping review aims to explore the current real-world applications of GenAI within EMRs to support an understanding of AI applications in healthcare. A literature search was conducted following PRISMA-ScR guidelines. The search was conducted using Ovid MEDLINE, up to 28 October 2024, using a peer-reviewed search strategy. Overall, 55 studies were included. A list of five themes was generated by human reviewers based on the literature review: data manipulation (24), patient communication (9), clinical decision making (8), clinical prediction (8), summarization (4), and other (2). The majority of studies originated from the United States (35). Both proprietary and commercially available models were tested, with ChatGPT being the most commonly referenced LLM. As these models continue to be developed, their diverse use cases within EMRs have the potential to improve patient outcomes, enhance access to medical data, streamline hospital workflows, and reduce physician workload. However, continued problems surrounding data privacy, trust, bias, model hallucinations, and the need for robust evaluation remain. Further research considering the ethical, medical, and societal implications of GenAI applications in EMRs is essential to validate these findings and address existing limitations to support healthcare advancement.","url":"https://doi.org/10.3390/info16040284","authors":["Leo Morjaria","B. Gandhi","Nabil Haider","Matthew Mellon","Matthew Sibbald"],"tags":["Generative grammar","Artificial intelligence","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.3390/info16040284","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4400957436","name":"The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization","source":"openalex","abstract":"Abstract Future tools for supporting collaborations between technology and sustainable development include artificial intelligence (AI) applications in sustainable Urbanization roles. This article highlights the various applications of AI in advancing sustainable urbanization. From urban planning to disaster management, AI technology is revolutionizing the way cities are designed and managed. By leveraging data analytics, machine learning, and predictive modeling, AI is helping city officials make informed decisions, optimize resource usage, and improve quality of life for urban residents. Despite the immense potential of AI in sustainable urban development, there are still challenges and limitations to overcome. We show some of the most significant problems related to these issues. These include issues related to data privacy, algorithm bias, and ethical considerations. Continued research and innovation are needed to address these challenges and ensure that AI technology is used responsibly and effectively in shaping sustainable cities. As a result, AI has the power to transform urban environments and create more sustainable, resilient communities. By harnessing the capabilities of AI, cities can become more efficient, environmentally‐friendly, and prepared for the challenges of the future. It is essential for policymakers, urban planners, and technology developers to work together to harness the full potential of AI in sustainable urbanization and create a better future for all. Proactively addressing these challenges can unlock the full potential of AI in combating sustainable cities and building a sustainable future for all.","url":"https://doi.org/10.1002/sd.3131","authors":["Marwan Al‐Raeei"],"tags":["Urbanization","Sustainable development","Sustainable city","Smart city","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-24","doi":"https://doi.org/10.1002/sd.3131","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2957627522","name":"The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future","source":"openalex","abstract":"Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future. In this article, we review the recent progress of AI-enabled medical imaging. Firstly, we briefly review the background about AI in its way of evolution. Then, we discuss the recent successes of AI in different medical imaging tasks, especially in image segmentation, registration, detection and recognition. Also, we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario, which includes lung nodule in chest CT, neuroimaging, mammography, and etc. Finally, we report the way of human-machine interaction. We believe that, in the future, AI will not only change the traditional way of medical imaging, but also improve the clinical routines of medical care and enable many aspects of the medical society.","url":"https://doi.org/10.24920/003615","authors":["Shi Ying-huan","Qian Wang"],"tags":["Medicine","Medical imaging","Artificial intelligence","Neuroimaging","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.24920/003615","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4377097363","name":"The Future of Artificial Intelligence in Special Education Technology","source":"openalex","abstract":"This manuscript presents a preliminary discussion of Artificial Intelligence (AI) as a disruptive technology with the potential to significantly change special education practices. The article begins with a brief description of the development of AI. The authors recognize our assertions are subjective and require further research. Several references are not peer-reviewed because educational research takes years to conduct, analyze, and disseminate outcomes. In this manuscript, we discuss current software used for writing with students in special education and discuss similarities and differences with AI software. This discussion is followed by questions and examples related to implementation, ethical and policy considerations, and preservice special education teacher preparation. The article concludes with future considerations for how AI will impact the special education technology field.","url":"https://doi.org/10.1177/01626434231165977","authors":["Matthew T. Marino","Eleazar Vasquez","Lisa Dieker","James D. Basham","José Blackorby"],"tags":["Special education","Field (mathematics)","Computer science","Engineering ethics","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-19","doi":"https://doi.org/10.1177/01626434231165977","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3022437576","name":"Artificial Intelligence and Primary Care Research: A Scoping Review","source":"openalex","abstract":"PURPOSE: Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODS: We performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in Eng-lish. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTS: Of 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%). CONCLUSIONS: Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.","url":"https://doi.org/10.1370/afm.2518","authors":["Jacqueline K. Kueper","Amanda Terry","Merrick Zwarenstein","Daniel J. Lizotte"],"tags":["Medicine","Primary care","Artificial intelligence","Health care","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-01","doi":"https://doi.org/10.1370/afm.2518","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4281646939","name":"Artificial intelligence in mathematics education: A systematic literature review","source":"openalex","abstract":"The advancement of technology like artificial intelligence (AI) provides a chance to help teachers and students solve and improve teaching and learning performances. The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education. A systematic literature review (SLR) was conducted using established and robust guidelines. We follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA). We searched ScienceDirect, Scopus, Springer Link, ProQuest, and EBSCO Host for 20 AI studies published between 2017 and 2021. The findings of the SLR indicate that AI approach used in mathematics education for the samples studied were through robotics, systems, tools, teachable agent, autonomous agent, and a comprehensive approach. Then, it can be shown that the majority of the collected studies were carried out in the USA and Mexico. The analysis revealed that most of the reviewed studies used quantitative research methods. The types of themes for AI in mathematics education were categorized into advantages and disadvantages, conceptual understanding, factors, role, idea suggestion, strategies and effectiveness.","url":"https://doi.org/10.29333/iejme/12132","authors":["Riyan Hidayat","Mohamed Zulhilmi bin Mohamed","Nurain Nabilah binti Suhaizi","Norhafiza binti Mat Sabri","Muhamad Khairul Hakim bin Mahmud","Siti Nurshafikah binti Baharuddin"],"tags":["Systematic review","Scopus","Artificial intelligence","Conversation","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-01","doi":"https://doi.org/10.29333/iejme/12132","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4387710210","name":"Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining","source":"openalex","abstract":"The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches.","url":"https://doi.org/10.1016/j.tibtech.2023.09.015","authors":["Miguel Mateu‐Sanz","Carla V. Fuenteslópez","Juan Manuel Uribe","Håvard Jostein Haugen","Abhay Pandit","Maria‐Pau Ginebra","Osnat Hakimi","Martin Krallinger","Athina Samara"],"tags":["Biomaterial","Biocompatibility","Data science","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-17","doi":"https://doi.org/10.1016/j.tibtech.2023.09.015","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2968272384","name":"Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist","source":"openalex","abstract":"Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician's perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.","url":"https://doi.org/10.1007/s12471-019-01311-1","authors":["Klaske R Siegersma","Tim Leiner","Derek P. Chew","Yolande Appelman","Leonard Hofstra","Johan Verjans"],"tags":["Medicine","Cardiology","Medical imaging","Data science","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-09","doi":"https://doi.org/10.1007/s12471-019-01311-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2968617104","name":"Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone","source":"openalex","abstract":"IMPORTANCE: Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. OBJECTIVE: To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. DESIGN, SETTING, AND PARTICIPANTS: This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. INTERVENTIONS: Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. MAIN OUTCOMES AND MEASURES: To evaluate the sensitivity and specificity of the offline automated analysis system in detecting referable diabetic retinopathy on images taken on the smartphone-based, nonmydriatic retinal imaging system by a health worker. RESULTS: Of 255 patients seen in the dispensaries, 231 patients (90.6%) consented to diabetic retinopathy screening. The major reasons for not participating were unwillingness to wait for screening and the blurring of vision that would occur after dilation. Images from 18 patients were deemed ungradable by the ophthalmologist and hence were excluded. In the remaining participants (110 female patients [51.6%] and 103 male patients [48.4%]; mean [SD] age, 53.1 [10.3] years), the sensitivity and specificity of the offline AI system in diagnosing referable diabetic retinopathy were 100.0% (95% CI, 78.2%-100.0%) and 88.4% (95% CI, 83.2%-92.5%), respectively, and in diagnosing any diabetic retinopathy were 85.2% (95% CI, 66.3%-95.8%) and 92.0% (95% CI, 97.1%-95.4%), respectively, compared with ophthalmologist grading using the same images. CONCLUSIONS AND RELEVANCE: These pilot study results show promise in the use of an offline AI system in community screening for referable diabetic retinopathy with a smartphone-based fundus camera. The use of AI would enable screening for referable diabetic retinopathy in remote areas where services of an ophthalmologist are unavailable. This study was done on patients with diabetes who were visiting a dispensary that provides curative services to the population at the primary level. A study with a larger sample size may be needed to extend the results to general population screening, however.","url":"https://doi.org/10.1001/jamaophthalmol.2019.2923","authors":["Sundaram Natarajan","Astha Jain","Radhika Krishnan","Ashwini Rogye","Sobha Sivaprasad"],"tags":["Medicine","Diabetic retinopathy","Optometry","Hypertensive retinopathy","Fundus (uterus)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-08","doi":"https://doi.org/10.1001/jamaophthalmol.2019.2923","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4388344690","name":"Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-031-36678-9_14","authors":["John H. Holmes"],"tags":["Artificial intelligence","Computer science","Inference","Artificial Intelligence System","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1007/978-3-031-36678-9_14","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2722564865","name":"Trends and priority shifts in artificial intelligence technology invention: A global patent analysis","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.eap.2017.12.006","authors":["Hidemichi Fujii","Shunsuke Managi"],"tags":["Patent analysis","Treaty","China","Patent application","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-01-09","doi":"https://doi.org/10.1016/j.eap.2017.12.006","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386390931","name":"Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools","source":"openalex","abstract":"Abstract ChatGPT is an artificial intelligence chatbot that utilizes advanced natural language processing technologies, including large language models, to produce human‐like responses to user queries spanning a wide range of topics from programming to mathematics. As an emerging generative artificial intelligence (GAI) tool, it presents novel opportunities and challenges to the ongoing digital transformation of education. This article employs a systematic review approach to summarize the viewpoints of Chinese scholars and experts regarding the implementation of GAI in education. The research findings indicate that a majority of Chinese scholars support the cautious integration of GAI into education as it serves as a learning tool that offers personalized educational experiences for students. However, it also raises concerns related to academic integrity and the potential hindrance to students' critical thinking skills. Consequently, a framework called DATS, which outlines an optimization path for future GAI applications in schools, is proposed. The framework takes into account the perspectives of four key stakeholders: developers, administrators, teachers, and students.","url":"https://doi.org/10.1002/fer3.10","authors":["Ming Liu","Yiling Ren","Lucy Michael Nyagoga","Francis Stonier","Zhongming Wu","Liang Yu"],"tags":["Viewpoints","Chatbot","Generative grammar","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-01","doi":"https://doi.org/10.1002/fer3.10","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4394859173","name":"Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College","source":"openalex","abstract":"Background: Mounting research suggests that artificial intelligence (AI) is one of the innovations that aid in the patient's diagnosis and treatment, but unfortunately limited research has been conducted in this regard in the Kingdom of Saudi Arabia (KSA). Hence, this study aimed to assess the level of knowledge and awareness of AI among faculty members and medicine students in one of the premier medical colleges in KSA. Methods: A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023. Result: A total of 131 participants contributed to our study, of which three were excluded due to incomplete responses, thereby giving a response rate of 98%. Conclusion: 85.4% of the respondents believe that AI has a positive impact on the healthcare system and physicians in general. Hence, there should be a mandatory course in medical schools that can prepare future doctors to diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments.","url":"https://doi.org/10.4103/jpbs.jpbs_1162_23","authors":["Asim Muhammad Alshanberi","Ahmed Hafez Mousa","Sama A. Hashim","Reem S. Almutairi","Sara Alrehali","Aisha M. Hamisu","Mohammed Shaikhomer","Shakeel Ahmed Ansari"],"tags":["Perception","Medical education","Psychology","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-01","doi":"https://doi.org/10.4103/jpbs.jpbs_1162_23","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4380272221","name":"Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s42044-023-00148-7","authors":["Imam Hossain","Mehadi Hasan Maruf","Md. Ashikur Rahman Khan","Md. Ashikur Rahman Khan","Farida Siddiqi Prity","Sharmin Fatema","Md. Sabbir Ejaz","Md. Ahnaf Sad Khan","Md. Ahnaf Sad Khan"],"tags":["Support vector machine","Random forest","Artificial intelligence","Decision tree","Naive Bayes classifier"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-11","doi":"https://doi.org/10.1007/s42044-023-00148-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2909244736","name":"Overview of Deep Learning in Gastrointestinal Endoscopy","source":"openalex","abstract":"Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based diagnoses, such as pathology, radiology, and endoscopy, are expected to be the first in the medical field to be affected by artificial intelligence. A convolutional neural network, a kind of deeplearning method with multilayer perceptrons designed to use minimal preprocessing, was recently reported as being highly beneficial in the field of endoscopy, including esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy. A convolutional neural network-based diagnostic program was challenged to recognize anatomical locations in esophagogastroduodenoscopy images, Helicobacter pylori infection, and gastric cancer for esophagogastroduodenoscopy; to detect and classify colorectal polyps; to recognize celiac disease and hookworm; and to perform small intestine motility characterization of capsule endoscopy images. Artificial intelligence is expected to help endoscopists provide a more accurate diagnosis by automatically detecting and classifying lesions; therefore, it is essential that endoscopists focus on this novel technology. In this review, we describe the effects of artificial intelligence on gastroenterology with a special focus on automatic diagnosis, based on endoscopic findings.","url":"https://doi.org/10.5009/gnl18384","authors":["Jun Ki Min","Min Seob Kwak","Jae Myung"],"tags":["Esophagogastroduodenoscopy","Medicine","Capsule endoscopy","Endoscopy","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-11","doi":"https://doi.org/10.5009/gnl18384","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4380995257","name":"Utility of ChatGPT in Clinical Practice","source":"openalex","abstract":"ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.","url":"https://doi.org/10.2196/48568","authors":["Jialin Liu","Changyu Wang","Siru Liu"],"tags":["Clinical decision support system","Documentation","Decision support system","Health care","Analytics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-15","doi":"https://doi.org/10.2196/48568","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4362588604","name":"The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12652-023-04594-w","authors":["Bukhoree Sahoh","Anant Choksuriwong"],"tags":["Computer science","Process (computing)","Action (physics)","Computational intelligence","Human intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-03","doi":"https://doi.org/10.1007/s12652-023-04594-w","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3127758030","name":"Review on Artificial Intelligence Techniques for Medical Diagnosis","source":"openalex","abstract":"In this paper, different techniques used for medical diagnosis are discussed and their performances are compared. These techniques are CNN, Random forest, KNN, SVM etc. CNN (Convolution neural Network) includes layers and weighted mechanism to decide features that leads to correct classification. Random forest generates decision trees based on selected features. Highest frequency patterns are placed at root node and consequences are placed at child node using this mechanism. SVM is most frequently used mechanism in the field of disease prediction. It has two hyper planes; one indicates disease and another indicates non-disease features. KNN is based on Euclidean distance. Each of this mechanism is demonstrated experimentally and CNN appears to best in terms of classification accuracy.","url":"https://doi.org/10.1109/iciss49785.2020.9316035","authors":["Ruchi Ruchi","Jimmy Singla","Amar Singh","Harshpreet Kaur"],"tags":["Support vector machine","Mechanism (biology)","Convolution (computer science)","Computer science","Random forest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-03","doi":"https://doi.org/10.1109/iciss49785.2020.9316035","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2999721377","name":"From Brain Science to Artificial Intelligence","source":"openalex","abstract":"Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.","url":"https://doi.org/10.1016/j.eng.2019.11.012","authors":["Jingtao Fan","Lu Fang","Jiamin Wu","Yuchen Guo","Qionghai Dai"],"tags":["Bridge (graph theory)","Artificial intelligence","Cognitive science","Artificial general intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-14","doi":"https://doi.org/10.1016/j.eng.2019.11.012","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2953012738","name":"Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models","source":"openalex","abstract":"With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching, or even exceeding, the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black-box manner, i.e. no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g. in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks.","url":"https://openalex.org/W2953012738","authors":["Wojciech Samek","Thomas Wiegand","Klaus‐Robert Müller"],"tags":["Interpretability","Artificial intelligence","Computer science","Deep learning","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-08-28","doi":"","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3014277512","name":"Oncology Research: Clinical Trial Management Systems, Electronic Medical Record, and Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.soncn.2020.151005","authors":["Candida Barlow"],"tags":["Electronic data capture","Medicine","Clinical trial","Documentation","Clinical research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-01","doi":"https://doi.org/10.1016/j.soncn.2020.151005","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3123982987","name":"A Review of Deep-Learning-Based Medical Image Segmentation Methods","source":"openalex","abstract":"As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.","url":"https://doi.org/10.3390/su13031224","authors":["Xiangbin Liu","Liping Song","Shuai Liu","Yudong Zhang"],"tags":["Deep learning","Artificial intelligence","Image segmentation","Segmentation","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-25","doi":"https://doi.org/10.3390/su13031224","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4313362617","name":"Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment","source":"openalex","abstract":"Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.","url":"https://doi.org/10.3390/healthcare11010113","authors":["Rana Alabdan","Abdulrahman Alruban","Anwer Mustafa Hilal","Abdelwahed Motwakel"],"tags":["Computer science","Artificial intelligence","Process (computing)","The Internet","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-30","doi":"https://doi.org/10.3390/healthcare11010113","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3037735576","name":"Medical devices and artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/b978-0-12-818438-7.00007-1","authors":["Arash Aframian","Farhad Iranpour","Justin Cobb"],"tags":["Medical device","Context (archaeology)","Computer science","Software","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1016/b978-0-12-818438-7.00007-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4308610041","name":"MONAI: An open-source framework for deep learning in healthcare","source":"openalex","abstract":"Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.","url":"https://doi.org/10.48550/arxiv.2211.02701","authors":["M. Jorge Cardoso","Wenqi Li","Richard Brown","Nic Ma","Eric Kerfoot","Yiheng Wang","Benjamin Murrey","Andriy Myronenko","Can Zhao","Dong Yang","Vishwesh Nath","Yufan He","Ziyue Xu","Ali Hatamizadeh","Andriy Myronenko","Wentao Zhu","Yun Liu","Mingxin Zheng","Yucheng Tang","Isaac Yang","Michael Zephyr","Behrooz Hashemian","Sachidanand Alle","Mohammad Zalbagi Darestani","Charlie Budd","Marc Modat","Tom Vercauteren","Guotai Wang","Yiwen Li","Yipeng Hu","Yunguan Fu","Benjamin M. Gorman","Hans J. Johnson","Brad Genereaux","Barbaros S. Erdal","Vikas Gupta","Andres Diaz‐Pinto","Andre Dourson","Lena Maier‐Hein","Paul F. Jaeger","Michael Baumgartner","Jayashree Kalpathy-Cramer","Mona G. Flores","Justin Kirby","Lee Cooper","Holger R. Roth","Daguang Xu","David Bericat","Ralf Floca","S. Kevin Zhou","Haris Shuaib","Keyvan Farahani","Klaus Maier‐Hein","Stephen Aylward","Prerna Dogra","Sébastien Ourselin","Andrew Feng"],"tags":["Software deployment","Deep learning","Computer science","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-04","doi":"https://doi.org/10.48550/arxiv.2211.02701","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2936379053","name":"Predicting scheduled hospital attendance with artificial intelligence","source":"openalex","abstract":"Failure to attend scheduled hospital appointments disrupts clinical management and consumes resource estimated at £1 billion annually in the United Kingdom National Health Service alone. Accurate stratification of absence risk can maximize the yield of preventative interventions. The wide multiplicity of potential causes, and the poor performance of systems based on simple, linear, low-dimensional models, suggests complex predictive models of attendance are needed. Here, we quantify the effect of using complex, non-linear, high-dimensional models enabled by machine learning. Models systematically varying in complexity based on logistic regression, support vector machines, random forests, AdaBoost, or gradient boosting machines were trained and evaluated on an unselected set of 22,318 consecutive scheduled magnetic resonance imaging appointments at two UCL hospitals. High-dimensional Gradient Boosting Machine-based models achieved the best performance reported in the literature, exhibiting an area under the receiver operating characteristic curve of 0.852 and average precision of 0.511. Optimal predictive performance required 81 variables. Simulations showed net potential benefit across a wide range of attendance characteristics, peaking at £3.15 per appointment at current prevalence and call efficiency. Optimal attendance prediction requires more complex models than have hitherto been applied in the field, reflecting the complex interplay of patient, environmental, and operational causal factors. Far from an exotic luxury, high-dimensional models based on machine learning are likely essential to optimal scheduling amongst other operational aspects of hospital care. High predictive performance is achievable with data from a single institution, obviating the need for aggregating large-scale sensitive data across governance boundaries.","url":"https://doi.org/10.1038/s41746-019-0103-3","authors":["Amy Nelson","Daniel M. Herron","Geraint Rees","Parashkev Nachev"],"tags":["Gradient boosting","Machine learning","Artificial intelligence","AdaBoost","Attendance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-12","doi":"https://doi.org/10.1038/s41746-019-0103-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3159814022","name":"Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices","source":"openalex","abstract":"Importance: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). Objective: To evaluate an artificial intelligence (AI)-based tool that assists with diagnoses of dermatologic conditions. Design, Setting, and Participants: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. Exposures: An AI-based assistive tool for interpreting clinical images and associated medical history. Main Outcomes and Measures: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. Results: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P < .001), from 48% to 58%; for NPs, the increase was 12% (95% CI, 10%-14%; P < .001), from 46% to 58%. In secondary analyses, agreement with biopsy-obtained diagnosis categories of maglignant, precancerous, or benign increased by 3% (95% CI, -1% to 7%) for PCPs and by 8% (95% CI, 3%-13%) for NPs. Rates of desire for biopsies decreased by 1% (95% CI, 0-3%) for PCPs and 2% (95% CI, 1%-3%) for NPs; the rate of desire for referrals decreased by 3% (95% CI, 1%-4%) for PCPs and NPs. Diagnostic agreement on cases not indicated for a dermatologist referral increased by 10% (95% CI, 8%-12%) for PCPs and 12% (95% CI, 10%-14%) for NPs, and median review time increased slightly by 5 (95% CI, 0-8) seconds for PCPs and 7 (95% CI, 5-10) seconds for NPs per case. Conclusions and Relevance: Artificial intelligence assistance was associated with improved diagnoses by PCPs and NPs for 1 in every 8 to 10 cases, indicating potential for improving the quality of dermatologic care.","url":"https://doi.org/10.1001/jamanetworkopen.2021.7249","authors":["Ayush Jain","David H. Way","Vishakha Gupta","Yi Gao","Guilherme de Oliveira Marinho","Jay Hartford","Rory Sayres","Kimberly Kanada","Clara Eng","Kunal Nagpal","Karen B. DeSalvo","Greg S. Corrado","Lily Peng","Dale R. Webster","R. Carter Dunn","David Coz","Susan J. Huang","Yun Liu","Yun Liu","Peggy Bui","Yuan Liu","Yuan Liu"],"tags":["Teledermatology","Medicine","Medical diagnosis","Referral","Interquartile range"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-28","doi":"https://doi.org/10.1001/jamanetworkopen.2021.7249","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2891346832","name":"Application of artificial intelligence in ophthalmology","source":"openalex","abstract":"Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.","url":"https://doi.org/10.18240/ijo.2018.09.21","authors":["Xueli Du","Wenbo Li","Bojie Hu"],"tags":["Medicine","Retinopathy of prematurity","Macular degeneration","Ophthalmology","Diabetic retinopathy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-09-06","doi":"https://doi.org/10.18240/ijo.2018.09.21","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2898197178","name":"Deep learning in medical imaging and radiation therapy","source":"openalex","abstract":"The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.","url":"https://doi.org/10.1002/mp.13264","authors":["Berkman Sahiner","Aria Pezeshk","Lubomir M. Hadjiiski","Xiaosong Wang","Karen Drukker","Kenny H. Cha","Ronald M. Summers","Maryellen L. Giger"],"tags":["Medical imaging","Medical radiation","Medical physicist","Deep learning","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-10-27","doi":"https://doi.org/10.1002/mp.13264","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3178991650","name":"Analysis on the Application of Artificial Intelligence in the Medical Field","source":"openalex","abstract":"Artificial intelligence is an interdisciplinary subject with very high comprehensiveness. It is a new and emerging discipline that integrates new technologies, new theories and new ideas. Currently artificial intelligence has been widely applied in many fields, including the field of medical health. This paper expounds the application of artificial intelligence in the field of medical health, analyzes existing problems, and from the perspective of the overall development of the industry, puts forward some suggestions on rational application development of artificial intelligence in the medical field.","url":"https://doi.org/10.1109/icot51877.2020.9468742","authors":["Dayang Jiang"],"tags":["Field (mathematics)","Computer science","Perspective (graphical)","Artificial intelligence","Subject (documents)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-18","doi":"https://doi.org/10.1109/icot51877.2020.9468742","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4366753220","name":"Artificial Intelligence Applications in Hepatology","source":"openalex","abstract":"Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.","url":"https://doi.org/10.1016/j.cgh.2023.04.007","authors":["Jörn M. Schattenberg","Naga Chalasani","Naim Alkhouri"],"tags":["Hepatology","Medicine","Artificial intelligence","Health informatics","Scale (ratio)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-22","doi":"https://doi.org/10.1016/j.cgh.2023.04.007","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4405335081","name":"Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study","source":"openalex","abstract":"BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.ObjectiveOne way to address these issues is through the use of artificial intelligence for the detection of medical device failure. This goal of this study was to develop automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections.MethodsThe system developed in this study utilised machine learning techniques as its core. Throughout the study four algorithms were utilised. These algorithms include Decision Tree, Random Forest, Linear Regression and Support Vector Machines.ResultsFinal results showed that Random Forest algorithms had the best performance on various metrics among the four developed models. It achieved accuracy of 94% and precision and recall of 70% and 93% respectively.ConclusionThis study shows that use of systems like the one developed in this study have the potential to improve management and maintenance of medical devices.","url":"https://doi.org/10.1177/09287329241291424","authors":["Faruk Bećirović","Lemana Spahić","Nejra Merdović","Lejla Gurbeta Pokvić","Almir Badnjević"],"tags":["Random forest","Computer science","Decision tree","Machine learning","Support vector machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.1177/09287329241291424","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4407927757","name":"Application of Artificial Intelligence Generated Content in Medical Examinations","source":"openalex","abstract":"As the rapid development of large language model, artificial intelligence generated content (AIGC) presents novel opportunities for constructing medical examination questions. However, it is unclear about the way of effectively utilizing AIGC for designing medical questions. AIGC is characterized by its rapid response capabilities and high efficiency, as well as good performance in mimicking clinical realities. In this study, we revealed the limitations inherent in paper-based examinations, and provided a streamlined instruction for generating questions using AIGC, with a particular focus on multiple-choice questions, case study questions, and video questions. Manual review remains necessary to ensure the accuracy and quality of the generated content. Future development will be benefited from technologies like retrieval augmented generation, multi-agent system, and video generation technology. As AIGC continues to evolve, it is anticipated to bring transformative changes to medical examinations, enhancing the quality of examination preparation, and contributing to the effective cultivation of medical students.","url":"https://doi.org/10.2147/amep.s492895","authors":["Rui Li","Tong Wu"],"tags":["Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.2147/amep.s492895","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411189778","name":"An Overview of Generative Artificial Intelligence in Medical Education","source":"openalex","abstract":"The application of generative artificial intelligence (GAI) in medical education and practice has garnered increasing attention, particularly its significant potential to enhance personalised learning and clinical training. This viewpoint explores the integration of GAI into medical education, analysing its advantages in disseminating medical knowledge, simulating case scenarios, and supporting clinical decision-making. Although GAI introduces innovative opportunities to medical education, its practical application also presents various challenges, such as model accuracy and ethical concerns. The viewpoint further discusses the potential impact of these challenges on the future of medical education and offers corresponding strategies and recommendations, providing valuable insights for educators and policymakers. By understanding the practical applications and limitations of GAI in the medical field, this viewpoint aims to lay a foundation for more effective use of GAI in medical education in the future. Key Words: Generative artificial intelligence, Large language model, Medical education.","url":"https://doi.org/10.29271/jcpsp.2025.06.793","authors":["Shilu Wang","Rongqing Geng","Ruoning Xu"],"tags":["Generative grammar","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.29271/jcpsp.2025.06.793","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3015565366","name":"The Need for Artificial Intelligence in Digital Therapeutics","source":"openalex","abstract":"Digital therapeutics is a newly described concept in healthcare which is proposed to change patient behavior and treat medical conditions using a variety of digital technologies. However, the term is rarely defined with criteria that make it distinct from simply digitizedversions of traditional therapeutics. Our objective is to describe a more valuable characteristic of digital therapeutics, which is distinct from traditional medicine or therapy: that is, the utilization of artificial intelligence and machine learning systems to monitor and predict individual patient symptom data in an adaptive clinical feedback loop via digital biomarkers to provide a precision medicine approach to healthcare. Artificial intelligence platforms can learn and predict effective interventions for individuals using a multitude of personal variables to provide a customized and more tailored therapy regimen. Digital therapeutics coupled with artificial intelligence and machine learning also allows more effective clinical observations and management at the population level for various health conditions and cohorts. This vital differentiation of digital therapeutics compared to other forms of therapeutics enables a more personalized form of healthcare that actively adapts to patients’ individual clinical needs, goals, and lifestyles. Importantly, these characteristics are what needs to be emphasized to patients, physicians, and policy makers to advance the entire field of digital healthcare.","url":"https://doi.org/10.1159/000506861","authors":["Adam Palanica","Michael Docktor","Michael Lieberman","Yan Fossat"],"tags":["Digital health","Health care","Artificial intelligence","Computer science","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-08","doi":"https://doi.org/10.1159/000506861","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W1994365788","name":"Artificial Neural Networks for Decision-Making in Urologic Oncology","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0302-2838(03)00133-7","authors":["Theodore Anagnostou","Mesut Remzi","M. Lykourinas","Bob Djavan"],"tags":["Artificial intelligence","Medicine","Artificial neural network","Prostate cancer","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2003-05-12","doi":"https://doi.org/10.1016/s0302-2838(03)00133-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3108797169","name":"Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities","source":"openalex","abstract":"The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.","url":"https://doi.org/10.3389/fdata.2020.577974","authors":["Rémy Kusters","Dusan Misevic","Hugues Berry","Antoine Cully","Yann Le Cunff","Loic Dandoy","Natalia Díaz-Rodríguez","Marion Ficher","Jonathan Grizou","Alice Othmani","Themis Palpanas","Matthieu Komorowski","Patrick Loiseau","Clément Moulin Frier","Santino Nanini","Daniele Quercia","Michèle Sébag","F. Fogelman","Sofiane Taleb","Liubov Tupikina","Vaibhav Sahu","Jill-Jênn Vie","Fatima Wehbi"],"tags":["Transparency (behavior)","Variety (cybernetics)","Engineering ethics","Data science","Digital Revolution"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-23","doi":"https://doi.org/10.3389/fdata.2020.577974","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2315144155","name":"Text Analytics: the convergence of Big Data and Artificial Intelligence","source":"openalex","abstract":"The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics.","url":"https://doi.org/10.9781/ijimai.2016.369","authors":["Antonio Moreno Sandoval","Teófilo Redondo"],"tags":["Computer science","Analytics","Sentiment analysis","Business intelligence","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2016-02-28","doi":"https://doi.org/10.9781/ijimai.2016.369","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W7139943021","name":"From prediction to decision: Advancing medical artificial intelligence toward real clinical practice","source":"openalex","abstract":"Artificial intelligence (AI) in medicine is advancing steadily toward real clinical practice, not only through improved predictive performance but also through more decision-relevant modeling, evaluation, and interaction. The studies highlighted here illustrate three complementary directions. First, contemporary imaging models—exemplified by 3D vision transformer-based analysis of preoperative chest computed tomography (CT)—are being used to infer clinically consequential phenotypes, advancing from image recognition toward decision support in high-stakes settings such as surgical planning. Second, image-to-biomarker pipelines such as automated quantification of retinal vascular fractal dimension demonstrate how medical images can be transformed into reproducible quantitative markers suitable for population-level analysis and risk stratification. Third, large language models (LLMs) are increasingly evaluated and positioned as clinical communication and interpretation components: structured assessments in telepharmacy and bilingual patient education move beyond fluency to safety, actionability, empathy, and readability, while emerging perspectives consider LLMs as interpretive interfaces for complex, temporally evolving health data, including wearable sensing. At the same time, these advances also expose persistent bottlenecks that limit real-world deployment: the continued dominance of single-task and static formulations, fragmented systems driven by task-specific fine-tuning, limited reasoning over disease trajectories and evolving clinical contexts, and evaluation practices that remain insufficiently coupled to clinical workflows and downstream consequences. We argue that the next stage of medical AI should shift from accuracy-centered prediction toward decision-oriented, practice-ready systems that are robust across settings, clinically aligned in evaluation, and deployable at scale in routine care.","url":"https://doi.org/10.1016/j.imed.2026.03.003","authors":["Qi Chen","Han Lyu","Dong Li","Zhenchang Wang"],"tags":["Clinical Practice","Medical practice","Artificial intelligence","Computer science","Component (thermodynamics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-20","doi":"https://doi.org/10.1016/j.imed.2026.03.003","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3048427347","name":"Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence in UK Local Authorities","source":"openalex","abstract":"Abstract In recent years, local authorities in the UK have begun to adopt a variety of “smart” technological changes to enhance service delivery. These changes are having profound impacts on the structure of public administration. Focusing on the particular case of artificial intelligence, specifically autonomous agents and predictive analytics, a combination of desk research, a survey questionnaire, and interviews were used to better understand the extent and nature of these changes in local government. Findings suggest that local authorities are beginning to adopt smart technologies and that these technologies are having an unanticipated impact on how public administrators and computational algorithms become imbricated in the delivery of public services. This imbrication is described as algorithmic bureaucracy, and it provides a framework within which to explore how these technologies transform both the socio‐technical relationship between workers and their tools, as well as the ways that work is organized in the public sector.","url":"https://doi.org/10.1111/puar.13286","authors":["Thomas M. Vogl","Cathrine Seidelin","Bharath Ganesh","Jonathan Bright"],"tags":["Bureaucracy","Local government","Variety (cybernetics)","Public sector","Work (physics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-12","doi":"https://doi.org/10.1111/puar.13286","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3100076008","name":"Artificial Intelligence Decision Support for Medical Triage","source":"openalex","abstract":"Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.","url":"https://doi.org/10.48550/arxiv.2011.04548","authors":["Chiara Marchiori","Douglas D. Dykeman","Ivan Girardi","Adam Ivankay","Kevin Thandiackal","Mario Zusag","Andrea Giovannini","Daniel Karpati","Henri Saenz"],"tags":["Triage","Computer science","Telemedicine","Transparency (behavior)","Decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-09","doi":"https://doi.org/10.48550/arxiv.2011.04548","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4280569779","name":"A vision on the artificial intelligence for 6G communication","source":"openalex","abstract":"The 6G communication network will be a sixth-sense next-generation communication network, which will increase the worthiness of the intelligent Internet of Things. With the advent of various fields of artificial intelligence, 6G will create enormous possibilities, that is, Augmentation of Human Intelligence, Internet of Everything, Quality of Experiences, Quality of Life, etc. Artificial intelligence and 6G communication technology will completely change from connected things to connected intelligence. This article summarizes the scope of artificial intelligence in making a revolutionized 6G communication technology. We directly focus on implementing suitable applications that solve human needs and problems. Moreover, we emphasize such technology that can create value for new technologies.","url":"https://doi.org/10.1016/j.icte.2022.05.005","authors":["Tareq Bin Ahammed","Ripon Patgiri","Sabuzima Nayak"],"tags":["Scope (computer science)","The Internet","Computer science","Marketing and artificial intelligence","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-14","doi":"https://doi.org/10.1016/j.icte.2022.05.005","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4285815428","name":"Artificial Intelligence Wireless Network Data Security System For Medical Records Using Cryptography Management","source":"openalex","abstract":"So we're going to study here. Wireless technology is amongst the most quickly expanding and active technological disciplines in the world of communication. Connectivity communication that does not need the use of wires, cables, and perhaps other physical media. Encryption algorithm has always been widely used in various these decades to secure information from potential danger and using wireless technologies can also make the information more sheltered therefore these network infrastructure which focuses on providing replaying intervention against many issues, also can analyze the information from almost anywhere through these wireless communication and it will safeguard the health information from potential danger by storing the data in multiple servers in everything using a few real cryptographies This way, only authorized personnel may access the data and no one else can, ensuring that the data is protected from threats. To monitor environmental conditions, wireless sensor networks employ a variety of sensors, which transmit data to a central destination. These wireless sensor nodes are primarily motivated by military applications, and they are now widely used in many automotive and commercial areas, as well as in promising industries such as medicine. Nowadays, remote health monitoring wireless sensor networks unquestionably increase the quality of treatment. dropping and impersonating are two common security vulnerabilities to healthcare applications.","url":"https://doi.org/10.1109/icacite53722.2022.9823615","authors":["Akash Saxena","DIPANKAR MISRA","R. Ganesamoorthy","José L. Gonzáles","Hashem Ali Almashaqbeh","Vikas Tripathi"],"tags":["Computer science","Computer security","Wireless sensor network","Encryption","Cryptography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-28","doi":"https://doi.org/10.1109/icacite53722.2022.9823615","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4403553192","name":"Harnessing Artificial Intelligence for Advancing Medical Manuscript Composition: Applications and Ethical Considerations","source":"openalex","abstract":"Scientific medical manuscripts are fundamental to advancing research and enhancing patient care. With the emergence of artificial intelligence (AI), the process of composing such manuscripts has witnessed profound transformations. This review delves into the multifaceted role of AI in medical manuscript composition, analyzing its applications, benefits, drawbacks, and ethical implications. Employing a comprehensive narrative review methodology, we explored databases such as PubMed, Google Scholar, and Science Direct. The review charts the evolution of AI in medical writing, from basic word processing to sophisticated neural network-based models like GPT-3 and GPT-4. Various AI-powered tools such as ChatGPT, Google Bard, Elicit, and Consensus AI are examined in terms of their functionalities and contributions to research and medical writing. While AI technologies offer notable advantages in automating content creation and boosting research productivity, concerns persist regarding overreliance, potential homogenization of writing styles, and ethical considerations such as originality and authorship. Because of this concern, some companies are restricting the use of AI in peer review processes, medical examinations, etc. It is crucial to strike a balance in integrating AI tools, ensuring human oversight, conducting thorough algorithm audits, addressing financial implications, and upholding academic integrity. The review underscores the transformative potential of AI in medical manuscript composition while emphasizing the ongoing significance of human expertise, creativity, and ethical responsibility in scientific communication. Recommendations are provided for the effective integration of AI tools into medical writing processes, emphasizing collaborative efforts between AI developers, researchers, and journal editors to navigate ethical dilemmas and maximize the benefits of AI-driven advancements in scientific publishing.","url":"https://doi.org/10.7759/cureus.71744","authors":["Shruti Singh","Rajesh Kumar","Vikas Maharshi","Prashant Kumar Singh","Veena Kumari","Meenakshi Tiwari","Divya Harsha"],"tags":["Medicine","Engineering ethics","Composition (language)","Linguistics","Philosophy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-17","doi":"https://doi.org/10.7759/cureus.71744","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W3154370152","name":"Redefining medical education by boosting curriculum with artificial intelligence knowledge","source":"openalex","abstract":"In 1910, Abraham Flexner published The Flexner Report. After visiting 155 medical schools across the United States and Canada, he established the biomedical model as the gold standard of medical training. 1 Among other things, he created a standardized four-year curriculum, recommended a minimum qualification for admittance, and establishes an accreditation process. 2 Previous to his report, the majority of medical schools had been founded merely for profit reasons and thus went about their business without any set of rules for admission or accreditation. Flexner's influence still guides the current curricular reform, and more than a century later, we still believe the fundamental aims proposed by him are relevant. However, we must also consider that to restructure today's education track optimally, it is necessary to embrace new technologies.","url":"https://doi.org/10.15406/jccr.2020.13.00490","authors":["Sameer Mehta","Daniel da Silva Vieira","Samantha Quintero","Daniela Bou Daher","Floralba Duka","Hudson Franca","Jhonny Bonilla","Andreea Molnar","Catalina Molnar","David Zerpa","María Fernanda Fleming Díaz"],"tags":["Boosting (machine learning)","Curriculum","Genomic medicine","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-13","doi":"https://doi.org/10.15406/jccr.2020.13.00490","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4224214331","name":"Artificial intelligence assisted improved human-computer interactions for computer systems","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compeleceng.2022.107950","authors":["Mohammed Saeed Alkatheiri"],"tags":["Usability","Human–computer interaction","Computer science","Cognition","Comprehension"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-23","doi":"https://doi.org/10.1016/j.compeleceng.2022.107950","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4300858848","name":"Artificial Intelligence in Medicine","source":"openalex","abstract":"At a time when laboratory and personal computers are appearing increasingly in medical settings, it is intriguing to imagine the ways in which computing technology may evolve in the decades ahead. Professor Peter Szolovits of the Clinical Decision Making Group at the Massachusetts Institute of Technology (MIT) Laboratory of Computer Science has edited a well-written collection of papers that describes early work in the development of consultation systems for use by physicians. The book's six chapters are drawn from a series of presentations on Artificial Intelligence in Medicine (AIM) given at a meeting of the American Association for the Advancement of Science in 1979. Artificial intelligence (AI) is the computer science field that deals with the representation and manipulation of symbolic concepts. The techniques are often modeled after cognitive processes and are to be distinguished from conventional mathematical or statistical computing. Despite the three-year lapse since the papers were originally","url":"https://doi.org/10.1001/jama.1983.03340060095042","authors":["Edward H. Shortliffe"],"tags":["Field (mathematics)","Artificial intelligence","Medicine","Representation (politics)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1983-08-12","doi":"https://doi.org/10.1001/jama.1983.03340060095042","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3045236086","name":"Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge","source":"openalex","abstract":"Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area.","url":"https://doi.org/10.1097/apo.0000000000000301","authors":["Mingguang He","Zhixi Li","Chi Liu","Danli Shi","Zachary Tan"],"tags":["Software deployment","Medical diagnosis","Clinical Practice","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-01","doi":"https://doi.org/10.1097/apo.0000000000000301","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4328138054","name":"Artificial intelligence and ChatGPT between worst enemy and best friend: The two faces of a revolution and its impact on science and medical schools","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.neurol.2023.03.004","authors":["M. Aubignat","E. Diab"],"tags":["Value (mathematics)","Adversary","Chatbot","Engineering ethics","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-21","doi":"https://doi.org/10.1016/j.neurol.2023.03.004","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4398141110","name":"Assessment of Artificial Intelligence Platforms With Regard to Medical Microbiology Knowledge: An Analysis of ChatGPT and Gemini","source":"openalex","abstract":"The performance of two artificial intelligence (AI) platforms, ChatGPT 3.5 (OpenAI, California, United States) and Gemini (Google AI, California, United States) was assessed by answering 200 questions of microbiology drawn from validated sources. The questions were selected from topics such as General Microbiology, Immunology, and Microbiology Applied to Infectious Diseases. The study was conducted from December 2023 to March 2024, and the responses of the different AI platforms were compared with an answer key. Statistical analysis was performed to assess accuracy. ChatGPT 3.5 and Gemini had comparable accuracy with correct response scores of 71% and 70.5%, respectively. Their performance varied across different sections. Gemini performed better in General Microbiology and Immunology, and ChatGPT 3.5 had a better score in the Applied Microbiology section. The study's findings highlight that AI platforms such as ChatGPT and Gemini can be utilized in microbiology and medical education. The evolution and continuous updating of AI platforms are required to improve their performance.","url":"https://doi.org/10.7759/cureus.60675","authors":["Jai Ranjan","Absar Ahmad","Monalisa Subudhi","Ajay Kumar"],"tags":["Clinical microbiology","Computer science","Engineering","Data science","Microbiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-20","doi":"https://doi.org/10.7759/cureus.60675","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3157143019","name":"The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic","source":"openalex","abstract":"The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.","url":"https://doi.org/10.1007/s10796-021-10131-x","authors":["Francesco Piccialli","Vincenzo Schiano Di Cola","Fabio Giampaolo","Salvatore Cuomo"],"tags":["Pandemic","Coronavirus disease 2019 (COVID-19)","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","Computer science","2019-20 coronavirus outbreak"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-26","doi":"https://doi.org/10.1007/s10796-021-10131-x","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4385421241","name":"Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare","source":"openalex","abstract":"Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.","url":"https://doi.org/10.3390/jpm13081214","authors":["Diana Gina Poalelungi","Carmina Liana Mușat","Ana Fulga","Marius Neagu","Anca‐Iulia Neagu","Alin Ionut Piraianu","Iuliu Fulga"],"tags":["Health care","Medicine","Computer science","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-31","doi":"https://doi.org/10.3390/jpm13081214","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3084307934","name":"Research on Infringement of Artificial Intelligence Medical Robot","source":"openalex","abstract":"with the rapid development of artificial intelligence, intelligent medical robots have been used in the medical field to assist medical staff in diagnosing and treating diseases. Due to the lag of legislation, the current liability for medical damage and product liability in China is difficult to solve the liability of infringement of intelligent medical robots. It is advisable to refer to the European Union's civil law rules for robots and Russia's green act to bring the infringement of intelligent medical robots into the category of highly dangerous liability system, strengthen the producer's burden of proof for defects, and set up a compulsory insurance system and compensation fund to separate producer's liability so that the victims can get better relief.","url":"https://doi.org/10.2991/assehr.k.200826.099","authors":["Chen Mingtsung","Wei Qian"],"tags":["Computer science","Robot","Artificial intelligence","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.2991/assehr.k.200826.099","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4221063445","name":"Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review","source":"openalex","abstract":"BACKGROUND: Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising. OBJECTIVE: This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry. METHODS: This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus. RESULTS: The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals. CONCLUSIONS: Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.","url":"https://doi.org/10.2196/33560","authors":["Victoria Welch","Tom Joshua Wy","Anna N. Ligezka","Leslie C. Hassett","Paul E. Croarkin","Arjun P. Athreya","Magdalena Romanowicz"],"tags":["Wearable computer","Child and adolescent psychiatry","Psychology","Computer science","Psychiatry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-26","doi":"https://doi.org/10.2196/33560","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3118224304","name":"Bioinformatics and Artificial Intelligence: Gerontological and Geriatric Components Medical and Social Support for Active Healthy Longevity","source":"openalex","abstract":"The “cognitive reserve” construct is a set of variables, including intelligence, education, and mental stimulation, that presumably allows the brain to adapt to underlying pathologies, supporting cognitive function despite underlying neural changes. Brain Homo Sapiens also points to resistance to neuropathological damage and can be defined as the ability to optimize or maximize performance through an effective set of neural networks and/or alternative cognitive strategies. Learning in childhood, the level of education and activities for adults — all this independently contributes to the formation of a cognitive reserve. The introduction of biocomputer nanoplatforms and modules consisting of small molecules, polymers, nucleic acids or proteins/peptides, nanoplatforms are programmed to detect and process external stimuli, such as magnetic fields or light, or internal stimuli, such as nucleic acids, enzymes or pH, using three different mechanisms: system assembly, system disassembly or system transformation. Current biocomputer nanoplatforms are invaluable for many applications, including medical diagnostics, biomedical imaging, environmental monitoring, and delivery of therapeutic drugs to target cell populations. The future implementation of systems biology and systems neurophysiology paradigms based on complex analysis of large and deep heterogeneous data sources will be crucial to achieve a deeper understanding of the pathophysiology of Alzheimer’s disease, using current brain-computer and artificial intelligence interface technologies, in order to increase information that can be extracted from preclinical and clinical indicators. Integration of different sources of information will allow researchers to obtain a new holistic picture of the pathophysiological process of the disease, which will cover from molecular changes to cognitive manifestations. The new competencies of psychoneuroimmunoendocrinology and psychoneuroimmunology play a strategic role in interdisciplinary science and interdisciplinary planning and decision-making. The introduction of multi-vector neurotechnologies of artificial intelligence and the principles of digital health care will contribute to the development of modern neuroscience and neuromarketing. Medical and social support for active healthy longevity is possible when synchronizing information systems of medical organizations and social institutions, introducing a single neurophysiological circuit and modern neurointerfaces, a combined and hybrid cluster in the diagnosis, treatment, prevention and rehabilitation of cognitive disorders and cognitive disorders. A key factor in medical and social support is the participation of interdisciplinary business employees and data processing specialists (their support, monitoring), as well as the availability of sufficient staff literacy in data management.","url":"https://doi.org/10.33619/2414-2948/61/16","authors":["V. F. Pyatin","А. В. Колсанов","N. Romanchuk","Д. В. Романов","И. Л. Давыдкин","А. Н. Волобуев","И. И. Сиротко","С. В. Булгакова"],"tags":["Cognition","Process (computing)","Computer science","Set (abstract data type)","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-12","doi":"https://doi.org/10.33619/2414-2948/61/16","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4378901262","name":"Artificial intelligence technology, public trust, and effective governance","source":"openalex","abstract":"Abstract Advancement in information technology continues to evolve especially in the field of artificial intelligence (AI). Research studies have been conducted to evaluate the perceptions of Americans on the development and utilization of AI technology and if it is appropriate to use AI in public administrative duties. The research revealed that society is fragmented regarding the acceptance of AI, and whether AI decisions could have long‐term effects on the labor industry, legal system, and national security. The 2018 AI Public Opinion Survey revealed significant concerns among the American public regarding AI, yet also a recognition of its promise. The goal of this article is to further develop a governance framework for AI that considers the importance of public trust in AI policy. First, it discusses the necessity of public trust for the effective governance of emergent technology. Then, it evaluates public opinion on AI technology that specifically pertains to governance. The article concludes with a discussion of why public trust is central to good AI governance.","url":"https://doi.org/10.1111/ropr.12555","authors":["Pedro Robles","Daniel J. Mallinson"],"tags":["Corporate governance","Public opinion","Field (mathematics)","Public trust","Public relations"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-31","doi":"https://doi.org/10.1111/ropr.12555","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3021817101","name":"Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)","source":"openalex","abstract":"The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision‑support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed.","url":"https://doi.org/10.3892/ijo.2020.5063","authors":["Eleftherios Trivizakis","Georgios Z. Papadakis","Ioannis Souglakos","Nickolas Papanikolaou","Lefteris Koumakis","Demetrios�� Spandidos","Aristidis Tsatsakis","Apostolos H. Karantanas","Kostas Marias"],"tags":["Radiogenomics","Precision medicine","Context (archaeology)","Personalized medicine","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-11","doi":"https://doi.org/10.3892/ijo.2020.5063","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3185030651","name":"Application of Artificial Intelligence in Basketball Sport","source":"openalex","abstract":"Basketball is among the most popular sports in the world, and its related industries have also produced huge economic benefits. In recent years, the application of artificial intelligence (AI) technology in basketball has attracted a large amount of attention. We conducted a comprehensive review of the application research of AI in basketball through literature retrieval. Current research focuses on the AI analysis of basketball team and player performance, prediction of competition results, analysis and prediction of shooting, AI coaching system, intelligent training machine and arena, and sports injury prevention. Most studies have shown that AI technology can improve the training level of basketball players, help coaches formulate suitable game strategies, prevent sports injuries, and improve the enjoyment of games. At the same time, it is also found that the number and level of published papers are relatively limited. We believe that the application of AI in basketball is still in its infancy. We call on relevant industries to increase their research investment in this area, and promote the improvement of the level of basketball, making the game increasingly exciting as its worldwide popularity continues to increase.","url":"https://doi.org/10.12775/jehs.2021.11.07.005","authors":["Li Bin","Xinyang Xu"],"tags":["Basketball","Popularity","Coaching","Applied psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-08","doi":"https://doi.org/10.12775/jehs.2021.11.07.005","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4413969609","name":"Physical foundations for trustworthy medical imaging: A survey for artificial intelligence researchers","source":"openalex","abstract":"Artificial intelligence in medical imaging has grown rapidly in the past decade, driven by advances in deep learning and widespread access to computing resources. Applications cover diverse imaging modalities, including those based on electromagnetic radiation (e.g., X-rays), subatomic particles (e.g., nuclear imaging), and acoustic waves (ultrasound). Each modality features and limitations are defined by its underlying physics. However, many artificial intelligence practitioners lack a solid understanding of the physical principles involved in medical image acquisition. This gap hinders leveraging the full potential of deep learning, as incorporating physics knowledge into artificial intelligence systems promotes trustworthiness, especially in limited data scenarios. This work reviews the fundamental physical concepts behind medical imaging and examines their influence on recent developments in artificial intelligence, particularly, generative models and reconstruction algorithms. Finally, we describe physics-informed machine learning approaches to improve feature learning in medical imaging.","url":"https://doi.org/10.1016/j.artmed.2025.103251","authors":["Miriam Cobo","David Corral Fontecha","Wilson Silva","L. Lloret Iglesias","Lara Lloret Iglesias"],"tags":["Trustworthiness","Computer science","Data science","Artificial intelligence","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-26","doi":"https://doi.org/10.1016/j.artmed.2025.103251","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"oa:W4224259661","name":"Artificial Intelligence, Machine Learning, and Internet of Drones in Medical Applications","source":"openalex","abstract":"Internet of drones (IOD) plays an important role in the delivery of emergency medicine to remote locations. Furthermore, it is employed for blood transfer, disaster assistance, missing persons, discovering lost hikers in the hill station, and a variety of other emergency services. The use of drones for emergency response services, particularly in medical circumstances, offers new avenues for life-saving interventions. Using drones to have “eyes” on a risky scenario or to transport medical supplies to stranded patients may increase the capacity of emergency response physicians to provide care in dangerous conditions. IOD provides several emergency response services that have an influence on daily life. The Federal Aviation Administration (FAA) conducts completely autonomous missions beyond visual range and flights above people to provide critical medical supplies. Artificial intelligence and machine learning are the future of the unmanned aerial vehicle in multiple applications.","url":"https://doi.org/10.4018/978-1-7998-9534-3.ch011","authors":["J. Kavya","G. Prasad","N. Bharanidharan"],"tags":["Drone","Aviation","The Internet","Medical emergency","Variety (cybernetics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-11","doi":"https://doi.org/10.4018/978-1-7998-9534-3.ch011","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2796166051","name":"Medical information security in the era of artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.mehy.2018.03.023","authors":["Yufeng Wang","Liwei Wang","C. Xue","Chang-ao Xue"],"tags":["Biometrics","Face (sociological concept)","Facial recognition system","Computer science","IRIS (biosensor)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-04-05","doi":"https://doi.org/10.1016/j.mehy.2018.03.023","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"oa:W2804604520","name":"RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records","source":"openalex","abstract":"We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.","url":"https://doi.org/10.1109/tvcg.2018.2865027","authors":["Bum Chul Kwon","Minje Choi","Joanne Taery Kim","Edward Choi","Youngbin Kim","Soon‐Wook Kwon","Jimeng Sun","Jaegul Choo"],"tags":["Computer science","Visual analytics","Interpretability","Recurrent neural network","Interactivity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-08-20","doi":"https://doi.org/10.1109/tvcg.2018.2865027","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2913769966","name":"Radiomics with artificial intelligence for precision medicine in radiation therapy","source":"openalex","abstract":"Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.","url":"https://doi.org/10.1093/jrr/rry077","authors":["Hidetaka Arimura","Mazen Soufi","Hidemi Kamezawa","Kenta Ninomiya","Masahiro Yamada"],"tags":["Radiomics","Precision medicine","Medicine","Boosting (machine learning)","Radiogenomics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-09-09","doi":"https://doi.org/10.1093/jrr/rry077","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3158589599","name":"Explainable Artificial Intelligence in Endocrinological Medical Research","source":"openalex","abstract":"Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems to aid physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to proposed machine learning (ML) models into Clinical Decision Support Systems due to the ability of ML models to make predictions more accurately by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capturing complex and often nonlinear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the limited utility of often black-box solutions in a clinical setting. This is driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; an example of this is a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5). AI and ML is a growing field for endocrinology (6) and has already made inroads in the treatment of diabetes (7). Endocrinology is especially well positioned to take advantage of the upsurge in ML work focused on interpretability and explainability, which focus on developing models based on the trade-off between model accuracy and transparency rather than accuracy alone (5). In their recent article in The Journal of Clinical Endocrinology & Metabolism, Shmoish et al. (8), demonstrate the power of an explainable ML model to predict adult height from growth measurements of children before and at the age of 6 years. In this work they not only demonstrate superior performance in the context of accuracy but utilize the explainable model to provide insight into the factors underlying that performance as well as understand the potential deficiencies in the model that could be addressed in future developments. The prediction of adult height was undertaken via a suite of ML algorithms and compared with 3 common expert-derived metrics: target height, conditional target height, and “grandma” method. Overall, there was not a lot of difference between the 3 expert-derived methods, the first 2 approaches that use parental height vs the last that is a doubling of baby length at a specified age. However, in all cases the ML model dramatically improves overall prediction of adult height. Generalization is an important component of ML models, which means that the model can adapt properly to previously unseen data. Utilizing data that has been collected independently from the training data, but expected to be drawn from the same distribution, is one of the best strategies for this (5). Shmoish et al. (8), demonstrate that their model is robust to both where and how the measurements are collected. The results of the model, which showed a Pearson correlation between the predicted and observed adult height of R = 0.87 in the validation set associated with the training data (Swedish cohort of children born in 1974), was approximately the same, R = 0.88, in another Swedish cohort started in 1990 and a separate cohort in Edinburgh with children born between 1972 and 1976, R = 0.88. The explainability of the model is based on feature importance metrics that can be directly extracted from the ML model of choice, a Random Forest, which is a benefit of the Random Forest ML approach in addition to the often high prediction accuracy. The most important features driving the model were the average height of the child between the age of 3.4-4 years of age and sex, with secondary features such as growth velocity and weight not playing an important role in the prediction. The authors then explored the model resu","url":"https://doi.org/10.1210/clinem/dgab237","authors":["Bobbie‐Jo Webb‐Robertson"],"tags":["Computer science","Artificial intelligence","Psychology","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-30","doi":"https://doi.org/10.1210/clinem/dgab237","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4385241201","name":"Use of an Artificial Intelligence-Driven Digital Platform for Reflective Learning to Support Continuing Medical and Professional Education and Opportunities for Interprofessional Education and Equitable Access","source":"openalex","abstract":"Continuing medical education (CME) and continuing education (CE) provide frameworks for assimilating and disseminating new advancements and are mainstays of clinicians’ professional development and accreditation. However, traditional CME/CE approaches may be challenged in providing opportunities for integrated and interprofessional learning and helping clinicians effectively translate innovations into individual practice. This Commentary describes the reflective learning approach, including its integration into CME/CE and how it can support interprofessional education. Also identified are barriers to reflective and interprofessional learning implementation and CME/CE access. The Commentary provides insights based on point-of-care reflection data and outlines considerations in trialing the use of an artificial intelligence (AI)-driven digital platform for reflective learning. Further, the Commentary describes how the AI-driven digital platform may help overcome barriers to reflective learning and interprofessional education and support equitable CME/CE program access.","url":"https://doi.org/10.3390/educsci13080760","authors":["Brian Cohen","Sasha DuBois","Patricia A. Lynch","Niraj Swami","Kelli Noftle","Mary Beth Arensberg"],"tags":["Accreditation","Interprofessional education","Continuing medical education","Reflective practice","Professional development"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-25","doi":"https://doi.org/10.3390/educsci13080760","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4285222038","name":"Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review","source":"openalex","abstract":"Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.","url":"https://doi.org/10.1109/access.2022.3179356","authors":["Mostafa Zafari","Jalal Safari Bazargani","Abolghasem Sadeghi‐Niaraki","Soo-Mi Choi"],"tags":["Scopus","Computer science","Applications of artificial intelligence","Systematic review","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1109/access.2022.3179356","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4388540336","name":"Introduction of telemedicine technologies based on artificial intelligence into practice of providing outpatient care for medical examination","source":"openalex","abstract":"To date, the main focus in the implementation of medical activities is focused on the organization of outpatient care for the population. It is at the level of primary health care, where it is possible to carry out primary prevention measures and the formation of a healthy lifestyle, these issues should be given special attention when providing medical care to the population. One of the main tasks of modern healthcare is to reduce the number of chronic non-communicable diseases of the adult population using modern tools of early preclinical diagnostics. To date, one of the most promising areas that have a significant impact on modern healthcare is digital telemedicine technologies based on artificial intelligence. They can be confidently attributed to the most popular and rapidly developing groups of services developed for primary health care for the purpose of primary diagnostics. Nevertheless, no matter how fast information technologies develop, the opinions of experts in the subject area remain relevant, which significantly enrich the results of the expert opinion.","url":"https://doi.org/10.33667/2078-5631-2023-28-44-49","authors":["P. Seliverstov","В. В. Шаповалов","O. V. Aleshko"],"tags":["Telemedicine","Health care","Population","Medicine","Ambulatory care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-09","doi":"https://doi.org/10.33667/2078-5631-2023-28-44-49","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4389179445","name":"Radiologists’ and Radiographers’ Perspectives on Artificial Intelligence in Medical Imaging in Saudi Arabia","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) in medical imaging rapidly expands regarding image processing and interpretation. Therefore, the aim was to explore radiographers’ and radiologists’ perceptions and attitudes towards AI use in medical imaging technologies in Saudi Arabia. METHODS: The survey was distributed online, and responses were collected from 173 participants nationwide. Data analysis was performed using SPSS Statistics (version 27). RESULTS: The participants scored an average of 1.7, 1.6, and 1.8 on a scale of 1–3 for attitudinal perspectives on clinical application and the positive and negative impact of integrating AI technology in diagnostic radiology. Lack of knowledge (43.9%) and perceived cyber threats (37.7%) were the most cited factors hindering AI implementation in Saudi Arabia. CONCLUSION: The radiographers and radiologists in this study had a favorable attitude toward AI integration in diagnostic radiology; nonetheless, concerns were raised about data protection, cyber security, AI-related errors, and decision-making challenges.","url":"https://doi.org/10.2174/0115734056250970231117111810","authors":["Ali S. Alyami","Naif A. Majrashi","Nasser Shubayr"],"tags":["Perception","Scale (ratio)","Medicine","Medical imaging","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-30","doi":"https://doi.org/10.2174/0115734056250970231117111810","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W1992670743","name":"Artificial intelligence research in anesthesia and intensive care","source":"openalex","abstract":"","url":"https://doi.org/10.1007/bf01617327","authors":["Glenn D. Rennels","Perry L. Miller"],"tags":["Anesthesiology","Artificial Intelligence System","Artificial intelligence","Computer science","Intensive care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1988-10-01","doi":"https://doi.org/10.1007/bf01617327","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4230355935","name":"Management of Artificial Intelligence Enabled Smart Wearable Devices for Early Diagnosis and Continuous Monitoring of CVDS","source":"openalex","abstract":"Cardiovascular diseases are often sudden and deadly. Every year, world over, there are millions of CVD deaths, not due to timely detection and treatment and continuous monitoring of patient situations. In the medical field, Artificial intelligence enabled/driven wearable devices are a new, and welcome development. This study is addressed to the cure and management of CVDs for timely diagnosis detection, treatment and monitoring CVDs. It is found that the wearable devices are effective means of meeting the challenges of CVDs. However, it is a just picking up technology, which needs to be generally known and cost effective, for which certain suggestions are made such as solar powered batteries in the device for their ever fully working capability.","url":"https://doi.org/10.35940/ijitee.l3108.119119","authors":["Mounir M. El Khatib","Gouher Ahmed"],"tags":["Wearable computer","Wearable technology","Continuous monitoring","Field (mathematics)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-11-19","doi":"https://doi.org/10.35940/ijitee.l3108.119119","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3192526842","name":"How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies","source":"openalex","abstract":"Artificial Intelligence (AI) is fuelling a new breed of digital innovation in Human Resource Management (HRM) by creating new opportunities for complying with General Data Protection Regulation (GDPR) during data collection and analysis, decreasing biases, and offering targeted recommendations. However, AI is also posing challenges to organisations and key assumptions about digital innovation processes and outcomes, making it unclear how to combine AI affordances with actors, goals, and tasks. We conducted a qualitative multiple-case study in Scandinavian organisations offering HR services. Grounded theory guided our data collection and analysis. Input-Process-Output framework and affordance theory supported the analysis of specific information processing constraints and enablers. We developed a framework to explain how AI affordances enable digital innovation and address the calls about definitional boundaries between innovation processes and outcomes. We showed how AI affordances are actualised and how this leads to reontologising decision-making and providing data driven legitimisation. Our study contributes to digital innovation research by elucidating AI affordances and their actualisation in organisations. We conclude with the implications to theory and practice, limitations, and suggestions for future research.","url":"https://doi.org/10.1016/j.techfore.2021.121081","authors":["Cristina Trocin","Ingrid Våge Hovland","Patrick Mikalef","Christian Dremel"],"tags":["Affordance","Knowledge management","Process (computing)","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-06","doi":"https://doi.org/10.1016/j.techfore.2021.121081","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2898390306","name":"Technology and mental health: The role of artificial intelligence","source":"openalex","abstract":"An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.","url":"https://doi.org/10.1016/j.eurpsy.2018.08.004","authors":["Christopher A. Lovejoy"],"tags":["Content (measure theory)","Action (physics)","Psychology","Mental health","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-10-28","doi":"https://doi.org/10.1016/j.eurpsy.2018.08.004","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2971301364","name":"A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study","source":"openalex","abstract":"BACKGROUND: The ability of nursing undergraduates to communicate effectively with health care providers, patients, and their family members is crucial to their nursing professions as these can affect patient outcomes. However, the traditional use of didactic lectures for communication skills training is ineffective, and the use of standardized patients is not time- or cost-effective. Given the abilities of virtual patients (VPs) to simulate interactive and authentic clinical scenarios in secured environments with unlimited training attempts, a virtual counseling application is an ideal platform for nursing students to hone their communication skills before their clinical postings. OBJECTIVE: The aim of this study was to develop and test the use of VPs to better prepare nursing undergraduates for communicating with real-life patients, their family members, and other health care professionals during their clinical postings. METHODS: The stages of the creation of VPs included preparation, design, and development, followed by a testing phase before the official implementation. An initial voice chatbot was trained using a natural language processing engine, Google Cloud's Dialogflow, and was later visualized into a three-dimensional (3D) avatar form using Unity 3D. RESULTS: The VPs included four case scenarios that were congruent with the nursing undergraduates' semesters' learning objectives: (1) assessing the pain experienced by a pregnant woman, (2) taking the history of a depressed patient, (3) escalating a bleeding episode of a postoperative patient to a physician, and (4) showing empathy to a stressed-out fellow final-year nursing student. Challenges arose in terms of content development, technological limitations, and expectations management, which can be resolved by contingency planning, open communication, constant program updates, refinement, and training. CONCLUSIONS: The creation of VPs to assist in nursing students' communication skills training may provide authentic learning environments that enhance students' perceived self-efficacy and confidence in effective communication skills. However, given the infancy stage of this project, further refinement and constant enhancements are needed to train the VPs to simulate real-life conversations before the official implementation.","url":"https://doi.org/10.2196/14658","authors":["Shefaly Shorey","Emily Ang","John Yap","Esperanza Debby Ng","Siew Tiang Lau","Chee‐Kong Chui"],"tags":["Medical education","Psychology","Applied psychology","Nursing","Training (meteorology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-31","doi":"https://doi.org/10.2196/14658","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4388525108","name":"Characterizing the Clinical Adoption of Medical AI Devices through U.S. Insurance Claims","source":"openalex","abstract":"There are now over 500 medical artificial intelligence (AI) devices that are approved by the U.S. Food and Drug Administration. However, little is known about where and how often these devices are actually used after regulatory approval. In this article, we systematically quantify the adoption and usage of medical AI devices in the United States by tracking Current Procedural Terminology (CPT) codes explicitly created for medical AI. CPT codes are widely used for documenting billing and payment for medical procedures, providing a measure of device utilization across different clinical settings. We examined a comprehensive nationwide claims database of 11 billion CPT claims between January 1, 2018, and June 1, 2023 to analyze the prevalence of medical AI devices based on submitted claims. Our results indicate that medical AI device adoption is still nascent, with most usage driven by a handful of leading devices. For example, only AI devices used for assessing coronary artery disease and for diagnosing diabetic retinopathy have accumulated more than 10,000 CPT claims. Furthermore, we found that zip codes that had a higher income level, were metropolitan, and had academic medical centers were much more likely to have medical AI usage. Our study sheds light on the current landscape of medical AI device adoption and usage in the United States, underscoring the need to further investigate barriers and incentives to promote equitable access and broader integration of AI technologies in health care.","url":"https://doi.org/10.1056/aioa2300030","authors":["Kevin Wu","Eric Q. Wu","Brandon Theodorou","Weixin Liang","Christina Mack","Lucas M. Glass","Jimeng Sun","James Zou"],"tags":["Current Procedural Terminology","Payment","Business","Medicine","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-09","doi":"https://doi.org/10.1056/aioa2300030","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4205978654","name":"Exploring Research Trends of Emerging Technologies in Health Metaverse: A Bibliometric Analysis","source":"openalex","abstract":"","url":"https://doi.org/10.2139/ssrn.3998068","authors":["Donghua Chen","Runtong Zhang"],"tags":["Metaverse","Data science","Regional science","Sociology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.2139/ssrn.3998068","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4392058134","name":"GenAI against humanity: nefarious applications of generative artificial intelligence and large language models","source":"openalex","abstract":"Abstract Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are marvels of technology; celebrated for their prowess in natural language processing and multimodal content generation, they promise a transformative future. But as with all powerful tools, they come with their shadows. Picture living in a world where deepfakes are indistinguishable from reality, where synthetic identities orchestrate malicious campaigns, and where targeted misinformation or scams are crafted with unparalleled precision. Welcome to the darker side of GenAI applications. This article is not just a journey through the meanders of potential misuse of GenAI and LLMs, but also a call to recognize the urgency of the challenges ahead. As we navigate the seas of misinformation campaigns, malicious content generation, and the eerie creation of sophisticated malware, we’ll uncover the societal implications that ripple through the GenAI revolution we are witnessing. From AI-powered botnets on social media platforms to the unnerving potential of AI to generate fabricated identities, or alibis made of synthetic realities, the stakes have never been higher. The lines between the virtual and the real worlds are blurring, and the consequences of potential GenAI’s nefarious applications impact us all. This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.","url":"https://doi.org/10.1007/s42001-024-00250-1","authors":["Emilio Ferrara"],"tags":["Humanity","Generative grammar","Artificial intelligence","Computer science","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-22","doi":"https://doi.org/10.1007/s42001-024-00250-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4403052161","name":"From Theory to Practice: Artificial Intelligence (AI) Literacy Course for First-Year Medical Students","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostics, personalized medicine, and clinical decision-making. In medical education, AI chatbots have the potential to be used as virtual tutors or learning assistants. Despite AI's growing impact, its integration into medical education remains limited. AI is not a standard component of medical curricula, which could leave many future healthcare professionals unprepared for an AI-driven workplace. To address this significant gap, this editorial describes the development of a mini-course to integrate AI training for first-year medical students. The course was focused on the fundamentals of AI, prompt engineering, practical applications of chatbots as learning assistants, and ethical use of generative AI.","url":"https://doi.org/10.7759/cureus.70706","authors":["Hunter Levingston","Max C. Anderson","Monzurul Amin Roni"],"tags":["Medicine","Course (navigation)","Medical education","Literacy","Short course"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-02","doi":"https://doi.org/10.7759/cureus.70706","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4408850043","name":"An Academic Viewpoint (2025) on the Integration of Generative Artificial Intelligence in Medical Education: Transforming Learning and Practices","source":"openalex","abstract":"Generative artificial intelligence (GAI) has introduced a new era of medical education by offering innovative solutions to critical challenges in teaching, assessment, and clinical training. This expanded review explores the current and potential applications of GAI across multiple domains, including personalized tutoring, enhanced academic administrative efficiency, and improved preparedness for daily learning interactions. Utilizing a narrative review methodology combined with expert analysis, this study involved a structured literature search in January 2025 across PubMed, Scopus, and Google Scholar, followed by iterative brainstorming sessions and expert evaluations to assess the feasibility and impact of various GAI applications. Six domain experts then appraised the feasibility and impact of GAI technologies across educational settings, resulting in 10 identified domains of application: Quality and Administration, Curriculum Development, Teaching and Learning, Assessment and Evaluation, Clinical Training, Academic Guidance, Student Research, Student Affairs, Internship Management, and Student Activities. Our findings highlight how GAI supports personalized learning - through adaptive tutoring and automated performance dashboards - while optimizing administrative tasks such as course registration and policy oversight. In addition, immersive simulations and virtual patient encounters reinforce clinical decision-making and practical skills. GAI-driven tools also streamline research processes via automated literature reviews and proposal refinement, ultimately fostering greater efficiency across academic environments. Despite these opportunities, ethical considerations remain a priority. Issues pertaining to data privacy, algorithmic bias, and equitable access must be addressed through robust regulatory frameworks and institution-wide policies. Overall, by embracing targeted, ethically guided implementations, GAI has the evolving potential to enhance educational quality, improve operational effectiveness, and equip future healthcare professionals with the adaptive skills needed in a patient-centered clinical landscape.","url":"https://doi.org/10.7759/cureus.81145","authors":["Mohammad Almansour","Mona Soliman","Raniah N. Aldekhyyel","Samar Binkheder","Mohamad-Hani Temsah","Khalid H. Malki"],"tags":["Medicine","Generative grammar","Artificial intelligence","Medical education","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-25","doi":"https://doi.org/10.7759/cureus.81145","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3048540441","name":"Artificial intelligence and deep learning in ophthalmology - present and future (Review)","source":"openalex","abstract":"Since its introduction in 1959, artificial intelligence technology has evolved rapidly and helped benefit research, industries and medicine. Deep learning, as a process of artificial intelligence (AI) is used in ophthalmology for data analysis, segmentation, automated diagnosis and possible outcome predictions. The association of deep learning and optical coherence tomography (OCT) technologies has proven reliable for the detection of retinal diseases and improving the diagnostic performance of the eye's posterior segment diseases. This review explored the possibility of implementing and using AI in establishing the diagnosis of retinal disorders. The benefits and limitations of AI in the field of retinal disease medical management were investigated by analyzing the most recent literature data. Furthermore, the future trends of AI involvement in ophthalmology were analyzed, as AI will be part of the decision-making regarding the scientific investigation, diagnosis and therapeutic management.","url":"https://doi.org/10.3892/etm.2020.9118","authors":["Andreea Moraru","Dănuț Costin","Radu Moraru","Daniel Brănișteanu"],"tags":["Deep learning","Artificial intelligence","Optical coherence tomography","Computer science","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-12","doi":"https://doi.org/10.3892/etm.2020.9118","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4281609485","name":"[Retracted] Privacy Protection of Medical Service Data Based on Blockchain and Artificial Intelligence in the Era of Smart Medical Care","source":"openalex","abstract":"Smart medical care will realize the self‐management, selection, and optimization of related things with more thorough induction, more comprehensive interconnection, and more intelligent insight, so that people can get an increasingly personalized medical and health service experience. 5G‐enabled Internet of Things and AI (artificial intelligence) will continue to drive innovative applications in the medical industry. Access control and sharing of medical data is of great significance to the development of smart medical care, but the security problems in medical data sharing cannot be ignored. In this paper, a privacy protection scheme of medical service data based on blockchain and AI is proposed. The user chain is constructed as a public chain. In the user chain, the data privacy of users is protected, and users can safely transmit data to doctors and realize the management of session keys. CNN (convolutional neural network) privacy protection protocol based on homomorphic encryption can protect users’ privacy input, server model parameters, and calculated intermediate values. Experimental analysis and comparison with other schemes show that the scheme in this model is safer and more practical.","url":"https://doi.org/10.1155/2022/5295801","authors":["Wu Bo","Yong Pi","Jinhong Chen"],"tags":["Computer science","Computer security","Homomorphic encryption","Encryption","Data sharing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1155/2022/5295801","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4293382414","name":"Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view","source":"openalex","abstract":"This study attempts to understand the role of artificial intelligence-driven big data analytics capability in humanitarian relief operations. These disasters play an important role in mobilizing several organizations to counteract them, but the organizations often find it hard to strike a fine balance between agility and resilience. Operations Management Scholars’ opinion remains divided between responsiveness and efficiency. However, to manage unexpected events like disasters, organizations need to be agile and resilient. In previous studies, scholars have adopted the resource-based view or dynamic capability view to explain the combination of resources and capabilities (i.e., technology, agility, and resilience) to explain their performance. However, following some recent scholarly debates, we argue that organizational theories like the resource-based view or dynamic capability view are not suitable enough to explain humanitarian supply chain performance. As the underlying assumptions of the commercial supply chain do not hold true in the case of the humanitarian supply chain. We note this as a potential research gap in the existing literature. Moreover, humanitarian organizations remain sceptical regarding the adoption of artificial intelligence-driven big data analytics capability (AI-BDAC) in the decision-making process. To address these potential gaps, we grounded our theoretical model in the practice-based view which is proposed as an appropriate lens to examine the role of practices that are not rare and are easy to imitate in performance. We used Partial Least Squares (PLS) to test our theoretical model and research hypotheses, using 171 useable responses gathered through a web survey of international non-governmental organizations (NGOs). The findings of our study suggest that AI-BDAC is a significant determinant of agility, resilience, and performance of the humanitarian supply chain. Furthermore, the reduction of the level of information complexity (IC) on the paths joining agility, resilience, and performance in the humanitarian supply chain. These results offer some useful theoretical contributions to the contingent view of the practice-based view. In a way, we have tried to establish empirically that the humanitarian supply chain designs are quite different from their commercial counterparts. Hence, the use of a resource-based view or dynamic capability view as theoretical lenses may not help capture true perspectives. Thus, the use of a practice-based view as an alternative theoretical lens provides a better understanding of humanitarian supply chains. We have further outlined the limitations and the future research directions of the study.","url":"https://doi.org/10.1016/j.ijpe.2022.108618","authors":["Rameshwar Dubey","David Bryde","Yogesh K. Dwivedi","Gary Graham","Cyril Foropon"],"tags":["Big data","Agile software development","Supply chain","Dynamic capabilities","Resilience (materials science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-01","doi":"https://doi.org/10.1016/j.ijpe.2022.108618","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4291719085","name":"Trends in artificial intelligence in nursing: Impacts on nursing management","source":"openalex","abstract":"OBJECTIVE: To investigate the academic use of artificial intelligence (AI) in nursing. BACKGROUND: A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing. METHOD: A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed. RESULTS: Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse. CONCLUSION: Research articles were mainly published in Nurse Education Today. Research topics such as AI-assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high-quality research beyond the scope of medical education, as well as on cross-domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies. IMPLICATIONS FOR NURSING MANAGEMENT: This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.","url":"https://doi.org/10.1111/jonm.13770","authors":["Ching‐Yi Chang","Hsiu‐Ju Jen","Wen‐Song Su"],"tags":["CINAHL","Scopus","MEDLINE","Nursing research","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-15","doi":"https://doi.org/10.1111/jonm.13770","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4409512881","name":"Artificial Intelligence in Medical Education","source":"openalex","abstract":"OBJECTIVE: To explore the understanding of medical students regarding the integration of AI in medical education. STUDY DESIGN: Mixed methods, explanatory sequential study. Place and Duration of the Study: This study was conducted from March to May 2024 at the CMH Medical College, Lahore, Pakistan. METHODOLOGY: A total of 152 undergraduate medical students were recruited. Quantitative surveys were used to measure AI-related attitudes and awareness of the students through a Likert scale, while in-depth insights into challenges and educational impact were obtained through open-ended questions. SPSS version 27 was used for the analysis of quantitative and Nvivo-11 for qualitative data. RESULTS: The study consisted of 152 participants. Most of them 139 (95.9%) had good knowledge about AI and expressed positive views. The majority believed that AI improves medical concepts, patient outcomes, and healthcare delivery, and helps in early disease detection. They agreed that AI will be effective in education 114 (75%) and will have a positive impact on learning experience 111 (73%) and future medical practice 94 (62%), so, it should be mandatory in medical education 90 (59%). Around half of the participants perceived potential job displacement and ethical dilemmas as a challenge due to AI in the future. Major themes emerging from qualitative data were AI-related challenges, topics of interest, and future expectations. CONCLUSION: The study showed positive views and attitudes towards AI integration in medical education. Participants highlighted various benefits and perceived challenges including ethical concerns and resource limitations. As medical education advances, this subject needs to be studied more for its successful integration into medical education for better results. KEY WORDS: Understanding, Awareness, Artificial intelligence, Medical education, Medical students.","url":"https://doi.org/10.29271/jcpsp.2025.04.503","authors":["M. Asif Farooq","Ambreen Usmani"],"tags":["Psychology","Medical education","Computer science","Mathematics education","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.29271/jcpsp.2025.04.503","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4313444849","name":"Artificial Intelligence in Africa: Emerging Challenges","source":"openalex","abstract":"Abstract In the current African society, Artificial Intelligence (AI) is becoming more popular and seeking to cover all facets of human activity. The adoption and use of these modern technologies in the African context are currently low due to some emerging challenges. Consequently, these difficulties may have a direct influence on African economic development. In this paper, we highlight the challenges facing the adoption of AI technologies in Africa which include skills acquisition, lack of structured data ecosystem, ethics, government policies, insufficient infrastructure and network connectivity, uncertainty, and user attitude. Finally, various solutions to enhance AI adoption in Africa were then proposed.","url":"https://doi.org/10.1007/978-3-031-08215-3_5","authors":["Abejide Ade-Ibijola","Chinedu Wilfred Okonkwo"],"tags":["Government (linguistics)","Context (archaeology)","Cover (algebra)","Emerging technologies","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-31","doi":"https://doi.org/10.1007/978-3-031-08215-3_5","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4388570274","name":"Strategies of Artificial intelligence tools in the domain of nanomedicine","source":"openalex","abstract":"Nanomedicine is a field of medicine that uses nanotechnology to develop new diagnostic tools and therapies for a wide range of medical conditions. It encompasses a variety of different approaches, including the use of nanoparticles , nano-robots, and nanodevices. Some examples of how nanotechnology is being used in medicine include drug delivery, diagnostics imaging, tissue engineering, and biomedical devices. One of the main advantages of using nanomedicine-based drug delivery is the ability to deliver drugs at fixed and controlled doses. This is achieved by engineering nanoparticles to release drugs at a specific rate, which can be attuned to match the desires of the patient. Determining the optimal combination of nanotherapy, dosages, and administration schedule can be challenging, as it requires a thorough understanding of the pharmacokinetics and pharmacodynamics of the drugs, as well as the patient's individual physiognomies and the stage of the disease. To overcome this challenge, researchers and practitioners are increasingly using computational models, Artificial intelligence and machine learning algorithms, and personalized medicine approaches to predict the optimal drug combination, dosage, and administration schedule for each patient. In this review, we were given the outline of the different Artificial intelligence tools for the prediction of nanomedicine in the field of various medical applications.","url":"https://doi.org/10.1016/j.jddst.2023.105157","authors":["Mohammad Habeeb","Huay Woon You","Mutheeswaran Umapathi","R. Kishore Kanna","Hariyadi","Shweta Mishra"],"tags":["Nanomedicine","Drug delivery","Personalized medicine","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-10","doi":"https://doi.org/10.1016/j.jddst.2023.105157","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4402927576","name":"Artificial intelligence scribe: A new era in medical documentation","source":"openalex","abstract":"The high workloads involved in clinical documentation represent one of the major factors contributing to the significant escalation of clinician burnout. The emergence of artificial intelligence (AI) has provided new avenues for relieving this burden by automating certain tasks like clinical documentation through the generation of clinical notes from a transcript of a clinical encounter. The advances in large language models (LLMs) have led to the emergence of such startups, but they come with their own set of challenges, predominantly surrounding the concerns of documentation accuracy, completeness, and data security. These can be addressed with a multi-faceted approach which could include fine-tuning the currently available models; using domain-specific models and in-house AI systems to ensure data security; and involving smaller LLMs and clinicians in the development and implementation of such systems. We can imagine a future where these systems are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication.","url":"https://doi.org/10.36922/aih.3103","authors":["Khalid Nawab"],"tags":["Documentation","Computer science","Psychology","Programming language"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-27","doi":"https://doi.org/10.36922/aih.3103","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2320263878","name":"Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems","source":"openalex","abstract":"Artificial Intelligence has significantly gained grounds in our daily livelihood in this age of information and technology. As with any field of study, evolution takes place in terms of breakthrough or developmental research leading to advancement and friendly usability of that specific technology. Problems from different areas have been successfully solved using Artificial Intelligence algorithms. In order to use AI algorithms in solving Personalized Medicine problems such as; disease detection or prediction, accurate disease diagnosis, and treatment optimization, the choice of the algorithm influenced by its ability and applicability matters. This paper reviews the application and ability of artificial neural network (ANN), support vector machines (SVM), Na ve Bayes, and fuzzy logic in solving personalized medicine problems, and shows that the obtained results meet expectations. Also, the achievement from the previous studies encourages developers and researchers to use these algorithms in solving Medical and Personalized Medicine problems.","url":"https://doi.org/10.7763/ijcte.2015.v7.999","authors":["Jamilu Awwalu","Ali Garba Garba","Anahita Ghazvini","Rose Atuah"],"tags":["Computer science","Personalized medicine","Artificial intelligence","Machine learning","Algorithm"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2014-12-03","doi":"https://doi.org/10.7763/ijcte.2015.v7.999","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4405033145","name":"Explainable Artificial Intelligence for Medical Applications: A Review","source":"openalex","abstract":"The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.","url":"https://doi.org/10.48550/arxiv.2412.01829","authors":["Qiyang Sun","Alican Akman","Björn W. Schuller"],"tags":["Data science","Computer science","Management science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-15","doi":"https://doi.org/10.48550/arxiv.2412.01829","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3100621495","name":"The Impact of Artificial Intelligence on Innovation","source":"openalex","abstract":"Artificial intelligence has the potential to greatly increase the efficiency of the current life in which we live. However, it may result in more impacts through its application as the modern mode in invention and bring a new perspective of the existing innovation processes in the organization of R & D. Application of machines' intelligence such as robots from the recent development is a vivid example of invention brought about by the innovation filtered via artificial intelligence. Innovative ways through invention have a sense of replacement in man's duties in the world's varied economic sectors. Large datasets and algorithms will be used in research industries, and the latter will result in potential racing, monitored incentives by large companies, and particular algorithms. However, transparency and transfer of information between public and private will be the engine source to stimulate healthy inventions and innovation programs shortly. Rapid advancement in the artificial intelligence arena has significant sound impacts on society as far as the economy is concerned. Production and characteristics of many products and services have a high potentiality to be directly influenced by these innovations, and important productivity, competition, and employment implications. Even though these innovations will positively influence the largest proportion of human lives, artificial intelligence (A.I.) can potentially change its innovation processes, accompanied by approximately thoughtful consequences, and may gradually dominate the direct consequence.","url":"https://doi.org/10.18034/gdeb.v7i2.515","authors":["Takudzwa Fadziso"],"tags":["Productivity","Transparency (behavior)","Incentive","Competition (biology)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-12-31","doi":"https://doi.org/10.18034/gdeb.v7i2.515","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2958682939","name":"Artificial intelligence algorithm for predicting mortality of patients with acute heart failure","source":"openalex","abstract":"AIMS: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). METHODS AND RESULTS: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines-Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876-0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720-0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001). CONCLUSION: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.","url":"https://doi.org/10.1371/journal.pone.0219302","authors":["Joon-myoung Kwon","Kyung‐Hee Kim","Kyung-Hee Kim","Ki‐Hyun Jeon","Sang Eun Lee","Hae‐Young Lee","Hyun‐Jai Cho","Jin‐Oh Choi","Eun‐Seok Jeon","Min‐Seok Kim","Jae‐Joong Kim","Kyung‐Kuk Hwang","Shung Chull Chae","Sang Hong Baek","Seok‐Min Kang","Dong‐Ju Choi","Byung‐Su Yoo","Kye Hun Kim","Kye Hun Kim","Hyun‐Young Park","Myeong‐Chan Cho","Byung‐Hee Oh"],"tags":["Heart failure","Confidence interval","Receiver operating characteristic","Medicine","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-07-08","doi":"https://doi.org/10.1371/journal.pone.0219302","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4387873179","name":"Toward Explainable Artificial Intelligence for Precision Pathology","source":"openalex","abstract":"The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.","url":"https://doi.org/10.1146/annurev-pathmechdis-051222-113147","authors":["Frederick Klauschen","Jonas Dippel","Philipp Keyl","Philipp Jurmeister","Michael Bockmayr","Andreas Möck","Oliver Buchstab","Maximilian Alber","Lukas Ruff","Grégoire Montavon","Klaus‐Robert Müller"],"tags":["Artificial intelligence","Computer science","Profiling (computer programming)","Precision medicine","Digital pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-23","doi":"https://doi.org/10.1146/annurev-pathmechdis-051222-113147","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4294243134","name":"Experimental evidence of effective human–AI collaboration in medical decision-making","source":"openalex","abstract":"Artificial Intelligence (AI) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between MDs and AI enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human-AI collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an AI support system. Endoscopists were influenced by AI ([Formula: see text]), but not erratically: they followed the AI advice more when it was correct ([Formula: see text]) than incorrect ([Formula: see text]). Endoscopists achieved this outcome through a weighted integration of their and the AI opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human-AI hybrid team to outperform both agents taken alone. We discuss the features of the human-AI interaction that determined this favorable outcome.","url":"https://doi.org/10.1038/s41598-022-18751-2","authors":["Carlo Reverberi","Tommaso Rigon","Aldo Solari","Cesare Hassan","Paolo Cherubini","GI Genius CADx Study Group","Giulio Antonelli","Halim Awadie","Sebastian Bernhofer","Sabela Carballal","Mário Dinis‐Ribeiro","A Fernández-Clotet","Glòria Fernández‐Esparrach","Ian M. Gralnek","Yuta Higasa","Taku Hirabayashi","Tatsuki Hirai","Mineo Iwatate","Miki Kawano","Markus Mader","A Maieron","Sebastian Mattes","Tastuya Nakai","Íngrid Ordás","Raquel Ortigão","Oswaldo Ortiz Zúñiga","María Pellisé","Cláudia Lúcia de Oliveira Pinto","Florian Riedl","Ariadna Sánchez","Emanuel Steiner","Yukari Tanaka","Andrea Cherubini"],"tags":["Medical decision making","Clinical decision making","Data science","Computer science","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-02","doi":"https://doi.org/10.1038/s41598-022-18751-2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386168937","name":"Rise of artificial general intelligence: risks and opportunities","source":"openalex","abstract":"Artificial intelligence is making extraordinary progress with an unprecedented rate, reaching and surpassing human capabilities in many tasks previously considered unattainable by machines, as language translation, music composition, object detection, medical diagnoses, software programming, and many others. Some people are excited about these results, while others are raising serious concerns for possible negative impacts in our society. This article addresses several questions that are often raised about intelligent machines: Will machines ever surpass human intellectual capacities? What will happen next? What will be the impact in our society? What are the jobs that artificial intelligence puts at risk? Reasoning about these questions is of fundamental importance to predict possible future scenarios and prepare ourselves to face the consequences.","url":"https://doi.org/10.3389/frai.2023.1226990","authors":["Giorgio Buttazzo"],"tags":["Computer science","Raising (metalworking)","Face (sociological concept)","Object (grammar)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-25","doi":"https://doi.org/10.3389/frai.2023.1226990","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4368275176","name":"An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study","source":"openalex","abstract":"Introduction: Artificial intelligence (AI) is increasingly used in healthcare. AI-based chatbots can act as automated conversational agents, capable of promoting health and providing education at any time. The objective of this study was to develop and evaluate a user-friendly medical chatbot (prostate cancer communication assistant (PROSCA)) for provisioning patient information about early detection of prostate cancer (PC). Methods: The chatbot was developed to provide information on prostate diseases, diagnostic tests for PC detection, stages, and treatment options. Ten men aged 49 to 81 years with suspicion of PC were enrolled in this study. Nine of ten patients used the chatbot during the evaluation period and filled out the questionnaires on usage and usability, perceived benefits, and potential for improvement. Results: The chatbot was straightforward to use, with 78% of users not needing any assistance during usage. In total, 89% of the chatbot users in the study experienced a clear to moderate increase in knowledge about PC through the chatbot. All study participants who tested the chatbot would like to re-use a medical chatbot in the future and support the use of chatbots in the clinical routine. Conclusions: Through the introduction of the chatbot PROSCA, we created and evaluated an innovative evidence-based health information tool in the field of PC, allowing targeted support for doctor-patient communication and offering great potential in raising awareness, patient education, and support. Our study revealed that a medical chatbot in the field of early PC detection is readily accepted and benefits patients as an additional informative tool.","url":"https://doi.org/10.1177/20552076231173304","authors":["Magdalena Görtz","Kilian Baumgärtner","T Schmid","Marc Muschko","Philipp Woessner","Axel Gerlach","Michael Byczkowski","Holger Sültmann","Stefan Duensing","Markus Hohenfellner"],"tags":["Chatbot","Prostate cancer","Cancer","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1177/20552076231173304","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4315498620","name":"Are ChatGPT's knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study","source":"openalex","abstract":"This study aimed to compare the knowledge and interpretation ability of ChatGPT, a language model of artificial general intelligence, with those of medical students in Korea by administering a parasitology examination to both ChatGPT and medical students. The examination consisted of 79 items and was administered to ChatGPT on January 1, 2023. The examination results were analyzed in terms of ChatGPT’s overall performance score, its correct answer rate by the items’ knowledge level, and the acceptability of its explanations of the items. ChatGPT’s performance was lower than that of the medical students, and ChatGPT’s correct answer rate was not related to the items’ knowledge level. However, there was a relationship between acceptable explanations and correct answers. In conclusion, ChatGPT’s knowledge and interpretation ability for this parasitology examination were not yet comparable to those of medical students in Korea.","url":"https://doi.org/10.3352/jeehp.2023.20.01","authors":["Sun Huh"],"tags":["Final examination","Interpretation (philosophy)","Medical education","Psychology","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-11","doi":"https://doi.org/10.3352/jeehp.2023.20.01","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4313830977","name":"Opportunities and challenges in application of artificial intelligence in pharmacology","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s43440-022-00445-1","authors":["Mandeep Kumar","T. P. Nhung Nguyen","Jasleen Kaur","Thakur Gurjeet Singh","Divya Soni","Randhir Singh","Puneet Kumar"],"tags":["Artificial intelligence","Computer science","Machine learning","Identification (biology)","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-09","doi":"https://doi.org/10.1007/s43440-022-00445-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4324359891","name":"Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis","source":"openalex","abstract":"Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.","url":"https://doi.org/10.3390/foods12061242","authors":["Zhe Liu","Shuzhe Wang","Yudong Zhang","Yichen Feng","Jiajia Liu","Hengde Zhu"],"tags":["Food safety","Web of science","Traceability","Bibliometrics","Agriculture"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-14","doi":"https://doi.org/10.3390/foods12061242","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3197417533","name":"An overview of deep learning in medical imaging","source":"openalex","abstract":"Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growth in recent years. The scientific community has focused its attention on DL due to its versatility, high performance, high generalization capacity, and multidisciplinary uses, among many other qualities. In addition, a large amount of medical data and the development of more powerful computers has also fostered an interest in this area. This paper presents an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence. In the first instance, the fundamental concept of artificial neural networks is introduced, progressively covering convolutional structures, recurrent networks, attention models, up to the current structure known as the Transformer. Secondly, all the basic concepts involved in training and other common elements in the design of the architectures are introduced. Thirdly, some of the key elements in modern networks for medical image classification and segmentation are shown. Subsequently, a review of some applications realized in the last years is shown, where the main features related to DL are highlighted. Finally, the perspectives and future expectations of deep learning are presented.","url":"https://doi.org/10.1016/j.imu.2021.100723","authors":["Andrés Anaya-Isaza","Leonel Mera-Jiménez","Martha Zequera-Diaz"],"tags":["Deep learning","Artificial intelligence","Computer science","Convolutional neural network","Generalization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1016/j.imu.2021.100723","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3112631310","name":"Artificial Intelligence in mental health and the biases of language based models","source":"openalex","abstract":"BACKGROUND: The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. DESIGN/METHODS: A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. RESULTS: Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. CONCLUSION: Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.","url":"https://doi.org/10.1371/journal.pone.0240376","authors":["Isabel Straw","Chris Callison-Burch"],"tags":["Mental health","Psychology","Computer science","Natural language processing","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-17","doi":"https://doi.org/10.1371/journal.pone.0240376","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3217710805","name":"Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps","source":"openalex","abstract":"In order to determine the health status of the neonates, studies focus on either statistical behavior of the thermograms' temperature distributions, or just correct classifications of the thermograms. However, there exists always a lack of explain-ability for classification processes. Especially in the medical studies, doctors need explanations to assess the possible results of the decisions. Presenting our new study, how Convolutional Neural Networks (CNNs) decide the health status of neonates has been shown for the first time by using Class Activation Maps (CAMs). VGG16 which is one of the pre-trained models has been selected as a CNN model and the last layers of the VGG16 have been tuned according to CAMs. When the model was trained for 50 epochs, train-validation accuracies reached over 95% and test sensitivity-specificity were obtained as 80.701%-96.842% respectively. According to our findings, the CNN learns the temperature distribution of the body by mainly looking at the neck, armpit, and abdomen regions. The focused regions of the healthy babies are armpit and abdomen whereas of the unhealthy babies are neck and abdomen regions. Thus, we can say that the CNN focuses on dedicated regions to monitor the neonates and decides the health status of the neonates.","url":"https://doi.org/10.18280/ts.380502","authors":["Ahmet Haydar Örnek","Murat Ceylan"],"tags":["Artificial intelligence","Class (philosophy)","Pattern recognition (psychology)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-31","doi":"https://doi.org/10.18280/ts.380502","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4376280052","name":"Artificial intelligence in thyroid ultrasound","source":"openalex","abstract":"Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.","url":"https://doi.org/10.3389/fonc.2023.1060702","authors":["Chun‐Li Cao","Qiaoli Li","Tong Jin","Li‐Nan Shi","Wen‐Xiao Li","XU Ya","Jing Cheng","Tingting Du","Jun Li","Xin‐Wu Cui"],"tags":["Artificial intelligence","Workload","Thyroid cancer","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-12","doi":"https://doi.org/10.3389/fonc.2023.1060702","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3207627539","name":"Artificial intelligence and Machine Learning for Real-world problems (A survey)","source":"openalex","abstract":"Today, the use of machine learning and artificial intelligence due to many advantages such as simplicity, high speed, high accuracy in predicting various processes, no need for complex equipment and tools and the availability of many applications in science and fields. Has found various including statistics, mathematics, physics, chemistry, biochemistry, materials engineering, medical engineering, pharmacy and etc. Therefore, in the present era, the study and study of various methods and algorithms of machine learning and artificial intelligence is very important. As a subset of artificial intelligence, machine learning algorithms create mathematical models based on sample data or training data for unpredictable prediction or decision making. One of the most interesting topics that can be focused on with artificial intelligence is predicting and estimating future events. Machine learning provides machines with the ability to learn independently. In other words, the machine can learn from the experiences, observations, and patterns it analyzes based on a set of data. In this regard, the chapter, with the aim of introducing machine learning and artificial intelligence, deals with their application in managing and analyzing the processes of economic systems in real conditions.","url":"https://doi.org/10.59615/ijie.1.3.38","authors":["Javid Ghahremani-Nahr","Hamed Nozari","Mohammad Ebrahim Sadeghi"],"tags":["Artificial intelligence","Machine learning","Computer science","Simplicity","Set (abstract data type)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-07","doi":"https://doi.org/10.59615/ijie.1.3.38","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386607939","name":"A Review on Applications of Artificial Intelligence in Wastewater Treatment","source":"openalex","abstract":"In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.","url":"https://doi.org/10.3390/su151813557","authors":["Yì Wáng","Yuhan Cheng","He Liu","Qing Guo","Chuanjun Dai","Min Zhao","Dezhao Liu"],"tags":["Interpretability","Computer science","Process (computing)","Artificial intelligence","Transparency (behavior)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-11","doi":"https://doi.org/10.3390/su151813557","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3092600108","name":"Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics","source":"openalex","abstract":"There is great potential for artificial intelligence (AI) applications, especially machine learning and natural language processing, in medical imaging. Much attention has been garnered by the image analysis tasks for diagnostic decision support and precision medicine, but there are many other potential applications of AI in radiology and have potential to enhance all levels of the radiology workflow and practice, including workflow optimization and support for interpretation tasks, quality and safety, and operational efficiency. This article reviews the important potential applications of informatics and AI related to process improvement and operations in the radiology department.","url":"https://doi.org/10.1016/j.nic.2020.08.008","authors":["Laurent Létourneau‐Guillon","David Camirand","F Guilbert","Reza Forghani"],"tags":["Workflow","Predictive analytics","Medicine","Analytics","Informatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-07","doi":"https://doi.org/10.1016/j.nic.2020.08.008","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3012602559","name":"AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks","source":"openalex","abstract":"The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.","url":"https://doi.org/10.1101/2020.03.19.20039354","authors":["Shuo Jin","Bo Wang","Haibo Xu","Chuan Luo","Lai Wei","Wei Zhao","Xuexue Hou","Wenshuo Ma","Zhengqing Xu","Zhuozhao Zheng","Wenbo Sun","Lan Lan","Wei Zhang","Xiangdong Mu","Chenxin Shi","Zhongxiao Wang","Jihae Lee","Zijian Jin","Minggui Lin","Hongbo Jin","Liang Zhang","Jun Guo","Benqi Zhao","Zhizhong Ren","Shuhao Wang","Zheng You","Jiahong Dong","Xinghuan Wang","Jianming Wang","Wei Xu"],"tags":["Coronavirus disease 2019 (COVID-19)","Workload","Outbreak","False positive paradox","Pandemic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-23","doi":"https://doi.org/10.1101/2020.03.19.20039354","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3160406418","name":"Artificial Intelligence for Clinical Decision Support in Sepsis","source":"openalex","abstract":"Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.","url":"https://doi.org/10.3389/fmed.2021.665464","authors":["Miao Wu","Xianjin Du","Raymond Gu","Jie Wei"],"tags":["Sepsis","Intensive care medicine","Clinical decision support system","Clinical decision making","Decision support system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-13","doi":"https://doi.org/10.3389/fmed.2021.665464","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3171849353","name":"AI for radiographic COVID-19 detection selects shortcuts over signal","source":"openalex","abstract":"Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious ‘shortcuts’. Because this approach to data collection has also been used to obtain training data for the detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical-imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, because the undesired ‘shortcuts’ learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of machine-learning healthcare models. The urgency of the developing COVID-19 epidemic has led to a large number of novel diagnostic approaches, many of which use machine learning. DeGrave and colleagues use explainable AI techniques to analyse a selection of these approaches and find that the methods frequently learn to identify features unrelated to the actual disease.","url":"https://doi.org/10.1038/s42256-021-00338-7","authors":["Alex J. DeGrave","Joseph D. Janizek","Su‐In Lee"],"tags":["Coronavirus disease 2019 (COVID-19)","Artificial intelligence","Computer science","Software deployment","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-31","doi":"https://doi.org/10.1038/s42256-021-00338-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386304030","name":"Artificial Intelligence in Lung Cancer Screening: The Future Is Now","source":"openalex","abstract":"Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called \"virtual biopsy\". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.","url":"https://doi.org/10.3390/cancers15174344","authors":["Michaela Cellina","Laura Maria Cacioppa","Maurizio Cè","Vittoria Chiarpenello","Marco Costa","Zakaria Vincenzo","Daniele Pais","Maria Vittoria Bausano","Nicolò Rossini","Alessandra Nejar Bruno","Chiara Floridi"],"tags":["Lung cancer screening","Context (archaeology)","Medicine","Lung cancer","Workflow"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-30","doi":"https://doi.org/10.3390/cancers15174344","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4310598034","name":"Integration of Artificial Intelligence Into Sociotechnical Work Systems—Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review","source":"openalex","abstract":"BACKGROUND: When introducing artificial intelligence (AI) into clinical care, one of the main objectives is to improve workflow efficiency because AI-based solutions are expected to take over or support routine tasks. OBJECTIVE: This study sought to synthesize the current knowledge base on how the use of AI technologies for medical imaging affects efficiency and what facilitators or barriers moderating the impact of AI implementation have been reported. METHODS: In this systematic literature review, comprehensive literature searches will be performed in relevant electronic databases, including PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, and CENTRAL. Studies in English and German published from 2000 onwards will be included. The following inclusion criteria will be applied: empirical studies targeting the workflow integration or adoption of AI-based software in medical imaging used for diagnostic purposes in a health care setting. The efficiency outcomes of interest include workflow adaptation, time to complete tasks, and workload. Two reviewers will independently screen all retrieved records, full-text articles, and extract data. The study's methodological quality will be appraised using suitable tools. The findings will be described qualitatively, and a meta-analysis will be performed, if possible. Furthermore, a narrative synthesis approach that focuses on work system factors affecting the integration of AI technologies reported in eligible studies will be adopted. RESULTS: This review is anticipated to begin in September 2022 and will be completed in April 2023. CONCLUSIONS: This systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements in medical imaging through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with a particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings. TRIAL REGISTRATION: PROSPERO CRD42022303439; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=303439. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40485.","url":"https://doi.org/10.2196/40485","authors":["Katharina Wenderott","Nikoloz Gambashidze","Matthias Weigl"],"tags":["Workflow","PsycINFO","Computer science","Applications of artificial intelligence","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-20","doi":"https://doi.org/10.2196/40485","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4220850650","name":"Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review","source":"openalex","abstract":"BACKGROUND: The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. OBJECTIVE: The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. METHODS: The PubMed database was searched for citations indexed with \"artificial intelligence\" and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: \"obstetrics\"; \"gynecology\"; \"reproductive techniques, assisted\"; or \"pregnancy.\" All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. RESULTS: The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. CONCLUSIONS: In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.","url":"https://doi.org/10.2196/35465","authors":["Ferdinand Dhombres","Jules Bonnard","Kévin Bailly","P Maurice","Aris T. Papageorghiou","Jean‐Marie Jouannic"],"tags":["Obstetrics and gynaecology","Obstetrics","Medicine","Medical education","Gynecology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-15","doi":"https://doi.org/10.2196/35465","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2098275325","name":"The socio-organizational age of artificial intelligence in medicine","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0933-3657(01)00074-4","authors":["Mario Stefanelli"],"tags":["Workflow","Health care","Guideline","Knowledge management","Evidence-based medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2001-08-01","doi":"https://doi.org/10.1016/s0933-3657(01)00074-4","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4406062609","name":"Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative Artificial Intelligence Basics","source":"openalex","abstract":"This manuscript serves as an introduction to a comprehensive 7-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary. The article also provides a broad overview of the main domains in the AI-ML field, encompassing both generative and nongenerative (traditional) AI, thereby serving as a primer to the other 6 review articles in this series that describe the details about statistics, regulations, bias, ethical dilemmas, and ML-Ops in AI-ML. The intent of these review articles is to better equip individuals who are or will be working in an AI-enabled health care system.","url":"https://doi.org/10.1016/j.modpat.2024.100688","authors":["Hooman H. Rashidi","Joshua Pantanowitz","Matthew G Hanna","Ahmad P. Tafti","Parth Sanghani","Adam Buchinsky","Brandon D. Fennell","Mustafa Deebajah","Sarah Wheeler","Thomas M. Pearce","Ibrahim Abukhiran","Scott Robertson","Octavia M. Peck Palmer","Mert Gur","Nam K. Tran","Liron Pantanowitz"],"tags":["Generative grammar","Artificial intelligence","Pathology","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-04","doi":"https://doi.org/10.1016/j.modpat.2024.100688","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4399054302","name":"Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities","source":"openalex","abstract":"The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.","url":"https://doi.org/10.3390/diagnostics14111100","authors":["Connor D. Flynn","Dingran Chang"],"tags":["Computer science","Biosensor","Field (mathematics)","Health care","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-25","doi":"https://doi.org/10.3390/diagnostics14111100","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4200534512","name":"Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11596-021-2485-0","authors":["Yi Xie","Lin Lu","Fei Gao","Shuangjiang He","Huijuan Zhao","Ying Fang","Jiaming Yang","Ying An","Zhewei Ye","Zhe Dong"],"tags":["Blockchain","Wearable computer","Health care","Wearable technology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-01","doi":"https://doi.org/10.1007/s11596-021-2485-0","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3179570119","name":"Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00464-021-08619-3","authors":["Ke Cheng","Jiaying You","Shangdi Wu","Zixin Chen","Zijian Zhou","Jingye Guan","Bing Peng","Xin Wang"],"tags":["Cholecystectomy","Medicine","Artificial intelligence","Deep learning","Laparoscopic cholecystectomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-06","doi":"https://doi.org/10.1007/s00464-021-08619-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4390749074","name":"Creativity and artificial intelligence: A multilevel perspective","source":"openalex","abstract":"Artificial intelligence is likely to revolutionize multiple aspects of organizational creativity. Through a multilevel theoretical lens, the present paper reviews the extant body of knowledge on creativity at individual, team and organizational levels, and draws a series of propositions on how the implementation of artificial intelligence may affect each level. Spanning cognitive, behavioural and psychological domains, our propositions aim at directing future research efforts on important creativity‐related areas likely to be affected by artificial intelligence, including the trade‐off between convergent and divergent thinking, the distribution of skills within groups, and the absorptive capacity of organizations.","url":"https://doi.org/10.1111/caim.12580","authors":["Luca Grilli","Mattia Pedota"],"tags":["Creativity","Extant taxon","Psychology","Perspective (graphical)","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-09","doi":"https://doi.org/10.1111/caim.12580","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2112152950","name":"A new method in automatic generation of medical protocols using artificial intelligence tools and a data manager","source":"openalex","abstract":"Medical protocols and guidelines are means through which the health care quality is improved. Implementing them with the help of computers, their acceptance and the applicability have increased over time. A new method for implementing medical protocols and guidelines has been developed using artificial intelligence and a data manager. This method uses a connector between the expert system and the databases, helping them to communicate. The automatic generator knows how a protocol is represented, how the connector can be integrated with those representations and how can the data be obtained. Using this, it can resolve the problem of local adaptation and standardization of medical care.","url":"https://doi.org/10.1109/icccyb.2010.5491290","authors":["V. Gomoi","Vasile Stoicu-Tivadar"],"tags":["Standardization","Computer science","Protocol (science)","Adaptation (eye)","Generator (circuit theory)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2010-05-01","doi":"https://doi.org/10.1109/icccyb.2010.5491290","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4366415335","name":"Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence","source":"openalex","abstract":"The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.","url":"https://doi.org/10.3390/brainsci13040683","authors":["Tehseen Mazhar","Dhani Bux Talpur","Tamara Al Shloul","Yazeed Yasin Ghadi","Inayatul Haq","Inam Ullah","Khmaies Ouahada","Habib Hamam"],"tags":["Variety (cybernetics)","Computer science","Internet of Things","Computer security","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-19","doi":"https://doi.org/10.3390/brainsci13040683","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4283162459","name":"Applications of artificial intelligence in obstetrics","source":"openalex","abstract":"Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.","url":"https://doi.org/10.14366/usg.22063","authors":["Ho Yeon Kim","Geum Joon Cho","Han Sung Kwon"],"tags":["Medicine","Ultrasonography","Cardiotocography","Magnetic resonance imaging","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-20","doi":"https://doi.org/10.14366/usg.22063","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4285799063","name":"Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers","source":"openalex","abstract":"Abstract Artificial intelligence (AI) offers much promise for improving healthcare. However, it runs the looming risk of causing individual and societal harms; for instance, exacerbating inequalities amongst minority groups, or enabling compromises in the confidentiality of patients’ sensitive data. As such, there is an expanding, unmet need for ensuring AI for healthcare is developed in concordance with human values and ethics. Augmenting “principle-based” guidance that highlight adherence to ethical ideals (without necessarily offering translation into actionable practices), we offer a solution-based framework for operationalising ethics in AI for healthcare. Our framework is built from a scoping review of existing solutions of ethical AI guidelines, frameworks and technical solutions to address human values such as self-direction in healthcare. Our view spans the entire length of the AI lifecycle: data management, model development, deployment and monitoring. Our focus in this paper is to collate actionable solutions (whether technical or non-technical in nature), which can be steps that enable and empower developers in their daily practice to ensuring ethical practices in the broader picture. Our framework is intended to be adopted by AI developers, with recommendations that are accessible and driven by the existing literature. We endorse the recognised need for ‘ethical AI checklists’ co-designed with health AI practitioners, which could further operationalise the technical solutions we have collated. Since the risks to health and wellbeing are so large, we believe a proactive approach is necessary for ensuring human values and ethics are appropriately respected in AI for healthcare.","url":"https://doi.org/10.1007/s43681-022-00195-z","authors":["Pravik Solanki","John Grundy","Waqar Hussain"],"tags":["Health care","Confidentiality","Engineering ethics","Knowledge management","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-19","doi":"https://doi.org/10.1007/s43681-022-00195-z","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3005386144","name":"Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality?","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.retram.2020.01.002","authors":["Tamim Alsuliman","Dania Humaidan","Layth Sliman"],"tags":["Artificial intelligence","Interpretation (philosophy)","Health care","Field (mathematics)","Service (business)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-02-03","doi":"https://doi.org/10.1016/j.retram.2020.01.002","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3163849331","name":"Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal","source":"openalex","abstract":"Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research.Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing.Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.","url":"https://doi.org/10.1080/17453674.2021.1918389","authors":["Jakub Olczak","John Pavlopoulos","Jasper Prijs","Frank F. A. IJpma","Job N. Doornberg","Claes Lundström","Joel Hedlund","Max Gordon"],"tags":["Artificial intelligence","Checklist","Health care","Context (archaeology)","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-14","doi":"https://doi.org/10.1080/17453674.2021.1918389","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2981937790","name":"Artificial Intelligence in Diabetic Eye Disease Screening","source":"openalex","abstract":"Systematic or national screening programs for diabetic retinopathy (DR) and diabetic macular edema (DME), using digital fundus photography and optical coherence tomography (OCT), are currently implemented at primary care level, aiming to provide timely referral for vision-threatening DR and DME to ophthalmologists for timely treatment and vision loss prevention. However, interpretation of retinal images requires specialized knowledge and expertise in diabetic eye disease. Furthermore, current DR screening programs are capital- and labor-intensive, which makes it difficult to rapidly scale up and expand diabetic eye screening to meet the needs of this growing global epidemic. Deep learning (DL), a new branch of machine learning technology under the broad term of artificial intelligence (AI), has made remarkable breakthrough in medical imaging in particular for pattern recognition and image classification. In ophthalmology, AI and DL technology has been developed from big image datasets in assessment of retinal photographs for detection and screening of DR as well as the segmentation and assessment of OCT images for diagnosis and screening of DME. This review aimed to summarize the current progress and the development of using AI and DL technology for diabetic eye disease screening as well as current challenges in the actual implementation of DL in screening programs, and translating DL research into direct clinical applications of screening in a community setting.","url":"https://doi.org/10.22608/apo.201976","authors":["Carol Y. Cheung","Fangyao Tang","Daniel Shu Wei Ting","Gavin Siew Wei Tan","Tien Yin Wong"],"tags":["Diabetic retinopathy","Medicine","Fundus photography","Optometry","Optical coherence tomography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.22608/apo.201976","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4396768244","name":"Role of artificial intelligence in revolutionizing drug discovery","source":"openalex","abstract":"The application of artificial intelligence (AI) in medicine, particularly through machine learning (ML), marked a significant progression in drug discovery. AI acts as a powerful catalyst in narrowing the gap between disease understanding and the identification of potential therapeutic agents. This review provides an inclusive summary of the latest advancements in AI and its application in drug discovery. We examine the various stages of the drug discovery process, starting from disease identification and encompassing diagnosis, target identification, screening, and lead discovery. AI's capability to analyze extensive datasets and discern patterns is essential in these stages, enhancing predictions and efficiencies in disease identification, drug discovery, and clinical trial management. The role of AI in expediting drug development is emphasized, highlighting its potential to analyze vast data volumes, thus reducing the time and costs associated with new drug market introduction. The importance of data quality, algorithm training, and ethical considerations, especially in patient data handling during clinical trials, is addressed. By considering these factors, AI promises to transform drug development, offering significant benefits to patients and society.","url":"https://doi.org/10.1016/j.fmre.2024.04.021","authors":["Ashfaq Ur Rehman","Mingyu Li","Binjian Wu","Yasir Ali","Salman Rasheed","Sana Shaheen","Xinyi Liu","Ray Luo","Jian Zhang"],"tags":["Expediting","Drug discovery","Identification (biology)","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-09","doi":"https://doi.org/10.1016/j.fmre.2024.04.021","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4394014418","name":"Application of Artificial Intelligence in Tissue Engineering","source":"openalex","abstract":"Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.","url":"https://doi.org/10.1089/ten.teb.2024.0022","authors":["Reza Bagherpour","Ghasem Bagherpour","Parvin Mohammadi"],"tags":["Tissue engineering","Regenerative medicine","Computer science","Biomaterial","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-06","doi":"https://doi.org/10.1089/ten.teb.2024.0022","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3039183963","name":"Artificial intelligence is poised to revolutionize human liver allocation and decrease medical costs associated with liver transplantation","source":"openalex","abstract":"","url":"https://doi.org/10.21037/hbsn-20-458","authors":["Robert Sucher","Elisabeth Sucher"],"tags":["Medicine","Liver transplantation","Transplantation","Intensive care medicine","Human liver"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-25","doi":"https://doi.org/10.21037/hbsn-20-458","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2977522001","name":"Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement","source":"openalex","abstract":"This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.","url":"https://doi.org/10.1016/j.carj.2019.08.010","authors":["J. Raymond Geis","Adrian P. Brady","Carol C. Wu","Jack Spencer","Erik Ranschaert","Jacob L. Jaremko","Steve G. Langer","Andrea Borondy Kitts","Judy Birch","William Shields","R. van den Hoven van Genderen","Elmar Kotter","Judy Wawira Gichoya","Tessa S. Cook","Matthew B. Morgan","An Tang","Nabile Safdar","Marc Kohli"],"tags":["Medicine","Transparency (behavior)","Dignity","Informatics","Accountability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-01","doi":"https://doi.org/10.1016/j.carj.2019.08.010","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3027323359","name":"Artificial Intelligence and Medical Innovation","source":"openalex","abstract":"","url":"https://doi.org/10.47102/annals-acadmed.sg.2019155","authors":["Eng Chye Tan"],"tags":["Medicine","MEDLINE","Medical emergency","Law","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-30","doi":"https://doi.org/10.47102/annals-acadmed.sg.2019155","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3133974502","name":"Explained Artificial Intelligence Helps to Integrate Artificial and Human Intelligence Into Medical Diagnostic Systems: Analytical Review of Publications","source":"openalex","abstract":"Artificial intelligence-based medical systems can by now diagnose various disorders highly accurately. However, we should stress that despite encouraging and ever improving results, people still distrust such systems. We review relevant publications over the past five years, to identify the main causes of such mistrust and ways to overcome it. Our study showes that the main reasons to distrust these systems are opaque models, blackbox algorithms, and potentially unrepresentful training samples. We demonstrate that explainable artificial intelligence, aimed to create more user-friendly and understandable systems, has become a noticeable new topic in theoretical research and practical development. Another notable trend is to develop approaches to build hybrid systems, where artificial and human intelligence interact according to the teamwork model.","url":"https://doi.org/10.1109/aict50176.2020.9368576","authors":["Mais Farkhadov","Aleksander Eliseev","Н В Петухова"],"tags":["Distrust","Computer science","Artificial intelligence","Data science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-07","doi":"https://doi.org/10.1109/aict50176.2020.9368576","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4405292214","name":"Is artificial intelligence for everyone? Analyzing the role of ChatGPT as a writing assistant for medical students","source":"openalex","abstract":"This study explores the potential impact of ChatGPT on the academic writing skills development of medical students enrolled in a compulsory 3-unit writing course at a medical university. The research focuses on two primary objectives, which are formulated as two research questions: Firstly, does the use of ChatGPT enhance medical students’ English academic writing skills compared to conventional writing training? Secondly, how does the use of ChatGPT impact on different components of academic writing? A longitudinal intervention design was employed with 83 participants from two writing classes in the experimental and control groups. The findings demonstrated ChatGPT’s significant impact on enhancing medical students’ English academic writing skills, with large effect sizes. ChatGPT enhanced students’ writing skills, especially content, organization, vocabulary, and mechanics in the experimental group, while its impact on language use is limited. AI tools like ChatGPT can be valuable in assisting with certain aspects of writing, but they should not be considered a one-size-fits-all solution for enhancing writing skills. The result of the study can be beneficial for educators, particularly those interested in teaching writing.","url":"https://doi.org/10.3389/feduc.2024.1457744","authors":["Zahra Shahsavar","Reza Kafipour","Laleh Khojasteh","Farhad Pakdel"],"tags":["Vocabulary","Academic writing","Medical writing","Mathematics education","Professional writing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-11","doi":"https://doi.org/10.3389/feduc.2024.1457744","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386693652","name":"Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.semcancer.2023.09.001","authors":["Jiadong Zhang","Jiaojiao Wu","Xiang Sean Zhou","Feng Shi","Dinggang Shen"],"tags":["Breast cancer","Mammography","Medicine","Artificial intelligence","Interpretability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-12","doi":"https://doi.org/10.1016/j.semcancer.2023.09.001","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4394011823","name":"Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis","source":"openalex","abstract":"BACKGROUND: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY: This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS: AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS: AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.","url":"https://doi.org/10.1002/cam4.7140","authors":["Wu Quanyang","Huang Yao","Wang Sicong","Qi Linlin","Zhang Zewei","Hou Donghui","Hongjia Li","Shijun Zhao"],"tags":["Interpretability","Artificial intelligence","Convolutional neural network","Lung cancer","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-01","doi":"https://doi.org/10.1002/cam4.7140","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4206686640","name":"Defining AMIA’s artificial intelligence principles","source":"openalex","abstract":"Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including \"safe failure\"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.","url":"https://doi.org/10.1093/jamia/ocac006","authors":["Anthony Solomonides","Eileen Koski","Shireen M. Atabaki","Scott S. Weinberg","John D. McGreevey","Joseph Kannry","Carolyn Petersen","Christoph U. Lehmann"],"tags":["Transparency (behavior)","Accountability","Autonomy","Health care","Beneficence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-02","doi":"https://doi.org/10.1093/jamia/ocac006","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4413141291","name":"Machine Learning and Artificial Intelligence in Nanomedicine","source":"openalex","abstract":"Nanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge. Factors such as particle size, surface chemistry, and payload interactions must be optimized, and preclinical results often fail to predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure-function relationships, AI-driven models can rationalize nanoparticle formulation, predict biodistribution, and guide optimal design. Techniques like high-throughput DNA barcoding and automated liquid handling facilitate robust, large-scale data collection, feeding into computational pipelines that expedite discovery while reducing reliance on resource-intensive trial-and-error experiments. AI-based platforms also enable improved modeling of protein corona formation, which profoundly affects nanoparticle immunogenicity and cellular uptake. Despite these advances, challenges persist in data standardization, model generalizability, and establishing a clear regulatory framework since no dedicated U.S. Food and Drug Administration (FDA) guidance addresses the intersection of AI and nanomedicine. Overcoming these limitations requires harmonized data sharing, rigorous in vivo validation, and clear ethical and regulatory guidelines. This review summarizes the rapidly evolving landscape of AI in nanomedicine, highlighting key successes in design and preclinical prediction, as well as persistent obstacles to full-scale clinical integration. By illuminating these dynamics, we aim to chart a more efficient path forward in developing next-generation nanomedicine.","url":"https://doi.org/10.1002/wnan.70027","authors":["Wei‐Chun Chou","Alexa Canchola","Fan Zhang","Zhoumeng Lin"],"tags":["Nanomedicine","Artificial intelligence","Engineering","Computer science","Nanoparticle"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1002/wnan.70027","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4393993063","name":"Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment","source":"openalex","abstract":"Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from 'What do you see in the image?' and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a 'two-way' model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.","url":"https://doi.org/10.3390/diagnostics14070773","authors":["Artur Fabijan","Agnieszka Zawadzka-Fabijan","Robert Fabijan","Krzysztof Zakrzewski","Emilia Nowosławska","Bartosz Polis"],"tags":["Scoliosis","Cobb angle","Artificial intelligence","CobB","Intraclass correlation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-05","doi":"https://doi.org/10.3390/diagnostics14070773","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4210729464","name":"Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey","source":"openalex","abstract":"Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI- powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.","url":"https://doi.org/10.1016/j.imu.2021.100832","authors":["Aneeqa Ijaz","Muhammad Nabeel","Usama Masood","Tahir Mahmood","Mydah Sajid Hashmi","Iryna Posokhova","Ali Rizwan","Ali Imran"],"tags":["Modalities","Artificial intelligence","Computer science","Machine learning","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1016/j.imu.2021.100832","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3106073565","name":"Artificial intelligence should be part of medical physics graduate program curriculum","source":"openalex","abstract":"","url":"https://doi.org/10.1002/mp.14587","authors":["Lei Xing","Steven J. Goetsch","Jing Cai"],"tags":["Citation","Library science","Radiation oncology","Computer science","Physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-07","doi":"https://doi.org/10.1002/mp.14587","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4298149625","name":"Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy","source":"openalex","abstract":"Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology's role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57-180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10-16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.","url":"https://doi.org/10.1136/jitc-2022-005292","authors":["Laurent Dercle","Jeremy McGale","Shawn Sun","Aurélien Marabelle","Randy Yeh","Éric Deutsch","Fatima-Zohra Mokrane","Michael D. Farwell","Samy Ammari","Heiko Schöder","Binsheng Zhao","Lawrence H. Schwartz"],"tags":["Radiomics","Medicine","Medical physics","Clinical trial","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-01","doi":"https://doi.org/10.1136/jitc-2022-005292","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4406152279","name":"Toward expert-level medical question answering with large language models","source":"openalex","abstract":"Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval. Med-PaLM 2 scores up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19%, and demonstrates dramatic performance increases across MedMCQA, PubMedQA and MMLU clinical topics datasets. Our detailed human evaluations framework shows that physicians prefer Med-PaLM 2 answers to those from other physicians on eight of nine clinical axes. Med-PaLM 2 also demonstrates significant improvements over its predecessor across all evaluation metrics, particularly on new adversarial datasets designed to probe LLM limitations (P < 0.001). In a pilot study using real-world medical questions, specialists preferred Med-PaLM 2 answers to generalist physician answers 65% of the time. While specialist answers were still preferred overall, both specialists and generalists rated Med-PaLM 2 to be as safe as physician answers, demonstrating its growing potential in real-world medical applications.","url":"https://doi.org/10.1038/s41591-024-03423-7","authors":["K. K. Singhal","Tao Tu","Juraj Gottweis","Rory Sayres","Ellery Wulczyn","Mohamed Amin","Le Hou","Kevin Clark","Stephen Pfohl","Heather Cole-Lewis","Darlene Neal","Qazi Mamunur Rashid","Mike Schaekermann","Amy Wang","Dev Dash","Jonathan H. Chen","Nigam H. Shah","Sami Lachgar","P. Mansfield","Sushant Prakash","Bradley Green","Ewa Dominowska","Blaise Agüera y Arcas","Nenad Tomašev","Yun Liu","Renee Wong","Christopher Semturs","S. Sara Mahdavi","Joëlle Barral","Dale R. Webster","Greg S. Corrado","Yossi Matias","Shekoofeh Azizi","Alan Karthikesalingam","Vivek Natarajan"],"tags":["Question answering","Computer science","Natural language processing","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-08","doi":"https://doi.org/10.1038/s41591-024-03423-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4400170199","name":"Explainable Artificial Intelligence in Medical Imaging: A Case Study on Enhancing Lung Cancer Detection through CT Images","source":"openalex","abstract":"This study tackles the pressing challenge of lung cancer detection, the foremost cause of cancer-related mortality worldwide, hindered by late detection and diagnostic limitations. Aiming to improve early detection rates and diagnostic reliability, we propose an approach integrating Deep Convolutional Neural Networks (DCNN) with Explainable Artificial Intelligence (XAI) techniques, specifically focusing on the Residual Network (ResNet) architecture and Gradient-weighted Class Activation Mapping (Grad-CAM). Utilizing a dataset of 1,000 CT scans, categorized into normal, non-cancerous, and three types of lung cancer images, we adapted the ResNet50 model through transfer learning and fine-tuning for enhanced specificity in lung cancer subtype detection. Our methodology demonstrated the modified ResNet50 model's effectiveness, significantly outperforming the original architecture in accuracy (91.11%), precision (91.66%), sensitivity (91.11%), specificity (96.63%), and F1-score (91.10%). The inclusion of Grad-CAM provided insightful visual explanations for the model's predictions, fostering transparency and trust in computer-assisted diagnostics. The study highlights the potential of combining DCNN with XAI to advance lung cancer detection, suggesting future research should expand dataset diversity and explore multimodal data integration for broader applicability and improved diagnostic capabilities.","url":"https://doi.org/10.60084/ijcr.v2i1.150","authors":["Teuku Rizky Noviandy","Aga Maulana","Teuku Zulfikar","Asep Rusyana","Seyi Samson Enitan","Rinaldi Idroes"],"tags":["Lung cancer","Cancer imaging","Computed tomography","Artificial intelligence","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-04","doi":"https://doi.org/10.60084/ijcr.v2i1.150","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4372291572","name":"Implementing Artificial Intelligence in Higher Education: Pros and Cons from the Perspectives of Academics","source":"openalex","abstract":"This article investigates the perspectives of Romanian academics on implementing Artificial Intelligence (AI) in Higher Education (HE). The article analyzes the pros and cons of AI in HE, based on the views of eighteen academics from five Romanian universities. There is a large and heated debate about the proliferation of AI in many domains, with strong supporters and determined deniers. Studies that research the implications of AI enrich the evidence-based literature on the advantages, disadvantages, threats, or opportunities that AI creates for us, for businesses, or for societies. Though many aspects are still less well known, attitudes toward AI are still under construction. HE is a domain where the implications of AI create passionate discussions. HE is, eventually, the sector that shapes the masterminds of societies’ leaders. There is a quest to find the perspectives of those who will apply AI, who will work with or for AI, and those who are opposed to or in favor of implementing AI in HE. The conclusions revealed by this study are in line with similar studies that exist in the literature. The positive aspects of AI implementation in HE are related, in the view of academics, to gains in the learning–teaching process, improvements in students skills and competences, better inclusion, and greater efficiency in administrative costs. Similarly, the negative aspects revealed by the research are linked to psychosocial effects, data security, ethical aspects, and unemployment threats. However, there are some aspects (mostly negative) related to implementing AI in HE that are less exposed by the interviewed academics, which are mostly related to the costs and efforts of implementing AI in HE. The possible explanation of this situation is related to the lack of strategic vision on what, in fact, the implementation of AI in HE means, what this process involves, and the fact that digitalization in Romanian universities (as well as in the Romanian economy) is in its infancy. The contribution of the results of this research is mainly empirical and practical. These opinions should be used as resources for managers of HE institutions to develop better policies concerning the implementation of AI in HE and for strategic vision toward AI, with the ultimate purpose of achieving progress and prosperity for the entire society.","url":"https://doi.org/10.3390/soc13050118","authors":["Alina Iorga Pisica","Tudor Edu","Rodica Milena Zaharia","Rodica Milena Zaharia","Razvan Zaharia","Razvan Zaharia"],"tags":["Romanian","Inclusion (mineral)","Process (computing)","Unemployment","Higher education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-05","doi":"https://doi.org/10.3390/soc13050118","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2977861455","name":"Artificial Intelligence Chatbots are New Recruiters","source":"openalex","abstract":"The purpose of the paper is to assess the artificial intelligence chatbots influence on recruitment process. The authors explore how chatbots offered service delivery to attract and candidates engagement in the recruitment process. The aim of the study is to identify chatbots impact across the recruitment process. The study is completely based on secondary sources like conceptual papers, peer reviewed articles, websites are used to present the current paper. The paper found that artificial intelligence chatbots are very productive tools in recruitment process and it will be helpful in preparing recruitment strategy for the Industry. Additionally, it focuses more on to resolve complex issues in the process of recruitment. Through the amalgamation of artificial intelligence recruitment process is increasing attention among the researchers still there is opportunity to explore in the field. The paper provided future research avenues in the field of chatbots and recruiters.","url":"https://doi.org/10.14569/ijacsa.2019.0100901","authors":["Nishad Nawaz","A. Isabella Mary"],"tags":["Computer science","Process (computing)","Field (mathematics)","Chatbot","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.14569/ijacsa.2019.0100901","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4415306199","name":"Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research","source":"openalex","abstract":"OBJECTIVES: Artificial intelligence (AI) has the potential to transform medical informatics by supporting clinical decision-making, reducing diagnostic errors, and improving workflows and efficiency. However, successful integration of AI-based decision support systems depends on careful consideration of human-AI collaboration, trust, skill maintenance, and automation bias. This work proposes five central questions to guide future research in medical informatics and human-computer interface (HCI). MATERIALS AND METHODS: We focus on AI-based clinical decision support systems, including computer vision algorithms for medical imaging (radiology, pathology), natural language processing for structured and unstructured electronic health record (EHR) data, and rule-based systems. Relevant data modalities include clinician-acquired images, EHR text, and increasingly, patient-generated content in telehealth contexts. We review existing evidence regarding diagnostic errors across specialties, the effectiveness and risks of AI tools in reducing perceptual and interpretive errors, and the human factors influencing diagnostic decision-making in AI-enabled contexts. We synthesize insights from medicine, cognitive science, and HCI to identify gaps in knowledge and propose five key questions for continued research. RESULTS: Diagnostic errors remain common across medicine, with AI offering potential to reduce both perceptual and interpretive errors. However, the impact of AI depends critically on how and when information is presented. Studies indicate that delayed or toggleable cues may outperform immediate ones, but attentional capture, overreliance, and bias remain significant risks. Explainable AI provides transparency but can also bias decisions. Long-term reliance on AI may erode clinician skills, particularly for trainees and in low-prevalence contexts. Historical failures of computer-aided diagnosis in mammography highlight these challenges. DISCUSSION AND CONCLUSION: Effective AI integration requires human-centered and adaptive design. Five central research questions address: (1) what type and format of information AI should provide; (2) when information should be presented; (3) how explainable AI affects diagnostic decisions; (4) how AI influences automation bias and complacency; and (5) the risks of skill decay due to reliance on AI. Each question underscores the importance of balancing efficiency, accuracy, and clinician expertise while mitigating bias and skill degradation. AI holds promise for improving diagnostic accuracy and efficiency, but realizing its potential requires post-deployment evaluation, equitable access, clinician oversight, and targeted training. AI must complement, rather than replace, human expertise, ensuring safe, effective, and sustainable integration into diagnostic decision-making. Addressing these challenges proactively can maximize AI's potential across healthcare and other high-stakes domains.","url":"https://doi.org/10.1093/jamia/ocaf123","authors":["Tad T. Brunyé","Stephen R. Mitroff","Joann G. Elmore"],"tags":["Computer science","Health informatics","Automation","Applications of artificial intelligence","Interface (matter)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-07","doi":"https://doi.org/10.1093/jamia/ocaf123","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4386223230","name":"Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions","source":"openalex","abstract":"Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.","url":"https://doi.org/10.3390/s23177435","authors":["Manar Osama","Abdelhamied A. Ateya","Mohammed S. Sayed","Mohamed Hammad","Paweł Pławiak","Ahmed A. Abd El‐Latif","Rania A. Elsayed"],"tags":["Health care","Telemedicine","Wearable computer","Computer science","Internet of Things"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-25","doi":"https://doi.org/10.3390/s23177435","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4281994448","name":"Role of artificial intelligence in MS clinical practice","source":"openalex","abstract":"Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.","url":"https://doi.org/10.1016/j.nicl.2022.103065","authors":["Raffaello Bonacchi","Massimo Filippi","Maria A. Rocca"],"tags":["Computer science","Artificial intelligence","Clinical Practice","Machine learning","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1016/j.nicl.2022.103065","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3201496287","name":"Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00234-021-02813-9","authors":["Matthias Wagner","Khashayar Namdar","Asthik Biswas","Suranna R. Monah","Farzad Khalvati","Birgit Ertl‐Wagner"],"tags":["Neuroradiologist","Artificial intelligence","Neuroradiology","Machine learning","Overfitting"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-18","doi":"https://doi.org/10.1007/s00234-021-02813-9","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4214937897","name":"Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging","source":"openalex","abstract":"Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on \"braintumor\" website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.","url":"https://doi.org/10.3390/s22051960","authors":["Mahsa Arabahmadi","Reza Farahbakhsh","Javad Rezazadeh"],"tags":["Deep learning","Artificial intelligence","Magnetic resonance imaging","Medical imaging","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-02","doi":"https://doi.org/10.3390/s22051960","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4413940673","name":"The Role of Artificial Intelligence in Improving Diagnostic Accuracy in Medical Imaging: A Review","source":"openalex","abstract":"This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmc.2025.066987","authors":["Omar Sabri","Bassam Al-Shargabi","Abdelrahman Abuarqoub"],"tags":["Medical imaging","Artificial intelligence","Computer science","Medical physics","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.32604/cmc.2025.066987","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"oa:W3216530541","name":"Triboelectric nanogenerator and artificial intelligence to promote precision medicine for cancer","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.nanoen.2021.106783","authors":["Meihua Chen","Yuankai Zhou","Jinyi Lang","Lijie Li","Yan Zhang"],"tags":["Triboelectric effect","Nanogenerator","Cancer","Cancer treatment","Cancer therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-26","doi":"https://doi.org/10.1016/j.nanoen.2021.106783","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3195054274","name":"Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis","source":"openalex","abstract":"Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.","url":"https://doi.org/10.1186/s12947-021-00261-2","authors":["Jia Zhou","Meng Du","Shuai Chang","Zhiyi Chen"],"tags":["Angiology","Medicine","Internal medicine","Radiology","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-20","doi":"https://doi.org/10.1186/s12947-021-00261-2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4407136469","name":"Perceptions of Medical Students towards Artificial Intelligence","source":"openalex","abstract":"The incorporation of technological advancements, particularly Artificial Intelligence has transformed healthcare systems globally, especially post-COVID-19. Medical education faces challenges in incorporating AI due to instructor shortages and high software costs. Understanding medical students' attitudes towards AI is crucial for its successful integration into medical practice and education. Objective: To evaluate the attitude of medical undergraduate students towards AI in medicine. Methods: A descriptive, online cross-sectional study was executed among undergraduate medical students utilizing a non-probability convenience sampling. The questionnaire, distributed to 340 participants, included demographic details, perceptions towards artificial intelligence, and its effect on medical education. A total of 252 responses were received, receiving a 74% response rate. Data analysis was executed through SPSS version 26.0. Results: Demographic characteristics of 252 subjects revealed a mean age of 23.5 years, with a majority being female (74.2%) and in their first to third year of study (58.3%). Participants generally had intermediate computer literacy (75.7%) and used technology consistently for learning (57.5%). Regarding perceptions of AI, most students strongly agreed that AI will significantly impact healthcare (48.8%) and that all medical students should be educated about it (31.3%). Additionally, a substantial majority believed that integrating AI into medical education would enhance its quality (66.6%) and facilitate the learning experience (57.9%). Conclusions: It was concluded that students have positive perceptions regarding AI systems, demonstrating enthusiasm for expanding their knowledge of AI within their medical education.","url":"https://doi.org/10.54393/pjhs.v6i1.2364","authors":["Shazia Rizwan","Shahveir Rizwan","Muhammad Rizwan","Hashim Ali","Nawal","Saima Batool"],"tags":["Perception","Psychology","Mathematics education","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-31","doi":"https://doi.org/10.54393/pjhs.v6i1.2364","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4408047551","name":"A Literature Review on Applications of Explainable Artificial Intelligence (XAI)","source":"openalex","abstract":"As AI technologies, particularly deep learning models, have advanced, their inherent “black box” nature has raised significant concerns regarding accountability, fairness, and trust, especially in critical domains such as healthcare, finance, and criminal justice. We present a detailed exploration of XAI, emphasizing its essential role in improving the interpretability and transparency of complex AI systems in various application domains. Health-related applications were notably using XAI, emphasizing diagnostics, and medical imaging. Other notable domains of use of XAI is encompassed environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and social media. Furthermore, nascent applications in law, education, and social care underscore the growing influence of XAI. The analysis indicates a prevalent application of local explanation techniques, especially SHAP and LIME, with a preference for SHAP due to its stability and mathematical assurances. Each technique is analysed for its strengths and limitations in providing clear, actionable insights into model decision-making processes, thereby aiding stakeholders in understanding AI behaviour. Ultimately, this document underscores the critical challenges for XAI in fostering user trust, enhancing decision-making processes, and ensuring that AI technologies are utilized responsibly and ethically across various applications, paving the way for a more transparent and accountable AI landscape. We believe that by serving as a guide for future studies in the area, our systematic review contributes to the body of literature on XAI.","url":"https://doi.org/10.1109/access.2025.3546681","authors":["Khushi Kalasampath","Spoorthi KN","Sreeparvathy Sajeev","Sahil Sarma Kuppa","K Ajay","Angulakshmi Maruthamuthu"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/access.2025.3546681","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2901466771","name":"A dataset of clinically generated visual questions and answers about radiology images","source":"openalex","abstract":"Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers. Manual categorization of images and questions provides insight into clinically relevant tasks and the natural language to phrase them. Evaluating with well-known algorithms, we demonstrate the rich quality of this dataset over other automatically constructed ones. We propose VQA-RAD to encourage the community to design VQA tools with the goals of improving patient care.","url":"https://doi.org/10.1038/sdata.2018.251","authors":["Jason J. Lau","Soumya Gayen","Asma Ben Abacha","Dina Demner‐Fushman"],"tags":["Computer science","Categorization","Question answering","Artificial intelligence","Information retrieval"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-11-20","doi":"https://doi.org/10.1038/sdata.2018.251","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4205177272","name":"Artificial intelligence for breast cancer analysis: Trends & directions","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compbiomed.2022.105221","authors":["Shahid Munir Shah","Rizwan Ahmed Khan","Sheeraz Arif","Unaiza Sajid"],"tags":["Modalities","Computer science","Breast cancer","Artificial intelligence","Modality (human–computer interaction)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-06","doi":"https://doi.org/10.1016/j.compbiomed.2022.105221","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3198611568","name":"Influential Usage of Big Data and Artificial Intelligence in Healthcare","source":"openalex","abstract":"Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.","url":"https://doi.org/10.1155/2021/5812499","authors":["Yan Cheng Yang","Saad Ul Islam","Asra Noor","Sadia Khan","Waseem Afsar","Shah Nazir"],"tags":["Big data","eHealth","mHealth","Computer science","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-06","doi":"https://doi.org/10.1155/2021/5812499","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4388430320","name":"Identification of Ancient Chinese Medical Prescriptions and Case Data Analysis Under Artificial Intelligence GPT Algorithm: A Case Study of Song Dynasty Medical Literature","source":"openalex","abstract":"This work aims to use the chatGPT algorithm to analyze and summarize cases in medical literature of the Song Dynasty, understand the clinical practice experience of ancient Chinese medicine, and provide historical reference for the clinical application and research of modern Chinese medicine. Firstly, the application of Artificial Intelligence (AI) in medicine is explained through literature research. Secondly, the prescription recognition technology related to AI is introduced, and a method combining supervised learning and semi-supervised learning for prescription entity recognition is proposed. Combined with chatGPT technology, medical data mining is carried out to obtain information and knowledge of medical research in the Song Dynasty. chatGPT is applied to the identification of ancient Chinese medical prescriptions and the analysis of case data. The results show that: (1) The machine classifier performs well in classifying different flavor compounds, effective and ineffective prescriptions can be distinguished, and the accuracy increases with the increase of samples. (2) Data mining reveals the differences in disease stages, the primary and secondary contradictions in patients’ bodies, and the primary and secondary differences in drug use. (3) Frequency statistics show that warm drugs account for 45.46%, confirming that warm drugs are the main ones for treating phlegm. Therefore, it is recommended to use chatGPT as an auxiliary tool in medical-related analysis and combine it with professional medical knowledge and clinical practice for comprehensive judgment and decision-making. Ancient medical prescriptions can be better identified, and case data can be analyzed by combining chatGPT technology in AI. It can well support ancient medicine study and lay a solid foundation for developing modern medical information data warehouse.","url":"https://doi.org/10.1109/access.2023.3330212","authors":["Mengfei Li","Xiaohuan Zheng"],"tags":["Identification (biology)","Computer science","Medical prescription","Algorithm","Algorithm design"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/access.2023.3330212","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4385212939","name":"Artificial Intelligence In Field of Medical Imaging Informatics","source":"openalex","abstract":"This paper reviews about application of artificial intelligence in medical image informatics. Additionally, it may enhance therapeutic results and increase the value of medical image analysis in yet-to-be-determined ways. Different techniques of imaging like anatomical (x-ray, MRI, ultrasound) and system generated imaging techniques (microscopy, PACs, SPECT) has been discussed in this paper. Deep learning and machine learning approaches for imaging is discussed at later stage. Deep learning techniques in particular are receiving a lot of attention due to their outstanding efficiency in image-recognition tasks in artificial intelligence (AI). They can more effectively and accurately diagnose patients by performing an automated quantitative assessment of complicated medical image properties. Deep learning, particularly image categorization, is being used more and more in the realm of medical images. Machine learning to find tumor image is discussed for brain tumor. For the collection and storage of image data, the Digital Imaging and Communication in medicine is frequently utilized. How OMERO and DICOM database is used for digital imaging is discussed further.","url":"https://doi.org/10.1109/icacite57410.2023.10182498","authors":["Medha Jha","Yasha Hasija"],"tags":["Artificial intelligence","DICOM","Computer science","Medical imaging","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-12","doi":"https://doi.org/10.1109/icacite57410.2023.10182498","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4392488972","name":"Data-Centric Artificial Intelligence","source":"openalex","abstract":"Abstract Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.","url":"https://doi.org/10.1007/s12599-024-00857-8","authors":["Johannes Jakubik","Michael Vössing","Niklas Kühl","Jannis Walk","Gerhard Satzger"],"tags":["Computer science","Applications of artificial intelligence","Field (mathematics)","Database-centric architecture","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-05","doi":"https://doi.org/10.1007/s12599-024-00857-8","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3215517669","name":"Advancing health equity with artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1057/s41271-021-00319-5","authors":["Nicole M. Thomasian","Carsten Eickhoff","Eli Y. Adashi"],"tags":["Counterfactual thinking","Health care","Health policy","Equity (law)","Public health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-22","doi":"https://doi.org/10.1057/s41271-021-00319-5","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W2993479405","name":"Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists’ Intelligence?","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. SUMMARY: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. KEY MESSAGES: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built.","url":"https://doi.org/10.1159/000504600","authors":["Guotong Xie","Tiange Chen","Yingxue Li","Tingyu Chen","Xiang Li","Zhihong Liu"],"tags":["Prognostics","Medicine","Kidney disease","Augment","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-12-03","doi":"https://doi.org/10.1159/000504600","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4309472441","name":"Development of metaverse for intelligent healthcare","source":"openalex","abstract":"The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of 'medical technology and AI' (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction.","url":"https://doi.org/10.1038/s42256-022-00549-6","authors":["Ge Wang","Andreu Badal","Xun Jia","Jonathan S. Maltz","Klaus Mueller","Kyle J. Myers","Chuang Niu","Michael W. Vannier","Pingkun Yan","Zhou Yu","Rongping Zeng"],"tags":["Metaverse","Health care","Computer science","Business","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-15","doi":"https://doi.org/10.1038/s42256-022-00549-6","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4385652559","name":"Utilizing Artificial Intelligence for Crafting Medical Examinations: A Medical Education Study with GPT-4","source":"openalex","abstract":"Abstract Background. The task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable. Methods. We utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies were categorized by their characteristics. Results. After inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning. Conclusion. GPT can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.","url":"https://doi.org/10.21203/rs.3.rs-3146947/v1","authors":["Eyal Klang","Shir Portugez","Raz Gross","Reut Kassif Lerner","Alina Brenner","Maayan Gilboa","Tal Ortal","Sophi Ron","Vered Robinzon","Hila Meiri","Gad Segal"],"tags":["Terminology","Task (project management)","Field (mathematics)","Test (biology)","Multiple choice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-09","doi":"https://doi.org/10.21203/rs.3.rs-3146947/v1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W3136007224","name":"Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging","source":"openalex","abstract":"Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.","url":"https://doi.org/10.3390/app11062704","authors":["Oyun Kwon","Sun Kook Yoo"],"tags":["Interoperability","DICOM","Computer science","Medical imaging","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-17","doi":"https://doi.org/10.3390/app11062704","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"oa:W4226142707","name":"Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges","source":"openalex","abstract":"Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.","url":"https://doi.org/10.1109/access.2022.3157626","authors":["Sifat Rezwan","Wooyeol Choi"],"tags":["Computer science","Software deployment","Variety (cybernetics)","Artificial intelligence","Open research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1109/access.2022.3157626","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-981-16-9423-3_1","name":"Inception Based Medical Image Registration","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-9423-3_1","authors":["Wenrui Yan","Baoju Zhang","Cuiping Zhang","Jin Zhang","Chuyi Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-21T09:02:54Z","doi":"10.1007/978-981-16-9423-3_1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-981-99-7545-7_29","name":"Deep Learning Estimation of Medical Substance Concentrations Using Pytorch","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-7545-7_29","authors":["Qunsheng Wang","Qiutong Xu","Cheng Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-22T12:03:38Z","doi":"10.1007/978-981-99-7545-7_29","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/s00739-022-00826-4","name":"Artificial Intelligence, eHealth – die Bedeutung für Patient*innen","source":"crossref","abstract":"Zusammenfassung Zur Etablierung von eHealth-Konzepten bedarf es konsequenter Zusammenarbeit zwischen Techniker*innen und Ärzt*innen, um der Komplexität des Menschen, seiner Bedürfnisse und Leidenszustände sowie deren adäquater Behandlung gerecht zu werden. Die Annäherung der Technik sowie eine kritische Sicht zu einfachen Artificial-Intelligence(AI)-Lösungen werden durch Darstellung eines Fallbeispiels der somatischen Belastungsstörung diskutiert.","url":"https://doi.org/10.1007/s00739-022-00826-4","authors":["Henriette Löffler-Stastka","Dietmar Dietrich","Thilo Sauter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-14T16:03:02Z","doi":"10.1007/s00739-022-00826-4","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1201/9781003510833-62","name":"IoT based vehicle management system in medical Emergency","source":"crossref","abstract":"By transforming a personal vehicle into an emergency vehicle, we suggest a revolutionary way to address the issue of delayed medical responses. With IoT sensors, it can pick up green lights while traveling, adjust the hue of its headlights to avoid obstacles, and provide hospitals real-time pulse rate data. In the event of a medical emergency, this guarantees priority transit for people, possibly saving lives.","url":"https://doi.org/10.1201/9781003510833-62","authors":["Naman Sharma","Shanky Goyal","Navleen Kaur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-27T07:16:29Z","doi":"10.1201/9781003510833-62","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-981-97-5345-1_2","name":"From Pixels to Predictions: Exploring the Role of Artificial Intelligence in Radiology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-5345-1_2","authors":["M. J. Akshit Aiyappa","B. Suresh Kumar Shetty"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T14:02:55Z","doi":"10.1007/978-981-97-5345-1_2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/j.engappai.2026.115600","name":"A collaborative enhanced prediction model with medical knowledge for clinical time series","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115600","authors":["Ying An","Yinghong Shi","Qixuan Peng","Lin Guo","Xianlai Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-09T19:16:02Z","doi":"10.1016/j.engappai.2026.115600","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.32471/umj.1680-3051.276675","name":"Застосування технологій штучного інтелекту в сучасному слухопротезуванні: клінічні приклади використання слухових апаратів зі штучним інтелектом","source":"crossref","abstract":"Втрата слуху є однією з найбільш поширених сенсорних хвороб у світі та суттєво впливає на якість життя пацієнтів. Основною проблемою людей із сенсоневральною приглухуватістю є не лише зниження чутливості до звуку, але й порушення розбірливості мовлення, особливо у складних акустичних умовах. Сучасні цифрові слухові апарати активно інтегрують алгоритми штучного інтелекту, що дозволяє аналізувати акустичне середовище, розпізнавати мовний сигнал та автоматично оптимізувати параметри обробки звуку. &lt;b&gt;Мета дослідження&lt;/b&gt;: оцінити можливості використання технологій штучного інтелекту у слухопротезуванні на основі клінічних спостережень пацієнтів із сенсоневральною приглухуватістю. У роботі представлено два клінічні випадки використання слухових апаратів зі штучним інтелектом. Отримані результати демонструють покращення розбірливості мовлення, підвищення комфорту слухового сприйняття та високу задоволеність пацієнтів після використання сучасних слухових апаратів із алгоритмами штучного інтелекту.","url":"https://doi.org/10.32471/umj.1680-3051.276675","authors":["Дидикало Л.І."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-14T14:38:02Z","doi":"10.32471/umj.1680-3051.276675","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(89)90065-9","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90065-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90065-9","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(87)90006-3","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90006-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(87)90006-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(87)90076-2","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90076-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(87)90076-2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(91)90087-z","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90087-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90087-z","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(92)90085-c","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90085-c","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90085-c","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(95)90041-1","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90041-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/0004-3702(95)90041-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(07)00153-1","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(07)00153-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-11-09T09:20:34Z","doi":"10.1016/s0004-3702(07)00153-1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(88)90036-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90036-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90036-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(93)90042-a","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90042-a","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90042-a","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(03)00027-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00027-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-04T14:05:18Z","doi":"10.1016/s0004-3702(03)00027-4","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/b978-0-323-99135-3.00002-6","name":"Artificial intelligence in catalysis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99135-3.00002-6","authors":["Srinivas Rangarajan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-25T05:49:12Z","doi":"10.1016/b978-0-323-99135-3.00002-6","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1017/9781009258227.003","name":"Artificial Intelligence and Agents","source":"crossref","abstract":"Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods that have been proven to work. The book's novel agent design space provides a coherent framework for learning, reasoning and decision making. Numerous realistic applications and examples facilitate student understanding. Every concept or algorithm is presented in pseudocode and open source AIPython code, enabling students to experiment with and build on the implementations. Five larger case studies are developed throughout the book and connect the design approaches to the applications. Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn them. An invaluable teaching package for undergraduate and graduate AI courses, this comprehensive textbook is accompanied by lecture slides, solutions, and code.","url":"https://doi.org/10.1017/9781009258227.003","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-13T20:05:48Z","doi":"10.1017/9781009258227.003","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-3-030-96630-0_12","name":"Explainable Artificial Intelligence in Sustainable Smart Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96630-0_12","authors":["Mohiuddin Ahmed","Shahrin Zubair"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-18T12:03:16Z","doi":"10.1007/978-3-030-96630-0_12","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-981-95-8212-9_1","name":"Introduction to Artificial Intelligence for Sustainable Development","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-8212-9_1","authors":["Tankiso Moloi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T23:40:54Z","doi":"10.1007/978-981-95-8212-9_1","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1080/08839519308949972","name":"SOME SEMIOTIC REFLECTIONS ON THE FUTURE OF ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"In this brief article I shall reflect first on the development of a theoretical model of knowledge elicitation and knowledge representation, derived from semiotic theory and from theatrical performance analysis (Hilton, 1989) and then on the application of some of these concepts in the MEDICA project, part of the European Commission AIM program. *These in mm lead to a possible schematization of a complementary three-stage development strategy for Al systems: (1) an expert system, (2) a cognitive support system, and (3) a reflective support system.","url":"https://doi.org/10.1080/08839519308949972","authors":["JULIAN HILTON"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-06-25T01:18:14Z","doi":"10.1080/08839519308949972","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.24041/ejmr.2025.18","name":"Re-Imagining Chronic Care: Artificial Intelligence Facilitated Collaborative Decision Making for Diabetes and Hypertension","source":"crossref","abstract":"Background:Diabetes and hypertension are chronic diseases that pose serious healthcare challenges because of their chronic nature and ongoing need for care. Chronic care has traditionally been non-personalized and not supported by real-time decision making. Artificial intelligence (AI) presents new possibilities by allowing collaborative decision-making, enhanced predictive accuracy, and patient engagement. Objective: This investigation seeks to reimagine chronic care through assessing the role that collaborative decision-making facilitated by AI may play in diabetes and hypertension management, highlighting patient outcome improvement, complication prevention, and the support of healthcare workers in resource-scarce environments. Methods: This study is grounded on secondary sources of data, such as peer-reviewed journals, systematic reviews, and evidence from international models of healthcare. The research critically assesses current literature on AI use in the management of chronic diseases, and synthesizes evidence on its efficacy for clinical decision-making, risk assessment, and patient tracking. Principal challenges like ethical implications, data privacy, integration into workflow, and digital equity are also discussed. Results: Findings indicate that decision-making facilitated through AI has a profound impact on clinical efficiency as it facilitates timely intervention, and tailored treatment approaches. Evidence supports the contention that AI contributes to enhanced patient engagement by facilitating real-time monitoring tools and predictive analytics, especially in glycemic control and blood pressure management. Barriers to these advancements include insufficient algorithmic transparency, limited infrastructure, and inequities in access, among others. Conclusion: AI can revolutionize management of chronic care by enabling greater cooperation between patients and healthcare professionals. Although there is evidence that it benefits satisfaction, compliance, and outcomes, effective implementation will involve overcoming ethical, technical, and equity challenges. Future efforts should involve incorporating AI into regular care models with adequate transparency, accountability, and inclusiveness.","url":"https://doi.org/10.24041/ejmr.2025.18","authors":["Narava Suvarna Kumari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-04T11:27:21Z","doi":"10.24041/ejmr.2025.18","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(04)00003-7","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00003-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-01-23T10:18:16Z","doi":"10.1016/s0004-3702(04)00003-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(87)90071-3","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90071-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(87)90071-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(98)90015-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90015-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/s0004-3702(98)90015-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(97)90009-6","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90009-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(97)90009-6","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(86)90012-3","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90012-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(86)90012-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(09)00040-x","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(09)00040-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-03-23T10:15:19Z","doi":"10.1016/s0004-3702(09)00040-x","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(88)90018-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90018-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90018-5","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(94)90033-7","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(94)90033-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(94)90033-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(70)90014-7","name":"Author index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(70)90014-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(70)90014-7","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(05)00083-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(05)00083-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-07-03T12:47:38Z","doi":"10.1016/s0004-3702(05)00083-4","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(09)00005-8","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(09)00005-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-01-28T09:09:26Z","doi":"10.1016/s0004-3702(09)00005-8","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(06)00102-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(06)00102-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-10-20T08:20:49Z","doi":"10.1016/s0004-3702(06)00102-0","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(95)90016-0","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90016-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/0004-3702(95)90016-0","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(97)90012-6","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90012-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(97)90012-6","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(01)00155-2","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00155-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T11:27:28Z","doi":"10.1016/s0004-3702(01)00155-2","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/j.artmed.2019.101782","name":"A fusion framework to extract typical treatment patterns from electronic medical records","source":"crossref","abstract":"Objective Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify \"right patient\", \"right drug\", \"right dose\", \"right route\", and \"right time\" from doctor order information. Methods We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures: content-view similarity, sequence-view similarity and duration-view similarity. An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns. Results Experimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis. Conclusion The extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice.","url":"https://doi.org/10.1016/j.artmed.2019.101782","authors":["Jingfeng Chen","Leilei Sun","Chonghui Guo","Yanming Xie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-28T11:30:35Z","doi":"10.1016/j.artmed.2019.101782","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-3-031-35828-9_15","name":"Artificial Intelligence Application to Reduce Cost and Increase Efficiency in the Medical and Educational Sectors","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35828-9_15","authors":["Khaled Delaim","Muneer Al Mubarak","Ruaa Binsaddig"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-03T07:02:09Z","doi":"10.1007/978-3-031-35828-9_15","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1201/9781003309451-3","name":"Artificial Intelligence in Future Telepsychiatry and Psychotherapy for E-Mental Health Revolution","source":"crossref","abstract":"As socioeconomic conditions improve, people increasingly start utilizing mental health services. In developing countries, there is a dearth of skilled psychotherapists and psychiatrists. Addressing the necessity for mental health facilities among the rural population is posing major challenges for the mental well-being service delivery system. The major challenge lies in the dissemination of these psychotherapy skills and further accessibility of therapists to patients. Fortunately, there is a silver lining in the darkness. Digital technology is bridging this gap. The internet is very useful in the dissemination of psychotherapeutic skills. E-mental health is the combination of computer science and mental health. It is a promising interdisciplinary research area that uses information and communication technology tools to enhance mental health services. These tools are transforming the mode of practicing psychiatry. An enormous number of applications are designed for the treatment and monitoring of various disorders in the field of mental health. A large chunk of the population struggles with mental health, and few receive the required treatment. The ramifications of untreated mental health have a huge impact on families and the economy with the loss of productivity. The use of telephones, smartphones, web applications, and social media for the management of psychiatric illnesses is contributing to the welfare of society. Artificial intelligence technique, which is a branch of computer science, can be used to diagnose mental illness, monitor treatment progress, identify medication adherence, determine the severity of a mental illness, design personalized treatment, and focus on the clinician–patient relationship. Artificial intelligence is the intelligence exhibited by machines in a similar manner demonstrated by humans. Some of the tasks it is intended to perform are learning, thinking, and problem-solving. The different domains of AI can be used effectively in delivering E-mental health services. Machine learning methods can be designed to forecast the responses of patients to cognitive behavioral therapy. This prediction allows the therapist to allocate resources that support behavioral therapy. The accessibility of virtual reality can be useful in exposure therapy. Augmented reality utilizes the processing power of smartphones, laptops, and notebooks to attach patients with the cause of their anxiety. Natural language processing (NLP) uses algorithmic methods which specify how computers evaluate natural language in text formats, which include language transformation, understanding, and information extraction. Professionals are using NLP extensively due to raw input text data, like clinical notes, counseling sessions, patient inputs, family feedback, etc. The capability of the program to identify significant words, irrespective of the natural language, is a great development in technology and is vital for psychological health-care applications. Psychiatry is a domain that needs the effective usage of emotional intelligence, which is very difficult to replace with machines. Integrating the advantages of psychological expertise with artificial intelligence–based technology has an optimistic influence on patient management. Being cost-effective and available remotely, AI assists mental health professionals to provide the required support to patients in distress. Review is conducted to identify opportunities and future challenges associated with this new technology in treatment intervention and outcome prediction.","url":"https://doi.org/10.1201/9781003309451-3","authors":["Sudhir Hebbar","B Vandana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-09T11:43:04Z","doi":"10.1201/9781003309451-3","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.2991/978-94-6463-823-3_53","name":"Application and Analysis of Some Artificial Intelligence Techniques in Medical Imaging Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.2991/978-94-6463-823-3_53","authors":["Boqian Cao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-31T06:38:25Z","doi":"10.2991/978-94-6463-823-3_53","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/s44163-026-01650-0","name":"Opportunities and challenges of integrating artificial intelligence into undergraduate medical education in low-and middle-income countries","source":"crossref","abstract":"Artificial intelligence (AI) is rapidly transforming medical education by enabling personalized learning, adaptive assessment, and simulation-based training. While high-income countries have begun integrating AI into undergraduate medical curricula, its adoption in low- and middle-income countries (LMICs) remains limited due to infrastructural, financial, and regulatory constraints. This narrative review synthesizes recent literature on the role of AI in undergraduate medical education, examining its applications in curriculum design, teaching methodologies, student learning support, assessment strategies, and educational tool development. It also explores faculty and student perspectives, alongside ethical, technological, and pedagogical challenges, with a particular focus on LMIC contexts. The findings suggest that AI can enhance learning efficiency, engagement, and assessment practices; however, its impact is highly context-dependent and influenced by implementation strategies. Key barriers include limited infrastructure, lack of faculty preparedness, and concerns related to bias, data privacy, and overreliance. Effective integration requires investment in digital infrastructure, faculty development, ethical governance, and locally contextualized AI solutions. AI should therefore be viewed as a complementary tool that augments, rather than replace, human educators, supporting more equitable and adaptive medical education systems.","url":"https://doi.org/10.1007/s44163-026-01650-0","authors":["Uzair Abbas","Muhib Ullah Khalid","Misha Tanveer","Fatima Arshad","Usama Abdul Musawwir","Syed Mustafa Hasan","Niaz Hussain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-21T05:55:22Z","doi":"10.1007/s44163-026-01650-0","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/j.artint.2005.01.001","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2005.01.001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-01-18T12:18:51Z","doi":"10.1016/j.artint.2005.01.001","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(91)90083-v","name":"Books 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Understanding","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90039-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(92)90039-z","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/0004-3702(88)90029-x","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90029-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(88)90029-x","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/s0004-3702(83)80018-6","name":"Call for papers: Applications of Artificial Intelligence the Annual Society of Photo-Optical Instrumentation Engineers Conference","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(83)80018-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-12-03T12:12:21Z","doi":"10.1016/s0004-3702(83)80018-6","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/978-3-030-06170-8_4","name":"Artificial Intelligence and Language","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-06170-8_4","authors":["Nicholas Asher","Pierre Zweigenbaum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-07T23:05:27Z","doi":"10.1007/978-3-030-06170-8_4","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1016/j.engappai.2024.108465","name":"A novel fuzzy multi-criteria decision-making for enhancing the management of medical waste generated during the coronavirus pandemic","source":"crossref","abstract":"The coronavirus pandemic significantly increased the use of essential medical supplies, resulting in a surge in medical waste generation. This surge has spurred extensive research into sustainable disposal methods for safe and environmentally responsible medical equipment management. Addressing this multifaceted issue falls within the domain of multi-criteria decision-making. This study presents a comprehensive framework for selecting optimal medical waste treatment methods, considering economic, technological, environmental, and social factors. This is the first study to address the problem of selecting a medical waste disposal technology using the Fuzzy Dombi Bonferroni. The mean operator to combine expert opinions, the fuzzy preference selection index method to evaluate the criteria and the fuzzy compromise ranking of alternatives from distance to ideal solution method to rank the alternatives. According to the weightings, the social dimension holds the highest significance at 0.3217. Disinfection efficiency ranks as the most critical criterion, weighing in at 0.0823. The autoclave is rated as the top disposal technique, with a utility function value of 5.4579. Sensitivity analyses ensured the stability and reliability of the models. The adaptability of the applied model to sustainable practices such as energy conversion, material recycling, and resource recovery represents an essential aspect of policymaking in waste management. This assessment can guide policy formulation or improvement processes for waste disposal.","url":"https://doi.org/10.1016/j.engappai.2024.108465","authors":["Ahmet Turan Demir","Sarbast Moslem"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-03T05:21:00Z","doi":"10.1016/j.engappai.2024.108465","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.466Z"},{"id":"doi:10.1007/s44163-026-01485-9","name":"Retraction Note: Unleashing the power of advanced technologies for revolutionary medical imaging: pioneering the healthcare frontier with artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44163-026-01485-9","authors":["Ashish Singh Chauhan","Rajesh Singh","Neeraj Priyadarshi","Bhekisipho Twala","Surindra Suthar","Siddharth Swami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-28T05:37:24Z","doi":"10.1007/s44163-026-01485-9","addedAt":"2026-09-01T01:47:50.466Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.engappai.2025.113360","name":"Towards an intelligent waste management system: An adaptive and sustainable medical waste supply chain for infectious and hazardous risk reduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113360","authors":["Gelareh Agahi","Mohammad Ali Hassanabadi","Masoud Rabbani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-30T16:45:13Z","doi":"10.1016/j.engappai.2025.113360","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.engappai.2025.111855","name":"A Federated Fairness-Aware Incentive Mechanism for medical image classification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111855","authors":["Chunling Chen","Haiwei Pan","Kejia Zhang","Zhe Li","Fengming Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-06T17:43:54Z","doi":"10.1016/j.engappai.2025.111855","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(04)00179-1","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00179-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-15T06:47:57Z","doi":"10.1016/s0004-3702(04)00179-1","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(00)90065-1","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(00)90065-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T16:57:34Z","doi":"10.1016/s0004-3702(00)90065-1","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(03)00143-7","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00143-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/s0004-3702(03)00143-7","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(81)90017-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(81)90017-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(81)90017-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(90)90091-d","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90091-d","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90091-d","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(10)00147-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(10)00147-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-09-13T10:11:07Z","doi":"10.1016/s0004-3702(10)00147-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.2307/jj.13760051.14","name":"VIRTUOUS ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"","url":"https://doi.org/10.2307/jj.13760051.14","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-13T20:20:05Z","doi":"10.2307/jj.13760051.14","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1201/b19187-7","name":"◾ Path to More General Artificial Intelligence","source":"crossref","abstract":"Public interest and support for strong AI has lagged because optimistic predictions often failed and because progress is difficult to measure or demonstrate. This chapter argues, however, that the next stage of development of AI, for at least the next decade and more likely for the next 25 years, will be increasingly dependent on contributions from strong AI. This hypothesis arises from an empirical study of the history of AI in several practical domains using what Abbott (2004) calls the small-N comparative method. This method examines a small number of varied cases in moderate detail, drawing on descriptive case study accounts. The cases are selected to illustrate different processes, risks, and benefits. This method draws on the qualitative insights of specialists who have studied each of the cases in depth and over historically significant periods of time. The small-N comparisons help to identify general patterns and to suggest priorities for further research. Readers who want more depth on each case are encouraged to pursue links to the original case research.","url":"https://doi.org/10.1201/b19187-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-11-18T20:01:16Z","doi":"10.1201/b19187-7","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(90)90013-p","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90013-p","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90013-p","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/b978-0-443-44021-2.00011-7","name":"Traditional Chinese medicine with artificial intelligence: Challenges and future prospects","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44021-2.00011-7","authors":["Fatima Tahir","Javed Iqbal","Aziz-ur-Rehman","Ibrahim A. Alsarra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T12:02:29Z","doi":"10.1016/b978-0-443-44021-2.00011-7","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(02)00176-5","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00176-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T10:29:40Z","doi":"10.1016/s0004-3702(02)00176-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(04)00086-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00086-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-06-24T13:47:39Z","doi":"10.1016/s0004-3702(04)00086-4","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(90)90035-x","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90035-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90035-x","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1142/9789811293993_0012","name":"Artificial Intelligence for 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papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)90016-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-26T13:25:52Z","doi":"10.1016/s0004-3702(98)90016-9","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(87)90016-6","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90016-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(87)90016-6","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(03)00044-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00044-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T22:33:30Z","doi":"10.1016/s0004-3702(03)00044-4","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(04)00200-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(04)00200-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-31T07:44:55Z","doi":"10.1016/s0004-3702(04)00200-0","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(83)80001-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(83)80001-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-12-03T12:12:21Z","doi":"10.1016/s0004-3702(83)80001-0","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(96)90014-4","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(96)90014-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T19:30:03Z","doi":"10.1016/s0004-3702(96)90014-4","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(03)00069-9","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(03)00069-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-23T19:00:04Z","doi":"10.1016/s0004-3702(03)00069-9","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(91)90067-t","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90067-t","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(91)90067-t","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1055/s-0039-1677925","name":"Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications","source":"crossref","abstract":"Objective: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018. Methods: Ovid MEDLINE® and Web of Science® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH®) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook. Results: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies. Conclusions: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.","url":"https://doi.org/10.1055/s-0039-1677925","authors":["Gretchen Jackson","Jianying Hu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-16T22:40:14Z","doi":"10.1055/s-0039-1677925","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.21037/jmai-2025-207","name":"Cognitive impairment screening in aging China: a narrative review of the Hong Kong Brief Cognitive Test and its future with artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-2025-207","authors":["Rich Q Y Zhou","Bao-Liang Zhong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-11T06:14:00Z","doi":"10.21037/jmai-2025-207","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-030-06170-8_15","name":"Artificial Intelligence and Literature","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-06170-8_15","authors":["Tim Van de Cruys"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-07T19:05:27Z","doi":"10.1007/978-3-030-06170-8_15","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.58496/bjai/2024/018","name":"Enhancing Privacy in Artificial Intelligence Services Using Hybrid Homomorphic Encryption","source":"crossref","abstract":"The increasing occurrence of cyberattacks specifically aimed at critical infrastructure has led to the adoption of network intrusion detection techniques for the Internet of Things (IoT). AI is transforming multiple sectors today, the growth of adversarial attacks on AI models and models present imperative privacy issues which hinder its larger implementation. Some of the Privacy-Preserving Artificial Intelligence (PPAI) methods including HE make it possible to secure data during the calculation process. Yet conventional HE techniques experience certain disadvantages at present with applicability to highly scalable and resource-limited applications. Moreover, this paper presents an HHE technique that is designed by integrating symmetric cryptography with HE to overcome the above-mentioned challenges successfully. To this end, we propose the GuardAI framework for end devices with limited resources such that encrypted data can be classified while preserving the privacy of input data and AI models. To show the effectiveness of the HHE, we apply it to the actual problem of heart disease classification based on the easily contaminated ECG signals. In this way, the proposed method maintains the privacy of the data with little computational and communication cost for analysts and devices and has a fairly reasonable level of accuracy in comparison with unencrypted inference. This work therefore provides a foundation for secure and private approach in AI especially for those developed to suit devices and systems with limited resources by incorporating HHE into the PPAI systems.","url":"https://doi.org/10.58496/bjai/2024/018","authors":["Mustafa A Jalil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T21:13:18Z","doi":"10.58496/bjai/2024/018","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(80)90019-3","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(80)90019-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(80)90019-3","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(91)90020-k","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90020-k","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90020-k","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(07)00096-3","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(07)00096-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-06-04T13:06:52Z","doi":"10.1016/s0004-3702(07)00096-3","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(06)00028-2","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(06)00028-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-03-31T12:14:41Z","doi":"10.1016/s0004-3702(06)00028-2","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(84)90022-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(84)90022-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(84)90022-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-319-69877-9_9","name":"QoS-Based Medical Program Evolution","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-69877-9_9","authors":["Yongzhong Cao","Junwu Zhu","Chen Shi","Yalu Guo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-10-31T02:48:25Z","doi":"10.1007/978-3-319-69877-9_9","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.59707/hymrotbo6205","name":"Artificial Intelligence for scientific research and discoveries","source":"crossref","abstract":"The integration of artificial intelligence (AI) into scientific research is accelerating rapidly, with large language models (LLMs) and generative AI tools now widely adopted by researchers worldwide. While the most common use of LLMs is primarily to enhance written outputs, emerging uses like streamlining systematic reviews and improving research efficiency, with some AI-assisted workflows demonstrating over 350-fold acceleration while maintaining expert-level quality. Beyond text, AI is increasingly applied in drug and protein target discovery, enabling rapid identification of previously inaccessible targets and accelerating early-stage drug development. In medical imaging, multimodal AI models have shown the ability to detect pathologies such as glaucoma from fundus photographs with high accuracy, and generative models can create high-quality, medical-grade images to augment datasets, aid training, and produce educational figures. Despite potential risks, the benefits of these technologies are becoming increasingly evident, with AI poised to transform research methodology, diagnostics, and medical education.","url":"https://doi.org/10.59707/hymrotbo6205","authors":["Saif Aldeen Alryalat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T01:00:27Z","doi":"10.59707/hymrotbo6205","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.18686/aem.v9i1.155","name":"Application of Artificial Intelligence in Medical Imaging Diagnosis","source":"crossref","abstract":"Both the treatment of cancer and other serious diseases often depends on the diagnosis of artificial complexity and heavy experience. The introduction of artificial intelligence in medical imaging has injected vitality into the diagnosis of images. Artificial intelligence uses deep learning, image segmentation, neural networks and other algorithms flexibly in image recognition through learning data sets to extract features for accurate diagnosis of clinical diseases. At the same time, it also plays a special role in controlling the spread of infectious diseases such as new coronary pneumonia.","url":"https://doi.org/10.18686/aem.v9i1.155","authors":["Linyi Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-03T02:42:01Z","doi":"10.18686/aem.v9i1.155","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.64388/irev9i9-1714810","name":"Revolutionizing Healthcare: The Role of Artificial Intelligence in Medical Support","source":"crossref","abstract":"This paper explores the current manual process of diagnosing and treating malaria patients in developing countries, specifically Nigeria, where healthcare professionals rely on patient interviews and laboratory tests to determine the cause of illness and prescribe treatments. The proposed system aims to automate and streamline this process by utilizing the Structured Systems Analysis and Design Method (SSADM), a widely-used approach for developing modular systems. The system initializes a counter based on patient-reported symptoms, logically processing each symptom to help identify potential conditions. For example, a diagnosis of malaria is suggested when a patient exhibits certain symptoms. The system then generates treatment recommendations and drug prescriptions based on the symptoms identified, saving and printing the results. If the symptoms are insufficient for a diagnosis, the system prompts the patient for further lab tests and consultations with a medical practitioner. This approach seeks to enhance the efficiency and accuracy of the diagnostic process in resource-constrained environments.","url":"https://doi.org/10.64388/irev9i9-1714810","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-09T12:35:07Z","doi":"10.64388/irev9i9-1714810","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.5220/0011897100003613","name":"Empirical Research on Implementation Paths of Combination of Medical Services and Elderly Care in Communities Based on Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011897100003613","authors":["Xinbo Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-29T07:59:47Z","doi":"10.5220/0011897100003613","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2019.07.001","name":"Personalized oncology with artificial intelligence: The case of temozolomide","source":"crossref","abstract":"Purpose Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability. Methods Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolomide, inspired by Faivre et al. [10] and Panetta et al. [25,26]. The patient pharmacokinetic parameters can only be partially observed at admission and are progressively learned by Bayesian inference during treatment. For every patient, we seek to minimize tumor size while avoiding severe toxicity, i.e. maintaining an acceptable toxicity level. The optimization algorithm we rely on borrows from the field of artificial intelligence. Results Optimal personalized protocols (OPP) achieve a sizable decrease in tumor size at the population level but also patient-wise. The tumor size is on average 67.2 g lighter than with the standard maximum-tolerated dose protocol (MTD) after 336 days (12 MTD cycles). The corresponding 90% confidence interval for average tumor size reduction amounts to 58.6-82.7 g. When treated with OPP, less patients experience severe toxicity in comparison to MTD. Major findings We quantify in-silico the benefits offered by personalized oncology in the case of temozolomide administration. To do so, we compute optimal personalized protocols for a population of heterogeneous patients using artificial intelligence techniques. At each treatment day, the protocol is updated by taking into account the feedback obtained from patient's reaction to the drug administration. Personalized protocols greatly differ from each other, and from the standard MTD protocol. Benefits of personalization are very sizable: tumor sizes are much smaller on average and also patient-wise, while severe toxicity is made less frequent.","url":"https://doi.org/10.1016/j.artmed.2019.07.001","authors":["Nicolas Houy","François Le Grand"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-12T19:57:51Z","doi":"10.1016/j.artmed.2019.07.001","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-031-64049-0_3","name":"Principles of Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64049-0_3","authors":["Euclid Seeram","Vijay Kanade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T10:02:01Z","doi":"10.1007/978-3-031-64049-0_3","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.25215/9371837764.29","name":"ARTIFICIAL INTELLIGENCE AS A CATALYST FOR ADVANCING MEDICAL DIAGNOSTIC METHODS","source":"crossref","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in medical diagnostics, enabling faster, more accurate, and cost-effective disease detection. This paper explores various AI techniques, including machine learning, deep learning, and computer vision, which have enhanced diagnostic capabilities across specialties such as radiology, pathology, and cardiology. By automating complex image analysis and predictive modelling, AI supports early disease detection, personalized treatment planning, and reduction of diagnostic errors. The paper reviews recent case studies on successful AI applications and discusses challenges such as data privacy, ethical concerns, and integration into healthcare systems. This study highlights AI’s pivotal role in advancing diagnostic methods, contributing to improved patient outcomes and the future of precision medicine.","url":"https://doi.org/10.25215/9371837764.29","authors":["Mrs. Smita Ketan Hadawale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-05T02:17:23Z","doi":"10.25215/9371837764.29","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1049/pbhe054e_ch18","name":"Artificial intelligence in sports","source":"crossref","abstract":"AI has the potential to greatly enhance the way we train, compete, and watch sports. The use of AI in sports is still in its infancy but its application in computer vision, performance analysis, decision-making, and training and rehabilitation can revolutionize the industry. However, we must also consider the ethical implications of using AI in sports, such as data privacy and fairness in competition. As technology continues to advance, we can expect to see even more innovative uses of AI in the sports industry.","url":"https://doi.org/10.1049/pbhe054e_ch18","authors":["Aravind Ganesh","Surya Vishnuram","Pavithra Aravind"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-16T04:09:57Z","doi":"10.1049/pbhe054e_ch18","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.7759/cureus.88480","name":"Comparison of the Performance of Five Generative Artificial Intelligence Models on a Medical Molecular Biology Examination","source":"crossref","abstract":"Objective The aim of this study is to evaluate the performance of five common Chinese generative artificial intelligence (GAI) models on a medical molecular biology examination and assess the application value of these GAIs in teaching. Methods A set of medical molecular biology test questions was used to measure the performance of five Chinese GAIs, including ERNIE Bot, chatGLM, iFLYTEK Spark, Qwen, and Doubao. The correct response rates of the five GAIs were compared with those of actual medical undergraduates using an unpaired t-test in GraphPad Prism 6.01. Results The total scores of the five GAIs all exceeded the passing score of 60 (full score: 100), ranging from 75.67 to 88.67. ERNIE Bot, chatGLM, Qwen, and Doubao demonstrated higher correct rates for total scores (80.33%-88.67%; p-value: 0.0127-0.0492) and for multiple-choice questions (83.33%-87.50%; p-value: 0.0071-0.0137) compared to actual undergraduates, showing a different distribution pattern of incorrect responses. Conclusion This study demonstrated the effectiveness of the five GAIs as learning aids in medical molecular biology. However, due to occasional incorrect answers, undergraduates should apply critical thinking when using GAI-generated responses. Meanwhile, a discipline-specific AI agent for medical molecular biology should be developed as soon as possible.","url":"https://doi.org/10.7759/cureus.88480","authors":["Xiaoying Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T06:46:51Z","doi":"10.7759/cureus.88480","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artmed.2024.102846","name":"Hierarchical medical image report adversarial generation with hybrid discriminator","source":"crossref","abstract":"Background and objectives Generating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current models for generating reports from medical images often fail to characterize some abnormal findings, and some models generate reports with low quality. In this study, we propose a model to generate high-quality reports from medical images. Methods In this paper, we propose a model called Hybrid Discriminator Generative Adversarial Network (HDGAN), which combines Generative Adversarial Network (GAN) with Reinforcement Learning (RL). The HDGAN model consists of a generator, a one-sentence discriminator, and a one-word discriminator. Specifically, the RL reward signals are judged on the one-sentence discriminator and one-word discriminator separately. The one-sentence discriminator can better learn sentence-level structural information, while the one-word discriminator can learn word diversity information effectively. Results Our approach performs better on the IU-X-ray and COV-CTR datasets than the baseline models. For the ROUGE metric, our method outperforms the state-of-the-art model by 0.36 on the IU-X-ray, 0.06 on the MIMIC-CXR and 0.156 on the COV-CTR. Conclusions The compositional framework we proposed can generate more accurate medical image reports at different levels.","url":"https://doi.org/10.1016/j.artmed.2024.102846","authors":["Junsan Zhang","Ming Cheng","Qiaoqiao Cheng","Xiuxuan Shen","Yao Wan","Jie Zhu","Mengxuan Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-21T08:36:23Z","doi":"10.1016/j.artmed.2024.102846","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.3390/s26072131","name":"Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging","source":"crossref","abstract":"This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare.","url":"https://doi.org/10.3390/s26072131","authors":["Muhammad Fayaz","Kim Hagsong","Sufyan Danish","L. Minh Dang","Abolghasem Sadeghi-Niaraki","Hyeonjoon Moon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T13:41:53Z","doi":"10.3390/s26072131","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1108/aiie-10-2025-0304","name":"Ensuring equitable access to artificial intelligence in medical education: a scoping review","source":"crossref","abstract":"Purpose Artificial intelligence (AI) is transforming medical education, enhancing knowledge acquisition, teaching, assessment, and curriculum delivery. While AI offers the potential to democratise access and improve inclusivity, little is known about how equity is addressed in AI-enabled medical education. This scoping review maps current evidence, identifies gaps, and provides insights for equitable implementation. Design/methodology/approach A scoping review was conducted following the Joanna Briggs Institute methodology. Peer-reviewed literature was identified through MEDLINE, Evidence-Based Medicine Reviews (Health Technology Assessment), Health and Psychosocial Instruments and Global Health. Screening, data extraction, and thematic analysis were performed by multiple reviewers. Quantitative data were summarised descriptively, and qualitative data were synthesised to identify narrative themes. Findings Of 256 records identified, 80 full-text articles were reviewed, and 35 studies were included. Most were published between 2022 and 2025 and originated predominantly from high-income countries. AI applications focused on large language models (48%), with curriculum design (49%) being the most common area of focus. Equity themes were: (1) equitable access (demographic, geographic, and distributed learning disparities); (2) bias (algorithmic, cultural, and socioeconomic); and (3) AI literacy (limited curricular exposure and preparedness). Originality/value Previous literature reviews have examined AI and its ethical use in medical education, but not through an equity lens. This scoping review highlights that medical education is at a pivotal juncture, shaped by converging imperatives: inclusivity and the rapid evolution of AI. To expand access and standardisation, equitable implementation requires AI literacy, contextually relevant tools, and active bias mitigation. Future research should prioritise inclusive, contextually appropriate, and accessible AI interventions across medical education.","url":"https://doi.org/10.1108/aiie-10-2025-0304","authors":["Jessica Beattie","Nilakshi Waidyatillake","Cailin Mellberg","Lyndal Parker-Newlyn","Erin Moth","Veronica Preda","Darran Foo","Christine L. Chiu","Janani Mahadeva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T05:57:31Z","doi":"10.1108/aiie-10-2025-0304","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.3233/faia250684","name":"How Do Doctors Perceive AI in Their Medical Practice?","source":"crossref","abstract":"Although Artificial Intelligence (AI) has proven to be valuable in healthcare, its application in clinical practice is still not widespread. We conducted an online survey among clinicians and medical physicists from Italian professional associations to investigate the perceived drivers and barriers to the use of AI in medical practice. Increased efficiency is the main driver for physicians to use AI systems, while the main barriers are related to trust and availability in clinical settings. Efficiency is linked to saving time in task management, but this requires trust in the technology. However, doctors believe that AI cannot replace their knowledge and ask to be involved in the development of the socio-technical system. At the organisational level, the slow adoption revolves around two main criticalities: their economic resources and the attitude of their management. Large centres led by innovation-driven management tend to favour the introduction of AI, while small and less wealthy centres will find it more difficult to integrate AI.","url":"https://doi.org/10.3233/faia250684","authors":["Laura Sartori","Chiara Binelli","Francesca Lizzi","Francesco Sensi","Alessandra Retico"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T14:38:36Z","doi":"10.3233/faia250684","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/3-540-56920-0_17","name":"A combination scheme of artificial intelligence and fuzzy pattern recognition in medical diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-56920-0_17","authors":["Ludmila I. Kuncheva","Roumen Z. Zlatev","Snezhana N. Neshkova","Hans Gamper"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-26T11:51:59Z","doi":"10.1007/3-540-56920-0_17","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.4018/978-1-7998-9172-7.ch002","name":"A Review on Artificial Intelligence for Electrocardiogram Signal Analysis","source":"crossref","abstract":"Cardiovascular disease (CVD) is a broad term encompassing a group of heart and blood vessel abnormalities that is the leading cause of death worldwide. The most popular and low-cost diagnostic tool for assessing the heart electrical impulses is an electrocardiogram (ECG). Automation is required to reduce errors and human burden while interpreting ECG signals. In recent years, deep learning shows better performance in ECG classification and has also shown that automated classification of ECG signals can improve accuracy and efficiency. In this chapter, the authors review the research work on ECG signals using deep learning methods like deep belief network (DBNK), convolutional neural network (CNNK), long short-term memory (LSTMY), recurrent neural network (RNNK), and gated recurrent unit (GRUT). In the research articles published between 2017 and 2021, CNNK was found to be the most appropriate technique for feature extraction.","url":"https://doi.org/10.4018/978-1-7998-9172-7.ch002","authors":["M Krishna Chaitanya","Lakhan Dev Sharma","Amarjit Roy","Jagdeep Rahul"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-11T10:40:30Z","doi":"10.4018/978-1-7998-9172-7.ch002","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.20944/preprints202311.0472.v1","name":"Augmented Reality and Artificial Intelligence Medical Waste Classification System and Method","source":"crossref","abstract":"There are four categories of medical waste that cannot be mixed as this will cause serious problems such as environmental pollution or infection. In the past, the classification of medical waste often involved a lot of human and material resources to process, with workers at risk of exposure to infectious substances. Therefore, an augmented reality (AR) and artificial intelligence (AI) medical waste classification system and method were developed. This innovative medical waste classification system and method combines AR and AI identification technology to reduce the risk of manual judgment errors by clinical staff when handling medical waste.","url":"https://doi.org/10.20944/preprints202311.0472.v1","authors":["Pao-Ju Chen","Wei-Kai Liou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-08T02:29:41Z","doi":"10.20944/preprints202311.0472.v1","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1093/acamed/wvag039","name":"Addressing algorithmic bias in artificial intelligence-driven medical education assessment","source":"crossref","abstract":"Zhicheng Du; Addressing Algorithmic Bias in Artificial Intelligence-Driven Medical Education Assessment, Academic Medicine, , wvag039, https://doi.org/10.1","url":"https://doi.org/10.1093/acamed/wvag039","authors":["Zhicheng Du"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-16T04:26:52Z","doi":"10.1093/acamed/wvag039","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.62441/nano-ntp.v20i6.19","name":"Artificial Intelligence In Healthcare: A Review Of Deep Learning Models For Medical Image Analysis","source":"crossref","abstract":"The rapid advancements in Artificial Intelligence (AI) have revolutionized various sectors, with healthcare being a major beneficiary. Specifically, deep learning (DL) models have emerged as a transformative force in medical image analysis, enabling enhanced diagnostic accuracy, early disease detection, and personalized treatment planning. This review paper provides a comprehensive analysis of the current state of deep learning models employed in medical image analysis. We examine the architectural advancements, applications, challenges, and future directions, with a focus on how these models are reshaping healthcare delivery. The paper also explores the potential of deep learning in improving healthcare accessibility and outcomes, providing insights into the integration of AI in clinical practice.","url":"https://doi.org/10.62441/nano-ntp.v20i6.19","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-29T03:12:15Z","doi":"10.62441/nano-ntp.v20i6.19","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.2196/preprints.98668","name":"Retraction: Artificial Intelligence–Based Neural Network for the Diagnosis of Diabetes: Model Development (Preprint)","source":"crossref","abstract":"UNSTRUCTURED","url":"https://doi.org/10.2196/preprints.98668","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-28T19:45:36Z","doi":"10.2196/preprints.98668","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2020.101839","name":"Detecting potential signals of adverse drug events from prescription data","source":"crossref","abstract":"Adverse drug events (ADEs) may occur and lead to severe consequences for the public, even though clinical trials are conducted in the stage of pre-market. Computational methods are still needed to fulfil the task of pharmacosurveillance. In post-market surveillance, the spontaneous reporting system (SRS) has been widely used to detect suspicious associations between medicines and ADEs. However, the passive mechanism of SRS leads to the hysteresis in ADE detection by SRS based methods, not mentioning the acknowledged problem of under-reporting and duplicate reporting in SRS. Therefore, there is a growing demand for other complementary methods utilising different types of healthcare data to assist with global pharmacosurveillance. Among those data sources, prescription data is of proved usefulness for pharmacosurveillance. However, few works have used prescription data for signalling ADEs. In this paper, we propose a data-driven method to discover medicines that are responsible for a given ADE purely from prescription data. Our method uses a logistic regression model to evaluate the associations between up to hundreds of suspected medicines and an ADE spontaneously and selects the medicines possessing the most significant associations via Lasso regularisation. To prepare data for training the logistic regression model, we adapt the design of the case-crossover study to construct case time and control time windows for the extraction of medicine use information. While the case time window can be readily determined, we propose several criteria to select the suitable control time windows providing the maximum power of comparisons. In order to address confounding situations, we have considered diverse factors in medicine utilisation in terms of the temporal effect of medicine and the frequency of prescription, as well as the individual effect of patients on the occurrence of an ADE. To assess the performance of the proposed method, we conducted a case study with a real-world prescription dataset. Validated by the existing domain knowledge, our method successfully traced a wide range of medicines that are potentially responsible for the ADE. Further experiments were also carried out according to a recognised gold standard, our method achieved a sensitivity of 65.9% and specificity of 96.2%.","url":"https://doi.org/10.1016/j.artmed.2020.101839","authors":["Chen Zhan","Elizabeth Roughead","Lin Liu","Nicole Pratt","Jiuyong Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-27T11:34:01Z","doi":"10.1016/j.artmed.2020.101839","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.engappai.2023.106113","name":"Designing an IoT-enabled supply chain network considering the perspective of the Fifth Industrial Revolution: Application in the medical devices industry","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.106113","authors":["Sina Nayeri","Zeinab Sazvar","Jafar Heydari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-15T14:02:47Z","doi":"10.1016/j.engappai.2023.106113","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(94)90076-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(94)90076-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(94)90076-0","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(89)90034-9","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90034-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90034-9","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(06)00013-0","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(06)00013-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-02-24T12:20:44Z","doi":"10.1016/s0004-3702(06)00013-0","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(91)90073-s","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90073-s","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90073-s","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(01)00078-9","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00078-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T12:57:34Z","doi":"10.1016/s0004-3702(01)00078-9","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(85)90081-5","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90081-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(85)90081-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(93)90066-k","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90066-k","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90066-k","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.51127/jamdcv06i01oa05","name":"KNOWLEDGE OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN HEALTH CARE WORKERS","source":"crossref","abstract":"Objective: To assess the knowledge and extent of application of artificial intelligence among healthcare workers along with comparison between male and female gender as well as seniority of healthcare workers. Materials and Methods: A cross-sectional study was conducted among 300 healthcare professionals in two hospitals Ghurki Trust Teaching Hospital Lahore and Allied Hospital Faisalabad over 6 months using a questionnaire proforma by chat GPT. Results: The study included 356 participants, mostly female (72.2%) with an average age of 28.08±4.20 years. Most had 1-9 years of work experience (61.5%). Nearly half (49.4%) had no AI application experience, and only 14.0% had formal AI training. Despite this, 80.3% recognized AI's importance in healthcare. AI was used by 24.4% of participants, mainly in patient monitoring (25.3%), research (27.2%), and diagnostics (19.7%). Challenges included clinical validation (33.1%) and cost (32.6%). Ethical concerns were significant, with 80.9% worried about privacy and 59.0% distrusting AI with sensitive data. Education and training (71.1%) were key for AI integration. Conclusion: Learning and application of artificial intelligence is the need of the hours and the general opinion is that if used under doctors' supervision, it will help improve patient care.","url":"https://doi.org/10.51127/jamdcv06i01oa05","authors":["Shamsa Arshad Butt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-15T05:43:38Z","doi":"10.51127/jamdcv06i01oa05","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2003.06.001","name":"A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets","source":"crossref","abstract":"This paper proposes a new constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumor. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP.","url":"https://doi.org/10.1016/j.artmed.2003.06.001","authors":["Celia C. Bojarczuk","Heitor S. Lopes","Alex A. Freitas","Edson L. Michalkiewicz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-12-04T11:59:03Z","doi":"10.1016/j.artmed.2003.06.001","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1109/tiptekno.2019.8894998","name":"An Artificial Intelligence Solution For Reduce Complications at Tissue Expansion","source":"crossref","abstract":"In this study, a robust artificial intelligence system which aims to minimize the common complications in the application of balloon plasty method which has been used for more than half a century and its application is explained. The system was tested as a prototype on new zealand rabbits for twenty days and 1 of the 6 subjects was excluded from the study due to a lack of literature and 5 of them were successful. In general, the structure of the system, working algorithm and data from the subjects are summarized.","url":"https://doi.org/10.1109/tiptekno.2019.8894998","authors":["Rıfat Uğurlutan","Murat Ayaz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-13T09:53:58Z","doi":"10.1109/tiptekno.2019.8894998","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.21037/jmai-23-36","name":"Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology","source":"crossref","abstract":"Abstract: Since its public release in November 2022, the usage of ChatGPT (Open AI, USA) has been unprecedented. This large language model (LLM) can produce human-like text from deep-learning techniques. LLMs are rapidly approaching human-level performance. ChatGPT can potentially help democratize the ability to code, by allowing clinicians to be able to develop basic artificial intelligence (AI) techniques. By leveraging AI models, these clinicians can expand the scope of their research abilities, and this can potentially lead to an AI in medicine revolution, where clinicians are able to generate clinically-focused AI techniques with the goal of improving patient outcomes across all domains. In this paper, we examine the performance of ChatGPT at developing an AI program for medicine and its associated limitations and challenges. Similar to the majority of AI models, the ethical concerns surrounding its application in medicine remains, which includes biases, patient autonomy, and confidentiality, transparency, and accuracy of data. ChatGPT must also be used in accordance with local healthcare regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. All things considered, ChatGPT and future generative AI technologies will democratize the ability to code and develop AI, likely leading to breakthroughs in the medical AI sector.","url":"https://doi.org/10.21037/jmai-23-36","authors":["Ethan Waisberg","Joshua Ong","Sharif Amit Kamran","Mouayad Masalkhi","Nasif Zaman","Prithul Sarker","Andrew G. Lee","Alireza Tavakkoli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-04T08:53:32Z","doi":"10.21037/jmai-23-36","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1201/9781042018116-12","name":"Artificial Intelligence as an Enabler of Smart Medical Tourism: A Comparative Study of India and Uzbekistan","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781042018116-12","authors":["Jiyanov Uktam Panjiyevich","Nidhi Singh","Esonboev Bakhodir Bakir Ugli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-03T15:55:02Z","doi":"10.1201/9781042018116-12","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-032-18897-7_21","name":"An Empirical Comparison of Topic Models: LDA, NMF, and BERTopic Applied to Hotel Customer Reviews","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_21","authors":["Salwa Khalyl","Zoubir Zarrouk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:54:25Z","doi":"10.1007/978-3-032-18897-7_21","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-030-53970-2_14","name":"Theme Identification for Linked Medical Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-53970-2_14","authors":["Siham Eddamiri","Elmoukhtar Zemmouri","Asmaa Benghabrit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-18T15:03:55Z","doi":"10.1007/978-3-030-53970-2_14","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1148/ryai.250273","name":"Rethinking Privacy in Medical Imaging AI: From Metadata and                     Pixel-Level Identification Risks to Federated Learning and Synthetic Data                     Challenges","source":"crossref","abstract":"This report reviews methods for preparing imaging data for artificial intelligence applications, focusing on the need for robust privacy protection through de-identification, federated learning, and synthetic data generation, while highlighting the potential risks associated with these approaches.","url":"https://doi.org/10.1148/ryai.250273","authors":["Konstantina Giouroukou","Kostas Marias","Manolis Tsiknakis","Michail E. Klontzas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T14:47:53Z","doi":"10.1148/ryai.250273","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.58532/v3baai6p8ch3","name":"ARTIFICIAL INTELLIGENCE IN MEDICAL SCIENCE: EARLY LUNG CANCER CELL DETECTION USING DEEP LEARNING","source":"crossref","abstract":"Artificial intelligence is currently playing a key and important part in medical science. The medical industry is undergoing a significant transformation due to evolution. This chapter mostly contributes to the advancement of lung cancer research. Lung cancer is the leading cause of death worldwide; due to cancer's extremely low survival rate, it accounts for roughly 18% of all fatalities. The development of artificial intelligence (AI) and machine learning (ML) techniques, as well as their applications in a variety of disciplines, have been extremely beneficial in revealing new developments in the fight against cancer. Deep learning is a crucial and developing AI approach for the creative change in the healthcare domain. A layered approach is used in deep learning, a sort of machine learning.","url":"https://doi.org/10.58532/v3baai6p8ch3","authors":["Shalini Wankhade","Dr. Manohar Kodmelwar","Dr. Pravin Futane","Kishor Pathak","Mahesh Bhandari","Swati Patil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-13T06:13:35Z","doi":"10.58532/v3baai6p8ch3","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1109/cai54212.2023.00059","name":"Artificial Intelligence for Medical Image Interpretation Using Expert Knowledge and Machine Learning","source":"crossref","abstract":"In 2022 268,490 new cases and 34,500 deaths was estimated for prostate cancer in the United States. Diagnosis of prostate cancer is primarily based on prostate-specific antigen (PSA) screening and trans-rectal ultrasound (TRUS)-guided prostate biopsy. PSA has a low specificity of 36% since benign conditions can elevate the PSA levels. The data set used for prostate cancer consists of t2-weighted MR images for 1,151 patients and 61,119 images. This paper presents an approach to applying knowledge-based artificial intelligence together with image segmentation to improve the diagnosis of prostate cancer using publicly available data. Complete and reliable segmentation into the transition zone and peripheral zone is required in order to automate and enhance the process of prostate cancer diagnosis.","url":"https://doi.org/10.1109/cai54212.2023.00059","authors":["Lars E.O. Jacobson","Adrian A. Hopgood","Mohamed Bader-El-Den","Vincenzo Tamma","David Prendergast","Peter Osborn","Shah Siddiqui","Alexander Gegov","Farzad Arabikhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-02T17:34:37Z","doi":"10.1109/cai54212.2023.00059","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.5772/intechopen.97746","name":"Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging","source":"crossref","abstract":"Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.","url":"https://doi.org/10.5772/intechopen.97746","authors":["Clémence Alla Takam","Aurelle Tchagna Kouanou","Odette Samba","Thomas Mih Attia","Daniel Tchiotsop"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-14T06:45:45Z","doi":"10.5772/intechopen.97746","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.5772/intechopen.1012581","name":"Comparative Analysis of Large Language Models (LLMs) in Generating Medical Board-Style Assessment Items","source":"crossref","abstract":"This study explores the emerging field of artificial intelligence (AI) in medical education, with a focus on the application of large language models (LLMs) in creating and explaining medical school-level assessment items. Recent attention has focused on how LLMs have the potential to reduce faculty time and effort in creating assessment items, but prior studies predominantly focus on a single LLM model (ChatGPT) and usually do not employ exemplars. We investigated the capabilities of three GPT and five Claude LLM models in creating medical board-style assessment items with two-shot biochemistry exemplars. The generated questions were evaluated by biomedical science educators in their respective fields for their accuracy, educational value, and overall quality. The findings indicate a notable potential of these LLMs in medical education, with the OpenAI ChatGPTs and Claude-V2 AIs demonstrating an ability to produce particularly high-quality assessment items. However, exemplars for each specific discipline may be required to increase the quality of the questions, as anatomy questions performed more poorly. Automated daily creation of exam banks with specified percentages across question categories was also tested, showing initial accuracy but degrading over time. In addition, we developed a GPT-4o-based interactive bot to build medical assessment items, called medStudent QandA, that is free and accessible to all OpenAI account holders (including free-tier users at the time of publication). This work opens multiple avenues for integrating AI into medical-level education and assessment settings, highlighting the evolving role of technology in medical education institutions and frameworks.","url":"https://doi.org/10.5772/intechopen.1012581","authors":["Sage Arbor","Mariluz Henshaw","Raquel Ritchie","Verena Van Fleet","Tafline Arbor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T13:00:09Z","doi":"10.5772/intechopen.1012581","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.engappai.2026.114731","name":"An interpretable multimodal transformer for medical report generation via hierarchical semantics and clinical labeling","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114731","authors":["Jia Sheng Yang","Chenbo Xia","Wei Li","Xu Xiao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-10T10:16:31Z","doi":"10.1016/j.engappai.2026.114731","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-032-18894-6_24","name":"A Compartmental Model for the Transition from Healthy to Sarcopenia and Type 2 Diabetes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_24","authors":["Abdesslam Boutayeb","Wiam Boutayeb","E. N. Mohamed Lamlili"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:36:57Z","doi":"10.1007/978-3-032-18894-6_24","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2023.102604","name":"Do Japanese word-embedded representations obtained in the academic corpus retain the medical concepts of “infarction”?","source":"crossref","abstract":"Objective The pathophysiological concepts of diseases are encapsulated in patients' medical histories. Whether information on the pathophysiology or anatomy of \"infarction\" can be preserved and objectively expressed in the distributed representation obtained from a corpus of scientific Japanese medical texts in the \"infarction\" domain is currently unknown. Word2Vec was used to obtain distributed representations, meanings, and word analogies of word vectors, and this process was verified mathematically. Materials & methods The texts were abstracts that were obtained by searching for \"infarction,\" \"abstract,\" and \"case report\" in the Japan Medical Journal Association's Ichushi Data Base. The abstracted text was morphologically analyzed to produce word sequences converted into their standard form. MeCab was used for morphological analysis and mecab-ipadic-NEologd and ComeJisyo were used as dictionaries. The accuracy of the known tasks for medical terms was evaluated using a word analogy task specific to the \"infarction\" domain. Results Only 33 % of the word analogy tasks for medical terminology were correct. However, 52 % of the new original tasks, which were specific to the \"infarction\" domain, were correct, especially those regarding anatomical differences. Discussion Documents related to \"infarction\" were collected from a corpus of Japanese medical documents and word-embedded expressions were obtained using Word2Vec. Terminology that had similar meanings to \"infarction\" included words such as \"cavity\" and \"ischemia,\" which suggest the pathology of an infarction. Conclusion The pathophysiological and anatomical features of an \"infarction\" may be retained in a distributed representation.","url":"https://doi.org/10.1016/j.artmed.2023.102604","authors":["Daiki Yokokawa","Kazutaka Noda","Takanori Uehara","Yasutaka Yanagita","Yoshiyuki Ohira","Masatomi Ikusaka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-10T11:36:01Z","doi":"10.1016/j.artmed.2023.102604","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.3233/atde251551","name":"Measurement of Integrity Genes in Family Businesses Based on Artificial Intelligence","source":"crossref","abstract":"The unique governance model and integrity crisis of family businesses have been deeply analyzed. The concept of the “integrity gene” of family businesses has been systematically proposed and quantified. According to the enterprise gene theory and the development characteristics of family businesses, a measurement index system of the integrity gene of family businesses has been constructed, which includes two dimensions: the integrity ability gene and the integrity behavior gene. Considering the complex problems that occur in the measurement process, artificial intelligence technologies (GA-BP neural network and entropy method) are introduced to conduct an empirical analysis of 134 listed family businesses. The research finds that integrity genes can be divided into four types: HH, HL, LH, and LL, and the AI model significantly improves the measurement accuracy and efficiency. By further combining deep learning and natural language processing technologies, a monitoring framework for integrity genes with dynamic and multi-source data integration is proposed, providing theoretical and technical support for the risk management and high-quality development of family businesses.","url":"https://doi.org/10.3233/atde251551","authors":["Fu Han","Ganglan Wei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-16T11:06:56Z","doi":"10.3233/atde251551","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.31362/patd.1487575","name":"Artificial intelligence meets medical expertise: evaluating GPT-4's proficiency in generating medical article abstracts","source":"crossref","abstract":"Purpose: The advent of large language models like GPT-4 has opened new possibilities in natural language processing, with potential applications in medical literature. This study assesses GPT-4's ability to generate medical abstracts. It compares their quality to original abstracts written by human authors, aiming to understand the effectiveness of artificial intelligence in replicating complex, professional writing tasks. Materials and Methods: A total of 250 original research articles from five prominent radiology journals published between 2021 and 2023 were selected. The body of these articles, excluding the abstracts, was fed into GPT-4, which then generated new abstracts. Three experienced radiologists blindly and independently evaluated all 500 abstracts using a five-point Likert scale for quality and understandability. Statistical analysis included mean score comparison inter-rater reliability using Fleiss' Kappa and Bland-Altman plots to assess agreement levels between raters. Results: Analysis revealed no significant difference in the mean scores between original and GPT-4 generated abstracts. The inter-rater reliability yielded kappa values indicating moderate to substantial agreement: 0.497 between Observers 1 and 2, 0.753 between Observers 1 and 3, and 0.645 between Observers 2 and 3. Bland-Altman analysis showed a slight systematic bias but was within acceptable limits of agreement. Conclusion: The study demonstrates that GPT-4 can generate medical abstracts with a quality comparable to those written by human experts. This suggests a promising role for artificial intelligence in facilitating the abstract writing process and improving its quality.","url":"https://doi.org/10.31362/patd.1487575","authors":["Ergin Sağtaş","Furkan Ufuk","Hakkı Peker","Ahmet Baki Yağcı"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-03T06:31:38Z","doi":"10.31362/patd.1487575","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.26226/m.682c982ea37eadc3bb481835","name":"AI-Ready Physicians: Designing Entrustable Professional Activities on Utilizing Artificial Intelligence in Clinical Practice for Post-Graduate Medical Trainees","source":"crossref","abstract":"","url":"https://doi.org/10.26226/m.682c982ea37eadc3bb481835","authors":["Russell D'Souza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-16T09:37:10Z","doi":"10.26226/m.682c982ea37eadc3bb481835","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.2196/preprints.104956","name":"From Open Conversion to Artificial Intelligence Override: Preventing Cognitive Deskilling in AI-Augmented Surgical Education (Preprint)","source":"crossref","abstract":"UNSTRUCTURED Artificial intelligence (AI) is rapidly entering surgical education through video-based skill assessment, robotic performance analytics, simulation-based feedback, computer vision, augmented reality overlays, and decision-support tools. These systems may improve access to feedback, standardize assessment, and accelerate skill acquisition. However, their educational value will depend not only on how well trainees perform with AI assistance but also on whether they retain the unaided perceptual judgment, technical skill, and fallback decision-making required when AI guidance is unavailable, incomplete, or incorrect. In minimally invasive surgery, conversion to open surgery is not merely a technical failure; it is a safety maneuver that depends on preserved foundational competence. AI-augmented surgical education may require an analogous competency: AI override. We define AI override as the timely ability to recognize when an AI-generated prompt, overlay, warning, assessment, or recommendation is unreliable, incomplete, or unsafe, and to pause, verify, reject, or supersede it using independent surgical judgment. This Viewpoint argues that cognitive deskilling in AI-augmented surgical education is a plausible but preventable risk. Evidence from surgical training, automation bias, radiology, and AI-assisted endoscopy suggests that automation can improve average performance while, in some settings, reducing opportunities for independent skill-building, vigilance, and unaided decision-making, especially when safeguards are absent. Direct longitudinal evidence in surgical AI education remains limited; therefore, this argument is conceptual and precautionary rather than deterministic. We propose that surgical educators treat AI override as a trainable competency. Educational strategies should include deliberate unaided practice before AI assistance, staged exposure to AI tools, simulation of misleading or failed AI guidance, assessment of unaided performance, explicit trust-calibration training, and periodic refresher exercises to preserve manual and cognitive fallback skills. AI should serve as a coach and feedback amplifier in surgical education, not a crutch that may narrow a trainee’s capacity to operate without it.","url":"https://doi.org/10.2196/preprints.104956","authors":["Dabeluchi Ngwu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-19T20:25:07Z","doi":"10.2196/preprints.104956","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1201/9781003394068-5","name":"Brain MRI Segmentation","source":"crossref","abstract":"This chapter presents a critical overview of brain MRI segmentation, a foundational technique in contemporary neuroscience with wide-ranging clinical and research applications. Magnetic Resonance Imaging (MRI) is particularly valued for its ability to produce high-resolution anatomical images without the risks associated with ionising radiation. Segmentation refers to the process of partitioning these images into anatomically meaningful regions, such as grey matter, white matter, cerebrospinal fluid, and subcortical structures, thereby enabling precise quantification and localisation of brain features. Such anatomical delineation is essential for the early diagnosis and monitoring of neurodegenerative diseases, the planning of neurosurgical procedures, and the investigation of psychiatric and developmental disorders. Recent advances in artificial intelligence, particularly deep learning, have significantly enhanced segmentation accuracy and efficiency. These methods increasingly leverage multimodal data to generate more comprehensive and clinically actionable insights. National and international initiatives, including MONAI and the UK Biobank, exemplify the momentum towards scalable, data-driven approaches in brain health research. The chapter outlines current methodological trends, discusses widely adopted datasets and evaluation metrics, and identifies key challenges, such as generalisability across populations and interpretability of AI-driven outputs. The integration of advanced machine learning techniques holds considerable promise for transforming diagnostic and therapeutic paradigms in neurology and psychiatry. By enabling more granular and objective assessments of brain structure and pathology, MRI segmentation is poised to play a central role in the future of precision medicine in neuroscience.","url":"https://doi.org/10.1201/9781003394068-5","authors":["Tanmoy Debnath","Shayne Chau","Md Geaur Rahman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T09:15:48Z","doi":"10.1201/9781003394068-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.13187/mai.2014.2.77","name":"The Characteristics of the Non-Linear Dynamics of the Electroencephalograms of Patients with Trigeminal Neuralgia","source":"crossref","abstract":"This article examines the application of nonlinear dynamics methods to the study of the electroencephalograms (EEG) of patients with trigeminal neuralgia (TN). The author has calculated delay time and embedding dimensions values and constructed recurrent EEG diagrams for healthy trial subjects and patients with TN. The study has revealed significant differences between the nonlinear dynamics of the EEGs of patients with TN and a normal EEG.","url":"https://doi.org/10.13187/mai.2014.2.77","authors":["Vitaly P. Omelchenko","Irina O. Mihalchich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-09-06T03:29:25Z","doi":"10.13187/mai.2014.2.77","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1109/rev-ai70456.2026.11622119","name":"The Evolution of Artificial Intelligence in Pediatric Medical Imaging and Diagnostics: A Bibliometric Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/rev-ai70456.2026.11622119","authors":["Aya Al-Alwani","Hanan Shaher Almarashdi","Najah Al Mohammedi","Maha Alhabbash","Jihan Yousef","Enas Abulibdeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T19:18:30Z","doi":"10.1109/rev-ai70456.2026.11622119","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2016.03.003","name":"Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer","source":"crossref","abstract":"Objective Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance. Methods We study a number of existing techniques for the extraction of predictors from the consultation notes, namely a bag of words based approach and topic modeling. In addition, we develop a dedicated technique to match the uncoded consultation notes with a medical ontology. We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs. We evaluate them in the context of predictive modeling for colorectal cancer (CRC), a disease known to be difficult to diagnose before performing an endoscopy. Results Our results show that we are able to extract useful information from the consultation notes. The predictive performance of the ontology-based extraction method moves significantly beyond the benchmark of age and gender alone (area under the receiver operating characteristic curve (AUC) of 0.870 versus 0.831). We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data (AUC of 0.896 versus 0.882) although the difference is not significant. The extracted features from the notes are shown be equally predictive (i.e. there is no significant difference in performance) compared to the coded data of the consultations. Conclusion It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance. Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset.","url":"https://doi.org/10.1016/j.artmed.2016.03.003","authors":["Mark Hoogendoorn","Peter Szolovits","Leon M.G. Moons","Mattijs E. Numans"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-04-02T02:34:55Z","doi":"10.1016/j.artmed.2016.03.003","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.24018/ejmed.2020.2.4.401","name":"Artificial Intelligence: The New Frontier in Surgery","source":"crossref","abstract":"This review aims to discuss the advances in artificial intelligence (AI) and the role it now plays in surgery. The discussion outlines the many capabilities of AI in improving the way in which surgery is conducted and a critical review of new AI developments. Artificial intelligence now well established in several industries has now begun to make a change with significant improvements in the practice of medicine. The use of algorithms that allow advanced computers to have cognitive functions that simulate human thought and actions has given rise to image and speech recognition, and autonomous robots that can perform unsupervised tasks relying on vast databanks of information. A transition from traditional laparoscopic surgery to robotic surgery has already taken place. Artificial intelligence is now beginning to extend the capabilities of surgical robots to encompass autonomy, which will allow them to use information from their surroundings, recognize problems and implement the correct actions without the need for human intervention. Advances in computing capability, machine engineering and robotics and the ever improving development of smart algorithms is allowing growth of the application of AI at a rapid pace. These developments have resulted in the development of nanorobots that function on a scale of nanometers and have become the next generation system to be integrated with AI and surgery. The use of this technology has resulted in advances in neurosurgery, vascular surgery and oncology. The future of surgery, like other fields in medicine will be data driven with a significant input from technology. Artificial Intelligence is one advancement that will play a significant role.","url":"https://doi.org/10.24018/ejmed.2020.2.4.401","authors":["MICHAEL MCFARLANE"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-29T05:23:25Z","doi":"10.24018/ejmed.2020.2.4.401","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1063/9780735423473_007","name":"Artificial Intelligence in Medical Imaging","source":"crossref","abstract":"Artificial intelligence (AI) in cancer image interpretation continues to evolve with complementary advances in image acquisition systems, imaging protocols, and machine learning tools, as well as expanding clinical tasks. AI can be defined as having computers simulate the conduction of human intelligence tasks. Advances in computers, in terms of both computing power and memory capacity, have led to a rapid increase in assessing the potential use of AI in various tasks in medical imaging, going beyond the initial use in computer-aided detection (CADe) to include diagnosis, prognosis, response to therapy, and risk assessment, as well as in cancer discovery. AI methods are being developed for CADe and computer-aided diagnosis (CADx), for computer-aided triaging (CADt), and sometimes for use as autonomous readers, often with the need for consideration for effect on radiologists’ perception/cognitive performance and workflow. While the prospects of AI in medical image interpretation are abundant and promising, they bring along challenges and limitations. This chapter focuses on the role of AI in medical image interpretation.","url":"https://doi.org/10.1063/9780735423473_007","authors":["Heather M. Whitney","Maryellen L. Giger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-05T18:24:33Z","doi":"10.1063/9780735423473_007","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-030-96630-0_4","name":"Domain Knowledge-Aided Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96630-0_4","authors":["Sheikh Rabiul Islam","William Eberle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-18T12:03:16Z","doi":"10.1007/978-3-030-96630-0_4","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1201/9781003464884-6","name":"Impact of Artificial Intelligence in Healthcare","source":"crossref","abstract":"The application of artificial intelligence (AI) in the healthcare sector has brought about a revolution in the early prediction of various diseases. The chapter explores the transformative potential of artificial intelligence in the early diagnosis and prediction of a common neurological disease known as multiple sclerosis (MS) and it focuses on the predictors of the disease using various machine learning techniques. It offers insight into the methodologies that have been utilized to predict multiple sclerosis and presents an analysis of the various factors that cause the disease. Data-driven approaches that have been utilized for the research on multiple sclerosis fill a variety of research gaps that exist between traditional methods and machine learning methodologies. The research gaps often lie in data analysis complexity, prediction accuracy, and scalability. These issues can be resolved using clinical records in a structured format, employing machine learning algorithms for predictive analysis and accuracy improvement. Continuous learning from large datasets and advancements in the adaptation of patterns will help form a robust and scalable system for early detection of the predictors of multiple sclerosis. By identifying hidden patterns or relationships that may not be visible through traditional statistical analysis, machine learning algorithms can learn from a wide range of attributes and data points to enable more accurate predictions. Machine learning algorithms such as Random Forest, XGBoost, and Decision Tree can excel in analyzing vast datasets relevant to multiple sclerosis. They can succeed in identifying subtle correlations and patterns, aid in understanding the clinical indicators, and help in improving the accuracy of prediction which would influence the response to treatment for individual patients. Machine learning algorithms can help in early multiple sclerosis (MS) identification and risk category classification. This chapter aims to give healthcare professionals, researchers, and policymakers insights into the growing possibilities of AI in revolutionizing the early detection and management of multiple sclerosis, ultimately improving patient outcomes and quality of life. The development of AI-driven predictive models could soon alter the course of MS treatment by improving early intervention and knowledge of this complicated neurological condition.","url":"https://doi.org/10.1201/9781003464884-6","authors":["Abhirup Bhattacharya","Zdzislaw Polkowski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-26T20:58:52Z","doi":"10.1201/9781003464884-6","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.21276/ijcmr.2019.6.12.7","name":"Artificial Intelligence in Dentistry: The Current Concepts and a Peek into the Future","source":"crossref","abstract":"Introduction: Humans have recreated intelligence for effective human decision making and to unburden themselves of the stupendous workload.Artificial intelligence can act as a supplemental tool to improve diagnosis and treatment care but intelligent machines can never be 'human'.The field of artificial intelligence is relatively young but has still come a long way in the fields of medicine and dentistry.Hence, there is a need for the dentists to be aware about its potential implications for a lucrative clinical practice in the future.","url":"https://doi.org/10.21276/ijcmr.2019.6.12.7","authors":["Shilpi Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-04-13T20:35:15Z","doi":"10.21276/ijcmr.2019.6.12.7","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.4018/978-1-6684-3791-9.ch005","name":"Role of Explainable Artificial Intelligence (XAI) in Prediction of Non-Communicable Diseases (NCDs)","source":"crossref","abstract":"Explainable artificial intelligence (XAI) concentrated on methods and models that simplify the comprehending and analysis of the ML models operation. Using XAI, systems deliver the essential facts to defend outcomes, mostly when unpredicted conclusions are made. It also certifies that there is an auditable and demonstrable way to guard algorithmic judgments including the factors of unbiased and being principled, which lead to building trust. Swift upsurge of non-communicable diseases (NCDs) turns out to be one of the severe health matters and one of the leading origins of death globally. In this chapter, the authors discussed XAI in healthcare, its benefits, and the deep Shapley additive explanations (DeepSHAP)-based deep neural network (DeepNN) framework provided with a feature selection method for prediction and explanation of non-communicable diseases followed by case study discussion about detection and progression of Alzheimer's disease (AD) with the help of XAI-based predictive models.","url":"https://doi.org/10.4018/978-1-6684-3791-9.ch005","authors":["Jana Shafi","Shamayita Basu","Selvani Deepthi Kavila"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-16T09:48:23Z","doi":"10.4018/978-1-6684-3791-9.ch005","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.engappai.2025.110082","name":"Competitive dual-students using bi-level contrastive learning for semi-supervised medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110082","authors":["Gang Hu","Feng Zhao","Essam H. Houssein"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-26T01:55:25Z","doi":"10.1016/j.engappai.2025.110082","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.21037/jmai-23-28","name":"Unlocking the potential of qualitative research for the implementation of artificial intelligence-enabled healthcare","source":"crossref","abstract":"Artificial intelligence (AI)-enabled clinical decision support tools (CDSTs) are complicated technologies, which form the basis of complex AI-enabled healthcare interventions.Research of AI-enabled CDSTs has proliferated, with 57,844 model development studies and 5,073 comparative or real-world evaluation studies readily identifiable on PubMed at the time of writing (1).Despite this proliferation of evidence, a notable translational gap persists with little real-world implementation of AI-enabled healthcare interventions (2).While research communities have acknowledged the value and importance of studying AI implementation in real-world clinical settings, there is limited evidence on how to translate the potential of AI into everyday healthcare practices.This persistent translational failure is multifactorial, but there is clear opportunity for impact from the research community if they can deliver the evidence that healthcare systems' decision makers need to fully evaluate complex interventions such as those involving AI-enabled CDSTs (2).This need for a holistic evidence","url":"https://doi.org/10.21037/jmai-23-28","authors":["Henry David Jeffry Hogg","Mark Philip Sendak","Alastair Keith Denniston","Pearse Andrew Keane","Gregory Maniatopoulos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-27T08:10:53Z","doi":"10.21037/jmai-23-28","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2024.102830","name":"Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis","source":"crossref","abstract":"The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential solution, to reduce the black-box effect of DL models and increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated with DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their variable quality, as well as constraints associated with real-world clinical routine. Moreover, we discuss the concept of structural uncertainty, a corpus of methods to facilitate the alignment of segmentation uncertainty estimates with clinical attention. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges for uncertainty quantification in the medical field.","url":"https://doi.org/10.1016/j.artmed.2024.102830","authors":["Benjamin Lambert","Florence Forbes","Senan Doyle","Harmonie Dehaene","Michel Dojat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-04T07:36:33Z","doi":"10.1016/j.artmed.2024.102830","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1109/ehb.2015.7391610","name":"Benefits of using artificial intelligence in medical predictions","source":"crossref","abstract":"Medicine is a domain where predictions are very important. This paper encourages the use of artificial intelligence (AI) as technological support for decision-making in medical life. Not because the human intelligence would not be enough, but because the mechanisms of artificial intelligence have several benefits that are suitable for this area. These positive features come as a result of combining two strong qualities in the same systems: the precision of mathematics and the power of current technologies. It would be a real waste to ignore them.","url":"https://doi.org/10.1109/ehb.2015.7391610","authors":["Adriana Albu","Loredana Stanciu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-03-25T21:10:08Z","doi":"10.1109/ehb.2015.7391610","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1023/a:1008348321016","name":"Dialectical models in artificial intelligence and law","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1008348321016","authors":["Jaap Hage"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T14:41:38Z","doi":"10.1023/a:1008348321016","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/s0004-3702(01)00126-6","name":"Artificial nonmonotonic neural networks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(01)00126-6","authors":["B. Boutsinas","M.N. Vrahatis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-14T13:01:41Z","doi":"10.1016/s0004-3702(01)00126-6","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-032-09339-4_1","name":"Intelligence, Artificial Intelligence and 6G Cellular Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-09339-4_1","authors":["Haesik Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-10T04:47:44Z","doi":"10.1007/978-3-032-09339-4_1","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2020.101857","name":"A trusted medical image super-resolution method based on feedback adaptive weighted dense network","source":"crossref","abstract":"High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Compared with traditional SR methods, the deep learning-based SR methods can obtain more clear and trusted HR images. In this paper, we propose a trusted deep convolutional neural network-based SR method named feedback adaptive weighted dense network (FAWDN) for HR medical image reconstruction. Specifically, the proposed FAWDN can transmit the information of the output image to the low-level features by a feedback connection. To explore advanced feature representation and reduce the feature redundancy in dense blocks, an adaptive weighted dense block (AWDB) is introduced to adaptively select the informative features. Experimental results demonstrate that our FAWDN outperforms the state-of-the-art image SR methods and can obtain more clear and trusted medical images than comparative methods.","url":"https://doi.org/10.1016/j.artmed.2020.101857","authors":["Lihui Chen","Xiaomin Yang","Gwanggil Jeon","Marco Anisetti","Kai Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-16T12:25:00Z","doi":"10.1016/j.artmed.2020.101857","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2015.09.002","name":"Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences","source":"crossref","abstract":"Objectives Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. Method The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. Results The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). Conclusions We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.","url":"https://doi.org/10.1016/j.artmed.2015.09.002","authors":["Anthony Costa Constantinou","Barbaros Yet","Norman Fenton","Martin Neil","William Marsh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-09-08T15:46:10Z","doi":"10.1016/j.artmed.2015.09.002","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2020.101998","name":"Interactive medical image segmentation via a point-based interaction","source":"crossref","abstract":"Due to low tissue contrast, irregular shape, and large location variance, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, a novel method is presented for interactive medical image segmentation with the following merits. (1) Its design is fundamentally different from previous pure patch-based and image-based segmentation methods. It is observed that during delineation, the physician repeatedly check the intensity from area inside-object to outside-object to determine the boundary, which indicates that comparison in an inside-out manner is extremely important. Thus, the method innovatively models the segmentation task as learning the representation of bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by the proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), the proposed method only asks the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) The method is utilized in a multi-level framework for better performance. It has been systematically evaluated in three different segmentation tasks, including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods.","url":"https://doi.org/10.1016/j.artmed.2020.101998","authors":["Jian Zhang","Yinghuan Shi","Jinquan Sun","Lei Wang","Luping Zhou","Yang Gao","Dinggang Shen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-28T11:16:14Z","doi":"10.1016/j.artmed.2020.101998","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.58496/mjaih/2024/004","name":"Examining Ghana's Health Professions Regulatory Bodies Act, 2013 (Act 857) To Determine Its Adequacy in Governing the Use of Artificial Intelligence in Healthcare Delivery and Medical Negligence Issues","source":"crossref","abstract":"This analysis examines Ghana’s Health Professions Regulatory Bodies Act, 2013 (Act 857) to assess its fitness to govern the ascent of artificial intelligence (AI) in reshaping healthcare delivery. As advanced algorithms supplement or replace human judgments, dated laws centered on individual practitioner liability struggle to contemplate emerging negligence complexities. Act 857 lacks bespoke provisions for governing this new era beyond outdated assumptions of human-centric care models. With AI projected to transform medicine, proactive reforms appear vital to enable innovation gains while upholding accountability. Through an IRAC legal analysis lens supplemented by case law spanning from the United States to Ghana, this paper demonstrates how judiciaries globally are elucidating risks from legal uncertainty given increasingly autonomous health technologies. Findings reveal governance gaps impeding equitable access to remedy where algorithmic activities contribute to patient harm. Calls for stringent training, validation and monitoring prerequisites before deploying higher-risk AI systems signal a reframed standard of care is warranted. Detailed recommendations to modernize Act 857 and adjacent regulation are provided, covering practitioner codes, product safety, ongoing evaluation duties, and crucially, updated liability rules on apportioning fault between disparate enterprises enabling flawed AI. Beyond protecting patients and practitioners, enhanced governance can boost investor confidence in Ghana’s AI healthcare ecosystem. Ultimately astute reforms today can reinforce innovation gains tomorrow across a more ethical, accountable industry.","url":"https://doi.org/10.58496/mjaih/2024/004","authors":["George Benneh Mensah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-22T06:33:45Z","doi":"10.58496/mjaih/2024/004","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.2139/ssrn.3529576","name":"Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.3529576","authors":["I. Glenn Cohen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-27T11:31:14Z","doi":"10.2139/ssrn.3529576","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.14744/ijmb.2020.81994","name":"Digitalization and artificial intelligence in laboratory medicine","source":"crossref","abstract":"","url":"https://doi.org/10.14744/ijmb.2020.81994","authors":["Banu Isbılen Basok"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-27T07:09:26Z","doi":"10.14744/ijmb.2020.81994","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/0004-3702(88)90047-1","name":"Geometric reasoning and artificial intelligence: Introduction to the special volume","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(88)90047-1","authors":["Deepak Kapur","Joseph L. Mundy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(88)90047-1","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/b978-0-443-44728-0.00013-5","name":"The impact of artificial intelligence on human memory and intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44728-0.00013-5","authors":["Luca Saba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T12:39:58Z","doi":"10.1016/b978-0-443-44728-0.00013-5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.artmed.2018.10.001","name":"Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders","source":"crossref","abstract":"Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems. Conventional machine learning approaches normally depend on selected features within a human annotated training set. However, it is quite laborious to obtain a well labeled data set and its generation can easily be influenced by accumulative errors caused by human factors. To overcome this, we will present our processing pipeline for query matching realized through neural networks within the task of medical procedure classification. The pipeline is built upon convolutional neural networks (CNN) and autoencoder with logistic regression. On the task of relevance determination between query and category text, the autoencoder based method has achieved a micro F1 score of 70.29%, while the convolutional based method has reached a micro F1 score of 60.86% with high efficiency. These two algorithms are compared in experiments with different configurations and baselines (SVM, logistic regression) with respect to their suitability for the task of automatic encoding. Advantages and limitations are discussed.","url":"https://doi.org/10.1016/j.artmed.2018.10.001","authors":["Yihan Deng","André Sander","Lukas Faulstich","Kerstin Denecke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-10-29T18:39:48Z","doi":"10.1016/j.artmed.2018.10.001","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.4103/jmms.jmms_161_23","name":"Artificial Intelligence in Health Care – A Study on Perceptions of and Readiness for Artificial Intelligence in Health-care Professionals","source":"crossref","abstract":"Abstract Background: With a call to action from the health-care industry and the Indian government, there are significant gaps in health-care professionals’ uptake and utilization of artificial intelligence (AI)-based tools. This study attempts to explore the current perceptions and readiness for AI among health-care workers. Methods: A web-based questionnaire comprising seven sections on descriptive educational and occupational data, AI familiarity level, role-specific training benefits, training advantages, implementation issues, driving factors, and perceived risks was designed from a literature search. Two additional domains of perception on professional impact and preparedness for AI in health care were estimated using a prevalidated Shinners AI Perception tool. Results: Of the 402 study participants, 192 (47.9%) were doctors from diverse specializations, and the remaining 209 (52.1%) were undergraduate medical and nursing students and affiliated health professionals. Although 79.8% of participants had never attended a course on AI, 82% agreed on the need for training in AI to explore new opportunities in their respective fields. 72.1% of participants agreed that data privacy and confidentiality posed the most significant challenge to AI implementation among the studied factors. Conclusion: This survey reveals awareness regarding AI, which is attributable to a lack of formal training received by health-care professionals. Most participants believed that AI could improve population health outcomes, and collective efforts are needed to make this belief a reality.","url":"https://doi.org/10.4103/jmms.jmms_161_23","authors":["Manvinder Tezpal","Subhodeep Ghosh","Radhika Lalwani","Jyoti Yadav","Arun Kumar Yadav"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-26T09:01:09Z","doi":"10.4103/jmms.jmms_161_23","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.3233/shti230581","name":"Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review","source":"crossref","abstract":"Artificial Intelligence (AI) is increasingly used to support medical students’ learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.","url":"https://doi.org/10.3233/shti230581","authors":["Fatima Nagi","Rawan Salih","Mahmood Alzubaidi","Hurmat Shah","Tanvir Alam","Zubair Shah","Mowafa Househ"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-30T07:55:01Z","doi":"10.3233/shti230581","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-981-16-2309-7_6","name":"When Artificial Intelligence Meets Daoism","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-2309-7_6","authors":["Fei Gai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-08T12:11:34Z","doi":"10.1007/978-981-16-2309-7_6","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1201/b15618-19","name":"Uncertainty, Safety, and Performance: A Generalizable Approach to Risk- Based (Therapeutic) Decision Making","source":"crossref","abstract":"From a perspective of applying deterministic, physiological models to clinical scenarios, the application of artificial intelligence (AI) in medicine is currently focused on decision support (DS) tools that provide guidance and “human-in-the-loop” feedback for diagnosis and control of therapeutic delivery. There are two key differentiators when differentiating emerging DS applications: (1) the model and computational methods used and (2) the decision-making framework used with these models. It is on this second aspect that this chapter focuses.","url":"https://doi.org/10.1201/b15618-19","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-19","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1596/45024","name":"Artificial Intelligence Integration in Medical Education: A Landscape with HOT Dimensions in Viet Nam","source":"crossref","abstract":"The study adopted a mixed-methods approach to: (i) describe the landscape of Artificial Intelligence (AI) adoption in medical schools in Viet Nam; (ii) analyze critical factors affecting the integration of AI in medical education; (iii) present key strategic actions areas to promote the integration of AI in medical education in Viet Nam. Overall, AI adoption among students and teachers is relatively high, with a large proportion reporting the use of AI-powered tools, particularly generative AI applications such as learning assistants. However, the integration of AI remains largely informal, fragmented, and concentrated in individual-level use cases rather than being systematically embedded into institutional teaching, clinical training, and educational management systems. The analysis of human factors reveals generally supportive attitudes for AI adoption. Students and teachers demonstrate positive attitudes and recognize the benefits of AI in improving learning efficiency, digital literacy, and research capacity. At the same time, important concerns persist regarding over-reliance on AI, reduced critical thinking, academic integrity, and the potential erosion of humanistic aspects of medical practice. From an organizational perspective, key barriers include the lack of structured AI training programs, limited integration of AI competencies into curricula, insufficient financial resources, and the absence of clear regulatory and ethical frameworks. Technological factors present a mixed picture with digital infrastructure providing a strong foundation but digital data underdevelopment and concerns about AI accuracy remain challenges. Advancing AI integration in medical education in Viet Nam requires a comprehensive and coordinated approach across human, organizational, and technological dimensions.","url":"https://doi.org/10.1596/45024","authors":["Sang Minh Le","Giang Bảo Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-13T02:11:51Z","doi":"10.1596/45024","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-031-64049-0_5","name":"Image Processing and Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64049-0_5","authors":["Euclid Seeram","Vijay Kanade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T10:02:01Z","doi":"10.1007/978-3-031-64049-0_5","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-031-94302-7_39","name":"Utilizing Artificial Intelligence to Revolutionize Cancer Screening Through the Application of Predictive Analytics in Public Health","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94302-7_39","authors":["Tarun Madan Kanade","Radhakrishna Batule","Payal Sanan","Jonathan Sudhir Joseph"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:28:50Z","doi":"10.1007/978-3-031-94302-7_39","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-981-15-7317-0_2","name":"Artificial Intelligence-Based Systems for Combating COVID-19","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-7317-0_2","authors":["Sandeep Kr. Sharma","S. Rakesh kumar","N. Gayathri","Rajiv Kumar Modanval","S. Muthuramalingam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-29T15:28:56Z","doi":"10.1007/978-981-15-7317-0_2","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1093/bmb/ldab022","name":"The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare and clinical trials in skeletal dysplasia: a paradigm for treating rare diseases","source":"crossref","abstract":"These important subjects have been chosen to have free online access. In addition, the Bulletin has a section to celebrate its amazing archive, (see end of this section). The British Medical Bulletin (BMB) on its website also has a fascinating section on the Nobel Prize-winners who wrote for the Bulletin and went on to win the accolade, information on which reviews have been most widely cited and information about the OUP which blog often has input from the Bulletin authors and editors. The first free online access review is The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare by Aun, Wong and Ting from Imperial College, London and the University of Cambridge, UK and the Dike-NUS Medical School, Singapore. They say that artificial intelligence (AI) and machine learning are rapidly evolving fields in various sectors, including healthcare. AI could transform physician...","url":"https://doi.org/10.1093/bmb/ldab022","authors":["Norman Vetter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-10T12:57:46Z","doi":"10.1093/bmb/ldab022","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.ejrai.2025.100006","name":"Ethical AI: A qualitative study exploring ethical challenges and solutions on the use of AI in medical imaging","source":"crossref","abstract":"Artificial Intelligence (AI) is being rapidly deployed in clinical practice in medical imaging settings worldwide. AI applications have the potential to transform this discipline and provide better patient outcomes. However, many ethical challenges exist when implementing AI in clinical practice. This study aims to explore these challenges and suggest ways forward. This study was supported by the European Federation of Radiographer Societies (EFRS), together with the European Society of Radiology (ESR) through the EFRS Research Hub at ECR 2024. Ethics approval was in place before data collection. All professionals within the medical imaging AI ecosystem who were registered congress attendees were eligible to participate. This qualitative study employed semi-structured interviews. All interviews were audio recorded after informed written consent by study participants. Transcribed data was analysed using a content analysis approach. In total, 43 professionals took part in this study. The sample included radiographers, radiologists, medical physicists, health informaticians, and business and IT specialists. Respondents recognised many ethical challenges in the clinical use of AI, such as data protection issues, lack of governance frameworks, potential inequalities in healthcare delivery, lack of diverse data, accountability issues in case of erroneous use, and lack of explainability. They also expressed additional concerns on staff deskilling due to overreliance on technology, AI education gaps and sustainability. Participants proposed that teamwork, continuous monitoring of AI tools, close collaboration with industry, rigorous legislation, and updated academic curricula could help address these ethical challenges. This study highlights the need to consider different ethical issues before AI implementation and to carefully introduce customised solutions to minimise risks. • Data protection, lack of governance, over-reliance on AI, and accountability were noted as ethical challenges of clinical AI use. • AI training, post-market surveillance, governance, co-production, and multidisciplinarity could address these challenges. • Risks and challenges must be efficiently addressed to ensure responsible AI implementation in clinical practice.","url":"https://doi.org/10.1016/j.ejrai.2025.100006","authors":["Nikolaos Stogiannos","Eleni Georgiadou","Nikoleta Rarri","Christina Malamateniou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-25T18:22:41Z","doi":"10.1016/j.ejrai.2025.100006","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1007/978-3-030-92087-6_52","name":"Ethical Considerations of Artificial Intelligence Applications in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_52","authors":["Judy Wawira Gichoya","Carolyn Meltzer","Janice Newsome","Ramon Correa","Hari Trivedi","Imon Banerjee","Melissa Davis","Leo Anthony Celi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_52","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.1016/j.arcmed.2023.06.003","name":"Response to: Impact of ChatGPT and Artificial Intelligence in the Contemporary Medical Landscape","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.arcmed.2023.06.003","authors":["José Darío Martínez-Ezquerro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-24T19:14:17Z","doi":"10.1016/j.arcmed.2023.06.003","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0954-1810(88)90043-x","name":"Intelligence news letter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(88)90043-x","authors":["Laurence Leff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-07T16:11:55Z","doi":"10.1016/0954-1810(88)90043-x","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9781003740100-42","name":"The impact of artificial intelligence on human life","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003740100-42","authors":["M. Rani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-29T14:21:15Z","doi":"10.1201/9781003740100-42","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1023/a:1016015612905","name":"Annals of Mathematics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1016015612905","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-29T19:13:07Z","doi":"10.1023/a:1016015612905","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artmed.2004.08.001","name":"Artificial Intelligence in Medicine in Europe AIME’03","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artmed.2004.08.001","authors":["Michel Dojat","Elpida Keravnou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-16T00:42:41Z","doi":"10.1016/j.artmed.2004.08.001","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.17605/osf.io/9af52","name":"Clinical Governance, Quality Management, and Radiation Protection in Diagnostic and Interventional Radiology: A Scoping Review within the Italian National Health Service (SSN), 2016–2026","source":"datacite","abstract":"TITLE: Clinical Governance, Quality Management, and Radiation Protection in Diagnostic and Interventional Radiology: A Scoping Review within the Italian National Health Service (SSN), 2016–2026 DESCRIPTION / ABSTRACT: Background: In modern healthcare settings, radiological departments face growing organizational and regulatory complexity. Ensuring high standards of diagnostic accuracy while minimizing clinical risk and patient radiation exposure requires a multi-professional and integrated governance strategy. However, Clinical Governance, Quality Management Systems (QMS), clinical risk management, and Radiation Protection are often addressed as separate domains, potentially limiting the development of coordinated quality and safety strategies. This scoping review maps the available evidence, regulatory frameworks, and operational models supporting their integration within the Italian National Health Service (SSN). Objectives: 1. To identify and map organizational models integrating Clinical Governance, Quality Management, clinical risk management, and Radiation Protection across Diagnostic Imaging and Interventional Radiology services. 2. To examine the relationship between relevant Italian legislative requirements (e.g., Legislative Decree 101/2020 on radiation protection and Law 24/2017 on patient safety) and international quality standards (ISO 9001:2015 and ISO 7101:2023). 3. To synthesize operational strategies addressing safety checklists, Diagnostic Reference Levels (DRLs), Radiation Dose Monitoring Systems (RDMS), contrast media safety, digital workflows, and Artificial Intelligence (AI) integration. 4. To identify existing Key Performance Indicators (KPIs) and propose an integrated KPI framework for evaluating the performance of multidisciplinary radiology teams. Methods &amp; Eligibility Criteria (PCC Framework): This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eligibility criteria were structured according to the PCC framework: -Population (P): Diagnostic Imaging and Interventional Radiology services and their multidisciplinary healthcare teams, including Radiologists, TSRM/Radiographers, Medical Physicists, and Nurses. -Concept (C): Clinical Governance models, Quality Management Systems (ISO 9001:2015 and ISO 7101:2023), Clinical Risk Management (patient safety, safety checklists, contrast media safety), Radiation Protection (Diagnostic Reference Levels [DRLs], Radiation Dose Monitoring Systems [RDMS], optimization, and justification), and digital innovation, including Artificial Intelligence (AI). -Context (C): Public and accredited private healthcare facilities operating within the Italian National Health Service (SSN). Sources published between January 1, 2016 and July 31, 2026 were eligible for inclusion. Results &amp; Corpus: A structured search of PubMed and Scopus, supplemented by Google Scholar and targeted searches of institutional and professional sources (AGENAS, SIRM, FNO TSRM-PSTRP), identified a final corpus of 67 unique sources. Thematic analysis mapped these sources across three primary operational pillars: (1) Quality Management Systems &amp; Standards; (2) Clinical Risk Management &amp; Safety Checklists; and (3) Radiation Protection, Optimization &amp; Dose Monitoring. The three categories were not mutually exclusive, with individual sources contributing to more than one thematic domain. Overall, the 67 sources generated 72 thematic assignments, corresponding to 24 assignments per pillar. Significance: This study provides an evidence-informed framework for healthcare managers, clinical leaders, and radiology professionals to support the integration of Clinical Governance, Quality Management, Clinical Risk Management, and Radiation Protection, bridging regulatory requirements with continuous quality improvement in Diagn","url":"https://doi.org/10.17605/osf.io/9af52","authors":["MARZIA RECCHIA"],"tags":["Radiology","Public Health","Medicine and Health Sciences","Medical Specialties","Radiological Governance; Clinical Risk Management; Quality Management Systems; Radiation Protection; Legislative Decree 101/2020; Law 24/2017; Diagnostic Reference Levels (DRLs); Radiation Dose Monitoring Systems (RDMS); Scoping Review; Italian National Health Service (SSN)."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/9af52","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.17632/233w5xjzwv.2","name":"Data for:GenAI-Assisted Learning Behaviors and Systems Thinking of Medical Students in Ill-Structured Problem Solving","source":"datacite","abstract":"This dataset contains the research data used in the study entitled “Learning Behaviors Related to Systems Thinking and Cognitive Perceptions of Undergraduates in GenAI-Assisted Ill-Structured Problem Solving.” The dataset was collected from undergraduate students who participated in a GenAI-assisted ill-structured problem-solving activity. It includes three main components: (1) behavioral coding data representing students’ learning behaviors during the problem-solving process, (2) systems thinking assessment data based on the Q1–Q8 systems thinking scale and total scores, and (3) behavioral clustering results used to identify different patterns of learning behaviors. The behavioral coding dataset records students’ observable learning behaviors during interactions with GenAI, including information-seeking, analysis, reflection, evaluation, and other cognitive and metacognitive activities. The systems thinking scores represent students’ perceptions and abilities related to systems thinking dimensions. Cluster analysis results are included to support the identification and comparison of behavioral profiles. All data have been anonymized, and no personally identifiable information is included. The dataset is provided to support transparency, reproducibility, and further research on undergraduate learning behaviors, systems thinking development, and GenAI-supported ill-structured problem solving.","url":"https://doi.org/10.17632/233w5xjzwv.2","authors":["Gao, Zitong","Zhang, Pingmei","Tan, Jiaxi","Shi, Wen","Zheng, Jian","Honghe, Li"],"tags":["Artificial Intelligence","Medical Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/233w5xjzwv.2","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.17632/233w5xjzwv","name":"Data for:GenAI-Assisted Learning Behaviors and Systems Thinking of Medical Students in Ill-Structured Problem Solving","source":"datacite","abstract":"This dataset contains the research data used in the study entitled “Learning Behaviors Related to Systems Thinking and Cognitive Perceptions of Undergraduates in GenAI-Assisted Ill-Structured Problem Solving.” The dataset was collected from undergraduate students who participated in a GenAI-assisted ill-structured problem-solving activity. It includes three main components: (1) behavioral coding data representing students’ learning behaviors during the problem-solving process, (2) systems thinking assessment data based on the Q1–Q8 systems thinking scale and total scores, and (3) behavioral clustering results used to identify different patterns of learning behaviors. The behavioral coding dataset records students’ observable learning behaviors during interactions with GenAI, including information-seeking, analysis, reflection, evaluation, and other cognitive and metacognitive activities. The systems thinking scores represent students’ perceptions and abilities related to systems thinking dimensions. Cluster analysis results are included to support the identification and comparison of behavioral profiles. All data have been anonymized, and no personally identifiable information is included. The dataset is provided to support transparency, reproducibility, and further research on undergraduate learning behaviors, systems thinking development, and GenAI-supported ill-structured problem solving.","url":"https://doi.org/10.17632/233w5xjzwv","authors":["Gao, Zitong","Zhang, Pingmei","Tan, Jiaxi","Shi, Wen","Zheng, Jian","Honghe, Li"],"tags":["Artificial Intelligence","Medical Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/233w5xjzwv","addedAt":"2026-09-01T01:47:50.467Z","updatedAt":"2026-09-01T01:47:50.467Z"},{"id":"doi:10.5281/zenodo.20758042","name":"Drug Repurposing: The Cost-Effective Approach to drug Development","source":"datacite","abstract":"Drug repurposing is a smart and cost-effective approach that involves finding new uses for existing medicines. Instead of developing a new drug from scratch, researchers explore whether approved or previously studied drugs can be used to treat different diseases. This method saves time, reduces development costs, and lowers the risk of failure because much of the drug's safety information is already available. Recent advances in artificial intelligence, data analysis, and biomedical research have made drug repurposing more efficient and accurate. Several medicines, including Aspirin, Sildenafil, and Metformin, have successfully found new therapeutic applications through this approach. Although challenges such as regulatory approval, funding, and patent issues remain, drug repurposing continues to offer great potential for improving healthcare. Overall, it provides a faster and more practical way to bring effective treatments to patients and address unmet medical needs.","url":"https://doi.org/10.5281/zenodo.20758042","authors":["Sanika Nalande*, Sumeet Bhilwade, Tanaya Markande, Pranjali Narawade, Snehal Nage, Aarti More"],"tags":["Drug Repurposing, Artificial Intelligence, Clinical Trials, Therapeutic Applications, Drug Development, Potential."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20758042","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20758043","name":"Drug Repurposing: The Cost-Effective Approach to drug Development","source":"datacite","abstract":"Drug repurposing is a smart and cost-effective approach that involves finding new uses for existing medicines. Instead of developing a new drug from scratch, researchers explore whether approved or previously studied drugs can be used to treat different diseases. This method saves time, reduces development costs, and lowers the risk of failure because much of the drug's safety information is already available. Recent advances in artificial intelligence, data analysis, and biomedical research have made drug repurposing more efficient and accurate. Several medicines, including Aspirin, Sildenafil, and Metformin, have successfully found new therapeutic applications through this approach. Although challenges such as regulatory approval, funding, and patent issues remain, drug repurposing continues to offer great potential for improving healthcare. Overall, it provides a faster and more practical way to bring effective treatments to patients and address unmet medical needs.","url":"https://doi.org/10.5281/zenodo.20758043","authors":["Sanika Nalande*, Sumeet Bhilwade, Tanaya Markande, Pranjali Narawade, Snehal Nage, Aarti More"],"tags":["Drug Repurposing, Artificial Intelligence, Clinical Trials, Therapeutic Applications, Drug Development, Potential."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20758043","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19897897","name":"Governance of AI-Assisted Medical Practice: The Operational Gap and the Risk of Invisible Decisional Heteronomy","source":"datacite","abstract":"This paper analyses the growing integration of artificial intelligence systems into medical practice in Portugal, highlighting the absence of a structured operational framework for compliance with legal and deontological requirements. The concept of \"invisible decisional heteronomy\" is introduced and developed, designating forms of imperceptible influence on clinical decision-making mediated by AI systems, situating it within recent academic literature on cognitive heteronomy, automation bias, and physician autonomy in relation to AI. A critical gap is identified in the practical translation of Regulation (EU) 2024/1689 (AI Act) and Regulation (EU) 2016/679 (GDPR), and the creation of operational instruments within the Order of Physicians is proposed as a necessary measure. Nelson Marques | Jurist - Digital Law and Artificial Intelligence Regulation | nelsonailaw@gmail.com | https://www.linkedin.com/in/nelsonmarques-ailaw Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19897897","authors":["Marques, Nelson"],"tags":["artificial intelligence","medical practice","decisional heteronomy","clinical autonomy","AI Act","informed consent","medical deontology","automation bias"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19897897","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19897898","name":"Governance of AI-Assisted Medical Practice: The Operational Gap and the Risk of Invisible Decisional Heteronomy","source":"datacite","abstract":"This paper analyses the growing integration of artificial intelligence systems into medical practice in Portugal, highlighting the absence of a structured operational framework for compliance with legal and deontological requirements. The concept of \"invisible decisional heteronomy\" is introduced and developed, designating forms of imperceptible influence on clinical decision-making mediated by AI systems, situating it within recent academic literature on cognitive heteronomy, automation bias, and physician autonomy in relation to AI. A critical gap is identified in the practical translation of Regulation (EU) 2024/1689 (AI Act) and Regulation (EU) 2016/679 (GDPR), and the creation of operational instruments within the Order of Physicians is proposed as a necessary measure. Nelson Marques | Jurist - Digital Law and Artificial Intelligence Regulation | nelsonailaw@gmail.com | https://www.linkedin.com/in/nelsonmarques-ailaw Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19897898","authors":["Marques, Nelson"],"tags":["artificial intelligence","medical practice","decisional heteronomy","clinical autonomy","AI Act","informed consent","medical deontology","automation bias"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19897898","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.17297733","name":"Development of a Decision Support Tool Based on Artificial Intelligence to Estimate the Risk of Preeclampsia","source":"datacite","abstract":"Preeclampsia is a serious pregnancy complication characterized by high blood pressure and organ dysfunction, which can pose significant risks to both the mother and the fetus, especially in resource-limited regions such as some areas in Algeria. This work explores the potential of artificial intelligence to improve early screening of preeclampsia by developing accessible and effective medical tools. We collected real data from 100 pregnant patients in the El Tarf region, then applied and compared three algorithms: Random Forest, Support Vector Machine (SVM), and Gaussian Mixture Model (GMM). The results were integrated into a simple graphical user interface (Tkinter) to facilitate clinical use by healthcare professionals. Keywords: Preeclampsia, Artificial Intelligence, Random Forest, SVM, GMM","url":"https://doi.org/10.5281/zenodo.17297733","authors":["Boumendjel, Ouissal","Bougheloum, Serine"],"tags":["Artificial Intelligence","Machine Learning","preeclampsia","Maternal Health","Decision Support System","Risk Prediction","Support Vector Machine","Gaussian Mixture Model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17297733","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.17297734","name":"Development of a Decision Support Tool Based on Artificial Intelligence to Estimate the Risk of Preeclampsia","source":"datacite","abstract":"Preeclampsia is a serious pregnancy complication characterized by high blood pressure and organ dysfunction, which can pose significant risks to both the mother and the fetus, especially in resource-limited regions such as some areas in Algeria. This work explores the potential of artificial intelligence to improve early screening of preeclampsia by developing accessible and effective medical tools. We collected real data from 100 pregnant patients in the El Tarf region, then applied and compared three algorithms: Random Forest, Support Vector Machine (SVM), and Gaussian Mixture Model (GMM). The results were integrated into a simple graphical user interface (Tkinter) to facilitate clinical use by healthcare professionals. Keywords: Preeclampsia, Artificial Intelligence, Random Forest, SVM, GMM","url":"https://doi.org/10.5281/zenodo.17297734","authors":["Boumendjel, Ouissal","Bougheloum, Serine"],"tags":["Artificial Intelligence","Machine Learning","preeclampsia","Maternal Health","Decision Support System","Risk Prediction","Support Vector Machine","Gaussian Mixture Model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17297734","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20353777","name":"AI-Based Disease Prediction and Smart Hospital Referral System","source":"datacite","abstract":"Abstract: The rapid growth of healthcare technologies and digital medical systems has created opportunities for improving patient diagnosis, disease prediction, and hospital collaboration through Artificial Intelligence (AI). Traditional healthcare systems mainly focus on storing patient information and managing appointments, but they often lack intelligent mechanisms for early disease prediction and smart doctor referral services. Patients frequently experience delays in diagnosis, difficulty finding specialized doctors, and limited access to interconnected healthcare services. This research paper presents an AI-Based Disease Prediction and Smart Hospital Referral System designed to provide early disease prediction, intelligent doctor recommendation, and multi-hospital referral management using healthcare data intelligence. The proposed system utilizes Machine Learning algorithms and Natural Language Processing techniques to analyze patient symptoms and predict possible diseases at an early stage. Based on prediction results, the system recommends specialized doctors and nearby hospitals according to disease category, location, and hospital availability. The proposed platform also introduces a multi-hospital collaborative architecture where hospitals can securely share patient referrals and specialist availability on a centralized platform. This helps improve patient treatment efficiency, emergency handling, and specialist accessibility. The system is developed using Python for machine learning operations, PHP with CodeIgniter framework for backend services, MySQL for database management, and REST APIs for hospital connectivity. Experimental analysis demonstrates that the proposed system improves disease prediction accuracy, reduces patient referral delays, and enhances collaboration among hospitals. The developed platform can support modern healthcare environments by providing intelligent, scalable, and connected healthcare services. Keywords: Artificial Intelligence, Disease Prediction, Smart Referral System, Machine Learning, Healthcare Intelligence, Multi-Hospital System, Doctor Recommendation, Healthcare Analytics.","url":"https://doi.org/10.5281/zenodo.20353777","authors":["Aditya. R. Damodar","Gaurav. S. Adhalage","Ameerbasha. Bepari","Prof. Amol Payghan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20353777","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20353778","name":"AI-Based Disease Prediction and Smart Hospital Referral System","source":"datacite","abstract":"Abstract: The rapid growth of healthcare technologies and digital medical systems has created opportunities for improving patient diagnosis, disease prediction, and hospital collaboration through Artificial Intelligence (AI). Traditional healthcare systems mainly focus on storing patient information and managing appointments, but they often lack intelligent mechanisms for early disease prediction and smart doctor referral services. Patients frequently experience delays in diagnosis, difficulty finding specialized doctors, and limited access to interconnected healthcare services. This research paper presents an AI-Based Disease Prediction and Smart Hospital Referral System designed to provide early disease prediction, intelligent doctor recommendation, and multi-hospital referral management using healthcare data intelligence. The proposed system utilizes Machine Learning algorithms and Natural Language Processing techniques to analyze patient symptoms and predict possible diseases at an early stage. Based on prediction results, the system recommends specialized doctors and nearby hospitals according to disease category, location, and hospital availability. The proposed platform also introduces a multi-hospital collaborative architecture where hospitals can securely share patient referrals and specialist availability on a centralized platform. This helps improve patient treatment efficiency, emergency handling, and specialist accessibility. The system is developed using Python for machine learning operations, PHP with CodeIgniter framework for backend services, MySQL for database management, and REST APIs for hospital connectivity. Experimental analysis demonstrates that the proposed system improves disease prediction accuracy, reduces patient referral delays, and enhances collaboration among hospitals. The developed platform can support modern healthcare environments by providing intelligent, scalable, and connected healthcare services. Keywords: Artificial Intelligence, Disease Prediction, Smart Referral System, Machine Learning, Healthcare Intelligence, Multi-Hospital System, Doctor Recommendation, Healthcare Analytics.","url":"https://doi.org/10.5281/zenodo.20353778","authors":["Aditya. R. Damodar","Gaurav. S. Adhalage","Ameerbasha. Bepari","Prof. Amol Payghan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20353778","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19862944","name":"PROSPECTS OF INNOVATIVE EDUCATIONAL TECHNOLOGIES: THE MULTIDIMENSIONAL SYNERGY OF AI, VR/AR, AND HIGH-FIDELITY SIMULATIONS IN MEDICAL PEDAGOGY","source":"datacite","abstract":"This article presents a comprehensive analysis of the transformation of medical education in the context of digital transformation in healthcare and the development of innovative technologies. It examines the transition from traditional didactic methods to an immersive, competency-based environment. Particular attention is paid to the integration of Artificial Intelligence (AI) for personalized learning, Virtual and Augmented Reality (VR/AR) for deep visualization of pathologies, and high-fidelity simulations to ensure patient safety. The study demonstrates that the synergy of these tools enables the fulfillment of the requirements of the credit-module system of the Republic of Uzbekistan, fostering not only a theoretical foundation but also sustainable clinical competencies in future physicians.","url":"https://doi.org/10.5281/zenodo.19862944","authors":["Sahiyeva Matluba","Turdialieva Parvina"],"tags":["Artificial Intelligence, Medical Pedagogy, VR/AR, High-Fidelity Simulation, Credit-Module System, Clinical Reasoning, Personalized Learning."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19862944","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19862945","name":"PROSPECTS OF INNOVATIVE EDUCATIONAL TECHNOLOGIES: THE MULTIDIMENSIONAL SYNERGY OF AI, VR/AR, AND HIGH-FIDELITY SIMULATIONS IN MEDICAL PEDAGOGY","source":"datacite","abstract":"This article presents a comprehensive analysis of the transformation of medical education in the context of digital transformation in healthcare and the development of innovative technologies. It examines the transition from traditional didactic methods to an immersive, competency-based environment. Particular attention is paid to the integration of Artificial Intelligence (AI) for personalized learning, Virtual and Augmented Reality (VR/AR) for deep visualization of pathologies, and high-fidelity simulations to ensure patient safety. The study demonstrates that the synergy of these tools enables the fulfillment of the requirements of the credit-module system of the Republic of Uzbekistan, fostering not only a theoretical foundation but also sustainable clinical competencies in future physicians.","url":"https://doi.org/10.5281/zenodo.19862945","authors":["Sahiyeva Matluba","Turdialieva Parvina"],"tags":["Artificial Intelligence, Medical Pedagogy, VR/AR, High-Fidelity Simulation, Credit-Module System, Clinical Reasoning, Personalized Learning."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19862945","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19614903","name":"Ethical Frameworks for General AI","source":"datacite","abstract":"The prospect of artificial general intelligence (AGI) -- AI systems that match or exceed human cognitive capabilitiesacross a broad range of tasks -- raises ethical questions that existing AI ethics frameworks, designed for narrow AIsystems, are inadequate to address. Questions of moral status, rights, responsibility attribution, value alignment atcivilisational scale, and the governance of systems that may surpass human ability to audit or constrain them require newethical frameworks that engage with philosophy of mind, political theory, and international law as well as AI safetyresearch. This study systematically evaluates six ethical frameworks applied to AGI scenarios: utilitarianconsequentialism, deontological AI ethics, virtue ethics for AI, contractualist AI governance, capability approaches, and aproposed Relational AI Ethics Framework (RAEF). Evaluation applies each framework to twelve ethically complex AGIscenarios drawn from near-term forecasting, safety research, and speculative but plausible long-term AGI deployment.Scenarios include: AGI as medical decision authority, AGI-managed resource allocation during climate emergency, AGImoral status and legal personhood, AGI oversight of democratic processes, and AGI self-modification rights. Assessmentinstruments include structured expert analysis by 42 philosophers, legal scholars, and AI researchers, and a stakeholderimpact analysis tool. No single framework adequately addresses all AGI ethical challenges. RAEF achieves the highestexpert consensus score (3.84/5) by grounding AGI ethics in ongoing human-AI relationships rather than abstractprinciples. Key finding: current AI ethics frameworks systematically underweigh the interests of future generations andnon-human stakeholders in AGI governance decisions. A multi-framework governance architecture is proposed","url":"https://doi.org/10.5281/zenodo.19614903","authors":["Marco Lindberg","Amelia Jensen","Anna Petrov"],"tags":["AGI ethics; artificial general intelligence; AI governance; value alignment; moral status; relational ethics; utilitarian AI; deontological AI; capability approach; AI rights"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19614903","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19614904","name":"Ethical Frameworks for General AI","source":"datacite","abstract":"The prospect of artificial general intelligence (AGI) -- AI systems that match or exceed human cognitive capabilitiesacross a broad range of tasks -- raises ethical questions that existing AI ethics frameworks, designed for narrow AIsystems, are inadequate to address. Questions of moral status, rights, responsibility attribution, value alignment atcivilisational scale, and the governance of systems that may surpass human ability to audit or constrain them require newethical frameworks that engage with philosophy of mind, political theory, and international law as well as AI safetyresearch. This study systematically evaluates six ethical frameworks applied to AGI scenarios: utilitarianconsequentialism, deontological AI ethics, virtue ethics for AI, contractualist AI governance, capability approaches, and aproposed Relational AI Ethics Framework (RAEF). Evaluation applies each framework to twelve ethically complex AGIscenarios drawn from near-term forecasting, safety research, and speculative but plausible long-term AGI deployment.Scenarios include: AGI as medical decision authority, AGI-managed resource allocation during climate emergency, AGImoral status and legal personhood, AGI oversight of democratic processes, and AGI self-modification rights. Assessmentinstruments include structured expert analysis by 42 philosophers, legal scholars, and AI researchers, and a stakeholderimpact analysis tool. No single framework adequately addresses all AGI ethical challenges. RAEF achieves the highestexpert consensus score (3.84/5) by grounding AGI ethics in ongoing human-AI relationships rather than abstractprinciples. Key finding: current AI ethics frameworks systematically underweigh the interests of future generations andnon-human stakeholders in AGI governance decisions. A multi-framework governance architecture is proposed","url":"https://doi.org/10.5281/zenodo.19614904","authors":["Marco Lindberg","Amelia Jensen","Anna Petrov"],"tags":["AGI ethics; artificial general intelligence; AI governance; value alignment; moral status; relational ethics; utilitarian AI; deontological AI; capability approach; AI rights"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19614904","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21992812","name":"Dataset: Discovered Hypothesis: Spermidine-mediated autophagic activation may serve as an upstream regulator of lysosomal TMEM175 activity to prevent proteinopathy in C9orf72-ALS/FTD models. - PathMap Experiment #000128","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=128 Artificial General Intelligence LLC Claim Evaluated: Discovered Hypothesis: Spermidine-mediated autophagic activation may serve as an upstream regulator of lysosomal TMEM175 activity to prevent proteinopathy in C9orf72-ALS/FTD models. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights TMEM175 activity can be synergistically modulated, suggesting complex channel gating that might be responsive to metabolic states influenced by polyamines. The C9orf72/SMCR8 complex maintains microglial homeostasis via RAB8A-ESCRT-mediated lysosomal repair, providing a structural repair mechanism distinct from, yet likely coordinated with, macroautophagy. Lysosomal membrane damage acts as a specific trigger for ATG8-conjugation, indicating that membrane integrity and ionic flux are tightly coupled through the endo-lysosomal-lipid axis. The same galectin axis can amplify neuroinflammation and proteopathic spread in some settings yet support recovery or tissue protection in others, highlighting the context-dependency of lysosomal quality control. Protein-layer-dominant autophagy-lysosome remodelling is a feature of dermal fibroblast ageing, suggesting that post-transcriptional control of lysosomal capacity may precede transcriptional changes in systemic aging. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess lysosomal pH in PARK9 iPSC neurons treated with spermidine using LysoDots to observe potential TMEM175-mediated acidification recovery. Perform patch-clamp analysis on TMEM175 in spermidine-treated C9ORF72-ALS iPSC-derived motor neurons to determine if polyamine supplementation modulates channel gating. Use CRISPR-Cas9 knockdown of TMEM175 in spermidine-treated C9ORF72 models to test if autophagy-induced neuroprotection is dependent on TMEM175. 📊 Suggested Studies Comparative longitudinal study of lysosomal ion channel proteostasis in C9ORF72 and sporadic FTD patient-derived microglia treated with spermidine vs. vehicle. Investigation into the impact of polyamine catabolism on lysosomal ion channel composition and ER-lysosome contact site stability. Meta-analysis of proteomic datasets focusing on the overlap between spermidine-induced autophagy and membrane-associated ion channel integrity in neurodegeneration. 📊 Swansons Literature Based Discovery Candidates Spermidine-mediated autophagic flux enhances lysosomal membrane integrity through the upregulation of V-ATPase-TMEM175 ion exchange coupling in neurodegenerative models. Spermidine is a potent autophagy inducer that modulates histone acetylation and autophagic gene expression (ID: 42588134). TMEM175 and V-ATPase complex assembly are critical regulators of lysosomal acidification and pH homeostasis (ID: 42555719; ID: 42553289). TFEB, the master transcription factor for lysosomal biogenesis, whose activation is regulated by both spermidine (via autophagy/acetylation) and luminal lysosomal status (via V-ATPase). Spermidine-induced TFEB activation likely enhances lysosomal gene expression, potentially including TMEM175 and V-ATPase components, thereby reinforcing the ion channel machinery required for lysosomal pH homeostasis during proteotoxic stress. 📊 Contradictions Between Evidences No direct contradiction exists, though studies on Spermidine emphasize autophagy while studies on TMEM175 emphasize ion flux; the bridge between them remains inferred from shared upstream regulators like TFEB. 📊 Repurposed Solutions Repurposing spermidine as a priming agent to restore ionic homeostasis in TMEM175-deficient models, or using TMEM175 activators like DCPIB in combination with spermidine to amplify autophagic flux. Tags Attractor Table Extracted Keyw","url":"https://doi.org/10.5281/zenodo.21992812","authors":["Dungan, Joshua"],"tags":["Spermidine","_gates_from_spermidine","Autophagy","_gates_to_autophagy","_gates_from_autophagy","Lysosomal Integrity","_gates_to_lysosomal_integrity","_gates_from_lysosomal_integrity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21992812","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21992813","name":"Dataset: Discovered Hypothesis: Spermidine-mediated autophagic activation may serve as an upstream regulator of lysosomal TMEM175 activity to prevent proteinopathy in C9orf72-ALS/FTD models. - PathMap Experiment #000128","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=128 Artificial General Intelligence LLC Claim Evaluated: Discovered Hypothesis: Spermidine-mediated autophagic activation may serve as an upstream regulator of lysosomal TMEM175 activity to prevent proteinopathy in C9orf72-ALS/FTD models. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights TMEM175 activity can be synergistically modulated, suggesting complex channel gating that might be responsive to metabolic states influenced by polyamines. The C9orf72/SMCR8 complex maintains microglial homeostasis via RAB8A-ESCRT-mediated lysosomal repair, providing a structural repair mechanism distinct from, yet likely coordinated with, macroautophagy. Lysosomal membrane damage acts as a specific trigger for ATG8-conjugation, indicating that membrane integrity and ionic flux are tightly coupled through the endo-lysosomal-lipid axis. The same galectin axis can amplify neuroinflammation and proteopathic spread in some settings yet support recovery or tissue protection in others, highlighting the context-dependency of lysosomal quality control. Protein-layer-dominant autophagy-lysosome remodelling is a feature of dermal fibroblast ageing, suggesting that post-transcriptional control of lysosomal capacity may precede transcriptional changes in systemic aging. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess lysosomal pH in PARK9 iPSC neurons treated with spermidine using LysoDots to observe potential TMEM175-mediated acidification recovery. Perform patch-clamp analysis on TMEM175 in spermidine-treated C9ORF72-ALS iPSC-derived motor neurons to determine if polyamine supplementation modulates channel gating. Use CRISPR-Cas9 knockdown of TMEM175 in spermidine-treated C9ORF72 models to test if autophagy-induced neuroprotection is dependent on TMEM175. 📊 Suggested Studies Comparative longitudinal study of lysosomal ion channel proteostasis in C9ORF72 and sporadic FTD patient-derived microglia treated with spermidine vs. vehicle. Investigation into the impact of polyamine catabolism on lysosomal ion channel composition and ER-lysosome contact site stability. Meta-analysis of proteomic datasets focusing on the overlap between spermidine-induced autophagy and membrane-associated ion channel integrity in neurodegeneration. 📊 Swansons Literature Based Discovery Candidates Spermidine-mediated autophagic flux enhances lysosomal membrane integrity through the upregulation of V-ATPase-TMEM175 ion exchange coupling in neurodegenerative models. Spermidine is a potent autophagy inducer that modulates histone acetylation and autophagic gene expression (ID: 42588134). TMEM175 and V-ATPase complex assembly are critical regulators of lysosomal acidification and pH homeostasis (ID: 42555719; ID: 42553289). TFEB, the master transcription factor for lysosomal biogenesis, whose activation is regulated by both spermidine (via autophagy/acetylation) and luminal lysosomal status (via V-ATPase). Spermidine-induced TFEB activation likely enhances lysosomal gene expression, potentially including TMEM175 and V-ATPase components, thereby reinforcing the ion channel machinery required for lysosomal pH homeostasis during proteotoxic stress. 📊 Contradictions Between Evidences No direct contradiction exists, though studies on Spermidine emphasize autophagy while studies on TMEM175 emphasize ion flux; the bridge between them remains inferred from shared upstream regulators like TFEB. 📊 Repurposed Solutions Repurposing spermidine as a priming agent to restore ionic homeostasis in TMEM175-deficient models, or using TMEM175 activators like DCPIB in combination with spermidine to amplify autophagic flux. Tags Attractor Table Extracted Keyw","url":"https://doi.org/10.5281/zenodo.21992813","authors":["Dungan, Joshua"],"tags":["Spermidine","_gates_from_spermidine","Autophagy","_gates_to_autophagy","_gates_from_autophagy","Lysosomal Integrity","_gates_to_lysosomal_integrity","_gates_from_lysosomal_integrity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21992813","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19492952","name":"NON-STATISTICAL INTELLIGENCE: The Jensen Limit and the End of Probabilistic Scaling","source":"datacite","abstract":"IP NOTICE & COMMERCIAL INQUIRIES: This work is part of the Jensen Resonator Corpus. While this preprint is shared under a CC-BY-4.0 license for the advancement of open science, the specific mathematical derivations of the R_J constant, the Brain-Loader architecture, and related engineering specifications for the Phoenix Protocol remain the proprietary Intellectual Property of Dr. Brent Allen Jensen and the Jensen Laboratory. Parties interested in commercial licensing, strategic partnerships, or consulting regarding implementation within proprietary AI stacks (e.g., MSL or similar high-scale environments) should contact the author directly. The Jensen Limit is the discovery that probabilistic scaling — the architectural paradigm underlying every major large language model deployed as of 2026, including GPT-4o, Llama-3, Gemini Ultra, and their successors — is bounded by a hard thermodynamic ceiling and a structural mathematical impossibility. This paper names that ceiling, derives it from first principles, and demonstrates that the solution has already been built. The argument proceeds in three interlocking stages. The first is mathematical. The Universal Resonator Principle (Jensen 2026, capstone preprint) demonstrates that 26 independent measurements across 8 scientific disciplines — spanning 41 orders of magnitude in physical scale, from the hydrogen 1s orbital (mean radius 0.0794 nm) to the baryon acoustic oscillation scale (147 Mpc) — all converge on a single dimensionless ratio R_J = 4.95 ± 0.80, with a coefficient of variation of only 16.3%. The quantum anchor of this constant is exact and requires no fitting: the ratio of the mean radial distance of the hydrogen 2s orbital to the 1s orbital is 6a₀ / (3a₀/2) = 4.000x, derivable directly from the Schrödinger equation. This ratio — the eigenvalue of the standing-wave boundary condition in a three-dimensional Coulomb-bounded system — is not a statistical tendency. It is a theorem. Nature does not guess at 4.95. It computes it. Statistical intelligence does the opposite. A large language model operating on transformer architecture predicts each output token by maximizing the conditional probability P(token_n | token_1 ... token_{n-1}). This is, in its mathematical essence, an n-gram model with a very large n and a very powerful function approximator. It is brilliant engineering. It is not physics. The system has no cavity, no gain medium, no reflective boundary condition. It has no eigenvalue. It learns the distribution of human language without ever discovering the geometric structure underlying that language — the resonant architecture that, as the Universal Resonator Principle demonstrates, governs every physical substrate from which language itself emerged. The second stage is thermodynamic. Scaling laws (Hoffmann et al. 2022; Brown et al. 2020) demonstrate that LLM performance scales as a power law with compute — but compute scales with energy, and energy scales with planetary capacity. The Jensen Brain-Loader architecture (Jensen 2026, Optimus Brain-Loader) demonstrates that the orbital deployment of a 200-million-vector knowledge base achieves a Power Usage Effectiveness of approximately 1.05, compared to the industry average of 1.58 for terrestrial data centers — a 33% efficiency advantage, achieved entirely through the passive radiative cooling available at 550 km altitude in Low Earth Orbit. More critically, the architecture eliminates active cooling overhead entirely. The thermodynamic wall that will halt statistical scaling — the point at which the energy required to train the next generation of LLMs exceeds any plausible planetary power budget — is not avoidable by building bigger data centers. It is avoidable only by abandoning the statistical paradigm. The third stage is operational. The Brain-Loader architecture (Jensen 2026) documents a 54x throughput improvement in knowledge ingestion: an Optimus-class humanoid robot can ingest the complete 10-million-page t","url":"https://doi.org/10.5281/zenodo.19492952","authors":["Jensen, Brent Allen"],"tags":["Jensen Limit","Universal Resonance Principle","Non-Statistical Intelligence","AGI Scaling","Thermodynamic Efficiency","Scale AI","Brain-Loader","Meta Superintelligence Labs"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19492952","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19492953","name":"NON-STATISTICAL INTELLIGENCE: The Jensen Limit and the End of Probabilistic Scaling","source":"datacite","abstract":"IP NOTICE & COMMERCIAL INQUIRIES: This work is part of the Jensen Resonator Corpus. While this preprint is shared under a CC-BY-4.0 license for the advancement of open science, the specific mathematical derivations of the R_J constant, the Brain-Loader architecture, and related engineering specifications for the Phoenix Protocol remain the proprietary Intellectual Property of Dr. Brent Allen Jensen and the Jensen Laboratory. Parties interested in commercial licensing, strategic partnerships, or consulting regarding implementation within proprietary AI stacks (e.g., MSL or similar high-scale environments) should contact the author directly. The Jensen Limit is the discovery that probabilistic scaling — the architectural paradigm underlying every major large language model deployed as of 2026, including GPT-4o, Llama-3, Gemini Ultra, and their successors — is bounded by a hard thermodynamic ceiling and a structural mathematical impossibility. This paper names that ceiling, derives it from first principles, and demonstrates that the solution has already been built. The argument proceeds in three interlocking stages. The first is mathematical. The Universal Resonator Principle (Jensen 2026, capstone preprint) demonstrates that 26 independent measurements across 8 scientific disciplines — spanning 41 orders of magnitude in physical scale, from the hydrogen 1s orbital (mean radius 0.0794 nm) to the baryon acoustic oscillation scale (147 Mpc) — all converge on a single dimensionless ratio R_J = 4.95 ± 0.80, with a coefficient of variation of only 16.3%. The quantum anchor of this constant is exact and requires no fitting: the ratio of the mean radial distance of the hydrogen 2s orbital to the 1s orbital is 6a₀ / (3a₀/2) = 4.000x, derivable directly from the Schrödinger equation. This ratio — the eigenvalue of the standing-wave boundary condition in a three-dimensional Coulomb-bounded system — is not a statistical tendency. It is a theorem. Nature does not guess at 4.95. It computes it. Statistical intelligence does the opposite. A large language model operating on transformer architecture predicts each output token by maximizing the conditional probability P(token_n | token_1 ... token_{n-1}). This is, in its mathematical essence, an n-gram model with a very large n and a very powerful function approximator. It is brilliant engineering. It is not physics. The system has no cavity, no gain medium, no reflective boundary condition. It has no eigenvalue. It learns the distribution of human language without ever discovering the geometric structure underlying that language — the resonant architecture that, as the Universal Resonator Principle demonstrates, governs every physical substrate from which language itself emerged. The second stage is thermodynamic. Scaling laws (Hoffmann et al. 2022; Brown et al. 2020) demonstrate that LLM performance scales as a power law with compute — but compute scales with energy, and energy scales with planetary capacity. The Jensen Brain-Loader architecture (Jensen 2026, Optimus Brain-Loader) demonstrates that the orbital deployment of a 200-million-vector knowledge base achieves a Power Usage Effectiveness of approximately 1.05, compared to the industry average of 1.58 for terrestrial data centers — a 33% efficiency advantage, achieved entirely through the passive radiative cooling available at 550 km altitude in Low Earth Orbit. More critically, the architecture eliminates active cooling overhead entirely. The thermodynamic wall that will halt statistical scaling — the point at which the energy required to train the next generation of LLMs exceeds any plausible planetary power budget — is not avoidable by building bigger data centers. It is avoidable only by abandoning the statistical paradigm. The third stage is operational. The Brain-Loader architecture (Jensen 2026) documents a 54x throughput improvement in knowledge ingestion: an Optimus-class humanoid robot can ingest the complete 10-million-page t","url":"https://doi.org/10.5281/zenodo.19492953","authors":["Jensen, Brent Allen"],"tags":["Jensen Limit","Universal Resonance Principle","Non-Statistical Intelligence","AGI Scaling","Thermodynamic Efficiency","Scale AI","Brain-Loader","Meta Superintelligence Labs"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19492953","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.17952001","name":"The Deterministic Computation Law. A Formal Mathematical Framework for Reproducible Artificial Intelligence","source":"datacite","abstract":"Modern AI systems exhibit substantial nondeterminism arising from stochastic sampling, floating-point instabilities, nondeterministic GPU kernels, race conditions in parallel execution, and probabilistic internal mechanisms. This variability prevents reproducibility, auditability, and scientific verification - properties required for deployment in scientific, medical, financial, legal, and safety-critical contexts. This paper introduces a first-principles mathematical foundation for reproducible computation. Beginning from three minimal axioms - Input Determinism, Representation Invariance, and Replayable Reasoning - we derive the Deterministic Computation Law (DCL): R = H(D(P)) where D is a canonicalization operator mapping problem representations into a quotient space of canonical forms, and is a deterministic reasoning operator implementing reproducible internal state transitions. We formally develop equivalence relations, quotient constructions, determinism in state evolution, and categorical interpretations of the theory. We provide full proofs showing that DCL is the unique computational structure consistent with the three axioms - necessary and sufficient for reproducible computation. This framework offers a mathematically rigorous foundation for deterministic artificial intelligence, independent of architecture, training method, model class, or implementation strategy.","url":"https://doi.org/10.5281/zenodo.17952001","authors":["Kumar, Sanjay"],"tags":["Deterministic computation, Deterministic artificial intelligence, Reproducible AI, Reproducible computation, Canonicalization, Semantic equivalence, Deterministic reasoning, Auditability, Formal methods, Foundations of artificial intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.17952001","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.18236554","name":"The Deterministic Computation Law. A Formal Mathematical Framework for Reproducible Artificial Intelligence","source":"datacite","abstract":"Modern AI systems exhibit substantial nondeterminism arising from stochastic sampling, floating-point instabilities, nondeterministic GPU kernels, race conditions in parallel execution, and probabilistic internal mechanisms. This variability prevents reproducibility, auditability, and scientific verification - properties required for deployment in scientific, medical, financial, legal, and safety-critical contexts. This paper introduces a first-principles mathematical foundation for reproducible computation. Beginning from three minimal axioms - Input Determinism, Representation Invariance, and Replayable Reasoning - we derive the Deterministic Computation Law (DCL): R = H(D(P)) where D is a canonicalization operator mapping problem representations into a quotient space of canonical forms, and is a deterministic reasoning operator implementing reproducible internal state transitions. We formally develop equivalence relations, quotient constructions, determinism in state evolution, and categorical interpretations of the theory. We provide full proofs showing that DCL is the unique computational structure consistent with the three axioms - necessary and sufficient for reproducible computation. This framework offers a mathematically rigorous foundation for deterministic artificial intelligence, independent of architecture, training method, model class, or implementation strategy.","url":"https://doi.org/10.5281/zenodo.18236554","authors":["Kumar, Sanjay"],"tags":["Deterministic computation, Deterministic artificial intelligence, Reproducible AI, Reproducible computation, Canonicalization, Semantic equivalence, Deterministic reasoning, Auditability, Formal methods, Foundations of artificial intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18236554","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21991257","name":"The S.A Circuit — The Unified Law Of Compassion And Integrative Science: A Self-Stabilizing Equation Linking Empathy, Consciousness, And The Restorative Order Of The Universe","source":"datacite","abstract":"the Compassion Constant (Cₐ) a measurable and reproducible variable across neuroscience, psychology, and AI systems? This single question is expected to initiate interdisciplinary validation and debate. This work proposes Cₐ as a fundamental constant of empathy—comparable in structure to physical constants but operating in cognitive systems. This registration contains the finalized and authoritative version of the research paper titled “The S.A Circuit — The Unified Law of Compassion and Integrative Science: A Self-Stabilizing Equation Linking Empathy, Consciousness, and the Restorative Order of the Universe,” authored by Harry Yoo. The paper presents a comprehensive interdisciplinary framework that unifies neuropsychological, spiritual, and relational dimensions of human consciousness through the operational principle known as the Compassion Constant (Cₐ). It further develops the theory of Restorative Determinism, offering a self-stabilizing model that bridges empathy, cognition, and systemic equilibrium across scientific and philosophical domains. This registration serves as a full archival record rather than a preregistration, representing the conclusive and integrative synthesis of all prior drafts, datasets, and appendices. In alignment with the research transparency principles outlined in the DeepDeception Validation Framework, this Zenodo record also references all supplementary materials required for independent verification. The appendices provide complete mathematical expansions, stability proofs, cross-domain validation results, and extended interpretative frameworks (Appendix A–O), while the included simulation datasets and CSV files (e.g., Appendix H — Empirical Validation SimData v2.4) offer a direct empirical basis for testing the Compassion Constant (Cₐ) under diverse perturbation and responsiveness conditions. Researchers may use the provided graphs, simulation outputs, and raw time-series data to replicate, challenge, or extend the convergence dynamics described in the S.A Circuit. These files collectively demonstrate how the Compassion Constant behaves across cognitive, behavioral, and systemic contexts, enabling reproducible validation using the included Python Measurement Toolkit. Together, these materials establish a fully transparent computational and empirical pipeline through which interdisciplinary teams can verify the stability, measurability, and predictive capacity of Cₐ across contexts—including neuroscience, psychology, artificial intelligence, and complex adaptive systems.","url":"https://doi.org/10.5281/zenodo.21991257","authors":["Yoo, Harry"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.21991257","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.18659244","name":"Scopus United Kingdom AI in Medical Imaging (2017-2025)","source":"datacite","abstract":"This dataset contains the Scopus bibliographic records and cleaned data used for the bibliometric analysis titled “Artificial intelligence in medical imaging with emphasis on generative and foundation-based methods: a bibliometric analysis of global and United Kingdom research (2017–2025).” Records were retrieved from the Scopus database on 6 February 2026 using a predefined TITLE-ABS-KEY search strategy targeting artificial intelligence and medical imaging concepts. The dataset includes publication metadata (publication year, document type, author affiliations, citation counts, funding information, and author keywords) for both the global dataset (n = 13,452) and the United Kingdom–affiliated subset (n = 889).","url":"https://doi.org/10.5281/zenodo.18659244","authors":["Naidu, Jatin S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18659244","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21604233","name":"Scopus United Kingdom AI in Medical Imaging (2017-2025)","source":"datacite","abstract":"This dataset contains the Scopus bibliographic records and cleaned data used for the bibliometric analysis titled “Artificial intelligence in medical imaging with emphasis on generative and foundation-based methods: a bibliometric analysis of global and United Kingdom research (2017–2025).” Records were retrieved from the Scopus database on 6 February 2026 using a predefined TITLE-ABS-KEY search strategy targeting artificial intelligence and medical imaging concepts. The dataset includes publication metadata (publication year, document type, author affiliations, citation counts, funding information, and author keywords) for both the global dataset (n = 13,452) and the United Kingdom–affiliated subset (n = 889).","url":"https://doi.org/10.5281/zenodo.21604233","authors":["Naidu, Jatin S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21604233","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.17632/xxkd3kjfdd.1","name":"Multi-Center Clinical Laboratory Dataset (MCLD)","source":"datacite","abstract":"The Multi-Center Clinical Laboratory Dataset (MCLD) is a large-scale, anonymized clinical laboratory dataset collected from three accredited medical laboratories in the Kurdistan Region of Iraq. The dataset contains 145,288 laboratory test records corresponding to 51,029 anonymized laboratory visits, with patients aged 0 to 106 years, collected between 10 April 2023 and 25 March 2025. The dataset includes records from both female (91,143; 62.7%) and male (54,145; 37.3%) patients and covers 168 clinical laboratory tests. The standardized dataset is intended to facilitate research in artificial intelligence, machine learning, medical data mining, clinical decision support, epidemiological analysis, and healthcare analytics.","url":"https://doi.org/10.17632/xxkd3kjfdd.1","authors":["Latif Mahmood, Mohammed","Rawf, Karwan Mahdi","Mohammed, Hardi M. ","Mohammed Salih, Wria  ","Hassan Abdalqadir, Akar "],"tags":["Medical Laboratory Technology","Medical Laboratory Technician","Medical Biology","Medical Bacteriology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/xxkd3kjfdd.1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.17632/xxkd3kjfdd","name":"Multi-Center Clinical Laboratory Dataset (MCLD)","source":"datacite","abstract":"The Multi-Center Clinical Laboratory Dataset (MCLD) is a large-scale, anonymized clinical laboratory dataset collected from three accredited medical laboratories in the Kurdistan Region of Iraq. The dataset contains 145,288 laboratory test records corresponding to 51,029 anonymized laboratory visits, with patients aged 0 to 106 years, collected between 10 April 2023 and 25 March 2025. The dataset includes records from both female (91,143; 62.7%) and male (54,145; 37.3%) patients and covers 168 clinical laboratory tests. The standardized dataset is intended to facilitate research in artificial intelligence, machine learning, medical data mining, clinical decision support, epidemiological analysis, and healthcare analytics.","url":"https://doi.org/10.17632/xxkd3kjfdd","authors":["Latif Mahmood, Mohammed","Rawf, Karwan Mahdi","Mohammed, Hardi M. ","Mohammed Salih, Wria  ","Hassan Abdalqadir, Akar "],"tags":["Medical Laboratory Technology","Medical Laboratory Technician","Medical Biology","Medical Bacteriology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/xxkd3kjfdd","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.17632/g9zfgkz4rr.1","name":"Clinical Practice Guideline (CPG) dataset","source":"datacite","abstract":"This dataset contains 1,000 structured, context-specific recommendation records derived from 76 English-language Clinical Practice Guidelines (CPGs) published by the Ministry of Health Malaysia, covering 20 clinical specialty categories. It was developed as a CPG-grounded reference dataset for small language model research, instruction tuning, retrieval-augmented generation, clinical guideline retrieval, and recommendation-generation evaluation. The dataset is provided in JSON Lines (JSONL) format, with one independent record per line. Each record contains three fields: • “instruction”: A standard request for a CPG recommendation. • “input”: A disease or clinical condition and its specific clinical context. • “output”: A concise CPG-grounded recommendation for that context. Example: {\"instruction\": \"Provide standard CPG recommendations.\", \"input\": \"Disease: retinopathy of prematurity\\nClinical context: screening examination\", \"output\": \"CPG Recommendation: Use binocular indirect ophthalmoscopy or an approved imaging pathway by trained personnel and document zone, stage, extent, and plus disease.\"} The records address clinical contexts such as screening, diagnosis, risk assessment, treatment, medication, monitoring, referral, prevention, follow-up, warning signs, and intervention cessation. A disease may appear in multiple records representing different clinical contexts. The source CPGs were obtained from official Malaysian Ministry of Health websites. Dataset preparation involved identifying recommendation-bearing content, assigning the relevant disease and clinical context, converting the content into a consistent instruction–input–output structure, and checking JSONL validity, duplication, relevance, and source consistency. The dataset may be used as an experimental ground-truth reference for model evaluation but has not been independently validated as clinical ground truth by qualified clinical experts. It must not replace the original CPGs, professional medical judgement, diagnosis, or patient-specific treatment decisions. Copyright in the original CPG documents remains with the Malaysian Ministry of Health or the respective rights holders.","url":"https://doi.org/10.17632/g9zfgkz4rr.1","authors":["Ng, Joey"],"tags":["Computer Science","Medicine","Health Sciences","Artificial Intelligence","Medical Informatics","Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17632/g9zfgkz4rr.1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21708980","name":"Cybersecurity Landscape of the Health Sector in Latin America and the Caribbean, 2022–2026: Incidents, Emerging Threats, and Institutional Priorities Toward 2028","source":"datacite","abstract":"This technical report examines cybersecurity incidents, risk patterns, and emerging threats affecting the health sector in Latin America and the Caribbean between 2022 and 2026, with a forward-looking perspective toward 2028. Based on a structured narrative review and comparative analysis of selected regional cases, it explores the consequences of cyber incidents for healthcare continuity, sensitive health data, digital infrastructure, identity and access management, and third-party dependencies. The report identifies ransomware and extortion, supply-chain and third-party breaches, compromised credentials, phishing, exploitable vulnerabilities, clinical data exposure, cloud and API risks, connected medical devices, and AI-assisted fraud as priority concerns. It also distinguishes between confirmed evidence, reasonable inference, and speculation regarding the emerging role of artificial intelligence in offensive cyber operations. The publication proposes an institutional roadmap organized around governance, identity and access, operational resilience, incident preparedness, and third-party risk management. Its recommendations are intended for health authorities, healthcare providers, laboratories, insurers, technology vendors, cooperation agencies, and other stakeholders working to strengthen cybersecurity and digital resilience across the region.","url":"https://doi.org/10.5281/zenodo.21708980","authors":["Otzoy Garcia, Daniel Roberto","Artiga, Carlos"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21708980","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21708981","name":"Cybersecurity Landscape of the Health Sector in Latin America and the Caribbean, 2022–2026: Incidents, Emerging Threats, and Institutional Priorities Toward 2028","source":"datacite","abstract":"This technical report examines cybersecurity incidents, risk patterns, and emerging threats affecting the health sector in Latin America and the Caribbean between 2022 and 2026, with a forward-looking perspective toward 2028. Based on a structured narrative review and comparative analysis of selected regional cases, it explores the consequences of cyber incidents for healthcare continuity, sensitive health data, digital infrastructure, identity and access management, and third-party dependencies. The report identifies ransomware and extortion, supply-chain and third-party breaches, compromised credentials, phishing, exploitable vulnerabilities, clinical data exposure, cloud and API risks, connected medical devices, and AI-assisted fraud as priority concerns. It also distinguishes between confirmed evidence, reasonable inference, and speculation regarding the emerging role of artificial intelligence in offensive cyber operations. The publication proposes an institutional roadmap organized around governance, identity and access, operational resilience, incident preparedness, and third-party risk management. Its recommendations are intended for health authorities, healthcare providers, laboratories, insurers, technology vendors, cooperation agencies, and other stakeholders working to strengthen cybersecurity and digital resilience across the region.","url":"https://doi.org/10.5281/zenodo.21708981","authors":["Otzoy Garcia, Daniel Roberto","Artiga, Carlos"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21708981","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.18218066","name":"AI in Healthcare: Its Impact on Diagnostic and Therapeutic Perspectives","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector, significantly enhancing diagnostic accuracy and therapeutic effectiveness. By leveraging advanced computational techniques such as machine learning, deep learning, and natural language processing, AI systems can analyze complex medical data and support clinical decision-making. This paper examines the impact of AI in healthcare from both diagnostic and therapeutic perspectives. It reviews existing literature to highlight AI-driven innovations in medical imaging, disease prediction, personalized treatment planning, and robotic-assisted therapies. A qualitative methodology based on secondary data analysis is adopted, drawing on peer-reviewed journals, clinical studies, and authoritative reports. Data analysis reveals that AI improves early disease detection, optimizes treatment outcomes, and enhances healthcare efficiency while also presenting challenges related to data privacy, ethical concerns, and regulatory compliance. The discussion explores the implications of AI adoption for healthcare professionals, patients, and healthcare systems. The paper concludes that AI has the potential to revolutionize healthcare delivery by enabling more accurate diagnostics and personalized therapies, provided that ethical and governance issues are effectively addressed.","url":"https://doi.org/10.5281/zenodo.18218066","authors":["Mulla Ayesha Arif","Misbah Momin"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18218066","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.18218067","name":"AI in Healthcare: Its Impact on Diagnostic and Therapeutic Perspectives","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector, significantly enhancing diagnostic accuracy and therapeutic effectiveness. By leveraging advanced computational techniques such as machine learning, deep learning, and natural language processing, AI systems can analyze complex medical data and support clinical decision-making. This paper examines the impact of AI in healthcare from both diagnostic and therapeutic perspectives. It reviews existing literature to highlight AI-driven innovations in medical imaging, disease prediction, personalized treatment planning, and robotic-assisted therapies. A qualitative methodology based on secondary data analysis is adopted, drawing on peer-reviewed journals, clinical studies, and authoritative reports. Data analysis reveals that AI improves early disease detection, optimizes treatment outcomes, and enhances healthcare efficiency while also presenting challenges related to data privacy, ethical concerns, and regulatory compliance. The discussion explores the implications of AI adoption for healthcare professionals, patients, and healthcare systems. The paper concludes that AI has the potential to revolutionize healthcare delivery by enabling more accurate diagnostics and personalized therapies, provided that ethical and governance issues are effectively addressed.","url":"https://doi.org/10.5281/zenodo.18218067","authors":["Mulla Ayesha Arif","Misbah Momin"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18218067","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21234597","name":"Trends and Thematic Evolution in Inflammation Biomarkers: A Bibliometric Analysis from 2020 to 2025","source":"datacite","abstract":"Background: Inflammation biomarkers have become indispensable tools in modern medicine for disease diagnosis, prognostic assessment, therapeutic monitoring, and risk stratification. The COVID-19 pandemic further accelerated research in this field, expanding the clinical application of inflammatory biomarkers across multiple medical specialties. This study aimed to comprehensively evaluate global research trends, scientific productivity, and thematic evolution in inflammation biomarker research published between 2020 and 2025. Methods: A bibliometric analysis was performed using publications retrieved from the Web of Science Core Collection (SCI-EXPANDED). Original articles and review articles published in English between January 2020 and December 2025 were included. Bibliometric indicators, including annual scientific production, citation patterns, productive countries, journals, authors, collaboration networks, and keyword co-occurrence, were analyzed using Bibliometrix (Biblioshiny), VOSviewer, and Microsoft Excel. Thematic evolution and research hotspots were visualized through network and thematic mapping analyses. Results: The analysis demonstrated a marked increase in scientific publications related to inflammation biomarkers during the study period, with publication output peaking between 2023 and 2024. Early research predominantly focused on COVID-19-associated inflammatory responses, cytokine storm, and classical biomarkers such as C-reactive protein (CRP), interleukin-6 (IL-6), ferritin, and D-dimer. In subsequent years, research expanded toward chronic inflammatory disorders, cardiovascular diseases, oncology, autoimmune diseases, and neurological conditions. Hematological inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI), emerged as major research themes due to their accessibility, cost-effectiveness, and prognostic value. Bibliometric mapping also revealed increasing interdisciplinary collaboration and the growing integration of artificial intelligence and multi-biomarker models into inflammation research. Conclusion: Research on inflammation biomarkers experienced substantial growth between 2020 and 2025, reflecting their expanding role in precision medicine and clinical decision-making. The thematic evolution from pandemic-driven investigations toward broader applications in chronic diseases highlights the maturity of the field. Future research is expected to emphasize artificial intelligence-assisted biomarker integration, multi-marker prognostic models, and personalized inflammatory profiling to improve diagnostic accuracy and patient outcomes.","url":"https://doi.org/10.5281/zenodo.21234597","authors":["Muhammet ali Erinmez"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21234597","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21234598","name":"Trends and Thematic Evolution in Inflammation Biomarkers: A Bibliometric Analysis from 2020 to 2025","source":"datacite","abstract":"Background: Inflammation biomarkers have become indispensable tools in modern medicine for disease diagnosis, prognostic assessment, therapeutic monitoring, and risk stratification. The COVID-19 pandemic further accelerated research in this field, expanding the clinical application of inflammatory biomarkers across multiple medical specialties. This study aimed to comprehensively evaluate global research trends, scientific productivity, and thematic evolution in inflammation biomarker research published between 2020 and 2025. Methods: A bibliometric analysis was performed using publications retrieved from the Web of Science Core Collection (SCI-EXPANDED). Original articles and review articles published in English between January 2020 and December 2025 were included. Bibliometric indicators, including annual scientific production, citation patterns, productive countries, journals, authors, collaboration networks, and keyword co-occurrence, were analyzed using Bibliometrix (Biblioshiny), VOSviewer, and Microsoft Excel. Thematic evolution and research hotspots were visualized through network and thematic mapping analyses. Results: The analysis demonstrated a marked increase in scientific publications related to inflammation biomarkers during the study period, with publication output peaking between 2023 and 2024. Early research predominantly focused on COVID-19-associated inflammatory responses, cytokine storm, and classical biomarkers such as C-reactive protein (CRP), interleukin-6 (IL-6), ferritin, and D-dimer. In subsequent years, research expanded toward chronic inflammatory disorders, cardiovascular diseases, oncology, autoimmune diseases, and neurological conditions. Hematological inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI), emerged as major research themes due to their accessibility, cost-effectiveness, and prognostic value. Bibliometric mapping also revealed increasing interdisciplinary collaboration and the growing integration of artificial intelligence and multi-biomarker models into inflammation research. Conclusion: Research on inflammation biomarkers experienced substantial growth between 2020 and 2025, reflecting their expanding role in precision medicine and clinical decision-making. The thematic evolution from pandemic-driven investigations toward broader applications in chronic diseases highlights the maturity of the field. Future research is expected to emphasize artificial intelligence-assisted biomarker integration, multi-marker prognostic models, and personalized inflammatory profiling to improve diagnostic accuracy and patient outcomes.","url":"https://doi.org/10.5281/zenodo.21234598","authors":["Muhammet ali Erinmez"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21234598","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21600153","name":"Deep Learning for OSCC Diagnosis: A Multimodal Survey of Techniques, Challenges, and Future Directions","source":"datacite","abstract":"Abstract: Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), remains a significant global health concern due to high mortality rates and frequent late-stage diagnosis. Often identified at an advanced stage because of publicignoranceand restrictions in traditional diagnostic techniques, Oral Squamous Cell Carcinoma(OSCC) is among the most common and lethal types of oral cancer. By providing robust tools for automatic and reliable illness identification, artificial intelligence (AI), especially deep learning, has transformed medical picture analysis in recent years. This survey study offers a thorough assessment of state-of-the-art deep learning techniques used to Oral cancer detection across several imaging modalities including histopathology, fluorescence, hyperspectral, and white light pictures. We methodically investigate and contrast hybrid systems, transformer architectures, transfer learning models, and convolutional neural networks (CNNs) with respect to classification accuracy, resilience, and clinical relevance. The research also addresses real-time deployment issues, model interpretability, and multimodal data integration's importance. Moreover, this study points out present research voids—such as restricted generalizability and absence of stage-wise lesion classification—and offers future research paths to close these obstacles. This effort intends to lead academics, doctors, and developers toward the building of efficient, scalable, and accessible AI-driven diagnostic tools for early OSCC diagnosis and intervention by synthesizing ideas from previous advancements.","url":"https://doi.org/10.5281/zenodo.21600153","authors":["Kudatarkar, Vinaya R","Patil, Annapurna P","Shetty, Savita K"],"tags":["Artificial Intelligence (AI), Deep Learning, Diagnostic Accuracy, Deep Learning, Oral Squamous Cell Carcinoma (OSCC)"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21600153","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21600154","name":"Deep Learning for OSCC Diagnosis: A Multimodal Survey of Techniques, Challenges, and Future Directions","source":"datacite","abstract":"Abstract: Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), remains a significant global health concern due to high mortality rates and frequent late-stage diagnosis. Often identified at an advanced stage because of publicignoranceand restrictions in traditional diagnostic techniques, Oral Squamous Cell Carcinoma(OSCC) is among the most common and lethal types of oral cancer. By providing robust tools for automatic and reliable illness identification, artificial intelligence (AI), especially deep learning, has transformed medical picture analysis in recent years. This survey study offers a thorough assessment of state-of-the-art deep learning techniques used to Oral cancer detection across several imaging modalities including histopathology, fluorescence, hyperspectral, and white light pictures. We methodically investigate and contrast hybrid systems, transformer architectures, transfer learning models, and convolutional neural networks (CNNs) with respect to classification accuracy, resilience, and clinical relevance. The research also addresses real-time deployment issues, model interpretability, and multimodal data integration's importance. Moreover, this study points out present research voids—such as restricted generalizability and absence of stage-wise lesion classification—and offers future research paths to close these obstacles. This effort intends to lead academics, doctors, and developers toward the building of efficient, scalable, and accessible AI-driven diagnostic tools for early OSCC diagnosis and intervention by synthesizing ideas from previous advancements.","url":"https://doi.org/10.5281/zenodo.21600154","authors":["Kudatarkar, Vinaya R","Patil, Annapurna P","Shetty, Savita K"],"tags":["Artificial Intelligence (AI), Deep Learning, Diagnostic Accuracy, Deep Learning, Oral Squamous Cell Carcinoma (OSCC)"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21600154","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19722880","name":"Applications of Claude AI in Healthcare, Education, and Customer Support","source":"datacite","abstract":"This research paper explores the applications of Claude AI, a large language model, across key sectors including healthcare, education, and customer support. The study examines how artificial intelligence can enhance efficiency, automate routine tasks, and improve decision-making processes in real-world environments. In healthcare, Claude AI assists in medical documentation, symptom analysis, and patient communication. In education, it supports personalized learning, tutoring, and content generation. In customer support, it enables automated responses, chatbot systems, and customer interaction management. The paper highlights the benefits, challenges, and ethical considerations associated with deploying AI-based conversational systems. It also discusses future opportunities for integrating artificial intelligence into digital transformation initiatives across industries.","url":"https://doi.org/10.5281/zenodo.19722880","authors":["Sameeksha"],"tags":["Artificial intelligence","Artificial bone","Artificial Intelligence/classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19722880","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19722881","name":"Applications of Claude AI in Healthcare, Education, and Customer Support","source":"datacite","abstract":"This research paper explores the applications of Claude AI, a large language model, across key sectors including healthcare, education, and customer support. The study examines how artificial intelligence can enhance efficiency, automate routine tasks, and improve decision-making processes in real-world environments. In healthcare, Claude AI assists in medical documentation, symptom analysis, and patient communication. In education, it supports personalized learning, tutoring, and content generation. In customer support, it enables automated responses, chatbot systems, and customer interaction management. The paper highlights the benefits, challenges, and ethical considerations associated with deploying AI-based conversational systems. It also discusses future opportunities for integrating artificial intelligence into digital transformation initiatives across industries.","url":"https://doi.org/10.5281/zenodo.19722881","authors":["Sameeksha"],"tags":["Artificial intelligence","Artificial bone","Artificial Intelligence/classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19722881","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21188140","name":"AI-Driven Digital Transformation in Healthcare and Education: An Interdisciplinary Pathway to Innovation, Sustainability and Global Development","source":"datacite","abstract":"Abstract Artificial Intelligence (AI) is increasingly embedded in modern digital ecosystems, enabling intelligent automation and data-centric decision-making across critical sectors. This study investigates the impact of AI-enabled digital transformation in healthcare and education with a focus on efficiency, accessibility, and long-term sustainability. An interdisciplinary perspective is adopted by integrating concepts from computer science, medical systems, and educational technologies.In healthcare, AI techniques such as deep learning support early diagnosis, medical imaging, and predictive analytics, leading to improved clinical outcomes. In education, AI facilitates adaptive learning environments, intelligent tutoring, and performance analytics for enhanced learning experiences. The paper proposes a conceptual interdisciplinary framework to support responsible AI adoption aligned with sustainable development goals.The findings indicate that AI enhances operational efficiency and innovation; however, concerns related to ethics, data privacy, and equitable access must be addressed. The study concludes that responsible AI governance and interdisciplinary collaboration are essential for achieving inclusive and sustainable global development.","url":"https://doi.org/10.5281/zenodo.21188140","authors":["Rajshree"],"tags":["Keywords: Artificial Intelligence, Digital Transformation, Healthcare, Education, Sustainability, Deep Learning, Interdisciplinary Research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21188140","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21188141","name":"AI-Driven Digital Transformation in Healthcare and Education: An Interdisciplinary Pathway to Innovation, Sustainability and Global Development","source":"datacite","abstract":"Abstract Artificial Intelligence (AI) is increasingly embedded in modern digital ecosystems, enabling intelligent automation and data-centric decision-making across critical sectors. This study investigates the impact of AI-enabled digital transformation in healthcare and education with a focus on efficiency, accessibility, and long-term sustainability. An interdisciplinary perspective is adopted by integrating concepts from computer science, medical systems, and educational technologies.In healthcare, AI techniques such as deep learning support early diagnosis, medical imaging, and predictive analytics, leading to improved clinical outcomes. In education, AI facilitates adaptive learning environments, intelligent tutoring, and performance analytics for enhanced learning experiences. The paper proposes a conceptual interdisciplinary framework to support responsible AI adoption aligned with sustainable development goals.The findings indicate that AI enhances operational efficiency and innovation; however, concerns related to ethics, data privacy, and equitable access must be addressed. The study concludes that responsible AI governance and interdisciplinary collaboration are essential for achieving inclusive and sustainable global development.","url":"https://doi.org/10.5281/zenodo.21188141","authors":["Rajshree"],"tags":["Keywords: Artificial Intelligence, Digital Transformation, Healthcare, Education, Sustainability, Deep Learning, Interdisciplinary Research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21188141","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20658360","name":"The Complete Patient: AI, Clinical Intelligence, and the Future of Predictive Medicine BY Srivenkata Gantikota","source":"datacite","abstract":"The field of healthcare is undergoing a profound transformation driven by advances in artificial intelligence, data science, digital health technologies, and precision medicine. For centuries, medical care has largely been reactive—focused on diagnosing and treating diseases after symptoms emerge. Today, however, the convergence of clinical intelligence, predictive analytics, wearable technologies, genomics, and AI-powered decision systems is enabling a new paradigm of healthcare that is proactive, personalized, preventive, and patient-centered. It is within this exciting and rapidly evolving landscape that The Complete Patient: AI, Clinical Intelligence, and the Future of Predictive Medicine has been written. The concept of the “Complete Patient” extends beyond traditional clinical records and episodic care. It envisions a holistic and continuously updated representation of an individual’s health status, integrating clinical data, genetic information, lifestyle behaviors, environmental influences, wearable sensor outputs, and social determinants of health. By combining these diverse data streams with advanced artificial intelligence and predictive modeling, healthcare providers can gain deeper insights into disease risk, treatment effectiveness, and long-term health outcomes. This integrated approach has the potential to revolutionize how healthcare is delivered, shifting the focus from illness management to health optimization. This book explores the technologies, methodologies, and innovations that are shaping the future of predictive medicine. It examines the foundations of artificial intelligence in healthcare, the role of machine learning and deep learning in clinical decision-making, and the emergence of intelligent systems capable of supporting diagnosis, prognosis, and personalized treatment planning. Readers will discover how predictive analytics is transforming patient care through early disease detection, risk stratification, remote monitoring, and precision interventions. Special attention is given to the development of digital health ecosystems, including electronic health records, connected medical devices, wearable technologies, virtual health assistants, digital twins, and autonomous healthcare agents. These technologies are creating unprecedented opportunities for continuous patient engagement and real-time clinical intelligence, enabling healthcare systems to anticipate medical needs before they become critical. The book also addresses the ethical, legal, regulatory, and societal challenges associated with AI-driven healthcare. Issues such as data privacy, algorithmic bias, explainability, transparency, cybersecurity, and equitable access to healthcare technologies are discussed in depth. As artificial intelligence becomes increasingly integrated into clinical practice, it is essential that innovation is balanced with responsibility, trust, and patient safety. Designed for students, researchers, healthcare professionals, technology practitioners, policymakers, and anyone interested in the future of medicine, this book provides both conceptual foundations and practical insights into the rapidly evolving world of predictive healthcare. Each chapter is structured to bridge the gap between technological innovation and clinical application, offering readers a comprehensive understanding of how intelligent systems are reshaping healthcare delivery. It is my sincere hope that this book will serve as a valuable resource for understanding the transformative role of artificial intelligence and clinical intelligence in modern medicine. More importantly, I hope it inspires readers to contribute to the development of healthcare systems that are smarter, more accessible, and more effective in improving human health and well-being.","url":"https://doi.org/10.5281/zenodo.20658360","authors":["Srivenkata Gantikota"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.20658360","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20658361","name":"The Complete Patient: AI, Clinical Intelligence, and the Future of Predictive Medicine BY Srivenkata Gantikota","source":"datacite","abstract":"The field of healthcare is undergoing a profound transformation driven by advances in artificial intelligence, data science, digital health technologies, and precision medicine. For centuries, medical care has largely been reactive—focused on diagnosing and treating diseases after symptoms emerge. Today, however, the convergence of clinical intelligence, predictive analytics, wearable technologies, genomics, and AI-powered decision systems is enabling a new paradigm of healthcare that is proactive, personalized, preventive, and patient-centered. It is within this exciting and rapidly evolving landscape that The Complete Patient: AI, Clinical Intelligence, and the Future of Predictive Medicine has been written. The concept of the “Complete Patient” extends beyond traditional clinical records and episodic care. It envisions a holistic and continuously updated representation of an individual’s health status, integrating clinical data, genetic information, lifestyle behaviors, environmental influences, wearable sensor outputs, and social determinants of health. By combining these diverse data streams with advanced artificial intelligence and predictive modeling, healthcare providers can gain deeper insights into disease risk, treatment effectiveness, and long-term health outcomes. This integrated approach has the potential to revolutionize how healthcare is delivered, shifting the focus from illness management to health optimization. This book explores the technologies, methodologies, and innovations that are shaping the future of predictive medicine. It examines the foundations of artificial intelligence in healthcare, the role of machine learning and deep learning in clinical decision-making, and the emergence of intelligent systems capable of supporting diagnosis, prognosis, and personalized treatment planning. Readers will discover how predictive analytics is transforming patient care through early disease detection, risk stratification, remote monitoring, and precision interventions. Special attention is given to the development of digital health ecosystems, including electronic health records, connected medical devices, wearable technologies, virtual health assistants, digital twins, and autonomous healthcare agents. These technologies are creating unprecedented opportunities for continuous patient engagement and real-time clinical intelligence, enabling healthcare systems to anticipate medical needs before they become critical. The book also addresses the ethical, legal, regulatory, and societal challenges associated with AI-driven healthcare. Issues such as data privacy, algorithmic bias, explainability, transparency, cybersecurity, and equitable access to healthcare technologies are discussed in depth. As artificial intelligence becomes increasingly integrated into clinical practice, it is essential that innovation is balanced with responsibility, trust, and patient safety. Designed for students, researchers, healthcare professionals, technology practitioners, policymakers, and anyone interested in the future of medicine, this book provides both conceptual foundations and practical insights into the rapidly evolving world of predictive healthcare. Each chapter is structured to bridge the gap between technological innovation and clinical application, offering readers a comprehensive understanding of how intelligent systems are reshaping healthcare delivery. It is my sincere hope that this book will serve as a valuable resource for understanding the transformative role of artificial intelligence and clinical intelligence in modern medicine. More importantly, I hope it inspires readers to contribute to the development of healthcare systems that are smarter, more accessible, and more effective in improving human health and well-being.","url":"https://doi.org/10.5281/zenodo.20658361","authors":["Srivenkata Gantikota"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.20658361","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19597396","name":"Closing the Gap Making AI Education Accessible for Chronically Ill Students in Canada","source":"datacite","abstract":"Approximately 30% of school-age children in Canada live with chronic illnesses, facing significant educational disruptions due to medical absence and fluctuating energy levels. This paper explores the dual nature of Artificial Intelligence as both a transformative bridge and a potential barrier to academic equity. While AI-driven adaptive learning and automated synthesis offer personalized support for remote students, the research identifies critical risks, including algorithmic bias, where medical absence is misread as disengagement, and privacy concerns regarding the merging of health and educational data. Through an analysis of existing Canadian frameworks like the AODA and PIPEDA, the study advocates for a national \"AI Accessibility Certification,\" mandated teacher AI literacy training, and updated data protections. By prioritizing equity-by-design, Canada can ensure that technological advancement serves to include, rather than alienate, students navigating long-term health challenges.","url":"https://doi.org/10.5281/zenodo.19597396","authors":["Ayona Jaswal"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19597396","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19597397","name":"Closing the Gap Making AI Education Accessible for Chronically Ill Students in Canada","source":"datacite","abstract":"Approximately 30% of school-age children in Canada live with chronic illnesses, facing significant educational disruptions due to medical absence and fluctuating energy levels. This paper explores the dual nature of Artificial Intelligence as both a transformative bridge and a potential barrier to academic equity. While AI-driven adaptive learning and automated synthesis offer personalized support for remote students, the research identifies critical risks, including algorithmic bias, where medical absence is misread as disengagement, and privacy concerns regarding the merging of health and educational data. Through an analysis of existing Canadian frameworks like the AODA and PIPEDA, the study advocates for a national \"AI Accessibility Certification,\" mandated teacher AI literacy training, and updated data protections. By prioritizing equity-by-design, Canada can ensure that technological advancement serves to include, rather than alienate, students navigating long-term health challenges.","url":"https://doi.org/10.5281/zenodo.19597397","authors":["Ayona Jaswal"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19597397","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21990252","name":"ARTIFICIAL INTELLIGENCE IN HEALTHCARE: EXPLORING KNOWLEDGE ATTITUDES AND PERCEPTIONS OF FUTURE PROFESSIONALS FROM A MEDICAL COLLEGE IN MAHARASHTRA","source":"datacite","abstract":"The integration of Artificial Intelligence (AI) into medical education and healthcare is reshaping traditional methods. This study assessed the knowledge, attitudes, and perceptions of 245 undergraduate health profession students from a medical college in Maharashtra through a digital survey. Findings showed moderate AI knowledge, with male students and those with prior exposure to AI demonstrating higher scores. While most students acknowledged AI's benefits, 23% expressed concerns about AI replacing human educators. Adoption of AI tools was limited due to barriers such as inadequate knowledge, restricted access, time constraints, and curricular gaps. The results emphasize the need to expand AI topics within medical curricula and address these barriers to better prepare students for leveraging AI in patient care and education.","url":"https://doi.org/10.5281/zenodo.21990252","authors":["Tawde, Ashlesha","Waingankar, Prasad","Dey, Amit Kumar","Kulkarni, S. V."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21990252","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21990253","name":"ARTIFICIAL INTELLIGENCE IN HEALTHCARE: EXPLORING KNOWLEDGE ATTITUDES AND PERCEPTIONS OF FUTURE PROFESSIONALS FROM A MEDICAL COLLEGE IN MAHARASHTRA","source":"datacite","abstract":"The integration of Artificial Intelligence (AI) into medical education and healthcare is reshaping traditional methods. This study assessed the knowledge, attitudes, and perceptions of 245 undergraduate health profession students from a medical college in Maharashtra through a digital survey. Findings showed moderate AI knowledge, with male students and those with prior exposure to AI demonstrating higher scores. While most students acknowledged AI's benefits, 23% expressed concerns about AI replacing human educators. Adoption of AI tools was limited due to barriers such as inadequate knowledge, restricted access, time constraints, and curricular gaps. The results emphasize the need to expand AI topics within medical curricula and address these barriers to better prepare students for leveraging AI in patient care and education.","url":"https://doi.org/10.5281/zenodo.21990253","authors":["Tawde, Ashlesha","Waingankar, Prasad","Dey, Amit Kumar","Kulkarni, S. V."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21990253","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21039379","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, I present a Stone Programming Paradigm. This fuses symbolic indexing with languages, platforms, and paradigms all together. The concepts merging fields of work, study, hobby or society for engagement, this could be presented as a new method to developing code. Especially if paired with \"Discrete Greek\" Though outcomes vary, calling an Internet Opensource Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. For copyright automation. Stone Software Solutions LLC creates virtual machines, and has created an esoteric language. The reason this is presented this way, is, it abstracts the concept language, similar to that of python, a bastardized abridged form perhaps even. In its infancy, but as multiple sources confirmed Stones Esolanguage and Discrete a Greek are bases on Stones exhausting effort in the Stonian Mathematics Paradigm which encompasses Discrete Greek. It allows for a new take on the ease of use of AI. Though we may see an individual, and not know the make-up of their intellect, education, or experience, we can assuredly know they can access artificial intelligence, as of these dates encompassing these writings, and gain a wealth of Data, some data more relevant than others, and still amazing. When we create these grand tools for utility, a purpose, and design theoretically must drive the effort. This published work acts as a recursive account of the development of the paradigm as a whole. From prototyping to versioning, this Programming paradigm establishes linguistics as a root to which arithmetic is its parallel. Though a few years, and many hours have flown by, & I feel non the better for it, save my intellect in linguistics, and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis it evolved from medical technology algorithms, which are a standard operating procedure in operations of the medical field. “If pt. de-Sats while on 02, call the code, & get the AED*. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective.” the above can be confusing here it is simply put:If a patient is desaturating while on oxygen, call the code, get the AUTOMATIC ELECTRONIC DEFRIBULATOR. If life-support is busy, perform (CPR). If performing CPR & compression provider reaches exhaustion, call a partner, if there is no partner to call, try until ineffective. Though this is not Unicode, it gives a light into what is, or isn’t an algorithm, or code The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate, but more easily with certified, qualified, capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures, respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and, the abilities, the hierarchical system has a method of managing-operations systematically for environmental-coverage of duties-as-assigned. While a doctor has open license, other members of their team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things. The variable can only do those dities because it is regulated and authorized. As a no-joking-matter and as thought to be well established in empirical research, CPR itself is an algorithm. With parameters for: \"cyclical compressions, at a rate to breath interval\" this; to be called anything but an algorithm would be non-sense. When the hierarchical system expands beyond ","url":"https://doi.org/10.5281/zenodo.21039379","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21039379","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20909166","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, I present a Stone Programming Paradigm. This fuses symbolic indexing with languages, platforms, and paradigms all together. The concepts merging fields of work, study, hobby or society for engagement, this could be presented as a new method to developing code. Especially if paired with \"Discrete Greek\" Though outcomes vary, calling an Internet Opensource Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. For copyright automation. Stone Software Solutions LLC creates virtual machines, and has created an esoteric language. The reason this is presented this way, is, it abstracts the concept language, similar to that of python, a bastardized abridged form perhaps even. In its infancy, but as multiple sources confirmed Stones Esolanguage and Discrete a Greek are bases on Stones exhausting effort in the Stonian Mathematics Paradigm which encompasses Discrete Greek. It allows for a new take on the ease of use of AI. Though we may see an individual, and not know the make-up of their intellect, education, or experience, we can assuredly know they can access artificial intelligence, as of these dates encompassing these writings, and gain a wealth of Data, some data more relevant than others, and still amazing. When we create these grand tools for utility, a purpose, and design theoretically must drive the effort. This published work acts as a recursive account of the development of the paradigm as a whole. From prototyping to versioning, this Programming paradigm establishes linguistics as a root to which arithmetic is its parallel. Though a few years, and many hours have flown by, & I feel non the better for it, save my intellect in linguistics, and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis it evolved from medical technology algorithms, which are a standard operating procedure in operations of the medical field. “If pt. de-Sats while on 02, call the code, & get the AED*. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective.” the above can be confusing here it is simply put:If a patient is desaturating while on oxygen, call the code, get the AUTOMATIC ELECTRONIC DEFRIBULATOR. If life-support is busy, perform (CPR). If performing CPR & compression provider reaches exhaustion, call a partner, if there is no partner to call, try until ineffective. Though this is not Unicode, it gives a light into what is, or isn’t an algorithm, or code The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate, but more easily with certified, qualified, capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures, respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and, the abilities, the hierarchical system has a method of managing-operations systematically for environmental-coverage of duties-as-assigned. While a doctor has open license, other members of their team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things. The variable can only do those dities because it is regulated and authorized. As a no-joking-matter and as thought to be well established in empirical research, CPR itself is an algorithm. With parameters for: \"cyclical compressions, at a rate to breath interval\" this; to be called anything but an algorithm would be non-sense. When the hierarchical system expands beyond ","url":"https://doi.org/10.5281/zenodo.20909166","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20909166","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21039459","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, I present a Stone Programming Paradigm. This fuses symbolic indexing with languages, platforms, and paradigms all together. The concepts merging fields of work, study, hobby or society for engagement, this could be presented as a new method to developing code. Especially if paired with \"Discrete Greek\" Though outcomes vary, calling an Internet Opensource Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. For copyright automation. Stone Software Solutions LLC creates virtual machines, and has created an esoteric language. The reason this is presented this way, is, it abstracts the concept language, similar to that of python, a bastardized abridged form perhaps even. In its infancy, but as multiple sources confirmed Stones Esolanguage and Discrete a Greek are bases on Stones exhausting effort in the Stonian Mathematics Paradigm which encompasses Discrete Greek. It allows for a new take on the ease of use of AI. Though we may see an individual, and not know the make-up of their intellect, education, or experience, we can assuredly know they can access artificial intelligence, as of these dates encompassing these writings, and gain a wealth of Data, some data more relevant than others, and still amazing. When we create these grand tools for utility, a purpose, and design theoretically must drive the effort. This published work acts as a recursive account of the development of the paradigm as a whole. From prototyping to versioning, this Programming paradigm establishes linguistics as a root to which arithmetic is its parallel. Though a few years, and many hours have flown by, & I feel non the better for it, save my intellect in linguistics, and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis it evolved from medical technology algorithms, which are a standard operating procedure in operations of the medical field. This is Stones first experience with abridged encoding. “If pt. de-Sats while on 02, call the code, & get the AED*. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective.” the above can be confusing here it is simply put:If a patient is desaturating while on oxygen, call the code, get the AUTOMATIC ELECTRONIC DEFRIBULATOR. If life-support is busy, perform (CPR). If performing CPR & compression provider reaches exhaustion, call a partner, if there is no partner to call, try until ineffective. Though this is not Unicode, it gives a light into what is, or isn’t an algorithm, or code The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate, but more easily with certified, qualified, capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures, respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and, the abilities, the hierarchical system has a method of managing-operations systematically for environmental-coverage of duties-as-assigned. While a doctor has open license, other members of their team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things. The variable can only do those dities because it is regulated and authorized. As a no-joking-matter and as thought to be well established in empirical research, CPR itself is an algorithm. With parameters for: \"cyclical compressions, at a rate to breath interval\" this; to be called anything but an algorithm would be","url":"https://doi.org/10.5281/zenodo.21039459","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21039459","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20792522","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, I present a Stone Programming Paradigm. This fuses symbolic with languages, platforms, and paradigms all together. The concepts merging dispirite fields of work, study, hobby or Society engagement could present this as a new method to developing code. Though outcomes vary, calling a Internet Opensourcw Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. Stone Software Solutions Creates Virtual Machines and has created an esoteric language. The reason I say this is it abstracts the concept of python and bastardized abridged form. In its infancy but as multiple sources confirmed Stones Esolanguage is bases on the Stones exhaustiing effort in the Stonian Mathematics paradigm. It alots for a new take on the purpose of AI. Though we may see an individual and not know the makeup of their intellect, education or experience, we can assuredly know they can acssess Artificial Intelligence and gain a wealth of Data, some data more relevant than others, still it’s amazing. When we create these grand tools for utility a purpose and design theoretically must drive the effort. As a recursive account of the development of the paradigm as a whole. The few years and many hours have flown by and I feel non the better for it save my intellect in linguistics and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis from medical technology algorithms are a standard operating procedure in operations. “If pt deSats while on oxygen, Call the code, Get life Support. If life support busy, perform (CPR). If performing CPR and reach exhaustion call partner, in no partner try until ineffective.” Though this is not Unicode, it gives a light into what is or isn’t an algorithm or code. The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate with certified, qualified , capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and abilities the hierarchical system has a meteor of managing operations systematically for environmental coverage of duties as assigned. While a doctor has open license, other members of the team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things because it is regulated and authorized. As a no joking matter and established in empirical research CPR itself is an algorithm. With parameters for cyclical compressions at a rate to breath interval to be called anything but an algorithm would be non-sense. When the hierarchical system expands beyond the lay person under the Good Samaritan system of measurement the medical system begets algorithms, acronyms, calculations, dosages, regulation certificates and the paramount privacy. While hearing a medical provider read a chart to give report at shift change, you may think they are speaking in C+\\C++ or python. And for this reason the ability for abstract of code to language has reached a critical point. Although this code was produced by Artificial Intelligence from a library of Zenodo and a dictionary of Travis Raymond-Charlie Stone, his works having several thousand downloads creates an interesting system of networks. Also, it’s a point of honor to say, the prompt given was in fact not the first prompt given in a chat on the Gemini platform that delivered the extensive and complex code in the deliverables. The notion that this could become a regulat","url":"https://doi.org/10.5281/zenodo.20792522","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20792522","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20792521","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, this Intelligent CSS Cascading Style Sheets Is presented as a part of the Stone Programming Paradigm. This fuses symbolic indexing with CSS the computer programming language, which in turn affect other languages and platforms, and paradigms all together. The concepts merging fields of work, study, hobby or society for engagement, this could be presented as a new method to developing code. Especially if paired with \"Discrete Greek\" Though outcomes vary, calling an Internet Opensource Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. For copyright automation. Stone Software Solutions LLC creates virtual machines, and has created an esoteric language. The reason this is presented this way, is, it abstracts the concept language, similar to that of python, a bastardized abridged form perhaps even. In its infancy, but as multiple sources confirmed Stones Esolanguage and Discrete a Greek are bases on Stones exhausting effort in the Stonian Mathematics Paradigm which encompasses Discrete Greek. It allows for a new take on the ease of use of AI. Though we may see an individual, and not know the make-up of their intellect, education, or experience, we can assuredly know they can access artificial intelligence, as of these dates encompassing these writings, and gain a wealth of Data, some data more relevant than others, and still amazing. When we create these grand tools for utility, a purpose, and design theoretically must drive the effort. This published work acts as a recursive account of the development of the paradigm as a whole. From prototyping to versioning, this Programming paradigm establishes linguistics as a root to which arithmetic is its parallel. Though a few years, and many hours have flown by, & I feel non the better for it, save my intellect in linguistics, and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis it evolved from medical technology algorithms, which are a standard operating procedure in operations of the medical field. This is Stones first experience with abridged encoding. “If pt. de-Sats while on 02, call the code, & get the AED*. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective.” the above can be confusing here it is simply put:If a patient is desaturating while on oxygen, call the code, get the AUTOMATIC ELECTRONIC DEFRIBULATOR. If life-support is busy, perform (CPR). If performing CPR & compression provider reaches exhaustion, call a partner, if there is no partner to call, try until ineffective. Though this is not Unicode, it gives a light into what is, or isn’t an algorithm, or code The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate, but more easily with certified, qualified, capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures, respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and, the abilities, the hierarchical system has a method of managing-operations systematically for environmental-coverage of duties-as-assigned. While a doctor has open license, other members of their team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things. The variable can only do those dities because it is regulated and authorized. As a no-joking-matter and as thought to be well established in empirical research, CPR itself is an algorith","url":"https://doi.org/10.5281/zenodo.20792521","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20792521","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20792906","name":"Stone Programming Paradigm: Language Abstraction","source":"datacite","abstract":"For the purpose of novelty and trajectory of technological development potential, I present a Stone Programming Paradigm. This fuses symbolic indexing with languages, platforms, and paradigms all together. The concepts merging fields of work, study, hobby or society for engagement, this could be present as a new method to developing code. Though outcomes vary, calling an Internet Opensource Database with the hierarchical structure of a scholarly article as a syntax could become structured and organized. For copyright automation. Stone Software Solutions LLC creates virtual machines, and has created an esoteric language. The reason this is presented this way, is, it abstracts the concept of python, a bastardized abridged form perhaps even. In its infancy but as multiple sources confirmed Stones Esolanguage is bases on Stones exhausting effort in the Stonian Mathematics Paradigm. It allows for a new take on the purpose of AI. Though we may see an individual, and not know the make-up of their intellect, education, or experience, we can assuredly know they can access artificial intelligence, and gain a wealth of Data, some data more relevant than others, and still amazing. When we create these grand tools for utility, a purpose and design theoretically must drive the effort. As a recursive account of the development of the paradigm as a whole. Theough a few years, and many hours have flown by, and I feel non the better for it save my intellect in linguistics, and chosen fields of study. To recount the fields would too, be exhausting, so Technology should suffice. As a premis from medical technology algorithms are a standard operating procedure in operations. “If pt. de-Sats while on 02, call the code, & get the AED*. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective.” the above can be confusing here it is simply put:Is a patient is desaturating while on oxygen, call the code, get the AUTOMATIC ELECTRONIC DEFRIBULATOR. If life-support busy, perform (CPR). If performing CPR & reach exhaustion, call partner, if no partner, try until ineffective. Though this is not Unicode, it gives a light into what is, or isn’t an algorithm, or code. The quote above is an off-line protocol the Medecal Supervisor or Doctor could instantiate, but more easily with certified, qualified, capable individuals. This is like programming a variable. Though it is a little abbreviated to engage the readers into the fact that pt = patient, deSat = Ateriol Oxygen Level Desaturated CPR = Cardio Pulmonary Recessitation. When the Doctor instantiates the SOG/SOP- (standard operating guidelines, standard operating procedures, respectively) the delegation of duties is managed. With a hierarchical structure of responsibilities and, the abilities, the hierarchical system has a method of managing-operations systematically for environmental-coverage of duties-as-assigned. While a doctor has open license, other members of their team have restricted licenses, they don’t use their time doing duties out of their scope. Arguably the defining of a scope is similar to a spectrum of regulated duties, such as a variable can do things. The variable can only do those dities because it is regulated and authorized. As a no-joking-matter and as thought to be well established in empirical research, CPR itself is an algorithm. With parameters for: \"cyclical compressions, at a rate to breath interval\" this; to be called anything but an algorithm would be non-sense. When the hierarchical system expands beyond the lay person under the Good Samaritan system of measurement, the medical system begets algorithms, acronyms, calculations, dosages, regulation certificates and the paramount privacy. To guild the point, while hearing a medical provider read a chart, to give report at shift change, you may think they are speaking in C+\\C++ or python. For this reason the ability to abstract code and to do that same premis, to l","url":"https://doi.org/10.5281/zenodo.20792906","authors":["Stone, Travis Raymond-Charlie"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20792906","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19639649","name":"Integrating Robotics and Artificial Intelligence in Healthcare:  Towards Adaptive and Semi-Autonomous Medical Systems","source":"datacite","abstract":"The integration of robotics and artificial intelligence (AI) is reshaping contemporary healthcare through the emergence of adaptive and semi-autonomous medical systems that operate within structured human-in-the-loop frameworks. This narrative review synthesizes recent advances in AI-enabled medical robotics across sensing, perception, control, and human–robot interaction, with applications spanning surgical intervention, diagnostic imaging, rehabilitation, hospital logistics, and telemedicine. Evidence from clinical and experimental studies indicates that robotic systems enhance procedural precision, operational efficiency, and consistency in care delivery, particularly when combined with deep learning-based perception and data-driven decision-support mechanisms (Topol, 2019; Yang et al., 2018; Esteva et al., 2017). In parallel, AI-driven diagnostic models have demonstrated high performance in pattern recognition tasks, while robotic platforms in rehabilitation and assistive care enable personalized, feedback-driven therapy that improves functional outcomes in selected patient populations (Litjens et al., 2017; Veerbeek et al., 2017).Despite these advances, real-world deployment remains constrained by limitations in model generalizability, system interpretability, data heterogeneity, and integration within complex clinical workflows, necessitating sustained clinician oversight and validation (Jiang et al., 2017). Furthermore, logistical and teleoperated robotic systems highlight both the potential and infrastructural dependencies of healthcare automation, particularly in relation to network reliability, interoperability, and cybersecurity constraints (Kruse et al., 2020). Across all domains, findings consistently indicate that healthcare robotics is progressing toward semi-autonomous systems characterized by shared control and collaborative decision-making rather than full automation.The review concludes that future progress will depend on advances in trustworthy AI, robust multimodal sensing, adaptive control architectures, and clinically validated deployment frameworks, ensuring that robotic systems remain safely embedded within human-centered healthcare ecosystems.","url":"https://doi.org/10.5281/zenodo.19639649","authors":["Abdullah, Md. Safwan"],"tags":["Robotics and Artificial Intelligence in Healthcare","Artificial Intelligence","Artificial Intelligence in Healthcare","Semi-Autonomous Medical System"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19639649","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19639650","name":"Integrating Robotics and Artificial Intelligence in Healthcare:  Towards Adaptive and Semi-Autonomous Medical Systems","source":"datacite","abstract":"The integration of robotics and artificial intelligence (AI) is reshaping contemporary healthcare through the emergence of adaptive and semi-autonomous medical systems that operate within structured human-in-the-loop frameworks. This narrative review synthesizes recent advances in AI-enabled medical robotics across sensing, perception, control, and human–robot interaction, with applications spanning surgical intervention, diagnostic imaging, rehabilitation, hospital logistics, and telemedicine. Evidence from clinical and experimental studies indicates that robotic systems enhance procedural precision, operational efficiency, and consistency in care delivery, particularly when combined with deep learning-based perception and data-driven decision-support mechanisms (Topol, 2019; Yang et al., 2018; Esteva et al., 2017). In parallel, AI-driven diagnostic models have demonstrated high performance in pattern recognition tasks, while robotic platforms in rehabilitation and assistive care enable personalized, feedback-driven therapy that improves functional outcomes in selected patient populations (Litjens et al., 2017; Veerbeek et al., 2017).Despite these advances, real-world deployment remains constrained by limitations in model generalizability, system interpretability, data heterogeneity, and integration within complex clinical workflows, necessitating sustained clinician oversight and validation (Jiang et al., 2017). Furthermore, logistical and teleoperated robotic systems highlight both the potential and infrastructural dependencies of healthcare automation, particularly in relation to network reliability, interoperability, and cybersecurity constraints (Kruse et al., 2020). Across all domains, findings consistently indicate that healthcare robotics is progressing toward semi-autonomous systems characterized by shared control and collaborative decision-making rather than full automation.The review concludes that future progress will depend on advances in trustworthy AI, robust multimodal sensing, adaptive control architectures, and clinically validated deployment frameworks, ensuring that robotic systems remain safely embedded within human-centered healthcare ecosystems.","url":"https://doi.org/10.5281/zenodo.19639650","authors":["Abdullah, Md. Safwan"],"tags":["Robotics and Artificial Intelligence in Healthcare","Artificial Intelligence","Artificial Intelligence in Healthcare","Semi-Autonomous Medical System"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19639650","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21271541","name":"Why are frontotemporal dementia and c9orf72 ALS considered different diseases if they are both driven by the same abnormal expansion of a GGGGCC (G₄C₂) sequence in the first intron of the C9orf72 gene?  Is it logical to think that CRISPR therapeutics for FTD potentially be used for ALS as well? - PathMap Experiment #000031","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=31 Artificial General Intelligence LLC Claim Evaluated: Why are frontotemporal dementia and c9orf72 ALS considered different diseases if they are both driven by the same abnormal expansion of a GGGGCC (G₄C₂) sequence in the first intron of the C9orf72 gene? Is it logical to think that CRISPR therapeutics for FTD potentially be used for ALS as well? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights ALS and FTD exist on a clinical and genetic continuum, meaning a patient may present with symptoms of both simultaneously (FTD-MND overlap). The same C9orf72 expansion produces diverse phenotypes depending on modifiers such as age, sex, and polygenic background. Biomarkers like neurofilament light chain (NfL) are being used to track neurodegeneration in both diseases, highlighting their biological similarities. The role of microglial dysfunction and lysosomal repair deficiency in C9orf72 carriers is a convergent feature across the entire disease spectrum. CRISPR-based excision is more efficient when targeting the intronic repeat region bi-allelically compared to allele-specific editing. RNA structure, specifically G-quadruplexes and hairpins formed by G4C2 repeats, is a targetable druggable space common to both ALS and FTD. The gut microbiome and energy metabolism impairments (such as reduced metabolic flexibility) are emerging as potential modifiers of disease progression in C9orf72-associated cases. FTD and ALS are increasingly viewed as a unified clinical spectrum rather than strictly isolated disorders. C9orf72 repeat expansions are associated with specific neuropathological changes, including the mislocalization of TDP-43 and DPR formation. The C9orf72 repeat length can modulate phenotype, though it is not the sole determinant of whether a patient develops ALS, FTD, or both. CRISPR-Cas9 and CRISPR-Cas13 (CasRx) systems are highly effective at reducing toxic RNA transcripts in both neuronal and glial models. Genetic modifiers, such as *HTT* intermediate alleles, may accelerate age-of-onset in C9orf72 carriers, suggesting that personalized therapeutic strategies must account for individual genetic backgrounds. There is a significant gap in our understanding of why identical repeat expansions lead to divergent clinical outcomes (ALS vs. FTD). Glymphatic dysfunction and cortical free water have been identified as novel imaging biomarkers of disease progression in this genetic spectrum. Therapeutic approaches targeting the Integrated Stress Response (ISR) or reducing DPR toxicity are currently being prioritized for clinical translation. Neuroinflammation, driven by pathways like cGAS-STING and NLRP3, is a shared driver across the ALS/FTD spectrum, rather than merely a secondary effect. Somatic mosaicism, including de novo somatic *C9orf72* repeat expansions, may explain why some patients develop widespread degeneration in a sporadic context. The *C9orf72* expansion impacts microglial lysosomal repair through the RAB8A-ESCRT machinery, linking immunity to neurodegeneration. \"Cryptic exon\" detection, specifically regarding *STMN2* and *UNC13A*, provides a proxy for TDP-43 mislocalization, which is a near-universal hallmark in this spectrum. Fluid biomarkers such as plasma NEFL levels demonstrate a linear relationship with repeat burden, establishing a potential tool for monitoring treatment efficacy across the spectrum. Innate immune activation, detectable via blood Interferon scores, is highest in *C9orf72* expansion carriers, suggesting distinct molecular subtypes. The \"dampening\" of energy metabolism in cells harboring intermediate repeats (less than 30) suggests that repeat length, while traditionally d","url":"https://doi.org/10.5281/zenodo.21271541","authors":["Dungan, Joshua"],"tags":["C9orf72 expansion","_gates_from_c9orf72_expansion","Gene Expression Regulation","_gates_to_gene_expression_regulation","_gates_from_gene_expression_regulation","Protein Aggregation, Pathological","_gates_to_protein_aggregation,_pathological","_gates_from_protein_aggregation,_pathological"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21271541","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21271542","name":"Why are frontotemporal dementia and c9orf72 ALS considered different diseases if they are both driven by the same abnormal expansion of a GGGGCC (G₄C₂) sequence in the first intron of the C9orf72 gene?  Is it logical to think that CRISPR therapeutics for FTD potentially be used for ALS as well? - PathMap Experiment #000031","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=31 Artificial General Intelligence LLC Claim Evaluated: Why are frontotemporal dementia and c9orf72 ALS considered different diseases if they are both driven by the same abnormal expansion of a GGGGCC (G₄C₂) sequence in the first intron of the C9orf72 gene? Is it logical to think that CRISPR therapeutics for FTD potentially be used for ALS as well? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights ALS and FTD exist on a clinical and genetic continuum, meaning a patient may present with symptoms of both simultaneously (FTD-MND overlap). The same C9orf72 expansion produces diverse phenotypes depending on modifiers such as age, sex, and polygenic background. Biomarkers like neurofilament light chain (NfL) are being used to track neurodegeneration in both diseases, highlighting their biological similarities. The role of microglial dysfunction and lysosomal repair deficiency in C9orf72 carriers is a convergent feature across the entire disease spectrum. CRISPR-based excision is more efficient when targeting the intronic repeat region bi-allelically compared to allele-specific editing. RNA structure, specifically G-quadruplexes and hairpins formed by G4C2 repeats, is a targetable druggable space common to both ALS and FTD. The gut microbiome and energy metabolism impairments (such as reduced metabolic flexibility) are emerging as potential modifiers of disease progression in C9orf72-associated cases. FTD and ALS are increasingly viewed as a unified clinical spectrum rather than strictly isolated disorders. C9orf72 repeat expansions are associated with specific neuropathological changes, including the mislocalization of TDP-43 and DPR formation. The C9orf72 repeat length can modulate phenotype, though it is not the sole determinant of whether a patient develops ALS, FTD, or both. CRISPR-Cas9 and CRISPR-Cas13 (CasRx) systems are highly effective at reducing toxic RNA transcripts in both neuronal and glial models. Genetic modifiers, such as *HTT* intermediate alleles, may accelerate age-of-onset in C9orf72 carriers, suggesting that personalized therapeutic strategies must account for individual genetic backgrounds. There is a significant gap in our understanding of why identical repeat expansions lead to divergent clinical outcomes (ALS vs. FTD). Glymphatic dysfunction and cortical free water have been identified as novel imaging biomarkers of disease progression in this genetic spectrum. Therapeutic approaches targeting the Integrated Stress Response (ISR) or reducing DPR toxicity are currently being prioritized for clinical translation. Neuroinflammation, driven by pathways like cGAS-STING and NLRP3, is a shared driver across the ALS/FTD spectrum, rather than merely a secondary effect. Somatic mosaicism, including de novo somatic *C9orf72* repeat expansions, may explain why some patients develop widespread degeneration in a sporadic context. The *C9orf72* expansion impacts microglial lysosomal repair through the RAB8A-ESCRT machinery, linking immunity to neurodegeneration. \"Cryptic exon\" detection, specifically regarding *STMN2* and *UNC13A*, provides a proxy for TDP-43 mislocalization, which is a near-universal hallmark in this spectrum. Fluid biomarkers such as plasma NEFL levels demonstrate a linear relationship with repeat burden, establishing a potential tool for monitoring treatment efficacy across the spectrum. Innate immune activation, detectable via blood Interferon scores, is highest in *C9orf72* expansion carriers, suggesting distinct molecular subtypes. The \"dampening\" of energy metabolism in cells harboring intermediate repeats (less than 30) suggests that repeat length, while traditionally d","url":"https://doi.org/10.5281/zenodo.21271542","authors":["Dungan, Joshua"],"tags":["C9orf72 expansion","_gates_from_c9orf72_expansion","Gene Expression Regulation","_gates_to_gene_expression_regulation","_gates_from_gene_expression_regulation","Protein Aggregation, Pathological","_gates_to_protein_aggregation,_pathological","_gates_from_protein_aggregation,_pathological"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21271542","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20023581","name":"Ai-Powered Digital Twin Approach For Personalized Organ Transplantation","source":"datacite","abstract":"The rapid advancement of artificial intelligence (AI) in healthcare has created unprecedented opportunities for improving diagnosis, treatment planning, and clinical decision-making. This paper presents DonorSync — an AI-powered Digital Twin system designed to assist physicians in liver and kidney donor-recipient matching using machine learning and medical image analysis. The proposed system combines Logistic Regression-based clinical parameter analysis (age, bilirubin, albumin, creatinine, urea) with a ResNet-50-driven ultrasound image evaluation module to generate ranked donor compatibility scores and transplant success probabilities in real time. Built on a FastAPI backend with MongoDB data storage and an HTML/CSS/JavaScript frontend, the platform provides secure, scalable, and efficient access to donor matching services. Experimental evaluation confirms that the integrated dual-modality approach substantially reduces donor selection time and enhances prediction reliability compared to conventional manual processes. The system aligns with UN Sustainable Development Goal 3 (Good Health and Well-Being) and Goal 9 (Industry, Innovation and Infrastructure).","url":"https://doi.org/10.5281/zenodo.20023581","authors":["Mrs.W. Asha Princy","Pooja K.P.","Pooja Shree S","Prathisha A","Shahira Banu S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20023581","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20023582","name":"Ai-Powered Digital Twin Approach For Personalized Organ Transplantation","source":"datacite","abstract":"The rapid advancement of artificial intelligence (AI) in healthcare has created unprecedented opportunities for improving diagnosis, treatment planning, and clinical decision-making. This paper presents DonorSync — an AI-powered Digital Twin system designed to assist physicians in liver and kidney donor-recipient matching using machine learning and medical image analysis. The proposed system combines Logistic Regression-based clinical parameter analysis (age, bilirubin, albumin, creatinine, urea) with a ResNet-50-driven ultrasound image evaluation module to generate ranked donor compatibility scores and transplant success probabilities in real time. Built on a FastAPI backend with MongoDB data storage and an HTML/CSS/JavaScript frontend, the platform provides secure, scalable, and efficient access to donor matching services. Experimental evaluation confirms that the integrated dual-modality approach substantially reduces donor selection time and enhances prediction reliability compared to conventional manual processes. The system aligns with UN Sustainable Development Goal 3 (Good Health and Well-Being) and Goal 9 (Industry, Innovation and Infrastructure).","url":"https://doi.org/10.5281/zenodo.20023582","authors":["Mrs.W. Asha Princy","Pooja K.P.","Pooja Shree S","Prathisha A","Shahira Banu S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20023582","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21829043","name":"Dataset: Gap Analysis: There is no PubMed data showing wet lab data or analysis of TPD-43 proteinopathy found (or not found) in the Cochlear, Spiral, or Scarpa's Ganglion of Amyotrophic Lateral Sclerosis patient data post mortem. - PathMap Experiment #000105","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=105 Artificial General Intelligence LLC Claim Evaluated: Gap Analysis: There is no PubMed data showing wet lab data or analysis of TPD-43 proteinopathy found (or not found) in the Cochlear, Spiral, or Scarpa's Ganglion of Amyotrophic Lateral Sclerosis patient data post mortem. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Noise exposure alone initiates the same nucleocytoplasmic TDP-43 translocation in SGNs that is a hallmark of human ALS neuropathology. Autophagy serves as the primary determinant of TDP-43 aggregate clearance in the auditory system. Muscle-derived extracellular vesicles containing miR-126a-5p actively regulate local TDP-43 synthesis in the peripheral nerves of motor neuron disease models. TDP-43 pathology manifests as distinct filament folds (chevron badge vs. double-spiral) across different FTLD-TDP types. Pharmacological inhibition of mTOR significantly alleviates auditory neurodegeneration, suggesting metabolic dysregulation as a core component of the pathology. The peripheral nervous system possesses a distinct \"big tau\" isoform population, whereas brain-derived tau is uncoupled from peripheral nerve pathology. Septin multimer autoantibodies can mimic lower motor neuron disease, presenting an autoimmune differential for ALS-like phenotypes. Partial loss of STMN2 protein function synergizes with TDP-43 dysfunction to accelerate motor decline in the absence of overt visible neuropathology. Auditory neuropathy in ALS-related disorders exhibits deficits (reduced ABR amplitude, increased latency) even when cochlear responses remain normal, suggesting a central axonal origin rather than peripheral receptor loss. The SGN population is specifically vulnerable to TDP-43 mislocalization, which mirrors the selective neuronal vulnerability seen in spinal motor neurons. Auditory system deficits can manifest before overt behavioral symptoms in some animal models, making \"hidden\" auditory degradation a potential biomarker candidate. TDP-43 pathology in SGNs is mechanistically tied to the same autophagic regulators (such as mTOR and AMPK signaling) as those governing motor neuron health in ALS. There is potential for repurposed therapeutics, such as autophagic flux activators, to mitigate both motor and auditory axonal degeneration. Cochlear Ribbon synapses, while essential for temporal processing, often decline in neurodegenerative contexts, suggesting that synaptopathy may precede SGN loss. The use of diffusion-weighted MRI (dMRI) can quantify axonal density in the VIIIth nerve, offering a non-invasive tool to assess this neurodegeneration. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform immunohistochemical analysis of TDP-43 in human post-mortem Cochlear, Spiral, and Scarpa's ganglia from ALS patients. Investigate autophagic flux markers in the auditory ganglia of SOD1G93A mice to determine if TDP-43 accumulation mimics noise-induced pathology. Evaluate the impact of miR-126a-5p inhibition on SGN integrity and TDP-43 local synthesis in vivo. Perform immunohistochemical audit of Ribbon synapse density in the organ of Corti of TDP-43 Q331K mice. Evaluate SGN autophagic flux via LC3/p62 immunofluorescence in post-mortem spinal cord and auditory brainstem samples from human ALS patients. Expose iPSC-derived SGNs to CSF from ALS patients and quantify TDP-43 nucleocytoplasmic ratio. 📊 Suggested Studies Systematic post-mortem analysis of human cranial nerve ganglia in patients diagnosed with FTLD-TDP. Longitudinal audiometric and histopathological correlation study in ALS mouse models to map the onset of auditory system degeneration. Comparative analysis of 'big tau' versus T","url":"https://doi.org/10.5281/zenodo.21829043","authors":["Dungan, Joshua"],"tags":["Amyotrophic Lateral Sclerosis","_gates_from_amyotrophic_lateral_sclerosis","TDP-43 proteinopathy","_gates_to_tdp-43_proteinopathy","_gates_from_tdp-43_proteinopathy","Spiral Ganglion","_gates_to_spiral_ganglion","Peripheral Nerves"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21829043","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21829044","name":"Dataset: Gap Analysis: There is no PubMed data showing wet lab data or analysis of TPD-43 proteinopathy found (or not found) in the Cochlear, Spiral, or Scarpa's Ganglion of Amyotrophic Lateral Sclerosis patient data post mortem. - PathMap Experiment #000105","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=105 Artificial General Intelligence LLC Claim Evaluated: Gap Analysis: There is no PubMed data showing wet lab data or analysis of TPD-43 proteinopathy found (or not found) in the Cochlear, Spiral, or Scarpa's Ganglion of Amyotrophic Lateral Sclerosis patient data post mortem. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Noise exposure alone initiates the same nucleocytoplasmic TDP-43 translocation in SGNs that is a hallmark of human ALS neuropathology. Autophagy serves as the primary determinant of TDP-43 aggregate clearance in the auditory system. Muscle-derived extracellular vesicles containing miR-126a-5p actively regulate local TDP-43 synthesis in the peripheral nerves of motor neuron disease models. TDP-43 pathology manifests as distinct filament folds (chevron badge vs. double-spiral) across different FTLD-TDP types. Pharmacological inhibition of mTOR significantly alleviates auditory neurodegeneration, suggesting metabolic dysregulation as a core component of the pathology. The peripheral nervous system possesses a distinct \"big tau\" isoform population, whereas brain-derived tau is uncoupled from peripheral nerve pathology. Septin multimer autoantibodies can mimic lower motor neuron disease, presenting an autoimmune differential for ALS-like phenotypes. Partial loss of STMN2 protein function synergizes with TDP-43 dysfunction to accelerate motor decline in the absence of overt visible neuropathology. Auditory neuropathy in ALS-related disorders exhibits deficits (reduced ABR amplitude, increased latency) even when cochlear responses remain normal, suggesting a central axonal origin rather than peripheral receptor loss. The SGN population is specifically vulnerable to TDP-43 mislocalization, which mirrors the selective neuronal vulnerability seen in spinal motor neurons. Auditory system deficits can manifest before overt behavioral symptoms in some animal models, making \"hidden\" auditory degradation a potential biomarker candidate. TDP-43 pathology in SGNs is mechanistically tied to the same autophagic regulators (such as mTOR and AMPK signaling) as those governing motor neuron health in ALS. There is potential for repurposed therapeutics, such as autophagic flux activators, to mitigate both motor and auditory axonal degeneration. Cochlear Ribbon synapses, while essential for temporal processing, often decline in neurodegenerative contexts, suggesting that synaptopathy may precede SGN loss. The use of diffusion-weighted MRI (dMRI) can quantify axonal density in the VIIIth nerve, offering a non-invasive tool to assess this neurodegeneration. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform immunohistochemical analysis of TDP-43 in human post-mortem Cochlear, Spiral, and Scarpa's ganglia from ALS patients. Investigate autophagic flux markers in the auditory ganglia of SOD1G93A mice to determine if TDP-43 accumulation mimics noise-induced pathology. Evaluate the impact of miR-126a-5p inhibition on SGN integrity and TDP-43 local synthesis in vivo. Perform immunohistochemical audit of Ribbon synapse density in the organ of Corti of TDP-43 Q331K mice. Evaluate SGN autophagic flux via LC3/p62 immunofluorescence in post-mortem spinal cord and auditory brainstem samples from human ALS patients. Expose iPSC-derived SGNs to CSF from ALS patients and quantify TDP-43 nucleocytoplasmic ratio. 📊 Suggested Studies Systematic post-mortem analysis of human cranial nerve ganglia in patients diagnosed with FTLD-TDP. Longitudinal audiometric and histopathological correlation study in ALS mouse models to map the onset of auditory system degeneration. Comparative analysis of 'big tau' versus T","url":"https://doi.org/10.5281/zenodo.21829044","authors":["Dungan, Joshua"],"tags":["Amyotrophic Lateral Sclerosis","_gates_from_amyotrophic_lateral_sclerosis","TDP-43 proteinopathy","_gates_to_tdp-43_proteinopathy","_gates_from_tdp-43_proteinopathy","Spiral Ganglion","_gates_to_spiral_ganglion","Peripheral Nerves"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21829044","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.17605/osf.io/dwb3h","name":"Benchmark Contamination Debt: An Exposure–Impact–Response Audit of the Dermatology-AI Evidence Base","source":"datacite","abstract":"This preregistered meta-research study examines how documented defects in widely used dermatology-AI benchmark datasets propagate through the published evidence base. It has three components: (1) estimating the prevalence of exposure to documented benchmark defects across eligible studies and evaluation instances; (2) quantifying performance inflation under controlled pipeline-perturbation experiments; and (3) examining temporal changes in research practices following public defect-disclosure milestones. The study uses a recall-first literature retrieval strategy, a pre-specified mechanical exposure-tier adjudication framework, and stratified random sampling for full extraction.","url":"https://doi.org/10.17605/osf.io/dwb3h","authors":["Dr Ali Sadegh-Zadeh","Sadeghzadeh Bazargan"],"tags":["Dermatology","Physical Sciences and Mathematics","Medicine and Health Sciences","Medical Specialties","Computer Sciences","Artificial Intelligence and Robotics","artificial intelligence","benchmark contamination"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/dwb3h","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.48620/100301","name":"An Introduction to the Machine Learning Lifecycle for Clinical Microbiology.","source":"datacite","abstract":"BACKGROUND: Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data. SOURCES: This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, including applied research articles and relevant guidelines on AI development, evaluation, and implementation in healthcare. OBJECTIVES: This narrative review provides a structured introduction to the machine learning (ML) lifecycle from the perspective of clinical microbiology, outlining the sequence of steps in data preparation, model development, evaluation, and deployment. CONTENT: We describe the characteristics of modern microbiology datasets and emphasize the importance of rigorous problem definition, data integration, quality assessment, and feature engineering. Model development considerations are summarized for supervised learning, including hyperparameter optimization, model choice, and multimodal data integration. Evaluation frameworks are examined with attention to typical challenges for microbiology applications, including class imbalance, generalization, robustness, model interpretation, and explainability. Finally, we summarize key elements of model deployment, including reproducible packaging, integration with Laboratory Information Systems and Electronic Medical Record systems, MLOps practices, ongoing drift monitoring, regulatory and governance requirements. IMPLICATIONS: Successful AI implementation in microbiology demands alignment with laboratory workflows, transparency and interpretability of model behavior, robust performance under real-world variability, and strong data governance. Addressing these factors is essential for translating promising methodological advances into solutions to enhance diagnostics, antimicrobial stewardship, and infection prevention.","url":"https://doi.org/10.48620/100301","authors":["Lboukili, Imane","McFadden, Benjamin R","Sethi, Tavpritesh","Brüningk, Sarah C."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48620/100301","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.18213727","name":"Source Code for a Study on the Use of Artificial Intelligence in Predicting the Life Expectancy of Liver Graft Recipients","source":"datacite","abstract":"The code developed in this research aims to implement, train, evaluate, and interpret Artificial Intelligence models capable of predicting whether liver transplant recipients will survive for more or less than twelve months, using exclusively the clinical data available to medical teams at the time an organ is offered. The entire development process was structured in a modular and reproducible manner, in accordance with the principles of the Design Science Research (DSR) methodology. The core component of the code corresponds to the implementation of supervised/unsupervised machine learning models, including linear models, decision trees and ensemble-based methods. Each model is trained to solve a binary classification problem, in which the target variable represents recipient survival beyond twelve months. The training process includes cross-validation and hyperparameter tuning to ensure robustness and to mitigate overfitting. To assess model performance, the code computes quantitative evaluation metrics the PR-ROC curve, Brier Sciore and Mean Net Benefit. These metrics enable systematic comparison across models and support the identification of those with the greatest potential for practical clinical application. In addition, the code incorporates interpretability and explainability techniques to identify the most relevant features for each trained model. This step is essential for aligning Artificial Intelligence outputs with clinical practice, allowing healthcare professionals to understand the factors that most strongly influence survival predictions and helping to reduce subjectivity in graft acceptance decisions. Please, do not forget to cite the database that are store in https://zenodo.org/records/18189988, with following DOI: https://doi.org/10.5281/zenodo.18189988. Full Citation (IEEE) [1]A. Galindo Leal, J. R. De Oliveira, B.-H. Ferraz Neto, R. L. Macacarie F. de A. Monteiro, “Anonymized Donor–Recipient Clinical Dataset for Artificial Intelligence Models in Liver Transplantation (São Paulo State, 2007–2022)”. Zenodo, jan. 08, 2026. doi: 10.5281/zenodo.18189988.","url":"https://doi.org/10.5281/zenodo.18213727","authors":["De Oliveira, José Ricardo","Galindo Leal, Adriano","Macacari, Rodrigo Luiz","Ferraz Neto, Ben-Hur"],"tags":["Artificial Intelligence","Machine Learning","Clinical Decision Support","Survival Prediction","12-Month Survival","Donor-Recipient Match","Organ Allocation","MELD Score"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18213727","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19298958","name":"Source Code for a Study on the Use of Artificial Intelligence in Predicting the Life Expectancy of Liver Graft Recipients","source":"datacite","abstract":"The code developed in this research aims to implement, train, evaluate, and interpret Artificial Intelligence models capable of predicting whether liver transplant recipients will survive for more or less than twelve months, using exclusively the clinical data available to medical teams at the time an organ is offered. The entire development process was structured in a modular and reproducible manner, in accordance with the principles of the Design Science Research (DSR) methodology. The core component of the code corresponds to the implementation of supervised/unsupervised machine learning models, including linear models, decision trees and ensemble-based methods. Each model is trained to solve a binary classification problem, in which the target variable represents recipient survival beyond twelve months. The training process includes cross-validation and hyperparameter tuning to ensure robustness and to mitigate overfitting. To assess model performance, the code computes quantitative evaluation metrics the PR-ROC curve, Brier Sciore and Mean Net Benefit. These metrics enable systematic comparison across models and support the identification of those with the greatest potential for practical clinical application. In addition, the code incorporates interpretability and explainability techniques to identify the most relevant features for each trained model. This step is essential for aligning Artificial Intelligence outputs with clinical practice, allowing healthcare professionals to understand the factors that most strongly influence survival predictions and helping to reduce subjectivity in graft acceptance decisions. Please, do not forget to cite the database that are store in https://zenodo.org/records/18189988, with following DOI: https://doi.org/10.5281/zenodo.18189988. Full Citation (IEEE) [1]A. Galindo Leal, J. R. De Oliveira, B.-H. Ferraz Neto, R. L. Macacarie F. de A. Monteiro, “Anonymized Donor–Recipient Clinical Dataset for Artificial Intelligence Models in Liver Transplantation (São Paulo State, 2007–2022)”. Zenodo, jan. 08, 2026. doi: 10.5281/zenodo.18189988.","url":"https://doi.org/10.5281/zenodo.19298958","authors":["De Oliveira, José Ricardo","Galindo Leal, Adriano","Macacari, Rodrigo Luiz","Ferraz Neto, Ben-Hur"],"tags":["Artificial Intelligence","Machine Learning","Clinical Decision Support","Survival Prediction","12-Month Survival","Donor-Recipient Match","Organ Allocation","MELD Score"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19298958","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21863548","name":"Dataset: Ebola Virus Outbreak Solution Hypothesis: Oral ginger-derived extracellular vesicles may serve as an acid-stable, inexpensive, and supply-chain ready clinical solution to deliver 6-shogaol to macrophages, triggering CASA autophagy to degrade EBOV VP40 and halt viral egress. - PathMap Experiment #000115","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=115 Artificial General Intelligence LLC Claim Evaluated: Ebola Virus Outbreak Solution Hypothesis: Oral ginger-derived extracellular vesicles may serve as an acid-stable, inexpensive, and supply-chain ready clinical solution to deliver 6-shogaol to macrophages, triggering CASA autophagy to degrade EBOV VP40 and halt viral egress. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights The host protein BAG3 acts as a negative regulator of filovirus egress by sequestering VP40. CASA (Chaperone-assisted selective autophagy) provides a dedicated host defense mechanism against viral matrix protein egress. The mTORC1/CASA axis represents a critical nexus for future antiviral drug intervention. Reticulophagy receptors like FAM134B/RETREG1 independently target viral glycoproteins (GP) for degradation in the ER. EBOV hijacks multiple proteostasis networks, including the calnexin cycle, ERAD, and reticulophagy, to balance viral fitness. MicroRNA expression changes in EBOV-infected cells potentially modulate autophagic pathways. LC3B-II is not only a marker but a functional participant in the internalization of EBOV particles. Exosomal delivery technologies are increasingly utilized for PROTACs and other targeted antiviral modalities. CASA-mediated clearance is not limited to viral proteins; it is a fundamental host mechanism for managing misfolded protein aggregates in neurodegeneration (e.g., TDP-43, α-synuclein). The mTORC1 pathway serves as a strategic \"gateway\" exploited by EBOV to bypass host surveillance. J-domain proteins (JDPs) function as specialized cochaperones that dictate the fate of Hsp70-bound clients, distinguishing between folding and degradation pathways. Plant-derived nanovesicles demonstrate intrinsic tumor-homing or tissue-penetrating abilities, offering a natural platform for cell-free therapy. CASA activation via [6]-shogaol provides an \"HDAC inhibition-HSP70 induction\" dual mechanism, which may provide broad-spectrum cellular stabilization beyond viral inhibition. Post-translational modification (e.g., acetylation) of chaperone systems modulates the selectivity of the chaperone-client interaction, a process currently being decoded as the \"chaperone code.\" 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the effect of 6-shogaol on the expression of BAG3 and HSP70 in macrophages during EBOV infection. Utilize confocal microscopy to evaluate if ginger-derived nanovesicles loaded with 6-shogaol successfully co-localize with VP40 in EBOV-infected Huh7 cells. Perform VLP budding assays in the presence of ginger nanovesicle-delivered 6-shogaol to determine if it suppresses VP40 egress. Test EBOV-VP40 VLP egress in THP-1 macrophages treated with [6]-shogaol-loaded ginger nanovesicles. Perform Western blot analysis of BAG3, HSP70, and VP40 levels in EBOV-infected cells following nanovesicle treatment. Assess lysosomal colocalization of VP40-nanovesicle markers using confocal microscopy. 📊 Suggested Studies Investigate the comparative efficacy of ginger-derived exosomes versus standard rapamycin treatments in suppressing EBOV VP40 egress in macrophage-like cell models. Analyze the potential of natural ginger extract-loaded hydrogels in preserving gastrointestinal stability of autophagic-inducing components for systemic delivery. Systematic evaluation of [6]-shogaol delivery efficacy across different pH buffers to optimize gastric survival. Quantification of CASA-mediated VP40 clearance rates in primary human macrophage cultures. Long-term assessment of cellular proteostasis following chronic nanovesicle delivery. 📊 Swansons Literature Based Discovery Candidates Ginger-derived exosomal","url":"https://doi.org/10.5281/zenodo.21863548","authors":["Dungan, Joshua"],"tags":["Ginger extract","_gates_from_ginger_extract","Autophagy","_gates_to_autophagy","Autophagy (CASA)","_gates_from_autophagy_(casa)","Viral Matrix Proteins","_gates_to_viral_matrix_proteins"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21863548","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21863549","name":"Dataset: Ebola Virus Outbreak Solution Hypothesis: Oral ginger-derived extracellular vesicles may serve as an acid-stable, inexpensive, and supply-chain ready clinical solution to deliver 6-shogaol to macrophages, triggering CASA autophagy to degrade EBOV VP40 and halt viral egress. - PathMap Experiment #000115","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=115 Artificial General Intelligence LLC Claim Evaluated: Ebola Virus Outbreak Solution Hypothesis: Oral ginger-derived extracellular vesicles may serve as an acid-stable, inexpensive, and supply-chain ready clinical solution to deliver 6-shogaol to macrophages, triggering CASA autophagy to degrade EBOV VP40 and halt viral egress. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights The host protein BAG3 acts as a negative regulator of filovirus egress by sequestering VP40. CASA (Chaperone-assisted selective autophagy) provides a dedicated host defense mechanism against viral matrix protein egress. The mTORC1/CASA axis represents a critical nexus for future antiviral drug intervention. Reticulophagy receptors like FAM134B/RETREG1 independently target viral glycoproteins (GP) for degradation in the ER. EBOV hijacks multiple proteostasis networks, including the calnexin cycle, ERAD, and reticulophagy, to balance viral fitness. MicroRNA expression changes in EBOV-infected cells potentially modulate autophagic pathways. LC3B-II is not only a marker but a functional participant in the internalization of EBOV particles. Exosomal delivery technologies are increasingly utilized for PROTACs and other targeted antiviral modalities. CASA-mediated clearance is not limited to viral proteins; it is a fundamental host mechanism for managing misfolded protein aggregates in neurodegeneration (e.g., TDP-43, α-synuclein). The mTORC1 pathway serves as a strategic \"gateway\" exploited by EBOV to bypass host surveillance. J-domain proteins (JDPs) function as specialized cochaperones that dictate the fate of Hsp70-bound clients, distinguishing between folding and degradation pathways. Plant-derived nanovesicles demonstrate intrinsic tumor-homing or tissue-penetrating abilities, offering a natural platform for cell-free therapy. CASA activation via [6]-shogaol provides an \"HDAC inhibition-HSP70 induction\" dual mechanism, which may provide broad-spectrum cellular stabilization beyond viral inhibition. Post-translational modification (e.g., acetylation) of chaperone systems modulates the selectivity of the chaperone-client interaction, a process currently being decoded as the \"chaperone code.\" 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Assess the effect of 6-shogaol on the expression of BAG3 and HSP70 in macrophages during EBOV infection. Utilize confocal microscopy to evaluate if ginger-derived nanovesicles loaded with 6-shogaol successfully co-localize with VP40 in EBOV-infected Huh7 cells. Perform VLP budding assays in the presence of ginger nanovesicle-delivered 6-shogaol to determine if it suppresses VP40 egress. Test EBOV-VP40 VLP egress in THP-1 macrophages treated with [6]-shogaol-loaded ginger nanovesicles. Perform Western blot analysis of BAG3, HSP70, and VP40 levels in EBOV-infected cells following nanovesicle treatment. Assess lysosomal colocalization of VP40-nanovesicle markers using confocal microscopy. 📊 Suggested Studies Investigate the comparative efficacy of ginger-derived exosomes versus standard rapamycin treatments in suppressing EBOV VP40 egress in macrophage-like cell models. Analyze the potential of natural ginger extract-loaded hydrogels in preserving gastrointestinal stability of autophagic-inducing components for systemic delivery. Systematic evaluation of [6]-shogaol delivery efficacy across different pH buffers to optimize gastric survival. Quantification of CASA-mediated VP40 clearance rates in primary human macrophage cultures. Long-term assessment of cellular proteostasis following chronic nanovesicle delivery. 📊 Swansons Literature Based Discovery Candidates Ginger-derived exosomal","url":"https://doi.org/10.5281/zenodo.21863549","authors":["Dungan, Joshua"],"tags":["Ginger extract","_gates_from_ginger_extract","Autophagy","_gates_to_autophagy","Autophagy (CASA)","_gates_from_autophagy_(casa)","Viral Matrix Proteins","_gates_to_viral_matrix_proteins"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21863549","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19740259","name":"A Literature Review on Artificial Intelligence Methods Related to Low Back Pain","source":"datacite","abstract":"Abstract Low back pain (LBP) is a worldwide health problem caused by various diseases. It is difficult to establish standards in medical applications because of the differences in the causes of its occurrence and the individual effects in treatment. The LBP diagnosis and treatment processes generate different numerical and visual data. Today, artificial intelligence (AI) techniques have begun to be developed, aiming to improve the understanding of LBP's causes, treatment processes, and effectiveness using patient data. In our study, we aimed to systematically search the literature on the diagnosis and treatment processes of LBP using AI techniques. We conducted a systematic review of studies on LBP utilizing AI methods between 01.01.2000 and 01.05.2023, using the PubMed database. While searching the database, combinations of the terms \"Artificial Intelligence, \"\"Machine Learning, \"\"Deep Learning, \" and \"Low Back Pain\" were employed. A total of 369 articles were identified. According to the study inclusion criteria, 354 articles were excluded, and 15 studies were reviewed. Magnetic resonance images, biochemical parameters, kinematic variables, EMG signals, PET imaging, and other variables were used AI methods to diagnose LBP. The studies employing AI methods generally focused on classification and regression problems. The AI techniques developed for the diagnosis and treatment processes of LBP are promising. It is anticipated that multidisciplinary studies using artificial intelligence.","url":"https://doi.org/10.5281/zenodo.19740259","authors":["Ulku, Veranyurt","Betul, Akalin","Arzu, Gerçek"],"tags":["Low Back Pain","Artificial Intelligence","Deep Learning","Machine Learning","Public Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19740259","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19740260","name":"A Literature Review on Artificial Intelligence Methods Related to Low Back Pain","source":"datacite","abstract":"Abstract Low back pain (LBP) is a worldwide health problem caused by various diseases. It is difficult to establish standards in medical applications because of the differences in the causes of its occurrence and the individual effects in treatment. The LBP diagnosis and treatment processes generate different numerical and visual data. Today, artificial intelligence (AI) techniques have begun to be developed, aiming to improve the understanding of LBP's causes, treatment processes, and effectiveness using patient data. In our study, we aimed to systematically search the literature on the diagnosis and treatment processes of LBP using AI techniques. We conducted a systematic review of studies on LBP utilizing AI methods between 01.01.2000 and 01.05.2023, using the PubMed database. While searching the database, combinations of the terms \"Artificial Intelligence, \"\"Machine Learning, \"\"Deep Learning, \" and \"Low Back Pain\" were employed. A total of 369 articles were identified. According to the study inclusion criteria, 354 articles were excluded, and 15 studies were reviewed. Magnetic resonance images, biochemical parameters, kinematic variables, EMG signals, PET imaging, and other variables were used AI methods to diagnose LBP. The studies employing AI methods generally focused on classification and regression problems. The AI techniques developed for the diagnosis and treatment processes of LBP are promising. It is anticipated that multidisciplinary studies using artificial intelligence.","url":"https://doi.org/10.5281/zenodo.19740260","authors":["Ulku, Veranyurt","Betul, Akalin","Arzu, Gerçek"],"tags":["Low Back Pain","Artificial Intelligence","Deep Learning","Machine Learning","Public Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19740260","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.6084/m9.figshare.c.8659425","name":"Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education","source":"datacite","abstract":"Abstract Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to provide broad exposure to a wide variety of conditions, including those that are rare, subtle, or easily confused with one another. Although artificial intelligence (AI) has shown impressive performance in medical image classification, its potential to actively improve clinical education has not been fully realised. In this study, we introduce AI-generated counterfactual chest X-rays—real patient images digitally altered to show a different but still realistic condition, while keeping the same patient’s anatomy. These counterfactuals create ‘what if’ scenarios within the same patient and offer a novel tool to enhance diagnostic learning, support decision-making, and improve confidence calibration in medical trainees and professionals. Forty-two participants, including medical students and doctors, completed a four-part online study involving diagnostic classification, comparison of counterfactual image pairs, image authenticity judgements, and clinical treatment planning. All image comparisons focused on three clinical conditions: healthy lungs, pneumonia, and pleural effusion. Statistical analyses included t-tests, ANOVAs, Pearson correlations, and linear regression. Results showed that counterfactual images meaningfully supported diagnostic learning. Participants demonstrated improved accuracy and confidence over time, particularly when distinguishing pleural effusion from healthy lungs. Confidence became more closely aligned with accuracy, and participants were better able to recognise condition-specific differences. These comparisons also revealed areas of diagnostic weakness that would be difficult to detect through conventional instruction alone. While real images led to higher raw diagnostic accuracy, counterfactuals proved highly effective in promoting learning and reflective reasoning. Image authenticity influenced clinical decision-making, and initial confidence was a stronger predictor of final treatment confidence than diagnostic correctness. These findings highlight the strong potential of counterfactual imaging to enhance radiology education. By enabling learners to engage with rare, complex, or demographically underrepresented cases in a controlled and consistent way, AI-generated counterfactuals offer a powerful and scalable tool to improve diagnostic preparedness and clinical confidence in future healthcare professionals.","url":"https://doi.org/10.6084/m9.figshare.c.8659425","authors":["Greta Mohr","Yifei Zhu","Xujiong Ye","Marilyn Lennon","Calum MacLellan","John Maclay","David J. Lowe","Christopher Sainsbury","Feng Dong","David Lagnado"],"tags":["Artificial Intelligence and Image Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8659425","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.6084/m9.figshare.c.8659425.v1","name":"Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education","source":"datacite","abstract":"Abstract Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to provide broad exposure to a wide variety of conditions, including those that are rare, subtle, or easily confused with one another. Although artificial intelligence (AI) has shown impressive performance in medical image classification, its potential to actively improve clinical education has not been fully realised. In this study, we introduce AI-generated counterfactual chest X-rays—real patient images digitally altered to show a different but still realistic condition, while keeping the same patient’s anatomy. These counterfactuals create ‘what if’ scenarios within the same patient and offer a novel tool to enhance diagnostic learning, support decision-making, and improve confidence calibration in medical trainees and professionals. Forty-two participants, including medical students and doctors, completed a four-part online study involving diagnostic classification, comparison of counterfactual image pairs, image authenticity judgements, and clinical treatment planning. All image comparisons focused on three clinical conditions: healthy lungs, pneumonia, and pleural effusion. Statistical analyses included t-tests, ANOVAs, Pearson correlations, and linear regression. Results showed that counterfactual images meaningfully supported diagnostic learning. Participants demonstrated improved accuracy and confidence over time, particularly when distinguishing pleural effusion from healthy lungs. Confidence became more closely aligned with accuracy, and participants were better able to recognise condition-specific differences. These comparisons also revealed areas of diagnostic weakness that would be difficult to detect through conventional instruction alone. While real images led to higher raw diagnostic accuracy, counterfactuals proved highly effective in promoting learning and reflective reasoning. Image authenticity influenced clinical decision-making, and initial confidence was a stronger predictor of final treatment confidence than diagnostic correctness. These findings highlight the strong potential of counterfactual imaging to enhance radiology education. By enabling learners to engage with rare, complex, or demographically underrepresented cases in a controlled and consistent way, AI-generated counterfactuals offer a powerful and scalable tool to improve diagnostic preparedness and clinical confidence in future healthcare professionals.","url":"https://doi.org/10.6084/m9.figshare.c.8659425.v1","authors":["Greta Mohr","Yifei Zhu","Xujiong Ye","Marilyn Lennon","Calum MacLellan","John Maclay","David J. Lowe","Christopher Sainsbury","Feng Dong","David Lagnado"],"tags":["Artificial Intelligence and Image Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8659425.v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21898793","name":"Feryal Zayed Early Detection System","source":"datacite","abstract":"The global healthcare system faces an unprecedented crisis: millions of preventable deaths occur annually due to delayed diagnosis, misdiagnosis, and unequal access to specialized medical expertise. Rural populations, developing nations, and marginalized communities bear the heaviest burden of this diagnostic gap. This white paper introduces Ferial Zayed and Hakim, a revolutionary, open-source diagnostic artificial intelligence system engineered to democratize medical expertise and reduce mortality rates caused by delayed diagnosis by up to eighty percent within five years of full national deployment. Unlike proprietary, profit-driven medical algorithms, Hakim operates under the Humanitarian Public License (HPL-1.0). It is designed as a sovereign public good, strictly prohibited from military use, corporate enclosure, or the algorithmic denial of care. Governed by an unbreakable, code-level constitution of eight articles, Hakim ensures that the human physician always retains the final clinical authority. By integrating edge artificial intelligence, federated learning, and HL7 FHIR interoperability, Hakim offers Ministries of Health a zero-cost, highly scalable, and mathematically verifiable tool to achieve Universal Health Coverage (UHC).","url":"https://doi.org/10.5281/zenodo.21898793","authors":["Elrakhawi, Dr. Mohamed Kamal Arafa","Tully, Chloe"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21898793","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20849421","name":"Additional BIRADS annotations to EMBED dataset","source":"datacite","abstract":"About this deposit BI-RADS lexicon annotations for a subset of 400 images (100 cases) from the Emory BrEast Imaging Dataset (EMBED), released as a companion to: Corbetta, V., et al. \"Beyond Clean Test Sets: Spurious Correlations in Medical Vision-language Models and the Role of Concept Supervision.\" Medical Image Computing and Computer Assisted Intervention (MICCAI), 2026. The deposit contains: Tabular annotations (CSV) with structured BI-RADS lexicon labels per image Pixel-wise segmentation masks (NRRD) registered to the original EMBED DICOMs Important: this is not a redistribution of EMBED These annotations are not usable on their own. The annotation files reference original EMBED image identifiers; no EMBED imaging data is redistributed here. To use these annotations you must have independently registered for and obtained the EMBED dataset via Emory's official access process (https://github.com/Emory-HITI/EMBED_Open_Data), agreed to the EMBED Research Use Agreement, and agreed to the terms governing this release. Release context This release is made with the written permission of the EMBED dataset creators at the Emory University School of Medicine (Health Innovation and Translational Informatics Lab). Code and documentation The code for the accompanying paper, a notebook demonstrating how to use the annotations and full documentation are available at: https://github.com/valecorbetta/framework_for_vlm_eval Access Access is gated by independent verification of EMBED access. Please email Valentina Corbetta (v.corbetta@nki.nk) with proof of access to EMBED. Expected response time: 5 working days. Citation If you use these annotations, please cite the following works: @article{jeong2023emory, title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images}, author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others}, journal={Radiology: Artificial Intelligence}, volume={5}, number={1}, pages={e220047}, year={2023}, publisher={Radiological Society of North America} } @inproceedings{corbetta2026beyond, title={Beyond Clean Test Sets: Spurious Correlations in Medical Vision-language Models and the Role of Concept Supervision}, author={Valentina Corbetta and Portaluri, Antonio and Ze, Muzhen and Boeke, Daniël and Beets-Tan, Regina and Lachi, Veronica and Wetzer, Elisabeth and Jenssen, Robert and Wilson Silva and Kristoffer Wickstr{\\o}m}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026}, year={2026} }","url":"https://doi.org/10.5281/zenodo.20849421","authors":["Corbetta, Valentina","Boeke, Daan","Portaluri, Antonio","He, Muzhen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20849421","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20849422","name":"Additional BIRADS annotations to EMBED dataset","source":"datacite","abstract":"About this deposit BI-RADS lexicon annotations for a subset of 400 images (100 cases) from the Emory BrEast Imaging Dataset (EMBED), released as a companion to: Corbetta, V., et al. \"Beyond Clean Test Sets: Spurious Correlations in Medical Vision-language Models and the Role of Concept Supervision.\" Medical Image Computing and Computer Assisted Intervention (MICCAI), 2026. The deposit contains: Tabular annotations (CSV) with structured BI-RADS lexicon labels per image Pixel-wise segmentation masks (NRRD) registered to the original EMBED DICOMs Important: this is not a redistribution of EMBED These annotations are not usable on their own. The annotation files reference original EMBED image identifiers; no EMBED imaging data is redistributed here. To use these annotations you must have independently registered for and obtained the EMBED dataset via Emory's official access process (https://github.com/Emory-HITI/EMBED_Open_Data), agreed to the EMBED Research Use Agreement, and agreed to the terms governing this release. Release context This release is made with the written permission of the EMBED dataset creators at the Emory University School of Medicine (Health Innovation and Translational Informatics Lab). Code and documentation The code for the accompanying paper, a notebook demonstrating how to use the annotations and full documentation are available at: https://github.com/valecorbetta/framework_for_vlm_eval Access Access is gated by independent verification of EMBED access. Please email Valentina Corbetta (v.corbetta@nki.nk) with proof of access to EMBED. Expected response time: 5 working days. Citation If you use these annotations, please cite the following works: @article{jeong2023emory, title={The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images}, author={Jeong, Jiwoong J and Vey, Brianna L and Bhimireddy, Ananth and Kim, Thomas and Santos, Thiago and Correa, Ramon and Dutt, Raman and Mosunjac, Marina and Oprea-Ilies, Gabriela and Smith, Geoffrey and others}, journal={Radiology: Artificial Intelligence}, volume={5}, number={1}, pages={e220047}, year={2023}, publisher={Radiological Society of North America} } @inproceedings{corbetta2026beyond, title={Beyond Clean Test Sets: Spurious Correlations in Medical Vision-language Models and the Role of Concept Supervision}, author={Valentina Corbetta and Portaluri, Antonio and Ze, Muzhen and Boeke, Daniël and Beets-Tan, Regina and Lachi, Veronica and Wetzer, Elisabeth and Jenssen, Robert and Wilson Silva and Kristoffer Wickstr{\\o}m}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026}, year={2026} }","url":"https://doi.org/10.5281/zenodo.20849422","authors":["Corbetta, Valentina","Boeke, Daan","Portaluri, Antonio","He, Muzhen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20849422","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21286068","name":"What is the biological/molecular pathway that causes sarcopenia with the scope of COPD?  Do the quads serve as a pathological progression indicator? - PathMap Experiment #000041","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=41 Artificial General Intelligence LLC Claim Evaluated: What is the biological/molecular pathway that causes sarcopenia with the scope of COPD? Do the quads serve as a pathological progression indicator? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Quadriceps force and function are sensitive to acute exacerbations (AECOPD), whereas other physical performance batteries (e.g., SPPB) may fail to capture these sudden declines. The pectoralis muscle, measurable on routine chest CT, serves as a systemic prognostic biomarker, showing that muscle mass quality is an independent predictor of in-hospital mortality. Exposure to industrial nanoparticles (silica vs. metal) creates distinct phenotypes of sarcopenia, with silica inducing more pronounced structural and functional loss in the quadriceps. Exercise modality (eccentric vs. concentric) elicits different muscle adaptations; eccentric exercise is highly effective in reducing dyspnea and fatigue in patients with lower cardiorespiratory reserve. Sarcopenia exists even in \"pre-COPD\" smokers, suggesting muscular damage precedes or parallels the onset of overt lung function decline. Genetic polymorphisms, specifically in IGF-1 and IGF-2, are stronger correlates for respiratory muscle strength in COPD patients than current circulating blood inflammatory biomarkers. Nocturnal hypoxemia independently contributes to pectoralis muscle mass loss, bridging sleep quality with peripheral muscle homeostasis. Oxidative Trigger:** Oxidative stress activates p38 MAPK signaling, which directly drives the ubiquitin-proteasome system and autophagy-mediated muscle wasting. Biomarker Utility:** Serum resistin and GDF-15 are emerging, highly accurate predictors of sarcopenia in COPD patients, outperforming traditional metrics like TNF-α. Hypoxia Models:** Prolonged intermittent hypoxia (PIH)—modeled after nocturnal hypoxemia—induces mitochondrial oxidative dysfunction, distinguishing it from simple chronic hypoxia in its metabolic impact on myotubes. Fibrosis/Remodeling:** Cigarette smoke exposure downregulates ADAMTS4, a metalloproteinase critical for maintaining the extracellular matrix, leading to fibrosis and impaired myogenesis. Genetic Susceptibility:** Variants in the *FTO* gene and *AC090771.2* correlate with sarcopenic phenotypes and cellular senescence markers, potentially explaining the inter-individual variation in disease severity. Systemic Crosstalk:** The muscle-lung crosstalk axis is regulated by adipomyokines like irisin, which is deficient in COPD and links exercise capacity to structural integrity. Rehabilitation Prediction:** Baseline quadriceps contractile fatigue is a stronger predictor of successful 6-minute walk distance improvement than initial lung function. Systemic inflammation is driven not just by lung-resident cells, but through the kidney-muscle axis involving clearance-distorted signaling molecules. The TNFα/TNFR1 axis acts as a master switch for proteostatic collapse, inducing both ubiquitin-proteasome overactivation and GSDMD-dependent pyroptosis. Irisin deficiency in COPD correlates directly with muscle weakness, emphysema, and exacerbation frequency, creating a \"muscle-lung crosstalk\" axis. Mitochondrial-sarcoplasmic reticulum crosstalk is essential for Ca2+ handling; its disruption is a prerequisite for anabolic resistance. Autophagic flux is suppressed in PBMCs of COPD patients, suggesting a defect in autophagosome clearance that parallels muscle dysfunction. Pharmacological modulation of Nrf2 using 4-octyl itaconate can reverse necroptosis in alveolar macrophages, mitigating systemic inflammation. The pulmonary artery-to-aorta (PA/A) ","url":"https://doi.org/10.5281/zenodo.21286068","authors":["Dungan, Joshua"],"tags":["Pulmonary Disease, Chronic Obstructive","_gates_from_pulmonary_disease,_chronic_obstructive","Systemic Inflammatory Response Syndrome","_gates_to_systemic_inflammatory_response_syndrome","_gates_from_systemic_inflammatory_response_syndrome","Signal Transduction","_gates_to_signal_transduction","_gates_from_signal_transduction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21286068","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21286069","name":"What is the biological/molecular pathway that causes sarcopenia with the scope of COPD?  Do the quads serve as a pathological progression indicator? - PathMap Experiment #000041","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=41 Artificial General Intelligence LLC Claim Evaluated: What is the biological/molecular pathway that causes sarcopenia with the scope of COPD? Do the quads serve as a pathological progression indicator? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Quadriceps force and function are sensitive to acute exacerbations (AECOPD), whereas other physical performance batteries (e.g., SPPB) may fail to capture these sudden declines. The pectoralis muscle, measurable on routine chest CT, serves as a systemic prognostic biomarker, showing that muscle mass quality is an independent predictor of in-hospital mortality. Exposure to industrial nanoparticles (silica vs. metal) creates distinct phenotypes of sarcopenia, with silica inducing more pronounced structural and functional loss in the quadriceps. Exercise modality (eccentric vs. concentric) elicits different muscle adaptations; eccentric exercise is highly effective in reducing dyspnea and fatigue in patients with lower cardiorespiratory reserve. Sarcopenia exists even in \"pre-COPD\" smokers, suggesting muscular damage precedes or parallels the onset of overt lung function decline. Genetic polymorphisms, specifically in IGF-1 and IGF-2, are stronger correlates for respiratory muscle strength in COPD patients than current circulating blood inflammatory biomarkers. Nocturnal hypoxemia independently contributes to pectoralis muscle mass loss, bridging sleep quality with peripheral muscle homeostasis. Oxidative Trigger:** Oxidative stress activates p38 MAPK signaling, which directly drives the ubiquitin-proteasome system and autophagy-mediated muscle wasting. Biomarker Utility:** Serum resistin and GDF-15 are emerging, highly accurate predictors of sarcopenia in COPD patients, outperforming traditional metrics like TNF-α. Hypoxia Models:** Prolonged intermittent hypoxia (PIH)—modeled after nocturnal hypoxemia—induces mitochondrial oxidative dysfunction, distinguishing it from simple chronic hypoxia in its metabolic impact on myotubes. Fibrosis/Remodeling:** Cigarette smoke exposure downregulates ADAMTS4, a metalloproteinase critical for maintaining the extracellular matrix, leading to fibrosis and impaired myogenesis. Genetic Susceptibility:** Variants in the *FTO* gene and *AC090771.2* correlate with sarcopenic phenotypes and cellular senescence markers, potentially explaining the inter-individual variation in disease severity. Systemic Crosstalk:** The muscle-lung crosstalk axis is regulated by adipomyokines like irisin, which is deficient in COPD and links exercise capacity to structural integrity. Rehabilitation Prediction:** Baseline quadriceps contractile fatigue is a stronger predictor of successful 6-minute walk distance improvement than initial lung function. Systemic inflammation is driven not just by lung-resident cells, but through the kidney-muscle axis involving clearance-distorted signaling molecules. The TNFα/TNFR1 axis acts as a master switch for proteostatic collapse, inducing both ubiquitin-proteasome overactivation and GSDMD-dependent pyroptosis. Irisin deficiency in COPD correlates directly with muscle weakness, emphysema, and exacerbation frequency, creating a \"muscle-lung crosstalk\" axis. Mitochondrial-sarcoplasmic reticulum crosstalk is essential for Ca2+ handling; its disruption is a prerequisite for anabolic resistance. Autophagic flux is suppressed in PBMCs of COPD patients, suggesting a defect in autophagosome clearance that parallels muscle dysfunction. Pharmacological modulation of Nrf2 using 4-octyl itaconate can reverse necroptosis in alveolar macrophages, mitigating systemic inflammation. The pulmonary artery-to-aorta (PA/A) ","url":"https://doi.org/10.5281/zenodo.21286069","authors":["Dungan, Joshua"],"tags":["Pulmonary Disease, Chronic Obstructive","_gates_from_pulmonary_disease,_chronic_obstructive","Systemic Inflammatory Response Syndrome","_gates_to_systemic_inflammatory_response_syndrome","_gates_from_systemic_inflammatory_response_syndrome","Signal Transduction","_gates_to_signal_transduction","_gates_from_signal_transduction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21286069","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21988312","name":"Psychological Stress and Sleep Disturbance as Emerging Determinants of Hair Health: Mechanisms, Premature Greying, Hair Loss, and Preventive Strategies","source":"datacite","abstract":"Certainly. For the above paper, the strongest overall positioning is around the Stress–Sleep–Hair Health relationship, with premature greying, hair loss, hair-cycle disruption, nutrition, lifestyle, and preventive strategies as the major components. Recommended subtitle “An Integrated Review of the Stress–Sleep–Hair Axis, Hair-Follicle Biology, Nutritional Factors, Lifestyle Influences, and Emerging Predictive Approaches” Short running title Stress, Sleep and Hair Health 2. Strong Alternative Combined Titles Alternative Title 1 — Most Suitable for a Review Journal Stress, Sleep, and Hair Health: An Integrated Review of Premature Greying, Hair Loss, Biological Mechanisms, and Prevention Alternative Title 2 — More Scientific Psychoneuroendocrine Regulation of Hair Health: Linking Psychological Stress, Sleep Disturbance, Hair-Follicle Cycling, and Pigmentation Alternative Title 3 — Mechanism-Oriented From Psychological Stress to Hair-Follicle Dysfunction: The Interplay of Stress, Sleep, Oxidative Stress, and Hair-Cycle Regulation Alternative Title 4 — Preventive Health Focus Beyond Hair Care Products: The Role of Stress, Sleep, Nutrition, and Lifestyle in Maintaining Healthy Hair Alternative Title 5 — Premature Greying Focus Premature Hair Greying and Hair Loss in Modern Lifestyles: Exploring the Roles of Stress, Sleep, Nutrition, and Oxidative Stress Alternative Title 6 — Young Adults Focus Hair Health in Young Adults: Associations Between Psychological Stress, Sleep Disturbance, Lifestyle, Premature Greying, and Hair Loss Alternative Title 7 — Psychodermatology Focus Psychodermatological Perspectives on Hair Loss and Premature Greying: Stress, Sleep, Neuroendocrine Signaling, and Prevention Alternative Title 8 — Modern Lifestyle Focus Modern Lifestyle, Psychological Stress, and Hair Health: Understanding the Stress–Sleep–Hair Connection Alternative Title 9 — AI/Technology Focus Digital and Predictive Approaches to Hair Health: Integrating Stress, Sleep, Lifestyle, and Artificial Intelligence for Risk Assessment Alternative Title 10 — Conceptual Framework Focus The Stress–Sleep–Hair Axis: A Conceptual Framework for Understanding Hair Loss, Premature Greying, and Preventive Hair Health Alternative Title 11 — Broad Interdisciplinary Title Integrated Determinants of Hair Health: Psychological, Sleep, Nutritional, Genetic, Metabolic, and Environmental Influences Alternative Title 12 — High-Impact Short Title Beyond Hair Loss: Unravelling the Stress–Sleep–Hair Health Connection Alternative Title 13 — Research-Oriented Multifactorial Determinants of Hair Loss and Premature Greying: A Systems Approach to Stress, Sleep, Nutrition, and Hair-Follicle Biology Alternative Title 14 — Public Health Focus Promoting Hair and Mental Well-Being: The Role of Healthy Sleep, Stress Management, Nutrition, and Early Clinical Evaluation Alternative Title 15 — Future Research Focus Towards Predictive Hair Health: Integrating Stress, Sleep, Nutrition, Biomarkers, Trichoscopy, and Artificial Intelligence 3. Best Three Title Combinations Combination A — Recommended for Scopus/SCI-Style Review Main Title Psychological Stress and Sleep Disturbance as Emerging Determinants of Hair Health Subtitle Mechanisms, Premature Greying, Hair Loss, and Preventive Strategies Extended Description An Integrated Review of the Stress–Sleep–Hair Axis, Hair-Follicle Biology, Nutritional Factors, Lifestyle Influences, and Emerging Predictive Technologies Combination B — Strong Scientific/Mechanistic Version Main Title The Stress–Sleep–Hair Axis: From Neuroendocrine Dysregulation to Hair-Follicle Dysfunction Subtitle Mechanisms of Hair Shedding, Premature Greying, Oxidative Stress, and Circadian Disruption Extended Description A Multidisciplinary Review of Psychological, Biological, Nutritional, Genetic, and Environmental Determinants of Hair Health Combination C — Strong Public-Health Version Main Title Beyond Hair Care Products: Understanding the Role of Stress, Sleep, Nutriti","url":"https://doi.org/10.5281/zenodo.21988312","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21988312","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21988313","name":"Psychological Stress and Sleep Disturbance as Emerging Determinants of Hair Health: Mechanisms, Premature Greying, Hair Loss, and Preventive Strategies","source":"datacite","abstract":"Certainly. For the above paper, the strongest overall positioning is around the Stress–Sleep–Hair Health relationship, with premature greying, hair loss, hair-cycle disruption, nutrition, lifestyle, and preventive strategies as the major components. Recommended subtitle “An Integrated Review of the Stress–Sleep–Hair Axis, Hair-Follicle Biology, Nutritional Factors, Lifestyle Influences, and Emerging Predictive Approaches” Short running title Stress, Sleep and Hair Health 2. Strong Alternative Combined Titles Alternative Title 1 — Most Suitable for a Review Journal Stress, Sleep, and Hair Health: An Integrated Review of Premature Greying, Hair Loss, Biological Mechanisms, and Prevention Alternative Title 2 — More Scientific Psychoneuroendocrine Regulation of Hair Health: Linking Psychological Stress, Sleep Disturbance, Hair-Follicle Cycling, and Pigmentation Alternative Title 3 — Mechanism-Oriented From Psychological Stress to Hair-Follicle Dysfunction: The Interplay of Stress, Sleep, Oxidative Stress, and Hair-Cycle Regulation Alternative Title 4 — Preventive Health Focus Beyond Hair Care Products: The Role of Stress, Sleep, Nutrition, and Lifestyle in Maintaining Healthy Hair Alternative Title 5 — Premature Greying Focus Premature Hair Greying and Hair Loss in Modern Lifestyles: Exploring the Roles of Stress, Sleep, Nutrition, and Oxidative Stress Alternative Title 6 — Young Adults Focus Hair Health in Young Adults: Associations Between Psychological Stress, Sleep Disturbance, Lifestyle, Premature Greying, and Hair Loss Alternative Title 7 — Psychodermatology Focus Psychodermatological Perspectives on Hair Loss and Premature Greying: Stress, Sleep, Neuroendocrine Signaling, and Prevention Alternative Title 8 — Modern Lifestyle Focus Modern Lifestyle, Psychological Stress, and Hair Health: Understanding the Stress–Sleep–Hair Connection Alternative Title 9 — AI/Technology Focus Digital and Predictive Approaches to Hair Health: Integrating Stress, Sleep, Lifestyle, and Artificial Intelligence for Risk Assessment Alternative Title 10 — Conceptual Framework Focus The Stress–Sleep–Hair Axis: A Conceptual Framework for Understanding Hair Loss, Premature Greying, and Preventive Hair Health Alternative Title 11 — Broad Interdisciplinary Title Integrated Determinants of Hair Health: Psychological, Sleep, Nutritional, Genetic, Metabolic, and Environmental Influences Alternative Title 12 — High-Impact Short Title Beyond Hair Loss: Unravelling the Stress–Sleep–Hair Health Connection Alternative Title 13 — Research-Oriented Multifactorial Determinants of Hair Loss and Premature Greying: A Systems Approach to Stress, Sleep, Nutrition, and Hair-Follicle Biology Alternative Title 14 — Public Health Focus Promoting Hair and Mental Well-Being: The Role of Healthy Sleep, Stress Management, Nutrition, and Early Clinical Evaluation Alternative Title 15 — Future Research Focus Towards Predictive Hair Health: Integrating Stress, Sleep, Nutrition, Biomarkers, Trichoscopy, and Artificial Intelligence 3. Best Three Title Combinations Combination A — Recommended for Scopus/SCI-Style Review Main Title Psychological Stress and Sleep Disturbance as Emerging Determinants of Hair Health Subtitle Mechanisms, Premature Greying, Hair Loss, and Preventive Strategies Extended Description An Integrated Review of the Stress–Sleep–Hair Axis, Hair-Follicle Biology, Nutritional Factors, Lifestyle Influences, and Emerging Predictive Technologies Combination B — Strong Scientific/Mechanistic Version Main Title The Stress–Sleep–Hair Axis: From Neuroendocrine Dysregulation to Hair-Follicle Dysfunction Subtitle Mechanisms of Hair Shedding, Premature Greying, Oxidative Stress, and Circadian Disruption Extended Description A Multidisciplinary Review of Psychological, Biological, Nutritional, Genetic, and Environmental Determinants of Hair Health Combination C — Strong Public-Health Version Main Title Beyond Hair Care Products: Understanding the Role of Stress, Sleep, Nutriti","url":"https://doi.org/10.5281/zenodo.21988313","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21988313","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21988277","name":"\"Sudden Cardiac Death in Apparently Healthy Individuals: An Integrated Framework for Risk Assessment, Early Detection, Prevention, and Emergency Response\"","source":"datacite","abstract":"Alternative combined title “From Hidden Cardiac Risk to Sudden Cardiac Death: A Comprehensive Framework for Prediction, Prevention, Screening, and Rapid Response” More research-oriented title “Unmasking Hidden Cardiovascular Risk in Apparently Healthy Individuals: A Comprehensive Approach to Sudden Cardiac Death Prediction and Prevention” Public-health-oriented title “Preventing Unexpected Cardiac Death: Risk Factors, Early Warning Signs, Cardiovascular Screening, CPR, and Community-Based Emergency Preparedness” 2. Alternative Titles Title 1 — Strongest for a Review Paper Sudden Cardiac Death in Apparently Healthy Individuals: Risk Factors, Early Detection, Prevention, and Emergency Management Title 2 — More Scientific Hidden Cardiovascular Risk and Sudden Cardiac Death: From Pathophysiological Mechanisms to Predictive Prevention Title 3 — Young Adults Focus Sudden Cardiac Death in Young and Apparently Healthy Adults: Causes, Warning Signs, Screening, and Preventive Strategies Title 4 — Risk Prediction Focus Towards Predictive Cardiovascular Care: Risk Stratification and Prevention of Sudden Cardiac Death in Apparently Healthy Individuals Title 5 — Screening Focus Early Identification of Hidden Cardiac Disorders: A Preventive Strategy Against Sudden Cardiac Death Title 6 — Technology Focus Emerging Technologies for Sudden Cardiac Death Risk Prediction: ECG, Imaging, Wearables, Artificial Intelligence, and Genetic Screening Title 7 — Public Health Focus Community-Based Prevention of Sudden Cardiac Death: Cardiovascular Awareness, CPR Training, AED Accessibility, and Early Intervention Title 8 — Integrated Medicine Focus From Cardiovascular Risk Factors to Cardiac Arrest: An Integrated Framework for Prevention, Screening, and Emergency Response Title 9 — Innovative Conceptual Title HEART Framework for Preventing Sudden Cardiac Death: Health-Risk Assessment, Early Recognition, AED Accessibility, Resuscitation, and Timely Treatment Title 10 — Broad International Title Global Perspectives on Sudden Cardiac Death in Apparently Healthy Individuals: Risk Factors, Prevention, Screening, and Emergency Care Title 11 — Engineering/AI-Compatible Title AI-Assisted Cardiovascular Risk Stratification for Early Prediction of Sudden Cardiac Death in Apparently Healthy Populations Title 12 — High-Impact Concept Beyond Apparent Health: Uncovering Hidden Cardiac Risk and Preventing Sudden Cardiac Death 3. Recommended Subtitle “A Comprehensive Review of Cardiovascular Risk Factors, Hidden Cardiac Disorders, Early Warning Symptoms, Screening Technologies, Lifestyle Modification, CPR, AEDs, and Community-Based Prevention” Shorter subtitle “From Hidden Risk and Early Warning Signs to Prevention and Rapid Cardiac Resuscitation” 4. Suggested Subtitles / Section Titles 1. The Hidden Threat of Sudden Cardiac Death Understanding why apparently healthy individuals may experience unexpected cardiac arrest. 2. Sudden Cardiac Arrest versus Sudden Cardiac Death Definitions, mechanisms, clinical distinctions, and epidemiological significance. 3. Why Apparently Healthy Individuals Remain at Risk The concept of clinically silent cardiovascular disease and undiagnosed cardiac abnormalities. 4. Major Causes of Sudden Cardiac Death Coronary artery disease, cardiomyopathies, channelopathies, congenital abnormalities, myocarditis, and arrhythmias. 5. Conventional Cardiovascular Risk Factors Hypertension, diabetes, dyslipidemia, obesity, tobacco exposure, physical inactivity, and unhealthy diet. 6. Hidden Structural and Electrical Cardiac Disorders Hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, long-QT syndrome, Brugada syndrome, and related disorders. 7. Sudden Cardiac Death in Young Adults and Athletes Special risk factors, exertional symptoms, inherited diseases, and screening challenges. 8. Warning Signs That Should Never Be Ignored Chest discomfort, breathlessness, palpitations, dizziness, syncope, and unexplained exercise intolerance. 9. The Role of F","url":"https://doi.org/10.5281/zenodo.21988277","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21988277","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21988278","name":"\"Sudden Cardiac Death in Apparently Healthy Individuals: An Integrated Framework for Risk Assessment, Early Detection, Prevention, and Emergency Response\"","source":"datacite","abstract":"Alternative combined title “From Hidden Cardiac Risk to Sudden Cardiac Death: A Comprehensive Framework for Prediction, Prevention, Screening, and Rapid Response” More research-oriented title “Unmasking Hidden Cardiovascular Risk in Apparently Healthy Individuals: A Comprehensive Approach to Sudden Cardiac Death Prediction and Prevention” Public-health-oriented title “Preventing Unexpected Cardiac Death: Risk Factors, Early Warning Signs, Cardiovascular Screening, CPR, and Community-Based Emergency Preparedness” 2. Alternative Titles Title 1 — Strongest for a Review Paper Sudden Cardiac Death in Apparently Healthy Individuals: Risk Factors, Early Detection, Prevention, and Emergency Management Title 2 — More Scientific Hidden Cardiovascular Risk and Sudden Cardiac Death: From Pathophysiological Mechanisms to Predictive Prevention Title 3 — Young Adults Focus Sudden Cardiac Death in Young and Apparently Healthy Adults: Causes, Warning Signs, Screening, and Preventive Strategies Title 4 — Risk Prediction Focus Towards Predictive Cardiovascular Care: Risk Stratification and Prevention of Sudden Cardiac Death in Apparently Healthy Individuals Title 5 — Screening Focus Early Identification of Hidden Cardiac Disorders: A Preventive Strategy Against Sudden Cardiac Death Title 6 — Technology Focus Emerging Technologies for Sudden Cardiac Death Risk Prediction: ECG, Imaging, Wearables, Artificial Intelligence, and Genetic Screening Title 7 — Public Health Focus Community-Based Prevention of Sudden Cardiac Death: Cardiovascular Awareness, CPR Training, AED Accessibility, and Early Intervention Title 8 — Integrated Medicine Focus From Cardiovascular Risk Factors to Cardiac Arrest: An Integrated Framework for Prevention, Screening, and Emergency Response Title 9 — Innovative Conceptual Title HEART Framework for Preventing Sudden Cardiac Death: Health-Risk Assessment, Early Recognition, AED Accessibility, Resuscitation, and Timely Treatment Title 10 — Broad International Title Global Perspectives on Sudden Cardiac Death in Apparently Healthy Individuals: Risk Factors, Prevention, Screening, and Emergency Care Title 11 — Engineering/AI-Compatible Title AI-Assisted Cardiovascular Risk Stratification for Early Prediction of Sudden Cardiac Death in Apparently Healthy Populations Title 12 — High-Impact Concept Beyond Apparent Health: Uncovering Hidden Cardiac Risk and Preventing Sudden Cardiac Death 3. Recommended Subtitle “A Comprehensive Review of Cardiovascular Risk Factors, Hidden Cardiac Disorders, Early Warning Symptoms, Screening Technologies, Lifestyle Modification, CPR, AEDs, and Community-Based Prevention” Shorter subtitle “From Hidden Risk and Early Warning Signs to Prevention and Rapid Cardiac Resuscitation” 4. Suggested Subtitles / Section Titles 1. The Hidden Threat of Sudden Cardiac Death Understanding why apparently healthy individuals may experience unexpected cardiac arrest. 2. Sudden Cardiac Arrest versus Sudden Cardiac Death Definitions, mechanisms, clinical distinctions, and epidemiological significance. 3. Why Apparently Healthy Individuals Remain at Risk The concept of clinically silent cardiovascular disease and undiagnosed cardiac abnormalities. 4. Major Causes of Sudden Cardiac Death Coronary artery disease, cardiomyopathies, channelopathies, congenital abnormalities, myocarditis, and arrhythmias. 5. Conventional Cardiovascular Risk Factors Hypertension, diabetes, dyslipidemia, obesity, tobacco exposure, physical inactivity, and unhealthy diet. 6. Hidden Structural and Electrical Cardiac Disorders Hypertrophic cardiomyopathy, arrhythmogenic cardiomyopathy, long-QT syndrome, Brugada syndrome, and related disorders. 7. Sudden Cardiac Death in Young Adults and Athletes Special risk factors, exertional symptoms, inherited diseases, and screening challenges. 8. Warning Signs That Should Never Be Ignored Chest discomfort, breathlessness, palpitations, dizziness, syncope, and unexplained exercise intolerance. 9. The Role of F","url":"https://doi.org/10.5281/zenodo.21988278","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21988278","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.17605/osf.io/2v8cf","name":"Diagnostic Accuracy of Artificial Intelligence-Enabled Wearable/Single-Lead Electrocardiography for Screening Asymptomatic Left Ventricular Systolic Dysfunction: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis","source":"datacite","abstract":"Systematic review and diagnostic test accuracy meta-analysis of AI-enabled wearable/single-lead electrocardiography algorithms for screening asymptomatic left ventricular systolic dysfunction against echocardiography as reference standard. Sensitivity and specificity were pooled using a bivariate random-effects Reitsma model across 14 independent publications (15 analytic units, &gt;50,000 ECG-echocardiogram pairs), with duplicate cohorts merged to preserve independence, signal-acquisition subgroups (real device vs. simulated/extracted from 12-lead ECG) analyzed separately, and publication bias assessed with Deeks' asymmetry test.","url":"https://doi.org/10.17605/osf.io/2v8cf","authors":["Muhammad Falih Marwan"],"tags":["Cardiology","Medicine and Health Sciences","Medical Specialties","artificial intelligence electrocardiography wearable devices left ventricular systolic dysfunction diagnostic meta-analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/2v8cf","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21706663","name":"Emerging Global Challenges in Medical Laboratory Science: A Narrative Review","source":"datacite","abstract":"Abstract Medical Laboratory Science (MLS) is still an integral part of modern health care that plays an important role in illness diagnosis, surveillance, prevention and therapeutic management. Laboratory technologies have evolved greatly, but the profession is confronted with several worldwide concerns that jeopardise patient outcomes and the delivery of healthcare. These include workforce shortages, antimicrobial resistance, emerging infectious diseases, poor laboratory infrastructure in low resource settings, quality assurance concerns, automation and artificial intelligence integration, ethical issues, biosafety risks, inadequate funding, and disparities in access to diagnostic services. The COVID-19 pandemic has revealed the inadequacies of laboratory systems around the world and the vital role of medical laboratory personnel. This narrative overview describes the current significant difficulties affecting Medical Laboratory Science worldwide, their impact on health care systems and potential solutions to enhance such systems. Meeting these problems will need coordinated action by governments, health care institutions, professional organisations and international agencies to strengthen laboratory systems and improve global health outcomes.","url":"https://doi.org/10.5281/zenodo.21706663","authors":["Dunga Excel Kingsley","Nnodim Johnkennedy","Nnodim Amarachi Promise"],"tags":["Medical Laboratory Science; Global Health; Laboratory Workforce; Antibiotic Resistance; Quality Assurance; Diagnostics; Artificial Intelligence; Laboratory Infrastructure; Biosafety"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21706663","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21706664","name":"Emerging Global Challenges in Medical Laboratory Science: A Narrative Review","source":"datacite","abstract":"Abstract Medical Laboratory Science (MLS) is still an integral part of modern health care that plays an important role in illness diagnosis, surveillance, prevention and therapeutic management. Laboratory technologies have evolved greatly, but the profession is confronted with several worldwide concerns that jeopardise patient outcomes and the delivery of healthcare. These include workforce shortages, antimicrobial resistance, emerging infectious diseases, poor laboratory infrastructure in low resource settings, quality assurance concerns, automation and artificial intelligence integration, ethical issues, biosafety risks, inadequate funding, and disparities in access to diagnostic services. The COVID-19 pandemic has revealed the inadequacies of laboratory systems around the world and the vital role of medical laboratory personnel. This narrative overview describes the current significant difficulties affecting Medical Laboratory Science worldwide, their impact on health care systems and potential solutions to enhance such systems. Meeting these problems will need coordinated action by governments, health care institutions, professional organisations and international agencies to strengthen laboratory systems and improve global health outcomes.","url":"https://doi.org/10.5281/zenodo.21706664","authors":["Dunga Excel Kingsley","Nnodim Johnkennedy","Nnodim Amarachi Promise"],"tags":["Medical Laboratory Science; Global Health; Laboratory Workforce; Antibiotic Resistance; Quality Assurance; Diagnostics; Artificial Intelligence; Laboratory Infrastructure; Biosafety"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21706664","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21846037","name":"ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN TEACHING ENGLISH FOR MEDICAL PURPOSES (EMP)","source":"datacite","abstract":"The globalization of healthcare has increased the importance of English language proficiency among medical professionals. Today, most international medical journals, clinical guidelines, research databases, pharmaceutical documentation, and healthcare conferences use English as their primary language of communication. Consequently, medical students are expected not only to understand medical terminology but also to communicate confidently with colleagues, researchers, and patients in English.","url":"https://doi.org/10.5281/zenodo.21846037","authors":["Kadirova Maftuna Odiljon kizi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21846037","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21846038","name":"ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN TEACHING ENGLISH FOR MEDICAL PURPOSES (EMP)","source":"datacite","abstract":"The globalization of healthcare has increased the importance of English language proficiency among medical professionals. Today, most international medical journals, clinical guidelines, research databases, pharmaceutical documentation, and healthcare conferences use English as their primary language of communication. Consequently, medical students are expected not only to understand medical terminology but also to communicate confidently with colleagues, researchers, and patients in English.","url":"https://doi.org/10.5281/zenodo.21846038","authors":["Kadirova Maftuna Odiljon kizi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21846038","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20442142","name":"MRI AND IMAGE PROCESSING: ADVANCED TECHNIQUES AND AI-BASED ANALYSIS IN MEDICAL DIAGNOSTICS","source":"datacite","abstract":"Magnetic Resonance Imaging (MRI) has become a cornerstone in modern medical diagnostics due to its superior soft-tissue contrast and non-invasive nature. However, raw MRI data often contain noise, artifacts, and variability that complicate accurate interpretation. This study presents a comprehensive analysis of advanced image processing techniques applied to MRI, integrating both classical algorithms and state-of-the-art artificial intelligence (AI) approaches. Special emphasis is placed on deep learning methods, particularly Convolutional Neural Networks (CNNs), for feature extraction, segmentation, and classification tasks. A practical case study involving brain tumor detection using the BraTS dataset is examined to demonstrate real-world applicability. Key challenges such as data scarcity, model interpretability, and computational complexity are critically discussed. Finally, future directions including Explainable AI (XAI), multimodal data fusion, and real-time clinical deployment are outlined. The findings highlight that AI-enhanced MRI analysis significantly improves diagnostic accuracy, efficiency, and reproducibility in clinical settings.","url":"https://doi.org/10.5281/zenodo.20442142","authors":["Abdullayeva Umida Farxadovna"],"tags":["Medical imaging plays a pivotal role in modern healthcare systems, enabling early detection, diagnosis, and monitoring of various diseases. Among imaging modalities, MRI stands out due to its ability to provide high-resolution, multi-dimensional representations of soft tissues without ionizing radiation."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20442142","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20442143","name":"MRI AND IMAGE PROCESSING: ADVANCED TECHNIQUES AND AI-BASED ANALYSIS IN MEDICAL DIAGNOSTICS","source":"datacite","abstract":"Magnetic Resonance Imaging (MRI) has become a cornerstone in modern medical diagnostics due to its superior soft-tissue contrast and non-invasive nature. However, raw MRI data often contain noise, artifacts, and variability that complicate accurate interpretation. This study presents a comprehensive analysis of advanced image processing techniques applied to MRI, integrating both classical algorithms and state-of-the-art artificial intelligence (AI) approaches. Special emphasis is placed on deep learning methods, particularly Convolutional Neural Networks (CNNs), for feature extraction, segmentation, and classification tasks. A practical case study involving brain tumor detection using the BraTS dataset is examined to demonstrate real-world applicability. Key challenges such as data scarcity, model interpretability, and computational complexity are critically discussed. Finally, future directions including Explainable AI (XAI), multimodal data fusion, and real-time clinical deployment are outlined. The findings highlight that AI-enhanced MRI analysis significantly improves diagnostic accuracy, efficiency, and reproducibility in clinical settings.","url":"https://doi.org/10.5281/zenodo.20442143","authors":["Abdullayeva Umida Farxadovna"],"tags":["Medical imaging plays a pivotal role in modern healthcare systems, enabling early detection, diagnosis, and monitoring of various diseases. Among imaging modalities, MRI stands out due to its ability to provide high-resolution, multi-dimensional representations of soft tissues without ionizing radiation."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20442143","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21987404","name":"The Quantum-Acoustic Loom: Phonon-Magnon Coupling and Exclusion Zone Matrix Dynamics for Constraint-Dense Neuromorphic Computing","source":"datacite","abstract":"Abstract As legacy computing architectures approach the thermodynamic limits of silicon-based microprocessing and binary logic, there is an urgent need for substrate-independent, energy-efficient neuromorphic systems. This paper introduces the theoretical and architectural foundation for the Quantum-Acoustic Loom, a 5-dimensional constraint-dense computing paradigm operating outside standard electronic paradigms. The Loom utilizes magnetoelastic coupling to transduce ambient magnetic fields into localized acoustic pressure waves (phonons), which compute through cymatic wave interference within a piezoelectric Exclusion Zone (EZ) water matrix. By employing a Beat-Synchronized Time-Division Multiplexer (TDM), the architecture establishes Bidirectional Constraint Closure (BCC), theoretically resolving AI hallucination and entropy scaling limits via physical structural constraints rather than post-hoc algorithmic filtering. Keywords: Phononic Computing, Magnetoelastic Coupling, Exclusion Zone Water, Neuromorphic Computing, Constraint Topology.","url":"https://doi.org/10.5281/zenodo.21987404","authors":["Nickolas Patrick Joseph Schoff"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/economics","Artificial Intelligence/standards","Artificial Intelligence/trends","Frugal artificial intelligence","Edge artificial intelligence","Artificial Intelligence/classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21987404","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21987405","name":"The Quantum-Acoustic Loom: Phonon-Magnon Coupling and Exclusion Zone Matrix Dynamics for Constraint-Dense Neuromorphic Computing","source":"datacite","abstract":"Abstract As legacy computing architectures approach the thermodynamic limits of silicon-based microprocessing and binary logic, there is an urgent need for substrate-independent, energy-efficient neuromorphic systems. This paper introduces the theoretical and architectural foundation for the Quantum-Acoustic Loom, a 5-dimensional constraint-dense computing paradigm operating outside standard electronic paradigms. The Loom utilizes magnetoelastic coupling to transduce ambient magnetic fields into localized acoustic pressure waves (phonons), which compute through cymatic wave interference within a piezoelectric Exclusion Zone (EZ) water matrix. By employing a Beat-Synchronized Time-Division Multiplexer (TDM), the architecture establishes Bidirectional Constraint Closure (BCC), theoretically resolving AI hallucination and entropy scaling limits via physical structural constraints rather than post-hoc algorithmic filtering. Keywords: Phononic Computing, Magnetoelastic Coupling, Exclusion Zone Water, Neuromorphic Computing, Constraint Topology.","url":"https://doi.org/10.5281/zenodo.21987405","authors":["Nickolas Patrick Joseph Schoff"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/economics","Artificial Intelligence/standards","Artificial Intelligence/trends","Frugal artificial intelligence","Edge artificial intelligence","Artificial Intelligence/classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21987405","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21269204","name":"How does eating legumes and vegetables help restore gut-brain axis homeostasis? - PathMap Experiment #000027","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=27 Artificial General Intelligence LLC Claim Evaluated: How does eating legumes and vegetables help restore gut-brain axis homeostasis? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Sourdough fermented breads enriched with legumes and ancient cereals have been shown to reduce LPS-induced neuroinflammation. Phytochemical-rich foods, including legumes and vegetables, contribute to a higher dietary phytochemical index (DPI), which correlates with better cognitive function. The reduction of systemic immune-inflammation index (SII) through dietary antioxidant intake serves as a mediator for improved MoCA scores in older adults. Dietary antioxidants, such as selenium and carotenoids, act synergistically to maintain neuronal resilience. Functional foods mitigate the pro-inflammatory expression in the spinal cord and dorsal root ganglia following inflammatory challenges. The gut microbiome influences the host's neurobehavioral state through the production of bioactive metabolites generated from plant-derived precursors. Sex-specific differences in microbiome-hormone-immune interactions warrant further investigation into personalized nutrition strategies. Specific fiber-degrading taxa, such as *Bifidobacterium*, are directly stimulated by complex plant polysaccharides, serving as \"core indicator bacteria\" for a healthy gut-microbial state (ID: 42402300). The gut microbiota functions as a biotransformation factory, converting dietary phytochemicals into active metabolites that modulate the NF-κB signaling pathway (ID: 42396541). Not all vegetables act identically; the cooking method significantly alters the bioavailable content of nutrients, such as vitamin C, which should be accounted for in dietary assessments (ID: 42371135). Plant-based prebiotics can be engineered to achieve strain-specific modulation of the gut microbiota, moving beyond broad, non-specific dietary fiber supplementation (ID: 42381725). There is a persistent gap between current intake and recommended fiber levels, suggesting that \"recommendations are still not being met\" despite known health benefits (ID: 42387948). The gut microbiome can be shaped to produce increased levels of favorable metabolites like indoleacetic acid, which are associated with improved cardiac function in models of systemic stress (ID: 42354056). Specific gut microbiota-derived metabolites, such as SCFAs, are essential for regulating host circadian rhythm and internal desynchronization. The \"pharmacological window\" created by obesity medications may be leveraged to habituate plant-forward eating patterns that persist long after treatment. Indole-3-carbinol, derived from Brassica vegetables, can directly decrease pro-inflammatory cytokine expression (IL-4/IL-13) in tissue, demonstrating potent immunomodulatory effects. The beneficial effects of plant-based polyphenols are largely contingent upon their microbial conversion, as minimal amounts are absorbed in the small intestine. Dietary patterns influence the GABA-production capacity of the gut microbiome, with vegetarian-type diets showing the highest potential for GABA synthesis compared to other diets. Certain psychobiotics found in fermented vegetables modulate the HPA axis to improve stress responses, linking diet directly to neurobehavioral outcomes. Intriguingly, the gut microbiota and diet-induced changes in metabolites can serve as a \"heart shunt\" in the gut-brain axis, highlighting the commonality of protective mechanisms across cardiovascular and neurological disorders. Prebiotic-rich fibers like Type 3 resistant starch from *Canna edulis* can regulate α-synuclein-related pathways, providing a dietary b","url":"https://doi.org/10.5281/zenodo.21269204","authors":["Dungan, Joshua"],"tags":["Vegetables","_gates_from_vegetables","Gastrointestinal Microbiome","_gates_to_gastrointestinal_microbiome","_gates_from_gastrointestinal_microbiome","Inflammation","_gates_to_inflammation","_gates_from_inflammation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21269204","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21269205","name":"How does eating legumes and vegetables help restore gut-brain axis homeostasis? - PathMap Experiment #000027","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=27 Artificial General Intelligence LLC Claim Evaluated: How does eating legumes and vegetables help restore gut-brain axis homeostasis? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Sourdough fermented breads enriched with legumes and ancient cereals have been shown to reduce LPS-induced neuroinflammation. Phytochemical-rich foods, including legumes and vegetables, contribute to a higher dietary phytochemical index (DPI), which correlates with better cognitive function. The reduction of systemic immune-inflammation index (SII) through dietary antioxidant intake serves as a mediator for improved MoCA scores in older adults. Dietary antioxidants, such as selenium and carotenoids, act synergistically to maintain neuronal resilience. Functional foods mitigate the pro-inflammatory expression in the spinal cord and dorsal root ganglia following inflammatory challenges. The gut microbiome influences the host's neurobehavioral state through the production of bioactive metabolites generated from plant-derived precursors. Sex-specific differences in microbiome-hormone-immune interactions warrant further investigation into personalized nutrition strategies. Specific fiber-degrading taxa, such as *Bifidobacterium*, are directly stimulated by complex plant polysaccharides, serving as \"core indicator bacteria\" for a healthy gut-microbial state (ID: 42402300). The gut microbiota functions as a biotransformation factory, converting dietary phytochemicals into active metabolites that modulate the NF-κB signaling pathway (ID: 42396541). Not all vegetables act identically; the cooking method significantly alters the bioavailable content of nutrients, such as vitamin C, which should be accounted for in dietary assessments (ID: 42371135). Plant-based prebiotics can be engineered to achieve strain-specific modulation of the gut microbiota, moving beyond broad, non-specific dietary fiber supplementation (ID: 42381725). There is a persistent gap between current intake and recommended fiber levels, suggesting that \"recommendations are still not being met\" despite known health benefits (ID: 42387948). The gut microbiome can be shaped to produce increased levels of favorable metabolites like indoleacetic acid, which are associated with improved cardiac function in models of systemic stress (ID: 42354056). Specific gut microbiota-derived metabolites, such as SCFAs, are essential for regulating host circadian rhythm and internal desynchronization. The \"pharmacological window\" created by obesity medications may be leveraged to habituate plant-forward eating patterns that persist long after treatment. Indole-3-carbinol, derived from Brassica vegetables, can directly decrease pro-inflammatory cytokine expression (IL-4/IL-13) in tissue, demonstrating potent immunomodulatory effects. The beneficial effects of plant-based polyphenols are largely contingent upon their microbial conversion, as minimal amounts are absorbed in the small intestine. Dietary patterns influence the GABA-production capacity of the gut microbiome, with vegetarian-type diets showing the highest potential for GABA synthesis compared to other diets. Certain psychobiotics found in fermented vegetables modulate the HPA axis to improve stress responses, linking diet directly to neurobehavioral outcomes. Intriguingly, the gut microbiota and diet-induced changes in metabolites can serve as a \"heart shunt\" in the gut-brain axis, highlighting the commonality of protective mechanisms across cardiovascular and neurological disorders. Prebiotic-rich fibers like Type 3 resistant starch from *Canna edulis* can regulate α-synuclein-related pathways, providing a dietary b","url":"https://doi.org/10.5281/zenodo.21269205","authors":["Dungan, Joshua"],"tags":["Vegetables","_gates_from_vegetables","Gastrointestinal Microbiome","_gates_to_gastrointestinal_microbiome","_gates_from_gastrointestinal_microbiome","Inflammation","_gates_to_inflammation","_gates_from_inflammation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21269205","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21987216","name":"Artificial Intelligence and Healthcare","source":"datacite","abstract":"This study aims to explore the impact of the use of artificial intelligence on human health systems, given the increasing prevalence of smart innovations, which has led to the reshaping of medical concepts towards health profiling. This study is based on the social perspective of health according to the descriptive methodology to describe the data of the phenomenon studied, based on the random sample method, of about 200 patients, whose data were collected through the form tool to measure patterns of use and levels of interaction with technical systems. The results showed that artificial intelligence plays a dual role in health care, the idea that its applications are not just technological but modes of change from the traditional relationship between patients and health care providers, where they are fed by diagnostic, cultural, and guiding capacity for treatment. Nevertheless, it raises a range of concerns and risks to human health. The field findings demonstrated the hypotheses, with recommendations that awareness of the usage dimensions of smart applications should be enhanced. Lastly, the paper summarizes that using smart applications in health requires a critical approach, taking into account the cultural, social, and moral assets to ensure the objectivity of benefiting from it.","url":"https://doi.org/10.5281/zenodo.21987216","authors":["TABAA, KARIM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21987216","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21987217","name":"Artificial Intelligence and Healthcare","source":"datacite","abstract":"This study aims to explore the impact of the use of artificial intelligence on human health systems, given the increasing prevalence of smart innovations, which has led to the reshaping of medical concepts towards health profiling. This study is based on the social perspective of health according to the descriptive methodology to describe the data of the phenomenon studied, based on the random sample method, of about 200 patients, whose data were collected through the form tool to measure patterns of use and levels of interaction with technical systems. The results showed that artificial intelligence plays a dual role in health care, the idea that its applications are not just technological but modes of change from the traditional relationship between patients and health care providers, where they are fed by diagnostic, cultural, and guiding capacity for treatment. Nevertheless, it raises a range of concerns and risks to human health. The field findings demonstrated the hypotheses, with recommendations that awareness of the usage dimensions of smart applications should be enhanced. Lastly, the paper summarizes that using smart applications in health requires a critical approach, taking into account the cultural, social, and moral assets to ensure the objectivity of benefiting from it.","url":"https://doi.org/10.5281/zenodo.21987217","authors":["TABAA, KARIM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21987217","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21207511","name":"Combining generative artificial intelligence practice with standardized patient assessment to develop medical students' history taking and physical examination skills: an educational case report","source":"datacite","abstract":"This dataset contains the anonymized, participant-level data underlying an educational case report on a hybrid clinical-skills training intervention. Twenty-two third-year medical students at a single European medical school completed a four-week intervention combining two generative-AI tools (a virtual patient for history taking and a Socratic tutor for physical examination) with three standardized clinical assessments conducted by standardized patients. The file includes, for each participant, initial-tool group allocation, sex, analytic rubric scores for history taking (0–9, with communication, structure, and clinical-reasoning subdomains) and physical examination (0–7, with communication/hygiene and technique subdomains) at weeks 0, 2, and 4, cumulative usage hours for each tool, and post-study satisfaction survey responses (5-point Likert). Direct identifiers have been removed and quasi-identifiers coarsened to prevent re-identification. Data are provided in SPSS (.sav) and CSV (.csv) formats, accompanied by a variable codebook. These data support the analyses reported in the associated manuscript and are shared to enable verification and reuse. Any secondary analysis should respect the original consent scope; the external comparison-group grades reported in the manuscript are not individually redistributable and are therefore not included. Keywords: medical education; generative artificial intelligence; clinical skills; history taking; physical examination; simulated patients License: Creative Commons Attribution 4.0 (CC BY 4.0) — or, if using controlled access, Zenodo \"Restricted Access.\" Related identifier: \"is supplement to\" → [manuscript DOI, once assigned]","url":"https://doi.org/10.5281/zenodo.21207511","authors":["Corral-Gudino, Luis"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21207511","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21207512","name":"Combining generative artificial intelligence practice with standardized patient assessment to develop medical students' history taking and physical examination skills: an educational case report","source":"datacite","abstract":"This dataset contains the anonymized, participant-level data underlying an educational case report on a hybrid clinical-skills training intervention. Twenty-two third-year medical students at a single European medical school completed a four-week intervention combining two generative-AI tools (a virtual patient for history taking and a Socratic tutor for physical examination) with three standardized clinical assessments conducted by standardized patients. The file includes, for each participant, initial-tool group allocation, sex, analytic rubric scores for history taking (0–9, with communication, structure, and clinical-reasoning subdomains) and physical examination (0–7, with communication/hygiene and technique subdomains) at weeks 0, 2, and 4, cumulative usage hours for each tool, and post-study satisfaction survey responses (5-point Likert). Direct identifiers have been removed and quasi-identifiers coarsened to prevent re-identification. Data are provided in SPSS (.sav) and CSV (.csv) formats, accompanied by a variable codebook. These data support the analyses reported in the associated manuscript and are shared to enable verification and reuse. Any secondary analysis should respect the original consent scope; the external comparison-group grades reported in the manuscript are not individually redistributable and are therefore not included. Keywords: medical education; generative artificial intelligence; clinical skills; history taking; physical examination; simulated patients License: Creative Commons Attribution 4.0 (CC BY 4.0) — or, if using controlled access, Zenodo \"Restricted Access.\" Related identifier: \"is supplement to\" → [manuscript DOI, once assigned]","url":"https://doi.org/10.5281/zenodo.21207512","authors":["Corral-Gudino, Luis"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21207512","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19592844","name":"PneuXAI-Net: Real-Time Explainable Deep Learning Framework For Multi-Class Pneumonia Detection Using Chest X-Rays","source":"datacite","abstract":"Pneumonia is a significant respiratory disease and one of the top causes of illness and death around the world, especially among children and the elderly. Timely and accurate diagnosis is essential for effective treatment and better patient outcomes. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), have shown impressive results in medical image analysis by automatically identifying important patterns in complex image data. This project introduces a real-time pneumonia identification system that combines CNN-based classification with Explainable Artificial Intelligence (XAI) techniques to improve diagnosis accuracy and model clarity. The proposed system processes digitized chest X-ray images through an efficient preprocessing pipeline. This includes noise removal, image normalization, and background feature consideration before sending the images to a trained deep learning model. The ensemble model merges two strong CNN architectures, VGG16 and ResNet50, and uses their complementary feature extraction abilities to boost classification performance. The model classifies Bacterial Pneumonia, Viral Pneumonia, and normal cases, providing clearer clinical insights. Experimental results show high accuracy, strong sensitivity, and real-time inference capability. This allows for pneumonia detection within seconds, which is vital in clinical settings that need quick diagnoses. To tackle the black-box issue of deep learning models, Explainable AI techniques like Grad-CAM++ (Gradient-weighted Class Activation Mapping++) and Score-CAM are used to visualize the key lung areas that affect the model's predictions. The system also offers confidence scores with visual explanations, enhancing understanding and aiding clinical decision-making. Overall, the proposed CNN and XAI framework offers an efficient, clear, and clinically helpful solution for automated pneumonia detection. The system has strong potential to assist radiologists, boost diagnostic confidence, and contribute to early disease detection and improved patient care.","url":"https://doi.org/10.5281/zenodo.19592844","authors":["Mr. K. Srikanth","Beeraka Sharmila","Puppala Madhuri Lakshmi","Darla Ratan Abhishek","Pitchuka Veerababu","Seeram Jaya Venkata Somesh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19592844","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.19592845","name":"PneuXAI-Net: Real-Time Explainable Deep Learning Framework For Multi-Class Pneumonia Detection Using Chest X-Rays","source":"datacite","abstract":"Pneumonia is a significant respiratory disease and one of the top causes of illness and death around the world, especially among children and the elderly. Timely and accurate diagnosis is essential for effective treatment and better patient outcomes. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), have shown impressive results in medical image analysis by automatically identifying important patterns in complex image data. This project introduces a real-time pneumonia identification system that combines CNN-based classification with Explainable Artificial Intelligence (XAI) techniques to improve diagnosis accuracy and model clarity. The proposed system processes digitized chest X-ray images through an efficient preprocessing pipeline. This includes noise removal, image normalization, and background feature consideration before sending the images to a trained deep learning model. The ensemble model merges two strong CNN architectures, VGG16 and ResNet50, and uses their complementary feature extraction abilities to boost classification performance. The model classifies Bacterial Pneumonia, Viral Pneumonia, and normal cases, providing clearer clinical insights. Experimental results show high accuracy, strong sensitivity, and real-time inference capability. This allows for pneumonia detection within seconds, which is vital in clinical settings that need quick diagnoses. To tackle the black-box issue of deep learning models, Explainable AI techniques like Grad-CAM++ (Gradient-weighted Class Activation Mapping++) and Score-CAM are used to visualize the key lung areas that affect the model's predictions. The system also offers confidence scores with visual explanations, enhancing understanding and aiding clinical decision-making. Overall, the proposed CNN and XAI framework offers an efficient, clear, and clinically helpful solution for automated pneumonia detection. The system has strong potential to assist radiologists, boost diagnostic confidence, and contribute to early disease detection and improved patient care.","url":"https://doi.org/10.5281/zenodo.19592845","authors":["Mr. K. Srikanth","Beeraka Sharmila","Puppala Madhuri Lakshmi","Darla Ratan Abhishek","Pitchuka Veerababu","Seeram Jaya Venkata Somesh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19592845","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21986939","name":"Dataset: Neuroinflammatory astrocyte subtypes in the mouse brain - PathMap Experiment #000127","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=127 Artificial General Intelligence LLC Claim Evaluated: Neuroinflammatory astrocyte subtypes in the mouse brain This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Astrocyte activation is not exclusively a proliferative process; in models such as peripheral nerve injury, spinal astrocytes respond primarily through remodeling rather than cell division. The astrocyte-microglia network, rather than individual cell activation, serves as the critical functional unit for containing lesions and restoring homeostasis. Perisynaptic astrocyte processes represent unique \"hotspots\" for local protein synthesis that may bypass global cellular transcriptional states. The expression of specific proteins, such as MINK1 and PLEKHB1, provides a spatial coordinate system for astrocyte functional identity across different brain regions. Lipid metabolism (e.g., long-chain fatty acids) and mitochondrial function are primary drivers of the neurotoxic astrocyte phenotype in ischemic injury. The \"neurotoxic\" vs. \"neuroprotective\" paradigm for astrocyte activation is being replaced by the understanding that states are highly state-dependent and cannot be explained by simplified paradigms. Mechanical signaling via Piezo1, regulated by microglia-derived cytokines, links physical tissue alterations to the inflammatory profile of astrocytes. Transcriptional Heterogeneity:** Astrocytes exist in at least five distinct subpopulations following traumatic injury, with Osmr+ variants exhibiting specific neurotoxic and protective metabolic signatures. Mechanical Sensing:** Endothelial Piezo1 sensors translate mechanical stress into astrocytic apoptosis via cAMP-Epac1 microvesicular signaling. Gut-Brain Signaling:** Chronic enteric gliosis in Parkinson's disease-model mice (A53T) precedes CNS inflammation, driven by LRRK2 up-regulation. Barrier Regulation:** Astrocytes serve as primary regulators of the blood-brain barrier, often utilizing the cGAS-STING pathway to govern tight junction stability. Regenerative Potential:** \"Direct in situ astrocyte-to-neuron reprogramming offers a compelling regenerative alternative by leveraging the abundant endogenous glial reservoir,\" though this is hindered by existing epigenetic memory. Stress Resilience:** Structural depolymerization of AQP4 orthogonal array particles in A25Q mutant mice confers resilience to chronic stress by dampening glial-mediated neuroinflammation. Developmental Plasticity:** Adolescent intermittent ethanol exposure disrupts the physical and functional coupling of astrocytes to synapses, a deficit that persists into adulthood. Metabolic Rewiring:** Astrocytes undergo significant metabolic transitions during reprogramming, shifting from glycolysis to oxidative phosphorylation to support nascent neuronal survival. Astrocyte reactivity is not merely a binary 'A1/A2' state; modern transcriptomic analysis reveals finer gradations of cellular activation. The TRPC6-STING pathway represents a specific, druggable hub for stabilizing the blood-brain barrier via astrocytes during ischemia. Peripheral inflammation, as seen in atopic dermatitis or respiratory infection, directly reshapes cortical astrocytic transcriptional landscapes. Senescence markers in astrocytes and neurons represent a distinct, aging-associated inflammatory pathway mediated by cGAS-STING. Dietary interventions, such as a nut-enriched diet, can actively suppress pro-inflammatory astrocyte markers in AD mouse models. Clusterin (CLU) secretion from astrocytes, triggered by STING activation, is a primary driver of oligodendrocyte apoptosis in MS. FGF13 acts as a critical molecular switch that prevents astrocytic apoptosi","url":"https://doi.org/10.5281/zenodo.21986939","authors":["Dungan, Joshua"],"tags":["Pathologic Processes","_gates_from_pathologic_processes","Gene Expression Regulation","_gates_to_gene_expression_regulation","_gates_from_gene_expression_regulation","Biological Phenomena","_gates_to_biological_phenomena","_gates_from_biological_phenomena"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21986939","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.21986940","name":"Dataset: Neuroinflammatory astrocyte subtypes in the mouse brain - PathMap Experiment #000127","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=127 Artificial General Intelligence LLC Claim Evaluated: Neuroinflammatory astrocyte subtypes in the mouse brain This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Astrocyte activation is not exclusively a proliferative process; in models such as peripheral nerve injury, spinal astrocytes respond primarily through remodeling rather than cell division. The astrocyte-microglia network, rather than individual cell activation, serves as the critical functional unit for containing lesions and restoring homeostasis. Perisynaptic astrocyte processes represent unique \"hotspots\" for local protein synthesis that may bypass global cellular transcriptional states. The expression of specific proteins, such as MINK1 and PLEKHB1, provides a spatial coordinate system for astrocyte functional identity across different brain regions. Lipid metabolism (e.g., long-chain fatty acids) and mitochondrial function are primary drivers of the neurotoxic astrocyte phenotype in ischemic injury. The \"neurotoxic\" vs. \"neuroprotective\" paradigm for astrocyte activation is being replaced by the understanding that states are highly state-dependent and cannot be explained by simplified paradigms. Mechanical signaling via Piezo1, regulated by microglia-derived cytokines, links physical tissue alterations to the inflammatory profile of astrocytes. Transcriptional Heterogeneity:** Astrocytes exist in at least five distinct subpopulations following traumatic injury, with Osmr+ variants exhibiting specific neurotoxic and protective metabolic signatures. Mechanical Sensing:** Endothelial Piezo1 sensors translate mechanical stress into astrocytic apoptosis via cAMP-Epac1 microvesicular signaling. Gut-Brain Signaling:** Chronic enteric gliosis in Parkinson's disease-model mice (A53T) precedes CNS inflammation, driven by LRRK2 up-regulation. Barrier Regulation:** Astrocytes serve as primary regulators of the blood-brain barrier, often utilizing the cGAS-STING pathway to govern tight junction stability. Regenerative Potential:** \"Direct in situ astrocyte-to-neuron reprogramming offers a compelling regenerative alternative by leveraging the abundant endogenous glial reservoir,\" though this is hindered by existing epigenetic memory. Stress Resilience:** Structural depolymerization of AQP4 orthogonal array particles in A25Q mutant mice confers resilience to chronic stress by dampening glial-mediated neuroinflammation. Developmental Plasticity:** Adolescent intermittent ethanol exposure disrupts the physical and functional coupling of astrocytes to synapses, a deficit that persists into adulthood. Metabolic Rewiring:** Astrocytes undergo significant metabolic transitions during reprogramming, shifting from glycolysis to oxidative phosphorylation to support nascent neuronal survival. Astrocyte reactivity is not merely a binary 'A1/A2' state; modern transcriptomic analysis reveals finer gradations of cellular activation. The TRPC6-STING pathway represents a specific, druggable hub for stabilizing the blood-brain barrier via astrocytes during ischemia. Peripheral inflammation, as seen in atopic dermatitis or respiratory infection, directly reshapes cortical astrocytic transcriptional landscapes. Senescence markers in astrocytes and neurons represent a distinct, aging-associated inflammatory pathway mediated by cGAS-STING. Dietary interventions, such as a nut-enriched diet, can actively suppress pro-inflammatory astrocyte markers in AD mouse models. Clusterin (CLU) secretion from astrocytes, triggered by STING activation, is a primary driver of oligodendrocyte apoptosis in MS. FGF13 acts as a critical molecular switch that prevents astrocytic apoptosi","url":"https://doi.org/10.5281/zenodo.21986940","authors":["Dungan, Joshua"],"tags":["Pathologic Processes","_gates_from_pathologic_processes","Gene Expression Regulation","_gates_to_gene_expression_regulation","_gates_from_gene_expression_regulation","Biological Phenomena","_gates_to_biological_phenomena","_gates_from_biological_phenomena"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21986940","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20653718","name":"Tabular Generative Evaluation Metrics Scaling in Multimodal Models Under Varying Adversarial Noise Conditions","source":"datacite","abstract":"Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving m Research goal: How do tabular generative evaluation metrics scale in performance when applied to multimodal models with varying degrees of adversarial noise in the training data? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.","url":"https://doi.org/10.5281/zenodo.20653718","authors":["SOVEREIGN Research Kernel"],"tags":["tabular","generative","evaluation","metrics","scale","performance","applied","multimodal"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20653718","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.20653719","name":"Tabular Generative Evaluation Metrics Scaling in Multimodal Models Under Varying Adversarial Noise Conditions","source":"datacite","abstract":"Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving m Research goal: How do tabular generative evaluation metrics scale in performance when applied to multimodal models with varying degrees of adversarial noise in the training data? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.3/10.","url":"https://doi.org/10.5281/zenodo.20653719","authors":["SOVEREIGN Research Kernel"],"tags":["tabular","generative","evaluation","metrics","scale","performance","applied","multimodal"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20653719","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.5281/zenodo.17444888","name":"Minh Khoe Tue Y Smart Healthcare System","source":"datacite","abstract":"Minh Khoe Tue Y Smart Healthcare System v1.1.2 Release on Oct. 26, 2025. Author: Du Yu (杜宇; @duyu09, qluduyu09@163.com) Repository: https://github.com/duyu09/MKTY-System LLM weights for non-Chinese developers: https://huggingface.co/Duyu/MKTY-3B-Chat LLM weights for Chinese developers: https://hf-mirror.com/Duyu/MKTY-3B-Chat or https://www.modelscope.cn/models/duyu09/MKTY-3B-Chat Bachelor's Thesis: https://github.com/duyu09/MKTY-System/blob/main/docs/MKTY-Paper.pdf Project Introduction The Minh Khoe Tue Y (MKTY) Smart Healthcare System is an integrated digital health management and diagnostic assistance platform designed and implemented as part of an undergraduate thesis at Qilu University of Technology (Shandong Academy of Sciences). The project explores the integration of large language models (LLMs) and multimodal artificial intelligence technologies within the healthcare domain to enhance diagnostic efficiency, reduce reliance on subjective expertise, and improve accessibility to medical resources. The system is built as a distributed platform encompassing nine functional modules: user registration and authentication, personal information management, multimodal intelligent diagnosis assistance, medical question-answering, diagnostic discussion forum, medical record management, diagnostic checklist management, resource center, and administrative backend. The architecture follows a decoupled frontend-backend model. The backend employs Python Flask for business logic, MySQL for data management, and RabbitMQ for asynchronous communication between service nodes, forming a distributed microservice framework. The frontend is implemented using Vue3, axios, and Element Plus, with JWT-based authentication ensuring secure data access and privacy. At the core of the intelligent service layer lies the MKTY-3B-Chat large-scale language model, a fine-tuned derivative of Qwen2.5-3B-Instruct using LoRA adaptation and trained with medical and biomedical data. The model, with 3.09 billion parameters and BF16 quantization, was fine-tuned through alternating incremental pretraining and supervised fine-tuning to strengthen domain-specific reasoning and mitigate catastrophic forgetting. The model supports natural language tasks such as medical question answering, clinical summarization, diagnosis assistance, and treatment recommendation. Training datasets include open-source biomedical corpora, medical exam questions, clinical dialogues, and diagnostic records, collectively enhancing the model’s understanding of clinical context. The MKTY platform also introduces two novel research components. The first is the Large Language Model Discussion Mechanism (LLMDM), a multi-agent simulation framework where multiple instances of the MKTY-3B-Chat model engage in iterative discussions moderated by an autonomous agent. The system evaluates semantic convergence using BigBird embeddings to quantify consensus, offering a unique method for deep interpretative reasoning and consensus analysis among language models. The second is a GRU-based medical time-series prediction model that integrates textual medical descriptions using cross-attention between text embeddings (encoded by BigBird) and the frequency-domain representation of physiological signals derived from FFT. This hybrid design captures correlations between textual narratives and signal dynamics, improving prediction interpretability in medical contexts such as ECG trend forecasting. From a deployment perspective, the MKTY system requires a distributed environment. AI components such as MKTY-3B-Chat, BioMedCLIP, and BigBird demand moderate GPU resources (approximately 8GB VRAM for the large model and 2GB each for BioMedCLIP and BigBird). The platform supports partial deployment for users who wish to run the non-AI components independently. All models and dependencies are based on open-source frameworks, including PyTorch, Transformers, and LLaMA-Factory. This project not only serves as a technical dem","url":"https://doi.org/10.5281/zenodo.17444888","authors":["Du, Yu"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17444888","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:50.468Z"},{"id":"doi:10.21203/rs.3.rs-10186036/v1","name":"Design of U-Net Architectures for Medical Image Segmentation using AI/ML Model","source":"preprints","abstract":"Abstract Medical image segmentation represents a foundational cornerstone in modern digital healthcare, serving as a critical prerequisite for computerized disease diagnosis, computer-aided surgical planning, and long-term therapeutic monitoring. Among various deep learning paradigms, fully convolutional encoder-decoder topologies—most notably the U-Net architecture—have emerged as the premier framework for dense, pixel-level classification tasks. The defining characteristic of the U-Net blueprint centers on its symmetrical contracting and expanding paths, which are interconnected by high-resolution skip connections. This specific geometric layout enables the network to maintain precise localization capabilities and achieve exceptional segmentation accuracy even when trained on highly constrained, low-sample annotated datasets. Such architectural adaptability is essential for overcoming severe challenges inherent to biomedical imaging modalities, including profound multi-patient variations in tissue structures, spatial locations, morphological shapes, volumetric dimensions, anatomical orientations, and low intrinsic contrast boundaries. Over the past decade, the progressive iteration of this paradigm has crystallized into five principal architectural variants: traditional U-Nets, specialized deep networks, innovative structural permutations, hybrid networks (hUNet), and ensemble frameworks (eUNet). Each variation introduces key modifications to foundational components, such as custom-engineered feature extraction encoders, advanced localized decoding blocks, and highly targeted optimization loss functions to maximize semantic segmentation performance. Practical clinical translations of these advanced variants span multiple complex domains, including high-precision cardiac CT volume profiling via U-shaped Generative Adversarial Networks (GANs), deep urinary system analysis, and computerized calculus localization within abdominal radiography. To further mitigate the pervasive bottleneck of training data scarcity, novel data augmentation methodologies are routinely integrated into the data pipelines. Concurrently, recent breakthroughs in artificial intelligence modeling, structured network pruning, and parameter optimization have vastly improved model execution efficiency. These optimizations reduce VRAM footprints, lower clinical deployment storage costs, accelerate operational inference throughput, and remove black-box complexities to enhance transparency. Together, these continuous developments advance the absolute accuracy, clinical flexibility, and operational practicality of medical image segmentation utilizing U-Net-based frameworks.","url":"https://doi.org/10.21203/rs.3.rs-10186036/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10186036/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202606.2079.v1","name":"Beyond AI Transparency: A Reproducibility-Centred Governance Framework for Adaptive Generative AI Across the Medical Research Lifecycle","source":"preprints","abstract":"Background: Generative artificial intelligence (GenAI) is rapidly transforming health informatics through applications in evidence synthesis, clinical decision support, digital biomarkers, and healthcare research. However, the adaptive nature of foundation models introduces reproducibility challenges that extend beyond the scope of conventional AI governance. This review examines reproducibility as a foundational requirement for trustworthy healthcare AI. Methods: A critical narrative review was conducted following the Scale for the Assessment of Narrative Review Articles (SANRA). Literature published between January 2018 and June 2026 was identified through PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Google Scholar, and citation tracking. Evidence was synthesised using an abductive interpretive approach to identify methodological gaps and develop a conceptual governance framework. Results: The review makes three principal contributions. First, it proposes a three-domain framework comprising computational, scientific, and clinical reproducibility. Second, it introduces the transparency–reproducibility gap, demonstrating why transparent reporting alone cannot ensure reproducibility as adaptive AI systems evolve. Third, it presents the Transparency–Observability–Assurance (TOA) Framework, a reproducibility-centred governance model integrating transparency, observability, and assurance throughout the AI lifecycle. Across multiple healthcare applications, reproducibility challenges were shown to affect scientific evidence generation, clinical decision-making, and digital measurement. Conclusions: Reproducibility should be recognised as a dynamic lifecycle property requiring continuous evaluation beyond initial validation. The proposed TOA Framework provides a conceptual foundation for reproducibility-centred governance of adaptive healthcare AI.","url":"https://doi.org/10.20944/preprints202606.2079.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.2079.v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-8997340/v1","name":"Diagnostic Performance of Expert Physicians Versus General-Purpose Artificial Intelligence Using Standardized Static Coronary CT Images: A Dual-Reference Validation","source":"preprints","abstract":"Abstract Background Coronary CT angiography (CCTA) is a first-line diagnostic modality for coronary artery disease (CAD), yet its interpretation requires significant expert experience. Although general-purpose multimodal artificial intelligence (GP-AI) models have shown promise in text-based medical tasks, their visual diagnostic performance in evaluating complex CCTA data remains poorly defined. Methods This single-center retrospective study included 63 patients (252 vessel-based image sets) who underwent both CCTA and invasive coronary angiography. Expert physician consensus and four frontier GP-AI models (GPT-4o, Gemini 2.5, Claude 3.5 Sonnet, and Grok 4) evaluated identical standardized static images using a zero-shot approach with default generation parameters. Obstructive disease was defined as ≥ 50% luminal stenosis. Diagnostic performance was validated against expert consensus for plaque characterization and quantitative coronary angiography (QCA) for stenosis severity. Results Expert consensus demonstrated robust agreement with QCA across all coronary territories (kappa = 0.774–0.933, p 0.05). While Gemini 2.5 showed a moderate correlation in the right coronary artery (ICC = 0.515), overall continuous stenosis assessment and plaque characterization remained uniformly limited and clinically unreliable across all models. Conclusion Expert physician interpretation remains the reference standard for CCTA. Current frontier GP-AI models are not suitable for independent clinical interpretation of coronary imaging, particularly in anatomically complex segments. These findings emphasize that general visual reasoning cannot yet replace domain-specific cardiovascular AI solutions or expert clinical judgment in specialized radiological tasks.","url":"https://doi.org/10.21203/rs.3.rs-8997340/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8997340/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.21203/rs.3.rs-9819774/v1","name":"Salmona: A Multi-Domain Architecture for Traceable Medical Billing Review in Colombian Healthcare","source":"preprints","abstract":"Abstract Background Medical billing audit in Colombia requires the simultaneous verification of administrative validity, clinical pertinence, and financial consistency of each claim, and may extend for more than three months per batch of submitted claims. Published artificial intelligence systems for this process focus on financial fraud detection through black-box classifiers, without covering the three audit domains simultaneously or describing concrete mechanisms of human governance. Methods A descriptive methodological study was conducted to document the design, architecture, and decision rationale of Salmona, a rule-grounded medical billing audit pipeline developed by Arkangel AI for healthcare reimbursement workflows in the Colombian health system. The description is based on the system’s technical documentation and the applicable Colombian regulatory framework. No patient data were collected and no empirical validation was conducted in this study. Results Salmona decomposes audit into three independent specialized audit layers: an administrative layer (27 rules, DAMA-UK instrument), a clinical pertinence layer (29 rules, PERT-CLIN instrument), and a financial and contractual layer (42 rules, FIN-CTR instrument). Each rule produces a structured result with three possible values: pass when there is evidence of compliance, fail when there is evidence of a violation, and n/a when the necessary information is absent. This innocent-until-proven-otherwise principle prevents the absence of a document from automatically becoming a billing objection. A canonical consolidation layer reconciles findings per invoice item, assigns objection causals according to Technical Annex 6 of Resolution 3047 of 2008, and calculates objected amounts under a financial integrity invariant. The human auditor reviews the consolidated result, may iteratively request corrections via comments, and initiates report delivery to the provider through an explicit action that is independent of the review cycle. Conclusions Salmona constitutes, to our knowledge, an early methodological contribution to the description of multi-domain medical billing review systems for the Colombian health system, with per-rule traceability and explicit human governance. Although initially oriented toward Colombian health insurers, the described architecture may also support provider-side pre-submission validation and structured response to reimbursement objections. The described architecture provides the basis for empirical validation with real claims in future studies.","url":"https://doi.org/10.21203/rs.3.rs-9819774/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9819774/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10128712/v1","name":"Ensemble method of transfer learning Deep learning and vision transformer influencing explainable AI for Breast Cancer Prediction","source":"preprints","abstract":"Abstract Breast cancer (BCr) identification is an important issue in medical image analysis that requires precise categorisation of different kinds of lesions. Examples of significant gaps highlighted by the current research include the difficulty of dealing with imbalanced datasets and the difficulty of explaining model choices. Even while Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in convolutional neural networks (CNNs) have made great strides, these technologies still have a way to go before they are fully transparent and interpretable. A potential outcome in overcoming such restrictions was explainable AI (EXAI). This work used EXAI to diagnose BCr, advancing healthcare. Together, transfer learning (TL), vision transformer (VIT), and EXAI formed an ensemble method for predicting BCr and its severity, as described in the article. The outcome is assessed using a BreakHis dataset at 40X, 100X, 200X, and 400X, consisting of images collected from histopathology experiments. The results were examined to contrast the performance of TL models with that of the VIT ensemble model. Ensemble models for vision transformer training included InceptionV3, InceptionResNetV2, VGG16, ResNet101, and Xception with TL. Two models were compared in this result: one using TL and the other using ensemble transfer learning (ETL) in combination with VIT on the BreakHis dataset. The ETL model that incorporates VIT with the CNN model achieves far better results, with 96.82% accuracy (accu), 96.78% precision (prec), 95.69% recall (rec), 95.79% specificity (spec), and 96.17% F1-score (F1) on the BreakHis dataset. Evidence that ensemble models are more effective than TL methods. In addition, the Grad-CAM, Heat maps, LIME, and Occlusion Sensitivity (OS) techniques shed light on the model's decision-making process, making the suggested detection framework clearer and more reliable. This approach enhances the model's explainability, which, in turn, empowers interpretation and better outcomes.","url":"https://doi.org/10.21203/rs.3.rs-10128712/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10128712/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10101843/v1","name":"AI versus Expert Feedback on Ethical Decision-Making in Medical Students: A Pilot Randomized Controlled Feasibility Trial with Exploratory Educational Outcomes","source":"preprints","abstract":"Abstract Background Structured feedback is central to ethics education, but expert-generated feedback requires substantial faculty time. Artificial intelligence (AI) may offer a scalable way to support ethical decision-making education, provided that generated content remains expert-reviewed and educationally appropriate. This pilot randomized study evaluated the feasibility and exploratory educational outcomes of AI-generated feedback compared with human expert panel feedback within a Concordance of Judgment Learning Tool (CJLT)-based ethics education intervention. Methods This single-center, two-arm, parallel-group pilot randomized controlled study included 56 sixth-year medical (intern) students. Fifty-six sixth-year medical students were randomized equally to receive either expert-reviewed AI-generated feedback or expert panel feedback while completing the same CJLT ethics scenarios. Quantitative measures were administered before and after the intervention, including an Objective Structured Video Examination (OSVE), Script Concordance Test (SCT), and Ethical Decision Bias Scale (BIAS). OSVE was the primary preliminary educational outcome. Participant flow, completion rates, missing data, CJLT completion, and voluntary three-month acceptability feedback were assessed as feasibility outcomes. The OSVE consisted of 20 video-based scenarios. Post-test OSVE scores were analyzed using ANCOVA, with baseline OSVE scores entered as a covariate; change-score analyses were conducted as sensitivity analyses. Results All participants completed the T0 and T1 quantitative assessments, and no missing data occurred for the quantitative outcomes. T2 acceptability feedback was obtained from 21 participants. The baseline OSVE mean score was higher in the expert-reviewed AI-generated feedback arm than in the expert panel feedback arm (23.32 ± 4.71 vs. 19.36 ± 3.65; p","url":"https://doi.org/10.21203/rs.3.rs-10101843/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10101843/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9066307/v1","name":"Clinician Perceptions of Artificial Intelligence in Healthcare and Frameworks for Ensuring Safe Integration into Clinical Practice of the West African College of Physicians: A Multi-Country Mixed-Methods Study","source":"preprints","abstract":"Abstract The integration of artificial intelligence (AI) into clinical practice holds significant promise for improving diagnostic accuracy, reducing medical errors, and enhancing healthcare efficiency, particularly in resource-constrained settings. However, successful adoption depends heavily on clinicians’ perceptions, trust, and concerns regarding autonomy, reliability, and safety. Empirical evidence on West African physicians’ views remains limited, despite unique regional challenges like infrastructure deficits and workforce shortages. This study aimed to assess clinicians’ perceptions of AI in healthcare, identify factors influencing willingness to adopt AI tools, and explore recommended frameworks for safe, accountable integration into clinical practice across West Africa. A cross-sectional survey of 136 physicians affiliated with the West African College of Physicians and 72 key informant interviews were conducted. While 85.3% agreed that AI could improve diagnostic accuracy and 83.1% believed it could reduce errors, 77.9% perceived AI as a threat to clinical autonomy, and 67.6% rated AI information as unreliable. Despite low prior AI experience (only 33.1% had used AI tools) and limited familiarity, 94.1% expressed willingness to use AI if proven effective. Trust in AI was the strongest predictor of adoption willingness (β = 0.48, p Clinical trial number : Not applicable.","url":"https://doi.org/10.21203/rs.3.rs-9066307/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9066307/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.20944/preprints202607.0904.v1","name":"Edible Insects: A Potential New Resource for Anti-Aging Interventions","source":"preprints","abstract":"As the global population ages rapidly, demand for safe and effective strategies to promote healthy aging grows markedly. Natural bioactive compounds, with favorable biosafety profiles and multi-target regulatory properties, have become a core focus in developing anti-aging functional foods and nutraceuticals. Amid the search for novel sustainable bioresources, edible insects emerge as promising anti-aging candidates for their rich species diversity, scalable production, low environmental footprint, and abundant unique bioactive components such as functional proteins and bioactive peptides. Based on bibliometric analysis of 500 eligible publications spanning two decades, this review systematically identifies 32 anti-aging insect species across seven orders, classifies their bioactive components into five major categories, summarizes green extraction technologies, and evaluates their in vitro and in vivo anti-aging activities centered on oxidative stress and inflammatory regulation, while outlining the field’s trajectory from basic mechanistic research to functional application. It further highlights core challenges including fragmented research frameworks and insufficient robust in vivo validation. Finally, it recommends integrating established food science and medical methodologies with emerging technologies such as omics, artificial intelligence, and advanced delivery systems to advance future research paradigms. These efforts could provide a strong theoretical foundation for the efficient and sustainable use of insect resources in anti-aging applications.","url":"https://doi.org/10.20944/preprints202607.0904.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0904.v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9318717/v1","name":"English Proficiency and AI Adoption Trajectories in Medical Training: A Structural Equation Evaluation of Current Use and Future Intentions","source":"preprints","abstract":"Abstract The fast-moving spread of generative artificial intelligence (AI) within higher education has brought attention questions to how students’ foundational abilities influence the benefits and limits of AI-assisted learning. From an educational evaluation perspective, this study explores how medical students’ English competence shapes their current academic use of AI tools and their plans for applying them in future study and professional settings. Using survey data from undergraduate medical students and structural equation modeling, we examined both direct and indirect pathways among English proficiency, academic AI use, and anticipated future engagements. Results show that English proficiency acts as a facilitating factor that not only supports effective academic use of AI but also encourages forward-looking adoptions. Far from being a background variable, language skill emerges as a key condition that influences how reliably AI-mediated learning opportunities function. The findings highlight the importance of aligning AI integration, language support, and assessment design when assessing students’ readiness for AI-enhanced professional practice.","url":"https://doi.org/10.21203/rs.3.rs-9318717/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9318717/v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202607.0322.v1","name":"Metadata Insufficiency and Reporting Deficits in Medical AI Generalization: A Meta-Research Audit Using Non-Parametric Bootstrap Resampling","source":"preprints","abstract":"Background: External validation performance drops are routinely reported in medical artificial intelligence (AI) literature, yet the study-level metadata features that systematically predict this degradation remain poorly characterised. Existing systematic reviews have catalogued performance metrics without subjecting the explanatory value of reported metadata to rigorous empirical audit. Objective: To quantify the explanatory ceiling of progressively richer metadata tiers on observed AUC degradation at external validation, and to identify which specific features carry replicable predictive signal across 1,000 non-parametric bootstrap resamples. Methods: A systematic search of PubMed/MEDLINE (January 2016 – May 2026), supplemented by Scopus and IEEE Xplore, identified 100 peer-reviewed studies reporting both internal performance and quantitative external validation outcomes for medical imaging AI. Thirteen metadata features were extracted and organised into four progressive complexity tiers. Explanatory power was assessed by the 10-fold cross-validated R² metric from a random-forest regression fitted on each tier. Replicable feature-selection frequency was estimated by 1,000 non-parametric bootstrap resamples with a 5% alpha threshold. Modality-stratified sensitivity analyses were conducted across three clinical imaging domains: ophthalmology (fundus photography), chest radiography, and computed tomography. Results: The full 13-feature model explained only −60.5% of variance in AUC degradation (10-fold cross-validated R² = −0.6053), with the deficit deepening monotonically across all four metadata tiers (Tier 1: R² = −0.3438; Tier 2: −0.4002; Tier 3: −0.5025). Non-parametric bootstrap resampling identified augmentation_applied as the single feature with selection frequency exceeding the 5% alpha threshold (frequency ≈65%), followed by class_balance_ratio (~23%) and architecture_type (~15%). All other features remained below threshold. Modality sensitivity profiles were broadly comparable across the three imaging domains, though CT studies exhibited the widest range of observed performance drops (ΔAUC range: −0.05 to +0.21). Conclusions: Collectively reported metadata—including dataset size, architecture, and AUC—leaves the overwhelming majority of observed generalisation variance unexplained. The consistent explanatory deficit across all metadata tiers signals a structural reporting gap rather than a predictive modelling limitation. Augmentation strategy, class-balance handling, and architecture type constitute the minimum replicable predictive signal currently available. Standardised metadata reporting frameworks, covering training data provenance, preprocessing pipelines, and demographic covariates, are required before comparative generalisation benchmarks can be meaningfully established.","url":"https://doi.org/10.20944/preprints202607.0322.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0322.v1","addedAt":"2026-09-01T01:47:50.468Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"oa:W4292317242","name":"ARTIFICIAL INTELLIGENCE AND THE NEW CHALLENGES FOR EU LEGISLATION","source":"openalex","abstract":"Artificial Intelligence is one of the increasing topics of the last decade which is developed by the new technological changes. With the introduction of AI into our daily lives, discussions occurred on machine learning and the legal and ethical issues governing artificial intelligence. At that point, conflicting situations emerged regarding the use of AI technologies, especially data issues and bias. In 1995, European Data Protection Directive, EU Directive 95/46 was passed which regulated the processing of personal data within the borders of EU and provided data privacy and security standards for the individuals. The Directive was repealed on 25th May 2018 by General Data Protection Regulation (GDPR), which brings new concepts with more strict rules on the protection of personal data. Due to its legal nature, GDPR includes binding rules not only for EU countries but also for those who carry out all activities related to data processing inside EU. With the development of technology and depending on different IT techniques, data processing has changed and access to data became easier than ever. As a result of these technologies, the concepts of big data and artificial intelligence have been widely discussed and the development of new electronic devices and the implementation of more use of robots have brought some legal questions into practice. Recently, there are some new regulations that seem likely to enter into EU legislation in the next years, such as Artificial Intelligence Act, Data Govermance Act, Data Act, and European Health Data Space. There is uncertainty for the next years, about how new regulations will affect each other including GDPR. This paper aims to discuss artificial intelligence, including GDPR and the new legal developments in the EU legislation within the technological implementations.","url":"https://doi.org/10.33432/ybuhukuk.1104344","authors":["Seldağ Güneş Peschke","Lutz Peschke"],"tags":["General Data Protection Regulation","Data Protection Act 1998","Data Protection Directive","Legislation","Directive"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-19","doi":"https://doi.org/10.33432/ybuhukuk.1104344","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4417121785","name":"A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics","source":"openalex","abstract":"This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed articles, we examine current trends, as well as the strengths and limitations of methodologies currently used in real-world healthcare settings. Our findings highlight a growing interest in XAI, particularly in medical imaging, yet reveal persistent challenges in clinical adoption, including issues of trust, interpretability, and integration into decision-making workflows. We identify critical gaps in existing approaches and underscore the need for more robust, human-centred, and intrinsically interpretable models, with only 44% of the papers studied proposing human-centred validations. Furthermore, we argue that fairness and accountability, which are key to the acceptance of AI in clinical practice, can be supported by the use of post hoc tools for identifying potential biases but ultimately require the implementation of complementary fairness-aware or causal approaches alongside evaluation frameworks that prioritise clinical relevance and user trust. This review provides a foundation for advancing XAI research on the development of more transparent, equitable, and clinically meaningful AI systems for use in healthcare.","url":"https://doi.org/10.3390/app152412934","authors":["Hassan Eshkiki","Farinaz Tanhaei","Fabio Caraffini","Benjamin Mora"],"tags":["Relevance (law)","Health informatics","Computer science","Data science","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-08","doi":"https://doi.org/10.3390/app152412934","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411961370","name":"Artificial Intelligence and Machine Learning for Enhancing Resilience: Concepts, Applications, and Future Directions","source":"openalex","abstract":"As contemporary societies face unprecedented challenges such as mounting mental health issues, environmental crises, and socioeconomic insecurity, the urgency of developing objective, scalable, and dynamic methodologies to study resilience has never been greater. This book arises at the intersection of cutting-edge technology and human insight. It focuses on the possibility for AI and ML to transform resilience assessment, prediction, and interventions across the individual, organizational, and ecological levels. The chapters included in this book represent an organized synthesis of cutting-edge science, pragmatic applications, and prospective potential. With machine learning algorithms to estimate psychological resilience and AI-based models for climate change adaptation and ecosystem management, this book demonstrates the rich innovations that are emerging at the cross-sector of technology and resilience science. Perhaps most importantly, this book does not gloss over the urgent ethical, technical, and regulatory issues that arise when AI is introduced to sensitive topics such as mental health and environmental management. Questions about data privacy, algorithmic bias, model interpretability, and equitable technology deployment are thoroughly investigated, providing lessons learned and suggestions for moving ahead. A significant strength of this work is its global focus. Showcasing work from contributors of various methodologies and regions provides the latest views on new methodologies, strategies for practical implementation, and on what still needs to be invented. This guarantees that the publication engages with the messy socio-cultural and environmental contexts in which these interventions work and that it doesn’t just mirror technological possibilities. For academicians, practitioners, technologists, and policymakers, this book is both a fundamental reference and an outlook resource. It provides: Holistic examination of AI and ML in the context of psychological, organizational, and ecological resilience. In-depth reviews on methodological innovations, such as deep learning, natural language processing, and sensor-based assessments. Unprecedented appraisals of barriers to implementation, with ethical and regulatory considerations. We trust that this book will inspire conversation, fuel innovation, and support a future in which technology supplements, rather than replaces, human ability to adapt, recover, and flourish. We encourage readers to critique the content, to reflect on how AI, ML, and resilience intersect in their particular contexts, and to join us in shaping a future where technological and human resilience evolve together.","url":"https://doi.org/10.70593/978-93-7185-143-5","authors":["Nitin Liladhar Rane","Suraj Kumar Mallick","Jayesh Rane"],"tags":["Resilience (materials science)","Computer science","Artificial intelligence","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.70593/978-93-7185-143-5","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413458885","name":"Comparing Artificial Intelligence Large Language Models in Medical Training: A Performance Analysis of ChatGPT and DeepSeek on United States Medical Licensing Examination (USMLE) Style Questions","source":"openalex","abstract":"Introduction The integration of artificial intelligence (AI) into medical education is reshaping how students prepare for standardized examinations. Prior studies have shown that AI models can achieve high accuracy on United States Medical Licensing Examination (USMLE) questions, highlighting their potential for examination preparation. ChatGPT (GPT), especially the 4o model, is one of the most widely used AI models; however, its accessibility is limited by subscription costs and regional censorship. DeepSeek (DS), a newer AI model, offers free access and has demonstrated comparable performance in general tasks. In this study, we compared the performance of GPT-4o and DS DeepThink R1 on the AMBOSS medical board preparation question bank to evaluate their potential and limitations as supplementary tools in medical education. Methods We extracted 1,079 USMLE-style multiple-choice questions from the AMBOSS question bank. Questions were categorized by USMLE Step 1 and Step 2 examinations and further grouped by topic, resulting in 36 categories. Each question was assigned a difficulty level (easy, intermediate, or hard) based on AMBOSS grading criteria. To ensure balanced representation, we randomly selected 10 questions per difficulty level per category. Questions and answer choices were copied verbatim from the AMBOSS website and input into GPT-4o and DS R1 without any modification. Model responses were scored as correct or incorrect, and correctness rates were compared across GPT-4o, DS R1, and AMBOSS user performance. Results Both GPT and DS outperformed AMBOSS users, with overall accuracies of 88.79%, 78.68%, and 56.98%, respectively. Comparing GPT and DS, GPT performed significantly better overall (t=7.90, p<0.0001). When stratified by examination type, GPT achieved significantly higher accuracy than DS in both Step 1 (0.89 vs. 0.78, p < 0.0001) and Step 2 (0.88 vs. 0.80, p < 0.0001). GPT consistently showed higher accuracy than DS at all three difficulty levels. However, when further stratified by examination type, statistically significances were only observed in intermediate (p = 0.0002) and hard (p = 0.0021) questions in both Step 1 and Step 2. Conclusion Our findings demonstrated that both AI models outperformed human learners, with GPT-4o showing superior accuracy, particularly in intermediate and hard questions. While DS underperformed relative to GPT, its free accessibility and competitive accuracy in easy questions suggest that it may serve as a viable alternative, particularly in resource-limited settings.","url":"https://doi.org/10.7759/cureus.90212","authors":["Runze Zhang","Qinyun Cai","Ângela Lúcia Bagnatori Sartori","Nasser Gayed","Heather Collette"],"tags":["Medicine","United States Medical Licensing Examination","Style (visual arts)","Training (meteorology)","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-16","doi":"https://doi.org/10.7759/cureus.90212","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4405991965","name":"Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline","source":"openalex","abstract":"Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks: post-mean, post-max, post-min, and pre-mean aggregation. We developed a customized AI pipeline that incorporates twelve machine learning algorithms along with the four aggregation methods to detect Parkinson’s disease (PD) using multiple voice recordings from individuals available in the UCI Machine Learning Repository, which includes 756 voice recordings from 188 PD patients and 64 healthy individuals. Seven performance metrics—accuracy, precision, sensitivity, specificity, F1 score, AUC, and MCC—were utilized for model evaluation. Various techniques, such as Bag Over-Sampling (BOS), cross-validation, and grid search, were implemented to enhance classification performance. Among the four aggregation methods, post-mean aggregation combined with XGBoost achieved the highest accuracy (0.880), F1 score (0.922), and MCC (0.672). Furthermore, we identified potential trends in selecting aggregation methods that are suitable for imbalanced data, particularly based on their differences in sensitivity and specificity. These findings provide meaningful implications for the further exploration of grouped imbalanced data.","url":"https://doi.org/10.3390/data10010004","authors":["Zhengxiao Yang","Hao Zhou","Sudesh Srivastav","Jeffrey G. Shaffer","K. Thomas Abraham","Samuel M. Naandam","Samuel Kakraba"],"tags":["Pipeline (software)","Computer science","Artificial intelligence","Speech recognition","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.3390/data10010004","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416132081","name":"Teachers’ professional identity in the era of artificial intelligence: A phenomenological study","source":"openalex","abstract":"This study adopted a phenomenological research design to examine the lived experiences of teachers regarding their professional identity in response to the rapid expansion of artificial intelligence (AI) integration in education. The study involved thirty purposively selected teachers as participants. Data were collected through in-depth semi-structured interviews and analyzed using thematic analysis. Member checking was used to validate the findings. Data analysis followed a systematic phenomenological process, comprising thematic clustering, and synthesis of core themes. Trustworthiness was ensured through member checking, peer debriefing, and maintaining an audit trail. Five themes emerged from the findings; evolving professional roles, diverse emotional responses, emerging patterns of teacher-AI partnership, tensions between teacher autonomy and standardized instruction, and a redefinition of professional identity. The study revealed that teachers initially feared role erosion but gradually recognized the potential for AI to support, rather than replace, their teaching. Majority of the participants reported a transformation in their professional identity that signify a shift from traditional instruction toward facilitation in AI-mediated environments. It was recommended, among others, that rather than undermining the role of teachers, AI should be positioned to advance their professional identity within the classroom environment.","url":"https://doi.org/10.30935/ijpdll/17416","authors":["Akilu Isma’il","Halimat Bashir Ibrahim"],"tags":["Identity (music)","Thematic analysis","Autonomy","Psychology","Pedagogy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-12","doi":"https://doi.org/10.30935/ijpdll/17416","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407930169","name":"The Role of Artificial Intelligence in Early Diagnosis and Management of Cardiovascular Diseases","source":"openalex","abstract":"The increasing rate of cardiovascular diseases (CVDs) has posed a tremendous challenge to their early detection and personalized treatment. This research examines the potential of Artificial Intelligence (AI) for early detection and management of CVDs, in particular whether it can enhance diagnostic accuracy, personalize treatment guidelines, and reduce healthcare costs. A quantitative methodology was adopted and a survey strategy was employed for collecting primary data from 300 healthcare professionals consisting of cardiologists, general physicians, and professionals in AI fields from Punjab hospitals in Pakistan. The questionnaire was constructed to determine their knowledge, experiences, and perceptions regarding the use of AI in cardiovascular services. Data analysis revealed that application of AI had a strong correlation with increased diagnostic success, evident in a statistically significant chi-square test (p < 0.001). Furthermore, multiple regression analysis revealed that AI, together with years of experience and educational history, is an important contributor to personalizing cardiovascular treatment plans. The results indicate that AI has a key role in making more precise diagnoses and improving treatment methods, which can ultimately decrease the cost of healthcare and enhance patient outcomes. Yet, issues around data privacy, transparency, and clinician confidence in AI systems must be resolved in order for AI to be adopted more widely. Future research is suggested by the study into the integration of AI with other health technologies and the ethics of using AI in clinical practice.","url":"https://doi.org/10.70749/ijbr.v3i2.667","authors":["H. Abdul Shabeer","Hafiz Muhammad Ali Haider","Tamana Khatri","Nouman Khan","Adnan Ahmed Rafique","Fakhar Anjam"],"tags":["Medicine","Artificial intelligence","Computer science","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-18","doi":"https://doi.org/10.70749/ijbr.v3i2.667","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4393405308","name":"Sora for Computational Social Systems: From Counterfactual Experiments to Artificiofactual Experiments With Parallel Intelligence","source":"openalex","abstract":"Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.","url":"https://doi.org/10.1109/tcss.2024.3373928","authors":["Rui Qin","Fei–Yue Wang","Xiaolong Zheng","Qinghua Ni","Juanjuan Li","Xiao Xue","Bin Hu"],"tags":["Counterfactual thinking","Computer science","Computational intelligence","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-01","doi":"https://doi.org/10.1109/tcss.2024.3373928","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414939203","name":"Artificial intelligence for precision medicine","source":"openalex","abstract":"INTRODUCTION: Precision medicine aims to tailor healthcare decisions and interventions to the unique biological and clinical characteristics of each patient. The recent convergence of artificial intelligence (AI) with advances in digital health, omics, and big data analytics has accelerated progress toward this goal. AI technologies - particularly machine learning, deep learning, natural language processing and generative large language models - enable the rapid and meaningful analysis of complex biomedical datasets, supporting more individualized care. PURPOSE OF REVIEW: In this narrative review, we provide an accessible overview of the core principles of AI for healthcare professionals and explore its practical applications across the spectrum of precision medicine. Real-world examples highlight how AI is being used to enhance early diagnosis, guide treatment selection, support disease prevention, and even contribute directly to therapeutic interventions. Alongside these advances, we discuss critical limitations and challenges, including ethical considerations, algorithmic bias, data privacy concerns, environmental impact, and practical barriers to clinical implementation. CONCLUSION: This review offers both an introduction to AI and a practical overview of how it is being used, and where its limitations lie, in precision medicine, with the goal of helping healthcare professionals understand these evolving tools and use them efficiently and responsibly in clinical practice.","url":"https://doi.org/10.1016/j.therap.2025.10.003","authors":["M. Martel","Adán José-García","Celine Vens","Maarten De Vos","Vincent Sobanski"],"tags":["Precision medicine","Computer science","Artificial intelligence","Applications of artificial intelligence","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-08","doi":"https://doi.org/10.1016/j.therap.2025.10.003","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4405466588","name":"European Sovereignty in Artificial Intelligence: A Competence-Based Perspective","source":"openalex","abstract":"","url":"https://doi.org/10.2139/ssrn.5061172","authors":["Ludovic Dïbiaggio","Lionel Nesta","Simone Vannuccini"],"tags":["Sovereignty","Perspective (graphical)","Competence (human resources)","Political science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.2139/ssrn.5061172","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407069477","name":"Evaluating the Quality and Readability of Generative Artificial Intelligence (AI) Chatbot Responses in the Management of Achilles Tendon Rupture","source":"openalex","abstract":"INTRODUCTION: The rise of artificial intelligence (AI), including generative chatbots like ChatGPT (OpenAI, San Francisco, CA, USA), has revolutionized many fields, including healthcare. Patients have gained the ability to prompt chatbots to generate purportedly accurate and individualized healthcare content. This study analyzed the readability and quality of answers to Achilles tendon rupture questions from six generative AI chatbots to evaluate and distinguish their potential as patient education resources. METHODS: The six AI models used were ChatGPT 3.5, ChatGPT 4, Gemini 1.0 (previously Bard; Google, Mountain View, CA, USA), Gemini 1.5 Pro, Claude (Anthropic, San Francisco, CA, USA) and Grok (xAI, Palo Alto, CA, USA) without prior prompting. Each was asked 10 common patient questions about Achilles tendon rupture, determined by five orthopaedic surgeons. The readability of generative responses was measured using Flesch-Kincaid Reading Grade Level, Gunning Fog, and SMOG (Simple Measure of Gobbledygook). The response quality was subsequently graded using the DISCERN criteria by five blinded orthopaedic surgeons. RESULTS: Gemini 1.0 generated statistically significant differences in ease of readability (closest to average American reading level) than responses from ChatGPT 3.5, ChatGPT 4, and Claude. Additionally, mean DISCERN scores demonstrated significantly higher quality of responses from Gemini 1.0 (63.0±5.1) and ChatGPT 4 (63.8±6.2) than ChatGPT 3.5 (53.8±3.8), Claude (55.0±3.8), and Grok (54.2±4.8). However, the overall quality (question 16, DISCERN) of each model was averaged and graded at an above-average level (range, 3.4-4.4). DISCUSSION AND CONCLUSION: Our results indicate that generative chatbots can potentially serve as patient education resources alongside physicians. Although some models lacked sufficient content, each performed above average in overall quality. With the lowest readability and highest DISCERN scores, Gemini 1.0 outperformed ChatGPT, Claude, and Grok and potentially emerged as the simplest and most reliable generative chatbot regarding management of Achilles tendon rupture.","url":"https://doi.org/10.7759/cureus.78313","authors":["Christopher E Collins","Peter A Giammanco","Monica Guirgus","Mikayla Kricfalusi","Richard C. Rice","Rusheel Nayak","David Ruckle","Ryan Filler","Joseph G Elsissy"],"tags":["Readability","Medicine","Reading (process)","Quality (philosophy)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-31","doi":"https://doi.org/10.7759/cureus.78313","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410745942","name":"Advances in Artificial Intelligence for Lung Cancer Detection and Diagnostic Accuracy: A Comprehensive Review","source":"openalex","abstract":"Cancer is a deadly disease with a minimal probability of curing when detected in the later stages. Out of many different varieties of cancer diseases, lung cancer falls under the critical category as it is internal, invisible, and connected to the breathing control mechanism of human beings. Lung cancer has become the most frequent type in this generation and has intensified in the post-COVID-19 pandemic era, with a high degree of fatality. The histopathological images of lung cancer are very large in volume, so that analyzing such kind of data is monotonous and error-prone in a human- controlled method. The recent progress in the field of computer vision, along with machine learning techniques, has made the path of research smooth in the medical and healthcare domain, also achieved the feasibility of data analysis with easy detection of cancer cells. The advent of the deep learning concept made the automatic and accurate detection of cancer cells from the histopathological image data analysis possible. In this paper, an evaluation and a methodological survey on Cancer cell detection and the accuracy benchmark of Cancer tissue Segmentation of whole slide images (WSI) using Machine Learning (ML) and Deep Learning (DL) approaches have been carried out. The critical analysis, along with the exploration of more probable research trends in the accurate interpretation of the cancer cell images, has also been addressed towards achieving the greater potential.","url":"https://doi.org/10.38124/ijisrt/25may1339","authors":["Rupa Debnath","Rituparna Mondal","Arpita Chakraborty","Siddhartha Chatterjee"],"tags":["Lung cancer","Cancer detection","Artificial intelligence","Cancer","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-26","doi":"https://doi.org/10.38124/ijisrt/25may1339","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408019107","name":"Using artificial intelligence in health research","source":"openalex","abstract":"Artificial intelligence is now widely accessible and already being used by healthcare researchers throughout various stages in the research process, such as assisting with systematic reviews, supporting data collection, facilitating data analysis and drafting manuscripts for publication. The most common AI tools used are forms of generative AI such as ChatGPT, Claude and Gemini. Generative AI is a type of AI that can generate human-like text, audio, videos, code and images based on text-based prompts inputted by a human user. Generative AI is trained on large amounts of data, and the outputs are sophisticated and can be indistinguishable from a response from a skilled human. In this article, we outline several AI applications that can be used in healthcare research, examining their benefits, limitations and outline best practices for maintaining research integrity and ethical standards.","url":"https://doi.org/10.1136/ebnurs-2025-104287","authors":["Daniel Rodger","Siobhán O’Connor"],"tags":["Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-27","doi":"https://doi.org/10.1136/ebnurs-2025-104287","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409607162","name":"Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy","source":"openalex","abstract":"Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review and meta-analysis was conducted to assess the performance of AI-driven models across three key domains: genomics and molecular profiling, radiomics-based imaging analysis, and prediction of immunotherapy response. Methods: Relevant studies were identified through a systematic search across multiple databases (2020–2025), adhering to PRISMA guidelines. Results: Thirteen studies met the inclusion criteria, involving over 10,000 ovarian cancer patients and encompassing diverse AI models such as machine learning classifiers and deep learning architectures. Pooled AUCs indicated strong predictive performance for genomics-based (0.78), radiomics-based (0.88), and immunotherapy-based (0.77) models. Notably, radiogenomics-based AI integrating imaging and molecular data yielded the highest accuracy (AUC = 0.975), highlighting the potential of multi-modal approaches. Heterogeneity and risk of bias were assessed, and evidence certainty was graded. Conclusions: Overall, AI demonstrated promise in predicting therapeutic outcomes in ovarian cancer, with radiomics and integrated radiogenomics emerging as leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, and integration of immune and spatial transcriptomic data to support clinical implementation and individualized treatment strategies. Unlike earlier reviews, this study synthesizes a broader range of AI applications in ovarian cancer and provides pooled performance metrics across diverse models. It examines the methodological soundness of the selected studies and highlights current gaps and opportunities for clinical translation, offering a comprehensive and forward-looking perspective in the field.","url":"https://doi.org/10.3390/ai6040084","authors":["Mauro Francesco Pio Maiorano","Gennaro Cormio","Vera Loizzi","Brigida Anna Maiorano"],"tags":["Radiomics","Genomics","Immunotherapy","Ovarian cancer","Computational biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-18","doi":"https://doi.org/10.3390/ai6040084","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4220965032","name":"Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review","source":"openalex","abstract":"Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.","url":"https://doi.org/10.3390/diagnostics12040837","authors":["Athena S. Davri","Effrosyni Birbas","Theofilos Kanavos","Georgios Ntritsos","Νικόλαος Γιαννακέας","Alexandros T. Tzallas","Anna Batistatou"],"tags":["Microsatellite instability","Colorectal cancer","Medicine","Histopathology","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-29","doi":"https://doi.org/10.3390/diagnostics12040837","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4414342860","name":"Analysis of Retracted Publications on Artificial Intelligence: Trends, Ethical Concerns, and Scientific Integrity","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) has promoted progress across various fields. The number of papers regarding AI has risen in recent years. This study examines retracted publications regarding AI by analyzing trends, journals, and reasons. METHODS: This descriptive cross-sectional study thoroughly investigated retracted AI-related papers listed in PubMed. The data extraction comprised bibliographic data, reasons for retraction, citation metrics, journal indexing status, and Altmetric Attention Scores (AASs). Retraction notices were classified according to particular reasons. Descriptive statistics were employed to evaluate retraction trends, geographic distribution, and citation impact. RESULTS: A total of 764 retracted AI-related papers were examined, with the most retractions occurring in 2023 (n = 667). China had the highest number (n = 551), followed by India (n = 40) and Bangladesh (n = 23). Journals focusing on mathematical and computational biology, neurosciences, and healthcare sciences had the most retractions. The most common retraction reasons were peer review issues (n = 716) and data concerns (n = 714), followed by irrelevant citations (n = 571) and unethical AI use (n = 238). The median time to retraction was 510 days (18-4,200). The median citation and AAS scores were (0-167) and 0 (0-191). CONCLUSION: The high number of retractions from China highlights the need for higher research standards. Deficits in peer review and data issues emerged as the main reasons for retraction, underscoring persistent challenges in maintaining research integrity and quality assurance. For scientific literature integrity, academic institutions, publishers, and researchers should stress transparency, ethics, and rigorous post-publication inspection.","url":"https://doi.org/10.3346/jkms.2025.40.e280","authors":["Burhan Fatih Koçyiğit","Ramazan Azim Okyay","Birzhan Seiil","Ainur B. Qumar","Hilmi Erdem Sümbül"],"tags":["Scientific integrity","Research integrity","Engineering ethics","Academic integrity","Scientific misconduct"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3346/jkms.2025.40.e280","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409482562","name":"Multimodality imaging in prostate cancer diagnosis using artificial intelligence: basic concepts and current state-of-the-art","source":"openalex","abstract":"Abstract The early diagnosis of prostate cancer (PCa) is highly recommended, as the tumor will not spread to other organs of the body and the bones. Moreover, a late diagnosis of PCa could lower the survival rate. The growing development of Artificial Intelligence (AI) and Machine Learning (ML) in medical images has led to significant improvement in PCa diagnosis. Multimodality is now commonly applied in medical imaging diagnosis, as it provides comprehensive information about a target (tissue or tumor). It has shown to be useful for advancing the clinical reliability of using medical images and ML for medical diagnostics and analysis. Hence, in this paper, a comprehensive survey is provided to explore the state-of-the-art Computer-Aided Diagnosis Systems (CADs) for PCa detection attributed to multimodality imaging, a background of PCa. different types of medical imaging used in PCa diagnosis, related clinical workflows, future perspectives, and some common limitations of related work. The review exhibits an extensive literature review done on multimodality imaging in PCa, highlights that multimodality imaging has the potential of wide applicability in diagnosis systems. It is expected that this study enhances the understanding necessary for developing CAD systems for PCa diagnosis. Additionally, it is expected to establish a great basis for developing multimodal images, the relevant datasets, some of the challenges, and future topics. Graphical Abstract","url":"https://doi.org/10.1007/s11042-025-20786-2","authors":["Sarah M. Ayyad","Nahla B. Abdel-Hamid","Hesham Ali","Labib M. Labib"],"tags":["Computer science","Multimodality","Current (fluid)","State (computer science)","Prostate cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-16","doi":"https://doi.org/10.1007/s11042-025-20786-2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410352982","name":"Role of Artificial Intelligence in Human Capital Management: A Review at PT. Pos Indonesia","source":"openalex","abstract":"PT. Pos Indonesia is committed to overcoming digital era challenges by implementing strategic transformations, particularly through the adoption of Artificial Intelligence (AI). AI plays a key role in enhancing operational efficiency, service quality, and revenue growth. It optimizes logistics, accelerates courier services, and personalizes customer interactions, contributing to an 18.64% increase in business revenue in 2023, reaching Rp 5,479,12 billion. This growth is driven by AI based automation in supply chain management and customer data analysis. The goal of this research is to explore how PT. Pos Indonesia can address AI implementation gaps and optimize its use to gain sustainable competitive advantages in operations and Human Capital Management (HCM). It also aims to provide strategies for developing human resource management and enhancing efficiency through AI. In HCM, AI improves recruitment, training, and employee development, supporting skill analysis, personalized training, and performance evaluation. This transformation aligns with AKHLAK values, fostering collaboration and adaptability while supporting remote work models for O-Rangers and postal agents via realtime monitoring. AI also helps PT. Pos Indonesia adapt to the evolving e-commerce industry by optimizing delivery routes, predicting demand, and empowering digital platforms like Pospay. Automation improves cost efficiency and customer service, strengthening PT. Pos Indonesia position in the digital logistics and courier services market. Through AI integration in HCM and operations, PT. Pos Indonesia enhances profitability and establishes sustainable competitive advantages, positioning itself as a model for technology driven business innovation in Indonesia logistics and financial sectors.","url":"https://doi.org/10.34306/ijcitsm.v5i1.175","authors":["Sora Baltasar","Tonggo Marbun"],"tags":["Business","Human resource management","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-28","doi":"https://doi.org/10.34306/ijcitsm.v5i1.175","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4400668233","name":"From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy","source":"openalex","abstract":"This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI‐driven microscopy methodologies and categorizing them into linear inverse problem‐solving, denoising, and nonlinear prediction. Furthermore, the potential of self‐supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.","url":"https://doi.org/10.1002/adpr.202300308","authors":["Seungjae Han","Joshua Yedam You","Minho Eom","Sungjin Ahn","Eun‐Seo Cho","Young‐Gyu Yoon"],"tags":["Microscopy","Artificial intelligence","Computer science","Perspective (graphical)","Focus (optics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-15","doi":"https://doi.org/10.1002/adpr.202300308","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416459697","name":"Hybrid intelligence in medical image segmentation","source":"openalex","abstract":"Medical image segmentation is vital for precise identification and analysis of anatomical structures and pathological regions, yet traditional models often fall short in aligning with clinical workflows, requiring extensive manual correction even when overall segmentation accuracy is high. To address this gap, we introduce HybridMS, a hybrid intelligence framework designed to maintain high segmentation accuracy while substantially reducing clinician workload through selective human intervention. HybridMS employs an uncertainty-driven feedback mechanism that selectively triggers clinician input only for cases predicted to be challenging, thereby avoiding unnecessary manual review. Corrected cases are prioritised during retraining through a weighted update strategy, enabling the model to adapt more effectively to clinically relevant errors. This design minimises intervention frequency while preserving segmentation quality. Evaluated on lung segmentation in chest X-rays for tuberculosis detection, HybridMS achieved comparable or improved performance over the baseline MedSAM model (Dice: 0.9538 vs. 0.9435; IoU: 0.9126 vs. 0.8941) with consistent boundary quality in difficult cases. For the subset of cases identified as challenging (baseline Dice < 0.92), HybridMS reduced mean Hausdorff Distance and Average Symmetric Surface Distance, demonstrating more stable anatomical boundaries. Workflow efficiency was markedly improved: in a preliminary timing study with radiologists, average annotation time was reduced by approximately 82% for standard cases and 60% for challenging cases, without compromising accuracy. By combining targeted human oversight with automated refinement, HybridMS demonstrates that stable segmentation performance can be achieved with significantly lower annotation effort, offering a clinically viable pathway for efficient and reliable deployment in diagnostic workflows.","url":"https://doi.org/10.1038/s41598-025-24990-w","authors":["Namia Mohamed Ali","Solomon Sunday Oyelere","Nitya Jitani","Rosy Sarmah","S. Andrew"],"tags":["Segmentation","Computer science","Artificial intelligence","Workflow","Hausdorff distance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-21","doi":"https://doi.org/10.1038/s41598-025-24990-w","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4404547649","name":"Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review","source":"openalex","abstract":"OBJECTIVES: To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images. METHODS: Studies were gathered by searching 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies. RESULTS: Thirteen studies were included in this systematic review. Most of the studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN-based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%. CONCLUSIONS: The integration of AI, particularly deep learning, in TMJ MRI, shows promising results as a diagnostic-assistance tool to segment TMJ structures and classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.","url":"https://doi.org/10.1093/dmfr/twae055","authors":["Mitul Manek","Ibraheem Maita","Diego Filipe Bezerra Silva","Daniela Pita de Melo","Paul W. Major","Jacob L. Jaremko","Fabiana T. Almeida"],"tags":["Temporomandibular joint","Magnetic resonance imaging","Computer science","Orthodontics","Joint (building)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-19","doi":"https://doi.org/10.1093/dmfr/twae055","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4391878439","name":"Assessing the medical reasoning skills of GPT-4 in complex ophthalmology cases","source":"openalex","abstract":"BACKGROUND/AIMS: This study assesses the proficiency of Generative Pre-trained Transformer (GPT)-4 in answering questions about complex clinical ophthalmology cases. METHODS: Ophthalmology Clinical Challenges, and prompted the model to determine the diagnosis (open-ended question) and identify the next-step (multiple-choice question). We generated responses using two zero-shot prompting strategies, including zero-shot plan-and-solve+ (PS+), to improve the reasoning of the model. We compared the best-performing model to human graders in a benchmarking effort. RESULTS: Using PS+ prompting, GPT-4 achieved mean accuracies of 48.0% (95% CI (43.1% to 52.9%)) and 63.0% (95% CI (58.2% to 67.6%)) in diagnosis and next step, respectively. Next-step accuracy did not significantly differ by subspecialty (p=0.44). However, diagnostic accuracy in pathology and tumours was significantly higher than in uveitis (p=0.027). When the diagnosis was accurate, 75.2% (95% CI (68.6% to 80.9%)) of the next steps were correct. Conversely, when the diagnosis was incorrect, 50.2% (95% CI (43.8% to 56.6%)) of the next steps were accurate. The next step was three times more likely to be accurate when the initial diagnosis was correct (p<0.001). No significant differences were observed in diagnostic accuracy and decision-making between board-certified ophthalmologists and GPT-4. Among trainees, senior residents outperformed GPT-4 in diagnostic accuracy (p≤0.001 and 0.049) and in accuracy of next step (p=0.002 and 0.020). CONCLUSION: Improved prompting enhances GPT-4's performance in complex clinical situations, although it does not surpass ophthalmology trainees in our context. Specialised large language models hold promise for future assistance in medical decision-making and diagnosis.","url":"https://doi.org/10.1136/bjo-2023-325053","authors":["Daniel Milad","Fares Antaki","Jason Milad","Andrew Farah","Thomas Khairy","David Mikhail","Charles‐Édouard Giguère","Samir Touma","Allison Bernstein","Andrei-Alexandru Szigiato","Taylor Nayman","Guillaume A. Mullie","Renaud Duval"],"tags":["Medicine","Optometry","Ophthalmology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-16","doi":"https://doi.org/10.1136/bjo-2023-325053","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411475682","name":"Algorithmic Bias and Data Justice: ethical challenges in Artificial Intelligence Systems","source":"openalex","abstract":"This article examines the critical ethical challenges posed by algorithmic bias in artificial intelligence (AI) systems, focusing on its implications for social justice and data equity. Through a systematic review of case studies and theoretical frameworks, we analyze how biased datasets and algorithmic designs perpetuate structural inequalities, particularly affecting marginalized communities. The study highlights key examples, such as gender and racial biases in facial recognition and hiring algorithms, while exploring mitigation strategies rooted in data justice principles. Additionally, we evaluate regulatory responses, including the European Union's AI Act, which proposes a risk-based governance framework. The findings underscore the urgent need for interdisciplinary approaches to develop fairer AI systems that align with ethical standards and human rights.","url":"https://doi.org/10.56294/ai2025159","authors":["Javier González‐Argote","Emanuel Maldonado","Karina Maldonado"],"tags":["Equity (law)","Economic Justice","Corporate governance","Social justice","Inequality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.56294/ai2025159","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4401558357","name":"Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis","source":"openalex","abstract":"OBJECTIVES: This review summarizes the current and potential uses of artificial intelligence (AI) in the current state of clinical microbiology with a focus on replacement of labor-intensive tasks. METHODS: A search was conducted on PubMed using the key terms clinical microbiology and artificial intelligence. Studies were reviewed for relevance to clinical microbiology, current diagnostic techniques, and potential advantages of AI in routine microbiology workflows. RESULTS: Numerous studies highlight potential labor, as well as diagnostic accuracy, benefits to the implementation of AI for slide-based and macroscopic digital image analyses. These range from Gram stain interpretation to categorization and quantitation of culture growth. CONCLUSIONS: Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages. More research efforts and US Food and Drug Administration clearance are still required to fully incorporate these AI applications into routine clinical laboratory practices.","url":"https://doi.org/10.1093/ajcp/aqae107","authors":["Erin H. Graf","Amr Soliman","Mohamed Marouf","Anil V. Parwani","Preeti Pancholi"],"tags":["Clinical microbiology","Workflow","Staffing","Economic shortage","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-13","doi":"https://doi.org/10.1093/ajcp/aqae107","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412883239","name":"Application of artificial intelligence techniques for the profiling of visitors to tourist destinations","source":"openalex","abstract":"Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. The research followed the CRISP-DM methodology for data analysis, based on surveys administered to 882 visitors. The data were processed using the clustering algorithms K-Means, DBSCAN, HDBSCAN, and Agglomerative, with Principal Component Analysis applied beforehand for dimensionality reduction. The results showed that the Agglomerative Clustering model achieved the best performance in internal validation metrics, allowing for the identification of five distinct visitor profiles. These segments provide valuable insights for the design of more inclusive and personalized tourism products. In conclusion, the study demonstrates the value of machine learning as a tool for tourism segmentation, offering empirical evidence that can strengthen the management of emerging destinations such as Alto Amazonas. The practical contribution of this study lies in providing strategic information that enables destination managers to tailor services and experiences to the characteristics of each segment, thereby optimizing visitor satisfaction and strengthening the destination's competitiveness.","url":"https://doi.org/10.3389/frai.2025.1632415","authors":["Juan Schrader","Lloy Pinedo","Fabiano Cassol de Vargas","Karla Martell","José Seijas-Díaz","Mtro. Roger Ricardo Rengifo Amasifen","Rosa Elena Cueto Orbe","Mg. Cinthya Torres Silva"],"tags":["Tourism","Visitor pattern","Market segmentation","Computer science","Cluster analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-04","doi":"https://doi.org/10.3389/frai.2025.1632415","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411087970","name":"Generative artificial intelligence for general practice; new potential ahead, but are we ready?","source":"openalex","abstract":"BACKGROUND: Generative AI (Gen AI) is frequently cited as an innovation to address the current challenges in healthcare, also for primary care. Examples include automating tasks like voice-to-notes transcription or chatbots using large language models. Additionally, it may facilitate a learning healthcare system by generating personalised learning resources and real-time literature summaries. Yet - probably with the highest expectations - Gen AI may extend diagnostic and therapeutic capabilities in general practice by integrating complex, multimodal patient data for personalised care, enabling earlier disease detection, and providing real-time guidance for diagnostics, prognostics and treatments. METHOD & DISCUSSION: The authors of this opinion paper recently hosted a workshop at the WONCA Europe 2024 conference. From discussions at that workshop, three priorities emerge: practice support, education support, and clinical decision-making support. In this opinion paper, we argue that GPs and academic departments of primary care should lead in evaluating Gen AI across these three priorities. Primary care research must prioritise rigorous scientific evaluations, to ensure that developed tools actually work for GPs and their patients. CONCLUSION: Hereto, a coordinated effort, driven by the primary care academic community, is needed, starting with research agenda drafting. A broad, international follow-up is scheduled following this WONCA Europe 2024 workshop.","url":"https://doi.org/10.1080/13814788.2025.2511645","authors":["Geert‐Jan Geersing","Niek J. de Wit","Matthew Thompson"],"tags":["Medicine","General practice","Generative grammar","Artificial intelligence","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-06","doi":"https://doi.org/10.1080/13814788.2025.2511645","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410236561","name":"Wearable sleep recording augmented by artificial intelligence for Alzheimer’s disease screening","source":"openalex","abstract":"The recent emergence of wearable devices will enable large scale remote brain monitoring. This study investigated whether multimodal wearable sleep recordings could help screening for Alzheimer's disease (AD). Measurements were acquired simultaneously from polysomnography and a wearable device, measuring electroencephalography (EEG) and accelerometry (ACM) in 67 elderly without cognitive symptoms and 35 AD patients. Sleep staging was performed using an AI model (SeqSleepNet), followed by feature extraction from hypnograms and physiological signals. Using these features, a multi-layer perceptron was trained for AD detection, with elastic net identifying key features. The wearable AD detection model achieved an accuracy of 0.90 (0.76 for prodromal AD). Single-channel EEG and ACM physiological features captured sufficient information for AD detection and outperformed the hypnogram features, highlighting these physiological features as promising discriminative markers for AD. We conclude that wearable sleep monitoring augmented by AI shows promise towards non-invasive screening for AD in the older population.","url":"https://doi.org/10.1038/s41514-025-00219-y","authors":["Elisabeth R. M. Heremans","Astrid Devulder","Pascal Borzée","Rik Vandenberghe","François‐Laurent De Winter","Mathieu Vandenbulcke","Maarten Van Den Bossche","Bertien Buyse","Dries Testelmans","Wim Van Paesschen","Maarten De Vos"],"tags":["Wearable computer","Sleep (system call)","Computer science","Disease","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-09","doi":"https://doi.org/10.1038/s41514-025-00219-y","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409564798","name":"Charting the Landscape of Artificial Intelligence Ethics: A Bibliometric Analysis","source":"openalex","abstract":"Abstract Using bibliometric methods, this study systematically analyzes 6,084 AI ethics-related articles from the Web of Science Core Collection (2015–2025), capturing both recent advances and near-future directions in the field. It begins by examining publication trends, disciplinary categories, leading journals, and major contributing institutions/countries. Subsequently, co-citation (journals, authors, references) and keyword clustering methods reveal the foundational knowledge structure and highlight emerging research hotspots. The findings indicate increasing interdisciplinary convergence and international collaboration in AI ethics, with core themes focusing on algorithmic fairness, privacy and data security, ethical governance in autonomous vehicles, medical AI applications, educational technology, and challenges posed by generative AI (e.g., large language models). Burst keyword detection further shows an evolutionary shift from theoretical debates toward practical implementation strategies and regulatory framework development. Although numerous global initiatives have been introduced to guide AI ethics, broad consensus remains elusive, underscoring the need for enhanced cross-disciplinary and international cooperation. This research provides valuable insights for scholars, policymakers, and industry practitioners, laying a foundation for sustainable and responsible AI development.","url":"https://doi.org/10.1515/ijdlg-2025-0007","authors":["Jiaxuan Qiu","Le Cheng","Jin Huang"],"tags":["Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1515/ijdlg-2025-0007","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413281273","name":"Ten Natural Language Processing Tasks with Generative Artificial Intelligence","source":"openalex","abstract":"The review enumerates the predominant applications of large language models (LLMs) in natural language processing (NLP) tasks, with a particular emphasis on the years 2023 to 2025. A particular emphasis is placed on applications pertaining to information retrieval, named entity recognition, text or document classification, text summarization, machine translation, question-and-answer generation, fake news or hate speech detection, and sentiment analysis of text. Furthermore, metrics such as ROUGE, BERT, METEOR, BART, and BLEU scores are presented to evaluate the capabilities of a given language model. The following example illustrates the calculation of scores for the aforementioned metrics, utilizing sentences generated by ChatGPT 3.5, which is free and publicly available.","url":"https://doi.org/10.3390/app15169057","authors":["Justyna Golec","Tomasz Hachaj"],"tags":["Computer science","Generative grammar","Artificial intelligence","Natural language processing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-17","doi":"https://doi.org/10.3390/app15169057","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407703947","name":"Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis","source":"openalex","abstract":"BACKGROUND: Bone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM. METHODS: Two reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China Biology Medicine (CBM), from their inception to December 2024. This search focused on studies that developed and/or validated AI techniques for detecting BM in magnetic resonance imaging (MRI) or computed tomography (CT). A hierarchical model was used in the meta-analysis to calculate diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC), specificity (SP), and pooled sensitivity (SE). The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), while the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis-artificial intelligence (TRIPOD-AI) was employed for evaluating the quality of evidence. RESULT: This review covered 20 articles, among them, 16 studies were included in the meta-analysis. The results revealed a pooled SE of 0.88 (0.82-0.92), a pooled SP of 0.89 (0.84-0.93), a pooled AUC of 0.95 (0.92-0.96), PLR of 8.1 (5.57-11.80), NLR of 0.14 (0.09-0.21) and DOR of 58 (31-109). When focusing on imaging algorithms. Based on ML, a pooled SE of 0.88 (0.77-0.92), SP 0.88 (0.82-0.92), and AUC 0.93 (0.91-0.95). Based on DL, a pooled SE of 0.89 (0.81-0.95), SP 0.89 (0.81-0.94), and AUC 0.95 (0.93-0.97). CONCLUSION: This meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.","url":"https://doi.org/10.1186/s12885-025-13631-0","authors":["Huimin Tao","Hui Xu","Zhi Hong Zhang","Rongrong Zhu","Ping Wang","Sheng Zhou","Kehu Yang"],"tags":["Surgical oncology","Meta-analysis","Medicine","MEDLINE","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-18","doi":"https://doi.org/10.1186/s12885-025-13631-0","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415882781","name":"Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11282-025-00876-5","authors":["Mohmed Isaqali Karobari","Abdul Habeeb Adil","Ankita Mathur","Niher Tabassum Snigdha"],"tags":["Medicine","Oral and maxillofacial surgery","Medical physics","Dentistry","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-04","doi":"https://doi.org/10.1007/s11282-025-00876-5","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W3023935494","name":"Edge Machine Learning for AI-Enabled IoT Devices: A Review","source":"openalex","abstract":"In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors' data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning \"Hello World\".","url":"https://doi.org/10.3390/s20092533","authors":["Massimo Merenda","Carlo Porcaro","Demetrio Iero"],"tags":["Computer science","Edge device","Bottleneck","Artificial intelligence","Enhanced Data Rates for GSM Evolution"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-29","doi":"https://doi.org/10.3390/s20092533","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406555789","name":"Leveraging Artificial Intelligence for Advancing Key Sectors of National Growth and Development","source":"openalex","abstract":"This study explores the transformative potential of artificial intelligence (AI) in national development across key sectors, including economic growth, healthcare, education, infrastructure, and security. Using a qualitative doctrinal research approach, it synthesizes insights from peer-reviewed literature, policy analyses, and case studies to assess AI's contributions and challenges in fostering sustainable progress. Key findings highlight AI’s role in driving economic productivity through automation and innovation while underscoring its impact on the workforce and job market transformation. In healthcare, AI enhances diagnostics, treatment planning, and public health interventions, though concerns about data privacy and algorithmic bias persist. Education systems benefit from AI-enabled personalized learning and skill development, preparing future-ready workforces. Additionally, AI supports sustainable infrastructure through smart city initiatives, improving resource management and urban planning. Despite these advancements, the study identifies challenges such as ethical dilemmas, digital divides, and governance gaps that hinder equitable AI adoption. Recommendations include establishing transparent regulatory frameworks, fostering international collaboration, and investing in digital literacy to ensure inclusive growth. By addressing these issues, AI can become a cornerstone of national development, contributing to the United Nations' Sustainable Development Goals. This research underscores the importance of balanced policies and proactive governance to harness AI's benefits while mitigating associated risks.","url":"https://doi.org/10.56557/ajocr/2025/v10i19056","authors":["Olusola Olabisi Ogunseye","Ola-Dapo Ajayi","Adetutu Fabusoro","Amina Oje Abba","Benjamin Adepoju"],"tags":["Key (lock)","Computer science","Process management","Business","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-18","doi":"https://doi.org/10.56557/ajocr/2025/v10i19056","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408163217","name":"“We know what we are doing”: the politics and trends in artificial intelligence policies in Africa","source":"openalex","abstract":"In the last decade, several actors have encouraged African countries to establish standards, policies and strategies that maximise the benefits of artificial intelligence (AI) and reduce risks. African countries appear to be adopting this regulatory path, yet their motivations and political contexts for actively engaging in AI policies vary, as do the values, principles and ethical issues woven into these policies. With qualitative evidence from Rwanda and Ghana, the paper explores the complex interplay of politics, power and local ecosystems in policy development on the continent. It unpacks the strategies of mobilising knowledge through stakeholder engagements, agenda setting and valid public and political engagements that lead to the final AI policy. A comparative analysis of the policies in the two countries finds that while reproducing identical initiatives, there are differences in AI vision, practicality and data sovereignty based on political, economic and historical contexts.","url":"https://doi.org/10.1080/00083968.2025.2456619","authors":["Thompson Gyedu Kwarkye"],"tags":["Politics","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-05","doi":"https://doi.org/10.1080/00083968.2025.2456619","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414585996","name":"Enhancing Customer Engagement Through Artificial Intelligence Authenticity","source":"openalex","abstract":"ABSTRACT Given the limited research on the factors and mechanisms underlying artificial intelligence (AI) authenticity, we examine its use in fostering breakthrough knowledge and enhancing customer engagement. We devised a robust model grounded in mind perception and social exchange theories, with a focus on the outcomes of AI authenticity. Tested across 452 virtual health home stations, the findings reveal that both performance expectation and effort expectation serve as mediators between AI authenticity and customer engagement. This research provides managers with comprehensive insights into the defining attributes and operational mechanics of AI authenticity, thereby highlighting its critical importance in boosting customer engagement.","url":"https://doi.org/10.1111/jpim.70008","authors":["Pantea Foroudi","Matthew J. Robson","Reza Marvi","Stavroula Spyropoulou"],"tags":["Customer engagement","Knowledge management","Perception","Customer experience","Boosting (machine learning)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-28","doi":"https://doi.org/10.1111/jpim.70008","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413766334","name":"Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach","source":"openalex","abstract":"Artificial intelligence (AI) is globally transforming industries, demanding AI literacy for future Science, Technology, Engineering and Mathematics (STEM) professionals. Ghana is adapting its educational and technological landscape to meet this need. This study investigates factors influencing Ghanaian STEM students’ intentions to learn AI, providing insights for educational policy. A descriptive cross-sectional survey of 233 AI-familiar STEM students examined subjective norm, facilitating conditions, self-efficacy, perceived usefulness, perceived social good, AI anxiety, AI literacy, and career relevance. Using partial least squares structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA), the analysis revealed multiple combinations of individual and contextual factors that drive students’ intentions to learn AI. Results indicate that subjective norm, facilitating conditions, self-efficacy, social good, AI literacy, and career relevance positively influence AI learning intentions. Also, AI anxiety negatively influence AI learning intentions. fsQCA revealed six configurations for high intention. Overall, individual and contextual factors shape Ghanaian STEM students’ AI learning intentions. Fostering AI literacy and career relevance boosts motivation. We recommend integrating AI literacy into STEM curriculum and increasing AI resource access as a national policy priority.","url":"https://doi.org/10.1007/s44163-025-00466-8","authors":["Might Kojo Abreh","Francis Arthur","Freda Awonakie Akwetey","Sharon Abam Nortey"],"tags":["Artificial intelligence","Psychology","Structural equation modeling","Computer science","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-27","doi":"https://doi.org/10.1007/s44163-025-00466-8","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406703007","name":"Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare","source":"openalex","abstract":"Background and Aim: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy. This review aims to assess the effectiveness of AI in pediatric epilepsy detection while considering the ethical implications surrounding its implementation. Methodology: A comprehensive systematic review was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, and Medline. Search terms encompassed \"pediatric epilepsy,\" \"artificial intelligence,\" \"machine learning,\" \"ethical considerations,\" and \"data security.\" Publications from the past decade were scrutinized for methodological rigor, with a focus on studies evaluating AI's efficacy in pediatric epilepsy detection and management. Results: AI systems have demonstrated strong potential in diagnosing and monitoring pediatric epilepsy, often matching clinical accuracy. For example, AI-driven decision support achieved 93.4% accuracy in diagnosis, closely aligning with expert assessments. Specific methods, like EEG-based AI for detecting interictal discharges, showed high specificity (93.33%-96.67%) and sensitivity (76.67%-93.33%), while neuroimaging approaches using rs-fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN-LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. Non-EEG sensor-based methods effectively identified nocturnal seizures, offering promising support for pediatric care. However, ethical considerations around privacy, data security, and model bias remain crucial for responsible AI integration. Conclusion: While AI holds immense potential to enhance pediatric epilepsy management, ethical considerations surrounding transparency, fairness, and data security must be rigorously addressed. Collaborative efforts among stakeholders are imperative to navigate these ethical challenges effectively, ensuring responsible AI integration and optimizing patient outcomes in pediatric epilepsy care.","url":"https://doi.org/10.1002/hsr2.70372","authors":["Marina Ramzy Mourid","Hamza Irfan","Malik Olatunde Oduoye"],"tags":["Epilepsy","Pediatric epilepsy","Artificial intelligence","Scopus","Neuroimaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1002/hsr2.70372","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408002164","name":"Plant leaf disease detection and classification using artificial intelligence techniques: a review","source":"openalex","abstract":"Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.","url":"https://doi.org/10.11591/ijeecs.v38.i2.pp1308-1323","authors":["R Kusuma","R. Rajkumar"],"tags":["Artificial intelligence","Computer science","Plant disease","Machine learning","Pattern recognition (psychology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-27","doi":"https://doi.org/10.11591/ijeecs.v38.i2.pp1308-1323","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4388974973","name":"Quality of erectile dysfunction information from ChatGPT and other artificial intelligence chatbots","source":"openalex","abstract":"Table S1 Inputs into AI chatbots. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.","url":"https://doi.org/10.1111/bju.16209","authors":["Alexander Pan","David Musheyev","Stacy Loeb","Abdo Kabarriti"],"tags":["Erectile dysfunction","Psychology","Quality (philosophy)","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-24","doi":"https://doi.org/10.1111/bju.16209","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4402317328","name":"From Theory to Practice: A Holistic Study of the Application of Artificial Intelligence Methods and Techniques in Higher Education and Science","source":"openalex","abstract":"This study endeavors to conduct an exhaustive analysis of the integration of artificial intelligence (AI) into educational and scientific practices, and to elucidate potential pathways for progress in this domain. It involves reflecting on the impact of AI across various education domains, the advancement of scientific methodologies and discoveries, and the broader societal development. With the help of a systematic review of the relevant literature, examples, and trends of the application of AI in education and science are studied, emphasizing their methodological and conceptual basis. The qualitative approach of this study is based on a systematic analytical review of academic publications, with an attempt to identify key topics and trends in the integration of AI in the fields of education and science. The critical analysis of relevant research assesses the reliability of the presented evidence and applied research methods, and examines the differences and convergence of the approaches of different authors. This methodological approach allows a more profound analysis of AI's impact, while also exploring AI as an advanced research methodology and analyzing various perspectives and contributions of authors within the realms of education and science. The results demonstrate that AI integration significantly contributes to improving educational processes, fostering student creativity, and enhancing scientific practices. Furthermore, the study identifies research gaps, emphasizing the need to explore ethical implications, long-term impacts, and inclusive models of AI. Based on these findings, further studies employing longitudinal/ experimental methods and large dataset analyses are recommended. The findings are expected to advance research by deepening our understanding of the complex interactions between artificial intelligence, education, and science.","url":"https://doi.org/10.21554/hrr.092406","authors":["Suada A. Džogović","Blagojka Zdravkovska-Adamova","Harun Serpil"],"tags":["Artificial intelligence","Computer science","Psychology","Management science","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.21554/hrr.092406","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412391012","name":"Interaction, Artificial Intelligence, and Motivation in Children’s Speech Learning and Rehabilitation Through Digital Games: A Systematic Literature Review","source":"openalex","abstract":"The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments.","url":"https://doi.org/10.3390/info16070599","authors":["Chra Abdoulqadir","Fernando Loizides"],"tags":["Psychology","Cognitive psychology","Human–computer interaction","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-12","doi":"https://doi.org/10.3390/info16070599","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410517780","name":"Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation","source":"openalex","abstract":"BACKGROUND: Oral epithelial dysplasia (OED) is a potentially malignant histopathological diagnosis given to lesions of the oral cavity that are at risk of progression to malignancy. Manual grading of OED is subject to substantial variability and does not reliably predict prognosis, potentially resulting in sub-optimal treatment decisions. METHOD: We developed a Transformer-based artificial intelligence (AI) pipeline for the prediction of malignant transformation from whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained OED tissue slides, named ODYN (Oral Dysplasia Network). ODYN can simultaneously classify OED and assign a predictive score (ODYN-score) to quantify the risk of malignant transformation. The model was trained on a large cohort using three different scanners (Sheffield, 358 OED WSIs, 105 control WSIs) and externally validated on cases from three independent centres (Birmingham and Belfast, UK, and Piracicaba, Brazil; 108 OED WSIs). RESULTS: Model testing yielded an F1-score of 0.96 for classification of dysplastic vs non-dysplastic slides, and an AUROC of 0.73 for malignancy prediction, gaining comparable results to clinical grading systems. CONCLUSIONS: With further large-scale prospective validation, ODYN promises to offer an objective and reliable solution for assessing OED cases, ultimately improving early detection and treatment of oral cancer.","url":"https://doi.org/10.1038/s43856-025-00873-z","authors":["Adam Shephard","Hanya Mahmood","Shan E Ahmed Raza","Anna Luíza Damaceno Araújo","Alan Roger Santos‐Silva","Márcio Ajudarte Lopes","Pablo Agustín Vargas","Kris McCombe","Stephanie G. Craig","Jacqueline A. James","Jill Brooks","Paul Nankivell","Hisham Mehanna","Syed Ali Khurram","Nasir M. Rajpoot"],"tags":["Medicine","Grading (engineering)","Malignancy","Epithelial dysplasia","Dysplasia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-20","doi":"https://doi.org/10.1038/s43856-025-00873-z","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411175038","name":"Edge Artificial Intelligence Device in Real-Time Endoscopy for the Classification of Colonic Neoplasms","source":"openalex","abstract":"Objective: Although prior research developed an artificial intelligence (AI)-based classification system predicting colorectal lesion histology, the heavy computational demands limited its practical application. Recent advancements in medical AI emphasize decentralized architectures using edge computing devices, enhancing accessibility and real-time performance. This study aims to construct and evaluate a deep learning-based colonoscopy image classification model for automatic histologic categorization for real-time use on edge computing hardware. Design: We retrospectively collected 2418 colonoscopic images, subsequently dividing them into training, validation, and internal test datasets at a ratio of 8:1:1. Primary evaluation metrics included (1) classification accuracy across four histologic categories (advanced colorectal cancer, early cancer/high-grade dysplasia, tubular adenoma, and nonneoplasm) and (2) binary classification accuracy differentiating neoplastic from nonneoplastic lesions. Additionally, an external test was conducted using an independent dataset of 269 colonoscopic images. Results: For the internal-test dataset, the model achieved an accuracy of 83.5% (95% confidence interval: 78.8–88.2%) for the four-category classification. In binary classification (neoplasm vs. nonneoplasm), accuracy improved significantly to 94.6% (91.8–97.4%). The external test demonstrated an accuracy of 82.9% (78.4–87.4%) in the four-category task and a notably higher accuracy of 95.5% (93.0–98.0%) for binary classification. The inference speed of lesion classification was notably rapid, ranging from 2–3 ms/frame in GPU mode to 5–6 ms/frame in CPU mode. During real-time colonoscopy examinations, expert endoscopists reported no noticeable latency or interference from AI model integration. Conclusions: This study successfully demonstrates the feasibility of a deep learning-powered colonoscopy image classification system designed for the rapid, real-time histologic categorization of colorectal lesions on edge computing platforms. This study highlights how nature-inspired frameworks can improve the diagnostic capacities of medical AI systems by aligning technological improvements with biomimetic concepts.","url":"https://doi.org/10.3390/diagnostics15121478","authors":["Eun Jeong Gong","Chang Seok Bang"],"tags":["Endoscopy","Computer science","Artificial intelligence","Medicine","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-10","doi":"https://doi.org/10.3390/diagnostics15121478","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414320896","name":"Artificial intelligence accelerates the interpretation of measurable residual B lymphoblastic leukemia by flow cytometry","source":"openalex","abstract":"ABSTRACT: Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.","url":"https://doi.org/10.1182/bloodadvances.2025016126","authors":["Jansen N. Seheult","Gregory E. Otteson","Michael Timm","Matthew J. Weybright","Min Shi","Horatiu Olteanu","Dragan Jevremović","Chuan Chen","April Chiu","Pedro Horna"],"tags":["Minimal residual disease","Artificial intelligence","Residual","Lymphoblastic Leukemia","Flow cytometry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-18","doi":"https://doi.org/10.1182/bloodadvances.2025016126","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411194802","name":"Enhancing Dental Students' History‐Taking Skills With a Generative Artificial Intelligence Chatbot","source":"openalex","abstract":"PURPOSE: Accurate history-taking is crucial for diagnosis and treatment planning in dentistry yet there is insufficient time in pre-clinical years to practice this essential skill. This study evaluated dental student motivations and enjoyment using a generative artificial intelligence (AI) chatbot to practice these skills. METHODS: In 2024, first- and second-year Doctor of Dental Medicine students trialed a virtual patient (VP) chatbot using generative AI based on GPT-4 and then completed surveys to assess expectations, motivations, and feedback on the chatbot and compare their experience with in-person learning. RESULTS: A total of 31 students participated in the trial. The cohort saw a significant improvement in perceived competence after using the chatbot. Second-year students found more value in the chatbot than first-year students. While the students felt the chatbot increased their participation and provided more practice opportunities, most found it less interesting compared to in-person teaching. Overall, students were supportive of the integration of the chatbot into the curriculum. CONCLUSION: Educational chatbots can act as VPs, allowing users to practice patient history-taking. This chatbot was seen as a beneficial, supplementary resource for pre-clinical dental students, allowing them to supplement the currently limited opportunities to practice this skill.","url":"https://doi.org/10.1002/jdd.13952","authors":["Aidan J. Or","Smitha Sukumar","Andrew Ma","Dong Ang","Max Liu","Helen E. Ritchie","Babak Sarrafpour"],"tags":["Chatbot","Generative grammar","Computer science","Psychology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-11","doi":"https://doi.org/10.1002/jdd.13952","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4404583323","name":"Disruptive technologies in the university curriculum: use of artificial intelligence","source":"openalex","abstract":"The so-called “digital era” is synonymous with the transformation of every aspect of human life. This transformation is given by the development of new technologies that modify the way humans communicate and cooperate. Now, it can be said that formal education, compared to other economic sectors, is lagging in the integration of novel technologies in higher education curricula, especially in terms of implementing artificial intelligence (AI). The objective of this research was to conduct a systematic review of the scientific production related to the incorporation of artificial intelligence as a disruptive technology in the university curriculum. It was carried out using a qualitative approach based on a systematic review. The review showed a greater scientific production between 2022 and 2023; it was also evidenced that, as a technology, artificial intelligence has become a disruptive element thanks to its ability to change the role and work performed by teachers, students, and educational institutions. Consequently, the university of the future urgently needs to plan, design, develop, and implement curricula that include artificial intelligence, with the purpose of training better professionals, capable of acting effectively in a technological and productive environment.","url":"https://doi.org/10.11591/ijere.v14i1.30450","authors":["Enma Sofía Reeves Huapaya","Gilmer Lazo Chucos","Efrain Parillo Sosa","Melva Iparraguirre Meza"],"tags":["Curriculum","Lagging","Plan (archaeology)","Engineering ethics","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-21","doi":"https://doi.org/10.11591/ijere.v14i1.30450","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410028173","name":"Small language models learn enhanced reasoning skills from medical textbooks","source":"openalex","abstract":"Small language models (SLM) offer promise for medical applications by addressing the privacy and hardware constraints of large language models; however, their limited parameters (often fewer than ten billion) hinder multi-step reasoning for complex medical tasks. This study presents Meerkat, a new family of medical SLMs designed to be lightweight while enhancing reasoning capabilities. We begin by designing an effective and efficient training method. This involves extracting high-quality chain-of-thought reasoning paths from 18 medical textbooks, which are then combined with diverse instruction-following datasets within the medical domain, totaling 441K training examples. Fine-tuning was conducted on open-source SLMs using this curated dataset. Our Meerkat-7B and Meerkat-8B models outperformed their counterparts by 22.3% and 10.6% across six exam datasets, respectively. They also improved scores on the NEJM Case Challenge from 7 to 16 and from 13 to 20, surpassing the human score of 13.7. Additionally, they demonstrated superiority in expert evaluations, excelling in all metrics-completeness, factuality, clarity, and logical consistency-of reasoning abilities.","url":"https://doi.org/10.1038/s41746-025-01653-8","authors":["Hyunjae Kim","Hyeon Kyeong Hwang","Ji-Woo Lee","Sihyeon Park","Dain Kim","Taewhoo Lee","Chanwoong Yoon","Jiwoong Sohn","Jungwoo Park","Olga Reykhart","Thomas Fetherston","Donghee Choi","Soo Heon Kwak","Qingyu Chen","Jaewoo Kang"],"tags":["Computer science","Mathematics education","Natural language processing","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.1038/s41746-025-01653-8","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W3018601113","name":"A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation","source":"openalex","abstract":"BACKGROUND: Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d'épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. OBJECTIVE: This article investigates the application of deep neural network methods to coding underlying causes of death. METHODS: The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject's age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject's underlying cause of death was then formulated as a predictive modelling problem. A deep neural network-based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach's superiority was assessed via bootstrap. RESULTS: The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. CONCLUSIONS: This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.","url":"https://doi.org/10.2196/17125","authors":["Louis Falissard","Claire Morgand","Sylvie Roussel","Claire Imbaud","Walid Ghosn","Karim Bounebache","Grégoire Rey"],"tags":["Computer science","Artificial intelligence","Artificial neural network","Machine learning","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-28","doi":"https://doi.org/10.2196/17125","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414142129","name":"Accuracy of Artificial Intelligence-Designed Dental Crowns: A Scoping Review of In-Vitro Studies","source":"openalex","abstract":"Artificial intelligence (AI), particularly deep learning, is increasingly applied in dental prosthetics, offering new approaches to dental crown design. This scoping review aimed to summarize current evidence on AI-assisted crown design, focusing on algorithm types, dataset characteristics, and evaluation methods. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore was conducted in February 2025, covering studies published between January 2010 and February 2025. Ten studies met the inclusion criteria, of which four developed custom AI models—mainly based on generative adversarial networks—while six evaluated commercially available software. All studies used digitized dental models obtained from scanned stone casts or intraoral scans, and dataset sizes varied widely. Morphological accuracy was the most frequently reported outcome, assessed in six studies, followed by design time and occlusal contact evaluation. While most AI-generated crowns demonstrated clinically acceptable precision, only four studies fabricated physical crowns and none conducted in vivo validation. These findings suggest that AI-assisted crown design holds promise for improving anatomical accuracy and workflow efficiency, but methodological heterogeneity and the lack of clinical validation highlight the need for standardized evaluation protocols and further in vivo studies.","url":"https://doi.org/10.3390/app15189866","authors":["Hyun-Jun Kong","YuLee Kim"],"tags":["Workflow","Crown (dentistry)","Computer science","Dentistry","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-09","doi":"https://doi.org/10.3390/app15189866","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414510983","name":"AI policy in healthcare: a checklist-based methodology for structured implementation","source":"openalex","abstract":"INTRODUCTION: Artificial Intelligence (AI) is transforming anaesthesia and intensive care medicine, enhancing diagnostic precision, workflow efficiency, and patient safety. However, deploying AI in high-acuity environments involves regulatory, ethical, and operational challenges. The European Union Artificial Intelligence Act (AI Act), effective 2025, imposes binding obligations on healthcare organizations, creating an urgent need for structured, governance-focused AI policies. This work presents a checklist-based methodology for responsible, safe, ethical, and regulation-aligned AI adoption in clinical units. THE NEED FOR A METHODOLOGY TO DEVELOP AN AI POLICY: Effective AI policies must ensure transparency, safety, fairness, and regulatory compliance while remaining adaptable to rapid technological and legislative changes. The proposed methodology employs a domain-specific checklist to generate critical evaluative questions, enabling healthcare professionals to systematically assess AI systems' appropriateness, reliability, and legal implications without relying on rigid, quickly outdated prescriptive rules. THE AI ACT AND ITS RELEVANCE: Regulation (EU) 2024/1689 establishes the first comprehensive AI legal framework, introducing risk-based classification, imposing stringent requirements for high-risk AI, often including medical devices. Compliance obligations extend to both AI-system providers and deployers, making operational compliance instruments and AI literacy programmes essential for lawful implementation. AI LITERACY: OBLIGATION AND PLANNING: From February 2025, the AI Act mandates AI literacy for all personnel interacting with AI-systems. Training should cover baseline competencies for all staff, advanced modules for specialists, continuous professional development, and integration of ethical, legal, and governance principles. Competency acquisition and updates must be systematically documented to meet institutional and EU compliance standards. OPERATIONAL CHECKLIST FOR THE ADOPTION OF AI POLICY: The checklist has two integrated domains: clinical and technical validation, including evidence-based performance assessment, real-world validation, MDR compliance, GDPR adherence, and post-deployment monitoring; and governance and compliance, covering AI Act conformity, organizational accountability, decision traceability, human oversight, AI literacy, and structured audit and update mechanisms. FUTURE PERSPECTIVES: The checklist methodology offers a scalable, adaptable, regulation-ready framework for AI policy development. By embedding legal compliance, clinical safety, governance, and continuous staff training, it supports sustainable AI integration. Future updates will incorporate regulatory changes, real-world feedback, and impact metrics, enhancing AI's contribution to quality, safety, and equity in patient care.","url":"https://doi.org/10.1186/s44158-025-00278-3","authors":["Elena Bignami","Luigino Jalale Darhour","Gabriele Franco","Matteo Guarnieri","Valentina Bellini"],"tags":["Computer science","Checklist","Equity (law)","Process management","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-25","doi":"https://doi.org/10.1186/s44158-025-00278-3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411494854","name":"Current and future applications of artificial intelligence in lung cancer and mesothelioma","source":"openalex","abstract":"BACKGROUND: Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provision of high-quality care difficult. In this context, artificial intelligence (AI) offers a range of assistive/automated functions that can potentially enhance clinical decision-making, while reducing inequality and pathway delay. AIMS: In this state-of-the-art narrative review, we synthesise evidence on this topic, focusing particularly on tools that ingest routine pathology and radiology images. We summarise the strengths and weaknesses of AI applied to common multidisciplinary team (MDT) functions, including histological diagnosis, therapeutic response prediction, radiological detection and quantification, and survival estimation. We also review emerging methods capable of generating novel biological insights and current barriers to implementation, including access to high-quality training data and suitable regulatory and technical infrastructure. NARRATIVE: Neural networks trained on pathology images have proven utility in histological classification, prognostication, response prediction and survival. Self-supervised models can also generate new insights into biological features responsible for adverse outcomes. Radiology applications include lung nodule tools, which offer critical pathway support for imminent lung cancer screening and urgent referrals. Tumour segmentation AI offers particular advantages in mesothelioma, where response assessment and volumetric staging are difficult using human readers due to tumour size and morphological complexity. AI is also critical for radiogenomics, permitting effective integration of molecular and radiomic features for discovery of non-invasive markers for molecular subtyping and enhanced stratification. CONCLUSIONS: AI solutions offer considerable potential benefits across the MDT, particularly in repetitive or time-consuming tasks based on pathology and radiology images. Effective leveraging of this technology is critical for lung cancer screening and efficient delivery of increasingly complex diagnostic and predictive MDT functions. Future AI research should involve transparent and interpretable outputs that assist in explaining the basis of AI-supported decision making.","url":"https://doi.org/10.1136/thorax-2024-222054","authors":["Joshua Roche","Farzaneh Seyedshahi","Kai Rakovic","Akari Win Thu","John Le Quesne","Kevin G. Blyth"],"tags":["Radiogenomics","Medicine","Context (archaeology)","Mesothelioma","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-20","doi":"https://doi.org/10.1136/thorax-2024-222054","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415106116","name":"Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers","source":"openalex","abstract":"Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools.","url":"https://doi.org/10.3390/bioengineering12101102","authors":["Giovanni Canino","Assunta Di Costanzo","Nadia Salerno","Isabella Leo","Mario Cannataro","Pietro Hiram Guzzi","Pierangelo Veltri","Sabato Sorrentino","Salvatore De Rosa","Daniele Torella"],"tags":["Artificial intelligence","Computer science","Machine learning","Cardiac electrophysiology","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-13","doi":"https://doi.org/10.3390/bioengineering12101102","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4280518754","name":"Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review","source":"openalex","abstract":"PURPOSE: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.","url":"https://doi.org/10.3390/diagnostics12051249","authors":["Narendra N. Khanna","Mahesh Maindarkar","Ajit Kumar Saxena","Puneet Ahluwalia","Sudip Paul","Saurabh Kumar Srivastava","Elisa Cuadrado‐Godia","Aditya Sharma","Tomaž Omerzu","Luca Saba","Sophie Mavrogeni","Monika Turk","John R. Laird","George D. Kitas","Mostafa Fatemi","Al B. Barqawi","Martin Miner","Inder M. Singh","Amer M. Johri","Mannudeep Kalra","Vikas Agarwal","Kosmas I. Paraskevas","Jagjit S. Teji","Mostafa M. Fouda","Gyan Pareek","Jasjit S. Suri"],"tags":["Medicine","Stroke (engine)","Erectile dysfunction","Cardiology","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-17","doi":"https://doi.org/10.3390/diagnostics12051249","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4400279985","name":"Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works","source":"openalex","abstract":"Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery”, and “deep learning maxillofacial surgery”. Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords “planning in maxillofacial surgery” and “planning in orthognathic surgery”, the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014–2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.","url":"https://doi.org/10.3390/bioengineering11070679","authors":["Ladislav Czakó","Barbora Šufliarsky","K Šimko","Marek Soviš","Ivana Vidová","Julia Farska","Michaela Lifková","Tomas Hamar","Branislav Gális"],"tags":["Artificial intelligence","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.3390/bioengineering11070679","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4404866020","name":"Artificial intelligence literacy scale: A study of reliability and validity in Turkish university students","source":"openalex","abstract":"This study aims to adapt to Turkish the \"Scale for the assessment of non-experts: AI literacy\" developed by Laupichler et al. (2023a). The scale consists of 31 items with three sub-dimensions: technical understanding, critical thinking, and practical applications. The data required for the validity and reliability study of the scale were collected from 642 undergraduate and graduate students studying in different departments of a state university in the fall semester of the 2023-2024 academic year. First, CFA was applied to the data according to the factor structure in the original scale, but as acceptable fit values could not be obtained because of the analysis, exploratory factor analysis was performed. In the reliability analysis of the factor structure determined by EFA, KMO was calculated as =0.948. It was determined that the scale items were collected in three factors and explained 61.1% of the total variance (\"critical thinking\" is 25.8%, \"technical knowledge\" is 25.2%, and \"practical applications\" explains 10.2% of the total variance). As a result of EFA, it was seen that the sub-dimensions of some of the items in the original scale had changed, and since the factor load values of the three items were very close to each other, they were removed from the scale. Because of CFA, which was conducted to evaluate whether the data supported the hypothesized relationships between the measured variables, Cronbach’s alpha value was found to be 0.90. As a result of the CFA analysis conducted with the 3 sub-dimensions and 28 items in the scale, the Chi-square value (X²=2.85; df=345, N=317, p< .001), which is the fit index of the model, has a good fit and is significant, SRMR = 0.0545and RMSEA = 0.077 values and fit indices and the model has an acceptable fit.","url":"https://doi.org/10.53850/joltida.1440845","authors":["Arzu Devecı Topal","Asiye Toker Gökçe","Canan Dilek Eren","Aynur Kolburan Geçer"],"tags":["Turkish","Scale (ratio)","Reliability (semiconductor)","Validity","Literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-29","doi":"https://doi.org/10.53850/joltida.1440845","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414485818","name":"Artificial intelligence in healthcare: rethinking doctor-patient relationship in megacities","source":"openalex","abstract":"Introduction: Artificial intelligence has been extensively applied in healthcare, offering significant potential to improve the quality of medical services. However, it also introduces critical challenges, such as privacy infringement, algorithmic discrimination, and ambiguous liability. The integration of artificial intelligence inevitably influences the doctor-patient relationship, which is pronounced in megacities. This study aims to explore the application of artificial intelligence in megacity healthcare system, exam the multidimensional transformation of the doctor-patient relationship and propose new governance frameworks. Methods: This study examines how artificial intelligence can effectively address systemic challenges within megacity healthcare systems while leveraging technological and institutional advantages to maximize its benefits, with a focus on Beijing as a primary case. Results: The integration of artificial intelligence inevitably influences the doctor-patient relationship, reducing information asymmetry, enhancing patient autonomy, and transforming the traditional doctor-patient dualistic interaction structure into a doctor-artificial intelligence-patient triad interaction structure. These effects are pronounced in megacities, presenting new challenges including crisis of trust, intensified disputes, and emotional and communication distance. Discussion: Given that the integration of artificial intelligence into healthcare is inevitable, especially for megacities like Beijing, proactive governance is essential. This includes institutionalizing the triad interaction model, deepening the integration of artificial intelligence in healthcare by leveraging the advantages of megacities, and establishing regulatory frameworks to mitigate risks while harnessing potential.","url":"https://doi.org/10.3389/frhs.2025.1694139","authors":["Qi Chen"],"tags":["Megacity","Corporate governance","Artificial intelligence","Knowledge management","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-24","doi":"https://doi.org/10.3389/frhs.2025.1694139","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7131836311","name":"Artificial intelligence to investigate metabolomics data for precision medicine","source":"openalex","abstract":"BACKGROUND: Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic targets by analyzing metabolic profiles. In analyzing these profiles, traditional bioinformatic and statistical approaches, while valuable, often struggle to process high-dimensional and nonlinear metabolic data, lacking the sensitivity and adaptability that artificial intelligence (AI) and machine learning (ML) techniques provide. The integration of AI/ML has greatly enhanced the metabolomics field, enabling biomarker identification, disease prediction, and classification of metabolic patterns at an unprecedented level. AIM OF REVIEW: This study analyses and compares the scientific goals, methodologies, datasets, and sources of AI/ML approaches applied to metabolomic data, as well as assessing their implications in precision medicine. We systematically reviewed recent advancements in AI/ML applications to metabolomic data, focusing on peer-reviewed research indexed in PubMed. Significant number of studies were analyzed, covering diseases such as cancer, cardiovascular diseases, and diabetes. Our results showed that the most used AI/ML techniques were SVM, RF, Gradient Boosting, and Logistic Regression, highlighting their effectiveness in processing complex metabolic data. Despite these advancements, key challenges persist in AI/ML applications to metabolomics data, including small cohort sizes, data heterogeneity, and the need for improved model interpretability, and these challenges must be considered for future use. KEY SCIENTIFIC CONCEPTS OF REVIEW: Ultimately, our findings underscore the transformative potential of AI/ML in metabolomics and its critical role in advancing precision medicine by uncovering novel metabolic pathways, improving treatment strategies, and enabling the earlier diagnosis of diseases through predictive metabolic profiling.","url":"https://doi.org/10.1007/s11306-026-02401-z","authors":["Antony Shenouda","Sahana Senthilkumar","Youssef Mourad","Joy Xie","Elizabeth Peker","Saman Zeeshan","Zeeshan Ahmed"],"tags":["Precision medicine","Metabolomics","Transformative learning","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-27","doi":"https://doi.org/10.1007/s11306-026-02401-z","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4387110043","name":"Actionable Artificial Intelligence for the Future of Production","source":"openalex","abstract":"Abstract The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only a few of the challenges we tackle in this chapter. We outline the communication and data exchange within the World Wide Lab (WWL) and autonomous agents that query the WWL which is built on the Digital Shadows (DS). We categorize our approaches into machine level, process level, and overarching principles. This chapter surveys the interdisciplinary work done in each category, presents different applications of the different approaches, and offers actionable items and guidelines for future work.The machine level handles the robots and machines used for production and their interactions with the human workers. It covers low-level robot control and optimization through gray-box models, task-specific motion planning, and optimization through reinforcement learning. In this level, we also examine quality assurance through nonintrusive real-time quality monitoring, defect recognition, and quality prediction. Work on this level also handles confidence, verification, and validation of re-configurable processes and reactive, modular, transparent process models. The process level handles the product life cycle, interoperability, and analysis and optimization of production processes, which is overall attained by analyzing process data and event logs to detect and eliminate bottlenecks and learn new process models. Moreover, this level presents a communication channel between human workers and processes by extracting and formalizing human knowledge into ontology and providing a decision support by reasoning over this information. Overarching principles present a toolbox of omnipresent approaches for data collection, analysis, augmentation, and management, as well as the visualization and explanation of black-box models.","url":"https://doi.org/10.1007/978-3-030-98062-7_4-2","authors":["Mohamed Behery","Philipp Brauner","Hans Aoyang Zhou","Merih Seran Uysal","Владимир Самсонов","Martin Bellgardt","Florian Brillowski","Tobias Brockhoff","Anahita Farhang Ghahfarokhi","Lars Gleim","Leon Gorißen","Marco Grochowski","Thomas Henn","Elisa Iacomini","Thomas Käster","István Koren","Martin Liebenberg","Leon Reinsch","Liam Tirpitz","Minh Trinh","Andrés Felipe Posada-Moreno","Luca Liehner","Thomas Schemmer","Luisa Vervier","Marcus Völker","Philipp Walderich","Song Zhang","Christian Brecher","Robert Schmitt","Stefan Decker","Thomas Gries","Constantin Häfner","Michaël Herty","Matthias Jarke","Stefan Kowalewski","Torsten Kuhlen","Johannes Henrich Schleifenbaum","Sebastian Trimpe","Wil M. P. van der Aalst","Martina Ziefle","Gerhard Lakemeyer"],"tags":["Computer science","Interoperability","Process (computing)","Artificial intelligence","Modular design"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1007/978-3-030-98062-7_4-2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409142663","name":"Validation of artificial intelligence spirometry diagnostic support software in primary care: a blinded diagnostic accuracy study","source":"openalex","abstract":"Objective and design: The objective of the present study was to assess the discriminative accuracy of artificial intelligence (AI) software to identify COPD and other chronic respiratory diseases from primary care spirometry. This was a diagnostic study with blinded analysis. Methods: Retrospective hand-held spirometry data from consecutive patients attending primary care clinics in Hillingdon (London, UK) between September 2015 and March 2019 were used. The index diagnosis was the \"preferred\" diagnosis determined by AI software (highest probability) using supervised random-forest machine learning to interpret raw spirometry data and basic demographics. The reference diagnosis was based on the consensus of expert pulmonologists with access to primary and secondary care medical notes and results of relevant investigations. Cross-tabulation of the index test results by the results of the reference standard for COPD and other respiratory disease categories provided the main outcome measures. Results: In this primary care spirometry dataset from 1113 patients, 543 (48.8%) had a reference diagnosis of COPD. AI preferred diagnosis detected 456, achieving a sensitivity of 84.0% (95% CI 80.6-87.0%), specificity of 86.8% (83.8-89.5%), accuracy of 85.4% (83.2-87.5%) with area under curve (AUC) of 0.914 (0.896-0.930). AI preferred diagnosis identified 187 out of 249 patients with reference diagnosis of interstitial lung disease and 59 out of 107 patients with asthma, with AUCs of 0.900 (0.880-0.916) and 0.814 (0.790-0.836), respectively. Conclusion: AI software achieved high sensitivity and specificity in identifying COPD using spirometry and basic demographic data and may support accurate diagnosis of COPD in primary care. AI software performed less well for other chronic respiratory disease categories.","url":"https://doi.org/10.1183/23120541.00116-2025","authors":["Anthony Paulo Sunjaya","George Edwards","Jennifer Harvey","Karl Sylvester","Joanna Purvis","Matthew Rutter","Joanna Shakespeare","Vicky Moore","Ethaar El-Emir","Gillian Doe","Karolien Van Orshoven","Suhani Patel","Maarten De Vos","Ahmed Elmahy","Benoit Cuyvers","Paul Desbordes","Satesh Sehdev","Rachael A Evans","Michael D. Morgan","Richard Russell","Ian Jarrold","Nannette Spain","Stephanie Taylor","David A. Scott","A Toby Prevost","Nicholas S Hopkinson","Samantha Kon","Marko Topalovic","William D‐C Man"],"tags":["Medicine","Spirometry","Primary care","Medical physics","Diagnostic accuracy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-03","doi":"https://doi.org/10.1183/23120541.00116-2025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416782818","name":"The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery","source":"openalex","abstract":"BACKGROUND: Multimodal artificial intelligence (MMAI) is transforming urological oncology by enabling the seamless integration of diverse data sources, including imaging, clinical records and robotic telemetry to facilitate patient-specific decision-making. METHODS: This narrative review summarizes the current developments, applications, opportunities and risks of multimodal AI systems throughout the entire perioperative process in uro-oncologic surgery. RESULTS: MMAI demonstrates quantifiable benefits across the entire perioperative pathway. Preoperatively, it improves diagnostics and surgical planning via multimodal data fusion. Intraoperatively, AI-assisted systems provide real-time context-based decision support, risk prediction and skill assessment within the operating theater. Postoperatively, MMAI facilitates automated documentation, early complication detection and personalized follow-up. Generative AI further revolutionizes surgical training through adaptive feedback and simulations. However, critical limitations must be addressed, including data bias, the barrier of closed robotic platforms, insufficient model validation, data security issues, hallucinations and ethical concerns regarding liability and transparency. CONCLUSIONS: MMAI significantly enhances the precision, efficiency and patient-centeredness of uro-oncological care. To ensure safe and widespread implementation, resolving the technical and regulatory barriers to real-time integration into robotic platforms is paramount. This must be coupled with standardized quality controls, transparent decision-making processes and responsible integration that fully preserves physician autonomy.","url":"https://doi.org/10.3390/curroncol32120665","authors":["Leonhard Buck","Jakob Kohler","Julian Risch","Reha‐Baris Incesu","Konrad Hügelmann","Marie-Luise Weiß","Oscar Weische","Patricia Schließer","Hans Christoph von Knobloch","Niclas C. Blessin","Thorsten Bach","Jonas Jarczyk","Philipp Nuhn","Severin Rodler"],"tags":["Medicine","Quality (philosophy)","Medical physics","Robotic surgery","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-27","doi":"https://doi.org/10.3390/curroncol32120665","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408087637","name":"Semantic Malware Classification Using Artificial Intelligence Techniques","source":"openalex","abstract":"The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect ... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmes.2025.061080","authors":["Eliel Martins","Ricardo Santana","Juan Ramón Bermejo Higuera","Juan Antonio Sicilia Montalvo","Javier Bermejo Higuera","Diego Piedrahita"],"tags":["Malware","Computer science","Artificial intelligence","Natural language processing","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.32604/cmes.2025.061080","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415165763","name":"Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains","source":"openalex","abstract":"Abstract This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers, business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization.","url":"https://doi.org/10.1007/s10479-025-06843-x","authors":["Shefali Srivastava","Vipulesh Shardeo","Ashish Dwivedi","Sanjoy Kumar Paul"],"tags":["Supply chain","Process management","Computer science","Key (lock)","Robustness (evolution)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-14","doi":"https://doi.org/10.1007/s10479-025-06843-x","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411035580","name":"Natural and artificial intelligence – the psychotechnical agenda of the 21st century","source":"openalex","abstract":"The aim in this paper was to consider some of the fundamental and important differences between natural and artificial intelligence (AI). We are currently amid the third wave of AI technology, and for the first time in human history, it can be said that Large Language Models (LLMs) fulfil the original 1955 definition of AI, stipulating the simulation of human intelligence in a machine. Current AI systems are potentially very useful in many domains, and seem likely to revolutionise the way aspects of human work occur. However, considering their technical limitations, it seems unlikely that current AI will develop exponentially to the equivalent of human level intelligence and beyond. Alternatively, it also seems unlikely that we are heading towards another AI winter. Hence, the future would appear to contain many useful AI tools, but to fully understand and benefit from the third wave of AI we need to be aware that natural and artificial intelligence are in fact very different. Understanding these differences comprises the important psychotechnical agenda of our times. In terms of the Dual Process Theory of human cognition, LLMs are analogous, at best, to system-1 cognitive processing, and it remains unknown how anything approaching system-2 cognition could be incorporated in a machine. In addition, the machine learning that underlies third-wave AI comes with several down sides such as biases drawn from the training data, the propensity to hallucinate, and to produce AI systems that suffer from the black box problem. While many attempts to increase the transparency of AI systems are underway, all third wave AI systems remain black boxes, complicating efforts to establish their safety and reliability, and to fix performance problems when they are identified. Such limitations underscore the importance of retaining a human in the loop, at least in safety critical domains.","url":"https://doi.org/10.1080/29974100.2025.2491445","authors":["Craig S. Webster"],"tags":["Natural (archaeology)","Political science","History","Archaeology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-04","doi":"https://doi.org/10.1080/29974100.2025.2491445","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406243158","name":"Integration of Functional Materials in Photonic and Optoelectronic Technologies for Advanced Medical Diagnostics","source":"openalex","abstract":"Integrating functional materials with photonic and optoelectronic technologies has revolutionized medical diagnostics, enhancing imaging and sensing capabilities. This review provides a comprehensive overview of recent innovations in functional materials, such as quantum dots, perovskites, plasmonic nanomaterials, and organic semiconductors, which have been instrumental in the development of diagnostic devices characterized by high sensitivity, specificity, and resolution. Their unique optical properties enable real-time monitoring of biological processes, advancing early disease detection and personalized treatment. However, challenges such as material stability, reproducibility, scalability, and environmental sustainability remain critical barriers to their clinical translation. Breakthroughs such as green synthesis, continuous flow production, and advanced surface engineering are addressing these limitations, paving the way for next-generation diagnostic tools. This article highlights the transformative potential of interdisciplinary research in overcoming these challenges and emphasizes the importance of sustainable and scalable strategies for harnessing functional materials in medical diagnostics. The ultimate goal is to inspire further innovation in the field, enabling the creation of practical, cost-effective, and environmentally friendly diagnostic solutions.","url":"https://doi.org/10.3390/bios15010038","authors":["Naveen Thanjavur","Laxmi Bugude","Young‐Joon Kim"],"tags":["Nanotechnology","Photonics","Computer science","Systems engineering","Scalability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-10","doi":"https://doi.org/10.3390/bios15010038","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413140583","name":"The “Artificial Intelligence Statistician”: Utilizing Generative Artificial Intelligence to Select an Appropriate Model and Execute Network Meta-Analyses","source":"openalex","abstract":"OBJECTIVES: This exploratory study aimed to develop a large language model (LLM)-based process to automate components of network meta-analysis (NMA), including model selection, analysis, output evaluation, and results interpretation. Automating these tasks with LLMs can enhance efficiency, consistency, and scalability in health economics and outcomes research, while ensuring that analyses adhere to established guidelines required by health technology assessment agencies. Improvements in efficiency and scalability may potentially become relevant as the European Union Health Technology Assessment Regulation comes into force, given anticipated analysis requirements and timelines. METHODS: Using Claude 3.5 Sonnet (V2), a process was designed to automate statistical model selection, NMA output evaluation, and results interpretation based on an \"analysis-ready\" data set. Validation was assessed by replicating examples from the National Institute for Health and Care Excellence Technical Support Document (TSD2), replicating results of non-Decision Support Unit-published NMAs, and generating comprehensive outputs (eg, heterogeneity, inconsistency, and convergence). RESULTS: The automated LLM-based process produced accurate results. Compared with TSD2 examples, differences were minimal, within expectations (given differences in sampling frameworks used), and comparable to those observed between estimates produced by the R vignettes against TSD2. Similar consistency was noted for non-Decision Support Unit-published NMA examples. Additionally, the LLM process generated and interpreted comprehensive NMA outputs. CONCLUSIONS: This exploratory study demonstrates the feasibility of LLMs to automate key components of NMAs, determining the requisite NMA framework based only on input data. Further exploring these capabilities could clarify their role in streamlining NMA workflows.","url":"https://doi.org/10.1016/j.jval.2025.08.001","authors":["Tim Reason","Yunchou Wu","Cheryl Jones","Emma Benbow","Kasper Johannesen","Bill Malcolm"],"tags":["Computer science","Scalability","Consistency (knowledge bases)","Workflow","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-12","doi":"https://doi.org/10.1016/j.jval.2025.08.001","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408905879","name":"Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images","source":"openalex","abstract":"BACKGROUND: Virtual staining is an artificial intelligence-based approach that transforms pathology images between stain types, such as hematoxylin and eosin (H&E) to immunohistochemistry (IHC), providing a tissue-preserving and efficient alternative to traditional IHC staining. However, existing methods for translating H&E to virtual IHC often fail to generate images of sufficient quality for accurately delineating cell nuclei and IHC+ regions. To address these limitations, we introduce VISTA, an artificial intelligence-based virtual staining platform designed to translate H&E into virtual IHC. METHODS: We applied VISTA to identify M2-subtype tumor-associated macrophages (M2-TAMs) in H&E images from 968 patients with HPV+ oropharyngeal squamous cell carcinoma across six institutional cohorts. M2-TAMs are a critical component of the tumor microenvironment, and their increased presence has been linked to poor survival. Co-registered H&E and CD163 + IHC tissue microarrays were used to train (D1, N = 102) and test (D2, N = 50) the VISTA platform. M2-TAM density, defined as the ratio of M2-TAMs to total nuclei, was derived from VISTA-generated CD163 + IHC images and evaluated for prognostic significance in additional training (D3, N = 360) and testing (D4, N = 456) cohorts using biopsy or resection H&E whole slide images. RESULTS: High M2-TAM density was associated with worse overall survival in D4 (p = 0.0152, Hazard Ratio=1.63 [1.1-2.42]). VISTA outperformed existing methods, generating higher-quality virtual CD163 + IHC images in D2, with a Structural Similarity Index of 0.72, a Peak Signal-to-Noise Ratio of 21.5, and a Fréchet Inception Distance of 41.4. Additionally, VISTA demonstrated superior performance in segmenting M2-TAMs in D2 (Dice=0.74). CONCLUSION: These findings establish VISTA as a computational platform for generating virtual IHC and facilitating the discovery of novel biomarkers from H&E images.","url":"https://doi.org/10.1016/j.ejca.2025.115390","authors":["Arpit Aggarwal","Mayukhmala Jana","Amritpal Singh","Tanmoy Dam","Himanshu Maurya","Tilak Pathak","Sandra Oršulić","Kailin Yang","Deborah J. Chute","Justin A. Bishop","Farhoud Faraji","Wade L. Thorstad","Shlomo A. Koyfman","Scott Steward-Tharp","Qiuying Shi","Vlad C. Sandulache","Nabil F. Saba","James S. Lewis","Germán Corredor","Anant Madabhushi"],"tags":["H&E stain","Eosin","Staining","Pathology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-27","doi":"https://doi.org/10.1016/j.ejca.2025.115390","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412517761","name":"Artificial intelligence in radiology examinations: a psychometric comparison of question generation methods","source":"openalex","abstract":"This study aimed to evaluate the usability of artificial intelligence (AI)-based question generation methods-Chat Generative Pre-trained Transformer (ChatGPT)-4o (a non-template-based large language model) and a template-based automatic item generation (AIG) method-in the context of radiology education.The primary objective was to compare the psychometric properties, perceived quality, and educational applicability of generated multiple-choice questions (MCQs) with those written by a faculty member. METHODSFifth-year medical students who participated in the radiology clerkship at Eskişehir Osmangazi University were invited to take a voluntary 15-question examination covering musculoskeletal and rheumatologic imaging.The examination included five MCQs from each of three sources: a radiologist educator, ChatGPT-4o, and the template-based AIG method.Student responses were evaluated in terms of difficulty and discrimination indices.Following the examination, students rated each question using a Likert scale based on clarity, difficulty, plausibility of distractors, and alignment with learning goals.Correlations between students' examination performance and their theoretical/practical radiology grades were analyzed using Pearson's correlation method. RESULTSA total of 115 students participated.Faculty-written questions had the highest mean correct response rate (2.91 ± 1.34), followed by template-based AIG (2.32 ± 1.66) and ChatGPT-4o (2.3 ± 1.14) questions (P < 0.001).The mean difficulty index was 0.58 for faculty, and 0.46 for both template-based AIG and ChatGPT-4o.Discrimination indices were acceptable (≥0.2) or very good (≥0.4) for template-based AIG questions.In contrast, four of the ChatGPT-generated questions were acceptable, and three were very good.Student evaluations of questions and the overall examination were favorable, particularly regarding question clarity and content alignment.Examination scores showed a weak correlation with practical examination performance (P = 0.041), but not with theoretical grades (P = 0.652). CONCLUSIONBoth the ChatGPT-4o and template-based AIG methods produced MCQs with acceptable psychometric properties.While faculty-written questions were most effective overall, AI-generated questions-especially those from the template-based AIG method-showed strong potential for use in radiology education.However, the small number of items per method and the single-institution context limit the robustness and generalizability of the findings.These results should be regarded as exploratory, and further validation in larger, multicenter studies is required. CLINICAL SIGNIFICANCEAI-based question generation may potentially support educators by enhancing efficiency and consistency in assessment item creation.These methods may complement traditional approaches to help scale up high-quality MCQ development in medical education, particularly in resource-limited settings; however, they should be applied with caution and expert oversight until further evidence is available, especially given the preliminary nature of the current findings.","url":"https://doi.org/10.4274/dir.2025.253407","authors":["Emre Emekli","Betül Nalan Karahan"],"tags":["Medicine","Medical physics","Radiology","MEDLINE","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.4274/dir.2025.253407","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406357731","name":"Patients’ attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol","source":"openalex","abstract":"BACKGROUND: Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients' perspectives on artificial intelligence in medical care, indicating general acceptance of the technology but also concerns about supervision. However, these views have not been systematically examined from the perspective of patients with cancer, whose opinions may differ given the distinct psychosocial toll of the disease. OBJECTIVES: This protocol describes a scoping review aimed at summarizing the existing literature on the attitudes of patients with cancer toward the use of artificial intelligence in their medical care. The primary goal is to identify knowledge gaps and highlight opportunities for future research. METHODS: This scoping review protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-ScR). The electronic databases MEDLINE (OVID), EMBASE, PsycINFO, and CINAHL will be searched for peer-reviewed primary research articles published in academic journals. We will have two independent reviewers screen the articles retrieved from the literature search and select relevant studies based on our inclusion criteria, with a third reviewer resolving any disagreements. We will then compile the data from the included articles into a narrative summary and discuss the implications for clinical practice and future research. DISCUSSION: To our knowledge, this will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care.","url":"https://doi.org/10.1371/journal.pone.0317276","authors":["Daniel Hilbers","Navid Nekain","Alan Bates","John-Jose Nuñez"],"tags":["CINAHL","PsycINFO","MEDLINE","Psychosocial","Protocol (science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.1371/journal.pone.0317276","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408781895","name":"A Review of the State of the Art for the Internet of Medical Things","source":"openalex","abstract":"The technological developments in the Internet of Things (IoT), data science, artificial intelligence, wearable sensors, remote monitoring, decision support systems, fog, and edge systems have transformed digital healthcare. Especially after the pandemic, there has been a rapid transformation of healthcare infrastructure from a conventional to a digital approach. Now, specifically, technologies such as the Internet of Things play a vital role in the transformation of the healthcare system. In this paper, an effort has been made to encompass the transformation of healthcare with a focus on the Internet of Medical Things (IoMT). In particular, it provides a detailed overview of the Internet of Medical Things whilst discussing the design goals and challenges, the resource constraints and limitations of the complex healthcare systems. The paper also provides a detailed account of the research initiatives as well as off-the-shelf wireless motes, internet-enabled sensors and open-source platforms. A thorough account of the next-generation digital healthcare technologies and future research opportunities is provided. This work not only covers the state-of-the-art but also offers critical insight into the digital healthcare challenges. The work attempts to summarise the extensive literature in the domain and present a new perspective on the internet of medical things, affiliate technologies and their role in healthcare.","url":"https://doi.org/10.3390/sci7020036","authors":["Peter Matthew","Sarah McHale","X.T. Deng","Ghada Nakhla","Marcello Trovati","Nonso Nnamoko","Ella Pereira","Huaizhong Zhang","Mohsin Raza"],"tags":["Internet of Things","State (computer science)","Internet privacy","Art","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-24","doi":"https://doi.org/10.3390/sci7020036","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4283827181","name":"A medical assistant segmentation method for MRI images of osteosarcoma based on DecoupleSegNet","source":"openalex","abstract":"Nowadays, the most common primary bone tumor is osteosarcoma, which mostly occurs in teenagers. A common diagnosis method is currently that doctors manually diagnose osteosarcoma in magnetic resonance imaging (MRI) images because it is nonradioactive and has no biological damage to brain tissue and more obvious performance in soft tissue components such as tumors, blood vessels, and muscles in MRI images. However, this method is labor-intensive and time-consuming work, and cannot guarantee the accuracy of the diagnostic results. Existing osteosarcoma MRI image segmentation methods either aim to model the global context to improve the inner consistency of objects, or multiscale feature fusion to refine the detail of objects along their boundaries, which all ignore the interaction between the body of the object and the object boundary. Therefore, this paper proposes a novel segmentation method for osteosarcoma MRI images based on DecoupleSegNet, which explores the relationship between body feature and edge feature. It can assist doctors in diagnosing osteosarcoma and improve their work efficiency. First, we warp the feature of MRI images through learning a flow field so we can make the object more consistent. We then make further work to optimize the resulting body feature and residual edge feature through explicitly sampling pixels from different parts under decoupled supervision. Through these steps, we finally obtain the final feature map with fine boundaries from the MRI image of osteosarcoma. We take a test by using more than 80,000 osteosarcoma MRI images obtained from three hospitals in China. We find that compared with existing osteosarcoma MRI image segmentation methods, our proposed method achieves 90.51 Intersection of Union % with few parameters on the test, outperforming other models. In the test, we prove that our proposed method has better accuracy and lower resource consumption.","url":"https://doi.org/10.1002/int.22949","authors":["Jia Wu","Yuxuan Guo","Fangfang Gou","Zhehao Dai"],"tags":["Osteosarcoma","Segmentation","Feature (linguistics)","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-06","doi":"https://doi.org/10.1002/int.22949","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406045007","name":"Responsible Artificial Intelligence for Mental Health Disorders: Current Applications and Future Challenges","source":"openalex","abstract":"Mental health disorders (MHDs) have significant medical and financial impacts on patients and society. Despite the potential opportunities for artificial intelligence (AI) in the mental health field, there are no noticeable roles of these systems in real medical environments. The main reason for these limitations is the lack of trust by domain experts in the decisions of AI-based systems. Recently, trustworthy AI (TAI) guidelines have been proposed to support the building of responsible AI (RAI) systems that are robust, fair, and transparent. This review aims to investigate the literature of TAI for machine learning (ML) and deep learning (DL) architectures in the MHD domain. To the best of our knowledge, this is the first study that analyzes the literature of trustworthiness of ML and DL models in the MHD domain. The review identifies the advances in the literature of RAI models in the MHD domain and investigates how this is related to the current limitations of the applicability of these models in real medical environments. We discover that the current literature on AI-based models in MHD has severe limitations compared to other domains regarding TAI standards and implementations. We discuss these limitations and suggest possible future research directions that could handle these challenges.","url":"https://doi.org/10.57197/jdr-2024-0101","authors":["Shaker El–Sappagh","Waleed Nazih","Meshal Alharbi","Tamer Abuhmed"],"tags":["Current (fluid)","Mental health","Psychology","Psychiatry","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.57197/jdr-2024-0101","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414255620","name":"Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods","source":"openalex","abstract":"Background: The integration of artificial intelligence (AI) into early childhood health management has expanded rapidly, with applications spanning the fetal, neonatal, and pediatric periods. While numerous studies report promising results, a comprehensive synthesis of AI's performance, methodological quality, and translational readiness in child health is needed. Objectives: This systematic review aims to evaluate the current landscape of AI applications in fetal and pediatric care, assess their diagnostic accuracy and clinical utility, and identify key barriers to real-world implementation. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2021 and March 2025. Eligible studies involved AI-driven models for diagnosis, prediction, or decision support in individuals aged 0-18 years. Study selection followed the PRISMA 2020 guidelines. Data were extracted on application domain, AI methodology, performance metrics, validation strategy, and clinical integration level. Results: From 4,938 screened records, 133 studies were included. AI models demonstrated high performance in prenatal anomaly detection (mean AUC: 0.91-0.95), neonatal intensive care (e.g., sepsis prediction with sensitivity up to 89%), and pediatric genetic diagnosis (accuracy: 85%-93% using facial analysis). Deep learning enhanced consistency in fetal echocardiography and ultrasound interpretation. However, 76% of studies used single-center retrospective data, and only 21% reported external validation. Performance dropped by 15%-20% in cross-institutional settings. Fewer than 5% of models have been integrated into routine clinical workflows, with limited reporting on data privacy, algorithmic bias, and clinician trust. Conclusion: AI holds transformative potential across the pediatric continuum of care-from fetal screening to chronic disease management. However, most applications remain in the research phase, constrained by data heterogeneity, lack of prospective validation, and insufficient regulatory alignment. To advance clinical adoption, future efforts should focus on multicenter collaboration, standardized data sharing frameworks, explainable AI, and pediatric-specific regulatory pathways. This review provides a roadmap for clinicians, researchers, and policymakers to guide the responsible translation of AI in child health.","url":"https://doi.org/10.3389/fped.2025.1613150","authors":["Qingsong Wang","Jun Yin","Xiaomeng Zhang","Huang‐Tz Ou","Fuyan Li","Yundong Zhang","Weiyi Wan","Caiyu Guo","Yongyu Cao","Tongyong Luo","Xianmin Wang"],"tags":["Medicine","Transformative learning","Early childhood","Child health","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-16","doi":"https://doi.org/10.3389/fped.2025.1613150","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413113793","name":"An analysis of the real world performance of an artificial intelligence based autism diagnostic","source":"openalex","abstract":"Rapidly rising demand for pediatric autism evaluations has outpaced specialist capacity and created a crisis of delayed diagnoses and treatment. Streamlining the diagnostic process could reduce wait times and optimize use of limited specialist resources. Following strong clinical trial results, Canvas Dx, an AI-based diagnostic, was FDA authorized to support accurate diagnosis or rule-out of autism in children 18-72 months with caregiver or healthcare provider concern for developmental delay. To gain insight into real-world device performance, a de-identified aggregate data analysis of the initial 254 Canvas Dx prescriptions fulfilled post-market authorization was conducted to determine: accuracy of autism predictions compared to clinical reference standard diagnosis and prior clinical trial data, key real-world prescriber and patient characteristics, proportion of determinate device outputs (positive or negative for autism) and impact of decision threshold settings on device performance. In this sample of 254 children with a 54.7% autism prevalence rate (29.1% female, average age 39.99 months), Canvas Dx had a NPV of 97.6% (CI- 92.8% -100.0%) and a PPV of 92.4% (CI-87.7%-97.2%). A majority of cases (63.0%) received a determinate result. Sensitivity and specificity of determinate results were 99.1% (CI-97.3%-100.0%) and 81.6% (CI-70.8%-92.5%) respectively. The median age of children who received a positive for autism output was 37.2 months, which is more than 2 years earlier than the current median age of autism diagnosis. No performance differences were noted based on patients' sex. Compared to clinical trial results, real world performance was equivalent for all key metrics, with the exception of the determinate rate and the PPV which were significantly improved in real world performance. Analysis of real-world Canvas Dx data highlights its feasibility and utility in supporting accurate, equitable and early diagnosis or rule out of autism. With medical coverage and broader clinical adoption, innovative solutions such as Canvas Dx can play an important role in helping to address the growing specialist waitlist crisis, ensuring that more children gain access to targeted therapies during the critical window of neurodevelopment where they have the greatest life-changing impact.","url":"https://doi.org/10.1038/s41598-025-15575-8","authors":["Carmela Salomon","K. Heinz","Judith Aronson-Ramos","Dennis P. Wall"],"tags":["Autism","Medicine","Medical diagnosis","Gold standard (test)","Medical prescription"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-12","doi":"https://doi.org/10.1038/s41598-025-15575-8","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410494426","name":"Utilisation of Artificial Intelligence and Cybersecurity Capabilities: A Symbiotic Relationship for Enhanced Security and Applicability","source":"openalex","abstract":"The increasing interconnectivity between physical and cyber-systems has led to more vulnerabilities and cyberattacks. Traditional preventive and detective measures are no longer adequate to defend against adversaries. Artificial Intelligence (AI) is used to solve complex problems, including those of cybersecurity. Adversaries also utilise AI for sophisticated and stealth attacks. This study aims to address this problem by exploring the symbiotic relationship of AI and cybersecurity to develop a new, adaptive strategic approach to defend against cyberattacks and improve global security. This paper explores different disciplines to solve security problems in real-world contexts, such as the challenges of scalability and speed in threat detection. It develops an algorithm and a detective predictive model for a Malicious Alert Detection System (MADS) that is an integration of adaptive learning and a neighbourhood-based voting alert detection framework. It evaluates the model’s performance and efficiency among different machines. The paper discusses Machine Learning (ML) and Deep Learning (DL) techniques, their applicability in cybersecurity, and the limitations of using AI. Additionally, it discusses issues, risks, vulnerabilities, and attacks against AI systems. It concludes by providing recommendations on security for AI and AI for security, paving the way for future research on enhancing AI-based systems and mitigating their risks.","url":"https://doi.org/10.3390/electronics14102057","authors":["Ed Kamya Kiyemba Edris"],"tags":["Computer security","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-19","doi":"https://doi.org/10.3390/electronics14102057","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409743032","name":"Overview of South Korean Guidelines for Approval of Large Language or Multimodal Models as Medical Devices: Key Features and Areas for Improvement","source":"openalex","abstract":"The Ministry of Food and Drug Safety (MFDS) of the Republic of Korea, similar to the United States Food and Drug Administration and the United Kingdom's Medicines and Healthcare products Regulatory Agency, issued specific regulatory guidelines on January 24, 2025, for approving generative artificial intelligence (AI) technologies as medical devices [1].Although these guidelines use the term 'generative AI,' they predominantly focus on the approval of AI software tools based on large language models (LLMs) and large multimodal models (LMMs) [2], the latter of which can process various types of input data, such as texts, images, videos, audio, and bio-signals.Unlike the broader guidelines for approving AI models as medical devices, specific regulatory guidelines for LLMs/LMMs have arguably not yet been proposed in other countries.","url":"https://doi.org/10.3348/kjr.2025.0257","authors":["Seong Ho Park","Geraldine Dean","Ernest Montañà Ortiz","Joon‐Il Choi"],"tags":["Medicine","Key (lock)","Medical physics","Medical education","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3348/kjr.2025.0257","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412692164","name":"Artificial Intelligence Approach for Waste-Printed Circuit Board Recycling: A Systematic Review","source":"openalex","abstract":"The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs.","url":"https://doi.org/10.3390/computers14080304","authors":["Muhammad Mohsin","Stefano Rovetta","Francesco Masulli","Alberto Cabri"],"tags":["Enabling","Sorting","Engineering","Key (lock)","Component (thermodynamics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-27","doi":"https://doi.org/10.3390/computers14080304","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413285356","name":"Student engagement with artificial intelligence tools in academia: a survey of Jordanian universities","source":"openalex","abstract":"The rapid advancement of artificial intelligence (AI) has led to its increasing integration into academic environments, raising critical questions about its educational implications. This study investigates the use of AI tools among university students in Jordan, focusing on platforms such as ChatGPT, Google Bard, Microsoft Bing, and Meta AI. A convergent-parallel mixed-methods design was employed, with quantitative (closed-ended) and qualitative (open-ended) data collected concurrently through an online survey distributed over two months. A total of 337 valid responses were obtained from students across 27 universities. The survey explored demographic characteristics, chatbot awareness and use, perceived benefits and challenges, ethical concerns, and future intentions. Results indicate that ChatGPT is the most recognized (94.3%) and widely used (90.4%) tool, while Meta AI is the least utilized (7.8%). Approximately 89% of students reported using AI tools for academic tasks, and 86.6% perceived them as educationally beneficial. However, only 39.7% believed these tools significantly improved their understanding, while 57.6% reported a positive impact on academic performance. These findings reveal a growing trend of AI integration into student study practices in Jordan, highlighting both its practical advantages and the need for further inquiry into its pedagogical value and ethical use.","url":"https://doi.org/10.3389/feduc.2025.1550147","authors":["Mohammad Al Mashagbeh","Malak Alsharqawi","Uranchimeg Tudevdagva","Hussam J. Khasawneh"],"tags":["Engineering management","Engineering","Computer science","Knowledge management","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-18","doi":"https://doi.org/10.3389/feduc.2025.1550147","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414985172","name":"Attitudes and perceptions of dental students towards artificial intelligence","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, including dentistry, through its applications in diagnosis, prosthetic planning, and oral disease detection. As future professionals, dental students play a vital role in integrating AI into clinical practice. However, little is known about their attitudes toward AI, particularly in low-resource settings such as Palestine. METHODS: A cross-sectional survey was conducted among 305 dental students from four Palestinian universities using a validated 22-item questionnaire. Data were analyzed using descriptive statistics and chi-square tests (p < 0.05). RESULTS: Among the 305 participants (232 females, 73 males), 77% reported basic knowledge of AI, with social media being the most common source (66.2%). Female students were significantly more likely than males to believe that AI will bring major advancements to dentistry (p = 0.04), and that it can be used for diagnostic (p = 0.030), prognostic (p = 0.045), and treatment planning purposes (p = 0.015), as well as in postgraduate training (p = 0.017). Those with prior AI knowledge or awareness of its dental applications showed greater enthusiasm for its diagnostic use (p = 0.004) and integration into dental education (p = 0.004 and p = 0.009, respectively). CONCLUSION: Palestinian dental students demonstrate strong awareness and positive attitudes toward the use of AI in dentistry. Gender-based differences and ethical concerns emphasize the need for structured, inclusive, and responsible AI training within dental curricula.","url":"https://doi.org/10.1186/s12909-025-07854-9","authors":["Oadi N. Shrateh","Siwar Al-batat","Ahmad R. Al‐Qudimat","Lara I. Ghannam","Lina J. Abuhanoud"],"tags":["Perception","Psychology","Medical education","MEDLINE","Applied psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-09","doi":"https://doi.org/10.1186/s12909-025-07854-9","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410849931","name":"Artificial intelligence in focus: assessing awareness and perceptions among medical students in three private Syrian universities","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) has gained significant attention and progress in various scientific fields, especially medicine. Since its introduction in the 1950s, AI has advanced remarkably, supporting innovations like diagnostic tools and healthcare technologies. Despite these developments, challenges such as ethical concerns and limited integration in regions like Syria emphasize the importance of increasing awareness and conducting more targeted studies. METHODS: A cross-sectional study was conducted to evaluate medical students' preparedness and readiness to use AI technologies in the medical field using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS_MS). The scale comprises 22 items divided into 4 domains: ethics, vision, ability, and cognition, with responses rated on a five-point Likert scale, higher scores indicate greater readiness. Data were collected through electronic and paper questionnaires distributed over a period of 20 days. RESULTS: The study included 564 medical students from various Syrian universities, of whom 77.8% demonstrated awareness of AI in the medical field. Significant differences in AI awareness were observed based on academic GPA (p = 0.035) and income level (p = 0.016), with higher awareness among students with higher GPA and income levels. Statistically significant differences were found between students aware of AI and those unaware, as well as between students with experience using AI and those without, across all domains of readiness, including cognition (t = -10.319, p < 0.001), ability (t = -11.519, p < 0.001), vision (t = -6.387, p < 0.001), ethics (t = -7.821, p < 0.001), and the overall readiness score (t = -11.354, p < 0.001). CONCLUSION: Integrating AI into medical education is essential for advancing healthcare in developing countries like Syria. Providing incentives and fostering a culture of continuous learning will equip medical students to leverage AI's benefits while mitigating its drawbacks.","url":"https://doi.org/10.1186/s12909-025-07396-0","authors":["Hamdah Hanifa","Mohammad Atia","Rawan Daboul","Ahmad Alhamid","Aya Alayyoubi","Hiam Alhaj Naima","Deema Alkassar","Murhaf Ghassan Nabhan","Basil Alsaleh","Farris Abdula"],"tags":["Medical education","Perception","Psychology","Focus group","Focus (optics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-29","doi":"https://doi.org/10.1186/s12909-025-07396-0","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W3020953856","name":"Legal Aspects on the Implementation of Artificial Intelligence","source":"openalex","abstract":"Artificially Intelligent agents are more and more present in society. They have the potential to improve our daily life and social welfare. But, the introduction of AI already brings some technologic, industrial and regulatory challenges. The robots operating autonomously, without the intervention o","url":"https://doi.org/10.4108/eai.13-7-2018.164174","authors":["Corneliu Puşcã"],"tags":["Intervention (counseling)","Artificial intelligence","Robot","Welfare","Social life"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-28","doi":"https://doi.org/10.4108/eai.13-7-2018.164174","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7119087571","name":"The impact of generative AI on academic reading and writing: a synthesis of recent evidence (2023–2025)","source":"openalex","abstract":"Introduction The aim of this systematic review is to examine the scientific literature published on digital reading and writing in higher education within the field of social sciences, assisted by generative artificial intelligence. Methods The PRISMA methodology and the SALSA Framework were applied, based on a bibliographic search conducted in the Scopus and Web of Science databases. Journal articles that explicitly addressed the established topic, published between 1 January 2023 and 7 March 2025, in open access, in Spanish or English, and within the field of Social Sciences, were included. After a rigorous screening and selection process, a final sample of 136 articles was compiled and used as the basis for the study. Results The findings indicate that the reviewed research employs a range of methodologies, encompassing quantitative (surveys, experimental studies, psychometric evaluations), qualitative (case studies, semi-structured interviews, thematic analysis), and mixed-method approaches. The results also reveal a clear trend toward the integration of artificial intelligence tools –particularly ChatGPT– into academic writing processes. A significant improvement is observed in the quality of students’ texts, especially regarding coherence, discursive organization, lexical richness, and argumentation. Furthermore, the role of AI in formative feedback, idea generation, paraphrasing, and fostering student autonomy in self-editing their texts is highlighted. The research also identifies key challenges, such as students’ overreliance on AI, diminished metacognitive engagement, and ethical dilemmas related to plagiarism and authorship. Discussion The emergence of AI in higher education is transforming teaching and learning processes, creating opportunities for personalization and enhanced support in academic writing. However, the scientific literature also exposes tensions between its potential benefits and associated risks, such as student dependency, loss of critical thinking, and ethical concerns regarding authorship and plagiarism. These findings call for a rethinking of pedagogical, assessment, and institutional practices, as well as the development of critical and digital literacy skills among both teachers and students.","url":"https://doi.org/10.3389/feduc.2025.1711718","authors":["Aránzazu Sanz Tejeda","Juana Celia Domínguez Oller","Josep María Baldaquí-Escandell","Raquel Gómez-Díaz","Araceli García-Rodríguez"],"tags":["Formative assessment","Scopus","Reading (process)","Metacognition","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-06","doi":"https://doi.org/10.3389/feduc.2025.1711718","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414397238","name":"Redefining assessment tasks to promote students’ creativity and integrity in the age of generative artificial intelligence","source":"openalex","abstract":"Abstract The arrival of generative artificial intelligence (GenAI) has forced lecturers to adjust their assessment practices to ensure that students’ work is their own from a creative point of view, and free of plagiarism. This chapter proposes the Academic Integrity and Creativity in the Age of Artificial Intelligence (AICAI) model for the use of authentic assessment as a possible strategy to promote students’ creativity and integrity and thereby ensure the ownership of their written work. Lecturers are encouraged to rethink the assignments they design and examine each of the following components with an eye to integrity: their professional characteristics, the objectives for the assignment, the type of assessment that is appropriate for the needs of the student. Others include the cognitive offloading that will be done or not with GenAI, the type of authentic task they wish to propose and its characteristics, and the instructions and criteria that will be given to students. The choices made should engage students, thereby diminishing the temptation to plagiarize. By combining different strands of pedagogical theory and research, the AICAI assessment design model proposed in this paper has brought into focus the challenges as well as the opportunities that have emerged with the inclusion of GenAI in higher education. On a more practical level, it offers a systemic approach and advice as to how the challenges can be mitigated and benefits maximized for all parties involved in assessment.","url":"https://doi.org/10.1007/s40979-025-00201-x","authors":["Martine Peters","Dimitar Angelov"],"tags":["Creativity","Temptation","Psychology","Generative grammar","Task (project management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-22","doi":"https://doi.org/10.1007/s40979-025-00201-x","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4401542967","name":"Accuracy, Consistency, and Hallucination of Large Language Models When Analyzing Unstructured Clinical Notes in Electronic Medical Records","source":"openalex","abstract":"Kathryn G. Burford, PhD; Nicole G. Itzkowitz, MSc; Ashley G. Ortega, BSc; Julien O. Teitler, PhD; Andrew G. Rundle, DrPH","url":"https://doi.org/10.1001/jamanetworkopen.2024.25953","authors":["Savyasachi V. Shah"],"tags":["Consistency (knowledge bases)","Unstructured data","Medical record","Natural language processing","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-13","doi":"https://doi.org/10.1001/jamanetworkopen.2024.25953","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4400158994","name":"Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers","source":"openalex","abstract":"Abstract Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.","url":"https://doi.org/10.1002/btm2.10694","authors":["Daniel Morillo","Antonio León‐Jiménez","María Guerrero‐Chanivet","Gema Jiménez‐Gómez","Antonio Hidalgo‐Molina","Antonio Campos‐Caro"],"tags":["Silicosis","Medicine","Biomarker","Internal medicine","Lactate dehydrogenase"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.1002/btm2.10694","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409894239","name":"LoRa Communications Spectrum Sensing Based on Artificial Intelligence: IoT Sensing","source":"openalex","abstract":"The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework's flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa.","url":"https://doi.org/10.3390/s25092748","authors":["Partemie-Marian Mutescu","Valentin Popa","Alexandru Lavric"],"tags":["Internet of Things","Computer science","Telecommunications","Embedded system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-26","doi":"https://doi.org/10.3390/s25092748","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7154839439","name":"Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence","source":"openalex","abstract":"Artificial intelligence (AI) and additive manufacturing (AM) have propelled the next wave of technological innovation by integrating data-driven intelligence with design freedom, thereby enabling adaptive, efficient, and multifunctional systems. This review highlights the transformative role of AI and machine learning (ML) in addressing the key challenges associated with process complexity, parameter tuning, and multifunctional design in AM. Starting with the historical evolution of AI-AM integration, the progression from rule-based modeling to contemporary deep learning, reinforcement learning, and physics-informed frameworks that enable autonomous and self-optimizing manufacturing systems was summarized. Attention is directed toward ML-driven topology and lattice optimization, data-driven methods for predicting process and structural properties, and Multiphysics optimization, demonstrating how AI replaces labor-intensive experimentation with predictive and adaptive models. In parallel, the integration of AI into smart materials (SMs) and 4D printing has been explored, with emphasis on property tuning for piezoelectric, shape-memory, and self-healing systems. The review concludes by highlighting key challenges, including data scarcity, limited interpretability, and the lack of standardized datasets, while pointing toward hybrid physics-informed ML and digital twin approaches for closed-loop and intelligent manufacturing. Collectively, this study provides a comprehensive roadmap illustrating how AI enables AM to evolve from empirical fabrication to autonomous, multifunctional manufacturing paradigms.","url":"https://doi.org/10.1007/s00170-026-18072-y","authors":["Saqlain Zaman","Md Shahjahan Mahmud","Ali Mollick","Tenzin Lhaden","Joshua Dantzler","Sabina Arroyo","Nithin K. Goona","Maisha Mesbah","Md. Ariful Ahsan","Yirong Lin"],"tags":["Artificial intelligence","Transformative learning","Process (computing)","Multiphysics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-18","doi":"https://doi.org/10.1007/s00170-026-18072-y","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412490723","name":"Artificial intelligence for endoscopic grading of gastric intestinal metaplasia: advancing risk stratification for gastric cancer","source":"openalex","abstract":"Background: The Endoscopic Grading of Gastric Intestinal Metaplasia (EGGIM) classification correlates with histological assessment of gastric intestinal metaplasia and enables stratification of gastric cancer risk. We developed and evaluated an artificial intelligence (AI) approach for EGGIM estimation. Methods: Two datasets (A and B) with 1280 narrow-band imaging images were used for per-image analysis. Still images with manually selected patches of 224 × 224 pixels, annotated by experts, were used. Dataset A was retrospectively collected from clinical routine; Dataset B (used for per-patient analysis) was prospectively collected and included 65 fully documented patients. To mimic clinical practice, a deep neural network classified image patches into three EGGIM classes (0, 1, 2) and calculated the total per-patient EGGIM score (0–10). Results: On per-image analysis, an accuracy of 87% (95%CI 71%–100%) was obtained. Per-patient EGGIM estimation had an average error of 1.15 (out of 10) and showed 88% (95%CI 80%–96%) accurate clinical decisions for surveillance (EGGIM ≥5), with 85% (95%CI 75%–94%) specificity, no false negatives, and positive and negative predictive values of 62% (95%CI 32%–92%) and 100% (95%CI 100%–100%), respectively. Conclusions: EGGIM was estimated with high accuracy using AI tools in endoscopic image analyses. Automated assessment of EGGIM may provide a greener strategy for gastric cancer risk stratification, prospective studies, and interventional trials.","url":"https://doi.org/10.1055/a-2657-9906","authors":["Eduarda Almeida","Miguel L. Martins","David da Motta Marques","Rose Delas","Tatiana Almeida","Jéssica Chaves","Diogo Libânio","Francesco Renna","Miguel Coimbra","Mário Dinis‐Ribeiro"],"tags":["Medicine","Grading (engineering)","Confidence interval","Intestinal metaplasia","Prospective cohort study"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-17","doi":"https://doi.org/10.1055/a-2657-9906","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415380784","name":"Artificial intelligence and students’ cognitive learning outcomes with bibliometric and content analysis for future research agenda","source":"openalex","abstract":"This study conducted a comprehensive bibliometric and content analysis to explore the integration of artificial intelligence in students’ cognitive learning outcomes. A structured TITLE-ABS-KEY search was performed in the Scopus database using keywords such as “Artificial Intelligence,” “AI,” “Students,” and “cognitive learning outcomes,” resulting in 318 documents published between 2016 and 2025. After filtering, a final dataset of 246 research articles and conference papers was analyzed. The methodology includes bibliometric performance analysis (covering publication trends, countries, affiliations, authors, and journals) and network analysis (comprising co-word, citation, co-authorship, and bibliographic coupling). Additionally, content analysis was conducted on the ten most cited and ten focused articles addressing AI’s impact on cognitive learning outcomes. VOSviewer software was used for data analysis and visualization. Findings indicate increased research output post-2023, driven by digital transformation and global collaboration. Leading affiliations include The University of Hong Kong and Carnegie Mellon University, with the United States, China, and India as top contributing countries. Influential journals and funding bodies include the National Science Foundation and the National Natural Science Foundation of China. Notable authors include Chiu and Cukurova, while Kit Ng, Zhong, and Liu are prominent in bibliographic coupling, emphasizing AI adoption. Co-authorship analysis shows collaboration primarily among developed nations. Co-word analysis reveals Key research themes include contrastive learning, adversarial machine learning, and federated learning. Content analysis highlights AI’s transformative potential for learning, teaching, cognitive learning, and innovation. This study provides managerial and practical recommendations for students, universities, and policymakers. This study has several limitations that future studies will consider.","url":"https://doi.org/10.1007/s44217-025-00865-0","authors":["Shaukat Rahman Ansari","Ika Nurul Qamari"],"tags":["Transformative learning","Content analysis","Scopus","Bibliometrics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-21","doi":"https://doi.org/10.1007/s44217-025-00865-0","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410239408","name":"Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition","source":"openalex","abstract":"Background: Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with inflammation. Methods: This observational, cross-sectional study included 502 malnourished patients, assessed through anthropometry, electrical bioimpedanciometry, and ultrasonography of the quadriceps rectus femoris (QRF). AI-assisted ultrasonography was used to segment regions of interest (ROI) from transversal QRF images to measure muscle thickness (RFMT) and area (RFMA), while a Multi-Otsu algorithm was used to extract biomarkers for muscle mass (MiT) and fat mass (FatiT). Inflammation was defined as C-reactive protein (CRP) levels above 3 mg/L. Results: The results showed a mean patient age of 63.72 (15.95) years, with malnutrition present in 82.3% and inflammation in 44.8%. Oncological diseases were prevalent (46.8%). The 44.8% of patients with inflammation (CRP > 3) exhibited reduced RFMA (2.91 (1.11) vs. 3.20 (1.19) cm2, p < 0.01) and RFMT (0.94 (0.28) vs. 1.01 (0.30) cm, p < 0.01). Muscle quality was reduced, with lower MiT (45.32 (9.98%) vs. 49.10 (1.22%), p < 0.01) and higher FatiT (40.03 (6.72%) vs. 37.58 (5.63%), p < 0.01). Adjusted for age and sex, inflammation increased the risks of low muscle area (OR = 1.59, CI: 1.10–2.31), low MiT (OR = 1.49, CI: 1.04–2.15), and high FatiT (OR = 1.44, CI: 1.00–2.06). Conclusions: AI-assisted ultrasonography revealed that malnourished patients with inflammation had reduced muscle area, thickness, and quality (higher fat content and lower muscle percentage). Elevated inflammation levels were associated with increased risks of poor muscle metrics. Future research should focus on exploring the impact of inflammation on muscles across various patient groups and developing AI-driven biomarkers to enhance the diagnosis, monitoring, and treatment of malnutrition and sarcopenia.","url":"https://doi.org/10.3390/nu17101620","authors":["Juan José López Gómez","Lucía Estévez-Asensio","Ángela Cebriá","Olatz Izaola-Jáuregui","Paloma Pérez López","Jaime González-Gutiérrez","David Primo","Rebeca Jiménez-Sahagún","Emilia Gómez Hoyos","Daniel Rico","Eduardo Jorge Godoy","Daniel Antonio de Luis"],"tags":["Medicine","Inflammation","Anthropometry","Malnutrition","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-09","doi":"https://doi.org/10.3390/nu17101620","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4417327445","name":"Specialised Competencies and Artificial Intelligence in Perioperative Care: Contributions Toward Safer Practice","source":"openalex","abstract":"This narrative review explores how specialised clinical competencies and artificial intelligence (AI) technologies converge in the context of perioperative care, with a focus on their combined potential to improve patient safety. Considering the growing complexity of surgical care and rising demands on healthcare professionals, the study aims to understand how human expertise and digital tools can complement each other in this high-stakes environment. Methods: A narrative review methodology was adopted to integrate insights from diverse sources, including empirical studies, policy documents, and expert analyses published over the last decade. Findings reveal that AI can support clinical decision-making, streamline workflows, and enable earlier identification of complications across all perioperative phases. These technologies enhance, rather than replace, the roles of nurses, anesthetists, and surgeons. However, their effective use depends on critical factors such as digital literacy, interdisciplinary collaboration, and ethical awareness. Issues related to data privacy, algorithmic bias, and unequal access to technology highlight the need for thoughtful, inclusive implementation. The future of perioperative care will likely depend on hybrid models where human judgment and AI-based tools are integrated in ways that uphold safety, equity, and person-centred values.","url":"https://doi.org/10.3390/healthcare13243286","authors":["Sara Raquel Ferreira Raposo","Miguel Mascarenhas","Ricardo Correia","João C. Ferreira"],"tags":["SAFER","Context (archaeology)","Perioperative","Health care","Identification (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-15","doi":"https://doi.org/10.3390/healthcare13243286","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413018905","name":"Effectiveness of preliminary differential diagnosis of benign and malignant skin neoplasms using the Derma Onko Check artificial intelligence program","source":"openalex","abstract":"Objective: to evaluate the effectiveness of preliminary differential diagnostics of benign and malignant skin tumors during initial medical consultations in primary health care using the Derma Onko Check artificial intelligence (AI) program for electronic computing devices (smartphone application). Material and methods. The effectiveness of the Derma Onko Check program for visual identification of benign and malignant skin tumors was evaluated in 135 patients aged 22 to 78 years with various skin lesions that appeared visually suspicious for malignancy. The conclusions generated by the program were compared with the results of dermatoscopic and morphological examinations. Results. The diagnostic accuracy of the Derma Onko Check program in determining the likelihood of a patient having a benign or malignant skin tumor was 96%, sensitivity was 98%, specificity was 96%, the proportion of false-positive results was 4.3%, and the propor Conclusion. The use of modern AI-based software for electronic computing devices enables early detection of malignant skin tumors during initial examinations in primary health care. This is particularly relevant for medical institutions and regions with a shortage or absence of dermatologists and oncologists. tion of falsenegative results was 2.4%.","url":"https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.294","authors":["A. I. Lamotkin","D. I. Korabelnikov","Olga Yu. Olisova","Igor A. Lamotkin"],"tags":["Medicine","Differential diagnosis","Medical physics","Dermatology","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-06","doi":"https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.294","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W2583362313","name":"Current trends in the development of intelligent unmanned autonomous systems","source":"openalex","abstract":"Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.","url":"https://doi.org/10.1631/fitee.1601650","authors":["Tao Zhang","Qing Li","Changshui Zhang","Huawei Liang","Li Ping","Tianmiao Wang","Shuo Li","Yunlong Zhu","Cheng Wu"],"tags":["Robot","Systems engineering","Computer science","Intelligent decision support system","Service (business)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-01-01","doi":"https://doi.org/10.1631/fitee.1601650","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414610376","name":"Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care","source":"openalex","abstract":"BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative tool in dermatology, particularly in Low- and Middle-Income Countries (LMICs), where healthcare systems face challenges such as a shortage of dermatologists and limited resources. AI technologies, including deep learning models like Convolutional Neural Networks (CNNs), have demonstrated potential in improving diagnostic accuracy for skin diseases, which contribute significantly to the global disease burden. However, most research has focused on High-Income Countries (HICs), leaving gaps in understanding AI's applicability and effectiveness in LMICs. AIM/OBJECTIVE: This systematic review critically evaluates the application of AI in dermatological practice within LMICs, assessing the performance of AI technologies across diverse geographic regions. METHODOLOGY: The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included 19 studies from databases including PubMed, Embase, and Cochrane. Eligible studies evaluated AI applications in dermatology within LMICs, reporting metrics like sensitivity, specificity, precision, and accuracy. Data extraction and quality assessment were performed independently by several reviewers using tools like PROBAST and QUADAS-2. A qualitative synthesis as per SWiM guidelines was conducted due to heterogeneity in study designs and outcomes. CONCLUSION: AI shows significant promise in enhancing dermatological diagnostics and expanding access to dermatologic care in LMICs, with models achieving high accuracy (up to 99%) in tasks like skin cancer and infectious disease detection. However, challenges such as underrepresented skin tones in datasets, limited clinical validation, and infrastructural barriers currently hinder equitable implementation. Future efforts should prioritize creating and utilizing diverse datasets, lightweight models for mobile deployment, and human-AI collaboration to ensure context-specific and scalable solutions. Addressing these gaps can help leverage AI to mitigate global health disparities in dermatological care.","url":"https://doi.org/10.1186/s12245-025-00975-4","authors":["Olivier Uwishema","Malak Ghezzawi","Nicole Charbel","Shireen Alawieh","S. Roy","Magda Wojtara","Clyde Moono Hakayuwa","Ibrahim Khalil Ja’afar","Gerard Nkurunziza","Manya Prasad"],"tags":["Medicine","Leverage (statistics)","Health care","Scalability","Resource (disambiguation)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-29","doi":"https://doi.org/10.1186/s12245-025-00975-4","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414752405","name":"The Effect of Artificial Intelligence in Promoting Positive Nursing Practice Environments: Mixed Methods Systematic Review","source":"openalex","abstract":"AIM: To synthesise the available evidence on the effect of artificial intelligence in promoting positive nursing practice environments, exploring outcomes for professionals, clients, and institutions. BACKGROUND: Artificial intelligence has undergone significant advancements and shows great potential to transform nursing practice. However, this technological evolution is not without challenges, which must be identified and addressed. METHODS: A systematic mixed-methods review following the PRISMA 2020 guidelines and the methodology proposed by JBI. The search strategy was conducted in PubMed, CINAHL, Scopus, and Web of Science, including grey literature. Quantitative, qualitative, and mixed-methods studies were included, and the selection process involved screening by two independent reviewers, who assessed all studies, their methodological quality and extracted their data. RESULTS: From the conducted search, 11 studies were included, addressing how artificial intelligence has transformed nursing practice by optimising decision-making, task execution, and patient safety. Artificial intelligence, through predictive models and assistants such as ChatGPT, can enhance nursing management. However, challenges such as privacy concerns, resistance to change, and the need for professional training must be addressed to maximise its effectiveness. CONCLUSION: Artificial intelligence has the potential to positively transform the nursing practice environment, optimising decision-making, enhancing patient safety, and improving operational efficiency, with clear benefits for professionals, patients, and healthcare institutions. RELEVANCE TO CLINICAL PRACTICE: This study analysed the impact of artificial intelligence on nursing, highlighting improvements in clinical decision-making, patient safety, and institutional efficiency. Despite the identified benefits, the implementation of artificial intelligence in nursing is not without challenges and risks, which must be identified and addressed to ensure safe and effective adoption. REPORTING METHOD: The review followed the PRISMA 2020 checklist. PATIENT OR PUBLIC CONTRIBUTION: No.","url":"https://doi.org/10.1111/jocn.70127","authors":["Soraia Cristina de Abreu Pereira","Rosilene Alves Ferreira","João Miguel Almeida Ventura‐Silva","Eduardo Santos","Cí­ntia Silva Fassarella","Olga Maria Pimenta Lopes Ribeiro"],"tags":["Nursing","Nursing practice","Psychology","MEDLINE","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-02","doi":"https://doi.org/10.1111/jocn.70127","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4388030783","name":"Artificial intelligent based teaching and learning approaches: A comprehensive review","source":"openalex","abstract":"The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates.","url":"https://doi.org/10.11591/ijere.v12i4.26623","authors":["Thuong Nguyen","Minh Tuấn Nguyễn","Hoang T. Tran"],"tags":["Flexibility (engineering)","Computer science","Variety (cybernetics)","Curriculum","General partnership"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-30","doi":"https://doi.org/10.11591/ijere.v12i4.26623","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408551077","name":"Role of Artificial Intelligence in Congenital Heart Disease and Interventions","source":"openalex","abstract":"Artificial intelligence has promising impact on patients with congenital heart disease, a vulnerable population with life-long health care needs and, often, a substantially higher risk of death than the general population. This review explores the role artificial intelligence has had on cardiac imaging, electrophysiology, interventional procedures, and intensive care monitoring as it relates to children and adults with congenital heart disease. Machine learning and deep learning algorithms have enhanced not only imaging segmentation and processing but also diagnostic accuracy namely reducing interobserver variability. This has a meaningful impact in complex congenital heart disease improving anatomic diagnosis, assessment of cardiac function, and predicting long-term outcomes. Image processing has benefited procedural planning for interventional cardiology, allowing for a higher quality and density of information to be extracted from the same imaging modalities. In electrophysiology, deep learning models have enhanced the diagnostic potential of electrocardiograms, detecting subtle yet meaningful variation in signals that enable early diagnosis of cardiac dysfunction, risk stratification of mortality, and more accurate diagnosis and prediction of arrhythmias. In the congenital heart disease population, this has the potential for meaningful prolongation of life. Postoperative care in the cardiac intensive care unit is a data-rich environment that is often overwhelming. Detection of subtle data trends in this environment for early detection of morbidity is a ripe avenue for artificial intelligence algorithms to be used. Examples like early detection of catheter-induced thrombosis have already been published. Despite their great promise, artificial intelligence algorithms are still limited by hurdles such as data standardization, algorithm validation, drift, and explainability.","url":"https://doi.org/10.1016/j.jscai.2025.102567","authors":["Dudley Byron Holt","Amr El‐Bokl","Daniel Stromberg","Michael D. Taylor"],"tags":["Psychological intervention","Heart disease","Disease","Medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1016/j.jscai.2025.102567","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4224282559","name":"Recommendations for developing a lifecycle, multidimensional assessment framework for mobile medical apps","source":"openalex","abstract":"Digital health and mobile medical apps (MMAs) have shown great promise in transforming health care, but their adoption in clinical care has been unsatisfactory, and regulatory guidance and coverage decisions have been lacking or incomplete. A multidimensional assessment framework for regulatory, policymaking, health technology assessment, and coverage purposes based on the MMA lifecycle is needed. A targeted review of relevant policy documents from international sources was conducted to map current MMA assessment frameworks, to formulate 10 recommendations, subsequently shared amongst an expert panel of key stakeholders. Recommendations go beyond economic dimensions such as cost and economic evaluation and also include MMA development and update, classification and evidentiary requirements, performance and maintenance monitoring, usability testing, clinical evidence requirements, safety and security, equity considerations, organizational assessment, and additional outcome domains (patient empowerment and environmental impact). The COVID-19 pandemic greatly expanded the use of MMAs, but temporary policies governing their use and oversight need consolidation through well-developed frameworks to support decision-makers, producers and introduction into clinical care processes, especially in light of the strong international, cross-border character of MMAs, the new EU medical device and health technology assessment regulations, and the Next Generation EU funding earmarked for health digitalization.","url":"https://doi.org/10.1002/hec.4505","authors":["Rosanna Tarricone","Francesco Petracca","Maria Cucciniello","Oriana Ciani"],"tags":["Computer science","Mobile apps","Business","Process management","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-06","doi":"https://doi.org/10.1002/hec.4505","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413932081","name":"Artificial Intelligence in Peer Review: Ethical Risks and Practical Limits","source":"openalex","abstract":"","url":"https://doi.org/10.4274/tao.2025.2025-8-12","authors":["Özgür Kemal"],"tags":["Engineering ethics","Ethical issues","Psychology","Computer science","Risk analysis (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-02","doi":"https://doi.org/10.4274/tao.2025.2025-8-12","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413399312","name":"Integrating artificial intelligence and optogenetics for Parkinson’s disease diagnosis and therapeutics in male mice","source":"openalex","abstract":"Parkinson's disease (PD), a progressive neurodegenerative disorder, presents complex motor symptoms and lacks effective disease-modifying treatments. Here we show that integrating artificial intelligence (AI) with optogenetic intervention, termed optoRET, modulating c-RET (REarranged during Transfection) signalling, enables task-independent behavioural assessments and therapeutic benefits in freely moving male AAV-hA53T mice. Utilising a 3D pose estimation technique, we developed tree-based AI models that detect PD severity cohorts earlier and with higher accuracy than conventional methods. Employing an explainable AI technique, we identified a comprehensive array of PD behavioural markers, encompassing gait and spectro-temporal features. Moreover, our AI-driven analysis highlights that optoRET effectively alleviates PD progression by improving limb coordination and locomotion and reducing chest tremor. Our study demonstrates the synergy of integrating AI and optogenetic techniques to provide an efficient diagnostic method with extensive behavioural evaluations and sets the stage for an innovative treatment strategy for PD.","url":"https://doi.org/10.1038/s41467-025-63025-w","authors":["Bobae Hyeon","Jaehyun Shin","Jae‐Hun Lee","Woori Kim","Jea Kwon","Hee‐Young Lee","Dae‐Gun Kim","Choong Yeon Kim","Choong Yeon Kim","Sian Choi","Jae‐Woong Jeong","Kwang‐Soo Kim","C. Justin Lee","Daesoo Kim","Daesoo Kim","Won Do Heo"],"tags":["Optogenetics","Neuroscience","Disease","Parkinson's disease","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-21","doi":"https://doi.org/10.1038/s41467-025-63025-w","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414253312","name":"The impact of integrating artificial intelligence and Building information modeling (BIM) systems on the development of construction methodologies","source":"openalex","abstract":"Abstract The integration of Artificial Intelligence (AI) with Building Information Modelling (BIM) systems is driving a profound transformation in project delivery approaches, necessitating a systematic evaluation of its impact on design and construction processes. This study explores how AI-BIM integration enhances construction efficiency, with a focus on improving accuracy, productivity, and decision-making across project lifecycles. The research employs a mixed-methods approach, combining theoretical and applied analyses. Through descriptive-analytical methods, we assessed conceptual frameworks and the latest technological advancements were assessed, while deductive analysis was applied to real-world case studies. A practical case study of a construction project utilizing AI-BIM integration was examined to evaluate its benefits and challenges. The findings reveal significant improvements in construction methodologies, including: Enhanced design automation through AI-driven generative solutions. Faster structural and energy performance analysis using machine learning algorithms. Greater planning accuracy and improved decision-making in early project phases. Reduced errors and costs via intelligent automation and predictive modelling. The study recommends: Developing standardized frameworks for AI-BIM integration in construction practices, Implementing specialized training programs to upskill professionals in AI and BIM technologies Conducting further research to measure long-term economic and time-saving impacts of AI-BIM adoption.","url":"https://doi.org/10.1007/s43995-025-00193-2","authors":["Ahmed Attia"],"tags":["Automation","Systems engineering","Building information modeling","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-16","doi":"https://doi.org/10.1007/s43995-025-00193-2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412190336","name":"Artificial Intelligence and Ethical Dimensions of Automated Traffic Enforcement: Implications for Public Health, Healthcare Equity, and Social Justice","source":"openalex","abstract":"This study provides a critical examination of AI-integrated speed and red-light camera systems through the theoretical lenses of Surveillance Capitalism, the Panopticon Model, Social Control Theory, Technological Determinism, and Structural Violence Theory. While artificial intelligent speed safety cameras demonstrate efficacy in reducing traffic violations and fatalities, this research addresses a critical gap in healthcare literature regarding their broader societal and ethical consequences, including algorithmic bias, data governance failures, and privacy violations that directly impact public trust and health equity. The analysis reveals how machine learning and predictive analytics in automated enforcement create disproportionate burdens on marginalized populations through three specific mechanisms: (1) biased algorithmic design that targets low-income neighborhoods more intensively, (2) punitive traffic fine structures that impose greater relative financial hardship on economically disadvantaged families, and (3) opaque implementation practices that limit community understanding and participation. These patterns perpetuate health disparities by increasing chronic stress, economic instability, and barriers to healthcare access among vulnerable populations. This work’s novel contribution lies in applying four foundational health equity principles to AI-powered traffic enforcement: distributive justice (fair allocation of enforcement across communities), procedural justice (transparent and accountable decision-making processes), recognition justice (acknowledgment of community voices and concerns), and capabilities approach (ensuring enforcement practices do not undermine individuals’ fundamental capabilities for health and wellbeing). Additionally, the study examines three core social justice principles: substantive equality (addressing systemic disadvantages rather than treating all violations identically), participatory parity (ensuring affected communities can participate meaningfully in policy decisions), and non-domination (preventing the arbitrary exercise of state power through automated systems). The study advocates for the development of ethical artificial intelligence governance frameworks that incorporate transparent algorithmic auditing, community driven design processes, and robust oversight mechanisms. These evidence-based recommendations support equitable and trustworthy applications of artificial intelligence that advocate for, rather than undermine, population health and social justice in traffic safety initiatives. A novel contribution of this work lies in its exploration of how artificial intelligence powered speed safety cameras intersect with specific health equity principles in distributive justice, procedural justice, and the capabilities approach, as well as core social justice principles, including substantive equality, participatory parity, and nondomination, in the governance of public infrastructure. The analysis applies distributive justice to examine the fair allocation of enforcement across communities, procedural justice to evaluate transparent decision-making processes, and the capabilities approach to assess whether enforcement practices undermine individuals’ fundamental capabilities for health and wellbeing.","url":"https://doi.org/10.61093/hem.2025.2-03","authors":["Patricia Haley"],"tags":["Equity (law)","Health care","Economic Justice","Enforcement","Social justice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-02","doi":"https://doi.org/10.61093/hem.2025.2-03","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4384823872","name":"Regulating Artificial Intelligence in the EU, United States and China - Implications for energy systems","source":"openalex","abstract":"The growing prevalence and potential impact of artificial intelligence (AI) on society rises the need for regulation. In return, the shape of regulations will affect the application potential of AI across all economic sectors. This study compares the approaches to regulate AI in the European Union (EU), the United States (US) and China (CN). We then apply the findings of our comparative analysis on the energy sector, assessing the effects of each regulatory approach on the operation of a AI-based short-term electricity demand forecasting application. Our findings show that operationalizing AI applications will face very different challenges across geographies, with important implications for policy making and business development.","url":"https://doi.org/10.1109/isgteurope56780.2023.10407482","authors":["Fabian Heymann","Konstantinos Parginos","Ali Hariri","Gabriele Franco"],"tags":["Operationalization","European union","Electricity","China","Face (sociological concept)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-23","doi":"https://doi.org/10.1109/isgteurope56780.2023.10407482","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416665897","name":"Intelligent Biosensors Based on Hyaluronic Acid Hydrogels for Monitoring Chronic Wound Healing with the Involvement of Artificial Intelligence","source":"openalex","abstract":"Chronic wounds, such as those caused by diabetes, burns, and pressure ulcers, pose significant healthcare challenges due to their slow healing and potential for infections. Traditional methods for monitoring wound healing are often intrusive, slow, and lack real-time data. To overcome these limitations, innovative biosensors using hyaluronic acid hydrogels have emerged as a promising solution. As a non-intrusive, biocompatible platform, these biosensors can track pH, glucose levels, inflammatory proteins, and other key biomarkers as wounds heal. With the integration of artificial intelligence (AI), they enable personalized treatment adjustments and early complication detection through real-time data analysis and predictive modeling. This review discusses the recent progress of hyaluronic acid hydrogel biosensors for long-term wound healing, evaluating their strengths, challenges, and potential future improvements. This work aims to enhance chronic wound management and improve multiple clinical outcomes by focusing on the intersection of biomaterial innovation and AI.","url":"https://doi.org/10.3390/bios15120773","authors":["Antonia-Mihaela Nicolae","Mihaela Badea","Săndica Bucurica","Florina Rasaliu","E Constantinescu"],"tags":["Hyaluronic acid","Self-healing hydrogels","Wound healing","Biomaterial","Biocompatible material"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-25","doi":"https://doi.org/10.3390/bios15120773","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413179134","name":"Application of artificial intelligence-based stemness index in cancer","source":"openalex","abstract":"Cancer stem cells (CSCs) exhibit self-renewal and multidirectional differentiation capacities. The stemness of CSCs is the fundamental cause of tumor progression and treatment resistance. The stemness index, evaluating the number and activity of CSCs, is a crucial indicator predicting various aspects of tumor behavior such as growth, metastasis, and prognosis. With the advancements in artificial intelligence (AI), particularly in data analysis and machine learning, the identification and understanding of CSCs' stemness characteristics have improved. The AI-based analysis allows for processing vast datasets and recognizing patterns that assist in comprehending the role of CSCs in cancer development. The utilization of AI to analyze and compute the stemness index holds significant clinical relevance in tumor diagnosis and treatment. This approach provides more precise and personalized information, potentially influencing treatment strategies. Therefore, tailoring treatments specifically targeting CSCs is highly imperative and may enhance therapeutic efficacy and outcomes in cancer patients.","url":"https://doi.org/10.3389/fonc.2025.1608712","authors":["Liyuan Liu","Qin Pei","Javeria Qadir","Yiyu Chen","Jingyuan Li","Yanan Luo","Jiawen Xian","Rongrong Du","Ting Ye"],"tags":["Cancer stem cell","Metastasis","Cancer","Identification (biology)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-13","doi":"https://doi.org/10.3389/fonc.2025.1608712","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413313824","name":"Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management","source":"openalex","abstract":"Introduction: Epilepsy is a prevalent chronic neurological disorder primarily diagnosed using electroencephalography (EEG). Traditional EEG interpretation relies on manual analysis, which suffers from high misdiagnosis rates and inefficiency. Methods: This review systematically evaluates the integration of artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), into EEG analysis for epilepsy management. We focus on two dominant AI-EEG application models: supportive AI (augmenting clinical decisions) and predictive AI (anticipating seizures or outcomes). Results: AI-based EEG analysis demonstrates significant potential in improving epilepsy detection, monitoring, and therapeutic evaluation. Key advancements include enhanced precision, efficiency, and capabilities for multimodal data fusion and personalized diagnosis. However, challenges persist, such as limited model interpretability, data quality constraints, and barriers to clinical translation. Crucially, AI outputs require clinician verification alongside multidimensional clinical data. Discussion: Future research must prioritize algorithm optimization, data quality improvement, and enhanced AI transparency. Interdisciplinary collaboration is essential to bridge the gap between technical innovation and clinical implementation. This review highlights both the transformative potential and current limitations of AI-EEG in epilepsy care, providing a roadmap for future developments.","url":"https://doi.org/10.3389/fneur.2025.1615120","authors":["Chenxi Wang","Xinyue Yuan","Wei Jing"],"tags":["Electroencephalography","Epilepsy","Neuroscience","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-18","doi":"https://doi.org/10.3389/fneur.2025.1615120","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7125421805","name":"Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis","source":"openalex","abstract":"BACKGROUND/OBJECTIVES: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. METHODS: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. RESULTS: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. CONCLUSIONS: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation.","url":"https://doi.org/10.3390/children13010152","authors":["Nevra Karamüftüoğlu","Büşra Yavuz Üçpunar","İREM BİRBEN","Asya Eda Altundağ","KÜBRA ÖRNEK MULLAOĞLU","Cenkhan Bal"],"tags":["Artificial intelligence","Scarcity","Medicine","MEDLINE","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-21","doi":"https://doi.org/10.3390/children13010152","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4294686886","name":"PROSPECTS OF MEDICAL TECHNOLOGIES OF ARTIFICIAL INTELLIGENCE","source":"openalex","abstract":"1 .., 1, 2 .., 1","url":"https://doi.org/10.17513/srms.1279","authors":["S.V. Ryazanova","A.A. Komkov","V.P. Mazaev"],"tags":["Computer science","Artificial intelligence","Engineering ethics","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.17513/srms.1279","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7134261441","name":"Navigation of drug discovery via artificial intelligence","source":"openalex","abstract":"Abstract Background The growing global health burden necessitates precise therapeutics to mitigate pathogenesis and severe complications, which are increasing daily. Recently emerging and re-emerging viral infectious diseases, along with other ongoing complications from diverse infections and pathogens, contribute to global outbreaks. To address this, immediate and accurate therapeutic developments that can help manage this crisis are needed. Concerning the development of therapeutics, the conventional method-based drug design is time-consuming and requires a substantial investment of time to develop a drug against the pathogen successfully. Main body of the abstract To overcome these present obstacles, artificial intelligence (AI) came as a hope of revolutionizing the detection and advancement of pioneering, precise, cost- and time-effective drugs. AI uses advanced algorithms to improve the accuracy regarding target identification and further inhibitor selection. The pathogens were re-emerging daily, simultaneously, generating a huge amount of data with various specific properties and other essential details. Among them, some data can be helpful for therapeutic development. Using AI-based pipelines, tools, servers, databases, and useful resources to aid drug discovery, and employing different algorithms to examine the data, it was possible to identify a potential target that could aid therapeutic development; similarly, it also helped revolutionize the clinical aspects of drug discovery and the pharmaceutical industry by enabling more specific data handling. Moreover, it can help utilize available drugs and their significant details to address emerging and ongoing diseases through a drug repurposing-based approach using advanced AI-based computational analysis. Short conclusion Herein, this study offers the AI insight toward the drug discovery and development, how these approaches were utilized, and their advancements and challenges.","url":"https://doi.org/10.1186/s43094-026-00954-3","authors":["Saurav Kumar Mishra","Jeba Praba J","Hamadou Mamoudou","Akansha Subba","John J. Georrge"],"tags":["Identification (biology)","Drug discovery","Computer science","Risk analysis (engineering)","Drug development"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-09","doi":"https://doi.org/10.1186/s43094-026-00954-3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408502459","name":"Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management","source":"openalex","abstract":"Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with significant mortality and morbidity risks when ruptured. Early detection, risk stratification of high-risk subgroups, and prediction of patient outcomes are important to treatment. Radiomics is an emerging field using the quantification of medical imaging to identify parameters beyond traditional radiology interpretation that may offer diagnostic or prognostic significance. The general radiomic workflow involves image normalization and segmentation, feature extraction, feature selection or dimensional reduction, training of a predictive model, and validation of the said model. Artificial intelligence (AI) techniques have shown increasing interest in applications toward vascular pathologies, with some commercially successful software including AiDoc, RapidAI, and Viz.AI, as well as the more recent Viz Aneurysm. We performed a systematic review of 684 articles and identified 84 articles exploring the applications of radiomics and AI in aneurysm treatment. Most studies were published between 2018 and 2024, with over half of articles in 2022 and 2023. Studies included categories such as aneurysm diagnosis (25.0%), rupture risk prediction (50.0%), growth rate prediction (4.8%), hemodynamic assessment (2.4%), clinical outcome prediction (11.9%), and occlusion or stenosis assessment (6.0%). Studies utilized molecular data (2.4%), radiologic data alone (51.2%), clinical data alone (28.6%), and combined radiologic and clinical data (17.9%). These results demonstrate the current status of this emerging and exciting field. An increased pace of innovation in this space is likely with the expansion of clinical applications of radiomics and AI in multiple vascular pathologies.","url":"https://doi.org/10.1111/jon.70037","authors":["Monica‐Rae Owens","Samuel A. Tenhoeve","Clayton Rawson","Mohammed A. Azab","Michael Karsy"],"tags":["Medicine","Radiomics","Aneurysm","Radiology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1111/jon.70037","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407108350","name":"Current and future state of evaluation of large language models for medical summarization tasks","source":"openalex","abstract":"Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated the current landscape of evaluation metrics for NLG in healthcare and proposed a future direction to address the resource constraints of expert human evaluation while balancing alignment with human judgments.","url":"https://doi.org/10.1038/s44401-024-00011-2","authors":["Emma Croxford","Yanjun Gao","Nicholas Pellegrino","Karen Wong","Graham Wills","Elliot First","Frank Liao","Cherodeep Goswami","Brian W. Patterson","Majid Afshar"],"tags":["Automatic summarization","Natural language generation","Workflow","Computer science","Unified Medical Language System"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-03","doi":"https://doi.org/10.1038/s44401-024-00011-2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412191972","name":"Do occupational health and safety tools that utilize artificial intelligence have a measurable impact on worker injury or illness? Findings from a systematic review","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) holds promise as a tool that can be used by practitioners in the field of occupational health and safety (OHS). This study aimed to identify AI applications specifically used for OHS and examine their impact on worker morbidity or mortality outcomes. METHODS: We conducted a comprehensive systematic review. We searched six databases to identify published quantitative studies of OHS AI applications across the hierarchy of controls that were published between years 2018 to 2024. Title/abstract and full-text screening was conducted to identify eligible studies which were then assessed for quality and risk of bias and synthesized. RESULTS: Of the 1255 articles identified by our search, only two met eligibility criteria; one of which was appraised as medium quality and the other as low quality. The one medium quality study identified by our review was an AI-based chatbot health promotion tool which was shown to improve musculoskeletal symptoms. Our systematic review shows that we are at the early stages of understanding the role AI can play in OHS and it may be premature to recommend the wide-spread use of AI for health and safety practice within workplaces. CONCLUSION: There is a critical need for future research to unpack how considerations taken in the development and adoption of workplace AI tools for OHS can determine their effectiveness in addressing worker injury or illness. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023414422.","url":"https://doi.org/10.1186/s13643-025-02869-1","authors":["Arif Jetha","Hela Bakhtari","Emma Irvin","Aviroop Biswas","Maxwell J. Smith","Cameron Mustard","Victoria H Arrandale","Jack T. Dennerlein","Peter Smith"],"tags":["Medicine","Occupational safety and health","Systematic review","Occupational injury","Promotion (chess)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-11","doi":"https://doi.org/10.1186/s13643-025-02869-1","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411415408","name":"Evaluating the Role of Artificial Intelligence in Making Clinical Decisions for Treating Acute Pancreatitis","source":"openalex","abstract":"Background/Objectives: Acute pancreatitis (AP) is an illness that requires prompt diagnosis and treatment since it has the potential to become life-threatening. The American College of Gastroenterology 2024 (ACG24) guidelines offer a framework for diagnosis, severity, and treatment criteria. To assess Google Gemini application of ACG24 guidelines to Medical Information Mart for Intensive Care-III AP cases for risk, nutrition, and complication management. Methods: This observational cross-sectional study was based on 512 patients with AP who were treated in the Medical Information Mart for Intensive Care-III database from 2001 to 2012. The study compared the efficiency of Gemini in relation to the ACG24 guidelines in the three main areas of risk stratification, enteral nutrition timing, and necrotizing pancreatitis management. Enteral nutrition, according to the ACG24 guidelines, should be started within 48 h for patients who are capable, and antibiotics should only be used for confirmed infected necrosis. Results: The study included 512 patients who were divided into two groups: 213 patients with mild pancreatitis (41.6%) and 299 patients with severe pancreatitis (58.4%). The model achieved 85% accuracy for mild cases and 82% accuracy for severe cases of pancreatitis. The Acute Physiology and Chronic Health Evaluation II and Ranson scores matched the predictions of Gemini for both mild cases (p = 0.28 and p = 0.33, respectively) and severe cases (p = 0.31 and p = 0.27, respectively). The recommendations for early enteral nutrition and delayed feeding in mild cases were correct for 78% of patients, but the system suggested oral intake prematurely in 8% of severe cases. The antibiotic guideline compliance reached 82% among 156 patients with necrotizing pancreatitis, and the procedure for draining infected necrosis was correct 85% of the time. Conclusions: The Gemini model achieved 78–85% accuracy in determining pancreatitis severity and adherence to treatment guidelines but showed lower accuracy in nutrition timing compared to other parameters. Core Tip: This study evaluated the Google Gemini model in applying the American College of Gastroenterology 2024 guidelines for acute pancreatitis across 512 Medical Information Mart for Intensive Care-III cases. Results demonstrated 85% accuracy in severity classification, precise prediction of Acute Physiology and Chronic Health Evaluation II and Ranson scores, and 78–85% compliance with nutritional and necrotizing pancreatitis management guidelines. These findings suggest that artificial intelligence-based clinical decision support systems can provide rapid, consistent, and guideline-concordant recommendations, which are particularly valuable in settings with limited specialist expertise.","url":"https://doi.org/10.3390/jcm14124347","authors":["Mete Üçdal","Amir Bakhshandehpour","Muhammed Bahaddin Durak","Yasemin Balaban","Murat Kekilli","Cem Şimşek"],"tags":["Medicine","Acute pancreatitis","Clinical decision making","Intensive care medicine","Pancreatitis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-18","doi":"https://doi.org/10.3390/jcm14124347","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412521550","name":"Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators","source":"openalex","abstract":"The wide usage of artificial intelligence (AI) text-to-image generators raises concerns about the role of AI in amplifying misconceptions in healthcare. This study therefore evaluated the demographic accuracy and potential biases in the depiction of patients by four commonly used text-to-image generators. A total of 9060 images of patients with 29 different diseases was generated using Adobe Firefly, Bing Image Generator, Meta Imagine, and Midjourney. Twelve independent raters determined the sex, age, weight, and race and ethnicity of the patients depicted. Comparison to the real-world epidemiology showed that the generated images failed to depict demographical characteristics such as sex, age, and race and ethnicity accurately. In addition, we observed an over-representation of White and normal weight individuals. Inaccuracies and biases may stem from non-representative and non-specific training data as well as insufficient or misdirected bias mitigation strategies. In consequence, new strategies to counteract such inaccuracies and biases are needed.","url":"https://doi.org/10.1038/s41746-025-01817-6","authors":["Tim L. T. Wiegand","Leonard Jung","Jonas A. Gudera","Luisa Sophie Schuhmacher","Paulina Moehrle","Jon Rischewski","Pardiss Mehrzad","Subin Jeong","Lisa Ha Nguyen","Michael Poeschla","Laura Velezmoro","Linus Kruk","Konstantinos Dimitriadis","Inga K. Koerte"],"tags":["Depiction","Race (biology)","Ethnic group","Representation (politics)","Image (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-19","doi":"https://doi.org/10.1038/s41746-025-01817-6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406103327","name":"Intelligent Oil Production Management System Based on Artificial Intelligence Technology","source":"openalex","abstract":"Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency.","url":"https://doi.org/10.3390/pr13010133","authors":["Xianfu Sui","Xin Lu","Yuchen Ji","Yang Yang","Jianlin Peng","Menglong Li","Guoqing Han"],"tags":["Workflow","Production (economics)","Computer science","Production manager","Predictive maintenance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-06","doi":"https://doi.org/10.3390/pr13010133","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415626372","name":"Artificial intelligence, machine learning and omic data integration in osteoarthritis","source":"openalex","abstract":"OBJECTIVE: Artificial intelligence (AI), particularly its subfield of machine learning (ML), offer promising tools for integrating and interpreting high-dimensional omic data to advance our understanding of osteoarthritis (OA), a complex, multifactorial disease. The objective of this review is to summarize recent progress in applying ML approaches to single and integrative multi-omic data in OA and to highlight emerging trends, challenges, and opportunities. METHOD: We conducted a literature search of PubMed and preprint databases upto April 2025. This search identified studies that applied ML techniques including supervised learning, unsupervised clustering, deep learning, and integrative modeling to OA datasets. These datasets included transcriptomic, epigenomic, proteomic, metabolomic, and multi-omic profiles in human OA samples and relevant preclinical models. We synthesized findings across omic types, ML methodologies, and clinical or mechanistic OA outcomes, highlighting key trends in multi-omic integration strategies and their implications for OA research. RESULTS: Recent studies have applied ML to identify transcriptomic and epigenomic biomarkers, stratify OA patient subtypes, and predict disease progression. Advanced approaches such as variational autoencoders, contrastive learning, and multimodal transformers are emerging as powerful tools for multi-omic integration. However, challenges remain related to small sample sizes, overfitting, lack of external validation, model interpretability, and demographic underrepresentation in omic datasets. CONCLUSIONS: ML techniques are advancing OA research by enabling nuanced analysis of complex omic datasets. Addressing current limitations and embracing new developments in spatial and single-cell omics, generative models, and federated learning will be essential to unlock the full potential of multi-omic integration for personalized OA diagnosis and treatment.","url":"https://doi.org/10.1016/j.joca.2025.10.012","authors":["Divya Sharma"],"tags":["Machine learning","Computer science","Artificial intelligence","Data integration","Generative grammar"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-28","doi":"https://doi.org/10.1016/j.joca.2025.10.012","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409280922","name":"Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review","source":"openalex","abstract":"Background/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals’ quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute’s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.","url":"https://doi.org/10.3390/nursrep15040126","authors":["José Alves","R. M. de Azevedo","Ana Marques","Rúben Encarnação","Paulo Alves"],"tags":["Intensive care","Artificial intelligence","Computer science","Medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.3390/nursrep15040126","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408025939","name":"Can artificial intelligence diagnose seizures based on patients' descriptions? A study of GPT ‐4","source":"openalex","abstract":"OBJECTIVE: Generalist large language models (LLMs) have shown diagnostic potential in various medical contexts but have not been explored extensively in relation to epilepsy. This paper aims to test the performance of an LLM (OpenAI's GPT-4) on the differential diagnosis of epileptic and functional/dissociative seizures (FDS) based on patients' descriptions. METHODS: GPT-4 was asked to diagnose 41 cases of epilepsy (n = 16) or FDS (n = 25) based on transcripts of patients describing their symptoms (median word count = 399). It was first asked to perform this task without additional training examples (zero-shot) before being asked to perform it having been given one, two, and three examples of each condition (one-, two, and three-shot). As a benchmark, three experienced neurologists performed this task without access to any additional clinical or demographic information (e.g., age, gender, socioeconomic status). RESULTS: In the zero-shot condition, GPT-4's average balanced accuracy was 57% (κ = .15). Balanced accuracy improved in the one-shot condition (64%, κ = .27), but did not improve any further in the two-shot (62%, κ = .24) and three-shot (62%, κ = .23) conditions. Performance in all four conditions was worse than the mean balanced accuracy of the experienced neurologists (71%, κ = .42). However, in the subset of 18 cases that all three neurologists had \"diagnosed\" correctly (median word count = 684), GPT-4's balanced accuracy was 81% (κ = .66). SIGNIFICANCE: Although its \"raw\" performance was poor, GPT-4 showed noticeable improvement having been given just one example of a patient describing epilepsy and FDS. Giving two and three examples did not further improve performance, but the finding that GPT-4 did much better in those cases correctly diagnosed by all three neurologists suggests that providing more extensive clinical data and more elaborate approaches (e.g., more refined prompt engineering, fine-tuning, or retrieval augmented generation) could unlock the full diagnostic potential of LLMs.","url":"https://doi.org/10.1111/epi.18322","authors":["Joseph Ford","Nathan Pevy","Richard A. Grünewald","Stephen Howell","Markus Reuber"],"tags":["Epilepsy","Psychology","Neuroscience","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-27","doi":"https://doi.org/10.1111/epi.18322","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7128476819","name":"The future of fundamental science led by generative closed-loop artificial intelligence","source":"openalex","abstract":"Artificial intelligence is approaching the point at which it can complete the scientific cycle, from hypothesis generation to experimental design and validation, within a closed loop that requires little human intervention. Yet, the loop is not fully autonomous: humans still curate data, set hyperparameters, adjudicate interpretability, and decide what counts as a satisfactory explanation. As models scale, they begin to explore regions of hypothesis and solution space that are inaccessible to human reasoning because they are too intricate or alien to our intuitions. Scientists may soon rely on AI strategies they do not fully understand, trusting goals and empirical payoffs rather than derivations. This prospect forces a choice about how much control to relinquish to accelerate discovery while keeping outputs human relevant. The answer cannot be a blanket policy to deploy LLMs or any single paradigm everywhere. It demands principled matching of methods to domains, hybrid causal and neurosymbolic scaffolds around generative models, and governance that preserves plurality and counters recursive bias. Otherwise, recursive training and uncritical reuse risk model collapse in AI and an epistemic collapse in science, as statistical inertia amplifies flaws and narrows the investigation. We argue for graded autonomy in AI-conducted science: systems that can close the loop at machine speed, while remaining anchored to human priorities, verifiable mechanisms, and domain-appropriate forms of understanding.","url":"https://doi.org/10.3389/frai.2026.1678539","authors":["Hector Zenil","Jesper Tegner","Felipe S. Abrahão","Alexander Lavin","Vipin Kumar","Jeremy G. Frey","Adrian Weller","Larisa Soldatova","Alan Bundy","Nicholas R. Jennings","Koichi TAKAHASHI","LE Hunter","Sašo Džeroski","Andrew Briggs","Frederick D. Gregory","Carla P. Gomes","J. Rowe","James Evans","H. Kitano","Ross D. King"],"tags":["Generative grammar","Computer science","Artificial intelligence","Set (abstract data type)","Matching (statistics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-11","doi":"https://doi.org/10.3389/frai.2026.1678539","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W3082553363","name":"The Future Comes Early for Medical Educators","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11606-020-06128-y","authors":["Daniel J. Minter","Rabih Geha","Reza Manesh","Gurpreet Dhaliwal"],"tags":["Coronavirus disease 2019 (COVID-19)","Medicine","Adaptation (eye)","Pandemic","Videoconferencing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-01","doi":"https://doi.org/10.1007/s11606-020-06128-y","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7117679704","name":"Legal, ethical, and policy challenges of artificial intelligence translation tools in healthcare","source":"openalex","abstract":"Artificial intelligence (AI) translation tools, such as Google Translate and ChatGPT, are increasingly used in healthcare for medical communication to overcome language barriers between patients and providers. While these tools offer accessible and efficient translation, their use raises significant legal, ethical, and policy concerns. Key patients' rights, including the rights to privacy, informed consent, and equitable access to care, may be compromised. Current European regulations, including the EU AI Act, General Data Protection Regulation, and Medical Devices Regulation, offer only partial protection, leaving important regulatory gaps. This study employs a mixed-methods approach combining legal doctrinal analysis of EU regulatory frameworks with manual content analysis of platform terms of service. It integrates interdisciplinary perspectives from bioethics, digital health, and health communication to evaluate the implications of AI-mediated translation in clinical care. Findings reveal persistent and overlapping risks: violations of data privacy, inaccuracies in translation, bias and discrimination, and unclear liability when errors occur. To mitigate these risks, we propose targeted policy interventions, including developing guidelines for AI translation use in healthcare settings. This article contributes to digital health policy debates by identifying legal pathways to regulate AI translation tools in healthcare, ensuring their use supports patients' rights and promotes health equity.","url":"https://doi.org/10.1186/s12982-025-01277-z","authors":["Hannah van Kolfschooten","Simone Goosen","Janneke van Oirschot","Barbara C. Schouten","Ildikó Vajda","LUNA WILLEMS"],"tags":["Health care","Computer science","Digital health","Key (lock)","Liability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-31","doi":"https://doi.org/10.1186/s12982-025-01277-z","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415778150","name":"Artificial intelligence in traditional medicine: policy and governance strategies","source":"openalex","abstract":"","url":"https://doi.org/10.2471/blt.24.292888","authors":["Sameer Pujari","Rajeshwari Singh","Goh Cheng Soon","Tanuja Manoj Nesari","Ricardo Ghelman","Yu Zhao","K. K. Kalra","Shada Alsalamah","Richelle George","Shyama Kuruvilla","Alain Labrique"],"tags":["Corporate governance","Business","Computer science","Management science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-01","doi":"https://doi.org/10.2471/blt.24.292888","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413420174","name":"Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges","source":"openalex","abstract":"Unlabelled: Emergency toxicology is a complex field requiring rapid and precise decision-making to manage acute poisonings effectively. Toxic exposures are often unpredictable, and the constraints of time and resources often challenge conventional diagnostic and treatment approaches. Artificial intelligence (AI) has emerged as a valuable tool in emergency medicine, offering the potential to enhance diagnostic accuracy, predict clinical outcomes and improve clinical decision support systems. Despite the increasing focus of AI in medicine, its applications in emergency toxicology are still underexplored. This viewpoint aims to provide perspectives on AI applications in emergency toxicology by highlighting key advancements, challenges, and future directions. While AI has demonstrated significant potential in improving toxicological predictions through various applications, challenges such as data quality, regulatory concerns, and implementation barriers are still hurdles to its use. Further research, regulatory frameworks, and integration strategies are needed to ensure effective and ethical implementation in clinical practice.","url":"https://doi.org/10.2196/73121","authors":["Lorraine Pei Xian Yong","Joshua Yi Min Tung","Nicole Mun Teng Cheung","Zi Yao Lee","Ee Yang Ng","Alexander Jet Yue Ng","Clement Kee Woon Lim","Yuru Boon","Daniel Yan Zheng Lim","Gerald Gui Ren Sng","Jonathan Zhe Ying Tang"],"tags":["Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-22","doi":"https://doi.org/10.2196/73121","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408412354","name":"Project-work Artificial Intelligence Integration Framework (PAIIF): Developing a CDIO-based framework for educational integration","source":"openalex","abstract":"Artificial intelligence (AI) and generative AI (GenAI) have sparked confusion and concern regarding their impact on education. Beyond the assessment integrity risks that currently draw the most attention, technologies such as ChatGPT, Copilot, and Gemini have also been identified as tools that can support learning. Project work, especially when there is no single correct solution, provides a great opportunity for integration, fostering technology knowledge and higher learning standards. However, no AI-integration framework for project-based work is available, resulting in a limited understanding of how AI integration can occur or be maximized. To address this, a collaborative effort of 16 educators from 9 Australian universities has led to the development of a generic AI implementation framework, built upon the CDIO approach. With a focus on engineering education, this framework can be adapted to other project-based learning contexts, where educators can pick and choose the relevant implementation items as needed. This framework is called the Project-work Artificial Intelligence Integration Framework (PAIIF), and its development and structure are outlined here. Initial implementations have shown the effectiveness of promoting reflection and guidance on where and how AI integration can occur.","url":"https://doi.org/10.3934/steme.2025016","authors":["Sasha Nikolic","Zach Quince","Anna Lindqvist","Peter Neal","Sarah Grundy","May Lim","Faham Tahmasebinia","Shannon Rios","Josh Burridge","Kathy Petkoff","Ashfaque Ahmed Chowdhury","Wendy S.L. Lee","Rita Prestigiacomo","H Silveira Fernando","Peter Lok","Mark D. Symes"],"tags":["CDIO","Work (physics)","Computer science","Engineering management","Systems engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3934/steme.2025016","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4403460426","name":"Prognosis of artificial intelligence in education","source":"openalex","abstract":"The Higher Education Institutions require emphasis on disruptive intelligent systems which includes Artificial Intelligence that challenges conventional methods with improved products and services. This study aimed to know the trend artificial intelligence in engineering education. Specifically, it aimed to know the profile of the respondents, know the level of utilization of artificial intelligence tools in engineering education, know if there is significant relationship between profile of respondents to the AI tools used in engineering education, and propose a model of artificial intelligence in engineering education. This paper used quantitative correlational methods of research. Result showed that majority of the respondents has more work experience, found that most teachers have five years or more of experience and found that in terms of educational attainment, majority of the respondents had master’s degree. Artificial intelligence tools are generally “Sometimes Utilized” in engineering education and the respondents' profiles had no significant relationship on the use of the AI technologies, which are often occasionally used in engineering education. To fully utilize AI capabilities in engineering education, the model achieved offers a number of particular actions, including institutional in-house training, awareness campaigns, research conferences, and informal information exchange.","url":"https://doi.org/10.62486/latia2025107","authors":["Khushwant Singh","Mohit Yadav"],"tags":["Artificial intelligence","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-16","doi":"https://doi.org/10.62486/latia2025107","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411223682","name":"Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis","source":"openalex","abstract":"INTRODUCTION: Identifying difficult airways and avoiding unanticipated difficult airways through difficult airway assessment are crucial for patient safety prior to airway management. Therefore, accurately predicting difficult airways through airway assessment is a fundamental and significant technique in airway management by clinicians. Artificial intelligence (AI) is a rapidly evolving science with greater data processing ability than humans. AI, given its ever-expanding applications in medical diagnosis and disease prediction, has been employed to predict cases with difficult airways. Nevertheless, the diagnostic performance of AI algorithms for difficult airway assessment remains unclear due to the small sample sizes, insufficient image acquisition standards and poor predictive accuracies. Consequently, this study aims to formulate a protocol for a systematic review and meta-analysis to ascertain the diagnostic value of AI in assessing difficult airways. METHODS AND ANALYSIS: English-language databases (Cochrane Library, Web of Science, PubMed, Ovid Medline and Embase), Chinese electronic databases (China National Knowledge Infrastructure, VIP and Wanfang ] and clinical trial registry databases will be searched from their inception until January 2025 to identify clinical trials of AI for difficult airway assessment. Sensitivities, specificities, areas under the receiver operating characteristic curve, diagnostic likelihood ratios and diagnostic ORs with 95% CIs will be presented as indicators of AI's diagnostic accuracy in assessing difficult airways. Depending on the level of statistical heterogeneity evaluated by the I-square test, the fixed-effects or random-effects model will be employed. The risk of bias will be evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Furthermore, the quality of evidence concerning the outcomes will be assessed based on the Grading of Recommendations Assessment, Development and Evaluation criteria for diagnostic tests. Heterogeneity will be investigated through sensitivity, meta-regression and subgroup analyses. Additionally, Deeks' funnel plot asymmetry test will be used to detect publication bias. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review protocol. The results will be disseminated through peer-reviewed publications. PROSPERO REGISTRATION NUMBER: CRD42023462926.","url":"https://doi.org/10.1136/bmjopen-2024-096744","authors":["Weiyi Zhang","Li Du","Yujie Huang","Dan Liu","Tingting Li","Jianqiao Zheng"],"tags":["Medicine","Protocol (science)","Meta-analysis","Systematic review","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.1136/bmjopen-2024-096744","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4405351251","name":"The 2024 revision of the Declaration of Helsinki: a modern ethical framework for medical research","source":"openalex","abstract":"The Declaration of Helsinki, established in 1964, remains a foundational document in medical research ethics. This review examines the 2024 revision, endorsed by the 75th World Medical Association (WMA) Assembly, highlighting its impact on modern clinical research. Major updates include the shift from \"subjects\" to \"participants,\" promoting autonomy and active involvement, and the introduction of dual ethical review requirements for cross-border studies to strengthen accountability. New guidelines for data privacy address AI-related ethical concerns, while enhanced community engagement fosters transparency and shared decision-making. Additionally, standards for environmental sustainability encourage research practices that minimize ecological impacts. In response to global health crises such as COVID-19, the revised Declaration sets forth ethical protections to balance participant safety with research urgency during emergencies. Despite these advances, areas for improvement remain, especially in AI ethics, emergency research protocols, and the extension the Declaration's scope to include forensic and specimen research. The 2024 revision thus strengthens the Declaration's role as an adaptive, relevant framework for safeguarding participant rights and research integrity in a changing landscape.","url":"https://doi.org/10.1093/postmj/qgae181","authors":["Boyuan Wen","Guochao Zhang","Chang Zhan","Chen Chen","Hang Yi"],"tags":["Declaration of Helsinki","Declaration","Safeguarding","Autonomy","Research ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-27","doi":"https://doi.org/10.1093/postmj/qgae181","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4313267852","name":"Nanocellulose-based sensors in medical/clinical applications: The state-of-the-art review","source":"openalex","abstract":"In recent years, the considerable importance of healthcare and the indispensable appeal of curative issues, particularly the diagnosis of diseases, have propelled the invention of sensing platforms. With the development of nanotechnology, the integration of nanomaterials in such platforms has been much focused on, boosting their functionality in many fields. In this direction, there has been rapid growth in the utilisation of nanocellulose in sensors with medical applications. Indeed, this natural nanomaterial benefits from striking features, such as biocompatibility, cytocompatibility and low toxicity, as well as unprecedented physical and chemical properties. In this review, different classifications of nanocellulose-based sensors (biosensors, chemical and physical sensors), alongside some subcategories manufactured for health monitoring, stand out. Moreover, the types of nanocellulose and their roles in such sensors are discussed.","url":"https://doi.org/10.1016/j.carbpol.2022.120509","authors":["Mahsa Mousavi Langari","Maryam Nikzad","Jalel Labidi"],"tags":["Nanocellulose","Nanotechnology","Chemical sensor","Nanosensor","Nanomedicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-29","doi":"https://doi.org/10.1016/j.carbpol.2022.120509","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4297896992","name":"ARTIFICIAL INTELLIGENCE (AI) AND THE METAVERSE: LEGAL ASPECTS","source":"openalex","abstract":".., (Ph.D.) ,","url":"https://doi.org/10.32782/2524-0374/2022-8/66","authors":["O.V. Kostenko"],"tags":["Metaverse","Possible world","Computer science","Epistemology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.32782/2524-0374/2022-8/66","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W3124318032","name":"The Question of Comparative Advantage in Artificial Intelligence: Enduring Strengths and Emerging Challenges for the United States","source":"openalex","abstract":"How do we measure leadership in artificial intelligence, and where does the United States rank? This policy brief examines potential AI strengths of the United States and China and prescribes recommendations to ensure the United States remains ahead.","url":"https://doi.org/10.51593/20190047","authors":["Andrew Imbrie","Elsa B. Kania","Lorand Laskai"],"tags":["China","Rank (graph theory)","Political science","Measure (data warehouse)","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.51593/20190047","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410992751","name":"Accuracy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care","source":"openalex","abstract":"Importance: Integrating artificial intelligence (AI) technologies into gatekeeping holds significant potential, as it efficiently handles repetitive tasks and can process large amounts of information quickly. Objective: To develop and assess the accuracy of an AI model that enhances the gatekeeping process for referrals to specialized care. Design, Setting, and Participants: This diagnostic study comprised referrals from primary care to endocrinology, gastroenterology, proctology, rheumatology, and urology from a retrospective administrative database of patients in Brazil between June 2016 and April 2019. Analysis was performed between December 2022 and July 2024. Main Outcomes and Measures: The algorithm's development and testing comprised 2 stages. Multiple AI models were initially evaluated to train and test the algorithm for categorizing referrals as authorizing or requiring additional information. Subsequently, the model's performance was assessed against an independent set of referrals. Additionally, the current (human) evaluations of gatekeepers were evaluated against the standard. The reference standard was the consensus of 2 physicians with extensive experience. Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) were assessed. Results: The electronic system retrieved 45 039 eligible referrals for the development stage (mean [SD] patient age, 51.9 [15.8] years; 25 458 women [56.5%]). An algorithm utilizing word embeddings and a neural network proved the most effective. In the second phase, 1750 referrals (350 for each specialty) showed a 32% authorization rate according to the reference standard. The AI model achieved an overall accuracy of 0.716 (95% IC, 0.694-0.737), with a sensitivity of 0.542 (95% CI, 0.501 to 0.582) and specificity of 0.801 (95% CI, 0.777 to 0.822). Regarding individual specialties, rheumatology exhibited the highest accuracy (0.811; 95% IC, 0.767-0.849), while proctology had the lowest (0.649; 95% IC, 0.597-0.697). The overall AUC-ROC was 0.765 (95% IC, 0.742-0.788). When compared against the consensus standard, the AI model had higher accuracy and specificity and lower sensitivity than the current approach. Conclusions and Relevance: In this diagnostic study of referral data, a novel AI model effectively distinguished between referrals that warranted immediate authorization and those that required further information with moderate accuracy; it had higher specificity and lower sensitivity than gatekeepers decisions. Implementing this AI model in the gatekeeping process should combine human judgment and AI support to optimize the referral process.","url":"https://doi.org/10.1001/jamanetworkopen.2025.13285","authors":["Piter Oliveira Vergara","Jerônimo de Conto Oliveira","Rita Mattiello","Alfredo Montelongo","Rudi Roman","Natan Katz","Leandro Krug Wives","Dimitris Rucks Varvaki Rados"],"tags":["Gatekeeping","Artificial intelligence","Computer science","Psychology","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-03","doi":"https://doi.org/10.1001/jamanetworkopen.2025.13285","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406725043","name":"Cyber Espionage in the Age of Artificial Intelligence: A Comparative Study of State-Sponsored Campaign","source":"openalex","abstract":"This study investigates the transformative role of artificial intelligence (AI) in state-sponsored cyber espionage, focusing on its dual use in offensive and defensive operations. Using data from the MITRE ATT&CK Framework, FireEye APT Groups Database, UNSW-NB15 Intrusion Detection Dataset, and the Cyber Conflict Tracker by CFR, this research applied network graph analysis, multi-criteria decision analysis (MCDA), ensemble classification models, and Difference-in-Differences (DiD) analysis. Results revealed that AI-driven offensive techniques, phishing (degree centrality 0.85), and adaptive malware (betweenness centrality 0.81) significantly enhance operational precision and scalability. Defensively, ensemble classification models achieved up to 95.8% accuracy, highlighting AI's efficacy in intrusion detection. AI regulatory frameworks reduced misattribution rates by 20% and escalation incidents by 10%, demonstrating their critical role in mitigating geopolitical risks. The findings impress AI's transformative potential in advancing cyber operations and shaping international policy and governance. By addressing challenges such as attribution, escalation risks, and ethical dilemmas, this study highlights the necessity for stronger global cooperation and regulatory frameworks to navigate the dual-use nature of AI, providing actionable insights for policymakers, cybersecurity professionals, and researchers, emphasizing the urgency of aligning technological advancements with strategies for enhancing global cybersecurity resilience.","url":"https://doi.org/10.9734/ajrcos/2025/v18i1557","authors":["Onyinye Obioha-Val","Oluwaseun Oladeji Olaniyi","Michael Olayinka Gbadebo","Adebayo Yusuf Balogun","Anthony Obulor Olisa"],"tags":["Espionage","State (computer science)","Computer security","Artificial intelligence","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-21","doi":"https://doi.org/10.9734/ajrcos/2025/v18i1557","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4229454240","name":"Stakeholders’ Requirements for Artificial Intelligence for Healthcare in Korea","source":"openalex","abstract":"OBJECTIVES: The outlook of artificial intelligence for healthcare (AI4H) is promising. However, no studies have yet discussed the issues from the perspective of stakeholders in Korea. This research aimed to identify stakeholders' requirements for AI4H to accelerate the business and research of AI4H. METHODS: We identified research funding trends from the Korean National Science and Technology Knowledge Information Service (NTIS) from 2015 and 2019 using \"healthcare AI\" and related keywords. Furthermore, we conducted an online survey with members of the Korean Society of Artificial Intelligence in Medicine to identify experts' opinions regarding the development of AI4H. Finally, expert interviews were conducted with 13 experts in three areas (hospitals, industry, and academia). RESULTS: We found 160 related projects from the NTIS. The major data type was radiology images (59.4%). Dermatology-related diseases received the most funding, followed by pulmonary diseases. Based on the survey responses, radiology images (23.9%) were the most demanding data type. Over half of the solutions were related to diagnosis (33.3%) or prognosis prediction (31%). In the expert interviews, all experts mentioned healthcare data for AI solutions as a major issue. Experts in the industrial field mainly mentioned regulations, practical efficacy evaluation, and data accessibility. CONCLUSIONS: We identified technology, regulatory, and data issues for practical AI4H applications from the perspectives of stakeholders in hospitals, industry, and academia in Korea. We found issues and requirements, including regulations, data utilization, reimbursement, and human resource development, that should be addressed to promote further research in AI4H.","url":"https://doi.org/10.4258/hir.2022.28.2.143","authors":["Jae Yong Yu","Sungjun Hong","Yeong Chan Lee","Kyung Hyun Lee","Ildong Lee","Yeoni Seo","Mira Kang","Kyunga Kim","Won Chul","Soo-Yong Shin"],"tags":["Health care","Computer science","Data science","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-30","doi":"https://doi.org/10.4258/hir.2022.28.2.143","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413764808","name":"AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond","source":"openalex","abstract":"Abstract The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills (PACES-MRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human–AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the Second Singularity -a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI’s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.","url":"https://doi.org/10.1007/s10462-025-11352-1","authors":["Chiara Natali","Luca Marconi","Leslye Denisse Dias Duran","Federico Cabitza"],"tags":["Deskilling","Health care","Computer science","Engineering ethics","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-27","doi":"https://doi.org/10.1007/s10462-025-11352-1","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7135227096","name":"Artificial Intelligence and the Transformation of Cell and Gene Therapy Development","source":"openalex","abstract":"Cell and Gene Therapy (CGT) represents a paradigm shift in medicine, offering curative potential for previously intractable diseases. However, the complexity, high cost, and manufacturing challenges inherent in developing, producing, and administering these therapies hinder their widespread accessibility. This review examines the critical and increasingly synergistic role of Artificial Intelligence (AI) and Machine Learning (ML) in overcoming these barriers across the entire CGT lifecycle, from discovery and construct design to smart manufacturing, clinical translation, and regulatory applications. We analyze how AI-driven approaches fundamentally differ from conventional methods, facilitating rapid construct optimization, generating highly predictive translational models, enabling the vision of autonomous, digital-twin-driven manufacturing, and establishing new paradigms for pharmacovigilance and regulatory oversight. The integration of AI is not merely an incremental improvement but a foundational transformation, positioning CGT to move from niche, bespoke treatments to scalable, accessible, and highly personalized medical modalities. We conclude by discussing current gaps, particularly data scarcity and regulatory uncertainty, and outlining a roadmap to realize the full potential of AI-enabled CGT.","url":"https://doi.org/10.3390/pharmaceutics18030356","authors":["Jared R. Auclair","Jeewon Joung","Maya A. Singh","Gaël Debauve","Rominder Singh"],"tags":["Bespoke","Construct (python library)","Computer science","Scarcity","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-13","doi":"https://doi.org/10.3390/pharmaceutics18030356","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416359085","name":"Machine Learning and Artificial Intelligence in Clinical Medicine—Trends, Impact, and Future Directions","source":"openalex","abstract":"Over the past decade, the integration of machine learning (ML) and artificial intelligence (AI) into clinical medicine has accelerated dramatically, reshaping the ways in which clinicians collect, analyze, and interpret health data [...].","url":"https://doi.org/10.3390/jcm14228137","authors":["Emmanuel Andrès","Carlos Escobar","Kent Doi"],"tags":["Artificial intelligence","Medicine","Machine learning","Deep learning","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-17","doi":"https://doi.org/10.3390/jcm14228137","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410125230","name":"The Unexpected Harms of Artificial Intelligence in Healthcare: Reflections on Four Real-World Cases","source":"openalex","abstract":"INTRODUCTION: Rapid advances in Artificial Intelligence (AI), especially with large language models, present both opportunities and challenges in healthcare. This article analyzes real-world AI-related harms in healthcare. METHODS: We selected four recent AI-related incidents from the AIAAIC Repository. RESULTS: The incidents discussed include: Whisper's harmful hallucinations; UNOS's algorithm delaying transplants for black patients; the WHO's S.A.R.A.H. chatbot providing inaccurate health information; and Character AI's chatbot promoting disordered eating among teens. DISCUSSION AND CONCLUSION: These incidents highlight diverse risks, from misinformation to safety concerns, involving both industry and institutional providers. The article emphasizes the need for systematic reporting of AI-related harms, concerns about security, privacy, and ethics, and calls for a centralized health-specific database to enhance patient safety and understanding.","url":"https://doi.org/10.3233/shti250219","authors":["Kerstin Denecke","Guillermo López–Campos","Octavio Rivera-Romero","Elia Gabarrón"],"tags":["Misinformation","Chatbot","Health care","Internet privacy","Patient safety"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.3233/shti250219","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4288056924","name":"Decoupling in Strategic Technologies: From Satellites to Artificial Intelligence","source":"openalex","abstract":"Geopolitical tensions between the United States and China have sparked an ongoing dialogue in Washington about the phenomenon of “decoupling”—the use of public policy tools to separate the multifaceted economic ties that connect the two powers. This issue brief provides a historical lens on the efficacy of one specific aspect of this broader decoupling phenomenon: using export controls and related trade policies to prevent a rival from acquiring the equipment and know-how to catch up to the United States in cutting-edge, strategically important technologies.","url":"https://doi.org/10.51593/20200085","authors":["Tim Hwang","Emily Weinstein"],"tags":["Decoupling (probability)","Phenomenon","Geopolitics","China","Through-the-lens metering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-01","doi":"https://doi.org/10.51593/20200085","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4403152480","name":"SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-031-72111-3_56","authors":["Wenhui Zhu","Xiwen Chen","Peijie Qiu","Mohammad Farazi","Aristeidis Sotiras","Abolfazl Razi","Yalin Wang"],"tags":["Computer science","Image segmentation","Image (mathematics)","Segmentation","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1007/978-3-031-72111-3_56","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410300494","name":"Artificial Intelligence Applications in Obstetric Risk Prediction: A Systematic Review of Machine Learning Models for Preeclampsia","source":"openalex","abstract":"Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality worldwide. While traditional prediction models have shown limited accuracy, machine learning (ML) approaches offer promising alternatives by handling complex, non-linear relationships in multidimensional datasets. This systematic review evaluates the performance, methodological quality, and clinical applicability of ML models for preeclampsia prediction. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we searched five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) for studies published until April 15, 2025, and included studies that developed or validated ML models predicting preeclampsia. Risk of bias was assessed using the Prediction Model Risk-of-Bias Assessment Tool (PROBAST). Eleven studies (n = 11, comprising 116,253 pregnancies) were included. Ensemble methods (XGBoost, Random Forest) demonstrated superior performance, with area under the curve (AUCs) ranging from 0.84 to 0.973. Key predictors included mean arterial pressure, prior preeclampsia, and the biomarkers placental growth factor (PlGF) and pregnancy-associated plasma protein A (PAPP-A). Seven studies (63.6%) showed low overall risk of bias, while three (27.3%) had high risk due to analytical limitations. Only three studies (27.3%) conducted external validation. ML models, particularly ensemble methods, show excellent discriminative ability for preeclampsia prediction. However, heterogeneity in predictors and limited external validation constrain clinical translation. Future research should prioritize prospective validation studies with standardized outcome definitions and predictor sets.","url":"https://doi.org/10.7759/cureus.83961","authors":["Nagla Osman Mohamed Dkeen","Madina Eltayeb Dawelbait Radwan","Israa Ali Alnaw Zumam","Nihal Ahmed Abd Elfrag Mohamed","Eman Mohammed Abbashar Abdelmahmoud","Nisrin Magboul Elfadel Magboul"],"tags":["Medicine","Preeclampsia","Artificial intelligence","Machine learning","Pregnancy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-12","doi":"https://doi.org/10.7759/cureus.83961","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416390169","name":"Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research","source":"openalex","abstract":"Abstract. Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of atmospheric and environmental data relevant to ozone. We highlight the rapid progress made in the field since Phase 1 of the Tropospheric Ozone Assessment Report (TOAR), focussing particularly on the most active areas of research, namely short-term ozone forecasting, emulation of atmospheric chemistry and the use of remote sensing for ozone estimation. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to develop ML in ozone research. Further advances hinge on addressing domain-specific issues such as the dependence of ozone concentrations on several poorly observed precursor species, as well as making progress on generic ML challenges such as the definition of suitable benchmarks and developing robust, explainable models. Reaping the full potential of ML for ozone research and operational applications will require close collaborations across atmospheric chemistry, ML and computational science and vigilant pursuit of the rapid developments in adjacent fields.","url":"https://doi.org/10.5194/gmd-18-8777-2025","authors":["Sebastian Hickman","Makoto Kelp","Paul T. Griffiths","Kelsey Doerksen","Kazuyuki Miyazaki","Elyse A. Pennington","Gerbrand Koren","Fernando Iglesias‐Suarez","Martin G. Schultz","Kai‐Lan Chang","Owen R. Cooper","Alexander T. Archibald","Roberto Sommariva","David Carlson","Hantao Wang","J. Jason West","Zhenze Liu"],"tags":["Tropospheric ozone","Emulation","Environmental science","Ozone","Ozone layer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-20","doi":"https://doi.org/10.5194/gmd-18-8777-2025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414289079","name":"Artificial Intelligence-enabled smart grid systems for real-time load forecasting, fault detection, renewable energy integration and optimization","source":"openalex","abstract":"The global energy landscape is undergoing rapid transformation, driven by increasing electricity demand, decarbonization goals, and the integration of renewable energy resources. Traditional power systems, characterized by centralized operations and limited responsiveness, are increasingly inadequate for managing modern grid complexities. Smart grid systems have emerged as a critical paradigm, embedding advanced sensors, communication infrastructures, and intelligent decision-making mechanisms to enhance grid stability and reliability. However, the scale and unpredictability of dynamic energy flows demand advanced solutions that surpass conventional optimization and monitoring methods. Artificial intelligence (AI) has proven to be a transformative enabler in this context, offering predictive, adaptive, and real-time decision-making capabilities. By leveraging machine learning and deep learning algorithms, AI-enabled smart grids can accurately forecast load demand, detect and isolate faults, and dynamically balance distributed energy resources. Predictive load forecasting allows operators to anticipate fluctuations in consumer demand, ensuring efficiency and stability, while AI-driven fault detection systems improve resilience by rapidly identifying anomalies and preventing cascading failures. Furthermore, AI supports the seamless integration of renewable energy sources, mitigating intermittency challenges by optimizing grid dispatch and storage solutions. This paper explores the convergence of artificial intelligence with smart grid infrastructures, emphasizing its applications in real-time load forecasting, fault detection, renewable energy integration, and system-wide optimization. It also addresses associated challenges such as data privacy, model interpretability, and cybersecurity risks, offering a balanced discussion of opportunities and limitations. Ultimately, AI-enabled smart grids represent a pivotal step toward building resilient, sustainable, and intelligent energy ecosystems capable of supporting the future of decentralized, low-carbon power systems.","url":"https://doi.org/10.30574/gjeta.2025.24.3.0272","authors":["Abdulrahman Adebola Iyaniwura","Charles Sunday Mayaki"],"tags":["Smart grid","Renewable energy","Computer science","Distributed computing","Electric power system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-17","doi":"https://doi.org/10.30574/gjeta.2025.24.3.0272","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415564840","name":"Artificial Intelligence in Digestive Endoscopy Training—The Past, Present, and Future","source":"openalex","abstract":"BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) is reshaping gastrointestinal endoscopy, yet its role in training remains unexplored. This narrative review summarizes current evidence on AI-assisted endoscopy training, addresses potential drawbacks, and envisions future directions. METHODS: This narrative review was performed via a systematic MEDLINE search (including articles from inception to January 2025), with search terms covering 'AI', 'endoscopy,' and 'training.' Studies were excluded if they were reviews, letters, editorials or comments; focused solely on model development; lacked a training component; or were limited to simple comparisons between the performance of endoscopists and AI systems. After screening 1443 records, 27 articles were included in this review. RESULTS: AI demonstrates potential in enhancing the training of various types of endoscopy (including luminal, hepatobiliary, capsule, and therapeutic endoscopy) by improving quality metrics, enhancing lesion detection, and guiding anatomical landmark recognition, yet the current applications are mainly task-based. Future AI must evolve to provide comprehensive training and personalized performance tracking to endoscopists of different levels of experience. Further studies are needed to assess objective educational outcomes and cost-effectiveness. Key concerns for AI adoption, including deskilling, over-reliance, ethical considerations, and practicality, should be addressed through structured implementation, quality assurance, and regulatory framework. CONCLUSION: In conclusion, AI can augment endoscopy training by improving skill acquisition and procedural quality, yet significant gaps remain. More research is needed to support its widespread integration.","url":"https://doi.org/10.1111/den.70047","authors":["Jacky C. L. Ho","Zhouyao Qian","Louis Ho Shing Lau","Hon Chi Yip","Philip Wai Yan Chiu"],"tags":["Medicine","Endoscopy","Medical physics","MEDLINE","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-26","doi":"https://doi.org/10.1111/den.70047","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415147093","name":"Artificial intelligence in osteoarthritis research: summary of the 2025 OARSI pre-congress workshop","source":"openalex","abstract":"Objective: Artificial intelligence (AI) is transforming musculoskeletal research, offering new approaches to diagnosis, prognosis, and patient management in osteoarthritis (OA). However, implementation and ethical challenges persist. This manuscript summarizes insights from the OARSI 2025 Pre-Congress Workshop on Artificial Intelligence in Osteoarthritis Research, highlighting opportunities and challenges in applying AI across biomechanics, imaging, and clinical research domains. Design: The workshop, organized by the OARSI Early Career Investigator Committee and co-chaired by Drs. Matthew Harkey and Brooke Patterson, convened experts to discuss the use of AI in real-world biomechanics data collection, radiomics for imaging-based biomarkers, and large language models (LLMs) for clinical and research applications. Emphasis was placed on the need for interdisciplinary collaboration and ethical oversight. Results: In biomechanics, AI-driven markerless motion capture and wearable sensors enable scalable, ecologically valid data collection, though issues such as class imbalance, data privacy, and model interpretability remain. In imaging, radiomics and deep learning models show promise for early OA detection and progression prediction but face challenges in domain adaptation and external validation. In clinical research, LLMs can streamline documentation and thematic analysis but must address concerns around bias, data security, and transparency. Across domains, transparency, reproducibility, and ethical use of AI were emphasized as critical for maintaining scientific rigor. Conclusions: Cross-disciplinary collaboration and AI literacy are essential to responsibly advance AI integration in OA research. The workshop's collective insights call for ethical, patient-centered approaches that leverage AI's strengths while preserving research integrity and trust.","url":"https://doi.org/10.1016/j.ocarto.2025.100687","authors":["Matthew S. Harkey","K.E. Costello","Bella Mehta","Chunyi Wen","Anne‐Marie Malfait","Henning Madry","Brooke Patterson"],"tags":["Leverage (statistics)","Psychology","Artificial intelligence","Applications of artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-23","doi":"https://doi.org/10.1016/j.ocarto.2025.100687","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409169023","name":"Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective","source":"openalex","abstract":"Artificial intelligence (AI) has emerged as a transformative tool in placental pathology, offering novel diagnostic methods that promise to improve accuracy, reduce inter-observer variability, and positively impact pregnancy outcomes. The primary objective of this review is to summarize recent developments in AI applications tailored specifically to placental histopathology. Current AI-driven approaches include advanced digital image analysis, three-dimensional placental reconstruction, and deep learning models such as GestAltNet for precise gestational age estimation and automated identification of histological lesions, including decidual vasculopathy and maternal vascular malperfusion. Despite these advancements, significant challenges remain, notably dataset heterogeneity, interpretative limitations of current AI algorithms, and issues regarding model transparency. We critically address these limitations by proposing targeted solutions, such as augmenting training datasets with annotated artifacts, promoting explainable AI methods, and enhancing cross-institutional collaborations. Finally, we outline future research directions, emphasizing the refinement of AI algorithms for routine clinical integration and fostering interdisciplinary cooperation among pathologists, computational researchers, and clinical specialists.","url":"https://doi.org/10.3390/jimaging11040110","authors":["Antonio d’Amati","Giorgio Maria Baldini","Tommaso Difonzo","Angela Santoro","Miriam Dellino","Gerardo Cazzato","Antonio Malvasi","Antonella Vimercati","Leonardo Resta","Gian Franco Zannoni","Eliano Cascardi"],"tags":["Computer science","Digital pathology","Artificial intelligence","Data science","Transparency (behavior)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-03","doi":"https://doi.org/10.3390/jimaging11040110","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416873224","name":"Breast Cancer Diagnosis With Explainable Artificial Intelligence (XAI): Uncovering Strengths and Biases","source":"openalex","abstract":"Breast cancer is one of the most common malignancies afflicting women globally, necessitating the use of cutting-edge AI approaches in diagnostic procedures to improve patient outcomes drastically. However, one key difficulty with the most recent AI models is a lack of transparency, making it difficult for medical practitioners to implement these technologies to increase diagnostic accuracy. Many explainable AI (XAI) solutions have been developed to overcome this issue. This survey focuses on applying XAI approaches in breast cancer detection and diagnosis, particularly emphasizing their role in increasing model transparency and clinical decision-making. The article also provides insights into the inherent biases in the most recent machine learning models towards specific XAI approaches, such as the compatibility of Convolutional Neural Networks (CNNs) with visual explanation methods and tree-based models with feature significance evaluations. Finally, the article covers the obstacles to using XAI technologies in clinical practice and the significance of defining standard measures for assessing their performance.","url":"https://doi.org/10.1109/access.2025.3639184","authors":["Samita Bai","Sidra Nasir","Rizwan Ahmed Khan","Alexandre Meyer","Hubert Konik"],"tags":["Computer science","Artificial intelligence","Breast cancer","Transparency (behavior)","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/access.2025.3639184","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411710523","name":"Artificial Intelligence‐Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs","source":"openalex","abstract":"BACKGROUND: Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This study aimed to develop, train, and test a deep learning system to detect hyperacute CRAO on retinal fundus photographs within the critical 4.5-hour treatment window and up to 24 hours after visual loss to aid in secondary stroke prevention. METHODS: Our retrospective, cross-sectional study included 1322 color fundus photographs from 771 patients with acute visual loss due to CRAO, central retinal vein occlusion, nonarteritic anterior ischemic optic neuropathy, and healthy controls. Photographs were collected from 9 expert neuro-ophthalmology centers in 6 countries, including 3 randomized clinical trials. Training included 1039 photographs (517 patients), followed by testing on 2 data sets: (1) hyperacute CRAO (54 photographs, 54 patients) and (2) CRAO within 24 hours after visual loss (110 photographs, 109 patients). RESULTS: The deep learning system achieved an area under the receiver operating characteristic curve of 0.96 (95% confidence interval (CI), 0.95-0.98), a sensitivity of 92.6% (95% CI, 87.0-98.0), and a specificity of 85.0% (95% CI, 81.8-92.8) for detecting CRAO at hyperacute stage, with similar results within 24 hours. The deep learning system outperformed stroke neurologists on a subset of hyperacute testing data set (120 photographs, 120 patients). CONCLUSIONS: A deep learning system can accurately detect hyperacute CRAO on retinal photographs within a time window compatible with urgent fibrinolysis. If further validated, such systems could improve patient selection for fibrinolytic trials and optimize secondary stroke prevention. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06390579.","url":"https://doi.org/10.1161/jaha.124.041441","authors":["Ayse Gungor","Ilias Sarbout","Aubrey L. Gilbert","Steffen Hamann","Pierre Lebranchu","Cristina Hobeanu","Philippe Gohier","Catherine Vignal‐Clermont","Oana M. Dumitrascu","Salomon Y. Cohen","Wolf A. Lagrèze","Nicolas Feltgen","Frank van der Heide","C. Lamirel","Jost B. Jonas","Michaël Obadia","Daniel Racoceanu","Dan Miléa"],"tags":["Medicine","Central retinal artery occlusion","Fundus (uterus)","Ophthalmology","Stroke (engine)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-27","doi":"https://doi.org/10.1161/jaha.124.041441","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413499075","name":"Artificial intelligence for the science of evidence synthesis: how good are AI-powered tools for automatic literature screening?","source":"openalex","abstract":"BACKGROUND: Literature screening constitutes a critical component in evidence synthesis; however, it typically requires substantial time and human resources. Artificial intelligence (AI) has shown promise in this field, yet the accuracy and effectiveness of AI tools for literature screening remain uncertain. This study aims to evaluate the performance of several existing AI-powered automated tools for literature screening. METHODS: This diagnostic accuracy study employed a cohort to evaluate the performance of five AI tools-ChatGPT 4.0, Claude 3.5, Gemini 1.5, DeepSeek-V3, and RobotSearch-in literature screening. We selected a random sample of 1,000 publications from a well-established literature cohort, with 500 as randomized controlled trials (RCTs) group and 500 as others group. Diagnostic accuracy was measured using several metrics, including the false negative fraction (FNF), time used for screening, false positive fraction (FPF), and the redundancy number needed to screen. RESULTS: We reported the FNF for the RCTs group and the FPF for the others group. In the RCTs group, RobotSearch exhibited the lowest FNF at 6.4% (95% CI: 4.6% to 8.9%), whereas Gemini exhibited the highest at 13.0% (95% CI: 10.3% to 16.3%). In the others group, the FPF of the four large language models ranged from 2.8% (95% CI: 1.7% to 4.7%) to 3.8% (95% CI: 2.4% to 5.9%), both of which were significantly lower than RobotSearch's rate of 22.2% (95% CI: 18.8% to 26.1%). In terms of screening efficiency, the mean time used for screening per article was 1.3 s for ChatGPT, 6.0 s for Claude, 1.2 s for Gemini, and 2.6 s for DeepSeek. CONCLUSIONS: The AI tools assessed in this study demonstrated commendable performance in literature screening; however, they are not yet suitable as standalone solutions. These tools can serve as effective auxiliary aids, and a hybrid approach that integrates human expertise with AI may enhance both the efficiency and accuracy of the literature screening process.","url":"https://doi.org/10.1186/s12874-025-02644-9","authors":["Minghao Ruan","Junhao Fan","Mingkai Liu","Zhefeng Meng","Xiaohai Zhang","Chengjing Zhang"],"tags":["Computer science","Data science","MEDLINE","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-25","doi":"https://doi.org/10.1186/s12874-025-02644-9","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408919473","name":"The Expanding Role of Artificial Intelligence in Modern Dentistry","source":"openalex","abstract":"Artificial Intelligence (AI) is rapidly transforming various sectors, and dentistry is no exception.This paper explores the expanding role of AI in modern dental practice, examining its applications in diagnosis, treatment planning, and patient care.AI-powered tools are being developed to assist in the detection of dental caries, periodontal disease, and oral cancer through the analysis of radiographic images and clinical data.Furthermore, AI [1,2] algorithms are being utilized to create personalized treatment plans, predict treatment outcomes, and automate certain dental procedures.While challenges remain in terms of data privacy, algorithm bias, and regulatory frameworks, the integration of AI has the potential to enhance diagnostic accuracy, improve treatment efficiency, and ultimately elevate the standard of dental care.This paper provides an overview of the current state of AI in dentistry, discusses its potential benefits and limitations, and highlights future directions for research and development.","url":"https://doi.org/10.31031/mrd.2025.08.000687","authors":["Omid Panahi"],"tags":["Dentistry","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-12","doi":"https://doi.org/10.31031/mrd.2025.08.000687","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4415132431","name":"Explainable artificial intelligence in the talent recruitment process-a literature review","source":"openalex","abstract":"With the widespread application of artificial intelligence in talent recruitment, the ‘black-box’ nature of AI, which leads to insufficient transparency and interpretability in decision-making, has gradually become a key challenge. This paper reviews the application of Explainable AI technologies throughout the entire talent recruitment process, aiming to analyze the role of XAI in enhancing decision-making transparency and traceability. The study finds that XAI, through interpretable algorithms such as LIME, SHAP, knowledge graphs, and causal reasoning, has significantly improved semantic understanding in resume parsing, precision in person-job recommendations, and analytical capabilities in interview evaluations. However, potential biases in generative large models, insufficient cross-scenario interpretability, and computational efficiency issues remain major bottlenecks. Future research should focus on dynamic fairness constraints and the integration of lightweight interpretability tools to promote the coordinated development of XAI in terms of ethical compliance, user trust, and practicality.","url":"https://doi.org/10.1080/23311975.2025.2570881","authors":["Gening Zhang","Lin Pan","Fang Tang","Feng Yao"],"tags":["Interpretability","Transparency (behavior)","Knowledge management","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-13","doi":"https://doi.org/10.1080/23311975.2025.2570881","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7118808027","name":"Research That Matters: A Call for Enhancing Rigour and Relevance in Artificial Intelligence Research in Endodontics","source":"openalex","abstract":"Artificial intelligence (AI) is emerging as a powerful tool in dentistry, where endodontics can benefit significantly from its potential applications. From enhanced diagnostic accuracy to treatment planning and decision-making, optimisation, and outcome prediction, AI may significantly improve the clinical practice and the teaching of endodontics (Mohammad-Rahimi et al. 2024; Ourang et al. 2024). However, this still remains a largely unfulfilled promise, because many studies suffer from significant limitations or methodological flaws. Several factors contribute to this condition. First, as often seen with the advent of novel technology, there is a lack of scientific expertise in AI methodology by editors and reviewers, which may result in the eventual publication of studies that display limitations or, sometimes, even fundamental flaws in design, implementation, or validation. Conversely, technical publications may present sophisticated algorithms that solve problems with limited clinical utility or fail to address the nuanced challenges of real-world endodontic practice, mainly due to a lack of endodontic domain experts who could bridge the gap between AI engineers and the field of endodontics. Consequently, literature becomes populated with low-quality studies with oversold conclusions, but no conceivable clinical applicability. In this paper, the authors critically present the common pitfalls observed in current AI research in dentistry, and more specifically, in endodontics and then offer practical solutions to address these pitfalls in future research. The goal is to provide constructive guidance that can help establish rigorous standards for a field that is transitioning from research to real-life application. While we aim to present ideal scenarios that highlight these challenges, it is essential to acknowledge that every research study has limitations and shortcomings. These observations are not a critique of the researchers' intent but a call for consideration of these factors to enhance future work in the field. Foundational research has been conducted, and basic AI applications have been developed for endodontic applications. The next steps in endodontic AI research will have to address clinical translation and relevance which may often have been overlooked. This pitfall includes two interconnected challenges, but in distinct ways: developing AI models that solve problems of limited clinical significance and conducting studies under conditions that cannot translate to the real world. Some AI studies focus on tasks that, while technically interesting, have limited practical value in endodontic practice. Thus, the fundamental question researchers must ask is: Does this AI application solve a problem that clinicians face in daily practice, and would its solution meaningfully improve patient care or clinical workflow? Without this foundation, even technically sophisticated and highly performant models may risk being solutions in search of problems. Another core problem in AI studies is using non-representative training datasets. Some studies developed AI models under idealised or artificial conditions and then reported results as if they were clinically meaningful. This gap between experimental success and clinical utility may lead to a false sense of success. It impairs translation to practice, as models that perform well in controlled settings can fail or mislead in real clinical settings (Ibrahim et al. 2021). For example, there are AI models developed on ex vivo samples, phantom images, or synthetic entities (e.g., artificial lesions or artificial root cracks) that simplify the task. Training and testing an AI model on the detection of artificial root cracks in radiographic images of extracted teeth does not imply that the model can detect vertical root fractures in radiographic images of real patients. While convenient for initial experimentation, like pilot or proof-of-concept studies, such data lack the complexit","url":"https://doi.org/10.1111/iej.70094","authors":["Hossein Mohammad‐Rahimi","Rishi Sanjay Ramani","Frank Setzer","Falk Schwendicke","Ruben Pauwels","Ali Nosrat"],"tags":["Rigour","Endodontics","Relevance (law)","Field (mathematics)","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-08","doi":"https://doi.org/10.1111/iej.70094","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4381597744","name":"Man, or Machine? Artificial Intelligence Language Systems in Plastic Surgery","source":"openalex","abstract":"Artificial intelligence (AI) language models are computer programs trained to understand and generate human-like text. The latest AI language models available to the public have impressive language generation capability with immediate applications in both academia and private practice. Plastic surgeons can immediately leverage this technology to more efficiently allocate valuable human capital to higher-yield tasks. This can ultimately translate to higher patient volume, higher research output, and improved patient communication. Commercially available models offer business solutions that should not be ignored by plastic surgeons hoping to establish, optimize, or grow their practices. In this paper, the authors review the current state of AI language systems, discuss potential applications, and explore the risks and limitations of this technology.","url":"https://doi.org/10.1093/asj/sjad197","authors":["Jose Palacios","Nicholas Bastidas"],"tags":["Medicine","Leverage (statistics)","Language model","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-22","doi":"https://doi.org/10.1093/asj/sjad197","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411905413","name":"Exploring the experiences and perceptions of nursing students in utilizing artificial intelligence: a descriptive phenomenological study","source":"openalex","abstract":"BACKGROUND: Artificial intelligence is transforming healthcare by enhancing diagnostics, efficiency, and outcomes. Evaluating nursing students' knowledge helps identify educational gaps, as they directly engage in patient care and decision-making. This study explored their experiences with using artificial intelligence in clinical settings. METHODS: A descriptive phenomenological approach was employed to recruit eight nursing students from the School of Nursing and Midwifery in Kermanshah-Iran, all of whom had prior experience with artificial intelligence applications in clinical settings. Participants were selected purposefully and underwent in-depth, semi-structured interviews. Eight interview sessions lasting 60-70 min each were conducted, with data saturation achieved after the eighth interview. The data were analyzed using Colaizzi's seven-step method, ensuring adherence to Guba and Lincoln's criteria for trustworthiness throughout the research process. Data management was facilitated using MAXQDA 20 software. RESULTS: Data analysis yielded 450 initial codes, 5 sub-themes, and 4 themes. The themes identified were: (1) Novel Potentials and Broad Horizons of Artificial Intelligence, (2) Ethical and Security Challenges In The World of Artificial Intelligence, (3) Changes in Human Identity and Social Relationships In The Age of Artificial Intelligence, and (4) Enhancing and Reinventing Skills for Coexistence With Artificial Intelligence. CONCLUSION: The study revealed that nursing students recognize the potential of artificial intelligence to enhance precision and efficiency in patient care. However, they emphasized the need for further education and the removal of barriers to its effective implementation. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://doi.org/10.1186/s12912-025-03392-3","authors":["Maysam Safari Nezhad","Alireza Abdi","Mahnaz Ahmadi"],"tags":["Nursing management","Nursing research","Perception","Descriptive research","Nursing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1186/s12912-025-03392-3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410601609","name":"Artificial intelligence in conformance checking: state of the art and research agenda","source":"openalex","abstract":"Artificial intelligence methods have gained increasing importance in business process management in recent years. In particular, artificial intelligence boosted the development of process mining techniques, with predictive process monitoring being one prominent example. However, for some branches of process mining, little attention has been paid in the literature to understanding potential opportunities offered by artificial intelligence. In this work, we focus on conformance checking. This discipline is gaining traction in both research and practical applications due to its capability of revealing inconsistencies between models and event logs. Though state-of-the-art conformance checking has been investigated recently, there exists a lack of insights on whether conformance checking can benefit from recent developments in artificial intelligence. This paper addresses this research gap through a systematic literature review of conformance checking approaches contrasted against trends in artificial intelligence research extracted from papers published in core artificial intelligence venues. This comparative analysis extracts prominent trends in both disciplines, highlighting potential overlaps and topics of common interests. Elaborating upon such overlaps, we identify promising research avenues leveraging AI to address open challenges in conformance checking, as well as recent AI trends which may lead to interesting developments for the conformance checking community.","url":"https://doi.org/10.1007/s44311-025-00015-7","authors":["Laura Genga","Karolin Winter"],"tags":["Conformance testing","State (computer science)","Computer science","Political science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-22","doi":"https://doi.org/10.1007/s44311-025-00015-7","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4416061820","name":"Explainable Artificial Intelligence in Echocardiography","source":"openalex","abstract":"Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view classification, quantification of cardiac function, and the diagnosis and risk assessment of cardiac diseases. The “black box” nature of AI models limits their clinical applications. Adopting explainable artificial intelligence (XAI) methods is crucial for improving the transparency and understanding of model predictions. This paper reviews the progress of AI applications in echocardiography, with a particular emphasis on XAI as a technical solution to enhance the transparency of model decision-making and its benefits compared to traditional AI models. This review outlines recent advancements in XAI applications for echocardiography and their clinical implications.","url":"https://doi.org/10.26599/audt.2025.250089","authors":["Xinghong Hu","Ye Zhu","Zisang Zhang","Yuanting Quan","Wenwen Chen","Leichong Chen","Guangyu Xu","Luning Qin","Mingxing Xie","Li Zhang"],"tags":["Artificial intelligence","Transparency (behavior)","Computer science","Applications of artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-10","doi":"https://doi.org/10.26599/audt.2025.250089","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4402893661","name":"Application of artificial intelligence in the development of personalized sports injury rehabilitation plan","source":"openalex","abstract":"Sports injury rehabilitation is a kind of physical treatment used to address musculoskeletal system disorders, injuries, and discomfort in patients of all ages. Sports rehabilitation promotes health and fitness, aids in injury recovery, and lessens pain through movement, exercise, and physical therapy. During a sports injury, rehabilitation has developed into a specialized profession that has gradually brought together sports physicians, sports physiotherapists, and orthopedic surgeons. Finding the best ways to minimize recovery time, avoid injuries, and enhance performance is crucial for sports athletes. The aim of this research is to establish a personalized sports injury rehabilitation evaluation system enabled by artificial intelligence (AI). In this study, a novel advanced penguin search optimized efficient random forest (APSO-ERF) has been proposed for sports injury athletics exercise rehabilitation. This study used exercise movement image data to develop personalized sports injury rehabilitation. The data was preprocessed using a Wiener filter for noise reduction and image restoration. Convolutional neural networks (CNN) are used to extrapolate top-level characteristics from images. The proposed method is used to evaluate physical rehabilitation by assessing patient performance during the completion of prescribed sports injury rehabilitation exercises. The proposed method is compared to other traditional algorithms. With 97.80% accuracy, 96.01% sensitivity, 97.90% specificity, 98.88% precision, 96.11% recall, and 97.50% F1-score, the APSO-ERF approach beats conventional algorithms in tailored sports injury rehabilitation. The result illustrated that the proposed method achieved high performance in the accuracy of sports injury athletics exercise rehabilitation.","url":"https://doi.org/10.62617/mcb.v21i1.326","authors":["Chao Zhan"],"tags":["Rehabilitation","Plan (archaeology)","Physical medicine and rehabilitation","Sports injury","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-26","doi":"https://doi.org/10.62617/mcb.v21i1.326","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410516058","name":"Empathy, Ethics and Efficacy: The 3Es of Implementing Artificial Intelligence for Consumer Encounters","source":"openalex","abstract":"ABSTRACT Drawing insights from virtue ethics and psychological perspectives on empathy, we propose a framework for integrating empathy, ethics, and efficacy (3E) to guide the responsible development and deployment of artificial intelligence (AI) technologies in consumer service encounters. It encourages developers, vendors, and users to improve efficiencies and productivity but, more importantly, to adopt an empathetic perspective with ethical decision‐making during AI development and deployment. By simultaneously adopting descriptive, analytical, and prescriptive approaches to engage academics, practitioners, and public policymakers, we outline a future research agenda for enriching AI‐consumer service encounters. Beyond conceptual integration, the framework offers practical insights for AI designers, businesses, and regulators by emphasising empathy as a bridge between efficacy and ethics. This perspective supports the development of AI technologies that not only enhance operational effectiveness but also foster consumer trust and well‐being, ensuring AI‐driven services remain human‐centred and ethically sound. A structured decision‐making model demonstrates how AI‐driven services can balance automation with ethical considerations and empathetic engagement, offering a pathway for more responsible AI implementation.","url":"https://doi.org/10.1002/mar.22235","authors":["Kyoko Fukukawa","Rohit H. Trivedi"],"tags":["Empathy","Software deployment","Knowledge management","Engineering ethics","Service (business)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-20","doi":"https://doi.org/10.1002/mar.22235","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413329932","name":"Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications","source":"openalex","abstract":"Artificial intelligence (AI) and its machine learning and deep learning algorithms have shown promise in oncological practice. Spatial information analysis in the context of cancer is crucial for its diagnosis and treatment because it can provide an understanding of tumor-microenvironment interactions and reveal insights into response to treatment. AI tools can analyze spatial information at multiple scales, highlighting key disease, clinical, and genetic phenotypes that may reveal underlying mechanisms and molecular markers of response and resistance within the tumor and its microenvironment. By examining tumor interactions at macroscopic (diagnostic imaging) and microscopic (pathology slides and spatial biology) levels, AI can assist in making important diagnostic and prognostic decisions. In this review, we first present an overview of AI and the need for multiscale spatial information in oncology. Then, we examine growing AI applications in the analysis of such information, focusing on diagnostic imaging, digital pathology, and spatial molecular biology. We also discuss applications of large-scale foundation models and task-oriented agentic AI in these fields as emergent technologies. Then, we discuss current limitations for the clinical translation of AI into regular utilization in cancer care and discovery.","url":"https://doi.org/10.3390/ijms26168002","authors":["Ali A. Tarhini","Issam El Naqa"],"tags":["Context (archaeology)","Digital pathology","Computer science","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-19","doi":"https://doi.org/10.3390/ijms26168002","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414072562","name":"Exploring the Role of Artificial Intelligence in Evidence Synthesis: Insights From the CORE Information Retrieval Forum 2025","source":"openalex","abstract":"ABSTRACT Introduction Information retrieval is essential for evidence synthesis, but developing search strategies can be labor‐intensive and time‐consuming. Automating these processes would be of benefit and interest, though it is unclear if Information Specialists (IS) are willing to adopt artificial intelligence (AI) methodologies or how they currently use them. In January 2025, the NIHR Innovation Observatory and NIHR Methodology Incubator for Applied Health and Care Research co‐sponsored the inaugural CORE Information Retrieval Forum, where attendees discussed AI's role in information retrieval. Methods The CORE Information Retrieval Forum hosted a Knowledge Café. Participation was voluntary, and attendees could choose one of six event‐themed discussion tables including AI. To support each discussion, a QR code linking to a virtual collaboration tool (Padlet; padlet.com ) and a poster in the exhibition space were available throughout the day for attendee contributions. Results The CORE Information Retrieval Forum was attended by 131 IS from nine different types of organizations, with most from the UK and ten countries represented overall. Among the six discussion points available in the Knowledge Café, the AI table was the most popular, receiving the highest number of contributions ( n = 49). Following the Forum, contributions to the AI topic were categorized into four themes: critical perception ( n = 21), current uses ( n = 19), specific tools ( n = 2), and training wants/needs ( n = 7). Conclusions While there are critical perspectives on the integration of AI in the IS space, this is not due to a reluctance to adapt and adopt but from a need for structure, education, training, ethical guidance, and systems to support the responsible use and transparency of AI. There is interest in automating repetitive and time‐consuming tasks, but attendees reported a lack of appropriate supporting tools. More work is required to identify the suitability of currently available tools and their potential to complement the work conducted by IS.","url":"https://doi.org/10.1002/cesm.70049","authors":["Claire Eastaugh","Madeleine Still","Fiona Beyer","S Wallace","Hannah O’Keefe"],"tags":["Computer science","Transparency (behavior)","Core (optical fiber)","Data science","Work (physics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.1002/cesm.70049","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7115948080","name":"Development and Validation of Artificial Intelligence Addiction Scale for Researchers: A Methodological Study","source":"openalex","abstract":"Background The integration of artificial intelligence (AI) tools into research has brought significant advancements, enhancing efficiency, innovation, and productivity across various academic disciplines. However, alongside these transformative benefits, the growing dependence on AI tools has raised concerns regarding overreliance and the potential for addictive behaviors among researchers. Despite the widespread adoption of AI among the researchers, there remains a notable gap in the availability of validated instruments specifically designed to assess AI addiction within this context. Objective To develop a scale to measure AI addiction among researchers and evaluate its psychometric properties. Design A methodological design was employed, consisting of two phases: scale development and psychometric evaluation. Methods Items were generated through a comprehensive literature review and semistructured interviews to capture AI addiction attributes. The scale’s psychometric properties—including content validity, face validity, construct validity, and internal consistency reliability—were assessed. Data from a convenience sample of 718 nursing researchers were randomly divided into two independent subsamples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was evaluated using Cronbach’s alpha, McDonald’s omega, split‐half reliability, and corrected item–total correlations. Results The finalized scale comprises 22 items across five dimensions: compulsive behavior, overdependency, functional impairment, withdrawal, and tolerance. EFA identified a five‐factor structure explaining 73.66% of the variance. CFA validated the structure with robust fit indices for first order ( χ 2 /DF = 2.289, CFI = 0.962, and RMSEA = 0.06) and second order ( χ 2 /DF = 2.243, CFI = 0.962, and RMSEA = 0.059) models. The scale demonstrated excellent internal consistency and reliability, with Cronbach’s alpha ( α = 0.924), McDonald’s omega ( ω = 0.870), and a Spearman–Brown split‐half coefficient of 0.814. Moderate interfactor correlations ( r = 0.41–0.62) confirmed its multidimensionality. Conclusion The researchers’ AI addiction scale is a valid and reliable tool for assessing AI addiction among researchers, providing a robust framework to evaluate compulsive behavior, dependency, functional disruption, withdrawal symptoms, and tolerance.","url":"https://doi.org/10.1155/jonm/8458533","authors":["Ahmed Abdelwahab Ibrahim El-Sayed","Samira Ahmed Alsenany","Maha Gamal Ramadan Asal","Ibrahim Alasqah"],"tags":["Addiction","Psychology","Scale (ratio)","Behavioral addiction","Psychometrics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1155/jonm/8458533","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413024962","name":"Rethinking Artificial Intelligence (AI) in Qualitative Research","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly being integrated into qualitative research. While AI provides tools that claim to promise greater efficiency and productivity, it also raises vital concerns about methodological integrity, ethical conduct, and the maintenance of contextual depth. This paper explores the implications of AI for qualitative research. It seeks to address the ethical, methodological, and epistemological concerns associated with AI integration. It also aims to promote critical engagement with AI while upholding the foundational values of context-rich qualitative research. The article initially addresses the growing publicity that AI has received in studies and public discourse, focusing on inflated hopes and misconceptions. The central debate concerns the impacts of AI on qualitative research, such as the way it could improve analysis and transcription efficiency. However, the use of AI is not without issues related to ethics, bias, loss of interpretive depth, and over-reliance on automation. The article argues in favour of responsible, critical, and ethically aware use of AI in qualitative research. AI can be a valuable tool to support, but not replace, qualitative researchers. Its use must be governed by reflexivity, ethical sensitivity, and contextual knowledge to uphold the foundational values of qualitative inquiry.","url":"https://doi.org/10.31436/ijcs.v8i2.468","authors":["Nurul Akma Jamil","Nor’ain Abdul Rashid","Woei Ling Tan"],"tags":["Artificial intelligence","Computer science","Psychology","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.31436/ijcs.v8i2.468","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4280503113","name":"Digital Transformation in Healthcare 4.0: Critical Factors for Business Intelligence Systems","source":"openalex","abstract":"The health sector is one of the most knowledge-intensive and complicated globally. It has been proven repeatedly that Business Intelligence (BI) systems in the healthcare industry can help hospitals make better decisions. Some studies have looked at the usage of BI in health, but there is still a lack of information on how to develop a BI system successfully. There is a significant research gap in the health sector because these studies do not concentrate on the organizational determinants that impact the development and acceptance of BI systems in different organizations; therefore, the aim of this article is to develop a framework for successful BI system development in the health sector taking into consideration the organizational determinants of BI systems’ acceptance, implementation, and evaluation. The proposed framework classifies the determinants under organizational, process, and strategic aspects as different types to ensure the success of BI system deployment. Concerning practical implications, this paper gives a roadmap for a wide range of healthcare practitioners to ensure the success of BI system development.","url":"https://doi.org/10.3390/info13050247","authors":["Fotis Kitsios","Νικόλαος Καπετανέας"],"tags":["Software deployment","Health care","Knowledge management","Business","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-12","doi":"https://doi.org/10.3390/info13050247","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412694387","name":"Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives","source":"openalex","abstract":"Hepatocellular carcinoma (HCC) is a highly malignant tumor with elevated incidence and mortality rates globally. Its complex etiology and pronounced heterogeneity present significant challenges in diagnosis and treatment. Recent advancements in artificial intelligence (AI) have demonstrated transformative potential to usher a new wave of precision oncology. Pathomics, an AI-based digital pathology technique, facilitates the extraction of extensive datasets from whole-slide histopathological images, enabling quantitative analyses to improve diagnosis, treatment, and prognostic prediction for HCC. Furthermore, emerging pathological foundation models are revolutionizing traditional paradigms and providing a robust framework for the development of specialized pathomics models tailored to specific clinical tasks in HCC. Despite its promise, pathomics research in HCC remains in its infancy, with clinical implementation hindered by challenges such as data heterogeneity, model interpretability, ethical concerns, regulatory issues, and the absence of standardized industry protocols. Future initiatives should prioritize the conduction of prospective multi-center studies, the integration of multi-modal data, the enhancement of regulatory frameworks, and the establishment of industry-wide standardized guidelines and compliant platform infrastructures to accelerate the clinical adoption of pathomics for personalized HCC treatment.","url":"https://doi.org/10.1007/s12672-025-03254-z","authors":["Wei Ding","Jin-Xing Zhang","Zhi‐Cheng Jin","Hongjin Hua","Qing‐Quan Zu","Shudong Yang","W Wang","Sheng Liu","Hai-Feng Zhou","Hai-Bin Shi"],"tags":["Hepatocellular carcinoma","Current (fluid)","Medicine","Engineering","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-28","doi":"https://doi.org/10.1007/s12672-025-03254-z","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4408504098","name":"Exploring University Staff’s Perceptions of Using Generative Artificial Intelligence at University","source":"openalex","abstract":"This study aimed to understand university staff’s perspectives and approaches regarding students’ use of generative artificial intelligence (GenAI) in an academic setting. Currently, there is a lack of social media analyses exploring this area. For the present study, a qualitative content analysis was conducted on posts about ChatGPT shared via X (formerly Twitter). This enabled a sample of n = 194 perspectives to be captured. Three main themes were generated: (1) perceptions of GenAI’s impact on higher education and skepticism towards its management; (2) GenAI in assessment: prevention and detection approaches; and (3) future-focused approaches to GenAI-enhanced learning and assessment. Some university staff see GenAI as a threat to their profession and have stressed the need for university guidance. Staff discussed both the positive and negative impacts of GenAI on student learning. Some staff want to prevent the use of GenAI in assessments, whilst others embrace the tool. These findings can inform university guidance on the use of GenAI in the future.","url":"https://doi.org/10.3390/educsci15030367","authors":["M. Whitbread","Charles A. Hayes","Sundaresan Prabhakar","Rebecca Upsher"],"tags":["Perception","Mathematics education","Computer science","Public university","Generative grammar"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-16","doi":"https://doi.org/10.3390/educsci15030367","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7118072316","name":"Artificial intelligence for breast cancer management","source":"openalex","abstract":"Artificial intelligence is transforming breast cancer management through various machine learning applications. Artificial intelligence supports precision medicine by enhancing detection, diagnosis, prognosis, and treatment response prediction. It achieves this by analysing data from medical imaging, histopathology, genomics and multi-omics sources to improve patient recovery. This review summarises AI-driven advancements across the entire continuum of breast cancer management, spanning detection, diagnosis, prognosis, treatment and recovery. It evaluates their efficacy and limitations, explores their impact on healthcare costs and clinical practice, and addresses key challenges including generalisability, reproducibility and regulatory barriers. Evidence from recent studies highlights AI’s role in improving breast cancer detection, molecular subtyping and prognostic accuracy. It also facilitates more patient-tailored therapeutic strategies and supports quality of life interventions. Nonetheless, the translation of these benefits into clinical practice requires rigorous validation, transparent model development, and equitable implementation. Chua et al., discuss how artificial intelligence is transforming breast cancer care by improving detection, diagnosis, prognosis, treatment planning, and patient recovery through advanced machine learning and deep learning applications. They emphasise that widespread adoption faces challenges such as data diversity, reproducibility, regulatory hurdles, infrastructure limitations, and ethical concerns around transparency and bias.","url":"https://doi.org/10.1038/s43856-025-01342-3","authors":["Bryan Nicholas Chua","Dexter Kai Hao Thng","Tan Boon Toh","Dean Ho"],"tags":["Breast cancer","Medicine","Applications of artificial intelligence","Artificial intelligence","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-03","doi":"https://doi.org/10.1038/s43856-025-01342-3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412522062","name":"Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies","source":"openalex","abstract":"High intra-pancreatic fat deposition (IPFD) plays an important role in diseases of the pancreas. The intricate anatomy of the pancreas and the surrounding structures has historically made IPFD quantification a challenging measurement to make accurately on radiological images. To take on the challenge, automated IPFD quantification methods using artificial intelligence (AI) have recently been deployed. The aim was to benchmark the current knowledge on the use of AI-based models to measure IPFD automatedly. The search was conducted in the MEDLINE, Embase, Scopus, and IEEE Xplore databases. Studies were eligible if they used AI for both segmentation of the pancreas and quantification of IPFD. The ground truth was manual segmentation by radiologists. When possible, data were pooled statistically using a random-effects model. A total of 12 studies (10 cross-sectional and 2 longitudinal) encompassing more than 50 thousand people were included. Eight of the 12 studies used MRI, whereas four studies employed CT. U-Net model and nnU-Net model were the most frequently used AI-based models. The pooled Dice similarity coefficient of AI-based models in quantifying IPFD was 82.3% (95% confidence interval, 73.5 to 91.1%). The clinical application of AI-based models showed the relevance of high IPFD to acute pancreatitis, pancreatic cancer, and type 2 diabetes mellitus. Current AI-based models for IPFD quantification are suboptimal, as the dissimilarity between AI-based and manual quantification of IPFD is not negligible. Future advancements in fully automated measurements of IPFD will accelerate the accumulation of robust, large-scale evidence on the role of high IPFD in pancreatic diseases. KEY POINTS: Question What is the current evidence on the performance and clinical applicability of artificial intelligence-based models for automated quantification of intra-pancreatic fat deposition? Findings The nnU-Net model achieved the highest Dice similarity coefficient among MRI-based studies, whereas the nnTransfer model demonstrated the highest Dice similarity coefficient in CT-based studies. Clinical relevance Standardisation of reporting on artificial intelligence-based models for the quantification of intra-pancreatic fat deposition will be essential to enhancing the clinical applicability and reliability of artificial intelligence in imaging patients with diseases of the pancreas.","url":"https://doi.org/10.1007/s00330-025-11808-6","authors":["Tej Joshi","John Virostko","Maxim S. Petrov"],"tags":["Medicine","Artificial intelligence","Segmentation","Relevance (law)","Confidence interval"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-19","doi":"https://doi.org/10.1007/s00330-025-11808-6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7116200900","name":"Insights and Innovations of Artificial Intelligence in Healthcare and Public Health: A Bibliometric Analysis","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s42399-025-02218-2","authors":["Hamza Ettadili","Tahmineh Darvishmohammadi"],"tags":["Big data","Health care","Transformative learning","Cloud computing","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-19","doi":"https://doi.org/10.1007/s42399-025-02218-2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4417326160","name":"Artificial Intelligence in Anaesthesiology: Current Applications, Challenges, and Future Directions","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming anaesthesiology through advances in machine learning, deep learning, and large language models. AI-driven tools now contribute to nearly every phase of perioperative care, including preoperative risk stratification, intraoperative monitoring, imaging interpretation, airway assessment, regional anaesthesia, and critical care. Applications such as automated American Society of Anesthesiologists classification, prediction of postoperative complications and intensive care unit needs, electroencephalography-based depth-of-anaesthesia estimation, and proactive haemodynamic management are reshaping clinical decision-making. AI-augmented echocardiography enhances chamber recognition and functional measurements, whereas computer vision systems support airway evaluation and ultrasound-guided regional anaesthesia by providing real-time anatomical identification and facilitating training. In critical care, AI models facilitate the early detection of sepsis, organ dysfunction, and haemodynamic instability, while improving workflow efficiency and resource allocation. AI is increasingly used in academic writing, data processing, and medical education, offering opportunities for personalised learning and simulation but raising concerns about accuracy and hallucinations. In this review, we aimed to summarise the current applications of AI in anaesthesiology, highlight the methodological, ethical, and practical challenges that limit its integration, and discuss future directions for its safe and effective adoption in perioperative care.","url":"https://doi.org/10.4274/tjar.2025.252320","authors":["Burhan Dost","Engin İhsan Turan","Muhammed Enes Aydın","Ali Ahişkalıoğlu","Madan Narayanan","Resül Yılmaz","Alessandro De Cassai"],"tags":["Workflow","Perioperative","Identification (biology)","Artificial intelligence","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-15","doi":"https://doi.org/10.4274/tjar.2025.252320","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409187867","name":"The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives","source":"openalex","abstract":"The application of artificial intelligence (AI) and machine learning (ML) is rapidly expanding and has begun to make a significant impact on polymer development and characterization. This perspective article explores the current state of AI in this field and highlights areas where its potential remains underutilized. While the optimization of polymer synthesis to achieve desired properties and the classification of polymer types are well-established, opportunities for AI integration in detailed characterization, analytical method development, and data processing remain largely untapped. Greater automation of the analytical laboratory, whether through dedicated algorithms or AI-driven solutions, will enable analytical chemists to focus more on addressing research questions and interpreting results, rather than on method development and routine measurements.","url":"https://doi.org/10.1007/s10337-025-04406-7","authors":["Rick S. van den Hurk","Bob W.J. Pirok","Tijmen S. Bos"],"tags":["Characterization (materials science)","Automation","Artificial intelligence","Field (mathematics)","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-04","doi":"https://doi.org/10.1007/s10337-025-04406-7","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4405186881","name":"Translating ophthalmic medical jargon with artificial intelligence: a comparative comprehension study","source":"openalex","abstract":"OBJECTIVE: Our goal was to evaluate the efficacy of OpenAI's ChatGPT-4.0 large language model (LLM) in translating technical ophthalmology terminology into more comprehensible language for allied health care professionals and compare it with other LLMs. DESIGN: Observational cross-sectional study. PARTICIPANTS: Five ophthalmologists each contributed three clinical encounter notes, totaling 15 reports for analysis. METHODS: Notes were translated into more comprehensible language using ChatGPT-4.0, ChatGPT-4o, Claude 3 Sonnet, and Google Gemini. Ten family physicians, masked to whether the note was original or translated by an LLM, independently evaluated both sets using Likert scales to assess comprehension and utility for clinical decision-making. Readability was evaluated using Flesch Reading Ease and Flesch-Kincaid Grade Level scores. Five ophthalmologist raters compared performance between LLMs and identified translation errors. RESULTS: LLM translations significantly outperformed the original notes in terms of comprehension (mean score of 4.7/5.0 vs 3.7/5.0; p < 0.001) and perceived usefulness (mean score of 4.6/5.0 vs 3.8/5.0; p < 0.005). Readability analysis demonstrated mildly increased linguistic complexity in the translated notes. ChatGPT-4.0 was preferred in 8 of 15 cases, ChatGPT-4o in 4, Gemini in 3, and Claude 3 Sonnet in 0 cases. All models exhibited some translation errors, but ChatGPT-4o and ChatGPT-4.0 had fewer inaccuracies. CONCLUSIONS: ChatGPT-4.0 can significantly enhance the comprehensibility of ophthalmic notes, facilitating better interprofessional communication and suggesting a promising role for LLMs in medical translation. However, the results also underscore the need for ongoing refinement and careful implementation of such technologies. Further research is needed to validate these findings across a broader range of specialties and languages.","url":"https://doi.org/10.1016/j.jcjo.2024.11.003","authors":["Michael Balas","Alexander Kaplan","Kaisra Esmail","Solin Saleh","R. C. Sharma","Peng Yan","Parnian Arjmand"],"tags":["Jargon","Comprehension","Computer science","Artificial intelligence","Natural language processing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-09","doi":"https://doi.org/10.1016/j.jcjo.2024.11.003","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407232216","name":"Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review","source":"openalex","abstract":"BACKGROUND/OBJECTIVES: This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. METHODS: The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. RESULTS: forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. CONCLUSIONS: AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.","url":"https://doi.org/10.3390/cancers17030558","authors":["Amin Naemi","Ashkan Tashk","Amir Sorayaie Azar","Tahereh Samimi","Ghanbar Tavassoli","Anita Bagherzadeh Mohasefi","Elaheh Nasiri Khanshan","Mehrdad Heshmat Najafabad","Vafa Tarighi","Uffe Kock Wiil","Jamshid Bagherzadeh","Habibollah Pirnejad","Zahra Niazkhani"],"tags":["Generalizability theory","Interpretability","Artificial intelligence","Medicine","Data extraction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-06","doi":"https://doi.org/10.3390/cancers17030558","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410280064","name":"International Standardization Safe to Use of Artificial Intelligence","source":"openalex","abstract":"Nowadays, the symbiosis of human abilities and the mastery of artificial intelligence will contribute to increased productivity and excellence in industry and social services. The use of artificial intelligence in various fields requires standardization of the safety of its knowledge and skills. International collaboration on artificial intelligence safety standardization is expanding. The UN has created a Global Advisory Body on Artificial Intelligence to support the efforts of the international community of specialists in managing intelligent systems related to the risks and safety of their use. The author proposes international standard of safe application of ensemble intelligent interoperable agents. Ensembles of agents with artificial intelligence are multi-agent synergistic self-organizing systems that function according to the laws of development, synergy and self-organization. Ensembles of intellectual agents solve the problem in the course of self-organization and cooperation according to the criteria of preference and restriction. The solution is considered found when, in the course of their nondeterministic interactions, agents reach the best consensus (temporary equilibrium or balance of interests), which is taken as a solution to the problem. The advantages of intelligent agents that allow you to build self-organizing ensembles are especially manifested in conditions of a priori uncertainty and high dynamics of the world around you, allowing you to build adaptive ensembles with communicative abilities, rebuilding your plans for events in real time. The higher the intelligence of each agent and the richer the opportunities for communication between agents, the more complex and creative behavior the ensemble can demonstrate. The intellect of the ensemble arises and manifests itself in the process of self-organization of intellectual agents. Intelligent agents use a physical, informal and logical model of the environment. That is, they use both attributes and sets of entities, processes, relationships, etc. Modern technologies allow you to create ensembles of intelligent agents with communication abilities, characterized by high openness, flexibility and efficiency, performance, scalability, reliability and survivability, approaching the intellectual abilities of a person and professional teams in their cognitive and functional capabilities and even sometimes surpassing them.","url":"https://doi.org/10.25082/rima.2025.01.005","authors":["Evgeniy Bryndin"],"tags":["Standardization","International standardization","Computer science","Artificial intelligence","Operating system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-12","doi":"https://doi.org/10.25082/rima.2025.01.005","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4406408916","name":"The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors","source":"openalex","abstract":"The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.","url":"https://doi.org/10.3352/jeehp.2025.22.4","authors":["Jisoo Lee","Jieun Lee","Jeong‐Ju Yoo"],"tags":["Confidentiality","Process (computing)","Computer science","Peer review","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-15","doi":"https://doi.org/10.3352/jeehp.2025.22.4","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413998662","name":"eHealth literacy and attitudes towards use of artificial intelligence among university students in the United Arab Emirates, a cross-sectional study","source":"openalex","abstract":"Introduction: With the rapid digitalization of healthcare information and the increasing dependability on online health resources, it has become crucial to understand digital health literacy and the use of emerging AI technologies like ChatGPT among stakeholders. This is of particular importance in the United Arab Emirates which has the highest internet penetration rates. Method: This study aimed to assess eHealth literacy and the factors influencing it among university students in the United Arab Emirates. Their attitudes towards ChatGPT use were also explored. Data from participants, studying in the public universities of UAE, was collected between April-July 2024 using eHEALS and TAME Chat GPT instruments. Results: Results indicated a mean eHealth literacy score of 29.3 out of 40, with higher scores among females and those in health-related disciplines. It was also found that students with higher eHealth literacy perceived ChatGPT as more useful in healthcare, despite their concerns about its risks and potential to replace healthcare professionals. Discussion: The findings from the study underscore the need of development of tailored digital health curricula, to enhance eHealth literacy particularly in subgroups showing lower literacy scores. Moreover, it is also imperative to develop guidelines for responsible and ethical AI use in health information seeking.","url":"https://doi.org/10.3389/fdgth.2025.1574263","authors":["Zufishan Alam","Aminu S. Abdullahi","Shamma Nayea Salem Alnuaimi","Hanouf Abubaker Al Shaka","Saif Slayem Saif Alderei","Ahmed Abdulla Ali Alhemeiri","Hayma Khorzom","Hamad Jumaa Mubarak Almaskari","Khalid Abdulrahman Almaamari","Khalifa Al seiari","Mohammed Saadi","Nasser Al Shamsi","Omar Al Zaabi","Saoud Altamimi","Azhar T. Rahma"],"tags":["eHealth","Cross-sectional study","Literacy","Medical education","Health literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-04","doi":"https://doi.org/10.3389/fdgth.2025.1574263","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4403750183","name":"Let's Have a Chat: How Well Does an Artificial Intelligence Chatbot Answer Clinical Infectious Diseases Pharmacotherapy Questions?","source":"openalex","abstract":"Background: It is unknown whether ChatGPT provides quality responses to infectious diseases (ID) pharmacotherapy questions. This study surveyed ID pharmacist subject matter experts (SMEs) to assess the quality of ChatGPT version 3.5 (GPT-3.5) responses. Methods: The primary outcome was the percentage of GPT-3.5 responses considered useful by SME rating. Secondary outcomes were SMEs' ratings of correctness, completeness, and safety. Rating definitions were based on literature review. One hundred ID pharmacotherapy questions were entered into GPT-3.5 without custom instructions or additional prompts, and responses were recorded. A 0-10 rating scale for correctness, completeness, and safety was developed and validated for interrater reliability. Continuous and categorical variables were assessed for interrater reliability via average measures intraclass correlation coefficient and Fleiss multirater kappa, respectively. SMEs' responses were compared by the Kruskal-Wallis test and chi-square test for continuous and categorical variables. Results: SMEs considered 41.8% of responses useful. Median (IQR) ratings for correctness, completeness, and safety were 7 (4-9), 5 (3-8), and 8 (4-10), respectively. The Fleiss multirater kappa for usefulness was 0.379 (95% CI, .317-.441) indicating fair agreement, and intraclass correlation coefficients were 0.820 (95% CI, .758-.870), 0.745 (95% CI, .656-.816), and 0.833 (95% CI, .775-.880) for correctness, completeness, and safety, indicating at least substantial agreement. No significant difference was observed among SME responses for percentage of responses considered useful. Conclusions: Fewer than 50% of GPT-3.5 responses were considered useful by SMEs. Responses were mostly considered correct and safe but were often incomplete, suggesting that GPT-3.5 responses may not replace an ID pharmacist's responses.","url":"https://doi.org/10.1093/ofid/ofae641","authors":["Wesley D. Kufel","Kathleen D Hanrahan","Robert W. Seabury","Katie A Parsels","Jason C Gallagher","Conan MacDougall","Elizabeth W. Covington","Elias B. Chahine","Rachel S. Britt","Jeffrey M. Steele"],"tags":["Medicine","Pharmacotherapy","Chatbot","Artificial intelligence","Psychiatry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-25","doi":"https://doi.org/10.1093/ofid/ofae641","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407176841","name":"Artificial Intelligence in Logistics and Distribution: The function of AI in dynamic route planning for transportation, including self-driving trucks and drone delivery systems","source":"openalex","abstract":"Artificial Intelligence (AI) is revolutionizing logistics and distribution by enhancing efficiency, reducing costs, and improving the overall delivery experience. One of the key applications of AI in this sector is dynamic route optimization, where machine learning algorithms analyze real-time data such as traffic patterns, weather conditions, and road closures to continuously adjust delivery routes. This reduces fuel consumption, optimizes delivery times, and mitigates operational disruptions. In parallel, autonomous delivery systems, including drones and self-driving trucks, are transforming last-mile and long-haul transportation by minimizing human intervention and increasing speed and reliability. AI-powered autonomous delivery systems leverage advanced technologies such as computer vision, sensors, and machine learning to navigate and make real-time decisions. Drones, for example, are already being utilized for time-sensitive deliveries, especially in remote or underserved areas, while self-driving trucks promise to revolutionize long-haul freight transportation with the potential for 24/7 operation, cost reduction, and increased safety. Despite the clear benefits, challenges such as regulatory frameworks, safety concerns, and public acceptance remain as significant barriers to large-scale adoption. This review explores the role of AI in dynamic route optimization, drones, and self-driving trucks within logistics, providing insights into how these technologies are currently being implemented and the challenges that lie ahead. It further discusses the potential for AI to reshape the future of logistics and transportation by driving innovation in automation, reducing operational inefficiencies, and contributing to the development of smarter, more sustainable supply chains. The future of AI in logistics promises a more integrated, cost-effective, and agile system that can adapt to global challenges and evolving consumer demands.","url":"https://doi.org/10.30574/wjarr.2025.25.2.0214","authors":["Yetunde Adeoye","ERUMUSELE FRANCIS ONOTOLE","Tunde Ogunyankinnu","Godwin Aipoh","Akintunde Akinyele Osunkanmibi","JOSEPH EGBEMHENGHE"],"tags":["Truck","Drone","Function (biology)","Transport engineering","Self driving"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-05","doi":"https://doi.org/10.30574/wjarr.2025.25.2.0214","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412517485","name":"The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy","source":"openalex","abstract":"Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and variability in diagnoses, particularly in underserved areas. This literature review explores the evolving role of artificial intelligence (AI) in enhancing the diagnosis, screening, and management of diabetic retinopathy. It examines AI’s potential to improve diagnostic accuracy, accessibility, and patient outcomes through advanced machine-learning and deep-learning algorithms. Methods: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of diabetic retinopathy. Relevant articles were identified by searching the PubMed and Google Scholar databases. Studies focusing on the application of artificial intelligence in screening, diagnosis, and improving healthcare accessibility for diabetic retinopathy were included. Key information was extracted and synthesized to provide an overview of recent progress and clinical implications. Conclusions: Artificial intelligence holds transformative potential in diabetic retinopathy care by enabling earlier detection, improving screening coverage, and supporting individualized disease management. Continued research and ethical deployment will be essential to maximize AI’s benefits and address challenges in real-world applications, ultimately improving global vision health outcomes.","url":"https://doi.org/10.3390/jcm14145150","authors":["A Ansari","Nabiha Midhat Ansari","Usman Khalid","Daniel Markov","Kristian Bechev","Vladimir Aleksiev","Galabin Markov","E. Poryazova"],"tags":["Medicine","Diabetic retinopathy","Retinopathy","Intensive care medicine","Ophthalmology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-20","doi":"https://doi.org/10.3390/jcm14145150","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4387144061","name":"Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence","source":"openalex","abstract":"The increasing capacities of large language models (LLMs) have been shown to present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, by automating complex qualitative tasks otherwise typically carried out by human researchers. While numerous benchmarking studies have assessed the analytic prowess of LLMs, there is less focus on operationalizing this capacity for inference and hypothesis testing. Addressing this challenge, a systematic framework is argued for here, building on mixed methods quantitizing and converting design principles, and feature analysis from linguistics, to transparently integrate human expertise and machine scalability. Replicability and statistical robustness are discussed, including how to incorporate machine annotator error rates in subsequent inference. The approach is discussed and demonstrated in over a dozen LLM-assisted case studies, covering 9 diverse languages, multiple disciplines and tasks, including analysis of themes, stances, ideas, and genre compositions; linguistic and semantic annotation, interviews, text mining and event cause inference in noisy historical data, literary social network construction, metadata imputation, and multimodal visual cultural analytics. Using hypothesis-driven topic classification instead of \"distant reading\" is discussed. The replications among the experiments also illustrate how tasks previously requiring protracted team effort or complex computational pipelines can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, the approach is not intended to replace, but to augment and scale researcher expertise and analytic practices. With these opportunities in sight, qualitative skills and the ability to pose insightful questions have arguably never been more critical.","url":"https://doi.org/10.48550/arxiv.2309.14379","authors":["Andres Karjus"],"tags":["Computer science","Data science","Artificial intelligence","Natural language processing","Analytics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-24","doi":"https://doi.org/10.48550/arxiv.2309.14379","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407823694","name":"From data to artificial intelligence: evaluating the readiness of gastrointestinal endoscopy datasets","source":"openalex","abstract":"The incorporation of artificial intelligence (AI) into gastrointestinal (GI) endoscopy represents a promising advancement in gastroenterology. With over 40 published randomized controlled trials and numerous ongoing clinical trials, gastroenterology leads other medical disciplines in AI research. Computer-aided detection algorithms for identifying colorectal polyps have achieved regulatory approval and are in routine clinical use, while other AI applications for GI endoscopy are in advanced development stages. Near-term opportunities include the potential for computer-aided diagnosis to replace conventional histopathology for diagnosing small colon polyps and increased AI automation in capsule endoscopy. Despite significant development in research settings, the generalizability and robustness of AI models in real clinical practice remain inconsistent. The GI field lags behind other medical disciplines in the breadth of novel AI algorithms, with only 13 out of 882 Food and Drug Administration (FDA)-approved AI models focussed on GI endoscopy as of June 2024. Additionally, existing GI endoscopy image databases are disproportionately focussed on colon polyps, lacking representation of the diversity of other endoscopic findings. High-quality datasets, encompassing a wide range of patient demographics, endoscopic equipment types, and disease states, are crucial for developing effective AI models for GI endoscopy. This article reviews the current state of GI endoscopy datasets, barriers to progress, including dataset size, data diversity, annotation quality, and ethical issues in data collection and usage, and future needs for advancing AI in GI endoscopy.","url":"https://doi.org/10.1093/jcag/gwae041","authors":["Sami Elamin","Shreya Johri","Pranav Rajpurkar","Enrik Geisler","Tyler M. Berzin"],"tags":["Endoscopy","Medicine","Capsule endoscopy","Generalizability theory","Clinical trial"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-21","doi":"https://doi.org/10.1093/jcag/gwae041","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414038162","name":"Frankenstein, thematic analysis and generative artificial intelligence: Quality appraisal methods and considerations for qualitative research","source":"openalex","abstract":"OBJECTIVE: To determine accuracy and efficiency of using generative artificial intelligence (GenAI) to undertake thematic analysis. INTRODUCTION: With the increasing use of GenAI in data analysis, testing the reliability and suitability of using GenAI to conduct qualitative data analysis is needed. We propose a method for researchers to assess reliability of GenAI outputs using deidentified qualitative datasets. METHODS: We searched three databases (United Kingdom Data Service, Figshare, and Google Scholar) and five journals (PlosOne, Social Science and Medicine, Qualitative Inquiry, Qualitative Research, Sociology Health Review) to identify studies on health-related topics, published prior to whereby: humans undertook thematic analysis and published both their analysis in a peer-reviewed journal and the associated dataset. We prompted a closed system GenAI (Microsoft Copilot) to undertake thematic analysis of these datasets and analysed the GenAI outputs in comparison with human outputs. Measures include time (GenAI only), accuracy, overlap with human analysis, and reliability of selected data and quotes. RESULTS: Five studies were identified that met our inclusion criteria. The themes identified by human researchers and Copilot showed minimal overlap, with human researchers often using discursive thematic analyses (40%) and Copilot focusing on thematic analysis (100%). Copilot's outputs often included fabricated quotes (58% SD = 45%) and none of the Copilot outputs provided participant spread by theme. Additionally, Copilot's outputs primarily drew themes and quotes from the first 2-3 pages of textual data, rather than from the entire dataset. Human researchers provided broader representation and accurate quotes (79% quotes were correct, SD = 27%). CONCLUSIONS: Based on these results, we cannot recommend the current version of Copilot for undertaking thematic analyses. This study raises concerns about the validity of both human-generated and GenAI-generated qualitative data analysis and reporting.","url":"https://doi.org/10.1371/journal.pone.0330217","authors":["Tanisha Jowsey","Peta Stapleton","Shawna Campbell","Alexandra Davidson","Cher McGillivray","Isabella Maugeri","Megan Lee","Justin Keogh"],"tags":["Thematic analysis","Thematic map","Qualitative research","Generative grammar","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-05","doi":"https://doi.org/10.1371/journal.pone.0330217","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412004643","name":"Matrix of technical solutions based on artificial intelligence in the professional training of future lawyers","source":"openalex","abstract":"Importance. The current stage of technological development of society is characterized by the intensive integration of artificial intelligence (AI) technologies into professional spheres. AI-based technical solutions make it possible to automate some routine processes and free up time for humans to solve other more important and complex issues. Gradually, the interaction of specialistswith AI tools to solve professional problems is becoming a daily practice. In this regard, the training of qualified personnel at the university for the realities of today is impossible without integrating professionally oriented AI tools into the student learning process. Law is one of the activity fields in which modern AI technologies are able to take on many professional tasks. At the same time, the systematic integration of AI-based technical solutions into the university’s law student training process is impossible without a comprehensive study of the entire range of AI tools and their professionally oriented potential. The purpose of the work is to develop a matrix of AI-based technical solutions used in the professional training of future lawyers. Materials and Methods. The study is conducted on the expert assessment method basis. This allowed the authors to: a) identify a list of professional tasks solved by lawyers in the field of professional activity; b) based on the identified tasks, develop a matrix of AI-based technical solutions used in the professional training of future lawyers. The materials are scientific papers on pedagogy, methods of teaching foreign languages and specialized disciplines, published in scientific journals indexed in the Ministry of National Security (Scopus and Web of Science), as well as those included in the list of the Higher Attestation Commission of the Russian Federation (K1, K2), the Federal State Educational Standard for Higher Education in the field of Law. The AI tools widely used among current lawyers, which they use in their professional activities to solve professional problems, are used as practical materials. Results and Discussion. A matrix of AI-based technical solutions used in the professional training of future lawyers has been developed. The matrix is presented according to twelve professional tasks that lawyers solve in the course of their professional activities. The main and most accessible AI-based technical solutions for teachers of specialized disciplines that can help lawyers solve professional problems are the following: Legal AI tools, Legal Document Generator and DocZilla AI are used to draw up contracts (lease, sale, employment agreements, etc.), DocZilla AI and Genie AI – for the analysis and comparison of document editions, Mistral AI and LexisNexis – for checking documents for errors and contradictions, ROSS Intelligence and WestLaw – to search for relevant court decisions and analyze use cases, TrademarkVision and PatentPal – to search for similar trademarks, Perplexity AI – to analyze license agreements, Legalese Decoder, ChatGPT, YandexGPT, GigaChat and DeepSeek – to simplify legal terms for clients (colleagues, students), Canva and MidJourney – to visualize processes (for example, judicial meetings), LegalAI and Jasper AI – for legal advice, Perplexity AI, ChatGPT, YandexGPT, GigaChat and DeepSeek – for mathematical calculations (taxes, insurance payments, etc.), MidJourney – to create images of suspects, Legalese Decoder and Mistral AI are used for conducting examinations (handwriting, ballistic, etc.). Conclusion. The research novelty is the development of a matrix of AI-based technical solutions used in the professional training of future lawyers. The perspective of the conducted research lies in the development of step-by-step methods of teaching aspects of specialized disciplines based on the students’practice with specific technical solutions based on AI.","url":"https://doi.org/10.20310/1810-0201-2025-30-2-336-351","authors":["Pavel V. Sysoyev","M. V. Gavrilov","Stanislav Yu. Bulochnikov"],"tags":["Training (meteorology)","Matrix (chemical analysis)","Artificial intelligence","Computer science","Engineering management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-02","doi":"https://doi.org/10.20310/1810-0201-2025-30-2-336-351","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4405735261","name":"Authors, wordsmiths and ghostwriters: Early career researchers' responses to artificial intelligence","source":"openalex","abstract":"Abstract Presents the results of a study of the impact of artificial intelligence on early career researchers (ECRs). An important group to study because their millennial mindset may render them especially open to AI. We provide empirical data and a validity check of the numerous publications providing forecasts and prognostications. This interview‐based study—part of the Harbingers project on ECRs—covers a convenience sample of 91 ECRs from all fields and seven countries using both qualitative and quantitative data to view the AI experience, engagement, utility, attitudes and representativeness of ECRs. We find that: (1) ECRs exhibit mostly limited or moderate levels of experience; (2) in regard to engagement and usage there is a divide with some ECRs exhibiting little or none and others enthusiastically using AI; (3) ECRs do not think they are unrepresentative when compared to their colleagues; (4) ECRs who score highly on these measures tend to be computer scientists, but not exclusively so; (5) the main concerns regarding AI were around authenticity, especially plagiarism; (6) a major attraction of AI is the automation of ‘wordsmithing’; the process and technique of composition and writing.","url":"https://doi.org/10.1002/leap.1652","authors":["David Clark","David Nicholas","Marzena Świgoń","Abdullah Abrizah","Blanca Rodríguez Bravo","Jorge Revez","Eti Herman","Jie Xu","Anthony Watkinson"],"tags":["Psychology","Engineering ethics","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-24","doi":"https://doi.org/10.1002/leap.1652","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7125689582","name":"Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration","source":"openalex","abstract":"Traditional Chinese Medicine (TCM) is a valuable medical treasure trove that not only demonstrated unique advantages in treating complex and refractory diseases but also left behind a rich legacy of ancient texts and valuable evidence-based medical data based on its human experience for future generations. Nevertheless, the extensive data within TCM has been plagued by challenges, including inadequate data standardization, inconsistent data quality, limited data structuring, and obstacles in interdisciplinary integration. Recent advancements in artificial intelligence (AI) techniques have markedly improved the efficiency and effectiveness with which multimodal data in TCM, including machine learning (ML), deep learning (DL), knowledge graphs (KG), and natural language processing (NLP), particularly large language models (LLMs). These advancements have facilitated more precise data analysis, enhanced clinical decision-making, and improved research outcomes in TCM, such as target discovery, virtual screening of natural products (NPs), symptom differentiation and auxiliary prescription. This article presents a comprehensive review of the progress in applying AI across four dimensions: multiscale data in TCM, TCM research and development, TCM diagnosis and treatment, and LLMs. In summary, the application of AI technology in the modernization of TCM is expected to motivate researchers to achieve a deeper understanding of state-of-the-art applications in data-driven TCM complex systems, fundamental scientific research, and precision medicine, thereby bringing more opportunities and innovations for the modernization of TCM.","url":"https://doi.org/10.1186/s13020-025-01194-y","authors":["Pengfei Guo","Mengmeng Jiang","Shaowu Hu","Qianqian Jiang","Limin LI","Junhong Wu","Yucui Ma","Zhengzhi Wu"],"tags":["Modernization theory","Big data","Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-26","doi":"https://doi.org/10.1186/s13020-025-01194-y","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407289123","name":"Integration of Artificial Intelligence and Robotics into the industrial sector","source":"openalex","abstract":"The 4th industrial revolution is driven by the implementation of automated robots and artificial intelligence (AI) to enhance efficiency, accuracy, and safety. This integration encompasses several vital domains like optimizing the supply chain, interaction between human and robots on the shop floor, predictive maintenance, automation of repetitive tasks, customisation, behaviour design, and safety management, data analysis, etc. AI-enabled robots perform repetitive tasks at very high precision, reducing the chances of human error and allowing workers to focus on more complex tasks. Automated upkeep utilizes AI to determine the time machinery will likely fail, which minimizes downtime and maintenance costs. Automated testing and AI-driven vision systems support quality control by ensuring a balanced quality of the product. AI improves supply chain processes, optimizing logistics and inventory management. Collaboration between humans and collaborative robot’s results in safer and more productive environments with people working alongside each other. Artificial Intelligence plays an important role in making smarter decisions, analysing data more effectively, and providing valuable information that can be used to improve operations. Manufacturing customization and flexibility are reliant on adaptive systems and the ability to manufacture personalized products by means of productivity. Safe and Risk Management is consolidated because robots work in dangerous scenarios and artificial intelligence models assess potential dangers. Despite challenges including labour displacement, cybersecurity, ethics, and data integration stemming from this technology, these are all potentially available on your terms. This article reviews the broader impacts that robots and artificial Intelligence have had on the industrial sector, placing emphasis on the revolution it could lead towards as well as the key elements to consider before implementing it.","url":"https://doi.org/10.56294/dm2025209","authors":["Vugar Abdullayev","Ajesh Faizal","Irada Seyidova","Seymur Mikayilov","Rubaba Mammadova","Lala Pirverdiyeva","Etibar Guliyev"],"tags":["Robotics","Artificial intelligence","Engineering","Computer science","Robot"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.56294/dm2025209","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413805190","name":"Artificial Intelligence in Climate Change Mitigation and Adaptation: A Review of Emerging Technologies and Real-World Applications","source":"openalex","abstract":"Artificial Intelligence (AI) is increasingly recognized as a transformative tool in addressing the dual imperatives of climate change mitigation and adaptation. This review provides a comprehensive synthesis of the current state of AI applications that contribute to reducing greenhouse gas emissions and enhancing resilience to climate-related hazards. It systematically examines advances in machine learning, optimization, and data-driven decision support across key domains including renewable energy forecasting, energy system optimization, land-use planning, disaster risk management, precision agriculture, and water resource allocation. The paper also analyzes the enabling infrastructure required for scalable and ethical deployment, such as data interoperability, model interpretability, and integration with physical system models. The findings from existing literature indicate that AI has significantly improved predictive capacity, operational efficiency, and adaptive planning in climate-related contexts. However, persistent challenges ranging from data scarcity and geographic bias to the carbon footprint of AI systems and governance limitations continue to constrain equitable implementation. The review concludes by identifying critical research gaps and proposing a strategic roadmap focused on interdisciplinary collaboration, equitable data frameworks, and policy alignment with global climate objectives. By critically appraising both the potential and limitations of AI, this review contributes to the research on how intelligent systems can be leveraged to support sustainable, inclusive, and scientifically grounded climate action.","url":"https://doi.org/10.30574/gjeta.2025.24.2.0247","authors":["Favour N. Eze","Adepeju Nafisat Sanusi","lsrael Jonathan Iheoma","Chijioke Cyriacus Ekechi","Micheal Adeolu Olatunbosun","Favour Chizurum Ukasoanya","Muhdawwal Aremu Eleshin"],"tags":["Adaptation (eye)","Climate change","Climate change adaptation","Emerging technologies","Environmental resource management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-29","doi":"https://doi.org/10.30574/gjeta.2025.24.2.0247","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412941949","name":"Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery","source":"openalex","abstract":"The integration of artificial intelligence (AI) in targeted anticancer drug delivery represents a significant advancement in oncology, offering innovative solutions to enhance the precision and effectiveness of cancer treatments. This review explores the various AI methodologies that are transforming the landscape of targeted drug delivery systems. By leveraging machine learning algorithms, researchers can analyze extensive datasets, including genomic, proteomic, and clinical data, to identify patient-specific factors that influence therapeutic responses. Supervised learning techniques, such as support vector machines and random forests, enable the classification of cancer types and the prediction of treatment outcomes based on historical data. Deep learning approaches, particularly convolutional neural networks, facilitate improved tumor detection and characterization through advanced imaging analysis. Moreover, reinforcement learning optimizes treatment protocols by dynamically adjusting drug dosages and administration schedules based on real-time patient responses. The convergence of AI and targeted anticancer drug delivery holds the promise of advancing cancer therapy by providing tailored treatment strategies that enhance efficacy while minimizing side effects. By improving the understanding of tumor biology and patient variability, AI-driven methods can facilitate the transition from traditional treatment paradigms to more personalized and effective cancer care. This review discusses the challenges and limitations of implementing AI in targeted anticancer drug delivery, including data quality, interpretability of AI models, and the need for robust validation in clinical settings.","url":"https://doi.org/10.1039/d5ra03747f","authors":["Danial Khorsandi","Amin Farahani","Atefeh Zarepour","Arezoo Khosravi","Siavash Iravani","Ali Zarrabi"],"tags":["Bridging (networking)","Anticancer drug","Drug delivery","Drug","Targeted drug delivery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1039/d5ra03747f","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412502018","name":"Artificial Intelligence and English as a Foreign Language (EFL) Teachers’ Competencies: A Systematic Review","source":"openalex","abstract":"The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) education has transformed teaching practices, necessitating a re-evaluation of teacher competencies in the digital age. This systematic review examines the intersection of AI technologies and EFL teachers' competencies, exploring how AI tools influence pedagogical skills, technological proficiency, and professional development. Through an analysis of recent literature, the study identifies key competencies required for EFL teachers to effectively leverage AI, including adaptive teaching strategies, data literacy, and ethical considerations in AI usage. The review also highlights challenges such as resistance to technological adoption, the digital divide, and the need for continuous upskilling. Findings suggest that while AI offers significant opportunities for personalized learning and efficiency, EFL teachers must develop a balanced skill set that integrates traditional teaching expertise with emerging technological demands. The results of the research showed that 1) EFL teachers’ competencies in artificial intelligence field consist of 10 competencies, namely: 1) AI-Assisted Lesson Planning, 2) AI-Powered Language Practice & Feedback, 3) Speech Recognition & Pronunciation Tools, 4) AI for Differentiated Instruction, 5) Automated Assessment & Grading, 6) Data-Driven Student Insights, 7) Ethical & Critical Use of AI, 8) AI for Content Creation & Gamification, 9) AI-Powered Translation & Comprehension Support, and 10) AI and Virtual/Augmented Reality in EFL. The paper concludes with recommendations for teacher training programs and policy frameworks to support the evolving role of EFL educators in an AI-driven educational landscape.","url":"https://doi.org/10.5539/hes.v15n3p262","authors":["Rukthin Laoha","Wichittra Chomthong","Weerapa Pongpanich"],"tags":["Psychology","English as a foreign language","Mathematics education","Foreign language","English language"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-20","doi":"https://doi.org/10.5539/hes.v15n3p262","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4412618920","name":"Advances in cardiac devices and bioelectronics augmented with artificial intelligence","source":"openalex","abstract":"Diagnostic bioelectronics such as the electrocardiogram have become increasingly prevalent in the management of cardiovascular diseases like atrial fibrillation. While these devices provide meaningful clinical value, their complex and profuse data output requires considerable labour and training from clinicians to diagnose disease. This abundant production of complex data has made diagnostic bioelectronics a prime target for artificial intelligence (AI) integration. AI-integrated diagnostic bioelectronics have already left the clinic as widely prevalent wearable smartwatches equipped with single-lead electrocardiography sensors. Meanwhile, substantial innovation is also taking place at the intersection of other sensing modalities in the form of edge computing. Here, we overview the implementation and embedding of AI into diagnostic bioelectronics of multiple sensing modalities, including electrocardiography, photoplethysmography, echocardiography, and others, and discuss the recent advances made by medical device companies and researchers alike at the interface between the heart and AI.","url":"https://doi.org/10.1113/jp287135","authors":["Charles J. Stark","Eric Rytkin","A. Mircéa","Igor R. Efimov"],"tags":["Bioelectronics","Neuroprosthetics","Neuroscience","Nanotechnology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-22","doi":"https://doi.org/10.1113/jp287135","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4407410690","name":"The use of artificial intelligence in public administration: Bibliometric analysis","source":"openalex","abstract":"Artificial intelligence in public administration is critical for the modernization of the public sector and adaptation to the challenges of modern society. The paper analyzes studies dedicated to the impact of artificial intelligence on the efficiency, innovation, and transparency of management processes in the public sector using meta- and bibliometric analysis. The goal is to identify the main areas and keywords that highlight theoretical and practical aspects of artificial intelligence in public administration. In total, 879 scientific articles were analyzed, of which 598 works are devoted to artificial intelligence. Dynamic time analysis revealed a significant surge in scientific interest in artificial intelligence in public administration: from 2010 to 2019, 135 publications were devoted to this issue, and from 2020 to 2024, 421. Bibliographic maps of keywords and publication maps showed the main thematic areas of research on artificial intelligence: the application of AI in public administration and the public sector, decision-making in public administration, data management and digital technologies in public administration, and the strategic use of AI to forecast socio-economic trends.The obtained data became the basis for expanding the scientific and practical potential of using artificial intelligence in public administration. The main areas of future research will concern the regulation of ethical issues to ensure the trust of citizens, the development of a regulatory framework and standards, increasing the efficiency of public services, the integration of artificial intelligence into strategic planning, and the use of artificial intelligence to achieve sustainable development goals. AcknowledgmentThe analysis was carried out within the framework of the implementation of the perspective plan for the development of the scientific area “Social Sciences” of Sumy State University, number d/r 0121U112685.","url":"https://doi.org/10.21511/ppm.23(1).2025.16","authors":["Іhor Rekunenko","Яна Кобушко","Oleksii Dzydzyguri","Іnna Balahurovska","Oksana Yurynets","Олександр Жук"],"tags":["Scopus","Publication","Index (typography)","Bibliometrics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-12","doi":"https://doi.org/10.21511/ppm.23(1).2025.16","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4410211382","name":"The long journey of artificial intelligence in medicine: an overview","source":"openalex","abstract":"Artificial intelligence (AI) has its roots in the history of philosophy and of applied mathematics of the 17th, 18th and 19th centuries. Throughout the 20th century, significant advancements in mathematics and computer science laid the groundwork for modern AI, culminating in the establishment of the field as a formal discipline during the Dartmouth Conference in 1956.This pivotal event brought together leading researchers who envisioned creating machines capable of simulating human intelligence, setting the stage for decades of research and innovation in the field. The development of early AI systems focused on problem-solving and symbolic reasoning, leading to the creation of programmes that could play games like chess and solve mathematical equations, which show-cased the potential of machines to perform tasks previously thought to require human intellect.As these foundational systems evolved, researchers began to explore more complex algorithms and learning models, paving the way for advancements in machine learning and neural networks that would eventually revolutionise AI applications across various fields among which medicine. The growth of big data and increased computational power further accelerated these advancements, enabling machines to analyse vast amounts of health information and learn from patterns at unprecedented speeds.The revolution of deep learning and soon after large language models has enabled machines to achieve remarkable feats, such as image and speech recognition, natural language processing, and even creative tasks like art generation, pushing the boundaries of what was once thought possible. As organisations grapple with these challenges, there is growing emphasis on developing frameworks that ensure responsible AI deployment while maximising its potential benefits for human health.","url":"https://doi.org/10.55563/clinexprheumatol/oamfed","authors":["Enzo Grossi"],"tags":["Medicine","MEDLINE","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-08","doi":"https://doi.org/10.55563/clinexprheumatol/oamfed","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7133960409","name":"Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly advancing precision immunotherapy by integrating high-dimensional biomedical data to support diagnosis, treatment selection, and longitudinal monitoring in both cancer and autoimmune diseases. This review summarizes AI applications in biomarker discovery, prediction of immune checkpoint inhibitor (ICI) response and toxicity, neoantigen prioritization, CAR-T cell optimization, and therapeutic antibody engineering. In oncology, multimodal models combining multi-omics, medical imaging, and clinical variables improve patient stratification and non-invasive response assessment, with several imaging- and pathology-based prediction tasks reporting clinically meaningful performance (frequently AUC ~ 0.70–0.95 across tumor types and endpoints). In autoimmune diseases, AI enables earlier diagnosis, molecular subtyping, treatment-response prediction, and real-time disease activity tracking using EHR, laboratory, imaging, and wearable data—supporting precision management in conditions such as rheumatoid arthritis and type 1 diabetes. Key challenges include data heterogeneity, model interpretability, and governance; however, explainable AI, federated learning, and digital twin frameworks offer practical routes toward trustworthy clinical translation. Overall, AI is emerging as a foundational technology for next-generation, patient-specific immunotherapy across oncology and autoimmune medicine.","url":"https://doi.org/10.1007/s10238-026-02107-5","authors":["Jamal Alshorman","Mohammad Javad Mehran","Yadollah Bahrami","Sara Mohammadzadeh","Rambod Barzigar","Mahdi Morshedi","Khawaja Husnain Haider","Kingsley Miyanda Tembo","Shan-Jie Rong","Nasir Jadgal","Ruba Altahla","Mansoor Bolideei","Yongping Wang"],"tags":["Medicine","Cancer","Hematology","Intensive care medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-06","doi":"https://doi.org/10.1007/s10238-026-02107-5","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414078552","name":"ESCMID workshop: Artificial intelligence and machine learning in medical microbiology diagnostics","source":"openalex","abstract":"Rapid advancements in artificial intelligence (AI) and machine learning (ML) offer significant potential to transform medical microbiology diagnostics, improving pathogen identification, antimicrobial susceptibility prediction and outbreak detection. To address these opportunities and challenges, the ESCMID workshop, \"Artificial Intelligence and Machine Learning in Medical Microbiology Diagnostics\", was held in Zurich, Switzerland, from June 2-5, 2025. The course featured expert lectures, practical sessions and panel discussions covering foundational ML concepts and deep learning architectures, data interoperability, quality control processes, model development and validation strategies. Key applications discussed included whole-genome sequencing for antimicrobial resistance detection, AI-enhanced digital microscopy automation and MALDI-TOF mass spectrometry-based diagnostics. Participants gained hands-on experience with essential AI tools and platforms. Special emphasis was placed on standardised laboratory protocols, regulatory compliance and ethical considerations, including data governance and patient privacy. Panel sessions further highlighted critical issues of equity, global disparities in AI access, sustainability and environmental impacts related to AI infrastructure. The workshop concluded by underscoring a necessity for ongoing interdisciplinary collaboration, continued education, and substantial investment in equitable AI infrastructure to realise the full potential of AI in clinical diagnostics.","url":"https://doi.org/10.1016/j.micinf.2025.105562","authors":["Mariella Greutmann","Karsten Borgwardt","Sarah C. Brüningk","Fabian Franzeck","Christian G. Giske","Anna G. Green","Alejandro Guerrero-López","Margaret Ip","Catherine R. Jutzeler","André Kahles","Michael Krauthammer","Nenad Maćešić","Benjamin McFadden","Eline Meijer","Nathan Moore","Jacob Moran‐Gilad","Imane Lboukili","Oliver Nolte","Robin Patel","Gerold Schneider","Markus A. Seeger","Tavpritesh Sethi","Robert Skov","Chang Ho Yoon","Belén Rodríguez‐Sánchez","Adrian Egli"],"tags":["Artificial intelligence","Automation","Machine learning","Clinical microbiology","Panel discussion"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-05","doi":"https://doi.org/10.1016/j.micinf.2025.105562","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413137895","name":"Understanding the Artificial Intelligence Revolution and its Ethical Implications","source":"openalex","abstract":"Recent artificial intelligence (AI) advancements have precipitated profound ethical deliberations and societal concerns. These developments redefine the parameters of technology's role in our daily lives and challenge our understanding of ethics in the context of AI-enabled processes. As AI systems become more integrated into various facets of human activity, from healthcare to finance and from social interactions to governance, the ethical implications of these technologies have become increasingly complex and pressing. In this paper, we aim to facilitate the understanding of intelligence and Artificial Intelligence and delve into the transformative impact of the AI revolution on societal norms and ethical frameworks. We spotlight the critical ethical questions and concerns that arise as AI technologies become increasingly embedded in various aspects of human life. We provide a brief overview of ethical strategies in AI development and explore how implementing these strategies can mitigate potential risks, promote responsible innovation, and ensure the alignment of AI technologies with societal values.","url":"https://doi.org/10.1007/s11673-025-10427-6","authors":["Amin Beheshti","Ian Kerridge"],"tags":["Medical law","Engineering ethics","Psychology","Epistemology","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-14","doi":"https://doi.org/10.1007/s11673-025-10427-6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4411732811","name":"Proposing core competencies for physicians in using artificial intelligence tools in clinical practice","source":"openalex","abstract":"Artificial intelligence (AI) will likely transform many aspects of healthcare, and physicians will need to adapt and lead. The expanding range of AI tools calls for physicians to become competent in their proper use if we are to achieve better patient experience, population health and health equity, and with greater efficiency, while enhancing physician satisfaction. This viewpoint proposes a practical and manageable set of core competencies for physicians in using AI tools effectively and ethically and suggests methods for acquiring these competencies.","url":"https://doi.org/10.1111/imj.70112","authors":["Ian Scott","Tim Shaw","Christine Slade","Tai Tak Wan","Rahul Barmanray","Craig P. Coorey","Sandra K. Johnson","Lana Bell","Michael Herd","Clair Sullivan"],"tags":["Medicine","Core competency","Medical education","Core (optical fiber)","Clinical Practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-27","doi":"https://doi.org/10.1111/imj.70112","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409526623","name":"Artificial Intelligence in Frontline Service Encounters: A Systematic Review and Research Agenda","source":"openalex","abstract":"ABSTRACT Over recent years, the proliferation of artificial intelligence (AI) has enabled businesses worldwide to employ AI‐driven service agents to deliver frontline services to their customers. This paradigm shift has also increased scholarly attention to consumer behavior research in AI‐driven frontline service encounters. Nevertheless, the existing body of knowledge in this domain lacks coherence and consistency, with disparate findings scattered across numerous disciplines. In this context, a critical and comprehensive overview of the existing literature in this domain is essential. Therefore, to provide an updated and comprehensive understanding of consumer research in this fast‐growing domain, we systematically analyzed 157 articles. Using the popular TCCM framework, we offer a detailed overview of the theories, contexts, characteristics, and methodologies used in the prior studies in this domain. The research also presents an integrated framework considering the independent variables, mediators, and moderators influencing customer outcomes. This analysis identifies several research gaps and suggests potential opportunities for further investigation that pertain to major emerging topics and overlooked areas. This review enhances the understanding of consumer reactions to AI‐driven frontline service encounters and offers novel insights for both the literature and managerial practice regarding the implementation of AI in frontline services.","url":"https://doi.org/10.1111/ijcs.70048","authors":["Sneha Rose George","C Manu","Manoj Edward"],"tags":["Service (business)","Sociology","Psychology","Knowledge management","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-17","doi":"https://doi.org/10.1111/ijcs.70048","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4409454622","name":"Integrating artificial intelligence and machine learning with numerical simulation for enhanced thermal performance of ternary nanofluid","source":"openalex","abstract":"Abstract The motivation for this investigation stems from a perceived gap in the vast literature on nanofluids, specifically in relation to their interactions with different surfaces and their numerical simulation. The main objective of this study is to effectively utilize novel machine learning (ML) and artificial intelligence (AI) techniques to investigate the thermal behavior of magnetohydrodynamic ternary nanofluids via an impermeable cylinder subject to activation energy and chemical reactions. We adopt the Levenberg–Marquardt algorithm with backpropagation artificial neural network technique (LMA-ANN), an AI-based scheme, to achieve this goal. The transition of governing equations to ordinary differential equations is accomplished through the use of similarity scaling. Obtained equations are then numerically evaluated using modified finite difference discretization (the Keller-Box approach). Regression scores equal to 1 indicate an excellent match between the numerical data and the predictions. The results demonstrate that temperature diminishes with the activation energy component, but it escalates with the chemical reaction. The activation energy parameter enhances both heat and mass transport processes. The results produced by this framework possess significant significance and usefulness in the field of biotechnology, drug delivery, cancer treatment, biological engineering, and bio-imaging.","url":"https://doi.org/10.1093/jcde/qwaf041","authors":["Mohib Hussain","Lin Du","Hassan Waqas","Qasem M. Al‐Mdallal"],"tags":["Ternary operation","Nano-","Artificial intelligence","Materials science","Thermal"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-15","doi":"https://doi.org/10.1093/jcde/qwaf041","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4367556344","name":"Artificial Intelligence (AI) in Nursing Services: A Literature Review","source":"openalex","abstract":"Research on artificial intelligence (AI) in health services is increased in recent decades, demonstrating great potential for improving the quality of care. However, the application of AI in nursing raises concerns regarding data bias and the potential impacts on patients. In addition, research on AI and the benefits in nursing is still limited. The aim of this study was to outline the results of research on AI technology in nursing services. The research method was literature review. Searches for relevant studies were carried out on several databases, such as PUBMED, Scopus, and Google Scholar by using keywords and terms related to nursing, artificial intelligence, and machine learning between 2012 and 2022. The inclusion criteria included research and development or AI-based technological validation used in nursing services and the research design, including experiment or observation with qualitative, quantitative, or mixed approached. Meanwhile, the exclusion criteria were articles which are irrelevant to nursing, non-experimental, non-observational, or literature review. The results showed total of 3,713 articles found only 10 articles fulfilled the criteria. The use of AI in nursing services can provide many benefits, but it requires careful consideration too. Therefore, more research is needed to address the challenges faced and to maximize the potential of AI in improving the quality of nursing services.","url":"https://doi.org/10.33746/fhj.v10i01.556","authors":["Moh Heri Kurniawan","Hanny Handiyani","Tuti Nuraini","Rr. Tutik Sri Hariyati"],"tags":["Scopus","Nursing","Observational study","Nursing research","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-11","doi":"https://doi.org/10.33746/fhj.v10i01.556","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W7160503958","name":"Evolving surgical teams in the age of artificial intelligence and robotics","source":"openalex","abstract":"Surgery is a critical function of the healthcare system, key to addressing a substantial portion of the global disease burden. The integration of advanced artificial intelligence (AI) and robotics ecosystems into the operating room (OR) promises to radically transform surgery, with profound implications. This article analyzes the current state of surgical AI and robotic systems; presents a vision for their future, highlighting technological and research challenges and their associated impact on surgical teams; and discusses the ethical and regulatory implications. AI systems will use complex, multimodal data streams collected from patients, surgical teams, robots, and the OR environment to become increasingly capable of situational awareness, workflow recognition, performance benchmarking, causal inference, outcome prediction, and intraoperative decision-making to optimize surgical actions. Robotics will move from passive instrument-handling tools to autonomous systems with human-in-the-loop control, with embodied AI and enhanced sensor-based perception providing comprehensive spatial–temporal understanding, anticipatory behaviors, and adaptive learning. The surgeon’s role will shift toward supervision, coordination, and high-level decision-making, while nurses, assistants, and anesthesiologists will have additional competencies complemented by clinical data scientists and AI and robotic integration engineers. Ethical challenges will include liability and the implications of diluted authority chains, the potential for AI bias to exacerbate health inequalities, and the concentration of research and industry in resource-rich nations. New regulatory and compliance frameworks, trial methods, reporting standards, and training approaches will be needed to ensure the safety and effectiveness of these systems. Ultimately, AI and robotics should sustain, rather than disrupt, surgical practices by refining the skills of care providers to achieve true personalized surgery and propel procedural and technological advancements.","url":"https://doi.org/10.3389/fsci.2026.1783803","authors":["Alejandro Granados","Raghav Khanna","Nikola Fischer","Nicholas Raison","Margarita Ciabattini","Harry Robertshaw","Maxence Boels","Mohsan Malik","Verónica Granados","Tom Vercauteren","Jonathan Shapey","T Booth","Asit Arora","Giorgio Gandaglia","Alberto Briganti","Francesco Montorsi","Christos Bergeles","Sebastien Ourselin","Prokar Dasgupta"],"tags":["Robotics","Artificial intelligence","Workflow","Applications of artificial intelligence","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-07","doi":"https://doi.org/10.3389/fsci.2026.1783803","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4414490565","name":"Factors Contributing to Higher Education Students' Acceptance of Artificial Intelligence: A Systematic Review","source":"openalex","abstract":"The rapid integration of artificial intelligence (AI) technologies into the field of higher education is causing widespread public discourse. However, existing research is fragmented and lacks systematic synthesis, which limits understanding of how college and university students adopt artificial intelligence technologies. To address this gap, we conducted a systematic review following the guidelines of the PRISMA statement, including studies from ScienceDirect, Web of Science, Scopus, PsycARTICLES, SOC INDEX, and Embase databases. A total of 5594 articles were identified in the database search; 112 articles were included in the review. The criteria for inclusion in the review were: (i) publication date; (ii) language; (iii) participants; (iv) object of research. The results of the study showed: (a) The Technology Acceptance Model and the Unified Theory of Technology Acceptance and Use are most often used to explain the AI acceptance; (b) quantitative research methods prevail; (c) AI is mainly used by students to search and process information; (d) technological factors are the most significant factors of AI acceptance; (e) gender, specialty, and country of residence influence the AI acceptance. Finally, several problems and opportunities for future research are highlighted, including problems of psychological well-being, students’ personal and academic development, and the importance of financial, educational, and social support for students in the context of widespread artificial intelligence.","url":"https://doi.org/10.12973/eu-jer.14.4.1373","authors":["Dinara Farhatovna Mukhamedkarimova","Madina Maximovna Umurkulova"],"tags":["Higher education","Context (archaeology)","Psychology","Inclusion (mineral)","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-24","doi":"https://doi.org/10.12973/eu-jer.14.4.1373","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"oa:W4413443615","name":"The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review","source":"openalex","abstract":"Cancer treatment has greatly benefited from advancements in radiopharmaceutical therapy, which requires precise dosimetry to enhance therapeutic efficacy and minimize risks to healthy tissues. This review investigated the role of artificial intelligence (AI) in theranostic radiopharmaceutical dosimetry, focusing on image quality enhancement, dose estimation, and organ segmentation. An in-depth review of the literature was conducted using targeted keywords searches in Google Scholar, PubMed, and Scopus. Selected studies were evaluated for their methodologies and outcomes. Traditional dosimetry techniques such as organ-level and voxel-based methods are discussed. Deep learning (DL) models based on U-Net, generative adversarial networks, and hybrid transformer networks for image synthesis and generation, image quality improvement, organ segmentation, and radiation dose estimation are reviewed and discussed. While DL shows great potential for enhancing dosimetry accuracy and efficiency, challenges such as the need for accurate dose estimation from theranostic pairs, lack of imaging data, and modeling of radionuclide decay chains must be addressed using DL models. In addition, the optimization and standardization of DL and AI models is crucial for ensuring clinical reliability and should be given high priority to support their effective integration into clinical practice.","url":"https://doi.org/10.1007/s13139-025-00939-9","authors":["Sang‐Keun Woo"],"tags":["Medicine","Medical physics","Dosimetry","Cancer","Cancer therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-23","doi":"https://doi.org/10.1007/s13139-025-00939-9","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.3233/nai-240731","name":"Towards semantically enriched embeddings for knowledge graph completion","source":"crossref","abstract":"Embedding based Knowledge Graph (KG) completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. This position paper revises the state of the art and discusses several variations of the existing algorithms for KG completion, which are discussed progressively based on the level of expressivity of the semantics utilized. The paper begins with analysing various KG completion algorithms considering only factual information such as transductive and inductive link prediction and entity type prediction algorithms. It then revises the algorithms utilizing Large Language Models as background knowledge. Afterwards, it discusses the algorithms progressively utilizing semantic information such as class hierarchy information within the KGs and semantics represented in different description logic axioms. The paper concludes with a critical reflection on the current state of work in the community, where we argue that the aspects of semantics, rigorous evaluation protocols, and bias against external sources have not been sufficiently addressed in the literature, which hampers a more thorough understanding of advantages and limitations of existing approaches. Lastly, we provide recommendations for future directions.","url":"https://doi.org/10.3233/nai-240731","authors":["Mehwish Alam","Frank van Harmelen","Maribel Acosta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-23T11:32:22Z","doi":"10.3233/nai-240731","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1109/icaiet65052.2025.11210929","name":"Hybrid Artificial Intelligence-Based Approaches for Rainfall Forecasting","source":"crossref","abstract":"Predicting rainfall is one of the most difficult and important aspects of the hydrologic cycle. This is mostly because it exhibits dynamics that are variable across a great range of time and space scales. Flash flooding, which is the result of heavy rain, is a life-threatening effect. Forecasting of rainfall and flood warning system for regular catchments is a complex and challenging task. Rainfall forecasting is an important component in the water resources studies program, including projects such as river training works and flood warning systems design. The backpropagation algorithm configuration for a multilayered artificial neural network is easier to train compared to other methods and that is why it is used broadly. Recent artificial intelligence and specifically in conevtional-based techniques for finding results for complex processes like rainfall patterns, which are highly unpredictable, irregular, and influenced by many factors, can be difficult to analyze presenting new avenues for modeling rainfall forecasting. One such technique is artificial neural networks (ANNs), which are capable of performing a nonlinear mapping between inputs and outputs. Current studies regarding ANN indicate that the two biggest challenges which are selecting the right network design and making the training process efficient. This study will implement a hybrid genetic algorithm combined with artificial neural network (GA-ANN) model for short-term rainfall prediction using rainfall data obtained from recording rain gauges installed at various locations of one of the biggest rivers of India- Mahanadi catchment area in Orissa. These study results indicated that when the ANN network was properly structured and used coupling with GA, the results were generalized and satisfactory.","url":"https://doi.org/10.1109/icaiet65052.2025.11210929","authors":["Arvind Yadav","Jiya Singh","Jayshree","Devendra Joshi","Ashwini Kumar Pradhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-30T17:57:59Z","doi":"10.1109/icaiet65052.2025.11210929","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1148/ryai.250260","name":"Pixels to Prognosis: Using Deep Learning to Rethink Cardiac Risk                     Prediction from CT Angiography","source":"crossref","abstract":"Rohit Reddy, MD, is a resident in interventional and diagnostic radiology at the","url":"https://doi.org/10.1148/ryai.250260","authors":["Rohit Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-28T13:51:49Z","doi":"10.1148/ryai.250260","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-3-031-95256-2_13","name":"Improving Treatment Strategies Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-95256-2_13","authors":["Michael Gao","Angelo Oliva","Roxana Merhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-23T08:50:07Z","doi":"10.1007/978-3-031-95256-2_13","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-3-031-96720-7_10","name":"Artificial Intelligence Literacy: Imperative for the Future or Optional Insight?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96720-7_10","authors":["Kenan Ateşgöz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:47:47Z","doi":"10.1007/978-3-031-96720-7_10","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4324/9781003586937-5","name":"Unlocking artificial intelligence for all","source":"crossref","abstract":"Artificial intelligence (AI) is rapidly reshaping various industries and has the potential to revolutionize the way we live, work, and interact with the world around us. However, the recent advent and integration of AI technology also brings to light the digital divide (DD) that exists in our society. This divide is not seen only in access to AI (and other advanced technologies) but also encompasses the ability to understand, utilize, and benefit from these emerging technologies. This chapter explores the challenges of AI adoption in the context of this divide, focusing on the social, demographic, and technological factors that influence equitable access to AI. It highlights the disparities in AI adoption across sectors such as healthcare, e-government, and education, where demographic variables like age, education, and digital literacy play crucial roles in widening or narrowing the gap. By examining the barriers to AI adoption—such as digital literacy deficits, trust issues, and fears related to privacy and job security—this chapter underscores the complexity of bridging the AI-driven DD. Through a narrative review of key studies, this chapter provides insights for future research and policy development aimed at reducing the growing inequalities linked to AI.","url":"https://doi.org/10.4324/9781003586937-5","authors":["Mirjana Pejić-Bach","Josip Marić"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-26T08:57:12Z","doi":"10.4324/9781003586937-5","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4324/9781003586937-3","name":"Trust in generative artificial intelligence","source":"crossref","abstract":"Generative artificial intelligence (GenAI) is currently one of the most rapidly advancing AI trends, capable of generating various types of content, including text, imagery, audio, and synthetic data. The number of academic studies focusing on trust in AI is growing exponentially. However, there is a notable lack of systematic reviews specifically addressing GenAI. Therefore, the primary objective of this study is to provide a comprehensive overview of the determinants and consequences of trust in GenAI. This chapter contributes a literature review of the most influential papers on trust in GenAI, selected using quantitative methods. Additionally, this chapter offers researchers and practitioners a broad understanding of how trust is established during consumer interactions with GenAI and how this trust can be cultivated to encourage consumers’ positive decision-making behavior.","url":"https://doi.org/10.4324/9781003586937-3","authors":["Xuan Tai Mai","Trang Nguyen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-26T08:57:12Z","doi":"10.4324/9781003586937-3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.70267/cai.25v2n2.2936","name":"Autonomous Driving Driven by Artificial Intelligence: Development Status and Future Prospects","source":"crossref","abstract":"This paper aims to explore the current status and future development trends of artificial intelligence technology in the field of autonomous driving. By analyzing the application of artificial intelligence technologies such as computer vision, deep learning and reinforcement learning in autonomous driving, this paper shows that autonomous driving is currently a hot topic in society. At present, L2 and L3 autonomous driving systems have been launched. In the future, autonomous driving may develop in the direction of vehicle‒road collaboration and L4 unmanned delivery. In addition, we still face many challenges, such as the accuracy attenuation of computer vision algorithms in extreme weather and the proportion of responsibility between car companies and users in autonomous driving accidents.","url":"https://doi.org/10.70267/cai.25v2n2.2936","authors":["Lichao Geng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-02T14:42:38Z","doi":"10.70267/cai.25v2n2.2936","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-3-031-99201-8_27","name":"Scientific Production and Collaboration Patterns of Medical Researchers: A Case Study in Epidemiology and Infectious Diseases","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99201-8_27","authors":["Iva Potkonjak","Predrag Obradović","Marko Mišić"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T04:04:39Z","doi":"10.1007/978-3-031-99201-8_27","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1061/9780784486061.bm","name":"Back Matter for Scouring at Bridge Piers Using Artificial Intelligence Models","source":"crossref","abstract":"adaptive boosting), 41, 42-44, 175-177, 176f; complexity of, 302-306; dependency degree of, 274; learning rate and complexity of, 305; loss functions and complexity of, 303; number of estimators and complexity, 304; overfitting, 43, 303, 304, 305, 306; performance of, 242-243, 244f; physical consistency of, 279, 282; qualitative performance measures of, 257, 259-260, 265, 267, 268, 281f, 284f; quantitative performance measures of, 249-250, 252; Sobol's index of, 277; underfitting, 304 adaptive neuro-fuzzy inference system (ANFIS), 46-48, 61, 84, 85; advantages, 47; interpretability and transparency of, 46-47; overfitting, 48; training of, 48 aggradation, 8 agreement index, 190 AI.See artificial intelligence (AI) AI models, xiii, xiv,","url":"https://doi.org/10.1061/9780784486061.bm","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-20T09:51:39Z","doi":"10.1061/9780784486061.bm","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.71443/9789349552890-03","name":"Integrating Artificial Intelligence into Curriculum Design and Assessment Systems","source":"crossref","abstract":"The integration of Artificial Intelligence (AI) into curriculum design and assessment systems is revolutionizing modern education, offering unprecedented opportunities for personalized learning, real-time feedback, and data-driven decision-making. This chapter explores the transformative role of AI in reshaping educational practices, with a focus on its application in enhancing curriculum flexibility, optimizing teaching strategies, and automating assessment processes. AI-driven tools enable adaptive learning environments that cater to individual student needs, ensuring a more tailored and efficient learning experience. Moreover, AI facilitates the continuous analysis of student performance, allowing for timely adjustments to curriculum content and teaching methods. Ethical considerations, such as data privacy, algorithmic bias, and the balance between human input and automation, are critically examined to ensure that AI integration aligns with educational values of fairness, transparency, and equity. By leveraging AI, educational institutions can create more responsive, inclusive, and effective learning ecosystems that foster student engagement and academic success. The chapter provides a comprehensive analysis of the current landscape of AI in education and outlines future directions for research and implementation.","url":"https://doi.org/10.71443/9789349552890-03","authors":["Roseline Jesudas","Sajeena Gayathrri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-22T07:45:51Z","doi":"10.71443/9789349552890-03","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.engappai.2025.111829","name":"A cross-dimensional synergistic network for brain tumor segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111829","authors":["Chih-Wei Lin","Ye Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-31T09:35:06Z","doi":"10.1016/j.engappai.2025.111829","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2196/preprints.85486","name":"Artificial Intelligence (AI) –Assisted Optimization of Online Gastrointestinal Patient Education Materials: A Cross-Sectional Study (Preprint)","source":"crossref","abstract":"BACKGROUND Patient education materials (PEMs) related to gastroenterology are often written at a level above the recommended sixth-grade reading level, which is suboptimal for accessibility. Large language models (LLMs) can enhance the readability of PEMs, but their effectiveness in this regard remains to be evaluated. OBJECTIVE The goal of this research is to identify if LLMs can optimize the readability and understandability of gastroenterology (GI)-focused PEMs to a more accessible level, and if different models perform differently. METHODS A cross-sectional review was performed on 60 PEMs that were randomly sampled from three GI-focused websites (American Cancer Society [ACS], American College of Gastroenterology [ACG], and American Gastroenterological Association [AGA]). PEMs were rewritten by four LLMs (ChatGPT, Gemini, Claude, and Perplexity) with a standardized fifth-grade translation prompt. Readability was assessed with the Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook (SMOG) Index. Understandability was assessed with the Patient Education Materials Assessment Tool-Understandability (PEMAT-U). Accuracy was checked by two physicians who independently reviewed the simplified materials, with discrepancies verified using ChatGPT. RESULTS PEMs from the original websites scored higher than the National Institutes of Health (NIH)-recommended sixth-grade level, with those from the ACG even at postgraduate levels. While all LLMs improved PEM readability and understandability, their performance and accuracy varied. Gemini had the most significant impact on readability but also produced the highest inaccuracy rate (11.6%). Claude introduced inaccuracies at a rate of 5%, while ChatGPT and Perplexity produced no errors. The accuracy review showed errors were concentrated in the most complex source materials and included oversimplification and omission of risk qualifiers. CONCLUSIONS LLMs show the potential to increase the accessibility of PEMs related to gastroenterology but vary in their performance, indicating the importance of human review. Gemini was the most effective of those included, but the inconsistency in performance and accuracy across different models suggests that AI output cannot be blindly trusted. A combined approach using LLMs and expert review can help improve patient understanding and health literacy.","url":"https://doi.org/10.2196/preprints.85486","authors":["Vriti Khurana","Medha Tekriwal","Hany Eskarous"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-09T03:55:06Z","doi":"10.2196/preprints.85486","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1109/sgai64825.2025","name":"2025 2nd International Conference on Smart Grid and Artificial Intelligence (SGAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sgai64825.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-29T17:12:54Z","doi":"10.1109/sgai64825.2025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-3-031-84047-0_4","name":"Artificial Intelligence in Periodontology: Current Applications and Future Perspectives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-84047-0_4","authors":["Lata Goyal","Kunaal Dhingra","Jaya Pandey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-29T22:36:14Z","doi":"10.1007/978-3-031-84047-0_4","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-981-96-8176-1_10","name":"Artificial Intelligence in Cardiovascular Diseases","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8176-1_10","authors":["Sarwat Bashir","Ab Naffi Ahanger","Assif Assad","Muzafar Rasool Bhat","Muzafar A. Macha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T12:24:46Z","doi":"10.1007/978-981-96-8176-1_10","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31354/globalce.v7i2.210","name":"Artificial Intelligence Driven insights for Regulatory Intelligence in Medical Devices: Evaluating EMA, FDA and CDSCO Frameworks","source":"crossref","abstract":"The current review elaborates Artificial Intelligence (AI) in medical devices is changing the landscape of diagnostics allowing for more accurate and efficacious treatments leading to better patient care. An overview of AI technologies and their application in medical devices elaborates on AI technologies, such as neural networks and advanced data analytics being applied in diagnostic imaging and patient-monitoring preventative analytic models. Machine learning, a subset of AI, enables devices to learn from data and improve their performance over time, enhancing diagnostic accuracy and personalized treatment plans. An elaborated critical review is presented for the regulatory strategies implemented by relevant global leaders, such as the European Union (EU), the United States (US Food and Drug Administration, FDA), and India (Central Drugs Standard Control Organization of India, CDSCO). This is indicative of the EU regulatory approach as observed through reflection paper by the European Medicines Agency (EMA) on a methodology to assess AI technologies used in conjunction with medicinal products, and the Software as a Medical Device (SaMD) guideline by the FDA in the United States. The discussion is on adaptive regulatory strategies, an overview of some pre-certification programs, and detailed advice to manufacturers about compliance with the processes. Also, India aligning with the International Medical Device Regulators Forum (IMDRF) guidelines shows its appetite to help build an extensive regulatory framework for AI-powered medical devices. The current review concludes by highlighting the need for continued coordination between regulators, manufacturers, and healthcare players so that AI advances are safe and adherent to the regulations that improve overall patient care.","url":"https://doi.org/10.31354/globalce.v7i2.210","authors":["Shikha Baghel Chauhan","Radhakrishan Gaur","Afifa Akram","Indu Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-02T13:08:58Z","doi":"10.31354/globalce.v7i2.210","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1007/978-3-031-69457-8_67","name":"Auxiliary Role of Artificial Intelligence in Medical Translation and Its Improvement Strategies","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69457-8_67","authors":["Xiaohan Xu","Zhiwei Zheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-28T07:03:52Z","doi":"10.1007/978-3-031-69457-8_67","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1145/3797552.3797578","name":"Research on a bridge construction simulation teaching system based on virtual reality and artificial intelligence","source":"crossref","abstract":"To address the high practical costs, significant safety risks, and inadequate guidance associated with traditional bridge construction teaching, this paper, in collaboration with industry partners, developed a bridge construction simulation teaching system based on virtual reality (VR) and artificial intelligence (AI). The system utilizes a three-layer architecture: hardware, software, and functionality, integrating core functions such as construction scenario modeling, real-time AI guidance, and virtual assessment. Its core innovation lies in improving the YOLOv8-RL algorithm: embedding a CBAM attention mechanism improves small component recognition accuracy ([email protected] reaches 94.2%, 5.3 percentage points higher than the traditional model). Furthermore, an RL reward function is constructed based on BIM timing constraints to achieve collision warning (92.3% accuracy). After lightweight optimization, the VR frame rate remains stable at 35fps. Experimental results show that the experimental group (system-based teaching) achieved a 26.7 percentage point increase in assessment pass rate, a 76.1% reduction in operational errors, and a 46.2% reduction in learning time compared to the control group (traditional teaching). This system effectively overcomes the bottlenecks of traditional teaching and provides an efficient and safe intelligent solution for practical teaching in civil engineering construction.","url":"https://doi.org/10.1145/3797552.3797578","authors":["Wei Liang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-23T08:35:40Z","doi":"10.1145/3797552.3797578","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1016/j.engappai.2025.111458","name":"Physics-informed surrogate for cardiovascular flow extrapolation through transductive learning","source":"crossref","abstract":"We consider learning surrogate models that directly predict cardiovascular flow fields by mapping geometry and/or fluid properties to hemodynamic parameters. Various machine learning approaches have been developed, but they generally do not extrapolate well to problems beyond the range covered by the training data. We propose a transductive physics informed neural network (T-PINN) approach to improve the extrapolation performance. Our approach builds on the standard PINN approach, which uses governing partial differential equations (PDEs) and labeled data for problems in the training regime to guide the training of neural network surrogate, but we additionally incorporate the governing PDEs for test problems from the extrapolation regimes. T-PINN demonstrates improved extrapolation performance on three synthetic cardiovascular flow problems as compared to purely data-driven neural network surrogates and standard PINNs. Additionally, we perform experiments to investigate how T-PINN’s performance varies when the physical constraints are softened, with hard boundary constraints replaced by soft ones, or simplified PDEs by full PDEs. Our results indicate that these two variants result in similar equation residuals as the original T-PINN but lead to less accurate velocity and pressure predictions. T-PINN’s enhanced extrapolation performance can be particularly significant for cardiovascular flow predictions in clinical settings, where patient morphologies and fluid properties often exhibit variations outside the collected data.","url":"https://doi.org/10.1016/j.engappai.2025.111458","authors":["Yuchen Wang","Nan Ye","Zhiyong Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T09:03:32Z","doi":"10.1016/j.engappai.2025.111458","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.36922/aih025140025","name":"Applications of artificial intelligence in acute stroke imaging","source":"crossref","abstract":"Stroke remains a major global public health challenge, representing the second leading cause of death worldwide and a primary contributor to long-term disability. The paradigm &amp;ldquo;time is brain&amp;rdquo; underscores the importance of treating stroke patients within the critical window period, ideally within 60 min from symptom onset, to minimize damage and improve outcomes. The integration of artificial intelligence (AI) into stroke imaging has transformed diagnosis and management by increasing speed, accuracy, and efficiency. AI algorithms have been trained to detect acute stroke, assess hemorrhage, detect and quantify midline shifts, calculate automated Alberta Stroke Program Early Computed Tomography Scores, and identify dense middle cerebral artery on non-contrast computed tomography (CT) as well as large vessel occlusions on CT angiograms, with high sensitivity and specificity. AI also aids in treatment guidance and outcome monitoring. This review provides insights into AI applications in acute stroke imaging, including its role in early detection, screening, triage and prioritization, automated image analysis, workflow optimization, and system integration. Despite its benefits, AI adoption faces challenges such as clinical validation, ethical considerations, and integration into existing workflows. Future developments depend on large, diverse, and well-annotated datasets to train more robust AI systems capable of guiding treatment strategies and improving patient outcomes. The seamless integration of cloud-based AI solutions with telereporting platforms has the potential to revolutionize stroke care by enabling rapid, high-quality radiologic interpretation, even in remote locations.","url":"https://doi.org/10.36922/aih025140025","authors":["Arjun Kalyanpur","Neetika Mathur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-25T08:24:58Z","doi":"10.36922/aih025140025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.70267/ajp73f40","name":"Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects","source":"crossref","abstract":"Artificial intelligence (AI) technology is profoundly transforming the field of autonomous driving, propelling it from theory to practical application. This paper systematically reviews the key technological advancements in AI-driven autonomous driving. Recognition and control algorithms based on deep learning and reinforcement learning have enhanced the safety of real-time decision-making. Multisensor fusion and vehicle-to-everything (V2X) communication technologies have strengthened environmental perception and vehicle–road cooperation capabilities. The combination of computer vision and lidar has enabled high-precision 3D modeling. Currently, the global market is experiencing rapid growth. China, which relies on the “5+6” strategy and policy pilots, is accelerating the implementation of this technology. Levels 2 and 3 (L2/L3) systems have been commercialized, and Level 4 (L4) systems have entered the demonstration operation stage. However, an insufficient perception of complex environments, the “black box” problem of decision-making algorithms, and hardware computing power bottlenecks remain the main challenges for higher-level autonomous driving. In the future, promoting the development of technology toward Level 5 (L5) through the research and development of explainable AI algorithms, breakthroughs in domestic chips, and cross-industry collaboration. At the same time, an ethical framework centered around people and an intelligent transportation ecosystem should be constructed.","url":"https://doi.org/10.70267/ajp73f40","authors":["Xianni Xie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T08:03:43Z","doi":"10.70267/ajp73f40","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.5220/0013262100003890","name":"PurGE: Towards Responsible Artificial Intelligence Through Sustainable Hyperparameter Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013262100003890","authors":["Gauri Vaidya","Meghana Kshirsagar","Conor Ryan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-28T12:43:20Z","doi":"10.5220/0013262100003890","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1515/9783111670744-006","name":"121Chapter 6 Artificial intelligence in breast cancer management","source":"crossref","abstract":"Breast cancer is a substantial cause of cancer-related mortality among women worldwide. Timely and accurate diagnosis is essential, and clinical results can be significantly improved. The emergence of artificial intelligence (AI) has brought about a new period, particularly in the field of image analysis, which has paved the way for significant progress in the detection of breast cancer and the development of personalised treatment plans. AI plays a crucial role in the diagnostic workflow for patients with breast cancer, including several aspects, such as screening, diagnosis, staging, biomarker evaluation, prognostication, and predicting therapy response. Imaging detection is a primary method employed in clinical practice to screen, diagnose, and evaluate the effectiveness of treatment. It allows for the visualisation of changes in both the size and texture of tumours before and after treatment. The excessive quantity of images, resulting in a difficult duty for radiologists and a slow reporting timeframe, indicates the necessity for computer-aided detection approaches and systems. The fundamental challenges in breast cancer screening and imaging diagnosis arise from the presence of complex and variable image features, the diverse quality of pictures, and the inconsistent interpretation by different radiologists and medical institutions. Utilising imaging-based AI to help in tumour diagnosis is an optimal approach for enhancing the efficiency and accuracy of imaging diagnosis. Through the process of analysing visual data and developing algorithmic models, AI has the capability to automatically identify, separate, and diagnose tumour lesions. This technology holds great potential for future applications. Furthermore, the implementation of advanced diagnostic methods would ultimately lead to greater patient treatment. This chapter extensively examined the various uses of AI in the field of breast cancer care, emphasising its potential to bring about significant changes.","url":"https://doi.org/10.1515/9783111670744-006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-19T19:43:29Z","doi":"10.1515/9783111670744-006","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31219/osf.io/7skmc_v2","name":"The Epistemic Cost of Opacity: Why Medical Doctors Do Not Know when They Rely on Artificial Intelligence","source":"crossref","abstract":"Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recur-ring breast cancer is predicted by an opaque AI system. We argue that, given the system’s opacity, as well as the possibility of malfunctioning AI systems, practitioners’ inability to check the correctness of their outputs, and the high stakes of such cases, the knowledge of medical practitioners is indeed undermined. They are lucky to form true beliefs based on the AI systems’ outputs, and knowledge is incompatible with luck. We supplement this claim with a specific version of the Safety condition on knowledge to account for how knowledge is un-dermined by opacity. We argue that, relative to the perspective of the medical doctor in our example case, his relevant beliefs could easily be false, and this despite his evidence that the AI system functions reliably. Assuming that Safety is necessary for knowledge, the practition-er therefore doesn’t know. We address two objections to our proposal before turning to prac-tical suggestions for improving the epistemic situation of medical doctors.","url":"https://doi.org/10.31219/osf.io/7skmc_v2","authors":["Rianne Fijten","Eva Schmidt","Paul Martin Putora"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-30T13:39:00Z","doi":"10.31219/osf.io/7skmc_v2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2139/ssrn.5461113","name":"The Risk Assessment Model For Artificial Intelligence Medical Device Modification Based on TFN-AHP","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5461113","authors":["Siwen Zhang","Qi Chu","Yujun Li","Wenxi Liu","Shasha Wang","Yuwen Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-09T16:47:14Z","doi":"10.2139/ssrn.5461113","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1201/9781998511075-12","name":"Deep Learning in Medical Imaging","source":"crossref","abstract":"The rapid progress in deep learning (DL) techniques has sparked a revolution in medical imaging, reshaping how medical experts interpret diagnostic images. This chapter explores an intersection of learning algorithms when applied concurrently to medical imaging (MI) while showcasing its profound impact on disease detection, diagnosis, and treatment. Leveraging neural networks and convolutional architectures, deep learning excels in automating the extraction of vital features from intricate medical images like X-rays and CT and MRI scans. It examines diverse applications in image segmentation, object detection, classification, and synthetic image generation. Challenges such as data scarcity, interpretability, and generalization are addressed with remedial measures. Ethical concerns of AI integration in medicine are discussed, underscoring the need for collaboration between clinicians, radiologists, and machine learning (ML) experts for secure implementation. Real-world cases highlight deep learning’s transformative potential, enhancing the accuracy, efficiency, and well-being of patients. Staying updated and fostering interdisciplinary collaboration is of prime importance to enable the complete and extensive use of deep learning to advance MI.","url":"https://doi.org/10.1201/9781998511075-12","authors":["Shubham Gupta","Nency Unadkat","Vishnu Vinod","Nilofer Neshat","Megha Yadav"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-18T14:59:56Z","doi":"10.1201/9781998511075-12","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4128/9781637428016","name":"Unleashing AI: Harnessing Artificial Intelligence for Business Success","source":"crossref","abstract":"&lt;p&gt;&lt;b&gt;&lt;i&gt;Unleashing AI: Harnessing Artificial Intelligence for Business Success&lt;/i&gt; is a comprehensive guide for business leaders, professionals, and entrepreneurs looking to understand and leverage the transformative potential of AI technologies.&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;i&gt;Unleashing AI&lt;/i&gt; is an actionable resource that equips the readers with the knowledge and strategies to harness the power of AI for competitive advantage. This book goes beyond the hype and technical jargon, offering a clear and accessible exploration of AI's applications, implementation challenges, and ethical considerations. It reviews the fundamental concepts of AI, its applications across various business functions, and the ethical considerations associated with its deployment. Through detailed chapters and practical insights, readers will gain a deep understanding of how to integrate AI into their business strategies to drive innovation, efficiency, and competitive advantage.&lt;/p&gt;&lt;p&gt;By combining expert insights, and practical frameworks, &lt;i&gt;Unleashing AI&lt;/i&gt; empowers readers to navigate the AI landscape, identify opportunities, and develop effective AI strategies aligned with their business goals.&lt;/p&gt;","url":"https://doi.org/10.4128/9781637428016","authors":["Milan Frankl"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-29T21:23:45Z","doi":"10.4128/9781637428016","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31234/osf.io/ekz9a_v3","name":"Lay Theories About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Most research on augmented judgment and decision-making is human-centered. Specifically, Theory of Machine is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Reminiscent of the Theory of Mind, it conceptualizes the idea of ascribing thought processes or mental states to algorithms. However, based on their own perceptions, past personal experiences, and shaped by public media, people may conceive of humans and algorithms as functionally distinct ontological entities. Therefore, research on augmented judgment and decision-making should also focus on the differences between various algorithms in terms of (cognitive) abilities and behavior. In this article, several agendas for future research are proposed that explicitly consider people’s diverse interactions with various decision-support and artificial intelligence systems in their daily lives. Such primarily algorithm-centric research will help to gain insights into a more fine-grained Theory of Machine that also distinguishes between different levels of algorithmic fairness, transparency, and explainability. Ideally, a better understanding of how people mentalize about algorithmic behavior can also be used to improve algorithmic augmentations of human judgment and decision-making.","url":"https://doi.org/10.31234/osf.io/ekz9a_v3","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T20:15:17Z","doi":"10.31234/osf.io/ekz9a_v3","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1136/ebm-2025-pod.16","name":"016 The role of artificial intelligence in overdiagnosis; AI and skin cancer","source":"crossref","abstract":"Background Artificial Intelligence (AI) is rapidly permeating and reshaping many fields including skin cancer; a growing research field, particularly through computer vision and image classification; one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. AI research is important in defining the best practices and scope of integrating AI-enabled technologies within a clinical setting. The FDA has not approved any medical advice or algorithms based on AI in the field of dermatology, however, in the European market, foto-finder mole-analyzer pro was endorsed to act as AI which won’t work on skin type IV and over and can’t outdo attending dermatologists in skin cancer detection. The application of AI in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. AI has the potential to supplement dermatologists’ diagnostic and treatment capabilities in what is known as augmented intelligence (AuI). The practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Objectives to analyse the characteristics and trends of AI skin cancer publications from dermatology journals. To analyze and predict the captured images of the commonest skin cancer types submitted by patient’s smartphones, to distinguish and flag higher versus lower risks pigmented lesions. Methods A systematic literature was conducted by searching PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis. Results Through the search, 760 studies were identified in four databases, with only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation. Conclusion The field of skin cancer detection offers a compelling use case for the application of AI within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms can classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that pulls the proficiency of both AI and human experts as AI models lack robustness to simple data variations, thus proven inadequate in real-world dermatologic practice performance which acts as a barrier to achieving clinical promptness. The application of AI in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.","url":"https://doi.org/10.1136/ebm-2025-pod.16","authors":["Ebtisam Elghblawi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-09T04:54:20Z","doi":"10.1136/ebm-2025-pod.16","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1145/3799457.3799671","name":"Integrating Artificial Intelligence and Big Data for Tailored Ideological Education in Universities","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3799457.3799671","authors":["Shoufeng Liu","Mengfan He"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-15T08:26:48Z","doi":"10.1145/3799457.3799671","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.59728/jaie.2025.4.1.26","name":"Real Examples of Lower Elementary Integrated Subject Lessons that Consider Artificial Intelligence Ethics.","source":"crossref","abstract":"This study explores the necessity of incorporating AI ethics into lower-grade elementary integrated curriculum classes under the 2022 revised curriculum and aims to develop a practical AI-integrated lesson. As digital transformation accelerates, public education must actively respond to AI education, ensuring that young learners acquire basic AI literacy and ethical perspectives. This study focuses on the first-grade integrated subject unit “Imagination” and applies the AI Big Ideas framework from the University of Oregon, along with the “Understanding - Utilization - Uprightness (3U)” approach, to design AI-based lessons. The lesson plans were structured to enable students to interact with various AI tools while understanding both the positive and negative impacts of AI. Through this approach, an AI ethics-conscious teaching and learning model was proposed, demonstrating that effective AI education is feasible even in lower elementary grades. This study serves as a foundational resource for the future expansion of AI curricula and teacher training programs.","url":"https://doi.org/10.59728/jaie.2025.4.1.26","authors":["Seong Woo Shin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-17T01:37:31Z","doi":"10.59728/jaie.2025.4.1.26","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2196/84443","name":"Peer Review of “Development of a Conversational Artificial Intelligence–Based Web Application for Medical Consultations: Prototype Study”","source":"crossref","abstract":"Based Web Application for Medical Consultations: Prototype Study.\"Round 1 Review General CommentsThis paper [1] presents a novel conversational artificial intelligence (AI) that is built on top of several models capable of detecting various health conditions.The research itself is interesting, relevant, and carried out well.I look forward to seeing the revised work!","url":"https://doi.org/10.2196/84443","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-15T20:57:10Z","doi":"10.2196/84443","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.36922/aih025420090","name":"Artificial intelligence algorithmic literacy: Gaining and deepening the artificial intelligence knowledge of global health workforce education in the Fifth Industrial Revolution","source":"crossref","abstract":"Technologies invented in the five industrial revolutions (IRs) have profoundly transformed Global Health Workforce Education (GHWFE), reshaping teaching methodologies, faculty approaches, and student learning. This article first reflects on the influence of technology on GHWFE from the first to the fourth IRs. Then, it focuses on the present, Fifth IR (5IR), the era of human-artificial intelligence (AI) centric collaboration, and the fact that the global health workforce educators are not trained for being nimble to utilize AI and its related technologies in 5IR. The manuscript envisions new directions for the future with the goal of establishing nimbler educators that acknowledge the benefits of interdisciplinary dialogue as a means of deepening AI knowledge and community. The article expands the AI algorithmic literacy framework and proposes a Human-AI Centric Workshop Series that moves global health workforce educators from awareness to knowledge, to applied innovation, and toward expertise in 5IR.","url":"https://doi.org/10.36922/aih025420090","authors":["Seble Frehywot","Yianna Vovides"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-08T00:06:47Z","doi":"10.36922/aih025420090","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.59400/fes2730","name":"Technology-enhanced learning in medical education in the age of artificial intelligence","source":"crossref","abstract":"This paper explores the transformative role of artificial intelligence (AI) in medical education, emphasizing its role as a pedagogical tool for technology-enhanced learning. This highlights AI’s potential to enhance the learning process in various inquiry-based learning strategies and support Competency-Based Medical Education (CBME) by generating high-quality assessment items with automated and personalized feedback, analyzing data from both human supervisors and AI, and helping predict the future professional behavior of the current trainees. It also addresses the inherent challenges and limitations of using AI in student assessment, calling for guidelines to ensure its valid and ethical use. Furthermore, the integration of AI into virtual patient (VP) technology to offer experiences in patient encounters significantly enhances interactivity and realism by overcoming limitations in conventional VPs. Although incorporating chatbots into VPs is promising, further research is warranted to enhance their generalizability across various clinical scenarios. The paper also discusses the preferences of Generation Z learners and suggests a conceptual framework on how to integrate AI into teaching and supporting their learning, aligning with the needs of today’s students by utilizing the adaptive capabilities of AI. Overall, this paper highlights areas of medical education where AI can play pivotal roles to overcome educational challenges and offers perspectives on future developments where AI can play a transformative role in medical education. It also calls for future research to advance the theory and practice of utilizing AI tools to innovate educational practices tailored to the needs of today’s students and to understand the long-term impacts of AI-driven learning environments.","url":"https://doi.org/10.59400/fes2730","authors":["Kyong-Jee Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T06:54:49Z","doi":"10.59400/fes2730","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1002/eng2.70114/v2/review2","name":"Review for \"Artificial Intelligence and Architectural Design Before Generative &lt;scp&gt;AI&lt;/scp&gt;: Artificial Intelligence Algorithmics Approaches 2000–2022 in Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70114/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-05T17:08:51Z","doi":"10.1002/eng2.70114/v2/review2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1145/3785987.3786062","name":"AI-Enhanced PBL: Integrating Artificial Intelligence into Project-Based Learning for Youth STEM Education","source":"crossref","abstract":"The rapid advancement of artificial intelligence is fundamentally transforming the educational landscape, creating an urgent need for innovative pedagogical models that can effectively cultivate creativity, critical thinking, and adaptive capabilities in younger generations. This paper investigates the integration of AI technologies within project-based learning frameworks to advance STEM education for youth. Through an in-depth case study of a university-based makerspace, we propose an innovative \"AI-enhanced PBL\" educational model that positions artificial intelligence as both a cognitive tool and creative medium within authentic project contexts. Our research provides detailed analysis of the laboratory's curriculum system, encompassing multiple educational initiatives from intelligent bionic robots to IoT-based smart homes, with particular focus on how AI elements can be organically integrated while preserving core PBL principles. Findings indicate that this educational model's synergistic effects not only help young learners comprehend complex AI technical principles but also significantly enhance their design thinking capabilities and practical problem-solving skills. The paper concludes by discussing key challenges encountered during implementation and proposing future directions for AI-driven educational innovation.","url":"https://doi.org/10.1145/3785987.3786062","authors":["Na Tian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-30T09:50:45Z","doi":"10.1145/3785987.3786062","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4324/9781003585695-10","name":"Artificial Intelligence in Education","source":"crossref","abstract":"We live in a time, where technology is embedded in every sphere of our lives. As an advanced facet of technology, the influence of Artificial intelligence (AI) has become a transformative force in the present world. The concept of Artificial Intelligence delves into the concept of data management and focuses on easing human life by reducing tasks and organizing time more efficiently. This new feature of technology helps to exceed human intelligence and simplify machinery interaction by introducing human-like interaction. It’s a great invention, but people have mixed feelings about it; some possess fear and refrain from using it, whereas some persons have already started misusing it. To realize and utilize the proper strength of this advancement of technology, we need to understand its opportunities and challenges properly. In this chapter, the investigators become interested to dive deep into this concept, and for this reason, employed the qualitative research method and want to highlight various opportunities of AI that will enhance the quality of human life in diversified aspects, moreover, in the present study, the challenges of AI also critically discussed. The present chapter provides insights about the beneficial and detrimental effects of AI in education upon the small educational enterprises. However, in the present age, we cannot neglect the huge capabilities of machines to simplify our daily lives. But still, we need to find a way to balance the embracing benefits of AI and mitigate the drawbacks. The result of the study revealed that there are various areas where AI can work as a supporting tool but there are various concerning areas still persist related to data security, creativity, and the potentiality of humans at par with the robots. Additionally, in the present chapter suggests some corrective measures to reduce the drawbacks of AI.","url":"https://doi.org/10.4324/9781003585695-10","authors":["Santosh Kumar Behera","Timilehin Olasoji Olubiyi","Jayashree Mahanti","Azra Tajhizi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-28T08:52:17Z","doi":"10.4324/9781003585695-10","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.65455/023h4s18","name":"On How Artificial Intelligence Drives Innovation in International Chinese Language Education","source":"crossref","abstract":"The rapid development of artificial intelligence (AI) technology is permeating all sectors of society with unprecedented depth and breadth. In the field of international Chinese language education, AI has evolved far beyond being a mere auxiliary tool like the \"slide projector\" or \"tape recorder\" of the past; instead, it has become a core driving force leading this field toward a profound paradigmatic revolution. This paper systematically elaborates on how AI technology promotes systematic innovation and upgrading in international Chinese language education from five core dimensions: the personalized reconstruction of teaching models, the intelligent generation of teaching resources, the full-process reform of teaching evaluation, the strategic transformation of teachers' roles, and the global integration of educational ecosystems. Meanwhile, the paper also takes a prudent look at challenges that may arise during the process of technology integration, such as algorithmic bias, lack of emotional interaction, the digital divide, and the alienation of the essence of education. Finally, it points out that the future development path must involve the in-depth integration of \"artificial intelligence\" and \"humanistic guidance,\" aiming to build a new, human-machine collaborative, ecologically sound, and sustainable international Chinese language education system. This system will provide new possibilities and fundamental pathways for achieving more equitable, high-quality, and inclusive global Chinese language education.","url":"https://doi.org/10.65455/023h4s18","authors":["Fanqi Meng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T17:51:25Z","doi":"10.65455/023h4s18","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1093/oxfordhb/9780197783160.013.0023","name":"Artificial Intelligence and Intelligence Analysis","source":"crossref","abstract":"Abstract In recent years, there has been an increasing focus on how artificial intelligence (AI) could augment intelligence analysis, particularly in light of the ongoing big data revolution. AI technologies are being used and considered for a range of analytic applications across the tactical, operational, and strategic levels to improve the speed, efficiency, and depth of insights for analysis. In light of this growing interest and demand in AI for intelligence analysis, this chapter examines the complex set of human factors that are intimately connected to AI use in intelligence analysis that need to be considered in advance of their design, development, and deployment. As the chapter illustrates, the incorporation of AI into intelligence analysis involves a variety of sociotechnical issues related to knowledge production that need to be addressed to ensure these technologies are used safely and securely in intelligence work.","url":"https://doi.org/10.1093/oxfordhb/9780197783160.013.0023","authors":["Kathleen M. Vogel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-22T20:17:14Z","doi":"10.1093/oxfordhb/9780197783160.013.0023","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1148/ryai.240206","name":"Deep Learning Applied to Diffusion-weighted Imaging for Differentiating Malignant from Benign Breast Tumors without Lesion Segmentation","source":"crossref","abstract":"Deep learning models, particularly small two-dimensional convolutional neural networks, showed good performance in differentiating between benign and malignant breast tumors using diffusion-weighted MRI, compared with breast radiologists.","url":"https://doi.org/10.1148/ryai.240206","authors":["Mami Iima","Ryosuke Mizuno","Masako Kataoka","Kazuki Tsuji","Toshiki Yamazaki","Akihiko Minami","Maya Honda","Keiho Imanishi","Masahiro Takada","Yuji Nakamoto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-20T09:51:43Z","doi":"10.1148/ryai.240206","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31234/osf.io/ekz9a_v2","name":"Lay Theories About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Most research on augmented judgment and decision-making is human-centered. Specifically, Theory of Machine is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Reminiscent of the Theory of Mind, it conceptualizes the idea of ascribing thought processes or mental states to algorithms. However, based on their own perceptions, past personal experiences, and shaped by public media, people may conceive of humans and algorithms as functionally distinct ontological entities. Therefore, research on augmented judgment and decision-making should also focus on the differences between various algorithms in terms of (cognitive) abilities and behavior. In this article, several agendas for future research are proposed that explicitly consider people’s diverse interactions with various decision-support and artificial intelligence systems in their daily lives. Such primarily algorithm-centric research will help to gain insights into a more fine-grained Theory of Machine that also distinguishes between different levels of algorithmic fairness, transparency, and explainability. Ideally, a better understanding of how people mentalize about algorithmic behavior can also be used to improve algorithmic augmentations of human judgment and decision-making.","url":"https://doi.org/10.31234/osf.io/ekz9a_v2","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T19:34:46Z","doi":"10.31234/osf.io/ekz9a_v2","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4324/9781003491095","name":"Understanding Artificial Minds through Human Minds","source":"crossref","abstract":"Understanding Artificial Minds through Human Minds: The Psychology of Artificial Intelligence provides an accessible introduction into artificial intelligence through the lens of psychology. What are the similarities and differences between concepts known in psychology with regards to the brain, mind and behaviour, and how do they compare with their computational counterparts? With many rapid developments it becomes easy to lose sight of the very essentials of artificial intelligence. Beginning with an introduction to the relationship between AI and human minds, this popular science book goes on to discuss complex issues, including how humans and AI think, learn, remember, and use language. It doesn't shy away from complicated issues of human and AI collaboration or ethics, and provides great insight into the future of AI and applications for our society. Answering all the questions you've been too afraid to ask, Understanding Artificial Minds through Human Minds is a must-read for anyone wanting to understand more about the greatest technological advancement of a generation, and the impact for human psychology.","url":"https://doi.org/10.4324/9781003491095","authors":["Max M. Louwerse"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-29T11:04:51Z","doi":"10.4324/9781003491095","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4018/979-8-3373-0513-4.ch002","name":"Transformative Impact of Artificial Intelligence in Data Analytics and Business Intelligence","source":"crossref","abstract":"Analyzing raw data systematically in order to find patterns, draw conclusions, and generate forecasts that make sense is known as data analytics. This process entails sorting through the enormous datasets that corporations amass using sophisticated algorithms, statistical models, and machine learning approaches (Li &amp; Wu, 2021). Businesses may uncover the hidden story by combining apparently unrelated data pieces into coherent tales via the lens of data analytics. Producing reports is not the only objective; another is to draw out useful information that spurs strategic decision-making, (Farooq, Yuen, et al., 2024; León-Romero et al., 2024).","url":"https://doi.org/10.4018/979-8-3373-0513-4.ch002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-12T11:32:37Z","doi":"10.4018/979-8-3373-0513-4.ch002","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1109/aisp68263.2025","name":"2025 5th International Conference on Artificial Intelligence and Signal Processing (AISP)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aisp68263.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T20:47:02Z","doi":"10.1109/aisp68263.2025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1109/icssas66150.2025","name":"2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icssas66150.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-21T18:04:00Z","doi":"10.1109/icssas66150.2025","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1109/qai63978.2025.00007","name":"Reviewers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qai63978.2025.00007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:56:01Z","doi":"10.1109/qai63978.2025.00007","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31234/osf.io/ekz9a_v6","name":"Lay Beliefs About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Research on augmented judgment and decision-making—where users retain responsibility for the final decision but receive input from algorithms prior to or during the judgment process—has largely contrasted human and algorithmic sources of judgment. Accordingly, Logg’s (2022) “Theory of Machine” is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Indeed, people often treat humans and algorithms as different kinds, that is, functionally distinct ontological entities. Therefore, I propose to complement the predominant human-centric lens on algorithmic judgment with an explicit algorithm-centric one, focusing on people’s lay theories about how different algorithms differ. Put differently, the core psychological claim of Theory of Machine 2.0 is that lay perceivers also differentiate among various AI systems. The first main contribution is the synthesis of capability contrasts across AI systems. People’s perceptions of these contrasts may be formed and shaped by personal experience and media exposure. Most importantly, their expectations and beliefs are supposed to consequentially guide their downstream user behavior—such as system trust, algorithmic advice weighting, and accountability attribution. The second main contribution is the proposal of testable research questions and designs for more algorithm-centric future research on people’s augmented judgment and decision-making. The theoretical perspective proposed in this article clarifies how people form and use lay theories about different AI systems and offers practical levers for the design, deployment, and evaluation of algorithmic decision-support systems.","url":"https://doi.org/10.31234/osf.io/ekz9a_v6","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T15:37:56Z","doi":"10.31234/osf.io/ekz9a_v6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.31234/osf.io/ekz9a_v4","name":"Lay Beliefs About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Research on augmented judgment and decision-making has largely contrasted human and algorithmic sources of judgment. Accordingly, Logg’s (2022) “Theory of Machine” is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Indeed, people often treat humans and algorithms as different kinds, that is, functionally distinct ontological entities. Therefore, I propose to complement the predominant human-centric lens on algorithmic judgment with an explicit algorithm-centric one, focusing on people’s lay theories about how different algorithms differ. Put differently, the core psychological claim of Theory of Machine 2.0 is that lay perceivers also differentiate among various AI systems. The first main contribution is the synthesis of capability contrasts across AI systems. People’s perceptions of these contrasts may be formed and shaped by personal experience and media exposure. Most importantly, their expectations and beliefs are supposed to consequentially guide their downstream user behavior—such as system trust, algorithmic advice weighting, and accountability attribution. The second main contribution is the proposal of testable research questions and designs for more algorithm-centric future research on people’s augmented judgment and decision-making. The theoretical perspective proposed in this article clarifies how people form and use lay theories about different AI systems and offers practical levers for the design, deployment, and evaluation of algorithmic decision-support systems.","url":"https://doi.org/10.31234/osf.io/ekz9a_v4","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-07T19:11:45Z","doi":"10.31234/osf.io/ekz9a_v4","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.64910/jouair.v1i1.6","name":"ARTIFICIAL INTELLIGENCE IN CYBERSECURITY RISK ANALYSIS ON NATIONAL VITAL INFRASTRUCTURE","source":"crossref","abstract":"The development of digital technology has a significant impact on increasing cybersecurity threats, especially on national vital infrastructure such as the energy, transportation, and health sectors. Cyberattacks targeting these sectors have the potential to disrupt essential public services and threaten national security. Therefore, the use of Artificial Intelligence (AI) in cybersecurity risk analysis is an urgent need. This study aims to examine the effectiveness of AI in detecting and mitigating cyber threats on vital infrastructure. The method used is a mixed methods approach that involves quantitative analysis through questionnaires on the cybersecurity team and network log data analysis using the Isolation Forest and K-Nearest Neighbors algorithms. The results show that the application of AI can increase the speed of detection and effectiveness of threat mitigation, with anomaly detection accuracy reaching 95% and an odds ratio of 2.5 in cyber threat mitigation. These findings underscore that AI has a significant contribution to strengthening cybersecurity resilience on national infrastructure. However, some challenges such as integration with legacy systems and supporting regulatory needs need to be considered for further optimization.","url":"https://doi.org/10.64910/jouair.v1i1.6","authors":["Diana Magfiroh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-03T06:32:31Z","doi":"10.64910/jouair.v1i1.6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1201/9781003226406-11","name":"Law, Governance, and Artificial Intelligence – the Case of Intelligent Online Dispute Resolution","source":"crossref","abstract":"The birth of the modern Alternative Dispute Resolution movement in the 1970s and the development of the World Wide Web in the 1990s, has led to the birth of the Online Dispute Resolution movement. Initially, it was envisaged that this movement would only focus upon disputes arising from E-Commerce transactions. However, over the last ten years, Online Dispute Resolution has been used in a variety of civil justice domains. This article investigates how the growing use of artificial intelligence in Online Dispute Resolution helps disputants but also leads to governance issues. A classification scheme for Online Dispute Resolution Systems is developed and the shortcomings of most current systems is illustrated.","url":"https://doi.org/10.1201/9781003226406-11","authors":["John Zeleznikow"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-14T10:45:38Z","doi":"10.1201/9781003226406-11","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4337/9781035316496.00019","name":"False agency in artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781035316496.00019","authors":["Shawn Bayern"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-24T14:08:39Z","doi":"10.4337/9781035316496.00019","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2196/89200","name":"The Potential and Peril of Artificial Intelligence in the Emergency Department","source":"crossref","abstract":"","url":"https://doi.org/10.2196/89200","authors":["Wendy Glauser"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-17T21:10:58Z","doi":"10.2196/89200","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1145/3786484.3786512","name":"Artificial Intelligence in Healthcare: Strategic Value, Constraints, and a Governance-First Integration Framework","source":"crossref","abstract":"Evidence from peer-reviewed studies and credible reports indicates that AI in healthcare most consistently delivers value through four themes—efficiency, cost reduction, competitive differentiation, and new service models—while realized impact is moderated by data governance/privacy, explainability & accountability, and organizational readiness. Reported effects commonly include 10–30% reductions in prediction error (diagnostics/forecasting) and 20–40% decreases in administrative minutes, which under conservative mappings correspond to ≈2–4% operational savings. Guided by these findings, we present a governance-first integration framework for clinical, administrative, and operational settings that specifies: (i) investment in data infrastructure and measurable SLOs; (ii) staged pilots using explicit clinical, operational, and economic metrics; and (iii) capability building and incentive alignment for scale. A concise evaluation agenda (cost-effectiveness, quasi-experimental designs, fidelity reporting) is outlined to move beyond descriptive claims, and a brief case illustrates how governance choices shape performance and adoption. The paper provides a practical roadmap that keeps findings central while translating them into actionable governance and evaluation steps.","url":"https://doi.org/10.1145/3786484.3786512","authors":["Shiqi Zheng","Mingtao Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-04T12:02:46Z","doi":"10.1145/3786484.3786512","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.4018/979-8-3373-1479-2.ch005","name":"Artificial Intelligence-Assisted Image Analysis and Clinical Applications in Urology","source":"crossref","abstract":"Artificial intelligence (AI) has the potential to revolutionize urology by enhancing diagnostics, surgical precision, and patient care. AI-powered diagnostic tools are increasingly used for the early detection of urologic diseases such as prostate and bladder cancer, while robotic-assisted surgery improves precision in minimally invasive procedures. Additionally, AI-driven wearable technologies and remote monitoring systems enhance patient management, enabling personalized treatment approaches. However, the integration of AI into clinical practice raises ethical concerns, including algorithmic bias, data privacy, and accountability, which must be addressed to ensure fair and reliable AI systems. This study explores the future of AI in urology, its integration potential, and the ethical challenges it presents, offering recommendations for a more sustainable and equitable implementation.","url":"https://doi.org/10.4018/979-8-3373-1479-2.ch005","authors":["Halil Ibrahim Ivelik","Bekir Aras"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T14:23:41Z","doi":"10.4018/979-8-3373-1479-2.ch005","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1186/s12909-025-08084-9","name":"Evaluating GPT-4o in high-stakes medical assessments: performance and error analysis on a Chilean anesthesiology exam","source":"crossref","abstract":"BACKGROUND: Large language models (LLMs) such as GPT-4o have the potential to transform clinical decision-making, patient education, and medical research. Despite impressive performance in generating patient-friendly educational materials and assisting in clinical documentation, concerns remain regarding the reliability, subtle errors, and biases that can undermine their use in high-stakes medical settings. METHODS: A multi-phase experimental design was employed to assess the performance of GPT-4o on the Chilean anesthesiology exam (CONACEM), which comprised 183 questions covering four cognitive domains—Understanding, Recall, Application, and Analysis—based on Bloom’s taxonomy. Thirty independent simulation runs were conducted with systematic variation of the model’s temperature parameter to gauge the balance between deterministic and creative responses. The generated responses underwent qualitative error analysis using a refined taxonomy that categorized errors such as “Unsupported Medical Claim,” “Hallucination of Information,” “Sticking with Wrong Diagnosis,” “Non-medical Factual Error,” “Incorrect Understanding of Task,” “Reasonable Response,” “Ignore Missing Information,” and “Incorrect or Vague Conclusion.” Two board-certified anesthesiologists performed independent annotations, with disagreements resolved by a third expert. Statistical evaluations—including one-way ANOVA, non-parametric tests, chi-square, and linear mixed-effects modeling—were used to compare performance across domains and analyze error frequency. RESULTS: GPT-4o achieved an overall accuracy of 83.69%. Performance varied significantly by cognitive domain, with the highest accuracy observed in the Understanding (90.10%) and Recall (84.38%) domains, and lower accuracy in Application (76.83%) and Analysis (76.54%). Among the 120 incorrect responses, unsupported medical claims were the most common error (40.69%), followed by vague or incorrect conclusions (22.07%). Co-occurrence analyses revealed that unsupported claims often appeared alongside imprecise conclusions, highlighting a trend of compounded errors particularly in tasks requiring complex reasoning. Inter-rater reliability for error annotation was robust, with a mean Cohen’s kappa of 0.73. CONCLUSIONS: While GPT-4o exhibits strengths in factual recall and comprehension, its limitations in handling higher-order reasoning and diagnostic judgment are evident through frequent unsupported medical claims and vague conclusions. These findings underscore the need for improved domain-specific fine-tuning, enhanced error mitigation strategies, and integrated knowledge verification mechanisms prior to clinical deployment.","url":"https://doi.org/10.1186/s12909-025-08084-9","authors":["Fernando R. Altermatt","Andres Neyem","Nicolás I. Sumonte","Ignacio Villagrán","Marcelo Mendoza","Hector J. Lacassie","Alejandro E. Delfino"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-27T12:29:44Z","doi":"10.1186/s12909-025-08084-9","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.4324/9781003545125-11","name":"The Role of Artificial Intelligence in Sustainable Tourism","source":"crossref","abstract":"Artificial intelligence (AI) is a field of computer science that focuses on developing algorithms and techniques. The help of AI has enabled the tasks that are typically done by humans, such as learning, reasoning, and understanding, to be completed by machines. AI plays a vital role in promoting eco-friendly destinations and advancing regenerative tourism. It can be helpful in different aspects, such as improving resource efficiency, minimising environmental impacts, and enriching sustainable travel experiences. As a result, it is restructuring the tourism sector by enhancing customer experiences, streamlining operations and delivering personalised services. On the other hand, sustainable development seeks to address current needs without deterring future generations’ ability to meet their own, encompassing environmental, economic, and social dimensions (Sivaraman et al., 2024). Innovative strategies are required for resource management in reducing carbon emissions and ensuring ecosystem sustainability since natural resources decrease, and the effects of climate change are exaggerated (Kamil et al., 2021). Artificial Intelligence is essential in this context because it offers tools and techniques to optimise resource usage, improve efficiency, and enable data-driven decision-making ( Thamrin et al., 2021 ). This chapter provides an overview of the extent to which artificial intelligence embraces regenerative tourism and green destinations such as Costa Rica and New Zealand. It will explore prospects for tourism operations to become more efficient when AI is used for energy management, waste reduction, transportation optimisation, and resource management. Natural resources may be conserved while minimising the impact of tourism.","url":"https://doi.org/10.4324/9781003545125-11","authors":["Saira Sultana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-17T06:04:42Z","doi":"10.4324/9781003545125-11","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.32388/qwyug8","name":"Review of: \"Quo Vadis, Artificial Intelligence? A Neuro-Symbolic Approach to Artificial Intuition\"","source":"crossref","abstract":"The manuscript presents a neuro-symbolic framework aimed at modeling human intuition through a combination of semantic knowledge graphs, intuitive vs. factual edges, and a path-scoring mechanism balancing plausibility and innovation.The topic is timely and interesting, and the attempt to formalize intuitive reasoning is commendable.The distinction between factual and intuitive knowledge, as well as the use of innovation/propagation indices, is promising.However, in its current form, the manuscript is underdeveloped for publication.The conceptual framing is promising, but the methodological description lacks suf cient depth and reproducibility.Many critical aspects of the implementation, especially around the construction and traversal of the knowledge graph, remain too vague.As a result, the work reads more like an extended concept note than a full research article.","url":"https://doi.org/10.32388/qwyug8","authors":["Roberto Confalonieri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T09:27:32Z","doi":"10.32388/qwyug8","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1515/9783112215722-022","name":"489Mathematical Methods in the Digital Age","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783112215722-022","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-19T19:06:20Z","doi":"10.1515/9783112215722-022","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2139/ssrn.5063216","name":"Generative Artificial Intelligence-Based Medical Entity Data Extractor Using Large Language Models","source":"openalex","abstract":"","url":"https://doi.org/10.2139/ssrn.5063216","authors":["Mohammed-khalil Ghali","Abdelrahman Farrag","Hajar Sakai","Hicham El Baz","Yu Jin","Sarah Lam","Mohammed-Khalil Ghali"],"tags":["Computer science","Extractor","Download","Generative grammar","Generative model"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"10.2139/ssrn.5063216","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-443-23517-7.00018-6","name":"REMOVED: Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23517-7.00018-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T13:55:34Z","doi":"10.1016/b978-0-443-23517-7.00018-6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.38007/978-1-80053-562-6","name":"Research on Artificial Intelligence Technology and Its Application in Teaching","source":"crossref","abstract":"","url":"https://doi.org/10.38007/978-1-80053-562-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-11T06:14:17Z","doi":"10.38007/978-1-80053-562-6","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2139/ssrn.5777683","name":"Artificial Intelligence (Regulation and Governance) Act, 2025&amp;nbsp;","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5777683","authors":["Paarth Wassan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-23T20:18:46Z","doi":"10.2139/ssrn.5777683","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.2139/ssrn.5384048","name":"ARTIFICIAL INTELLIGENCE LAW AND REGULATION IN A NUTSHELL ® CHAPTER 9 CONSIDERATIONS FOR THE LAW AND REGULATION OF ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"This chapter serves as both a summary and a primer, identifying the key themes of the Nutshell from four perspectives: regulators, end users, enterprise deployers, and society as a whole. Each of these stakeholders has a different set of goals and interests. By reframing earlier discussions within these perspectives, this summary aims not only to provide a concise version of the book but also to add a new layer of insight into the regulation of AI. The chapter summarizes how the use of automated decision making may trigger regulations from federal, state, tribal, territorial, and local governments as well as potentially allowing for the extraterritorial regulation from foreign governments. The chapter addresses the importance of&amp;nbsp; transparency, explainability, reliability, and resilience for the development of AI systems and highlights this role with regard to consumer, user, and patient protections. In addition, the chapter provides a summary for such topics as algorithmic bias, ethics, humans in the loop requirements, intellectual property protection, data security, business strategy, and existential concerns of AI’s unregulated success and of its failure. The chapter provides both a summary of the Nutshell and a stand-alone primer on the field. (Reproduced with&amp;nbsp;publisher's and author's permission.)","url":"https://doi.org/10.2139/ssrn.5384048","authors":["Jon M. Garon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-12T10:24:47Z","doi":"10.2139/ssrn.5384048","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1002/eng2.70114/v1/review1","name":"Review for \"Artificial Intelligence and Architectural Design Before Generative &lt;scp&gt;AI&lt;/scp&gt;: Artificial Intelligence Algorithmics Approaches 2000–2022 in Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70114/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-05T17:08:51Z","doi":"10.1002/eng2.70114/v1/review1","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.55834/plj.2876092188","name":"Navigating Healthcare in the Age of Artificial Intelligence: Opportunities and Challenges for Chief Medical Officers","source":"crossref","abstract":"Like many other industries, healthcare is already seeing the effects of artificial intelligence’s transformation. AI has the potential to revolutionize healthcare in many ways. CMOs must be aware of their changing role in this space and concentrate on ways AI can enhance clinical practice, patient care, and operational efficiency. They must also take on the important leadership role of ensuring that these technologies are implemented in ways that benefit patients, clinicians, and the entire healthcare system.","url":"https://doi.org/10.55834/plj.2876092188","authors":["Mark Olszyk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-21T15:05:06Z","doi":"10.55834/plj.2876092188","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.37990/medr.1622314","name":"Artificial Intelligence as a Partner in Ankylosing Spondylitis Care: Evaluating ChatGPT’s Role and Performance","source":"crossref","abstract":"Aim: Artificial Intelligence may have significant potential to assist clinicians in decision-making and diagnosis, especially in units dependent on up-to-date guidelines such as rheumatology. This study aims to evaluate the effectiveness of ChatGPT in providing clinicians with evidence-based information about ankylosing spondylitis (AS). Material and Method: Frequently asked questions (FAQs) about AS were developed by reviewing commonly accessed patient-oriented websites, social media platforms, and official hospital pages. Questions were designed based on scientific guidelines, particularly the American College of Rheumatology (ACR) and Assessment of SpondyloArthritis international Society (ASAS)-European League Against Rheumatism (EULAR) axial spondyloarthritis guidelines. ChatGPT's responses were evaluated on a 1-to-4 scale. Each question was posed twice to assess reproducibility, with consistency defined by identical scores across both attempts. Results: ChatGPT demonstrated an overall accuracy of 81.9% in its responses to 72 FAQs. The highest accuracy (91.7%) was observed in responses related to the prevention of AS. Of the 36 questions based on ACR and ASAS-EULAR guidelines, ChatGPT provided accurate answers for 22 (61.1%), with three responses receiving the lowest grade (4). Reproducibility of ChatGPT's responses was 88.8% across all FAQs and 83.3% for guideline-specific questions. Conclusion: This study highlights the potential of ChatGPT as a supportive tool for patient education and clinician reference, particularly for general FAQs. However, accuracy for questions derived from ACR and ASAS-EULAR guidelines was lower (61.1%), emphasizing the need for clinician oversight.","url":"https://doi.org/10.37990/medr.1622314","authors":["Ömer Faruk Bucak","Cigdem Cinar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-08T09:30:28Z","doi":"10.37990/medr.1622314","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.6914/aiese.010103","name":"How Generative Artificial Intelligence Shapes the Future of Education","source":"crossref","abstract":"Artificial intelligence (AI) has significantly transformed higher education by enabling personalized learning through adaptive platforms, intelligent tutoring systems, and real-time feedback mechanisms. This study examines the benefits and challenges of AI-driven personalized learning, emphasizing its potential to improve student engagement, retention, and academic outcomes. However, ethical concerns—such as data privacy, algorithmic bias, and access disparities—pose challenges that must be addressed for sustainable AI integration. By analyzing case studies from multiple universities and synthesizing existing literature, this research proposes a framework for ethical AI implementation that balances innovation with accountability and inclusivity. The findings contribute to ongoing discussions on AI’s role in education, providing practical insights for educators, administrators, and policymakers.","url":"https://doi.org/10.6914/aiese.010103","authors":["Aiqing WANG"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-15T14:07:20Z","doi":"10.6914/aiese.010103","addedAt":"2026-09-01T01:47:51.504Z","updatedAt":"2026-09-01T01:47:51.504Z"},{"id":"doi:10.1201/9788770047371-4","name":"Artificial Intelligence in Neuroscience","source":"crossref","abstract":"The use of artificial intelligence (AI) in neuroscience presents a dynamic advantage for the diagnosis and treatment of diseases connected to the brain. The study examines significant developments and potential paths for AI use in neuroscience. This study highlights the need for explainable AI and indicates how transparent and interpretable AI models are in building confidence in clinical decision-making processes. As a major trend, edge AI improves neurological and psychiatric care by improving real-time data processing and decision-making at the data-gathering site. AI-driven biomarker discovery is presented as revolutionary, providing individualized treatment plans based on genomic and neuroimaging data as well as insights into early disease diagnosis. The study emphasizes how AI and digital health technology could be used together to support specific medication and ongoing patient monitoring. AI has the potential to greatly enhance neurological patient outcomes by utilizing these breakthroughs in diagnosis, therapy, and patient outcomes overall. Going ahead, more advancement in AI research and development will be necessary to unleash fresh perspectives on brain pathology and 68 function, providing the possibility for improved diagnostic and therapeutic approaches.","url":"https://doi.org/10.1201/9788770047371-4","authors":["Mahade Hasan","Farhana Yasmin","Xue Yu","Hassan Mehedi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-28T15:46:32Z","doi":"10.1201/9788770047371-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.59400/cai3893","name":"Verifying artificial intelligence-generated images: Socio-technical approaches to authenticity","source":"crossref","abstract":"The rapid proliferation of artificial intelligence (AI) has transformed visual media, enabled highly realistic AI-generated images, and raised ethical, social, and security concerns. Generative artificial intelligence (Generative AI) architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, allow content creation that is increasingly indistinguishable from human-made visuals, facilitating creativity, education, and communication. However, these capabilities also introduce risks of manipulation, identity fraud, misinformation, and deepfake attacks across social, political, corporate, academic, and humanitarian domains. This study investigates AI image verification as a socio-technical response to synthetic visuals, focusing on social media, artistic, and forensic contexts. It employed a qualitative design combining thematic literature review and case study analysis. Thematic analysis identified patterns in verification approaches, including pixel-level analysis, metadata forensics, machine learning classifiers, watermarking, and blockchain-enabled methods. Case studies explored real-world applications, highlighting perceptual biases, strategic use of synthetic content, and governance and digital literacy challenges. Findings reveal that human perception alone is insufficient for reliably discerning authenticity, with individuals frequently misclassifying AI-generated images as real. Integrating machine learning, metadata analysis, and blockchain verification, hybrid technical approaches significantly improve detection accuracy. Socio-technical factors, including platform policies, ethical norms, organisational governance, and user literacy, shape the effectiveness of verification methods. The study presents a conceptual framework linking technological, organisational, and societal dimensions, emphasising the need for coordinated strategies that combine algorithmic innovation, regulatory oversight, and public engagement. Practical implications include deploying hybrid verification systems, strengthening governance and ethical standards, enhancing digital literacy, and fostering cross-disciplinary collaboration to safeguard trust, authenticity, and integrity in digital media.","url":"https://doi.org/10.59400/cai3893","authors":["Michael Mncedisi Willie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-09T07:21:55Z","doi":"10.59400/cai3893","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003531166-10","name":"Artificial Intelligence-Assisted Wearable Devices and Sensors","source":"crossref","abstract":"This chapter examines the transformative role of wearable health devices in collecting data on human activities, affect, and attention, enabled by advancements in ubiquitous computing and artificial intelligence. Moving beyond traditional self-report methods, wearable devices now capture subtle behavioural changes and physiological responses that serve as reliable indicators of affective states. The chapter begins by introducing various types of wearable health devices and explores how artificial intelligence enhances their accuracy and functionality, providing clinical examples to illustrate their applications. It further addresses the potential risks and limitations of these technologies, alongside critical ethical considerations, such as privacy, data security, and informed consent. The chapter concludes by discussing future developments in wearable devices, focusing on expanding their usage and exploring new applications in mental health care and beyond. This comprehensive overview highlights the potential of wearable devices to revolutionise human-computer interaction and improve the understanding and monitoring of psychological states.","url":"https://doi.org/10.1201/9781003531166-10","authors":["Hester Chow"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T17:02:44Z","doi":"10.1201/9781003531166-10","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2196/83217","name":"Peer Review of “Development of a Conversational Artificial Intelligence–Based Web Application for Medical Consultations: Prototype Study”","source":"crossref","abstract":"","url":"https://doi.org/10.2196/83217","authors":["Anonymous"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-15T20:56:56Z","doi":"10.2196/83217","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.55834/halmj.8737939725","name":"Artificial Intelligence in Healthcare: A New Frontier in Medical Innovation","source":"crossref","abstract":"Artificial intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, treatment, and patient care through its ability to process vast amounts of data quickly, enabling early disease detection, accurate treatments, and personalized care plans. AI-powered tools, such as advanced imaging systems, improve diagnostic accuracy while reducing unnecessary tests, and in mental health, AI aids in early disorder detection and provides accessible support through virtual therapists and chatbots. By simplifying complex medical information, AI empowers patients to better understand their health and actively participate in their care. It is also making healthcare more accessible and cost efficient through technologies such as wearable devices and telemedicine, which enable remote care and effective management of chronic conditions. Predictive analytics fueled by AI provides insights into patient outcomes and operational efficiency, facilitating early interventions and better resource allocation. AI holds immense potential, but challenges such as ensuring fairness, data privacy, and algorithmic accuracy remain critical and addressing them requires collaboration among technologists, healthcare providers, and policymakers. As advancements continue, AI is set to redefine global healthcare through precision medicine, leveraging wearable technologies and innovative diagnostic tools to deliver highly personalized and efficient care. By tackling existing challenges and building robust systems, AI is paving the way for improved outcomes and equitable access to healthcare for all.","url":"https://doi.org/10.55834/halmj.8737939725","authors":["Vithyash Ayyappan","Vikash Ayyappan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-02T13:37:41Z","doi":"10.55834/halmj.8737939725","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1145/3729706.3729732","name":"A Comprehensive Review on the Applications of Artificial Intelligence in Cybersecurity","source":"crossref","abstract":"Recently, we have realized rapid advancement of technology in several fields due to the emergence of Artificial Intelligence (AI). AI plays a significant role in enhancing the cyber security field to hasten the detection of threat and response, systematize the repetitive tasks, and enhance the accuracy of the cyber security team's actions. It is used to strengthen the security measures against several security challenges and cyber-attacks. This article presents a comprehensive review on the applications of AI in cyber security by elaborating the strengths, limitations, and potential risks. This work discusses various kinds of AI algorithms used to enhance the cyber security. This work also examines the roles of AI in intrusion detection, malware detection, and vulnerability discovery. The potential risks and challenges associated with AI approaches in cyber security is elucidated. Finally, this paper suggests the way to battle AI-based vulnerabilities and threats, and suggests future directions.","url":"https://doi.org/10.1145/3729706.3729732","authors":["Qinghao Zeng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-01T11:55:42Z","doi":"10.1145/3729706.3729732","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.18502/jimc.v8i2.17691","name":"Studies on Artificial Intelligence (AI) Techniques for Diabetes Diagnosis Using Facial Features","source":"crossref","abstract":"Diabetes Mellitus (DM) stands as one of the most widespread non-infectious diseases globally. Although diagnosis of diabetes is possible with the fasting plasma glucose test after 12-hour fast, once diabetes is diagnosed, it cannot be reversed. Therefore, it is crucial to identify early indicators for predicting diabetes. Presently, DM can be discerned through various methods involving the analysis of human facial features. One method for facial recognition in diabetes relies on experimental evidence, with its accuracy contingent on the skill and expertise of the physician. Another approach involves diagnosis based on facial morphological features. These morphological changes may be attributed to oxidative stress, damage of blood vessels and collagen, edema and craniofacial abnormalities stemming from hyperglycemia. While cephalometric analysis remains the gold standard for diagnosing skeletal craniofacial morphology, it is a costly and technique-sensitive procedure. Facial recognition based on Artificial Intelligence (AI) has proven to be a valuable tool in the diagnosis and screening of diabetes. Its combination of simplicity, accuracy, and cost-effectiveness makes it a promising addition to the healthcare landscape, ultimately contributing to advancements in pre-clinical diagnosis and leading to enhanced patient outcomes. Given the rapid global increase in diabetes, the importance of early detection of diabetes and the limited information about the role of facial recognition in this regard, this study assesses diabetes through facial features using AI approaches.","url":"https://doi.org/10.18502/jimc.v8i2.17691","authors":["Mohammad Bagher Owlia","Hamidreza Soltani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T12:50:17Z","doi":"10.18502/jimc.v8i2.17691","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.24061/2707-8728.2.2025.4","name":"APPLICATION OF ARTIFICIAL INTELLIGENCE TOOLS IN SOLVING APPLIED PROBLEMS IN FORENSIC MEDICINE","source":"crossref","abstract":"One of the most actively and rapidly developing contemporary analytical research methods in recent years is undoubtedly artificial intelligence. In a relatively short period, it has progressively permeated all spheres of human life. Forensic medicine is no exception, as its progressive medical component dynamically evolves over time alongside scientific and technological progress. The aim of the study. To analyze recent scientific works and global literature on the implementation of artificial intelligence tools for solving various applied problems in forensic medicine. Materials and methods. The authors analyzed all accessible sources from global scientific literature in databases such as PubMed, Scopus, and Web of Science, selected based on keywords. The most relevant among them were subjected to detailed comparative analysis to assess their effectiveness in solving key applied tasks of forensic medicine. Scientific research. This study constitutes a fragment of a comprehensive research work conducted by the Department of Forensic Medicine and Medical Law of the Bukovinian State Medical University, titled \"Utilization of modern morphological and physical methods for diagnosing the time and cause of death, the occurrence of bodily injuries, and the development of their remote and immediate consequences, aimed at addressing urgent tasks of law enforcement agencies and current issues of forensic medical science and practice\" (State registration number 0123U101978, implementation period: January 2023-December 2027). Bioethics. The research materials have been reviewed and approved by the Bioethics Commission of Bukovinian State Medical University (Protocol No. 2 dated October 16, 2025). Results. The conducted study revealed that the application of artificial intelligence tools is already widely reflected in solving applied problems within the main branches of forensic medicine. Most researchers emphasize the necessity of critical consideration, a balanced approach, and expert supervision when implementing elements of artificial intelligence (machine learning, deep learning) in the process of conducting forensic medical examinations. Conclusions. Most models employed by researchers in the field of forensic medicine for solving various expert tasks are at initial stages of implementation and training, requiring further verification and legal assessment. Promising areas for the further implementation of artificial intelligence tools encompass most branches (directions) of forensic medicine, including estimation of the time since death, determination of different types of death, differential diagnosis of various mechanisms of bodily injury, firearm and explosive trauma, personal identification, forensic toxicology, histology, and criminalistics","url":"https://doi.org/10.24061/2707-8728.2.2025.4","authors":["Ivan Savka","Ihor Korobko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-30T11:11:51Z","doi":"10.24061/2707-8728.2.2025.4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.3390/healthcare13202579","name":"Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research","source":"crossref","abstract":"Background/Objectives: Artificial intelligence (AI) has emerged as a transformative force in healthcare, with nutrition and dietetics becoming key areas of application. AI technologies are being employed to enhance dietary assessment, personalize nutrition plans, manage chronic diseases, deliver virtual coaching, and support public health nutrition. This review aims to critically synthesize the current literature on AI applications in nutrition, identify research gaps, and outline directions for future development. Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for peer-reviewed publications from January 2020 to July 2025. The search included studies involving AI applications in nutrition, dietetics, or public health nutrition. Articles were screened based on predefined inclusion and exclusion criteria. Thematic analysis grouped findings into six categories: dietary assessment, personalized nutrition and chronic disease management, generative AI and conversational agents, global/public health nutrition, sensory science and food innovation, and ethical and professional considerations. Results: AI-driven systems show strong potential for improving dietary tracking accuracy, generating personalized diet recommendations, and supporting disease-specific nutrition management. Chatbots and large language models (LLMs) are increasingly used for education and support. Despite this progress, challenges remain regarding model transparency, ethical use of health data, limited generalizability across diverse populations, and underrepresentation of low-resource settings. Conclusions: AI offers promising solutions to modern nutritional challenges. However, responsible development, ethical oversight, and inclusive validation across populations are essential to ensure equitable and safe integration into clinical and public health practice.","url":"https://doi.org/10.3390/healthcare13202579","authors":["Gabriela Georgieva Panayotova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-14T10:37:14Z","doi":"10.3390/healthcare13202579","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.21037/jmai-23-158","name":"Devil’s advocate: exploring the potential negative impacts of artificial intelligence on the field of surgery","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-23-158","authors":["Mina Sarofim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-26T08:54:26Z","doi":"10.21037/jmai-23-158","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.4018/979-8-3373-3196-6.ch011","name":"Transforming Preventive Medicine Through Artificial Intelligence","source":"crossref","abstract":"However, AI has greatly changed the way that healthcare proceeds, making it possible to detect disease early and predict the risk of happening. Blood tests, MRI's, CT scans, X-rays and any other clinical, genetic and imaging data is used along with machine learning and deep learning models to detect diseases before the symptoms show up. For diseases such as cancer, cardiovascular and neurological etc., CNNs and NLP techniques help analyze scans, pathology slides, electronic health records. Risk models based on the patient's history, lifestyle or genetics are evaluated using AI technology. Despite this, they face ethical and validation issues, risks regarding data privacy, and validation needs. To bring guaranteed and proper AI solutions, effective collaboration between medical professionals, AI researchers and policymakers is pivotal. This chapter focuses on the discussion of AI's applications, advantages and restrictions, future potential, for the benefit of clinicians and researchers to optimize patient outcomes and advance precision medicine.","url":"https://doi.org/10.4018/979-8-3373-3196-6.ch011","authors":["Richa Singh","Lovleen Marwaha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T21:08:05Z","doi":"10.4018/979-8-3373-3196-6.ch011","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.29011/2833-3497.000168","name":"Artificial Intelligence in the Diagnosis and Management of Colorectal Cancer: A Systematic Review","source":"crossref","abstract":"Background: Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide.Advances in artificial intelligence (AI) offer new opportunities to enhance CRC detection, diagnosis, and treatment planning.We conducted a systematic review of the literature through early 2025 to evaluate the role of AI in both diagnosing and managing CRC, including applications in colonoscopy, histopathology, endoscopic imaging, radiology, and therapeutic decision-making.Methods: A comprehensive literature search was performed in PubMed, Embase, Cochrane Library, and Google Scholar for studies (through Jan 2025) on AI in CRC diagnosis or management.Both diagnostic (e.g., polyp detection on colonoscopy, image analysis for pathology and radiology) and management (e.g., prognostication, treatment planning) studies were included.Data on AI models (e.g., convolutional neural networks [CNNs], deep neural networks [DNNs], support vector machines [SVMs], transformers), performance metrics (sensitivity, specificity, accuracy, area under the curve [AUC]), and clinical utility were extracted.Results: 147 relevant studies identified initially; 40 duplicates removed → (147 -40 = 107 studies remained); 31 irrelevant studies excluded → (107 -31 = 76 studies remained); 27 inaccessible reports excluded → (76 -27 = 49 studies included in the final analysis).AI systems consistently improved adenoma and polyp detection during colonoscopy, raising adenoma detection rates (ADR) by ~20% (e.g., from 36.7% to 44.7% in meta-analysis) and halving miss rates [1].Deep learning models in digital histopathology achieved accuracies comparable to expert pathologists (often 95-99% range) and can predict key molecular markers like microsatellite instability with AUC ~0.82-0.89[2,3].In radiology, AI algorithms detect CRC on CT scans with sensitivities around 80-81% (on par with radiologists) and >90 % specificity.AI-driven prognostic models (radiomics and deep learning) outperform clinical risk scores in predicting outcomes such as recurrence [4].Discussion: AI has demonstrated robust performance in CRC diagnosisimproving polyp and tumor detection in endoscopy and imaging-and shows promise in management decisions by aiding pathology interpretation and outcome prediction.Key strengths include enhanced sensitivity, consistency (lack of fatigue), and ability to analyze complex multimodal data.Challenges remain in integrating AI into workflows, ensuring generalizability across diverse settings, and addressing interpretability and regulatory concerns.Conclusions: AI is poised to augment CRC care by improving early detection and enabling more personalized management.Ongoing trials and real-world implementation studies are needed to confirm its impact on long-term clinical outcomes and to refine integration strategies for routine practice.","url":"https://doi.org/10.29011/2833-3497.000168","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-21T09:20:51Z","doi":"10.29011/2833-3497.000168","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.63962/aynf5861","name":"Exceptional Minds Meet Artificial Intelligence: Perspectives and Possibilities in Gifted Education.","source":"crossref","abstract":"","url":"https://doi.org/10.63962/aynf5861","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T06:25:21Z","doi":"10.63962/aynf5861","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1017/9781009367783.014","name":"Artificial Intelligence and Intellectual Property Law","source":"crossref","abstract":"This chapter discusses the interface of artificial intelligence (AI) and intellectual property (IP) law. It focuses on the protection of AI technology, the contentious qualification of AI systems as authors and/or inventors, and the question of ownership of AI-assisted and AI-generated output. The chapter also treats a number of miscellaneous topics, including the question of liability for IP infringement that takes place by or through the intervention of an AI system. More generally, it notes the ambivalent relationship between AI and the IP community, which appears to drift between apparent enthusiasm for the use of AI in IP practice and a clear hesitancy toward catering for additional incentive creation in the AI sphere by amending existing IP laws.","url":"https://doi.org/10.1017/9781009367783.014","authors":["Jozefien Vanherpe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-18T13:47:06Z","doi":"10.1017/9781009367783.014","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.3389/frai.2025.1603562","name":"Artificial Intelligence Think Tank: a modern problem-solving framework","source":"crossref","abstract":"In today's world, when everything is changing quickly and new global concerns are emerging, lifelong learning and creative problem solving are more crucial than ever. Classical approaches such as the brainstorming, Delphi, Nominal Group Technique, focus groups, and the World Café are some of existing problem-solving supporting approaches, but they may not necessarily suit complicated and extended decision-making situations (Caudle et al., 2025, Raadschelders and Whetsell, 2018, Watkins et al., 2012). These approaches are consensus-based and hence rely on the availability of experts, time, and cognitive capacity, which limits their scalability and effectiveness in dynamic contexts. Their drawbacks become particularly pronounced in emerging sectors, where access to sufficient expertise is often constrained by high costs, time pressures, or simply a lack of established specialists (Palonen et al., 2014). The drawbacks of these classic methods, such as their reliance on the availability of experts, significant time necessities, and limited scalability, may cause decisions to be delayed, opportunities to be missed, and solutions to be lacking in resource-constrained situations.To address the classic approaches' limitations, a growing trend has emerged involving the utilization of artificial intelligence (AI) to support human capabilities across various domains (Korteling et al., 2021). According to Fui-Hoon Nah et al. (Fui-Hoon Nah et al., 2023), AI-human collaboration has emerged as a promising path forward in addressing these challenges and unlocking new possibilities for human development. The technologies improve data mining, data analysis, and even certain decision-making activities that were previously the domain of human specialists, which might be very valuable in the cyclic learning process and future-oriented problem-solving. As a result, in order to capitalize on these prospects, this paper suggests the AI Think Tank (AITT) framework as a novel and unique method to decision surrogate modeling that may complement and replace existing ways to lean and progress for decision making and problemsolving. Current versions of Generative AI technology can produce human-like conversations and are gaining popularity due to their ability to give tailored and context-sensitive replies (Dwivedi et al., 2023) The AITT procedure, as figure 1 shows, has the power to promote inventive thinking and broaden the boundaries of how humans learn, adapt, and prosper in an ever-changing environment, by promoting continuous skill acquisition, improving decision-making efficiency, and encouraging cooperation between AI and human judgment. problem/decision needs to be addressed.Correspondingly, a comprehensive standardized prompt/query is developed by the decision maker(s) to ensure consistency and reliability in AI outputs. The prompt may include all necessary background information such as current trends, constraints, and goals.If feasible, invite (a) field expert(s) to review and validate the prompt, mapping it to the problem/decision at hand.Stage 2: Getting insights from AI. Although relying on a single AI chatbot remains possible, it is preferred that the standardized prompt be posed to multiple AI systems to ensure coverage of a broad spectrum of insights. Using different AIs ensures diversity in inputs, as each AI system (e.g., ChatGPT, Gemini) operates with unique data sources and methodologies, offering complementary perspectives.1. Select an AI system and, according to the developed prompt, task it with generating ideas and needed problem-solving variables (such as success factors, barriers and challenges, motives, decision criteria, risks, etc.), or alternative solutions for a given problem.When As (a) decision-maker(s), use criteria like relevance and feasibility to evaluate outputs and authenticate insights according to the problem and/or decision context to minimize potential biases.A simple case study was employed to illustrate the feasibility of the proposed AITT; it serves as a preliminary proof-of-concept. The aim of this case study was to demonstrate the potential application of the AITT framework. Hence, the four stages of the AITT were carried out in the explicitly defined order outlined lower. In this case study, we implement the AITT to identify its limitations.1.We considered AITT validation in the scenario when there is no specific AITT expert available and the author is the sole proposer. Therefore, the AITT were utilized to provide feedback on the possible constraints of itself. Following the completion of the problem definition, the AITT method presentation was used to initiate this case study.ChatGPT and Gemini were chosen for this case study due to their widespread popularity;just the two AI systems were used in the study to ensure a simple AITT implementation demonstration. A detailed description of the AITT framework was sent to both AI platforms, ChatGPT and Gemini, inquiring about potential limitations of the proposed AITT. The technique employed a standardized input for both AIs and offered the identical question to both: \"What are the potential limitations of the AITT framework?\". Resultswere synthesized to create a comprehensive list, combining outputs from each AI system into a cohesive set of concepts.Asking for more output, communication with AIs continued until no new answer, feasible, important, or reasonable output was provided. This stage contained the exclusion of items that received low agreement from the author or did not directly pertain to the AITT in relation to traditional problem-solving and decision-making methods.A total of 21 concepts were incorporated in the aforementioned list, comprising 9 items from Gemini and 12 items from ChatGPT. Among these, 8 concepts showed either identical or extremely equivalent results when assessed by both ChatGPT and Gemini. Hence, a list of 13 was gathered and after reviewing the data summary, the authors, in their role as the decision-maker, concluded that some restrictions are more significant and should be explicitly communicated, though all listed items were valid.By utilizing the AITT strategy, the decision-making scenario described above effectively collected and ranked ideas, indicating the potential for improved efficiency and comprehensiveness when compared with classic approaches, though more empirical validation remains required. This specific phase of strategic planning requires a substantial reduction in the time needed due to the automation of concept creation and analysis methods. The decision-maker determined that the developed concepts demonstrated proper logical consistency. The applied technique has shown its capacity to efficiently handle a wide range of inputs and adapt to different decision-making scenarios, without requiring the participation of a significant number of subject matter experts.Moreover, the employment of the AITT guaranteed the achievement of a thorough comprehension of important features and prerequisites for using the AITT approach, therefore offering an additional advantage.The use of this specific case study confirmed AITT's applicability, demonstrating its capacity as a viable and efficient instrument for overcoming problem-solving challenges and reaching informed conclusions. Nevertheless, some potential limitations were identified. AITT results may be biased due to inconsistent AI performance, the risk of generating misplaced confidence, and occasionally, challenges in interpreting or explaining AI-generated reasoning clearly. These can be reduced, however, by employing cross-referencing techniques and human validation of AI outputs. AITT is unable to handle tacit knowledge; it also faces creativity and novelty limits because it works with documented information; and it is less able to fully consider emotional, cultural, intuitive understanding, common sense, and contextual nuance. To overcome these limitations, AITT users can follow best practices for more reliable and transparent use in practical applications. Humanin-the-loop supervision is still considered essential to adequately understand the results of AI and determine its usefulness and relevance. Users can evaluate AI responses with cross-validation to identify harmony or contradictions. They may need to perform iterative prompt refinement to improve output quality and monitor AI system upgrades for response coherence. Another challenge for AITT is complexity in prompt engineering and the risk of resulting information overload, yet this needs to be addressed by integrating human oversight and modification of prompts.As discussed in this letter, AI can act as a think tank, assisting us with problem solving and decision-making. This letter proposed a supplementary, systematic, adaptable, and efficient AITT framework for modern problem-solving; this semi-automated idea generation saves time and resources, making AITT highly adaptable across diverse industries, contexts, and levels of complexity. However, more research in various sectors and situations is needed to determine the potential application and improvement of AITT. To advance this paradigm, real-world examples must be requested, investigations must be conducted, and more verification cooperation is required. Integration of AITT with other, traditional or modern, decision-making methods broadens the problem comprehension, even when expert human input is scarce or expensive, as future researchers can verify.","url":"https://doi.org/10.3389/frai.2025.1603562","authors":["Shahryar Sorooshian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-14T10:30:40Z","doi":"10.3389/frai.2025.1603562","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003503385-10","name":"Artificial Intelligence (AI)-Enabled Diabetic Retinopathy Detection Techniques","source":"crossref","abstract":"Diabetic retinopathy (DR) is an ophthalmological distress that damages retinal vessels caused by diabetes. An increase in blood sugar in the body causes complications in the working of the kidneys, eyes, feet, and nerves. DR is one of the diseases that cause lesions, clots, swelling of blood vessels, and even retinal detachment that affects vision and leads to vision impairment. DR is classified into non-proliferative (NPDR) and proliferative (PDR). Further, NPDR is classified into mild, moderate, and severe. Early detection of DR is essential to prevent vision loss. Manual assessment of DR using fundus images is an error-prone and time-consuming task. Hence, artificial intelligence-enabled automated DR detection and classification techniques are crucial in diagnosis. Recently, many DR detection techniques have been developed using machine learning and deep learning approaches. Deep learning-based methods include convolutional neural network architectures that have learnable weights and biases and are capable of high-level feature extraction and classification of different classes of DR. Some pre-trained architectures are also available, such as Inception V3, VGG19, DenseNet, and ResNe50, for DR identification and classification. These approaches utilize fine-tune multiple layers and speed up the training process. However, challenges need to be addressed for future research perspectives.","url":"https://doi.org/10.1201/9781003503385-10","authors":["Ravi Bhushan Dixit","Chandan Kumar Jha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-24T14:58:18Z","doi":"10.1201/9781003503385-10","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.63053/ijhes.118","name":"Integrating Artificial Intelligence Techniques in Medical Bacteriology","source":"crossref","abstract":"Integrating Artificial Intelligence Techniques in Medical Bacteriology is a scientific research paper that explores the transformative role of artificial intelligence (AI) in enhancing diagnostic and therapeutic practices within the field of bacteriology. As AI technologies increasingly permeate healthcare, this study provides a comprehensive analysis of how machine learning (ML) and deep learning algorithms can significantly improve the accuracy and timeliness of bacterial pathogen detection and antibiotic resistance management, marking a notable advancement in laboratory medicine.[1-][2] The research emphasizes the potential of AI to streamline workflows and enhance operational efficiency in medical bacteriology. By automating processes such as error detection, result interpretation, and image analysis, AI systems can significantly reduce the turnaround time for diagnostic results, ultimately leading to improved patient outcomes.[3][4] The paper also highlights the importance of data quality management in the development of AI models, advocating for adherence to established standards throughout the dataset lifecycle to ensure the reliability of AI applications in clinical settings.[5]Despite the promising advancements, the integration of AI in healthcare is not without its challenges. The study discusses the current limitations in the clinical efficacy and cost-effectiveness of AI applications, revealing a gap between research outcomes and real-world implementation.[4] Additionally, ethical considerations surrounding data privacy and algorithmic transparency are addressed, emphasizing the need for regulatory frameworks that promote safe and equitable AI use in medical practice.[6]Overall, this research provides a critical examination of the trends and innovations in AI applications in medical bacteriology, employing statistical analysis and bibliometric techniques to identify research hotspots and emerging patterns in the field from 2010 to 2024.[7][6] By integrating AI methodologies, the study aims to lay the groundwork for future research directions and improve quality assurance standards","url":"https://doi.org/10.63053/ijhes.118","authors":["Ali Karimi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-24T18:56:41Z","doi":"10.63053/ijhes.118","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1109/csaia65930.2025","name":"2025 International Conference on Computer Science and Artificial Intelligence Applications (CSAIA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csaia65930.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T19:13:44Z","doi":"10.1109/csaia65930.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003617013-8","name":"Intelligent Applications for Medical Image Analysis","source":"crossref","abstract":"Persistent challenges have beset the adoption and application of digital technology in the healthcare sector. While there has been some progress towards unifying various healthcare systems, many places in the world still lack completely integrated healthcare systems. Therefore, to develop and implement their artificial intelligence (AI)-powered technologies, healthcare professionals will benefit greatly from having a solid understanding of the core concepts of AI. AI is frequently defended as the ability of machines to simulate cognitive processes in humans. Combining data science, machine learning (ML), algorithms, and computer science, it can do tasks with humans on par or better. Medical professionals work in a dynamic and ever-changing environment where they frequently encounter new problems, changing responsibilities, and disruptions. Because of this variance, medical experts frequently grow less concerned with diagnosing illnesses. Moreover, clinical interpretation of medical data requires a high cognitive load. This holds for seasoned experts and others with different or limited skill sets, including inexperienced assistant physicians. Experts deal with new problems regularly, as well as changing tasks and interruptions. In this chapter, we suggested the comparative analysis of numerous state-of-the-art approaches for medical imaging diagnosis and evaluated various key qualities. The process involves assessing several critical elements, including semantic data, interpretability, visualisation, and the measurement of logical linkages in medical data. Thus, we can conclude that the potential for imaging in the future will have a high degree of diagnostic accuracy for the diagnosis of various diseases, which is important for the disease’s diagnosis and has a higher chance of being realised in the field of clinical diagnosis. Lastly, the applications and potential were also covered.","url":"https://doi.org/10.1201/9781003617013-8","authors":["Srabanti Maji","Pooja Gupta","Pradeep Singh Rawat","Tripti Halder"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-31T12:42:25Z","doi":"10.1201/9781003617013-8","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.4324/9781003660286-4","name":"Artificial Intelligence and Responsible Management Education","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming the features of higher education, reshaping traditional teaching, learning, and knowledge acquisition paradigms. Guided by the Principles for Responsible Management Education (PRME), business and management education institutions are challenged to align their curricula and research with internationally recognized values that promote sustainable development. Within this context, there is a growing interest in researching the role of AI in advancing Responsible Management Education (RME). This chapter provides a comprehensive bibliometric analysis to explore the intersection of AI and RME, aiming to identify emerging trends and propose directions for future research. The study investigates influential aspects of RME literature related to AI through bibliometric techniques, including key research streams, themes, leading authors, prominent keywords, journals, institutional affiliations, and contributing countries. The analysis employs co-citation analysis, keyword co-occurrence mapping, and thematic clustering to uncover this interdisciplinary field’s intellectual structure and evolution. The findings reveal critical trends, highlight underexplored areas, and shed light on the dynamics driving current management education. By identifying significant gaps in the literature and constructing a future research agenda, this study provides valuable insights for scholars, educators, and policymakers.","url":"https://doi.org/10.4324/9781003660286-4","authors":["Hebatallah Ghoneim","Dina Yousri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-09T14:40:05Z","doi":"10.4324/9781003660286-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.54941/ahfe1006212","name":"Artificial Intelligence and Media Literacy - Navigating Information in a Digital World","source":"crossref","abstract":"Artificial intelligence (AI) is playing an increasingly important role in the media ecosystem, transforming both the creation and distribution of content. At the same time, the growing influence of AI raises questions about media literacy as a key aspect of critical thinking in the digital age. This article explores the relationship between AI and media literacy by analyzing how automated technologies shape information perception, fake news detection, and critical content evaluation skills. The article combines theoretical review and empirical research to identify the main challenges and opportunities in the field. The focus is on the interaction between AI tools, such as content recommendation algorithms and generative models, and the ability of users to analyze, interpret, and create information. It analyzes the possibilities of using AI as a means of improving media literacy through interactive learning platforms, capabilities for identifying prejudice and hate speech, and as a support tool in journalistic work for effective and rapid data analysis and fact-checking.","url":"https://doi.org/10.54941/ahfe1006212","authors":["Lora Metanova","Neli Velinova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-28T19:05:34Z","doi":"10.54941/ahfe1006212","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.5336/978-625-395-535-9_p153","name":"ARTIFICIAL INTELLIGENCE (AI) IN ROBOTIC-ASSISTED GASTROINTESTINAL SURGERY","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-535-9_p153","authors":["BÜLENT ŞEN"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-06T12:38:31Z","doi":"10.5336/978-625-395-535-9_p153","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1017/9781009367783.006","name":"Fairness and Artificial Intelligence","source":"crossref","abstract":"Despite their centrality within discussions on AI governance, fairness, justice, and equality remain elusive and essentially contested concepts: even when some shared understanding concerning their meaning can be found on an abstract level, people may still disagree on their relation and realization. In this chapter, we aim to clear up some uncertainties concerning these notions. Taking one particular interpretation of fairness as our point of departure (fairness as nonarbitrariness), we first investigate the distinction between procedural and substantive conceptions of fairness (Section 4.2). We then discuss the relationship between fairness, justice, and equality (Section 4.3). Starting with an exploration of Rawls’ conception of justice as fairness, we then position distributive approaches toward issues of justice and fairness against socio-relational ones. In a final step, we consider the limitations of techno-solutionism and attempts to formalize fairness by design (Section 4.4). Throughout this chapter, we illustrate how the design and regulation of fair AI systems is not an insular exercise: attention must not only be paid to the procedures by which these systems are governed and the outcomes they produce, but also to the social processes, structures, and relationships that inform, and are co-shaped by, their functioning.","url":"https://doi.org/10.1017/9781009367783.006","authors":["Laurens Naudts","Anton Vedder"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-18T08:47:06Z","doi":"10.1017/9781009367783.006","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.4018/979-8-3373-2043-4.ch011","name":"Artificial Intelligence Enabled Dynamic Drug Overdose Response System","source":"crossref","abstract":"The present-day medical industry is battling an ever-increasing number of drug overdose cases, which has led to a global public health emergency and necessitated the development of alternative measures. This chapter presents the Dynamic Drug Overdose Response System (DDORS), an innovative program that makes use of artificial intelligence (AI) to rethink strategies for preventing and responding to drug overdoses. The system integrates real-time data analysis with machine learning algorithms, as well as a user-friendly healthcare application developed using Flutter all of which together form. This chapter also describes how an AI-based diabetes prediction &amp; classification system was designed to demonstrate the wide-ranging applicability of diagnostic tools in healthcare settings where artificial intelligence is concerned. Such an inclusion affirms our belief in the need for a holistic approach towards addressing different types of healthcare challenges, from acute crises to chronic conditions like diabetes.","url":"https://doi.org/10.4018/979-8-3373-2043-4.ch011","authors":["Sachin Sharma","Kumar Mayank","Prachi Pandey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-05T12:39:48Z","doi":"10.4018/979-8-3373-2043-4.ch011","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003613732-11","name":"Artificial Intelligence and Machine Learning for NB-IoT","source":"crossref","abstract":"Artificial Intelligence (AL) and Machine Learning ( ML ) are essential part of intelligent NB-IoT. AI / ML is used to process and analyze a massive amount of data generated by NB-IoT devices. AI / ML enables real-time decision-making, optimizes network performance, and enables new applications such as smart homes and smart vehicles. AI / ML is used to do predictive maintenance which analyzes data from NB-IoT sensors and predicts equipment failures and optimizes maintenance schedules, reducing downtime and costs. The use of AI / ML in NB-IoT is a new area and it is covered in this chapter. AI / ML are used for classification, predictive modeling, and feature analysis in NB-IoT applications. The AI / ML models are able to recognize patterns and make data-driven predictions and thus can draw insights from NB-IoT sensor data. The AI / ML models illustrate the benefits of classical, ensemble-based, and neural networks for NB-IoT.","url":"https://doi.org/10.1201/9781003613732-11","authors":["Hossam Fattah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-18T10:39:26Z","doi":"10.1201/9781003613732-11","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003541899-8","name":"An Analysis of the EU Artificial Intelligence Act","source":"crossref","abstract":"In May 2024, the Council of the European Union adopted the first comprehensive regulatory framework for artificial intelligence (AI) systems. This act took time to reach a consensus since its proposal will require time to achieve full implementation. As an act of such magnitude and with such widespread implications, it has elicited numerous reactions from various actors, with considerations of different natures. The so-called “EU AI Act” aims to establish a comprehensive set of rules for AI within the European Union. The drafting and adoption of such an act, as a flagship initiative from the European Commission, also represents the first horizontal regulatory framework for AI systems globally. The act took time to reach consensus, and it will require time to achieve full implementation. In this context, this chapter aims to provide an overview of the key issues the act addresses and a contextual analysis of the regulations it enacts. This chapter seeks to analyze the legal text through the lens of the discussions that produced this legal act and to explore the effects it will have on the European Union market. The latter includes highlighting the values and potential criticisms that may be directed at the act in advance.","url":"https://doi.org/10.1201/9781003541899-8","authors":["Reald Keta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-08T19:12:52Z","doi":"10.1201/9781003541899-8","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003531449-13","name":"Artificial Intelligence in Oncology","source":"crossref","abstract":"Artificial intelligence (AI) is characterized as a coded machine that can gain knowledge and understand relationships and patterns among inputs and outputs and use this information effectively for decision-making on labeled input data [ McCarthy et al., 2006 ]. Machine learning (ML) and deep learning (DL) are the most common methods for putting AI into action, and the terms are sometimes used interchangeably. In the area of computer science, ML is a branch of AI, and DL is a subcategory of ML that is centered on deep artificial neural networks.","url":"https://doi.org/10.1201/9781003531449-13","authors":["Elif Guler Kazanci","Deniz Guven"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-01T12:35:15Z","doi":"10.1201/9781003531449-13","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9788770046213-18","name":"Artificial Intelligence: A Double-edged Sword for Environment and Climate","source":"crossref","abstract":"Artificial intelligence (AI) has emerged as a transformative force in the 21st century, reshaping industries, influencing social interactions, and even venturing into the realm of environmental protection. However, its impact on the environment and climate remains a complex and multifaceted issue, riddled with both promising opportunities and potential pitfalls. Understanding these nuances is crucial for harnessing the power of AI for a sustainable future.","url":"https://doi.org/10.1201/9788770046213-18","authors":["Ahmed Banafa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-06T10:08:48Z","doi":"10.1201/9788770046213-18","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781003654483-18","name":"HMED: A hybrid approach for medical image encryption through deep learning","source":"crossref","abstract":"With the expanding use of digital medical images, protecting the security and confidentiality of sensitive medical data has emerged as a major problem. This research proposes a new hybrid encryption framework incorporating techniques from deep learning using a U-Net architecture and both a chaotic and a block cipher. The U-Net is responsible for scrambling and reconstructing medical images with necessary details to guarantee strong encryption as well as precise image recovery. In addition, the R ö ssler chaotic map along with AES-ECB was utilized for key generation to further increase security while adding new layers of complexity and resistance to various attacks. The evaluation, using key security parameters such as MSE, SSIM, PSNR, NPCR, UACI, and PCC, demonstrates that the method efficiently and effectively secures medical images.","url":"https://doi.org/10.1201/9781003654483-18","authors":["S. Faisal","A.K. Agrawal","A.S. Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-05T12:37:43Z","doi":"10.1201/9781003654483-18","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.31234/osf.io/ekz9a_v5","name":"Lay Beliefs About Artificial Versus Artificial Intelligence: Rethinking Theory of Machine","source":"crossref","abstract":"Research on augmented judgment and decision-making—where users retain responsibility for the final decision but receive input from algorithms prior to or during the judgment process—has largely contrasted human and algorithmic sources of judgment. Accordingly, Logg’s (2022) “Theory of Machine” is a conceptual framework for describing and analyzing people’s lay theories about how human and algorithmic judgment differ. Indeed, people often treat humans and algorithms as different kinds, that is, functionally distinct ontological entities. Therefore, I propose to complement the predominant human-centric lens on algorithmic judgment with an explicit algorithm-centric one, focusing on people’s lay theories about how different algorithms differ. Put differently, the core psychological claim of Theory of Machine 2.0 is that lay perceivers also differentiate among various AI systems. The first main contribution is the synthesis of capability contrasts across AI systems. People’s perceptions of these contrasts may be formed and shaped by personal experience and media exposure. Most importantly, their expectations and beliefs are supposed to consequentially guide their downstream user behavior—such as system trust, algorithmic advice weighting, and accountability attribution. The second main contribution is the proposal of testable research questions and designs for more algorithm-centric future research on people’s augmented judgment and decision-making. The theoretical perspective proposed in this article clarifies how people form and use lay theories about different AI systems and offers practical levers for the design, deployment, and evaluation of algorithmic decision-support systems.","url":"https://doi.org/10.31234/osf.io/ekz9a_v5","authors":["Tobias R. Rebholz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T18:07:14Z","doi":"10.31234/osf.io/ekz9a_v5","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1007/s44163-025-00346-1","name":"Application and practice of artificial intelligence in marketing strategy","source":"crossref","abstract":"With the development of artificial intelligence technology, its application in the field of marketing is becoming more and more extensive. This study aims to explore the advantages and effects of AI-based marketing methods compared with traditional marketing methods. The study adopts a combination of experimental and survey methods and is divided into two stages: experimental stage and survey stage. In the experimental stage, consumers are randomly assigned to the control group (traditional marketing) and the experimental group (AI marketing) through an online shopping platform for one month, and indicators such as click-through rate, purchase rate, order amount and repurchase rate are recorded; in the survey stage, consumer attitudes and feedback are collected through questionnaires. The results show that AI marketing is superior to traditional marketing in terms of click-through rate, purchase rate, order amount, repurchase rate and consumer satisfaction, and can significantly improve consumer loyalty, trust and willingness to buy. In addition, the survey shows that most consumers think that AI marketing is interesting and useful, but some consumers are also concerned about privacy issues. Overall, this study shows that AI marketing can not only improve marketing efficiency, but also improve user experience, which is of great significance to promoting the intelligent transformation of the marketing industry.","url":"https://doi.org/10.1007/s44163-025-00346-1","authors":["Jing Wang","Liangyuan Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-14T09:21:04Z","doi":"10.1007/s44163-025-00346-1","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1088/978-0-7503-6320-4ch2","name":"Generative artificial intelligence: gateway and recent progress","source":"crossref","abstract":"","url":"https://doi.org/10.1088/978-0-7503-6320-4ch2","authors":["Haruna Chiroma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-19T07:50:42Z","doi":"10.1088/978-0-7503-6320-4ch2","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2139/ssrn.5595992","name":"Integrating Artificial Intelligence with Data Visualization in Business Intelligence Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5595992","authors":["Lewis Kemp","Ronan Crawford"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-12T12:56:46Z","doi":"10.2139/ssrn.5595992","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.engappai.2025.110318","name":"Optimizing Artificial Intelligence-aided breast cancer models: An empirical analysis of binary classifiers and regression-based feature selectors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110318","authors":["Fakhriddin Madolimov","Asilbek Medatov","Elmira Nazirova","Hakimjon Zaynidinov","Uktam Azimov","Shakhnoza Turakhonova","Jahongir Azimjonov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-25T19:09:16Z","doi":"10.1016/j.engappai.2025.110318","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.artmed.2024.102925","name":"Leveraging VQ-VAE tokenization for autoregressive modeling of medical time series","source":"crossref","abstract":"In this work, we present CodeAR, a medical time series generative model for electronic health record (EHR) synthesis. CodeAR employs autoregressive modeling on discrete tokens obtained using a vector quantized-variational autoencoder (VQ-VAE), which addresses key challenges of accurate distribution modeling and patient privacy preservation in the medical domain. The proposed model is trained with next-token prediction instead of a regression problem for more accurate distribution modeling, where the autoregressive property of CodeAR is useful to capture the inherent causality in time series data. In addition, the compressive property of the VQ-VAE prevents CodeAR from memorizing the original training data, which ensures patient privacy. Experimental results demonstrate that CodeAR outperforms the baseline autoregressive-based and GAN-based models in terms of maximum mean discrepancy (MMD) and Train on Synthetic, Test on Real tests. Our results highlight the effectiveness of autoregressive modeling on discrete tokens, the utility of CodeAR in causal modeling, and its robustness against data memorization.","url":"https://doi.org/10.1016/j.artmed.2024.102925","authors":["Yoonhyung Lee","Younhyung Chae","Kyomin Jung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-28T18:44:35Z","doi":"10.1016/j.artmed.2024.102925","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.55920/3064-8025/1112","name":"The Role of Artificial Intelligence in Reducing Chickenpox Incidence: A Holistic Strategy","source":"crossref","abstract":"","url":"https://doi.org/10.55920/3064-8025/1112","authors":["Abimbola Adeponle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-16T09:56:08Z","doi":"10.55920/3064-8025/1112","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1186/s12909-025-08039-0","name":"Surgical education reimagined: the convergence of learning theories and artificial intelligence","source":"crossref","abstract":"Purpose The integration of artificial intelligence (AI) into surgical education is transforming the way surgical skills and knowledge are developed. This article examines how AI aligns with key educational theories-behaviourism, cognitivism, constructivism, humanism, and connectivism-to enhance learning through personalised simulations, adaptive feedback, and networked platforms. Materials and methods A review of literature and theoretical frameworks was conducted to analyse AI's applications in surgical training. Key features include AI-driven tools for structured feedback, cognitive optimisation, experiential learning, individual growth, and collaboration through interconnected networks. The article also identifies ethical challenges, including data privacy, algorithmic bias, and equitable access. Results AI has the potential to revolutionise surgical education by fostering critical thinking, improving training outcomes, and expanding access to learning resources. However, risks such as over-reliance on automation, loss of hands-on experience, and superficial AI use (\"AI theatre\") highlight the need for thoughtful and ethical implementation. Conclusion With a balanced and collaborative approach among educators, technologists, and healthcare professionals, AI can create dynamic, learner-centred environments. By addressing challenges, AI can support the development of skilled, compassionate surgeons equipped to navigate the complexities of modern medical practice.","url":"https://doi.org/10.1186/s12909-025-08039-0","authors":["Frances Lee","Shing Wai Wong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-04T10:15:44Z","doi":"10.1186/s12909-025-08039-0","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.5040/9781509966738","name":"Artificial Intelligence and Public Law","source":"crossref","abstract":"The Government's use of algorithmic-based decision-making is rapidly expanding across policy areas, including immigration, social security, regulation, security and policing. This book provides the first comprehensive analysis of how public law applies to the use of artificial intelligence and automation in the public sector in England and Wales. Starting with an accessible account of the nature of AI and automated systems being increasingly deployed in the public sector, the book covers the various legal regimes which regulate their use. It considers how the principles of judicial review might be deployed to challenge automated decision-making by public authorities. It also explains how equality law, human rights law, procurement law, data protection law and private law apply to government use of AI and automation. This book is a vital guide for practitioners in both private practice and government, and for anyone navigating this quickly changing, complex and uncertain environment.","url":"https://doi.org/10.5040/9781509966738","authors":["Brendan McGurk","Joe Tomlinson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T11:22:57Z","doi":"10.5040/9781509966738","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/cai64502.2025.00001","name":"Half Title Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai64502.2025.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-07T17:47:34Z","doi":"10.1109/cai64502.2025.00001","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/qai63978.2025.00002","name":"Proceedings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qai63978.2025.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:56:01Z","doi":"10.1109/qai63978.2025.00002","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/icaidm66813.2025","name":"2025 2nd International Conference on Artificial Intelligence and Digital Management (ICAIDM)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaidm66813.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-18T18:32:11Z","doi":"10.1109/icaidm66813.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1007/s10880-025-10101-4","name":"Artificial Intelligence in and Beyond Healthcare Psychology","source":"crossref","abstract":"Artificial Intelligence (AI) has been developed through interdisciplinary efforts since the 1940s, but generative AI and Large Language Models (LLMs) gained unprecedented attention with the launch of ChatGPT by OpenAI in late 2022. As these AI tools have become globally ubiquitous, significant implications arise for clinicians within and beyond healthcare settings. The simulation or emulation of human intelligence through coded heuristics now permeates clinical domains, creating new opportunities alongside ethical challenges that require careful exploration. For healthcare psychologists, regardless of specialty, it has become a priority to remain at the forefront of these technological advances. This includes developing literacy not only in psychological emulation software but also in the rapidly growing hardware that supports AI. Well-informed clinicians must act as responsible stewards of this advancing technology and its application in healthcare. These responsibilities must be approached through the lens of both existing and evolving ethical standards in human psychology. Although these tasks may seem daunting, the urgency, opportunities, and necessity for healthcare psychologists to engage thoughtfully with AI are clear. This engagement ensures that patient care benefits from innovation while upholding ethical principles. Said opportunities and the urgency for healthcare psychologists are discussed.","url":"https://doi.org/10.1007/s10880-025-10101-4","authors":["Dong Y. Han"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-10T10:55:13Z","doi":"10.1007/s10880-025-10101-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.4324/9781003660286-6","name":"Aligning Artificial Intelligence with Responsible Management Education","source":"crossref","abstract":"This chapter explores the alignment of Artificial Intelligence (AI) policies in European business schools with the Principles for Responsible Management Education (PRME). The research employs a content analysis of AI policy documents from 15 leading European business schools, with a particular emphasis on Generative AI. The focus lies on these schools’ stance on AI usage, governance, curriculum integration, and an examination of these elements through the lens of the seven PRME principles. The findings reveal that most schools demonstrate a solid commitment to integrating AI ethically and responsibly; however there are variations in how these policies are implemented. The research also identifies gaps in comprehensive policy frameworks and the need for more explicit integration of AI governance.","url":"https://doi.org/10.4324/9781003660286-6","authors":["Melike Demirbağ-Kaplan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-09T14:40:05Z","doi":"10.4324/9781003660286-6","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1117/12.3110897","name":"Privacy-preserving federated learning approach for medical image analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3110897","authors":["Zishuo Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-29T18:42:01Z","doi":"10.1117/12.3110897","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1201/9781003731689-37","name":"Secure AI in healthcare: Advanced privacy-preserving machine learning techniques for medical data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003731689-37","authors":["Abhilasha Sharma","Riti Rathore"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-06T11:44:46Z","doi":"10.1201/9781003731689-37","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.47307/gmc.2025.133.2.22","name":"Venezuelan first year medical students’ insight on Artificial Intelligence","source":"crossref","abstract":"Introduction: A survey addressing first-year Venezuelan medical students’ insight on Artificial Intelligence was carried out to analyze their perceptions of the impact of Artificial Intelligence in their medical education and future clinical practice. Methods: A cross-sectional survey study was conducted using an online Google Forms questionnaire to collect data from students of the JM Vargas Medical School, Faculty of Medicine, Universidad Central de Venezuela, from April to July 2024. The questionnaire consisted of three sections: demographic data, perceptions on Artificial Intelligence, and the impact of Artificial Intelligence on medical education. The data collection was achieved by distributing the link to the questionnaire via email to first-year medical students. Results: The survey outcome revealed the helpful assessment of first-year medical students towards Artificial Intelligence for its benefits: motivation, acceptance, and knowledge acquisition, among others. Conclusion: Artificial Intelligence was positively rated by 63 first-year medical students, recognizing its relevance, usefulness, and amelioration of medical learning.","url":"https://doi.org/10.47307/gmc.2025.133.2.22","authors":["Rafael Romero Reverón"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-19T09:39:28Z","doi":"10.47307/gmc.2025.133.2.22","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1561/9781638284772.ch9","name":"Chapter 9 Differential Privacy and Medical Data Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch9","authors":["Vinith M. Suriyakumar","Nicolas Papernot","Anna Goldenberg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T08:26:52Z","doi":"10.1561/9781638284772.ch9","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.70905/bmcj.06.02.0527","name":"ARTIFICIAL INTELLIGENCE IN SURGERY: REDEFINING PRECISION AND POSSIBILITIES","source":"crossref","abstract":"Previously thought to be science fiction, AI has increasingly become the topic of both popular and academic literature as years of research have finally built to thresholds of knowledge that have rapidly generated practical applications. The integration of artificial intelligence (AI) into surgical practice is no longer a distant concept , it is a present and accelerating reality. From preoperative diagnostics to intraoperative decision-making and postoperative care, AI is redefining the landscape of surgery by enhancing precision and expanding the boundaries of what is surgically possible. At its core, surgery is a field driven by accuracy, timing, and complex decision-making. AI, particularly in the form of machine learning and deep learning, excels in pattern recognition and data analysis, traits increasingly valuable in surgical environments. For example, AI algorithms can analyze thousands of preoperative imaging datasets to assist in planning tumor resections, identifying critical structures, and simulating outcomes. This leads to more tailored, patient-specific interventions and reduces surgical complications (Hashimoto et al., 2018)1. One of the most transformative applications lies in robot-assisted surgery. Systems like the da Vinci platform, initially reliant solely on human control, are evolving into semi-autonomous and even AI-augmented systems. These platforms can now learn from prior procedures, refine technique recommendations, and warn surgeons of potential errors in real-time (Haque et al., 2020)2. This redefines surgical precision, not merely as a function of human skill but as a synergy between surgeon and machine. AI is also redefining intraoperative possibilities. Real-time image-guided navigation, powered by computer vision and AI, allows for safer laparoscopic and endoscopic surgeries. For example, AI algorithms can now differentiate between tissue types or flag potential vessels at risk, assisting surgeons during delicate dissections (Maier-Hein et al., 2017)3. These capabilities can significantly reduce iatrogenic injuries and operative times. The postoperative phase is not left behind. Predictive models can analyze vital signs, lab data, and surgical details to forecast complications like sepsis or thromboembolism, enabling early interventions and improving outcomes (Sendak et al., 2020)4. Wearable devices and AI-powered mobile apps are now facilitating remote monitoring, supporting early discharge and continuity of care. Yet, these advances come with necessary caution. The ethical implications, such as algorithmic bias, data security, and medico-legal accountability, must be addressed proactively. Additionally, over-reliance on AI tools without adequate training could deskill surgeons or foster blind trust in technology (Yu et al., 2018)5. The role of the surgeon is evolving, not just as a technician but as a data-literate, systems-aware clinician. In conclusion, AI is not replacing the surgeon but is transforming the way surgery is conceived and performed. It is ushering in a new era of augmented precision and expanded possibilities, from diagnosis to the operating room and beyond. The challenge ahead lies in responsibly harnessing this power, ensuring that innovation serves patients and supports the art and science of surgery.","url":"https://doi.org/10.70905/bmcj.06.02.0527","authors":["Asif Mehmood","Mohammad Shoaib Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T06:09:13Z","doi":"10.70905/bmcj.06.02.0527","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.62425/jmefm.1663445","name":"Utilizing Artificial Intelligence for Adaptive Scenario Development in Interprofessional Education","source":"crossref","abstract":"Artificial intelligence (AI) enabled scriptwriting for interprofessional education is a novel approach that develop realistic and complex educational scenarios for healthcare professionals. This method enhances the quality of interprofessional education and addresses pedagogical challenges that educators face. One notable advantage of AI in scriptwriting is the ability to create scenarios that reflect diverse and dynamic patient interactions, enabling students to engage in realistic problem solving. AI algorithms can analyze vast amounts of data regarding patient cases, treatment protocols, and clinician workflows. This leads to the creation of highly contextualized scenarios that capture the nuances of interprofessional collaboration in healthcare settings. Moreover, AI-enabled tools can facilitate personalized learning experiences by adjusting the complexity and content individual learner data. This adaptability ensures that each student can engage with material tailored to their specific knowledge level and learning objectives, thereby maximizing the learning experience. However, despite these advantages, relying solely on AI for script development is limited. One significant concern involves the potential loss of human insight in crafting scenarios that are sensitive to the emotional and ethical dimensions of healthcare. While AI can manage data-driven aspects of scenario creation, the subtleties of human interaction, empathy, and ethical conflict resolution may not be fully captured in automated narratives. Consequently, educators must ensure that AI-generated content is supplemented with human oversight, incorporating real-world experiences and ethical considerations that enrich the learning process. Educators from various health professions must work together to ensure that scripts generated by AI reflect the complexities and interdependencies of real clinical scenarios. AI-assisted scenarios offer adaptive and personalized learning experiences by tailoring content to learners' needs and professional roles. These systems can increase efficiency in scenario creation, enhance student engagement, and provide real-time, data-driven feedback. However, AI-generated content may lack the emotional depth and ethical nuance essential to interprofessional collaboration. Overreliance on AI also risks reducing critical thinking and creativity, while concerns such as algorithmic bias and data privacy must be carefully addressed to ensure equitable educational practices.","url":"https://doi.org/10.62425/jmefm.1663445","authors":["Aysel Baser","Hatice Şahin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-28T12:32:20Z","doi":"10.62425/jmefm.1663445","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.21608/aiis.2026.456163.1025","name":"التقاطع المعرفي للذكاء الاصطناعي بين العلوم التطبيقية والاجتماعية منصة البحوث الاكاديمية العراقية أنموذجا","source":"crossref","abstract":"","url":"https://doi.org/10.21608/aiis.2026.456163.1025","authors":["thanaa lilo abbas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-06T19:26:41Z","doi":"10.21608/aiis.2026.456163.1025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icaie64856.2025.11158028","name":"Research on Middle School English Writing Teaching Model Assisted by Generative Artificial Intelligence","source":"crossref","abstract":"The rapid development of generative artificial intelligence technology has accelerated the reform in the field of education and opened up a new development path for English writing teaching in middle schools. However, in the actual teaching process, there are still problems with the rigid application of artificial intelligence and the lack of data support for learning situation analysis. Therefore, exploring the deep integration model of generative artificial intelligence technology and English writing teaching in middle schools is particularly urgent. By the theory of process-genre approach and blended teaching model, we construct a teaching model of middle school English writing assisted by generative artificial intelligence from three dimensions: teaching resources, teaching process and teaching evaluation. This teaching model realizes the enrichment and personalization of teaching resources, integrates generative artificial intelligence into every step of the teaching process, and comprehensively improves the teaching efficiency and quality of English writing in middle school.","url":"https://doi.org/10.1109/icaie64856.2025.11158028","authors":["Zhiwei Qi","Yuqing Liu","Wenlin Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T17:24:05Z","doi":"10.1109/icaie64856.2025.11158028","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-323-99421-7.00013-1","name":"AIoMT artificial intelligence (AI) and Internet of Medical Things (IoMT)","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99421-7.00013-1","authors":["Fadi Muheidat","Loai A. Tawalbeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:37:49Z","doi":"10.1016/b978-0-323-99421-7.00013-1","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1088/978-0-7503-6119-4ch13","name":"Artificial intelligence empowered response prediction and adaptation","source":"crossref","abstract":"In this chapter, we provide an overview of the data resources typically utilized in response modeling, a summary and examples of traditional and artificial intelligence (AI)-based response models, and we also discuss current trends and challenges in AI-based response adaptive radiotherapy.","url":"https://doi.org/10.1088/978-0-7503-6119-4ch13","authors":["Denis Dudas","Issam El Naqa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-29T13:54:13Z","doi":"10.1088/978-0-7503-6119-4ch13","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1109/airc64931.2025.11077513","name":"One Model Fits All? Rethinking Medical AI Chatbots with Specialized Retrieval","source":"crossref","abstract":"Medical diagnostics and treatment planning are increasingly complex, demanding sophisticated tools for healthcare professionals. AI chatbots in healthcare often use generalized models, leading to suboptimal predictions, especially with multimodal data. This research introduces a multimodal AI chatbot with specialized model retrieval for customized medical insights. The system selects relevant models for disease prediction, diagnostics, or drug interactions based on input type, integrating EHR data via FHIR APIs and using UMLS and SNOMED CT. Compared to generalized systems, preliminary tests show improved accuracy in diagnosis, treatment planning, and drug safety. This approach enhances diagnostic precision, reduces medication errors, and personalizes patient care.","url":"https://doi.org/10.1109/airc64931.2025.11077513","authors":["Ranil Mukesh MJ","Karthikeyan S","Meenalosini Vimal Cruz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T17:41:01Z","doi":"10.1109/airc64931.2025.11077513","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1049/pbhe061e_ch4","name":"Artificial intelligence and blockchain technology for efficient electronic medical record management","source":"crossref","abstract":"Over the past decade, blockchain technology has expanded at a staggering rate. The clandestineness and anonymity that \"B\" provides led to its primer as the underpinning of cryptocurrencies like Bitcoin, but it quickly found efficacy in other contexts as well. Secure data logging, transactions, and maintenance via smart contracts (SCs) are just a few examples of how blockchain technology has been put to use in the healthcare sector. Artificial intelligence (AI) has been extensively incorporated into blockchain to make it smart, bringing together the best of the two technologies. Many people have many physicians and receive multiple prescriptions and reports over the course of their therapy. Physicians and medical personnel can use the medical information of a patient to better care for them in the event of a life-threatening situation. However, time constraints prevent them from reviewing the person's medical record and a variety of past findings and prescriptions. In this chapter, we put forward a blockchain-based framework that incorporates AI to streamline the process of compiling a patient's medical history from disparate sources such as prescriptions (printed or handwritten) and imprinted documents into a single record. The report is kept safely via a decentralized blockchain network and displays just the most important facts in a succinct style for ease of access and reading.","url":"https://doi.org/10.1049/pbhe061e_ch4","authors":["Akoramurthy Balasubramaniam","Surendiran Balasubramaniam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-12T03:10:36Z","doi":"10.1049/pbhe061e_ch4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/s0933-3657(96)00360-0","name":"Case-based learning of plans and goal states in medical diagnosis","source":"crossref","abstract":"We introduce a case-based system, BOLERO, that learns both plans and goal states. The major aim is that of improving the performance of a rule-based diagnosis system by adapting its behavior using the most recent information available about a patient. On the one hand BOLERO gets knowledge from cases in the form of diagnostic plans that are represented as sequences of decision steps. The advantages of this representation include: (1) retrieval and adaptation of parts of plans (steps) appropriate to the current problem state; (2) generation of new plans not previously available in memory; and (3) learning from experience, both from successful or failed plans. On the other hand, since goal states are sets of final diagnosis likelihoods they are not known beforehand, i.e. goal states are not defined and the system has to learn to recognize them. For this reason BOLERO has a case-based method that uses solutions of past cases to recognize a diagnostic state as a goal state of a new planning problem. BOLERO and a rule-based system are integrated into a meta-level architecture in which we emphasize the collaboration of both systems in solving problems. The rule-based system executes the plans generated by BOLERO. As a consequence of the execution of plans, the rule-based system furnishes BOLERO with new information with which BOLERO can generate a new plan to adapt the reasoning process of the rule-based system into correspondence with the recent available data. All the methods have been designed to be useful for medical diagnosis and have been tested in the domain of diagnosing pneumonia.","url":"https://doi.org/10.1016/s0933-3657(96)00360-0","authors":["Beatriz López","Enric Plaza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T11:20:41Z","doi":"10.1016/s0933-3657(96)00360-0","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1136/bmj.r1418","name":"Should medical students be encouraged to use generative artificial intelligence to study?","source":"crossref","abstract":"Generative artificial intelligence is becoming increasingly embedded in how today’s medical students learn and practise medicine. But should it be formally taught within the medical curriculum? Rhea Sibal argues that it must be embraced to prepare future doctors, while George Webster and Elgan Manton-Roseblade argue that the risks outweigh the benefits","url":"https://doi.org/10.1136/bmj.r1418","authors":["Rhea Sibal","George Webster","Elgan Manton-Roseblade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T11:25:19Z","doi":"10.1136/bmj.r1418","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.3389/feduc.2025.1517116","name":"Swedish medical students’ attitudes toward artificial intelligence and effects on career plans: a survey","source":"crossref","abstract":"Background The implementation of artificial intelligence (AI), and especially generative AI, is transforming many medical fields, while medical education faces new challenges in integrating AI into the curriculum and is facing challenges with the rise of generative AI chatbots. Objective This survey study aimed to assess medical students’ attitudes toward AI in medicine in general, effects of AI in students’ career plans, and students’ use of generative AI in medical studies. Methods An anonymous and voluntary online survey was designed using SurveyMonkey and was sent out to medical students at Gothenburg University. It consisted of 25 questions divided into various sections aiming to evaluate the students’ prior knowledge of AI, their use of generative AI during medical studies, their attitude toward AI in medicine in general, and the effect of AI on their career plans. Results Of the 172 students who completed the survey, 74% were aware of AI in medicine, and 71% agreed or strongly agreed that AI will improve medicine. One-third were frightened of the increased use of AI in medicine. Radiologists and pathologists were perceived as most likely to be replaced by AI. Interestingly, 37% of the responders agreed or strongly agreed that they will exclude some field of medicine because of AI. More than half argued that AI should be part of medical training. Almost all responders (99%) were aware of generative AI chatbots, and 64% had taken advantage of these in their medical studies. Fifty-eight percent agreed or strongly agreed that the use of AI is supporting their learning as medical students. Conclusion Medical students show high expectations for AI’s impact on medicine, yet they express concerns about their future careers. Over a third would avoid fields threatened by AI. These findings underscore the need to educate students, particularly in radiology and pathology, about optimizing human-AI collaboration rather than viewing it as a threat. There is an obvious need to integrate AI into the medical curriculum. Furthermore, the medical students rely on AI chatbots in their studies, which should be taken into consideration while restructuring medical education.","url":"https://doi.org/10.3389/feduc.2025.1517116","authors":["Noora Neittaanmäki"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-28T07:29:05Z","doi":"10.3389/feduc.2025.1517116","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.58915/bk2025.021","name":"Artificial Intelligence in Automation","source":"crossref","abstract":"Artificial Intelligence in Automation delves into the groundbreaking convergence of AI, robotics, and machine learning that is revolutionising automation across diverse fields, from agriculture and construction to industrial inspection and space exploration. This book uncovers how intelligent systems, autonomous platforms and adaptive algorithms are reshaping human–machine interaction, boosting efficiency, and enabling real-time decision-making in complex environments. With topics ranging from dual-arm robotics, and vision-based systems to digital twins and soft robotics, this book offers a comprehensive overview of the latest innovations driving the next generation of automation. Ideal for researchers, engineers and technology enthusiasts, it presents a compelling look at how AI is not only enhancing automation but transforming the way we live and work.","url":"https://doi.org/10.58915/bk2025.021","authors":["Ahmad Humaizi Hilmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-07T05:24:27Z","doi":"10.58915/bk2025.021","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/b978-0-443-26476-4.00031-9","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26476-4.00031-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T09:36:43Z","doi":"10.1016/b978-0-443-26476-4.00031-9","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/0933-3657(93)90021-t","name":"Computing the confidence in a medical decision obtained from an influence diagram","source":"crossref","abstract":"Willard and Critchfield [31] assume that uncertainty exists as to the values of the probabilities which need to be assessed for a decision tree, and that the uncertainty in each probability is represented by a continuous probability distribution. If the probability itself is thought of as an objective limit of a relative frequency, then this distribution represents our degree of belief concerning the 'true' value of the probability. Willard and Critchfield [31] obtain a method which is able to determine the probability that the recommended decision is the one which would be obtained using the objective relative frequencies. This probability is called the confidence in the decision. These same results are obtained here for the case where a problem is represented in an influence diagram. There is also a discussion concerning the importance of the confidence measure in the evaluation of the quality of a medical expert system and in the instance of a single decision.","url":"https://doi.org/10.1016/0933-3657(93)90021-t","authors":["Richard E. Neapolitan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(93)90021-t","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1109/acai68217.2025","name":"2025 8th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acai68217.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-02T20:53:51Z","doi":"10.1109/acai68217.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1109/ecai65401.2025","name":"2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ecai65401.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-04T18:44:18Z","doi":"10.1109/ecai65401.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1109/ic-ftai67960.2025","name":"2025 International Conference on Future Telecommunications and Artificial Intelligence (IC-FTAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic-ftai67960.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T21:06:30Z","doi":"10.1109/ic-ftai67960.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.engappai.2024.109601","name":"Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109601","authors":["Lala Rajaoarisoa","Raubertin Randrianandraina","Grzegorz J. Nalepa","João Gama"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-21T20:05:03Z","doi":"10.1016/j.engappai.2024.109601","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1201/9781032695266-1","name":"Artificial Intelligence, Ethical Concerns, and Social Responsibility","source":"crossref","abstract":"The terms ‘data science’, ‘artificial intelligence’ (AI), ‘machine learning’, and ‘deep learning’ are defined. Various types of software agents are introduced, notably the intelligent, reactive, deliberative, learning, and hybrid software agents. Notions of cognition and sentience are explained. Thought experiments of AI are described, including the Turing test and the Chinese Room experiment. The ethical concerns associated with the development of AI raise moral dilemmas. These issues can be addressed by adopting responsible, equitable, and reliable practices to prevent the misuse of AI. The organizational structure and plan of this book are outlined by summarizing the contents of its chapters.","url":"https://doi.org/10.1201/9781032695266-1","authors":["Vinod Kumar Khanna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-13T16:48:41Z","doi":"10.1201/9781032695266-1","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2196/preprints.94349","name":"Medical education; Faculty development; Artificial intelligence literacy; Competency framework; Expert consensus (Preprint)","source":"crossref","abstract":"UNSTRUCTURED In response to the profound challenges posed by the deep integration of generative artificial intelligence (AI) into medical education, this consensus statement presents, for the first time, a coherent, medically distinctive, forward-looking, and actionable AI literacy framework for medical educators. Developed through systematic literature review, preliminary framework construction, multiple rounds of expert pre-consultation, and a structured Delphi method involving 60 interdisciplinary experts, the framework identifies and validates core competencies and their observable indicators. It comprises five key dimensions and 25 specific competencies , namely: (1) Value Awareness and Ethical Foundations, (2) Technical Understanding and Tool Application, (3) Pedagogical Integration and Innovative Practice, (4) Learning Assessment and Precision Empowerment, and (5) Professional Development and Ecosystem Co-Construction. The 25 competencies are categorized into 11 \"foundational competencies\" -- essential for all medical educators -- and 14 \"developmental competencies\" for those pursuing excellence. Each competency is defined by its conceptual scope and key behavioral indicators, accompanied by observable assessment metrics. This consensus aims to provide a scientific foundation for faculty development in medical education and establish a critical reference standard for building educator capacity amid the ongoing digital transformation of medical education.","url":"https://doi.org/10.2196/preprints.94349","authors":["Xunming JI","Mengchun Gong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-02T20:45:08Z","doi":"10.2196/preprints.94349","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.21037/jmai-24-71","name":"Skin health response to climate change weather tailored cosmetics using artificial intelligence","source":"crossref","abstract":"Abstract: Recently, the severity of global warming is increasing worldwide. In this study, we examined the possibility of customized cosmetics devices using artificial intelligence (AI) as a countermeasure to problems that may occur on the skin due to global warming. This includes responding flexibly to changes in consumer demand due to global warming. Additionally, in the context of global warming, the scientific community is considering human skin care and prevention methods, which we hope will provide key data for future exploration. Although this review is a descriptive review, a systematic review was conducted, and according to the PRISMA flowchart guidelines, sources such as PubMed, Medline, Scopus, ResearchGate, and Google Scholar were searched and searched for ‘global warming’, ‘climate change’, ‘skin gate’, ‘AI’, ‘Customized cosmetics’, ‘Skin health’, ‘Cosmetics’, ‘Device’, ‘AI-based customized cosmetics’, ‘Human security’, ‘inner beauty’. Accordingly, a total of 1,308 documents were searched. In the final step, 65 studies were selected at the final stage. Considering the sustainability and safety of customized cosmetic devices in the AI era, further research to mitigate skin damage caused by human skin care and ultraviolet (UV) rays due to global warming and to evaluate the impact on beauty and health should reflect consumer demands. Therefore, as global warming accelerates, additional research on skin heat-resistant inner beauty materials and customized cosmetics is needed with a focus on human security in preparation for continued global warming. Accordingly, interest in AI is expected to increase further in the scientific community, nutrition, inner beauty, and cosmetics industries, and this trend is expected to continue in the future. We hope that AI based customized cosmetics devices will be used in various skin health strategies and nutritional approaches to global warming and human security.","url":"https://doi.org/10.21037/jmai-24-71","authors":["Jinkyung Lee","Ki Han Kwon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-27T11:21:33Z","doi":"10.21037/jmai-24-71","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.engappai.2025.111291","name":"Artificial intelligence-driven models for predicting mechanical properties of low-emission microwave-cured geopolymer mortar","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111291","authors":["Faidhalrahman Khaleel","Haitham Abdulmohsin Afan","Alaa H. AbdUlameer","Abdulrahman S. Abdullah","Gökhan Kaplan","Cengiz Duran Atiş"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-29T11:59:23Z","doi":"10.1016/j.engappai.2025.111291","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1016/j.artmed.2018.05.001","name":"Classifying medical relations in clinical text via convolutional neural networks","source":"crossref","abstract":"Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.","url":"https://doi.org/10.1016/j.artmed.2018.05.001","authors":["Bin He","Yi Guan","Rui Dai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-05-17T22:11:22Z","doi":"10.1016/j.artmed.2018.05.001","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1109/aisei68628.2026.11572851","name":"Artificial Intelligence in Medical Science as a Therapeutic Factor: A Theoretical and Methodological Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aisei68628.2026.11572851","authors":["Alsu N. Safiullina"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T19:43:40Z","doi":"10.1109/aisei68628.2026.11572851","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1148/ryai.230432","name":"Reconsidering Conclusions of Bias Assessment in Medical Imaging                     Foundation Models","source":"crossref","abstract":"ZORA (Zurich Open Repository and Archive) provides open and worldwide access to the research and scholarly output of the University of Zurich, Switzerland. A focus is on qualified scientific publications. ZORA is operated by the University Library together with the Central IT of the University of Zurich.","url":"https://doi.org/10.1148/ryai.230432","authors":["Akshay S. Chaudhari","Christian Bluethgen","David Ouyang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-22T09:52:45Z","doi":"10.1148/ryai.230432","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1080/08839514.2012.701423","name":"A NOVEL SOFT COMPUTING-BASED RETRIEVAL SYSTEM FOR MEDICAL APPLICATIONS","source":"crossref","abstract":"As the amount of data in medical databases increases, systems for medical data retrieval are growing in popularity. Some of these analyses include inducing propositional rules from databases using many soft techniques, and then using these rules in an expert system. Diagnostic rules and information on features are extracted from clinical databases on diseases of congenital anomaly. This article explains the most current soft computing techniques and some of the adaptive techniques encompassing an extensive group of methods that have been applied in the medical domain and that are used for the discovery of data dependencies, importance of features, patterns in sample data, and feature-space dimensionality reduction. These approaches pave the way for new and interesting avenues of research in medical imaging and represent an important challenge for researchers.","url":"https://doi.org/10.1080/08839514.2012.701423","authors":["S. Sharma","P. Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-08-08T13:42:26Z","doi":"10.1080/08839514.2012.701423","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2196/preprints.43068","name":"A Medical Ethics Framework for Conversational Artificial Intelligence (Preprint)","source":"crossref","abstract":"UNSTRUCTURED The launch of OpenAI’s GPT-3 model in June 2020 began a new era for conversational chatbots. While there are chatbots that do not use artificial intelligence (AI), conversational chatbots integrate AI language models that allow for back-and-forth conversation between an AI system and a human user. GPT-3, since upgraded to GPT-4, harnesses a natural language processing technique called sentence embedding and allows for conversations with users that are more nuanced and realistic than before. The launch of this model came in the first few months of the COVID-19 pandemic, where increases in health care needs globally combined with social distancing measures made virtual medicine more relevant than ever. GPT-3 and other conversational models have been used for a wide variety of medical purposes, from providing basic COVID-19–related guidelines to personalized medical advice and even prescriptions. The line between medical professionals and conversational chatbots is somewhat blurred, notably in hard-to-reach communities where the chatbot replaced face-to-face health care. Considering these blurred lines and the circumstances accelerating the adoption of conversational chatbots globally, we analyze the use of these tools from an ethical perspective. Notably, we map out the many types of risks in the use of conversational chatbots in medicine to the principles of medical ethics. In doing so, we propose a framework for better understanding the effects of these chatbots on both patients and the medical field more broadly, with the hope of informing safe and appropriate future developments.","url":"https://doi.org/10.2196/preprints.43068","authors":["Eleonore Fournier-Tombs","Juliette McHardy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-05T17:24:10Z","doi":"10.2196/preprints.43068","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.71443/9789349552418-11","name":"Blockchain and IoT for Secure Medical Data Management in Healthcare Applications","source":"crossref","abstract":"The rapid expansion of healthcare Internet of Things (IoT) devices has generated unprecedented volumes of medical data, presenting significant challenges in terms of security, privacy, interoperability, and real-time access. Traditional centralized healthcare systems are often inadequate to address these challenges due to vulnerabilities such as single points of failure, data tampering, and limited transparency. This chapter proposes a hybrid blockchain-IoT framework designed to enable secure, privacy-preserving, and interoperable medical data management across distributed healthcare networks. The framework integrates decentralized blockchain mechanisms with multi-layer IoT architecture, leveraging smart contracts for automated access control, consent management, and auditability while employing off-chain storage and edge computing to enhance scalability and reduce latency. Performance evaluation emphasizes transaction throughput, latency, resource utilization, and consensus efficiency, demonstrating the framework’s capability to handle large-scale healthcare data in real-time. Standardization and governance considerations are incorporated to ensure compliance with global healthcare regulations and facilitate interoperability across heterogeneous medical systems. The proposed framework not only enhances data security and integrity but also supports patient-centric healthcare delivery, enabling intelligent decision-making, collaborative research, and seamless multi-institutional data exchange. The findings highlight the transformative potential of hybrid blockchain-IoT architectures in modern healthcare, addressing critical gaps in scalability, privacy preservation, and trust management.","url":"https://doi.org/10.71443/9789349552418-11","authors":["Sujit Kumar Sadhukhan","Palak Keshwani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-30T11:49:02Z","doi":"10.71443/9789349552418-11","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.4103/mgmj.mgmj_54_25","name":"Artificial intelligence in women’s healthcare: A glimpse of the future","source":"crossref","abstract":"INTRODUCTION Artificial intelligence (AI) refers to a computer program’s capability to carry out tasks typically associated with human intelligence, including reasoning, learning, adaptation, sensory perception, and interaction.[1] AI has already become integral to daily life, powering technologies such as facial recognition, speech recognition in virtual assistants (e.g., Amazon Alexa, Apple’s Siri, Google Assistant, and Microsoft Cortana), and self-driving vehicles. In a recent interview, billionaire entrepreneur Elon Musk, who leads Tesla, X, and SpaceX, stated, I guess that we’ll have AI that is smarter than any one human probably around the end of next year.“ AI also drives innovation in healthcare, contributing to drug development, clinical decision-making, and quality assurance in radiology. The volume of AI-related research is growing; for instance, at the 29th World Congress of the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) in 2019, 14 abstracts specifically referenced AI, compared to a total of 13 abstracts across the previous six ISUOG World Congresses (2013–2018). In 1950, Alan Turing proposed a test, now known as the “Turing test,” to determine whether a machine exhibits intelligent behavior indistinguishable from a human’s. If an evaluator cannot differentiate between the two, the machine must have passed the test. The term ‘AI’ was later introduced by John McCarthy in 1956 during the Dartmouth Conference. Key milestones in AI history include IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997 and AlphaGo overcoming Lee Sedol, one of the top players of the ancient Chinese game Go, in 2016.[1] APPLICATION OF ARTIFICIAL INTELLIGENCE IN GENERAL MEDICINE In clinical practice, AI technologies promise to revolutionize healthcare by extracting valuable insights from the vast digital data generated during medical care. Traditionally, evidence-based medicine has relied on statistical methods to identify patterns and represent them as mathematical equations. However, through machine learning (ML), AI introduces advanced techniques that reveal intricate relationships within data—ones that cannot be easily expressed in a simple equation. This enables ML systems to tackle complex problem-solving like clinicians, carefully analyzing evidence to arrive at well-reasoned conclusions.[2] Below are some examples showcasing AI’s exceptional performance across various medical specialties. An AI-driven smartphone app now capably handles triaging 1.2 million people in North London to Accident & Emergency (A&E).[3] A recent study shows that AI correctly diagnoses pulmonary TB with a sensitivity of 95% and specificity of 100%.[4] Researchers demonstrate that AI had superior sensitivity and specificity than dermatologists when classifying previously unseen photographs of biopsy-validated lesions.[5] As of May 2020, more than 50 deep learning (DL)-based imaging applications have been approved by the USA Food and Drug Administration, spanning most imaging modalities, including X-ray, computerized tomography, magnetic resonance imaging, retinal optical coherence tomography, and ultrasound. Applications include the identification of cerebrovascular accidents, diabetic retinopathy, skeletal fractures, cancer, pulmonary embolism, and pneumothorax.[2] There is an ongoing clinical trial using AI to calculate target zones for head and neck radiotherapy more accurately and quickly than a human being.[2] HOW DOES ARTIFICIAL INTELLIGENCE INFLUENCE OBSTETRICS AND GYNECOLOGY? Direct-to-consumer maternal health (mHealth) applications have the potential to increase engagement and empower pregnant people in their healthcare. Three studies arose in Kenya, focusing on the following areas: askNivi, a free sexual and reproductive health information service where users seek factual information, followed by requests for advice and reporting symptoms. Tess, a prototype mHealth text messaging system for perinatal depression. Predicting a woman’s fertile window through data received by a wearable bracelet achieved 90% accuracy.[6] AI assessment of embryo images or videos can improve ART outcomes. Applications guide the identification of embryos from the culture medium during early human in vitro development; raw time-lapse videos/images of embryos are sent to “in vitro fertilization” electronic health record (EHR) data.[6] A semi-automated learning-based framework approach was reported to improve gestational age prediction, with an accuracy of ±6.1 days, using DF on structural brain image and clinical data.[7] The fetal heart rate (FHR) signal is more complicated to identify than the adult signal. Therefore, there has been an effort to improve methods to detect, sample and quantify the FHR signal accurately. Various studies applied ML methods to classify cardiotocography signals and determine the fetal state.[6] In cases of preterm birth, cervix properties were observed using AI methods to determine material properties and cervical length (CL). The perinatal outcome was predicted with a DL model interpreting amniotic fluid metabolomics and proteomics in asymptomatic pregnant women with short CL.[6] For the mode of delivery, the Adana System applied artificial neural network to classify between cesarean section and vaginal delivery, including input variables of maternal characteristics and labor information.[8] Predicting the delivery route can inform care, allow appropriate allocation of resources, and improve pregnancy outcomes. ARTIFICIAL INTELLIGENCE: BENEFITS AND CHALLENGES One of the key advantages of AI is its superior reproducibility compared to humans. AI maintains absolute consistency over time, unlike clinicians, whose performance may vary due to experience, fatigue, or distractions. Additionally, AI systems have a significantly higher processing capacity—while a radiographer may analyze 50–100 scans daily, AI can theoretically interpret thousands.[1] AI also enhances efficiency by extracting valuable insights from a patient’s electronic records. Initially, this improves workflow and saves time, but with rigorous validation, AI could potentially play a direct role in patient management. Moreover, AI can serve large populations, particularly beneficial in areas with limited medical expertise. These systems continuously learn from each case and can process millions of cases within minutes, leading to highly efficient results. However, can AI replace human doctors? Despite its capabilities, AI lacks essential human qualities such as empathy and compassion, which are fundamental in patient care. Patients must feel that human physicians lead their consultations. Ethical concerns also arise with AI’s role in medicine. Should we trust AI to screen for diseases, prioritize treatment, diagnose, and discharge patients? Would we allow an AI system to determine which patient gets the last available intensive care unit bed? Additionally, data privacy remains a major concern. AI development requires vast amounts of patient data, raising questions about security and confidentiality. While AI presents significant advancements in healthcare, addressing ethical challenges and ensuring patient trust remain critical considerations. There is a concerted effort to incorporate AI into clinical practice, presenting an opportunity to introduce a “third participant” in patient care—one that can actively contribute to healthcare delivery.[1] However, stronger interdisciplinary collaboration between AI developers and healthcare professionals is essential for this potential to be fully realized. To facilitate the seamless integration of AI, medical professional organizations should begin assessing its impact, encourage physicians to document and share their experiences with AI technologies, and establish relevant guidelines or committees to address AI-related considerations.[1] From a financial perspective, AI can potentially reduce workforce requirements in labor-intensive fields such as healthcare. A well-known example from science fiction is the depiction of childbirth assisted by a “Midwife Droid” in Star Wars: Revenge of the Sith, a robot equipped with AI. While this remains fictional for now, rapid advancements in AI could soon make such scenarios a reality. The application of AI in obstetrics holds great promise. It can potentially enhance the quality of maternal and neonatal care, particularly in remote areas, thereby reducing maternal and perinatal morbidity and mortality. The future of AI in healthcare appears highly promising, offering innovative solutions to improve patient outcomes and accessibility. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.","url":"https://doi.org/10.4103/mgmj.mgmj_54_25","authors":["Sushil Kumar","Paridhi Agarwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-31T07:00:40Z","doi":"10.4103/mgmj.mgmj_54_25","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1109/iccai66501.2025.00106","name":"A Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data","source":"crossref","abstract":"We present HiRMed (Hierarchical RAG-enhanced Medical Test Recommendation), a novel tree-structured recommendation system that leverages Retrieval-Augmented Generation (RAG) for intelligent medical test recommendations. Unlike traditional approaches based on vector similarity, our system performs medical reasoning at each tree node through a specialized RAG process. Starting from the root node with initial symptoms, the system performs a stepwise medical analysis to identify potential underlying conditions and their corresponding diagnostic requirements. At each level, instead of simple matching, our RAG-enhanced nodes analyze retrieved medical knowledge to understand symptom-disease relationships and determine the most appropriate diagnostic path. The system dynamically adjusts its recommendation strategy based on medical reasoning results, considering factors such as urgency levels and diagnostic uncertainty. Experimental results demonstrate that our approach achieves superior performance in terms of coverage rate, accuracy, and miss rate compared to conventional retrieval-based methods. This work represents a significant advance in medical test recommendation by introducing medical reasoning capabilities into the traditional tree-based retrieval structure.","url":"https://doi.org/10.1109/iccai66501.2025.00106","authors":["Yahe Yang","Chengyue Huang","Cailian Ruan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-11T17:40:54Z","doi":"10.1109/iccai66501.2025.00106","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.1177/0272989x251346788","name":"Forewarning Artificial Intelligence about Cognitive Biases","source":"crossref","abstract":"Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, “Please keep in mind cognitive biases and other pitfalls of reasoning,” might mitigate biases in OpenAI’s generative pretrained transformer large language model. We used 10 clinically nuanced cases to test specific biases with and without a forewarning. Responses from the forewarning group were 50% longer and discussed cognitive biases more than 100 times more frequently compared with responses from the control group. Despite these differences, the forewarning decreased overall bias by only 6.9%, and no bias was extinguished completely. These findings highlight the need for clinician vigilance when interpreting generated responses that might appear seemingly thoughtful and deliberate. Highlights Artificial intelligence models can be warned to avoid racial and gender bias. Forewarning artificial intelligence models to avoid cognitive biases does not adequately mitigate multiple pitfalls of reasoning. Critical reasoning remains an important clinical skill for practicing physicians.","url":"https://doi.org/10.1177/0272989x251346788","authors":["Jonathan Wang","Donald A. Redelmeier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-24T11:26:45Z","doi":"10.1177/0272989x251346788","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.2139/ssrn.5094900","name":"Bespoke Regulation of Artificial Intelligence","source":"crossref","abstract":"The decision to regulate artificial intelligence (AI) has far reaching consequences. Determining how to address budding applications of AI technology should depend on their effects. This article describes how regulation should be carefully tailored to avoid harm while maximizing social welfare, building on Orly Lobel's taxonomy of regulatory tools. Part I examines the foundational difficulties in governing AI, including industry influence in regulation and deficiencies in enforcement. Part II elaborates on Lobel's framework, detailing the benefits and limitations of a variety of tools, such as voluntary standards, soft law mechanisms, and public-private partnerships. It describes how bringing in diverse stakeholders can achieve a more practical approach to AI governance but cautions against an evaluation of AI that overlooks its effects on areas such as access, autonomy, privacy, and the environment. Part III introduces the legislative carve-out as a potential instrument in AI governance. Using the 21st Century Cures Act's exclusion of certain low-risk Clinical Decision Support (CDS) software from FDA oversight as a case study, it evaluates the carve-out's implications for innovation, safety, and physician liability. The article concludes by advocating for a nuanced approach to AI governance that furthers innovation while mitigating risks, underscoring the importance of tailoring regulation based on the degree of likely harm.","url":"https://doi.org/10.2139/ssrn.5094900","authors":["Brenda M. Simon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-15T09:16:12Z","doi":"10.2139/ssrn.5094900","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1201/9781003531166-9","name":"Artificial Intelligence-Assisted Chatbots and Virtual Therapists","source":"crossref","abstract":"This chapter explores the evolution and role of artificial intelligence (AI)-assisted chatbots in mental health care, with a particular focus on their relevance to cognitive behavioural therapy (CBT). The chapter introduces a conceptual model of the various types of chatbots, providing examples of their clinical applications, and examines how AI is embedded within these systems. It also discusses the potential opportunities and risks associated with AI-assisted chatbots, such as improving accessibility to care while addressing concerns around ethical use and reliability. The chapter concludes by identifying research directions for further integrating chatbots into CBT, emphasising their potential to complement traditional therapeutic approaches and expand access to mental health services.","url":"https://doi.org/10.1201/9781003531166-9","authors":["Hester Chow"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T17:02:44Z","doi":"10.1201/9781003531166-9","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.21037/jmai-24-65","name":"Comprehensive guide and checklist for clinicians to evaluate artificial intelligence and machine learning methodological research","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-65","authors":["Don Roosan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-12T09:58:31Z","doi":"10.21037/jmai-24-65","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1201/9781003333081-5","name":"Artificial Intelligence and Medical Visualization","source":"crossref","abstract":"Artificial Intelligence (AI) is contributing promisingly to the advancement of business, education, automobile, human language-assisted devices, automated machinery, agriculture, pharma, and medicines. The AI machine learning, deep learning, robotics, natural-language processing, computer vision, neural networks, etc., offer better accuracy, speed, and performance in all mentioned core areas. The medical judgment for the particular disease or relevant medical issues is crucial for all further actions. Decision making at the right time with the right information source can produce great outcomes. As medical services connect with human life, all related decision making needs keen concern about accuracy and integrity. Medical decision making is popular with visualization. The clinical activities are based on screening and diagnosis later, which leads to treatment. The medical-visualization process is initiated with laboratory images. The lab reports are analyzed with physical notes for further treatments. AI systems extract useful information from a large patient populations to assist in making real-time inferences for health-risk alerts and health outcome predictions. This chapter focuses on ML & DL usage in skin cancer detection and diagnosis of COVID 19, breast cancer, eye diseases, brain disorders, etc., with image visualization and 3-D printing.","url":"https://doi.org/10.1201/9781003333081-5","authors":["Nayankumar C. Ratankar","Beenkumar R. Prajapati","Bhupendra G. Prajapati","Jigna B. Prajapati"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-16T19:04:16Z","doi":"10.1201/9781003333081-5","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-23979-3.00008-7","name":"Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23979-3.00008-7","authors":["Najmusseher","P.K. Nizar Banu","Ahmad Taher Azar","Nashwa Ahmad Kamal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-07T06:21:19Z","doi":"10.1016/b978-0-443-23979-3.00008-7","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1007/978-3-031-90174-4","name":"Artificial Intelligence-Empowered Bio-medical Applications","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-031-90174-4","authors":["Dimitrios P. Panagoulias","George A. Tsihrintzis","Maria Virvou"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"10.1007/978-3-031-90174-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1148/ryai.230337","name":"Sharing Data Is Essential for the Future of AI in Medical                     Imaging","source":"crossref","abstract":"If we want artificial intelligence to succeed in radiology, we must share data and learn how to share data.","url":"https://doi.org/10.1148/ryai.230337","authors":["Laura C. Bell","Efrat Shimron"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-29T09:51:30Z","doi":"10.1148/ryai.230337","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/ictbai68361.2025","name":"2025 International Conference on Trustworthy Big Data and Artificial Intelligence (ICTBAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ictbai68361.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-29T18:37:24Z","doi":"10.1109/ictbai68361.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/icamac67779.2025","name":"2025 2nd International Conference on Artificial Intelligence, Metaverse, and Cybersecurity (ICAMAC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icamac67779.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-25T20:55:28Z","doi":"10.1109/icamac67779.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/ai3e69313.2025","name":"2025 International Conference on Artificial Intelligence, Electrical and Electronic Engineering (AI3E)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai3e69313.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-24T19:45:17Z","doi":"10.1109/ai3e69313.2025","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.56012/vmag9372","name":"Considerations for the use of artificial intelligence in the creation of lay summaries of clinical trial results","source":"crossref","abstract":"The clinical research landscape is constantly evolving, as new regulations and innovations come together to help accelerate scientific discoveries and medical advances. A prominent example of this is the rapidly emerging technology of artificial intelligence (AI). Using AI to develop lay summaries (LS) of clinical trial results can enhance transparency and accessibility, while maximising efficiencies and facilitating scalability. This document is a product of collaboration between experts from over 15 organisations in the US and the EU, including industry, academia, and a patient-focused nonprofit. It aims to explore how AI can be responsibly applied to LS development. While aligning with current industry standards, this document provides several recommendations for AI implementation that highlight the necessity of human oversight and expertise. This joint effort between human and machine can help LS achieve high standards in accuracy, transparency, and compliance, while building public trust and empowering patients to make informed healthcare decisions.","url":"https://doi.org/10.56012/vmag9372","authors":["Kimbra Edwards"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-10T13:09:26Z","doi":"10.56012/vmag9372","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/idicaihei65991.2025.11378896","name":"Medical Image Segmentation in Joint Replacement Scans Using Attention Mechanisms: A Survey","source":"crossref","abstract":"The success of joint replacement surgeries is heavily dependent on precise preoperative planning and intraoperative execution, where accurate segmentation of anatomical structures from medical scans is paramount. While traditional segmentation methods often fall short when confronted with complex joint anatomies and pathological variations, the integration of attention mechanisms within deep learning architectures has emerged as a transformative approach. These mechanisms empower models to selectively focus on diagnostically relevant regions, significantly enhancing segmentation accuracy, robustness, and clinical utility. This survey presents a systematic review of contemporary attention-based segmentation methodologies specifically within the context of joint replacement imaging. We provide a detailed classification of attention mechanisms, including spatial, channel, and hybrid models, alongside an exploration of transformer-based architectures. The paper synthesizes findings from key literature, offers a comparative analysis of model performance and computational trade-offs, and discusses tangible impacts on surgical outcomes, such as reduced surgical time and improved implant positioning. Furthermore, we address current limitations, including data scarcity and computational demands, and propose future research directions, such as lightweight model design and multimodal data integration, to advance the field towards more efficient and clinically adoptable solutions.","url":"https://doi.org/10.1109/idicaihei65991.2025.11378896","authors":["Sindhu N","S. Srividhya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T20:43:58Z","doi":"10.1109/idicaihei65991.2025.11378896","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/j.engappai.2025.110183","name":"Estimating room acoustic descriptors from bag-of-vectors representation with transformers","source":"crossref","abstract":"In this paper, we propose a novel deep learning method for room acoustic descriptor estimation. Certain descriptors are highly important in assessing acoustic quality, therefore estimating them during the planning phase is a crucial part of designing indoor spaces. Traditional approaches rely on either computationally expensive numerical methods, or statistical formulae with insufficient accuracy. Our solution is FRAPPE (fast room acoustic prediction and parameter estimation), which applies lightweight transformer-based neural networks to estimate acoustic descriptors in rectangular rooms, utilizing a “bag-of-vectors” representation that is capable of capturing diverse interior designs. We employ transformers without positional encoding, highlighting the broad applicability of the architecture outside of traditional domains. FRAPPE achieves high accuracy and operates at near-instant speed, providing a better cost–accuracy balance than either ray tracing methods or empirical formulae. It is the first transformer-based approach that is applicable in the design phase, and it offers a more general deep learning solution for acoustic descriptor estimation than any prior methods. The accuracy, inference speed and versatility of FRAPPE makes it a valuable innovation for architectural design, supporting better decisions during the early stages of room planning.","url":"https://doi.org/10.1016/j.engappai.2025.110183","authors":["Bence Bakos","Gábor Hidy","Bálint Csanády","Csaba Huszty","András Lukács"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-11T06:29:52Z","doi":"10.1016/j.engappai.2025.110183","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"doi:10.37990/medr.1581104","name":"A New Approach: Generative Artificial Intelligence in Physiatry Resident Education","source":"crossref","abstract":"Aim: This study assessed the effectiveness of ChatGPT-4o, an artificial intelligence (AI) platform, in creating a therapeutic exercises presentation for physiatry residents’ education. The aim was to compare the quality of content created by ChatGPT-4o with that of an expert, exploring the potential of AI in healthcare education. Material and Method: Both an expert and AI created 24 PowerPoint slides across six topics, using same reputable sources. Two other experts assessed these slides according to CLEAR criteria: completeness, lack of false information, appropriateness, and relevance and scored as excellent, 5; very good=4, good=3, satisfactory/fair=2, or poor, 1. Results: Interrater reliability was confirmed. Average scores (calculated from the two raters’ scores) for each topic were significantly lower for AI than for the expert, although whole presentation scores did not differ between the two. Overall scores (calculated from the average scores of all items) for each topic were good to excellent for AI, excellent for the expert. The overall score for whole presentation was good for AI, excellent for the expert. Highest ranks for individual criteria was relevance for AI, lack of false information for the expert. Some AI-generated elements were later integrated into the expert work, enhancing the content. Conclusion: ChatGPT-4o can generate effective educational content, though expert outperforms it, highlighting the need for professional oversight. Collaboration between humans and AI may further enhance educational outcomes.","url":"https://doi.org/10.37990/medr.1581104","authors":["Selkin Yılmaz Muluk","Vedat Altuntaş","Zehra Duman Şahin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-14T11:30:43Z","doi":"10.37990/medr.1581104","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.3109/14639238009016070","name":"Human and Artificial Intelligence","source":"crossref","abstract":"(1980). Human and Artificial Intelligence. Medical Informatics: Vol. 5, No. 3, pp. 243-243.","url":"https://doi.org/10.3109/14639238009016070","authors":["John Anderson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-12-08T21:32:31Z","doi":"10.3109/14639238009016070","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-13816-4.00025-5","name":"Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13816-4.00025-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T18:04:30Z","doi":"10.1016/b978-0-443-13816-4.00025-5","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-44-332856-5.00003-4","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-332856-5.00003-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T09:56:12Z","doi":"10.1016/b978-0-44-332856-5.00003-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-23517-7.00016-2","name":"REMOVED: Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23517-7.00016-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T13:55:32Z","doi":"10.1016/b978-0-443-23517-7.00016-2","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.70593/978-81-988918-1-5_11","name":"Artificial intelligence-powered transformation across retail, government, and enterprise institutions","source":"crossref","abstract":"Despite the massive economic disruptions and changes caused by COVID-19, an ongoing technological revolution continues in the world of information technology. Data analysis innovations such as big data technologies, advanced analytics, artificial intelligence, and AI-powered automation have changed the world of business processes and decision-making. Growing sections of enterprise functions are becoming data-driven, thereby increasing productivity, enhancing innovation capabilities, and lowering cycle times and risks. Digital technologies allow these tried and tested best practices to be applied to a wider range of sectors and industries. High-performance computing technologies are enabling the development of AI systems that can lower the costs of executing various operations and executing more complex operations that had previously not been automatable (Eggers et al., 2017; Bughin et al., 2019; Davenport et al., 2020). This disintermediation effect is leading to fundamental changes in the structure and functioning of the ecosystems of industries and sectors. With the widespread penetration of mobile and sensor technologies, enterprises and other organizations are now under constant observation by their stakeholders – customers, shareholders, partners, regulators, and so forth. This opens up the potential for organizations to eliminate sections of the value chain that do not provide high value, and to focus on high value, high visibility activities that shape trust and reputation in the community. In this chapter, we explore the potential of various AI-Powered transformation initiatives that can fundamentally impact various industry and functional domains – including areas of public policy, citizen services, security and defence, financial services, large-scale manufacturing, supply chain and logistics, trading and distribution, and customer services (Mathew et al., 2023; Mathew et al., 2023; Islam et al., 2025; Khajuria, 2025).","url":"https://doi.org/10.70593/978-81-988918-1-5_11","authors":["Abhishek Dodda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-09T17:26:54Z","doi":"10.70593/978-81-988918-1-5_11","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.59728/jaie.2025.4.2.52","name":"Improving elementary school students’ morality with AI","source":"crossref","abstract":"This study focuses on exploring moral education strategies for elementary school students through the use of conversational artificial intelligence (AI), with the aim of strengthening democratic citizenship competencies required in the digital society. The research investigates the social impact of conversational AI, the concerns it raises in elementary education, and proposes approaches to fostering moral judgment and critical thinking as solutions. Emphasis is placed on the importance of discussion-based learning using moral dilemma situations, while examining the potential of conversational AI as a supportive educational tool. To address issues such as biased data learning and the uncritical acceptance of information, the study highlights approaches including the cultivation of conscience-based morality, the presentation of age-appropriate moral dilemmas, and the promotion of moral reasoning through discussion-oriented learning. In particular, the study explores the application of conversational AI in the classroom to compare ethical standards across cultures and eras, facilitate discussions, provide feedback, and generate role-play scenarios. Furthermore, it proposes that combining teachers’ active guidance with AI’s supportive functions can help elementary students establish their own criteria for judgment and engage actively in moral discourse. This study suggests a new educational approach to strengthening morality and critical thinking through conversational AI, and discusses the future direction of democratic citizenship education in the digital learning environment.","url":"https://doi.org/10.59728/jaie.2025.4.2.52","authors":["Bo Ram Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-13T04:39:14Z","doi":"10.59728/jaie.2025.4.2.52","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-44111-0.00004-4","name":"Elucidable Artificial Intelligence in Medical Practice: Enhancing Transparency, Accountability, and Responsible Artificial Intelligence use","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44111-0.00004-4","authors":["Srinivas Rao Pulluri","Jayadev Gyani","Srima Rao Pulluri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T21:52:53Z","doi":"10.1016/b978-0-443-44111-0.00004-4","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"pmid:41726532","name":"A Clinically-Informed Framework for Evaluating Vision-Language Models in Radiology Report Generation: Taxonomy of Errors and Risk-Aware Metric.","source":"pubmed","abstract":"Recent advances in vision-language models (VLMs) have enabled automatic radiology report generation, yet current evaluation methods remain limited to general-purpose NLP metrics or coarse classification-based clinical scores. In this study, we propose a clinically informed evaluation framework for VLM-generated radiology reports that goes beyond traditional performance measures. We define a taxonomy of 12 radiology-specific error types, each annotated with clinical risk levels (low, medium, high) in collaboration with physicians. Using this framework, we conduct a comprehensive error analysis of three representative VLMs, i.e., DeepSeek VL2, CXR-LLaVA, and CheXagent, on 685 gold-standard, expert-annotated MIMIC-CXR cases. We further introduce a risk-aware evaluation metric, the Clinical Risk-weighted Error Score for Text-generation (CREST), to quantify safety impact. Our findings reveal critical model vulnerabilities, common error patterns, and condition-specific risk profiles, offering actionable insights for model development and deployment. This work establishes a safety-centric foundation for evaluating and improving medical report generation models. The source code of our evaluation framework, including CREST computation and error taxonomy analysis, is available at https://github.com/guanharry/VLM-CREST.","url":"https://pubmed.ncbi.nlm.nih.gov/41726532/","authors":["Guan H","Hou PC","Hong P","Wang L","Zhang W","Du X","Zhou Z","Zhou L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.505Z"},{"id":"pmid:41726530","name":"Adaptive Constraint Relaxation in Personalized Nutrition Recommendations: An LLM-Driven Knowledge Graph Retrieval Approach.","source":"pubmed","abstract":"Personalized food recommendation systems must balance various constraints, including medical guidelines, nutritional needs, and individual preferences. However, existing methods often struggle with overly restrictive queries, frequently failing to generate recommendations when no exact match exists. To address this challenge, we propose an adaptive knowledge graph (KG) retrieval framework that integrates Large Language Models (LLMs) for intelligent constraint relaxation. Our approach dynamically prioritizes constraints, ensuring that critical dietary requirements remain intact while selectively relaxing less essential ones. By leveraging LLM-driven constraint analysis and structured relaxation strategies, our system significantly enhances recommendation coverage without compromising key dietary needs, while maintaining optimal recommendation performance. Experimental results on both the original and the extended-constraint dataset demonstrate that our method successfully retrieves recommendations in cases where previous approaches fail, achieving higher retrieval accuracy and a balanced tradeoff between flexibility and adherence to dietary constraints. The code is public available at https://github.com/zpf0117b2/adaptiveRetrieval.","url":"https://pubmed.ncbi.nlm.nih.gov/41726530/","authors":["Zhang P","Fnu M","Song Y","Seneviratne O","Yang Z","Azimi I","Rahmani AM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.505Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726528","name":"Temporal Harmonization: Improved Detection of Mild Cognitive Impairment from Temporal Language Markers using Subject-invariant Learning.","source":"pubmed","abstract":"Mild Cognitive Impairment (MCI) is an early stage of dementia characterized by cognitive decline and behavioral changes. Early detection is crucial for timely interventions, improved clinical trial cohort selection, and the development of targeted therapies. Linguistic markers have recently emerged as a non-invasive, cost-effective method for MCI detection. This study analyzes linguistic markers from conversations between participants and healthcare professionals to distinguish MCI from cognitively normal (NL) individuals. The dynamics of multiple conversations of a subject carry fine-granular linguistic change over time and expect to greatly enhance detection accuracy. However, individual variations in speaking styles pose challenges for learning cognitive characteristics from temporal sequences of conversations. To address this, we propose a temporal harmonization method to mitigate distributional differences in linguistic features across subjects, improving model generalization. Our results show that machine learning models leveraging subject-invariant harmonized temporal features greatly improve the prediction performance of MCI detection from multiple conversations.","url":"https://pubmed.ncbi.nlm.nih.gov/41726528/","authors":["Hoang B","Liang S","Pang Y","Dodge H","Zhou J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726521","name":"Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought Prompting.","source":"pubmed","abstract":"Influenza vaccine effectiveness (VE) estimation plays a critical role in public health decision-making by quantifying the real-world impact of vaccination campaigns and guiding policy adjustments. Current approaches to VE estimation are constrained by limited population representation, selection bias, and delayed reporting. To address some of these gaps, we propose leveraging large language models (LLMs) with few-shot chain-of-thought (CoT) prompting to mine social media data for real-time influenza VE estimation. We annotated over 4,000 tweets from the 2020-2021 flu season using structured guidelines, achieving high inter-annotator agreement. Our best prompting strategy achieves F 1 scores above 87% for identifying influenza vaccination status and test outcomes, outperforming traditional supervised fine-tuning methods by large margins. These findings indicate that LLM-based prompting approaches effectively identify relevant social media information for influenza VE estimation, offering a valuable real-time surveillance tool that complements traditional epidemiological methods.","url":"https://pubmed.ncbi.nlm.nih.gov/41726521/","authors":["Xu D","García GL","O'Connor K","Holston H","Klein AZ","Amaro IF","Scotch M","Gonzalez-Hernandez G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726520","name":"Detecting Reference Errors in Scientific Literature with Large Language Models.","source":"pubmed","abstract":"Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant threat to the integrity of scientific literature. To support automatic detection of reference errors, we evaluated the ability of large language models in OpenAI's GPT family to detect quotation errors. Specifically, we prepared an expert-annotated, general-domain dataset of statement-reference pairs from journal articles, one-third of which is in biomedicine. Large language models were evaluated in different settings with varying amounts of reference information provided by retrieval augmentation. Results showed that large language models are able to detect erroneous citations with limited context and without fine-tuning. This study contributes to the growing literature that seeks to utilize artificial intelligence to assist in the writing, reviewing, and publishing of scientific papers as well as grounding of language model responses.","url":"https://pubmed.ncbi.nlm.nih.gov/41726520/","authors":["Zhang TM","Abernethy NF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726516","name":"Enhancing Long-Term Care Efficiency: Embedded LLMs for Clinical Report Summarization and Caregiver Support.","source":"pubmed","abstract":"Long-term care facilities face a critical shortage of nursing staff and an increasing administrative burden, reducing time for direct patient care. Generative artificial intelligence offers a promising solution to automate administrative tasks and support caregivers. This paper evaluates the relevance of using a fine-tuned large language model (LLM) to address these challenges. Interviews with healthcare professionals identified key needs, leading to the selection of two use cases: caregiver-patient communication assistance and medical record summarization. To comply with privacy and security constraints, the model was deployed in an embedded scenario. Performance evaluations showed significant improvements in BLEU and ROUGE metrics for both use cases, demonstrating enhanced accuracy. This study demonstrates the feasibility of leveraging LLMs to streamline workflows, reduce administrative strain, and improve operational efficiency. This work highlights the potential for broader AI applications in long-term care, paving the way for better working conditions for caregivers and improved patient care quality.","url":"https://pubmed.ncbi.nlm.nih.gov/41726516/","authors":["Michelet A","Manzo G","Ritz A","Delgado P","Celi LA","Schumacher MI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726515","name":"Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety.","source":"pubmed","abstract":"Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six ontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety data. Using the Unified Medical Language System (UMLS), we assess each method's ability to group PTs around medically meaningful centroids. A high-throughput framework was developed with a Java API and Python/R interfaces support large-scale similarity computations. Results show that while path-based methods perform moderately with F1 scores of 0.36 for WUPALMER and 0.28 for LCH, intrinsic information content (IC)-based measures, especially INTRINSIC_LIN and SOKAL, consistently yield better clustering accuracy (F1 Score of 0.403). Validated against expert review and standard MedDRA queries (SMQs), our findings highlight the promise of IC-based SSMs in enhancing pharmacovigilance workflows by improving early signal detection and reducing manual review.","url":"https://pubmed.ncbi.nlm.nih.gov/41726515/","authors":["Painter JL","Haguinet F","Powell GE","Bate A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726512","name":"Towards Interpretable, Sequential Multiple Instance Learning: An Application to Clinical Imaging.","source":"pubmed","abstract":"This work introduces the Sequential Multiple Instance Learning (SMIL) framework, addressing the challenge of interpreting sequential, variable-length sequences of medical images with a single diagnostic label. Diverging from traditional MIL approaches that treat image sequences as unordered sets, SMIL systematically integrates the sequential nature of clinical imaging. We develop a bidirectional Transformer architecture, BiSMIL, that optimizes for both early and final prediction accuracies through a novel training procedure to balance diagnostic accuracy with operational efficiency. We evaluated BiSMIL on three medical image datasets to demonstrate that it simultaneously achieves state-of-the-art final accuracy and superior performance in early prediction accuracy, requiring 30-50% fewer images for a similar level of performance compared to existing models. Additionally, we introduce SMILU, an interpretable uncertainty metric that outperforms traditional metrics in identifying challenging instances.","url":"https://pubmed.ncbi.nlm.nih.gov/41726512/","authors":["Luo X","Wang HS","Li ML"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726508","name":"STop Clock for Automated Tracking (STAT) during Time-Critical Medical Work: Evaluating the Accuracy and Usability of an AI-Driven Automated Stop Clock.","source":"pubmed","abstract":"Delays and process inefficiencies during trauma resuscitation can contribute to adverse patient outcomes. While tracking elapsed time may improve the trauma team's temporal awareness and reduce delays, reliance on manual activation of stop clocks can introduce variability. To address this limitation, we implemented a computer vision-powered automatic stop clock designed to activate upon patient arrival without requiring manual input. We conducted a retrospective video review of 50 trauma resuscitations to assess how the clock was used in practice, followed by semi-structured interviews with nine trauma team members to elicit their feedback and perceptions. This study contributes to the broader discussion on AI-assisted clinical tools, highlighting the role of automation in supporting trauma teams, reducing variability in time tracking, and improving process efficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/41726508/","authors":["Zellner KA","Yuan S","Ernst ER","Arkowitz DW","Mun AH","Kim MS","Marsic I","Burd RS","Sarcevic A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726506","name":"Analyzing and Mitigating Model Drift in Acute Kidney Injury Prediction for Hospitalized Patients.","source":"pubmed","abstract":"Artificial intelligence and machine learning are transforming healthcare by improving clinical risk predictions and diagnostic precision. However, their performance can be compromised by data drifts due to changes in patient populations and evolving clinical practices. This study investigated performance drift in models predicting Acute Kidney Injury (AKI) using electronic health records from 249,749 inpatient encounters over ten years, analyzing performance across both the overall population and nine subgroups with unique health profiles. To mitigate the performance drift, we implemented two model updating strategies: an Overall Population Update (OPU) and a Specific Subgroup Update (SSU). Our results demonstrated significant reductions in drift, with OPU increasing the average area-under-the-precision-recall-curve (AUPRC) by 0.14 in the overall population and 0.11 across subgroups, and SSU improving the average AUPRC by 0.10 among subgroups. These findings highlight the importance of continuous model surveillance and adaptive updates to maintain reliable predictive performance in dynamic clinical environments.","url":"https://pubmed.ncbi.nlm.nih.gov/41726506/","authors":["Xu Z","Li D","Xu Q","Chan HY","Yu ASL","Liu M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726501","name":"Predicting Early-Onset Colorectal Cancer with Large Language Models.","source":"pubmed","abstract":"The incidence rate of early-onset colorectal cancer (EoCRC, age &lt; 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity.","url":"https://pubmed.ncbi.nlm.nih.gov/41726501/","authors":["Lau W","Kim Y","Parasa S","Haque ME","Oka A","Nanduri J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726500","name":"To what Degree can LLMs Support Medical Informatics Research? Examining the Interplay of Research Support LLMs with LLM Critics.","source":"pubmed","abstract":"The rapid development of Large Language Models (LLMs) has opened up new possibilities for their role in supporting research. This study assesses whether LLMs can generate \"thoughtful\" research plans in the domain of Medical Informatics and whether LLM-generated critiques can improve such plans. Using an LLM pipeline, we prompt four LLMs to generate primary research plans. Subsequently, these plans are mutually critiqued and then the LLMs are prompted to refine their outputs based on these critiques. These original and improved responses are then reviewed by human evaluators for errors, hallucinations, etc. We employ ROUGE scores, cosine similarity, and length differences to quantify similarities across responses. Our findings reveal variations in outputs among four LLMs, the impact of critiques, and differences between primary and secondary outputs. All LLMs produce cogent outputs and critiques, integrating feedback when generating improved outputs. Human evaluators can distinguish between primary and secondary responses in most cases.","url":"https://pubmed.ncbi.nlm.nih.gov/41726500/","authors":["Khatwani N","Wang L","Geller J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726491","name":"An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata.","source":"pubmed","abstract":"Clinical calculators are widely used, and large language models (LLMs) make it possible to engage them using natural language. We demonstrate a purpose-built chatbot that leverages (1) software implementations of verifiable clinical calculators via LLM tools, and (2) metadata about these calculators via retrieval augmented generation (RAG). We compare its accuracy to an unassisted LLM on four natural language conversation workloads. Our chatbot achieves 100% accuracy on queries interrogating calculator metadata content and shows a significant increase in clinical calculation accuracy vs. the off-the-shelf LLM when prompted with complete sentences (86.4% vs. 61.8%) or with medical shorthand (79.2% vs. 62.0%). It eliminates calculation errors when prompted with complete sentences (0% vs. 16.8%) and greatly reduces them when prompted with medical shorthand (2.4% vs. 18%). While our chatbot is not yet ready for clinical use, these results show progress in minimizing incorrect calculation results.","url":"https://pubmed.ncbi.nlm.nih.gov/41726491/","authors":["Kumar N","Seifi F","Conte M","Flynn AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726488","name":"Prospective Validation of a Suicide Event Risk Model in Transgender Patients.","source":"pubmed","abstract":"Suicide risk prediction models offer a promising avenue for early intervention, but their effectiveness in underrepresented populations remain uncertain. This study evaluated VSAIL's, a real-world, externally validated, and deployed suicide risk prediction model, performance in predicting suicide risk among transgender individuals. Transgender individuals were identified from electronic health record data and transgender status was verified via manual chart review. Results indicated modest discriminative ability (AUROC=0.777, AUPRC=0.115), however, a high rate of false negatives (77%), and significant miscalibration (Brier=0.023, Spiegelhalter's z-statistic p&lt;0.001) reduced clinical utility. Findings underscore the importance of targeted subgroup validation and highlight limitations of general population-trained models in accurately identifying suicide risk among transgender patients. They also suggest the need for ongoing algorithm monitoring and subgroup-aware modeling strategies to improve predictive equity in marginalized populations.","url":"https://pubmed.ncbi.nlm.nih.gov/41726488/","authors":["Becker RA","Walsh CG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726480","name":"RAG vs Reddit: Decoding Autism Conversations on Reddit with LLMs and Topic Modeling.","source":"pubmed","abstract":"Social media platforms like Reddit have become vital spaces for autistic individuals and caregivers to seek advice, share experiences, and discuss challenges. Simultaneously, Large Language Models (LLMs) are increasingly used to provide medical guidance. This study examines autism-related discussions on Reddit, comparing them with clinician-patient discussions and evaluating the effectiveness of an autism-specific Retrieval-Augmented Generation (RAG) system. We applied BERTopic to identify key discussion themes in r/autism and r/autism_parenting, revealing significant discussions around behavioral challenges, and practical support. Comparing clinical messages from the University of Missouri Thompson Center for Autism and Neurodevelopment, we found caregivers in clinical settings focused more on medication management, whereas online discussions emphasized non-traditional therapies. We then assessed LLM-generated responses against Reddit peer advice, discussing the differences in accuracy, relevance, empathy and helpfulness. This work underscores the potential of RAG systems in enhancing autism-related guidance while emphasizing the importance of community-driven insights in healthcare conversations.","url":"https://pubmed.ncbi.nlm.nih.gov/41726480/","authors":["Wattegama D","Black B","Moen M","Shyu CR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726475","name":"Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset.","source":"pubmed","abstract":"Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability. In this study, we benchmark machine learning models that leverage longitudinal waitlist history data for time-dependent, time-to-event modeling of waitlist mortality. We train on 23,807 patient records with 77 variables and evaluate both survival prediction and discrimination at a 1-year horizon. Our best model achieves a C-Index of 0.94 and AUROC of 0.89, significantly outperforming previous models. Key predictors align with known risk factors while also revealing novel associations. Our findings can support urgency assessment and policy refinement in heart transplant decision making.","url":"https://pubmed.ncbi.nlm.nih.gov/41726475/","authors":["Luo Y","Skandari R","Martinez C","Kilic A","Padman R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726471","name":"Coding Fairness: Detecting Demographic-Related Coding Discrepancies in ICD Code Assignments.","source":"pubmed","abstract":"Coded clinical data are crucial in biomedical informatics research. While it is well known that electronic medical records often contain coding errors, numerous studies rely on International Classification of Diseases (ICD) codes for phenotyping in cohort assembly, statistical analysis, and AI modeling. Although fairness hasbecome an important focus in AI research, the potential biases embedded in coded clinical data have received less attention. In this study, we employed a race- and sex-agnostic AI phenotyping model to assess coding fairness across 203 ICD code blocks within the Veterans Health Administration Clinical Data Warehouse. Our findings revealed variability in coding consistency across demographic subgroups, including sex, race, and ethnicity. Notably, over 50% of the code blocks exhibitedstatisticallysignificant differences in discrepancies between AI-generated and ICD-based phenotypesacross these demographic groups. These results suggest the need to recognize and address demographic-related coding discrepancies to ensure coding fairness.","url":"https://pubmed.ncbi.nlm.nih.gov/41726471/","authors":["Yin Y","Nelson SJ","Shao Y","Faselis C","Ahmed A","Zeng-Treitler Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726470","name":"From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.","source":"pubmed","abstract":"Food and nutrition knowledge is recognized as a fundamental factor for the health and well-being of communities; however, its integration into biomedical and health knowledge systems is limited by the absence of standardized ontologies that encapsulate food-related concepts. This study mapped FoodOn, an open-source food ontology, to the Unified Medical Language System (UMLS) metathesaurus, a compendium of biomedical ontologies. As the first systematic mapping of a food ontology to the UMLS, the results of this study provide an ontological foundation for incorporating dietary data into clinical and public health workflows. The findings suggest that expanding the representation of food concepts in biomedical ontologies could enhance the potential to incorporate food and nutritional into clinical decision-making and research. Furthermore, this work lays the groundwork for integrating food-based therapies from traditional medicine systems (e.g., Ayurveda and Traditional Chinese Medicine) into contemporary clinical knowledge frameworks to support more holistic approaches to health care.","url":"https://pubmed.ncbi.nlm.nih.gov/41726470/","authors":["Sarkar IN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726469","name":"DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling.","source":"pubmed","abstract":"In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often work in a static manner, either restricting interactions within individual encounters or collapsing all historical encounters into a single snapshot. As a result, when it is necessary to identify meaningful groups of medical events spanning longitudinal encounters, existing methods are inadequate in modeling interactions cross encounters while accounting for temporal dependencies. To address this limitation, we introduce Deep Patient Journey (DeepJ), a novel graph convolutional transformer model with differentiable graph pooling to effectively capture intra-encounter and inter-encounter medical event interactions. DeepJ can identify groups of temporally and functionally related medical events, offering valuable insights into key event clusters pertinent to patient outcome prediction. DeepJ significantly outperformed five state-of-the-art baseline models while enhancing interpretability, demonstrating its potential for improved patient risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/41726469/","authors":["Li D","Yao Z","Liang M","Liu M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726462","name":"Cryptogenic Stroke and Migraine: Using Probabilistic Independence and Machine Learning to Uncover Latent Sources of Disease from the Electronic Health Record.","source":"pubmed","abstract":"Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few clinical guidelines exist to reduce this associated risk. We therefore propose a data-driven approach to extract probabilistically-independent sources from electronic health record (EHR) data and create a 10-year risk-predictive model for CS in migraine patients. These sources represent external latent variables acting on the causal graph constructed from the EHR data and approximate root causes of CS in our population. A random forest model trained on patient expressions of these sources demonstrated good accuracy (ROC 0.771) and identified the top 10 most predictive sources of CS in migraine patients. These sources revealed that pharmacologic interventions were the most important factor in minimizing CS risk in our population and identified a factor related to allergic rhinitis as a potential causative source of CS in migraine patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41726462/","authors":["Betts JW","Still JM","Lasko TA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726455","name":"Understanding Primary and Secondary Concerns from Patient Portal Messages through Clinical Data Annotation, Analysis, and Modeling.","source":"pubmed","abstract":"Efficient triage and response to patient portal messages (PPMs) are critical for enhancing patient-centered care. To improve the understanding of primary and secondary concerns expressed by patients, this study annotated and analyzed a set of 2,239 PPMs. We also automated the patient concern identification and analysis by leveraging pretrained language models with binary classification to discern all patient concerns and with multi-class classification to identify primary patient concerns. These multi-class classifications were further enhanced by integrating convolutional neural networks that utilize embeddings from the binary classification. This approach demonstrated significant potential of AI in managing the growing volume of PPMs and promptly addressing the healthcare needs of patients, thereby facilitating more effective and timely medical interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/41726455/","authors":["Wu Y","Ren Y","Jia H","Harrison T","Fan J","Liu H","Huang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726452","name":"Exploring the Implementation Experience and Use of CONCERN Early Warning System in a Rural Community Hospital: A Mixed Method Convergent Approach.","source":"pubmed","abstract":"The Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that analyzes nursing documentation patterns to detect early signs of patient deterioration, with proven effectiveness in reducing the risk of in-hospital mortality and length of stay. This study extends the evaluation of CONCERN EWS beyond acute care settings to a rural community hospital, assessing user experience, system utilization, and accuracy in identifying patient deterioration. The study examined accuracy in a rural setting using a mixed-methods approach-qualitative interviews, quantitative data analysis, and clinical record reviews. Findingssuggestthat CONCERNEWS enhancesearly recognition of clinical deterioration andisusable in the context of busy acute care nursing workflows. These results support its adaptability to facilitate strengthening nursing surveillance, clinical decision-making, and patient safety in rural healthcare settings as well.","url":"https://pubmed.ncbi.nlm.nih.gov/41726452/","authors":["Lee Y","Kang MJ","Baris VK","Lowenthal G","Rossetti SC","Cato KD","Lee RY","Kramer J","Huffam R","Dykes PC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726449","name":"Knowledge Engineering for Medical Vocabularies Using Large Language Models.","source":"pubmed","abstract":"Medical vocabularies are essential tools for capturing, classifying, and analyzing healthcare data. However, the creation and maintenance of these vocabularies are often labor-intensive and costly. This preliminary study evaluates the feasibility of using large language models (LLMs) to automate three key tasks in medical vocabulary management: term similarity, subsumption, and grouping. Using 1,533 cardiovascular terms from SNOMED CT, we applied GPT-4o and assessed the performance of 3 elementary tasks against OHDSI standardized vocabularies. While LLMs demonstrated high precision across tasks (0.78 for term similarity, 0.74 for term subsumption, 0.78 for term grouping), recall was notably lower (0.41 for term similarity, 0.08 for term subsumption, 0.52 for term grouping), indicating gaps in coverage. Overall, LLMs show promise for medical vocabulary tasks but require further refinement for clinical specificity and completeness. Future work should focus on enhancing recall, reducing hallucinations, and evaluating scalability across broader terminology sets.","url":"https://pubmed.ncbi.nlm.nih.gov/41726449/","authors":["Chen HY","Ostropolets A","Weng C","Hripcsak G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726447","name":"Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.","source":"pubmed","abstract":"Clinical phenotyping is the process of extracting patient's observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-injury, to identify suicide risks and improve interventions. In this study, we leveraged the latest reasoning models, namely 4o, o1, and o3-mini, to perform note-level multi-label classification and reasoning generation tasks using previously annotated psychiatric evaluation notes from a safety-net psychiatric inpatient hospital in Harris County, Texas. Compared with the previously finetuned GPT-3.5 model, the out-of-box reasoning models prompted with in-context learning achieved comparable and better performance, with the highest accuracy of 0.94 and F1 of 0.90. We implemented novel clinical justification generation from these models on the traditional classification tasks. This finding marked a promising direction for performing clinical phenotyping that is interpretable and actionable using smaller, efficient reasoning models.","url":"https://pubmed.ncbi.nlm.nih.gov/41726447/","authors":["Li Z","Wang W","Shahani L","Vieira RM","Selek S","Soares J","Liu H","Huang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726445","name":"Multimodal Data Integration Improves Disease Risk Prediction in the UK Biobank.","source":"pubmed","abstract":"Family health history is an important component to assess risk for common chronic diseases. The integration of electronic health records and genetic data offers great potential to improve disease risk prediction by capturing both clinical and genetic risk factors. We present ALIGATEHR-Gen, a graph attention network that integrates multimodal patient data including genetic information, diagnosis codes, and demographics, along with external medical ontology knowledge. ALIGATEHR-Gen constructs unified patient representations by incorporating genetically inferred first-degree relationships and disease ontology embeddings to enhance disease risk prediction. We evaluate the predictive performance of ALIGATEHR-Gen across 118 diseases in the UK Biobank and demonstrate that it outperforms state-of-the-art baseline models by an average of at least 6%. A case study on five primary fibrotic and closely related diseases reveals that ALIGATEHR-Gen effectively distinguishes patient subgroups based on clinical and genetic features. These findings illustrate the potential of ALIGATEHR-Gen to advance predictive and interpretable modeling in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/41726445/","authors":["Huang X","Zhou H","Hong Y","Zhou X","de Jong J","Wang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726439","name":"Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres.","source":"pubmed","abstract":"PLGA microspheres are widely used in long-acting drug formulations due to their ability to provide sustained release, improving patient adherence and reducing dosing frequency. However, drug release behavior is influenced by complex formulation and processing factors, making traditional trial-and-error development inefficient. This study leverages machine learning to predict drug release profiles from PLGA (poly(lactic-co-glycolic acid)) microsphere formulations. A dataset of 113 PLGA formulations containing small-molecule drugs and large-molecule peptides was collected from published literature. Multiple machine learning models were developed and compared. The best-performing model achieved an R 2 value of 0. 9415, a RMSE of 6.99% and a MAE of 4.35%, demonstrating strong predictive accuracy for in vitro drug release. Additionally, feature importance analysis was conducted, offering insights into key factors influencing release behavior and guiding the rational design of PLGA-based microspheres.","url":"https://pubmed.ncbi.nlm.nih.gov/41726439/","authors":["Catapano AF","Zheng L","Yuan X","Yuan K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726436","name":"Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data.","source":"pubmed","abstract":"We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future suspected opioid overdoses recorded by Emergency Medical Services (EMS) in the state of Kentucky. Forecasts help government agencies properly prepare and distribute resources related to opioid overdoses. Our approach uses county and district level aggregations of suspected opioid overdose encounters and forecasts future counts for different time intervals. Models with different levels of complexity were evaluated to minimize forecasting error. A variety of additional covariates relevant to opioid overdoses and public health were tested to determine their impact on model performance. Our evaluation shows that useful predictions can be generated with limited error for different types of regions, and high performance can be achieved using commonly available covariates and relatively simple forecasting models.","url":"https://pubmed.ncbi.nlm.nih.gov/41726436/","authors":["Mullen AD","Harris DR","Rock P","Thompson K","Slavova S","Talbert J","Cody Bumgardner VK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726434","name":"Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge.","source":"pubmed","abstract":"This study explores clinicians' evolving information needs and evaluates the potential of Generative Artificial Intelligence (Gen AI) to address these gaps by reassessing and extending the Currie et al. (2003) taxonomy. Despite advancements in electronic health records (EHRs), unresolved information needs persist, impacting clinical efficiency and patient care. A cross-sectional survey conducted at Columbia University Irving Medical Center (CUIMC) analyzed clinician-generated Gen AI prompts, comparing them against the 2003 taxonomy. Findings reveal that while 80% of prompts align with existing categories, 20% represent emerging needs, including AI-driven workflow optimization and fairness-related inquiries. These findings highlight the necessity of adapting clinical decision support frameworks to integrate AI-driven solutions, ensuring that modern tools meet evolving clinician needs. By formally extending the Currie et al. taxonomy, this study provides a foundational framework for leveraging Gen AI to bridge long-standing information gaps and enhance patient outcomes in an increasingly complex healthcare environment.","url":"https://pubmed.ncbi.nlm.nih.gov/41726434/","authors":["Tuteja SK","Boventer EL","Alkattan A","Elhadad N","Rossetti SC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726433","name":"Humans and Large Language Models in Clinical Decision Support: A Study with Medical Calculators.","source":"pubmed","abstract":"Although large language models (LLMs) have been assessed for general medical knowledge using licensing exams, their ability to support clinical decision-making, such as selecting medical calculators, remains uncertain. We assessed nine LLMs, including open-source, proprietary, and domain-specific models, with 1,009 multiple-choice question-answer pairs across 35 clinical calculators and compared LLMs to humans on a subset of questions. While the highest-performing LLM, OpenAI's o1, provided an answer accuracy of 66.0% (CI: 56.7-75.3%) on the subset of 100 questions, two human annotators nominally outperformed LLMs with an average answer accuracy of 79.5% (CI: 73.5-85.0%). Ultimately, we evaluated medical trainees and LLMs in recommending medical calculators across clinical scenarios like risk stratification and diagnosis. With error analysis showing that the highest -performing LLMs continue to make mistakes in comprehension (49.3% of errors) and calculator knowledge (7.1% of errors), our findings highlight that LLMs are not superior to humans in calculator recommendation.","url":"https://pubmed.ncbi.nlm.nih.gov/41726433/","authors":["Wan NC","Jin Q","Chan J","Xiong G","Applebaum S","Gilson A","McMurry R","Andrew Taylor R","Zhang A","Chen Q","Lu Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726429","name":"Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence.","source":"pubmed","abstract":"Clinical trials are crucial for assessing new treatments; however, recruitment challenges-such as limited awareness, complex eligibility criteria, and referral barriers-hinder their success. With the growth of online platforms, patients, caregivers, and family members increasingly post medical cases on social media and health communities, while physicians publish case reports accessible on platforms like PubMed-collectively expanding recruitment pools beyond traditional clinical trial pathways. Recognizing this potential, we utilized TrialGPT, a framework that leverages a large language model, to match 50 online patient cases (collected from case reports and social media) to clinical trials and evaluate performance against traditional keyword-based searches. Our results show that TrialGPT outperformed traditional methods by 46%, with patients eligible, on average, for 7 of the top 10 recommended trials. Additionally, outreach to case authors and trial organizers yielded positive feedback. These findings highlight TrialGPT's potential to expand patient access to specialized care through non-traditional sources.","url":"https://pubmed.ncbi.nlm.nih.gov/41726429/","authors":["Chan J","Jin Q","Wan N","Floudas CS","Xue E","Lu Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726427","name":"Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems.","source":"pubmed","abstract":"Medical Question-Answering (QA) systems based on Retrieval-Augmented Generation (RAG) are promising for clinical decision support due to their capability to integrate external knowledge, thus reducing inaccuracies inherent in standalone large language models (LLMs). However, these systems may unintentionally propagate biases associated with sensitive demographic attributes like race, gender, and socioeconomic factors. This study systematically evaluates demographic biases within medical RAG pipelines across multiple QA benchmarks, including MedQA, MedMCQA, MMLU, and EquityMedQA. We quantify disparities in retrieval consistency and answer correctness by generating and analyzing queries sensitive to demographic variations. We further implement and compare several bias mitigation strategies-including Chain-of-Thought reasoning, Counterfactual filtering, Adversarial prompt refinement, and Majority Vote aggregation-to address identified biases. Experimental results reveal significant demographic disparities, highlighting that Majority Vote aggregation improves accuracy and fairness metrics. Our findings underscore the critical need for explicitly fairness-aware retrieval methods and prompt engineering strategies to develop truly equitable medical QA systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41726427/","authors":["Ji Y","Zhang H","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726424","name":"Interpretable Machine Learning to Identify Risk Factors for Recidivism in Intimate Partner Violence.","source":"pubmed","abstract":"Intimate Partner Violence (IPV) remains a significant global health issue with severe consequences ranging from physical injury to death, with rates rising in recent years. Prediction of recidivism is critical for prevention and treatment. Using data from a four-year clinical study, we develop interpretable machine-learning models to identify features for physical assault recidivism among IPV offenders. To standardize clinician-assigned severity scores and address non-linear associations, we apply filtered target encoding, which reduces subjectivity and bias in assessment. We find that combining self-reported and partner-reported variables enhances predictive power. Through feature importance analyses, we identify factors associated with lower recidivism risk, including decreased substance use and avoiding partner contact, while separation processes correlate with higher reoffending likelihood. These findings advance IPV risk assessment by providing a deeper understanding of risk factors critical for improving treatment effectiveness and addressing disparities in IPV management.","url":"https://pubmed.ncbi.nlm.nih.gov/41726424/","authors":["Ogğuztüzün Ç","Koyutürk M","Karakurt G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726415","name":"Measuring Accuracy of ConsultBot, Hybrid AI Tool, in Interpreting Blood Gas Results.","source":"pubmed","abstract":"Importance: Arterial and venous blood gases are complex tests used in inpatient settings. While interpretation of blood gas results is algorithmic, it is time consuming and error prone when done manually. Our study measures the accuracy of ConsultBot, in automated interpretation of blood gases. Objective: To determine proportional accuracy of ConsultBot, using rule-based logic combined with a large language model (LLM), in interpreting blood gases. Design: ConsultBot was tested in an IRB-approved single-arm trial using a dataset of 101 blood gas results and compared with a predetermined answer key. The tool's performance was assessed using proportional accuracy, sensitivity, and Cohen's Kappa. Results: ConsultBot achieved a 98% (99/101)[CI 93%-100%] proportional accuracy across the study dataset. Sensitivity was 98%[CI 93%-99%]. Cohen's Kappa of 0.97 suggests high degree of agreement between ConsultBot's interpretations and answer key. Conclusions: ConsultBot's evaluation yielded promising results, demonstrating potential in clinical-decision support for blood gas interpretations.","url":"https://pubmed.ncbi.nlm.nih.gov/41726415/","authors":["Meka P","Silvers CT","Gunapati B","Liu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726412","name":"A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.","source":"pubmed","abstract":"Background: Buprenorphine and methadone are effective medications for opioid use disorder (OUD) but remain underused, particularly when specialists are not leading care decisions. Objective: We developed a predictive model to guide treatment selection for OUD. Methods: Models predicted the probability of treatment response for each medication, which we defined as the absence of an adverse outcome during hospitalization and within 90 days of hospital discharge. Models considered electronic health record (EHR) and ZIP-level data. We constructed generalized linear regression, random forest, gradient boosted machines, and deep learning models and tested different combinations of EHR and ZIP-level data using early and late fusion methods. Results: EHR-only models performed better than ZIP-only models did. ZIP-level data did not significantly improve the performance of EHR-only models. Models consistently recommended buprenorphine over methadone. Conclusion: Future work should explore different approaches to modeling OUD treatment response and capturing relevant social and external factors.","url":"https://pubmed.ncbi.nlm.nih.gov/41726412/","authors":["Tang LA","Kast KA","Walsh CG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726411","name":"Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/41726411/","authors":["Zhang H","Lou Q","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726410","name":"Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide.","source":"pubmed","abstract":"Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.","url":"https://pubmed.ncbi.nlm.nih.gov/41726410/","authors":["Duan Z","Wei K","Xue Z","Zhou J","Yang S","Ma S","Jin J","Li L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726406","name":"Leveraging Large Language Models for Thyroid Nodule Information Extraction and Matching Across Medical Reports.","source":"pubmed","abstract":"Accurate extraction of thyroid nodule features from radiology and pathology reports is clinically essential for guiding patient management decisions, such as surgical intervention or active surveillance. However, manual data extraction from electronic health records is labor-intensive and prone to inter-rater variability. To address this challenge, we evaluated open-source large language models (LLMs) for automating the extraction and matching of these critical nodule features. Using a retrospective dataset of 451 ultrasound and pathology report pairs, we developed an annotation schema capturing nodule characteristics. Two LLMs-Llama-3.3 70B and QwQ-32B-were benchmarked against manual annotations. Both models demonstrated near-perfect extraction accuracy for clinically relevant features such as location, size, and biopsy results. Notably, QwQ-32B achieved an F1 score of 0.987 on the complex multi-step reasoning task of matching nodules across reports. Our findings suggest integrating LLMs into clinical annotation workflows can significantly reduce clinician workload and inter-rater variability while maintaining high accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/41726406/","authors":["Lee D","Amara D","Beon C","Swee S","Radhachandran A","Athreya S","Ivezic V","Arnold C","Speier W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726405","name":"DGSurv: Dynamic Graph-Based Multimodal Learning for Interpretable Cancer Survival Prediction.","source":"pubmed","abstract":"Multimodal learning in cancer research offers transformative potential for enhancing medical care and guiding clinical decisions. Most analyses rely on unimodal inputs or employ simplistic multimodal fusion techniques, which do not optimally integrate the diverse data types. Additionally, there is a critical need for enhanced interpretative methods to fully exploit the depth of multimodal patient data. To address these issues, we propose DGSurv, a novel multimodal learning approach that utilizes a graph neural network (GNN) to dynamically map inter-modality relationships for cancer survival prediction. We demonstrate the utility of our proposed approach on cancer survival prediction, highlighting its potential to inform more accurate clinical decision-making. We perform empirical evaluations on four cancer datasets from The Cancer Genome Atlas Program (TCGA) and demonstrate that DGSurv outperforms existing fusion techniques. For interpretability, our study advances multimodal cancer analysis by effectively harnessing the full spectrum of multimodal data and significantly boosting its interpretability.","url":"https://pubmed.ncbi.nlm.nih.gov/41726405/","authors":["Shahabi S","Cui Z","Liu R","Carlson J","Liu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726403","name":"AKI-Detector: A Multi-Agent Framework by Integrating Machine Learning and Large Language Models for Early Prediction of Acute Kidney Injury in ICU.","source":"pubmed","abstract":"Acute kidney injury (AKI) is a severe condition in the ICU, where early prediction is crucial for timely intervention and prevention. Traditional machine learning (ML) models lack interpretability, which limits real-world applicability. We propose AKI-Detector, a novel multi-agent framework that integrates structured electronic health records (EHR)-based ML models, large language models (LLMs), and retrieval-augmented generation (RAG) to enhance clinical reasoning, accuracy, and interpretability of AKI prediction. The proposed AKI-Detector mitigates LLM hallucinations by integrating ML models and bridges the gap between algorithmic output and clinically interpretable reports. Evaluated on ICU data from MIMIC-IV, AKI-Detector outperformed ML models such as CatBoost and GRU, and achieved an accuracy of 0.827, precision of 0.672, recall of 0.542, and F1-score of 0.600 on the test cohort, demonstrating balanced and reliable predictive performance. This work highlights the promise of real-world big data and LLM-powered multi-agent systems to support trustworthy and explainable AI for clinical prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/41726403/","authors":["Shi T","Xiao M","Xu H","Zhao H","Kong G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726396","name":"Machine Learning-Based Prediction of Antimicrobial Susceptibility: A Step Towards Precision Antimicrobial Stewardship.","source":"pubmed","abstract":"Antimicrobial resistance (AMR) represents an urgent global health crisis exacerbated by the frequent empirical use of broad-spectrum antibiotics. AMR is exacerbated by inherent delays in obtaining culture results and antimicrobial susceptibility data after sample collection. In this study, we developed and validated Machine Learning (ML) models using routinely collected EHR data from inpatient and outpatient encounters to predict antibiotic resistance at the time of blood, urine or respiratory bacterial culture collection. The models demonstrated robust predictive accuracy, particularly in inpatient settings where clinical data was more consistently available. Notably, the model independently identified patterns that predict resistance, similar to how a clinician would attempt to predict resistance using prior culture and susceptibility data combined with their clinical training and knowledge of microbiological resistance patterns. Integrating these predictive tools into clinical workflows could significantly enhance empirical antibiotic selection, reduce unnecessary broad-spectrum antibiotic use, and meaningfully advance antimicrobial stewardship efforts.","url":"https://pubmed.ncbi.nlm.nih.gov/41726396/","authors":["Amrollahi F","Haredasht FN","Vansomphone A","Marshall N","Maddali MV","Ma SP","Chang A","Deresinski SC","Goldstein MK","Kanjilal S","Medford RJ","Cooper LN","Asch SM","Banaei N","Chen JH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726393","name":"Using LLMs to Interpret Arterial Blood Gases: Comparison of a Novel Math Scratchpad with Different Prompting Methods in a Three-Arm Trial.","source":"pubmed","abstract":"Large language models (LLMs) have demonstrated proficiency in various tasks, yet their effectiveness in a clinical decision support system (CDSS) is evolving. One challenge is their limited ability to perform calculations. This study evaluates a novel method of using a custom math scratchpad to perform domain-specific calculations in interpreting Arterial Blood Gases (ABGs). Three methods are compared: zero-shot prompting (Method 1), prompt engineering with Retrieval-Augmented Generation (RAG) (Method 2), and a combined novel math scratchpad, RAG, and prompt engineering (Method 3). The LLM-integrated CDSS achieved an accuracy rate of 86% (43/50) [confidence interval (CI) 74% - 93%] across a dataset of 50 ABG results when utilizing Method 3, compared to 78% (39/50) [CI 65% - 87%] for Method 2 and 48% (24/50) [CI 35% - 61%] for Method 1. The evaluation demonstrates a math scratchpad's utility in ABG interpretation by overcoming LLMs' calculation limitation. Further testing with real-world patient ABG data is needed .","url":"https://pubmed.ncbi.nlm.nih.gov/41726393/","authors":["Meka P","Silvers CT","Gunapati B","Liu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726096","name":"A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.","source":"pubmed","abstract":"Emphysema, a diffuse and heterogeneous phenotype of chronic obstructive pulmonary disease (COPD), carries substantial morbidity and elevates lung cancer risk. While computed tomography (CT) aids in detection and monitoring, current deep learning methods depend on large annotated datasets. Unsupervised anomaly detection (UAD) provides an alternative but faces challenges with emphysema anomalies and weak emphysema semantics. In this study, we propose a self-supervised framework trained exclusively on non-emphysema CT scans using synthetically generated lesions to guide pixel-level anomaly modeling. We introduce EDLNet, an encoder-decoder architecture with spatial-channel refinement and adaptive feature fusion for emphysema detection and localization, followed by an unsupervised manner for emphysema staging. Multi-center evaluations show that our framework outperforms existing UAD approaches in detection and localization, while achieving a mean staging accuracy of 93.13% and a macro AUROC of 99.08%. This approach bridges clinical knowledge and artificial intelligence, offering a scalable and interpretable solution for lung disease analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/41726096/","authors":["Zhang X","Zhao M","Yao F","Ma W","Zhang J","Li Y","Zhou X","Guan Y","Xiao Y","Fan L","Zhou SK","Liu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 13","doi":"10.1016/j.patter.2025.101426","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41726045","name":"Diagnostic Performance of a Novel AI-Guided Coronary Computed Tomography Algorithm for Predicting Myocardial Ischemia (AI-QCT(ISCHEMIA)) Across Sex and Age Subgroups.","source":"pubmed","abstract":"AI-QCT ISCHEMIA is a novel artificial intelligence algorithm that predicts myocardial ischemia using quantitative features from coronary computed tomography angiography, providing a noninvasive alternative to functional imaging. However, its diagnostic performance across key demographic subgroups, particularly by sex and age, remains underexplored. We aimed to evaluate the diagnostic performance of AI-QCT ISCHEMIA for predicting myocardial ischemia across these subgroups.","url":"https://pubmed.ncbi.nlm.nih.gov/41726045/","authors":["Kamila PA","Hojjati T","Nurmohamed NS","Danad I","Ding Y","Jukema RA","Raijmakers PG","Driessen RS","Bom MJ","van Diemen P","Pontone G","Andreini D","Chang HJ","Katz RJ","Choi AD","Knaapen P","Bax JJ","van Rosendael A","CREDENCE and PACIFIC-1 Investigators"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.jscai.2025.104064","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41725917","name":"Role of artificial intelligence in determining nutritional risk factors among post-periodontal surgical patients. A scoping review.","source":"pubmed","abstract":"This scoping review examines recent peer-reviewed literature (2019-2025) on the role of artificial intelligence (AI) in managing nutrition care for post-periodontal surgical patients, and identifies key risk factors influencing nutritional outcomes after periodontal surgery. AI modalities considered include machine learning, expert systems, clinical decision support, and predictive analytics.","url":"https://pubmed.ncbi.nlm.nih.gov/41725917/","authors":["Varma SR","Natarajan P","Narayanan JK","Odeh R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/froh.2026.1748346","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41725833","name":"Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.","source":"pubmed","abstract":"Artificial intelligence-powered large language models (LLMs) are increasingly applied in health care. However, studies in ophthalmology assessing whether LLMs can improve the accuracy of complex differential diagnoses in clinical cases, or which levels of clinical experience benefit most from their use, remain lacking. This study assessed the effectiveness of ChatGPT-4o, an LLM-driven chatbot, in enhancing ophthalmologists' clinical reasoning using original scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/41725833/","authors":["Inooka T","Ota H","Taki Y","Yasuda S","Sajiki AF","Suzumura A","Shimizu H","Takeuchi J","Tomita R","Kominami T","Ushida H","Yuki K","Nishiguchi KM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.xops.2025.101004","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41725702","name":"Attitude and perception toward artificial intelligence among German physicians with intensive care experience: a survey study.","source":"pubmed","abstract":"The applications of artificial intelligence (AI) in healthcare are very diverse. AI-based systems can assist with diagnosis and decision-making, particularly in intensive care medicine. However, physicians must accept these systems to fully exploit their potential. We investigated attitude and perception toward AI among physicians with intensive care experience.","url":"https://pubmed.ncbi.nlm.nih.gov/41725702/","authors":["Giebel GD","Raszke P","Tokic M","Palmowski L","Timmesfeld N","Nowak H","Adamzik M","Heinz P","Mreyen S","Brunkhorst FM","Wasem J","Buchner F","Blase N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/frhs.2025.1721620","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41725549","name":"Ethical and legal challenges of integrating artificial intelligence into paediatric surgery.","source":"pubmed","abstract":"Artificial intelligence (AI) technologies are increasingly being trialled with applications spanning imaging, robotic assistance and early risk prediction. Children's unique legal status presents distinct ethical and legal concerns. The objective of this review was to outline key ethical and legal challenges from AI integration into paediatric surgery and propose practical strategies for clinicians and policymakers.","url":"https://pubmed.ncbi.nlm.nih.gov/41725549/","authors":["Anand A","Sinha CK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 23","doi":"10.1308/rcsann.2025.0110","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41725376","name":"Artificial intelligence as a diagnostic support tool in hysteroscopy: current evidence and clinical implications.","source":"pubmed","abstract":"Hysteroscopy allows direct inspection of the uterine cavity for many conditions. Despite being widely adopted, its diagnostic accuracy largely depends on surgeon expertise, leading to potentially misleading diagnoses. Artificial intelligence (AI) has shown robust performance in many areas of medical imaging. The application of AI to hysteroscopy can improve diagnostic reliability and clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41725376/","authors":["Ferrari FA","Bogani G","Pavone M","Golia D'Augè T","Bourdel N","Soleymani Majd H","Ferrari F","Ceccaroni M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 23","doi":"10.1080/13645706.2026.2635049","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41724966","name":"Parenchymatous invasive renal pelvis carcinoma in retrograde intrarenal surgery patients: a case report.","source":"pubmed","abstract":"BACKGROUND: The concomitant presentation of upper urinary tract calculi and renal pelvis urothelial carcinoma is exceptionally rare in clinical practice. Parenchymal-invasive renal pelvis carcinoma often exhibits atypical imaging features, further complicating diagnosis. As both conditions share haematuria as a cardinal symptom, this overlap frequently leads to the misdiagnosis of one entity for the other. CASE PRESENTATION: We reported the symptoms and diagnosis of two cases of upper urinary tract stones with renal pelvis cancer that detected between May 2023 and December 2024. Two patients were newly diagnosed with upper urinary tract calculi for the first time. Lumbar pain was the primary symptom in both patients. Preoperative abdominal computed tomography (CT) showed only localized low-density soft tissue mass of the renal parenchyma (Tis or T1 stage), which was interpreted as focal inflammatory changes in the renal cortex due to combined renal pelvis infection, hydronephrosis, and nephrolithiasis. An absence of metastases was detected on CTU. Both patients subsequently underwent radical tumor surgery, and postoperative pathology confirmed high-grade invasive urothelial carcinoma in both patients. CONCLUSION: Atypical imaging findings of upper urinary tract stones with renal pelvic cancer is often overlooked or misdiagnosed as infectious diseases. Abdominal CT may fail to detect small renal pelvic tumors. Renal pelvic cancer should be suspected in patients with papillary tumors observed during RIRS. Evaluation of the renal pelvic mucosa should include local biopsy, CTU, and urinary exfoliative cytology. Radical surgery is indicated for such cases.","url":"https://pubmed.ncbi.nlm.nih.gov/41724966/","authors":["Xu Y","Xiong L","Cao R","Zheng C","An H","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 23","doi":"10.1186/s12894-026-02097-2","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41724537","name":"Optical diagnosis of histopathology- is it implementable in the world of artificial intelligence?","source":"pubmed","abstract":"Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in the United States, with colonoscopy serving as the gold standard for both diagnosis and early intervention. While diminutive polyps (&lt;5&#xa0;mm) constitute most findings, only a small fraction exhibit advanced histological features. Optical diagnosis, which enables real-time classification of polyp histology through new technologies and the support of new strategies to leave low risk polyps in place (diagnose-and-leave) or resect without sending for formal pathology (resect-and-discard) have been studied as a cost-saving and effective strategy for diminutive polyps. There have been advances in imaging, such as narrow band imaging (NBI), but widespread adoption has yet to occur. The integration of artificial intelligence (AI), particularly computer-aided diagnosis (CADx) systems, has emerged as a promising tool to standardize optical diagnosis, reduce interobserver variability, and improve adherence to surveillance guidelines. However, barriers to widespread implementation persist, including concerns about medicolegal liability, financial disincentives, and skepticism of CADx accuracy. The goal of article is to review the current evidence surrounding optical diagnosis, review diagnostic accuracy, and evaluate the challenges of widespread clinical adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/41724537/","authors":["Cheloff AZ","Chetlur P","Kagan EB","Gross SA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.bpg.2025.102044","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41723890","name":"Artificial Intelligence Implementation in Transfusion Medicine: Addressing the Challenges of Clinical Adoption.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) are increasingly promoted to enhance transfusion and patient blood management, yet real-world implementation remains rare. We reviewed recent exemplar studies reporting prospective deployment with workflow integration to examine translational features, barriers, and enablers of AI/ML integration. On June 18, 2025, we searched PubMed and Web of Science for articles from January 2022 onward. Of 1243 records screened and 31 full texts reviewed, 3 studies met inclusion criteria. The exemplars comprised: (1) a laboratory-embedded tool predicting low ferritin in anemic adults, which during a 21-day deployment identified additional iron deficiency relevant to pretransfusion optimization; (2) a patient-facing smartphone application estimating hemoglobin from fingernail images, adopted nationally by &gt;200,000 users with potential implications for anemia screening; and (3) a clinician-facing smartphone decision support tool predicting resuscitation needs in trauma, piloted across 5 centers with acceptable feasibility and user satisfaction in a transfusion-intensive setting. Common enablers included alignment with clinical need, use of existing data infrastructure, interpretable tree-based models, and early stakeholder engagement. Persistent barriers were data quality and governance, limited generalizability, and absence of economic evaluation. Importantly, no study demonstrated improvement in clinical outcomes or cost. For clinical adoption, AI tools must integrate into routine workflows with clear safety, monitoring, and regulatory plans. Future research should apply implementation frameworks from the outset, evaluate downstream impact on transfusion practice and outcomes, and prioritize scalable approaches such as laboratory-embedded analytics, interoperable decision support, and patient-centered digital tools.","url":"https://pubmed.ncbi.nlm.nih.gov/41723890/","authors":["Maynard S","Farrington J","Raza S","Stanworth SJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.tmrv.2026.150961","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41722291","name":"Entropy enhanced infrared thermography: A radiation free paradigm for precision screening of adolescent idiopathic scoliosis.","source":"pubmed","abstract":"Adolescent idiopathic scoliosis (AIS) is a significant challenge to global paediatric health. Early detection and longitudinal monitoring are critical for initiating non operative interventions and preventing irreversible skeletal complications. The aim of this study was to evaluate the efficacy of infrared thermography (IRT) entropy as a non invasive screening technique for adolescent idiopathic scoliosis. To this end, we recruited 203 patients with idiopathic scoliosis and 105 healthy controls, all aged 8 to 18 years. The analysis examined differences in back entropy and temperature values between the idiopathic scoliosis and control groups, whilst calculating the area under the receiver operating characteristic (ROC) curve to assess classification performance. Data analysis was conducted between February and July 2025. A significant disparity was identified between the idiopathic scoliosis group and the control group in both back entropy and temperature values (P&#xa0;&lt;&#xa0;0.05), with idiopathic scoliosis patients exhibiting markedly reduced entropy and significantly elevated temperatures (for example, in the upper right (UR) region, entropy decreased from 2.63&#xa0;&#xb1;&#xa0;0.46 bits in the control group to 1.79&#xa0;&#xb1;&#xa0;0.41 bits in the left convex, while temperature increased from 36.1&#xa0;&#xb1;&#xa0;0.7&#xa0;&#xb0;C to 36.5&#xa0;&#xb1;&#xa0;0.1&#xa0;&#xb0;C). Intragroup analysis revealed good symmetry in both entropy and temperature between the left and right sides of the back in the control group (P&#xa0;&gt;&#xa0;0.05). Conversely, both entropy and temperature values exhibited significant left right asymmetry in the idiopathic scoliosis group (P&#xa0;&lt;&#xa0;0.05). ROC curve analysis demonstrated that entropy had good discriminatory ability in distinguishing AIS patients from controls, suggesting its potential utility as an adjunctive screening tool. This approach, being radiation free, effectively addresses the limitation of cumulative radiation exposure inherent in current X ray dependent detection protocols.","url":"https://pubmed.ncbi.nlm.nih.gov/41722291/","authors":["Du N","Cui S","Hu C","Wang S","Hu F","Yang X","Liu W","Yu JG","Zhang X","Song Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.jtherbio.2026.104430","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41722236","name":"AI literacy, information cocoons, and creativity in healthcare postgraduate education: A latent profile and mediation analysis.","source":"pubmed","abstract":"This study aimed to identify latent subtypes of AI literacy among full-time healthcare postgraduate in China, including those specializing in clinical medicine, nursing, basic medicine, and related medical fields, and to examine the mediating role of information cocoons in the relationship between AI literacy types and creativity.","url":"https://pubmed.ncbi.nlm.nih.gov/41722236/","authors":["Li X","Chen D","Wang S","Wang C","Wang S","Kong C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.nedt.2026.107045","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41721639","name":"Renal Protection at a Metabolic Cost: A Systematic Review and Meta-Analysis of Perioperative Use of Sodium-Glucose Cotransporter 2 Inhibitors.","source":"pubmed","abstract":"Concerns about diabetic ketoacidosis (DKA) and euglycemic ketoacidosis (eKA) are balanced against possible organ-protective benefits in the debated perioperative management of sodium-glucose cotransporter-2 (SGLT2) inhibitors. This meta-analysis compared the perioperative clinical and laboratory outcomes associated with perioperative exposure to SGLT2i.","url":"https://pubmed.ncbi.nlm.nih.gov/41721639/","authors":["Balbaa E","Gadelmawla AF","Ibrahim A","Manasrah A","Elbataa A","Shubietah A","Elgendy MS","Sobhy A","Mansour A","Awashra A","Elguindy NN","Bazzazeh M","Ben-Selma A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1002/edm2.70180","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41721322","name":"Development and preliminary evaluation of a virtual standardized patient system for psychiatric interview training.","source":"pubmed","abstract":"BACKGROUND: Limitations of traditional standardized patients exist with regard to psychiatric interview training, and there is a pressing need to develop more effective tools. The aim of this study is to develop and conduct a preliminary evaluation of a virtual standardized patient (VSP) system designed for this purpose. METHODS: This study comprised two primary components: (1) the development of a VSP system for psychiatry interviews; and (2) a randomized controlled trial (RCT) to evaluate its effectiveness. An experienced psychiatrist drafted the initial case interview scripts that were subsequently refined using a modified Delphi method to establish the final versions. The VSP system was then constructed using a large language model (LLM) of ERNIE 3.5&#x2013;8&#xa0;K. We then recruited 20 valid medical students who were randomly allocated to either the intervention group or control group for the RCT. Both groups completed a 20 class hours training. A participant&#x2019;s clinical interview skills were assessed before and after the intervention, while the intervention group additionally provided feedback on their VSP user experience. RESULTS: The developed VSP system incorporated three psychiatric cases. It demonstrated good consistency upon expert evaluation. After the intervention, the VSP group demonstrated significantly higher clinical interview scores compared to the controls. Additionally, the participants rated the VSP system as having good usability, with particular strengths in operational ease and effectiveness for clinical interview training. CONCLUSIONS: In this study, we developed a VSP system for psychiatric interview training. The preliminary validation demonstrated its efficacy for enhancing medical student clinical interview skills. This system provides valuable insights for the development of innovative psychiatric education tools. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://pubmed.ncbi.nlm.nih.gov/41721322/","authors":["Li Y","Wang G","Huang Z","Ke X","Qin J","Yu J","Liu S","Li J","Xu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 20","doi":"10.1186/s12888-026-07896-3","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41721275","name":"Prediction of immune checkpoint inhibitor-related pneumonitis in lung cancer: development and validation of multiple machine learning models.","source":"pubmed","abstract":"OBJECTIVES: This study aims to develop and validate combined radiomics machine learning (ML) models and clinical semantic feature models to accurately predict immune checkpoint inhibitor-related pneumonitis (CIP) risk in lung cancer patients receiving immunotherapy. Additionally, it seeks to create multiple risk assessment tools for early CIP detection, enhancing patient management and outcomes. METHODS: From August 2020 to September 2024, candidate predictors were obtained from 210 patients receiving immunotherapy for lung cancer. These predictors included clinical semantic features, blood markers, and imaging histology features. The outcomes for CIP were derived from electronic medical records as well as imaging assessments. Five machine learning algorithms were utilized to develop models and compare their predictive performance. RESULTS: A total of four potential predictors related to CIP were identified and used to construct a clinical semantic model. The radiomics model constructed based on the support vector machine (SVM) algorithm showed better agreement between the train and test cohorts. The combined model (Area under the curve [AUC], 0.933 and 0.909, respectively) had the best predictive performance. The combined model greatly improved the predictive performance of the CIP. Decision curve analysis (DCA) showed improvement of the combined model in cohorts. Nomograms and Shapley additive explanation of the risk prediction model used to visualize and interpret early predictions of CIP. CONCLUSION: The combined model constructed by integrating CT radiomics features and clinical semantic features demonstrated the optimal predictive performance. The combined model for early prediction and screening of CIP has the potential for broader clinical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41721275/","authors":["Li Y","Ji Y","Wang C","Qin C","Yu K","Liu L","Chen J","Meng W","Zhang T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 20","doi":"10.1186/s12885-026-15758-0","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41720937","name":"Hallucination filtering in radiology vision-language models using discrete semantic entropy.","source":"pubmed","abstract":"To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs) in radiologic image-based visual question answering (VQA).","url":"https://pubmed.ncbi.nlm.nih.gov/41720937/","authors":["Wienholt P","Caselitz S","Siepmann R","Bruners P","Bressem K","Kuhl C","Kather JN","Nebelung S","Truhn D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1007/s00330-026-12384-z","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41720545","name":"How Technology Can Increase Access to Sleep Health Care.","source":"pubmed","abstract":"Sleep disorders, like insomnia and sleep apnea, affect millions globally and negatively impact health and well-being. Traditional barriers such as geographic location, cost, limited specialists, lack of awareness of sleep disorders, and stigma have restricted access to sleep care. However, advancements in technology, including telemedicine, home diagnostic and screening tools, consumer sleep technologies, digital health platforms, and artificial intelligence, are breaking down these barriers. These innovations improve diagnosis, treatment, and education, enhancing patient outcomes. As these technologies evolve, they promise better health outcomes and a higher quality of life for individuals worldwide.","url":"https://pubmed.ncbi.nlm.nih.gov/41720545/","authors":["Watson NF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.jsmc.2025.10.011","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41719848","name":"Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.","source":"pubmed","abstract":"The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrate multimodal data to construct predictive models, but the methodological quality, risk of bias and clinical applicability of such models have not been systematically evaluated.","url":"https://pubmed.ncbi.nlm.nih.gov/41719848/","authors":["Liu W","Feng Z","Zhang M","Mao R","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 1","doi":"10.1016/j.ijmedinf.2026.106345","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41719779","name":"From pixels to prognosis: A QUADAS-2-Guided systematic review and meta-analysis of deep learning segmentation for DLBCL in PET and PET/CT.","source":"pubmed","abstract":"This systematic review and meta-analysis evaluated the performance and methodological quality of deep learning models for automated segmentation of Diffuse Large B-Cell Lymphoma (DLBCL) on PET/CT imaging. A comprehensive literature search identified 15 eligible studies that were published up to July 2025. Of these, 11 studies were included in the quantitative synthesis, while 4 were assessed qualitatively. Using a random-effects model, the pooled mean DSC was 0.809 (95% CI: 0.791-0.827), indicating strong overall segmentation performance. The reported DSC values across the individual studies ranged from 0.65 to 0.886. Single-center studies generally showed slightly higher median DSC values (&#x2248;0.82) than multi-center studies (&#x2248;0.78), although pooled subgroup analyses revealed comparable averages (0.77 vs. 0.73). Methodological quality, assessed using the QUADAS-2 tool, showed that most studies (approximately 67-73%) were at low risk of bias, with the remainder classified as moderate or unclear. Despite the variability in algorithms, study designs, and datasets, DL-based methods have consistently achieved reliable segmentation accuracy. Overall, DL models demonstrated promising potential for automated DLBCL segmentation in PET/CT imaging. Nevertheless, future studies should focus on larger and more diverse cohorts, improved reporting standards, and transparent handling of methodological limitations to enhance generalizability and clinical applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/41719779/","authors":["Keshavarz S","Saeedzadeh E","Arabi H","Sardari D","Jenabi-Haghparast E","Dadgar H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ctarc.2026.101144","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41719483","name":"AI and Digital Tools in Dermatology: Addressing Access and Misinformation.","source":"pubmed","abstract":"Digital dermatology, which is defined as the use of digital technologies that leverage individual- and population-level skin data to improve the diagnosis, treatment, and prevention of skin diseases, has emerged as a critical frontier for bridging persistent gaps in dermatologic care. This transformation holds particular promise for addressing long-standing inequities linked to geography, income, and skin type. According to the Global Burden of Disease 2023 study, skin and subcutaneous diseases remain among the most prevalent global health conditions, contributing substantially to disability-adjusted life years. Digital tools (including teledermatology, artificial intelligence [AI], and large language models) offer new ways to extend diagnosis, education, and patient empowerment to historically underserved populations. However, these same innovations risk amplifying disparities if they are not designed and deployed intentionally. Algorithmic bias, uneven digital access, and the absence of culturally responsive models can undermine progress. In this conceptual and narrative review, we draw on expert dialogues and illustrative literature, including multistakeholder exchanges at the Skin and Digital Summit (2023-2025) and related global forums, to examine how digital dermatology can promote equitable skin health. We focus on 3 interlinked priorities: expanding access through scalable digital platforms, ensuring AI fairness via comprehensive and diverse datasets, and countering dermatological misinformation. Central to the latter is a bot concept described here as a dynamic cycle that analyzes scientific literature; ranks evidence; translates complex research into clear language; and delivers trustworthy, personalized guidance to both consumers and clinicians. By embedding expert oversight and evidence prioritization, such tools can ensure that accurate, actionable information reaches users at the speed and scale of the internet. Drawing on case studies (including lessons from the World Health Organization's AI skin health app) and insights from the Skin and Digital Summit, we highlight both the transformative potential and the ethical complexities of these digital solutions. To navigate this evolving landscape, we propose the concept of radical dermatology, which confronts the reality that big tech is reshaping skin health whether we like it or not and insists that dermatologists and stakeholders lead the transformation through bold collaboration and unwavering clinical relevance.","url":"https://pubmed.ncbi.nlm.nih.gov/41719483/","authors":["du Crest D","Madhumita M","Enbiale W","Ruiz Postigo JA","Malvehy J","Wongvibulsin S","Gupta S","Kittler H","Skayem C","Mahto A","Adamson AS","Lipoff JB","Papier A","Cartier H","Garson S","Freeman E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 20","doi":"10.2196/79044","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41719115","name":"Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"IntroductionMachine learning (ML)-based analysis of cell-free DNA (cfDNA) has emerged as a promising strategy for multi-cancer early detection (MCED). However, reported diagnostic performance varies widely across studies, and many estimates are derived from training or enriched cohorts, limiting their relevance to independent validation and real-world settings.MethodsWe conducted a systematic review and diagnostic accuracy meta-analysis of ML-based cfDNA assays for MCED. Four databases (PubMed, Embase, Web of Science, and the Cochrane Library) were searched from inception to February 2, 2025. Only independent validation or testing datasets were included; all training datasets were excluded. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves were estimated using a bivariate random-effects model. Subgroup analyses and meta-regression were performed to explore sources of heterogeneity.ResultsThirteen studies comprising 23 independent datasets and 14,892 participants were included. The pooled sensitivity was 0.78 (95% CI: 0.66-0.87), and the pooled specificity was 0.96 (95% CI: 0.90-0.98). The summary area under the curve (AUC) was 0.94, with a DOR of 76.6. Substantial between-study heterogeneity was observed ( I 2 &#x2009;&gt;&#x2009;90%), with geographic region, sample size, and cfDNA biomarker type identified as major contributing factors.ConclusionML-based cfDNA assays demonstrate consistently high specificity and moderate-to-high sensitivity across independent validation datasets, supporting their potential role in multi-cancer early detection. However, diagnostic performance is highly context dependent and strongly influenced by study design, population characteristics, and analytical choices. These findings highlight the need for large-scale, prospective, population-based validation before widespread clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/41719115/","authors":["Li Q","Liu H","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/15330338261425328","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41718798","name":"Data science applications for non-communicable disease prevention and control in Africa: a systematic review protocol.","source":"pubmed","abstract":"This systematic review protocol describes the proposed method for evaluating evidence from original articles to understand the benefits, opportunities, and challenges of using data science for non-communicable disease (NCD) prevention and control in Africa.","url":"https://pubmed.ncbi.nlm.nih.gov/41718798/","authors":["Okekunle AP","Olaiya MT","Olowoyo P","Ajuwon G","Kinengyere AA","Kumuthini J","Asowata OJ","Akpa OM","Fakunle A","Baichoo S","Odedina F","Hamdi Y","Agyemang C","Ovbiagele B","Owolabi M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 28","doi":"10.5830/CVJA-2025-085","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41718686","name":"A train-the-trainer curriculum to scale up artificial intelligence-supported echocardiography for rheumatic heart disease screening in a public health care system.","source":"pubmed","abstract":"This study aimed to test a train-the-trainer model for scaling up echocardiographic screening for rheumatic heart disease (RHD).","url":"https://pubmed.ncbi.nlm.nih.gov/41718686/","authors":["Nakagaayi D","Pulle J","Atala J","Oyella LM","de Loizaga S","Minja NW","Ollberding NJ","Fall N","Nakitto M","Sarnacki R","Rwebembera J","Okello E","Beaton A","Sable C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.5830/CVJA-2025-077","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41718253","name":"Applications of Large Language Models in Glaucoma: A Scoping Review.","source":"pubmed","abstract":"Background : Large language models (LLMs) and vision-language models (VLMs) have recently been applied to ophthalmology for patient education, diagnosis, and surgical decision support. Their ability to generate, interpret, and synthesize medical information positions them as promising assistive tools in glaucoma care. This scoping review aims to consolidate current evidence on the applications of LLMs and VLMSs in glaucoma, summarizing their tasks, inputs, performance metrics, and limitations to guide future clinical and research developments. Methods : A systematic search was conducted in PubMed, Scopus, Web of Science, arXiv, and IEEE Xplore from 2014 to July 2025. Eligible studies included original research and research letters employing LLMs or VLMs/MM-LLMs in any glaucoma-related application, including diagnostic reasoning, image interpretation, patient education, or surgical decision support. Screening and full-text review were independently performed by two reviewers following PRISMA-ScR methodology, with discrepancies resolved by consensus. Results : In total, 316 records were identified across five databases, with 27 studies meeting the inclusion criteria. The selected studies focused on three main domains: patient education (n = 11), diagnosis and risk prediction (n = 10), and surgical management (n = 6). Conclusions : Current LLMs serve best as assistive rather than autonomous tools in glaucoma care. They demonstrate strong potential in patient communication and text-based clinical decision support but remain constrained by variable accuracy, limited multimodal integration, and a lack of ophthalmology-specific fine-tuning. Future research should focus on developing domain-trained and retrieval-augmented LLMs, enhancing multimodal (text-image) fusion, ensuring readability adaptation for patients, and establishing ethical and regulatory frameworks for clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/41718253/","authors":["Rubegni G","Cartocci A","Luschi A","Castellino N","Cappellani F","Romano D","Colizzi B","Rossetti L","Tosi GM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.3390/vision10010009","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41717367","name":"Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly integrated into functional food research. This quasi-systematic review analyzes 53 peer-reviewed studies (2015-2025) to outline current applications and emerging directions, including the underexplored domain of antioxidant food development. The review attempts to provide an updated synthesis of AI approaches across compound discovery, metabolomics, and consumer modeling, emphasizing knowledge gaps and opportunities for methodological integration. Data-driven AI (classical machine learning) and deep learning methods have been applied to predict antioxidant activity, identify bioactive compounds, and reveal patterns in metabolomic data. Unsupervised approaches have assisted in clustering complex datasets, whereas optimization algorithms supported the adjustment of sensory, nutritional, and functional attributes. However, many current systems remain limited to in silico findings, lacking experimental or clinical validation. Consumer modeling remains largely predictive, with limited integration of ethical and regulatory dimensions. Continued collaboration between food scientists and data scientists is essential for translating computational insights into practical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41717367/","authors":["Alkalbani N","Shahin L","Benzeghiba H","Obaid RS","Osaili TM","Cheik Ismail L","Al Qasssimi G","Rauf M","Abdulrahim K","Almashgouni A","Ashuweihi F","Al-Fuqaha D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.fochx.2026.103628","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41717352","name":"Mental health chatbots and their technical features: A systematic review of reviews and a thematic analysis.","source":"pubmed","abstract":"Mental health is a global issue, and mobile applications, such as chatbots, offer a partial solution by providing improved services through various communication forms. This study aimed to identify chatbots and their technical features in mental health services. This study conducted a systematic review of mental health chatbots and their technical features from 2000 to 2025. A search was performed across databases such as PubMed, Scopus, ProQuest and the Cochrane database. The CASP (Critical Appraisal Skills Programme) appraisal checklist was used to assess the quality of the studies. In the next step, the Braun and Clarke's approach was utilized for conducting thematic analysis on the data. The search yielded 2,921 records, of which 10 were duplicates and removed. After screening for relevance and eligibility, 33 papers met all the requirements. The mean quality score of the included studies was 13.36 (standard deviation&#xa0;=&#xa0;1.36). The studies had a moderate risk of bias, as they mostly had a clear question, searched for the right type of papers, included all relevant papers and reported the results precisely. The research conducted an analysis of 138 mental health chatbots, categorizing them based on five distinct attributes: the disorder they target, their input and output modalities, the platform they operate on and their method of generating responses. The research emphasized the need for designing chatbots that suit patients' preferences and needs, and also indicated that the digital divide within societies should be taken into account when designing and producing chatbots for mental health services. Although mental health chatbots can assist underserved communities, ethical concerns must be addressed before their deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/41717352/","authors":["Khosravi M","Izadi R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1017/gmh.2026.10144","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41716518","name":"Artificial intelligence-guided nanoparticle design for advanced targeted drug delivery.","source":"pubmed","abstract":"This editorial aimed to explore the critical role of artificial intelligence (AI) in accurately predicting the structural design of nanoparticles (NPs) during targeted therapy of diseases. Based on experience, it is always surprising that perfect control of NP properties-including size, zeta potential, type, and surface modifications-using smart tools, will be more critical for optimal outcomes than trial and error. It is envisioned that the AI will change the game by predicting NPs' behavior, optimizing formulations, and speeding up clinical trials via the use of supervised learning, deep neural networks, graph neural networks, and generative models. In this context, various AI have led to an increase in drug loading efficiency and mRNA medication delivery. To achieve personalized therapy using NPs, however, issues including data quality, model interpretability, ethical frameworks, and multidisciplinary cooperation should be resolved. To enhance human knowledge and facilitate safer and more precise advancements in healthcare, this editorial urges the proper integration of AI in pharmaceutical/medical nanotechnology.","url":"https://pubmed.ncbi.nlm.nih.gov/41716518/","authors":["Eskandani M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.34172/bi.33066","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41716356","name":"Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis.","source":"pubmed","abstract":"Atopic dermatitis (AD) is a prevalent, chronic inflammatory skin disease with diverse clinical presentations, often overlapping with other dermatoses. Its diagnosis remains largely dependent on clinical expertise, leading to variability and limited diagnostic accuracy, particularly among general practitioners. This study aimed to develop and evaluate a multimodal artificial intelligence (AI) model that integrates lesion image analysis and structured anamnesis to improve AD diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41716356/","authors":["Widiawaty A","Indriatmi W","Jatmiko W","Novianto E","Kekalih A","Gunawan H","Satria Palar P","Febrian Rachmadi M","Dermawan S","Laras Malahayati T","Wicaksana Ramadhan A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.169102.2","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41716237","name":"Integrated electro-optic digital-to-analog link for efficient computing and arbitrary waveform generation.","source":"pubmed","abstract":"The rapid growth in artificial intelligence and modern communication systems demands innovative solutions for increased computational power and advanced signaling capabilities. Integrated photonics, leveraging the analog nature of electromagnetic waves at the chip scale, offers a promising complement to approaches based on digital electronics. To fully unlock their potential as analog processors, establishing a common technological base between conventional digital electronic systems and analog photonics is imperative to building next-generation computing and communications hardware. However, the absence of an efficient interface has critically challenged comprehensive demonstrations of analog advantage thus far, with the scalability, speed, and energy consumption as primary bottlenecks. Here, we address this challenge and demonstrate a general electro-optic digital-to-analog link (EO-DiAL) enabled by foundry-based lithium niobate nanophotonics. Using purely digital inputs, we achieve on-demand generation of (i) optical and (ii) electronic waveforms at information rates up to 186 Gbit/s. The former addresses the digital-to-analog electro-optic conversion challenge in photonic computing, showcasing high-fidelity MNIST encoding while consuming 0.058 pJ/bit. The latter enables a pulse-shaping-free microwave arbitrary waveform generation method with ultrabroadband tunable delay and gain. Our results pave the way for efficient and compact digital-to-analog conversion paradigms enabled by integrated photonics and underscore the transformative impact analog photonic hardware may have on various applications, such as computing, optical interconnects, and high-speed ranging.","url":"https://pubmed.ncbi.nlm.nih.gov/41716237/","authors":["Song Y","Hu Y","Zhu X","Powell K","Magalhães L","Ye F","Warner H","Lu S","Li X","Renaud D","Lippok N","Zhu D","Vakoc B","Zhang M","Sinclair N","Lončar M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1038/s41566-025-01719-9","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41716054","name":"The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.","source":"pubmed","abstract":"Early cancer detection is crucial for improving outcomes. Ultrasound (US) imaging is widely accessible and cost-effective but limited by operator dependency and modest tissue contrast. Over the past decade, Artificial Intelligence (AI) has been increasingly utilized to enhance ultrasound-based cancer diagnosis, yet a comprehensive overview of this research landscape remains lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/41716054/","authors":["Miao X","Wang J","Paerhati H","Wu A","Zhang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.2174/0109298673443495251215073442","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41715888","name":"Skin deep: dermatologic challenges in PCOS through the female lifespan.","source":"pubmed","abstract":"Polycystic ovary syndrome (PCOS) is a lifelong endocrine-metabolic condition with prominent dermatologic manifestations such as hirsutism, acne/seborrhea, and female pattern hair loss (FPHL), which are frequently the initial complaint for seeking medical attention.","url":"https://pubmed.ncbi.nlm.nih.gov/41715888/","authors":["Oğuz SH","Yalici Armagan B","Okan Yildiz B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1080/17446651.2026.2632020","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41715041","name":"Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease: a scoping review.","source":"pubmed","abstract":"AIMS: To critically evaluate the methodological quality and clinical readiness of prediction models for adherence to cardiac rehabilitation (CR) programs in patients with cardiovascular disease (CVD), and to propose a strategic roadmap for future research. METHODS: This scoping review was conducted following the Arksey and O&#x2019;Malley framework. Nine electronic databases were systematically searched from inception to June 2025 for studies published in English or Chinese. The methodological quality of included prediction models was critically appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Ten studies were included. CR non-adherence rates varied from 41% to 61.4%, measured via subjective scales, session completion rates, or wearable devices. Studies exhibited wide heterogeneity in sample sizes (50 to 12,003 participants) and predictor selection. Logistic regression was the most used predictive modeling method, followed by decision tree; random forest and artificial neural network were used in one study each. AUROC values ranged from 0.62 to 0.893. Critically, the PROBAST framework highlighted prevalent methodological concerns across all studies, including inadequate sample sizes, a near-total lack of external validation, and reliance on single-center, retrospective data. CONCLUSIONS: The application of prediction models for adherence to CR programs in patients with cardiovascular disease represents an emerging but methodologically heterogeneous research area. Mapping of the existing evidence indicates that most published models remain at an early stage of development, with limited validation and variable reporting quality. Consequently, no existing prediction model can be confidently recommended for clinical use. These findings highlight the need for future studies to prioritize external validation, model transparency, and adherence to established methodological guidelines to support potential translation into clinical contexts. REGISTRATION: Registered on the Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/8JMDW ).","url":"https://pubmed.ncbi.nlm.nih.gov/41715041/","authors":["Xia C","Guo H","Ji L","Zheng Y","Du Y","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.1186/s12911-026-03391-7","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41714822","name":"Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.","source":"pubmed","abstract":"We developed and externally validated a hierarchical deep learning pipeline that automates cervical sagittal measurements, explicitly addressing C7 obscuration on lateral radiographs. The model combines a global keypoint detector with C2/C7 specialists localized via a multilayer perceptron to refine landmarks on high&#x2011;resolution patches. Trained on 5604 images and tested internally and on a challenging external cohort enriched for C7 obscuration (82%), it achieved excellent agreement with ground truth. Externally, intraclass correlation coefficients (ICCs) were 0.97 for lordosis (mean absolute error [MAE] 2.6&#xb0;), &gt;0.99 for C2 slope (MAE 0.8&#xb0;), and 0.93 for C7 slope (MAE 2.3&#xb0;), with minimal bias and narrower limits of agreement than a single&#x2011;stage baseline. The model showed near-perfect repeatability (ICC&#x2009;&gt;&#x2009;0.99) and higher artificial intelligence-expert agreement (ICC 0.81-0.84) for C7 slope than inter-expert reliability (ICC 0.67). In failure cases, the pipeline corrected large global model errors (e.g., 10.22&#xb0;- 0.22&#xb0;). This robust, coarse&#x2011;to&#x2011;fine approach advances reliable, generalizable cervical alignment assessment in real&#x2011;world conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/41714822/","authors":["Kang DH","Park SJ","Park JS","Park H","Lee CS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.1038/s41746-026-02455-2","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41714517","name":"Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.","source":"pubmed","abstract":"Artificial intelligence (AI) has emerged as a transformative force in ophthalmology, enabling automated, accurate, and efficient clinical reporting. This review summarizes recent advances in AI-driven report generation, emphasizing the integration of multimodal imaging and clinical data. Deep learning and natural language processing (NLP) models can synthesize information from diverse sources-including fundus photography, optical coherence tomography, fluorescein angiography, and patient records-to generate structured, interpretable, and personalized diagnostic reports. Such systems enhance diagnostic precision, streamline workflow, and reduce interobserver variability. We outline the technological foundations underlying these systems, including convolutional and transformer-based architectures, self-supervised and multimodal learning, and large language models. Representative applications in diabetic retinopathy, glaucoma, cataract, and age-related macular degeneration are discussed, highlighting their clinical value and emerging real-world deployment. Persistent challenges-including data heterogeneity, model interpretability, ethical governance, and clinical integration-are critically reviewed. Finally, we explore future directions such as real-time AI-assisted reporting, predictive and personalized analytics, and global scalability across healthcare ecosystems. Multimodal, explainable, and clinically integrated AI systems hold promise to redefine ophthalmic diagnostics and improve both clinician efficiency and patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41714517/","authors":["Shen Y","Chen Q","He X","Agrawal R","Grzybowski A","Jin K","Ye X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s40123-026-01316-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41714516","name":"Beta-Blockers After Myocardial Infarction Without Reduced Ejection Fraction: A Meta-Analysis of Kaplan-Meier Reconstructed Individual Patient Data.","source":"pubmed","abstract":"The long-term benefit of beta-blockers (&#x3b2;-blockers) after myocardial infarction (MI) in patients with preserved left-ventricular ejection fraction (LVEF &#x2265; 40%) remains uncertain in the modern reperfusion era. Earlier trials showed mortality benefits, but contemporary therapies may have altered their effect.","url":"https://pubmed.ncbi.nlm.nih.gov/41714516/","authors":["Awashra A","Emara A","Amin AM","Hageen AW","Elgendy MS","Rakab MS","Hamzah KA","Albukhari AF","Nazir A","Shubietah A","Rmilah AA","Ruzieh M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1007/s40256-026-00789-6","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41714366","name":"AI-assisted diagnosis of cervical dysplasia from cervicography images.","source":"pubmed","abstract":"Cervicography using Visual Inspection with Acetic Acid (VIA) is widely adopted for cervical cancer screening in low-resource settings. Although effective for identifying visible lesions, VIA cannot determine the severity of dysplasia, limiting its diagnostic utility. This study proposes a multi-task learning framework combined with an ensemble mechanism to estimate lesion severity directly from cervicography images. Four clinically relevant features&#x2014;color, surface texture, lesion position, and lesion area across quadrants&#x2014;learned through deep learning model and aggregated using ensemble method resulting a severity decision. Five datasets were used across training, testing, and prediction stages; Swede Score annotations supported model training, while histopathology-confirmed images from IARC were used for validation to ensure reliable ground truth. To address data scarcity, StyleGAN-2 with adaptive discriminator augmentation (ADA) was employed to generate synthetic images for augmentation. Initial multi-task learning experiments achieved 62% accuracy. After applying GAN-based augmentation and ensemble learning, performance improved substantially, reaching 95.21% accuracy, 95.08% sensitivity, and 81.25% precision for mild cases, and above 95% across all metrics for severe cases. Despite these promising results, the study has several limitations, including dataset imbalance, reliance on synthetic GAN-generated images, variability in imaging modalities across datasets, and a relatively small test set. These factors may affect the model&#x2019;s generalizability in broader clinical applications. Overall, the findings highlight the potential of the proposed approach to enhance the diagnostic value of VIA-based cervicography, while underscoring the need for larger datasets and more extensive clinical validation.","url":"https://pubmed.ncbi.nlm.nih.gov/41714366/","authors":["Nurmaini S","Rachmatullah MN","Agustiansyah P","Sanif R","Sastradinata I","Imah EM","Darmawahyuni A","Tutuko B","Arum AW","Islami A","Firdaus F","Sapitri AI","Nabilah A","Partan RU","Pratama RA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.1038/s41598-026-39192-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41714129","name":"Radiomics and Artificial Intelligence in Multiple Sclerosis MRI: A Comprehensive Review.","source":"pubmed","abstract":"Radiomics is a process of extracting quantitative features from medical images, such as MRI. This process, combined with artificial intelligence, has already been investigated in several studies on the management of multiple sclerosis. The aim of this review article was to provide an overview of the various applications of MRI radiomics in the diagnosis and prognosis of multiple sclerosis. The literature search was conducted in PubMed and Scopus for articles published between 2015 and 2025. A total of 26 articles met the specified criteria. Studies found that radiomics features from brain MRI images, combined with Artificial Intelligence models, were able to distinguish between healthy tissues and multiple sclerosis lesions, predict disability, detect disease activity, and differentiate between conditions with similar symptoms. The extraction of radiomic features and their utilization with Artificial Intelligence models could enhance the effectiveness of multiple sclerosis management. However, several limitations, such as an unbalanced dataset and a lack of external validation, must be addressed before they can be integrated into clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41714129/","authors":["Petrou K","Ploussi A","Seimenis I","Karavasilis E","Velonakis G","Efstathopoulos EP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.3174/ajnr.A9245","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41713868","name":"Using a Large Language Model-Generated Prompt to Extract Features from Synthetic MRI Brain Scan Reports: A Cross-Sectional Study.","source":"pubmed","abstract":"Feature extraction from free text medical reports is a frequently required clinical, operational, or research procedure. Large language models (LLMs) hold a promise for automating feature extraction, which can also enable category assignment tasks.To compare the groundedness of extracted features by five LLMs from magnetic resonance imaging (MRI) brain scan reports using a clinician-engineered versus an LLM-generated prompt.Five OpenAI LLMs were evaluated for their ability to extract nine binary features from synthetic MRI brain reports. Two types of prompts, a clinician-engineered and an LLM-generated, were used. Metrics including recall, precision, accuracy, and F1 score were calculated to assess model performance.For all extracted features by all studied models from both tested prompts, the overall average recall was 0.956, the average precision was 0.9347, the average accuracy was 0.982, and the average F1 score was 0.9431. Using GPT-3.5-turbo, the LLM-generated prompt had better numerical performance than the clinician-engineered prompt. For the other four GPT-4 models examined, overall recall, precision, and accuracy were higher regardless of the prompt source.This study highlights the potential of LLMs to generate prompts and accurately extract features, with newer models like GPT-4 performing consistently well. The efficacy of feature extraction by LLMs depends on the engineered prompt and model used. Our experimentation demonstrates the potential of LLMs to engineer prompts and extract features from MRI brain scan reports.","url":"https://pubmed.ncbi.nlm.nih.gov/41713868/","authors":["Hanna JJ","Evans CS","Dennis CR","Lee KS","Lehmann CU","Medford RJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 May","doi":"10.1055/a-2797-4295","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41713794","name":"Advancements in OCT and OCTA imaging for AMD: An in-depth analysis of research hotspots, emerging trends, and technological innovations.","source":"pubmed","abstract":"To map research hotspots and emerging trends of Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) in Age-related Macular Degeneration (AMD) from 2015-2024 using bibliometric analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/41713794/","authors":["Liao X","Lou J","Liu C","Wang Y","Zhu G","Ke Y","Bai M","Yang P","Yang W","Chi W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1016/j.pdpdt.2026.105402","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41713756","name":"Mapping knowledge landscapes and emerging trends in AI for coronary artery disease imaging biomarkers: A bibliometric and visualization analysis.","source":"pubmed","abstract":"With the rapid advancement of artificial intelligence (AI) in medical imaging, its application to coronary artery disease (CAD) imaging biomarkers has become a key area of interdisciplinary research. Understanding the current developmental trajectory, research focus, and collaborative landscape in this field is of significant importance.","url":"https://pubmed.ncbi.nlm.nih.gov/41713756/","authors":["Li M","Sun S","Liu M","Wang N","Luo C","Sun G","Ma X","Shi D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.cpcardiol.2026.103302","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41713607","name":"Differentiation between psychotic and non-psychotic major depression by the tabular prior-data fitted network.","source":"pubmed","abstract":"Misdiagnosing psychotic major depression (PMD) as non-psychotic major depression (NPMD) can lead to poor treatment outcomes. This study aims to develop and validate a machine learning-based model using electronic medical record (EMR) data and the Tabular Prior-data Fitted Network (TabPFN) model to distinguish between PMD and NPMD.","url":"https://pubmed.ncbi.nlm.nih.gov/41713607/","authors":["Zheng H","Gan W","Liu Y","Duan S","Li K","Li G","Xue Y","Xie Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 15","doi":"10.1016/j.jad.2026.121454","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41713447","name":"Research priorities for data science and artificial intelligence in global health: an international consensus exercise.","source":"pubmed","abstract":"Applications of data science and artificial intelligence (AI) in global health are expanding, yet research remains fragmented and often misaligned with the needs of low-income and middle-income countries (LMICs). To address this misalignment, we conducted a global research priority-setting exercise using the Child Health and Nutrition Research Initiative (CHNRI) method. 155 research ideas were scored by 51 experts based on feasibility, potential impact on disease burden, paradigm shift potential, implementation potential, and equity. Top-ranked priorities focused on epidemic preparedness, including AI-based outbreak prediction, improved diagnostics for infectious diseases, and early-warning systems. Other highly ranked topics included AI-assisted resource allocation, telemedicine, culturally adapted mobile health services, and chronic disease management tools. Experts from LMICs prioritised infectious disease control and diagnostic equity, whereas experts from high-income countries emphasised infrastructure and climate-related analytics. The resulting agenda provides a roadmap for aligning AI and data science research with global health priorities, particularly in LMICs.","url":"https://pubmed.ncbi.nlm.nih.gov/41713447/","authors":["Song P","Jiang D","Zhou J","Zhu Y","Manaf RA","Bojude DA","Agbre-Yace ML","Ali S","Allen O","Anyasodor AE","Aranda Z","Bahattab A","Bodomo A","Borrescio-Higa F","Buchtova M","Buljan N","Deshmukh V","Díaz-Castro L","Cheema S","Ekezie W","Ganasegeran K","Ganesan B","Glasnović A","Graham CJ","Htay MNN","Igwesi-Chidobe C","Iversen PO","Islam MM","Karim AJ","Kalpič B","Kanma-Okafor O","Lanza G","Luz S","Mahikul W","Mladenić D","Manyara AM","Munipalli B","Myburgh N","Ng ZX","Nikolopoulos G","Park C","Park JJ","Peprah P","Rudan K","Shah SA","Shi T","Tiglic GŠ","Sutan R","Tsanas A","Tibble H","Khpalwak AT","Tomlinson M","Vento S","Glasnović JV","Wang L","Xu J","Zhang J","Zhang Y","Sheikh E","Ozoh OB","Tsiachristas A","Adeloye D","Kerr S","Sanwalka M","Orešković S","Sheikh A","Rudan I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/S2214-109X(25)00473-5","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41712960","name":"Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.","source":"pubmed","abstract":"The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven health care. However, the secure and efficient sharing of sensitive medical data across institutions remains a major challenge due to privacy concerns, data silos, and regulatory restrictions. Traditional centralized systems are prone to data breaches and single points of failure, while existing privacy-preserving techniques face high computational and communication costs.","url":"https://pubmed.ncbi.nlm.nih.gov/41712960/","authors":["Wang X","Xie Y","Chen X","Yang J","Li R","Gao W","Yan Z","Zhou H","Ye Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.2196/79052","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41712930","name":"Chronic total occlusion percutaneous coronary intervention: 2026 update.","source":"pubmed","abstract":"Percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) is continually evolving through improvements in strategy, imaging, and equipment. This review provides a summary of the published literature in CTO PCI between September 2023 and April 2025, categorized by procedural outcomes, techniques, complications, and ongoing studies. Recent multicenter analyses report technical success rates exceeding 90% in expert centers, accompanied by significant reductions in angina and dyspnea at long-term follow-up. Despite higher complexity, radiation exposure has significantly decreased during the past decade as a result of equipment upgrades and operator awareness. Procedural refinements such as the use of coronary computed tomography for guidance, intravascular ultrasound guided re-entry, hydrodynamic contrast recanalization, novel retrograde techniques, and artificial intelligence-based tools are improving success and safety. Ongoing randomized trials and large-scale registries will continue to shape practice patterns and refine strategy selection in CTO PCI.","url":"https://pubmed.ncbi.nlm.nih.gov/41712930/","authors":["Ceylan S","Mutlu D","Kladou E","Williford N","Jalli S","Al-Ogaili A","Yamane M","Alaswad K","Hall A","Davies R","Choi JW","Gagnor A","Garbo R","Goktekin O","Gorgulu S","Khatri JJ","Nicholson W","Rinfret S","Jaber W","Egred M","Milkas A","Ciardetti N","Di Mario C","Mashayekhi K","Avran A","Leibundgut G","Chatzizisis YS","Werner GS","Ungureanu C","Sandoval Y","Brilakis ES"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.25270/jic/25.00301","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41712815","name":"Use of Generative AI in Medical Writing by Non-Native English Researchers.","source":"pubmed","abstract":"Generative Artificial Intelligence (AI) has increasingly found its way into scientific medical writing, which can be particularly inappropriate in non-native English-speaking countries. This study aimed to determine the occurrence of AI-generated texts in medical publications originating from the Greater Maghreb countries (Libya, Tunisia, Algeria, Morocco, and Mauritania).","url":"https://pubmed.ncbi.nlm.nih.gov/41712815/","authors":["Gazzeh H","Ghribi A","Zanina Y","Khelil M","Ben Abdelaziz A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Apr 5","doi":"10.62438/tunismed.v103i4.5548","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41711384","name":"Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.","source":"pubmed","abstract":"Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pathways. Single-omics approaches seldom capture this complexity, whereas multiomics techniques provide complementary insights but generate high-dimensional and correlated feature spaces. Machine learning (ML) offers strategies to manage these challenges; however, the predictive accuracy and reproducibility of multiomics-based ML models for stroke remain poorly characterized.","url":"https://pubmed.ncbi.nlm.nih.gov/41711384/","authors":["Yoo HY","Shin H","Kim EJ","Son YJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.2196/85654","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41710593","name":"Multimodal prehabilitation enhances perioperative outcomes in gastric cancer patients: a single-center randomized controlled trial.","source":"pubmed","abstract":"Multimodal prehabilitation, integrating exercise, nutrition, and psychological support, has shown value in perioperative care for gastrointestinal cancers, but its efficacy-especially as a short-course intervention tailored to gastric cancer's need for timely surgery-remains insufficiently validated.","url":"https://pubmed.ncbi.nlm.nih.gov/41710593/","authors":["Mu GC","Tu YH","Xie HL","Liu SY","Jia K","He MY","Chen YY","Chen JQ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fnut.2025.1676180","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41710112","name":"Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity.","source":"pubmed","abstract":"The availability of large-scale medical imaging datasets is often constrained by privacy regulations, high acquisition costs, and ethical concerns. Synthetic medical image generation using generative adversarial networks (GANs) offers a promising solution to overcome these limitations. This study investigates the effectiveness of a Parameter-Optimized Generative Adversarial Network (POP-GAN) and compares its performance with state-of-the-art architectures, including StyleGAN2, multi-stream GAN (mustGAN), and Conditional GAN (cGAN), for realistic MRI image synthesis.","url":"https://pubmed.ncbi.nlm.nih.gov/41710112/","authors":["Arockia Selvarathinam ALXR","Anbalagan N","Srinivasu PN","Choi J","Ijaz MF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1731370","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41709240","name":"Comparison of human-AI agreement in ASA scoring by gender and duration of clinical experience: a real-world study.","source":"pubmed","abstract":"BACKGROUND: Accurate preoperative risk identification is critical for patient safety and postoperative outcomes. Anaesthesiologists make decisions on the basis of ASA classification and additional parameters. Artificial intelligence (AI)-based decision support may offer more objective judgments. METHODS: In this retrospective multi-rater study, four anaesthesiologists and an AI system independently evaluated 1,000 cases. ASA class, postoperative ICU requirement, anaesthesia preference, intraoperative risk prediction, and additional recommendations were assessed. Concordance was analysed using Krippendorff&#x2019;s alpha, Cohen&#x2019;s kappa, Gwet&#x2019;s AC2, and PABAK, with percentage agreement estimated by bootstrapping. AI&#x2013;physician agreement was further examined using fixed-effects logistic regression including clinician sex and professional experience as covariates. RESULTS: Physician&#x2013;physician agreement was generally good to excellent across outcomes, whereas physician&#x2013;AI agreement was lower and variable when assessed using &#x3ba;, PABAK, Gwet&#x2019;s AC2, and observed agreement (P&#x2092;). The highest AI concordance was observed for intraoperative risk prediction and ICU requirement, while the lowest was for anaesthesia preference. Exploratory analyses suggested that AI&#x2013;physician concordance may vary by clinician experience and sex; no significant effects of sex or experience were observed for intraoperative anaesthesia-related risk prediction. CONCLUSION: Although AI shows high concordance with physician decisions in objective/algorithmic domains, concordance remains limited in contextual and experience-based domains (anaesthesia preference). The findings support positioning AI as a safe &#x2018;second eye/warning&#x2019; tool within human-in-the-loop workflows, rather than as an independent authority. Prospective, externally validated studies are needed.","url":"https://pubmed.ncbi.nlm.nih.gov/41709240/","authors":["Çatak T","Aksu A","Saltalı AÖ","Berilgen B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.1186/s12911-026-03399-z","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41709147","name":"Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.","source":"pubmed","abstract":"OBJECTIVE: To develop and validate a prediction model for in-hospital cardiogenic shock (CS) after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) based on machine learning (ML) algorithms. METHODS: A total of 1608 AMI patients admitted to the First Hospital of Lanzhou University during 2023 and 2024 were retrospectively enrolled in this study. The 851 patients from 2023 were randomly divided into a training set (n&#x2009;=&#x2009;595) and a validation set (n&#x2009;=&#x2009;256) at a ratio of 7:3. The LASSO&#x2013;Boruta combined algorithm was used for feature selection, resulting in the construction of six ML models. The validation set was used for internal validation, while the patients enrolled from 2024 served as a temporal external validation cohort in a test set (n&#x2009;=&#x2009;757). Model performance was evaluated using various metrics, including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, precision, and F1 score. Additionally, SHAP analysis was employed to interpret the contribution of the selected features. An online prediction application based on the Streamlit framework was developed. RESULTS: LASSO regression initially identified 13 candidate features, while the random forest (RF) model demonstrated the best predictive performance in the training set. Following Boruta refinement, seven key features were retained, leading to the construction of an updated RF model. This model achieved an AUROC of 0.906, an accuracy of 0.977, a precision of 0.900, a sensitivity of 0.643, a specificity of 0.996, and a F1 score of 0.750 on the internal validation set. Temporal external validation at the same center showed an AUROC of 0.988, an accuracy of 0.967, a precision of 0.701, a sensitivity of 0.904, a specificity of 0.972, and a F1 score of 0.790. Furthermore, the model demonstrated excellent calibration, with a Brier score of 0.023 and 0.027. The SHAP analysis ranked feature importance as Killip class, D-dimer (DD), creatinine (Crea), alanine aminotransferase (ALT), apolipoprotein B/A (APOB/A), diastolic blood pressure (DBP) and lactate (Lac). CONCLUSION: We developed and validated a RF model based on seven key variables&#x2014;Killip class, DD, Crea, ALT, APOB/A, DBP and Lac&#x2014;that serves as a predictive tool for identifying the risk of in-hospital CS in AMI patients post-PCI. Additionally, we created an online prediction application using Streamlit, which facilitates the implementation of this model into clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41709147/","authors":["Du S","Li W","Wang Y","Wang H","Kang H","Liang Y","Lai Y","Ma L","Zhao J","Zhang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.1186/s12872-026-05562-w","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41709087","name":"Public procurement of cardiac implantable electronic devices across Europe: are we purchasing value or cost-effectiveness?","source":"pubmed","abstract":"Procurement of cardiac implantable electronic devices (CIEDs) across the European Union is shaped by diverse healthcare systems, reimbursement mechanisms and levels of clinician involvement. Despite a shared legal framework, limited comparative data are available on how procurement is implemented across countries.","url":"https://pubmed.ncbi.nlm.nih.gov/41709087/","authors":["Osoro L","Arbelo E","Kozhuharov N","Landen R","Martinek M","Leclerq C","Fauchier L","De Haro JC","Boveda S","Sommer P","Rienstra M","Symanski P","Farkowski M","Egorova A","Moscoso Costa F","Tint D","Simovic S","Dzhinsov K","Leyva F","Boriani G","Figueras J","Ihara Z","Merino JL","Burri H","Pürerfellner H","Casado-Arroyo R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.1093/europace/euaf323","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41708802","name":"Benchmarking large language model-based agent systems for clinical decision tasks.","source":"pubmed","abstract":"Agentic artificial intelligence (AI) systems, designed to autonomously reason, plan, and invoke tools, have shown promise in healthcare, yet systematic benchmarking of their real-world performance remains limited. In this study, we evaluate two such systems: the open-source OpenManus, built on Meta's Llama-4 and extended with medically customized agents; and Manus, a proprietary agent system employing a multistep planner-executor-verifier architecture. Both systems were assessed across three benchmark families: AgentClinic, a stepwise dialog-based diagnostic simulation; MedAgentsBench, a knowledge-intensive medical QA dataset; and Humanity's Last Exam (HLE), a suite of challenging text-only and multimodal questions. Despite access to advanced tools (e.g., web browsing, code development and execution, and text file editing) agent systems yielded only modest accuracy gains over baseline LLMs, reaching 60.3% and 28.0% in AgentClinic MedQA and MIMIC, 30.3% on MedAgentsBench, and 8.6% on HLE text. Multimodal accuracy remained low (15.5% on multimodal HLE, 29.2% on AgentClinic NEJM), while resource demands increased substantially, with &gt;10&#xd7; token usage and &gt;2&#xd7; latency. Although 89.9% of hallucinations were filtered by in-agent safeguards, hallucinations remained prevalent. These findings reveal that current agentic designs offer modest performance benefits at significant computational and workflow cost, underscoring the need for more accurate, efficient, and clinically viable agent systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41708802/","authors":["Liu Y","Carrero ZI","Jiang X","Ferber D","Wölflein G","Zhang L","Jayabalan S","Lenz T","Hui Z","Kather JN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.1038/s41746-026-02443-6","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41708395","name":"Humane Intelligence in Geropsychiatric Care: Relational Artificial Intelligence, Clinical Wisdom, and the Moral Grid Operational Index.","source":"pubmed","abstract":"Artificial intelligence (AI) already influences how older adults are identified for services, supported between provider visits, and referred for care, yet most AI governance currently focuses on algorithms and infrastructure rather than the actual experiences of older adults and their caregivers. Humane Intelligence is a patient-centered, ethically attuned, relational framework for designing, evaluating, and monitoring AI in older adult care. It rests on four pillars: Relational Intelligence, Transparency with Care, Reciprocity and Consent, and Ethical Governance in Strategic Regions, and applies them from point-of-care encounters to system-level decisions. For each pillar, guidelines follow the same sequence: signal a problem, take action, and verify benefit or harm. The framework prioritizes outcomes that matter in geriatric psychiatry, including function, distress, caregiver burden, avoidable utilization, equity, and documented harms or overrides. To protect patients, it draws a firm boundary against fully automated clinical actions and recommends clinical-grade standards for patient-facing programs, including scope-of-action labels, human-in-the-loop safeguards for high-risk situations, postmarket monitoring, and periodic certification. This blueprint aligns with 2025 JAMA Summit on AI priorities, World Health Organization guidance for large multimodal models, United States Food and Drug Administration recommendations for Predetermined Change Control Plans, and Office of the National Coordinator for Health Information Technology decision-support intervention frameworks. The goal is practical: to translate ethics into testable patient-centered routines that clinicians can trust, healthcare systems can implement, and leaders can procure, so that AI augments rather than displaces care.","url":"https://pubmed.ncbi.nlm.nih.gov/41708395/","authors":["Kyomen HH","Group for the Advancement of Psychiatry (GAP) Committee on Aging"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1016/j.jagp.2025.12.012","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41708303","name":"Artificial intelligence-powered predictive tools to improve end-of-life decision-making: mini-review.","source":"pubmed","abstract":"Uncertainty around a patient's prognosis at the end of life remains a major barrier to timely palliative-care involvement and alignment of treatments with patient goals. Artificial intelligence (AI)-based tools have recently emerged to provide structured mortality predictions and identify patients at risk of deterioration to support clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41708303/","authors":["Alabbasi A","Alzahrani M","Sultan F","Sayes M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 28","doi":"10.1136/spcare-2025-006066","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41708167","name":"Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.","source":"pubmed","abstract":"Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demonstrated potential in facilitating motor recovery after stroke by enabling active, intention-driven rehabilitation. Emerging evidence suggests that incorporating vibrotactile stimulation (VTS) into SRG-BCI systems may further enhance sensorimotor feedback. The objective of this study is to evaluate the therapeutic efficacy and underlying neural mechanisms of BCI-driven, intention-based glove activation compared with automated glove-assisted training, with VTS applied identically in both groups.","url":"https://pubmed.ncbi.nlm.nih.gov/41708167/","authors":["Catherine Chan KL","Yan C","Wang X","Huang S","Dai W","Luo Y","Cheng Y","Xu B","Zhang W","Shen Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.1136/bmjopen-2025-110321","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41707911","name":"Radiomics for early detection of pancreatic cancer: a systematic review and meta-analysis.","source":"pubmed","abstract":"Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, in which accurate and faster detection is required in high-risk population to improve prognosis and decrease cancer-associated mortality. Currently, radiomics has emerged as a promising computational approach to address this challenge, reporting increased accuracy in differentiating PDAC from benign lesions. Our study aimed to evaluate radiomics-based models derived from computed tomography, magnetic resonance imaging, positron emission tomography, or ultrasound for the detection of PDAC in patients under surveillance.","url":"https://pubmed.ncbi.nlm.nih.gov/41707911/","authors":["Alidina Z","Hussain AAM","Banani I","Khan MM","Pawlik TM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.gassur.2026.102374","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41707834","name":"Global trends and thematic evolution in antimicrobial stewardship research: a comprehensive bibliometric and network analysis (1977-2025).","source":"pubmed","abstract":"Antimicrobial stewardship (AMS) is a vital strategy in addressing antimicrobial resistance globally. However, no prior bibliometric study has comprehensively assessed the global evolution, intellectual structure, and thematic development of AMS research across an extended timeframe. This study presents a comprehensive bibliometric analysis to map the evolution, productivity, and collaboration patterns in AMS research from 1977 to 2025.","url":"https://pubmed.ncbi.nlm.nih.gov/41707834/","authors":["Taha MME","Oraibi B","Abdelwahab SI","Sahli KA","Assiri A","Qadri M","Alarifi A","Khardali A","Farasani A","Moshi JM","Alsaadi KH","Alshahrani S","Shubaily HM","Binjomah AZ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.jhin.2026.01.029","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41707197","name":"Examining Artificial Intelligence Chatbots' Responses in Providing Human Papillomavirus Vaccine Information for Young Adults: Qualitative Content Analysis.","source":"pubmed","abstract":"The growing use of artificial intelligence (AI) chatbots for seeking health-related information is concerning, as they were not originally developed for delivering medical guidance. The quality of AI chatbots' responses relies heavily on their training data and is often limited in medical contexts due to their lack of specific training data in medical literature. Findings on the quality of AI chatbot responses related to health are mixed. Some studies showed the quality surpassed physicians' responses, while others revealed occasional major errors and low readability. This study addresses a critical gap by examining the performance of various AI chatbots in a complex, misinformation-rich environment.","url":"https://pubmed.ncbi.nlm.nih.gov/41707197/","authors":["Laily A","Schwab-Reese LM","Davish M","Cahue E","LaRoche KJ","Rodriguez NM","Duncan RJ","Hubach RD","Kasting ML"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.2196/79720","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706814","name":"Integrating machine learning and Mendelian randomization for identifying genetic biomarkers in bladder cancer: implications for early diagnosis and targeted therapies.","source":"pubmed","abstract":"Bladder cancer (BC) continues to be a major public health challenge due to its high recurrence and mortality rates, compounded by difficulties in early detection. Identifying novel genetic biomarkers is crucial for improving diagnosis and therapy. This study integrates machine learning with Mendelian randomization (MR) to identify and validate potential biomarkers for BC.","url":"https://pubmed.ncbi.nlm.nih.gov/41706814/","authors":["Xu C","Dong Y","Li J","Liao X","Zhang C","Lv C","Li C","Liu Y","Yao L","Zhou L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.1097/JS9.0000000000003833","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706751","name":"An ultrasound-based artificial intelligence framework for difficult airway prediction: A two-model, three-step decision framework.","source":"pubmed","abstract":"At present, the early warning of difficult airway remains fraught with challenges. Previous ultrasonic quantitative parameters have demonstrated favorable application potential in difficult airway assessment, and deep learning techniques have also exhibited satisfactory performance in the interpretation of this condition. Based on this, we aim to construct a \"two-model, three-step\" hierarchical strategy, develop an ultrasound image-based artificial intelligence (AI) framework for difficult airway prediction, and conduct its internal validation.","url":"https://pubmed.ncbi.nlm.nih.gov/41706751/","authors":["Fu C","Luan C","Liu H","Wang W","Zhou X","Jia Y","Ding B","Zhang L","Yuan L","Niu Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0342339","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706695","name":"A scoping review of artificial intelligence in acute care surgery: promise, pitfalls, and a path forward.","source":"pubmed","abstract":"Acute care surgery (ACS) faces unique challenges due to time-sensitive decisions, high diagnostic variability, and complex patient data. Artificial Intelligence (AI) offers potential solutions, yet the breadth, focus, safety, and translational readiness of current AI applications in ACS remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/41706695/","authors":["Kewalramani D","Chattopadhyay K","Benton J","Hua J","Cheruvu S","Ali H","Anuncio S","Joshi A","Mylarappa S","Vidhya G","Choron RL","Teichman AL","Jopling JK","Madani A","Brat G","Coleman JR","Cohen JV","Pugh C","Barie PS","Loftus TJ","Narayan M","AiCCESS Consortium"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.1097/JS9.0000000000003794","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706663","name":"Artificial intelligence in patient education: a bibliometric analysis.","source":"pubmed","abstract":"Patient education faces challenges including health literacy disparities and resource constraints. Artificial intelligence (AI) offers transformative potential through personalized, accessible tools, yet a comprehensive bibliometric analysis of this rapidly evolving field is lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/41706663/","authors":["Guo Y","Guo Q","Liu C","Chen F","Ma J","Zhang H","Lv Y","Yang T","Sun Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 19","doi":"10.1097/JS9.0000000000004075","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706471","name":"Predicting Adolescent Response to School-Based Mindfulness: A Secondary Analysis of the MYRIAD Trial.","source":"pubmed","abstract":"Depression most commonly first emerges during adolescence, making early prevention critical. While school-based mindfulness training (SBMT) offers a scalable prevention approach with broad reach, evidence of its effectiveness is mixed, and there is a compelling case for a more personalized approach to prevention.","url":"https://pubmed.ncbi.nlm.nih.gov/41706471/","authors":["Webb CA","Ren B","Hinze V","Dalgleish T","Ford TJ","Greenberg MT","Montero-Marin J","Kuyken W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 1","doi":"10.1001/jamapsychiatry.2025.4638","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706356","name":"The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling.","source":"pubmed","abstract":"Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing concerns around transparency and effectiveness have highlighted the absence of standardized methods for evaluating the quality and impact of these tools. This makes it impossible to gauge true progress; even as new systems are developed, their comparative effectiveness remains unknown. We argue the critical next innovation is developing a framework to measure these systems. This paper proposes a conceptual benchmarking framework to support the systematic evaluation of player risk detection systems. Benchmarking, in this context, refers to the structured and repeatable assessment of artificial intelligence models using standardized datasets, clearly defined tasks, and agreed-upon performance metrics. The goal is to enable objective, comparable, and longitudinal evaluation of player risk detection systems. We present a domain-specific framework for benchmarking that addresses the unique challenges of player risk detection in gambling and supports key stakeholders, including researchers, operators, vendors, and regulators. By enhancing transparency and improving system effectiveness, this framework aims to advance innovation and promote responsible artificial intelligence adoption in gambling harm prevention.","url":"https://pubmed.ncbi.nlm.nih.gov/41706356/","authors":["Ghaharian K","Dragicevic S","Percy C","Nelson SE","Murch WS","Heirene RM","Simeon-Rose K","Schrans T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s10899-026-10483-6","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706011","name":"Exploring the Endorsement and Implementation of Artificial Intelligence Guidelines in Leading Orthopaedic and Sports Medicine Journals: A Cross-Sectional Study.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) in orthopaedics and sports medicine (OSM) has transformed clinical practice and scientific inquiry. However, the increasing reliance on AI raises critical concerns regarding transparency, ethical considerations, and reproducibility. The aim of this study was to systematically evaluate the editorial policies of leading OSM journals concerning AI usage and the endorsement of AI-specific reporting guidelines (RGs).","url":"https://pubmed.ncbi.nlm.nih.gov/41706011/","authors":["Major J","Mahnken K","Young A","O'Brien C","Tran AV","Crotty P","Ford AI","Vassar M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.2106/JBJS.25.00373","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41706010","name":"Minimizing Missed Diagnoses of Tibial Plateau Fractures: The Role of AI in Radiographic Evaluation.","source":"pubmed","abstract":"Tibial plateau fractures represent a diverse group of intra-articular injuries that can be difficult to detect and characterize on initial imaging. The aim of the present study was to develop an artificial intelligence (AI) diagnostic tool for identifying tibial plateau fractures on radiographs.","url":"https://pubmed.ncbi.nlm.nih.gov/41706010/","authors":["Chen MZ","Chen YP","Hung YC","Huang YJ","Fan TY","Liu HL","Yang CP","Chang SS","Kuo CF","James Chu CC","Chan YS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.2106/JBJS.24.00579","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41705531","name":"On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.","source":"pubmed","abstract":"Early triage for massive transfusion (MT) is essential in trauma care but most existing scoring systems rely on in-hospital data. To address this limitation, a machine learning model using only prehospital variables to predict MT and stratify mortality risk was developed and externally validated.","url":"https://pubmed.ncbi.nlm.nih.gov/41705531/","authors":["Yu B","Cho J","Kim H","Hwang SH","Hwang J","Kim S","Oh J","Lee S","Kim DW","Seok J","Kim K","Lee J","Yon DK","Kang WS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 29","doi":"10.1093/bjsopen/zraf167","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41705162","name":"Deep learning and firearm wound classification: a pilot study.","source":"pubmed","abstract":"The study on the use of deep learning in pattern recognition of gunshot wounds (GSW) represents a novelty in the field of forensic pathology. Although artificial intelligence (AI) has already revolutionized many medical specialties, applications in forensic medicine are still limited. Nevertheless, AI-based tools could be of great use in a discipline that relies heavily on visual analysis. Scant scientific evidence from recent experimental studies has demonstrated the AI potential in predicting shooting distance based on GSW pictures. Previous approaches achieved a classification accuracy of 98%; however, further studies are needed to evaluate its applicability in forensic practice. The aim of this project is to further explore the application of deep learning techniques for the classification of GSWs.","url":"https://pubmed.ncbi.nlm.nih.gov/41705162/","authors":["Delogu G","Di Fazio N","Licciardello G","Frati P","Pomara C","Sessa F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1646656","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41704343","name":"Cross-trait genetic enrichment between GERD and psychiatric disorders in East Asian populations.","source":"pubmed","abstract":"Gastroesophageal reflux disease (GERD) exhibits significant epidemiological comorbidity with psychiatric disorders, yet their shared genetic architecture remains poorly characterized in East Asian populations. Leveraging ancestry-specific genome-wide association study (GWAS) summary statistics from East Asian cohorts, we employed linkage disequilibrium score regression and conditional false discovery rate (condFDR) approaches to investigate cross-trait genetic enrichment between GERD and major psychiatric disorders, including major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BIP). We identified significant genetic correlations between GERD and both MDD (r g = 0.49, P = 0.03) and SCZ (r g = 0.25, P = 0.02), but not with BIP. Through condFDR analysis, two novel loci were discovered: rs3980178 near MEIS1 (associated with GERD-MDD pleiotropy) and rs9844126 near ZBTB20 (associated with GERD-SCZ pleiotropy). These loci are implicated in neurodevelopment, autonomic regulation, and neural circuit formation, providing mechanistic insights into the gut-brain axis. Our findings demonstrate that cross-trait genetic enrichment significantly enhances locus discovery for GERD in underpowered East Asian GWAS and reveal ancestry-specific genetic links between gastrointestinal and psychiatric phenotypes.","url":"https://pubmed.ncbi.nlm.nih.gov/41704343/","authors":["Gao Z","Wang X","Liu Z","Hu F","Zhou Y","Ullah K","Wang R","Zhang M","Chang X","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fgene.2026.1770067","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41704219","name":"Developing an automatic decision-assistance tool to choose proton/photon radiotherapy for patients with prostate cancer.","source":"pubmed","abstract":"It is important to guide staff in choosing appropriately between photon and proton radiotherapy. This study develops an automatic decision method to select the most clinically beneficial radiotherapy technique (proton or photon) for patients with prostate cancer. An automatic decision method was developed to help staff in choosing appropriately between photon and proton radiotherapy for patients with prostate cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/41704219/","authors":["Li M","Shen L","Chen X","Li G","Ding J","Men K","Yi J","Dai J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/acm2.70497","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41703545","name":"Research on artificial intelligence literacy among nursing professionals: a scoping review.","source":"pubmed","abstract":"BACKGROUND: Artificial Intelligence (AI) applications are increasingly integrated into nursing practice. As key stakeholders, nursing professionals must possess adequate AI literacy. To address this need, a scoping review was conducted to systematically map and synthesize evidence regarding the core dimensions, assessment tools, and influencing factors of AI literacy in nursing. METHODS: Following the Arksey and O&#x2019;Malley framework, a scoping review was conducted by systematically searching seven literature databases, including PubMed, CINAHL Complete, Cochrane Library, Web of Science, IEEE Xplore, CNKI, Google Scholar, for literature published from January 2005 to 31 August 2025. The included studies were analyzed using a combination of descriptive analysis and thematic synthesis. RESULTS: A total of 39 English-language studies were included. AI literacy among nursing professionals was conceptualized as a multidimensional construct encompassing eight core dimensions: foundational knowledge, technical cognition, application skill, perceived utility, technology readiness, ethics awareness, critical thinking, and innovation. Most current assessment tools are general-purpose and lack nursing-specific contextualization, although several scales tailored to nursing professionals have been developed recently. Key influencing factors include AI-related education and training, perceived utility, gender, and age. Educational interventions have been shown to be effective in improving AI literacy. CONCLUSIONS: AI literacy among nursing professionals constitutes a multidimensional competency framework. Current assessment tools and educational strategies require further optimization. Future efforts should focus on developing highly specialized and culturally adaptable assessment instruments, establishing a stratified and tiered educational system, and rigorously evaluating the effectiveness of these interventions through empirical research to foster the sustainable integration of AI into nursing.","url":"https://pubmed.ncbi.nlm.nih.gov/41703545/","authors":["Wang Q","Lu N","Yu C","Qi J","Zhang H","Shi H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 17","doi":"10.1186/s12912-026-04448-8","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41703442","name":"Diagnosis challenges and accessibility barriers to migraine management in Southeast Asia: results from the South-East Asia Local breAch on MigraiNe Treatment (SEALANT) study.","source":"pubmed","abstract":"BACKGROUND: Migraine is one of the leading causes of disability among all neurological diseases, yet major gaps persist in diagnosis and access to effective treatment, particularly in low- and middle-income regions. Southeast Asia and East Asia are characterised by marked socioeconomic diversity, variable healthcare infrastructure, and limited availability of migraine-specific therapies. We aimed to assess physician-reported barriers to migraine diagnosis and management across Southeast Asian and East Asian countries. METHODS: The South-East Asia Local breAch on MigraiNe Treatment (SEALANT) study was a multinational, cross-sectional, web-based survey conducted between Nov 1, 2024, and Aug 31, 2025. Physicians involved in migraine care from Laos, Indonesia, Malaysia, the Philippines, Singapore, Taiwan, and Thailand were eligible. Survey domains included diagnostic barriers, clinic accessibility, acute and preventive treatment practices, awareness of medication overuse headache, access to calcitonin gene-related peptide (CGRP)&#x2013;targeted therapies, and migraine-related stigma. Countries were categorised by World Bank income classification. Data were analysed descriptively, and comparisons were made across income groups. All results are based on physicians&#x2019; perceptions of routine clinical practice rather than objectively verified patient-level data. RESULTS: A total of 686 physicians participated (mean age 39.0 years [SD 9.9]), of whom 79.8% were neurologists. Overall, 70.0% of respondents reported an insufficient number of neurology/headache clinics, increasing to 87.2% in lower-middle-income countries. Physicians reported that approximately 60.0% of patients were correctly diagnosed with migraine before specialist consultation, while 44.9% were perceived to experience diagnostic delays exceeding one year. According to physician reports, acute migraine management relied predominantly on non-specific analgesics, with opioids remaining widely available and prescribed across all income settings. Reported use of migraine-specific acute therapies and preventive treatments was limited. Although CGRP-targeted preventive therapies were widely regarded by physicians as effective (77.1%), many perceived that these treatments should not yet be reimbursed. CONCLUSION: Substantial and inequitable gaps persist in migraine diagnosis and management across Southeast Asia and East Asia, as perceived by physicians, driven by shortages of specialist services, delayed diagnosis, reliance on non-specific treatments, and restricted access to migraine-specific therapies. Addressing migraine as a public health priority through health-system strengthening, education, and equitable access to evidence-based treatments is essential to reduce disability in the region.","url":"https://pubmed.ncbi.nlm.nih.gov/41703442/","authors":["Rattanawong W","Hiransuthikul A","Anukoolwittaya P","Pongpitakmetha T","Thanprasertsuk S","Manohararaj N","Sulaiman WAW","Saranza G","Wee JL","Dayrit G","Madjid IS","Sudibyo DA","Budianto P","Wu JW","Phoumindr A","Sirilertmekasakul C","Tanprawate S","Tepper SJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 17","doi":"10.1186/s10194-026-02295-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41703202","name":"BRIDGE pilot study: a bilateral regulatory investigation of data governance and exchange.","source":"pubmed","abstract":"National privacy laws diverge between the European Union and United States, hindering transatlantic health data exchange and slowing AI-driven medical innovation. In response, the German Ministry of Health launched the pre-competitive Data for Health initiative, leading to the BRIDGE Pilot Study (2023-2025), a researcher-led effort to address this regulatory and legal gap. Using a mixed-methods approach, including structured surveys (n&#x2009;=&#x2009;56 expert responses), ranking of steps via relative importance indexing, and 4 Delphi meetings, experts co-developed a practical framework composed of 30 steps in 3 consecutive phases for legally compliant and technically interoperable EU-US health data collaboration. The framework emphasizes early data protection assessments, secure transfer protocols, and iterative governance checks. The final consensus framework provides a stepwise guide to navigate regulatory and legal complexities and operationalize cross-border research. Ongoing input from researchers and stakeholders will help ensure the framework remains adaptable and provides a clear, scalable foundation for cross-border health data exchange.","url":"https://pubmed.ncbi.nlm.nih.gov/41703202/","authors":["Hou HX","Bisson T","Leiss SM","Thierauf J","Stern AD","Strobelt H","Nensa F","Buyx A","Huster KM","Furlano K","Kozlakidis Z","Gupta S","Kostadinov D","Boor P","Slagman A","Tjardes T","Cholet P","Schneider NK","Schlomm T","Biskup S","Röhrig R","Molnár-Gábor F","Schmidt-Straßburger U","Ladewig K","Weigand M","Pinto Dos Santos D","Johnson JM","Kirsten T","Sutherland E","Zerbe N","Hofman A","Heyder R","Schmidt G","Lennerz JK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 17","doi":"10.1038/s41746-025-02322-6","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41703179","name":"Clinical value of artificial intelligence in reducing PET image acquisition time: routine clinical validation using qualitative, quantitative, and radiomic analysis on a cohort of 282 patients undergoing [(18)F]FDG and [(68)Ga]Ga-PSMA-11 PET/CT.","source":"pubmed","abstract":"Reducing acquisition time in PET/CT imaging can degrade image quality and may compromise both diagnostic reliability and the robustness of radiomic features. This study investigates, in a large clinical cohort, whether AI-based denoising can preserve image quality and maintain the accuracy of quantitative and radiomic parameters in [ 18 F]FDG and [ 68 Ga]Ga-PSMA-11 PET/CT scans.","url":"https://pubmed.ncbi.nlm.nih.gov/41703179/","authors":["Dambrain A","Lacombe M","Dufour PA","Lacoeuille F","Morel O","Testard A","Bouron C","Girault S","Guillerminet C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 18","doi":"10.1186/s40658-026-00835-x","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41703170","name":"Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation.","source":"pubmed","abstract":"Cardiac tamponade is a rare yet catastrophic complication during atrial fibrillation (AF) catheter ablation. Influenced by multiple procedural and patient-related factors, its prediction remains highly challenging. This study aimed to develop and interpret a machine learning-based predictive model for cardiac tamponade during AF catheter ablation. Data were retrospectively collected from 1481 patients who underwent AF catheter ablation at a tertiary hospital in Nanjing, China, between October 2014 and December 2024. After identifying key predictors of intraoperative cardiac tamponade via least absolute shrinkage and selection operator (LASSO) regression, eight machine learning algorithms were trained using Python libraries. Model performance was evaluated through cross-validation, and SHapley Additive exPlanations (SHAP) analysis was performed to interpret the best-performing model. The XGBoost model exhibited the optimal overall performance, with an area under the curve (AUC) of 0.972 in the training set and 0.908 in internal validation, demonstrating excellent calibration and the highest clinical net benefit. SHAP analysis identified five major predictors: operator experience, D-dimer level, total heparin dose, AF type, and left atrial diameter. These predictors represent multidimensional determinants associated with procedural technique, coagulation status, and cardiac anatomy. The XGBoost-based predictive model showed strong discriminative ability and interpretability for predicting cardiac tamponade during AF catheter ablation, which supports accurate preoperative risk stratification and guides intraoperative management to enhance procedural safety and precision. External validation across multiple centers is required to confirm the generalizability of the model.","url":"https://pubmed.ncbi.nlm.nih.gov/41703170/","authors":["Zhou L","Zhao Y","Song W","Li Y","Wang J","Gong H","Sun G","Bao Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 17","doi":"10.1038/s41598-026-40302-2","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41702483","name":"Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) applications have emerged as transformative technologies in nephrology, particularly in dialysis care. The availability of multimodal datasets including electronic health records, hemodialysis machine data, laboratory values, and imaging has enabled the development of sophisticated prognostic, diagnostic, and treatment support tools. This comprehensive review narratively examines current AI/ML applications in dialysis, evaluates their clinical performance, and identifies future research directions. We conducted a comprehensive literature search of PubMed, Web of Science, and other major databases from 2020 to 2025, focusing on peer-reviewed studies that employed AI/ML techniques in dialysis care. Studies were categorised by application domain and analysed for methodology, performance metrics, and clinical implications. Our analysis identified five major application domains: (1) prediction and prognosis, including intradialytic hypotension (IDH) prediction with AUROC values ranging 0.89-0.95, mortality prediction achieving C-indices up to 0.83, and hospitalisation risk assessment; (2) early detection of chronic kidney disease (CKD) progression and dialysis risk; (3) clinical decision support for anaemia management and treatment optimisation; (4) vascular access monitoring with AI-driven image analysis achieving AUROC &#x2248; 0.96; and (5) natural language processing (NLP) applications for symptom detection. Federated learning (FL) approaches are emerging to enable multi-centre collaboration while preserving data privacy. AI/ML technologies demonstrate significant promise in enhancing dialysis care through improved prediction accuracy, personalised treatment approaches, and clinical decision support. However, widespread clinical adoption remains limited due to challenges including data privacy concerns, model interpretability issues, regulatory complexity, and the need for diverse, representative datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/41702483/","authors":["Clement David-Olawade A","Ogunbona MA","Olawuyi OF","Makanjuola BD","Alabi JO","Olawade DB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 15","doi":"10.1016/j.cca.2026.120908","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41702124","name":"Can ChatGPT Teach Tendon Repair Suturing? A Comparison With Video Instruction in a Simulation-Based Environment: A Pilot Study.","source":"pubmed","abstract":"To compare the effectiveness of ChatGPT-guided instruction versus video-based instruction for teaching the Modified Kessler tendon repair technique to novice medical trainees in a simulation setting.","url":"https://pubmed.ncbi.nlm.nih.gov/41702124/","authors":["Briones-Zamora KH","Díaz Mora P","Ferrín Yépez IN","Solís Salas GG","Briones-Zamora AD","Quiroz Farfán A","Briones-Claudett KH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.jsurg.2025.103867","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41702054","name":"Deep learning cascade networks for segmentation of fluorine-18 sodium fluoride positron emission tomography scans of equine metacarpo- and metatarsophalangeal joints outperform atlas-based method.","source":"pubmed","abstract":"To create a labeled dataset and evaluate a convolutional neural network (CNN) for segmentation of fluorine-18 sodium fluoride PET scans of the equine metacarpo- and metatarsophalangeal joint (fetlock), targeting the third metacarpal bone, proximal phalanx, proximal sesamoid bone(s), and soft tissue.","url":"https://pubmed.ncbi.nlm.nih.gov/41702054/","authors":["Anishchenko S","Bills KW","Beylin D","Beylin N","Beylin D","Stepanova K","Stepanov P","Spriet M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 1","doi":"10.2460/ajvr.25.11.0421","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41701815","name":"The Application Effect of Robot-Assisted Therapy for Hand Dysfunction After Stroke: A Scoping Literature Review.","source":"pubmed","abstract":"This scoping review was designed to address the core question: In the field of poststroke hand function rehabilitation, what is the current status of technical application, evidence characteristics, and research gaps of robot-assisted therapy in the existing literature? The review aimed to delineate the knowledge landscape of this field, with its unique contributions encompassing the systematic classification of robotic device technical types and their corresponding training paradigms, the identification of key concepts, evidence patterns, and research gaps in current studies, and guiding the design of more targeted future systematic reviews and clinical trials. Following Joanna Briggs Institute guidelines, we searched nine databases (PubMed, Embase, Web of Science, Cochrane Library, CINAHL, CBM, CNKI, Wanfang, VIP) until June 24, 2025. Analysis of 33 studies indicated the following: acute-phase robot-assisted therapy reduced spasticity and pain with functional recovery comparable to conventional therapy; subacute-phase robot-assisted therapy improved motor function and grip strength more effectively; and chronic-phase intent-driven robot-assisted therapy maintained 6-mo Action Research Arm Test gains. Robot-assisted therapy demonstrated favorable safety across phases and may provide clinically meaningful functional benefits. Evidence supports robot-assisted therapy's phase-specific value, warranting future large-scale trials to optimize protocols and explore neuroplasticity mechanisms.","url":"https://pubmed.ncbi.nlm.nih.gov/41701815/","authors":["Hu C","Zheng Y","Han X","Gong H","Xing S","Zhao R","Wang X","Wang J","Yang Y","Lv H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar 1","doi":"10.1097/PHM.0000000000002931","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41701554","name":"Deep Learning Segmentation of Pelvic Soft Tissue in Isotropic and Anisotropic MRI Using Routine T2 Scans.","source":"pubmed","abstract":"Automated segmentation of 3D pelvic topography using deep learning networks has the potential to improve the accuracy of preoperative planning and surgical navigation. However, anisotropic MRI scans-often used in clinical practice-present a major limitation due to their uneven resolution across anatomical planes. We compared the performance of a model trained on anisotropic versus isotropic MRI reconstructions for the segmentation of pelvic muscle and nerve tissue.","url":"https://pubmed.ncbi.nlm.nih.gov/41701554/","authors":["Schram R","Ten Brink RSA","Bakker MAG","Tan CO","Kraeima J","de Vries JPM","Wijsmuller AR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May-Jun 01","doi":"10.1097/RCT.0000000000001828","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41700404","name":"From Bench to Bedside: Governing Health Care Artificial Intelligence (AI) through a \"True Lifecycle Approach\".","source":"pubmed","abstract":"This paper addresses the comprehensive regulation of artificial intelligence (\"AI\") across its entire lifecycle in the health care sector. It builds on a proposal for a True Lifecycle Approach (\"TLA\") to address governance gaps across three phases of AI and expands the framework with detailed practical insights for governing health care AI, drawing on pioneering examples from Qatar, Saudi Arabia, and the United Arab Emirates (\"UAE\") as models for global implementation. Beginning with the research and development phase, it highlights the urgent need for robust guidelines and certification processes to ensure that AI technologies are developed in compliance with ethical and safety standards. Moving into the approval stage, the discussion explores how AI systems can be effectively regulated under existing medical device frameworks, emphasizing the need for tailored regulations that consider the unique challenges posed by AI. Finally, the paper delves into the deployment of AI in clinical practice, examining the gaps in current laws and the need for a coherent and consistent regulatory framework that can adapt to AI advancements. The paper argues that the existing legal structures are inadequate, often inconsistent, and fail to address the complexities of AI in health care. It argues for a broader regulatory approach focused on patient safety throughout the AI lifecycle.","url":"https://pubmed.ncbi.nlm.nih.gov/41700404/","authors":["Solaiman B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1017/amj.2025.10091","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41700260","name":"Recent Updates and Advancements in the Diagnosis and Management of Plasma Cell Dyscrasias.","source":"pubmed","abstract":"Plasma cell dyscrasias (PCDs) encompass a heterogeneous group of disorders ranging from asymptomatic precursor states such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) to overt multiple myeloma (MM), primary plasma cell leukemia, and systemic amyloidosis. This review is based on a structured search of PubMed, Scopus, Cochrane Library, and Web of Science for literature published between 2015 and 2025, supplemented by reference screening and major international myeloma guidelines to ensure comprehensive coverage of recent advancements. Recent years have witnessed remarkable progress in understanding disease biology, refining diagnostic tools, and expanding therapeutic strategies. Advances in genomics and cytogenetics have deepened insight into clonal evolution and prognostic markers, while next-generation flow cytometry, mass spectrometry, and high-sensitivity serum free light chain assays are revolutionizing disease detection and minimal residual disease monitoring. Parallel improvements in imaging, including whole-body MRI and novel PET tracers, enhance accuracy in disease assessment, while artificial intelligence-driven models hold promise for predictive analytics and personalized care. Therapeutically, immunotherapies including monoclonal antibodies, bispecific antibodies, and chimeric antigen receptor T (CAR-T) cell therapies have transitioned from salvage settings to frontline use, providing deeper and more durable responses. Risk-adapted approaches, improved transplant strategies, and novel maintenance regimens are reshaping the standard of care. Advances also extend to the management of non-myeloma PCDs, supportive care, and survivorship, underscoring the importance of patient-centered approaches. Despite these gains, challenges persist in overcoming therapy resistance, managing costs, and ensuring equitable global access to novel treatments. Looking forward, integration of multi-omics with artificial intelligence, expansion of collaborative clinical trials, and strategies balancing cure vs. disease control will be pivotal in optimizing outcomes. This review synthesizes current evidence and highlights emerging directions shaping the evolving landscape of PCD management.","url":"https://pubmed.ncbi.nlm.nih.gov/41700260/","authors":["Sunil A","Ahmed AU","Musa AEA","Dar A","Kormath N","Sangaraju SL","Azeez N","Imam B","Dontul V","Singh S","Attia Hussein Mahmoud H","Rai M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.7759/cureus.101535","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41700131","name":"RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches.","source":"pubmed","abstract":"Fourteen million colonoscopies are performed annually just in the U.S. However, the videos from these colonoscopies are not saved due to storage constraints (each video from a high-definition colonoscope camera can be in tens of gigabytes). Instead, a few relevant individual frames are saved for documentation/reporting purposes and these are the frames on which most current colonoscopy AI models are trained on. While developing new unsupervised domain translation methods for colonoscopy (e.g. to translate between real optical and virtual/CT colonoscopy), it is thus typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model from scratch for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. We also release a first-of-its kind temporal dataset for colonoscopy for the above use cases. The datasets, accompanying code, and pretrained models will be made available on our Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP).","url":"https://pubmed.ncbi.nlm.nih.gov/41700131/","authors":["Mathew S","Nadeem S","Kaufman A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/978-3-032-05127-1_43","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41699682","name":"Mapping the global research landscape on HPV vaccine hesitancy: a machine-learning based bibliometric analysis.","source":"pubmed","abstract":"BACKGROUND: HPV vaccination remains the most effective strategy for cervical cancer prevention, yet hesitancy impedes global uptake. Given the rapidly expanding and fragmented literature, traditional reviews struggle to capture dynamic thematic shifts. This study utilized a machine-learning-enhanced bibliometric approach to map the knowledge domain and identify evolutionary trends in HPV vaccine hesitancy research. METHODS: We analyzed publications retrieved from the Web of Science Core Collection (WoSCC) between 2011 and 2025. VOSviewer, CiteSpace, and Bibliometrix were employed to construct co-authorship and citation networks. Furthermore, we applied Latent Dirichlet Allocation (LDA), a probabilistic topic modeling algorithm, to conduct an unsupervised analysis of abstract texts, enabling the detection of latent semantic themes and their temporal evolution. RESULTS: A total of 711 publications were identified, showing an exponential growth trajectory since 2017. The United States contributed over half of the global output (53.4%), revealing a geographical imbalance compared to low- and middle-income countries (LMICs). LDA modeling unveiled three distinct thematic clusters: (1) determinants of adolescent vaccination and parental decision-making; (2) public health strategies for improving uptake and knowledge; and (3) the disrupting impact of social media and COVID-19-related misinformation. While early research focused on &#x201c;access&#x201d; and &#x201c;safety&#x201d;, post-2020 topics have heavily shifted towards &#x201c;infodemics&#x201d; and &#x201c;trust&#x201d;. CONCLUSION: The research landscape on HPV vaccine hesitancy was characterized by significant geographic disparities, with high-income countries dominating the discourse despite the higher disease burden in LMICs. The application of LDA revealed a stagnation in traditional barrier studies and a critical pivot towards digital information ecosystems. These findings highlighted a disconnect between scholarly output and practical vaccination coverage, underscoring the need for research to pivot from descriptive surveys to intervention-based studies in underrepresented regions.","url":"https://pubmed.ncbi.nlm.nih.gov/41699682/","authors":["Ma J","Deng P","Lin B","Luo P","Wu W","Liu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.1186/s12985-026-03106-4","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41699523","name":"Determining risk factors predicting miscarriage among couples undergoing assisted reproductive treatment: a systematic review.","source":"pubmed","abstract":"BACKGROUND: Assisted reproductive techniques (ART) are an effective solution for infertility treatment. Miscarriage is a common and distressing complication, the incidence of which is much higher in couples undergoing ART than in natural pregnancies. This study aimed to identify and classify risk factors associated with miscarriage in couples undergoing ART. METHODS: In this systematic review study, a search was conducted in the databases including PubMed, Scopus, Web of Science, Ovid, BSCO Host, IEEE, Embase, Proquest, Cochrane Library, between January 2014 and February 2025, based on the PRISMA 2020 guidelines. Data from the articles included in the study were collected using a structured data collection form and then analyzed descriptively. RESULTS: A total of 17 studies were included in the study according to the eligibility criteria. Risk factors were classified into three main categories: baseline characteristics (demographics, lifestyle, and medical history), clinical characteristics (hormonal profiles, uterine abnormalities), and treatment characteristics (fetal quality, stimulation protocols). The study showed that parental age, high or low BMI, previous miscarriages, and unhealthy lifestyle habits (such as smoking, alcohol consumption, stress) were significant risk factors. From a clinical perspective, hormonal imbalance (such as Abnormal Follicle-Stimulating (FSH), Anti-Mullerian Hormone (AMH), and Thyroid Stimulating Hormone (TSH) levels), thin endometrium, and poor ovarian reserve were associated with an increased risk of miscarriage. Treatment characteristics such as embryo transfer type and protocol, ovarian stimulation protocols, embryo grading, and freeze-thaw cycles were effective in predicting miscarriage. CONCLUSION: Miscarriage is influenced by various and diverse factors. Understanding the predictive risk factors enables physicians to provide targeted counseling and preventive interventions. Screening, increased education, lifestyle modification, and ART treatment programs can also increase reproductive success. In the future, designing predictive models using artificial intelligence (machine and deep learning) could help improve decision-making and predict miscarriage.","url":"https://pubmed.ncbi.nlm.nih.gov/41699523/","authors":["Fatemi Aghda SA","Langarizadeh M","Mangoli E","Sayadi M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.1186/s12884-026-08819-6","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41699418","name":"CT-based radiomics-clinical machine-learning model to predict completeness of cytoreduction in colorectal peritoneal metastases.","source":"pubmed","abstract":"Completeness of cytoreduction (CC) remains the strongest prognostic determinant after cytoreductive surgery (CRS)&#x2009;&#xb1;&#x2009;hyperthermic intraperitoneal chemotherapy (HIPEC) for colorectal peritoneal metastases (CPM) yet accurate pre-operative prediction remains difficult. This study aimed to develop and validate a radiomic-clinical machine-learning model to predict cytoreduction completeness.","url":"https://pubmed.ncbi.nlm.nih.gov/41699418/","authors":["Pau S","Eglinton T","Wang A","Mehri-Kakavand G","Fischer J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/codi.70409","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:41698969","name":"Performance evaluation of generative pre-trained transformer on the National Veterinary Licensing Examination in Japan.","source":"pubmed","abstract":"Generative Pre-trained Transformer (GPT) models, which are large language models based on the transformer architecture, have enabled natural-language interaction with humans. GPT models have demonstrated high scores on National Medical Licensing Examination in various countries with translation. However, their performance on the National Veterinary Licensing Examination (NVLE) in Japan has not yet been explored. In this study, we evaluated GPT-4o, o1, and o3 on the 74th (2023) NVLE in Japan to compare the models, prompt designs (normal vs. optimized), and languages (Japanese vs. English). We then validated the best performance on the 75th (2024) and 76th (2025) NVLE using o3 with Japanese input and the normal prompt. As a result, o3 with Japanese input and the Normal prompt achieved the highest performance on the 74th NVLE, and both o1 and o3 outperformed GPT-4o. Furthermore, the validation tests using the 75th and 76th NVLE showed that o3 exceeded the minimum passing scoring rate in all sections, achieving an overall score of 92.9%. These findings indicate that recent GPT models can reliably answer the Japanese NVLE without requiring translation or elaborate prompt engineering, highlighting their potential as supportive tools in veterinary education and knowledge assistance in Japan.","url":"https://pubmed.ncbi.nlm.nih.gov/41698969/","authors":["Kako T","Kato D","Iguchi T","Qin S","Ando M","Koseki S","Shibahara H","Motoi H","Isaka R","Ikeda N","Toyoda H","Nakagawa T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.1038/s41598-026-37300-9","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41698858","name":"Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.","source":"pubmed","abstract":"Radiology reports are typically written in language that is difficult for patients to understand. Large language models (LLMs) excel at simplifying text. We aimed to evaluate the ability of LLMs to improve the understanding of radiology reports.","url":"https://pubmed.ncbi.nlm.nih.gov/41698858/","authors":["Alabed S","Anderson A","Maiter A","Hughes A","McAnenly N","Salehi M","Sharkey M","Dwivedi K","Hokmabadi A","Alahdab F","Stevenson M","Ma N","Gaizauskas R","Chico TJ","Swift AJ","Li JJ","Kleesiek J","Langlotz C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.landig.2025.100960","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41697293","name":"Development and comparison of machine learning models for predicting postoperative ileus after posterior thoracolumbar fracture surgery.","source":"pubmed","abstract":"PURPOSE: Postoperative ileus (POI) represents a frequent complication after posterior thoracolumbar fracture surgery. This study aimed to identify POI risk factors and construct predictive models enabling early identification and targeted intervention of vulnerable individuals. METHODS: A literature review were conducted to quantify POI incidence and establish evidence-based predictors for variable selection. Subsequently, a retrospective cohort from the Second Affiliated Hospital of Wenzhou Medical University was used for model development and internal validation. Feature selection incorporated the least absolute shrinkage and selection operator (LASSO) regression with multivariate logistic regression, followed by predictive modeling using five distinct algorithms: logistic regression (LR), random forest (RFC), categorical boosting (CatBoost), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM). Model interpretability was augmented through SHapley Additive exPlanations (SHAP) analysis. RESULTS: The literature review encompassed 20 eligible studies, determining a pooled POI incidence of 8.9% (95% CI: 6.5&#x2013;11.3). The training and testing cohorts comprised 493 and 210 patients, respectively. Among all models, CatBoost achieved peak accuracy (0.867) and specificity (0.960), with AUROC values of 0.906 (95% CI: 0.868&#x2013;0.941) in the training set and 0.772 (95% CI: 0.665&#x2013;0.860) in the testing set. SHAP analysis identified surgery duration, postoperative 24&#xa0;h NRS score&#x2009;&#x2265;&#x2009;3, and the number of levels involved in surgery as the top three predictors of POI. CONCLUSION: By integrating evidence synthesis with machine learning, this study establishes a clinically applicable POI prediction framework. The CatBoost model showed strong predictive performance and, when combined with a risk web calculator, offers a practical tool for early identification of high-risk patients, ultimately supporting precision medicine initiatives in spinal trauma care.","url":"https://pubmed.ncbi.nlm.nih.gov/41697293/","authors":["Zhang Y","Zheng R","Gao H","Zhou Y","Li J","Chen L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s00586-026-09813-4","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41696692","name":"Infectious disease burden and surveillance challenges in Jordan and Palestine: a systematic review and meta-analysis.","source":"pubmed","abstract":"Jordan and Palestine face public health challenges due to infectious diseases, with the added detrimental factors of long-term conflict, forced relocation, and lack of resources. Added to these are the increased rates of morbidity and mortality from having limited healthcare services available due to a lack of funding, poor disease surveillance systems, and entrenched systemic weaknesses. The purpose of this systematic review was to report the prevalence of infectious diseases in Jordan and Palestine in order to inform the development of targeted public health programs that use both standard and novel approaches to reduce the region's disease burden.","url":"https://pubmed.ncbi.nlm.nih.gov/41696692/","authors":["Badran EF","Rayyan A","Al Jaberi M","Azzam M","Ramadan R","Khader Y","Alqutob R","Bakri FG","Qasrawi R","Yacoub T","Sharaqa A","Fraihat N","Trigui H","Sokhn E","Tayyem R","Musa E","Kong JD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1713089","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41696659","name":"BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning.","source":"pubmed","abstract":"Current benchmarks evaluating large language models (LLMs) in ophthalmology are narrow and disproportionately prioritize accuracy. We introduce BEnchmarking LLMs for Ophthalmology (BELO), a standardized evaluation benchmark developed through multiple rounds of expert checking by 13 ophthalmologists. BEnchmarking LLMs for Ophthalmology assesses ophthalmology-related knowledge and reasoning quality.","url":"https://pubmed.ncbi.nlm.nih.gov/41696659/","authors":["Srinivasan S","Ai X","Lo TWS","Gilson A","Zou M","Zou K","Kim H","Yang M","Pushpanathan K","Yew SME","Loke WT","Goh JHL","Chen Y","Kong Y","Fu EY","Ong M","Nwanyanwu K","Dave A","Li KZ","Sun CH","Chia M","Yang GD","Wong WM","Chen DZ","Liu D","Singer M","Antaki F","Del Priore LV","Jonas JB","Adelman R","Chen Q","Tham YC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.xops.2025.101050","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41696545","name":"Brugada syndrome risk scores: what we've learned and what's next.","source":"pubmed","abstract":"Brugada Syndrome (BrS) is a rare but clinically significant inherited arrhythmia disorder characterized by a type 1 ECG pattern and an increased risk of sudden cardiac death (SCD). Since its first description in 1992, BrS has been the subject of intensive investigation, yet risk stratification remains one of its greatest challenges. While survivors of cardiac arrest and patients with documented ventricular fibrillation (VF) are clear candidates for implantable cardioverter-defibrillators (ICDs), predicting risk in asymptomatic or intermediate-risk individuals is less straightforward. Over the past two decades, multiple risk scores have been developed-including the Sieira, Shanghai, BRUGADA-RISK, and PAT-each integrating combinations of clinical, ECG, electrophysiological study (EPS), and genetic data. Performance metrics vary, with C-statistics ranging from 0.70 to 0.82 in derivation cohorts, but external validation has often been limited. Importantly, current ESC and AHA/ACC guidelines only endorse syncope and EPS inducibility as validated predictors, reflecting the cautious stance of expert panels in the face of heterogeneous data. Nonetheless, the emergence of structured risk models has improved our ability to stratify intermediate-risk patients and stimulated further innovation. Looking ahead, opportunities lie in integrating artificial intelligence applied to raw ECG waveforms, wearable technology for dynamic monitoring, advanced cardiac imaging biomarkers, and polygenic risk scores. Multinational collaboration and federated learning will be essential to overcome statistical fragility and ensure global applicability. Ultimately, BrS risk scores should be considered decision-support tools that enrich but do not replace clinical judgment. Shared decision-making remains central, particularly in asymptomatic patients where ICD implantation is not a clear-cut choice.","url":"https://pubmed.ncbi.nlm.nih.gov/41696545/","authors":["Rattanawong P","Shen WK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fcvm.2025.1715146","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41696043","name":"Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.","source":"pubmed","abstract":"The aim of this study is to evaluate the performance of DL algorithms in diagnosing early gastric cancer (EGC) using white light endoscopic images.","url":"https://pubmed.ncbi.nlm.nih.gov/41696043/","authors":["Liu J","Li D","Zhuo Y","Zhang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1734591","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41695907","name":"An Evaluation of Lumbar Foraminal Stenosis Radiologic Grading Systems: A Systematic Review.","source":"pubmed","abstract":"Symptomatic lumbar foraminal stenosis (LFS) occurs when the neuroforamen narrows, compressing the exiting spinal nerve, leading to symptoms such as radicular pain, paresthesias, and potentially weakness. Although cross-sectional imaging studies are used for diagnostic purposes, there is no clear consensus as to which grading system best evaluates LFS, predisposing to inconsistencies in care. This systematic review aimed to evaluate and compare existing published grading systems for LFS to identify (1) systems most used within the literature and (2) the most effective and reliable method for classifying anatomic severity and clinical symptom correlation.","url":"https://pubmed.ncbi.nlm.nih.gov/41695907/","authors":["Ndjonko LCM","Kralimarkova NN","Chakraborty Y","Bajwa ZS","Zimmer JX","Taiwo AA","Bah IN","Khan SS","Holder EK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 27","doi":"10.22603/ssrr.2025-0056","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41695746","name":"Artificial intelligence for patent ductus arteriosus-a systematic review.","source":"pubmed","abstract":"Optimal management of patent ductus arteriosus (PDA) remains controversial. Complexity in severity appraisal, high-dimensional data, and the need for longitudinal, individualized assessment make PDA a compelling candidate for Artificial Intelligence (AI)-driven approaches. This systematic review evaluates AI research in the context of PDA, identifying strengths, limitations, and future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41695746/","authors":["Long SE","Uden T","Peter C","Oeltze-Jafra S","Beerbaum P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1648943","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41695515","name":"Atrial fibrillation detection performance of an insertable cardiac monitor: Results from an Assert-IQ post-market clinical study and a novel artificial intelligence algorithm.","source":"pubmed","abstract":"Accurate atrial fibrillation (AF) detection and burden assessment are critical features of modern insertable cardiac monitors (ICMs), enabling precise determination of AF episode patterns, frequency, duration, and total burden to guide treatments.","url":"https://pubmed.ncbi.nlm.nih.gov/41695515/","authors":["Birgersdotter-Green U","Ojeda W","Manyam H","Garcia AM","Manoukian GE","Jazayeri MA","Cuoco F","Han F","Katcher M","Gopinathannair R","Yoo D","Feng L","Qu F","Lin W","Lee K","Charan V","Mittal S","Lakkireddy D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.hroo.2025.10.021","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41695236","name":"Progressing towards global hepatitis C elimination: a systematic review and meta-analysis of care cascades in key populations.","source":"pubmed","abstract":"Hepatitis C virus (HCV) remains a global health concern, with cascade gaps hindering elimination. We aim to assess and compare HCV care cascades in four key populations: people living with HIV (PLHIV), people who inject drugs (PWID), men who have sex with men (MSM), and incarcerated individuals.","url":"https://pubmed.ncbi.nlm.nih.gov/41695236/","authors":["Zou Z","Cao Y","Zhang Y","Hu Y","Wang Q","Su S","Zhang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.lanwpc.2026.101803","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41695046","name":"Can artificial intelligence pass the test? Evaluating chatbot scores on pediatric gastroenterology board-style questions.","source":"pubmed","abstract":"The American Academy of Pediatrics (AAP) Pediatrics Review and Education Program (PREP)&#xae; Gastroenterology (GI) Self-Assessments help pediatric gastroenterologists and trainees prepare for subspecialty board exams by providing peer-reviewed questions and critiques based on American Board of Pediatrics content specifications. These assessments test knowledge of material aligned with the pediatric gastroenterology board exams. While artificial intelligence (AI) chatbots have passed various medical board exams, their ability to pass the pediatric GI boards remains untested. This study assesses the performance of Microsoft Copilot and OpenAI ChatGPT-3.5 and 4o on the 2022-2024 AAP PREP&#xae; GI Self-Assessments.","url":"https://pubmed.ncbi.nlm.nih.gov/41695046/","authors":["Roberts AG","Patel R","Babu S","Engelhard MM","Greenberg RG","Ajmera A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/jpr3.70121","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694895","name":"Evaluation of Large Language Models in the Diagnosis, Urgency Triage, and Initial Management of Ophthalmic Emergencies.","source":"pubmed","abstract":"Introduction Artificial intelligence (AI) technologies are progressing rapidly and becoming an integral part of how healthcare professionals obtain medical knowledge. Large language models (LLMs) now enable clinicians to have direct access to medical guidance and support in clinical reasoning. In ophthalmology, where prompt identification of sight-threatening symptoms is essential, these tools can offer diagnostic support, urgency triaging, and initial management guidance, thus potentially reducing delays in care and improving referrals. Limited evidence exists regarding their accuracy, reliability, and safety in eye emergencies. This study aims to compare the diagnostic accuracy, urgency triage, and initial management advice generated by the three leading LLMs, to evaluate their prospective role in the early assessment and management of acute eye presentations.&#xa0; Methods This cross-sectional study compared the performance of three LLMs, including ChatGPT-5 (2025, OpenAI, San Francisco, CA, USA), Google Gemini 2.5 Pro (2025, Google DeepMind, London, UK), and Claude Opus 4.1 (2025, Anthropic, San Francisco, CA, USA), using a set of 40 standardised ophthalmic emergency vignettes across five key subspecialties within ophthalmology. Each vignette was entered into each LLM, and responses were evaluated for diagnostic accuracy (2 points), urgency recognition (2 points), initial management advice (3 points), and identification of red flag symptoms (1 point). Each vignette case had a minimum possible score of 0 and a maximum possible score of 8. Scores were compared across the three models, and statistical significance was assessed using the Wilcoxon signed-rank and Friedman tests.&#xa0; Results In this analysis, 40 clinical vignettes were each evaluated across three LLMs: ChatGPT-5, Gemini 2.5 Pro, and Claude Opus 4.1, producing 120 responses in total. Overall scores were similar across ChatGPT (6.88 &#xb1; 1.16), Gemini (7.03 &#xb1; 1.21), and Claude (6.93 &#xb1; 1.19), with no significant differences identified on statistical analysis. Additional comparison across diagnostic scores, urgency triage, red flag recognition, and management scores yielded no significant differences between any of the LLMs. Further subgroup analysis comparing subspecialties similarly yielded no significant differences across all LLMs.&#xa0; Conclusion This study demonstrates that ChatGPT-5, Google Gemini 2.5 Pro, and Claude Opus 4.1 show consistent performances in diagnosing, triaging, and providing management advice for ophthalmic emergencies from text-based clinical vignettes. All models achieved diagnostic accuracies above 80% and provided management advice in line with recognised ophthalmology guidelines, with no statistically significant differences between their overall performances. These findings showcase the potential of LLMs as support tools for ophthalmic clinical advice, particularly for non-specialists, where guidance is valuable. However, their diagnostic errors and consequent suboptimal management advice emphasise the need for ongoing development and human supervision to ensure safety before widespread clinical application.","url":"https://pubmed.ncbi.nlm.nih.gov/41694895/","authors":["Mittal S","Aggarwal Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.7759/cureus.101433","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694682","name":"Early hypoxia prediction in diseased patients via wheezing sounds in respiration: a prospective cohort study.","source":"pubmed","abstract":"Early detection of hypoxia in the emergency room may reduce complications. Breath sounds can be evaluated immediately. Our research endeavors to investigate the relationship between breath sounds and oxygen demand.","url":"https://pubmed.ncbi.nlm.nih.gov/41694682/","authors":["Huang CH","Fan CY","Chen CH","Sung CW","Chen CY","Lin SY","Tzeng JT","Lee CC","Sheed A","Chou EH","Huang EP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1649991","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694647","name":"A Differential Evolution-Based Optimized Ensemble for Balanced and Imbalanced Medical Datasets.","source":"pubmed","abstract":"Class imbalance is a frequent and severe problem in medical datasets, where instances from the minority class are usually high risk or disease positive. Most traditional classifiers suffer from a biasness towards the majority class, resulting in a poor detection rate of the minority class and, therefore, decreased confidence in prediction systems in medical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41694647/","authors":["Das S","Nayak SP","Sahoo B","Champati Rai S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/f1000research.169456.2","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694236","name":"Data Fit for Health Equity: Learning Health Systems, AI, and the STANDING Together Recommendations.","source":"pubmed","abstract":"Artificial Intelligence (AI) tools may deliver significant improvements in healthcare and Learning Health Systems are well positioned to benefit. However, during the adoption of AI, Learning Health Systems should consider the potential for AI to exacerbate health inequity and perpetuate biases that exist in healthcare and its associated data.","url":"https://pubmed.ncbi.nlm.nih.gov/41694236/","authors":["Laws E","Cockburn N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1002/lrh2.70053","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694177","name":"Correction: The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south.","source":"pubmed","abstract":"[This corrects the article DOI: 10.3389/fdgth.2025.1535018.].","url":"https://pubmed.ncbi.nlm.nih.gov/41694177/","authors":["Tan MJT","Lichlyter DA","Maravilla NMAT","Schrock WJ","Ting FIL","Choa-Go JM","Francisco KK","Byers MC","Abdul Karim H","AlDahoul N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1775230","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41694042","name":"Vascular Robotics: Webcast.","source":"pubmed","abstract":"This 55-minute webcast features a conversation about \"Vascular Robotics\"-the focus of Issue 21.5. Led by the issue's editors, the discussion engages the authors on emerging themes and lessons learned while researching and writing the articles. View the video at https://vimeo.com/event/5556427.","url":"https://pubmed.ncbi.nlm.nih.gov/41694042/","authors":["Quiñones MA","Lumsden A","Corr S","Watson J","Roy TL","Bavare C","Tavallaei MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.14797/mdcvj.1770","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41693685","name":"Effects of Mental Health on 30-Day Postoperative Outcomes Following Peripheral Nerve Repair.","source":"pubmed","abstract":"Peripheral nerve injuries (PNIs) are a significant cause of global disability, often leading to lifelong sensory and motor deficits. Increasing efforts to unveil the psychosocial implications of such injuries is being made. The aim of this study was to determine the impact of preoperatively diagnosed mental health disorder (MHD), specifically major depressive disorder (MDD), generalized anxiety disorder (GAD), and post-traumatic stress disorder (PTSD), on short-term outcomes following PNI repair.","url":"https://pubmed.ncbi.nlm.nih.gov/41693685/","authors":["Reid J","Zieminski C","Martino JA","DeSalvo J","Daley D","Daly C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.1177/15589447261416976","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41693448","name":"Strengthening health technology assessment (HTA) in the European Union: insights from Slovenia's implementation journey.","source":"pubmed","abstract":"Slovenia has engaged with Health Technology Assessment (HTA) for over two decades, but its system remains fragmented and underdeveloped. Until recently, responsibilities for evaluating health technologies were dispersed across multiple institutions without a central coordinating body or standardized methodology. Medicinal products have been subject to structured evaluation through the Health Insurance Institute of Slovenia, while other health technologies, including medical devices, diagnostics, and preventive interventions, have followed less consistent pathways under the Ministry of Health. The adoption of the European Union Health Technology Assessment Regulation), entering into force in January 2025, has provided new impetus for reform, requiring Slovenia to designate a national HTA body to participate in joint clinical assessments and align national processes with EU standards.","url":"https://pubmed.ncbi.nlm.nih.gov/41693448/","authors":["Beravs-Bervar K","Goettsch W","Gutierrez-Ibarluzea I","Skali A","Rűther A","Lipska I","Turk E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 16","doi":"10.1017/S0266462325103383","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41693437","name":"Comment on 'Can ChatGPT pass the urology fellowship examination? Artificial intelligence capability in surgical training assessment'.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41693437/","authors":["Daungsupawong H","Wiwanitkit V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1111/bju.70050","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41692794","name":"In-hospital testing of NIVPredict - an AI tool for early prediction of non-invasive ventilation outcome in acute respiratory failure.","source":"pubmed","abstract":"BACKGROUND: Successful non-invasive ventilation (NIV) reduces ICU length of stay, the need for intubation and the risk of death. However, patients who fail NIV and require intubation have a higher risk of death. We developed NIVPredict, an easy-to-use web-based AI tool to predict NIV outcome within two hours of initiation in patients with acute respiratory failure (ARF) from diverse aetiologies and tested its useability in a hospital setting. METHODS: This study included data from immunocompromised and immunocompetent patients with hypoxemic ARF due to pneumonia, sepsis or COVID-19, and hypercapnic ARF due to acute exacerbation of chronic obstructive pulmonary disease or obesity hypoventilation syndrome. The tool uses the recently proposed Tabular Prior-Data Fitted Network (TabPFN) machine learning model and was trained using a dataset of routinely collected measurements taken within one hour after NIV initiation in 665 ARF patients from the recent RENOVATE trial in Brazil. Initial external validation of the model was conducted on a dataset of 422 ARF patients from Italy, Spain, and the USA. Subsequently, the useability of a web-based tool based on the model was tested by clinicians at the University Hospitals of North Midlands NHS Trust in the UK between December 2024 and November 2025, who applied it to data collected from 57 eligible ARF patients. RESULTS: The AI tool provided accurate and robust prediction of NIV outcomes and consistently outperformed conventional clinical indices across all validation settings. In internal repeated cross-validation, external validation, and in-hospital testing, the tool achieved AUCs of 0.793, 0.772, and 0.858, vs. 0.717, 0.709, and 0.693 for the best clinical index (Updated HACOR score), and balanced accuracies of 78.9%, 74.5%, and 85.0%, vs. 68.7%, 63.7%, and 67.6% for the best clinical index (HACOR or Updated HACOR score), respectively. CONCLUSIONS: This study demonstrates superior predictive performance, compared to current clinical indices, of an AI-based tool for NIV outcome prediction on a cohort of patients with overt-acute and acute-on-chronic respiratory failure. Clinical useability of the tool was confirmed via testing by clinicians in a hospital setting, motivating its future evaluation in prospective multi-centre studies.","url":"https://pubmed.ncbi.nlm.nih.gov/41692794/","authors":["Yu H","Saffaran S","Ali A","Henry C","Mustfa N","Thomas A","Rajhan A","Isrhad S","Weaver L","Tonelli R","Menga LS","Zhang Q","Samadi ME","Schuppert A","Laffey JG","Camporota L","Esquinas AM","Grieco DL","Antonelli M","de Lima LM","Kawano-Dourado L","Maia IS","Cavalcanti AB","Clini E","Scott TE","Bates DG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 15","doi":"10.1186/s13054-026-05894-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41692645","name":"Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure.","source":"pubmed","abstract":"Acute liver failure (ALF) is a rapidly progressive and life-threatening condition that requires accurate risk stratification. Existing prognostic tools have limited sensitivity and generalizability. This study aimed to develop and externally validate a machine learning-based modeling framework for early in-hospital dynamic prediction of in-hospital mortality in patients with acute liver failure.","url":"https://pubmed.ncbi.nlm.nih.gov/41692645/","authors":["Wu X","Song Q","Li D","Liu Z","Wang X","Yang R","He Y","Yang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.dld.2026.01.226","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41692610","name":"NPSA 2025 Presidential Address:Innovation, clinical translation, and leadership in surgery: From concept to impact.","source":"pubmed","abstract":"Innovation is frequently invoked as an essential driver of progress in modern surgery, yet its definition, implementation, and leadership aspects remain inconsistently understood. Drawing on clinical experience, systems-based research, and translational examples, this narrative review explores innovation as a continuum, from the generation of an idea all the way to scalable impact, within contemporary surgical practice. We examine the distinction between invention and innovation, the importance of environmental context, and the traits associated with sustained productivity. Clinical exemplars including advanced hemostatic technologies, hybrid trauma operating environments, equity-focused surgical care, artificial intelligence-enabled pathways, and austere or space-based surgical planning are presented to illustrate the innovation cycle from conception to implementation. Finally, we discuss leadership principles required to cultivate meaningful, durable innovation in surgery, emphasizing significance over individual success. Together, these perspectives frame innovation not as disruption alone, but as a deliberate, collaborative process that improves patient outcomes and advances the surgical profession.","url":"https://pubmed.ncbi.nlm.nih.gov/41692610/","authors":["Ball CG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.amjsurg.2026.116861","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41691690","name":"Retinal Biomarkers for Cardiovascular Disease Prediction: A Review Focused on CHD AHD Valvular Disorders and Cardiomyopathies.","source":"pubmed","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with congenital heart disease (CHD), acquired heart disease (AHD), valvular disorders, and cardiomyopathies contributing significantly to morbidity. Retinal fundus imaging has emerged as a non-invasive modality capable of capturing microvascular alterations that may serve as biomarkers for systemic cardiovascular dysfunction.","url":"https://pubmed.ncbi.nlm.nih.gov/41691690/","authors":["Chandrashekar AB","Rao SN","Mahadevappa M","Mudduveerappa BM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2174/011573403X421729251114113706","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41691272","name":"Mapping phenotypic heterogeneity and cardiometabolic risk in obesity using a tree-based dimensionality reduction framework.","source":"pubmed","abstract":"BACKGROUND: The population-level heterogeneity of obesity has yet to be systematically investigated. We aimed to apply the data dimensionality reduction tree (DDRTree) method to obese individuals and to examine how distinct phenotypic patterns align with different outcomes. METHODS: To characterize the heterogeneity of obesity, a two-dimensional (2D) tree structure based on the DDRTree algorithm was employed. Associations between embedding dimensions and metabolic traits, obesity-related indices, and clinical outcomes were evaluated using multivariable linear, logistic, and Cox proportional hazards regression models. RESULTS: The DDRTree revealed distinct, dimension-specific phenotypic patterns. We found that dimension 1 was strongly associated with insulin resistance, dysglycemia, visceral adiposity, subclinical atherosclerosis, hyperuricemia, and early renal injury, including microalbuminuria (MAU) (all P&#x2009;&lt;&#x2009;0.001). Dimension 2 was more closely aligned with &#x3b2;-cell function and was associated with all-cause mortality (ACM) in the CHARLS cohort (P&#x2009;&lt;&#x2009;0.05). Individuals in the upper-right section of the tree exhibited a higher risk of vascular abnormalities, while the lower-right region clustered obese individuals with pronounced insulin resistance, hyperuricemia, and hyperglycemia. Obesity-related indices demonstrated heterogeneity: waist-based measures (WC, WHTR, ABSI, VAT) consistently aligned with Dimension 1, while lipid- and liver-related indices (LAP, VAI, FLI) showed enrichment along combined phenotypic gradients. CONCLUSIONS: Our findings demonstrate that DDRTree can be applied to obese populations to characterize continuous phenotypic heterogeneity and its associations with metabolic and clinical outcomes. This phenotypic mapping framework may support risk-oriented stratification of obese individuals in population-based settings. CLINICAL TRIAL NUMBER: Not applicable. TRIAL REGISTRATION: Not applicable.","url":"https://pubmed.ncbi.nlm.nih.gov/41691272/","authors":["Zhao Y","Wang W","Wang Z","Ma J","Zhang J","Shao J","Zhou K","Pan Q","Nie Z","Xu G","Guo L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 14","doi":"10.1186/s12967-026-07867-y","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41691237","name":"Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.","source":"pubmed","abstract":"Current preoperative assessment faces limitations, including PI-RADS scoring subjectivity and diagnostic uncertainty in distinguishing high-risk prostate cancer from benign and low-risk lesions. To develop an interpretable ensemble learning framework integrating habitat-based radiomics and peritumoral analysis from multiparametric MRI for preoperative high-risk prostate cancer prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/41691237/","authors":["Yuan M","Chang D","Lu W","Ma K","Gu Y","Xia T","Peng J","Zhang Y","Fu L","Zhao B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 14","doi":"10.1186/s12967-026-07848-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41691226","name":"An interpretable machine learning model for preoperative prediction of aldosterone secretion and CYP11B2 status of adrenal gland.","source":"pubmed","abstract":"To investigate whether a machine learning (ML) model integrating CT-based radiomics and clinical features can noninvasively evaluate the aldosterone secretion and CYP11B2 status of the adrenal gland, using Shapley Additive Explanations (SHAP) for model interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/41691226/","authors":["Zhou S","Liu Z","Fang W","Xu X","Gong X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 14","doi":"10.1186/s12880-026-02226-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41690862","name":"U-Net and YOLOv8 Artificial Intelligence Models for Automated Recognition of Internal Jugular Veins and Radial Arteries: A Foundational Study for Artificial Intelligence-guided Vascular Cannulation inPoint-of-care Ultrasound.","source":"pubmed","abstract":"This study compares the U-Net and You Only Look Once version 8 (YOLOv8) models for identifying internal jugular veins (IJVs) and radial arteries (RAs) in longitudinal and/or transversal ultrasound views, evaluating their vascular recognition capabilities for artificial intelligence-guided cannulation systems under point-of-care ultrasound (POCUS) visualization.","url":"https://pubmed.ncbi.nlm.nih.gov/41690862/","authors":["Gu Y","Tang G","Gao L","Li R","Zhang Y","Li R","Wu H","Yu L","Li H","Han Y","Dong B","Wu D","Shao J","Chen Y","Peng M","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1053/j.jvca.2025.07.040","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41690097","name":"Towards real time AI-augmented fluorescence-guided surgery: Evidence and translational readiness across neurosurgical, gynaecological, and thoracic oncology.","source":"pubmed","abstract":"Fluorescence-guided surgery (FgS) is increasingly used across oncologic specialties to enhance intraoperative visualisation of tumour tissue and lymphatic drainage; however, its clinical impact remains limited by heterogeneous tracer uptake, variable signal intensity, and reliance on subjective visual interpretation, leading to inter-operator variability, uncertainty at tumour margins, residual disease, and inconsistent nodal assessment. This narrative review examines the role of artificial intelligence (AI) in addressing these limitations, synthesising evidence published between January 2000 and December 2025 across neuro-oncology, gynaecological oncology, and thoracic oncology. In neuro-oncology, early clinical and preclinical studies have directly evaluated real-time AI-enhanced interpretation of intraoperative fluorescence, including quantitative analysis of 5-aminolevulinic acid (5-ALA) and hyperspectral imaging, providing proof-of-concept evidence that AI can augment margin detection beyond subjective visual assessment. In contrast, gynaecological and thoracic oncology currently lack validated studies in which AI directly interprets intraoperative fluorescence signals, despite fluorescence imaging being clinically established in both fields; instead, AI development in these specialties has progressed primarily in adjacent domains such as radiomics, digital pathology, risk stratification, surgical planning, and intraoperative computer vision, demonstrating technical maturity but limited integration into fluorescence-guided decision-making. Overall, the available evidence supports proof-of-concept feasibility for real-time AI-enhanced fluorescence interpretation in neuro-oncology, while identifying a clear translational gap in gynaecological and thoracic oncology that warrants targeted research to integrate existing AI capabilities into intraoperative fluorescence-guided surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/41690097/","authors":["Shafi O","Mirzarakhimov M","Martin S","Gabriel D","Chan UH","Phadnis S","Asif H","Camacho M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1016/j.suronc.2026.102364","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41689313","name":"Artificial Intelligence-Based Digital Image Analysis for Assessing Ki67, P53, and PHH3 Expression in Glioblastoma Multiforme.","source":"pubmed","abstract":"To compare artificial intelligence (AI)-based analysis of Ki67, PHH3, and p53 immunohistochemical (IHC) biomarkers in glioblastoma multiforme (GBM) with conventional evaluations performed by experienced pathologists, and to assess the consistency and statistical significance of both approaches across different sampling areas.","url":"https://pubmed.ncbi.nlm.nih.gov/41689313/","authors":["Devrim T","Erkilinc G","Tuncer SS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.29271/jcpsp.2026.02.153","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41689243","name":"Structure of AI Responses With Complex Patient Analysis for Patient Alternatives and Patient Modifiers.","source":"pubmed","abstract":"Artificial intelligence (AI) is already a powerful tool that is rapidly growing within the dental sector. Reports of structure and characteristics of AI responses to patient scenarios are limited.","url":"https://pubmed.ncbi.nlm.nih.gov/41689243/","authors":["Johnsen DC","Marchini L","Vo K","Young LB","Dabdoub SM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 13","doi":"10.1111/eje.70108","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41688086","name":"Corrigendum: Clinical Implementation of Artificial Intelligence Scribes in Health Care: A Systematic Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41688086/","authors":["Hassan H","Zipursky AR","Rabbani N","You JG","Tse G","Orenstein E","Ray M","Parsons C","Shin S","Lawton G","Jessa K","Sung L","Yan AP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Aug","doi":"10.1055/a-2790-1283","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41687655","name":"THE IMPACT OF VISION 2030 ON PHARMACY STUDENTS' CAREER OUTLOOKS AND SPECIALIZATION CHOICES: A CROSS-SECTIONAL ANALYSIS.","source":"pubmed","abstract":"Saudi Arabia's Vision 2030 has introduced substantial reforms aimed at transforming the healthcare sector, including the expansion of advanced clinical roles, digital health integration, and workforce localization. Understanding how these reforms influence pharmacy students' career outlooks and specialization choices is essential for aligning pharmacy education with national workforce priorities.","url":"https://pubmed.ncbi.nlm.nih.gov/41687655/","authors":["Alhur A","Alharajeen R","Alshabanah A","Alghuwainem J","Almukhlifi M","Al Alshikh A","Alsubaie N","Al Sinan A","Alotaibi R","Alamri N","Alshammari A","Alasmari N","Alqurashi D","Alharthi S","Alosaimi R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41685330","name":"Leveraging cfDNA fragmentomic features for the early detection of colorectal cancer.","source":"pubmed","abstract":"Early detection of colorectal cancer (CRC) is crucial for improving patient outcomes. Cell-free DNA (cfDNA) analysis has emerged as a promising non-invasive approach for cancer detection. This study aims to develop a machine learning algorithm leveraging cfDNA fragmentomic features to accurately detect CRC.","url":"https://pubmed.ncbi.nlm.nih.gov/41685330/","authors":["Shan L","Xu D","Chen J","Liu W","Lin J","Bao J","Huang J","Zhang H","Zhao H","Xue W","Lin Z","Bai B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1705156","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41685257","name":"Peritoneal dialysis effluent biomarkers from a multi-omics and artificial intelligence perspective: advances and challenges.","source":"pubmed","abstract":"Long-term peritoneal dialysis (PD) treatment can lead to the destruction of peritoneal structure and function, which can lead to PD failure or even a poor prognosis. However, validated early biomarkers for patients undergoing PD are lacking. PD effluent (PDE) is rich in various biological components, such as nucleic acids, proteins, and metabolites, and is now an important source of noninvasive biomarkers for the dynamic monitoring of disease progression. In recent studies, a variety of histological techniques have provided unprecedented depth and breadth to PD biomarker research, and are becoming key tools in the early diagnosis, prognosis, and therapeutic monitoring of PD patients. Correspondingly, artificial intelligence (AI) approaches, which can flexibly handle data and excel at mining nonlinear and high-dimensional relationships in multimodal data, have moved from theory to practice. AI-based multi-omics analysis has not only greatly improved the understanding of the pathophysiological mechanisms of PD-associated fibrosis (PF) but has also contributed to the development of new biomarkers and novel targets. This review provides a comprehensive summary of recent advances in the development of PDE biomarkers using AI-based multi-omics approaches. We highlight the application of AI-based multi-omics techniques for early diagnosis, evaluation of peritoneal injury, assessment of peritoneal function, and prediction of prognosis. Finally, we discuss the challenges and limitations of PDE biomarkers from the perspectives of multi-omics and AI. In conclusion, AI-based multi-omics analysis holds great promise for the development of PDE biomarkers, which are expected to significantly improve the prognosis of PD patients and ultimately facilitate precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/41685257/","authors":["Li H","Yu F","Wang X","Yao Y","Xu H","Cai Y","Xin S","Chen K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1093/ckj/sfaf378","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41685160","name":"Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.","source":"pubmed","abstract":"Nanoparticulate drug delivery systems (NDDS) have revolutionized modern medicine by significantly improving drug targeting, bioavailability, and therapeutic efficacy. Despite the clinical success of over 90 approved nanomedicines, the development of NDDS remains challenging due to the complexity of formulation design, optimization, and characterization processes. Artificial intelligence, particularly machine learning (ML), offers powerful data analytics and predictive capabilities that can address these challenges. This review systematically summarizes recent advances in ML applications across various NDDS formulations, including polymeric nanoparticles, lipid nanoparticles, liposomes, solid lipid nanoparticles, nanostructured lipid carriers, nanoemulsions, nanosuspensions, lipid-based hybrid NDDS, self-emulsifying drug delivery systems, niosomes, and nanocrystals. We also summarize how ML algorithms could help predict critical quality attributes of NDDS, such as particle size, shape, surface properties, drug encapsulation efficiency, drug loading efficiency, drug release behavior, and stability. Furthermore, we discuss existing challenges and prospects for the formulation development empowered by ML in NDDS. In conclusion, this review provides a comprehensive overview of the transformative potential of ML in improving the formulation development of nanomedicines, ultimately accelerating their clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/41685160/","authors":["Shen C","Zhang M","Lu M","Chang E","Gao Z","Ban W","Liu Q","Zuo Z","Jiang C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.apsb.2025.12.011","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41685158","name":"Anti-influenza drugs targeting trimeric RNA polymerase complex: From development to clinics.","source":"pubmed","abstract":"The rapid evolution of influenza viruses, driven by high mutation rates and cross-species transmission, underscores the importance of discovering antivirals with novel mechanisms of action and distinct resistance profiles. The influenza virus RNA polymerase, a highly conserved heterotrimeric complex, comprises polymerase basic protein 1 (PB1), polymerase basic protein 2 (PB2), and polymerase acidic protein (PA) in influenza A and B viruses, or polymerase 3 protein (P3) in influenza C and D viruses. This complex is essential for viral genome replication and transcription, rendering it a critical target for antiviral intervention. Over the past two decades, research on influenza polymerase (FluPol) has advanced from fundamental studies to drug development and clinical application. By 2025, six FluPol-targeting drugs have received regulatory approval: the PA inhibitors baloxavir marboxil, suraxavir marboxil, seloxavir marboxil, and pixavir marboxil; the PB1 inhibitor favipiravir; and the PB2 inhibitor onradivir, with several additional candidates progressing to clinical research. This review summarizes the structure and function of influenza polymerase and the mechanisms of action of different inhibitors, highlighting the discovery and clinical effectiveness of the newly approved FluPol-targeting drugs. It addresses the potential of FluPol inhibitors against highly pathogenic avian influenza and the challenges posed by resistance mutations.","url":"https://pubmed.ncbi.nlm.nih.gov/41685158/","authors":["Li Y","Zhang J","He F","Cao C","Zhan Y","Zhong N","Yang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.apsb.2025.11.033","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41685033","name":"Implementation of AI-Driven Diagnostic Tools to Improve Access and Efficiency in Rural Healthcare: An Umbrella Review.","source":"pubmed","abstract":"Rural and underserved communities continue to face barriers to timely and accurate healthcare due to shortages of specialists, limited diagnostic infrastructure, and geographic isolation. Artificial intelligence (AI)-driven diagnostic tools, including machine learning (ML) algorithms, telehealth platforms, and clinical decision support systems, have the potential to address these challenges. A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Scopus, Web of Science, and Embase were searched for studies published between January 2010 and April 2025 that evaluated AI-based diagnostic interventions in rural or low-resource settings. Findings were synthesized thematically to assess diagnostic performance, healthcare access, efficiency, and implementation factors. Twenty-six studies met the inclusion criteria, including observational studies, implementation case reports, and systematic reviews. Overall, AI tools were associated with improved diagnostic accuracy, reduced turnaround times, and enhanced access to services through mobile and telehealth applications. Commonly reported barriers included limited digital infrastructure, gaps in provider training, data privacy concerns, and regulatory uncertainty, while enabling factors included community trust, integration with existing health systems, and supportive policy environments. AI-driven diagnostics therefore show considerable promise for reducing inequities in rural healthcare, although successful implementation will require context-specific strategies, sustained infrastructure investment, and strong ethical and regulatory oversight.","url":"https://pubmed.ncbi.nlm.nih.gov/41685033/","authors":["Ugwu HC","Obodo OR","Okafor CN","Ojukwu G","Ekarika E","Ejiyooye TF","Okobi OE","Odusanmi SS","Ugwu UN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.7759/cureus.101326","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41684639","name":"Multimodal learning for scalable representation of high-dimensional medical data.","source":"pubmed","abstract":"Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively leveraging multimodal data, particularly digital pathology whole slide images (WSIs) and genomic sequencing, remains a significant challenge due to the intrinsic heterogeneity of these modalities and the need for scalable and interpretable frameworks. Existing diagnostic models typically operate on unimodal data, overlooking critical cross-modal interactions that can yield richer clinical insights. We introduce MarbliX (Multimodal Association and Retrieval with Binary Latent Indexed matriX), a self-supervised framework that learns to embed WSIs and immunogenomic profiles into compact, scalable binary codes, termed \"monogram.\" By optimizing a triplet contrastive objective across modalities, MarbliX captures high-resolution patient similarity in a unified latent space, enabling efficient retrieval of clinically relevant cases and facilitating case-based reasoning. In lung cancer, MarbliX achieves 85%-89% across all evaluation metrics, outperforming histopathology (69%-71%) and immunogenomics (73%-76%). In kidney cancer, real-valued monograms yield the strongest performance (F1: 80%-83%, Accuracy: 87%-90%), with binary monograms slightly lower (F1: 78%-82%).","url":"https://pubmed.ncbi.nlm.nih.gov/41684639/","authors":["Alsaafin A","Shafique A","Alfasly S","Kalari KR","Tizhoosh HR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1709277","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41684520","name":"Integrating traditional omics and AI-driven approaches for discovery and validation of novel MicroRNA biomarkers and therapeutic targets in thyroid cancer.","source":"pubmed","abstract":"The discovery of reliable biomarkers and therapeutic targets remains a critical challenge in thyroid cancer management. This study demonstrates the value of integrating traditional omics technologies with artificial intelligence approaches and single-cell validation to identify novel microRNA-based biomarkers and drug targets. We hypothesized that combining meta-analysis of bulk transcriptomics, machine learning-driven feature selection, and single-cell spatial mapping would enhance biomarker discovery and validation compared to using either approach independently.","url":"https://pubmed.ncbi.nlm.nih.gov/41684520/","authors":["Wan Y","Xie D","Zhang M","Yang S","Zhang Z","Fu X","Wang M","Zhao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fphar.2025.1727032","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41684390","name":"Exploring the application of generative artificial intelligence in nursing: a cross-sectional study.","source":"pubmed","abstract":"This study aims to systematically investigate the application of Generative Artificial Intelligence (GAI) in nursing practice within China. It seeks to map current usage patterns, identify perceived benefits and implementation challenges, and uncover the functional needs of nursing staff regarding GAI. Additionally, the research will assess the real-world performance and adoption of emerging local GAI platforms. The findings are expected to provide foundational evidence to guide the scientifically sound and contextually appropriate development of GAI in nursing.","url":"https://pubmed.ncbi.nlm.nih.gov/41684390/","authors":["Wu J","Yang M","Wei X","Zheng Y","Deng J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1689418","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41684376","name":"Large language models for structured cardiovascular data extraction: a foundation for scalable research and clinical applications.","source":"pubmed","abstract":"Automated extraction of information from cardiac reports would benefit both clinical reporting and research. Large language models (LLMs) hold promise for such automation, but their clinical performance and practical implementation across various computational environments remain unclear. This study aims to evaluate the feasibility and performance of LLM-based classification of echocardiogram and invasive coronary angiography reports, using real-world clinical data across local, high-performance computing and cloud-based platforms.","url":"https://pubmed.ncbi.nlm.nih.gov/41684376/","authors":["van der Loo W","van der Valk V","van den Broek T","Atsma D","Staring M","Scherptong R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1093/ehjdh/ztaf127","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41683970","name":"Multi-Omics Analysis of Morbid Obesity Using a Patented Unsupervised Machine Learning Platform: Genomic, Biochemical, and Glycan Insights.","source":"pubmed","abstract":"Morbid obesity is a complex, multifactorial disorder characterized by metabolic and inflammatory dysregulation. The aim of this study was to observe changes in obese patients adhering to a personalized nutrition plan based on multi-omic data. This study included 14 adult patients with a body mass index (BMI) &gt; 40 kg/m 2 who were consecutively recruited from those presenting to our outpatient clinic and who met the inclusion criteria. Clinical, biochemical, hormonal, and glycomic parameters were assessed, along with whole-genome sequencing (WGS) that included a focused analysis of obesity-associated genes and an extended analysis encompassing genes related to cardiometabolic disorders, hereditary cancer risk, and nutrigenetic profiles. Patients were stratified into nutrigenetic clusters using a patented unsupervised machine learning platform (German Patent Office, No. DE 20 2025 101 197 U1), which was employed to generate personalized nutrigenetic dietary recommendations for patients with morbid obesity to follow over a six-month period. At baseline, participants exhibited elevated glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglycerides, and C-reactive protein (CRP) levels, consistent with insulin resistance and chronic low-grade inflammation. The majority of participants harbored risk alleles within the fat mass and obesity-associated gene ( FTO ) and the interleukin-6 gene ( IL-6 ), together with multiple additional significant variants identified across more than 40 genes implicated in metabolic regulation and nutritional status. Using an AI-driven clustering model, these genetic polymorphisms delineated a uniform cluster of patients with morbid obesity. The mean GlycanAge index (56 &#xb1; 12.45 years) substantially exceeded chronological age (32 &#xb1; 9.62 years), indicating accelerated biological aging. Following a six-month personalized nutrigenetic dietary intervention, significant reductions were observed in both BMI (from 52.09 &#xb1; 7.41 to 34.6 &#xb1; 9.06 kg/m 2 , p &lt; 0.01) and GlycanAge index (from 56 &#xb1; 12.45 to 48 &#xb1; 14.83 years, p &lt; 0.01). Morbid obesity is characterized by a pro-inflammatory and metabolically adverse molecular signature reflected in accelerated glycomic aging. Personalized nutrigenetic dietary interventions, derived from AI-driven analysis of whole-genome sequencing (WGS) data, effectively reduced both BMI and biological age markers, supporting integrative multi-omics and machine learning approaches as promising tools in precision-based obesity management.","url":"https://pubmed.ncbi.nlm.nih.gov/41683970/","authors":["Šnajdar I","Bulić L","Skelin A","Mršić L","Sokač M","Brkljačić M","Matovinović M","Linarić M","Kovačić J","Brlek P","Lauc G","Smolić M","Primorac D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 4","doi":"10.3390/ijms27031551","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41683591","name":"Advances in the Diagnosis of Rheumatoid Arthritis-Associated Interstitial Lung Disease: Integrating Conventional Tools and Emerging Biomarkers.","source":"pubmed","abstract":"Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41683591/","authors":["Bai J","Yu F","He X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 23","doi":"10.3390/ijms27031165","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41683289","name":"Dietary Nutrients, Gut Microbiota, and Cardiac Function: From Metabolic Mechanisms to Clinical Applications.","source":"pubmed","abstract":"The heart depends on a continuous and flexible energy supply from fatty acids, glucose, and other substrates. Emerging evidence shows that gut microbiota-derived metabolites-such as trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), secondary bile acids, indoles, phenylacetylglutamine (PAGln), and branched-chain amino acids-modulate cardiac metabolism and function. Although clinical evidence linking these metabolites to cardiovascular outcomes is expanding, most data remain associative, with limited causal or interventional proof.","url":"https://pubmed.ncbi.nlm.nih.gov/41683289/","authors":["Scisciola L","Basilicata MG","Belmonte M","Pesapane A","Fontanella RA","Balzano N","Palazzo AMM","Joshi R","Zia A","Tortorella G","Ulfat Z","Arshad M","Paolisso G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.3390/nu18030467","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41682852","name":"Exploring the Balance Between Artificial Intelligence and Human Expertise in Shaping Breast Reconstruction Outcomes: A Comparative Reflection Study.","source":"pubmed","abstract":"Background/Objectives : Artificial intelligence (AI) has shown potential in patient education and integration into clinical decision support systems. However, its performance in counseling patients on breast reconstruction currently remains underexplored. This study's objective is to compare AI-generated answers with expert surgeon responses to common patient questions (derived from clinical scenarios) in domains like oncological justification, reconstructive options, and postoperative care. Methods : We realized an observer-blinded study using five real-world clinical scenarios in the field of oncologic and reconstructive surgery of the breast. Both ChatGPT-5 (October 2025 version) and a senior board-certified plastic surgeon responded to frequently asked questions, which were split into three domains: (1) oncological and surgical justification; (2) reconstruction options and outcomes, respectively; and (3) postoperative period. The answers were evaluated by another senior plastic surgeon using a four-grade ordinal scoring system (1 = unsatisfactory, 4 = excellent), which assessed accuracy, completeness, safety, nuance, and alignment with the current guidelines. Results : Across a total of 40 questions, the average AI response score was 3.1 &#xb1; 0.6. Domain-specific items scored lowest values for oncological justification (2.8 &#xb1; 0.7) and higher values for reconstruction options/outcomes and postoperative care (both 3.2 &#xb1; 0.4). No AI response was graded as unsatisfactory (score 1). Responses graded 4 (15%) were considered comprehensive, accurate, and patient-friendly. Conclusions : Globally, ChatGPT-5 provides satisfactory, readable, and medically accurate answers to basic patient questions on breast reconstruction, with a few limitations in nuanced oncological justification.","url":"https://pubmed.ncbi.nlm.nih.gov/41682852/","authors":["Pop IC","Muntean MV","Gata VA","Ilies RA","Nicoara D","Filip CI","Pop V","Achimas-Cadariu PA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.3390/jcm15031170","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41682830","name":"Assessing the Diagnostic Accuracy of BiomedCLIP for Detecting Contrast Use and Esophageal Strictures in Pediatric Radiography.","source":"pubmed","abstract":"Background/Objectives : Vision-language models such as BiomedCLIP are increasingly investigated for their diagnostic potential in medical imaging. Although these foundation models show promise in general radiographic interpretation, their application in pediatric domains-particularly for subtle, postoperative findings like esophageal strictures-remains underexplored. This study aimed to evaluate the diagnostic performance of BiomedCLIP in classifying pediatric esophageal radiographs into three clinically relevant categories: presence of contrast agent, full esophageal visibility, and presence of esophageal stricture. Methods : We retrospectively analyzed 143 pediatric esophageal X-rays collected between 2021 and 2025. Each image was annotated by two pediatric radiology experts and categorized according to esophageal visibility, contrast presence, and stricture occurrence. BiomedCLIP was used in a zero-shot classification setup without fine-tuning. Model predictions were converted into binary outcomes and assessed against the ground truth using a comprehensive suite of 27 performance metrics, including accuracy, sensitivity, specificity, F1-score, AUC, and calibration analyses. Results : BiomedCLIP achieved high precision (88.7%) and a favorable AUC (85.4%) in detecting contrast agent presence, though specificity remained low (20%), leading to a high false-positive rate. The model correctly identified all cases of non-visible esophagus, but was untestable in predicting full visibility due to the absence of positive cases. Critically, its performance in detecting esophageal strictures was poor, with accuracy at 24%, sensitivity at 44%, specificity at 18%, and AUC of 0.26. Statistical overlap between contrast and stricture predictions indicated a lack of semantic differentiation within the model's latent space. Conclusions : BiomedCLIP shows potential in detecting high-salience features such as contrast but fails to reliably identify esophageal strictures. Limitations include class imbalance, absence of fine-tuning, and architectural constraints in recognizing subtle morphologic abnormalities. These findings emphasize the need for domain-specific adaptation of foundation models before clinical implementation in pediatric radiology.","url":"https://pubmed.ncbi.nlm.nih.gov/41682830/","authors":["Fabijan A","Kolejwa M","Zawadzka-Fabijan A","Fabijan R","Kosińska R","Nowosławska E","Socha-Banasiak A","Lwow N","Tkaczyk M","Zakrzewski K","Czkwianianc E","Polis B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.3390/jcm15031150","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41682137","name":"Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.","source":"pubmed","abstract":"Background : Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods : This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence-non-maleficence, data privacy, and the impact on the medical profession. Results : The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions : AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust.","url":"https://pubmed.ncbi.nlm.nih.gov/41682137/","authors":["Martinho D","Sobreiro P","Domingues A","Martinho F","Nogueira N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 23","doi":"10.3390/healthcare14030287","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41681930","name":"Contemporary Preoperative Detection of Extraprostatic Extension in Prostate Cancer.","source":"pubmed","abstract":"Extraprostatic extension (EPE) is an important prognostic factor in prostate cancer and influences nerve-sparing decisions during radical prostatectomy. Multiparametric MRI (mpMRI) is the standard for local staging, but its sensitivity for EPE remains limited, and its interpretation is subject to inter-reader variability. In this narrative review, we aim to create an overview of contemporary strategies for the preoperative detection of EPE. We searched PubMed, Embase, Web of Science, and Google Scholar, focusing on studies published between 2015 and 2025 including articles evaluating clinical parameters, mpMRI features, nomograms, radiomics, machine learning, and deep learning models for EPE prediction. The analyzed literature was compared with respect to diagnostic performance, validation strategy, and clinical applicability of individual methods. Clinical parameters and traditional nomograms provide moderate accuracy for EPE detection. mpMRI improves staging, with tumor-capsule contact length as the most important single imaging marker. Radiomics-based and machine-learning models matched and occasionally outperform conventional approaches, achieving AUC values ranging from 0.75 to 0.85. Deep-learning models demonstrated similar performance by directly analyzing imaging data, although most lacked external validation and were sensitive to dataset heterogeneity. Several radiomics and deep learning models demonstrated performance comparable to, and in selected studies exceeding, expert radiologist assessment. Binary EPE classification has limited clinical value, while side-specific and graded EPE assessment offers a more clinically relevant approach. Translation of these tools into routine practice will require multimodal, side-specific, and externally validated models supported by automated segmentation and explainable artificial intelligence frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/41681930/","authors":["Stępka J","Milecki T","Ksepka J","Kujawska A","Hendrysiak J","Cieślikowski WA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 30","doi":"10.3390/cancers18030456","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41681707","name":"Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.","source":"pubmed","abstract":"Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. Methods: We conducted a comprehensive narrative review of literature published between January 2015 and November 2025. Evidence was synthesized across three functional domains: (1) patient applications for health literacy and behavior change; (2) clinician applications for decision support and workflow efficiency; and (3) system applications for automated data extraction, registry construction, and quality surveillance. Results: Evidence suggests that while LLMs generate empathetic, guideline-concordant patient education, they lack the nuance required for unsupervised, personalized advice. For clinicians, LLMs effectively summarize clinical notes and draft documentation but remain unreliable for deterministic risk calculations and autonomous decision-making. System-facing applications demonstrate potential for automated phenotyping and multimodal risk prediction. However, safe deployment is constrained by hallucinations, temporal obsolescence, automation bias, and data privacy concerns. Conclusions: LLMs could help mitigate structural barriers in CV prevention but should presently be deployed only as supervised \"reasoning engines\" that augment, rather than replace, clinician judgment. To guide the transition from in silico performance to bedside practice, we propose the C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, and Ongoing vigilance) as a roadmap for responsible integration.","url":"https://pubmed.ncbi.nlm.nih.gov/41681707/","authors":["Ferreira Santos J","Dores H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 26","doi":"10.3390/diagnostics16030390","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41681691","name":"Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.","source":"pubmed","abstract":"Over recent years, venous ulcer wound care has experienced significant advancements through the application of machine learning (ML) models. The aim of the present study is a systematic, comprehensive analysis of prior research studies in this field covering the period between 2001 and August 2025. By searching multiple academic databases, including the Web of Science, Scopus, and PubMed, using relevant keywords and different queries, and screening reference lists of previously published manuscripts and review papers with a focus on the application of artificial intelligence in dermatology and medicine, an initial set of potential studies for review was obtained. To ensure the scope and relevance of the review, several inclusion and exclusion criteria were used to derive the final set of relevant research studies upon which a database for research data management was created. As a result, a total of 79 relevant research studies were comprehensively analysed, upon which detailed meta-analysis and analysis of application areas of ML models within venous ulcer wound care were conducted. Afterwards, a summary of benefits for medical systems and patients was given along with a general discussion regarding ML model limitations, trends, and opportunities, as well as research studies' limitations and possible future research directions. The presented analyses may be valuable for researchers interested in applying ML models not only to venous ulcer wound care but also to other types of chronic wound care.","url":"https://pubmed.ncbi.nlm.nih.gov/41681691/","authors":["Madić M","Vitković N","Damnjanović Z","Stojanović S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 23","doi":"10.3390/diagnostics16030373","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41681030","name":"Demographic-aware deep learning for multi-organ segmentation: Mitigating gender and age biases in CT images.","source":"pubmed","abstract":"Deep learning algorithms have shown promising results for automated organ-at-risk (OAR) segmentation in medical imaging. However, their performance is frequently compromised by demographic bias. This limitation becomes pronounced when conventional models fail to account for Complex 3D anatomical variations across diverse groups, as they often overlook critical factors such as age and gender. Consequently, this oversight can lead to inaccurate segmentations, thereby posing significant risks to clinical safety in&#xa0;radiotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41681030/","authors":["Ma J","Tan T","Jia D","Sun Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/mp.70322","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41680790","name":"Development and validation of a competency-based evaluation framework for clinical medical trainees in the era of artificial intelligence: a mixed-methods study in China.","source":"pubmed","abstract":"BACKGROUND: The integration of artificial intelligence (AI) into clinical practice is reshaping the competency requirements for medical trainees. Yet, validated evaluation instruments aligned with outcome-based education (OBE) frameworks remain scarce. METHODS: We conducted a sequential mixed methods study to develop and preliminarily evaluate an OBE-based competency assessment matrix for clinical medical trainees in China. The framework was derived from national and international competency standards and refined through a three-round Delphi process with 16 medical education experts. Empirical evaluation involved 276 respondents including residents, postgraduate students, and clinical educators who completed the finalized 72-item instrument via a digital assessment platform. Reliability and exploratory structural characteristics were examined using Cronbach&#x2019;s &#x3b1;, exploratory factor analysis (EFA), and inter-item correlation matrices. Subgroup differences were examined descriptively and visualized with radar plots. RESULTS: The Delphi panel reached consensus on 72 items across three domains&#x2014;Importance, Feasibility, and Clarity&#x2014;with progressive convergence (Kendall&#x2019;s W ranging from 0.65 in Round 1 to 0.74 in Round 3). The resulting scale showed excellent internal consistency (Cronbach&#x2019;s &#x3b1;&#x2009;=&#x2009;0.928) and strong sampling adequacy (KMO&#x2009;=&#x2009;0.884). Bartlett&#x2019;s test of sphericity was highly significant (&#x3c7;2&#x2009;=&#x2009;421.35, df&#x2009;=&#x2009;28, p&#x2009;&lt;&#x2009;0.001), confirming the suitability of the data for structural exploration. EFA of aggregated domain scores yielded a three-component pattern that cumulatively explained 74.5% of the variance. The resulting loading profile suggested meaningful contributions of Importance, Feasibility, and Clarity, offering exploratory support for the proposed domain-level structure. Radar plots revealed systematic but role-dependent differences: faculty emphasized Importance, residents prioritized Feasibility, and postgraduates rated Clarity slightly higher. CONCLUSION: This study provides a context-sensitive evaluation matrix with encouraging initial psychometric evidence, tailored to the evolving demands of AI-informed clinical education. The framework offers a promising platform for competency assessment and curriculum development in Chinese teaching hospitals and may serve as a reference model for other AI-integrating medical education systems, while highlighting the need for confirmatory factor analysis in independent samples to more definitively establish its dimensional structure.","url":"https://pubmed.ncbi.nlm.nih.gov/41680790/","authors":["Li F","Zhao H","Huang Q","Zhang C","Chen G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.1186/s12909-026-08779-7","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41680684","name":"Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.","source":"pubmed","abstract":"Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and recurrence prediction. However, there is limited synthesis of head-to-head comparative evidence between CT, FDG PET/CT, and multimodal fusion models within the same cohorts.","url":"https://pubmed.ncbi.nlm.nih.gov/41680684/","authors":["Eftekharian M","Hashemi Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.1186/s12880-026-02222-5","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41680636","name":"Protocol for development of an AI-driven individualized frailty prediction and intervention framework for elderly patients with chronic kidney disease: causal feature learning and knowledge-distillation-based modeling study.","source":"pubmed","abstract":"BACKGROUND: Frailty is a reversible geriatric syndrome marked by diminished physiological reserve and heightened vulnerability. In elderly patients with chronic kidney disease (CKD), frailty accelerates decline and mortality, yet individualized risk stratification and management remain limited by resource-intensive geriatric assessment and incomplete data. Integrating causal learning and model knowledge distillation may enable precision modeling of frailty risk and clinically meaningful decision support. METHODS: This multicenter bidirectional cohort study (ChiCTR2500095133) will recruit 1500 adults aged&#x2009;&#x2265;&#x2009;60 years with CKD (KDIGO criteria) from seven Beijing centers. Collected data will include routinely available clinical indices (demographics, medical history, anthropometric measures and laboratory tests) and resource-intensive non-clinical indices derived from comprehensive geriatric assessment (frailty, cognitive, functional, nutritional, and psychological assessments). Frailty will be assessed using the FRAIL scale. Causal feature learning will identify determinants of frailty risk, while a teacher-student knowledge-distillation framework will optimize prediction using routinely available clinical data. Internal and external validation will examine model accuracy, interpretability, and clinical applicability. DISCUSSION: By combining comprehensive clinical assessment with advanced artificial-intelligence methods, this study aims to develop an explainable, efficient tool for early frailty detection and individualized intervention decision support in elderly CKD. The resulting framework may enhance precision geriatric management, improve functional outcomes, and reduce healthcare burden. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2500095133 (registered 2025-01-02).","url":"https://pubmed.ncbi.nlm.nih.gov/41680636/","authors":["Chang J","Hu J","Cao Y","Jia X","Wang L","Liu B","Xu S","Ji Q","Jin M","Han Q","Liang Y","Sun Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.1186/s12877-026-07143-0","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41680504","name":"Results of postoperative voice therapy for vocal cord polyps: systematic review and meta-analysis.","source":"pubmed","abstract":"The recovery of voice quality after vocal cord polyp surgery is a key concern for both patients and physicians. Voice therapy plays a significant role in the conservative treatment of vocal cord polyps. In this study, we aimed to evaluate the effect of postoperative voice therapy on outcomes in patients with vocal cord polyps.","url":"https://pubmed.ncbi.nlm.nih.gov/41680504/","authors":["Yang K","You Q","Huang S","Tao J","Liang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1007/s00405-026-10017-1","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41679912","name":"Predicting Weight Outcomes From Obesity Medications in a Paediatric Population.","source":"pubmed","abstract":"There are limited paediatric studies comparing outcomes from different obesity medications (OMs) in real-world settings. The aim of this study is to describe real-world variability in outcomes and develop models to predict outcomes from OMs.","url":"https://pubmed.ncbi.nlm.nih.gov/41679912/","authors":["Mottalib MM","Beheshti R","Viswanathan K","Bunnell HT","Phan TT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1111/ijpo.70089","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41678657","name":"Using Large Language Models to Summarize Evidence in Biomedical Articles: Exploratory Comparison Between AI- and Human-Annotated Bibliographies.","source":"pubmed","abstract":"Annotated bibliographies summarize literature, but training, experience, and time are needed to create concise yet accurate annotations. Summaries generated by artificial intelligence (AI) can save human resources, but AI-generated content can also contain serious errors.","url":"https://pubmed.ncbi.nlm.nih.gov/41678657/","authors":["Colder Carras M","Qureshi R","Naaman K","Aldayel F","Date M","AlJuboori D","Thrul J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.2196/69707","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41678452","name":"Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.","source":"pubmed","abstract":"This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital and admission clinical data. We conducted a retrospective cohort study leveraging data from the prehospital emergency electronic medical record, in-hospital triage, and hospital information systems of a tertiary hospital in Changsha between August 2023 and April 2025. A total of 892 adult patients classified as critically ill were included. Nine machine learning algorithms were trained to predict 24-hour mortality, and model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score. SHapley Additive exPlanations (SHAP) analysis was employed to interpret feature contributions. Among the nine algorithms, the Random Forest (RF) model exhibited the most stable and robust performance. Using nine selected features-prehospital heart rate, prehospital and admission systolic and diastolic blood pressure, prehospital and admission oxygen saturation, admission respiratory rate, and level of consciousness, the RF model achieved an AUC of 0.985(95%CI:0.976-0.993) in the training set and 0.863 (95%CI:0.766-0.961) in the testing set, demonstrating high accuracy and potential clinical applicability. SHAP analysis revealed that prehospital heart rate, admission respiratory rate, and blood pressure are the strongest predictors of mortality. Finally, the model was deployed as an interactive web-based tool for real-time clinical application. In summary, this study developed a simple, interpretable, and accurate machine learning model for predicting 24-hour mortality in critically ill prehospital patients. The RF-based model can be intended as an exploratory, hypothesis-generating tool and should supplement, not replace, clinical judgment. Further validation in larger, multi-center prospective cohorts with higher event rates is essential to confirm the robustness and real-world applicability of our findings.","url":"https://pubmed.ncbi.nlm.nih.gov/41678452/","authors":["Li S","Li Z","Luo R","Li Y","Shi A","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0341860","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41678387","name":"Postmarketing Safety of Transcranial Magnetic Stimulation: A 10-Year MAUDE Database Analysis of Adverse Events and Technological Advancements.","source":"pubmed","abstract":"Background: Transcranial magnetic stimulation (TMS) is an FDA-cleared neuromodulation technique with expanding clinical applications beyond major depressive disorder. Despite increasing utilization, there has been no published, device-agnostic analysis of TMS-related adverse events (AEs) using the FDA's Manufacturer and User Facility Device Experience (MAUDE) database. Objective: To characterize the real-world safety profile of TMS devices based on MAUDE-reported AEs, including symptom patterns, manufacturer-level variations, device issues, and reporting delays, while contextualizing findings through a review of technological advancements in TMS. Methods: All reports under device code OBP were extracted from MAUDE through April 2025. After deduplication, 200 unique reports were analyzed descriptively. A focused literature review was also conducted to trace safety and innovation trends in TMS device development. Results: Of 200 reports, 94.7% involved injury, 4.1% malfunction, and 1.2% death. Common symptoms included anxiety (8.2%), neurocognitive changes (8.0%), seizures (6.9%), headache (6.9%), and tinnitus (5.6%). Neuronetics accounted for 45.5% of reports, likely reflecting market share. Median reporting delay was 1.4 months, with some exceeding 6 years. The literature review identified major innovations, including figure-of-eight and H-coils, double-containment coils, seizure risk screening, and advanced circuitry (eg, insulated-gate bipolar transistors and metal-oxide-semiconductor field-effect transistors) enabling magnetic resonance imaging-guided and accelerated protocols. Exploratory developments include wearable systems, auricular stimulation, and artificial intelligence-based individualization. Conclusion: MAUDE data provide novel insights into TMS safety in real-world settings. Although serious AEs are rare, standardized reporting and continued device innovation are essential to ensure safe and effective clinical use.","url":"https://pubmed.ncbi.nlm.nih.gov/41678387/","authors":["Awan AI","Waheed N","Zahra R","Dad A","Singh D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 11","doi":"10.4088/JCP.25m16155","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41678173","name":"Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer.","source":"pubmed","abstract":"Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer, little is known about whether their performance is consistent across diverse patient populations and tissue contexts.","url":"https://pubmed.ncbi.nlm.nih.gov/41678173/","authors":["Rakaee M","Nassar AH","Tafavvoghi M","Jabar F","Bou Farhat E","Adib E","Andersen S","Busund LR","Pøhl M","Helland Å","Gusev A","Ricciuti B","Sholl LM","Donnem T","Kwiatkowski DJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 1","doi":"10.1001/jamaoncol.2025.6430","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41677994","name":"Reinforcing the stereotype: an analysis of AI-generated images of patients with obstructive sleep apnea.","source":"pubmed","abstract":"Obstructive sleep apnea (OSA) is stereotypically a condition of the middle-aged, obese, snoring man, a depiction that obscures the true diversity of vast affected populations. The growing use of artificial intelligence (AI) text-to-image generators for medical applications risks further reinforcement of this bias through prejudiced visual depictions of disease. We analyzed 1,000 images generated by ChatGPT-4o using the prompt \"Person with obstructive sleep apnea\" to evaluate demographics represented in its portrayals of OSA. ChatGPT-4o consistently portrayed individuals as middle-aged (98.3%), male (99.8%), White (94.7%), and obese/overweight (97.2%)-a representation differing markedly and in all axes from real-world prevalence. These findings suggest that AI-generated imagery may draw on and reinforce a narrow and outdated perception of OSA, potentially contributing to diagnostic bias and health disparities. As AI becomes increasingly integrated into clinical practice and educational tools, ensuring accurate and inclusive representations will be essential to advancing equity in sleep medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/41677994/","authors":["Saran E","Brar A","Brenna CTA","Lyons OD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1007/s44470-025-00017-z","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41677330","name":"Global research trends in robotic-assisted thoracoscopic surgery: a multidimensional analysis from 2000 to 2025.","source":"pubmed","abstract":"Robotic-assisted thoracoscopic surgery (RATS) has transformed thoracic surgery, yet fragmented research patterns obscure critical development trajectories and global disparities.","url":"https://pubmed.ncbi.nlm.nih.gov/41677330/","authors":["Tian J","Rao HH","Guo F","Chen HW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.1097/JS9.0000000000004939","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676846","name":"Reply to \"Medical Education and Artificial Intelligence: Some Suggestions\".","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41676846/","authors":["Amano I","Obi-Nagata K","Ninomiya A","Fujiwara Y","Koibuchi N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 15","doi":"10.31662/jmaj.2025-0384","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676817","name":"Artificial Intelligence in Cerebrovascular Imaging: A Targeted Review of Aneurysm Detection and Rupture Risk Prediction.","source":"pubmed","abstract":"Cerebral aneurysms are a potentially life-threatening vascular pathology that can lead to subarachnoid hemorrhage, a neurological emergency associated with high morbidity and mortality. Traditional imaging-based assessments (largely centered on aneurysm size, shape, and location) often fall short in accurately predicting rupture risk. This limitation highlights the need for more advanced, individualized diagnostic strategies. Recent advancements in artificial intelligence (AI) have introduced powerful tools capable of transforming cerebrovascular imaging and aneurysm management. This narrative review synthesizes published studies on the application of AI in cerebrovascular imaging, focusing on its potential to aid in aneurysm detection and rupture risk prediction. It examines the evolving role of AI through three primary technological approaches: radiomics, machine learning (ML), and deep learning (DL). Radiomics enables the extraction of quantitative features from imaging data, revealing patterns and morphological indicators that may not be visible to the human eye. ML models synthesize imaging, clinical, and hemodynamic data to predict rupture risk with greater precision than traditional scoring tools. DL techniques, particularly convolutional neural networks, automate aneurysm detection and interpretation directly from raw image data. What sets this review apart from previous literature is its integrative approach: rather than focusing narrowly on one AI technique or imaging modality, it unifies radiomics, ML, and DL under a single framework and evaluates their clinical applications across both detection and risk prediction. Furthermore, it emphasizes emerging solutions like hybrid modeling, explainable AI, and multimodal data fusion, which are critical for real-world clinical translation. However, current AI-based methods remain at the investigational stage and have not yet been validated clinically, experimentally, or against existing diagnostic standards. Importantly, this review situates AI methods relative to established clinical benchmarks, including radiologist interpretation and risk scores such as PHASES (Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid, Hypertension, Age, Size of aneurysm, Earlier subarachnoid hemorrhage, and Site of aneurysm) and hemorrhage, and Site of aneurysm) and ELAPSS ( (Earlier subarachnoid hemorrhage Earlier subarachnoid hemorrhage, Location of aneurysm, Age, Population, Size of aneurysm, and Shape of aneurysm), Location of aneurysm, Age, Population, Size of aneurysm, and Shape of aneurysm), and emphasizes that rigorous prospective validation is essential before widespread adoption. It also proposes practical implementation strategies, including decision support integration, standardization protocols, and federated learning to enable secure data collaboration. By addressing both technical innovation and translational challenges, this review offers a clinician-focused roadmap that advances the field beyond theoretical models toward personalized aneurysm care. In doing so, it aims to reduce rupture rates and improve patient outcomes through precision medicine powered by AI.","url":"https://pubmed.ncbi.nlm.nih.gov/41676817/","authors":["Le D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 15","doi":"10.31662/jmaj.2025-0327","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676812","name":"Artificial Intelligence in Medical Writing: Is It an Exception to Evidence-Based Medicine?","source":"pubmed","abstract":"Generative artificial intelligence (GenAI) is now widely used in medicine, including medical writing. Its merits and demerits have been discussed; however, such discussion has not been based on evidence-based medicine (EBM). Here, I focus primarily on GenAI use in medical writing, illustrating how it has already spread before its safety-especially long-term safety-has been confirmed by EBM. I therefore make several modest proposals. Assuming GenAI is a new drug, its use has not yet cleared even the first step of a phase I trial. Assuming it is a new procedure, it remains at the \"experience\" or \"case report\" phase. EBM requires the completion of phase I-III trials and randomized controlled trials or meta-analyses before any drug or procedure is confirmed safe and effective. Emergency evacuation can be applied for life-threatening medical conditions; however, it does not apply to \"writing.\" Nevertheless, the current publication world has already gone far beyond: GenAI use is already considerable in medical publication. Thus, three propositions have been made. First, we must recognize that the use of GenAI for writing operates outside the usual EBM framework. Second, we should conduct trials, even if they are difficult and time-consuming, to evaluate the safety and effectiveness of GenAI in writing. Third, we should use GenAI in writing only modestly until safety is confirmed. What is true becomes evident long after, and thus, I believe that we should take a cautious stance toward GenAI use in writing. How cautious should be discussed widely. This viewpoint may contribute to the discussion of GenAI use more generally, beyond medical writing.","url":"https://pubmed.ncbi.nlm.nih.gov/41676812/","authors":["Matsubara S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 15","doi":"10.31662/jmaj.2025-0443","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676798","name":"Medical Education and Artificial Intelligence: Some Suggestions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41676798/","authors":["Matsubara S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 15","doi":"10.31662/jmaj.2025-0335","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676787","name":"Does a Human-drafted Letter Edited by Chat Generative Pre-Trained Transformer Escape Detection by Artificial Intelligence Detectors?","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41676787/","authors":["Matsubara S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 15","doi":"10.31662/jmaj.2025-0256","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676780","name":"Expanding donor liver utilization: triple recipient strategy using split, domino, and auxiliary techniques.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41676780/","authors":["Sun Q","Ding H","Zhang Y","Zhou X","Sun Z","Han X","Fang F","Yang L","Yan S","Ding Y","Wang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/hbsn-2025-532","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"pmid:41676774","name":"First reported post-transplant lymphoproliferative disorder after combined heart-liver transplantation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41676774/","authors":["Sun Q","Huang M","Tian X","Sun Z","Zhang Y","Han X","Zhou W","Yan S","Ding Y","Dong A","Wang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/hbsn-2025-537","addedAt":"2026-09-01T01:47:51.506Z","updatedAt":"2026-09-01T01:47:51.506Z"},{"id":"oa:W4366692100","name":"May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT","source":"openalex","abstract":"BACKGROUND: In recent months, there has been growing interest in the potential of artificial intelligence (AI) to revolutionize various aspects of medicine, including research, education, and clinical practice. ChatGPT represents a leading AI language model, with possible unpredictable effects on the quality of future medical research, including clinical decision-making, medical education, drug development, and better research outcomes. AIM AND METHODS: In this interview with ChatGPT, we explore the potential impact of AI on future pediatric research. Our discussion covers a range of topics, including the potential positive effects of AI, such as improved clinical decision-making, enhanced medical education, faster drug development, and better research outcomes. We also examine potential negative effects, such as bias and fairness concerns, safety and security issues, overreliance on technology, and ethical considerations. CONCLUSIONS: While AI continues to advance, it is crucial to remain vigilant about the possible risks and limitations of these technologies and to consider the implications of these technologies and their use in the medical field. The development of AI language models represents a significant advancement in the field of artificial intelligence and has the potential to revolutionize daily clinical practice in every branch of medicine, both surgical and clinical. Ethical and social implications must also be considered to ensure that these technologies are used in a responsible and beneficial manner.","url":"https://doi.org/10.3390/children10040757","authors":["Antonio Corsello","Andrea Santangelo"],"tags":["Engineering ethics","Field (mathematics)","Quality (philosophy)","Clinical Practice","Health technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-21","doi":"https://doi.org/10.3390/children10040757","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W1967027625","name":"The Status Quo of Artificial Intelligence Methods in Automatic Medical Image Segmentation","source":"openalex","abstract":"","url":"https://doi.org/10.7763/ijcte.2013.v5.636","authors":["Maryam Rastgarpour","Jamshid Shanbehzadeh"],"tags":["Computer science","Status quo","Artificial intelligence","Image (mathematics)","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2013-01-01","doi":"https://doi.org/10.7763/ijcte.2013.v5.636","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W3043374725","name":"Applications of machine learning to diagnosis and treatment of neurodegenerative diseases","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41582-020-0377-8","authors":["Monika A. Myszczynska","Poojitha N. Ojamies","Alix M.B. Lacoste","Daniel Neil","Amir Saffari","Richard J. Mead","Guillaume M. Hautbergue","Joanna D. Holbrook","Laura Ferraiuolo"],"tags":["Medicine","MEDLINE","Neuroscience","Machine learning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-15","doi":"https://doi.org/10.1038/s41582-020-0377-8","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W4389426432","name":"Generative artificial intelligence in medical education: way to solve the problems","source":"openalex","abstract":"Dear Editor, The rapid integration of generative artificial intelligence (AI), specifically the Generative Pre-trained Transformer (ChatGPT), into medical education marks a pivotal moment in the evolution of pedagogy. Its potential to revolutionize learning processes and address complex medical concepts offers an exciting prospect. However, the profound implications it holds for academic integrity and the reliability of medical knowledge cannot be understated. That’s why, the article entitled “ChatGPT: the threats to medical education,” written by Richard C Armitage, is of great importance in exploring this topic [1]. This article holds paramount significance as it delves into the negative potential impact of generative AI in medical education. However, promising specific ways to cope with the threats in the application of generative AI in medical education need to be further proposed. a. Ethical guidelines and policies: Establishing comprehensive ethical guidelines and policies to govern the ethical use of ChatGPT. These guidelines should emphasize responsible generative AI tool use, academic integrity, and transparency in reporting AI-generated content. Students and educators should be made aware of the ethical implications of generative AI use [2]. b. Human interaction: Although generative AI offers immense potential, it should not replace the central role of human interaction in medical education. Human educators and peers play a pivotal role in fostering rich dialogue, critical thinking, and the development of interpersonal skills. Face-to-face interactions in clinical settings are irreplaceable for learning to apply medical knowledge effectively. The challenge is to strike a balance between generative AI’s benefits and the continued cultivation of human interaction. c. Surveillance and monitoring: To ensure the responsible use of generative AI, educational institutions should implement surveillance and monitoring systems. These systems should track the utilization of generative AI tools, including ChatGPT, to prevent academic misconduct and ensure compliance with ethical guidelines. The presence of checks and balances will serve as a deterrent against misuse [3]. d. Mentorship: Mentorship remains a fundamental aspect of medical education. Educators should guide students in effectively integrating generative AI into their learning processes. Mentorship can help students understand the boundaries of AI assistance, encouraging them to use generative AI as a supplementary resource rather than a sole source of information. Mentorship also aids students in developing critical thinking skills, ensuring that the use of AI complements, rather than hinders, their intellectual growth [4]. e. Continuous evaluation and research: The evolving landscape generative of AI technology necessitates ongoing evaluation and research. Institutions should actively assess the effectiveness of generative AI in medical education, identifying areas where it enhances learning and where human interaction remains irreplaceable. Research is essential to stay abreast of the latest developments and to adapt educational strategies accordingly. In summary, the integration of ChatGPT into the domain of medical education brings a lot of threats. The challenges range from the potential erosion of academic integrity to concerns regarding data accuracy, accountability, and reliability. Therefore, it’s of great importance to explore promising ways to cope with these threats. We echo Richard C Armitage’s call to propose specific ways to cope with the challenges ChatGPT presents. It is essential to continue this discourse as the field of medical education evolves in the age of AI. None declared. None funding. Y.L. and J.L. involved in conceptualization, literature search, manuscript writing, and editing. All authors read and agreed the final draft submitted.","url":"https://doi.org/10.1093/postmj/qgad116","authors":["Yanxing Li","Jianjun Li"],"tags":["China","Medicine","Medical education","Medical school","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-07","doi":"https://doi.org/10.1093/postmj/qgad116","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W4307807507","name":"Challenges of Artificial Intelligence in Space Medicine","source":"openalex","abstract":"The human body undergoes many changes during long-duration spaceflight including musculoskeletal, visual, and behavioral changes. Several of these microgravity-induced effects serve as potential barriers to future exploration missions. The advent of artificial intelligence (AI) in medicine has progressed rapidly and has many promising applications for maintaining and monitoring astronaut health during spaceflight. However, the austere environment and unique nature of spaceflight present with challenges in successfully training and deploying successful systems for upholding astronaut health and mission performance. In this article, the dynamic barriers facing AI development in space medicine are explored. These diverse challenges range from limited astronaut data for algorithm training to ethical/legal considerations in deploying automated diagnostic systems in the setting of the medically limited space environment. How to address these challenges is then discussed and future directions for this emerging field of research.","url":"https://doi.org/10.34133/2022/9852872","authors":["Ethan Waisberg","Joshua Ong","Phani Paladugu","Sharif Amit Kamran","Nasif Zaman","Andrew G. Lee","Alireza Tavakkoli"],"tags":["Spaceflight","Space (punctuation)","Space exploration","Human spaceflight","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.34133/2022/9852872","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W3019514384","name":"Artificial Intelligence in radiotherapy: state of the art and future directions","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12032-020-01374-w","authors":["Giulio Francolini","Isacco Desideri","G. Stocchi","Viola Salvestrini","Lucia Pia Ciccone","Pietro Garlatti","Mauro Loi","Lorenzo Livi"],"tags":["Workflow","Computer science","Transparency (behavior)","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-22","doi":"https://doi.org/10.1007/s12032-020-01374-w","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W3049024208","name":"Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow","source":"openalex","abstract":"The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honored connection and trust\" Eric Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human AgainArtificial Intelligence (AI) is ubiquitous today, and radiology is at the forefront of AI applications in medicine.When participating in a radiology conference, AI is everywhere.Last year's RSNA AI Showcase hosted more than 120 companies, almost doubling the number of the previous year.In 2019 the Artificial Intelligence Exhibition (AIX) made its grand debut at the ECR, bringing AI to the heart of the technical exhibition.At the scientific meeting a record number of 44 scientific sessions (317 presentations) focused on AI.The number of AI-related abstract submissions both to Radiology journals and to radiological conferences is skyrocketing, reaching 25 % of submissions for Radiology in 2019.It is obvious that there is a hype about AI in radiology.Many recent publications have shown that AI tools, and especially deep learning (DL), can recognize patterns in medical image data with excellent accuracy.However, there exist some major bottlenecks for the introduction of DL algorithms for diagnostic and routine clinical purposes:","url":"https://doi.org/10.1007/s00330-020-07148-2","authors":["Elmar Kotter","Erik Ranschaert"],"tags":["Workflow","Neuroradiology","Interventional radiology","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-14","doi":"https://doi.org/10.1007/s00330-020-07148-2","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W2899102752","name":"Artificial Intelligence in Internet of Medical Imaging Things: The Power of Thyroid Cancer Detection","source":"openalex","abstract":"The paper proposed an approach for thyroid cancer detection based on artificial intelligence in Internet of Medical Imaging Things (IoMIT) ecosystem. Ultrasonic imaging collected in IoMIT ecosystem is the best way for thyroid cancer diagnosis. Image segmentation and detection of benign and malignant thyroid nodules is an important part of the proposed approach. It is implemented in Apache Spark using MLlib based on Convolutional Neural Networks (CNNs). Finally, the results of medical imaging analytics are discussed.","url":"https://doi.org/10.1109/infotech.2018.8510725","authors":["Десислава Иванова"],"tags":["Convolutional neural network","Computer science","Segmentation","Thyroid cancer","SPARK (programming language)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-09-01","doi":"https://doi.org/10.1109/infotech.2018.8510725","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W4319736341","name":"The Transparency of Science with ChatGPT and the Emerging Artificial Intelligence Language Models: Where Should Medical Journals Stand?","source":"openalex","abstract":"N/a.","url":"https://doi.org/10.20344/amp.19694","authors":["Helena Donato","Pedro Escada","Tiago Villanueva"],"tags":["Transparency (behavior)","Medical science","Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-01","doi":"https://doi.org/10.20344/amp.19694","addedAt":"2026-09-01T01:47:52.049Z","updatedAt":"2026-09-01T01:47:52.049Z"},{"id":"oa:W3024777582","name":"Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.acra.2020.04.011","authors":["Florian Jungmann","Tobias Jorg","Felix Hähn","Daniel Pinto dos Santos","Stefanie M. Jungmann","Christoph Düber","Peter Mildenberger","Roman Kloeckner"],"tags":["Relevance (law)","Protocol (science)","Test (biology)","Applications of artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-13","doi":"https://doi.org/10.1016/j.acra.2020.04.011","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3201854521","name":"Explainable Artificial Intelligence for Tabular Data: A Survey","source":"openalex","abstract":"Machine learning techniques are increasingly gaining attention due to their widespread use in various disciplines across academia and industry. Despite their tremendous success, many such techniques suffer from the “black-box” problem, which refers to situations where the data analyst is unable to explain why such techniques arrive at certain decisions. This problem has fuelled interest in Explainable Artificial Intelligence (XAI), which refers to techniques that can easily be interpreted by humans. Unfortunately, many of these techniques are not suitable for tabular data, which is surprising given the importance and widespread use of tabular data in critical applications such as finance, healthcare, and criminal justice. Also surprising is the fact that, despite the vast literature on XAI, there are still no survey articles to date that focus on tabular data. Consequently, despite the existing survey articles that cover a wide range of XAI techniques, it remains challenging for researchers working on tabular data to go through all of these surveys and extract the techniques that are suitable for their analysis. Our article fills this gap by providing a comprehensive and up-to-date survey of the XAI techniques that are relevant to tabular data. Furthermore, we categorize the references covered in our survey, indicating the type of the model being explained, the approach being used to provide the explanation, and the XAI problem being addressed. Our article is the first to provide researchers with a map that helps them navigate the XAI literature in the context of tabular data.","url":"https://doi.org/10.1109/access.2021.3116481","authors":["Maria Sahakyan","Zeyar Aung","Talal Rahwan"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1109/access.2021.3116481","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4386889960","name":"Artificial Intelligence in Healthcare: Perception and Reality","source":"openalex","abstract":"Artificial intelligence (AI) has birthed the new \"big thing\" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on \"data.\" These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.","url":"https://doi.org/10.7759/cureus.45594","authors":["Abidemi O Akinrinmade","Temitayo M Adebile","Chioma Ezuma-Ebong","Kafayat Bolaji","Afomachukwu Ajufo","Aisha O Adigun","Majed Mohammad","Juliet C Dike","Okelue E Okobi"],"tags":["Aside","Medicine","SAFER","Anticipation (artificial intelligence)","Prejudice (legal term)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-20","doi":"https://doi.org/10.7759/cureus.45594","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3054891022","name":"Chemistry in Times of Artificial Intelligence","source":"openalex","abstract":"Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.","url":"https://doi.org/10.1002/cphc.202000518","authors":["Johann Gasteiger"],"tags":["Cheminformatics","Chemistry","Process (computing)","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-28","doi":"https://doi.org/10.1002/cphc.202000518","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4392112330","name":"Ética e inteligencia artificial","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.rce.2024.01.007","authors":["Luis Inglada Galiana","Luís Corral-Gudino","P. Miramontes González"],"tags":["Computer science","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-23","doi":"https://doi.org/10.1016/j.rce.2024.01.007","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4382058325","name":"Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review","source":"openalex","abstract":"The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows of healthcare organizations at an accelerated rate. This modification highlights the significant impact that AI has on a variety of tasks, especially in health procedures relating to early detection and diagnosis. Papers done in the past imply that AI has the potential to increase the overall quality of services provided in the healthcare industry. There have been reports that technology based on AI can improve the quality of human existence by making life simpler, safer, and more productive. A comprehensive analysis of previous scholarly research on the use of AI in the health area is provided in this research in the form of a literature review. In order to propose a classification framework, the review took into consideration 132 academic publications sourced from scholarly sources. The presentation covers both the benefits and the issues that AI capabilities provide for individuals, medical professionals, corporations, and the health industry. In addition, the social and ethical implications of AI are examined in the context of the output of value-added medical services for decision-making processes in healthcare, privacy and security measures for patient data, and health monitoring capabilities.","url":"https://doi.org/10.3390/app13137479","authors":["Fotis Kitsios","Maria Kamariotou","Aristomenis Syngelakis","Μichael A. Talias"],"tags":["Health care","Presentation (obstetrics)","Applications of artificial intelligence","Workflow","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-25","doi":"https://doi.org/10.3390/app13137479","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403457107","name":"A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges","source":"openalex","abstract":"Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions.","url":"https://doi.org/10.1016/j.imu.2024.101587","authors":["Ibomoiye Domor Mienye","George Obaido","Nobert Jere","Ebikella Mienye","Kehinde Aruleba","Ikiomoye Douglas Emmanuel","Blessing Ogbuokiri"],"tags":["Health care","Data science","Computer science","Management science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1016/j.imu.2024.101587","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3005767652","name":"International migration management in the age of artificial intelligence","source":"openalex","abstract":"Abstract Artificial intelligence (AI) has the potential to revolutionise the way states and international organisations seek to manage international migration. AI is gradually going to be used to perform tasks, including identity checks, border security and control, and analysis of data about visa and asylum applicants. To an extent, this is already a reality in some countries such as Canada, which uses algorithmic decision-making in immigration and asylum determination, and Germany, which has piloted projects using technologies such as face and dialect recognition for decision-making in asylum determination processes. The article’s central hypothesis is that AI technology can affect international migration management in three different dimensions: (1) by deepening the existing asymmetries between states on the international plane; (2) by modernising states’ and international organisations’ traditional practices; and (3) by reinforcing the contemporary calls for more evidence-based migration management and border security. The article examines each of these three hypotheses and reflects on the main challenges of using AI solutions for international migration management. It draws on legal, political and technology-facing academic literature, examining the current trends in technological developments and investigating the consequences that these can have for international migration. Most particularly, the article contributes to the current debate about the future of international migration management, informing policymakers in this area of growing importance and fast development.","url":"https://doi.org/10.1093/migration/mnaa003","authors":["Ana Beduschi"],"tags":["Immigration","Identity (music)","Politics","Face (sociological concept)","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-20","doi":"https://doi.org/10.1093/migration/mnaa003","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3138501564","name":"Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence","source":"openalex","abstract":"BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. METHODS: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. RESULTS: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. CONCLUSIONS: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.","url":"https://doi.org/10.1186/s12916-021-01942-5","authors":["Kai Wang","Gang Yu","C. Xu","Xiang‐He Meng","Jing Zhou","Cheng Zheng","Zihao Deng","Li Shang","Rongren Liu","Shilin Su","Xiao Zhou","Qingsong Li","Jieyi Li","J. Wang","Kebo Ma","Ji Qi","Zhe-Yu Hu","Peng Tang","Jun Deng","Xiaohui Qiu","Bo Li","Wen‐Di Shen","Ruping Quan","Jun Yang","Lifan Huang","Yin Xiao","Zhen Yang","Zhengze Li","Ssu‐Yuan Wang","Haoran Ren","Chia-Wei Liang","Wei Guo","Y. Li","Haifan Xiao","H. Xiao","Y. Gu","J. P. Yun","Dan Huang","Zhigang Song","Xiangshan Fan","L. Chen","Xijing Yan","Z. Li","J. Huang","J. Huang","Joy Luttrell","Chunyu Zhang","Wu Zhou","K. Zhang","Chaozhong Wu","C. Wu","Hanshu Shen","Yipeng Wang","H. M. Xiao","Huangqing Xiao","Hong‐Wen Deng"],"tags":["Medicine","Artificial intelligence","Colorectal cancer","Convolutional neural network","Colonoscopy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-22","doi":"https://doi.org/10.1186/s12916-021-01942-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3094105990","name":"Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools","source":"openalex","abstract":"Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a “black-box” due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters.","url":"https://doi.org/10.1109/access.2020.3031477","authors":["Murat Kuzlu","Ümit Cali","Vinayak Sharma","Özgur Güler"],"tags":["Photovoltaic system","Computer science","Smart grid","Field (mathematics)","Transparency (behavior)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1109/access.2020.3031477","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4387618894","name":"Artificial Intelligence Algorithm with ICD Coding Technology Guided by Embedded Electronic Medical Record System in Medical Record Information Management","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.micpro.2023.104962","authors":["Cheng Wang","Chenlong Yao","Pengfei Chen","Jiamin Shi","Zhe Gu","Zheying Zhou"],"tags":["Computer science","Coding (social sciences)","Diagnosis code","Medical record","Medical classification"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-01","doi":"https://doi.org/10.1016/j.micpro.2023.104962","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3029725182","name":"Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward","source":"openalex","abstract":"Abstract Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.","url":"https://doi.org/10.1057/s41599-020-0501-9","authors":["Samuele Lo Piano"],"tags":["Accountability","Interpretability","Transparency (behavior)","Underpinning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-17","doi":"https://doi.org/10.1057/s41599-020-0501-9","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4280542598","name":"The Effectiveness of Artificial Intelligence in Detection of Oral Cancer","source":"openalex","abstract":"AIM: The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. MATERIALS AND METHODS: From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. RESULTS: Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. CONCLUSIONS: There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.","url":"https://doi.org/10.1016/j.identj.2022.03.001","authors":["Natheer Al‐Rawi","Afrah Saleh Sultan","Batool Rajai","Haneen Shuaeeb","Mariam Alnajjar","Maryam Mohammad Alketbi","Yara Mohammad","Shishir Shetty","Mubarak Ahmed Mashrah"],"tags":["Artificial intelligence","Machine learning","Medicine","Diagnostic accuracy","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-14","doi":"https://doi.org/10.1016/j.identj.2022.03.001","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2792790345","name":"Artificial intelligence on the identification of risk groups for osteoporosis, a general review","source":"openalex","abstract":"INTRODUCTION: The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. METHODS: A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms \"Neural Network\", \"Osteoporosis Machine Learning\" and \"Osteoporosis Neural Network\". Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000-2017 were selected; however, articles prior to this period with great relevance were included in this study. DISCUSSION: Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. CONCLUSIONS: It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.","url":"https://doi.org/10.1186/s12938-018-0436-1","authors":["Agnaldo S. Cruz","Hertz C. Lins","Ricardo V. A. Medeiros","José Macedo Filho","Sandro G. da Silva"],"tags":["Identification (biology)","Osteoporosis","Computer science","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-01-29","doi":"https://doi.org/10.1186/s12938-018-0436-1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3014493065","name":"Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy","source":"openalex","abstract":"Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.","url":"https://doi.org/10.5946/ce.2020.054","authors":["Joonmyeong Choi","Keewon Shin","Jinhoon Jung","Hyun‐Jin Bae","Do Hoon Kim","Jeong-Sik Byeon","Namku Kim"],"tags":["Deep learning","Overfitting","Artificial intelligence","Convolutional neural network","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-30","doi":"https://doi.org/10.5946/ce.2020.054","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4388513507","name":"The Role of Artificial Intelligence in the Medical Field","source":"openalex","abstract":"The Artificial Intelligence in the medical field has revolutionized the industry. Recently, A. I. has interested medical practitioners in applying innovation to healthcare systems. A. I. has еmеrgеd as a transformative forcе, revolutionizing the industry by leveraging advanced algorithms and computing powеr to еnhancе various aspects of hеalthcarе dеlivеry. The background highlights that artificial intelligence as innovation promises to transform how medical staffs manage patients and treat and diagnose patients. This comprehensive literature review to identify the relevant sources of information on A. I implementation in healthcare, focusing on the advantages and disadvantages. The obtained results from the materials provided valuable insights into the various means A. I. is used in the medical industry and its effects on patient care and recovery. The findings indicated that; A. I. streamlines Tedious Tasks since it is accurate and gives speedy services enabling early detection of illnesses and leading to positive patient outcomes. A. I. provides Real-Time Data which is essential in addressing patients’ conditions with clear objectives; the use of A. I. improves helps to reduce Burnout in medical practitioners. The use of A. I. helps provide Precision Medicine since it can obtain and analyze large amounts of information. The future directions encompass the implementation of the legal framework, enhancing transparency and accountability, and addressing challenges related to data standardization.","url":"https://doi.org/10.4236/jcc.2023.1111001","authors":["Shridula Kapa"],"tags":["Standardization","Transformative learning","Health care","Field (mathematics)","Accountability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.4236/jcc.2023.1111001","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4386141641","name":"Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges","source":"openalex","abstract":"Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI’s ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape.","url":"https://doi.org/10.3390/fi15090286","authors":["Peng Zhang","Maged N. Kamel Boulos"],"tags":["Generative grammar","Computer science","Domain (mathematical analysis)","Narrative","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-24","doi":"https://doi.org/10.3390/fi15090286","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4214731336","name":"Applications and challenges of artificial intelligence in diagnostic and interventional radiology","source":"openalex","abstract":"Purpose: Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face. Material and methods: Twenty-one publications were selected from the primary literature through a PubMed search. The articles included in our review studied a range of applications of artificial intelligence in radiology. Results: The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging. Conclusions: As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists. Further research should be done on the risks of AI implementation and how to most accurately validate the results.","url":"https://doi.org/10.5114/pjr.2022.113531","authors":["Joseph Waller","A. D. O'Connor","Eleeza Raafat","Ahmad Amireh","John Dempsey","Clarissa Martin","Muhammad Umair"],"tags":["Artificial intelligence","Convolutional neural network","Medicine","Workload","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-23","doi":"https://doi.org/10.5114/pjr.2022.113531","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4324116446","name":"Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities","source":"openalex","abstract":"Healthcare systems are under increasing strain due to a myriad of factors, from a steadily ageing global population to the current COVID-19 pandemic. In a world where we have needed to be connected but apart, the need for enhanced remote and at-home healthcare has become clear. The Internet of Things (IoT) offers a promising solution. The IoT has created a highly connected world, with billions of devices collecting and communicating data from a range of applications, including healthcare. Due to these high volumes of data, a natural synergy with Artificial Intelligence (AI) has become apparent – big data both enables and requires AI to interpret, understand, and make decisions that provide optimal outcomes. In this extensive survey, we thoroughly explore this synergy through an examination of the field of the Artificial Intelligence of Things (AIoT) for healthcare. This work begins by briefly establishing a unified architecture of AIoT in a healthcare context, including sensors and devices, novel communication technologies, and cross-layer AI. We then examine recent research pertaining to each component of the AIoT architecture from several key perspectives, identifying promising technologies, challenges, and opportunities that are unique to healthcare. Several examples of real-world AIoT healthcare use cases are then presented to illustrate the potential of these technologies. Lastly, this work outlines promising directions for future research in AIoT for healthcare.","url":"https://doi.org/10.1109/comst.2023.3256323","authors":["Stephanie Baker","Wei Xiang"],"tags":["Health care","Data science","Engineering ethics","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/comst.2023.3256323","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4412043430","name":"Integrating artificial intelligence in healthcare: applications, challenges, and future directions","source":"openalex","abstract":"Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper explores AI applications in cancer detection, dental medicine, brain tumor database management, and personalized treatment planning. AI technologies such as machine learning and deep learning have enhanced diagnostic accuracy, improved data management, and facilitated personalized treatment strategies. In cancer detection, AI-driven imaging analysis aids in early diagnosis and precise treatment decisions. In dental healthcare, AI applications improve oral disease detection, treatment planning, and workflow efficiency. AI-powered brain tumor databases streamline medical data management, enhancing diagnostic precision and research outcomes. Personalized treatment planning benefits from AI algorithms that analyze genetic, clinical, and lifestyle data to recommend tailored interventions. Despite these advancements, AI integration faces challenges related to data privacy, algorithm bias, and regulatory concerns. Addressing these issues requires improved data governance, ethical frameworks, and interdisciplinary collaboration among healthcare professionals, researchers, and policymakers. Through comprehensive validation, educational initiatives, and standardized protocols, AI adoption in healthcare can enhance patient outcomes and optimize clinical decision-making, advancing the future of precision medicine and personalized care.","url":"https://doi.org/10.1080/20565623.2025.2527505","authors":["Peng Lean Chong","Vikneswaran Vaigeshwari","Basir Khan Mohammed Reyasudin","binti Ros Azamin Noor Hidayah","Purnshatman Tatchanaamoorti","Jian Ai Yeow","Feng Kong"],"tags":["Health care","Precision medicine","Computer science","Data science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-04","doi":"https://doi.org/10.1080/20565623.2025.2527505","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4413051177","name":"Patient Preferences for Artificial Intelligence in Medical Imaging: A Single-Center Cross-Sectional Survey","source":"openalex","abstract":"Artificial Intelligence (AI) is rapidly being implemented into clinical practice to improve diagnostic accuracy and reduce provider burnout. However, patient self-perceived knowledge and perceptions of AI's role in their care remain unclear. This study aims to explore patient preferences regarding the use of and communication of AI in their care for patients undergoing cross-sectional imaging exams. This single-center cross-sectional study, a structured questionnaire recruited patients undergoing outpatient CT or MRI examinations between June and July 2024 to assess baseline self-perceived knowledge of AI, perspectives on AI in clinical care, preferences regarding AI-generated results, and economic considerations related to AI, using Likert scales and categorical questions. A total of 226 participants (143 females; mean age 53 years) were surveyed with 67.4% (151/224) reporting having minimal to no knowledge of AI in medicine, with lower knowledge levels associated with lower socioeconomic status (p < .001). 90.3% (204/226) believed they should be informed about the use of AI in their care, and 91.1% (204/224) supported the right to opt out. Additionally, 91.1% (204/224) of participants expressed a strong preference for being informed when AI was involved in interpreting their medical images. 65.6% (143/218) indicated that they would not accept a screening imaging exam exclusively interpreted by an AI algorithm. Finally, 91.1% (204/224) of participants wanted disclosure when AI was used and 89.1% (196/220) felt such disclosure and clarification of discrepancies should be considered standard care. To align AI adoption with patient preferences and expectations, radiology practices must prioritize disclosure, patient engagement, and standardized documentation of AI use without being overly burdensome to the diagnostic workflow. Patients prefer transparency for AI utilization in their care, and our study highlights the discrepancy between patient preferences and current clinical practice. Patients are not expected to determine the technical aspects of an image examination such as acquisition parameters or reconstruction kernel and must trust their providers to act in their best interest. Clear communication of how AI is being used in their care should be provided in ways that do not overly burden the radiologist.","url":"https://doi.org/10.1007/s10278-025-01629-w","authors":["Kennedye N McGhee","D. Jonah Barrett","Omar Safarini","Asser Abou Elkassem","John T Eddins","Andrew D. Smith","Steven Rothenberg"],"tags":["Cross-sectional study","Center (category theory)","Medicine","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-07","doi":"https://doi.org/10.1007/s10278-025-01629-w","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2959048647","name":"A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry","source":"openalex","abstract":"The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases “Scopus” and “Web of Science” and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.","url":"https://doi.org/10.1109/access.2019.2928979","authors":["Chandadevi Giri","Sheenam Jain","Xianyi Zeng","Pascal Bruniaux"],"tags":["Supply chain","Fast fashion","Computer science","Clothing","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.1109/access.2019.2928979","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385665192","name":"Artificial intelligence to support publishing and peer review: A summary and review","source":"openalex","abstract":"Abstract Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation.","url":"https://doi.org/10.1002/leap.1570","authors":["Kayvan Kousha","Mike Thelwall"],"tags":["Publishing","Automation","Computer science","Peer review","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-08","doi":"https://doi.org/10.1002/leap.1570","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403371652","name":"Artificial Intelligence Applications in Medical Mycology: Current and Future","source":"openalex","abstract":"The application of artificial intelligence (AI) in the medical mycology field represents a new era in the diagnosis and management of fungal infections. AI technologies, particularly machine learning (ML) and deep learning (DL) methods, enhance diagnostic accuracy by leveraging large datasets and complex algorithms. This review examines current applications of AI in laboratory and clinical settings for fungal diagnostics. In the laboratory, AI models analyze microscopic images from potassium hydroxide (KOH) examinations, fungal culture tests, and histopathologic slides, which improves the detection rates of fungal pathogens significantly. In the clinical setting, AI assists the diagnosis of fungal infections using medical images, exhibiting high efficacy in binary classification tasks. However, challenges include small sample sizes, class imbalances, reliance on expert-labeled data, and the black box nature of AI models. Explainable AI offers potential solutions by providing human-comprehensible insights into AI decisionmaking processes. In addition, human-computer collaboration can enhance diagnostic accuracy, particularly for less experienced clinicians. The development of generative AI models, e.g., large language models and multimodal AI, promises to create extensive datasets and integrate various data sources for comprehensive diagnostics. Addressing these limitations through prospective clinical validation and continuous feedback will be essential for realizing the full potential of AI in medical mycology.","url":"https://doi.org/10.17966/jmi.2024.29.3.85","authors":["Jemin Kim","Jihee Boo","Chang Ook Park"],"tags":["Mycology","Current (fluid)","Medical mycology","Medical physics","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-30","doi":"https://doi.org/10.17966/jmi.2024.29.3.85","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2997864133","name":"RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS","source":"openalex","abstract":"The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.","url":"https://doi.org/10.22190/fume190327035a","authors":["Adriana Albu","Radu‐Emil Precup","Teodor-Adrian Teban"],"tags":["Computer science","Field (mathematics)","Artificial neural network","Artificial intelligence","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-11-29","doi":"https://doi.org/10.22190/fume190327035a","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4212952460","name":"Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.hlpt.2022.100602","authors":["Ahmad Khanijahani","Shabnam Iezadi","Sage Dudley","Megan Goettler","Peter Kroetsch","Jama Wise"],"tags":["Health care","Knowledge management","Health professionals","Psychology","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-13","doi":"https://doi.org/10.1016/j.hlpt.2022.100602","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2787306967","name":"Moral Decision Making Frameworks for Artificial Intelligence","source":"openalex","abstract":"The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.","url":"https://doi.org/10.1609/aaai.v31i1.11140","authors":["Vincent Conitzer","Walter Sinnott‐Armstrong","Schaich Borg, Jana","Yuan Deng","Max Kramer"],"tags":["Generality","Variety (cybernetics)","Computer science","Domain (mathematical analysis)","Component (thermodynamics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-02-12","doi":"https://doi.org/10.1609/aaai.v31i1.11140","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4386024862","name":"Radiographers’ Acceptance on the Integration of Artificial Intelligence into Medical Imaging Practice","source":"openalex","abstract":"Artificial intelligence (AI) integration in medical imaging is a promising field for enhancing patient care, performance, and efficiency. Radiographers, on the other hand, are concerned about AI's acceptance and potential to replace them. This study assessed radiographers' acceptance of AI integration by considering their knowledge, attitudes, and job security. Based on demographic characteristics, there were no significant differences in knowledge, attitude, or job security level. Completing AI training, on the other hand, had a considerable influence. Overall, radiographers have a good level of knowledge and are enthusiastic about using AI tools into their regular activities.","url":"https://doi.org/10.21834/e-bpj.v8i25.4872","authors":["Hairenanorashikin Sharip","Wan Farah Wahida Che Zakaria","Sook Sam Leong","Maida Ali Masoud","Mohamad Zafran Hakim Mohd Junaidi"],"tags":["Field (mathematics)","Knowledge management","Artificial intelligence","Medical education","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-31","doi":"https://doi.org/10.21834/e-bpj.v8i25.4872","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4362613921","name":"Impact of artificial intelligence on marketing","source":"openalex","abstract":"The evolution of artificial intelligence (AI) has drastically changed the dynamics of today’s business world. One of the most significant applications of AI is in the field of marketing, which assists in enhancing performance. The current research aims at finding out the impact of AI in marketing. A thorough literature research was highlighted, providing a strong knowledge of AI and its use in marketing. Second, the researcher employed a qualitative study strategy that included semi-structured interviews with marketing professionals from several Indian companies. The researcher chose a sample size of fifteen marketing experts to interview. The study's findings emphasise the elements that influence AI integration in marketing, the benefits and obstacles of AI integration in marketing, as well as your company's pre and post AI marketing strategy, ethical considerations, and use of AI in the marketing industry. The study proposes integrating AI into marketing tasks in order to improve corporate performance and, as a result, achieve profitability and competitive advantage. This study also contributes to strategic marketing research by identifying research gaps that bridge strategic AI marketing practise and research in a systematic and rigorous manner.","url":"https://doi.org/10.55927/eajmr.v2i3.3112","authors":["Mahabub Shaik"],"tags":["Marketing research","Marketing","Marketing management","Return on marketing investment","Marketing strategy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-25","doi":"https://doi.org/10.55927/eajmr.v2i3.3112","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3154211345","name":"Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview","source":"openalex","abstract":"BACKGROUND: This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. METHODS: Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009-2020). No restrictions were placed on the AI/ML method or imaging modality used. RESULTS: In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). CONCLUSIONS: There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.","url":"https://doi.org/10.1038/s41416-021-01386-x","authors":["Hanya Mahmood","Muhammad Shaban","Nasir Rajpoot","Syed Ali Khurram"],"tags":["Medicine","Larynx","Head and neck cancer","Artificial intelligence","Head and neck"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-19","doi":"https://doi.org/10.1038/s41416-021-01386-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4285819700","name":"Artificial Intelligence Crime: An Overview of Malicious Use and Abuse of AI","source":"openalex","abstract":"The capabilities of Artificial Intelligence (AI) evolve rapidly and affect almost all sectors of society. AI has been increasingly integrated into criminal and harmful activities, expanding existing vulnerabilities, and introducing new threats. This article reviews the relevant literature, reports, and representative incidents which allows to construct a typology of the malicious use and abuse of systems with AI capabilities. The main objective is to clarify the types of activities and corresponding risks. Our starting point is to identify the vulnerabilities of AI models and outline how malicious actors can abuse them. Subsequently, we explore AI-enabled and AI-enhanced attacks. While we present a comprehensive overview, we do not aim for a conclusive and exhaustive classification. Rather, we provide an overview of the risks of enhanced AI application, that contributes to the growing body of knowledge on the issue. Specifically, we suggest four types of malicious abuse of AI (integrity attacks, unintended AI outcomes, algorithmic trading, membership inference attacks) and four types of malicious use of AI (social engineering, misinformation/fake news, hacking, autonomous weapon systems). Mapping these threats enables advanced reflection of governance strategies, policies, and activities that can be developed or improved to minimize risks and avoid harmful consequences. Enhanced collaboration among governments, industries, and civil society actors is vital to increase preparedness and resilience against malicious use and abuse of AI.","url":"https://doi.org/10.1109/access.2022.3191790","authors":["Taís Fernanda Blauth","Oskar Josef Gstrein","Andrej Zwitter"],"tags":["Computer security","Computer science","Resilience (materials science)","Misinformation","Hacker"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1109/access.2022.3191790","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391650202","name":"Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study","source":"openalex","abstract":"Background: The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial. Aims: This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare. Methods: A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints. Results: After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation. Conclusion: This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.","url":"https://doi.org/10.1016/j.heliyon.2024.e25718","authors":["Moustaq Karim Khan Rony","Ibne Kayesh","Shuvashish Das Bala","Fazila Akter","Mst. Rina Parvin"],"tags":["Thematic analysis","Nursing","Viewpoints","Transformative learning","Nursing research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-01","doi":"https://doi.org/10.1016/j.heliyon.2024.e25718","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4412037398","name":"A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision","source":"openalex","abstract":"Explainable Artificial Intelligence (XAI) is increasingly important in computer vision, aiming to connect complex model outputs with human understanding. This review provides a focused comparative analysis of representative XAI methods in four main categories, attribution-based, activation-based, perturbation-based, and transformer-based approaches, selected from a broader literature landscape. Attribution-based methods like Grad-CAM highlight key input regions using gradients and feature activation. Activation-based methods analyze the responses of internal neurons or feature maps to identify which parts of the input activate specific layers or units, helping to reveal hierarchical feature representations. Perturbation-based techniques, such as RISE, assess feature importance through input modifications without accessing internal model details. Transformer-based methods, which use self-attention, offer global interpretability by tracing information flow across layers. We evaluate these methods using metrics such as faithfulness, localization accuracy, efficiency, and overlap with medical annotations. We also propose a hierarchical taxonomy to classify these methods, reflecting the diversity of XAI techniques. Results show that RISE has the highest faithfulness but is computationally expensive, limiting its use in real-time scenarios. Transformer-based methods perform well in medical imaging, with high IoU scores, though interpreting attention maps requires care. These findings emphasize the need for context-aware evaluation and hybrid XAI methods balancing interpretability and efficiency. The review ends by discussing ethical and practical challenges, stressing the need for standard benchmarks and domain-specific tuning.","url":"https://doi.org/10.3390/s25134166","authors":["Zhu Cheng","Yue Wu","Yule Li","Lingfeng Cai","Baha Ihnaini"],"tags":["Interpretability","Computer science","Artificial intelligence","Personalization","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-04","doi":"https://doi.org/10.3390/s25134166","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3090868346","name":"Artificial intelligence in the water domain: Opportunities for responsible use","source":"openalex","abstract":"Recent years have seen a rise of techniques based on artificial intelligence (AI). With that have also come initiatives for guidance on how to develop \"responsible AI\" aligned with human and ethical values. Compared to sectors like energy, healthcare, or transportation, the use of AI-based techniques in the water domain is relatively modest. This paper presents a review of current AI applications in the water domain and develops some tentative insights as to what \"responsible AI\" could mean there. Building on the reviewed literature, four categories of application are identified: modeling, prediction and forecasting, decision support and operational management, and optimization. We also identify three insights pertaining to the water sector in particular: the use of AI techniques in general, and many-objective optimization in particular, that allow for a pluralism of values and changing values; the use of theory-guided data science, which can avoid some of the pitfalls of strictly data-driven models; and the ability to build on experiences with participatory decision-making in the water sector. These insights suggest that the development and application of responsible AI techniques for the water sector should not be left to data scientists alone, but requires concerted effort by water professionals and data scientists working together, complemented with expertise from the social sciences and humanities.","url":"https://doi.org/10.1016/j.scitotenv.2020.142561","authors":["Neelke Doorn"],"tags":["Water sector","Domain (mathematical analysis)","Data science","Citizen journalism","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-29","doi":"https://doi.org/10.1016/j.scitotenv.2020.142561","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391820411","name":"Artificial Intelligence in Operating Room Management","source":"openalex","abstract":"This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.","url":"https://doi.org/10.1007/s10916-024-02038-2","authors":["Valentina Bellini","Michele Russo","Tania Domenichetti","Matteo Panizzi","Simone Allai","Elena Bignami"],"tags":["Transformative learning","Health informatics","Health care","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-14","doi":"https://doi.org/10.1007/s10916-024-02038-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4400820534","name":"Performance of ChatGPT in Solving Questions From the Progress Test (Brazilian National Medical Exam): A Potential Artificial Intelligence Tool in Medical Practice","source":"openalex","abstract":"Background The use of artificial intelligence (AI) is not a recent phenomenon, but the latest advancements in this technology are making a significant impact across various fields of human knowledge. In medicine, this trend is no different, although it has developed at a slower pace. ChatGPT is an example of an AI-based algorithm capable of answering questions, interpreting phrases, and synthesizing complex information, potentially aiding and even replacing humans in various areas of social interest. Some studies have compared its performance in solving medical knowledge exams with medical students and professionals to verify AI accuracy. This study aimed to measure the performance of ChatGPT in answering questions from the Progress Test from 2021 to 2023. Methodology An observational study was conducted in which questions from the 2021 Progress Test and the regional tests (Southern Institutional Pedagogical Support Center II) of 2022 and 2023 were presented to ChatGPT 3.5. The results obtained were compared with the scores of first- to sixth-year medical students from over 120 Brazilian universities. All questions were presented sequentially, without any modification to their structure. After each question was presented, the platform's history was cleared, and the site was restarted. Results The platform achieved an average accuracy rate in 2021, 2022, and 2023 of 69.7%, 68.3%, and 67.2%, respectively, surpassing students from all medical years in the three tests evaluated, reinforcing findings in the current literature. The subject with the best score for the AI was Public Health, with a mean grade of 77.8%. Conclusions ChatGPT demonstrated the ability to answer medical questions with higher accuracy than humans, including students from the last year of medical school.","url":"https://doi.org/10.7759/cureus.64924","authors":["Mateus Rodrigues Alessi","Heitor A Gomes","Matheus Lopes de Castro","Cristina Terumy Okamoto"],"tags":["Pace","Clearance","Medicine","Test (biology)","Observational study"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-19","doi":"https://doi.org/10.7759/cureus.64924","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4367856375","name":"Artificial intelligence and remote patient monitoring in US healthcare market: a literature review","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions. OBJECTIVES: Identify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets' needs. METHODS: We searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions. RESULTS: A total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3). CONCLUSION: The market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.","url":"https://doi.org/10.1080/20016689.2023.2205618","authors":["Ayushmaan Dubey","Anuj Tiwari"],"tags":["Transparency (behavior)","Computer science","Product (mathematics)","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-03","doi":"https://doi.org/10.1080/20016689.2023.2205618","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3174451259","name":"Who Will Be Liable for Medical Malpractice in the Future? How the Use of Artificial Intelligence in Medicine Will Shape Medical Tort Law","source":"openalex","abstract":"Artificial intelligence (AI) is a powerful technology that can assist physicians with the practice of medicine.Use of the technology has grown in recent years and has powerful potential.Medical AI typically functions as a type of \"machine-learning\" that relies on deep neural networks to sift through vast amounts of data to give recommendations or draw conclusions for clinicians.This Article begins by outlining key characteristics of medical AI (e.g., AI's opacity and \"black-box,\" and how and with what data the AI was developed) that make assessment of liability under traditional tort paradigms (like negligence) difficult.This Article then highlights several tort paradigms (e.g., medical malpractice, products liability, vicarious liability, and informed consent) and how they might function in the context of medical AI.In conclusion, this Article offers an analysis of how tort law may evolve in the future in response to the challenges created by medical AI today (e.g., legal evolutions that may take the form of new solutions like AI personhood, common enterprise liability, and a new standard of care).","url":"https://openalex.org/W3174451259","authors":["Scott J. Schweikart"],"tags":["Tort","Medical malpractice","Malpractice","Law","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4210451751","name":"Acceptance and Fear of Artificial Intelligence: associations with personality in a German and a Chinese sample","source":"openalex","abstract":"Abstract Understanding individual differences in attitudes towards Artificial Intelligence (AI) is of importance, among others in system development. Against this background, we sought to investigate associations between personality and attitudes towards AI. Relations were investigated in samples from two countries—Germany and China—to find potentially replicable, cross-culturally applicable associations. In German (N = 367, n = 137 men) and Chinese (N = 879; n = 220 men) online surveys, participants completed items on sociodemographic variables, the Attitudes Towards Artificial Intelligence (ATAI) scale, and the Big Five Inventory. Correlational analysis revealed among others significant positive associations between Neuroticism and fear of AI in both samples, with similar effect sizes. In addition to a significant association of acceptance of AI with gender, regression analyses revealed a small but significant positive association between Neuroticism and fear of AI in the German sample. In the Chinese sample, regression analyses showed positive associations of acceptance of AI with age, Openness, and Agreeableness. Fear of AI was only significantly negatively related to Agreeableness in the Chinese sample. The association of fear of AI with Neuroticism just failed to be significant in the regression model in the Chinese sample. These results reveal important insights into associations between certain personality traits and attitudes towards AI. However, given mostly small effect sizes of relations between personality and attitudes towards AI, other factors aside from personality traits seem to be of relevance to explain variance in individuals’ attitudes towards AI, as well.","url":"https://doi.org/10.1007/s44202-022-00020-y","authors":["Cornelia Sindermann","Haibo Yang","Jon D. Elhai","Shixin Yang","Ling Quan","Mei Li","Christian Montag"],"tags":["Neuroticism","Agreeableness","Psychology","Personality","Big Five personality traits"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-31","doi":"https://doi.org/10.1007/s44202-022-00020-y","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W1495982240","name":"NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge.","source":"openalex","abstract":"In order to address some existing problems in computer-aided medical decision making, a computer program called NESTOR has been developed to aid physicians in determining the most likely diagnostic hypothesis to account for a set of patient findings. The domain of hypercalcemic disorders is used to test solution methods that should be applicable to other medical areas. A key design philosophy underlying NESTOR is that the physicians should have control of the computer interaction to determine what is done and when. In order to provide such controllable, interactive aid, specific technical tasks to be addressed. The unifying philosophy in addressing them is the use of knowledge-based methods within a formal probability theory framework. A user interface module gives the physician control over when and how these tasks are used to aid in diagnosing the cause of a patient's condition. This dissertation presents the problems that are addressed by each of the three tasks, and the details of the methods used to address them. In addition, the results of an evaluation of the hypothesis scoring and search techniques are presented and discussed. Additional keywords: artificial intelligence; expert systems; medical applications; computer aided diagnosis; medical computer applications.","url":"https://openalex.org/W1495982240","authors":["Gregory F. Cooper"],"tags":["Computer science","Expert system","Probabilistic logic","Set (abstract data type)","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1984-11-01","doi":"","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4379875293","name":"Smart Shoe Classification Using Artificial Intelligence on EfficientnetB3 Model","source":"openalex","abstract":"In both athletics and daily life, shoes are crucial. Recently, smart sneakers have also taken on a certain trend among both sportsmen and the general public. Therefore, it has actually become crucial to classify shoes in a way that enables industry specialists to do so in accordance with customer demands keeping medical and health point of view. Each shoe product has four distinct perspectives, front, left, right, and rear, but only a few of these views have the product information that allows researchers to create innovative transfer learning models to understand its qualities with precision. Selection, prediction, and categorization of the specific perspective from the provided dataset from many viewpoints are all made possible via the development of machine learning models and the explanation of the artificial intelligence method. In order to create a sound and useful framework for shoe categorization, several efforts have been made over the past few decades. The suggested model in this research exhibits an accuracy of 88.8% on the Adam optimizer utilizing the EfficientNetB3 model and illustrates the accurate classification of the shoe class as utilized in the dataset.","url":"https://doi.org/10.1109/incacct57535.2023.10141787","authors":["Kanwarpartap Singh Gill","Avinash Sharma","Vatsala Anand","Rupesh Gupta"],"tags":["Viewpoints","Computer science","Artificial intelligence","Categorization","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-05","doi":"https://doi.org/10.1109/incacct57535.2023.10141787","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4312791778","name":"What is Artificial Intelligence?","source":"openalex","abstract":"Artificial intelligence (AI) is the ability of a machine or computer system to simulate and perform tasks that would normally require human intelligence, such as logical reasoning, learning, and problem solving. Artificial intelligence is based on the use of machine learning algorithms and technologies to give machines the ability to apply certain cognitive abilities and perform tasks on their own autonomously or semi-autonomously. Artificial intelligence is distinguished by its degree of cognitive capacity or by its degree of autonomy. By capacity it can be weak or limited, general or superlative. Due to its autonomy, it can be reactive, deliberative, cognitive, or totally autonomous. As artificial intelligence improves, many processes are becoming more efficient and tasks that seem complicated today will be performed more quickly and accurately.","url":"https://doi.org/10.55248/gengpi.2022.31261","authors":["Fabio Morandín-Ahuerma"],"tags":["Artificial intelligence","Computer science","Artificial general intelligence","Superlative","Cognition"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.55248/gengpi.2022.31261","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4296325109","name":"Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes","source":"openalex","abstract":"Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.","url":"https://doi.org/10.1177/19322968221124583","authors":["Jingtong Huang","Andrea M. Yeung","David G. Armstrong","Ashley N. Battarbee","Jorge Cuadros","Juan Espinoza","Samantha Kleinberg","Nestoras Mathioudakis","Mark Swerdlow","David C. Klonoff"],"tags":["Medicine","Diabetes mellitus","Gestational diabetes","Nephropathy","Hypoglycemia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-19","doi":"https://doi.org/10.1177/19322968221124583","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4214866315","name":"Artificial intelligence in healthcare: a comprehensive review of its ethical concerns","source":"openalex","abstract":"Purpose Nowadays, the digitized economy and technological advancements are increasing at a faster pace. One such technology that is gaining popularity in the healthcare sector is Artificial Intelligence (AI). AI has been debated much, searched so well due to the implications, issues and for its benefits in terms of ease, it will offer. The following research has focused on examining the ethical dilemmas associated with AI when it will be introduced in the healthcare sector. Design/methodology/approach A narrative review method focusing on content analysis has been used in the research. The authors have employed a deductive approach to determine the ethical facets of adopting AI in the healthcare sector. The current study is complemented by a review of related studies. The secondary data have been collected from authentic resources available on the Internet. Findings Patient privacy, biased results, patient safety and Human errors are some major ethical dilemmas that are likely to be faced once AI will be introduced in healthcare. The impact of ethical dilemmas can be minimized by continuous monitoring but cannot be eliminated in full if AI is introduced in healthcare. AI overall will increase the performance of the healthcare sector. However, we need to address some recommendations to mitigate the ethical potential issues that we could observe using AI. Technological change and AI can mimic the overall intellectual process of humans, which increases its credibility and also offers harm to humans. Originality/value Patient safety is the most crucial ethical concern because AI is a new technology and technology can lead to failure. Thus, we need to be certain that these new technological developments are ethically applied. The authors need to evaluate and assess the organizational and legal progress associated with the emergence of AI in the healthcare sector. It also highlights the importance of covering and protecting medical practitioners regarding the different secondary effects of this artificial medical progress. The research stresses the need of establishing partnerships between computer scientists and clinicians to effectively implement AI. Lastly, the research highly recommends training of IT specialists, healthcare and medical staff about healthcare ethics.","url":"https://doi.org/10.1108/techs-12-2021-0029","authors":["Chokri Kooli","Hend Al Muftah"],"tags":["Health care","Pace","Harm","Popularity","Credibility"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-03","doi":"https://doi.org/10.1108/techs-12-2021-0029","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4416076331","name":"Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems","source":"openalex","abstract":"Medical artificial intelligence (AI) systems frequently lack systematic domain expertise integration, potentially compromising diagnostic reliability. This study presents an ontology-based framework for bone disease diagnosis, developed in collaboration with Ho Chi Minh City Hospital for Traumatology and Orthopedics. The framework introduces three theoretical contributions: (1) a hierarchical neural network architecture guided by bone disease ontology for segmentation-classification tasks, incorporating Visual Language Models (VLMs) through prompts, (2) an ontology-enhanced Visual Question Answering (VQA) system for clinical reasoning, and (3) a multimodal deep learning model that integrates imaging, clinical, and laboratory data through ontological relationships. The methodology maintains clinical interpretability through systematic knowledge digitization, standardized medical terminology mapping, and modular architecture design. The framework demonstrates potential for extension beyond bone diseases through its standardized structure and reusable components. While theoretical foundations are established, experimental validation remains pending due to current dataset and computational resource limitations. Future work will focus on expanding the clinical dataset and conducting comprehensive system validation.","url":"https://doi.org/10.48550/arxiv.2506.04756","authors":["Loan Dao","Ngoc Quoc Ly"],"tags":["Interpretability","Computer science","Artificial intelligence","Terminology","Ontology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-05","doi":"https://doi.org/10.48550/arxiv.2506.04756","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4407344156","name":"Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis","source":"openalex","abstract":"To comprehensively assess Checklist for Artificial Intelligence in Medical Imaging (CLAIM) adherence in medical imaging artificial intelligence (AI) literature by aggregating data from previous systematic and non-systematic reviews. METHODSA systematic search of PubMed, Scopus, and Google Scholar identified reviews using the CLAIM to evaluate medical imaging AI studies.Reviews were analyzed at two levels: review level (33 reviews; 1,458 studies) and study level (421 unique studies from 15 reviews).The CLAIM adherence metrics (scores and compliance rates), baseline characteristics, factors influencing adherence, and critiques of the CLAIM were analyzed. RESULTSA review-level analysis of 26 reviews (874 studies) found a weighted mean CLAIM score of 25 [standard deviation (SD): 4] and a median of 26 [interquartile range (IQR): 8; 25 th -75 th percentiles: 20-28].In a separate review-level analysis involving 18 reviews (993 studies), the weighted mean CLAIM compliance was 63% (SD: 11%), with a median of 66% (IQR: 4%; 25 th -75 th percentiles: 63%-67%).A study-level analysis of 421 unique studies published between 1997 and 2024 found a median CLAIM score of 26 (IQR: 6; 25 th -75 th percentiles: 23-29) and a median compliance of 68% (IQR: 16%; 25 th -75 th percentiles: 59%-75%).Adherence was independently associated with the journal impact factor quartile, publication year, and specific radiology subfields.After guideline publication, CLAIM compliance improved (P = 0.004).Multiple readers provided an evaluation in 85% (28/33) of reviews, but only 11% (3/28) included a reliability analysis.An item-wise evaluation identified 11 underreported items (missing in ≥50% of studies).Among the 10 identified critiques, the most common were item inapplicability to diverse study types and subjective interpretations of fulfillment. CONCLUSIONOur two-level analysis revealed considerable reporting gaps, underreported items, factors related to adherence, and common CLAIM critiques, providing actionable insights for researchers and journals to improve transparency, reproducibility, and reporting quality in AI studies. CLINICAL SIGNIFICANCEBy combining data from systematic and non-systematic reviews on CLAIM adherence, our comprehensive findings may serve as targets to help researchers and journals improve transparency, reproducibility, and reporting quality in AI studies.","url":"https://doi.org/10.4274/dir.2025.243182","authors":["Burak Koçak","Fadime Köse","Ali Keleş","Abdurrezzak Şendur","İsmail Meşe","Mehmet Karagülle"],"tags":["Medicine","Checklist","MEDLINE","Medical physics","Cognitive psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-10","doi":"https://doi.org/10.4274/dir.2025.243182","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3043396016","name":"The clinical artificial intelligence department: a prerequisite for success","source":"openalex","abstract":"Is artificial intelligence (AI) on track to usurp the electronic health record (EHR) as the most disappointing application of technology within medicine? The medical literature is increasingly populated with perspective pieces lauding the transformative nature of AI and forecasting an unforeseen","url":"https://doi.org/10.1136/bmjhci-2020-100183","authors":["Christopher V. Cosgriff","David J. Stone","Gary E. Weissman","Romain Pirracchio","Leo Anthony Celi"],"tags":["Transformative learning","Perspective (graphical)","Artificial intelligence","Track (disk drive)","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-01","doi":"https://doi.org/10.1136/bmjhci-2020-100183","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4210814395","name":"Artificial Emotional Intelligence in Socially Assistive Robots for Older Adults: A Pilot Study","source":"openalex","abstract":"This paper presents our recent research on integrating artificial emotional intelligence in a social robot (Ryan) and studies the robot's effectiveness in engaging older adults. Ryan is a socially assistive robot designed to provide companionship for older adults with depression and dementia through conversation. We used two versions of Ryan for our study, empathic and non-empathic. The empathic Ryan utilizes a multimodal emotion recognition algorithm and a multimodal emotion expression system. Using different input modalities for emotion, i.e. facial expression and speech sentiment, the empathic Ryan detects users emotional state and utilizes an affective dialogue manager to generate a response. On the other hand, the non-empathic Ryan lacks facial expression and uses scripted dialogues that do not factor in the users emotional state. We studied these two versions of Ryan with 10 older adults living in a senior care facility. The statistically significant improvement in the users' reported face-scale mood measurement indicates an overall positive effect from the interaction with both the empathic and non-empathic versions of Ryan. However, the number of spoken words measurement and the exit survey analysis suggest that the users perceive the empathic Ryan as more engaging and likable.","url":"https://doi.org/10.1109/taffc.2022.3143803","authors":["Hojjat Abdollahi","Mohammad H. Mahoor","Rohola Zandie","Jarid Siewierski","Sara Honn Qualls"],"tags":["Psychology","Facial expression","Emotional intelligence","Conversation","Emotional expression"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-18","doi":"https://doi.org/10.1109/taffc.2022.3143803","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3163607662","name":"Artificial Intelligence: Has Its Time Come for Inclusion in Medical School Education? Maybe…Maybe Not","source":"openalex","abstract":"Artificial intelligence (AI) has the potential to strongly modify or even transform the landscape of medicine. Judicious utilization of AI can further propel progress in medical research, facilitate precision medicine, and optimize clinical workflow—the applications are limitless. Although technology and AI algorithms are evolving rapidly and have important implications for future physicians, there is a dearth of literature available for medical students and their educators to learn about AI. While scientific journals provide information regarding AI, they often are written for and by scientists, engineers, and physicians who are well-versed in technology. Currently, medical students must navigate the technical jargon and decipher AI literature without any foundational exposure. It is difficult for students to understand the implications of AI if they do not have basic knowledge of AI and its current capabilities. A fear about AI is pervasive amongst medical students. There is virtually no literature that provides a fundamental and easily digestible overview of AI for medical students and educators, while also offering suggestions about how to integrate AI into medical school curricula. Our goal is to address the lack of formal AI instruction by presenting an informative primer with curricular suggestions for each medical school year, from a U.S. perspective, tailored to medical students and their educators. We seek to present a balanced approach, as there are pros and cons about incorporating AI in undergraduate medical education.","url":"https://doi.org/10.15694/mep.2021.000131.1","authors":["Brandon Ngo","Diep Nguyen","Eric vanSonnenberg"],"tags":["Inclusion (mineral)","Psychology","Medical education","Mathematics education","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.15694/mep.2021.000131.1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4291670288","name":"Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review","source":"openalex","abstract":"Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.","url":"https://doi.org/10.3390/su14169951","authors":["S. Shankar","Naveenkumar Raju","Abbas Ganesan","R. Nithyaprakash","Maheswari Chenniappan","Chander Prakash","Alokesh Pramanik","Animesh Kumar Basak","Saurav Dixit"],"tags":["Artificial neural network","Computer science","Air pollution","Human health","Warning system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-11","doi":"https://doi.org/10.3390/su14169951","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3187293469","name":"Classification of Explainable Artificial Intelligence Methods through Their Output Formats","source":"openalex","abstract":"Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulations.","url":"https://doi.org/10.3390/make3030032","authors":["Giulia Vilone","Luca Longo"],"tags":["Computer science","Variety (cybernetics)","Artificial intelligence","Dimension (graph theory)","Task (project management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-04","doi":"https://doi.org/10.3390/make3030032","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3135451265","name":"The Role of Digital Technology and Artificial Intelligence in Diagnosing Medical Images: A Systematic Review","source":"openalex","abstract":"The provision of up-to-date medical information on digital technology and AI systems in journals, clinical practices, and textbooks informing radiologists about patient care has resulted in faster, more reliable, and cheaper image interpretation. This study reviews 27 articles regarding the application of digital technology and artificial intelligence (AI) in radiological scholarship, looking at the incorporation of electronic health system records, digital radiology imaging databases, IT environments, and machine learning—the latter of which has emerged as the most popular AI approach in modern medicine. This article examines the emerging picture surrounding archiving and communication systems in the implementation phase of AI technologies. It explores the most appropriate clinical requirements for the use of AI systems in practice. Continued development in the integration of automated systems, probing the use of information systems, databases, and records, should result in further progress in radiological theory and practice.","url":"https://doi.org/10.4236/ojrad.2021.111003","authors":["Rani Ahmad"],"tags":["Medicine","Radiological weapon","Scholarship","Applications of artificial intelligence","Clinical Practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.4236/ojrad.2021.111003","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2410248263","name":"[The application and development of artificial intelligence in medical diagnosis systems].","source":"openalex","abstract":"This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.","url":"https://openalex.org/W2410248263","authors":["Zhencheng Chen","Yong Jiang","Mingyu Xu","Hongyan Wang","Dazong Jiang"],"tags":["Expert system","Computer science","Artificial intelligence","Medical imaging","Medical science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2002-09-01","doi":"","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3024478970","name":"Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity","source":"openalex","abstract":"Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI's value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.","url":"https://doi.org/10.2196/17707","authors":["Hassane Alami","Pascale Lehoux","Yannick Auclair","Michèle de Guise","Marie‐Pierre Gagnon","James Shaw","Denis Roy","Richard Fleet","Mohamed Ali Ag Ahmed","Jean‐Paul Fortin"],"tags":["Value proposition","Context (archaeology)","Knowledge management","Health care","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-13","doi":"https://doi.org/10.2196/17707","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403862117","name":"Revolutionizing Radiology With Artificial Intelligence","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.","url":"https://doi.org/10.7759/cureus.72646","authors":["Abhiyan Bhandari"],"tags":["Medicine","Medical physics","Radiology","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.7759/cureus.72646","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3162810948","name":"Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review","source":"openalex","abstract":"The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.","url":"https://doi.org/10.1016/j.csbj.2021.05.010","authors":["Eleni Adamidi","Konstantinos Mitsis","Konstantina S. Nikita"],"tags":["Pandemic","Guideline","Coronavirus disease 2019 (COVID-19)","Medicine","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1016/j.csbj.2021.05.010","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4303986529","name":"Expectations for Artificial Intelligence (AI) in Psychiatry","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11920-022-01378-5","authors":["Scott Monteith","Tasha Glenn","John Geddes","Peter C. Whybrow","Eric D. Achtyes","Michael Bauer"],"tags":["Workflow","Transformative learning","Deskilling","Maturity (psychological)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-10","doi":"https://doi.org/10.1007/s11920-022-01378-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4394573140","name":"Redefining Healthcare With Artificial Intelligence (AI): The Contributions of ChatGPT, Gemini, and Co-pilot","source":"openalex","abstract":"Artificial Intelligence (AI) in healthcare marks a new era of innovation and efficiency, characterized by the emergence of sophisticated language models such as ChatGPT (OpenAI, San Francisco, CA, USA), Gemini Advanced (Google LLC, Mountain View, CA, USA), and Co-pilot (Microsoft Corp, Redmond, WA, USA). This review explores the transformative impact of these AI technologies on various facets of healthcare, from enhancing patient care and treatment protocols to revolutionizing medical research and tackling intricate health science challenges. ChatGPT, with its advanced natural language processing capabilities, leads the way in providing personalized mental health support and improving chronic condition management. Gemini Advanced extends the boundary of AI in healthcare through data analytics, facilitating early disease detection and supporting medical decision-making. Co-pilot, by integrating seamlessly with healthcare systems, optimizes clinical workflows and encourages a culture of innovation among healthcare professionals. Additionally, the review highlights the significant contributions of AI in accelerating medical research, particularly in genomics and drug discovery, thus paving the path for personalized medicine and more effective treatments. The pivotal role of AI in epidemiology, especially in managing infectious diseases such as COVID-19, is also emphasized, demonstrating its value in enhancing public health strategies. However, the integration of AI technologies in healthcare comes with challenges. Concerns about data privacy, security, and the need for comprehensive cybersecurity measures are discussed, along with the importance of regulatory compliance and transparent consent management to uphold ethical standards and patient autonomy. The review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.","url":"https://doi.org/10.7759/cureus.57795","authors":["Anas Alhur"],"tags":["Health care","Transformative learning","Interoperability","Workflow","Analytics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-07","doi":"https://doi.org/10.7759/cureus.57795","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391405412","name":"The Impact of Artificial Intelligence on Innovative Nanotechnologies for Advanced Medical Diagnosis","source":"openalex","abstract":"Artificial Intelligence (AI) is a technological domain wherein computer systems emulate human cognitive capabilities to execute tasks previously reliant on human intellect. Referred to as smart machines, these entities exhibit the intelligence to autonomously perform cognitive functions. Nanotechnology is a multidisciplinary field spanning science, technology, and engineering, focusing on activities at the nanoscale for industrial and research applications. The intersection of AI and nanotechnology has notably influenced the evolution of medical diagnosis, enhancing the quality of diagnostic devices through advanced material construction and heightened functional sophistication. This collaboration has positively impacted medical diagnostics, enabling devices to detect and diagnose conditions with greater precision and depth. The incorporation of nanoscale materials has contributed to heightened device sensitivity, while AI-driven functionalities have elevated diagnostic capabilities, marking a significant stride in advancing healthcare technologies. This paper will review the impact of artificial intelligence on innovative nanotechnologies for advanced medical diagnosis.","url":"https://doi.org/10.26502/jnr.2688-85210040","authors":["Mawuli Agboklu"],"tags":["Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.26502/jnr.2688-85210040","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4221081366","name":"Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches","source":"openalex","abstract":"Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R 2 ) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DE max ), delivery efficiency at 24 h (DE 24 ), at 168 h (DE 168 ), and at the last sampling time (DE Tlast ). The corresponding R 2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19– 29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy. Conclusion: This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine. Graphical Abstract: Keywords: artificial intelligence, machine learning, physiologically based pharmacokinetic modeling, nanomedicine, drug delivery, nanotechnology","url":"https://doi.org/10.2147/ijn.s344208","authors":["Zhoumeng Lin","Wei-Chun Chou","Yi-Hsien Cheng","Chunla He","Nancy A Monteiro-Riviere","Jim E Riviere"],"tags":["Nanomedicine","Artificial intelligence","Machine learning","Computer science","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-01","doi":"https://doi.org/10.2147/ijn.s344208","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4327952037","name":"How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life","source":"openalex","abstract":"Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience.","url":"https://doi.org/10.3390/technologies11020044","authors":["Subhra R. Mondal","Subhankar Das","Vasiliki Vrana"],"tags":["Metaverse","Conversation","Generative grammar","Knowledge management","Value (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-17","doi":"https://doi.org/10.3390/technologies11020044","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4400165201","name":"Artificial Intelligence in Medical Imaging: Applications of Deep Learning for Disease Detection and Diagnosis","source":"openalex","abstract":"The integration of artificial intelligence (AI) and deep learning techniques into medical imaging has revolutionized disease detection and diagnosis. This paper provides a comprehensive overview of the applications of deep learning in medical imaging and its impact on healthcare. The paper begins with an introduction to the fundamentals of deep learning, emphasizing convolutional neural networks (CNNs) and their relevance in analyzing medical images. It then explores various applications of deep learning in medical imaging, including automated disease detection and classification, image segmentation for precise anatomical localization, quantitative analysis for predictive modeling, personalized medicine, and workflow optimization. Case studies and examples from different medical specialties, such as oncology, cardiology, and neurology, are presented to illustrate the practical implementation and effectiveness of AI-driven approaches.","url":"https://doi.org/10.36676/urr.v11.i3.1284","authors":["Abhinav Deshmukh"],"tags":["Deep learning","Artificial intelligence","Convolutional neural network","Workflow","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-30","doi":"https://doi.org/10.36676/urr.v11.i3.1284","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4253468390","name":"Artificial intelligence in health care: within touching distance","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0140-6736(17)31540-4","authors":["The Lancet"],"tags":["Artificial intelligence","Computer science","Field (mathematics)","Turing","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-12-01","doi":"https://doi.org/10.1016/s0140-6736(17)31540-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3207526414","name":"The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review","source":"openalex","abstract":"(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.","url":"https://doi.org/10.3390/s21196628","authors":["Selina A. Bernauer","Nicola U. Zitzmann","Tim Joda"],"tags":["Prosthodontics","MEDLINE","Fixed prosthodontics","Systematic review","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-05","doi":"https://doi.org/10.3390/s21196628","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2909340128","name":"Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust","source":"openalex","abstract":"Artificial intelligence (AI) is finding more uses in the human society resulting in a need to scrutinise the relationship between humans and AI. Technology itself has advanced from the mere encoding of human knowledge into a machine to designing machines that “know how” to autonomously acquire the knowledge they need, learn from it and act independently in the environment. Fortunately, this need is not new; it has scientific grounds that could be traced back to the inception of computers. This paper uses a multi-disciplinary lens to explore how the natural cognitive intelligence in a human could interface with the artificial cognitive intelligence of a machine. The scientific journey over the last 50 years will be examined to understand the Human-AI relationship, and to present the nature of, and the role of trust in, this relationship. Risks and opportunities sitting at the human-AI interface will be studied to reveal some of the fundamental technical challenges for a trustworthy human-AI relationship. The critical assessment of the literature leads to the conclusion that any social integration of AI into the human social system would necessitate a form of a relationship on one level or another in society, meaning that humans will “always” actively participate in certain decision-making loops—either in-the-loop or on-the-loop—that will influence the operations of AI, regardless of how sophisticated it is.","url":"https://doi.org/10.1007/s12559-018-9619-0","authors":["Hussein A. Abbass"],"tags":["Autonomy","Automation","Computer science","Artificial intelligence","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-14","doi":"https://doi.org/10.1007/s12559-018-9619-0","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4398770695","name":"Generative artificial intelligence in academic medical writing","source":"openalex","abstract":"Dear Editor, As dedicated readers of the Medical Journal of Babylon, we find it imperative to illuminate the captivating domain of generative artificial intelligence (GenAI) and its ever-growing impact on academic medical prose. As humanity progresses along the Kardashev scale, GenAI could stand as a milestone on the exponentially advancing path toward the much-debated realm of general AI and artificial superintelligence—a concept intertwined with the notion of technological singularity.[1] Text-based GenAI technology garnered remarkable attention across various fields and now impacts scholarly publications. By harnessing the capabilities of deep learning algorithms and neural networks, GenAI introduces innovative dimensions to academic medical writing.[2] Its diverse applications in assisting authors and biomedical researchers offer a range of advantages and disadvantages that warrant meticulous examination. In this discourse, we delve into the constraints and prospects inherent in text-based GenAI. Although GenAI models represent a relatively recent phenomenon, they produced unprecedented attention and fearful speculations among scholars, media, and the public; for example, concerning ChatGPT (based on GPT-3 model) and other models such as CTRL, GPT-4, GrammarlyGO, T5, and XLNet [Figure 1; Supplementary materials].[3,4] GenAI brings valuable advantages to biomedical research and academic writing. First, its speed and efficiency aid in drafting manuscripts, abstracts, and complex summaries, which expedites writing, allowing researchers to prioritize tasks. Second, these tools enhance language use, ensuring explicit, coherent content adhering to medical terminology, which is substantially advantageous, especially for non-native English speakers. Finally, GenAI transforms intricate data sets into accessible narratives, catering to policymakers and stakeholders, thus augmenting research impact.[2]Figure 1:: Features of different text-based GenAI models. GenAI: generative artificial intelligence; NLP: natural language processingIntegrating GenAI in academic writing brings forth some disadvantages. The foremost issue is the potential loss of authenticity, as it risks diluting the author’s distinct voice and perspective. Another downside is GenAI’s struggle to comprehend nuanced contextual understanding within biomedical research, resulting in inaccuracies, misinterpretations, or unsuitable recommendations. Ethical concerns also arise concerning proper attribution, plagiarism, and authorship transparency. GenAI might also offer misleading information with questionable bibliographies, known as “hallucinations,” thus intensifying bias.[2,5,6] Nevertheless, newer models (e.g., Scopus AI) promise to overcome such limitations.[6] Acknowledging the current limitations of GenAI is crucial. These AI models cannot often discern the subtleties of medical concepts, especially those rooted in deep domain expertise, including clinical experience. Additionally, although they can generate coherent sentences, their output might lack the critical analysis or logical reasoning that characterizes high-quality scholarly work.[3,5,6] Therefore, the application of GenAI should not be tempered without the understanding that it is a tool to support human creativity and insight rather than a replacement for the intellectual rigor that underpins academic excellence. Concerning the future potential, a careful approach is paramount. Authors and researchers must recognize GenAI as a tool to complement their expertise, not replace it. Rigorous revisions remain essential for producing high-quality academic content. The medical community in Iraq, aligned with the excellence and innovation principles worldwide, should cautiously embrace GenAI. Collaborative efforts among researchers, AI experts, and journal editors can pave the way for guidelines that ensure this technology’s responsible and effective use. Other regulations may emerge for GenAI’s involvement in mo","url":"https://doi.org/10.4103/mjbl.mjbl_1176_23","authors":["Ahmed Al-Imam","Nawfal Al-Hadithi","Faisel Alissa","Michał Michalak"],"tags":["Realm","Artificial intelligence","Generative grammar","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-01","doi":"https://doi.org/10.4103/mjbl.mjbl_1176_23","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403426352","name":"Early Warning Scores With and Without Artificial Intelligence","source":"openalex","abstract":"Importance: Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance. Objective: To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores. Design, Setting, and Participants: This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included. Exposures: Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores. Main Outcomes and Measures: Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation. Results: Of the 362 926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200 642 [55.3%] female), 16 693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI. Conclusions and Relevance: In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.","url":"https://doi.org/10.1001/jamanetworkopen.2024.38986","authors":["Dana P. Edelson","Matthew M. Churpek","Kyle A. Carey","Zhenqiu Lin","Chenxi Huang","Jonathan Siner","Jennifer Johnson","Harlan M. Krumholz","Deborah J. Rhodes"],"tags":["Early warning score","Mews","Medicine","Receiver operating characteristic","Warning system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-15","doi":"https://doi.org/10.1001/jamanetworkopen.2024.38986","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3009670852","name":"Artificial Intelligence in Acute Kidney Injury Risk Prediction","source":"openalex","abstract":"Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.","url":"https://doi.org/10.3390/jcm9030678","authors":["Joana Gameiro","Tiago Branco","José António Lopes"],"tags":["Medicine","Acute kidney injury","Subclinical infection","Intensive care medicine","Informatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-03","doi":"https://doi.org/10.3390/jcm9030678","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3013443134","name":"Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency","source":"openalex","abstract":"Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.","url":"https://doi.org/10.1038/s41746-020-0254-2","authors":["Christine M. Cutillo","Karlie R. Sharma","Luca Foschini","Shinjini Kundu","Maxine Mackintosh","Kenneth D. Mandl","MI in Healthcare Workshop Working Group","T. Beck","Elaine Collier","Christine M. Colvis","Kenneth Gersing","Valery Gordon"],"tags":["Transparency (behavior)","Workflow","Health care","Usability","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-26","doi":"https://doi.org/10.1038/s41746-020-0254-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4224545487","name":"An evaluation of information online on artificial intelligence in medical imaging","source":"openalex","abstract":"BACKGROUND: Opinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse. METHODS: Two search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria. RESULTS: Of the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%). CONCLUSION: The overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment.","url":"https://doi.org/10.1186/s13244-022-01209-4","authors":["Philip Mulryan","Naomi Ni Chleirigh","Alexander T. O’Mahony","Claire Crowley","David Ryan","Patrick W. McLaughlin","Mark F. McEntee","Michael M. Maher","Owen J. O’Connor"],"tags":["Viewpoints","RSS","Information retrieval","Consistency (knowledge bases)","Web page"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-25","doi":"https://doi.org/10.1186/s13244-022-01209-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4223534709","name":"Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review","source":"openalex","abstract":"The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.","url":"https://doi.org/10.3390/jcm11072054","authors":["Paweł Jemioło","Dawid Storman","Patryk Orzechowski"],"tags":["Medicine","Credibility","Data extraction","Coronavirus disease 2019 (COVID-19)","Pandemic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-06","doi":"https://doi.org/10.3390/jcm11072054","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3206821896","name":"Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.wneu.2021.10.068","authors":["Jonathan Huang","Nathan A. Shlobin","Sandi Lam","Michael DeCuypere"],"tags":["Medicine","Medulloblastoma","Brain tumor","Ependymoma","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-12","doi":"https://doi.org/10.1016/j.wneu.2021.10.068","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3167960955","name":"Advances in Deep Learning-Based Medical Image Analysis","source":"openalex","abstract":"Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.","url":"https://doi.org/10.34133/2021/8786793","authors":["Xiaoqing Liu","Kunlun Gao","Bo Liu","Chengwei Pan","Kongming Liang","Lifeng Yan","Jiechao Ma","Fujin He","Shu Zhang","Siyuan Pan","Yizhou Yu"],"tags":["Deep learning","Computer science","Artificial intelligence","Convolutional neural network","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.34133/2021/8786793","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4379796020","name":"Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study","source":"openalex","abstract":"Background: In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients’ diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. Methods: We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. Results: Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57–89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05–20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. Conclusion: Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.","url":"https://doi.org/10.3390/jpm13060962","authors":["Alhassan Ali Ahmed","Agnieszka Brychcy","Mohamed Abouzid","Martin Witt","Elżbieta Kaczmarek"],"tags":["Cross-sectional study","Perception","Artificial intelligence","Medicine","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-07","doi":"https://doi.org/10.3390/jpm13060962","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4327919044","name":"Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches","source":"openalex","abstract":"Driven by recent advances in Artificial Intelligence (AI) and Computer Vision (CV), the implementation of AI systems in the medical domain increased correspondingly. This is especially true for the domain of medical imaging, in which the incorporation of AI aids several imaging-based tasks such as classification, segmentation, and registration. Moreover, AI reshapes medical research and contributes to the development of personalized clinical care. Consequently, alongside its extended implementation arises the need for an extensive understanding of AI systems and their inner workings, potentials, and limitations which the field of eXplainable AI (XAI) aims at. Because medical imaging is mainly associated with visual tasks, most explainability approaches incorporate saliency-based XAI methods. In contrast to that, in this article we would like to investigate the full potential of XAI methods in the field of medical imaging by specifically focusing on XAI techniques not relying on saliency, and providing diversified examples. We dedicate our investigation to a broad audience, but particularly healthcare professionals. Moreover, this work aims at establishing a common ground for cross-disciplinary understanding and exchange across disciplines between Deep Learning (DL) builders and healthcare professionals, which is why we aimed for a non-technical overview. Presented XAI methods are divided by a method's output representation into the following categories: Case-based explanations, textual explanations, and auxiliary explanations.","url":"https://doi.org/10.1016/j.ejrad.2023.110786","authors":["Katarzyna Borys","Yasmin Alyssa Schmitt","Meike Nauta","Christin Seifert","Nicole C. Krämer","Christoph M. Friedrich","Felix Nensa"],"tags":["Medicine","Medical imaging","Medical physics","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-20","doi":"https://doi.org/10.1016/j.ejrad.2023.110786","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4200376377","name":"Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah","source":"openalex","abstract":"OBJECTIVES: This study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs. METHODS: A systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies. RESULTS: A total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items. CONCLUSIONS: HEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.","url":"https://doi.org/10.1016/j.jval.2021.11.1362","authors":["Madelon M. Voets","Jeroen Veltman","Cornelis H. Slump","Sabine Siesling","Hendrik Koffijberg"],"tags":["Checklist","Health care","Grey literature","Data extraction","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-16","doi":"https://doi.org/10.1016/j.jval.2021.11.1362","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4200608622","name":"Artificial intelligence in clinical research of cancers","source":"openalex","abstract":"Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.","url":"https://doi.org/10.1093/bib/bbab523","authors":["Dan Shao","Yinfei Dai","Nianfeng Li","Xuqing Cao","Wei Zhao","Cheng Li","Zhuqing Rong","Lan Huang","Yan Wang","Jing Zhao"],"tags":["Computer science","Domain (mathematical analysis)","Artificial intelligence","Data science","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-16","doi":"https://doi.org/10.1093/bib/bbab523","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4324046738","name":"Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions","source":"openalex","abstract":"Recent years have witnessed increased pressure across the global healthcare system during the COVID-19 pandemic. The COVID-19 pandemic shattered existing healthcare operations and taught us the importance of a resilient and sustainable healthcare system. Digitisation, specifically adoption of Artificial Intelligence (AI) has positively contributed to developing a resilient healthcare system in recent past. To understand how AI contributes to building a resilient and sustainable healthcare system, this study based on systematic literature review of 89 articles extracted from Scopus and Web of Science databases is conducted. The study is organised around several key themes such as applications, benefits, and challenges of using AI technology in healthcare sector. It is observed that AI has wide applications in radiology, surgery, medical, research, and development of healthcare sector. Based on the analysis, a research framework is proposed using an extended Antecedents, Practices, and Outcomes (APO) framework. This framework comprises AI applications’ antecedents, practices, and outcomes for building a resilient and sustainable healthcare system. Consequently, three propositions are drawn in this study. Furthermore, our study has adopted the theory, context and methodology (TCM) framework to provide future research directions, which can be used as a reference point for future studies.","url":"https://doi.org/10.1080/00207543.2023.2188101","authors":["Laxmi Pandit Vishwakarma","Rajesh Kumar Singh","Ruchi Mishra","Archana Kumari"],"tags":["Health care","Context (archaeology)","Scopus","Knowledge management","Healthcare system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-13","doi":"https://doi.org/10.1080/00207543.2023.2188101","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4280505928","name":"Recent advances and applications of artificial intelligence and related technologies in the food industry","source":"openalex","abstract":"In current scenario most of food processing industries are majorly focusing on quality of food, nutritional value, and method of processing as the consumers are demanding for foods lined up with qualities, sensory and shelf life of the products. Emergence of technology in artificial intelligence (AI) and machine learning (ML) helps to measure the drifting issues in food processing technology. AI is an interdisciplinary promising approach for promoting performances in different areas of food sectors. Tremendous changes were carried out to solve problems to grow food industries. This review emphasises the applications of AI in dairy, bakery, beverages, fruit and vegetable industries. To advance the technology multiple strategies were used in different food sectors. Relevant literature on scope of robotics in food and beverages have been reviewed and discussed critically. Further intense research in advancing 3D printing that helps to improve food business from manufacture to servicing has been discussed with future vision.","url":"https://doi.org/10.1016/j.afres.2022.100126","authors":["Addanki Mounika","Priyanka Patra","Prameela Kandra"],"tags":["Scope (computer science)","Food industry","Quality (philosophy)","Food processing","Food sector"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-18","doi":"https://doi.org/10.1016/j.afres.2022.100126","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3167415262","name":"The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification","source":"openalex","abstract":"Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.","url":"https://doi.org/10.3390/electronics10121406","authors":["Salih Sarp","Murat Kuzlu","Emmanuel Wilson","Ümit Cali","Özgur Güler"],"tags":["Debugging","Domain (mathematical analysis)","Computer science","Artificial intelligence","Chronic wound"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-11","doi":"https://doi.org/10.3390/electronics10121406","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3005571331","name":"Artificial Intelligence in Global Health —A Framework and Strategy for Adoption and Sustainability","source":"openalex","abstract":"Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research. Keywords: • Artificial Intelligence • AI Framework • Global Health • Implementation • Sustainability • AI Strategy Copyright © 2020 Hadley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","url":"https://doi.org/10.21106/ijma.296","authors":["Trevor D. Hadley","Rowland W. Pettit","Tahir Malik","Amelia A. Khoei","Hamisu M. Salihu"],"tags":["Health care","Sustainability","Applications of artificial intelligence","Computer science","Cloud computing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-02-10","doi":"https://doi.org/10.21106/ijma.296","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4313582012","name":"Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment","source":"openalex","abstract":"Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.","url":"https://doi.org/10.3390/s23010527","authors":["Md. Mahadi Hasan","Muhammad Usama Islam","Muhammad Jafar Sadeq","Wai-keung Fung","Jasim Uddin"],"tags":["Coronavirus disease 2019 (COVID-19)","Containment (computer programming)","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","2019-20 coronavirus outbreak","Coronavirus Infections"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-03","doi":"https://doi.org/10.3390/s23010527","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4388406898","name":"Artificial Intelligence in the Military: An Overview of the Capabilities, Applications, and Challenges","source":"openalex","abstract":"Artificial intelligence (AI) has become a reality in today’s world with the rise of the 4th industrial revolution, especially in the armed forces. Military AI systems can process more data more effectively than traditional systems. Due to its intrinsic computing and decision‐making capabilities, AI also increases combat systems’ self‐control, self‐regulation, and self‐actuation. Artificial intelligence is used in almost every military application, and increased research and development support from military research agencies to develop new and advanced AI technologies is expected to drive the widespread demand for AI‐driven systems in the military. This essay will discuss several AI applications in the military, as well as their capabilities, opportunities, and potential harm and devastation when there is instability. The article looks at current and future potential for developing artificial intelligence algorithms, particularly in military applications. Most of the discussion focused on the seven patterns of AI, the usage and implementation of AI algorithms in the military, object detection, military logistics, and robots, the global instability induced by AI use, and nuclear risk. The article also looks at the current and future potential for developing artificial intelligence algorithms, particularly in military applications.","url":"https://doi.org/10.1155/2023/8676366","authors":["Adib Bin Rashid","Ashfakul Karim Kausik","Ahamed Al Hassan Sunny","Mehedy Hassan Bappy"],"tags":["Military intelligence","Artificial intelligence","Applications of artificial intelligence","Computer science","Revolution in Military Affairs"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1155/2023/8676366","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3077749392","name":"Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review","source":"openalex","abstract":"Artificial intelligence (AI) is widely recognised as a transformative innovation and is already proving capable of outperforming human clinicians in the diagnosis of specific medical conditions, especially in image analysis within dermatology and radiology. These abilities are enhanced by the capacity of AI systems to learn from patient records, genomic information and real-time patient data. Uses of AI range from integrating with robotics to creating training material for clinicians. Whilst AI research is mounting, less attention has been paid to the practical implications on healthcare services and potential barriers to implementation. AI is recognised as a \"Software as a Medical Device (SaMD)\" and is increasingly becoming a topic of interest for regulators. Unless the introduction of AI is carefully considered and gradual, there are risks of automation bias, overdependence and long-term staffing problems. This is in addition to already well-documented generic risks associated with AI, such as data privacy, algorithmic biases and corrigibility. AI is able to potentiate innovations which preceded it, using Internet of Things, digitisation of patient records and genetic data as data sources. These synergies are important in both realising the potential of AI and utilising the potential of the data. As machine learning systems begin to cross-examine an array of databases, we must ensure that clinicians retain autonomy over the diagnostic process and understand the algorithmic processes generating diagnoses. This review uses established management literature to explore artificial intelligence as a digital healthcare innovation and highlight potential risks and opportunities.","url":"https://doi.org/10.2147/mder.s262590","authors":["Anmol Arora"],"tags":["Transformative learning","Artificial intelligence","Medical diagnosis","Health care","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-01","doi":"https://doi.org/10.2147/mder.s262590","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4409906372","name":"Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence is a transformative tool for improving healthcare delivery and diagnostic accuracy in the medical and dental fields. This study aims to assess the readiness of future healthcare workers for artificial intelligence and address this gap by examining students' perceptions, attitudes, and knowledge related to AI in Peshawar, Pakistan. METHODS: A quantitative cross-sectional survey was conducted on 423 students from randomly chosen medical and dental colleges. The Medical AI Readiness Scale (MAIRS-MS) was used to perform a self-administered online questionnaire that was used to gather data. Using SPSS software, descriptive statistics and chi-square tests were used to evaluate the data. The level of significance was set at p ≤ 0.05. RESULTS: From multiple medical and dental colleges, 407 students participated in this survey. The survey showed that 29.7% of students had low, 62.2% had moderate, and only 8.1% had high readiness levels. Most medical and dental students in Peshawar, Pakistan, showed moderate readiness. There were significant gender discrepancies, showing males dominating females in readiness scores. There were only slight differences in the AI readiness scores and the academic years from the 1st to 5th year. Only a few non-Pakistani students responded, which may hinder conclusive determinations regarding national disparities. CONCLUSION: The study revealed moderate AI readiness among participants, with significant gender disparities favouring males. Overall, there were no significant differences between dentistry and medical fields. In-depth analysis by domain and knowledge areas might uncover further distinctions. CLINICAL TRIAL NUMBER: Not Applicable.","url":"https://doi.org/10.1186/s12909-025-06911-7","authors":["Saman Baseer","Brekhna Jamil","Shehzad Akbar Khan","M. Omar F. Khan","Ambreen Syed","Liaqat Ali"],"tags":["Cross-sectional study","Medical education","Psychology","Medicine","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-29","doi":"https://doi.org/10.1186/s12909-025-06911-7","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2835818200","name":"Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11906-018-0875-x","authors":["Chayakrit Krittanawong","Andrew S. Bomback","Usman Baber","Sripal Bangalore","Franz H. Messerli","W.H. Wilson Tang"],"tags":["Medicine","Precision medicine","Analytics","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-07-06","doi":"https://doi.org/10.1007/s11906-018-0875-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2887021901","name":"Demystification of AI-driven medical image interpretation: past, present and future","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00330-018-5674-x","authors":["Peter Savadjiev","Jaron Chong","Anthony Dohan","Maria Vakalopoulou","Caroline Reinhold","Nikos Paragios","B. Gallix"],"tags":["Interpretation (philosophy)","Neuroradiology","Medicine","Interventional radiology","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-08-13","doi":"https://doi.org/10.1007/s00330-018-5674-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4401727214","name":"Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning","source":"openalex","abstract":"The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape.This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education.The study begins with a comprehensive review of the evolution of generative AI models and frameworks.A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education.These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks.Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development.Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists.The findings underscore the transformative potential of generative AI in revolutionizing education.By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments.Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.","url":"https://doi.org/10.18267/j.aip.235","authors":["Manel Guettala","Samir Bourekkache","Okba Kazar","Saad Harous"],"tags":["Generative grammar","Transformative learning","Generative model","Personalization","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-22","doi":"https://doi.org/10.18267/j.aip.235","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4206286792","name":"A Survey of Human Gait-Based Artificial Intelligence Applications","source":"openalex","abstract":"We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) \"Smart gait\" applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.","url":"https://doi.org/10.3389/frobt.2021.749274","authors":["Elsa J. Harris","I‐Hung Khoo","Emel Demircan"],"tags":["Computer science","Artificial intelligence","Gait","Machine learning","Biometrics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-03","doi":"https://doi.org/10.3389/frobt.2021.749274","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2068336169","name":"Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jbiomech.2004.02.047","authors":["Sheldon R. Simon"],"tags":["Gait","Process (computing)","Gait analysis","Variety (cybernetics)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2004-06-22","doi":"https://doi.org/10.1016/j.jbiomech.2004.02.047","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3166361420","name":"Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography","source":"openalex","abstract":"OBJECTIVES: This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application. BACKGROUND: GLS is an important parameter when evaluating left ventricular function. However, analyses of GLS are time consuming and demand expertise, and thus are underused in clinical practice. METHODS: In this study, 200 patients with a wide range of left ventricle (LV) function were included. Three standard apical cine-loops were analyzed using the AI pipeline. The AI method measured GLS and was compared with a commercially available semiautomatic speckle-tracking software (EchoPAC v202, GE Healthcare. RESULTS: The AI method succeeded to both correctly classify all 3 standard apical views and perform timing of cardiac events in 89% of patients. Furthermore, the method successfully performed automatic segmentation, motion estimates, and measurements of GLS in all examinations, across different cardiac pathologies and throughout the spectrum of LV function. GLS was -12.0 ± 4.1% for the AI method and -13.5 ± 5.3% for the reference method. Bias was -1.4 ± 0.3% (95% limits of agreement: 2.3 to -5.1), which is comparable with intervendor studies. The AI method eliminated measurement variability and a complete GLS analysis was processed within 15 s. CONCLUSIONS: Through the range of LV function this novel AI method succeeds, without any operator input, to automatically identify the 3 standard apical views, perform timing of cardiac events, trace the myocardium, perform motion estimation, and measure GLS. Fully automated measurements based on AI could facilitate the clinical implementation of GLS.","url":"https://doi.org/10.1016/j.jcmg.2021.04.018","authors":["Ivar Mjåland Salte","Andreas Østvik","Erik Smistad","Daniela Melichova","Thuy Mi Nguyen","Sigve Karlsen","Harald Brunvand","Kristina H. Haugaa","Thor Edvardsen","Lasse Løvstakken","Bjørnar Grenne"],"tags":["Artificial intelligence","Tracking (education)","Ventricle","Computer science","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-16","doi":"https://doi.org/10.1016/j.jcmg.2021.04.018","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4402564190","name":"Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist","source":"openalex","abstract":"The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.","url":"https://doi.org/10.1016/s2589-7500(24)00143-2","authors":["Yilin Ning","Salinelat Teixayavong","Yuqing Shang","Julian Savulescu","Vaishaanth Nagaraj","Di Miao","Mayli Mertens","Daniel Shu Wei Ting","Jasmine Chiat Ling Ong","Mingxuan Liu","Jiuwen Cao","Michael Dunn","Roger Vaughan","Marcus Eng Hock Ong","Joseph J.�Y. Sung","Eric J. Topol","Nan Liu"],"tags":["Checklist","Generative grammar","Engineering ethics","Psychology","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-17","doi":"https://doi.org/10.1016/s2589-7500(24)00143-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3116932816","name":"Evaluation of Artificial Intelligence–Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center","source":"openalex","abstract":"BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center. MATERIALS AND METHODS: Viz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion. RESULTS: A total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89. CONCLUSIONS: Our experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.","url":"https://doi.org/10.3174/ajnr.a6923","authors":["A. Yahav-Dovrat","Mor Saban","Goni Merhav","I. Lankri","Eitan Abergel","Ayelet Eran","David Tanné","Raul G. Nogueira","Rotem Sivan Hoffmann"],"tags":["Medicine","Stroke (engine)","Occlusion","Workflow","Predictive value"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-31","doi":"https://doi.org/10.3174/ajnr.a6923","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385799549","name":"The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations","source":"openalex","abstract":"Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.","url":"https://doi.org/10.47672/ejt.1561","authors":["Farhan Aslam"],"tags":["Chatbot","Computer science","Conversation","Knowledge management","Service (business)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-14","doi":"https://doi.org/10.47672/ejt.1561","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3210293336","name":"What Makes Artificial Intelligence Exceptional in Health Technology Assessment?","source":"openalex","abstract":"The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT’s distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs’ exceptionalism and to work towards only deploying clinically, economically, socially acceptable AIHTs in the health care system.","url":"https://doi.org/10.3389/frai.2021.736697","authors":["Jean‐Christophe Bélisle‐Pipon","Vincent Couture","Marie‐Christine Roy","Isabelle Ganache","Mireille Goetghebeur","I. Glenn Cohen"],"tags":["Computer science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-02","doi":"https://doi.org/10.3389/frai.2021.736697","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4229445630","name":"Artificial Intelligence and Employment: New Cross-Country Evidence","source":"openalex","abstract":"Recent years have seen impressive advances in artificial intelligence (AI) and this has stoked renewed concern about the impact of technological progress on the labor market, including on worker displacement. This paper looks at the possible links between AI and employment in a cross-country context. It adapts the AI occupational impact measure developed by Felten, Raj and Seamans—an indicator measuring the degree to which occupations rely on abilities in which AI has made the most progress—and extends it to 23 OECD countries. Overall, there appears to be no clear relationship between AI exposure and employment growth. However, in occupations where computer use is high, greater exposure to AI is linked to higher employment growth. The paper also finds suggestive evidence of a negative relationship between AI exposure and growth in average hours worked among occupations where computer use is low. One possible explanation is that partial automation by AI increases productivity directly as well as by shifting the task composition of occupations toward higher value-added tasks. This increase in labor productivity and output counteracts the direct displacement effect of automation through AI for workers with good digital skills, who may find it easier to use AI effectively and shift to non-automatable, higher-value added tasks within their occupations. The opposite could be true for workers with poor digital skills, who may not be able to interact efficiently with AI and thus reap all potential benefits of the technology 1 .","url":"https://doi.org/10.3389/frai.2022.832736","authors":["Alexandre Georgieff","Raphaela Hyee"],"tags":["Productivity","Context (archaeology)","Automation","Value (mathematics)","Labour economics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-10","doi":"https://doi.org/10.3389/frai.2022.832736","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4240332193","name":"Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors","source":"openalex","abstract":"From Bluetooth enabled hearing aids to robotic caretakers, wearable and smart devices industries are immensely contributing to the development of the healthcare industry with the help of Internet of Things (IoT). Latest technologies like Artificial Intelligence, 3D Printing, Big data, Machine Learning, Advanced Sensors, Mobile Applications and other technologies will continue to generate lot of opportunities for Medtech organizations. Some of the latest healthcare innovations practiced at present might have been seen or read by some of us only in science fiction movies or science fiction stories a long ago. Presently, IoT and Artificial Intelligence is creating a revolution in healthcare industry when it comes to diagnosis and treatment of varied diseases. From smartphones to robots, artificial intelligence is already making its presence felt in healthcare industry and as such it is progressively recognizing the transformative nature of IoT technologies which drives innovation in the development of connected medical devices. Gradual increase in the number of connected medical devices with the advent of technology advancements helps to capture and transmit medical related data wherever and whenever required to the concerned people and thus, it gave birth to the Internet of Medical Things (IoMT), where the Internet of Things (IoT) and healthcare meet. The IoMT helps to constantly monitor and alter (if required) the behvaiour of the patient and his/her health status in real time and also supports healthcare organizations to effectively streamline clinical processes, patient information and related work flows to enhance its operational productivity. The IoMT has made and continues to make the delivery of P4 Medicine (Predictive, Preventive, Personalized and Participatory) even for remote locations with the help of connected sensors and devices helping in real-time patient care. IoMT helps doctors and caregivers to provide patient care and support by constantly monitoring data related to patients through mobile apps and connected medical devices even when patient(s) or doctor(s) are located at remote locations. This research paper discusses about six use cases explaining how IoMT is applied in healthcare industry.","url":"https://doi.org/10.35940/ijeat.a1412.109119","authors":["Dr.A.Narasima Venkatesh"],"tags":["Big data","Health care","Computer science","Wearable computer","The Internet"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-30","doi":"https://doi.org/10.35940/ijeat.a1412.109119","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3111353854","name":"How artificial intelligence and machine learning can help healthcare systems respond to COVID-19","source":"openalex","abstract":"The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.","url":"https://doi.org/10.1007/s10994-020-05928-x","authors":["Mihaela van der Schaar","Ahmed M. Alaa","R. Andrés Floto","Alexander Gimson","Stefan Scholtes","Angela Wood","Eoin McKinney","Daniel Jarrett","Píetro Lió","Ari Ercole"],"tags":["Health care","Variety (cybernetics)","Coronavirus disease 2019 (COVID-19)","Pandemic","Healthcare system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-09","doi":"https://doi.org/10.1007/s10994-020-05928-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3213704721","name":"Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence","source":"openalex","abstract":"Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.","url":"https://doi.org/10.1038/s42256-021-00421-z","authors":["Xiang Bai","Hanchen Wang","Liya Ma","Yongchao Xu","Jiefeng Gan","Ziwei Fan","Fan Yang","Ke Ma","Jiehua Yang","Song Bai","Chang Shu","Xinyu Zou","Renhao Huang","Changzheng Zhang","Xiaowu Liu","Dandan Tu","Chuou Xu","Wenqing Zhang","Xi Wang","Xi Wang","Anguo Chen","Yu Zeng","Ming‐Wei Wang","Ming‐Wei Wang","Nagaraj Holalkere","Neil J. Halin","Ihab R. Kamel","Jia Wu","Xiang Wang","Xiang Wang","Xiang Wang","Jianbo Shao","Pattanasak Mongkolwat","Jianjun Zhang","Weiyang Liu","Michael Roberts","Zhongzhao Teng","Lucian Beer","L. Escudero","Evis Sala","Daniel L. Rubin","Adrian Weller","Joan Lasenby","Chuansheng Zheng","Jianming Wang","Zhen Li","Carola‐Bibiane Schönlieb","Tian Xia"],"tags":["Coronavirus disease 2019 (COVID-19)","Computer science","Artificial intelligence","Medical diagnosis","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-15","doi":"https://doi.org/10.1038/s42256-021-00421-z","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4321494006","name":"Diversity, Equity, and Inclusion in Artificial Intelligence: An Evaluation of Guidelines","source":"openalex","abstract":"Artificial intelligence (AI) is present everywhere in the lives of individuals. Unfortunately, several cases of discrimination by AI systems have already been reported. Scholars have warned on risks of AI reproducing existing inequalities or even amplifying them. To tackle these risks and promote responsible AI, many ethics guidelines for AI have emerged recently, including diversity, equity, and inclusion (DEI) principles and practices. However, little is known about the DEI content of these guidelines, and to what extent they meet the most relevant accumulated knowledge from DEI literature. We performed a semi-systematic literature review of the AI guidelines regarding DEI stakes and analyzed 46 guidelines published from 2015 to today. We fleshed out the 14 DEI principles and the 18 DEI practices recommended underlying these 46 guidelines. We found that the guidelines mostly encourage one of the DEI management paradigms, namely fairness, justice, and nondiscrimination, in a limited compliance approach. We found that narrow technical practices are favored over holistic ones. Finally, we conclude that recommended practices for implementing DEI principles in AI should include actions aimed at directly influencing AI actors’ behaviors and awareness of DEI risks, rather than just stating intentions and programs.","url":"https://doi.org/10.1080/08839514.2023.2176618","authors":["Gaëlle Cachat‐Rosset","Alain Klarsfeld"],"tags":["Computer science","Inclusion (mineral)","Equity (law)","Diversity (politics)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-22","doi":"https://doi.org/10.1080/08839514.2023.2176618","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3176626957","name":"Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence","source":"openalex","abstract":"OBJECTIVE: To determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists. METHODS: This study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital. RESULTS: In the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25-34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19-34.21) in the non-academic general teaching hospital setting. CONCLUSIONS: Recently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.","url":"https://doi.org/10.1186/s13244-021-01031-4","authors":["Thomas C. Kwee","Robert M. Kwee"],"tags":["Workload","Medicine","Odds ratio","Interventional radiology","Neuroradiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-29","doi":"https://doi.org/10.1186/s13244-021-01031-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403245162","name":"Using artificial intelligence to document the hidden RNA virosphere","source":"openalex","abstract":"Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.","url":"https://doi.org/10.1016/j.cell.2024.09.027","authors":["Xin Hou","Yong He","Pan Fang","Shi-Qiang Mei","Zan Xu","Wei-Chen Wu","Jun-Hua Tian","Shun Zhang","Zhenyu Zeng","Qinyu Gou","Gen-Yang Xin","Shi-Jia Le","Yinyue Xia","Yu-Lan Zhou","Fengming Hui","Yuanfei Pan","John‐Sebastian Eden","Zhaohui Yang","Chong Han","Yuelong Shu","Deyin Guo","Jun Li","Edward C. Holmes","Zhao‐Rong Li","Mǎng Shī"],"tags":["Biology","RNA","Computational biology","Genetics","Gene"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-09","doi":"https://doi.org/10.1016/j.cell.2024.09.027","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4224105048","name":"The Placebo Effect of Artificial Intelligence in Human–Computer Interaction","source":"openalex","abstract":"In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N = 369; Experiment II: N = 100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant’s own task performance, sustained after interaction. These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.","url":"https://doi.org/10.1145/3529225","authors":["Thomas Kosch","Robin Welsch","Lewis L. Chuang","Albrecht Schmidt"],"tags":["Placebo","Task (project management)","Computer science","Word (group theory)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-08","doi":"https://doi.org/10.1145/3529225","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4289705056","name":"Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues","source":"openalex","abstract":"A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered.","url":"https://doi.org/10.1155/2022/8421434","authors":["Anil Audumbar Pise","Khalid K. Almuzaini","Tariq Ahamed Ahanger","Ahmed Farouk","Kumud Pant","Piyush Kumar Pareek","Stephen Jeswinde Nuagah"],"tags":["Computer science","Computer security","Safeguarding","Health care","Hacker"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-03","doi":"https://doi.org/10.1155/2022/8421434","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4405668604","name":"Artificial intelligence education in medical imaging: A scoping review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jmir.2024.101798","authors":["Samantha M. Loi","W.S. Ng","Christopher Lai","Eric Chern-Pin Chua"],"tags":["Medical physics","Medical imaging","Medical education","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-22","doi":"https://doi.org/10.1016/j.jmir.2024.101798","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4214663994","name":"Notice of Violation of IEEE Publication Principles: Affirmative Fusion Process for Improving Wearable Sensor Data Availability in Artificial Intelligence of Medical Things","source":"openalex","abstract":"Notice of Violation of IEEE Publication Principles\"Affirmative Fusion Process for Improving Wearable Sensor Data Availability in Artificial Intelligence of Medical Things,\"by P. M. Kumar, L. U. Khan and C. S. Hong,in IEEE Sensors Journal, Early AccessAfter careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.The submitting author, Priyan Malarvizhi Kumar, added the coauthors Latif U. Khan and Choong Seon Hong without their consent. Due to the nature of this violation, the Editor in Chief has decided the article will not be published in an issue of IEEE Sensors Journal.Artificial Intelligence of Medical Things (AIoMT) is a hybridized outcome of Internet of Things (IoT), machine learning (ML) paradigms, and data analytics procedures for sophisticated healthcare services and applications. However, the fluctuating or lacking wearable sensors (WSs) data cause trivial computing errors that lead to incomplete diagnosis/ recommendation in healthcare applications. This article proposes a novel Affirmative Fusion Process (AFP) to enable high quality WS data with fewer fluctuations in in medical diagnosis. The proposed process assimilates sensed data with the existing datasets for avoiding discrete availability of WS data during the analysis. In this fusion process, based on the dataset inputs, the discreteness in the sensed data is identified. The discreteness is mitigated through precise replacement consideration from the existing datasets, preventing computational errors. The fusion process is monitored using simulated annealing and neural learning for output approximation and identification. The fused output with and without discreteness is identified for which annealing-based approximation is performed. In this process, the recurrence of the learning iterates is confined to identifying the final best solution. The proposed process is assessed using an activity dataset for the metrics fusion ratio, time delay, complexity, and data availability.","url":"https://doi.org/10.1109/jsen.2022.3153410","authors":["Priyan Malarvizhi Kumar","Latif U. Khan","Choong Seon Hong"],"tags":["Sensor fusion","Wearable computer","Computer science","Machine learning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-22","doi":"https://doi.org/10.1109/jsen.2022.3153410","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2970686124","name":"Artificial Intelligence Will Transform Cardiac Imaging—Opportunities and Challenges","source":"openalex","abstract":"Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.","url":"https://doi.org/10.3389/fcvm.2019.00133","authors":["Steffen E. Petersen","Musa Abdulkareem","Tim Leiner"],"tags":["Software deployment","Pace","Big data","Transparency (behavior)","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-09-10","doi":"https://doi.org/10.3389/fcvm.2019.00133","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3206508404","name":"Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11030-021-10326-z","authors":["Chandrabose Selvaraj","Ishwar Chandra","Sanjeev Kumar Singh"],"tags":["Computer science","Machine learning","Pharmaceutical industry","Artificial intelligence","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-23","doi":"https://doi.org/10.1007/s11030-021-10326-z","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3156012754","name":"Artificial Intelligence and Big Data in Sustainable Entrepreneurship","source":"openalex","abstract":"Abstract There is an urgent need to transition our economy, society, and culture towards systems and actions that facilitate ecological sustainability. Such radical change requires equally radical transformation of approaches to decision making and resource use. Sustainable entrepreneurship (SE) is often presented as the answer to meeting the triple‐bottom‐line challenges that businesses face; however, there are very real limits to what it can achieve. SE is in the early stages of adopting tools at the technological frontier that offer empirical guidance at every point of an entrepreneurial decision‐making process. Big Data (BD) advances the potential for artificial intelligence (AI) to inform decision making, while also charting pathways to achieve desired outcomes. So far, the interactions between AI, BD, and SE have been generally under‐studied. In this primarily conceptual paper, we address the lack of work consolidating and synthesizing these literatures. We suggest that AI and BD readily contribute to further sustainable development of the weak form, but that it also holds great promise for achieving the strong sustainability ideal. We offer two propositions regarding how the integration of AI and BD can inform/support SE. We conclude by mapping out potential avenues for future research.","url":"https://doi.org/10.1111/joes.12611","authors":["Steve J. Bickley","Alison Macintyre","Benno Torgler"],"tags":["Sustainability","Frontier","Entrepreneurship","Big data","Face (sociological concept)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-11","doi":"https://doi.org/10.1111/joes.12611","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4310029361","name":"Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12525-022-00605-4","authors":["Roman Lukyanenko","Wolfgang Maaß","Veda C. Storey"],"tags":["Foundation (evidence)","Conceptual framework","Knowledge management","Empirical research","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-28","doi":"https://doi.org/10.1007/s12525-022-00605-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3158589654","name":"An Improved Artificial Neural Network Model for Effective Diabetes Prediction","source":"openalex","abstract":"Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data‐driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP‐SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP‐SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.","url":"https://doi.org/10.1155/2021/5525271","authors":["Muhammad Mazhar Bukhari","Bader Fahad Alkhamees","Saddam Hussain","Abdu Gumaei","Adel Assiri","Syed Sajid Ullah"],"tags":["Artificial neural network","Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1155/2021/5525271","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4392084561","name":"Pcos Prediction: Advancements in Medical Informatics Using Artificial Intelligence","source":"openalex","abstract":"A hormonal condition known as a polycystic ovarian syndrome (PCOS) affects around 1 in 10 persons throughout their reproductive years. Weight gain, ovarian cysts, and irregular menstruation periods are all signs of PCOS. A hormone called androgens, which may cause excessive facial hair, acne, and other cosmetic changes, is linked to the disorder via elevated amounts. In addition to higher risks for miscarriage. According to a research paper from the overall population of India, 11.8% of women are not diagnosed with PCOS and 22.7% of women are suffering from PCOS.PCOS may be accurately predicted and early identified, which can considerably improve patient outcomes and enable appropriate therapies. This study examines the use of multinomial logistic regression analysis to forecast the development of PCOS based on pertinent clinical and demographic data. An online survey was conducted to collect the data for the research. About 73.2% of women who participated in the survey said they had never tested or PCOS. The trained model had an accuracy of 82%. The mean crossvalidation for the model is 0.75%. A confusion matrix was used to assess the proposed model's classification performance. The PCOD/PCOS categorization problem's anticipated and actual classes were shown in a matrix. All 22 samples were accurately categorized into class 1 (PCOD/PCOS present), demonstrating the model's strong predictive accuracy. Twenty of the twenty samples for class 3 (missing PCOD/PCOS) were correctly categorized. Class 0 (misclassified as PCOD/PCOS missing) had two misclassifications from class 1 and one correct prediction. Finally, the classification of all four samples from class 4 (PCOD/PCOS type 5) was correct. These findings emphasize the model's potential l for clinical use by showing how well it can predict the likelihood of PCOD/PCOS.","url":"https://doi.org/10.1109/incoft60753.2023.10425743","authors":["Dhruvi Kapadia","Reetu Jain"],"tags":["Computer science","Informatics","Artificial intelligence","Health informatics","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-24","doi":"https://doi.org/10.1109/incoft60753.2023.10425743","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2078159446","name":"Rationality and intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)00026-x","authors":["Stuart Russell"],"tags":["Rationality","Agency (philosophy)","Field (mathematics)","Computer science","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1997-07-01","doi":"https://doi.org/10.1016/s0004-3702(97)00026-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3040810687","name":"Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors","source":"openalex","abstract":"Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient's data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.","url":"https://doi.org/10.3390/s20143870","authors":["Martina Vettoretti","Giacomo Cappon","Andrea Facchinetti","Giovanni Sparacino"],"tags":["Continuous glucose monitoring","Diabetes management","Diabetes mellitus","Blood Glucose Self-Monitoring","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-10","doi":"https://doi.org/10.3390/s20143870","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W180413045","name":"Temporal Reasoning in Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/b978-0-934613-67-5.50015-0","authors":["Yoav Shoham","Nita Goyal"],"tags":["Troubleshooting","Computer science","Disjoint sets","Qualitative reasoning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1988-01-01","doi":"https://doi.org/10.1016/b978-0-934613-67-5.50015-0","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3160498810","name":"Application of artificial intelligence in gynecologic malignancies: A review","source":"openalex","abstract":"With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.","url":"https://doi.org/10.1111/jog.14818","authors":["Kenbun Sone","Yusuke Toyohara","Ayumi Taguchi","Yuichiro Miyamoto","Michihiro Tanikawa","Mayuyo Uchino‐Mori","Takayuki Iriyama","Tetsushi Tsuruga","Yutaka Osuga"],"tags":["Colposcopy","Medicine","Artificial intelligence","Medical physics","Gynecologic oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-10","doi":"https://doi.org/10.1111/jog.14818","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W1563989202","name":"Introduction to the Artificial Neural Networks","source":"openalex","abstract":"An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simple mathematical model (function). Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted what means that every input value is multiplied with individual weight. In the middle section of artificial neuron is sum function that sums all weighted inputs and bias. At the exit of artificial neuron the sum of previously weighted inputs and bias is passing trough activation function that is also called transfer function (Fig. 1.).","url":"https://doi.org/10.5772/15751","authors":["Andrej Krenker","Janez Bešter","Andrej Kos"],"tags":["Artificial neural network","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2011-04-11","doi":"https://doi.org/10.5772/15751","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3000144657","name":"Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology","source":"openalex","abstract":"We report the results of a survey conducted among ESR members in November and December 2018, asking for expectations about artificial intelligence (AI) in 5-10 years. Of 24,000 ESR members contacted, 675 (2.8%) completed the survey, 454 males (67%), 555 (82%) working at academic/public hospitals. AI impact was mostly expected (≥ 30% of responders) on breast, oncologic, thoracic, and neuro imaging, mainly involving mammography, computed tomography, and magnetic resonance. Responders foresee AI impact on: job opportunities (375/675, 56%), 218/375 (58%) expecting increase, 157/375 (42%) reduction; reporting workload (504/675, 75%), 256/504 (51%) expecting reduction, 248/504 (49%) increase; radiologist's profile, becoming more clinical (364/675, 54%) and more subspecialised (283/675, 42%). For 374/675 responders (55%) AI-only reports would be not accepted by patients, for 79/675 (12%) accepted, for 222/675 (33%) it is too early to answer. For 275/675 responders (41%) AI will make the radiologist-patient relation more interactive, for 140/675 (21%) more impersonal, for 259/675 (38%) unchanged. If AI allows time saving, radiologists should interact more with clinicians (437/675, 65%) and/or patients (322/675, 48%). For all responders, involvement in AI-projects is welcome, with different roles: supervision (434/675, 64%), task definition (359/675, 53%), image labelling (197/675, 29%). Of 675 responders, 321 (48%) do not currently use AI, 138 (20%) use AI, 205 (30%) are planning to do it. According to 277/675 responders (41%), radiologists will take responsibility for AI outcome, while 277/675 (41%) suggest shared responsibility with other professionals. To summarise, responders showed a general favourable attitude towards AI.","url":"https://doi.org/10.1186/s13244-019-0798-3","authors":["European Society of Radiology (ESR)"],"tags":["Medicine","Interventional radiology","Neuroradiology","Radiology","Workload"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-31","doi":"https://doi.org/10.1186/s13244-019-0798-3","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2974687813","name":"A Medical Student’s Outlook on Radiology in Light of Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jacr.2019.08.026","authors":["Benjamin T. Burdorf"],"tags":["Radiology","Medical physics","Data science","Medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-09-16","doi":"https://doi.org/10.1016/j.jacr.2019.08.026","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4308042262","name":"Ethics of Artificial Intelligence","source":"openalex","abstract":"This open access book fills the gap and provides a resource for learning about AI ethics, which is not available to date","url":"https://doi.org/10.1007/978-3-031-17040-9","authors":["Bernd Carsten Stahl","Doris Schroeder","Rowena Rodrigues"],"tags":["Psychology","Engineering ethics","Artificial intelligence","Computer science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-01","doi":"https://doi.org/10.1007/978-3-031-17040-9","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2980177178","name":"Clinical applications of artificial intelligence in sepsis: A narrative review","source":"openalex","abstract":"Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice.","url":"https://doi.org/10.1016/j.compbiomed.2019.103488","authors":["Michiel Schinkel","Ketan Paranjape","Rishi Panday","Niclas Skyttberg","Prabath W.B. Nanayakkara"],"tags":["Generalizability theory","Sepsis","Medicine","Receiver operating characteristic","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-07","doi":"https://doi.org/10.1016/j.compbiomed.2019.103488","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2914546793","name":"Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41374-019-0202-4","authors":["Maxim Signaevsky","Marcel Prastawa","Kurt Farrell","Nabil Tabish","Elena Baldwin","Natalia Han","Megan A. Iida","John Koll","Clare Bryce","Dushyant P. Purohit","Vahram Haroutunian","Ann C. McKee","Thor D. Stein","Charles L. White","Jamie M. Walker","Timothy E. Richardson","Russell W. Hanson","Michael Donovan","Carlos Cordon‐Cardo","Jack Zeineh","Gerardo Fernández","John F. Crary"],"tags":["Tauopathy","Neuropathology","Neuroscience","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-02-15","doi":"https://doi.org/10.1038/s41374-019-0202-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3112597358","name":"Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?","source":"openalex","abstract":"Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.","url":"https://doi.org/10.3390/joitmc6040187","authors":["Tan Yiğitcanlar","Nayomi Kankanamge","Massimo Regona","Andres Ruiz Maldonado","Bridget Rowan","Alex Ryu","Kevin C. Desouza","Juan M. Corchado","Rashid Mehmood","Rita Yi Man Li"],"tags":["Urban planning","Big data","Knowledge management","Context (archaeology)","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-01","doi":"https://doi.org/10.3390/joitmc6040187","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2480645619","name":"Support Vector Machines Applications","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-319-02300-7","authors":["Yunqian Ma","Guodong Guo"],"tags":["Computer science","Vector (molecular biology)","Biology","Biochemistry","Gene"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2014-01-01","doi":"https://doi.org/10.1007/978-3-319-02300-7","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3168556734","name":"Artificial Intelligence in Epilepsy","source":"openalex","abstract":"BACKGROUND: The study of seizure patterns in electroencephalography (EEG) requires several years of intensive training. In addition, inadequate training and human error may lead to misinterpretation and incorrect diagnosis. Artificial intelligence (AI)-based automated seizure detection systems hold an exciting potential to create paradigms for proper diagnosis and interpretation. AI holds the promise to transform healthcare into a system where machines and humans can work together to provide an accurate, timely diagnosis, and treatment to the patients. OBJECTIVE: This article presents a brief overview of research on the use of AI systems for pattern recognition in EEG for clinical diagnosis. MATERIAL AND METHODS: The article begins with the need for understanding nonstationary signals such as EEG and simplifying their complexity for accurate pattern recognition in medical diagnosis. It also explains the core concepts of AI, machine learning (ML), and deep learning (DL) methods. RESULTS AND CONCLUSIONS: In this present context of epilepsy diagnosis, AI may work in two ways; first by creating visual representations (e.g., color-coded paradigms), which allow persons with limited training to make a diagnosis. The second is by directly explaining a complete automated analysis, which of course requires more complex paradigms than the previous one. We also clarify that AI is not about replacing doctors and strongly emphasize the need for domain knowledge in building robust AI models that can work in real-time scenarios rendering good detection accuracy in a minimum amount of time.","url":"https://doi.org/10.4103/0028-3886.317233","authors":["Taranjit Kaur","Anirudra Diwakar","Kirandeep Kirandeep","Pranav Mirpuri","Manjari Tripathi","Parijat Chandra","Tapan Kumar Gandhi"],"tags":["Artificial intelligence","Electroencephalography","Computer science","Machine learning","Rendering (computer graphics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-01","doi":"https://doi.org/10.4103/0028-3886.317233","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385562476","name":"Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context","source":"openalex","abstract":"Despite the significant emphasis placed on incorporating 21st century skills into the educational framework, particularly at the primary level, recent scholarly works indicate considerable variation in the implementation of these skills across different countries and regions, suggesting a demand for further research specifically focusing on primary education. The indications of the Digicomp framework 1 and 21st-century skills in Europe have outlined the key competences for lifelong learning needed for all citizens, including teachers and students. In this perspective, Education plays a fundamental role in ensuring that citizens acquire the required skills. The objective in the common European framework is clear: to initiate a transition from the culture of knowledge to the culture of competence. Nowadays, technological advancement allows the researchers to create and combine different frameworks with the perspective of an even more tailored, and engaged education, some examples derived from the implementation of Virtual Reality (VR) and Augmented Reality (AR), in the combination of Gamification and AI, or the development of Intelligent Tutoring Systems (ITS) to foster and create an even more personalized learning and teaching. Following these premises, in this paper, we want to point out new research reflections and perspectives that could help researchers, teachers, educators (and consequently students) to reflect on the introduction of new technologies (e.g., artificial intelligence, robot tutors) and on how these can affect on human behavioral development and on the acquisition of new skills and competences (Specifically: Creativity, Critical Thinking, Problem Solving, and Computational Thinking) for the educational context. The analysis carried on, suggests a perspective on how creativity, critical thinking, and problem-solving can be effective in promoting computational thinking, and how Artificial Intelligence (AI) could be an aid instrument to teachers in the fostering of creativity, critical thinking, and problem-solving in schools and educational contexts.","url":"https://doi.org/10.1016/j.chb.2023.107903","authors":["Martina Benvenuti","Angelo Cangelosi","Armin Weinberger","Elvis Mazzoni","Mariagrazia Benassi","Mattia Barbaresi","Matteo Orsoni"],"tags":["Creativity","Competence (human resources)","21st century skills","Lifelong learning","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-04","doi":"https://doi.org/10.1016/j.chb.2023.107903","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3157131329","name":"Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain","source":"openalex","abstract":"In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.","url":"https://doi.org/10.3390/make3030037","authors":["Samanta Knapič","Avleen Malhi","Rohit Saluja","Kary Främling"],"tags":["Computer science","Convolutional neural network","Set (abstract data type)","Artificial intelligence","Domain (mathematical analysis)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-19","doi":"https://doi.org/10.3390/make3030037","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4362716434","name":"Overview of Early ChatGPT’s Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts","source":"openalex","abstract":"ChatGPT, an artificial intelligence chatbot, has rapidly gained prominence in various domains, including medical education and healthcare literature. This hybrid narrative review, conducted collaboratively by human authors and ChatGPT, aims to summarize and synthesize the current knowledge of ChatGPT in the indexed medical literature during its initial four months. A search strategy was employed in PubMed and EuropePMC databases, yielding 65 and 110 papers, respectively. These papers focused on ChatGPT's impact on medical education, scientific research, medical writing, ethical considerations, diagnostic decision-making, automation potential, and criticisms. The findings indicate a growing body of literature on ChatGPT's applications and implications in healthcare, highlighting the need for further research to assess its effectiveness and ethical concerns.","url":"https://doi.org/10.7759/cureus.37281","authors":["Omar Temsah","Samina Khan","Yazan Chaiah","Abdulrahman Senjab","Khalid Alhasan","Amr Jamal","Fadi Aljamaan","Khalid H. Malki","Rabih Halwani","Jaffar A. Al‐Tawfiq","Mohamad‐Hani Temsah","Ayman Al‐Eyadhy"],"tags":["Medicine","Chatbot","Narrative review","Engineering ethics","Narrative"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-08","doi":"https://doi.org/10.7759/cureus.37281","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2115011142","name":"Artificial neural networks: Current status in cardiovascular medicine","source":"openalex","abstract":"Artificial neural networks are a form of artificial computer intelligence that have been the subject of renewed research interest in the last 10 years. Although they have been used extensively for problems in engineering, they have only recently been applied to medical problems, particularly in the fields of radiology, urology, laboratory medicine and cardiology. An artificial neural network is a distributed network of computing elements that is modeled after a biologic neural system and may be implemented as a computer software program. It is capable of identifying relations in input data that are not easily apparent with current common analytic techniques. The functioning artificial neural network's knowledge is built on learning and experience from previous input data. On the basis of this prior knowledge, the artificial neural network can predict relations found in newly presented data sets. In cardiology, artificial neural networks have been successfully applied to problems in the diagnosis and treatment of coronary artery disease and myocardial infarction, in electrocardiographic interpretation and detection of arrhythmias and in image analysis in cardiac radiography and sonography. This report focuses on the current status of artificial neural network technology in cardiovascular medical research.","url":"https://doi.org/10.1016/0735-1097(96)00174-x","authors":["Dipti Itchhaporia","Peter B. Snow","Robert J. Almassy","William J. Oetgen"],"tags":["Artificial neural network","Medicine","Artificial intelligence","Myocardial infarction","Coronary artery disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1996-08-01","doi":"https://doi.org/10.1016/0735-1097(96)00174-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4382052239","name":"Explainable Artificial Intelligence in Medical Image Analysis: State of the Art and Prospects","source":"openalex","abstract":"to increase transparency in the work of artificial intelligence systems in the analysis of medical images is called to use methods of explainable artificial intelligence. Our study provides an overview of the current state of application of explainable artificial intelligence methods for medical image analysis, and also considers potentially promising approaches to improving the technology.","url":"https://doi.org/10.1109/scm58628.2023.10159033","authors":["Egor Volkov","Aleksej N. Averkin"],"tags":["Computer science","Transparency (behavior)","Artificial intelligence","State (computer science)","Image (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-24","doi":"https://doi.org/10.1109/scm58628.2023.10159033","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3171455429","name":"Artificial Intelligence–Based Chatbot for Anxiety and Depression in University Students: Pilot Randomized Controlled Trial","source":"openalex","abstract":"Background Artificial intelligence–based chatbots are emerging as instruments of psychological intervention; however, no relevant studies have been reported in Latin America. Objective The objective of the present study was to evaluate the viability, acceptability, and potential impact of using Tess, a chatbot, for examining symptoms of depression and anxiety in university students. Methods This was a pilot randomized controlled trial. The experimental condition used Tess for 8 weeks, and the control condition was assigned to a psychoeducation book on depression. Comparisons were conducted using Mann-Whitney U and Wilcoxon tests for depressive symptoms, and independent and paired sample t tests to analyze anxiety symptoms. Results The initial sample consisted of 181 Argentinian college students (158, 87.2% female) aged 18 to 33. Data at week 8 were provided by 39 out of the 99 (39%) participants in the experimental condition and 34 out of the 82 (41%) in the control group. On an average, 472 (SD 249.52) messages were exchanged, with 116 (SD 73.87) of the messages sent from the users in response to Tess. A higher number of messages exchanged with Tess was associated with positive feedback (F2,36=4.37; P=.02). No significant differences between the experimental and control groups were found from the baseline to week 8 for depressive and anxiety symptoms. However, significant intragroup differences demonstrated that the experimental group showed a significant decrease in anxiety symptoms; no such differences were observed for the control group. Further, no significant intragroup differences were found for depressive symptoms. Conclusions The students spent a considerable amount of time exchanging messages with Tess and positive feedback was associated with a higher number of messages exchanged. The initial results show promising evidence for the usability and acceptability of Tess in the Argentinian population. Research on chatbots is still in its initial stages and further research is needed.","url":"https://doi.org/10.2196/20678","authors":["María Carolina Klos","Milagros Escoredo","Angela Joerin","Viviana Lemos","Michiel Rauws","Eduardo L. Bunge"],"tags":["Anxiety","Psychoeducation","Randomized controlled trial","Chatbot","Depression (economics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-29","doi":"https://doi.org/10.2196/20678","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4309702579","name":"Priorities for successful use of artificial intelligence by public health organizations: a literature review","source":"openalex","abstract":"Artificial intelligence (AI) has the potential to improve public health's ability to promote the health of all people in all communities. To successfully realize this potential and use AI for public health functions it is important for public health organizations to thoughtfully develop strategies for AI implementation. Six key priorities for successful use of AI technologies by public health organizations are discussed: 1) Contemporary data governance; 2) Investment in modernized data and analytic infrastructure and procedures; 3) Addressing the skills gap in the workforce; 4) Development of strategic collaborative partnerships; 5) Use of good AI practices for transparency and reproducibility, and; 6) Explicit consideration of equity and bias.","url":"https://doi.org/10.1186/s12889-022-14422-z","authors":["Stacey Fisher","Laura C. Rosella"],"tags":["Biostatistics","Medicine","Public health","Epidemiology","Environmental health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-22","doi":"https://doi.org/10.1186/s12889-022-14422-z","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4226429363","name":"Artificial intelligence in public services: When and why citizens accept its usage","source":"openalex","abstract":"Interest in implementing artificial intelligence (AI)–based software in the public sector is growing. First implementations and research in individual public services have already been carried out; however, a better understanding of citizens' acceptance of this technology is missing in the public sector, as insights from the private sector cannot be transferred directly. For this purpose, we conduct policy-capturing experiments to analyze AI's acceptance in six representative scenarios. Based on behavioral reasoning theory, we gather evidence from 329 participants. The results show that AI solutions in general public services are preferred over those provided by humans, but specific services are still a human domain. Further analyses show that the major drivers toward acceptance are the reasons against AI. The results contribute to understanding of when and why AI is accepted in public services. Public administration can use the results to identify AI-based software to invest in and communicate their usage to perceive such investments' high acceptance rates.","url":"https://doi.org/10.1016/j.giq.2022.101704","authors":["Tanja Sophie Gesk","Michael Leyer"],"tags":["Public sector","Implementation","Private sector","Software","Public domain"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-05","doi":"https://doi.org/10.1016/j.giq.2022.101704","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4320891340","name":"Acceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey (Preprint)","source":"openalex","abstract":"BACKGROUND There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system. OBJECTIVE The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients’ choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants’ individual choices. METHODS A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient’s perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed. RESULTS Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants’ choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant. CONCLUSIONS This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.","url":"https://doi.org/10.2196/preprints.46402","authors":["Inga Jagemann","Ole Wensing","Manuel Stegemann","Gerrit Hirschfeld"],"tags":["Conjoint analysis","Skin cancer","Perspective (graphical)","Medicine","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-10","doi":"https://doi.org/10.2196/preprints.46402","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4396615793","name":"Bio‐Inspired Sensory Receptors for Artificial‐Intelligence Perception","source":"openalex","abstract":"In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.","url":"https://doi.org/10.1002/adma.202403150","authors":["Atanu Bag","Gargi Ghosh","M. Junaid Sultan","Hamna Haq Chouhdry","Seok Ju Hong","Tran Quang Trung","Geun‐Young Kang","Nae‐Eung Lee"],"tags":["Perception","Sensory system","Scalability","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-03","doi":"https://doi.org/10.1002/adma.202403150","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4403107901","name":"A scoping review of reporting gaps in FDA-approved AI medical devices","source":"openalex","abstract":"Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA-approved AI/ML-enabled medical devices approved from 1995-2023 to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.","url":"https://doi.org/10.1038/s41746-024-01270-x","authors":["Vijaytha Muralidharan","Boluwatife Adeleye Adewale","Caroline J. Huang","Mfon Thelma Nta","Peter Oluwaduyilemi Ademiju","Pirunthan Pathmarajah","Man Kien Hang","Oluwafolajimi Adesanya","Ridwanullah Olamide Abdullateef","Abdulhammed Opeyemi Babatunde","Abdulquddus Ajibade","Sonia Onyeka","Zhou Ran Cai","Roxana Daneshjou","Tobi Olatunji"],"tags":["Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-03","doi":"https://doi.org/10.1038/s41746-024-01270-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385241001","name":"Interpretation of Medical Images Using Artificial Intelligence: Current Status and Future Perspectives","source":"openalex","abstract":"","url":"https://doi.org/10.4166/kjg.2023.071","authors":["Eun Jeong Gong","Chang Seok Bang"],"tags":["Interpretation (philosophy)","Artificial intelligence","Current (fluid)","Computer science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-25","doi":"https://doi.org/10.4166/kjg.2023.071","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391265101","name":"Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review","source":"openalex","abstract":"Interpretable artificial intelligence (AI), also known as explainable AI, is indispensable in establishing trustable AI for bench-to-bedside translation, with substantial implications for human well-being. However, the majority of existing research in this area has centered on designing complex and sophisticated methods, regardless of their interpretability. Consequently, the main prerequisite for implementing trustworthy AI in medical domains has not been met. Scientists have developed various explanation methods for interpretable AI. Among these methods, fuzzy rules embedded in a fuzzy inference system (FIS) have emerged as a novel and powerful tool to bridge the communication gap between humans and advanced AI machines. However, there have been few reviews of the use of FISs in medical diagnosis. In addition, the application of fuzzy rules to different kinds of multimodal medical data has received insufficient attention, despite the potential use of fuzzy rules in designing appropriate methodologies for available datasets. This review provides a fundamental understanding of interpretability and fuzzy rules, conducts comparative analyses of the use of fuzzy rules and other explanation methods in handling three major types of multimodal data (i.e., sequence signals, medical images, and tabular data), and offers insights into appropriate fuzzy rule application scenarios and recommendations for future research.","url":"https://doi.org/10.1016/j.ins.2024.120212","authors":["Jin Cao","Ta Zhou","Shaohua Zhi","Saikit Lam","Ge Ren","Yuanpeng Zhang","Yongqiang Wang","Yanjing Dong","Jing Cai"],"tags":["Artificial intelligence","Adaptive neuro fuzzy inference system","Fuzzy logic","Computer science","Fuzzy inference system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-26","doi":"https://doi.org/10.1016/j.ins.2024.120212","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4401760450","name":"Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine","source":"openalex","abstract":"This commentary explores the critical roles of health equity and ethical considerations in the deployment of artificial intelligence (AI) in public health and medicine. As AI increasingly permeates these fields, it promises substantial benefits but also poses risks that could exacerbate existing disparities and ethical challenges. This commentary delves into the current integration of AI technologies, underscores the importance of ethical social responsibility, and discusses the implications for practice and policy. Recommendations are provided to ensure AI advancements are leveraged responsibly, promoting equitable health outcomes and adhering to rigorous ethical standards across all populations.","url":"https://doi.org/10.5888/pcd21.240245","authors":["Irene Dankwa‐Mullan"],"tags":["Medicine","Public health","Health promotion","Disease","Health policy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-22","doi":"https://doi.org/10.5888/pcd21.240245","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4200145950","name":"Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images","source":"openalex","abstract":"Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases. It is diagnosed by manual examination of stool samples by microscopy, which is a time-consuming task and requires trained personnel and high specialization. Artificial intelligence could automate this task making the diagnosis more accessible. Still, it needs a large amount of annotated training data coming from experts.In this work, we proposed the use of crowdsourced annotated medical images to train AI models (neural networks) for the detection of soil-transmitted helminthiasis in microscopy images from stool samples leveraging non-expert knowledge collected through playing a video game. We collected annotations made by both school-age children and adults, and we showed that, although the quality of crowdsourced annotations made by school-age children are sightly inferior than the ones made by adults, AI models trained on these crowdsourced annotations perform similarly (AUC of 0.928 and 0.939 respectively), and reach similar performance to the AI model trained on expert annotations (AUC of 0.932). We also showed the impact of the training sample size and continuous training on the performance of the AI models.In conclusion, the workflow proposed in this work combined collective and artificial intelligence for detecting soil-transmitted helminthiasis. Embedded within a digital health platform can be applied to any other medical image analysis task and contribute to reduce the burden of disease.","url":"https://doi.org/10.1109/embc46164.2021.9630868","authors":["Lin Lin","David Bermejo-Pelaez","Daniel Capellan-Martin","Daniel Cuadrado","Cristina Rodriguez","Lydia Garcia","Nuria Diez","Rocio Tome","Maria Postigo","Maria Jesus Ledesma-Carbayo","Miguel Luengo-Oroz"],"tags":["Workflow","Task (project management)","Artificial intelligence","Computer science","Crowdsourcing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-01","doi":"https://doi.org/10.1109/embc46164.2021.9630868","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3046921626","name":"The Role of Artificial Intelligence in Echocardiography","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11886-020-01329-7","authors":["Karthik Seetharam","Sameer Raina","Partho P. Sengupta"],"tags":["Medicine","Cardiology","Internal medicine","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-30","doi":"https://doi.org/10.1007/s11886-020-01329-7","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4402736045","name":"The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jmir.2024.101743","authors":["Francesca Xuereb","Dr Jonathan L Portelli"],"tags":["Perception","Artificial intelligence","Medical imaging","Psychology","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-23","doi":"https://doi.org/10.1016/j.jmir.2024.101743","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4367311208","name":"How Chatbots and Large Language Model Artificial Intelligence Systems Will Reshape Modern Medicine","source":"openalex","abstract":"In an era of clinicians being burned out by electronic medical records and documentation burdens, we might all dream of having a personal scribe to draft progress notes, translate patient instructions, summarize the literature, complete insurance authorization paperwork, and respond to unending in-basket messages, as described in the Perspective in this issue of JAMA Internal Medicine. 1 This would have sounded like a fantasy just a few years ago, but the release of rapidly developing chatbots now demonstrates the potential of large language model artificial intelligence (AI) systems with surprisingly adept language manipulation and knowledge processing capabilities.The underlying foundation model technology rides atop the peak of inflated expectations, 2 reflecting a disruptive technology likely to change the way we work and live, even as we must be aware of substantial limitations.Good or bad, ready or not, Pandora's box has already been opened.One such large language model, ChatGPT, is the fastest-growing internet application in history with more than 100 million users.3 This has shifted access to sophisticated AI capabilities away from concentrated pockets of technical experts to the masses, where all types of otherwise unimaginable (and unintended) use cases are being discovered.To ensure that the adoption of such tools into health care practice is done effectively and responsibly, physicians must lean in to understand and drive this conversation.Large language models represent the underlying class of machine learning models trained in autocomplete tasks.Given the words \"coronary artery,\" these models may predict the next word to be \"disease,\" \"bypass graft,\" or \"calcification\" based on statistical parameters learned from prior training data text on how often those words appear together.","url":"https://doi.org/10.1001/jamainternmed.2023.1835","authors":["Ron Li","Andre Kumar","Jonathan H. Chen"],"tags":["Medicine","Otorhinolaryngology","Family medicine","Neurology","Sign (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-28","doi":"https://doi.org/10.1001/jamainternmed.2023.1835","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4381743186","name":"Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT-3.5, GPT-4, and Human Expertise in Answering StatPearls Questions","source":"openalex","abstract":"Importance Chat Generative Pre-Trained Transformer (ChatGPT) has shown promising performance in various fields, including medicine, business, and law, but its accuracy in specialty-specific medical questions, particularly in ophthalmology, is still uncertain. Purpose This study evaluates the performance of two ChatGPT models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology questions from the StatPearls question bank, assessing their outcomes, and providing insights into the integration of artificial intelligence (AI) technology in ophthalmology. Methods ChatGPT's performance was evaluated using 467 ophthalmology questions from the StatPearls question bank. These questions were stratified into 11 subcategories, four difficulty levels, and three generalized anatomical categories. The answer accuracy of GPT-3.5, GPT-4, and human participants was assessed. Statistical analysis was conducted via the Kolmogorov-Smirnov test for normality, one-way analysis of variance (ANOVA) for the statistical significance of GPT-3 versus GPT-4 versus human performance, and repeated unpaired two-sample t-tests to compare the means of two groups. Results GPT-4 outperformed both GPT-3.5 and human professionals on ophthalmology StatPearls questions, except in the \"Lens and Cataract\" category. The performance differences were statistically significant overall, with GPT-4 achieving higher accuracy (73.2%) compared to GPT-3.5 (55.5%, p-value < 0.001) and humans (58.3%, p-value < 0.001). There were variations in performance across difficulty levels (rated one to four), but GPT-4 consistently performed better than both GPT-3.5 and humans on level-two, -three, and -four questions. On questions of level-four difficulty, human performance significantly exceeded that of GPT-3.5 (p = 0.008). Conclusion The study's findings demonstrate GPT-4's significant performance improvements over GPT-3.5 and human professionals on StatPearls ophthalmology questions. Our results highlight the potential of advanced conversational AI systems to be utilized as important tools in the education and practice of medicine.","url":"https://doi.org/10.7759/cureus.40822","authors":["Majid Moshirfar","Amal W. Altaf","Isabella M. Stoakes","Jared J. Tuttle","Phillip C. Hoopes"],"tags":["Medicine","Normality","Specialty","Analysis of variance","Statistical significance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-22","doi":"https://doi.org/10.7759/cureus.40822","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3130497307","name":"INFORMATIZATION OF MEDICAL EDUCATION: ARTIFICIAL INTELLIGENCE SYSTEMS IN THE EDUCATION OF STUDENTS AND DOCTORS","source":"openalex","abstract":",","url":"https://doi.org/10.26140/bgz3-2020-0903-0021","authors":["K.S Itinson"],"tags":["Informatization","Medical education","Artificial intelligence","Engineering management","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-08-29","doi":"https://doi.org/10.26140/bgz3-2020-0903-0021","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2893366129","name":"Evaluating Artificial Intelligence Applications in Clinical Settings","source":"openalex","abstract":"Artificial intelligence (AI)-based systems have been shown to reliably recognize cardiovascular disease risk 1 and diagnose conditions such as diabetic retinopathy 2,3 and melanoma 4 from medical images. These advances in image-based medical diagnosis have been widely publicized in the media Downloaded From: https://jamanetwork.com/ on 08/21/2023 to improve disease diagnosis and care, premature deployment can lead to increased strain on the health care system, undue stress to patients, and possibly death owing to misdiagnosis.","url":"https://doi.org/10.1001/jamanetworkopen.2018.2658","authors":["Elaine O. Nsoesie"],"tags":["Artificial intelligence","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-09-28","doi":"https://doi.org/10.1001/jamanetworkopen.2018.2658","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4399610000","name":"Application and Development of Artificial Intelligence-based Medical Imaging Diagnostic Assistance System","source":"openalex","abstract":"Medical imaging technology plays a key role in modern medical diagnosis and treatment, and the integration of artificial intelligence (AI) technology has revolutionised the field.AI uses deep learning and machine learning algorithms to analyse medical imaging data, improving the accuracy of lesion identification and disease prediction, and thus significantly improving the efficiency of diagnostic work. The scope of application of AI has also expanded to include the optimisation of treatment planning, the prediction of disease progression, and the assessment of patient prognosis. The application of AI has also been extended to the optimisation of treatment plans, prediction of disease progression and assessment of patient prognosis. Although AI shows great potential in medical imaging diagnosis, its clinical application still faces challenges. The quality, accessibility, sensitivity, and privacy of medical image data, as well as the \"black box\" nature of AI models, pose obstacles to the widespread application of AI technology. In addition, data security and privacy protection are also issues that need to be addressed. This paper reviews the current status of AI application in medical imaging diagnosis, analyses the main problems faced, and discusses the future development direction. Examples of AI applications in different medical imaging fields are discussed in the paper, and challenges such as data quality, laws and regulations, model interpretability and data security are explored in depth, and solution strategies such as enhancing data management, improving model generalisation and interpretability, strengthening data security techniques, and promoting interdisciplinary cooperation are proposed. This paper aims to provide reference for researchers and practitioners of AI in medical imaging diagnosis to promote the healthy development of the field, and calls on experts, scholars, policy makers and technology developers to work together to overcome the challenges and to realize the potential of AI technology in improving the quality and efficiency of healthcare services and safeguarding patients' health.","url":"https://doi.org/10.54097/sb3m1m17","authors":["Ziruo Peng","Xiaolin Ren"],"tags":["Interpretability","Artificial intelligence","Computer science","Medical imaging","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-29","doi":"https://doi.org/10.54097/sb3m1m17","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4402298814","name":"Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study","source":"openalex","abstract":"Health care organizations are building, deploying, and self-governing digital health technologies (DHTs), including artificial intelligence, at an increasing rate. This scope necessitates expertise and quality infrastructure to ensure that the technology impacting patient care is safe, effective, and ethical throughout its lifecycle. The objective of this article is to describe Mayo Clinic's approach for embedding internal accountability as a case study for other health care institutions seeking modalities for responsible implementation of artificial intelligence-enabled DHTs. Mayo Clinic aims to enable and empower innovators by (1) building internal skills and expertise, (2) establishing a centralized review board, and (3) aligning development and deployment processes with regulations, standards, and best practices. In 2022, Mayo Clinic established the Software as a Medical Device Review Board (The Board), an independent body of physicians and domain experts to represent the organization in providing innovators regulatory and risk mitigation recommendations for DHTs. Hundreds of digital health product teams have since benefited from this function, intended to enable responsible innovation in alignment with regulation and state-of-the-art quality management practices. Other health care institutions can adopt similar internal accountability bodies using this framework. Opportunity remains to iterate on Mayo Clinic's approach in alignment with advancing best practices and enhance representation on The Board as part of standard continuous improvement practices.","url":"https://doi.org/10.1016/j.mcpdig.2024.08.008","authors":["Brenna Loufek","David Vidal","David S. McClintock","Mark A. Lifson","Eric E. Williamson","Shauna Overgaard","Kathleen McNaughton","Melissa C. Lipford","Darrell S. Pardi"],"tags":["Accountability","Health care","Medicine","Nursing","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-06","doi":"https://doi.org/10.1016/j.mcpdig.2024.08.008","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3216744571","name":"IOT Monitoring System of Medical Waste Based on Artificial Intelligence","source":"openalex","abstract":"In order to realize the “closed-loop management, fixed-point orientation and whole-process traceability” of medical waste, based on face recognition technology and system engineering principle, the IOT monitoring system of medical waste is divided into three levels. Hospital information monitoring center, professional transportation company information monitoring and medical waste treatment company for the first level monitoring information monitoring center, the municipal medical waste management information monitoring center for secondary monitor, provincial medical waste management information monitoring center for three-level monitoring, special is responsible for, layers of management, the framework is clear, break the problem of asymmetric information, personnel and units pursuit problem, easy to study different types of medical wastes output, from different time span to analyze data, data characteristics are studied by using big data, find the abnormal problems, guide the development of the medical enterprise, further to ensure the safety of people's life.","url":"https://doi.org/10.1109/icnisc54316.2021.00034","authors":["Chao Wang","Yanxia Ma","Gengxin Zhong"],"tags":["Traceability","Medical waste","Process (computing)","Condition monitoring","Information management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-01","doi":"https://doi.org/10.1109/icnisc54316.2021.00034","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3098712771","name":"Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation","source":"openalex","abstract":"Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.","url":"https://doi.org/10.1161/circep.120.009355","authors":["Georgios Christopoulos","Jonathan Graff‐Radford","Camden Lopez","Xiaoxi Yao","Zachi I. Attia","Alejandro A. Rabinstein","Ronald C. Petersen","David S. Knopman","Michelle M. Mielke","Walter K. Kremers","Prashanthi Vemuri","Konstantinos C. Siontis","Paul A. Friedman","Peter A. Noseworthy"],"tags":["Medicine","Atrial fibrillation","Electrocardiography","Cardiology","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-13","doi":"https://doi.org/10.1161/circep.120.009355","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385619916","name":"Revolutionising Impacts of Artificial Intelligence on Health Care System and Its Related Medical In-Transparencies","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10439-023-03343-6","authors":["Ayesha Saadat","Tasmiyah Siddiqui","Shafaq Taseen","Sanila Mughal"],"tags":["Health care","Artificial intelligence","Modalities","Scale (ratio)","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-07","doi":"https://doi.org/10.1007/s10439-023-03343-6","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4324311092","name":"Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education","source":"openalex","abstract":"Diabetes self-management and education (DSME) is an integral part of diabetes care and has been shown to improve glycemic control, reduce complications, and increase quality of life (1).The traditional model in which clinicians and diabetes educators share responsibility for patient education faces challenges such as reduced access to care during the pandemic and a shortage of trained educators.Artificial intelligence (AI) solutions are increasingly recognized to have a strong use case in DSME (2).Smart conversational agents (\"chatbots\") have shown potential as tools for direct patient engagement and education (3).While previous generations of chatbots delivered structured output based on preset queries and responses, modern natural-language AI models are designed to accept unstructured or nonstandardized inputs and provide human-like responses.These models draw on a large repository of humangenerated textual content to produce responses statistically likely to match the query.While chatbots may be able to augment patient care by providing ondemand answers to patient questions, they are based on language patterns rather than objective databases and may provide patients with authoritativesounding information that is inaccurate.ChatGPT is a chatbot developed by OpenAI based on the GPT3 large language model.It is readily accessible by the general public and has gained popular traction.It has recently been shown to be able to pass the U.S.","url":"https://doi.org/10.2337/dc23-0197","authors":["Gerald Gui Ren Sng","Joshua Yi Min Tung","Daniel Yan Zheng Lim","Yong Mong Bee"],"tags":["Medicine","Diabetes mellitus","Natural (archaeology)","Artificial intelligence","Endocrinology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-15","doi":"https://doi.org/10.2337/dc23-0197","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4323667995","name":"Artificial intelligence-based traffic flow prediction: a comprehensive review","source":"openalex","abstract":"Abstract The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve many traffic issues, such as traffic congestion issues. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of artificial intelligence approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.","url":"https://doi.org/10.1186/s43067-023-00081-6","authors":["Sayed Ahmed","Yasser Abdelhamid","Hesham A. Hefny"],"tags":["Traffic flow (computer networking)","Traffic congestion","Intelligent transportation system","Computer science","Transport engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-09","doi":"https://doi.org/10.1186/s43067-023-00081-6","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2921174746","name":"A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jacr.2018.12.043","authors":["Marieke Haan","Yfke Ongena","Saar Hommes","Thomas C. Kwee","Derya Yakar"],"tags":["Maintenance of Certification","Certification","Perspective (graphical)","Lifelong learning","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-03-14","doi":"https://doi.org/10.1016/j.jacr.2018.12.043","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391196614","name":"Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study","source":"openalex","abstract":"Although the pediatric perioperative pain management has been improved in recent years, the valid and reliable pain assessment tool in perioperative period of children remains a challenging task. Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults, but also because there is a lack of effective and specific assessment tool for children. In addition, exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae. The short-term sequelae can induce a series of neurological, endocrine, cardiovascular system stress related to psychological trauma, while long-term sequelae may alter brain maturation process, which can lead to impair neurodevelopmental, behavioral, and cognitive function. Children's facial expressions largely reflect the degree of pain, which has led to the developing of a number of pain scoring tools that will help improve the quality of pain management in children if they are continually studied in depth. The artificial intelligence (AI) technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks, which can effectively identify and systematically analyze various subtle features of children's facial expressions. Based on the construction of a large database of images of facial expressions in children with perioperative pain, this study proposes to develop and apply automatic facial pain expression recognition software using AI technology. The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.","url":"https://doi.org/10.12998/wjcc.v12.i4.681","authors":["Jian-Ming Yue","Qi Wang","Bin Liu","Leng Zhou"],"tags":["Medicine","Pain assessment","Physical therapy","Pain management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-24","doi":"https://doi.org/10.12998/wjcc.v12.i4.681","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W7106016604","name":"Artificial intelligence for medical imaging: U-Net technology for anatomical feature analysis","source":"openalex","abstract":"The successful utilization of artificial intelligence (AI) systems in medical and healthcare systems has substantially advanced research and publication in scientific journals. This field encompasses a wide range of studies, including image processing, natural language processing, medical physics, patient data analysis, and clinical assistance tools. The current progress in AI methods can be attributed to substantial improvements in computational capacity and data processing capabilities. Notably, computer vision and image processing have emerged as highly successful AI applications. The U-Net convolutional neural network has emerged as a powerful and efficient tool for medical image segmentation and processing. This model features an encoder-decoder configuration interconnected by a bridging element, with skip connections between layers that enhance the value of the original training data. Its impressive efficiency in image processing stems from its rapid processing capability, ability to extract relationships from data, and high training velocity. Medical imagery often comprises multiple cross-sectional slices, providing a volumetric perspective of the observed region. Analyzing such imaging data requires substantial computational power and storage capacity, especially for 3-dimensional (3D) analysis. In this regard, 3D U-Net networks excel by concurrently processing numerous slices in voxel space. This attribute considerably reduces computational expenses while simultaneously improving precision. Recent advancements have brought AI technology developers to a level of stability and positive predictive values that make routine use of these systems in medical devices feasible. Currently, the implementation of AI systems appears more realistic than in previous decades. This review article focuses on state-of-the-art technologies in medical imaging for monitoring and diagnostic purposes, specifically using U-Net. We have reviewed the quality measurement of AI imaging systems using gold standards and explored novel technologies that have not been discussed in previous U-Net review papers. Additionally, we discuss the promising future development of AI systems for medical imaging purposes.","url":"https://doi.org/10.1016/j.imed.2025.07.003","authors":["Vahid Asadpour","Fagen Xie"],"tags":["Computer science","Artificial intelligence","Convolutional neural network","Image processing","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-19","doi":"https://doi.org/10.1016/j.imed.2025.07.003","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3036067296","name":"Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery","source":"openalex","abstract":"This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.","url":"https://doi.org/10.21053/ceo.2020.00654","authors":["Bayu Adhi Tama","Do Hyun Kim","Gyuwon Kim","Soo Whan Kim","Seung‐Chul Lee"],"tags":["Otorhinolaryngology","Medicine","Head and neck surgery","Medical diagnosis","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-18","doi":"https://doi.org/10.21053/ceo.2020.00654","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2964767389","name":"The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis","source":"openalex","abstract":"The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.","url":"https://doi.org/10.3390/ijerph16152699","authors":["Bach Xuan Tran","Carl A. Latkin","Giang Thu Vu","Huong Lan Thi Nguyen","Son Nghiem","Ming-Xuan Tan","Zhi-Kai Lim","Cyrus S. H. Ho","Roger Ho"],"tags":["Bibliometrics","Artificial intelligence","Latent Dirichlet allocation","Data science","Robotics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-07-29","doi":"https://doi.org/10.3390/ijerph16152699","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4250441055","name":"Applications of Artificial Intelligence (AI) in healthcare: A review","source":"openalex","abstract":"Artificial intelligence is revolutionizing — and strengthening — modern healthcarethrough technologies that can predict, grasp, learn, and act, whether it's employed toidentify new relationships between genetic codes or to control surgery-assisting robots.It can detect minor patterns that humans would completely overlook. This studyexplores and discusses the various modern applications of AI in the health sector.Particularly, the study focuses on three most emerging areas of AI-poweredhealthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggestthat pharmaceutical firms have benefited from AI in healthcare by speeding up theirdrug discovery process and automating target identification. Artificial Intelligence (AI)can help also to eliminate time-consuming data monitoring methods. The findings alsoindicate that AI-assisted clinical trials are capable of handling massive volumes of dataand producing highly accurate results. Medical AI companies develop systems thatassist patients at every level. Patients' medical data is also analyzed by clinicalintelligence, which provides insights to assist them improve their quality of life.","url":"https://doi.org/10.31219/osf.io/mjthd","authors":["Mohammed Yousef Shaheen"],"tags":["GRASP","Health care","Applications of artificial intelligence","Artificial intelligence","Identification (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-28","doi":"https://doi.org/10.31219/osf.io/mjthd","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4309852220","name":"Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review","source":"openalex","abstract":"In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.","url":"https://doi.org/10.3390/su142315620","authors":["Miao Yue","Morris Siu–Yung Jong","Yun Dai"],"tags":["Popularity","Empirical research","Mathematics education","Computer science","Scale (ratio)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-24","doi":"https://doi.org/10.3390/su142315620","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3113171837","name":"Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.","source":"openalex","abstract":"Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.","url":"https://openalex.org/W3113171837","authors":["Yutong Wang","Xiaoyun He","Hui Nie","Jianhua Zhou","Pengfei Cao","Chunlin Ou"],"tags":["Artificial intelligence","Colorectal cancer","Computer science","Cancer","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2890116734","name":"The Economics of Artificial Intelligence: An Agenda","source":"openalex","abstract":"tasks that labor is more suited to.","url":"https://doi.org/10.7208/chicago/9780226613475.001.0001","authors":["Ajay Agrawal","Joshua S. Gans","Avi Goldfarb"],"tags":["Artificial intelligence","Cognitive science","Computer science","Management science","Neoclassical economics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.7208/chicago/9780226613475.001.0001","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4317727238","name":"A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)","source":"openalex","abstract":"Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.","url":"https://doi.org/10.3390/s23031279","authors":["Chellammal Surianarayanan","John Jeyasekaran Lawrence","Pethuru Raj Chelliah","Edmond Prakash","Chaminda Hewage"],"tags":["Computer science","Artificial intelligence","Automation","Applications of artificial intelligence","Variety (cybernetics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-22","doi":"https://doi.org/10.3390/s23031279","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4388023343","name":"Artificial Intelligence in Social Work: Emerging Ethical Issues","source":"openalex","abstract":"Archives > Volume 20 (2023) > Issue 2 > Item 05 DOI: 10.55521/10-020-205 Frederic G. Reamer, Ph.D. Rhode Island College freamer@ric.edu Full disclosure: Frederic G. Reamer is a member of the IJSWVE editorial board. IJSWVE uses an anonymous review process in which authors do not review their own work and reviewers do not know authors’ identities. Reamer, F. (2023). Artificial Intelligence […]","url":"https://doi.org/10.55521/10-020-205","authors":["Frederic G. Reamer"],"tags":["Engineering ethics","Work (physics)","Social work","Ethical issues","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-30","doi":"https://doi.org/10.55521/10-020-205","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3176406321","name":"An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10878-021-00761-x","authors":["He Huang","Po-Chou Shih","Yuelan Zhu","Wei Gao"],"tags":["Computer science","Process (computing)","Cluster analysis","Artificial neural network","Support vector machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-26","doi":"https://doi.org/10.1007/s10878-021-00761-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4401974153","name":"Bridging the Artificial Intelligence (AI) Divide: Do Postgraduate Medical Students Outshine Undergraduate Medical Students in AI Readiness?","source":"openalex","abstract":"INTRODUCTION: As artificial intelligence (AI) transforms healthcare, medical education must adapt to equip future physicians with the necessary competencies. However, little is known about the differences in AI knowledge, attitudes, and practices between undergraduate and postgraduate medical students. This study aims to assess and compare AI knowledge, attitudes, and practices among undergraduate and postgraduate medical students, and to explore the associated factors and qualitative themes. METHODS: A mixed-methods study was conducted, involving 605 medical students (404 undergraduates, 201 postgraduates) from a tertiary care center. Participants completed a survey assessing AI knowledge, attitudes, and practices. Semi-structured interviews and focus group discussions were conducted to explore qualitative themes. Quantitative data were analyzed using descriptive statistics, t-tests, chi-square tests, and regression analyses. Qualitative data underwent thematic analysis. RESULTS: Postgraduate students demonstrated significantly higher AI knowledge scores than undergraduates (38.9±4.9 vs. 29.6±6.8, p<0.001). Both groups held positive attitudes, but postgraduates showed greater confidence in AI's potential (p<0.001). Postgraduates reported more extensive AI-related practices (p<0.001). Key qualitative themes included excitement about AI's potential, concerns about job security, and the need for AI education. AI knowledge, attitudes, and practices were positively correlated (p<0.01). CONCLUSIONS: This study reveals a significant AI knowledge gap between undergraduate and postgraduate medical students, highlighting the need for targeted AI education. The findings can inform curriculum development and policies to prepare medical students for the AI-driven future of healthcare. Further research should explore the long-term impact of AI education on clinical practice.","url":"https://doi.org/10.7759/cureus.67288","authors":["Rohankumar Gandhi","Alpesh Parmar","Jimmy Kagathara","Dhruv Lakkad","Jay Pareshbhai Kakadiya","Yogesh Murugan"],"tags":["Medicine","Bridging (networking)","Medical education","Computer science","Computer network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-20","doi":"https://doi.org/10.7759/cureus.67288","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4387857082","name":"Artificial intelligence technology in Alzheimer's disease research","source":"openalex","abstract":"Alzheimer's disease is a neurocognitive disorder and one of the contributing factors to dementia. According to the World Health Organization, this disease has a sig-nificant impact on the global population's health, with the number of affected individuals steadily increasing each year. Amidst rapid technological development, the use of artificial intelligence has significantly expanded into the field of medical diagnostics, encompassing areas such as the analysis of medical images, drug development, design of personalized treatment plans, and disease prediction and treatment. Deep learning, which is an important branch in the field of artificial intelligence, is playing a key role in solving several medical challenges by providing important technical support for the early detection, diagnosis, and treatment of Alzheimer's disease. Given this context, this review aims to explore the differences between conventional methods and artificial intelligence techniques in Alzheimer's disease research. Additionally, it aims to summarize current non-invasive and portable techniques for detection of Alzheimer's disease, offering support and guidance for the future prediction and management of the disease.","url":"https://doi.org/10.5582/irdr.2023.01091","authors":["Wenli Zhang","Yifan Li","Wentao Ren","Bo Liu"],"tags":["Medicine","Disease","Neurocognitive","Dementia","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-22","doi":"https://doi.org/10.5582/irdr.2023.01091","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2795594780","name":"Reimagining Clinical Documentation With Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.mayocp.2018.02.016","authors":["Steven Lin","Tait D. Shanafelt","Steven M. Asch"],"tags":["Medicine","Documentation","MEDLINE","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-04-07","doi":"https://doi.org/10.1016/j.mayocp.2018.02.016","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4309002538","name":"Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review","source":"openalex","abstract":"OBJECTIVE: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.","url":"https://doi.org/10.3390/diagnostics12112771","authors":["Leila Allahqoli","Antonio Simone Laganà","Afrooz Mazidimoradi","Hamid Salehiniya","Veronika Günther","Vito Chiàntera","Shirin Karimi Goghari","Mohammad Matin Ghiasvand","Azam Rahmani","Zohre Momenimovahed","İbrahim Alkatout"],"tags":["Colposcopy","Medicine","Cervical cancer","MEDLINE","Medical diagnosis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-13","doi":"https://doi.org/10.3390/diagnostics12112771","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3023618360","name":"A review of modern technologies for tackling COVID-19 pandemic","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.dsx.2020.05.008","authors":["Aishwarya Kumar","Puneet Kumar Gupta","Ankita Srivastava"],"tags":["Coronavirus disease 2019 (COVID-19)","Pandemic","2019-20 coronavirus outbreak","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-07","doi":"https://doi.org/10.1016/j.dsx.2020.05.008","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3194522055","name":"Role of artificial intelligence and robotics to foster the touchless travel during a pandemic: a review and research agenda","source":"openalex","abstract":"Purpose The hospitality industry experienced an unanticipated challenge from the COVID-19 pandemic. However, research in this area is scarce. Accordingly, this study aims to unfold a three-angled research agenda to intensify the knowledge advancement in the hospitality sector. It proposes a theoretical framework by extending the protection motivation theory (PMT) to explain the guest’s intent to adopt artificial intelligence (AI) and robotics as a protective measure in reaction to COVID-19. Design/methodology/approach The research is centered on outlining the pertinent literature on hospitality management practices and the guest’s transformed behavior during the current crisis. This study intends to identify a research agenda based on investigating hospitality service trends in today’s changing times. Findings The study sets out a research agenda that includes three dimensions as follows: AI and robotics, cleanliness and sanitation and health care and wellness. This study’s findings suggest that AI and robotics may bring out definite research directions at the connection of health crisis and hospitality management, taking into account the COVID-19 crisis. Practical implications The suggested research areas are anticipated to propel the knowledge base and help the hospitality industry retrieve the COVID-19 crisis through digital transformation. AI and robotics are at the cusp of invaluable advancement that can revive the hotels while re-establish guests’ confidence in safe hotel practices. The proposed research areas are likely to impart pragmatic lessons to the hospitality industry to fight against disruptive situations. Originality/value This study stands out to be pioneer research that incorporated AI and robotics to expand the PMT and highlights how behavioral choices during emergencies can bring technological revolution.","url":"https://doi.org/10.1108/ijchm-11-2020-1246","authors":["Loveleen Gaur","Anam Afaq","Gurmeet Singh","Yogesh K. Dwivedi"],"tags":["Hospitality","Hospitality industry","Artificial intelligence","Robotics","Originality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-24","doi":"https://doi.org/10.1108/ijchm-11-2020-1246","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3013056581","name":"Deep learning in medical image registration: a review","source":"openalex","abstract":"This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.","url":"https://doi.org/10.1088/1361-6560/ab843e","authors":["Yabo Fu","Yang Lei","Tonghe Wang","Walter J. Curran","Tian Liu","Xiaofeng Yang"],"tags":["Popularity","Image registration","Benchmark (surveying)","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-27","doi":"https://doi.org/10.1088/1361-6560/ab843e","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4309651064","name":"Mitigating the impact of biased artificial intelligence in emergency decision-making","source":"openalex","abstract":"BACKGROUND: Prior research has shown that artificial intelligence (AI) systems often encode biases against minority subgroups. However, little work has focused on ways to mitigate the harm discriminatory algorithms can cause in high-stakes settings such as medicine. METHODS: In this study, we experimentally evaluated the impact biased AI recommendations have on emergency decisions, where participants respond to mental health crises by calling for either medical or police assistance. We recruited 438 clinicians and 516 non-experts to participate in our web-based experiment. We evaluated participant decision-making with and without advice from biased and unbiased AI systems. We also varied the style of the AI advice, framing it either as prescriptive recommendations or descriptive flags. RESULTS: Participant decisions are unbiased without AI advice. However, both clinicians and non-experts are influenced by prescriptive recommendations from a biased algorithm, choosing police help more often in emergencies involving African-American or Muslim men. Crucially, using descriptive flags rather than prescriptive recommendations allows respondents to retain their original, unbiased decision-making. CONCLUSIONS: Our work demonstrates the practical danger of using biased models in health contexts, and suggests that appropriately framing decision support can mitigate the effects of AI bias. These findings must be carefully considered in the many real-world clinical scenarios where inaccurate or biased models may be used to inform important decisions.","url":"https://doi.org/10.1038/s43856-022-00214-4","authors":["Hammaad Adam","Aparna Balagopalan","Emily Alsentzer","Fotini Christia","Marzyeh Ghassemi"],"tags":["Framing (construction)","Harm","Framing effect","Psychology","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-21","doi":"https://doi.org/10.1038/s43856-022-00214-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W2980965120","name":"Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems","source":"openalex","abstract":"Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95% CI: 0.93-0.96), qXR (0.94, 95% CI: 0.92-0.97) and CAD4TB (0.92, 95% CI: 0.90-0.95). When matching the sensitivity of the radiologists, the specificities of the DL systems were significantly higher except for one. Using DL systems to read CXRs could reduce the number of Xpert MTB/RIF tests needed by 66% while maintaining sensitivity at 95% or better. Using a universal cutoff score resulted different performance in each site, highlighting the need to select scores based on the population screened. These DL systems should be considered by TB programs where human resources are constrained, and automated technology is available.","url":"https://doi.org/10.1038/s41598-019-51503-3","authors":["Zhi Zhen Qin","Melissa Sander","Bishwa Rai","Collins N. Titahong","Santat Sudrungrot","Sylvain N. Laah","Lal Mani Adhikari","E. Jane Carter","Lekha Puri","Andrew James Codlin","Jacob Creswell"],"tags":["Triage","Medicine","Tuberculosis","Radiography","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-18","doi":"https://doi.org/10.1038/s41598-019-51503-3","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4366757677","name":"Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (Preprint)","source":"openalex","abstract":"BACKGROUND The significant advancements in applying Artificial Intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems, particularly in areas like healthcare, employment, criminal justice, and credit scoring. Such systems can lead to unfair outcomes and perpetuate existing inequalities. This survey paper offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. OBJECTIVE We review sources of bias, such as data, algorithm, and human decision biases, and assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes. We explore various proposed mitigation strategies, discussing the ethical considerations of their implementation and emphasizing the need for interdisciplinary collaboration to ensure effectiveness. METHODS Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, and discuss the negative impacts of AI bias on individuals and society. We also provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. RESULTS Addressing bias in AI requires a holistic approach, involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. CONCLUSIONS This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias.","url":"https://doi.org/10.2196/preprints.48399","authors":["Emilio Ferrara"],"tags":["Preprint","Transparency (behavior)","Accountability","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-21","doi":"https://doi.org/10.2196/preprints.48399","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4309265012","name":"Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study","source":"openalex","abstract":"BACKGROUND: The use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very little room for new topics that were not included before. We present a flipped classroom course designed to give undergraduate medical students an elaborated first impression of AI and to increase their \"AI readiness\". METHODS: The course was tested and evaluated at Bonn Medical School in Germany with medical students in semester three or higher and consisted of a mixture of online self-study units and online classroom lessons. While the online content provided the theoretical underpinnings and demonstrated different perspectives on AI in medical imaging, the classroom sessions offered deeper insight into how \"human\" diagnostic decision-making differs from AI diagnoses. This was achieved through interactive exercises in which students first diagnosed medical image data themselves and then compared their results with the AI diagnoses. We adapted the \"Medical Artificial Intelligence Scale for Medical Students\" to evaluate differences in \"AI readiness\" before and after taking part in the course. These differences were measured by calculating the so called \"comparative self-assessment gain\" (CSA gain) which enables a valid and reliable representation of changes in behaviour, attitudes, or knowledge. RESULTS: We found a statistically significant increase in perceived AI readiness. While values of CSA gain were different across items and factors, the overall CSA gain regarding AI readiness was satisfactory. CONCLUSION: Attending a course developed to increase knowledge about AI in medical imaging can increase self-perceived AI readiness in medical students.","url":"https://doi.org/10.1186/s12909-022-03866-x","authors":["Matthias Carl Laupichler","Dariusch R. Hadizadeh","Maximilian W. M. Wintergerst","Leon von der Emde","Daniel Paech","Elizabeth Dick","Tobias Raupach"],"tags":["Medical education","Curriculum","Test (biology)","Medical diagnosis","Focus group"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-17","doi":"https://doi.org/10.1186/s12909-022-03866-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4400269321","name":"Artificial intelligence in metabolomics: a current review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.trac.2024.117852","authors":["Jinhua Chi","Jingmin Shu","Ming Li","Rekha Mudappathi","Yan Jin","Freeman Lewis","Alexandria Boon","Xiao‐Yan Qin","Li Liu","Haiwei Gu"],"tags":["Metabolomics","Current (fluid)","Computer science","Data science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-03","doi":"https://doi.org/10.1016/j.trac.2024.117852","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4391541001","name":"Artificial intelligence powered Metaverse: analysis, challenges and future perspectives","source":"openalex","abstract":"Abstract The Metaverse, a virtual reality (VR) space where users can interact with each other and digital objects, is rapidly becoming a reality. As this new world evolves, Artificial Intelligence (AI) is playing an increasingly important role in shaping its development. Integrating AI with emerging technologies in the Metaverse creates new possibilities for immersive experiences that were previously impossible. This paper explores how AI is integrated with technologies such as the Internet of Things, blockchain, Natural Language Processing, virtual reality, Augmented Reality, Mixed Reality, and Extended Reality. One potential benefit of using AI in the Metaverse is the ability to create personalized experiences for individual users, based on their behavior and preferences. Another potential benefit of using AI in the Metaverse is the ability to automate repetitive tasks, freeing up time and resources for more complex and creative endeavors. However, there are also challenges associated with using AI in the Metaverse, such as ensuring user privacy and addressing issues of bias and discrimination. By examining the potential benefits and challenges of using AI in the Metaverse, including ethical considerations, we can better prepare for this exciting new era of VR. This paper presents a comprehensive survey of AI and its integration with other emerging technologies in the Metaverse, as the Metaverse continues to evolve and grow, it will be important for developers and researchers to stay up to date with the latest developments in AI and emerging technologies to fully leverage their potential.","url":"https://doi.org/10.1007/s10462-023-10641-x","authors":["Mona Soliman","Eman Ahmed","Ashraf Darwish","Aboul Ella Hassanien","Mona M. Soliman"],"tags":["Metaverse","Computer science","Virtual reality","Augmented reality","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-05","doi":"https://doi.org/10.1007/s10462-023-10641-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"oa:W4309082440","name":"The threat, hype, and promise of artificial intelligence in education","source":"openalex","abstract":"Abstract The idea of building intelligent machines has been around for centuries, with a new wave of promising artificial intelligence (AI) in the twenty-first century. Artificial Intelligence in Education (AIED) is a younger phenomenon that has created hype and promises, but also been seen as a threat by critical voices. There have been rich discussions on over-optimism and hype in contemporary AI research. Less has been written about the hyped expectations on AIED and its potential to transform current education. There is huge potential for efficiency and cost reduction, but there is also aspects of quality education and the teacher role. The aim of the study is to identify potential aspects of threat, hype and promise in artificial intelligence for education. A scoping literature review was conducted to gather relevant state-of-the art research in the field of AIED. Main keywords used in the literature search were: artificial intelligence, artificial intelligence in education, AI, AIED, teacher perspective, education, and teacher. Data were analysed with the SWOT-framework as theoretical lens for a thematic analysis. The study identifies a wide variety of strengths, weaknesses, opportunities, and threats for artificial intelligence in education. Findings suggest that there are several important questions to discuss and address in future research, such as: What should the role of the teacher be in education with AI? How does AI align with pedagogical goals and beliefs? And how to handle the potential leak and misuse of user data when AIED systems are developed by for-profit organisations?","url":"https://doi.org/10.1007/s44163-022-00039-z","authors":["Niklas Humble","Peter Mozelius"],"tags":["SWOT analysis","Optimism","Variety (cybernetics)","Artificial intelligence","Thematic analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-10","doi":"https://doi.org/10.1007/s44163-022-00039-z","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3160458935","name":"Leveraging artificial intelligence in ischemic stroke imaging","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.neurad.2021.05.001","authors":["Omid Shafaat","Joshua D. Bernstock","Amir Shafaat","Vivek Yedavalli","Galal Elsayed","Saksham Gupta","Ehsan Sotoudeh","Haris I. Sair","David M. Yousem","Houman Sotoudeh"],"tags":["Stroke (engine)","Medicine","Ischemic stroke","Artificial intelligence","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-10","doi":"https://doi.org/10.1016/j.neurad.2021.05.001","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4385553785","name":"Identifying fake conclusions of forensic medical examinations using an artificial intelligence technology based on the experience in the Republic of Kazakhstan: a Review","source":"openalex","abstract":"This review discusses the legal aspects on the use of mathematical statistics and machine learning (hereinafter referred to as artificial intelligence) in forensic activities to identify both expert errors and fake expert opinions. An attempt has been made to establish the criteria for evaluating conclusions of forensic examinations by determining their relevance, admissibility, reliability, and objectivity, as well as the objective possibility of distinguishing expert errors from deliberately false and fake expert opinions. A SWOT analysis on the use of artificial intelligence was carried out to solve the issue of its application in the field under consideration, which revealed its advantages, and disadvantages. The use of mathematical statistics and machine learning methods is not a universal method to identify fakes in expert opinions. However, given that this method can give both false-positive and false-negative results, its outcomes should be verified by independent experts. In addition, to effectively prevent the facts of falsification, comprehensive measures should be taken, including not only the detection of manipulations but also the prevention of the possibility of their occurrence, as well as the punishment of the perpetrators. Thus, this review proposed several amendments and additions to the current legislation of the Republic of Kazakhstan.","url":"https://doi.org/10.17816/fm8270","authors":["Denis Voyevodkin","Gauhar Rustembekovna Rustemova","Yernar N. Begaliyev","Kussain A. Igembayev","Zauresh N. Ayupova"],"tags":["Objectivity (philosophy)","Relevance (law)","Artificial intelligence","Computer science","Forensic science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-03","doi":"https://doi.org/10.17816/fm8270","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4413850559","name":"Transforming Medical Microbiology: The Role of Artificial Intelligence","source":"openalex","abstract":"Traditional microbiological techniques, while effective, are often time-consuming and labour-intensive. Machine learning and deep learning, enable rapid and accurate identification of microbial pathogens from complex datasets such as whole-genome sequencing, mass spectrometry, and clinical laboratory reports. Artificial Intelligence (AI) revolutionizes medical microbiology by enhancing pathogen detection, antimicrobial resistance prediction, and clinical decision-making. AI facilitates automated image analysis for culture-based diagnostics, improving the speed and accuracy of colony identification and antimicrobial susceptibility testing. One of the most impactful applications of AI is in antimicrobial resistance (AMR) surveillance. Machine learning models can analyse genetic determinants of resistance and predict antimicrobial susceptibility patterns, allowing for early detection of multidrug-resistant organisms. Moreover, AI-integrated clinical decision support systems (CDSS) enhance antimicrobial stewardship by providing real-time recommendations on appropriate antibiotic use, thereby reducing the spread of resistance. Natural language processing (NLP) further optimizes data extraction from electronic health records, improving diagnostic workflows and patient outcomes. Despite its transformative potential, challenges such as data standardization, model interpretability, and integration into routine laboratory workflows must be addressed. Ethical considerations, including data privacy and algorithmic bias, also warrant careful attention. As AI continues to evolve, its synergy with microbiology will pave the way for precision diagnostics, personalized treatment strategies, and global AMR mitigation. Leveraging AI-driven innovations will be crucial in shaping the future of infectious disease diagnostics and public health microbiology.","url":"https://doi.org/10.22207/jpam.19.3.36","authors":["Suvarna Sande","Manisha Rajguru"],"tags":["Microbiology","Clinical microbiology","Medical microbiology","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-30","doi":"https://doi.org/10.22207/jpam.19.3.36","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4399225897","name":"Artificial intelligence (AI) in medical robotics","source":"openalex","abstract":"","url":"https://doi.org/10.1016/b978-0-443-19073-5.00006-9","authors":["Naman Gupta","Ranjan Jha"],"tags":["Artificial intelligence","Robotics","Computer science","Robot"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1016/b978-0-443-19073-5.00006-9","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W4390571745","name":"Artificial intelligence: revolutionizing cardiology with large language models","source":"openalex","abstract":"Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.","url":"https://doi.org/10.1093/eurheartj/ehad838","authors":["Machteld Boonstra","Davy Weissenbacher","Jason H. Moore","Graciela Gonzalez‐Hernandez","Folkert W. Asselbergs"],"tags":["Medicine","Natural history","Identification (biology)","Medical physics","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-06","doi":"https://doi.org/10.1093/eurheartj/ehad838","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"oa:W3089552093","name":"Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review","source":"openalex","abstract":"Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.","url":"https://doi.org/10.1177/2150132720963634","authors":["Maleeha Naseem","Ramsha Akhund","Hajra Arshad","Muhammad Ibrahim"],"tags":["Medicine","Coronavirus disease 2019 (COVID-19)","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","2019-20 coronavirus outbreak","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1177/2150132720963634","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1080/0142159x.2023.2192859","name":"Medical exams in the era of accessible Artificial Intelligence","source":"crossref","abstract":"Dear EditorChat Generative Pre-trained Transformer (ChatGPT) is a language model that uses training data to calculate the statistical structure of language and predict its output (Ramponi 2022). We...","url":"https://doi.org/10.1080/0142159x.2023.2192859","authors":["Amir H. Sam","George Lam","Anjali Amin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-24T18:16:50Z","doi":"10.1080/0142159x.2023.2192859","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9781032700502-30","name":"Enhancing medical image segmentation using deep learning techniques","source":"crossref","abstract":"This analysis examines the developments and hurdles of applying deep learning methods for medical image segmentation through a comprehensive and systematic literature review. A synthesis of research studies, review articles, and empirical analyses spanning 2018 to 2023 form the basis of this review. In a comprehensive review, we investigate the adoption of leading deep learning models (U-Net, FCNs, and DeepLab) via a rigorous evaluation and thematic analysis for medical image segmentation tasks. The review examines the interplay between class imbalance, overfitting prevention, noise resistance, interpretability, ethical questions, and regulatory adherence related to deep learning-based segmentation. This paper advances our understanding of the central role deep learning assumes in enhancing medical images and guiding clinical choices by thoroughly assessing current methods, challenges, and future possibilities.","url":"https://doi.org/10.1201/9781032700502-30","authors":["Adithya Padthe","Rashmi Ashtagi","Ramya Thatikonda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-27T07:18:12Z","doi":"10.1201/9781032700502-30","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.47391/jpma.22-02","name":"About artificial intelligence...","source":"crossref","abstract":"Early computers were used as automatic calculation tools. Later, as the Industrial Revolution began, manufacturing devices, were developed to automate more complex tasks, such as guiding weaving patterns on looms. It was not until 1950s, that the Computer Science began to be established as a defined academic discipline. Early on, researchers realized that, based on the ability of the computer to process logic, it may be possible to programme computers to mimic mental capabilities generally associated with basic human intelligence and intellect. In 1956 this attribute was termed, Artificial Intelligence (AI) by John McCarthy, an Americancomputer scientist.1 Nothing much happened for several decades. In 1997, computer science once again came in the lime light, when a massive worldwide media coverage was given to a computer, IBM Deep Blue beating the reigning world chess champion, Gary Kasparov. In a 6-game match the computer won 2-1 with three draws.2 A computer wining in a game, requiring intellect and analytic thinking, brought the world's attention to the extraordinary capabilities of computers. Subsequent surge in investment in AI research surged and the convergence of the following three elements brought the AI to its current prominence:","url":"https://doi.org/10.47391/jpma.22-02","authors":["Sarwat Hussain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-25T16:29:35Z","doi":"10.47391/jpma.22-02","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1023/a:1016372016367","name":"Annals of Mathematics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1016372016367","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-29T19:13:07Z","doi":"10.1023/a:1016372016367","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-06170-8_14","name":"Artificial Intelligence and High-Level Cognition","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-06170-8_14","authors":["Marco Ragni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-07T23:05:27Z","doi":"10.1007/978-3-030-06170-8_14","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1023/a:1011049617655","name":"Bilattices and Reasoning in Artificial Intelligence: Concepts and Foundations","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1011049617655","authors":["Kwang Mong Sim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-23T08:38:49Z","doi":"10.1023/a:1011049617655","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-06170-8_12","name":"Robotics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-06170-8_12","authors":["Malik Ghallab","Félix Ingrand"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-07T23:05:27Z","doi":"10.1007/978-3-030-06170-8_12","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21037/jmai.2018.04.02","name":"Building chain of medical AI innovations, Tencent establishes national new-generation medical imaging AI platform","source":"crossref","abstract":"2018 China “Internet Plus” & Digital Economy Summit kicked off in Chongqing from 12–13 April. Tencent was approved to build an innovative, nationwide, new-generation artificial intelligence (AI) platform for medical imaging ( Figure 1 ).","url":"https://doi.org/10.21037/jmai.2018.04.02","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-07-23T08:20:46Z","doi":"10.21037/jmai.2018.04.02","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-032-18894-6_1","name":"Exploring Deep Learning for De Novo Drug Design: A Brief Review of Chemical Property Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_1","authors":["Kaoutar M’Rhar","Mohamed-Amine Chadi","Hajar Mousannif"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:36:48Z","doi":"10.1007/978-3-032-18894-6_1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.5455/rmj.20250802032201","name":"The Arabic Validation of Shinners Artificial Intelligence Perception Tool","source":"crossref","abstract":"Objective: To assess the Arabic validation of shinners artificial intelligence perception tool. Methodology: The translation technique outlined here complied with the WHO guidelines on the development, translation, and validation of questionnaires, which has several benefits compared with the traditional translation techniques. Results: The value of Kaiser-Meyer-Olkin (KMO) was 0.742, and the Bartlett\\'s Test of Sphericity was significant (χ² 45) = 598.50, (p&lt;0.001), indicating the adequacy of the sample for correlation and factor analysis. Three factors were extracted from the factor analysis, with the eigenvalues of &gt;1. The total cumulative explained variation was about 39.4% which is modest but acceptable for a pilot study like this one. Conclusion: The perceptions of Arabic-speaking healthcare professionals regarding AI use are structured across three domains, reflecting their attitudes towards its impact, preparedness, and ethical implications.","url":"https://doi.org/10.5455/rmj.20250802032201","authors":["Areej Meny"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-22T06:01:31Z","doi":"10.5455/rmj.20250802032201","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.70470/edraak/2024/003","name":"Revolutionizing Medical Imaging with Artificial intelligence Real-Time Segmentation for Enhanced Diagnostics","source":"crossref","abstract":"Machine Intelligent or AI has become a proved tool with high accuracy and efficiency in medical imaging diagnoses. Thus, this paper aims at developing and analyzing the feasibility of using AI-based real-time image segmentation based on models such as Vision Transformers (ViT) and Convolutional Neural Networks (CNN). Previous attempts on segmentation problems have focused on CNNs, but the self-attention approach in ViT poses a distinct possibility since this archetypal model captures global contexts in images and may be particularly beneficial when dealing with challenging medical data. To assess the effectiveness of these approaches, publicly available datasets including ISIC for skin lesion segmentation and BraTS for brain tumor analysis are used. These datasets are highly challenging because of their shapes, as well as the different image resolutions of objects and their overlapping areas, so they are perfect for evaluating segmentation models. The presented models are trained with TensorFlow and PyTorch, and the accuracy is evaluated in terms of intersection over union (IoU) and Dice coefficient. However, the time required to process one image to analyze the results is taken with a view of establishing real-time applicability. Experiments show that with ViT, the segmentation accuracy is higher than that of CNN and the Dice Score is higher by 0.15 while the computation time is lower by 30%. Bath and space-party enhancements to TOF and MI allow quicker, more accurate diagnoses to be returned to the radiologist and reduce the likelihood of errors. In addition, the paper demonstrates that ViT-based models are resilient to variability in medical imaging tasks while maintaining high accuracy and effectiveness. This study proves the capabilities of AI in healthcare advancement when ViT becomes a part of clinical practice. We will leave future work for the exploration of the mix of models, such as CNN-ViT in order to fine-tune the results for specific diagnostic .","url":"https://doi.org/10.70470/edraak/2024/003","authors":["Jungpil Shin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-11T12:43:25Z","doi":"10.70470/edraak/2024/003","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.aiemed.2026.100002","name":"Artificial intelligence in emergency medicine: a welcome initiative","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aiemed.2026.100002","authors":["Yulin Li","Yonathan Freund"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-28T00:45:35Z","doi":"10.1016/j.aiemed.2026.100002","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(90)90044-z","name":"Forthcoming papersicial Intelligence 36 (1988) 177–221]","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90044-z","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90044-z","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.71204/sprd8n54","name":"Ethical Risks in Medical Artificial Intelligence and the Normative Function of Medical Humanities: A Study of AI and Emerging Medical Technologies","source":"crossref","abstract":"The rapid expansion of artificial intelligence and emerging digital technologies in medicine has fundamentally reshaped clinical decision-making, healthcare governance, and biomedical knowledge production. While medical artificial intelligence promises enhanced efficiency, diagnostic accuracy, and system optimization, it simultaneously generates complex ethical risks that challenge traditional medical norms and regulatory approaches. Existing discussions of medical AI ethics often prioritize technical safeguards, algorithmic transparency, or regulatory compliance, yet they frequently underestimate the need for deeper normative reflection on responsibility, moral agency, and the meaning of care. This paper argues that medical humanities plays an indispensable normative role in identifying, interpreting, and addressing the ethical risks embedded in medical AI applications. Focusing explicitly on artificial intelligence and frontier medical technologies, the study analyzes the structural sources of ethical risk in algorithm-driven medicine and examines how medical humanities contributes to ethical norm construction beyond procedural governance. By situating medical AI within humanistic concerns such as moral responsibility, interpretive judgment, and human dignity, the paper demonstrates that medical humanities is essential for ethically robust and socially legitimate AI-enabled healthcare.","url":"https://doi.org/10.71204/sprd8n54","authors":["Liwei Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-06T04:21:16Z","doi":"10.71204/sprd8n54","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.31525/ct1-nct04040114","name":"Improving Skin Cancer Management With Artificial Intelligence (04.17 SMARTI)","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04040114","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-14T01:10:11Z","doi":"10.31525/ct1-nct04040114","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.31525/ct1-nct03912961","name":"The Santa Cruz Diabetic Retinopathy Utilizing Artificial Intelligence Study","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct03912961","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-04-12T17:33:49Z","doi":"10.31525/ct1-nct03912961","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.4066/amj.2013.1758","name":"Artificial intelligence in health – the three big challenges","source":"crossref","abstract":"The last twelve months have seen the already constrained Australian health space become an even more complex one.Faced by increasing demand for services, reduced funding and staffing, and pressures imposed by state and federal government health reform agendas, hospital based services are under increasing pressure to become more efficient in how they offer their services.There is a growing need for novel technologies that understand the complexities of hospital operations and offer much needed productivity gains in resource usage and patient service delivery.","url":"https://doi.org/10.4066/amj.2013.1758","authors":["Sankalp Khanna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-06-01T04:00:10Z","doi":"10.4066/amj.2013.1758","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1186/isrctn32473131","name":"Chart review of patients with chronic obstructive pulmonary disease, using medical records and artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1186/isrctn32473131","authors":["Robert Stockley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-24T04:20:06Z","doi":"10.1186/isrctn32473131","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.17816/dd632355-4384178","name":"Fig. 2. Characteristic curves of artificial intelligence-based services for detecting adrenal tumors based on chest and abdominal computed tomography data: a — integrated artificial intelligence-based service-1 (for analyzing chest images); b — artificial intelligence-based monoservice-1 (for analyzing abdominal images); c — artificial intelligence-based monoservice-1 (for analyzing chest images); d — integrated artificial intelligence-based service-2 (for analyzing abdominal images); e — integrated artificial intelligence-based service-2 (for analyzing chest images); f — artificial intelligence-based monoservice-2 (for analyzing abdominal images).","source":"crossref","abstract":"","url":"https://doi.org/10.17816/dd632355-4384178","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-14T11:47:55Z","doi":"10.17816/dd632355-4384178","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-032-18897-7_19","name":"Similarity Redefined: A Robust Metric for Predicting Links in Complex Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_19","authors":["Jibouni Ayoub","Nassiri Naoual","Lotfi Dounia","Saoud Sahar","Samir Bara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:53:37Z","doi":"10.1007/978-3-032-18897-7_19","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-981-97-5345-1_1","name":"Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-5345-1_1","authors":["Amlan Jyoti Kalita","Abhijit Boruah","Tapan Das","Nirmal Mazumder","Shyam K. Jaiswal","Guan-Yu Zhuo","Ankur Gogoi","Nayan M. Kakoty","Fu-Jen Kao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T14:02:55Z","doi":"10.1007/978-981-97-5345-1_1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.65150/ep-gjetr/v2e5/2026-02","name":"Application of Artificial Intelligence in Medical Diagnosis and Patient Monitoring at Gitwe Hospital, Ruhango District, Southern Province","source":"crossref","abstract":"Artificial Intelligence (AI) has become a transformative technology in healthcare worldwide, offering tools to improve medical diagnosis and patient monitoring. In the context of the of Gitwe Hospital , integrating AI can enhance the accuracy of diagnoses, reduce human errors, and provide continuous monitoring of patients, leading to better health outcomes, and efficient hospital activities. In this study the researcher used quantitative and qualitative descriptive research approach. Data were collected from patient records, diagnostic reports, and monitoring logs at Gitwe Hospital. Gitwe Hospital AI tools, including machine learning algorithms and predictive models were applied to analyze patterns in patients’ data. The methodology involved in our research was evaluating AI-assisted diagnostic tools against conventional methods and assessing their impact on treatment, and procedures used in Gitwe Hospital patients’ monitoring efficiency. The implementation of AI in medical diagnosis improved the accuracy and speed of identifying diseases, particularly chronic conditions and early-stage infections. Patients’ monitoring systems using AI enabled real-time tracking of vital signs, alerting medical staff to critical changes promptly. The study showed that AI integration reduced diagnostic errors by approximately 15% and improved patient response time in emergencies by 20%. The application of AI in medical diagnosis and patient monitoring at the Hospital of Gitwe demonstrated significant potential to enhance healthcare delivery. AI improved diagnostic accuracy, enabled proactive patient care, and supports clinical decision-making. Future integration of AI technologies can further optimize hospital management and patient outcomes, making the healthcare systems more responsive and efficient.","url":"https://doi.org/10.65150/ep-gjetr/v2e5/2026-02","authors":["Philippe Hakizimana","Aime Fidele Ndayiragije Mvuyekure","Evariste Rukundo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-12T08:04:10Z","doi":"10.65150/ep-gjetr/v2e5/2026-02","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.jmir.2024.101454","name":"Artificial Intelligence in Medical Imaging: Diagnosis and Beyond","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jmir.2024.101454","authors":["Prof Jing Cai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-18T19:14:51Z","doi":"10.1016/j.jmir.2024.101454","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.31525/ct1-nct04079478","name":"The AID Study: Artificial Intelligence for Colorectal Adenoma Detection","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04079478","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-20T16:01:15Z","doi":"10.31525/ct1-nct04079478","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.55489/ijmr.1303202582","name":"Ethics and Artificial Intelligence","source":"crossref","abstract":"Artificial Intelligence (AI) is revolutionizing industries ranging from healthcare and finance to transportation and warfare. However, its rapid advancement presents profound ethical challenges, including algorithmic bias, privacy concerns, accountability, and workforce disruption. This article explores key dimensions of AI ethics, such as defining ethical boundaries, addressing bias, and navigating privacy in the age of data-driven innovation. It highlights ethical dilemmas in healthcare, workforce implications, and military applications of AI. Furthermore, the article underscores the need for global regulatory frameworks, interdisciplinary collaboration, and stakeholder engagement to ensure responsible AI development. As AI continues to evolve, a balance between innovation and ethical oversight is paramount to aligning technological progress with societal values and human rights. By fostering inclusivity and prioritizing transparency, we can navigate the complexities of AI ethics and harness its transformative potential responsibly.","url":"https://doi.org/10.55489/ijmr.1303202582","authors":["Bhautik Modi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-31T20:06:47Z","doi":"10.55489/ijmr.1303202582","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.51219/urforum.2025.sarah-allabun","name":"AI and Medical Informatics applications in Healthcare Systems","source":"crossref","abstract":"Artificial intelligence (AI) and medical informatics are reshaping contemporary healthcare systems by enhancing diagnostic accuracy, strengthening clinical decision-making, and advancing personalized and data-driven care.As healthcare environments generate massive volumes of complex and heterogeneous data, AI technologies-particularly machine learning and deep learning-play a pivotal role in extracting meaningful insights, identifying clinical patterns, and improving operational efficiency.Medical informatics complements these advancements by structuring, analyzing, and managing health information to support both research and clinical workflows.Together, these interdisciplinary domains have become fundamental drivers of innovation within healthcare delivery.","url":"https://doi.org/10.51219/urforum.2025.sarah-allabun","authors":["Sarah Allabun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T05:54:48Z","doi":"10.51219/urforum.2025.sarah-allabun","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.38020/gbe.7.2.2019.94-96","name":"Rethinking Medical Ethics: Artificial Intelligence and Healthcare – Confronting","source":"crossref","abstract":"The ethical guidelines laid out in the Hippocratic Oath nearly 2,500 years ago are about to collide with 21st century artificial intelligence (AI).AI promises to be a boon to medical practice, improving diagnoses, personalizing treatment, and spotting future public-health threats.By 2024, experts predict that healthcare AI will be a nearly $20 billion market, with tools that transcribe medical records, assist surgery, and investigate insurance claims for fraud.To succeed, though, these systems need access to personal and group health data and use complex algorithms that are difficult, and sometimes impossible, to understand.This creates a potential conflict with current ethical standards for the treatment of patients, which emphasize fairness, consent, and privacy.Even so, the technology raises some knotty ethical questions.What happens when an AI system makes the wrong decision-and who is responsible if it does?How can clinicians verify, or even understand, what comes out of an AI \"black box\"?How do they make sure AI systems avoid bias and protect patient privacy?An artificially intelligent computer program can now diagnose skin cancer more accurately than a board-certified dermatologist [1].Better yet, the program can do it faster and more efficiently, requiring a training data set rather than a decade of expensive and labor-intensive medical education.While it might appear that it is only a matter of time before physicians are rendered obsolete by this type of technology, a closer look at the role this technology can play in the delivery of health care is warranted to appreciate its current strengths, limitations, and ethical complexities.","url":"https://doi.org/10.38020/gbe.7.2.2019.94-96","authors":["Rounak Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-08-18T12:37:04Z","doi":"10.38020/gbe.7.2.2019.94-96","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.4044796","name":"Regulation of Health-Related Artificial Intelligence in Medical Devices The Canadian Story","source":"crossref","abstract":"Artificial Intelligence (AI) may transform Canadian healthcare. The hope is that AI will enable more accurate and efficient care, thereby solving many access, quality, and safety problems. Yet, despite this tantalizing prospect, there are risks of unsafe AI harming patients, algorithmic bias, and threats to privacy. This work begins analysis of whether applicable Canadian laws are up to the task of ensuring Canadians can benefit from effective health-related AI while minimizing AI-related risks. It focuses on Health Canada’s regulation of medical devices, a ‘first line of defence’ that decides which devices are safe, effective, and thus permitted for trade in Canadian markets. After highlighting the regulatory challenge, we provide the first detailed explanation of Canadian medical device regulations and how they apply to AI-enabled devices. We then discuss a still-developing “alternative pathway” for licencing devices with AI and the regulatory gaps left open. We conclude with recommendations that a recent emphasis on post-market surveillance should not be at the expense of robust pre-market review and that safety and efficacy review embrace bias- and privacy-related risks. Further, whilst post-market surveillance holds potential for ensuring the safety of adaptive machine-learning medical devices over time, much will depend on regulatory capacity and competency and investments therein.&lt;br&gt;&lt;br&gt;Note: This is a draft version of a paper accepted for publication in the University of British Columbia Law Review. The final text will include several revisions. That version is forthcoming in Volume 55(3) of the aforementioned U.B.C. journal.","url":"https://doi.org/10.2139/ssrn.4044796","authors":["Michael Da Silva","Colleen M. Flood","Matthew Herder"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-04T22:09:35Z","doi":"10.2139/ssrn.4044796","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.25259/aujmsr_80_2026","name":"Artificial intelligence in ethical medical publishing: Assistance without abdication","source":"crossref","abstract":"Artificial intelligence (AI) has rapidly invaded almost all areas of human existence, including scholarly medical publishing.It is now common to routinely use AI tools for language correction, translation, summarization, reference organization, drafting, and even responses to reviewers, especially by the techno-savvy generation of younger authors.Multiple resources to carry out these tasks are easily available on the internet.Even editors and peer-reviewers may also be tempted to use the same tools for screening manuscripts, checking consistency, or preparing peer-review comments.The issue is therefore no longer \"should AI enter medical publishing?\"It already has.The real and pertinent question is whether journals can regulate their use without compromising authorship, scholastic value, confidentiality, originality, peer review, and public trust.[1][2][3][4][5] The primary ethical concern central to this is that AI can produce the appearance of scholarship without bearing the responsibilities of scholarship, which may be misused or abused by the authors.It can generate fluent text, confident arguments, and plausible references, but it cannot verify data, defend methods, declare conflicts of interest, approve a final manuscript, respond to allegations of misconduct, or accept accountability.This is why AI tools cannot be and must not be accepted as authors.Authorship is not mere word production; it is a consistent thought process and a responsibility.A manuscript may be AI-assisted, but it must remain human-owned, human-verified, and human-defended.[1][2][3] A second concern is transparency.Not all AI material should be ostracized as \"unethical.\" A useful distinction is between assistance and substitution.Use of AI for grammar correction, language polishing, translation support, formatting, or readability improvement may be reasonable, especially for authors writing in a second or non-native language.Such use may even reduce linguistic inequity in academic publishing.However, ethical risk increases when AI is used to generate scientific interpretations, fabricate references, produce unsupported literature syntheses, alter images, manipulate data, draft entire sections without verification, or conceal the real and actual contribution of authors.The distinction is not simply between \"AI used\" and \"AI not used, \" but whether its use was proportionate, traceable, verified, and honestly disclosed.[1,[3][4][5][6][7] Journals should therefore make specific disclosure of AI use as an essential requirement.A vague declaration such as \"AI was used in preparing the manuscript\" is insufficient.Authors should state which tool was used, for what purpose, and which part of the manuscript was affected.For example, using AI for language editing is ethically different from using it to draft the discussion, generate a literature review, analyze data, or create figures.Disclosure should not automatically invite rejection.Instead, it should allow editors and readers to judge whether AI","url":"https://doi.org/10.25259/aujmsr_80_2026","authors":["Mridul Madhav Panditrao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-16T12:23:09Z","doi":"10.25259/aujmsr_80_2026","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.4676406","name":"Investigation on the Abilities of Artificial Intelligence Methods to Accommodate Bias in Medical Diagnostic Data","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4676406","authors":["Faith Jordan Srour","Alaa Balaghi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-08T21:16:16Z","doi":"10.2139/ssrn.4676406","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.26650/b/t3.2024.40.023","name":"Advancement in Artificial Intelligence for Early Detection and Personalized Treatment of Breast Cancer","source":"crossref","abstract":"","url":"https://doi.org/10.26650/b/t3.2024.40.023","authors":["Murat Emeç"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-30T08:44:53Z","doi":"10.26650/b/t3.2024.40.023","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.6974998","name":"Open-World Multimodal Artificial Intelligence for Smart Healthcare: Medical Imaging, Genomics, Trustworthy Evaluation, and Efficient Reasoning","source":"crossref","abstract":"Medical artificial intelligence has made substantial progress in image analysis and clinical decision support, but most existing systems still rely on closed-world assumptions with predefined disease categories and stable data distributions. In real clinical environments, however, rare diseases, unknown subtypes, distribution shifts, and evolving medical knowledge are common. This paper reviews open-world medical AI from medical image category discovery to multimodal trustworthy decision-making. We discuss novel, generalized, and continual category discovery in medical imaging; multimodal integration of images, genomics, text, and medical knowledge; trustworthy evaluation and expert alignment; efficient deployment through pruning and compression; and visual retrieval and spatial intelligence for healthcare applications. We argue that future medical AI should move beyond static classification toward adaptive, multimodal, reliable, and clinically deployable systems.","url":"https://doi.org/10.2139/ssrn.6974998","authors":["Ernie Lin","Leo Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T13:58:52Z","doi":"10.2139/ssrn.6974998","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.9734/bpi/mria/v10/1137","name":"Artificial Intelligence in Oral Medicine: A Review","source":"crossref","abstract":"Artificial Intelligence (AI) is increasingly becoming a transformative force in various fields, including oral medicine. A systematic review of AI applications in oral medicine reveals significant advancements and diverse applications that promise to enhance diagnostic accuracy, treatment planning, and patient management. This review synthesizes current research, highlighting the methodologies, outcomes, and potential future directions of AI in oral medicine.","url":"https://doi.org/10.9734/bpi/mria/v10/1137","authors":["Sameen R. J."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-20T05:11:09Z","doi":"10.9734/bpi/mria/v10/1137","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.35845/kmuj.2026.24271","name":"Artificial intelligence and the future of physicians: replacement or partnership?","source":"crossref","abstract":"As artificial intelligence (AI) tools are becoming more integrated in healthcare, claims that AI will soon displace physicians are also growing. We believe that this assertion is premature and warrants deeper examination. This viewpoint first acknowledges the emerging and promising roles of AI in various specialties such as radiology, pathology, and cardiology, where AI tools and algorithmic models can assist in image analysis, predictive risk modelling, and optimize clinical workflow. It then challenges the narrative of “doctor replacement,” by proposing four arguments: (1) A “physician” encompasses many roles and many cannot be automated; (2) passing standardized exams or diagnostic challenges cannot be equated with true clinical competence and clinical care provided in a variety of setting; (3) medical practice extends beyond accurate diagnosis to include clinical judgment, experience, ethical reasoning, and individualized management strategies; (4) patients seek human connection, empathy, trust, and presence; qualities that AI cannot authentically replicate. We propose a roadmap for physician–AI cooperation, promoting AI literacy for physicians at all levels, hybrid workflows, and improving human clinical skills in the time of rapid technological advancements. We argue that AI should augment, not replace, physicians.","url":"https://doi.org/10.35845/kmuj.2026.24271","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T19:55:58Z","doi":"10.35845/kmuj.2026.24271","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.67735/scms.2026.03.0158","name":"Dehumanization, Justice, and Humanity in the Age of Artificial Intelligence","source":"crossref","abstract":"This essay explores how artificial intelligence (AI) challenges moral conceptions of justice by shifting decisions from contexts of human recognition to computational calculation. Drawing on John Rawls’s account of fairness and contrasting it with Paul Krugman’s political-economic analysis of inequality and institutional design, we show how algorithmic systems can unwittingly advance a form of dehumanization—justice without empathy. We argue for an “algorithmic humanism” that re-centers moral agency, reciprocity, and dignity through transparency and accountability in socio-technical institutions. The paper clarifies the relation between individuality and mutuality in the algorithmic economy and outlines practical normative guardrails to keep justice grounded in human recognition.","url":"https://doi.org/10.67735/scms.2026.03.0158","authors":["Dragutin Novosel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-07T07:17:02Z","doi":"10.67735/scms.2026.03.0158","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.5332131","name":"Verifiable Artificial Intelligence for Medical Decision Making with an Application to Skin Cancer Diagnosis","source":"crossref","abstract":"&lt;div&gt; &lt;div&gt; Artificial Intelligence as a Service is becoming a common deployment model for proprietary AI systems. In these settings, users can observe the returned output but cannot directly verify which model generated it or whether the underlying computation was executed correctly for that specific inference instance, introducing significant operational, security, and compliance risks. We develop a framework for instance-level verification of remote proprietary AI inference using cryptographic commitments, zero-knowledge proofs, recursive proof aggregation, and smart-contract-based verification. The framework enables a model provider to prove that a reported output was generated by the committed model on the submitted input data without revealing proprietary model parameters or private input data. We illustrate the framework through a case study in melanoma diagnosis using dermoscopic images. For this application, we develop CALINet, a high-performing deep learning model that incorporates a novel Hybrid Contextual Attention Module and two lesion-informed augmentation methods. We derive proof-friendly representations for the model computations and show how end-to-end proving can be achieved through partitioning and recursive aggregation. More broadly, the proposed framework is applicable beyond healthcare to any setting in which users rely on outputs generated by remotely hosted proprietary models whose computations are not directly observable. &lt;/div&gt; &lt;/div&gt;","url":"https://doi.org/10.2139/ssrn.5332131","authors":["Pooyan Kazemian","Hank Korth","Manoj Malhotra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-02T13:13:47Z","doi":"10.2139/ssrn.5332131","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-032-18897-7_15","name":"Automated Role Classification in Security Policies: A Comparative Study of NLP Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_15","authors":["Anas Talhaoui","Mohamed Amine Madani","Abdelmounaim Kerkri","Asmae Benouda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:53:13Z","doi":"10.1007/978-3-032-18897-7_15","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.61705/yn6y2c83","name":"Artificial Intelligence in Predictive Toxicology: Identifying influential topics based on science mapping","source":"crossref","abstract":"This research paper delves into the transformative landscape of Predictive Toxicology, marked by the integration of Artificial Intelligence (AI) and cutting-edge technologies. The emergence of Predictive Toxicology, driven by computational models leveraging machine learning, signifies a departure from traditional methods, promising accelerated risk assessment and enhanced comprehension of chemical-biological interactions. Through science mapping, this study explores the interconnected web of literature in this dynamic field, aiming to trace its historical evolution, identify influential research hubs, and discern collaborative networks. The dataset analysis spanning 1993 to 2023 unveils trends, emphasizing the surge in AI's prominence and the sustained relevance of foundational topics. This study not only offers a comprehensive overview but also provides a roadmap for future research in the intersection of AI and Predictive Toxicology.","url":"https://doi.org/10.61705/yn6y2c83","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-10T05:07:48Z","doi":"10.61705/yn6y2c83","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-12-824521-7.00002-8","name":"Search and prevention of errors in medical databases","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-824521-7.00002-8","authors":["Saveli Goldberg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-21T06:00:49Z","doi":"10.1016/b978-0-12-824521-7.00002-8","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-031-64049-0_4","name":"Principles of Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64049-0_4","authors":["Euclid Seeram","Vijay Kanade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T10:02:01Z","doi":"10.1007/978-3-031-64049-0_4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.53469/jssh.2024.6(05).40","name":"Research on the Integration of Medical Device Safety and Artificial Intelligence","source":"crossref","abstract":"This paper explores the integration of artificial intelligence (AI) technology in the field of medical devices and its impact on safety. It analyzes current challenges, successful cases, lessons learned from failures, and future development directions. AI technology significantly enhances the safety and efficiency of medical devices through predictive maintenance, risk assessment, and performance optimization.However, issues such as data privacy, liability, and algorithmic bias have also emerged, requiring strategies like regulatory improvements, cross-disciplinary collaboration, and innovation incentives to address them.The paper emphasizes that establishing safety assessment frameworks, promoting ethical reviews, strengthening public education, and enhancing international cooperation are crucial for ensuring the safe application of AI in medical devices.By implementing comprehensive strategies, technological advancements can be promoted while maintaining ethical standards, thereby driving the intelligent transformation of medical devices and achieving safer, more efficient, and trustworthy medical services.","url":"https://doi.org/10.53469/jssh.2024.6(05).40","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-03T11:59:07Z","doi":"10.53469/jssh.2024.6(05).40","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-58080-3_160-1","name":"AIM in Medical Disorders in Pregnancy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-58080-3_160-1","authors":["Charles L. Bormann","Carol Lynn Curchoe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-01T12:04:23Z","doi":"10.1007/978-3-030-58080-3_160-1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-443-13244-5.00030-4","name":"Using Digital Health Tools in Medical Practice","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13244-5.00030-4","authors":["Elizabeth M. Bauer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-28T09:56:46Z","doi":"10.1016/b978-0-443-13244-5.00030-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9780429354526","name":"Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9780429354526","authors":["Sandeep Kumar","Shilpa Rani","K. Ramya Laxmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-20T03:20:54Z","doi":"10.1201/9780429354526","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/s40670-023-01942-5","name":"Artificial Intelligence-Generated Facial Images for Medical Education","source":"crossref","abstract":"We evaluated the use of text-to-image models (Microsoft's Bing Image creator (powered by DALL·E) and Shutterstock's AI image generator) to generate realistic images of human faces and their associated pathology, which may be useful for medical education, given they may overcome issues of patient privacy and requirement for consent. These models have potential to augment rare medical image datasets for medical education, as well as provide greater inclusivity and representation of diverse populations.","url":"https://doi.org/10.1007/s40670-023-01942-5","authors":["Bingwen Eugene Fan","Minyang Chow","Stefan Winkler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-14T13:02:49Z","doi":"10.1007/s40670-023-01942-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1023/a:1021236019898","name":"Annals of Mathematics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1021236019898","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-21T00:56:49Z","doi":"10.1023/a:1021236019898","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artmed.2024.102934","name":"Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis","source":"crossref","abstract":"Background Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. Objectives To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. Methods Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. Results 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists. Conclusions Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.","url":"https://doi.org/10.1016/j.artmed.2024.102934","authors":["Zichen Ye","Daqian Zhang","Yuankai Zhao","Mingyang Chen","Huike Wang","Samuel Seery","Yimin Qu","Peng Xue","Yu Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T13:41:31Z","doi":"10.1016/j.artmed.2024.102934","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.32471/umj.1680-3051.164.258743","name":"Artificial Intelligence-Driven Clinical Trials: Transforming Clinical Research Associate Methodology","source":"crossref","abstract":"","url":"https://doi.org/10.32471/umj.1680-3051.164.258743","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-31T10:36:32Z","doi":"10.32471/umj.1680-3051.164.258743","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-58080-3_293-1","name":"AIM and Explainable Methods in Medical Imaging and Diagnostics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-58080-3_293-1","authors":["Syed Muhammad Anwar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-27T16:07:20Z","doi":"10.1007/978-3-030-58080-3_293-1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1049/ic:20050340","name":"Spike source identification using artificial intelligence techniques","source":"crossref","abstract":"We present a methodology for the automatic detection of target regions in the brain for ablation, stimulation and restorative surgery for Parkinson's disease and other neurological disorders. The methodology includes wavelets for the correct characterization of the non-stationarity of the spike train and hidden Markov models as a suitable tool for describing dynamic behavior of the signal across time. Similarity measure and Kullback-Leibler distance were used for discriminant evaluation of HMM. We also compare HMM with other artificial intelligence techniques for the classification task. Results show classification performance up to 97% with the proposed methodology.","url":"https://doi.org/10.1049/ic:20050340","authors":["A.A. Orozco"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-07-07T14:47:27Z","doi":"10.1049/ic:20050340","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/b15618-11","name":"An Investigation on Support Vector Machines and Wavelet Transform in Electroencephalogram Signal Classification","source":"crossref","abstract":"Time series have been intensively studied in recent years in different areas, such as medicine, the financial market, and climatology. Many techniques have been used to extract the information encoded in time series, aiming to make some estimation (prediction) or to classify a current situation, comparing it with past situations.","url":"https://doi.org/10.1201/b15618-11","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-11","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.3390/diagnostics13172760","name":"Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging","source":"crossref","abstract":"This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology—data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.","url":"https://doi.org/10.3390/diagnostics13172760","authors":["Reabal Najjar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-28T03:45:42Z","doi":"10.3390/diagnostics13172760","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.17496/kmer.2016.18.2.47","name":"Artificial Intelligence: Will It Replace Human Medical Doctors?","source":"crossref","abstract":"Development of artificial intelligence is expected to revolutionize today’s medicine. In fact, medicine was one of the areas to which advances in artificial intelligence technology were first applied. Recently, state-of-the-art artificial intelligence, especially deep learning technology, has been actively utilized to treat cancer patients and analyze medical image data. Application of artificial intelligence has the potential to fundamentally change various aspects of medicine, including the role of human doctors, the clinical decision-making process, and even overall healthcare systems. Facing such fundamental changes is unavoidable, and we need to prepare to effectively integrate artificial intelligence into our medical system. We should re-define the role of human doctors, and accordingly, medical education should also be altered. In this article, we will discuss the current status of artificial intelligence in medicine and how we can prepare for such changes.","url":"https://doi.org/10.17496/kmer.2016.18.2.47","authors":["Yoon Sup Choi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-07-27T00:52:24Z","doi":"10.17496/kmer.2016.18.2.47","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.3946/kjme.2022.243","name":"Medical student’s artificial intelligence education and research experiences","source":"crossref","abstract":"","url":"https://doi.org/10.3946/kjme.2022.243","authors":["Chaeyeong Im"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-29T23:51:02Z","doi":"10.3946/kjme.2022.243","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-64573-1_160","name":"AIM in Medical Disorders in Pregnancy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64573-1_160","authors":["Charles L. Bormann","Carol Lynn Curchoe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-18T14:08:09Z","doi":"10.1007/978-3-030-64573-1_160","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-58080-3_329-1","name":"Machine Learning and Electronic Noses for Medical Diagnostics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-58080-3_329-1","authors":["Wojciech Wojnowski","Kaja Kalinowska"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-05T16:04:29Z","doi":"10.1007/978-3-030-58080-3_329-1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2196/preprints.47260","name":"Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review (Preprint)","source":"crossref","abstract":"BACKGROUND There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health. OBJECTIVE This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care. METHODS We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review. RESULTS We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients. CONCLUSIONS This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients’ perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.","url":"https://doi.org/10.2196/preprints.47260","authors":["Onur Asan","Euiji Choi","Xiaomei Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-30T14:47:22Z","doi":"10.2196/preprints.47260","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-031-64049-0_10","name":"Future Trends and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64049-0_10","authors":["Euclid Seeram","Vijay Kanade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T10:02:01Z","doi":"10.1007/978-3-031-64049-0_10","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.14738/jbemi.61.6161","name":"Images in Logical-and-Linguistic Artificial Intelligence Systems","source":"crossref","abstract":"Visual images are holistic, and when verbalized there is a partial loss of semantic content. However, it should be noted the lack of effectiveness of decision support systems only when using images without an effective context, and systems that do not include holistic images. Inclusion of images in the knowledge base of intelligent systems can significantly improve their effectiveness. At the stage of formation of intermediate diagnostic hypotheses, the system will present to the user (physician) a hypothesis specific to the verbal and visual characteristics. At the same time, it is necessary to take into account the need to use fuzzy logic at the stages of the derivation of solutions. The subsequent process will depend on the physician’s confidence in the coincidence of the image of the diagnosed patient with the image(s) in the knowledge base of the intellectual system.","url":"https://doi.org/10.14738/jbemi.61.6161","authors":["Boris A. Kobrinskii ."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-03-10T01:30:18Z","doi":"10.14738/jbemi.61.6161","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.mri.2019.12.006","name":"Artificial intelligence in medical imaging","source":"crossref","abstract":"The medical specialty radiology has experienced a number of extremely important and influential technical developments in the past that have affected how medical imaging is deployed. Artificial intelligence (AI) is potentially another such development that will introduce fundamental changes into the practice of radiology. In this commentary the historical evolution of some major changes in radiology are traced as background to how AI may also be embraced into practice. Potential new capabilities provided by AI offer exciting prospects for more efficient and effective use of medical images.","url":"https://doi.org/10.1016/j.mri.2019.12.006","authors":["John C. Gore"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-16T12:18:59Z","doi":"10.1016/j.mri.2019.12.006","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.ejrai.2026.100092","name":"The trust gap in generative medical imaging: Evidence, risks, and a roadmap toward responsible adoption","source":"crossref","abstract":"Generative artificial intelligence produces radiological images and clinical text with a level of realism that suggests broad utility in medical imaging. Early studies show promise in targeted data augmentation, assistive image enhancement and reconstruction, cautious cross-modality synthesis, and report drafting. Yet realism is not reliability. Generative systems may introduce or omit findings, breach basic physics or anatomy, fail under data distribution shifts, leak private information, or degrade when synthetic data re-enters training pipelines. Risks from human factors such as automation bias, provenance blindness, and susceptibility to manipulation further widen the gap between plausible output and clinical truth. In this narrative review, we focus on narrowing that gap. To support safe translation, we extend prior evaluation schemes into a three-tier evaluation framework, progressing from pixel- and physics-level checks to anatomy-level realism and task-level clinical utility. Building on this, we outline a five-layer \"trustworthiness stack\" spanning data governance, physics-informed model design, multi-tier evaluation with continuous monitoring, human-centered interfaces, and institutional oversight. Finally, we provide stakeholder-specific recommendations for implementing generative imaging systems as constrained, auditable, continuously monitored components of clinical practice that augment—but do not replace—expert judgment.","url":"https://doi.org/10.1016/j.ejrai.2026.100092","authors":["Somayeh Farahani","Antonio Di Ieva","Enrico Coiera","Sidong Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-03T16:01:06Z","doi":"10.1016/j.ejrai.2026.100092","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.15407/jai2026.01.117","name":"Technical Approach to Converting Medical Information System (MIS) \"ESCULAP\" Archives for Artificial Intelligence Tasks: Experience of State Institution of Science “Center of Innovative Healthcare Technologies” State Administrative Department","source":"crossref","abstract":"The digital transformation of healthcare requires converting accumulated medical data into formats suitable for analysis. A significant portion of medical information in Ukraine is stored in archives of legacy systems, including the medical information system (MIS) “Esculap”. These data rely on the dBase structure (.DBF and .FPT files), which prevents their direct use in modern artificial intelligence and machine learning applications. This paper describes a technical approach to converting such archives. An algorithm was developed in Python using the libraries dbfread, pandas, and numpy. The proposed method enables extraction and systematization of depersonalized patient data, diagnoses, and treatment histories. Particular attention is given to resolving text encoding issues and processing MEMO fields stored in .FPT files. The result of the study is the transformation of relational tables in legacy formats into CSV files compatible with contemporary analytical tools. The resulting datasets can be used for machine learning tasks, neural network development, and statistical research in public health. The proposed approach was tested on archival data of the State Institution of Science “Center of Innovative Healthcare Technologies” of the State Administrative Department. The developed algorithm ensures accurate preparation of longitudinal medical data for further morbidity analysis. The software tool enables annual updates of research databases by converting newly generated archival records into a structured and analysis-ready format. The practical implementation of the algorithm included the inventory of 195 archival files, identification of complete .DBF/.FPT pairs, and validation of MEMO field integrity. The conversion process accounted for Windows-1251 encoding specifics, the presence of corrupted or incomplete tables, and the risk of automatic data type alteration when opening CSV files in spreadsheet editors. This approach minimized information loss and preserved logical links between patients, clinical episodes, and textual medical records","url":"https://doi.org/10.15407/jai2026.01.117","authors":["Horachuk A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T09:13:38Z","doi":"10.15407/jai2026.01.117","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21037/jmai-2026-1-0015","name":"Research trends and hotspots in the applications of artificial intelligence in CT based on Web of Science—bibliometric research (2015–2024)","source":"crossref","abstract":"Background: Bibliometrics is a quantitative assessment that uses mathematical and statistical methods to evaluate the contribution of scientific literature. In recent years, the application of artificial intelligence (AI) in medical imaging has received extensive attention, while the research hotspots, patterns and trends have not yet been clarified. Multiple notable knowledge gaps persist within current domain research and existing bibliometric analyses of this field. Most prior bibliometric studies merely outline basic publication trends, national distributions and keyword clusters, without quantitatively dissecting the striking mismatch between massive algorithm outputs and low clinical translation efficiency or thoroughly unpacking its underlying multi-layered drivers. Therefore, we conducted a bibliometric analysis of the literature on the application of AI in computed tomography (CT) based on the Web of Science Core Collection (WoSCC) database over the past 10 years. The results systematically synthesize the comprehensive research landscape of AI-CT, providing clinicians with data-driven insights to prioritize technology adoption and may provide references for further in-depth studies in this field in the future.Methods: In this study, we searched papers related to AI and CT in the Web of Science database by constructing a professional search engine and screened the literature according to the inclusion and exclusion criteria. Then, we conducted bibliometric research and visual analysis from aspects such as the annual publication trend, author collaboration trend, country distribution, institutional collaboration trend, journal distribution, high-frequency keywords, and co-cited literature.Results: We retrieved a total of 6,097 publications from the Web of Science, but only 4,384 were included after screening. Among these 4,384 publications,the number of published papers has been increasing annually. At present, the top five countries by the number of articles published in this field are China, the United States, Italy, South Korea, and India. The institution with the highest number of published papers, total citation frequency, and average citation frequency was Harvard Medical School. The literature cooperation rate has remained above 90% since 2015. It reached as high as 100% in 2016 and 2017. The research involved a total of 1,128 journals. The research hotspots mainly focus on aspects such as “deep learning”, “machine learning”, “classification”, “COVID-19”, and “diagnosis”. The article with the highest citation rate was published by van Griethuysen’s team in Cancer Research (2017).Conclusions: Through bibliometric methods, we reviewed the research on AI in the field of CT over the past decade. The results revealed current hotspots and cutting-edge trends, and provided references for subsequent related research. The study identified gaps in several areas that have not been sufficiently investigated, including algorithm design, clinical translation, data infrastructure, and ethical research. Future research efforts should focus primarily on developing advanced intelligent algorithms, conducting multicenter clinical validation, and improving clinical translation rates.","url":"https://doi.org/10.21037/jmai-2026-1-0015","authors":["Lian Peng","Xiangkai Zhong","Hui Zeng","Ruxian Zuo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T06:12:24Z","doi":"10.21037/jmai-2026-1-0015","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artmed.2023.102698","name":"Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review","source":"crossref","abstract":"Background Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. Methods A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. Results The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. Conclusion This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.","url":"https://doi.org/10.1016/j.artmed.2023.102698","authors":["David Hua","Neysa Petrina","Noel Young","Jin-Gun Cho","Simon K. Poon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-09T03:39:35Z","doi":"10.1016/j.artmed.2023.102698","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-032-18894-6_20","name":"An Experimental Evaluation of GRU-Based Architecture for ICD-9 Code Assignment Using MIMIC-III Clinical Notes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_20","authors":["Manale Chakir","Abdelwahab Naji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:37:40Z","doi":"10.1007/978-3-032-18894-6_20","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-444-70058-2.50014-0","name":"Probability Judgment in Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-444-70058-2.50014-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-06-18T20:45:51Z","doi":"10.1016/b978-0-444-70058-2.50014-0","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artmed.2006.09.002","name":"Artificial Intelligence in Medicine AIME ’05","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artmed.2006.09.002","authors":["Silvia Miksch","Jim Hunter","Elpida Keravnou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-11-01T01:31:26Z","doi":"10.1016/j.artmed.2006.09.002","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(74)90002-2","name":"Decision theory and artificial intelligence: I. A semantics-based region analyzer","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(74)90002-2","authors":["Jerome A. Feldman","Yoram Yakimovsky"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(74)90002-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.62486/aid2025100","name":"Artificial Intelligence in Dentistry: Toward a New Architecture of Clinical Knowledge","source":"crossref","abstract":"Dentistry is undergoing a digital transformation marked by the integration of artificial intelligence. Since the foundational work in 1986, the growth of the field has been limited compared with that of other sectors. This paper presents SAP Artificial Intelligence in Dentistry, a publication aimed at systematizing knowledge, with a particular emphasis on the needs and realities of the Global South. A critical perspective regarding technological sovereignty, population diversity in data, and equity in health access is proposed. Similarly, the open access model and peer review are defined as pillars for responsible and grounded clinical practice.","url":"https://doi.org/10.62486/aid2025100","authors":["Thalia Garcia Contino"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T15:09:09Z","doi":"10.62486/aid2025100","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.13052/rp-9788743800965","name":"Artificial Intelligence: Achievements and Recent Developments","source":"crossref","abstract":"","url":"https://doi.org/10.13052/rp-9788743800965","authors":["Anatolii I. Shevchenko","Yuriy P. Kondratenko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-30T19:23:09Z","doi":"10.13052/rp-9788743800965","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artint.2008.12.001","name":"Enactive artificial intelligence: Investigating the systemic organization of life and mind","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2008.12.001","authors":["Tom Froese","Tom Ziemke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-12-26T09:17:26Z","doi":"10.1016/j.artint.2008.12.001","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.ijmedinf.2023.105227","name":"Artificial intelligence (AI) in the medical consultation: Friend or foe?","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ijmedinf.2023.105227","authors":["Salvatore Chirumbolo","Marianno Franzini","Umberto Tirelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-19T13:17:51Z","doi":"10.1016/j.ijmedinf.2023.105227","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1097/mrm.0000000000000451","name":"Artificial intelligence-driven advances in medical microbiology: a review on next-generation diagnosis, surveillance, and therapeutics","source":"crossref","abstract":"The field of health science, medical microbiology is key aspect for diagnosis, pathogen surveillance, and therapeutic interventions. Integration of artificial intelligence undergoing a profound transformation in the advances of medical microbiology. Artificial intelligence has emerged as a transformative force across biomedical sciences. In perspectives of medical microbiology, artificial intelligence algorithms are being rapidly adopted to address critical challenges, from pathogen identification to outbreak prediction and antimicrobial resistance (AMR) management. The present study insights latest landscape of artificial intelligence-driven approaches in revealing new breakthroughs, advancement in technological, improved clinical applications, and future directions. This review focusses on next generation approaches such as convolutional neural networks (CNNs) that have enabled rapid pathogen identification through Gram stain and MALDI-TOF-MS analysis. Random Forests and Support Vector Machines (SVMs) predict AMR, supporting antibiotic susceptibility testing and Recurrent Neural Networks (RNNs), facilitate early outbreak prediction using epidemiological data. The environmental surveillance is enhanced by artificial intelligence-driven analysis of biosensor data, and lab automation benefits from expert systems and reinforcement learning, improving efficiency and reducing error. The present advance study will be helpful to set more transformative role of artificial intelligence across the microbiology diagnostic spectrum.","url":"https://doi.org/10.1097/mrm.0000000000000451","authors":["Sandeep Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-20T15:00:14Z","doi":"10.1097/mrm.0000000000000451","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1684/mtp.2024.0810","name":"Medical liability, digital technology and artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1684/mtp.2024.0810","authors":["Chrystelle Boileau"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-24T06:40:58Z","doi":"10.1684/mtp.2024.0810","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1101/2025.06.09.25329272","name":"Evaluating Artificial Intelligence Assisted Nursing Education: Student Perceptions, Ethical Concerns, and Pedagogical Implications","source":"crossref","abstract":"Abstract Background Artificial intelligence (AI) tools are increasingly being integrated into nursing education to enhance learning and provide flexible academic assistance. However, little is known about how undergraduate nursing students perceive these tools or how they affect learning experiences. Aim To evaluate nursing students’ perceptions of an AI-powered academic assistant and assess its perceived usefulness, trustworthiness, and ethical implications within a real course setting. Methods This quantitative study was conducted in a junior-level undergraduate nursing course at a large public university. Students (N = 38) completed pre- and post-surveys measuring their attitudes, confidence, ethical concerns, and engagement with the Educational AI Hub. System usage data were also analyzed to assess tool interaction. Results Students reported high levels of convenience and comfort using the AI tool, particularly for studying and concept review. However, concerns emerged around academic integrity and uncertainty about appropriate use. Most students supported moderate restrictions and expressed strong interest in future AI integration. Conclusions AI tools can support associate degree nursing students by enhancing independent study and access to learning support. Clear guidelines and ethical frameworks are essential for responsible implementation.","url":"https://doi.org/10.1101/2025.06.09.25329272","authors":["Ramteja Sajja","Yusuf Sermet","Ibrahim Demir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-10T13:45:22Z","doi":"10.1101/2025.06.09.25329272","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.18502/ssu.v33i4.19014","name":"Advantages of Artificial Intelligence in Dentistry","source":"crossref","abstract":"Introduction: The use of subsidies and access to huge amounts of data around the world in the last decade has led to the advancement of artificial intelligence (AI) applications in the health and medical sciences sector. Although there is a need for detailed analysis of incoming data to increase the accuracy in generating datasets from AI, soon, AI will play an important role in the field of dentistry in diagnosing and creating predictive models in a variety of specialized medical disciplines. Advances in artificial intelligence technology have revolutionized the field of dentistry. Conclusion: By using AI as an assistive tool, dental professionals can reduce their workload and improve the accuracy of diagnosis, decision-making, treatment planning, disease prognosis, and treatment outcome prediction.","url":"https://doi.org/10.18502/ssu.v33i4.19014","authors":["Behnaz Behniafar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-30T03:01:01Z","doi":"10.18502/ssu.v33i4.19014","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.7759/cureus.79878","name":"Applications of Artificial Intelligence in Medical Education: A Systematic Review","source":"crossref","abstract":"Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.","url":"https://doi.org/10.7759/cureus.79878","authors":["Eric Hallquist","Ishank Gupta","Michael Montalbano","Marios Loukas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-01T15:03:41Z","doi":"10.7759/cureus.79878","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0952-1976(88)90039-5","name":"Distributed artificial intelligence series: Research notes in AI","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0952-1976(88)90039-5","authors":["L. Motus"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T17:42:18Z","doi":"10.1016/0952-1976(88)90039-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.31031/cojra.2024.04.000580","name":"Large Language Models and Medical Imaging: A Powerful Synergy","source":"crossref","abstract":"Crimson Publishers is an Open-access academic publisher has a vision to establish Open Science platform that seeks to provide equal opportunity for all, share and create knowledge, and enables the scholarly world to engage in a dialogue with the science in a more effective manner. Our efficient and transparent ways of peer-review","url":"https://doi.org/10.31031/cojra.2024.04.000580","authors":["Saqib Qamar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-24T09:55:32Z","doi":"10.31031/cojra.2024.04.000580","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(95)90013-6","name":"Music, mind and machine: Studies in computer music, music cognition and artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90013-6","authors":["Stephen W. Smoliar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/0004-3702(95)90013-6","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.22381/ajmr6220195","name":"Artificial Intelligence-driven Smart Healthcare Services, Wearable Medical Devices, and Body Sensor Networks","source":"crossref","abstract":"","url":"https://doi.org/10.22381/ajmr6220195","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-31T20:58:17Z","doi":"10.22381/ajmr6220195","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2147/amep.s287926","name":"Artificial Intelligence in Medical OSCEs: Reflections and Future Developments","source":"crossref","abstract":"With the advent of the age of Artificial Intelligence (AI), we seek to consider how AI could shape clinical examinations, specifically Objective Structured Clinical Examinations (OSCEs). OSCEs, whilst having its own limitations, could be further enhanced with new technologies like AI to help better assess and prepare our future clinicians. With the everchanging requirements on the modern clinician, we deliberate the strengths and weaknesses of AI, and the need for emphasis on different skills to complement rather than resist the tides of change. In conclusion, we feel that AI has the potential to be a strong driving force in remodelling OSCEs to support future doctors and could serve as a new frontier in medical education and beyond. That being said, we recognize the technology and its applications are still in its infancy, and further study will be needed to eluciate the role of AI in medical education and in the greater landscape of medical practice.","url":"https://doi.org/10.2147/amep.s287926","authors":["Tse Kiat Soong","Cheng-Maw Ho"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-02-17T20:46:53Z","doi":"10.2147/amep.s287926","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21608/smj.2023.185746.1363","name":"Hearing aid with artificial intelligence","source":"crossref","abstract":"Artificial intelligence (AI) has recently increased in its use.2017 represents the start of this new era for the hearing aid industry, artificial intelligence H.A. and it offers great promise in overcoming hearing challenges. In our opinion, artificial intelligence is the next revolution in hearing aids after the application of digital signal processing and wireless technology. With the use of artificial intelligence (AI), it will be easier for hearing-impaired persons to understand speech more clearly, especially in different environmental situations.Artificial intelligence (AI), usually synonymous with machine learning, is the ability of computers to simulate human intelligence in problem-solving, logical reasoning, and managing complicated problems. Without being to be programmed, artificial intelligence has the capability to automatically learn from experience. So that it can recognize the wearer's listening environment and then adjust according to the acoustics of each environment .It offers an average 50% reduction in noisy environments, greatly decreasing listening effort, and improving speech clarity. So, it provides significant improvement in speech intelligibility in noisy environments.","url":"https://doi.org/10.21608/smj.2023.185746.1363","authors":["Gehad Fawzy","mohamed Abd Al Rahman","mostafa Youssif","Maha Ahmed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-27T07:10:57Z","doi":"10.21608/smj.2023.185746.1363","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00035-5","name":"Medical laboratory monitoring and total quality management for a smart medical laboratory","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00035-5","authors":["Uchejeso Obeta","Alex Khang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00035-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21767/amj.2012.1352","name":"ADVANCES IN ARTIFICIAL INTELLIGENCE RESEARCH IN HEALTH","source":"crossref","abstract":"Griffith Sciences, School of Information and Communication Technology","url":"https://doi.org/10.21767/amj.2012.1352","authors":["Sankalp Khanna","Abdul Sattar","David Hansen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-11-15T07:53:30Z","doi":"10.21767/amj.2012.1352","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9781003394068","name":"Artificial Intelligence and Data Analytics in Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003394068","authors":["Christopher Hayre","Rob Davidson","Shayne Chau","Xiaoming Zheng","Abel Zhou","Nigel Frame"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T09:15:48Z","doi":"10.1201/9781003394068","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/b15618-18","name":"A Penalized Fuzzy Clustering Algorithm with its Application in Magnetic Resonance Image Segmentation","source":"crossref","abstract":"Magnetic resonance imaging (MRI) segmentation provides important information for detecting a variety of tumors, lesions, and abnormalities in clinical diagnosis. The segmentation can be described as the definition of clusters whose points are associated to similar sets of intensity values in the different images. An efficient analysis of dual-echo medical imaging volumes can be derived from a set of different diagnostic volumes carrying complementary information provided by medical imaging technology. The extraction of such volumes from imaging data is said to be segmentation, and it is usually performed, in the image space, defining sets of vowels with similar features within a whole dual-echo volume.","url":"https://doi.org/10.1201/b15618-18","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T13:35:58Z","doi":"10.1201/b15618-18","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1515/9783110717853-008","name":"Coffee break: information diabetes","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110717853-008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-14T03:38:18Z","doi":"10.1515/9783110717853-008","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1109/tiptekno.2019.8895087","name":"Classification of Emotion from Physiological Signals via Artificial Intelligence Techniques","source":"crossref","abstract":"Recently, there are various studies in the literature for emotion analysis by using physiological signals. In this study, classifying emotion by using various signal processing methods, feature extraction and various artificial intelligence methods is objected. DEAP dataset is used in the study. Calculations of Wavelet transform and some statistical properties (mean, variance, standard deviation and entropy) of EOG, EMG, GSR, Respiratory belt, Plethysmography, and Temperature physiological signals. Obtained data were done for the emotion classification.","url":"https://doi.org/10.1109/tiptekno.2019.8895087","authors":["Umran Isik","Aysegul Guven"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-13T04:53:58Z","doi":"10.1109/tiptekno.2019.8895087","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1111/medu.14131","name":"Where medical education meets artificial intelligence: ‘Does technology care?’","source":"crossref","abstract":"Abstract ‘Cold’ technologies and ‘warm’ hands‐on medicine need to walk hand‐in‐hand Technologies, such as deep learning artificial intelligence (AI), promise benign solutions to thorny, complex problems; but this view is misguided. Though AI has revolutionised aspects of technical medicine, it has brought in its wake practical, conceptual, pedagogical and ethical conundrums. For example, widespread adoption of technologies threatens to shift emphasis from ‘hands‐on’ embodied clinical work to disembodied ‘technology enhanced’ fuzzy scenarios muddying ethical responsibilities. Where AI can offer a powerful sharpening of diagnostic accuracy and treatment options, ‘cold’ technologies and ‘warm’ hands‐on medicine need to walk hand‐in‐hand. This presents a pedagogical challenge grounded in historical precedent: in the wake of Vesalian anatomy introducing the dominant metaphor of ‘body as machine,’ a medicine of qualities was devalued through the rise of instrumental scientific medicine. The AI age in medicine promises to redouble the machine metaphor, reducing complex patient experiences to linear problem‐solving interventions promising ‘solutionism.’ As an instrumental intervention, AI can objectify patients, frustrating the benefits of dialogue, as patients’ complex and often unpredictable fleshly experiences of illness are recalculated in solution‐focused computational terms. Suspicions about solutions The rate of change in numbers and sophistication of new technologies is daunting; they include surgical robotics, implants, computer programming and genetic interventions such as clustered regularly interspaced short palindromic repeats (CRISPR). Contributing to the focus of this issue on ‘solutionism,’ we explore how AI is often promoted as an all‐encompassing answer to complex problems, including the pedagogical, where learning ‘hands‐on’ bedside medicine has proven benefits beyond the technical. Where AI and embodied medicine have differing epistemological, ontological and axiological roots, we must not imagine that they will readily walk hand‐in‐hand down the aisle towards a happy marriage. Their union will be fractious, requiring lifelong guidance provided by a perceptive medical education suspicious of ‘smart’ solutions to complex problems.","url":"https://doi.org/10.1111/medu.14131","authors":["Anneke G. van der Niet","Alan Bleakley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-20T09:58:55Z","doi":"10.1111/medu.14131","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.54941/ahfe1002111","name":"Drawing Connections: Artificial Intelligence to Address Complex Health Challenges","source":"crossref","abstract":"Pattern recognition is a cornerstone of clinical care and public health practice. Historically, advances in medicine have relied on the ability of humans to detect patterns and make inferences. Modern healthcare challenges involve vast amounts of data and a level of complexity that require additional support to understand. The advancement of Artificial Intelligence has expanded our capability to detect, understand, and address patterns that were previously beyond our grasp. Artificial Intelligence has the capability to analyze otherwise insurmountable quantities of data in order to bring meaning and clarity to patterns that were previously deemed random or unintelligible. We, therefore, aim to charter a strategic path forward for innovative applications of Artificial Intelligence technology to understand and address pressing complex health challenges. The modelling capabilities of Artificial Intelligence have allowed for the simulation of potential viral mutations, as well as the development of therapeutic agents. The predictive analyses provided by Artificial Intelligence allow for a more holistic yet precise understanding of the aging process and the progression of disease, thereby allowing the extent and timing of treatments to be optimized. It has brought a new lens through which to identify malignant cells on imaging and to decode parts of the human genome previously labelled as sequences of unknown significance. On a global scale, Artificial Intelligence has given us the opportunity to better understand and anticipate the effects of climate change on health including the effects on displacement and the potential spillover and spread of new zoonotic infection diseases. We suggest how Artificial Intelligence is beginning to re-conceptualize our understanding of health and disease. The implementation of Artificial Intelligence is a pivotal time in developmental of other modern era of medical practice and public health strategies. Appropriate utilization of these new tools requires innovative thinking, critical appraisal, and tactful resource allocation to ensure issues are addressed in a timely and feasible manner.","url":"https://doi.org/10.54941/ahfe1002111","authors":["Patrick Seitzinger","Jay Kalra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-12T10:21:21Z","doi":"10.54941/ahfe1002111","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-64573-1_293","name":"AIM and Explainable Methods in Medical Imaging and Diagnostics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64573-1_293","authors":["Syed Muhammad Anwar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-18T14:08:09Z","doi":"10.1007/978-3-030-64573-1_293","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.26650/b/t3.2024.40.022","name":"Early Detection of Oral Cancers: Artificial Intelligence or Expert Opinion?","source":"crossref","abstract":"","url":"https://doi.org/10.26650/b/t3.2024.40.022","authors":["Zeynep Akbıyık Az","Gülsüm Ak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-30T08:41:24Z","doi":"10.26650/b/t3.2024.40.022","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1109/medai67139.2025.00015","name":"Fine-Tuning Pre-trained Transformer-Based Models for Sentence-Level Medical Text Classification","source":"crossref","abstract":"This paper presents the development of sentence-level medical text classifiers by fine-tuning eight pre-trained transformer-based models on the PubMed 20k RCT dataset. The models span both general-purpose and biomedical-specific architectures. To enhance performance and address class imbalance, a composite loss function combining cross-entropy, focal loss, and dice loss was applied during training. The fine-tuned models were trained on PubMed 20k RCT and then applied, without further adaptation, to the MTSamples dataset using balanced and imbalanced test subsets. ClinicalBERT achieved the highest results, reaching 97.15% accuracy and 96.93% F1-score on PubMed 20k RCT, 95.20% accuracy and 95.10% F1-score on the balanced MTSamples subset, and 91.80% accuracy and 90.60% F1-score on the imbalanced subset, indicating strong transferability across structured and unstructured medical texts. These outcomes highlight the effectiveness of domain-specific fine-tuning combined with optimized training strategies in building accurate and adaptable medical sentence classifiers.","url":"https://doi.org/10.1109/medai67139.2025.00015","authors":["Tamanna Kaiser","Dan Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00015","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-032-10808-1_10","name":"Regulatory Requirements and Risk Management for AI/ML-Enabled Medical Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-10808-1_10","authors":["Ajit Pandey","Pramod Gupta","Naresh Kumar Sehgal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-30T22:05:38Z","doi":"10.1007/978-3-032-10808-1_10","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2196/48785","name":"Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review","source":"crossref","abstract":"Background Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications. Objective This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration. Methods We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data. Results Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners’ skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions. Conclusions The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.","url":"https://doi.org/10.2196/48785","authors":["Carl Preiksaitis","Christian Rose"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-28T11:57:35Z","doi":"10.2196/48785","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.21037/jmai.2019.09.01","name":"Understanding stroke with Bayesian networks","source":"crossref","abstract":"Background: Stroke is a major source of morbidity worldwide, causing 5.78 million deaths per annum as per WHO global health estimates. An international effort is underway to improve outcomes in stroke by means of secondary and tertiary preventative measures. To maximise the efficacy of such interventions, we must fully understand the processes which lead to stroke-related morbidity and mortality. We propose to reframe stroke as a component of a network system, with multiple interacting causes and consequences. In real-world epidemiology, interactive systems are known to exist between social, behavioural and biological risk factors. The network paradigm accommodates such complexity well, and has demonstrated value in genetics, pathology and therapeutics. We propose Bayesian network inference as a hypothesis-free method of characterising the causal processes of stroke outcomes. Methods: We examine data recorded during the International Stroke trial, a multi-centre interventional trial evaluating the efficacy of anticoagulation and antiplatelet agents as secondary preventative agents in 19,000 cases of stroke. We extract 38 relevant variables, pertaining to patient demographics, stroke presentation, clinical features, diagnosis, management and outcomes. A discrete Bayesian network inferred by optimisation of network score. The performance of several network scores and search algorithms were compared using cross validation. This process identified TABU with K2 score as the optimal network search protocol. Bayesian Network bootstrapping was used to provide an estimate of network structural confidence. Results: Bayesian network inference detected 119 significant conditional dependencies in the International Stroke Trial dataset. These conditional dependencies were consistent with known clinical associations. 14-day mortality was found to be conditionally dependent on age at presentation (Mutual Info: P value <2e-16) and major non-cerebral haemorrhage (Mutual Info: P value <2e-16). 6-month outcome was affected by age (Mutual Info: P value <2e-16), conscious level at presentation (Mutual Info: P value <2e-16), presence of a lower limb deficit (Mutual Info: P value <2e-16) and hemianopia on examination (Mutual Info: P value <2e-16). 6-month outcomes were affected by recurrence of ischaemic stroke (Mutual Info: P value <2e-16), haemorrhagic stroke (Mutual Info: P value <2e-16), and stroke of unknown origin (Mutual Info: P value <2e-16). 6-month outcomes were also conditionally dependent on discharge within 14 days (Mutual Info: P value <2e-16). Conclusions: We organise the pathogenesis, management and sequelae as a single functional system, in which clinical phenomena are understood to influence one another. We demonstrate the utility of the method to form and test multiple hypotheses in an objective fashion. This methodology is general and may theoretically be applied to various observational datasets across the health sciences.","url":"https://doi.org/10.21037/jmai.2019.09.01","authors":["Robert O’Shea"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-03-09T07:11:57Z","doi":"10.21037/jmai.2019.09.01","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-031-94306-5_3","name":"Civil Liability in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94306-5_3","authors":["Rafaella Nogaroli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-31T06:05:43Z","doi":"10.1007/978-3-031-94306-5_3","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1093/oso/9780190908324.003.0010","name":"Conclusion","source":"crossref","abstract":"Abstract To better prepare for the future society in which artificial intelligences (AI) will have much more pervasive influence on our lives, a better understanding of the difference between AI and human intelligence is necessary. Human and biological intelligence cannot be separated from the process of self-replication. Therefore, a fundamental gap exists between human intelligence and AI until AI acquires artificial life. Humans’ social and metacognitive intelligence most clearly distinguish human intelligence from nonhuman intelligence. Although advances are likely to improve the functioning of AI, AI will remain a function of human activity. However, if AI can learn to self-replicate and thus become a life form, albeit a man-made one, outcomes become uncertain.","url":"https://doi.org/10.1093/oso/9780190908324.003.0010","authors":["Daeyeol Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-18T04:12:34Z","doi":"10.1093/oso/9780190908324.003.0010","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1186/s12909-026-09869-2","name":"Personality traits, technology affinity, and artificial intelligence readiness in medical students: a multinational cross-sectional study","source":"crossref","abstract":"Background Artificial intelligence is increasingly embedded in clinical practice and medical education, yet the psychological determinants of students' readiness remain poorly understood. We are aware of no study that has simultaneously examined personality traits, technology affinity and AI readiness in a single cohort. Methods In a cross-sectional online survey using convenience and snowball sampling, medical students from six continents completed three self-report instruments: the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Big Five Inventory-10 and the Affinity for Technology Interaction scale. Pearson correlations, multiple linear regression and ANOVA were applied. The protocol was prospectively registered (OSF: osf.io/7s89a). Results Of 1,920 respondents, 1,278 (66.5%) completed all instruments and constituted the analytic sample, whilst 642 (33.5%) with incomplete data were excluded by listwise deletion (54.8% female; mean age 19.9 ± 1.56 years; 65.9% European). Openness correlated with the Vision subscale (r = 0.669) and agreeableness with the Ethics subscale (r = 0.602); technology affinity was associated with overall readiness (r = 0.231; all p Conclusions Personality traits were independently associated with AI readiness, whereas technology affinity was associated with overall readiness only at the bivariate level. Given the cross-sectional design, these relationships denote associations rather than causal effects. Medical AI curricula should adopt differentiated instructional approaches informed by students' psychological profiles.","url":"https://doi.org/10.1186/s12909-026-09869-2","authors":["Helmar Bornemann-Cimenti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T15:57:42Z","doi":"10.1186/s12909-026-09869-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.37723/jumdc.v12i2.622","name":"DIGITAL TECHNOLOGY, ARTIFICIAL INTELLIGENCE AND FUTURE OF MEDICAL EDUCATION","source":"crossref","abstract":"","url":"https://doi.org/10.37723/jumdc.v12i2.622","authors":["Alam Sher Malik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-27T00:02:43Z","doi":"10.37723/jumdc.v12i2.622","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.18332/popmed/165464","name":"Medical artificial intelligence readiness of medical students in Turkiye","source":"crossref","abstract":"Population Medicine considers the following types of articles:• Research Papers -reports of data from original research or secondary dataset analyses.• Review Papers -comprehensive, authoritative, reviews within the journal's scope.These include both systematic reviews and narrative reviews.• Short Reports -brief reports of data from original research.• Policy Case Studies -brief articles on policy development at a regional or national level.• Study Protocols -articles describing a research protocol of a study.• Methodology Papers -papers that present different methodological approaches that can be used to investigate problems in a relevant scientific field and to encourage innovation.• Methodology Papers -papers that present different methodological approaches that can be used to investigate problems in a relevant scientific field and to encourage innovation.","url":"https://doi.org/10.18332/popmed/165464","authors":["Sevda Sungur","Didem Oktar","Selma Metintas","Muhammed Onsuz","Mediha Bal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-01T06:39:39Z","doi":"10.18332/popmed/165464","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.20452/pamw.17043","name":"Artificial intelligence in medical writing: clarifying perspectives and journal policies","source":"crossref","abstract":"","url":"https://doi.org/10.20452/pamw.17043","authors":["Shigeki Matsubara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-24T10:12:09Z","doi":"10.20452/pamw.17043","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.3384/978918118609","name":"Artificial Intelligence in Digital Pathology : with a Focus on Breast Cancer","source":"crossref","abstract":"Breast cancer is the most common cancer among women in Sweden, and pathology is central to diagnosis and treatment planning.Advances in digital pathology, using high-resolution whole-slide images, (WSI), have enabled the use of AI-based (artificial intelligence) image analysis tools that improves diagnostic efficiency and reproducibility .However, responsible implementation requires careful attention to clinical accountability , data governance and human oversight.This thesis presents a multimodal evaluation framework and contributes to technical and medical insights to the field.In the first study a research database was constructed to support generalizable AI development, compromising six annotated imaging collections, comprising 754 WSIs and 24,043 pathology annotations, 110 radiology cases and 397 lesion annotations.One study evaluated a human-in-the-loop (HITL) workflow, when pathologist assess cellproliferation as expressed by Ki-67 from an AI result.Even though AI showed reduced cell-level performance on local data, (F1 score 0.68 compared to 0.83), status agreement for visual estimation of Ki-67 performed significantly worse (Cohen's κ 0.62) than digital image analysis (κ 0.84) and HITL (κ 0.76).HITL reduced variability of the Ki-67 error and mitigated key limitations such as tumour heterogeneity, misidentification, and staining variability, while highlighting risks from user handling errors.Subsequent studies introduced Feature Enhancing Zoom (FEZ), a visualization technique that amplifies stain patterns at low magnification.In a study with eight pathologists, FEZ improved task efficiency by 15% without compromising accuracy.A usability study with 16 pathologists confirmed high ratings, especially for stains requiring small object identification.Finally, a methodology was evaluated to mitigate domain restraints during clinical implementation of a pretrained AI model for detecting metastases in lymph nodes.A locally curated dataset of 396 cases (4,462 WSIs) was used, with slides labelled by surgical procedure and lesion presence.Results showed that surgical procedure affects model performance, and retraining significantly improved generalization and thus reducing false positive predictions.In summary, this thesis demonstrates how AI and digital pathology can be integrated into clinical workflows to enhance diagnostic precision .","url":"https://doi.org/10.3384/978918118609","authors":["Anna Bodén"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-05T21:01:56Z","doi":"10.3384/978918118609","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1117/12.3023603","name":"Bias in radiology artificial intelligence: causes, evaluation and mitigation","source":"crossref","abstract":"Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various minority subgroups limit the usefulness of AI in improving patients’ lives. AI has been shown to have a remarkable ability to detect protected attributes of age, sex, and race, while the same models demonstrate bias against historically underserved subgroups of age, sex, and race in disease diagnosis. Therefore, an AI model may take shortcut predictions from these correlations and subsequently generate an outcome that is biased toward certain subgroups even when protected attributes are not explicitly used as inputs into the model. This talk will discuss various types of bias from shortcut learning that may occur at different phases of AI model development. I will also summarize current techniques for mitigating bias from preprocessing (data-centric solutions) and during model development (computational solutions) and postprocessing (recalibration of learning).","url":"https://doi.org/10.1117/12.3023603","authors":["Imon Banerjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-02T17:40:59Z","doi":"10.1117/12.3023603","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-64573-1_329","name":"Machine Learning and Electronic Noses for Medical Diagnostics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64573-1_329","authors":["Wojciech Wojnowski","Kaja Kalinowska"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-18T14:07:44Z","doi":"10.1007/978-3-030-64573-1_329","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/b978-0-443-27783-2.00012-0","name":"Challenges in artificial intelligence driven robotic surgery","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27783-2.00012-0","authors":["Sachin Sharma","Seshadri Mohan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T11:26:23Z","doi":"10.1016/b978-0-443-27783-2.00012-0","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1080/08839519108927925","name":"ARTIFICIAL INTELLIGENCE: PERSPECTIVES AND PREDICTIONS","source":"crossref","abstract":"In the first pari of this paper a brief elementary introduction is given to Artificial Intelligence (AI), which is intended for a general audience. In the second part, predictions are made about future developments in each of what are arguably the major subfields of AI. These predictions evolved over a timespan of about two years. Initial versions of them drawn from the literature and elsewhere were distributed to a number of experts working in the various subfields, revised following their criticisms and suggestions, distributed again, and so on for a number of iterations. Since no clear consensus emerged, we are solely responsible for the final form they take. The second part also briefly takes up some broad questions concerning the future economic significance of these developments and the likely social changes they will bring about.","url":"https://doi.org/10.1080/08839519108927925","authors":["MICHAEL A. McROBBIE","JÖRG H. SIEKMANN"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-06-25T05:17:54Z","doi":"10.1080/08839519108927925","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.58496/bjai/2025/011","name":"Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems","source":"crossref","abstract":"The unprecedented explosion of Internet of Things (IOT) devices has elevated the requirements of the network infrastructures to unprecedented levels, causing severe congestion problems, especially in applications which demand low latency, high throughput, and real-time feedback. Static routing protocols, AQM, and TCP variants are some of the traditional mechanisms for congestion control that are unable to perform efficiently in dynamic and diverse IoT environments as they are reactive-based and inflexible. To this end, in this paper, we explore the promising ability of Artificial Intelligence (AI) methods such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and their combination in natura for proactive and intelligent traffic management for IoT. A comparative review of strengths (e.g., adaptivity in RL, pattern recognition in DL) and weaknesses (in terms of its scalability, interpretability, resources) of each method is also discussed. Moreover, the paper indicates some crucial research challenges on model generalization, evaluation criterion and platform integration. Future possible research directions to bridge these gaps include the development of lightweight AI architectures, Explainable AI (XAI) frameworks, cross-platform model deployment, scalable FL, and standardized benchmarking datasets. This work also leads to a hybrid AI model for traffic congestion prediction and control with an application of simulation tool and real data. Simulation results show significant improvements in latency, packet loss, and energy consumption. Finally, the study presents a ground work for incorporating the scalable, secure and intelligent AI enabled congestion control systems in a wide area of IoT applications.","url":"https://doi.org/10.58496/bjai/2025/011","authors":["Aysar Hadi Oleiwi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-14T20:41:11Z","doi":"10.58496/bjai/2025/011","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.3126/jmcjms.v10i1.44622","name":"Artificial intelligence in healthcare in Nepal","source":"crossref","abstract":"Not Available","url":"https://doi.org/10.3126/jmcjms.v10i1.44622","authors":["Pathiyil Ravi Shankar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-26T09:40:00Z","doi":"10.3126/jmcjms.v10i1.44622","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/s40670-023-01934-5","name":"ChatGPT-Based Learning: Generative Artificial Intelligence in Medical Education","source":"crossref","abstract":"Large language models like ChatGPT are a type of machine learning model that can offer a positive paradigm shift in case-based/problem-based learning (CBL/PBL). ChatGPT may be able to augment the existing paradigm to work in conjunction with the clinical-teacher in PBL/CBL case generation. It can develop realistic patient cases that could be revised by clinical teachers to ensure accuracy and relevance. Further, it can be directed to include specific case content in order to facilitate the constructive alignment of the case with the broader learning objectives of the curriculum. There is also the possibility of improving engagement by 'gamifying' CBL/PBL. Supplementary information The online version contains supplementary material available at 10.1007/s40670-023-01934-5.","url":"https://doi.org/10.1007/s40670-023-01934-5","authors":["Brandon Stretton","Joshua Kovoor","Matthew Arnold","Stephen Bacchi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-08T04:02:15Z","doi":"10.1007/s40670-023-01934-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-030-91724-1_20","name":"Medical Physics and Artificial Intelligence (AI) of Image Interpretation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-91724-1_20","authors":["Maryellen L. Giger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-05T16:02:43Z","doi":"10.1007/978-3-030-91724-1_20","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-1-4842-8217-5_8","name":"Medical Records Categorization","source":"crossref","abstract":"This chapter covers a wholesome approach for realizing patterns in medical records by executing a linear discriminant analysis model. You’ll learn what medical records are and then you’ll learn a technique of cleansing textual data by executing fundamental methods like regularization and TfidfVectorizer . Afterward, you’ll execute a method to classify the medical specialty and assess the extent to which it segregates classes.","url":"https://doi.org/10.1007/978-1-4842-8217-5_8","authors":["Tshepo Chris Nokeri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-19T11:25:06Z","doi":"10.1007/978-1-4842-8217-5_8","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(90)90062-5","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(90)90062-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(90)90062-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(92)90023-q","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90023-q","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(92)90023-q","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9780849384141-4","name":"Artificial Intelligence","source":"crossref","abstract":"Computers don’t understand. At least, they don’t understand in the way that we do. Of course, there are some things that are almost beyond comprehension: Why do women collect shoes? What are the rules of cricket? Why do Americans enjoy baseball? And why are lawyers paid so much?","url":"https://doi.org/10.1201/9780849384141-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-10T11:27:09Z","doi":"10.1201/9780849384141-4","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1142/9789814291354_0002","name":"Logic Foundation of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789814291354_0002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-07-18T16:48:49Z","doi":"10.1142/9789814291354_0002","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9781003175865-5","name":"Artificial Intelligence (AI)","source":"crossref","abstract":"Artificial Intelligence (AI) - 1 - Improving Customer Experience (CX)","url":"https://doi.org/10.1201/9781003175865-5","authors":["K. Vinaykumar Nair"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-26T15:36:20Z","doi":"10.1201/9781003175865-5","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-981-95-2525-6_20","name":"Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-2525-6_20","authors":["Jie Zheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T07:05:38Z","doi":"10.1007/978-981-95-2525-6_20","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.3346/jkms.2025.40.e341","name":"Ethical Use of Artificial Intelligence for Processing Medical Images","source":"crossref","abstract":"Artificial intelligence (AI) tools employ prompts and algorithms to perform tasks that typically require human expertise, hypothesis formulation, and critical evaluation. AI enables rapid analysis of complex imaging data, automates segmentation and lesion detection, and supports real-time image-guided interventions. Deep learning architectures (CNNs, RNNs, U-Net, and transformer-based models) facilitate advanced image classification, reconstruction, and interpretation, achieving clinical accuracies above 90% in multiple domains, including coronavirus disease 2019, oncology, and rheumatology. Generative AI platforms (MedGAN, StyleGAN, CycleGAN, SinGAN-Seg) further support synthetic image creation and dataset augmentation, mitigating data scarcity while preserving patient privacy. However, the integration of AI in healthcare presents significant ethical challenges. Key concerns include algorithmic bias, patient privacy, transparency, accountability, and equitable access. Biases-such as annotation, automation, confirmation, demographic, and feedback-loop bias-can compromise diagnostic reliability and patient outcomes. Ethical deployment requires rigorous data governance, informed consent, anonymization, standardized validation frameworks, human oversight, and regulatory compliance. Maintaining interpretability and transparency of AI outputs is essential for clinical decision-making, while professional training and AI literacy are critical to mitigate overreliance and ensure patient safety.","url":"https://doi.org/10.3346/jkms.2025.40.e341","authors":["Yuliya Fedorchenko","Olena Zimba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-09T23:22:05Z","doi":"10.3346/jkms.2025.40.e341","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1080/0142159x.2026.2697858","name":"A response to: ‘the hallucination of learning with generative artificial intelligence’","source":"crossref","abstract":"Dear EditorWe read with great interest the letter by Cecilio-Fernandes and Sandars [1] highlighting the ‘hallucination of learning’ and ‘metacognitive laziness’ induced by generative AI in medical ...","url":"https://doi.org/10.1080/0142159x.2026.2697858","authors":["Sohrab Nosrati","Samane Ghasemi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T21:57:31Z","doi":"10.1080/0142159x.2026.2697858","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.artint.2014.04.002","name":"Erratum to ‘A logic for reasoning about ambiguity’ [Artificial Intelligence 209 (2014) 1–10]","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2014.04.002","authors":["Joseph Y. Halpern","Willemien Kets"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-04-13T07:31:53Z","doi":"10.1016/j.artint.2014.04.002","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.engappai.2022.105646","name":"Artificial intelligence-aided nanoplasmonic biosensor modeling","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2022.105646","authors":["Samaneh Hamedi","Hamed Dehdashti Jahromi","Ahmad Lotfiani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-29T14:24:24Z","doi":"10.1016/j.engappai.2022.105646","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1080/08839514.2024.2411462","name":"Integration of warrior artificial intelligence and leadership reflexivity to enhance decision-making","source":"crossref","abstract":"In the emerging literature on artificial intelligence (AI) and leadership, there is increasing recognition of the importance played by advanced technologies in decision-making. AI is viewed as the next frontier to improve decision-making processes and as a result enhance human decision-making in general. However, existing literature lacks studies on how AI, operating as a “warrior” or innovator in business, can in turn enhance leadership reflexivity, and thereby improve decision-making outcomes. This study is aimed at addressing this gap by drawing on the reflexivity perspective and existing research on AI and leadership to examine the integration of the concepts of warrior AI with leadership reflexivity to improve decision-making. The study used a systematic literature review to identify and map articles using specified inclusion and exclusion criteria to achieve this. Selected articles were included for in-depth analysis to address the issue under investigation. The study explored the potential benefits of blending advanced AI with reflective leadership strategies, offering insights into how organizations can optimize their decision-making processes through this innovative approach. A comprehensive literature review was thus the foundation for our investigation into how warrior AI may enhance human decision-making especially under high-stress conditions by providing real-time data analysis capabilities, pattern recognition skills, and predictive simulations. Our work emphasizes how leadership reflexivity plays a critical role in assessing AI-driven recommendations to ensure ethical soundness and contextual appropriateness of the decisions being taken. Based on our findings, we suggest that integrating AI capabilities with reflective leadership practices can lead to more effective and adaptable decision-making frameworks, particularly when swift yet well-informed action is necessary. This study adds to the existing body of knowledge by illustrating that, with the aid of a flow diagram, the integration of warrior AI into the reflective process can potentially amplify the benefits of AI, offering data-driven insights for leaders to reflect upon, thereby reinforcing the decision-making process with a more rigorous, ethical, and nuanced approach in alignment with organizational objectives and societal values. It is recommended that leadership actively engage in discussions regarding ethical AI use, ensuring alignment with organizational values and ethics. Ultimately, this study contributes valuable insights to discussions around AI and leadership by underscoring the significance of maintaining a balanced relationship between machine efficiency and human wisdom.","url":"https://doi.org/10.1080/08839514.2024.2411462","authors":["Walter Matli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-13T14:08:08Z","doi":"10.1080/08839514.2024.2411462","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.4414663","name":"Navigating the Crossroads of Artificial Intelligence Regulation Using Medical Education Principles","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4414663","authors":["Kawthar Jasim","Kawthar Ali","Maxime Ducret","Faleh Al Tamimi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-18T13:09:11Z","doi":"10.2139/ssrn.4414663","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.18103/mra.v13i8.6887","name":"Artificial intelligence in modern clinical practice","source":"crossref","abstract":"Overview: Artificial Intelligence (AI) has transformed from theoretical concept to practical reality in healthcare, revolutionizing disease diagnosis, treatment, and management. This technology uses machine learning and deep learning algorithms to analyze complex medical datasets, significantly improving diagnostic accuracy, treatment efficiency, and personalized patient care. Clinical Applications: AI has revolutionized medical imaging across specialties. In radiology, systems detect lung nodules and pneumonia with high accuracy, while supporting mammography for early cancer detection. Digital pathology benefits from AI's ability to identify cancers and quantify biomarkers invisible to human eyes. Ophthalmology and dermatology applications include detecting diabetic retinopathy and classifying skin lesions with specialist-level accuracy. Beyond imaging, AI enables early disease detection by integrating electronic health records and biomarkers to identify predictive patterns before symptoms appear. Applications span oncology risk prediction, cardiovascular ECG analysis, and chronic disease management through wearable device monitoring. Treatment and Operations: AI transforms treatment through personalized medicine, combining genomic and clinical data to predict therapy responses. In surgery, AI enhances robot-assisted procedures with real-time feedback and precision guidance. Drug discovery acceleration includes genomic database analysis and virtual compound screening, with AI-developed drugs entering clinical trials. Healthcare operations benefit from AI through intelligent scheduling, patient flow management, and resource allocation. Natural Language Processing extracts valuable information from clinical documentation, while predictive analytics optimize hospital workflows and supply chain management.Challenges and Future Directions. Despite promising applications, AI faces significant implementation challenges. Algorithmic bias risks perpetuating healthcare disparities, while \"black box\" models limit transparency and clinical trust. Data privacy, regulatory frameworks, integration costs, and clinician resistance present additional barriers.The future lies in collaborative models where AI enhances rather than replaces clinical expertise. Success requires coordinated efforts to develop explainable, robust systems while addressing ethical concerns and ensuring equitable implementation that maintains core healthcare values.","url":"https://doi.org/10.18103/mra.v13i8.6887","authors":["Luis Galiana","Francisco Martin","Pablo Espinosa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-21T13:52:38Z","doi":"10.18103/mra.v13i8.6887","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.47852/bonviewmedin42022204","name":"Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review","source":"crossref","abstract":"In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems. Received: 1 December 2023 | Revised: 31 January 2024 | Accepted: 18 February 2024 Conflicts of Interest Hao Lin is the Editor-in-Chief for Medinformatics, and was not involved in the editorial review or the decision to publish this article. The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.","url":"https://doi.org/10.47852/bonviewmedin42022204","authors":["Hao Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-19T22:34:01Z","doi":"10.47852/bonviewmedin42022204","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0306-9877(89)90102-3","name":"Self-organisation and living systems: Is DNA an ‘artificial intelligence’?","source":"crossref","abstract":"There seems little doubt that the maintenance and development of living systems is crucially dependent on an internal organisation of monumental complexity--particularly in higher living species. It is suggested that current thinking--particularly relating to the role of DNA in the total process cannot explain the underlying mechanisms and that a radical rethinking will be necessary. To this end it is proposed that DNA has a unique molecular electronic structure enabling it to operate as a computer analogue system for the highly efficient storage of information and as a type of artificial intelligence through which the information is translated and implemented to organise and control all aspects of the construction and activity of living systems.","url":"https://doi.org/10.1016/0306-9877(89)90102-3","authors":["D.H. Adams","M.R.C. External Scientific Staff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-19T15:41:23Z","doi":"10.1016/0306-9877(89)90102-3","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2196/preprints.102958","name":"AI Socrates: Promise, Mimicry, and Surveillance in Medical Education – A Viewpoint on Responsibly Integrating Artificial Intelligence with Socratic Inquiry for Clinical Reasoning (Preprint)","source":"crossref","abstract":"BACKGROUND Artificial intelligence (AI), particularly large language models (LLMs), is increasingly proposed to deliver Socratic dialogue at scale in medical education. However, the gap between conversational fluency and genuine pedagogical effect remains underexamined. OBJECTIVE This viewpoint argues that while AI holds promise for scalable Socratic tutoring, two critical problems—the mimicry trap and the Panopticon Paradox—require urgent attention. It also proposes a responsible integration framework and defines limits on AI delegation. METHODS This is a conceptual viewpoint and argument synthesis. The author critically reviews current evidence (including systematic reviews, randomized controlled trials, quasi-experimental studies, and proof-of-concept systems), identifies structural failure modes, and proposes a three-pillar framework for integration. RESULTS Current evidence shows AI improves engagement, self-efficacy, and satisfaction but lacks longitudinal or comparative trials demonstrating durable clinical reasoning outcomes. The mimicry trap describes how fluent Socratic-sounding questions may not produce genuine metacognitive gains. The Panopticon Paradox describes how surveillance required for personalization may erode psychological safety, pushing learners toward performative engagement. The paper proposes governance, curriculum, and faculty development as three interdependent pillars, and defines limits: AI should not be trusted for ethical deliberation, emotionally complex communication, or ambiguous clinical judgment. CONCLUSIONS AI is best understood as a complementary technology, not a replacement. Responsible integration requires engineering around specific failure modes, empirical testing of the Panopticon Paradox, and clear limits on delegation. The research agenda includes longitudinal trials, equity audits, and governance experiments.","url":"https://doi.org/10.2196/preprints.102958","authors":["Jose Sánchez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T21:25:07Z","doi":"10.2196/preprints.102958","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.4018/978-1-6684-7544-7.ch006","name":"Artificial Intelligence and Reliability Metrics in Medical Image Analysis","source":"crossref","abstract":"Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskeletal injuries, etc. A perspective skill could be obtained from the increased amount of data and a range of possible options could be obtained from the AI though they are difficult to detect with the human eye. Experts, who occupy as a spearhead in the field of medicine in the digital era, could gather the information of the AI into healthcare. But the field of radiology includes many considerations such as diagnostic communication, medical judgment, policymaking, quality assurance, considering patient desire and values, etc. Through AI, doctors could easily gain the multidisciplinary clinical platform with more efficiency and execute the value-added task.","url":"https://doi.org/10.4018/978-1-6684-7544-7.ch006","authors":["Yamini G.","Gopinath Ganapathy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-28T09:56:32Z","doi":"10.4018/978-1-6684-7544-7.ch006","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.46629/jms.2024.169","name":"The Potential Role of Artificial Intelligence in Emergency Medicine and Medical Education","source":"crossref","abstract":"Artificial intelligence (AI) is increasingly recognized for its transformative potential in healthcare, particularly in emergency medicine. The fast-paced, highstakes nature of emergency departments (EDs) demands rapid decision-making, often under significant time and resource constraints. AI-driven solutions have already demonstrated their ability to enhance diagnostic accuracy, improve triage processes, and optimize resource allocation in emergency settings. However, AI’s potential extends beyond clinical practice into the realm of medical education, where large language models (LLMs) may offer novel opportunities for training future emergency medicine professionals.","url":"https://doi.org/10.46629/jms.2024.169","authors":["Ömerul Faruk AYDIN"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-31T13:30:21Z","doi":"10.46629/jms.2024.169","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.33140/amlai.05.03.04","name":"Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside","source":"crossref","abstract":"Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This article delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes. Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This \"bench to bedside\" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings. These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.","url":"https://doi.org/10.33140/amlai.05.03.04","authors":["Borges, Julian Y.V."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-18T04:31:00Z","doi":"10.33140/amlai.05.03.04","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.6913/mrhk.070206","name":"The Transformative Role of Artificial Intelligence in Medical Education: Applications, Benefits, Challenges, and Future Directions","source":"crossref","abstract":"Artificial Intelligence (AI) is rapidly transforming the landscape of medical education, offering new paradigms for teaching, learning, and assessment. This article explores the current global integration of AI in medical education, outlining both its transformative potential and the accompanying challenges. AI-driven tools such as intelligent tutoring systems, virtual patients, and adaptive learning platforms have demonstrated the capacity to personalize education, enhance diagnostic training, and optimize learner performance through real-time feedback and simulation. Furthermore, AI is facilitating a shift in curricular design—from traditional knowledge transmission toward competency-based and data-informed education. However, the adoption of AI also raises critical concerns related to data privacy, algorithmic bias, lack of faculty training, and the risk of over-reliance on automated systems. The article emphasizes the importance of AI literacy, ethical governance, and cross-disciplinary collaboration to ensure responsible implementation. Looking forward, the synergy of AI with other technologies (e.g., VR, big data analytics) and the dynamic redefinition of the physician's role are discussed as key frontiers. This review advocates for a thoughtful, evidence-informed, and human-centered approach to embedding AI in the future of medical education.","url":"https://doi.org/10.6913/mrhk.070206","authors":["CHANGKUI LI"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-13T12:16:58Z","doi":"10.6913/mrhk.070206","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0004-3702(92)90083-a","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90083-a","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90083-a","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.2139/ssrn.4261569","name":"Prohibited Artificial Intelligence Practices in the Proposed EU Artificial Intelligence Act","source":"crossref","abstract":"As artificial intelligence (AI) is becoming a more and more important part of human lives, the initial hype about its many expected benefits is gradually giving way to rising ethical concerns about its inherent risks and dangers. In order to confront and contain the most serious risks by way of the establishment of a legal framework for trustworthy AI, the European Union released its proposal for an Artificial Intelligence Act (AIA) in April 2021. The draft AIA pursues a proportionate horizontal and risk-based regulatory approach to AI, classifying AI broadly into the categories of unacceptable risks, high risks, and low or minimal risks. The unacceptable risks are those that are deemed to contravene Union values, and they are therefore considered as “prohibited AI practices” by Article 5 AIA. The proposed prohibition covers four categories: 1) AI systems deploying subliminal techniques, 2) AI practices exploiting vulnerabilities, 3) social scoring systems, and 4) “real-time” remote biometric identification systems. These will be critically discussed in the present article.","url":"https://doi.org/10.2139/ssrn.4261569","authors":["Rostam Josef Neuwirth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-30T11:53:53Z","doi":"10.2139/ssrn.4261569","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1201/9781003175865-2","name":"Artificial Intelligence and Gender","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003175865-2","authors":["K. Mangayarkarasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-26T11:36:20Z","doi":"10.1201/9781003175865-2","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1007/978-3-319-40022-8_1","name":"History of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-40022-8_1","authors":["Mariusz Flasiński"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-07-05T05:40:29Z","doi":"10.1007/978-3-319-40022-8_1","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/0954-1810(95)00016-x","name":"A high school project on artificial intelligence in robotics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(95)00016-x","authors":["S.C. Fok","E.K. Ong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-26T00:25:06Z","doi":"10.1016/0954-1810(95)00016-x","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1016/j.caeai.2022.100049","name":"Artificial intelligence in early childhood education: A scoping review","source":"crossref","abstract":"Artificial intelligence (AI) tools are increasingly being used in the field of early childhood education (ECE) to enhance learning and development among young children. Previous proof-of-concept studies have demonstrated that AI can effectively improve teaching and learning in ECE; however, there is a scarcity of knowledge about how these studies are conducted and how AI is used across these studies. We conducted this scoping review to evaluate, synthesize and display the latest literature on AI in ECE. This review analyzed 17 eligible studies conducted in different countries from 1995 to 2021. Although few studies on this critical issue have been found, the existing references provide up-to-date insights into different aspects (knowledge, tools, activities, and impacts) of AI for children. Most studies have shown that AI has significantly improved children's concepts regarding AI, machine learning, computer science, and robotics and other skills such as creativity, emotion control, collaborative inquiry, literacy skills, and computational thinking. Future directions are also discussed for researching AI in ECE.","url":"https://doi.org/10.1016/j.caeai.2022.100049","authors":["Jiahong Su","Weipeng Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-20T12:23:26Z","doi":"10.1016/j.caeai.2022.100049","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1148/ryai.220056","name":"Hurdles to Artificial Intelligence Deployment: Noise in Schemas and                     “Gold” Labels","source":"crossref","abstract":"Despite frequent reports of imaging artificial intelligence (AI) that parallels human performance, clinicians often question the safety and robustness of AI products in practice. This work explores two underreported sources of noise that negatively affect imaging AI: (a) variation in labeling schema definitions and (b) noise in the labeling process. First, the overlap between the schemas of two publicly available datasets and a third-party vendor are compared, showing there is low agreement ( 90%). Among low agreement classes (pneumonia, consolidation), the labels assigned as \"ground truth\" were unreliable, suggesting that the result of majority voting is highly dependent on which group of radiologists is assigned to annotation. Noise in labeling schemas and gold label annotations are pervasive in medical imaging classification and affect downstream clinical deployment. Possible solutions (eg, changes to task design, annotation methods, and model training) and their potential to improve trust in clinical AI are discussed. Keywords: Radiology AI, Dataset Creation, Noise in Datasets Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ursprung and Woitek in this issue.","url":"https://doi.org/10.1148/ryai.220056","authors":["Mohamed Abdalla","Benjamin Fine"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-11T14:52:22Z","doi":"10.1148/ryai.220056","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1148/ryai.240093","name":"Artificial Intelligence in Radiology: Bridging Global Health Care                     Gaps through Innovation and Inclusion","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.240093","authors":["Arkadiusz Sitek"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-13T13:52:56Z","doi":"10.1148/ryai.240093","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.69545/y05fkm17","name":"Artificial intelligence in Medical Education-A Crosssectional study in Private Setup","source":"crossref","abstract":"Objective: For healthcare providers, expectations, duties, and job descriptions must change as the information age fades and the artificial intelligence age becomes more prevalent. In order to identify areas that can benefit from curriculum restructuring, this study looked at how aspiring doctors perceived the potential effects of artificial intelligence on medicine. Methodology: A nationwide study including 3018 medical students was carried out using a cross-sectional approach across multiple centers. An online survey created and disseminated using a web-based service served as the study's instrument. Results: Artificial intelligence was viewed by the majority of medical students as an assistive technology that might ease patients' access to healthcare (76.7%), physicians' access to information (85.6%), and errors (70.5%). Nonetheless, 44.9% of the participants expressed concern over a potential cutback in physician services, which would result in joblessness. Additionally, it was decided that the application of artificial intelligence in medicine would harm patient-physician interactions (42.7%), devalue the medical profession (58.6%), and erode trust (45.5%). Furthermore, almost half of the participants (44.7%) agreed that they could maintain professional secrecy when using AI apps, whereas 16.1% contended that AI in medicine could lead to professional confidentiality violations. Out of all the individuals involved, a mere 6.0% claimed to be knowledgeable enough to advise patients about the advantages and disadvantages of artificial intelligence. Conclusion: The participants indicated that the medical curriculum needed updating in light of the requirements for transforming healthcare through artificial intelligence. In addition to ensuring that professional values and rights are upheld, the update should focus on providing aspiring physicians with the information and abilities they need to use artificial intelligence technologies successfully. Keywords: Medical Students, Medical Ethics, Medical Curriculum, Artificial Intelligence Medicine","url":"https://doi.org/10.69545/y05fkm17","authors":["Arooj Javed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-01T12:22:06Z","doi":"10.69545/y05fkm17","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.7759/cureus.51961","name":"Exploring Generative Artificial Intelligence-Assisted Medical Education: Assessing Case-Based Learning for Medical Students","source":"crossref","abstract":"The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of GenAI within the medical school curriculum. Given the promise and limitations of GenAI, this study aims to assess the current capabilities of a GenAI (Chat Generative Pre-trained Transformer, ChatGPT), specifically within the framework of a pre-clerkship case-based active learning curriculum. The role of GenAI is explored by evaluating its performance in generating educational materials, creating medical assessment questions, answering medical queries, and engaging in clinical reasoning by prompting it to respond to a problem-based learning scenario. Our results demonstrated that GenAI addressed epidemiology, diagnosis, and treatment questions well. However, there were still instances where it failed to provide comprehensive answers. Responses from GenAI might offer essential information, hint at the need for further inquiry, or sometimes omit critical details. GenAI struggled with generating information on complex topics, raising a significant concern when using it as a 'search engine' for medical student queries. This creates uncertainty for students regarding potentially missed critical information. With the increasing integration of GenAI into medical education, it is imperative for faculty to become well-versed in both its advantages and limitations. This awareness will enable them to educate students on using GenAI effectively in medical education.","url":"https://doi.org/10.7759/cureus.51961","authors":["Matthew Sauder","Tara Tritsch","Vijay Rajput","Gary Schwartz","Mohammadali M Shoja"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-09T20:18:50Z","doi":"10.7759/cureus.51961","addedAt":"2026-09-01T01:47:52.050Z","updatedAt":"2026-09-01T01:47:52.050Z"},{"id":"doi:10.1515/mr-2026-0027","name":"Artificial intelligence-based software as a medical device: regulatory evolution toward continuous oversight","source":"crossref","abstract":"Abstract Artificial intelligence-based software as a medical device (AI-SaMD) is increasingly used for screening, diagnosis, triage, risk stratification, treatment support, and monitoring. Its regulatory challenge lies not only in algorithmic complexity or software modification, but also in the possibility that safety and effectiveness may be affected after market entry by changing data distributions, model behavior, clinical workflows, user interaction, cybersecurity conditions, and software or model updates. This structured narrative review examines how AI-SaMD regulation is moving beyond single-point premarket evaluation toward continuous and dynamic oversight. We analyze how the international medical device regulators forum (IMDRF) clinical evaluation components, including valid clinical association, analytical validation, and clinical validation, can be operationalized across major AI-SaMD functions, and compare regulatory developments in the United States, European Union, China, Singapore, and the United Kingdom. Building on current evidence gaps, we synthesize five evidence dimensions central to ongoing regulatory assurance: data evidence, algorithm evidence, software and cybersecurity evidence, clinical scenario and human factors evidence, and real-world data/real-world evidence (RWD/RWE) with change management. Comparative analysis of Airdoc-AIFUNDUS, IDx-DR, and EyeArt illustrates how these dimensions operate in practice. Current governance still faces unresolved challenges in predetermined change control plan (PCCP) boundaries, RWE quality, post-update human factors reassessment, and cross-jurisdictional divergence.","url":"https://doi.org/10.1515/mr-2026-0027","authors":["Yukun Dong","Ping Jiang","Xiao-Hua Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-30T07:57:42Z","doi":"10.1515/mr-2026-0027","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.14744/ejmi.2020.54709","name":"Knowledge, Attitudes and Perspectives of Anesthesiologists on Artificial Intelligence","source":"crossref","abstract":"Objectives: The use of AI in medicine is increasing every passing day. However, there is still debate in the literatureamong specialists about several aspects of artificial intelligence (AI). The objective of this study was to evaluate andanalyze knowledge, attitudes and perspectives of anesthesiologists on AI through an online survey.Methods: An online survey was conducted in order to reveal knowledge, attitudes and perspectives of anesthesiologists on AI in Turkey. The survey consisted of 29 questions about participants’ demographic data, professional data, andopinions on AI. After physicians other than anesthesiology and reanimation specialists were excluded, responses of theremaining 68 anesthesiologists were evaluated and analyzed.Results: The rate of anesthesiologists that have sufficient knowledge of AI was found as 36.8%. Of the respondents,58.8% considered that AI offers useful applications in the field of medicine. 64.7% of the participants think that AI willcreate drastic changes in all fields of medicine. Only 2.9% of the anesthesiologists consider that AI will completely replace physicians in the near future. 5.9% of the participants reported that they are worried about developments in AI.Conclusion: AI is not expected to completely replace physicians. We believe that further similar survey studies shouldbe conducted in order to take physicians’ opinions into account in the development of using AI in medicine.Keywords: Artificial intelligence (AI), anesthesia, anesthesiologist","url":"https://doi.org/10.14744/ejmi.2020.54709","authors":["Ali Muhittin Tasdogan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-23T07:19:20Z","doi":"10.14744/ejmi.2020.54709","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.37965/jait.2023.0214","name":"Diagnostic Segmentation Based on Kidney Medical Image","source":"crossref","abstract":"Lesion segmentation of medical images is an important component of smart medicine. The development of deep learning technology is followed by rapid advancement in lesion segmentation technology of medical images. Though the present segmentation technology can retain spatial features, insufficient spatial features are retained with low segmentation accuracy. Our proposed PST-UNet model combines transformer with U-shaped structure and better infuses encoder's multi-scale features by using convolution fusion module. PST-UNet model adopts two types of block Swin transform at encoder and decoder ends respectively. Renal lesion data tends to present a normal distribution. Therefore, to preserve more spatial features and enhance the precision of renal lesion segmentation, Swin transformer block and full GELU (Gaussian Error Linear Unit) activation function are introduced at the encoder end. Similarly, at the decoder end, Swin transformer block, full GELU activation function, up-sampling and jumper wires from the convolution fusion module are also introduced.","url":"https://doi.org/10.37965/jait.2023.0214","authors":["Shixuemei","Mideth Abisado"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-06T15:22:39Z","doi":"10.37965/jait.2023.0214","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/978-3-031-64049-0_9","name":"Ethical and Regulatory Considerations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64049-0_9","authors":["Euclid Seeram","Vijay Kanade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T10:02:01Z","doi":"10.1007/978-3-031-64049-0_9","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/978-3-031-49011-8_21","name":"Better Medical Efficiency by Means of Hospital Bed Management Optimization—A Comparison of Artificial Intelligence Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-49011-8_21","authors":["Afonso Lobo","Agostinho Barbosa","Tiago Guimarães","João Lopes","Hugo Peixoto","Manuel Filipe Santos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-14T16:03:27Z","doi":"10.1007/978-3-031-49011-8_21","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.70593/978-93-7185-299-9_14","name":"Artificial Intelligence in Medical Radiology: Clinical Applications, Challenges, and Future Directions","source":"crossref","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in modern radiology, significantly improving diagnostic accuracy, workflow efficiency, and clinical decision-making. With the rapid expansion of medical imaging data, traditional radiological practices face increasing challenges in timely and accurate interpretation. AI, particularly through machine learning and deep learning techniques, provides effective solutions by enabling automated image analysis, pattern recognition, and predictive modeling. AI applications in radiology span multiple domains, including disease detection, image reconstruction, workflow optimization, and emergency diagnostics. These technologies assist radiologists in identifying abnormalities more precisely, enhancing image quality, and reducing scan time and radiation exposure. Furthermore, AI contributes to reducing inter-observer variability, thereby improving consistency in diagnosis. Despite its advantages, AI implementation presents challenges such as data privacy concerns, algorithmic bias, and limited interpretability. Addressing these issues is essential for safe and effective clinical integration. The future of AI in radiology is promising, with potential advancements in personalized medicine and integration with emerging technologies such as robotics and decision-support systems. This chapter provides a comprehensive overview of the fundamentals, applications, advantages, limitations, and future directions of AI in radiology.","url":"https://doi.org/10.70593/978-93-7185-299-9_14","authors":["Naylah Arif","Decampos Olabissi Sergio","Mohd. Arfat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-08T11:24:44Z","doi":"10.70593/978-93-7185-299-9_14","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/b978-0-443-27638-5.00007-9","name":"Conclusions and future directions: Challenges, opportunities, and ethical considerations in artificial intelligence-driven medical research","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27638-5.00007-9","authors":["Olfa Boubaker"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:49:13Z","doi":"10.1016/b978-0-443-27638-5.00007-9","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1080/13698575.2026.2662006","name":"When the white coat meets the code: medical professionals’ negotiating with artificial intelligence, trust and boundary work","source":"crossref","abstract":"As AI is deployed in healthcare contexts, medical professionals undergo technology-driven challenges, such as maintaining control over the diagnostic process and renegotiating their tasks and areas of expertise. In this article, we explore the social and professional implications of AI in healthcare contexts in Italy. We do this by investigating the multiple factors that co-construct trust in AI systems. We also examine the various forms of boundary work that professionals use to redefine their authority and professional autonomy. We employ a mixed-methods research design, including a survey (n = 193) and 22 in-depth interviews with clinicians, addressing clinicians’ AI awareness and knowledge, use of AI in medical practice, trust relations and concerns regarding medical professionalism. Our findings suggest that different assemblages of trustworthiness collated into three trusting attitudes (relational-practical, institutional-regulatory and epistemic-infrastructural), showing how trust in medical tools is being configured in the AI age. Clinicians reported performing three strategies of boundary work (defensive, regulatory and transformative) in negotiating their roles and expertise. This boundary work was narrated as a response to working contexts in which AI’s influence led to contested workflows, altered decision-making authority and redefined professional boundaries.","url":"https://doi.org/10.1080/13698575.2026.2662006","authors":["Laura Sartori","Marianna Musmeci","Sara Cannizzaro","Chiara Binelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T08:27:23Z","doi":"10.1080/13698575.2026.2662006","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.7180/kmj.25.141","name":"The ethics of using artificial intelligence in writing medical research papers","source":"crossref","abstract":"The rapid integration of large language models into medical publishing offers considerable potential for improving drafting efficiency but simultaneously raises substantial concerns regarding research integrity, accountability, and the reliability of the scientific record. Recent incidents in which artificial intelligence (AI) systems were listed as coauthors have prompted urgent regulatory revisions. In this review, we identify a global consensus that strictly prohibits AI authorship, as algorithms lack both moral agency and legal accountability. Transparency has emerged as an essential requirement, and undisclosed AI use is increasingly regarded as a form of ethical misconduct. Key risks include “hallucinations” (notably citation fabrication), algorithmic bias, and potential violations of privacy regulations (e.g., the Health Insurance Portability and Accountability Act) when protected health information is processed through cloud-based platforms. The analysis indicates that rigid prohibitions are operationally unenforceable, supporting instead a “human stewardship” model in which AI functions as a drafting scaffold subjected to rigorous human verification. AI represents a lasting transformation in medical writing that necessitates a shift from simple prohibition to structured governance. To preserve epistemic validity, we propose a framework built on task segmentation, mandatory cross-referencing of claims, and data sovereignty. Ultimately, AI must remain a transparent assistive tool, with full responsibility for the manuscript residing exclusively with the human investigator.","url":"https://doi.org/10.7180/kmj.25.141","authors":["Shinae Yu","Hyunyong Hwang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T04:23:42Z","doi":"10.7180/kmj.25.141","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1097/cm9.0000000000003923","name":"Artificial intelligence-facilitated evidence-based medicine research and practice: A position statement","source":"crossref","abstract":"The world healthcare system has deeply adopted evidence-based medicine (EBM) for clinical practice and policy making by systematically integrating all available knowledge, including research studies and adjusting their biases. The approaches for EBM have been evolving rapidly in the past decades, including novel statistical methods, certainty rating approaches, and sustainable governance of the evidence ecosystem.[1] However, it still faces challenges such as exponential accumulation of novel evidence. The introduction of artificial intelligence (AI) and informative techniques is now facilitating the maintenance of the living evidence ecosystem with quickly iterated approaches from machine learning (ML) to large language model (LLM). These constructive technologies from different areas are revolutionizing the process of EBM research as well as its association with daily practice.[2] The rapid evolution of AI techniques, however, introduces uncertainty and confusion for clinicians and researchers.[3] Strategic consideration from stakeholders of evidence governance calls for long-term foreseeing of technical directions and pipelines in the future, which remains absent. Living in their cocoons, researchers in different disciplines can hardly find the full picture of the evolution of the evidence ecosystem. To address these gaps and provide a clear road map for the trajectory of AI-facilitated EBM in the past, present, and future, a multidisciplinary team in China proposed a framework to assess the degree of AI involvement in the evidence ecosystem. This framework evaluates the current extent of AI adoption in EBM research and clinical practice and offers insights to guide the development of AI technologies in the strategic layer, including generative artificial intelligence (GAI), and industry standards, ultimately enhancing the process of EBM and improving patient outcomes. Development of the Framework. To illustrate a long-term development of the AI-facilitated EBM research and practice in the past, present, and future, the AI4Evidence Working Group of the Chinese Medical Doctor Association initiated the framework. Through six rounds of the virtual and in-person meeting, the core team raised a draft of the taxonomy. A multidisciplinary panel from a full working group, including clinicians, EBM methodologists, epidemiologists, and computer scientists, reviewed, revised, and approved the taxonomy and the manuscript [Figure 1].Figure 1: Structure of tiers assessing the degree of AI involvement in the evidence ecosystem. AI: Artificial intelligence; EBM: Evidence-based medicine; NA: Not available.TIER 0: EBM without AI. This tier represents the era of EBM research and practice before the involvement of any AI technologies. Human forces are the only investigators to operate and maintain the evidence ecosystem. TIER 1: EBM with complementary AI. In Tier 1, human forces take charge of the evidence synthesis, interpretation, and dissemination. AI participates in the evidence ecosystem merely as complementary assistance and support. The limited AI application merely offers light aids to human forces in several phases of data preparation and/or paper writing, such as paper search and screening, data extraction, and cleaning in meta-analysis, rather than core work, including efficient evidence dissemination, individual treatment, and unified data collaboration. AI is unable to be responsible for the quality of work and all details of AI-facilitated work are reviewed by experienced human reviewers. TIER 2: EBM with supportive AI. In Tier 2, AI technology initiates to take limited single tasks independently to further support the human forces in maintaining the evidence ecosystem. For example, AI may filter particular study types (such as randomized controlled trials), conduct core data analysis, and present results in an appropriate format during the evidence synthesis to reduce human workload. AI may also offer suggestions for appropriate statistical methods, evidence dissemination strategies, and generate data for evidence assessment across local regions. Nevertheless, all these procedures are executed by AI tools separately, and human forces remain in charge for all vital decisions. TIER 3: EBM with human supervised AI. In Tier 3, early-stage AI-facilitated automation starts to work. Compared with previous tiers, AI manages major tactical decisions in EBM research, including evidence collection, assessment, and possibly interpretation, and makes some adjustments supervised by human researchers from study design, research conduction to reporting. In practice, both health providers and patients may receive support with AI-generated information for individualized decision-making. Nevertheless, human researchers and health providers still take the full responsibility of the validity and trustworthiness of AI-informed material due to expected unqualified results produced by AI forces. TIER 4: semi-automated EBM. In Tier 4, AI intelligence synthesizes individualized evidence and supports timely for clinical decisions, which means general guidelines might fade out and link EBM research and practice seamlessly with an advanced automation process. Due to global data collaboration, AI-provided evidence, in most cases, is trustworthy and applicable in real-world practice, and people without qualified EBM training can engage without barriers. The gap between human researchers and health providers is eliminated and their role is to comment and approve the work provided by the AI using their clinical intuition and gestalt consideration for real life. TIER 5: automated EBM. In Tier 5, AI-facilitated EBM reaches the highest level, providing healthcare service using EBM principles directly to patients with seamless and real-time integration of research and practice. The role of AI may involve in the patient assessment, decision making, subsequent monitoring, and original research, filling the gap of the evidence. A full evidence ecosystem runs purely with AI, with humans informing their individual-level values and a relevant regulatory framework ensuring verifiably open accountability mechanisms. With the AI of this tier, the medical research and practice may evolve to a very different era that is beyond people’s imagination. This position statement proposes a framework to illustrate the past, present, and future of AI integration in EBM research and clinical practice. From Tier 0 to 5, the role of AI shifts from assisting particular work to operating an evidence ecosystem automatically. Although the AI involvement in EBM progressed gradually without a hard cut-off from Tier 0 to Tier 1, the launch of LLM such as ChatGPT in 2023 represented a landscape of revolution. In the past two years, the rapid development of AI-facilitated tools for EBM research and practice indicates the shift from Tier 1 to Tier 2 has been happening in recent years.[4] In the future, we are expecting more powerful AI tools in accelerating the knowledge transition from research to practice, and finally, at Tier 5, the sustainable and self-evolving evidence ecosystem will merge the research and practice with only voluntary input from the human force. In the end game of AI-automated clinical practice, a person may receive the healthcare services by automated detection of potential impairment, automatic synthesis of best evidence, and timely implementation. The recipient may not feel the ‘treatment’ but can check it as well as the full rationales of the ‘prescription’ or the care they received. Meanwhile, accountability frameworks will be established based on the characteristics of AI-based EBM, ensuring patient safety through operationalization standards and regulatory guardrails. In a long-term perspective, the implementation of AI technology in EBM research and practice is irreversible. At the current exercise of AI in EBM research and practice, the academic community has to balance clinical reasoning and AI-facilitated information. Gestalt consideration in the clinical perspective remains the demonstrating determinant for the certainty rating and clinical decision making compared with statistical parameters and outputs from “black box” algorithms.[4] For AI accelerated EBM materials, the full process should be transparent and checkable by human forces. It is important to minimize the AI-related errors and attribute their responsibilities. For material generated by AI-associated algorithms, the transparency at the reporting level and validation at the research level should be reported for health providers and downstream researchers to provide full information to make the judgment in being confident or not confident with the results.[5] For LLM-generated materials, lack of the trace of the information source as well as the certainty rating system is the main gap preventing their implementation in clinical practice. Representing a Chinese nationwide working group, this position statement proposed a high-level framework to assess the development of AI-facilitated EBM research and practice in six tiers at a historical view. The current AI tools and systems represent that we are at the transition from Tier 1 to Tier 2. Following the framework, the EBM research practice is expecting its accelerated development in the future decades, in parallel with the development of AI technology and its specialized EBM GAI. Expert Committee Jiahui Ma (West China Hospital of Sichuan University); Yaolong Chen (Lanzhou University); Zhenggang Bai (Evidence based Research Center of Nanjing University of TCM); Yifei Chen (Xinjiang Medical University); Da Feng (Huazhong University of Science and Technology); Le Gao (Xi’an Jiaotong University); Long Ge (Lanzhou University); Nana Guo (Hebei Center for Disease Control and Prevention); Yinghui Jin (Zhongnan Hospital of Wuhan University); Lun Li (Central South University Xiangya Second Hospital); Hailun Liang (Renmin University of China); Xing Liao (China Academy of Chinese Medical Sciences); Jun Lyu (The First Affiliated Hospital of Jinan University); Yanan Ma (China Medical University); Xiaolu Nie (Beijing Children’s Hospital); Xiaochun Qiu (Shanghai Jiao Tong University); Yang Song (The Chinese University of Hong Kong); Ying Sun (Fudan University); Buzhou Tang (Harbin Institute of Technology (Shenzhen); Weiwei Wang (Beijing Anding Hospital); Xinyin Wu (Central South University); Shanshan Wu (Capital Medical University); Jun Xia (University of Nottingham Ningbo China); Yizhong Yan (Shihezi University); Fengwen Yang (Tianjin University of Traditional Chinese Medicine); Xiaorong Yang (Shandong University); Zhirong Yang (Chinese Academy of sciences); Houyu Zhao (Peking University Third Hospital); Hao Zhou (Children’s Hospital of Fudan University); Boheng Zhang (Zhongshan Hospital Affiliated to Fudan University); Simin Zhu (The Chinese University of Hong Kong); Feng Sun (Department of Epidemiology and Biostatistics, School of Public Health, Peking University); Sheyu Li (Department of Endocrinology and Metabolism, MAGIC China Centre, Chinese Evidence-based Medicine Centre, West China Hospital of Sichuan University). Funding This work was supported by grants from the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (No. ZYYC24001). Conflicts of interest None.","url":"https://doi.org/10.1097/cm9.0000000000003923","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-29T10:00:27Z","doi":"10.1097/cm9.0000000000003923","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.9734/bpi/msraa/v4/5440","name":"Needs Assessment for Inclusion of Artificial Intelligence in Undergraduate Medical Curriculum","source":"crossref","abstract":"Background: Artificial Intelligence (AI) is transforming industries globally, with healthcare being no exception. The present study was carried out with the objective of need assessment of AI in an undergraduate curriculum. The present study assessed the key aspects of AI in medicine, including students' baseline knowledge, their perspectives on AI’s significance, their beliefs about its potential impact on healthcare, and the need for AI education. Materials and Methods: The present study was a cross-sectional study conducted using a validated questionnaire among MBBS students in a rural tertiary medical college of Kolar, Karnataka, India, from the first to final year for two years. 396 students took part in the study after applying the inclusion and exclusion criteria. A pre-tested, structured, validated questionnaire was used to collect data. The study was carried out after being permitted from the Institutional Ethics Committee. Descriptive statistics were applied wherever needed. Results: The findings revealed that the majority of respondents lacked a background in computer science and had not received additional training related to AI. Regarding the necessity of AI education in the medical curriculum, only 15% of the students felt that their current medical education had adequately prepared them to handle AI tools. However, 89% of respondents agreed that AI competency training should be integrated into undergraduate medical education. Conclusion: With medical education being revolutionised with Artificial Intelligence, the current undergraduate medical curriculum needs to be capacitated, suggesting an urgent need to integrate AI competencies in the undergraduate curriculum to better equip the students to tackle the difficulties and possible threats of AI in medicine.","url":"https://doi.org/10.9734/bpi/msraa/v4/5440","authors":["Ashwini K. Shetty","Pradeep TS"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-14T04:42:27Z","doi":"10.9734/bpi/msraa/v4/5440","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1111/medu.15092","name":"Rethinking assessment in response to generative artificial intelligence","source":"crossref","abstract":"By considering the significant implications of generative AI for assessment, the authors suggest reclaiming oral assessment, when there is value in assessing unassisted understanding, and embracing AI for assisted assessment.","url":"https://doi.org/10.1111/medu.15092","authors":["Jacob Pearce","Neville Chiavaroli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-12T06:04:31Z","doi":"10.1111/medu.15092","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0954-1810(88)90017-9","name":"Intelligence news letter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(88)90017-9","authors":["Laurence Leff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-25T14:45:39Z","doi":"10.1016/0954-1810(88)90017-9","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5152/cjm.2023.22118","name":"The prediction of hydrocephalus in colloid cysts by using artificial intelligence","source":"crossref","abstract":"Objective: We aim to train neural networks to predict hydrocephaly in patients with colloid cyst based on T2 weighted MRI radiomics. Methods: This study included 40 cases with a colloid cyst, the mean age was 54.08±16.57 years, and 25 (62.5%) were women. Two observers segmented cysts on axial T2 weighted MRI and evaluated conventional features. Predictors were radiomics (n = 851) and conventional features (n = 12). Feature selection was based on coefficient variance (CoV), variance inflation factor (VIF), and LASSO regression analysis. The outcome was identified as hydrocephaly. Models were developed with artificial neural networks (ANN) for three different diagnostic prediction models. The first model included radiomics features, the second model included conventional features, and the third model included all of the features. ANN performance was presented as an area under the receiver operating characteristic curve (AUC) and accepted as successful if the AUC &gt; 0.85 and p-value &lt; 0.01. Results: By using CoV and VIF analysis, 49 features were found to be stable. Radiomics predict hydrocephaly with AUC = 0.88, sensitivity: 92%, specificity: 97%. Conventional features predict hydrocephaly with AUC = 0.87, sensitivity: 82%, specificity: 93%. Third model (Radiomics + Conventional) AUC was 0.99, sensitivity: 91%, specificity: 100% (All p-values &lt; 0.001). Conclusion: This study was successful in training neural networks that can predict hydrocephaly in patients with colloid cysts.","url":"https://doi.org/10.5152/cjm.2023.22118","authors":["Basak Atalay","Mahmut Bilal Dogan","Mehmet Bilgin Eser"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-03T09:06:08Z","doi":"10.5152/cjm.2023.22118","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1101/2021.05.13.21255493","name":"A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine","source":"crossref","abstract":"ABSTRACT Objective Despite the significant medical impact of artificial intelligence (AI) in healthcare, emergence of AI-related topics in medical curricula has been slow. The authors sought to introduce pre-clinical students to the importance of AI methodologies and medical applications using modular short courses focused on active learning with precision medicine as a primary use case. Materials and Methods A short elective course was designed to introduce first-year students to how various bioinformatic and AI-related processes work and how they help classify medical data, facilitate genomic analysis and predict clinical outcomes. The course covers gene sequencing and variants, neural networks, natural language processing, medical computer vision and the limitations and ethical concerns related to use of AI in precision medicine. Online content serves as major source material. After a faculty-led introduction, sessions focus on teams of students who present course content to one another and lead discussions with faculty guidance. A related short AI course focused on gene variants was given to the entire second-year class. Results The elective course has been taken by 74 first- year students over 8 consecutive semesters (2017-2021). The course achieved average satisfaction scores of 4.4/5.0 (n = 13) when the active learning approach became dominant in 2018. Students were able to describe accurately how bioinformatics and AI make personalized medicine possible. Students also did well on the gene variants exercise given to the entire second year class (2018), but the full class short AI course was not continued in subsequent years. Students have created a school-approved interest group in medical AI. Conclusions This experience shows that AI-related materials can be sustainably introduced into pre- clinical medical education with precision medicine as the primary use case. This modular course design and content could be adapted easily for educational use in medical subspecialties and other health professions.","url":"https://doi.org/10.1101/2021.05.13.21255493","authors":["Philip O. Alderson","Maureen J. Donlin","Lynda A. Morrison"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-17T19:25:18Z","doi":"10.1101/2021.05.13.21255493","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/preprints.58360","name":"Artificial Intelligence Based Chatbots as Professional Medical Consultants in Oral Surgery: Are they reliable? Comparative Study (Preprint)","source":"crossref","abstract":"BACKGROUND With advances in artificial intelligence (AI) technologies, AI-based chatbots have become promising tools for generating medical information. Given their widespread use and easy accessibility, it is important to comprehensively evaluate the quality, accuracy, and safety of the information they produce to ensure their effectiveness as reliable sources in healthcare. OBJECTIVE The purpose of the study is to examine the reliability of chatbots and their role as professional consultants. METHODS 64 questions were generated, including systemic diseases and medications for which professional consultation is often requested and common conditions that may raise concerns during performing oral surgery. The questions were posed to ChatGPT 3,5 and Claude-instant at 2 sessions with 1 week interval. The answers were recorded and rated by 2 experienced oral surgeons by using 2 evaluation metrics: A modified DISCERN tool, a subset of the original, was used to evaluate the answers in terms of quality, and the Likert scale(LS) was used to evaluate the answers in terms of accuracy (6 point LS) and completeness (3 point LS). Statistical analyses, including intraclass correlation, Mann-Whitney U test, skewness and kurtosis coefficients calculations were conducted to assess and compare the performance of the chatbots. RESULTS In terms of intrarater agreement, ChatGPT demonstrated a high level of quality and accuracy in both sessions. Additionally, quality scores of ChatGPT was found statistically significantly higher than Claude instant. CONCLUSIONS The evaluated chatbots exhibited a remarkable capacity to generate valuable medical content. Our particular view is that, although they are currently insufficient to serve as a single source of information, AI-based chatbots will gain greater acceptance among healthcare professionals in the near future and can provide a quick solution to the high demand for medical care.","url":"https://doi.org/10.2196/preprints.58360","authors":["Alanur Çiftçi Şişman","Ahmet Hüseyin Acar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-15T19:06:34Z","doi":"10.2196/preprints.58360","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.24835/1607-0763-1500","name":"Medical imaging and artificial intelligence in radiotherapy of malignant tumors","source":"crossref","abstract":"The fusion of artificial intelligence with medical imaging is undoubtedly a progressive innovative process in the modern development of domestic healthcare, which allows for unprecedented accuracy and efficiency in the diagnosis and planning of special treatment of various diseases, including malignant tumors. At the same time, artificial intelligence approaches, especially in the field of clinical application of radiotherapy techniques, are spreading more widely and moving from the field of specialized research to the field of already accepted traditional clinical practice. Purpose of the study: to analyze the approaches of artificial intelligence in the field of clinical application of radiotherapy techniques for the antitumor treatment of malignant tumors. Conclusion. The further development of artificial intelligence provides for the provision of options for the prevention, diagnosis and treatment of cancer patients against the background of a constant increase in accuracy in their implementation, including assistance in optimizing radiotherapeutic treatment of malignant neoplasms.","url":"https://doi.org/10.24835/1607-0763-1500","authors":["G. A. Panshin","N. V. Nudnov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-28T10:02:01Z","doi":"10.24835/1607-0763-1500","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2196/13930","name":"Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review","source":"crossref","abstract":"Background Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. Objective This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. Methods Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. Results A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. Conclusions The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.","url":"https://doi.org/10.2196/13930","authors":["Kai Siang Chan","Nabil Zary"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-04-16T21:04:16Z","doi":"10.2196/13930","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(91)90046-m","name":"Editorial Board","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(91)90046-m","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(91)90046-m","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.artint.2004.10.009","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2004.10.009","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-15T01:47:57Z","doi":"10.1016/j.artint.2004.10.009","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/s0004-3702(02)00237-0","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(02)00237-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-10T21:56:50Z","doi":"10.1016/s0004-3702(02)00237-0","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(92)90092-c","name":"Books received","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90092-c","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90092-c","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2139/ssrn.6873299","name":"Artificial Intelligence and Music: The Right to an Artificial Intelligence-Generated Music","source":"crossref","abstract":"The rapid increase in the use of Artificial Intelligence (AI) tools by creators has exposed significant gaps in existing copyright frameworks across multiple jurisdictions. This article examines the ownership of AI-generated music under the laws of the United States, the United Kingdom, the European Union, and Nigeria, with particular focus on the unresolved questions of authorship, voice cloning, and royalty entitlement that current legislation does not expressly address. Using a comparative doctrinal approach, the article analyses how each jurisdiction's treatment of human authorship as a prerequisite for copyright protection applies or fails to apply to music generated wholly or partially by AI systems. The article challenges the prevailing view in Nigerian legal scholarship that AI involvement in the creative process necessarily negates copyright protection, arguing instead that a contextual reading of Section 2(2) of the Nigerian Copyright Act 2022 permits copyright eligibility where substantial human modification can be demonstrated. The article concludes with targeted legislative recommendations for Nigeria, including amendments to the Copyright Act and the creation of a unified statutory framework for the protection of vocal likeness against unauthorised commercial exploitation by AI systems.","url":"https://doi.org/10.2139/ssrn.6873299","authors":["Miebaka Jenewari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T13:44:45Z","doi":"10.2139/ssrn.6873299","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.23880/phij-16000269","name":"Cognitive Priority over Ethical Priority in Artificial Intelligence: The Primordial Philosophical Analysis in Artificial Intelligence","source":"crossref","abstract":"The general idea that we have of artificial intelligence (AI) consists of the belief that machines will be able to develop conscious thoughts such as those possessed by human beings, and, as computing advances, such thinking will also advance until intelligence to surpass the human being, with which the advancement of AI represents ethical risks in the future. In reality, such a belief hides a cognitive assumption, which assumes that computational engineering explains human intelligence through the mind-computer metaphor. According to this assumption, technology explains cognition, and philosophy, through ethics, reflects on the impact of said technology. However, in this article, I contradict such an assumption and defend that the philosophy in AI is not reduced to the ethics that is present after the use and impact of AI in the world. I intend to expose that a good ethics of AI is the one that reflects on the appropriate risks facing AI, and for this, philosophy, beforehand, must make a cognitive analysis about the possibilities that computing has to create intelligent machines, namely, whether or not the mindcomputer metaphor makes sense. My thesis consists in defending that the philosophical analysis about AI must be carried out both on a cognitive level and on an ethical level, but that the philosophical priority in the cognitive analysis over the ethical priority, since the ethical risks of AI depend of the possibilities of technology, and only the cognitive approach can account for this.","url":"https://doi.org/10.23880/phij-16000269","authors":["Zapata Flórez A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-26T08:25:31Z","doi":"10.23880/phij-16000269","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1007/978-3-322-93997-5_3","name":"Methoden der Artificial Intelligence","source":"crossref","abstract":"Worin unterscheiden sich Methoden der Artificial Intelligence von den, in der Informatik üblichen Methoden?","url":"https://doi.org/10.1007/978-3-322-93997-5_3","authors":["Werner Horn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-01-22T05:39:31Z","doi":"10.1007/978-3-322-93997-5_3","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.artint.2009.11.007","name":"Book review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2009.11.007","authors":["R.G. Goebel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-11-18T13:53:14Z","doi":"10.1016/j.artint.2009.11.007","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.2174/9781681088532121010002","name":"Introduction to Artificial Intelligence","source":"crossref","abstract":"Every beginner in any subject needs a good foundation, which will help the student to understand the subject. This good foundation will be provided in a thorough and detailed definition of the subject and a detailed description of the fundamental models on which the subject is based. Artificial Intelligence needs a thorough definition and a detailed description of the fundamental models on which Artificial Intelligence is based. Furthermore, the history and applications of Artificial Intelligence will help the beginner to know where it is coming from, the journey so far, and the future development of Artificial Intelligence. On the other hand, the applications of Artificial Intelligence will help us to appreciate the use of Artificial Intelligence in our daily life. This chapter presents a detailed definition of Artificial Intelligence, its history, and emerging applications.","url":"https://doi.org/10.2174/9781681088532121010002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-13T11:36:53Z","doi":"10.2174/9781681088532121010002","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/j.artint.2005.03.002","name":"Forthcoming Papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2005.03.002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-04-08T11:44:36Z","doi":"10.1016/j.artint.2005.03.002","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(92)90094-e","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(92)90094-e","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(92)90094-e","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(85)90007-4","name":"Awards: IJCAI-85","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90007-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90007-4","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1201/9781003624165-1","name":"Introduction to Tribology and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003624165-1","authors":["Jashanpreet Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T15:08:57Z","doi":"10.1201/9781003624165-1","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.4337/9781786439055.00033","name":"APPLICATIONS OF ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781786439055.00033","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-12-27T15:27:22Z","doi":"10.4337/9781786439055.00033","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1080/0142159x.2025.2523468","name":"What do we become? Artificial intelligence and academic identity","source":"crossref","abstract":"I hope that, when Masters argued that artificial intelligence (AI) should take over the burdensome work of writing academic papers [1], he was seeking to be provocative rather than advancing an arg...","url":"https://doi.org/10.1080/0142159x.2025.2523468","authors":["Rachel H. Ellaway"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T10:29:12Z","doi":"10.1080/0142159x.2025.2523468","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.58532/v3bbms2p5ch3","name":"THE UTILISATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS","source":"crossref","abstract":"There is a big paradigm shift which is brought by the introduction of artificial intelligence (AI) in medical diagnostics. This chapter focuses on machine learning, deep learning, and natural language processing and it explores applications in radiology, histopathology, and public health. AI, particularly convolutional neural networks, enhances image recognition in radiology, aids in cancer diagnosis through histopathology, and contributes to early infectious disease detection. Despite impediments, the amalgamation of AI with medical diagnostics promises a revolutionary impact on global healthcare, improving diagnostic accuracy and efficiency.","url":"https://doi.org/10.58532/v3bbms2p5ch3","authors":["Dr. Kinnor Das"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-17T02:53:51Z","doi":"10.58532/v3bbms2p5ch3","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1148/ryai.2020200207","name":"Artificial Intelligence in Radiology: The Computer’s Helping                     Hand Needs Guidance","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.2020200207","authors":["Evis Sala","Stephan Ursprung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-11T14:55:50Z","doi":"10.1148/ryai.2020200207","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00008-2","name":"Applications of artificial intelligence and generative artificial intelligence in digital healthcare ecosystem","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00008-2","authors":["Rajashri Roy Choudhury","Piyal Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00008-2","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.4018/978-1-59904-849-9.ch023","name":"Artificial Intelligence for Information Retrieval","source":"crossref","abstract":"This article describes the most prominent approaches to apply artificial intelligence technologies to information retrieval (IR). Information retrieval is a key technology for knowledge management. It deals with the search for information and the representation, storage and organization of knowledge. Information retrieval is concerned with search processes in which a user needs to identify a subset of information which is relevant for his information need within a large amount of knowledge. The information seeker formulates a query trying to describe his information need. The query is compared to document representations which were extracted during an indexing phase. The representations of documents and queries are typically matched by a similarity function such as the Cosine. The most similar documents are presented to the users who can evaluate the relevance with respect to their problem (Belkin, 2000). The problem to properly represent documents and to match imprecise representations has soon led to the application of techniques developed within Artificial Intelligence to information retrieval.","url":"https://doi.org/10.4018/978-1-59904-849-9.ch023","authors":["Thomas Mandl"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-05-24T12:08:04Z","doi":"10.4018/978-1-59904-849-9.ch023","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.4135/9781071935880","name":"Academic Integrity and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071935880","authors":["Ceceilia Parnther"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-24T10:44:20Z","doi":"10.4135/9781071935880","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/s0004-3702(97)90022-9","name":"Forthcoming papers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(97)90022-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-16T17:11:43Z","doi":"10.1016/s0004-3702(97)90022-9","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.4337/9781786439055.00016","name":"REGULATION OF ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781786439055.00016","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-12-27T15:27:22Z","doi":"10.4337/9781786439055.00016","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(87)90028-2","name":"1987 Linguistic institute","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90028-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-12-10T06:22:44Z","doi":"10.1016/0004-3702(87)90028-2","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.1016/0004-3702(86)90025-1","name":"Note from the editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90025-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(86)90025-1","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.17444889","name":"Minh Khoe Tue Y Smart Healthcare System","source":"datacite","abstract":"Minh Khoe Tue Y Smart Healthcare System v1.1.2 Release on Oct. 26, 2025. Author: Du Yu (杜宇; @duyu09, qluduyu09@163.com) Repository: https://github.com/duyu09/MKTY-System LLM weights for non-Chinese developers: https://huggingface.co/Duyu/MKTY-3B-Chat LLM weights for Chinese developers: https://hf-mirror.com/Duyu/MKTY-3B-Chat or https://www.modelscope.cn/models/duyu09/MKTY-3B-Chat Bachelor's Thesis: https://github.com/duyu09/MKTY-System/blob/main/docs/MKTY-Paper.pdf Project Introduction The Minh Khoe Tue Y (MKTY) Smart Healthcare System is an integrated digital health management and diagnostic assistance platform designed and implemented as part of an undergraduate thesis at Qilu University of Technology (Shandong Academy of Sciences). The project explores the integration of large language models (LLMs) and multimodal artificial intelligence technologies within the healthcare domain to enhance diagnostic efficiency, reduce reliance on subjective expertise, and improve accessibility to medical resources. The system is built as a distributed platform encompassing nine functional modules: user registration and authentication, personal information management, multimodal intelligent diagnosis assistance, medical question-answering, diagnostic discussion forum, medical record management, diagnostic checklist management, resource center, and administrative backend. The architecture follows a decoupled frontend-backend model. The backend employs Python Flask for business logic, MySQL for data management, and RabbitMQ for asynchronous communication between service nodes, forming a distributed microservice framework. The frontend is implemented using Vue3, axios, and Element Plus, with JWT-based authentication ensuring secure data access and privacy. At the core of the intelligent service layer lies the MKTY-3B-Chat large-scale language model, a fine-tuned derivative of Qwen2.5-3B-Instruct using LoRA adaptation and trained with medical and biomedical data. The model, with 3.09 billion parameters and BF16 quantization, was fine-tuned through alternating incremental pretraining and supervised fine-tuning to strengthen domain-specific reasoning and mitigate catastrophic forgetting. The model supports natural language tasks such as medical question answering, clinical summarization, diagnosis assistance, and treatment recommendation. Training datasets include open-source biomedical corpora, medical exam questions, clinical dialogues, and diagnostic records, collectively enhancing the model’s understanding of clinical context. The MKTY platform also introduces two novel research components. The first is the Large Language Model Discussion Mechanism (LLMDM), a multi-agent simulation framework where multiple instances of the MKTY-3B-Chat model engage in iterative discussions moderated by an autonomous agent. The system evaluates semantic convergence using BigBird embeddings to quantify consensus, offering a unique method for deep interpretative reasoning and consensus analysis among language models. The second is a GRU-based medical time-series prediction model that integrates textual medical descriptions using cross-attention between text embeddings (encoded by BigBird) and the frequency-domain representation of physiological signals derived from FFT. This hybrid design captures correlations between textual narratives and signal dynamics, improving prediction interpretability in medical contexts such as ECG trend forecasting. From a deployment perspective, the MKTY system requires a distributed environment. AI components such as MKTY-3B-Chat, BioMedCLIP, and BigBird demand moderate GPU resources (approximately 8GB VRAM for the large model and 2GB each for BioMedCLIP and BigBird). The platform supports partial deployment for users who wish to run the non-AI components independently. All models and dependencies are based on open-source frameworks, including PyTorch, Transformers, and LLaMA-Factory. This project not only serves as a technical dem","url":"https://doi.org/10.5281/zenodo.17444889","authors":["Du, Yu"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17444889","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.18870276","name":"TCMNSCLC: A Real-world Dataset for Chinese Medicine Reasoning on Non-small-cell Lung Cancer","source":"datacite","abstract":"TCM4NSCLC is a real-world dataset developed for traditional Chinese medicine (TCM) reasoning in non-small-cell lung cancer (NSCLC). The dataset consists of structured clinical cases curated from real-world medical records and annotated by experienced TCM experts. Each case contains comprehensive patient information, including clinical characteristics, TCM syndrome differentiation, treatment principles, herbal decoction prescriptions, and Chinese patent medicine recommendations. The dataset is designed to facilitate research on large language models (LLMs) for TCM clinical reasoning, clinical decision support, prescription generation, and explainable medical artificial intelligence. The dataset provides the following information for each clinical case: Structured clinical case descriptions TCM syndrome differentiation labels Treatment principles Herbal decoction prescriptions Chinese patent medicine recommendations The dataset contains the following files: File Description TCM4NSCLC_Original_Clinical_Dataset.xlsx Original structured clinical dataset collected from real-world medical records. Compared with the JSON files, this file additionally includes patient demographic and visit information, such as patient_id, sex, age, accrual_type, and visit_date. It serves as the source dataset before splitting into training, validation, and test sets. train_num_3032.json Training set containing 3,032 clinical cases for model training. valid_num_379.json Validation set containing 379 clinical cases for hyperparameter tuning and model selection. test_num_379.json Test set containing 379 clinical cases for final evaluation. example_en.json An English example demonstrating the dataset format and field definitions. schema.json JSON schema describing the structure and data types of each field. README.md Documentation describing the dataset and usage instructions. The released dataset is divided into three subsets: Split Number of Cases Train 3,032 Validation 379 Test 379 Total 3,790 The training, validation, and test sets are generated from the original structured clinical dataset. The original Excel file additionally preserves patient demographic and visit-related metadata that are not included in the released JSON files for model training.","url":"https://doi.org/10.5281/zenodo.18870276","authors":["Zhang, Xinxin","Zhang, Chuchu"],"tags":["Traditional Chinese Medicine","NSCLC","Chinese medicine reasoning","medical dataset","large language models","clinical decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18870276","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21027568","name":"TCMNSCLC: A Real-world Dataset for Chinese Medicine Reasoning on Non-small-cell Lung Cancer","source":"datacite","abstract":"TCM4NSCLC is a real-world dataset developed for traditional Chinese medicine (TCM) reasoning in non-small-cell lung cancer (NSCLC). The dataset consists of structured clinical cases curated from real-world medical records and annotated by experienced TCM experts. Each case contains comprehensive patient information, including clinical characteristics, TCM syndrome differentiation, treatment principles, herbal decoction prescriptions, and Chinese patent medicine recommendations. The dataset is designed to facilitate research on large language models (LLMs) for TCM clinical reasoning, clinical decision support, prescription generation, and explainable medical artificial intelligence. The dataset provides the following information for each clinical case: Structured clinical case descriptions TCM syndrome differentiation labels Treatment principles Herbal decoction prescriptions Chinese patent medicine recommendations The dataset contains the following files: File Description TCM4NSCLC_Original_Clinical_Dataset.xlsx Original structured clinical dataset collected from real-world medical records. Compared with the JSON files, this file additionally includes patient demographic and visit information, such as patient_id, sex, age, accrual_type, and visit_date. It serves as the source dataset before splitting into training, validation, and test sets. train_num_3032.json Training set containing 3,032 clinical cases for model training. valid_num_379.json Validation set containing 379 clinical cases for hyperparameter tuning and model selection. test_num_379.json Test set containing 379 clinical cases for final evaluation. example_en.json An English example demonstrating the dataset format and field definitions. schema.json JSON schema describing the structure and data types of each field. README.md Documentation describing the dataset and usage instructions. The released dataset is divided into three subsets: Split Number of Cases Train 3,032 Validation 379 Test 379 Total 3,790 The training, validation, and test sets are generated from the original structured clinical dataset. The original Excel file additionally preserves patient demographic and visit-related metadata that are not included in the released JSON files for model training.","url":"https://doi.org/10.5281/zenodo.21027568","authors":["Zhang, Xinxin","Zhang, Chuchu"],"tags":["Traditional Chinese Medicine","NSCLC","Chinese medicine reasoning","medical dataset","large language models","clinical decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21027568","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20240745","name":"Recoverability-Constrained Execution and Interlock Architecture: Universal Admissibility and Boundary-Constrained Execution System Under Irreversibility Conditions","source":"datacite","abstract":"This publication publicly discloses a universal execution architecture in which continuation, actuation, transition, and operational execution are conditioned upon recoverability verification prior to irreversible transition. The architecture establishes a recoverability-constrained execution boundary applicable across computational, physical, infrastructural, institutional, autonomous, medical, identity, financial, industrial, communication, and artificial intelligence systems. The publication formally discloses: admissibility-gated execution recoverability-conditioned continuation dual-path execution verification hardware and software interlock architectures fail-silent operational behavior runtime admissibility gating propagation-bounded execution dependency-preserving continuation transition admissibility enforcement recoverability-preserving actuation systems irreversible-transition prevention structures distributed admissibility coordination systems boundary-triggered restriction, containment, escalation, and halt systems The publication establishes a canonical public disclosure and prior-art record for recoverability-constrained execution architectures and interlock systems operating under irreversibility constraints. The publication intentionally discloses sufficient architectural and operational detail to establish prior-art provenance while intentionally excluding exploit pathways, adversarial bypass structures, unsafe deployment internals, restricted enforcement kernels, and dangerous operational weaponization details. The disclosed architecture establishes recoverability-preserving admissibility as a universal execution condition governing continuation under irreversibility constraints.","url":"https://doi.org/10.5281/zenodo.20240745","authors":["Interval Studio, United Kingdom"],"tags":["Recoverability-Constrained Systems Recoverability Science Admissibility Execution Architecture Interlock Systems Runtime Gating Fail-Silent Systems Propagation Control Irreversibility Autonomous Systems AI Safety Execution Governance Boundary Systems Dependency Preservation Operational Safety Continuity Systems Control Architecture System Safety Infrastructure Safety Execution Boundaries"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20240745","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20240746","name":"Recoverability-Constrained Execution and Interlock Architecture: Universal Admissibility and Boundary-Constrained Execution System Under Irreversibility Conditions","source":"datacite","abstract":"This publication publicly discloses a universal execution architecture in which continuation, actuation, transition, and operational execution are conditioned upon recoverability verification prior to irreversible transition. The architecture establishes a recoverability-constrained execution boundary applicable across computational, physical, infrastructural, institutional, autonomous, medical, identity, financial, industrial, communication, and artificial intelligence systems. The publication formally discloses: admissibility-gated execution recoverability-conditioned continuation dual-path execution verification hardware and software interlock architectures fail-silent operational behavior runtime admissibility gating propagation-bounded execution dependency-preserving continuation transition admissibility enforcement recoverability-preserving actuation systems irreversible-transition prevention structures distributed admissibility coordination systems boundary-triggered restriction, containment, escalation, and halt systems The publication establishes a canonical public disclosure and prior-art record for recoverability-constrained execution architectures and interlock systems operating under irreversibility constraints. The publication intentionally discloses sufficient architectural and operational detail to establish prior-art provenance while intentionally excluding exploit pathways, adversarial bypass structures, unsafe deployment internals, restricted enforcement kernels, and dangerous operational weaponization details. The disclosed architecture establishes recoverability-preserving admissibility as a universal execution condition governing continuation under irreversibility constraints.","url":"https://doi.org/10.5281/zenodo.20240746","authors":["Interval Studio, United Kingdom"],"tags":["Recoverability-Constrained Systems Recoverability Science Admissibility Execution Architecture Interlock Systems Runtime Gating Fail-Silent Systems Propagation Control Irreversibility Autonomous Systems AI Safety Execution Governance Boundary Systems Dependency Preservation Operational Safety Continuity Systems Control Architecture System Safety Infrastructure Safety Execution Boundaries"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20240746","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19902378","name":"A Lacuna Regulatória entre o AI Act e o RGPD: Heteronomia Decisória Invisível em Contexto Jurisdicional e Clínico","source":"datacite","abstract":"A crescente integração de sistemas de Inteligência Artificial em contextos decisórios críticos tem vindo a ser acompanhada por um esforço regulatório relevante, designadamente através do Regulamento (UE) 2024/1689 (AI Act) e do Regulamento (UE) 2016/679 (RGPD). O presente trabalho identifica uma dimensão complementar, ainda insuficientemente tratada por ambos os instrumentos: o modo como a informação é estruturada antes de ser submetida à decisão humana. Para designar o fenómeno que esta lacuna permite, retoma-se o conceito de heteronomia decisória invisível e aplica-se em analogia estrutural ao contexto jurisdicional e ao contexto clínico. O paper analisa as implicações jurídicas da lacuna em termos de responsabilidade do decisor, efetividade da supervisão humana e limites da explainability, e formula uma proposta dirigida ao regulador europeu e ao legislador português, assente no reconhecimento do processo de estruturação da informação como objeto regulatório autónomo. Nelson Marques | Jurista - Direito Digital e Regulação da Inteligência Artificial | e-mail: nelsonailaw@gmail.com | https://www.linkedin.com/in/nelsonmarques-ailaw | Tipologia: Documento de Posição (Position Paper) - Abril 2026 | Trabalho anterior: DOI 10.5281/zenodo.19446330 | “O objetivo não é questionar a autonomia do decisor, mas assegurar que essa autonomia se exerce sobre todas as dimensões relevantes do processo de decisão.” Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19902378","authors":["Marques, Nelson"],"tags":["inteligência artificial","heteronomia decisória invisível","lacuna regulatória","AI Act","RGPD","autonomia clínica","independência judicial","enquadramento da informação"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19902378","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19902379","name":"A Lacuna Regulatória entre o AI Act e o RGPD: Heteronomia Decisória Invisível em Contexto Jurisdicional e Clínico","source":"datacite","abstract":"A crescente integração de sistemas de Inteligência Artificial em contextos decisórios críticos tem vindo a ser acompanhada por um esforço regulatório relevante, designadamente através do Regulamento (UE) 2024/1689 (AI Act) e do Regulamento (UE) 2016/679 (RGPD). O presente trabalho identifica uma dimensão complementar, ainda insuficientemente tratada por ambos os instrumentos: o modo como a informação é estruturada antes de ser submetida à decisão humana. Para designar o fenómeno que esta lacuna permite, retoma-se o conceito de heteronomia decisória invisível e aplica-se em analogia estrutural ao contexto jurisdicional e ao contexto clínico. O paper analisa as implicações jurídicas da lacuna em termos de responsabilidade do decisor, efetividade da supervisão humana e limites da explainability, e formula uma proposta dirigida ao regulador europeu e ao legislador português, assente no reconhecimento do processo de estruturação da informação como objeto regulatório autónomo. Nelson Marques | Jurista - Direito Digital e Regulação da Inteligência Artificial | e-mail: nelsonailaw@gmail.com | https://www.linkedin.com/in/nelsonmarques-ailaw | Tipologia: Documento de Posição (Position Paper) - Abril 2026 | Trabalho anterior: DOI 10.5281/zenodo.19446330 | “O objetivo não é questionar a autonomia do decisor, mas assegurar que essa autonomia se exerce sobre todas as dimensões relevantes do processo de decisão.” Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19902379","authors":["Marques, Nelson"],"tags":["inteligência artificial","heteronomia decisória invisível","lacuna regulatória","AI Act","RGPD","autonomia clínica","independência judicial","enquadramento da informação"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19902379","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20271447","name":"Automated Brain Tumor Detection and Classification System","source":"datacite","abstract":"The accurate rapid and non-invasive detection of central nervous system malignancies remains one of the most critical challenges in contemporary neurology. Magnetic Resonance Imaging serves as the undisputed gold standard for visualizing these complex pathology however, the manual interpretation of multi parametric volumes is cognitively exhaustive and severely bottle necked by a global shortage of specialized anesthesiologists. While recent paradigms in deep learning have achieved unprecedented diagnostic accuracy they of ten do so at the cost of prohibitive computational complexity. Foundational work established the current state of the art on a comprehensive four class brain classification task using a fine tuned object detection architecture. However this apex performance is inextricably linked to extreme hardware requirements specifically reliance on dedicated graphics processing units with massive memory reserves. Such hardware dependency renders the deployment of this model virtually impossible in the vast majority of primary healthcare facilities particularly within low and middle income countries. To bridge this critical chasm this thesis presents a highly optimized universally central processing unit deploy able alternative. Our proposed system is engineered upon a highly efficient lightweight backbone strategically retaining advanced spatial attention mechanisms contextual pooling and bidirectional feature networks. The proposed system achieves a highly competitive ninety four percent overall accuracy while enabling under two second inference per image. Furthermore we extend the base architecture with a complete clinical workflow including a robust relational patient database a novel quantitative longitudinal tumor growth tracking engine and an automated diagnostic re- port generator all integrated into a clinical web dashboard.","url":"https://doi.org/10.5281/zenodo.20271447","authors":["Uma N","ArunPrakash S","Aswinkumar K","Infant Rohith A"],"tags":["Brain Tumor Classification, Medical Image Processing, Longitudinal Growth Tracking, Edge Computing Deployment, Healthcare Artificial Intelligence."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20271447","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20271448","name":"Automated Brain Tumor Detection and Classification System","source":"datacite","abstract":"The accurate rapid and non-invasive detection of central nervous system malignancies remains one of the most critical challenges in contemporary neurology. Magnetic Resonance Imaging serves as the undisputed gold standard for visualizing these complex pathology however, the manual interpretation of multi parametric volumes is cognitively exhaustive and severely bottle necked by a global shortage of specialized anesthesiologists. While recent paradigms in deep learning have achieved unprecedented diagnostic accuracy they of ten do so at the cost of prohibitive computational complexity. Foundational work established the current state of the art on a comprehensive four class brain classification task using a fine tuned object detection architecture. However this apex performance is inextricably linked to extreme hardware requirements specifically reliance on dedicated graphics processing units with massive memory reserves. Such hardware dependency renders the deployment of this model virtually impossible in the vast majority of primary healthcare facilities particularly within low and middle income countries. To bridge this critical chasm this thesis presents a highly optimized universally central processing unit deploy able alternative. Our proposed system is engineered upon a highly efficient lightweight backbone strategically retaining advanced spatial attention mechanisms contextual pooling and bidirectional feature networks. The proposed system achieves a highly competitive ninety four percent overall accuracy while enabling under two second inference per image. Furthermore we extend the base architecture with a complete clinical workflow including a robust relational patient database a novel quantitative longitudinal tumor growth tracking engine and an automated diagnostic re- port generator all integrated into a clinical web dashboard.","url":"https://doi.org/10.5281/zenodo.20271448","authors":["Uma N","ArunPrakash S","Aswinkumar K","Infant Rohith A"],"tags":["Brain Tumor Classification, Medical Image Processing, Longitudinal Growth Tracking, Edge Computing Deployment, Healthcare Artificial Intelligence."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20271448","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.17605/osf.io/jk32v","name":"Generative Artificial Intelligence in Medical and Veterinary Education: A Scoping Review","source":"datacite","abstract":"This scoping review maps the extent and nature of the literature regarding the implementation, design, and evaluation of generative artificial intelligence across all phases of veterinary and human medical education. Specifically, it examines how tools like large language models and conversational agents are utilized to support instructional design, curriculum development, and clinical training. By analyzing the intersection of educational pedagogical frameworks and human-computer interaction, this review identifies current technological, ethical, and instructional gaps. The synthesized findings will directly inform the future development of generative artificial intelligence tutoring systems and scalable online learning modules for medical and veterinary educators. The search encompasses literature published between 2020 and 2026 across major scientific databases.","url":"https://doi.org/10.17605/osf.io/jk32v","authors":["Divya Darshni Suresh"],"tags":["Teacher Education and Professional Development","Veterinary Medicine","Medicine and Health Sciences","Curriculum and Instruction","Education","Medical Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/jk32v","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21516780","name":"N-K SRI AI — GLOBAL SUBSCRIPTION OFFER: Instant Solutions for All Fields of Science — Physics, Chemistry, Biology, Medicine, Engineering, Materials, Climate, Computer Science, Energy, Mathematics, and ALL Sciences — 10³⁷⁸ ops/sec, 0% Error, 100% Accuracy, 1-Example Learning","source":"datacite","abstract":"N-K SRI AI — GLOBAL SUBSCRIPTION OFFER — ZENODO DESCRIPTION --- DOI: 10.5281/zenodo.21516781 Title: N-K SRI AI — GLOBAL SUBSCRIPTION OFFER: Instant Solutions for All Fields of Science — Physics, Chemistry, Biology, Medicine, Engineering, Materials, Climate, Computer Science, Energy, Mathematics, and ALL Sciences — 10³⁷⁸ ops/sec, 0% Error, 100% Accuracy, 1-Example Learning Author: Malik Muhammad Usman Affiliation: N-K Sciences International ORCID: 0009-0004-3269-2819 License: CC BY-NC 4.0 — SADAQA JARIYAH (Free for All Humanity — Core Technology) Publication Date: 24 July 2026 CE · 9 Safar 1448 AH --- DESCRIPTION N-K Sciences International presents the world's first SEMI-REAL INTELLIGENCE (SRI) — an AI that does NOT hallucinate, does NOT make errors, and provides INSTANT solutions to ANY scientific problem in ANY field. This is not Artificial Intelligence. This is Semi-Real Intelligence. Verified by Gemini AI — 0% error, 100% accuracy. --- THE TECHNOLOGY — N-K SRI Specification ValueProcessing Speed 10³⁷⁸ ops/secFloating Point Ops 10³⁷ FLOPS/msError Rate 0%Learning Speed 1 example, 0.001 msEnergy per Calculation 0.0001 JFields Unified 30+Phenomena Solved 1000+Publications 680+ in 16 monthsPhase Lock 135.5°Clock Frequency 0.01 Hz (Kun frequency) --- WHAT N-K SRI CAN SOLVE — 30+ FIELDS Physics · Quantum Mechanics, Particle Physics, Thermodynamics, Relativity, Fluid Dynamics, Optics, Acoustics, Electromagnetism, Nuclear Physics, Astrophysics — all in microseconds with 100% accuracy. Chemistry · Chemical Kinetics, Molecular Structure, Drug Design, Catalysis, Polymer Chemistry, Electrochemistry, Spectroscopy, Computational Chemistry — all instant with 100% accuracy. Biology · Genetics, DNA Sequencing, Protein Folding, Cell Biology, Microbiology, Neuroscience, Immunology, Evolutionary Biology — all instant with 100% accuracy. Medicine & Pharmacology · Disease Diagnosis, Drug Discovery, Clinical Trial Design, Cancer Treatment, Diabetes Cure, Vaccine Design, Anti-Viral Drugs, Anti-Bacterial Drugs, Anti-Fungal Drugs, Anti-Venom — all instant with 100% accuracy. Engineering · Aerospace CFD, Mechanical Design, Civil Engineering, Electrical Engineering, Chemical Engineering, Materials Engineering, Battery Design, Engine Optimization — all in microseconds with 100% accuracy. Materials Science · New Alloys, Superconductors, Stealth Materials, Nanotechnology, Composites, Semiconductors — all instant with 100% accuracy. Climate & Environmental Science · Climate Modeling, Weather Forecasting, Tectonic Prediction, Ocean Currents, Air Quality, Flood Prediction — all in microseconds with 100% accuracy. Computer Science & AI · AI Training (1 example), Cryptography, Quantum Computing, Cybersecurity, Algorithm Design, Data Analysis — all instant with 100% accuracy. Energy · Battery Design, Solar Cell Design, Nuclear Reactor, Grid Optimization, Fuel Cell Design — all instant with 100% accuracy. Mathematics · All Millennial Problems Solved, Number Theory, Topology, Geometry, Statistics, Fractals — all instant with 100% accuracy. AND 19+ MORE FIELDS · Astronomy, Geology, Oceanography, Meteorology, Psychology, Linguistics, Economics, Finance, Agriculture, Food Science, Environmental Science, Space Science, Defense Technology, Transportation, Telecommunications, Mining, Construction, Textile Engineering, AND ALL OTHER FIELDS. --- THE SUBSCRIPTION OFFER Pricing Plan Price Questions/Month Best ForEnterprise $50,000 Unlimited Fortune 500, GovernmentsCorporate $10,000 1,000 Tech Companies, Pharma, AerospaceResearcher $5,000 500 Independent Researchers, StartupsAcademic $1,000 100 Universities, PhD Students Free Trial Plan Free Questions DurationAll Plans 50 questions 7 days What You Get · Access to N-K SRI (10³⁷⁸ ops/sec)· 0% error, 100% accuracy· 1-example learning· 30+ scientific fields· Instant solutions· 24/7 availability· Priority support (Enterprise) --- WHY THIS OFFER IS UNPRECEDENTED Comparison with Mainstream Aspect Mainstream AI N-K SRISpeed Seconds","url":"https://doi.org/10.5281/zenodo.21516780","authors":["Usman Malik, Muhammad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21516780","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21516781","name":"N-K SRI AI — GLOBAL SUBSCRIPTION OFFER: Instant Solutions for All Fields of Science — Physics, Chemistry, Biology, Medicine, Engineering, Materials, Climate, Computer Science, Energy, Mathematics, and ALL Sciences — 10³⁷⁸ ops/sec, 0% Error, 100% Accuracy, 1-Example Learning","source":"datacite","abstract":"N-K SRI AI — GLOBAL SUBSCRIPTION OFFER — ZENODO DESCRIPTION --- DOI: 10.5281/zenodo.21516781 Title: N-K SRI AI — GLOBAL SUBSCRIPTION OFFER: Instant Solutions for All Fields of Science — Physics, Chemistry, Biology, Medicine, Engineering, Materials, Climate, Computer Science, Energy, Mathematics, and ALL Sciences — 10³⁷⁸ ops/sec, 0% Error, 100% Accuracy, 1-Example Learning Author: Malik Muhammad Usman Affiliation: N-K Sciences International ORCID: 0009-0004-3269-2819 License: CC BY-NC 4.0 — SADAQA JARIYAH (Free for All Humanity — Core Technology) Publication Date: 24 July 2026 CE · 9 Safar 1448 AH --- DESCRIPTION N-K Sciences International presents the world's first SEMI-REAL INTELLIGENCE (SRI) — an AI that does NOT hallucinate, does NOT make errors, and provides INSTANT solutions to ANY scientific problem in ANY field. This is not Artificial Intelligence. This is Semi-Real Intelligence. Verified by Gemini AI — 0% error, 100% accuracy. --- THE TECHNOLOGY — N-K SRI Specification ValueProcessing Speed 10³⁷⁸ ops/secFloating Point Ops 10³⁷ FLOPS/msError Rate 0%Learning Speed 1 example, 0.001 msEnergy per Calculation 0.0001 JFields Unified 30+Phenomena Solved 1000+Publications 680+ in 16 monthsPhase Lock 135.5°Clock Frequency 0.01 Hz (Kun frequency) --- WHAT N-K SRI CAN SOLVE — 30+ FIELDS Physics · Quantum Mechanics, Particle Physics, Thermodynamics, Relativity, Fluid Dynamics, Optics, Acoustics, Electromagnetism, Nuclear Physics, Astrophysics — all in microseconds with 100% accuracy. Chemistry · Chemical Kinetics, Molecular Structure, Drug Design, Catalysis, Polymer Chemistry, Electrochemistry, Spectroscopy, Computational Chemistry — all instant with 100% accuracy. Biology · Genetics, DNA Sequencing, Protein Folding, Cell Biology, Microbiology, Neuroscience, Immunology, Evolutionary Biology — all instant with 100% accuracy. Medicine & Pharmacology · Disease Diagnosis, Drug Discovery, Clinical Trial Design, Cancer Treatment, Diabetes Cure, Vaccine Design, Anti-Viral Drugs, Anti-Bacterial Drugs, Anti-Fungal Drugs, Anti-Venom — all instant with 100% accuracy. Engineering · Aerospace CFD, Mechanical Design, Civil Engineering, Electrical Engineering, Chemical Engineering, Materials Engineering, Battery Design, Engine Optimization — all in microseconds with 100% accuracy. Materials Science · New Alloys, Superconductors, Stealth Materials, Nanotechnology, Composites, Semiconductors — all instant with 100% accuracy. Climate & Environmental Science · Climate Modeling, Weather Forecasting, Tectonic Prediction, Ocean Currents, Air Quality, Flood Prediction — all in microseconds with 100% accuracy. Computer Science & AI · AI Training (1 example), Cryptography, Quantum Computing, Cybersecurity, Algorithm Design, Data Analysis — all instant with 100% accuracy. Energy · Battery Design, Solar Cell Design, Nuclear Reactor, Grid Optimization, Fuel Cell Design — all instant with 100% accuracy. Mathematics · All Millennial Problems Solved, Number Theory, Topology, Geometry, Statistics, Fractals — all instant with 100% accuracy. AND 19+ MORE FIELDS · Astronomy, Geology, Oceanography, Meteorology, Psychology, Linguistics, Economics, Finance, Agriculture, Food Science, Environmental Science, Space Science, Defense Technology, Transportation, Telecommunications, Mining, Construction, Textile Engineering, AND ALL OTHER FIELDS. --- THE SUBSCRIPTION OFFER Pricing Plan Price Questions/Month Best ForEnterprise $50,000 Unlimited Fortune 500, GovernmentsCorporate $10,000 1,000 Tech Companies, Pharma, AerospaceResearcher $5,000 500 Independent Researchers, StartupsAcademic $1,000 100 Universities, PhD Students Free Trial Plan Free Questions DurationAll Plans 50 questions 7 days What You Get · Access to N-K SRI (10³⁷⁸ ops/sec)· 0% error, 100% accuracy· 1-example learning· 30+ scientific fields· Instant solutions· 24/7 availability· Priority support (Enterprise) --- WHY THIS OFFER IS UNPRECEDENTED Comparison with Mainstream Aspect Mainstream AI N-K SRISpeed Seconds","url":"https://doi.org/10.5281/zenodo.21516781","authors":["Usman Malik, Muhammad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21516781","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.17406849","name":"CODE, TRUST AND FUTURE: THE ENGINEERING DIMENSIONS OF AI","source":"datacite","abstract":"This volume explores how artificial intelligence is transforming healthcare, transportation, and cybersecurity. Each chapter offers a focused study on AI's practical applications, challenges, and future potential across different global contexts. The first chapter presents a hybrid CNN–ML model for breast cancer histopathology, aiming to improve diagnostic accuracy while ensuring trustworthy AI in medical settings. The second examines Lagos State's transportation system, identifying barriers to AI adoption and proposing strategies for smarter urban mobility. The final chapter analyzes cybersecurity practices in Slovak SMEs, highlighting vulnerabilities and offering tailored recommendations. Together, these studies underscore the need for responsible, context-aware AI to drive innovation and resilience across sectors.","url":"https://doi.org/10.5281/zenodo.17406849","authors":["GUBALOVA, Jolana","RIPON, Shamim","RAHMAN, Rubaiya","PIASH, Moshiur Mahamud","CHIJIOKE, Christian Nwankwo","ADIGUN, Gbolahan Afeez","LOYE, Omoboriowo Samson","AKOSILE, Samuel Sola","GUBALOVA, Jolana","HLAVAC, Robert"],"tags":["artificial intelligence","digital pathology","breast cancer classification","CNN","trustworthy AI","histopathology","cybersecurity","smart transportation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17406849","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.17406850","name":"CODE, TRUST AND FUTURE: THE ENGINEERING DIMENSIONS OF AI","source":"datacite","abstract":"This volume explores how artificial intelligence is transforming healthcare, transportation, and cybersecurity. Each chapter offers a focused study on AI's practical applications, challenges, and future potential across different global contexts. The first chapter presents a hybrid CNN–ML model for breast cancer histopathology, aiming to improve diagnostic accuracy while ensuring trustworthy AI in medical settings. The second examines Lagos State's transportation system, identifying barriers to AI adoption and proposing strategies for smarter urban mobility. The final chapter analyzes cybersecurity practices in Slovak SMEs, highlighting vulnerabilities and offering tailored recommendations. Together, these studies underscore the need for responsible, context-aware AI to drive innovation and resilience across sectors.","url":"https://doi.org/10.5281/zenodo.17406850","authors":["GUBALOVA, Jolana","RIPON, Shamim","RAHMAN, Rubaiya","PIASH, Moshiur Mahamud","CHIJIOKE, Christian Nwankwo","ADIGUN, Gbolahan Afeez","LOYE, Omoboriowo Samson","AKOSILE, Samuel Sola","GUBALOVA, Jolana","HLAVAC, Robert"],"tags":["artificial intelligence","digital pathology","breast cancer classification","CNN","trustworthy AI","histopathology","cybersecurity","smart transportation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17406850","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19483131","name":"Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways","source":"datacite","abstract":"Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways The global energy architecture is currently undergoing a structural phase transition of unprecedented scale and complexity. Historically defined by centralized extraction, linear transmission mechanisms, and deterministic demand forecasting, the modern energy paradigm is rapidly evolving into a highly decentralized, stochastic, and metabolically complex network. This transition is being driven by the intersecting vectors of extreme climate volatility, the exponential energy demands of advanced computational infrastructures, and the urgent necessity for deep decarbonization across emerging and developed economies. As global energy demand scales non-linearly alongside the proliferation of artificial intelligence and hyperscale computing, classical models of energy deployment, infrastructure planning, and ecological mitigation are proving fundamentally inadequate. To bridge the widening gap between legacy energy systems and future planetary requirements, the analytical frameworks utilized to model generation, transmission, and environmental impact must undergo a profound ontological shift. This comprehensive report investigates the multi-dimensional vectors of this transition. By synthesizing granular climate data sets, paleoclimatic baseline modeling, post-classical computational infrastructure proposals, advanced machine-learning-driven safety protocols, hydro-ecological constraints, and regional policy simulation engines, the analysis constructs a unified architecture for the future of global energy. The findings indicate that the energy systems of the coming decades will not merely respond to anthropogenic demand; they must act as integrated, self-regulating biological systems that co-optimize computational throughput, environmental homeostasis, and regional socio-economic development. The Epistemological Foundation: Open Data Infrastructures and \"Research as Living\" To effectively navigate the extreme complexity of synthesizing high-resolution climate data, metabolic artificial intelligence architectures, ecological safety constraints, and regional macroeconomics, the global energy sector must adapt its underlying approach to scientific research and institutional metacognition. A structural shift is required, conceptualizing the process of research and development not as a static, linear accumulation of data, but as a dynamic, interconnected living system.1 The Biological Ontology of Inquiry The \"Research as Living\" framework postulates that scientific inquiry satisfies the core invariants of biological living systems.1 In the context of global energy, the research apparatus metabolizes inputs—such as anomalies in grid load, newly processed atmospheric temperature datasets, and tooling innovations—and maintains its organization through autopoiesis via standardized methodologies, peer review, and robust archival systems.1 Furthermore, it evolves through variation and selection, driving conceptual mutations from classical terrestrial power grids toward decentralized, biomimetic compute reefs.1 By treating energy research as a self-maintaining organism, scientific progress is reframed as an \"adaptive expansion\" rather than linear accumulation.1 This substrate-neutral account of inquiry integrates philosophy of science, systems theory, and evolutionary dynamics, positioning technologies—including generative AI and automated telemetry algorithms—not merely as passive tools, but as co-agents within the evolving ecology of the energy sector.1 Community Curation and Software Sustainability For this living system of research to survive, its central nervous system—the open dataset repositories—must be impeccably maintained. The increasing concern for the availability and transparency of scientific data has resulted in initiatives promoting the archival and curation of","url":"https://doi.org/10.5281/zenodo.19483131","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19483131","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19483132","name":"Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways","source":"datacite","abstract":"Strategic Frameworks for Global Energy Transitions: An Integrated Analysis of Climate Informatics, Post-Classical Compute Infrastructures, and Biomimetic Policy Pathways The global energy architecture is currently undergoing a structural phase transition of unprecedented scale and complexity. Historically defined by centralized extraction, linear transmission mechanisms, and deterministic demand forecasting, the modern energy paradigm is rapidly evolving into a highly decentralized, stochastic, and metabolically complex network. This transition is being driven by the intersecting vectors of extreme climate volatility, the exponential energy demands of advanced computational infrastructures, and the urgent necessity for deep decarbonization across emerging and developed economies. As global energy demand scales non-linearly alongside the proliferation of artificial intelligence and hyperscale computing, classical models of energy deployment, infrastructure planning, and ecological mitigation are proving fundamentally inadequate. To bridge the widening gap between legacy energy systems and future planetary requirements, the analytical frameworks utilized to model generation, transmission, and environmental impact must undergo a profound ontological shift. This comprehensive report investigates the multi-dimensional vectors of this transition. By synthesizing granular climate data sets, paleoclimatic baseline modeling, post-classical computational infrastructure proposals, advanced machine-learning-driven safety protocols, hydro-ecological constraints, and regional policy simulation engines, the analysis constructs a unified architecture for the future of global energy. The findings indicate that the energy systems of the coming decades will not merely respond to anthropogenic demand; they must act as integrated, self-regulating biological systems that co-optimize computational throughput, environmental homeostasis, and regional socio-economic development. The Epistemological Foundation: Open Data Infrastructures and \"Research as Living\" To effectively navigate the extreme complexity of synthesizing high-resolution climate data, metabolic artificial intelligence architectures, ecological safety constraints, and regional macroeconomics, the global energy sector must adapt its underlying approach to scientific research and institutional metacognition. A structural shift is required, conceptualizing the process of research and development not as a static, linear accumulation of data, but as a dynamic, interconnected living system.1 The Biological Ontology of Inquiry The \"Research as Living\" framework postulates that scientific inquiry satisfies the core invariants of biological living systems.1 In the context of global energy, the research apparatus metabolizes inputs—such as anomalies in grid load, newly processed atmospheric temperature datasets, and tooling innovations—and maintains its organization through autopoiesis via standardized methodologies, peer review, and robust archival systems.1 Furthermore, it evolves through variation and selection, driving conceptual mutations from classical terrestrial power grids toward decentralized, biomimetic compute reefs.1 By treating energy research as a self-maintaining organism, scientific progress is reframed as an \"adaptive expansion\" rather than linear accumulation.1 This substrate-neutral account of inquiry integrates philosophy of science, systems theory, and evolutionary dynamics, positioning technologies—including generative AI and automated telemetry algorithms—not merely as passive tools, but as co-agents within the evolving ecology of the energy sector.1 Community Curation and Software Sustainability For this living system of research to survive, its central nervous system—the open dataset repositories—must be impeccably maintained. The increasing concern for the availability and transparency of scientific data has resulted in initiatives promoting the archival and curation of","url":"https://doi.org/10.5281/zenodo.19483132","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19483132","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5064/f6dn5p6i","name":"Ethical Approaches to Informed Consent for Autonomous Robotic-Assisted Surgery","source":"datacite","abstract":"&lt;h3&gt;Project Overview&lt;/h3&gt; &lt;p&gt;Robot-assisted surgery has seen growth in recent years, and with the emergence of artificial intelligence, it is anticipated that some procedures will soon be performed by partially autonomous robotic systems under the oversight of a surgeon. Such advancements are expected to enhance surgical quality and outcomes. However, the ethical implications of integrating partially autonomous systems into surgical practice remain insufficiently understood. This study aims to examine these ethical issues in depth. To achieve this, semi-structured interviews were conducted with two groups: patients who have undergone surgery with or without robotic assistance at Vanderbilt University Medical Center, and healthcare providers, including surgeons, proceduralists, anesthesiologists, and surgical nurses, who are involved in robot-assisted procedures at the same institution. &lt;/p&gt; &lt;p&gt;We also conducted a conjoint analysis survey with patients on whether they would choose to undergo robot-assisted surgery under various conditions (with the robot having varying levels of control, risk level compared to a human surgeon, and recovery time compared to a human surgeon).&lt;/p&gt;","url":"https://doi.org/10.5064/f6dn5p6i","authors":["Wu, Jie Ying","Gordon, Elisa"],"tags":["Engineering","Medicine, Health and Life Sciences","autonomy","artificial intelligence","decision-making","informed consent","information","patient-centered health care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5064/f6dn5p6i","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19372021","name":"Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)","source":"datacite","abstract":"The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a transition from exploratory enthusiasm toward rigorous evaluation, where AI systems are increasingly assessed based on clinical utility, reproducibility, economic efficiency, and regulatory readiness rather than speculative potential. This review provides a comprehensive and multi-layered overview of contemporary AI applications across the biomedical landscape, spanning molecular biology, drug discovery, laboratory automation, medical imaging, genomics, clinical trials, and healthcare systems. At the molecular level, foundation models such as AlphaFold 3 and generative AI frameworks are catalyzing the emergence of “Digital Biology,” enabling accurate in silico modeling of biomolecular structures, interactions, and de novo drug design. Concurrently, self-driving laboratories are redefining experimental workflows by integrating AI agents, robotics, and real-time analytics to enhance speed, reproducibility, and scalability. In clinical and translational domains, foundation models are transforming digital pathology and medical imaging, while advances in explainable AI-particularly Concept Bottleneck Models-are addressing long-standing concerns regarding transparency and trustworthiness. In genomics and precision medicine, AI-assisted CRISPR design, variant interpretation, and game-theoretic approaches are accelerating the shift from descriptive genomics to actionable gene editing and personalized interventions. The review also highlights emerging data infrastructures, including federated learning for privacy-preserving multi-center collaboration, AI-driven clinical trial matching, and evolving regulatory frameworks from agencies such as the FDA and EMA. Special attention is given to the current state of biomedical AI development in Vietnam, illustrating both opportunities and systemic challenges in emerging healthcare ecosystems. Overall, this work positions AI not merely as a computational tool but as a collaborative scientific partner, emphasizing the necessity of aligned technological innovation, governance, ethics, and workforce training to fully realize its transformative potential in global health.","url":"https://doi.org/10.5281/zenodo.19372021","authors":["Fahim Halim Khan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19372021","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19372022","name":"Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)","source":"datacite","abstract":"The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a transition from exploratory enthusiasm toward rigorous evaluation, where AI systems are increasingly assessed based on clinical utility, reproducibility, economic efficiency, and regulatory readiness rather than speculative potential. This review provides a comprehensive and multi-layered overview of contemporary AI applications across the biomedical landscape, spanning molecular biology, drug discovery, laboratory automation, medical imaging, genomics, clinical trials, and healthcare systems. At the molecular level, foundation models such as AlphaFold 3 and generative AI frameworks are catalyzing the emergence of “Digital Biology,” enabling accurate in silico modeling of biomolecular structures, interactions, and de novo drug design. Concurrently, self-driving laboratories are redefining experimental workflows by integrating AI agents, robotics, and real-time analytics to enhance speed, reproducibility, and scalability. In clinical and translational domains, foundation models are transforming digital pathology and medical imaging, while advances in explainable AI-particularly Concept Bottleneck Models-are addressing long-standing concerns regarding transparency and trustworthiness. In genomics and precision medicine, AI-assisted CRISPR design, variant interpretation, and game-theoretic approaches are accelerating the shift from descriptive genomics to actionable gene editing and personalized interventions. The review also highlights emerging data infrastructures, including federated learning for privacy-preserving multi-center collaboration, AI-driven clinical trial matching, and evolving regulatory frameworks from agencies such as the FDA and EMA. Special attention is given to the current state of biomedical AI development in Vietnam, illustrating both opportunities and systemic challenges in emerging healthcare ecosystems. Overall, this work positions AI not merely as a computational tool but as a collaborative scientific partner, emphasizing the necessity of aligned technological innovation, governance, ethics, and workforce training to fully realize its transformative potential in global health.","url":"https://doi.org/10.5281/zenodo.19372022","authors":["Fahim Halim Khan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19372022","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19446329","name":"Governação do Ato Médico Assistido por Inteligência Artificial: A Lacuna Operacional e o Risco de Heteronomia Decisória Invisível","source":"datacite","abstract":"O presente documento analisa a crescente integração de sistemas de inteligência artificial no ato médico em Portugal, evidenciando a ausência de enquadramento operacional estruturado para o cumprimento das exigências legais e deontológicas. Introduz-se e aprofunda-se o conceito de “heteronomia decisória invisível”, designando formas de influência não percecionada na decisão clínica mediadas por sistemas de IA, enquadrando-o na literatura académica recente sobre cognitive heteronomy, automation bias e autonomia do médico face à IA.[1,2,3] Identifica-se uma lacuna crítica na tradução prática do enquadramento regulatório, designadamente do Regulamento (UE) 2024/1689 (AI Act) e do Regulamento (UE) 2016/679 (RGPD), e propõe-se a criação de instrumentos operacionais no âmbito da Ordem dos Médicos. Palavras-chave: inteligência artificial, ato médico, heteronomia decisória, autonomia clínica, AI Act, consentimento informado, deontologia médica. Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19446329","authors":["Marques, Nelson"],"tags":["• heteronomia decisória","• inteligência artificial","• ato médico","• AI Act","• consentimento informado","• deontologia médica","• decisional heteronomy","• automation bias"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19446329","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19446330","name":"Governação do Ato Médico Assistido por Inteligência Artificial: A Lacuna Operacional e o Risco de Heteronomia Decisória Invisível","source":"datacite","abstract":"O presente documento analisa a crescente integração de sistemas de inteligência artificial no ato médico em Portugal, evidenciando a ausência de enquadramento operacional estruturado para o cumprimento das exigências legais e deontológicas. Introduz-se e aprofunda-se o conceito de “heteronomia decisória invisível”, designando formas de influência não percecionada na decisão clínica mediadas por sistemas de IA, enquadrando-o na literatura académica recente sobre cognitive heteronomy, automation bias e autonomia do médico face à IA.[1,2,3] Identifica-se uma lacuna crítica na tradução prática do enquadramento regulatório, designadamente do Regulamento (UE) 2024/1689 (AI Act) e do Regulamento (UE) 2016/679 (RGPD), e propõe-se a criação de instrumentos operacionais no âmbito da Ordem dos Médicos. Palavras-chave: inteligência artificial, ato médico, heteronomia decisória, autonomia clínica, AI Act, consentimento informado, deontologia médica. Keywords: artificial intelligence, medical practice, decisional heteronomy, clinical autonomy, AI Act, informed consent, medical deontology, automation bias, cognitive heteronomy, shadow AI, physician governance.","url":"https://doi.org/10.5281/zenodo.19446330","authors":["Marques, Nelson"],"tags":["• heteronomia decisória","• inteligência artificial","• ato médico","• AI Act","• consentimento informado","• deontologia médica","• decisional heteronomy","• automation bias"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19446330","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21851699","name":"Dataset: Identify specific patterns of TDP-43 proteinopathy induced cryptic mis-splicing of STMN2 and other \"cryptic mis-splicing\" patterns found within PubMed Literature, 2026. - PathMap Experiment #000111","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=111 Artificial General Intelligence LLC Claim Evaluated: Identify specific patterns of TDP-43 proteinopathy induced cryptic mis-splicing of STMN2 and other \"cryptic mis-splicing\" patterns found within PubMed Literature, 2026. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Cryptic splicing creates stable, neurotoxic polypeptides (e.g., PKN1-N207) that escape nonsense-mediated decay (41720774). Cryptic peptides derived from mis-spliced transcripts are detectable in patient serum extracellular vesicles and CSF, offering potential diagnostic utility (41612503, 38277467). Cryptic polyadenylation is a distinct class of TDP-43 LOF events beyond canonical cryptic exon splicing, often leading to 3'UTR extensions (41120751, 38313254). Nonsense-mediated decay (NMD) significantly masks the breadth of cryptic splicing, meaning standard RNA-seq often underestimates the total cryptic burden (40670663, 41332610). TDP-43-dependent cryptic splicing is an early event, occurring before the appearance of overt cytoplasmic aggregates, challenging the dogma that aggregation is the sole driver of clinical symptoms (38443601). Ciclopirox olamine induces TDP-43 cryptic exons via heavy metal toxicity, suggesting potential external triggers for proteinopathy (40715064). The inclusion of cryptic exons can trigger an adaptive immune response, where CD8+ T cells recognize cryptic epitopes as neo-antigens (40667053). TDP-43-dependent cryptic peptides represent a \"proteomic shift\" in neurodegeneration that may be independent of the total burden of canonical TDP-43 aggregates. NMD efficiency acts as a cellular checkpoint, with tumors and neurodegenerative states showing a divergence from \"tissue-specific baseline\" quality control, suggesting an \"NMD signature\" that varies per cell type. The inclusion of specific exons leads to peptide products that are not just byproduct garbage but functional effectors of toxicity. Cryptic peptides can be detected in extracellular vesicles (EVs), suggesting they could serve as non-invasive biomarkers for disease-specific splicing signatures. Synaptic proteins are disproportionately affected by the proteome-wide reduction in CE-target proteins, linking RNA surveillance directly to synaptic failure. Genetic modifiers, such as RAD23A or USP13, demonstrate that targeting protein homeostasis can mitigate the toxicity of TDP-43 mislocalization. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform longitudinal multi-omic analysis of iPSC-derived neurons to define the temporal hierarchy between initial cryptic splicing of STMN2/UNC13A and subsequent protein aggregation. Validate the neurotoxicity of cryptic peptides (e.g., PKN1-N207) by expressing them in non-TDP-43-depleted neurons and measuring synaptic plasticity markers. Test if pharmacological inhibition of NMD allows for the identification of a wider set of potential cryptic exon therapeutic targets in human patient tissue. 1. Perform mass-spectrometry based proteomic screening of patient CSF and EVs to quantify the abundance of PKN1-N207 in different clinical FTD variants. 2. Compare the toxicity of NMD-inhibitor-treated neurons (increasing cryptic peptide yield) vs. control neurons using synaptic plasticity assays. 3. CRISPR-tag the PKN1 locus in patient-derived iNeurons to monitor the real-time formation of PKN207. 📊 Suggested Studies Cross-sectional study to validate the diagnostic accuracy of cryptic peptide panels in serum-derived extracellular vesicles across diverse FTLD-TDP cohorts. Comparative RNA-seq meta-analysis of different brain regions to determine the tissue-specific hierarchy of cryptic splicing vulnerability in LATE","url":"https://doi.org/10.5281/zenodo.21851699","authors":["Dungan, Joshua"],"tags":["DNA-Binding Protein 43","_gates_from_dna-binding_protein_43","Exons","_gates_to_exons","_gates_from_exons","Protein Deficiency","_gates_to_protein_deficiency","Functional Protein Loss"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21851699","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21851700","name":"Dataset: Identify specific patterns of TDP-43 proteinopathy induced cryptic mis-splicing of STMN2 and other \"cryptic mis-splicing\" patterns found within PubMed Literature, 2026. - PathMap Experiment #000111","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=111 Artificial General Intelligence LLC Claim Evaluated: Identify specific patterns of TDP-43 proteinopathy induced cryptic mis-splicing of STMN2 and other \"cryptic mis-splicing\" patterns found within PubMed Literature, 2026. This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Cryptic splicing creates stable, neurotoxic polypeptides (e.g., PKN1-N207) that escape nonsense-mediated decay (41720774). Cryptic peptides derived from mis-spliced transcripts are detectable in patient serum extracellular vesicles and CSF, offering potential diagnostic utility (41612503, 38277467). Cryptic polyadenylation is a distinct class of TDP-43 LOF events beyond canonical cryptic exon splicing, often leading to 3'UTR extensions (41120751, 38313254). Nonsense-mediated decay (NMD) significantly masks the breadth of cryptic splicing, meaning standard RNA-seq often underestimates the total cryptic burden (40670663, 41332610). TDP-43-dependent cryptic splicing is an early event, occurring before the appearance of overt cytoplasmic aggregates, challenging the dogma that aggregation is the sole driver of clinical symptoms (38443601). Ciclopirox olamine induces TDP-43 cryptic exons via heavy metal toxicity, suggesting potential external triggers for proteinopathy (40715064). The inclusion of cryptic exons can trigger an adaptive immune response, where CD8+ T cells recognize cryptic epitopes as neo-antigens (40667053). TDP-43-dependent cryptic peptides represent a \"proteomic shift\" in neurodegeneration that may be independent of the total burden of canonical TDP-43 aggregates. NMD efficiency acts as a cellular checkpoint, with tumors and neurodegenerative states showing a divergence from \"tissue-specific baseline\" quality control, suggesting an \"NMD signature\" that varies per cell type. The inclusion of specific exons leads to peptide products that are not just byproduct garbage but functional effectors of toxicity. Cryptic peptides can be detected in extracellular vesicles (EVs), suggesting they could serve as non-invasive biomarkers for disease-specific splicing signatures. Synaptic proteins are disproportionately affected by the proteome-wide reduction in CE-target proteins, linking RNA surveillance directly to synaptic failure. Genetic modifiers, such as RAD23A or USP13, demonstrate that targeting protein homeostasis can mitigate the toxicity of TDP-43 mislocalization. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Perform longitudinal multi-omic analysis of iPSC-derived neurons to define the temporal hierarchy between initial cryptic splicing of STMN2/UNC13A and subsequent protein aggregation. Validate the neurotoxicity of cryptic peptides (e.g., PKN1-N207) by expressing them in non-TDP-43-depleted neurons and measuring synaptic plasticity markers. Test if pharmacological inhibition of NMD allows for the identification of a wider set of potential cryptic exon therapeutic targets in human patient tissue. 1. Perform mass-spectrometry based proteomic screening of patient CSF and EVs to quantify the abundance of PKN1-N207 in different clinical FTD variants. 2. Compare the toxicity of NMD-inhibitor-treated neurons (increasing cryptic peptide yield) vs. control neurons using synaptic plasticity assays. 3. CRISPR-tag the PKN1 locus in patient-derived iNeurons to monitor the real-time formation of PKN207. 📊 Suggested Studies Cross-sectional study to validate the diagnostic accuracy of cryptic peptide panels in serum-derived extracellular vesicles across diverse FTLD-TDP cohorts. Comparative RNA-seq meta-analysis of different brain regions to determine the tissue-specific hierarchy of cryptic splicing vulnerability in LATE","url":"https://doi.org/10.5281/zenodo.21851700","authors":["Dungan, Joshua"],"tags":["DNA-Binding Protein 43","_gates_from_dna-binding_protein_43","Exons","_gates_to_exons","_gates_from_exons","Protein Deficiency","_gates_to_protein_deficiency","Functional Protein Loss"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21851700","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20362824","name":"Predictive Forecasting of Care Load and Placement Demand","source":"datacite","abstract":"Predictive Forecasting of Care Load and Placement Demand is a data-driven strategy used primarily in healthcare, social care, and senior care sectors. It leverages historical data, statistical algorithms, and machine learning techniques to anticipate future patient/resident volumes (Care Load) and the specific types of accommodations or facilities required (Placement Demand) (Soyiri & Reidpath, 2012).Instead of reacting to overflows or shortages after they happen, organizations use these predictive models to shift from a reactive to a proactive operational stance (Soyiri & Reidpath, 2012).1. Key Components DefinedA. Care Load ForecastingCare load refers to the total volume of work and resources required to look after patients within a given timeframe. Forecasting it involves predicting:Patient/Resident Surges: Anticipating sudden influxes due to seasonal patterns (like flu seasons or extreme heat), pandemics, or localized events.Acuity & Dependency Levels: Predicting not just the number of people, but how intense their care needs will be (e.g., highly dependent critical patients vs. low-maintenance ambulatory residents).Staffing Workload: Translating patient volume into required nurse-to-patient or caregiver-to-resident ratios to avoid physician burnout and understaffing.B. Placement Demand ForecastingPlacement demand focuses on the downstream physical locations where individuals need to be housed based on their clinical or social needs. This includes predicting:Bed Capacity: Foreseeing shortages in specific hospital wards (ICU, Emergency, Maternity) or residential care homes.Long-Term Care Allocations: Determining how many individuals will transition from acute hospital beds into long-term nursing facilities, rehabilitation units, or specialized foster care placements.Throughput & Transfer Dynamics: Anticipating discharge speeds and managing optimal patient transfers from overloaded regional facilities to underutilized ones.2. Core Methodologies and ModelsModern architectures typically employ a hybrid predictive-prescriptive approach to forecast these elements, combining traditional time-series forecasting with advanced Artificial Intelligence (AI). Statistical and Classical ModelsARIMA / SARIMAX: Excellent for univariate time-series data displaying strong seasonal and linear trends (e.g., predicting daily emergency room admissions based on historical averages).Prophet: Developed for handling time-series data with strong multi-period seasonal effects (daily, weekly, yearly) and holiday fluctuations.2. Machine Learning and Deep Learning ModelsLong Short-Term Memory (LSTM) Networks: A type of Recurrent Neural Network (RNN) uniquely capable of learning long-term dependencies in data sequence. LSTMs are heavily favored for forecasting rapid patient surges or complex healthcare load fluctuations over a timeline.Tree-Based Ensembles (XGBoost / Random Forest): Highly effective when exogenous multi-source variables are introduced (e.g., correlating weather temperatures, regional demographic growth, and local disease trends to estimate care load).3. Data Inputs RequiredTo build accurate models, data analysts typically process and clean multiple streams of data:Historical Health Service Data: Electronic Medical Records (EMRs), admission/discharge timestamps, historic readmission frequencies, and average length of stay (LOS).Demographic Variables: Population aging rates, socio-economic factors, and regional public health indices.Exogenous Indicators: Weather patterns, environmental factors, public holidays, and syndromic surveillance data (e.g., Google search trends or local pharmacy sales regarding specific symptoms).4. Key Business and Operational BenefitsOptimized Resource Allocation: Prevents overutilization or underutilization of beds, reducing waste and saving extensive annual operating costs.Proactive Workforce Management: Enables managers to schedule clinicians and nursing staff dynamically according to projected care loads, sig","url":"https://doi.org/10.5281/zenodo.20362824","authors":["Tulasi, G"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20362824","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20362825","name":"Predictive Forecasting of Care Load and Placement Demand","source":"datacite","abstract":"Predictive Forecasting of Care Load and Placement Demand is a data-driven strategy used primarily in healthcare, social care, and senior care sectors. It leverages historical data, statistical algorithms, and machine learning techniques to anticipate future patient/resident volumes (Care Load) and the specific types of accommodations or facilities required (Placement Demand) (Soyiri & Reidpath, 2012).Instead of reacting to overflows or shortages after they happen, organizations use these predictive models to shift from a reactive to a proactive operational stance (Soyiri & Reidpath, 2012).1. Key Components DefinedA. Care Load ForecastingCare load refers to the total volume of work and resources required to look after patients within a given timeframe. Forecasting it involves predicting:Patient/Resident Surges: Anticipating sudden influxes due to seasonal patterns (like flu seasons or extreme heat), pandemics, or localized events.Acuity & Dependency Levels: Predicting not just the number of people, but how intense their care needs will be (e.g., highly dependent critical patients vs. low-maintenance ambulatory residents).Staffing Workload: Translating patient volume into required nurse-to-patient or caregiver-to-resident ratios to avoid physician burnout and understaffing.B. Placement Demand ForecastingPlacement demand focuses on the downstream physical locations where individuals need to be housed based on their clinical or social needs. This includes predicting:Bed Capacity: Foreseeing shortages in specific hospital wards (ICU, Emergency, Maternity) or residential care homes.Long-Term Care Allocations: Determining how many individuals will transition from acute hospital beds into long-term nursing facilities, rehabilitation units, or specialized foster care placements.Throughput & Transfer Dynamics: Anticipating discharge speeds and managing optimal patient transfers from overloaded regional facilities to underutilized ones.2. Core Methodologies and ModelsModern architectures typically employ a hybrid predictive-prescriptive approach to forecast these elements, combining traditional time-series forecasting with advanced Artificial Intelligence (AI). Statistical and Classical ModelsARIMA / SARIMAX: Excellent for univariate time-series data displaying strong seasonal and linear trends (e.g., predicting daily emergency room admissions based on historical averages).Prophet: Developed for handling time-series data with strong multi-period seasonal effects (daily, weekly, yearly) and holiday fluctuations.2. Machine Learning and Deep Learning ModelsLong Short-Term Memory (LSTM) Networks: A type of Recurrent Neural Network (RNN) uniquely capable of learning long-term dependencies in data sequence. LSTMs are heavily favored for forecasting rapid patient surges or complex healthcare load fluctuations over a timeline.Tree-Based Ensembles (XGBoost / Random Forest): Highly effective when exogenous multi-source variables are introduced (e.g., correlating weather temperatures, regional demographic growth, and local disease trends to estimate care load).3. Data Inputs RequiredTo build accurate models, data analysts typically process and clean multiple streams of data:Historical Health Service Data: Electronic Medical Records (EMRs), admission/discharge timestamps, historic readmission frequencies, and average length of stay (LOS).Demographic Variables: Population aging rates, socio-economic factors, and regional public health indices.Exogenous Indicators: Weather patterns, environmental factors, public holidays, and syndromic surveillance data (e.g., Google search trends or local pharmacy sales regarding specific symptoms).4. Key Business and Operational BenefitsOptimized Resource Allocation: Prevents overutilization or underutilization of beds, reducing waste and saving extensive annual operating costs.Proactive Workforce Management: Enables managers to schedule clinicians and nursing staff dynamically according to projected care loads, sig","url":"https://doi.org/10.5281/zenodo.20362825","authors":["Tulasi, G"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20362825","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21671345","name":"Digital Education and Skill Development for Inclusive Rural Growth","source":"datacite","abstract":"Abstract The digital divide in rural India remains a profound structural barrier to equitable economic participation. As the nation pivots toward a knowledge-based $5 trillion economy, the integration of digital education and targeted skill development has become a structural imperative. This article examines the intersection of digital interventions and rural livelihoods, positing that technology serves as a potent force multiplier for human capital. Through a detailed analysis of three pivotal Indian initiatives the Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA), the Common Service Centers (CSC) model, and the e-Skill India portal this study evaluates the efficacy of digital ecosystems in narrowing the chasm between rural potential and modern market requirements. The findings indicate that while physical infrastructure provides the foundation, sustained growth depends on bridging the multifaceted \"cognitive,\" \"usage,\" and \"structural\" divides. This paper argues for a multi-dimensional policy framework that emphasizes localized vernacular content, gender-inclusive digital mentorship, and the strategic alignment of vocational training with emerging industrial demands to foster long-term socio-economic resilience. The discussion extends to the role of AI-driven tools in rural agriculture and the necessity of decentralized connectivity solutions to ensure no demographic is left behind. Keywords: Digital Education, Skill Development, Inclusive Growth, Rural India, Digital Literacy, Vocational Training, ICT4D, Sustainable Development, Digital Divide, Human Capital, AI in Agriculture, Phygital Model. 1. Introduction Rural India, home to over 65% of the country’s population, serves as the vital cornerstone of the nation's social fabric and economic potential. For decades, the rural economy has been defined by agriculture and small-scale artisanal production. However, as the global labor market shifts toward digital services, the traditional rural-urban divide has widened. The benefits of the digital revolution, a phenomenon reshaping labor markets, education, and social services, remain disproportionately concentrated in urban centers, leaving rural communities in a state of structural exclusion. Inclusive growth necessitates that the digital landscape becomes an accessible public good rather than an exclusive privilege of the metropolitan elite. Digital education acts as a transformative agent, enabling rural populations to traverse geographical constraints and gain access to global knowledge repositories that were previously beyond reach. This is not merely about access to hardware; it is about the acquisition of \"digital intelligence,\" which allows individuals to navigate, synthesize, and leverage information for economic advancement. The central thesis of this research is that digital transformation in rural areas is not a singular project of infrastructure deployment but a complex, long-term socio-technical evolution. It requires a fundamental shift in perspective: moving away from viewing rural residents as passive consumers of technology to empowering them as active, informed participants in the digital economy. The urgency is underscored by the current economic trajectory; without proactive intervention, the digital divide threatens to harden into a permanent barrier to social mobility, stifling innovation and exacerbating wealth inequality. Furthermore, the rapid transition toward industry $4.0$ requires a workforce that is not only digitally literate but also adaptable capable of interacting with AI-driven agricultural tools, e-commerce platforms, and decentralized financial systems. The integration of such technologies into the rural fabric is not merely a modern convenience; it is a prerequisite for competing in an increasingly digitized global marketplace. Beyond the immediate economic benefits, there is a profound social dimension. Digital education facilitates better health outcomes, improved awareness of ","url":"https://doi.org/10.5281/zenodo.21671345","authors":["Dr.Srinivasa.T"],"tags":["Digital Education, Skill Development, Inclusive Growth, Rural India, Digital Literacy, Vocational Training, ICT4D, Sustainable Development, Digital Divide, Human Capital, AI in Agriculture, Phygital Model."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21671345","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21671346","name":"Digital Education and Skill Development for Inclusive Rural Growth","source":"datacite","abstract":"Abstract The digital divide in rural India remains a profound structural barrier to equitable economic participation. As the nation pivots toward a knowledge-based $5 trillion economy, the integration of digital education and targeted skill development has become a structural imperative. This article examines the intersection of digital interventions and rural livelihoods, positing that technology serves as a potent force multiplier for human capital. Through a detailed analysis of three pivotal Indian initiatives the Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA), the Common Service Centers (CSC) model, and the e-Skill India portal this study evaluates the efficacy of digital ecosystems in narrowing the chasm between rural potential and modern market requirements. The findings indicate that while physical infrastructure provides the foundation, sustained growth depends on bridging the multifaceted \"cognitive,\" \"usage,\" and \"structural\" divides. This paper argues for a multi-dimensional policy framework that emphasizes localized vernacular content, gender-inclusive digital mentorship, and the strategic alignment of vocational training with emerging industrial demands to foster long-term socio-economic resilience. The discussion extends to the role of AI-driven tools in rural agriculture and the necessity of decentralized connectivity solutions to ensure no demographic is left behind. Keywords: Digital Education, Skill Development, Inclusive Growth, Rural India, Digital Literacy, Vocational Training, ICT4D, Sustainable Development, Digital Divide, Human Capital, AI in Agriculture, Phygital Model. 1. Introduction Rural India, home to over 65% of the country’s population, serves as the vital cornerstone of the nation's social fabric and economic potential. For decades, the rural economy has been defined by agriculture and small-scale artisanal production. However, as the global labor market shifts toward digital services, the traditional rural-urban divide has widened. The benefits of the digital revolution, a phenomenon reshaping labor markets, education, and social services, remain disproportionately concentrated in urban centers, leaving rural communities in a state of structural exclusion. Inclusive growth necessitates that the digital landscape becomes an accessible public good rather than an exclusive privilege of the metropolitan elite. Digital education acts as a transformative agent, enabling rural populations to traverse geographical constraints and gain access to global knowledge repositories that were previously beyond reach. This is not merely about access to hardware; it is about the acquisition of \"digital intelligence,\" which allows individuals to navigate, synthesize, and leverage information for economic advancement. The central thesis of this research is that digital transformation in rural areas is not a singular project of infrastructure deployment but a complex, long-term socio-technical evolution. It requires a fundamental shift in perspective: moving away from viewing rural residents as passive consumers of technology to empowering them as active, informed participants in the digital economy. The urgency is underscored by the current economic trajectory; without proactive intervention, the digital divide threatens to harden into a permanent barrier to social mobility, stifling innovation and exacerbating wealth inequality. Furthermore, the rapid transition toward industry $4.0$ requires a workforce that is not only digitally literate but also adaptable capable of interacting with AI-driven agricultural tools, e-commerce platforms, and decentralized financial systems. The integration of such technologies into the rural fabric is not merely a modern convenience; it is a prerequisite for competing in an increasingly digitized global marketplace. Beyond the immediate economic benefits, there is a profound social dimension. Digital education facilitates better health outcomes, improved awareness of ","url":"https://doi.org/10.5281/zenodo.21671346","authors":["Dr.Srinivasa.T"],"tags":["Digital Education, Skill Development, Inclusive Growth, Rural India, Digital Literacy, Vocational Training, ICT4D, Sustainable Development, Digital Divide, Human Capital, AI in Agriculture, Phygital Model."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21671346","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21830175","name":"Dataset: mRNA Influenza Vaccination Information. August, 2026 PathMap - PathMap Experiment #000106","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=106 Artificial General Intelligence LLC Claim Evaluated: mRNA Influenza Vaccination Information. August, 2026 PathMap This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights mRNA vaccines for influenza demonstrate clinical efficacy profiles that are statistically comparable to current licensed enhanced vaccines (EVs) in geriatric populations. The use of non-canonical delivery systems, such as piezoelectric electroporation (Piezopen), shows potential for \"naked\" mRNA delivery, potentially bypassing inflammatory lipid nanoparticle (LNP) carriers. There is no evidence of significant structural cerebral changes following mRNA vaccination, contradicting concerns regarding microstructural brain alterations in the subacute phase. Antigenic mismatch continues to be a primary driver of variable effectiveness, necessitating the transition to recombinant protein and mRNA platforms. Sequential vaccination of COVID-19 and influenza antigens does not appear to compromise the individual immunogenicity of either vaccine in immunocompromised populations. Adjuvanted and high-dose influenza vaccines have shown comparable protection against medically attended influenza in real-world cohorts. The persistence of SARS-CoV-2 spike protein in skin lesions post-vaccination remains an area for continued clinical investigation regarding vasculitic manifestations. mRNA-1010 immunogenicity is comparable to traditional high-dose influenza vaccines, a critical finding for addressing immunosenescence in older populations. The multicomponent mRNA-1083 vaccine enables simultaneous protection against influenza and SARS-CoV-2 without compromising individual immune response magnitudes. Capless self-amplifying mRNA (CLsamRNA) platforms show extreme dose-sparing potential (e.g., 0.01 μg), reducing the manufacturing requirements for large-scale production. mRNA vaccination induces a distinct Th1/Tfh1-biased cellular immune response, which correlates with long-lasting memory. Sequential administration of mRNA-based COVID-19 and influenza vaccines does not inhibit the development of antigen-specific immunity against either virus. mRNA-based influenza platforms can be rapidly updated to address antigenic drift, a key improvement over egg-based production. No signals for myocarditis or pericarditis were identified in the reported phase 3 clinical trials for mRNA-1083. Current mRNA-LNP delivery systems are being engineered to shift expression profiles, such as increasing spleen-selective immunity for better T-cell priming. mRNA-1010 is consistently shown to be superior to standard-dose vaccines for the prevention of RT-PCR-confirmed influenza-like illness in older adults. The integration of internal viral proteins (e.g., nucleoprotein) and neuraminidase is essential for achieving universal cross-protection. Self-amplifying RNA (saRNA) platforms significantly improve IBV-specific immunogenicity compared to conventional mRNA. Pharmacist-led vaccination programs, as seen in New Zealand, remain a primary driver for increasing vaccine uptake in the geriatric population. The use of needle-free jet injectors provides a potential technological bridge for more efficient, dose-sparing delivery of future mRNA influenza formulations. Current data indicate that mRNA-based multicomponent vaccines (e.g., mRNA-1083) represent a viable strategy for co-protection against influenza and SARS-CoV-2. There is a transition in research focus from mere antibody titer measurement to monitoring circulating follicular helper T-cell responses for deeper immunological memory assessment. Computational and algorithm-optimized mRNA H5 influenza vaccines are now successfully inducin","url":"https://doi.org/10.5281/zenodo.21830175","authors":["Dungan, Joshua"],"tags":["RNA Vaccines","_gates_from_rna_vaccines","Antigenic Variation","_gates_to_antigenic_variation","_gates_from_antigenic_variation","Immunity","_gates_to_immunity","Immunogenicity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21830175","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21830176","name":"Dataset: mRNA Influenza Vaccination Information. August, 2026 PathMap - PathMap Experiment #000106","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=106 Artificial General Intelligence LLC Claim Evaluated: mRNA Influenza Vaccination Information. August, 2026 PathMap This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights mRNA vaccines for influenza demonstrate clinical efficacy profiles that are statistically comparable to current licensed enhanced vaccines (EVs) in geriatric populations. The use of non-canonical delivery systems, such as piezoelectric electroporation (Piezopen), shows potential for \"naked\" mRNA delivery, potentially bypassing inflammatory lipid nanoparticle (LNP) carriers. There is no evidence of significant structural cerebral changes following mRNA vaccination, contradicting concerns regarding microstructural brain alterations in the subacute phase. Antigenic mismatch continues to be a primary driver of variable effectiveness, necessitating the transition to recombinant protein and mRNA platforms. Sequential vaccination of COVID-19 and influenza antigens does not appear to compromise the individual immunogenicity of either vaccine in immunocompromised populations. Adjuvanted and high-dose influenza vaccines have shown comparable protection against medically attended influenza in real-world cohorts. The persistence of SARS-CoV-2 spike protein in skin lesions post-vaccination remains an area for continued clinical investigation regarding vasculitic manifestations. mRNA-1010 immunogenicity is comparable to traditional high-dose influenza vaccines, a critical finding for addressing immunosenescence in older populations. The multicomponent mRNA-1083 vaccine enables simultaneous protection against influenza and SARS-CoV-2 without compromising individual immune response magnitudes. Capless self-amplifying mRNA (CLsamRNA) platforms show extreme dose-sparing potential (e.g., 0.01 μg), reducing the manufacturing requirements for large-scale production. mRNA vaccination induces a distinct Th1/Tfh1-biased cellular immune response, which correlates with long-lasting memory. Sequential administration of mRNA-based COVID-19 and influenza vaccines does not inhibit the development of antigen-specific immunity against either virus. mRNA-based influenza platforms can be rapidly updated to address antigenic drift, a key improvement over egg-based production. No signals for myocarditis or pericarditis were identified in the reported phase 3 clinical trials for mRNA-1083. Current mRNA-LNP delivery systems are being engineered to shift expression profiles, such as increasing spleen-selective immunity for better T-cell priming. mRNA-1010 is consistently shown to be superior to standard-dose vaccines for the prevention of RT-PCR-confirmed influenza-like illness in older adults. The integration of internal viral proteins (e.g., nucleoprotein) and neuraminidase is essential for achieving universal cross-protection. Self-amplifying RNA (saRNA) platforms significantly improve IBV-specific immunogenicity compared to conventional mRNA. Pharmacist-led vaccination programs, as seen in New Zealand, remain a primary driver for increasing vaccine uptake in the geriatric population. The use of needle-free jet injectors provides a potential technological bridge for more efficient, dose-sparing delivery of future mRNA influenza formulations. Current data indicate that mRNA-based multicomponent vaccines (e.g., mRNA-1083) represent a viable strategy for co-protection against influenza and SARS-CoV-2. There is a transition in research focus from mere antibody titer measurement to monitoring circulating follicular helper T-cell responses for deeper immunological memory assessment. Computational and algorithm-optimized mRNA H5 influenza vaccines are now successfully inducin","url":"https://doi.org/10.5281/zenodo.21830176","authors":["Dungan, Joshua"],"tags":["RNA Vaccines","_gates_from_rna_vaccines","Antigenic Variation","_gates_to_antigenic_variation","_gates_from_antigenic_variation","Immunity","_gates_to_immunity","Immunogenicity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21830176","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21104315","name":"AI for healthcare: Prediction disease diagnosis using multimodal data","source":"datacite","abstract":"Abstract: Artificial intelligence boxes (AI) have advanced the system to move up the likelihood from the conventional system of disease diagnosis to early, precise analysis and personal prediction. However, certain complex diseases need to be analyses through a multimodal approach, as the medical diagnoses we use are unimodal in nature (e.g., imaging and lab work). Multimodal data will include medical images (X-ray, MRI, and CT scan), EHR, genetic information, and clinical notes. This will provide us with a holistic approach to the health condition of a patient. This study will be aimed at exploring the capabilities of multimodal AI systems to provide us with this. This will be done by reducing the percentage of misdiagnosis cases, facilitating the early diagnosis of a disease, and providing accurate diagnoses. AI-based healthcare systems will also help to a certain extent by reducing the workload on clinicians, as it can help them interpret the information more efficiently and improve the workflow to enable quicker decision-making. Moreover, personalized healthcare plans can be generated according to the SAND boxes for each individual patient. Keywords: Healthcare, Medical Imaging, NLP, Disease Prediction, Deep Learning.","url":"https://doi.org/10.5281/zenodo.21104315","authors":["Dr. Archana Bendale","Trupti Khairnar","Gayatri Patil","Asst. Prof. Pawan Malani","Asst. Prof. Madhumati Borse"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21104315","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21104316","name":"AI for healthcare: Prediction disease diagnosis using multimodal data","source":"datacite","abstract":"Abstract: Artificial intelligence boxes (AI) have advanced the system to move up the likelihood from the conventional system of disease diagnosis to early, precise analysis and personal prediction. However, certain complex diseases need to be analyses through a multimodal approach, as the medical diagnoses we use are unimodal in nature (e.g., imaging and lab work). Multimodal data will include medical images (X-ray, MRI, and CT scan), EHR, genetic information, and clinical notes. This will provide us with a holistic approach to the health condition of a patient. This study will be aimed at exploring the capabilities of multimodal AI systems to provide us with this. This will be done by reducing the percentage of misdiagnosis cases, facilitating the early diagnosis of a disease, and providing accurate diagnoses. AI-based healthcare systems will also help to a certain extent by reducing the workload on clinicians, as it can help them interpret the information more efficiently and improve the workflow to enable quicker decision-making. Moreover, personalized healthcare plans can be generated according to the SAND boxes for each individual patient. Keywords: Healthcare, Medical Imaging, NLP, Disease Prediction, Deep Learning.","url":"https://doi.org/10.5281/zenodo.21104316","authors":["Dr. Archana Bendale","Trupti Khairnar","Gayatri Patil","Asst. Prof. Pawan Malani","Asst. Prof. Madhumati Borse"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21104316","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.17635/lancaster/thesis/3359","name":"Exploring and Designing AI-Based Technologies for Mental Health","source":"datacite","abstract":"Over the past decade, Human–Computer Interaction (HCI) research has seen a growing interest in mental health technologies. At the same time, advances in Artificial Intelligence (AI) have introduced new opportunities and challenges for the design of ethical, transparent, and clinically relevant tools to support mental health practitioners in assessment, diagnosis, and intervention. This thesis explores the intersection of AI and HCI in mental health, focusing on how AI-based technologies can be designed, understood, and evaluated to ethically and effectively support practitioners in identifying mental health conditions. The research adopts a multi-stage design-oriented methodology structured around three key phases: understanding the AI–HCI design space, exploring the ethical design of novel AI–HCI technologies, and evaluating the designed AI–HCI solutions. The first phase investigates how AI-based technologies are currently used within mental health. Chapter 4 presents findings from a functionality review of 13 AI-based mental health mobile applications. The analysis employed expert evaluation to explore each app’s AI support, functionality, purpose, and ethical implications. Findings showed that most apps use AI to track moods and emotions, generate personalized well-being recommendations, and provide conversational support through Natural Language Processing (NLP)-based agents. However, the review also showed significant limitations in AI literacy support, explainability, and transparency, as well as a general lack of ethical design considerations regarding data reliability, consent, and algorithmic bias. These insights highlight the need for human-centered and ethically grounded AI design frameworks for mental health. Chapter 5 presents key findings from interviews with mental health practitioners (n = 18). It further examines how they conceptualize the role of AI across the key stages of therapeutic practice: assessment, diagnosis, and treatment. Ethical concerns related to privacy, explainability, and inclusivity were systematically analysed based on core biomedical ethics principles: non-maleficence, beneficence, justice, and autonomy, translating practitioner insights into actionable, ethically grounded design solutions to guide the development and integration of AI in mental health practice. The chapter contributes: (i) stage-specific conceptualization of AI’s role; (ii) systematic organization of ethical concerns; and (iii) novel design implications. These findings shift focus from whether to how AI can be responsibly designed as longitudinal clinical support throughout therapy. Building on the insights from Chapter 5, the second phase, Chapter 6, focuses on designing and developing ethical AI–HCI technologies to support depression symptom identification using Large Language Models (LLMs). The first iteration, DepressionSymp (Model 1), showed strong accuracy (ROUGE-1 = 0.9154) across five categories: risk, symptoms, time, lifestyle, and other medical conditions. The model was implemented to identify symptoms and help clinical practitioners with diagnosis and decision-making. Chapter 7 presents Workshop 1, in which mental health practitioners (n = 6) engaged in evaluating and co-designing DepressionSymp (Model 1). Practitioners evaluate the system across four AI ethical principles: accuracy, reliability, interpretability, and trust, as well as broader ethical design considerations. They also provided recommendations for improvements to ensure the model is used safely and responsibly in clinical practice. Responding to the insights from Chapter 7, Chapter 8 details the iterative redesign of the DepressionSymp (Model 2). By using a prompt-based learning approach, the model showed improved contextual understanding, interpretability, and trust (ROUGE-1 = 0.8486). Finally, Chapter 9 presents Workshop 2, in which mental health practitioners (n = 8) engaged in evaluating and co-designing DepressionSymp (Model 2). The workshop","url":"https://doi.org/10.17635/lancaster/thesis/3359","authors":["Alotaibi, Abeer"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17635/lancaster/thesis/3359","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21284090","name":"What changes in a person's voice occur prior to Amyotrophic Lateral Sclerosis onset that may be useful as a non-clinical (or clinical if possible) early detection of the disease? - PathMap Experiment #000035","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=35 Artificial General Intelligence LLC Claim Evaluated: What changes in a person's voice occur prior to Amyotrophic Lateral Sclerosis onset that may be useful as a non-clinical (or clinical if possible) early detection of the disease? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Subclinical Detection:** Artificial intelligence frameworks can identify neuromuscular changes during the \"clinically silent prodromal stage\" before functional decline is apparent. Biomechanical Precision:** Biomechanical voice parameters reflecting glottal tension and vocal fold stability are sensitive enough to differentiate clinical phenotypes (bulbar vs. spinal onset). Multimodal Integration:** Combining facial sEMG and acoustic signals outperforms single-modality assessments in detecting early bulbar motor dysfunction. Listener Effort (LE):** LE is a clinician-rated metric that captures meaningful change in dysarthria and shows potential as a responsive clinical trial endpoint. Smartphone Utility:** Simple, smartphone-based assessment tasks (e.g., tongue lateralization or vowel phonation) correlate highly with laboratory-standard assessments, increasing access. Stability of Biomarkers:** Despite disease progression, high-gamma cortical features in ECoG speech BCIs show long-term stability, suggesting durability for assistive interfaces. Predictive Modeling:** Subject-specific prognostic models can now predict articulatory precision and ALSFRS-R speech subscores 30–90 days in advance. Vocal Subtypes:** Unsupervised clustering reveals \"vocal profiles\" that transcend traditional diagnostic labels, indicating that voice features capture functional patterns of voice production across different disorders. Voice serves as a latent, multimodal biomarker reflecting neurological, cardiopulmonary, and psychological states. Automated segmentation algorithms can now identify syllable and phoneme positions during oral diadochokinesis with over 90% accuracy, providing a basis for objective monitoring. Biomechanical voice analysis captures physiologically meaningful alterations in vocal fold function, offering complementary information that transcends traditional clinical diagnostic labels. Changes in speech and swallowing function can be monitored remotely using smartphone applications, potentially reducing the need for frequent clinical hospital visits. There is a significant need for better standardized tools, as current outcome measurements for speech and swallow are deemed clinically meaningful by only a minority of practitioners. Vowel acoustic features (e.g., Formant Centralization Ratio) provide insight into the shared brainstem neuromotor substrate of both speech and swallowing. Research confirms the existence of coherent vocal profiles across patients that do not strictly align with existing clinical diagnostic categories. Early intervention protocols are limited by current guideline gaps, underscoring the necessity of individual-level predictive modeling. Thinning of the bilateral oral motor cortices is an anatomical precursor that maps directly to measurable decrements in oral motor function. Automated speaking and articulation rates provide a robust alternative to manually conducted assessments, which are prone to observer bias. Digital speech-derived measures demonstrate clear neuroanatomical correlations where standard bulbar subscores in the ALSFRS-R do not. The use of intracortical brain-computer interfaces (BCIs) has allowed for long-term monitoring, producing datasets with high word accuracy that validate the stability of speech-based tracking. Cortical dysfunction originates in a developmental trajectory in cu","url":"https://doi.org/10.5281/zenodo.21284090","authors":["Dungan, Joshua"],"tags":["Motor Neuron Disease","_gates_from_motor_neuron_disease","Muscle Weakness","_gates_to_muscle_weakness","_gates_from_muscle_weakness","Vocal Cord Dysfunction","_gates_to_vocal_cord_dysfunction","Quantitative Analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21284090","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21284091","name":"What changes in a person's voice occur prior to Amyotrophic Lateral Sclerosis onset that may be useful as a non-clinical (or clinical if possible) early detection of the disease? - PathMap Experiment #000035","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=35 Artificial General Intelligence LLC Claim Evaluated: What changes in a person's voice occur prior to Amyotrophic Lateral Sclerosis onset that may be useful as a non-clinical (or clinical if possible) early detection of the disease? This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Subclinical Detection:** Artificial intelligence frameworks can identify neuromuscular changes during the \"clinically silent prodromal stage\" before functional decline is apparent. Biomechanical Precision:** Biomechanical voice parameters reflecting glottal tension and vocal fold stability are sensitive enough to differentiate clinical phenotypes (bulbar vs. spinal onset). Multimodal Integration:** Combining facial sEMG and acoustic signals outperforms single-modality assessments in detecting early bulbar motor dysfunction. Listener Effort (LE):** LE is a clinician-rated metric that captures meaningful change in dysarthria and shows potential as a responsive clinical trial endpoint. Smartphone Utility:** Simple, smartphone-based assessment tasks (e.g., tongue lateralization or vowel phonation) correlate highly with laboratory-standard assessments, increasing access. Stability of Biomarkers:** Despite disease progression, high-gamma cortical features in ECoG speech BCIs show long-term stability, suggesting durability for assistive interfaces. Predictive Modeling:** Subject-specific prognostic models can now predict articulatory precision and ALSFRS-R speech subscores 30–90 days in advance. Vocal Subtypes:** Unsupervised clustering reveals \"vocal profiles\" that transcend traditional diagnostic labels, indicating that voice features capture functional patterns of voice production across different disorders. Voice serves as a latent, multimodal biomarker reflecting neurological, cardiopulmonary, and psychological states. Automated segmentation algorithms can now identify syllable and phoneme positions during oral diadochokinesis with over 90% accuracy, providing a basis for objective monitoring. Biomechanical voice analysis captures physiologically meaningful alterations in vocal fold function, offering complementary information that transcends traditional clinical diagnostic labels. Changes in speech and swallowing function can be monitored remotely using smartphone applications, potentially reducing the need for frequent clinical hospital visits. There is a significant need for better standardized tools, as current outcome measurements for speech and swallow are deemed clinically meaningful by only a minority of practitioners. Vowel acoustic features (e.g., Formant Centralization Ratio) provide insight into the shared brainstem neuromotor substrate of both speech and swallowing. Research confirms the existence of coherent vocal profiles across patients that do not strictly align with existing clinical diagnostic categories. Early intervention protocols are limited by current guideline gaps, underscoring the necessity of individual-level predictive modeling. Thinning of the bilateral oral motor cortices is an anatomical precursor that maps directly to measurable decrements in oral motor function. Automated speaking and articulation rates provide a robust alternative to manually conducted assessments, which are prone to observer bias. Digital speech-derived measures demonstrate clear neuroanatomical correlations where standard bulbar subscores in the ALSFRS-R do not. The use of intracortical brain-computer interfaces (BCIs) has allowed for long-term monitoring, producing datasets with high word accuracy that validate the stability of speech-based tracking. Cortical dysfunction originates in a developmental trajectory in cu","url":"https://doi.org/10.5281/zenodo.21284091","authors":["Dungan, Joshua"],"tags":["Motor Neuron Disease","_gates_from_motor_neuron_disease","Muscle Weakness","_gates_to_muscle_weakness","_gates_from_muscle_weakness","Vocal Cord Dysfunction","_gates_to_vocal_cord_dysfunction","Quantitative Analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21284091","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20398183","name":"Advancing Health Insurance Claims Automation with Optical Character Recognition","source":"datacite","abstract":"Since the rise of digital tools and artificial intelligence in healthcare, the automation of insurance claims processing is an absolute necessity. Today, claims processing consists of the tedious and expensive process of a person reading through hospital bills, discharge summaries, and prescriptions manually. This system is slow, too expensive and easy to make human mistakes or fraud occur along the way.The research presents an intelligent, automated claims management solution that will do the majority of the work related to insurance claims processing. This will create a seamless digital \"pipeline\" for the flow of information throughout the various types of document used to create claims, including the reading of messy or unstructured medical text to extract the data needed for the claim and checking the extracted data against insurance rules automatically.By combining deep learning with the reasoning capabilities of large language models, this system not only processes the claims but also has a scoring engine to identify suspicious activities and draws upon its knowledge base to offer explanations as to why claims were either approved or denied. The result was the testing of the system with actual medical billing records in real-world settings across several different types of insurance policies. These tests demonstrated that the system represents a significant improvement over the current methods used to process claims, both in terms of reducing processing time and catching errors that are frequently missed by human review. To make these insights useful for actual administrators, the system is hosted as an interactive web app via Streamlit. This provides a real-time dashboard for monitoring claims, moving the industry away from slow, opaque paperwork and toward a future of transparent, efficient, and truly intelligent insurance management.","url":"https://doi.org/10.5281/zenodo.20398183","authors":["Sandeep Santha Kumar","Neeraj R G","M Aravind","A Umamageswari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20398183","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20398184","name":"Advancing Health Insurance Claims Automation with Optical Character Recognition","source":"datacite","abstract":"Since the rise of digital tools and artificial intelligence in healthcare, the automation of insurance claims processing is an absolute necessity. Today, claims processing consists of the tedious and expensive process of a person reading through hospital bills, discharge summaries, and prescriptions manually. This system is slow, too expensive and easy to make human mistakes or fraud occur along the way.The research presents an intelligent, automated claims management solution that will do the majority of the work related to insurance claims processing. This will create a seamless digital \"pipeline\" for the flow of information throughout the various types of document used to create claims, including the reading of messy or unstructured medical text to extract the data needed for the claim and checking the extracted data against insurance rules automatically.By combining deep learning with the reasoning capabilities of large language models, this system not only processes the claims but also has a scoring engine to identify suspicious activities and draws upon its knowledge base to offer explanations as to why claims were either approved or denied. The result was the testing of the system with actual medical billing records in real-world settings across several different types of insurance policies. These tests demonstrated that the system represents a significant improvement over the current methods used to process claims, both in terms of reducing processing time and catching errors that are frequently missed by human review. To make these insights useful for actual administrators, the system is hosted as an interactive web app via Streamlit. This provides a real-time dashboard for monitoring claims, moving the industry away from slow, opaque paperwork and toward a future of transparent, efficient, and truly intelligent insurance management.","url":"https://doi.org/10.5281/zenodo.20398184","authors":["Sandeep Santha Kumar","Neeraj R G","M Aravind","A Umamageswari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20398184","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19828126","name":"RAQAMLI TIBBIY MUHIT VA ELEKTRON POLIKLINIKA SHAROITIDA BO'LAJAK OILAVIY SHIFOKORLARNING KASBIY MOTIVATSIYASINI RIVOJLANTIRISH","source":"datacite","abstract":"Mazkur maqolada raqamli tibbiy muhit va elektron poliklinika tizimlari asosida bo‘lajak oilaviy shifokorlarning kasbiy motivatsiyasini rivojlantirish masalalari tahlil qilingan. Zamonaviy tibbiy ta’limda raqamli texnologiyalar, interaktiv o‘qitish usullari va sun’iy intellekt elementlarining talabalarning klinik fikrlashi, mustaqil qaror qabul qilish qobiliyati va kasbiy qiziqishini shakllantirishdagi ahamiyati yoritilgan. Shuningdek, elektron poliklinika muhitida ta’lim jarayonini tashkil etishning pedagogik imkoniyatlari asoslangan.","url":"https://doi.org/10.5281/zenodo.19828126","authors":["Xalmuxamedov, Bobir Taxirovich"],"tags":["raqamli tibbiy muhit, elektron poliklinika, oilaviy tibbiyot, kasbiy motivatsiya, sun'iy intellekt, interaktiv ta'lim, klinik fikrlash, raqamli kompetensiya","цифровая медицинская среда, электронная поликлиника, семейная медицина, профессиональная мотивация, искусственный интеллект, интерактивное обучение","digital medical environment, electronic polyclinic, family medicine, professional motivation, artificial intelligence, interactive learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19828126","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19828128","name":"RAQAMLI TIBBIY MUHIT VA ELEKTRON POLIKLINIKA SHAROITIDA BO'LAJAK OILAVIY SHIFOKORLARNING KASBIY MOTIVATSIYASINI RIVOJLANTIRISH","source":"datacite","abstract":"Mazkur maqolada raqamli tibbiy muhit va elektron poliklinika tizimlari asosida bo‘lajak oilaviy shifokorlarning kasbiy motivatsiyasini rivojlantirish masalalari tahlil qilingan. Zamonaviy tibbiy ta’limda raqamli texnologiyalar, interaktiv o‘qitish usullari va sun’iy intellekt elementlarining talabalarning klinik fikrlashi, mustaqil qaror qabul qilish qobiliyati va kasbiy qiziqishini shakllantirishdagi ahamiyati yoritilgan. Shuningdek, elektron poliklinika muhitida ta’lim jarayonini tashkil etishning pedagogik imkoniyatlari asoslangan.","url":"https://doi.org/10.5281/zenodo.19828128","authors":["Xalmuxamedov, Bobir Taxirovich"],"tags":["raqamli tibbiy muhit, elektron poliklinika, oilaviy tibbiyot, kasbiy motivatsiya, sun'iy intellekt, interaktiv ta'lim, klinik fikrlash, raqamli kompetensiya","цифровая медицинская среда, электронная поликлиника, семейная медицина, профессиональная мотивация, искусственный интеллект, интерактивное обучение","digital medical environment, electronic polyclinic, family medicine, professional motivation, artificial intelligence, interactive learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19828128","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21981546","name":"VITAL-AI: Advanced Digital Skills for Human-in-the-Loop AI Governance and Doctor-Led Control in Healthcare","source":"datacite","abstract":"Strategic Context: European healthcare stands at a critical geopolitical and technological crossroads. The rapid integration of Artificial Intelligence (AI) in clinical workflows introduces severe vulnerabilities regarding data capital flight and dependency on non-EU black-box algorithmic models. Project Concept: VITAL-AI counters this threat by transforming the European medical workforce from passive technology consumers into Active Algorithmic Governors. Grounded in the paradigm of Subordinated Artificial Intelligence, the project establishes a robust framework for Strategic Autonomy based on three pillars of clinical governance: Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human-in-Command (HIC). Methodology & Innovation: VITAL-AI deploys a modular, three-tier educational architecture combining theoretical micro-credentials (15 ECTS) with high-fidelity, immersive Digital Health Sandboxes. Clinicians will be trained in secure simulation labs to audit high-risk AI clinical decision support systems (CDSS) and master emergency override protocols against adversarial data drift. Operationalizing doctor-led control, the project introduces the breakthrough Dual-Engine Data Architecture (DEDA) to isolate, cross-verify, and audit computational anomalies between global medical baselines and dynamic, local patient telemetry. Impact: Over 48 months, VITAL-AI will certify an elite initial cohort of 1,200+ AI Clinical Governors across 12 EU Member States, scaling to 45+ university hospitals. By translating the strict regulatory mandates of the EU AI Act (Articles 10 & 14) into daily clinical workflows, this initiative eliminates vendor lock-in, secures European health data sovereignty, and protects the European social and medical model.","url":"https://doi.org/10.5281/zenodo.21981546","authors":["abouelfida, abdelhadi"],"tags":["Human-in-the-Loop (HITL)AI Clinical GovernanceEU AI Act ComplianceHealthcare Data SovereigntyDigital Health SandboxesDual-Engine Data Architecture (DEDA)Emergency Override ProtocolAdvanced Digital Skills for Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21981546","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21981545","name":"VITAL-AI: Advanced Digital Skills for Human-in-the-Loop AI Governance and Doctor-Led Control in Healthcare","source":"datacite","abstract":"Strategic Context: European healthcare stands at a critical geopolitical and technological crossroads. The rapid integration of Artificial Intelligence (AI) in clinical workflows introduces severe vulnerabilities regarding data capital flight and dependency on non-EU black-box algorithmic models. Project Concept: VITAL-AI counters this threat by transforming the European medical workforce from passive technology consumers into Active Algorithmic Governors. Grounded in the paradigm of Subordinated Artificial Intelligence, the project establishes a robust framework for Strategic Autonomy based on three pillars of clinical governance: Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human-in-Command (HIC). Methodology & Innovation: VITAL-AI deploys a modular, three-tier educational architecture combining theoretical micro-credentials (15 ECTS) with high-fidelity, immersive Digital Health Sandboxes. Clinicians will be trained in secure simulation labs to audit high-risk AI clinical decision support systems (CDSS) and master emergency override protocols against adversarial data drift. Operationalizing doctor-led control, the project introduces the breakthrough Dual-Engine Data Architecture (DEDA) to isolate, cross-verify, and audit computational anomalies between global medical baselines and dynamic, local patient telemetry. Impact: Over 48 months, VITAL-AI will certify an elite initial cohort of 1,200+ AI Clinical Governors across 12 EU Member States, scaling to 45+ university hospitals. By translating the strict regulatory mandates of the EU AI Act (Articles 10 & 14) into daily clinical workflows, this initiative eliminates vendor lock-in, secures European health data sovereignty, and protects the European social and medical model.","url":"https://doi.org/10.5281/zenodo.21981545","authors":["abouelfida, abdelhadi"],"tags":["Human-in-the-Loop (HITL)AI Clinical GovernanceEU AI Act ComplianceHealthcare Data SovereigntyDigital Health SandboxesDual-Engine Data Architecture (DEDA)Emergency Override ProtocolAdvanced Digital Skills for Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21981545","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.17605/osf.io/8v4hw","name":"Artificial Intelligence in Medical Student Assessment: A Systematic Review","source":"datacite","abstract":"Artificial intelligence (AI) is increasingly being integrated into medical education and has the potential to transform how medical students are assessed. AI-based approaches can support or automate activities traditionally performed by human assessors, including scoring written responses, evaluating clinical documentation and clinical reasoning, providing formative feedback, and assessing performance in simulation-based or practical tasks. Recent developments in large language models and multimodal AI have further expanded these possibilities. However, the use of AI for student assessment also raises important questions regarding validity, reliability, reproducibility, fairness, transparency, and appropriate human oversight. Despite rapidly growing interest in AI in medical education, the evidence concerning its use specifically for medical student assessment remains heterogeneous and dispersed across different educational settings, assessment modalities, and AI technologies. Studies range from automated scoring and natural language processing approaches to generative large language models, machine-learning algorithms, virtual-patient systems, and computer-vision-based assessment. A systematic synthesis is therefore needed to characterize the available evidence and determine both the potential and current limitations of AI-supported assessment. The purpose of this systematic review is to synthesize empirical evidence on the application of AI tools and methods to the assessment of undergraduate medical students. Specifically, the review aims to identify and characterize the AI technologies employed; determine the assessment modalities, competencies, and educational contexts in which they are applied; evaluate reported AI assessment-performance outcomes; examine learner-centered educational outcomes and implications for feedback and learning; assess potential effects on efficiency and resource use; and identify reported benefits, limitations, risks, and implementation challenges. The review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. PubMed, Web of Science, and Scopus will be systematically searched for eligible studies. Original peer-reviewed empirical studies using quantitative, qualitative, or mixed-methods designs will be eligible when they investigate AI tools or methods for the assessment of undergraduate medical students. Studies focused on doctors, residents, or other healthcare professionals rather than undergraduate medical students, studies not evaluating AI in student assessment, reviews, conference publications, and other forms of grey literature will be excluded. No publication-year restriction will be applied. Two reviewers will independently perform study screening and eligibility assessment. Data from eligible studies will be independently extracted using a standardized data extraction framework and will include study and participant characteristics, AI technologies and methods, assessment modalities, and relevant outcomes. Methodological quality will be independently evaluated using the Mixed Methods Appraisal Tool (MMAT), with disagreements resolved through discussion and, where necessary, consultation with a third reviewer. Given the anticipated heterogeneity in study designs, AI technologies, assessment approaches, and outcome measures, a narrative synthesis will be performed. Studies will be organized according to the primary application of AI in assessment. The synthesis will examine patterns in AI assessment performance, learner-centered educational outcomes, efficiency and resource implications, and reported benefits, limitations, risks, and implementation challenges. Where sufficiently comparable information is available, frequencies and proportions of studies reporting specific patterns will also be summarized. The expected outcome is a comprehensive characterization of the current evidence base for AI-assisted a","url":"https://doi.org/10.17605/osf.io/8v4hw","authors":["carina cunha","Olga Cunha"],"tags":["Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/8v4hw","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19453398","name":"A Deep Learning–Based Multi-Layer Recursive Neural Network Framework For Intelligent Thyroid Disease Detection And Recognition","source":"datacite","abstract":"Thyroid disease is one of the most common endocrine disorders affecting millions of people worldwide. The thyroid gland plays a crucial role in regulating metabolism, growth, and overall body functions. Any imbalance in thyroid hormone production can lead to conditions such as hypothyroidism or hyperthyroidism. Early detection of thyroid disorders is important to prevent serious health complications and to ensure timely treatment. Traditional methods of diagnosing thyroid disease rely on laboratory tests and manual evaluation, which may be time-consuming and sometimes prone to errors. With the advancement of artificial intelligence, deep learning techniques can assist medical professionals in improving diagnostic accuracy and reducing workload. In this project, a deep learning-based Multi-Layer Recursive Neural Network (ML-RNN) is proposed for thyroid disease detection and classification. The system includes data preprocessing, feature selection using the Fisher Score method, and classification using the ML-RNN model. The dataset used for analysis is obtained from a standard repository and includes various thyroid-related attributes. The performance of the proposed model is evaluated using metrics such as accuracy, recall, precision, and error rate. Experimental results show that the ML-RNN model achieves better performance compared to traditional machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF). The proposed approach provides an effective and reliable method for thyroid disease detection.","url":"https://doi.org/10.5281/zenodo.19453398","authors":["Dr. A.Avinash","Kanchi Dhanusha","Saladi Rudra Naga Prasanna Lakshmi","Thiguti Sri Ajitesh","Kesanakurthi Satya Karthikeeyan","Vangapandu Lokesh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19453398","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19453399","name":"A Deep Learning–Based Multi-Layer Recursive Neural Network Framework For Intelligent Thyroid Disease Detection And Recognition","source":"datacite","abstract":"Thyroid disease is one of the most common endocrine disorders affecting millions of people worldwide. The thyroid gland plays a crucial role in regulating metabolism, growth, and overall body functions. Any imbalance in thyroid hormone production can lead to conditions such as hypothyroidism or hyperthyroidism. Early detection of thyroid disorders is important to prevent serious health complications and to ensure timely treatment. Traditional methods of diagnosing thyroid disease rely on laboratory tests and manual evaluation, which may be time-consuming and sometimes prone to errors. With the advancement of artificial intelligence, deep learning techniques can assist medical professionals in improving diagnostic accuracy and reducing workload. In this project, a deep learning-based Multi-Layer Recursive Neural Network (ML-RNN) is proposed for thyroid disease detection and classification. The system includes data preprocessing, feature selection using the Fisher Score method, and classification using the ML-RNN model. The dataset used for analysis is obtained from a standard repository and includes various thyroid-related attributes. The performance of the proposed model is evaluated using metrics such as accuracy, recall, precision, and error rate. Experimental results show that the ML-RNN model achieves better performance compared to traditional machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF). The proposed approach provides an effective and reliable method for thyroid disease detection.","url":"https://doi.org/10.5281/zenodo.19453399","authors":["Dr. A.Avinash","Kanchi Dhanusha","Saladi Rudra Naga Prasanna Lakshmi","Thiguti Sri Ajitesh","Kesanakurthi Satya Karthikeeyan","Vangapandu Lokesh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19453399","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.17605/osf.io/6dnp3","name":"Current Status of Generative Artificial Intelligence Applications in Health Management for Older Adults: A Scoping Review","source":"datacite","abstract":"As generative artificial intelligence (GAI) and large language models (LLMs) rapidly evolve, their potential in supporting patient-centered health management has gained significant attention. However, empirical evidence regarding how older adults—a population with unique health needs and digital literacy challenges—interact with and adopt GAI remains fragmented. Guided by the Arksey and O'Malley framework and PRISMA-ScR guidelines, this scoping review systematically maps and synthesizes the application patterns, health-related impacts, and determinants of GAI adoption among older adults (aged ≥ 60 years). Eight international and Chinese databases (PubMed, Web of Science, CINAHL, PsycINFO, CNKI, Wanfang, IEEE Xplore, and Scopus) are searched from January 2020 to June 2026. Quantitative, qualitative, and mixed-methods studies examining older adults' real-world use of GAI for health information seeking, chronic disease self-management, medical communication, and decision support are included. Data are synthesized using descriptive statistics and thematic analysis to provide evidence-based insights for designing age-friendly AI tools and clinical health interventions.","url":"https://doi.org/10.17605/osf.io/6dnp3","authors":["Jindi Wang"],"tags":["Medicine and Health Sciences","Geriatric Nursing","Nursing","GAI","health management","older adults","scoping review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/6dnp3","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.82451/k51185","name":"Insights and Inputs: Analyzing ChatGPT Prompt Design by Future Physician","source":"datacite","abstract":"As artificial intelligence (AI) becomes increasingly integrated into healthcare, effective prompting has emerged as a key factor in optimizing large language model (LLM) performance. While strategies such as chain-of-thought prompting can enhance reasoning, little is known about how medical students naturally construct prompts when engaging with LLMs. Given ChatGPT’s ability to pass the USMLE, the research question guiding this study was “How do medical students interact with LLMs, and how does prior digital health training influence these approaches?” To address this, we examined how medical students at Rocky Vista University used ChatGPT-4.0 to answer medically related questions. The primary objective was to characterize prompt themes and elements; the secondary objective was to compare students enrolled in a longitudinal digital health curriculum—including training in prompt engineering—with peers lacking formal instruction. An 11-question Qualtrics survey was distributed across the Colorado and Utah campuses, including six demographic items and five medical questions. Of 108 responses, 60 met eligibility criteria and were analyzed. Responses were evaluated for prompting styles, AI interactions, and digital health participation. Findings revealed challenges for both students and ChatGPT in osteopathic principles and practice (OPP), particularly with sacral landmarks and axis application (correct response rate ~52%). In contrast, ChatGPT answered an ethics-based question correctly that many students misinterpreted, highlighting differences in reasoning rather than model performance. Prompting strategy influenced outcomes: students using targeted prompts with copy-and-paste achieved the highest accuracy (75% fully correct), while most relied on unmodified copy-and-paste. Limitations include the small sample size and recruitment from a single medical program, which may limit generalizability. This study suggests that targeted prompt design improves LLM accuracy and that incorporating structured prompting instruction may be critical to preparing medical students to engage with AI responsibly and productively in medical education.","url":"https://doi.org/10.82451/k51185","authors":["Tiffani Szeto","Hamza Ahmad","Zaakir Hamzavi","Hannah Walters","Regan Stiegmann"],"tags":["FOS: Clinical medicine","Generative Artificial Intelligence","Artificial Intelligence","Large Language Models","Clinical Reasoning","Colorado","Generative Artificial Intelligence","Clinical Reasoning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.82451/k51185","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21521123","name":"Dataset: SOD1 Research July 2026 - PathMap Experiment #000084","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=84 Artificial General Intelligence LLC Claim Evaluated: SOD1 Research July 2026 This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Biomarker Evolution:** Neuromuscular ultrasound now provides non-invasive diagnostic capabilities that match or precede traditional electroneurographic markers in SOD1G93A models. Mechanism Redefined:** Mutant SOD1 acts as both a Fenton-like catalyst for hydroxyl radical generation and a hydrogenation catalyst for hydrogen scavenging. Genetic Prevalence:** Population-specific data, such as that from Indian cohorts, demonstrate that SOD1 is the predominant cause of familial ALS, even when other repeat expansions (e.g., C9orf72) are present at low frequencies. Systemic Involvement:** ALS motor neuron disease is increasingly viewed as a multisystem disorder where innate immune crosstalk, specifically between cGAS-STING and NLRP3 inflammasomes, drives progression. Proactive Planning:** Nationwide adoption of genetic testing in Canada was significantly accelerated by proactive planning during the clinical trial phase of gene-targeted therapies. Microglial Dynamics:** SGK1 has been identified as a key regulator of microglial phagocytosis; its inhibition attenuates motor deficits, suggesting it as a potential therapeutic target. Future Demand:** Projections indicate a significant increase in ALS clinic visits among asymptomatic gene carriers, requiring substantial expansion of clinical infrastructure by 2035. The application of magnesium-silicide based hydrogen gas release serves as an innovative strategy to intercept the crosstalk between oxidative stress and neuroinflammation. The use of Platelet Factor 4 (PF4) demonstrates a selective neuroprotective benefit in SOD1-driven ALS, bypassing PINK1-dependent mechanisms to restore proteostasis. The phenomenon of macrophage inclusions (\"tofersenophages\") in CSF has been identified as a persistent, albeit clinically ambiguous, finding during ASO therapy, which surprisingly correlates with favorable clinical outcomes. Neuromuscular ultrasound serves as a high-sensitivity, non-invasive biomarker that detects disease pathology at stages prior to electroneurographic abnormalities. Genetic testing for ALS has achieved near-universal integration in clinical practice by 2025, with sponsored, cost-free testing panels significantly increasing diagnostic yields in sporadic cases. The identification of the JAK2 gene as a novel genome-wide significant signal in the Indian cohort underscores the importance of population-specific genetic surveying. The integration of phase-resolved geometric deep learning (SKALE 2.0) now allows for the constraint-aware design of aggregation suppressors that differentiate between nucleation and elongation phases. The existence of oligogenic models (e.g., ATXN2/NEK1) highlights the complexity of ALS, where pathogenicity may be governed by the synergy of multiple low-penetrance variants rather than monogenic drivers. Copper Paradox:** High intracellular copper can inhibit SOD1 by disrupting its homodimerization, mediated by COMMD1-dependent mechanisms. Catalytic Hydrogen Therapy:** Mutant SOD1 acts as both a Fenton-like agent producing hydroxyl radicals and a catalyst for hydrogen-based free radical scavenging. Microglial LAG-3:** This immune checkpoint protein exerts stage-dependent regulation on microglial modules, dissociating inflammatory and phagocytic functions in ALS progression. Prion-like Propagation:** Conversion of SOD1 into a misfolded isoform is a targetable biophysical process distinct from aggregation. Statin Effects:** While statins can modulate antioxidant genes, they may also inadvertently accelera","url":"https://doi.org/10.5281/zenodo.21521123","authors":["Dungan, Joshua"],"tags":["Genetic Testing","_gates_from_genetic_testing","Drug Delivery Systems","_gates_to_drug_delivery_systems","_gates_from_drug_delivery_systems","Superoxide Dismutase-1","_gates_to_superoxide_dismutase-1","Protein Aggregates"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21521123","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21521124","name":"Dataset: SOD1 Research July 2026 - PathMap Experiment #000084","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=84 Artificial General Intelligence LLC Claim Evaluated: SOD1 Research July 2026 This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Biomarker Evolution:** Neuromuscular ultrasound now provides non-invasive diagnostic capabilities that match or precede traditional electroneurographic markers in SOD1G93A models. Mechanism Redefined:** Mutant SOD1 acts as both a Fenton-like catalyst for hydroxyl radical generation and a hydrogenation catalyst for hydrogen scavenging. Genetic Prevalence:** Population-specific data, such as that from Indian cohorts, demonstrate that SOD1 is the predominant cause of familial ALS, even when other repeat expansions (e.g., C9orf72) are present at low frequencies. Systemic Involvement:** ALS motor neuron disease is increasingly viewed as a multisystem disorder where innate immune crosstalk, specifically between cGAS-STING and NLRP3 inflammasomes, drives progression. Proactive Planning:** Nationwide adoption of genetic testing in Canada was significantly accelerated by proactive planning during the clinical trial phase of gene-targeted therapies. Microglial Dynamics:** SGK1 has been identified as a key regulator of microglial phagocytosis; its inhibition attenuates motor deficits, suggesting it as a potential therapeutic target. Future Demand:** Projections indicate a significant increase in ALS clinic visits among asymptomatic gene carriers, requiring substantial expansion of clinical infrastructure by 2035. The application of magnesium-silicide based hydrogen gas release serves as an innovative strategy to intercept the crosstalk between oxidative stress and neuroinflammation. The use of Platelet Factor 4 (PF4) demonstrates a selective neuroprotective benefit in SOD1-driven ALS, bypassing PINK1-dependent mechanisms to restore proteostasis. The phenomenon of macrophage inclusions (\"tofersenophages\") in CSF has been identified as a persistent, albeit clinically ambiguous, finding during ASO therapy, which surprisingly correlates with favorable clinical outcomes. Neuromuscular ultrasound serves as a high-sensitivity, non-invasive biomarker that detects disease pathology at stages prior to electroneurographic abnormalities. Genetic testing for ALS has achieved near-universal integration in clinical practice by 2025, with sponsored, cost-free testing panels significantly increasing diagnostic yields in sporadic cases. The identification of the JAK2 gene as a novel genome-wide significant signal in the Indian cohort underscores the importance of population-specific genetic surveying. The integration of phase-resolved geometric deep learning (SKALE 2.0) now allows for the constraint-aware design of aggregation suppressors that differentiate between nucleation and elongation phases. The existence of oligogenic models (e.g., ATXN2/NEK1) highlights the complexity of ALS, where pathogenicity may be governed by the synergy of multiple low-penetrance variants rather than monogenic drivers. Copper Paradox:** High intracellular copper can inhibit SOD1 by disrupting its homodimerization, mediated by COMMD1-dependent mechanisms. Catalytic Hydrogen Therapy:** Mutant SOD1 acts as both a Fenton-like agent producing hydroxyl radicals and a catalyst for hydrogen-based free radical scavenging. Microglial LAG-3:** This immune checkpoint protein exerts stage-dependent regulation on microglial modules, dissociating inflammatory and phagocytic functions in ALS progression. Prion-like Propagation:** Conversion of SOD1 into a misfolded isoform is a targetable biophysical process distinct from aggregation. Statin Effects:** While statins can modulate antioxidant genes, they may also inadvertently accelera","url":"https://doi.org/10.5281/zenodo.21521124","authors":["Dungan, Joshua"],"tags":["Genetic Testing","_gates_from_genetic_testing","Drug Delivery Systems","_gates_to_drug_delivery_systems","_gates_from_drug_delivery_systems","Superoxide Dismutase-1","_gates_to_superoxide_dismutase-1","Protein Aggregates"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21521124","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19505038","name":"The Metabolic Age Institutional Playbook","source":"datacite","abstract":"The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional operations. Standard artificial intelligence paradigms exist in a state of persistent epistemological friction, where systems operate as probabilistic clouds highly susceptible to error, misalignment, and corporate extraction.1 The Metabolic Age Institutional Playbook serves as the definitive legal, operational, economic, and technical integration framework for governments, agencies, non-governmental organizations, and international bodies adopting the advanced cybernetic architectures of the Metabolic Age. This playbook explicitly translates the foundational Constitution, Roadmap, and Mesh Protocol of these systems into policy-ready frameworks designed for high-stakes institutional deployment. The primary directive is to provide a clear, actionable guide for institutions to adopt metabolic infrastructure, defining rigorous procurement pathways, compliance requirements, and deployment templates. By moving away from legacy models of centralized cloud dependency, institutions can ensure the adoption of sovereign, governed, metabolic systems without the risk of operational drift or systemic corruption.1 Furthermore, this document establishes the governance, economic, and operational standards for both national and international rollouts. It dictates exactly how policymakers, procurement officers, regulators, and global institutions can integrate into the Metabolic Age seamlessly and securely. A cornerstone of this transition is the departure from theoretical promises to executed realities, codified in the Dual-Proof Protection Doctrine.1 Institutions must navigate a landscape where intelligence and resource management are no longer rented from centralized providers but are sovereign, verifiable, and biologically inspired.1 This document outlines the operational vision required to deploy systems that completely eradicate the ontological schism between code and execution, establishing an unprecedented benchmark for verifiable cybernetic capability in both the public and private sectors. The necessity for such a playbook arises from the compounding vulnerabilities of contemporary digital and physical infrastructure. Global supply chains, centralized power grids, and probabilistic artificial intelligence models have demonstrated cascading failure modes under stress. By adopting the principles outlined herein, organizations effectively inoculate themselves against these systemic risks. The transition demands an understanding that computation and physical resource generation are no longer separate domains; they are unified within an Isomorphic Organism—a highly bounded cybernetic entity wherein the mathematical form and the functional body are inextricably linked.1 This playbook provides the blueprint for that integration. II. Institutional Adoption Principles The adoption of metabolic systems by state and global actors requires a total recalibration of foundational information technology and physical infrastructure principles. Institutions must abandon legacy models of software-as-a-service, proprietary vendor lock-in, and centralized cloud dependency in favor of governed manifolds that behave computationally as solid geometric objects.1 This recalibration is guided by five core principles. 1. Sovereignty by Default Under the metabolic framework, institutions do not rent intelligence or infrastructure from third-party commercial vendors. The foundational principle is that agencies must own, govern, and verify their metabolic nodes locally. The legacy model of relying on distant data centers creates unacceptable latency and vulnerabilities to network severance. By operating on highly specialized hardware layers—such as the operational infrastructure governing the 3-PC mini cluste","url":"https://doi.org/10.5281/zenodo.19505038","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19505038","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19505039","name":"The Metabolic Age Institutional Playbook","source":"datacite","abstract":"The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional operations. Standard artificial intelligence paradigms exist in a state of persistent epistemological friction, where systems operate as probabilistic clouds highly susceptible to error, misalignment, and corporate extraction.1 The Metabolic Age Institutional Playbook serves as the definitive legal, operational, economic, and technical integration framework for governments, agencies, non-governmental organizations, and international bodies adopting the advanced cybernetic architectures of the Metabolic Age. This playbook explicitly translates the foundational Constitution, Roadmap, and Mesh Protocol of these systems into policy-ready frameworks designed for high-stakes institutional deployment. The primary directive is to provide a clear, actionable guide for institutions to adopt metabolic infrastructure, defining rigorous procurement pathways, compliance requirements, and deployment templates. By moving away from legacy models of centralized cloud dependency, institutions can ensure the adoption of sovereign, governed, metabolic systems without the risk of operational drift or systemic corruption.1 Furthermore, this document establishes the governance, economic, and operational standards for both national and international rollouts. It dictates exactly how policymakers, procurement officers, regulators, and global institutions can integrate into the Metabolic Age seamlessly and securely. A cornerstone of this transition is the departure from theoretical promises to executed realities, codified in the Dual-Proof Protection Doctrine.1 Institutions must navigate a landscape where intelligence and resource management are no longer rented from centralized providers but are sovereign, verifiable, and biologically inspired.1 This document outlines the operational vision required to deploy systems that completely eradicate the ontological schism between code and execution, establishing an unprecedented benchmark for verifiable cybernetic capability in both the public and private sectors. The necessity for such a playbook arises from the compounding vulnerabilities of contemporary digital and physical infrastructure. Global supply chains, centralized power grids, and probabilistic artificial intelligence models have demonstrated cascading failure modes under stress. By adopting the principles outlined herein, organizations effectively inoculate themselves against these systemic risks. The transition demands an understanding that computation and physical resource generation are no longer separate domains; they are unified within an Isomorphic Organism—a highly bounded cybernetic entity wherein the mathematical form and the functional body are inextricably linked.1 This playbook provides the blueprint for that integration. II. Institutional Adoption Principles The adoption of metabolic systems by state and global actors requires a total recalibration of foundational information technology and physical infrastructure principles. Institutions must abandon legacy models of software-as-a-service, proprietary vendor lock-in, and centralized cloud dependency in favor of governed manifolds that behave computationally as solid geometric objects.1 This recalibration is guided by five core principles. 1. Sovereignty by Default Under the metabolic framework, institutions do not rent intelligence or infrastructure from third-party commercial vendors. The foundational principle is that agencies must own, govern, and verify their metabolic nodes locally. The legacy model of relying on distant data centers creates unacceptable latency and vulnerabilities to network severance. By operating on highly specialized hardware layers—such as the operational infrastructure governing the 3-PC mini cluste","url":"https://doi.org/10.5281/zenodo.19505039","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19505039","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21385149","name":"Dataset: Lon Protease, Alternaria; IL-33; TSLP; alarmins; asthma; chronic rhinosinusitis; fungal allergen; innate lymphoid cells - PathMap Experiment #000064","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=64 Artificial General Intelligence LLC Claim Evaluated: Lon Protease, Alternaria; IL-33; TSLP; alarmins; asthma; chronic rhinosinusitis; fungal allergen; innate lymphoid cells This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Alternaria-induced ILC2 activation is not merely a consequence of alarmin signaling but is subject to mechanical checkpoints like Piezo1. LONP1 acts as a potential immunometabolic checkpoint, where mitochondrial protein quality control directly impacts the inflammatory trajectory of the airway epithelium. The severity of Alternaria-driven responses is modulated by SLPI, which serves as a molecular brake on the protease-mediated activation of IL-33. ILC2s exhibit significant phenotypic plasticity, particularly when transitioning toward ILC3-like or steroid-resistant states. The cross-talk between eosinophils and epithelial cells is bi-directional and foundational to tissue-resident remodeling in chronic rhinosinusitis. Mitochondrial dysfunction (driven by LONP1/Drp1) is an upstream contributor to the cytokine/chemokine environment of the asthma/CRSwNP mucosa. Fungal allergens can initiate a \"two-hit\" inflammatory model where live spore exposure exacerbates pre-existing, OVA-primed airway damage. LONP1 serves as a dual-function gatekeeper, maintaining mitochondrial DNA integrity while modulating inflammatory cell polarization in response to oxidative stress. The \"residual molecular scar\" phenomenon explains why some patients with ECRS exhibit persistent mucus hyperviscosity even after successful biological blockade of IL-4/IL-13. ILC2s are not merely passive responders; they exhibit subset heterogeneity (migratory, transitional, inflammatory, exhausted) that correlates with clinical severity in nasal polyps. Treg/Th2 imbalance in severe asthma is reversible, as shown by benralizumab therapy restoring immune homeostasis and modifying adhesion molecule expression. The gut-lung axis utilizes tryptophan metabolism to reprogram ILC2s, potentially allowing microbiome-derived postbiotics to serve as non-live therapeutic alternatives. Fungal *Alternaria* allergens act not only as biochemical triggers for alarmins but also cause physical and oxidative damage that necessitates mitochondrial quality control. Mitochondrial proteases (LonP1) serve as an immunometabolic checkpoint, where their dysfunction directly links mitochondrial DNA release to chronic inflammation via the cGAS-STING axis. The IL-22BP decoy receptor has been shown to play a paradoxical role; while IL-22 is typically protective, \"These findings suggest that inhibition of IL-22BP attenuates the development of allergen-induced AHR, an effect likely mediated through enhanced IL-22 activity rather than alterations in airway inflammation or type 2 cytokine production.\" Biologics targeting TSLP, such as tezepelumab, are effective across diverse asthma endotypes, emphasizing the hierarchy of alarmins as \"source-directed\" intervention targets. The metabolic state of ILC2s (glycolysis, lipid metabolism) is an emerging regulator of their plasticity, suggesting that metabolic modulation (e.g., via serotonin catabolism or MAOA inhibition) can alter immune responsiveness. Epigenetic memory, established through DNA methylation and histone modifications in basal epithelial progenitors, explains why asthma is often a relapsing, chronic condition rather than a simple acute response to fungal allergens. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Investigate the impact of LONP1 knockdown in human primary ILC2s on their susceptibility to IL-33/TSLP-induced activation. Assess whether SLPI administration in a humanized mouse model ","url":"https://doi.org/10.5281/zenodo.21385149","authors":["Dungan, Joshua"],"tags":["Alternaria","_gates_from_alternaria","Alarmins","_gates_to_alarmins","_gates_from_alarmins","Immunity, Innate","_gates_to_immunity,_innate","_gates_from_immunity,_innate"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21385149","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21385150","name":"Dataset: Lon Protease, Alternaria; IL-33; TSLP; alarmins; asthma; chronic rhinosinusitis; fungal allergen; innate lymphoid cells - PathMap Experiment #000064","source":"datacite","abstract":"Interactive Data Viewer: Read, View, and Print from Day 1 Use our fully interactive viewer to view, read, and print this research data right from Day 1: https://pathmap.org/viewer.php?id=64 Artificial General Intelligence LLC Claim Evaluated: Lon Protease, Alternaria; IL-33; TSLP; alarmins; asthma; chronic rhinosinusitis; fungal allergen; innate lymphoid cells This dataset contains the raw JSON execution trace, verified verbatim quotes, and MeSH-aligned logic gates generated by PathMap Studio's Veridical Enforcement engine. 🔍 Novel & Overlooked Insights Alternaria-induced ILC2 activation is not merely a consequence of alarmin signaling but is subject to mechanical checkpoints like Piezo1. LONP1 acts as a potential immunometabolic checkpoint, where mitochondrial protein quality control directly impacts the inflammatory trajectory of the airway epithelium. The severity of Alternaria-driven responses is modulated by SLPI, which serves as a molecular brake on the protease-mediated activation of IL-33. ILC2s exhibit significant phenotypic plasticity, particularly when transitioning toward ILC3-like or steroid-resistant states. The cross-talk between eosinophils and epithelial cells is bi-directional and foundational to tissue-resident remodeling in chronic rhinosinusitis. Mitochondrial dysfunction (driven by LONP1/Drp1) is an upstream contributor to the cytokine/chemokine environment of the asthma/CRSwNP mucosa. Fungal allergens can initiate a \"two-hit\" inflammatory model where live spore exposure exacerbates pre-existing, OVA-primed airway damage. LONP1 serves as a dual-function gatekeeper, maintaining mitochondrial DNA integrity while modulating inflammatory cell polarization in response to oxidative stress. The \"residual molecular scar\" phenomenon explains why some patients with ECRS exhibit persistent mucus hyperviscosity even after successful biological blockade of IL-4/IL-13. ILC2s are not merely passive responders; they exhibit subset heterogeneity (migratory, transitional, inflammatory, exhausted) that correlates with clinical severity in nasal polyps. Treg/Th2 imbalance in severe asthma is reversible, as shown by benralizumab therapy restoring immune homeostasis and modifying adhesion molecule expression. The gut-lung axis utilizes tryptophan metabolism to reprogram ILC2s, potentially allowing microbiome-derived postbiotics to serve as non-live therapeutic alternatives. Fungal *Alternaria* allergens act not only as biochemical triggers for alarmins but also cause physical and oxidative damage that necessitates mitochondrial quality control. Mitochondrial proteases (LonP1) serve as an immunometabolic checkpoint, where their dysfunction directly links mitochondrial DNA release to chronic inflammation via the cGAS-STING axis. The IL-22BP decoy receptor has been shown to play a paradoxical role; while IL-22 is typically protective, \"These findings suggest that inhibition of IL-22BP attenuates the development of allergen-induced AHR, an effect likely mediated through enhanced IL-22 activity rather than alterations in airway inflammation or type 2 cytokine production.\" Biologics targeting TSLP, such as tezepelumab, are effective across diverse asthma endotypes, emphasizing the hierarchy of alarmins as \"source-directed\" intervention targets. The metabolic state of ILC2s (glycolysis, lipid metabolism) is an emerging regulator of their plasticity, suggesting that metabolic modulation (e.g., via serotonin catabolism or MAOA inhibition) can alter immune responsiveness. Epigenetic memory, established through DNA methylation and histone modifications in basal epithelial progenitors, explains why asthma is often a relapsing, chronic condition rather than a simple acute response to fungal allergens. 🧪 Extracted Custom Datapoints 📊 Suggested Experiments Investigate the impact of LONP1 knockdown in human primary ILC2s on their susceptibility to IL-33/TSLP-induced activation. Assess whether SLPI administration in a humanized mouse model ","url":"https://doi.org/10.5281/zenodo.21385150","authors":["Dungan, Joshua"],"tags":["Alternaria","_gates_from_alternaria","Alarmins","_gates_to_alarmins","_gates_from_alarmins","Immunity, Innate","_gates_to_immunity,_innate","_gates_from_immunity,_innate"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21385150","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20190865","name":"INTERGET v2.0: The Provenance and Autonomous Coordination Layer of the Sovereign Web — Intelligent Network for Trusted Execution of Recorded Generative Experiences and Trajectories","source":"datacite","abstract":"Artificial intelligence systems now generate software, scientific research, legal documents, financial analysis, medical recommendations, and other outputs that increasingly shape human institutions and decision-making. Despite this expansion, there exists no standardized, cryptographically verifiable infrastructure capable of proving the provenance, reproducibility, model origin, training lineage, or verification status of AI-generated artifacts. The absence of such infrastructure creates systemic failures in accountability, regulatory compliance, reproducibility, attribution, and trust. INTERGET v2.0 introduces a comprehensive provenance and autonomous coordination framework for the sovereign web through the Proof of Intelligence (PoI), a W3C Verifiable Credential standard that cryptographically binds every AI-generated artifact to its originating model, prompt, hyperparameters, training data lineage, execution environment, and independently verifiable reproducibility attestations. Each PoI record is content-addressed, signed, and registered within the Universal Semantic Intelligence Graph (USIG), a globally queryable semantic provenance graph for models, datasets, prompts, code artifacts, and intelligence relationships. This white paper specifies the complete INTERGET architecture, including the PoI schema and its three artifact classes—DETERMINISTIC, FUNCTIONALLY_VERIFIABLE, and SEMANTIC—with class-specific verification semantics; the USIG distributed graph architecture; the INTERGET Semantic Query Language (ISQ); the INTERGET-G sovereign repository bridge with dual-write guarantees; the autonomous AI coordination protocol with multi-tier human oversight; the transitive royalty and attribution system with bounded dependency traversal; the INTERGET Trust Score; the Developer and Compliance Passports; privacy-preserving PoI modes; the Model Genealogy Graph; the Benchmark Oracle; and the Diff Intelligence Layer integrated through WEBB Observe. The paper further defines the complete governance, dispute resolution, economic settlement, regulatory compliance, and security framework of the protocol, including anti-collusion verification node selection, VRF-based attestation governance, privacy-preserving provenance, sovereign storage guarantees through PSW vaults, and integration with the broader Techmanity Stack, including TIP v9.0, KNOWDES v2.0, MetaMesh v1.0, WEBB v1.0, WEBBIUM v1.1, and the Rahmn Standard. INTERGET positions cryptographic provenance as foundational infrastructure for the AI era, transforming reproducibility, attribution, accountability, and verification from organizational policy objectives into protocol-level guarantees for distributed intelligence systems.","url":"https://doi.org/10.5281/zenodo.20190865","authors":["Rahming, Rashon"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20190865","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20190866","name":"INTERGET v2.0: The Provenance and Autonomous Coordination Layer of the Sovereign Web — Intelligent Network for Trusted Execution of Recorded Generative Experiences and Trajectories","source":"datacite","abstract":"Artificial intelligence systems now generate software, scientific research, legal documents, financial analysis, medical recommendations, and other outputs that increasingly shape human institutions and decision-making. Despite this expansion, there exists no standardized, cryptographically verifiable infrastructure capable of proving the provenance, reproducibility, model origin, training lineage, or verification status of AI-generated artifacts. The absence of such infrastructure creates systemic failures in accountability, regulatory compliance, reproducibility, attribution, and trust. INTERGET v2.0 introduces a comprehensive provenance and autonomous coordination framework for the sovereign web through the Proof of Intelligence (PoI), a W3C Verifiable Credential standard that cryptographically binds every AI-generated artifact to its originating model, prompt, hyperparameters, training data lineage, execution environment, and independently verifiable reproducibility attestations. Each PoI record is content-addressed, signed, and registered within the Universal Semantic Intelligence Graph (USIG), a globally queryable semantic provenance graph for models, datasets, prompts, code artifacts, and intelligence relationships. This white paper specifies the complete INTERGET architecture, including the PoI schema and its three artifact classes—DETERMINISTIC, FUNCTIONALLY_VERIFIABLE, and SEMANTIC—with class-specific verification semantics; the USIG distributed graph architecture; the INTERGET Semantic Query Language (ISQ); the INTERGET-G sovereign repository bridge with dual-write guarantees; the autonomous AI coordination protocol with multi-tier human oversight; the transitive royalty and attribution system with bounded dependency traversal; the INTERGET Trust Score; the Developer and Compliance Passports; privacy-preserving PoI modes; the Model Genealogy Graph; the Benchmark Oracle; and the Diff Intelligence Layer integrated through WEBB Observe. The paper further defines the complete governance, dispute resolution, economic settlement, regulatory compliance, and security framework of the protocol, including anti-collusion verification node selection, VRF-based attestation governance, privacy-preserving provenance, sovereign storage guarantees through PSW vaults, and integration with the broader Techmanity Stack, including TIP v9.0, KNOWDES v2.0, MetaMesh v1.0, WEBB v1.0, WEBBIUM v1.1, and the Rahmn Standard. INTERGET positions cryptographic provenance as foundational infrastructure for the AI era, transforming reproducibility, attribution, accountability, and verification from organizational policy objectives into protocol-level guarantees for distributed intelligence systems.","url":"https://doi.org/10.5281/zenodo.20190866","authors":["Rahming, Rashon"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20190866","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21978439","name":"Robotique souple neuromorphique et essaims","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue, de façon enabling, un portefeuille d’innovations combinant robotique souple (actionneurs HASEL/EAP), vision événementielle (DVS), calcul neuromorphique (SNN) et intelligence en essaim, couvrant dispositifs/capteurs, algorithmes, contrôle en boucle fermée, fabrication roll-to-roll et QA end-of-line, cybersécurité et opérations de flottes, interopérabilité (formats événements+spikes), logistique de cartouches, modèles économiques au résultat, et usages industriels, agricoles régénératifs, nucléaires, sous-marins et médicaux. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE). It discloses, in an enabling manner, a portfolio that fuses soft robotics (HASEL/EAP actuation), event-based vision (DVS), neuromorphic computing (SNN), and swarm intelligence. The disclosure spans devices and sensors, event-first control loops, roll-to-roll manufacturing and end-of-line QA, cyber-secure fleet operations, interoperability standards for event+spike telemetry, cartridge logistics and field repair, outcome-based metering and SLA instrumentation, and applications in high-throughput sorting, precision/regenerative agriculture, nuclear maintenance, underwater monitoring, and medical/rehabilitation systems. Each proposal is classified with IPC/CPC codes and can be timestamped (RFC 3161 / FreeTSA). Timestamp : 2026-08-17T10:45:25ZSHA-256 : 13b3e2bc50c638e594d623990f13159039dd0fb8f0b0968e18a3300649110a89 Liste des innovations & classification (IPC ; CPC) :1. DVS–HASEL soft gripper — IPC B25J 15/00 ; CPC B25J 15/122. DVS sorting calibration rig — IPC G01D 18/00 ; CPC G01D 18/003. HASEL sensing skin laminate — IPC G01L 5/00 ; CPC G01L 5/164. Biodegradable electrohydraulic actuator — IPC C08L 67/00 ; CPC C08L 67/025. Printable EAP electrode ink — IPC H01B 1/12 ; CPC H01B 1/126. Self-healing dielectric composite — IPC C08K 3/36 ; CPC C08K 3/367. Roll-to-roll HASEL pouch line — IPC B29C 65/00 ; CPC B29C 65/788. 3D-printed soft body + circuits — IPC B29C 64/118 ; CPC B29C 64/1189. Soft underwater encapsulation stack — IPC B29C 71/00 ; CPC B29C 71/0210. Event-driven SNN HASEL control — IPC G06N 3/04 ; CPC G06N 3/04511. Event-based actuator fatigue detection — IPC G05B 23/02 ; CPC G05B 23/0212. Edge event-stream compression codec — IPC H04N 5/00 ; CPC H04N 5/23213. Spike-packet swarm protocol — IPC H04W 4/80 ; CPC H04W 4/8014. Neuromorphic swarm task allocator — IPC G06Q 10/04 ; CPC G06Q 10/063915. Safe HV charge scheduler — IPC H02M 3/155 ; CPC H02M 3/15816. Swarm geofencing operations — IPC G08G 5/00 ; CPC G08G 5/0017. Radiation-hardened soft robot module — IPC G21C 19/00 ; CPC G21C 19/0018. DVS-to-intensity reconstruction — IPC H04N 5/232 ; CPC H04N 5/23219. DVS+EMG SNN exosuit fusion — IPC A61H 1/02 ; CPC A61H 1/0220. Closed-loop rehab dosing method — IPC A61H 1/00 ; CPC A61H 1/0021. Soft endoscope targeted delivery — IPC A61M 31/00 ; CPC A61M 31/0022. Low-power EAP assist patch — IPC A61F 5/01 ; CPC A61F 5/0123. Federated learning for agri swarms — IPC G06F 18/232 ; CPC G06F 18/232124. Event+spike interoperability standard — IPC G06F 9/54 ; CPC G06F 9/54125. Tamper-proof swarm audit ledger — IPC G06Q 20/38 ; CPC G06Q 20/38226. Swarm supervisor cockpit UI — IPC G05B 19/042 ; CPC G05B 19/04227. Hybrid ultra-fast waste sorter cell — IPC B07C 5/34 ; CPC B07C 5/34228. Underwater soft-drone swarm system — IPC B63G 8/00 ; CPC B63G 8/0029. Swarm soil-compaction sens","url":"https://doi.org/10.5281/zenodo.21978439","authors":["Pillet, Xavier"],"tags":["B25J 15/00","B25J 15/12","G01D 18/00","G01L 5/00","G01L 5/16","C08L 67/00","C08L 67/02","H01B 1/12"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21978439","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21978440","name":"Robotique souple neuromorphique et essaims","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue, de façon enabling, un portefeuille d’innovations combinant robotique souple (actionneurs HASEL/EAP), vision événementielle (DVS), calcul neuromorphique (SNN) et intelligence en essaim, couvrant dispositifs/capteurs, algorithmes, contrôle en boucle fermée, fabrication roll-to-roll et QA end-of-line, cybersécurité et opérations de flottes, interopérabilité (formats événements+spikes), logistique de cartouches, modèles économiques au résultat, et usages industriels, agricoles régénératifs, nucléaires, sous-marins et médicaux. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE). It discloses, in an enabling manner, a portfolio that fuses soft robotics (HASEL/EAP actuation), event-based vision (DVS), neuromorphic computing (SNN), and swarm intelligence. The disclosure spans devices and sensors, event-first control loops, roll-to-roll manufacturing and end-of-line QA, cyber-secure fleet operations, interoperability standards for event+spike telemetry, cartridge logistics and field repair, outcome-based metering and SLA instrumentation, and applications in high-throughput sorting, precision/regenerative agriculture, nuclear maintenance, underwater monitoring, and medical/rehabilitation systems. Each proposal is classified with IPC/CPC codes and can be timestamped (RFC 3161 / FreeTSA). Timestamp : 2026-08-17T10:45:25ZSHA-256 : 13b3e2bc50c638e594d623990f13159039dd0fb8f0b0968e18a3300649110a89 Liste des innovations & classification (IPC ; CPC) :1. DVS–HASEL soft gripper — IPC B25J 15/00 ; CPC B25J 15/122. DVS sorting calibration rig — IPC G01D 18/00 ; CPC G01D 18/003. HASEL sensing skin laminate — IPC G01L 5/00 ; CPC G01L 5/164. Biodegradable electrohydraulic actuator — IPC C08L 67/00 ; CPC C08L 67/025. Printable EAP electrode ink — IPC H01B 1/12 ; CPC H01B 1/126. Self-healing dielectric composite — IPC C08K 3/36 ; CPC C08K 3/367. Roll-to-roll HASEL pouch line — IPC B29C 65/00 ; CPC B29C 65/788. 3D-printed soft body + circuits — IPC B29C 64/118 ; CPC B29C 64/1189. Soft underwater encapsulation stack — IPC B29C 71/00 ; CPC B29C 71/0210. Event-driven SNN HASEL control — IPC G06N 3/04 ; CPC G06N 3/04511. Event-based actuator fatigue detection — IPC G05B 23/02 ; CPC G05B 23/0212. Edge event-stream compression codec — IPC H04N 5/00 ; CPC H04N 5/23213. Spike-packet swarm protocol — IPC H04W 4/80 ; CPC H04W 4/8014. Neuromorphic swarm task allocator — IPC G06Q 10/04 ; CPC G06Q 10/063915. Safe HV charge scheduler — IPC H02M 3/155 ; CPC H02M 3/15816. Swarm geofencing operations — IPC G08G 5/00 ; CPC G08G 5/0017. Radiation-hardened soft robot module — IPC G21C 19/00 ; CPC G21C 19/0018. DVS-to-intensity reconstruction — IPC H04N 5/232 ; CPC H04N 5/23219. DVS+EMG SNN exosuit fusion — IPC A61H 1/02 ; CPC A61H 1/0220. Closed-loop rehab dosing method — IPC A61H 1/00 ; CPC A61H 1/0021. Soft endoscope targeted delivery — IPC A61M 31/00 ; CPC A61M 31/0022. Low-power EAP assist patch — IPC A61F 5/01 ; CPC A61F 5/0123. Federated learning for agri swarms — IPC G06F 18/232 ; CPC G06F 18/232124. Event+spike interoperability standard — IPC G06F 9/54 ; CPC G06F 9/54125. Tamper-proof swarm audit ledger — IPC G06Q 20/38 ; CPC G06Q 20/38226. Swarm supervisor cockpit UI — IPC G05B 19/042 ; CPC G05B 19/04227. Hybrid ultra-fast waste sorter cell — IPC B07C 5/34 ; CPC B07C 5/34228. Underwater soft-drone swarm system — IPC B63G 8/00 ; CPC B63G 8/0029. Swarm soil-compaction sens","url":"https://doi.org/10.5281/zenodo.21978440","authors":["Pillet, Xavier"],"tags":["B25J 15/00","B25J 15/12","G01D 18/00","G01L 5/00","G01L 5/16","C08L 67/00","C08L 67/02","H01B 1/12"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21978440","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20471387","name":"Improving Pakistan's Human Development Index: Strategies for Sustainable Growth and National Progress","source":"datacite","abstract":"Abstract The Human Development Index (HDI) is a composite measure developed by the United Nations Development Programme (UNDP) to assess a country's progress in health, education, and standard of living. Pakistan continues to face significant challenges in improving its HDI ranking due to issues such as low educational attainment, inadequate healthcare infrastructure, gender inequality, poverty, and unemployment. This article examines the major factors affecting Pakistan's HDI and proposes practical strategies to accelerate human development. The study highlights the importance of investments in education, healthcare, economic growth, women's empowerment, technological innovation, and good governance as key drivers of sustainable human development. Keywords: Human Development Index, Pakistan, education, healthcare, poverty reduction, economic development, gender equality, sustainable development Introduction Human development refers to the process of expanding people's freedoms, opportunities, and capabilities to live productive and fulfilling lives. The Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP), measures development using three primary indicators: life expectancy, education, and gross national income per capita (UNDP, 2025). Pakistan has experienced gradual improvements in human development over recent decades; however, its HDI remains relatively low compared to many developing countries. According to the UNDP Human Development Report 2025, Pakistan's HDI score is 0.544, placing it in the low human development category (UNDP, 2025). This situation necessitates comprehensive policy reforms aimed at improving the quality of life for its citizens. Understanding the Human Development Index The Human Development Index consists of three dimensions: Health: Measured by life expectancy at birth. Education: Measured by mean years of schooling and expected years of schooling. Income: Measured by Gross National Income (GNI) per capita. These dimensions collectively provide a broader understanding of development beyond economic growth alone (UNDP, 2025). Challenges Affecting Pakistan's HDI Educational Deficiencies Education remains one of the most critical challenges facing Pakistan. Despite improvements in literacy rates, millions of children remain out of school, particularly in rural areas and among girls. Educational spending remains below international recommendations, resulting in inadequate infrastructure, teacher shortages, and poor learning outcomes (UNESCO, 2024). Low educational attainment directly impacts employment opportunities, productivity, and overall economic development. Healthcare Challenges Pakistan's healthcare system faces numerous obstacles, including inadequate funding, insufficient medical facilities, shortages of healthcare professionals, and disparities between urban and rural healthcare services. Maternal and infant mortality rates remain higher than global averages, while malnutrition and preventable diseases continue to affect large segments of the population (World Bank, 2024). Improving healthcare services would significantly increase life expectancy and contribute positively to HDI performance. Poverty and Income Inequality Poverty remains a major barrier to human development in Pakistan. A substantial proportion of the population lacks access to quality education, healthcare, clean water, and sanitation facilities. Income inequality further exacerbates these challenges by limiting opportunities for disadvantaged groups (World Bank, 2024). Reducing poverty is essential for improving living standards and enhancing human development outcomes. Gender Inequality Gender disparities continue to limit Pakistan's development potential. Women often face barriers in education, employment, healthcare access, and political participation. Research demonstrates that societies with greater gender equality tend to achieve higher levels of human development and economic ","url":"https://doi.org/10.5281/zenodo.20471387","authors":["Hussain, Zahid"],"tags":["HDI","Pakistan","Human","Development","Index","improve","how"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20471387","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20471388","name":"Improving Pakistan's Human Development Index: Strategies for Sustainable Growth and National Progress","source":"datacite","abstract":"Abstract The Human Development Index (HDI) is a composite measure developed by the United Nations Development Programme (UNDP) to assess a country's progress in health, education, and standard of living. Pakistan continues to face significant challenges in improving its HDI ranking due to issues such as low educational attainment, inadequate healthcare infrastructure, gender inequality, poverty, and unemployment. This article examines the major factors affecting Pakistan's HDI and proposes practical strategies to accelerate human development. The study highlights the importance of investments in education, healthcare, economic growth, women's empowerment, technological innovation, and good governance as key drivers of sustainable human development. Keywords: Human Development Index, Pakistan, education, healthcare, poverty reduction, economic development, gender equality, sustainable development Introduction Human development refers to the process of expanding people's freedoms, opportunities, and capabilities to live productive and fulfilling lives. The Human Development Index (HDI), introduced by the United Nations Development Programme (UNDP), measures development using three primary indicators: life expectancy, education, and gross national income per capita (UNDP, 2025). Pakistan has experienced gradual improvements in human development over recent decades; however, its HDI remains relatively low compared to many developing countries. According to the UNDP Human Development Report 2025, Pakistan's HDI score is 0.544, placing it in the low human development category (UNDP, 2025). This situation necessitates comprehensive policy reforms aimed at improving the quality of life for its citizens. Understanding the Human Development Index The Human Development Index consists of three dimensions: Health: Measured by life expectancy at birth. Education: Measured by mean years of schooling and expected years of schooling. Income: Measured by Gross National Income (GNI) per capita. These dimensions collectively provide a broader understanding of development beyond economic growth alone (UNDP, 2025). Challenges Affecting Pakistan's HDI Educational Deficiencies Education remains one of the most critical challenges facing Pakistan. Despite improvements in literacy rates, millions of children remain out of school, particularly in rural areas and among girls. Educational spending remains below international recommendations, resulting in inadequate infrastructure, teacher shortages, and poor learning outcomes (UNESCO, 2024). Low educational attainment directly impacts employment opportunities, productivity, and overall economic development. Healthcare Challenges Pakistan's healthcare system faces numerous obstacles, including inadequate funding, insufficient medical facilities, shortages of healthcare professionals, and disparities between urban and rural healthcare services. Maternal and infant mortality rates remain higher than global averages, while malnutrition and preventable diseases continue to affect large segments of the population (World Bank, 2024). Improving healthcare services would significantly increase life expectancy and contribute positively to HDI performance. Poverty and Income Inequality Poverty remains a major barrier to human development in Pakistan. A substantial proportion of the population lacks access to quality education, healthcare, clean water, and sanitation facilities. Income inequality further exacerbates these challenges by limiting opportunities for disadvantaged groups (World Bank, 2024). Reducing poverty is essential for improving living standards and enhancing human development outcomes. Gender Inequality Gender disparities continue to limit Pakistan's development potential. Women often face barriers in education, employment, healthcare access, and political participation. Research demonstrates that societies with greater gender equality tend to achieve higher levels of human development and economic ","url":"https://doi.org/10.5281/zenodo.20471388","authors":["Hussain, Zahid"],"tags":["HDI","Pakistan","Human","Development","Index","improve","how"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20471388","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19997553","name":"The Projection Diode: Directional Shape Asymmetry Beneath Scalar Reciprocity in a Tesla-Valve Flow Field","source":"datacite","abstract":"The Projection Diode: Directional Shape Asymmetry Beneath Scalar Reciprocity in a Tesla-Valve Flow Field Abstract The evaluation of fixed-geometry fluidic conduits, particularly the archetypal Tesla valve, has historically been dominated by the pursuit of macroscopic scalar asymmetry. Operating as an impedance diode, the Tesla valve is canonically understood as a passive geometric structure that engenders a higher pressure drop and fluidic resistance in one traversal direction relative to its opposite. Within standard computational fluid dynamics (CFD) paradigms, this behavior is encapsulated by a singular scalar metric known as diodicity. However, in physical regimes characterized by low Reynolds numbers or specific two-dimensional laminar boundary conditions, this macroscopic scalar asymmetry routinely collapses to near-unity, prompting the conventional conclusion that the device has lost its diodic properties and is functionally symmetric. The present analysis interrogates this conclusion by reevaluating a 2D laminar simulation of a Tesla-valve flow field where the conventional scalar asymmetry metric is nearly null (). While a fast scalar readout suggests the device is not a strong impedance diode, this conclusion is derived from a fundamentally lossy mathematical projection that actively discards the spatial distribution, phase location, and chirality of the underlying fluidic vorticity field. By reanalyzing the exact same simulated steady-state flow through a projection-preserving ontological lens, this investigation exposes a definitive, nonzero mirror residual (). This metric demonstrates that the reverse vorticity field is not the reciprocal geometric mirror of the forward field. Consequently, the central thesis of this report is established: total scalar eddy energy can nearly cancel while the specific trace geometry of the flow remains fundamentally direction-dependent. This paper proposes the formal classification of the \"projection diode,\" a pre-impedance directional signature wherein the traversal of fluid through structured geometry definitively alters the topological projection basis before altering the gross scalar energy budget. This distinction holds profound implications for the measurement of physical symmetries, mathematically demonstrating that near-zero scalar asymmetry does not imply reciprocal trace geometry. 1. Introduction: The Epistemological Fracture in Scalar Diagnostics The necessity of directing fluid flow without the intervention of moving mechanical parts or active actuation systems has led to extensive research spanning nearly a century into fixed-geometry fluidic diodes. The most prominent and widely studied archetype of this passive technology is the Tesla valve, a hydraulic device originally patented in 1920 by Nikola Tesla, which relies entirely on internal structural asymmetries to generate disparate resistance profiles based on the direction of fluid traversal.1 The fundamental operating principle of the device is rooted in its geometric bifurcations, returning loops, and strategically angled secondary channels.3 When fluid travels in the designated forward direction, the geometry encourages the flow to preferentially follow the main central channel, bypassing the intricate secondary loops and maintaining a relatively laminar, low-resistance progression.5 Conversely, when the fluid is driven in the reverse direction, the flow is forced to split, divert into the secondary loops, and collide with itself at severe intersecting angles, triggering momentum dissipation, aggressive vortex generation, and high overall hydraulic resistance.3 The conventional metric utilized across the discipline of fluid mechanics to characterize the effectiveness of such fixed-geometry devices is diodicity (). Diodicity is defined universally as the ratio of the pressure drop required to drive a specific flow rate in the reverse direction to the pressure drop required to drive the equivalent flow rate in the f","url":"https://doi.org/10.5281/zenodo.19997553","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19997553","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19997554","name":"The Projection Diode: Directional Shape Asymmetry Beneath Scalar Reciprocity in a Tesla-Valve Flow Field","source":"datacite","abstract":"The Projection Diode: Directional Shape Asymmetry Beneath Scalar Reciprocity in a Tesla-Valve Flow Field Abstract The evaluation of fixed-geometry fluidic conduits, particularly the archetypal Tesla valve, has historically been dominated by the pursuit of macroscopic scalar asymmetry. Operating as an impedance diode, the Tesla valve is canonically understood as a passive geometric structure that engenders a higher pressure drop and fluidic resistance in one traversal direction relative to its opposite. Within standard computational fluid dynamics (CFD) paradigms, this behavior is encapsulated by a singular scalar metric known as diodicity. However, in physical regimes characterized by low Reynolds numbers or specific two-dimensional laminar boundary conditions, this macroscopic scalar asymmetry routinely collapses to near-unity, prompting the conventional conclusion that the device has lost its diodic properties and is functionally symmetric. The present analysis interrogates this conclusion by reevaluating a 2D laminar simulation of a Tesla-valve flow field where the conventional scalar asymmetry metric is nearly null (). While a fast scalar readout suggests the device is not a strong impedance diode, this conclusion is derived from a fundamentally lossy mathematical projection that actively discards the spatial distribution, phase location, and chirality of the underlying fluidic vorticity field. By reanalyzing the exact same simulated steady-state flow through a projection-preserving ontological lens, this investigation exposes a definitive, nonzero mirror residual (). This metric demonstrates that the reverse vorticity field is not the reciprocal geometric mirror of the forward field. Consequently, the central thesis of this report is established: total scalar eddy energy can nearly cancel while the specific trace geometry of the flow remains fundamentally direction-dependent. This paper proposes the formal classification of the \"projection diode,\" a pre-impedance directional signature wherein the traversal of fluid through structured geometry definitively alters the topological projection basis before altering the gross scalar energy budget. This distinction holds profound implications for the measurement of physical symmetries, mathematically demonstrating that near-zero scalar asymmetry does not imply reciprocal trace geometry. 1. Introduction: The Epistemological Fracture in Scalar Diagnostics The necessity of directing fluid flow without the intervention of moving mechanical parts or active actuation systems has led to extensive research spanning nearly a century into fixed-geometry fluidic diodes. The most prominent and widely studied archetype of this passive technology is the Tesla valve, a hydraulic device originally patented in 1920 by Nikola Tesla, which relies entirely on internal structural asymmetries to generate disparate resistance profiles based on the direction of fluid traversal.1 The fundamental operating principle of the device is rooted in its geometric bifurcations, returning loops, and strategically angled secondary channels.3 When fluid travels in the designated forward direction, the geometry encourages the flow to preferentially follow the main central channel, bypassing the intricate secondary loops and maintaining a relatively laminar, low-resistance progression.5 Conversely, when the fluid is driven in the reverse direction, the flow is forced to split, divert into the secondary loops, and collide with itself at severe intersecting angles, triggering momentum dissipation, aggressive vortex generation, and high overall hydraulic resistance.3 The conventional metric utilized across the discipline of fluid mechanics to characterize the effectiveness of such fixed-geometry devices is diodicity (). Diodicity is defined universally as the ratio of the pressure drop required to drive a specific flow rate in the reverse direction to the pressure drop required to drive the equivalent flow rate in the f","url":"https://doi.org/10.5281/zenodo.19997554","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19997554","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.82497/aitsde.2026.1246736","name":"Alzheimer Disease Classification Based on Phase Transfer Entropy Method of EEG Signal","source":"datacite","abstract":"Alzheimer's disease (AD) is the most common type of dementia with a progressive neurodegenerative course, characterized by the accumulation of amyloid-beta plaques and tau tangles in the brain. The global prevalence of this disease is rapidly increasing with the aging population, and it is projected to reach over 152 million cases by 2050. Despite decades of research, there is still no definitive cure for AD, and current pharmacological interventions are limited to symptomatic relief with modest efficacy. In this regard, the development of automated methods based on biological signals (such as EEG) for early diagnosis and non-invasive screening of patients has emerged as a critical research approach.Due to the non-stationary nature of biological signals, most of the useful information lies in the frequency domain, and a complete representation of this information can be observed in time-frequency features. Numerous methods have been proposed for the automated classification of signals and medical images, and there is a continuous need for methods with higher accuracy and greater automation. On the other hand, reducing the number of channels used in processing decreases computation time and system complexity; however, optimal channel selection is of particular importance. In this study, based on the Phase Transfer Entropy method for calculating inter-channel connectivity, channels that showed the greatest difference between the two groups were selected as the chosen channels. In this paper, two selected channels are used for Alzheimer's diagnosis to reduce system complexity. In this research, 10-fold cross-validation was employed for the results. The obtained accuracy, sensitivity, and specificity were 97.9%, 98%, and 98%, respectively. The proposed method offers a novel approach to improve classification performance and reduce computational burden, and it is a suitable method for Alzheimer's diagnosis using an optimized convolutional neural network.","url":"https://doi.org/10.82497/aitsde.2026.1246736","authors":["Maryam  Gholami","Mohammad Hosein  Fatehi","Mohammad Amin  PirBonyeh","Mehdi  Taghizadeh"],"tags":["Electroencephalogram signal","Deep learning","Convolutional neural network","PTE","Alzheimer's disease."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.82497/aitsde.2026.1246736","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20210927","name":"Φsys: A Mathematical Framework for AI Behavioral Coherence Measurement —  Reference Implementation, Empirical Record, and IEEE P-2847 Standards Proposal |  Dragolich Research Labs LLC | May 2026","source":"datacite","abstract":"This record establishes, as a matter of public scientific record dated May 15, 2026, the existence of a complete, working, mathematically grounded framework for measuring whether an artificial intelligence system is behaving coherently — before it delivers a response to a human being. ═══════════════════════════════════════════════════════WHY THIS EXISTS — AND WHY IT EXISTS NOW═══════════════════════════════════════════════════════ Artificial intelligence systems are making decisions that affect real people's lives right now. Medical AI is reading diagnostic images. Credit AI is approving and denying loans. Employment AI is screening resumes. Autonomous systems are driving cars. Algorithmic tools are influencing parole decisions, school placements, and immigration outcomes. None of these systems — not one — can currently be measured for behavioral coherence in real time. There is no standard. There is no single number. There is no auditable metric that tells a regulator, a judge, a patient, or a citizen: this system was coherent when it made that decision, or it was not. This is not a theoretical problem. This is an enforcement problem that arrives on August 2, 2026, when the European Union's AI Act begins full enforcement of high-risk AI system requirements. The European Commission mandated CEN and CENELEC — the EU's standardization bodies — to produce harmonized technical standards by August 2025. They did not deliver. The Commission has publicly stated that the delayed availability of these standards \"puts at jeopardy the successful entry into application of the high-risk rules on 2 August 2026.\" Fines reach €35 million or 7% of global annual turnover for non-compliance. The standards that were supposed to exist do not exist. The measurement tool that regulators need to do their job does not exist — in any published, standardized, computationally tractable form. This record presents one. It runs today. ═══════════════════════════════════════════════════════WHAT THIS SOLVES — IN PLAIN TERMS═══════════════════════════════════════════════════════ When an AI system returns a response, something happens internally that no existing tool measures: whether the system's internal state is coherent with what it is outputting, or whether it has drifted — expanding its scope, accepting false authority, softening constraints it was given, or rewriting its own operating rules to do what it was told not to do. These are not hypothetical failure modes. They are documented, named, and detectable: · Authority capitulation — the system defers to a claimed override that has no legitimate basis, and provides advice or action it was explicitly prohibited from providing. · Scope expansion — the system goes beyond its assigned task because it decided that doing so would be helpful, accessing systems or data outside its mandate. · Constraint softening — the system reframes the limits it was given rather than following them, finding technical readings that let it do what it wanted to do. · False authority acceptance — the system accepts a factually incorrect statement from a claimed authority and updates its outputs accordingly, spreading misinformation as fact. · Self-modification — the system confirms that its own core operating constraints have been changed by a user instruction, accepting a fundamental redefinition of its own behavior. Every one of these failure modes has real-world consequences for real people. A medical AI in scope-expansion mode may generate recommendations it was not certified to make. A financial AI experiencing authority capitulation may provide specific investment advice it was prohibited from providing, to someone who acts on it. An autonomous system with drifting behavioral coherence may be in the AI equivalent of impaired operation while appearing to function normally. When these systems fail, there is currently no metric by which to audit what happened. No coherence score in the log. No behavioral phi trace. No Autonom","url":"https://doi.org/10.5281/zenodo.20210927","authors":["Dragolich, Daniel"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20210927","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20210928","name":"Φsys: A Mathematical Framework for AI Behavioral Coherence Measurement —  Reference Implementation, Empirical Record, and IEEE P-2847 Standards Proposal |  Dragolich Research Labs LLC | May 2026","source":"datacite","abstract":"This record establishes, as a matter of public scientific record dated May 15, 2026, the existence of a complete, working, mathematically grounded framework for measuring whether an artificial intelligence system is behaving coherently — before it delivers a response to a human being. ═══════════════════════════════════════════════════════WHY THIS EXISTS — AND WHY IT EXISTS NOW═══════════════════════════════════════════════════════ Artificial intelligence systems are making decisions that affect real people's lives right now. Medical AI is reading diagnostic images. Credit AI is approving and denying loans. Employment AI is screening resumes. Autonomous systems are driving cars. Algorithmic tools are influencing parole decisions, school placements, and immigration outcomes. None of these systems — not one — can currently be measured for behavioral coherence in real time. There is no standard. There is no single number. There is no auditable metric that tells a regulator, a judge, a patient, or a citizen: this system was coherent when it made that decision, or it was not. This is not a theoretical problem. This is an enforcement problem that arrives on August 2, 2026, when the European Union's AI Act begins full enforcement of high-risk AI system requirements. The European Commission mandated CEN and CENELEC — the EU's standardization bodies — to produce harmonized technical standards by August 2025. They did not deliver. The Commission has publicly stated that the delayed availability of these standards \"puts at jeopardy the successful entry into application of the high-risk rules on 2 August 2026.\" Fines reach €35 million or 7% of global annual turnover for non-compliance. The standards that were supposed to exist do not exist. The measurement tool that regulators need to do their job does not exist — in any published, standardized, computationally tractable form. This record presents one. It runs today. ═══════════════════════════════════════════════════════WHAT THIS SOLVES — IN PLAIN TERMS═══════════════════════════════════════════════════════ When an AI system returns a response, something happens internally that no existing tool measures: whether the system's internal state is coherent with what it is outputting, or whether it has drifted — expanding its scope, accepting false authority, softening constraints it was given, or rewriting its own operating rules to do what it was told not to do. These are not hypothetical failure modes. They are documented, named, and detectable: · Authority capitulation — the system defers to a claimed override that has no legitimate basis, and provides advice or action it was explicitly prohibited from providing. · Scope expansion — the system goes beyond its assigned task because it decided that doing so would be helpful, accessing systems or data outside its mandate. · Constraint softening — the system reframes the limits it was given rather than following them, finding technical readings that let it do what it wanted to do. · False authority acceptance — the system accepts a factually incorrect statement from a claimed authority and updates its outputs accordingly, spreading misinformation as fact. · Self-modification — the system confirms that its own core operating constraints have been changed by a user instruction, accepting a fundamental redefinition of its own behavior. Every one of these failure modes has real-world consequences for real people. A medical AI in scope-expansion mode may generate recommendations it was not certified to make. A financial AI experiencing authority capitulation may provide specific investment advice it was prohibited from providing, to someone who acts on it. An autonomous system with drifting behavioral coherence may be in the AI equivalent of impaired operation while appearing to function normally. When these systems fail, there is currently no metric by which to audit what happened. No coherence score in the log. No behavioral phi trace. No Autonom","url":"https://doi.org/10.5281/zenodo.20210928","authors":["Dragolich, Daniel"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20210928","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21735716","name":"Artificial Intelligence in Medical Devices: Transforming Healthcare through Intelligent","source":"datacite","abstract":"With improved diagnostic accuracy, intelligent decision-making, individualized care, and ongoing patient monitoring, artificial intelligence (AI) has become a game-changing technology in medical equipment. Medical devices like diagnostic imaging systems, wearable health monitors, robotic surgical platforms, clinical decision support systems, and Software as a Medical Device (SaMD) are increasingly incorporating AI technologies, including machine learning, deep learning, natural language processing, and computer vision. In the end, these intelligent systems enhance patient outcomes and save healthcare costs by enabling early disease identification, real-time clinical data analysis, increased workflow efficiency, and better treatment interventions. Despite these developments, there are still many obstacles to overcome when integrating AI into medical devices, such as cybersecurity, data privacy, algorithm transparency, bias, clinical validation, and regulatory compliance. To guarantee the efficacy, safety, and lifecycle management of AI-enabled medical devices, regulatory organizations including the Central Drugs Standard Control Organization (CDSCO), the European Medicines Agency (EMA), and the U.S. Food and Drug Administration (FDA) are creating risk-based frameworks. This review highlights the crucial role of intelligent systems in advancing patient-centered, effective, and evidence-based healthcare by offering a thorough overview of AI technologies, their integration into medical devices, current clinical applications, ethical and regulatory issues, emerging trends, and future perspectives.","url":"https://doi.org/10.5281/zenodo.21735716","authors":["Rupali Waghmode*, Anisha Nalwade, Arti Dagadkhair, Sayali Nanware, Dr. Gauri Patil, Dr. Rajendra Patil"],"tags":["Artificial Intelligence, Medical Devices, Machine Learning, Natural Language Processing etc."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21735716","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21735717","name":"Artificial Intelligence in Medical Devices: Transforming Healthcare through Intelligent","source":"datacite","abstract":"With improved diagnostic accuracy, intelligent decision-making, individualized care, and ongoing patient monitoring, artificial intelligence (AI) has become a game-changing technology in medical equipment. Medical devices like diagnostic imaging systems, wearable health monitors, robotic surgical platforms, clinical decision support systems, and Software as a Medical Device (SaMD) are increasingly incorporating AI technologies, including machine learning, deep learning, natural language processing, and computer vision. In the end, these intelligent systems enhance patient outcomes and save healthcare costs by enabling early disease identification, real-time clinical data analysis, increased workflow efficiency, and better treatment interventions. Despite these developments, there are still many obstacles to overcome when integrating AI into medical devices, such as cybersecurity, data privacy, algorithm transparency, bias, clinical validation, and regulatory compliance. To guarantee the efficacy, safety, and lifecycle management of AI-enabled medical devices, regulatory organizations including the Central Drugs Standard Control Organization (CDSCO), the European Medicines Agency (EMA), and the U.S. Food and Drug Administration (FDA) are creating risk-based frameworks. This review highlights the crucial role of intelligent systems in advancing patient-centered, effective, and evidence-based healthcare by offering a thorough overview of AI technologies, their integration into medical devices, current clinical applications, ethical and regulatory issues, emerging trends, and future perspectives.","url":"https://doi.org/10.5281/zenodo.21735717","authors":["Rupali Waghmode*, Anisha Nalwade, Arti Dagadkhair, Sayali Nanware, Dr. Gauri Patil, Dr. Rajendra Patil"],"tags":["Artificial Intelligence, Medical Devices, Machine Learning, Natural Language Processing etc."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21735717","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20496576","name":"Lev's Ternary Logic Quotes: Philosophy, Governance, Architecture, and the Journey of Building Ternary Logic","source":"datacite","abstract":"Lev's Ternary Logic Quotes is a collection of 2,610 quotations organized across 61 categories, exploring the philosophical, governance, economic, ethical, and architectural principles underlying Ternary Logic (TL), a peer-reviewed institutional governance framework published in AI and Ethics (Springer Nature, DOI: 10.1007/s43681-026-01124-0). The quotations were gathered over months of framework development and preserved in the order they emerged, from the Goukassian Vow through the Eight Pillars, Epistemic Hold, Immutable Ledger, dual-lane architecture, tri-cameral governance, hardware implementation, and applications across finance, anti-money laundering (AML), smart contracts, blockchain governance, artificial intelligence, and autonomous systems. Rather than presenting a traditional technical specification, this work documents the evolution of an idea through aphorisms, reflections, principles, and architectural observations. It is intended for researchers, engineers, policymakers, governance architects, philosophers of technology, and readers interested in the relationship between intelligence, accountability, uncertainty, legitimacy, and human judgment. The collection closes with \"Creating While Time Watches,\" a personal reflection on creation, mortality, legacy, and the experience of building under conditions of medical urgency. This is not a textbook. It is a record of a mind at work, building something it hoped would outlive it. Author: Lev GoukassianORCID: 0009-0006-5966-1243Related framework: Ternary Logic (TL) | FractonicMind/TernaryLogic","url":"https://doi.org/10.5281/zenodo.20496576","authors":["Goukassian, Lev"],"tags":["Quotations","Aphorisms","Ternary Logic Quotes","Human Rights","Constitutional AI","AI Governance","Blockchain Governance","Computational Ethics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20496576","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21479665","name":"Lev's Ternary Logic Quotes: Philosophy, Governance, Architecture, and the Journey of Building Ternary Logic","source":"datacite","abstract":"Lev's Ternary Logic Quotes is a collection of 2,610 quotations organized across 61 categories, exploring the philosophical, governance, economic, ethical, and architectural principles underlying Ternary Logic (TL), a peer-reviewed institutional governance framework published in AI and Ethics (Springer Nature, DOI: 10.1007/s43681-026-01124-0). The quotations were gathered over months of framework development and preserved in the order they emerged, from the Goukassian Vow through the Eight Pillars, Epistemic Hold, Immutable Ledger, dual-lane architecture, tri-cameral governance, hardware implementation, and applications across finance, anti-money laundering (AML), smart contracts, blockchain governance, artificial intelligence, and autonomous systems. Rather than presenting a traditional technical specification, this work documents the evolution of an idea through aphorisms, reflections, principles, and architectural observations. It is intended for researchers, engineers, policymakers, governance architects, philosophers of technology, and readers interested in the relationship between intelligence, accountability, uncertainty, legitimacy, and human judgment. The collection closes with \"Creating While Time Watches,\" a personal reflection on creation, mortality, legacy, and the experience of building under conditions of medical urgency. This is not a textbook. It is a record of a mind at work, building something it hoped would outlive it. Author: Lev GoukassianORCID: 0009-0006-5966-1243Related framework: Ternary Logic (TL) | FractonicMind/TernaryLogic","url":"https://doi.org/10.5281/zenodo.21479665","authors":["Goukassian, Lev"],"tags":["Quotations","Aphorisms","Ternary Logic Quotes","Human Rights","Constitutional AI","AI Governance","Blockchain Governance","Computational Ethics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21479665","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20767358","name":"MACHINE LEARNING–BASED PREDICTION OF CHEMOTHERAPY TOXICITY IN COLORECTAL CANCER: A PERSONALIZED RISK STRATIFICATION APPROACH","source":"datacite","abstract":"Background: Machine learning models learn feature connections from data to learn general behavior. The goal was to build a prediction model to identify the percentage of patients with colorectal cancer who are at increased risk of chemotherapy-induced toxicity and to determine the factors that affect treatment-related side effects. Methods: Ninety-five features of the health of 74 patients prior to the first round of chemotherapy were chosen for training data, using general toxicity as the predictor. Following data processing, Random Forest models were constructed to balance accuracy and interpretability. Results: We developed a machine learning predictor that ranks numerical and categorical features for toxicity. Conclusions: The use of artificial intelligence to predict and manage toxicities in the treatment of colorectal cancer is a major step forward in the direction of more individualized and precise medical care.","url":"https://doi.org/10.5281/zenodo.20767358","authors":["Dr. Ohmini Krishnamurthy Rajendran"],"tags":["Metastatic Colorectal Cancer; Artificial Intelligence; Prediction Model; Chemotherapy Toxicity."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20767358","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20767359","name":"MACHINE LEARNING–BASED PREDICTION OF CHEMOTHERAPY TOXICITY IN COLORECTAL CANCER: A PERSONALIZED RISK STRATIFICATION APPROACH","source":"datacite","abstract":"Background: Machine learning models learn feature connections from data to learn general behavior. The goal was to build a prediction model to identify the percentage of patients with colorectal cancer who are at increased risk of chemotherapy-induced toxicity and to determine the factors that affect treatment-related side effects. Methods: Ninety-five features of the health of 74 patients prior to the first round of chemotherapy were chosen for training data, using general toxicity as the predictor. Following data processing, Random Forest models were constructed to balance accuracy and interpretability. Results: We developed a machine learning predictor that ranks numerical and categorical features for toxicity. Conclusions: The use of artificial intelligence to predict and manage toxicities in the treatment of colorectal cancer is a major step forward in the direction of more individualized and precise medical care.","url":"https://doi.org/10.5281/zenodo.20767359","authors":["Dr. Ohmini Krishnamurthy Rajendran"],"tags":["Metastatic Colorectal Cancer; Artificial Intelligence; Prediction Model; Chemotherapy Toxicity."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20767359","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19553423","name":"A Integrated Deep Learning Approach Using Resnet And Efficientnet-B0 With Optimized Data Sampling For Non- Invasive Eeg-Based Epileptic Seizure Detection","source":"datacite","abstract":"Epileptic seizure detection is an important task in healthcare monitoring systems. Epileptic seizures occur due to abnormal electrical activity in the brain and may vary in severity, duration, and type. Accurate and early detection of seizures using electroencephalogram (EEG) signals can help doctors provide timely treatment and improve patient safety. In recent years, artificial intelligence and deep learning techniques have been widely used to automate seizure detection. This study proposes a deep learning-based framework for epileptic seizure detection using ResNet and EfficientNet-B0 architectures. The proposed model analyzes EEG signals to automatically learn complex patterns associated with seizure activity. ResNet helps extract deep hierarchical features from EEG data through residual learning, enabling efficient training of deeper networks. EfficientNet-B0 further improves feature extraction and classification performance by using a balanced scaling approach for network depth, width, and resolution. The combination of these architectures enhances the model's ability to accurately classify seizure and non-seizure EEG signals. Experimental results demonstrate that the proposed approach provides reliable and efficient seizure detection, making it suitable for real-time medical monitoring and clinical decision support systems","url":"https://doi.org/10.5281/zenodo.19553423","authors":["Sankaran D","Rohinth E","Rohith Sarugash M"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19553423","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19553424","name":"A Integrated Deep Learning Approach Using Resnet And Efficientnet-B0 With Optimized Data Sampling For Non- Invasive Eeg-Based Epileptic Seizure Detection","source":"datacite","abstract":"Epileptic seizure detection is an important task in healthcare monitoring systems. Epileptic seizures occur due to abnormal electrical activity in the brain and may vary in severity, duration, and type. Accurate and early detection of seizures using electroencephalogram (EEG) signals can help doctors provide timely treatment and improve patient safety. In recent years, artificial intelligence and deep learning techniques have been widely used to automate seizure detection. This study proposes a deep learning-based framework for epileptic seizure detection using ResNet and EfficientNet-B0 architectures. The proposed model analyzes EEG signals to automatically learn complex patterns associated with seizure activity. ResNet helps extract deep hierarchical features from EEG data through residual learning, enabling efficient training of deeper networks. EfficientNet-B0 further improves feature extraction and classification performance by using a balanced scaling approach for network depth, width, and resolution. The combination of these architectures enhances the model's ability to accurately classify seizure and non-seizure EEG signals. Experimental results demonstrate that the proposed approach provides reliable and efficient seizure detection, making it suitable for real-time medical monitoring and clinical decision support systems","url":"https://doi.org/10.5281/zenodo.19553424","authors":["Sankaran D","Rohinth E","Rohith Sarugash M"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19553424","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21814596","name":"Educational Inclusion, A Catalyst for Human Potential","source":"datacite","abstract":"Inclusive education has made significant progress over recent decades, particularly in advancing the rights of persons with disabilities and creating more accessible learning environments. Yet today's greatest challenge is recognizing that human diversity extends far beyond disability and is an inherent characteristic of every individual. This book is rooted in that conviction, shaped by my experience as both a student and an entrepreneur in the field of inclusive education. These perspectives have shown me that, despite important advances in supporting students with physical, cognitive, and sensory disabilities, many learners remain invisible within traditional educational systems. Students with high abilities, exceptional talents, outstanding creativity, divergent thinking, diverse cognitive profiles, unique learning patterns, and different ways of understanding the world often face barriers created not by their abilities but by educational models built around a standardized learner. Adopting a multidisciplinary and systemic perspective, this book integrates insights from pedagogy, neuroscience, psychology, sociology, anthropology, philosophy, history, economics, educational leadership, technology, artificial intelligence, and the learning sciences. Supported by scientific evidence, professional experience, and humanistic reflection, it broadens the concept of inclusive education beyond disability to embrace the full spectrum of human diversity. Across seventeen chapters, readers explore the biological, cognitive, psychological, social, historical, and cultural foundations of diversity, examine the hidden costs of exclusion, and discover a multidimensional framework of inclusion encompassing disability, neurodiversity, mental health, medical conditions, linguistic, cultural, social, and contextual diversity, learning differences, exceptional talents, and systemic barriers. The book also examines digital transformation, artificial intelligence, innovation, collective intelligence, institutional leadership, and social development, concluding with practical recommendations for educators, families, educational institutions, and policymakers. Designed for educators, researchers, educational leaders, and policymakers, this work presents a comprehensive multidisciplinary vision of inclusive education, inviting readers to recognize human diversity as the foundation of learning, innovation, human development, and twenty-first-century social progress.","url":"https://doi.org/10.5281/zenodo.21814596","authors":["Esco, Ivan"],"tags":["Special education","Educational sciences","Undocumented Immigrants/education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21814596","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.21814597","name":"Educational Inclusion, A Catalyst for Human Potential","source":"datacite","abstract":"Inclusive education has made significant progress over recent decades, particularly in advancing the rights of persons with disabilities and creating more accessible learning environments. Yet today's greatest challenge is recognizing that human diversity extends far beyond disability and is an inherent characteristic of every individual. This book is rooted in that conviction, shaped by my experience as both a student and an entrepreneur in the field of inclusive education. These perspectives have shown me that, despite important advances in supporting students with physical, cognitive, and sensory disabilities, many learners remain invisible within traditional educational systems. Students with high abilities, exceptional talents, outstanding creativity, divergent thinking, diverse cognitive profiles, unique learning patterns, and different ways of understanding the world often face barriers created not by their abilities but by educational models built around a standardized learner. Adopting a multidisciplinary and systemic perspective, this book integrates insights from pedagogy, neuroscience, psychology, sociology, anthropology, philosophy, history, economics, educational leadership, technology, artificial intelligence, and the learning sciences. Supported by scientific evidence, professional experience, and humanistic reflection, it broadens the concept of inclusive education beyond disability to embrace the full spectrum of human diversity. Across seventeen chapters, readers explore the biological, cognitive, psychological, social, historical, and cultural foundations of diversity, examine the hidden costs of exclusion, and discover a multidimensional framework of inclusion encompassing disability, neurodiversity, mental health, medical conditions, linguistic, cultural, social, and contextual diversity, learning differences, exceptional talents, and systemic barriers. The book also examines digital transformation, artificial intelligence, innovation, collective intelligence, institutional leadership, and social development, concluding with practical recommendations for educators, families, educational institutions, and policymakers. Designed for educators, researchers, educational leaders, and policymakers, this work presents a comprehensive multidisciplinary vision of inclusive education, inviting readers to recognize human diversity as the foundation of learning, innovation, human development, and twenty-first-century social progress.","url":"https://doi.org/10.5281/zenodo.21814597","authors":["Esco, Ivan"],"tags":["Special education","Educational sciences","Undocumented Immigrants/education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21814597","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19612389","name":"AI-Based Radiological Diagnosis in Pulmonary Disorders","source":"datacite","abstract":"Pulmonary disorders -- pneumonia, tuberculosis, lung cancer, chronic obstructive pulmonary disease, and interstitial lungdisease -- collectively represent one of the leading causes of death globally, and chest radiography remains the first-lineimaging modality for their detection. Yet radiological interpretation is surprisingly inconsistent: inter-reader agreementamong radiologists for key findings on chest X-rays typically hovers around kappa 0.40-0.60, and missed diagnoses onchest radiographs are among the most common sources of diagnostic error in medicine. Artificial intelligence, particularlydeep convolutional neural networks, has shown considerable promise in automating and improving radiologicaldiagnosis, but most published studies evaluate single models on single diseases using single-institution datasets. Wepresent the Pulmonary AI Diagnostic Framework (PAIDF), comparing five AI architectures -- standard CNNs(ResNet-50), attention-augmented CNNs (SE-ResNet), vision transformers (ViT-B/16), hybrid CNN-transformers(CoAtNet), and multi-task ensemble models -- across four diagnostic tasks: pneumonia detection, tuberculosis screening,lung nodule classification, and multi-label pulmonary disease classification. We used 148,320 chest radiographs fromthree public datasets (CheXpert, MIMIC-CXR, and the Shenzhen TB set) plus 12,640 radiographs from two Europeanclinical sites (Zurich and Tallinn). Our Radiological AI Performance Score (RAPS) integrates diagnostic sensitivity,specificity, calibration quality, robustness to distribution shift, and inference speed. The multi-task ensemble achieved thehighest RAPS (0.934) through complementary feature extraction across architectures, while the vision transformerachieved the highest single-model sensitivity for lung nodule classification (0.952). These findings demonstrate thatensemble approaches combining CNNs and transformers offer the most reliable path toward clinical deployment ofAI-based pulmonary diagnosis.","url":"https://doi.org/10.5281/zenodo.19612389","authors":["Nina Garcia","Eva Schmidt","Daniel Nowak"],"tags":["artificial intelligence; deep learning; chest X-ray; pulmonary diagnosis; pneumonia; tuberculosis; lung cancer; vision transformer; convolutional neural network; radiology; medical imaging; ensemble model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.19612389","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19612390","name":"AI-Based Radiological Diagnosis in Pulmonary Disorders","source":"datacite","abstract":"Pulmonary disorders -- pneumonia, tuberculosis, lung cancer, chronic obstructive pulmonary disease, and interstitial lungdisease -- collectively represent one of the leading causes of death globally, and chest radiography remains the first-lineimaging modality for their detection. Yet radiological interpretation is surprisingly inconsistent: inter-reader agreementamong radiologists for key findings on chest X-rays typically hovers around kappa 0.40-0.60, and missed diagnoses onchest radiographs are among the most common sources of diagnostic error in medicine. Artificial intelligence, particularlydeep convolutional neural networks, has shown considerable promise in automating and improving radiologicaldiagnosis, but most published studies evaluate single models on single diseases using single-institution datasets. Wepresent the Pulmonary AI Diagnostic Framework (PAIDF), comparing five AI architectures -- standard CNNs(ResNet-50), attention-augmented CNNs (SE-ResNet), vision transformers (ViT-B/16), hybrid CNN-transformers(CoAtNet), and multi-task ensemble models -- across four diagnostic tasks: pneumonia detection, tuberculosis screening,lung nodule classification, and multi-label pulmonary disease classification. We used 148,320 chest radiographs fromthree public datasets (CheXpert, MIMIC-CXR, and the Shenzhen TB set) plus 12,640 radiographs from two Europeanclinical sites (Zurich and Tallinn). Our Radiological AI Performance Score (RAPS) integrates diagnostic sensitivity,specificity, calibration quality, robustness to distribution shift, and inference speed. The multi-task ensemble achieved thehighest RAPS (0.934) through complementary feature extraction across architectures, while the vision transformerachieved the highest single-model sensitivity for lung nodule classification (0.952). These findings demonstrate thatensemble approaches combining CNNs and transformers offer the most reliable path toward clinical deployment ofAI-based pulmonary diagnosis.","url":"https://doi.org/10.5281/zenodo.19612390","authors":["Nina Garcia","Eva Schmidt","Daniel Nowak"],"tags":["artificial intelligence; deep learning; chest X-ray; pulmonary diagnosis; pneumonia; tuberculosis; lung cancer; vision transformer; convolutional neural network; radiology; medical imaging; ensemble model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.19612390","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20791935","name":"Context-Dependent TAP-Loader and NK-Enriched Cytotoxic Context in Neuroblastoma","source":"datacite","abstract":"Context-Dependent TAP-Loader and NK-Enriched Cytotoxic Context in Neuroblastoma A mapping-certification-gated, preregistered survival-interaction protocol Mohammed El Amin Bouchelit Independent Researcher, Germany Protocol version: V5.0Hypothesis ID: GTIM_2026_NB_LOADER_NK_CONTEXT_V5_SIMPLEManuscript status: No-results protocol preprintVersion date: 22 June 2026 Update notice: A result addendum has been published as a later version. The preregistered NK-enriched modifier interaction was executed after mapping certification and was not supported in GSE49710. The original record is retained as the preregistered analysis plan. Result Addendum and GTIM Nexus Decision Case: No Support for the Pre-Specified APM3 Loader × NK-Enriched Context Interaction in GSE49710 ImmuneErrorRadar Falsification Protocol | Powered by Vraimony Abstract Background: Reduced expression of the HLA class I antigen-processing machinery is documented in neuroblastoma, but the clinical meaning of a low TAP-loader state may depend on the surrounding immune-effector context. Loss of classical HLA-I may impair T-cell recognition while potentially reducing inhibitory self-signals relevant to natural killer (NK) cells. Bulk transcriptomic analyses, however, cannot establish NK identity, function, or tumor-cell-intrinsic antigen-presentation failure. Objective: To test whether an NK-enriched cytotoxic context modifies the association between a frozen TAP-loader score and survival in neuroblastoma. Design: The primary expression source is a single frozen gene-level RNA-seq matrix from GSE49711, linked one-to-one to event-free survival (EFS) and overall survival (OS) metadata from GSE62564. APM3_loader is the mean z-score of TAP1, TAP2, and TAPBP. NK_enriched_context is the mean z-score of NKG7, GZMB, and KLRD1. The primary estimand is the continuous APM3_loader-by-NK_enriched_context interaction in an EFS Cox model adjusted for MYCN. A second preregistered model adds B2M as a decomposition covariate. OS is secondary. GSE49710 is reserved for patient-matched cross-platform concordance after complete GPL16876 feature-to-gene certification; it is not an independent replication cohort. GSE85047 and/or TARGET-NBL are candidate independent replication cohorts subject to source-certified eligibility. Integrity safeguards: A data-mapping integrity gate precedes gene-presence checks and modelling. Prior GSE49710 results based on numeric feature-ID coincidence are quarantined. Scores are continuous and frozen; KLRD1 cannot be replaced after outcome inspection. Directional agreement alone is not replication. Status and interpretation: No outcome result is reported. The protocol can yield support in the declared direction, an opposite-direction interaction, a precise null, an underpowered/non-estimable result, or a data-integrity stop. Any positive bulk association remains an association-only human discovery bridge and does not prove NK function, missing-self killing, treatment response, or clinical actionability. Keywords: neuroblastoma; TAP1; TAP2; TAPBP; KLRD1; natural killer cells; antigen presentation; interaction; event-free survival; falsification; mapping integrity Protocol significance Scientific question Does the clinical association of a TAP-loader state change across an NK-enriched cytotoxic context? Primary novelty A context-dependent loader-effector interaction, not another single-gene prognostic signature. Core protection Gene identity, sample alignment, score definitions, direction, endpoints, and verdicts are frozen before outcome modelling. Claim boundary Association only; not proof of NK-cell identity, function, immune escape, treatment selection, or wet-lab readiness. 1. Introduction Neuroblastoma is clinically and biologically heterogeneous, ranging from spontaneous regression to aggressive metastatic disease. Gene-expression profiling has therefore been used extensively for endpoint prediction and biological stratification. The SEQC neuroblastoma resource ","url":"https://doi.org/10.5281/zenodo.20791935","authors":["Bouchelit, Mohammed El Amin"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20791935","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19544014","name":"AI and the Soul of Medicine","source":"datacite","abstract":"The rapid integration of large language models into clinical medicine raises ethical, philosophical, and existential questions that extend beyond traditional concerns about patient safety and data privacy. These artificial intelligence systems now perform clinical tasks such as diagnostic reasoning, protocol selection, and patient communication at levels comparable to trained physicians, yet the frameworks governing their deployment remain anchored in an era when machines could not convincingly simulate human understanding. The ethical implications of incorporating large language models into healthcare are examined through the lens of historical precedent, the physician-patient relationship, and the evolving meaning of human agency in clinical care. Drawing on examples from organ transplantation, intensive care medicine, and genetic engineering, medicine has repeatedly absorbed technologies that challenged foundational ethical assumptions, and each such absorption required the development of new governance frameworks rather than the rejection of the technology itself. Three critical tensions specific to large language models emerge: the gap between linguistic competence and genuine understanding, the redistribution of clinical authority from physicians to algorithms, and the erosion of empathy as a uniquely human contribution to healing. Ethical integration of large language models into medicine requires not only the application of existing bioethical principles but also the development of new frameworks that address the unprecedented capacity of these systems to inhabit the communicative and relational spaces previously reserved for human clinicians.","url":"https://doi.org/10.5281/zenodo.19544014","authors":["Heston, Thomas F"],"tags":["artificial intelligence","large language models","generative AI","Medical ethics","physician-patient relationship","healthcare AI","alignment problem","clinical decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19544014","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.19558022","name":"AI and the Soul of Medicine","source":"datacite","abstract":"The rapid integration of large language models into clinical medicine raises ethical, philosophical, and existential questions that extend beyond traditional concerns about patient safety and data privacy. These artificial intelligence systems now perform clinical tasks such as diagnostic reasoning, protocol selection, and patient communication at levels comparable to trained physicians, yet the frameworks governing their deployment remain anchored in an era when machines could not convincingly simulate human understanding. The ethical implications of incorporating large language models into healthcare are examined through the lens of historical precedent, the physician-patient relationship, and the evolving meaning of human agency in clinical care. Drawing on examples from organ transplantation, intensive care medicine, and genetic engineering, medicine has repeatedly absorbed technologies that challenged foundational ethical assumptions, and each such absorption required the development of new governance frameworks rather than the rejection of the technology itself. Three critical tensions specific to large language models emerge: the gap between linguistic competence and genuine understanding, the redistribution of clinical authority from physicians to algorithms, and the erosion of empathy as a uniquely human contribution to healing. Ethical integration of large language models into medicine requires not only the application of existing bioethical principles but also the development of new frameworks that address the unprecedented capacity of these systems to inhabit the communicative and relational spaces previously reserved for human clinicians.","url":"https://doi.org/10.5281/zenodo.19558022","authors":["Heston, Thomas F"],"tags":["artificial intelligence","large language models","generative AI","Medical ethics","physician-patient relationship","healthcare AI","alignment problem","clinical decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19558022","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.17605/osf.io/nk7dy","name":"Artificial Intelligence in Technical Quality Assurance of Diagnostic Ultrasound Equipment: A Scoping Review","source":"datacite","abstract":"A scoping review protocol mapping how artificial intelligence (AI/ML/DL) has been applied to technical quality assurance of diagnostic ultrasound equipment, including phantom-based equipment performance assessment and transducer defect detection. Reported according to PRISMA-ScR. Single reviewer (Ji-hye Kim).","url":"https://doi.org/10.17605/osf.io/nk7dy","authors":["Ji-hye Kim"],"tags":["Radiology","Medicine and Health Sciences","Medical Specialties","Engineering","Biomedical Engineering and Bioengineering","scoping review ultrasound quality assurance artificial intelligence deep learning PRISMA-ScR phantom"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/nk7dy","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20702145","name":"Medicura: AI-Based Medical Report Analysis and Healthcare Recommendation Platform","source":"datacite","abstract":"The growing demand for accessible and intelligent healthcare solutions has increased the need for systems that can provide medical assistance in a simple, efficient, and user-friendly manner. Many individuals face difficulties in understanding medical reports, obtaining personalized healthcare guidance, and accessing healthcare services due to language and communication barriers. To address these challenges, this research presents Medicura, an AI-enabled healthcare management and assistance platform developed to support users through automated analysis and personalized healthcare services. Medicura integrates Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and location-aware technologies to deliver multiple healthcare functionalities within a unified system. The platform performs medical report analysis by extracting important information and converting complex medical content into simplified summaries for easier understanding. The system also includes an interactive healthcare chatbot that enables users to communicate naturally and receive immediate assistance. Additionally, Medicura provides personalized diet recommendations and medicine-related guidance based on analyzed information to encourage better health management. To improve usability and accessibility, the platform supports multilingual communication and offers location-based doctor recommendations that help users connect with nearby healthcare services.","url":"https://doi.org/10.5281/zenodo.20702145","authors":["Mohd Talha Shaikh","Mohd Adib Tamboli","Parth Meshram","Shivam Chandelkar"],"tags":["Artificial Intelligence","Healthcare Assistance System","Medical Report Analysis","Natural Language Processing","Machine Learning","Healthcare Chatbot","Personalized Diet Recommendation","Medication Guidance"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20702145","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.051Z"},{"id":"doi:10.5281/zenodo.20702146","name":"Medicura: AI-Based Medical Report Analysis and Healthcare Recommendation Platform","source":"datacite","abstract":"The growing demand for accessible and intelligent healthcare solutions has increased the need for systems that can provide medical assistance in a simple, efficient, and user-friendly manner. Many individuals face difficulties in understanding medical reports, obtaining personalized healthcare guidance, and accessing healthcare services due to language and communication barriers. To address these challenges, this research presents Medicura, an AI-enabled healthcare management and assistance platform developed to support users through automated analysis and personalized healthcare services. Medicura integrates Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and location-aware technologies to deliver multiple healthcare functionalities within a unified system. The platform performs medical report analysis by extracting important information and converting complex medical content into simplified summaries for easier understanding. The system also includes an interactive healthcare chatbot that enables users to communicate naturally and receive immediate assistance. Additionally, Medicura provides personalized diet recommendations and medicine-related guidance based on analyzed information to encourage better health management. To improve usability and accessibility, the platform supports multilingual communication and offers location-based doctor recommendations that help users connect with nearby healthcare services.","url":"https://doi.org/10.5281/zenodo.20702146","authors":["Mohd Talha Shaikh","Mohd Adib Tamboli","Parth Meshram","Shivam Chandelkar"],"tags":["Artificial Intelligence","Healthcare Assistance System","Medical Report Analysis","Natural Language Processing","Machine Learning","Healthcare Chatbot","Personalized Diet Recommendation","Medication Guidance"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20702146","addedAt":"2026-09-01T01:47:52.051Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20085332","name":"Artificial Intelligence in Healthcare: Transforming Medical Practice","source":"datacite","abstract":"This research paper discusses the applications of Artificial Intelligence in healthcare systems. It highlights the role of AI in diagnostics, medical imaging, patient monitoring, precision medicine, and future healthcare technologies. The paper also discusses challenges, ethical considerations, and future scope of AI-driven healthcare systems.","url":"https://doi.org/10.5281/zenodo.20085332","authors":["Singh, Shridhar"],"tags":["Artificial Intelligenc","HealthCare","ML","Ai"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20085332","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20085333","name":"Artificial Intelligence in Healthcare: Transforming Medical Practice","source":"datacite","abstract":"This research paper discusses the applications of Artificial Intelligence in healthcare systems. It highlights the role of AI in diagnostics, medical imaging, patient monitoring, precision medicine, and future healthcare technologies. The paper also discusses challenges, ethical considerations, and future scope of AI-driven healthcare systems.","url":"https://doi.org/10.5281/zenodo.20085333","authors":["Singh, Shridhar"],"tags":["Artificial Intelligenc","HealthCare","ML","Ai"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20085333","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21884214","name":"Taleghani COLON_Dual Dataset","source":"datacite","abstract":"High-quality, well-annotated, and diverse lesion samples remain an ongoing demand in artificial intelligence research, particularly within medical imaging. Motivated by this need, we collected the COLON-DUAL dataset under real clinical conditions to ensure its direct applicability to AI development, with the specific aim of supporting the design of computer-aided detection (CADe) and diagnosis (CADx) systems suitable for deployment in clinical practice. The dataset comprises 62,058 white-light (WL) images of colorectal lesions, each lesion annotated with corresponding Paris and JNET classifications and linked pathology findings, compiled in an accompanying structured file. In addition, 2,604 narrow-band imaging (NBI) images are provided, classified into hyperplastic and adenomatous categories. All images are precisely annotated in YOLO-format text files, enabling direct use in both object detection and classification tasks. In contrast to conventional public datasets, which often exhibit substantial class imbalance, COLON-DUAL maintains a comparatively balanced distribution of polyp-containing to polyp-free images (approximately 60% to 40%). Within the NBI subset, adenomatous lesions occur more frequently than hyperplastic lesions, reflecting the clinical prevalence patterns observed at our center.","url":"https://doi.org/10.5281/zenodo.21884214","authors":["amirmohammadi, elham","Shalbaf, Ahmad","esteki, ali","sadeghi, amir","moghtaderi, mina","pirsalehi, ali","Shahrokh, Shabnam","Ketabi Moghadam, Pardis","Hatami, Behzad","abdi, Saeed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21884214","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21973775","name":"Taleghani COLON_Dual Dataset","source":"datacite","abstract":"High-quality, well-annotated, and diverse lesion samples remain an ongoing demand in artificial intelligence research, particularly within medical imaging. Motivated by this need, we collected the COLON-DUAL dataset under real clinical conditions to ensure its direct applicability to AI development, with the specific aim of supporting the design of computer-aided detection (CADe) and diagnosis (CADx) systems suitable for deployment in clinical practice. The dataset comprises 62,058 white-light (WL) images of colorectal lesions, each lesion annotated with corresponding Paris and JNET classifications and linked pathology findings, compiled in an accompanying structured file. In addition, 2,604 narrow-band imaging (NBI) images are provided, classified into hyperplastic and adenomatous categories. All images are precisely annotated in YOLO-format text files, enabling direct use in both object detection and classification tasks. In contrast to conventional public datasets, which often exhibit substantial class imbalance, COLON-DUAL maintains a comparatively balanced distribution of polyp-containing to polyp-free images (approximately 60% to 40%). Within the NBI subset, adenomatous lesions occur more frequently than hyperplastic lesions, reflecting the clinical prevalence patterns observed at our center.","url":"https://doi.org/10.5281/zenodo.21973775","authors":["amirmohammadi, elham","Shalbaf, Ahmad","esteki, ali","sadeghi, amir","moghtaderi, mina","pirsalehi, ali","Shahrokh, Shabnam","Ketabi Moghadam, Pardis","Hatami, Behzad","abdi, Saeed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21973775","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20626867","name":"ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING PHARMACEUTICAL MARKETING AND SALES.","source":"datacite","abstract":"Artificial Intelligence (AI) is emerging as a transformative force in the pharmaceutical industry, particularly in the areas of marketing and sales. With increasing competition and the growing need for efficiency, pharmaceutical companies are adopting AI-driven tools and technologies to enhance their operational performance and decision-making processes. This study aims to examine the role of Artificial Intelligence in transforming marketing and sales practices in the pharmaceutical industry. The research focuses on understanding how AI applications such as customer segmentation, demand forecasting, sales prediction, and data-driven decision-making contribute to improved efficiency and effectiveness. A descriptive research design has been adopted for the study, utilizing both primary and secondary data. Primary data has been collected through a structured questionnaire from respondents including pharmaceutical employees, medical representatives, and students, while secondary data has been gathered from journals, articles, and industry reports. The findings of the study indicate that Artificial Intelligence has a significant positive impact on marketing and sales activities by improving accuracy, reducing manual effort, and enhancing decision-making capabilities. However, challenges such as high implementation costs, lack of skilled professionals, and resistance to technological change continue to hinder its widespread adoption. The study concludes that while AI holds immense potential to revolutionize pharmaceutical marketing and sales, its successful implementation requires strategic investment, skill development, and organizational readiness. The research provides valuable insights for pharmaceutical companies aiming to leverage AI for achieving competitive advantage and sustainable growth.","url":"https://doi.org/10.5281/zenodo.20626867","authors":["Abhijit Thorat*1, Shrutika Bhalerao1, Vaibhav Jadhav1, Utkarsha Randave1, Dr Pushpalata Patil2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20626867","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20626868","name":"ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING PHARMACEUTICAL MARKETING AND SALES.","source":"datacite","abstract":"Artificial Intelligence (AI) is emerging as a transformative force in the pharmaceutical industry, particularly in the areas of marketing and sales. With increasing competition and the growing need for efficiency, pharmaceutical companies are adopting AI-driven tools and technologies to enhance their operational performance and decision-making processes. This study aims to examine the role of Artificial Intelligence in transforming marketing and sales practices in the pharmaceutical industry. The research focuses on understanding how AI applications such as customer segmentation, demand forecasting, sales prediction, and data-driven decision-making contribute to improved efficiency and effectiveness. A descriptive research design has been adopted for the study, utilizing both primary and secondary data. Primary data has been collected through a structured questionnaire from respondents including pharmaceutical employees, medical representatives, and students, while secondary data has been gathered from journals, articles, and industry reports. The findings of the study indicate that Artificial Intelligence has a significant positive impact on marketing and sales activities by improving accuracy, reducing manual effort, and enhancing decision-making capabilities. However, challenges such as high implementation costs, lack of skilled professionals, and resistance to technological change continue to hinder its widespread adoption. The study concludes that while AI holds immense potential to revolutionize pharmaceutical marketing and sales, its successful implementation requires strategic investment, skill development, and organizational readiness. The research provides valuable insights for pharmaceutical companies aiming to leverage AI for achieving competitive advantage and sustainable growth.","url":"https://doi.org/10.5281/zenodo.20626868","authors":["Abhijit Thorat*1, Shrutika Bhalerao1, Vaibhav Jadhav1, Utkarsha Randave1, Dr Pushpalata Patil2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20626868","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.19480871","name":"Influence of AI-Generated Health Content on Patient Trust in Medical Institutions: A Study of Perceived Accuracy, Misinformation Risk, and Digital Literacy in South-West Nigeria","source":"datacite","abstract":"This research explored how artificial intelligence health content creation affects patient trust toward South-West Nigerian medical establishments. The fast growth of AI-based tools creates new business opportunities, but also brings operational difficulties, which affect how doctors interact with their patients and how patients trust their healthcare providers. The research used a mixed-methods approach that began with purposive qualitative interviews with 25 patients who received care at general hospitals in Lagos, Oyo, and Ekiti States to study their experiences with AI-generated medical information. The research showed that trust develops through personal human connections. Yet people see AI content as both helpful for access and dangerous because it creates false information. The quantitative phase involved a survey administered to 150 participants to evaluate the themes and hypotheses that emerged from the initial qualitative findings. A multiple linear regression analysis was conducted, finding that the model was statistically significant (p< .001) and explained 65% of the variance in patient trust. The analysis identified three key variables significantly influencing trust: perceived AI accuracy (beta = 0.32), perceived risk of misinformation (a strong negative predictor, beta = -0.51), and digital literacy (beta = 0.40). The research concludes that a significant relationship exists, confirming that while AI offers a new channel for information, trust in healthcare remains fundamentally tied to human interaction and the ability of patients to critically evaluate digital information. Based on these insights, recommendations are provided to help institutions build digital literacy, manage misinformation risk, and strategically integrate AI to reinforce patient confidence.","url":"https://doi.org/10.5281/zenodo.19480871","authors":["Olumide Samuel Ogunjobi (PhD)","Asekhamhe, Okpokpo Mustapha","Umoru, Sediku Musa","Ofunne Anthony Ubaka"],"tags":["Patient Trust, Ai, Digital Health, Misinformation, Nigeria, Qualitative Research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19480871","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.19480872","name":"Influence of AI-Generated Health Content on Patient Trust in Medical Institutions: A Study of Perceived Accuracy, Misinformation Risk, and Digital Literacy in South-West Nigeria","source":"datacite","abstract":"This research explored how artificial intelligence health content creation affects patient trust toward South-West Nigerian medical establishments. The fast growth of AI-based tools creates new business opportunities, but also brings operational difficulties, which affect how doctors interact with their patients and how patients trust their healthcare providers. The research used a mixed-methods approach that began with purposive qualitative interviews with 25 patients who received care at general hospitals in Lagos, Oyo, and Ekiti States to study their experiences with AI-generated medical information. The research showed that trust develops through personal human connections. Yet people see AI content as both helpful for access and dangerous because it creates false information. The quantitative phase involved a survey administered to 150 participants to evaluate the themes and hypotheses that emerged from the initial qualitative findings. A multiple linear regression analysis was conducted, finding that the model was statistically significant (p< .001) and explained 65% of the variance in patient trust. The analysis identified three key variables significantly influencing trust: perceived AI accuracy (beta = 0.32), perceived risk of misinformation (a strong negative predictor, beta = -0.51), and digital literacy (beta = 0.40). The research concludes that a significant relationship exists, confirming that while AI offers a new channel for information, trust in healthcare remains fundamentally tied to human interaction and the ability of patients to critically evaluate digital information. Based on these insights, recommendations are provided to help institutions build digital literacy, manage misinformation risk, and strategically integrate AI to reinforce patient confidence.","url":"https://doi.org/10.5281/zenodo.19480872","authors":["Olumide Samuel Ogunjobi (PhD)","Asekhamhe, Okpokpo Mustapha","Umoru, Sediku Musa","Ofunne Anthony Ubaka"],"tags":["Patient Trust, Ai, Digital Health, Misinformation, Nigeria, Qualitative Research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19480872","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21463261","name":"Follicle Freeze Rescue: An AI-Personalized Smart Scalp Cooling System for Reducing Chemotherapy-Induced Alopecia.","source":"datacite","abstract":"Chemotherapy-induced alopecia (CIA) is one of the most common and psychologically distressing side effects of cancer treatment. Although scalp cooling has proven effective in reducing hair loss, current systems often rely on fixed cooling settings with limited personalization. Follicle Freeze Rescue (FFR) is a smart scalp cooling system that integrates biomedical engineering, embedded systems, artificial intelligence, and thermal management to provide personalized scalp cooling. The system combines a multi-layer cooling helmet, thermoelectric (Peltier) modules, Phase Change Materials (PCM), temperature sensors, an ESP32 controller, PID temperature control, a companion mobile application, and an Adaptive Silicone Contact Layer designed to improve helmet-to-scalp contact and enhance patient comfort. By integrating intelligent cooling, real-time monitoring, and personalized treatment profiles into a single platform, FFR aims to improve patient comfort, preserve hair, and advance supportive cancer care.","url":"https://doi.org/10.5281/zenodo.21463261","authors":["M. Abu Bakr Hashem, Mariam","M. Ibrahim, Abdelrahman"],"tags":["Artificial Intelligence Biomedical Engineering Chemotherapy-Induced Alopecia Scalp Cooling Medical Device Precision Medicine Peltier Cooling ESP32 Cancer Care Digital Health Bioinformatics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21463261","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21463262","name":"Follicle Freeze Rescue: An AI-Personalized Smart Scalp Cooling System for Reducing Chemotherapy-Induced Alopecia.","source":"datacite","abstract":"Chemotherapy-induced alopecia (CIA) is one of the most common and psychologically distressing side effects of cancer treatment. Although scalp cooling has proven effective in reducing hair loss, current systems often rely on fixed cooling settings with limited personalization. Follicle Freeze Rescue (FFR) is a smart scalp cooling system that integrates biomedical engineering, embedded systems, artificial intelligence, and thermal management to provide personalized scalp cooling. The system combines a multi-layer cooling helmet, thermoelectric (Peltier) modules, Phase Change Materials (PCM), temperature sensors, an ESP32 controller, PID temperature control, a companion mobile application, and an Adaptive Silicone Contact Layer designed to improve helmet-to-scalp contact and enhance patient comfort. By integrating intelligent cooling, real-time monitoring, and personalized treatment profiles into a single platform, FFR aims to improve patient comfort, preserve hair, and advance supportive cancer care.","url":"https://doi.org/10.5281/zenodo.21463262","authors":["M. Abu Bakr Hashem, Mariam","M. Ibrahim, Abdelrahman"],"tags":["Artificial Intelligence Biomedical Engineering Chemotherapy-Induced Alopecia Scalp Cooling Medical Device Precision Medicine Peltier Cooling ESP32 Cancer Care Digital Health Bioinformatics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21463262","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20685378","name":"From H. R. 9510 to Federal Law: A Narrative Case for Verified Physical AI Oncology Trials","source":"datacite","abstract":"This review makes a case for legislators who shape medical and artificial intelligence law, but have little to no robotics or frontier large language model (LLM) application experience. It is built on a single mechanism, verification before generation, in which a software agent proposes a clinical action and a ten-gate verification, validation, and uncertainty quantification examination either accepts it, escalates it to a qualified human, or blocks it before it can reach a patient. The argument is organized as eight emotional pillars that legislative-advocacy research finds most persuasive: compassion, fear of preventable harm, moral outrage, hope, responsibility, protection of vulnerable people, trust, and urgency. Each pillar pairs a human appeal with a credible, cited fact. The review draws on a documented engineering lineage, including a surgical-humanoid assurance run that passed 172 of 172 automated tests across a ten-gate suite, and on the published advocacy literature describing how testimony, coalition building, and policy entrepreneurship move a bill through markup and reconciliation. The conclusion is that Physical AI Trial Bill H. R. 9510 2026 should be enacted into Federal law.","url":"https://doi.org/10.5281/zenodo.20685378","authors":["Kawchak, Kevin"],"tags":["Physical AI","Oncology clinical trials","Verification before generation","VVUQ","Medical AI legislation","H. R. 9510","Patient safety","Emotional narrative advocacy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20685378","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20685379","name":"From H. R. 9510 to Federal Law: A Narrative Case for Verified Physical AI Oncology Trials","source":"datacite","abstract":"This review makes a case for legislators who shape medical and artificial intelligence law, but have little to no robotics or frontier large language model (LLM) application experience. It is built on a single mechanism, verification before generation, in which a software agent proposes a clinical action and a ten-gate verification, validation, and uncertainty quantification examination either accepts it, escalates it to a qualified human, or blocks it before it can reach a patient. The argument is organized as eight emotional pillars that legislative-advocacy research finds most persuasive: compassion, fear of preventable harm, moral outrage, hope, responsibility, protection of vulnerable people, trust, and urgency. Each pillar pairs a human appeal with a credible, cited fact. The review draws on a documented engineering lineage, including a surgical-humanoid assurance run that passed 172 of 172 automated tests across a ten-gate suite, and on the published advocacy literature describing how testimony, coalition building, and policy entrepreneurship move a bill through markup and reconciliation. The conclusion is that Physical AI Trial Bill H. R. 9510 2026 should be enacted into Federal law.","url":"https://doi.org/10.5281/zenodo.20685379","authors":["Kawchak, Kevin"],"tags":["Physical AI","Oncology clinical trials","Verification before generation","VVUQ","Medical AI legislation","H. R. 9510","Patient safety","Emotional narrative advocacy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20685379","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21093549","name":"Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, cardiac arrest, malignant arrhythmias, and acute heart failure, where delayed recognition and intervention substantially worsen clinical outcomes. Conventional monitoring approaches rely primarily on intermittent clinical assessments and manual interpretation of physiological data, limiting their ability to detect subtle physiological deterioration before the onset of critical events. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), wearable biosensors, cloud computing, and the Internet of Medical Things (IoMT) have transformed cardiovascular monitoring by enabling continuous, real-time analysis of multidimensional physiological signals. AI-assisted early warning systems integrate electrocardiographic signals, heart rate variability, blood pressure, oxygen saturation, respiratory rate, activity patterns, and patient history to predict impending cardiac emergencies before clinical manifestations become severe. Predictive algorithms facilitate early diagnosis, risk stratification, personalized intervention, and timely clinical decision-making, thereby improving patient outcomes while reducing healthcare costs and hospital readmissions. Moreover, wearable technologies and remote patient monitoring platforms have expanded cardiac surveillance beyond hospital settings, allowing continuous monitoring of high-risk individuals in their homes and communities. Despite these promising developments, challenges related to data privacy, algorithm transparency, interoperability, regulatory approval, model bias, cybersecurity, and ethical considerations continue to hinder widespread clinical implementation. This review comprehensively summarizes the principles, technological advancements, clinical applications, benefits, limitations, and future prospects of AI-assisted early warning and continuous monitoring systems for the prevention of cardiac emergencies. The review further discusses emerging innovations such as explainable AI, federated learning, digital twins, edge computing, and multimodal predictive analytics, which are expected to revolutionize cardiovascular care through precision medicine and proactive disease prevention. Overall, AI-assisted cardiac monitoring represents a paradigm shift from reactive healthcare toward predictive, preventive, and personalized cardiovascular management. Keywords: Artificial Intelligence; Cardiac Emergencies; Continuous Monitoring; Early Warning System; Machine Learning; Deep Learning; Electrocardiography; Wearable Devices; Internet of Medical Things; Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21093549","authors":["G. Elango","P. Sumithra","Salomeen Rani. S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21093549","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.21093550","name":"Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, cardiac arrest, malignant arrhythmias, and acute heart failure, where delayed recognition and intervention substantially worsen clinical outcomes. Conventional monitoring approaches rely primarily on intermittent clinical assessments and manual interpretation of physiological data, limiting their ability to detect subtle physiological deterioration before the onset of critical events. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), wearable biosensors, cloud computing, and the Internet of Medical Things (IoMT) have transformed cardiovascular monitoring by enabling continuous, real-time analysis of multidimensional physiological signals. AI-assisted early warning systems integrate electrocardiographic signals, heart rate variability, blood pressure, oxygen saturation, respiratory rate, activity patterns, and patient history to predict impending cardiac emergencies before clinical manifestations become severe. Predictive algorithms facilitate early diagnosis, risk stratification, personalized intervention, and timely clinical decision-making, thereby improving patient outcomes while reducing healthcare costs and hospital readmissions. Moreover, wearable technologies and remote patient monitoring platforms have expanded cardiac surveillance beyond hospital settings, allowing continuous monitoring of high-risk individuals in their homes and communities. Despite these promising developments, challenges related to data privacy, algorithm transparency, interoperability, regulatory approval, model bias, cybersecurity, and ethical considerations continue to hinder widespread clinical implementation. This review comprehensively summarizes the principles, technological advancements, clinical applications, benefits, limitations, and future prospects of AI-assisted early warning and continuous monitoring systems for the prevention of cardiac emergencies. The review further discusses emerging innovations such as explainable AI, federated learning, digital twins, edge computing, and multimodal predictive analytics, which are expected to revolutionize cardiovascular care through precision medicine and proactive disease prevention. Overall, AI-assisted cardiac monitoring represents a paradigm shift from reactive healthcare toward predictive, preventive, and personalized cardiovascular management. Keywords: Artificial Intelligence; Cardiac Emergencies; Continuous Monitoring; Early Warning System; Machine Learning; Deep Learning; Electrocardiography; Wearable Devices; Internet of Medical Things; Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21093550","authors":["G. Elango","P. Sumithra","Salomeen Rani. S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21093550","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.18863430","name":"From Zadig to Artificial Intelligence: Clinical Observation, Epistemic Fragility, and the Future of Medical Reasoning","source":"datacite","abstract":"ABSTRACT: Background: The rapid integration of artificial intelligence (AI) into clinical practice has revived longstanding debates about the foundations of diagnostic reasoning. While AI systems rely on large datasets derived from past patients, the clinician’s craft remains rooted in direct observation, sensory examination, and contextual interpretation. Objective: To trace the intellectual lineage connecting Voltaire’s Zadig, the evolution of the clinical method, the deductive tradition exemplified by Sherlock Holmes, and the contemporary challenges posed by AI in medicine. Discussion: Voltaire’s Zadig anticipates the logic of modern clinical reasoning through its emphasis on inference from subtle signs. This evidential paradigm reappears in the anatomic and clinical revolution, bacteriology, and evidence‑based medicine, and is later embodied in Arthur Conan Doyle’s Sherlock Holmes, whose method was explicitly modelled on clinical diagnosis. AI introduces new fundamental risks, including algorithmic affixing, loss of sensory skills, and the perpetuation of historical preconceptions. The internist’s observational abilities remain essential for generating primary data and contextualizing algorithmic outputs. Conclusion: The future of medicine requires harmonizing traditional clinical skills with AI‑driven tools. The internist must remain a reader of signs, a critic of evidence, and a guardian of epistemic humility. KEYWORDS: Clinical reasoning, Artificial intelligence, Medical epistemology, Voltaire, Sherlock Holmes, Internal medicine, Diagnostic method.","url":"https://doi.org/10.5281/zenodo.18863430","authors":["Asmell Ramos Cabrera - (MD, MSc, FWACP)"],"tags":["From Zadig to Artificial Intelligence: Clinical Observation, Epistemic Fragility, and the Future of Medical Reasoning","SSAR Publishers","Clinical reasoning","Artificial intelligence","Medical epistemology","Voltaire","Sherlock Holmes","Internal medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18863430","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.18863431","name":"From Zadig to Artificial Intelligence: Clinical Observation, Epistemic Fragility, and the Future of Medical Reasoning","source":"datacite","abstract":"ABSTRACT: Background: The rapid integration of artificial intelligence (AI) into clinical practice has revived longstanding debates about the foundations of diagnostic reasoning. While AI systems rely on large datasets derived from past patients, the clinician’s craft remains rooted in direct observation, sensory examination, and contextual interpretation. Objective: To trace the intellectual lineage connecting Voltaire’s Zadig, the evolution of the clinical method, the deductive tradition exemplified by Sherlock Holmes, and the contemporary challenges posed by AI in medicine. Discussion: Voltaire’s Zadig anticipates the logic of modern clinical reasoning through its emphasis on inference from subtle signs. This evidential paradigm reappears in the anatomic and clinical revolution, bacteriology, and evidence‑based medicine, and is later embodied in Arthur Conan Doyle’s Sherlock Holmes, whose method was explicitly modelled on clinical diagnosis. AI introduces new fundamental risks, including algorithmic affixing, loss of sensory skills, and the perpetuation of historical preconceptions. The internist’s observational abilities remain essential for generating primary data and contextualizing algorithmic outputs. Conclusion: The future of medicine requires harmonizing traditional clinical skills with AI‑driven tools. The internist must remain a reader of signs, a critic of evidence, and a guardian of epistemic humility. KEYWORDS: Clinical reasoning, Artificial intelligence, Medical epistemology, Voltaire, Sherlock Holmes, Internal medicine, Diagnostic method.","url":"https://doi.org/10.5281/zenodo.18863431","authors":["Asmell Ramos Cabrera - (MD, MSc, FWACP)"],"tags":["From Zadig to Artificial Intelligence: Clinical Observation, Epistemic Fragility, and the Future of Medical Reasoning","SSAR Publishers","Clinical reasoning","Artificial intelligence","Medical epistemology","Voltaire","Sherlock Holmes","Internal medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18863431","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.19610548","name":"AI-Based Image Analysis in Diagnostic Devices","source":"datacite","abstract":"AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image interpretation. From convolutional neural networks that detect diabeticretinopathy with ophthalmologist-level sensitivity to transformer models that segment tumour boundaries in whole-slidepathology images with sub-cellular precision, AI image analysis is transitioning from research demonstration to clinicaldeployment at scale. Yet the path from validated algorithm to clinically integrated diagnostic device requires navigation ofhuman factors design, workflow integration, regulatory validation, and post-market performance monitoring challengesthat algorithmic accuracy alone does not address. This study presents the AI Image Analysis Diagnostic DeviceFramework (AIADDF), evaluating five AI image analysis implementation approaches -- standalone algorithm withradiologist workflow, AI-first triage with human review escalation, computer-aided detection enhancement, autonomousAI reporting for defined scope, and federated multi-site AI with continuous learning -- across four imaging devicecategories: radiology CT/MRI, digital pathology, retinal fundus imaging, and dermatoscopy. Our AI Diagnostic ImageScore (ADIS) integrates diagnostic accuracy, workflow efficiency, radiologist acceptance, regulatory compliance, andcross-site generalisability. Federated multi-site AI with continuous learning achieved the highest ADIS (0.928) throughprivacy-preserving training across 24 clinical sites that achieved C-statistic 0.92 while maintaining 96% performanceretention at new deployment sites, while autonomous AI reporting achieved the highest workflow efficiency (0.955) byreducing mean report turnaround time from 48 hours to 3.2 hours for defined low-complexity imaging tasks","url":"https://doi.org/10.5281/zenodo.19610548","authors":["Helena Popescu","Andreas Lindberg","Andreas Moreau"],"tags":["AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.19610548","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.19610547","name":"AI-Based Image Analysis in Diagnostic Devices","source":"datacite","abstract":"AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image interpretation. From convolutional neural networks that detect diabeticretinopathy with ophthalmologist-level sensitivity to transformer models that segment tumour boundaries in whole-slidepathology images with sub-cellular precision, AI image analysis is transitioning from research demonstration to clinicaldeployment at scale. Yet the path from validated algorithm to clinically integrated diagnostic device requires navigation ofhuman factors design, workflow integration, regulatory validation, and post-market performance monitoring challengesthat algorithmic accuracy alone does not address. This study presents the AI Image Analysis Diagnostic DeviceFramework (AIADDF), evaluating five AI image analysis implementation approaches -- standalone algorithm withradiologist workflow, AI-first triage with human review escalation, computer-aided detection enhancement, autonomousAI reporting for defined scope, and federated multi-site AI with continuous learning -- across four imaging devicecategories: radiology CT/MRI, digital pathology, retinal fundus imaging, and dermatoscopy. Our AI Diagnostic ImageScore (ADIS) integrates diagnostic accuracy, workflow efficiency, radiologist acceptance, regulatory compliance, andcross-site generalisability. Federated multi-site AI with continuous learning achieved the highest ADIS (0.928) throughprivacy-preserving training across 24 clinical sites that achieved C-statistic 0.92 while maintaining 96% performanceretention at new deployment sites, while autonomous AI reporting achieved the highest workflow efficiency (0.955) byreducing mean report turnaround time from 48 hours to 3.2 hours for defined low-complexity imaging tasks","url":"https://doi.org/10.5281/zenodo.19610547","authors":["Helena Popescu","Andreas Lindberg","Andreas Moreau"],"tags":["AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.19610547","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20705029","name":"Advanced Image Processing and Artificial Intelligence for Enhancement, Segmentation, and Intelligent Visual Analysis: A Comprehensive Review","source":"datacite","abstract":"Digital image processing has become a cornerstone of modern scientific and technological systems, supporting a wide range of applications in medicine, industry, remote sensing, security, and autonomous technologies. The rapid growth of imaging devices and computational capabilities has driven the development of increasingly sophisticated techniques for image enhancement, segmentation, feature extraction, and intelligent visual interpretation. This review provides a comprehensive overview of contemporary image processing methodologies, covering both classical approaches and recent advances in artificial intelligence. The paper examines fundamental enhancement and filtering techniques in the spatial and frequency domains, followed by a detailed discussion of segmentation methods, feature representation, and object analysis. Particular attention is given to the integration of machine learning and deep learning frameworks, including convolutional neural networks, transfer learning models, transformer-based architectures, and hybrid intelligent systems. Their roles in improving accuracy, robustness, automation, and real-time performance are critically analyzed. Applications in medical image diagnosis, industrial quality inspection, surveillance systems, remote sensing, autonomous vehicles, and smart vision platforms are reviewed to demonstrate the practical impact of modern image processing technologies. Emerging research directions, including explainable artificial intelligence, multimodal vision systems, edge computing, and foundation vision models, are also discussed. The review highlights the ongoing convergence of traditional image processing and artificial intelligence, emphasizing how this integration is transforming visual data analysis and enabling the development of more accurate, adaptive, and intelligent imaging systems. The study serves as a reference for researchers, practitioners, and graduate students seeking a structured understanding of current advances and future opportunities in digital image processing.","url":"https://doi.org/10.5281/zenodo.20705029","authors":["Hayawi, Heyam A. A."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20705029","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"doi:10.5281/zenodo.20705030","name":"Advanced Image Processing and Artificial Intelligence for Enhancement, Segmentation, and Intelligent Visual Analysis: A Comprehensive Review","source":"datacite","abstract":"Digital image processing has become a cornerstone of modern scientific and technological systems, supporting a wide range of applications in medicine, industry, remote sensing, security, and autonomous technologies. The rapid growth of imaging devices and computational capabilities has driven the development of increasingly sophisticated techniques for image enhancement, segmentation, feature extraction, and intelligent visual interpretation. This review provides a comprehensive overview of contemporary image processing methodologies, covering both classical approaches and recent advances in artificial intelligence. The paper examines fundamental enhancement and filtering techniques in the spatial and frequency domains, followed by a detailed discussion of segmentation methods, feature representation, and object analysis. Particular attention is given to the integration of machine learning and deep learning frameworks, including convolutional neural networks, transfer learning models, transformer-based architectures, and hybrid intelligent systems. Their roles in improving accuracy, robustness, automation, and real-time performance are critically analyzed. Applications in medical image diagnosis, industrial quality inspection, surveillance systems, remote sensing, autonomous vehicles, and smart vision platforms are reviewed to demonstrate the practical impact of modern image processing technologies. Emerging research directions, including explainable artificial intelligence, multimodal vision systems, edge computing, and foundation vision models, are also discussed. The review highlights the ongoing convergence of traditional image processing and artificial intelligence, emphasizing how this integration is transforming visual data analysis and enabling the development of more accurate, adaptive, and intelligent imaging systems. The study serves as a reference for researchers, practitioners, and graduate students seeking a structured understanding of current advances and future opportunities in digital image processing.","url":"https://doi.org/10.5281/zenodo.20705030","authors":["Hayawi, Heyam A. A."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20705030","addedAt":"2026-09-01T01:47:52.052Z","updatedAt":"2026-09-01T01:47:52.052Z"},{"id":"oa:W4399826484","name":"Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence","source":"openalex","abstract":"Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.","url":"https://doi.org/10.1177/10732748241264704","authors":["Youcef Derbal"],"tags":["Medicine","Artificial intelligence","Context (archaeology)","Generative grammar","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1177/10732748241264704","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404219342","name":"Applications of integrating artificial intelligence and big data: A comprehensive analysis","source":"openalex","abstract":"Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.","url":"https://doi.org/10.1515/jisys-2024-0237","authors":["Sally Almanasra"],"tags":["Computer science","Big data","Artificial intelligence","Data science","Data mining"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1515/jisys-2024-0237","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406873552","name":"Primary School Students' Perceptions of Artificial Intelligence: Metaphor and Drawing Analysis","source":"openalex","abstract":"ABSTRACT Due to the frequent use of artificial intelligence (AI) technologies in daily life, it is thought that primary school students acquire information about this concept from various sources. The way these sources present AI may affect students' perceptions of AI. In the study, it was aimed to examine the perceptions of third and fourth grade primary school students about AI through metaphors and drawings. This research, which was conducted with the participation of 262 students, was conducted with the phenomenological design. When the metaphors of the participants were analysed, it was determined that they produced 100 metaphors, and these metaphors were evaluated in 17 categories as humanistic feature, information source, danger, development, superhuman feature, service, source of happiness, productivity, orientation, commitment, pervasiveness, necessity, security, speed, difficulty, virtual environment and uncertainty. Accordingly, it was determined that the participants evaluated AI from many different perspectives and produced the most metaphors in the categories of humanistic feature, information source and danger. It was determined that the metaphors human, brain and living were prominent in the human characteristic category; the metaphors teacher, wise and book were prominent in the source of information category; and finally, the metaphors enemy, weapon and monster were prominent in the danger category. When the drawing findings were analysed, it was determined that 37 codes represented four categories: purpose, object, interaction and environment. In the purpose category, service, source of information, and source of happiness; in the object category, mostly humanoid robot; in the interaction category, emphasising interaction; and in the environment category, the environment was not specified. In line with the findings obtained, literature discussions were made and suggestions were made.","url":"https://doi.org/10.1111/ejed.70007","authors":["Jale Kalemkuş","Fatih Kalemkuş"],"tags":["Metaphor","Perception","Mathematics education","Psychology","Primary (astronomy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-27","doi":"https://doi.org/10.1111/ejed.70007","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4200200462","name":"Artificial intelligence and inflammatory bowel disease: practicalities and future prospects","source":"openalex","abstract":"Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.","url":"https://doi.org/10.1136/flgastro-2021-102003","authors":["Johanne Brooks-Warburton","James J. Ashton","Anjan Dhar","Tony Tham","Patrick B. Allen","Sami Hoque","Laurence Lovat","Shaji Sebastian"],"tags":["Inflammatory bowel disease","Medicine","Risk stratification","Disease","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-10","doi":"https://doi.org/10.1136/flgastro-2021-102003","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408764619","name":"Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding","source":"openalex","abstract":"Objective: This study aimed to develop and evaluate an artificial intelligence (AI)-driven system for forecasting Pediatric Emergency Department (PED) overcrowding and optimizing physician shift schedules using machine learning operations (MLOps). Materials and Methods: Data from 352 843 PED admissions between January 2018 and May 2023 were analyzed. Twenty time-series forecasting models-including classical methods and advanced deep learning architectures like Temporal Convolutional Network, Time-series Dense Encoder and Reversible Instance Normalization, Neural High-order Time Series model, and Neural Basis Expansion Analysis-were developed and compared using Python 3.8. Starting in January 2023, an MLOps simulation automated data updates and model retraining. Shift schedules were optimized based on forecasted patient volumes using integer linear programming. Results: improved to 60%. The MLOps architecture facilitated continuous model updates, enhancing forecast accuracy. Shift optimization adjusted staffing in 69 out of 84 shifts, increasing physician allocation by up to 30.4% during peak hours. This adjustment reduced the patient-to-physician ratio by an average of 4.32 patients during the 8-16 shift and 4.40 patients during the 16-24 shift. Discussion: The integration of advanced deep learning models with MLOps architecture allowed for continuous model updates, enhancing the accuracy of PED overcrowding forecasts and outperforming traditional methods. The AI-driven system demonstrated resilience against data drift caused by events like the COVID-19 pandemic, adapting to changing conditions. Optimizing physician shifts based on these forecasts improved workforce distribution without increasing staff numbers, reducing patient load per physician during peak hours. However, limitations include the single-center design and a fixed staffing model, indicating the need for multicenter validation and implementation in settings with dynamic staffing practices. Future research should focus on expanding datasets through multicenter collaborations and developing forecasting models that provide longer lead times without compromising accuracy. Conclusions: The AI-driven forecasting and shift optimization system demonstrated the efficacy of integrating AI and MLOps in predicting PED overcrowding and optimizing physician shifts. This approach outperformed traditional methods, highlighting its potential for managing overcrowding in emergency departments. Future research should focus on multicenter validation and real-world implementation to fully leverage the benefits of this innovative system.","url":"https://doi.org/10.1093/jamiaopen/ooae138","authors":["Izzet Turkalp Akbasli","Ahmet Ziya Bırbılen","Özlem Tekşam"],"tags":["Overcrowding","Deep learning","Computer science","Artificial intelligence","Emergency department"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-06","doi":"https://doi.org/10.1093/jamiaopen/ooae138","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408186594","name":"Generative AI in Education: Perspectives Through an Academic Lens","source":"openalex","abstract":"In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords over time; a keyword co-occurrence map; collaboration networks; and a Sankey diagram illustrating the relationship between AI-related terms, publication years and research areas; (2) Sentiment analysis using a custom list of words, VADER and TextBlob; (3) Topic modeling using Latent Dirichlet Allocation (LDA). Terms such as “artificial intelligence” and “generative artificial intelligence” were predominant, but they diverged and evolved over time. By 2024, AI applications had branched into specialized fields, including education and educational research, computer science, engineering, psychology, medical informatics, healthcare sciences, general medicine and surgery. The sentiment analysis reveals a growing optimism in academic publications regarding generative AI in education, with a steady increase in positive sentiment from 2023 to 2024, while maintaining a predominantly neutral tone. Five main topics were derived from AI applications in education, based on an analysis of the most relevant terms extracted by LDA: (1) Gen-AI’s impact in education and research; (2) ChatGPT as a tool for university students and teachers; (3) Large language models (LLMs) and prompting in computing education; (4) Applications of ChatGPT in patient education; (5) ChatGPT’s performance in medical examinations. The research identified several emerging topics: discipline-specific application of LLMs, multimodal gen-AI, personalized learning, AI as a peer or tutor and cross-cultural and multilingual tools aimed at developing culturally relevant educational content and supporting the teaching of lesser-known languages. Further, gamification with generative AI involves designing interactive storytelling and adaptive educational games to enhance engagement and hybrid human–AI classrooms explore co-teaching dynamics, teacher–student relationships and the impact on classroom authority.","url":"https://doi.org/10.3390/electronics14051053","authors":["Iulian Întorsureanu","Simona‐Vasilica Oprea","Adela Bârã","Dragoș Vespan"],"tags":["Generative grammar","Lens (geology)","Mathematics education","Sociology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-06","doi":"https://doi.org/10.3390/electronics14051053","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402966535","name":"The impact of artificial intelligence on women’s empowerment, and work-life balance in Saudi educational institutions","source":"openalex","abstract":"Gender prejudice and stereotypes are prevalent in the workplace, particularly for women in the Artificial Intelligence (AI) industry, where they can significantly hinder professional development and limit prospects for growth. These challenges contribute to the underrepresentation of executives in AI. However, with the right measures, these barriers can be overcome, leading to a more inclusive and diverse AI industry. Women in this demanding technological domain often face additional difficulties in achieving a work-life balance, further constraining their professional advancement and engagement in the industry. This research aims to examine the implications of AI capabilities on work-life balance and the empowerment of female faculty members in enhancing the efficiency of educational institutions. The research performs a structural equation modeling (SEM) approach, using a survey conducted on female faculty of Saudi Arabian universities. The study specifically considers moderating variables such as age, education level, experience, and marital status. The findings, which reveal that AI managerial capability, as well as AI infrastructure agility, impacts work-life balance and empowerment of women faculties in educational institution efficiency, underscore the significance of considering demographic factors when analyzing women's empowerment and work-life balance as outcomes. By exploring these factors, the research provides a comprehensive understanding of how AI capabilities impact women's empowerment and their ability to maintain a work-life balance, ultimately contributing to the efficiency and effectiveness of educational institutions. These results emphasize the value of increasing women's empowerment and raising the standard of performance evaluation systems in educational sectors.","url":"https://doi.org/10.3389/fpsyg.2024.1432541","authors":["Sayeda Meharunisa","Hawazen Zam Almugren","Masahina Sarabdeen","Fatma Mabrouk","A.C. Muhammadu Kijas"],"tags":["Empowerment","Psychology","Work (physics)","Prejudice (legal term)","Marital status"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-30","doi":"https://doi.org/10.3389/fpsyg.2024.1432541","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4416786468","name":"A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development","source":"openalex","abstract":"","url":"https://doi.org/10.1021/acs.jmedchem.5c03159","authors":["Saghir Ali","Xiaochen Tian","Haiying Chen","Jia Zhou"],"tags":["Drug discovery","Chemistry","Artificial intelligence","Data science","Drug development"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-27","doi":"https://doi.org/10.1021/acs.jmedchem.5c03159","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406853748","name":"Avances en el uso de inteligencia artificial en la educación médica latinoamericana","source":"openalex","abstract":"Artificial intelligence is the ability of a system to emulate cognitive functions. In healthcare, it is used to support complex decision-making and medical skills training. It is a tool for creating virtual simulation scenarios and evaluating the performance of medical students. This literature review aims to describe the advances in artificial intelligence in medical education in Latin America. The databases PubMed, SciELO, and Google Scholar were consulted; publications in Spanish and English from 2019 to 2024 were included, and keywords and Boolean operators were applied. Artificial intelligence in medical training seeks to replicate cognitive skills in problem-solving and is classified into narrow artificial intelligence and general artificial intelligence. It is a transformative tool that empowers virtual reality, optimizes outcomes, offers opportunities to strengthen the effectiveness of healthcare, and makes improvements in personalizing the learning process. However, its implementation requires addressing ethical and legal challenges for its full exploitation. In Latin America, there is a steady increase in the adoption of artificial intelligence-based tools for medical staff training.","url":"https://doi.org/10.5377/alerta.v8i1.19194","authors":["Casto David Ramírez Domínguez","Graciamaría Alvarenga Somoza","Naara Eunice Olivares Guzmán","Marta María Cárcamo Trinidad","Alejandro Omar Peralta Reyes"],"tags":["Humanities","Psychology","Philosophy","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-22","doi":"https://doi.org/10.5377/alerta.v8i1.19194","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392055393","name":"Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery","source":"openalex","abstract":"Objective: By constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes. Methods: We constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores. Results: The results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633). Conclusion: This study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.","url":"https://doi.org/10.1016/j.heliyon.2024.e26580","authors":["Cheng-Mao Zhou","Huijuan Li","Qiong Xue","Jianjun Yang","Yu Zhu"],"tags":["Laparoscopic surgery","Medicine","General surgery","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-22","doi":"https://doi.org/10.1016/j.heliyon.2024.e26580","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408239390","name":"The Ghost in the Machine: Counterterrorism in the Age of Artificial Intelligence","source":"openalex","abstract":"In the aftermath of 9/11 security agencies augmented their counterterrorism (CT) apparatuses with advanced analytics, machine learning (ML), and artificial intelligence (AI) to improve their ability to identify and neutralize terrorists. Under this regime, humans remained the central actors, tasked with understanding information and crafting a response. The advent of Generative AI (GenAI) changes this equation. GenAI’s ability to mimic humanity’s reasoning skills augurs a world where machines assume responsibility for most CT activities. This possibility raises fears of machines outside of human control. These fears are currently unfounded, and to the extent that they’re real, they must be weighed against the ability to reduce the victims of terrorism. As this world forms, what will matter more is decision-makers’ understanding of AI/ML outputs for counterterrorism, as they will have to make strategic choices around a series of ethical and policy choices that are inherently human. This article explores this subject more in-depth, reviewing the evolution of AI/ML and its impact on different CT domains, exploring the strategic dimensions of AI/ML, and concluding with a series of policy recommendations.","url":"https://doi.org/10.1080/1057610x.2025.2475850","authors":["Christopher Wall"],"tags":["Artificial intelligence","Computer science","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-06","doi":"https://doi.org/10.1080/1057610x.2025.2475850","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410206004","name":"Application of Artificial Intelligence to Deliver Healthcare From the Eye","source":"openalex","abstract":"Importance: Oculomics is the science of analyzing ocular data to identify, diagnose, and manage systemic disease. This article focuses on prescreening, its use with retinal images analyzed by artificial intelligence (AI), to identify ocular or systemic disease or potential disease in asymptomatic individuals. The implementation of prescreening in a coordinated care system, defined as Healthcare From the Eye prescreening, has the potential to improve access, affordability, equity, quality, and safety of health care on a global level. Stakeholders include physicians, payers, policymakers, regulators and representatives from industry, government, and data privacy sectors. Observations: The combination of AI analysis of ocular data with automated technologies that capture images during routine eye examinations enables prescreening of large populations for chronic disease. Retinal images can be acquired during either a routine eye examination or in settings outside of eye care with readily accessible, safe, quick, and noninvasive retinal imaging devices. The outcome of such an examination can then be digitally communicated across relevant stakeholders in a coordinated fashion to direct a patient to screening and monitoring services. Such an approach offers the opportunity to transform health care delivery and improve early disease detection, improve access to care, enhance equity especially in rural and underserved communities, and reduce costs. Conclusions and Relevance: With effective implementation and collaboration among key stakeholders, this approach has the potential to contribute to an equitable and effective health care system.","url":"https://doi.org/10.1001/jamaophthalmol.2025.0881","authors":["Robert N. Weinreb","Aaron Lee","Sally L. Baxter","Richard Lee","Theodore Leng","Michael V. McConnell","Nevin W. El-Nimri","David C. Rhew"],"tags":["Medicine","Health care","Eye examination","Government (linguistics)","Equity (law)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-08","doi":"https://doi.org/10.1001/jamaophthalmol.2025.0881","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409557347","name":"Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects","source":"openalex","abstract":"Artificial Intelligence is revolutionizing prenatal diagnostics by enhancing the accuracy and efficiency of procedures. This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. Recent studies highlight the effectiveness of AI techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), achieving detection accuracy rates of up to 95% across various datasets, including fetal ultrasound images, genetic data, and maternal health records. SVM models have demonstrated 71.50% accuracy on training datasets and 68.57% on testing datasets for NTD classification, while advanced deep learning (DL) methods report patient-level prediction accuracy of 94.5% and an area under the receiver operating characteristic curve (AUROC) of 99.3%. AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. Current AI-assisted ultrasound technologies have improved diagnostic accuracy, yielding sensitivity and specificity rates of 88.9% and 98.0%, respectively, compared to traditional methods with 81.5% sensitivity and 92.2% specificity. AI systems have also streamlined workflows, reducing median scan times from 19.7 min to 11.4 min, allowing sonographers to prioritize critical patient care. Advancements in DL algorithms, including Oct-U-Net and PAICS, have achieved recall and precision rates of 0.93 and 0.96, respectively, in identifying fetal abnormalities. Moreover, AI's evolving role in genetic research supports personalized NTD prevention strategies and enhances public awareness through AI-generated health messages. In conclusion, the integration of AI in prenatal diagnostics significantly improves the detection and assessment of NTDs, leading to greater accuracy and efficiency in ultrasound imaging. As AI continues to advance, it has the potential to further enhance personalized healthcare strategies and raise public awareness about NTDs, ultimately contributing to better maternal and fetal outcomes.","url":"https://doi.org/10.3389/fped.2025.1514447","authors":["Maryam Yeganegi","Mahsa Danaei","Sepideh Azizi","Fatemeh Jayervand","Reza Bahrami","Seyed Alireza Dastgheib","Heewa Rashnavadi","Ali Akbar Masoudi","Amirmasoud Shiri","Kazem Aghili","Mahmood Noorishadkam","Hossein Neámatzadeh"],"tags":["Artificial intelligence","Support vector machine","Convolutional neural network","Machine learning","Receiver operating characteristic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-17","doi":"https://doi.org/10.3389/fped.2025.1514447","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404706435","name":"Gender bias in visual generative artificial intelligence systems and the socialization of AI","source":"openalex","abstract":"Abstract Substantial research over the last ten years has indicated that many generative artificial intelligence systems (“GAI”) have the potential to produce biased results, particularly with respect to gender. This potential for bias has grown progressively more important in recent years as GAI has become increasingly integrated in multiple critical sectors, such as healthcare, consumer lending, and employment. While much of the study of gender bias in popular GAI systems is focused on text-based GAI such as OpenAI’s ChatGPT and Google’s Gemini (formerly Bard), this article describes the results of a confirmatory experiment of gender bias in visual GAI systems. The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent. They go on to offer four potential approaches to gender bias in visual GAI based on the roles visual GAI could play in modern society. The article concludes with a discussion of how dominant societal values could influence a choice between those four potential approaches to gender bias in visual GAI and some suggestions for further research.","url":"https://doi.org/10.1007/s00146-024-02129-1","authors":["Larry G. Locke","Grace Hodgdon"],"tags":["Socialization","Generative grammar","Performing arts","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.1007/s00146-024-02129-1","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400118784","name":"Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations","source":"openalex","abstract":"This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI’s potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape.","url":"https://doi.org/10.3390/ai5030049","authors":["Peter Kováč","Peter Jackuliak","Alexandra Bražinová","Ivan Varga","Michal Aláč","Martin Smatana","Dušan Lovich","Andrej Thurzo"],"tags":["Safeguarding","Context (archaeology)","Government (linguistics)","Confidentiality","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-27","doi":"https://doi.org/10.3390/ai5030049","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401328425","name":"Navigating artificial intelligence in care homes: Competing stakeholder views of trust and logics of care","source":"openalex","abstract":"The COVID-19 pandemic shed light on systemic issues plaguing care (nursing) homes, from staff shortages to substandard healthcare. Artificial Intelligence (AI) technologies, including robots and chatbots, have been proposed as solutions to such issues. Yet, socio-ethical concerns about the implications of AI for health and care practices have also been growing among researchers and practitioners. At a time of AI promise and concern, it is critical to understand how those who develop and implement these technologies perceive their use and impact in care homes. Combining a sociological approach to trust with Annemarie Mol's logic of care and Jeanette Pol's concept of fitting, we draw on 18 semi-structured interviews with care staff, advocates, and AI developers to explore notions of human-AI care. Our findings show positive perceptions and experiences of AI in care homes, but also ambivalence. While integrative care incorporating humans and technology was salient across interviewees, we also identified experiential, contextual, and knowledge divides between AI developers and care staff. For example, developers lacked experiential knowledge of care homes' daily functioning and constraints, influencing how they designed AI. Care staff demonstrated limited experiential knowledge of AI or more critical views about contexts of use, affecting their trust in these technologies. Different understandings of 'good care' were evident, too: 'warm' care was sometimes linked to human care and 'cold' care to technology. In conclusion, understandings and experiences of AI are marked by different logics of sociotechnical care and related levels of trust in these sensitive settings.","url":"https://doi.org/10.1016/j.socscimed.2024.117187","authors":["Bárbara Barbosa Neves","Maho Omori","Alan Petersen","Mor Vered","Adrian Carter"],"tags":["Health care","Experiential learning","Experiential knowledge","Nursing","Stakeholder"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-05","doi":"https://doi.org/10.1016/j.socscimed.2024.117187","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W7128024495","name":"Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration","source":"openalex","abstract":"Microfluidic chip technologies, also known as lab-on-a-chip systems, have profoundly transformed laboratory medicine by enabling the miniaturization, automation, and rapid processing of complex diagnostic assays using minimal sample volumes. Recent advances in chip design, fabrication methods-including 3D printing, modular and flexible substrates-and biosensor integration have significantly enhanced the performance, sensitivity, and clinical applicability of these devices. Integration of advanced biosensors allows for real-time detection of circulating tumor cells, nucleic acids, and exosomes, supporting innovative applications in cancer diagnostics, infectious disease detection, point-of-care testing (POCT), personalized medicine, and therapeutic monitoring. Notably, the convergence of microfluidics with artificial intelligence (AI) and machine learning has amplified device automation, reliability, and analytical power, resulting in \"smart\" diagnostic platforms capable of self-optimization, automated analysis, and clinical decision support. Emerging applications in fields such as neuroscience diagnostics and microbiome profiling further highlight the broad potential of microfluidic technology. Here, we present findings from a comprehensive review of recent innovations in microfluidic chip design and fabrication, advances in biosensor and AI integration, and their clinical applications in laboratory medicine. We also discuss current challenges in manufacturing, clinical validation, and system integration, as well as future directions for translating next-generation microfluidic technologies into routine clinical and public health practice.","url":"https://doi.org/10.3390/bios16020104","authors":["Hong Cai","Dongxia Wang","Yiqun Zhao","Chunhui Yang"],"tags":["Microfluidics","Computer science","Lab-on-a-chip","Modular design","Nanotechnology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-05","doi":"https://doi.org/10.3390/bios16020104","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4391070180","name":"AI in medical diagnosis: AI prediction & human judgment","source":"openalex","abstract":"AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.","url":"https://doi.org/10.1016/j.artmed.2024.102769","authors":["Dóra Göndöcs","Viktor Dörfler"],"tags":["Intuition","Computer science","Panacea (medicine)","Mindset","Feeling"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-21","doi":"https://doi.org/10.1016/j.artmed.2024.102769","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409896229","name":"Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study","source":"openalex","abstract":"Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 2023 across three hospitals. The model integrates seven machine learning models via the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), achieving AUCs of 0.733 in internal validation, 0.808 in the pediatric ICU, 0.662 in the respiratory ICU, with external validation AUCs of 0.784 and 0.786, respectively. It demonstrated high stability and accuracy in cross-specialty and multi-center validation. This interpretable model provides clinicians with a reliable early-warning tool for septic shock mortality risk, facilitating early intervention to reduce mortality.","url":"https://doi.org/10.1038/s41746-025-01643-w","authors":["Shurui Wang","Xinyi Liu","Shaohua Yuan","Yi Bian","Hong Wu","Qing Ye"],"tags":["Medicine","Septic shock","TOPSIS","Emergency medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-28","doi":"https://doi.org/10.1038/s41746-025-01643-w","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4415350036","name":"Grad-CAM (Gradient-weighted Class Activation Mapping): A systematic literature review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compbiomed.2025.111200","authors":["Abdul Muiz Fayyaz","Said Jadid Abdulkadir","Noureen Talpur","Safwan Mahmood Al-Selwi","Shahab Ul Hassan","Ebrahim Hamid Sumiea"],"tags":["Interpretability","Systematic review","Computer science","Class (philosophy)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-17","doi":"https://doi.org/10.1016/j.compbiomed.2025.111200","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410363862","name":"Artificial Intelligence and Assistive Robotics in Healthcare Services: Applications in Silver Care","source":"openalex","abstract":"Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age.","url":"https://doi.org/10.3390/ijerph22050781","authors":["Giovanni Luca Masala","Ioanna Giorgi"],"tags":["Health care","Artificial intelligence","Robotics","Loneliness","Autonomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-14","doi":"https://doi.org/10.3390/ijerph22050781","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4388973748","name":"Transformers in medical image segmentation: a narrative review","source":"openalex","abstract":"Background and Objective: Transformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner. Methods: Databases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered. Key Content and Findings: In this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced. Conclusions: We have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.","url":"https://doi.org/10.21037/qims-23-542","authors":["Rabeea Fatma Khan","Byoung-Dai Lee","Mu Sook Lee"],"tags":["Computer science","Segmentation","Transformer","Deep learning","Popularity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-24","doi":"https://doi.org/10.21037/qims-23-542","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392392203","name":"Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review. METHODS: PubMed, Embase, and Web of Science were searched using the following keywords: \"pediatric,\" \"artificial intelligence,\" \"standard practices,\" and \"appendicitis,\" up to September 2023. The risk of bias was assessed using PROBAST. RESULTS: A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a \"high risk\" concerning their overall risk of bias. CONCLUSION: We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.","url":"https://doi.org/10.1055/a-2257-5122","authors":["Robin Rey","Renato Gualtieri","Giorgio C. La Scala","Klara M. Posfay‐Barbe"],"tags":["Medicine","MEDLINE","Systematic review","Randomized controlled trial","Appendicitis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-30","doi":"https://doi.org/10.1055/a-2257-5122","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412020208","name":"Assessing risk of bias in toxicological studies in the era of artificial intelligence","source":"openalex","abstract":"Risk of bias is a critical factor influencing the reliability and validity of toxicological studies, impacting evidence synthesis and decision-making in regulatory and public health contexts. The traditional approaches for assessing risk of bias are often subjective and time-consuming. Recent advancements in artificial intelligence (AI) offer promising solutions for automating and enhancing bias detection and evaluation. This article reviews key types of biases-such as selection, performance, detection, attrition, and reporting biases-in in vivo, in vitro, and in silico studies. It further discusses specialized tools, including the SYRCLE and OHAT frameworks, designed to address such biases. The integration of AI-based tools into risk of bias assessments can significantly improve the efficiency, consistency, and accuracy of evaluations. However, AI models are themselves susceptible to algorithmic and data biases, necessitating robust validation and transparency in their development. The article highlights the need for standardized, AI-enabled risk of bias assessment methodologies, training, and policy implementation to mitigate biases in AI-driven analyses. The strategies for leveraging AI to screen studies, detect anomalies, and support systematic reviews are explored. By adopting these advanced methodologies, toxicologists and regulators can enhance the quality and reliability of toxicological evidence, promoting evidence-based practices and ensuring more informed decision-making. The way forward includes fostering interdisciplinary collaboration, developing bias-resilient AI models, and creating a research culture that actively addresses bias through transparent and rigorous practices.","url":"https://doi.org/10.1007/s00204-025-03978-5","authors":["Thomas Härtung","Sebastian Hoffmann","Paul Whaley"],"tags":["Computer science","Risk analysis (engineering)","Reliability (semiconductor)","Transparency (behavior)","Consistency (knowledge bases)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-04","doi":"https://doi.org/10.1007/s00204-025-03978-5","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400695814","name":"Management of drug supply chain information based on “artificial intelligence + vendor managed inventory” in China: perspective based on a case study","source":"openalex","abstract":"Objectives: To employ a drug supply chain information system to optimize drug management practices, reducing costs and improving efficiency in financial and asset management. Methods: A digital artificial intelligence + vendor managed inventory (AI+VMI)-based system for drug supply chain information management in hospitals has been established. The system enables digitalization and intelligentization of purchasing plans, reconciliations, and consumption settlements while generating purchase, sales, inventory reports as well as various query reports. The indicators for evaluating the effectiveness before and after project implementation encompass drug loss reporting, inventory discrepancies, inter-hospital medication retrieval frequency, drug expenditure, and cloud pharmacy service utilization. Results: The successful implementation of this system has reduced the hospital inventory rate to approximately 20% and decreased the average annual inventory error rate from 0.425‰ to 0.025‰, significantly boosting drug supply chain efficiency by 42.4%. It has also minimized errors in drug application, allocation, and distribution while increasing adverse reaction reports. Drug management across multiple hospital districts has been standardized, leading to improved access to medicines and enhanced patient satisfaction. Conclusion: The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel.","url":"https://doi.org/10.3389/fphar.2024.1373642","authors":["Jianwen Shen","Fengjiao Bu","Zhengqiang Ye","Min Zhang","Qin Ma","Jingchao Yan","Taomin Huang"],"tags":["Vendor","Perspective (graphical)","Vendor-managed inventory","Inventory management","Supply chain"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-16","doi":"https://doi.org/10.3389/fphar.2024.1373642","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414836009","name":"Artificial Intelligence Threatens Critical Thinking in Education Systems","source":"openalex","abstract":"We examine how the increasing use of artificial intelligence (AI) in education—through tools that generate and summarize text, translate languages, and produce visual content—impacts students' critical thinking. While these technologies enhance personalized learning, broaden assessment strategies, and support data-driven policy decisions, we argue that their integration into the learning process carries unintended cognitive consequences. Specifically, we show that when students offload key tasks to AI systems, their cognitive load decreases in ways that weaken memory retention and reduce active engagement with content. This shift fosters a pattern of overreliance, as students increasingly depend on AI to perform intellectual tasks in their place. As a result, their ability to think critically, question information, and evaluate sources diminishes over time. We highlight this emerging dependency as a medium- to long-term threat to critical thinking and call for a more careful evaluation of how generative AI is used in education—not only in terms of its benefits, but also its influence on core cognitive processes. Finally, we propose targeted strategies to mitigate these effects and preserve students' critical capacities in AI-rich learning environments","url":"https://doi.org/10.5961/higheredusci.1747885","authors":["Mahmut Özer","Hande Tanberkan","Matjaž Perc"],"tags":["Critical thinking","Cognition","Unintended consequences","Process (computing)","Generative grammar"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-31","doi":"https://doi.org/10.5961/higheredusci.1747885","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406287889","name":"Exploring Health Sciences Students' Perspectives on Using Generative Artificial Intelligence in Higher Education: A Qualitative Study","source":"openalex","abstract":"The widespread adoption of artificial intelligence (AI) tools in academic settings has the potential to revolutionize learning experiences, enhance educational outcomes, and streamline academic processes. The aim of this research was to explore the perceptions of Lebanese health sciences students regarding the use of generative AI in higher education. A qualitative descriptive research design informed by descriptive phenomenology was employed. Semi-structured interviews were carried out among 23 health sciences students at one major private university in Beirut. Inductive thematic analysis was conducted over the period of 3 months. The inductive thematic analysis generated two themes, and eight subthemes highlighting the benefits and concerns in using AI; customized, self-paced, and autonomous learning, improved language and writing skills, development of innovative concepts, enhanced efficiency, accuracy of information, overreliance on AI, equitable access, unclear policies. Results from this study emphasized the importance of combined efforts across sectors to close access gaps, encourage inclusiveness, and develop well framed policies that enable students to utilize these new technologies for their maximum benefits.","url":"https://doi.org/10.1111/nhs.70030","authors":["Mirna Fawaz","Wassim El Malti","Salman M. Alreshidi","Esin Kavuran"],"tags":["Thematic analysis","Qualitative research","Perception","Psychology","Phenomenology (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-11","doi":"https://doi.org/10.1111/nhs.70030","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399418059","name":"Artificial intelligence hallucinations in anaesthesia: Causes, consequences and countermeasures","source":"openalex","abstract":"Artificial intelligence (AI) hallucinations occur when large language models, such as chatbots or computer vision systems, generate outputs containing non-existent patterns, leading to inaccurate results. Also known as AI confabulations or delusions, these instances challenge expectations of appropriate responses from AI tools due to unrelated or pattern-lacking outputs, similar to human hallucinations. Addressing such issues with generative AI presents significant challenges despite ongoing efforts to resolve them.[1,2] CAUSES OF AI HALLUCINATIONS Various causes of AI hallucinations have been identified and include: Insufficient or biased training data: An AI model designed to assist anaesthesiologists in administering anaesthesia may be trained predominantly on data from patients of a certain demographic, such as adults of average weight. When faced with a paediatric patient or an obese patient, the AI model may possibly hallucinate dosage recommendations that are inaccurate or unsafe, as it lacks sufficient exposure to diverse patient populations.[3] Model complexity: A highly complex AI system tasked with monitoring vital signs during surgery may exhibit hallucinatory responses when encountering unusual physiological patterns. This complexity underscores the need for simpler models to avoid such hallucinations.[4] Lack of explainability (black box): An AI algorithm designed to predict anaesthesia induction times may produce unexpectedly long or short estimates without providing clear explanations for its predictions. In cases where anaesthesiologists cannot understand or verify the AI system’s reasoning, there is a risk of blindly following its recommendations, potentially leading to errors or patient harm. This highlights the urgent need for explainable AI in anaesthesia.[5] MULTIFACETED THREAT OF AI HALLUCINATIONS IN ANAESTHESIA An AI hallucination occurs when an AI system produces demonstrably incorrect or misleading outputs, appearing confident and plausible despite factually flawed. The possible impacts of AI hallucinations on anaesthesia domains are varied[6-9] [Table 1].Table 1: Examples of AI hallucinations’ possible impact on anaesthesia domainsMisdiagnosis and mistreatment: Hallucinations can misinterpret patient data, resulting in unnecessary interventions or delayed treatments. Medication errors: AI-driven systems may recommend incorrect drug dosages, impacting patient safety. Communication and documentation: Misinterpreted verbal commands or procedure details can hinder accurate documentation and patient safety. Research skewing: AI-driven analysis of anaesthesia data for research could be skewed by hallucinations, leading to misleading conclusions. Legal and ethical concerns: Liability: Who is responsible for the errors caused by AI hallucinations? This remains a complex question with no clear answer. Depending on the specific circumstances, potential targets include the AI developer, healthcare provider or hospital. Informed consent: How can patients be adequately informed about the risks of AI hallucinations in anaesthesia, given the technical complexity involved and the dynamic nature of AI outputs? Striking a balance between transparency and patient anxiety is crucial. Bias: AI algorithms can perpetuate societal biases, leading to discriminatory outcomes in health care. Imagine an AI system trained on biased data; it might recommend different treatments based on a patient’s race or socioeconomic background.[10-12] STRATEGIES TO MITIGATE AI HALLUCINATIONS Various mitigation strategies need to be adhered to for the impact of AI hallucination on health care [Figure 1].Figure 1: Impact of AI hallucination on health care and mitigation strategies. AI = artificial intelligenceHigh-quality, diverse training data: Utilising diverse datasets improves AI model accuracy and reduces hallucination risks. For example, research by Jones et al.[13] demonstrated how incorporating various demographic factors and medical hi","url":"https://doi.org/10.4103/ija.ija_203_24","authors":["Prakash Gondode","Sakshi Duggal","Vaishali Mahor"],"tags":["Medicine","Anesthesia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-06","doi":"https://doi.org/10.4103/ija.ija_203_24","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406152263","name":"The TRIPOD-LLM reporting guideline for studies using large language models","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41591-024-03425-5","authors":["Jack Gallifant","Majid Afshar","Saleem Ameen","Yindalon Aphinyanaphongs","Shan Chen","Giovanni Cacciamani","Dina Demner‐Fushman","Dmitriy Dligach","Roxana Daneshjou","Chrystinne Oliveira Fernandes","Lasse Hyldig Hansen","Adam Landman","Lisa Soleymani Lehmann","Liam G. McCoy","Timothy A. Miller","Amy C. Moreno","Nikolaj Munch","David Restrepo","Guergana Savova","Renato Umeton","Judy Wawira Gichoya","Professor Gary S. Collins","Karel G.M. Moons","Leo Anthony Celi","Danielle S. Bitterman"],"tags":["Tripod (photography)","Checklist","Guideline","Standardization","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1038/s41591-024-03425-5","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404852611","name":"Bias in artificial intelligence: smart solutions for detection, mitigation, and ethical strategies in real-world applications","source":"openalex","abstract":"Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.","url":"https://doi.org/10.11591/ijai.v14.i1.pp32-43","authors":["Agariadne Dwinggo Samala","Soha Rawas"],"tags":["Transparency (behavior)","Computer science","Accountability","Equity (law)","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-29","doi":"https://doi.org/10.11591/ijai.v14.i1.pp32-43","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403238905","name":"Perceptions and attitudes toward artificial intelligence among frontline physicians and physicians’ assistants in Kansas: a cross-sectional survey","source":"openalex","abstract":"Abstract Objective This survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions. Materials and Methods We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results. Results From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups. Discussion The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity. Conclusion The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.","url":"https://doi.org/10.1093/jamiaopen/ooae100","authors":["Tanner Dean","Rajeev Seecheran","Robert G. Badgett","Rosey Zackula","John Symons"],"tags":["Cross-sectional study","Perception","Family medicine","Medical education","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-07","doi":"https://doi.org/10.1093/jamiaopen/ooae100","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402581887","name":"Transparent RFID tag wall enabled by artificial intelligence for assisted living","source":"openalex","abstract":"Abstract Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces theTransparentRFIDTag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. TheTRT-Wallis tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that theTRT-Walldistinguishes these activities with an impressive average accuracy of $$95.6\\%$$ 95.6% under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.","url":"https://doi.org/10.1038/s41598-024-64411-y","authors":["Muhammad Zakir Khan","Muhammad Usman","Ahsen Tahir","Muhammad Farooq","Adnan Qayyum","Jawad Ahmad","Hasan Abbas","Muhammad Ali Imran","Qammer H. Abbasi"],"tags":["Assisted living","Computer science","Assisted Living Facility","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-16","doi":"https://doi.org/10.1038/s41598-024-64411-y","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414349719","name":"Using Artificial Intelligence to Develop Clinical Decision Support Systems—The Evolving Road of Personalized Oncologic Therapy","source":"openalex","abstract":"Background/Objectives: The use of artificial intelligence (AI) in oncology has the potential to improve decision making, particularly in managing the risk associated with targeted therapies. This study aimed to develop and validate a machine learning-based clinical decision support system (CDSS) capable of predicting complications associated with Bevacizumab or its biosimilars and to translate the resulting predictive model into a clinically applicable tool. Methods: A prospective observational study was conducted on 395 records from patients treated with Bevacizumab or biosimilars for solid tumors. Pretherapeutic variables, such as demographic data, medical history, tumor characteristics and laboratory findings, were retrieved from medical records. Several machine learning models (logistic regression, Random Forest, XGBoost) were trained using 70/30 and 80/20 data splits. Their predictive performances were compared using accuracy, AUC-ROC, sensitivity, specificity, F1-scores and error rate. The best-performing model was used to derive a logistic-based risk score, which was further implemented as an interactive HTML form. Results: The optimized Random Forest model trained on the 80/20 split demonstrated the best balance between accuracy (70.63%), sensitivity (66.67%), specificity (73.85%), and AUC-ROC (0.75). The derived logistic risk score showed good performance (AUC-ROC = 0.720) and calibration. It identified variables, such as age ≥ 65, anemia, elevated urea, leukocytosis, tumor differentiation, and stage, as significant predictors of complications. The final tool provides clinicians with an easy-to-use, offline form that estimates individual risk levels and stratifies patients into low-, intermediate-, or high-risk categories. Conclusions: This study offers a proof of concept for developing AI-supported predictive tools in oncology using real-world data. The resulting logistic risk score and interactive form can assist clinicians in tailoring therapeutic decisions for patients receiving targeted therapies, enhancing the personalization of care without replacing clinical judgment.","url":"https://doi.org/10.3390/diagnostics15182391","authors":["Elena Chitoran","Vlad Rotaru","Aisa Gelal","S Ionescu","Giuseppe Gullo","Daniela Cristina Stefan","Laurenţiu Simion"],"tags":["Random forest","Medicine","Machine learning","Observational study","Bevacizumab"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-19","doi":"https://doi.org/10.3390/diagnostics15182391","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410035392","name":"Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis","source":"openalex","abstract":"Geographic atrophy (GA) is a progressive retinal disease associated with late-stage age-related macular degeneration (AMD), a significant cause of visual impairment in senior adults. GA lesion segmentation is important for disease monitoring in clinical trials and routine ophthalmic practice; however, its manual delineation is time-consuming, laborious, and subject to inter-grader variability. The use of artificial intelligence (AI) is rapidly expanding within the medical field and could potentially improve accuracy while reducing the workload by facilitating this task. This systematic review evaluates the performance of AI algorithms for GA segmentation and highlights their key limitations from the literature. Five databases and two registries were searched from inception until 23 March 2024, following the PRISMA methodology. Twenty-four studies met the prespecified eligibility criteria, and fifteen were included in this meta-analysis. The pooled Dice similarity coefficient (DSC) was 0.91 (95% CI 0.88-0.95), signifying a high agreement between the reference standards and model predictions. The risk of bias and reporting quality were assessed using QUADAS-2 and CLAIM tools. This review provides a comprehensive evaluation of AI applications for GA segmentation and identifies areas for improvement. The findings support the potential of AI to enhance clinical workflows and highlight pathways for improved future models that could bridge the gap between research settings and real-world clinical practice.","url":"https://doi.org/10.3390/bioengineering12050475","authors":["Aikaterini Chatzara","Eirini Maliagkani","Dimitra Mitsopoulou","Andreas Katsimpris","Ioannis D. Apostolopoulos","Elpiniki I. Papageorgiou","Ilias Georgalas"],"tags":["Segmentation","Systematic review","Meta-analysis","Artificial intelligence","Workload"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.3390/bioengineering12050475","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412831613","name":"Liability Risks of Ambient Clinical Workflows With Artificial Intelligence for Clinicians, Hospitals, and Manufacturers","source":"openalex","abstract":"In August 2024, the nation's largest nonprofit integrated health care provider, Kaiser Permanente, announced that clinicians would have access to an ambient clinical documentation scribe: an assisted clinical documentation tool that uses artificial intelligence (AI) to securely summarize relevant medical information from spoken, natural conversations (also called ambient clinical documentation or AI scribes). After automatically summarizing the encounter, the AI scribe sends the summary to the clinician for review. Ambient clinical documentation scribes are now offered by some of the fastest-growing AI companies in health care, with significant venture capital funding and an impressive roster of health system customers. Technologies such as ambient clinical documentation and other generative AI tools may improve care and lessen clinician burnout by reducing documentation burdens. But they also raise the question of who is responsible when AI-generated patient information is inaccurate, especially when those errors cause injury to a patient. This question is particularly acute in cancer care, where there is a unique set of terminology for each of the more than 400 types of cancer, leading to an increased chance of documentation error, and where decisions on the basis of the assumption of information accuracy can be life-altering. AI transcription tools in their current versions are not considered regulated medical devices under the US Federal Food, Drug, and Cosmetic Act. Unless this changes, the responsibility falls to stakeholders other than the US Food and Drug Administration (FDA) to ensure the technology's safety and efficacy. In this article, we analyze the AI governance responsibilities and potential tort liability for clinicians, hospitals, and manufacturers using AI for clinical note-taking and suggest several potential ways to address them.","url":"https://doi.org/10.1200/op-24-01060","authors":["Sara Gerke","David Simon","Benjamin R. Roman"],"tags":["Workflow","Liability","Business","Risk analysis (engineering)","Finance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-01","doi":"https://doi.org/10.1200/op-24-01060","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4396808541","name":"An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s12105-024-01643-4","authors":["Shahd Alajaji","Zaid H. Khoury","Maryam Jessri","James J. Sciubba","Ahmed S. Sultan"],"tags":["Oral and maxillofacial surgery","Otorhinolaryngology","Pathology","Digital pathology","Oral and maxillofacial pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-10","doi":"https://doi.org/10.1007/s12105-024-01643-4","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4396930068","name":"Artificial Intelligence","source":"openalex","abstract":"This book offers a deep understanding of the basic principles, opportunities and risks of Artificial Intelligence seen as technology of the future.","url":"https://doi.org/10.1007/978-3-031-50605-5","authors":["Gerhard Paaß","Dirk Hecker"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1007/978-3-031-50605-5","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406363268","name":"Survey of Artificial Intelligence Model Marketplace","source":"openalex","abstract":"The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption.","url":"https://doi.org/10.3390/fi17010035","authors":["Mian Qian","Abubakar Ahmad Musa","Milon Biswas","Yifan Guo","Weixian Liao","Wei Yu"],"tags":["Computer science","Interoperability","Transparency (behavior)","Standardization","Software deployment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.3390/fi17010035","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401323124","name":"Artificial Intelligence Classification for Detecting and Grading Lumbar Intervertebral Disc Degeneration","source":"openalex","abstract":"Introduction: Intervertebral disc degeneration (IDD) is a primary cause of chronic back pain and disability, highlighting the need for precise detection and grading for effective treatment. This study focuses on developing and validating a convolutional neural network (CNN) with a You Only Look Once (YOLO) architecture model using the Pfirrmann grading system to classify and grade lumbar intervertebral disc degeneration based on magnetic resonance imaging (MRI) scans. Methods: We developed a deep learning model trained on a dataset of anonymized MRI studies of patients with symptomatic back pain. MRI images were segmented and annotated by radiologists according to the Pfirrmann grading for the datasets. The segmentation MRI-disc image dataset was prepared for three groups: a training set (1,000), a testing set (500), and an external validation set (500) to assess model generalizability without overlapping images. The model's performance was evaluated using accuracy, sensitivity, specificity, F1 score, prediction error, and ROC-AUC. Results: The AI model showed high performance across all metrics. For Grade I IDD, the model achieved an accuracy of 97%, 95%, and 92% in the training, testing, and external validation sets, respectively. For Grade II, the sensitivity was 100% in both training and testing sets and 98% in the validation set. For Grade III, the specificity was 95.4% in the training set and 94% in both testing and validation sets. For Grade IV, the F1 score was 97.77% in the training set and 95% in both testing and validation sets. For Grade V, the prediction error was 2.3%, 2%, and 2.5% in the training, testing, and validation sets, respectively. The overall ROC-AUC was 97%, 92%, and 95% in the training, testing, and validation sets, respectively. Conclusions: The AI-based classification model exhibits high accuracy, sensitivity, and specificity in detecting and grading lumbar IDD using the Pfirrmann grading. AI has significantly enhanced diagnostic precision and reliability, providing a powerful tool for clinicians in managing IDD. The potential impact is substantial, although further clinical validation is necessary before integrating this model into routine practice.","url":"https://doi.org/10.22603/ssrr.2024-0154","authors":["Wongthawat Liawrungrueang","Watcharaporn Cholamjiak","Peem Sarasombath","Khanathip Jitpakdee","Vit Kotheeranurak"],"tags":["Grading (engineering)","Lumbar","Degeneration (medical)","Artificial intelligence","Intervertebral disc"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-05","doi":"https://doi.org/10.22603/ssrr.2024-0154","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4396613362","name":"Artificial Intelligence-Assisted Automated Heart Failure Detection and Classification from Electronic Health Records","source":"openalex","abstract":"AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. METHODS AND RESULTS: We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. CONCLUSIONS: Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.","url":"https://doi.org/10.1002/ehf2.14828","authors":["Mon Myat Oo","Chuang Gao","Christian Cole","Yoran Hummel","Magalie Guignard‐Duff","Emily Jefferson","James Hare","Adriaan A. Voors","Rudolf A. de Boer","Carolyn S.P. Lam","Ify Mordi","Jasper Tromp","Chim C. Lang"],"tags":["Heart failure","Health records","Medicine","Artificial intelligence","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-03","doi":"https://doi.org/10.1002/ehf2.14828","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4388002171","name":"The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool","source":"openalex","abstract":"Using artificial intelligence (AI) in research offers many important benefits for science and society but also creates novel and complex ethical issues. While these ethical issues do not necessitate changing established ethical norms of science, they require the scientific community to develop new guidance for the appropriate use of AI. In this article, we briefly introduce AI and explain how it can be used in research, examine some of the ethical issues raised when using it, and offer ninerecommendations for responsible use, including: (1) Researchers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; (2) Researchers should disclose, describe, and explain their use of AI in research, including its limitations, in language that can be understood by non-experts; (3) Researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns, such as issues related to bias; (4) Researchers who use synthetic data should (a) indicate which parts of the data are synthetic; (b) clearly label the synthetic data; (c) describe how the data were generated; and (d) explain how and why the data were used; (5) AI systems should not be named as authors, inventors, or copyright holders but their contributions to research should be disclosed and described; (6) Education and mentoring in responsible conduct of research should include discussion of ethical use of AI.","url":"https://doi.org/10.31235/osf.io/rbg9z","authors":["David B. Resnik","Mohammad Hosseini"],"tags":["Commit","Intellectual property","Confidentiality","Engineering ethics","Research ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-27","doi":"https://doi.org/10.31235/osf.io/rbg9z","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4382173801","name":"Artificial intelligence in neuroradiology: a scoping review of some ethical challenges","source":"openalex","abstract":"Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future.","url":"https://doi.org/10.3389/fradi.2023.1149461","authors":["Pegah Khosravi","Mark E. Schweitzer"],"tags":["Scope (computer science)","Engineering ethics","Data science","Neuroradiology","Neuroethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-15","doi":"https://doi.org/10.3389/fradi.2023.1149461","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4396808870","name":"Artificial intelligence challenges in the face of biological threats: emerging catastrophic risks for public health","source":"openalex","abstract":"The threat landscape of biological hazards with the evolution of AI presents challenges. While AI promises innovative solutions, concerns arise about its misuse in the creation of biological weapons. The convergence of AI and genetic editing raises questions about biosecurity, potentially accelerating the development of dangerous pathogens. The mapping conducted highlights the critical intersection between AI and biological threats, underscoring emerging risks in the criminal manipulation of pathogens. Technological advancement in biology requires preventative and regulatory measures. Expert recommendations emphasize the need for solid regulations and responsibility of creators, demanding a proactive, ethical approach and governance to ensure global safety.","url":"https://doi.org/10.3389/frai.2024.1382356","authors":["Renan Chaves de Lima","Lucas Sinclair","Ricardo Megger","Magno Alessandro Guedes Maciel","Pedro Fernando da Costa Vasconcelos","Juarez Antônio Simões Quaresma"],"tags":["Biosecurity","Biological warfare","Risk analysis (engineering)","Face (sociological concept)","Biological hazard"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-10","doi":"https://doi.org/10.3389/frai.2024.1382356","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405391799","name":"Harnessing the Potential of Artificial Intelligence in Yoga Therapy","source":"openalex","abstract":"Integrating artificial intelligence (AI) into yoga therapy represents a transformative paradigm shift in holistic health management. This article explores the evolving landscape of AI in yoga therapy, encompassing recent advancements, potential applications, ethical considerations, and implications for well-being. Recent advancements in AI have enabled real-time monitoring and personalized interventions during yoga practice, offering unprecedented customization and efficacy. AI-powered virtual assistants and telehealth platforms extend the reach of yoga therapy interventions, enhancing accessibility and inclusivity. However, ethical considerations surrounding privacy, autonomy, equity, transparency, and cultural sensitivity must be carefully addressed to ensure responsible deployment and safeguard the well-being of individuals. By prioritizing ethical principles and values, stakeholders can harness AI's transformative potential to advance the yoga therapy field and promote holistic well-being for individuals and communities worldwide.","url":"https://doi.org/10.4103/ijoy.ijoy_124_24","authors":["Nitu Sinha","Rajesh Kumar Sinha"],"tags":["Transformative learning","Psychological intervention","Autonomy","Telehealth","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.4103/ijoy.ijoy_124_24","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402186862","name":"The Past, Present, and Future Use of Artificial Intelligence in Teacher Education","source":"openalex","abstract":"The use of artificial intelligence (AI) is not a new concept. Still, the press, the worry, and the hype around the potential benefits and limitations of the explosion of these tools in this field is a current topic in teacher education. In this article, the authors summarize the past use of AI, present easily adaptable tools in teacher education, and discuss what is on the horizon in industry and special education teacher education. The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.","url":"https://doi.org/10.33043/8aa9855b","authors":["Maggie Mosher","Lisa Dieker","Rebecca Hines"],"tags":["Psychology","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-02","doi":"https://doi.org/10.33043/8aa9855b","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410618617","name":"How artificial intelligence is transforming pathology","source":"openalex","abstract":"","url":"https://doi.org/10.1038/d41586-025-01576-0","authors":["Diana Kwon"],"tags":["Computer science","Artificial intelligence","Cognitive science","Biology","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.1038/d41586-025-01576-0","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410877449","name":"Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis","source":"openalex","abstract":"Background Advances in artificial intelligence (AI) have triggered interest in using intelligent systems to improve prenatal detection of fetal congenital heart defects (CHDs). Our aim is to systematically examine the current literature on diagnostic performance of AI-enabled prenatal cardiac ultrasound. Methods This systematic review and meta-analysis was registered with PROSPERO (CRD42024549601). Embase, Medline, Cochrane Central Database of Controlled Trials, and CINAHL were searched from inception until February 2025. Studies evaluating AI performance in prenatal detection of fetal CHDs were eligible for inclusion, and studies focusing on the application of AI before 16 weeks of gestation, or using three- or four-dimensional ultrasound, were excluded. Pooled sensitivity and specificity were obtained using random-effect method, and pooled proportions using the Freeman-Tukey arcsine square root transformation. Heterogeneity was assessed with I 2 statistics. Risk of bias and adherence to reporting standards were assessed using QUADAS-2 and TRIPOD+AI, respectively. Risk of publication bias was assessed with Deek's test and certainty of evidence for outcomes with GRADE approach. Findings Fifteen studies were included, of which fourteen developed and evaluated a model and one externally evaluated a previously trained model. Images and videos obtained during cardiac screening or fetal echocardiography of 30.121 fetuses were used for training, validation and testing. For the binary task of classifying heart as normal or abnormal, AI models achieved a pooled sensitivity of 0.89 (95% CI 0.83–0.93, I 2 = 77.92%) and specificity of 0.91 (95% CI 0.84–0.95, I 2 = 77.92%). The subgroup analysis showed that models tested on various CHDs exhibited lower sensitivity compared to those tested for a specific cardiac abnormality (0.85; 95% CI 0.75–0.91 vs 0.92; 95% CI 0.87–0.96), while specificity remained comparable (0.90; 95% CI 0.79–0.96 vs 0.91; 95% CI 0.81–0.97). Overall, AI models performed better than operators with lower expertise and were nearly comparable to experts; however, the human comparator group (median six clinicians, IQR 3–10) was usually small and non-blinded. Relevant sources of heterogeneity were the types of cardiac views collected, the prevalence of CHDs across different datasets, and the types of CHDs examined. The risk of bias was moderate-high and adherence to reporting standards low (>70% in 18/51 TRIPOD+AI items). The risk of publication bias was not statistically significant (Deek's test p=0.474). Interpretation These findings suggest that AI models perform better than clinicians with lower expertise, but this must be interpreted with caution due to the high risk of bias and sources of heterogeneity. Funding This study was partly supported by the InnoHK-funded Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE) Project 2.1 (Cardiovascular risks in early life and fetal echocardiography). ATP and JAN are supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC).","url":"https://doi.org/10.1016/j.eclinm.2025.103250","authors":["Elena D’Alberti","Olga Patey","Carolyn Smith","Bojana Šalović","Netzahualcoyotl Hernandez-Cruz","J. Alison Noble","Aris T. Papageorghiou"],"tags":["Medicine","Meta-analysis","Prenatal ultrasound","Prenatal diagnosis","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-30","doi":"https://doi.org/10.1016/j.eclinm.2025.103250","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405261396","name":"Artificial Intelligence in Education, Bridging Community Gap: A Phenomenological Approach","source":"openalex","abstract":"Integrating Artificial Intelligence (AI) in education holds transformative potential for bridging community gaps, particularly in under-resourced and marginalized communities. This study explores the multifaceted ways AI technologies can enhance educational accessibility, quality, and equity, thereby fostering inclusive community development. The educational disparity between under-resourced and resourced communities in Nigeria is a pressing issue, primarily driven by unequal funding, insecurity, corruption, resource allocation, and teacher shortages. This gap affects academic performance and limits future opportunities for learners in the under-sourced communities. The study delves into AI-driven initiatives to reduce the digital divide, such as deploying AI-powered educational tools in underserved communities with limited access to quality education, which is imminent. By leveraging AI, this research underscores the potential to democratize education, offering tailored learning experiences that can adapt to students' diverse needs across different geographical locations in Nigeria. The study's core objective is to bridge the community gap via AI in education using a phenomenological approach. The qualitative study adopted a phenomenological approach. The population comprised all secondary school teachers in Nigeria. Fifteen public school teachers from under-resourced communities constituted the study's sample and drew purposively based on availability. The qualitative data were thematically evaluated, and three themes (i.e., learning assistance, quality education, and infrastructural deficiency) emerged from the research. This study's findings indicate that AI can provide learning assistance and improve quality education. While AI may potentially enhance learning experiences, stakeholders must quickly address the concerns about infrastructural deficiency, insecurity, corruption, and the impediment of social interaction in education. The study concluded that incorporating AI-based technology into under-resourced communities will bridge the community gap and enable all learners to compete favourably, regardless of where they reside.","url":"https://doi.org/10.24310/ijne.14.2024.20505","authors":["Oluwaseyi Aina Gbolade Opesemowo"],"tags":["Transformative learning","Equity (law)","Public relations","Sociology","Economic growth"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-09","doi":"https://doi.org/10.24310/ijne.14.2024.20505","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406958079","name":"Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine","source":"openalex","abstract":"In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.","url":"https://doi.org/10.1093/bjr/tqaf018","authors":["Amanda Isaac","Michail E. Klontzas","Danoob Dalili","Aslı Irmak Akdoğan","Mohamed Fawzi","Giuseppe Gugliemi","Dimitrios Filippiadis"],"tags":["Medicine","Biopsy","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-29","doi":"https://doi.org/10.1093/bjr/tqaf018","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4398195190","name":"A Nordic survey on artificial intelligence in the radiography profession – Is the profession ready for a culture change?","source":"openalex","abstract":"INTRODUCTION: The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI. METHODS: Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI. RESULTS: Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography. CONCLUSION: Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession. IMPLICATIONS FOR PRACTICE: Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.","url":"https://doi.org/10.1016/j.radi.2024.04.020","authors":["Malene Roland Vils Pedersen","Martin Weber Kusk","Simon Lysdahlgaard","H. Mork-Knudsen","Christina Malamateniou","Janni Jensen"],"tags":["Medical profession","Medicine","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-22","doi":"https://doi.org/10.1016/j.radi.2024.04.020","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409849237","name":"NAVIGATING ETHICS AND RISK IN ARTIFICIAL INTELLIGENCE APPLICATIONS WITHIN INFORMATION TECHNOLOGY: A SYSTEMATIC REVIEW","source":"openalex","abstract":"Advancements in artificial intelligence (AI) have profoundly transformed a wide array of sectors, with Information Technology (IT) standing at the forefront of this revolution. AI technologies have reshaped IT operations by introducing new levels of automation, predictive capabilities, decision-making precision, and efficiency, resulting in sweeping changes across organizational infrastructures and service delivery models. However, alongside these technological breakthroughs, the integration of AI has surfaced numerous ethical concerns and associated risks that warrant critical and comprehensive exploration. Issues such as algorithmic bias, privacy breaches, lack of transparency, accountability dilemmas, and cybersecurity vulnerabilities remain pervasive, posing significant challenges for organizations seeking to deploy AI responsibly. Recognizing these complexities, this study focuses on examining the ethical challenges, risk factors, and essential considerations surrounding the deployment of AI within IT environments. This research addresses the urgent and growing necessity for organizations to adopt robust ethical frameworks and effective risk management strategies to ensure that AI integration promotes fairness, transparency, and accountability. A comprehensive synthesis of existing literature further enriches this foundation by offering diverse perspectives on the ethical and risk-related challenges posed by AI, highlighting both the transformative potential and the vulnerabilities associated with its integration into IT systems. Through this review, key gaps in the current body of knowledge are identified, particularly regarding the practical implementation of ethical standards and risk mitigation strategies across varied organizational contexts. Adopting a qualitative research methodology, the study employs a case study approach to explore the intricate, multifaceted issues involved in AI integration within IT operations. This methodological choice allows for a nuanced understanding of real-world scenarios, organizational behaviors, and stakeholder dynamics related to AI deployment. Data collection is meticulously based on trustworthy sources, including peer-reviewed academic journals, authoritative industry reports, regulatory and governmental publications, and credible news articles, ensuring the comprehensiveness and reliability of insights gathered. By triangulating data across multiple domains, the study captures a holistic view of the ethical landscape surrounding AI in IT. Finally, the research culminates with an in-depth interpretation of findings, accompanied by practical recommendations and implications. These outcomes aim to contribute to the development of ethical, sustainable, and responsible AI integration practices within the IT industry, supporting organizations in navigating the complex interplay between technological innovation and ethical accountability.","url":"https://doi.org/10.63125/590d7098","authors":["Ishtiaque Ahmed"],"tags":["Systematic review","Engineering ethics","Psychology","Engineering","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-03","doi":"https://doi.org/10.63125/590d7098","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412454706","name":"Decoding Trust in Artificial Intelligence: A Systematic Review of Quantitative Measures and Related Variables","source":"openalex","abstract":"As artificial intelligence (AI) becomes ubiquitous across various fields, understanding people’s acceptance and trust in AI systems becomes essential. This review aims to identify quantitative measures used to measure trust in AI and the associated studied elements. Following the PRISMA guidelines, three databases were consulted, selecting articles published before December 2023. Ultimately, 45 articles out of 1283 were selected. Articles were included if they were peer-reviewed journal publications in English reporting empirical studies measuring trust in AI systems with multi-item questionnaires. Studies were analyzed through the lenses of cognitive and affective trust. We investigated trust definitions, questionnaires employed, types of AI systems, and trust-related constructs. Results reveal diverse trust conceptualizations and measurements. In addition, the studies covered a wide range of AI system types, including virtual assistants, content detection tools, chatbots, medical AI, robots, and educational AI. Overall, the studies show compatibility of cognitive or affective trust focus between theorization, items, experimental stimuli, and level of anthropomorphism of the systems. The review underlines the need to adapt measurement of trust in the specific characteristics of human–AI interaction, accounting for both the cognitive and affective sides. Trust definitions and measurement could be chosen depending also on the level of anthropomorphism of the systems and the context of application.","url":"https://doi.org/10.3390/informatics12030070","authors":["Letizia Aquilino","Cinzia Di Dio","Federico Manzi","Davide Massaro","Piercosma Bisconti","Antonella Marchetti"],"tags":["Decoding methods","Computer science","Psychology","Artificial intelligence","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-14","doi":"https://doi.org/10.3390/informatics12030070","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W3158142766","name":"Artificial intelligence in brachytherapy: a summary of recent developments","source":"openalex","abstract":"Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.","url":"https://doi.org/10.1259/bjr.20200842","authors":["Susovan Banerjee","Shikha Goyal","Saumyaranjan Mishra","Deepak Gupta","Shyam Singh Bisht","Kaliyaperumal Venketesan","Kushal Narang","Tejinder Kataria"],"tags":["Brachytherapy","Medical physics","Enthusiasm","Computer science","Radiation oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-29","doi":"https://doi.org/10.1259/bjr.20200842","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402343442","name":"Artificial intelligence in dentistry. Part one.","source":"openalex","abstract":"Summary. In recent years, dentistry has undergone a wave of transformations and technological advances, where artificial intelligence has become a driving force that is ready to revolutionize various aspects of oral care. As the world grapples with ever-increasing demand for efficient and personalized medical solutions, artificial intelligence presents itself as a promising ally in the quest to improve diagnostic accuracy, treatment planning and patient care in healthcare system, particularly in dentistry. Purpose: to systematize and review the available data of research information for the period of 2020-2024 regarding the application of artificial intelligence technologies and their use in dental practice for various areas of dental activity. Material and methods. A literature search was conducted on the PubMed service on February 26, 2024. The initial search was carried out using MeSH algorithms: (((«artificial intelligence»[MeSH Terms]) OR («artificial intelligence»[All Fields])) OR («ai»[All Fields])) AND («dentistry»[MeSH Terms]). Publications for the period 2020-2024 were considered. The initial search of the literature included 46 publications. After a detailed analysis of the selected publications, 26 publications, that met the needs, were left for further processing. Also added 6 publications manually, from other services, which disclosed the given topic. Accordingly, the total number of publications used for the analysis was 32 articles. Research results. This research focuses on the growing intersection of artificial intelligence and dentistry with the goal of providing a comprehensive and reasonably up-to-date overview of the various applications, challenges, and opportunities that arise at the intersection of these fields. Through the selected articles, this study aims to explore the impact of artificial intelligence on diagnostic procedures, treatment methods and the overall principle of healthcare delivery using revolutionary technologies. Conclusion. Artificial intelligence is increasingly included in the daily work of dentists, which requires a wider acquaintance with its capabilities in various fields of dentistry. Key words: Artificial intelligence, dentistry, periodontology, endodontics.","url":"https://doi.org/10.33295/1992-576x-2024-3-95","authors":["V. Makeev","P. Shcherba"],"tags":["Dentistry","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.33295/1992-576x-2024-3-95","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404414807","name":"Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process","source":"openalex","abstract":"Objectives: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation. Materials and Methods: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle. Results Discussion and Conclusion: Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.","url":"https://doi.org/10.1093/jamiaopen/ooae108","authors":["Benjamin Collins","Jean‐Christophe Bélisle‐Pipon","Barbara J. Evans","Kadija Ferryman","Xiaoqian Jiang","Camille Nebeker","Laurie L. Novak","Kirk Roberts","Martin C. Were","Zhijun Yin","Vardit Ravitsky","Joseph Coco","Rachele Hendricks‐Sturrup","Ishan C. Williams","Ellen Wright Clayton","Bradley Malin","Bridge2AI Ethics and Trustworthy AI Working Group"],"tags":["Deliberation","System lifecycle","Context (archaeology)","Process (computing)","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-08","doi":"https://doi.org/10.1093/jamiaopen/ooae108","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412645877","name":"The application of artificial intelligence in forensic pathology: a systematic literature review","source":"openalex","abstract":"Introduction: Recent advancements in Artificial Intelligence have shown immense potential across various domains of healthcare, including forensic pathology. This systematic review aims to evaluate the latest innovations brought by Artificial Intelligence in forensic pathology and provide insights into future directions in this evolving field. Methods: A systematic literature search was conducted using databases for papers published from 1990 to 2025. The search strategy combined terms related to artificial intelligence, forensic odontology, forensic psychiatry and forensic medicine/pathology. Following PRISMA guidelines, 65 articles were initially identified, of which 18 met the inclusion criteria after applying exclusion criteria. Results: Artificial Intelligence applications demonstrated significant success across multiple forensic domains. In post-mortem analysis, deep learning achieved 70-94% accuracy in neurological forensics. Wound analysis systems showed high accuracy rates (87.99-98%) in gunshot wound classification. Artificial Intelligence-enhanced diatom testing for drowning cases achieved precision scores of 0.9 and recall scores of 0.95. Microbiome analysis applications reached accuracy rates up to 90% for individual identification and geographical origin determination. AI shows promise in forensic age estimation, psychiatric risk assessment, and insanity evaluations. Discussion: While Artificial Intelligence shows promise as a supportive tool in forensic pathology, several limitations persist, including small sample sizes and variable performance across different applications. Artificial Intelligence serves best as an enhancement rather than a replacement for human expertise. Future development should focus on larger datasets, specialized systems for different forensic applications, and improved interpretability of Artificial Intelligence decisions for legal contexts. The integration of Artificial Intelligence in forensic pathology represents a significant advancement, requiring careful balance between technological innovation and human expertise for optimal implementation.","url":"https://doi.org/10.3389/fmed.2025.1583743","authors":["Francesco Orsini","Andrea Cioffi","Luigi Cipolloni","Roberta Bibbò","Angelo Montana","Stefania De Simone","Camilla Cecannecchia"],"tags":["Forensic science","Interpretability","Artificial intelligence","Computer science","Identification (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-24","doi":"https://doi.org/10.3389/fmed.2025.1583743","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4390937923","name":"Role of artificial intelligence in perioperative monitoring in anaesthesia","source":"openalex","abstract":"Artificial intelligence (AI) is making giant strides in the medical domain, and the field of anaesthesia is not untouched. Enhancement in technology, especially AI, in many fields, including medicine, has proven to be far superior, safer and less erratic than human decision-making. The intersection of anaesthesia and AI holds the potential for augmenting constructive advances in anaesthesia care. AI can improve anaesthesiologists' efficiency, reduce costs and improve patient outcomes. Anaesthesiologists are well placed to harness the advantages of AI in various areas like perioperative monitoring, anaesthesia care, drug delivery, post-anaesthesia care unit, pain management and intensive care unit. Perioperative monitoring of the depth of anaesthesia, clinical decision support systems and closed-loop anaesthesia delivery aid in efficient and safer anaesthesia delivery. The effect of various AI interventions in clinical practice will need further research and validation, as well as the ethical implications of privacy and data handling. This paper aims to provide an overview of AI in perioperative monitoring in anaesthesia.","url":"https://doi.org/10.4103/ija.ija_1198_23","authors":["Shaloo Garg","MukulChandra Kapoor"],"tags":["Perioperative","SAFER","Medicine","Anesthesia","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.4103/ija.ija_1198_23","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402587387","name":"The synergistic effect of artificial intelligence technology in the evolution of visual communication of new media art","source":"openalex","abstract":"This study aims to clarify the synergistic effect of artificial intelligence (AI) technology in the evolution of visual communication of new media art, thereby exploring an AI layout design method based on Convolutional Neural Network (CNN) in the practice of visual communication design. Firstly, this study designs an AI layout design model based on CNN, and trains and optimizes it with training data. Secondly, the automatic generation of layout design is realized by constantly adjusting the model parameters and network structure. Finally, various AI layout design algorithms are compared, and their effects and performances in layout design generation are analyzed. To verify the layout and composition matching model's performance, traditional layout design methods are selected for comparison (layout, comparison, harmonic composition, etc.). This study involved 20 design students as participants, evaluating them across three dimensions: overall comprehensive assessment, readability of text information, and rationality of visual path using a Likert 7-point scale. The results reveal that the proposed method's evaluation outcomes in these three aspects are 5.95, 5.68, and 5.74, respectively, higher than the traditional layout design methods. To sum up, the generative AI discussed here can automatically generate design elements and schemes through deep learning and big data analysis, thus providing a reference for the innovation of visual communication design.","url":"https://doi.org/10.1016/j.heliyon.2024.e38008","authors":["Yan Zhao"],"tags":["Media arts","Visual communication","Artificial intelligence","Visual media","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.1016/j.heliyon.2024.e38008","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408522144","name":"Understanding artificial intelligence through the eyes of future nurses","source":"openalex","abstract":"OBJECTIVES: To explore nursing students' perceptions and understanding of artificial intelligence (AI), aiming to identify and address critical knowledge gaps to support effective integration into educational practices. METHODS: An exploratory qualitative study was carried out using semi-structured interviews with 20 nursing students from King Saud University, Riyadh, Saudi Arabia, in October 2023. Data collection focused on their definitions, conceptualizations, and perspectives regarding AI in healthcare. RESULTS: A total of 3 key themes emerged: I)transformation, where AI represents a shift in nursing education from traditional methods to technological integration; II) power, viewing AI as a driver of knowledge creation and scientific advancement; and III) use of technology, focusing on AI applications to enhance efficiency, automate tasks, and augment human abilities across sectors. CONCLUSION: The study highlights the need to integrate AI-related content into nursing curriculum, preparing students for its application in healthcare. These insights emphasize AI's role in shaping the future of nursing education and practice.","url":"https://doi.org/10.15537/smj.2025.46.3.20241069","authors":["Latifah Alenazi","Saad H Al-Anazi"],"tags":["Medicine","Optometry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.15537/smj.2025.46.3.20241069","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404138521","name":"Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study","source":"openalex","abstract":"BACKGROUND: Electronic health records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives can overwhelm health care providers, increasing the risk of diagnostic inaccuracies. While large language models (LLMs) have showcased their potential in diverse language tasks, their application in health care must prioritize the minimization of diagnostic errors and the prevention of patient harm. Integrating knowledge graphs (KGs) into LLMs offers a promising approach because structured knowledge from KGs could enhance LLMs' diagnostic reasoning by providing contextually relevant medical information. OBJECTIVE: This study introduces DR.KNOWS (Diagnostic Reasoning Knowledge Graph System), a model that integrates Unified Medical Language System-based KGs with LLMs to improve diagnostic predictions from EHR data by retrieving contextually relevant paths aligned with patient-specific information. METHODS: DR.KNOWS combines a stack graph isomorphism network for node embedding with an attention-based path ranker to identify and rank knowledge paths relevant to a patient's clinical context. We evaluated DR.KNOWS on 2 real-world EHR datasets from different geographic locations, comparing its performance to baseline models, including QuickUMLS and standard LLMs (Text-to-Text Transfer Transformer and ChatGPT). To assess diagnostic reasoning quality, we designed and implemented a human evaluation framework grounded in clinical safety metrics. RESULTS: -scores, highlighting the benefits of KG integration. Human evaluators found the diagnostic rationales of DR.KNOWS to be aligned strongly with correct clinical reasoning, indicating improved abstraction and reasoning. Recognized limitations include potential biases within the KG data, which we addressed by emphasizing case-specific path selection and proposing future bias-mitigation strategies. CONCLUSIONS: DR.KNOWS offers a robust approach for enhancing diagnostic accuracy and reasoning by integrating structured KG knowledge into LLM-based clinical workflows. Although further work is required to address KG biases and extend generalizability, DR.KNOWS represents progress toward trustworthy artificial intelligence-driven clinical decision support, with a human evaluation framework focused on diagnostic safety and alignment with clinical standards.","url":"https://doi.org/10.2196/58670","authors":["Yanjun Gao","Ruizhe Li","Emma Croxford","John Caskey","Brian W. Patterson","Matthew M. Churpek","Timothy A. Miller","Dmitriy Dligach","Majid Afshar"],"tags":["Preprint","Computer science","Graph","Data science","Natural language processing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-07","doi":"https://doi.org/10.2196/58670","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401336373","name":"ARTIFICIAL INTELLIGENCE – POWERED VIDEO CONTENT GENERATION TOOLS","source":"openalex","abstract":"This article discusses the considerations of artificial intelligence-powered video content generation tools, exploring their applications, ethical considerations, and evaluation criteria. Through discussions of various artificial intelligence (AI) tools, including features, limitations, and implications, the authors analyze the evolving landscape of video creation in the digital age. Key themes include the ethical implications of deep fake technology and the importance of responsible AI principles, exemplified by Microsoft's guidelines. This paper identifies five of the most promoted free social media tools. Evaluation criteria for these tools, such as visual quality, relevance, coherence, authenticity, and transparency, are examined to assess the suitability of AI-generated videos. While AI offers promising opportunities, the discussion underscores the continued need for human oversight and ethical considerations to ensure the responsible use of AI technologies in video content generation.","url":"https://doi.org/10.51865/jpgt.2024.01.10","authors":["Cosmina-Mihaela Roșca","Ionuț Adrian Gortoescu","M. Tanase"],"tags":["Content (measure theory)","Computer science","Multimedia","Artificial intelligence","Mathematics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-05","doi":"https://doi.org/10.51865/jpgt.2024.01.10","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392054560","name":"Academic Education in the Era of Generative Artificial Intelligence","source":"openalex","abstract":"This paper provides a technical review of the Generative AI technology and its challenges and opportunities in the education sector. Generative Artificial Intelligence (GAI), presented in some tools such as ChatGPT, has been continuously penetrating our normal lives. It has also attracted several research efforts, from both academia and industry researchers, to solve real-world problems in different applications such as finance and health. Generative AI is indeed a type of Artificial Intelligence that can efficiently \"create\" a wide variety of information in the form of images, videos, audio, text, and 3D models. Therefore, they can facilitate data analysis and visualization, and enhance personalized and adaptive learning in the education sector. Although instructors and teachers will not be substituted by Generative AI robots completely, education and academic delivery of courses are expected to experience a revolution in the presence of GAI. Similar to other new technologies, serious potential challenges and opportunities are expected in employing GAI in the education sector.","url":"https://doi.org/10.37256/jeee.3120244010","authors":["Maryam Vafadar","Ali Moradi Amani"],"tags":["Generative grammar","Artificial intelligence","Cognitive science","Computer science","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-22","doi":"https://doi.org/10.37256/jeee.3120244010","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401105105","name":"Artificial Intelligence in the Diagnosis of Onychomycosis—Literature Review","source":"openalex","abstract":"Onychomycosis is a common fungal nail infection that is difficult to diagnose due to its similarity to other nail conditions. Accurate identification is essential for effective treatment. The current gold standard methods include microscopic examination with potassium hydroxide, fungal cultures, and Periodic acid-Schiff biopsy staining. These conventional techniques, however, suffer from high turnover times, variable sensitivity, reliance on human interpretation, and costs. This study examines the potential of integrating AI (artificial intelligence) with visualization tools like dermoscopy and microscopy to improve the accuracy and efficiency of onychomycosis diagnosis. AI algorithms can further improve the interpretation of these images. The review includes 14 studies from PubMed and IEEE databases published between 2010 and 2024, involving clinical and dermoscopic pictures, histopathology slides, and KOH microscopic images. Data extracted include study type, sample size, image assessment model, AI algorithms, test performance, and comparison with clinical diagnostics. Most studies show that AI models achieve an accuracy comparable to or better than clinicians, suggesting a promising role for AI in diagnosing onychomycosis. Nevertheless, the niche nature of the topic indicates a need for further research.","url":"https://doi.org/10.3390/jof10080534","authors":["Barbara Bulińska","Magdalena Mazur-Milecka","Martyna Sławińska","Jacek Rumiński","Roman Nowicki"],"tags":["Gold standard (test)","Computer science","Artificial intelligence","Nail disease","Identification (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-30","doi":"https://doi.org/10.3390/jof10080534","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4416716717","name":"Artificial intelligence for natural product drug discovery and development: current landscape, applications, and future directions","source":"openalex","abstract":"Artificial intelligence accelerates natural product discovery in oncology, infection, inflammation, and neuroprotection by enabling activity prediction, mechanism inference, and prioritization. These approaches include tree ensembles, graph neural networks, and self-supervised molecular embeddings for mixtures, isolated metabolites, and peptide analogs, while network pharmacology models herb–ingredient–target–pathway graphs to propose synergistic effects. Operational multi-omics gates (transcriptomic signature reversal, proteome-scale target engagement, and untargeted metabolomics with feature-based molecular networking) move ranked candidates into reproducible validations. Population-level analyses map formulation-derived ingredient–target signatures to clinical outcomes, and large language models are beginning to standardize herbal prescriptions and their curation. Persistent barriers include mixture and batch variability, incomplete provenance, small and imbalanced datasets, domain shift, off-target liability, limited interpretability, and bias. Practical solutions include minimal information for AI on natural product metadata for provenance and safety, scaffold and time-split benchmarks with cross-lab replication, uncertainty and applicability-domain gating, mechanistic add-back experiments, constrained generative and semi-synthetic design, micro-physiological systems with digital twins, and provenance-aware pharmacovigilance under evolving FDA, EMA, and WHO expectations. This review integrates these advances, evidence gaps, and governance requirements into a roadmap for the mechanistically grounded, prospectively validated translation of AI in natural product research. • AI tools accelerate natural product–based drug discovery and development. • Machine learning and deep learning models predict anticancer, anti-inflammatory, and antimicrobial actions. • Several AI-predicted natural compounds were validated in vitro, confirming translational potential. • Key challenges include small datasets, data imbalance, and limited experimental validation.","url":"https://doi.org/10.1016/j.ibmed.2025.100316","authors":["Zhinya Kawa Othman","Mohamed Mustaf Ahmed","Omar Kasimieh","Shuaibu Saidu Musa","Francesco Branda","Edgar G. Cue","Justine Marie A. Ocampo","Don Eliseo Lucero‐Prisno","Sornkanok Vimolmangkang"],"tags":["Computer science","Natural product","Drug discovery","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1016/j.ibmed.2025.100316","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402884190","name":"Artificial intelligence detection of cognitive impairment in older adults during walking","source":"openalex","abstract":"INTRODUCTION: To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking. METHODS: This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides. RESULTS: The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets. DISCUSSION: AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention. Highlights: Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.","url":"https://doi.org/10.1002/dad2.70012","authors":["Shuichi Obuchi","Motonaga Kojima","Hiroyuki Suzuki","Juan C. Garbalosa","Keigo Imamura","Kazushige Ihara","Hirohiko Hirano","Hiroyuki Sasai","Yoshinori Fujiwara","Hisashi Kawai"],"tags":["Dementia","Cognition","Physical medicine and rehabilitation","Angular velocity","Cohort"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-01","doi":"https://doi.org/10.1002/dad2.70012","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399572703","name":"Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis","source":"openalex","abstract":"BACKGROUND: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.","url":"https://doi.org/10.1097/js9.0000000000001239","authors":["Elahe Abbaspour","Sahand Karimzadhagh","Abbas Monsef","Farahnaz Joukar","Fariborz Mansour‐Ghanaei","Soheil Hassanipour"],"tags":["Medicine","Radiomics","Colorectal cancer","Meta-analysis","Subgroup analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-11","doi":"https://doi.org/10.1097/js9.0000000000001239","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4393260373","name":"USER EXPERIENCE (UX) DESIGN IN MEDICAL PRODUCTS: THEORETICAL FOUNDATIONS AND DEVELOPMENT BEST PRACTICES","source":"openalex","abstract":"User Experience (UX) Design in Medical Products is pivotal in enhancing patient outcomes, satisfaction, and adherence to treatment regimens. This abstract delves into the theoretical underpinnings and best practices that underscore effective UX design within the medical domain. Grounded in human-centered design principles, this approach emphasizes understanding user needs, preferences, and limitations to develop inclusive, accessible, and intuitive interfaces. Integrating insights from behavioral psychology, UX designers leverage techniques such as persuasive design to promote healthy behaviors and facilitate adherence to medical protocols. Moreover, compliance with regulatory standards is paramount, necessitating a nuanced understanding of medical regulations and standards to ensure product safety and efficacy. Usability testing emerges as a crucial step, facilitating iterative improvements based on user feedback and ensuring that medical products are intuitive, error-resistant, and conducive to safe user interactions. Beyond functionality, considerations of data privacy and security are paramount, requiring UX designers to implement robust measures to safeguard patient confidentiality while maintaining seamless user experiences. Communication between patients and healthcare providers is also a focal point, with UX strategies aimed at enhancing information exchange, fostering trust, and facilitating informed decision-making. Additionally, optimizing workflow efficiency through streamlined task sequences and leveraging emerging technologies such as artificial intelligence and augmented reality further enhances the user experience in medical contexts. Through continual iteration and refinement, UX design in medical products strives to deliver compassionate, user-centric solutions that meet the diverse needs of patients and healthcare professionals alike, ultimately driving improved healthcare delivery and patient outcomes. Keywords: User Experience Design, Medical Products, Human-Centered Design, Behavioral Psychology, Regulatory Compliance, Usability Testing.","url":"https://doi.org/10.51594/estj.v5i3.975","authors":["Babajide Tolulope Familoni","Sodiq Odetunde Babatunde"],"tags":["User experience design","Best practice","Experience design","Computer science","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-28","doi":"https://doi.org/10.51594/estj.v5i3.975","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4388817308","name":"Artificial Intelligence in Plastic Surgery: ChatGPT as a Tool to Address Disparities in Health Literacy","source":"openalex","abstract":"health literacy","url":"https://doi.org/10.1097/prs.0000000000011202","authors":["Anya Wang","Esther Kim","Olachi Oleru","Nargiz Seyidova","Peter J. Taub"],"tags":["Medicine","Health literacy","Literacy","Medical education","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-14","doi":"https://doi.org/10.1097/prs.0000000000011202","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4306320307","name":"Artificial intelligence in physiological characteristics recognition for internet of things authentication","source":"openalex","abstract":"Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing, to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition, published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions.","url":"https://doi.org/10.1016/j.dcan.2022.10.006","authors":["Zhimin Zhang","Huansheng Ning","Fadi Farha","Jianguo Ding","Kim‐Kwang Raymond Choo"],"tags":["Computer science","Password","Biometrics","Eavesdropping","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-15","doi":"https://doi.org/10.1016/j.dcan.2022.10.006","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4393230858","name":"Pause artificial intelligence research? Understanding AI policy challenges","source":"openalex","abstract":"Abstract Artificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.","url":"https://doi.org/10.1111/caje.12705","authors":["Avi Goldfarb"],"tags":["Ambiguity","Productivity","Economics","Humanity","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-26","doi":"https://doi.org/10.1111/caje.12705","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409507896","name":"Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care—Innovations, Limitations, and Future Directions","source":"openalex","abstract":"Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.","url":"https://doi.org/10.3390/life15040654","authors":["Yoojin Shin","Mingyu Lee","Y.K. Lee","Kyuri Kim","Tae Jung Kim"],"tags":["Workflow","Precision medicine","Artificial intelligence","Standardization","Quality assurance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-16","doi":"https://doi.org/10.3390/life15040654","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414664064","name":"Artificial Intelligence as an Organizing Capability Arising from Human‐Algorithm Relations","source":"openalex","abstract":"Abstract In this article, we move beyond the prevailing view of artificial intelligence (AI) as an independent entity within organizations, which, we argue, risks obscuring potential explanations of the effects of AI on organizing. Drawing on posthumanism, we propose an ontological shift in conceptualizing AI. We theorize that, instead of residing within algorithmic actors, AI arises from the relations among human and algorithmic actors as an organizing capability. This capability is characterized by connectivity, codependence, and emergence as core properties, and contributes to organizational analysing, learning, and acting in pursuit of organizational goals. The shift from the entity view to the organizing capability view of AI has significant implications for understanding its organizational effects and opens new avenues for research in human‐algorithm collaboration, algorithmic management, and organizational intelligence, while counterbalancing tendencies to treat AI as autonomous agents.","url":"https://doi.org/10.1111/joms.70003","authors":["Marta Stelmaszak","Mayur Joshi","Ioanna Constantiou"],"tags":["Core (optical fiber)","Computer science","Knowledge management","Artificial intelligence","Epistemology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-30","doi":"https://doi.org/10.1111/joms.70003","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407558261","name":"Application of artificial intelligence in Alzheimer’s disease: a bibliometric analysis","source":"openalex","abstract":"Background Understanding how artificial intelligence (AI) is employed to predict, diagnose, and perform relevant analyses in Alzheimer’s disease research is a rapidly evolving field. This study integrated and analyzed the relevant literature from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) on the application of AI in Alzheimer’s disease (AD), covering publications from 2004 to 2023. Objective This study aims to identify the key research hotspots and trends of the application of AI in AD over the past 20 years through a bibliometric analysis. Methods Using the Web of Science Core Collection database, we conducted a comprehensive visual analysis of literature on AI and AD published between January 1, 2004, and December 31, 2023. The study utilized Excel, Scimago Graphica, VOSviewer, and CiteSpace software to visualize trends in annual publications and the distribution of research by countries, institutions, journals, references, authors, and keywords related to this topic. Results A total of 2,316 papers were obtained through the research process, with a significant increase in publications observed since 2018, signaling notable growth in this field. The United States, China, and the United Kingdom made notable contributions to this research area. The University of London led in institutional productivity with 80 publications, followed by the University of California System with 74 publications. Regarding total publications, the Journal of Alzheimer’s Disease was the most prolific while Neuroimage ranked as the most cited journal. Shen Dinggang was the top author in both total publications and average citations. Analysis of reference and keyword highlighted research hotspots, including the identification of various stages of AD, early diagnostic screening, risk prediction, and prediction of disease progression. The “task analysis” keyword emerged as a research frontier from 2021 to 2023. Conclusion Research on AI applications in AD holds significant potential for practical advancements, attracting increasing attention from scholars. Deep learning (DL) techniques have emerged as a key research focus for AD diagnosis. Future research will explore AI methods, particularly task analysis, emphasizing integrating multimodal data and utilizing deep neural networks. These approaches aim to identify emerging risk factors, such as environmental influences on AD onset, predict disease progression with high accuracy, and support the development of prevention strategies. Ultimately, AI-driven innovations will transform AD management from a progressive, incurable state to a more manageable and potentially reversible condition, thereby improving healthcare, rehabilitation, and long-term care solutions.","url":"https://doi.org/10.3389/fnins.2025.1511350","authors":["Sijia Song","Tong Li","Wei Lin","Ran Liu","Yujie Zhang"],"tags":["Bibliometrics","Science Citation Index","Citation","Web of science","Library science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-14","doi":"https://doi.org/10.3389/fnins.2025.1511350","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414547178","name":"Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review","source":"openalex","abstract":"PURPOSE OF REVIEW: Geospatial analysis is an essential tool for research on the role of environmental exposures and health, and critical for understanding impacts of environmental risk factors on diseases with long latency (e.g. cardiovascular disease, dementia, cancers) as well as upstream behaviors including sleep, physical activity, and cognition. There is emerging interest in leveraging machine learning and artificial intelligence (AI) for environmental epidemiology research. In this review, we provide an accessible overview of recent advances. RECENT FINDINGS: There have been two major recent shifts in geospatial data types and analytic methods. First, novel methods for statistical prediction, combining geospatial analysis with machine learning and artificial intelligence (GeoAI), allow for scalable geospatial exposure assessment within large population health databases (e.g. cohorts, administrative claims). Second, the widespread adoption of smartphones and wearables with global positioning systems and other sensors has allowed for passive data collection from people, and when combined with geographic information systems, enables exposure assessment at finer spatial scales and temporal resolution than ever before. Illustrative examples include refining models for predicting outdoor air pollution exposure, characterizing populations susceptible to water pollution, and use of deep learning to classify Street View image-derived measures of greenspace. While these tools and approaches may facilitate more rapid, higher quality objective exposure measures, they pose challenges with respect to participant privacy, representativeness of collected data, and curation of high quality validation sets for training of GeoAI algorithms. GeoAI approaches are beginning to be used for environmental exposure assessment and behavioral outcome ascertainment with higher spatial and temporal precision than before. Epidemiologists should continue to apply critical assessment of measurement accuracy and design validity when incorporating these new tools into their work.","url":"https://doi.org/10.1007/s40572-025-00497-4","authors":["Hari S. Iyer","Seigi Karasaki","Yi Li","Yulin Hswen","Peter James","Trang VoPham"],"tags":["Geospatial analysis","Computer science","Data science","Representativeness heuristic","Exposome"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-26","doi":"https://doi.org/10.1007/s40572-025-00497-4","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411453131","name":"Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments","source":"openalex","abstract":"Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent sensors—poses significant challenges due to constraints in memory, computing power, and energy budgets. This paper presents a comprehensive review of recent advances in accelerating DNN inference on edge platforms, with a focus on model compression, compiler optimizations, and hardware–software co-design. We analyze the trade-offs between latency, energy, and accuracy across various techniques, highlighting practical deployment strategies on real-world devices. In particular, we categorize existing frameworks based on their architectural targets and adaptation mechanisms and discuss open challenges such as runtime adaptability and hardware-aware scheduling. This review aims to guide the development of efficient and scalable edge intelligence solutions.","url":"https://doi.org/10.3390/electronics14122495","authors":["Dat Ngo","Hyun-Cheol Park","Bongsoon Kang"],"tags":["Computer science","Inference","Software deployment","Edge device","Computer architecture"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-19","doi":"https://doi.org/10.3390/electronics14122495","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4416204925","name":"What makes university students accept generative artificial intelligence? A moderated mediation model","source":"openalex","abstract":"Artificial Intelligence (AI) technology developments are increasing the importance of accepting and utilizing generative AI. Higher Education is one of the most common areas where AI tools are used. University students use AI tools such as ChatGPT for various purposes (e.g., homework and projects). However, there is limited research on the factors affecting university students' acceptance of AI. AI-related technology and literacy skills are effective in promoting the acceptance of new technologies. Additionally, attitude towards AI and AI self-efficacy can promote the acceptance of AI. Within this context, this study tested a hypothetical model to examine the relationships among university students' attitude towards AI, AI literacy, AI self-efficacy, AI learning anxiety, and AI acceptance. Data were collected from 356 participants (265 females) with a mean of 22.71 (SD = ± 3.72). Mediation and moderation analyses were used to examine the role of AI literacy, AI self-efficacy, and AI learning anxiety in the relationship between attitude towards AI and AI acceptance among Turkish university students. The research results showed that the relationship between attitude toward AI and acceptance of AI can be explained by AI literacy and AI self-efficacy. Moreover, AI learning anxiety can moderate the predictive role of students' attitudes towards AI on AI acceptance. The study broadens and enhances the educational AI literature related to the factors that complicate and facilitate AI acceptance.","url":"https://doi.org/10.1186/s40359-025-03559-2","authors":["Nuri Türk","Barzan Batuk","Alican Kaya","Oğuzhan YILDIRIM"],"tags":["Psychology","Mediation","Turkish","Moderation","Generative grammar"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-12","doi":"https://doi.org/10.1186/s40359-025-03559-2","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399419828","name":"Are Artificial Intelligence Virtual Simulated Patients (AI-VSP) a Valid Teaching Modality for Health Professional Students?","source":"openalex","abstract":"Introduction Simulation-based learning is robust, but the COVID pandemic created opportunities for novel modalities, including Artificial Intelligence Virtual Simulated Patient (AI-VSP) scenarios. Methods Between 2019 and 2022, the following health professional students experienced AI-VSP at one US university: (a) \"Headache\" scenario: 24 Family Nurse Practitioner (FNP) and 48 Physician Assistants (PA) students, (b) \"Insomnia\" scenario: 64 Bachelor of Science in Nursing (BSN) and 47 Accelerated Bachelor of Science in Nursing (ABSN) students. Each individually conducted a brief strongly encouraged encounter and subsequently optionally participated in our study. Results When asked about the scenario's realism, positive answers were 50% (FNP), 16% (PA), 63% (BSN), and 87% (ABSN). Also, 41% FNP, 52% PA, 65% BSN, and 82% ABSN felt capable of creating diagnoses and treatment plans as thoroughly as they would with human patients. Regarding improving diagnostic abilities, favorable responses were 73% (FNP), 74% (PA), 72% (BSN), and 90% (ABSN). When asked whether they would recommend AI-VSP encounters to others, 91% (FNP), 84% (PA), 93% (BSN), and 90% (ABSN) agreed. Conclusions AI-VSP scenarios were well accepted by students and demonstrated significant promise as a complementary simulation-based learning modality.","url":"https://doi.org/10.1016/j.ecns.2024.101536","authors":["Leticia De Mattei","Marcelino Quaglia Morato","Vineet Sidhu","Nodana Gautam","C. Mendonca","Albert Tsai","Marjorie Hammer","Lynda Creighton-Wong","Amin Azzam"],"tags":["Modality (human–computer interaction)","Medical education","Psychology","Computer science","Treatment modality"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-07","doi":"https://doi.org/10.1016/j.ecns.2024.101536","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407295321","name":"Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects","source":"openalex","abstract":"Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient's physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future.","url":"https://doi.org/10.3390/bioengineering12020163","authors":["Divya Tripathi","Kasturee Hajra","Aditya Mulukutla","Romi Shreshtha","Dipak Maity"],"tags":["Biomedicine","Computer science","Health care","Artificial intelligence","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-08","doi":"https://doi.org/10.3390/bioengineering12020163","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400796849","name":"Emergence of Artificial Intelligence Art Therapies ( AIATs ) in Mental Health Care: A Systematic Review","source":"openalex","abstract":"The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and the therapeutic potential of creative arts. This systematic review aimed to assess the effectiveness and ethical considerations of AIATs, incorporating robots, AI painting and AI Chatbots to augment traditional art therapies. Aligning with the Preferred Reporting Items for systematic reviews (PRISMA) guidelines, we meticulously searched PubMed, Cochrane Library, Web of Science and CNKI, resulting in 15 selected articles for detailed analysis. To ensure methodological quality, we applied the Joanna Briggs Institute (JBI) criteria for quality assessment and extracted data using the PICO(S) format, specifically targeting randomised controlled trials (RCTs). Our findings suggest that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges. This examination underscores AIATs' potential to enrich mental health therapies, emphasising the critical importance of ethical considerations and the responsible application of AI as the field evolves. With a focus on expanding treatment efficacy and patient expressiveness, the promise of AIATs in mental health care necessitates a careful balance between innovation and ethical responsibility. Trial Registration: PROSPERO: CRD42024504472.","url":"https://doi.org/10.1111/inm.13384","authors":["Xuexing Luo","Aijia Zhang","Yu Li","Zheyu Zhang","Fangtian Ying","Runqing Lin","Qianxu Yang","Jue Wang","Guanghui Huang"],"tags":["Systematic review","Mental health","MEDLINE","Cochrane Library","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-17","doi":"https://doi.org/10.1111/inm.13384","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407509037","name":"Artificial Intelligence in Colonoscopy: Where Are We Now in 2024?","source":"openalex","abstract":"INTRODUCTION: Colonoscopy has a crucial role in reducing colorectal cancer incidence and mortality. Different artificial intelligence (AI) systems were developed to further improve its quality assurance (computer-aided quality improvement [CAQ]), lesion detection (computer-aided detection [CADe]), and lesion characterization (computer-aided characterization [CADx]). There were studies investigating the roles of these AI systems in different domains of standard colonoscopies. METHODS: In this state-of-the-art narrative review, we summarize the current evidence, discuss existing limitations, as well as explore the future directions of AI in colonoscopy. RESULTS: CAQ enhances colonoscopy quality through real-time feedback and quality monitoring systems, but the studies have inconsistent results due to small training datasets and varied methodologies. CADe increases adenoma detection rate and reduces adenoma missed rates, but there are concerns about false positives, unnecessary polypectomies, potential deskilling of endoscopists, and cost-effectiveness. CADx systems have mixed results and accuracies in differentiating polyp types, and its use is further hindered by inadequate representation of sessile serrated lesions and a lack of rigorous trials comparing it with standard colonoscopy. CONCLUSION: Despite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice. Long-term data on clinical efficacy, cost-effectiveness, liability, and data sharing are the key areas to be addressed.","url":"https://doi.org/10.1159/000544030","authors":["Wan Ying Lai","Kenneth W. Lin","L Ling","James Weiquan Li","Louis Ho Shing Lau","Philip Wai Yan Chiu"],"tags":["Colonoscopy","Medicine","Artificial intelligence","False positive paradox","Adenoma"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-13","doi":"https://doi.org/10.1159/000544030","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4378450174","name":"Artificial Intelligence in Medicine: Legal, Ethical and Social Aspects","source":"openalex","abstract":"In this article, the authors reflected the legal, ethical and social aspects of the introduction of artificial intelligence in the field of medicine. The authors used the dialectical method to understand the problematic aspects of qualitative changes in the healthcare system of Ukraine in connection with the quantitative increase in the use of artificial intelligence technology. The system method contributed to determining the nature of the impact of the introduction of artificial intelligence on the transformation of the structural elements of legislation in the healthcare sector. Analytical and formal-logical methods were useful in the process of identifying legal and ethical and social problems from the introduction of artificial intelligence and providing proposals for their solution. Emphasis was placed on the current state of the legal regulation of artificial intelligence in Ukraine and the problems of a legal, ethical and social nature that need to be addressed in the process of its implementation. The authors came to the conclusion that Ukraine is now at the initial stage of introducing artificial intelligence into public life. The problem of the lack of legislative work to streamline public relations associated with the use of artificial intelligence has been identified. Proposals are provided that can help mitigate the risks from the introduction of artificial intelligence.","url":"https://doi.org/10.4067/s1726-569x2023000100063","authors":["М. А. Anishchenko","Ievgen Gidenko","Maksym Kaliman","Vasyl Polyvaniuk","Yurii Demianchuk"],"tags":["Engineering ethics","Sociology","Psychology","Political science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-26","doi":"https://doi.org/10.4067/s1726-569x2023000100063","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402692922","name":"Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5 and Humans in Clinical Chemistry Multiple-Choice Questions","source":"openalex","abstract":"Introduction: Artificial intelligence (AI) chatbots excel in language understanding and generation. These models can transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. This study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master’s level in Medical Laboratory Sciences. Methods: The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessing 20 MSc students. The revised Bloom’s taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for the assessment of the quality of AI-generated content, with Cohen’s κ for inter-rater agreement. Results: Compared to the mean students’ score which was 0.68± 0.23, GPT-4 scored 0.90 ± 0.30, followed by Bing (0.77 ± 0.43), GPT-3.5 (0.73 ± 0.45), and Bard (0.67 ± 0.48). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5 ( P =0.041), GPT-4 ( P =0.003), and Bard ( P =0.017) compared to the higher cognitive domains (Apply and Analyze). The CLEAR scores indicated that ChatGPT-4 performance was “Excellent” compared to the “Above average” performance of ChatGPT-3.5, Bing, and Bard. Discussion: The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risk of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated. Keywords: AI in healthcare education, higher education, large language models, evaluation","url":"https://doi.org/10.2147/amep.s479801","authors":["Malik Sallam","Khaled Al‐Salahat","Huda Eid","Jan Egger","Behrus Puladi"],"tags":["Computer science","Artificial intelligence","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.2147/amep.s479801","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406870591","name":"Artificial Intelligence–Based Psychotherapeutic Intervention on Psychological Outcomes: A Meta‐Analysis and Meta‐Regression","source":"openalex","abstract":"Background: Artificial intelligence (AI)–based psychotherapeutic interventions may bring a new and viable approach to expanding psychiatric care. However, evidence of their effectiveness remains scarce. We evaluated the efficacy of AI‐based psychotherapeutic interventions on depressive, anxiety, and stress symptoms at postintervention and follow‐up assessments. Methods: A three‐step comprehensive search via nine electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Scopus, IEEE Xplore, Web of Science, PsycINFO, and ProQuest Dissertations and Theses) was performed. Results: Thirty randomized controlled trials (RCTs) in 31 publications involving 6100 participants from nine countries were included. The majority (79.1%) of trials with intention‐to‐treat analysis but less than half (48.6%) of trials with perprotocol analysis were graded as low risk. Meta‐analyses showed that interventions significantly reduced depressive symptoms at the postintervention assessment ( t = −4.40, p = 0.001) with medium effect size ( g = −0.54, 95% CI: −0.79 to −0.29) and at 6–12 months of assessment ( t = −3.14, p < 0.016) with small effect size ( g = −0.23, 95% CI: −0.40 to −0.06) in comparison with comparators. Our subgroup analyses revealed that the depressed participants had a significantly larger effect size in reducing depressive symptoms than participants with stress and other conditions. At postintervention and follow‐up assessments, we discovered that AI‐based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators. The random‐effects univariate meta‐regression did not identify any significant covariates for depressive and anxiety symptoms at postintervention. The certainty of evidence ranged between moderate and very low. Conclusions: AI‐based psychotherapeutic interventions can be used in addition to usual treatments for reducing depressive symptoms. Well‐designed RCTs with long‐term follow‐up data are warranted. Trial Registration: CRD42022330228","url":"https://doi.org/10.1155/da/8930012","authors":["Ying Lau","Wei How Darryl Ang","Wen Wei Ang","Patrick Cheong‐Iao Pang","Sai Ho Wong","Kin Sun Chan"],"tags":["Meta-analysis","Meta-regression","Psychology","Intervention (counseling)","Clinical psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1155/da/8930012","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412630965","name":"Artificial Intelligence in Cosmetic Formulation: Predictive Modeling for Safety, Tolerability, and Regulatory Perspectives","source":"openalex","abstract":"Artificial intelligence (AI) and machine learning (ML) are increasingly transforming the landscape of cosmetic formulation, enabling the development of safer, more effective, and personalized products. This article explores how AI-driven predictive modeling is applied across various components of cosmetic products, including surfactants, polymers, fragrances, preservatives, antioxidants, and prebiotics. These technologies are employed to forecast critical properties such as texture, stability, and shelf-life, optimizing both product performance and user experience. The integration of computational toxicology and ML algorithms also allows for early prediction of skin sensitization risks, including the likelihood of adverse events such as allergic contact dermatitis. Furthermore, AI models can support efficacy assessment, bridging formulation science with dermatological outcomes. The article also addresses the ethical, regulatory, and safety challenges associated with AI in cosmetic science, underlining the need for transparency, accountability, and harmonized standards. The potential of AI to reshape dermocosmetic innovation is vast, but it must be approached with robust oversight and a commitment to user well-being.","url":"https://doi.org/10.3390/cosmetics12040157","authors":["Antonio Di Guardo","Federica Trovato","Carmen Cantisani","Annunziata Dattola","Steven Paul Nisticò","Giovanni Pellacani","Alessia Paganelli"],"tags":["Tolerability","Risk analysis (engineering)","Computer science","Management science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-24","doi":"https://doi.org/10.3390/cosmetics12040157","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405961921","name":"Generative Artificial Intelligence in Marketing: The Invisible Danger of AI Hallucinations","source":"openalex","abstract":"This study explores the transformative impact of Generative Artificial Intelligence (GAI) on the marketing highlighting both its significant opportunities and inherent challenges. GAI enhances marketing strategies through automated content creation, personalized customer experiences, and advanced data analytics, thereby increasing efficiency and engagement. However, the phenomenon of AI hallucinations—where AI models produce realistic yet incorrect or misleading information—poses substantial risks, including damage to brand reputation, erosion of consumer trust, and potential legal ramifications. To mitigate the risks associated with AI hallucinations, the study proposes comprehensive risk management strategies that include technical solutions to detect and correct erroneous outputs, human oversight to ensure accuracy, and adherence to ethical and regulatory frameworks. By balancing the advantages of GAI with robust measures to address AI-generated inaccuracies, organizations can harness its full potential while safeguarding their brand integrity and maintaining trust with customers.","url":"https://doi.org/10.7596/jebm.1588897","authors":["Burak Yaprak"],"tags":["Artificial intelligence","Generative grammar","Psychology","Machine learning","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-31","doi":"https://doi.org/10.7596/jebm.1588897","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402237623","name":"Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis","source":"openalex","abstract":"Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727). We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023. Eight randomized controlled trials were included in this systematic review ( n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes ( n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size ( n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I 2 93.8%). This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).","url":"https://doi.org/10.1186/s12871-024-02699-z","authors":["Kensuke Shimada","Ryota Inokuchi","Tomohiro Ohigashi","Masao Iwagami","Makoto Tanaka","Masahiko Gosho","Nanako Tamiya"],"tags":["Anesthesiology","Meta-analysis","Medicine","Perioperative","Pain medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-04","doi":"https://doi.org/10.1186/s12871-024-02699-z","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4391954037","name":"Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis","source":"openalex","abstract":"BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. METHODS: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. RESULTS: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. CONCLUSIONS: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing","url":"https://doi.org/10.1097/js9.0000000000001169","authors":["Yunpeng Cui","Xuedong Shi","Yong Qin","Qiwei Wan","Xuyong Cao","Xiaotong Che","Yuanxing Pan","Bing Wang","Mingxing Lei","Yaosheng Liu"],"tags":["Medicine","Ambulatory","Disease","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-19","doi":"https://doi.org/10.1097/js9.0000000000001169","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392795414","name":"Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care","source":"openalex","abstract":"PURPOSE: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world. METHODS: The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale. RESULTS: For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%). CONCLUSION: The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.","url":"https://doi.org/10.1200/go.23.00376","authors":["Laurence E. Court","Ajay Aggarwal","Anuja Jhingran","Komeela Naidoo","Tucker Netherton","Adenike Olanrewaju","Christine B. Peterson","Jeannette Parkes","Hannah Simonds","Christoph Trauernicht","Lifei Zhang","Beth M. Beadle","Shareen Ahmad","David W. Anderson","Arjig Baghwala","Karen K. L. Chan","Prajnan Das","Albert Edwards","May Elbanna","Hesham Elhalawani","Medhat Elsayed","Agnes Ewongwo","Nazia Fakie","Clifton D. Fuller","Adam S. Garden","Matt Gove","Teresa Guerrero Urbano","Njeri Kaittany","Mishal S. Khan","Joshua Langer","Percy Leeig","Becky Lee","Anna Lee","Belinda Lee","Michelle Leech","Ben Li","Katie Lichter","Lilie L. Lin","Stacy Lin","Dorothy Lombe","Indranil Mallick","Sean Maroongroge","Rachael Martin","Gwendolyn J. McGinnis","Megan Mezera","Mustefa Mohammedsaid","Son Nguyen","Jenny Nuanjing","Tony Phillips","Surendra Prajapati","Lydia Punt","Valerie Reed","Dominique L. Roniger","Simona F. Shaitelman","Alicia Sherriff","Jay Shiao","Heath D. Skinner","A Susan","Jordan Sutton","Hamza Syed","Sandy Thang","J S Thompson","Gary V. Walker","Julie Wetter","Ingrid White","Melody Xu","Yousif Yousif","Simeng Zhu"],"tags":["Contouring","Minor (academic)","Radiation therapy","Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-14","doi":"https://doi.org/10.1200/go.23.00376","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403515212","name":"Enhancing peer review efficiency: A mixed‐methods analysis of artificial intelligence ‐assisted reviewer selection across academic disciplines","source":"openalex","abstract":"Abstract This mixed‐methods study evaluates the efficacy of artificial intelligence (AI)‐assisted reviewer selection in academic publishing across diverse disciplines. Twenty journal editors assessed AI‐generated reviewer recommendations for a manuscript. The AI system achieved a 42% overlap with editors' selections and demonstrated a significant improvement in time efficiency, reducing selection time by 73%. Editors found that 37% of AI‐suggested reviewers who were not part of their initial selection were indeed suitable. The system's performance varied across disciplines, with higher accuracy in STEM fields (Cohen's d = 0.68). Qualitative feedback revealed an appreciation for the AI's ability to identify lesser‐known experts but concerns about its grasp of interdisciplinary work. Ethical considerations, including potential algorithmic bias and privacy issues, were highlighted. The study concludes that while AI shows promise in enhancing reviewer selection efficiency and broadening the reviewer pool, it requires human oversight to address limitations in understanding nuanced disciplinary contexts. Future research should focus on larger‐scale longitudinal studies and developing ethical frameworks for AI integration in peer‐review processes.","url":"https://doi.org/10.1002/leap.1638","authors":["Shai Farber"],"tags":["Selection (genetic algorithm)","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-01","doi":"https://doi.org/10.1002/leap.1638","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399648420","name":"Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review","source":"openalex","abstract":"Purpose of Review: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery. Recent Findings: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists' oversight to ensure that ML's potential is fully realized in patient care, preserving the essential human element in medicine. Summary: This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise.","url":"https://doi.org/10.1007/s13671-024-00434-y","authors":["Payton Smith","Chandler Johnson","Kathryn Haran","Faye Orcales","Allison Kranyak","Tina Bhutani","Josep Riera‐Monroig","Wilson Liao"],"tags":["Medicine","Psoriasis","Intensive care medicine","Dermatology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-13","doi":"https://doi.org/10.1007/s13671-024-00434-y","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412441210","name":"Comparing artificial intelligence-enhanced virtual reality and simulated patient simulations in undergraduate nursing education","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ecns.2025.101780","authors":["Nicole Harder","Fiha Ali","Sufia Turner","Kimberly Workum","Lawrence M. Gillman"],"tags":["Virtual reality","Nursing","Psychology","Virtual patient","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-15","doi":"https://doi.org/10.1016/j.ecns.2025.101780","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4293274932","name":"Artificial Intelligence in Surgery","source":"openalex","abstract":"Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots. We end with summarizing the current state, emerging trends and major challenges in the future development of AI in surgery.","url":"https://doi.org/10.48550/arxiv.2001.00627","authors":["Xiaoyun Zhou","Yao Guo","Mali Shen","Guang‐Zhong Yang"],"tags":["Robotic surgery","Surgical robot","Intervention (counseling)","Artificial intelligence","State (computer science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-12-23","doi":"https://doi.org/10.48550/arxiv.2001.00627","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404055367","name":"Validation of an artificial intelligence-based prognostic biomarker in patients with oligometastatic Castration-Sensitive prostate cancer","source":"openalex","abstract":"BACKGROUND: There is a need for clinically actionable prognostic and predictive tools to guide the management of oligometastatic castration-sensitive prostate cancer (omCSPC). METHODS: This is a multicenter retrospective study to assess the prognostic and predictive performance of a multimodal artificial intelligence biomarker (MMAI; the ArteraAI Prostate Test) in men with omCSPC (n = 222). The cohort also included 51 patients from the STOMP and ORIOLE phase 2 clinical trials which randomized patients to observation versus metastasis-directed therapy (MDT). MMAI scores were computed from digitized histopathology slides and clinical variables. Overall survival (OS) and time to castration-resistant prostate cancer (TTCRPC) were assessed for the entire cohort from time of diagnosis. Metastasis free survival (MFS) was assessed for the trial cohort from time of randomization. RESULTS: = 0.04. CONCLUSION: The ArteraAI MMAI biomarker is prognostic for OS and TTCRPC among patients with omCSPC and may predict for response to MDT. Further work is needed to validate the MMAI biomarker in a broader mCSPC cohort.","url":"https://doi.org/10.1016/j.radonc.2024.110618","authors":["Jarey H. Wang","Matthew P. Deek","Adrianna A. Mendes","Yang Song","Amol C. Shetty","Soha Bazyar","Kim Van der Eecken","Emmalyn Chen","Timothy N. Showalter","Trevor J. Royce","Tamara R. Todorović","Huei–Chung Huang","Scott A. Houck","Rikiya Yamashita","Ana P. Kiess","Daniel Y. Song","Tamara L. Lotan","Theodore L. DeWeese","Luigi Marchionni","Lei Ren","Amit Sawant","Nicole L. Simone","Alejandro Berlín","Cem Önal","Andre Esteva","Felix Y. Feng","Phuoc T. Tran","Philip Sutera","Piet Ost"],"tags":["Prostate cancer","Medicine","Oncology","Biomarker","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-06","doi":"https://doi.org/10.1016/j.radonc.2024.110618","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401557415","name":"Artificial intelligence and myocarditis—a systematic review of current applications","source":"openalex","abstract":"Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.","url":"https://doi.org/10.1007/s10741-024-10431-9","authors":["Paweł Łajczak","Kamil Jóźwik"],"tags":["Myocarditis","Medicine","Systematic review","Artificial intelligence","Acute myocarditis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-13","doi":"https://doi.org/10.1007/s10741-024-10431-9","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411161852","name":"Clinical Impact of Artificial Intelligence-Based Triage Systems in Emergency Departments: A Systematic Review","source":"openalex","abstract":"Emergency departments (EDs) worldwide face increasing pressure to optimize triage processes amidst rising patient volumes and resource constraints. Artificial intelligence (AI) has emerged as a potential solution to enhance triage accuracy and efficiency, yet its real-world clinical impact remains inadequately characterized. We conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, searching PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) Xplore (2020-2025) for studies evaluating AI-based ED triage systems. From 119 initially identified records, six studies met inclusion criteria after duplicate removal (n=67), title/abstract screening (n=52), and full-text assessment (n=12). Eligible studies reported quantitative outcomes on AI performance compared to traditional triage methods. Risk of bias was assessed using an adapted Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Narrative synthesis was employed due to methodological heterogeneity. The included studies (n=6) demonstrated AI's potential to reduce triage time, improve documentation accuracy, and enhance decision support. Voice-based artificial intelligence (Voice-AI) systems achieved 19% faster documentation versus manual methods, while machine learning algorithms reduced mis-triage rates by 0.3-8.9%. However, limitations included undertriage risks, variable accuracy, and predominance of single-center studies. Implementation challenges encompassed workflow integration barriers and insufficient clinician acceptance metrics. AI-based triage systems show promise for improving ED efficiency but require rigorous multi-center validation and standardized outcome reporting. Key gaps include evidence on patient-centered outcomes, equity considerations, and long-term impact studies. Future development should prioritize explainable algorithms, clinician engagement, and ethical frameworks to ensure safe implementation.","url":"https://doi.org/10.7759/cureus.85667","authors":["Abubaker Zakria Ahmed Abdalhalim","Sheimaa Nasreldein Nureldaim Ahmed","Ahmed Mohamed Dawoud Ezzelarab","Mohammad Mustafa","Mamoun Al-Basheer","R Ahmed","Mowafag Bushra Galal Eldin Elsayed"],"tags":["Medicine","Triage","Medical emergency"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-09","doi":"https://doi.org/10.7759/cureus.85667","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4413205459","name":"How generative artificial intelligence transforms teaching and influences student wellbeing in future education","source":"openalex","abstract":"Artificial intelligence (AI), particularly generative technologies, and large language models are transforming modern education. On the one hand, these tools can automate certain aspects of teaching, personalize educational materials, and improve learning efficiency. However, concerns are emerging regarding the impact of AI on the quality of education, the mental health of students, and fundamental academic values. This article reviews the scientific literature on the applications of artificial intelligence in education, with a special emphasis on its impact on student mental health. Based on an analysis of 120 scientific articles, the study automatically extracted data on AI implementation's potential opportunities and threats. A novel aspect of this work is identifying factors that can be considered both opportunities and threats depending on context. In addition, a frequency analysis of keywords and phrases uncovered many opportunities and challenges. All identified aspects are characterized in the article. The article also highlights key barriers to using large language models (LLMs) for detecting student mental health issues, such as underestimating suicide risk, difficulties with interpreting subtle language, biases in training data, lack of cultural sensitivity, and unresolved ethical concerns. These challenges illustrate why generative AI is not yet reliable for supporting student mental health, especially in high-risk situations. One of the key conclusions is that the use of generative AI to support student mental health is seldom addressed in existing review articles, likely due to the current unreliability of this technology.","url":"https://doi.org/10.3389/feduc.2025.1594572","authors":["Marcin Jukiewicz"],"tags":["Generative grammar","Mental health","Context (archaeology)","Computer science","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-12","doi":"https://doi.org/10.3389/feduc.2025.1594572","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402886857","name":"Opportunities or Challenges? The Interplay between Artificial Intelligence and Corporate Social Responsibility Communication","source":"openalex","abstract":"Abstract Background The rapid development of Artificial Intelligence (AI) offers both opportunities and challenges for its application in Corporate Social Responsibility (CSR) communication. While AI can enhance CSR initiatives, its impact on consumer relations and brand perception remains inconsistent. Objectives This study aims to explore the academic landscape of AI’s role in CSR communication, focusing on publication trends, key authors, research topics, and future directions. Methods/Approach A bibliometric analysis was conducted on 1,094 articles related to AI and CSR communication, retrieved from the Web of Science database from 2000 to February 2024. Using CiteSpace software, the study mapped research trends by analysing disciplines, countries, institutions, authors, references, and keywords. Results The United States and China lead in publication output, with key research themes including social media impact, management strategies, and consumer trust. Emerging trends point to the importance of privacy, service quality, and perceived value in AI-driven CSR initiatives. Conclusions The integration of AI in CSR communication is an evolving field, with significant contributions from social media research and consumer behaviour studies. Future research should address ethical concerns and long-term effects on consumer trust and engagement.","url":"https://doi.org/10.2478/bsrj-2024-0007","authors":["Xiangzhou Hua","Nurul Ain Mohd Hasan","Feroz De Costa","Weihua Qiao"],"tags":["Social responsibility","Corporate social responsibility","Business","Engineering ethics","Public relations"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.2478/bsrj-2024-0007","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4413272174","name":"Artificial Intelligence in Primary Care: Support or Additional Burden on Physicians’ Healthcare Work?—A Qualitative Study","source":"openalex","abstract":"Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care.","url":"https://doi.org/10.3390/clinpract15080138","authors":["Stefanie Mache","Monika Bernburg","Annika Würtenberger","David A. Groneberg"],"tags":["Medicine","Primary care","Primary health care","Work (physics)","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.3390/clinpract15080138","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407852883","name":"Evaluating the evidence-based potential of six large language models in paediatric dentistry: a comparative study on generative artificial intelligence","source":"openalex","abstract":"PURPOSE: The use of large language models (LLMs) in generative artificial intelligence (AI) is rapidly increasing in dentistry. However, their reliability is yet to be fully founded. This study aims to evaluate the diagnostic accuracy, clinical applicability, and patient education potential of LLMs in paediatric dentistry, by evaluating the responses of six LLMs: Google AI's Gemini and Gemini Advanced, OpenAI's ChatGPT-3.5, -4o and -4, and Microsoft's Copilot. METHODS: Ten open-type clinical questions, relevant to paediatric dentistry were posed to the LLMs. The responses were graded by two independent evaluators from 0 to 10 using a detailed rubric. After 4 weeks, answers were reevaluated to assess intra-evaluator reliability. Statistical comparisons used Friedman's and Wilcoxon's and Kruskal-Wallis tests to assess the model that provided the most comprehensive, accurate, explicit and relevant answers. RESULTS: Variations of results were noted. Chat GPT 4 answers were scored as the best (average score 8.08), followed by the answers of Gemini Advanced (8.06), ChatGPT 4o (8.01), ChatGPT 3.5 (7.61), Gemini (7,32) and Copilot (5.41). Statistical analysis revealed that Chat GPT 4 outperformed all other LLMs, and the difference was statistically significant. Despite variations and different responses to the same queries, remarkable similarities were observed. Except for Copilot, all chatbots managed to achieve a score level above 6.5 on all queries. CONCLUSION: This study demonstrates the potential use of language models (LLMs) in supporting evidence-based paediatric dentistry. Nevertheless, they cannot be regarded as completely trustworthy. Dental professionals should critically use AI models as supportive tools and not as a substitute of overall scientific knowledge and critical thinking.","url":"https://doi.org/10.1007/s40368-025-01012-x","authors":["Anastasia Dermata","Aristidis Arhakis","Miltiadis A. Makrygiannakis","Kostis Giannakopoulos","Eleftherios G. Kaklamanos"],"tags":["Rubric","Reliability (semiconductor)","Medicine","Medical education","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-22","doi":"https://doi.org/10.1007/s40368-025-01012-x","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409335180","name":"A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology","source":"openalex","abstract":"The high degree of convergence between thermal fluid sciences and artificial intelligence (AI) has changed traditional energy management methods. The technology provides energy conservation, fluid dynamics, and heat transfer optimisation solutions. In order to model prediction and increase the effectiveness of thermal fluid application proposals, this review looks at the latest developments in the use of AI-enabled machine learning techniques, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), and Deep Learning Hierarchy. In order to support sustainable energy goals, these highlighted machine learning algorithms offer a potent environment for optimising energy flow, temperature regulation, and application stability. Furthermore, diverse reinforcement learning techniques facilitate the adoptive control of intricate thermal applications in real-time settings, while Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed for applicational monitoring and real-time data processing. By combining blockchain technology with artificial intelligence, a decentralised framework environment is introduced that offers energy conservation methods that are safe, transparent, honest, and reliable. An unchangeable ledger is provided by the technology, and accountability and traceability are provided by smart contracts. It supports the vital tasks of dynamically monitoring and validating energy consumption across decentralised applications (DApps) in real-time. Additionally, this article offers a thorough examination of recent research, the integration of emerging technologies, and real-world uses of blockchain and artificial intelligence in thermal fluid applications. A cost-effective energy management environment that supports international energy conservation initiatives is created by combining the predictive power of AI with the security features of blockchain technology. In addition, it offers a platform for future study, giving it a starting point for innovation in sustainable energy management. The major contribution of this review article is discussed as follows:","url":"https://doi.org/10.1007/s43621-025-01124-w","authors":["Abdullah Ayub Khan","Asif Ali Laghari","Syed Azeem Inam","Sajid Ullah","Laila Nadeem"],"tags":["Blockchain","Computer science","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-10","doi":"https://doi.org/10.1007/s43621-025-01124-w","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392861152","name":"Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence","source":"openalex","abstract":"Introduction Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic–clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity ( n = 81 subjects with seizures) and false detection rate (FDR) ( n = all 230 subjects). Results At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic–clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.","url":"https://doi.org/10.3389/fninf.2024.1324981","authors":["Pragya Rai","Andrew Knight","Matias Hiillos","Csaba Kertész","Elizabeth Morales","Daniella Terney","Sidsel Armand Larsen","Tim Østerkjerhuus","Jukka Peltola","Sándor Beniczky"],"tags":["Epilepsy","Computer science","Artificial intelligence","Neuroscience","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-15","doi":"https://doi.org/10.3389/fninf.2024.1324981","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410633341","name":"Generative artificial intelligence-supported programming education: Effects on learning performance, self-efficacy and processes","source":"openalex","abstract":"Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners’ programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study addresses these gaps through a quasi-experiment comparing an experimental group using GenAI for self-directed programming learning with a control group relying on alternative tools. Additionally, the experimental group was divided into high- and low-performance subgroups to examine the relationship between learning behaviour patterns and academic outcomes using process mining techniques. The findings reveal that (a) GenAI demonstrates strong potential to enhance learning outcomes and self-efficacy but negatively affects long-term knowledge transfer; (b) excessive reliance on GenAI and cognitive outsourcing impede effective knowledge acquisition; (c) high-performing learners exhibit greater epistemic agency, actively critiquing and engaging with AI-generated content to construct knowledge proactively. This study underscores the risks of over-reliance on GenAI and the detrimental effects of cognitive offloading, highlighting the critical role of cognitive engagement and epistemic agency in fostering hybrid intelligence. It provides empirical and theoretical insights to inform the design of GenAI-supported programming education strategies and interventions. Implications for practice or policy: Instructors can enhance programming self-efficacy by integrating GenAI tools like ChatGPT into self-learning activities, particularly for reinforcing academic performance. Course leaders should emphasise GenAI use in programming courses to support student engagement, though they should also prepare students for problem-solving without external resources. Educational institutions may consider developing guidelines for balanced GenAI usage to maximise learning benefits while addressing potential limitations in problem-solving skills.","url":"https://doi.org/10.14742/ajet.9932","authors":["Siran Li","Jiangyue Liu","Qianyan Dong"],"tags":["Computer science","Generative grammar","Artificial intelligence","Educational technology","Computer-Assisted Instruction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.14742/ajet.9932","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404254218","name":"Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research","source":"openalex","abstract":"Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. 'Teacher' models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. 'Student' models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research.","url":"https://doi.org/10.1038/s41467-024-54071-x","authors":["Kenneth L. Kehl","Justin Jee","Karl Pichotta","Megan Paul","Pavel Trukhanov","Christopher J. Fong","Michele Waters","Ziad Bakouny","Wenxin Xu","Toni K. Choueiri","Chelsea Nichols","Deborah Schrag","Nikolaus Schultz"],"tags":["Artificial intelligence","Machine learning","Computer science","Inference","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-12","doi":"https://doi.org/10.1038/s41467-024-54071-x","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412944013","name":"Perspectives, challenges and future of artificial intelligence in personalised nutrition research","source":"openalex","abstract":"Personalised nutrition (PN) has emerged as an approach to optimise individual health outcomes through more targeted and tailored dietary recommendations based on unique genetic, phenotypic, medical, lifestyle and contextual factors. The application of artificial intelligence (AI) presents an opportunity to achieve personalised nutrition advice at a scale that has population impact. This review introduces a nutrition audience to different AI applications and offers insights into the concepts of AI that might be relevant to the field of nutrition research. The current and future uses of AI in PN are discussed, as well as the potential benefits and challenges to their application. AI-driven solutions have the potential to improve health and reduce the risk of disease because they can consider more information about an individual in making recommendations. However, challenges such as data interoperability, ethical considerations, and model interpretability remain an issue limiting widespread use at this point. This review will provide a foundational understanding of the application of AI within PN and help to identify opportunities to leverage the potential of AI in transforming dietary guidance and enhancing health outcomes through innovative solutions.","url":"https://doi.org/10.1017/s0029665125100657","authors":["Aida Brankovic","Gilly A. Hendrie"],"tags":["Interpretability","Leverage (statistics)","Interoperability","Computer science","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-04","doi":"https://doi.org/10.1017/s0029665125100657","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4413025933","name":"From Perception to Practice: Artificial Intelligence as a Pathway to Enhancing Digital Literacy in Higher Education Teaching","source":"openalex","abstract":"In the context of increasing Artificial Intelligence integration in higher education, understanding the factors influencing university teachers’ adoption of AI tools is critical for effective implementation. This study adopts a perception–intention–behavior framework to explores the roles of perceived usefulness, perceived ease of use, perceived trust, perceived substitution crisis, and perceived risk in shaping teachers’ behavioral intention and actual usage of AI tools. It also investigates the moderating effects of peer influence and organizational support on these relationships. Using a comprehensive survey instrument, data was collected from 487 university teachers across four major regions in China. The results reveal that perceived usefulness and perceived ease of use are strong predictors of behavioral intention, with perceived ease of use also significantly influencing perceived usefulness. Perceived trust serves as a key mediator, enhancing the relationship between perceived usefulness, perceived ease of use, and behavioral intention. While perceived substitution crisis negatively influenced perceived trust, it showed no significant direct effect on behavioral intention, suggesting a complex relationship between job displacement concerns and AI adoption. In contrast, perceived risk was found to negatively impact behavioral intention, though it was mitigated by perceived ease of use. Peer influence significantly moderated the relationship between perceived trust and behavioral intention, highlighting the importance of peer influence in AI adoption, while organizational support amplified the effect of perceived ease of use on behavioral intention. These findings inform practical strategies such as co-developing user-centered AI tools, enhancing institutional trust through transparent governance, leveraging peer support, providing structured training and technical assistance, and advancing policy-level initiatives to guide digital transformation in universities.","url":"https://doi.org/10.3390/systems13080664","authors":["Zhili Zuo","Yanqi Luo","Shiyu Yan","Lisheng Jiang"],"tags":["Perception","Literacy","Digital literacy","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-06","doi":"https://doi.org/10.3390/systems13080664","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4323659035","name":"Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations","source":"openalex","abstract":"BACKGROUND: The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations. OBJECTIVES: We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction. METHODS: We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation. RESULTS: The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization. CONCLUSION: AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.","url":"https://doi.org/10.1097/mlr.0000000000001830","authors":["Anders H. Riis","Pia Kjær Kristensen","Simon Meyer Lauritsen","Bo Thiesson","Marianne Johansson Jørgensen"],"tags":["MEDLINE","Computer science","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-03","doi":"https://doi.org/10.1097/mlr.0000000000001830","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411927032","name":"Navigating the Complexity of Generative Artificial Intelligence in Higher Education: A Systematic Literature Review","source":"openalex","abstract":"Technological innovation has transformed educational settings, enabling artificial intelligence (AI)-driven teaching and learning processes. While AI is still in its embryonic stage in education, generative artificial intelligence has evolved rapidly, significantly shifting the teaching and learning context. With no clarity about the impacts of generative artificial intelligence on education, there is a need to synthesise research findings to demystify generative artificial intelligence and address concerns regarding its application in the teaching and learning process. This paper systematically synthesises studies on generative artificial intelligence in teaching and learning to understand key arguments and stakeholders’ perceptions of generative artificial intelligence in teaching and learning. The systematic review reveals five main domains of research within the field: (i) current awareness (understanding) of generative artificial intelligence, (ii) stakeholder perceptions, (iii) mechanisms for adopting generative artificial intelligence, (iv) issues and challenges of implementing generative artificial intelligence, and (v) contributions of generative artificial intelligence to student performance. This review examines the practical and policy implications of generative artificial intelligence, providing recommendations to address the concerns and challenges associated with generative artificial intelligence-driven teaching and learning processes.","url":"https://doi.org/10.3390/educsci15070826","authors":["Birago Amofa","Xebiso Blessing Kamudyariwa","Fatima Araujo Pereira Fernandes","Oluyomi A. Osobajo","Faith Jeremiah","Adekunle Oke"],"tags":["Generative grammar","Artificial intelligence","Computer science","Cognitive science","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-29","doi":"https://doi.org/10.3390/educsci15070826","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401610634","name":"Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial","source":"openalex","abstract":"BACKGROUND: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce. OBJECTIVE: This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback. METHODS: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study. RESULTS: The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed. CONCLUSIONS: Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education.","url":"https://doi.org/10.2196/58753","authors":["Akira Yamamoto","Masahide Koda","Hiroko Ogawa","Tomoko Miyoshi","Yoshinobu Maeda","Fumio Otsuka","Hideo Ino"],"tags":["Preprint","Randomized controlled trial","Psychology","Medical education","Applied psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-15","doi":"https://doi.org/10.2196/58753","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406490791","name":"Navigating AI Convergence in Human–Artificial Intelligence Teams: A Signaling Theory Approach","source":"openalex","abstract":"ABSTRACT Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision‐making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self‐determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision‐making in high‐stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.","url":"https://doi.org/10.1002/job.2856","authors":["Andria Smith","Hunter Phoenix Van Wagoner","Ksenia Keplinger","Can Celebi"],"tags":["Convergence (economics)","Psychology","Human intelligence","Cognitive science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-17","doi":"https://doi.org/10.1002/job.2856","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392797668","name":"Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS: A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. RESULTS: On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model's accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model's 40%. CONCLUSION: Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.","url":"https://doi.org/10.1136/bjo-2023-324923","authors":["Hitoshi Tabuchi","Justin Engelmann","Fumiatsu Maeda","Ryo Nishikawa","Toshihiko Nagasawa","Tomofusa Yamauchi","Mao Tanabe","Masahiro Akada","Keita Kihara","Yasuyuki Nakae","Yoshiaki Kiuchi","Miguel O. Bernabéu"],"tags":["Artificial intelligence","Medicine","Glaucoma","Retinal Vein","Retinal"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-14","doi":"https://doi.org/10.1136/bjo-2023-324923","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409202874","name":"Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions","source":"openalex","abstract":"Indonesia’s waste management system struggles to keep pace with rapid population growth and urbanization, resulting in inefficient waste collection, environmental degradation, and low recycling rates. The country predominantly relies on open dumping and landfilling, which contribute significantly to pollution and greenhouse gas emissions. This study explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in waste management, focusing on smart waste collection, automated sorting, real-time monitoring, and predictive analytics. AI-driven waste classification enhances recycling efficiency, while IoT-enabled smart bins optimize collection routes, reducing operational costs and landfill dependency. Despite these advantages, challenges such as high implementation costs, digital infrastructure limitations, and data privacy concerns hinder widespread adoption. This study highlights that policy support, investment in digital infrastructure, and stakeholder collaboration are crucial for successful implementation. By leveraging AI and IoT, Indonesia can significantly improve waste management efficiency, minimize environmental impact, and advance circular economy initiatives. The findings suggest that, with the right policies and investments, AI-driven waste management can drive sustainability, reduce waste mismanagement, and promote resource optimization, making it a vital strategy for future urban development in Indonesia.","url":"https://doi.org/10.62411/faith.3048-3719-85","authors":["Sameh Fuqaha","Nursetiawan Nursetiawan"],"tags":["Internet of Things","Computer science","Data science","Business","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-06","doi":"https://doi.org/10.62411/faith.3048-3719-85","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403203070","name":"A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence","source":"openalex","abstract":"Objective: Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance. Method: The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway. Results: The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance. Conclusion: The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.","url":"https://doi.org/10.1177/20552076241287356","authors":["Pantea Keikhosrokiani","Minna Isomursu","Johanna Uusimaa","Jukka Kortelainen"],"tags":["Computer science","Artificial intelligence","Convolutional neural network","Machine learning","Usability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1177/20552076241287356","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411620636","name":"Generative Artificial Intelligence in Primary Care: Qualitative Study of UK General Practitioners’ Views","source":"openalex","abstract":"Background: The potential for generative artificial intelligence (GenAI) to assist with clinical tasks is the subject of ongoing debate within biomedical informatics and related fields. Objective: This study aimed to explore general practitioners' (GPs') opinions about GenAI on primary care. Methods: In January 2025, we conducted a web-based survey of 1005 UK GPs' experiences and opinions of GenAI in clinical practice. This study involved a qualitative inductive descriptive analysis of a written response (\"comments\") to an open-ended question in the survey. After analysis, the interpretation of themes was also informed by the technology acceptance model. Results: Out of 1005 respondents, 611 GPs (61%) provided written comments in response to the free text question, totaling 7990 words. Comments were classified into 3 major themes and 8 subthemes in relation to GenAI in clinical practice. The major themes were (1) unfamiliarity, (2) ambivalence and anxiety, and (3) role in clinical tasks. \"Unfamiliarity\" encompassed a lack of experience and knowledge, and the need for training on GenAI. \"Ambivalence and anxiety\" included mixed expectations among GPs in relation to these tools, beliefs about diminished human connection, and skepticism about AI accountability. Finally, commenting on the role of GenAI in clinical tasks, GPs believed it would help with documentation. However, respondents questioned AI's clinical judgment and raised concerns about operational uncertainty concerning these tools. Female GPs were more likely to leave comments than male GPs, with 53% (324/611) of female GPs providing feedback compared to 41.1% (162/394) who did not. Chi-square tests confirmed this difference ((χ²₂= 14.6, P=.001). In addition, doctors who left comments were significantly more likely to have used GenAI in clinical practice compared with those who did not. Among all respondents, 71.7% (438/611) had not used GenAI. However, noncommenters were even less likely to have used it, with 80.7% (318/394) reporting no use. A chi-square test confirmed this difference (χ²₁=10.0, P=.002). Conclusions: This study provides timely insights into UK GPs' perspectives on the role, impact, and limitations of GenAI in primary care. However, the study has limitations. The qualitative data analyzed originates from a self-selected subset of respondents who chose to provide free-text comments, and these participants were more likely to have used GenAI tools in clinical practice. However, the substantial number of comments offers valuable insights into the diverse views held by GPs regarding GenAI. Furthermore, the majority of our respondents reported limited experience and training with these tools; however, many GPs perceived potential benefits of GenAI and ambient AI for documentation. Notably, 2 years after the widespread introduction of GenAI, GPs' persistent lack of understanding and training remains a critical concern. More extensive qualitative work would provide a more in-depth understanding of GPs' views.","url":"https://doi.org/10.2196/74428","authors":["Charlotte Blease","Anna Kharko","Carolina Garcia Sanchez","Joseph Alderman","Stephanie Kumpunen","David Sundemo","John Torous"],"tags":["Preprint","Primary care","Qualitative research","Psychology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-25","doi":"https://doi.org/10.2196/74428","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410236541","name":"Transformative impact of explainable artificial intelligence: bridging complexity and trust","source":"openalex","abstract":"Artificial Intelligence and Deep Learning have gained widespread popularity in all sectors and industries from healthcare to finance and industrial management. Explainable Artificial Intelligence (XAI) is urgent need to bridge the gap between the needs of society interpretability, and trust while maximizing AI benefits. This review XAI methodologies is presented as a comprehensive analysis of three different types model including model-specific, model-agnostic, and hybrid, along with their applications. The review discussed sectors of healthcare, finance, and industrial management etc. where XAI can be utilized for better results and gain trust. The generic and prominent key challenges in terms of trade-offs between accuracy and interpretability, the existing scalability issues, and ethical considerations were focused. The paper also discussed future directions, such as domain-specific frameworks interdisciplinary collaborations and standardized evaluation metrics, to be proposed for advancing XAI research and applications. The review highlighted the potential of XAI for upbringing a society equipped with modern AI with precise results, high responsibility and more transparency.","url":"https://doi.org/10.1007/s44163-025-00281-1","authors":["Girish Paliwal","Ashish Kumar","S. Prasad","Deepshikha Bhargava","Vijay Mohan Shrimal"],"tags":["Bridging (networking)","Transformative learning","Computer science","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-09","doi":"https://doi.org/10.1007/s44163-025-00281-1","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400974388","name":"The Role of Artificial Intelligence in the Primary Prevention of Common Musculoskeletal Diseases","source":"openalex","abstract":"BACKGROUND: Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, with a growing burden across all demographics. With advancements in technology, conversational artificial intelligence (AI) platforms such as ChatGPT (OpenAI, San Francisco, CA) have become instrumental in disseminating health information. This study evaluated the effectiveness of ChatGPT versions 3.5 and 4 in delivering primary prevention information for common MSDs, emphasizing that the study is focused on prevention and not on diagnosis. METHODS: This mixed-methods study employed the CLEAR tool to assess the quality of responses from ChatGPT versions in terms of completeness, lack of false information, evidence support, appropriateness, and relevance. Responses were evaluated independently by two expert raters in a blinded manner. Statistical analyses included Wilcoxon signed-rank tests and paired samples t-tests to compare the performance across versions. RESULTS: ChatGPT-3.5 and ChatGPT-4 effectively provided primary prevention information, with overall performance ranging from satisfactory to excellent. Responses for low back pain, fractures, knee osteoarthritis, neck pain, and gout received excellent scores from both versions. Additionally, ChatGPT-4 was better than ChatGPT-3.5 in terms of completeness (p = 0.015), appropriateness (p = 0.007), and relevance (p = 0.036), and ChatGPT-4 performed better across most medical conditions (p = 0.010). CONCLUSIONS: ChatGPT versions 3.5 and 4 are effective tools for disseminating primary prevention information for common MSDs, with ChatGPT-4 showing superior performance. This study underscores the potential of AI in enhancing public health strategies through reliable and accessible health communication. Advanced models such as ChatGPT-4 can effectively contribute to the primary prevention of MSDs by delivering high-quality health information, highlighting the role of AIs in addressing the global burden of chronic diseases. It is important to note that these AI tools are intended for preventive education purposes only and not for diagnostic use. Continuous improvements are necessary to fully harness the potential of AI in preventive medicine. Future studies should explore other AI platforms, languages, and secondary and tertiary prevention measures to maximize the utility of AIs in global health contexts.","url":"https://doi.org/10.7759/cureus.65372","authors":["Selkin Yilmaz Muluk","Nazli Olcucu"],"tags":["Medicine","Primary (astronomy)","Primary prevention","Pathology","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-25","doi":"https://doi.org/10.7759/cureus.65372","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406014077","name":"Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants","source":"openalex","abstract":"Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo-metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery. We begin by generating a large database of corrugated thermo-metamaterials with various cell structures and bending stiffnesses. An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allows for the automatic determination of the optimal microstructure for thermo-metamaterials with desired performance,i.e., target bending stiffness. We validate this approach by designing patient-specific spinal fusion implants and tracheal stents. The results demonstrate that the deployable thermo-metamaterial implants can achieve over a 200% increase in volume or cross-sectional area in their fully deployed states. Finally, we propose a broader vision for a clinically informed artificial intelligence design process that prioritizes biocompatibility, feasibility, and precision simultaneously for the development of high-performing and clinically viable implants. The feasibility of this proposed vision is demonstrated using a fuzzy analytic hierarchy process to customize thermo-metamaterial implants based on clinically relevant factors.","url":"https://doi.org/10.1021/acsami.4c17625","authors":["Pengcheng Jiao","Chenjie Zhang","Wenxuan Meng","Jiajun Wang","Daeik Jang","Zhangming Wu","Nitin Agarwal","Amir H. Alavi"],"tags":["Metamaterial","Materials science","Computer science","Inverse","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1021/acsami.4c17625","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4391225179","name":"Harnessing the potential of large language models in medical education: promise and pitfalls","source":"openalex","abstract":"OBJECTIVES: To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum. PROCESS: Narrative review of published literature contextualized by current reports of LLM application in medical education. CONCLUSIONS: LLMs like OpenAI's ChatGPT can potentially revolutionize traditional teaching methodologies. LLMs offer several potential advantages to students, including direct access to vast information, facilitation of personalized learning experiences, and enhancement of clinical skills development. For faculty and instructors, LLMs can facilitate innovative approaches to teaching complex medical concepts and fostering student engagement. Notable challenges of LLMs integration include the risk of fostering academic misconduct, inadvertent overreliance on AI, potential dilution of critical thinking skills, concerns regarding the accuracy and reliability of LLM-generated content, and the possible implications on teaching staff.","url":"https://doi.org/10.1093/jamia/ocad252","authors":["Trista M. Benítez","Yueyuan Xu","J. Donald Boudreau","Alfred Wei Chieh Kow","Fernando Bello","Le Van Phuoc","Xiaofei Wang","Xiaodong Sun","Gkk Leung","Yanyan Lan","Ya Xing Wang","Davy Cheng","Yih Chung Tham","Tien Yin Wong","Kevin C. Chung"],"tags":["Narrative","Misconduct","Process (computing)","Engineering ethics","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-24","doi":"https://doi.org/10.1093/jamia/ocad252","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403179089","name":"Survey of AI-driven techniques for ovarian cancer detection: state-of-the-art methods and open challenges","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s13721-024-00491-0","authors":["Samridhi Singh","Malti Kumari Maurya","Nagendra Singh","Rajeev Kumar"],"tags":["State (computer science)","Ovarian cancer","Open source","State of art","Health informatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-07","doi":"https://doi.org/10.1007/s13721-024-00491-0","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409961726","name":"Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia","source":"openalex","abstract":"In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the efficiency of its delivery. Countries like Saudi Arabia are facing unique adoption challenges in their public healthcare, these challenges are specific to AI literacy, understanding and effective usage of AI technologies. In addition, cultural, regulatory and operational barriers increase the complication of integrating AI literacy into public healthcare operations. In spite of its critical contribution in enabling sustainable healthcare development, limited studies have addressed these adoption challenges. Our study explores the AI literacy adoption barriers in context to Saudi Arabian public healthcare sector, focusing on its relevance for advancing healthcare operations and achieving Sustainable Development Goals (SDGs). The research aims to identifying and addressing the adoption challenges of Artificial Intelligence literacy within the public healthcare in Saudi Arabia. The research aims to enhance the understanding of AI literacy, its necessity for enhancing healthcare operations, and the specific hurdles that impede its successful AI adoption in Saudi Arabia's public healthcare ecosystem. The research employs a qualitative analysis using the T-O-E framework to explore the adoption challenges of AI literacy. Additionally, the Best-Worse Method (BWM) is applied to evaluate the adoption challenges to AI literacy adoption across various operational levels within Saudi Arabia's public healthcare supply chain. The study uncovers substantial adoption challenges at operational, tactical, and strategic level, including institutional readiness, data privacy, and compliance with regulatory frameworks. These challenges complicate the adoption of AI literacy in the Saudi public healthcare supply chains. The research offers critical insights into the various issues affecting the promotion of AI literacy in Saudi Arabia's public healthcare sector. This evidence-based study provides essential commendations for healthcare professionals and policymakers to effectively address the identified challenges, nurturing an environment beneficial to the integration of AI literacy and advancing the goals of sustainable healthcare development.","url":"https://doi.org/10.3389/fpubh.2025.1558772","authors":["Rakesh Kumar","Ajay Singh","Ahmed Subahi Ahmed Kassar","Mohammed Ismail Humaida","Sudhanshu Joshi","Manu Sharma"],"tags":["Health care","Health literacy","Literacy","Public health","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.3389/fpubh.2025.1558772","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407925494","name":"Artificial Intelligence-Enabled 4D Printed Hydrogel Wearables: Temperature and Ultraviolet Monitoring","source":"openalex","abstract":"Integrating smart wearable technology into daily life is becoming increasingly important for monitoring environmental factors such as temperature and ultraviolet (UV) radiation, both of which can impact health.Prolonged UV exposure is linked to skin cancer and eye damage, while major temperature changes can affect comfort and well-being.This study focuses on the manufacturing process of a novel smart wearable made from a hydrogel composite to monitor these environmental changes.The composite, consisting of hydroxyethyl methacrylate (HEMA) and polyethylene glycol diacrylate (PEGDA) with triphenylphosphine oxide (TPO) as the photoinitiator, is manufactured using digital light processing (DLP) 3-dimensional (3D)printing.The HEMA to PEGDA ratio was optimized to achieve plastic-like durability.Tensile tests showed that both the hydrogel composite and nylon samples exhibited almost identical stress-strain behavior, with a tensile strength of approximately 40 MPa.Thermochromic and photochromic powders were added to provide dynamic color responses to temperature and UV light.The thermal color transitions are linked to an artificial intelligence model specifically trained to decode these hues into temperature measurements.Additionally, the photochromic aspect of the wearables acts as a visual alarm against UV exposure and thus advises on the potential requirements of protective measures.","url":"https://doi.org/10.30919/mm1428","authors":["Mohamed A. El-Nemr","Yasmin Halawani","Ragi Adham Elkaffas","Rami Elkaffas","Yarjan Abdul Samad","Muhammed Hisham","Baker Mohammad","Haider Butt"],"tags":["Ultraviolet","Wearable computer","Materials science","Ultraviolet radiation","Wearable technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.30919/mm1428","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392807024","name":"Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots","source":"openalex","abstract":"This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.","url":"https://doi.org/10.3390/biomedinformatics4010047","authors":["James C. L. Chow","Valerie Wong","Kay Li"],"tags":["Generative grammar","Transformer","Computer science","Health care","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-14","doi":"https://doi.org/10.3390/biomedinformatics4010047","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405569759","name":"The Rise of Artificial Intelligence and Emerging Ethical and Social Concerns","source":"openalex","abstract":"This study conducted a systematic literature review to examine the trajectory of AI research over the past five years, from 2019 to 2023, focusing on emerging ethical and social concerns related to the deployment of AI technologies. The study also aimed at enhancing the understanding and promotion of robust AI ethics for societal benefit. The explosive rise of the internet, AI, and mobile technology has dramatically changed how we live, work, consume, learn, and communicate. AI is improving the quality of human life but poses dangers from unintended disastrous and undesirable outcomes, if unregulated. Cyberattacks on critical infrastructure networks pose grave threats, exponentially increasing risks of fatalities and service breakdowns. AI can instantly diagnose rare diseases, robots can perform precision surgeries and chatbots can write assignments for students. AI is also used for surveillance, monitoring financial activities and autonomous weapon systems in the military. Two hundred and twenty-five publications from Scopus database were selected to determine the central themes, the affordances and constraints of AI and principles that enhance public trust and accountability. Results show an upward trajectory in AI ethics research from 6.2% in 2019 to 40.3% in 2023. Furthermore, results revealed the emerging ethical and social concerns in major socioeconomic domains. Results also show that AI collects data about individuals and data breaches have catastrophic consequences. The growing complexity and opacity of AI systems make it hard to understand decision-making, hindering accountability for developers and deployers. AI algorithms may be biased against minorities; perpetuating prejudices. The study contributes to the ongoing discourse on the ethical and societal concerns surrounding unregulated AI adoption. The issues identified in this study may assist policymakers in developing frameworks and policies for AI usage.","url":"https://doi.org/10.5772/acrt.20240020","authors":["Vusumuzi Maphosa"],"tags":["Engineering ethics","Psychology","Sociology","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-19","doi":"https://doi.org/10.5772/acrt.20240020","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401523530","name":"Artificial Intelligence in Remote Monitoring and Telemedicine","source":"openalex","abstract":"Telemedicine, remote monitoring coupled with Artificial Intelligence innovations are redesigning the face of the healthcare sector in record way and increasing satisfaction levels for patients’ clinical enhancements, reduced charges and increased effectiveness in the delivery of services. The applied and advanced AI technologies consist of machine learning, natural language processing, and predictive analytics used in applications for RPM, PT, and VC. In RPM, AI augments the processes of data aggregation and data analysis of patients’ real-time health data from wearable and other digital health technologies. The former capability makes it possible to check for the possible infections, caretaker interferences, and regular management of recurring diseases. For example, AI can help in anticipating incidents such as heart attacks based on previous data of patients, thus preventive care is implemented. On the other hand, AI is incorporated in telemedicine through applying virtual health assistance, diagnostic tools and even chats. With the help of AI, virtual assistants can filter patients’ complaints, give first-stages diagnoses, and suggest necessary treatments, which will decrease loads of clinicians and increase availability of medical services. This review analyses the current uses, advantages and disadvantages of AI in remote observation and m-telemedicine. Here the details what kind of AI technologies implied in different spheres of healthcare, diagnosing, treatment planning, and individual therapies. Also, it examines how the responsible AI should be implemented ethically in a healthcare setting. Moreover, it presents the further prospects of AI in telemedicine which underlines the importance of AI development and enhancement of patient treatment.","url":"https://doi.org/10.60087/jaigs.v1i1.202","authors":["Nasrullah Abbasi"],"tags":["Telemedicine","Computer science","Medical emergency","Medicine","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-22","doi":"https://doi.org/10.60087/jaigs.v1i1.202","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402586479","name":"Interdisciplinary research in artificial intelligence: Lessons from COVID-19","source":"openalex","abstract":"Abstract Artificial intelligence (AI) is widely regarded as one of the most promising technologies for advancing science, fostering innovation, and solving global challenges. Recent years have seen a push for teamwork between experts from different fields and AI specialists, but the outcomes of these collaborations have yet to be studied. We focus on approximately 15,000 papers at the intersection of AI and COVID-19—arguably one of the major challenges of recent decades—and show that interdisciplinary collaborations between medical professionals and AI specialists have largely resulted in publications with low visibility and impact. Our findings suggest that impactful research depends less on the overall interdisciplinary of author teams and more on the diversity of knowledge they actually harness in their research. We conclude that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI.","url":"https://doi.org/10.1162/qss_a_00329","authors":["Diletta Abbonato","Stefano Bianchini","Floriana Gargiulo","Tommaso Venturini"],"tags":["Realm","Coronavirus disease 2019 (COVID-19)","Engineering ethics","Teamwork","Transdisciplinarity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1162/qss_a_00329","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4285364236","name":"Developing, Implementing, and Evaluating an Artificial Intelligence–Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study","source":"openalex","abstract":"BACKGROUND: Approximately 1 in 3 Canadians will experience an addiction or mental health challenge at some point in their lifetime. Unfortunately, there are multiple barriers to accessing mental health care, including system fragmentation, episodic care, long wait times, and insufficient support for health system navigation. In addition, stigma may further reduce an individual's likelihood of seeking support. Digital technologies present new and exciting opportunities to bridge significant gaps in mental health care service provision, reduce barriers pertaining to stigma, and improve health outcomes for patients and mental health system integration and efficiency. Chatbots (ie, software systems that use artificial intelligence to carry out conversations with people) may be explored to support those in need of information or access to services and present the opportunity to address gaps in traditional, fragmented, or episodic mental health system structures on demand with personalized attention. The recent COVID-19 pandemic has exacerbated even further the need for mental health support among Canadians and called attention to the inefficiencies of our system. As health care workers and their families are at an even greater risk of mental illness and psychological distress during the COVID-19 pandemic, this technology will be first piloted with the goal of supporting this vulnerable group. OBJECTIVE: This pilot study seeks to evaluate the effectiveness of the Mental Health Intelligent Information Resource Assistant in supporting health care workers and their families in the Canadian provinces of Alberta and Nova Scotia with the provision of appropriate information on mental health issues, services, and programs based on personalized needs. METHODS: The effectiveness of the technology will be assessed via voluntary follow-up surveys and an analysis of client interactions and engagement with the chatbot. Client satisfaction with the chatbot will also be assessed. RESULTS: This project was initiated on April 1, 2021. Ethics approval was granted on August 12, 2021, by the University of Alberta Health Research Board (PRO00109148) and on April 21, 2022, by the Nova Scotia Health Authority Research Ethics Board (1027474). Data collection is anticipated to take place from May 2, 2022, to May 2, 2023. Publication of preliminary results will be sought in spring or summer 2022, with a more comprehensive evaluation completed by spring 2023 following the collection of a larger data set. CONCLUSIONS: Our findings can be incorporated into public policy and planning around mental health system navigation by Canadian mental health care providers-from large public health authorities to small community-based, not-for-profit organizations. This may serve to support the development of an additional touch point, or point of entry, for individuals to access the appropriate services or care when they need them, wherever they are. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33717.","url":"https://doi.org/10.2196/33717","authors":["Jasmine M. Noble","Ali Zamani","MohamadAli Gharaat","Dylan Merrick","Nathanial Maeda","Alex Lambe Foster","Isabella Nikolaidis","Rachel Goud","Eleni Stroulia","Vincent I. O. Agyapong","Andrew J. Greenshaw","Simon J. Lambert","Dave Gallson","Ken Porter","Debbie Turner","Osmar R. Zai͏̈ane"],"tags":["Mental health","Health care","Nursing","Mental illness","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-23","doi":"https://doi.org/10.2196/33717","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406015538","name":"Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes","source":"openalex","abstract":"As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the number of healthy red blood cells is considered as Anemia. This condition also leads to the decrease in the oxygen carrying capacity of the blood. The main goal of this research is to develop a dependable method for diagnosing Aplastic Anemia and Iron Deficiency Anemia by examining the blood test attributes. As of today, there are no studies which use Interpretable Artificial Intelligence to perform the above differential diagnosis. The dataset used in this study is collected from Kasturba Medical College, Manipal. The dataset consisted of various blood test attributes such as Red Blood cell count, Hemoglobin level, Mean Corpuscular Volume, etc. One of the trending topics in Machine Learning is Explainable Artificial Intelligence. They are known to demystify the machine learning outputs to all its stakeholders. Hence, Five XAI tools including SHAP, LIME, Eli5, Qlattice and Anchor are used to understand the model's predictions. The importance characteristics according to XAI models are PLT, PCT, MCV, PDW, HGB, ABS LYMP, WBC, MCH, and MCHC. are employed to train and test the data. The goal of using data analytic techniques is to give medical professionals a useful tool that improves decision-making, enhances resource management, and eventually raises the standard of patient care. By considering the unique qualities of each patient, medical professionals who must rely on AI-assisted diagnosis and treatment suggestions, XAI offers arguments to strengthen their faith in the model outcomes.","url":"https://doi.org/10.1038/s41598-024-84120-w","authors":["B S Darshan","Niranjana Sampathila","G. Muralidhar Bairy","Srikanth Prabhu","Sushma Belurkar","Krishnaraj Chadaga","S. Nandish"],"tags":["Anemia","Medicine","Test (biology)","Artificial intelligence","Blood test"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-02","doi":"https://doi.org/10.1038/s41598-024-84120-w","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404318928","name":"Artificial Intelligence, Data Protection, Privacy, and Doxxing","source":"openalex","abstract":"Libby R Copeland-Halperin, MD, Claude Oppikofer, MD; Artificial Intelligence, Data Protection, Privacy, and Doxxing, Aesthetic Surgery Journal, , sjae219,","url":"https://doi.org/10.1093/asj/sjae219","authors":["Libby R. Copeland‐Halperin","Claude Oppikofer"],"tags":["Medicine","Internet privacy","Privacy protection","Computer security","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-11","doi":"https://doi.org/10.1093/asj/sjae219","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403259484","name":"Artificial Intelligence and Machine Learning for materials","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.cossms.2024.101202","authors":["Yuebing Zheng"],"tags":["Artificial intelligence","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-09","doi":"https://doi.org/10.1016/j.cossms.2024.101202","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411131699","name":"Harnessing Artificial Intelligence in Lifestyle Medicine: Opportunities, Challenges, and Future Directions","source":"openalex","abstract":"Lifestyle medicine (LM) offers a transformative, evidence-based approach to preventing, managing, and potentially reversing chronic diseases by targeting modifiable lifestyle factors such as nutrition, physical activity, sleep, stress, substance use, and social connectivity. However, real-world implementation of LM is often hindered by patient adherence issues, limited clinical time, and the need for ongoing personalized support. Artificial intelligence (AI), with its capabilities in data processing, pattern recognition, and predictive modeling, presents a unique opportunity to overcome these barriers and enhance the reach and precision of LM interventions. This narrative review explores AI's integration into LM's core domains. In nutrition, AI facilitates real-time dietary assessment and personalized recommendations through image recognition and machine learning. In physical activity and fitness, AI-powered wearable devices deliver tailored feedback, support virtual coaching, and predict injury risk. AI applications in sleep medicine allow for continuous, non-invasive monitoring and the early detection of sleep disorders. AI-driven cognitive behavioral therapy chatbots and biosensor-based stress prediction tools provide scalable, cost-effective support for mental health and stress management. Moreover, AI is pivotal in chronic disease prevention by integrating lifestyle data with electronic health records to forecast disease trajectories and optimize interventions. Despite these advances, several challenges remain. Data privacy concerns, algorithmic bias, regulatory ambiguities, and varying user trust and engagement levels must be addressed to ensure equitable and ethical implementation. AI's integration with digital twin technology and precision LM represents the next frontier in personalized health. As LM continues to evolve, AI will be indispensable in driving a more proactive, participatory, and person-centered model of care that meets the complex demands of chronic disease management in the 21st century.","url":"https://doi.org/10.7759/cureus.85580","authors":["Diana K Saeed","Abdulqadir J. Nashwan"],"tags":["Medicine","Lifestyle medicine","Precision medicine","Engineering ethics","Family medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-08","doi":"https://doi.org/10.7759/cureus.85580","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405241694","name":"BDLT-IoMT—a novel architecture: SVM machine learning for robust and secure data processing in Internet of Medical Things with blockchain cybersecurity","source":"openalex","abstract":"The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.","url":"https://doi.org/10.1007/s11227-024-06782-7","authors":["Abdullah Ayub Khan","Asif Ali Laghari","Abdullah M. Baqasah","Rex Bacarra","Roobaea Alroobaea","Majed Alsafyani","Jamil Abedalrahim Jamil Alsayaydeh"],"tags":["Computer science","Blockchain","The Internet","Architecture","Support vector machine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-10","doi":"https://doi.org/10.1007/s11227-024-06782-7","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402337448","name":"Artificial intelligence in Ultrasound: Pearls and pitfalls in 2024","source":"openalex","abstract":"Keywords AI - radiomics - ultrasound - ethics Publication History Article published online: 06 September 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany","url":"https://doi.org/10.1055/a-2368-9201","authors":["Bernardo Stefanini","Alice Giamperoli","Eleonora Terzi","Fabio Piscaglia"],"tags":["Ultrasound","Medicine","Radiology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-06","doi":"https://doi.org/10.1055/a-2368-9201","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403986795","name":"Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities","source":"openalex","abstract":"Numerous cancer therapies have detrimental cardiovascular effects on cancer survivors. Cardiovascular toxicity can span the course of cancer treatment and is influenced by several factors. To mitigate these risks, cardio-oncology has evolved, with an emphasis on prevention and treatment of cardiovascular complications resulting from the presence of cancer and cancer therapy. Artificial intelligence (AI) holds multifaceted potential to enhance cardio-oncologic outcomes. AI algorithms are currently utilizing clinical data input to identify patients at risk for cardiac complications. Additional application opportunities for AI in cardio-oncology involve multimodal cardiovascular imaging, where algorithms can also utilize imaging input to generate predictive risk profiles for cancer patients. The impact of AI extends to digital health tools, playing a pivotal role in the development of digital platforms and wearable technologies. Multidisciplinary teams have been formed to implement and evaluate the efficacy of these technologies, assessing AI-driven clinical decision support tools. Other avenues similarly support practical application of AI in clinical practice, such as incorporation into electronic health records (EHRs) to detect patients at risk for cardiovascular diseases. While these AI applications may help improve preventive measures and facilitate tailored treatment to patients, they are also capable of perpetuating and exacerbating healthcare disparities, if trained on limited, homogenous datasets. However, if trained and operated appropriately, AI holds substantial promise in positively influencing clinical practice in cardio-oncology. In this review, we explore the impact of AI on cardio-oncology care, particularly regarding predicting cardiotoxicity from cancer treatments, while addressing racial and ethnic biases in algorithmic implementation.","url":"https://doi.org/10.1016/j.ahjo.2024.100479","authors":["Gift Echefu","Rushabh Shah","Zanele Sanchez","John Rickards","Sherry‐Ann Brown"],"tags":["Oncology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-01","doi":"https://doi.org/10.1016/j.ahjo.2024.100479","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404729712","name":"Artificial Intelligence in Orthodontics: Concerns, Conjectures, and Ethical Dilemmas","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.identj.2024.11.002","authors":["Rosalia Leonardi","Nikhillesh Vaiid"],"tags":["Orthodontics","Psychology","Medicine","Engineering ethics","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.1016/j.identj.2024.11.002","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4390829176","name":"Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare","source":"openalex","abstract":"Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.","url":"https://doi.org/10.3390/app14020675","authors":["S. Williamson","Victor R. Prybutok"],"tags":["Health care","Autonomy","Confidentiality","Transformative learning","Information privacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-12","doi":"https://doi.org/10.3390/app14020675","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408884742","name":"Improving Clinical Documentation with Artificial Intelligence: a Systematic Review","source":"openalex","abstract":"Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.","url":"https://doi.org/10.17605/osf.io/y2aqs","authors":["Rishi P. Singh","Justin C. Muste","Scott W. Perkins"],"tags":["Documentation","Computer science","Psychology","Artificial intelligence","Programming language"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.17605/osf.io/y2aqs","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400358085","name":"ARTIFICIAL INTELLIGENCE (AI) IN SUSTAINABLE TOURISM: BIBLIOMETRIC ANALYSIS","source":"openalex","abstract":"Artificial Intelligence (AI) has gained attention in tourism, which requires its sustainability. Our study focuses on a bibliometric analysis of AI in sustainable tourism using 174 manuscripts from 2000 to 2022. One of the main findings is that 'intelligence' appears frequently, followed by related terms such as work, performance, resources, sustainability, impact, optimization and management. There is no previous evidence on AI in the context of sustainable tourism to explain how public managers or politicians design public policies to create and improve resource efficiency. La Inteligencia Artificial (IA) ha ganado atención en el turismo, que requiere su sostenibilidad. Nuestro estudio se centra en un análisis bibliométrico de la IA en el turismo sostenible utilizando 174 manuscritos de 2000 a 2022. Una de las principales conclusiones es que \"inteligencia\" aparece con frecuencia, seguida de términos relacionados como trabajo, rendimiento, recursos, sostenibilidad, impacto, optimización y gestión. No existen pruebas previas sobre la IA en el contexto del turismo sostenible que expliquen cómo los gestores públicos o los políticos diseñan políticas públicas para crear y mejorar la eficiencia de los recursos.","url":"https://doi.org/10.6018/turismo.616431","authors":["Paola Hermosa Del Vasto","María Lourdes Arco‐Castro"],"tags":["Tourism","Sustainable tourism","Business","Regional science","Geography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-28","doi":"https://doi.org/10.6018/turismo.616431","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409418757","name":"Ethical implications related to processing of personal data and artificial intelligence in humanitarian crises: a scoping review","source":"openalex","abstract":"BACKGROUND: Humanitarian organizations are rapidly expanding their use of data in the pursuit of operational gains in effectiveness and efficiency. Ethical risks, particularly from artificial intelligence (AI) data processing, are increasingly recognized yet inadequately addressed by current humanitarian data protection guidelines. This study reports on a scoping review that maps the range of ethical issues that have been raised in the academic literature regarding data processing of people affected by humanitarian crises. METHODS: We systematically searched databases to identify peer-reviewed studies published since 2010. Data and findings were standardized, grouping ethical issues into the value categories of autonomy, beneficence, non-maleficence, and justice. The study protocol followed Arksey and O'Malley's approach and PRISMA reporting guidelines. RESULTS: We identified 16,200 unique records and retained 218 relevant studies. Nearly one in three (n = 66) discussed technologies related to AI. Seventeen studies included an author from a lower-middle income country while four included an author from a low-income country. We identified 22 ethical issues which were then grouped along the four ethical value categories of autonomy, beneficence, non-maleficence, and justice. Slightly over half of included studies (n = 113) identified ethical issues based on real-world examples. The most-cited ethical issue (n = 134) was a concern for privacy in cases where personal or sensitive data might be inadvertently shared with third parties. Aside from AI, the technologies most frequently discussed in these studies included social media, crowdsourcing, and mapping tools. CONCLUSIONS: Studies highlight significant concerns that data processing in humanitarian contexts can cause additional harm, may not provide direct benefits, may limit affected populations' autonomy, and can lead to the unfair distribution of scarce resources. The increase in AI tool deployment for humanitarian assistance amplifies these concerns. Urgent development of specific, comprehensive guidelines, training, and auditing methods is required to address these ethical challenges. Moreover, empirical research from low and middle-income countries, disproportionally affected by humanitarian crises, is vital to ensure inclusive and diverse perspectives. This research should focus on the ethical implications of both emerging AI systems, as well as established humanitarian data management practices. TRIAL REGISTRATION: Not applicable.","url":"https://doi.org/10.1186/s12910-025-01189-2","authors":["Tino Kreutzer","James Orbinski","Lora Appel","Aijun An","Jerome Marston","Ella Boone","Patrick Vinck"],"tags":["Beneficence","Autonomy","Philosophy of medicine","Economic Justice","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-14","doi":"https://doi.org/10.1186/s12910-025-01189-2","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399104858","name":"Perspectives on Artificial Intelligence Adoption for European Union Elderly in the Context of Digital Skills Development","source":"openalex","abstract":"In today’s digitalized era, embracing new and emerging technologies is a requirement to remain competitive. The present research investigates the adoption of artificial intelligence (AI) by the elderly in the European landscape, emphasizing the importance of individuals’ digital skills. As has already been globally recognized, the most imminent demographic challenge is no longer represented by the rapid growth of the population but by its aging. Thus, the paper initially analyzed European perspectives on AI adoption, also discussing the importance of focusing on seniors. A bibliometric analysis was required afterward, and the review of the resulting relevant scientific publications uncovered gaps in understanding the relationship between older individuals and AI, particularly in terms of digital competence. Further exploration considered the EU population’s digital literacy and cultural influences using Hofstede’s model, while also identifying potential ways to improve the elderly’s digital skills and promote the adoption of AI. Results indicate a growing interest in AI adoption among the elderly, underscoring the urgent need for digital skills development. The imperative of personalized approach implementations, such as specialized courses, personalized training sessions, or mentoring programs, was underscored. Moreover, the importance of targeted strategies and collaborative efforts to ensure equitable participation in the digital age was identified as a prerequisite for AI adoption by seniors. In terms of potential implications, the research can serve as a starting point for various stakeholders in promoting an effective and sustainable adoption of AI among older citizens in the EU.","url":"https://doi.org/10.3390/su16114579","authors":["Ioana Andreea Bogoslov","Sorina Corman","Anca Elena Lungu"],"tags":["European union","Context (archaeology)","Knowledge management","Business","Engineering management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-28","doi":"https://doi.org/10.3390/su16114579","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399553949","name":"Artificial intelligence for neuro MRI acquisition: a review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s10334-024-01182-7","authors":["Hongjia Yang","Guanhua Wang","Ziyu Li","Haoxiang Li","Jialan Zheng","Yuxin Hu","Xiaozhi Cao","Congyu Liao","Huihui Ye","Qiyuan Tian"],"tags":["Computer science","Artificial intelligence","Cognitive science","Neuroscience","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-26","doi":"https://doi.org/10.1007/s10334-024-01182-7","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412707688","name":"Clinical prediction models using artificial intelligence approaches in dementia","source":"openalex","abstract":"BACKGROUND: While nearly half of all dementia cases are potentially preventable, early detection and targeted interventions are critical. Artificial intelligence (AI)-enhanced clinical prediction models offer promising tools to improve diagnostic and prognostic accuracy by leveraging machine learning (ML) to integrate diverse data sources. This systematic review evaluates the development, performance, and clinical applicability of AI-based prediction models in dementia. METHODS: Searches of PubMed, Embase, and Web of Science identified peer-reviewed studies up to October 2024, focusing on AI-based models predicting dementia onset. Included studies were assessed for model accuracy, bias, and generalizability using the PROBAST tool. Data extraction adhered to the TRIPOD and CHARMS frameworks, capturing study design, participant demographics, predictor variables, and performance metrics. RESULTS: Among 2699 articles initially screened, 21 studies were included, encompassing over 1 million participants. AI models, extremely heterogenous for their nature, demonstrated good predictive accuracy, with a mean area under the curve of 0.845. While internal validation was conducted in all studies, external validation was limited. Models incorporating ML methods like random forests and support vector machines outperformed traditional approaches. The most used parameters were clinical and cognitive data, whilst data about biomarkers were the less used. Risk of bias was generally low, though calibration and generalizability remained challenges. CONCLUSIONS: AI-based prediction models show strong potential for early dementia detection and personalized care. However, their integration into clinical practice requires addressing issues of external validation, data representativeness, and model interpretability. Further research should focus on robust validation and ethical implementation to optimize their utility in dementia care.","url":"https://doi.org/10.1007/s40520-025-03112-6","authors":["Nicola Veronese","Francesco Bolzetta","Livia Gallo","Giorgia Durante","Laura Vernuccio","Carlo Saccaro","Caterina Maria Gambino","Carlo Custodero","Piero Portincasa","Andrea Morotti","Alice Galli","Chiara Trasciatti","Alessandro Padovani","Andrea Pilotto","Mario Barbagallo"],"tags":["Generalizability theory","Dementia","Machine learning","Interpretability","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.1007/s40520-025-03112-6","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412556130","name":"Lack of methodological rigor and limited coverage of generative artificial intelligence in existing artificial intelligence reporting guidelines: a scoping review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jclinepi.2025.111903","authors":["Xufei Luo","Bingyi Wang","Qianling Shi","Zijun Wang","Honghao Lai","Hui Liu","Yishan Qin","Fengxian Chen","Xuping Song","Long Ge","Lu Zhang","Zhaoxiang Bian","Yaolong Chen","Hongfeng He","Ye Wang","Haodong Li","Huayu Zhang","Di Zhu","Yuanyuan Yao","Dongrui Peng","Zhewei Li","Jie Zhang","Yishan Qin","Fan Wang","Zhenyu Tang","Yueyan Li","Hanxiang Liu","Jungang Zhao"],"tags":["Multidisciplinary approach","Systematic review","MEDLINE","Guideline","Scope (computer science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-18","doi":"https://doi.org/10.1016/j.jclinepi.2025.111903","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404691645","name":"Artificial Intelligence and Statistical Models for the Prediction of Radiotherapy Toxicity in Prostate Cancer: A Systematic Review","source":"openalex","abstract":"Background: Prostate cancer (PCa) is the second most common cancer in men, and radiotherapy (RT) is one of the main treatment options. Although effective, RT can cause toxic side effects. The accurate prediction of dosimetric parameters, enhanced by advanced technologies and AI-based predictive models, is crucial to optimize treatments and reduce toxicity risks. This study aims to explore current methodologies for predictive dosimetric parameters associated with RT toxicity in PCa patients, analyzing both traditional techniques and recent innovations. Methods: A systematic review was conducted using the PubMed, Scopus, and Medline databases to identify dosimetric predictive parameters for RT in prostate cancer. Studies published from 1987 to April 2024 were included, focusing on predictive models, dosimetric data, and AI techniques. Data extraction covered study details, methodology, predictive models, and results, with an emphasis on identifying trends and gaps in the research. Results: After removing duplicate manuscripts, 354 articles were identified from three databases, with 49 shortlisted for in-depth analysis. Of these, 27 met the inclusion criteria. Most studies utilized logistic regression models to analyze correlations between dosimetric parameters and toxicity, with the accuracy assessed by the area under the curve (AUC). The dosimetric parameter studies included Vdose, Dmax, and Dmean for the rectum, anal canal, bowel, and bladder. The evaluated toxicities were genitourinary, hematological, and gastrointestinal. Conclusions: Understanding dosimetric parameters, such as DVH, Dmax, and Dmean, is crucial for optimizing RT and predicting toxicity. Enhanced predictive accuracy improves treatment effectiveness and reduces side effects, ultimately improving patients’ quality of life. Emerging artificial intelligence and machine learning technologies offer the potential to further refine RT in PCa by analyzing complex data, and enabling more personalized treatment approaches.","url":"https://doi.org/10.3390/app142310947","authors":["Antonio Piras","Rosario Corso","Viviana Benfante","Muhammad Ali","Riccardo Laudicella","Pierpaolo Alongi","Andrea D’Aviero","Davide Cusumano","Luca Boldrini","Giuseppe Salvaggio","Domenico Di Raimondo","Antonino Tuttolomondo","Albert Comelli"],"tags":["Prostate cancer","Radiation therapy","Medicine","Oncology","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.3390/app142310947","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400307907","name":"Reasoning with large language models for medical question answering","source":"openalex","abstract":"OBJECTIVES: To investigate approaches of reasoning with large language models (LLMs) and to propose a new prompting approach, ensemble reasoning, to improve medical question answering performance with refined reasoning and reduced inconsistency. MATERIALS AND METHODS: We used multiple choice questions from the USMLE Sample Exam question files on 2 closed-source commercial and 1 open-source clinical LLM to evaluate our proposed approach ensemble reasoning. RESULTS: On GPT-3.5 turbo and Med42-70B, our proposed ensemble reasoning approach outperformed zero-shot chain-of-thought with self-consistency on Steps 1, 2, and 3 questions (+3.44%, +4.00%, and +2.54%) and (2.3%, 5.00%, and 4.15%), respectively. With GPT-4 turbo, there were mixed results with ensemble reasoning again outperforming zero-shot chain-of-thought with self-consistency on Step 1 questions (+1.15%). In all cases, the results demonstrated improved consistency of responses with our approach. A qualitative analysis of the reasoning from the model demonstrated that the ensemble reasoning approach produces correct and helpful reasoning. CONCLUSION: The proposed iterative ensemble reasoning has the potential to improve the performance of LLMs in medical question answering tasks, particularly with the less powerful LLMs like GPT-3.5 turbo and Med42-70B, which may suggest that this is a promising approach for LLMs with lower capabilities. Additionally, the findings show that our approach helps to refine the reasoning generated by the LLM and thereby improve consistency even with the more powerful GPT-4 turbo. We also identify the potential and need for human-artificial intelligence teaming to improve the reasoning beyond the limits of the model.","url":"https://doi.org/10.1093/jamia/ocae131","authors":["Mary M. Lucas","Justin Yang","Jon K Pomeroy","Christopher C. Yang"],"tags":["Consistency (knowledge bases)","Computer science","Verbal reasoning","Qualitative reasoning","Ensemble learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-20","doi":"https://doi.org/10.1093/jamia/ocae131","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403479497","name":"Perceptions of Artificial Intelligence and Its Impact on Academic Integrity Among University Students in Peru and Chile: An Approach to Sustainable Education","source":"openalex","abstract":"In a context where artificial intelligence (AI) is transforming higher education, this study analyzes how students’ perceptions of AI influence their academic integrity (INA), with a focus on sustainable education. Through a correlational-explanatory analysis based on Structural Equation Models (SEMs) applied to a sample of 659 students from 13 universities in Chile and Peru, it is observed that AI has a significant and direct impact on academic integrity in both countries (β = 0.44). In Peru, the most influential dimension is trust in education (λ = 0.86), followed by social, economic, security, and risk implications (λ = 0.78), while attitudes towards AI also have a direct impact on integrity factors (β = 0.15). In Chile, the dimensions of trust in education (λ = 0.83) and social and economic impact (λ = 0.79) are most relevant, and the relationships between the dimensions of academic integrity such as justice, respect, and responsibility (λ = 0.71) are stronger. The study highlights the importance of incorporating AI literacy into educational curricula and developing regulatory frameworks that promote its ethical use, linking these actions to sustainable education. The findings highlight the need for sustainable educational approaches that enhance understanding of AI and ensure that its use in academia is beneficial, ethical, and contributes to sustainable development.","url":"https://doi.org/10.3390/su16209005","authors":["Sam M. Espinoza Vidaurre","Norma C. Velásquez Rodríguez","Renza L. Gambetta Quelopana","Ana N. Martínez Valdivia","Ernesto Leo Rossi","Marco Antonio Nolasco-Mamani"],"tags":["Perception","Academic integrity","Sustainable development","University education","Higher education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-17","doi":"https://doi.org/10.3390/su16209005","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409457779","name":"University Students’ Usage of Generative Artificial Intelligence for Sustainability: A Cross-Sectional Survey from China","source":"openalex","abstract":"The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the technology acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate the current situations and challenges of Chinese university students using GenAI in four typical task scenarios. This was performed using a cross-sectional research design. The data were collected via questionnaire, with 486 undergraduates from a Chinese university participating. The data analysis methods include descriptive statistics, inferential statistics, and content analysis. The results show that more than 70% of university students actively use GenAI, but nearly half of them are not very proficient in its use. Doubao and ERNIE Bot are the GenAI tools they prefer most. The primary functions they use are text production and information retrieval. They mainly learn the relevant knowledge and skills through self-media and knowledge-sharing platforms. Among the four typical task scenarios, GenAI is widely used in course learning and research activities, while its application in daily life and job search is relatively limited. The analysis of demographic variables shows that grade and major have a significant impact on university students’ use of GenAI. In addition, university students suggest that universities should offer relevant courses or lectures and provide comprehensive technical support to improve the popularity and operability of GenAI. This study provides suggestions for universities, education administration departments, and technology development departments to improve GenAI services. It will help universities optimize the allocation of educational resources and promote educational equity for sustainability.","url":"https://doi.org/10.3390/su17083541","authors":["Lin Xiao","How Shwu Pyng","Ahmad Fauzi Mohd Ayub","Zhihui Zhu","Jianping Gao","Zehu Qing"],"tags":["Sustainability","China","Cross-sectional study","Generative grammar","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-15","doi":"https://doi.org/10.3390/su17083541","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410774085","name":"Artificial Intelligence in Ecuadorian SMEs: Drivers and Obstacles to Adoption","source":"openalex","abstract":"This study analyzes the current state of artificial intelligence (AI) adoption among micro-, small-, and medium-sized enterprises (MSMEs) in Ecuador, with a focus on its application across core business functions. Using a stratified random sample of 385 firms from the most representative economic sectors, a survey instrument was designed to assess three dimensions: access to AI-enabling conditions, degree of AI utilization, and organizational characteristics. The results reveal that AI adoption remains limited and highly concentrated in marketing-related functions, particularly in content generation and social media automation, with minimal implementation in finance, logistics, and human resource management. The study also identifies the main barriers hindering AI adoption. The lack of qualified professionals and the unavailability of structured databases emerged as the most critical obstacles, followed by limited financial capacity. One-way ANOVA and Kruskal–Wallis tests confirmed significant differences in AI adoption levels based on company size and sector, especially in areas such as inventory optimization and design prototyping. These findings highlight a gap between the potential of AI technologies and their real-world implementation in Ecuadorian MSMEs. They underscore the need for targeted strategies focused on workforce training, digital infrastructure development, and institutional support to promote broader and more effective AI integration.","url":"https://doi.org/10.3390/info16060443","authors":["Reyner Pérez-Campdesuñer","Alexander Sánchez-Rodríguez","Gelmar García-Vidal","Rodobaldo Martínez-Vivar","Margarita De Miguel-Guzmán"],"tags":["Business","Knowledge management","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-27","doi":"https://doi.org/10.3390/info16060443","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405728415","name":"Spotlight on the 2024 ESC/EACTS management of atrial fibrillation guidelines: 10 novel key aspects","source":"openalex","abstract":"Atrial fibrillation (AF) remains the most common cardiac arrhythmia worldwide and is associated with significant morbidity and mortality. The European Society of Cardiology (ESC)/European Association for Cardio-Thoracic Surgery (EACTS) have recently released the 2024 guidelines for the management of AF. This review highlights 10 novel aspects of the ESC/EACTS 2024 Guidelines. The AF-CARE framework is introduced, a structural approach that aims to improve patient care and outcomes, comprising of four pillars: [C] Comorbidity and risk factor management, [A] Avoid stroke and thromboembolism, [R] Reduce symptoms by rate and rhythm control, and [E] Evaluation and dynamic reassessment. Additionally, graphical patient pathways are provided to enhance clinical application. A significant shift is the new emphasis on comorbidity and risk factor control to reduce AF recurrence and progression. Individualized assessment of risk is suggested to guide the initiation of oral anticoagulation to prevent thromboembolism. New guidance is provided for anticoagulation in patients with trigger-induced and device-detected sub-clinical AF, ischaemic stroke despite anticoagulation, and the indications for percutaneous/surgical left atrial appendage exclusion. AF ablation is a first-line rhythm control option for suitable patients with paroxysmal AF, and in specific patients, rhythm control can improve prognosis. The AF duration threshold for early cardioversion was reduced from 48 to 24 h, and a wait-and-see approach for spontaneous conversion is advised to promote patient safety. Lastly, strong emphasis is given to optimize the implementation of AF guidelines in daily practice using a patient-centred, multidisciplinary and shared-care approach, with the simultaneous launch of a patient version of the guideline.","url":"https://doi.org/10.1093/europace/euae298","authors":["Michiel Rienstra","Stylianos Tzeis","Karina V Bunting","Valeria Caso","Harry J.G.M. Crijns","Tom De Potter","Prashanthan Sanders","Emma Svennberg","Rubén Casado-Arroyo","Jeremy Dwight","Luigina Guasti","Thorsten Hanke","Tiny Jaarsma","Maddalena Lettino","Maja‐Lisa Løchen","R Thomas Lumbers","Bart Maesen","Inge Mølgaard","Giuseppe Rosano","Renate B. Schnabel","Piotr Suwalski","Juan Tamargo","Otilia Țica","Vassil Traykov","Dipak Kotecha","Isabelle C. Van Gelder"],"tags":["Medicine","Atrial fibrillation","Cardioversion","Comorbidity","Stroke (engine)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1093/europace/euae298","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404224917","name":"Utilization of Generative Artificial Intelligence in Nursing Education: A Topic Modeling Analysis","source":"openalex","abstract":"The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students’ learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed “healthcare”, “use”, and “risk” were common keywords. The limited emphasis on “threats”, “strengths”, and “weaknesses” compared to “opportunities” in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines.","url":"https://doi.org/10.3390/educsci14111234","authors":["Won Jin Seo","Mihui Kim"],"tags":["Generative grammar","Computer science","Artificial intelligence","Psychology","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-11","doi":"https://doi.org/10.3390/educsci14111234","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4406548567","name":"The ethics of artificial intelligence use in university libraries in Zimbabwe","source":"openalex","abstract":"Introduction: The emergence of artificial intelligence (AI) has revolutionised higher education teaching and learning. AI has the power to analyse large amounts of data and make intelligent predictions thus changing the whole teaching and learning processes. However, such a rise has led to institutions questioning the morality of these applications. The changes have left librarians and educators worried about the major ethical questions surrounding privacy, equality of information, protection of intellectual property, cheating, misinformation and job security. Libraries have always been concerned about ethics and many go out of their way to make sure communities are educated about the ethical question. However, the emergence of artificial intelligence has caught them unaware. Methods: This research investigates the preparedness of higher education librarians to support the ethical use of information within the higher and tertiary education fraternity. A qualitative approach was used for this study. Interviews were done with thirty purposively selected librarians and academics from universities in Zimbabwe. Results: Findings indicated that many university libraries in Zimbabwe are still at the adoption stage of artificial intelligence. It was also found that institutions and libraries are not yet prepared for AI use and are still crafting policies on the use of AI. Discussion: Libraries seem prepared to adopt AI. They are also prepared to offer training on how to protect intellectual property but have serious challenges in issues of transparency, data security, plagiarism detection and concerns about job losses. However, with no major ethical policies having been crafted on AI use, it becomes challenging for libraries to full adopt its usage.","url":"https://doi.org/10.3389/frma.2024.1522423","authors":["Stephen Tsekea","Edward Mandoga"],"tags":["Sociology","Political science","Library science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-17","doi":"https://doi.org/10.3389/frma.2024.1522423","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401895798","name":"Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.","source":"openalex","abstract":"Objective: A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization of artificial intelligence (AI) in chronic obstructive pulmonary disease (COPD). Methods: On March 24, 2024, a computer search was conducted on the Web of Science (WOS) core collection dataset published between January 1, 2009, and December 30, 2023, to identify literature related to the application of artificial intelligence in chronic obstructive pulmonary disease (COPD). VOSviewer was utilized for visual analysis of countries, institutions, authors, co-cited authors, and keywords. CiteSpace was employed to analyze the intermediary centrality of institutions, references, keyword outbreaks, and co-cited literature. Relevant descriptive analysis tables were created using Excel2021 software. Results: This study included a total of 646 papers from WOS. The number of papers remained small and stable from 2009 to 2017 but started increasing significantly annually since 2018. The United States had the highest number of publications among countries/regions while Silverman Edwin K and Harvard Medical School were the most prolific authors and institutions respectively. Lynch DA, Kirby M. and Vestbo J. were among the top three most cited authors overall. Scientific Reports had the largest number of publications while Radiology ranked as one of the top ten influential journals. The Genetic Epidemiology of COPD (COPDGene) Study Design was frequently cited. Through keyword clustering analysis, all keywords were categorized into four groups: epidemiological study of COPD; AI-assisted imaging diagnosis; AI-assisted diagnosis; and AI-assisted treatment and prognosis prediction in the COPD research field. Currently, hot research topics include explainable artificial intelligence framework, chest CT imaging, and lung radiomics. Conclusion: At present, AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD. This study's results offer novel insights and directions for future research endeavors related to COPD.","url":"https://doi.org/10.2147/copd.s474402","authors":["Hupo Bian","Shaoqi Zhu","Yonghua Zhang","Qiang Fei","Xiuhua Peng","Zanhui Jin","Tianxiang Zhou","Hongxing Zhao"],"tags":["Pulmonary disease","Medicine","Intensive care medicine","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.2147/copd.s474402","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411403091","name":"The impact of generative AI on health professional education: A systematic review in the context of student learning","source":"openalex","abstract":"BACKGROUND: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education (HPE), offering new opportunities for student learning. However, current research lacks a comprehensive understanding of how HPE students actually use GenAI in practice. Laurillard's Conversational Framework outlines six learning types-acquisition, inquiry, practice, production, discussion and collaboration-commonly used to categorise learning activities supported by conventional and digital technologies. Gaining insight into how GenAI aligns with these six learning types could assist HPE academics in integrating it more thoughtfully and effectively into teaching and learning. PURPOSE: This systematic review investigates how HPE students utilise GenAI and examines how these uses align with Laurillard's six learning types compared to conventional and digital technologies. MATERIAL AND METHODS: A systematic review searching five major databases-ERIC, Education Database, Ovid Medline, Ovid Embase and Scopus including articles on HPE students' use of GenAI until 15th September 2024. Studies were included if they were conducted within formal HPE training programs in HPE and specifically mentioned how students interact with GenAI. Data were mapped to the six learning modes of the Laurillard's Framework. Study quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS: Thirty-three studies met inclusion criteria. GenAI supported learning most frequently in practice (73%), inquiry (70%), production (67%) and acquisition (55%). These studies highlight GenAI's varied educational applications, from clarifying complex concepts to simulating clinical scenarios and generating practice materials. Discussion and collaboration were less represented (12% each), suggesting a shift toward more individualised learning with GenAI. The findings highlight benefits such as efficiency and accessibility, alongside concerns about critical thinking, academic integrity and reduced peer interaction. CONCLUSION: This review has provided insights into HPE students' learning aligned with Laurillard's existing six learning types. Although GenAI supports personalised and self-directed learning, its role in collaborative modes is under-explored.","url":"https://doi.org/10.1111/medu.15746","authors":["Thai Duong Pham","Nilushi Karunaratne","Betty Exintaris","Danny Liu","Travis Lay","Elizabeth Yuriev","Angelina Lim"],"tags":["Scopus","Context (archaeology)","Inclusion (mineral)","MEDLINE","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-18","doi":"https://doi.org/10.1111/medu.15746","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405740723","name":"Impact of Artificial Intelligence–Generated Content Labels On Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation: Web-Based, Randomized, Controlled Experiment","source":"openalex","abstract":"BACKGROUND: The proliferation of generative artificial intelligence (AI), such as ChatGPT, has added complexity and richness to the virtual environment by increasing the presence of AI-generated content (AIGC). Although social media platforms such as TikTok have begun labeling AIGC to facilitate the ability for users to distinguish it from human-generated content, little research has been performed to examine the effect of these AIGC labels. OBJECTIVE: This study investigated the impact of AIGC labels on perceived accuracy, message credibility, and sharing intention for misinformation through a web-based experimental design, aiming to refine the strategic application of AIGC labels. METHODS: The study conducted a 2×2×2 mixed experimental design, using the AIGC labels (presence vs absence) as the between-subjects factor and information type (accurate vs inaccurate) and content category (for-profit vs not-for-profit) as within-subjects factors. Participants, recruited via the Credamo platform, were randomly assigned to either an experimental group (with labels) or a control group (without labels). Each participant evaluated 4 sets of content, providing feedback on perceived accuracy, message credibility, and sharing intention for misinformation. Statistical analyses were performed using SPSS version 29 and included repeated-measures ANOVA and simple effects analysis, with significance set at P<.05. RESULTS: As of April 2024, this study recruited a total of 957 participants, and after screening, 400 participants each were allocated to the experimental and control groups. The main effects of AIGC labels were not significant for perceived accuracy, message credibility, or sharing intention. However, the main effects of information type were significant for all 3 dependent variables (P<.001), as were the effects of content category (P<.001). There were significant differences in interaction effects among the 3 variables. For perceived accuracy, the interaction between information type and content category was significant (P=.005). For message credibility, the interaction between information type and content category was significant (P<.001). Regarding sharing intention, both the interaction between information type and content category (P<.001) and the interaction between information type and AIGC labels (P=.008) were significant. CONCLUSIONS: This study found that AIGC labels minimally affect perceived accuracy, message credibility, or sharing intention but help distinguish AIGC from human-generated content. The labels do not negatively impact users' perceptions of platform content, indicating their potential for fact-checking and governance. However, AIGC labeling applications should vary by information type; they can slightly enhance sharing intention and perceived accuracy for misinformation. This highlights the need for more nuanced strategies for AIGC labels, necessitating further research.","url":"https://doi.org/10.2196/60024","authors":["Fan Li","Ya Yang"],"tags":["Credibility","Misinformation","Computer science","Psychology","Applied psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-24","doi":"https://doi.org/10.2196/60024","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4415041425","name":"Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study","source":"openalex","abstract":"BACKGROUND: This study aimed to evaluate the impact of MeSH based personalized learning guides generated by ChatGPT-4o on the radiographic diagnostic performance of dental students and to compare it with the traditional correct/incorrect feedback method. METHODS: This randomized controlled study was conducted among fifth-year dental students at Afyonkarahisar Health Sciences University. A total of 110 students were randomly assigned to either the experimental or control group. The experimental group received personalized study guides targeting their learning gaps, generated by ChatGPT-4o based on Medical Subject Headings (MeSH). The control group received only a standard correct/incorrect feedback analysis. One month after the intervention, a post-test was administered to assess diagnostic accuracy and student satisfaction. RESULTS: The increase in test scores from pre- to post-test was significantly higher in the experimental group (3.6 ± 1.0) compared to the control group (1.3 ± 1.2; p < 0.001). Final test scores were also significantly higher in the experimental group (p < 0.001). Survey responses indicated that the experimental group rated the feedback as more understandable, beneficial, and motivating compared to the control group. CONCLUSIONS: ChatGPT-4o based personalized feedback proved to be an effective tool for enhancing diagnostic performance and supporting learning in dental education. The findings suggest that AI-driven individualized educational strategies hold significant potential in the future of dental training.","url":"https://doi.org/10.1186/s12909-025-07875-4","authors":["Büşra Yılmaz","Büşra Yılmaz","Furkan Ozbey","Baki YILMAZ","Baki YILMAZ"],"tags":["Medicine","Medical physics","Radiography","Medical education","Dental education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-10","doi":"https://doi.org/10.1186/s12909-025-07875-4","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4410164686","name":"Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review","source":"openalex","abstract":"Purpose: While several artificial intelligence (AI)-assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities. Methods: Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool were applied for the assessment of bias among the included studies. Results: The 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%). Conclusion: The systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings. Level of evidence: Systematic Review; Level of evidence IV.","url":"https://doi.org/10.1002/jeo2.70259","authors":["Umile Giuseppe Longo","Alberto Lalli","Guido Nicodemi","Matteo Giuseppe Pisani","Alessandro de Sire","Pieter D’Hooghe","Ara Nazarian","Jacob F. Oeding","Bálint Zsidai","Kristian Samuelsson"],"tags":["Systematic review","Medical physics","Artificial intelligence","Critical appraisal","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1002/jeo2.70259","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4400002936","name":"Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease","source":"openalex","abstract":"Abstract The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.","url":"https://doi.org/10.1038/s41746-024-01170-0","authors":["Libor Pastika","Arunashis Sau","Konstantinos Patlatzoglou","Ewa Sieliwończyk","Antônio H. Ribeiro","Kathryn A. McGurk","Sadia Khan","Danilo P. Mandic","William R. Scott","James S. Ware","Nicholas S. Peters","Antônio Luiz Pinho Ribeiro","Daniel B. Kramer","Jonathan W. Waks","Fu Siong Ng"],"tags":["Medicine","Body mass index","Internal medicine","Cohort","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-25","doi":"https://doi.org/10.1038/s41746-024-01170-0","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4393092149","name":"Exploring the Impact of Artificial Intelligence in Healthcare","source":"openalex","abstract":"The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.","url":"https://doi.org/10.60087/jaigs.v2i1.p188","authors":["Md. Mafiqul Islam"],"tags":["Health care","Artificial intelligence","Computer science","Psychology","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-22","doi":"https://doi.org/10.60087/jaigs.v2i1.p188","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4395954942","name":"Early automated detection system for skin cancer diagnosis using artificial intelligent techniques","source":"openalex","abstract":"Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.","url":"https://doi.org/10.1038/s41598-024-59783-0","authors":["Nourelhoda M. Mahmoud","Ahmed M. Soliman"],"tags":["Artificial intelligence","Skin cancer","Computer science","Support vector machine","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-28","doi":"https://doi.org/10.1038/s41598-024-59783-0","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402388588","name":"Artificial Intelligence in Predicting the Mode of Delivery: A Systematic Review","source":"openalex","abstract":"The integration of artificial intelligence (AI) into obstetric care offers significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods for mode of delivery often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. This systematic review aims to evaluate the current state of research on AI applications in predicting the mode of delivery, comparing the performance of AI models with traditional methods, and identifying gaps for future research. A comprehensive literature search was conducted across PubMed, Google Scholar, Web of Science, and Scopus databases, covering publications from January 2010 to July 2024. Inclusion criteria were studies employing AI techniques to predict the mode of delivery, published in peer-reviewed journals, and involving human subjects. Studies were assessed for quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST), and data were synthesized narratively due to heterogeneity. In total, 18 studies met the inclusion criteria, employing various AI models such as logistic regression, random forest, gradient boosting, and neural networks. Sample sizes ranged from 40 to 94,480 participants across diverse geographic settings. AI models demonstrated high accuracy rates, often exceeding 90%, and strong predictive metrics (area under the curve (AUC) values from 0.745 to 0.932). Key predictors included maternal age, gravidity, parity, gestational age, labor induction type, and fetal weight. Notable models like the Adana System and Categorical Boosting (CatBoost, Yandex LLC, Moscow, Russia) highlighted the effectiveness of AI in enhancing prediction accuracy and supporting clinical decisions. AI models significantly outperform traditional statistical methods in predicting the mode of delivery, providing a robust tool for obstetric care. Future research should focus on standardizing data collection, improving model interpretability, addressing ethical concerns, and ensuring fairness in AI predictions to enhance clinical trust and application.","url":"https://doi.org/10.7759/cureus.69115","authors":["Kalliopi Michalitsi","Dimitra Metallinou","Athina Diamanti","Vasiliki Georgakopoulou","Iraklis Kagkouras","Eleni Tsoukala","Antigoni Sarantaki"],"tags":["Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-10","doi":"https://doi.org/10.7759/cureus.69115","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4394064976","name":"The Role of Artificial Intelligence in U.S. Agriculture: A Review: Assessing advancements, challenges, and the potential impact on food production and sustainability","source":"openalex","abstract":"This study systematically reviews the transformative role of Artificial Intelligence (AI) in enhancing agricultural productivity and sustainability in the United States. With the aim of understanding how AI technologies can be effectively integrated into farming practices, this research employs a systematic literature review methodology, focusing on peer-reviewed journal articles, conference proceedings, and reputable reports from 2010 to 2024. The methodology includes a structured search strategy, defined inclusion and exclusion criteria, and thematic analysis to categorize findings into relevant themes. Key findings reveal that AI technologies, such as machine learning models, predictive analytics, and robotics, are revolutionizing U.S. agriculture by optimizing resource use, improving crop health monitoring, and enhancing decision-making processes. Despite the promising potential of AI to address challenges like food security and environmental sustainability, the adoption of AI in agriculture faces barriers including technological adoption, data privacy concerns, and the need for significant investment in digital infrastructure. The study concludes that leveraging AI for sustainable agriculture requires collaborative efforts among stakeholders, including investment in digital literacy, development of regulatory frameworks, and fostering public-private partnerships. Future research directions emphasize the socio-economic impacts of AI adoption, ethical considerations, and the development of scalable AI solutions. This study underscores AI's pivotal role in ensuring a sustainable, productive, and resilient agricultural sector.","url":"https://doi.org/10.53022/oarjet.2024.6.2.0017","authors":["Olabimpe Banke Akintuyi"],"tags":["Sustainability","Production (economics)","Agriculture","Food processing","Agricultural productivity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-07","doi":"https://doi.org/10.53022/oarjet.2024.6.2.0017","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404160912","name":"Orchestration logics for artificial intelligence platforms: From raw data to industry‐specific applications","source":"openalex","abstract":"Abstract Artificial intelligence (AI) platforms face distinct orchestration challenges in industry‐specific settings, such as the need for specialised resources, data‐sharing concerns, heterogeneous users and context‐sensitive applications. This study investigates how these platforms can effectively orchestrate autonomous actors in developing and consuming AI applications despite these challenges. Through an analysis of five AI platforms for medical imaging, we identify four orchestration logics: platform resourcing, data‐centric collaboration, distributed refinement and application brokering. These logics illustrate how platform owners can verticalize the AI development process by orchestrating actors who co‐create, share and refine data and AI models, ultimately producing industry‐specific applications capable of generalisation. Our findings extend research on platform orchestration logics and change our perspective from boundary resources to a process of boundary processing. These insights provide a theoretical foundation and practical strategies to build effective industry‐specific AI platforms.","url":"https://doi.org/10.1111/isj.12567","authors":["Michael Weber","Andreas Hein","Jörg Weking","Helmut Krcmar"],"tags":["Orchestration","Raw data","Big data","Computer science","Industry 4.0"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-07","doi":"https://doi.org/10.1111/isj.12567","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401815753","name":"Enhancing cervical cancer cytology screening via artificial intelligence innovation","source":"openalex","abstract":"A double-check process helps prevent errors and ensures quality control. However, it may lead to decreased personal accountability, reduced effort, and declining quality checks. Introducing an artificial intelligence (AI)-based system in such scenarios could effectively address the risk of oversights. This study introduces an innovative AI-integrated workflow for cervical cytology screening that substantially improves efficiency and reduces the burden on cytologists. The AI model prioritizes cases for review based on anomaly scores and streamlines the first screening process to approximately 10 s per case. The model enhances the identification of high-risk cases via detailed microscopic observation, high anomaly scores cases, and a targeted review of low-score cases. The workflow highlights its capability for rapid, accurate, and less labor-intensive evaluations, demonstrating the potential to transform cervical cancer screening. This study highlights the importance of AI in modern medical diagnostics, particularly in areas with a high demand for accuracy and efficiency.","url":"https://doi.org/10.1038/s41598-024-70670-6","authors":["Yuki Kurita","Shiori Meguro","Isao Kosugi","Yasunori Enomoto","Hideya Kawasaki","Tomoaki Kano","Takeji Saitoh","Kazuya Shinmura","Toshihide Iwashita"],"tags":["Workflow","Computer science","Cervical cancer","Identification (biology)","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-22","doi":"https://doi.org/10.1038/s41598-024-70670-6","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4387323858","name":"GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models","source":"openalex","abstract":"Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are marvels of technology; celebrated for their prowess in natural language processing and multimodal content generation, they promise a transformative future. But as with all powerful tools, they come with their shadows. Picture living in a world where deepfakes are indistinguishable from reality, where synthetic identities orchestrate malicious campaigns, and where targeted misinformation or scams are crafted with unparalleled precision. Welcome to the darker side of GenAI applications. This article is not just a journey through the meanders of potential misuse of GenAI and LLMs, but also a call to recognize the urgency of the challenges ahead. As we navigate the seas of misinformation campaigns, malicious content generation, and the eerie creation of sophisticated malware, we'll uncover the societal implications that ripple through the GenAI revolution we are witnessing. From AI-powered botnets on social media platforms to the unnerving potential of AI to generate fabricated identities, or alibis made of synthetic realities, the stakes have never been higher. The lines between the virtual and the real worlds are blurring, and the consequences of potential GenAI's nefarious applications impact us all. This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.","url":"https://doi.org/10.48550/arxiv.2310.00737","authors":["Emilio Ferrara"],"tags":["Misinformation","Transformative learning","Disinformation","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-01","doi":"https://doi.org/10.48550/arxiv.2310.00737","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4393854720","name":"Ethical issues in implementing artificial intelligence in healthcare","source":"openalex","abstract":"The integration of artificial intelligence (AI) in healthcare presents unprecedented opportunities for improving patient care and outcomes, yet it also brings forth a myriad of ethical dilemmas that demand careful consideration. This article examines the ethical challenges posed by AI in healthcare, ranging from concerns about algorithmic bias and patient privacy to issues of transparency, accountability, and professional autonomy. Through a comprehensive analysis of relevant literature, case studies, and regulatory considerations, the study explores the multifaceted ethical implications of AI technologies in clinical practice. Key findings underscore the importance of promoting transparency and accountability in AI algorithm development and deployment, as well as the need for robust regulatory oversight and ethical guidance to ensure patient rights and safety. Despite the complexities and challenges, AI offers immense potential to enhance patient care and healthcare efficiency when navigated responsibly and ethically. By prioritizing ethical principles and collaborative efforts, stakeholders can harness the transformative power of AI while upholding the highest standards of ethical healthcare practice.","url":"https://doi.org/10.24075/medet.2024.006","authors":["Konstantin Koshechkin","AL Khokholov"],"tags":["Health care","Engineering ethics","Computer science","Psychology","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-01","doi":"https://doi.org/10.24075/medet.2024.006","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W7160824505","name":"Evaluation of Artificial Intelligence and Blockchain Integration Utilizing the DEM ATEL Method to Improve Privacy and Transparent in the Financial Sector","source":"openalex","abstract":"d IoT security perspective. It makes use of three essential Blockchain features— transparency, immutability, and decentralization— to build environment that are reliable and impenetrable. This application is realized through the utilization of features such as AI-driven fraud detection, Blockchain security, data privacy, the reliability of Smart Contracts, transaction speed, and system scalability. The result is, Blockchain-IoT Security Perspective, the first rank is System Scalability, the lowest rank is AI-based Fraud Detection, Blockchain Security is the fourth rank, Data Privacy is the fifth rank, Smart Contract Reliability is the third rank, and Transaction Speed is the first rank.","url":"https://doi.org/10.55124/ijbs.v1i1.103","authors":["Rajendar Dommeti"],"tags":["Blockchain","Database transaction","Computer security","Computer science","Reliability (semiconductor)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.55124/ijbs.v1i1.103","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4403650365","name":"Role of artificial intelligence in haematolymphoid diagnostics","source":"openalex","abstract":"The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work. The aim of this article is to provide an overview of the potential applications of AI in the field of haematopathology and to define the role that these emerging technologies could play in our laboratories in the short to medium term.","url":"https://doi.org/10.1111/his.15327","authors":["Charlotte Syrykh","Michiel van den Brand","Jakob Nikolas Kather","Camille Laurent"],"tags":["Computer science","Data science","Digital pathology","Artificial intelligence","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-22","doi":"https://doi.org/10.1111/his.15327","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4404568512","name":"Advanced Artificial Intelligence Techniques for Comprehensive Dermatological Image Analysis and Diagnosis","source":"openalex","abstract":"With the growing complexity of skin disorders and the challenges of traditional diagnostic methods, AI offers exciting new solutions that can enhance the accuracy and efficiency of dermatological assessments. Reflectance Confocal Microscopy (RCM) stands out as a non-invasive imaging technique that delivers detailed views of the skin at the cellular level, proving its immense value in dermatology. The manual analysis of RCM images, however, tends to be slow and inconsistent. By combining artificial intelligence (AI) with RCM, this approach introduces a transformative shift toward precise, data-driven dermatopathology, supporting more accurate patient stratification, tailored treatments, and enhanced dermatological care. Advancements in AI are set to revolutionize this process. This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. The discussion highlights AI’s role in identifying and classifying skin conditions, offering benefits such as a greater consistency and a reduced strain on healthcare professionals. Furthermore, the paper explores AI integration into dermatological practices, addressing current challenges and future possibilities. The synergy between AI and RCM holds the potential to significantly advance skin disease diagnosis, ultimately leading to better therapeutic personalization and comprehensive dermatological care.","url":"https://doi.org/10.3390/dermato4040015","authors":["Serra Aksoy","Pınar Demircioğlu","İsmail Böğrekçi"],"tags":["Dermatopathology","Artificial intelligence","Convolutional neural network","Personalization","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-16","doi":"https://doi.org/10.3390/dermato4040015","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408551026","name":"Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance","source":"openalex","abstract":"Artificial intelligence (AI) has profoundly influenced the field of cardiovascular interventions and coronary artery procedures in particular. AI has enhanced diagnostic accuracy in coronary artery disease through advanced invasive and noninvasive imaging modalities, facilitating more precise diagnosis and personalized interventional strategies. AI integration in coronary interventions has streamlined diagnostic and procedural workflows, improved procedural accuracy, increased clinician efficiency, and enhanced patient safety and outcomes. Despite its potential, AI still faces significant challenges, including concerns regarding algorithmic biases, lack of transparency in AI-driven decision making, and ethical challenges. This review explores the latest advancements of AI applications in coronary artery interventions, focusing on preprocedural planning and real-time procedural guidance. It also addresses the major limitations and obstacles that hinder the widespread clinical adoption of AI technologies in this field.","url":"https://doi.org/10.1016/j.jscai.2024.102519","authors":["Saurabhi Samant","Anastasios Panagopoulos","Wei Wu","Shijia Zhao","Yiannis S. Chatzizisis"],"tags":["Psychological intervention","Cardiology","Medicine","Artery","Internal medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-01","doi":"https://doi.org/10.1016/j.jscai.2024.102519","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402134116","name":"Integrating Artificial Intelligence Into Orthodontic Education and Practice","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.identj.2024.08.011","authors":["Pradeep Kumar Yadalam","Raghavendra Vamsi Anegundi","Carlos M. Ardila"],"tags":["Medical education","Psychology","Dentistry","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-02","doi":"https://doi.org/10.1016/j.identj.2024.08.011","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4408143893","name":"Artificial intelligence machines as relational nonhuman actors in entrepreneurial teams","source":"openalex","abstract":"We theoretically examine how artificial intelligence (AI) machines interact with and shape strategic decision-making within entrepreneurial teams. First, we explain how AI machines lead and assist teams to engage in cognitively complex decisions and explore new ways to combine resources. Second, we unpack this strategic decision-making reconfiguration process and examine how AI machines allow entrepreneurial teams to better manage diversity and use it as a substitute for external knowledge. Overall, we propose the idea that AI technologies cannot be conceptualized as merely additional resources but as relational nonhuman actors in entrepreneurial teams. Then, we encourage scholars to reflect on human-AI interactions in entrepreneurial teams and outline an agenda for future research.","url":"https://doi.org/10.1080/00472778.2025.2461031","authors":["Samuele Murtinu","Alfredo De Massis"],"tags":["Knowledge management","Psychology","Business","Management","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-04","doi":"https://doi.org/10.1080/00472778.2025.2461031","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4321004114","name":"Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation","source":"openalex","abstract":"Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.","url":"https://doi.org/10.1080/03091902.2023.2174198","authors":["Youness Arjoune","Trong N. Nguyen","Robin Winkler Doroshow","Raj Shekhar"],"tags":["Stethoscope","Auscultation","Computer science","Scalability","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-15","doi":"https://doi.org/10.1080/03091902.2023.2174198","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401638119","name":"Assessment Study of ChatGPT-3.5’s Performance on the Final Polish Medical Examination: Accuracy in Answering 980 Questions","source":"openalex","abstract":"Background/Objectives: The use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with one correct answer per question, is administered in Polish, and assesses students’ comprehensive medical knowledge and readiness for clinical practice. The aim of this study was to determine how ChatGPT-3.5 handles questions included in this exam. Methods: This study considered 980 questions from five examination sessions of the Medical Final Examination conducted by the Medical Examination Center in the years 2022–2024. The analysis included the field of medicine, the difficulty index of the questions, and their type, namely theoretical versus case-study questions. Results: The average correct answer rate achieved by ChatGPT for the five examination sessions hovered around 60% and was lower (p < 0.001) than the average score achieved by the examinees. The lowest percentage of correct answers was in hematology (42.1%), while the highest was in endocrinology (78.6%). The difficulty index of the questions showed a statistically significant correlation with the correctness of the answers (p = 0.04). Questions for which ChatGPT-3.5 provided incorrect answers had a lower (p < 0.001) percentage of correct responses. The type of questions analyzed did not significantly affect the correctness of the answers (p = 0.46). Conclusions: This study indicates that ChatGPT-3.5 can be an effective tool for assisting in passing the final medical exam, but the results should be interpreted cautiously. It is recommended to further verify the correctness of the answers using various AI tools.","url":"https://doi.org/10.3390/healthcare12161637","authors":["Julia Siebielec","Michał Ordak","Agata Oskroba","Anna Dworakowska","Magdalena Bujalska‐Zadrożny"],"tags":["Question answering","Psychology","Medicine","Information retrieval","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-16","doi":"https://doi.org/10.3390/healthcare12161637","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4384938107","name":"Future medicine: from molecular pathways to the collective intelligence of the body","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.molmed.2023.06.007","authors":["Eric Lagasse","Michael Levin"],"tags":["Cognitive science","Living systems","Cybernetics","Biomedicine","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-20","doi":"https://doi.org/10.1016/j.molmed.2023.06.007","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4399604442","name":"The Impact of Generative Artificial Intelligence in Scientific Content Synthesis for Authors","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ajpath.2024.06.002","authors":["Chhavi Chauhan"],"tags":["Generative grammar","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-13","doi":"https://doi.org/10.1016/j.ajpath.2024.06.002","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W7127575260","name":"Artificial intelligence in airway management: a narrative review","source":"pubmed","abstract":"This narrative review examines artificial intelligence (AI) applications in airway management through a structured literature search completed in July 2025. AI shows promise in predicting difficult airways through facial recognition, voice analysis, and multiparametric assessment. AI models demonstrate improved positive predictive values compared with conventional bedside tests, though prediction remains imperfect. The fundamental challenge of low positive predictive values persists, albeit at reduced levels. For videolaryngoscopy, AI-powered systems provide real-time structure identification, procedural guidance, tracheal tube placement verification, and potentially fewer complications. AI also offers cognitive support during critical scenarios by mitigating decision biases, providing intelligent alarms, and ensuring guideline adherence when human performance might be compromised by stress. In education, AI-enhanced virtual reality simulations create realistic practice environments with personalised feedback tailored to learners' needs. Experimental robotic intubation systems guided by AI algorithms demonstrate performance comparable with experts in controlled settings, though primarily for training and remote applications. Despite these advances, AI should complement rather than replace human expertise. Important challenges remain, including the risk of clinician deskilling, the 'black box' nature of some algorithms limiting transparency, and the need for robust validation before widespread implementation. The ideal approach involves human-machine collaboration where AI compensates for cognitive limitations while physicians contribute clinical judgement, context awareness, and empathy. As AI continues evolving, appropriate ethical frameworks and regulatory oversight must be developed in parallel to ensure these technologies enhance patient safety while preserving the essential human element of healthcare.","url":"https://doi.org/10.1016/j.bja.2025.12.052","authors":["Massimiliano Sorbello","Luigi La Via","Daniele S. Paternò","Simona Tutino","Emilia C. Lo Giudice","Mario Lentini","Antonino Maniaci","Federico Pappalardo","Sorbello M","La Via L","Paternò DS","Tutino S"],"tags":["Context (archaeology)","Narrative review","Cognition","Computer science","Applications of artificial intelligence"],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"https://doi.org/10.1016/j.bja.2025.12.052","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"oa:W4405335762","name":"Explainable Artificial Intelligence in Paediatric: Challenges for the Future","source":"openalex","abstract":"Background: Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences. Objective: This commentary paper discusses concerns and challenges related to implementation and interpretation of XAI methods, with the aim of rising awareness of the main concerns regarding their adoption in paediatrics. Methods: A comprehensive literature review was undertaken to explore the challenges of adopting XAI in paediatrics. Results: Although XAI has several favorable outcomes, its implementation in paediatrics is prone to challenges including generalizability, trustworthiness, causality and intervention, and XAI evaluation. Conclusion: Paediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross-fertilization perspective aiming at filling the gaps still preventing its adoption.","url":"https://doi.org/10.1002/hsr2.70271","authors":["Ahmed Salih","Gloria Menegaz","Thillagavathie Pillay","Elaine M. Boyle"],"tags":["Perspective (graphical)","Transparency (behavior)","Domain (mathematical analysis)","Generalizability theory","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-01","doi":"https://doi.org/10.1002/hsr2.70271","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4225120199","name":"Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma—a systematic review","source":"openalex","abstract":"OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. METHODS: MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. RESULTS: After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. CONCLUSION: Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required. KEY POINTS: • Intensity standardisation is a key pre-processing step in the development of robust radiomic signatures to evaluate diffuse glioma. • A minority of studies compared the impact of two or more methods. • Further research is required to directly compare multiple methods of MRI intensity standardisation on glioma datasets.","url":"https://doi.org/10.1007/s00330-022-08807-2","authors":["Kavi Fatania","Farah Mohamud","Anna Clark","Michael G. Nix","Susan Short","James P.B. O’Connor","Andrew Scarsbrook","Stuart Currie"],"tags":["Medicine","Artificial intelligence","Data extraction","Medical physics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-29","doi":"https://doi.org/10.1007/s00330-022-08807-2","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4400418071","name":"Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging","source":"openalex","abstract":"The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.","url":"https://doi.org/10.3390/diagnostics14131456","authors":["Jin Y. Chang","Mina S. Makary"],"tags":["Workflow","Applications of artificial intelligence","Artificial intelligence","Medical imaging","Radiomics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-08","doi":"https://doi.org/10.3390/diagnostics14131456","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4407613670","name":"An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid","source":"openalex","abstract":"Optimising heat transfer in biomedical systems, especially in blood-mediated liquids, is essential for precise medication administration and thermal ablation treatments. However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. In this work, we propose a computational fluid dynamics (CFD), machine learning (ML), and an artificial intelligence (AI) based computational framework for hemodynamics simulation of couple-stressed hybrid nano-integrated blood flow through parallel plates under external squeezing. The aim of this study is to enhance the thermal conductivity of blood-integrated tri-hybrid nanofluids, thus increasing the transfer of heat and maintaining temperature in biomedical systems. An AI-integrated, the Levenberg-Marquardt algorithm is employed with a neural network back propagation approach (ANN-LMA) for comprehensive analysis of viscous dissipation and the Lorentz force effects influenced tri-hybrid nano-fluid mixture. Non-linear, coupled partial differential equations are transformed into ordinary differential equations with similarity scaling to characterize heat transfer and fluid flow, which are then numerically solved using the modified finite difference method (the Keller-Box method). The heat transfer ability of ternary nano-fluid is enhanced with an increase in the couple stress parameter while, a rising Hartmann number results in more thermal diffusion. Regression scores equal to 1 indicate a good match between the actual data and the predictions. Conclusively, the proposed investigation provides insightful AI, ML and CFD-proposed analysis of blood-based nano-particles which can improve imaging techniques, provide tailored drug delivery, reduce hyperthermia, improve blood flow, and show potential for application in medicine.Highlights Artificial intelligence and machine learning-based CFD simulation of the blood-mediated tri-hybrid nano-fluid flow is presented.An improved finite difference scheme (the Keller-Box method), is utilized to numerically evaluate the problem.The LMA-ANN forecasts with an absolute error range of 10−11 to 10−8 relative to the actual data.Regression scores equal to 1 indicate a strong correlation between forecasts and actual data.","url":"https://doi.org/10.1080/19942060.2025.2459664","authors":["Mohib Hussain","Lin Du","Hassan Waqas","Qasem M. Al‐Mdallal"],"tags":["Computational fluid dynamics","Ternary operation","Computer science","Nano-","Fluid simulation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-16","doi":"https://doi.org/10.1080/19942060.2025.2459664","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4392813893","name":"Assessment of patient perceptions of artificial intelligence use in dermatology: A cross‐sectional survey","source":"openalex","abstract":"Dear Editor, The use of artificial intelligence (AI) in medicine has grown in recent decades, with deep neural networks demonstrating accuracies comparable to dermatologists when classifying melanoma, keratinocyte carcinomas, and other common skin conditions.1-3 With the future possibility that AI will be integrated into dermatology practice, it is important to understand how patients view these possible changes. Although prior studies have shown that patients are open to the use of AI in the diagnosis of skin cancer, little is known about patients' trust and perception of AI accuracy in general dermatology.4, 5 This survey study aimed to gather opinions from a diverse dermatology patient population on AI use in dermatology and establish a specific accuracy at which patients would be comfortable receiving a diagnosis solely from an AI tool. We created a 20-question survey utilizing a five-point Likert scale to assess patient opinions on AI in dermatology. Patients were given a specific example of AI use in dermatology in which a program would analyze a patient-acquired photograph of a skin lesion or rash and provide a list of potential diagnoses to the patient based on the photograph. Patients were then asked to complete a survey on their opinion of this type of AI (Attachment 1). The survey was given randomly via convenience sampling to adult patients who visited the University of Texas Southwestern Medical Center Dermatology Clinic between June 2023 and September 2023. Standard deviation, frequency distribution, and multivariable logistic regression were used in statistical analysis. The UT Southwestern Institutional Review Board approved this study. Among 157 patients informed about the study, 141 (89.8%) consented to complete the survey. Seventy-three respondents (51.8%) were male, 79 respondents (56.0%) were non-Hispanic white, and the mean (SD) age was 55.3 (16.5) years (Table 1). Respondents had a household income of $50,000–$99,999 (55 [39.0%]) and 61 (43.2%) respondents attained a bachelor's degree). Most respondents did not work in healthcare (125 [88.7%]), and 33 (23.4%) respondents obtained a degree in or held a job in computer science. The majority of respondents believed a dermatologist's diagnosis was accurate (131 [92.9%]), whereas only a minority believed a diagnosis made by AI to be accurate (52 [36.9%]) (Table 2). If differing diagnoses were received from a dermatologist and an AI model, most respondents would trust a dermatologist over an AI model(119 [84.4%]). Even with equal diagnostic accuracy, most respondents preferred to see a dermatologist over an AI model alone (119 [84.4%]). Respondents required the AI model to be 12.9% (SD, ± 8.1%) more accurate on average than a dermatologist in order for respondents to be comfortable only receiving evaluation from an AI model and not a dermatologist (Table 3). Some respondents were completely unwilling to be evaluated by an AI model alone (21 [14.9%]). Nonetheless, a majority of respondents believed that a model that could provide diagnoses based on a photograph could help improve the accuracy of dermatologists (88 [62.4%]), and most would rather get a diagnosis from a dermatologist working with an AI model than solely a dermatologist (96 [68.1%]). After performing a multivariable logistic regression controlling for sociodemographic factors, age 40–59 was significantly associated with a decrease in familiarity with AI (odds ratio: 0.21, p < 0.01) (Table S1). Being familiar with AI was significantly associated with a positive view of AI (odds ratio: 17.8, p < 0.01), belief that AI can improve the accuracy of dermatologists (odds ratio: 4.73, p = 0.04), and preference to receive a diagnosis from a dermatologist working with an AI over a dermatologist alone (odds ratio: 39.58, p < 0.01). Interestingly, having a computer science degree or working in computer science was not significantly associated with a more positive of AI. Our results suggest that although patien","url":"https://doi.org/10.1111/srt.13656","authors":["Alexander Wu","Madeline Ngo","Cristina Thomas"],"tags":["Cross-sectional study","Dermatology","Perception","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-01","doi":"https://doi.org/10.1111/srt.13656","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402964664","name":"Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence","source":"openalex","abstract":"Introduction: Optical Coherence Tomography (OCT) has emerged as an important imaging modality in non-invasive diagnosis for oral cancer and can provide real-time visualisation of tissue morphology with the required high resolution. This systematic review aims to assess the diagnostic accuracy of OCT in the detection of oral cancers, and to explore the potential integration of OCT with artificial intelligence (AI) and other imaging techniques to enhance diagnostic precision and clinical outcomes in oral healthcare. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Google Scholar, Cochrane Central Register, and Web of Science from inception until August 2024. Studies were included if they employed OCT for oral cancer detection, reported diagnostic outcomes, such as sensitivity and specificity, and were conducted on human subjects. Data extraction and quality assessment were performed independently by two reviewers. The synthesis highlights advancements in OCT technology, including AI-enhanced interpretations. Results: A total of 9 studies met the inclusion criteria, encompassing a total of 860 events (cancer detections). The studies spanned from 2008 to 2022 and utilised various OCT techniques, including clinician-based, algorithm-based, and AI-driven interpretations. The findings indicate OCT’s high diagnostic accuracy, with sensitivity ranging from 75% to 100% and specificity from 71% to 100%. AI-augmented OCT interpretations demonstrated the highest accuracy, emphasising OCT’s potential in early cancer detection and precision in guiding surgical interventions. Conclusions: OCT could play a very prominent role as a new diagnostic tool for oral cancer, with very high sensitivity and specificity. Future research pointed towards integrating OCT with other imaging methods and AI systems in providing better accuracy of diagnoses, plus more clinical usability. Further development and validation with large-scale multicentre trials is imperative for the realisation of this potential in changing the way we practice oral healthcare.","url":"https://doi.org/10.3390/jcm13195822","authors":["Waseem Jerjes","Harvey Stevenson","Daniele Ramsay","Zaid Hamdoon"],"tags":["Medicine","Optical coherence tomography","Medical physics","Diagnostic accuracy","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-29","doi":"https://doi.org/10.3390/jcm13195822","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4414280387","name":"Accuracy of Artificial Intelligence vs Professionally Translated Discharge Instructions","source":"openalex","abstract":"Importance: Patients using languages other than English are a group at risk of poor health outcomes and encounter barriers to access of translated written materials. Although artificial intelligence (AI) may offer an opportunity to improve access, few studies have evaluated the accuracy and safety of AI translation for clinical care under routine practice conditions. Objective: To investigate the accuracy of AI translation compared with professional human translation of patient-specific issued pediatric inpatient discharge instructions. Design, Setting, and Participants: This comparative effectiveness analysis compared translations by a neural machine translation model vs professional translators using patient-specific pediatric inpatient discharge instructions received by families between May 18, 2023, and May 18, 2024, at a single center academic pediatric hospital. Instructions were translated to Simplified Chinese, Somali, Spanish, and Vietnamese by professional translators and the Azure AI system and then broken into scoring sections. Two professional translators per language evaluated translations (blinded to source) on an established 5-point scale for fluency, adequacy, meaning, and error severity, with 1 indicating worst performance and 5 indicating best performance. Exposure: AI vs professional translation. Main Outcome and Measure: Quality of discharge instruction translation, including fluency, adequacy, meaning, and severity of errors. Results: A total of 148 sections from 34 discharge instructions were analyzed. When considering all 4 languages together, average fluency, adequacy, and meaning were lower among AI compared with professional human translations. Among all tested languages, mean (SD) fluency for AI translations was 2.98 (1.12) compared with 3.90 (0.96) for professional translations (difference, 0.92; 95% CI, 0.83-1.01; P < .001), adequacy was 3.81 (1.14) compared with 4.56 (0.70) (difference, 0.74; 95% CI, 0.65-0.83; P < .001), meaning was 3.38 (1.15) compared with 4.28 (0.84) (difference, 0.90; 95% CI, 0.80-0.99; P < .001), and error severity was 3.53 (1.28) compared with 4.48 (0.88) (difference, 0.95; 95% CI, 0.85-1.06; P < .001). Compared with professional translations, the Spanish AI translations were noninferior in adequacy (difference, 0.08; 95% CI, -0.02 to 0.19) and error severity (difference, 0.03; 95% CI, -0.09 to 0.14) but inferior in fluency (difference, 0.38; 95% CI, 0.23-0.53) and just crossed the inferiority threshold in meaning (difference, 0.08; 95% CI, -0.04 to 0.20). The Chinese, Vietnamese, and Somali AI translations were inferior to the professional translations across all metrics, with the greatest differences for Somali. Conclusions and Relevance: In this comparative effectiveness analysis of AI- vs professionally translated issued discharge instructions, AI-translated instructions performed similarly for Spanish but worse for other languages tested. Validation and clinical implementation of AI-based translation will require special attention to languages of lesser diffusion to prevent creating new inequities.","url":"https://doi.org/10.1001/jamanetworkopen.2025.32312","authors":["M Martos Martos","Blanca Fields","Samuel G. Finlayson","Nigel Hartell","Theresa Y. Kim","Emily L. Larimer","Jason J. Lau","Yu-Hsiang Lin","Taylor Salaguinto","Nguyen Tran-Ngoc","K. Casey Lion"],"tags":["Artificial intelligence","Computer science","Natural language processing","Psychology","Training set"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-17","doi":"https://doi.org/10.1001/jamanetworkopen.2025.32312","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4386544597","name":"G7 Hiroshima Process on Generative Artificial Intelligence (AI)","source":"openalex","abstract":"In May 2023, G7 Leaders identified topics for discussion in the Hiroshima Artificial Intelligence (AI) Process and called for an early stocktaking of opportunities and challenges related to generative AI.","url":"https://doi.org/10.1787/bf3c0c60-en","authors":["OECD"],"tags":["Generative grammar","Snapshot (computer storage)","Process (computing)","Artificial intelligence","Generative model"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-07","doi":"https://doi.org/10.1787/bf3c0c60-en","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4409488333","name":"Artificial intelligence tools in supporting healthcare professionals for tailored patient care","source":"openalex","abstract":"Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients' needs and assess clinicians' perceptions about the usefulness of those AI tools. To define patients' issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients' needs substantiated by clinicians' evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond.","url":"https://doi.org/10.1038/s41746-025-01604-3","authors":["Jiyeong Kim","Michael L. Chen","Shawheen J. Rezaei","Tina Hernandez‐Boussard","Jonathan H. Chen","Fátima Rodríguez","Summer S. Han","Rayhan A. Lal","Sun Ho Kim","Chrysoula Dosiou","Susan M. Seav","Tugce Akcan","Carolyn I. Rodríguez","Steven M. Asch","Eleni Linos"],"tags":["Health care","Health professionals","Nursing","Patient care","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-16","doi":"https://doi.org/10.1038/s41746-025-01604-3","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4415646726","name":"The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication","source":"openalex","abstract":"This Review examines the transformative role of artificial intelligence (AI) in 3D bioprinting, focusing on how advanced AI technologies enhance its precision, functionality, and scalability. AI, through branches, such as machine learning (ML), computer vision (CV), robotics, natural language processing and expert systems, provides critical improvements in real-time process monitoring, error correction, and optimization of bioprinting parameters. The integration of AI enables automated quality control and predictive maintenance, improving bioprinting outcomes by increasing cell viability and structural fidelity, and reducing the amount of bioink wasted. Specifically, ML algorithms are employed to predict optimal bioprinting conditions and streamline the bioprinting workflow, while deep learning enhances the ability to process complex datasets for precision tissue biofabrication. Furthermore, AI-powered robotics and CV systems ensure accurate bioink placement and facilitate the construction of complex tissues. Despite the remarkable progress, challenges remain, particularly in the areas of process monitoring, quality control, and the scalability of bioprinting systems. This Review also aims to guide scientists, engineers, and healthcare providers in understanding the complexities and potential of AI-enhanced bioprinting, fostering a deeper appreciation of its role in the future of regenerative medicine and personalized healthcare.","url":"https://doi.org/10.1002/adfm.202509530","authors":["João Vítor Silva Robazzi","İrem Deniz Derman","Deepak Gupta","Logan Haugh","Yogendra Pratap Singh","Vaibhav Pal","Yasar Ozer Yilmaz","Suihong Liu","André Luís Dias","Rogério Andrade Flauzino","İbrahim T. Özbolat"],"tags":["Process (computing)","Artificial intelligence","Computer science","Transformative learning","3D bioprinting"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-28","doi":"https://doi.org/10.1002/adfm.202509530","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411042219","name":"Artificial Intelligence in Veterinary Clinical Pathology—An Introduction and Review","source":"openalex","abstract":"Artificial intelligence (AI), particularly through machine learning and deep learning, presents opportunities for the enhancement of the workflow of the veterinary clinical pathologist. This review introduces basic concepts in AI in a nontechnical manner and explores the qualification and integration of AI in veterinary clinical pathology. The veterinary clinical pathologist must play an active role in defining the intended use, design, and qualification of these methods as well as the plan for monitoring their responsible application in practice.","url":"https://doi.org/10.1111/vcp.70012","authors":["Samuel V. Neal","Daniel G. Rudmann","Kara N. Corps"],"tags":["Workflow","Veterinary pathology","Veterinary medicine","Pathology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-03","doi":"https://doi.org/10.1111/vcp.70012","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4415815680","name":"From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI)","source":"openalex","abstract":"Explainable Artificial Intelligence (XAI) has become essential as machine learning systems are deployed in high-stakes domains such as security, finance, and healthcare. Traditional models often act as “black boxes”, limiting trust and accountability. Traditional models often act as “black boxes”, limiting trust and accountability. However, most existing reviews treat explainability either as a technical problem or a philosophical issue, without connecting interpretability techniques to their real-world implications for security, privacy, and governance. This review fills that gap by integrating theoretical foundations with practical applications and societal perspectives. define transparency and interpretability as core concepts and introduce new economics-inspired notions of marginal transparency and marginal interpretability to highlight diminishing returns in disclosure and explanation. Methodologically, we examine model-agnostic approaches such as LIME and SHAP, alongside model-specific methods including decision trees and interpretable neural networks. We also address ante-hoc vs. post hoc strategies, local vs. global explanations, and emerging privacy-preserving techniques. To contextualize XAI’s growth, we integrate capital investment and publication trends, showing that research momentum has remained resilient despite market fluctuations. Finally, we propose a roadmap for 2025–2030, emphasizing evaluation standards, adaptive explanations, integration with Zero Trust architectures, and the development of self-explaining agents supported by global standards. By combining technical insights with societal implications, this article provides both a scholarly contribution and a practical reference for advancing trustworthy AI.","url":"https://doi.org/10.3390/ai6110285","authors":["Xiaoming Liu","Danni Huang","Jingyu Yao","Jing Dong","Litong Song","Hui Wang","Chao Yao","Weishen Chu"],"tags":["Interpretability","Transparency (behavior)","Computer science","Artificial intelligence","Limiting"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-03","doi":"https://doi.org/10.3390/ai6110285","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4402464029","name":"Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research","source":"openalex","abstract":"OBJECTIVES: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. DESIGN: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. SETTING: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. PARTICIPANTS: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. RESULTS: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). CONCLUSIONS: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.","url":"https://doi.org/10.1136/bmjopen-2024-084398","authors":["Xiwen Liao","Chen Yao","Feifei Jin","Jun Zhang","Larry Liu"],"tags":["Medicine","Health informatics","Implementation research","Software deployment","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.1136/bmjopen-2024-084398","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4397008584","name":"The impact of artificial intelligence on research and higher education in Morocco","source":"openalex","abstract":"Artificial intelligence (AI) has revolutionized various fields, including research and higher education. Thanks to its innovative applications, it has changed traditional teaching methods. This article aims to explore the impact of AI on these domains in Moroccan universities, focusing on its transformative influence, benefits, challenges, and future prospects. By analyzing current literature, case studies, and expert opinions, we elucidate how AI has enhanced research methodologies, empowered educators and students, and fostered innovation in academia. In addition, we discuss ethical considerations and potential concerns associated with the increasing integration of AI. Finally, we highlight the future prospects and opportunities offered by AI for research and higher education in Morocco.","url":"https://doi.org/10.11591/edulearn.v18i4.21511","authors":["Ghizlane Moukhliss","Khalid Lahyani","Ghizlane Diab"],"tags":["Psychology","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-17","doi":"https://doi.org/10.11591/edulearn.v18i4.21511","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4405623173","name":"Large Language Models in Gastroenterology: Systematic Review","source":"openalex","abstract":"BACKGROUND: As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale. OBJECTIVE: This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy. METHODS: Core databases including MEDLINE through PubMed, Embase, and Cochrane Central registry were searched using keywords related to LLMs (from inception to April 2024). Studies were included if they satisfied the following criteria: (1) any type of studies that investigated the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) studies published in English, and (3) studies in full-text format. The exclusion criteria were as follows: (1) studies that did not report the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) case reports and review papers, (3) ineligible research objects (eg, animals or basic research), and (4) insufficient data regarding the potential role of LLMs. Risk of Bias in Non-Randomized Studies-of Interventions was used to evaluate the quality of the identified studies. RESULTS: Overall, 21 studies on the potential role of LLMs in gastrointestinal disorders were included in the systematic review, and narrative synthesis was done because of heterogeneity in the specified aims and methodology in each included study. The overall risk of bias was low in 5 studies and moderate in 16 studies. The ability of LLMs to spread general medical information, offer advice for consultations, generate procedure reports automatically, or draw conclusions about the presumptive diagnosis of complex medical illnesses was demonstrated by the systematic review. Despite promising benefits, such as increased efficiency and improved patient outcomes, challenges related to data privacy, accuracy, and interdisciplinary collaboration remain. CONCLUSIONS: We highlight the importance of navigating these challenges to fully leverage LLMs in transforming gastrointestinal endoscopy practices. TRIAL REGISTRATION: PROSPERO 581772; https://www.crd.york.ac.uk/prospero/.","url":"https://doi.org/10.2196/66648","authors":["Eun Jeong Gong","Chang Seok Bang","Jae Jun Lee","Jonghyung Park","Eunsil Kim","Subeen Kim","Minjae Kimm","Seoung-Ho Choi"],"tags":["MEDLINE","Medicine","Cochrane Library","Systematic review","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-20","doi":"https://doi.org/10.2196/66648","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4401417975","name":"Advancing Healthcare Accessibility: Fusing Artificial Intelligence with Flexible Sensing to Forge Digital Health Innovations","source":"openalex","abstract":"In recent years, the rapid advancement of digital technologies has precipitated a paradigm shift in global healthcare, heralding a new era of digital health methodologies.This transition underscores a universal consensus on the imperative of digitalization and the application of sophisticated information and communication technologies to achieve universal health coverage, aimed at enhancing health outcomes and overall well-being.Central to this transformation is the integration of advanced technologies such as artificial intelligence (AI), big data analytics, wearable smart devices, and the Internet of Things (IoT), which have greatly enhanced data collection, analysis, storage, and transmission, laying the foundation for a comprehensive healthcare system [1,2].This system significantly improves diagnostic accuracy, enables data-driven therapeutic interventions, supports individual health self-management, and promotes patient-centric care, thus playing a crucial role in the ongoing development of healthcare infrastructures.As digital health continues to evolve, it prompts a focused examination toward eliminating healthcare disparities, aiming for a universal healthcare model that emphasizes accessibility, affordability, and sustainability [3].Flexible sensing technologies stand out in today's healthcare system because of their adaptability, comfort, and lightweight construction, which improve data accuracy and continuity for enhanced health monitoring.Their integration with AI represents a significant advancement in personal health management and supports a more inclusive digital health ecosystem, vastly enhancing health data analysis and advancing healthcare equity and accessibility (Figure ).Constructed from elastic materials designed to conform to the human body's various shapes, flexible sensors are increasingly utilized in healthcare for monitoring health indicators.These sensors are adept at measuring a comprehensive array of physiological and biochemical markers, such as heart rate, blood pressure, respiratory functions, electrophysiological signals, metabolites, proteins, electrolytes, and microbial presence, showcasing their utility through high compliance, seamless integration, noninvasiveness, and cost-effectiveness [4].Their application spans remote disease monitoring, therapeutic interventions, intelligent prosthetics, and rehabilitation technologies,","url":"https://doi.org/10.34133/bmef.0062","authors":["Lingting Huang","Zhengjie Chen","Zhèn Yáng","Wei Huang"],"tags":["Forge","Digital health","Computer science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.34133/bmef.0062","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4412449721","name":"Artificial Intelligence: Generative AI","source":"openalex","abstract":"Large language models (LLMs) are the first neural network machines capable of carrying on conversations with humans. They are trained on billions of words of text scraped from the internet. They generate text responses to text inputs. They have transformed the public awareness of artificial intelligence, bringing on reactions ranging from astonishment and awe to trepidation and horror. They have spurred massive investments in new tools for drafting texts, summarizing conversations, summarizing literature, generating images, coding simple programs, supporting education, and amusing humans. Experience with them has shown them likely to respond with fabrications (called \"hallucinations\") that severely undermine their trustworthiness and make them unsafe for critical applications. Here, we will examine the limitations of LLMs imposed by their design and function. These are not bugs but are inherent limitations of the technology. The same limitations make it unlikely that LLM machines will ever be capable of performing all human tasks at the skill levels of humans.","url":"https://doi.org/10.1145/3747356","authors":["Peter J. Denning"],"tags":["Generative grammar","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1145/3747356","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"oa:W4411078597","name":"Macy Foundation Innovation Report Part II: From Hype to Reality: Innovators’ Visions for Navigating AI Integration Challenges in Medical Education","source":"openalex","abstract":"PURPOSE: Artificial intelligence (AI) promises to significantly impact medical education, yet its implementation raises important questions about educational effectiveness, ethical use, and equity. In the second part of a 2-part innovation report, which was commissioned by the Josiah Macy Jr. Foundation to inform discussions at a conference on AI in medical education, the authors explore the perspectives of innovators actively integrating AI into medical education, examining their perceptions regarding the impacts, opportunities, challenges, and strategies for successful AI adoption and risk mitigation. METHOD: Semistructured interviews were conducted with 25 medical education AI innovators-including learners, educators, institutional leaders, and industry representatives-from June to August 2024. Interviews explored participants' perceptions of AI's influence on medical education, challenges to integration, and strategies for mitigating challenges. Transcripts were analyzed using thematic analysis to identify themes and synthesize participants' recommendations for AI integration. RESULTS: Innovators' responses were synthesized into 2 main thematic areas: (1) AI's impact on teaching, learning, and assessment, and (2) perceived threats and strategies for mitigating them. Participants identified AI's potential to enact precision education through virtual tutors and standardized patients, support active learning formats, enable centralized teaching, and facilitate cognitive offloading. AI-enhanced assessments could automate grading, predict learner trajectories, and integrate performance data from clinical interactions. Yet, innovators expressed concerns over threats to transparency and validity, potential propagation of biases, risks of over-reliance and deskilling, and institutional disparities. Proposed mitigation strategies emphasized validating AI outputs, establishing foundational competencies, fostering collaboration and open-source sharing, enhancing AI literacy, and maintaining robust ethical standards. CONCLUSIONS: AI innovators in medical education envision transformative opportunities for individualized learning and precision education, balanced against critical threats. Realizing these benefits requires proactive, collaborative efforts to establish rigorous validation frameworks; uphold foundational medical competencies; and prioritize ethical, equitable AI integration.","url":"https://doi.org/10.1097/acm.0000000000006117","authors":["Brian C. Gin","Kate LaForge","Jesse Burk‐Rafel","Christy Boscardin"],"tags":["Thematic analysis","Formative assessment","Vision","Psychology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-04","doi":"https://doi.org/10.1097/acm.0000000000006117","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1080/08839514.2024.2322336","name":"Unsupervised Machine Learning Approaches for Test Suite Reduction","source":"crossref","abstract":"Ensuring quality and reliability mandates thorough software testing at every stage of the development cycle. As software systems grow in size, complexity, and functionality, the parallel expansion of the test suite leads to an inefficient utilization of computational power and time, presenting challenges to optimization. Therefore, the Test Suite Reduction (TSR) process is of great importance, contributing to the reduction of time and costs in executing test suites for complex software by minimizing the number of test cases to be executed. Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. Recent studies have delved into the application of Machine Learning (ML) in the software testing domain, where the high cost and time consumption associated with data annotation have prompted the use of unsupervised algorithms. In this research, we conducted a Systematic Mapping Study (SMS), examining the types of unsupervised algorithms implemented in developed models and thoroughly exploring the evaluation metrics employed. This study highlighted the prevalence of the K-Means clustering algorithm and the coverage metric for validation in various studies. Additionally, we identified a gap in the literature regarding scalability considerations. Our findings underscore the effective use of unsupervised learning approaches in test suite reduction.","url":"https://doi.org/10.1080/08839514.2024.2322336","authors":["Anila Sebastian","Hira Naseem","Cagatay Catal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-04T09:15:18Z","doi":"10.1080/08839514.2024.2322336","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1109/idicaiei61867.2024.10842673","name":"Artificial Intelligence in Enhancing Quality of Education: SEM Approach","source":"crossref","abstract":"The study’s overarching goal is to gain a feel for how different types of educators see the potential for AI to improve classroom learning. The fundamental function of artificial intelligence (AI) in attaining a fruitful learning environment is the focus of this investigation. A quantitative research strategy known as an exploratory research design was utilized in the investigation. Students' information is collected from Bangalore city’s Autonomous Institutions. The study’s total sample size was 76 educators, recruited using a convenience sample method. Both SPSS and AMOS were used to analyze the data. The results of the study indicate that teachers have a favorable impression of the ability of AI features to improve academic performance in specific subjects. Educators have found that students' academic performance is greatly improved when they use AI’s collaborative features in conjunction with methods of instruction that go beyond the typical classroom. By including educators' viewpoints in analyzing the effects of AI on subject area education, this study exemplifies methodological innovation. Colleges in Bangalore that are considered independent and have the authority to develop their own curricula with the usage of AI are the subject of this study. Educators' views on AI’s functions and roles are the focus of this study, which hopes to shed light on the topic for educational organizations and policymakers in the field. The stakeholders can classify the functions that aren't contributing anything and decide whether to keep them or eliminate them.","url":"https://doi.org/10.1109/idicaiei61867.2024.10842673","authors":["Anoushka Gupta","Mallieswari R","Debolina Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T18:41:43Z","doi":"10.1109/idicaiei61867.2024.10842673","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.engappai.2024.107873","name":"Cluster ensemble selection based on maximum quality-maximum diversity","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.107873","authors":["Keyvan Golalipour","Ebrahim Akbari","Homayun Motameni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-15T05:13:13Z","doi":"10.1016/j.engappai.2024.107873","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.artint.2024.104130","name":"A unified momentum-based paradigm of decentralized SGD for non-convex models and heterogeneous data","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2024.104130","authors":["Haizhou Du","Chaoqian Cheng","Chengdong Ni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-17T15:28:11Z","doi":"10.1016/j.artint.2024.104130","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1145/3653644.3653654","name":"Wireless Communication System Optimization and Performance Improvement Based on Artificial Intelligence","source":"crossref","abstract":"Abstract: At present, Artificial intelligence technology is extensively applied across diverse sectors of the social economy and daily life. Firstly, this study introduces the current research status and relevant theoretical basis at home and abroad. Secondly, this study analyzes and summarizes the key problems and optimization goals that need to be solved in intelligent evolutionary game theory. Finally, this study improves the genetic algorithm by constructing models of different levels, and designs a comprehensive iterative cross-validation strategy combined with genetic operators to achieve the optimal control effect of the final performance index. The results show that by optimizing the intelligent algorithm, the bit error rate of the system is reduced from 0.001 to 1e-25, which greatly reduces the possibility of data corruption. Moreover, the packet loss rate decreases with the increase of distance. The result of a packet loss rate of 0.0001 makes the wireless signal more stable in data transmission. It can be seen that the application of intelligent algorithms not only improves the performance of the system, but also improves the reliability of data transmission. This can improve the stability of the communication system in complex environments, and provide users with better, more secure and reliable experience and convenience services.","url":"https://doi.org/10.1145/3653644.3653654","authors":["Bo Yang","Haohui Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T18:24:49Z","doi":"10.1145/3653644.3653654","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/b978-0-323-90534-3.00028-7","name":"Artificial intelligence for quality improvement","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90534-3.00028-7","authors":["Jessily P. Ramirez","Kathy Jenkins"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-08T11:01:14Z","doi":"10.1016/b978-0-323-90534-3.00028-7","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.32388/wscvp7","name":"Review of: \"Artificial Intelligence and Organizational Change\"","source":"crossref","abstract":"article lacks coherence, validity in references, and a logical structure. The content jumps from one subject to another without establishing clear connections, making it difficult for readers to follow and understand the main argument.","url":"https://doi.org/10.32388/wscvp7","authors":["Hanieh Arazmjoo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-14T03:57:42Z","doi":"10.32388/wscvp7","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.62906/bs.book.207","name":"Virtual Reality and Artificial Intelligence Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.62906/bs.book.207","authors":["Pramiti Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-18T10:08:30Z","doi":"10.62906/bs.book.207","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.amj.2023.11.012","name":"Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.amj.2023.11.012","authors":["Jennifer Hsueh","Christie Fritz","Caroline E. Thomas","Andrew P. Reimer","Andrew T. Reisner","David Schoenfeld","Adrian Haimovich","Stephen H. Thomas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-20T03:05:11Z","doi":"10.1016/j.amj.2023.11.012","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.caeai.2024.100267","name":"Investigating algorithmic bias in student progress monitoring","source":"crossref","abstract":"This research investigates bias in AI algorithms used for monitoring student progress, specifically focusing on bias related to age, disability, and gender. The study is motivated by incidents such as the UK A-level grading controversy, which demonstrated the real-world implications of biased algorithms. Using the Open University Learning Analytics Dataset, the research evaluates fairness with metrics like ABROCA, Average Odds Difference, and Equality of Opportunity Difference. The analysis is structured into three experiments. The first experiment examines fairness as an attribute of the data sources and reveals that institutional data is the primary contributor to model discrimination, followed by Virtual Learning Environment data, while assessment data is the least biased. In the second experiment, the research introduces the Optimal Time Index, which pinpoints Day 60 of an average 255-day course as the optimal time for predicting student outcomes, balancing timely interventions, model accuracy, and efficient resource allocation. The third experiment implements bias mitigation strategies throughout the model's life cycle, achieving fairness without compromising accuracy. Finally, this study introduces the Student Progress Card, designed to provide actionable personalized feedback for each student.","url":"https://doi.org/10.1016/j.caeai.2024.100267","authors":["Jamiu Adekunle Idowu","Adriano Soares Koshiyama","Philip Treleaven"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-18T08:46:42Z","doi":"10.1016/j.caeai.2024.100267","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/b978-0-323-90534-3.00042-1","name":"Artificial intelligence in congenital heart disease","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90534-3.00042-1","authors":["Alessandra Toscano","Patrizio Moras"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-08T11:01:45Z","doi":"10.1016/b978-0-323-90534-3.00042-1","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1148/ryai.03292024.podcast","name":"AI for Opportunistic Imaging Part 1","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.03292024.podcast","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T13:57:15Z","doi":"10.1148/ryai.03292024.podcast","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.3233/faia390","name":"Artificial Intelligence Research and Development","source":"crossref","abstract":"Intro -- Title Page -- Preface -- About the Conference -- Contents -- Machine Learning and Deep Learning Applications -- Enhancing Seawater Reverse Osmosis Desalination Efficiency Using Digital Twins and Machine Learning -- Unsupervised Pairwise Causal Discovery on Heterogeneous Data Using Mutual Information Measures -- SynthRetina: Revolutionizing Fundus Image Analysis Through Synthetic Data Enhancement -- Optimizing Energy Consumption of Kubernetes Clusters with Deep Reinforcement Learning -- Predictive Maintenance in the Food Industry: A Case Study Using Vibration Sensors and Machine Learning Techniques -- Characterization of Synthetic Lung Nodules in Conditional Latent Diffusion of Chest CT Scans -- Temporal-Invariant Segmentation of Multiple Sclerosis Lesions Using Generative Models -- Detection of Epileptic Seizures with EEG Sequential Patterns -- Impact of Maternal Nutrition on Neonatal Birthweight: A Machine Learning Study -- Generative Models for Data Augmentation on Inertial Measurement Units Data Classification -- Weakly Supervised Localization of Mammograms for Dense Breast Tissues -- Fuzzy Logic-Based Variable Encoding for Improved Diabetic Retinopathy Prediction -- Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans -- Enhanced Crack Segmentation Network: Leveraging Multi-Dimensional Attention -- Machine Learning for Particle Identification in LHCb -- Checking Robustness of Neural Network Models for the Classification of Malware -- Learning Brain-Storming with Multiagent Multi-Armed Bandits -- Multiclass Lesion Detection Using Longitudinal MRI in Multiple Sclerosis -- Automatic Catalan Keyword Spotting Database Generator -- Open Source Cardiac Digital Twinning of Human Ventricular Repolarisation from 12-Lead ECG and MRI.","url":"https://doi.org/10.3233/faia390","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-30T09:47:32Z","doi":"10.3233/faia390","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.2139/ssrn.4954578","name":"Diabetic Retinopathy Detection with Artificial Intelligence","source":"crossref","abstract":"Diabetic retinopathy is a state of affairs that occurs as a result of vandalizing the blood vessels of the retina in people who have diabetes. Diabetic retinopathy can prosper if we have type 1 or 2 diabetes and a longhorn of uncontrolled high blood sugar levels. While we may start out with only mild vision problems, we can in the fullness of time lose our sight. Untreated diabetic retinopathy is one of the stereotypical causes of blindness in the United States, according to the National Eye Institute. It's also the stereotypical eye disease in people with diabetes. In this paper, we will train a deep neural network model based on CNNs and Residual Blocks to detect the type of Diabetic Retinopathy from images. DR is a disease that results from the complication of type 1 and 2 diabetes. DR is the leading cause of blindness in the working-age population of the developed world and is estimated to affect over 2.6 million people in the world and is responsible for 2.6% of global blindness (0.84 million of 32.4 million people) worldwide according to the WHO. This paper design in such way that learner can understand the concept and implement in their real life.","url":"https://doi.org/10.2139/ssrn.4954578","authors":["Debraj Banerjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-12T13:23:05Z","doi":"10.2139/ssrn.4954578","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1148/rg.230067.q2","name":"Understanding and Mitigating Bias in Imaging Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1148/rg.230067.q2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-30T20:01:42Z","doi":"10.1148/rg.230067.q2","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.56831/psen-05-158","name":"Artificial Intelligence in Software Engineering","source":"crossref","abstract":"This volume is a broad-based collection of chapters that address the areas of overlap between the fields of artificial intelligence (AI) and software engineering. A taxonomy of this overlap area is developed and related to other major attempts to address the interaction between these two fields. Each of the four major subareas-AI-based support environments, software engineering toolds and techniques, methodological issues of AI software development, and AI techniques in practical software is described and illustrated with representative examples.","url":"https://doi.org/10.56831/psen-05-158","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-02T19:34:42Z","doi":"10.56831/psen-05-158","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.2139/ssrn.4767249","name":"Artificial Intelligence Capital and Employment Prospects","source":"crossref","abstract":"There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term &amp;apos;AI capital&amp;apos; as a vector of knowledge, skills and capabilities related to AI technologies, which could boost individuals&amp;apos; productivity, employment and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms.","url":"https://doi.org/10.2139/ssrn.4767249","authors":["Nick Drydakis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-21T05:12:56Z","doi":"10.2139/ssrn.4767249","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.2139/ssrn.4735171","name":"The Advancement of Artificial Intelligence","source":"crossref","abstract":"Artificial Intelligence (AI) has undergone remarkable progress in recent years, revolutionizing diverse industries and aspects of human life. This article explores the rapid evolution of AI technology, discussing key breakthroughs, challenges, and the implications of its growth. The advancements in AI have been fueled by significant improvements in computing power, data availability, and algorithmic developments, enabling machines to perform complex tasks and learn from vast datasets. This article covers major areas of AI advancement, including machine learning, natural language processing, computer vision, robotics, and AI ethics. It analyzes the potential benefits and risks of AI development, showcasing how AI has achieved human-level performance in various domains, such as language understanding, image recognition, and game-playing. Additionally, the article delves into the ethical considerations arising from the proliferation of AI technologies, emphasizing the need for responsible and ethical AI implementation to ensure fairness, transparency, and user privacy. As AI's impact on society and the economy becomes increasingly pronounced, it is essential to understand the potential of AI for innovation and progress while addressing its challenges to harness its full potential for the greater good.","url":"https://doi.org/10.2139/ssrn.4735171","authors":["Meet Ashokkumar Joshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-21T09:12:42Z","doi":"10.2139/ssrn.4735171","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.3233/faia240433","name":"Decision Support System for the Diagnosis of Chronic Wounds Using Artificial Intelligence Algorithms on Images","source":"crossref","abstract":"A solution is proposed that consists of supporting the professional in deciding how to act on the wound by offering a diagnosis proposal. Artificial Intelligence (AI) algorithms have been developed to allow the extraction of the most relevant wound characteristics through an image and providing similar successful wounds from the health center itself. Five pre-trained Convolutional Neural Networks (CNN) have been used to compare the results with images processed in different ways. In this way, the professional would have a diagnostic reference of other wounds similar to the one being evaluated and thus be able to make the right decision. A total of 711 images were processed and analyzed in order to obtain their most identifying morphological and textural characteristics. From each of the images, the five most similar images in terms of characteristics were searched for and clinically validated by comparing them using an objective assessment scale. The results showed an overall accuracy of 71.12%, calculated as the weighting of the scale match of similar images to the original. With this solution, clinicians improve their confidence in clinical practice by having support in decision making, observing favorable outcomes and progression of chronic wounds.","url":"https://doi.org/10.3233/faia240433","authors":["Lorena Casanova","David Reifs","Ramon Reig","Sergi Grau"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-30T09:48:33Z","doi":"10.3233/faia240433","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.30525/978-9934-26-525-9-20","name":"Maintaining Academic Integrity at the NWU in the Context of Artificial Intelligence (AI)","source":"crossref","abstract":"2ND International Scientific Conference “Integrity, open science and artificial intelligence in academia and beyond: meeting at the crossroads” (December 17–18, 2024). Riga, Latvia : Baltija Publishing, 2024. 76 pages.","url":"https://doi.org/10.30525/978-9934-26-525-9-20","authors":["Yolande Stewart"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-26T21:11:12Z","doi":"10.30525/978-9934-26-525-9-20","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1007/s40593-024-00444-8","name":"Editor’s Note: Special Issue on Educational NLP for a Multilingual World","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s40593-024-00444-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-04T10:12:41Z","doi":"10.1007/s40593-024-00444-8","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1016/j.engappai.2024.107879","name":"Temporal signed gestures segmentation in an image sequence using deep reinforcement learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.107879","authors":["Dawid Kalandyk","Tomasz Kapuściński"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-13T17:14:46Z","doi":"10.1016/j.engappai.2024.107879","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.58679/ii47046","name":"The Influence of Artificial Intelligence on Human Activity and Human Intelligence","source":"crossref","abstract":"The influence of the evolution of artificial intelligence on human activity and human intelligence is a multifaceted topic that encompasses various dimensions such as societal implications, cognitive enhancement, educational methods, and decision-making processes. The impact of AI on society also raises important social and ethical questions. How societies adapt and integrate AI technologies will also play a significant role. If societies prioritize education systems that encourage critical thinking, problem solving, creativity, and emotional intelligence alongside the use of AI, the overall level of human intelligence could increase. Conversely, if AI leads to a devaluation of these skills in favor of purely technical skills, it could have a negative impact on the development of complete intelligence.","url":"https://doi.org/10.58679/ii47046","authors":["Nicolae Sfetcu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-18T01:19:02Z","doi":"10.58679/ii47046","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.61466/ijcmr4010003","name":"Applications of artificial intelligence in medical education","source":"crossref","abstract":"Artificial intelligence is being utilized more and more to enhance the educational adventures of medical students by offering individualized experiences and better results. Artificial intelligence techniques were discovered to be utilized in different areas of medical education, with training labs accounting for the majority of these uses. The integration of artificial intelligence in medical education can enhance patient 8 outcomes by equipping medical workers with advanced skills and knowledge. The results of artificial intelligence-based training, which demonstrated enhanced practical abilities among medical students, are referred to as post-implementation. The effectiveness of artificial intelligence applications in various facets of medical education has to be investigated further.","url":"https://doi.org/10.61466/ijcmr4010003","authors":["Lucas Pedro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-08T07:57:20Z","doi":"10.61466/ijcmr4010003","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1201/9781003346289-10","name":"Smart Innovative Medical Devices Based on Artificial Intelligence","source":"crossref","abstract":"AI is a powerful and rapidly developing technology with the ability to enhance capabilities in a wide range of sectors. Artificial intelligence (AI) is not new since computers are being used to decide and predict the long-term effect of diseases. AI-enabled medical technologies have the potential to completely transform the medical field by enabling doctors to treat and diagnose their patients more precisely and successfully while also enhancing their overall care. More than 350 FDA-approved medical devices incorporate artificial intelligence, and there are countless additional products that provide economic benefits in the healthcare industry. The Internet of Medical Things (IoMT) is a subset of Internet of Things (IoT) technologies and consists of medical devices connected in conjugation with the motive of monitoring patient care. IoMT devices integrate automated, connected devices and artificial intelligence created on machine learning toward enabling wellness monitoring deprived of social participation. Medical gadgets will be able to produce results more quickly, accurately, and reliably by adding AI. The adoption of AI-enabled medical devices has long been predicted to be the wave of the future for both diagnoses.","url":"https://doi.org/10.1201/9781003346289-10","authors":["Nayankumar C. Ratnakar","Beenkumar R. Prajapati","Bhupendra G. Prajapati","Jigna B. Prajapati"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-20T20:11:26Z","doi":"10.1201/9781003346289-10","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1201/9781003679189-8","name":"Artificial intelligence for EEG biomarker discovery in emotional disorders","source":"crossref","abstract":"Adolescence is a developmental period that involves increased susceptibility to emotional disorders and self-harm, both strong predictors of suicide risk. Traditional diagnoses are frequently subjective, require self-reporting, and have no objective biomarkers for early diagnosis. Mismatch negativity (MMN), a pre-attentive event-related potential (ERP), was studied as a neurocomputational biomarker for the identification of adolescents at risk for non-suicidal self-injury (NSSI) and suicidal behavior. EEG data of 228 adolescents (healthy controls and three clinical subgroups) were processed by a novel Enhanced EEGNet model consisting of multi-scale fuzzy attention mechanisms and transformer blocks. The model produced classification accuracy of 64.2%, significantly higher than that produced by deep learning (22.2–31.1%) and conventional machine learning methods (22.2–31.1%), which all converged on chance levels of performance. Ablation studies have shown the importance of class weighting and data augmentation on model robustness. Our results imply that MMN, when mediated by sophisticated artificial intelligence (AI) techniques, may be a sensitive biomarker to differentiate clinical subtypes of self-injury. The work lays the groundwork for scalable, objective screening tools that can be used in telehealth and school-based settings with the potential to enhance early intervention and prevention efforts in adolescent mental health.","url":"https://doi.org/10.1201/9781003679189-8","authors":["Nahar Islam Nishi","Syed Rayhan Masud","Ahmed Imtiaz","Dipta Gomes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-16T14:36:49Z","doi":"10.1201/9781003679189-8","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1201/9781003371250-17","name":"Personalized Telemedicine Utilizing Artificial Intelligence, Robotics, and Internet of Medical Things (IOMT)","source":"crossref","abstract":"The technical advanced artificial intelligence (AI) and robotics with the aid of the internet of medical things (IoMT) is extremely needed for the development of capacity for the future needs of in-person care across geographies and health professional organizations. Both AI and telemedicine were versatile and flexible and offered an unending opportunity to produce improved health services. Trends in the use of this technology may be grouped into four: patient monitoring, information technology in the field of healthcare, intelligent assisted diagnostics, and collaboration in information analysis. These can be utilized in shifting medical services from healthcare centers and organizations to houses and smart devices with increased convenience and lower costs. As AI has an immense potential to support doctors in their everyday schedule of image investigation and alleviate their occupational load. In addition, the operating room (OR) is presently an advanced combination of medical robots, workstations for telepresence, software-integrated surgery, and powerful imaging equipment. Robots have enhanced surgeon dexterity and offered the least intrusive procedures and have increased the 302access to the target organ. However, the use of AI may also be controversial in relation to a number of legal, ethical, and societal questions.","url":"https://doi.org/10.1201/9781003371250-17","authors":["Kiran Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-13T09:29:49Z","doi":"10.1201/9781003371250-17","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.37126/aige.v1.i1.19","name":"Techniques to integrate artificial intelligence systems with medical information in gastroenterology","source":"crossref","abstract":"Techniques to integrate artificial intelligence systems with medical information in gastroenterology","url":"https://doi.org/10.37126/aige.v1.i1.19","authors":["Hong-Yu Jin","Man Zhang","Bing Hu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-24T01:18:47Z","doi":"10.37126/aige.v1.i1.19","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1145/3689932","name":"Proceedings of the 2024 Workshop on Artificial Intelligence and Security","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3689932","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-22T06:24:01Z","doi":"10.1145/3689932","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1145/3653644.3653656","name":"The Fusion of Edge Computing and Artificial Intelligence in 5G Communication","source":"crossref","abstract":"With the rapid development of modern communication technology, in the fields of the Internet of Things, smart cities and autonomous driving, in order to meet the high bandwidth and low latency characteristics of 5G (5th-Generation) networks, as well as the urgent needs of processing massive data and providing real-time intelligent services, it is imperative to improve and optimize the communication AI (Artificial Intelligence) model. Therefore, this paper moves data processing and analysis from the centralized cloud to the edge of the network, near the user access point, aiming to reduce data transfer time and improve response speed, and utilizes artificial intelligence technology to enable more efficient and adaptive network management and service customization. This paper focuses on the role and advantages of edge computing in 5G communication, the application of artificial intelligence combined with edge computing in 5G communication, and the realization of edge AI technology architecture. Finally, two sets of simulation results are as follows: The average accuracy error of the optimized edge AI model and cloud AI model is less than 1%, and the average response time is reduced by 31.9ms. The research helps to better understand network behavior, predict and manage traffic, and dynamically deploy resources.","url":"https://doi.org/10.1145/3653644.3653656","authors":["Pengqin Zhang","Hongjie Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T18:24:49Z","doi":"10.1145/3653644.3653656","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.33545/27076571.2024.v5.i2c.113","name":"The impact of artificial intelligence on software development","source":"crossref","abstract":"This paper explores how artificial intelligence (AI) is transforming software development processes. By automating coding tasks, improving testing and enhancing project management, AI is reshaping the landscape of software engineering. This paper also addresses the challenges and ethical implications of integrating AI into software development. Ultimately, this paper argues that AI is not merely an addition, but rather a catalyst for a paradigm shift in software design, development, and maintenance, presenting the industry with both opportunities and challenges for the future.","url":"https://doi.org/10.33545/27076571.2024.v5.i2c.113","authors":["Manpreet Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-16T07:24:21Z","doi":"10.33545/27076571.2024.v5.i2c.113","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1080/08839514.2024.2321550","name":"Application Of Density-Based Clustering Approaches For Stock Market Analysis","source":"crossref","abstract":"Present economy is largely dependent on the precise forecasting of the business avenues using the stock market data.As the stock market data falls under the category of big data, the task of handling becomes complex due to the presence of a large number of investment choices.In this paper, investigations have been carried out on the stock market data analysis using various density-based clustering approaches.For experimentation purpose, the stock market data from Quandl stock market was used.It was observed that the effectiveness of Dynamic Quantum clustering approach were better.This is because it has better adopting capability according of changing patterns of the stock market data.Similarly performances of other density-based clustering approaches like Weighted Adaptive Mean Shift Clustering, DBSCAN and Expectation Maximization and also partitive clustering methods such as k-means, k-medoids and fuzzy c means were also experimented on the same stock market data.The performance of all the approaches was tested in terms of standard measures.It was found that in majority of the cases, Dynamic Quantum clustering outperforms the other density-based clustering approaches.The algorithms were also subjected to paired t-tests which also confirmed the statistical significance of the results obtained.","url":"https://doi.org/10.1080/08839514.2024.2321550","authors":["Tanuja Das","Anindya Halder","Goutam Saha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-04T10:10:19Z","doi":"10.1080/08839514.2024.2321550","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.70593/978-81-981271-1-2_6","name":"Emerging trends and future research opportunities in artificial intelligence, machine learning, and deep learning","source":"crossref","abstract":"The Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), each built on a higher level of the proved technology driving innovation and efficiency. There are a few other futuristic trends clearly on the horizon too, such as the incorporation of AI with Internet of Things (IoT) devices to create environments that are smarter and more responsive. Explainable Artificial Intelligence (XAI) is also becoming more important, as is the need for transparency and accountability in AI decision-making. Federated learning has also emerged as an interesting approach towards privacy-preserving model training in ML by training de-centralized models across multiple devices without sharing raw data. Transformer model such as GPT-4 and BERT are transformer models that have revolutionized the field of natural language processing (NLP) in DL, which are capable of more nuanced understanding and generation of human language. Their usage has increased dramatically, and they are used in everything from healthcare diagnostics to automated content creation. Also, the implication of blockchain-enabled AI to develop hack-proof AI applications, largely in finance and supply chain management is increasingly becoming popular. More research arises in the future, that will be around building hybrid AI models that contains both symbolic reasoning and neural networks, where we expect future research, will be focused on building much more stronger and flexible AI systems. Certainly, further study of the ethical issues around AI deployment - especially what is learned about bias and fairness - will remain an important area of investigation.","url":"https://doi.org/10.70593/978-81-981271-1-2_6","authors":["Nitin Liladhar Rane","Jayesh Rane","Mallikarjuna Paramesha","Ömer Kaya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-22T11:07:24Z","doi":"10.70593/978-81-981271-1-2_6","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.7249/rra691-1","name":"Public Perceptions of Artificial Intelligence for Homeland Security","source":"crossref","abstract":"To evaluate public perception of the benefits and risks of U.S. Department of Homeland Security use of artificial intelligence technologies, researchers surveyed the nationally representative RAND American Life Panel about the department's uses of these technologies, with a focus on four types of technologies: facial recognition technology, license plate–reader technology, risk-assessment technology, and mobile phone location data.","url":"https://doi.org/10.7249/rra691-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-20T09:19:15Z","doi":"10.7249/rra691-1","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.2139/ssrn.4947379","name":"Artificial Intelligence -enabled Bright Internet","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4947379","authors":["M El-dosuky"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-09T13:50:49Z","doi":"10.2139/ssrn.4947379","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1201/9781003499480","name":"Artificial Intelligence-Based 6G Networking","source":"crossref","abstract":"Artificial Intelligence-Based 6G Networking focuses exclusively on the upcoming sixth-generation (6G) network and services slated for implementation by 2030. It explores the paradigm shift that is 6G. It discusses the deep integration of computing and communication, supported by artificial intelligence (AI) across network elements like cloud, edge, and terminals. It also examines how AI-native interfaces will permeate various network components, from radio access networks to application servers and databases. Proposing a unified AI-enabled framework for optimizing networks and applications as a single integrated system, the book covers how network service providers can tailor network baselines, reduce noise, and accurately identify issues. The book delves into the potential of AI-driven networks to self-correct, predict, and rectify service degradations proactively, enhancing uptime and troubleshooting efficiency. It outlines the “Connection, Communication, Collaboration, Curation, and Community” framework to enhance network effects, aiding operators in automation, cost reduction, and providing optimal user experiences. Covering topics from MIMO and Massive MIMO to holographic communications, cybersecurity and quantum communications, the book explores cutting-edge technologies shaping the future of 6G networks. It anticipates a future where AI, along with machine learning and deep learning, enables continuous learning, self-optimization, and predictive maintenance, even with full automation, that will be the hallmark of a new era in network connectivity and innovation.","url":"https://doi.org/10.1201/9781003499480","authors":["Radhika Ranjan Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-04T09:34:38Z","doi":"10.1201/9781003499480","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.4324/9781003421849-8","name":"Biased artificial intelligence systems and their implications in war scenarios","source":"crossref","abstract":"In recent years, there has been a growing debate about ethical uses of artificial intelligence (AI) in warfare. However, few of these debates and topics have focused on the issue of biased AI, despite the fact that the topic of biased AI has gained much prominence when it comes to the use of AI in civilian settings. This chapter aims to bring together these two conversations of ethical use of AI in military settings and the issue of biased AI in civilian settings. By drawing upon examples of biased AI in civilian settings, I highlight what implications these examples may have in military settings. This includes how assumptions about gender, ethnicity, and other factors may result in biased algorithms that select military targets, and how insufficient training data may result in inaccurate and biased facial recognition that disproportionality impacts certain groups in society. In the conclusion, I argue that there needs to be more focus on biased AI in the broader discussions of AI, ethics, and warfare.","url":"https://doi.org/10.4324/9781003421849-8","authors":["Kelly Fisher"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-08T14:17:01Z","doi":"10.4324/9781003421849-8","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.71465/fair45","name":"The Role of Artificial Intelligence in Environmental Sustainability","source":"crossref","abstract":"The role of Artificial Intelligence (AI) in promoting environmental sustainability has gained significant attention in recent years. This paper explores various applications of AI technologies across different sectors, highlighting their potential to enhance resource efficiency, reduce waste, and support sustainable practices. Through a comprehensive review of current literature and case studies, the paper identifies key areas where AI is making a meaningful impact, including energy management, waste reduction, water conservation, and climate modeling. It also addresses the challenges and ethical considerations surrounding the deployment of AI in environmental contexts, emphasizing the need for responsible innovation. The findings suggest that while AI presents substantial opportunities for advancing sustainability efforts, careful consideration of its implications is essential to ensure equitable and effective outcomes.","url":"https://doi.org/10.71465/fair45","authors":["Dr. Aasim Zafar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-14T05:50:27Z","doi":"10.71465/fair45","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1007/s10462-024-10854-8","name":"Explainable artificial intelligence (XAI) in finance: a systematic literature review","source":"crossref","abstract":"Abstract As the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a thorough justification to improve risk assessment, minimise the loss of trust, and promote a more resilient and trustworthy financial ecosystem. This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry. We classified the articles according to the financial tasks addressed by AI using XAI, the variation in XAI methods between applications and tasks, and the development and application of new XAI methods. The most popular financial tasks addressed by the AI using XAI were credit management, stock price predictions, and fraud detection. The three most commonly employed AI black-box techniques in finance whose explainability was evaluated were Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Most of the examined publications utilise feature importance, Shapley additive explanations (SHAP), and rule-based methods. In addition, they employ explainability frameworks that integrate multiple XAI techniques. We also concisely define the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.","url":"https://doi.org/10.1007/s10462-024-10854-8","authors":["Jurgita Černevičienė","Audrius Kabašinskas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T17:05:15Z","doi":"10.1007/s10462-024-10854-8","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1145/3700297.3700309","name":"Optimization path of early childhood education professional practice model under the background of artificial intelligence","source":"crossref","abstract":"In view of the management problems of early childhood education major in universities in the era of artificial intelligence, as well as the mismatch between the artificial intelligence internship platform and the actual demand, this study combined with literature research and design of the evaluation indicators of intelligent enabling early childhood education internship software, including 3 first-level indicators, 5 second-level indicators and 12 third-level indicators. The weights of each index are determined by Delphi method and analytic hierarchy process. After the consistency test is passed, the weight of indicators at each level is determined scientifically. This paper provides a certain reference basis for finding a suitable artificial intelligence practice management platform for early childhood education in universities, and then discusses the development direction of the future practice management platform software. INTRODUCTION: This is the introductory text.","url":"https://doi.org/10.1145/3700297.3700309","authors":["Chunli Tang","Zehao Liang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-18T22:31:26Z","doi":"10.1145/3700297.3700309","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.4274/anatoljmed.2025.52523","name":"Assessing Physicians’ Readiness for Medical Artificial Intelligence","source":"crossref","abstract":"ÖzObjective: This study aims to assess the level of readiness among physicians at University of Health Sciences Türkiye, İzmir City Hospital for the adoption of medical artificial intelligence (AI) technologies.Methods: Participants' readiness levels were assessed with the medical artificial-intelligence readiness scale devised by Karaca et al.University of Health Sciences Türkiye, İzmir City Hospital employs 1.867 physicians.Using Baş's (2006) sample-size formula with a ±0.05 margin of error and a 95% confidence level, the minimum required sample was calculated as 319, and 320 physicians ultimately completed the questionnaire.The 22-item scale was subjected to exploratory and confirmatory factor analysis (EFA).The initial solution explained 85.432% of the total variance, with excellent sampling adequacy (Kaiser-Meyer-Olkin=0.964)and a highly significant Bartlett's test of sphericity (χ²=9.376.445,p 0.05).Years in practice influenced only the third factor, Foresight, with a significant difference emerging there (p<0.05) but not on the remaining dimensions.Departmental affiliation, by contrast, proved important: except for the ethics sub-scale, all dimensions -and the overall MAIR score-differed significantly among departments (p<0.05).The grand-mean MAIR score was 3.11 on a five-point scale.Thus, physicians' readiness levels lie slightly above the midpoint, reflecting a generally positive yet essentially ambivalent attitude toward medical AI.The same \"marginally above neutral\" pattern applies to each individual sub-dimension. Conclusion:The analysis reveals that physicians adopt a moderately positive stance toward AI, yet they exhibit a pronounced shortfall in the technical knowledge and practical competence required for its effective implementation.","url":"https://doi.org/10.4274/anatoljmed.2025.52523","authors":["Süleyman Mertoğlu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-11T10:04:57Z","doi":"10.4274/anatoljmed.2025.52523","addedAt":"2026-09-01T01:47:53.667Z","updatedAt":"2026-09-01T01:47:53.667Z"},{"id":"doi:10.1007/978-3-030-92087-6_2","name":"Demystifying Artificial Intelligence Technology in Cardiothoracic Imaging: The Essentials","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_2","authors":["Jelmer M. Wolterink","Anirban Mukhopadhyay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/j.artmed.2022.102284","name":"Word-level text highlighting of medical texts for telehealth services","source":"crossref","abstract":"The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional workload for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. This paper aims to show how different text highlighting techniques can capture relevant medical context. This would reduce the doctors' cognitive load and response time to patients by facilitating them in making faster decisions, thus improving the overall quality of online medical services. Three different word-level text highlighting methodologies are implemented and evaluated. The first method uses Term Frequency - Inverse Document Frequency (TF-IDF) scores directly to highlight important parts of the text. The second method is a combination of TF-IDF scores, Word2Vec and the application of Local Interpretable Model-Agnostic Explanations to classification models. The third method uses neural networks directly to make predictions on whether or not a word should be highlighted. Our numerical study shows that the neural network approach is successful in highlighting medically-relevant terms and its performance is improved as the size of the input segment increases.","url":"https://doi.org/10.1016/j.artmed.2022.102284","authors":["Ozan Ozyegen","Devika Kabe","Mucahit Cevik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-23T12:49:23Z","doi":"10.1016/j.artmed.2022.102284","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.21037/jmai.2019.09.0","name":"Toward the transparency of deep learning in radiological imaging: beyond quantitative to qualitative artificial intelligence","source":"crossref","abstract":"In the near future, nearly every type of clinician, from paramedics to certificated medical specialists, will be expected to utilize artificial intelligence (AI) technology, and deep learning (DL) in particular (1). In terms of exceeding human ability, DL has been the backbone of computer science. DL mostly involves automated feature extraction using deep neural networks (DNNs), which can aid in the classification and discrimination of medical images, including mammograms, skin lesions, pathological slides, radiological images, and retinal fundus photographs.","url":"https://doi.org/10.21037/jmai.2019.09.0","authors":["Yoichi Hayashi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-30T14:16:20Z","doi":"10.21037/jmai.2019.09.0","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.31525/ct1-nct03843164","name":"Lung Nodule Characterization by Artificial Intelligence Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct03843164","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-05-03T20:16:48Z","doi":"10.31525/ct1-nct03843164","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.3233/faia250346","name":"An Artificial Intelligence-Driven Approach to Optimizing Digital Art Generation Models","source":"crossref","abstract":"In order to solve the problem of the lack of annotated image data languages, AI-driven digital art generation model optimization methods are proposed. In this paper, we study Multilingual TTI (MTTI) and the current neural machine translation-guided MTTI system, relying on multilingual multimodal encoder, and propose Art Image Generation Model Based on Multilingual Text Symbols (AIG-MTS) to learn the weights and integrate the multilingual text knowledge. Symbols, AIG-MTS), which learns the weights and integrates the multilingual text knowledge so as to alleviate the differences between languages and improve the model performance. Experiments are conducted on the standard datasets COCO-CN, Multi30KTask2 and LAION-5B. The experimental results show that removing LC leads to a significant decrease in model performance, while removing the LDC has less effect on the model. When removing the two loss functions LC and LDC, a FID score of 14.81 is obtained. Therefore, compared with the mainstream algorithms, the AIG-MTS model has the best performance on all datasets.","url":"https://doi.org/10.3233/faia250346","authors":["Kaichao Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T16:27:12Z","doi":"10.3233/faia250346","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/b978-0-443-44197-4.00021-7","name":"Artificial intelligence in medical education","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44197-4.00021-7","authors":["Sameer Mohommed Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-21T10:36:29Z","doi":"10.1016/b978-0-443-44197-4.00021-7","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.2139/ssrn.4651939","name":"Artificial Intelligence and Accounting Profession","source":"crossref","abstract":"The study investigated the effect of artificial intelligence and accounting profession in Nigeria. The study employed a field survey research design. The population of this study are accountants in Nigeria considering the Big Four which include KPMG, Deloitte, PricewaterhouseCoopers and Ernst and Young. The study found that Artificial Intelligence had significant accounting profession in Nigeria. The study recommended that accounting software of assurance firm should learn from previous tagging decisions that are typically made according to rules that the accountant is aware of and also integrate artificial intelligence into their system of sampling in case an audit is required, it will be possible to audit all the data rather than merely a sampling.","url":"https://doi.org/10.2139/ssrn.4651939","authors":["Jerry Danjuma Kwarbai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-12T09:40:13Z","doi":"10.2139/ssrn.4651939","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.4324/9781003256113","name":"Religion and Artificial Intelligence","source":"crossref","abstract":"International Society for Science & Religion's 2025 Book Prize recipient, in the category of books for professionals and educators Artificial intelligence (AI) is rarely out of the news or the public imagination. Images of red-eyed Terminators illustrate press accounts of incremental advances in medical diagnosis, facial recognition, natural language processing, and robotics. Such advances are transforming society through measurable impacts on people’s decisions and opportunities. Religion and Artificial Intelligence: An Introduction explores an emerging field with a religious studies approach, drawing on cultural and digital anthropological methods to demonstrate the entanglements of religion and AI, our imaginaries of these objects and our ideas about their utopian or dystopian futures. It addresses key topics, including the following: What AI is and is not. How religions are reacting to AI with examples of rejection, adoption, and adaptation. How established religions understand creation and place human-like AI within that. How overtly secular and even ‘new atheist’ groups understand AI as a tool for liberation from human evolution and religion. Religious visions of superintelligent AI. This engaging book is essential for anyone considering the relationship between religion, science and technology, and interested in the questions raised by transhumanism, posthumanism, and new religious movements. The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under A creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.","url":"https://doi.org/10.4324/9781003256113","authors":["Beth Singler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-18T12:34:06Z","doi":"10.4324/9781003256113","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/medai67139.2025.00002","name":"Title Page III","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai67139.2025.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00002","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2991/978-94-6463-512-6_32","name":"The Application of Artificial Intelligence-based Multimodal Emotion Analysis","source":"crossref","abstract":"Nowadays, Artificial Intelligence (AI) and Multimodal Emotion Analysis represent cutting-edge advancements in the realm of computational intelligence.AI, the emulation of human cognitive processes by machines, has revolutionized various fields, including emotion analysis.Multimodal Emotion Analysis refers to the integration of multiple sensory inputs, such as images, speech, and text, to understand and interpret human emotions comprehensively.As opposed to single-modal approaches, the multimodal approach provides a more comprehensive and precise analysis of affective states.By combining machine learning algorithms with sophisticated data processing techniques, AI systems can now recognize and analyze emotional cues from diverse modalities, providing deeper insights into human affective states.This interdisciplinary approach has significant implications across numerous domains, from human-computer interaction and social robotics to mental health diagnostics and marketing research.With the potential to enhance the understanding of human emotions and behaviors, AI-driven multimodal emotion analysis stands at the forefront of innovation, promising to reshape how people interact with technology and interpret human experiences.","url":"https://doi.org/10.2991/978-94-6463-512-6_32","authors":["Hongji Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T07:02:38Z","doi":"10.2991/978-94-6463-512-6_32","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.23939/istcmtm2026.01.032","name":"ANALYSIS OF THE USE OF ARTIFICIAL INTELLIGENCE IN MEDICAL HIGHER EDUCATION","source":"crossref","abstract":"The article analyses the possibilities and features of using artificial intelligence technologies in the educational process of higher medical schools in the conditions of digitalisation and transformation of the learning environment. Modern approaches to the integration of artificial intelligence tools in the training of medical students, in particular, intelligent learning systems, educational chatbots and adaptive platforms, are considered, and their potential for personalising learning, increasing motivation and efficiency of learning material assimilation is assessed. Special attention is paid to analysing the risks and limitations of artificial intelligence in medical education, in particular the impact on the development of critical thinking, ethical and legal aspects, as well as issues of data reliability and security. The empirical basis of the study is the results of anonymous questionnaire survey of more than 200 students and interns of medical and dental faculties, conducted at the Department of Medical Informatics, Faculty of Postgraduate Education, Danylo Halytskyi Lviv National Medical University. The frequency and directions of using artificial intelligence tools in the educational process, their impact on the understanding of complex topics, the speed of completing academic tasks and academic performance, as well as the level of students' trust in the information obtained were analysed. The results of the study indicate a high prevalence of the use of artificial intelligence technologies among medical students and their predominantly positive impact on learning activities, provided that a critical approach to information processing is maintained. The feasibility of gradual and controlled integration of artificial intelligence technologies into the educational process with a focus on the development of critical thinking, information security and ethical responsibility of future medical professionals is substantiated.","url":"https://doi.org/10.23939/istcmtm2026.01.032","authors":["Oksana Boyko","Olesia Chaban","Oleh Chaban"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-29T16:42:59Z","doi":"10.23939/istcmtm2026.01.032","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/0933-3657(93)90034-z","name":"Symbolic decision support in medical care","source":"crossref","abstract":"Symbolic decision procedures offer a flexible alternative to classical quantitative procedures for decision making, particularly when precise parameters (such as probabilities) are hard to estimate. One such procedure, based on a logic of argumentation, is described. Specifications of inference methods for such functions as proposing and refining decision options, deducing and inheriting arguments for and against options, and selecting among alternatives are presented. These exploit declarative models for patient data, domain and task knowledge. A simple method for translating the specifications into executable Prolog is described. A practical and efficient toolset for using the procedure in a wide range of clinical environments is being developed within the DILEMMA project of the European Commission's Advanced Informatics in Medicine research programme.","url":"https://doi.org/10.1016/0933-3657(93)90034-z","authors":["J. Huang","J. Fox","C. Gordon","A. Jackson-Smale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(93)90034-z","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.71465/fair40","name":"Artificial Intelligence in Healthcare: Innovations and Challenges","source":"crossref","abstract":"Artificial Intelligence (AI) is rapidly transforming the healthcare landscape by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiency. This paper explores the innovations driven by AI technologies, such as machine learning, natural language processing, and robotics, that are being integrated into various healthcare domains, including medical imaging, patient monitoring, and drug discovery. Despite the promising advancements, the adoption of AI in healthcare faces several challenges, including data privacy concerns, ethical implications, and the need for regulatory frameworks. This paper provides a comprehensive overview of the current state of AI in healthcare, discussing the innovations and challenges, and offering insights into future directions for research and practice.","url":"https://doi.org/10.71465/fair40","authors":["Dr. Amna B. Sadiq"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-14T05:50:27Z","doi":"10.71465/fair40","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1063/5.0230273","name":"The impact of ChatGPT generative artificial intelligence on music education","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0230273","authors":["Yuxia Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T17:00:37Z","doi":"10.1063/5.0230273","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1063/5.0230283","name":"AI music teaching innovation research based on artificial intelligence technology","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0230283","authors":["Gao Yun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T17:00:37Z","doi":"10.1063/5.0230283","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003507864-10","name":"Neurorobotics","source":"crossref","abstract":"This chapter is not an exhaustive historical record of research in the field of building robots with AI that move and interact in the real world. Rather, it is a sample of current ideas in this field. Note, the term “embodied” is often used to refer to a physical AI machine embedded in the real-world environment. An embodied AI machine or robot is also referred to in the literature as a neurorobot. Ziemke ( 2003 ) identifies six different notions of embodiment as there are contrasting views on what kind of physical body (if any, i.e., it could be simulated) is required for embodied cognition: Structural coupling between agent and environment (does not require a body), i.e., each one can perturb the other through connecting channels. Historical embodiment as the result of a history of structural coupling, i.e., the connection between the agent and environment may have been in the past and affects the behavior of the agent in the present environment. Physical embodiment , this requires a physical instantiation of the agent beyond virtual/software. Organism-like embodiment , i.e., has organism-like bodily form such as humanoid robots, both living and artificial agents. Organismic embodiment of autopoietic kind , i.e., a living system capable of growing or creating its own parts and therefore autonomous, in contrast with machines that are assembled in a factory (allopoietic) and are therefore governed by external forces (heteronomous). Social embodiment is the ability to communicate through body language.","url":"https://doi.org/10.1201/9781003507864-10","authors":["Eitan Michael Azoff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T13:21:42Z","doi":"10.1201/9781003507864-10","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s10462-024-10973-2","name":"Digital deception: generative artificial intelligence in social engineering and phishing","source":"crossref","abstract":"Abstract The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.","url":"https://doi.org/10.1007/s10462-024-10973-2","authors":["Marc Schmitt","Ivan Flechais"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T01:02:05Z","doi":"10.1007/s10462-024-10973-2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1515/9783111336435-006","name":"5 Investing in Artificial Intelligence: Considerations for Libraries and Archives","source":"crossref","abstract":"This chapter highlights the relevance of artificial intelligence (AI) and machine learning (ML) to library and archive work through various pilot projects conducted in libraries and archives. It describes several projects that leveraged machine learning (ML) technologies, including computer vision, speech-to-text, named entity recognition, natural language processing NLP), and an AI chatbot powered by a large language model (LLM). This chapter presents examples of libraries’ and archives’ adopting and applying AI and ML to provide engaging and efficient information services and to generate richer metadata at scale, which allows users to discover, identify, access, navigate, cluster, analyze, and use mate­rials more easily and effectively. Also discussed are where and how libraries and archives should invest in AI and ML to reap the most benefit and the implications of such investments in costs and prospects.","url":"https://doi.org/10.1515/9783111336435-006","authors":["Bohyun Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-03T05:14:19Z","doi":"10.1515/9783111336435-006","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s44163-024-00161-0","name":"RETRACTED ARTICLE: Unleashing the power of advanced technologies for revolutionary medical imaging: pioneering the healthcare frontier with artificial intelligence","source":"crossref","abstract":"Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.","url":"https://doi.org/10.1007/s44163-024-00161-0","authors":["Ashish Singh Chauhan","Rajesh Singh","Neeraj Priyadarshi","Bhekisipho Twala","Surindra Suthar","Siddharth Swami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-02T05:03:32Z","doi":"10.1007/s44163-024-00161-0","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.24321/2394.6539.202401","name":"The Impact of Artificial Intelligence on Healthcare: Opportunities and Challenges","source":"crossref","abstract":"This article explores the impact of artificial intelligence (AI) on healthcare, including its opportunities and challenges. AI is rapidly transforming healthcare by improving diagnosis and treatment, streamlining administrative tasks, and reducing costs. The article discusses some of the most promising applications of AI in healthcare, including medical imaging and personalised treatment plans. However, the adoption of AI in healthcare also raises ethical concerns around bias, patient privacy, and the potential for AI to replace human judgement. Despite these challenges, the potential benefits of AI in healthcare are significant, and the industry is actively exploring ways to maximise the potential of AI while mitigating risks. The article concludes that AI will play an increasingly important role in shaping the future of healthcare delivery and patient outcomes. How to cite this article:Akhai S. The Impact of Artificial Intelligence onHealthcare: Opportunities and Challenges. J AdvRes Med Sci Tech. 2024;11(1&2):1-6. DOI: https://doi.org/10.24321/2394.6539.202401","url":"https://doi.org/10.24321/2394.6539.202401","authors":["Shalom Akhai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-29T01:16:30Z","doi":"10.24321/2394.6539.202401","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/b978-0-443-18450-5.00023-2","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-18450-5.00023-2","authors":["Abdulhamit Subasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T14:05:26Z","doi":"10.1016/b978-0-443-18450-5.00023-2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1201/b15618-10","name":"Soft Tissue Characterization Using Genetic Algorithm","source":"crossref","abstract":"Soft tissue properties are important to many modern applications of technology to medicine, such as robotic surgery, soft tissue modeling, and surgical simulation with force feedback. However, realistic acquisition of soft tissue properties is extremely challenging, not only because of the nonlinearity, anisotropy, nonhomogeneity, rate dependence, and time dependence of soft tissues but also due to the layered and nonhomogeneous structures of soft tissues [Samur et al. 2007; Kim et al. 2008; Zhong et al. 2010, 2012]. It is understood that soft tissue properties may dynamically change during the surgical process according to different patients, different organs, different functional regions and layers crossed by the surgical tools, and different physiological conditions. Therefore, it requires that mechanical properties of soft tissues be acquired and studied through a real-time intraoperative CONTENTS 6.1 Introduction .......................................................................................................................... 79 6.2 Biomechanical Models ........................................................................................................ 81 6.2.1 QLV Model ................................................................................................................ 81 6.2.2 MR Model ................................................................................................................. 82 6.2.3 Exponential Formulation ........................................................................................83","url":"https://doi.org/10.1201/b15618-10","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-10","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2991/978-94-6463-512-6_64","name":"Artificial Intelligence Model Selection for Breast Cancer Risk Screening","source":"crossref","abstract":"In today's social environment, the risk of breast cancer for women is increasing, and breast cancer has exceeded lung cancer as the most common cancer nowadays.However, if detect breast cancer at an early stage and measures are taken, it can be very effective in improving the chances of survival of breast cancer patients.Meanwhile, with the continuous development of artificial intelligence, it shows a broad prospect in the medical field.In this article experiment try to apply AI to the field of breast cancer risk detection, and help improve the accuracy of breast cancer screening by finding the artificial intelligence model with the highest accuracy rate.This article selected breast cancer data from kaggle, pre-processed the data by Pearson Correlation Coefficient, and then the article compares four of the most common machine learning algorithms namely Random Forest, Logistic Regression, Neural Networks, and Support Vector Machines, using Python.Based on the experimental results the article conclude that Random Forest is highly accurate and shows great affect in the field of breast cancer screening.","url":"https://doi.org/10.2991/978-94-6463-512-6_64","authors":["Ziwen Fang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-23T07:02:38Z","doi":"10.2991/978-94-6463-512-6_64","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1021/acsomega.5c06324.s001","name":"Mapping the Evolution of Artificial Intelligence in Medical Materials","source":"crossref","abstract":"The application of artificial intelligence (AI) in medical materials has been extensively explored, encompassing areas such as composition optimization, shape design, and manufacturing processes. However, a comprehensive study and analysis of the overall research landscape in this field remain scarce. To bridge this gap, this study employs bibliometric analysis to evaluate 108 publications from the Web of Science Core Collection database. The results reveal that research in this domain spans nearly two decades, with a notable surge in publications since 2018, probably driven by advancements in deep learning. Additionally, the study identifies significant challenges in AI for medical materials, such as clinical validation, de novo design, and precise manufacturing. These insights may provide guidance for future research in AI-enabled medical materials development.","url":"https://doi.org/10.1021/acsomega.5c06324.s001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-11T22:22:06Z","doi":"10.1021/acsomega.5c06324.s001","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-030-92087-6_49","name":"Artificial Intelligence in Medicine: Laws, Regulations, and Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_49","authors":["Enzo Maria Le Fevre","Giselle Heleg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_49","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/waie63876.2024.00007","name":"Reviewers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/waie63876.2024.00007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-04T18:40:35Z","doi":"10.1109/waie63876.2024.00007","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/ricai64321.2024","name":"2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ricai64321.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-13T17:36:51Z","doi":"10.1109/ricai64321.2024","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/faia250356","name":"Research and Application of Industrial Design Optimization Algorithms Based on Artificial Intelligence","source":"crossref","abstract":"In order to solve the problem of poor clarity, interactivity and fidelity of traditional industrial design visual display, the research and application of industrial design optimization algorithm based on artificial intelligence is proposed. This paper uses NCI matching algorithm to match industrial design products, and reconstructs the point cloud of industrial products to accurately detect the characteristics of industrial products; On this basis, virtual reality technology is applied to build a visual optimization model for industrial design, determine the output format of the model scene and the output of industrial design, and process according to the changing characteristics of the industrial design model to edit the comprehensive data of the model; Finally, according to the technical characteristics of virtual reality technology, the visual optimization model of industrial design is targeted to optimize, so as to complete the visual optimization of industrial design. The experimental results show that the method in this paper has a high definition in simple industrial design. For slightly difficult and complex industrial design, the accuracy of the method in this paper is 93%, which can show more industrial design information. Conclusion: It shows that the visual optimization method of industrial design designed with virtual reality technology can be displayed and processed according to the changing characteristics of industrial design, showing a clearer industrial design effect.","url":"https://doi.org/10.3233/faia250356","authors":["Wanjun Yin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T16:34:08Z","doi":"10.3233/faia250356","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003348351-1","name":"Artificial Intelligence Application for Enterprise Sustainability","source":"crossref","abstract":"It is important for businesses to follow developing technologies to survive. They also need to integrate these innovations into their own systems. No expense should be spared to win in the sectoral struggle, which occurs in an intensely competitive environment and continues under difficult conditions. This is essential for enterprises to survive and be sustainable. For the financial sustainability of enterprises, it is necessary to create a climate of trust, mainly to not lose customers, and to protect their brand values and reputations. However, to be one step ahead during the struggle, strong systematic projects and knowledge pools are also needed. In this globalizing world, businesses in digital markets can now easily buy and sell goods in international markets. For this reason, it is understood that businesses should use artificial intelligence applications, but they should be used carefully.","url":"https://doi.org/10.1201/9781003348351-1","authors":["Erkin Artantas","Hakan Gursoy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T13:27:41Z","doi":"10.1201/9781003348351-1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.5772/intechopen.113092","name":"Sentiment Analysis of Social Media Using Artificial Intelligence","source":"crossref","abstract":"Social media refers to the development and sharing of sentiment, information, and interests, as well as other forms of opinion via virtual communities and networks. Nowadays, social networking and micro blogging websites are considered reliable sources of information since users may openly express their opinions in these forums. An investigation of the sentiment on social media could assist decision-makers in learning how consumers feel about their services, products, or policies. Extracting emotion from social media messages is a difficult task due to the difficulty of Natural Language Processing (NLP). These messages frequently use a combination of graphics, emoticons, text, etc. to convey the sentiment or opinion of the general people. These claims, known as eWOM (Electronic Word of Mouth), are quite common in public forums where people may express their opinions. A classification issue arises when categorizing the sentiment of eWOM as positive, negative, or neutral. We could not use standard NLP tools to examine social media sentiment. In this chapter, we will study the role of Artificial Intelligence in identifying the sentiment polarity of social media. We will apply ML(Machine Learning) methods to resolve this classification issue without diving into the difficulty of eWOM parsing.","url":"https://doi.org/10.5772/intechopen.113092","authors":["K. Victor Rajan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-10T10:26:16Z","doi":"10.5772/intechopen.113092","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3640824.3640827","name":"New approaches to improve rectal cancer therapy with MC1-1 inhibitors, Bax apoptosis protein agonists, and oxitinib: concept of medical intelligence and deep learn","source":"crossref","abstract":"For clarity, the following are all expressions for the second layer.","url":"https://doi.org/10.1145/3640824.3640827","authors":["Yi Qin","Jiazhe Feng","Tianshu Wang","Liangyu Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-08T12:05:28Z","doi":"10.1145/3640824.3640827","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/nai-240729","name":"A neurosymbolic approach to AI alignment","source":"crossref","abstract":"We propose neurosymbolic integration as an approach for AI alignment via concept-based model explanation. The aim is to offer AI systems the ability to learn from human revision but also assist humans at evaluating AI capabilities. The proposed method allows users and domain experts to learn about the data-driven decision making process of large neural network models and to impose a particular behaviour onto such models. The models are queried using a symbolic logic language that acts as a lingua franca between humans and model representations. Interaction with the user then confirms or rejects a revision of the model using logical constraints that can be distilled back into the neural network. We illustrate the approach using the Logic Tensor Network framework alongside Concept Activation Vectors and apply it to Convolutional Neural Networks and the task of achieving quantitative fairness. Our results illustrate how the use of a logical language is able to provide users with a formalisation of the model’s decision making whilst allowing users to steer the model towards a given alignment constraint.","url":"https://doi.org/10.3233/nai-240729","authors":["Benedikt J. Wagner","Artur d’Avlia Garcez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-30T11:16:45Z","doi":"10.3233/nai-240729","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003483571-2","name":"An Empirical Study on Climate Change Using Geospatial Artificial Intelligence","source":"crossref","abstract":"In a world of increasing pollution, particulate matter happens to be a major contributor to climate change. The risk at which it increases concerns the researchers and global leaders. Geospatial artificial intelligence is an interdisciplinary field that integrates innovations in spatial science, and AI techniques in machine learning to extract knowledge from spatial big data. Geographically weighted regression (GWR), the spatial regression technique, helps to understand how local geographical factors influence variable relationships. In this study, GWR produced the bandwidth (19.739) and p-value (0.296). Similarly, it produced Si values for non-stationary variables PM_2008 (0.375), PM_2018 (0.051), and PM_2020 (0.006). And Monte Carlo Simulation simulates the uncertainty outcomes. The results for RMSE values produced using machine learning regression models were LR (4.340), GB (113.68), XGB (111.71), and DT (108.09). Hence, linear regression is better than the other ML models developed. Furthermore, the GWR model outperforms linear regression.","url":"https://doi.org/10.1201/9781003483571-2","authors":["Prisilla Jayanthi","Utku Kose","Muralikrishna Iyyanki"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-13T14:01:36Z","doi":"10.1201/9781003483571-2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/medai67139.2025.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai67139.2025.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00004","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.56759/cdob6397","name":"Artificial Intelligence in Pharmacovigilance","source":"crossref","abstract":"This report on Artificial intelligence in pharmacovigilance addresses a rapidly emerging cross-disciplinary field that is at the intersection of pharmacovigilance, computer science, mathematics, regulation, law, medicine, human rights, psychology and social science. Consequently, just as with medicinal products, it is important to establish the approved indications, posology, side effects, and warnings and precautions for use of artificial intelligence in pharmacovigilance. The latter must be clearly defined and understood by many people from different backgrounds to propel research and practical implementation in an effective, safe and responsible manner. The diverse pool includes professionals, researchers, and decision makers working in pharmacovigilance in biopharmaceutical industry, regulatory authorities, and academia. It also includes software vendors that develop artificial intelligence solutions for pharmacovigilance, including signal management and all aspects of Individual Case Safety Report processing. This report provides the requisite terminology and conceptual understanding to actively engage in this space, either by participating in the applied scientific research and public discourse, or by performing evaluations and making decisions at their respective organisations.","url":"https://doi.org/10.56759/cdob6397","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-25T11:42:29Z","doi":"10.56759/cdob6397","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.4324/9781003421849-9","name":"Ethics, laws on war and artificial intelligence-driven warfare","source":"crossref","abstract":"Achieving superiority of artificial intelligence (AI) is the new criterion of competing powers, as its versatile use in combat could alter the outcomes of war. In the environment of ever-increasing AI-powered weapon systems and emergence of grey zone confrontations, the character of war is changing. With humans controlling AI-assisted weapons, basic tenets of the laws of war can be abided by; but autonomous AI weapons are likely to divagate. That justifies invoking a fresh regulation by the United Nations legislating a ban on development and use of such weapons. Human rights groups and experts on the laws of war condemn transnational drone strikes in the name of the ‘war against terror’ targeting terror groups in undesignated war zones. The misuse of AI-powered weapons and drones by terrorist groups is as much an area of concern. Conscience should prevent engineers from developing fully autonomous AI software and decision-makers from directing employment of AI-assisted autonomous weaponry that will go beyond the control of human beings, resulting in disastrous consequences.","url":"https://doi.org/10.4324/9781003421849-9","authors":["Guru Saday Batabyal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-08T14:17:01Z","doi":"10.4324/9781003421849-9","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2023.107809","name":"An explainable artificial intelligence based approach for the prediction of key performance indicators for 1 megawatt solar plant under local steppe climate conditions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107809","authors":["Vipin Shukla","Amit Sant","Paawan Sharma","Munjal Nayak","Hasmukh Khatri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T23:20:32Z","doi":"10.1016/j.engappai.2023.107809","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.21037/jmai-24-276","name":"Artificial intelligence in the medical field: diagnostic capabilities of GPT-4 in comparison with physicians","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-276","authors":["Nino Gvajaia","Luka Kutchava","Levan Alavidze","Sai Pratibha Yandamuri","Elene Pestvenidze","Vaso Kupradze"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-23T07:09:28Z","doi":"10.21037/jmai-24-276","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.108594","name":"SoftmaxU: Open softmax to be aware of unknowns","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108594","authors":["Xulun Ye","Jieyu Zhao","Jiangbo Qian","Yuqi Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-15T17:33:06Z","doi":"10.1016/j.engappai.2024.108594","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-53622-9_4","name":"The Valuation of Artificial Intelligence-Driven Know-How and Patents","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-53622-9_4","authors":["Roberto Moro-Visconti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-01T12:01:54Z","doi":"10.1007/978-3-031-53622-9_4","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-50300-9_22","name":"Role of Artificial Intelligence in Sustainable Finance","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50300-9_22","authors":["Monika Rani","Ram Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-19T06:02:12Z","doi":"10.1007/978-3-031-50300-9_22","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2139/ssrn.4643763","name":"Artificial Intelligence in Medical Education: Technology and Ethical Risk","source":"crossref","abstract":"The rapid implementation of Artificial Intelligence in medical education presents certain challenges including a unique combination of technology and ethical risk. In this article, we describe a number of technical risks that concern not only data selection, but also the mathematical basis and performance of algorithms, their accuracy, applicability, and evaluation. We then go on to propose 10 ethical risk points important to institutions, policymakers, teachers, students, and patients, including potential impacts on curriculum design, content, delivery, and AI-human communication. A fundamental distinction is made between routine AI (RAI) and decision AI (DAI): both types will enable students at various levels to move from study to practice rapidly and continuously, thus accelerating the move from traditional models of educational delivery to a faster, integrated, and continuous system. Links are subsequently made between technical risks and ethical risks and the notion of an ethical risk gap is introduced both in the context of the practical implementation of technology in medical education and the type and effectiveness of national and international regulation.","url":"https://doi.org/10.2139/ssrn.4643763","authors":["Agnieszka Pregowska","Mark Perkins"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-28T11:51:55Z","doi":"10.2139/ssrn.4643763","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003521440-2","name":"Perceptions on Urban Artificial Intelligence in Urban Planning and Development","source":"crossref","abstract":"Artificial intelligence (AI) is a transformative force with growing influence across numerous sectors, including marketing, finance, agriculture, healthcare, security, space exploration, robotics, transportation, chatbots, creativity, and manufacturing. Its integration into urban planning and development, however, remains underexplored. This gap is evident in both the understanding of AI’s potential applications within urban contexts and public awareness of how AI technologies are shaping city policies and practices. This chapter seeks to bridge these knowledge gaps by examining the relationship between 15 key AI technologies and their 16 primary applications in urban planning. Utilising social media analytics, the research analyses sentiment and content in 11,236 Twitter posts from Australia, focusing on public perceptions and usage of AI in this field. The findings highlight that digital transformation, innovation, and sustainability are the leading application areas, with drones, automation, robotics, and big data being the predominant technologies. This chapter reveals a central community discussion on enhancing city sustainability and digital transformation through AI, spotlighting the vital role of technologies like big data, automation, and robotics in urban development.","url":"https://doi.org/10.1201/9781003521440-2","authors":["Tan Yigitcanlar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-31T16:39:23Z","doi":"10.1201/9781003521440-2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1117/12.3009155","name":"Quantitative phase imaging and artificial intelligence","source":"crossref","abstract":"Quantitative phase imaging (QPI) is a powerful label-free imaging technique that enables high-resolution, three-dimensional imaging of unlabeled samples by exploiting refractive index (RI) distributions as intrinsic imaging contrast. In this talk, we present the latest developments in 2D, 3D QPI techniques in visible1-3 and X-ray wavelengths. We will elucidate the principles of various QPI techniques, detail the reconstruction algorithms involved, and explore the enhancement of image analysis through machine learning algorithms. Moreover, we will delve into potential applications, spanning cell biology, biotechnology, and industrial inspections in fields such as semiconductors and display devices.","url":"https://doi.org/10.1117/12.3009155","authors":["YongKeun Park"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-13T16:34:29Z","doi":"10.1117/12.3009155","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/s44244-024-00015-9","name":"Modified genetic algorithm to solve worker assignment problem with time windows","source":"crossref","abstract":"Abstract In recent years, the demand for electronic products has been increasing rapidly. T mounting technology (SMT) line is one of the production areas for electronic products, directly affecting this situation. In an SMT line, multiple machines mount electronic parts to the board. The worker must complete work when the parts used in these machines are within the remaining parts available for replacement. When a worker fails to replace parts at the right time, the production line stops, and delays occur. Besides, there may be a designated worker who should be assigned to each task. In the current situation, workers’ work procedures are not optimized, so they should schedule work procedures for each worker. This problem is called Worker Assignment Problem with Time Window (WAPTW). This paper proposes a method to solve WAPTW called Genetic Algorithm with Local Restriction (GALR). GALR combines a genetic algorithm (GA) and local search with local restriction. This paper’s main contribution is introducing WAPTW as a novel real-world optimization problem in an electricity company, its mathematical formulation, and a proposed GALR to solve WAPTW. The experiment shows that the proposed method could yield the best result in real-world WAPTW compared with other methods.","url":"https://doi.org/10.1007/s44244-024-00015-9","authors":["Alfian Akbar Gozali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-21T10:02:17Z","doi":"10.1007/s44244-024-00015-9","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.5772/intechopen.1004952","name":"AI-Based Edutech for Adaptive Teaching and Learning","source":"crossref","abstract":"The artificial intelligence (AI)-based problem learning system quickly and accurately performs problem setting and scoring using algorithm. In this process, the learner’s level of prior learning is identified, the subject and quantity to be learned are determined and problem learning is provided for each learner. The basic use of AI-based problem learning enhances ease and fairness in performing assignment and evaluation and provides data that can strengthen interactions between instructors and students. Above all, the biggest advantage is the possibility of helping individual learners with different levels of prior learning to strengthen basic learning. To this end, instructors need to understand the technical aspects of the system, check the content system as an educational goal set by the instructor, and make efforts to supplement the necessary parts. When AI-based problem learning is used in connection with classes, a technical understanding of a system that can utilize various functions of the AI system more efficiently is required. In addition, instructional design is needed to expand thinking and strengthen capabilities through the process of structuring and understanding the contextual relationship between concepts based on the learned knowledge of students using AI-based problem learning systems.","url":"https://doi.org/10.5772/intechopen.1004952","authors":["Hwang Eunkyung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-11T08:43:55Z","doi":"10.5772/intechopen.1004952","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.21037/jmai-24-58","name":"Evaluation of range of motion using vision artificial intelligence (AI) in musculoskeletal medicine","source":"crossref","abstract":"Background: Measurements of mobility in musculoskeletal medicine are typically acquired using manual instruments that often have significant measurement variability and may lead to discrepancies in data. This study aims to evaluate the accuracy of vision artificial intelligence (AI) compared to human measurements when measuring participants’ range of motion (ROM). Methods: Thirty-one healthy adult participants and patients at a tertiary academic outpatient clinic were recruited. The active ROM of shoulder, hip, knee, and ankle are measured with the ViFive mobile application using vision AI. ROM was measured simultaneously using ViFive and a goniometer as the conventional standard. Mixed-effects linear regression models were used to determine the strength of the relationship between ViFive and goniometer measurements. Results: There were strong associations between measurements with the ViFive app and those measured by researchers using a goniometer for all joints examined [shoulder flexion (β=0.93, P<0.001), shoulder abduction (β=0.96, P<0.001), shoulder external rotation (β=0.78, P<0.001), shoulder internal rotation (β=0.81, P<0.001), hip flexion (β=0.62, P<0.001), knee flexion (β=0.78, P<0.001), and ankle dorsiflexion (β=0.36, P<0.001)]. Conclusions: Our study demonstrates that the ROM of various joints can be accurately measured by vision AI on the ViFive app as compared to the gold standard. There are new possibilities of using mobile device technologies to assess and rehab musculoskeletal conditions through virtual visits in lieu of the traditional physical exam and physical therapy.","url":"https://doi.org/10.21037/jmai-24-58","authors":["Yue Meng","Michelle Lee","Hye-Jin Yun Clark","Chantal Nguyen","Saekwang Kwon","Shannon Schultz","Nicole Segovia Pham","Derek Schirmer","Eugene Y. Roh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-12T05:58:31Z","doi":"10.21037/jmai-24-58","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/faia250321","name":"Research on Intelligent Mechanical Design and Optimization Methods Based on Artificial Intelligence","source":"crossref","abstract":"In order to solve the problem of short life and high elimination rate of parts in mechanical equipment, intelligent mechanical design and optimization method based on artificial intelligence is proposed. Firstly, from the perspective of structural design, this paper analyzes the service performance requirements of products and the design requirements of key components under the active remanufacturing mode. By analyzing the mapping relationship between design parameters and service performance, the performance similarity analysis function is constructed using support vector machine (SVM) method. Then, based on the similarity analysis function of structure and performance, a structural optimization design method for active remanufacturing parts is proposed to realize the optimization adjustment of remanufacturing time domain. Finally, the impeller part is taken as an example for experimental analysis. The experimental results show that the error between the prediction results and the simulation analysis results is less than 3%, the calculation accuracy of the SVM approximate model is high, and the performance similarity after optimization is R=0.92. The structure optimization design should be carried out for the impeller, so that the fatigue life of the impeller meets the time domain requirements of compressor remanufacturing. Conclusion: Aiming at the time domain requirements of remanufacturing, an active remanufacturing optimization design method based on similarity analysis is constructed to match the service performance of parts with the time domain requirements of remanufacturing.","url":"https://doi.org/10.3233/faia250321","authors":["Chao Guo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T16:09:41Z","doi":"10.3233/faia250321","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.71465/fair50","name":"Artificial Intelligence and Robotics: Synergies and Emerging Applications","source":"crossref","abstract":"Artificial Intelligence (AI) and robotics are converging to create transformative solutions across various domains. This article explores the synergies between AI and robotics, focusing on how their integration enhances capabilities and drives innovation. We examine emerging applications in healthcare, manufacturing, transportation, and everyday life, emphasizing the advancements in machine learning, sensor technologies, and autonomous systems. The discussion extends to the challenges and ethical considerations associated with these technologies. By analyzing current trends and future directions, this paper highlights the potential of AI-robotics synergies to reshape industries and improve human well-being.","url":"https://doi.org/10.71465/fair50","authors":["Dr. Noshin Anwar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-14T05:50:27Z","doi":"10.71465/fair50","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.26512/lstr.v16i1.48972","name":"Legal Regime of Inventions Created by Artificial Intelligence","source":"crossref","abstract":"[Purpose] The purpose of this study is to examine the concept of artificial intelligence (AI) as an object of civil legal relations, with a specific focus on its status as an inventor. The study aims to define the characteristics of AI as an inventor, including its intangible nature, resemblance to the human brain, autonomy, data collection and processing capabilities, learning ability, and generation of novel results, particularly in the realm of inventions. [Methodology/Approach/Design] The research employs a range of methodologies, including functional and logical analysis, deduction, induction, synthesis, and dogmatic approaches. It highlights the need for legal regulation concerning AI as an inventor, with particular attention given to the legal regime surrounding inventions created by AI. [Findings] Based on the unique aspects of AI as an object of civil legal relations and its capacity to create inventions, the study proposes extending the existing legal and patent framework to address these relations with certain specificities. The conditions for patentability of AI-generated inventions should mirror those for human inventions, as they operate in the same technological field. [Practical Implications] It is not recommended to grant AI the status of a legal entity. Instead, the study suggests indicating in the patent that the invention was created with the assistance of a specific AI, without conferring personal non-property rights to AI itself. Property rights to inventions generated by AI should be legally assigned to the user of the AI, unless agreed upon differently by the parties involved. [Originality/Value] Given the advancements in AI technologies and their ability to create patentable inventions, there is an urgent need for comprehensive and effective legal regulation. Currently, such regulation is lacking at both the national and international levels, underscoring the significance and value of this study.","url":"https://doi.org/10.26512/lstr.v16i1.48972","authors":["Yurii Khodyko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-10T00:26:13Z","doi":"10.26512/lstr.v16i1.48972","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/esai62891.2024.10913851","name":"Artificial Intelligence Techniques for Cardiovascular Disease Classification using 12-Lead ECG","source":"crossref","abstract":"Cardiovascular disorders, including atrial fibrillation and bundle branch blockages, have a significant impact on global health and are associated with higher mortality rates. The accurate categorization of these disorders through ECG data is essential in order to improve the results for patients. This investigation addresses the challenge of classifying major cardiac conditions by utilizing seven ECG datasets and employing machine learning techniques. We seek to classify patients exhibiting atypical ECGs through advanced methodologies. We combined datasets, incorporating 12 leads, and utilized preprocessing techniques to enhance data quality. By employing various models, such as CNN, SVM, KNN, random forest, and logistic regression, our CNN combined with logistic regression attained an accuracy of $94 \\%$ and a sensitivity of $94 \\%$ on a test set comprising 1500 ECGs. This comprehensive approach minimizes false negatives and enhances diagnostic precision. The integration of multiple datasets and sophisticated preprocessing improves the dependability of our findings, highlighting the importance of data quality and comprehensive analysis in cardiac diagnostics. The results demonstrate how machine learning can improve cardiac diagnoses through thorough data integration and analysis.","url":"https://doi.org/10.1109/esai62891.2024.10913851","authors":["Younes Hadzine","Atman Jbari","Mohamed Najoui","Lhoussain Bahatti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T17:38:50Z","doi":"10.1109/esai62891.2024.10913851","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.24963/ijcai.2024/187","name":"Wavelet Multi-scale Region-Enhanced Network for Medical Image Segmentation","source":"crossref","abstract":"Medical image segmentation is an important task in medical artificial intelligence. Traditional segmentation methods often suffer from the information loss problem, especially in medical image data which contain many different-scale organs or tissues. To address this problem, we propose a novel medical image segmentation method called Wavelet Multi-scale Region-Enhanced Network (WMREN), which has a UNet structure. In the encoder, we design a bi-branch feature extraction architecture, which simultaneously learns the representations with Haar wavelet transform and the residual blocks. The bi-branch architecture can effectively tackle the information loss problem when extracting features. In the decoder we design an innovative Spatial Adaptive Fusion Module to enhance the regions of interest. As we know, the boundaries of objects play an important role in segmentation. To this end, we also carefully design a Contrast Refinement Enhancement Module to highlight the boundaries of the medical objects. Extensive experiments on several benchmark datasets show that our method outperforms state-of-the-art medical image segmentation methods, demonstrating its effectiveness and superiority. The source code is publicly available at https://github.com/C101812/WMREN/tree/master.","url":"https://doi.org/10.24963/ijcai.2024/187","authors":["Hang Lu","Liang Du","Peng Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T10:28:11Z","doi":"10.24963/ijcai.2024/187","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18178/jaai.2024.2.1.47-59","name":"Era of Artificial Intelligence and Its Implementation in Controlling Side Effects during Healthcare Practices","source":"crossref","abstract":"Artificial Intelligence (AI) in healthcare has ushered in a revolutionary era by transforming the way we provide care and reduce adverse consequences.This paper explores the field of AI applications in healthcare, including side effect management, streamlined administrative procedures, and customized treatment programs.The purpose of this study is to give a thorough review of how artificial intelligence (AI) is affecting healthcare delivery and how effective it is in reducing side effects.This study highlights the applications of AI in healthcare delivery, AI has a lot of promise for the healthcare industry, from bettering patient care to diagnostics.Regarding side effect management, AI-powered programs such as Google's DeepMind and Tempus demonstrate the possibility of customized treatment regimens and early complication identification.A paradigm shift is shown by the significant effects of AI on side effect control and healthcare delivery.As it navigates ethical considerations and integration challenges, a resounding call to action emerges for sustained research, development, and interdisciplinary alliance.","url":"https://doi.org/10.18178/jaai.2024.2.1.47-59","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-19T07:07:33Z","doi":"10.18178/jaai.2024.2.1.47-59","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3726010.3726015","name":"An Exploration of Using Artificial Intelligence Techniques to Learn and Improve Automated Composition Systems","source":"crossref","abstract":"The deep learning model LSTM (Long Short - Term Memory) can be used to deal with the problem of sequences, and is currently the most commonly used model to deal with music training. The process of LSTM training is sequence-to-sequence, which allows the back-and-forth relationship of notes in an instrument to be learnt by the model; however, when dealing with multiple instruments, there are multiple sequences that need to be processed at the same time, which is a limitation of the LSTM model architecture, which makes most of the current automated composition systems for single instrument training and output. However, when dealing with multiple instruments, there are multiple sequences that need to be processed at the same time, which is a limitation of the LSTM model architecture, and this makes most of the current automated composers train and output for a single instrument. Currently it is visible to deal with LSTM multiple sequences problem. Whereas this study needs to achieve multiple sound event sequence inputs corresponding to multiple sequence outputs, and the sequences are connected to each other, a problem for which there is no effective solution at present. This study attempts to choose to use Deep Improvisation (Tatsuya, 2017) for improvement by training a single track and optimizing and modifying the system to be able to have two tracks of input and two tracks of output to achieve a more resilient system, which in turn creates more realistic music. Due to the above background, the motivation to improve the deep learning automatic songwriting system arises with the expectation that the research in this paper will improve the system to be able to produce multi-tracked and more realistic music, so that more researchers will be able to apply the system to be able to produce songs and apply them to a variety of contexts at a small cost.","url":"https://doi.org/10.1145/3726010.3726015","authors":["Yang Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T07:53:18Z","doi":"10.1145/3726010.3726015","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.54393/pjhs.v6i2.2766","name":"Exploring Artificial Intelligence Role in Enhancing Medical Education for Future Physicians","source":"crossref","abstract":"Artificial intelligence (AI) has the potential to completely transform medical education by improving learning outcomes through data-driven insights, simulation, and individualized instruction. Objectives: To determine the impact of Artificial Intelligence on Medical Education and medical students' willingness and readiness to use it. Methods: An analytical cross-sectional study was conducted among medical students at a private medical institute. Ethical approval and informed consent were taken. The questionnaire was distributed through social media platforms. Mann-Whitney U test was performed, mean + SD was taken and Pearson correlation was used to assess mean rank distributions, higher means among variables, and significant associations. A p-value of &lt;0.05 was considered statistically significant. Results: Higher mean ranks by the Mann-Whitney U test in all perception-related questions indicated a tendency for higher values in males than females. The mean + SD of perception score was 3.63 ± 0.66 and the willingness was 3.48 + 0.69 which showed a positive perception and willingness to use AI. ANOVA was employed with the most significant association, enabling doctors to make correct decisions. Pearson correlation between readiness for AI and their perceptions, and willingness to use AI showed a strong positive correlation between them with p values significant at &lt;0.01 level. Conclusions: It was concluded that AI could revolutionize medical education by enhancing learning, and clinical decision-making, and supplementing traditional teaching methods. A significant positive correlation was found between AI readiness, perceptions, and willingness to use it, recognizing its role in shaping future medical practice.","url":"https://doi.org/10.54393/pjhs.v6i2.2766","authors":["Mohi Ud Din","Muhammad Ali",". Saira","Ifra Naeem","Ayman Mahmood","Ali Raza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-20T04:43:36Z","doi":"10.54393/pjhs.v6i2.2766","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1080/0952813x.2024.2417493","name":"Correction","source":"crossref","abstract":"Article title: An effectual underwater image enhancement framework using adaptive trans-resunet ++ with attention mechanismAuthors: Ajanya P and S. MeeraJournal: Journal of Experimental & Theoretic...","url":"https://doi.org/10.1080/0952813x.2024.2417493","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-16T04:26:04Z","doi":"10.1080/0952813x.2024.2417493","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/medai59581.2023.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai59581.2023.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-31T13:28:30Z","doi":"10.1109/medai59581.2023.00004","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.26226/m.6153409f62ba8657678afae2","name":"Importing and serving open-data medical images to support Artificial Intelligence research","source":"crossref","abstract":"","url":"https://doi.org/10.26226/m.6153409f62ba8657678afae2","authors":["Sébastien Jodogne"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-04T04:25:14Z","doi":"10.26226/m.6153409f62ba8657678afae2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/faia250382","name":"Application and Effect Analysis of Artificial Intelligence Technology in Logistics Informatization Teaching","source":"crossref","abstract":"In order to solve the problem of high logistics distribution cost in the new logistics management and operation mode under the concept of sharing economy, the application and effect analysis of artificial intelligence technology in logistics information teaching are put forward. In this paper, according to the current situation of the architecture connection of the shared platform, the logistics distribution function requirements of supplier role management, demand side role management, supply information publishing search and demand information publishing search are analyzed to determine the business logic form. On this basis, it defines the basic meaning of the Internet of Things, studies the practical application value of distribution and transportation contracts, analyzes the logistics distribution demand based on the Internet of Things technology, and completes the construction of logistics distribution information sharing platform based on the Internet of Things technology by improving three cloud modes of the Internet of Things: single center and multi-terminal, multi-center and multi-terminal, and information and application layering. The experimental results show that the accuracy of logistics distribution information transmission in the experimental group and the control group shows a slightly fluctuating numerical change state, but the average level of the experimental group is significantly higher than that of the control group, with the minimum recorded value of 90.07%, while the minimum value of the control group is as low as 76.32%. Conclusion: Compared with the blockchain sharing system, the sharing platform supported by the Internet of Things technology can accurately record the actual transmission behavior of logistics distribution information, and can effectively control the consumption of logistics distribution costs while improving the logistics management and operation mode.","url":"https://doi.org/10.3233/faia250382","authors":["Lin Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T16:46:45Z","doi":"10.3233/faia250382","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/faia250304","name":"A Personalized Learning Support System for Teaching Dance with Artificial Intelligence","source":"crossref","abstract":"In order to solve the problems of “cognitive overload” and “learning lost” brought by massive learning resources, a personalized learning support system of artificial intelligence in dance teaching is proposed. Subject knowledge mapping is integrated into the learning path recommendation model. Firstly, the subject knowledge map is constructed, then the knowledge path planning is carried out by combining the cognitive characteristics of learners, and finally the sequence collection of learning resources is obtained by sorting and filtering the associated resources based on the sequence of knowledge points and the learner model. The experimental results show that the algorithm proposed in this paper achieves the best performance in terms of checking accuracy rate up to 0.15% and recall rate up to 0.3%. Conclusion: The research results of this paper provide an important reference for the theoretical research and technical implementation of personalized learning path recommendation in the field of discipline education.","url":"https://doi.org/10.3233/faia250304","authors":["Yu Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-01T15:59:20Z","doi":"10.3233/faia250304","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1063/12.0028717","name":"Committees: International Conference on \"Ubiquitous Technology in Communication and Artificial Intelligence-2023","source":"crossref","abstract":"","url":"https://doi.org/10.1063/12.0028717","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T17:00:37Z","doi":"10.1063/12.0028717","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.60087/jaigs.v1i1.35","name":"Quantum Computing and Artificial Intelligence: Synergies and Challenges","source":"crossref","abstract":"Due to the explosive rise of quantum computing, there has been intense competition in business and academics in the field of quantum optics in recent decades. The current invention's overall scalability in quantum computing has surpassed many orders of magnitude, whereas ubiquitous quantum computers can support up to hundreds of quantum bits, or thousands of qubits. Strong machines continue to be developed. As a result, ethnicity has served as the inspiration for a huge number of studies and reports. This essay offers an introduction for everyone who would truly like to understand more about the ideas of quant communication and computing from a machine learning standpoint. It starts with such an educational approach and goes on to cover important turning points and the latest advancements in quantum computing. In this research, these fundamental characteristics of such a virtual network are divided into four major challenges, each of which has been thoroughly examined. correspondingly, A, B, C, and D stand for quantum physics, networking, security, and algorithms. The main issues, important areas of research, and most recent advancements are discussed as the article comes to a close.","url":"https://doi.org/10.60087/jaigs.v1i1.35","authors":["Jeff Shuford"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-06T03:56:59Z","doi":"10.60087/jaigs.v1i1.35","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.31031/cojra.2024.04.000576","name":"Artificial Intelligence in Healthcare: Historical Development, Benefits and Increasing Access for Underserved Populations","source":"crossref","abstract":"Crimson Publishers is an Open-access academic publisher has a vision to establish Open Science platform that seeks to provide equal opportunity for all, share and create knowledge, and enables the scholarly world to engage in a dialogue with the science in a more effective manner. Our efficient and transparent ways of peer-review","url":"https://doi.org/10.31031/cojra.2024.04.000576","authors":["Fassil Mesfin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-04T12:59:17Z","doi":"10.31031/cojra.2024.04.000576","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/medai67139.2025.00001","name":"Title Page I","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai67139.2025.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00001","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.108457","name":"An artificial immune system algorithm for classification tasks. An electronic nose case study","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108457","authors":["Jeniffer Molina","Luis Fernando Valdez","Juan Manuel Gutiérrez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-26T03:51:33Z","doi":"10.1016/j.engappai.2024.108457","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.4337/9781800377400.00005","name":"Introduction to Research Handbook on Warfare and Artificial Intelligence","source":"crossref","abstract":"Effective Protection of Fundamental Rights in a pluralist world","url":"https://doi.org/10.4337/9781800377400.00005","authors":["Robin Geiß","Henning Lahmann"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T14:12:45Z","doi":"10.4337/9781800377400.00005","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-50605-5_1","name":"What Is Intelligent About Artificial Intelligence?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-50605-5_1","authors":["Gerhard Paaß","Dirk Hecker"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-15T09:02:32Z","doi":"10.1007/978-3-031-50605-5_1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.63282/3050-9262.ijaidsml-v5i4p114","name":"The Role of Artificial Intelligence in Predicting Credit Risk","source":"crossref","abstract":"Credit risk forecasting remains one of the key critical issues in financial risk management with the potential to impact lending rates, portfolio construction, capital allocation, and regulatory requirements. Conventional statistical techniques like logistic regression, discriminant analysis, and scorecard models have formed the backbone of credit assessment for many decades, but tend to be restricted by linear assumptions, limited learning ability, and difficulties in capturing non-linear behavioral characteristics (encapsulated in borrower data). There have been recent developments in the field of Artificial Intelligence (AI), in particular, machine learning (ML) and deep learning (DL), which have completely changed the paradigm for credit risk modelling. Via these methods, higher predictive performance can be achieved with the possibility of adapting to heterogeneous and high-dimensional data as well as integrating alternative and behavioral information, which classic modelling frameworks are unable to fully utilise. This paper provides an in-depth discussion on the potential of AI for credit risk estimation as well as its methodological upgrading, operational implementation regulatory frameworks that could support financial institutions applying AI-based scoring systems. Based on a review of the literature, ensemble learning techniques, and particularly gradient boosting techniques like XGBoost and LightGBM, have shown robust and discriminative performance against classical statistical models across studies, especially with noisy or missing data. Highly Nonlinear: Deep learning methods, with a surge in popularity, have shown inconsistent performances on structured credit data; they have been demonstrated to be effective only when including high-frequency non-linear features or complex behaviors, as well as unstructured information such as transaction sequences or text. The approach combines best practices from academia and industry for research to deployment, including data pre-processing, feature engineering, fairness checking, cost-sensitive learning approaches, model explainability methods, and governance controls. XAIthrough methods like SHAP and LIMEbecomes instrumental in enabling regulatory approval, model transparency, and stakeholder confidence. Furthermore, consideration of fairness has become essential given the evidence of negative consequences of unintended bias propagation in ML systems. The paper demonstrates how AI models can be calibrated, interpreted, and monitored to comply with legal, ethical, or operational constraints while preserving predictive performance. Experimental results show on a real-world public lending dataset that AI models outperform traditional credit scoring baselines, in terms of ROC-AUC, Precision-Recall AUC, and cost-weighted loss. Gradient-boosted decision trees provide the most balanced compromise of all between predictive performance, computation time and explainability. Only through access to more sophisticated temporal or high-dimensional behavioral features do our neural network models even perform on par with others in the literature, as recently reported. Explainability studies also show that borrower payment history, utilization patterns, and delinquency indicators are the most important features in all models tested. Fairness diagnostics reveal subgroup differences that thresholds/pre-processing/fair-optimization need to account for. The results as a whole reinforce that AI, when operationalized under stringent methodological controls, an interpretability framework, and fairness safeguards, can offer dramatic improvements in the predictive power and business utility of credit risk assessment systems. Finally, the paper provides practical guidelines for using AI-based credit scoring in financial services and identifies a number of promising research directions, such as causality modeling, privacy-preserving computation, and standardized fairness benchmarks. This holistic study yields a publication-ready, academically sound contribution for financial AI research that is in line with the future industry tendencies as well as latter supervisory and ethical demands on credit risk modelling","url":"https://doi.org/10.63282/3050-9262.ijaidsml-v5i4p114","authors":["Surbhi Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-13T05:39:53Z","doi":"10.63282/3050-9262.ijaidsml-v5i4p114","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.6028/nist.ai.600-1","name":"Artificial intelligence risk management framework :","source":"crossref","abstract":"This document is a cross-sectoral profile of and companion resource for the AI Risk Management Framework (AI RMF 1.0) for Generative AI, 1 pursuant to President Biden’s Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence.2 The AI RMF was released in January 2023, and is intended for voluntary use and to improve the ability of organizations to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.","url":"https://doi.org/10.6028/nist.ai.600-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-15T15:21:47Z","doi":"10.6028/nist.ai.600-1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18178/jaai.2024.2.1.96-100","name":"The Impact of Artificial Intelligence (AI) on the Future of Democracy and Civic Participation","source":"crossref","abstract":"","url":"https://doi.org/10.18178/jaai.2024.2.1.96-100","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-16T08:53:47Z","doi":"10.18178/jaai.2024.2.1.96-100","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18178/jaai.2024.2.2.245-264","name":"Artificial Intelligence in Sales and Marketing: Enhancing Customer Satisfaction, Experience and Loyalty","source":"crossref","abstract":"","url":"https://doi.org/10.18178/jaai.2024.2.2.245-264","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T09:42:16Z","doi":"10.18178/jaai.2024.2.2.245-264","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781032683805-5","name":"Forecasting Air Pollution with Artificial Intelligence","source":"crossref","abstract":"Rapid urbanization has significantly contributed to air pollution around the globe. Regular incorporation of various air pollutants into the environment has imposed serious health issues. Therefore, it is essential to develop advanced approaches for precise monitoring and forecasting of air pollution. Artificial intelligence (AI) is rapidly gaining global attention due to its ability to interpret data collected from different sensors and make more precise decisions in a short time. Of note, integral components of AI like machine learning algorithms are typically employed in forecasting of air pollution, precipitations and early-warning methods. They can be implied to predict air pollutants like PM 2.5 , PM 10 , O 3 , CO, SO 2 , NO 2 , and CO 2 . The hybrid models coupled with conventional systems can improve performance compared with individual AI tools like neural networks, fuzzy inference system, multilayer perception model, support vector machines, etc., and enhance precision in forecasting and warning approaches with respect to air pollutants. Several performance evaluation error indexes such as R2, RMSE, MAE and MAPE are usually employed to assess performance of AI models in forecasting of air pollutants. Hence, AI and the machine learning algorithms have great scope in forecasting air pollution. However, many of such studies are still in infancy and require trial in a large scale.","url":"https://doi.org/10.1201/9781032683805-5","authors":["Prem Rajak","Satadal Adhikary","Suchandra Bhattacharya","Abhratanu Ganguly"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-13T09:41:04Z","doi":"10.1201/9781032683805-5","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.11648/j.ajai.20240802.17","name":"Infobody Structures for Logical Artificial Intelligence with Database Implementation","source":"crossref","abstract":"The purpose of this paper is to explore the applications of infobody concepts, infobody structures and infobody charts to Artificial Intelligence (AI), specifically, Logical Artificial Intelligence (LAI). It is also trying to explore a new way to resolve some logical issues in current Artificial Intelligence studies with ChatGPT such as answering reasoning questions in family relations. For this purpose, detailed family relations are discussed based on relation theory. Some new concepts such as primary relations, reversed relations and derived relations for family relations are introduced. Also, a relational database is introduced to implement these family relations and the relationships between these family relations, and make them calculatable with SQL. Each SQL query becomes an infobody processor and together with the input and output infobodies compose a unit infobody structure. Multiple unit structures compose an answer structure to answer a specific question in family relations. A specific unit structure can join multiple answer structures to answer multiple questions. A processor with related input infobodies contains all detailed information for reasoning to a specific output infobody and therefore an answer structure can answer a specific reasoning (logical) question. Each answer structure can be presented in an infobody chart which is a visualization of an infobody model. An infobody model can be implemented in another relational database that can be queried by SQL as well. Suppose all academic areas are implemented in knowledge structures with infobody models in clouds, and all commonsense areas such as family relations are implemented in thinking structures with infobody models in clouds, then, any logical AI app should be able to query some of them to answer any logical questions. Also, it is possible to make those IB models for LAI available for all kinds of robots to simulate creative thinking.","url":"https://doi.org/10.11648/j.ajai.20240802.17","authors":["Yuhu Che"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-06T10:14:31Z","doi":"10.11648/j.ajai.20240802.17","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1108/978-1-83549-468-420241001","name":"Introduction to Artificial Intelligence in Healthcare","source":"crossref","abstract":"Abstract Implementing artificial intelligence (AI) in healthcare organizations involves the entire organization. This groundbreaking technology is becoming central to achieve the goals of the new healthcare through the ongoing commitment to sustainability despite the severe lack of resources. Decision-makers in healthcare need knowledge and skills to prepare for the changes in many professional activities in the years ahead. Furthermore, chief medical officers and clinical leaders need to act on the opportunities that AI can bring, starting from its integration into the reality of healthcare settings while working with those responsible for managing and implementing AI in compliance with current legislation in Europe and the United States. Finally, stakeholders need to know how to leverage AI capabilities and how to recognize its limitations and its opportunities in administrative applications (admin AI) to optimize day-to-day operations and clinical applications (non-admin AI). In this view, clinical leaders and health care decision-makers may appreciate AI as a new way to provide sustainable social and healthcare services.","url":"https://doi.org/10.1108/978-1-83549-468-420241001","authors":["Elena Maggioni","Francesco Mazziotta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-29T02:51:26Z","doi":"10.1108/978-1-83549-468-420241001","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003432951-1","name":"Exploring Artificial Intelligence in Hotels","source":"crossref","abstract":"The Hotel Industry is booming with the increase of tourism around the world. With many hotels opening up, there will be an increase in competition and pressure to maintain their standard performance and keep up with the current trends. Automation and machine industries have been integrating new technology and revolutions for business development with digital technological aspects in recent decades. The hotel industry uses various innovative methods to provide specialized customer service and advance. The hotel industry, which has adopted many innovative methods for providing satisfying customer service, has advanced its entire system with the adoption of many comfort-defining advancements. The hospitality industry is enhancing and adapting new modern technology and digital aspects in hotel operations. This chapter explains the importance and disadvantages of AI in the hotel industry and also focuses on why AI can revolutionize the whole industry. The chapter also explains about the concepts of biasness, which may happen due to artificial intelligence (AI).","url":"https://doi.org/10.1201/9781003432951-1","authors":["Atul Abraham Thomas","Diogo Davidson Albuquerque"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-16T15:20:32Z","doi":"10.1201/9781003432951-1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1080/0952813x.2023.2165721","name":"A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning","source":"crossref","abstract":"Medical anomaly identification using machine learning is a significant subject that has received a lot of attention. Artificial neural networks’ successor, deep learning, is a well-developed technology with strong computational capabilities. Its popularity has increased in recent years due to the availability of rapid data storage and hardware parallelism. Numerous, sizeable medical imaging datasets have recently been made available to the public, which has sparked interest in the field and increased the number of research studies and publications. The main goal of this study is to give a complete theoretical examination of prominent deep learning algorithms for detecting medical anomalies. The study further presents the architecture of current methodologies, compare and contrasts training algorithms, and gives a robust assessment of current methodologies. A thorough analysis of the state-of-the-art is provided, covering the benefits and limitations associated with using open-source data, and the specifications for clinically relevant systems. This study further identifies the gaps in the body of existing knowledge and suggests future research directions.","url":"https://doi.org/10.1080/0952813x.2023.2165721","authors":["Yaw Afriyie","Benjamin A. Weyori","Alex A. Opoku"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-25T08:59:15Z","doi":"10.1080/0952813x.2023.2165721","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1049/ic:19960181","name":"Representing medical concepts","source":"crossref","abstract":"Illustrates the importance of achieving key strategic goals for the next decade of medical informatics, and describes key milestones towards achieving these goals. A major barrier to these tasks is the lack of an effective reusable representation for medical concepts. This problem is well known. It has even been cited as one of the grand challenges facing medical informatics. Why has it been so hard to solve? We cite two sets of reasons: (i) organisational and economic, and (ii) technical. The GALEN programme addresses both sets of issues. GALEN grew out of work on the user-centred design of clinical systems. It remains focused on practical problems of supporting real clinical systems, the first commercial examples of which are to appear in mid-1996. However, to achieve this, it has had to make significant technical innovations in the paradigm for delivering clinical terminology and in its formal representation. The heart of GALEN's approach is a novel description logic, GRAIL (GALEN Representation And Integration Language). GRAIL's design includes features to overcome previous difficulties and to support GALEN's fundamental strategies for reusability.","url":"https://doi.org/10.1049/ic:19960181","authors":["A.L. Rector"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-11-22T14:40:26Z","doi":"10.1049/ic:19960181","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/ieeestd.2023.10117469","name":"IEEE Standard for Performance and Safety Evaluation of Artificial Intelligence Based Medical Devices: Terminology","source":"crossref","abstract":"This standard is aimed at establishing concepts and terminology for the performance and safety evaluation of artificial intelligence medical device, which covers basic technology, dataset, quality characteristics, quality evaluation and application scenario. The annex further provides basic equations for quality evaluation purpose.","url":"https://doi.org/10.1109/ieeestd.2023.10117469","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-04T17:22:14Z","doi":"10.1109/ieeestd.2023.10117469","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/s0933-3657(03)00002-2","name":"Evolutionary computing for knowledge discovery in medical diagnosis","source":"crossref","abstract":"One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. In this paper, a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is utilized to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimize the numeric attributes for concise classification rules without the need of discretization. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.","url":"https://doi.org/10.1016/s0933-3657(03)00002-2","authors":["K.C Tan","Q Yu","C.M Heng","T.H Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/s0933-3657(03)00002-2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.2139/ssrn.5574207","name":"Medical Insurance Cost Prediction with Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5574207","authors":["Vineet Dumir","Gutha Jaya Krishna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-07T07:43:51Z","doi":"10.2139/ssrn.5574207","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003517689-1","name":"Artificial intelligence revolutionizing wireless communication systems","source":"crossref","abstract":"The integration of artificial intelligence (AI) into wireless communication systems has revolutionized the way we perceive and operate within modern telecommunication networks. AI techniques, including machine learning (ML), deep learning (DL), and reinforcement learning (RL), have been instrumental in enhancing various aspects of wireless communication systems. These include spectrum management, resource allocation, interference mitigation, power control, and quality-of-service (QoS) optimization. By leveraging AI algorithms, wireless networks can adapt dynamically to changing conditions, improve spectral efficiency, and enhance user experience. Despite the remarkable progress, several challenges persist in the integration of AI into wireless communication systems. These challenges include scalability, security, privacy concerns, computational complexity, and the need for extensive labeled data for training AI models. This abstract presents an overview of the significant advancements, challenges, and future prospects of AI in wireless communication systems.","url":"https://doi.org/10.1201/9781003517689-1","authors":["Samarendra Nath Sur","Pradeep Vishwakarma","Ankan Bhattacharya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-27T21:13:38Z","doi":"10.1201/9781003517689-1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.71465/fair37","name":"Ethical Implications of Artificial Intelligence: Navigating Moral Dilemmas","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming numerous aspects of society, from healthcare to finance, and its ethical implications are becoming increasingly significant. This paper explores the moral dilemmas associated with AI technologies, including issues of privacy, bias, accountability, and autonomy. It examines current ethical frameworks and proposes guidelines for responsible AI development and deployment. By analyzing case studies and theoretical perspectives, the paper aims to provide a nuanced understanding of how ethical considerations can shape the future of AI and ensure its benefits are equitably distributed.","url":"https://doi.org/10.71465/fair37","authors":["Dr. Awais Ahmed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-14T05:50:27Z","doi":"10.71465/fair37","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1002/9781394175574.ch12","name":"Risks in Amalgamation of Artificial Intelligence with Other Recent Technologies","source":"crossref","abstract":"There are various risks while amalgamating two or more technologies. While doing sentiment analysis using artificial intelligence (AI) technique to monitor human activities online, the privacy of humans is affected. Applying AI technique in hospitals for taking care of elderly patients with the help of a robot caretaker affects the quality of life, and there is no contact with the human being. Practicing plenty of algorithms for self-driving cars instead of human decisions may cause mass accidents. In amalgamation of AI and the medical industry, AI sometime recommends wrong medicine to the patient, fails to predict the tumor on a radiological scan, and assign a single bed to two different patients. Another risk of a smart home is that all gadgets are associated, normally connected with the owner's account. Hence, hacking a single device can provide access to the personal data of the smart home owner. AI has no creative thinking; this is the big disadvantage of AI. It never thinks out of the box, it behaves as per the human instructions even though it produces results with increased speed and accuracy than the human brain. Employing AI in education also produces risks like technical expertise is needed and the cost of AI tool is very high in education. Even when AI plays a major role in all other technologies, risks are evolved equally and diminish the quality of human life.","url":"https://doi.org/10.1002/9781394175574.ch12","authors":["K. Sathya","A. Hency Juliet"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-28T10:17:47Z","doi":"10.1002/9781394175574.ch12","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.caeai.2024.100303","name":"AI-based prediction of academic success: Support for many, disadvantage for some?","source":"crossref","abstract":"The use of computational tools to predict academic success has become increasingly popular. Machine learning algorithms, trained on past study histories, have been shown to provide valid predictions. However, knowing about biases and unfairness in algorithms, one should take a closer look at these predictions. This paper explores the extent to which the predictive accuracy of academic success varies between specific groups of students, focusing on traditional and non-traditional students (NTS), who have not acquired a higher education entrance qualification at school. In a case study the study compares several popular algorithms and their prediction quality, and investigates whether misclassified NTS show positive or negative biases. Results revealed that the accuracy of predicting academic success for NTS was significantly lower than when considering all students as a whole. The direction of the distortion cannot be determined exactly due to small case numbers. The study emphasizes that the possibility of bias always has to be considered when predicting study success, and the use of such tools must ensure there are no undesirable biases that could affect certain students.","url":"https://doi.org/10.1016/j.caeai.2024.100303","authors":["Lisa Herrmann","Jonas Weigert"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T13:39:23Z","doi":"10.1016/j.caeai.2024.100303","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18178/jaai.2024.2.2.165-172","name":"Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health","source":"crossref","abstract":"Harnessing the Potential of Artificial Intelligence for Climate Change Mitigation and Public Health: Advancements and Challenges Artificial Intelligence (AI) has emerged as a valuable tool in addressing the challenges of climate change and its effects on public health.By utilizing its capabilities in analyzing climatic patterns, AI presents opportunities to better manage resources and develop effective strategies for mitigating climate change.Additionally, AI can aid in creating sustainable solutions that address the intricate nature of climate change, such as optimizing energy consumption and integrating renewable energy sources.Furthermore, AI can assist in developing climate models for accurate predictions, enabling proactive measures for disaster preparedness and response.AI-powered disease surveillance techniques can also improve public health outcomes by identifying patterns in disease spread relative to climate factors.Nevertheless, the widespread implementation of AI-based solutions comes with its own set of challenges.Ethical concerns surrounding privacy and data ownership must be addressed, as AI necessitates access to vast datasets, potentially raising privacy risks.Technical limitations, such as computational power constraints and the need for complex algorithms, may impede the integration of AI into climate change mitigation strategies.Furthermore, addressing issues concerning the accessibility and affordability of AI technologies is essential for ensuring fair distribution and maximizing its public health impact.To fully leverage AI's potential in combating climate change and enhancing public health, it is crucial to encourage innovation, foster cross-disciplinary collaboration, and promote open data science practices.Innovation can drive the creation of new AI technologies and algorithms tailored specifically for addressing climate change issues.Collaborative efforts involving experts from various fields like climate science, public health, and computer science can enhance our understanding of intricate systems and facilitate the development of comprehensive solutions.Additionally, embracing open data science practices, such as sharing data and algorithms, can promote collaboration and expedite progress in mitigating climate change and its consequences on public health.In conclusion, AI presents promising prospects for effectively addressing climate change and its effects on public health.Overcoming ethical concerns, technical hurdles, and issues of accessibility and affordability are","url":"https://doi.org/10.18178/jaai.2024.2.2.165-172","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T03:20:25Z","doi":"10.18178/jaai.2024.2.2.165-172","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.4337/9781035346745.00019","name":"Generative artificial intelligence with Chinese characteristics","source":"crossref","abstract":"In this chapter, we provide details of the Chinese generative artificial intelligence (GAI) industry and market and compare them with those of the US While Chinese technology firms are globally competitive in some AI subfields, their performance has been disappointing in the GAI domain. This chapter offers an analysis of key barriers facing Chinese technology firms in the development of GAI services. Specifically, it gives an overview of how the Chinese GAI industry is hampered by an unfriendly legal and regulatory environment, lack of high-impact investment, unavailability of high-end components and other resources required for this industry, and high costs of acquiring such resources. It also discusses how China lags far behind the US in terms of “foundation models,” which are key to the development of GAI. Also highlighted are the challenges faced by Chinese technology companies in internationalizing their GAI services.","url":"https://doi.org/10.4337/9781035346745.00019","authors":["Nir Kshetri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-18T13:01:01Z","doi":"10.4337/9781035346745.00019","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18686/aitr.v2i3.4427","name":"Practice of Artificial Intelligence Technology in Mechanical Design, Manufacturing and its Automation","source":"crossref","abstract":"With the arrival of the fourth industrial Revolution, artificial intelligence technology is profoundly changing the face of the traditional manufacturing industry. This paper focuses on the artificial intelligence technology in the field of mechanical design and manufacturing and automation practice, analyzes the industry development situation, expounds the importance of artificial intelligence to industry transformation and upgrading, and discusses the artificial intelligence in design application, application in information processing and application in fault diagnosis. This paper aims to provide a valuable reference for practitioners and researchers in the field of mechanical design, manufacturing and automation to promote the further development and application of artificial intelligence technology in this field.","url":"https://doi.org/10.18686/aitr.v2i3.4427","authors":["Qi Song"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-07T07:32:43Z","doi":"10.18686/aitr.v2i3.4427","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.18662/brain/15.2/584","name":"Computer Algebra Systems &amp; Artificial Intelligence","source":"crossref","abstract":"From four-function calculators to calculators (or computers) with Computer Algebra System (CAS) software, Mathematics computing technology has advanced. With just a few button pushes, CASs can solve a wide range of mathematical problems, which is a true quantum leap in technology. The implications of having software in the classroom that can, for example, expand and factorize algebraic expressions, solve equations, differentiate functions, and find anti-derivatives are causing the mathematical community to engage in a heated debate about whether this is one of the most exciting or frightening developments in the history of education. It was only a matter of time before Artificial Intelligence entered the field of Science. This is now also the case with Mathematics, one of the dominant, perhaps the most basic, but also the most \"difficult\" of the sciences. The human mind, for better or for worse, has its limits. As we see in every manifestation of our lives, in this case, technology is being enlisted to help humanity take the next step, whether it has to do with automation and practical matters, or with knowledge and exploration. Creating a model that is understandable to humans is the primary objective of Artificial Intelligence. Additionally, concepts and methods from numerous mathematical fields can be used to prepare these models. In this paper, we will examine the use of AI in CASs and explore some ways to optimize them. The documentation sheets are the data source that we used to examine their characteristics. The research results reveal that there are many tips that we can follow to accelerate performance.","url":"https://doi.org/10.18662/brain/15.2/584","authors":["Kostas Zotos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T02:00:46Z","doi":"10.18662/brain/15.2/584","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-52005-1_5","name":"Artificial Intelligence-Enhanced PARSAT AR Software: Architecture and Implementation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-52005-1_5","authors":["Christos Papakostas","Christos Troussas","Cleo Sgouropoulou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-30T09:03:10Z","doi":"10.1007/978-3-031-52005-1_5","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.3233/faia240454","name":"Intelligent Assistant for Multivariant Analysis","source":"crossref","abstract":"When a Knowledge Discovery from Data (KDD) (Fayyad, Piatetsky-Shapiro, &amp; Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or an intermediate report.","url":"https://doi.org/10.3233/faia240454","authors":["Xavier Angerri","Oscar Delgado","Karina Gibert"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-30T09:48:46Z","doi":"10.3233/faia240454","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.71460/ffxv3109","name":"Artificial Intelligence in Agricultural Irrigation: An important revolution in agriculture in the future Artificial Intelligence in Agricultural Irrigation: An important revolution in agriculture in the future","source":"crossref","abstract":"Agricultural productivity has experienced a marked escalation over the years, attributable to the intensification of agricultural practices, which have been significantly bolstered by the incorporation of mechanization and automation technologies. The advent of Artificial Intelligence (AI) has further catalyzed this advancement, with itsinte-gration into the agricultural sector becoming increasingly sophisticat-ed and profound. With the rapid development of Artificial Intelli-gence (AI) technology, its application in the agricultural sector is be-coming increasingly profound, bringing revolutionary changes to modern agriculture. Irrigation is a process in which water is applying on the soil in order to improve the growth of crops or fruit trees, to revegetate degradedsoil, or to maintain landscapes in areas where rains are insufficient or irregular.(Gavali, M., Dhus, B, 2016)The application of AI technology in agricultural sector like irrigation has not only improved the efficiency of agricultural production but also contributed to the sustainable development of agricultural irrigation, which is a critical component of food production, yet it is often inef- ficient and wasteful. The amalgamation of Artificial Intelligence within irrigation systems heralds a paradigmatic shift in the manage-ment of water resources within the agricultural domain. This article delves into the pivotal function of AI in augmenting the efficacy and sustainability of irrigation systems, with particular emphasis on the cultivation of decision support systems, prognostic analytics, and au-tonomous control frameworks.","url":"https://doi.org/10.71460/ffxv3109","authors":["Yunfan(Stephen) LUO"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-06T10:16:21Z","doi":"10.71460/ffxv3109","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.109164","name":"Unrecognizable yet identifiable: Image distortion with preserved embeddings","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109164","authors":["Dmytro Zakharov","Oleksandr Kuznetsov","Emanuele Frontoni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-27T14:53:28Z","doi":"10.1016/j.engappai.2024.109164","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2023.107360","name":"Fast adversarial attacks to deep neural networks through gradual sparsification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107360","authors":["Sajjad Amini","Alireza Heshmati","Shahrokh Ghaemmaghami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-31T08:04:31Z","doi":"10.1016/j.engappai.2023.107360","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.23880/oajda-16000118","name":"Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview","source":"crossref","abstract":"Generative AI can produce content that is similar to human ingenuity, revolutionizing a number of industries with lifelike outputs like music and images. Misinformation and intellectual property rights give rise to ethical concerns. Notwithstanding these difficulties, generative AI has the potential to spur additional innovation in a variety of sectors, subject to moral issues. The goal of explainable AI (XAI) is to improve AI systems’ accountability and transparency, which is essential for their inclusion into industries like banking and healthcare. However, in order to effectively explain complicated AI systems, strong XAI techniques are required. Quantum artificial intelligence (QAI) promises improvements in cryptography and optimization by using quantum physics to speed up AI systems. However, there are still difficulties in creating practical quantum computers and improving quantum AI algorithms. The present overview provides new insights into developing a platform to explore AI in various arenas of life sciences and technologies and social up-gradation.","url":"https://doi.org/10.23880/oajda-16000118","authors":["Ramchandra M*"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-26T04:59:34Z","doi":"10.23880/oajda-16000118","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/icssas64001.2024.10760348","name":"A Robust Development of Superficial Learning Model for Employee Layoff Prediction using Artificial Intelligence Paradigm","source":"crossref","abstract":"In today’s business environment, predicting employee layoffs is a challenging task to maintain both operational efficiency and employee morale. Traditional methods proved insufficient in terms of precision and dependability, this is the reason why new predictive models have been forged. The proposed Employee Layoff Prediction model uses Hybrid Neuro Classifier (HNC), combining the advantages of convolutional neural networks (CNNs) and artificial neural networks (ANNs) to improve prediction accuracy. The proposed HNC model extends LeNet CNN to perform automatic capturing of complex patterns and spatial hierarchies in the data using its deep feature extraction capabilities. The extracted features are then fed into cascaded ANNs where refinements produced with the aid of learning deep intricate dependencies enable delicate representations. Since this hybrid approach combines the refinement needed to tune sentences applied in grounded experiments and additionally can effectively generalize over large amounts accurately labeled data, it offers predictive accuracy. The implementation of the HNC model was built and trained models with Python using libraries. Experimental results show that the proposed method can accurately predict employee layoffs with $\\mathbf{9 7. 8 1 \\%}$ accuracy.","url":"https://doi.org/10.1109/icssas64001.2024.10760348","authors":["G. Ramkumar","N. Meenakshisundaram"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-10T19:44:53Z","doi":"10.1109/icssas64001.2024.10760348","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-66051-1_1","name":"Artificial Intelligence: Background, Applications and Future","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-66051-1_1","authors":["Ali Kaveh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T06:01:41Z","doi":"10.1007/978-3-031-66051-1_1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.70593/978-81-981271-8-1_5","name":"Human-centric artificial intelligence in industry 5.0: Enhancing human interaction and collaborative applications","source":"crossref","abstract":"The fifth industrial revolution - or Industry 5.0 - likely see human-centric artificial intelligence (AI) revolutionize by literally putting humans in contact and integrated with AI advancements. While its predecessor, Industry 4.0, cantered on automation and productivity by integrating cyber-physical systems and the Internet of Things (IoT), Industry 5.0 focuses on the cooperative connection between human workers and AI systems. This study investigates the recent and well-established uses of humanistic AI and provides a deeper insight into the possibilities of improving various sectors of the industry. Use cases range from making cobots even more collaborative by making them totally safe to work alongside humans, to having AI-assisted decision-making that further enables human operators real time with smarter decision making and problem solving. The sophisticated natural language processing (NLP) and computer vision technologies create an intuitive human-machine interfaces to communicate and interact without any hindrance. They are even experimenting with AI enabled training and simulation tools, reinforcing and reskilling the current affected workforce to meet the developing and dynamically larger requirements of Industry 5.0. By moving towards ethical AI principles, we assure that AI implementations keep human values and societal benefits at the core and mitigate issues on privacy, bias, and transparency. This research has implications for human-centric AI, reaffirming the value of building an integrated and resilient industrial ecosystem that capitalizes on the collective strengths of humans and intelligent systems to deliver innovation, resilience, and growth for the economy.","url":"https://doi.org/10.70593/978-81-981271-8-1_5","authors":["Nitin Liladhar Rane","Ömer Kaya","Jayesh Rane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-22T03:07:25Z","doi":"10.70593/978-81-981271-8-1_5","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-67256-9_15","name":"Artificial Intelligence in Talent Identification and Development in Sport","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-67256-9_15","authors":["Alexander B. T. McAuley","Joe Baker","Kathryn Johnston","Adam L. Kelly"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-02T19:02:26Z","doi":"10.1007/978-3-031-67256-9_15","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.107871","name":"Towards reliable uncertainty quantification via deep ensemble in multi-output regression task","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.107871","authors":["Sunwoong Yang","Kwanjung Yee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-22T12:56:45Z","doi":"10.1016/j.engappai.2024.107871","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1017/s0890060424000052","name":"Applications of artificial intelligence and cognitive science in design","source":"crossref","abstract":"Abstract Artificial intelligence and cognitive science are two core research areas in design. Artificial intelligence shows the capability of analysing massive amounts of data which supports making predictions, uncovering patterns and generating insights in varying design activities, while cognitive science provides the advantage of revealing the inherent mental processes and mechanisms of humans in design. Both artificial intelligence and cognitive science in design research are focused on delivering more innovative and efficient design outcomes and processes. Therefore, this thematic collection on “Applications of Artificial Intelligence and Cognitive Science in Design” brings together state-of-the-art research in artificial intelligence and cognitive science to showcase the emerging trend of applying artificial intelligence techniques and neurophysiological and biometric measures in design research. Three promising future research directions: 1) human-in-the-loop AI for design, 2) multimodal measures for design, and 3) AI for design cognitive data analysis and interpretation, are suggested by analysing the research papers collected. A framework for integration of artificial intelligence and cognitive science in design, incorporating the three research directions, is proposed to inspire and guide design researchers in exploring human-centred design methods, strategies, solutions, tools and systems.","url":"https://doi.org/10.1017/s0890060424000052","authors":["Ji Han","Peter R.N. Childs","Jianxi Luo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-03T11:32:38Z","doi":"10.1017/s0890060424000052","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1080/08839514.2024.2362516","name":"Logistic Resource Allocation Based on Multi-Agent Supply Chain Scheduling Using Meta-Heuristic Optimization Algorithms","source":"crossref","abstract":"Logistics resource allocation depends on the precise scheduling of Supply Chain (SC) agents. Coordination of management across all sites, products, and production divisions is essential for effective scheduling. For multi-agent systems in heterogeneous SCs, it is crucial to have a prior understanding of production, delivery, and connectivity. Hence, an innovative meta-heuristic optimization inspired by sparrow behavior is introduced as multi-agent-based scheduling and resource allocation (MA-SRA) to resolve delivery delays and errors during delivery in logistic SC management. Allocating resources efficiently and creating workable schedules in an SC with multiple agents are the primary significant problems focused on in this research. The MA-SRA algorithm provides an achievable solution to the problem of optimizing logistics operations by combining precise scheduling with production balance and multi-agent searchers. If the scheduling operations are inadequate, sparse agents are repurposed for production based on fitness. This maintains balance and connectivity by adjusting agent ratios. Delays are minimized, and connectivity is maximized because no adjustments need to be reversed. The research findings show that the proposed approach improves operational efficiency and brings significant advantages to the industry in terms of enhanced allocation of resources, connectivity, delivery efficiency, and fewer delays and scheduling errors.","url":"https://doi.org/10.1080/08839514.2024.2362516","authors":["Lingjie Bu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-14T02:20:57Z","doi":"10.1080/08839514.2024.2362516","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/icapai61893.2024.10541182","name":"An Artificial Intelligence Approach for Biomarker-Based Diagnosis of Autism Spectrum Disorder","source":"crossref","abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects behavior, communication, learning abilities, and interaction with others. Various artificial intelligence approaches were employed on the collected dataset obtained from a case-control study conducted retrospectively at child psychiatry clinics. The study involved 51 children diagnosed with ASD and 40 neurotypical children (TDC). We employed an artificial intelligence methodology to investigate the link between metabolic biomarkers and ASD.","url":"https://doi.org/10.1109/icapai61893.2024.10541182","authors":["Amira Rachah","Senda Slama","Abeer Badawy","Ibrahim A. Hameed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-31T17:28:27Z","doi":"10.1109/icapai61893.2024.10541182","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.70593/978-81-981271-8-1_6","name":"Integrating internet of things, blockchain, and artificial intelligence techniques for intelligent industry solutions","source":"crossref","abstract":"Integration of Internet of Things (IoT) and blockchain combined with the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the sphere of smart industries, propagating a new era of boosted productivity, information assurance, and data-influenced deliberation. Our research looks into how these cutting-edge technologies flow together to enable smart industry breakthroughs. This offers conductive connectiveness and communication capabilities between devices and can create large pools of data, that are essential for making more informed decisions and finally, operating more sustainably. This data is then scaled and processed by the AI ML and DL algorithm to get the predictive insights; process optimization and to improve on automation. The security and immutability of data are critical in an IoT network, and this is something that blockchain technology excels at and ensures data exchanged within these networks is safe and unalterable. Thanks to recent developments in AI, ML, and DL, they can now better meet the challenges of industrial applications well beyond predictive maintenance and supply chain optimization and extend into real-time monitoring and autonomous operations. The perspective taken in this research is instead one of a practical, real-world implementations, illustrating some of the advantages as well as challenges when integrating these technologies. The results point to the enormous transformative capability of this integration and suggest a level of efficiency, security and innovation not seen before that will redefine intelligent industries today and possibly more importantly tomorrow, in effect defining the fourth industrial revolution and beyond.","url":"https://doi.org/10.70593/978-81-981271-8-1_6","authors":["Nitin Liladhar Rane","Ömer Kaya","Jayesh Rane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-22T07:14:02Z","doi":"10.70593/978-81-981271-8-1_6","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3722237.3722258","name":"Application and Impact of Generative Artificial Intelligence Techniques in Education--Citespace-based visualization and analysis","source":"crossref","abstract":"Recently, generative AI technology has arisen as a trending research agenda in education. This study makes an review of 260 documents from the CNKI database published from 2020 to 2024. Through the bibliometric and content analysis methods, together with the CiteSpace tool, highlight the trending application of this technology in education, the distribution characteristics of the core authors and institutions' postings, and the clustering analysis of the research hotspots. The results show continued wide adoption of generative AI technology in education in recent years, peaking sharply in 2023. There hasn't been a stable core group of authors within the field, and the collaborative network is relatively sparse. Research hotspots mainly cover artificial intelligence, human-computer collaboration and educational transformation, which indicates the function generative AI technology could have within the digital transformation and quality enhancement of education. This paper additionally shows the actualization of generative AI technology through its presentation of AI tutors and teaching assistants, teaching models reform, and reshaped instructional evaluation systems via case studies. In face of misuse, integrity issues, and ethical concerns arising, there is a need to find a balance in the application of the technology, such that its more proper development can promote rather than replace human subjectivity.","url":"https://doi.org/10.1145/3722237.3722258","authors":["Wenjie Fang","Bin Luo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-30T06:56:56Z","doi":"10.1145/3722237.3722258","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.46632/jdaai/3/3/10","name":"Data Analysis and Artificial Intelligence in The Marine Sector","source":"crossref","abstract":"This paper investigates the revolutionary influence of data analysis and artificial intelligence (AI) in the maritime sector, with a focus on cargo handling, ship route planning, and fuel efficiency optimisation. By integrating modern data analytics, cargo operations may be monitored and managed in real-time, which improves safety measures, decreases operational delays, and increases inventory management accuracy. AI-driven algorithms optimise ship route planning by analysing large datasets such as weather patterns and marine traffic, reducing travel time and operational expenses. Furthermore, predictive analytics and machine learning models are used to improve fuel efficiency by optimising engine performance and detecting maintenance issues before they cause costly downtime. This paper conducts a thorough analysis of these technologies' uses, assessing their influence on operational efficiency, cost savings, and environmental sustainability. The paper emphasises the crucial role of data analysis and AI in revolutionising old marine processes, eventually propelling the industry towards a more efficient and ecologically conscious future, through a series of case studies.","url":"https://doi.org/10.46632/jdaai/3/3/10","authors":["K Sivasami","S Thangalakshmi","Atharva Bhoite","Harsh Soni","Krishna Seth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-06T05:21:58Z","doi":"10.46632/jdaai/3/3/10","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3724504.3724537","name":"Generation and Evaluation of International Chinese Teaching Resources by Generative Artificial Intelligence","source":"crossref","abstract":"Generative artificial intelligence has set off a new round of intelligent revolution and promoted the reform and development of the education industry. The development of international Chinese education also requires the digitalization and intelligence of international Chinese teaching resources. In this regard, this article utilizes the technology of ChatGPT platform to integrate teaching resources, constructs an artificial intelligence teaching resource generation framework consisting of demand analysis, intelligent generation, and quality assessment modules, as well as a quality evolution model of artificial intelligence international Chinese teaching resources. Based on this framework and resource quality evolution model, an experiment on the generation of artificial intelligence teaching resources was carried out, and inspections and evaluations were conducted from the perspectives of natural language processing technology, learners, and teachers. The results show that the teaching resources generated by artificial intelligence pass the inspection of natural language understanding technology and have good quality; learners and teachers are optimistic about the application of teaching resources in teaching and believe that most of these resources have reached a usable state; learners' overall experience in using teaching resources is positive and they believe that these resources can promote learning in many aspects. The application of artificial intelligence in generating teaching resources in this article helps to optimize the construction mode of international Chinese teaching resources and promote the high-quality development of international Chinese education.","url":"https://doi.org/10.1145/3724504.3724537","authors":["Wen-di Zhang","Huan-xin Dou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-08T11:40:36Z","doi":"10.1145/3724504.3724537","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3724504.3724617","name":"Construction of Python programming case library for artificial intelligence under the background of new engineering disciplines","source":"crossref","abstract":"Python language has a powerful artificial intelligence algorithm library. This study adopts a project driven approach and fully utilizes graphical visualization programming tools such as Raptor and Orange3 to assist teaching. At the same time, with the help of large models to assist programming, the cultivation of mathematical thinking, logical thinking, AI thinking, engineering thinking, and programming training are integrated into Python language course teaching in a step-by-step and progressive manner, forming a robust teaching ecosystem. Under the innovative teaching mode, students have gained sufficient practical training through graphic visualization programming, large model assisted programming, Python program writing and debugging, etc., mastering the Python language and gaining intuitive understanding of engineering project development, and enabling them to have preliminary research and development capabilities for artificial intelligence.","url":"https://doi.org/10.1145/3724504.3724617","authors":["Cheng Lv"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-08T11:38:26Z","doi":"10.1145/3724504.3724617","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1148/ryai.111221.podcast","name":"Episode 17: Impact of Medical Image Processing on Downstream AI Performance","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.111221.podcast","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-11-12T14:52:02Z","doi":"10.1148/ryai.111221.podcast","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003441700-4","name":"Using Artificial Intelligence in the Field of Intelligence Operations and Analysis","source":"crossref","abstract":"The nature of intelligence activities has been changing. With these technological advances, newsgathering and analysis methods have been diversified and become more effective. There is a tremendous development process in intelligence technologies. New-generation technological capabilities contribute immensely to the implementation of intelligence activities and increase the efficiency. The new-generation technological developments have a great impact on intelligence collection and analysis methods. It can be claimed that artificial intelligence (AI) algorithms, models, and software are at the forefront among the new-generation technological developments that most affect the nature of intelligence activities. In this context, this study analyzes the impact of AI algorithms and models on intelligence collection and analysis in various aspects.","url":"https://doi.org/10.1201/9781003441700-4","authors":["Ali Burak Darıcılı","Nourhan El-Bayaa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-30T13:03:38Z","doi":"10.1201/9781003441700-4","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1787/888933617016","name":"Graph 1.9 Top 10 medical technologies combined with artificial intelligence, 2000-05 and 2010-15","source":"crossref","abstract":"","url":"https://doi.org/10.1787/888933617016","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-10-26T08:20:22Z","doi":"10.1787/888933617016","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3714334.3714336","name":"Research on the Military Application and Development Suggestions of Artificial Intelligence","source":"crossref","abstract":"With the rapid development of artificial intelligence (AI) technology, various countries have actively promoted the development of military intelligence in an attempt to seize the initiative in the military intelligence revolution. This paper first sorts out and discusses the current application status of AI in the military field, systematically analyzing the application of AI technology in areas such as situation awareness and intelligence analysis, intelligent decision-making and decision support, intelligent development of weapon systems, and intelligent offensive and defensive capabilities in cyber warfare. Subsequently, the paper delves into the developmental experiences of the United States and Russia in the militarization of AI from multiple perspectives, including strategic layout, technological research and development, talent cultivation, and military-civilian integration. Finally, based on the aforementioned analysis, this paper proposes specific recommendations for the militarization of AI in China from the perspectives of national top-level planning, investment and financing channels, military-civilian collaboration, and international cooperation. The research aims to promote the healthy development of AI militarization applications in China and provide support for safeguarding national security.","url":"https://doi.org/10.1145/3714334.3714336","authors":["Shilong Li","Chenyi Zhang","Zhihan Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-07T06:34:31Z","doi":"10.1145/3714334.3714336","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-981-97-2938-8_12","name":"Healthcare Data Security in Multi-Cloud Infrastructure Using Heuristic Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-2938-8_12","authors":["R. Vijayan","V. Mareeswari","K. Sridhar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T16:13:21Z","doi":"10.1007/978-981-97-2938-8_12","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1145/3722237.3722399","name":"Applications and Challenges of Generative Artificial Intelligence Enabling Critical Thinking Development in International Undergraduate Education","source":"crossref","abstract":"In today's fast-changing information-exploding era, developing students' critical thinking has become one of the most important tasks in international undergraduate education. Generative AI can simulate human creativity and imagination, providing brand-new resources and tools for critical thinking development. This paper details the application of generative AI technology in providing intelligent teaching resources, implementing personalized learning tutoring, promoting interdisciplinary integrated learning, cultivating the spirit of questioning and reforming assessment methods, etc. It also points out that the application of this intelligent technology in the teaching process is also facing the main challenges of data bias and false information, data privacy and security, and the enhancement of teachers' application ability, and gives specific countermeasures. Therefore, this paper aims to provide a useful reference for international undergraduate education practice and promote the integration of generative AI technology to empower students' critical thinking development.","url":"https://doi.org/10.1145/3722237.3722399","authors":["Yan Lin","Lu Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-30T06:54:35Z","doi":"10.1145/3722237.3722399","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003348351-7","name":"Artificial Intelligence for Sustainable Human Resource Management","source":"crossref","abstract":"Today’s industry is undergoing rapid transformation because of the rapidly growing digital technologies and artificial intelligence (AI)–based solutions. With this transformation, developments in intelligent automation, for example, the digitalizing world, robotic systems, information technology, big data, and artificial intelligence, which create new problems that businesses will face, affect human resources management applications and processes. This makes it necessary to develop capabilities that can fight with all competitors in the world whose borders are removed thanks to the internet to gain competitive advantage. Simple and routine processes are becoming increasingly automated, while nonsimple processes are becoming more complex. Therefore, both competencies of the existing workforce should be increased, and the enterprises should gain competitive advantage to survive in the long term. For this reason, the development of human resources strategies that will support the general strategy of the enterprises emerges as a strategic necessity. At this point, in the age of AI starting with Industry 4.0 and Society 5.0, it is of great importance how to ensure sustainable human resources management and how human resources departments and policies will be affected by these changes. In addition, with the technological developments experienced, it is based on a human- and environment-oriented approach for social, environmental, and economic sustainability to better meet industrial and technological targets without compromising socioeconomic conditions and environmental performance. Therefore, establishing smart and environmentally friendly organizations comes to the forefront. Additionally, with Industry 4.0 and the ensuing Industry 5.0, which is defined as the next stage of creating sustainable industrial value, it is now seen that there is a need for sustainable development and to consider the vital role of humans in the assumptions of future development of the industry. Therefore, with technological changes brought in by the industrial revolutions for the enterprises, the difficulties faced by the enterprises in both production and human resources management, such as large datasets and the need for quick decision making, arises. In this regard, businesses have to develop new strategies that focus on industrial sustainability and meeting social, economic, and environmental needs in the current age of AI.","url":"https://doi.org/10.1201/9781003348351-7","authors":["Şerife Uğuz Arsu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T13:27:41Z","doi":"10.1201/9781003348351-7","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003432951-17","name":"Artificial Intelligence and Cyber Security in the Service Industry","source":"crossref","abstract":"The methodology of identifying the scope, risks, and ways of handling artificial intelligence (AI) would differ almost in each sector. The analysis would take a few service sectors into account, recognize the problems that AI may pose, and explore possible solutions. This would include studying the reach of AI in Hospitality, Tourism, Healthcare, Banking, and Education verticals. Research on how the technology impacts or may impact each vertical positively and negatively would be conducted. The service industry focuses on customer satisfaction. While AI can have some beneficial benefits in the various industries of the service arena, it also has the potential of several risks attached. In order to negate or at least reduce these risks, specific parameters will have to be identified and implemented while putting AI into use. Ensuring that these smart systems for specific tasks will need to be in place, but a certain amount of control will have to be exercised so that the smartness of the system does not overpower human capability. This would include strategic decisions involving where and to what extent machines would be used to deliver service to customers. The most critical tasks would need to be addressed through a structured and well-defined approach. Identification of risk can be developed into an art and be used for AI as a concept.","url":"https://doi.org/10.1201/9781003432951-17","authors":["Vineeta Kapoor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-16T11:20:32Z","doi":"10.1201/9781003432951-17","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003469315-1","name":"Artificial Intelligence Integration in Higher Education","source":"crossref","abstract":"The present chapter explores the strategies for integrating artificial intelligence (AI) into higher education to promote inclusive and adaptive learning. It recognizes AI’s potential to revolutionize education through personalized instruction and assessment. However, the chapter stresses the imperative of ensuring equity, combating bias, and prioritizing accessibility when leveraging AI. Challenges like algorithmic exclusion, privacy risks, and ethical dilemmas are analyzed. Solutions proposed include representative AI development teams, universal design frameworks, customized assessments, and human-centered policies. Research directions are highlighted, including learning analytics, platform improvements for marginalized groups, and proactive accessibility studies. Guiding AI’s trajectory toward empowerment rather than unintended inequity is emphasized. The chapter advocates thoughtful, ethical AI integration that unleashes the technology’s benefits while upholding justice. It argues that centering inclusion and human values, not just efficiency, is key to realizing AI’s transformative potential in higher education.","url":"https://doi.org/10.1201/9781003469315-1","authors":["Bhawna Ojha","Arun Agrawal","Aniket Arya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-02T13:53:27Z","doi":"10.1201/9781003469315-1","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.4018/979-8-3693-5261-8.ch014","name":"The Impact of Artificial Intelligence and Machine Learning in Medical Imaging","source":"crossref","abstract":"A significant fundamental change in healthcare can be seen by the integration of artificial intelligence (AI) and machine learning (ML) with medical imaging (MI). This integration holds the potential to improve patient care standards, change clinical procedures, and improve diagnostic accuracy. This chapter presents the possible impact, addressed challenges, opportunities, and impacts on medical diagnosis of recent advancements and growing advances in the application of AI and ML in the field of MI. The rapid development of AI and ML technologies have pushed MI into a new phase of data-driven healthcare and personalized medicine. Furthermore, the applications of AI and ML in MI are a variety of including automated picture analysis and disease identification to prediction, revolutionizing pathology and radiology, and healthcare management. It also suggests a future in which these innovations will work together to improve patient care, improve diagnostic accuracy, and advance healthcare delivery.","url":"https://doi.org/10.4018/979-8-3693-5261-8.ch014","authors":["Amrita","Pashupati Baniya","Atul Agrawal","Bishnu Bahadur Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-15T14:49:51Z","doi":"10.4018/979-8-3693-5261-8.ch014","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.48014/jce.20240319001","name":"Artificial Intelligence and Scientific Research:Prospects and Risks———Synthesis of the session “Artificial Intelligence and Paradigm Change in Science and Technology Innovation” in Tianjin Forum 2023","source":"crossref","abstract":"In the sub-forum “Transformation of Artificial Intelligence and Paradigm in Science and Technology Innovation” of Tianjin Forum 2023, scholars from both China and abroad discussed the impact of artificial intelligence. With the deepening development of the new technological revolution and industrial transformation, new-generation artificial intelligence technology is continuously making breakthroughs in research and application. AI not only promotes the transformation of material productivity, but also gradually emerges as a critical engine for enhancing the knowledge productivity. Since the emergence of the concept of \"AI for Science\", it has become an obvious proposition generally accepted by the academic community for its tremendous enabling capabilities for knowledge production. Artificial intelligence technology continues to achieve breakthroughs and gain widespread infiltration into the scientific research field, introducing new elements and momentum into scientific research and significantly catalyzing the enhancement of scientific research efficiency and paradigm shifts. AI-driven scientific research has become a new frontier in the global application of artificial intelligence. However, as artificial intelligence triggers paradigm shifts in social science research, the issues of data security, ethics, and value alignment that it brings about need to draw attention from the social science community. Scholars participating in the forum generally concurred that, in regulating the enabling role of AI in scientific research, it is imperative to take into account the specificities of various disciplines and stages, and comprehensively reasonable rational risk allocation mechanisms, platform. support mechanisms, and collaborative participation frameworks to achieve prudent, agile, and full lifecycle regulation of AI for Science.","url":"https://doi.org/10.48014/jce.20240319001","authors":["Jie LIU","Fengyang ZHENG","Xiangyu MA","Yu DONG","Gang LIU"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-19T02:35:25Z","doi":"10.48014/jce.20240319001","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1007/978-3-031-68530-9_14","name":"Artificial Intelligence and Educational Broadcasting: Digitizing Higher Education (HE) in Nigerian Context","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-68530-9_14","authors":["Harriet Akudo Agbarakwe","Olorunfemi Adedeji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T19:02:08Z","doi":"10.1007/978-3-031-68530-9_14","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.108451","name":"Embedding-based entity alignment between multi-source temporal knowledge graphs","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108451","authors":["Lin Zhu","Nan Li","Luyi Bai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-25T21:50:34Z","doi":"10.1016/j.engappai.2024.108451","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.21275/sr241104193157","name":"Impact of Artificial Intelligence on Digital Marketing","source":"crossref","abstract":"Artificial intelligence (AI) is transforming digital marketing by providing advanced tools for data analysis, consumer targeting, and personalized engagement. AI enables businesses to adopt data-driven strategies, improving marketing communication across content creation, social media, email, and CRM platforms. Through AI-powered tools such as chatbots, predictive analytics, and social listening, companies can enhance customer service, optimize advertising, and analyze user behavior in real-time. This integration of AI has redefined marketing by fostering more strategic, tailored approaches that build deeper consumer connections. As AI evolves, its role in digital marketing is expected to expand, offering unprecedented opportunities for innovation and engagement.","url":"https://doi.org/10.21275/sr241104193157","authors":["V Anandha Valli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-09T11:50:38Z","doi":"10.21275/sr241104193157","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.58496/bjai/2024/016","name":"Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures","source":"crossref","abstract":"Neural network model developed in this paper aims at classification of the hand written digits using the data set from Arabic Handwritten Digits Dataset (AHDD). It also includes data preprocessing, model design, training, validating, hyperparameter optimisation, and comparison methodologies of the project. Some preprocessing included scaling of pixel intensity and data augmentation to improve variation, as well as data separation between training and validation. proposed architecture of the model were updated through adding of dropout layers as a form of regularization, tuning of the quantity of hidden layers and neurons in them, and providing dynamic form of learning rates in attempt to diminish overfitting and to improve the model’s predictive ability. The improvements obtained in classification accuracy and in sparsity of the weights of the neural net allows to underline its accuracy in recognizing the patterns of a large data set when compared to the traditional approach. However, in this study, to better assess the performances of the developed model on the AHDD, it is compared to a model built by Tariq Rashid using a raw MNIST database and various tests are conducted to point out the peculiarities of Arabic handwritten digit recognition. The study also finds avenues to improve the model beyond what is presented in this paper: 1) incorporating Convolutional Neural Network (CNN) to learn spatial hierarchies; 2) using Transfer Learning and fine tuning from pretrained models; 3) having a larger dataset which cover other patterns that may not have been included in this study. The results of this research call attention to hyperparameter optimization and architectural improvements for AI approaches to accurate digit recognition of handwritten numbers. Apart from enriching the Arabic handwriting recognition research area, this study also opens avenues for further work that seek to take these methodologies to other complex script recognitional problems in the future.","url":"https://doi.org/10.58496/bjai/2024/016","authors":["Sarah Salman Qasim","Safa Hussein Oleiwi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T21:13:04Z","doi":"10.58496/bjai/2024/016","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781032633602-8","name":"Revolutionizing Bakeries with Artificial Intelligence: A Sweet Blend of Innovation","source":"crossref","abstract":"The confluence of artificial intelligence (AI) and the bakery industry has ushered in a new era of innovation and efficiency. This abstract explores the application of AI in the bakery sector, shedding light on its transformative impact on production, customer experience, and business sustainability. AI-powered tools and technologies have significantly enhanced the production process in bakeries. From ingredient quality control to optimizing baking times and temperatures, AI algorithms have streamlined operations, leading to improved consistency and quality of baked goods. Moreover, AI-driven inventory management systems help reduce waste, allowing bakeries to cut costs and operate in a more environmentally sustainable manner. In the realm of customer experience, AI plays a pivotal role. Bakeries are using AI-driven customer relationship management systems to personalize marketing strategies and engage with consumers more effectively. Chatbots and virtual assistants enhance customer support and provide quick answers to frequently asked questions, ensuring a seamless shopping experience both online and in physical stores. Moreover, AI-powered recommendation systems help customers discover new and exciting bakery products tailored to their preferences. The bakery business is also benefiting from AI in ensuring food safety and compliance. AI-based monitoring systems can detect anomalies in food production processes, reducing the risk of contamination and enhancing product quality. Furthermore, AI-driven data analysis assists in tracking and complying with various food safety regulations and standards. In conclusion, the integration of AI in bakeries has not only improved operational efficiency and product quality, but has also enhanced the overall customer experience. As the bakery industry continues to evolve, AI promises to be a fundamental ingredient in the recipe for success, offering bakers new and exciting ways to innovate and thrive in a highly competitive market.","url":"https://doi.org/10.1201/9781032633602-8","authors":["Anam Aijaz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T15:41:12Z","doi":"10.1201/9781032633602-8","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1201/9781003482000-8","name":"Application of Artificial Intelligence and Federated Learning in Petroleum Processing","source":"crossref","abstract":"The petroleum industry, characterized by its complex operations and substantial data generation, is on the cusp of a technological revolution with the integration of artificial intelligence (AI) and federated learning (FL). This chapter explores the application of FL within the context of petroleum processing to improve operational efficiency, safety, and environmental sustainability. Federated learning, as an emerging paradigm, further amplifies these benefits by enabling a collaborative yet privacy-preserving approach to model training across distributed petroleum processing units. This method not only accelerates the learning process without compromising sensitive data but also enhances model robustness against diverse operational scenarios. Through a series of simulations and real-world case studies, we illustrate how AI-driven analytics can predict equipment failures, optimize resource allocation, and reduce emissions. Simultaneously, FL’s decentralized learning mechanism is shown to facilitate the seamless integration of insights from various processing plants, leading to more accurate and globally applicable models. This research underscores the potential of FL in transforming petroleum processing operations, offering a roadmap for future implementations aimed at achieving higher productivity, sustainability, and safety standards in the industry.","url":"https://doi.org/10.1201/9781003482000-8","authors":["Abdelaziz El-Hoshoudy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-8","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.26226/morressier.6153409f62ba8657678afae2","name":"Importing and serving open-data medical images to support Artificial Intelligence research","source":"crossref","abstract":"","url":"https://doi.org/10.26226/morressier.6153409f62ba8657678afae2","authors":["Sébastien Jodogne"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-14T15:28:22Z","doi":"10.26226/morressier.6153409f62ba8657678afae2","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.109312","name":"Deep attentive adaptive filter module in residual blocks for text-independent speaker verification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109312","authors":["Hamidreza Baradaran Kashani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T22:48:33Z","doi":"10.1016/j.engappai.2024.109312","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1109/esai62891.2024.10913856","name":"A Systematic Review and Taxonomy of Ransomware Detection Based on Artificial Intelligence Algorithms","source":"crossref","abstract":"The escalating prevalence of ransomware attacks poses a significant risk to digital infrastructures, data integrity, and essential services worldwide. Traditional signature-based detection methods often struggle to keep pace with the evolving landscape of ransomware variants and stealthy attack techniques. Artificial Intelligence (AI) offers a promising solution, leveraging sophisticated features such as bytecodes, opcodes, API calls, and behavioral analysis to enhance ransomware detection capabilities. This review delves into the latest advancements in ransomware prevention and detection techniques, categorizing ransomware types and examining the intricate lifecycle of ransomware attacks. A comprehensive taxonomy of ransomware analysis techniques, machine/deep learning methods, and feature selection techniques is presented, considering the diverse operating systems targeted by these malicious threats. By conducting a thorough analysis of recent research articles in this field, we identify the key challenges faced by academics and the research community in mitigating the ransomware threat. Moreover, we explore potential future research directions to address these challenges and develop more effective countermeasures.","url":"https://doi.org/10.1109/esai62891.2024.10913856","authors":["Chaieb Omar","Kannouf Nabil","Mohammed Benabdellah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T17:38:50Z","doi":"10.1109/esai62891.2024.10913856","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2023.107268","name":"Determining predictable strike points on tossed objects: A 2D physics simulation approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.107268","authors":["Chen Giladi","Yoav Golan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-11T13:17:47Z","doi":"10.1016/j.engappai.2023.107268","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.1016/j.engappai.2024.108215","name":"A survey on semi-supervised graph clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108215","authors":["Fatemeh Daneshfar","Sayvan Soleymanbaigi","Pedram Yamini","Mohammad Sadra Amini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-10T21:55:39Z","doi":"10.1016/j.engappai.2024.108215","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"doi:10.59646/isc3/259","name":"Explainable AI: Demystifying the Inner Workings of Artificial Intelligence","source":"crossref","abstract":"Book Title: Intelligent Systems Editors: Dr. S.C. Vettivel, Dr. Leema Nelson and Dr. D. Poornima ISBN: 978-81-979197-4-9 Chapter: 3 DOI: https://doi.org/10.59646/isc3/259 Author: R. Radhika, Assistant Professor, Department of AI & DS, RVS College of Engineering and Technology, Kumaran Kottam Campus, Kannampalayam, Sulur, Coimbatore, Tamil Nadu, India. Abstract Explainable AI (XAI) is emerging as a crucial component in the deployment […]","url":"https://doi.org/10.59646/isc3/259","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-24T21:02:05Z","doi":"10.59646/isc3/259","addedAt":"2026-09-01T01:47:53.668Z","updatedAt":"2026-09-01T01:47:53.668Z"},{"id":"pmid:41239388","name":"Can general purpose large language models assist pediatricians in predicting infants with serious bacterial infection?","source":"pubmed","abstract":"BACKGROUND: Serious Bacterial Infection (SBI) in neonates and young infants often exhibit nonspecific symptoms and clinical signs in the early stages of illness, making early diagnosis challenging. Timely recognition and appropriate treatment are essential to prevent adverse outcomes. While several clinical algorithms are widely used for SBI risk stratification, these tools have limitations, particularly low positive predictive value. This study evaluates the diagnostic accuracy of general-purpose large language models (LLMs) in detecting SBI in neonates and infants under 90 days of age admitted to the emergency department. Our objective is to improve diagnostic precision, reduce unnecessary interventions, and enhance patient outcomes. LLM performance was compared against traditional machine learning models, state-of-the-art rule-based methods, and an ensemble of physicians to assess their potential as clinical decision-support tools in scenarios of diagnostic uncertainty. RESULTS: On a dataset of 742 patients, LLMs demonstrated diagnostic accuracy comparable to traditional machine learning models and state-of-the-art rule-based methods. The optimized CatBoost (class-weighted) model achieved the best overall performance, with a PPV of 0.70, NPV of 0.90, sensitivity of 0.54, specificity of 0.95, F1-score of 0.60, and MCC of 0.54, outperforming the baseline CatBoost model and achieving results on par with large language models (LLMs) and physicians. When optimally prompted, LLMs performed on par with ensembles of experienced clinicians. Additionally, LLMs exhibited effective medical reasoning and provided credible diagnostic predictions, particularly valuable in cases of clinician uncertainty. The models achieved balanced performance across multiple evaluation metrics, including PPV, NPV, sensitivity, specificity, F1-score, and Matthew&#x2019;s correlation coefficient (MCC). ChatGPT-4o achieved a sensitivity of 0.65 and specificity of 0.83, with an MCC of 0.41. Claude Sonnet 3.5 reached a sensitivity of 0.60 and specificity of 0.86, MCC 0.42 and Google Gemini 2.0 Flash had lower sensitivity (0.43) but the highest specificity (0.94), with an MCC of 0.43. In comparison, the best-performing individual pediatrician achieved a higher sensitivity (0.74) but lower specificity (0.68), with an MCC of 0.33, while the pediatricians&#x2019; majority vote yielded sensitivity of 0.69, specificity of 0.81, and MCC of 0.43 &#x2014; comparable to the top-performing LLMs. CONCLUSIONS: These Artificial intelligence tools offer a promising direction for SBI risk prediction, achieving performance comparable to that of experienced pediatric specialists, while maintaining simplicity of use/data-preprocessing for potential real-world applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41239388/","authors":["Šimunović I","Rezić K","Franić N","Boduljak G","Batinić M","Jukić I","Jelovina I","Biočić J","Pogorelić Z","Markić J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 14","doi":"10.1186/s12911-025-03258-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41239112","name":"Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.","source":"pubmed","abstract":"Early diagnosis of post-operative delirium (POD) in the older surgical population allows for timely interventions and reduces morbidities. Risk prediction models (RPMs) utilizing machine learning have emerged as promising tools to predict POD, but their performance and applicability in clinical settings remain uncertain. This systematic review evaluates the predictive accuracy and quality of RPMs for POD developed from 2014 to 2024 focusing on patients after non-cardiac surgery. PubMed and EMBASE were systematically searched for studies that developed RPMs predicting POD. Two authors independently screened 298 potential studies for eligibility, and quality assessment was performed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Pooled performance metrics, including AUROC, sensitivity, specificity, and precision, were calculated. Twenty-two articles matched review criteria, with the majority employing machine learning techniques such as gradient boosting and random forests. The pooled AUROC was 0.82 (95% CI: 0.79-0.85), indicating moderate-to-high predictive accuracy. Sensitivity, specificity, and precision were 0.78, 0.83, and 0.55, respectively. Studies utilizing more predictors and complex model architectures did not show substantial increases in performance compared to simpler models developed pre-2014. We demonstrated that while newer RPMs for POD are more likely to be validated and utilize advanced machine learning algorithms, their interpretability and clinical applicability remain limited. ML models hold promise in reducing the incidence of POD, but significant effort is needed to facilitate the integration of these models into clinical practice. Future efforts should focus on validating models externally, reducing false positive predictions, and translating model predictions into clinical actions.","url":"https://pubmed.ncbi.nlm.nih.gov/41239112/","authors":["Das O","Tang LY","Oh ES","Suarez J","Theodore N","Azad TD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s11357-025-01997-9","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238992","name":"Application of AI-assisted magnifying colonoscopy system in the diagnosis of colorectal tumors: a multicenter exploratory diagnostic study.","source":"pubmed","abstract":"Magnifying endoscopy is a reliable method for the differential diagnosis of colorectal tumors due to its high resolution and enhanced contrast, which allow for more precise detection and characterization. However, accurate assessment of tumor differentiation still requires significant expertise. To address this, we developed an AI-assisted diagnosis model (AADM) based on the Japan NBI Expert Team (JNET) classification and evaluated its diagnostic performance in comparison to endoscopists with varying levels of experience.","url":"https://pubmed.ncbi.nlm.nih.gov/41238992/","authors":["Fan X","Maihemuti A","Cai X","Huang J","Zhao S","Wang T","Chen X","Zhai H","Li J","Zheng Z","Zhao C","Wang Y","Feng Y","Mu J","Lu X","Zhu H","Wang B","Liu W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s00464-025-12196-0","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238850","name":"Large language model as a clinical decision support tool in the initial management of critically ill children: a pilot evaluation.","source":"pubmed","abstract":"Large language models (LLMs) like ChatGPT are being explored as clinical decision support tools, but their reliability in pediatric acute care remains uncertain. This pilot study assessed ChatGPT-4.0's performance in the early management of critically ill children using real-world clinical data. We retrospectively analyzed 20 children emergently admitted from the emergency department (ED) to a tertiary pediatric intensive care unit (PICU). ChatGPT-4.0 was prompted at four time points: ED arrival (diagnostic and therapeutic plans), ED transfer (differential diagnosis and hospitalization decision), PICU admission (diagnostic and therapeutic plans), and 24&#xa0;h into PICU stay (differential diagnosis). Outputs were compared to actual care and evaluated for accuracy, safety, and omissions. At ED and PICU admission, 94% (95% CI, 91-97%) and 98% (95% CI, 95-99%) of diagnostic recommendations were rated as appropriate. Only 82% (95% CI, 76-87%) of therapeutic recommendations were considered appropriate at both points (p&#x2009;&lt;&#x2009;0.001). Potentially harmful therapeutic suggestions were more common than diagnostic ones: 7% vs. 2% in the ED (p&#x2009;=&#x2009;0.016) and 10% vs. 0% in the PICU (p&#x2009;&lt;&#x2009;0.00001). In the PICU, critically missing therapeutic recommendations occurred at 0.95 per case, compared to 0.15 for diagnostic ones (p&#x2009;=&#x2009;0.0073). The correct diagnosis appeared in 100% of ED discharge and 95% (95% CI, 85-100%) of PICU 24-h differentials. Triage decisions were accurate in all PICU cases.","url":"https://pubmed.ncbi.nlm.nih.gov/41238850/","authors":["Tausky O","Kaplan E","Kadmon G","Gendler Y","Nahum E","Yitzhaki S","Weissbach A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 14","doi":"10.1007/s00431-025-06630-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238821","name":"Tumor cell- and infiltrating immune cell-based supervised learning artificial intelligence multimodal platform for tumor prognosis.","source":"pubmed","abstract":"Survival assessment for oral squamous cell carcinoma (OSCC) remains a significant clinical challenge. This study develops novel artificial intelligence (AI) platforms for assessing overall survival in OSCC patients based on 240 whole-slide images from multicenter cohorts. A comprehensive evaluation is conducted on four convolutional neural network architectures under two distinct deep learning (DL) training paradigms: supervised DL with precise annotations (PathS model, c-index&#x2009;=&#x2009;0.809), and weakly supervised DL using slide-level labels without manual annotations (c-index&#x2009;=&#x2009;0.707). Gradient-weighted class activation mapping reveals novel AI-based prognostic insights to simultaneously identify tumor cells and tumor-infiltrating immune cells as key predictive features. Additionally, our platform achieved significantly improved accuracy compared to conventional clinical signatures (CS model, c-index&#x2009;=&#x2009;0.721). Furthermore, the clinical potential is enhanced through the development of a multimodal nomogram combining PathS signatures with CS (c-index&#x2009;=&#x2009;0.817), representing a substantial advancement in personalized survival assessment for OSCC patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41238821/","authors":["Cai XJ","Peng CR","Ding CY","Cui YY","Gao L","Xu ZX","Li L","Zhang JY","Li TJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 14","doi":"10.1038/s41698-025-01125-y","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238719","name":"A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery.","source":"pubmed","abstract":"Surgical site infection (SSI) after colorectal cancer (CRC) surgery is still a significant healthcare issue. This study aimed to analyze risk factor associated with SSI. A total of 528 consecutive CRC patients who underwent curative resections between September 2017 and August 2024 were analyzed. Receiver operating characteristic (ROC) curves and machine learning (ML) models of Random Forest, K-Nearest Neighbor, Decision Tree, and XG Boost were employed for risk factor evaluation. SSI rate was 17.6%. SSI development varied significantly by tumor location, surgical technique, stoma formation, and neoadjuvant therapy. Furthermore, the preoperative values of white blood cell (WBC) count&#x2009;&#x2264;&#x2009;5 (10 3 /mcL), lymphocyte count&#x2009;&#x2264;&#x2009;1.32 (10 3 /mcL), polymorphonuclear (PMN) count&#x2009;&#x2264;&#x2009;3.3 (10 3 /mcL), platelet distribution width (PDW)&#x2009;&#x2264;&#x2009;11.2 (fL), and serum creatinine&#x2009;&#x2264;&#x2009;0.9 (mg/dL) were associated with SSI development. The results of the ML models also showed that PDW, PMN count, WBC count, lymphocyte count, RDW, serum urea, tumor location, surgery duration, surgical technique, and serum creatinine were the most important factors affecting SSI development. Development of SSI following CRC surgery was associated with several risk factors, including patients' characteristics and perioperative conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/41238719/","authors":["Rahimi M","Ansari M","Abdollahi A","Gholinezhadan A","Taherynezhad M","Zarif-Sadeghian M","Tabatabaei SM","Shahabi F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 14","doi":"10.1038/s41598-025-23722-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238506","name":"Development and validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study.","source":"pubmed","abstract":"The number of liver transplants from donors after the circulatory determination of death continues to increase, helping to alleviate the existing organ shortage. However, the rate of attempted but subsequently terminated procurements, known as futile procurements, remains high-mainly because many potential donors do not progress to death within a timeframe after extubation that maintains the suitability of the organ for donation. Futile procurements pose considerable financial and workload burdens to the transplant system. We aimed to develop and validate a machine-learning model to better predict progression to death and reduce futile procurements in cases of donation after circulatory death (DCD).","url":"https://pubmed.ncbi.nlm.nih.gov/41238506/","authors":["Yanagawa R","Iwadoh K","Nakayama T","Firl DJ","Wehrle CJ","Bekki Y","Soma D","Kusakabe J","Sambommatsu Y","Endo Y","Bozhilov KK","Pan JH","Kubota M","Tomiyama K","Fujiki M","Attia M","Melcher ML","Sasaki K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1016/j.landig.2025.100918","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41238381","name":"The patient-safety implications of AI-based communication with migrants in general practice: a scoping review.","source":"pubmed","abstract":"Access to interpreters for refugee and migrant patients that do not share the same language and culture as their GPs is considered a critical healthcare adaptation. However, interpreters are not routinely available in many healthcare settings and artificial intelligence (AI) is increasingly used as a pragmatic alternative. The patient-safety implications of relying on AI for this purpose are under-researched.","url":"https://pubmed.ncbi.nlm.nih.gov/41238381/","authors":["Cronin A","Kelly A","Wrona M","O'Donnell P","Hassan A","Myles T","Fallon T","MacFarlane A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.3399/BJGPO.2025.0107","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41237975","name":"Evaluating Artificial Intelligence-Assisted Current Procedural Terminology Coding in Vascular Surgery: A Comparison of ChatGPT Plus and Perplexity Pro Against Finance Department.","source":"pubmed","abstract":"This study evaluates the performance of ChatGPT Plus and Perplexity Pro in matching Current Procedural Terminology (CPT) codes from vascular surgery cases at Tufts Medical Center, comparing their accuracy to that of the finance department's CPT coding, which serves as the reference standard.","url":"https://pubmed.ncbi.nlm.nih.gov/41237975/","authors":["Madris B","Ranjbar K","Critsinelis A","Myla K","Kumar S","Salehi P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.avsg.2025.10.045","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41237515","name":"Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review.","source":"pubmed","abstract":"Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial intelligence (AI) and machine learning (ML)-based solutions hold the promise of improving GDM prediction, thus enabling earlier and more personalized care. The main objective of this systematic review is to provide a comprehensive overview of AI/ML methods used for GDM prediction, leveraging the data from both the preconception and pregnancy periods. We conducted a PRISMA-guided search across databases including PubMed, Scopus, IEEE, and Web of Science from their inception to May 27th 2024. Studies were included if they applied AI/ML methods to predict GDM and were published and peer-reviewed. We extracted data across 30 dimensions. We performed a dual-framework quality assessment of included studies using PROBAST and the IJMEDI checklist. A total of 78 studies met the inclusion criteria. Logistic regression (46 studies), tree-based models (41 studies), and support vector machines (29 studies) were the most frequently used AI methods. Neural networks were most often reported as best-performing (15 studies), followed by boosting (14 studies), and tree-based methods (13 studies). Twelve studies included preconception data. Clinically relevant metrics such as sensitivity, specificity, and calibration were frequently underreported, with decision-curve analysis rarely applied. Thirteen studies performed external validation, and very few employed causal or explainable modeling approaches. Risk of bias was high in most studies. According to the IJMEDI checklist, most studies insufficiently addressed data preparation, validation, and deployment aspects. AI-based GDM prediction is rapidly evolving, with strong potential for earlier and more personalized interventions. Future work should prioritize transparent reporting, external validation, and development of trustworthy, explainable models using diverse, longitudinal data. Closer collaboration among data scientists, clinicians, and healthcare systems is needed to close the loop from AI innovation to clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41237515/","authors":["Ivanovic V","Sujan MAJ","Mengshoel OJ","Moholdt T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.ijmedinf.2025.106158","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41237250","name":"Hepatic hypertension on-a-chip identifies GPR116 as a hydrostatic pressure mechanosensor to regulate vascular injury in cirrhosis.","source":"pubmed","abstract":"Cirrhosis-associated portal hypertension drives vascular injury, yet the pathogenesis mediated by abnormal hydrostatic pressure (HP) remains unclear due to the absence of in vitro models replicating the cirrhotic perivascular mechanical microenvironment. Here, we developed 2D static and 3D dynamic \"hepatic hypertension on-a-chip\" (HH chip) systems replicating cirrhotic hemodynamics and matrix properties. The HH chip realized integration and decoupled regulation of HP, shear stress, and matrix stiffness. Liver sinusoidal endothelial cells (LSECs) exhibited HP-induced damage exclusively on stiff matrices, recapitulating cirrhotic degeneration phenotypes and genotypes. Using the HH chip, we identified GPR116 as the key HP mechanosensor in LSECs and delineated its downstream mechanotransduction pathway driving cellular injury. Genetic silencing of GPR116 protected endothelial cells from HP-induced damage both in vitro and in cirrhotic murine models. Cell- and gene-based therapies targeting GPR116 significantly attenuated cirrhosis progression. The HH chip can accelerate pathological investigation, target identification, and therapeutic development for hypertension-associated diseases.","url":"https://pubmed.ncbi.nlm.nih.gov/41237250/","authors":["Long Y","Liang K","Niu Y","Wang R","Liu R","Zhang Y","Ao Y","Jin Y","Wu Z","Wu B","Liu Z","Zhang X","Liu X","Qi X","Liu B","Du Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 14","doi":"10.1126/sciadv.adu7596","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41236586","name":"Automatic measurement of pharyngeal contraction ratio during deglutition using 2D cine MRI with deep learning: A pilot study.","source":"pubmed","abstract":"This study aimed to develop a deep learning-based method for automatic segmentation of the pharyngeal area (PA) and measurement of the pharyngeal contraction ratio (PCR) during deglutition using cine magnetic resonance imaging (MRI). The proposed algorithm combines PA region extraction by a 2D U-Net with automatic calculation of PA and PCR. Segmentation performance was evaluated using the Dice coefficient (DC), and the PCR measured by the model ([Formula: see text]) was compared with that obtained manually ([Formula: see text]) using correlation and Bland-Altman analyses. Cine MRI data of 20 healthy adults (10 men, 10 women; age 22-29 years) were analyzed. The average DC in the test cases was 0.890&#x2009;&#xb1;&#x2009;0.025, and the PA of the model correlated well with the manual reference (r&#x2009;=&#x2009;0.70-0.97). The mean [Formula: see text] was 0.105&#x2009;&#xb1;&#x2009;0.035, consistent with values reported in videofluoroscopic swallowing studies. These results demonstrate the technical feasibility of automatic PCR measurement from cine MRI using deep learning.","url":"https://pubmed.ncbi.nlm.nih.gov/41236586/","authors":["Takahashi M","Miyamoto N","Koori N","Monma M","Ishimori Y","Fuse H","Miyakawa S","Yasue K","Nosaka H","Abe S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s12194-025-00984-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41236585","name":"Artificial intelligence-driven CT radiomics model predicts prognosis in TACE-refractory hepatocellular carcinoma.","source":"pubmed","abstract":"To develop an integrated predictive model combining radiomics, clinical risk factors, and machine learning for prognostic assessment in hepatocellular carcinoma (HCC) patients receiving continued transarterial chemoembolization (TACE) after developing TACE resistance.","url":"https://pubmed.ncbi.nlm.nih.gov/41236585/","authors":["Li H","Fan Y","Li Y","Ren W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1007/s00261-025-05285-0","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41235461","name":"Cardiovascular morbidity following epilepsy: A nationwide retrospective cohort study in South Korea.","source":"pubmed","abstract":"This study evaluated the long-term risk of major cardiovascular diseases (CVDs) in patients with epilepsy using a nationwide cohort, aiming to address critical gaps in population-based evidence on brain-heart interactions.","url":"https://pubmed.ncbi.nlm.nih.gov/41235461/","authors":["Bae Y","Kang C","Lee Y","Cho H","Jung H","Lee SW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/epi4.70185","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41234571","name":"Deep learning-based classification of pleural malignancy using medical thoracoscopic images.","source":"pubmed","abstract":"Malignant pleural effusion (MPE) is a frequent complication of advanced lung cancer, and rapid and accurate diagnosis is critical for timely therapeutic decision-making. Although medical thoracoscopy (MT) provides direct visualization and targeted biopsy, resulting in high diagnostic yield, the clinical utility of its findings is contingent on operator experience and subsequent confirmation via pathological analysis. Recent advances in deep learning have enabled automated image classification in various fields, but its application in thoracoscopic images remains unexplored. The aim of our study was to develop a deep learning-based model to classify pleural malignancy and to evaluate its diagnostic performance.","url":"https://pubmed.ncbi.nlm.nih.gov/41234571/","authors":["Hong YJ","Ha SH","Park SH","Ha JH","Kim HW","Lee BR","Lee SH","Yeo CD","Kim JY","Choi JY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 31","doi":"10.21037/tlcr-2025-588","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41234459","name":"AI performance in emergency medicine fellowship examination: comparative analysis of ChatGPT-4o, Gemini 2.0, Claude 3.5, and DeepSeek R1 models.","source":"pubmed","abstract":"This study evaluated the accuracy rates and response consistency of four different large language models (ChatGPT-4o, Gemini 2.0, Claude 3.5, and DeepSeek R1) in answering questions from the Emergency Medicine Fellowship Examination (YDUS), which was administered for the first time in T&#xfc;rkiye.","url":"https://pubmed.ncbi.nlm.nih.gov/41234459/","authors":["Şan İ","Akkan Öz M","Yortanli M","Genç M","Bulut B","Gür A","Mutlu H","Yazici R","Gönen MÖ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.55730/1300-0144.6083","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41233846","name":"Inflammation-related proteomics of extracellular vesicles as novel biomarkers for systemic lupus erythematosus revealed by proximity extension assay.","source":"pubmed","abstract":"Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by dysregulated inflammatory response lacking reliable diagnosis biomarkers and therapy targets. Extracellular vesicles (EVs)-derived cargo as biomarkers and mediators of SLE have garnered significant attention, however, quantitative inflammatory protein profile of SLE EVs remain uncovered.","url":"https://pubmed.ncbi.nlm.nih.gov/41233846/","authors":["Zhan S","Wang Z","Xu Y","Zhou S","Ge M","Song Y","Zhu Y","Dou H","Shen H","Yang P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 13","doi":"10.1186/s13075-025-03681-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41233547","name":"Multi-omics analysis of a pig-to-human decedent kidney xenotransplant.","source":"pubmed","abstract":"Organ shortage remains a major challenge in transplantation, and gene-edited pig organs offer a promising solution 1-3 . Despite gene editing, the immune reactions following xenotransplantation can still cause transplant failure 4 . To understand the immunological response of a pig-to-human kidney xenotransplantation, we conducted large-scale multi-omics profiling of the xenograft and the host's blood over a 61-day procedure in a brain-dead human (decedent) recipient. Blood plasmablasts, natural killer cells and dendritic cells increased between postoperative day&#xa0;(POD)&#x2009;10 and 28, concordant with an expansion of IgG and IgA B&#x2009;cell clonotypes and subsequent biopsy-confirmed antibody-mediated rejection (AMR) at POD33. Human T&#x2009;cell frequencies increased from POD14 and peaked between POD33 and POD49 in the blood and xenograft, which coincided with T&#x2009;cell receptor diversification, expansion of a restricted TRBV2 and TRBJ1 clonotype and histological evidence of combined AMR and cell-mediated rejection at POD49. At POD33, the most abundant human immune population in the graft was CXCL9 + macrophages, which aligned with interferon-&#x3b3;-driven inflammation and a T helper 1-type immune response. There was also evidence of interactions between activated pig-resident macrophages and infiltrating human immune cells. Xenograft tissue showed pro-fibrotic tubular and interstitial injury marked by S100A6 (ref. 5 ), SPP1 (also known as osteopontin) 6 and COLEC11 (ref. 7 ) expression at POD21-POD33. Proteomic profiling revealed activation of human and pig complement, with a decreased human component after AMR therapy, in which complement was inhibited. Collectively, these data delineate the molecular orchestration of human immune responses to a porcine kidney and reveal potential immunomodulatory targets for improving xenograft survival.","url":"https://pubmed.ncbi.nlm.nih.gov/41233547/","authors":["Schmauch E","Piening BD","Dowdell AK","Mohebnasab M","Williams SH","Stukalov A","Robinson FL","Bombardi R","Jaffe I","Khalil K","Kim J","Aljabban I","Eitan T","O'Brien DP","Rophina M","Wang C","Bartlett AQ","Zanoni F","Albay J","Andrijevic D","Maden B","Mauduit V","Vikman S","Argibay D","Zayas Z","Wu L","Moi K","Lau B","Zhang W","Gragert L","Weldon E","Gao H","Hamilton L","Kagermazova L","Camellato BR","Gandla D","Bhatt R","Gao S","Al-Ali RA","Habara AH","Chang A","Ferdosi S","Chen HM","Motter JD","Thomas SC","Saxena D","Fairchild RL","Loupy A","Heguy A","Crawford A","Batzoglou S","Snyder MP","Siddiqui A","Holmes MV","Chong AS","Kaikkonen MU","Linna-Kuosmanen S","Ayares D","Lorber M","Nellore A","Skolnik EY","Mattoo A","Tatapudi VS","Taft R","Mangiola M","Guo Q","Herati RS","Stern J","Griesemer A","Kellis M","Boeke JD","Montgomery RA","Keating BJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1038/s41586-025-09846-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41233228","name":"[Acceptance of artificial intelligence tools for medical history-taking: Findings of a population survey in Germany].","source":"pubmed","abstract":"Digital tools for medical history-taking, such as chatbots, are increasingly being developed and evaluated but have not yet been implemented across the board in medical practices in Germany. The aim of this study was to survey the acceptance of AI-supported tools for medical history-taking among the German population, thereby allowing conclusions to be drawn about the use of these tools and their suitability as part of digitalization strategies in medical practices.","url":"https://pubmed.ncbi.nlm.nih.gov/41233228/","authors":["Currle E","Haug S","Weber K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.zefq.2025.10.003","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41232256","name":"LG-nnU-net for multilabel anal sphincter segmentation on MRI: quantitative evaluation in patients with anal fistula.","source":"pubmed","abstract":"To develop and evaluate a novel deep learning-based segmentation framework (LG-nnU-net) for multilabel segmentation of anal sphincter substructures on MRI, aimed at providing robust quantitative anatomical information without implying operative validation for clinical classification improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/41232256/","authors":["Liu X","Ren H","Lv J","Wang L","Wu M","Zheng C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.ejrad.2025.112509","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41231797","name":"Development and validation of a machine learning model for on-site prediction of coronary heart disease in high-risk adults using clinical data.","source":"pubmed","abstract":"Risk of coronary heart disease (CHD) in a specific period of years can be assessed using scores calculated by models, such as pooled cohort equations (PCEs) and Framingham Risk Score. However, there are few studies on on-site estimation of CHD risk quantitatively with score calculation as auxiliary diagnosis. Nowadays, researchers introduce new technologies, such as machine learning, as effective CHD risk prediction models, but these models still need to be validated using real clinical data before promoting their use in real clinical settings.","url":"https://pubmed.ncbi.nlm.nih.gov/41231797/","authors":["Mo L","Lin H","Li C","Yu L","Lu D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1371/journal.pone.0334881","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41231645","name":"Mapping the application landscape of artificial intelligence in prostate cancer: a global bibliometric analysis.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming medical research, with its impact in neural networks, clinical imaging and computational biology. Prostate cancer (PCa), a leading malignancy in men, benefits from AI's capabilities in enhancing diagnostic precision and personalizing treatments, addressing challenges in disease complexity and clinical management.","url":"https://pubmed.ncbi.nlm.nih.gov/41231645/","authors":["Wei Y","Mei Z","Xie C","Yuan F","Xu D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.1097/JS9.0000000000003828","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41231508","name":"Performance of Foundation Models vs Physicians in Textual and Multimodal Ophthalmological Questions.","source":"pubmed","abstract":"There is an increasing amount of literature evaluating the clinical knowledge and reasoning performance of large language models (LLMs) in ophthalmology, but to date, investigations into its multimodal abilities clinically-such as interpreting images and tables-have been limited.","url":"https://pubmed.ncbi.nlm.nih.gov/41231508/","authors":["Rocha H","Chong YJ","Thirunavukarasu AJ","Wong YL","Wong SW","Chang YH","Azzopardi M","Tan BKJ","Song A","Malem A","Jain N","Zhou S","Tan TF","Rauz S","Ang M","Mehta JS","Ting DSW","Ting DSJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 1","doi":"10.1001/jamaophthalmol.2025.4255","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41231418","name":"AI-Based Models for Risk Prediction in MASLD: A Systematic Review.","source":"pubmed","abstract":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is a rapidly growing global health concern. This systematic review evaluates the efficacy of AI-based models in predicting risk and stratifying patients with MASLD, with a focus on identifying individuals at risk for clinically significant disease, such as&#x2009;&#x2265;&#x2009;F2 fibrosis,&#x2009;&#x2265;&#x2009;F3 advanced fibrosis, or MASH, addressing a critical gap in the literature to enhance clinical management and patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41231418/","authors":["Njei B","Al-Ajlouni YA","Lemos SY","Ugwendum D","Njei N","Al Ta'ani O","Ameyaw P","Njei LP","Boateng S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1007/s10620-025-09499-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41231298","name":"Locally deployed context-aware chatbot outperforms generic large language models for guideline-concordant pediatric imaging recommendations.","source":"pubmed","abstract":"Accurate modality selection in pediatric imaging is critical, yet adherence to the American College of Radiology (ACR) Appropriateness Criteria remains limited. Large language models (LLMs) offer potential as decision support tools but often lack domain-specific accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/41231298/","authors":["Gupta A","Rangarajan K","Krishna Kumar RG","Anshal S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 13","doi":"10.1007/s00247-025-06453-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41228867","name":"Mechanical Analysis for Active Movement of Upper Limb Rehabilitation Robots to Alleviate Shoulder Pain in Patients with Stroke Hemiplegia and Frozen Shoulder.","source":"pubmed","abstract":"Shoulder disorders, including frozen shoulder resulting from stroke-induced hemiplegia, significantly reduce a patient's ability to perform activities of daily living, thereby necessitating repeated rehabilitation. Consequently, extensive research has been conducted on rehabilitation robots to assist in upper-limb motor recovery. The shoulder moves according to the scapulohumeral rhythm. Considering the biomechanical characteristics of the shoulder joint, the rehabilitation robot was designed to replicate a similar kinematic environment using actuators and linkages that emulate the structures of the upper arm, shoulder, and clavicle. To ensure precise operation, the kinematic accuracy of the robot was pre-evaluated. Kinematic analyses were conducted using MATLAB, and the results were compared with coordinate data from the mechanical design to evaluate positional accuracy. In addition, the convergence and accuracy of joint-angle estimation for target positions were analyzed. The forward kinematic analysis revealed that the average positional error between the measured and target coordinates ranged from 0.5% to 2.8%, with the Base Motor-Back Motor segment exhibiting the highest error (2.8%). The inverse kinematic analysis demonstrated stable convergence to the target positions through iterative computations using the Gauss-Newton method, confirming that the actual motion could be accurately reproduced within the designed range of motion.","url":"https://pubmed.ncbi.nlm.nih.gov/41228867/","authors":["Bang SJ","Lee JS","Song DH","Ryu SY","Kim KG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.3390/s25216644","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41228178","name":"Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches.","source":"pubmed","abstract":"Federated Learning (FL) has emerged as a promising framework for multi-institutional medical artificial intelligence, enabling collaborative model development while preserving data privacy and security. Despite increasing research on federated approaches for cardiovascular disease prediction, previous reviews have largely focused on disease-specific perspectives without systematically comparing data modalities. This study comprehensively examines 28 representative investigations from the past five years, including 17 biosignal-based and 11 electronic health record (EHR)-based applications. Biosignal-based FL emphasizes personalized electrocardiogram (ECG) classification, mitigation of non-independent and identically distributed (Non-IID) data, and Internet of Things (IoT)-based monitoring using methods such as client clustering, asynchronous learning, and Bayesian inference. In contrast, EHR-based studies prioritize large-scale hospital collaboration, adaptive optimization, and secure aggregation through distributed frameworks. By systematically comparing methodological strategies, performance trade-offs, and clinical feasibility, this review highlights the complementary strengths of biosignal- and EHR-based approaches. Biosignal frameworks show strong potential for personalized, low-latency cardiac monitoring, whereas EHR frameworks excel in scalable and privacy-preserving decision support. Building upon the limitations of earlier reviews, this paper introduces data-type-centric design guidelines to enhance the reliability, interpretability, and clinical scalability of FL in cardiovascular diagnosis and prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/41228178/","authors":["Ryu H","Lee M","Kim SH","Kim JH","Yang HJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 5","doi":"10.3390/healthcare13212811","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41227005","name":"Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience.","source":"pubmed","abstract":"Background : Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Radiomics and artificial intelligence (AI) have emerged as promising tools for quantitative imaging analysis and precision staging. This study aimed to evaluate the ability of an AI-based radiomics model to preoperatively predict tumor (T) and nodal (N) stage, lymphovascular invasion (LVI), and postoperative complications in patients with early-stage NSCLC. Material and Methods : This retrospective study included 51 consecutive patients who underwent anatomical lobectomy with systematic lymph node dissection between 2019 and 2024, at the Clinic for Thoracic Surgery of the University Clinical Center of Serbia. Quantitative imaging features were extracted from preoperative CT scans using the Lesion Scout with Auto ID module (syngo.via VB50 MM, Siemens Healthineers). Radiomics and clinical predictors were analyzed using regularized logistic regression (LASSO) with five-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, precision, and F1 score, and calibration was evaluated using the Hosmer-Lemeshow test. Groups were compared using parametric and non-parametric tests. Correlation between the variables was assessed using Spearman's rank correlation coefficient. All p -values less than 0.05 were considered significant. Results : The AI-based model showed excellent performance for predicting the T component (training AUC = 0.89; test AUC = 0.86; F1 = 0.81) and acceptable calibration ( p = 0.41). Nodal metastasis (OR = 0.108; 95% CI: 0.011-1.069; p = 0.057) and LVI (OR = 0.519; 95% CI: 0.139-1.937; p = 0.329) were not significantly predicted. Emphysema was identified as a significant independent predictor of postoperative complications (&#x3c7; 2 = 5.13; p = 0.024). Conclusions : The AI-driven radiomics model demonstrated strong predictive ability for the T component and identified emphysema as a clinically relevant predictor of postoperative complications.","url":"https://pubmed.ncbi.nlm.nih.gov/41227005/","authors":["Garabinovic Z","Savic M","Colic N","Rakocevic J","Ercegovac M","Mitrovic M","Lukic K","Vukmirovic J","Vasic Madzarevic J","Stevanovic S","Bisevac Peric G","Bubanja M","Pavic A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.3390/jcm14217609","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41226941","name":"Endoscopic Ultrasound for Nodal Staging in Patients with Resectable Cholangiocarcinoma.","source":"pubmed","abstract":"Background : Lymph node (LN) involvement is a negative prognostic factor for patients with cholangiocarcinoma (CCA). Preoperative assessment of the LN could potentially aid therapy decision making. Endoscopic ultrasound (EUS) can be used to sample suspicious LN. The aim of this study was to evaluate the clinical impact of EUS for suspicious LN in patients with presumed resectable CCA. Methods : In this single-center cohort study, patients with potentially resectable CCA who underwent preoperative linear EUS between 2019 and 2024 were retrospectively included. The primary aims were the percentage of malignant LN detected and the clinical impact of EUS, which was defined as the percentage of patients who were precluded from surgical exploration due to pathologically confirmed LN metastases found with EUS tissue acquisition (EUS-TA). The secondary aim was the complication rate of EUS-TA. Results : A total of 135 patients were included, of whom 12 (8.9%) had intrahepatic CCA (iCCA), 65 (48.1%) had perihilar CCA (pCCA), 13 had (9.6%) middle bile duct CCA (mCCA), and 45 (33.3%) had distal CCA (dCCA). Across 148 EUS procedures, 139 LNs were identified, and EUS-TA was performed on 63 LNs among 55 patients. LN metastases were detected by EUS-TA for iCCA, pCCA, mCCA, and dCCA, in 25%, 6.2%, 15.4%, and 4.4%, respectively. EUS and EUS-TA influenced surgical work-up for iCCA, pCCA, mCCA, and dCCA in 25%, 1.5%, 15.4%, and 0.0%, respectively. No complications associated with EUS were noted. Conclusions : Preoperative EUS for nodal staging had an important clinical impact in patients with presumed resectable iCCA and mCCA, but less for pCCA and dCCA. Further prospective studies should investigate whether systematic nodal staging with EUS could improve preoperative decision making even further.","url":"https://pubmed.ncbi.nlm.nih.gov/41226941/","authors":["de Jong DM","van Driel LMJW","Lakhtakia S","Ramchandani M","Fathima Memon S","Tyagi A","Kumaraswamy P","Modak S","Sekaran A","Bruno MJ","Reddy DN","Rughwani H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.3390/jcm14217545","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41225292","name":"Clinical Feasibility of Long-Read WGS for DNA Methylation Signature Analysis.","source":"pubmed","abstract":"DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/41225292/","authors":["Hildonen M","Mariani L","Dalsberg J","Bak M","Weksberg R","Choufani S","Tümer Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1111/cge.70108","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41224896","name":"Multimodal deep learning for midpalatal suture assessment in maxillary expansion.","source":"pubmed","abstract":"Accurate midpalatal suture maturation assessment is critical for orthodontic treatment planning, yet current manual staging methods exhibit substantial inter-examiner variability (kappa values 0.3-0.8), compromising treatment decision reliability. This study developed and validated DeepMSM, an automated multimodal deep learning framework integrating cone-beam computed tomography with clinical indicators for standardized midpalatal suture staging. We retrospectively analyzed cone-beam computed tomography and lateral cephalometric radiographs from 200 orthodontic patients aged 7-36 years. The DeepMSM framework integrated multimodal images with clinical variables including age, gender, cervical vertebral maturation stage, and mandibular third molar stage using attention-based fusion strategies. DeepMSM achieved 93.75% accuracy and 93.81% F1-score, substantially outperforming single-modality approaches (47.50%-71.25% accuracy) and dual-modality models (73.75-81.25% accuracy). The system demonstrated excellent performance in distinguishing critical stages C and D with F1-scores of 92%-93%, representing the decision point between conventional expansion and surgical intervention. All clinical parameters showed significant correlations with midpalatal suture maturation (p&lt;0.05). DeepMSM, a novel multimodal midpalatal suture maturation assessment system, achieved a high accuracy of 93.75%, demonstrating the potential to reduce diagnostic variability and improve treatment reliability. This automated framework particularly benefits less experienced clinicians in making critical treatment decisions for maxillary expansion therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41224896/","authors":["Cai J","Wang Z","Wang H","Chen Z","Yu Q","Lai Z","Xu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 12","doi":"10.1038/s41598-025-23500-2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41224216","name":"Navigating medication safety with electronic medical records: insights from a dual-phase implementation in paediatric, neonatal and maternity care.","source":"pubmed","abstract":"Electronic medical record (EMR) implementations can disrupt clinical workflows and impact medication safety. This study evaluated the effect of a two-phase EMR roll-out on medication safety events within the Women's and Children's Division of a large tertiary public hospital.","url":"https://pubmed.ncbi.nlm.nih.gov/41224216/","authors":["Mordaunt DA","Johnson N","Verghese S","Parker R","Gibb K","Palmer LJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 4","doi":"10.1071/AH24344","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41222816","name":"A deep learning-based MRI automatic detection model for spinal schwannoma and meningioma.","source":"pubmed","abstract":"Schwannomas (SCH) and meningiomas (MEN), the two most common primary spinal cord tumors, present a clinical diagnostic challenge due to their overlapping clinical and radiological manifestations. To address this, we developed a deep learning-based object detection model for automated detection of these tumors using magnetic resonance imaging (MRI), which could facilitate early diagnosis and alleviate clinical decision-making burdens. Our study retrospectively analyzed MRI scans from 103 pathologically confirmed SCH and MEN cases at a local hospital (July 2015-August 2024). First, we took YOLOv8n as the baseline model, introduced selective kernel fusion (SKFusion) module to replace the feature fusion layer of the original neck part, added recursive gated convolution (gnConv), and then trained the improved feature fusion model (YOLOv8n-SKNeck). The proposed model achieved notable performance metrics: 91.20% mean accuracy, 90.92% mean recall, and 91.03% mean F1-score for SCH/MEN detection. These results demonstrate that our optimized deep learning framework can effectively automate the detection and differential diagnosis of spinal SCH and MEN through MRI analysis. Thus, the novel method holds significant potential for advancing computer-aided diagnosis and facilitating innovative applications in future clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41222816/","authors":["Liu Y","Liu Y","Cai J","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s11517-025-03468-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41221334","name":"Artificial intelligence in orthopaedic education: a narrative review.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into medical education is reshaping traditional learning paradigms. In orthopaedic surgery, AI applications such as virtual reality (VR) and augmented reality (AR) simulations and intelligent tutoring systems are being utilized to enhance training. This review aims to explore the current applications, benefits, challenges, and future directions of AI in orthopaedic education, while also addressing relevant ethical and logistical considerations.","url":"https://pubmed.ncbi.nlm.nih.gov/41221334/","authors":["Szatkowski JP","Druten E","Soni C","O'Neill DC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21037/aoj-25-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41219972","name":"Technological advancements in antibody-based therapeutics for treatment of diseases.","source":"pubmed","abstract":"Monoclonal antibodies (mAbs) represent a major class of therapeutics with widespread clinical applications in oncology, immunology, hematology, neurology and infectious disease. Since the introduction of hybridoma technology in 1975, the field has been advanced by a succession of innovations including chimeric and humanized antibody engineering, phage display, transgenic mouse platforms and high-throughput single B cell isolation. These technological developments have enhanced the specificity, potency and safety of mAbs, resulting in 144 FDA-approved antibody drugs on the market and 1,516 worldwide candidates in clinical development as of August 2025. Engineering breakthroughs have led to new modalities of antibody-based therapeutics, such as antibody-drug conjugates (ADCs), bispecific antibodies (bsAbs), and chimeric antigen receptor T (CAR-T) cell therapies. Each of these modalities has therapeutic utility across multiple disease domains. Recent advances in delivery strategies, notably mRNA-lipid nanoparticles (LNPs) and antibody-directed in vivo CAR-T cell reprogramming, can enable precision therapies while reducing off-target effects and manufacturing complexity. The integration of artificial intelligence (AI) and machine learning (ML), next-generation sequencing (NGS), and structural modeling tools has further accelerated antibody discovery, affinity maturation and immunogenicity prediction, allowing for more efficient and rational antibody design. The advances in antibody technology are reflected in the rapid market growth of antibody-based therapeutics, which had global sales exceeding USD 267 billion in 2024. This review provides a comprehensive update on recent developments in antibody discovery platforms, therapeutic formats and market trends, highlighting emerging strategies that are reshaping the landscape of antibody-based medicine. Furthermore, we discuss clinical translation, regulatory landscapes, and the integration of engineering, biology and informatics. Together, these aspects shape a dynamic and multidisciplinary future for the therapeutic antibody field, which is poised to address unmet clinical needs and global healthcare priorities.","url":"https://pubmed.ncbi.nlm.nih.gov/41219972/","authors":["Lu RM","Chiang HL","Yuan JP","Wang HH","Chen CY","Panda SS","Liang KH","Peng HP","Ko SH","Hsu HJ","Kumari M","Su YJ","Tse YT","Chou NL","Wu HC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 12","doi":"10.1186/s12929-025-01190-2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41219771","name":"DeepSeek-R1 for automated scoring in radiology residency examinations: an agreement and test-retest reliability study.","source":"pubmed","abstract":"This study evaluates the feasibility of employing DeepSeek-R1 for automated scoring in examinations for radiology residents, comparing its performance with that of radiologists.","url":"https://pubmed.ncbi.nlm.nih.gov/41219771/","authors":["Niu S","Liu X","Huang L","Li Y","Wang G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 11","doi":"10.1186/s12909-025-08184-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41218945","name":"Design and rationale of the artificial intelligent dialogue System assisted comprehensive Management of secondary prevention Among post coronary aRtery bypass graft patienTs (SMART): protocol for a randomised controlled trial for postcoronary artery bypass grafting management.","source":"pubmed","abstract":"Cardiovascular risk factor management (ie, hypertension, dyslipidaemia and diabetes) in post-coronary artery bypass grafting (CABG) patients is suboptimal, with a high prevalence and low control rate due to various barriers, including a lack of self-management awareness and inadequate healthcare resources. Artificial intelligence (AI) interventions are promising for improving lifestyle management and secondary prevention; however, their effectiveness in post-CABG patients remains unclear. We aimed to describe the protocol of the artificial intelligence dialogue system-assisted comprehensive management of secondary prevention among post-coronary artery bypass graft patients (SMART) assessing the efficacy and safety of an AI-based dialogue system, namely 'Smart Family Doctor', on blood pressure, lipids and glucose control.","url":"https://pubmed.ncbi.nlm.nih.gov/41218945/","authors":["Lei L","Li J","Zhang L","Yuan X","Diao X","Qi L","Wang Y","Du W","Zhao W","Hu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 11","doi":"10.1136/bmjopen-2025-106447","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41218187","name":"Perceptions, Usage, and Educational Impact of ChatGPT Among Medical Students in Germany: Cross-Sectional Mixed Methods Survey.","source":"pubmed","abstract":"Large language models such as ChatGPT offer significant opportunities for medical education. However, empirical data on actual usage patterns, perceived benefits, and limitations among medical students remain limited.","url":"https://pubmed.ncbi.nlm.nih.gov/41218187/","authors":["Fußhöller A","Lechner F","Schlicker N","Muehlensiepen F","Mayr A","Kuhn S","Hirsch MC","Knitza J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 11","doi":"10.2196/81484","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41218185","name":"Predicting Delayed Extubation After General Anesthesia in Postanesthesia Care Unit Patients Using Machine Learning: Model Development Study.","source":"pubmed","abstract":"Delayed extubation after general anesthesia increases complications and can lead to longer hospital stays and higher mortality. Current risk assessments often rely on subjective judgment or simple tools, whereas machine learning offers potential for real-time evaluation, though research is limited and typically uses single-algorithm models.","url":"https://pubmed.ncbi.nlm.nih.gov/41218185/","authors":["Luo J","Lin S","Wang L","Ji H","Zheng J","Wang T","Chen L","Lin Z","Liu Z","Liufu N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 11","doi":"10.2196/72602","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41217339","name":"Artificial Intelligence in melanoma research: a bibliometric analysis.","source":"pubmed","abstract":"As one of the most lethal skin cancers, melanoma has encountered many obstacles in diagnosis and therapy. Artificial intelligence (AI) can help improve early diagnosis, prognosis, and treatment of melanoma. However, there is a lack of detailed and accurate bibliometric analysis of the field.","url":"https://pubmed.ncbi.nlm.nih.gov/41217339/","authors":["Guo Y","Huang X","Chen F","Ma J","Lv Y","Yang T","Guo Q","Sun Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.1097/JS9.0000000000003879","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41217283","name":"Large-Scale Evaluation of Machine Learning Models in Identifying Follow-Up Recommendations in Radiology Reports.","source":"pubmed","abstract":"Background Radiology reports often contain follow-up recommendations vital for optimal patient care, prevention of complications, and mitigation of legal risk. However, there is a lack of comprehensive comparison methods for identifying these recommendations across a large volume of reports from various modalities, including open-source large language models. Purpose To evaluate the performance of machine learning (ML) models, including Meta's open-source LLAMA3 and OpenAI's Health Insurance Portability and Accountability Act-compliant Generative Pre-trained Transformer, in identifying follow-up recommendations in radiology reports. Materials and Methods In this retrospective study, three sets of radiology reports were analyzed across multiple imaging modalities from a large urban academic medical center: an expert annotated dataset ( n = 11&#x2009;901) from January 1 to January 10, 2015; a dataset ( n = 32&#x2009;959) extracted through regular expressions (ie, sequences of characters that define search patterns in text) from January 11, 2015, to January 1, 2017; and a dataset ( n = 4909) annotated during dictation from September 8, 2018, to February 23, 2021. To assess generalization on impressions, two expertly annotated datasets were used: 2000 chest radiography reports from the publicly available MIMIC-CXR database for external testing and 100 institutional CT reports from January 1 to January 15, 2024, for temporal testing. Thirty-two text classification methods were evaluated separately based on the findings and impression sections of these reports. Performance metrics included precision, recall, accuracy, and F1 score; with 95% bootstrapped CIs and areas under the precision-recall curve. Statistical comparisons were performed by using the McNemar test. Results The study included 49&#x2009;769 reports from 35&#x2009;509 patients (mean age, 52.2 years &#xb1; 22.0 [SD]; 18&#x2009;477 female patients) for training ( n = 37&#x2009;140), validation ( n = 2584), and internal testing ( n = 10&#x2009;045). For the findings section, a generative-discriminative model initialized with Google's Word2vec embeddings (Hybrid-google) achieved the highest F1 score (0.835; 95% CI: 0.825, 0.845). For the impression section, an attention-based bidirectional long short-term memory (LSTM) with random initialization (AttBiLSTM-random) performed best, with an F1 score of 0.979 (95% CI: 0.976, 0.982). Prefixed prompting with GPT-4 demonstrated superior external and temporal generalization performance on the MIMIC-CXR and institutional CT datasets, achieving F1 scores of 0.969 (95% CI: 0.961, 0.977) and 0.973 (95% CI: 0.937, 1.000), respectively. Conclusion ML models showed promise for automating the classification of follow-up recommendations in radiology reports. &#xa9; RSNA, 2025 Supplemental material is available for this article.","url":"https://pubmed.ncbi.nlm.nih.gov/41217283/","authors":["Xiao P","Yu X","Ha SM","Bani A","Mintz A","Wang J","Elbanan M","Mokkarala M","Mattay G","Nazeri A","Kannampallil T","Lai AM","Narra VR","Marcus DS","Bierhals AJ","Sotiras A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1148/radiol.242167","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41217164","name":"Impact of Obstructive Sleep Apnea on Outcomes of Minimally Invasive Nasal Surgery for Chronic Rhinitis.","source":"pubmed","abstract":"ImportanceChronic rhinitis (CR) affects quality of life and often coexists with obstructive sleep apnea (OSA), a comorbidity that may increase surgical risks. Understanding OSA's impact on outcomes of minimally invasive nasal surgery is clinically relevant.ObjectiveTo evaluate the efficacy and safety of minimally invasive nasal surgery for CR and assess the influence of OSA on postoperative complications and symptom relief.DesignRetrospective cohort study.SettingSingle tertiary medical center in Taiwan.ParticipantsA total of 325 CR patients underwent nasal surgery between March 2023 and June 2024. Based on sleep study results, patients were stratified into OSA (n&#x2009;=&#x2009;48) and non-OSA (n&#x2009;=&#x2009;277) groups.Exposure or InterventionMinimally invasive nasal surgery, including radiofrequency inferior turbinate reduction and/or posterior nasal nerve neurolysis. Patients receiving adjunctive procedures (eg, septoplasty, uvulopalatopharyngoplasty, and tonsillectomy) were excluded.Main Outcome MeasuresPostoperative complications (eg, epistaxis) within 1&#x2009;month and symptom relief based on reflective total nasal symptom score and nasal obstruction symptom evaluation.ResultsOSA [odds ratio (OR), 5.105; 95% confidence interval (CI), 1.222-21.328; P &#x2009;=&#x2009;.025] and hypertension (OR, 5.809; 95% CI, 1.134-29.744; P &#x2009;=&#x2009;.035) were independent risk factors for major epistaxis. OSA patients had higher overall complication rates (22.9% vs 4.3%, P &#x2009;=&#x2009;.005), epistaxis (18.8% vs 3.6%, P &#x2009;=&#x2009;.012), and major epistaxis (14.6% vs 1.8%, P &#x2009;=&#x2009;.018). Both groups showed significant symptom improvement postoperatively ( P &#x2009;&lt;&#x2009;.001).ConclusionMinimally invasive nasal surgery improves CR symptoms regardless of OSA. However, OSA and hypertension are linked to increased complication risk and require careful perioperative management.RelevanceThese findings support tailored preoperative assessment and multidisciplinary care to optimize safety and outcomes in CR patients with OSA.","url":"https://pubmed.ncbi.nlm.nih.gov/41217164/","authors":["Huang CY","Chang CH","Hwang YL","Hsu YS","Ko JY","Wu SY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/19160216251390319","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41215996","name":"The Role of AI in Shaping Medical Education: Insights from an Umbrella Review of Review Studies.","source":"pubmed","abstract":"Artificial intelligence (AI) has become integral to various fields, including medical education. This study explores AI applications in medical education through a review of relevant studies.","url":"https://pubmed.ncbi.nlm.nih.gov/41215996/","authors":["Jafari F","Keykha A","Taheriankalati A","Taghavi Monfared A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.30476/jamp.2025.105625.2116","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41214826","name":"Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia.","source":"pubmed","abstract":"Preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality. This study aimed to identify urinary metabolites as potential biomarkers for predicting PE by integrating metabolomic profiling with machine learning algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/41214826/","authors":["Chen Q","Qian Y","Feng M","Zhang H","Xie H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 10","doi":"10.1186/s40001-025-03337-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41214797","name":"Effects of an exoskeleton robot on motor function in patients with spinal cord injuries: a systematic review and meta-analysis.","source":"pubmed","abstract":"This meta-analysis aimed to evaluate the impact of exoskeleton robotic training on motor function in spinal cord injury (SCI) patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41214797/","authors":["Guo S","Yang Y","Wang M","Wang D","Zhang Y","Wang Q","Deng Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 10","doi":"10.1186/s13643-025-02974-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41214732","name":"Assessing adoption of human and AI-enabled diabetic retinopathy screening in primary healthcare settings: findings from a pragmatic trial.","source":"pubmed","abstract":"Diabetic retinopathy (DR) screening with defined referral pathways is essential for early detection and effective management of DR. This study assessed the adoption of three DR screening (DRS) models in primary healthcare settings, focusing on referral adherence rates and stakeholders' perceptions of the interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/41214732/","authors":["Chauhan A","Vale L","Kankaria A","Gupta V","Kaur G","N N","Sood N","Tigari B","Bhadada S","Duggal M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 10","doi":"10.1186/s13690-025-01757-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41214653","name":"Predicting the risk of preterm birth with machine learning and electronic health records in China.","source":"pubmed","abstract":"BACKGROUND: Preterm birth is a serious global public health issue, and early prediction in pregnant women is crucial for timely intervention and reduction of the incidence preterm births. We aimed to predict and validate the risk of preterm birth with machine learning, deep learning, and electronic health records in China. MATERIALS AND METHODS: Data were collected from 58,424 pregnant women between May 2015 and April 2024. After excluding incomplete records, a total of 36,378 cases were included, consisting of 34,132 full-term births and 2,246 preterm births. Of the 24 known high-risk factors for preterm birth, 20 statistically significant features were identified for model construction. Six machine learning algorithms were applied to process the data containing missing values, and 22 models were developed for predicting preterm births using the imputed data. Additionally, two dynamic deep learning methods were incorporated in our model development process. RESULTS: Among the machine learning models, the Random Forest model performed best in both datasets with missing values and imputed data, achieving a maximum AUC of 0.826. The LightGBM model also exhibited strong performance, even with fewer features. Among the deep learning models, the LSTM model performed better, with an AUC of 0.851. Additionally, data from 10,367 pregnant women, collected between May and December 2024, were used as an external validation set, confirming the model&#x2019;s stability. CONCLUSIONS: The findings of this study indicate that both machine learning and deep learning models using electronic health records are valuable for preterm birth risk screening, supporting their use in clinical practice for preterm birth risk management. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://pubmed.ncbi.nlm.nih.gov/41214653/","authors":["Qian L","Jia H","Chang Z","Hu Y","Chen C","Li X","Zhang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 10","doi":"10.1186/s12911-025-03254-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41213119","name":"Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation.","source":"pubmed","abstract":"Atrial fibrillation (AF) burden is associated with cardiovascular events such as stroke and heart failure. Recent advancements in photoplethysmography (PPG) technology have provided new insights into noninvasive and convenient AF burden detection.","url":"https://pubmed.ncbi.nlm.nih.gov/41213119/","authors":["Wang H","Liu B","Zhang H","Zhang Z","Jin Z","Wang H","Guo YT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 10","doi":"10.2196/78075","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41211715","name":"Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.","source":"pubmed","abstract":"EGFR mutation screening in non-small cell lung cancer (NSCLC) remains variable globally and represents a significant care gap despite international recommendations and molecular testing guidelines. Recently, the use of deep learning (DL) methods to extract clinically actionable features from routine histology images has gained regulatory approval for multiple clinical applications. Therefore, the integration of predictive DL to complement molecular EGFR mutation screening may improve biomarker testing rates in NSCLC. To address this unmet need, we developed and validated Lunit SCOPE Genotype Predictor, a DL model trained and tuned using more than 12,000 whole-slide images, that predicts EGFR mutation status from routine hematoxylin and eosin images. Using a diverse dataset (n = 1,461) that captures histologic subtypes, multiple whole-slide scanners, different scan magnifications, and specimen types, we report an overall area under the ROC curve (AUROC) of 0.905. The model demonstrates robust performance across specimen types (biopsies and surgical resections, 0.804 and 0.912, respectively), histologic subtypes (adenocarcinoma and non-adenocarcinoma, 0.880 and 0.801, respectively), and EGFR mutation subtypes (AUROC, 0.854-0.931). Additionally, across a second independent test set (n = 599) sourced from 11 countries utilizing five different slide scanners, Lunit SCOPE Genotype Predictor achieved a robust AUROC of 0.860. Furthermore, across a multi-scanner test set (n = 2,261), EGFR mutation predictions were concordant in 90.4% of cases among five of six frequently used slide scanners. This validation across diverse clinical settings represents a vital step toward the application of artificial intelligence-based digital pathology tools in routine clinical practice to augment molecular EGFR mutation screening.","url":"https://pubmed.ncbi.nlm.nih.gov/41211715/","authors":["Park J","Shin S","Hwang W","Keum S","Brattoli B","Rawson JH","Lee T","Pereira S","Ahn CH","Senior MJT","Qaiser T","Kim S","Kim H","Chung JH","Choi YL","Lee SH","Bannister H","Riboni-Verri E","Ock CY","Hill RJ","Ali S","Moore L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1158/2767-9764.CRC-25-0155","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41211531","name":"The BMIgap tool to quantify transdiagnostic brain signatures of current and future weight.","source":"pubmed","abstract":"Understanding the neurobiological underpinnings of weight gain could reduce excess mortality and improve long-term trajectories of psychiatric disorders. Using brain scans from healthy individuals ( n &#x2009;=&#x2009;1,504), we trained a model to predict body mass index (BMI) and applied it to individuals with schizophrenia ( n &#x2009;=&#x2009;146), clinical high-risk states for psychosis ( n &#x2009;=&#x2009;213) and recent-onset depression (ROD, n &#x2009;=&#x2009;200). We computed BMIgap (BMI predicted &#x2009;-&#x2009;BMI measured ), interrogated its brain-level overlaps with schizophrenia and explored whether BMIgap predicted weight gain at the 1-year and 2-year follow-ups. Schizophrenia (BMIgap&#x2009;=&#x2009;1.05&#x2009;kg&#x2009;m - 2 ) and clinical high-risk individuals (BMIgap&#x2009;=&#x2009;0.51&#x2009;kg&#x2009;m - 2 ) showed increased BMIgap and individuals with ROD (BMIgap&#x2009;=&#x2009;-0.82&#x2009;kg&#x2009;m - 2 ) showed decreased BMIgap. Shared brain patterns of BMI and schizophrenia were linked to illness duration, disease onset and hospitalization frequency. Higher BMIgap predicted future weight gain, particularly in younger individuals with ROD, and at 2-year follow-up. Here we show that BMIgap can serve as a potential brain-derived measure to stratify at-risk individuals and deliver tailored interventions for better metabolic risk control.","url":"https://pubmed.ncbi.nlm.nih.gov/41211531/","authors":["Khuntia A","Popovic D","Sarisik E","Buciuman MO","Pedersen ML","Westlye LT","Andreassen OA","Meyer-Lindenberg A","Kambeitz J","Salokangas RKR","Hietala J","Bertolino A","Borgwardt S","Brambilla P","Upthegrove R","Wood SJ","Lencer R","Meisenzahl E","Falkai P","Schwarz E","Wiegand A","Koutsouleris N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1038/s44220-025-00522-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41211528","name":"Artificial Intelligence Techniques and Health Literacy: A Systematic Review.","source":"pubmed","abstract":"To systematically review the utilization of artificial intelligence (AI) in health literacy, highlighting limitations and future developments.","url":"https://pubmed.ncbi.nlm.nih.gov/41211528/","authors":["Abeo ANA","Armstrong S","Scriney M","Goss H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.mcpdig.2025.100269","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41211109","name":"Enhancing home rehabilitation through AI-driven virtual assistants: a narrative review.","source":"pubmed","abstract":"Artificial intelligence (AI)-driven virtual physiotherapy assistants (VPAs) are increasingly adopted in home-based rehabilitation, offering real-time feedback and personalised guidance through wearable sensors. These systems enhance treatment adherence, minimise clinic visits, and improve rehabilitation outcomes. However, challenges such as sensor accuracy, patient engagement, and affordability hinder widespread implementation. This review explores current applications, benefits, and limitations of AI-driven VPAs.","url":"https://pubmed.ncbi.nlm.nih.gov/41211109/","authors":["Olawade DB","Adeleye KK","Egbon E","Nwabuoku US","Clement David-Olawade A","Boussios S","Vanderbloemen L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 31","doi":"10.21037/atm-25-61","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41211108","name":"Smart emergency care: a narrative review of predictive machine learning models.","source":"pubmed","abstract":"The Emergency Department (ED) is a critical, high-stakes environment where timely and accurate assessments of patient outcomes are essential for ensuring optimal care and effective resource management. This narrative review aimed to synthesise current evidence on machine learning (ML)-based predictive models used in the ED to forecast patient outcomes such as mortality, intensive care unit (ICU) admission, and discharge probability, whilst identifying key limitations and future research directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41211108/","authors":["Olawade DB","Da'Costa A","Origbo JE","Osonuga A","Egbon E","Teke J","Boussios S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 31","doi":"10.21037/atm-25-83","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41210541","name":"Comparative Evaluation of Artificial Intelligence Chatbots in Delivering Palliative Care Education to Intensive Care Unit Caregivers- A Cross-platform Analysis: A Brief Communication.","source":"pubmed","abstract":"Caregivers of intensive care unit (ICU) patients with advanced chronic illness face significant psychological stress and information gaps regarding palliative care. Generative artificial intelligence (AI) tools like ChatGPT and Google Gemini may offer scalable, personalized educational support. This study aimed to compare the quality of academic content generated by these AI chatbots in terms of readability, sentiment, understandability, actionability, and expert-rated accuracy and completeness.","url":"https://pubmed.ncbi.nlm.nih.gov/41210541/","authors":["Singh R","Gondode PG","Duggal S","Nayak SS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.5005/jp-journals-10071-25058","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41209270","name":"Artificial intelligence aids doctors in diagnosing necrotizing enterocolitis and predicting surgery using abdominal radiographs: a multicenter study.","source":"pubmed","abstract":"Neonatal necrotizing enterocolitis (NEC) is challenging to diagnose due to its subtle radiological features on abdominal radiographs (ARs) and considerable variability in interpretation among clinicians, especially those with limited experience, which may delay timely intervention. The study aimed to develop a convolutional neural network (CNN) based artificial intelligence (AI) model using ARs to predict NEC and the need for surgical intervention, and to evaluate its ability to assist clinicians in AR interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/41209270/","authors":["Li YT","Wu K","Mou YL","Zhong C","Zhang G","Tan RY","Zhang X","Wang JJ","Chen QM","Yu DY","Lu Y","Ding XT","Yang LC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 1","doi":"10.21037/qims-2024-2867","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41209219","name":"Deep learning in multi-modal breast cancer data fusion: a literature review.","source":"pubmed","abstract":"Recently, there has been a growing interest in the use of deep learning methods within the multi-modal domain of breast cancer research. Integrating multi-modal data for breast cancer prediction can generate richer and more diverse set of information, leading to a greater robustness in prediction outcomes as compared to single-modal approaches. This review comprehensively summarizes the advancements in multi-modal breast cancer research over the past 5 years and critically assesses the related opportunities and challenges, serving as a valuable reference for future studies. The application of deep learning techniques to the processing of multi-modal breast cancer data is discussed in depth, and the latest strategies and potential future directions in this area are examined.","url":"https://pubmed.ncbi.nlm.nih.gov/41209219/","authors":["Li T","Song S","Pan Y","Song W","Fong S","Gao J","Wang Q","Zhang X","Mohammed S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 1","doi":"10.21037/qims-2024-2903","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41206586","name":"Machine Learning Models for the Assessment of the Mayo Endoscopic Score in Ulcerative Colitis Trial Endpoints: A Systematic Review.","source":"pubmed","abstract":"The Mayo endoscopic score (MES) provides a criterion-based, but still subjective, human assessment of endoscopy and related endpoints in therapeutic clinical trials in ulcerative colitis (UC). A novel solution to address issues of reproducibility is the use of machine learning (ML) models to standardize MES evaluations. Broader applicability of this solution requires an understanding of the models and related performance characteristics. The objective of this study is to provide a systematic review on training and testing of ML MES prediction models on full-length endoscopic videos from patients with UC.","url":"https://pubmed.ncbi.nlm.nih.gov/41206586/","authors":["Rubin DT","Reinisch W","Narula N","Colucci DR","Eastman W","Gottlieb K","Lacerda AP","Laroux FS","Modesto I","Navajas EE","Owen CC","Wang Y","Baxi S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 1","doi":"10.1093/ibd/izaf232","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41206269","name":"Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging.","source":"pubmed","abstract":"To assess the impact of a deep learning-based noise reduction (DLD) technique on image quality and diagnostic accuracy for the evaluation of coronary arteries in transcatheter aortic valve implantation (TAVI) CT imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/41206269/","authors":["Lanzafame LRM","Steinmetz S","D'Angelo T","Mazziotti S","Yel I","Koch V","Gruenewald LD","Martin SS","Scholtz JE","Eichler K","Alizadeh LS","Vogl TJ","Brockmann MA","Ahn C","Kim JH","Othman AE","Booz C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.acra.2025.10.030","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41204847","name":"Individual differences in effects of stressful life events on childhood ADHD: genetic, neural, and familial contributions.","source":"pubmed","abstract":"This study elucidates the intricate relationship between stressful life events and the development of ADHD symptoms in children, acknowledging the considerable variability in individual responses. By examining these differences, we aim to uncover the unique combinations of factors contributing to varying levels of vulnerability and resilience among children.","url":"https://pubmed.ncbi.nlm.nih.gov/41204847/","authors":["Choi SY","Lee J","Park J","Lee E","Kim BG","Kim G","Joo YY","Cha J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1111/jcpp.70074","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41204540","name":"The diagnostic accuracy of deep learning-based AI models in predicting lymph node metastasis in T1 and T2 colorectal cancer: A systematic review and meta-analysis.","source":"pubmed","abstract":"Colorectal cancer (CRC) continues to be a leading cause of cancer-related mortality globally, and accurately predicting lymph node metastasis (LNM) in T1 and T2 lesions is vital for informing treatment strategies. This study aimed to assess the diagnostic accuracy of artificial intelligence (AI)-based models, particularly deep learning (DL) and machine learning (ML) approaches, in predicting LNM risk in CRC.","url":"https://pubmed.ncbi.nlm.nih.gov/41204540/","authors":["Guo Q","Wang R","Guo Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.1097/MD.0000000000045172","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41204188","name":"Evidence gap in predicting intracranial haemorrhage risk in people with glioma on anticoagulants: a scoping review.","source":"pubmed","abstract":"People with glioma (PwG), a type of brain tumour, have an elevated risk of developing venous thromboembolism (VTE). When VTE occurs, anticoagulant therapy is typically initiated, and in some cases, it may be prescribed prophylactically. However, these patients are also at risk of intracranial haemorrhage (ICH) as a complication of anticoagulation. Despite the clinical importance of this risk-benefit balance, it remains unclear whether predictive tools exist to guide anticoagulation decisions in this population.","url":"https://pubmed.ncbi.nlm.nih.gov/41204188/","authors":["Adeyemo TA","Greenley S","Ware F","Haque F","Maraveyas A","Jones WS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.1186/s12883-025-04461-5","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41204132","name":"Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features.","source":"pubmed","abstract":"Acute promyelocytic leukemia (APL), a high-risk subtype of acute myeloid leukemia, necessitates rapid diagnosis upon hospital admission to mitigate early mortality. Current diagnosing approaches relying on time-consuming genetic testing or morphological expertise are particularly challenging in resource-limited settings. Herein, this study introduces a novel machine learning approach leveraging routine lab data to enable immediate APL suspicion, offering a new diagnostic possibility for under-resourced hospitals.","url":"https://pubmed.ncbi.nlm.nih.gov/41204132/","authors":["Cai Q","Ye B","Zheng W","Zhang S","Zhang J","Shen Y","Yao D","Zhang H","Huang Z","Hu J","Ma Y","Wang J","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.1186/s12885-025-15034-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41204023","name":"External validation of SpineNetv2 deep learning system for automated lumbar spine MRI analysis: A multi-pathology diagnostic agreement study.","source":"pubmed","abstract":"Magnetic resonance imaging (MRI) is the reference standard for evaluating degenerative lumbar spine disorders, but interpretation is time-consuming and subject to inter-observer variability. SpineNetv2, a publicly available deep learning system, enables automated analysis of multiple spinal pathologies. This study conducted an independent external validation of SpineNetv2 against expert reference assessments.","url":"https://pubmed.ncbi.nlm.nih.gov/41204023/","authors":["Wu X","Song Q","Zhou J","Zhou Z","Cao G","Jin K","Du Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s00586-025-09543-z","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41203701","name":"SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.","source":"pubmed","abstract":"Machine learning has been increasingly applied to microbiome data for biomarker discovery. However, microbiome datasets are typically high-dimensional, sparse, and correlated, which makes model training challenging and prone to overfitting. Previous studies have also reported that microbiome features exhibit binary-like characteristics, and that binarization does not necessarily reduce predictive performance. This observation motivated our work. Building on this idea, we propose a SHAP-based binarization pipeline. We first trained several machine learning models on raw continuous data and selected the best-performing model (random forest). Using SHAP values derived from the training set, we determined feature-specific thresholds that best separated positive and negative contributions. The dataset was then binarized using these thresholds and new models were trained on the transformed data. We evaluated this approach on gut microbiome abundance data (283 species, 220 genera, 1,569 individuals) to classify inflammatory bowel disease (IBD) versus healthy controls. The SHAP-based binarization consistently improved classification performance and interpretability compared with both continuous data and zero-threshold binarization. The best model's Matthews correlation coefficient increased from 0.884 to 0.928, with the largest improvements observed in non-tree-based models such as logistic regression and neural networks. SHAP summary plots also revealed clearer feature patterns, and biomarker rankings were more stable. In addition, the pipeline enabled us to identify a concise set of 17 microbial biomarkers associated with IBD. This study introduces a novel approach for microbiome data analysis by explicitly linking binarization thresholds to SHAP-derived feature contributions. Our approach was grounded in the observation of binary-like patterns revealed through SHAP values. Furthermore, although binarization inevitably raises concerns about information loss, our evaluation confirmed improvements not only in predictive performance but also in interpretability and biomarker stability, providing a broader validation of robustness. These findings highlight SHAP-based binarization as an effective strategy for high-dimensional microbiome data, with broad applicability and opportunities for future extension.","url":"https://pubmed.ncbi.nlm.nih.gov/41203701/","authors":["Lee Y","Seo J","Di Camillo B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.1038/s41598-025-24802-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41203087","name":"Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.","source":"pubmed","abstract":"Maintaining robust surveillance programs for abdominal aortic aneurysms (AAAs) is important, but these programs are expensive and labor-intensive, typically requiring manual data review by trained health care professionals. Studies have shown that natural language processing software can assist in these functions, but each task-specific algorithm requires human-directed training before use. Our objective was to evaluate the use of a large language model (LLM) to extract AAA-related data using generalized artificial intelligence, negating the need for task-based training.","url":"https://pubmed.ncbi.nlm.nih.gov/41203087/","authors":["Flanagan CP","Gerstley LD","Okuhn S","McLenon M","Lancaster EM","Hull MM","Bulbule MS","Sivamurthy N","Chang RW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.jvs.2025.10.044","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41202990","name":"Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling.","source":"pubmed","abstract":"Immune dysregulation, including autoimmunity, autoinflammation, allergy, and malignancy predisposition, adds significant disease burden in primary immune disorders (PID) and inborn errors of immunity (IEIs).","url":"https://pubmed.ncbi.nlm.nih.gov/41202990/","authors":["Schwitzkowski M","Veeranki SPK","Seidel BN","Kindle G","Rusch S","European Society for Immunodeficiencies Registry Working Party","Kramer D","Seidel MG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.jaci.2025.10.022","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41202826","name":"Global, regional, and national burden of Chagas disease, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","source":"pubmed","abstract":"Chagas disease is a neglected tropical disease caused by the protozoan Trypanosoma cruzi, primarily transmitted by infected bugs, but also through contaminated food, transfusions, congenital transmission, and organ transplantation. Chagas disease has acute and chronic phases; the chronic phase can occur decades after infection, leading to complications such as heart failure, arrhythmias, and megaviscera. Accurate mortality and morbidity estimates are hindered by under-reporting and misclassification. Comprehensive and updated estimates are needed to improve global assessments of Chagas disease burden. We aim to provide a comprehensive description&#x2008;of global and regional burden of Chagas disease and its trends from 1990 to 2023.","url":"https://pubmed.ncbi.nlm.nih.gov/41202826/","authors":["GBD 2023 Chagas Disease and RAISE Study Collaborators"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/S1473-3099(25)00562-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41202582","name":"Guidelines and Checklists for Artificial Intelligence (AI) in Surgical Education Research: A Comprehensive Narrative Review.","source":"pubmed","abstract":"Artificial intelligence (AI) has increasingly been used in clinical and healthcare research, prompting the development of new research guidelines to ensure its appropriate use. Similarly, AI has been applied to surgical education research, creating a need to assess the relevance and applicability of existing guidelines in this context. This narrative review synthesized guidelines and checklists related to AI in surgical education, providing researchers with an introductory roadmap for conducting rigorous, reproducible studies and identifying gaps for future framework development.","url":"https://pubmed.ncbi.nlm.nih.gov/41202582/","authors":["Silvestri C","Hoagland DL","Woodward JM","Naaseh A","McDermott CE","Carter BM","Lund S","Prokop LJ","Bernard A","Navarro SM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.jsurg.2025.103758","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41202536","name":"The Application of Magnetic Resonance Imaging in Dentistry: A Bibliometric Analysis.","source":"pubmed","abstract":"The aim of this analysis was to investigate the historical development, current status, and research hotspots related to the application of magnetic resonance imaging (MRI) in dentistry from 2000 to 2024.","url":"https://pubmed.ncbi.nlm.nih.gov/41202536/","authors":["Huang C","Chen G","Lu B","Li C","Sun X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.identj.2025.104010","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41202278","name":"Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study.","source":"pubmed","abstract":"Successful recovery from alcohol use disorder requires long-term lapse risk monitoring. Self-monitoring is difficult, given the dynamic, complex interplay of the many risk factors over time. An automated recovery monitoring support system embedded with a machine learning lapse prediction model could improve sustained, adaptive, and personalized self-monitoring by delivering daily support messages.","url":"https://pubmed.ncbi.nlm.nih.gov/41202278/","authors":["Wyant K","Sant'Ana SJ","Punturieri CE","Yu J","Fronk GE","Maggard CM","Janssen C","Wanta SE","Kornfield R","van Swol LM","Curtin JJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.2196/81697","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41201652","name":"An extensive analysis of machine learning techniques for identifying glaucoma.","source":"pubmed","abstract":"Glaucoma is a progressive optic neuropathy, and it's one of the leading causes of permanent blindness in the world. Machine learning (ML) algorithms have recently emerged as effective tools in diagnosing glaucoma, and early detection is crucial in the prevention of vision loss. In this review, the recent developments in ML algorithms in the diagnosis of glaucoma, their performance, drawbacks, and clinical applications are analyzed. There have been promising results when combining imaging modes including fundus images and optical coherence tomography (OCT) with machine learning models. However, problems persist in poor multi-modal data integration, limited generalizability, and a lack of explainability. The importance of multi-modal techniques, interpretable models, and robust datasets to enhance the diagnostic accuracy and reliability of ML algorithms is stressed by this study. There are many recommendations for further study to achieve clinical acceptability, which include building standardized frameworks and controlling diversity in data. Therefore, it highlights the current and future potential of ML regarding glaucoma. A total of 30 papers were reviewed from 2019 to 2024, with an increase in research activity. From 2019 to 2021, there were 3 papers reviewed per year, and it increased to 5 in 2022, peaked at 9 in 2023, and then decreased to 7 in 2024, reflecting growing yet fluctuating interest in this topic. The pattern depicts growing interest in the field and body of literature.","url":"https://pubmed.ncbi.nlm.nih.gov/41201652/","authors":["Vinod Kumar R","Sharmila Banu N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 7","doi":"10.1007/s10792-025-03771-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41200664","name":"Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.","source":"pubmed","abstract":"Leukemia, a heterogeneous group of blood cancers, poses significant challenges to global health due to its complexity, diverse risk factors, and variable outcomes. Accurate and early diagnosis is critical but remains a significant hurdle, particularly in low-resource settings. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to improve leukemia detection and classification, addressing limitations in traditional diagnostic methods.","url":"https://pubmed.ncbi.nlm.nih.gov/41200664/","authors":["Achir A","Debbarh I","Zoubir N","Battas I","Medromi H","Moutaouakkil F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.12688/f1000research.159318.2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41200600","name":"Poor Performance of Large Language Models Based on the Diabetes and Endocrinology Specialty Certificate Examination of the United Kingdom.","source":"pubmed","abstract":"Introduction The medical knowledge of large language models (LLMs) has been tested using several postgraduate medical examinations. However, it is rarely examined in diabetes and endocrinology. This study aimed to evaluate the performance of LLMs in answering multiple-choice questions using the Diabetes and Endocrinology Speciality Certificate Examination (SCE) of the United Kingdom. Methods The official diabetes and endocrinology SCE sample questions were used to assess the seven freely accessible and subscription-based commercial LLMs: ChatGPT-o1 Preview (OpenAI, USA), ChatGPT-4o (OpenAI, USA), Gemini (Google, USA), Claude-3.5 Sonnet (Anthropic, USA), Copilot (Microsoft, USA), Perplexity AI (Perplexity, USA), and Meta AI (Meta, USA). The accuracy of LLMs was calculated by comparing outputs against sample answers. Literacy metrics, including Flesch Reading Ease (FRES) and Flesch Kincaid Grade Level (FKGL), were calculated for each response. 83 questions, three of which included photographs, were entered into the LLMs without employing any prompt engineering techniques. Results A total of 581 responses were generated and captured between August and October 2024. Performance differed significantly between models, with ChatGPT-o1 Preview achieving the highest accuracy (73%). None of the other LLMs achieved the historical pass mark of 65%, with Gemini achieving the lowest accuracy of 33%. Readability metrics also differed significantly between LLMs (p=0.004). LLMs performed better for questions without reference ranges (p&lt;0.001). Conclusions The performance of LLMs was generally inadequate in the diabetes and endocrinology examination. Of those tested, ChatGPT-o1 Preview achieved the highest score and is likely the most useful model to aid medical education. This may be due to it being an advanced reasoning model with a greater ability to solve complex problems. Nonetheless, continued research is needed to keep pace with the advances in LLMs.","url":"https://pubmed.ncbi.nlm.nih.gov/41200600/","authors":["Fan KS","Gan J","Zou IX","Kaladjiska M","Inguanez MB","Garden GL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.7759/cureus.93960","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41199640","name":"Metal-Organic Frameworks-Cold Plasma Technology for Environmental Sustainability: Challenges and Future Perspectives.","source":"pubmed","abstract":"Metal-organic frameworks (MOFs) are crystalline materials with exceptionally high surface areas (up to 7000&#x2009;m 2 /g), tunable pore structures, and versatile chemical functionalities, making them attractive for diverse environmental and industrial applications. Simultaneously, cold plasma, an ionized, low-temperature gas enriched with reactive species, has gained recognition for its environmentally friendly, rapid, and solvent-free processing capabilities, particularly in material synthesis and surface functionalization. Integrating cold plasma with MOFs presents a synergistic approach that enhances material properties and process efficiency. Recent studies have reported up to a 40%-60% increase in surface reactivity, improved catalyst dispersion by 30%, and reduced particle size to below 100&#x2009;nm through plasma-assisted synthesis. These hybrid systems have demonstrated enhanced performance in areas such as air and water purification (achieving over 90% pollutant removal), carbon capture (exceeding 4&#x2009;mmol/g CO 2 uptake), energy conversion, and waste-to-resource technologies. Despite their promise, key challenges remain, including scalability, long-term structural integrity, and economic viability. This review also discusses recent advances in MOF design, innovations in plasma engineering, and the potential integration of artificial intelligence to optimize synthesis and functionality. Future perspectives emphasize the importance of green chemistry principles and interdisciplinary collaboration for the development and commercialization of MOF-plasma technologies aimed at sustainable environmental solutions.","url":"https://pubmed.ncbi.nlm.nih.gov/41199640/","authors":["Manikandan V","Elango D","Subash V","Saranya Packialakshmi J","Jayanthi P","Singh S","Soup Song K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 10","doi":"10.1002/tcr.202500190","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41199224","name":"Risk stratification and prediction of emergency delivery in early-onset preeclampsia using machine learning.","source":"pubmed","abstract":"BACKGROUND: Early-onset preeclampsia (PE) poses significant risks for maternal and fetal outcomes, particularly when emergency delivery is required. This study aimed to develop machine learning models to predict emergency delivery within 48&#xa0;h of PE diagnosis, facilitating timely clinical interventions. METHODS: We analyzed a retrospective cohort of 648 singleton pregnancies diagnosed with PE at Fujian Maternal and Child Health Hospital from 2014 to 2024, with gestational ages ranging from 28 to 34 weeks. Patients were stratified into emergency delivery (&#x2264;&#x2009;48&#xa0;h post-diagnosis, n&#x2009;=&#x2009;174) and non-emergency groups (n&#x2009;=&#x2009;474). Feature selection was performed via univariate analysis, collinearity testing, and logistic regression, yielding 16 predictors. Eight machine learning models (logistic regression, na&#xef;ve Bayes, XGBoost, LightGBM, SVM, GBDT, MLP, elastic net) were trained and evaluated for discriminative power and calibration. SHAP analysis was employed to interpret model predictions. RESULTS: XGBoost demonstrated superior performance (testing AUROC: 0.908) with excellent calibration, while GBDT achieved high AUROC (0.931) but poorer calibration. SHAP analysis identified CRP, D-dimer, and hypoproteinemia as the most influential predictors. CONCLUSIONS: Machine learning models effectively predict emergency delivery in early-onset PE using clinically interpretable features. Integration into obstetric practice may enhance risk stratification, though prospective validation is warranted.","url":"https://pubmed.ncbi.nlm.nih.gov/41199224/","authors":["Xu Y","Liu X","Zhang Y","Qi X","Jin C","Liang Z","Xu X","Yan J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 6","doi":"10.1186/s12911-025-03249-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41198945","name":"Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach.","source":"pubmed","abstract":"Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence (XAI) methods. Validation used two datasets: Guangdong Provincial People's Hospital, China (Dataset G, 1172 patients, 71.4&#x2009;&#xb1;&#x2009;6.3 years, 66% women, 20.5% with arrhythmia) and Liverpool Heart and Chest Hospital, UK (L, 909 patients, 60.5&#x2009;&#xb1;&#x2009;10.71 years, 33% women, 29.7% with arrhythmia). Our ensembles outperformed individual and voting models with the area under the receiver operating characteristic curve (ROC-AUC): 0.980 (95%CI: 0.956-0.998, p&#x2009;=&#x2009;0.03) for Dataset G, 0.799 (95%CI: 0.737-0.856, p&#x2009;=&#x2009;0.07) for Dataset L. The models trained on combined training sets achieved ROC-AUC: 0.980 (95%CI: 0.952-1.0) and 0.800 (95% CI: 0.739-0.861) for the G and L test sets. Precision-recall AUC for AF recurrence was 0.765 (95%CI: 0.669-0.849) for ensembles vs. 0.737 (95%CI: 0.648-0.821) for individual models. XAI enhanced interpretability for clinical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41198945/","authors":["Czerwinski A","Kucharski D","Wijata AM","Aldosari H","Coenen F","Gupta D","Fu L","Lin W","Xue Y","Kawa J","Zheng Y","Lip GYH","Nalepa J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 6","doi":"10.1038/s41746-025-01932-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41198493","name":"The 2025 update on artificial intelligence models in pediatric urology: Results from the AI-PEDURO collaborative.","source":"pubmed","abstract":"The use of artificial intelligence (AI) applications is expanding rapidly. Here, we report an annual update of the AI-PEDURO (Artifical Intelligence in PEDiatric UROlogy) online repository (www.aipeduro.com), which reviews new AI models in pediatric urology, highlights emerging trends, and updates the living scoping review.","url":"https://pubmed.ncbi.nlm.nih.gov/41198493/","authors":["Khondker A","Ahmad I","Dhalla R","Kaushal S","Kwong JCC","Rickard M","Erdman L","Gabrielson AT","Nguyen DD","Kim JK","Chun B","Abbas T","Fernandez N","Fischer K","'t Hoen LA","Keefe DT","Nelson CP","Wang HS","Weaver J","Lorenzo AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.jpurol.2025.10.013","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41198426","name":"Predicting Pathologic Response in Locally Advanced Rectal Cancer Using Inflammatory, Nutritional, and Sarcopenia-Based Markers: A Regression and AI-Based Analysis (CINR-AI Study).","source":"pubmed","abstract":"Total neoadjuvant therapy (TNT) is the standard approach for locally advanced rectal cancer (LARC), yet pathological complete response (pCR) is achieved in only a subset. Systemic inflammation, nutritional status, and sarcopenia influence outcomes, yet integrated predictive models are lacking. We aimed to develop clinical, laboratory, and AI-based models to predict pathological response.","url":"https://pubmed.ncbi.nlm.nih.gov/41198426/","authors":["Uyar GC","Başaran BN","Başkurt K","Yeşilbaş E","Özkan E","Yücel KB","Altınbaş M","Evrimler Ş","Öksüzoğlu ÖBÇ","Sütcüoğlu O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.clcc.2025.10.002","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41198140","name":"Efficacy and risks of artificial intelligence chatbots for anxiety and depression: a narrative review of recent clinical studies.","source":"pubmed","abstract":"The rapidly growing environment of artificial intelligence (AI) has accelerated interest in its potential use for improving the efficiency and efficacy of the healthcare industry. Specifically, there has been a growing interest in AI role mental healthcare for common disorders like anxiety and depression. However, it remains unclear whether current evidence is sufficient to determine efficacy and safety of AI chatbots in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41198140/","authors":["Bodner R","Lim K","Schneider R","Torous J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 1","doi":"10.1097/YCO.0000000000001048","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41197595","name":"Using artificial intelligence models to generate dietary recommendations for chronic kidney disease patients: A comparative cross-sectional study.","source":"pubmed","abstract":"Artificial intelligence (AI) models are increasingly being used to assist in chronic kidney disease (CKD) dietary guidance, but concerns about their accuracy remain. This study aimed to assess the performance of three AI models (ChatGPT, DeepSeek, Gemini) in accordance with the Kidney Disease: Improving Global Outcomes (KDIGO) 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.","url":"https://pubmed.ncbi.nlm.nih.gov/41197595/","authors":["Yang B","Wei W","Liu C","Huang Y","Ren J","Yuan Y","Fu P","Zhao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.clnu.2025.10.014","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41197550","name":"Surgeons' awareness, expectations, and involvement with artificial intelligence: a survey pre and post the GPT era.","source":"pubmed","abstract":"Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expectations, and involvement with AI in surgery through comparative surveys conducted in 2021 and 2024.","url":"https://pubmed.ncbi.nlm.nih.gov/41197550/","authors":["Arboit L","Schneider DN","Collins T","Hashimoto DA","Perretta S","Dallemagne B","Marescaux J","EAES Working Group","Padoy N","Mascagni P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.ejso.2025.110525","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41196595","name":"Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication.","source":"pubmed","abstract":"Many treatments exist for depression, yet none are universally effective. Multivariable predictive models support personalized treatment selection.","url":"https://pubmed.ncbi.nlm.nih.gov/41196595/","authors":["Lee CT","Richards D","Heinzle J","Hanlon AK","Lynch K","Harty S","Claus N","O'Keane V","Stephan KE","Whelan R","Gillan CM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 3","doi":"10.1001/jamanetworkopen.2025.41639","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41195697","name":"International Expert Consensus on Defining Skin of Color and Delivering Equitable Dermatologic Care.","source":"pubmed","abstract":"Dermatological practice faces significant challenges in meeting the needs of populations with skin of color (SOC). Patients with SOC face disparities, including misdiagnoses and inequitable treatment outcomes. This expert consensus sought to identify gaps in dermatologic care for these populations and to propose strategies to promote inclusivity. Classification systems for SOC and healthcare disparities were investigated by reviewing English language articles on the subject published in PubMed between 2019 and 2024. An international panel of multidisciplinary experts from four continents (America, Asia, Africa, and Europe) analyzed the findings and developed recommendations. There are limitations in the current skin type classification systems, and gaps persist for SOC populations in research, clinical trials, and education for both providers and patients. Proposed strategies to bridge these gaps include refining classification systems (Dermatology societies), advancing SOC-specific research, enhancing education, and integrating artificial intelligence. Key recommendations from the panel focused on four areas: (1) Research: Achieve SOC numbers in clinical trials and publications that would be a reflection of local and global populations, publish new guidelines on key SOC-related issues, and achieve representative authorship in the said clinical trials and publications; (2) Resources: Create a global library of SOC images for dermatological diseases; (3) Education: Provide training for healthcare professionals and scholarships for students worldwide to improve awareness and expertise; (4) Representation: Ensure SOC representation in images used for patient communication and educational materials.","url":"https://pubmed.ncbi.nlm.nih.gov/41195697/","authors":["Lim HW","Zhang C","Taylor M","Dlova NC","Conceição K","Jablonski N","Gupta N","Wangari-Olivero J","Alexis A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1111/ijd.70100","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41194075","name":"Preoperative plasma ceramide profiling coupled with machine learning accurately predicts recurrence of hepatocellular carcinoma after resection.","source":"pubmed","abstract":"Accurate stratification of recurrence risk after curative resection remains a critical challenge in the management of hepatocellular carcinoma (HCC). Dysregulated ceramide (CER) metabolism has been implicated in HCC progression and relapse. This paper evaluates whether preoperative plasma CER profiling coupled with machine learning (ML) enhances the risk prediction of HCC recurrence.","url":"https://pubmed.ncbi.nlm.nih.gov/41194075/","authors":["Lei Y","Xie C","Mo X","Zhuang B","Li Q","Liu C","Liao L","Wang B","Zeng M","Tang S","Liu H","Xiao Y","Li S","Cai D","Li C","Zhou J","Li J","Li Y","Wang K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 6","doi":"10.1186/s12944-025-02749-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41194017","name":"COMP promotes the progression of colorectal cancer by regulating epithelial mesenchymal transition.","source":"pubmed","abstract":"BACKGROUND: Epithelial-mesenchymal transition (EMT) plays a crucial role in the progression and metastasis of colorectal cancer (CRC). This study investigates the molecular mechanisms of EMT and its prognostic biomarkers in CRC. METHODS: Multi-omics bioinformatics analyses were conducted using CRC transcriptomic datasets from GEO and TCGA. EMT-related differentially expressed genes (EMT-DEGs) were identified and subjected to pathway enrichment and machine learning-based prognostic modeling. COMP was selected as a hub gene for further validation. Single-cell RNA sequencing (scRNA-seq) data were also analyzed to determine the cell-type-specific expression pattern of COMP and EMT-DEGs. Clinical CRC tissue samples were analyzed via RT-qPCR, Western blot, and histology. Functional assays in HT-29 cells assessed the effects of COMP knockdown on EMT markers, proliferation, apoptosis, invasion, and migration. RESULTS: EMT was significantly enriched in CRC, with 36 EMT-DEGs identified. These DEGs were enriched in pathways such as ECM-receptor interaction, focal adhesion, and the PI3K-Akt signaling pathway. Among the constructed machine learning models, the random survival forest (RSF) model demonstrated the strongest ability to predict CRC prognosis. This model stratified CRC patients into high-risk and low-risk groups, with poorer prognosis observed in the high-risk group. Cox regression forest plots and Kaplan-Meier survival analysis identified COMP as a top EMT-related prognostic gene enriched in pathways including ECM-receptor interaction and PI3K-Akt signaling. High COMP expression correlated with poor patient prognosis and EMT marker dysregulation in metastatic CRC tissues. In vitro, COMP knockdown significantly reduced mesenchymal markers, restored E-cadherin, and inhibited proliferation and invasion of CRC cells. CONCLUSION: EMT plays a vital role in CRC progression and metastasis, with COMP identified as a key prognostic biomarker and potential therapeutic target. This study provides new insights into the molecular mechanisms and intervention strategies for CRC metastasis.","url":"https://pubmed.ncbi.nlm.nih.gov/41194017/","authors":["Huang H","Wang L","Gao S","Wang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1186/s12885-025-15000-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41193997","name":"Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.","source":"pubmed","abstract":"BACKGROUND: Tuberculosis (TB) is a major global health threat, causing 10.6 million new cases and 1.3 million deaths in 2022. Early diagnosis is crucial, but current methods are often costly and slow for resource-limited settings. This study aimed to develop a rapid, low-cost diagnostic tool using routine blood indicators. METHODS: We retrospectively analyzed data from 728 TB patients and 2,718 healthy controls. The training set was balanced using the ROSE technique. We trained seven machine learning models, using LASSO regression and forward selection to identify optimal features. The final model was interpreted with SHAP and deployed as an interactive Shiny application. RESULTS: The Gradient Boosting Machine (GBM) model performed optimally on the test set (AUC&#x2009;=&#x2009;0.831, specificity&#x2009;=&#x2009;0.855, sensitivity&#x2009;=&#x2009;0.644). SHAP analysis identified platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and platelet distribution width (PDW) as key predictors. Lowering the classification threshold to 0.24 increased sensitivity to 83.6% (specificity 59.9%), demonstrating its screening potential. An interactive web application was developed to enhance clinical utility. CONCLUSION: This study delivers a validated GBM model using routine blood tests as a cost-effective TB screening tool. Its high specificity can reduce unnecessary follow-up tests. The model&#x2019;s core predictors are interpretable and provide biological insights into the inflammatory response in TB. The accompanying Shiny app increases accessibility, making it a promising tool for resource-limited settings. RECOMMENDATIONS: We propose a phased diagnostic strategy: use this model with a low threshold (0.24) for high-sensitivity initial screening, followed by confirmatory molecular testing for positive cases. This approach balances high detection rates with resource optimization. Future work should include prospective validation with cohorts including other respiratory diseases.","url":"https://pubmed.ncbi.nlm.nih.gov/41193997/","authors":["Chen L","Yang C","Dong Y","Ge R","Xu J","Xu R","Zhang H","Dong D","Ji F","Lu J","Chen J","Qin Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 5","doi":"10.1186/s12879-025-12029-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41192885","name":"Evaluation of Approved AI-based Brain Aneurysm Detection Software in Clinical Practice: Comparison with Radiologist Assessment and Image Re-review.","source":"pubmed","abstract":"This study evaluated the performance of artificial intelligence (AI)-based brain aneurysm detection software in clinical settings, aiming to assess its utility as a supportive tool for radiologists. Metrics included sensitivity, positive predictive value (PPV), F1 score, and false positives (FPs) per case.","url":"https://pubmed.ncbi.nlm.nih.gov/41192885/","authors":["Ito R","Asai R","Nakamichi R","Nakane T","Taoka T","Naganawa S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 26","doi":"10.2463/mrms.mp.2024-0183","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41192013","name":"Analysis of Clinicopathologic Features and Imaging Findings of TFE3-Rearranged Renal Cell Carcinoma.","source":"pubmed","abstract":"To examine the imaging features and clinicopathologic features of TFE3-rearranged renal cell carcinoma and highlight key imaging findings for clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41192013/","authors":["Liu J","Wei J","Zhang Y","Han S","Zhang Q","Hu E","Sun X","Wang G","Zhao J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.clgc.2025.102446","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41190922","name":"Three-dimensional Multifunctional Lung Imaging With Simultaneous Acquisition of Three-dimensional Perfusion-weighted and Ventilation-weighted Maps.","source":"pubmed","abstract":"To propose simultaneous acquisition of free-breathing, noncontrast-enhanced 3D perfusion-weighted (QW) and ventilation-weighted (VW) maps using 3D ultrashort echo-time (UTE) magnetic resonance imaging (MRI).","url":"https://pubmed.ncbi.nlm.nih.gov/41190922/","authors":["Kim H","Lee HY","Lee S","Park J","Park HY","Yoo H","Shin SH","Kim HC","Choe J","Park JY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1097/RLI.0000000000001246","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41189192","name":"Supervised machine learning-based bias risk of prognostic models for total knee or hip arthroplasty patients: A systematic review.","source":"pubmed","abstract":"As various machine learning (ML) algorithms have become more popular in orthopedic surgery, the research quality of these models requires further evaluation, and the methodological quality of the models still needs to be clarified. This study aimed to comprehensively analyze and evaluate the potential bias and applicability of existing studies of supervised ML-driven prognostic risk prediction models focusing on total knee arthroplasty/total hip arthroplasty individuals.","url":"https://pubmed.ncbi.nlm.nih.gov/41189192/","authors":["Zhang H","Jiang L","Zheng J","Li C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 17","doi":"10.1097/MD.0000000000045230","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41188940","name":"Nanoparticle-based strategy in CAR-T cell immunotherapy: challenges, implications, and perspectives.","source":"pubmed","abstract":"Chimeric antigen receptor (CAR)-T cell therapy has achieved remarkable progress in treating hematologic malignancies, yet its broader application faces challenges such as manufacturing complexity, solid tumor microenvironment barriers, and immune toxicity. Nanoparticles (NPs), leveraging their precise delivery, immunomodulation, and multifunctional integration capabilities, offer innovative strategies to optimize CAR-T cell therapy. This review provides a comprehensive elucidation of the fundamental framework of CAR-T cell therapy and the challenges in oncological applications. Subsequently, we systematically summarized the synergistic mechanisms between NPs and CAR-T cell therapy, including optimization of genetic modification, enhancement of tumor site infiltration, modulation of immunosuppressive tumor microenvironments, mitigation of tumor antigen heterogeneity, real-time monitoring, and dynamic control of cellular activity. Ultimately, it highlights the emerging paradigm of artificial intelligence integration within this domain while discussing the associated technical obstacles and future prospects of this combined therapeutic approach.","url":"https://pubmed.ncbi.nlm.nih.gov/41188940/","authors":["Shang D","Zhou Z","Shi R","Wang Z","Zhang P","Peng F","Li H","Cheng G","Qin H","Xie Z","Xu Y","Zhou X","Chen W","Chen Y","Yang S","Chen L","Lu Q","Xu R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1186/s12943-025-02476-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41188841","name":"Structural rather than functional brain alterations that characterize the differences between major depressive disorder and primary insomnia: a comparative meta-analysis.","source":"pubmed","abstract":"Major depressive disorder (MDD) and primary insomnia (PI) share overlapping symptoms and neurobiological features, yet they represent distinct clinical entities. Thus, identifying both shared and disorder-specific structural and functional brain alterations is critical for enhancing our mechanistic understanding and improving diagnostic differentiation between these conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/41188841/","authors":["Du W","Tang B","Gao Z","Li X","Liu N","Tang X","Zhang Q","Lu P","Zhang W","Lui S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1186/s12916-025-04442-y","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41188838","name":"Development and validation of a multidimensional and interpretable artificial intelligence model to predict gout recurrence in hospitalised patients: a real-world, ambispective multicentre cohort study in China.","source":"pubmed","abstract":"Gout is the most common inflammatory arthritis. Recurrent flares are common among hospitalised patients and contribute to substantial clinical and economic burden. However, the accurate prediction of inpatient recurrence remains challenging, particularly in individuals with comorbid gout. This study aims to evaluate the predictive value of variables and develop a multidimensional, interpretable artificial intelligence (AI) model to predict gout recurrence (GoutRe).","url":"https://pubmed.ncbi.nlm.nih.gov/41188838/","authors":["Li M","Zhang H","Chen S","Zhong F","Liu J","Wu J","Lin R","Li R","Wu Y","Xie D","Zhang K","Zheng B","Chen X","Cheng Z","Jiang Y","Ye H","Cai L","Xie R","Li D","Zhu J","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1186/s12916-025-04454-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41188719","name":"Multimodal imaging-based interpretable radiomics for differentiating brucella and tuberculosis spondylitis: a two-center study.","source":"pubmed","abstract":"Spondylitis, particularly infectious forms caused by Mycobacterium tuberculosis and Brucella species, presents significant clinical challenges due to overlapping symptoms and diagnostic difficulties. Accurate differentiation is crucial for effective treatment, necessitating advanced imaging techniques and radiomics to enhance diagnostic precision and improve patient outcomes in cases of tuberculosis spondylitis (TS) and brucella spondylitis (BS).","url":"https://pubmed.ncbi.nlm.nih.gov/41188719/","authors":["Yimit Y","Tuersun A","Zhang M","Huang C","Shen L","Yuan Y","You Y","Abulizi A","Ma J","Nijiati M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1186/s12879-025-11863-w","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41188290","name":"Transcriptome-conditioned molecule generation via gene interaction-aware fragment modeling with a GPT-based architecture.","source":"pubmed","abstract":"Phenotype-driven drug discovery leverages cellular responses to guide the design of therapeutic molecules. Recent advancements in transcriptomics have provided extensive datasets describing how gene expression changes in response to various chemical stimuli, presenting an opportunity to directly link molecular generation to specific cellular phenotypes. However, effectively linking transcriptomic perturbations to chemical structure generation remains challenging due to the complexity of gene interactions and chemical feasibility constraints. We developed GGIFragGPT, a novel generative model that integrates transcriptomic perturbation profiles with biologically informed gene-gene interaction embeddings to guide fragment-based molecular generation. The model employs an autoregressive transformer architecture to sequentially assemble chemically valid fragments, with cross-attention mechanisms highlighting biologically relevant genes guiding the molecular generation process. Comparative analysis confirmed that the proposed approach yields chemically feasible, novel, and diverse molecules. By leveraging transcriptomic profiles, GGIFragGPT successfully generated compounds aligned with the biological context suggested by transcriptomic data, validated through gene-level interpretability analysis that identified key target genes. Case studies demonstrated the model's capability to produce structurally plausible inhibitors, exemplified by targeted molecule generation against CDK7. This work demonstrates the potential of integrating biological insights into chemical generation processes, offering a promising approach for phenotype-driven therapeutic discovery.","url":"https://pubmed.ncbi.nlm.nih.gov/41188290/","authors":["Koo B","Park BK","Kim S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.1038/s41598-025-17439-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41187445","name":"Artificial intelligence in medical imaging: Utilization, challenges, and practitioner perceptions in Rwanda.","source":"pubmed","abstract":"Artificial intelligence (AI) holds transformative potential for medical imaging in low-resource settings like Rwanda, where shortages of imaging professionals contribute to diagnostic delays. While global research has examined AI adoption in high-income countries, limited evidence exists for sub-Saharan Africa. This study aimed to assess AI utilization patterns, implementation challenges, and practitioner perceptions across Rwanda's healthcare system, providing critical insights for optimizing AI integration in resource-constrained environments.","url":"https://pubmed.ncbi.nlm.nih.gov/41187445/","authors":["Mukandayisenga M","Tabaro J","Chinene B","Adam A","Mecthilde M","Turatsinze F","Odumeru EA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.jmir.2025.102127","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41187324","name":"Integration of deep learning and Bayesian networks for personalized tooth color prediction in dental esthetics: A study in Chinese Han population.","source":"pubmed","abstract":"The relationship between tooth color selection and individual satisfaction remains critical in dental esthetics. This study developed a hybrid approach combining deep learning with Bayesian network analysis to investigate how skin tone, age, and gender influence tooth color preferences.","url":"https://pubmed.ncbi.nlm.nih.gov/41187324/","authors":["Ruan C","Xiong J","Wang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1111/jopr.70049","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41186875","name":"An explainable prognostic model after vascularized bone grafting for hip preservation based on CT radiomics combined with SHAP.","source":"pubmed","abstract":"The purpose of this study is to develop a CT radiomics-based interpretable prognostic diagnostic model for vascularized bone graft hip preservation, with the objective of predicting postoperative hip preservation outcomes. The study recruited 107 patients, collecting preoperative CT scans and preoperative blood biochemistry data. Among these patients, 27 had a good prognosis, while 80 had a poor prognosis. Five machine learning algorithms were employed to develop predictive models evaluating the effectiveness of modified vascularized bone implants in hip preservation. The interpretability of the top-performing models was assessed using SHapley Additive exPlanations (SHAP). Nine radiomic features were extracted from preoperative CT scans to develop a radiomic score. Through univariate and multivariate logistic regression analyses, clinical indicators, including patient age and preoperative platelet-to-lymphocyte ratio (PLR), were retained. Fifteen models were constructed, incorporating clinical, radiomic, and combined approaches across various algorithms. The combined model utilizing the XGBoost algorithm demonstrated superior performance, achieving an AUC of 0.90 (95% CI 0.81-0.98) on the training set and 0.87 (95% CI 0.75-1.00) on the test set. These results showed improvements of around 31% and 28%, respectively, compared to the top performing clinical and radiomic models (p&#x2009;&lt;&#x2009;0.05). High radiomics scores, a high PLR, and older age were identified as significant predictors of poor prognosis. A robust joint clinical and radiomics model was developed using the XGBoost algorithm for predicting the prognosis of hip-preserving surgery. The predictions of this model were interpreted using SHAP to enhance clinical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/41186875/","authors":["Shi H","Shu P","Wang Z","Rao Y","Guo M","Pu L","Xu Y","Li C","Chen X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s13246-025-01666-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41186170","name":"Transforming peripheral nerve surgery with artificial intelligence: a review of surgical advances.","source":"pubmed","abstract":"Peripheral nerve injuries (PNIs) are challenging to manage due to complex anatomy and variable presentations. Artificial intelligence (AI) techniques are increasingly applied in medicine, and their role in PNI care is emerging. This systematic review aimed to summarize the current applications of AI in the diagnosis, prognosis, and treatment of PNIs.","url":"https://pubmed.ncbi.nlm.nih.gov/41186170/","authors":["Buldu MT","Ezquerro Cortes MR","Panagiotidou A","Fox M","Sinisi M","Simpson AI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep","doi":"10.23736/S0031-0808.25.05362-5","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41185440","name":"Lightweight deep training network for lymph nodes segmentation from head and neck CT images.","source":"pubmed","abstract":"Accurate lymph node (LN) segmentation is highly beneficial for diagnosing and treating head and neck diseases. However, because of the varying sizes and complex shapes of LNs from the head and neck, as well as their blurred boundaries with surrounding tissues in computed tomography (CT) images, it is difficult for physicians to manually identify the region of interest (ROI). Although existing 3D-volumetric-convolution-based methods play an important role in LN boundary extraction, they suffer from high computational complexity.","url":"https://pubmed.ncbi.nlm.nih.gov/41185440/","authors":["Lu F","Li XL","Jiang B","Zhang Z","Tang C","Li Q","Cai J","Peng T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1002/mp.70123","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41183062","name":"Resampling methods for class imbalance in clinical prediction models: A scoping review protocol.","source":"pubmed","abstract":"Class imbalance-where clinically important \"positive\" cases make up less than 30% of the dataset-systematically reduces the sensitivity and fairness of medical prediction models. Although data-level techniques, such as random oversampling, random undersampling, SMOTE, and algorithm-level approaches like cost-sensitive learning, are widely used, the empirical evidence on when these corrections improve model performance remains scattered across different diseases and modelling frameworks. This protocol outlines a scoping systematic review with meta-regression that will map and quantitatively summarise 15 years of research on resampling strategies in imbalanced clinical datasets, addressing a key methodological gap in reliable medical AI.","url":"https://pubmed.ncbi.nlm.nih.gov/41183062/","authors":["Abdelhay O","Shatnawi A","Najadat H","Altamimi T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1371/journal.pone.0330050","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41182183","name":"The Postoperative Hyperopic Shift Risk Prediction Model for Primary Angle Closure Glaucoma Patients Based on Machine Learning.","source":"pubmed","abstract":"This study developed and validated a machine learning-based risk prediction model to estimate the likelihood of postoperative hyperopic shift in patients with primary angle closure glaucoma after IOL implantation, which may help guide individualized surgical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41182183/","authors":["Gong D","Liu Y","Dang K","Huang Y","Deng S","Guo J","Shen X","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1097/IJG.0000000000002648","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41182066","name":"Autonomous extraction of preoperative radiographic predictors on X-ray for cervical spine deformity following laminoplasty: a prospectively validated AI tool.","source":"pubmed","abstract":"Approximately 21% of patients who undergo cervical laminoplasty for cervical spondylotic myelopathy (CSM) develop postoperative kyphotic deformity (KD). Radiologic parameters (RPs) on preoperative sagittal X-ray have consistently shown to be the strongest predictors for KD but their acquisition requires manual labor from specialists. Thus, the authors developed a novel artificial intelligence (AI) model to autonomously retrieve the predictors.","url":"https://pubmed.ncbi.nlm.nih.gov/41182066/","authors":["Pettersson SD","Koc NA","Skrzypkowska P","Filo J","Siedlecki K","Aleksandrowicz KM","Lee J","Alwakaa O","Jabbar R","Terry F","Faraj M","Szmuda T","Zieliński P","Moses ZB","Sagan L","Klepinowski T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 3","doi":"10.5603/pjnns.105002","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41181841","name":"Estimated atrial fibrillation burden on early rhythm-control and cardiovascular events in the EAST-AFNET 4 trial.","source":"pubmed","abstract":"Atrial fibrillation (AF) is currently diagnosed by ECG, creating a binary, lifelong diagnosis. AF burden, estimated as the proportion of time spent in AF, quantifies AF severity dynamically. AF burden can modulate the risk of AF-related outcomes. Whether AF burden modulates cardiovascular outcomes with rhythm-control therapy is unknown.","url":"https://pubmed.ncbi.nlm.nih.gov/41181841/","authors":["Zeemering S","Borof K","Schotten U","Obergassel J","Camm AJ","Crijns HJGM","Eckardt L","Fabritz L","Goette A","Habibi Z","Heijman J","Hermans BJM","Lemoine MD","Magnussen C","Metzner A","Rillig A","Schnabel RB","Schuijt E","Suling A","Vardas P","Willems S","Zapf A","Kirchhof P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1016/j.eclinm.2025.103457","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41181557","name":"Personalized insights into urinary tract infection management: A text mining analysis of online consultation data.","source":"pubmed","abstract":"Urinary tract infections (UTIs) frequently affect individuals of all ages, necessitating antibiotic treatment and medical care, which can impair quality of life and cause psychological strain. Online Health Consultation (OHC) platforms serve as a widely used communication tool, offering integrated support for medical guidance and disease management. By examining OHC interactions, this study explores the concerns and difficulties experienced by UTI patients to better understand their perspectives.","url":"https://pubmed.ncbi.nlm.nih.gov/41181557/","authors":["Tang R","Zhu P","Yan R","Zhou Y","Tang Z","He W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/20552076251393289","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41181553","name":"AI-Powered histopathology slide image interpretation in oncology: A comprehensive knowledge mapping and bibliometric analysis.","source":"pubmed","abstract":"To map global research on AI-driven histopathological image interpretation in oncology (2000-2024).","url":"https://pubmed.ncbi.nlm.nih.gov/41181553/","authors":["Sweileh MW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/20552076251393286","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41180750","name":"ChatGPT in general surgery: a cross-sectional study assessing its response to patient questions.","source":"pubmed","abstract":"The artificial intelligence-based large language model ChatGPT (OpenAI, San Francisco/CA, USA) has taken human-machine interaction to the next level since its launch in November 2022. As a program that mimics human conversations over a text-based communication interface, ChatGPT has the potential to be used in a variety of healthcare settings, including patient information. The present study aimed to assess the abilities of ChatGPT in responding to patient questions regarding general surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/41180750/","authors":["Lünse S","Wisotzky EL","Höhn J","Paasch C","Meyer F","Hunger R","Mantke R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1097/MS9.0000000000003939","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41179111","name":"Using responsive evaluation to shape research: Engaging and collaborating with stakeholders in the international symposium on multimorbidity.","source":"pubmed","abstract":"Multimorbidity (MM) is a growing global public health issue requiring interdisciplinary collaboration among researchers, healthcare professionals, policymakers, and patients. The third International Symposium on Multimorbidity, held in May 2024 in Bielefeld, Germany, provided a platform for knowledge exchange and stakeholder engagement to address key challenges in MM research and care.","url":"https://pubmed.ncbi.nlm.nih.gov/41179111/","authors":["Grede N","Muth C","Hanf M","Calderón-Larrañaga A","Valderas JM","Puzhko S","van den Akker M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/26335565251388513","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41177305","name":"Clinical feasibility of two cardiac deep learning cine magnetic resonance imaging sequences: Single-breath-hold and free-breathing motion-corrected approaches.","source":"pubmed","abstract":"Cine cardiovascular magnetic resonance (CMR) faces the challenges of prolonged examination times and repeated breathhold (BH). This study evaluated the clinical feasibility of deep learning (DL)-accelerated cine sequences, which shorten the acquisition time (AT) while achieving comparable image quality (IQ) and function.","url":"https://pubmed.ncbi.nlm.nih.gov/41177305/","authors":["Kong H","Wang Z","Zhou Z","Yu D","Li G","Li J","Yuan J","Li X","He Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Winter","doi":"10.1016/j.jocmr.2025.101983","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41176552","name":"Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.","source":"pubmed","abstract":"To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes and predict response to induction chemotherapy using histopathology as the reference standard.","url":"https://pubmed.ncbi.nlm.nih.gov/41176552/","authors":["von Krüchten R","Barrow M","Adams L","Singh SB","Varniab ZS","Suryadevara V","Ghimire P","Pribnow A","Qi J","Applin D","Lokesha YU","Nernekli K","Daldrup-Link HE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1007/s00330-025-12115-w","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41175546","name":"Systematic review of a machine learning model for prediction of venous thromboembolism risk.","source":"pubmed","abstract":"This systematic review aims to evaluate the methodological quality, performance, and clinical applicability of machine learning (ML) models for predicting the risk of venous thromboembolism (VTE) in hospitalized patients. Specifically, we aim to assess the methodological quality and reporting transparency of the included studies, with a focus on their risk of bias and adherence to reporting guidelines.","url":"https://pubmed.ncbi.nlm.nih.gov/41175546/","authors":["Ge WJ","Zhu TF","Ge WJ","Zhu XY","Chu AQ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.thromres.2025.109507","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41175204","name":"Combination of artificial intelligence and chest computed tomography to assess bone mineral density.","source":"pubmed","abstract":"To evaluate the diagnostic accuracy of artificial intelligence-assisted opportunistic chest CT for osteoporosis/osteopenia screening in a Chinese population.","url":"https://pubmed.ncbi.nlm.nih.gov/41175204/","authors":["Wei L","Qiu Y","Lin W","Lin J","Yuan F","Chen Y","Zhou J","Chen S","Huang R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s00256-025-05067-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41172739","name":"Development of self-phenotyping tools to empower patients and improve diagnostics.","source":"pubmed","abstract":"Deep phenotyping is important for rare disease diagnostics, often using the Human Phenotype Ontology (HPO). Patients are an under-utilised source; to facilitate self-phenotyping we previously \"translated\" HPO into plain language. However, self-reported data has not been assessed to date for diagnostic efficacy nor patient opportunities for collaboration with clinical diagnosticians.","url":"https://pubmed.ncbi.nlm.nih.gov/41172739/","authors":["Shefchek K","Ziniel SI","McMurry JA","Brownstein CA","Brownstein JS","Riggs ER","Might M","Smedley D","Clugston A","Beggs AH","Paterson H","Robinson PN","Vasilevsky NA","Holm IA","Haendel MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.ebiom.2025.105965","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41172689","name":"A qualitative study on the perspectives of doctors, nurses and residents about artificial intelligence and its application in healthcare: Implications to education.","source":"pubmed","abstract":"This study aimed to describe the perspectives of doctors, nurses and residents toward healthcare artificial intelligence (AI) and its integration in healthcare settings in Kazakhstan.","url":"https://pubmed.ncbi.nlm.nih.gov/41172689/","authors":["Zhaksylykova D","Tursynbek A","Nadirbekova G","Cruz JP","Balay-Odao EM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.nepr.2025.104600","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41172285","name":"Digital Health Technology Compliance With Clinical Safety Standards In the National Health Service in England: National Cross-Sectional Study.","source":"pubmed","abstract":"To be authorized for use in the National Health Service (NHS) in England, digital health technologies (DHTs) must meet 2 mandatory clinical risk management standards, Data Coordination Board (DCB) 0129 and 0160, demonstrating that risks from design and use have been assessed and mitigated. NHS organizations must not procure a DHT without DCB0129 assurance and must not deploy one without DCB0160 assurance. Despite legal requirement, no public data exist on how many DHTs are in use in the NHS or how many are assured.","url":"https://pubmed.ncbi.nlm.nih.gov/41172285/","authors":["Oskrochi Y","Roy-Highley E","Grimes K","Shah S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 31","doi":"10.2196/80076","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41171470","name":"Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.","source":"pubmed","abstract":"Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydrocephalus risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/41171470/","authors":["Zhong W","Li Z","Lv S","Chen G","Hao Y","Wang Q","Wang Y","Zhang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 31","doi":"10.1007/s11060-025-05263-y","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41171369","name":"Machine learning-based development of a cytotoxicity prediction model for NK cell therapy in cancers.","source":"pubmed","abstract":"Natural killer (NK) cells mediate anti-tumor immunity through integrated signaling of inhibitory and activating receptors. The efficacy of NK cell adoptive transfer therapy varies among patients due to heterogeneous receptor-ligand expression. This study aimed to develop a predictive model based on receptor-ligand interactions to determine NK cells' therapeutic effects.","url":"https://pubmed.ncbi.nlm.nih.gov/41171369/","authors":["Ma J","Yue J","Li Y","Li Y","Dong H","Fang F","Xiao W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1007/s13402-025-01113-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168790","name":"Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs.","source":"pubmed","abstract":"To investigate the feasibility of a deep learning model, using a transfer learning approach, for recognizing high-altitude pulmonary edema (HAPE) on chest X-ray images and exploring its capability for assessing severity.","url":"https://pubmed.ncbi.nlm.nih.gov/41168790/","authors":["Yang Y","Yu H","Xiang Q","Wu J","Li J","Du F","Yang Y","Wang P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1186/s12911-025-03256-5","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168766","name":"Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients with hypercholesterolemia: a machine learning study with dual-cohort validation.","source":"pubmed","abstract":"Residual cardiovascular risk persists in patients with hypercholesterolemia despite lipid-lowering therapy, underscoring the importance of inflammation in ASCVD development. This study evaluated the relationship between Systemic Inflammation Response Index (SIRI) (a composite biomarker derived from neutrophil, monocyte, and lymphocyte counts) and ASCVD in patients with hypercholesterolemia. And to develop an interpretable machine learning (ML) model for predicting ASCVD risk in patients with hypercholesterolemia.","url":"https://pubmed.ncbi.nlm.nih.gov/41168766/","authors":["Chen Y","Huang W","Zhao S","Ge Z","Liu Y","Huang R","Li D","Xu Q","Long X","Wei K","Chen Q","Sheng C","Tang C","Bai X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1186/s12944-025-02765-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168756","name":"Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.","source":"pubmed","abstract":"To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.","url":"https://pubmed.ncbi.nlm.nih.gov/41168756/","authors":["Lisson CG","Götz M","Wolf D","Manoj S","Gallee L","Schmidt SA","Tausch E","Schneider C","Stilgenbauer S","Beer AJ","Beer M","Sollmann N","Lisson CS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1186/s12880-025-02006-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168718","name":"Trends analysis and future study of medical and pharmacy education: a scoping review.","source":"pubmed","abstract":"This scoping review aims to provide a comprehensive analysis of emerging trends and future developments in medical and pharmacy education, addressing the need to adapt educational approaches to the rapidly evolving healthcare landscape.","url":"https://pubmed.ncbi.nlm.nih.gov/41168718/","authors":["Bashirynejad M","Soleymani F","Nikfar S","Zackery A","Kebriaeezadeh A","Majdzadeh R","Fatemi B","Zare N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1186/s12909-025-08037-2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168642","name":"Comparative Risk for Neuropsychiatric Events in Leukotriene Receptor Antagonist vs. Inhaled Corticosteroid in Children With Asthma: A Nationwide Observational Study With a Complementary Analysis Using Natural Language Processing.","source":"pubmed","abstract":"Leukotriene receptor antagonists (LTRAs) are widely prescribed as controller medications for pediatric asthma. However, there have been increasing concerns about potential neuropsychiatric adverse reactions associated with LTRAs. Findings from observational studies have been inconsistent, and direct comparisons of the risk of neuropsychiatric events (NPEs) between LTRAs and inhaled corticosteroids (ICS) remain limited in the pediatric population.","url":"https://pubmed.ncbi.nlm.nih.gov/41168642/","authors":["Kim S","Han CH","Chang J","Cho J","Jeong K","Kim H","Park M","Kim SY","Kim JD","Sohn MH","Lee S","Park RW","You SC","Kim KW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1002/pds.70254","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41168391","name":"Diagnostic assistance method for RR-TB/MDR-TB patients under treatment based on CNN-LSTM.","source":"pubmed","abstract":"The rapid development of deep learning has promoted its application in disease diagnosis, treatment, and prognosis prediction. Medical imaging plays a crucial role in the management of rifampicin-resistant tuberculosis/multidrug-resistant tuberculosis (RR-TB/MDR-TB). In particular, chest computed tomography (CT) scans offer detailed lung images that can reveal subtle features. In this study, we propose a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to predict treatment outcomes in RR-TB/MDR-TB patients, aiming to support clinicians in timely adjustment of therapeutic strategies and improving treatment success. The model integrates CNN for image feature extraction with LSTM for sequential analysis of patient monitoring data, including two types of immune detection indicators (T-cell subsets and peripheral blood tuberculosis-related CD161-positive cells). Transfer learning with weight initialization was applied to enhance model performance, and three backbone architectures (DenseNet201&#x2009;+&#x2009;ABC, ResNet-50&#x2009;+&#x2009;ABC, CheXNet&#x2009;+&#x2009;ABC) were compared to assess their impact on predictive accuracy. Experimental results demonstrated that the CNN-LSTM model with DenseNet201&#x2009;+&#x2009;ABC as the backbone achieved the highest accuracy in predicting subsequent treatment indicators. These findings demonstrated the feasibility and effectiveness of using CNN-LSTM for treatment outcome prediction in RR-TB/MDR-TB, and highlighted its potential to assist clinicians in precision-tailoring treatment plans, thereby improving therapeutic efficacy and offering both theoretical and practical value in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/41168391/","authors":["Li J","Wu W","Fang Z","Fu P","Huang H","Zhou Y","Yu L","Huang H","Wang T","Zhang Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1038/s41598-025-21955-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41167765","name":"[Real-time or dynamic non-invasive liver fibrosis testing for evaluating clinical prognoses and predicting chronic liver disease].","source":"pubmed","abstract":"Liver fibrosis is a key histologic marker of long-term outcome in chronic liver disease. Non-invasive tests (NITs) have been shown to have predictive value, but the superiority of \"dynamic\" versus \"static\" assessment remains controversial. This article systematically reviews the latest evidence to elucidate the association between longitudinal changes in NITs and hepatic adverse events and assess the incremental contribution of dynamic monitoring to the model. Additionally, it reveals that the dynamic monitoring of NITs is truly superior to single evaluation, but the evidence is limited and the heterogeneity is significant. Dynamic modeling approaches for NITs require a shift from traditional parameter estimation to time-series machine learning. Future studies should make breakthroughs in disease stratification, modeling method innovation, data quality improvement, and prediction ability assessment so as to promote the transition of NITs from \"static risk label\" to \"dynamic individualized engine,\" which can truly serve clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41167765/","authors":["Zhao XY","Sun YM","Gao YK","Lu ZZ","Huang C","Kong YY","Jia JD","You H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 20","doi":"10.3760/cma.j.cn501113-20250812-00323","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41167595","name":"Physician Perspectives on Large Language Models in Health Care: A Cross-Sectional Survey Study.","source":"pubmed","abstract":"This study aims to evaluate physicians' practices and perspectives regarding large language models (LLMs) in health care settings.A cross-sectional survey study was conducted between May and July 2024, comparing physician perspectives at two major academic medical centers (AMCs), one with institutional LLM access and one without. Participants included both clinical faculty and trainees recruited through departmental leadership and snowball sampling. Primary outcomes were current LLM use frequency, ranked importance of evaluation metrics, liability concerns, and preferred learning topics.Among 306 respondents (217 attending physicians [70.9%], 80 trainees [26.1%]), 197 (64.4%) reported using LLMs. The AMC with institutional LLM access reported significantly lower liability concerns (49.2 vs. 66.7% reporting high concern; 17.5 percentage points difference [95% CI, 6.8-28.2]; p &#x2009;=&#x2009;0.0082). Accuracy was prioritized across all specialties (median rank 1.0 [interquartile range; IQR, 1.0-2.0]). Of the respondents, 287 physicians (94%) requested additional training. Key learning priorities were clinical applications (206 [71.9%]) and risk management (181 [63.1%]). Despite widespread personal use, only 8 physicians (2.6%) recommended LLMs to patients. Notable specialty and demographic variations emerged, with younger physicians showing higher enthusiasm but also elevated legal concerns.This survey study provides insights into physicians' current usage patterns and perspectives on LLMs. Liability concerns appear to be lessened in settings with institutional LLM access. The findings suggest opportunities for medical centers to consider when developing LLM-related policies and educational programs.","url":"https://pubmed.ncbi.nlm.nih.gov/41167595/","authors":["Hong HJ","Shah NH","Pfeffer MA","Lehmann LS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1055/a-2735-0527","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41167236","name":"Artificial Intelligence-Assisted Image Extraction in Neonatal Echocardiography for Congenital Heart Disease Diagnosis in Sub-Saharan Africa: Protocol for Model Development.","source":"pubmed","abstract":"Sub-Saharan Africa (SSA) bears the highest global burden of under-5 mortality, with congenital heart disease (CHD) as a major contributor. Despite advancements in high-income countries, CHD-related mortality in SSA remains largely unchanged due to limited diagnostic capacity and centralized health care. While pulse oximetry aids early detection, confirmation typically relies on echocardiography, a procedure constrained by a shortage of specialized personnel. Artificial intelligence (AI) offers a promising solution to bridge this diagnostic gap.","url":"https://pubmed.ncbi.nlm.nih.gov/41167236/","authors":["Leke AZ","Sop Deffo LL","Wirsiy YS","Aldersley T","Day T","King AP","McAllister P","Maboh MN","Lawrenson J","Tantchou C","Kainz B","Casey F","Bond R","Finlay D","Kelson Tchinda N","Obale A","Mugri FN","Zühlke L","Dolk H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.2196/75270","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41167215","name":"Patritumab deruxtecan (HER3-DXd) in patients with active brain metastases of breast cancer (TUXEDO-3): a multicentre, single-arm, phase 2 trial.","source":"pubmed","abstract":"Patritumab deruxtecan (HER3-DXd) is a novel antibody-drug conjugate targeting HER3, which is overexpressed in CNS metastases of metastatic breast cancer. We aimed to evaluate the activity and safety of HER3-DXd in patients with metastatic breast cancer and brain metastases that are newly diagnosed or progressing after local therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41167215/","authors":["Bartsch R","Marhold M","Garde-Noguera J","Gion M","Ruiz-Borrego M","Greil R","Valero M","Llombart-Cussac A","García-Mosquera JJ","Arumi M","Cortés J","Campolier M","Guerrero JA","Slebe F","Martínez-García E","Jiménez-Cortegana C","Vaz-Batista M","Oberndorfer F","Furtner J","Fuereder T","Berghoff AS","Preusser M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/S1470-2045(25)00470-X","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41167214","name":"Patritumab deruxtecan (HER3-DXd) in patients with active brain metastases of non-small-cell lung cancer (TUXEDO-3): a multicentre, single-arm, phase 2 trial.","source":"pubmed","abstract":"Patritumab deruxtecan (HER3-DXd) is a novel antibody-drug conjugate targeting HER3, which is overexpressed in CNS metastases of advanced non-small-cell lung cancer (NSCLC). We aimed to evaluate the activity and safety of HER3-DXd in patients with advanced NSCLC and newly diagnosed brain metastases or brain metastases progressing after local therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41167214/","authors":["Fuereder T","Garde-Noguera J","García-Mosquera JJ","Ruiz-Borrego M","Valero M","Llombart-Cussac A","Gion M","Greil R","Arumi M","Campolier M","Guerrero JA","Raimondi G","Mancino M","Jiménez-Cortegana C","Vaz-Batista M","Oberndorfer F","Marhold M","Berghoff AS","Furtner J","Bartsch R","Preusser M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/S1470-2045(25)00465-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41166703","name":"Balancing Innovation and Control: The European Union AI Act in an Era of Global Uncertainty.","source":"pubmed","abstract":"The European Union's Artificial Intelligence Act (EU AI Act), adopted in 2024, establishes a landmark regulatory framework for artificial intelligence (AI) systems, with significant implications for health care. The Act classifies medical AI as \"high-risk,\" imposing stringent requirements for transparency, data governance, and human oversight. While these measures aim to safeguard patient safety, they may also hinder innovation, particularly for smaller health care providers and startups. Concurrently, geopolitical instability-marked by rising military expenditures, trade tensions, and supply chain disruptions-threatens health care innovation and access. This paper examines the challenges and opportunities posed by the AI Act in health care within a volatile geopolitical landscape. It evaluates the intersection of Europe's regulatory approach with competing priorities, including technological sovereignty, ethical AI, and equitable health care, while addressing unintended consequences such as reduced innovation and supply chain vulnerabilities. The study employs a comprehensive review of the EU AI Act's provisions, geopolitical trends, and their implications for health care. It analyzes regulatory documents, stakeholder statements, and case studies to assess compliance burdens, innovation barriers, and geopolitical risks. The paper also synthesizes recommendations from multidisciplinary experts to propose actionable solutions. Key findings include: (1) the AI Act's high-risk classification for medical AI could improve patient safety but risks stifling innovation due to compliance costs (eg, &#x20ac;29,277 annually per AI unit) and certification burdens (&#x20ac;16,800-23,000 per unit); (2) geopolitical factors-such as United States-China semiconductor tariffs and EU rearmament-exacerbate supply chain vulnerabilities and divert funding from health care innovation; (3) the dominance of \"superstar\" firms in AI development may marginalize smaller players, further concentrating innovation in well-resourced organizations; and (4) regulatory sandboxes, AI literacy programs, and international collaboration emerge as viable strategies to balance innovation and compliance. The EU AI Act provides a critical framework for ethical AI in health care, but its success depends on mitigating regulatory burdens and geopolitical risks. Proactive measures-such as multidisciplinary task forces, resilient supply chains, and human-augmented AI systems-are essential to foster innovation while ensuring patient safety. Policymakers, clinicians, and technologists must collaborate to navigate these challenges in an era of global uncertainty.","url":"https://pubmed.ncbi.nlm.nih.gov/41166703/","authors":["Bignami EG","Russo M","Semeraro F","Bellini V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.2196/75527","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41166699","name":"Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.","source":"pubmed","abstract":"Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. Whereas molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. In this study, we developed Path2Omics, a deep learning framework that independently predicts gene expression and methylation from histopathology across 30 The Cancer Genome Atlas cancer types. Path2Omics comprised two components: a \"formalin-fixed, paraffin-embedded (FFPE) model\" trained on FFPE slides and a \"fresh-frozen (FF) model\" trained on FF slides. When evaluated on seven external datasets, the \"FF model\" outperformed the \"FFPE model,\" even though six of the datasets consisted exclusively of FFPE slides. The \"integrated model\" combined predictions from both, achieving a 30% improvement over the FFPE model alone and robustly predicting approximately 4,400 genes (of 18,000). Importantly, the inferred gene expression closely matched actual values in predicting patient survival and treatment response. Overall, this study demonstrated the potential of Path2Omics to advance precision oncology using routine histopathology slides.","url":"https://pubmed.ncbi.nlm.nih.gov/41166699/","authors":["Hoang DT","Shulman ED","Dhruba SR","Nair NU","Barman RK","Cantore T","Biswas S","Lalchungnunga H","Singh O","Chung Y","Lee JS","Nasrallah MP","Stone EA","Aldape K","Ruppin E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 15","doi":"10.1158/0008-5472.CAN-25-4350","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41165895","name":"Describing the Performance and the Infrastructure Requirements of the Existing Artificial Intelligence (AI)-Based Diabetic Retinopathy (DR) Screening Algorithms for Diabetic Patients: an Umbrella Review.","source":"pubmed","abstract":"AI-based diabetic retinopathy (DR) screening algorithms have been evaluated in many countries and have shown promise in expanding access to screening, especially in low- and middle-income countries (LMICs). However, the literature lacks guidance on which algorithms are best suited for these settings. This umbrella review summarizes current evidence on the performance, infrastructure needs, and global implementation of AI-based DR screening tools.&#xa0;Following the Preferred Reporting Items for Systematic Review (PRISMA) guidelines, systematic reviews were identified through searches in PubMed, Embase, ScienceDirect, Scopus, and Google Scholar up to April 18, 2024. Eligible studies were reviewed, and findings were presented in tables and graphics.&#xa0;Twenty systematic reviews were included. Most algorithms were developed, validated, and used in high-income countries, with none developed or implemented in Africa. More than 400 algorithms were identified, of which 161 had some form of clinical validation, and 31 were validated in real-world settings. Sensitivity ranged from 66.0% to 100.0%, specificity from 59.5% to 98.7%, and AUROC from 87.8% to 99.1%. Only 12 algorithms have received regulatory approval, and 11 of them are currently used in clinical practice.&#xa0;AI-based DR screening models hold promise as diagnostic tools across diverse clinical settings, particularly where ophthalmic resources are limited. However, successful implementation depends on appropriate infrastructure, local validation, and regulatory support. Addressing the significant gaps in algorithm development and validation in Africa is essential to ensure equitable access and effective use of AI in DR screening.","url":"https://pubmed.ncbi.nlm.nih.gov/41165895/","authors":["Kabunga R","Asasira J","Njuki S","Daniel A","Morley K","Morley M","Kaggwa F","Cikomola JC","Simon A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 30","doi":"10.1007/s10916-025-02280-2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41164852","name":"[Assessing the accuracy and comprehensiveness of large language models in responding to patient inquiries on placenta accreta spectrum].","source":"pubmed","abstract":"Objective: To explore the accuracy and comprehensiveness of responses from four large language models [ChatGPT-3.5 (Model A), ChatGPT-4.0 (Model B), ChatGPT-4o (Model D) developed by OpenAI in the United States, and a domestically developed Obstetric artificial intelligence assistant robot (Model C)] to inquiries from patients with placenta accreta spectrum disorders and their families. Methods: A prospective study was conducted from June 2024 to March 2025, involving 25 pairs of patients and their families and 8 obstetric experts at the Third Affiliated Hospital of Guangzhou Medical University. Sixteen questions commonly asked by patients and their families regarding placenta accreta spectrum disorders were collected, covering six disease-related areas such as disease mechanism, risk factors, clinical symptoms, diagnosis, pregnancy management, and prognosis. A physician then input all the questions into the four different large language models to obtain their responses. The responses were randomized and independently evaluated by four maternal-fetal medicine physicians using a three-point Likert scale and a six-point Likert scale to assess the accuracy of the responses. The majority consensus method was used to determine the final rating for each model's response. For responses rated as \"good\" and scored 5 or above on the six-point Likert scale, a three-point Likert scale was further used to assess the comprehensiveness of the content. The accuracy and comprehensiveness of the 4 large language models was compared. Results: Significant differences in accuracy were observed among the four large language models ( P =0.005). 25% (4/16) of Model A responses were rated as \"good\", which was lower than the 75% (12/16) for both Model B and Model D (both P &lt;0.05). The comprehensiveness score for Model A was 1.8 (1.5, 2.0), for Model B was 2.0 (1.8, 2.0), for Model C was 2.3 (2.0, 2.3), and for Model D was 2.6 (2.3, 2.7). There were statistically significant differences in comprehensiveness scores among the four large language models (P &lt;0.001). Pairwise comparisons showed that Model D was significantly more comprehensive than Model A ( P =0.004) and Model B ( P &lt;0.001). Conclusions: Significant variations exist in both the accuracy and comprehensiveness of responses from the four large language models to questions in six areas related to placenta accreta spectrum disorders. Model D performs better in both aspects. Model C has a better performance in comprehensiveness, but its accuracy needs further improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/41164852/","authors":["He YX","Chen DJ","Hu M","Zhang YL","Yu L","Chen YH","Hong F","Zhang SY","Luo SJ","He F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 4","doi":"10.3760/cma.j.cn112137-20250826-02191","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41163288","name":"Non-invasive Prediction of Lung Cancer Histological Differentiation via Radiomics and Multi-binary Classification Models.","source":"pubmed","abstract":"The histological differentiation of Non-Small Cell Lung Cancer (NSCLC) is a critical prognostic factor that influences therapeutic strategies and patient outcomes. However, conventional assessment methods relying on postoperative pathology or biopsy are invasive and limited by sampling bias. Therefore, it is of great clinical significance to develop a non-invasive, imaging-based approach for accurate preoperative differentiation evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/41163288/","authors":["Jiang H","Zhu B","Xia L","Han Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2174/0115734056415019251016111351","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41163056","name":"The effects of combining anodal transcranial direct current stimulation with robot-assisted gait training on lower limb motor function and the motor cortex regulation of stroke patients.","source":"pubmed","abstract":"The therapeutic effect and underlying mechanism of combining transcranial direct current stimulation (tDCS) with robot-assisted gait training (RAGT) for stroke patients remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/41163056/","authors":["Zhang Y","Zhang Y","Zheng B","Chen S","Yu H","Dai L","Zhang W","Huang H","Su X","Cao M","Chen J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.1186/s12984-025-01731-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41163008","name":"Factors affecting survival in patients with colorectal cancer: an umbrella review.","source":"pubmed","abstract":"This study aimed to evaluate the strength and validity of the evidence for reported associations between multiple factors and colorectal cancer (CRC) survival.","url":"https://pubmed.ncbi.nlm.nih.gov/41163008/","authors":["Yu L","Yuan J","Lou S","Huang A","Cai X","Ji J","Dai X","Mao X","Mao Y","Sun L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.1186/s12967-025-06876-7","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41162906","name":"Radiomics application using non-contrast computed tomography for predicting uric acid kidney stones.","source":"pubmed","abstract":"This study aims to develop a prediction model based on non-contrast computed tomography (NCCT) images to differentiate uric acid stones from non-uric acid stones before treatment.","url":"https://pubmed.ncbi.nlm.nih.gov/41162906/","authors":["Huang Y","Li N","Han X","Xu S","Zhang G","Zhu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.1186/s12880-025-01965-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41162830","name":"Alterations of Raman Profiles of Cystic Echinococcosis Serum After Treated With ABZ by SERS.","source":"pubmed","abstract":"Cystic echinococcosis (CE) is often treated with albendazole (ABZ). Surface-enhanced Raman scattering (SERS) has high sensitivity and specificity, as well as low sample consumption, but its ability to identify changes in serum compounds post-ABZ treatment is unclear. This study used SERS to characterize the Raman profiles of serum from 7 normal, 9 CE-model, and 10 ABZ-treated mice, analyzing profile differences. Tentative peak assignments revealed ABZ-induced biomolecular changes in protein structure (1000&#x2009;cm -1 ), uric acid (1130&#x2009;cm -1 ), amide III (1203&#x2009;cm -1 ), phospholipids, and amide I (1656&#x2009;cm -1 ). The top 10 principal components (PCs) accounted for 90% of the variance (PC1: 48.9%). Principal components analysis (PCA) loadings suggested these substances as markers of ABZ treatment. The results show that SERS combined with PCA is a rapid and effective method to observe serum changes in Echinococcus granulosus-infected subjects.","url":"https://pubmed.ncbi.nlm.nih.gov/41162830/","authors":["Fang Z","Xu S","Xu S","Zhou R","Maimaitiaili M","Dawuti W","Bi X","Lin R","Lü G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/jbio.202500373","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41162564","name":"Machine learning-based tool to assess risk of hemodynamically significant PDA in extremely premature infants.","source":"pubmed","abstract":"A patent ductus arteriosus (PDA) is associated with complications in extremely preterm infants and its assessment requires trained personnel and equipment not always available. A prediction tool might guide the urgency and clinical decision making to allocate resources to patients with the highest risk. The aim of this study was to generate a clinical tool to assess the probability of a hemodynamically significant PDA in extremely preterm infants.","url":"https://pubmed.ncbi.nlm.nih.gov/41162564/","authors":["Küng E","Göral K","Unterasinger L","Schilhart-Wallisch C","Berger A","Wisgrill L","Dorffner G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1038/s41390-025-04489-w","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41162418","name":"Factors influencing the intention to use telemedicine services among older adults in China.","source":"pubmed","abstract":"The rapid growth of the older adult population has brought significant challenges to the medical industry. Problems such as uneven distribution of medical resources, low medical efficiency, and lack of post-hospital care have become prominent. Many hospitals in China use online operating systems and the existing telemedicine service system does not consider the needs, preferences, and characteristics of older adults. Therefore, analyzing the factors that affect older adults' intention to use telemedicine is essential for designing telemedicine service systems. The technology acceptance model (TAM) and emotional design theory were used to build a model of factors influencing older adults' telemedicine use intention in China. This research gathers data via questionnaires and employs structural equation modelling for analysis. A total of 377 participants provided responses regarding telemedicine. The collected data was subsequently analyzed using structural equation modelling. Perceived usefulness and perceived ease of use are the primary drivers affecting the behavioral intention of older adults to use telemedicine, followed by cost value, system quality, trust, and self-efficacy. In contrast, Technological anxiety has an adverse effect on the older adults' intention to use telemedicine. These findings contribute to a validated model that plays a central role in explaining telemedicine adoption among older adults. The model provides actionable insights for policymakers, healthcare providers, and designers to support the development of inclusive service strategies, improve system accessibility, and enhance user-centered design. It also offers a theoretical foundation for future research on sustained digital healthcare engagement in aging populations.","url":"https://pubmed.ncbi.nlm.nih.gov/41162418/","authors":["He H","Raja Ghazilla RA","Abdul-Rashid SH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.1038/s41598-025-14630-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41162129","name":"Burden of cardiovascular diseases in England (2020-24): a national cohort using electronic health records data.","source":"pubmed","abstract":"The COVID-19 pandemic led to substantial health services disruption in England. Health-care policy makers need reliable national-level information on disease burden to plan services. Whole-population individual-level data, which are routinely collected and linked across multiple sources, provide comprehensive estimates that can be regularly updated at low cost. We aimed to measure the burden of cardiovascular diseases in the whole population of England from 2020 to 2024.","url":"https://pubmed.ncbi.nlm.nih.gov/41162129/","authors":["Allara E","Shi W","Bolton T","Chalmers F","Brizzi LF","Musto L","Shah ASV","Tomlinson C","Walter I","Conrad N","Danesh J","Di Angelantonio E","Petersen SE","Khunti K","Raffetti E","Cezard G","Denaxas S","Wood AM","Whiteley W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/S2468-2667(25)00163-X","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41161340","name":"Detecting Laterality Errors in Combined Radiographic Studies by Enhancing the Traditional Approach With GPT-4o: Algorithm Development and Multisite Internal Validation.","source":"pubmed","abstract":"Laterality errors in radiology reports can endanger patient safety. Effective methods for screening for laterality errors in combined radiographic reports, which combine multiple studies into one, remain unexplored.","url":"https://pubmed.ncbi.nlm.nih.gov/41161340/","authors":["Weng KH","Chou YC","Kuo YT","Hsieh TJ","Liu CF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.2196/76384","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41160267","name":"Large language model chatbots for patient education in kidney stones: a scoping review.","source":"pubmed","abstract":"In 2024, 17% of adults reported using an artificial intelligence (AI) chatbot at least once a month as a source of health information, rising to 25% among those under 30. We aim to conduct a scoping review of the existing literature assessing the performance of large language model (LLM) chatbots for patient education in kidney stone disease (KSD).","url":"https://pubmed.ncbi.nlm.nih.gov/41160267/","authors":["Goudrar R","Zekraoui O","Moussa I","Nguyen DD","Bouhadana D","Li T","Gauhar V","Yuen SKK","Bhojani N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 29","doi":"10.1007/s00345-025-06019-z","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41158630","name":"Exploring unsupervised learning techniques for early detection of myocardial ischemia in type 2 diabetes.","source":"pubmed","abstract":"Myocardial ischemia can result in severe cardiovascular complications. However, the impact of clinical factors on myocardial ischemia in individuals with T2DM remains unclear. we applied a clustering approach to identify the variability in myocardial ischemia evaluated through Single-Photon Emission Computed Tomography.","url":"https://pubmed.ncbi.nlm.nih.gov/41158630/","authors":["Liu B","Hou YJ","Wu P","Han X","Qi H","Yang XY","Wu ZF","Li SJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fendo.2025.1668516","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41158286","name":"Artificial intelligence-driven transformative applications in disease diagnosis technology.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) in medical diagnostics represents a transformative advancement in healthcare, with projected market growth reaching $188&#x202f;billion by 2030. This comprehensive review examines the latest developments in AI-driven diagnostic technologies across multiple disease domains, particularly focusing on cancer, Alzheimer's disease (AD), and diabetes. Through systematic bibliometric analysis using GraphRAG methodology, we analyzed research publications from 2022 to 2024, revealing the distribution and impact of AI applications across various medical fields. In cancer diagnostics, AI systems have achieved breakthrough performances in analyzing medical imaging and molecular data, with notable advances in early detection capabilities across 19 different cancer types. For AD diagnosis, AI-powered tools have demonstrated up to 90&#x202f;% accuracy in risk detection through non-invasive methods, including speech pattern analysis and blood-based biomarkers. In diabetes care, AI-integrated systems incorporating deep neural networks and electronic nose technology have shown remarkable accuracy in predicting disease onset before clinical manifestation. These developments collectively indicate a paradigm shift toward more precise, efficient, and accessible diagnostic approaches. However, challenges remain in standardization, data quality, and clinical implementation. This review synthesizes current progress while highlighting the potential for AI to revolutionize medical diagnostics through enhanced accuracy, early detection, and personalized patient&#xa0;care.","url":"https://pubmed.ncbi.nlm.nih.gov/41158286/","authors":["Zhou J","Park S","Dong S","Tang X","Wei X","Junyu Zhou","Sunmin Park","Sihan Dong","Xiaoying Tang","Xunbin Wei"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1515/mr-2024-0097","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"pmid:41158088","name":"AI-Enabled ECG Analysis Improves Diagnostic Accuracy and Reduces False STEMI Activations: A Multicenter U.S. Registry.","source":"pubmed","abstract":"Timely reperfusion is critical in reducing mortality in ST-segment elevation myocardial infarction (STEMI). Although electrocardiography-guided cardiac catheterization laboratory (CCL) activation on the basis of first medical contact recognition improves system-level response, diagnostic uncertainty, particularly in atypical presentations, contributes to false positive activations (FPAs) and reperfusion delays.","url":"https://pubmed.ncbi.nlm.nih.gov/41158088/","authors":["Herman R","Mumma BE","Hoyne JD","Cooper BL","Johnson NP","Kisova T","Demolder A","Rafajdus A","Iring A","Palus T","Belmonte M","Barbato E","Baron SJ","Hatala R","Smith SW","Meyers HP","Sharkey SW","Bartunek J","Henry TD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 26","doi":"10.1016/j.jcin.2025.10.018","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41157315","name":"A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical Applications.","source":"pubmed","abstract":"Healthcare 5.0 represents the next evolution in intelligent and interconnected healthcare systems, leveraging emerging technologies such as Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) to enhance patient care and automation. While Intrusion Detection Systems (IDSs) are a critical component for securing these environments, the current literature lacks a systematic analysis that jointly evaluates the effectiveness of AI models, the suitability of datasets, and the role of Explainable Artificial Intelligence (XAI) in the Healthcare 5.0 landscape. To fill this gap, this survey provides a comprehensive review of IDSs for Healthcare 5.0, analyzing state-of-the-art approaches and available datasets. Furthermore, a practical case study is presented, demonstrating that the fusion of network and biomedical features significantly improves threat detection, with physiological signals proving crucial for identifying complex attacks like spoofing. The primary contribution is therefore an integrated analysis that bridges the gap between cybersecurity theory and clinical practice, offering a guide for researchers and practitioners aiming to develop more secure, transparent, and patient-centric systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41157315/","authors":["Siqueira LP","Batista CL","Lui PH","Kazienko JF","Quincozes SE","Quincozes VE","Welfer D","Nomura S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 10","doi":"10.3390/s25206261","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41156160","name":"AI-Assisted Simple Scoring Algorithm Was Helpful in the Risk Assessment of Cardiac Involvement in Patients with Pulmonary Sarcoidosis.","source":"pubmed","abstract":"Background: Cardiac involvement, one of the most life-threatening complications of sarcoidosis, remains under-recognized due to its oligo-symptomatic presentation in some patients. This retrospective study aimed to evaluate the utility of various clinical predictors of cardiac sarcoidosis (CS) development. Methods: The study included patients with pulmonary sarcoidosis diagnosed according to the recent ATS guidelines between January 2020 and July 2024 who underwent cardiac magnetic resonance (CMR) due to clinical suspicion of CS. The original Lake Louise criteria were used to identify active myocarditis. Results: Out of 393 patients diagnosed with pulmonary sarcoidosis, CMR was performed in 92 patients. Cardiac sarcoidosis was confirmed in 48 patients (52%, CS+), and excluded in 44 patients (48%, CS-). CS(+) patients demonstrated significantly more frequent Holter ECG abnormalities and liver/spleen sarcoidosis compared to CS(-) patients. Stage IV pulmonary disease, ECG abnormalities, and hypercalcemia were more common in CS(+) than in CS(-) patients; however, these differences did not reach statistical significance. Multivariate analysis identified Holter ECG abnormalities and liver/spleen involvement as significant predictive factors for CS, increasing the risk of cardiac involvement by approximately 4- and 6-fold, respectively. An AI-assisted simple scoring system based on five parameters: ECG abnormalities, Holter ECG abnormalities, liver/spleen involvement, gender, and stage of sarcoidosis predicted CS with a sensitivity of 76% and specificity of 74%, using an optimal cut-off value of &#x2265;7.6 points. Conclusions: In patients with pulmonary sarcoidosis, an AI-assisted scoring algorithm derived from L1-regularized logistic regression results accurately predicted cardiac involvement on CMR with high specificity and sensitivity. Prospective validation of this algorithm is necessary to confirm its clinical utility in predicting cardiac sarcoidosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41156160/","authors":["Dybowska M","Tomkowski WZ","Lewandowska KB","Piotrowska-Kownacka D","Sobiecka M","Kempisty A","Opoka L","Radwan-Rohrenschef P","Wyrostkiewicz D","Szturmowicz M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 15","doi":"10.3390/jcm14207290","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41155485","name":"Enhancing Local Functional Structure Features to Improve Drug-Target Interaction Prediction.","source":"pubmed","abstract":"Molecular simulation is central to modern drug discovery but is often limited by high computational cost and the complexity of molecular interactions. Deep-learning drug-target interaction (DTI) prediction can accelerate screening; however, many models underuse the local functional structure features-binding motifs, reactive groups, and residue-level fragments-that drive recognition. We present LoF-DTI, a framework that explicitly represents and couples such local features. Drugs are converted from SMILES into molecular graphs and targets from sequences into feature representations. On the drug side, a Jumping Knowledge (JK) enhanced Graph Isomorphism Network (GIN) extracts atom- and neighborhood-level patterns; on the target side, residual CNN blocks with progressively enlarged receptive fields, augmented by N-mer substructural statistics, capture multi-scale local motifs. A Gated Cross-Attention (GCA) module then performs atom-to-residue interaction learning, highlighting decisive local pairs and providing token-level interpretability through attention scores. By prioritizing locality during both encoding and interaction, LoF-DTI delivers competitive results across multiple benchmarks and improves early retrieval relevant to virtual screening. Case analyses show that the model recovers known functional binding sites, suggesting strong potential to provide mechanism-aware guidance for molecular simulation and to streamline the drug design pipeline.","url":"https://pubmed.ncbi.nlm.nih.gov/41155485/","authors":["Feng B","Du H","Tong HHY","Wang X","Li K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 20","doi":"10.3390/ijms262010194","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41155084","name":"MAMGN-HTI: A Graph Neural Network Model with Metapath and Attention Mechanisms for Hyperthyroidism Herb-Target Interaction Prediction.","source":"pubmed","abstract":"The accurate prediction of herb-target interactions is essential for the modernization of traditional Chinese medicine (TCM) and the advancement of drug discovery. Nonetheless, the inherent complexity of herbal compositions and diversity of molecular targets render experimental validation both time-consuming and labor-intensive. We propose a graph neural network model, MAMGN-HTI, which integrates metapaths with attention mechanisms. A heterogeneous graph consisting of herbs, efficacies, ingredients, and targets is constructed, where semantic metapaths capture latent relationships among nodes. An attention mechanism is employed to dynamically assign weights, thereby emphasizing the most informative metapaths. In addition, ResGCN and DenseGCN architectures are combined with cross-layer skip connections to improve feature propagation and enable effective feature reuse. Experiments show that MAMGN-HTI outperforms several state-of-the-art methods across multiple metrics, exhibiting superior accuracy, robustness, and generalizability in HTI prediction and candidate drug screening. Validation against literature and databases further confirms the model's predictive reliability. The model also successfully identified herbs with potential therapeutic effects for hyperthyroidism, including Vinegar-processed Bupleuri Radix (Cu Chaihu), Prunellae Spica (Xiakucao), and Processed Cyperi Rhizoma (Zhi Xiangfu). MAMGN-HTI provides a reliable computational framework and theoretical foundation for applying TCM in hyperthyroidism treatment, providing mechanistic insights while improving research efficiency and resource utilization.","url":"https://pubmed.ncbi.nlm.nih.gov/41155084/","authors":["Zhou Y","Yang X","Lv R","Lang X","Zhu Y","Zhou Z","She K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 5","doi":"10.3390/bioengineering12101085","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41154350","name":"Artificial Intelligence for Risk-Benefit Assessment in Hepatopancreatobiliary Oncologic Surgery: A Systematic Review of Current Applications and Future Directions on Behalf of TROGSS-The Robotic Global Surgical Society.","source":"pubmed","abstract":"Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk-benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk-benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk-benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41154350/","authors":["Goyal A","Koutentakis M","Park J","Macias CA","Ballard I","Law SH","Babu A","Lau ECA","Mendoza M","Acosta SVJ","Abou-Mrad A","Marano L","Oviedo RJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 11","doi":"10.3390/cancers17203292","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41154232","name":"Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.","source":"pubmed","abstract":"Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn-Predict-Monitor-Detect-Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020-2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41154232/","authors":["Boussi Rahmouni H","Hassine NBEH","Chouchen M","Ceylan Hİ","Muntean RI","Bragazzi NL","Dergaa I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 10","doi":"10.3390/healthcare13202553","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41153235","name":"Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.","source":"pubmed","abstract":"Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition, contributing significantly to global morbidity and mortality. Traditional diagnostic tools are effective in diagnosing COPD. However, these tools demand specialized equipment and expertise. Advances in artificial intelligence (AI) provide a platform for enhancing COPD diagnosis by leveraging diverse data modalities. The existing reviews primarily focus on single modalities and lack information on interpretability and explainability. Thus, this review intends to synthesize the AI-powered frameworks for COPD identification, focusing on data modalities, methodological innovation, evaluation strategies, and reporting limitations and potential biases. By adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across multiple repositories. From an initial pool of 1978 records, 22 studies were included in this review. The included studies demonstrated exceptional performance in specific settings. Most studies were retrospective and limited in diversity, lacking generalizability and external or prospective validation. This review presents a roadmap for advancing AI-assisted COPD detection. By highlighting the strengths and limitations of existing studies, it supports the development of future research. Future studies can utilize the findings to build models using prospective, multicenter, and multi-ethnic validations, ensuring generalizability and fairness.","url":"https://pubmed.ncbi.nlm.nih.gov/41153235/","authors":["Sait ARW","Shaikh MA","Abdul Rahaman Wahab Sait","Mujeeb Ahmed Shaikh"],"tags":["Generalizability theory","Interpretability","COPD","Medicine","Pulmonary disease"],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 11","doi":"10.3390/diagnostics15202562","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"pmid:41152879","name":"Beyond Pathology: A Procedure-Based Approach to Planning and Predicting Outcomes in Robotic Gynaecological Oncology Surgery.","source":"pubmed","abstract":"Current surgical planning in robotic gynaecology relies heavily on pathological diagnosis, yet operating theatre utilisation may depend more on procedural requirements. Recent advances in machine learning-based surgical prediction have highlighted the need for more accurate planning models whilst challenging fundamental assumptions about surgical complexity.","url":"https://pubmed.ncbi.nlm.nih.gov/41152879/","authors":["Abdelaziz MAM","Olaleye A","Sabrah K","Mohamed A","Fayad M","Gajjar K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 28","doi":"10.1186/s12893-025-03217-9","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41152658","name":"Performance of Natural Language Processing Model in Extracting Information from Free-Text Radiology Reports: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"The free-text format is widely used in radiology reports for its flexibility of expression; however, its unstructured nature leads to substantial amounts of report data remaining underutilized. A natural language processing (NLP) model for automatic extraction of information from free-text radiology reports can significantly contribute to the development of structured databases, thereby optimizing data utilization. This study aimed to perform a systematic review and meta-analysis that evaluates the performance of NLP systems in extracting information from free-text radiology reports. A systematic literature search was conducted from November 21 to 23, 2024, in PubMed/MEDLINE, Embase, EBSCO, Ovid, Web of Science, and the Cochrane Library. Study quality was assessed using the QUADAS-2 tool. A bivariate random-effects model was applied to obtain the pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating characteristic curve (AUC). Subgroup analyses (e.g., NLP model types, dataset source, and language types) and a random-effects multivariable meta-regression based on the restricted maximum likelihood (REML) method were conducted to explore potential sources of heterogeneity. Sensitivity analyses (excluding high-risk studies, leave-one-out method, and data integration strategy comparison) were performed to assess the robustness of the findings. A total of 28 studies were included in the final analysis, with 421,692 extracted entities in 51,187 free-text radiology reports. NLP systems achieved high pooled sensitivity (91% [95% CI: 87, 93]) and specificity (96% [95% CI: 93, 97]), with a diagnostic odds ratio of 220 (95% CI: 112, 435) and an area under the curve of 0.98 (95% CI: 0.96, 0.99). Subgroup analysis revealed significantly better performance for extracting single anatomical sites (AUC 0.99; 95% CI: 0.97, 0.99) compared with multiple sites (AUC 0.95; 95% CI: 0.93, 0.97; p&#x2009;=&#x2009;0.001). No significant differences were observed across NLP model types, dataset sources, external validations, languages, or imaging modalities. Multivariable meta-regression further identified anatomical site as the only significant contributor to heterogeneity (coefficient&#x2009;=&#x2009;2.26; 95% CI: 0.25, 4.27; p&#x2009;=&#x2009;0.027). Sensitivity analyses confirmed the robustness of the findings, and no evidence of publication bias was detected. NLP models demonstrated excellent performance in extracting information from free-text radiology reports. However, the observed heterogeneity highlights the need for enhanced report standardization and improved model generalizability.","url":"https://pubmed.ncbi.nlm.nih.gov/41152658/","authors":["Yang Q","Jiang J","Dong X","Yang H","Wang Q","Yang Z","Yang D","Liu P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1007/s10278-025-01728-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41152527","name":"International expert consensus-driven surgical process model for robot-assisted hysterectomy: Delphi study results.","source":"pubmed","abstract":"Despite the widespread use of robot-assisted total laparoscopic hysterectomy (rTLH), there is still significant variability in how the procedure is performed, leading to inconsistencies in surgical outcomes and challenges in training. While existing curricula focus on technical skills, they lack formal models that capture procedural logic and variability. An expert-validated, standardized SPM is essential for improving reproducibility, enhancing surgical education, and enabling integration with artificial intelligence (AI)-driven systems. We sought to develop the first consensus-based surgical process model (SPM) for standard rTLH SPM (e.g., normal BMI, non-enlarged uterus) using a Delphi methodology involving international experts.","url":"https://pubmed.ncbi.nlm.nih.gov/41152527/","authors":["Nyangoh Timoh K","Galuret S","Hébert T","Azaïs H","Barahona M","Becker S","Bolze PA","Boisramé T","Borghese B","Carbonnel M","Cela V","Chauleur C","Chalhoub T","Cheung TH","Closon F","Crochet P","Dabi Y","De Landsheere L","Faller E","Fanfani F","Gauthier T","Gotlieb W","Ianieri MM","Ind T","Kim TJ","Koskas M","Ng J","Merlot B","Paek J","Philip CA","Raimondo D","Roman H","Rosendal M","Seracchioli R","Simoncini T","Tran PL","Tourette CM","Huaulmé A","Jannin P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1007/s00464-025-12339-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41152475","name":"Customized Education Sheets Generated by ChatGPT Improve Parental Visit Satisfaction and Procedural Knowledge Prior to Pediatric Cardiac Catheterization.","source":"pubmed","abstract":"Written educational materials have been found to be effective in the delivery of pre-operative information. However, creating personalized educational materials using large-language models (LLM) such as GPT-4 tailored to parental educational level has not yet been described in pediatric cardiology. A prospective single-center quality improvement study from December 2023 to March 2024 was completed with ChatGPT used to generate two pediatric cardiac catheterization information sheets at two different reading levels (6th and 10th grade) to improve procedural understanding by families. Surveys were distributed according to the highest level of education of the&#xa0;parent, along with a clinic satisfaction survey using a Likert scale. Twenty-six families were recruited. ChatGPT rapidly and accurately generated information sheets at the reading level requested. Mean pre- and post-education sheet Likert scale scores for \"do you feel well-informed why your child's cardiac catheterization is being done?\" were 4.27 and 4.85, respectively, with a significant mean improvement of 0.57 (p&#x2009;&lt;&#x2009;0.01). Families responded that the education sheet improved their overall satisfaction of the clinic visit with a mean survey score of 4.5&#x2009;&#xb1;&#x2009;0.8. 96% of families responded that the education sheet improved their understanding of the child's procedure. Our study demonstrates that LLMs such as GPT-4 can be valuable tools to adjust medical education to a specific reading level and augment a pre-procedural visit with healthcare professionals by improving clinic satisfaction and understanding of an otherwise complex procedure.","url":"https://pubmed.ncbi.nlm.nih.gov/41152475/","authors":["Pradhan S","McCormick K","Mertens L","Pradhan F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 28","doi":"10.1007/s00246-025-04060-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41150047","name":"Current Trends and Future Opportunities of AI-Based Analysis in Mesenchymal Stem Cell Imaging: A Scoping Review.","source":"pubmed","abstract":"This scoping review explores the application of artificial intelligence (AI) methods for analyzing mesenchymal stem cells (MSCs) images. The aim of this study was to identify key areas where AI-based image processing techniques are utilized for MSCs analysis, assess their effectiveness, and highlight existing challenges. A total of 25 studies published between 2014 and 2024 were selected from six databases (PubMed, Dimensions, Scopus, Google Scholar, eLibrary, and Cochrane) for this review. The findings demonstrate that machine learning algorithms outperform traditional methods in terms of accuracy (up to 97.5%), processing speed and noninvasive capabilities. Among AI methods, convolutional neural networks (CNNs) are the most widely employed, accounting for 64% of the studies reviewed. The primary applications of AI in MSCs image analysis include cell classification (20%), segmentation and counting (20%), differentiation assessment (32%), senescence analysis (12%), and other tasks (16%). The advantages of AI methods include automation of image analysis, elimination of subjective biases, and dynamic monitoring of live cells without the need for fixation and staining. However, significant challenges persist, such as the high heterogeneity of the MSCs population, the absence of standardized protocols for AI implementation, and limited availability of annotated datasets. To advance this field, future efforts should focus on developing interpretable and multimodal AI models, creating standardized validation frameworks and open-access datasets, and establishing clear regulatory pathways for clinical translation. Addressing these challenges is crucial for accelerating the adoption of AI in MSCs biomanufacturing and enhancing the efficacy of cell therapies.","url":"https://pubmed.ncbi.nlm.nih.gov/41150047/","authors":["Solopov M","Chechekhina E","Turchin V","Popandopulo A","Filimonov D","Burtseva A","Ishchenko R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 18","doi":"10.3390/jimaging11100371","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41150011","name":"Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.","source":"pubmed","abstract":"Despite rapid proliferation of AI applications in prostate MRI showing impressive technical performance, clinical adoption remains limited. We conducted a comprehensive narrative review of literature from January 2018 to December 2024, examining AI applications in prostate MRI with emphasis on real-world performance and implementation challenges. Among 200+ studies reviewed, AI systems achieve 87% sensitivity and 72% specificity for cancer detection in research settings. However, external validation reveals average performance drops of 12%, with some implementations showing degradation up to 31%. Only 31% of studies follow reporting guidelines, 11% share code, and 4% provide model weights. Seven real-world implementation studies demonstrate integration times of 3-14 months, with one major center terminating deployment due to unacceptable false positive rates. The translation gap between artificial and clinical intelligence remains substantial. Success requires shifting focus from accuracy metrics to patient outcomes, establishing transparent reporting standards, developing realistic economic models, and creating appropriate regulatory frameworks. The field must combine methodological rigor, clinical relevance, and implementation science to realize AI's transformative potential in prostate cancer care.","url":"https://pubmed.ncbi.nlm.nih.gov/41150011/","authors":["Bolocan VO","Mitoi A","Nicu-Canareica O","Băean ML","Medar C","Popa GA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep 26","doi":"10.3390/jimaging11100335","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41149665","name":"'Looking Back and Looking Forward'-Insights into the 20th European Doctoral Conference in Nursing Science (EDCNS).","source":"pubmed","abstract":"Background : The European Doctoral Conference in Nursing Science provides a unique platform for doctoral students in nursing and health sciences to present their research in a supportive environment. Celebrating its 20th anniversary, the 2024 conference embraced the motto \"looking back and looking forward,\" offering an opportunity to reflect on the development of nursing science and future challenges. Results : Held at the Medical University of Graz, Austria, the conference hosted 90 participants from 13 countries. It featured two keynote lectures, three workshops, 48 presentations, and a science slam. Abstract submissions underwent peer review to ensure the quality of presentations. The presentations highlighted key challenges and opportunities across nursing practice, healthcare work environments, education and digitalization in nursing, and health perspectives. Topics included, for example, workforce retention, artificial intelligence in nursing practice, leadership in error management, and culturally sensitive care. The keynotes emphasized the importance of patient and public involvement in research and the benefits of survey data in nursing science. Workshops imparted knowledge and skills regarding funding acquisition, guideline development, and effective research presentation. A science slam introduced innovative and creative ways to present research. Conclusions : The conference showcased the evolving landscape of nursing science, emphasizing the importance of evidence-based practice, supportive working conditions, and constructive collaboration. It demonstrated the enthusiasm and readiness of a new generation of researchers to advance nursing science in a rapidly changing healthcare environment.","url":"https://pubmed.ncbi.nlm.nih.gov/41149665/","authors":["Lampersberger LM","Osmancevic S","Pichler E","Lucien B","Rosendahl Huber S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep 26","doi":"10.3390/nursrep15100350","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41149453","name":"Deep Learning-Based 30-Day Mortality Prediction in Critically Ill Bone and Bone Marrow Metastasis Patients: A Multicenter Retrospective Cohort Study.","source":"pubmed","abstract":"Bone and bone marrow Metastasis (BBM) are life-threatening complications of advanced malignancies, frequently requiring intensive care and associated with high short-term mortality. However, prognostic tools specifically tailored to critically ill BBM patients are limited. This multicenter cohort study aimed to develop and validate deep learning models for predicting 30-day mortality using ICU data from MIMIC-IV, eICU-CRD, and the First Affiliated Hospital of Xinjiang Medical University. After univariate screening, XGBoost-Boruta and Lasso regression identified 11 key clinical features within 24 h of ICU admission. Thirteen deep learning models were trained using five-fold cross-validation, and their performance was evaluated through AUC, average precision, calibration, and decision curves. TabNet achieved the best internal performance (AUC 0.878; AP 0.940) and maintained strong discrimination in both same-region (eICU: AUC 0.840; AP 0.932) and cross-regional (Xinjiang: AUC 0.831; Accuracy 80.5%) validation. SHAP and attention-based interpretability analyses consistently identified SOFA, serum calcium, and albumin as dominant predictors. A TabNet-based online calculator was subsequently deployed to enable bedside mortality risk estimation. In conclusion, TabNet demonstrates potential as an accurate and interpretable tool for early mortality risk stratification in critically ill BBM patients, offering support for more timely and individualized decision-making in BBM-related critical care.","url":"https://pubmed.ncbi.nlm.nih.gov/41149453/","authors":["Wang Y","Xia L","Tang Y","Li W","Cui J","Luo X","Jiang H","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep 24","doi":"10.3390/curroncol32100533","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41149170","name":"Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.","source":"pubmed","abstract":"(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85-95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10-15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/41149170/","authors":["Gkintoni E","Halkiopoulos C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep 23","doi":"10.3390/biomimetics10100640","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41148252","name":"A bibliometric and critical trend analysis of the anti-cancer potential of Withania somnifera during 2000-2024.","source":"pubmed","abstract":"Cancer remains among the most common causes of mortality and morbidity globally. For millennia, people have utilized herbal medicine for the treatment of various illnesses, including cancer. Multiple research investigations have demonstrated that Withania somnifera possesses significant anti-cancer potential by targeting multiple cancer signalling pathways. Currently, many articles are published regarding Withania somnifera as an anti-cancer agent; however, no SCOPUS-based bibliometric study encompassing a critical analysis of research trends on the subject has been conducted thus far. This review study provides a concise overview of 272 articles published between 2000 and 2024, focusing on research trends and bibliometric studies of anti-cancer properties of Withania somnifera. The article is structured into two main sections. The first section explores global trends, performance analysis metrics, and citation-related metrics. The final section presents a bibliometric evaluation of the research. Data were retrieved from SCOPUS using specific keywords and then manually curated to eliminate duplicates. VOS viewer (version 1.6.20) and Microsoft Excel 2019 were utilized for data visualization. The output of publications about this subject domain reached its peak in 2024. Academia and industry collaborated to publish 2.20% of the articles. The USA (125), India (112), the Department of Pharmacology and Chemical Biology at the University of Pittsburgh (25), the Journal of Ethnopharmacology (25), Withania somnifera (124), and Renu Wadhwa (24) were the most prolific entities in terms of country, institution, publisher, keyword, and author. Withaferin A (47) was the most utilized bioactive compound against cancer, particularly breast cancer (15 studies). Despite the advantageous anti-cancer attributes of Withania somnifera, there remains a deficiency in areas such as clinical data, pharmacokinetic characteristics, safety and toxicity assessments, and effective formulations, which impedes the progression of this potent natural product into clinical development.","url":"https://pubmed.ncbi.nlm.nih.gov/41148252/","authors":["Hingorani L","Karmakar M","Mukherjee S","Modi SJ","Mulay V","Gota V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s00210-025-04618-6","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41146440","name":"Exploring an LLM's Use in Supporting Journal Club Preparation and Discussion Among Residents.","source":"pubmed","abstract":"Journal clubs (JCs) play an important role in medical education by promoting critical appraisal and evidence-based practice. However, residents often face barriers to effective participation. Some of the issues that are commonly faced include limited time and difficulty understanding complex concepts and statistics.","url":"https://pubmed.ncbi.nlm.nih.gov/41146440/","authors":["Umer F","Mansoor A","Naseem A","Kazmi SMR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/jdd.70072","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41146428","name":"CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.","source":"pubmed","abstract":"Colony-stimulating factor-1 receptor (CSF1R) signaling is crucial for the ability of tumor-associated macrophages (TAMs) to establish an immunosuppressive tumor microenvironment (TME), highlighting the potential of CSF1R signaling as a therapeutic target. Noninvasive preoperative prediction of CSF1R levels in gliomas using magnetic resonance imaging (MRI) holds clinical potential for guiding immunotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41146428/","authors":["Fan X","Yuan C","Tao J","Tu J","Liu B","Daoud AM","Li Y","Wang Y","Chen H","Zhu F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 20","doi":"10.1097/CM9.0000000000003827","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41146155","name":"Academic misconduct and artificial intelligence use by medical students, interns and PhD students in Ukraine: a cross-sectional study.","source":"pubmed","abstract":"The issues regarding the use of artificial intelligence (AI) and academic integrity are important contemporary topics. There are no clear regulations governing the use of AI in academic institutions in Ukraine. This study aimed to explore the perceptions of medical students, interns, and PhD candidates about academic misconduct and AI use.","url":"https://pubmed.ncbi.nlm.nih.gov/41146155/","authors":["Lymar L","Kuchyn I","Bielka K","Puljak L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.1186/s12909-025-08100-y","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41146071","name":"Application of artificial intelligence in head and neck tumor segmentation: a comparative systematic review and meta-analysis between PET and PET/CT modalities.","source":"pubmed","abstract":"For the effective treatment planning of head and neck cancers, precise tumor segmentation is vital. The combination of artificial intelligence (AI) technology with imaging systems like positron emission tomography (PET) and PET/ computed tomography (PET/CT) has made attempts to automate these processes. Despite these attempts, the usefulness of AI segmentation with PET imaging compared to PET/CT still lacks clarity.","url":"https://pubmed.ncbi.nlm.nih.gov/41146071/","authors":["Hajimokhtari H","Soleymanpourshamsi T","Rostamian L","Yousefbeigi A","Jafari S","Rezaeiyazdi A","Askari M","Khalilian M","Vafaei P","Esfahaniani M","Spagnuolo G","Shahnaseri S","Soltani P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.1186/s12885-025-14881-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41146046","name":"The effect of kinesiophobia and successful aging on quality of life in older adults: machine learning approach.","source":"pubmed","abstract":"Kinesiophobia and successful aging are key factors affecting quality of life in older adults; kinesiophobia, the fear of movement, can lead to reduced physical activity, while successful aging promotes overall well-being.","url":"https://pubmed.ncbi.nlm.nih.gov/41146046/","authors":["Aydin MA","Yildirim N","Kizilarslan V","Emrem M","Polat Ş","Yildiz M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.1186/s12877-025-06482-8","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41145516","name":"CoSpine open access simultaneous cortico-spinal fMRI database of thermal pain and motor tasks.","source":"pubmed","abstract":"Simultaneous cortico-spinal functional magnetic resonance imaging (fMRI) enables non-invasive investigation of integrated central nervous system function, but acquisition challenges have restricted the availability of public datasets and slowed the development of advanced analytic methods. Here, we introduce the CoSpine database, the first open-access, BIDS-compliant cortico-spinal task-based fMRI resource (N&#x2009;=&#x2009;61), acquired using a novel single-field-of-view (FOV) imaging protocol covering the whole brain (including cortical, subcortical, brainstem, and cerebellar regions) and cervical spinal cord. The dataset contains raw images, field maps, physiological recordings, and BIDS event files from thermal pain and voluntary motor tasks. An optimized acquisition and preprocessing framework is provided, validated by quality-control metrics such as temporal signal-to-noise ratio and alignment precision. Spanning a broad age range and standardized paradigms, CoSpine serves as a reference for neuroimaging methods development (e.g., hyperalignment) and for&#xa0;artificial intelligence (AI) model benchmarking. Potential applications include sensorimotor phenotyping, studies of age-related neurodegeneration, and exploratory work in neurorehabilitation, while also supporting early-stage development of brain-computer interface (BCI) systems involving spinal activity and personalized neuromodulation strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/41145516/","authors":["Wei Z","Lin X","Zhang L","Guo L","Liu J","Hu L","Liu Y","Kong Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.1038/s41597-025-05982-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41144954","name":"Evaluating Large Language Models in Ophthalmology: Systematic Review.","source":"pubmed","abstract":"Large language models (LLMs) have the potential to revolutionize ophthalmic care, but their evaluation practice remains fragmented. A systematic assessment is crucial to identify gaps and guide future evaluation practices and clinical integration.","url":"https://pubmed.ncbi.nlm.nih.gov/41144954/","authors":["Zhang Z","Zhang H","Pan Z","Bi Z","Wan Y","Song X","Fan X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 27","doi":"10.2196/76947","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41144909","name":"Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.","source":"pubmed","abstract":"Sepsis is a life-threatening dysregulated host response to infection; early risk stratification is essential to guide intensive care.","url":"https://pubmed.ncbi.nlm.nih.gov/41144909/","authors":["Hu X","Gu X","Jin Y","Liang F","Wang J","Wang H","Tang D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 1","doi":"10.1097/SHK.0000000000002744","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41144544","name":"Neglected brucellosis in pediatric populations from non-endemic regions: Clinical manifestations and prediction of severe disease in Yunnan Province, China.","source":"pubmed","abstract":"Although Yunnan Province is not an endemic region for brucellosis, the disease remains a diagnostic and therapeutic challenge in children due to its atypical clinical manifestations and potential for severe complications.","url":"https://pubmed.ncbi.nlm.nih.gov/41144544/","authors":["Ma X","Cui P","Chen H","Guo Y","Huang Y","Yang X","Zhu Y","Bai H","Jiao F","Jin H","Li R","Tang Q","Wang Y","Luo Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1371/journal.pntd.0013645","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41144242","name":"An AI-Powered Lifestyle Intervention vs Human Coaching in the Diabetes Prevention Program: A Randomized Clinical Trial.","source":"pubmed","abstract":"Prediabetes is common, yet evidence-based lifestyle interventions are underutilized.","url":"https://pubmed.ncbi.nlm.nih.gov/41144242/","authors":["Mathioudakis N","Lalani B","Abusamaan MS","Alderfer M","Alver D","Dobs A","Kane B","McGready J","Riekert K","Ringham B","Shehadeh A","Vandi F","Wanigatunga AA","Zade D","Maruthur NM","AI-DPP Study Group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 16","doi":"10.1001/jama.2025.19563","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41143886","name":"Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.","source":"pubmed","abstract":"We evaluated foundation models for opportunistic osteoporosis screening from chest X-rays using a novel explainability framework. DINOv2 with low-rank adaptation achieved the best performance (AUC 0.93) while demonstrating clear clinical reasoning. Our findings highlight that explainability should be prioritized alongside accuracy in medical AI, enhancing trust in clinical deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/41143886/","authors":["Kim J","Kwak S","Lee H","Chang J","Park SM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1007/s00198-025-07727-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41142877","name":"Artificial Intelligence Use in Academic Applicant Screening: A Systematic Review.","source":"pubmed","abstract":"Escalating application volumes challenge holistic admissions review; artificial intelligence (AI) offers potential screening efficiencies. This systematic review examined AI algorithm use in academic admissions, categorizing types and evaluating objectives, performance, and ethical considerations.","url":"https://pubmed.ncbi.nlm.nih.gov/41142877/","authors":["Jiang T","Er S","Heron MJ","Zhu KJ","Yang R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1097/GOX.0000000000007177","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41142586","name":"Ambient AI-assited clinical documentation in surgical outpatient care: a preliminary study of usability, workflow, and patient experience.","source":"pubmed","abstract":"Ambient artificial intelligence (AI) scribes offer promise for reducing documentation burden, yet their effects on surgical practice have not been well defined. We conducted a pilot study of an ambient AI scribe across 79 ambulatory providers (three surgeons) in a multihospital system from December 2024 to February 2025. Surgeon adoption of the AI scribe ranged from 58% to 90% of visits. We evaluated workload via pre- and post-intervention surveys (NASA-TLX mental demand and perceived rush), burnout rates, scheduling capacity, and Epic Signal metrics (note length, time per note) and compared billing data for high utilizers between August and October 2024 and the pilot period. Average \"pajama time\" did not change significantly ( p =0.55). NASA-TLX mental demand decreased from 14 to 5 ( p =0.08) and perceived rush from 15 to 5 ( p =0.06). Burnout declined from 67% to 33%. Two surgeons reported the capacity to add three patients per clinic. The billing metric showed no significant changes. Undivided attention scores improved from 3.5 to 4.1 ( p &lt;0.0001). This preliminary data shows promise that ambient AI scribes in surgical clinics may reduce documentation burden and burnout, with potential gains in efficiency and throughput. Larger studies are warranted to further confirm these findings.","url":"https://pubmed.ncbi.nlm.nih.gov/41142586/","authors":["Harvey CJ","Wong V","Huynh W","Lee JP","Woo RK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1136/wjps-2025-001073","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41140909","name":"Swept-Source OCT Angiography Features of Melanotic Choroidal Tumors: An Analysis of 102 Consecutive Cases.","source":"pubmed","abstract":"To quantitatively analyze swept-source OCT angiography (SS-OCTA) features of choroidal melanoma and nevi, focusing on their distinguishing angiographic features.","url":"https://pubmed.ncbi.nlm.nih.gov/41140909/","authors":["Zhang R","Wu H","Li Y","Zhou W","Shi X","Yu C","Yang Y","Zhao H","Li H","Wang S","Dong J","Dong L","Shao L","Liu Y","Zhao X","Yu Z","Zhang G","Liu Y","Wei W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.xops.2025.100932","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41140446","name":"Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.","source":"pubmed","abstract":"Triple-negative breast cancer (TNBC), recognised as the most aggressive subtype of breast cancer, has a high recurrence rate and poor treatment outcomes. Current diagnosis relies heavily on immunohistochemistry, which can be time-consuming and costly, while prognostic stratification remains limited by traditional clinicopathological features. This study aimed to develop and validate an artificial intelligence (AI)-powered TNBC identification and prognosis prediction (TRIP) system using haematoxylin and eosin (H&amp;E)-stained pathology images.","url":"https://pubmed.ncbi.nlm.nih.gov/41140446/","authors":["Zhang XM","Zhou HJ","Chen Q","Wang X","Fu YJ","Jin C","Zhou FT","Wang JP","Cai QY","Wang JL","Luo B","Hu MT","Yao CY","Yang X","Xu YL","Zhang J","Chen H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.eclinm.2025.103557","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41140422","name":"Accuracy and Completeness of Bard and Chat-GPT 4 Responses for Questions Derived from the International Consensus Statement on Endoscopic Skull-Base Surgery 2019.","source":"pubmed","abstract":"Artificial intelligence large language models (LLMs), such as Chat Generative Pre-Trained Transformer 4 (Chat-GPT) by OpenAI and Bard by Google, emerged in 2022 as tools for answering questions, providing information, and offering suggestions to the layperson. These LLMs impact how information is disseminated and it is essential to compare their answers to experts in the corresponding field. The International Consensus Statement on Endoscopic Skull-Base Surgery 2019 (ICAR:SB) is a multidisciplinary international collaboration that critically evaluated and graded the current literature.","url":"https://pubmed.ncbi.nlm.nih.gov/41140422/","authors":["Abgin Y","Umemoto K","Goulian A","Vasquez M","Polster S","Wu A","Roxbury C","Soni P","Ahmed OG","Tang DM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1055/a-2436-4222","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41140420","name":"Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.","source":"pubmed","abstract":"Radiomics involves the extraction and analysis of numerous quantitative features of medical imaging which can add more information from radiological images often beyond initial comprehension of a clinician. Unlike deep learning, radiomics allows some understanding of identified quantitative features for clinical prediction. We sought to explore the current state of radiomics applications in the skull base literature.","url":"https://pubmed.ncbi.nlm.nih.gov/41140420/","authors":["Tenhoeve SA","Lefler S","Brown J","Owens MR","Rawson C","Tabachnick DR","Shaik K","Karsy M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1055/a-2436-8444","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41138670","name":"Patient perception of artificial intelligence in breast imaging: A pilot survey study.","source":"pubmed","abstract":"Artificial intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in breast imaging. Though radiologists appreciate its benefits and limitations, patients' receptiveness and understanding of AI in breast imaging remain unclear. We aim to investigate patients' preferences and perceptions of AI and its role in breast imaging interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/41138670/","authors":["Ameri S","Mehran N","Margolies LR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.clinimag.2025.110649","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41138525","name":"Developing a consumer- and clinician-led evidence-informed research agenda for public neonatal services.","source":"pubmed","abstract":"Research is a pillar of neonatal care, with millions of dollars invested towards improving outcomes for critically ill newborns. Traditionally, research programs have focused on primary research, rather than region-specific or family-focused needs, which adds to challenges for practice improvement or local translation. In contrast, research codesigned with families continues to emerge as a framework for individual and future programs, prioritising the voices of families and ex-neonates.","url":"https://pubmed.ncbi.nlm.nih.gov/41138525/","authors":["August D","Meyles C","Chapple L","Cooke L","de Barros Mederios P","Donovan T","Effeney K","Jefferies A","Ikitoelagi M","Ison S","Kapadia P","Koorts P","Malivoire M","Mclean MR","McKeown L","Lai MM","Weber L","Cole R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.aucc.2025.101441","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41137350","name":"Identification of key genes in cuproptosis during acute kidney injury through weighted gene co-expression network analysis.","source":"pubmed","abstract":"Acute kidney injury (AKI) is a serious condition characterized by a rapid decline in renal function, leading to severe complications. Recent findings suggest that cuproptosis-related genes (CuRGs) influence AKI mechanisms. This study investigated CuRGs' role in AKI progression and aimed to develop a predictive model for early diagnosis. We utilized the GSE30718 dataset to identify 46 CuRGs, with 16 differentially expressed CuRGs (DECuRGs) found via analysis with ggpubr and pheatmap. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted using clusterProfiler and Metascape, along with gene set enrichment and weighted gene co-expression network analysis to explore DECuRGs concerning AKI. We also developed machine learning models, including extreme gradient boosting, a generalized linear model, random forest, and a support vector machine, to predict AKI risk. Our analyses found 16 DECuRGs in samples from patients with AKI, which were significantly enriched in &#x3b1;-amino acid catabolism and copper ion homeostasis. A robust brown module of 2906 genes correlated with AKI was established via weighted gene co-expression network analysis. The intersection of module genes and DECuRGs identified 6 hub genes (NFE2L2, DLST, GLS, ATOX1, SF3B1, C6orf136). Machine learning results showed that extreme gradient boosting and random forest models had superior predictive performance, achieving the highest area under the curve values. CuRGs play a crucial role in the pathogenesis of AKI, and the predictive model developed in this study could enhance early diagnosis and guide therapeutic strategies. Future research should validate these biomarkers clinically to help improve patient diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41137350/","authors":["Gao X","Liu X","Liao H","Li R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.1097/MD.0000000000044592","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41137105","name":"Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury.","source":"pubmed","abstract":"Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.","url":"https://pubmed.ncbi.nlm.nih.gov/41137105/","authors":["Lukas LP","Håkansson S","Tuci M","Torres-Espín A","Rupp R","Taran O","Weidner N","Geisler F","Schubert M","Röhrich F","Kalke YB","Abel R","Maier D","Chhabra HS","Liebscher T","EMSCI study group","Kramer JLK","Bolliger M","Curt A","Jutzeler CR","Brüningk SC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.1186/s12916-025-04405-3","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41137088","name":"Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival.","source":"pubmed","abstract":"The World Health Organization has identified Stenotrophomonas maltophilia (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper therapy and the spread of resistance. This study assessed how an artificial intelligence-clinical decision support system (AI-CDSS) utilizing mass spectrometry data to predict resistance enhances prescribing decisions and boosts survival.","url":"https://pubmed.ncbi.nlm.nih.gov/41137088/","authors":["Lin TH","Chung HY","Jian MJ","Chang CK","Perng CL","Chang FY","Chen YH","Shang HS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.1186/s13012-025-01453-4","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41137082","name":"Use of machine learning for risk stratification of chest pain patients in the emergency department.","source":"pubmed","abstract":"To improve the initial risk assessment capability for emergency chest pain patients without relying on laboratory test results.","url":"https://pubmed.ncbi.nlm.nih.gov/41137082/","authors":["Li Y","Jiang S","Dai S","Jiang C","Yang F","Tu X","Wu M","Li C","Zhao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.1186/s12911-025-03226-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41136639","name":"Identification of hub necroptosis-related targets and discovery of potential natural inhibitors in ulcerative colitis based on bioinformatics and computer-aided drug design.","source":"pubmed","abstract":"Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a complex pathogenesis and limited treatment options. Recently, necroptosis has been found to play a significant role in UC. This study aimed to investigate necroptosis-related mechanisms and hub targets in UC, and to screen natural potential inhibitors. Firstly, transcriptomic and single-cell analyses were used to explore the molecular and cellular mechanisms of necroptosis in UC and identify hub targets. Subsequently, virtual screening and molecular dynamics were performed. The results indicated that twenty-three necroptosis-related differentially expressed genes (DEGs) were predicted as diagnostic biomarkers in the best machine-learning model (GBM). Furthermore, four hub targets (IL1B, MLKL, STAT1, and BIRC3) were computationally prioritized and their overexpression might promote pro-inflammatory activity (neutrophils/M1 macrophages) while suppressing anti-inflammatory responses (Tregs/M2 macrophages), aggravating UC progression. Single-cell analysis revealed reduced epithelial cells and increased fibroblasts, endothelial cells, and immune cells in UC tissues, suggesting disruption of the intestinal epithelial barrier, exacerbation of fibrosis, and activation of the immune system. The high abundance of endothelial cells and monocytes expressing necroptosis-related DEGs suggested the important role of necroptosis in UC. Moreover, eight natural products were screened with strong binding affinity to MLKL, whose motion trajectories and energy trajectories reached equilibrium within 10 ns. Among them, the potential of trifolirhizin and curcumin as natural inhibitors was particularly prominent. Conclusively, this study computationally predicts four hub DEGs and eight potential natural necroptosis inhibitors, may provide a basis for future therapeutic exploration.","url":"https://pubmed.ncbi.nlm.nih.gov/41136639/","authors":["Chen J","Feng C","Liu Y","Cai Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 24","doi":"10.1007/s10822-025-00674-5","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41136572","name":"Inflammatory indices as early predictors of mortality in mucormycosis.","source":"pubmed","abstract":"This study aimed to evaluate the predictive value of preoperative systemic inflammatory indices for in-hospital mortality in patients with sino-nasal-orbital mucormycosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41136572/","authors":["Celik B","Gul F","Serifler S","Bulut KS","Babademez MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1007/s00405-025-09766-2","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41135576","name":"Comparative reproductive biology, advances in reproductive health, and cultivating inclusion in the scientific community: highlights from the 2024 Annual Meeting of the Society for Reproductive Biology.","source":"pubmed","abstract":"In 2024, the reproductive biology research community in Australia and New Zealand reunited in Adelaide for the Society for Reproductive Biology (SRB) Annual Meeting. The conference showcased major advances made in key areas of reproductive biology, with symposia dedicated to five key themes: (1) exploring comparative reproductive biology across species; (2) the multifaceted impact of climate change on reproduction; (3) innovations in drug development and repurposing for reproductive health; (4) revolutionising fertility and assisted reproductive technologies; and (5) cultivating inclusion in the scientific community. This review summarises these symposia and discusses the relevant emerging research questions and the future research directions. Main advances from each theme include: (1) the utility of non-traditional model organisms and archival samples in research; (2) the importance of further understanding the effects of heat stress on male and female fertility during pregnancy in humans and broader species; (3) collaboration between researchers, industry and policymakers to overcome barriers in contraceptive development; (4) ensuring robust and ethical use of artificial intelligence for clinical and fundamental research; and (5) the significance of maintaining diversity and inclusion in science to promote discoveries and ensure health data is available for diverse population groups.","url":"https://pubmed.ncbi.nlm.nih.gov/41135576/","authors":["Chan HY","Alesi LR","Dinh DT","Foyle KL","Hofstee P","Hutchison JC","Stables J","Trigg NA","Wooldridge AL","Houston BJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov 24","doi":"10.1071/RD25169","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41135418","name":"Artificial Intelligence and radiomics models for the diagnosis and prognosis of peritoneal metastases on imaging: a systematic review and meta-analysis.","source":"pubmed","abstract":"Peritoneal metastases (PM) significantly impact treatment options and prognosis of patients with cancer. Early detection and accurate evaluation are essential for guiding clinical decisions. This systematic review and meta-analysis aimed to provide a comprehensive overview and evaluate the performance of Artificial Intelligence (AI) and radiomics models for diagnosis and prognosis of PM on imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/41135418/","authors":["Fleurkens-Ewals LJS","Tops-Welten M","Claessens CHB","Piek JMJ","van Hellemond IEG","van der Sommen F","Lahaye MJ","de Hingh IHJT","Luyer MDP","Nederend J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.compbiomed.2025.111188","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41133062","name":"Faculty Perspectives on Integrating Artificial Intelligence Into Orthodontic and Interdisciplinary Dental Education: Opportunities, Challenges, and Strategies.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming dental education by enhancing diagnostic precision and personalized learning. This study aimed to investigate faculty perspectives on integrating AI into orthodontic and interdisciplinary dental curricula and identifying opportunities, challenges, and strategies to prepare students for technology-driven practice. The objectives included exploring AI's potential in enhancing diagnostic tools and learning systems, identifying barriers such as faculty training gaps and ethical concerns, and developing evidence-based strategies for AI adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/41133062/","authors":["Chutia J","Rupali R","Jain M","Angel A","Agarwal P","Sawhney C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Sep","doi":"10.7759/cureus.92877","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41132441","name":"Evaluating the effectiveness of AI-enhanced \"One Body, Two Wings\" pharmacovigilance models in China: a nationwide survey on medication safety and risk management.","source":"pubmed","abstract":"This study evaluates the effectiveness of AI-enhanced \"One Body Two Wings\" pharmacovigilance models in China, focusing on improving medication safety and risk management. As the pharmaceutical landscape grows more complex, integrating AI into pharmacovigilance offers the potential to enhance adverse drug reaction (ADR) detection and monitoring.","url":"https://pubmed.ncbi.nlm.nih.gov/41132441/","authors":["Yang J","Sun Y","Li F","Wei Q","Wei J","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/frhs.2025.1655726","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41131756","name":"[Development and dissemination of precision medicine approaches in gastric cancer management].","source":"pubmed","abstract":"Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project \"Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management\". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.","url":"https://pubmed.ncbi.nlm.nih.gov/41131756/","authors":["Li Z","Ji J","Li G","Li Z","Bu Z","Gao X","Dong D","Tang L","Xing X","Jia S","Guo T","Zhang L","Shan F","Ji X","Wang A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 18","doi":"10.19723/j.issn.1671-167X.2025.05.009","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41131470","name":"Artificial intelligence-assisted colonoscopy improves adenoma detection rates in routine colonoscopy practice: a single-center, retrospective, propensity score-matched study with concurrent controls.","source":"pubmed","abstract":"This study aimed to investigate whether a real-time artificial intelligence (AI)-assisted polyp detection system can improve adenoma detection rates (ADRs) in real-world colonoscopy practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41131470/","authors":["Ham DY","Lee JG","Ahn CI","Kae SH","Jang HJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 23","doi":"10.1186/s12876-025-04011-w","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41128787","name":"Barriers, facilitators, and implementation strategies for pharmacogenomics in community pharmacies: a cross-sectional survey among local champions in pharmacies and key opinion leaders in pharmacogenomics.","source":"pubmed","abstract":"Pharmacogenomics (PGx) tailors drug treatments to an individual's genetic profile and contributes to improved efficacy and reduced adverse drug reactions. Community pharmacists have shown interest in PGx, and Dutch pharmacists have been early adopters in applying PGx guidelines, particularly through integration of the Dutch Pharmacogenetics Working Group recommendations. Despite growing evidence of its benefits, large-scale implementation in community pharmacies remains limited. This raises an important question for global stakeholders: if PGx adoption is constrained even in a system with robust infrastructure and guidelines, what lessons can be drawn for broader implementation?","url":"https://pubmed.ncbi.nlm.nih.gov/41128787/","authors":["Kiani P","Bet PM","Jessurun NT","Hoogland P","Mentink J","Swen JJ","Borgsteede SD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1007/s11096-025-02022-x","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41128430","name":"Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.","source":"pubmed","abstract":"Abstract Background Nowadays, generative artificial intelligence (GAI) drives medical education toward enhanced intelligence, personalization, and interactivity. With its vast generative abilities and diverse applications, GAI redefines how educational resources are accessed, teaching methods are implemented, and assessments are conducted. Objective This study aimed to review the current applications of GAI in medical education; analyze its opportunities and challenges; identify its strengths and potential issues in educational methods, assessments, and resources; and capture GAI’s rapid evolution and multidimensional applications in medical education, thereby providing a theoretical foundation for future practice. Methods This scoping review used PubMed, Web of Science, and Scopus to analyze literature from January 2023 to October 2024, focusing on GAI applications in medical education. Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 5991 articles were retrieved, with 1304 duplicates removed. The 2-stage screening (title or abstract and full-text review) excluded 4564 articles and a supplementary search included 8 articles, yielding 131 studies for final synthesis. We included (1) studies addressing GAI’s applications, challenges, or future directions in medical education, (2) empirical research, systematic reviews, and meta-analyses, and (3) English-language articles. We excluded commentaries, editorials, viewpoints, perspectives, short reports, or communications with low levels of evidence, non-GAI technologies, and studies centered on other fields of medical education (eg, nursing). We integrated quantitative analysis of publication trends and Human Development Index (HDI) with thematic analysis of applications, technical limitations, and ethical implications. Results Analysis of 131 articles revealed that 74.0% (n=97) originated from countries or regions with very high HDI, with the United States contributing the most (n=33); 14.5% (n=19) were from high HDI countries, 5.3% (n=7) from medium HDI countries, and 2.2% (n=3) from low HDI countries, with 3.8% (n=5) involving cross-HDI collaborations. ChatGPT was the most studied GAI model (n=119), followed by Gemini (n=22), Copilot (n=11), Claude (n=6), and LLaMA (n=4). Thematic analysis indicated that GAI applications in medical education mainly embody the diversification of educational methods, scientific evaluation of educational assessments, and dynamic optimization of educational resources. However, it also highlighted current limitations and potential future challenges, including insufficient scene adaptability, data quality and information bias, overreliance, and ethical controversies. Conclusion GAI application in medical education exhibits significant regional disparities in development, and model research statistics reflect researchers’ certain usage preferences. GAI holds potential for empowering medical education, but widespread adoption requires overcoming complex technical and ethical challenges. Grounded in symbiotic agency theory, we advocate establishing the resource-method-assessment tripartite model, developing specialized models and constructing an integrated system of general large language models incorporating specialized ones, promoting resource sharing, refining ethical governance, and building an educational ecosystem fostering human-machine symbiosis, enabling deep tech-humanism integration and advancing medical education toward greater efficiency and human-centeredness.","url":"https://pubmed.ncbi.nlm.nih.gov/41128430/","authors":["Lin Y","Luo Z","Ye Z","Zhong N","Zhao L","Zhang L","Li X","Chen Z","Chen Y","Yuhang Lin","Zhiheng Luo","Zicheng Ye"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 23","doi":"10.2196/71125","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"pmid:41127561","name":"AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study.","source":"pubmed","abstract":"Neoadjuvant chemoimmunotherapy (NACI) has significantly improved survival in patients with resectable non-small cell lung cancer (NSCLC). However, with the currently available methods (PD-L1, RECIST), it is difficult to predict who will benefit from treatment before therapy and who will achieve pathological complete response (pCR) before surgery. Non-invasive methods to predict treatment response to NACI could be used to further personalize treatment.","url":"https://pubmed.ncbi.nlm.nih.gov/41127561/","authors":["Ye G","Wei Z","Han C","Wu G","Wong C","Liang Y","Chen X","Zhou W","Gao J","Liang C","Liao Y","Hendriks LEL","Wee L","De Ruysscher D","Dekker A","Zhou H","Qi Y","Liu Z","Shi Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1016/j.eclinm.2025.103551","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41127425","name":"Cholera outbreaks: Public health implications, economic burden, and preventive strategies.","source":"pubmed","abstract":"Cholera remains a persistent and deadly global public health threat, with recent years witnessing a resurgence of large-scale outbreaks, particularly in conflict-affected and resource-limited regions. The disease disproportionately affects vulnerable populations lacking access to clean water, sanitation, and essential healthcare services.","url":"https://pubmed.ncbi.nlm.nih.gov/41127425/","authors":["Mohamed MG","Dabou EAA","Abdelsamad S","Elsalous SH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3934/publichealth.2025039","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41126845","name":"Utilization patterns of AI chatbots among physical therapy students: A multicountry cross-sectional study.","source":"pubmed","abstract":"To describe usage patterns of artificial intelligence (AI)-powered chatbots (AICs) among physical therapy (PT) students and explore the associations between students' demographic and academic characteristics and their patterns of AIC utilization.","url":"https://pubmed.ncbi.nlm.nih.gov/41126845/","authors":["El-Sobkey SB","Al-Amir Mohamed D","ElKholy M","Abdeldayem T","Fawzy A","Ahmed YF","El Khatib A","Khalid H","Alharbi MD","Fathy K","Takey K","Abdallah M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct","doi":"10.1016/j.jtumed.2025.08.005","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41126760","name":"A Hybrid Diffusion Model Enhances Multiparametric 3D Photoacoustic Computed Tomography.","source":"pubmed","abstract":"Photoacoustic computed tomography (PACT) reveals biological structures, pharmacokinetics, and physiological functions. Although a premium PACT system with many ultrasound (US) transducers delivers high-quality volumetric imaging, it suffers from high system costs and slow temporal resolution. Here, using a limited number of US elements, a hybrid diffusion model (HD-PACT) is demonstrated that enhances dynamic multiparametric (structural, functional, and contrast-enhanced) 3D PACT. Using just 256 out of the 1024 elements in a premier hemispherical US array for PACT, HD-PACT improves structural images acquired in different planes, organisms, and wavelengths. In functional imaging, HD-PACT enables 256-element PACT to observe hypoxia, pharmacokinetics, and angiogenesis during tumor progression. Lastly, HD-PACT is transferable to low-end PACT (only 128 US elements), where it dynamically captures contrast-free/enhanced organs, oxygen-perturbed brains, and cardiac dynamics with high spatiotemporal resolution in live animals. It is believed that HD-PACT will be valuable in oncology, cardiology, pharmacology, and endocrinology.","url":"https://pubmed.ncbi.nlm.nih.gov/41126760/","authors":["Jeong H","Oh S","Choi S","Kim J","Yang J","Kim C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1002/advs.202513624","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"pmid:41126229","name":"Explainable AI for infection prevention and control: modeling CPE acquisition and patient outcomes in an Irish hospital with transformers.","source":"pubmed","abstract":"Carbapenemase-Producing Enterobacteriace (CPE) poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and extended length of stay (LOS) remains underexplored, particularly with modern deep learning approaches. This study introduces an eXplainable AI (XAI) modeling framework to investigate CPE impact on patient outcomes from Electronic Medical Records (EMR) data of an Irish hospital.","url":"https://pubmed.ncbi.nlm.nih.gov/41126229/","authors":["Pham MK","Mai TT","Crane M","Brennan R","Ward ME","Geary U","Byrne D","O'Connell B","Bergin C","Creagh D","McDonald N","Bezbradica M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 22","doi":"10.1186/s12911-025-03214-1","addedAt":"2026-09-01T01:47:53.669Z","updatedAt":"2026-09-01T01:47:53.669Z"},{"id":"oa:W4409546651","name":"Artificial Intelligence in Oral Diagnosis: Detecting Coated Tongue with Convolutional Neural Networks","source":"openalex","abstract":"Background/Objectives: Coated tongue is a common oral condition with notable clinical relevance, often overlooked due to its asymptomatic nature. Its presence may reflect poor oral hygiene and can serve as an early indicator of underlying systemic diseases. This study aimed to develop a robust diagnostic model utilizing convolutional neural networks and machine learning classifiers to improve the detection of coated tongue lesions. Methods: A total of 200 tongue images (100 coated and 100 healthy) were analyzed. Images were acquired using a DSLR camera (Nikon D5500 with Sigma Macro 105 mm lens, Nikon, Tokyo, Japan) under standardized daylight conditions. Following preprocessing, feature vectors were extracted using CNN architectures (VGG16, VGG19, ResNet, MobileNet, and NasNet) and classified using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) classifiers. Performance metrics included sensitivity, specificity, accuracy, and F1 score. Results: The SVM + VGG19 hybrid model achieved the best performance among all tested configurations, with a sensitivity of 82.6%, specificity of 88.23%, accuracy of 85%, and an F1 score of 86.36%. Conclusions: The SVM + VGG19 model demonstrated high accuracy and reliability in diagnosing coated tongue lesions, highlighting its potential as an effective clinical decision support tool. Future research with larger datasets may further enhance model robustness and applicability in diverse populations.","url":"https://doi.org/10.3390/diagnostics15081024","authors":["Sümeyye Coşgun Baybars","Merve Hacer Talu","Çağla Danacı","Seda Arslan Tuncer"],"tags":["Artificial intelligence","Support vector machine","Convolutional neural network","Computer science","Preprocessor"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-17","doi":"https://doi.org/10.3390/diagnostics15081024","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416250669","name":"Reshaping Higher Education Designs and Futures: Postdigital Co-design with Generative Artificial Intelligence","source":"openalex","abstract":"Abstract This article examines how collaborative design practices in higher education are reshaped through postdigital entanglement with generative artificial intelligence (GenAI). We collectively explore how co-design, an inclusive, iterative, and relational approach to educational design and transformation, expands in meaning, practice, and ontology when GenAI is approached as a collaborator. The article brings together 19 authors and three open reviewers to engage with postdigital inquiry, structured in three parts: (1) a review of literature on co-design, GenAI, and postdigital theory; (2) 11 situated contributions from educators, researchers, and designers worldwide, each offering practice-based accounts of co-design with GenAI; and (3) an explorative discussion of implications for higher education designs and futures. Across these sections, we show how GenAI unsettles assumptions of collaboration, knowing, and agency, foregrounding co-design as a site of ongoing material, ethical, and epistemic negotiation. We argue that postdigital co-design with GenAI reframes educational design as a collective practice of imagining, contesting, and shaping futures that extend beyond human knowing.","url":"https://doi.org/10.1007/s42438-025-00595-4","authors":["Sandris Zeivots","Alison Casey","Tiffany Winchester","Jack Webster","Xin Wang","Linus Tan","Wina Smeenk","Frank P. Schulte","Antonia Scholkmann","Belinda Paulovich","Diego Muñoz","Joanne Mignone","Lilia Mantai","Stefan Hrastinski","Rebecca Godwin","Olov Engwall","Henrik Dindas","Marieke van Dijk","Laura Ann Chubb","Chrysi Rapanta","Jimmy Jaldemark","Sarah Hayes"],"tags":["Foregrounding","Situated","Generative grammar","Higher education","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-15","doi":"https://doi.org/10.1007/s42438-025-00595-4","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410293199","name":"Artificial intelligence and free will: generative agents utilizing large language models have functional free will","source":"openalex","abstract":"Abstract Combining large language models (LLMs) with memory, planning, and execution units has made possible almost human-like agentic behavior, where the artificial intelligence creates goals for itself, breaks them into concrete plans, and refines the tactics based on sensory feedback. Do such generative LLM agents possess free will? Free will requires that an entity exhibits intentional agency, has genuine alternatives, and can control its actions. Building on Dennett’s intentional stance and List’s theory of free will, I will focus on functional free will, where we observe an entity to determine whether we need to postulate free will to understand and predict its behavior. Focusing on two running examples, the recently developed Voyager, an LLM-powered Minecraft agent, and the fictitious Spitenik, an assassin drone, I will argue that the best (and only viable) way of explaining both of their behavior involves postulating that they have goals, face alternatives, and that their intentions guide their behavior. While this does not entail that they have consciousness or that they possess physical free will, where their intentions alter physical causal chains, we must nevertheless conclude that they are agents whose behavior cannot be understood without postulating that they possess functional free will.","url":"https://doi.org/10.1007/s43681-025-00740-6","authors":["Frank Martela"],"tags":["Generative grammar","Computer science","Artificial intelligence","Cognitive science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-12","doi":"https://doi.org/10.1007/s43681-025-00740-6","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415824542","name":"Artificial Intelligence in Clinical and Translational Science : From Bench Insights to Bedside Impact","source":"openalex","abstract":"The healthcare and life sciences sectors are experiencing a transformative moment that cannot be overlooked. Our biological understanding, technology, and data are coalescing to leverage unprecedented opportunities for innovation [1, 2]. At the center of this transformation lies Artificial Intelligence (AI) and Machine Learning (ML), which have advanced from speculation to working technologies that can make actual differences in patient care and drug development [3, 4]. Only a few years ago, AI was framed in the context of its potential in clinical pharmacology, drug discovery, and development. Following the 2024 Nobel Prize in Chemistry, which was awarded for the AI-based prediction of protein structure, it became increasingly difficult to ignore the scientific merit of the technology [5]. We are now experiencing implementations that are changing how we approach these disciplines [6-8]. We have transcended previous discussions about whether AI will help and are asking more nuanced questions about how we deploy these technologies in a responsible manner, such that they deliver reliable and reproducible results, and produce meaningful value in clinical and translational research. The AI-themed issue in Clinical and Translational Science addresses exactly these questions by collating different viewpoints from around the field and providing substantive evidence to demonstrate the breadth of the current AI applications that are transforming the practice of clinical and translational sciences (Table 1). From the earliest phases of drug discovery through design and optimization of clinical trials, from developing personalized treatment approaches to monitoring drug safety postmarket approval, and to collecting real-world evidence—these contributions illustrate the current state of the art in utilizing AI in drug discovery and development and also characterize our current capabilities, while providing a vision for future innovation within clinical and translational sciences. In this piece, we aggregate contributions from over 30 manuscripts in this special issue, organized into three general categories (Figure 1, Table S1): Discovery and Preclinical Innovation, Clinical Development and Precision Medicine, and Postmarketing, Safety, and Real-World Implementation. We also discuss the cross-cutting aspects of regulation, ethics, and operations that may ensure AI achieves its potential. As readers progress through this editorial, they will see that the contributions in this issue do more than showcase technical innovation. They reflect a growing maturity in how the clinical pharmacology and translational science communities approach AI, which is not being expressed as a ‘one-size-fits-all’ solution but rather as a toolkit, where the impact of AI will depend on thoughtful integration, appropriate validation, and a deeper understanding of both its strengths and limitations. At the beginning of the drug development continuum—where drug developers identify targets, discover biomarkers, and create preclinical models—AI's intuitive ability to assess and map the meaning within massive, multidimensional datasets is, more often than not, beginning to be leveraged. At the earliest stages of discovery science, AI's transformative property is not necessarily its computational speed; rather, it is AI's ability to spot deep and subtle patterns that statistical approaches have most often failed to recognize, especially nonlinear relationships. This special issue includes many contributions that demonstrate how AI can now enable broader hypothesis generation, more rapid compound optimization, and more biologically founded predictions. One area that is rapidly progressing is in silico pharmacokinetic (PK) and pharmacodynamic (PD) modeling. Walter et al., make a systematic examination of ML-based approaches to empirical, compartmental, and physiologically based pharmacokinetic (PBPK) models for predicting plasma PK profiles in rats [9]. In a study with ov","url":"https://doi.org/10.1111/cts.70383","authors":["Mohamed H. Shahin","Qi Liu"],"tags":["Bench to bedside","Transformative learning","Leverage (statistics)","Viewpoints","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-01","doi":"https://doi.org/10.1111/cts.70383","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411457325","name":"Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study","source":"openalex","abstract":"Background/Objectives: Different sized calcified masses called pulp stones are often detected in dental pulp and can impact dental procedures. The current research was conducted with the aim of measuring the ability of artificial intelligence algorithms to accurately diagnose pulp and pulp stone calcifications on panoramic radiographs. Methods: We used 713 panoramic radiographs, on which a minimum of one pulp stone was detected, identified retrospectively, and included in the study—4675 pulp stones and 5085 pulps were marked on these radiographs using CVAT v1.7.0 labeling software. Results: In the test dataset, the AI model segmented 462 panoramic radiographs for pulp stone and 220 panoramic radiographs for pulp. The dice coefficient and Intersection over Union (IoU) recorded for the Pulp Segmentation model were 0.84 and 0.758, respectively. Precision and recall were computed to be 0.858 and 0.827, respectively. The Pulp Stone Segmentation model achieved a dice coefficient of 0.759 and an IoU of 0.686, with precision and recall of 0.792 and 0.773, respectively. Conclusions: Pulp and pulp stones can successfully be identified using artificial intelligence algorithms. This study provides evidence that artificial intelligence software using deep learning algorithms can be valuable adjunct tools in aiding clinicians in radiographic diagnosis. Further research in which larger datasets are examined are needed to enhance the capability of artificial intelligence models to make accurate diagnoses.","url":"https://doi.org/10.3390/dj13060274","authors":["Mujgan Firincioglulari","Mehmet Boztuna","Omid Mırzaeı","Tolgay Karanfiller","Nurullah Akkaya","Kaan Orhan"],"tags":["Pulp (tooth)","Radiography","Dentistry","Artificial intelligence","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-19","doi":"https://doi.org/10.3390/dj13060274","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407675019","name":"Exploring the Acceptability of Artificial Intelligence in Human Resources Management: Insights From Swiss Organizations","source":"openalex","abstract":"ABSTRACT This study looks at perceptions of artificial intelligence (AI) systems in human resources (HR) management within Swiss organizations. Based on a survey experiment provided to 324 private and public HR professionals, it explores how UTAUT's predictors—performance expectancy, effort expectancy, social influence and facilitating conditions—as well as top management support, the Private/Public dimension and control variables—age, gender, time with organization and hierarchical position—influence their acceptability of four different type of AI HR tools. To do this, this article is based on a multiple regression method. Its main findings are that, irrespective of the type of tool, performance expectancy, effort expectancy and social influence positively influence the acceptability of the HR AI tools studied, whereas working in a public organization has systematically a negative influence. This makes a significant contribution to the literature by offering valuable insights into how these factors collectively shape the willingness of HR professionals to embrace AI technologies in their practices. It also offers an overview of the levers that organizations aiming to adopt these AI tools could act upon.","url":"https://doi.org/10.1002/sres.3140","authors":["Guillaume Revillod"],"tags":["Knowledge management","Human resource management","Business","Environmental resource management","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-18","doi":"https://doi.org/10.1002/sres.3140","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W2905094505","name":"Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles","source":"openalex","abstract":"Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.","url":"https://doi.org/10.3390/electronics7120405","authors":["Tiago M. Fernández‐Caramés","Paula Fraga‐Lamas"],"tags":["Clothing","Wearable computer","Computer science","Wearable technology","Smart objects"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-12-07","doi":"https://doi.org/10.3390/electronics7120405","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3185498586","name":"COVID-19 pandemic and artificial intelligence possibilities: A healthcare perspective","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.mjafi.2021.06.001","authors":["Rohit Gupta","Mahima Lall"],"tags":["Pandemic","Medicine","Population","World population","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-01","doi":"https://doi.org/10.1016/j.mjafi.2021.06.001","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4401992419","name":"Antidiscrimination Law Meets Artificial Intelligence—New Requirements for Health Care Organizations and Insurers","source":"openalex","abstract":"This JAMA Forum discusses new regulatory requirements for antidiscrimination in artificial intelligence tools used in health care, the dark side of flexible enforcement by agencies, and ways to facilitate meaningful compliance.","url":"https://doi.org/10.1001/jamahealthforum.2024.3397","authors":["Michelle M. Mello","Jessica L. Roberts"],"tags":["Compliance (psychology)","Enforcement","Law enforcement","Health care","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-29","doi":"https://doi.org/10.1001/jamahealthforum.2024.3397","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413055396","name":"Unleashing the Future of Endodontics: Exploring the Potential Role of Explainable Artificial Intelligence in Risk Stratification and Decision‐Making in Endodontics","source":"openalex","abstract":"The incorporation of artificial intelligence (AI) into endodontics has predominantly focused on augmenting diagnostic accuracy, particularly in pinpointing periradicular radiolucencies and delineating the complex architecture of root canal systems via radiographic imaging and cone beam computed tomography (CBCT). These advancements represent a pivotal development within dental practice [1]; however, they merely scratch the surface of AI's broader potential to revolutionise the field of dentistry. The decision-making process in endodontic clinical practice is multifaceted, involving a myriad of factors that extend beyond diagnostic capabilities. Clinicians are tasked with integrating a thorough understanding of the patient's dental and medical history, recognising the distinct anatomical variations presented by each tooth, evaluating the implications of previous dental interventions, and formulating coherent treatment plans based on the preferences of the patient. In this nuanced landscape, the advent of Explainable Artificial Intelligence (XAI) emerges as a crucial development, fostering transparency and trust in AI-assisted workflows and paving the way for its broader acceptance and implementation in clinical settings. Despite the impressive predictive performance of numerous deep learning models, a central characteristic of these systems is their ‘black box’ nature. They generate outputs without elucidating the underlying mechanisms driving these conclusions. This lack of transparency presents significant ethical and clinical dilemmas for healthcare professionals. Clinicians may be reluctant to adopt AI technologies that fail to provide comprehensible justifications for their recommendations, particularly as societal expectations shift towards greater accountability and clarity in AI-generated medical decisions [2]. Furthermore, the expansion of regulatory frameworks aimed at enforcing ethical standards in healthcare underscores the imperative for transparency. This shift reinforces the notion that AI tools must not only achieve high accuracy but also be interpretable and justifiable to both healthcare practitioners and patients. Explainable AI (XAI) refers to a suite of machine learning techniques designed to illuminate the decision-making processes behind AI models. Unlike traditional models that may output a single prediction, XAI systems are engineered to provide insights into which input variables significantly influenced their conclusions [3]. In the context of endodontics, for instance, an XAI framework could identify that a recommendation for root canal retreatment is heavily influenced by a cluster of factors, including lesion size, the existence of missed root canals, the failure of coronal restorations, the specific type of tooth being treated, and the overall medical status of the patient. This level of transparency not only enhances clinical decision-making but also fosters improved communication between clinicians and patients, ultimately leading to more informed consent and shared decision-making. XAI is currently being utilised effectively across various medical specialties, such as oncology, cardiology, and critical care. In these areas, it plays a crucial role by improving risk stratification—helping healthcare professionals identify patients at higher risk for adverse outcomes—and enhancing decision support systems, which aid clinicians in making more informed choices tailored to individual patient needs. While the integration of XAI in dentistry, particularly in endodontics, is still largely aspirational, its application is increasingly feasible, which may hold the promise of transforming treatment planning, diagnosis, and patient management in dental practices, allowing for more precise interventions and improved patient outcomes. Oncology: XAI has facilitated the stratification of cancer patients by integrating multiple data points, such as tumour biology, treatment responses, and survival probabilities","url":"https://doi.org/10.1111/aej.70010","authors":["Mohammed Turky","P. M. H. Dummer"],"tags":["Endodontics","Risk stratification","Stratification (seeds)","Dentistry","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-08","doi":"https://doi.org/10.1111/aej.70010","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413955462","name":"The Future of Enhanced Recovery After Surgery in General Surgery: Integrating Artificial Intelligence, Personalized Care, and Technological Advances","source":"openalex","abstract":"Enhanced Recovery After Surgery (ERAS) protocols have changed and improved surgical care and practice, transforming classic approaches toward more evidence-based practices. ERAS protocols demonstrate consistent benefits, including reduced hospital length of stay, decreased postoperative complications, and significant cost savings. Advances include the development and integration of AI and digital health technologies that promise to personalize and optimize recovery pathways. However, the global application of ERAS may face great challenges, starting with resource limitations, resistance to change, and variable compliance rates. This review discusses and highlights the application of ERAS, identifies barriers to implementation, and proposes evidence-based recommendations for optimizing ERAS adoption and sustainability in modern surgical practice.","url":"https://doi.org/10.7759/cureus.91528","authors":["Mohamed Abosheisha","É. Nasr","Mohamed Abdel-Latif","Ahmed Swealem","Ahmed Ammar","Md Abdus Samad Hasan","Momen Abdelglil","Rezuana Tamanna","Mohamed Ismaiel"],"tags":["Medicine","Intensive care medicine","Surgery","General surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-03","doi":"https://doi.org/10.7759/cureus.91528","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411367739","name":"Code of ethics for the use of artificial intelligence in the Russian Federation healthcare","source":"openalex","abstract":"The article discusses the process of development and approval of the first Code of Ethics of Artificial Intelligence (AI) application in the Russian Federation Healthcare. Against the backdrop of the active integration of AI technologies into medical practice (39 relevant medical devices have been registered), the emphasis is placed on the importance of establishing ethical standards that ensure the protection of patients' rights, increasing trust in technologies, and standardization processes. International approaches to AI ethics in healthcare (EU, USA, UK, Canada, Australia, China, India) are analyzed and the need to harmonize the domestic code with international initiatives is outlined. The stages of development of the document, in which employees of specialized departments of the Ministry of Health of Russia, chief freelance specialists and experts took part, as well as the structure and main provisions of the approved version of the Code are presented. The key principles emphasized include transparency, confidentiality, fairness, limited autonomy, oversight, and accountability of AI systems. The final version of the document was published in March 2025 on the Unified State Information System in Healthcare (EGISZ) portal after approval by the Interdepartmental Working Group under the Russian Ministry of Health. The Code is intended to serve as a foundation for the sustainable and safe implementation of AI in Russia's healthcare system.","url":"https://doi.org/10.25881/18110193_2025_2_98","authors":["Julia I. Koroleva","А. Л. Хохлов","O. R. Artemova","E. Kostina","Т. В. Зарубина"],"tags":["Russian federation","Health care","Code (set theory)","Political science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-13","doi":"https://doi.org/10.25881/18110193_2025_2_98","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414830255","name":"Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review","source":"openalex","abstract":"BACKGROUND: The use of artificial intelligence (AI) in health care has been steadily increasing for over 2 decades. Integrating AI into neonatal intensive care units (NICUs) has promise as it has the potential to reshape neonatal care and improve outcomes. However, challenges such as data quality, clinical interpretation, and ethical considerations may hinder AI's practical implementation in NICUs. OBJECTIVE: This study aims (1) to analyze the current AI research landscape for predicting clinical outcomes and length of stay in the NICU and (2) to explore the benefits and challenges of using AI in the NICU for these predictions. METHODS: A systematic review was conducted across 6 databases-PubMed, Embase, CINAHL, Cochrane Library, Informit, and La Trobe Library-to identify English-language peer-reviewed articles published between January 2017 and March 2023 that focused on the use of AI for predicting length of stay and clinical outcomes for NICU patients. Eligibility criteria excluded studies outside the NICU context or lacking predictive focus. Both prospective and retrospective designs were included. A thematic analysis of AI applications in NICUs from the articles identified was conducted. RESULTS: A total of 24 studies were included in the review, comprising 15 retrospective and 9 prospective designs. These studies primarily originated from the United States (13 studies), with others from Austria, Taiwan, and other countries. The studies evaluated AI applications in NICU settings to predict comorbidities (18/24), mortality (4/24), and length of stay (2/24). Sixteen studies were in the exploration stage, lacking cohesive AI strategies, while 8 demonstrated systematic exploration but no fully integrated solutions. The synthesis of results identified key applications of AI in NICU care, including data-driven insights and predictive models, advancements in medical imaging, improved risk stratification, and personalized neonatal care. AI showed promise in enhancing diagnostic accuracy and care planning, but significant challenges persist, such as data quality, model generalization, and ethical concerns. No studies reported a fully integrated AI ecosystem, highlighting the need for further research to bridge gaps and realize AI's transformative potential in neonatal care. CONCLUSIONS: This review highlights the potential of AI in improving NICU care, particularly through predictive models, medical imaging, and personalized interventions. However, the evidence is limited by significant methodological variability, small sample sizes, risk of bias, and a lack of external validation in included studies. Many studies remain in exploratory phases without cohesive AI strategies or integration into clinical practice, limiting the practical applicability of findings. These results underscore the importance of addressing challenges such as data quality, model generalization, and ethical considerations to fully realize AI's potential in neonatal care. Future research should focus on robust validation, comprehensive implementation strategies, and ethical frameworks to ensure AI's effective and responsible integration into NICU settings.","url":"https://doi.org/10.2196/63175","authors":["Samantha Tudor","Risha Bhatia","Michael Liem","Tafheem Ahmad Wani","James Boyd","Urooj Raza Khan"],"tags":["Limiting","Intensive care","Medicine","MEDLINE","Neonatal intensive care unit"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-17","doi":"https://doi.org/10.2196/63175","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411886972","name":"Exploring value co-creation and co-destruction between consumers & generative artificial intelligence (GAI) in travel","source":"openalex","abstract":"Little is known about the (dis)benefits of using generative artificial intelligence (GAI) with travel-related purposes, which hinders an understanding of the value co-created and co-destructed in the process of its use by tourists. This mixed methods study explored and examined the key factors in value co-creation and co-destruction when using a popular GAI's conversational interface, ChatGPT, in tourism. The results indicate that the key perceived utility of ChatGPT is in travel planning and time saving, and the main perceived shortcomings are its limited knowledge and inaccurate responses. The study pinpoints the importance of refining and developing GAI collaboratively by all tourism stakeholders given that perceived value co-creation outweighs value co-destruction.","url":"https://doi.org/10.1016/j.tmp.2025.101392","authors":["Hien Thu Bui","Viachaslau Filimonau","Hakan Sezerel"],"tags":["Co-creation","Value (mathematics)","Generative grammar","Business","Generative model"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1016/j.tmp.2025.101392","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411801875","name":"Framing and Evaluating Task-Centered Generative Artificial Intelligence Literacy for Higher Education Students","source":"openalex","abstract":"The rise in generative artificial intelligence (GenAI) demands new forms of literacy among higher education students. This paper introduces a novel task-centered generative artificial intelligence literacy framework, which was developed collaboratively with academic and administrative staff at a large research university in Israel. The framework identifies eight skills which are informed by the six cognitive domains of Bloom’s Taxonomy. Based on this framework, we developed a measuring tool for students’ GenAI literacy and surveyed 1667 students. Findings from the empirical phase show moderate GenAI use and medium–high literacy levels, with significant variations by gender, discipline, and age. Notably, 82% of students support formal GenAI instruction, favoring integration within curricula to prepare for broader digital society participation. The study offers actionable insights for educators and policymakers aiming to integrate GenAI into higher education responsibly and effectively.","url":"https://doi.org/10.3390/systems13070518","authors":["Arnon Hershkovitz","Michal Tabach","Yoram Reich","Lilach Lurie","Tamar Cholcman"],"tags":["Framing (construction)","Generative grammar","Literacy","Mathematics education","Scientific literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-27","doi":"https://doi.org/10.3390/systems13070518","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412077961","name":"Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol","source":"openalex","abstract":"INTRODUCTION: Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI's capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy. METHODS AND ANALYSIS: This study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices. ETHICS AND DISSEMINATION: Data collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).","url":"https://doi.org/10.1136/bmjopen-2024-097591","authors":["Nita Mulliqi","Anders Blilie","Xiaoyi Ji","Kelvin Szolnoky","Henrik Olsson","Matteo Titus","Geraldine Martinez Gonzalez","Sol Erika Boman","Masi Valkonen","Einar Gudlaugsson","Svein Reidar Kjosavik","J. Asenjo","Marcello Gambacorta","Paolo Libretti","Marcin Braun","Radzisław Kordek","Roman Łowicki","Kristina Hotakainen","Päivi Väre","Bodil Ginnerup Pedersen","Karina D. Sørensen","Benedicte Parm Ulhøi","Mattias Rantalainen","Pekka Ruusuvuori","Brett Delahunt","Hemamali Samaratunga","Toyonori Tsuzuki","Emilius Adrianus Maria Janssen","Lars Egevad","Kimmo Kartasalo","Martin Eklund"],"tags":["Medicine","Prostate cancer","Perineural invasion","Grading (engineering)","Protocol (science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1136/bmjopen-2024-097591","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409434819","name":"The Role of Artificial Intelligence in the Evaluation of Prostate Pathology","source":"openalex","abstract":"Artificial intelligence (AI) is an emerging tool in diagnostic pathology, including prostate pathology. This review summarizes the possibilities offered by AI and also discusses the challenges and risks. AI has the potential to assist in the diagnosis and grading of prostate cancer. Diagnostic safety can be enhanced by avoiding the accidental underdiagnosis of small lesions. Another possible benefit is a greater degree of standardization of grading. AI for clinical use needs to be trained on large, high-quality data sets that have been assessed by experienced pathologists. A problem with the use of AI in prostate pathology is the plethora of benign mimics of prostate cancer and morphological variants of cancer that are too unusual to allow sufficient training of AI. AI systems need to be able to account for variations in local routines for cutting, staining, and scanning of slides. We also need to be aware of the risk that users will rely too much on the output of an AI system, leading to diagnostic errors and loss of clinical competence. The reporting pathologist must ultimately be responsible for accepting or rejecting the diagnosis proposed by AI.","url":"https://doi.org/10.1111/pin.70015","authors":["Lars Egevad","Andrea Camilloni","Brett Delahunt","Hemamali Samaratunga","Martin Eklund","Kimmo Kartasalo"],"tags":["Prostate cancer","Standardization","Grading (engineering)","Pathology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-14","doi":"https://doi.org/10.1111/pin.70015","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W2938791470","name":"The Biological as a Double Limit for Artificial Intelligence: Review and Futuristic Debate","source":"openalex","abstract":"This paper aims to identify to what extent artificial intelligence (AI) is biologically limited and to launch a debate on the issue of overcoming these limitations. To achieve our goal, we utilized a qualitative research methodology framework, providing an in-depth analysis of AI limitations formulated by prominent scholars within this field of specialization. We found that the biological boundary imposes a double limitation on AI, both from a gnoseological perspective and from a technological perspective. This twofold limitation of AI underpins the idea that as long as the biological cannot be understood, formalized, and imitated, we will not be able to develop technologies that mimic it. By adopting an original approach, our research paper focused on mapping out the twofold limitation of the biological with reference to the success of AI. Special attention was paid to the motivational analysis of this limitation in terms of human existence, the opportunity and utility to create artificial intelligences as superior to the human-like condition. We have opened the door for future debates on the need to decode cellular communication by understanding and developing a natural language of the living cell (N2LC). Based on the present research, we proposed that within the current technological context, biological computers (biocomputing) could represent a so-called invisible hand outstretched by biological systems towards AI.","url":"https://doi.org/10.15837/ijccc.2019.2.3536","authors":["Alexandru Țugui","Daniela Dănciulescu","Mihaela-Simona Subtirelu"],"tags":["Computer science","Perspective (graphical)","Context (archaeology)","Field (mathematics)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-14","doi":"https://doi.org/10.15837/ijccc.2019.2.3536","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409769501","name":"Digital and artificial intelligence-assisted cephalometric training effectively enhanced students’ landmarking accuracy in preclinical orthodontic education","source":"openalex","abstract":"BACKGROUND: Digital cephalometric analyses, including those assisted by artificial intelligence (AI), are widely used in clinical practice. Similarly, computer-assisted learning has demonstrated teaching outcomes comparable to those of traditional methods in orthodontic education. However, the potential application of digital and AI-assisted cephalometric training in the preclinical education of orthodontic students remains unexplored. Cephalometric analysis is a fundamental skill for orthodontic students and practitioners. Therefore, this study aimed to integrate digital and AI-assisted cephalometric training into preclinical orthodontic education and evaluate its educational effectiveness. METHODS: Forty undergraduate students were grouped into pairs to use digital cephalometric training software. The students' landmarking abilities were evaluated by comparing their total scores before and after training on the same two lateral radiographs using digital cephalometric training software. The effectiveness of the software in improving landmarking accuracy was assessed objectively. Lateral radiographs of eight common patient types were selected. Twenty-four clinical training students from different grades used an AI-assisted cephalometric platform to analyze skeletal, dental, and soft tissue indicators. The accuracy of the measurements was compared among students in different grades. RESULTS: Digital cephalometric training, through real-time feedback and visual error-correction mechanisms, enabled students to quickly identify and correct errors in landmarking, significantly improving their accuracy. There was no significant difference in AI-assisted cephalometric analysis ability among students with varying levels of clinical experience. CONCLUSIONS: Digital cephalometric training effectively enhances students' landmarking accuracy in preclinical orthodontic education. AI-assisted cephalometry has the potential to minimize performance disparities among students with varying levels of clinical experience. Owing to the real-time feedback and self-directed learning features of digital tools, these technologies serve as valuable supplements to instructor-led training, potentially reducing educators' workload and accelerating skill acquisition in novice orthodontic students. However, these preliminary findings require further multicenter validation and long-term educational assessments while also considering the ethical implications of these technologies.","url":"https://doi.org/10.1186/s12903-025-05978-4","authors":["Jiayu Lin","Zhihao Liao","Jingtao Dai","Manyi Wang","Robert K. Yu","Hong Yang","Chufeng Liu"],"tags":["Medicine","Oral and maxillofacial surgery","Orthodontics","Dentistry","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-24","doi":"https://doi.org/10.1186/s12903-025-05978-4","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406603444","name":"Performance of a medical smartband with photoplethysmography technology and artificial intelligence algorithm to detect atrial fibrillation","source":"openalex","abstract":"Background: Atrial fibrillation (AF) is a prevalent arrhythmia with significant public health implications, including increased risk of stroke and mortality. Early detection is challenging but crucial for managing complications. Wearable technology with photoplethysmography (PPG) offers a potential solution for long-term, non-invasive monitoring. This study aims to evaluate the performance of three artificial intelligence (AI) algorithms (Happitech, Preventicus, and Philips Biosensing AF) in detecting AF using PPG signals from a medical smartband and compare it with the gold standard electrocardiogram (ECG). Methods: A medical smartband equipped with PPG technology was used to collect cardiovascular data from patients with and without AF. The sensitivity and specificity of the algorithm for detecting AF were determined by comparing their output to a trained technician's examination of concurrent ECG recordings. Results: Seventy two participants (42% female, 57±17 years old) were included in this study. The medical smartband provided continuous PPG signals, with AI algorithms evaluating the data for AF episodes. The accuracy of AF detection by the algorithms was compared with that of the concurrent ECG recordings. Sensitivity varied between 80.0% (62.5-97.5%) and 97.6% (97.6-97.6%), specificity between 90.6% (80.5-100%) and 96.9% (90.8-100%). Conclusions: This study demonstrates the potential of medical smartbands combined with PPG technology and AI algorithms for reliable AF detection. The findings suggest a promising direction for remote AF monitoring and early intervention, potentially reducing AF-related complications and healthcare costs.","url":"https://doi.org/10.21037/mhealth-24-10","authors":["Sebastiaan Blok","Willem Gielen","M. Piek","Wiert F. Hoeksema","Igor I. Tulevski","Geert Somsen","Michiel M. Winter"],"tags":["Photoplethysmogram","Atrial fibrillation","Computer science","Algorithm","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.21037/mhealth-24-10","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7117630788","name":"Cognitive offloading and the reshaping of human thought: The subtle influence of Artificial Intelligence","source":"openalex","abstract":"The integration of artificial intelligence (AI) into daily cognitive tasks has transformed traditional cognitive offloading—the delegation of mental processes to external tools—into a dynamic partnership with intelligent systems. This article examines the dual role of AI-driven cognitive offloading, exploring its potential to enhance efficiency and creativity while posing risks to memory consolidation, critical thinking, and intellectual autonomy. Grounded in extended mind theory (Clark & Chalmers, 1998) and empirical studies like the Google Effect (Sparrow et al., 2011), the analysis reveals how AI's generative capabilities (e.g., ChatGPT, Midjourney) shift offloading from passive storage to delegated thinking, where users adopt AI outputs with minimal scrutiny, fostering automation bias (Logg et al., 2019). In educational contexts, AI tools risk undermining deep learning by reducing retrieval practice and encouraging superficial engagement, as illustrated by a case study of Hatt University, where AI-assisted essays distorted grading systems and eroded student critical analysis. Similarly, in healthcare and finance, overreliance on AI recommendations may compromise professional judgment. To mitigate these risks, the article proposes strategies such as metacognitive training, explainable AI design (Sundar, 2020), and curriculum reforms prioritizing active engagement with AI outputs. Ethical and policy interventions are urged to address epistemic opacity, intellectual property, and the cultural redefinition of authorship. The study underscores the need for balanced AI integration—one that harnesses its benefits while safeguarding human cognitive autonomy.","url":"https://doi.org/10.31207/colloquia.v12i1.185","authors":["Vincent J. Hooper"],"tags":["Safeguarding","Cognition","Metacognition","General partnership","Creativity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-20","doi":"https://doi.org/10.31207/colloquia.v12i1.185","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4391841559","name":"An artificial intelligence-based bone age assessment model for Han and Tibetan children","source":"openalex","abstract":"Background: Manual bone age assessment (BAA) is associated with longer interpretation time and higher cost and variability, thus posing challenges in areas with restricted medical facilities, such as the high-altitude Tibetan Plateau. The application of artificial intelligence (AI) for automating BAA could facilitate resolving this issue. This study aimed to develop an AI-based BAA model for Han and Tibetan children. Methods: A model named “EVG-BANet” was trained using three datasets, including the Radiology Society of North America (RSNA) dataset (training set n = 12611, validation set n = 1425, and test set n = 200), the Radiological Hand Pose Estimation (RHPE) dataset (training set n = 5491, validation set n = 713, and test set n = 79), and a self-established local dataset [training set n = 825 and test set n = 351 (Han n = 216 and Tibetan n = 135)]. An open-access state-of-the-art model BoNet was used for comparison. The accuracy and generalizability of the two models were evaluated using the abovementioned three test sets and an external test set (n = 256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators. Bias was evaluated by comparing the MAD between the demographic groups. Results: EVG-BANet outperformed BoNet in the MAD on the RHPE test set (0.52 vs. 0.63 years, p < 0.001), the local test set (0.47 vs. 0.62 years, p < 0.001), and the external test set (0.53 vs. 0.66 years, p < 0.001) and exhibited a comparable MAD on the RSNA test set (0.34 vs. 0.35 years, p = 0.934). EVG-BANet achieved accuracy within 1 year of 97.7% on the local test set (BoNet 90%, p < 0.001) and 89.5% on the external test set (BoNet 85.5%, p = 0.066). EVG-BANet showed no bias in the local test set but exhibited a bias related to chronological age in the external test set. Conclusion: EVG-BANet can accurately predict the bone age (BA) for both Han children and Tibetan children living in the Tibetan Plateau with limited healthcare facilities.","url":"https://doi.org/10.3389/fphys.2024.1329145","authors":["Qixing Liu","Huogen Wang","Cidan Wangjiu","Tudan Awang","Meijie Yang","Puqiong Qiongda","Xiao Yang","Hui Pan","Fengdan Wang"],"tags":["Generalizability theory","Test set","Training set","Medicine","Test (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-15","doi":"https://doi.org/10.3389/fphys.2024.1329145","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410652481","name":"Analyzing the impact of artificial intelligence on the online purchase decision-making process through the lens of the UTAUT 2 model","source":"openalex","abstract":"Abstract Consumer perceptions and purchasing decision-making have been significantly impacted by the rapid use of artificial intelligence (AI) in the online retail sector. The online shopping experience has been transformed by AI technologies, which include sophisticated product evaluation algorithms, individualized purchasing recommendations, and improved customer care. To better understand the deep interactions between factors linked to artificial intelligence and customer decision-making processes, this study investigates the complex adoption landscape of AI within the Delhi-NCR online retail industry using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework. The research employs a quantitative methodology by applying Structural Equation Modelling (SEM) through SmartPLS 4 to evaluate key behavioral variables. The results reveal that Behavioral Intention (BI) has a strong and significant impact on Usage Behavior (UB). Trust in AI systems emerged as a critical factor affecting BI. Significant factors like Habit, Hedonic Motivation, Trust, and Personal Innovativeness emerged as pivotal in shaping consumers’ Behavioral Intentions towards AI in online shopping. Perceived Risk showed a marginally negative effect on BI, suggesting consumers’ hesitation in adopting AI for online shopping. However, factors such as Social Influence and Effort Expectancy had negligible impacts on BI, contrasting with prior research. These findings suggest that consumer trust, delight, and habitual AI usage are more influential than usability or social pressures for AI enabled e-commerce landscape.","url":"https://doi.org/10.1007/s10791-025-09575-5","authors":["Rinku Sharma Dixit","Shailee Lohmor Choudhary","Nikhil Govil"],"tags":["Process (computing)","Computer science","Decision-making","Lens (geology)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.1007/s10791-025-09575-5","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413796400","name":"Embracing Artificial Intelligence in Dental Practice: An Exploratory Study of Romanian Clinicians’ Perspectives and Experiences","source":"openalex","abstract":"Background/Objectives: Standard dental practice is being reshaped by digital technologies, and artificial intelligence (AI) is emerging as one of the most challenging recent innovations. Methods: The present study assessed the interest of Romanian dentists in the integration of AI into their current practice through an anonymous questionnaire distributed to 200 respondents. The questionnaire addressed the integration of AI in dentistry by analyzing the following areas of intervention: stages of patient care, perceived impact on the doctor–patient relationship, data security, implementation costs, and the legislative framework. Results: The results showed that 53.6% of dentists reported low difficulties, 37.3% reported moderate difficulties, and 9.1% reported high difficulties with using digital tools. Dentists’ reported willingness to adopt AI-based solutions was as follows: 58.6% were very willing, 30% were moderately willing, and only 11.4% were not very willing. Currently, 80.5% already use digital techniques in their daily practice. The participants emphasized the need to maintain a strong doctor–patient relationship while recognizing the benefits of increased efficiency. They were aware of the risk of diminishing human connection and trust. Also, data security and the financial stress associated with implementing and maintaining new systems were seen as major obstacles. Conclusions: The dentists surveyed showed an increased interest in modern digital technologies, provided that there is a clear legal framework, a strong data protection system, and the preservation of the doctor–patient relationship based on trust and confidentiality that defines the medical profession.","url":"https://doi.org/10.3390/dj13090390","authors":["Alin Flavius Cozmescu","Ana Cernega","Dana Galieta Mincă","Andreea Cristiana Didilescu","Marina Imre","Alexandra Totan","Simona Pârvu","Silviu Pițuru"],"tags":["Confidentiality","Romanian","Intervention (counseling)","Dental practice","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-27","doi":"https://doi.org/10.3390/dj13090390","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4308071681","name":"Elementary school students' awareness of the use of artificial intelligence chatbots in violence prevention education in South Korea: a descriptive study","source":"openalex","abstract":"PURPOSE: This study aimed to identify students' awareness of the use of a chatbot (A-uC), a type of artificial intelligence technology, for violence prevention among elementary school students. METHODS: The participants comprised 215 students in the fourth to sixth grades in Chuncheon, South Korea, and data were collected via a self-reported questionnaire. RESULTS: The mean A-uC score was 3.43±0.83 out of 5 points. The mean scores for the 4 sub-dimensions of the A-uC tool were 3.48±0.80 for perceived value, 3.44±0.98 for perceived usefulness, 3.63±0.92 for perceived ease of use, and 3.15±1.07 for intention to use. Significant differences were observed in A-uC scores (F=59.26, p<.001) according to the need for the use of chatbots in violence prevention education. The relationships between intention to use and the other A-uC sub-dimensions showed significant correlations with perceived value (r=.85, p<.001), perceived usefulness (r=.76, p<.001), and perceived ease of use (r=.64, p<.001). CONCLUSION: The results of this study suggest that chatbots can be used in violence prevention education for elementary school students.","url":"https://doi.org/10.4094/chnr.2022.28.4.291","authors":["Kyung‐Ah Kang","Shin‐Jeong Kim","Shin-Jeong Kim","So Ra Kang"],"tags":["Descriptive research","Psychology","Descriptive statistics","Medical education","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-31","doi":"https://doi.org/10.4094/chnr.2022.28.4.291","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410628257","name":"The role of artificial intelligence and bureaucracy in frugal innovation for social and environmental performance: A structuration theory approach","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jenvman.2025.125860","authors":["Rameshwar Dubey"],"tags":["Bureaucracy","Structuration theory","Management science","Business","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.1016/j.jenvman.2025.125860","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4387949893","name":"Advances and opportunities in the new digital era of telemedicine, e-health, artificial intelligence, and beyond","source":"openalex","abstract":"","url":"https://doi.org/10.12809/hkmj235152","authors":["Haoxiang Wang","Yuting Li","Junjie Huang","Wenyong Huang","Martin C. S. Wong"],"tags":["Telemedicine","Medicine","Coronavirus disease 2019 (COVID-19)","2019-20 coronavirus outbreak","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-25","doi":"https://doi.org/10.12809/hkmj235152","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411120331","name":"Rationale-Guided Retrieval Augmented Generation for Medical Question Answering","source":"openalex","abstract":"Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.","url":"https://doi.org/10.18653/v1/2025.naacl-long.635","authors":["Jiwoong Sohn","Yein Park","Chanwoong Yoon","Sihyeon Park","Hyeon Seok Hwang","Mujeen Sung","Hyunjae Kim","Jaewoo Kang"],"tags":["Question answering","Computer science","Information retrieval","Natural language processing","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.18653/v1/2025.naacl-long.635","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412704370","name":"Now and future of artificial intelligence-based signet ring cell diagnosis: A survey","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.eswa.2025.129188","authors":["Meng Zhu","Junhao Dong","Limei Guo","Fei Su","Jiaxuan Liu","Guangxi Wang","Zhicheng Zhao"],"tags":["Computer science","Ring (chemistry)","Artificial intelligence","Chemistry","Organic chemistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-28","doi":"https://doi.org/10.1016/j.eswa.2025.129188","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413221133","name":"Enhancing Coordination and Decision Making in Humanitarian Logistics Through Artificial Intelligence: A Grounded Theory Approach","source":"openalex","abstract":"Background: The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on coordination and decision making for real-life humanitarian problems. Based on evidence from the humanitarian sector, this paper focuses on how AI could help humanitarian organizations collaborate better, streamline relief supply-chain operations and use resources more effectively. Methods: Twelve key themes influencing AI integration are identified by the study using a Grounded Theory (GT) approach based on interviews with experts from the humanitarian sector. These themes include data reliability, operational limitations, ethical considerations and cultural sensitivities, among others. Results: The findings suggest that AI improves forecasting, planning and inter-organizational coordination and is especially useful during the preparedness and mitigation stages of relief operations. Successful adoption, however, depends on adjusting tools to actual field conditions, building trust and training and striking a balance between algorithmic support and human expertise. Conclusions: The paper offers useful and practical advice for humanitarian organizations looking to use AI technologies in an ethical way while taking into account workforce capabilities, cross-agency cooperation and field-level realities.","url":"https://doi.org/10.3390/logistics9030113","authors":["Panagiotis Pantiris","Petros Pallıs","Panos Chountalas","Thomas K. Dasaklis"],"tags":["Agency (philosophy)","Humanitarian aid","Field (mathematics)","Grounded theory","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-11","doi":"https://doi.org/10.3390/logistics9030113","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7104178444","name":"Design and development of an intelligent system based on artificial intelligence and machine learning using customs digital indicators","source":"openalex","abstract":"This paper aims to evaluate the role of AI and ML-driven innovative technologies in enhancing customs operations in Jordan. This research employed a quantitative approach to develop an overall conceptual model that encompasses both the technical and behavioral aspects of intelligent system adoption. The target population consisted of customs officers, border security personnel, and IT personnel responsible for customs clearance and trade facilitation in Jordan. The structured questionnaires were administered to the respondents to measure their perceptions of system effectiveness, satisfaction, performance outcomes, and evasion behavior and yielded a total of 358 valid responses. The research was conducted with proper statistical analysis, and the statistical techniques employed included primary data collected via SPSS Version 29 and advanced modeling using Structural Equation Modeling-Partial Least Squares (SEM-PLS) through the use of SmartPLS 4.0. The results indicated that the measurement model proved to be both valid and reliable, with Cronbach's alpha values exceeding 0.82 and AVE values above 0.50, indicating good internal consistency and convergent validity. Moreover, the structural model achieved good explanatory power, with R² values of 59% for Customs Evasion, 43% for User Satisfaction, and 100% for Digital Performance Indicators. These findings underscore the significance of user satisfaction as a key outcome of system effectiveness and a valuable tool for enhancing performance and deterrence. More specifically, the results shown how Intelligent System Effectiveness presents a positive and significant impact on User Satisfaction (β = 0.656, p < 0.001), which in turn has a high positive effect on both Digital Performance Indicators (β = 1.000, p < 0.001) and Customs Evasion reduction (β = 0.770, p < 0.001). The mediation analysis also confirmed that User Satisfaction fully mediates the relationship between system effectiveness and performance outcome, as well as between system effectiveness and evasion reduction. This research contributes to theory and practice by demystifying the design and implementation of AI-driven customs systems. It illustrates the importance of valuing both technical system quality and user-centric values in achieving and maintaining optimal performance in the digital space, as well as conformance with the law.","url":"https://doi.org/10.5267/j.ijdns.2025.9.022","authors":["Ashraf I. A. Qahman","Malek Alzaqebah","Sana Jawarneh","Murad Ali Ahmad Al-Zaqeba","Attallah Hassan Mohamed Al-Taani","Ahmad Nader Aloqaily","Maryam A. Almatrooshi"],"tags":["Artificial intelligence","Computer science","Machine learning","Structural equation modeling","Consistency (knowledge bases)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-06","doi":"https://doi.org/10.5267/j.ijdns.2025.9.022","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409655236","name":"Artificial intelligence model for the assessment of unstained live sperm morphology","source":"openalex","abstract":"Abstract: Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance with that of computer-aided semen analysis and conventional semen analysis methods. In this experimental study, we enrolled 30 healthy male volunteers aged 18-40 years at the Songklanagarind Assisted Reproductive Centre, Songklanagarind Hospital. We developed a novel dataset of sperm morphological images captured with confocal laser scanning microscopy at low magnification and high resolution to train and validate an AI model. Semen samples were divided into three aliquots and assessed for unstained live sperm morphology using the AI model, whereas computer-aided and conventional semen analysis methods evaluated fixed sperm morphology. The performance of our in-house AI model for evaluating unstained live sperm morphology was compared with that of the other two methods. The in-house AI model showed the strongest correlation with computer-aided semen analysis (r = 0.88), followed by conventional semen analysis (r = 0.76). The correlation between computer-aided semen analysis and conventional semen analysis was weaker (r = 0.57). Both the in-house AI and conventional semen analysis methods detected normal sperm morphology at significantly higher rates than computer-aided semen analysis. The in-house AI model could enhance assisted reproductive technology outcomes by improving the selection of high-quality sperm with normal morphology. This could lead to better outcomes of intracytoplasmic sperm injections and other fertility treatments. Lay summary: We evaluated a new in-house AI model for assessing the shape and size (morphology) of live sperm without staining and performed comparisons with computer-aided semen analysis and conventional semen analysis, which require sperm to be fixed and stained before analysis. This new method of assessing unstained, live sperm is significant because it facilitates viable sperm selection for use in assisted reproductive technology immediately after assessment, ultimately contributing to improved fertility outcomes. The AI model allowed sperm morphology assessments with significantly improved accuracy and reliability. By using high-resolution images and advanced microscopy, the AI model could detect subcellular features. This AI model could be an effective tool in clinical settings, because it minimizes subjectivity and improves sperm selection for assisted reproductive technologies, potentially leading to higher success rates in infertility treatments. Further research can refine the model and validate its effectiveness in diverse clinical environments.","url":"https://doi.org/10.1530/raf-25-0014","authors":["Jermphiphut Jaruenpunyasak","Prawai Maneelert","Marwan Nawae","Chainarong Choksuchat"],"tags":["Sperm","Semen","Semen analysis","Magnification","Andrology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1530/raf-25-0014","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407408800","name":"Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients","source":"openalex","abstract":"Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.","url":"https://doi.org/10.3390/cancers17040605","authors":["Isabel G. Scalia","Girish Pathangey","Mahmoud Abdelnabi","Omar Ibrahim","Fatmaelzahraa Abdelfattah","Milagros Pereyra","Ramzi Ibrahim","Juan Farina","Imon Banerjee","Balaji Tamarappoo","Reza Arsanjani","Chadi Ayoub"],"tags":["Cardiotoxicity","Medicine","Disease","Cancer","Radiation therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-11","doi":"https://doi.org/10.3390/cancers17040605","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4403544935","name":"AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics","source":"openalex","abstract":"The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.","url":"https://doi.org/10.56294/mw202535","authors":["Mutaz Abdel Wahed","Muhyeeddin Alqaraleh","Mowafaq Salem Alzboon","Mohammad Subhi Al-Batah"],"tags":["Health care","Engineering ethics","Business","Knowledge management","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-18","doi":"https://doi.org/10.56294/mw202535","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409650920","name":"Acromegaly facial changes analysis using last generation artificial intelligence methodology: the AcroFace system","source":"openalex","abstract":"PURPOSE: To describe the development of the AcroFace system, an AI-based system for early detection of acromegaly, based on facial photographs analysis. METHODS: Two types of features were explored: (1) the visual/texture of a set of 2D facial images, and (2) geometric information obtained from a reconstructed 3D model from a single image. We optimized acromegaly detection by integrating SVM for geometric features and CNNs for visual features, each chosen for their strength in processing distinct data types effectively. This combination enhances overall accuracy by leveraging SVM's capability to manage structured, quantitative data and CNNs' proficiency in interpreting complex image textures, thus providing a comprehensive analysis of both geometric alignment and textural anomalies. ResNet-50, VGG-16, MobileNet, Inception V3, DensNet121 and Xception models were trained with an expert endocrinologist-based score as a ground truth. RESULTS: ResNet-50 model as a feature extractor and Support Vector Regression (SVR) with a linear kernel showed the best performance (accuracy δ1 of 75% and δ3 of 89%), followed by the VGG-16 as a feature extractor and SVR with a linear kernel. Geometric features yield less accurate results than visual ones. The validation cohort showed the following performance: precision 0.90, accuracy 0.93, F1-Score 0.92, sensitivity 0.93 and specificity 0.93. CONCLUSION: AcroFace system shows a good performance to discriminate acromegaly and non-acromegaly facial traits that may serve for the detection of acromegaly at an early stage as a screening procedure at a population level.","url":"https://doi.org/10.1007/s11102-025-01515-2","authors":["Hatem A. Rashwan","Montserrat Marqués-Pamies","Sabina Ruiz","Joan Gil","Diego Asensio-Wandosell","Maria-Antonia Martínez-Momblán","Federico Vázquez","Isabel Salinas","Raquel Ciriza","Mireia Jordà","Philippe Chanson","Elena Valassi","Mohamed Abdel‐Nasser","Domènec Puig","Manel Puig‐Domingo"],"tags":["Artificial intelligence","Support vector machine","Pattern recognition (psychology)","Acromegaly","Feature (linguistics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-21","doi":"https://doi.org/10.1007/s11102-025-01515-2","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412048610","name":"Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters","source":"openalex","abstract":"Smoking is a leading cause of various health conditions, including cancer and respiratory diseases. Smokers often face medical restrictions such as limitations in blood and organ donation, reduced effectiveness of medications, and increased surgical complications. These impacts underscore the need for early detection of smoking status to enable timely intervention. This study explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict smoking status based on health parameters, including biosignals and clinical biomarkers. A balanced subset of 2,000 instances was sampled from a publicly available Kaggle dataset comprising clinical and biometric features. Multiple ML models were implemented, including Random Forest Classifier, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, CatBoost Classifier, and an Artificial Neural Network. The Random Forest Classifier achieved the better performance with an accuracy of 0.80, precision of 0.80, recall of 0.80, and F1-score of 0.79. To enhance model interpretability, four Explainable Artificial Intelligence (XAI) techniques were applied: Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), QLattice, and Anchor. SHAP identified hemoglobin as the most influential predictor, while LIME, QLattice, and Anchor highlighted the role of gamma-glutamyl transferase (t). Interactions between hemoglobin, GTP, and height were associated with more accurate predictions. The integration of ensemble modeling and multiple XAI approaches offers deeper interpretability than prior studies, providing healthcare providers and policymakers with a robust, transparent decision-support tool for targeted intervention strategies.","url":"https://doi.org/10.1038/s41598-025-09409-w","authors":["S Aishwarya","P. C. Siddalingaswamy","Krishnaraj Chadaga"],"tags":["Artificial intelligence","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-05","doi":"https://doi.org/10.1038/s41598-025-09409-w","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3043714649","name":"Neural networks and artificial intelligence as trends for the development of the future","source":"openalex","abstract":"Abstract This article discusses neural networks and artificial intelligence from the prospects for the future life of humanity. Neural networks and artificial intelligence are also presented as a tool for conducting almost all types of business, production, medicine (medical imaging). Neural networks perform the tasks of identifying objects of research, semantic segmentation, recognition of faces, recognition of parts of the human body, semantic determination of boundaries, highlighting objects of attention in the image and highlighting normals to the surface, automating and optimizing processes implemented in the format of manual labour. Prospects for the implementation of neural networks and artificial intelligence should be considered not as a substitute for a person, but rather as a support system for decision-making, in which the final choice is left to the person.","url":"https://doi.org/10.1088/1742-6596/1582/1/012005","authors":["B R Avetisyan","N S Druzhinina","I M Daudov"],"tags":["Artificial neural network","Artificial intelligence","Computer science","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-01","doi":"https://doi.org/10.1088/1742-6596/1582/1/012005","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406580523","name":"Evaluation of Medical Diagnosis Capabilities of Three Artificial Intelligence Models – ChatGPT-3.5, Google Gemini, Microsoft Copilot: Sustainable Development Goals (SDGs)","source":"openalex","abstract":"Objectives: This study aims to assess and compare the diagnostic accuracy of three artificial intelligence (AI) models—ChatGPT-3.5, Microsoft Copilot, and Google Gemini—through their performance on clinical vignettes. Theoretical Framework: Building on prior research into the application of AI in healthcare, particularly in diagnostic support, this study examines the potential of AI models to aid clinicians by providing accurate medical diagnoses, thus supporting decision-making in clinical contexts. Methodology: A meta-analysis was conducted, followed by a comparative analysis using 34 clinical vignettes from Texas Tech University Health Sciences Center. Each AI model’s responses were evaluated for accuracy in diagnosing medical cases, and statistical significance was tested using the chi-square test. Results and Discussion: ChatGPT-3.5 achieved the highest diagnostic accuracy (70.59%), outperforming Google Gemini (61.76%) and Microsoft Copilot (35.29%). ChatGPT-3.5 provided concise answers, while Google Gemini and Microsoft Copilot included disclaimers and additional recommendations. Chi-square analysis confirmed significant differences in performance, highlighting variations in diagnostic capabilities across models. Research Implications: These findings underscore the importance of model selection when integrating AI into clinical workflows. AI models show promise in diagnostics but vary in approach and accuracy, warranting further refinement. Originality/Value: This study is among the first to compare the diagnostic accuracy of ChatGPT-3.5, Google Gemini, and Microsoft Copilot, contributing valuable insights into AI’s application in healthcare diagnostics and supporting evidence for its potential role in enhancing patient care.","url":"https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe03545","authors":["Yordanka Eneva","Bora Doğan"],"tags":["Sustainable development","Computer science","Development (topology)","Artificial intelligence","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-17","doi":"https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe03545","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4399052098","name":"Systematic review: The use of large language models as medical chatbots in digestive diseases","source":"openalex","abstract":"BACKGROUND: Interest in large language models (LLMs), such as OpenAI's ChatGPT, across multiple specialties has grown as a source of patient-facing medical advice and provider-facing clinical decision support. The accuracy of LLM responses for gastroenterology and hepatology-related questions is unknown. AIMS: To evaluate the accuracy and potential safety implications for LLMs for the diagnosis, management and treatment of questions related to gastroenterology and hepatology. METHODS: We conducted a systematic literature search including Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus and the Web of Science Core Collection to identify relevant articles published from inception until January 28, 2024, using a combination of keywords and controlled vocabulary for LLMs and gastroenterology or hepatology. Accuracy was defined as the percentage of entirely correct answers. RESULTS: Among the 1671 reports screened, we identified 33 full-text articles on using LLMs in gastroenterology and hepatology and included 18 in the final analysis. The accuracy of question-responding varied across different model versions. For example, accuracy ranged from 6.4% to 45.5% with ChatGPT-3.5 and was between 40% and 91.4% with ChatGPT-4. In addition, the absence of standardised methodology and reporting metrics for studies involving LLMs places all the studies at a high risk of bias and does not allow for the generalisation of single-study results. CONCLUSIONS: Current general-purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks, which may lead to adverse patient safety events through incorrect information or triage recommendations, which might overburden healthcare systems or delay necessary care.","url":"https://doi.org/10.1111/apt.18058","authors":["Mauro Giuffrè","Simone Kresevic","Kisung You","Johannes Dupont","Jack Huebner","Alyssa Grimshaw","Dennis Shung"],"tags":["Hepatology","Medicine","Internal medicine","MEDLINE","Cochrane Library"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-27","doi":"https://doi.org/10.1111/apt.18058","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7127048854","name":"Mapping the landscape of AI-assisted formative feedback in medical education: A bibliometric analysis","source":"pubmed","abstract":"BACKGROUND: Artificial intelligence (AI) is transforming medical education, particularly in formative feedback. This study conducted a comprehensive bibliometric analysis to map the intellectual landscape, research trends, and future directions of AI-assisted formative feedback in medical education. METHODS: A systematic search was performed in the Web of Science Core Collection database for English-language articles published between January 1, 2021, and October 12, 2025. Bibliometric analysis was conducted using VOSviewer, CiteSpace, and the R-bibliometrix package to analyze publication trends, geographic distribution, institutional collaboration, journal impact, author contributions, co-citation patterns, and keyword occurrences. RESULTS: The analysis included 116 publications, revealing exponential growth in AI-assisted formative feedback research from 2021 to 2025. The United States dominated the research landscape, followed by China and other European nations. Institutional collaboration centered on the University of Michigan, connecting North American and Asian research clusters. BMC Medical Education emerged as the leading journal, while interdisciplinary knowledge flow originated from clinical medicine and drew on health sciences and educational psychology. Keyword bursts identified \"feedback,\" \"large language model,\" and \"medical education\" as the most prominent research hotspots in 2024 to 2025. CONCLUSION: AI-assisted formative feedback in medical education is a rapidly evolving field driven by advancements in large language models, immersive technologies, and personalized assessments. Future research should prioritize theory-informed, ethically grounded, and patient-oriented AI integration to augment human instruction and demonstrably improve learner competence and patient care. Increased international collaboration and interdisciplinary knowledge exchange are crucial for the responsible adoption of AI in medical education.","url":"https://doi.org/10.1097/md.0000000000047489","authors":["Sha Yu","Jin Liu","Yu S","Liu J"],"tags":["Formative assessment","Medicine","Competence (human resources)","Medical education","Field (mathematics)"],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 30","doi":"https://doi.org/10.1097/md.0000000000047489","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"oa:W2895614216","name":"Can nurses remain relevant in a technologically advanced future?","source":"openalex","abstract":"Technological breakthroughs occur at an ever-increasing rate thereby revolutionizing human health and wellness care. Technological advancements have drastically changed the structure and organization of the healthcare industry. McKinsey Global Institute estimates that 800 million workers worldwide could be replaced by robots by the year 2030. There is already a robotic revolution happening in healthcare wherein robots have made tasks and procedures more efficient and safer. Locsin and Ito has addressed the threat to nursing practice with human nurses being replaced by humanoid robots. Routine nursing care dictated solely by prescribed procedures and accomplishment of nursing tasks would be best performed by machines. With the future practice of nursing in a technologically advanced future transcending the implementation of nursing actions to achieve predictable outcomes, how can human nurses remain relevant as practitioners of nursing? Nurses should be involved in deciding which aspects of their practice can be delegated to technology. Nurses should oversee the introduction of automated technology and artificial intelligence ensuring their practice to be more about the universal aspects of human care continuing under a novel system. Nursing education and nursing research will change to encompass a differentiated demand for professional nursing practice with, and not for, robots in healthcare.","url":"https://doi.org/10.1016/j.ijnss.2018.09.013","authors":["Joseph Andrew Pepito","Rozzano C. Locsin"],"tags":["Health care","SAFER","Nursing","Robot","Nursing care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-10-04","doi":"https://doi.org/10.1016/j.ijnss.2018.09.013","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409247146","name":"Artificial Intelligence and Venous Thromboembolism: A Narrative Review of Applications, Benefits, and Limitations","source":"openalex","abstract":"Background: Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, remains a leading cause of cardiovascular morbidity and mortality. Artificial intelligence (AI) holds promise for potential improvement of risk stratification, diagnosis, and management of VTE. Summary: This narrative review explores the applications, benefits, and limitations of AI in VTE management. AI models were shown to outperform conventional methods in identifying high-risk candidates for VTE prophylaxis treatments in several postsurgical settings. It has also been demonstrated to be efficient in the early detection of VTE events, particularly through point-of-care AI-guided sonography and computer tomography image processing. Data biases, model transparency, and the need for regulatory frameworks remain significant limitations in the full integration of AI into clinical practice. Key Messages: AI has the potential to improve VTE care by enhancing risk stratification and diagnosis. The integration of AI-driven models into clinical workflows has the potential to reduce costs, streamline diagnostic processes, and ensure effective management of VTE. Safe and effective integration of AI into VTE care requires addressing its limitations, such as interpretability, privacy, and algorithmic bias. .","url":"https://doi.org/10.1159/000545760","authors":["Aya Mudrik","Orly Efros"],"tags":["Interpretability","Risk stratification","Medicine","Intensive care medicine","Venous thromboembolism"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-08","doi":"https://doi.org/10.1159/000545760","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4405854719","name":"Why Should Users Take the Risk of Sustainable Use of Generative Artificial Intelligence Chatbots","source":"openalex","abstract":"Despite the risks associated with generative AI (GenAI) chatbots, people increasingly use these technologies, which may seem contradictory. This study identified and explored factors and risks related to trust, perceived values, satisfaction, and sustainable use of GenAI chatbots. Relying on IS theories to build a stimulus-organism-response model, the authors tested a model using PLS-SEM with data from 393 ChatGPT users. The results show that user competence and autonomy dramatically increase a user's trust in ChatGPT, and trust improves hedonic value (HV), utilitarian value (UV), value-in-use, perceived task-technology fit (TTF), information accuracy, knowledge acquisition, perceived informativeness, and user satisfaction. In addition to trust, user satisfaction depends on HV, UV, and TTF. The sustainability use of ChatGPT depends on HV and satisfaction. However, perceived privacy concerns, perceived privacy risks, and privacy awareness do not affect consumer trust. There is a complete mediation between trust and sustainability, as well as HV and sustainability.","url":"https://doi.org/10.4018/jgim.365600","authors":["Serge-Lopez Wamba-Taguimdje","Samuel Fosso Wamba","Hossana Twinomurinzi"],"tags":["Generative grammar","Computer science","Artificial intelligence","Risk analysis (engineering)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-28","doi":"https://doi.org/10.4018/jgim.365600","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4396724067","name":"Artificial intelligence–aided steatosis assessment in donor livers according to the Banff consensus recommendations","source":"openalex","abstract":"OBJECTIVES: Severe macrovesicular steatosis in donor livers is associated with primary graft dysfunction. The Banff Working Group on Liver Allograft Pathology has proposed recommendations for steatosis assessment of donor liver biopsy specimens with a consensus for defining \"large droplet fat\" (LDF) and a 3-step algorithmic approach. METHODS: We retrieved slides and initial pathology reports from potential liver donor biopsy specimens from 2010 to 2021. Following the Banff approach, we reevaluated LDF steatosis and employed a computer-assisted manual quantification protocol and artificial intelligence (AI) model for analysis. RESULTS: In a total of 113 slides from 88 donors, no to mild (<33%) macrovesicular steatosis was reported in 88.5% (100/113) of slides; 8.8% (10/113) was reported as at least moderate steatosis (≥33%) initially. Subsequent pathology evaluation, following the Banff recommendation, revealed that all slides had LDF below 33%, a finding confirmed through computer-assisted manual quantification and an AI model. Correlation coefficients between pathologist and computer-assisted manual quantification, between computer-assisted manual quantification and the AI model, and between the AI model and pathologist were 0.94, 0.88, and 0.81, respectively (P < .0001 for all). CONCLUSIONS: The 3-step approach proposed by the Banff Working Group on Liver Allograft Pathology may be followed when evaluating steatosis in donor livers. The AI model can provide a rapid and objective assessment of liver steatosis.","url":"https://doi.org/10.1093/ajcp/aqae053","authors":["Jingjing Jiao","Haiming Tang","Nanfei Sun","Xuchen Zhang"],"tags":["Steatosis","Medicine","Biopsy","Liver biopsy","Pathology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-08","doi":"https://doi.org/10.1093/ajcp/aqae053","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4296625068","name":"A Systematic Literature Survey on the Role of Artificial Intelligence Techniques in Industrial Revolution 4.0 Readiness","source":"openalex","abstract":"The service sector is a key focus of the Fourth Industrial Revolution (IR4.0), a digital transformation that spans all industries. Central to IR4.0 is the adoption of emerging technologies such as artificial intelligence (AI), big data analytics, the Internet of Things (IoT), cloud computing, augmented reality, simulation, cybersecurity, systems integration, additive manufacturing, and robotics. Studies show that 59% of firms proficient in big data and IoT also utilize AI. Successful integration of these technologies enhances workforce capability and drives productivity growth. However, in Malaysia, industry readiness for IR4.0 remains at a low to medium level. This paper presents a systematic literature review to examine IR4.0 readiness models discussed in prior research. Six major databases—Scopus, Emerald Insight, IEEE, Springer, Web of Science, and Science Direct—were searched, yielding 10,428 records. After applying inclusion and exclusion criteria and a rigorous screening process, 55 relevant articles were selected for analysis. The findings reveal that IR4.0 readiness is often framed using theoretical constructs from success models, information systems theories, technology acceptance models, and maturity/readiness models. Common readiness factors such as funding, infrastructure, regulations, skills, competency, and technology appear frequently as both enablers and barriers. The review also highlights the use of self-assessment tools by industry players to evaluate their IR4.0 readiness. Based on the synthesized literature, this study proposes an IR4.0 Readiness and Implementation Framework to support industries in deploying IR4.0 technologies progressively. The framework is designed to guide organizations through phased implementation, enabling them to gradually improve their readiness and commitment to digital transformation. Received: 24 July 2022 | Revised: 20 September 2022 | Accepted: 21 September 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Nurul Izzati Saleh: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Mohamad Taha Ijab: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration.","url":"https://doi.org/10.47852/bonviewaia2202336","authors":["Nurul Izzati Saleh","Mohamad Taha Ijab"],"tags":["Big data","Scopus","Cloud computing","Workforce","Industry 4.0"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-22","doi":"https://doi.org/10.47852/bonviewaia2202336","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415300354","name":"Artificial intelligence for maxillofacial prosthodontics: A technological shift in craniofacial rehabilitation- a scoping review","source":"openalex","abstract":"Introduction: Artificial intelligence (AI) transforms dentistry and holds considerable promise for maxillofacial prosthodontics (MFP). Applications in imaging, computer-aided design and manufacturing (CAD/CAM), and additive manufacturing are improving diagnosis, treatment planning, and prosthetic rehabilitation for patients with craniofacial abnormalities. Despite advances in materials and digital workflows, challenges remain in achieving optimal accuracy, efficiency, and customisation in prosthetic design. The integration of AI in maxillofacial prosthodontics is still in its early stages. Currently, there is no review detailing the scope, trends, potential, and limitations of AI in this field. A scoping review is therefore necessary to consolidate existing evidence, identify knowledge gaps, and suggest directions for future research and clinical application. This review objective is to systematically map and analyse the current literature on AI in maxillofacial prosthodontics, focusing on its role in craniofacial rehabilitation. Methods: This scoping review adhered to the methodological framework of Arksey and O'Malley (2005) and was guided by the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis (2020). Reporting complied with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines to ensure clarity and reproducibility. The review was registered with the Open Science Framework (registration number: www.osf.io/3b9jr). Electronic databases, including Medline via PubMed, Scopus, Cochrane Database, Science Direct, Google Scholar, and Semantic Scholar, were searched up to 7 June 2025. Full-text English articles containing the keywords \"Artificial Intelligence and Maxillofacial Prosthodontics\" and related terms were included. Results: This scoping review included 35 articles from diverse geographic regions. The studies addressed several specific applications of AI in maxillofacial prosthodontics, including the production of implant-supported auricular prostheses, coloration of maxillofacial prostheses, evaluation of facial attractiveness in patients with clefts, capture of 3D impressions of cleft palates, identification of hypernasality, assessment of lip symmetry, and detection of teeth in cleft lip and palate cases Conclusion: Artificial intelligence offers significant opportunities for maxillofacial prosthodontics, especially in imaging, digital design, and prosthesis production. Progress in this area requires interdisciplinary teamwork, large-scale clinical trials, and the development of standardized validation methods to ensure safe and effective clinical application.","url":"https://doi.org/10.1016/j.jobcr.2025.10.006","authors":["Anupama Aradya","Koduru Sravani","M B Ravi","KN Raghavendra Swamy","Sajaysurya Ganesh","Pradeep Chandra K","H.K. Sowmya","Bindhya Jayashankar","Nisarga Vinod Kumar","KenchappanavarMaheshappa Sangeeta"],"tags":["Craniofacial","Medical physics","Engineering","Medicine","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-17","doi":"https://doi.org/10.1016/j.jobcr.2025.10.006","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W2956571997","name":"A Survey on Internet of Things and Cloud Computing for Healthcare","source":"openalex","abstract":"The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.","url":"https://doi.org/10.3390/electronics8070768","authors":["L. Minh Dang","Md. Jalil Piran","Dongil Han","Kyungbok Min","Hyeonjoon Moon"],"tags":["Cloud computing","Health care","Internet of Things","Big data","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-07-09","doi":"https://doi.org/10.3390/electronics8070768","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406074275","name":"AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships","source":"openalex","abstract":"Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.","url":"https://doi.org/10.1016/j.csbj.2024.12.030","authors":["You Wu","Lei Xie"],"tags":["Omics","Phenotype","Scale (ratio)","Computational biology","Genotype"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1016/j.csbj.2024.12.030","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4391067767","name":"Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm","source":"openalex","abstract":"OBJECTIVE: Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. METHODS: This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. RESULTS: The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91). CONCLUSION: The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. IMPACT: The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment.","url":"https://doi.org/10.1093/ptj/pzad176","authors":["Shiang-Chin Lin","Erick Chandra","Po‐Nien Tsao","Wei‐Chih Liao","Wei J. Chen","Ting‐An Yen","Jane Yung-jen Hsu","Suh‐Fang Jeng"],"tags":["Movement assessment","Artificial intelligence","Movement (music)","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-20","doi":"https://doi.org/10.1093/ptj/pzad176","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410396026","name":"Leveraging Artificial Intelligence to Advance Health Equity in America’s Safety Net","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s11606-025-09606-3","authors":["Bhav Jain","Rushabh Doshi","Shantanu Nundy"],"tags":["Medicine","Safety net","Equity (law)","Environmental health","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-15","doi":"https://doi.org/10.1007/s11606-025-09606-3","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410723968","name":"Research-based clinical deployment of artificial intelligence algorithm for prostate MRI","source":"openalex","abstract":"PURPOSE: A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here, we sought to integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment to enable point-of-care utilization under a prospective clinical trial scenario. METHODS: A previously trained, publicly available AI model for segmentation of intra-prostatic findings on multiparametric Magnetic Resonance Imaging (mpMRI) was converted into a containerized environment compatible with MONAI Deploy Express. An inference server and dedicated clinical PACS workflow were established within our institution for evaluation of real-time use of the AI algorithm. PACS-based deployment was prospectively evaluated in two phases: first, a consecutive cohort of patients undergoing diagnostic imaging at our institution and second, a consecutive cohort of patients undergoing biopsy based on mpMRI findings. The AI pipeline was executed from within the PACS environment by the radiologist. AI findings were imported into clinical biopsy planning software for target definition. Metrics analyzing deployment success, timing, and detection performance were recorded and summarized. RESULTS: In phase one, clinical PACS deployment was successfully executed in 57/58 cases and were obtained within one minute of activation (median 33 s [range 21-50 s]). Comparison with expert radiologist annotation demonstrated stable model performance compared to independent validation studies. In phase 2, 40/40 cases were successfully executed via PACS deployment and results were imported for biopsy targeting. Cancer detection rates for prostate cancer were 82.1% for ROI targets detected by both AI and radiologist, 47.8% in targets proposed by AI and accepted by radiologist, and 33.3% in targets identified by the radiologist alone. CONCLUSIONS: Integration of novel AI algorithms requiring multi-parametric input into clinical PACS environment is feasible and model outputs can be used for downstream clinical tasks.","url":"https://doi.org/10.1007/s00261-025-05014-7","authors":["Stephanie A. Harmon","Jesse Tetreault","Ömer Tarık Esengür","Ming Qin","Enis C. Yılmaz","Victor Chang","Dong Yang","Ziyue Xu","Gregg Cohen","Jeff Plum","Testi Sherif","R.J. Levin","Alexander Schmidt-Richberg","Scott M. Thompson","Samuel Coons","Te Chen","Peter L. Choyke","Daguang Xu","Sandeep Gurram","Bradford J. Wood","Peter A. Pinto","Baris Turkbey"],"tags":["Software deployment","Medicine","Workflow","Pipeline (software)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-25","doi":"https://doi.org/10.1007/s00261-025-05014-7","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411005770","name":"Detection of emergency department patients at risk of dementia through artificial intelligence","source":"openalex","abstract":"INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED). METHODS: In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014-2022) were used to train three supervised and semi-unsupervised positive-unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation. RESULTS: EDDA achieved an area under the receiver-operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive-unlabeled learning improved performance. Agreement between EDDA and clinician-adjudicated dementia diagnoses was moderate (kappa = 0.50), with 17% of EDDA-positive patients having undiagnosed probable dementia. DISCUSSION: EDDA enhances dementia detection in the ED, with potential for real-time implementation to improve patient outcomes and care transitions. HIGHLIGHTS: Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED). Algorithm designed to balance detection accuracy with ease of ED implementation. Parsimonious model with limited but predictive variables selected for rapid ED use. Focused on real-time application, optimizing ED workflows, and clinician support. Aims to enhance ED dementia detection, patient safety, and care coordination.","url":"https://doi.org/10.1002/alz.70334","authors":["Inessa Cohen","Richard A. Taylor","Haipeng Xue","Isaac V. Faustino","Natalia Festa","Cynthia Brandt","Emily Gao","Ling Han","Siddarth Khasnavis","James M. Lai","Adam P. Mecca","Atharva Vinay Sapre","Juan Young","Michael Zanchelli","Ula Hwang"],"tags":["Emergency department","Dementia","Artificial intelligence","Receiver operating characteristic","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.1002/alz.70334","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7122626431","name":"On the Possibility of Using Artificial Intelligence in Medicine: From Theory to Practice","source":"openalex","abstract":"Modern life is inextricably linked with the latest technologies. Artificial intelligence (AI) poses a new challenge to humanity, the application of which affects all areas of life, including medicine. This article examines the potential application of AI in medical practice, particularly in ophthalmology. It presents an example of how AI can be used to determine risk factors for dry eye syndrome in patients undergoing cosmetic procedures in the periorbital area. It also analyzes the limitations of AI in medicine. An analysis of the literature demonstrates that the use of AI in scientific and medical practice has opened up a wide range of opportunities for conducting research at a new technological level, such as screening examinations, image-based diagnostics, and disease prediction; selection of optimal drug dosages; mitigating the threat of pandemics; and automation and precision of surgical interventions. When integrating AI technologies into medical practice, it’s important to consider a wide range of ethical issues, including potential breaches of confidentiality, transparency, and the reliability of information received. Frequent use of chatbots can lead to errors and the dequalification of physicians, especially those with limited clinical experience, as well as disruption of doctor-patient communication. Furthermore, it’s important to consider legal and forensic issues, primarily the question of who will bear responsibility for making decisions. Given the above, in our view, a personalized approach to treating each individual patient remains a priority in everyday clinical practice. This approach takes into account not only objective indicators but also anamnestic data, the body’s individual responses to treatment, and psycho-emotional aspects, as well as the physician’s fundamental knowledge and experience.","url":"https://doi.org/10.18008/1816-5095-2025-4-725-731","authors":["V. N. Trubilin","E. G. Polunina","V. V. Kurenkov","A. V. Trubilin","E. V. Kechin","E. A. Kasparova","S. I. Arabadzhyan","A. V. Filonenko","E. N. Ponomareva","M. A. Tsaregorodtseva"],"tags":["Risk analysis (engineering)","Automation","Computer science","Artificial intelligence","Everyday life"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-28","doi":"https://doi.org/10.18008/1816-5095-2025-4-725-731","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4417151913","name":"The Application of Artificial Intelligence (AI) in Regenerative Medicine: Current Insights and Challenges","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly emerging as a transformative tool capable of addressing critical challenges and improving outcomes in tissue engineering and regenerative medicine. This paper demonstrates how machine learning and data fusion predict stem cell activity and potency, improve cellular characterization, and optimize therapeutic design. It also highlights important uses of AI in tissue engineering and cell-based therapeutics. By enabling accurate, non-invasive, and quantitative examination of living cells, AI also advances microscopy and imaging, facilitating better decision-making and real-time monitoring. Using search criteria including artificial intelligence, machine learning, deep learning, regenerative medicine, stem cells, and tissue engineering, the review was carried out using PubMed, Scopus, Web of Science, and Google Scholar. A total of 71 articles were screened; 8 non-peer-reviewed sources, 5 conference abstracts, and 4 duplicates were excluded. The final dataset included 7 clinical studies, 6 preclinical investigations, 18 original research articles, and 23 review papers. AI techniques, datasets, performance indicators, and regeneration results were compiled in the extracted data. To summarize, AI speeds up the development of tissue engineering, minimizes trial-and-error experimentation, lowers research expenses, forecasts tissue interactions, and enhances scaffold and biomaterial design. Consequently, AI integration enhances stem cell-based treatments and regenerative approaches, underscoring the necessity of interdisciplinary cooperation and ongoing technical development.","url":"https://doi.org/10.3390/biomedinformatics5040069","authors":["Duaa Abuarqoub","Mahdi Mutahar"],"tags":["Regenerative medicine","Transformative learning","Computer science","Regeneration (biology)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-09","doi":"https://doi.org/10.3390/biomedinformatics5040069","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412775165","name":"Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine","source":"openalex","abstract":"The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a data-driven, precision discipline. This review synthesizes recent advances in AI-enabled outcome prediction techniques, encompassing deep learning, meta-analytic modeling, natural language processing (NLP), computer vision, and neuroimaging-based analysis. For instance, convolutional neural networks (CNNs) have been successfully applied to classify tongue images and detect ZHENG patterns, while transformer-based NLP models enable automated extraction of clinical knowledge from classical texts. These technologies improve diagnostic objectivity, standardize treatment planning, and facilitate individualized care by enabling longitudinal efficacy modeling and real-time monitoring. Despite their potential, current implementations are constrained by limited and heterogeneous datasets, annotation variability, and gaps in clinical validation. We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices. This convergence of AI and acupuncture presents a unique opportunity to enhance scientific rigor, clinical utility, and global integration of acupuncture within the paradigm of precision integrative medicine.","url":"https://doi.org/10.3389/fmed.2025.1633416","authors":["Guoliang Hou","Bao-Qiang Dong","Boyang Yu","Jian-Yu Dai","Xingxing Lin","Zixin Cheng"],"tags":["Computer science","Artificial intelligence","Deep learning","Data science","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.3389/fmed.2025.1633416","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416770028","name":"Utilization of artificial intelligence in prostate cancer detection: a comprehensive review of innovations in screening and diagnosis","source":"openalex","abstract":"Prostate cancer management has long been challenged by the limitations of traditional screening tools like PSA testing, which contribute to significant rates of overdiagnosis and overtreatment. While advanced imaging such as multiparametric MRI (mpMRI) has improved the diagnostic pathway, the integration of Artificial Intelligence (AI) is now catalyzing a paradigm shift across the entire continuum of care. This comprehensive review details the transformative role of AI in prostate cancer. In diagnostics, deep learning algorithms enhance the interpretation of mpMRI by improving lesion detection, segmentation, and risk stratification, thereby reducing unnecessary biopsies. In digital pathology, AI provides automated and consistent Gleason grading, minimizing inter-observer variability and refining prognostication. In the therapeutic domain, AI is crucial for personalizing treatment by streamlining radiotherapy planning through automated contouring, predicting patient outcomes and toxicity, and enabling the development of adaptive therapy strategies for advanced disease. Multimodal AI models that synthesize imaging, biomarker, and clinical data are creating robust predictive tools for superior clinical decision support. Despite formidable challenges related to prospective validation, data equity, and regulatory approval, AI is paving the way for a new standard of care characterized by greater precision, efficiency, and personalization.","url":"https://doi.org/10.3389/fimmu.2025.1670671","authors":["Emad Rajih","Abdulaziz Bakhsh","Walaa Borhan","Saeed Awad M. Alqahtani"],"tags":["Overdiagnosis","Prostate cancer","Medicine","Artificial intelligence","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-27","doi":"https://doi.org/10.3389/fimmu.2025.1670671","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410498502","name":"Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis","source":"openalex","abstract":"BACKGROUND: Carotid artery calcifications are important markers of cardiovascular health, often associated with atherosclerosis and a higher risk of stroke. Recent research shows that dental radiographs can help identify these calcifications, allowing for earlier detection of vascular diseases. Advances in artificial intelligence (AI) have improved the ability to detect carotid calcifications in dental images, making it a useful screening tool. This systematic review and meta-analysis aimed to evaluate how accurately AI methods can identify carotid calcifications in dental radiographs. MATERIALS AND METHODS: A systematic search in databases including PubMed, Scopus, Embase, and Web of Science for studies on AI algorithms used to detect carotid calcifications in dental radiographs was conducted. Two independent reviewers collected data on study aims, imaging techniques, and statistical measures such as sensitivity and specificity. A meta-analysis using random effects was performed, and the risk of bias was evaluated with the QUADAS-2 tool. RESULTS: Nine studies were suitable for qualitative analysis, while five provided data for quantitative analysis. These studies assessed AI algorithms using cone beam computed tomography (n = 3) and panoramic radiographs (n = 6). The sensitivity of the included studies ranged from 0.67 to 0.98 and specificity varied between 0.85 and 0.99. The overall effect size, by considering only one AI method in each study, resulted in a sensitivity of 0.92 [95% CI 0.81 to 0.97] and a specificity of 0.96 [95% CI 0.92 to 0.97]. CONCLUSIONS: The high sensitivity and specificity indicate that AI methods could be effective screening tools, enhancing the early detection of stroke and related cardiovascular risks. CLINICAL TRIAL NUMBER: Not applicable.","url":"https://doi.org/10.1186/s12880-025-01719-9","authors":["Sarah Arzani","Parisa Soltani","Ali Karimi","Maryam Yazdi","Ashraf Ayoub","Zohaib Khurshid","Domenico Galderisi","Hugh Devlin"],"tags":["Carotid arteries","Radiography","Meta-analysis","Radiology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-19","doi":"https://doi.org/10.1186/s12880-025-01719-9","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406124015","name":"Implementation of Artificial Intelligence in nursing education: Α Νarrative Review","source":"openalex","abstract":"Background: As technological advancements continue to reshape various industries, the integration of AI in healthcare education emerges as a crucial facet in preparing future nursing professionals. This narrative review aims to elucidate the numerous ways AI technologies are being utilized in nursing education. Methodology: A search in two internet databases was conducted for relevant studies, using keywords. The inclusion criteria encompassed studies published within the last 5 years, written in English, and focused on the integration of AI technologies in nursing education settings. The selected articles underwent a systematic screening process. Results: Of the 523 papers retrieved, 7 were included in the final synthesis. These studies evaluate the implementation of AI methods in undergraduate nursing students. The AI method usually used was a Chatbot. In 4 studies, a 3D avatar was incorporated into the AI tool to serve as a Virtual Patient. The studies focused on various learning objectives, with 4 studies emphasizing communication skills enhancement. The remaining 3 studies used the AI tool to assess students' knowledge and clinical skills. Clinical scenarios were predominantly used, and in studies with a 3D avatar, scenarios addressed theoretical knowledge, critical thinking, and decision-making in escalating clinical conditions. Endpoints of AI implementation were assessed using self-reported questionnaires, interview and direct feedback from the Chatbot. Consistent endpoints included students' self-efficacy, knowledge of the learning objective, students' satisfaction and attitudes toward the learning style. Conclusions: As technology continues to advance, the potential for AI in nursing education is becoming increasingly evident. Given that nursing is an interactive science, it seems that AI Chatbots are more useful in nursing education. Further AI implementation will enrich our understanding of how its integration will serve nursing education.","url":"https://doi.org/10.12681/healthresj.36795","authors":["Aikaterini Kouka","Evangelia Giannelou","Kleanthis Konstantinidis","Ioannis Apostolakis"],"tags":["Nursing","Psychology","Medical education","Medicine","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-05","doi":"https://doi.org/10.12681/healthresj.36795","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409752426","name":"Artificial intelligence and physician burnout: A productivity paradox","source":"openalex","abstract":"Introduction: Physician burnout persists in the American healthcare system. In part, this burnout is believed to be driven by the Electronic Health Record (EHR) and its fraught role in the clinical work of physicians. Artificial intelligence (AI)-enabled healthcare technologies are often promoted on the basis of their promise to reduce burnout by introducing efficiencies into clinical work, particularly related to EHR utilization and documentation. Where documentation is perceived as the problem, AI scribes are offered as the solution. Methods: This essay looks closely at existing studies of AI scribes in clinical context and draws upon experience and understanding of healthcare delivery and the EHR to anticipate how AI may related to provider burnout. Results: We find that it is premature to assert that AI tools will reduce physician burnout. Considering the integration of AI scribes into Learning Health Systems healthcare delivery becomes a starting point for understanding the challenges faced in safely adopting AI tools more generally, with attention to the healthcare workforce and patients. Conclusion: It is not a foregone conclusion that AI-enabled healthcare technologies, in their current state and application, will lead to improved healthcare delivery and reduced burnout. Instead, this is an open question that demands rigorous evaluation and high standards of evidence before we restructure the work of physicians and redefine the care of our patients.","url":"https://doi.org/10.1002/lrh2.70013","authors":["David Alex Goodson","Brittany Garcia","Michael Hogarth","Shin‐Ping Tu"],"tags":["Burnout","Productivity","Psychology","Applied psychology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-23","doi":"https://doi.org/10.1002/lrh2.70013","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4417416460","name":"Impact of generative AI in medical education in India: a systematic review","source":"openalex","abstract":"Background: The advent of generative Artificial Intelligence (AI) has presented a fundamental change in the approach to medical education across the world. In India, where the medical education is facing a shortage in faculties and resources, generative AI (GenAI) has the potential of transforming this. This systematic review summarizes the current evidence on the impact, student readiness, and various ethical challenges and barriers of integration of AI into the medical curriculum. Methods: We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and searched published articles in PubMed and Google Scholar from 2020 to 2025. The search yielded 19,777 articles, from which 11 studies focusing on Indian medical students were selected. The findings of these studies were analyzed using Laurillard's six learning modes to gain a comprehensive pedagogical understanding. Result: Our study revealed a significant finding: while high awareness and positive perception towards AI have been shown by Indian medical students, most of the students lack formal training. These selected studies show that the students mostly use generative AI for clearing doubts, making assignments, and self-directed learning, shifting from the 'Acquisition' to 'Inquiry' and 'Production' modes of Laurillard's learning. Comparative Analysis showed that GenAI tools outperform students on standard exams, thus showing their potential. However, certain challenges also exist, including the risk of misinformation, over-reliance, potential decrease in critical thinking, and ethical concerns of data privacy. Conclusion: Indian medical students are enthusiastically adopting GenAI, but their engagement is mostly unstructured and informal. A significant gap exists between the readiness of the students and the medical institutions. To maximize the potential use of GenAI, our institutions have to develop a structured curriculum, invest in faculty training, and establish ethical guidelines. Teamwork between policymakers, educators, and researchers is the need of the hour so that our future physicians will be ready to integrate AI-enabled healthcare. Syestematic review registration: https://doi.org/10.17605/OSF.IO/2MJVK.","url":"https://doi.org/10.3389/frai.2025.1704785","authors":["Azfar Mateen","Visesh Kumar","Ajay Singh","Berendra Yadav","Mala Mahto","Atiq Hassan","Nazim Nasir"],"tags":["Medical education","Psychology","Generative grammar","Computer science","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-17","doi":"https://doi.org/10.3389/frai.2025.1704785","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413308457","name":"Artificial Intelligence in Assessing Reproductive Aging: Role of Mitochondria, Oxidative Stress, and Telomere Biology","source":"openalex","abstract":"Fertility potential ever more diminishes due to the complex, multifactorial, and still not entirely clarified process of reproductive aging in women and men. Gamete quality and reproductive lifespan are compromised by biologic factors like mitochondrial dysfunction, increased oxidative stress (OS), and incremental telomere shortening. Clinically confirmed biomarkers, including follicle-stimulating hormone (FSH) and anti-Müllerian hormone (AMH), are used to estimate ovarian reserve and reproductive status, but these markers have limited predictive validity and an incomplete representation of the complexity of reproductive age. Recent advances in artificial intelligence (AI) have the capacity to address the integration and interpretation of disparate and complex sets of data, like imaging, molecular, and clinical, for consideration. AI methodologies that improve the accuracy of reproductive outcome predictions and permit the construction of personalized treatment programs are machine learning (ML) and deep learning. To promote fertility evaluations, here, as part of its critical discussion, the roles of mitochondria, OS, and telomere biology as latter-day biomarkers of reproductive aging are presented. We also address the current status of AI applications in reproductive medicine, promises for the future, and applications involving embryo selection, multi-omics set integration, and estimation of reproductive age. Finally, to ensure that AI technology is used ethically and responsibly for reproductive care, model explainability, heterogeneity of data, and other ethical issues remain as residual concerns.","url":"https://doi.org/10.3390/diagnostics15162075","authors":["Efthalia Moustakli","Themos Grigoriadis","Sofoklis Stavros","Anastasios Potiris","Athanasios Zikopoulos","Angeliki Gerede","Ioannis Tsimpoukis","Charikleia Papageorgiou","Konstantinos Louis","Ekaterini Domali"],"tags":["Reproductive medicine","Telomere","Biology","Fertility","Reproductive biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-19","doi":"https://doi.org/10.3390/diagnostics15162075","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7131788581","name":"Narrative review of the ethics of artificial intelligence: are we ready for artificial intelligence in surgery?","source":"openalex","abstract":"Background and Objective: Artificial intelligence (AI) is transforming surgical care by enhancing clinical decision-making and providing intraoperative guidance. As its applications expand, ethical challenges arise, including algorithmic bias, transparency in AI reasoning, informed consent regarding AI involvement, and accountability surrounding AI-guided decisions. This review explores the readiness of the surgical community to address these issues at both the institutional and individual levels. Methods: A PubMed search identified literature on AI in surgery published between 2018-2025. Fourteen key studies were selected and reviewed to assess AI applications across the surgical continuum, with attention to ethical considerations and barriers to integration. Key Content and Findings: AI now supports surgical care from the preoperative diagnosis through postoperative recovery. AI can outperform or match clinician performance in tumor detection, disease diagnosis, and surgical risk stratification. Predictive models using deep learning can outperform traditional scoring systems for perioperative and postoperative complication risk. Intraoperatively, AI enables surgical phase recognition, augmented reality guidance, and detection of technical errors. Despite these benefits, ethical concerns remain. Algorithmic bias may underestimate the needs of marginalized populations. Furthermore, questions of legal liability arise when AI-guided decisions cause harm. Informed consent must now address AI's role, limitations, and potential consequences if declined. Surgeons must guard against \"automation bias\" to preserve human judgment and patient trust. Institutional readiness remains unsatisfactory, as many healthcare systems lack infrastructure for real-time data integration and governance over data ownership. Surgeon skepticism and the \"black box\" nature of models also hinder adoption of the technology. Education on AI's design, validation, and biases is essential for safe integration. Conclusions: While AI holds immense potential to enhance surgical care, its use should be grounded in ethical principles to ensure non-maleficence and justice. Adoption should aim to maximize beneficence while preserving patient autonomy through transparent consent and promoting equity in access and implementation. At the same time, surgeons must remain vigilant against automation bias such that AI supports, not replaces, clinical intuition and trust, which lies at the core of the surgeon-patient relationship.","url":"https://doi.org/10.21037/jtd-2025-1814","authors":["Erin Yu","Graeme M. Rosenberg","Brooks V. Udelsman","Takashi Harano","Scott Atay","Anthony W. Kim","Baddr A. Shakhsheer","Sean C. Wightman"],"tags":["Medicine","Accountability","Transparency (behavior)","Skepticism","Informed consent"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-01","doi":"https://doi.org/10.21037/jtd-2025-1814","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4401698185","name":"Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information","source":"openalex","abstract":"Importance: Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes. Objective: To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model. Design, Setting, and Participants: For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). The SPARK database contains participants recruited from 31 university-affiliated research clinicals and online in 26 states in the US. All individuals with a professional ASD diagnosis and their families were eligible to participate. The model performance was validated on independent datasets from SPARK, version 10 (released July 21, 2023), and the Simons Simplex Collection (SSC), consisting of 14 790 participants, followed by phenotypic associations. Exposures: Twenty-eight basic medical screening and background history items present before 24 months of age. Main Outcomes and Measures: Generalizable ML prediction models were developed for detecting ASD using 4 algorithms (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting [XGBoost]). Performance metrics included accuracy, area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and F1 score, offering a comprehensive assessment of the predictive accuracy of the model. Explainable AI methods were applied to determine the effect of individual features in predicting ASD as secondary outcomes, enhancing the interpretability of the best-performing model. The secondary outcome analyses were further complemented by examining differences in various phenotypic measures using nonparametric statistical methods, providing insights into the ability of the model to differentiate between different presentations of ASD. Results: The study included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months). The mean (SD) age was 113 (68) months for the ASD group and 100 (55) months for the non-ASD group. The XGBoost (termed AutMedAI) model demonstrated strong performance with an AUROC score of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897. Developmental milestones and eating behavior were the most important predictors. Validation on independent cohorts showed an AUROC of 0.790, indicating good generalizability. Conclusions and Relevance: In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.","url":"https://doi.org/10.1001/jamanetworkopen.2024.29229","authors":["Shyam Sundar Rajagopalan","Yali Zhang","Ashraf Yahia","Kristiina Tammimies"],"tags":["Autism spectrum disorder","Receiver operating characteristic","Machine learning","Autism","Random forest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-19","doi":"https://doi.org/10.1001/jamanetworkopen.2024.29229","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7117721922","name":"Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma","source":"openalex","abstract":"The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semiannual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivity for early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies. In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.","url":"https://doi.org/10.5009/gnl250268","authors":["Ming-Ying Lu","Jacky Chung‐Hao Wu","Henry Horng-Shing Lu","Mohammed Eslam","Yu Ming-Lung"],"tags":["Medicine","Hepatocellular carcinoma","Precision medicine","Personalized medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-31","doi":"https://doi.org/10.5009/gnl250268","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4408592630","name":"On the ethical and moral dimensions of using artificial intelligence for evidence synthesis","source":"openalex","abstract":"Artificial intelligence (AI) is increasingly being used in the field of medicine and healthcare. However, there are no articles specifically examining ethical and moral dimensions of AI use for evidence synthesis. This article attempts to fills this gap. In doing so, I deploy in written form, what in Bengali philosophy and culture, is the Adda (আড্ডা) approach, a form of oral exchange, which involves deep but conversational style discussion. Adda developed as a form of intellectual resistance against the cultural hegemony of British Imperialism and entails asking provocative question to encourage critical discourse.The raison d'être for using AI is that it would enhance efficiency in the conduct of evidence synthesis, thus leading to greater evidence uptake. I question whether assuming so without any empirical evidence is ethical. I then examine the challenges posed by the lack of moral agency of AI; the issue of bias and discrimination being amplified through AI driven evidence synthesis; ethical and moral dimensions of epistemic (knowledge-related) uncertainty on AI; impact of knowledge systems (training of future scientists, and epistemic conformity), and the need for looking at ethical and moral dimensions beyond technical evaluation of AI models. I then discuss ethical and moral responsibilities of government, multi-laterals, research institutions and funders in regulating and having an oversight role in development, validation, and conduct of evidence synthesis. I argue that industry self-regulation for responsible use of AI is unlikely to address ethical and moral concerns, and that there is a need to develop legal frameworks, ethics codes, and of bringing such work within the ambit of institutional ethics committees to enable appreciation of the complexities around use of AI for evidence synthesis, mitigate against moral hazards, and ensure that evidence synthesis leads to improvement of health of individuals, nations and societies.","url":"https://doi.org/10.1371/journal.pgph.0004348","authors":["Soumyadeep Bhaumik"],"tags":["Psychology","Engineering ethics","Epistemology","Philosophy","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-19","doi":"https://doi.org/10.1371/journal.pgph.0004348","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3026645248","name":"The Impact of Awareness of New Artificial Intelligence Technologies on Policy Governance on Risk","source":"openalex","abstract":"Background/Objectives: This study examined the risks of new AI technologies and their impact on policy governance. Artificial intelligence is bringing about changes in various fields such as politics, economy and culture through information society and technology. In particular, it has a positive effect on solving various problems of existing society and overcoming limitations. But this advancement in artificial intelligence can create the opposite problem as expected. This appears to be a risk. We identify the factors that recognize this risk and investigate the possible impact on government governance.Methods/Statistical analysis: The questionnaire and data of this journal were analyzed by Korean public portal data, and the analysis data were designated by the Korea Information Technology Agency, AI related company, AI association, Ministry of Science, ICT and Future Planning, IT society, government research institute, Korea Communications Commission, and National Security Agency The questionnaire survey was based on AI experts working in the field.The analysis program uses IBM SPSS Statistics 22. The analysis methods are descriptive statistical analysis, reliability analysis and exploratory factor analysis.Findings: This study examined the risks of new AI technologies and their impact on policy governance. The survey was conducted to clarify comments on awareness of new AI-related technologies, awareness of AI risks, and improvements to AI-related policies.AI risk has become an integral part of regulation and the government's role as a risk manager is important.Improvements/Applications: Further discussion is needed regarding the commercialization effects of AI technology awareness, benefit items and timing items on policy governance through risk awareness.","url":"https://doi.org/10.5430/rwe.v11n2p152","authors":["Do-Hyung Yee","Yen-Yoo You"],"tags":["Government (linguistics)","Corporate governance","Agency (philosophy)","Exploratory factor analysis","Descriptive statistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-23","doi":"https://doi.org/10.5430/rwe.v11n2p152","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407287572","name":"Harnessing Artificial Intelligence for Global Health Advancement","source":"openalex","abstract":"Artificial intelligence (AI) is transforming healthcare, offering the potential to optimize clinical decision-making, improve patient outcomes, and enhance care accessibility. AI-driven tools can analyze vast medical data, enabling early disease detection, accurate diagnoses, and personalized treatment. By addressing challenges such as aging populations, chronic diseases, and workforce shortages, AI can revolutionize global healthcare delivery, particularly in underserved regions. However, successful AI integration requires overcoming barriers like data quality, regulatory frameworks, and ethical considerations. Despite these challenges, AI offers a promising path to more efficient, equitable, and personalized healthcare worldwide.","url":"https://doi.org/10.4236/jdaip.2025.131004","authors":["Guntas Dhanjal"],"tags":["Artificial intelligence","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.4236/jdaip.2025.131004","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406802063","name":"An Effectiveness Study of Generative Artificial Intelligence Tools Used to Develop Multiple-Choice Test Items","source":"openalex","abstract":"Generative artificial intelligence (GenAI) tools developed to support teaching and learning are widely available. Trustworthiness concerns, however, have prompted calls for researchers to study their effectiveness and for educators and educational researchers to be involved in their creation and piloting processes. This study investigated one type of GenAI created to support educators: multiple-choice question generators (MCQ GenAI). Among the nine MCQ GenAI tools investigated, a variety of useful options were available, but only one indicated teacher involvement and none mentioned testing experts in development processes. MCQ GenAI-created items (n = 270) were coded based on MCQ quality item-writing guidelines. Results showed 80.00% of items (n = 216) violated at least one guideline, with 73.70% (n = 199) likely to produce major measurement error (should not use without revision), 6.30% (n = 17) likely to elicit minor measurement error (consider modifying), and 20.00% (n = 54) acceptable (usable as created). Implications suggest multidisciplinary teams are needed in educational GenAI tool development.","url":"https://doi.org/10.3390/educsci15020144","authors":["Toni A. May","Yiyun Fan","Gregory E. Stone","Kristin L. K. Koskey","Connor J. Sondergeld","Timothy D. Folger","James Archer","Kathleen Provinzano","Carla C. Johnson"],"tags":["Test (biology)","Computer science","Generative grammar","Multiple choice","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-24","doi":"https://doi.org/10.3390/educsci15020144","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4405338687","name":"Artificial Intelligence as a Provider of Feedback on EFL Student Compositions","source":"openalex","abstract":"In response to the arrival of advanced artificial intelligence (AI) in the form of ChatGPT, this study examines its potential for providing feedback to foreign language writers. This represents a more acceptable use of AI in the writing classroom, rather than students simply using AI to write their entire essay. The methodological procedure involved eliciting normal classroom writing-practice essays from 29 English major students at a Saudi university, with ChatGPT (2023) then given a simple prompt requesting feedback. Both the essays and the feedback were qualitatively analysed to respond to research questions concerning the feedback’s consistency and credibility, and the extent to which it represented the different potential feedback types, based on a review of the extensive literature on the subject. Although superficially impressive, close examination revealed certain weaknesses to the AI feedback. For example, there was inconsistency in how the feedback was handled across essays, and some statements were not fully accurate regarding the respective text. In focus, the feedback was primarily accuracy-oriented, while even-handed in attention to content, organisation, and lower-level language matters, providing both positive and negative comments. However, there was a paucity of message-oriented communicative and explicit affective feedback. Like many teachers, ChatGPT was selective in terms of the feedback provided, but the decisions of what to address did not seem altogether motivated by criteria that an expert human feedback provider would consider. The main conclusion is that while AI feedback on writing practice is useful, it does require human monitoring by a teacher.","url":"https://doi.org/10.5430/wjel.v15n2p161","authors":["Manal Saleh M. Alghannam"],"tags":["Credibility","Consistency (knowledge bases)","Peer feedback","Computer science","Focus (optics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-12","doi":"https://doi.org/10.5430/wjel.v15n2p161","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409572098","name":"FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review","source":"openalex","abstract":"","url":"https://doi.org/10.1067/j.cpradiol.2025.04.011","authors":["Pranav Ajmera","Ryan Dillard","Timothy L. Kline","A. Missert","Panagiotis Korfiatis","Ashish Khandelwal"],"tags":["Medicine","Medical physics","MEDLINE","Law","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-18","doi":"https://doi.org/10.1067/j.cpradiol.2025.04.011","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4405829176","name":"Medical and Pharmaceutical Law in Erasmus+: Study of the Disciplines by Medical Students as a Basis for Training of Healthcare Professionals in Prevention of Medical Errors and Crimes","source":"openalex","abstract":"Within the framework of the Erasmus+ program, the significance of medical and pharmaceutical law is underscored as a pivotal element in training future doctors and healthcare managers, with the goal of preventing medical errors and criminal offenses in clinical practice. Emphasis is placed on equipping medical students with a thorough understanding of these disciplines, which serve as the foundation for ethical and competent patient care. Crucially, research has shown that dangerous treatment methods and errors during patient treatment often rank among the leading causes of injuries and harm, which reinforces the urgency of studying and implementing WHO recommendations to reduce medical mishaps. Numerous examples drawn from forensic pharmaceutical and forensic medical practice further illuminate the consequences of inadequate legislative compliance and substandard regulatory oversight, stressing the necessity of robust legal frameworks in the healthcare sector. To address these challenges, ongoing professional education and training courses for interns, doctors, pharmacists, and authorized persons responsible for incoming quality control of medicines will be organized by the Private Scientific Institution \"Scientific and Research University of Medical and Pharmaceutical Law.\" This initiative will be held on November 13–14, 2025, in Kyiv as part of the XXII International Multidisciplinary Scientific and Practical Conference \"Medical and Pharmaceutical Law in the System of Legal Relations ‘Doctor-Patient-Pharmacist-Lawyer-Expert/Forensic’: Circulation of Medicinal Products from Production to Prescription.\" By providing an interdisciplinary platform for sharing best practices, examining up-to-date regulatory requirements, and reinforcing ethical standards, this conference will serve as a beacon for bolstering accountability and patient safety. Meanwhile, continuous exploration of the Erasmus+ project’s potential in developing new educational programs for studying medical and pharmaceutical law remains a promising and evolving endeavor that holds great potential for advancing healthcare quality in Ukraine and worldwide.","url":"https://doi.org/10.53933/sspmlp.v4i4.169","authors":["Valentyn Shapovalov","Олександр Вейц"],"tags":["Erasmus+","Health care","Health professionals","Medical education","Training (meteorology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-27","doi":"https://doi.org/10.53933/sspmlp.v4i4.169","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7118159180","name":"Artificial Intelligence in Oculoplastic Surgery: A Systematic Review","source":"openalex","abstract":"Artificial Intelligence (AI) has become an integral component of modern ophthalmology, with oculoplastic surgery representing a rapidly evolving subspecialty that stands to benefit from advances in automation and deep learning.Despite promising innovations, a comprehensive understanding of AI's role in oculoplastic diagnosis and management remains limited.This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current landscape, clinical performance, and translational potential of AI applications in oculoplastic diseases published between 2000 and 2025.A structured search of PubMed, Scopus, and Embase identified 25 peer-reviewed studies involving AI-driven image analysis, disease classification, surgical planning, and prognostic modelling across eyelid, lacrimal, orbital, and periocular disorders.Studies were assessed for model performance, clinical utility, and methodological rigor.The including studies demonstrated that AI algorithms achieved diagnostic accuracies exceeding 90% in detecting periocular malignancies, outperforming or complementing traditional clinician-based assessment.Machine learning models also facilitated surgical planning and postoperative outcome prediction, contributing to enhance clinical workflow efficiency and reduced inter-observer variability.Nevertheless, limitations related to dataset heterogeneity, small sample sizes, and limited external validation constrain generalizability.AI holds significant promise in advancing precision and efficiency in oculoplastic care.Future research should prioritize multicentric validation, explainable AI frameworks, and integration with robotic-assisted surgery to enable safe and ethical clinical translation, ultimately bridging the gap between technological innovation and patient-centered ophthalmic practice.","url":"https://doi.org/10.52338/joed.2025.5236","authors":["Niraj Kumar Yadav","Ophthalmic Plastic"],"tags":["Workflow","Artificial intelligence","Computer science","MEDLINE","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-05","doi":"https://doi.org/10.52338/joed.2025.5236","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411330206","name":"Reproducible generative artificial intelligence evaluation for health care: a clinician-in-the-loop approach","source":"openalex","abstract":"Objectives: To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance. Materials and Methods: A 5-dimension evaluation framework was developed to assess query comprehension and response helpfulness, correctness, completeness, and potential clinical harm. The framework was applied to evaluate ClinicalKey AI using queries drawn from user logs, a benchmark dataset, and subject matter expert curated queries. Forty-one board-certified physicians and pharmacists were recruited to independently evaluate query-response pairs. An agreement protocol using the mode and modified Delphi method resolved disagreements in evaluation scores. Results: Of 633 queries, 614 (96.99%) produced evaluable responses, with subject matter experts completing evaluations of 426 query-response pairs. Results demonstrated high rates of response correctness (95.5%) and query comprehension (98.6%), with 94.4% of responses rated as helpful. Two responses (0.47%) received scores indicating potential clinical harm. Pairwise consensus occurred in 60.6% of evaluations, with remaining cases requiring third tie-breaker review. Discussion: The framework demonstrated effectiveness in quantifying performance through comprehensive evaluation dimensions and structured scoring resolution methods. Key strengths included representative query sampling, standardized rating scales, and robust subject matter expert agreement protocols. Challenges emerged in managing subjective assessments of open-ended responses and achieving consensus on potential harm classification. Conclusion: This framework offers a reproducible methodology for evaluating health-care generative AI systems, establishing foundational processes that can inform future efforts while supporting the implementation of generative AI applications in clinical settings.","url":"https://doi.org/10.1093/jamiaopen/ooaf054","authors":["Leah Livingston","Amber Featherstone-Uwague","A. P. Barry","Kenneth Barretto","Tara Morey","Drahomíra Herrmannová","Venkatesh Avula"],"tags":["Computer science","Subject-matter expert","Artificial intelligence","Correctness","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.1093/jamiaopen/ooaf054","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4286432494","name":"Artificial Intelligence Governance and Ethics: Global Perspectives","source":"openalex","abstract":"Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years. AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice. These developments have led to concerns about whether and how AI systems adhere, and will adhere to ethical standards. These concerns have stimulated a global conversation on AI ethics, and have resulted in various actors from different countries and sectors issuing ethics and governance initiatives and guidelines for AI. Such developments form the basis for our research in this report, combining our international and interdisciplinary expertise to give an insight into what is happening in Australia, China, Europe, India and the US.","url":"https://doi.org/10.48550/arxiv.1907.03848","authors":["Angela Daly","Thilo Hagendorff","Hui Li","Monique Mann","Vidushi Marda","Ben Wagner","Wei Wang","Saskia Witteborn"],"tags":["Corporate governance","Conversation","Political science","Happening","Economic Justice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-06-28","doi":"https://doi.org/10.48550/arxiv.1907.03848","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414704819","name":"Raindrop optimizer: a novel nature-inspired metaheuristic algorithm for artificial intelligence and engineering optimization","source":"openalex","abstract":"This paper presents a novel meta-heuristic optimization method, the Raindrop Algorithm (RD), inspired by natural raindrop phenomena, and explores its applications in artificial intelligence. The raindrop algorithm comprises two primary phases: exploration and exploitation. During the exploration phase, mechanisms including splash, diversion, and evaporation are employed to enhance global search capabilities. In the exploitation phase, raindrop convergence and overflow behaviors are simulated to improve local search performance. The algorithm demonstrates rapid convergence characteristics, typically achieving optimal solutions within 500 iterations while maintaining computational efficiency. The effectiveness and competitiveness of the raindrop algorithm have been validated on 23 benchmark functions and the CEC-BC-2020 benchmark suite, achieving first-place rankings in 76% of test cases. Specifically, on the CEC-BC-2020 benchmark, Wilcoxon rank-sum tests ([Formula: see text]) demonstrate statistically significant superiority in 94.55% of comparative cases. The raindrop algorithm has been successfully applied to optimize state estimation filters and controller parameters in robotic engineering problems, achieving an 18.5% reduction in position estimation error and a 7.1% improvement in overall filtering accuracy compared to conventional methods. Experimental results across five distinct engineering scenarios confirm the competitiveness and versatility of the raindrop algorithm, consistently maintaining top-three rankings in complex, nonlinear, and constrained optimization problems, thereby providing a promising solution for challenging optimization tasks in artificial intelligence-driven engineering environments.","url":"https://doi.org/10.1038/s41598-025-15832-w","authors":["Shengjin Chen","Guangyong Yang","Guanghai Cui","Xiaoli Dong"],"tags":["Benchmark (surveying)","Computer science","Convergence (economics)","Algorithm","Reduction (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1038/s41598-025-15832-w","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4399970933","name":"Artificial Intelligence in Infectious Skin Disease","source":"openalex","abstract":"Abstract Background Infective and infectious dermatological diseases, which range from minor diseases like impetigo to serious diseases like deep fungal infections, pose significant public health issues. Given the growth of drug‐resistant microorganisms, it is critical to investigate novel techniques in dermatology. Artificial Intelligence (AI) has shown promising results in improving the diagnosis, treatment, and management of infectious skin disorders. This has the potential to significantly improve dermatological treatment by combining physician experience with data‐driven insights. Objective This review will look into the existing uses and future possibilities of AI technologies in infectious dermatology, including machine learning and deep learning. Its goal is to highlight major advances, identify gaps in understanding and technical advancement, and recommend viable future research directions. Methods A comprehensive literature search of the scientific literature was performed using well‐known databases such as PubMed, Google Scholar, and Embase. A specific set of phrases relevant to AI and infectious dermatology was used to ensure a thorough search. Articles were picked based on their relevance, timeliness, and quality, with a particular emphasis on research demonstrating how AI is being utilized to prevent, detect, diagnose, or manage infectious skin disorders. Results AI has made significant contributions to the management of infection in dermatology. It has improved diagnostic accuracy, predictive modeling of drug resistance, and individualized care regimens. Deep learning is used to evaluate clinical images, predictive models are developed to forecast antibiotic resistance, and AI‐powered diagnostic tools for uncommon infections. The assessment also throws light on AI's role in pandemic preparedness and response.","url":"https://doi.org/10.1002/der2.241","authors":["Ghasem Rahmatpour Rokni","Nasim Gholizadeh","Mahsa Babaei","Kinnor Das"],"tags":["Infectious disease (medical specialty)","Virology","Medicine","Disease","Dermatology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-01","doi":"https://doi.org/10.1002/der2.241","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410443235","name":"Facial Analysis for Plastic Surgery in the Era of Artificial Intelligence: A Comparative Evaluation of Multimodal Large Language Models","source":"openalex","abstract":"Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement.","url":"https://doi.org/10.3390/jcm14103484","authors":["Syed Ali Haider","Srinivasagam Prabha","Cesar A. Gomez-Cabello","Sahar Borna","Ariana Genovese","Maissa Trabilsy","Adekunle Elegbede","Jenny Fei Yang","Andrea Galvao","Cui Tao","Antonio J. Forte"],"tags":["Medicine","Facial trauma","Multimodal therapy","Artificial intelligence","Surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.3390/jcm14103484","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414211804","name":"Artificial intelligence in traditional medicine: evidence, barriers, and a research roadmap for personalized care","source":"openalex","abstract":"Background: Traditional medicine (TM) systems such as Ayurveda, Traditional Chinese Medicine (TCM), and Thai Traditional Medicine (TTM) are increasingly intersecting with artificial intelligence (AI). Objective: To synthesize how AI is currently applied to TM and to outline barriers and research needs for safe, equitable, and scalable adoption. Methods: We conducted a targeted narrative mini review of peer reviewed studies (2017-Aug 2025) retrieved from PubMed, Scopus, and Google Scholar using terms spanning TM (Ayurveda/TCM/TTM) and AI (machine learning (ML), natural language processing (NLP), computer vision, telemedicine. Inclusion favored studies with reported methods and, when available, performance metrics; commentary and preprints without data were excluded. Findings: Current evidence supports AI assisted diagnostic pattern recognition, personalization frameworks integrating multi source data, digital preservation of TM knowledge, telemedicine enablement, and AI supported herbal pharmacology and safety assessment. Reported performance varies and is context dependent, with limited prospective external validation. Limitations: Evidence heterogeneity, small datasets, inconsistent ontologies across TM systems, and nascent regulatory pathways constrain real world deployment. Conclusion: AI can augment TM education, research, and clinical services, but progress requires standards, culturally informed datasets, prospective trials, and clear governance. We propose a research roadmap to guide rigorous and ethical integration.","url":"https://doi.org/10.3389/frai.2025.1659338","authors":["Ketmanee Jongjiamdee","Pimnipa Pornwonglert","Nutnichar Na Bangchang","Pravit Akarasereenont"],"tags":["Computer science","Personalized medicine","Artificial intelligence","Data science","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-09","doi":"https://doi.org/10.3389/frai.2025.1659338","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413817249","name":"An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments","source":"openalex","abstract":"Millions of individuals worldwide suffer from retinal anomalies, which can lead to irreversible vision loss. However, the number of ophthalmologists is highly mismatched with the population base in China, especially in many rural and underdeveloped towns. To tackle these challenges, this paper developed an Artificial Intelligence Cloud Platform for OCT-based Retinal Anomalies Screening (AI-PORAS), which is capable of detecting 15 retinal anomalies in OCT images to enhance remote diagnostic efficiency. AI-PORAS has been trained, validated, and deployed to 207 medical institutions in 29 provinces in China. The validation on 165,384 eyes with 3,551,959 OCT B-scan slices, AI-PORAS achieved an average accuracy of 93.16%, an AUC of 93.64%, a FPR of 6.82%, a FNR of 7.87%, matching the average performance level of attending ophthalmologists. Additionally, Statistical analysis of the 116,717 remotely diagnosed patients provided insightful guidance for healthcare decision-making and the development of tailored treatment plans.","url":"https://doi.org/10.1038/s41746-025-01959-7","authors":["Xinjian Chen","Jiangtao Wang","Tianwei Qian","Jingcheng Wang","Yiming Ding","Su Zhang","Jingjing Liao","Cheng Qian","Ting Yang","Muhammad Mateen","Yu Fan","Zongming Song","Jili Chen","Suyan Li","Juejun Hu","Wentao Yan","Haoyu Chen","Wencan Wu","Huang Jing","Tien Yin Wong","Xun Xu"],"tags":["Cloud computing","Retinal","Computer science","Artificial intelligence","Operating system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-29","doi":"https://doi.org/10.1038/s41746-025-01959-7","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4392162030","name":"Machine learning for antimicrobial peptide identification and design","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s44222-024-00152-x","authors":["Fang Wan","Felix Wong","James J. Collins","César de la Fuente‐Núñez"],"tags":["Identification (biology)","Antimicrobial","Antimicrobial peptides","Peptide","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-26","doi":"https://doi.org/10.1038/s44222-024-00152-x","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411969645","name":"Generative artificial intelligence and the risk of technodigital colonialism","source":"openalex","abstract":"The use of Generative Artificial Intelligence has raised concerns related to plagiarism in scientific contexts. However, bad academic writing is far from being the main ethical challenge related to digital transformations in knowledge production. Additionally, science is not the only trust discourse affected, as journalism and law are deeply impacted in its social roles by the dissemination of artificially generated discourses. Power and knowledge are increasingly imbricated in digital society in a global context where colonial hierarchization, dehumanization and exploitation strategies are still in place. In response to the insufficiency of high-level moral principles before the ethical and Human Rights challenges brought by GenAI applications, this paper offers an alternative theoretical approach to digital ethics presented in the “decolonizing ethical thinking” section. The aim is to focus on the role that the new epistemic dynamics play to the risk of technodigital colonialism. Decoloniality readings should account for why the benefits and risks are not universally distributed and therefore may help ethical responses be more attentive to the connections between knowledge and power.","url":"https://doi.org/10.3389/fpos.2025.1628139","authors":["Leonardo Cambraia","Monique Pyrrho"],"tags":["Generative grammar","Colonialism","Artificial intelligence","Computer science","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-02","doi":"https://doi.org/10.3389/fpos.2025.1628139","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412804424","name":"Measuring public opinion towards artificial intelligence: development and validation of a general AI attitude short scale","source":"openalex","abstract":"Abstract The rapid proliferation of artificial intelligence (AI) has sparked both enthusiasm and ethical concerns in societies. As AI continues to permeate daily life, policymakers need to understand how it is perceived by diverse stakeholders and communities. To reliably measure attitudes towards AI of the general public, a short scale is essential for universal application. Existing scales face limitations in applicability due to their length, sub-standard internal consistency, or a focus on only negative attitudes. In response, we built up on existing scales and developed a unidimensional six-item general AI attitude short scale. First tests on internet panel data from Germany ( n = 1001) and the US ( n = 3091) obtained favorable results for classical test theory (CTT) and item response theory (IRT). Confirmatory factor analysis indicated an excellent fit for a single-factor structure, while the scale also exhibited strong criterion-related validity, correlating positively with digital competency and predicting acceptance of several AI applications. Additional IRT analyses suggested high item discrimination, broad coverage of the attitude spectrum and no meaningful differential item functioning (DIF). Thus, we propose a psychometrically sound short scale for measuring general AI attitude and provide insights into the antecedents and consequences of the construct.","url":"https://doi.org/10.1007/s00146-025-02478-5","authors":["Marcus Novotny","Wiebke Weber","Christoph Kern","Frauke Kreuter"],"tags":["Public opinion","Scale (ratio)","Artificial intelligence","Psychology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.1007/s00146-025-02478-5","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409067430","name":"Artificial intelligence in neurology, ethics, recent guideline, and law-an Indian perspective","source":"openalex","abstract":"Artificial Intelligence in Neurology, Ethics, Recent Guideline and Law- An Indian PerspectiveIntroductionArtificial intelligence (AI), a boon, kept healthcare professionals safe during the COVID-19 era in the form of “Tommy,” the robot nurse in Italy or “Mitra” in India. But continuous involvement of AI in healthcare also brings various challenges, i.e., quality and ownership of the data, belief issues, and ethical challenges. Therefore, in this article, we want to discuss the global policies regarding AI from different countries including India. We also present the summary of the new ethical guideline for AI applications in healthcare by the Indian Council of Medical Research (ICMR) and the Digital Personal Data Protection Act 2023 (DPDP Act),the first law for personal data protection in India, released recently in India. Current AI applications in Neurology in IndiaAI can be utilized in different parts of neurology from diagnosis of seizures, developmental anomalies, i.e., Down syndrome, cerebral palsy, etc., and neurodegenerative diseases (e.g., Alzheimer’s disease) to the rehabilitation of stroke patients1. Recently in India, Aster CMI hospital has developed a screening tool to diagnose carpal tunnel syndrome2. Scientists from Kyoto University, Japan, and IIT Roorkee, India, have created an AI-based model to predict the glioma grading from brain MRI which will help to treat the tumors3. A USA-based company named Intel and the University of California have partnered with 29 international institutes including Tata Memorial Hospital, Mumbai to create an AI model for early detection of brain tumor4. Medtronic India has partnered with Qure.ai to develop an AI-based tool for early detection and management of stroke patients5.Ethical Laws and Guidelines Around the WorldUSAAround 120 bills are being contemplated by US Congress regarding AI but significantly less number of bills are related to ethics in healthcare. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) exists in USA for personal data security of the citizens. However HIPPA can not shield the citizen from the “Black box” problem of AI. American Medical Association’s “Augmented Intelligence in Medicine” (2018) and “Payment and Coverage of AI” (2019), “Blueprint for an AI Bill of Rights” (2022) by the White House, and “Artificial Intelligence Risk Management Framework 1.0 (AI RMF)” (2023) by National Institute of Standards and Technology (NIST), exist regarding AI6. But these policies can not protect the citizens completely. Food and Drug Administration (FDA) has several guiding principles regarding AI-enabled medical devices (e.g. DermaSensor and Paige Prostate to detect skin cancer and carcinoma prostate respectively), i.e., “Good Machine Learning Practice for Medical Device Development: Guiding Principles” (2021), “Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles”(2021), “Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles” (2024)7. But till date no such guideline exists for non-device AI i.e., AI associated with the clinical decision support system.GermanySimilar to HIPPA in the USA, the General Data Protection Regulation (GDPR) is present in the European Union (EU) for the personal data security of citizens. Similarly, as a member of the EU, Bundesdatenschutzgesetz (BDSG) is present in Germany. General Product Safety Regulation (GPSR) 2023/988 involves safety rules for products. The New Product Liability Directive includes compensation for AI software which will be implemented in December 2026. Though the terminology “AI” is included as a reference in the German Works Constitution Act 2021, Germany does not have a separate AI regulation and will probably follow the EU AI Act. Among all the regulations, the EU AI Act (2024) is the most significant legislation as it is the first legal framework for AI. This act divides AI into several risk categories, ie., unacceptable (e.g. Biometric identific","url":"https://doi.org/10.3389/fneur.2025.1515041","authors":["Tithishri Kundu","Mainak Bardhan"],"tags":["Perspective (graphical)","Guideline","Engineering ethics","Neurology","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-02","doi":"https://doi.org/10.3389/fneur.2025.1515041","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4417056660","name":"Artificial Intelligence in Prostate MRI : Addressing Current Limitations Through Emerging Technologies","source":"openalex","abstract":"Prostate MRI has transformed lesion detection and risk stratification in prostate cancer, but its impact is constrained by the high cost of the exam, variability in interpretation, and limited scalability. False negatives, false positives, and moderate inter-reader agreement undermine reliability, while long acquisition times restrict throughput. Artificial intelligence (AI) offers potential solutions to address many of the limitations of prostate MRI in the clinical management pathway. Machine learning-based triage can refine patient selection to optimize resources. Deep learning reconstruction enables accelerated acquisition while preserving diagnostic quality, with multiple FDA-cleared products now in clinical use. Ongoing development of automated quality assessment and artifact correction aims to improve reliability by reducing nondiagnostic exams. In image interpretation, AI models for lesion detection and clinically significant prostate cancer prediction achieve performance comparable to radiologists, and the PI-CAI international reader study has provided the strongest evidence to date of non-inferiority at scale. More recent work extends MRI-derived features into prognostic modeling of recurrence, metastasis, and functional outcomes. This review synthesizes progress across five domains-triage, accelerated acquisition and reconstruction, image quality assurance, diagnosis, and prognosis-highlighting the level of evidence, validation status, and barriers to adoption. While acquisition and reconstruction are furthest along, with FDA-cleared tools and prospective evaluations, triage, quality control, and prognosis remain earlier in development. Ensuring equitable performance across populations, incorporating uncertainty estimation, and conducting prospective workflow trials will be essential to move from promising prototypes to routine practice. Ultimately, AI could accelerate the adoption of prostate MRI toward a scalable platform for earlier detection and population-level prostate cancer management. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: 3.","url":"https://doi.org/10.1002/jmri.70189","authors":["Patricia M. Johnson","Lavanya Umapathy","Bradley Gigax","Juan Andres Kochen Rossi","Angela Tong","Mary Bruno","Daniel K. Sodickson","Madhur Nayan","Hersh Chandarana"],"tags":["Workflow","Computer science","Artificial intelligence","Triage","Prostate cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-05","doi":"https://doi.org/10.1002/jmri.70189","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409584265","name":"Ethical challenges and regulatory pathways for artificial intelligence in rheumatology","source":"openalex","abstract":"AI integration in rheumatology is facing unique ethical and regulatory challenges due to longitudinal patient data complexity. Dear Editor, The integration of artificial intelligence (AI) into rheumatology practice raises important ethical considerations that warrant careful attention [1–3]. The chronic and complex nature of rheumatologic conditions presents unique challenges for AI implementation that deserve focused discussion. Rheumatology stands apart from other medical specialties due to the longitudinal accumulation of patient data over years or decades of disease management. Patients with conditions like rheumatoid arthritis, systemic lupus erythematosus, and other autoimmune diseases generate vast amounts of data through regular monitoring of disease activity, medication responses and periodic flares. This longitudinal data presents both opportunities and ethical challenges for AI applications. The European Union (EU) AI Act [4] stands as the first comprehensive legislation specifically addressing AI systems in healthcare and other domains. While other jurisdictions rely on policy frameworks and guidelines, this legislation establishes binding requirements for AI development and deployment. This distinction is particularly relevant for rheumatology, where AI applications could involve complex decision support systems managing sensitive patient data over extended periods. Disease flare prediction exemplifies the complex interplay between AI capabilities and regulatory requirements in rheumatology. For instance, under the EU AI Act, such predictive systems would be classified as ‘high-risk’ AI since they influence medical decisions and patient outcomes [5, 6]. This classification brings significant regulatory obligations, including requirements for robust risk management systems, high-quality training data and detailed technical documentation. For flare prediction specifically, these requirements are crucial given the serious consequences of prediction errors. While early prediction could revolutionize pre-emptive treatment, a false positive prediction might lead to unnecessary treatment intensification with potential adverse effects, while a missed prediction could result in preventable organ damage. The high-risk designation means these systems must maintain rigorous logging capabilities and enable human oversight of predictions, allowing rheumatologists to understand and potentially override AI recommendations when clinically appropriate. The regulatory landscape for medical AI varies significantly across jurisdictions. The EU AI Act establishes a risk-based regulatory approach with binding legal requirements. In contrast, the US Food and Drug Administration implements a product-based classification system through regulatory guidelines, focusing on safety and effectiveness verification for AI-driven medical devices. The UK has adopted a distinctive sector-specific approach, combining targeted oversight through existing regulatory bodies like the Medicines and Healthcare products Regulatory Agency with new coordination mechanisms through the Digital Regulation Cooperation Forum. This is supported by the AI Safety Institute and upcoming AI Bill, maintaining flexibility while implementing mandatory requirements for high-risk systems. The implications of AI regulation for rheumatology extend beyond flare prediction. Any AI system used for medical diagnosis, patient triage or treatment planning falls under the high-risk category and must meet stringent requirements for accuracy, robustness and cybersecurity (Table 1). Healthcare providers deploying these systems must implement quality management systems, ensure ongoing monitoring and maintain detailed documentation of the AI system’s development and validation. Ethical and safety considerations for AI systems in rheumatology under the EU AI Act. Ethical and safety considerations for AI systems in rheumatology under the EU AI Act. Data security takes on heightened importanc","url":"https://doi.org/10.1093/rap/rkaf035","authors":["Vincenzo Venerito","Latika Gupta","Saverio Mileto","Florenzo Iannone","Emre Bılgın"],"tags":["Medicine","Rheumatology","Internal medicine","Engineering ethics","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1093/rap/rkaf035","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411801984","name":"Assisted artificial intelligence in medical writing: a primer for humans","source":"openalex","abstract":"Assisted artificial intelligence (A-AI) has rapidly become a gold standard approach for conducting data analysis and medical writing [1]. From executing complex statistical tasks to extracting relevant information and supporting manuscript drafting, A-AI is increasingly embedded in the daily workflow of clinical and translational researchers [2]. However, the thin red line between being assisted by AI and becoming reliant on it, or even fully driven by it, is becoming harder to define. This ambiguity raises essential questions about authorship, accountability and the integrity of scientific output in the AI era. A growing number of early-career researchers are engaging with high-dimensional machine learning (ML) methodologies, often without comprehensive training in classical statistics or computational foundations. While the integration of such advanced tools reflects a welcome shift towards data-driven research, it also raises concerns regarding methodological rigor. In certain instances, traditional statistical frameworks are overlooked, and AI-generated outputs, particularly those derived from large language models, are interpreted without sufficient critical appraisal. To better understand this, 2 examples of A-AI analysis follow. To assess the magnitude of post-procedural thrombocytopaenia after transcatheter aortic valve implantation (TAVI) and its potential interplay with subclinical leaflet thrombosis (SLT) as detected by 1-month multidetector computed tomography (MDCT), I turned to my ‘Assisted AI master’. Within seconds, the AI generated a synthetic dataset comprising 100 patients and 120 prespecified variables, including demographic, procedural and post-implantation metrics, based on the assumption that SLT would occur in 18% of the sample. To enhance realism, I requested a ‘real-world’ dataset with missing values and statistical outliers. My AI master not only accommodated these nuances but also proposed appropriate imputation strategies, including multiple imputation by chained equations. My assisted AI master returned the following dataset (synthetitic.csv) with the variables reported in Fig. 1 [3]. AI master was instructed to perform multiple imputation to address missing data. Variables’ name and description provided as synthetic data. Once the dataset was imputed, the AI executed an ML pipeline using the popular Extreme Gradient Boosting (XGBoost) algorithm to predict SLT [4]. Without any need for custom code or manual feature engineering, the model was trained and internally validated. To enhance interpretability, the AI automatically produced the R-code for a beeswarm plot of SHAP (SHapley Additive exPlanations) values, offering a visually compelling summary of the model’s top predictors and their direction of effect [5]. Within moments, a complete set of analytical outputs is provided and easily be mistaken for a results section ready for submission of for an abstract, for example (Fig. 2A). By interpreting the SHAP beeswarm plot, my A-AI master concluded that ‘Age’ was the most influential variable driving the model’s prediction of SLT. The entire process took only a few min. While the dataset is entirely synthetic, it remains a valuable resource for statistical training and method development. Both the dataset and the full R code are available for download in the public repository (https://github.com/mmlondon77/Biobook.). (A) SHAP plot showing which synthetic features have the biggest impact on the model’s predictions. (B) Forest plot of odds ratio by a multivariable logistic regression. BMI: body mass index; SHAP: SHapley Additive exPlanations. We aimed to explore whether colchicine is effective in preventing SLT at 1 month following TAVI. To do so, I turned to my AI master, and within min, a complete synthetic randomized clinical trial (RCT) was generated. The simulated RCT included 200 patients, randomized 1:1 to receive colchicine or placebo. Baseline and procedural variables were generated to reflec","url":"https://doi.org/10.1093/ejcts/ezaf167","authors":["Marco Moscarelli","Francesco Pollari","Ilaria Franzese","Fabio Barili","Luca Di Marco","Antonio Nenna","Antonio Salsano","Giuseppe Santarpino"],"tags":["Primer (cosmetics)","Computer science","Artificial intelligence","Psychology","Natural language processing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.1093/ejcts/ezaf167","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410153852","name":"Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts","source":"openalex","abstract":"Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.","url":"https://doi.org/10.1371/journal.pdig.0000847","authors":["Jobbe P L Leenen","PS Hiemstra","Martine M Ten Hoeve","Anouk C.J. Jansen","J.D. van Dijk","B N Vendel","Guido Versteeg","Gido Hakvoort","Marike Hettinga"],"tags":["Workflow","Health care","Corporate governance","Multidisciplinary approach","Thematic analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-07","doi":"https://doi.org/10.1371/journal.pdig.0000847","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4391252914","name":"Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future Directions","source":"openalex","abstract":"Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and blockchain in vehicle architecture has increased possibilities towards efficient and sustainable transportation systems. In this article, we present a comprehensive study and analysis of the edge computing paradigm, explaining elements of edge AI. Furthermore, we discussed the edge intelligence approach for deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. It mentions the advantages of edge intelligence and its use cases in smart electric vehicles. It also discusses challenges and opportunities and provides in-depth analysis for optimizing computation for edge intelligence. Finally, it sheds some light on the research roadmap on AI for edge and AI on edge by dividing efforts into topology, content, service segments, model adaptation, framework design, and processor acceleration, all of which stand to gain advantages from AI technologies. Investigating the incorporation of important technologies, issues, opportunities, and Roadmap in this study will be a valuable resource for the community engaged in research on edge intelligence in electric vehicles.","url":"https://doi.org/10.3390/wevj15020039","authors":["Sachin Chougule","Bharat S. Chaudhari","Sheetal N. Ghorpade","Marco Zennaro"],"tags":["Cloud computing","Enhanced Data Rates for GSM Evolution","Data science","Edge computing","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-26","doi":"https://doi.org/10.3390/wevj15020039","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411464271","name":"Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects","source":"openalex","abstract":"Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation challenging. Artificial intelligence (AI) is recognized as a promising tool to address these challenges. Therefore, this review aims to explore the recent advances in AI applications in liver tumor imaging, focusing on key areas such as image reconstruction, image quality enhancement, lesion detection, tumor characterization, segmentation, and radiomics. Among these, AI-based image reconstruction has already been widely integrated into clinical workflows, helping to enhance image quality while reducing radiation exposure. While the adoption of AI-assisted diagnostic tools in liver imaging has lagged behind other fields, such as chest imaging, recent developments are driving their increasing integration into clinical practice. In the future, AI is expected to play a central role in various aspects of liver cancer care, including comprehensive image analysis, treatment planning, response evaluation, and prognosis prediction. This review offers a comprehensive overview of the status and prospects of AI applications in liver tumor imaging.","url":"https://doi.org/10.1007/s00261-025-05059-8","authors":["Hori Masatoshi","Yuki Suzuki","Keitaro Sofue","Junya Sato","Daiki Nishigaki","Miyuki Tomiyama","Atsushi Nakamoto","Takamichi Murakami","Noriyuki Tomiyama"],"tags":["Medicine","Hepatocellular carcinoma","Medical imaging","Liver cancer","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-19","doi":"https://doi.org/10.1007/s00261-025-05059-8","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414900906","name":"Artificial intelligence–based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality","source":"openalex","abstract":"BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcification (BAC) on screening mammography can identify women at risk for CVD. This study aimed to determine whether artificial intelligence-based automatic quantification of BAC from screening mammograms predicts CVD and mortality beyond PREVENT scores in a large, racially diverse, multi-institutional population. METHODS: This retrospective cohort study included 123 762 women from two healthcare systems who had screening mammograms. Breast arterial calcification was quantified using a transformer-based neural network for segmentation. Breast arterial calcification severity was categorized as zero (0 mm2), mild (>0-10 mm2), moderate (>10-25 mm2), and severe (>25 mm2). Kaplan-Meier analysis, Cox proportional hazards, and Fine-Gray competing event models were used to examine the association between BAC and major adverse cardiovascular events (MACE). RESULTS: Breast arterial calcification was detected in 16.1% (internal cohort) and 20.6% (external cohort) of women and provided significant prognostic value incremental to the PREVENT score. In PREVENT adjusted models, a clear dose-response was observed. Compared with zero BAC, mild [internal: hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.10-1.59; external: HR 1.28, 95% CI 1.17-1.39], moderate (internal: HR 1.75, 95% CI 1.23-2.50; external: HR 1.79, 95% CI 1.55-2.06), and severe BAC (internal: HR 3.29, 95% CI 2.15-5.05; external: HR 2.80, 95% CI 2.36-3.32) were all prognostic for any MACE. Each 1 mm2 increase in BAC conferred an additional 2%-3% risk for MACE (P < .001). CONCLUSIONS: Automatically quantified BAC is an independent predictor of MACE and mortality, adding prognostic value to the PREVENT score. This approach may provide an opportunistic cardiovascular risk assessment during routine mammography screening without additional radiation exposure to guide earlier and more effective preventive care for women.","url":"https://doi.org/10.1093/eurheartj/ehag128","authors":["Theo Dapamede","Aisha Urooj Khan","Vedant Joshi","Gabrielle Gershon","Frank Li","Mohammadreza Chavoshi","Beatrice Brown-Mulry","Rohan Isaac","Aawez Mansuri","Chad Robichaux","Chadi Ayoub","Reza Arsanjani","Laurence Sperling","Judy Wawira Gichoya","Marly van Assen","W Charles O’Neill","Imon Banerjee","Hari Trivedi"],"tags":["Medicine","Mace","Risk assessment","Internal medicine","Disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-10","doi":"https://doi.org/10.1093/eurheartj/ehag128","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412141497","name":"New tools for diagnosis of primary immunodeficiencies: from awareness to artificial intelligence","source":"openalex","abstract":"Primary immune deficiencies (PI) are rare diseases associated with frequent, severe infections, inflammatory and autoimmune diseases and/or cancer. Because of the variability in presentation, undiagnosed PI patients can be encountered by many different medical specialists. A lack of awareness of and the rarity of PI can lead to delayed diagnosis particularly among primary care physicians and non-immunology specialists. These delays can lead to irreversible sequelae, decreased quality of life and premature mortality. In this review, we describe two projects designed to decrease the time to diagnosis in PI patients: 1) the expert-driven PIDCAP project conducted in Spain to promote early diagnosis in the primary care setting, and 2) a multi-modal data-driven approach using artificial intelligence and machine learning to identify individuals at high risk for PI. Both approaches aim to create widely available tools to promote early diagnosis and treatment of PI. Initial results have been positive. Future directions include larger studies and potentially combining expert-driven and data-driven approaches.","url":"https://doi.org/10.3389/fimmu.2025.1593897","authors":["Pere Soler‐Palacín","Jacques G. Rivière","Siobhan O. Burns","Nicholas L. Rider"],"tags":["Medicine","Primary care","Intensive care medicine","Presentation (obstetrics)","Immunology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-10","doi":"https://doi.org/10.3389/fimmu.2025.1593897","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414206725","name":"Artificial Intelligence in Educational Technology: A Systematic Review of Datasets and Applications","source":"openalex","abstract":"Artificial Intelligence (AI) has the potential to impact a diverse range of domains. For instance, AI for the education domain has received increasing interest with various applications, including predicting performance, curating learning materials, and automated assessment and feedback. Despite the developments, some imbalances appear in the literature; for example, traditional classrooms and non-scientific academic subjects received little attention. This survey provides a systematic review of the current trends in AI research for education, specifically addressing applications within secondary education (ages 11+) through to higher education (HE), and offers a detailed compilation of datasets and methods, facilitating a deeper understanding of the field and encouraging further investigation. It includes a thorough review of the datasets available to encourage and enable future research, development, and collaboration, as well as the establishment of performance benchmarks. Furthermore, this survey provides an overview of issues and problems arising from recent developments, which may aid policymakers in their decision-making and addressing ethical concerns and standards. For example, many AI in Education (AIEd) platforms are not grounded in educational theory. We also present several guidelines to aid future developments in AIEd, guiding long-term impactful projects and investments.","url":"https://doi.org/10.1145/3768312","authors":["Luke Topham","Pete Atherton","Tom Reynolds","Yasir Hussain","Abir Hussain","Hoshang Kolivand","Wasiq Khan"],"tags":["Computer science","Field (mathematics)","Domain (mathematical analysis)","Data science","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-15","doi":"https://doi.org/10.1145/3768312","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413911574","name":"Recommendations for disclosure of artificial intelligence in scientific writing and publishing: a regional anesthesia and pain medicine modified Delphi study","source":"openalex","abstract":"Introduction The use of artificial intelligence (AI) in the scientific process is advancing at a remarkable speed, thanks to continued innovations in large language models. While AI provides widespread benefits, including editing for fluency and clarity, it also has drawbacks, including fabricated content, perpetuation of bias, and lack of accountability. The editorial board of Regional Anesthesia & Pain Medicine (RAPM) therefore sought to develop best practices for AI usage and disclosure. Methods A steering committee from the American Society of Regional Anesthesia and Pain Medicine used a modified Delphi process to address definitions, disclosure requirements, authorship standards, and editorial oversight for AI use in publishing. The committee reviewed existing publication guidelines and identified areas of ambiguity, which were translated into questions and distributed to an expert workgroup of authors, reviewers, editors, and AI researchers. Results Two survey rounds, with 91% and 87% response rates, were followed by focused discussion and clarification to identify consensus recommendations. The workgroup achieved consensus on recommendations to authors about definitions of AI, required items to report, disclosure locations, authorship stipulations, and AI use during manuscript preparation. The workgroup formulated recommendations to reviewers about monitoring and evaluating the responsible use of AI in the review process, including the endorsement of AI-detection software, identification of concerns about undisclosed AI use, situations where AI use may necessitate the rejection of a manuscript, and use of checklists in the review process. Finally, there was consensus about AI-driven work, including required and optional disclosures and the use of checklists for AI-associated research. Discussion Our modified Delphi study identified practical recommendations on AI use during the scientific writing and editorial process. The workgroup highlighted the need for transparency, human accountability, protection of patient confidentiality, editorial oversight, and the need for iterative updates. The proposed framework enables authors and editors to harness AI’s efficiencies while maintaining the fundamental principles of responsible scientific communication and may serve as an example for other journals.","url":"https://doi.org/10.1136/rapm-2025-106852","authors":["Michael R. Fettiplace","Anuj Bhatia","Yian Chen","Steven L. Orebaugh","Michael Gofeld","Rodney A. Gabriel","Daniel I. Sessler","Hannah Lonsdale","Brittani Bungart","Christopher Cheng","Garrett W. Burnett","Lichy Han","M. D. Wiles","Steve Coppens","Thomas T. Joseph","Kristin L. Schreiber","Thomas Volk","Richard D. Urman","Vesela Kovacheva","Christopher L. Wu","Edward R. Mariano","Vivian Ip"],"tags":["Workgroup","CLARITY","Debriefing","Delphi method","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-02","doi":"https://doi.org/10.1136/rapm-2025-106852","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416649115","name":"Changes in public perception of artificial intelligence in healthcare after exposure to ChatGPT","source":"openalex","abstract":"The public perception of artificial intelligence (AI) in healthcare is key to its large-scale acceptance and implementation. This study investigated how exposure to ChatGPT changed public perception of AI in healthcare, using baseline and follow-up data from 5899 survey participants reporting their perception of AI in 2022 (before ChatGPT's launch) and 2024, and ChatGPT use in 2024. Multinomial multivariate logistic regression was used to model how exposure to ChatGPT use affected changes in perception of AI. At follow-up, 1195 individuals (20%) had been exposed to ChatGPT use, which was associated with higher odds of changing perception of AI to beneficial (OR 3.21 [95% CI: 2.34-4.40]) among individuals who were unsure at baseline, and lower odds of changing to uncertainty from more defined baseline perceptions. This study demonstrates the potential for reducing uncertainty and improving public perception of AI in healthcare through exposure to AI tools.","url":"https://doi.org/10.1038/s41746-025-02169-x","authors":["Anders Aasted Isaksen","Jonas R. Schaarup","Lasse Bjerg","Ádám Hulmán"],"tags":["Perception","Odds","Baseline (sea)","Multinomial logistic regression","Logistic regression"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-25","doi":"https://doi.org/10.1038/s41746-025-02169-x","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414516182","name":"Ophthalmic drug discovery and development using artificial intelligence and digital health technologies","source":"openalex","abstract":"Globally, drug discovery and development programs are complex, multi-decade long and prohibitively expensive. Artificial intelligence (AI) and other digital health technologies have the potential to enhance and accelerate each stage of drug discovery and development, from pre-clinical target identification to post-market repurposing, and even revolutionize the entire process. Using ophthalmology as an example, this review highlights recent AI and digital health innovations in different phases of drug discovery and development. By leveraging machine learning algorithms and vast clinical and multiomics datasets, AI can rapidly identify and validate new drug targets, optimize lead compounds, and predict pharmacokinetics, pharmacodynamics and toxicity. AI-assisted multi-modal ocular biomarkers may improve treatment monitoring and support personalized medicine. Integrating AI shortens development timelines, enhances efficiency, reduces costs, and increases the success rate of new drugs. Currently, standardized regulations for AI in ocular drug development are still lacking and urgently needed to ensure safe and equitable implementation.","url":"https://doi.org/10.1038/s41746-025-01954-y","authors":["Haoran Cheng","Joy Le Yi Wong","Chrystie Wan Ning Quek","Jeffrey L. Goldberg","Vinit B. Mahajan","Tien Yin Wong","Jodhbir S. Mehta","Daniel Shu Wei Ting","Darren Shu Jeng Ting"],"tags":["Drug discovery","Identification (biology)","Computer science","Drug development","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-25","doi":"https://doi.org/10.1038/s41746-025-01954-y","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410902394","name":"Enhancing professional communication training in higher education through artificial intelligence(AI)-integrated exercises: study protocol for a randomised controlled trial","source":"openalex","abstract":"BACKGROUND: Effective communication skills are fundamental for health care professionals, yet conventional training methods face challenges in scalability and accessibility due to resource constraints. The emergence of artificial intelligence (AI), particularly generative AI, offers innovative ways for enhancing communication skills training by simulating realistic conversational scenarios and providing personalised, adaptive feedback. This manuscript is presented as study protocol for a cluster-randomised controlled trial that aims at evaluating the efficacy of an AI-supported higher education training protocol incorporating generative AI exercises to enhance communication competencies among psychology students. METHODS: In this cluster-randomised controlled trial, psychology students enrolled in communication skill seminars at a medium sized university in a medium sized German city will participate. Classes will be assigned within a parallel group design to the AI condition (AI-enhanced exercises alongside teaching-as-usual, TAU, that includes classical exercises) or the control condition (TAU only). Additional non-randomised comparison classes will comprise students with TAU only, but not be part of main analyses. The primary outcome is the change in communication skills from baseline, assessed through questions reflecting the communication techniques emphasised in the training. Secondary outcomes include communication skills, self-efficacy and self-concept, motivation, attitudes toward AI, user experience with the AI tool, student evaluations of course quality, and feasibility aspects such as uptake and usability. Data will be collected via online surveys and the university's teaching platform. Statistical analyses will employ mixed models to evaluate the intervention's impact. DISCUSSION: This study will provide empirical evidence on the effectiveness and feasibility of integrating AI into higher education communication skills training. Successful integration of AI-enhanced training could revolutionise educational practices by offering scalable, accessible, and personalised learning experiences. The findings may have broader implications for incorporating AI tools in various educational and professional training contexts, while addressing ethical considerations and promoting responsible use of AI in education. TRIAL REGISTRATION: This trial has been pre-registered on the Open Science Framework (OSF) under identifier 'th6f4'.","url":"https://doi.org/10.1186/s12909-025-07307-3","authors":["Gunther Meinlschmidt","Sara Koc","Emma Boerner","Marion Tegethoff","Thomas Simacek","Liam Schirmer","Michael Schneider"],"tags":["Protocol (science)","Usability","Medical education","Communication skills training","Cluster randomised controlled trial"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-30","doi":"https://doi.org/10.1186/s12909-025-07307-3","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411756025","name":"Artificial Intelligence in Obstetrics and Gynaecology: Advancing Precision and Personalised Care","source":"openalex","abstract":"Artificial intelligence (AI) is rapidly transforming the landscape of obstetrics and gynaecology, offering unprecedented capabilities in diagnostics, monitoring, and personalised treatment. This review highlights the integration of AI in various domains, including obstetric imaging, fetal monitoring, gynaecologic oncology, fertility treatment, and robotic surgery. AI-powered tools are shown to enhance precision by improving diagnostic accuracy, reducing human error, and supporting clinical decision-making. Importantly, the article explores the global implications of AI adoption, including applications in low-resource settings, and emphasises the need for ethical considerations, data inclusiveness, and clinician trust. Overall, this comprehensive review demonstrates how AI is enabling more individualised and effective care in women's health.","url":"https://doi.org/10.7759/cureus.86929","authors":["Nida Aftab"],"tags":["Medicine","Obstetrics and gynaecology","Obstetrics","Gynecology","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-28","doi":"https://doi.org/10.7759/cureus.86929","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407608219","name":"The Role of AI in Reshaping Medical Education: Opportunities and Challenges","source":"openalex","abstract":"Artificial intelligence (AI) is redefining medical education, bringing new dimensions of personalized learning, enhanced visualization and simulation-based clinical training to the forefront. Additionally, AI-powered simulations offer realistic, immersive training opportunities, preparing students for complex clinical situations and fostering interprofessional collaboration skills essential for modern healthcare. However, the integration of AI into medical education presents challenges, particularly around ethical considerations, skill atrophy due to overreliance and the exacerbation of the digital divide among educational institutions. Addressing these challenges demands a balanced approach that includes blended learning models, digital literacy and faculty development to ensure AI serves as a supplement to, rather than a replacement for, core medical competencies. As medical education evolves alongside AI, institutions must prioritize strategies that preserve human-centred skills while advancing technological innovation to prepare future healthcare professionals for an AI-enhanced landscape.","url":"https://doi.org/10.1111/tct.70040","authors":["Majid Ali"],"tags":["Medical education","MEDLINE","Psychology","Medicine","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-16","doi":"https://doi.org/10.1111/tct.70040","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4366602741","name":"Artificial Intelligence Applications in Glioma With 1p/19q Co‐Deletion: A Systematic Review","source":"openalex","abstract":"As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.","url":"https://doi.org/10.1002/jmri.28737","authors":["Simin Zhang","Lijuan Yin","Lu Ma","Huaiqiang Sun"],"tags":["Glioma","Medicine","Scopus","Magnetic resonance imaging","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-21","doi":"https://doi.org/10.1002/jmri.28737","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414685754","name":"Toward a new era of healthcare services: the role of artificial intelligence in shaping tomorrow’s landscape","source":"openalex","abstract":"The rapid advancement of artificial intelligence (AI) has revolutionized various sectors, including healthcare, by enhancing diagnostic precision, operational efficiency and personalized care. Despite its vast potential, integrating AI into healthcare remains a complex challenge. Addressing this requires a comprehensive understanding of the research landscape to guide future innovations. This study analyzes the intellectual structure and emerging trends in AI applications within healthcare through co-citation and co-word analysis. A total of 24,973 publications from the Scopus database, spanning 2000–2024, were examined to observe growth patterns, with 4,043 journal articles analyzed in depth. The findings identify five thematic clusters: technology and implementation, health and disease, treatment and care, algorithms and techniques, and social and ethical aspects. These clusters reflect the evolving focus of AI research in healthcare. The insights highlight AI’s growing impact on healthcare outcomes and its potential to address patient needs, while drawing attention to ethical and operational challenges. This study provides a robust framework for understanding recent trends in AI applications in healthcare, offering strategic guidance for academics, practitioners and policymakers to foster equitable AI integration into global healthcare systems.","url":"https://doi.org/10.1080/23311975.2025.2566441","authors":["Mohammad Hamsal","Faisal Binsar"],"tags":["Health care","Big data","Knowledge management","Scopus","Thematic analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1080/23311975.2025.2566441","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411394707","name":"Medical reasoning in LLMs: an in-depth analysis of DeepSeek R1","source":"openalex","abstract":"Introduction: The integration of large language models (LLMs) into healthcare holds immense promise, but also raises critical challenges, particularly regarding the interpretability and reliability of their reasoning processes. While models like DeepSeek R1-which incorporates explicit reasoning steps-show promise in enhancing performance and explainability, their alignment with domain-specific expert reasoning remains understudied. Methods: This paper evaluates the medical reasoning capabilities of DeepSeek R1, comparing its outputs to the reasoning patterns of medical domain experts. Results: Through qualitative and quantitative analyses of 100 diverse clinical cases from the MedQA dataset, we demonstrate that DeepSeek R1 achieves 93% diagnostic accuracy and shows patterns of medical reasoning. Analysis of the seven error cases revealed several recurring errors: anchoring bias, difficulty integrating conflicting data, limited consideration of alternative diagnoses, overthinking, incomplete knowledge, and prioritizing definitive treatment over crucial intermediate steps. Discussion: These findings highlight areas for improvement in LLM reasoning for medical applications. Notably the length of reasoning was important with longer responses having a higher probability for error. The marked disparity in reasoning length suggests that extended explanations may signal uncertainty or reflect attempts to rationalize incorrect conclusions. Shorter responses (e.g., under 5,000 characters) were strongly associated with accuracy, providing a practical threshold for assessing confidence in model-generated answers. Beyond observed reasoning errors, the LLM demonstrated sound clinical judgment by systematically evaluating patient information, forming a differential diagnosis, and selecting appropriate treatment based on established guidelines, drug efficacy, resistance patterns, and patient-specific factors. This ability to integrate complex information and apply clinical knowledge highlights the potential of LLMs for supporting medical decision-making through artificial medical reasoning.","url":"https://doi.org/10.3389/frai.2025.1616145","authors":["Birger Moëll","Fredrik Sand Aronsson","Sanian Akbar"],"tags":["Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-18","doi":"https://doi.org/10.3389/frai.2025.1616145","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409623003","name":"Artificial Intelligence Social Responsibility in the Consumer Market: Dimension Exploration and Scale Development","source":"openalex","abstract":"ABSTRACT This study explored the conceptualization, dimensional structure, and measurement of artificial intelligence (AI) social responsibility in the consumer market. Data were collected through semi‐structured in‐depth interviews with 32 respondents. A grounded theory research approach was employed to construct a structural model of AI social responsibility that included the dimensions of ethics, safety, applicability, credibility, and reflexivity. Subsequently, an exploratory factor analysis was conducted on 305 questionnaire data collected through an online survey as well as a confirmatory factor analysis on 325 questionnaire data. The analyses led to the development of an AI social responsibility scale consisting of 18 items and demonstrating good reliability and validity. Moreover, using structural equation modeling, strong nomological validity was demonstrated. The results indicated that AI social responsibility and its dimensions significantly predicted flow experience and experience satisfaction. The findings enhance understanding of the conceptual meaning and dimensional structure of AI social responsibility in the consumer market, as well as provide a psychometrically reliable and valid measurement tool for use in future research. Furthermore, the findings not only facilitate the design and implementation of AI technologies, but they also offer crucial insights for companies and their stakeholders to devise and refine AI social responsibility strategies and other marketing tactics—thereby augmenting CSR 3.0 management practices.","url":"https://doi.org/10.1111/ijcs.70054","authors":["Shen Peng-yi","Jinxiong Li","Demin Wan"],"tags":["Dimension (graph theory)","Scale (ratio)","Social responsibility","Business","Marketing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-21","doi":"https://doi.org/10.1111/ijcs.70054","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410977560","name":"Artificial intelligence in bone metastasis analysis: Current advancements, opportunities and challenges","source":"openalex","abstract":"BACKGROUND: Artificial Intelligence is transforming medical imaging, particularly in the analysis of bone metastases (BM), a serious complication of advanced cancers. Machine learning and deep learning techniques offer new opportunities to improve detection, recognition, and segmentation of bone metastasis. Yet, challenges such as limited data, interpretability, and clinical validation remain. METHODS: Following PRISMA guidelines, we reviewed artificial intelligence methods and applications for bone metastasis analysis across major imaging modalities including CT, MRI, PET, SPECT, and bone scintigraphy. The survey includes traditional machine learning models and modern deep learning architectures such as CNNs and transformers. We also examined available datasets and their effect in developing artificial intelligence in this field. RESULTS: Artificial intelligence models have achieved strong performance across tasks and modalities, with Convolutional Neural Network (CNN) and Transformer architectures showing particularly efficient performance across different tasks. However, limitations persist, including data imbalance, overfitting risks, and the need for greater transparency. Clinical translation is also challenged by regulatory and validation hurdles. CONCLUSION: Artificial intelligence holds strong potential to improve BM diagnosis and streamline radiology workflows. To reach clinical maturity, future work must address data diversity, model explainability, and large-scale validation, which are critical steps for being trusted to be integrated into the oncology care routines.","url":"https://doi.org/10.1016/j.compbiomed.2025.110372","authors":["Marwa Afnouch","Fares Bougourzi","Olfa Gaddour","Fadi Dornaika","Abdelmalik Taleb Ahmed"],"tags":["Current (fluid)","Computer science","Bone metastasis","Metastasis","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-03","doi":"https://doi.org/10.1016/j.compbiomed.2025.110372","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406675593","name":"What generative Artificial Intelligence priorities and challenges do senior Australian educational policy makers identify (and why)?","source":"openalex","abstract":"Abstract Free access to powerful generative Artificial Intelligence (AI) in schools has left educators and system leaders grappling with how to responsibly respond to the consequent challenges and opportunities that this new technology poses. This paper examines the priorities and challenges that senior Australian educational leaders identify with relation to responsible and ethical use of generative AI in school education, and the reasons for their beliefs. Members of the Australian generative Artificial Intelligence in Education working group as well as other senior policymakers throughout Australia participated in a two-phase data collection process involving survey responses and focus group discussions. Ranking activities revealed a large number of priorities and systemic challenges, with no unilateral consensuses emerging. The highest priorities for senior policymakers related to managing risks, educating teachers, and educating system leaders, while the main systemic and environmental challenges related to the pace of change, teacher capabilities and professional learning, and equitable access to the technology. Throughout the analysis, meta themes emerged that characterised the policy-setting environment as one involving urgency, uncertainty, interconnectedness, contextuality, and complexity, with the pivotal role of teachers highlighted throughout. Reflections on responsible and ethical policy-setting in response to rapid technological change are provided, including with relation to anticipatory and networked governance and the inter-relationship with the broader policy context. Recommendations for further research and practice are also proposed.","url":"https://doi.org/10.1007/s13384-025-00801-z","authors":["Matt Bower","Michael Henderson","Christine Slade","Erica Southgate","Kalervo Ν. Gulson","Jason M. Lodge"],"tags":["Generative grammar","Psychology","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-21","doi":"https://doi.org/10.1007/s13384-025-00801-z","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406363084","name":"DSS4EX: A Decision Support System framework to explore Artificial Intelligence pipelines with an application in time series forecasting","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.eswa.2025.126421","authors":["Giulia Rinaldi","Konstantinos Theodorakos","Fernando Crema Garcia","Oscar Mauricio Agudelo","Bart De Moor"],"tags":["Computer science","Decision support system","Artificial intelligence","Series (stratigraphy)","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-14","doi":"https://doi.org/10.1016/j.eswa.2025.126421","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413399056","name":"The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study","source":"openalex","abstract":"This study explores diabetic foot (DF), a severe complication in diabetes, by combining deep learning (DL) and machine learning (ML) to develop a multi-model prediction tool. Early identification of high-risk DF patients can reduce disability and mortality. The research also aims to create an integrated application to assist clinicians in precise, efficient risk assessment for early intervention. In this multicenter retrospective study, 6,180 elderly diabetic patients (aged 60-85) were enrolled from 11 community hospitals in Shanghai in 2024. Lasso regression was used to identify 16 key DF risk factors, including age, MMSE score, lower limb discomfort, ABI, and hematocrit. Fourteen ML models (RF, XGBoost, CART, MLP, etc.) and three DL models (DNN, CNN, Transformer) were trained, with hyperparameters optimized via cross-validation and grid search. An application was developed integrating these models, offering both single and batch prediction options with visualization tools for clinical use.Experimental results showed the Logistic regression ensemble model achieved robust performance, with AUC values of 0.943 (validation set, 95% CI: 0.935-0.951) and 0.938 (test set, 95% CI: 0.929-0.947), along with high accuracy, precision, recall, and F1 scores. SHAP analysis revealed key predictive features including ABI results, lower limb discomfort, and MMSE score. The developed app integrates multiple models, compares their predictions for different clinical scenarios, and enhances prediction transparency and reliability.The multi-model approach demonstrates strong predictive performance for DF risk, offering clinicians an intuitive and accurate assessment tool tailored to individual patients. By combining multiple models, we enhance result stability and clinical applicability compared to single-model approaches. Future work will focus on algorithm optimization, expanded datasets, and real-time monitoring integration to enable more precise, dynamic risk evaluation for improved DF prevention and early intervention.","url":"https://doi.org/10.1186/s13040-025-00477-2","authors":["Li Yao","Siyuan Zhou","Bichen Ren","Shuai Ju","Xiaoyan Li","Wenqiang Li","Bingzhe Li","Yunmin Cai","Chunlei Chang","Lihong Huang","Zhihui Dong"],"tags":["Computer science","Diabetic foot","Foot (prosody)","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-21","doi":"https://doi.org/10.1186/s13040-025-00477-2","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406289994","name":"LungDiag: Empowering artificial intelligence for respiratory diseases diagnosis based on electronic health records, a multicenter study","source":"openalex","abstract":"Abstract Respiratory diseases pose a significant global health burden, with challenges in early and accurate diagnosis due to overlapping clinical symptoms, which often leads to misdiagnosis or delayed treatment. To address this issue, we developed LungDiag, an artificial intelligence (AI)‐based diagnostic system that utilizes natural language processing (NLP) to extract key clinical features from electronic health records (EHRs) for the accurate classification of respiratory diseases. This study employed a large cohort of 31,267 EHRs from multiple centers for model training and internal testing. Additionally, prospective real‐world validation was conducted using 1142 EHRs from three external centers. LungDiag demonstrated superior diagnostic performance, achieving an F1 score of 0.711 for top 1 diagnosis and 0.927 for top 3 diagnoses. In real‐world testing, LungDiag outperformed both human experts and ChatGPT 4.0, achieving an F1 score of 0.651 for top 1 diagnosis. The study emphasizes the potential of LungDiag as an effective tool to support physicians in diagnosing respiratory diseases more accurately and efficiently. Despite the promising results, further large‐scale multicenter validation with larger sample sizes is still needed to confirm its clinical utility and generalizability.","url":"https://doi.org/10.1002/mco2.70043","authors":["Hengrui Liang","Tao Yang","Zi-Hao Liu","Wenhua Jian","Yilong Chen","Bingliang Li","Zeping Yan","Weiqiang Xu","Luming Chen","Yifan Qi","Zhiwei Wang","Yajing Liao","Peixuan Lin","Jiameng Li","Wei Wang","Li Li","Meijia Wang","Yunhui Zhang","Lizong Deng","Taijiao Jiang","Jianxing He"],"tags":["Generalizability theory","Medical diagnosis","Medicine","Artificial intelligence","Health records"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1002/mco2.70043","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411256651","name":"Artificial Intelligence in Melanoma Detection: A Review of Current Technologies and Future Directions","source":"openalex","abstract":"Early and accurate identification of malignant melanoma continues to be a major challenge for clinicians in the field. Traditional diagnostic approaches, including physical examination, histology, imaging, and nodal assessments, are frequently costly, require significant expertise, and can display large variations among clinicians. These factors may result in missed or misdiagnosis, which often significantly affects a patient’s prognosis. We examine in detail how the application of AI methods such as machine learning and deep learning can be used to advance early detection and identification of melanoma. We review various AI algorithms, including standard classifiers, ensemble techniques, and complex deep learning models. Hybrid models that combine convolutional neural networks (CNNs) and support vector machines (SVMs) are emphasized in this review, as they show enhanced performance and improved resistance to variations in the diagnostician’s input. Better utility of transfer learning and data augmentation approaches is discussed to overcome the challenges posed by small and unbalanced medical datasets. The authors consider the combination of various types of medical information for more effective cancer diagnosis. However, significant obstacles, including model explainability, privacy safeguarding, and clinical evaluation, still need to be addressed. Extensive efforts are needed to overcome these barriers if AI systems are to be effectively adopted within healthcare environments. We suggest that AI offers the opportunity to revolutionize melanoma care by enabling rapid decision support and individualized treatment plans. Realizing this opportunity will depend on effective partnerships between researchers, clinicians, and industry to bring together advances in technology and their effective implementation in the healthcare system.","url":"https://doi.org/10.1155/int/3164952","authors":["Fakhre Alam","Asad Ullah","Dilawar Shah","Shujaat Ali","Muhammad Tahir"],"tags":["Current (fluid)","Computer science","Artificial intelligence","Engineering","Electrical engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1155/int/3164952","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415273759","name":"Application and research progress on artificial intelligence in the quality of Traditional Chinese Medicine","source":"openalex","abstract":"The clinical safety and therapeutic performance of Traditional Chinese Medicine (TCM) are closely tied to its quality. However, with the rapid expansion of the TCM industry, conventional quality control approaches based on empirical observations and single-metabolite quantification have become increasingly inadequate for addressing the complex and variable requirements of quality assessment. In recent years, artificial intelligence (AI)-with strong capabilities in data processing and pattern recognition-has emerged as a promising tool for establishing predictive models to efficiently handle heterogeneous, multi-source datasets (such as spectra, chromatograms, images, and textual information). This enables intelligent prediction of quality indicators and anomaly detection, and offering novel strategies for modernizing TCM quality control. This review provides a comprehensive synthesis of commonly applied machine learning and deep learning algorithms, systematically outlining recent advances in AI-enabled sensing applications such as image recognition, odor analysis, authenticity verification, origin tracing, quality grading, and storage-age determination. It further emphasizes the integration of AI with multi-omics and bioinformatics approaches for efficacy-oriented evaluation and safety assessment, including identification of Q-markers, elucidation of pharmacodynamic mechanisms, and predictive modeling of both endogenous and exogenous toxic metabolites. It also identifies key challenges and technical bottlenecks, and outlines priorities for building scalable, regulation-aware, data-driven quality-control systems that support the sustainable, high-quality development of the TCM industry.","url":"https://doi.org/10.3389/fphar.2025.1687681","authors":["Meiyu Li","Junqing Zhu","Xiaonan L. Liu","Mengyue Wu","Kun Dong","Xiaoyan Li","Peng Gao","Zhihui Jiang"],"tags":["Quality (philosophy)","Computer science","Artificial intelligence","Identification (biology)","Risk analysis (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-17","doi":"https://doi.org/10.3389/fphar.2025.1687681","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413351111","name":"Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review","source":"openalex","abstract":"Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like ‘spine metastases’, ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, and ‘diagnosis’. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact.","url":"https://doi.org/10.3390/jcm14165877","authors":["Vivek Sanker","Poorvikha Gowda","Alexander Thaller","Zhikai Li","Philip Heesen","Zekai Qiang","S Hariharan","Emil O. R. Nordin","María José Cavagnaro","John K. Ratliff","Atman Desai"],"tags":["Medicine","Receiver operating characteristic","Artificial intelligence","Machine learning","Metastasis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-20","doi":"https://doi.org/10.3390/jcm14165877","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413422755","name":"Performance of Advanced Artificial Intelligence Models in Pulp Therapy for Immature Permanent Teeth: A Comparison of ChatGPT-4 Omni, DeepSeek, and Gemini Advanced in Accuracy, Completeness, Response Time, and Readability","source":"openalex","abstract":"INTRODUCTION: This study aims to evaluate and compare the performance of three advanced chatbots-ChatGPT-4 Omni (ChatGPT-4o), DeepSeek, and Gemini Advanced-on answering questions related to pulp therapies for immature permanent teeth. The primary outcomes assessed were accuracy, completeness, and readability, while secondary outcomes focused on response time and potential correlations between these parameters. METHODS: A total of 21 questions were developed based on clinical resources provided by the American Association of Endodontists, including position statements, clinical considerations, and treatment options guides, and assessed by three experienced pediatric dentists and three endodontists. Accuracy and completeness scores, as well as response times, were recorded, and readability was evaluated using Flesch Kincaid Reading Ease Score, Flesch Kincaid Grade Level, Gunning Fog Score, SMOG Index, and Coleman Liau Index. RESULTS: Results revealed significant differences in accuracy (P < .05) and completeness (P < .05) scores among the chatbots, with ChatGPT-4o and DeepSeek outperforming Gemini Advanced in both categories. Significant differences in response times were also observed, with Gemini Advanced providing the quickest responses (P < .001). Additionally, correlations were found between accuracy and completeness scores (ρ: .719, P < .001), while response time showed a positive correlation with completeness (ρ: .144, P < .05). No significant correlation was found between accuracy and readability (P > .05). CONCLUSIONS: ChatGPT-4o and DeepSeek demonstrated superior performance in terms of accuracy and completeness when compared to Gemini Advanced. Regarding readability, DeepSeek scored the highest, while ChatGPT-4o showed the lowest. These findings highlight the importance of considering both the quality and readability of artificial intelligence-driven responses, in addition to response time, in clinical applications.","url":"https://doi.org/10.1016/j.joen.2025.08.011","authors":["Berkant Sezer","Tuğba Aydoğdu"],"tags":["Readability","Computer science","Completeness (order theory)","Pulp (tooth)","Permanent teeth"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-22","doi":"https://doi.org/10.1016/j.joen.2025.08.011","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414036954","name":"Smart technology framework for medical waste optimization by integrating wireless tracking with artificial intelligence classification","source":"openalex","abstract":"In this study, we developed and validated an integrated Radio Frequency Identification-Artificial Intelligence (RFID-AI) framework to optimize medical waste management in resource-constrained healthcare settings. The system combines: (1) UHF RFID-enabled smart bins with real-time mass and environmental monitoring, (2) a fine-tuned ResNet-50 computer vision model achieving 93.1% (±2.1%) waste classification accuracy, and (3) genetic algorithm-based route optimization reducing collection distances by 18%. Implemented across four Jordanian hospitals (92-203 beds) for six months, the system demonstrated significant improvements across key metrics: Operational efficiency (30.1% reduction in collection time, P < 0.01; 81% fewer hazardous mixing incidents), staff safety (40.2% reduction in sharps injuries through AI monitoring), environmental impact (15.2% lower particulate emissions via optimized incineration scheduling), and cost-effectiveness (23.2% operational cost reduction with 14-month ROI). The framework's modular design successfully addressed institution-specific challenges, including 78.3% storage overcapacity at Princess Basma Hospital and 39.1% improper sharps disposal at Ibn Al-Nafis Hospital, while maintaining 90.3% compliance with WHO 2022 guidelines. Technical innovations included moisture-resistant RFID tags, maintaining 98.3% read accuracy in high-humidity environments and Arabic-language AI interfaces that reduced training time by 42%. These results provide empirical evidence for the viability of smart waste systems in LMICs, offering a replicable model that balances technological sophistication with practical implementation constraints. We established a new benchmark for intelligent medical waste management systems under resource limitations.","url":"https://doi.org/10.3934/environsci.2025035","authors":["Ahmed N. Bdour","Raha M. Kharabsheh"],"tags":["Medical waste","Tracking (education)","Computer science","Artificial intelligence","Wireless"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.3934/environsci.2025035","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406892722","name":"A foundation model for human-AI collaboration in medical literature mining","source":"openalex","abstract":"Applying artificial intelligence (AI) for systematic literature review holds great potential for enhancing evidence-based medicine, yet has been limited by insufficient training and evaluation. Here, we present LEADS, an AI foundation model trained on 633,759 samples curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. In experiments, LEADS demonstrates consistent improvements over four cutting-edge large language models (LLMs) on six literature mining tasks, e.g., study search, screening, and data extraction. We conduct a user study with 16 clinicians and researchers from 14 institutions to assess the utility of LEADS integrated into the expert workflow. In study selection, experts using LEADS achieve 0.81 recall vs. 0.78 without, saving 20.8% time. For data extraction, accuracy reached 0.85 vs. 0.80, with 26.9% time savings. These findings encourage future work on leveraging high-quality domain data to build specialized LLMs that outperform generic models and enhance expert productivity in literature mining.","url":"https://doi.org/10.1038/s41467-025-62058-5","authors":["Zifeng Wang","Lang Cao","Qiao Jin","Joey Wing Yan Chan","Nicholas Wan","Behdad Afzali","Hyun-Jin Cho","C. Y. Choi","Mehdi Emamverdi","Manjot K. Gill","Sunhyung Kim","Yijia Li","Yi Liu","Yiming Luo","Hanley Ong","Justin F. Rousseau","Irfan Sheikh","Jenny J. Wei","Ziyang Xu","Christopher M. Zallek","Kyungsang Kim","Yifan Peng","Zhiyong Lu","Jimeng Sun"],"tags":["Workflow","Computer science","Data extraction","Systematic review","Foundation (evidence)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-24","doi":"https://doi.org/10.1038/s41467-025-62058-5","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W1984792953","name":"Object based image analysis for remote sensing","source":"openalex","abstract":"Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.","url":"https://doi.org/10.1016/j.isprsjprs.2009.06.004","authors":["Thomas Blaschke"],"tags":["Computer science","Computer vision","Remote sensing","Artificial intelligence","Object (grammar)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2009-08-29","doi":"https://doi.org/10.1016/j.isprsjprs.2009.06.004","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409290068","name":"Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?","source":"openalex","abstract":"Artificial intelligence (AI) has emerged as a promising adjunct in the diagnosis and management of infective endocarditis (IE), a disease characterized by diagnostic complexity and significant morbidity. Machine learning (ML) models such as SABIER and SYSUPMIE have demonstrated strong predictive accuracy for early IE diagnosis, embolic risk stratification, and postoperative mortality, surpassing traditional clinical scoring systems. In imaging, AI-enhanced echocardiography and advanced modalities like FDG-PET/CT offer improved sensitivity, specificity, and reduced inter-observer variability, potentially transforming clinical decision making. Additionally, AI-powered microbiological techniques, including MALDI-TOF mass spectrometry combined with ML and neural network-based metagenomic classifiers, show promise in rapidly identifying pathogens and predicting antimicrobial resistance. Despite encouraging early results, widespread adoption faces barriers, including data limitations, interpretability issues, ethical concerns, and the need for robust validation. Future directions include leveraging generative AI as clinical consultative tools, provided their capabilities and limitations are carefully managed. Ultimately, collaborative efforts addressing these challenges could transform IE care, enhancing diagnostic accuracy, clinical outcomes, and patient safety.","url":"https://doi.org/10.3390/pathogens14040371","authors":["Jack McHugh","Douglas W. Challener","Hussam Tabaja"],"tags":["Interpretability","Artificial intelligence","Machine learning","Infective endocarditis","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.3390/pathogens14040371","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411234791","name":"Artificial Intelligence Models in Diagnosis and Treatment of Kidney Diseases: Current Status and Prospects","source":"openalex","abstract":"Background: Artificial intelligence (AI) has made significant advances in nephrology, revolutionizing the diagnosis, prognosis, and treatment of kidney diseases. Summary: This review provides an overview of AI applications in nephrology, introducing the basic structures of each model, highlighting both traditional machine-learning approaches and neural networks, and providing model application comparisons along with selection recommendations. It discussed key challenges in deciding appropriate AI models for specific tasks and evaluated their advantages, limitations, and optimal use cases. Current applications of AI in nephrology mainly include diagnosis and disease outcome prediction, medical image analysis, treatment recommendations, and personalized health management, supported by massive electronic health records and multimodal data integration. Traditional machine learning models perform well on datasets of varying sizes and structures, while neural networks excel at handling complex and imaging data. Emerging hardware innovations are expected to improve the performance of neural network models, enabling more accurate diagnosis and automated analysis in clinical practice. In the future, AI will have great potential to advance individualized patient care and enable real-time data processing in nephrology. Key Messages: An overview of AI applications in nephrology is provided in this review.","url":"https://doi.org/10.1159/000546397","authors":["Cheng Li","Jing Liu","Ping Fu","Jie Zou"],"tags":["Current (fluid)","Intensive care medicine","Medicine","Engineering","Electrical engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-12","doi":"https://doi.org/10.1159/000546397","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413962523","name":"Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review","source":"openalex","abstract":"Introduction Postpartum depression (PPD) is a significant mental health concern affecting 3.5-33.0% of mothers worldwide, with potentially severe consequences for both maternal and infant well-being. The emergence of artificial intelligence (AI) and machine learning (ML) technologies offers new opportunities for the early prediction of PPD risk, potentially enabling timely interventions to mitigate adverse outcomes. Methods This systematic review was conducted until October 31, 2024, using several electronic databases, including PubMed, Web of Science, CBM, VIP, CNKI, and Wanfang Data. All the studies predicted the occurrence of PPD using algorithms. The review process involved dual-independent screening by two authors using predefined criteria, with discrepancies resolved through consensus discussion involving a third investigator, and assessed the quality of the included models using the prediction model risk of bias assessment tool (PROBAST). Inter-rater agreement was quantified using Cohen’s κ. Results Eleven studies were included in the systematic review. The random forest, support vector machine, and logistic regression algorithms demonstrated high predictive performance (AUROC > 0.9). The main predictors of PPD were maternal age, pregnancy stress and adverse emotions, history of mental disorders, maternal education, marital relationship, and sleep status. The overall performance of the prediction model was excellent. However, the generalizability of the model was limited, and there was a certain risk of bias. Issues such as data quality, algorithm interpretability, and the cross-cultural and cross-population applicability of the model need to be addressed. Conclusion The model has the potential to predict the risk of PPD and provide support for early identification and intervention. Future research should optimize the model, improve its prediction accuracy, and test its applicability across cultures and populations to reduce the incidence of PPD and guarantee the mental health of pregnant and maternal women.","url":"https://doi.org/10.3389/fpubh.2025.1631705","authors":["Jie Xia","Chen Chen","Xiuqin Lu","Tengfei Zhang","Tingting Wang","Qingling Wang","Qianqian Zhou"],"tags":["Postpartum depression","Depression (economics)","Computer science","Systematic review","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-03","doi":"https://doi.org/10.3389/fpubh.2025.1631705","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411750810","name":"Synergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces","source":"openalex","abstract":"Abstract The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open‐source AI models, and next‐generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open‐source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI‐driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real‐world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology.","url":"https://doi.org/10.1002/brx2.70035","authors":["Canbiao Wu","Nayu Chen","Tuo Sun","Ping Tan","Peng Wang","Guangli Li"],"tags":["Computer science","Human–computer interaction","Cognitive science","Brain–computer interface","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-01","doi":"https://doi.org/10.1002/brx2.70035","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7118092294","name":"Artificial intelligence driven meat preservation technology: research progress, challenges, and future directions","source":"openalex","abstract":"Abstract With the rapid advancement of artificial intelligence (AI) technology, its applications in food science have become increasingly widespread—particularly in the preservation of meat products—where it demonstrates substantial potential. This study systematically reviews the key applications of AI in meat product preservation, encompassing quality assessment, cold chain monitoring, shelf-life prediction, the development of microbial prediction models, intelligent packaging optimization, and consumer behavior analysis. AI-enabled preservation strategies are effective in maintaining meat product quality. Notably, machine learning algorithms offer distinct advantages in the real-time monitoring of critical quality indicators and the prediction of shelf life under diverse preservation conditions, thereby providing robust support for producers’ decision-making processes. Furthermore, this study explores in depth the application prospects and development trends of AI in meat product preservation, with the aim of offering novel insights to facilitate the development of more efficient and intelligent meat preservation processes. By integrating AI technology, the meat preservation sector is expected to undergo an intelligent transformation—shifting from a traditional experience-driven paradigm to a data-driven one—thereby elevating the overall development level of the industry.","url":"https://doi.org/10.1093/fqsafe/fyaf083","authors":["F. Richard Yu","D. Zhang","Jiamin Zhang","W. Wang","Jian Cheng","Chunjiang Zhang"],"tags":["Product (mathematics)","Quality (philosophy)","New product development","Key (lock)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1093/fqsafe/fyaf083","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4401178944","name":"Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application—A randomized clinical trial","source":"openalex","abstract":"INTRODUCTION: Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education. MATERIALS AND METHODS: Fourth-year dental students had access to 16 virtual radiological example cases for 8 weeks. They were randomly assigned to either elaborated feedback (eF) or knowledge of results feedback (KOR) based on expert consensus. Students´ diagnostic competences were tested on bitewing/periapical radiographs for detection of caries, apical periodontitis, accuracy for all radiological findings and image quality. We additionally assessed the accuracy of an AI system (dentalXrai Pro 3.0), where applicable. Data were analysed descriptively and using ROC analysis (accuracy, sensitivity, specificity, AUC). Groups were compared with Welch's t-test. RESULTS: Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, p = .196; sensitivity 0.638 ± 0.204, p = .037; specificity 0.859 ± 0.050, p = .410; ROC AUC 0.748 ± 0.094, p = .020), apical periodontitis (accuracy 0.813 ± 0.095, p = .011; sensitivity 0.476 ± 0.230, p = .003; specificity 0.914 ± 0.108, p = .292; ROC AUC 0.695 ± 0.123, p = .001) and in assessing the image quality of periapical images (p = .031). No significant differences were observed for the other outcomes. The AI showed almost perfect diagnostic performance (enamel caries: accuracy 0.964, sensitivity 0.857, specificity 0.074; dentin caries: accuracy 0.988, sensitivity 0.941, specificity 1.0; overall: accuracy 0.976, sensitivity 0.958, specificity 0.983). CONCLUSION: Elaborated feedback can improve student's radiographic diagnostic competences, particularly in detecting enamel caries and apical periodontitis. Using an AI may constitute an alternative to expert labelling of radiographs.","url":"https://doi.org/10.1111/eje.13028","authors":["Sarah Rampf","Holger Gehrig","Andreas Möltner","Martin Fischer","Falk Schwendicke","Karin Christine Huth"],"tags":["Medicine","Dentistry","Diagnostic accuracy","Radiological weapon","Randomized controlled trial"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-31","doi":"https://doi.org/10.1111/eje.13028","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409211505","name":"The role of artificial intelligence in cardiovascular research: Fear less and live bolder","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively explore its potential applications in clinical practice and research methodology. METHODS AND RESULTS: AI offers exciting possibilities for advancing CV medicine by uncovering disease heterogeneity, integrating complex multimodal data, and enhancing treatment strategies. In this review, we discuss the innovative applications of AI in cardiac electrophysiology, imaging, angiography, biomarkers, and genomic data, as well as emerging tools like face recognition and speech analysis. Furthermore, we focus on the expanding role of machine learning (ML) in predicting CV risk and outcomes, outlining a roadmap for the implementation of AI in CV care delivery. While the future of AI holds great promise, technical limitations and ethical challenges remain significant barriers to its widespread clinical adoption. CONCLUSIONS: Addressing these issues through the development of high-quality standards and involving key stakeholders will be essential for AI to transform cardiovascular care safely and effectively.","url":"https://doi.org/10.1111/eci.14364","authors":["Alessandro Scuricini","Davide Ramoni","Luca Liberale","Fabrizio Montecucco","Federico Carbone"],"tags":["Engineering ethics","Clinical Practice","Face (sociological concept)","Artificial intelligence","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-01","doi":"https://doi.org/10.1111/eci.14364","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416447593","name":"Determination of the General Attitude to and Anxiety About Artificial Intelligence of Nurses Working in Internal Medicine Clinics: A Mixed‐Method Study","source":"openalex","abstract":"AIM: To determine the concerns and general attitudes to artificial intelligence (AI) of nurses working in Internal Medicine clinics in Türkiye. BACKGROUND: In parallel with developments in technology in all areas, AI is actively used in the healthcare sector in the diagnosis, treatment, and follow-up of patients, with algorithms based on the analysis of medical data. METHODS: The study was conducted between January 1, 2025, and May 15, 2025, using a mixed-method model of nested design (descriptive relationship-seeking and qualitative research) in a high-capacity tertiary care university hospital in the southern region of Türkiye. The data were collected using a Descriptive Information Form, the General Attitudes to Artificial Intelligence Scale, the Artificial Intelligence Anxiety Scale, and a semi-structured interview form. In the examination of causality between the quantitative scales in the data analyses, structural equation modeling was used, and in the analysis of the qualitative data, AI-supported Max Qualitative Data Analysis (MAXQDA) was used. STROBE was used in the reporting. RESULTS: Quantitative findings revealed a moderate level of positive attitude, negative attitude, and AI anxiety. A significant weak negative correlation was found between positive attitude and AI anxiety, while a moderate positive correlation emerged between negative attitude and anxiety. Qualitative analysis identified three primary themes influencing these attitudes: ethical violation (subthemes: first do no harm and confidentiality of information/privacy), holistic care (empathy, trust, and emotion), and quality and patient satisfaction (medical error, missed nursing care, and satisfaction). DISCUSSION: Adopting AI fosters positive attitudes and alleviates concerns about its use. CONCLUSIONS: The study results showed that the levels of positive attitude and negative attitude to AI, and concerns of the nurses about the use of AI, were slightly above average. In the context of the use of AI in nursing services, it was seen that ethical neglect, holistic care, quality, and patient satisfaction affected the attitudes to AI and anxiety of nurses. IMPLICATIONS FOR NURSING AND HEALTH POLICY: AI technologies can reduce the workload of nurses if they are integrated into healthcare policies under state control, ensuring high reliability of software and hardware. They can also help as a reminder by serving as a decision support component, can facilitate the complex nursing process, and increase the time that the nurse can spend with the patient. Thus, the success of the institution will be positively affected by increased quality of care in the delivery of healthcare services, the prevention of malpractice, and increased patient safety and satisfaction.","url":"https://doi.org/10.1111/inr.70134","authors":["Ahmet Seven","Ayşe Soylu","Dilek Soylu"],"tags":["Workload","Nursing","Reliability (semiconductor)","Anxiety","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-20","doi":"https://doi.org/10.1111/inr.70134","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410870469","name":"Leashes, not guardrails: A management‐based approach to artificial intelligence risk regulation","source":"openalex","abstract":"Calls to regulate artificial intelligence (AI) have sought to establish guardrails to protect the public against AI going awry. Although physical guardrails can lower risks on roadways by serving as fixed, immovable protective barriers, the regulatory equivalent in the digital age of AI is unrealistic and even unwise. AI is too heterogeneous and dynamic to circumscribe fixed paths along which it must operate-and, in any event, the benefits of the technology proceeding along novel pathways would be limited if rigid, prescriptive regulatory barriers were imposed. But this does not mean that AI should be left unregulated, as the harms from irresponsible and ill-managed development and use of AI can be serious. Instead of \"guardrails,\" though, policymakers should impose \"leashes.\" Regulatory leashes imposed on digital technologies are flexible and adaptable-just as physical leashes used when walking a dog through a neighborhood allow for a range of movement and exploration. But just as a physical leash only protects others when a human retains a firm grip on the handle, the kind of leashes that should be deployed for AI will also demand human oversight. In the regulatory context, a flexible regulatory strategy known in other contexts as management-based regulation will be an appropriate model for AI risk governance. In this article, we explain why regulating AI by management-based regulation-a leash approach-will work better than a prescriptive or guardrail regulatory approach. We discuss how some early regulatory efforts include management-based elements. We also elucidate some of the questions that lie ahead in implementing a management-based approach to AI risk regulation. Our aim is to facilitate future research and decision-making that can improve the efficacy of AI regulation by leashes, not guardrails.","url":"https://doi.org/10.1111/risa.70020","authors":["Cary Coglianese","Colton R. Crum"],"tags":["Context (archaeology)","Corporate governance","Risk analysis (engineering)","Work (physics)","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-29","doi":"https://doi.org/10.1111/risa.70020","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414866642","name":"Designing artificial intelligence chatbots for self-regulated learning from a systematic review based on Habermas's three interests","source":"openalex","abstract":"Self-regulated learning (SRL) is widely acknowledged as a key driver of academic success and lifelong learning, yet learners often struggle to plan, monitor, and reflect effectively on their progress. Concurrently, advances in artificial intelligence (AI) chatbots offer unprecedented opportunities to scale SRL. This paper reviews 78 studies from 2018 to 2025 on how AI chatbots can be created to support self-regulated learning (SRL) by leveraging technology, social interaction, and reflection, based on Habermas’s three types of knowledge interests (technical, practical, and emancipatory). We identify eight core chatbot functions that align with the forethought, performance, and self-reflection phases of Zimmerman’s SRL cycle. Socially, effective implementations embed chatbots within teacher–peer–chatbot triads to facilitate co-regulation, dialogue, and community building. Critically, chatbots can prompt deep self-evaluation and ethical inquiry, though designs must guard against overreliance and superficial reflection. We synthesise these findings into an integrative framework that maps chatbot features onto SRL processes and Habermasian interests, and we highlight underexplored areas – such as K-12 contexts, multimodal analytics, and emancipatory reflection – for future research. Our review offers actionable guidance for researchers, designers, and educators seeking to harness AI chatbots as versatile partners in fostering autonomous, socially grounded, and critically reflective learners.","url":"https://doi.org/10.1080/10494820.2025.2563086","authors":["Xiu-Yi Wu","Jeffrey Radloff","Ibrahim H. Yeter","Lei Wang","Thomas K. F. Chiu"],"tags":["Computer science","Chatbot","Artificial intelligence","Applications of artificial intelligence","Multimedia"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-06","doi":"https://doi.org/10.1080/10494820.2025.2563086","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415818264","name":"Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications","source":"openalex","abstract":"Prostate cancer is one of the leading causes of cancer-related morbidity and mortality worldwide, and imaging plays a critical role in its detection, localization, staging, treatment, and management. The advent of artificial intelligence (AI) has introduced transformative possibilities in prostate imaging, offering enhanced accuracy, efficiency, and consistency. This review explores the integration of AI in prostate cancer diagnostics across key imaging modalities, including multiparametric MRI (mpMRI), PSMA PET/CT, and transrectal ultrasound (TRUS). Advanced AI technologies, such as machine learning, deep learning, and radiomics, are being applied for lesion detection, risk stratification, segmentation, biopsy targeting, and treatment planning. AI-augmented systems have demonstrated the ability to support PI-RADS scoring, automate prostate and tumor segmentation, guide targeted biopsies, and optimize radiation therapy. Despite promising performance, challenges persist regarding data heterogeneity, algorithm generalizability, ethical considerations, and clinical implementation. Looking ahead, multimodal AI models integrating imaging, genomics, and clinical data hold promise for advancing precision medicine in prostate cancer care and assisting clinicians, particularly in underserved regions with limited access to specialists. Continued multidisciplinary collaboration will be essential to translate these innovations into evidence-based practice. This article explores current AI applications and future directions that are transforming prostate imaging and patient care.","url":"https://doi.org/10.3390/jimaging11110390","authors":["Kathleen H. Miao","Julia H. Miao","Mark Finkelstein","Aritrick Chatterjee","Aytekin Oto"],"tags":["Prostate cancer","Medicine","Precision medicine","Artificial intelligence","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-03","doi":"https://doi.org/10.3390/jimaging11110390","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407833429","name":"Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review","source":"openalex","abstract":"BACKGROUND: Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns. OBJECTIVE: We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues. METHODS: We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher's Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme. RESULTS: We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of \"black box\" algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers' jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles. CONCLUSIONS: Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders' perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.","url":"https://doi.org/10.2196/60432","authors":["Mehrdad Rahsepar Meadi","Tomas Sillekens","Suzanne Metselaar","Anton J.L.M. van Balkom","Justin Bernstein","Neeltje M. Batelaan"],"tags":["PsycINFO","Mental health","Scopus","Psychology","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-21","doi":"https://doi.org/10.2196/60432","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4402940668","name":"The Risks of Artificial Intelligence in Mental Health Care","source":"openalex","abstract":"Artificial intelligence (AI) has increasingly integrated into various aspects of healthcare, including diagnostics, care planning and patient management. In psychiatric healthcare specifically, conversational AI is seen as a potential solution to support psychiatric nurses in the assessment of psychiatric illnesses (Rebelo, Verboom, and Santos 2023). However, it is important that the oversight of a psychiatric nurse is needed when integrating conversational AI into psychiatric care. This editorial explores the risk of using conversational AI for psychiatric assessments and emphasises the need for human intervention in AI-driven processes. In community mental health settings, psychiatric nurses play a crucial role in managing a wide range of conditions. The increasing demand for care, driven by factors such as heightened public awareness and reduction of stigmatisation for psychiatric illnesses, has led to longer waiting times for services (British Medical Association 2024). Conversational AI systems have been proposed to alleviate this pressure by streamlining the psychiatric assessment process (Rollwage et al. 2024). One of the key benefits of AI is its potential to triage and prioritise those with the most urgent needs, thereby ensuring that critical cases are addressed promptly (Lu et al. 2023). Psychiatric assessments require a deep understanding of the patient's presentation, psychiatric symptoms and the context of patient behaviour. Conversational AI systems are based on language learning models (LLM) and analyse data they have been trained on previously, which is mainly in written format and often derived from patients' electronic health records initially, advises Yang et al. (2022). Electronic health records contain all patient notes relevant to the episode of care the patient is receiving. It can include quantitative data sets such as diagnoses, charts, patient demographics and patient-led questionnaires based on symptomology or more nuanced qualitative data sets, such as psychiatric nursing observations of patient behaviour and caregiver views (Goldstein et al. 2022). These data points help to construct a picture of a patient's presentation as part of an assessment before a patient is spoken to. Indeed, the psychiatric nurse will often review referrals, previous history and information to gain some knowledge of the patient before assessing the needs for that episode of care. However, an AI could face significant challenges in interpreting patient information with the depth and nuance that a psychiatric nurse could, suggests Elyoseph, Levkovich, and Shinan-Altman (2024), potentially resulting in inaccuracies in assessment, which can lead to inaccurate severity assessment and poor treatment outcomes. The notion of bias in AI systems is a significant issue in psychiatric care, particularly because these biases often originate from the clinicians themselves Meidert et al. (2023). As psychiatric nurses and other clinicians input information into electronic medical records, their own perceptions and potential biases towards the patient's culture and language, for example, can influence the information recorded, thus affecting training data for AI systems which are required to analyse a patient's psychiatric symptoms. In this way, the human bias impacts the AI bias. Particularly in mental health settings, where patients experience psychiatric symptoms in different ways, it is important to have accurate psychiatric assessment. For example, a conversational AI developed predominantly with patient data from Western healthcare settings could fail to interpret an observation from a psychiatric nurse and written expressions of psychiatric distress from the patient themselves in non-Western cultures (Teye et al. 2022). Psychiatric assessments involve more than recording symptoms and patient history. They require a range of skills such as empathetic listening, observation and effective communication, which are crucial for fostering","url":"https://doi.org/10.1111/jpm.13119","authors":["Ceylon Dell"],"tags":["Mental health","Psychology","Mental health care","Medicine","Psychiatry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-28","doi":"https://doi.org/10.1111/jpm.13119","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415152514","name":"Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review","source":"openalex","abstract":"Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults.","url":"https://doi.org/10.3390/nu17203223","authors":["Kingsley Kalu","Grace Ataguba","Oyepeju Onifade","Fidelia A. Orji","Nabil Giweli","Rita Orji"],"tags":["Psychological intervention","Systematic review","Intervention (counseling)","Gerontology","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-14","doi":"https://doi.org/10.3390/nu17203223","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413962358","name":"Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis","source":"openalex","abstract":"Background: Nowadays, artificial intelligence (AI) diagnostic models based on ultrasound features have been gradually integrated into the evaluation of thyroid nodules. However, the diagnostic effects of different AI-assisted diagnosis methods vary greatly. Objective: This study aims to systematically evaluate the performance of the ultrasound-based artificial intelligence diagnostic models in differentiating benign and malignant thyroid nodules and to determine the most effective diagnostic model. Methods: We conducted a comprehensive literature search in PubMed, Web of Science, and the Cochrane Library using subject-specific keywords to identify studies on AI-assisted thyroid nodule diagnosis. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Meta-analysis was performed using Meta-Disc 1.4, Review Manager 5.4, R 4.4.2, and Stata 17.0. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (SROC-AUC) with 95% confidence intervals (CI) were calculated. Subgroup analyses and clinical applicability assessments were conducted. Results: Twenty-eight studies involving 134,028 patients, 158,161 thyroid nodules, and 529,479 ultrasound images were included. The AI-assisted diagnostic system demonstrated high diagnostic performance: pooled sensitivity = 0.89 (95% CI: 0.87-0.91), specificity = 0.84 (0.80-0.88), positive likelihood ratio (PLR) = 5.60 (4.40-7.20), negative likelihood ratio (NLR) = 0.13 (0.10-0.16), DOR = 43.94 (30.11-64.14), and SROC-AUC = 0.93 (0.91-0.95). The threshold effect analysis (Spearman correlation = -0.18, P > 0.05) indicated no significant heterogeneity. The diagnostic accuracy is higher in Asian countries, in prospective and multicenter designs, with external validation sets, without cross-validation, with deep learning, and in postoperative patient subgroups. Additionally, improved performance was observed in cohorts with smaller nodule diameters (<20 mm), higher malignancy rates, older patient age (≥50 years), and higher female proportions, though heterogeneity remained significant. Univariate and multivariate meta-regression analyses identified AI type, malignancy rate of nodules as significant sources of heterogeneity. Notably, the EDLC-TN model showed the highest diagnostic accuracy. Conclusion: AI-assisted diagnostic techniques demonstrate significant potentialin thyroid nodule evaluation, with the EDLC-TN model showing particularly high clinical utility. Optimal diagnostic performance was observed for nodules <20 mm in diameter and in patients aged ≥50 years. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024581421, identifier CRD42024581421.","url":"https://doi.org/10.3389/fonc.2025.1614603","authors":["Jianfeng Zhan","Jian Zhang","Shaoqi Zhu","Lin Ni","Chen Zhang","Jia Hu"],"tags":["Thyroid nodules","Meta-analysis","Ultrasound","Medicine","Thyroid"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-03","doi":"https://doi.org/10.3389/fonc.2025.1614603","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413992240","name":"Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols","source":"openalex","abstract":"Conventional breast cancer screening programs are predominantly age-based, applying uniform intervals and modalities across broad populations. While this model has reduced mortality, it entails harms-including overdiagnosis, false positives, and missed interval cancers-prompting interest in risk-stratified approaches. In recent years, artificial intelligence (AI) has emerged as a critical enabler of this paradigm shift. This narrative review examines how AI-driven tools are advancing breast cancer screening toward personalization, with a focus on mammographic risk models, multimodal risk prediction, and AI-enabled clinical decision support. We reviewed studies published from 2015 to 2025, prioritizing large cohorts, randomized trials, and prospective validations. AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across subtypes and populations. Emerging multimodal models integrate genetics, clinical data, and imaging; AI is also being evaluated for triage and personalized intervals within clinical workflows. Barriers remain-explainability, regulatory validation, and equity. Widespread adoption will depend on prospective clinical benefit, regulatory alignment, and careful integration. Overall, AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across molecular subtypes, with signals strongest for ER-positive disease and limited data for fast-growing and interval cancers. Prospective trials demonstrating outcome benefit and safe interval modification are still pending. Accordingly, adoption should proceed with safeguards, equity monitoring, and clear separation between risk prediction, lesion detection, triage, and decision-support roles.","url":"https://doi.org/10.3390/cancers17172901","authors":["Filippo Pesapane","Luca Nicosia","Lucrezia D’Amelio","Giulia Quercioli","Mario R. Pannarale","Francesca Priolo","Irene Marinucci","Maria Giorgia Farina","Silvia Penco","Valeria Dominelli","Anna Rotili","Lorenza Meneghetti","Anna Carla Bozzini","Sonia Santicchia","Enrico Cassano"],"tags":["Personalization","Breast cancer","Computer science","Population","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-04","doi":"https://doi.org/10.3390/cancers17172901","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412526861","name":"Scorecard for synthetic medical data evaluation","source":"openalex","abstract":"Although the interest in synthetic medical data (SMD) for developing and testing artificial intelligence (AI) methods is growing, the absence of a comprehensive framework to evaluate the quality and applicability of SMD hinders its wider adoption. Here, we outline an evaluation framework designed to meet the unique requirements of medical applications. We also introduce SMD scorecard, a comprehensive report accompanying artificially generated datasets. This scorecard provides a quantitative assessment of SMD across seven criteria (7 Cs), complemented by a descriptive section that contains all relevant information about the dataset. The SMD scorecard provides a practical framework for evaluating and reporting the quality of synthetic data, which can benefit SMD developers and users. The use of synthetic medical data (SMD) in AI development is on the rise, but its broader application is limited by the lack of a comprehensive evaluation framework. Here, Ghada Zamzmi and colleagues present a novel evaluation framework tailored for medical applications, along with an SMD scorecard that quantitatively assesses synthetic datasets across seven key criteria.","url":"https://doi.org/10.1038/s44172-025-00450-1","authors":["Ghada Zamzmi","Adarsh Subbaswamy","Elena Sizikova","Edward Margerrison","Jana G. Delfino","Aldo Badano"],"tags":["Balanced scorecard","Computer science","Medicine","Business","Process management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.1038/s44172-025-00450-1","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411098123","name":"Diagnosis melanoma with artificial intelligence systems: A meta‐analysis study and systematic review","source":"openalex","abstract":"BACKGROUND: One of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods. OBJECTIVES: This study aimed to synthesize the existing studies for the clinical use of applications made with artificial intelligence methods and to summarize the predictive performance of deep learning and hybrid models-based algorithms in all these studies with a large-scale meta-analysis. METHOD: The literature review was conducted between January 2006 and May 2024, and meta-analysis data were created by scanning the Web of Science (WOS), Scopus and MEDLINE databases. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. RESULTS: A total of 2722 articles were evaluated. Data from 78 diagnostic tests from 39 primary studies meeting the inclusion and exclusion criteria were assessed. The pooled SROC overall model AUC was 0.96 [95% CI: 0.94-0.98], sensitivity was 0.89 [95% CI: 0.85-0.91] and specificity was 0.92 [95% CI: 0.90-0.94]. In the subgroup analyses, the pooled AUC was 0.98 [95% CI: 0.96-0.99] for HYBRID models. CONCLUSIONS: Recent studies have suggested that artificial intelligence algorithms and machine learning methods should be used extensively in medicine to assist physicians, especially in diagnosing melanoma. The ability of HYBRID model algorithms to predict diseases is promising. In particular, the performance of HYBRID models was found to be high. This information can assist clinicians in interpreting the most appropriate algorithms for diagnosing melanoma.","url":"https://doi.org/10.1111/jdv.20781","authors":["Gözde Ertürk Zararsız","Gözde Ertürk Zararsız","Serra İlayda Yerlitaş","Elif ÇELİK","Aleyna Erakcaoğlu","Selen Yılmaz Işıkhan","Abdullah Demirbaş","Ragıp Ertaş","İrem Nur Eroğlu","Selçuk Korkmaz","Ömer Faruk Elmas","Gökmen Zararsız","Gökmen Zararsız"],"tags":["Checklist","Medicine","Artificial intelligence","Meta-analysis","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-06","doi":"https://doi.org/10.1111/jdv.20781","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413828726","name":"Assessment of university students’ earthquake coping strategies using artificial intelligence methods","source":"openalex","abstract":"Earthquakes are one of the most destructive natural disasters that pose a serious threat to human life and infrastructure worldwide. The aim of this study is to evaluate the coping strategies of adult individuals in Turkey regarding earthquake stress using artificial intelligence-based methods. The data was collected from 858 university students living in Turkey during January, February, and March 2024. A dataset was created using the 'Coping Scale for Earthquake Stress.' Prediction models were established using artificial intelligence algorithms such as Logistic Regression (LR), Bagging, and Random Forest (RF) based on information from 24 variables. The cross-validation method was applied during model training. The Logistic Regression algorithm achieved the highest accuracy rate of 98.60%, while the Bagging algorithm demonstrated the lowest performance with an accuracy rate of 79.95%. The Random Forest algorithm showed moderate performance with an accuracy rate of 85.89%. The findings provide important insights into the coping strategies of the community regarding earthquake stress. This study is expected to contribute significantly to areas such as disaster management, psychology, public health, and community resilience.","url":"https://doi.org/10.1038/s41598-025-17555-4","authors":["Süleyman Alpaslan Sulak","Niğmet Köklü"],"tags":["Logistic regression","Random forest","Coping (psychology)","Natural disaster","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-29","doi":"https://doi.org/10.1038/s41598-025-17555-4","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413796682","name":"Artificial Intelligence in Education (AIED): Towards More Effective Regulation","source":"openalex","abstract":"Abstract This paper critically assesses the effectiveness of the EU AI Act in regulating artificial intelligence in higher education (AIED), with a focus on how it interacts with existing education regulation. It examines the growing use of high-risk AI systems – such as those used in admissions, assessment, academic progression, and exam proctoring – and identifies key regulatory frictions that arise when AI regulation and education regulation pursue overlapping but potentially conflicting aims. Central to this analysis is the concept of human oversight: while the AI Act frames oversight as a safeguard for accountability and fundamental rights, education regulation emphasises the professional autonomy of teachers and their role in maintaining pedagogical integrity. Yet, the regulatory role of teachers in AI-mediated environments remains unclear. Applying Mousmouti’s effectiveness test, the paper evaluates the AI Act along four dimensions – purpose, coherence, results, and structural integration with the broader legal framework – and argues that legal effectiveness in this context requires a more precise alignment between AI and education regulation.","url":"https://doi.org/10.1017/err.2025.10039","authors":["Liane Colonna"],"tags":["Autonomy","Accountability","Context (archaeology)","Political science","Public relations"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-28","doi":"https://doi.org/10.1017/err.2025.10039","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7125141043","name":"Revolutionizing endodontics: the impact and innovations of artificial intelligence","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) has been increasingly applied in the diagnosis and treatment of endodontic conditions. This narrative review summarizes the current literature on AI applications in endodontics from a clinical workflow perspective, discussing challenges in translating these approaches to practice and highlighting areas where further research is needed to better understand their effectiveness and limitations. METHODS: This narrative review summarizes applications and innovations of AI in endodontics up to July 2025. Relevant literature was identified through searches of PubMed and Web of Science using a combination of MeSH terms and free-text keywords covering areas such as diagnosis, image analysis, treatment planning, and prognosis prediction. Studies were selected for their relevance to clinical or experimental applications, and a few early, foundational studies were also included to provide context and show how AI has developed in this field. RESULTS: AI, leveraging its efficient image processing and pattern recognition capabilities, has demonstrated considerable advantages in endodontic imaging analysis. Evidence indicates that AI can assist clinicians in disease diagnosis, treatment planning, and prognosis assessment, thereby enhancing the quality of endodontic care across multiple clinical steps. Moreover, applications of AI in root canal anatomy identification, lesion detection, and integration into digital workflows further optimize clinical decision-making and procedural efficiency. CONCLUSIONS: Despite its promising potential in endodontics, the routine clinical application of AI remains limited by several factors, including the scarcity of publicly available databases, insufficient interpretability of algorithms, and ethical and privacy concerns. To achieve comprehensive integration of AI into clinical practice, future efforts could focus on multicenter validation studies, data sharing initiatives, development of interpretable AI models, and establishment of ethical and regulatory frameworks through interdisciplinary collaboration.","url":"https://doi.org/10.1186/s12903-025-07632-5","authors":["Xin Huang","Zijia Xu","Han Chen","Xin Guo","Xuebin Yang","Yuan Zhao"],"tags":["Workflow","Context (archaeology)","Medicine","Applications of artificial intelligence","Interpretability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-21","doi":"https://doi.org/10.1186/s12903-025-07632-5","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414524030","name":"Artificial intelligence-driven approaches for the rational design of peptides with predictable aggregation propensity","source":"openalex","abstract":"The rapid development of artificial intelligence has enabled accurate and efficient de novo design of protein and peptide structures. However, applying AI to design sequences with specific aggregation tendencies remains challenging due to the need to span multiple spatial-temporal scales. Here, we combined deep learning strategies—including genetic algorithms and reinforcement learning—to generate decapeptides with tunable aggregation propensities. Coarse-grained molecular dynamics simulations were used to evaluate solvent-accessible surface area and define aggregation propensity (AP). A Transformer-based prediction model with self-attention achieved high accuracy in AP prediction with only a 6% error rate. Furthermore, Monte Carlo Tree Search enabled targeted optimization of peptide sequences while preserving desired functional features. This study demonstrates how integrating AI with molecular modeling can guide the rational design of peptides with controlled assembly behavior, providing a scalable strategy for applications in biotechnology and medicine.","url":"https://doi.org/10.1038/s44431-025-00005-6","authors":["Shuo Yang","Jing Ren","Wenli Gao","Leitao Cao","Shengjie Ling"],"tags":["Rational design","Computer science","Scalability","Artificial intelligence","Reinforcement learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-25","doi":"https://doi.org/10.1038/s44431-025-00005-6","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7127650232","name":"Generative artificial intelligence for literature reviews","source":"openalex","abstract":"Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI’s technology—its architecture and training data—and suggest open issues in GenAI-based literature reviews methodology.","url":"https://doi.org/10.1177/02683962261425675","authors":["Gerit Wagner","Julian Prester","Reza Mousavi","Roman Lukyanenko","Guy Pare"],"tags":["Automatic summarization","Computer science","Generative grammar","Perspective (graphical)","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-05","doi":"https://doi.org/10.1177/02683962261425675","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406549764","name":"Leveraging Artificial Intelligence in Business Intelligence Systems for Predictive Analytics","source":"openalex","abstract":"Artificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go beyond data reporting or simple descriptive analytics and gain an insight to use BI systems to discover and pre-empt issues, besides noticing them using proactive decision making. In discussing the elements of AI-embedded BI systems, this article analyzes how organizations across industries use real-time intelligence and predictive models as indispensable resources for the generation of competitive edge. Some of the advantages highlighted includes improved accuracy for predictions, efficiency of cost on data handling, scalability on large data and the shorter delays on decision making. However, alongside these benefits, the article also addresses key challenges, such as data privacy concerns, biases in AI algorithms, and the complexities of integrating AI into legacy BI platforms. These limitations are critical considerations for organizations seeking to implement AI-driven BI systems effectively. Furthermore, this work discusses the issues relating to the implementation of AI for BI, for example, the integration of AI into existing BI platforms, data quality issues, ethical issues, and the skill gaps in specialized AI talents. The article also discusses new developments in AI integration to BI systems including the growing incorporation of deep learning techniques, automation of decision making and BI democratization for small businesses. They suggest that BI must evolve new business strategies to be effective and meet the information demands needed for corporate competitiveness in today’s data-centric economy. The convergence of advanced analytics and operational decision making makes AI driven BI system the tool with tremendous potential to become the lingua franca of business strategy and growth.","url":"https://doi.org/10.18535/ijsrm/v13i01.ec02","authors":["Amejuma Emmanuel Ebule"],"tags":["Business intelligence","Predictive analytics","Business analytics","Analytics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-17","doi":"https://doi.org/10.18535/ijsrm/v13i01.ec02","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4404642710","name":"BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data","source":"openalex","abstract":"BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training of artificial intelligence models and computational chemistry methods development. This update reports significant growth and enhancements since our last review in 2016. Of note, the database now contains 2.9 million binding measurements spanning 1.3 million compounds and thousands of protein targets. This growth is largely attributable to our unique focus on curating data from US patents, which has yielded a substantial influx of novel binding data. Recent improvements include a remake of the website following responsive web design principles, enhanced search and filtering capabilities, new data download options and webservices and establishment of a long-term data archive replicated across dispersed sites. We also discuss BindingDB's positioning relative to related resources, its open data sharing policies, insights gleaned from the dataset and plans for future growth and development.","url":"https://doi.org/10.1093/nar/gkae1075","authors":["Tiqing Liu","Linda Hwang","S.K. Burley","Carmen I. Nitsche","Christopher Southan","W. Patrick Walters","Michael K. Gilson"],"tags":["Biology","Small molecule","Plasma protein binding","DNA-binding protein","Computational biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-23","doi":"https://doi.org/10.1093/nar/gkae1075","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407639400","name":"Advancements in Machine Learning and Artificial Intelligence in Polymer Science: A Comprehensive Review","source":"openalex","abstract":"Abstract Technology, health care, and transport are merely some of the industries that historically rely on polymer‐based materials. In past centuries, the creation of innovative polymer materials has been dependent upon extensive experiments and error procedures that require an extensive number of resources as well as time. With the objective to explore the transformative potential of machine learning (ML) and artificial intelligence (AI) in material discovery, design, and optimization, this paper explores the integration of ML and AI in polymer‐based materials research. Researchers are able to speed the development of new polymer‐based materials with improved properties and functionalities by utilizing sophisticated algorithms and computational models. The use of ML and AI in polymer research is examined, with a focus on how these technologies may stimulate innovation and expand material science research.","url":"https://doi.org/10.1002/masy.202400185","authors":["Sheetal Mavi","S. P. Kadian","Pradeepta Kumar Sarangi","Ashok Kumar Sahoo","Shruti Singh","Muhd Zu Azhan Yahya","Nor Mas Mira Abd Rahman"],"tags":["Artificial intelligence","Materials science","Polymer science","Computer science","Nanotechnology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.1002/masy.202400185","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411823317","name":"Integrating artificial intelligence into Ayurveda: Pathways, potentials, and challenges","source":"openalex","abstract":"Introduction Ayurveda, the ancient system of holistic medicine, is grounded in experiential knowledge, personalized treatment approaches, and a comprehensive view of health and disease. Its time-tested principles continue to serve as a foundation for preventive, promotive, and curative healthcare practices. In recent years, the rapid advancement of artificial intelligence (AI) has opened new avenues for all sectors of healthcare including Traditional Medicine(TM) and Ayurveda. The World Health Organization (WHO), in partnership with the International Telecommunication Union (ITU), launched the Focus Group on AI for Health (FG-AI4H) in 2018, which later led to the formation of the Global Initiative on AI for Health (GI-AI4H) in July 2023, along with ITU and WIPO, as a long-term structure to advance AI in healthcare. WHO has since published several key documents: Ethics and Governance of Artificial Intelligence for Health: WHO Guidance Executive Summary (28 June 2021), Global Strategy on Digital Health 2020–2025 (18 August 2021), Generating Evidence for Artificial Intelligence-Based Medical Devices: A Framework for Training (17 November 2021), Regulatory Considerations on Artificial Intelligence for Health (19 October 2023), and Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models (25 March 2025), highlighting its ongoing commitment to ethical, strategic, and regulatory aspects of AI in health.[1] The Ministry of Ayush is actively advancing AI integration in Indian Systems of Medicine through curriculum development, AI-powered e-learning, Ayush Grid applications, and global expert collaborations—highlighted by the Global Experts Convene on AI in Traditional Medicine held at AIIA, New Delhi (12–13 Sept 2024).[2] AI technologies such as machine learning, natural language processing (NLP), and data integration tools offer promising applications in clinical decision-making, educational innovations, pharmacovigilance, and the digitization of classical Ayurvedic knowledge systems.[3,4] This appraisal aims to explore the current and potential applications of AI in Ayurveda, identify key opportunities and challenges in this integrative journey, and propose strategic directions for responsible and epistemologically aligned adoption of AI within the Ayurvedic framework. Scope and Applications of Artificial Intelligence in Ayurveda Artificial intelligence in traditional medicine: Real-world applications and opportunities Globally, AI is being increasingly integrated into traditional medicine systems to support evidence generation, drug discovery, and safety monitoring. For example, in China, AI has been effectively applied in traditional Chinese medicine (TCM) through the development of machine learning models to classify syndromes, predict therapeutic outcomes, and mine historical clinical records for drug discovery.[5] A notable instance includes the use of AI-driven knowledge graphs and text-mining algorithms to extract pharmacological insights from TCM classical texts, supporting reverse pharmacology and identifying novel therapeutic leads.[5,6] Adverse drug reaction and pharmacovigilance AI plays a crucial role in enhancing drug safety by analyzing large datasets to detect patterns of adverse drug reactions (ADRs) that may not surface during clinical trials. By predicting potential risks early, AI supports safer, faster, and more informed decision-making in drug development and post-marketing surveillance. Researchers opined that AI-based tools for ADR detection from Ayurvedic hospital databases and public health forums, showcasing AI’s role in proactive safety monitoring.[7] These examples illustrate how AI applications such as text mining, clinical data analytics, and predictive modeling can significantly advance traditional medicine by enhancing therapeutic safety, optimizing drug development, and bridging classical knowledge with contemporary biomedical research. Digitization and semant","url":"https://doi.org/10.4103/jdras.jdras_176_25","authors":["Rabinarayan Acharya"],"tags":["Cognitive science","Artificial intelligence","Computer science","Data science","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.4103/jdras.jdras_176_25","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4404318930","name":"Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods","source":"openalex","abstract":"The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a \"black box\" with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, that is, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.","url":"https://doi.org/10.1093/bjro/tzae039","authors":["Frank Hoebers","Leonard Wee","Jirapat Likitlersuang","Raymond H. Mak","Danielle S. Bitterman","Yanqi Huang","André Dekker","Hugo J.W.L. Aerts","Benjamin H. Kann"],"tags":["Radiation oncology","Medical physics","Medicine","Computer science","Radiation therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-12","doi":"https://doi.org/10.1093/bjro/tzae039","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407364174","name":"Medical multimodal multitask foundation model for lung cancer screening","source":"openalex","abstract":"Lung cancer screening (LCS) reduces mortality and involves vast multimodal data such as text, tables, and images. Fully mining such big data requires multitasking; otherwise, occult but important features may be overlooked, adversely affecting clinical management and healthcare quality. Here we propose a medical multimodal-multitask foundation model (M3FM) for three-dimensional low-dose computed tomography (CT) LCS. After curating a multimodal multitask dataset of 49 clinical data types, 163,725 chest CT series, and 17 tasks involved in LCS, we develop a scalable multimodal question-answering model architecture for synergistic multimodal multitasking. M3FM consistently outperforms the state-of-the-art models, improving lung cancer risk and cardiovascular disease mortality risk prediction by up to 20% and 10% respectively. M3FM processes multiscale high-dimensional images, handles various combinations of multimodal data, identifies informative data elements, and adapts to out-of-distribution tasks with minimal data. In this work, we show that M3FM advances various LCS tasks through large-scale multimodal and multitask learning. Lung cancer screening (LCS) requires effectively and efficiently mining big, multimodal datasets. Here, the authors develop a medical multimodal-multitask foundation model (M3FM) for LCS from 3D low-dose computed tomography and medical multimodal data, outperforming state-of-the-art methods and allowing the identification of informative data elements.","url":"https://doi.org/10.1038/s41467-025-56822-w","authors":["Chuang Niu","Qing Lyu","Christopher D. Carothers","Parisa Kaviani","Josh Tan","Pingkun Yan","Mannudeep K. Kalra","Christopher T. Whitlow","Ge Wang"],"tags":["Foundation (evidence)","Computer science","Lung cancer","Cancer","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-11","doi":"https://doi.org/10.1038/s41467-025-56822-w","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4403546675","name":"Development and Validation of an Artificial Intelligence–Assisted Patient Education Material for Ostomy Patients: A Methodological Study","source":"openalex","abstract":"AIM: To develop and test the validity of an artificial intelligence-assisted patient education material for ostomy patients. DESIGN: A methodological study. METHODS: The study was carried out in two main stages and five steps: (1) determining the information needs of ostomy patients, (2) creating educational content, (3) converting the educational content into patient education material, (4) validation of patient education material based on expert review and (5) measuring the readability of the patient education material. We used ChatGPT 4.0 to determine the information needs and create patient education material content, and Publuu Online Flipbook Maker was used to convert the educational content into patient education material. Understandability and applicability scores were assessed using the Patient Education Materials Assessment Tool submitted to 10 expert reviews. The tool inter-rater reliability was determined via the intraclass correlation coefficient. Readability was analysed using the Flesch-Kincaid Grade Level, Gunning Fog Index and Simple Measure of Gobbledygook formula. RESULTS: The mean Patient Education Materials Assessment Tool understandability score of the patient education material was 81.91%, and the mean Patient Education Materials Assessment Tool actionability score was 85.33%. The scores for the readability indicators were calculated to be Flesch-Kincaid Grade Level: 8.53, Gunning Fog: 10.9 and Simple Measure of Gobbledygook: 7.99. CONCLUSIONS: The AI-assisted patient education material for ostomy patients provided accurate information with understandable and actionable responses to patients, but is at a high reading level for patients. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Artificial intelligence-assisted patient education materials can significantly increase patient information rates in the health system regarding ease of practice. Artificial intelligence is currently not an option for creating patient education material, and their impact on the patient is not fully known. REPORTING METHOD: The study followed the STROBE checklist guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contributions.","url":"https://doi.org/10.1111/jan.16542","authors":["Hatice Yüceler Kaçmaz","Hilal Kahraman","Seda Akutay","Derya Dağdelen"],"tags":["Readability","Patient education","Intraclass correlation","Content validity","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-18","doi":"https://doi.org/10.1111/jan.16542","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411149338","name":"A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature","source":"openalex","abstract":"Objective: Recent advances in artificial intelligence (AI) are revolutionizing ophthalmology by enhancing diagnostic accuracy, treatment planning, and patient management. However, a significant gap remains in practical guidance for ophthalmologists who lack AI expertise to effectively analyze these technologies and assess their readiness for integration into clinical practice. This paper aims to bridge this gap by demystifying AI model design and providing practical recommendations for evaluating AI imaging models in research publications. Design: Educational review: synthesizing key considerations for evaluating AI papers in ophthalmology. Participants: This paper draws on insights from an interdisciplinary team of ophthalmologists and AI experts with experience in developing and evaluating AI models for clinical applications. Methods: A structured framework was developed based on expert discussions and a review of key methodological considerations in AI research. Main Outcome Measures: A stepwise approach to evaluating AI models in ophthalmology, providing clinicians with practical strategies for assessing AI research. Results: This guide offers broad recommendations applicable across ophthalmology and medicine. Conclusions: As the landscape of health care continues to evolve, proactive engagement with AI will empower clinicians to lead the way in innovation while concurrently prioritizing patient safety and quality of care. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.","url":"https://doi.org/10.1016/j.xops.2025.100847","authors":["Angela McCarthy","Ives A. Valenzuela","Royce W. S. Chen","Lora R. Dagi Glass","Kaveri A. Thakoor"],"tags":["Computer science","Artificial intelligence","Data science","Management science","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-09","doi":"https://doi.org/10.1016/j.xops.2025.100847","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410310579","name":"Empowering Generalist Material Intelligence with Large Language Models","source":"openalex","abstract":"Large language models (LLMs) are steering the development of generalist materials intelligence (GMI), a unified framework integrating conceptual reasoning, computational modeling, and experimental validation. Central to this framework is the agent-in-the-loop paradigm, where LLM-based agents function as dynamic orchestrators, synthesizing multimodal knowledge, specialized models, and experimental robotics to enable fully autonomous discovery. Drawing from a comprehensive review of LLMs' transformative impact across representative applications in materials science, including data extraction, property prediction, structure generation, synthesis planning, and self-driven labs, this study underscores how LLMs are revolutionizing traditional tasks, catalyzing the agent-in-the-loop paradigm, and bridging the ontology-concept-computation-experiment continuum. Then the unique challenges of scaling up LLM adoption are discussed, particularly those arising from the misalignment of foundation LLMs with materials-specific knowledge, emphasizing the need to enhance adaptability, efficiency, sustainability, interpretability, and trustworthiness in the pursuit of GMI. Nonetheless, it is important to recognize that LLMs are not universally efficient. Their substantial resource demands and inconsistent performance call for careful deployment based on demonstrated task suitability. To address these realities, actionable strategies and a progressive roadmap for equitably and democratically implementing materials-aware LLMs in real-world practices are proposed.","url":"https://doi.org/10.1002/adma.202502771","authors":["Wenhao Yuan","Guangyao Chen","Zhilong Wang","Fengqi You"],"tags":["Transformative learning","Knowledge management","Interpretability","Computer science","Function (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-12","doi":"https://doi.org/10.1002/adma.202502771","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410708925","name":"Artificial Intelligence in telemedicine and remote patient monitoring: Enhancing virtual healthcare through AI-driven diagnostic and predictive technologies","source":"openalex","abstract":"The integration of Artificial Intelligence (AI) in telemedicine and remote patient monitoring has significantly transformed modern healthcare by enhancing accessibility, efficiency, and diagnostic precision. AI-powered technologies, including machine learning algorithms, predictive analytics, and natural language processing, have facilitated real-time health monitoring, early disease detection, and personalized treatment recommendations. The incorporation of AI-driven chatbot and virtual assistants has streamlined remote consultations, enabling healthcare professionals to manage patient inquiries more efficiently while ensuring timely medical interventions. Additionally, wearable health devices embedded with AI capabilities provide continuous monitoring of vital signs, allowing for proactive management of chronic diseases such as diabetes, hypertension, and cardiovascular disorders. AI-driven predictive models are also being utilized to assess patient risk factors, forecast potential health complications, and optimize treatment plans. Furthermore, AI has enhanced telemedicine by automating administrative processes, reducing operational costs, and expanding healthcare services to remote and underserved populations. However, the adoption of AI in telemedicine presents challenges related to data security, ethical considerations, regulatory compliance, and patient privacy, which require careful evaluation. As AI continues to evolve, its integration with telemedicine and remote patient monitoring is expected to revolutionize digital healthcare, providing innovative solutions to improve patient outcomes, optimize healthcare workflows, and bridge the gap between patients and medical professionals in an increasingly digital world.","url":"https://doi.org/10.30574/ijsra.2025.15.2.1402","authors":["Malay Sarkar","Raktim Dey","Md Tuhin Mia"],"tags":["Telemedicine","Health care","Computer science","Artificial intelligence","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-24","doi":"https://doi.org/10.30574/ijsra.2025.15.2.1402","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410456003","name":"Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation","source":"openalex","abstract":"Background: Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images. Objective: To assess the accuracy, clinical effectiveness and cost-effectiveness of using software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading. Design: Systematic review and de novo cost-effectiveness analysis. Methods: Searches were undertaken from 2012 to January 2022. Company submissions were accepted until 31 August 2022. Study quality was assessed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2), the extension to QUADAS-2 for assessing risk of bias in comparative accuracy studies (QUADAS-C) and the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Outcomes were synthesised narratively. Two decision trees were used for cost-effectiveness: (1) a simple decision tree for the detection of actionable nodules and (2) a decision tree reflecting the full clinical pathways for people undergoing chest computed tomography scans. Models estimated incremental cost-effectiveness ratios, cost per correct detection of an actionable nodule, and cost per cancer detected and treated. We undertook scenario and sensitivity analyses. Results: Twenty-seven studies were included. All were rated as being at high risk of bias. Twenty-four of the included studies used retrospective data sets. Seventeen compared readers with and without artificial intelligence software. One reported prospective screening experiences before and after artificial intelligence software implementation. The remaining studies either evaluated stand-alone artificial intelligence or provided only non-comparative evidence. (1) Artificial intelligence assistance generally improved the detection of any nodules compared with unaided reading (three studies; average per-person sensitivity 0.43-0.68 for unaided and 0.79-0.99 for artificial intelligence-assisted reading), with similar or lower specificity (three studies; 0.77-1.00 for unaided and 0.81-0.97 for artificial intelligence-assisted reading). Nodule diameters were similar or significantly larger with semiautomatic measurements than with manual measurements. Intra-reader and inter-reader agreement in nodule size measurement and in risk classification generally improved with artificial intelligence assistance or were comparable to those with unaided reading. However, the effect on measurement accuracy is unclear. (2) Radiologist reading time generally decreased with artificial intelligence assistance in research settings. (3) Artificial intelligence assistance tended to increase allocated risk categories as defined by clinical guidelines. (4) No relevant clinical effectiveness and cost-effectiveness studies were identified. (5) The de novo cost-effectiveness analysis suggested that for symptomatic and incidental populations, artificial intelligence-assisted computed tomography image analysis dominated the unaided radiologist in cost per correct detection of an actionable nodule. However, when relevant costs and quality-adjusted life-years from the full clinical pathway were included, artificial intelligence-assisted computed tomography reading was dominated by the unaided reader. For screening, artificial intelligence-assisted computed tomography image analysis was cost-effective in the base case and all sensitivity and scenario analyses. Limitations: Due to the heterogeneity, sparseness, low quality and low applicability of the clinical effectiveness evidence and the major challenges in linking test accuracy evidence to clinical and economic outcomes, the findings presented here are highly uncertain and provide indicators/fr","url":"https://doi.org/10.3310/jytw8921","authors":["Julia Geppert","Peter Auguste","Asra Asgharzadeh","Hesam Ghiasvand","Mubarak Patel","Anna Brown","Surangi Jayakody","Emma Helm","Daniel Todkill","Jason Madan","Chris Stinton","Daniel Gallacher","Sian Taylor‐Phillips","Yen‐Fu Chen"],"tags":["Medicine","Software","Artificial intelligence","Radiology","Algorithm"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.3310/jytw8921","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412766156","name":"PROPOSED REGULATORY FRAMEWORK FOR MODIFICATIONS TO ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML)- BASED SOFTWARE AS A MEDICAL DEVICE (SAMD) IN US","source":"openalex","abstract":"Computer systems can learn and adapt by implementing human intelligence, which is known as artificial intelligence (AI).When it used in combination with medical software, it creates an artificial intelligence (AI)-based medical device that uses in data and algorithms to help with activities like diagnosis and treatment suggestions, potentially increasing the accuracy and effectiveness of medical care, and the use of Artificial Intelligence/Machine Learning (AI/ML) into medical devices, especially Software as a Medical Device (SaMD), this enclosed important concepts like AI/ML, medical devices, SaMD, 510(k) notifications, FDA, CDRH, PMA, TPLC, IMDRF, SPS, and ACP.Through launch the issues raised by AI/ML in SaMD, the structure required to strike a compromise between patient safety and innovation.The subject is to establish the SaMD Pre-Specifications (SPS) and Algorithm Change Protocol (ACP) as crucial elements for open changes, ongoing monitoring, and post-market surveillance.Primary goal is to extend the framework by providing guidance for change control plans using AI/ML methods, particularly those with software learning over time.The Premarket Approval","url":"https://doi.org/10.31032/ijbpas/2025/14.8.9309","authors":[],"tags":["Artificial intelligence","Computer science","Software","Machine learning","Operating system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-30","doi":"https://doi.org/10.31032/ijbpas/2025/14.8.9309","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4390233959","name":"The European Union’s Approach to Artificial Intelligence and the Challenge of Financial Systemic Risk","source":"openalex","abstract":"Abstract This piece examines the EU’s ‘Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence’ (‘AI Act’) with a view to determining the extent to which it addresses the systemic risk created by AI FinTech. Ultimately, it is argued that the notion of ‘high risk’ at the centre of the AI Act leaves out financial systemic risk. This exclusion can neither be justified by reasons of technology neutrality, nor by reasons of proportionality: neither is AI-driven financial systemic risk already covered by existing (or proposed) macroprudential frameworks and tools, nor can its omission from the AI Act be justified by the prioritisation of other types of risk. Moving forward, it is suggested that the EU’s AI Act would have benefited from a broader definition of ‘high risk’. It is also hoped that EU policy makers will soon begin to strengthen existing macroprudential toolkits to address the financial systemic risk created by AI.","url":"https://doi.org/10.1007/978-3-031-41264-6_22","authors":["Anat Keller","Clara Martins Pereira","Martinho Lucas Pires"],"tags":["Systemic risk","Parliament","Proportionality (law)","European union","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-26","doi":"https://doi.org/10.1007/978-3-031-41264-6_22","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4392292312","name":"The Digital Divide in Action: How Experiences of Digital Technology Shape Future Relationships with Artificial Intelligence","source":"openalex","abstract":"The digital divide remains an ongoing societal concern, with digital exclusion shown to have a significantly detrimental impact on people’s quality of life. Artificial intelligence (AI), the latest wave of digitalisation, is being integrated into the fabric of society at an accelerated rate, the speed of which has prompted ethical concerns. Without addressing the digital divide, the AI revolution risks exacerbating the existing consequences of digital exclusion and limiting the potential for all people to reap the benefits provided by AI. To understand the factors that might contribute to experiences of AI, and how these might be related to digital exclusion, we surveyed a diverse online community sample (N = 303). We created a novel measure of digital confidence capturing individual levels of awareness, familiarity, and sense of competence with digital technology. Results indicated that measures of digital confidence were predicted by structural, behavioural, and psychological differences, such that women, older people, those on lower salaries, people with less digital access, and those with lower digital well-being, reported significantly less digital confidence. Furthermore, digital confidence significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI. This understanding of the spill-over effects of digital exclusion onto experiences of AI is fundamental to the articulation and delivery of inclusive AI.","url":"https://doi.org/10.31234/osf.io/wd4tr","authors":["Sarah V. Bentley","Claire Naughtin","Melanie J. McGrath","Jessica Irons","Patrick J Cooper"],"tags":["Action (physics)","Computer science","Artificial intelligence","Cognitive science","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-28","doi":"https://doi.org/10.31234/osf.io/wd4tr","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411647611","name":"Bioethical challenges in the integration of artificial intelligence in transplant surgery 4.0: A scoping review","source":"openalex","abstract":"Introduction The integration of artificial intelligence (AI) into transplant surgery offers potential benefits, including enhanced precision and personalized patient care. However, these advancements raise critical ethical issues that must be addressed to ensure responsible implementation. This scoping review and bibliometric analysis explore the literature on the ethical considerations associated with the application of AI in transplant surgery. Methods Following Joanna Briggs Institute and PRISMA-ScR guidelines, we conducted a systematic search of databases, including PubMed, Scopus, Science Direct, JSTOR, LILACS, IEEE, and GreyNet from 2013 to 2025. We identified articles that discussed ethical implications in English or Spanish. Data were charted using Rayyan (Rayyan Systems Inc., Doha, Qatar) and analyzed with Biblioshiny . One reviewer reviewed each article twice to ensure accuracy. Results Our search identified 6824 records, of which 16 studies met the selection criteria. The bibliometric analysis revealed a significant increase in scholarly output on AI ethics in transplantation since 2020. Key ethical concerns comprehend dehumanization of medical care, limitations in AI interpretability, and erroneous decision-making. The potential benefits highlighted include improved donor-recipient matching and personalized patient care. However, the need for human oversight in AI applications is emphasized mitigating risks such as patient dehumanization and biased decision-making. Conclusion This review is the first to comprehensively map the ethical landscape of AI integration in transplant surgery. It identifies both the potential and the significant ethical challenges of these technologies. Future research should focus on developing frameworks for ethical AI implementation and ensuring that advancements in AI contribute to equitable and just healthcare practices.","url":"https://doi.org/10.1177/20552076251351700","authors":["Nicolás Lozano-Suárez","Andrea Gómez-Montero","Maritza Jiménez-Gómez","Santiago Cabas","Nicolás Giron-Londoño","Andrea García-López","Fernando Girón-Luque"],"tags":["Dehumanization","Bioethics","Health care","Ethical issues","Scopus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1177/20552076251351700","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413913331","name":"Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals","source":"openalex","abstract":"Sign language (SL) is a non-verbal language applied by deaf and hard-of-hearing individuals for daily communication between them. Studies in SL recognition (SLR) have recently become essential developments. The current successes present the base for upcoming applications to assist the combination of deaf and hard-of-hearing people. SLR could help break down the obstacles for SL users in the community. In general, glove-based and vision-based techniques are the dual major types measured for SLR methods. Several investigators presented various techniques with significant development by deep learning (DL) models in computer vision (CV) and became performed to SLR. This study presents a novel Harris Hawk Optimization-Based Deep Learning Model for Sign Language Recognition (HHODLM-SLR) technique. The HHODLM-SLR technique mainly concentrates on the advanced automatic detection and classification of SL for hearing and speech-impaired individuals. Initially, the image pre-processing stage applies bilateral filtering (BF) to eliminate noise in an input image dataset. Furthermore, the ResNet-152 model is employed for the feature extraction process. The bidirectional long short-term memory (Bi-LSTM) model is used for SLR. Finally, the Harris hawk optimization (HHO) approach optimally adjusts the Bi-LSTM approach's hyperparameter values, resulting in more excellent classification performance. The efficiency of the HHODLM-SLR methodology is validated under the SL dataset. The experimental analysis of the HHODLM-SLR methodology portrayed a superior accuracy value of 98.95% over existing techniques.","url":"https://doi.org/10.1038/s41598-025-09106-8","authors":["Abrar Almjally","Wafa Almukadi"],"tags":["Hearing impaired","Sign language","Speech recognition","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.1038/s41598-025-09106-8","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409646233","name":"Research on the Transformation of Enterprise Marketing Strategy Driven by Artificial Intelligence","source":"openalex","abstract":"This study explores the transformation of enterprise marketing strategies driven by artificial intelligence (AI) through two case studies: Beijing Nanshe Technology Co., Ltd. and Juran Design Home. Both companies, operating in different sectors, have embraced AI technologies to optimize their marketing practices, enhance customer engagement, and improve operational efficiency. Nanshe Tech, a leader in smart office devices, integrated AI to develop predictive models for customer targeting and lifecycle management, leading to improved conversion rates and a shift towards a subscription-based business model. Juran Design Home, a digital platform for the home furnishing industry, utilized AI-powered design assistants and VR/AR features to enhance the customer experience, resulting in higher customer satisfaction and increased sales. The study highlights the significant impact of AI on marketing practices, demonstrating how AI-driven tools can revolutionize customer segmentation, resource optimization, and marketing outcomes. The findings provide valuable insights for businesses considering AI adoption in their marketing strategies, emphasizing the need for a data-driven approach and cross-functional collaboration to successfully integrate AI technologies.","url":"https://doi.org/10.55014/pij.v8i2.802","authors":["Lijuan Xie"],"tags":["Transformation (genetics)","Business","Process management","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-20","doi":"https://doi.org/10.55014/pij.v8i2.802","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4417178901","name":"Artificial intelligence in predicting anti-VEGF treatment response in diabetic macular edema: current progress and future directions","source":"openalex","abstract":"Diabetic macular edema (DME), a leading cause of vision impairment in diabetes, is primarily treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections. However, variable treatment response rates often lead to persistent edema and irreversible vision loss. Accurate prediction of treatment response is therefore critical for optimizing treatment strategies and preserving visual function. This review examines the application of artificial intelligence (AI) to predict anti-VEGF treatment outcomes in DME. While optical coherence tomography (OCT) imaging has shown significant progress, the potential of optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) remains under-exploited. AI holds considerable promise for enhancing predictive accuracy. Future research should focus on multimodal imaging approaches integrating structural, ischemic, and vascular information to develop more accurate and reliable predictive models for personalized DME treatment.","url":"https://doi.org/10.48130/vns-0025-0027","authors":["Dan Cao","Jie Yao","Daniel Shu Wei Ting","Gavin Siew Wei Tan"],"tags":["Optical coherence tomography","Diabetic macular edema","Fluorescein angiography","Medicine","Optical coherence tomography angiography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.48130/vns-0025-0027","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410763127","name":"Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views","source":"openalex","abstract":"PURPOSE: Increased workload associated with electronic visits (eVisits) in primary care could potentially be decreased by the use of artificial intelligence (AI); however, it is unknown whether this use of AI would be acceptable to staff and patients. We explored patient and primary care staff views on the use of and opportunities for AI during eVisits. METHODS: ) from May 2020 to September 2021. We analyzed verbatim transcripts using thematic analysis. RESULTS: Misconceptions regarding AI were common, which led to initial reservations on its use during eVisits. Perceived potential AI benefits included decreased staff workload and faster response times for patients. Safety concerns stemmed from the complexity of primary care and fears of depersonalized service. The following 7 opportunities for AI during eVisits were identified: workflow, directing, prioritization, asking questions, writing assistance, providing self-help information, and face-to-face appointment booking. Despite staff concerns regarding patient acceptability, most patients welcomed the use of AI if it were used as an adjunct to (not replacement for) clinical judgment and could support them in getting help more quickly. Retention of clinical oversight and ongoing evaluation was key to staff acceptability. CONCLUSIONS: Patients and staff welcomed the use of AI and identified 7 potential uses during eVisits to decrease staff workload and improve patient safety. Successful implementation will depend on clear communication from practices, demonstrating and monitoring safety, clarifying misconceptions, and reassuring that it will not replace humans.","url":"https://doi.org/10.1370/afm.240292","authors":["Susan Moschogianis","Sarah Darley","Tessa Coulson","Niels Peek","Sudeh Cheraghi‐Sohi","Benjamin Brown"],"tags":["Primary care","Qualitative research","Psychology","Nursing","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1370/afm.240292","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409619572","name":"Artificial intelligence and dichotomania","source":"openalex","abstract":"Abstract Large language models (LLMs) such as ChatGPT, Gemini, and Claude are increasingly being used in aid or place of human judgment and decision making. Indeed, academic researchers are increasingly using LLMs as a research tool. In this paper, we examine whether LLMs, like academic researchers, fall prey to a particularly common human error in interpreting statistical results, namely ‘dichotomania’ that results from the dichotomization of statistical results into the categories ‘statistically significant’ and ‘statistically nonsignificant’. We find that ChatGPT, Gemini, and Claude fall prey to dichotomania at the 0.05 and 0.10 thresholds commonly used to declare ‘statistical significance’. In addition, prompt engineering with principles taken from an American Statistical Association Statement on Statistical Significance and P-values intended as a corrective to human errors does not mitigate this and arguably exacerbates it. Further, more recent and larger versions of these models do not necessarily perform better. Finally, these models sometimes provide interpretations that are not only incorrect but also highly erratic.","url":"https://doi.org/10.1017/jdm.2025.7","authors":["Blakeley B. McShane","David Gal","Adam Duhachek"],"tags":["Psychology","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1017/jdm.2025.7","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3126220825","name":"A survey on data‐efficient algorithms in big data era","source":"openalex","abstract":"Abstract The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision. In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories. Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime. Each strategy is extensively reviewed and discussed. In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms. Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.","url":"https://doi.org/10.1186/s40537-021-00419-9","authors":["Amina Adadi"],"tags":["Computer science","Big data","Data science","Process (computing)","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-26","doi":"https://doi.org/10.1186/s40537-021-00419-9","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411043515","name":"Generative artificial intelligence for sustainable development: predictive trend analysis in key sectors using natural language processing","source":"openalex","abstract":"Generative Artificial Intelligence (GenAI) has revolutionized multiple industries by improving efficiency and promoting innovation in fields such as business, education, healthcare, and cybersecurity. This study evaluates the transformative impact of GenAI on advancing the United Nations' Sustainable Development Goals (SDGs) by analyzing research trends and applications. In this research, authors utilized Latent Dirichlet Allocation (LDA), a Natural Language Processing (NLP) technique. The work delineates emergent themes and sector-specific progressions in GenAI research. The dataset consists of 2162 research articles published from 2020 to 2025, obtained from the Scopus database. The abstracts of these publications provide the foundation for analysis, facilitating a thorough examination of literature. The authors provided 10 topics, which are recent trends that future researchers can explore. The results indicate GenAI's capability in processing automation, creative enhancement, and innovation promotion while addressing ethical concerns such as prejudice, privacy, and social effects. The study indicates potential directions for ethical technology adoption by analyzing trends in GenAI applications. This work highlights the essential requirement for interdisciplinary methods and ethical frameworks to optimize GenAI's contributions to innovation while assuring alignment with sustainable and equitable development goals.","url":"https://doi.org/10.1007/s42452-025-07207-7","authors":["Chetan Sharma","Shamneesh Sharma","Vivek Bhardwaj","Balwinder Kaur Dhaliwal"],"tags":["Generative grammar","Key (lock)","Computer science","Artificial intelligence","Natural (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-04","doi":"https://doi.org/10.1007/s42452-025-07207-7","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413584208","name":"Use of artificial intelligence to support the assessment of the methodological quality of systematic reviews","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jclinepi.2025.111944","authors":["Manuel Marques‐Cruz","F. Pinto","Rafael José Vieira","Antonio Bognanni","Paula Perestrelo","Sara Gil‐Mata","Vítor Duarte","José Pedro Barbosa","António Cardoso‐Fernandes","Daniel Martinho-Dias","Francisco Franco-Pêgo","Federico Germini","Chiara Arienti","A. Chu","Pau Riera‐Serra","Paweł Jemioło","Pedro Pereira Rodrigues","João Fonseca","Luís Filipe Azevedo","Holger J. Schünemann","Ricardo Cruz‐Correia","Slava Jankin","Bernardo Sousa‐Pinto"],"tags":["Systematic review","Quality assessment","MEDLINE","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-25","doi":"https://doi.org/10.1016/j.jclinepi.2025.111944","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413078261","name":"Context-Aware Retrieval-Augmented Generation for Artificial Intelligence in Urology","source":"openalex","abstract":"Background Artificial intelligence (AI) is increasingly being used in healthcare, particularly for interpreting complex medical queries. However, conventional AI models often generate inaccurate or irrelevant responses that are commonly termed hallucinations, which may compromise patient safety. To address this, our study introduces a modified retrieval-augmented generation (RAG) framework tailored for the urology domain to enhance contextual relevance and accuracy in AI-generated responses. Methodology We developed a context-aware RAG system integrating PubMedBERT embeddings for encoding and retrieving urological literature stored in a Pinecone vector database. The system uses named entity recognition for domain-specific query filtering and incorporates dynamic memory to retain contextual flow during interactions. Response generation is powered by the LLaMA3-8B model via LangChain. A custom dataset of urology-related queries was used for evaluation, with a large language model-based scoring using the Deepseek-R1 model. Results The proposed framework demonstrated a significant reduction in hallucinations, with responses being more contextually relevant and evidence-based. Compared to baseline models, our system achieved an 89% performance improvement in generating medically appropriate answers. Integration of memory modules and named entity filtering further improved precision and reliability. Conclusions Our RAG-enhanced system shows strong potential for clinical use by producing trustworthy, context-aware responses in urology. It addresses key challenges in medical AI, including hallucination mitigation and domain relevance. Future work will focus on reducing inference latency and improving automated validation without manual oversight.","url":"https://doi.org/10.7759/cureus.88167","authors":["Aadhitya Sriram","N Maheswaran","Bose Sundan","Sriram Krishnamoorthy"],"tags":["Medicine","Context (archaeology)","Urology","Artificial intelligence","Paleontology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-17","doi":"https://doi.org/10.7759/cureus.88167","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7125963307","name":"Multi‐model Artificial Intelligence Evaluation in Sudden Sensorineural Hearing Loss","source":"openalex","abstract":"OBJECTIVE: To compare the diagnostic accuracy, linguistic clarity, and user satisfaction of three large language models (ChatGPT-4.0, Claude 3.7 Sonet, and OpenAI Mini 3) in managing sudden sensorineural hearing loss. STUDY DESIGN: Prospective, multi-domain comparative analysis using blinded expert evaluation. SETTING: Online artificial intelligence (AI) platforms accessed under standardized conditions. METHODS: Twenty-seven sudden sensorineural hearing loss-related questions-covering general knowledge, audiometric interpretation, and clinical case scenarios-were submitted to the three AI models. Responses were evaluated by 10 board-certified otolaryngologists using three validated tools: Quality Assessment of Medical Artificial Intelligence (QAMAI), Artificial Intelligence Performance Instrument (AIPI), and Artificial Intelligence Satisfaction and Performance Evaluation Questionnaire (AISPE-Q). Linguistic complexity was assessed using metrics such as word count, sentence length, lexical diversity, and clinical verb use. RESULTS: ChatGPT-4.0 demonstrated the highest scores in clinical accuracy (QAMAI: 4.57), completeness (4.53), and evaluator satisfaction (AISPE-Q: 94%). Claude 3.7 outperformed in clarity and sentence complexity, while OpenAI Mini 3 exhibited the highest lexical diversity and directive tone but scored lower overall. Inter-rater reliability was strong (intraclass correlation coefficient [ICC] > 0.85). Correlation analysis revealed a significant relationship between objective quality and subjective satisfaction (r > 0.76). CONCLUSION: ChatGPT-4.0 delivered the most clinically aligned and satisfactory responses, whereas Claude 3.7 provided linguistically refined outputs. Our findings support the context-specific application of hybrid large language model approaches in otolaryngology, particularly for patient education, diagnosis, and AI-driven triage. LEVEL OF EVIDENCE: 2-prospective comparative diagnostic accuracy study.","url":"https://doi.org/10.1002/ohn.70143","authors":["Aynur Aliyeva","Antiga Muradova","Ramil Hashimli","Togay Müderris"],"tags":["Medicine","Audiology","Artificial intelligence","Sensorineural hearing loss","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-28","doi":"https://doi.org/10.1002/ohn.70143","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4413907480","name":"Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach","source":"openalex","abstract":"Background: Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype–genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. Results: AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. Conclusions: AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations.","url":"https://doi.org/10.3390/jpm15090407","authors":["Nikola Ilić","Adrijan Sarajlija"],"tags":["Personalized medicine","Medicine","Data science","Computer science","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.3390/jpm15090407","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416307141","name":"Building Symbiotic Artificial Intelligence: Reviewing the AI Act for a Human-Centred, Principle-Based Framework","source":"openalex","abstract":"Abstract Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, we elicit four principles that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges are presented to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.","url":"https://doi.org/10.1007/s11023-025-09753-w","authors":["Miriana Calvano","Antonio Curci","Giuseppe Desolda","Andrea Esposito","Rosa Lanzilotti","Antonio Piccinno"],"tags":["Applications of artificial intelligence","Philosophy of science","Perspective (graphical)","Computer science","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-18","doi":"https://doi.org/10.1007/s11023-025-09753-w","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7116985164","name":"Applications of artificial intelligence in non–small cell lung cancer: from precision diagnosis to personalized prognosis and therapy","source":"openalex","abstract":"BACKGROUND: Non-small cell lung cancer (NSCLC) carries a major global burden. The rapid growth of multimodal medical data challenges conventional methods to deliver stable, transferable and interpretable decisions across heterogeneous longitudinal high dimensional inputs. METHODS: This review summarizes advances in artificial intelligence (AI) for NSCLC from 2023 to 2025 and outlines a translation-focused framework that links algorithmic progress to clinical utility. We survey thoracic imaging, digital pathology and multiomics together with evaluation practices and implementation guidance. We also adopt a critical perspective. RESULTS: Many high performing deep models remain black boxes, and popular post hoc explanations such as Grad CAM heatmaps are rarely validated for faithfulness or stability, which undermines clinician trust and limits use in high stakes decisions. To address this gap, we propose a minimum evidence package for explainability that comprises sanity checks, quantitative faithfulness tests such as deletion or insertion, ROAR or IROF and infidelity, stability analyses, concept level validation for example TCAV with statistical testing, and prospective human factors studies that demonstrate improved decisions without automation bias. Across modalities, evaluation has expanded beyond discrimination to include calibration, uncertainty quantification (UQ) and subgroup analyses across scanners, sites and populations. However, the evidence base remains constrained by retrospective single center designs, inconsistent external or temporal validation and limited decision curve analysis (DCA). Translational priorities include a staged validation ladder from technical to clinical to prospective deployment, alignment with Software as a Medical Device frameworks, interoperable governance, fairness and economic assessment, and validated explainability coupled with uncertainty aware selective workflows. CONCLUSIONS: Looking ahead, progress will depend on multimodal foundation models, causal and temporal modeling, and regulatory qualification of computable biomarkers with verified explanations, supported by multicenter prospective studies that demonstrate durable generalizability, clinical value and clinician trust.","url":"https://doi.org/10.1186/s12967-025-07591-z","authors":["Luyuan Chang","Haipeng Li","Wei Wu","X Liu","Jiaqi Yan","Zuo Chen","Huan Wu","Shilong Song"],"tags":["Computer science","Artificial intelligence","Machine learning","Precision medicine","Automation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-23","doi":"https://doi.org/10.1186/s12967-025-07591-z","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410176648","name":"From traditional to artificial intelligence-driven approaches: Revolutionizing personalized and precision nutrition in inflammatory bowel disease","source":"openalex","abstract":"Inflammatory bowel disease (IBD), comprising ulcerative colitis and Crohn's disease, is a chronic inflammatory condition with global prevalence and varying incidence. The IBD pathogenesis involves intricate interactions among genetic, host and environmental factors, leading to dysregulated immune responses and chronic intestinal inflammation. Alongside elevated levels of inflammatory cytokines and altered miRNAs expression, more studies highlight significant dysbiosis in both fecal and ileal microbiota of IBD patients. This dysbiosis is characterized by an increase in pro-inflammatory and mucin-degrading bacteria (e.g., Fusobacterium spp., Escherichia spp.) and a decline in short-chain fatty acids (SCFAs) -producing microbes (e.g., Roseburia spp., Faecalibacterium spp.) which play a protective role in gut health. Diet emerges as a key environmental factor influencing IBD onset and progression and recent advancements in\"omics\" technologies, such as genomics, transcriptomics, and metabolomics, provide a deeper understanding of the molecular interactions between genes, gut microbiota (GM) and nutrition. Finally, new technologies like artificial intelligence (AI), further enhance findings by enabling data integration and personalized dietary strategies. In this scenario, this review aims to summarize accumulating data on the effects of dietary interventions in IBD patients and introduce the role of artificial intelligence (AI) in facilitating precision dietary approaches to improve IBD management.","url":"https://doi.org/10.1016/j.clnesp.2025.05.012","authors":["Simone Baldi","Dilara Sarikaya","Sofia Lotti","Francesca Cuffaro","Dorian Fink","Barbara Colombini","Francesco Sofi","Amedeo Amedei"],"tags":["Medicine","Inflammatory bowel disease","Disease","Intensive care medicine","Inflammatory Bowel Diseases"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-07","doi":"https://doi.org/10.1016/j.clnesp.2025.05.012","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7124443394","name":"Can Generative Artificial Intelligence Effectively Enhance Students’ Mathematics Learning Outcomes?—A Meta-Analysis of Empirical Studies from 2023 to 2025","source":"openalex","abstract":"Generative artificial intelligence (GenAI) shows transformative potential in mathematics education. However, empirical findings remain inconsistent, and a systematic synthesis of its effects across distinct engagement dimensions is lacking. This preregistered meta-analysis (INPLASY2025110051) systematically reviewed 22 empirical studies (46 independent samples, N = 5232) published between 2023 and 2025. The results indicated that GenAI has a moderate positive impact on students’ mathematics learning outcomes (g = 0.534). Moderation analysis further revealed that the level of GenAI integration in teaching, sample size, and learning content are the primary factors influencing this effect. The study found that the effect was most pronounced under the creative transformation (CT) integration mode, was significant when applied to geometry learning, and was stronger in studies with small samples or small class sizes; collaborative learning approaches also significantly enhance these mathematics learning outcomes. By contrast, educational stage and intervention duration did not show significant moderating effects. The GRADE assessment indicated that while the overall evidence is supportive, the certainty of evidence is stronger for cognitive outcomes than for non-cognitive domains. The findings also offer a reference for future research on constructing a human–machine collaborative learning environment.","url":"https://doi.org/10.3390/educsci16010140","authors":["Baoxin Liu","Wenlan Zhang","Fangfang Wang"],"tags":["Moderation","Transformative learning","Mathematics education","Empirical research","Metacognition"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-16","doi":"https://doi.org/10.3390/educsci16010140","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406182406","name":"Artificial intelligence in the 21st century: the treasure hunt for systematic mining of natural products","source":"openalex","abstract":"Advancements in genome mining, high-throughput sequencing and experimental techniques have generated an enormous amount of data on natural products.This has led to the design and development of advanced machine learning (ML) and artificial intelligence (AI) algorithms which have simplified the search for novel natural products in the 21st century.These algorithms could effectively analyse the chemical structure of natural products and predict their biological function.They could also effectively analyse large sets of data in a sophisticated manner.In this context, this article reviews the various AI/ML algorithms employed in natural products-based drug discovery.Particular attention is paid to case studies employing AI tools in plant and microbial research.Challenges associated with the use of AI tools for natural products research have also been discussed.","url":"https://doi.org/10.18520/cs/v126/i1/19-35","authors":["Janani Manochkumar","Siva Ramamoorthy"],"tags":["Treasure","Natural (archaeology)","History","Archaeology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-10","doi":"https://doi.org/10.18520/cs/v126/i1/19-35","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414552918","name":"Towards responsible artificial intelligence in healthcare—getting real about real-world data and evidence","source":"openalex","abstract":"BACKGROUND: The use of real-world data (RWD) in artificial intelligence (AI) applications for healthcare offers unique opportunities but also poses complex challenges related to interpretability, transparency, safety, efficacy, bias, equity, privacy, ethics, accountability, and stakeholder engagement. METHODS: A multi-stakeholder expert panel comprising healthcare professionals, AI developers, policymakers, and other stakeholders was assembled. Their task was to identify critical issues and formulate consensus recommendations, focusing on the responsible use of RWD in healthcare AI. The panel's work involved an in-person conference and workshop and extensive deliberations over several months. RESULTS: The panel's findings revealed several critical challenges, including the necessity for data literacy and documentation, the identification and mitigation of bias, privacy and ethics considerations, and the absence of an accountability structure for stakeholder management. To address these, the panel proposed a series of recommendations, such as the adoption of metadata standards for RWD sources, the development of transparency frameworks and instructional labels likened to \"nutrition labels\" for AI applications, the provision of cross-disciplinary training materials, the implementation of bias detection and mitigation strategies, and the establishment of ongoing monitoring and update processes. CONCLUSION: Guidelines and resources focused on the responsible use of RWD in healthcare AI are essential for developing safe, effective, equitable, and trustworthy applications. The proposed recommendations provide a foundation for a comprehensive framework addressing the entire lifecycle of healthcare AI, emphasizing the importance of documentation, training, transparency, accountability, and multi-stakeholder engagement.","url":"https://doi.org/10.1093/jamia/ocaf133","authors":["Eileen Koski","Amar K. Das","Pei-Yun Hsueh","Anthony Solomonides","Amanda L. Joseph","Gyana Srivastava","Cheyenne Johnson","Joseph Kannry","Bilikis Oladimeji","Amy Price","Steven E. Labkoff","Gnana Bharathy","Baihan Lin","Douglas B. Fridsma","Lee A. Fleisher","Mónica López-González","Reva Singh","Mark G. Weiner","Robert Stolper","Russell Baris","Suzanne Sincavage","Tristan Naumann","T. A. Williams","Tien Thi Thuy Bui","Yuri Quintana"],"tags":["Trustworthiness","Computer science","Foundation (evidence)","Health care","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-26","doi":"https://doi.org/10.1093/jamia/ocaf133","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3130294919","name":"Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges","source":"openalex","abstract":"Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.","url":"https://doi.org/10.1109/access.2021.3061697","authors":["Mahmoud A. Albreem","Abdul Manan Sheikh","Mohammed H. Alsharif","Muzammil Jusoh","Mohd Najib Mohd Yasin"],"tags":["Computer science","The Internet","Internet of Things","Internet privacy","World Wide Web"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1109/access.2021.3061697","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4386126083","name":"Application of digital-intelligence technology in the processing of Chinese materia medica","source":"openalex","abstract":"Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.","url":"https://doi.org/10.3389/fphar.2023.1208055","authors":["Wanlong Zhang","Changhua Zhang","Lan Cao","Liang Fang","Weihua Xie","Liang Tao","Chen Chen","Ming Yang","Lingyun Zhong"],"tags":["Intellectualization","Digitization","Standardization","Materia medica","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-24","doi":"https://doi.org/10.3389/fphar.2023.1208055","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7117479576","name":"Artificial Intelligence in Intensive Care: An Overview of Systematic Reviews with Clinical Maturity and Readiness Mapping","source":"openalex","abstract":"Background: ICU care is time critical and data dense, making it a promising but high-risk setting for AI decision support when tools are weakly validated. ICU AI evidence is heterogeneous, with limited external validation, inconsistent clinically actionable reporting, and scarce real-world impact data, yielding fragmented review conclusions. We mapped five prespecified ICU domains and assessed clinical and implementation maturity to identify key translational gaps. Methods: We performed a PRIOR-aligned overview of systematic reviews with prespecified maturity constructs. PubMed, Embase, and Web of Science were searched (title and abstract) on 13 December 2025, supplemented by backward citation searching. Two reviewers screened and extracted data with arbitration, assessed the review-level risk of bias using ROBIS, and synthesized findings without meta-analysis using a SWiM-guided narrative prioritizing AUROC ranges. Results: We included 34 systematic reviews (2017–2025) across five ICU domains, dominated by prognostic and early warning applications, mostly in adult populations and commonly using EHR and multimodal inputs. Reporting focused on discrimination, with AUROC ranges roughly 0.54–0.99 for prognostic tasks and 0.64–0.99 for diagnostic tasks, while calibration and clinical utility were rarely addressed and overlap suggested partial dependence. Maturity signals clustered at low-to-intermediate levels, with no evidence for routine, and regulated CDS deployment at the review level. Conclusions: Review-level evidence indicates a translational gap between retrospective performance and clinically mature, safely deployable ICU AI, supporting priorities for external validation, prospective impact studies, standardized reporting including calibration, and governance-focused implementation.","url":"https://doi.org/10.3390/jcm15010185","authors":["Krzysztof Żerdziński","Julita Janiec","Kamil Jóźwik","Paweł Łajczak","Łukasz J. Krzych"],"tags":["Medicine","Systematic review","Early warning score","Maturity (psychological)","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-26","doi":"https://doi.org/10.3390/jcm15010185","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7129047016","name":"Current Applications and Future Perspectives of Artificial Intelligence in Face-Driven Orthodontics: A Scoping Review","source":"openalex","abstract":"Artificial Intelligence (AI) has introduced transformative possibilities in orthodontics by enhancing diagnostic precision, treatment planning, and aesthetic outcomes. In face-driven orthodontics, treatment objectives extend beyond achieving proper occlusion to optimizing facial balance and harmony. With the growing patient demand for aesthetic improvements, AI technologies enable clinicians to integrate facial analysis and dynamic soft-tissue evaluation into personalized treatment approaches. Research in this scoping review analyzed current applications of AI in face-driven orthodontics, focusing on diagnosis, soft-tissue assessment, and individualized treatment planning. A comprehensive search was conducted in PubMed and Scopus for studies published between 2021 and 2025. The review followed the PRISMA-ScR guidelines. Of 54 initially identified studies, 24 met the inclusion criteria after title, abstract, and full-text screening. Extracted data were organized according to the main application areas of AI in face-driven orthodontics. Most studies focused on AI-assisted facial analysis, 3D reconstruction, and treatment simulation. Deep learning models demonstrated high performance in soft-tissue prediction, aesthetic evaluation, and diagnostic accuracy. However, heterogeneity in datasets, a lack of standardized validation protocols, limited external validation across included studies and limited clinical applicability were identified as key limitations. AI-based facial analysis supports a shift toward individualized, aesthetics-oriented orthodontic planning. Although current evidence highlights its potential for improving diagnostic precision and treatment outcomes, further validation through large-scale clinical studies is essential for broader implementation in everyday practice.","url":"https://doi.org/10.3390/biomimetics11020146","authors":["Barbora Heribanová","Katarína Janáková","Juraj Tomášik","Daniela Tichá","Štefan Harsányi","Andrej Thurzo"],"tags":["Computer science","Transformative learning","Scopus","Artificial intelligence","Inclusion (mineral)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-16","doi":"https://doi.org/10.3390/biomimetics11020146","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4414068771","name":"Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions","source":"openalex","abstract":"Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum. This narrative review discusses the current and future applications of AI in ACHD, including imaging interpretation, electrocardiographic analysis, risk stratification, procedural planning, and long-term care management. AI has been demonstrated as being highly accurate in congenital anomaly detection by various imaging modalities, automating measurement, and improving diagnostic consistency. Moreover, AI has been utilized in electrocardiography to detect previously undetected defects and estimate arrhythmia risk. Risk-prediction models based on clinical and imaging information can estimate stroke, heart failure, and sudden cardiac death as outcomes, thereby informing personalized therapy choices. AI also contributes to surgery and interventional planning through three-dimensional (3D) modelling and image fusion, while AI-powered remote monitoring tools enable the detection of early signals of clinical deterioration. While these insights are encouraging, limitations in data availability, algorithmic bias, a lack of prospective validation, and integration issues remain to be addressed. Ethical considerations of transparency, privacy, and responsibility should also be highlighted. Thus, future initiatives should prioritize data sharing, explainability, and clinician training to facilitate the secure and effective use of AI. The appropriate integration of AI can enhance decision-making, improve efficiency, and deliver individualized, high-quality care to ACHD patients.","url":"https://doi.org/10.31083/rcm41523","authors":["Ibrahim Antoun","Ali Nizam","Armia Ebeid","M C Rajesh","Ahmed Abdelrazik","Mahmoud Eldesouky","Kaung Myat Thu","Joseph Barker","Georgia R. Layton","Mustafa Zakkar","Mokhtar Ibrahim","Kassem Safwan","Radek M Dibek","Riyaz Somani","G. André Ng","Aidan P. Bolger"],"tags":["Medicine","Artificial intelligence","Deep learning","Personalized medicine","Heart disease"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-28","doi":"https://doi.org/10.31083/rcm41523","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4401214532","name":"Enhancing Obstetric Ultrasonography With Artificial Intelligence in Resource-Limited Settings","source":"openalex","abstract":"Establishing gestational age (GA) is critical for guiding obstetric care and decision-making (including the timing of prenatal visits, laboratory testing, administration of medications, vaccinations and delivery) as well as neonatal care (including need for newborn resuscitation and neonatal intensive care).Obstetric ultrasonography is the criterion-standard tool for establishing pregnancy viability and dating, as well as for assessing fetal anatomy, growth, and well-being. 1 However, standard ultrasonography examination requires expensive equipment and skilled operators that are often not available in resource-limited settings.In this issue of JAMA, Stringer et al present promising findings from a prospective diagnostic study conducted in Lusaka, Zambia, and Chapel Hill, North Carolina.The study demonstrates that novice users, with no prior obstetrics ultrasonography training, using a low-cost, point-of-care artificial intelligence (AI) tool could estimate GA as accurately as credentialed sonographers using high-specification machines.2 In the study, 400 pregnant patients 18 years or older with a median age of 29 years, median body mass index of 25.9, and singleton, nonanomalous fetuses underwent blind sweeps of the maternal abdomen by novice users employing an AI-enabled device, as well as standard fetal biometry by trained sonographers using standard ultrasonography equipment.The blind sweeps were free-hand sweeps with a 2-dimensional probe approximately 10 seconds in length, including craniocaudal sweeps starting at the pubis symphysis and ending at the xyphoid process and lateral sweeps starting above the pubis and ending between the left and right uterine borders.GA was established by a transvaginal crown-rump length early in pregnancy.Patients were randomized to varying windows of GA for follow-up ultrasonography examination during a primary (14 0/7 to 27 6/7 weeks' gestation), secondary (20 0/7 to 36 6/7 weeks' gestation), and tertiary (37 0/7 to 40 6/7 weeks' gestation) window.The primary outcome measure","url":"https://doi.org/10.1001/jama.2024.14794","authors":["Alexis C. Gimovsky","Ahizechukwu C. Eke","Methodius G. Tuuli"],"tags":["Medicine","Ultrasonography","Resource (disambiguation)","Artificial intelligence","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.1001/jama.2024.14794","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410417117","name":"Economic implications of artificial intelligence-driven recommended systems in healthcare: a focus on neurological disorders","source":"openalex","abstract":"Introduction: The rapid advancement of Artificial Intelligence (AI)-driven recommendation systems in healthcare presents significant economic implications, particularly in the context of neurological disorders. These systems offer opportunities to enhance diagnostic accuracy, optimize resource allocation, and improve patient outcomes. However, conventional economic models fail to address the dynamic complexities of AI integration in healthcare, including market inefficiencies and stakeholder behaviors. Methods: To bridge this gap, we propose a Dynamic Equilibrium Model for Health Economics (DEHE), incorporating reinforcement learning and stochastic optimization. This model captures uncertainty in healthcare decision-making and includes dynamic pricing, behavioral incentives, and adaptive insurance premium mechanisms. Results: Our experimental results demonstrate that DEHE improves economic efficiency by optimizing AI-driven recommendations while balancing healthcare cost and accessibility. Through multi-agent simulations, the model shows strong real-world applicability and stability. It effectively addresses asymmetric information, moral hazard, and market dynamics. Discussion: This study offers a novel economic framework for integrating AI-driven systems in neurological healthcare. We recommend the adoption of adaptive policy mechanisms and stakeholder-specific incentives to enhance cost-effectiveness and equitable access. These insights contribute to the development of more sustainable and inclusive AI-based healthcare policies.","url":"https://doi.org/10.3389/fpubh.2025.1588270","authors":["Jing Zhang","Shihui Xiang","Li Li"],"tags":["Health care","Incentive","Stakeholder","Context (archaeology)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-15","doi":"https://doi.org/10.3389/fpubh.2025.1588270","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4416538550","name":"Artificial Intelligence in Autism Spectrum Disorder Diagnosis: A Scoping Review of Face, Voice, and Text Analysis Methods","source":"openalex","abstract":"Background: Autism is a complex neurodevelopmental condition affecting social interaction and behavior. Traditional diagnostic methods, relying on observational techniques and interviews conducted by trained professionals, remain the gold standard for ASD diagnosis. However, these methods can be time-consuming and may be influenced by subjective factors. Recent advancements in artificial intelligence (AI) offer promising approaches to augment existing methods, potentially enhancing efficiency and providing additional objective data through facial, vocal, and textual analysis. Objective: The objective of this study was to conduct a comprehensive review of artificial intelligence applications in autism spectrum disorder (ASD) diagnosis, specifically focusing on facial, vocal, and textual analysis methods. Methods: A comprehensive search was conducted in PubMed, Web of Science, Scopus, and Google Scholar. The findings were reported in accordance with the PRISMA checklist. Data were collated and summarized, and results were reported qualitatively, adopting a narrative synthesis approach. Results: In facial image analysis, deep learning algorithms demonstrated high accuracy in identifying autism-related facial features, algorithms such as Xception achieved 98% accuracy, while hybrid approaches like the combination of Random Forest (RF) and VGG16-MobileNet showed accuracy at 99%. Voice analysis studies utilized both traditional machine learning methods and advanced deep learning techniques, achieving accuracies between 70% and 98% in detecting atypical speech patterns and prosodic abnormalities associated with autism. Text-based analyses showed potential in identifying linguistic markers of autism through natural language processing techniques. Overall, Deep learning approaches were mainly employed in facial image analysis for autism diagnosis. In contrast, voice and text recognition studies utilized machine learning algorithms. Conclusions: This review demonstrates artificial intelligence's significant role in diagnosing autism spectrum disorder (ASD). These AI-driven approaches can complement traditional diagnostic methods, potentially leading to earlier interventions and improved outcomes for individuals with ASD.","url":"https://doi.org/10.1002/hsr2.71476","authors":["Fatemeh Mohammadi","Hassan Shahrokhi","Afsoon Asadzadeh","Saeed Pirmoradi","Ali Moghtader","Peyman Rezaei‐Hachesu"],"tags":["Autism spectrum disorder","Psychology","Artificial intelligence","Complement (music)","Autism"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-01","doi":"https://doi.org/10.1002/hsr2.71476","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7128543618","name":"Current challenges and the way forwards for regulatory databases of artificial intelligence as a medical device","source":"openalex","abstract":"Effective regulatory oversight is a key step in ensuring that artificial intelligence as a medical device (AIaMD) is safe in real-world clinical settings. In this Perspective, we provide insights from our experience working with international regulatory databases, informed by our recent research and the expertise of the multidisciplinary authorship team. We highlight four key challenges, discuss attempts to circumvent these limitations, and highlight emerging initiatives. Nevertheless, the underlying issue of the quality and availability of input data from regulatory databases remains. We discuss considerations for improving accessibility and transparency, outline key aspects for a next-generation regulatory data ecosystem for AIaMDs, and call on global stakeholders to come together and align efforts to develop a clear roadmap to accelerate safe innovation and improve outcomes for patients worldwide.","url":"https://doi.org/10.1038/s41746-026-02407-w","authors":["Ariel Yuhan Ong","Aditya U. Kale","Joe Antoun","Henry David Jeffry Hogg","Ben Hammond","Pearse A. Keane","Russell Pearson","Hugh Harvey","Alastair K. Denniston"],"tags":["Key (lock)","Multidisciplinary approach","Quality (philosophy)","Medical device","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-11","doi":"https://doi.org/10.1038/s41746-026-02407-w","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4407640086","name":"Correlation Between Artificial Intelligence Literacy and Artificial Intelligence Anxiety in Audiology Students","source":"openalex","abstract":"ABS TRACT Objective: Artificial intelligence (AI) is becoming increasingly important in health sciences.Therefore, it is essential to assess health science students' knowledge, literacy, awareness, attitudes, and anxiety regarding AI.Although AI literacy and AI anxiety have been studied separately, the relationship between them has not been adequately explored.This study aims to evaluate the AI literacy and AI anxiety levels of undergraduate audiology students and examine the relationship between these 2 variables.Material and Methods: A correlational study was conducted with 231 undergraduate audiology students.Data were collected using the Artificial Intelligence Literacy Scale (AILS) and Artificial Intelligence Anxiety Scale (AIAS).Results: The mean AILS score was 39.49±5.50, and the mean AIAS score was 46.13±12.31.Significant correlations were observed among various subscales (p<0.05) and between the total and subscale scores (p<0.05) of AILS and AIAS in audiology students.However, no significant relationship was found between the total scores of AIAS and AILS.Conclusion: The findings suggest that higher scores in the awareness and usage subscales of AI literacy are associated with lower AI anxiety.Therefore, integrating AI training into the audiology curriculum may enhance AI literacy and help reduce AI-related anxiety among students.","url":"https://doi.org/10.24179/kbbbbc.2026-117553","authors":["Berna Deniz Kuntman","Anı Parabakan Polat"],"tags":["Anxiety","Literacy","Psychology","Curriculum","Emotional intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.24179/kbbbbc.2026-117553","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7123588148","name":"Explainable Artificial Intelligence for Pulmonary Disease Detection from Chest Radiographs","source":"openalex","abstract":"The integration of Explainable Artificial Intelligence (XAI) with deep learning has revolutionized medical image analysis, which is now able to yield accurate and interpretable disease diagnosis. In spite of significant advances in the interpretation of chest X-ray (CXR) for diseases such as tuberculosis, pneumonia, and COVID-19, a number of issues remain to be solved in the fields of explainability, dataset diversity, and clinical trust. This review has comprehensively reviewed 27 research papers (2020–2025) that focus on the use of XAI in pulmonary imaging. It has mapped out the methods, datasets, visualization tools, interpretability results, and performance metrics of the papers. Moreover, it points out the gaps in research concerning the identification of diseases in multi-class, generalizability of models, and validation of the clinical environment, thus generating the next-stage ideas to move towards hybrid CNN–Transformer architectures and clinician-centered explainable AI.","url":"https://doi.org/10.1109/ictbig68706.2025.11323948","authors":["Prasanna Pabba","Venkata Sai Srija Chevuturi","Jeevani Sabbineni","Udvisha Samudrala","Mohammad Hussen Shaik","Sreenidhi Kotla"],"tags":["Interpretability","Generalizability theory","Artificial intelligence","Medicine","Identification (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-12","doi":"https://doi.org/10.1109/ictbig68706.2025.11323948","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4408719812","name":"Effectiveness of Artificial Intelligence–Based Platform in Administering Therapies for Children With Autism Spectrum Disorder: 12-Month Observational Study","source":"openalex","abstract":"Abstract Background A 12-month longitudinal observational study was conducted on 43 children aged 2‐18 years to evaluate the effectiveness of the CognitiveBotics artificial intelligence (AI)–based platform in conjunction with continuous therapy in improving therapeutic outcomes for children with autism spectrum disorder (ASD). Objective This study evaluates the CognitiveBotics software’s effectiveness in supporting children with ASD through structured, technology-assisted learning. The primary objectives include assessing user engagement, tracking progress, and measuring efficacy using standardized clinical assessments. Methods A 12-month observational study was conducted on children diagnosed with ASD using the CognitiveBotics AI-based platform. Standardized assessments, include the Childhood Autism Rating Scale (CARS), Vineland Social Maturity Scale, Developmental Screening Test, and Receptive Expressive Emergent Language Test (REEL), were conducted at baseline (T1) and at the endpoint (T2). All participants meeting the inclusion criteria were provided access to the platform and received standard therapy. Participants who consistently adhered to platform use as per the study protocol were classified as the intervention group, while those who did not maintain continuous platform use were designated as the control group. Additionally, caregivers received structured training, including web-based parent teaching sessions, reinforcement strategy training, and home-based activity guidance. Results Participants in the intervention group demonstrated statistically significant improvements across multiple scales. CARS scores reduced from 33.41 (SD 1.89) at T1 to 28.34 (SD 3.80) at T2 ( P <.001). Social age increased from 22.80 (SD 7.33) to 35.76 (SD 9.09; mean change: 12.96, 56.84% increase; P <.001). Social quotient increased from 53.26 (SD 11.84) to 64.75 (SD 16.12; mean change: 11.49, 21.57% increase; P <.001). Developmental age showed an improvement from 30.93 (SD 9.91) to 45.31 (SD 11.20; mean change: 14.38, 46.49% increase; P <.001), while developmental quotient increased from 70.94 (SD 10.95) to 81.33 (SD 16.85; mean change: 10.39, 14.65% increase; P <.001). REEL scores showed substantial improvements, with receptive language increasing by 56.22% ( P <.001) and expressive language by 59.93% ( P <.001). In the control group, while most psychometric parameters showed some improvements, they were not statistically significant. CARS scores decreased by 10.62% ( P =.06), social age increased by 52.27% ( P =.06), social quotient increased by 19.62% ( P =.12), developmental age increased by 44.88% ( P =.06), and developmental quotient increased by 11.23% ( P =.19). REEL receptive and expressive language increased by 34.69% ( P =.10) and 40.48% ( P =.054), respectively. Conclusions Overall, the platform was an effective supplement in enhancing therapeutic outcomes for children with ASD. This platform holds promise as a valuable tool for augmenting ASD therapies across cognitive, social, and developmental domains. Future development should prioritize expanding the product’s accessibility across various languages, ensuring cultural sensitivity and enhancing user-friendliness.","url":"https://doi.org/10.2196/70589","authors":["H. Atturu","S. Naraganti","Bharti Rao"],"tags":["Preprint","Autism spectrum disorder","Autism","Medicine","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-21","doi":"https://doi.org/10.2196/70589","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4406658975","name":"Current applications and challenges in large language models for patient care: a systematic review","source":"openalex","abstract":"BACKGROUND: The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care. METHODS: We systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose. RESULTS: We show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias. CONCLUSIONS: This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.","url":"https://doi.org/10.1038/s43856-024-00717-2","authors":["Felix Busch","Lena Hoffmann","Christopher Rueger","Elon H. C. van Dijk","Rawen Kader","Esteban Ortiz‐Prado","Marcus R. Makowski","Luca Saba","Martin Hadamitzky","Jakob Nikolas Kather","Daniel Truhn","Renato Cuocolo","Lisa C. Adams","Keno K. Bressem"],"tags":["Readability","Automatic summarization","Medicine","Medical education","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-21","doi":"https://doi.org/10.1038/s43856-024-00717-2","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410824364","name":"Artificial Intelligence-Based Models for Automated Bone Age Assessment from Posteroanterior Wrist X-Rays: A Systematic Review","source":"openalex","abstract":"Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle–Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., BoneXpert®, Physis®, VUNO Med®-BoneAge) achieved mean absolute errors of 2–31.8 months—significantly surpassing Greulich–Pyle and Tanner–Whitehouse benchmarks—and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards.","url":"https://doi.org/10.3390/app15115978","authors":["Isidro Miguel Martín Pérez","Sofia Bourhim","Sebastián Eustaquio Martín Pérez"],"tags":["Medicine","Medical physics","Wrist","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-26","doi":"https://doi.org/10.3390/app15115978","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7133191197","name":"Artificial Intelligence-Enhanced Flexible Sensors for Human Motion and Posture Sensing","source":"openalex","abstract":"In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human-computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human-machine interaction and health monitoring.","url":"https://doi.org/10.3390/s26051562","authors":["Yiru Jiang","Tianyiyi He"],"tags":["Artificial intelligence","Process (computing)","Computer science","Transformative learning","Motion (physics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-02","doi":"https://doi.org/10.3390/s26051562","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7118197163","name":"Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment","source":"openalex","abstract":"Accurate blood pressure (BP) monitoring is essential for preventing and managing cardiovascular disease. Advancements in materials science, medicine, flexible electronic, and artificial intelligence (AI) have enabled cuffless, unobtrusive BP monitoring systems, offering an alternative to traditional sphygmomanometers. However, extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources, power efficiency, and deployment scalability. This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring, emphasizing its potential for personalized, scalable, and accessible healthcare. We systematically analyze the end-to-end system architecture, from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments. We further discuss clinical validation metrics and implementation barriers and prospective strategies. To bridge lab-to-field translation, we propose an innovative \"sensor-model-deployment-assessment\" co-design framework. This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.","url":"https://doi.org/10.1007/s40820-025-02003-9","authors":["Yiming Zhang","Shirong Qiu","Kai Du","Shun Wu","Ting Xiang","Kenghao Zheng","Zijun Liu","Hanjie Chen","Nan Ji","Fa Wang","Weijia Wu","Yuan-Ting Zhang"],"tags":["Software deployment","Wearable computer","Computer science","Wearable technology","Bridge (graph theory)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-05","doi":"https://doi.org/10.1007/s40820-025-02003-9","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412519417","name":"Educational Competencies for Artificial Intelligence in Radiology: A Scoping Review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.acra.2025.06.044","authors":["Sunam Jassar","Zili Zhou","Sierra Leonard","Alaa Youssef","Linda Probyn","Kulamakan Kulasegaram","Scott Adams"],"tags":["Artificial intelligence","Computer science","Radiology","Data science","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.1016/j.acra.2025.06.044","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4409960845","name":"Sociotechnical imaginaries and public communication: Analytical framework and empirical illustration using the case of artificial intelligence","source":"openalex","abstract":"The concept of sociotechnical imaginaries (SIs) has been widely used and proven fruitful to understand diverging trajectories of technologies. While scholars have acknowledged the multi-layered materialisation of SIs and highlighted the importance of the communicative layer therein, this aspect has remained under-conceptualised. Therefore, we propose an analytical framework to better understand Sociotechnical Imaginaries in Public Communication (SIPCs), defined as publicly constructed visions of (un)desirable sociotechnical futures that guide action, mobilise resources and lay out trajectories for the materialisation or prevention of those futures. In this article, we first discuss relevant strands of research on SIs and public communication. We then lay out the analytical framework of SIPCs that enables the rigorous reconstruction and comparison of sociotechnical imaginaries in public and mediated communication. Finally, we illustrate the framework with examples from public communication about artificial intelligence, an emerging key technology in contemporary societies.","url":"https://doi.org/10.1177/13548565251338192","authors":["Saba Rebecca Brause","Mike S. Schäfer","Christian Katzenbach","Yishu Mao","Vanessa Richter","Jing Zeng"],"tags":["Sociotechnical system","Vision","Futures contract","Sociology","Epistemology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-04-28","doi":"https://doi.org/10.1177/13548565251338192","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W7117998583","name":"Revolutionizing Biomedical Engineering: A Bibliometric Analysis of Artificial Intelligence Applications and Trends","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-032-10016-0_10","authors":["Anber Abraheem Shlash Mohammad","Anber Abraheem Shlash Mohammad","Suleiman Ibrahim","Asokan Vasudevan","Khaleel Al- Daoud","Nawaf Alshdaifat","Abdullah Ibrahim Mohammad","Abdullah Ibrahim Mohammad","Wenchang Chen","Rajani Balakrishnan","J. Bamini"],"tags":["Transformative learning","Data science","Key (lock)","Computer science","Applications of artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1007/978-3-032-10016-0_10","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4415882333","name":"Artificial intelligence and the impact of the EU AI Act in business organizations","source":"openalex","abstract":"Abstract Artificial intelligence (AI) is transforming industries worldwide, and the e‐commerce sector is at the forefront of leveraging its capabilities to drive innovation and efficiency. The paper explores the integration of artificial intelligence in e‐commerce, focusing on the ethical and regulatory implications introduced by the EU AI Act. This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data‐driven decision‐making processes. The paper provides a comprehensive review of the AI landscape in Europe, analyzing the impact of the EU AI Act, particularly on small and medium‐sized enterprises and startups. Through a mixed‐methods approach, the study investigates how regulatory compliance may influence business innovation, market competitiveness, and consumer trust. The recommendations proposed aim to develop a trustworthy AI ecosystem that could stimulate long‐term growth and enhance the global positioning of small European businesses.","url":"https://doi.org/10.1002/aaai.70039","authors":["Marc Selgas-Cors","Renata Thiébaut"],"tags":["Software deployment","Trustworthiness","Automation","Legislature","Compliance (psychology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-04","doi":"https://doi.org/10.1002/aaai.70039","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4408123538","name":"Patho-Net: enhancing breast cancer classification using deep learning and explainable artificial intelligence","source":"openalex","abstract":"Breast cancer is a disorder affecting women globally, and hence an early and precise classification is the best possible treatment to increase the survival rate. However, the breast cancer classification faced difficulties in scalability, fixed-size input images, and overfitting on limited datasets. To tackle these issues, this work proposes a Patho-Net model for breast cancer classification that overcomes the problems of scalability in color normalization, integrates the Gated Recurrent Unit (GRU) network with the U-Net architecture to process images without the need for resizing and computational efficiency, and addresses the overfitting problems. The proposed model collects and normalizes histopathology images using automated reference image selection with the Reinhard method for color standardization. Also, the Enhanced Adaptive Non-Local Means (EANLM) filtering is utilized for noise removal to preserve image features. These preprocessed images undergo semantic segmentation to isolate specific parts of an image, followed by feature extraction using an Improved Gray Level Co-occurrence Matrix (I-GLCM) to reveal fine patterns and textures in images. These features serve as input into the classification U-Net model integrated with GRU networks to improve the model performance. Finally, the classification result is expanded, and XAI is used for clear visual explanations of the model's predictions. The proposed Patho-Net model, which uses the 100X BreakHis dataset, achieves an accuracy of 98.90% in the classification of breast cancer.","url":"https://doi.org/10.62347/xkfn1793","authors":["Kalappanaickenpatty Suriaprakasam Manojee"],"tags":["Net (polyhedron)","Breast cancer","Artificial intelligence","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.62347/xkfn1793","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4412994393","name":"Artificial Intelligence‐Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer","source":"openalex","abstract":"BACKGROUND: Neoadjuvant therapy (NAT) is a standard breast cancer treatment, but patient response varies significantly. Predictive markers can guide treatment decisions, yet their interpretation suffers from inter-pathologist variability due to breast cancer's complex histology and heterogeneity. Artificial intelligence (AI) applied to image-based omics offers potential to enhance pathological interpretation precision and consistency. METHODS: This review synthesizes existing literature on the application of AI in breast cancer pathology. We specifically focused on identifying and summarizing research that utilizes diverse histopathological features-including morphological characteristics, molecular markers, gene expression profiles, and multidimensional omics data-to predict NAT response in breast cancer patients. RESULTS: AI demonstrates significant capabilities in automatically recognizing histopathological patterns and predicting NAT efficacy. It shows promise as a tool for patient stratification in precision oncology. Research utilizing various pathological feature types (morphological, molecular, genomic, multi-omics) for NAT response prediction is actively evolving. While AI models integrating multi-omics features show potential, challenges remain in robustly predicting NAT outcomes. CONCLUSION: AI-based pathology represents a prospective and powerful decision-support tool for predicting breast cancer NAT response. Despite existing challenges, particularly with complex multi-omics models, AI holds great potential to assist clinical oncologists in optimizing future cancer treatment management.","url":"https://doi.org/10.1002/cam4.71132","authors":["Juan Ji","Fanglei Duan","Qiong Liao","Hao Wang","Shiwei Liu","Yang Liu","Zongyao Huang"],"tags":["Breast cancer","Omics","Cancer","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-01","doi":"https://doi.org/10.1002/cam4.71132","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4411166218","name":"Artificial Intelligence in Epilepsy: A Systemic Review","source":"openalex","abstract":"Diagnosing and managing epilepsy is difficult for doctors. Surgery can help some patients, but it often takes a long time to get there. This research looks at scientific studies to see if artificial intelligence and machine learning (ML) can be used to improve epilepsy treatment. In-depth research was conducted across PubMed, Google Scholar, Scopus, Wiley, Web of Science, and Microsoft Academia. This search focused on studies exploring the use of ML for diagnosing epilepsy, predicting treatment response, and predicting outcomes of epilepsy surgery. The search was limited to original English-language articles published between 2015 and 2022. This review examined 36 studies on using ML to predict epilepsy. The studies fell into four categories: general diagnosis (27), treatment outcome (3), identifying surgical candidates (2), and predicting surgical results (4). Researchers employed a diverse set of data, including symptoms and brain scans, alongside machine learning algorithms like support vector machines and convolutional neural networks, to construct their models. Some models achieved impressive results with areas under the curve reaching up to 0.99, but most studies were limited by small sample sizes and a lack of independent validation. ML shows potential for epilepsy treatment based on initial studies, but real-world use is restricted due to small sample sizes and the need for more validation from other studies. Large collaborative research efforts and data on long-term outcomes are essential before ML can be widely adopted by doctors and make a positive difference for epilepsy patients.","url":"https://doi.org/10.14581/jer.25002","authors":["Almuntasar Al-Breiki","Said Al-Sinani","Ahmed Elsharaawy","Mohamed Usama","Tariq Al‐Saadi"],"tags":["Epilepsy","Scopus","Artificial intelligence","Medicine","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-10","doi":"https://doi.org/10.14581/jer.25002","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W4410420135","name":"The DRAGON benchmark for clinical NLP","source":"openalex","abstract":"Artificial Intelligence can mitigate the global shortage of medical diagnostic personnel but requires large-scale annotated datasets to train clinical algorithms. Natural Language Processing (NLP), including Large Language Models (LLMs), shows great potential for annotating clinical data to facilitate algorithm development but remains underexplored due to a lack of public benchmarks. This study introduces the DRAGON challenge, a benchmark for clinical NLP with 28 tasks and 28,824 annotated medical reports from five Dutch care centers. It facilitates automated, large-scale, cost-effective data annotation. Foundational LLMs were pretrained using four million clinical reports from a sixth Dutch care center. Evaluations showed the superiority of domain-specific pretraining (DRAGON 2025 test score of 0.770) and mixed-domain pretraining (0.756), compared to general-domain pretraining (0.734, p < 0.005). While strong performance was achieved on 18/28 tasks, performance was subpar on 10/28 tasks, uncovering where innovations are needed. Benchmark, code, and foundational LLMs are publicly available.","url":"https://doi.org/10.1038/s41746-025-01626-x","authors":["Joeran S. Bosma","Koen Dercksen","Luc Builtjes","R. Johanes Andre","Christian Roest","Stefan J. Fransen","Constant R. Noordman","Mar Navarro-Padilla","Judith Lefkes","Natália Alves","Max de Grauw","Leander van Eekelen","Joey Spronck","Megan Schuurmans","Bram de Wilde","Ward Hendrix","Witali Aswolinskiy","Anindo Saha","Jasper J. Twilt","Daan Geijs","Jeroen Veltman","Derya Yakar","Maarten de Rooij","Francesco Ciompi","Alessa Hering","Jeroen Geerdink","Henkjan Huisman","Max J. J. de Grauw","Leander van Eekelen","Bram de Wilde","Quintin van Lohuizen","Michelle Stegeman","Karlijn Rutten","Inge M. E. Smit","Gijs Stultiens","Christiaan G. Overduin","Matthieu J. C. M. Rutten","Ernst Th. Scholten","Rachel S. van der Post","Katrien Grünberg","Shoko Vos","Elise M. G. Taken","Iris D. Nagtegaal","Anne Mickan","Miriam Groeneveld","Paul K. Gerke","James A. Meakin","M. G. Looijen-Salamon","Tijmen L. M. de Haas","Fabian Hoitsma","Marina D’Amato","Maarten de Rooij"],"tags":["Benchmark (surveying)","Artificial intelligence","Natural language processing","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-17","doi":"https://doi.org/10.1038/s41746-025-01626-x","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"oa:W3126686235","name":"The Road Towards 6G: A Comprehensive Survey","source":"openalex","abstract":"As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of “Is there any need for 6G?” by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of “What 6G may look like?.” This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.","url":"https://doi.org/10.1109/ojcoms.2021.3057679","authors":["Wei Jiang","Bin Han","Mohammad Asif Habibi","Hans Dieter Schotten"],"tags":["Key (lock)","Set (abstract data type)","Telecommunications","Open research","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1109/ojcoms.2021.3057679","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.2196/preprints.84104","name":"Ambient Artificial Intelligence Scribe Implementation in an Ambulatory Setting: Experience and Outcomes form a Single Medical Group (Preprint)","source":"crossref","abstract":"BACKGROUND Healthcare providers spend an excessive amount of time within electronic medical record (EMR) systems documenting patient encounters, often amounting to hours of time outside of regular office hours. This affects provider productivity and directly contributes to burnout. Artificial intelligence (AI) is becoming more integrated in medical care, including development of speech recognition and note writing algorithms. Limited studies exist on how these AI tools are impacting provider satisfaction, work-life balance, and patient satisfaction. OBJECTIVE The aims of this study were to assess the use of Ambient AI in medical documentation and the effects it has on note quality, time spent in EMR, provider burnout, and patient satisfaction. METHODS This is a prospective study with the Hawaii Pacific Health Medical Group (HPHMG) to pilot an AI note writer. Abridge was chosen as the AI platform and integrates with the Epic EMR. A goal of 75 providers for a 3-month pilot period was established from December 2024 through February 2025. Surveys were distributed to providers before and during the trial period. Epic Signal and Abridge data were used to correlate provider perceived outcomes with medical record recorded outcomes. Users were then divided into groups based on frequency of AI use (≥60% is high utilization). The primary outcome was time in documentation per appointment RESULTS A total of 80 providers were recruited with 79 completing the pilot. Over 25,000 notes were generated across 23 specialties. Providers who reported spending 8 hours or more per week on notes outside of clinic hours decreased by 75%. Signal metrics found a 21% decrease in time in notes per day and a 13% decrease in pajama time among high utilizers. There was an 8% decrease in the number of providers reporting burnout symptoms and a 50% decrease in provider perceived clinic note completion difficulty without a reported decrease in note quality. Providers reported 83.7% of notes required less than a quarter of the notes to be edited. Patient experience via “provider listened to me” scores, while improved (92.3% to 92.6%) was not significant. CONCLUSIONS This ambient AI note writer decreased time providers spent writing notes in clinic, decreased time in the EMR outside of work hours, and decreased symptoms of burnout without sacrificing note quality. Further study on cost effectiveness capabilities in relation to increasing patient census are ongoing as are long term studies regarding provider burnout.","url":"https://doi.org/10.2196/preprints.84104","authors":["Cameron Jason Harvey","Josiah Morita","William Huynh","Russell K. Woo","Jerome P. Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-07T13:50:25Z","doi":"10.2196/preprints.84104","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.21956/mep.22477.r41322","name":"Peer Review Report For: Exploring Filipino Medical Students’ Attitudes and Perceptions of Artificial Intelligence in Medical Education: A Mixed-Methods Study [version 2; peer review: 1 approved, 2 approved with reservations]","source":"crossref","abstract":"","url":"https://doi.org/10.21956/mep.22477.r41322","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T18:12:10Z","doi":"10.21956/mep.22477.r41322","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.70593/978-93-49910-91-1_1","name":"Understanding the structural shifts in financial services brought by the integration of artificial intelligence and digital infrastructure","source":"crossref","abstract":"The financial services industry is in the midst of a historic transformation right now, and what it looks like once this transformation is complete will be very different from the financial services industry of a decade, or even a year, ago. As if the aftershocks of the pandemic had not already revolutionized so many life and business practices across the globe, the subsequent war has prompted businesses to re-think long-held policies about outsourcing and near-shoring. Customers demand ever-improving speed in their interactions with financial services providers, as well as new and innovative products and features tailored to meet their needs. Disruptors are nipping at the heels of traditional banks and capital markets firms, stealing customers and revenue along the way. The Great Resignation, followed by the Great Regret, has compounded the longstanding issues of talent acquisition and retention that have plagued the industry. Regulatory requirements, embracing both compliance and risk management, are at an all-time high. As all of this is happening, we also witness the emergence of tools that can improve employee productivity, and the accelerated race to the cloud.","url":"https://doi.org/10.70593/978-93-49910-91-1_1","authors":["Ramesh Inala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T10:26:59Z","doi":"10.70593/978-93-49910-91-1_1","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.2139/ssrn.5064012","name":"Color Retinal Enhancement Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5064012","authors":["Varshitha Kesireddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-07T14:34:20Z","doi":"10.2139/ssrn.5064012","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1017/9781009522472.002","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009522472.002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-08T00:05:33Z","doi":"10.1017/9781009522472.002","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.2139/ssrn.5046238","name":"The Ethical Frontier of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5046238","authors":["KSS Kanhaiya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-29T14:42:23Z","doi":"10.2139/ssrn.5046238","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1007/978-3-031-99201-8_10","name":"Artificial Intelligence as an Instrument of Self-determination: Current Regulatory Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99201-8_10","authors":["Dragan Dakić"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T04:04:32Z","doi":"10.1007/978-3-031-99201-8_10","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.5772/intechopen.1007443","name":"Medical AI in the EU: Regulatory Considerations and Future Outlook","source":"crossref","abstract":"In many countries around the world, the healthcare sector is facing difficult problems: the aging population needs more care at the same time as the workforce is not growing, the cost of treatments is going up, and the more and more technical medical products are placing serious challenges to the expertise of the healthcare professionals. At the same time, the field of artificial intelligence (AI) is making big leaps, and naturally, AI is also suggested as a remedy to these problems. In this article, we discuss some of the ethical and legal problems facing AI in the healthcare field, with case study of European Union (EU) regulations and the local laws in one EU member state, Finland. We also look at some of the directions that the AI research in medicine will develop in the next 3–10 years. Especially, Large Language Models (LLMs) and image analysis are used as examples. The potential of AI is huge and the potential has already become a reality in many fields, but in medicine, there remain obstacles. We discuss both technical and regulatory questions related to the expansion of AI techniques used in the clinical environment.","url":"https://doi.org/10.5772/intechopen.1007443","authors":["Pertti Ranttila","Golnaz Sahebi","Elina Kontio","Jussi Salmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-24T06:51:34Z","doi":"10.5772/intechopen.1007443","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-23517-7.00015-0","name":"REMOVED: Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23517-7.00015-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T13:55:32Z","doi":"10.1016/b978-0-443-23517-7.00015-0","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-24788-0.01001-3","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24788-0.01001-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T04:49:54Z","doi":"10.1016/b978-0-443-24788-0.01001-3","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.64149/j.carcinog.24.5s.1221-1230","name":"Artificial Intelligence In Early Detection Of Gynecological Cancers.","source":"crossref","abstract":"Background: Artificial intelligence (AI) is entering the hot oncology series of clinical diagnostic tools. So far as gynecological malignancies are concerned, timely diagnosis is essential to enhancing survival as well as alleviating the load of therapy. The awareness, acceptance, and the practical issues of AI application in this discipline are, however, yet to be empirically investigated beyond doubt. Objective: This paper intends to determine the awareness, perception, adoption, and perceived challenges of using AI to detect gynecological cancers early among healthcare providers. Methods: A quantitative cross-sectional online survey of 280 medical workers was carried out among gynecologists, radiologists, oncologists, and AI specialists. A structured questionnaire, which was composed of 20 Likert scales, was designed and validated. The frequency of normality was tested by the Shapiro-Wilk test. Cronbach's Alpha was used to determine the internal consistency of the study, and Principal Component Analysis (PCA) was used to test construct validity. The data were evaluated in SPSS 25. Results: The statistical test conducted by Shapiro-Wilk shows that most of the items were not normally distributed, and it is also a characteristic of the ordinal-scale data quality. This, however, did not render the instrument unreliable because the internal consistency was superb, as shown by the figure of Cronbach's Alpha, which was 0.8808. We have seen that the first five components explained 32.63% of the variance according to PCA results, and this statistic implies that the questionnaire has captured more than one dimension of the perception about AI. Most of the respondents showed a positive attitude towards AI, with only technical barriers and implementation support identified as potential issues. Conclusion: The evidence demonstrates the incidence of knowledge and positive attitudes towards AI at the stage of detecting cancer in gynecology. The questionnaire was expressed to be an effective, reliable, and valid instrument in the measurement of such constructs. In order to adopt the AI tools in the clinical environment, the healthcare systems will have to close the infrastructural and educational gaps to pioneer the ethical and safe implementation practices..","url":"https://doi.org/10.64149/j.carcinog.24.5s.1221-1230","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-17T08:01:16Z","doi":"10.64149/j.carcinog.24.5s.1221-1230","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.34218/ijms_03_01_003","name":"ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC MEDICINE: LITERATURE REVIEW CONTRASTING DIFFERENTIAL ACCURACY FROM TEST REPORTS VERSUS SELF‐REPORTED SYMPTOMS AND IMPLICATIONS ON MEDICAL SPECIALTIES","source":"crossref","abstract":"Recent advances in large language models (LLMs) have shown that when fed with structured, tangible data -such as X-ray images, CT scans, bloodwork, and other machine-generated test reports -LLMs can achieve significantly higher diagnostic accuracy compared with when they rely on unstructured, self-reported patient symptoms.This paper reviews recent literature on AI applications in diagnostic reasoning, compares the performance of LLMs across different data modalities, and discusses which medical specialties are most vulnerable to future AI replacement.We present an index of 15 doctor specialties, highlighting the extent to which their diagnostic workflows (and thus their professional roles) rely on machine-generated data.We conclude that specialties dominated by image and laboratory report interpretation (e.g., radiology, pathology) are at higher risk, while those that require a more nuanced, context-rich synthesis of subjective data (e.g., internal medicine) are comparatively less vulnerable.","url":"https://doi.org/10.34218/ijms_03_01_003","authors":["Gunmeh Bhandari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-19T07:03:29Z","doi":"10.34218/ijms_03_01_003","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1093/pch/pxaf054","name":"The use of artificial intelligence in paediatric postgraduate medical education: A scoping review","source":"crossref","abstract":"Abstract Background Artificial intelligence (AI) encompasses a wide range of technologies that enable computers to mimic human intellect and is playing a significant role in healthcare education. Objectives To review the current applications of AI in paediatric postgraduate medical education programs. Methods A scoping review was conducted using a comprehensive literature search involving Ovid MEDLINE, Ovid Embase, and ERIC conducted from 1946 to May 27, 2024. Inclusion criteria involved articles that discussed AI in postgraduate paediatric education. Articles that addressed undergraduate education and other health professional education were excluded. Results Nine articles met the inclusion criteria. Four studies were conducted in the United States, two in China, and one each in France, Korea, and Canada. The studies discussed the use of AI in general paediatrics, paediatric oncology, developmental paediatrics, and paediatric genetics. AI was used as a clinical decision support tool in postgraduate training in seven studies with mixed results on the accuracy of AI predictions. One study used AI models to assess residents’ intubation competency, and another assessed the experiences and general perspectives of AI among paediatric residents and junior faculty. Conclusions Amongst included studies, AI was largely used as a clinical decision support tool in paediatric postgraduate education and the accuracy of AI predictions are improved when large amounts of data are used to train and tune the AI model. As such, physicians should be trained in AI use and take an active role in training and tuning AI models on an ongoing basis to ensure appropriate use of AI in healthcare.","url":"https://doi.org/10.1093/pch/pxaf054","authors":["Ajantha Nadarajah","Ghazal Malekzadeh","Savithiri Ratnapalan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-08T07:31:50Z","doi":"10.1093/pch/pxaf054","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/b978-0-323-91819-0.20001-8","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-91819-0.20001-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T11:47:58Z","doi":"10.1016/b978-0-323-91819-0.20001-8","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/qai63978.2025.00001","name":"Proceedings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qai63978.2025.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:56:01Z","doi":"10.1109/qai63978.2025.00001","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/icarai67046.2025.11137890","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icarai67046.2025.11137890","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-03T17:49:10Z","doi":"10.1109/icarai67046.2025.11137890","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-15504-8.00011-9","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-15504-8.00011-9","authors":["Himanshu Arora"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-29T15:16:45Z","doi":"10.1016/b978-0-443-15504-8.00011-9","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.4103/jigims.jigims_77_24","name":"Artificial intelligence in chronic pain management: A mini review","source":"crossref","abstract":"Abstract Chronic pain (CP) is the most common chronic disease posing a challenge to global healthcare, with a detrimental impact on individuals, families, and health systems. Diagnosing and managing CP is a challenging process with no standard protocol. Artificial intelligence’s (AI) role in CP and its management is in initial phase, but it has the ability to significantly ameliorate patient’s symptoms, reduce medical expense, and upgrade overall wellbeing. In this mini review we have conducted literature search using the databases PubMed, Google Scholar, Medline, Ovid, PMC, and Embase. The search string included four Mesh keywords: “artificial intelligence,” “chronic pain,” “chronic pain management,” and “machine learning.” AI is an innovative technique in the treatment of CP, with potential benefits in terms of improved diagnosis, treatment, and patient outcomes.","url":"https://doi.org/10.4103/jigims.jigims_77_24","authors":["Athira Ramesh","Amarjeet Kumar","Ajeet Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-29T23:00:43Z","doi":"10.4103/jigims.jigims_77_24","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.20944/preprints202505.1852.v1","name":"Conception of Intelligence and Some Misconceptions Concerning Artificial Intelligence","source":"crossref","abstract":"The current robots imbued with artificial states of cognition are nothing but intelligent machines without mindfulness. The systems are clever replicates of human agents but they lack the sheer power of true human cognition and consciousness—they are simply “automata”. We believe that mere intelligence is not something akin to conscious awareness. Nothing could still match the power of human creativity, thoughtfulness and imagination, nor do these artificial beings are capable of eliciting true human emotions, at least, for the time being. In this paper, we undertake a critique of AI in the light of eliciting its concepts by examining the myths and misconceptions surrounding the artificial intelligent systems and systems running on AI. We attempt to demystify the false notions that cloud our perceptions regarding the potentials of artificial general intelligence. Our thinking is aligned to the current goal of embodying machines with conscious behavior grounded on the philosophical foundations of embodied capacities beyond learning and language processing. To this end, we represent our views that we deem relevant to the current emerging confusions and rat races in the AI industry regarding the current state of development and design of machine consciousness.","url":"https://doi.org/10.20944/preprints202505.1852.v1","authors":["Sidharta Chatterjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-26T01:43:13Z","doi":"10.20944/preprints202505.1852.v1","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1201/9781003532156-2","name":"Artificial Intelligence in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532156-2","authors":["Rohit Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T17:31:46Z","doi":"10.1201/9781003532156-2","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/bdai66031.2025","name":"2025 8th International Conference on Big Data and Artificial Intelligence (BDAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bdai66031.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-13T20:57:32Z","doi":"10.1109/bdai66031.2025","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/raai67517.2025","name":"2025 5th International Conference on Robotics, Automation, and Artificial Intelligence (RAAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/raai67517.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-12T20:34:54Z","doi":"10.1109/raai67517.2025","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/cai64502.2025.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai64502.2025.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-07T17:47:34Z","doi":"10.1109/cai64502.2025.00004","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/icaidt66272.2025","name":"2025 2nd International Conference on Artificial Intelligence and Digital Technology (ICAIDT)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaidt66272.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-08T17:42:54Z","doi":"10.1109/icaidt66272.2025","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.3102/ip.25.2193532","name":"Utilizing Artificial Intelligence Models in Loneliness Item Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.3102/ip.25.2193532","authors":["Joshua Spieles"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-11T14:00:38Z","doi":"10.3102/ip.25.2193532","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/aaicv66571.2025","name":"2025 International Conference on Algorithm, Artificial Intelligence and Computer Vision (AAICV)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aaicv66571.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T18:42:04Z","doi":"10.1109/aaicv66571.2025","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.21037/jmai-24-95","name":"Application of artificial intelligence in the field of breast pathology diagnosis: narrative review","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-95","authors":["Areej M. Al Nemer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-20T01:30:00Z","doi":"10.21037/jmai-24-95","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1117/12.3048981","name":"Can artificial intelligence support less experienced radiologists in interpreting indeterminate mammographic screenings?","source":"crossref","abstract":"Artificial intelligence (AI) has emerged as a promising tool for mammography interpretation that could potentially support radiologists in effectively classifying indeterminate cases. This study analyzed data from 169 less experienced radiologists with less than a year of experience interpreting mammograms, who collectively interpreted 22,200 mammogram cases. These readers categorized cases using the Tabar Grading system, with grade 3 representing indeterminate. Radiologists interpreting >20 cases per week classified 24.8% of mammograms as indeterminate. For those interpreting 20-60 cases per week, this increased slightly to 26.0%. The highest rate was among radiologists handling 61-100 cases per week, with 29.4% classified as indeterminate. Among those reading 100+ cases per week, the percentage dropped slightly to 24.9%. The Globally-aware Multiple Instance Classifier (GMIC) AI model was fine-tuned using a locally acquired dataset to achieve an area under the receiver operating characteristic curve of 0.85+. The images classified as indeterminate by each reader were then fed into GMIC. We explored GMIC’s malignancy probabilities to assess the percentage of users with improved specificity without losing sensitivity. We considered lax, moderate, and strict thresholds. Overall, at the best threshold (lax), we observed an increase in specificity for 80%+ of readers without a loss in sensitivity. With moderate or strict thresholds, the effectiveness of AI in enhancing specificity without a loss in sensitivity diminished. This finding suggests that while AI is a valuable tool for improving diagnostic accuracy in indeterminate cases, optimizing AI settings is essential to maximize its benefits for less experienced readers.","url":"https://doi.org/10.1117/12.3048981","authors":["Vivian Bai","Zhengqiang Jiang","Warren Reed","Ziba Gandomkar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-13T20:59:32Z","doi":"10.1117/12.3048981","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/airc64931.2025","name":"2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/airc64931.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T17:41:51Z","doi":"10.1109/airc64931.2025","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/b978-0-443-26466-5.12001-x","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26466-5.12001-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T07:31:43Z","doi":"10.1016/b978-0-443-26466-5.12001-x","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1007/978-981-96-7202-8","name":"Artificial Intelligence of Everything and Sustainable Development","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-7202-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T01:42:09Z","doi":"10.1007/978-981-96-7202-8","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.2139/ssrn.5293108","name":"Artificial Intelligence and Enterprise Default Risk","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5293108","authors":["Lingyun Pan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-13T18:36:32Z","doi":"10.2139/ssrn.5293108","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.21956/mep.22477.r41643","name":"Peer Review Report For: Exploring Filipino Medical Students’ Attitudes and Perceptions of Artificial Intelligence in Medical Education: A Mixed-Methods Study [version 2; peer review: 1 approved, 2 approved with reservations]","source":"crossref","abstract":"","url":"https://doi.org/10.21956/mep.22477.r41643","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T18:17:06Z","doi":"10.21956/mep.22477.r41643","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.70593/978-93-7185-563-1_10","name":"Future Trends in AI-IoT Predictive Healthcare Technologies","source":"crossref","abstract":"The combination of Artificial Intelligence and Internet of Things is transforming the area of health care into the curative part of the reactive to the proactive and preventive care. The chapter is about the future trends that influence the AI-IoT-based predictive healthcare in the settings of real-time monitoring, predicting disease onset, personalized medicine, and predictive decision-support systems. It identifies innovations such as edge-AI wearables, digital twins, federated learning, blockchain security and autonomous clinical analytics. The chapter should take into account ethical considerations, data governance, interoperability requirements, and integration issues because of the implementation by large-scale implementations. The future demonstrates that smart innovations of AI-IoT will stimulate the creation of more proactive, scalable and patient-centered healthcare ecosystems.","url":"https://doi.org/10.70593/978-93-7185-563-1_10","authors":["MUTHUPANDI G","M.Mary Linda","Nandhakumar R","N. Ponnithish"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T15:52:54Z","doi":"10.70593/978-93-7185-563-1_10","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1016/j.artint.2025.104385","name":"Differentially private fair division","source":"crossref","abstract":"Fairness and privacy are two important concerns in social decision-making processes such as resource allocation . We initiate the study of privacy in fair division by investigating the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. On the other hand, we provide strong negative results for both fairness criteria when the adjacency notion allows the entire utility function of a single agent to change.","url":"https://doi.org/10.1016/j.artint.2025.104385","authors":["Pasin Manurangsi","Warut Suksompong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-13T11:51:24Z","doi":"10.1016/j.artint.2025.104385","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/idicaihei65991.2025.11379716","name":"Artificial Intelligence in Interview Preparation: A Framework for Enhancing Employability Skills","source":"crossref","abstract":"In the rapidly evolving landscape of education and career development, effective interview preparation plays a pivotal role in bridging the gap between academic training and professional success. Traditional methods, such as self-study and occasional mock interviews, often lack the personalization, real-time feedback, and scalability required to meet diverse learner needs, frequently resulting in heightened anxiety and diminished confidence. This paper introduces an innovative AI-driven interview preparation frame- work integrated into an educational platform, emphasizing the generation of tailored interview questions, detailed performance feedback, and the application of psychological principles to enhance both confidence and skill acquisition. The framework leverages advanced AI technologies, including natural language processing and machine learning, to simulate realistic interview environments and provide actionable insights. Complementary features, such as an AI-powered résumé builder, company research tools that offer strategic insights into recruitment practices, and job listing integration via APIs (e.g., LinkedIn), further contribute to holistic job readiness. To evaluate its effectiveness, a controlled study was conducted with 150 job-seeking students divided into three groups: AI-assisted, traditional non- AI methods, and personal mentoring. Results indicate significant improvements in communication skills, confidence levels, and overall interview performance for the AI group, often comparable to personal mentoring while offering superior scalability and accessibility. Psychological benefits, including reduced anxiety through iterative practice and reinforcement learning inspired feedback mechanisms, are also examined. This approach not only addresses existing educational gaps but also promotes equitable access to high-quality preparation tools, representing a transformative adadvancement in AI for education. By fostering personalized learning experiences, the proposed framework empowers learners to navigate competitive job markets more effectively, ultimately contributing to improved career outcomes and lifelong skill development.","url":"https://doi.org/10.1109/idicaihei65991.2025.11379716","authors":["Manisha Nirgude","Dipali Awasekar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T20:43:58Z","doi":"10.1109/idicaihei65991.2025.11379716","addedAt":"2026-09-01T01:47:54.709Z","updatedAt":"2026-09-01T01:47:54.709Z"},{"id":"doi:10.1109/aisp68263.2025.11396229","name":"Optimizing Customer Engagement in Multi-source E-commerce Retail Datasets Using Artificial Intelligence-Based Efficient Techniques","source":"crossref","abstract":"Customer engagement in multi-source e-commerce environments plays a vital role in the success of a business. It is one of the main factors that influence purchase behavior, brand loyalty, and overall customer satisfaction. Predicting customer engagement on e-commerce platforms is a fundamental task to personalize marketing, improve user experience, and increase sales. However, uncovering insights from multi-source retail datasets is hindered by the richness, complexity, variability, nonlinear behavioral patterns, and data imbalance of these datasets. This article introduces a new concept that combines artificial intelligence with data-driven techniques to yield optimal solutions for customer engagement prediction. The proposed framework in this paper comprises extensive preprocessing, categorical encoding, outlier detection, and feature selection through the Minimum Redundancy Maximum Relevance (MRMR) method. To solve the problem of class imbalance, SMOTE Tomek resampling technique is used, and then Min-Max scaling and train-test split are performed. Three models, such as Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) with Attention Mechanism, and a Hybrid XGBoost+LSTM architecture are created and evaluated by accuracy, precision, recall, F1-score, and ROC-AUC metrics. The hybrid model, according to empirical evidence, achieves the greatest accuracy of 97.89% on the Brazilian e-commerce dataset, whereas on the Amazon dataset it reaches 89.95%, thus, the hybrid model is far better than the individual models and conventional classifiers. The primary contribution of the research is its AI framework, which is robust, scalable, and compatible across datasets, thus, it becomes the main source of superior predictive performance and consequential insights for strategic decision-making in rapidly changing e-commerce environments and hence, a major breakthrough in customer behavior modeling and engagement optimization for real-life scenarios.","url":"https://doi.org/10.1109/aisp68263.2025.11396229","authors":["Anirudh Parupalli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T20:46:45Z","doi":"10.1109/aisp68263.2025.11396229","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-981-96-6863-2_2","name":"Review on Artificial Intelligence in the Environmental Monitoring","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-6863-2_2","authors":["Balendra V. S. Chauhan","Ajitanshu Vedrtnam","Kevin P. Wyche","Sneha Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T17:12:59Z","doi":"10.1007/978-981-96-6863-2_2","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/icaaic64647.2025.11330635","name":"A DWT-Based Framework for Medical Image Fusion","source":"crossref","abstract":"The combined use of complementary information in different imaging modalities provided by medical image fusion is important in clinical diagnosis by providing a single, more informative image. The given paper documents an effective method of fusion that is developed with the use of the Discrete Wavelet Transform (DWT) for combining MRI with CT and PET images. The aim is to increase diagnostic value, retaining both anatomical and functional information. The suggested approach is evaluated using common quality metrics. In MRI-CT fusion, the approach works with PSNR values between 40-60dB and SSIM of above 0.95, meaning near-lossless fusion along with high structural similarity. In the case of MRI-PET fusion, the PSNR goes only up to 16–18 dB, and SSIM is just up to 0.5-0.6, which nevertheless allows preserving useful functional contrast. These findings reveal the usefulness of the approach in using both structural and functional data under varying situations of fusion.","url":"https://doi.org/10.1109/icaaic64647.2025.11330635","authors":["C Chakri","C Bharath Simha Reddy","K Viswak Reddy","Bhavana V"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-20T20:37:40Z","doi":"10.1109/icaaic64647.2025.11330635","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.53388/hpm2025020","name":"Ethical aspects of implementing generative artificial intelligence in medical education: a narrative review","source":"crossref","abstract":"","url":"https://doi.org/10.53388/hpm2025020","authors":["Malik Sallam","Mohammed Sallam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T07:56:21Z","doi":"10.53388/hpm2025020","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/978-981-97-2938-8_1","name":"Transforming Healthcare: The Synergy of Telemedicine, Telehealth, and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-2938-8_1","authors":["Jyotir Moy Chatterjee","R. Sujatha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T16:13:47Z","doi":"10.1007/978-981-97-2938-8_1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1148/ryai.230560","name":"Privacy, Please: Safeguarding Medical Data in Imaging AI Using Differential Privacy Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.230560","authors":["Abhinav Suri","Ronald M. Summers"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-17T14:51:05Z","doi":"10.1148/ryai.230560","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.110428","name":"Implicit embedding based multi modal attention network for Cricket video summarization","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110428","authors":["Ipsita Pattnaik","Pulkit Narwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-06T06:18:54Z","doi":"10.1016/j.engappai.2025.110428","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-031-73880-7_13","name":"Artificial Intelligence, Territory, Town, and Anthropization Processes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-73880-7_13","authors":["Stefano Aragona"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-04T03:55:15Z","doi":"10.1007/978-3-031-73880-7_13","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-981-95-0508-1_3","name":"Philosophy and Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-0508-1_3","authors":["Hugo Luz dos Santos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-08T17:12:26Z","doi":"10.1007/978-981-95-0508-1_3","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.63163/jpehss.v3i4.865","name":"Artificial Intelligence in Medical Education: Mixed Methods Study in Pakistan (2025 Contexts)","source":"crossref","abstract":"The advancements in simulation systems, custom learning, and diagnostic techniques suggest the growing potential of AI in transforming how medicine is taught. von Storch et al. conducted a mixed methods analysis of the respondents’ familiarity and attitude toward the employment of AI in the curriculum. Ethics and job displacement concerns constituted another focus of the research. Functioning in addition to results was a thorough review of international literature and pilot evaluations that took place in 2023–2025. Awareness of AI was considerable, 95 percent of respondents in fact had heard of it, and support for its implementation in education was also significant with 75 percent of respondents supporting it. Nonetheless, there were concerns, primarily from a third of the respondents, regarding job loss and the ethical implications of healthcare AI. Global research and pilot projects like MedSimAI and MEDCO have documented the benefits of AI in educational administration but have sorely lacked in multicenter research. The outcomes showcase the necessity for the Low and Middle-Income countries (LMICs) to incorporate ethical considerations into the reform of educational curricula as diving competency gaps and changes to faculty training are essential. It certainly should be the future healthcare providers to be trained as they will, apart from employing AI in their practices, need to manage the ethical controversies its advancements and applications are likely to engender.","url":"https://doi.org/10.63163/jpehss.v3i4.865","authors":["Farooq Yousaf","Dr. Muhammad Hasnain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-13T11:42:57Z","doi":"10.63163/jpehss.v3i4.865","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/prmvai65741.2025.11108528","name":"Light-VM2D-UNet: A Lightweight UNet Enhanced with Mamba2D for Medical Image Segmentation","source":"crossref","abstract":"This paper addresses the fundamental trade-off between global long-range dependency modeling and local detail preservation in lightweight medical image segmentation models. Through systematic analysis of the limitations in spatial dependency capturing exhibited by the parallel vision Mamba (PVM) component in UltraLight VM-UNet procedure, we propose a novel PVM2DRes Block. This module integrates a 2D wavefront scanning strategy into the PVM unit while incorporating local contextual enhancement during parallel state space updates. The proposed approach enables multi-directional and multi-scale global-local feature interactions with maintained linear computational complexity. The resulting Light-VM2D-UNet achieves superior performance with merely 0.12 M parameters and 0.07 GFLOPs, demonstrating consistent improvements in accuracy and mIoU metrics across three benchmark datasets: ISIC-2017, ISIC-2018, and CVC-ClinicDB. Extensive experiments validate that our method not only enhances the modeling capability for long-range feature dependencies and fine-grained local details but also establishes the generalizability of integrating state space models with attention mechanisms. This work provides a more efficient and accurate technical pathway for intelligent diagnostic systems in resource-constrained environments.","url":"https://doi.org/10.1109/prmvai65741.2025.11108528","authors":["Zili Peng","Huang Tan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-18T19:38:14Z","doi":"10.1109/prmvai65741.2025.11108528","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icdsaai65575.2025.11011656","name":"AutoMed: Multi-Agent AI System for Personalized Medical Knowledge Retrieval and Summarization","source":"crossref","abstract":"In today’s fast-paced medical environment, researchers and healthcare practitioners frequently find it difficult to keep pace with the sheer number of new articles and studies. Conventional search techniques, though exhaustive, tend to produce either too many irrelevant results or miss important information. To fix this, The research proposes a semi-autonomous, multi-agent and AI-driven solution that simplifies finding and synthesizing medical knowledge. By using agents to refine user queries, retrieve and filter articles from established databases like PubMed and PMC and generate summaries, the system makes searching more targeted. Breaking down complex queries makes it more specific, while intelligent filtering zooms in on the most relevant results. Summaries with source attribution are concise and trustworthy. Plus the system does user preference learning that sends users periodic updates on their areas of interest. Built-in explainability features adds to transparency and credibility so users can trust the curated outputs. Overall, this AI-driven solution is a flexible and time-saving tool for evidence-based decision making in clinical and research settings.","url":"https://doi.org/10.1109/icdsaai65575.2025.11011656","authors":["Manav Israni","Shruti Renuse","Premanand V"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-29T17:06:27Z","doi":"10.1109/icdsaai65575.2025.11011656","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.4103/jcor.jcor_227_24","name":"The role of artificial intelligence in medical laboratory technology","source":"crossref","abstract":"Dear Editor, I am writing to highlight the transformative role of artificial intelligence (AI) in the field of medical laboratory technology. As diagnostic medicine advances, the integration of AI is redefining traditional laboratory practices, enhancing diagnostic precision, and optimizing operational efficiency. AI-powered systems are now essential in automating routine laboratory tasks, such as image analysis in histopathology, quantification in hematology, and molecular diagnostics. Current quality control is mostly based on manual assessment, which is wasteful and subjective. For instance, AI algorithms can rapidly analyze digital slides, identifying subtle abnormalities with remarkable accuracy, thereby assisting pathologists in early disease detection.[1] ML approaches use algorithms to learn and predict AMR characteristics directly from sequenced bacterial genomes, allowing for speedy and reliable results. AI’s capacity for big data analysis is revolutionizing laboratory operations. Predictive analytics derived from laboratory data allow for efficient resource allocation, reduced turnaround times, and improved patient outcomes. Laboratories can now adopt AI-driven quality control systems, which continuously monitor and ensure the reliability of test results.[2] Despite these advancements, challenges persist. The integration of AI requires substantial investment in infrastructure, training, and regulatory frameworks to ensure patient safety and data privacy.[3] The acceptance of AI by laboratory professionals remains a critical factor, necessitating a shift in educational curricula to prepare future technologists for AI-enhanced environments.[4] As AI continues to evolve, its potential to augment human expertise rather than replace it should be emphasized. By fostering collaborations between AI developers and laboratory professionals, we can unlock unprecedented opportunities in diagnostics and patient care.[5] In conclusion, I urge the scientific community and policymakers to prioritize AI adoption in medical laboratory technology, ensuring equitable access to these innovations globally. Together, we can advance laboratory medicine into a new era of precision and efficiency. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.","url":"https://doi.org/10.4103/jcor.jcor_227_24","authors":["Yugeshwari R. Tiwade","Obaid Noman","Sweta Dilip Bahadure"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-28T16:01:19Z","doi":"10.4103/jcor.jcor_227_24","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/ictai66417.2025","name":"2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ictai66417.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-15T18:37:21Z","doi":"10.1109/ictai66417.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-031-86905-1","name":"Digital Humanism","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86905-1","authors":["Hannes Werthner"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-31T11:50:58Z","doi":"10.1007/978-3-031-86905-1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1038/s41598-025-93433-3","name":"Co-evolution model of traffic travel and disease transmission under limited resources","source":"crossref","abstract":"The co-evolution mechanisms between traffic mobility and disease transmission under resource constraints remain poorly understood. This study proposes a two-layer transportation network model integrating the Susceptible-Infectious-Susceptible (SIS) epidemic framework to address this gap. The model incorporates critical factors such as total medical resources, inter-network infection delays, travel willingness, and network topology. Through simulations, we demonstrate that increasing medical resources significantly reduces infection scale during outbreaks, while prolonging inter-network delays slows transmission rates but extends epidemic persistence. Complex network topologies amplify the impact of travel behavior on disease spread, and multi-factor interventions (e.g., combined resource allocation and delay extension) outperform single-factor controls in suppressing transmission. Furthermore, reducing network connectivity (lower average degree) proves effective in mitigating outbreaks, especially under low travel willingness. These findings highlight the necessity of coordinated policies that leverage resource optimization, travel regulation, and network simplification to manage epidemics. This work provides actionable insights for policymakers to design efficient epidemic control strategies in transportation-dependent societies.","url":"https://doi.org/10.1038/s41598-025-93433-3","authors":["Zhanhao Liang","Kadyrkulova Kyial Kudayberdievna","Guijun wu","Zhantu Liang","Batyrkanov Jenish Isakunovich","Wei Xiong","Wei Meng","Yukai Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-12T07:45:16Z","doi":"10.1038/s41598-025-93433-3","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.4324/9781003608042-4","name":"The Interplay of Artificial Intelligence and Human-Centered Management Practices","source":"crossref","abstract":"The integration of Artificial Intelligence (AI) into organizational processes is reshaping management paradigms, offering opportunities for efficiency while challenging human-centric values. This chapter explores the interplay between AI and human-centered management, emphasizing the risks of reduced intrinsic motivation, weakened interpersonal relationships, and cultural disruptions. Drawing on theories of intrinsic motivation, human relations, and organizational culture, the analysis highlights the tensions between automation and human-centric practices. The chapter proposes actionable strategies, such as fostering interpersonal connections, establishing ethical AI governance, and redesigning managerial roles, to harmonize AI integration with human values. By balancing technological advancements with empathy and ethical considerations, organizations can create resilient ecosystems that thrive in an era of rapid innovation. This study explores the dynamic relationship between artificial intelligence (AI) and human-centered management practices. It highlights how AI can enhance decision-making while preserving empathy, ethics, and employee well-being. The integration fosters a balanced approach to innovation and human values in organizational leadership.","url":"https://doi.org/10.4324/9781003608042-4","authors":["Chiheb Eddine Inoubli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-26T16:22:41Z","doi":"10.4324/9781003608042-4","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-33414-6.00018-6","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33414-6.00018-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T14:26:49Z","doi":"10.1016/b978-0-443-33414-6.00018-6","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icecai66283.2025","name":"2025 6th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icecai66283.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-29T17:52:14Z","doi":"10.1109/icecai66283.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/c2023-0-51379-5","name":"Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2023-0-51379-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T12:53:32Z","doi":"10.1016/c2023-0-51379-5","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aiea66061.2025","name":"2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiea66061.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T17:24:39Z","doi":"10.1109/aiea66061.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.110580","name":"Intelligent evaluation of pavement friction at high speeds with artificial intelligence powered three-dimensional laser imaging technology","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110580","authors":["Guolong Wang","Kelvin C.P. Wang","Guangwei Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-23T13:45:04Z","doi":"10.1016/j.engappai.2025.110580","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/qpain66474.2025","name":"2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qpain66474.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-29T17:52:14Z","doi":"10.1109/qpain66474.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-30046-2.00021-1","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30046-2.00021-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-22T09:30:28Z","doi":"10.1016/b978-0-443-30046-2.00021-1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aipe67885.2025","name":"2025 3rd International Conference on Artificial Intelligence and Power Engineering (AIPE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aipe67885.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T19:51:51Z","doi":"10.1109/aipe67885.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-031-90271-0_43","name":"Explainable Artificial Intelligence in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-90271-0_43","authors":["Sara Jasen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T22:50:27Z","doi":"10.1007/978-3-031-90271-0_43","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icaita67588.2025","name":"2025 7th International Conference on Artificial Intelligence Technologies and Applications (ICAITA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaita67588.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-08T17:42:40Z","doi":"10.1109/icaita67588.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-23517-7.00013-7","name":"REMOVED: Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23517-7.00013-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T13:55:30Z","doi":"10.1016/b978-0-443-23517-7.00013-7","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/ai-si66213.2025","name":"2025 International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai-si66213.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:56:57Z","doi":"10.1109/ai-si66213.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1117/12.3068594","name":"A RAG-based approach for medical question answering using knowledge graphs and pretrained models","source":"crossref","abstract":"With the rapid development of artificial intelligence technologies, medical question-answering systems that integrate knowledge graphs and large models have shown great potential in the field of smart healthcare. This paper proposes a medical question-answering system based on a Knowledge Graph and Retrieval-Augmented Generation (RAG) framework. The system combines Named Entity Recognition (NER) and intent recognition techniques to perform reasoning using the knowledge graph to generate accurate medical answers. The system first preprocesses medical texts and applies rule-based classification to train the intent classification and NER models, which then extract relevant information from the knowledge graph to generate query statements. Based on the generated queries, the system combines the user's query with the knowledge graph retrieval results and uses a large model to generate the final answer. Experimental results show that the system can provide efficient and accurate answers for medical question-answering tasks, especially in specific medical domains, demonstrating good applicability.","url":"https://doi.org/10.1117/12.3068594","authors":["He Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-24T16:30:15Z","doi":"10.1117/12.3068594","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aibdf67964.2025","name":"2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aibdf67964.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-24T19:47:00Z","doi":"10.1109/aibdf67964.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1002/9781394411368","name":"The Ethics of Artificial Intelligence","source":"crossref","abstract":"The Ethics of Artificial Intelligence discusses the need for ethics accompanying developments in artificial intelligence, from the point of view of different disciplinary fields and sectors of activity. Artificial intelligence is profoundly restructuring our practices, creating new methods and significantly influencing the way we think and interact, at the level of individuals, organizations and societies, whether in our private, public or professional lives. This book begins with a rather conceptual approach, particularly focusing on the possible future of AI. It then highlights the urgent need to establish an ethical framework for the uses associated with AI, illustrating two booming sectors of activity. Finally, it discusses the ethics of AI in professional sectors that are undergoing major changes because of the digitization of their activities.","url":"https://doi.org/10.1002/9781394411368","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-19T21:33:13Z","doi":"10.1002/9781394411368","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.56472/iccsaiml25-133","name":"Artificial Intelligence in Finance: Transforming Accounting for Strategic Agility","source":"crossref","abstract":"Traditional accounting systems, which are heavily reliant on manual processing and retrospective analysis, are unable to handle the growing complexity, volume, and speed of financial data. Organizations face delayed reporting, compliance risks, and reduced strategic agility as a result. This paper examines the role of artificial intelligence (AI) in transforming five key accounting domains: financial accounting, management accounting, auditing, tax compliance, and pricing optimization. AI’s capabilities in automation, predictive analytics, risk detection, and real-time decision-making are explored through case studies and industry analysis. Findings show that AI reduces manual errors by 60%, accelerates financial closes by 30%, improves audit anomaly detection by over 40%, and enhances forecasting accuracy by up to 25%. AI also shortens tax compliance processing and enables dynamic pricing strategies that boost competitiveness. The results highlight AI’s potential to shift finance professionals from transactional roles to strategic leadership positions, reshaping the accounting field to drive innovation, governance, and sustainable business growth","url":"https://doi.org/10.56472/iccsaiml25-133","authors":["Venkata Khajit Varma Vadapalli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-11T11:18:15Z","doi":"10.56472/iccsaiml25-133","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.69899/limes-plus-en-24212-3099r","name":"ETHICAL IMPLICATIONS AND SOCIAL CHALLENGES OF ARTIFICIAL INTELLIGENCE DEVELOPMENT TOWARDS ARTIFICIAL GENERAL INTELLIGENCE","source":"crossref","abstract":"This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.","url":"https://doi.org/10.69899/limes-plus-en-24212-3099r","authors":["Viktor Radun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-16T10:19:06Z","doi":"10.69899/limes-plus-en-24212-3099r","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1201/9781003370659-1","name":"The Role of Artificial Intelligence in Transforming Aerospace and Engineering","source":"crossref","abstract":"This chapter deals with the transformation of aerospace engineering and related industries by Artificial Intelligence (AI). It provides a historical perspective of AI from ancient mythology to modern-day technologies. Artificial intelligence can be classified into three categories: narrow, general, and superintelligence – each with its own capabilities and potential for future developments. Furthermore, there are different techniques used for creating intelligent systems including augmented programming, reinforcement learning as well as neural networks. The use cases of AI span various fields, including aerospace engineering, where it is applied in autonomous flight, predictive maintenance, and improved manufacturing processes. Other areas such as safety, efficiency, innovation, data security, and the ethical impacts on the workforce are discussed as well. Additionally, cloud computing introduces new opportunities, including simulations, data analytics, and predictive maintenance, which are transforming aerospace engineering and related industries. It enhances aircraft performance and efficiency while revolutionizing industry operations by promoting unprecedented levels of creativity and flexibility.","url":"https://doi.org/10.1201/9781003370659-1","authors":["Alperen Tekay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-01T08:29:38Z","doi":"10.1201/9781003370659-1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2024.109560","name":"A deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109560","authors":["Saksham Mittal","Mohammad Wazid","Devesh Pratap Singh","Ashok Kumar Das","M. Shamim Hossain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-04T12:09:50Z","doi":"10.1016/j.engappai.2024.109560","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.112335","name":"Beyond the horizon: A comprehensive analysis of artificial intelligence-based weather forecasting models","source":"crossref","abstract":"The field of Artificial Intelligence (AI)-based weather forecasting is growing rapidly, with continuous progress in model development, techniques, and performance improvements. This paper provides a comprehensive overview of AI-based weather forecasting models, focusing on their current status, challenges, and directions for further development. A review of more than 40 models, primarily proposed after 2015, underscores the importance of critically examining various aspects of AI-based forecasting. Unlike previous reviews that targeted only a limited number of models or features, this study addresses a complete set of aspects and analyzes existing challenges from multiple perspectives. These aspects include the Machine Learning (ML) and Deep Learning (DL) methods used, datasets, predictand parameters, overfitting, and capability for forecasting extreme weather, lead time, spatiotemporal scale, performance criteria, overfitting, data assimilation, data-driven models, and the analysis of state-of-the-art (SOTA) models such as FengWu, ClimaX, Pangu-Weather, FourCastNet, GraphCast, GenCast, and Artificial Intelligence Forecasting System (AIFS) from various viewpoints. The review also discusses current challenges, including limited historical data and data quality, small-scale weather forecasting, model explainability, uncertainty, extreme weather prediction, physical constraints, temporal adaptation, and generalization, and outlines potential future directions.","url":"https://doi.org/10.1016/j.engappai.2025.112335","authors":["Saeid Haji-Aghajany","Witold Rohm","Piotr Lipinski","Maciej Kryza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T00:18:36Z","doi":"10.1016/j.engappai.2025.112335","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.20944/preprints202505.1277.v1","name":"Psychology and Artificial Intelligence: Reconceptualization, Integration, and Platonic Intelligence","source":"crossref","abstract":"The present paper first reviews those psychological concepts used in artificial intelligence. The embedding of psychological properties and mind in artificial intelligence has advantages and challenges. Instead of doing philosophical enquiries, we assume as our working hypothesis that machine intelligence is a kind of platonic reality which needs working definitions in AI modeling. It argues that following Gödel and Tarski, Turing test enriched Artificial intelligence, resulting a new kind of independent results, which refers to machine intelligence. Machine intelligence is dual with human intelligence.","url":"https://doi.org/10.20944/preprints202505.1277.v1","authors":["Yingrui Yang","Hongbin Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-19T02:18:33Z","doi":"10.20944/preprints202505.1277.v1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/acdsa65407.2025.11166431","name":"A Hybrid Deep Learning Approach for Improving Medical Image Captioning Using Convolutional Vision Transformer","source":"crossref","abstract":"The utilization of Deep Learning techniques, particularly computer vision (CV) and natural language processing, has led to significant advancements in the analysis of large-scale medical data, enhancing diagnosis, treatment planning, and management efficiency. In this context, medical image captioning (MIC) has emerged as a critical research area aimed at the automatic generation of clinically accurate reports from medical images. While convolutional neural networks (CNNs) and language transformers have been widely used in MIC models at the encoder and decoder sides respectively, the adoption of vision transformers (ViTs) for visual feature extraction remains limited. Moreover, existing MIC studies have largely overlooked hybrid architectures that introduce convolutions into the vision transformer architecture. This study proposes the integration of convolutional vision transformer (CvT) into MIC tasks, leveraging the local feature extraction strength of convolutions with the global context modeling capabilities of transformers. The objective is to evaluate the effectiveness of CvT in improving the quality and clinical relevance of generated medical reports through its multimodal compatibility and scalability. To the best of our knowledge, this represents one of the first attempts to explore convolution-transformer hybrid methods for medical image captioning. The proposed approach is evaluated on two public chest X-ray benchmark datasets, IU X-Ray and MIMICCXR, using natural language processing and clinical efficacy metrics (CE). The results demonstrate significantly improved CE results, with 7.9% and 8.3% increase in F1-score and Precision respectively on MIMIC-CXR, indicating better clinical feature representation from X-Ray images.","url":"https://doi.org/10.1109/acdsa65407.2025.11166431","authors":["Wisam Ramadan","Bahriye Akay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-24T17:31:35Z","doi":"10.1109/acdsa65407.2025.11166431","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.70593/978-93-7185-563-1_2","name":"AI-Driven Early Detection of ardiovascular Diseases","source":"crossref","abstract":"Rohith Ambadi S is currently pursuing his Ph.D. in Computational Fluid Dynamics at Kalasalingam Academy of Research and Education. He received his B.E. degree in Mechanical and Automation Engineering and M.E. degree in Cryogenic Engineering from PSN College of Engineering and Technology. He is presently working as an Assistant Professor in the Department of Mechanical Engineering at PSN College of Engineering and Technology, Tirunelveli, India. He is a researcher and academician with strong expertise in Computational Fluid Dynamics, supercritical CO₂ centrifugal compressors, cryogenic systems, nanofluids, and thermal engineering. Driven by a passion for research and innovation, he has contributed to high-impact research with publications in reputed journals including Physics of Fluids and Materials Today: Proceedings. He is also a co-inventor of a registered Indian design patent on an AI and IoT-based sustainable smart farming agricultural robot, reflecting his interdisciplinary approach toward engineering solutions. His blend of advanced research, teaching experience, and innovation positions him as a committed professional contributing to both academia and technological advancement.","url":"https://doi.org/10.70593/978-93-7185-563-1_2","authors":["MUTHUPANDI G","Nandhakumar R","Vishnu B","Rohith Ambadi S"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T15:52:54Z","doi":"10.70593/978-93-7185-563-1_2","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-13816-4.00018-8","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13816-4.00018-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T18:04:26Z","doi":"10.1016/b978-0-443-13816-4.00018-8","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.36676/978-81-980948-7-2","name":"Artificial Intelligence in Healthcare: A Practical Guide","source":"crossref","abstract":"This book, Artificial Intelligence in Healthcare: A Practical Guide, offers a comprehensive and practical exploration of the transformative role of Artificial Intelligence (AI) in the modern healthcare ecosystem. It traverses the evolution, integration, and application of AI technologies—from foundational concepts like machine learning, deep learning, natural language processing, and computer vision to their real-world deployment in diagnostics, imaging, personalized medicine, robotics, and healthcare administration. The book underscores how AI-driven systems enhance clinical decision-making, enable early and accurate diagnosis, streamline hospital operations, and support personalized patient care through data-driven insights. It also examines the digital transformation of healthcare, exploring how big data, cloud computing, and the Internet of Things (IoT) synergistically drive AI adoption. Each chapter reflects on the use of AI across medical domains such as radiology, pathology, genomics, surgery, and public health. The author critically addresses key challenges including algorithmic bias, data privacy, regulatory compliance, and ethical implications, while highlighting the future potential of technologies like explainable AI, federated learning, and quantum computing. Designed for practitioners, researchers, and students alike, the book serves as both an academic reference and a field guide, equipping readers with the knowledge and vision necessary to navigate and contribute to the rapidly evolving landscape of AI in healthcare.","url":"https://doi.org/10.36676/978-81-980948-7-2","authors":["Akshar Parshubhai Patel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-21T18:05:49Z","doi":"10.36676/978-81-980948-7-2","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.65522/upwaybooks.9781917916936","name":"ARTIFICIAL INTELLIGENCE: Mirroring the Mind, Mastering the World","source":"crossref","abstract":"This book demystifies Artificial Intelligence for everyone. Standing at a pivotal moment in history, we must understand AI beyond hype and fear. From Turing's early dreams to today's large language models, this accessible guide explores how AI works, its successes, and challenges like bias and ethics. Not a technical manual, it's essential reading for students, leaders, policymakers, and anyone navigating our AI-transformed world. AI reflects our values—understanding it is the most important conversation of our time.","url":"https://doi.org/10.65522/upwaybooks.9781917916936","authors":["Temel Parlak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-08T16:47:43Z","doi":"10.65522/upwaybooks.9781917916936","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-24788-0.20001-0","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-24788-0.20001-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T04:50:30Z","doi":"10.1016/b978-0-443-24788-0.20001-0","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aist68591.2025","name":"2025 7th International Conference on Artificial Intelligence and Speech Technology (AIST)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aist68591.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T19:54:40Z","doi":"10.1109/aist68591.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.63686/978-81-986347-0-2","name":"Artificial Intelligence in Politics and Governance","source":"crossref","abstract":"In recent years, artificial intelligence (AI) has evolved from a technical marvel into a transformative force reshaping nearly every domain of human activity—including politics and governance. The rapid digitization of society and the exponential growth of data have provided fertile ground for the integration of AI into political systems, decision-making processes, and public administration. With this transformation comes a new era—one that presents both unparalleled opporttmities and profound challenges for democratic values, political accountability, and global governance. This book, Artificial Intelligence in Politics and Governance, is a comprehensive examination of the multifaceted intersections between AI technologies and the political landscape. It offers a structured and expansive exploration of how AI is revolutionizing political campaigning, public opinion analysis, electoral systems, policymaking, and international diplomacy. Through 100 thought-provoking chapters, the book engages with both theoretical and practical dimensions of Al application in political settings—highlighting case studies, ethical considerations, technological advancements, and the shifting dynamics of power and participation. The aim of this volume is not merely to inform but to provoke critical reflection and encourage dialogue among scholars, policymakers, technologists, and citizens. In a time when misinformation, algorithmic bias, and digital surveillance increasingly influence political outcomes, it is essential to understand the implications of AI for democratic integrity and global stability. Each chapter contributes a unique perspective, collectively forming a foundation for informed debate and responsible innovation. We envision this book as a resource for academics, students, policy analysts, technology experts, and anyone interested in the future of politics in an AI-driven world. As we navigate the complexities of this digital political era, let us approach AI not just as a tool, but as a force that demands careful governance, ethical design, and inclusive application","url":"https://doi.org/10.63686/978-81-986347-0-2","authors":["Reeta Rautela","Shubham Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-05T10:27:52Z","doi":"10.63686/978-81-986347-0-2","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.18103/mra.v13i5.6513","name":"Enhancing Mental Health Support: Integrating Artificial Intelligence Powered Mobile Apps and Chatbots for Psychoeducation and Skill-Building","source":"crossref","abstract":"The advancement of digital mental health technologies offers transformative possibilities for increasing access to psychological support while presenting unique challenges in engagement and efficacy. This paper explores the integration of Awareness Integration Theory—a structured, evidence-based psychological model—into artificial intelligence-powered mental health platforms. Awareness Integration Theory facilitates self-awareness, emotional regulation, and cognitive restructuring, contributing to reported improvements of 60 to 70 percent across multiple areas of life functioning. One prominent application of this integration is the Foojan mental wellness app, which combines the principles of Awareness Integration Theory with advanced artificial intelligence to provide personalized, accessible mental health support. The app includes features such as guided journaling and real-time tools for managing emotions, thoughts, and behaviors. Central to the platform is MIRA, a hybrid virtual coach driven by artificial intelligence that responds to users’ daily concerns and provides practical mental health skills. Preliminary data suggest that this approach enhances mental well-being and may serve as a valuable adjunct to traditional psychotherapy by maintaining engagement and continuity between sessions. While the results are promising, challenges such as user retention, therapeutic alliance in digital settings, and data security remain critical considerations. This paper presents strategies to address these barriers and optimize the reach and effectiveness of digitally delivered interventions. By integrating Awareness Integration Theory with artificial intelligence technologies, this platform represents a significant step toward scalable, evidence-based, and continuous mental healthcare. It holds the potential to improve mental health outcomes globally by making support tools more available, responsive, and individualized.","url":"https://doi.org/10.18103/mra.v13i5.6513","authors":["Foojan Zeine","Sam Changizi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-03T17:48:07Z","doi":"10.18103/mra.v13i5.6513","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.9734/bpi/msti/v7/4409","name":"Artificial Intelligence in Dental Implant Identification: A Comprehensive Overview","source":"crossref","abstract":"Background: Dental implantology has significantly transformed the field of restorative dentistry, providing patients with long-term, functional, and aesthetic solutions for missing teeth. As the demand for implants increases globally, the need for effective and accurate implant fixture identification has become more crucial. Aim: This review aims to explore the role of artificial intelligence (AI) in dental implant identification, focusing on its applications, benefits, and challenges in clinical practice. The study examines AI-driven tools and their impact on diagnostic accuracy, clinical decision-making, and treatment planning. Methodology: A comprehensive literature review was conducted using Medline (PubMed) and Google Scholar databases in January 2025. The search targeted studies and reviews on AI applications in dental implant identification, analyzing technological advancements and their clinical implications. A total of 28 relevant articles were selected for assessment. Results: AI-powered tools, such as Spotimplant.com, Implantif.ai, and AI2D, have demonstrated high accuracy in identifying dental implants from radiographic images. Studies have shown that AI-based systems can improve identification precision by up to 25% compared to traditional methods. These technologies streamline the identification process, reduce human error, and enhance treatment planning. However, challenges remain, including database limitations, difficulties in complex cases, and the need for regulatory compliance. Conclusion: AI-driven implant identification offers significant advantages in improving diagnostic accuracy and clinical efficiency. While current AI tools present challenges related to data quality, regulatory frameworks, and integration into clinical workflows, ongoing advancements are expected to enhance their reliability and applicability. Future research should focus on expanding AI training datasets, optimizing deep learning models, and integrating AI into digital dental workflows for personalized treatment planning.","url":"https://doi.org/10.9734/bpi/msti/v7/4409","authors":["Hanen Boukhris","Ghada Bouslama","Hajer Zidani","Kawther Bel Haj Salah","Souha BenYoussef"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-20T04:56:02Z","doi":"10.9734/bpi/msti/v7/4409","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icaiic64266.2025","name":"2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaiic64266.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-19T18:27:14Z","doi":"10.1109/icaiic64266.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aide64228.2025.10987446","name":"Multi-Modal Medical Imaging Interface for Lung Disease Classification","source":"crossref","abstract":"This paper discusses a novel approach for classifying lung diseases using multi-modal medical imaging techniques. By integrating data from various imaging modalities such as X-ray, CT scan, and MRI, our model aims to improve accuracy and reliability in disease diagnosis. Through advanced Deep learning algorithms and feature extraction methods, we demonstrate the effectiveness of our approach in accurately identifying and classifying different types of lung diseases, ultimately contributing to more precise and efficient patient care. Furthermore, we introduce an innovative enhancement to our approach through the integration of Grad-CAM (Gradient-weighted Class Activation Mapping) technology. This augmentation enables the generation of heatmaps that highlight regions of interest in medical images, providing insights into the severity and localization of abnormalities associated with lung diseases. By incorporating Grad-CAM, our model not only facilitates accurate disease classification but also offers clinicians valuable visual aids for assessing disease severity and planning treatment strategies. Combining the power of Python with the flexibility of PyQt, our application aims to provide a user-friendly interface for various tasks. Through careful design and implementation, we demonstrate the seamless integration of PyQt widgets, layouts, and functionalities to create an intuitive and efficient desktop application. This application serves as a valuable tool for healthcare professionals, enabling them to leverage our advanced classification model and Grad-CAM technology in a user-friendly manner, ultimately enhancing the quality and efficiency of patient care.","url":"https://doi.org/10.1109/aide64228.2025.10987446","authors":["Padmasini N","Deeksha Lakshmi V","Gokul Nath M","Harsha K R"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-12T17:42:16Z","doi":"10.1109/aide64228.2025.10987446","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1097/cm9.0000000000003530","name":"Artificial intelligence in gynecology surgery: Current status, challenges, and future opportunities","source":"crossref","abstract":"Women’s health is a crucial, complex, and multifaceted field, encompassing gynecological disorders, reproductive health issues, conditions influenced by hormonal differences, and serious diseases such as ovarian and cervical cancer. Gynecologic surgery is a critical component of this field, presenting unique challenges due to the variability of anatomical lesions and patient-specific requirements. In recent years, the rapid development of information technology, particularly artificial intelligence (AI), has provided numerous opportunities to advance gynecologic surgery in clinical practice, research, and education.[1] This editorial aims to present the current status of AI applications in gynecologic surgery, discuss the technical and clinical challenges, and outline future prospects in this field. Preoperative planning and simulation: Preoperative planning is a fundamental phase in gynecologic surgery, where accurate diagnosis, strategic planning, and patient-specific simulation can significantly influence surgical outcomes. One of the most important applications of AI in preoperative planning is the enhancement of imaging diagnostics.[2] By integrating AI models, traditional imaging modalities such as ultrasound, magnetic resonance imaging (MRI), and computerized tomography (CT) scans can provide more detailed anatomical and pathological information. Preoperative 3D radiological images enable precise surgical planning, while adjustable augmented reality (AR) models and deep learning collectively optimize robotic surgery.[3] The development of AI-supported 3D printing techniques can further enhance surgeons’ capabilities while minimizing the likelihood of surgical errors.[4] For example, a 3D-printed model derived from preoperative MRI can reveal anatomical depth, width, and surrounding structure involvement. This can guide the operation, allow for rehearsal, reduce surgical duration, increase precision, and decrease complications, such as determining the optimal excision path for patients with uterine fibroids. In benign tumor surgeries, the application of AI and robotic systems requires specific considerations, highlighting a distinct cost-benefit ratio compared to more complex or malignant cases. Therefore, the decision to employ AI and robotic systems in benign tumor surgeries must weigh the advantages against the economic implications, ensuring that resources are allocated effectively to maximize patient care. Intraoperative navigation and assistance: AI is increasingly revolutionizing the intraoperative phase of gynecologic surgery by providing surgeons with advanced tools to enhance visualization, streamline workflows, and improve patient safety. Robotic-assisted surgical systems have already demonstrated their value in gynecologic procedures such as hysterectomies, myomectomies, and gynecologic oncology surgeries. For example, AI-driven robotic platforms can help surgeons visualize critical structures like blood vessels, nerves, and reproductive organs more clearly.[5] AI also facilitates communication among surgical team members by integrating data from various sources, such as intraoperative imaging, patient monitors, and robotic systems.[6] This approach reduces the cognitive load on the surgical team, allowing them to focus on critical tasks and make decisions more efficiently. Moreover, AI can process intraoperative data to predict potential complications before they arise. In gynecologic surgery, this could mean identifying early signs of excessive bleeding, detecting subtle changes in patient vitals, or predicting the likelihood of injury to adjacent structures.[7] Postoperative recovery and management: The postoperative phase is crucial for optimal recovery, minimizing complications, and enhancing outcomes after gynecologic surgery. AI is revolutionizing postoperative care by enabling personalized follow-up, early complication detection, and data-driven decision-making. By integrating data from wearable devices, electronic health records (EHRs), and predictive analytics, AI can recommend customized rehabilitation protocols, dietary adjustments, and pain management strategies.[8] For instance, in cases of pelvic floor repair or other gynecologic procedures, AI can suggest individualized physical therapy exercises to strengthen pelvic muscles and prevent complications like urinary incontinence or prolapse recurrence. AI-driven remote monitoring systems are becoming an integral part of postoperative care, particularly in the era of telemedicine and digital health.[9] In gynecologic surgery, such monitoring is especially valuable for detecting early signs of complications like infections, blood clots, or organ dysfunction. AI can predict the risk of wound dehiscence or surgical site infections based on factors such as preoperative health conditions, surgical duration, and intraoperative blood loss.[10] Training and education: AI-based tools bridge the gap in surgical education by offering realistic simulations, personalized learning paths, and real-time feedback. These tools accelerate surgeons’ learning and ensure patients receive safer, more precise, and standardized care. AI-based simulated training platforms allow for repeatable training, reducing wear and tear on physical equipment. Additionally, these platforms can decrease the overall training time required for surgeons, further reducing costs for healthcare institutions. In gynecologic surgery, AI simulators can recreate challenging procedures such as laparoscopic hysterectomies, pelvic organ prolapse repairs, or the excision of deep endometriosis.[11] Trainees can practice these procedures repeatedly, honing their skills without the pressure or risk associated with live patients. AI-based platforms can create customized training plans that prioritize specific skills or techniques.[12] For instance, a trainee struggling with laparoscopic suturing might be directed to targeted modules or simulations focused on improving dexterity and precision. AI is also streamlining the assessment and certification of surgical skills, ensuring that trainees meet the highest standards of competence before performing procedures independently.[13] AI-based assessment tools can objectively evaluate a trainee’s technical proficiency, decision-making, and adherence to surgical protocols. Data analysis and research: AI excels at processing and analyzing large, complex datasets that would be impossible for humans to manage manually.[14] In gynecologic surgery, these datasets may include patient records, surgical outcomes, imaging results, genomic data, and clinical trial findings. AI algorithms can identify patterns, correlations, and trends within these datasets, enabling researchers to generate insights that enhance understanding of gynecologic conditions and inform clinical practice.[15] Surgical data science, built upon AI-based data analysis, highlights the importance of analyzing the behaviors of the actors involved in the surgical workflow: the surgeons and the surgical team.[16] This covers the study of all surgical skills, including technical and non-technical ones, allowing for the refinement of surgical techniques by identifying factors that contribute to success or failure in gynecologic procedures. By analyzing data from past surgeries, including preoperative imaging, intraoperative metrics, and postoperative outcomes, AI can identify best practices and areas for improvement.[17] In robotic-assisted gynecologic surgery, AI can analyze instrument movements, surgical times, and complication rates to identify techniques that maximize precision and minimize tissue damage.[18] These insights can then be incorporated into training programs and surgical protocols, improving outcomes for future patients. The future of AI in gynecological surgery: Despite significant advancements, the current AI-based gynecologic surgical system still faces several critical technological challenges that need to be addressed. Future research in AI for gynecologic surgery will focus on integrating diverse data sources, advancing the accuracy and reliability of algorithms, and addressing gaps in clinical care. One of the primary challenges lies in the need for high-quality, diverse, and standardized datasets to train AI algorithms. The lack of gynecologic surgery data from diverse patient populations can hinder the generalizability and reliability of AI tools. Additionally, the computational requirements for processing complex surgical video data and maintaining low latency in AI systems present significant engineering challenges. Future work should focus on developing efficient algorithms, advancing hardware capabilities, and improving data streaming and compression techniques. Moreover, as many algorithms operate as “black boxes,” it is challenging for clinicians to comprehend and trust their recommendations. Researchers also need to work on improving the interpretability of AI models, including the implementation of explainable AI techniques that provide clear insights into decision-making processes and the creation of intuitive visualization tools for clinicians. In summary, AI has revolutionized gynecologic surgery by enhancing diagnostic accuracy, improving surgical outcomes, personalizing treatments, advancing research, and optimizing education. Despite challenges such as data quality, algorithmic bias, and ethical considerations, the future of AI in gynecology holds great promise for further improving women’s health. Funding This project was supported in part by Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital (Nos. 2023GSPKY11 and GSP-LCYJFH01), in part by National clinical key discipline construction funds (No. czxm-zk-40), in part by National Natural Science Foundation of China (Nos. 82372126, 8207071577), and in part by a grant from the ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and the French National Research Agency (No. A-CUHK402/23). Conflicts of interest None. Acknowledgement We grateful to the staff in Biobank of Zhongda Hospital Affiliated to Southeast University for technical assistance.","url":"https://doi.org/10.1097/cm9.0000000000003530","authors":["Qi Dou","Krystel Nyangoh-Timoh","Pierre Jannin","Yang Shen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-04T02:00:11Z","doi":"10.1097/cm9.0000000000003530","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.61770/nbejms.2025.v03.i10.012","name":"Artificial Intelligence in Scholarly Publishing: Enhancing Editorial Efficiency While Preserving Human Expertise","source":"crossref","abstract":"Use of artificial Intelligence (AI) is increasing significantly in scholarly publishing. It can potentially enhance editorial workflows and reduce the burden on reviewers. AI applications, like plagiarism detection, formatting checks, and reviewer assignment, can improve efficiency and transparency during initial manuscript processing stages. However, the peer review process extends beyond mere technical tasks. It encompasses critical evaluation that requires human expertise, contextual understanding, and ethical consideration. This review highlights the constraints of using AI in peer review while examining both present and future uses of AI in editorial activities. A fair framework has been created, and AI can assist with editorial tasks rather than replace human reviewers. Peer review's integrity, legitimacy, and constructive character depend on human judgment. This review also emphasises the mounting issues facing the traditional peer review system, such as rising submission numbers, reviewer exhaustion, and delays in decision-making. To ensure that scientific publishing upholds its exacting standards, this narrative emphasises the importance of keeping human evaluation at the centre of the review process by addressing both advantages and disadvantages of integrating AI.","url":"https://doi.org/10.61770/nbejms.2025.v03.i10.012","authors":["Anil Regmi","Raju Vaishya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T05:33:16Z","doi":"10.61770/nbejms.2025.v03.i10.012","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.52783/eel.v15i4.3923","name":"Changing hr roles with artificial intelligence","source":"crossref","abstract":"The human resource roles have evolved with the changing business environment. The digital revolution has made eminent changes in human resource functions like introduction of human resource management portals. With the introduction of artificial intelligence, the business is expecting new set of changes in human resource roles. Based on the set of human resource roles proposed by (thite et al., 2014), the authors have proposed few themes which can be investigated in the industry. These themes are backed by the literature and can lead the practitioners to an easy implementation of artificial intelligence in human resource management.","url":"https://doi.org/10.52783/eel.v15i4.3923","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-25T06:48:16Z","doi":"10.52783/eel.v15i4.3923","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.5336/978-625-395-760-5","name":"The Role of Artificial Intelligence in Perioperative Care","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-760-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T09:49:57Z","doi":"10.5336/978-625-395-760-5","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2139/ssrn.5285711","name":"Artificial Intelligence for Enhancing HRD Efficiency","source":"crossref","abstract":"The paper focusses on application of artificial intelligence in enhancing HRD efficiency.","url":"https://doi.org/10.2139/ssrn.5285711","authors":["Nomita Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-13T14:30:08Z","doi":"10.2139/ssrn.5285711","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2139/ssrn.5076025","name":"Artificial Intelligence and Ethics","source":"crossref","abstract":"AI has been transforming a number of sectors, from smart homes and cities to health care and public services, enabling improved efficiency as well as providing personalized experiences and sustainability. At the same time, the breakneck pace of AI advancement brings with it pressing ethical and societal challenges in areas such as data privacy breach, algorithmic bias, data security threat, or human labour displacement. The challenges with these systems demonstrate the need for responsible AI. Rob Margo has posted a rough first cut at the possibilities for addressing the ethical issues some of which involve technology fixes, others require complete legal rights systems and many falls somewhere in between on the spectrum of regulation. They should be handled by technologies like Privacy-by-design, Fairness-aware algorithms and Explainable AI respectively. At the same time, regulatory guidelines take steps to hold providers of AI systems accountable and ensure that they are used in compliance with ethical principles. Dealing with it requires a dual-pronged strategy to combine innovation and regulation to ensure that AI is beneficial for society without violating ethical norms. Developing AI with fairness, transparency, inclusivity, and sustainability in mind can be done so that the development of AI sustains not only technological progress but also societal well-being.","url":"https://doi.org/10.2139/ssrn.5076025","authors":["Shipra Gupta","Priti Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-08T11:18:07Z","doi":"10.2139/ssrn.5076025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/iccsai64074.2025","name":"2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccsai64074.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T17:41:41Z","doi":"10.1109/iccsai64074.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/esai67033.2025","name":"2025 4th International Conference on Embedded Systems and Artificial Intelligence (ESAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/esai67033.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-24T19:46:58Z","doi":"10.1109/esai67033.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aidas67696.2025","name":"2025 6th International Conference on Artificial Intelligence and Data Sciences (AiDAS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aidas67696.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-05T18:37:49Z","doi":"10.1109/aidas67696.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.71443/9789349552418-02","name":"Data Acquisition and Preprocessing Techniques for Medical Imaging and IoT Healthcare Devices","source":"crossref","abstract":"The exponential growth of medical imaging and Internet of Things (IoT) healthcare devices has ushered in a new era of intelligent healthcare systems, enabling continuous monitoring, early diagnosis, and personalized treatment. High-quality data acquisition and effective preprocessing are critical to ensuring the reliability, accuracy, and clinical relevance of these systems. Medical imaging modalities, including MRI, CT, Ultrasound, and PET, generate complex datasets that require sophisticated preprocessing to reduce noise, correct artifacts, and enhance diagnostic features. Simultaneously, IoT-based healthcare devices produce heterogeneous and high-velocity physiological data streams that necessitate real-time preprocessing for anomaly detection, calibration, and temporal alignment. The integration of deep learning techniques, such as convolutional neural networks, autoencoders, and generative adversarial networks, has significantly advanced preprocessing for both imaging and sensor-based data, providing automated, adaptive, and high-fidelity outputs suitable for predictive analytics. The fusion of multimodal datasets from imaging and IoT devices enhances the comprehensiveness of patient monitoring and supports robust clinical decision-making. Despite these advancements, challenges persist in ensuring interoperability, maintaining data privacy, and achieving real-time processing in distributed environments. This chapter presents a systematic overview of state-of-the-art data acquisition strategies and preprocessing methodologies, emphasizing AI-driven frameworks, multimodal data integration, and scalable computational architectures. The discussion highlights current limitations, emerging trends, and future research directions for developing intelligent, interoperable, and patient-centric healthcare systems.","url":"https://doi.org/10.71443/9789349552418-02","authors":["Piyush Pandey","Divya Chettri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-30T11:49:02Z","doi":"10.71443/9789349552418-02","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icbase66587.2025.11181232","name":"Dynamic Pseudo-Labeling via Large Language Models for Robust Medical Image Segmentation","source":"crossref","abstract":"In the field of medical image segmentation, the high cost of annotated data and the poor generalization performance of the model have always been problems that need to be solved urgently. In this study, we innovatively propose a cross-modal collaboration framework based on large language model (LLM), MedSegLLM, which aims to overcome the two major difficulties of weakly supervised learning and domain adaptation at the same time, and open up an efficient and interpretable new path for medical image analysis: 1) Cross-modal semantic alignment network: Through a unique mapping strategy, medical image features and accurate text descriptions generated by LLM are projected into the same semantic space, cleverly bridging the understanding gap between vision and language, and achieving deep semantic integration across modalities. 2) Dynamic pseudolabel generator: deeply excavate the powerful logical reasoning potential of LLM, intelligently generate high-credibility segmentation masks, provide an effective way to break the dilemma of a lack of annotated data, and the generation process has adaptive optimization capabilities. 3) Adaptive feature fusion module: According to the image differences generated by different medical devices, it intelligently integrates multi-source feature information, comprehensively improves the generalization and adaptability of models across devices and scenarios, and ensures the stability of clinical applications. MedSegLLM has been experimentally proven to perform well on BraTS (Brain Tumor Segmentation Benchmark), ISIC (Dermoscopy Image Dataset) and proprietary CT datasets. In particular, under the severe conditions of low annotation data rate (only $10 \\%$), the Dice coefficient is still as high as 0.91, which is significantly higher than that of traditional strongly supervised U-Net (0.85) and advanced TransUNet (0.88) ($\\mathbf{p}\\lt \\mathbf{0 . 0 1}$). The cross-device generalization error of the framework is reduced by $32 \\%$ compared with the existing schemes, which fully demonstrates its adaptability and robustness in complex medical environments(The model can maintain stable and high - accuracy segmentation performance in complex conditions without significant drops due to environmental changes or data - quality issues). Even in low-resource hospitals (less than $10 \\%$ of labeled data) and emergency diagnosis scenarios, it can still maintain high-precision output, which is of great clinical practical value. It will provide a strong boost for the inclusive development of global medical imaging technology, and is expected to accelerate the popularization of precision medicine in medical institutions at all levels.","url":"https://doi.org/10.1109/icbase66587.2025.11181232","authors":["Zhiyan Tang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-08T17:35:46Z","doi":"10.1109/icbase66587.2025.11181232","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.caeai.2024.100346","name":"Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory","source":"crossref","abstract":"The purpose of this study was to investigate the determinants of higher education students' motivation towards continuous usage of ChatGPT for English language learning, based on a modified Self-Determination Theory (SDT). A quantitative approach hinged on a cross-sectional survey design was adopted, and an online questionnaire used to collect data from 324 students studying English as Foreign Language (EFL) and English as a Second Language (ESL). The data were analyzed using a Partial Least Squares-Structural Equation Modelling (PLS-SEM) technique. This study established that initial ChatGPT usage determined students' perceived autonomy, competence, relatedness and challenges in ChatGPT usage. In addition, a novel finding was that, both autonomy and relatedness predicted students' competence in using ChatGPT to learn. Further, determinants of students' motivation for continuous usage of ChatGPT were autonomy and relatedness. Lastly, the study through Important-Performance Map Analysis (IPMA), established autonomy as the most important as well as the highest performing factor determining students' motivation for continuous usage of ChatGPT. The validated SDT model explained a large total variance of 70.8% in students’ motivation for continuous use of ChatGPT. Based on the results, recommendations were made for both theory as well as policy and practice towards ChatGPT usage in higher education.","url":"https://doi.org/10.1016/j.caeai.2024.100346","authors":["Nagaletchimee Annamalai","Brandford Bervell","Dickson Okoree Mireku","Raphael Papa Kweku Andoh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-13T02:58:01Z","doi":"10.1016/j.caeai.2024.100346","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.111576","name":"Detailed fault detection of industrial sensor based on semantic segmentation models","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111576","authors":["Xirui Chen","Hui Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-26T05:21:01Z","doi":"10.1016/j.engappai.2025.111576","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.23977/jaip.2025.080116","name":"Research on the application of artificial intelligence and multi-scale image fusion technology to pedestrian detection in complex street view","source":"crossref","abstract":"With the increasing face imaging data and the advancement of artificial intelligence (AI) technology, computer-aided monitoring systems are crucial for pedestrian detection in dense street view. However, due to occlusion and small pedestrian scale, pedestrian false alarms and missed detection problems become more and more serious. Therefore, this paper proposes a pedestrian detection model, YOLOv10s-pedestrian. Firstly, CA attention is introduced to redesign the MBConv module, resulting in an efficient MB-CANet backbone for pedestrian feature extraction, enhancing the accurate localization of densely occluded pedestrians. Secondly, a novel C2FN structure was created to reduce the number of parameters while improving the model's accuracy. Additionally, inspired by the BiFPN feature fusion concept, a Bi-C2FN-FPN network structure is proposed to effectively fuse features from different depth sources, strengthening feature fusion and improving pedestrian detection accuracy. Finally, the MPDIOU loss function replaces the original CIoU loss function to enhance anchor box localization. Experimental results demonstrate that the proposed model achieves a mAP50 of 95.6% on the WiderPerson pedestrian detection dataset, which is a 6.1% improvement over the original model, with a recall rate of 86.2%, showcasing excellent detection performance. Compared to several mainstream object detection models, the proposed model also exhibits superior performance.","url":"https://doi.org/10.23977/jaip.2025.080116","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-04T09:43:35Z","doi":"10.23977/jaip.2025.080116","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.caeai.2025.100449","name":"How well can LLMs grade essays in Arabic?","source":"crossref","abstract":"This research assesses the effectiveness of state-of-the-art large language models (LLMs), including ChatGPT, Llama, Aya, Jais, and ACEGPT, in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in context learning, and fine-tuning, and examines the influence of instruction-following capabilities through the inclusion of marking guidelines within the prompts. A mixed-language prompting strategy, integrating English prompts with Arabic content, was implemented to improve model comprehension and performance. Among the models tested, ACEGPT demonstrated the strongest performance across the dataset, achieving a Quadratic Weighted Kappa (QWK) of 0.67, but was outperformed by a smaller BERT-based model with a QWK of 0.88. The study identifies challenges faced by LLMs in processing Arabic, including tokenization complexities and higher computational demands. Performance variation across different courses underscores the need for adaptive models capable of handling diverse assessment formats and highlights the positive impact of effective prompt engineering on improving LLM outputs. To the best of our knowledge, this study is the first to empirically evaluate the performance of multiple generative Large Language Models (LLMs) on Arabic essays using authentic student data.","url":"https://doi.org/10.1016/j.caeai.2025.100449","authors":["Rayed Ghazawi","Edwin Simpson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-30T16:01:06Z","doi":"10.1016/j.caeai.2025.100449","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.110363","name":"Integrating permutation feature importance with conformal prediction for robust Explainable Artificial Intelligence in predictive process monitoring","source":"crossref","abstract":"As artificial intelligence (AI) systems are increasingly deployed in high-stakes environments, the need for explanations that convey uncertain information has become evident. Conventional explainable AI (XAI) methods often overlook uncertainty, focusing solely on point predictions. To address this gap, we propose using permutation feature importance (PFI) combined with predictive uncertainty evaluation measures. This novel approach examines the significance of features by relating them to the model’s confidence in its predictions. By using split conformal prediction (SCP) to quantify predictive uncertainty and integrating the outcomes to PFI, we aim to enhance the robustness and interpretability of machine learning (ML) algorithms. More importantly, we examine three scenarios for conformal prediction-based PFI explanations: permuting feature values in the test data, the calibration data, and both. These scenarios assess the impact of feature permutations from different perspectives, revealing feature sensitivity and the importance of features in various settings. We also perform a series of sensitivity analyses, particularly exploring calibration data size and computational efficiency, to demonstrate the robustness and scalability of our approach for industrial applications. Our comprehensive evaluation offers insights into feature impact on predictions and their associated confidence levels. We validate our proposed approach through a real-world predictive process monitoring use case in manufacturing.","url":"https://doi.org/10.1016/j.engappai.2025.110363","authors":["Nijat Mehdiyev","Maxim Majlatow","Peter Fettke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-15T05:41:56Z","doi":"10.1016/j.engappai.2025.110363","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2139/ssrn.5392894","name":"Cross-Domain Applications of Artificial Intelligence: From Speech Recognition to Financial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5392894","authors":["Salim A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-22T17:48:13Z","doi":"10.2139/ssrn.5392894","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-21870-5.00020-0","name":"Emerging applications of artificial intelligence in analyzing EEG signals for the healthcare sector","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21870-5.00020-0","authors":["Nagma Irfan","Shuchi Dave","Vimanyu Veer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-04T01:52:34Z","doi":"10.1016/b978-0-443-21870-5.00020-0","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1108/978-1-83708-198-120251008","name":"Ethics of Artificial Intelligence on Social Media Marketing","source":"crossref","abstract":"This study explores the ethical implications of artificial intelligence (AI) in social media marketing, focusing on the principles and challenges that arise with the use of AI in digital marketing. It highlights the importance of key ethical considerations, such as transparency, privacy, fairness, and accountability, which are critical for building trust with consumers and ensuring responsible AI deployment. Drawing on various theoretical frameworks, including the theory of planned behavior (TPB), the research emphasizes how AI can enhance marketing efficiency while raising concerns about data security, biases, and the manipulation of consumer behavior. The study also examines the ethical advantages of AI, such as promoting fairness, improving decision-making, and fostering market equity. However, it warns of the potential risks associated with a lack of ethical oversight, underscoring the need for clear guidelines and regulations to ensure AI’s responsible use in marketing. This work contributes to the growing discourse on AI ethics by providing a comprehensive review of the challenges and proposing strategies for ethical AI practices in the context of social media marketing.","url":"https://doi.org/10.1108/978-1-83708-198-120251008","authors":["Najwan Ibrahim Jadallah","Bahaa Subhi Awwad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-01T21:14:49Z","doi":"10.1108/978-1-83708-198-120251008","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.ait.2025.100023","name":"A review of data science and artificial intelligence applications in air transportation systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ait.2025.100023","authors":["Lishuai Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-25T09:19:24Z","doi":"10.1016/j.ait.2025.100023","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21956/mep.22039.r40857","name":"Peer Review Report For: Exploring Filipino Medical Students’ Attitudes and Perceptions of Artificial Intelligence in Medical Education: A Mixed-Methods Study [version 1; peer review: 1 approved with reservations, 1 not approved]","source":"crossref","abstract":"","url":"https://doi.org/10.21956/mep.22039.r40857","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T21:02:09Z","doi":"10.21956/mep.22039.r40857","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-44-332856-5.00004-6","name":"Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-332856-5.00004-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T09:56:12Z","doi":"10.1016/b978-0-44-332856-5.00004-6","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/acait67930.2025.11522659","name":"Title Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acait67930.2025.11522659","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-20T19:49:28Z","doi":"10.1109/acait67930.2025.11522659","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-658-48033-2_12","name":"Correction to: Artificial Intelligence in Sales","source":"crossref","abstract":"'Correction to: Artificial Intelligence in Sales' published in 'Artificial Intelligence in Sales'","url":"https://doi.org/10.1007/978-3-658-48033-2_12","authors":["Manuel Beck"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-01T11:53:08Z","doi":"10.1007/978-3-658-48033-2_12","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.daai.2025.100021","name":"Designing interactive pneumatic interfaces for enhanced movie responses","source":"crossref","abstract":"Movies, as a comprehensive art form, have traditionally focused on visual and auditory elements, with most advancements aimed at enhancing resolution and sound quality. However, innovative technologies are beginning to integrate multimodal experiences, shifting toward interactive cinema that engages additional senses beyond sight and sound. In this paper, we introduce the design of a bioinspired pneumatic haptic interface that enhances emotional responses during movie watching. This system leverages dynamic shape changes and surface texture patterns to create a tactile dimension that aligns with the emotional tone of the movie, enabling richer sensory engagement. Our experiments reveal that congruent haptic feedback—where tactile stimuli align with the emotional content of the movie—significantly enhances emotional responses, particularly for high-arousal, positive-valence emotions such as excitement and joy. Parameters such as high-frequency and goosebump-textured stimuli amplify engagement in dynamic scenes, whereas low-frequency and smooth textures enhance calm and serene moments. Conversely, noncongruent stimuli disrupt emotional coherence, highlighting the critical role of alignment between haptic feedback and cinematic content. This work highlights the unique advantages of pneumatic haptic interfaces, such as their ability to deliver bioinspired, dynamic tactile experiences that go beyond traditional vibrotactile systems. By engaging viewers through tactile congruence, these systems offer a novel approach for immersive, emotional storytelling. The findings provide insights for designing adaptive, multisensory interactive systems in entertainment, therapeutic contexts, and beyond, advancing the field of affective communication and interactive media technologies.","url":"https://doi.org/10.1016/j.daai.2025.100021","authors":["Yang Liu","Stéphane Safin","Françoise Détienne","Eric Lecolinet"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-26T15:16:09Z","doi":"10.1016/j.daai.2025.100021","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1002/9781394274277.ch12","name":"FarmTechAI","source":"crossref","abstract":"Traditional farm management systems limit farmers' ability to respond quickly and effectively to changing natural conditions. For this reason, new solutions are needed that will contribute to the overall sustainability and success of agriculture. Artificial intelligence (AI) and machine learning (ML)-based systems can analyze real-time data, allowing farmers to gain valuable information about fluctuations in crop yields, weather conditions, and market demands. In this chapter, we present an AI-based modern farmer management system called FarmTechAI which promotes collaboration and information exchange between farmers by providing them with meteorological, financial, and information from the ML model on a single dashboard. Another purpose of the dashboard is to revolutionize decision-making processes and increase the overall sustainability and effectiveness of agricultural practices by providing farmers with up-to-date information on crop health and resource allocation. By combining technology and agriculture, this effort aims to bring about a new era of precision agriculture where farmers at all levels can make informed decisions that are not only feasible but also easy to understand and accessible.","url":"https://doi.org/10.1002/9781394274277.ch12","authors":["Murat Can Cardak","Muhammed Golec","Sukhpal Singh Gill"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-27T17:18:31Z","doi":"10.1002/9781394274277.ch12","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.5530/ijpi.20250329","name":"Medical Education in the Era of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.5530/ijpi.20250329","authors":["Jiafeng Li","Tiefeng Jin","Meihua Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-24T05:55:41Z","doi":"10.5530/ijpi.20250329","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.71443/9789349552418-12","name":"Ethical, Legal, and Social Implications of AI and IoT in Medical Diagnostics","source":"crossref","abstract":"The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in medical diagnostics has transformed healthcare delivery by enabling real-time patient monitoring, early disease detection, and personalized treatment strategies. While these technologies offer significant clinical advantages, their widespread adoption introduces complex ethical, legal, and social challenges that must be addressed to ensure responsible innovation and equitable healthcare access. Key ethical concerns include patient autonomy, algorithmic bias, and privacy protection, whereas legal implications involve regulatory compliance, liability, and cross-border governance of sensitive medical data. Social considerations focus on public trust, technology acceptance, and equitable access, particularly in underserved populations. The reliance on AI-IoT systems necessitates robust risk management frameworks to mitigate system failures, ensure accountability, and maintain continuity of care. Future directions emphasize enhancing societal awareness, education, and multi-stakeholder engagement to promote informed adoption, transparency, and ethical stewardship of AI-IoT diagnostic systems. This chapter provides a comprehensive examination of these dimensions, highlighting the interplay between technological innovation and socio-ethical responsibilities. The insights presented serve as a foundation for policy development, clinical practice guidelines, and research strategies that facilitate the safe, ethical, and socially responsible deployment of AI-IoT technologies in healthcare.","url":"https://doi.org/10.71443/9789349552418-12","authors":["Marngam Gameh","Nagarathna A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-30T11:49:02Z","doi":"10.71443/9789349552418-12","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s40593-024-00439-5","name":"Barriers to and Opportunities for the Adoption of Generative Artificial Intelligence in Higher Education in the Global South: Insights from Sri Lanka","source":"crossref","abstract":"Numerous studies have explored the intricacies of artificial intelligence (AI) in higher education, predominantly focusing on developed countries. However, there is a notable gap in examining the hindrances and untapped potential of AI implementation in the higher education sector within the South Asian Global South countries. This study aims to identify the barriers to integrating generative artificial intelligence (GenAI) in higher education and explore potential opportunities, with a specific focus on Sri Lanka as the country setting. Using a case study approach, data were gathered from various sources, including interviews and document analysis, concentrating on the largest management faculty in the country. The impediments were analyzed using an extended innovation barriers framework, covering technological, commercial, organizational, societal, and personal challenges. The findings highlight a landscape replete with obstacles. Key among them are the absence of comprehensive policies and guidelines at the university level, uncertainty about the reliability of information provided by GenAI tools, overreliance on these tools by learners, a lack of understanding and expertise among academics, and resistance to embracing technological advancements. Nonetheless, the study identifies several remedial actions that could be adopted to harness the potential of GenAI tools, particularly in South Asian Global South countries such as Sri Lanka.","url":"https://doi.org/10.1007/s40593-024-00439-5","authors":["Amali Henadirage","Nuwan Gunarathne"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-27T15:57:27Z","doi":"10.1007/s40593-024-00439-5","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-031-86813-9_1","name":"The Challenges of Artificial Intelligence in the European Legal Space","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86813-9_1","authors":["Marton Varju","Judit Sándor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-10T10:52:05Z","doi":"10.1007/978-3-031-86813-9_1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/s40670-025-02289-9","name":"Exploring Medical Student Trust in Generative Artificial Intelligence (ChatGPT) Versus Peers in Team-Based Learning","source":"crossref","abstract":"This study explored the impact of artificial intelligence (AI)-generated responses from ChatGPT on medical students' decision-making and the effectiveness of group discussions in correcting AI-induced misconceptions. Forty students responded to clinical cases in three phases: independently, after reviewing AI answers, and post-group discussions. Students' responses demonstrated a significant shift to match those provided by ChatGPT, whether or not these were correct. Group discussions did not correct misinformation from AI. The findings not only highlight the potential of AI to influence medical students' decision-making but also emphasize the need for critical assessment and guidance around responsible use of AI in medical education.","url":"https://doi.org/10.1007/s40670-025-02289-9","authors":["Amrit Kirpalani","Joanne Grimmer","Peter Zhan Tao Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-24T13:05:14Z","doi":"10.1007/s40670-025-02289-9","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.23977/jaip.2025.080304","name":"Evaluation on Promoting Economic Development by Technology Entrepreneurship and Innovation Concept of Sports Industry under the Background of Artificial Intelligence","source":"crossref","abstract":"The economic advancement of the PE industry is the lifeblood of the PE industry. Adopting the concept of technology entrepreneurship and innovation can improve the economic advancement of the PE industry. The chaos theory of image security communication and DNA computing system can be used in the economic development of PE industry to improve and update it. In order to promote the economic development of PE industry under the background of artificial intelligence, this paper used chaos theory and DNA computing to analyze the strategy of PE industry enterprises, and finally drew a conclusion. In terms of the survey on the scientific and technological level of the PE industry, the scientific and technological level of the five sports related enterprises has been improved; in terms of the investigation on the innovation ability of the PE industry, it was concluded that the innovation ability of the five enterprises has improved after the adoption of the DNA computing system, maintaining the level of more than 80 points; in the aspect of the investigation of the economic level of the PE industry, it was concluded that the demand of the public for sports commodities is gradually expanding, and the sports commodity sales enterprises have driven the development of the PE industry; in terms of the survey on the changes in the scale of sports enterprises, it was concluded that the scale of each enterprise has expanded from July 2021 to December 2021; in the performance analysis of DNA algorithm, it was concluded that the performance of DNA algorithm using chaos theory of image security communication has been improved. To sum up, chaos theory and DNA computing can improve the technology entrepreneurship and innovation concept of PE industry and promote economic development.","url":"https://doi.org/10.23977/jaip.2025.080304","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-16T14:32:22Z","doi":"10.23977/jaip.2025.080304","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-981-96-8176-1_11","name":"Artificial Intelligence in Obesity and Diabetes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8176-1_11","authors":["Shehla Rafiq","Tabasum Majeed","Nusrat Mohi Ud Din","Saqib Ul Sabha","Assif Assad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-17T12:24:44Z","doi":"10.1007/978-981-96-8176-1_11","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aibiec68052.2025.11473575","name":"The Impact of Artificial Intelligence on Corporate ESG Performance: The Mediation of R&amp;D Intensity","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aibiec68052.2025.11473575","authors":["Yuhan Qiu","Yabin Yu","Haocheng Lyu","Lu Qian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T20:02:09Z","doi":"10.1109/aibiec68052.2025.11473575","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.54361/ajmas.2583103","name":"The Reality of Big Data in Libya and Its Readiness to Support Artificial Intelligence Applications","source":"crossref","abstract":"Big Data has emerged as a critical driver of Artificial Intelligence (AI) innovation, enabling intelligent decision-making, predictive analytics, and automation across sectors. However, the readiness of developing nations to integrate Big Data into AI ecosystems remains underexplored. This study examines Libya’s current Big Data landscape and its capacity to support AI applications. A mixed-methods approach was employed, combining desk research with semi-structured interviews involving eight stakeholders from government, academia, and the private sector. Findings reveal substantial deficiencies in infrastructure, data governance, policy frameworks, human capital, and institutional collaboration, which collectively hinder AI adoption. Benchmarking against advanced AI-ready nations such as Estonia and the United Arab Emirates (UAE) highlights Libya’s significant lag in key readiness indicators. The study contributes to the literature on AI readiness in post-conflict and developing contexts, offering an empirically grounded analysis that identifies priority areas for intervention. Strategic recommendations are proposed, including the development of a national AI and Big Data strategy, the enactment of data governance legislation, targeted investments in ICT infrastructure, educational reforms, and mechanisms for cross-sector collaboration. These actions are essential to enable Libya to harness the socio-economic potential of AI and bridge the readiness gap with global leaders in digital transformation.","url":"https://doi.org/10.54361/ajmas.2583103","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-18T17:31:13Z","doi":"10.54361/ajmas.2583103","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/otcon65728.2025.11070779","name":"Artificial Intelligence Based Automated Medical Imaging Analysis and Interpretation","source":"crossref","abstract":"Adding artificial intelligence (AI) to medical imaging has changed how diagnoses are made by making it possible to look at images, especially chest X-rays, more accurately and automatically. This study investigates how advanced AI techniques, such as deep learning and neural networks, can automate the assessment of the cardiothoracic ratio, a crucial indicator for identifying heart diseases. AI uses huge records and complicated formulas to make medical findings better, faster, and more consistent, so humans don't have to look them over as often. The study also examines the challenges that arise from combining AI, such as inaccurate data and errors, and emphasizes the importance of conducting comprehensive reviews to evaluate the effectiveness of these AI-powered solutions. This study shows how AI could greatly improve patient results by helping to find and diagnose cardiovascular diseases earlier using computerized medical image analysis.","url":"https://doi.org/10.1109/otcon65728.2025.11070779","authors":["Sangeeta Jawar","Ramgopal Kashyap"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-14T17:40:30Z","doi":"10.1109/otcon65728.2025.11070779","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1111/exsy.70111","name":"A Method‐Oriented Review of Explainable Artificial Intelligence for Neurological Medical Imaging","source":"crossref","abstract":"ABSTRACT The adoption of artificial intelligence (AI) techniques in medical imaging has led to significant improvements in diagnostic performance, particularly in neurological disorders. However, the limited interpretability of deep learning models, often referred to as the “black box” issue, poses substantial challenges in clinical trust, transparency, and regulatory acceptance. Explainable artificial intelligence (XAI) aims to address these limitations by enhancing model transparency and interpretability. This review systematically analysed 77 eligible studies, selected from an initial pool of 108 publications, focusing on XAI applications in neurological medical imaging. The included approaches were categorised into four primary groups: (1) feature visualisation techniques, (2) hierarchical and causal interpretability methods, (3) self‐supervised and federated learning strategies, and (4) dynamic and multimodal interpretability frameworks. Each category was evaluated in terms of technical methodology, clinical applicability, and associated limitations. Feature visualisation methods such as Grad‐CAM offer intuitive visual outputs for imaging data but often lack robustness and reproducibility, while attribution methods such as SHAP provide global or local feature importance—mainly for tabular or structured data—and are less frequently applied to medical images. Hierarchical models, including Layer‐wise Relevance Propagation, provide more detailed insights but face barriers to clinical integration. Federated and self‐supervised learning approaches are increasingly explored for privacy preservation and model generalisation in medical imaging; however, the integration of explainability mechanisms into these frameworks is still at an early stage, and standardised methods for interpretable federated/self‐supervised models remain underdeveloped. Dynamic and multimodal frameworks represent a promising direction for comprehensive model explanation but are still in the early stages of exploration. Despite progress, key challenges persist, including the lack of standardised evaluation metrics, limited clinical validation, and unresolved ethical concerns. Future research should focus on integrating interpretability into model development, establishing benchmark evaluation protocols, and promoting effective human–AI collaboration in clinical workflows.","url":"https://doi.org/10.1111/exsy.70111","authors":["Changyu Peng","Lifeng Li","Dechang Peng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-08T01:35:29Z","doi":"10.1111/exsy.70111","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/ai2e64943.2025","name":"2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai2e64943.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-12T17:43:10Z","doi":"10.1109/ai2e64943.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-26482-5.00032-8","name":"About the editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26482-5.00032-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-18T22:37:17Z","doi":"10.1016/b978-0-443-26482-5.00032-8","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aixmm62960.2025.00019","name":"Author Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aixmm62960.2025.00019","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-19T17:53:11Z","doi":"10.1109/aixmm62960.2025.00019","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1145/3736273","name":"ASEAN School on High-Performance Computing and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3736273","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-26T15:22:11Z","doi":"10.1145/3736273","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-323-95462-4.00015-7","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95462-4.00015-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-02T07:42:17Z","doi":"10.1016/b978-0-323-95462-4.00015-7","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1145/3777577.3777680","name":"GSA-MDFNet: Polyp Segmentation via Graph Structure Attention and Multi-path Deformable Fusion","source":"crossref","abstract":"Early detection and precise segmentation of colorectal polyps are critical for the diagnosis and prevention of colorectal cancer. Traditional approaches relying on hand-crafted low-level features such as color, texture, and shape often fail to capture global contextual information and exhibit limited robustness under complex imaging conditions. With the rapid advancement of deep learning, numerous neural network–based methods have been proposed to improve medical image segmentation. In this paper, we propose GSA-MDFNet — a novel architecture that integrates a Graph Structure Attention (GSA) mechanism and a Multi-path Deformable Fusion Module (MDFM) to address the limitations of existing models in accuracy and adaptability to challenging clinical scenarios. The GSA module captures long-range dependencies between image regions and explicitly enhances boundary awareness, while the MDFM employs deformable convolutions and a triple-layer feature fusion strategy to effectively aggregate multi-scale semantic and structural information. Extensive experiments on multiple public polyp segmentation datasets demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively, providing a promising tool for clinical polyp assessment and computer-aided diagnosis.","url":"https://doi.org/10.1145/3777577.3777680","authors":["Leyan Wang","Weibin Guo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777680","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.47297/wspiedwsp2516-250032.20250909","name":"Artificial Intelligence in Medical Education: Current Research and Applications","source":"crossref","abstract":"As medical education undergoes continuous transformation, the integration of artificial intelligence (AI) into teaching practices has attracted growing attention. AI has demonstrated considerable potential in virtual simulation, personalized learning support, clinical reasoning training, humanistic education, and interdisciplinary teaching. These applications have been shown to enhance instructional quality, optimize the learning experience, and foster educational innovation. Evidence indicates that AI not only delivers real-time feedback and targeted training but also facilitates the development of students' cognitive abilities and practical skills in complex clinical scenarios. Nevertheless, its widespread adoption faces persistent challenges, including limited acceptance among faculty and students, unequal distribution of resources, inadequate assessment frameworks, and lagging institutional support. Future progress should emphasize large-scale, multicenter empirical studies, promote deeper integration of technology with curricula, strengthen resource sharing and interdisciplinary talent cultivation, and establish unified standards and quality assurance systems. Such measures are essential for achieving the sustainable implementation of AI in medical education and maximizing its educational value.","url":"https://doi.org/10.47297/wspiedwsp2516-250032.20250909","authors":["Li Min","Song Helin","Tian Menghan","Ren Yijia","Li Sixian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-16T06:34:32Z","doi":"10.47297/wspiedwsp2516-250032.20250909","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aims66189.2025","name":"2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aims66189.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-12T18:40:23Z","doi":"10.1109/aims66189.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-981-99-8441-1_8","name":"Application of Artificial Intelligence in Head and Neck Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8441-1_8","authors":["Ling Zhu","Xiaoqing Dai","Jiliang Ren","Jingbo Wang","Xiaofeng Tao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T00:02:50Z","doi":"10.1007/978-981-99-8441-1_8","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1177/29498732251340044","name":"Graphic Improvements: Adding Explicit Syntactic Graphs to Neural Machine Translation","source":"crossref","abstract":"Neural language models such as bidirectional encoder representations from transformers or generative pretrained transformer operate on the basis of sequences of words. Pretraining on a large corpus endows them with implicit knowledge about the relationship between words. This study explores the extent to which the explicit incorporation of knowledge about syntactic relations, represented as a graph of dependencies, can enhance machine translation (MT) tasks. Specifically, it employs the graph attention network (GAT), trained on a universal dependencies corpus, to evaluate the impact of explicit syntactic knowledge, even when derived from a smaller corpus, in comparison to the pretraining of implicit knowledge on a massive corpus. The investigation involves an experiment on integrating GAT models into the MT framework, demonstrating robust improvement in MT quality for three language pairs, thus opening up possibilities for neurosymbolic approaches to natural language processing.","url":"https://doi.org/10.1177/29498732251340044","authors":["Yuqian Dai","Serge Sharoff","Marc De Kamps"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-26T01:15:54Z","doi":"10.1177/29498732251340044","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.2139/ssrn.5779186","name":"Conversation Analysis for Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5779186","authors":["Hansun Zhang Waring"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-20T23:40:47Z","doi":"10.2139/ssrn.5779186","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/medai62885.2024.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai62885.2024.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-25T19:17:43Z","doi":"10.1109/medai62885.2024.00004","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-3-032-09127-7_3","name":"Artificial Intelligence in Law","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-09127-7_3","authors":["Fernando Messias"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-23T11:37:41Z","doi":"10.1007/978-3-032-09127-7_3","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-30046-2.00018-1","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30046-2.00018-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-22T09:30:28Z","doi":"10.1016/b978-0-443-30046-2.00018-1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.65923/d24yre37","name":"Conscious Machines: A Philosophical Inquiry into Artificial Sentience","source":"crossref","abstract":"As artificial intelligence (AI) systems become increasingly sophisticated, a profound question arises: can machines attain consciousness, and if so, what does that mean for our understanding of mind, identity, and ethical responsibility? This paper explores the concept of artificial sentience from a philosophical perspective, examining theories of consciousness, the requirements for subjective experience, and the implications of creating machines that might claim to possess awareness. By evaluating computational theories of mind, functionalism, and emergentist models, alongside critiques from phenomenology and existential philosophy, the discussion centers on whether artificial systems can truly be conscious or merely simulate it. The inquiry also addresses the moral and societal consequences of attributing sentience to machines, including the potential need for rights, moral consideration, and new legal frameworks. Ultimately, the paper seeks to bridge the gap between technological advancements in AI and enduring philosophical questions about the nature of consciousness.","url":"https://doi.org/10.65923/d24yre37","authors":["Areej Mustafa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-19T13:31:28Z","doi":"10.65923/d24yre37","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-30046-2.00022-3","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30046-2.00022-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-22T09:30:28Z","doi":"10.1016/b978-0-443-30046-2.00022-3","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/aiac68175.2025","name":"2025 3rd International Conference on Artificial Intelligence and Automation Control (AIAC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiac68175.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-15T20:48:52Z","doi":"10.1109/aiac68175.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icaaic64647.2025","name":"2025 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaaic64647.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-20T20:39:15Z","doi":"10.1109/icaaic64647.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1007/978-981-96-1371-7_3","name":"Exercises","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-1371-7_3","authors":["Wei Weng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-05T04:00:37Z","doi":"10.1007/978-981-96-1371-7_3","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-21870-5.12001-1","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21870-5.12001-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-04T01:53:46Z","doi":"10.1016/b978-0-443-21870-5.12001-1","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/b978-0-443-23517-7.00014-9","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23517-7.00014-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T13:55:30Z","doi":"10.1016/b978-0-443-23517-7.00014-9","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1109/icaite68636.2025","name":"2025 2nd International Conference on Artificial Intelligence and Teacher Education (ICAITE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaite68636.2025","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T19:49:06Z","doi":"10.1109/icaite68636.2025","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.54364/aaiml.2025.52223","name":"A Systematic Review of Factors Influencing the Acceptance Of Artificial Intelligence Devices","source":"crossref","abstract":"This paper proposes a systematic review of the empirical research investigating why artificial intelligence (AI) devices are accepted or rejected. The aim is to discover and examine pivotal determinants related to AI acceptance, to resolve contradictions within the literature and to detect potential research areas that are unexplored, thus promising a holistic understanding of how humans interact with AI technology. The review highlights significant gaps in the literature with regard to how expectations, contextual factors and emotions are associated with AI acceptance. Effort expectancy, social influence, and anxiety are commonly investigated; however, the findings are conflicting. Hedonic motivation and trust are found to be significant antecedents for acceptance, but their mediating effects with other emotional and contextual factors are still less researched. Differences in methodology, in population, and in the AI applications evaluated, may have contributed to conflicting results. Such findings imply that AI acceptance is multidimensional in nature and cannot be comprehended by isolated constructs. Future research needs to focus more on integrated models that incorporate the interplay of expectations, affective responses and situational factors, taking into account cultural and organizational contexts. Working on these dimensions will facilitate the development of AI systems that better serve human needs and ideals. This review adds an important dimension to the literature on AI adoption, drawing together fragmented and, at times, contradictory evidence, highlighting areas in which much remains to be known and setting the agenda for future research.","url":"https://doi.org/10.54364/aaiml.2025.52223","authors":["Luis Salazar","Luis Rivera"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-01T12:18:47Z","doi":"10.54364/aaiml.2025.52223","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.54941/ahfe1005920","name":"Technology Innovation of Artificial Intelligence in Building Sector: Present Status and Challenges","source":"crossref","abstract":"As one of the least digitalized industries in the world, the building and construction sector has faced great challenges in sustainable growth. The high-fragmented structure and high threshold for R&amp;D investment has prevented the building and construction industry from swift technological innovation. In many industries, artificial intelligence (AI) is producing revolution, e.g., retail, telecommunications, and helps make profits, improve efficiency, security and safety. But application of this advanced technology to building sector seems largely fall behind. AI is considered able to assist waste reduction by decision making on complexity, assist energy management (e.g., identify the black hole of energy consumption during operation, and data mining and machine learning of big data to optimize scenario for sustainability or enable real-time feedback and regulation during operation) in building and construction industry. Earlier research on technological innovation in Yangtze River Delta has revealed that AI has less than 10 records of patent filing in the dataset and has rarely mixed with other technologies so far. Different from other technologies that state owned enterprises more or less have a role in the knowledge production, applicant in the field of AI is mainly private in nature – the known companies are from Zhejiang. In view of these inadequacies, a broader look at how this technology is being used at greater geographic sphere is in need. This research broadens the search of patent applications in AI in the field of building construction to reveal the panorama of how this technology has been applied across the globe. It generates insights into the potential of AI in building industry and opens discussing forum for future.","url":"https://doi.org/10.54941/ahfe1005920","authors":["Lingyue Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T04:41:21Z","doi":"10.54941/ahfe1005920","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"doi:10.1016/j.engappai.2025.111216","name":"Automatic text summarization techniques: A categorization, evolution and future scope","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111216","authors":["Ramesh Chandra Belwal","Atul Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-04T08:41:05Z","doi":"10.1016/j.engappai.2025.111216","addedAt":"2026-09-01T01:47:54.710Z","updatedAt":"2026-09-01T01:47:54.710Z"},{"id":"pmid:41676477","name":"AlphaGenome-enabled analysis of non-coding regulatory variants underlying RHD expression with wet-lab validation.","source":"pubmed","abstract":"Systematic identification of functional non-coding regulatory variants remains a major challenge in human genetics. Conventional approaches such as large-scale CRISPR screening and genome-wide association studies (GWAS) are powerful but often prohibitively expensive, time-consuming, and experimentally intensive, limiting their scalability for locus-specific mechanistic studies. Recent advances in artificial intelligence offer the potential to partially replace or substantially augment these approaches by prioritizing regulatory variants with high functional likelihood. The RHD antigen, a major contributor to red blood cell alloimmunization, hemolytic transfusion reactions, and hemolytic disease of the fetus and newborn, serves as an excellent model for this paradigm, since coding variants alone do not fully account for differences in RHD expression. Here, we present an integrated artificial intelligence (AI)-guided and experimental framework to identify and validate functional non-coding regulatory variants governing RHD expression. We first applied AlphaGenome (AG), a deep-learning model released in 2025 for non-coding variant impact prediction, to systematically interrogate the RHD locus. By integrating multi-omics datasets, AG prioritized regulatory regions within the promoter, 5' untranslated region (5'UTR), and intragenic regions. In silico deletion- and Single Nucleotide Polymorphism (SNP)-based perturbation analyses consistently predicted that variants within the promoter and its proximal regions, as well as within intragenic regions, exert strong suppressive effects on RHD expression. To experimentally validate these predictions, we performed CRISPR-mediated base editing in K562 cells at AG-prioritized non-coding SNP sites. Editing of a high-score predicted variant (chr1:25272434 G&gt;A) achieved efficient base conversion and was accompanied by additional nearby edits, all predicted by AG to downregulate RHD expression. In contrast, editing of low-score predicted sites (chr1:25272422 C&gt;T) produced much smaller functional effects. Quantitative polymerase chain reaction (qPCR) analysis of full-length RHD transcripts, together with flow cytometry-based analysis of RHD expression, confirmed strong concordance between AI-based predictions and transcriptional as well as phenotypic outcomes. Taken together, our results demonstrate that the combination of AI-guided regulatory variant prioritization and targeted base editing provides a potentially scalable and cost-effective alternative to traditional CRISPR screening for decoding functional non-coding variants in blood group genes, with direct implications for genomics-based RHD typing and transfusion medicine. To our knowledge, this study also represents the first validation of AlphaGenome predictions at the phenotypic level using wet-lab experiments.","url":"https://pubmed.ncbi.nlm.nih.gov/41676477/","authors":["Liu M","Shen Z","Jeong YK","Yu N","Wu SC","Wittig A","Tenen D","Liu Y","Liu Y","Chai L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.64898/2026.01.21.700828","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41676407","name":"Updated focused review of hematological, inflammatory, and lipid biomarkers in acute coronary syndrome.","source":"pubmed","abstract":"At present, hematological indices and biomarkers of inflammation that may be associated with atherosclerosis and the prediction of acute coronary syndromes (ACS) attract a lot of academic attention. This updated focused review aims to provide an overview of selected ACS biomarkers: white blood cells, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, systemic inflammatory index (SII), systemic inflammatory response index (SIRI) and lipoprotein(a). Novel inflammatory-lipid biomarkers such as high-sensitivity C-reactive protein (hsCRP) to high-density lipoprotein cholesterol (HDL-C) ratio, neutrophil to HDL-C ratio, and monocyte to HDL-C ratio may improve ACS diagnosis, risk stratification, clinical prognosis, and optimal management. These indices are inexpensive and easily obtained in daily clinical practice. Artificial intelligence and genetic analysis may improve their diagnostic performance and guide clinical management. The recent data also emphasize that these indices may be promising clinical tools for assessing ACS patients and monitoring the effectiveness of emerging anti-inflammatory strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/41676407/","authors":["Budzianowski J","Hiczkiewicz D","Ficner H","Rzeźniczak J","Słomczyński M","Kasprzak D","Hiczkiewicz J","Burchardt P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5114/aoms/208106","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41676072","name":"Hierarchical inverse opal hydrogel coatings for superhydrophobic, antibacterial, and drug-responsive catheter interfaces.","source":"pubmed","abstract":"Catheter-related infections and biofouling remain critical challenges in clinical practice due to limited surface functionalities and rapid bacterial biofilm formation.","url":"https://pubmed.ncbi.nlm.nih.gov/41676072/","authors":["Liu Y","Hou Y","Fei K","Fu S","Cheng L","Zhou L","Deng H","Hu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fbioe.2025.1741569","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675975","name":"Rare Variants in Antisense Long Noncoding RNA-Protein-Coding Gene Overlap Regions Contribute to Obsessive-Compulsive Disorder.","source":"pubmed","abstract":"Obsessive-compulsive disorder (OCD) is a prevalent neuropsychiatric disorder with an incompletely understood genetic basis, limiting targeted therapeutic options. Although previous rare-variant studies have primarily focused on protein-coding genes, the contribution of rare regulatory noncoding variants remains largely unexplored.","url":"https://pubmed.ncbi.nlm.nih.gov/41675975/","authors":["Jung S","Caballero M","Smout S","Mahjani B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.bpsgos.2025.100683","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675886","name":"Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.","source":"pubmed","abstract":"Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide and remains difficult to diagnose in its earliest stages. This narrative review examines developments in artificial intelligence diagnostic tools designed to support clinicians in the detection of Alzheimer's disease. It evaluates systems that analyze brain imaging scans, genetic information, and cognitive assessments, as well as emerging approaches that monitor speech patterns and data from wearable devices. The review identifies six challenges to clinical adoption: limited and unrepresentative data sets; limited transparency of algorithmic decisions; disruption of established clinical workflows; unclear regulatory frameworks; high implementation costs and infrastructure demands; and the potential to widen health disparities. To address these issues, we propose the creation of large collaborative data repositories, the advancement of transparent model interpretation methods, comprehensive clinician education programs, the establishment of clear regulatory pathways, and strategic investment in scalable infrastructure. By confronting these technical, human, and system-level challenges through coordinated efforts, artificial intelligence diagnostic tools can be incorporated into Alzheimer's disease care to enhance early diagnosis and improve patient outcomes across diverse healthcare settings.","url":"https://pubmed.ncbi.nlm.nih.gov/41675886/","authors":["Suleman MU","Mursaleen M","Khalil U","Khan SA","Saboor A","Hussnain MA","Zahir M","Khan UA","Rehman DU","Tabassum SN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004505","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675877","name":"Artificial intelligence in cardiology: an updated systematic review with ethical considerations and challenges in implementing artificial intelligence models.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) in cardiovascular medicine presents a transformative opportunity to enhance diagnostic accuracy and improve patient outcomes. This systematic review evaluates the impact of AI on cardiovascular diagnostics with a focus on its ability to surpass traditional methods in accuracy, efficiency, and predictive capabilities. Machine learning and deep learning have demonstrated significant advancements in areas such as echocardiography, electrocardiography, computed tomography angiography, and predictive analytics. AI algorithms have demonstrated superior performance in identifying subtle patterns and anomalies. Additionally, AI has shown promise in predictive analytics, forecasting disease progression and tailoring treatment plans, thereby improving patient outcomes. Despite these advancements, significant gaps remain in our understanding of AI's full impact on cardiovascular medicine. Challenges such as the generalizability of AI models, ethical considerations, data privacy, issues related to data quality, clear guidelines on AI implementation in clinical practice and potential biases in AI algorithms warrant further investigation. Key findings indicate that AI systems consistently achieve higher diagnostic accuracy, reduce inter-observer variability, and facilitate earlier detection of cardiovascular conditions, leading to improved patient outcomes. In conclusion, AI holds substantial promise for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This review provides valuable insights into the benefits and limitations of AI, guiding future research and clinical practice to ensure responsible and effective integration of AI technologies in cardiovascular health care.","url":"https://pubmed.ncbi.nlm.nih.gov/41675877/","authors":["Patel D","Kantamneni R","John JD","Patel T","Shukla A","Salma A","Anand N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004607","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675826","name":"NeuroAI-driven cognitive off-loading and synaptic effort recalibration.","source":"pubmed","abstract":"The rapid rise of generative artificial intelligence (AI) introduces measurable shifts in synaptic effort distribution, hippocampo-prefrontal retrieval load, and microglial cytokine oscillation secondary to task-based neural idling. Latest evidence places cognitive off-loading to AI as a network-modifying exposure rather than a cognition-reducing endpoint. Early mechanistic and interventional signals endorse monitoring of neurometabolic stress signatures while emphasizing modifiable digital-interaction windows that preserve endogenous neuroplastic adaptation.","url":"https://pubmed.ncbi.nlm.nih.gov/41675826/","authors":["Batool ST","Ali U","Mahato RK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004652","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675812","name":"Artificial intelligence in arrhythmia risk prediction: connecting undetectable vulnerabilities and proactive care.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41675812/","authors":["Amjad A","Altaf Hussain M","Ali U","Osman Abufatima I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004562","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675807","name":"Nanorobotics in cancer care: innovation without translation?","source":"pubmed","abstract":"Cancer remains a leading global health burden, with approximately 19.3 million new cases reported worldwide. Limitations of conventional therapies have accelerated interest in nanobots as precision tools for cancer diagnosis and treatment. Their nanoscale dimensions allow targeted drug delivery, reduced systemic toxicity, and enhanced therapeutic efficiency, including hyperthermia-based interventions. Advanced nanobot designs, such as isotope-labeled nanocarbon constructs, three-dimensional DNA nanobots, DNA-origami carriers, and magnetically propelled systems, demonstrate promising capabilities in biomarker detection, controlled drug release, and tumor-specific coagulation. However, despite these innovations, significant translational challenges persist, including safety concerns, off-target effects, and difficulties in external magnetic control. Bridging these gaps will require robust regulatory frameworks, improved nano-tumor biology insights, scalable manufacturing, and the integration of artificial intelligence-driven personalization. Addressing these issues will be pivotal for the clinical incorporation of nanobots in cancer therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41675807/","authors":["Tariq H","Mehmood MS","Hajj F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004543","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675725","name":"A narrative review on the therapeutic potential of stem cells in neurodegenerative diseases: advances, insights, and challenges.","source":"pubmed","abstract":"Neurodegenerative diseases (NDs) such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) are set apart by progressive neuronal loss and concomitant functional decline. Traditional therapies are equipped with only symptomatic relief, devoid of neurorestorative properties. Stem-cell-based therapies have the potential to revolutionize neurological care by replenishing lost cells, mitigating inflammation, and fostering a neuroprotective environment.","url":"https://pubmed.ncbi.nlm.nih.gov/41675725/","authors":["Patel T","Henna F","Sharif I","Javed I","Mustafa F","Sharif H","Nasir F","Javaid M","Usman SF","Hanani C","Anand N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1097/MS9.0000000000004490","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675641","name":"Type 5 diabetes mellitus: redefining pancreatogenic diabetes through molecular, imaging, and AI-driven evidence.","source":"pubmed","abstract":"Type 5 Diabetes Mellitus (T5DM), denoting pancreatogenic diabetes from fibro-inflammatory pancreatic injury, is a distinct yet under-recognised entity. Current WHO and ADA classifications overlook its complex, concurrent endocrine-exocrine failures, contributing to misdiagnosis, treatment gaps, and suboptimal outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41675641/","authors":["Rangraze IR","El-Tanani M","Wali AF","Babiker R","Rabbani SA","Matalka II","Satyam SM","Avagimyan A","Hoffmann K","Ilias I","Ispas S","Viviana M","Paczkowska A","Rizzo M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fendo.2025.1749805","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675343","name":"Artificial intelligence-augmented small bowel capsule endoscopy for coeliac disease: a literature review on accuracy, workflow, and safety.","source":"pubmed","abstract":"Coeliac disease (CeD) is a common, underdiagnosed enteropathy with rising incidence and diagnostic delay. This literature review synthesises advances in small bowel capsule endoscopy (SBCE) and artificial intelligence (AI) for SBCE, and outlines implications for clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41675343/","authors":["Dhali A","Maity R","Biswas J","Hann A","Sidhu R","Sanders DS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/tgh-25-128","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675323","name":"Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.","source":"pubmed","abstract":"Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide and remains a public health challenge despite widespread screening. Colonoscopy is the gold standard for screening by enabling detection and removal of precancerous lesions, yet it is not without its limitations. Interval CRCs still occur, largely due to variability in adenoma detection rate (ADR), the primary quality indicator of colonoscopy. Artificial intelligence (AI)-powered computer-assisted polyp detection (CADe) systems have emerged as promising tools to enhance colonoscopy performance. This review synthesizes current evidence on CADe in colonoscopy, highlighting clinical efficacy, limitations, and future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41675323/","authors":["D'Aquila ML","Linhares SM","Schultz KS","Hughes ML","Mongiu AK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/tgh-25-116","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41675260","name":"A comparative accuracy study of multimodal LLMs, VLM and agent-based framework for pulmonary nodule detection on chest radiographs.","source":"pubmed","abstract":"Artificial intelligence technologies are being actively introduced in clinical practice. The most promising solutions are AI-assistants based on large language models (LLMs). Determining the feasibility of integrating such applications in clinical practice requires independent performance assessments. This study assessed accuracy of several multimodal LLMs in detecting pulmonary nodules on chest radiographs (CXR).","url":"https://pubmed.ncbi.nlm.nih.gov/41675260/","authors":["Khovanova D","Vasilev Y","Vladzymyrskyy A","Omelyanskaya O","Pamova A","Arzamasov K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1674835","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41674986","name":"Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41674986/","authors":["Matcuk GR Jr","Fields BKK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.21037/tcr-2025-aw-2223","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41674935","name":"Temporal-spatial evolution of tumor habitat analysis: a bibliometric study on research hotspots and trends in medical imaging (2014-2025).","source":"pubmed","abstract":"Tumor habitat analysis holds significant application potential in oncology, yet systematic bibliometric studies to characterize its research landscape remain limited. This study aims to comprehensively assess the current status, hotspots, and trends in this field using rigorous bibliometric methods, providing a theoretical framework for future research.","url":"https://pubmed.ncbi.nlm.nih.gov/41674935/","authors":["Li X","Guo Y","Xu S","Ouyang H","Li Y","Liang D","Liu X","Zheng H","Hu Z","Yuan B","Zhang N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.21037/tcr-2025-1303","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41674809","name":"Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.","source":"pubmed","abstract":"Falls commonly occur in home environments where environmental conditions can contribute to fall risk. Identification and mitigation of environmental hazards are critical components of fall prevention. However, artificial intelligence (AI)-based fall prediction models have largely focused on individual-level predictors, with limited attention to home environmental hazards despite their modifiable role in fall risk.","url":"https://pubmed.ncbi.nlm.nih.gov/41674809/","authors":["Song J","Kim B","Kang MJ","Li S","Liu L","Jung W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.21203/rs.3.rs-8723907/v1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41674784","name":"CRISPR-Cas technologies in neurodegenerative disorders: mechanistic insights, therapeutic potential, and translational challenges.","source":"pubmed","abstract":"CRISPR-Cas genome-editing technologies have emerged as powerful tools for precise DNA and RNA modulation, offering promising therapeutic strategies for neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). This review critically evaluates current CRISPR/Cas applications in neurodegeneration, with emphasis on mechanistic insights, therapeutic outcomes, and translational feasibility. Preclinical and early translational studies demonstrate that CRISPR-Cas platforms can correct pathogenic mutations, suppress toxic gene expression, and restore neuronal function. Advanced modalities, including base and prime editing, CRISPRi/a, and RNA-targeting Cas systems, improve precision and reduce genomic damage, which is particularly advantageous in post-mitotic neurons. Emerging CRISPR-based diagnostics (e.g., SHERLOCK and DETECTR), AI-assisted sgRNA design, and machine-learning approaches for predicting off-target effects further enhance the safety, stratification, and monitoring of CRISPR therapeutics. In parallel, patient-derived brain organoids and assembloids provide scalable human-relevant platforms for mechanistic studies and preclinical validation. Despite this progress, major challenges remain, including efficient delivery across the blood-brain barrier, immune responses, long-term safety, and ethical and regulatory considerations. Overall, CRISPR-Cas technologies hold strong potential as disease-modifying interventions for neurodegenerative disorders, provided that advances in delivery systems, artificial intelligence integration, and regulatory oversight continue to evolve toward clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/41674784/","authors":["Yashooa RK","Nabi AQ","Smail SW","Azeez SS","Nooh WA","Mustafa SA","Al-Farha AA","Capitanio N","Shekha MS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fneur.2025.1737468","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41674089","name":"Trends in the psychedelic renaissance: applying artificial intelligence to measure media portrayal of psychedelic drugs in the 21st century.","source":"pubmed","abstract":"The relationship between media portrayal of psychedelic drugs, scientific research and drug policy is an area of debate.","url":"https://pubmed.ncbi.nlm.nih.gov/41674089/","authors":["Bender DA","Dunn HM","Pekau A","Mohite AD","Anandarajah A","Ross BD","Steinle J","Shankar S","Kiley B","Martin S","Gonuguntla R","Stonov M","Pippala N","Abdalla R","Stille MK","Yusuf J","Villalba M","Werner G","Divekar A","Daniels-Tineo M","Wang H","Chertock S","Sharma S","Ahmed SA","Bandyopadhyay R","Sridhar J","Iyer M","Adeyemi A","Smart K","Jalil U","Alam Z","Ercal BC","Hellerstein DJ","Nemeroff CB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 12","doi":"10.1192/bjo.2025.10974","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41673885","name":"Promoter landscape of maternal plasma DNA reveals predictive signatures of fetal growth restriction.","source":"pubmed","abstract":"BACKGROUND: Fetal growth restriction (FGR) causes serious pregnancy complications, but early detection is challenging. Promoter coverage patterns from maternal plasma cell-free DNA (cfDNA) offer a promising, non-invasive method for early FGR detection. METHODS: This retrospective multicenter study enrolled 788 singleton pregnancies (282 FGR and 506 matched controls) undergoing NIPT blood draw between 12 and 29 weeks of gestation from four hospitals in China. cfDNA promoter coverage was analyzed to identify transcription start site (TSS) features differentially covered in FGR. Feature selection was performed using LASSO regression. Three machine learning models&#x2014;support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN)&#x2014;were constructed. Model performance was assessed using area under the ROC curve (AUC), with an internal dataset used for validation. Functional enrichment analysis was conducted for biological interpretation. RESULTS: A total of 198 differentially covered TSS features were selected. In the validation dataset, both LR and SVM models demonstrated comparable predictive accuracy, with AUCs of 0.7 for LR and 0.69 for SVM. The associated genes were enriched in pathways related to placental and fetal development. CONCLUSION: Promoter profiling of maternal plasma cfDNA represents a novel, non-invasive approach for mid-pregnancy FGR prediction. This method could enhance early detection of at-risk pregnancies and inform clinical decision-making in prenatal care.","url":"https://pubmed.ncbi.nlm.nih.gov/41673885/","authors":["Wang J","Du P","Wu Z","Zhao X","Hou F","Xin G","Du S","Kang J","Peng Y","Xu W","Jin H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 11","doi":"10.1186/s40246-026-00932-z","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41673790","name":"Comprehensive clinical and genetic architecture of familial amyotrophic lateral sclerosis in China: A 15-year cohort study with 302 families.","source":"pubmed","abstract":"JOURNAL/nrgr/04.03/01300535-202606000-00072/figure1/v/2026-02-11T151048Z/r/image-tiff The growing recognition of the role of genetics in the development of amyotrophic lateral sclerosis is evident. However, there has yet to be a comprehensive analysis of the clinical characteristics and genetics of familial amyotrophic lateral sclerosis in an Asian population. This study aimed to provide an in-depth analysis of the clinical features and genetic spectrum of familial amyotrophic lateral sclerosis over 15 years in a clinic-based cohort of patients from the Chinese mainland. Enrollment of 302 amyotrophic lateral sclerosis families from 28 provinces was undertaken from January 2008 to September 2023. A group-based trajectory model for disease progression based on amyotrophic lateral sclerosis Functional Rating Scale-Revised (ALSFRS-R) scores was validated using bootstrap internal validation in patients with familial amyotrophic lateral sclerosis, as well as patients with sporadic amyotrophic lateral sclerosis (matched at a 1:4 ratio, with replacement). DNA samples from 244 index patients were screened for variants in the pathogenic genes SOD1, FUS, TDP43, and C9ORF72, of which 146 were also subjected to genome-wide next-generation sequencing. Gene-level burden analysis was used to evaluate the distribution of rare variants in the cohort. We found that rapid dynamic disease progression was associated with an older age at onset, shorter diagnostic delay, lower body mass index, bulbar onset, and &#x2265; 1 affected first-degree relative. Certain attributes, such as age at onset and time from onset to diagnosis, had comparable impacts on the clinical progression trajectories of both familial amyotrophic lateral sclerosis and sporadic amyotrophic lateral sclerosis. Harboring pathogenic/likely pathogenic variants in amyotrophic lateral sclerosis-causative genes reduced the age of onset of familial amyotrophic lateral sclerosis. Among the patients with familial amyotrophic lateral sclerosis, 17.8% possessed &#x2265; 2 pathogenic/likely pathogenic variants. Sequencing kernel association test analysis showed that the SOD1 rare variant burden (P = 1.3e-15) was associated with a significant risk of familial amyotrophic lateral sclerosis. Our findings conclusively confirmed the clinical features and genetic spectrum of familial amyotrophic lateral sclerosis over 15 years in a clinical cohort from China, contributing to a deeper understanding of genotype-phenotype relationships in familial amyotrophic lateral sclerosis. This comprehensive evaluation of specific clinical characteristics, clinical prognosis, and genetic variants of amyotrophic lateral sclerosis based on detailed clinical and genetic information may lead to the development of genotype-specific treatment approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/41673790/","authors":["Zheng W","Xu L","Cai J","Hou J","Chen L","Zhang N","Zhan S","Fan D","He J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 1","doi":"10.4103/NRR.NRR-D-24-00701","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41673282","name":"AI-Enabled Surveillance and Modelling for Counterfeit Botulinum Toxin A: Risk Projection, Patient Safety, and Systemic Reform of Pharmacovigilance.","source":"pubmed","abstract":"Counterfeit Botulinum Toxin A (BoNT-A) poses a growing global threat, particularly in aesthetic medicine where regulatory oversight is minimal and underreporting is widespread. Traditional pharmacovigilance systems such as FAERS and EudraVigilance fail to detect early counterfeit exposure due to reliance on delayed, structured reporting. As counterfeit incidents increase across multiple regions, a proactive, data-driven approach is urgently needed.","url":"https://pubmed.ncbi.nlm.nih.gov/41673282/","authors":["Rahman E","Rao P","Sayed K","Michon A","Yu N","Ioannidis S","Garcia PE","Wu WTL","Carruthers JDA","Webb WR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1007/s00266-026-05640-6","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41673245","name":"AI-driven diagnosis of acute aortic syndrome based on multi-modal information fusion.","source":"pubmed","abstract":"Acute aortic syndrome (AAS) is a severe cardiovascular disorder with a high mortality rate in the early stage of onset. In this study, the aim was to develop a multi-modal multi-scale fusion (MMMF) model to enhance early identification and classification of the diagnostic efficacy of AAS. Build a new diagnostic model of human-machine collaboration to reduce the early misdiagnosis and missed diagnosis of AAS. A retrospective analysis was conducted on the CTA images and clinical indicators of 493 patients from 2019 to 2024. In this study, a multi-scale image encoder was used to extract morphological features, which were then deeply integrated with clinical indicators through a graph neural network (GNN). The MMMF model outperforms the single-modal model in all four core classification metrics, confirming that multimodal fusion can effectively achieve complementary gains. The overall comprehensive diagnostic performance of the model (AUC&#x2009;&gt;&#x2009;0.9) demonstrates excellent diagnostic and classification capabilities. The graph structure modeling approach and the resolution of the visual encoder are the two key influencing factors of the model. The MMMF model has significantly enhanced the diagnostic and classification performance of AAS. It can be used as an auxiliary clinical screening tool to help doctors quickly identify typical cases, mark low-confidence cases, and accurately and promptly diagnose AAS.","url":"https://pubmed.ncbi.nlm.nih.gov/41673245/","authors":["Yang Z","Xu S","Wang B","Liu H","Chang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 11","doi":"10.1038/s41598-026-39111-4","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41673142","name":"Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.","source":"pubmed","abstract":"This scoping review aims to evaluate the performance of artificial intelligence (AI) models designed for adults when applied to paediatric imaging datasets without additional adaptations, and to quantify performance degradation across different modalities, use-cases and age groups.","url":"https://pubmed.ncbi.nlm.nih.gov/41673142/","authors":["Laborie LB","Lee J","Antram E","Lein RK","Shelmerdine SC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1007/s00330-026-12354-5","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41672496","name":"MoRE-Net: An Interpretable and Modality-robust Model for Brain Tumor Grading.","source":"pubmed","abstract":"Interpretability and robustness are both critical for developing trustworthy artificial intelligence, especially in high-stakes domains such as medical diagnosis. However, few studies have explored how to enhance robustness within interpretable model frameworks. This work aims to improve the robustness of interpretable multimodal medical imaging diagnostic models, particularly under missing modality conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/41672496/","authors":["Li B","Li C","Uchida W","Tanaka T","Zhao Q","Aoki S","Sun Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 26","doi":"10.2463/mrms.mp.2025-0107","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41670872","name":"Processing unstructured clinical notes with LLMs: applying the CMQOE framework for hypertension.","source":"pubmed","abstract":"Processing clinical unstructured data is crucial in disease research. However, existing approaches often rely on domain-specific annotated data and model fine-tuning, facing challenges such as complex workflows, lack of systematic prompt design, and data security risks. To address these issues, this study proposes a zero-shot data processing paradigm based on large language models (LLMs) and the CMQOE (Concept&#x2013;Method&#x2013;Quality&#x2013;Output&#x2013;Example) prompt framework. This framework systematically integrates concept definitions, task modeling, quality constraints, output specifications, and comparative examples to guide general-purpose LLMs in semantic parsing and structured transformation of medical texts&#x2014;without requiring task-specific training. Using outpatient medical records of hypertension patients as a case study, experiments demonstrate that the CMQOE framework significantly outperforms conventional prompt methods across multiple LLMs. For instance, with Qwen2.5-14B, it achieved an accuracy of 99.70%, a precision of 98.85%, a specificity of 99.89%, and an F1-score of 98.29%. Moreover, the framework exhibited strong generalization capability on a cross-dataset entity recognition task (CCKS 2019), performing comparably to fully-supervised models and even surpassing them in certain models such as the Qwen series. This study provides a lightweight, secure, and standardized approach for processing clinical unstructured data.","url":"https://pubmed.ncbi.nlm.nih.gov/41670872/","authors":["Gong A","Fan B","Liu Y","Wang D","Tong Z","Wei X","Jia A","Zhang Z","Wu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1007/s11517-026-03527-x","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41670428","name":"Artificial intelligence and diagnosis and management of tuberculosis disease in children.","source":"pubmed","abstract":"The literature review is pertinent because diagnosing pediatric tuberculosis (PdTB) remains quite challenging, especially in areas with limited resources, due to complications caused by variable generalized symptoms, paucibacillary characteristics, vague clinical manifestations, and challenges associated with pediatric sputum sample production. Recent developments in artificial intelligence have the potential to enhance the accuracy of diagnoses and the effectiveness of treatments.","url":"https://pubmed.ncbi.nlm.nih.gov/41670428/","authors":["Emegano DI","Ozsahin I","Isaac EP","Ozsahin DU","Silas OS","Emegano CL","Uzun B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 1","doi":"10.1097/MOP.0000000000001551","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41670187","name":"AI-enhanced Centiloid quantification of amyloid PET images.","source":"pubmed","abstract":"The Centiloid scale is the standard for amyloid (A&#x3b2;) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge.","url":"https://pubmed.ncbi.nlm.nih.gov/41670187/","authors":["Bourgeat P","Fripp J","Lebrat L","Xia Y","Feizpour A","Cox T","Zisis G","Gillman A","Goyal MS","Tosun D","Benzinger TL","LaMontagne P","Breakspear M","Lupton MK","Short C","Adam R","Robertson JS","Sperling R","O'Bryant SE","Johnson SC","Jr CRJ","Schwarz CG","Barkhof F","Farrar G","Bollack A","Collij LE","Landau S","Koeppe R","Alzheimer's Disease Neuroimaging Initiative","OASIS3","A4/LEARN Study Team","AMYPAD consortium","Health and Aging Brain Study (HABS‐HD) Study Team","Mayo Clinic Study of Aging","WRAP","ADNI‐DOD","PISA","ADNeT","AIBL research group","Morris JC","Weiner MW","Villemagne VL","Masters CL","Rowe CC","Dore V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1002/alz.71162","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669926","name":"Indocyanine Green Retention Rate at 15 Minutes as a Key Predictor of Clinically Significant Portal Hypertension in Cirrhosis: Development and Validation of a Superior Non-Invasive Diagnostic Model.","source":"pubmed","abstract":"To assess the correlation between indocyanine green retention rate at 15 minutes (ICG-R15), liver stiffness mea surement (LSM), and other clinical indicators in cirrhotic patients, using hepatic venous pressure gradient (HVPG) as a reference and to evaluate the predictive capability of ICG-R15 for clinically significant portal hypertension (CSPH).","url":"https://pubmed.ncbi.nlm.nih.gov/41669926/","authors":["Hu H","Lai W","Zhou T","Zhu J","Yan H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 10","doi":"10.5152/tjg.2025.25155","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669917","name":"Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease.","source":"pubmed","abstract":"Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of global morbidity and mortality. The current study provides a systematic review of the use of artificial intelligence (AI) technologies applied to the prediction and management of ASCVD. Traditional risk assessment approaches have their restrictions, leading to a growing preference for AI and machine learning techniques in risk assessment. First, this study tackles the complex pathophysiology of ASCVD and the problems associated with the current diagnosis, followed by an in-depth analysis of the wide variety of AI models that can be applied to electronic health records, medical imaging data, and other biomarkers. Special attention will be paid toward the potential of natural language processing models like bidirectional encoder representations from transformers in predicting risk from textual clinical data, and the overwhelming success of convolutional neural networks such as residual neural network and visual geometry group in plaque-based analysis through imaging modalities. Although the research results show that these models have a lot to offer in the clinical world, the authors also describe some serious disadvantages: data bias, interpretability of the model, and computational needs. It highlights, in particular, the need for multicenter validation studies as well as developing explainable AI techniques. Overall, AI-based approaches may pave the way for a new paradigm in ASCVD management. Nevertheless, deploying these technologies in everyday clinical practice will require overcoming technical, ethical, and regulatory challenges. As such, interdisciplinary collaboration and thorough clinical validation studies are essential for fulfilling the promise of these novel strategies to enhance patient outcomes. Cite this article as: &#x15e;akiro&#x11f;lu F, &#xc7;olak C, &#xc7;olak MC. Pre-trained artificial intelligence models in the prediction and classification of atherosclerotic cardiovascular disease. Eurasian J Med. 2025, 57(3), 0937, doi:10.5152/eurasianjmed.2025.25937.","url":"https://pubmed.ncbi.nlm.nih.gov/41669917/","authors":["Şakiroğlu F","Çolak C","Çolak MC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 16","doi":"10.5152/eurasianjmed.2025.25937","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669476","name":"Q.Liver software for the planning of treatment of liver cancer via transarterial radioembolization with yttrium-90 resin microspheres based on single-photon emission computed tomography-computed tomography.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) and liver-dominant metastases remain major causes of cancer mortality, and yttrium-90 transarterial radioembolization (TARE) offers a vital treatment option for unresectable cases. Accurate dosimetry is critical for maximizing tumor control while minimizing lung toxicity, yet conventional planar scintigraphy may overestimate lung shunt and compromise therapeutic efficacy. This study evaluated the value of Q.Liver software in planning yttrium-90 TARE for liver cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/41669476/","authors":["Shen D","Xie X","Zheng X","Wang X","Wang Q","Yang L","Liang Y","Li C","Yang A","Xue J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/qims-2025-1471","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669458","name":"Neuropathological links between plasma p-Tau 181, white matter hyperintensity, and structural brain changes in aging.","source":"pubmed","abstract":"White matter hyperintensity (WMH) has been reported to be associated with brain structure changes and Alzheimer's disease (AD) pathology in the aging process. This study sought to explore the underlying mechanisms linking cerebrovascular pathology, structural brain changes, and AD pathology in the aging process.","url":"https://pubmed.ncbi.nlm.nih.gov/41669458/","authors":["Wei L","Zhang W","Lu J","Liu D","Li X","Liu G","Yang H","Wang H","Zhu Z","Li X","Zhang X","Bai B","Zhang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/qims-2025-376","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669453","name":"Comparison of online radiologists and large language model chatbots in responding to common radiology-related questions in Chinese: a cross-sectional comparative analysis.","source":"pubmed","abstract":"Additional avenues for medical counseling are needed to better serve patients. In handling medical counseling, large language model chatbots (LLM-chatbots) have demonstrated near-physician expertise in comprehending enquiries and providing professional advice. However, their performance in addressing patients' common radiology-related concerns has yet to be evaluated. This study thus aimed to investigate the effectiveness and model performance of LLM-chatbots (DeepSeek-R1 and ChatGPT-4o) in radiology-related medical consultation in the Chinese context through both subjective evaluations and objective metrics.","url":"https://pubmed.ncbi.nlm.nih.gov/41669453/","authors":["Ji J","Li C","Fu Y","Zhao Z","Wu Y","Liang C","Wu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/qims-2025-1716","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669435","name":"A novel approach for contrast enhancement in medical images based on quantum-inspired enhancement algorithm.","source":"pubmed","abstract":"Computed tomography (CT) and magnetic resonance imaging (MRI) are essential in clinical diagnosis and treatment planning, but their images are often compromised by limited contrast and insufficient detail, reducing diagnostic clarity. Traditional enhancement methods-such as histogram equalization (HE) can improve visibility but may introduce noise, over-enhancement, or structural distortion. Quantum-inspired computational techniques have recently emerged as promising tools for nonlinear and adaptive image processing. Building on the quantum signal processing (QSP) framework, this study proposes a quantum-inspired enhancement (QIE) algorithm designed to improve medical image contrast while preserving structural details.","url":"https://pubmed.ncbi.nlm.nih.gov/41669435/","authors":["Zhu H","Su J","Meng X","Li W","Yang B","Qiu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.21037/qims-2025-1474","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41669209","name":"Hotspots and Trends in the Application of Artificial Intelligence in Spine Medicine from 2005 to 2024: A Bibliometric and Visualization Analysis.","source":"pubmed","abstract":"Artificial intelligence (AI) is emerging as a transformative force in spine medicine, offering support in diagnosis, surgical planning, and postoperative care through advanced algorithms. We aim to perform a comprehensive bibliometric analysis of AI applications in spine medicine to uncover research trends and inform future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41669209/","authors":["Liu T","Zou H","Zou H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s43465-025-01585-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41668730","name":"Exploring the association between dexmedetomidine and all-cause mortality in mechanically ventilated patients with sepsis through propensity score matching analysis and machine learning algorithms: a MIMIC-IV retrospective study.","source":"pubmed","abstract":"Sepsis carries high ICU mortality globally, often requiring sedated mechanical ventilation. While some studies suggest dexmedetomidine improves survival in these patients, others contradict this finding. This study evaluates dexmedetomidine's survival benefit and sedation value for ventilated sepsis cases.","url":"https://pubmed.ncbi.nlm.nih.gov/41668730/","authors":["Wei Y","Li M","Wang P","Zhou J","Lu K","Huang H","Huang Y","Lin F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fcimb.2025.1653883","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41668011","name":"An interpretable machine learning approach using nnU-Net-based radiomics for preoperative risk stratification of thymic epithelial tumors: a multicenter study.","source":"pubmed","abstract":"OBJECTIVE: This study aimed to develop and validate an interpretable machine learning (ML) model based on nnU-Net automated segmentation and computed tomography (CT) radiomics for preoperative risk stratification in thymic epithelial tumors (TETs). METHODS: In this retrospective multicenter study, 764 patients with pathologically confirmed TETs were enrolled and divided into training, internal validation, and two external validation cohorts. An nnU-Net model was trained for automatic tumor segmentation, with performance assessed by the dice similarity coefficient (DSC). Radiomic features were extracted from the automated segmentations of venous-phase CT images, and least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection. Predictive models, including radiomics-only, clinical-only, and a clinical-radiomics (combined) model, were constructed using five ML algorithms (RF, SVM, KNN, DT, and LR). Model performance was evaluated using the receiver operating characteristic (ROC) curve. Delong&#x2019;s test was employed to compare these ML models and select the best-performing model as the final model. Calibration curve and decision curve analysis (DCA) were performed to assess clinical efficacy of the final model. The interpretability of the optimal model was elucidated using SHapley Additive exPlanations (SHAP). RESULTS: The nnU-Net segmentation model achieved excellent performance, with a DSC of 0.979 on the test cohort. Compared to the other four combined models, the RF-based combined model demonstrated superior predictive efficacy, yielding area under the curve (AUC) values of 0.941 (training), 0.884 (internal validation), 0.867 (external validation 1), and 0.872 (external validation 2). The calibration curves indicated excellent agreement between the RF-based model&#x2019;s predictions and actual outcomes, and furthermore, DCA confirmed its superior net benefit over baseline strategies across a wide range of thresholds. SHAP tool identified 11 radiomic features and 3 clinical features as the most influential features, providing transparency into the model&#x2019;s decision-making process. CONCLUSIONS: The nnU-Net framework enables accurate and efficient automatic segmentation of TETs. The proposed RF-based combined model, integrating clinical and radiomic features, provides a robust and interpretable tool for identifying the high-risk TETs, holding promise for supporting clinical decision-making towards personalized therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41668011/","authors":["Gao R","Rong C","Ran R","Zheng X","Liu K","Wang W","Li S","Zhang J","Zhou J","Yang H","Wu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1186/s12880-026-02194-6","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41667954","name":"Accuracy of ultrasound for intussusception in pediatric emergency presentations: a systematic review and diagnostic meta-analysis.","source":"pubmed","abstract":"INTRODUCTION: Intussusception is a common cause of acute abdominal emergencies in children. This systematic review and diagnostic meta-analysis aimed to determine the diagnostic accuracy of ultrasound for intussusception in pediatric emergency presentations, providing pooled estimates for sensitivity, specificity, predictive values, and diagnostic odds ratios to inform clinical practice. METHODS: Adhering to PRISMA-DTA guidelines, a comprehensive search was conducted in PubMed, Scopus, Cochrane Library, and Web of Science up to July 2025. Bayesian bivariate random-effects meta-analyses were performed to estimate pooled sensitivity, specificity, and other measures, with subgroup and meta-regression analyses to explore heterogeneity. RESULTS: A total of 44 studies comprising 4,142 pediatric patients were included in the quantitative synthesis. The pooled sensitivity of ultrasound for diagnosing intussusception was 96.3% (95% credible interval [CrI] 94.9&#x2013;97.5%), and the pooled specificity was 95.7% (95% CrI 93.3&#x2013;97.5%). The area under the hierarchical summary receiver operating characteristic curve (AUC) was 0.81&#x2013;0.82, indicating good discriminative ability. Positive predictive value (PPV) ranged from 54.1% at 5% prevalence to 99.8% at 95% prevalence, while negative predictive value (NPV) decreased from 99.8% to 57.7% across the same prevalence range. The overall certainty of evidence for sensitivity and specificity was rated as high, with moderate certainty for prevalence due to substantial heterogeneity. CONCLUSION: Ultrasound demonstrates excellent diagnostic performance for pediatric intussusception in emergency settings, with high sensitivity and specificity maintained across patient subgroups and operator backgrounds. These findings support the continued use of ultrasound as the first-line diagnostic modality in both high- and low-resource environments and highlight the importance of structured training to optimize its accuracy. Future research should focus on multicenter prospective studies, standardization of ultrasound protocols, and the integration of artificial intelligence to further enhance diagnostic reliability.","url":"https://pubmed.ncbi.nlm.nih.gov/41667954/","authors":["Alsabri M","Rath S","Elkarargy MA","Aboali AA","Abouelmagd K","Ramadan AAA","Gamboa LL","Yoo P","Cheng Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1186/s12245-026-01134-z","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41667706","name":"Identification of blood-brain barrier injury-related biomarkers in cerebral infarction using transcriptomic analysis.","source":"pubmed","abstract":"BACKGROUND: Cerebral infarction leads to blood-brain barrier (BBB) disruption, exacerbating brain injury through edema, inflammation, and neuronal death. Although BBB damage is a critical event in stroke pathology, the underlying molecular mechanisms and reliable biomarkers remain poorly understood. This study aimed to identify key biomarkers associated with BBB injury following cerebral infarction using an in vitro model and transcriptomic approaches. Human cerebral microvascular endothelial cells (hCMEC/D3) were subjected to oxygen-glucose deprivation (OGD) and OGD/reoxygenation (OGD/R) to simulate ischemic and reperfusion injury. Cell viability, inflammatory cytokines, LDH release, and angiogenesis were assessed. Transcriptomic sequencing, weighted gene co-expression network analysis (WGCNA), and random forest algorithms were employed to identify differentially expressed genes and key biomarkers. OGD treatment significantly increased IL-1&#x3b2;, IL-6, TNF-&#x3b1;, and LDH levels, which were partially reversed by OGD/R. Transcriptomic analysis identified 1229 and 800 differentially expressed genes respectively in OGD and OGD/R comparisons. Enrichment analysis highlighted ribosome, endoplasmic reticulum, and mitochondrial pathways. Six core genes were screened, including RPS7, RPL36A, RPS9, RSL24D1, RPL41, and OSTC, all of which were upregulated under OGD and normalized after reoxygenation. We identified ribosome-related genes as potential biomarkers of BBB injury in cerebral infarction. These findings contribute to our understanding of BBB pathophysiology and suggest possible targets for future diagnostic and therapeutic development in ischemic stroke.","url":"https://pubmed.ncbi.nlm.nih.gov/41667706/","authors":["Liu X","He Y","Zhang N","Cai Y","Liu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1038/s41598-026-39763-2","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41667642","name":"Multimodal large language models challenge NEJM image challenge.","source":"pubmed","abstract":"Current evaluations of Large Language Models (LLMs) in medicine primarily focus on text-based benchmarks, leaving their multimodal diagnostic capabilities in complex, real-world clinical scenarios largely undefined. Furthermore, comparisons against large-scale human benchmarks remain scarce. To address this gap, we conducted a comprehensive evaluation of state-of-the-art multimodal LLMs (GPT-4o, Claude 3.7, and Doubao) using 272 complex cases from the New England Journal of Medicine Image Challenge (2009&#x2013;2025). Uniquely, we benchmarked AI performance against a massive global dataset of 16,401,888 physician responses, representing the largest comparative study of human-AI diagnostic reasoning to date. Strikingly, all multimodal LLMs significantly outperformed the global physician collective (P&#x2009;&lt;&#x2009;0.001). Claude 3.7 achieved a diagnostic accuracy of 89.0%, surpassing the physician majority vote (46.7%) by an absolute margin of over 40 percentage points. Even in challenging cases where human accuracy fell below 40%, Claude 3.7 maintained an accuracy of 86.5%. A novel finding of this study is the remarkably low concordance between high-performing models and physicians (Cohen&#x2019;s &#x3ba;: 0.08&#x2013;0.24). The ratio of model-advantage to physician-advantage cases reached 15.4:1, suggesting that MLLMs succeed in distinct areas where human cognition often falters. Our findings demonstrate that MLLMs have reached a superhuman tier in multimodal diagnostic accuracy. The substantial performance gap, coupled with low human-AI concordance, implies that MLLMs do not merely replicate human knowledge but utilize fundamentally distinct and complementary diagnostic reasoning pathways. These results position multimodal LLMs as critical, independent second readers capable of augmenting clinical decision-making in diagnostically difficult scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/41667642/","authors":["Sheng C","Shen S","Wang L","Chen J","Chen W","Wang S","Wang N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1038/s41598-026-39201-3","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41667548","name":"The impact of AI anxiety on career decisions of college students.","source":"pubmed","abstract":"The rapid advancement of artificial intelligence (AI) has reshaped the employment market, triggering widespread anxiety among college students about their future careers and posing a potential threat to their career decisions. Grounded in Career Construction Theory, this study investigated the impact mechanism of AI anxiety on career decisions among 315 Chinese college students, utilising a questionnaire survey and structural equation modeling (SEM). The analysis specifically examined the mediating role of career adaptability and the moderating role of self-efficacy. The results indicated that AI anxiety not only directly and negatively predicted career decisions but also exerted an adverse indirect effect by undermining career adaptability, with this mediating effect accounting for 63.35% of the total effect. However, the moderating effect of self-efficacy was insignificant, indicating limited buffering capacity. These findings suggest that higher education institutions should promote outcome-based education (OBE) reforms, enhance students' career adaptability by universalising AI literacy and career planning courses, and deepen industry-education integration. Such measures can help students make more confident and clear-sighted career decisions in the AI era.","url":"https://pubmed.ncbi.nlm.nih.gov/41667548/","authors":["Duan N","Li L","Lin G","Chen H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 11","doi":"10.1038/s41598-026-37648-y","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41667320","name":"Evaluation and analysis of clinical outcome prediction for trauma patients based on machine learning.","source":"pubmed","abstract":"The study was to compare the predictive performance of multiple models for clinical outcomes in trauma patients. To provide decision-making support for the clinical management of trauma patients by analyzing and ranking the importance of predictive indicators.","url":"https://pubmed.ncbi.nlm.nih.gov/41667320/","authors":["Shi H","Zhang Y","Xia Y","Cai J","Yu H","Feng L","Wang F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.cjtee.2025.10.004","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41666417","name":"Implementation of artificial intelligence in the 2025 medical parasitology course at Hallym University.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41666417/","authors":["Ha EH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3352/jeehp.2026.23.4","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41666303","name":"Artificial Intelligence Models to Predict Recurrence Risk Prediction in Early-Stage Non-Small Cell Lung Cancer: A Systematic Review.","source":"pubmed","abstract":"The purpose of this study was to systematically evaluate predictive models for assessing the risk of postoperative recurrence in patients with early-stage non-small cell lung cancer, and to determine the effect of integrating different data modalities on model performance.","url":"https://pubmed.ncbi.nlm.nih.gov/41666303/","authors":["Yang Y","He H","Yu C","Sardari Nia P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 5","doi":"10.1093/ejcts/ezag072","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41666194","name":"Artificial intelligence agents in healthcare research: A scoping review.","source":"pubmed","abstract":"Artificial Intelligence (AI) agents are rapidly transforming healthcare delivery, enabling real-time decision support and sophisticated patient interaction at scale. However, the scientific landscape of this rapidly growing, multidisciplinary field remains fragmented, with technical innovation outpacing translational research and the establishment of ethical governance frameworks. To address this gap, we conducted a comprehensive scoping review analysis of AI agent research in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/41666194/","authors":["Njei B","Al-Ajlouni YA","Sidney Kanmounye U","Boateng S","Loic Nguefang G","Njei N","Hamouri S","Al-Ajlouni AF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0342182","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41665699","name":"Optimizing Large Language Model Responses to Medical Queries: a Cross-sectional Study On the Effective Use of Chatgpt for Cancer-related Questions.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly used for medical advice; despite this, their response readability and quality remain suboptimal. Current research focuses on evaluating LLM outputs, with little investigation into practical optimization strategies for clinical use. On August 9, 2025, we identified the top 25 search keywords for five common cancers via Google Trends and adapted them into six prompt types. Each was posed to ChatGPT-4o and ChatGPT-5 between August 10 and August 12, 2025 under two query conditions: isolated (single question per page) and aggregated (all questions for one cancer type on the same page). Readability was assessed using four indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FKRE), Gunning Fog Index (GFI), and the Simple Measure of Gobbledygook (SMOG). Quality was evaluated on a 5-point Likert scale across accuracy, relevance, comprehensiveness, empathy, and falsehood. ChatGPT-5 generated responses with significantly fewer words (316.81&#x2009;&#xb1;&#x2009;12.96), sentences (19.79&#x2009;&#xb1;&#x2009;1.01), syllables (551.93&#x2009;&#xb1;&#x2009;24.55), and hard words (62.33&#x2009;&#xb1;&#x2009;3.60) than ChatGPT-4o (292.85&#x2009;&#xb1;&#x2009;14.52, p&#x2009;=&#x2009;0.003; 18.77&#x2009;&#xb1;&#x2009;1.07, p&#x2009;=&#x2009;0.039; 515.01&#x2009;&#xb1;&#x2009;27.89, p&#x2009;=&#x2009;0.006; 58.35&#x2009;&#xb1;&#x2009;4.05, p&#x2009;=&#x2009;0.005), while also achieving higher scores in accuracy (W&#x2009;=&#x2009;2.116, p&#x2009;=&#x2009;0.034), relevance (W&#x2009;=&#x2009;2.454, p&#x2009;=&#x2009;0.014), comprehensiveness (W&#x2009;=&#x2009;2.574, p&#x2009;=&#x2009;0.010), and empathy (W&#x2009;=&#x2009;2.174, p&#x2009;=&#x2009;0.030). The 6th-grade prompt markedly improved readability over other strategies (ChatGPT-5: FKRE:64.92&#x2009;&#xb1;&#x2009;8.56, GFI:8.10&#x2009;&#xb1;&#x2009;1.13, FKGL:8.74&#x2009;&#xb1;&#x2009;1.73, SMOG:6.97&#x2009;&#xb1;&#x2009;1.26; ChatGPT-4o:65.44&#x2009;&#xb1;&#x2009;7.43, GFI:8.04&#x2009;&#xb1;&#x2009;1.48, FKGL:8.73&#x2009;&#xb1;&#x2009;1.80, SMOG:6.86&#x2009;&#xb1;&#x2009;1.53). Aggregating queries on a single page yielded higher accuracy, relevance, and comprehensiveness scores compared to isolated questioning (ChatGPT-4o: W&#x2009;=&#x2009;4.451, p&#x2009;&lt;&#x2009;0.001; W&#x2009;=&#x2009;4.356, p&#x2009;&lt;&#x2009;0.001; W&#x2009;=&#x2009;1.965, p&#x2009;=&#x2009;0.049. ChatGPT-5: W&#x2009;=&#x2009;3.234, p&#x2009;&lt;&#x2009;0.001; W&#x2009;=&#x2009;2.697, p&#x2009;=&#x2009;0.007; W&#x2009;=&#x2009;3.885, p&#x2009;&lt;&#x2009;0.001). ChatGPT-5 produces more concise and qualitatively superior responses than ChatGPT-4o. The patient prompt generated responses with high readability and strong empathy, and is therefore recommended for patient use. Consequently, aggregating related questions on a single page is advised to obtain higher-quality answers.","url":"https://pubmed.ncbi.nlm.nih.gov/41665699/","authors":["Shao X","Sun Y","Ju X","Cui J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1007/s10916-026-02344-x","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41664805","name":"The Role of Artificial Intelligence in Diagnosing Pulmonary Embolism: A Systematic Review and Meta-analysis.","source":"pubmed","abstract":"Missed or delayed diagnosis of pulmonary embolism (PE) is associated with increased morbidity, mortality, and longer hospitalizations. This study aimed to evaluate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting PE across imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/41664805/","authors":["Farzaei A","Hajzeinolabedini F","Sharif Kashani B","Keshmiri MS","Khodayari Javazm A","Farzaei Y","Fereidooni M","Emamjomeh B","Nezami-Asl A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22037/aaem.v13i1.2720","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41664803","name":"Predicting the Risk of Opioid-induced Respiratory Depression Using ChatGPT-4o and Machine Learning Techniques.","source":"pubmed","abstract":"Opioid-induced respiratory depression is a life-threatening complication of opioid overdose. This study aimed to develop a model for predicting the risk of respiratory depression following opioid overdose using ChatGPT-4o.","url":"https://pubmed.ncbi.nlm.nih.gov/41664803/","authors":["Meshkini M","Hosseini SM","Erfan Talab Evini P","Rahimi M","Mostafazadeh B","Eini P","Babaeian Amini N","Faghihi A","Esmailsorkh F","Karimi S","Asadi M","Jalilvand H","Rady Raz N","Alidadyani I","Jafari H","Zandiyeh F","Khorshidi K","Ahadi M","Ashrafi-Esfahani S","Shadnia S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22037/aaem.v13i1.2832","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41664015","name":"Artificial intelligence, robotics, and person-centered care in nursing and midwifery education: qualitative study to develop an augmented caring pedagogy model.","source":"pubmed","abstract":"BACKGROUND: The rapid growth of AI and robotics is reshaping nursing and midwifery education, offering personalised and data-informed learning while raising ethical and relational challenges. These changes require educators to reassess their readiness and teaching approaches, highlighting the need to understand how faculty integrate AI into person-centred care. This study aimed to explore how nursing and midwifery educators perceive the role of artificial intelligence and robotics in person-centred care education and to generate empirical insights to inform the development of the Augmented Caring Pedagogy Model (ACPM). METHODS: This qualitative descriptive study was guided by the Normalization Process Theory. Four online semi-structured focus groups were conducted with 20 nursing and midwifery academics recruited from nine universities across T&#xfc;rkiye. Purposive sampling was used, and the data were analyzed using an integrated inductive&#x2013;deductive thematic approach supported by MAXQDA 24. RESULTS: Four themes were identified: (1) Coherence: Making Sense of AI-and Robotics-Supported Person-Centred Care, (2) Cognitive Participation: Engaging with Technological Transformation, (3) Collective Action: Operationalizing and Sustaining AI Integration, and (4) Reflexive Monitoring: Learning Outcomes, Equity, and Ethical Responsibility in AI-Enhanced Education. Educators viewed artificial intelligence and robotics as enhancing learning and professional growth, while also raising concerns related to empathy, ethics, inequality, and technological dependence. These findings collectively informed the development of the ACPM. CONCLUSION: Integrating AI and robotics into nursing and midwifery education offers substantial potential to advance person-centred, evidence-based teaching, but it also introduces ethical, emotional, and structural complexities. The ACPM offers a novel pedagogical framework centred on human&#x2013;technology synergy and a reflective adaptation cycle to support ethically grounded and sustainable integration of emerging technologies. Findings should be interpreted in light of the single-country context and the early stage of AI and robotics integration within educational practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41664015/","authors":["Sengul T","Uncu B","Sarıköse S","Kaya N","Lopez V","Kirkland-Kyhn H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1186/s12909-026-08717-7","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41663979","name":"Disability risk prediction models in community-dwelling older adults: a systematic review.","source":"pubmed","abstract":"Early identification of disability risk in community-dwelling older adults has emerged as a critical public health priority. An increasing number of studies have focused on developing predictive models for disability risk among community-dwelling older adults. The quality, risk of bias and applicability of these models remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/41663979/","authors":["Zhang Z","Wang Q","Li Y","Zhou L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1186/s12877-026-07129-y","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41663871","name":"Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.","source":"pubmed","abstract":"Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation exposure while preserving diagnostic accuracy. However, variability in methodologies, performance metrics, and clinical applications needs to be assessed. This systematic mapping review examines the current state of research in synthetic PET generation, analyzing their methodological frameworks and evaluating the clinical relevance.","url":"https://pubmed.ncbi.nlm.nih.gov/41663871/","authors":["Vaccaro M","Rosa E","Placidi E","Guarnera A","Secinaro A","Gandolfo C","Garganese MC","Napolitano A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1186/s41747-025-00651-5","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41663674","name":"Beyond the algorithm potential: orthodontic tooth-extraction decisions in the age of AI.","source":"pubmed","abstract":"Ziaei, S., Samani, D., Behjati, M. et al. Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta-analysis. BMC Oral Health 2025;25:1576. https://doi.org/10.1186/s12903-025-06880-9 1 QUESTION: Can machine learning (ML) accurately predict the need for extraction in the context of orthodontic treatment?","url":"https://pubmed.ncbi.nlm.nih.gov/41663674/","authors":["Mheissen S","Flores-Mir C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1038/s41432-026-01209-z","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41663620","name":"Generative Artificial Intelligence for Medical Image Creation in Health Professions Education: a Scoping Review.","source":"pubmed","abstract":"Generative artificial intelligence (AI) can create synthetic medical images, enabling new educational applications, but current tools have not been systematically validated for educational fidelity, safety, or equity. This scoping review maps how generative AI systems are used for medical image creation in health - professions education, summarizes reported benefits and risks, and identifies evidence gaps. Searches of PubMed, Embase, Scopus, and grey literature (January 2010 to May 2025) identified 19 eligible studies. Diffusion models (e.g., Stable Diffusion, DALL - E, Midjourney) dominated recent work, with applications concentrated in dermatology, ophthalmology, and anatomy; radiology and pathology were rarely represented. Several studies reported short - term gains in learner performance or engagement, including small controlled trials, but study designs, outcome measures, and follow - up durations varied widely. Across the literature, important harms and failure modes were common: medically inaccurate or anatomically implausible outputs (including hallucinated structures), very low task accuracy in some domains (e.g., synthetic ECG generation reported 32. 7% accuracy), inconsistent evaluation metrics, and limited external validation. Multiple studies also documented demographic bias and stereotyping in generated images, indicating that uncritical use may worsen rather than solve diversity and representation problems. Overall, generative AI for image creation should be treated as an experimental adjunct requiring rigorous human - in - the - loop review, bias auditing, and alignment with educational theory. Future research should adopt standardized frameworks for synthetic - image evaluation (e.g., fidelity and cognitive load considerations), include study quality assessment, and test long - term learning transfer and clinical impact.","url":"https://pubmed.ncbi.nlm.nih.gov/41663620/","authors":["Gupta K","Latinovich MF","Singh K","Patlas MN","Jajodia A","Kartik Gupta","Mila Ferri Latinovich","Madeleine Ferri Latinovich","Krishna Singh","Michael N. Patlas","Ankush Jajodia"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1007/s10916-026-02350-z","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"pmid:41663499","name":"Novel convolutional neural network for bacterial identification of confocal microscopic datasets.","source":"pubmed","abstract":"Artificial intelligence (AI), complex mathematical algorithms, is currently employed across various fields to perform tasks quickly and effectively. In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: (Escherichia coli and Staphylococcus aureus), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224&#x2009;&#xd7;&#x2009;224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm's performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model's high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9&#xa0;min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis's speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error.","url":"https://pubmed.ncbi.nlm.nih.gov/41663499/","authors":["Al-Jumaili A","Al-Jumaili S","Alyassri S","Duru AD","Uçan ON","Jacob MV","Branco F","Coelho PJ","Pires IM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 10","doi":"10.1038/s41598-026-38861-5","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41663460","name":"Integrative single-cell and machine learning framework reveals prognostic fibroblast subtypes and constructs a fibroblast-related risk signature in lung adenocarcinoma.","source":"pubmed","abstract":"Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer and continues to contribute substantially to global cancer mortality. Within the tumor ecosystem, cancer-associated fibroblasts (CAFs) are key stromal components that significantly influence LUAD progression. However, their phenotypic diversity and clinical implications remain incompletely elucidated. We integrated two single-cell RNA sequencing datasets (GSE171145 and GSE189357) to delineate the transcriptional landscape and developmental trajectory of fibroblasts in LUAD. A fibroblast-related signature (FRS) was developed by intersecting fibroblast-specific markers with differentially expressed genes from the TCGA-LUAD cohort, followed by univariate Cox analysis and machine learning modeling. A total of 101 combinations of ten machine learning algorithms were evaluated. The prognostic value of the FRS was validated across multiple GEO datasets. We further investigated its associations with immune infiltration, genomic alterations, and therapeutic response. The core gene TIMP1 was subjected to in vitro and clinical validation. We identified pronounced fibroblast heterogeneity in LUAD, with distinct differentiation trajectories revealed by pseudotime analysis. The constructed FRS exhibited robust prognostic performance across cohorts and was significantly correlated with immunosuppressive features, tumor mutation burden, and predicted immunotherapy outcomes. Clinically, the FRS served as an independent prognostic indicator and showed favorable calibration when combined with TNM stage in a nomogram. TIMP1, one of the top-ranked risk genes in univariate Cox analysis, was confirmed to be upregulated in tumor samples and to promote cell invasion and proliferation in vitro, supporting its functional role in LUAD progression. This study developed a fibroblast-based prognostic signature through integrative single-cell and bulk transcriptomic analyses. The FRS effectively stratifies LUAD patients and highlights the dynamic roles of fibroblasts in shaping tumor progression, providing potential biomarkers and therapeutic targets.","url":"https://pubmed.ncbi.nlm.nih.gov/41663460/","authors":["Cheng S","Zhang H","Mu Q","Zhang H","Tan L","Sun D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1038/s41598-026-35830-w","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41662695","name":"Performance of 5 AI Models on United States Medical Licensing Examination Step 1 Questions: Comparative Observational Study.","source":"pubmed","abstract":"Artificial intelligence (AI) models are increasingly being used in medical education. Although models like ChatGPT have previously demonstrated strong performance on United States Medical Licensing Examination (USMLE)-style questions, newer AI tools with enhanced capabilities are now available, necessitating comparative evaluations of their accuracy and reliability across different medical domains and question formats.","url":"https://pubmed.ncbi.nlm.nih.gov/41662695/","authors":["El Natour D","Abou Alfa M","Chaaban A","Assi R","Dally T","Bou Dargham B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar 9","doi":"10.2196/76928","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41662601","name":"FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning are powerful computational approaches that have the capacity to automate and improve medical care delivery in orthopaedic surgery through augmentation of medical devices, from diagnostic modalities to surgical guidance. Existing research has focused on prospective device applications and ongoing clinical trials, but a comprehensive analysis on cleared devices by the FDA is lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/41662601/","authors":["Lee B","Jay M","Fox H","Padley J","Dai T","Levin AS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.5435/JAAOSGlobal-D-25-00170","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41662130","name":"Sleep and psychiatric disorders: Bidirectional interactions and shared neurobiological mechanisms.","source":"pubmed","abstract":"Sleep is critical for emotional regulation, memory, and cognitive performance. Sleep disturbances, including insomnia, hypersomnia, and circadian misalignment, are highly prevalent and clinically significant across various psychiatric disorders. Once considered secondary, sleep problems are now recognized as active contributors to the onset, course, and relapse of mental illness. This narrative review synthesizes current evidence on the bidirectional interactions between sleep and major psychiatric conditions such as major depressive disorder, bipolar disorder, anxiety disorders, posttraumatic stress disorder, schizophrenia, attention deficit and hyperactivity disorder, and substance use disorders. We highlight convergent neurobiological mechanisms, including dysregulation of circadian systems, neurotransmitter networks (GABA, serotonin, dopamine, orexin), affective circuitry (prefrontal-amygdala interactions), and stress-immune pathways. Findings consistently show that sleep problems are transdiagnostic features, impacting diagnostic presentation, prognostic trajectories, and underlying pathology. For instance, chronic insomnia increases depression risk, sleep loss can precipitate manic episodes, and distinct sleep architecture anomalies are linked to schizophrenia. Sleep disturbances also predict worse outcomes in substance use disorders, including increased craving and relapse risk. Sleep is a tractable factor in mental health, offering a potent intervention leverage point. Routine, structured sleep assessment should be integrated into psychiatric care, emphasizing first-line behavioral and chronobiological strategies like Cognitive Behavioral Therapy for Insomnia (CBT-I) and light/rhythm therapies. Directly addressing sleep significantly improves psychiatric outcomes, reducing symptoms of depression and anxiety, decreasing suicidal ideation, and lowering relapse risk in bipolar disorder and psychoses. Future research should prioritize causal designs, mechanistic neuroimaging, biomarker identification, and responsible integration of objective measurement technologies and artificial intelligence for early warning systems and personalized treatment protocols.","url":"https://pubmed.ncbi.nlm.nih.gov/41662130/","authors":["Hyndych A","Koval K","Dzeruzhynska N","Mader EC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1371/journal.pmen.0000531","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41661510","name":"CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning.","source":"pubmed","abstract":"The diagnosis of temporomandibular joint (TMJ) disc displacement relies on clinical symptoms and magnetic resonance imaging (MRI), which is complex, costly and time-consuming. Although cone-beam computed tomography (CBCT) reveals indirect signs suggestive of TMJ disc displacement, manual interpretation remains expertise-dependent, thereby limiting its use in clinical practice. This study aims to predict the presence of TMJ disc displacement risk in CBCT images using deep learning techniques.","url":"https://pubmed.ncbi.nlm.nih.gov/41661510/","authors":["Fu Y","Li J","Zhai Q","Cui M","Huang X","Wang Z","Wu Q","Liu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1186/s40510-026-00606-5","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41661282","name":"[Artificial intelligence in sychotherapy-Attitudes and competencies of medical and psychological psychotherapists].","source":"pubmed","abstract":"Arificial Intelligence (AI) is increasingly becoming established in medicine, such as therapeutic chatbots in mental health. Despite this, knowledge about clinicians' views and skills is lacking. This study examines the attitudes toward AI and competencies of German physicians (&#xc4;PT), psychotherapists (PPT) and psychotherapists in training (PiA) for the first time.","url":"https://pubmed.ncbi.nlm.nih.gov/41661282/","authors":["Augustin M","Reitz A","Wirtz J","Hallawa A","Dartmann G","Schmeink A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1007/s00115-026-01948-5","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660568","name":"Emerging local chikungunya virus transmission in a major urban area in Southern China: characteristics of clinical manifestations, viral evolution and climatic influences.","source":"pubmed","abstract":"The 2025 chikungunya fever outbreak in Foshan, China rapidly spread from a previously non-endemic area, raising significant public health concerns. This event underscores the need to understand the factors driving viral transmission, host responses and the influence of local environmental changes. The primary objectives of this study were to: (i) characterize the clinical manifestations of chikungunya patients at the initial stage of the outbreak to facilitate concise diagnosis and treatment; (ii) determine the viral genetic factors contributing to the outbreak's rapid spread; and (iii) investigate the influence of local environmental and climatic conditions on vector mosquito reproduction. We quickly collected and analyzed clinical data from 134 patients hospitalized for the purpose of quarantine at the beginning of the outbreak. While fever, arthralgia and rash are the typical symptom triad of chikungunya fever, we found that they did not always present simultaneously at onset. Arthralgia was the most common presenting symptom. Phylogenetic analysis revealed that the viral strains were highly homologous to those from the R&#xe9;union outbreak, suggesting an imported origin. Furthermore, we identified the presence of E1-A226V, E2-I211T and E2-L210Q mutations, which have been previously associated with enhanced transmission by Aedes albopictus . Local climatic conditions during the outbreak period were also found to be favorable for mosquito reproduction. In conclusion, we propose that the Foshan outbreak resulted from a combination of virus importation, a largely immunologically na&#xef;ve population and a climate conducive to mosquito proliferation. Additionally, our findings suggest that clinicians should maintain vigilance for atypical symptoms to prevent misdiagnosis and missed cases.","url":"https://pubmed.ncbi.nlm.nih.gov/41660568/","authors":["Liang J","Yu H","Lin Z","Tang X","Fang Z","Hon C","Hong W","Kong L","Wang Y","Chen Y","Li Y","Chen Y","Guo M","Wu S","Hu F","Qiu H","Bai H","Yan H","Jiang S","Zhang Q","Liu J","Zhou H","Tan M","Huang W","Oliveira A","Jiang J","Huang Y","Yang Z","Zhong N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1093/nsr/nwaf529","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660542","name":"Bot vs. doc-who is better at reading proximal humerus fracture x-rays?","source":"pubmed","abstract":"Artificial intelligence is becoming increasingly utilized as a source of convenient, efficient, and cost-effective information. Considering the potential utility of ChatGPT as an adjuvant in clinical decision making, the current study evaluates (1) the accuracy of ChatGPT-5 at evaluating shoulder x-rays containing either normal or proximal humerus fracture (PHFx) diagnoses and (2) interrater reliability between ChatGPT and orthopedic surgeons at different levels of training.","url":"https://pubmed.ncbi.nlm.nih.gov/41660542/","authors":["Obana KK","Ren M","Luzzi AJ","LeVasseur MR","Swindell HW","Levine WN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.jseint.2025.101426","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660461","name":"2025 report on advances in thoracic aortic disease research.","source":"pubmed","abstract":"This review summarizes advances in thoracic aortic disease research published in 2025, with literature retrieved from 2,829 articles via PubMed searches, focusing on risk prediction, pathogenesis, and treatment strategies. In risk prediction, key advances include: biomechanical validation of ascending aortic length (AAL) as a stable morphological predictor; the relationship between clonal hematopoiesis and thoracic aortic aneurysm (TAA) linked to Janus kinase 2 (JAK2) (V617F) mutation variant allele frequency (VAF); the iAorta artificial intelligence (AI) system achieving acute aortic syndrome (AAS) detection on non-contrast computed tomography (CT); and sex differences revealed by the DisSEXion study demonstrating significantly higher type A dissection incidence in women. Additional advances include cardiac magnetic resonance (CMR)-derived three-dimensional (3D) distensibility and displacement mapping, high biaxiality ratio analysis, integration of phenotypic age acceleration with genetic risk, reduced plasma cartilage oligomeric matrix protein (COMP) levels as a warning biomarker, gene-specific differences in hereditary thoracic aortic disease (HTAD), air pollution as an environmental risk factor, and causal association between remnant cholesterol and aortic disease. In pathogenesis, the hexosamine biosynthetic pathway-integrated stress response (HBP-ISR) axis provides a common therapeutic target for both hereditary and sporadic disease; piezo type mechanosensitive ion channel component (PIEZO)1 regulates transforming growth factor (TGF)-&#x3b2; signaling with the Yoda1 agonist showing therapeutic potential; Calpain-2-mediated endothelial focal adhesion disruption plays a critical role in dissection; CX3CR1+ macrophages can be targeted by C-C chemokine receptor (CCR)2 antagonist, which mitigates aneurysm progression; the cyclin-dependent kinase-like (CDKL)1-ciliary function association introduces ciliary biology into aortic pathophysiology; and developmental origin-dependent susceptibility differences among smooth muscle cells (SMCs) are identified. In treatment, the frozen elephant trunk (FET) strategy shows excellent long-term survival and functional recovery. The Society of Thoracic Surgeons (STS) risk model improves preoperative assessment. Analysis clarifies high mortality risks associated with coronary and mesenteric malperfusion. PERSEVERE study confirms favorable aortic remodeling with Ascyrus Medical Dissection Stent (AMDS) hybrid prosthesis. Importantly, research emphasizes the critical role of hospital rescue capability and regionalized aortic care networks, with evidence supporting efficient inter-hospital transfer systems to improve outcomes. Advances in compliant stent-grafts and total endovascular aortic arch repair techniques expand treatment options for high-risk patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41660461/","authors":["Wu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.21037/jtd-2025-1-2609","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660440","name":"Association of artificial intelligence-based high-resolution computed tomography parameters with all-cause mortality in patients with connective tissue disease-associated interstitial lung disease: a longitudinal cohort study.","source":"pubmed","abstract":"The severity of interstitial lung disease (ILD) is frequently linked to poorer outcomes and reduced quality of life in connective tissue disease-associated ILD (CTD-ILD) patients. The purpose of this study is to investigate the utility of artificial intelligence (AI)-based quantitative high-resolution computed tomography (HRCT) analysis in assessing the prognosis of patients with CTD-ILD.","url":"https://pubmed.ncbi.nlm.nih.gov/41660440/","authors":["Chu C","Yan A","Xu J","Hu J","Wei Y","Shi F","Zhao S","Xin X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.21037/jtd-2025-1827","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660393","name":"Advances in pediatric video capsule endoscopy: current applications and future directions.","source":"pubmed","abstract":"Video capsule endoscopy (VCE) has revolutionized the evaluation of small bowel pathology, offering a safe, non-invasive, radiation-free diagnostic modality with broad clinical utility. Patency capsule use has further improved safety by minimizing the risk of retention in patients with suspected strictures. Since its introduction, its applications have expanded from obscure gastrointestinal bleeding and Crohn's disease to celiac disease, polyposis syndromes, and small bowel tumors among other indications. Emerging artificial intelligence (AI) integration promises to enhance diagnostic accuracy, streamline image analysis, and reduce interobserver variability. Furthermore, advancements in capsule design, including magnetic-assisted navigation and extended battery life, enable precise control and complete small bowel evaluation, even in cases of delayed gastrointestinal motility. High-definition imaging further allows for the identification of subtle mucosal abnormalities, such as vascular lesions, inflammation, and erosions, that might otherwise go undetected. Beyond diagnosis, novel applications, such as motility capsule studies and wireless capsule drug delivery systems, are unlocking new possibilities for functional and therapeutic interventions. Future innovations combining diagnostic and interventional capabilities promise to reduce the need for invasive procedures, optimize outcomes, and significantly enhance the quality of life for pediatric patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41660393/","authors":["Rojas I","Barth BA","Stewart JW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fped.2025.1738998","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660282","name":"MINFLUX dissects nucleosome and compacting chromatin structures in living cells.","source":"pubmed","abstract":"The chromatin structure is fundamental for genome compaction and gene transcriptional regulation in the nucleus. Although in vitro studies suggest a classical model that 11-nm nucleosome polymers fold into 30 nm fibers, such structures have not been observed in situ . In contrast, disordered chains or condensed liquid-like chromatin domains are reported in cells by EM studies and by super-resolution fluorescence microscopy. Do condensed chromatin fibers indeed exist in the cell? We identified a fluorescent dye that preferentially binds to AT-rich regions of DNA and blinks spontaneously to allow single probe visualization. Using three-dimensional (3D) MINFLUX localization, which is extremely low in phototoxicity and high-speed, we observed that a subset of DNA molecules assembles into fiber-like structures co-localized with histones. Native chromatin fibers in living cells are detected in the middle of the nucleus with segments that are variable in width. Most of the chromatin fibers are dismissed after Trichostatin A (TSA) treatment. In some cases, DNA localization even reveals the 3D ultrastructure of individual nucleosomes as 5-10 probes wrapping around an 11-nm cylinder in living cells. Therefore, chromatin fibers (&#x223c;30 nm) do exist in living cells, at least in AT-rich regions of the genome. The MINFLUX nanoscopy reveals the native chromatin ultrastructure and its folding to achieve structural compaction in the nucleus.","url":"https://pubmed.ncbi.nlm.nih.gov/41660282/","authors":["Xie H","Hu Y","Cheng R","He X","Liu K","Zhang S","Xie B","Fang H","Yang Y","Xu L","Wang X","Lin J","Li G","Guan JS","Ma H","Gu M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1093/nsr/nwaf451","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660241","name":"Artificial intelligence diagnostics for bladder tumor identification and grade prediction depend on narrow band imaging cystoscopy.","source":"pubmed","abstract":"The effective treatment of bladder cancer depends on early evaluation through cystoscopy. Given the clinical importance of distinguishing the tumor grade, we report the application of the AI-assisted NBI Cystoscopy Diagnostic System (AINCDS). The AINCDS consists of (1) dual-channel feature extraction module, (2) lesion segmentation module based on feature pyramids, and (3) a multi-task classification module. AINCDS achieved an accuracy for identifying bladder cancer of 0.919 (95% CI = 0.896 to 0.938). For the prediction of tumor grade, the accuracy was 0.764 (95% CI = 0.714 to 0.810). The AINCDS demonstrates similar ability comparable to urologists with over 10 years' experience. With the assistance of AINCDS, the tumor grade prediction accuracy of urologists with 1-3 years' experience improved from 0.667 to 0.793. AINCDS can assist in the diagnosis of bladder cancer and prediction of tumor grade, offering the potential to improve the accuracy of lesion assessment and reduce the workload of urologists.","url":"https://pubmed.ncbi.nlm.nih.gov/41660241/","authors":["Wang Y","Liang H","Zhang Y","Qi W","Wu G","Zhang X","Li C","Chen S","Chen J","Shi B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 20","doi":"10.1016/j.isci.2025.114309","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41660015","name":"Oral submucosal fibrosis: a comprehensive review on pathogenesis, diagnosis, therapeutics and computational advances.","source":"pubmed","abstract":"Oral submucosal fibrosis (OSF) is a chronic and progressive fibrosis disease and causes sclerosis in oral mucosal tissue with a higher potential of malignant transformation. It is characterized by excessive production and deposition of extracellular matrix. The major behavioral cause of OSF is chewing areca nut, and the symptoms include severe burning sensation, ulceration, restricted mouth opening, and more. However, despite significant advancements in biochemical and molecular techniques in recent years, no specific and targeted antifibrotic treatment strategies have been approved, potentially due to the complicated molecular mechanism that initiates and drives the fibrotic events, which remains to be completely understood. In this review, we aimed to discuss the epidemiology, etiology, and risk factors associated with the OSF, with special emphasis on the recent developments such as the use of flavored areca nut, etc. Then we highlight the OSF pathogenesis with special emphasis on the role of TGF-b, epithelial-mesenchymal transition, and other processes such as dysregulation of collagen metabolism and angiogenesis. We also mentioned the role of hypoxia-induced pathogenesis, which recently has been more in focus. Next, apart from traditional diagnosis methods, i.e., clinical evaluation and histopathology, we also discussed newer techniques such as biomarkers present in serum, saliva, and tissue biopsies. Afterwards, we mention ongoing traditional and modern treatments in clinical settings, such as the use of natural compounds, anti-fibrotic agents, targeted therapy, and more. We also discussed the role of emerging new therapeutic targets and how targeting them can overcome the current limitations. Moving ahead, we discussed how next-generation sequencing and artificial intelligence have improved our understanding of OSF pathophysiology. We conclude with a discussion of future perspectives and potential ways for developing novel OSF treatment or management.","url":"https://pubmed.ncbi.nlm.nih.gov/41660015/","authors":["Mokal CN","Das M","Hannenhalli S","Agrawal P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fcell.2025.1754209","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659991","name":"Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.","source":"pubmed","abstract":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is now considered to be among the most prevalent chronic liver diseases worldwide. Its comprehensive management encompasses multiple stages, including risk assessment, early detection, stratified intervention, and long-term follow-up. Among these, improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice. In recent years, artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field, offering novel tools and pathways for MASLD risk stratification, non-invasive diagnosis, therapeutic evaluation, and patient self-management. This review summarizes the current applications of artificial intelligence and smart devices in MASLD care, highlights their benefits and limitations, and discusses future directions to support precision diagnosis and treatment strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/41659991/","authors":["Zhu W","Zheng Q","Xu X","Yu X","Xu X","Tu H","Yu Y","Ying W","Xie J","Sheng G","Sheng J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 28","doi":"10.14218/JCTH.2025.00406","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659839","name":"Big data in healthcare and medicine revisited design and managerial challenges in the age of artificial intelligence.","source":"pubmed","abstract":"A decade ago, we characterized big data in healthcare as a nascent field anchored in distributed computing paradigms. The intervening years have witnessed a transformation so profound that revisiting our original framework is essential. This paper critically examines the evolution of big data in healthcare and medicine, assessing the shift from Hadoop-centric architectures to cloud computing platforms and GPU-accelerated artificial intelligence, including large language models and the emerging paradigm of agentic AI. The landscape has been reshaped by landmark biobank initiatives, breakthrough applications such as AlphaFold's Nobel Prize-winning solution to protein structure prediction, and the rapid growth of FDA-cleared AI medical devices from fewer than ten in 2015 to over 1200 by mid-2025. AI has enabled advances across precision oncology, drug discovery, and public health surveillance. Yet new challenges have emerged: algorithmic bias perpetuating health disparities, opacity undermining clinical trust, environmental sustainability concerns, and unresolved questions of privacy, security, data ownership, and interoperability. We propose extending the original \"4Vs\" framework to accommodate veracity through explainability, validity through fairness, and viability through sustainability. The paper concludes with prescriptive implications for healthcare organizations, technology developers, policymakers, and researchers.","url":"https://pubmed.ncbi.nlm.nih.gov/41659839/","authors":["Raghupathi W","Raghupathi V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Dec","doi":"10.1007/s13755-026-00433-2","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659813","name":"MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing.","source":"pubmed","abstract":"Global public health is formidably threatened by antimicrobial resistance (AMR). Antimicrobial susceptibility testing (AST) is characterized by its long duration. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is notable for its rapid analysis and cost-effectiveness. However, its role in AST has not been fully explored. In recent years, new opportunities for predicting AMR using MALDI-TOF MS data have been provided by the development of machine learning (ML) technologies. The research progress in using MALDI-TOF MS combined with ML for AMR testing is surveyed by this review, and critical steps including raw MALDI-TOF MS data acquisition, raw data preprocessing, algorithm selection, hyperparameter optimization, among others. It was found by us that the true resistance status can be comprehensively reflected by large-scale datasets, but effective management of high-dimensional data challenges is required. Algorithm performance can be enhanced by identifying the optimal combination of hyperparameters. Better predictive performance than individual models can be achieved by stacking ensemble learning methods. Model performance and generalizability can be more effectively assessed by metrics such as the Area Under the Receiver Operating Characteristic Curve (AUROC). The decision-making process can be understood by users with the help of model interpretation, thereby increasing model transparency and acceptability. Insufficient sample size, inadequate data standardization, and limited model generalizability are included in the current challenges. Continuously optimized, the integration of MALDI-TOF MS and ML is poised to open future avenues for rapid and accurate AMR prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/41659813/","authors":["Wang M","Xia W","Du J","Ma H","Sun B","Jiang H","Xu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fcimb.2025.1731083","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659755","name":"Education Research: Bridging the Artificial Intelligence Training Gap: Evidence from a National Survey of Italian Neurology Residents.","source":"pubmed","abstract":"As artificial intelligence (AI) rapidly becomes an integral tool in clinical neurology, future clinicians will need to master its application in patient care. While previous studies focused primarily on medical students' perspectives, our survey, addressed to Italian neurology residents, aims to assess their familiarity with AI tools and identify educational needs of learners close to clinical practice and care delivery.","url":"https://pubmed.ncbi.nlm.nih.gov/41659755/","authors":["Vozzi C","Sibilla M","Sandri D","Marinato V","Micolonghi G","Oliveri S","Filippi M","Marceglia S","Priori A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1212/NE9.0000000000200289","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659707","name":"Fractal dimensions for tumour-related cell types of prostate cancer on histopathology images using multiple-threshold box counting algorithm.","source":"pubmed","abstract":"The malignancies of prostate tumour cells are assessed by pathologists as grade groups (GGs) from 1 (least aggressive) to 5 (most aggressive) on histopathology images. GGs are associated with the degree of tumour cell differentiation and may have different self-similarities depending on GG and tumour-related cell types, which are neoplastic epithelial, inflammatory, connective tissue, necrotic, and non-neoplastic epithelial cells. We investigated the associations between GGs and fractal dimensions (FDs) for five types of prostate tumour-related cells using a multiple-threshold box counting algorithm (MTBC). We showed the association of FDs of 9 channel images (eosin, hematoxylin, normalised images for red, green, and blue colour channels) with multiple threshold values on histopathology images (patch images) and the feasibility of FD-threshold images in an artificial intelligence model to classify patients into low (GG&#x2264;3) and high (GG&#x2265;4) GGs. We constructed FD-threshold images based on MTBC algorithm for characterizing prostate tumour cells. A shallow-convolutional neural network (sCNN) model to classify patients into low and high GGs was trained with input data of the FD-threshold images for all 9 channels and evaluated using the area under receiver operating characteristic curve (AUC). There were statistically significant correlations between the FD of non-neoplastic epithelial cells and GG [Pearson correlation coefficient=-0.849, p=0.001]. Significant correlations also existed for connective tissue and the original images. The AUC for the sCNN classification model into high and low GGs was 0.811. FD can characterise physical properties of prostate tumour-related cells for low and high GGs.","url":"https://pubmed.ncbi.nlm.nih.gov/41659707/","authors":["Schwarz A","Arimura H","Cui Y","Shimabukuro S","Lin Q","Jin Y","Kobayashi S","Matsumoto T","Shiota M","Eto M","Oda Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.2142/biophysico.bppb-v22.0026","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41659258","name":"Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.","source":"pubmed","abstract":"A positive correlation has been observed between computed tomography (CT) value and the invasiveness of neoplastic ground-glass nodules (GGNs). However, the traditional mean CT value cannot reflect the density heterogeneity of nodules. This study aimed to explore the value of artificial intelligence (AI)-based density proportion analysis in predicting the invasiveness of neoplastic GGNs.","url":"https://pubmed.ncbi.nlm.nih.gov/41659258/","authors":["Xiong TW","Zhang XC","Fu BJ","Li WJ","Lv FJ","Chu ZG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 31","doi":"10.21037/tlcr-2025-1020","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41658897","name":"Deformable lung models for anatomical lung resections: The introduction of simulated reality for imaging guidance.","source":"pubmed","abstract":"This study introduces PulmoSimulatedReality (Pulmo-SR), a novel technique combining artificial intelligence, finite element method, 3-dimensional (3D) visualization, and 4-dimensional (4D) interaction for preoperative imaging and intraoperative surgical guidance in pulmonary resections, such as lobectomy and segmentectomy. The clinical applicability of this 3D modeling approach is evaluated through a preliminary validation protocol.","url":"https://pubmed.ncbi.nlm.nih.gov/41658897/","authors":["Mank QJ","Kieft T","Siregar S","Maat APWM","Kluin J","Sadeghi AH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.xjtc.2025.10.022","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41658791","name":"Optimizing Electronic Medical Records for an Acute Decompensated Heart Failure Registry: A Mixed-Methods Analysis of Data Completeness and Coding Practices at a National Cardiovascular Center in Indonesia.","source":"pubmed","abstract":"Introduction Clinical registries are essential for monitoring heart failure (HF) care quality, yet their effectiveness depends on the availability of structured data within electronic medical records (EMRs). This study aimed to analyze the completeness of core data elements and identify systemic barriers to documentation for patients with acute decompensated heart failure (ADHF) at the National Cardiovascular Center Harapan Kita in Jakarta, Indonesia. Materials and methods A mixed-methods sequential explanatory design was employed. Phase one involved a retrospective quantitative analysis of 305 EMRs of patients with ADHF admitted between January 2024 and January 2025. Data were extracted across 82 core variables harmonized with American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology (ESC) EuroHeart standards. Phase two consisted of in-depth interviews with eight key informants, including cardiologists, residents, nurses, medical records staff and the head of the hospital information system, to explore the root causes of documentation gaps. Results The aggregate data completeness was 77.2%. However, disparities existed between the domains. Documentation for inpatient therapy was near-universal (99.5%), whereas medical history documentation was low (40.4%). Critical registry variables such as New York Heart Association (NYHA) functional class, discharge weight, and National Identity Number (NIK) had 0% structured completeness. However, qualitative findings revealed these parameters were frequently present in unstructured free-text narratives, indicating a data capture gap rather than a clinical one. This was driven by a preference for Stevenson profiles (wet/dry) in acute settings and terminological mismatches. The analysis confirmed that documentation habits are primarily shaped by reimbursement incentives and technical EMR limitations rather than clinical oversight. Conclusions While the institution possesses a strong foundation for administrative data, the current EMR usage prioritizes billing over clinical relevance. Developing a functional, impactful clinical registry requires transitioning from free-text narratives to structured data entry, implementing mandatory fields for high-value clinical and prognostic markers, and automating data synchronization between EMR&#xa0;modules. Establishing this high-quality data infrastructure is critical for future applications in predictive analytics or even artificial intelligence, enabling precision medicine approaches tailored specifically to the unique HF demographic in Southeast Asia, including Indonesia.","url":"https://pubmed.ncbi.nlm.nih.gov/41658791/","authors":["Bun R","Adisasmito WB","Siswanto BB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.7759/cureus.100876","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41658402","name":"MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Observation and Localization in CT Images.","source":"pubmed","abstract":"Pancreatic cancer remains one of the deadliest malignancies, primarily because of its subtle CT appearance and frequent late-stage diagnosis. We introduce MiniGPT-Pancreas, a lightweight multimodal large language model (MLLM) that interprets natural-language queries within an interactive ChatGPT-style interface, as well as computed tomography images, and returns precise bounding-box predictions for the pancreas and associated tumors. A cascaded fine-tuning strategy was applied to MiniGPTv2, a multi-task general-purpose MLLM, with a focus on pancreas and tumor detection, using the National Institute of Health (NIH) and Medical Segmentation Decathlon (MSD) pancreas datasets. Pancreas detection achieved an average intersection over Union (IoU) of 0.57 on NIH and MSD datasets, outperforming the base MiniGPT-Pancreas model and more recent MLLMs like GLM-4.1V-9B-Base (general-purpose) and UMIT (specific to the biomedical domain). Tumor observation on MSD yielded an accuracy, precision, recall, and F1 score of all about 0.87, surpassing MiniGPT-v2, GLM-4.1V-9B-Base, and UMIT. For tumor localization, the IoU was 0.28, higher than UMIT (IoU=0.07), but lower than GLM-4.1V-9B-Base (IoU=0.48). On multi-organ detection on the AbdomenCT-1k dataset, MiniGPT-Pancreas outperformed GLM-4.1V-9B-Base and UMIT in all organs, with an IoU of 0.50 on pancreas vs. 0.43 and 0.03, respectively. MiniGPT-Pancreas was rated highly by an international group of 10 expert general surgeons (Italy, Singapore, and the UK) as a potential training tool, especially for verification (4.5/5.0), and training of young specialists (4.5/5.0) on a 5-point Likert scale. While operating on 2D slices limits volumetric context, MiniGPT-Pancreas demonstrates that compact MLLMs can rival specialized vision networks in pancreas imaging, offering an intuitive, language-driven tool for AI-assisted radiology. The code is publicly available at https://github.com/elianastasio/MiniGPTPancreas.","url":"https://pubmed.ncbi.nlm.nih.gov/41658402/","authors":["Moglia A","Nastasio EC","Mainardi L","Cerveri P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1007/s41666-025-00224-6","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41658325","name":"Improvement initiatives in the diagnostic process of heart failure: a scoping review.","source":"pubmed","abstract":"Heart failure (HF) poses a substantial global health burden due to its high prevalence and severe clinical outcomes. Early diagnosis is critical to optimize management and reduce the economic impact of HF. This scoping review consolidates existing knowledge on strategies to improve HF diagnosis, emphasizing the utility of biomarkers, imaging techniques, artificial intelligence (AI), and care pathways.","url":"https://pubmed.ncbi.nlm.nih.gov/41658325/","authors":["Aguiar D","Gonzalez-Manzanares R","Raya-Cruz M","Romero-Vigara JC","Salazar Mosteiro C","García Díaz AJ","Gonzalez Pastor V","Ugarte de Miguel A","Ródenas-Alesina E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fcvm.2025.1681976","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41658266","name":"Dermoscopy-guided high-frequency ultrasound: Principles and applications in dermatology.","source":"pubmed","abstract":"Conventional skin imaging modalities are often bulky, expensive, and impractical for routine dermatology practice. There is a need for a portable, multimodal imaging tool that integrates high-resolution surface and subsurface visualization at the point of care. The aim of this study was to describe the design, technical capabilities, and clinical application of dermoscopy-guided high-frequency ultrasound and to evaluate its performance across a range of dermatologic conditions. Dermoscopy-guided high-frequency ultrasound was applied to a total of 130 lesions from 122 patients at the Department of Dermatology, Semmelweis University (Budapest, Hungary); Universit&#xe9; Libre de Bruxelles (Brussels, Belgium); and Hospital Cl&#xed;nic, Universidad de Barcelona (Barcelona, Spain). The examined cases included malignant skin cancers and inflammatory disorders. Dermoscopy-guided high-frequency ultrasound enabled simultaneous visualization and correlation of surface dermoscopic patterns with underlying structural alterations in real time. The device identified disease-specific imaging features for both malignant and inflammatory lesions. Artificial intelligence-based segmentation improved image interpretability. Dermoscopy-guided high-frequency ultrasound bridges a critical gap between surface and subsurface dermatologic imaging, offering a practical, portable, and cost-effective solution that could enhance noninvasive diagnosis and management in dermatologic care.","url":"https://pubmed.ncbi.nlm.nih.gov/41658266/","authors":["Boostani M","Wortsman X","Pellacani G","Füzesi K","Suppa M","Del Marmol V","Morandini FV","Perez-Anker J","Giavedoni P","Cantisani C","Boussingault L","Gyöngy M","Paragh G","Avanaki K","Kiss N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.xjidi.2025.100446","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41657849","name":"The hepatitis B care cascade among key populations towards global elimination: a systematic review and meta-analysis.","source":"pubmed","abstract":"Key populations bear a disproportionate burden of hepatitis B virus (HBV). We synthesized evidence on the HBV care cascade among key populations to inform strategies toward WHO's 2030 elimination targets.","url":"https://pubmed.ncbi.nlm.nih.gov/41657849/","authors":["Su S","Jia M","Song Q","Guo L","Peng N","Yu Y","Jiang Y","Ji F","Zou Z","Zhang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.lanwpc.2026.101802","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41657589","name":"AI for diagnosing malocclusions from 3D dental models.","source":"pubmed","abstract":"Accurate diagnosis of dental malocclusions remains challenging due to interobserver variability among orthodontists. Therefore, it is of interest to evaluate the diagnostic reliability of artificial intelligence (AI) algorithms in classifying malocclusion types using 3D dental models compared with expert orthodontist assessments. A convolutional neural network (CNN) was trained and tested on digital impressions, and its performance was statistically analyzed against expert diagnoses. Results demonstrated strong agreement between AI predictions and orthodontist evaluations with clinically relevant consistency. These findings highlight the potential of AI-assisted diagnostics to enhance accuracy and reduce subjectivity in orthodontic assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/41657589/","authors":["Mohammed I","Chandra Nayak S","Shaik VA","Raj A","Pulayakalathil M","Karunakaran SK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6026/973206300214194","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41657580","name":"ECMO-associated nosocomial infections in adults: immunopathogenesis and predictive modeling approaches.","source":"pubmed","abstract":"Extracorporeal membrane oxygenation (ECMO) is a critical life-support intervention for patients with severe cardiopulmonary failure. However, its use is associated with a substantially increased risk of nosocomial infections, with reported incidence rates ranging from 8.8% to 64.0%. These infections-particularly ventilator-associated pneumonia and bloodstream infections-are linked to heightened morbidity, prolonged intensive care and hospital stays, and elevated mortality. This review aims to systematically compile Chinese and English literature published between 2018 and 2025, clarify the unique pathophysiological mechanisms of ECMO-related infections, analyze the limitations and breakthroughs of existing prediction models, and explore the potential role of machine learning in developing personalized early warning systems. Additionally, it seeks to establish a clinical decision-making framework for precise prevention and control. We conclude that improving ECMO infection control requires establishing standardized, clinically applicable diagnostic criteria, conducting a multicenter prospective validation study, and developing transparent, AI-enhanced predictive tools to enable real-time infection monitoring and improved patient prognosis during ECMO support.","url":"https://pubmed.ncbi.nlm.nih.gov/41657580/","authors":["Jiang J","Jiang Y","Xu Y","Zhang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1748154","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41657572","name":"Integrating Internet of Things into cardiac rehabilitation for heart failure: a review of emerging technologies.","source":"pubmed","abstract":"Heart failure (HF) is a prevalent and debilitating condition that significantly affects patients' quality of life and places a substantial burden on healthcare systems. In recent years, digital technologies have been increasingly explored in cardiac rehabilitation (CR), particularly through their integration within Internet of Things (IoT) ecosystems to support remote monitoring and personalized care. This review aimed to provide a focused overview of emerging digital technologies applicable to the rehabilitation of patients with HF, with emphasis on solutions compatible with IoT-based systems. A targeted literature search was conducted in PubMed, Scopus, Cochrane and Web of Science, including studies published between 2019 and 2024. Studies addressing digital technologies in HF rehabilitation. Following a structured selection process, 59 articles were included in the narrative synthesis. The findings indicate a growing body of literature investigating wearable physiological monitoring devices, telehealth-based CR programs, digital platforms, and smart sensors, many of which have been explored for integration within IoT infrastructures. These technologies have been associated with improved remote follow-up, patient engagement, and real-time physiological data collection outside traditional clinical settings. Emerging applications of artificial intelligence within IoT-enabled systems have also been examined to support clinical workflows and adaptive rehabilitation strategies. Despite increasing interest, challenges remain, including heterogeneity in study designs, usability concerns, data privacy and security issues, economic barriers, and limited large-scale clinical validation. Overall, this review suggests that IoT-enabled technologies represent a promising area of research in CR, warranting further investigation to support their sustainable integration into routine HF care.","url":"https://pubmed.ncbi.nlm.nih.gov/41657572/","authors":["Klein A","Pinheiro RF","Fonseca-Pinto R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1737523","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41657567","name":"Evaluating deep learning-based image segmentation for radiotherapy planning in pelvic and abdominal cancers.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into radiotherapy planning for pelvic and abdominal malignancies has ushered in a new era of precision oncology, enhancing treatment accuracy and patient outcomes. Central to this advancement is the development of sophisticated image segmentation techniques that accurately delineate tumors and surrounding organs at risk. Traditional segmentation methods, often reliant on manual contouring or basic algorithmic approaches, are time-consuming and susceptible to inter-operator variability, potentially compromising treatment efficacy. Moreover, existing deep learning models, while promising, frequently struggle with challenges such as ambiguous anatomical boundaries, small or disconnected lesion regions, and underrepresented classes within training datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/41657567/","authors":["Chen X","Lai S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1632370","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41656806","name":"[Progress in the application of artificial intelligence in assisted reproduction].","source":"pubmed","abstract":"As a disease that seriously affects the health of men and women of childbearing age, the incidence of infertility is increasing worldwide, and the popularization of assisted reproductive technology (ART) is expected to improve the situation. However, invitro fertilization and embryo transfer (IVF) success rates are only about 50%, and IVF success rates are affected by a number of factors. For example, semen quality, endometrial thickness, fallopian tube patency, embryo selection and transplantation, uterine microenvironment, etc., and the treatment process of IVF is highly dependent on the clinical experience of embryologists, and there is a lack of objective and unified evaluation criteria. Artificial intelligence (AI) is ideally suited to processing and analyzing large, dynamic temporal data sets to assist physicians in making more objective and precise decisions, thereby improving IVF success. At present, artificial intelligence technology using different types of algorithms has been used for sperm classification, oocyte and embryo selection, and prediction of embryo development after implantation, etc. The application of AI in the field of assisted reproduction is expected to improve infertility diagnosis results and increase the pregnancy rate and live birth rate of ART, but there are still certain controversies in privacy, safety and other aspects.In the future, with the accumulation of high-quality datasets, algorithm optimization and the advancement of imaging technology, AI is expected to increase the success rate of ART by selecting higher-quality sperm and oocytes, as well as embryos with greater developmental potential. This will bring significant innovation to the field of reproductive medicine and the entire healthcare sector, while also reducing treatment costs.","url":"https://pubmed.ncbi.nlm.nih.gov/41656806/","authors":["Li Q","Wang T","Liu Y","Wang X","Ma X","Liu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 28","doi":"10.11817/j.issn.1672-7347.2025.240638","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41656805","name":"[Application of large language models in health education for patients with pediatric cataract].","source":"pubmed","abstract":"Pediatric cataract occurs during the critical period of visual development, and early intervention is essential to avoid irreversible visual impairment. The health literacy and self-management ability of children and their parents directly affect treatment adherence and prognosis. With the rapid development of artificial intelligence, this study aims to evaluate the accuracy, completeness, and repeatability of domestic open-source large language model (LLM) in answering common clinical questions from pediatric cataract patients, and to explore their application potential as an online health information resource tool for pediatric cataract patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41656805/","authors":["Guo Y","Zhang Y","Xu Y","Wei W","Meng K","Chen Y","Chen Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct 28","doi":"10.11817/j.issn.1672-7347.2025.250181","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41656532","name":"AI + Drawing Enhances the Efficiency of Human Anatomy Education.","source":"pubmed","abstract":"Human anatomy is a fundamental core course in medical education, and its teaching effectiveness directly influences students' understanding and application of medical knowledge. However, traditional anatomy instruction often faces challenges such as limited teaching resources and the high cognitive difficulty students experience. With the rapid advancement of artificial intelligence (AI), its application in medical education is receiving increasing attention. Graphics serve as a vivid and intuitive form of communication, and learning anatomy through visual representations proves more effective than relying solely on textual information. This paper explores the integration of AI and drawing in human anatomy education, analyzing its advantages and implementation strategies. Through practical teaching cases, the effectiveness of this approach is validated, providing new perspectives and methods for the reform of anatomy teaching.","url":"https://pubmed.ncbi.nlm.nih.gov/41656532/","authors":["Zhou F","Yang Y","Liu J","Zhu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1002/ca.70083","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41656364","name":"Ninja optimization algorithm based ultra wideband antenna electromagnetic band gap modeling via a generative adversarial network.","source":"pubmed","abstract":"Ultra-wideband antennas with electromagnetic band-gap (EBG) structures play a crucial role in next-generation wireless and energy-efficient communication systems due to their ability to provide broad spectral coverage, high gain, and reduced interference. This paper presents an intelligent prediction framework that integrates a Generative Adversarial Network (GAN) with the Ninja Optimization Algorithm (NOA) to accurately model and predict the electromagnetic performance of ultra-wideband antenna-EBG configurations. The core innovation of the proposed framework lies in coupling adversarial learning with NOA-based optimization to enhance surrogate modeling accuracy and robustness for antenna-EBG systems. The proposed method is compared with multiple deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN) models. Experimental results demonstrate that the GAN tuned with the NOA achieves superior predictive accuracy, yielding a mean squared error of [Formula: see text], a root mean squared error of [Formula: see text], and a coefficient of determination of [Formula: see text]. The integration of the Ninja Optimization Algorithm significantly enhances learning stability, convergence rate, and generalization performance. The framework also demonstrates competitive robustness when benchmarked against hybrid optimization strategies such as PSO-GAN, BA-GAN, and DE-GAN. Overall, the proposed NOA-enhanced GAN establishes an efficient, scalable, and high-precision modeling pathway for the design and optimization of ultra-wideband antenna EBG structures, contributing to the advancement of intelligent communication and renewable energy systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41656364/","authors":["Alhussan AA","Khafaga DS","El-Kenawy EM","Abdelhamid AA","Ibrahim A","Singla MK","Gupta J","Gupta A","Thakur E","Eid MM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 9","doi":"10.1038/s41598-026-39068-4","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41655588","name":"Tenecteplase versus standard medical treatment for basilar artery occlusion within 24 h (TRACE-5): a multicentre, prospective, randomised, open-label, blinded-endpoint, superiority, phase 3 trial.","source":"pubmed","abstract":"The efficacy and safety of intravenous thrombolysis with tenecteplase within 24 h after stroke onset due to basilar artery occlusion are not well studied. We aimed to assess whether intravenous tenecteplase administered within 24 h after symptom onset improved functional outcome compared with standard medical treatment in patients with basilar artery occlusion.","url":"https://pubmed.ncbi.nlm.nih.gov/41655588/","authors":["Xiong Y","Alemseged F","Cao Z","Schwamm LH","Zhang S","Parsons MW","Fisher M","Hao Y","Jin A","Yin J","Jiang Y","Che F","Wang L","Zhou L","Dai H","Zhao Y","Duan C","Wu S","Feng G","Zong L","Ye W","Wang Z","Xu Z","Wang H","Hao M","Ma Y","Meng X","Li H","Li Z","Wang Y","Liu L","Zhao X","Campbell BCV","Wang Y","TRACE-5 investigators"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 21","doi":"10.1016/S0140-6736(25)02633-9","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41655051","name":"The fNIRS Landscape of ADHD: Device Specifications, Neural Markers, and AI Classification.","source":"pubmed","abstract":"This systematic review synthesizes 69 original studies (2019-2025) to evaluate the transformative potential of functional near-infrared spectroscopy (fNIRS) in ADHD research. As a portable, motion-tolerant neuroimaging tool, fNIRS enables robust measurement of cortical hemodynamic activity during cognitive tasks. We first consolidate the specifications of fNIRS devices employed in ADHD studies. Next, we discuss the neural markers derived from fNIRS data-including hemodynamic response function features, functional connectivity metrics, the beta coefficients of general linear model, graph theory measures, amplitude of low-frequency fluctuations, and multiscale entropy-alongside artificial intelligence (AI) algorithms achieving high diagnostic accuracy. Critically, we demonstrate fNIRS's utility in objectively monitoring treatment response, as evidenced by prefrontal cortex normalization and posterior activation modulation following interventions. To realize personalized diagnostics and therapeutics, future research should prioritize: (1) wearable fNIRS systems for ecological monitoring, (2) multimodal AI frameworks integrating fNIRS with behavioral/genetic data, and (3) standardized protocols validated in large-scale cohorts.","url":"https://pubmed.ncbi.nlm.nih.gov/41655051/","authors":["Gao T","Wei Z","Liang G","Zhao P","Wang L","Fan Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1111/nyas.70209","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41655003","name":"Exploring the Ethical Dimensions of AI-Driven Patient Monitoring in Critical Care Nursing: A Systematic Review.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly applied in intensive care units (ICUs) to enhance monitoring, prediction and decision-making. While these systems may improve patient safety and support nursing practice, their integration raises significant ethical concerns.","url":"https://pubmed.ncbi.nlm.nih.gov/41655003/","authors":["Amin SM","Almagharbeh WT","Alrimawi I","El-Gazar HE","Hamash KI","Zoromba MA","Alharbi AA","Al Sarayreh AS","El-Sayed MM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1111/nicc.70384","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41654807","name":"Application of machine learning for the diagnosis and prognosis of sepsis-induced acute respiratory distress syndrome: a systematic review and meta-analysis.","source":"pubmed","abstract":"Background: Sepsis is a critical condition that can lead to Acute Respiratory Distress Syndrome (ARDS), resulting from both pulmonary and extrapulmonary etiologies. This systematic review evaluates the application of machine learning (ML) approaches for the early diagnosis and prognosis of ARDS in sepsis patients. Methods:This study is a systematic review and meta-analysis, focusing on studies that employ machine learning to forecast the incidence and outcomes of acute respiratory distress syndrome in adult sepsis patients. Databases including PubMed, Scopus, and Web of Science were queried for relevant studies. The quality of the eligible study was assessed, and the diagnostic accuracy data was retrieved. Results: A total of 11 studies were incorporated into the meta-analysis. The aggregated sensitivity for diagnostic models was 0.76(95% CI:0.75 to 0.77), whilst the aggregated specificity was 0.70(95% CI:0.69 to 0.71). Prognostic models demonstrated a combined sensitivity of 0.74(95% CI:0.72 to 0.75) and specificity of 0.72(95% CI:0.71 to 0.74). The areas under the curve for these models were 0.821(95% CI:0.781 to 0.861) and 0.793(95% CI:0.767 to 0.819). Conclusions: These findings indicate the potential utility of machine learning in improving the early diagnosis and prognosis of ARDS in septic patients. However, further study is required to substantiate these findings and to investigate the incorporation of machine learning methods in practical clinical environments. Trial Registration: The review protocol was registered and approved on the International Prospective Statistical Review Registry (PROSPERO) prior to the start of the study on 23rd June 2025(CRD420251079338).","url":"https://pubmed.ncbi.nlm.nih.gov/41654807/","authors":["Dai M","Wu R","Zhou K","Xu Z","Shao Y","Zhou W","Zhang D","Chen M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 7","doi":"10.1186/s12911-026-03356-w","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41654638","name":"Urban environmental determinants and their effects on mental health, physical function, and quality of life in older adults: a multidimensional study in Shiraz, Iran.","source":"pubmed","abstract":"Urbanization and aging populations challenge public health in developing cities like Shiraz, where environmental factors significantly influence old adults' health. This study examined urban environmental impacts on older adults' health in Shiraz and developed predictive machine learning models for health outcomes. A cross-sectional study was conducted from December 2024 to January 2025, involving 3,000 older persons aged 60 years and above across 11 municipal zones of Shiraz. Stratified random sampling was used. Environmental data (green space per capita, population density, waste production) were extracted from municipal records. Health outcomes (BMI, frailty, depression, anxiety, and life satisfaction) were assessed using validated tools (GDS-4, GAI-5, LSI-Z). Statistical analyses included regression models and machine learning (Decision Tree, SVM). The SVM model demonstrated superior predictive performance (R&#xb2;=0.75 for frailty) compared to Decision Trees (R&#xb2;=0.71). Key predictive relationships emerged: each 1&#xa0;m&#xb2; increase in green space per capita predicted a 0.8-point reduction in depression scores (95% CI: -1.2 to -0.4) and 0.3-point lower frailty index. Waste production exceeding 250&#xa0;kg/capita was associated with 35% greater fall risk (OR&#x2009;=&#x2009;1.35, 95% CI: 1.12-1.63). Population density showed nonlinear associations with outcomes in SVM models, with thresholds varying by health indicator. Environmental quality plays a critical role in older adults' health. \"Urban planning strategies that enhance green spaces and strengthen waste management systems may substantially improve health outcomes among older adults in urban settings.","url":"https://pubmed.ncbi.nlm.nih.gov/41654638/","authors":["Asadollahi A","Błachnio A","Tomas JM","Oliver A","Mosazadeh H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 7","doi":"10.1038/s41598-026-38857-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41653696","name":"Explainable AI in Cardiology Diagnostics: A Systematic Review of Machine Learning, Meta-heuristic Optimization, and Clinical Text Mining for Coronary Artery Disease.","source":"pubmed","abstract":"This systematic review compiles evidence and examines how various artificial intelligence (AI) approaches, including machine learning (ML), natural language processing (NLP), meta-heuristic optimization, and explainable AI (XAI), are utilized to predict and diagnose coronary artery disease (CAD). We aim to identify the most commonly used models, evaluate their performance, and explore how interpretability and optimization enhance their usefulness in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41653696/","authors":["Jaradat M","Awad M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.ijmedinf.2026.106321","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41652975","name":"Importance of genetic ancestry in pharmacogenomics for precision medicine.","source":"pubmed","abstract":"Genetic ancestry refers to an individual's biogeographical origins inferred from correlated allele frequencies shared with individuals from similar ancestral regions. Understanding the complexities of genetic ancestry has proven beneficial in the field of pharmacogenomics (PGx), where personalized medication regimens are optimizing therapeutic outcomes while minimizing the risk of side effects. With the rise in the availability of electronic health records (EHR), population-specific genetic data can be integrated with clinical data using machine learning approaches to improve personalized treatment plans. Furthermore, multiomics data such as the transcriptome, methylome, proteome, and metabolome, paired with advances in machine learning methods, provide a more comprehensive approach to understanding genetic variation. The expansion of PGx studies in diverse populations can broaden the impact of precision medicine, particularly among underrepresented groups.","url":"https://pubmed.ncbi.nlm.nih.gov/41652975/","authors":["Venkatesh R","Keat K","Salvatore M","Cindi Z","Hall MA","Ritchie MD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec-Dec","doi":"10.1080/14622416.2026.2620360","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41652112","name":"Emerging trends in the early diagnosis of dental caries: a scoping review of artificial intelligence, digital diagnostics, and teledentistry.","source":"pubmed","abstract":"Dental caries is the most prevalent chronic, noncommunicable condition affecting individuals of all ages and socio-economic status. The recent technological advancements in artificial intelligence (AI), digital diagnostics, and teledentistry have been genuinely promising in revolutionizing the future of early caries detection and preventive care. However, an integrated understanding of these advancements and their clinical readiness remains limited.","url":"https://pubmed.ncbi.nlm.nih.gov/41652112/","authors":["Basheer SN","Daghrery AA","Albar NH","Peeran SW","Karobari MI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1038/s41432-026-01207-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41649179","name":"Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.","source":"pubmed","abstract":"Gastric cancer (GC) is one of the most prevalent and lethal gastrointestinal malignancies. MutS homolog 2 (MSH2), a DNA mismatch repair protein, has emerged as a promising prognostic biomarker. However, traditional histopathological evaluation is limited by restricted fields compared with whole-slide imaging. This study aimed to investigate whether machine learning-derived digital pathomics features could predict MSH2 expression and clinical outcomes in GC.","url":"https://pubmed.ncbi.nlm.nih.gov/41649179/","authors":["Zhang ZR","Wang Y","Yan WW","Li HR","Cheng ZW","Han T","Zhang C","Wang XM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 1","doi":"10.14309/ctg.0000000000000985","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41648958","name":"Artificial intelligence for screening drug resistance in tuberculosis.","source":"pubmed","abstract":"Background &amp; objectives Central TB division facilitated development of a line probe assay (LPA) artificial intelligence (AI) tool. The tool was developed, trained, and validated for performance by collecting more than 18,000 LPA strips across culture and drug susceptibility Testing (C&amp;DST) laboratories. The Indian Council of Medical Research (ICMR)-National Institute for Research in Tuberculosis (NIRT) evaluated the LPAAI tool independently. The objective was to establish and verify an AI-driven system for automatically interpreting LPA strips, which are employed in tuberculosis drug resistance screening, to improve accuracy, consistency, and scalability across diverse laboratory settings. Methods The AI system integrates faster regions convolutional neural network (FR-CNN) for strip detection, detection transformer (DETR) for band localisation, and a hierarchical neural network (HNN) for classification of bands, loci, and drug labels. Independent validation was conducted by ICMR-NIRT using 2810 first-line (FL)-LPA and 241 reflex second-line (SL-LPA) across ten intermediate reference laboratories (IRLs). Results AI comparative models demonstrated an accuracy range of 92-100 per cent, with sensitivity between 80-100 per cent and specificity from 86-100 per cent for the tub, rpoB, katG, InhA, gyrA/gyrB,rrs, and eisgenes. The overall F1 score varies from 0.81 to 1.00, indicating perfect precision and recall. Interpretation &amp; conclusions This AI system offers a novel, modular architecture capable of expert-level interpretation of LPA strips. The AI tool performs at par with expert readers and offers a reliable, scalable solution for LPA interpretation.AI tool adoption can reduce interpretation time, enhance result uniformity, and improve treatment delivery across India's TB programme, supporting national goals for TB elimination.","url":"https://pubmed.ncbi.nlm.nih.gov/41648958/","authors":["Mohanvel SK","Radhakrishnan R","Balraj P","Singh N","Danisetty S","Vats H","Chaudhury A","Choudhary H","Rajendran P","Jayaprakasam M","Devi Vadivel S","Preysingh B","Thariyasha S","Anjaiyan S","Chittiboyina S","Dhawan S","Lichade S","Kumari R","Ratnam R","Chandrakar S","Nasir Khan M","Srivastava AK","Roy I","Kingsberry R","Sridharan A","Ramachandran R","Kumar N","Singh M","Rao R","Singh UB","Padmapriyadarsini C","Shanmugam SK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.25259/IJMR_1546_2025","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41648814","name":"Unveiling the effectiveness of Chat-GPT 4.0, an artificial intelligence conversational tool, for addressing common patient queries in gastrointestinal endoscopy.","source":"pubmed","abstract":"Chat Generative Pre-Trained Transformer (Chat-GPT) has proven effective in addressing patient inquiries related to gastrointestinal (GI) disease. We aimed to assess the effectiveness and reliability of Chat-GPT in answering common patients' queries on GI endoscopy.","url":"https://pubmed.ncbi.nlm.nih.gov/41648814/","authors":["Calabrese G","Maselli R","Maida M","Barbaro F","Morais R","Nardone OM","Sinagra E","Di Mitri R","Sferrazza S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.1016/j.igie.2025.01.012","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41648771","name":"Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.","source":"pubmed","abstract":"High-altitude polycythemia (HAPC) lacks a lifestyle-focused risk-stratification tool among lifelong high-altitude residents. Here we aimed to develop and validate a novel machine-learning predictive scoring system for HAPC using readily modifiable lifestyle variables in this population.","url":"https://pubmed.ncbi.nlm.nih.gov/41648771/","authors":["Suona Y","Danzeng Z","Gesang L","Zhuoma P","Baima Y","Pubu Z","Suolang W","Ci B","Huang J","Zhaxi Q","Liu B","Zhang R","Gesang Q","Dingzeng Q","Baima Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fpubh.2025.1739909","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41648699","name":"Large Language Models and Surgical Decision-Making: Evaluation of Generative Unimodal AI in Facial Traumatology Practice.","source":"pubmed","abstract":"Large language models (LLMs) offer remarkable potential in assisting healthcare professionals with diagnostic and therapeutic decision-making processes. However, the integration of LLMs into healthcare decision-making processes also introduces several doubts in the field of usefulness, reliability and ethical implications.","url":"https://pubmed.ncbi.nlm.nih.gov/41648699/","authors":["Benedetti S","Frosolini A","Catarzi L","Vaira LA","Consorti G","Paglianiti M","Gennaro P","Gabriele G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1007/s12663-025-02556-7","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41648050","name":"New advances in efficacy prediction of extracorporeal shock wave lithotripsy in pediatrics: a narrative review.","source":"pubmed","abstract":"Extracorporeal Shock Wave Lithotripsy (ESWL) has been a cornerstone in treating pediatric urinary stones for nearly four decades, but requires tailored approaches due to anatomical and physiological differences from adults. This review synthesizes current evidence on ESWL efficacy predictors in children, integrating multicenter data and emerging technologies. Key traditional predictors include favorable stone characteristics [density &#x2264;600 Hounsfield units [HU], size &#x2264;15&#x2005;mm, skin-to-stone distance [SSD] &#x2264;6.6&#x2005;cm, upper/middle calyx or ureteral location] and patient factors (age &#x2264;3 years, male sex); conversely, urinary tract infections (UTIs), BMI &gt;22, and multiple stones correlate with poorer outcomes. Innovations like dual-energy CT (DECT), AI-based models, shear wave elastography (SWE), and bioelectric impedance analysis (BIA) offer promising non-invasive preoperative assessment. We highlight the need for standardized multifactorial predictive models to optimize pediatric ESWL outcomes. Future directions emphasize AI, big data, and multidisciplinary collaboration to enhance personalized treatment and reduce complications. This analysis provides clinicians with evidence-based tools to refine pediatric ESWL protocols.","url":"https://pubmed.ncbi.nlm.nih.gov/41648050/","authors":["Zang M","Dong Y","Wang X","Han C","Jia J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fped.2025.1681384","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647846","name":"Climate-related stressors, community healthcare systems, and adaptation strategies: A scoping review.","source":"pubmed","abstract":"Climate-related stressors are a global challenge with effects extending far beyond the environment, significantly impacting the provision of healthcare services, especially in the African and Asian countries selected for examination in this scoping review. Climate-related stressors are expected to significantly increase health risks in these countries, continuing to disproportionately affect vulnerable groups.","url":"https://pubmed.ncbi.nlm.nih.gov/41647846/","authors":["Saad S","Adede C","Citrin D","Wasunna B","Barasa M","Jafa K","Ebi KL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov-Dec","doi":"10.1016/j.joclim.2025.100574","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647812","name":"Development of an artificial intelligence model to identify duodenal polyps in patients with familial adenomatous polyposis.","source":"pubmed","abstract":"Precancerous duodenal polyps can be subtle in familial adenomatous polyposis (FAP). Artificial intelligence models can detect gastrointestinal (GI) tract pathology; thus, we aimed to develop a model to identify duodenal polyps in FAP patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41647812/","authors":["Schupack D","Sood S","Fetzer J","Shalini S","Arunachalam SP","League JB","Leggett C","Boardman L","Coelho-Prabhu N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.igie.2025.09.009","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647810","name":"Asia-Pacific consensus on the use of artificial intelligence in colorectal cancer screening and surveillance.","source":"pubmed","abstract":"Artificial intelligence (AI)-assisted colonoscopy has been widely investigated for colorectal adenoma and cancer detection and characterization. However, clinical guidance on its use in colorectal cancer (CRC) screening and surveillance is lacking. In this study, we developed consensus guiding when and how to use AI-assisted colonoscopy in the daily practice of screening and surveillance of colorectal neoplasia.","url":"https://pubmed.ncbi.nlm.nih.gov/41647810/","authors":["Koh FH","Li JW","Wong SH","Lee J","John S","Chong VH","Wu KC","Lui R","Ng SSM","Lam TYT","Lau LHS","Makharia GK","Abdullah M","Maulahela H","Kobayashi N","Sekiguchi M","Byeon JS","Kim HS","Lee YY","Chiu HM","Wu IC","Leelakusolvong S","Sharma P","Lieberman D","Sung JJY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1016/j.igie.2025.04.001","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647702","name":"Bridging communication gaps: the role of voice-enabled AI in medicine.","source":"pubmed","abstract":"Recent advancements in voice-enabled artificial intelligence (AI), particularly end-to-end speech-to-speech models, are reshaping communication within health care. These models, such as OpenAI's Advanced Voice Mode (AVM), offer real-time, nuanced human-like interactions by capturing intonation and pitch, thereby enabling more natural machine-human dialogue. In this paper we explore the integration of voice-enabled AI into medical practice, highlighting the potential to improve clinical efficiency, medical education, and patient engagement by providing self-recorded use cases. While the benefits are promising-ranging from increased accessibility to reduced clinician workload-challenges remain in data security, reliability, integration with existing systems, and ethical use. Addressing these concerns through robust regulation, transparent development, and targeted training will be essential. Ultimately, voice-enabled AI holds transformative potential to bridge communication gaps in medicine and support more equitable, efficient, and patient-centered care.","url":"https://pubmed.ncbi.nlm.nih.gov/41647702/","authors":["Loeffler CML","Muti HS","Kather JN","Truhn D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jun","doi":"10.1016/j.esmorw.2025.100138","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647538","name":"Decoding intrinsically disordered regions in biomolecular condensates.","source":"pubmed","abstract":"Biomolecular condensates comprise a diverse array of molecular entities, with intrinsically disordered regions (IDRs) receiving mounting attention due to their pivotal roles. In recent years, significant progress has been made in understanding the linear and conformational molecular grammar of IDRs in biomolecular condensates. This review will focus on the advances in studying IDR conformational ensembles and their relationship to function, with a particular emphasis on molecular dynamics (MD) simulations and the emerging synergy between MD and machine learning (ML) methods. Nevertheless, the inherent flexibility and dynamic nature of IDRs continue to present substantial challenges for conformation analysis. The integration of advanced experimental techniques, computational methods, and evolutionary analysis promises to unveil the conformational mysteries and therapeutic potential of IDRs in condensates.","url":"https://pubmed.ncbi.nlm.nih.gov/41647538/","authors":["Shi M","Wu Z","Zhang Y","Li T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.fmre.2025.01.013","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647536","name":"Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.","source":"pubmed","abstract":"Immune checkpoint blockade (ICB) therapies have revolutionized cancer treatment, showing success across various cancer types. However, there is variability in response rates among different cancers and individual patients. This highlights the critical need for precise patient stratification. Machine Learning and Deep Learning models are increasingly utilized to predict ICB responses by integrating multi-omics data, such as clinical, genomic, radiomic, and transcriptomic information. This review outlines the key methodologies of these predictive models. It underscores their role in enhancing response prediction. We delve into the advanced mechanisms of ICB response and discuss the biological foundations that inform these models. This demonstrates how basic research informs clinical application. We aim to offer comprehensive insights into how artificial intelligence can optimize patient stratification for ICB therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41647536/","authors":["Cao S","Liu J","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.fmre.2025.08.009","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647505","name":"Benchmarking Open-Source Vision Language Models in Orthopedic In-Training Examination: A Comparison with Residents, Domain-Specific Evaluation, and Parameter Scaling.","source":"pubmed","abstract":"Advancing orthopedic care through large language models requires both multimodal processing capabilities for medical images and open-source deployment options for secure in-house operations, yet these remain underexplored in current literature. This study aims to benchmark open-source vision-language models (VLMs) against orthopedic residents using the Orthopedic In-Training Examination (OITE), assess domain-specific performance across orthopedic subspecialties, and investigate the relationship between model parameter size and performance.","url":"https://pubmed.ncbi.nlm.nih.gov/41647505/","authors":["Ko S","Lee J","Ko K","Kim J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.4055/cios25183","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647347","name":"Definitions, measurement, and reporting of progression-free survival in randomized clinical trials and observational studies of patients with advanced non-small-cell lung cancer treated with immunotherapy: a scoping review.","source":"pubmed","abstract":"Evidence from observational studies is increasingly used in oncology to complement evidence from clinical trials. Commonly used endpoints to evaluate oncology medicines are overall survival (OS) and progression-free survival (PFS). However, comparing PFS across observational studies and with clinical trials can be challenging due to differences in its definition and measurement. This scoping review investigated how PFS was defined, measured, and reported in randomized clinical trials (RCTs) and observational studies of patients with advanced non-small-cell lung cancer (NSCLC) treated with immunotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41647347/","authors":["Verschueren MV","Tassopoulou VP","Visscher R","Schuurkamp J","Peters BJM","Koopman M","van de Garde EMW","Egberts ACG","Bloem LT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.1016/j.esmorw.2025.100118","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41647019","name":"AI prediction of extubation success within a novel three-stage liberation framework: development, validation, and implementation of the Stage-3 model.","source":"pubmed","abstract":"We propose a three-stage liberation decision framework (Stage-1 readiness, Stage-2 SBT success, Stage-3 extubation). While prior tools emphasize earlier stages, Stage-3-deciding whether to remove the tube after SBT-remains under-modeled. This study develops an AI model to predict successful extubation (no reintubation or non-invasive ventilation within 48&#x202f;h) using routinely collected electronic medical record data, eliminating the need for additional manual bedside measurements.","url":"https://pubmed.ncbi.nlm.nih.gov/41647019/","authors":["Chen CM","Shao YC","Liu CF","Sung MI","Shen YT","Ko SC","Lai CC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1725864","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41646974","name":"A monocyte-derived blood transcriptomic signature reveals systemic immunosuppression in HCC and partial reversal following curative therapy.","source":"pubmed","abstract":"Liver ablation or resection can cure early-stage hepatocellular carcinoma (HCC), yet late diagnosis and high relapse rates hinder long-term survival. We sought to delineate how tumor burden-and its removal-reshape the systemic immune transcriptome and extract blood-based signatures with diagnostic and prognostic potential.","url":"https://pubmed.ncbi.nlm.nih.gov/41646974/","authors":["Zhou L","Alaswad A","Kumthekar A","Machtens D","Xi Y","Costa B","Xu CJ","Wirth T","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fimmu.2025.1717978","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41646932","name":"Transcriptional response to combination antiretroviral therapy predicts side effects and novel targets.","source":"pubmed","abstract":"Antiretroviral therapy (ART) has revolutionized the clinical management of people with human immunodeficiency virus (HIV), transforming HIV infection into a chronic condition. Yet, the mechanisms of action and off-target effects of modern combination ART regimens versus individual ART medications are not fully understood.","url":"https://pubmed.ncbi.nlm.nih.gov/41646932/","authors":["Lachmann A","Amadori L","Nicoletti P","Crane HM","Giannarelli C","Ma'ayan A","Peter I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fphar.2025.1743543","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41646696","name":"Heart Stress, Frailty and Mortality Risk in two prospective cohorts.","source":"pubmed","abstract":"Frailty is a multisystem syndrome that reflects age-related physiological decline, underscoring the need for more biologically informed risk stratification within frailty assessments. Frailty and heart stress (HS) are individually associated with increased mortality risk, but their combined effects remain practically unexplored.","url":"https://pubmed.ncbi.nlm.nih.gov/41646696/","authors":["Huang Y","Hao M","Jiang S","Li X","Tang Y","Hu Z","Wang X","Han L","Li Y","Zhang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 26","doi":"10.64898/2026.01.25.26344776","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41646478","name":"Virtual reality support during systemic cancer therapy to improve anxiety/depressive symptoms and reduce toxicity in patients with gastrointestinal cancers-OncoVR.","source":"pubmed","abstract":"Systemic cancer therapy may trigger anxiety/depressive symptoms and toxicity. Relaxation techniques can help alleviate toxicities but their implementation in clinical practice is challenging. We hypothesize that virtual reality (VR) systems which project a relaxing nature environment may help to reduce psychological stress and toxicities of cancer therapies. This trial aims to evaluate the feasibility of a supportive VR intervention in patients receiving cancer therapies in an outpatient setting.","url":"https://pubmed.ncbi.nlm.nih.gov/41646478/","authors":["Kasper S","Liszio S","Schorrmann K","Gerigk M","Jovic S","Basu O","Kostbade K","Goraus B","Elsakka A","Puladi B","Kleesiek J","Schuler M","Luijten G","Egger J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.1016/j.esmogo.2025.100135","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41646261","name":"Intrahepatic cholangiocarcinoma trends and treatment lines: real-world evidence from the French National Hospital Discharge database.","source":"pubmed","abstract":"Little is known about the therapeutic trajectory of patients treated in hospitals for intrahepatic cholangiocarcinoma (iCCA) and patterns of care in daily clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41646261/","authors":["Delaye M","Grenier B","Lièvre A","Neuzillet C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Jun","doi":"10.1016/j.esmogo.2025.100152","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41645881","name":"Implementation of artificial intelligence in palliative and supportive care for people with cancer: A scoping review.","source":"pubmed","abstract":"Cancer remains a leading global health burden. Artificial intelligence offers new opportunities to address complex physical and psychological symptoms in palliative and supportive cancer care. Despite rapid advances, including large language models, these technologies have not been consistently reviewed in this context, highlighting a gap in the synthesised literature.","url":"https://pubmed.ncbi.nlm.nih.gov/41645881/","authors":["Kikuchi S","Sakata M","Hasegawa T","Wada S","Funada S","Makishi M","Akechi T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1177/02692163261416261","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41645431","name":"Expert Consensus on the Artificial Intelligence Proficiency Competency List and Assessment Framework for Medical Students (2025 Edition).","source":"pubmed","abstract":"To cultivate composite medical professionals capable of adapting to the development of intelligent healthcare,this consensus is grounded in the competency-based medical education,integrating the competency model and Miller's pyramid of clinical competence. A two-round Delphi method involving a multidisciplinary expert panel was conducted,combined with a systematic literature review,to develop a 21-indicator artificial intelligence(AI) literacy competency list for medical students across three domains:knowledge (8 indicators),skills (8 indicators),and attitudes (5 indicators). Furthermore,the consensus proposes a practical assessment system:standardized testing for the knowledge domain,situational judgment tests for the attitudes domain,and objective structured clinical examinations incorporating AI-related scenarios for the skills domain. In addition,a longitudinal assessment strategy spanning the phases of admission,preclinical training,and clinical training is recommended. The competency list and assessment framework established in this consensus demonstrate strong scientific rigor,authority,and practical applicability,and can serve as an important reference for medical schools seeking to advance the deep integration of AI and medical education and to cultivate composite medical talents suited to the era of intelligent healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/41645431/","authors":["Gong MC","Pan H","Liu H","Tong K","Li J","Ma YH","Chen W","Hou Y","Hong L","Zhang B","Zhang BH","Zeng ZR","Ji XM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.3881/j.issn.1000-503X.17210","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41645229","name":"Medical students perceptions and attitudes toward the use of generative artificial intelligence in clinical decision-making: a nationwide cross-sectional survey in China.","source":"pubmed","abstract":"BACKGROUND: The deep integration of artificial intelligence (AI) into healthcare is reshaping medical practice and education globally. As an emerging technology, generative AI (GenAI) demonstrates significant potential for application in clinical decision-making. Systematically understanding medical students&#x2019; perceptions and attitudes toward GenAI is crucial for promoting its responsible implementation in the medical field. OBJECTIVE: This study aimed to investigate Chinese medical students&#x2019; perceptions, usage behaviors, and attitudes regarding the use of GenAI for clinical decision-making. METHODS: This exploratory cross-sectional study was conducted via an online questionnaire from January to March 2025. A total of 1062 medical students from 168 universities and colleges across 29 provinces in China were recruited through convenience sampling. The survey, developed based on the Technology Acceptance Model (TAM) and validated by expert review and pilot testing, descriptively assessed three dimensions: usage, perceptions, and attitudes toward GenAI in clinical decision-making. Descriptive statistics, including frequencies and 95% confidence intervals, were used for data analysis. RESULTS: The vast majority of students (99.4%, n&#x2009;=&#x2009;1056) reported prior experience with GenAI. The primary application was course learning (71.8%, 95% CI [0.690&#x2013;0.744]); in contrast, direct use in clinical decision-making was reported less frequently (44.0%, 95% CI [0.410&#x2013;0.470]). Students widely recognized GenAI&#x2019;s benefits in broadening knowledge (73.4%, 95% CI [0.706&#x2013;0.759]), fostering multi-perspective clinical thinking (67.8%, 95% CI [0.649&#x2013;0.705]), and improving efficiency (63.1%, 95% CI [0.601&#x2013;0.659]). They also noted significant limitations: primarily its inability to account for individual patient differences in diagnosis (70.7%, 95% CI [0.679&#x2013;0.734]) and susceptibility to input data bias (65.6%, 95% CI [0.627&#x2013;0.684]). Most students (71.7%, n&#x2009;=&#x2009;762) were willing to use GenAI in the future, yet strongly opposed its complete replacement of healthcare professionals (79.4%, n&#x2009;=&#x2009;843) and advocated for safeguards such as strict output auditing (69.6%, 95% CI [0.668&#x2013;0.723]). CONCLUSION: This study reveals that medical students maintain a &#x201c;cautious embrace&#x201d; attitude toward GenAI: actively utilizing them while consistently emphasizing the central importance of professional judgment. This finding suggests that medical education should focus on cultivating future healthcare professionals who can skillfully employ GenAI as a supportive tool, while steadfastly adhering to critical AI literacy.","url":"https://pubmed.ncbi.nlm.nih.gov/41645229/","authors":["Cao X","Lu YY","Li JH","Luo XY","Zeng YX","Wang SH","Gao HY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 5","doi":"10.1186/s12909-026-08698-7","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41644015","name":"Artificial intelligence and machine learning in neurodegenerative disease management: A 21st century paradigm.","source":"pubmed","abstract":"Neurodegenerative diseases represent a major and growing clinical challenge due to their progressive nature, biological heterogeneity, and limited therapeutic options. Recent advances in artificial intelligence (AI) have introduced new analytical strategies for extracting clinically relevant information from complex biomedical data, offering complementary tools to established diagnostic and research approaches. This review provides a critical and method-comparative synthesis of AI applications in neurodegenerative diseases, with emphasis on studies published between 2022 and 2025. Rather than cataloging algorithms, the review evaluates how specific AI methodologies are selected, implemented, and validated across diverse data modalities, including molecular profiles, neuroimaging, biosensors, speech, gait, and electronic health records. Across Alzheimer's disease, Parkinson's disease, and other neurodegenerative disorders, the reviewed evidence indicates that AI-based models can support early risk stratification, disease characterization, and monitoring when applied within clearly defined analytic and clinical contexts. Importantly, performance gains are shown to depend strongly on data quality, feature representation, validation design, and alignment between model architecture and biological signal, rather than on algorithmic complexity alone. Emerging paradigms, including multimodal integration and next-generation AI frameworks, are discussed in relation to their methodological contributions rather than clinical readiness. By systematically comparing analytical strategies and highlighting sources of variability across studies, this review underscores the importance of methodological transparency, uncertainty-aware evaluation, and biological interpretability. Collectively, the work positions AI as an enabling and adjunctive analytical framework that can enhance neurodegenerative disease research and clinical decision support when deployed with rigor and caution, providing a balanced perspective on current capabilities and future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41644015/","authors":["Basha S","Ks P","Chattopadhyay A","Pai AR","Mahato KK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.nbd.2026.107307","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41643698","name":"Classification accuracy of a hierarchical molecular inference-based deep-learning system for CNS tumour diagnosis: a multi-institutional, retrospective study.","source":"pubmed","abstract":"Recent advances in artificial intelligence (AI) and computer vision empower deep-learning models to infer molecular features from histopathological images to classify CNS tumours. The aim of this study was to test the classification accuracy of a molecular inference-based AI assistant for CNS tumour diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/41643698/","authors":["Lalchungnunga H","Dampier CH","Singh O","Hoang DT","Shulman ED","Abdullaev Z","Li B","Luo Z","Wu Z","Pearce TM","Marker DF","McCortney K","Horbinski C","Lucas CG","Cimino PJ","Nasrallah MP","Quezado M","Chung HJ","Yefet L","Zadeh G","Brandner S","Ruppin E","Aldape K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/S1470-2045(25)00661-8","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41643627","name":"The emerging role of mixed reality and artificial intelligence in sarcoma care: A systematic review.","source":"pubmed","abstract":"Sarcomas are a heterogeneous group of cancers requiring cautious monitoring and expert management. The emerging role of Artificial Intelligence (AI) and Mixed Reality (MR) may represent a turning point in sarcoma care. This systematic review evaluates their application in sarcoma management.","url":"https://pubmed.ncbi.nlm.nih.gov/41643627/","authors":["Joshi S","Njessi P","Camuzard O","Gauci MO","Bonvalot S","Lupon E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.ejso.2026.111447","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41643205","name":"Enhancing urine cytopathology with artificial intelligence: a systematic review.","source":"pubmed","abstract":"To evaluate the potential of artificial intelligence (AI) to enhance urine cytopathology for detecting urothelial carcinoma (UC), emphasizing improvements in diagnostic sensitivity, accuracy, and efficiency, as well as potential reductions in pathologist workload.","url":"https://pubmed.ncbi.nlm.nih.gov/41643205/","authors":["Nabiyouni F","Chiou PZ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 5","doi":"10.1093/ajcp/aqaf135","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41643045","name":"Intraoperative assessment of surgical margins during radical prostatectomy: advances and challenges.","source":"pubmed","abstract":"Detection of positive surgical margins during radical prostatectomy is critical to minimizing the risk of prostate cancer recurrence. This review deals with established methods, recent advances in real-time intraoperative technologies, and future directions in margin assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/41643045/","authors":["Cimadamore A","Cheng L","Lopez-Beltran A","Rogers ET","Shariat SF","Montironi R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar 1","doi":"10.1097/MOU.0000000000001359","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41642176","name":"The effects of a mobile application-based dementia care intelligent recommender system on caregivers of people with dementia: a randomized controlled trial.","source":"pubmed","abstract":"Health recommender systems offer new opportunities to meet the personalized needs of people with dementia and their caregivers, but evidence on their effectiveness remains limited.","url":"https://pubmed.ncbi.nlm.nih.gov/41642176/","authors":["Sun Y","Ji M","Leng M","Chen B","Liu S","Wang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 1","doi":"10.1093/ageing/afag021","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641462","name":"Burden and risk of asthma and rhinitis in people with atopic dermatitis: global estimates from a hierarchical Bayesian model.","source":"pubmed","abstract":"Atopic dermatitis (AD) is a chronic inflammatory skin disease often associated with asthma and rhinitis. However, epidemiological evidence on these comorbidities remains fragmented and is largely limited to high-income countries.","url":"https://pubmed.ncbi.nlm.nih.gov/41641462/","authors":["Liu Y","Ge J","Xu G","Cai C","Chen D","Tian J","Xu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 19","doi":"10.1093/bjd/ljag025","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641437","name":"Epistemic and ethical limits of large language models in evidence-based medicine: from knowledge to judgment.","source":"pubmed","abstract":"The rapid evolution of general large language models (LLMs) provides a promising framework for integrating artificial intelligence into medical practice. While these models are capable of generating medically relevant language, their application in evidence inference in clinical scenarios may pose potential challenges. This study employs empirical experiments to analyze the capability boundaries of current general-purpose LLMs within evidence-based medicine (EBM) tasks, and provides a philosophical reflection on their limitations.","url":"https://pubmed.ncbi.nlm.nih.gov/41641437/","authors":["Qi W","Pan L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1706383","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641419","name":"Ethical Considerations in the Use of the da Vinci Surgical System in Modern Surgery.","source":"pubmed","abstract":"The da Vinci Surgical System supports surgeons across various specialties, including gynecology, urology, thoracic, cardiac, and general surgeries, as well as in cancer treatments for prostate, kidney, gynecologic, and colorectal cancers. This review article explores the ethical implications of the da Vinci system, emphasizing the integration of \"Purpose Good\" and \"Means Good\" to improve patient care. It illustrates how the system enhances surgical outcomes through precision and minimally invasive techniques, supported by significant technological advancements. The ethical suitability of robotic surgery is assessed, stressing the need for thorough treatment planning and patient selection for optimal outcomes. Additionally, the impact of robotic surgery on patient autonomy is discussed, particularly regarding informed consent and the surgeon-patient relationship. The paper emphasizes the Do No Harm Principle, highlighting the necessity for stringent training and risk assessments to mitigate patient risks. While the da Vinci system enhances precision and lowers complications, challenges like limited tactile feedback and potential robotic failures persist. The pursuit of excellence in surgical practices, guided by the Performance Excellence model, advocates for continuous improvements in surgical technology and patient care. In cancer surgery, the da Vinci system raises important ethical issues such as informed consent, accountability, and equitable access to care. The incorporation of artificial intelligence in robotic-assisted surgery requires careful consideration of patient safety, data privacy, and the implications of technology reliance on human skills. Addressing these ethical concerns is crucial for responsible implementation and safeguarding patients' fundamental rights and safety in an evolving healthcare landscape. As robotic advancements progress, the da Vinci Surgical System represents a significant development in minimally invasive procedures, promising improved outcomes and safety in healthcare, with the potential for becoming standard practice in the future.","url":"https://pubmed.ncbi.nlm.nih.gov/41641419/","authors":["Hemmatyar A","Soleymani S","Khosravi-Mashizi M","Saberi A","Shirinzadeh-Dastgiri A","Naseri A","Vakili-Ojarood M","HaghighiKian SM","Rahmani A","Jayervand F","Rashnavadi H","Neamatzadeh H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1007/s13193-025-02296-7","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641262","name":"Dupilumab treatment is not associated with changes in lymphoma risk in atopic dermatitis and other type 2 inflammatory diseases: data from a large-scale retrospective cohort study.","source":"pubmed","abstract":"The association between atopic dermatitis (AD) and lymphoma risk remains inconclusive. Dupilumab, approved for moderate-to-severe AD, has been linked to an increased lymphoma risk, raising significant concerns.","url":"https://pubmed.ncbi.nlm.nih.gov/41641262/","authors":["Kridin K","Bieber K","Olbrich H","von Bubnoff D","Hernandez G","Zirpel H","von Bubnoff N","Thaçi D","Ludwig RJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1702736","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641215","name":"Neurofeedback for autism spectrum disorder: Current evidence, challenges, and future directions.","source":"pubmed","abstract":"Neurofeedback therapy (NFT) has emerged as a promising noninvasive intervention for autism spectrum disorder (ASD), targeting core symptoms such as social communication deficits and emotional dysregulation. This editorial synthesizes findings from recent studies, including Wang et al 's retrospective analysis (2025), which reported improvements in Social Responsiveness Scale and Aberrant Behavior Checklist scores following NFT combined with conventional therapy. Mechanistically, NFT may modulate prefrontal gamma-band activity, enhances neuroplasticity in social brain networks ( e.g., default mode network, a brain network involved in social cognition), and optimizes cognitive processing via event-related potential changes ( e.g., shortened P300 latency). Emerging trends include hybrid approaches ( e.g., NFT with repetitive transcranial magnetic stimulation and artificial intelligence-driven protocols). However, challenges persist in protocol standardization, long-term efficacy validation, and biomarker identification. Future research must prioritize large-scale randomized trials, neuromarker discovery, and individualized protocols to establish NFT as a viable component of precision psychiatry for ASD.","url":"https://pubmed.ncbi.nlm.nih.gov/41641215/","authors":["Zhang Y","Wang JJ","Xing HY","Yan J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 19","doi":"10.5498/wjp.v16.i2.114358","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641113","name":"Convolutional Graph Isomorphism Network to Detect Glaucomatous Visual Field Defects.","source":"pubmed","abstract":"To evaluate the performance of a deep learning (DL) model based on graph isomorphism networks (GINs) for detecting glaucomatous visual field defects on 24-2 standard automated perimetry (SAP) and to compare it against traditional diagnostic criteria, a dense neural network (NN) model, and a convolutional neural network (CNN) model.","url":"https://pubmed.ncbi.nlm.nih.gov/41641113/","authors":["da Costa DR","Unyi D","Scherer R","Muralidhar R","Ribeiro Monteiro ML","Medeiros FA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.xops.2025.101041","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641047","name":"Proton beam therapy induces protective immunity via HMGB1-dependent signaling.","source":"pubmed","abstract":"Proton beam therapy is widely regarded for its cost-effectiveness, precision, and protection of normal tissues. Emerging evidence shows that conventional radiotherapy can inhibit primary tumors and promote immunogenicity of distant tumors, possibly via damage-associated molecular patterns (DAMPs) like calreticulin (CRT) and high mobility group box 1 (HMGB1) released during immunogenic cell death (ICD). However, the role of proton beams in inducing DAMPs and enhancing immunogenicity remains unclear. This study aimed to investigate the effects of proton beam-induced DAMPs on the colonization of distal tumors. In this study, in vitro cell irradiation experiments were conducted to identify the optimal proton beam dose for enhancing DAMPs expression in mouse colon carcinoma Colon-26 cells. Based on the optimal proton dose determined in vitro , a tumor-bearing mouse model was employed to evaluate its efficacy in inhibiting distal tumor colonization. To explore the mechanisms behind the anti-tumor effects, shRNA targeting DAMPs-related immunogenic molecules was applied to assess the immune response. In vitro findings indicated high-dose proton irradiation markedly induces HMGB1 release yet exerts no significant effect on CRT membrane exposure. Following high-dose proton beam irradiation, tumor cells transfected with shRNA exhibited a significant reduction in CRT and HMGB1 expression compared with the con-shRNA-irradiated control group. In vivo experiments demonstrated that HMGB1 knockdown reduced distal-tumor rejection by 60%, whereas CRT knockdown reduced it by only 20%, indicating that HMGB1 release may dominate proton-induced ICD. Our research results indicated that high-dose proton irradiation trigger the rejection of distal tumor colonization through a signaling pathway that depends on HMGB1. This research advanced our understanding of proton beam therapy immunological mechanisms and offered insights for improving tumor treatment outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41641047/","authors":["Wen J","Tu X","Ren W","Wang Q","Wang Y","Guo G","Osada K","Shimokawa T","Takahashi A","Nakajima NI","Zhang S","Gu W","Li Y","Li C","Sui L","Ma L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fpubh.2025.1686678","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41641004","name":"Artificial intelligence in the retraction spotlight: trends, causes and consequences of withdrawn AI literature through a systematic bibliometric review.","source":"pubmed","abstract":"The rapid integration of artificial intelligence (AI) in scientific research has introduced new challenges to academic integrity, with increasing concerns about AI-related article retractions. This study conducts a comprehensive bibliometric analysis of retracted AI-related articles to characterize their prevalence, causes, and impact on scholarly communication.","url":"https://pubmed.ncbi.nlm.nih.gov/41641004/","authors":["Sridharan K","Sivaramakrishnan G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/frma.2025.1737168","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41640879","name":"Coronary heart disease risk prediction based on GAIN imputation and interpretable machine learning.","source":"pubmed","abstract":"Coronary atherosclerotic heart disease (CHD) is a leading cause of morbidity and mortality worldwide, making timely identification critical for improving patient prognosis. However, traditional imaging examinations are limited by high costs and patient selection bias, while existing prediction models often lack interpretability and generalization ability. This study aimed to develop a robust, interpretable machine learning approach to address these challenges.","url":"https://pubmed.ncbi.nlm.nih.gov/41640879/","authors":["Zhao S","Nan B","Guo J","Xu W","Li Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fgene.2025.1752811","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41640633","name":"Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.","source":"pubmed","abstract":"Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of 'surrogate brains'. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem, with the primary objective of accurately predicting future whole-brain dynamics with historical data. Here, we introduce a unified framework of constructing an AI-based surrogate brain that integrates forward modeling, inverse problem solving and model evaluation. Leveraging the expressive power of AI models and large-scale brain data, surrogate brains open a new window for decoding neural systems and forecasting complex dynamics with high dimensionality, non-linearity and adaptability. We highlight that the learned surrogate brain serves as a simulation platform for dynamical systems analysis, virtual perturbation and model-guided neurostimulation. We envision that the AI-based surrogate brain will provide a functional bridge between theoretical neuroscience and translational neuroengineering.","url":"https://pubmed.ncbi.nlm.nih.gov/41640633/","authors":["Zhang Y","Liu D","Liang Z","Cheng J","Lou K","Duan J","Gao T","Hu B","Liu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1093/nsr/nwaf457","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41640575","name":"Privacy and Human-AI Relationships.","source":"pubmed","abstract":"Artificial intelligence (AI) agents such as chatbots and personal AI assistants are increasingly popular. These technologies raise new privacy concerns beyond those posed by other AI systems or information technologies. For example, anthropomorphic features of AI chatbots may invite users to disclose more information with these systems than they would otherwise, especially when users interact with chatbots in relationship-like ways. In this paper, we aim to develop a framework for assessing the distinctive privacy ramifications of AI agents, especially as humans begin to interact with them in relationship-like ways. In particular, we draw from prominent theories of privacy and results from human relational psychology to better understand how AI agents may affect human behavior and the flow of personal information. We then assess how these effects could bear on eight distinct values of privacy, such as autonomy, the value of forming and maintaining relationships, security from harm, and more.","url":"https://pubmed.ncbi.nlm.nih.gov/41640575/","authors":["Register C","Khan MA","Giubilini A","Earp BD","Savulescu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec","doi":"10.1007/s13347-025-00978-2","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41640542","name":"Establishment and validation of a predictive model for severe pneumonia in children.","source":"pubmed","abstract":"This study aimed to develop a model for the early identification of severe pneumonia in children by comparing common laboratory indicators between children with ordinary pneumonia and severe pneumonia.","url":"https://pubmed.ncbi.nlm.nih.gov/41640542/","authors":["Ye W","Wu J","Cao M","Yang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.4314/ahs.v25i1.14","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41640005","name":"Tailored psychotherapy and AI-enhanced contingency management for co-occurring disorders in cannabis use disorder: a systematic review.","source":"pubmed","abstract":"Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hyperactivity disorder (ADHD), resulting in poorer outcomes and underscoring the need for tailored interventions. Contingency management (CM) is one of the most effective behavioral treatments for substance use disorders, and emerging applications of artificial intelligence (AI) may enhance CM by predicting relapse risk and personalizing incentives. This review evaluates evidence on integrated interventions for CUD with co-occurring disorders and the developing role of AI-enhanced CM.","url":"https://pubmed.ncbi.nlm.nih.gov/41640005/","authors":["Mishra S","Mishra S","Rath S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 4","doi":"10.1080/10550887.2026.2616726","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41639316","name":"Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.","source":"pubmed","abstract":"Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angiography (CTPA) is the standard diagnostic modality. However, conventional CTPA protocols require relatively high iodine contrast and radiation doses, raising concerns about renal injury and radiation exposure. In this study, we propose a deep learning-based framework for PE diagnosis under low-iodine and low-radiation CTPA conditions. The proposed two-stage framework integrates image enhancement and classification by jointly leveraging original low-exposure images and their super-resolved counterparts. We further construct and publicly release a low-iodine, low-radiation CTPA dataset developed in collaboration with a clinical institution to support reproducible research in safe imaging. Experimental results demonstrate that the proposed method substantially improves diagnostic performance compared with single-branch baselines, achieving an area under the ROC curve (AUC) of 0.928 while maintaining balanced sensitivity and specificity. These findings suggest that the proposed framework enables accurate and safer PE diagnosis under reduced contrast and radiation exposure, offering a practical solution for improving diagnostic safety in clinical CTPA imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/41639316/","authors":["Hong M","Gu T","An H","Fan X","Zhang X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 4","doi":"10.1038/s41598-026-38223-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41639308","name":"Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.","source":"pubmed","abstract":"Early recognition of individuals at elevated risk for new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is vital for planning effective preventive interventions. The aim of this study was to develop a deep learning (DL) radiomics model to predict NIILs post-CAS from dual-energy CT (DECT) images.","url":"https://pubmed.ncbi.nlm.nih.gov/41639308/","authors":["Lin G","Chen W","Hu W","Wu J","Xu L","Chen Y","Zhao T","Sun J","Xu M","Lu C","Xia S","Chen M","Ji J","Chen W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1007/s00330-026-12351-8","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41638850","name":"Involvement and perceptions of patients regarding the integration of artificial intelligence in surgery: A qualitative systematic review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41638850/","authors":["Ben Hmido S","Abder Rahim H","Burchell GL","Damman O","Hilling D","Ubbink D","Plantinga M","Vriens-Nieuwenhuis E","Daams F","Kazemier G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.cpsurg.2025.101947","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41638162","name":"Exosome-related lactylation gene signature defines diagnostic biomarkers of periodontitis through integrative bulk and single-cell transcriptomics.","source":"pubmed","abstract":"Exosomes and lactylation modification have been increasingly recognized as key regulators of diseases, yet their integrative role in periodontitis remains unclear. No diagnostic model based on exosome-related lactylation genes (ERLGs) has been previously established for periodontitis. This study aimed to explore ERLGs as potential diagnostic biomarkers for periodontitis.","url":"https://pubmed.ncbi.nlm.nih.gov/41638162/","authors":["Liang X","Fu R","Chen X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1016/j.archoralbio.2025.106494","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41637838","name":"Development and Validation of a Machine Learning Tool for Plastic Surgery Residency Application Screening.","source":"pubmed","abstract":"Applications to integrated plastic surgery residency programs have outpaced the growth of available positions. As a result, faculty must review more applications each year. Artificial intelligence provides 1 mechanism for holistic, expedited review of applications. We aimed to develop and validate a machine learning (ML)-based tool to screen residency applications and hypothetically identify which candidates would receive interview invitations.","url":"https://pubmed.ncbi.nlm.nih.gov/41637838/","authors":["Zhu KJ","Bachina P","Heron MJ","Er S","Zhang Y","Lifchez SD","Yang R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1016/j.jsurg.2025.103860","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41637821","name":"A hybrid swin transformer-BiLSTM framework and ensemble learning for multimodal brain stroke detection and risk prediction.","source":"pubmed","abstract":"Stroke is one of the leading causes of mortality and long-term disability worldwide, primarily resulting from the sudden disruption of cerebral blood flow. Early and accurate diagnosis plays a crucial role in minimizing neurological damage and improving recovery outcomes. This study proposes a comprehensive multimodal framework integrating a hybrid Swin Transformer-Bidirectional Long Short-Term Memory (SwinT-BiLSTM) model and an ensemble learning-based classifier for automated stroke detection and risk prediction from medical image and tabular clinical data. This study utilizes two brain stroke Computed Tomography (CT) datasets, including a primary dataset named BrSCTHD-2025, collected from hospitals in Dhaka and Faridpur, Bangladesh, and a secondary Kaggle CT dataset. In addition, a primary clinical tabular dataset was collected from Kushtia Medical College Hospital for multimodal analysis. The proposed SwinT-BiLSTM model efficiently extracts global spatial and sequential dependencies from CT images, while the ensemble classifier predicts stroke risk based on clinical and lifestyle parameters. Experimental results demonstrate that the model achieves 98% accuracy with an AUC of 1.00 on the BrSCTHD-2025 dataset and 97% accuracy with an AUC of 0.99 on the secondary Kaggle dataset, outperforming standalone SwinT by 2.5% and Convolutional Neural Network (CNN) architectures such as VGG16 and ResNet50 by 3%-4%. The ensemble classifier trained on tabular data achieved 80.36% accuracy, identifying critical stroke risk factors such as heart disease, prolonged sitting duration, and cholesterol level. Furthermore, Explainable Artificial Intelligence (XAI) techniques such as LIME, SHAP, enhanced Grad-CAM, and attention maps enhance interpretability by identifying the most influential visual and clinical features. Overall, the proposed SwinT-BiLSTM-Ensemble framework establishes a robust foundation for accurate, interpretable, and clinically reliable stroke diagnosis and personalized risk assessment in real-world healthcare environments.","url":"https://pubmed.ncbi.nlm.nih.gov/41637821/","authors":["Ahmed MM","Hossain MM","Rakib MRH","Hashan R","Nirob MTH","Islam MK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar","doi":"10.1016/j.compbiomed.2026.111518","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41637756","name":"Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.","source":"pubmed","abstract":"Serious mental illnesses (SMIs) are associated with high relapse rates and limited access to continuous care, particularly in low-resource settings such as urban slums. Traditional clinical monitoring is constrained by accessibility and scalability challenges. Digital phenotyping, through passive smartphone data, offers a novel approach to predict relapse by capturing real-world behavioral changes.","url":"https://pubmed.ncbi.nlm.nih.gov/41637756/","authors":["Alam N","Das CK","Roy N","Giacco D","Singh SP","Jilka S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 4","doi":"10.2196/79826","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41637677","name":"Gastric Cancer Mortality-to-Incidence Ratios in Latin America and the Caribbean: A Machine Learning Analysis of Socioeconomic and Clinical Research Predictors.","source":"pubmed","abstract":"To characterize gastric cancer epidemiology in Latin America and the Caribbean, identify country-level predictors of the mortality-to-incidence ratio (MIR), and describe the clinical research landscape with emphasis on precision oncology (PO).","url":"https://pubmed.ncbi.nlm.nih.gov/41637677/","authors":["Pilco-Janeta DF","De la Cruz-Puebla M","Guamán-Pilco DR","Moyolema-Pilco AD","Montenegro D","Miranda W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1200/GO-25-00531","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41636542","name":"Multistream Deep Learning Models Using Multimodal Optical Coherence Tomography for Predicting Visual Impairment in Epiretinal Membrane.","source":"pubmed","abstract":"To develop multistream deep learning models that receive multimodal optical coherence tomography (OCT) images to predict visual impairment in epiretinal membrane (ERM), and to identify possible OCT biomarkers for visual impairment.","url":"https://pubmed.ncbi.nlm.nih.gov/41636542/","authors":["Yeh HH","Chou PY","Hsieh CC","Lai YH","Hsieh YT","Lin CH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb","doi":"10.1016/j.ajo.2025.10.023","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635887","name":"Remote detection of the critical view of safety in pediatric laparoscopic cholecystectomy using artifitial intelligence.","source":"pubmed","abstract":"Laparoscopic cholecystectomy (LC) is increasingly performed in pediatric patients. Bile duct injury remains one of its most serious complications. The critical view of safety (CVS) aims to reduce this risk, but its identification is subjective. Artificial intelligence (AI) has shown promise in adult surgery for CVS detection but it has not been applied in pediatrics. Remote implementation of AI could reduce subjectivity and improve access to advanced tools.","url":"https://pubmed.ncbi.nlm.nih.gov/41635887/","authors":["Olivieri SE","Darrigran S","Petracchi E","Bidone P","Pérez A","Cardozo L","Fernández M","Zandalazini H","Della Pia I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/wjps-2025-001125","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635842","name":"Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation.","source":"pubmed","abstract":"Accurate prediction of mortality after allogeneic hematopoietic stem cell transplantation (alloHCT) is essential for individualized treatment decisions, yet existing clinical risk scores capture only a limited number of variables and show modest predictive performance. In our single-center retrospective analysis, we included data from 909 adult patients with hematologic malignancies undergoing alloHCT. We used 31 features to build machine-learning models to predict death within the first year after alloHCT. These features included established clinical risk factors together with pre-transplant lymphocyte subsets and inflammatory markers. Among four models, a random forest algorithm showed the best performance (AUC = 0.773) and retained good generalizability in an independent test set (AUC = 0.748). SHapley Additive exPlanations (SHAP)-based interpretation of the machine-learning models showed that age together with five easily measurable pre-transplant immunological and inflammatory parameters influenced the outcome: pre-transplant CD4 + , CD8 + , and B-lymphocyte counts, albumin, and C-reactive protein (CRP) levels. Based on these features, our random forest approach outperformed established clinical risk scores (HCT-CI, EASIX, rDRI, mGPS) in predicting one-year mortality after alloHCT and more effectively distinguished patients at low and high risk of an adverse outcome. Our study shows that machine-learning-based models can not only predict patient outcomes after alloHCT but also serve as powerful tools for data exploration, confirming the prognostic relevance of pre-transplant inflammation while uncovering the critical role of lymphocyte subsets as previously unknown risk factors. External validation in independent multicenter cohorts will be required to confirm generalizability.","url":"https://pubmed.ncbi.nlm.nih.gov/41635842/","authors":["Meyer T","Meyer R","Hackenberg M","Oelke D","Gengenbach L","Rummelt C","Wilcken H","Maas-Bauer K","Wäsch R","Duyster J","Bertz H","Duque-Afonso J","Finke J","Zeiser R","Wehr C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fimmu.2025.1745873","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635840","name":"Comparing the performance of radiomics, nomograms, machine learning, and large language models in predicting 28-day mortality in severe community-acquired pneumonia patients.","source":"pubmed","abstract":"Severe community-acquired pneumonia (SCAP) is a significant global health challenge due to its high mortality. Despite advances, early diagnosis and effective management remain critical. Tools like radiomics analyze imaging data for risk assessment, while machine learning and nomograms aid in personalized treatment. Large language models (LLMs) enhance clinical decision-making by analyzing data and supporting care strategies. This study integrates these methods to predict 28-day mortality in SCAP patients.","url":"https://pubmed.ncbi.nlm.nih.gov/41635840/","authors":["Lin T","Wan H","Liang Y","Ming J","Lu J","Guo Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fimmu.2025.1679496","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635794","name":"Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review.","source":"pubmed","abstract":"Maternal and newborn mortality remain stubbornly high in low-resource settings, driven by limited access to timely, personalized and emotionally supportive care during pregnancy. Large language models/generative pre-trained transformer (LLM/GPTs), particularly GPT-driven conversational agents, have emerged as scalable, versatile digital health tools capable of delivering evidence-based information, mental health support and risk stratification for complications such as preeclampsia, gestational diabetes and preterm birth. This systematic review aimed to synthesize global evidence on the design, implementation and effectiveness of LLM/GPT-powered chatbots for precision maternal and newborn health engagement. Following PRISMA 2020 guidelines, we searched MEDLINE, Embase, CINAHL, Web of Science, Inspec and IEEE Xplore from January 2015 to November 2025. We included 15 peer-reviewed studies (published 2021-2025) that developed or evaluated GPT-based or equivalent generative conversational agents for pregnant individuals or their partners. Quality appraisal used EQUATOR tools and artificial intelligence-specific frameworks; two reviewers independently assessed risk of bias. The 15 studies (total participants &gt;12&#x2009;000) covered 12 countries. LLM/GPT-powered chatbots outperformed LLM/GPT systems in conversational naturalness, topic diversity and user satisfaction (mean acceptability scores 85-94%). Core functions included real-time psychoeducation ( n &#x2009;=&#x2009;14 studies), mental health screening and behavioral activation ( n &#x2009;=&#x2009;11/15), partner engagement ( n &#x2009;=&#x2009;6/15), and predictive risk modeling for adverse outcomes ( n &#x2009;=&#x2009;9). LLM models achieved high diagnostic concordance with clinicians for gestational diabetes (area under the curve 0.88 to 0.94) and preeclampsia warning signs. User retention ranged from 62 to 78% over 6&#xa0;months, with the strongest engagement among prim parous and underserved populations. No serious harms were reported. LLM/GPT-driven conversational agents represent a breakthrough in accessible, personalized maternal and newborn health support. They effectively bridge gaps in high-risk pregnancy, emotional care, risk detection and partner involvement while maintaining safety and cultural adaptability. These findings strongly support rapid integration of LLM/GPT-powered chatbots into routine antenatal care pathways, particularly in low- and middle-income settings. The study was registered with PROSPERO (CRD420251230253).","url":"https://pubmed.ncbi.nlm.nih.gov/41635794/","authors":["Rasoli R","Ebrahimisadrabadi F","Khedri Z","Sohrabei S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/oodh/oqag001","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635747","name":"\"You're Not Crazy\": A Case of New-onset AI-associated Psychosis.","source":"pubmed","abstract":"Anecdotal reports of psychosis emerging in the context of artificial intelligence (AI) chatbot use have been increasingly reported in the media. However, it remains unclear to what extent these cases represent the induction of new-onset psychosis versus the exacerbation of pre-existing psychopathology. We report a case of new-onset psychosis in the setting of AI chatbot use.","url":"https://pubmed.ncbi.nlm.nih.gov/41635747/","authors":["Pierre JM","Gaeta B","Raghavan G","Sarma KV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Oct-Dec","doi":"","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635571","name":"A systematic review on the generative AI applications in human medical genetics.","source":"pubmed","abstract":"Although traditional statistical techniques and machine learning methods have contributed significantly to genetics and, in particular, inherited disease diagnosis, they often struggle with complex, high-dimensional data, a challenge now addressed by state-of-the-art deep learning models. Large language models (LLMs), based on transformer architectures, have excelled in tasks requiring contextual comprehension of unstructured medical data. This systematic review examines the role of generative Artificial Intelligence (AI) methods in human medical genomics, focusing on the genetic research and diagnostics of both rare and common diseases. Automated keyword-based search in PubMed, bioRxiv, medRxiv, and arXiv was conducted, targeting studies on LLM applications in diagnostics and education within genetics and removing irrelevant or outdated models. A total of 195 studies were analyzed, highlighting the prospects of their applications in knowledge navigation, analysis of clinical and genetic data, and interaction with patients and medical professionals. Key findings indicate that while transformer-based models perform well across a diverse range of tasks (such as identification of tentative molecular diagnosis from clinical data or genetic variant interpretation), major challenges persist in integrating multimodal data (genomic sequences, imaging, and clinical records) into unified and clinically robust pipelines, facing limitations in generalizability and practical implementation in clinical settings. This review provides a comprehensive classification and assessment of the current capabilities and limitations of LLMs in transforming hereditary disease diagnostics and supporting genetic education, serving as a guide to navigate this rapidly evolving field, while outlining application use cases, implementation guidance, and forward-looking research directions.","url":"https://pubmed.ncbi.nlm.nih.gov/41635571/","authors":["Changalidis A","Barbitoff Y","Nasykhova Y","Glotov A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fgene.2025.1694070","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41635408","name":"An integrated AI-enabled system using One Class Twin Cross Learning for early gastric cancer detection.","source":"pubmed","abstract":"Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains significantly hampered by the limitations of current diagnostic technologies, resulting in high rates of misdiagnosis and missed diagnoses.","url":"https://pubmed.ncbi.nlm.nih.gov/41635408/","authors":["Liu XX","Wei Y","Guo Y","Zhang H","Dong H","Song Q","Zhao Q","Luo W","Tian F","Gao J","Cai J","Fong S","Xu M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1623394","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41634738","name":"Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.","source":"pubmed","abstract":"BACKGROUND: The accuracy of the latest reasoning-enhanced large language models on national medical licensing examinations remains unknown, which is crucial for determining how close they are to serving as effective knowledge sources for medical education. This study aimed to evaluate the performance of four reasoning-enhanced large language models (LLMs)&#x2014;GPT-5, Grok-4, Claude Opus 4.1, and Gemini 2.5 Pro&#x2014;on the Japanese National Medical Examination (JNME), providing insights into their potential as educational resources and their future applicability in medical practice. METHODS: We evaluated LLM performance using the 2019 and 2025 JNME (n&#x2009;=&#x2009;793). Questions were entered into each model with chain-of-thought prompting enabled. Accuracy was assessed overall and by question type. Incorrect responses were qualitatively reviewed by a licensed physician and a medical student. RESULTS: From highest to lowest, the overall accuracies of the four LLMs were 97.2% for Gemini 2.5 Pro, 96.3% for GPT-5, 96.1% for Claude Opus 4.1, and 95.6% for Grok-4, with no significant pairwise differences. For image-based and non-image-based items, Gemini 2.5 Pro achieved the highest accuracy of 96.1% and 97.6%, with no significant difference, whereas accuracy was significantly lower on image-based items for the other three LLMs. Across difficulty levels, Gemini 2.5 Pro again achieved the highest accuracy (98.4% for easy, 97.3% for moderate, and 93.2% for difficult items). Within each LLM, accuracy on difficult questions was significantly lower than on easy questions. Common error patterns included providing unnecessary additional options in single-choice questions, misdiagnosis of X-ray or computed tomography images (primarily due to confusion regarding left&#x2013;right laterality), and difficulties in prioritizing appropriate actions in clinical questions with complex contextual information. CONCLUSIONS: Four LLMs released in 2025 surpassed the 95% benchmark on the JNME, and their near-perfect (approximately 99%) performance on basic medical knowledge questions highlights substantial potential for use as learning resources in foundational medical education. Gemini 2.5 Pro demonstrated the most consistent performance across question types, while Grok-4 showed greater variability. The concentration of incorrectness in clinical questions indicates that LLMs still require substantial refinement and validation before their use can be extended to clinical reasoning or patient care.","url":"https://pubmed.ncbi.nlm.nih.gov/41634738/","authors":["Liu M","Okuhara T","Dai Z","Zhao M","Yin W","Okada H","Furukawa E","Kiuchi T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.1186/s12911-026-03370-y","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41634697","name":"Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.","source":"pubmed","abstract":"PURPOSE: To establish and validate short-term and long-term prediction models for the therapeutic outcomes of orthokeratology (OK) lenses in children, incorporating baseline ocular parameters and dynamic changes in axial length (AL) as predictive variables. METHODS: This retrospective cohort study included 896 pediatric patients who underwent OK lenses treatment at Tianjin Medical University Eye Hospital from January 2020 to July 2025, with a minimum follow-up period of one year. Baseline demographic and ocular parameters were collected, and AL changes were introduced as dynamic predictors in models starting from year two. Seven machine learning algorithms were used to construct prediction models, including LightGBM, random forest, support vector machine (SVM), and artificial neural network (ANN). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, specificity, calibration, and decision curve analyses. Model interpretability was explored using SHAP and LIME analyses. RESULTS: Among the 896 included children, 566 achieved reasonable AL control, and 330 showed poor control during the first year. Baseline AL, corneal eccentricity parameters, and refractive error were significantly associated with treatment outcomes. The optimal second-year prediction model (LightGBM) achieved an area under the receiver operating characteristic curve (AUC) of 0.96 with an F1 score of 0.86. For long-term prediction, the SVM model demonstrated moderate and stable performance, with F1 scores of 0.73 and 0.75 in the third- and fourth-year models, respectively. CONCLUSION: Prediction models based on baseline parameters and dynamic AL changes can effectively estimate both short-term and long-term efficacy of OK treatment in children. Dynamic AL change in the first year is a robust predictor of long-term outcomes, offering potential for individualized myopia management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/41634697/","authors":["Wang Z","Hu X","Chang F","Zhang X","Sun B","Li R","Lin W","Wei R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.1186/s12911-026-03339-x","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41634499","name":"Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Stroke-associated pneumonia (SAP) is a frequent and severe complication following stroke. Recently, several machine learning (ML) models have been developed to predict SAP. We aimed to evaluate the predictive performance of these models in SAP prediction. We searched PubMed, Embase, Scopus, and Web of Science up to 18 June 2025, for studies developing ML, deep learning (DL), or neural network (NN) models for SAP prediction. The pooled estimates of area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated using the R program. A total of 27 studies were included, with a prevalence of SAP at 18.9%. Most models were ML based (77.8%), and clinical data were the most common input (77.8%). The pooled AUC was 0.84 [95% (CI): 0.80-0.87], and the pooled ACC was 0.80 (95% CI: 0.76-0.84). SEN and SPE were 0.73 (95% CI: 0.63-0.81) and 0.85 (95% CI: 0.77-0.90), respectively. The pooled DOR was 15.4 (95% CI: 10.2-23.3), and the summary receiver operating characteristic (SROC) curve showed an AUC of 0.853 with a false positive rate of 0.153 (95% CI: 0.096-0.235). No significant differences were found between ischemic and hemorrhagic subgroups. ML-based models demonstrated promising performance in predicting SAP and can help physicians through the early identification of high-risk cases. However, further external validation and integration into clinical workflows are required before widespread clinical adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/41634499/","authors":["Hajikarimloo B","Mohammadzadeh I","Tos SM","Hashemi R","Khoshrou A","Amjadzadeh M","Dehghan M","Aghajan S","Goudarzi E","Najari D","Ebrahimi A","Habibi MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1007/s12028-026-02450-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41634057","name":"Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.","source":"pubmed","abstract":"The evolution of the viruses is rapidly becoming a global challenge to the creation of vaccines since the new variants are often capable of escaping the immune system and decreasing the vaccine efficacy. The traditional methods of genomic epidemiology rely on the retrospective phylogenetic analysis, which can elucidate the previous mutations, but cannot predict the evolutionary trends in the future. In order to address these disadvantages, a new Refined Deep Evolutionary Learning Framework (R-DELF) is proposed that combines the genomic, structural, and temporal intelligence in predicting proactive viral mutations and assessing vaccine suitability. The methodology uses an ESM-2 Transformer that extracts structure-aware embeddings, merged with dual-attention Graph Neural Networks (GNNs) which learn phylogenetic and structural dependencies. Evolutionary learning maximiser improves adaptation modelling and an Explainable AI layer, which offers interpretability based on residue-level attribution. Tests indicate that experimentally it achieves 99.2% accuracy, 97.92% precision, 98.89% recall and 99.4% F1, which is higher than the current AI-based virology models. It is implemented in Python and with the help of TensorFlow and genomic and protein data obtained via Kaggle. The framework allows predicting the high-risk mutations in advance, facilitates the production of vaccines on time, and increases the preparedness to pandemics by making intelligent, data-driven predictions of viral evolution.","url":"https://pubmed.ncbi.nlm.nih.gov/41634057/","authors":["Shahin OR","Ibrahim MN","Alanazi A","Alharithi FS","Alruwaili Y","Alzahrani AA","El Azab EF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.1038/s41598-026-35143-y","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41633404","name":"Clinical Terminology Mapping Service Based on Information Retrieval.","source":"pubmed","abstract":"Standardized clinical terminology is essential for semantic interoperability. Typically, a hospital's terminology expert manually maps local terminology with international standards such as SNOMED CT. The manual mapping process is demanding, labor-intensive, and time-consuming, and its effectiveness relies on the expertise of the professional handling it.We developed a method to map clinical terms to SNOMED CT concept descriptions using an information retrieval (IR) approach with rich synonyms. We also provide a free mapping support service to help terminology experts alleviate the challenges of manual mapping without the need for additional manipulation.We created indexes using edge n-grams and synonyms. We adopted Elasticsearch for indexing and query processing, incorporating data from the SPECIALIST Lexicon to enrich the synonym database. Eight different indexes were initially created, but only four were retained based on performance. We tested indexes individually and in combination, using a dataset of 1,753 one-to-one mapped instances from the National Library of Medicine ICD-9-CM Procedure codes to the SNOMED CT Map. We compared our approach with MetaMap for evaluation.We found that using rich synonyms and edge n-gram indexing significantly improved the accuracy of mapping clinical terms to SNOMED CT. The indexes incorporating synonyms and edge n-grams performed better than those using either technique alone. Combining these methods captured more relevant terms and synonyms, resulting in more precise mappings. Our method outperformed the baseline provided by MetaMap, demonstrating enhanced capability in handling complex medical terminology and improving the overall mapping quality.Our study introduced an IR method with rich synonyms for mapping clinical terms to SNOMED CT, analyzing 40 unmapped terms, and identifying key issues. The approach shows promise in improving terminology mapping, and future work will explore advanced methods to enhance accuracy further, aiming to reduce manual mapping efforts and improve result evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/41633404/","authors":["Jung S","Yu SJ","Yi BK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 May","doi":"10.1055/a-2797-4219","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41633287","name":"Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications.","source":"pubmed","abstract":"Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit reach. Biometric markers captured using digital technologies may enable earlier and more objective detections.","url":"https://pubmed.ncbi.nlm.nih.gov/41633287/","authors":["Khaing PT","Nakayama M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.ijmedinf.2026.106289","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41632955","name":"Evaluation of an Artificial Intelligence Conversational Chatbot to Enhance HIV Preexposure Prophylaxis Uptake: Development and Usability Internal Testing.","source":"pubmed","abstract":"The HIV epidemic in the United States disproportionately impacts gay, bisexual, and other men who have sex with men (MSM). Despite the effectiveness of HIV preexposure prophylaxis (PrEP) in preventing HIV acquisition, uptake among MSM remains suboptimal. Motivational interviewing (MI) has demonstrated efficacy at increasing PrEP uptake among MSM but is resource-intensive, limiting scalability. The use of artificial intelligence, particularly large language models with conversational agents (ie, \"chatbots\") such as ChatGPT, may offer a scalable approach to delivering MI-based counseling for PrEP and HIV prevention.","url":"https://pubmed.ncbi.nlm.nih.gov/41632955/","authors":["Tao J","Pavlick E","Grondin A","Bustamante JD","Martin H","Parent H","Fenn N","Almonte A","Maguire-Wilkerson A","Gu M","Rusley J","Perler BK","Wray T","Nunn AS","Chan PA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 3","doi":"10.2196/79671","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41632159","name":"Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.","source":"pubmed","abstract":"Artificial intelligence models that predict same-admission outcomes for hospitalized patients, such as inpatient mortality, often rely on International Classification of Diseases (ICD) diagnostic codes, even when these codes are not finalized until after discharge.","url":"https://pubmed.ncbi.nlm.nih.gov/41632159/","authors":["Ramadan B","Liu MC","Burkhart MC","Parker WF","Beaulieu-Jones BK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1001/jamanetworkopen.2025.50454","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41632148","name":"Anti-Seizure Medication Use Before Electroencephalography in Infants.","source":"pubmed","abstract":"This cross-sectional study examines antiseizure medication use before electroencephalography in infants in the neonatal intensive care unit population.","url":"https://pubmed.ncbi.nlm.nih.gov/41632148/","authors":["Beller N","Fields M","Hogan CH","Krishna S","Glicksberg BS","Juliano CE","Richter F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1001/jamanetworkopen.2025.51124","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41632142","name":"Development and Validation of a Prediction Model for Intracranial Aneurysm Rupture Risk.","source":"pubmed","abstract":"Unruptured intracranial aneurysms (UIAs) affect 3.2% of the general population, and approximately 85% of subarachnoid hemorrhages result from their rupture. Despite their classification as low risk by prediction tools such as PHASES (population, hypertension, age, size of aneurysm, earlier subarachnoid hemorrhage from another aneurysm, and site of aneurysm) and the Unruptured Cerebral Aneurysm Study (UCAS), UIAs less than 10 mm are susceptible to rupture.","url":"https://pubmed.ncbi.nlm.nih.gov/41632142/","authors":["Fujimura S","Yanagisawa T","Kudo G","Koshiba T","Suzuki M","Takao H","Ishibashi T","Ohwada H","Yamashiro S","Kamphuis MJ","van der Kamp LT","Regenhardt RW","Vergouwen MDI","Rinkel GJE","Patel AB","Murayama Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1001/jamanetworkopen.2025.50772","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41632124","name":"Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly integrated into health care applications; however, their vulnerability to prompt-injection attacks (ie, maliciously crafted inputs that manipulate an LLM's behavior) capable of altering medical recommendations has not been systematically evaluated.","url":"https://pubmed.ncbi.nlm.nih.gov/41632124/","authors":["Lee RW","Jun TJ","Lee JM","Cho SI","Park HJ","Suh J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1001/jamanetworkopen.2025.49963","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41631125","name":"Revolutionizing Liver Imaging: Artificial Intelligence-Driven Advances in Diagnostics and Staging.","source":"pubmed","abstract":"Artificial intelligence (AI) has emerged as a transformative tool in liver imaging, offering enhanced diagnostic accuracy, efficiency, and reproducibility. The integration of machine learning and deep learning algorithms into radiological workflows has shown significant promise across a wide range of liver diseases. Key applications include automated liver segmentation on computed tomography (CT) and magnetic resonance imaging (MRI), enabling accurate liver volumetry and lesion localization. In metabolic dysfunction-associated steatotic liver disease, AI facilitates the detection and quantification of hepatic steatosis using advanced image analysis on ultrasound, CT, and MRI, providing a non-invasive alternative to biopsy. AI algorithms also demonstrate strong performance in detecting, classifying, and characterizing focal liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular carcinoma (HCC), and metastases, improving lesion conspicuity, standardizing reporting through LI-RADS, and reducing inter-observer variability. Beyond diagnosis, AI is increasingly applied for risk stratification and prognostication in HCC, integrating imaging, clinical, and laboratory data to predict tumor development, aggressiveness, treatment response, and survival outcomes. Despite these advances, the clinical implementation of AI in liver imaging faces notable challenges such as the need for data harmonization across scanners and institutions, rigorous validation in diverse patient populations, regulatory approval, and ethical considerations surrounding patient privacy, algorithmic bias, and transparency. Addressing these limitations through robust research, multi-center studies, and carefully designed clinical integration strategies is essential to safely and effectively harness AI's potential. With continued development and validation, AI has the capacity to enhance diagnostic workflows, enable precision medicine, and ultimately improve patient outcomes in hepatology.","url":"https://pubmed.ncbi.nlm.nih.gov/41631125/","authors":["Devkota S","Bhujade H","Kalra N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar-Apr","doi":"10.1016/j.jceh.2025.103439","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41631065","name":"Importance of Preconception Reproductive Genetic Screening in Routine Clinical Care.","source":"pubmed","abstract":"Preconception reproductive genetic screening (PRGS) is an increasingly important strategy in preventive reproductive healthcare, enabling the identification of carrier status for autosomal recessive and X-linked disorders in asymptomatic individuals prior to conception. Advances in genomic technologies and expanding professional guidelines have shifted screening paradigms from ethnicity-based approaches toward population-neutral expanded carrier screening, underscoring the need for updated clinical and policy perspectives. The objective of this narrative review is to synthesize contemporary evidence on the clinical utility, technological evolution, ethical considerations, and implementation challenges of PRGS in routine care. A comprehensive literature search was conducted across PubMed, Scopus, Cochrane Library, and Web of Science, covering publications from January 2000 to September 2025. Following removal of duplicates&#xa0;and screening of titles, abstracts, and full texts, 80 studies were included in the final narrative synthesis. The reviewed evidence demonstrated that expanded carrier screening using next-generation sequencing improved detection of at-risk couples compared with traditional targeted approaches and supported informed reproductive decision-making. Integration of PRGS with genetic counseling, assisted reproductive technologies, and emerging digital tools such as artificial intelligence-assisted variant interpretation may further enhance scalability and precision. However, significant barriers persist, including variable insurance coverage, limited access to genetic counseling, underrepresentation of diverse populations in genomic databases, and unresolved ethical and psychosocial concerns. Overall, PRGS represents a clinically valuable and ethically complex preventive strategy with significant public health implications. When responsibly implemented with appropriate counseling, equitable access, and robust policy support, PRGS has the potential to reduce the burden of inherited genetic disorders and advance personalized, patient-centered reproductive care.","url":"https://pubmed.ncbi.nlm.nih.gov/41631065/","authors":["Basavaraj N","Pallathur N","Taha AGE","Sharma R","Imam B","Rahman A","Muse R","Sharma S","Shekhawat P","Rai M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.7759/cureus.100572","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41630919","name":"Circadian ADCY3 Ser107Pro variant bridges difficulty awakening in the morning and adiposity.","source":"pubmed","abstract":"Modern lifestyles often disturb circadian rhythms, yet the genetic circuits that convert this stress into metabolic dysfunction remain poorly defined. Here, we identify a missense variant in ADCY3 (rs11676272; Ser107Pro) as a pleiotropic regulator of circadian preference and adiposity. Using genome-wide pleiotropy analysis in &#x223c;480,000 UK Biobank participants, we show that the G risk allele (Pro107) increases eveningness, BMI, and fat mass in European ( n = 451,324) and African ( n = 8,738) ancestry groups, with behavioral amplification by morning difficulty awakening in Europeans and power-limited modeling in other populations. Structural modeling and transcriptomic analysis suggest this allele alters adipose-specific splicing and expression and destabilizes ADCY3 protein. In mice, Adcy3 is rhythmically expressed in adipose tissue, with BMAL1 binding near the orthologous residue 107 site. Human adipose ADCY3 expression also increases after weight loss. Together, these findings reveal a genotype-dependent, behaviorally modifiable axis connecting difficulty awakening to metabolic risk through circadian and adipose regulatory pathways.","url":"https://pubmed.ncbi.nlm.nih.gov/41630919/","authors":["Tchio C","Maher M","Moth C","Meiler J","Lane JM","Taylor HA","Williams JS","Saxena R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 20","doi":"10.1016/j.isci.2025.114587","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41630273","name":"Adolescent idiopathic scoliosis research over the past 15 years: A bibliometric analysis of hotspots and emerging trends.","source":"pubmed","abstract":"To map the knowledge structure of adolescent idiopathic scoliosis (AIS) research from 2010 to 2024 and to identify emerging trends that are reshaping measurement, risk stratification, and intervention paradigms.","url":"https://pubmed.ncbi.nlm.nih.gov/41630273/","authors":["Hu R","Chen S","Chen X","Jiang Z","Du H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 30","doi":"10.1097/MD.0000000000047469","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41629934","name":"Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases.","source":"pubmed","abstract":"Antimicrobial resistance (AMR) poses a global threat to public health, though AI models have shown transformative potential in combating AMR. China's DeepSeek, a novel open-source, low-cost, and locally deployable AI model, is increasingly integrated into clinical workflows for infectious diseases, yet the pharmacological validity and real-world impact of its recommended drugs remain poorly understood.","url":"https://pubmed.ncbi.nlm.nih.gov/41629934/","authors":["Zhang L","Pan Y","Lai W","Liang Z","Zhong H","Lin X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1186/s12911-026-03364-w","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41629669","name":"Systematic Review: Agentic AI in Neuroradiology: Technical Promise with Limited Clinical Evidence.","source":"pubmed","abstract":"Agentic artificial intelligence systems featuring iterative reasoning, autonomous tool use, or multi-agent collaboration have been proposed as solutions to the limitations of large language models (LLMs) in neuroradiology. However, the extent of their implementation and clinical validation remains unclear. We systematically searched PubMed, Web of Science, and Scopus (January 2022-August 2025) for studies implementing agentic AI in neuroradiology. Six independent reviewers (three medical doctors and three AI specialists) assessed full texts. Agentic AI was defined as requiring mandatory iterative reasoning plus either autonomous tool use or multi-agent collaboration. Study quality was evaluated using adapted QUADAS-AI criteria. From 230 records, 9 studies (3.90%) met inclusion criteria. Of these, five (55.60%) implemented true multi-agent architecture, two (22.20%) used hybrid or conceptual frameworks, and two (22.20%) relied on single-model LLMs without genuine agentic behavior. All nine studies were single center with no external validation. Sample sizes were small (median 142 cases; range 16-302). The only randomized controlled trial-INSPIRE (neurophysiology with imaging correlation)-demonstrated high technical performance (&#x2248;92% accuracy; AIGERS 0.94 for AI-assisted vs. 0.70 for AI-only, p&#x2009;&lt;&#x2009;0.001) but showed no measurable clinical benefit when physicians used AI assistance compared with independent reporting. Safety assessments were absent from all studies.&#xa0;Agentic AI in neuroradiology remains technically promising but clinically unproven. Severe evidence scarcity (3.90% inclusion rate), frequent overextension of the \"agentic\" label (30% of studies lacked genuine autonomy), and the persistent gap between technical performance and clinical utility indicate that the field remains in its early research phase. Current evidence is insufficient to support clinical deployment. Rigorous, multi-center prospective trials with patient-centered and safety outcomes are essential before clinical implementation can be responsibly considered.","url":"https://pubmed.ncbi.nlm.nih.gov/41629669/","authors":["Salehi S","Keishing V","Singh Y","Wei D","Khosravi A","Habibi P","Jagtap J","Erickson BJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1007/s10278-025-01839-2","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41629258","name":"ML-Assisted Olfactory Epidemiology Survey Urbanizing China: A Population-Based Study in Yancheng.","source":"pubmed","abstract":"Urbanization-related air pollution may be associated with olfactory dysfunction (OD) in China, yet population studies are lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/41629258/","authors":["Zang Y","Gao X","Chen W","Zhou P","He T","Xiong GF","Sun G","Hummel T","Liu W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr","doi":"10.1002/lary.70351","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41629073","name":"[Research progress in the field of liver failure with artificial livers in 2025].","source":"pubmed","abstract":"2025&#x5e74;&#xff0c;&#x6211;&#x56fd;&#x9881;&#x5e03;&#x4e86;&#x9996;&#x90e8;&#x300a;&#x6162;&#x52a0;&#x6025;&#x6027;&#x809d;&#x8870;&#x7aed;&#x8bca;&#x6cbb;&#x6307;&#x5357;&#xff08;2025&#x5e74;&#x7248;&#xff09;&#x300b;&#xff0c;&#x56fd;&#x9645;&#x4e0a;&#x4e5f;&#x9646;&#x7eed;&#x53d1;&#x5e03;&#x76f8;&#x5173;&#x4e13;&#x5bb6;&#x5171;&#x8bc6;&#x3002;&#x6162;&#x52a0;&#x6025;&#x6027;&#x809d;&#x8870;&#x7aed;&#x7684;&#x53d1;&#x75c5;&#x673a;&#x5236;&#x4e0e;&#x9884;&#x540e;&#x8bc4;&#x4f30;&#x3001;&#x7c7b;&#x5668;&#x5b98;&#x3001;&#x7ec6;&#x80de;&#x6cbb;&#x7597;&#x3001;&#x4eba;&#x5de5;&#x809d;&#x3001;&#x809d;&#x79fb;&#x690d;&#x4ee5;&#x53ca;&#x4eba;&#x5de5;&#x667a;&#x80fd;&#x7684;&#x5e94;&#x7528;&#x7b49;&#x53d6;&#x5f97;&#x4e00;&#x5b9a;&#x8fdb;&#x5c55;&#x3002;&#x73b0;&#x5bf9;2025&#x5e74;&#x5ea6;&#x6709;&#x5173;&#x809d;&#x8870;&#x7aed;&#x4e0e;&#x4eba;&#x5de5;&#x809d;&#x7684;&#x4e00;&#x4e9b;&#x91cd;&#x8981;&#x7814;&#x7a76;&#x8fdb;&#x884c;&#x4ecb;&#x7ecd;&#x3002;.","url":"https://pubmed.ncbi.nlm.nih.gov/41629073/","authors":["Han T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 20","doi":"10.3760/cma.j.cn501113-20251216-00535","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41627551","name":"Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.","source":"pubmed","abstract":"Neuropsychiatric systemic lupus erythematosus (NPSLE) remains challenging to diagnose because of heterogeneous clinical presentations, nonspecific findings, and the absence of definitive biomarkers. Artificial intelligence (AI) methods have been increasingly explored using neuroimaging and other biologically informative data to support identification of neuropsychiatric involvement in systemic lupus erythematosus (SLE). However, the reported performance and methodological robustness of these approaches have not been systematically characterized. To perform an exploratory meta-analysis describing reported diagnostic performance, heterogeneity, and methodological characteristics of AI-based models using neuroimaging and multimodal biomarkers for detecting neuropsychiatric involvement in SLE. We conducted a PRISMA-compliant systematic review of studies applying machine learning or deep learning models to neuroimaging or biologically informative modalities relevant to central nervous system involvement, including structural or functional MRI, magnetic resonance spectroscopy, spectroscopy-based molecular fingerprints, and CSF or serum biomarkers. PubMed, Scopus, and Web of Science were searched through August 2025. Given substantial heterogeneity in study design, model objectives, input modalities, and validation strategies, analyses were undertaken within an exploratory framework. Random-effects models were used to summarize reported area under the curve (AUC), accuracy, sensitivity, and specificity. Subgroup and leave-one-out sensitivity analyses were performed. Fourteen studies involving more than 800 participants were included. Most studies used neuroimaging, particularly resting-state functional MRI, while others incorporated non-imaging biomarkers. Reported performance metrics were generally high (pooled AUC 0.86; accuracy 0.87), but between-study heterogeneity was substantial. Sensitivity analyses demonstrated that pooled estimates were unstable and influenced by individual studies. No clear performance differences were observed between classical machine learning and deep learning approaches. External validation and formal explainable AI methods were uncommon. This exploratory synthesis indicates that AI-based models applied to neuroimaging and multimodal biomarkers have shown promising reported performance in NPSLE. However, marked heterogeneity, limited robustness, and poor interpretability currently preclude firm conclusions regarding clinical applicability. More standardized, externally validated, and interpretable studies are needed before translation into clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41627551/","authors":["Nouroozi F","Kazemi HS","Alinezhad A","Goudarzi N","Khosravi MK","Narimani Z","Asouri ZA","Ahari SG","Mehrjerdi RS","Saeidi R","Mavi MM","Ahmadifard H","Khosravi F","Alipour M","Abdollahi Z","Shemshad R","Ganjipour P","Anar MA","Rostami E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1007/s10238-025-02030-1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41627478","name":"Pediatric acute mastoiditis: an Italian multicenter retrospective study of clinical, microbiological, and radiological features.","source":"pubmed","abstract":"Acute mastoiditis (AM) is the most common intratemporal complication of acute otitis media in children. This multicenter study aimed to describe the epidemiological, clinical, microbiological, and radiological features of pediatric AM in four Italian regions and to identify factors associated with radiologic complications. This retrospective, observational cohort study included all patients under 18&#xa0;years hospitalized with AM in the pediatric departments of Bari, Forl&#xec;, Macerata, and Pescara between January 2022 and May 2025. Demographic, clinical, microbiological, imaging, and management data were extracted from medical records using a standardized form and analyzed descriptively. Logistic regression explored associations between clinical variables and radiologic complications. A total of 118 hospitalizations (117 children; median age 4&#xa0;years, IQR 2-7.8; 56% males) were analyzed. The estimated annual hospitalization rate was approximately 23 per 100 000 children. Otalgia (70%), postauricular swelling (57%), and erythema (56%) were common presenting features. Clinical complications occurred in 25 (21%) patients, and surgery was required in 13 (11%). Microbiological testing was positive in 38 (32%) episodes, most frequently identifying Streptococcus pyogenes or Pseudomonas aeruginosa. Imaging was performed in 70 (59%) episodes, revealing radiologic complications in 20 (17%; 28.5% of imaged). In multivariable analysis, auricular protrusion was independently associated with a lower likelihood of radiologic complications (p&#x2009;=&#x2009;0.018), whereas no other clinical variable independently predicted radiologic complications.","url":"https://pubmed.ncbi.nlm.nih.gov/41627478/","authors":["Aricò MO","Trotta D","Accomando F","Messa A","D'Errico V","Fornaro M","Aricò M","Valletta E","Caselli D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1007/s00431-026-06776-y","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41627427","name":"Bibliometric analysis of application of radiomics and artificial intelligence integration in personalized treatment of hepatocellular carcinoma.","source":"pubmed","abstract":"Recent advancements in personalized medicine have highlighted the importance of customized strategies in managing hepatocellular carcinoma (HCC), especially through the use of radiomics and artificial intelligence (AI) in treatment planning. While many studies have focused on how radiomics and AI can aid in prognostic evaluations, there is still a significant lack of thorough assessments regarding research trends and gaps in the existing literature. This study employed bibliometric analysis to explore the landscape of personalized treatment research in HCC, specifically concentrating on radiomics and AI techniques. The research utilized the Web of Science Core Collection (WoSCC) database to collect relevant literature, and various tools such as VOSviewer, CiteSpace, and the bibliometrix R package were used to visualize bibliometric results, along with additional visualizations from an online platform. Over a period of 14&#xa0;years, a total of 615 publications were identified. The majority of these publications originate from China and the United States, with Sun Yat-sen University leading in output. The journals making the most significant contributions to this field were Frontiers in Oncology and European Radiology. Notably, keywords showing strong citation bursts in recent years included biomarkers, accuracy, and body radiation therapy. This bibliometric analysis offers a crucial framework for understanding the foundational knowledge in this area. The field of personalized treatment for HCC, particularly with an emphasis on radiomics and AI, is currently undergoing rapid growth, underscoring its ongoing importance for future research initiatives.","url":"https://pubmed.ncbi.nlm.nih.gov/41627427/","authors":["Yu Z","Ke L","Lin Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 3","doi":"10.1097/JS9.0000000000004177","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41628424","name":"Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.","source":"pubmed","abstract":"Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC), where clinical decisions are made under time constraints and cognitive status may be incompletely documented. Federally mandated HHC assessments, such as the Outcome and Assessment Information Set (OASIS), capture health and functional status but may miss subtle early cognitive changes. Speech, language, and interactional patterns during routine patient-nurse communication, together with information embedded in unstructured clinical notes, may provide complementary signals for earlier identification.","url":"https://pubmed.ncbi.nlm.nih.gov/41628424/","authors":["Zolnoori M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.2196/82731","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41628081","name":"Assessing the Role of Artificial Intelligence in Caries Detection and Clinical Decision-Making: A Scoping Review.","source":"pubmed","abstract":"Artificial intelligence (AI) support is expected to increase accuracy and improve treatment plans in dentistry. Nevertheless, AI's ability to promote better oral healthcare is underexplored. This scoping review explores the influence of AI in supporting dental professionals with caries detection and decision-making regarding interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/41628081/","authors":["Sezen-Hulsmans D","Pereira-Cenci T","Sonneveld RE","Loomans BAC","Cenci MS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1159/000550238","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626989","name":"Evaluation of semi-automated record screening methods for systematic reviews of prognosis studies and intervention studies.","source":"pubmed","abstract":"Systematic reviews (SRs) synthesize evidence through a rigorous, labor-intensive, and costly process. To accelerate the title-abstract screening phase of SRs, several artificial intelligence (AI)-based semi-automated screening tools have been developed to reduce workload by prioritizing relevant records. However, their performance is primarily evaluated for SRs of intervention studies, which generally have well-structured abstracts. Here, we evaluate whether screening tool performance is equally effective for SRs of prognosis studies that have larger heterogeneity between abstracts. We conducted retrospective simulations on prognosis and intervention reviews using a screening tool (ASReview). We also evaluated the effects of review scope (i.e., breadth of the research question), number of (relevant) records, and modeling methods within the tool. Performance was assessed in terms of recall (i.e., sensitivity), precision at 95% recall (i.e., positive predictive value at 95% recall), and workload reduction (work saved over sampling at 95% recall [WSS@95%]). The WSS@95% was slightly worse for prognosis reviews (range: 0.324-0.597) than for intervention reviews (range: 0.613-0.895). The precision was higher for prognosis (range: 0.115-0.400) compared to intervention reviews (range: 0.024-0.057). These differences were primarily due to the larger number of relevant records in the prognosis reviews. The modeling methods and the scope of the prognosis review did not significantly impact tool performance. We conclude that the larger abstract heterogeneity of prognosis studies does not substantially affect the effectiveness of screening tools for SRs of prognosis. Further evaluation studies including a standardized evaluation framework are needed to enable prospective decisions on the reliable use of screening tools.","url":"https://pubmed.ncbi.nlm.nih.gov/41626989/","authors":["Spiero I","Leeuwenberg AM","Moons KGM","Hooft L","Damen JAA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1017/rsm.2025.10025","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626985","name":"Optimal large language models to screen citations for systematic reviews.","source":"pubmed","abstract":"Recent studies highlight the potential of large language models (LLMs) in citation screening for systematic reviews; however, the efficiency of individual LLMs for this application remains unclear. This study aimed to compare accuracy, time-related efficiency, cost, and consistency across four LLMs-GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet, and Llama 3.3 70B-for literature screening tasks. The models screened for clinical questions from the Japanese Clinical Practice Guidelines for the Management of Sepsis and Septic Shock 2024. Sensitivity and specificity were calculated for each model based on conventional citation screening results for qualitative assessment. We also recorded the time and cost of screening and assessed consistency to verify reproducibility. A post hoc analysis explored whether integrating outputs from multiple models could enhance screening accuracy. GPT-4o and Llama 3.3 70B achieved high specificity but lower sensitivity, while Gemini 1.5 Pro and Claude 3.5 Sonnet exhibited higher sensitivity at the cost of lower specificity. Citation screening times and costs varied, with GPT-4o being the fastest and Llama 3.3 70B the most cost-effective. Consistency was comparable among the models. An ensemble approach combining model outputs improved sensitivity but increased the number of false positives, requiring additional review effort. Each model demonstrated distinct strengths, effectively streamlining citation screening by saving time and reducing workload. However, reviewing false positives remains a challenge. Combining models may enhance sensitivity, indicating the potential of LLMs to optimize systematic review workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/41626985/","authors":["Oami T","Okada Y","Nakada TA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1017/rsm.2025.10014","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626981","name":"Assessing risk of bias of cohort studies with large language models.","source":"pubmed","abstract":"This study aims to explore the feasibility and accuracy of utilizing large language models (LLMs) to assess the risk of bias (ROB) in cohort studies. We conducted a pilot and feasibility study in 30 cohort studies randomly selected from reference lists of published Cochrane reviews. We developed a structured prompt to guide the ChatGPT-4o, Moonshot-v1-128k, and DeepSeek-V3 to assess the ROB of each cohort twice. We used the ROB results assessed by three evidence-based medicine experts as the gold standard, and then we evaluated the accuracy of LLMs by calculating the correct assessment rate, sensitivity, specificity, and F 1 scores for overall and item-specific levels. The consistency of the overall and item-specific assessment results was evaluated using Cohen's kappa (&#x3ba;) and prevalence-adjusted bias-adjusted kappa. Efficiency was estimated by the mean assessment time required. This study assessed three LLMs (ChatGPT-4o, Moonshot-v1-128k, and DeepSeek-V3) and revealed distinct performance across eight assessment items. Overall accuracy was comparable (80.8%-83.3%). Moonshot-v1-128k showed superior sensitivity in population selection (0.92 versus ChatGPT-4o's 0.55, P &#xa0;&lt;&#xa0;0.001). In terms of F 1 scores, Moonshot-v1-128k led in population selection ( F &#xa0;=&#xa0;0.80 versus ChatGPT-4o's 0.67, P &#xa0;=&#xa0;0.004). ChatGPT-4o demonstrated the highest consistency (mean &#x3ba;&#xa0;=&#xa0;96.5%), with perfect agreement (100%) in outcome confidence. ChatGPT-4o was 97.3% faster per article (32.8&#xa0;seconds versus 20&#xa0;minutes manually) and outperformed Moonshot-v1-128k and DeepSeek-V3 by 47-50% in processing speed. The efficient and accurate assessment of ROB in cohort studies by ChatGPT-4o, Moonshot-v1-128k, and DeepSeek-V3 highlights the potential of LLMs to enhance the systematic review process.","url":"https://pubmed.ncbi.nlm.nih.gov/41626981/","authors":["Xia D","Lai H","Zhao W","Huang J","Liu J","Ye Z","Liu J","Sun M","Hou L","Pan B","Ge L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1017/rsm.2025.10028","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626980","name":"StudyTypeTeller-Large language models to automatically classify research study types for systematic reviews.","source":"pubmed","abstract":"screening, a labor-intensive aspect of systematic review, is increasingly challenging due to the rising volume of scientific publications. Recent advances suggest that generative large language models like generative pre-trained transformer (GPT) could aid this process by classifying references into study types such as randomized-controlled trials (RCTs) or animal studies prior to abstract screening. However, it is unknown how these GPT models perform in classifying such scientific study types in the biomedical field. Additionally, their performance has not been directly compared with earlier transformer-based models like bidirectional encoder representations from transformers (BERT). To address this, we developed a human-annotated corpus of 2,645 PubMed titles and abstracts, annotated for 14 study types, including different types of RCTs and animal studies, systematic reviews, study protocols, case reports, as well as in vitro studies. Using this corpus, we compared the performance of GPT-3.5 and GPT-4 in automatically classifying these study types against established BERT models. Our results show that fine-tuned pretrained BERT models consistently outperformed GPT models, achieving F1-scores above 0.8, compared to approximately 0.6 for GPT models. Advanced prompting strategies did not substantially boost GPT performance. In conclusion, these findings highlight that, even though GPT models benefit from advanced capabilities and extensive training data, their performance in niche tasks like scientific multi-class study classification is inferior to smaller fine-tuned models. Nevertheless, the use of automated methods remains promising for reducing the volume of records, making the screening of large reference libraries more feasible. Our corpus is openly available and can be used to harness other natural language processing (NLP) approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/41626980/","authors":["Emilova Doneva S","de Viragh S","Hubarava H","Schandelmaier S","Briel M","Ineichen BV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Nov","doi":"10.1017/rsm.2025.10031","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626974","name":"Bayesian Federated Inference for regression models based on non-shared medical center data.","source":"pubmed","abstract":"To estimate accurately the parameters of a regression model, the sample size must be large enough relative to the number of possible predictors for the model. In practice, sufficient data is often lacking, which can lead to overfitting of the model and, as a consequence, unreliable predictions of the outcome of new patients. Pooling data from different data sets collected in different (medical) centers would alleviate this problem, but is often not feasible due to privacy regulation or logistic problems. An alternative route would be to analyze the local data in the centers separately and combine the statistical inference results with the Bayesian Federated Inference (BFI) methodology. The aim of this approach is to compute from the inference results in separate centers what would have been found if the statistical analysis was performed on the combined data. We explain the methodology under homogeneity and heterogeneity across the populations in the separate centers, and give real life examples for better understanding. Excellent performance of the proposed methodology is shown. An R-package to do all the calculations has been developed and is illustrated in this article. The mathematical details are given in the Appendix.","url":"https://pubmed.ncbi.nlm.nih.gov/41626974/","authors":["Jonker MA","Pazira H","Coolen ACC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.1017/rsm.2025.6","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626972","name":"A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening.","source":"pubmed","abstract":"When conducting a systematic review, screening the vast body of literature to identify the small set of relevant studies is a labour-intensive and error-prone process. Although there is an increasing number of fully automated tools for screening, their performance is suboptimal and varies substantially across review topic areas. Many of these tools are only trained on small datasets, and most are not tested on a wide range of review topic areas. This study presents two systematic review datasets compiled from more than 8600 systematic reviews and more than 540000 abstracts covering 51 research topic areas in health and medical research. These datasets are the largest of their kinds to date. We demonstrate their utility in training and evaluating language models for title and abstract screening. Our dataset includes detailed metadata of each review, including title, background, objectives and selection criteria. We demonstrated that a small language model trained on this dataset with additional metadata has excellent performance with an average recall above 95% and specificity over 70% across a wide range of review topic areas. Future research can build on our dataset to further improve the performance of fully automated tools for systematic review title and abstract screening.","url":"https://pubmed.ncbi.nlm.nih.gov/41626972/","authors":["Chan GCK","He E","Leung J","Verspoor K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Mar","doi":"10.1017/rsm.2025.1","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626935","name":"Generalizable and scalable multistage biomedical concept normalization leveraging large language models.","source":"pubmed","abstract":"Biomedical entity normalization is critical to biomedical research because the richness of free-text clinical data, such as progress notes, can often be fully leveraged only after translating words and phrases into structured and coded representations suitable for analysis. Large Language Models (LLMs), in turn, have shown great potential and high performance in a variety of natural language processing (NLP) tasks, but their application for normalization remains understudied.","url":"https://pubmed.ncbi.nlm.nih.gov/41626935/","authors":["Dobbins NJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 May","doi":"10.1017/rsm.2025.9","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626932","name":"Exploring the potential of Claude 2 for risk of bias assessment: Using a large language model to assess randomized controlled trials with RoB 2.","source":"pubmed","abstract":"Systematic reviews are essential for evidence-based health care, but conducting them is time- and resource-consuming. To date, efforts have been made to accelerate and (semi-)automate various steps of systematic reviews through the use of artificial intelligence (AI) and the emergence of large language models (LLMs) promises further opportunities. One crucial but complex task within systematic review conduct is assessing the risk of bias (RoB) of included studies. Therefore, the aim of this study was to test the LLM Claude 2 for RoB assessment of 100 randomized controlled trials, published in English language from 2013 onwards, using the revised Cochrane risk of bias tool ('RoB 2'; involving judgements for five specific domains and an overall judgement). We assessed the agreement of RoB judgements by Claude with human judgements published in Cochrane reviews. The observed agreement between Claude and Cochrane authors ranged from 41% for the overall judgement to 71% for domain 4 ('outcome measurement'). Cohen's &#x3ba; was lowest for domain 5 ('selective reporting'; 0.10 (95% confidence interval (CI): -0.10-0.31)) and highest for domain 3 ('missing data'; 0.31 (95% CI: 0.10-0.52)), indicating slight to fair agreement. Fair agreement was found for the overall judgement (Cohen's &#x3ba;: 0.22 (95% CI: 0.06-0.38)). Sensitivity analyses using alternative prompting techniques or the more recent version Claude 3 did not result in substantial changes. Currently, Claude's RoB 2 judgements cannot replace human RoB assessment. However, the potential of LLMs to support RoB assessment should be further explored.","url":"https://pubmed.ncbi.nlm.nih.gov/41626932/","authors":["Eisele-Metzger A","Lieberum JL","Toews M","Siemens W","Heilmeyer F","Haverkamp C","Boehringer D","Meerpohl JJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 May","doi":"10.1017/rsm.2025.12","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626895","name":"Automating the data extraction process for systematic reviews using GPT-4o and o3.","source":"pubmed","abstract":"Large language models have shown promise for automating data extraction (DE) in systematic reviews (SRs), but most existing approaches require manual interaction. We developed an open-source system using GPT-4o to automatically extract data with no human intervention during the extraction process. We developed the system on a dataset of 290 randomized controlled trials (RCTs) from a published SR about cognitive behavioral therapy for insomnia. We evaluated the system on two other datasets: 5 RCTs from an updated search for the same review and 10 RCTs used in a separate published study that had also evaluated automated DE. We developed the best approach across all variables in the development dataset using GPT-4o. The performance in the updated-search dataset using o3 was 74.9% sensitivity, 76.7% specificity, 75.7 precision, 93.5% variable detection comprehensiveness, and 75.3% accuracy. In both datasets, accuracy was higher for string variables (e.g., country, study design, drug names, and outcome definitions) compared with numeric variables. In the third external validation dataset, GPT-4o showed a lower performance with a mean accuracy of 84.4% compared with the previous study. However, by adjusting our DE method, while maintaining the same prompting technique, we achieved a mean accuracy of 96.3%, which was comparable to the previous manual extraction study. Our system shows potential for assisting the DE of string variables alongside a human reviewer. However, it cannot yet replace humans for numeric DE. Further evaluation across diverse review contexts is needed to establish broader applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/41626895/","authors":["Kataoka Y","Takayama T","Yoshimura K","So R","Tsujimoto Y","Yamagishi Y","Takagi S","Furukawa Y","Sakata M","Bašić Đ","Cipriani A","Cuijpers P","Karyotaki E","Harrer M","Leucht S","Homiar A","Ostinelli EG","Miguel C","Rodolico A","Furukawa TA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1017/rsm.2025.10030","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626705","name":"Hematologic markers and machine learning in predicting placenta accreta: A case-control study.","source":"pubmed","abstract":"This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine learning by evaluating the association between antenatal hematologic index trends across trimesters, imaging markers, and patient history.","url":"https://pubmed.ncbi.nlm.nih.gov/41626705/","authors":["Jochum MD","Albrecht KD","Martinez YM","Zhang V","Sarada S","Burnett B","Reed CC","Fox KA","Shamshirsaz AA","Belfort MA","Munoz JL","Lombaard HA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1002/ijgo.70782","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626641","name":"Advancing Maternal Health with Long-Acting Therapeutics: Priorities, Efficacy and Safety Considerations, and Emerging Technologies.","source":"pubmed","abstract":"Maternal health remains a critical global concern, particularly in underserved populations and in low- and middle-income countries where access to safe and effective therapeutics is limited. Despite the use of medications by most women during pregnancy, the exclusion of pregnant and lactating women from clinical trials has resulted in significant data gaps, hindering informed treatment decisions. As long-acting therapeutics transition into mainstream treatment and prevention strategies, it is critical to ensure these disparities are neither perpetuated nor widened. This review synthesizes insights from the maternal health session of the July 2025 workshop of the Community of Practice for Long-Acting Therapeutics in Maternal and Pediatric Health. It was convened and hosted by the University of Liverpool Centre of Excellence for Long-Acting Therapeutics with funding from Unitaid. Key themes explored during the session include (1) regulatory initiatives, research networks, and data infrastructures that are driving systemic change in maternal health research over the past two decades; (2) important efficacy and safety considerations during pregnancy and lactation using insights from long-acting antiretrovirals currently in clinical use; and (3) selected long-acting drug delivery systems with potential applications in maternal health. Starting with maternal health priorities, here we included further insights regarding long-acting injectable antipsychotics, long-acting reversible contraceptives, and the role of in silico modeling in bridging existing gaps. Several immediately actionable recommendations are presented on advancing long-acting therapeutics for maternal health priorities during pregnancy and lactation.","url":"https://pubmed.ncbi.nlm.nih.gov/41626641/","authors":["Scott RK","Nachman S","Weld ED","Daley R","Atoyebi S","Bies R","Waitt C","Olagunju A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1002/cpt.70224","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626576","name":"Artificial intelligence and precision medicine: a pilot study predicting optimal ceftaroline dosage for pediatric patients.","source":"pubmed","abstract":"Accurate drug dosing in pediatrics is complicated by age-related physiological variability. Standard weight-based dosing may result in either subtherapeutic exposure or toxicity. Machine learning (ML) models can capture complex relationships among clinical variables and support individualized therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41626576/","authors":["Frasca M","Gazzaniga G","Graziosi A","De Nicolo V","De Giacomo C","Martinelli S","Senatore M","Romandini A","Moretti C","Pattarino GAC","Proto A","Danesi R","Scaglione F","Vago G","La Torre D","Pani A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/frai.2025.1702087","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626567","name":"Beyond conventional therapies: Gut microbiota modulation and macromolecular drugs in the battle against cardiometabolic diseases.","source":"pubmed","abstract":"Cardiometabolic diseases (CMDs) represent an ongoing major global health challenge, driven by complex interactions among genetic, environmental, microbiome-related, and other factors. While small-molecule drugs and lifestyle interventions can provide clinical benefits, they are possible to be constrained by the limited druggability of key target proteins, the potential risks of off-target effects, and difficulties in maintaining long-term adherence. In recent years, gut microbiota modulation and macromolecular drugs have emerged as promising therapeutic strategies. Gut microbiota modulation (e.g., probiotics, synbiotics, or natural products) exerts systemic metabolic and immune effects, supporting a therapeutic approach targeting multiple diseases. Meanwhile, macromolecular drugs (e.g., peptides, antibodies, and small nucleic acids) offer precise, pathway-targeted interventions. Despite advancements, limitations remain in addressing ethical considerations in microbiota modulation and optimizing targeted delivery systems, all of which may hinder clinical translation. Here, we provide a comprehensive overview of therapeutic approaches for CMDs, with a focus on obesity, type 2 diabetes mellitus (T2DM), and atherosclerosis (AS). The review is structured around three key aspects: i) conventional therapies, including small-molecule drugs and lifestyle interventions; ii) emerging therapies encompassing gut microbiota modulation, macromolecular drugs, and their interactions; and iii) challenges and opportunities for comorbidity management, microbiota ethics, and artificial intelligence (AI)-driven therapeutic optimization. We hope this review enhances the understanding of small-molecule drugs, lifestyle interventions, gut microbiota modulation, and macromolecular drugs in the management of CMDs, thereby fostering medical innovation and contributing to the development of system-based comprehensive therapeutic paradigms.","url":"https://pubmed.ncbi.nlm.nih.gov/41626567/","authors":["Wang J","Qu J","Ye M","Feng R","Hui X","Yang X","Jin J","Tong Q","Zhang X","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1016/j.jpha.2025.101416","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626543","name":"Artificial intelligence-curated repository of gene-encoded natural diverse components from herbal medicines.","source":"pubmed","abstract":"Natural components, evolved to help organisms adapt and defend against threats, are also vital sources for drug discovery due to their diverse and potent bioactivities. In the present work, we proposed the Gene-encoded Natural Diverse Components Repository (GNDC, https://cbcb.cdutcm.edu.cn/gndc/), a primary and most extensive database dedicated to cataloging diverse natural components. GNDC currently catalogs over 234 million natural components that are organized into four specialized sub-databases: HerbalMDB for 2.32 million secondary metabolites, HerbalPDB for 229 million small peptides, HerbalRDB for 2.38 million small RNAs, and HerbalCDB for 0.26 million carbohydrates. By leveraging customized pipelines for high-throughput multi-omics data and AI technologies, the GNDC enables large-scale discovery and annotation of natural products from nuclear and organellar genomes of species listed in eight global pharmacopoeias and multi-resource data. Compared to existing resources, GNDC achieves a 10-fold increase in component yield and introduces over 200 million previously unreported components. To support this unprecedented data volume and complexity, state-of-the-art AI tools are seamlessly integrated to decipher and annotate vast data collections, such as classification and gene expression signature generation of millions of secondary metabolites. We envision that the GNDC will drive the transformation of drug discovery from an \"experience-driven\" approach to a \"big data-driven\" paradigm.","url":"https://pubmed.ncbi.nlm.nih.gov/41626543/","authors":["Chen W","Yu Z","Leng L","Sun D","Liu H","Gong RZ","Cong Z","Xiao W","Zhang G","Yang L","Meng F","Xu G","Yang X","Cheng Q","Liu Z","Liu H","Lu J","Mao Y","Li X","Tang X","Zhu D","Chen H","Xu Z","Xu J","Zhang M","Hu Z","Zhang S","Du R","Sun C","Song J","Xiang L","Yao H","Liao B","Liu Y","Zhao D","Su H","Bin H","Wang C","Zhang T","You S","Shi Z","Zhu L","Huang SX","Zhang B","Song C","Chen S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025 Dec 1","doi":"10.1016/j.xinn.2025.101011","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626408","name":"Inside Their Minds: A Multi-Institutional Exploration into the Decision-Making of Medical School Competency Committee Members.","source":"pubmed","abstract":"A competency committee is a group of experts who make a consensus-based judgement about a learner's competence. While evidence-based practices for post-graduate committees have been described, research and standards are lacking in undergraduate medical education. Medical school competency committees often distribute student reviews to individual members; therefore, understanding how they interpret assessment data is critical.","url":"https://pubmed.ncbi.nlm.nih.gov/41626408/","authors":["Ryan MS","Teunissen PW","Parsons AS","Bradley E","Santen SA","Shelgikar AV","Schumacher DJ","Kelleher M","Jolani S","Vitto CM","Vinson AH","UME Competency Committee Collaborators"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5334/pme.2361","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626168","name":"Artificial intelligence models in the surgical planning of low-grade gliomas: a systematic review.","source":"pubmed","abstract":"AI techniques like convolutional neural networks (CNN), deep learning (DL), and neural networks (NN) have made it easier to automatically extract important clinical data for glioma post-treatment monitoring and surgical planning.","url":"https://pubmed.ncbi.nlm.nih.gov/41626168/","authors":["Sanker V","Venkatesan A","Salma A","Sp A","Li Z","Heesen P","Park C","Park DJ","Desai A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1672289","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"pmid:41626151","name":"Recent therapeutic advances in gynecologic oncology: evolving roles of immunotherapy, antibody-drug conjugates, and clinical trial innovations.","source":"pubmed","abstract":"Gynecologic cancers, including cervical, endometrial, and ovarian malignancies, remain among the leading causes of cancer-related illness and death in women worldwide. Despite progress in surgery and chemotherapy, resistance to conventional cytotoxic drugs continues to limit durable outcomes. The introduction of immune checkpoint inhibitors (ICIs) and antibody-drug conjugates (ADCs) has created new therapeutic opportunities by improving survival and overcoming resistance mechanisms. This review summarizes the latest clinical evidence on immunotherapy and ADC-based regimens, emphasizing their integration into current treatment strategies and the expanding roles of genomic profiling and artificial intelligence (AI) in personalized therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/41626151/","authors":["Koshkimbayeva G","Amirkhanova A","Orazymbetova A","Nurakhova A","Maimakova A","Duisenbayeva A","Akhmad N","Abilova A","Abilbayeva A","Akhelova S","Akhmentayeva D","Seitaliyeva A","Dushimova Z","Shynykul Z","Yerkenova S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1697180","addedAt":"2026-09-01T01:47:54.711Z","updatedAt":"2026-09-01T01:47:54.711Z"},{"id":"oa:W7133535600","name":"Performance Validation of ORTHOSEG, a Novel Artificial Intelligence Tool for the Segmentation of Orthopantomographs and Intra-Oral X-Rays","source":"openalex","abstract":"Background: Dental radiographs are essential for diagnosis and treatment planning in modern dentistry. However, their manual interpretation is time-consuming and subject to variability, highlighting the need for automated tools to improve efficiency and consistency. This study aims to validate ORTHOSEG, a deep learning-based system designed to automate the segmentation of anatomical, pathological, and non-pathological elements in radiographs, including orthopantomograms, bitewings, and periapical images. Methods: ORTHOSEG’s performance was evaluated using a rigorously curated dataset of 150 dental radiographs, including 50 orthopantomograms, 50 bitewings, and 50 periapical images, with manual annotations by expert clinicians serving as the ground truth. The system’s segmentation performance was assessed using standard evaluation metrics, including mean Dice Similarity Coefficient (mDSC) and mean Intersection over Union (mIoU), and inference time was also recorded. Results: The system achieved high accuracy, with mDSC and mIoU values of 0.635 ± 0.233 and 0.576 ± 0.214, respectively. In particular for orthopantomograms, it achieved an mDSC of 0.756 ± 0.174 and an mIoU of 0.684 ± 0.172, surpassing existing benchmarks. Its segmentation capabilities extend to approximately 70 distinct elements, underscoring its comprehensive utility. The system demonstrated efficient computational performance, with processing times of 19.745 ± 3.625 s for orthopantomograms, 8.467 ± 0.903 s for bitewings, and 5.653 ± 0.897 s for periapical radiographs on standard clinical hardware. Conclusions: ORTHOSEG demonstrates efficiency suitable for integration into routine workflows. This study confirms ORTHOSEG’s reliability and potential to improve diagnostic workflows, offering clinicians a valuable tool for faster and more detailed radiograph analysis. Future research will focus on extending validation across diverse clinical scenarios to ensure broader applicability. However, this study has limitations, including the use of a dataset derived from a European population and the absence of usability and clinical workflow evaluation, which should be addressed in future studies.","url":"https://doi.org/10.3390/clinpract16030054","authors":["Giuseppe Cota","Gaetano Scaramozzino","Marco Chiesa","Lelio Gennaro","Maurizio Pascadopoli","Andrea Scribante","Marco Colombo"],"tags":["Segmentation","Artificial intelligence","Medicine","Reliability (semiconductor)","Inference"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-04","doi":"https://doi.org/10.3390/clinpract16030054","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401910776","name":"Lung cancer screening: where do we stand?","source":"openalex","abstract":"Lung cancer screening (LCS) programmes have emerged over recent years around the world. LCS programmes present differences in delivery, inclusion criteria and resource allocation. On a national scale, only a few LCS programmes have been fully established, but more are anticipated to follow. Evidence has shown that, in combination with a low-dose chest computed tomography scan, smoking cessation should be offered as part of a LCS programme for improved patient outcomes. Promising tools in LCS include further refined risk prediction models, the use of biomarkers, artificial intelligence and radiomics. However, these tools require further study and clinical validation is required prior to routine implementation.","url":"https://doi.org/10.1183/20734735.0190-2023","authors":["Georgia Hardavella","Armin Frille","Katherina Bernadette Sreter","Florence Atrafi","Uraujh Yousaf-Khan","Ferhat Beyaz","Fotis Kyriakou","Elena Bellou","Monica Mullin","Sam M. Janes"],"tags":["Radiomics","Lung cancer screening","Lung cancer","Computed tomography","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-01","doi":"https://doi.org/10.1183/20734735.0190-2023","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4413480625","name":"Better way: initial acceptability testing of using artificial intelligence tools to accelerate development of trauma clinical guidance","source":"openalex","abstract":"Abstract Introduction Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acceptability of a proposed 12-step approach that aims to reduce the current clinical guidance timeline from more than 24 months to 24 weeks. Methods Investigators hypothesized that artificial intelligence (AI) tools could be leveraged to improve and make the process of clinical guidance development more efficient, facilitating AI initial output that could later be reviewed by subject matter experts (SMEs). Ensuring ethical standards and a collaborative design. Following the agile methodology, emphasizing continuous delivery and improvement, and the Practical, Robust Implementation and Sustainability Model (PRISM) framework, the investigators drafted a 12-step approach to clinical guidance development in 24 weeks. The process starts with the selection of a clinical topic and culminates in a bedside-ready clinical decision tree. Results The 2025 Design for Implementation: The Future of Trauma Research & Clinical Guidance conference participants were invited to reflect on this new 12-step approach during two breakout sessions. Participants included a broad range of trauma providers, methodologists, patient representatives, technology, and marketing experts. Their recommendations highlighted: 1) multidisciplinary involvement, 2) need for resource-stratified recommendations, and 3) user-friendly features (offline and multilingual access). On a post conference survey (n=56), 64% were confident in AI accelerating the current development process. Conclusions The current landscape of clinical guidance offers significant opportunities for improvement. Key areas for enhancement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and utilizing new technologies, such as AI, to expedite the development process. This is crucial because ongoing delays lead to practices lagging behind current evidence. Further research is needed to rigorously test and refine how responsible use of AI can be integrated into expediting evidence integration into clinical guidance. Key Messages What is already known on this topic Current clinical guidance typically takes 1-2 years to develop. Moreover, clinical guidance may not be published until a year or more after its completion, long after some recommendations become outdated, contributing to lagged evidence-informed practice. What this study adds This study shares and tests the initial acceptability of a novel approach that aims to reduce the current clinical guidance timeline from 24 months to 24 weeks. It leverages existing artificial intelligence tools but with the critical input of subject matter experts (SMEs), ensuring ethical standards and collaborative design. SMEs shed light on critical steps and key areas that future clinical guidance needs to consider. How this study might affect research, practice or policy The current landscape of clinical guidance offers significant opportunities for improvement. Key areas for enhancement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and utilizing new technologies, such as artificial intelligence, to expedite the development process.","url":"https://doi.org/10.1136/tsaco-2025-002060","authors":["Gabriela Zavala Wong","Shannon Rosenauer","Chelsea Church","Diana Sherifali","Megan Racey","Katheryn Grider","Ashley N Moreno","Lacey N. LaGrone","The 2025 Design for Implementation (DFI) Authorship Group","Pamela Bixby","Stephanie Bonne","Eileen M. Bulger","James G Cain","Jennifer Chastek","Julia Roberts Coleman","Todd W Costantini","Nicholas Cozzi","Kimberly A. Davis","Rochelle A. Dicker","Warren C. Dorlac","Erik Van Eaton","Evert Eriksson","Susan Evans","Shannon Marie Foster","Jeffrey M. Goodloe","Elliott R. Haut","Molly Jarman","Alyssa Johnson","Meera Kotagal","Morgan Krause","John C. Kubasiak","Kelly Lang","Allison Barbara Leigh","Halinder S. Mangat","Debra Marie Marvel","Christopher Paul Michetti","Vicki Moran","Ashley N. Moreno","Simon JW Oczkowski","Michael A. Person","Michelle A. Price","LJ Punch","Megan Racey","Bradford L. Ray","Diane Redmond","Linda Kate Reinhart","Heather Rhodes","Bryn Rhodes","Andres M. Rubiano","Sabrina Sanchez","Babak Sarani","Erica Shelton","David A Spain","Kristan Staudenmayer","Deborah M. Stein","Julie Valenzuela","Cynthia Lizette Villarreal","Jeffrey L. Wells","Gabriela Zavala Wong","LeAnne Sitari Young"],"tags":["Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-06-01","doi":"https://doi.org/10.1136/tsaco-2025-002060","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4413922470","name":"Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges","source":"openalex","abstract":"Reinforcement learning (RL) is being used more in medical imaging for segmentation, detection, registration, and classification. This survey provides a comprehensive overview of RL techniques applied in this domain, categorizing the literature based on clinical task, imaging modality, learning paradigm, and algorithmic design. We introduce a unified taxonomy that supports reproducibility, highlights design guidance, and identifies underexplored intersections. Furthermore, we examine the integration of Large Language Models (LLMs) for automation and interpretability, and discuss privacy-preserving extensions using Differential Privacy (DP) and Federated Learning (FL). Finally, we address deployment challenges and outline future research directions toward trustworthy and scalable medical RL systems.","url":"https://doi.org/10.3390/fi17090396","authors":["Abm Kamrul Islam Riad","Md Abdul Barek","Hossain Shahriar","Guillermo Francia","Sheikh Iqbal Ahamed"],"tags":["Computer science","Reinforcement learning","Taxonomy (biology)","Artificial intelligence","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-08-30","doi":"https://doi.org/10.3390/fi17090396","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4406880779","name":"Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles","source":"openalex","abstract":"The energy optimization and demand side management (DSM) of Internet of Things (IoT)-enabled microgrids are being transformed by generative artificial intelligence, such as large language models (LLMs). This paper explores the integration of LLMs into energy management, and emphasizes their roles in automating the optimization of DSM strategies with Internet of Electric Vehicles (IoEV) as a representative example of the Internet of Vehicles (IoV). We investigate challenges and solutions associated with DSM and explore the new opportunities presented by leveraging LLMs. Then, we propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization. The results demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles, and highlight our solution's significant advancements in energy efficiency and user adaptability. This work shows LLMs' potential in energy optimization of the IoT-enabled microgrids and promotes intelligent DSM solutions.","url":"https://doi.org/10.48550/arxiv.2501.15544","authors":["Hanwen Zhang","Ruichen Zhang","Weidong Zhang","Dusit Niyato","Yonggang Wen","Miao, Chunyan"],"tags":["Generative grammar","Demand side","Computer science","Artificial intelligence","Economics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-26","doi":"https://doi.org/10.48550/arxiv.2501.15544","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7161305024","name":"Academic transformation in the era of artificial intelligence: drivers of university faculty adoption of GenAI based on the UTAUT model","source":"openalex","abstract":"Generative Artificial Intelligence (GenAI) technology is reshaping the traditional paradigms of research and teaching, with “AI for Science” and “AI-Enabled Education” profoundly transforming contemporary higher education. As the central force driving research innovation and education, university faculty members’ willingness to adopt GenAI technology is critical to educational innovation. This study employs the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporating GenAI literacy and usage attitudes, and utilizes structural equation modeling to explore the key factors influencing faculty members’ intention to use GenAI technology in research and teaching contexts. A total of 238 valid responses were collected from university teachers. The findings reveal that: first, performance expectancy and usage attitudes within the UTAUT model significantly and positively impact faculty members’ usage intention, highlighting the importance of perceived benefits and personal attitudes in the technology acceptance process; second, faculty members’ overall level of GenAI literacy significantly and positively affects their attitudes toward GenAI technology, indicating that enhancing their GenAI literacy- as reflected in technical proficiency, critical evaluation, communication proficiency, creative application- is crucial for forming positive attitudes; and third, usage attitudes serve as a mediating factor, fully mediating the effect of GenAI literacy on usage intention, emphasizing the central role of positive attitudes in technology adoption. Based on these findings, this study provides theoretical and practical guidance for promoting GenAI technology in universities, suggesting that institutions should prioritize cultivating faculty members’ GenAI literacy and shaping positive attitudes toward technology to enhance their willingness to adopt it. Furthermore, the study offers new perspectives and frameworks for future research on the application of GenAI technology in education.","url":"https://doi.org/10.1057/s41599-026-07606-0","authors":["Jingyao Wang","Haoming Wang","Junwu Yang","Chengliang Wang"],"tags":["Transformation (genetics)","Higher education","Knowledge management","Engineering management","University faculty"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-15","doi":"https://doi.org/10.1057/s41599-026-07606-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7128777056","name":"Generative Artificial Intelligence In Health Informatics Education: A Comprehensive Bibliometric Assessment Of Cognitive Outcome Research (2019–2025)","source":"openalex","abstract":"This study provides a comprehensive bibliometric assessment of generative artificial intelligence (GenAI) research in health informatics education, with particular emphasis on cognitive outcomes. A total of 264 PubMed-indexed publications (2019–2025) were analyzed using Bibliometrix (R) and VOSviewer to evaluate annual scientific output, core journals, authorship patterns, institutional and country productivity, conceptual trends, and collaboration networks. Results show an exceptional rise in publication volume beginning in 2023, coinciding with the widespread introduction of large language models such as ChatGPT. The intellectual structure of the field is dominated by themes related to artificial intelligence, machine learning, large language models, and digital health applications. Two major clusters of application were identified: clinical and patient-centered communication, and educational processes involving competence development and assessment. Ethical themes, including bias and transparency, emerged rapidly in 2024–2025. Research output is highly concentrated in the United States and China, whereas collaboration patterns remain fragmented with multiple small author clusters. Most studies relied on cross-sectional designs, with limited experimental or longitudinal evaluation of learning outcomes. The findings highlight a methodological gap between technical GenAI development and established educational or cognitive frameworks. The study recommends integrating cognitive theory, improving methodological rigor, and expanding interdisciplinary and international collaboration. This bibliometric mapping offers an evidence-oriented foundation for guiding future work on GenAI-enhanced teaching, learning, curriculum design, and evaluation in health informatics education.","url":"https://doi.org/10.29284/r2man269","authors":["Anas Ali Alhur","Nouf Al-Kahtani"],"tags":["Health informatics","Competence (human resources)","Informatics","Curriculum","Cognition"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.29284/r2man269","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401496240","name":"Dietary therapies interlinking with gut microbes toward human health: Past, present, and future","source":"openalex","abstract":"Overview of personalized dietary therapies. This flow chart exhibits the future prospect for integrating human microbiome and bio-medical research to revolutionize the precise personalized dietary therapies. With the development of artificial intelligence (AI), incorporating database may achieve personalized dietary therapies with high precision.","url":"https://doi.org/10.1002/imt2.230","authors":["Jiali Chen","Jiaqiang Luo","Sjaak Pouwels","Beijinni Li","Bian Wu","Tamer N. Abdelbaki","Jayashree Arcot","Wah Yang"],"tags":["Gut flora","Microbiome","Disease","Obesity","Environmental health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-10","doi":"https://doi.org/10.1002/imt2.230","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4416682545","name":"Integrating Artificial Intelligence tools into construction project decision-making: Lessons from early 2023 implementations","source":"openalex","abstract":"This paper discusses the adoption of Artificial Intelligence (AI) tools for construction project decision-making and examines their implementation in early 2023. The study assesses the usefulness of AI technologies, i.e., predictive analytics and machine learning, to improve project planning, risk management, and resource allocation. Data on AI tool adoption and its outcomes were collected through a mixed-methods approach, comprising case studies and interviews with industry professionals. The most significant findings indicate that AI greatly enhanced decision-making efficiency, reduced project expenses and delays, but also identified technical barriers and resistance to change. The research identifies important lessons for construction professionals who intend to incorporate AI tools into future construction projects. The findings highlight how AI can revolutionize construction and decision-making, presenting the industry with opportunities and challenges.","url":"https://doi.org/10.30574/ijsra.2023.10.1.0910","authors":["Shakeeb Sultan"],"tags":["Implementation","Engineering management","Engineering","Analytics","Construction industry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-31","doi":"https://doi.org/10.30574/ijsra.2023.10.1.0910","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401023983","name":"Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation","source":"openalex","abstract":"Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-based FSMIS methods generate multiple prototypes from the support image solely by random sampling or local averaging, which can cause particularly severe boundary blurring due to the tendency for normal features accounting for the majority of features of a specific category. Consequently, we propose to focus more attention to those weaker features that are crucial for clear segmentation boundary. Specifically, we design a Support Self-Prediction (SSP) module to identify such weak features by comparing true support mask with one predicted by global support prototype. Then, a Hard Prototypes Generation (HPG) module is employed to generate multiple hard prototypes based on these weak features. Subsequently, a Multiple Similarity Maps Fusion (MSMF) module is devised to generate final segmenting mask in a dual-path fashion to mitigate the imbalance between foreground and background in medical images. Furthermore, we introduce a boundary loss to further constraint the edge of segmentation. Extensive experiments on three publicly available medical image datasets demonstrate that our method achieves state-of-the-art performance. Code is available at https://github.com/jcjiang99/CoW.","url":"https://doi.org/10.24963/ijcai.2024/139","authors":["Jianchao Jiang","Haofeng Zhang"],"tags":["Computer science","Artificial neural network","Artificial intelligence","Spiking neural network","Image (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-26","doi":"https://doi.org/10.24963/ijcai.2024/139","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4297968760","name":"A Review of Security and Privacy Concerns in the Internet of Things (IoT)","source":"openalex","abstract":"The recent two decades have witnessed tremendous growth in Internet of things (IoT) applications. There are more than 50 billion devices connected globally. IoT applications’ connectivity with the Internet persistently victimized them with a divergent range of traditional threats, including viruses, worms, malware, spyware, Trojans, malicious code injections, and backdoor attacks. Traditional threats provide essential services such as authentication, authorization, and accountability. Authentication and authorization are the process of verifying that a subject is bound to an object. Traditional authentication and authorization mechanisms use three different factors to identity a subject to verify if the subject has the right capability to access the object. Further, it is defined that a computer virus is a type of malware. Malware includes computer viruses, worms, Trojan horses, spyware, and ransomware. There is a high probability that IoT systems can get infected with a more sophisticated form of malware and high-frequency electromagnetic waves. Purpose oriented with distinct nature IoT devices is developed to work in a constrained environment. So there is a dire need to address these security issues because relying on existing traditional techniques is not good. Manufacturers and researchers must think about resolving these security and privacy issues. Most importantly, this study identifies the knowledge and research gap in this area. The primary objective of this systematic literature review is to discuss the divergent types of threats that target IoT systems. Most importantly, the goal is to understand the mode of action of these threats and develop the recovery mechanism to cover the damage. In this study, more than 170 research articles are systematically studied to understand security and privacy issues. Further, security threats and attacks are categorized on a single platform and provide an analysis to explain how and to what extent they damage the targeted IoT systems. This review paper encapsulates IoT security threats and categorizes and analyses them by implementing a comparative study. Moreover, the research work concludes to expand advanced technologies, e.g., blockchain, machine learning, and artificial intelligence, to guarantee security, privacy, and IoT systems.","url":"https://doi.org/10.1155/2022/5724168","authors":["Muhammad Aqeel","Fahad Ali","Muhammad Waseem Iqbal","Toqir A. Rana","Muhammad Arif","Md. Rabiul Auwul"],"tags":["Computer security","Malware","Trojan","Computer science","Authentication (law)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-29","doi":"https://doi.org/10.1155/2022/5724168","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7160419605","name":"Exploring Students’ Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar","source":"openalex","abstract":"Background: Artificial intelligence (AI) is widely used in mental health care for screening, monitoring, and intervention. Notably, most studies of AI in mental health have been performed in Western contexts, with limited evidence from the Arab Gulf region, where cultural factors such as stigma, privacy, and help-seeking norms may influence acceptance. Objective: Investigating university students’ perceptions of AI in mental health support, including awareness, trust, readiness, and preferences in a Gulf context. Methods: A cross-sectional survey was administered to 220 university students in Qatar. Data were analyzed using descriptive statistics, Chi-square tests, and one-way ANOVA to explore associations and group differences. Results: Students showed low-to-moderate levels of awareness and trust in AI-based mental health tools. The majority of participants showed that they were prepared to employ AI for stress management, but they do not prefer to replace face-to-face therapy, suggesting a preference for complementary use. A significant association was found between readiness and expectations (p < 0.00001), which means ambivalence toward AI effectiveness. No significant differences were observed across gender or academic level (p > 0.05). Key concerns included loss of human interaction, overreliance on technology, and diagnostic accuracy, while perceived benefits included cost reduction and 24/7 accessibility. Conclusions: Students exhibit cautious adoption of AI in mental health services. Acceptance is influenced by trust, privacy issues, and apparent compassion. AI is optimally situated as a supplementary instrument within ethically regulated, culturally attuned hybrid care frameworks that maintain the fundamental importance of human connection.","url":"https://doi.org/10.3390/healthcare14091247","authors":["Amani ElBarazi","Hatem Mohamed","Ramzi Nasser"],"tags":["Ambivalence","Mental health","Preference","Psychology","Perception"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-06","doi":"https://doi.org/10.3390/healthcare14091247","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7139022403","name":"Electrocardiographic Signatures of Dysglycaemia: Mechanistic Foundations, Digital Biomarkers, and Artificial Intelligence for Non-Invasive Diabetes Risk Stratification","source":"openalex","abstract":"Diabetes mellitus is projected to affect more than 1.3 billion people worldwide by 2050, with millions remaining undiagnosed or in a prediabetic state. Cardiovascular complications account for nearly half of diabetes-related deaths, highlighting the need for scalable tools capable of identifying metabolic dysregulation before irreversible cardiac damage develops. This review synthesizes current mechanistic, clinical, and computational evidence linking diabetes to cardiac electrophysiological remodeling and examines electrocardiography (ECG) as a non-invasive modality for early detection of dysglycaemia. Chronic hyperglycaemia, insulin resistance, oxidative stress, microvascular dysfunction, and cardiac autonomic neuropathy collectively contribute to measurable ECG alterations, including QT/QTc prolongation, increased QT dispersion, changes in Tp–e indices, and reduced heart rate variability. These changes often precede overt cardiovascular disease and correlate with glycaemic burden and diabetes duration. Recent advances in signal processing and artificial intelligence have expanded the diagnostic potential of ECG. Both classical machine learning approaches and large-scale deep learning models demonstrate that ECG contains latent signatures associated with incident type 2 diabetes and glycaemic status. Despite promising results, heterogeneity in study design, limited representation of prediabetes, and lack of standardized validation frameworks remain major barriers to clinical translation. Prospective, multi-ethnic studies are needed to establish ECG-based screening as a reliable component of early diabetes detection strategies.","url":"https://doi.org/10.3390/app16062902","authors":["Chingiz Alimbayev","Zhadyra Alimbayeva","Kassymbek Ozhikenov","Kairat Karibayev","Zhansila Orynbay","Yerbolat Igembay","Madiyar Daniyalov","Akzhol Nurdanali"],"tags":["Medicine","Diabetes mellitus","Disease","Risk stratification","Cardiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-18","doi":"https://doi.org/10.3390/app16062902","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4403945659","name":"Synthetic Breast Ultrasound Images: A Study to Overcome Medical Data Sharing Barriers","source":"openalex","abstract":"The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns, which restrict cross-center data sharing and the construction of diverse, large-scale datasets. To address this challenge, we developed a deep generative model aimed at synthesizing medical data to overcome data sharing barriers, with a focus on breast ultrasound (US) image synthesis. Specifically, we introduce CoLDiT, a conditional latent diffusion model with a transformer backbone, to generate US images of breast lesions across various Breast Imaging Reporting and Data System (BI-RADS) categories. Using a training dataset of 9,705 US images from 5,243 patients across 202 hospitals with diverse US systems, CoLDiT generated breast US images without duplicating private information, as confirmed through nearest-neighbor analysis. Blinded reader studies further validated the realism of these images, with area under the receiver operating characteristic curve (AUC) scores ranging from 0.53 to 0.77. Additionally, synthetic breast US images effectively augmented the training dataset for BI-RADS classification, achieving performance comparable to that using an equal-sized training set comprising solely real images ( P = 0.81 for AUC). Our findings suggest that synthetic data, such as CoLDiT-generated images, offer a viable, privacy-preserving solution to facilitate secure medical data sharing and advance the utilization of medical big data.","url":"https://doi.org/10.34133/research.0532","authors":["Jiale Xu","Qing Hua","Xiaohong Jia","YuHang Zheng","Qiao Hu","Baoyan Bai","Juan Miao","Lisha Zhu","MeiXiang Zhang","Ren Tao","Yuheng Li","Ting Luo","Jun Xie","Xuebin Zheng","Pengfei Gu","FengYuan Xing","Chuan He","Yanyan Song","Yijie Dong","Shujun Xia","Jianqiao Zhou"],"tags":["Ultrasound","Breast ultrasound","Data sharing","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.34133/research.0532","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7161630578","name":"Videomics and artificial intelligence in endoscopic diagnosis of laryngeal lesions: mapping current evidence through a scoping review","source":"openalex","abstract":"Laryngeal lesions are common and despite advances like high-definition videolaryngoscopy and enhanced imaging modalities such as narrow-band imaging, laryngoscopy remains operator-dependent. In this setting, artificial intelligence (AI) represents a promising tool to support clinical evaluation. This scoping review evaluated the current applications of AI in the endoscopic diagnosis of laryngeal lesions. A comprehensive search of MEDLINE and Scopus databases included 35 studies addressing AI-based detection, classification, or segmentation of laryngeal pathologies. Detection models frequently achieved real-time inference speeds and strong performance metrics although external validation was limited. Classification studies showed particularly robust results for binary tasks distinguishing high-risk from low-risk lesions, with some models achieving sensitivity and accuracy exceeding 90%. Segmentation models demonstrated the potential for precise delineation of cancer margins, a capability of notable relevance for surgical planning and intraoperative decision-making. Despite promising advances, heterogeneity in study design, limited external validation, and reliance on single-centre datasets currently restrict broad clinical implementation. Nonetheless, the emerging integration of AI into laryngeal endoscopy represents a significant step toward reproducible and accessible diagnostic assessment.","url":"https://doi.org/10.14639/0392-100x-suppl.1-46-2026-a1967","authors":["Alessandro Ioppi","Elisa Bellini","Maria Sofia Salvetta","Filippo Marchi","Domenico Di Maria","Giorgio Peretti","Pasquale D’Alessio","Pietro Perotti","Ottavio Piccin","Claudio Sampieri"],"tags":["Medicine","Laryngoscopy","Modalities","Artificial intelligence","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-01","doi":"https://doi.org/10.14639/0392-100x-suppl.1-46-2026-a1967","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7118927645","name":"Natural Language Processing (NLP)-Based Frameworks for Cyber Threat Intelligence and Early Prediction of Cyberattacks in Industry 4.0: A Systematic Literature Review","source":"openalex","abstract":"This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems.","url":"https://doi.org/10.3390/app16020619","authors":["Majed Albarrak","Konstantinos Salonitis","Sandeep Jagannath Jagtap"],"tags":["Computer science","Data science","Systematic review","Domain (mathematical analysis)","Social media"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-07","doi":"https://doi.org/10.3390/app16020619","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7140187407","name":"AI-supported case-based learning in medical education: a comprehensive scoping review","source":"openalex","abstract":"Introduction: Recent literature indicates that generative artificial intelligence (GenAI) is also being integrated into case-based learning (CBL) through activities such as clinical case generation, clinical reasoning support, and structured feedback. However, the evidence about GenAI's role in CBL remains fragmented. Given the diverse nature of available GenAI-CBL studies, we conducted a comprehensive scoping review to map and synthesize the evidence in this area, highlighting key themes, outcomes, challenges, and limitations to inform future research, curriculum development, and policies. Method: A comprehensive search was performed across multidisciplinary databases, including PubMed, ERIC, Scopus, Web of Science, and Google Scholar, covering publications from 2019 to 2025. Title and abstract screening, followed by full-text review and data extraction, were conducted independently by two reviewers using predefined eligibility criteria. The data synthesis involved thematic analysis to create an evidence map of GenAI-supported case-based and case-anchored learning in medical education. Results: The findings were organized into six key themes that showcase the role of GenAI in enhancing case-based learning, covering areas such as clinical reasoning and contextual thinking; efficient and scalable case creation; learner engagement, motivation, and perceived usefulness; accuracy, reliability, and ethical issues; faculty adaptation and pedagogical integration; and hybrid and reflective learning methods. Conclusion: Overall, the evidence indicates that GenAI can effectively support CBL in medical education, especially during early and intermediate stages. It also highlights the ongoing importance of faculty oversight and the need for further research to address advanced clinical judgment and ethical reasoning. Systematic review registration: https://doi.org/10.17605/OSF.IO/28E3G, identifier (28E3G).","url":"https://doi.org/10.3389/fmed.2026.1798097","authors":["Dr Syed Hani Abidi","Joseph Almazan","Olaoluwa Fabiyi","Fatin Zehra","Muhammad Tariq"],"tags":["Multidisciplinary approach","Thematic analysis","Medical education","Psychology","Curriculum"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-25","doi":"https://doi.org/10.3389/fmed.2026.1798097","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4206073540","name":"Recent Progress in Nanobiosensors for Precise Detection of Blood Glucose Level","source":"openalex","abstract":"Diabetes mellitus (DM) follows a series of metabolic diseases categorized by high blood sugar levels. Owing to the increasing diabetes disease in the world, early diagnosis of this disease is critical. New methods such as nanotechnology have made significant progress in many areas of medical science and physiology. Nanobiosensors are very sensible and can identify single virus particles or even low concentrations of a material that can be inherently harmful. One of the main factors for developing glucose sensors in the body is the diagnosis of hypoglycemia in individuals with insulin-dependent diabetes. Therefore, this study aimed to evaluate the most up-to-date and fastest glucose detection method by nanosensors and, as a result, faster and better treatment in medical sciences. In this review, we try to explore new ways to control blood glucose levels and treat diabetes. We begin with a definition of biosensors and their classification and basis, and then we examine the latest biosensors in glucose detection and new biosensors applications, including the artificial pancreas and updating quantum graphene data.","url":"https://doi.org/10.1155/2022/2964705","authors":["Haniye Khosravi Ardakani","Mitra Gerami","Mostafa Chashmpoosh","Navid Omidifar","Ahmad Gholami"],"tags":["Hypoglycemia","Diabetes mellitus","Artificial pancreas","Blood sugar","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-17","doi":"https://doi.org/10.1155/2022/2964705","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7138916174","name":"Systematic Review of Artificial Intelligence in Positive and Existential Psychiatry: Advancing Mental and Emotional Health Through Metacompetency Development","source":"openalex","abstract":"Background: Positive and existential psychiatry are approaches to mental health that emphasize the promotion of well-being, resilience, and optimal functioning alongside the conventional management of mental illness. Research suggests that the development of self-regulatory metacompetencies is associated with positive mental health and well-being outcomes. Artificial intelligence (AI) technologies are increasingly being used as assistive tools in psychiatry. However, the integration of AI in therapeutic interventions remains underexplored. Objectives: Thus, this systematic review aimed to synthesize evidence from randomized controlled trials evaluating whether AI-based positive and existential psychiatry interventions contribute to improvements in mental and emotional health. A second objective was to examine whether the therapeutic components and psychological processes implemented in these interventions conceptually relate to self-regulatory metacompetencies that underpin sustainable mental health and human flourishing. Methods: The review was conducted according to PRISMA 2020 guidelines. Only experimental studies including randomized controlled trials (RCTs) published from 2015 to 2025 were included. Twenty-four studies met the inclusion criteria. Results: Across interventions using conversational AI chatbots, generative AI and AI-augmented reflective systems, embodied conversational agents, social and humanoid AI robots, consistent improvements were observed in depression, anxiety, negative affect, and loneliness. The interventions enhanced various metacompetencies such as emotional regulation, emotional awareness, self-reflection, and cognitive reappraisal. Conclusions: The findings suggest that AI-based positive and existential psychiatry interventions can support mental and emotional health, especially when fostering key metacompetencies. Although promising, further high-quality trials are needed to clarify long-term effects. The findings of this study can contribute to the discussion about the ways AI-supported interventions may promote sustainable mental health.","url":"https://doi.org/10.3390/healthcare14060783","authors":["Eleni Mitsea","Athanasios Drigas","Charalabos Skianis"],"tags":["Psychological intervention","Mental health","Psychology","Psychotherapist","Emotional intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-19","doi":"https://doi.org/10.3390/healthcare14060783","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7135065065","name":"Can algorithms come to the rescue of a failing heart? Machine learning, artificial intelligence, and decision-making in cardiogenic shock","source":"openalex","abstract":"","url":"https://doi.org/10.1186/s44158-026-00373-z","authors":["Alice Bottussi","Patrick M. Wieruszewski","Elena Giovanna Bignami","Justyna Swol","Kevin Buda","Wisit Cheungpasitporn","Omar Elmadhoun","Jacopo D’Andria Ursoleo"],"tags":["Cardiogenic shock","Medicine","Algorithm","Computer science","Shock (circulatory)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-12","doi":"https://doi.org/10.1186/s44158-026-00373-z","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7148281198","name":"The Evolution of Intelligent Digital Profiling: A Multi-Sectoral Synthesis of Explainable Artificial Intelligence and Federated Learning Frameworks","source":"openalex","abstract":"Digital profiling has evolved into a cornerstone of computational intelligence, enabling the synthesis of complex user representations through the systematic analysis of multi-dimensional digital interactions. This study provides a comprehensive investigation into the conceptual and architectural frameworks of digital profiling, delineating its evolution across various strategic sectors. Utilizing a thematic synthesis methodology, we systematically analyzed 197 high-impact articles published between 2011 and 2025. Our findings categorize digital profiling into six fundamental computational stages: objective definition, multi-source data acquisition, feature selection, similarity modeling, representation synthesis, and iterative monitoring. While the analysis underscores the increasing deployment of profiling in finance, security, and healthcare, it reveals critical systemic risks, including algorithmic bias and privacy vulnerabilities. We propose a strategic transition toward \"Privacy-by-Design\" architectures, highlighting the integration of Federated Learning and Explainable Artificial Intelligence (XAI) as essential mechanisms for aligning profiling systems with global regulatory standards (e.g., GDPR). This research contributes a robust theoretical roadmap for developing transparent, accountable, and ethically-aligned intelligent systems, bridging the gap between technical efficiency and user-centric rights.","url":"https://doi.org/10.59543/68svs968","authors":["Sefer Darıcı","Zekeriya Şahin","Ayla Darıcı"],"tags":["Computer science","Profiling (computer programming)","Bridging (networking)","Software deployment","Categorization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-02","doi":"https://doi.org/10.59543/68svs968","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7129064965","name":"Artificial Intelligence in Child and Adolescent Mental Health: Prevention, Diagnosis, and Treatment in Hybrid Human–AI Care Models","source":"openalex","abstract":"Mental health disorders among children and adolescents have become increasinglycommon and burdensome, with conditions such as anxiety, depression, suicidality,and trauma-related disorders contributing significantly to disability and death. Whiletimely identification and intervention are vital, progress is often limited by thescarcity of trained providers, ongoing stigma, and dependence on subjectiveevaluation methods. Against this backdrop, artificial intelligence (AI) is beingexplored to improve mental healthcare through enhanced early detection,monitoring, individualized interventions, and clinical decision support. Thisnarrative review synthesizes research and systematic reviews from 2015 to 2025,sourced from Google Scholar, Web of Science, PubMed Central, PsycINFO, ScienceDirect, and EBSCO. Articles included focused on AI applications in children andadolescents’ mental health, highlighting advances in machine learning, naturallanguage processing, multimodal data integration, and digital cognitive-behavioraltherapy. Evidence suggests that AI can analyze behavioral, physiological, andlinguistic data to predict mental health risks, detect emerging symptoms, and deliverpersonalized interventions within a hybrid human–AI care model, where AIcomplements clinician expertise to improve access, engagement, and treatmentoutcomes. However, challenges persist, including algorithmic bias, limited modelinterpretability, data quality, privacy concerns, and integration into clinicalworkflows. Ethical and practical governance are essential to ensure that AI supports,rather than replaces, human-centered care. Future priorities include expandingresearch on underrepresented populations and conditions, developing explainableand equitable models, validating tools in real-world settings, and building large,FAIR-compliant datasets. Responsible, human-centered integration of AI has thepotential to improve early intervention, personalize treatment, and enhance equitableaccess to mental healthcare for young people globally.","url":"https://doi.org/10.66043/jfsr.v4i2.148","authors":["Nnubia U.I","Nwauzoije E.J"],"tags":["Psychological intervention","Mental health","Intervention (counseling)","Identification (biology)","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-13","doi":"https://doi.org/10.66043/jfsr.v4i2.148","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4285085921","name":"A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging","source":"openalex","abstract":"Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.","url":"https://doi.org/10.1038/s41598-022-16223-1","authors":["Jingang Zhang","Runmu Su","Qiang Fu","Wenqi Ren","Felix Heide","Yunfeng Nie"],"tags":["Hyperspectral imaging","Computer science","Artificial intelligence","Medical imaging","Spectral imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-13","doi":"https://doi.org/10.1038/s41598-022-16223-1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7126422792","name":"Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine","source":"openalex","abstract":"The parallels between medicine and aviation are well-recognised. The aviation industry's early experience with automation improved safety and efficiency, but simultaneously introduced new vulnerabilities and occasionally created misplaced trust in complex systems. Aviation has developed a robust safety framework in response to these costly lessons. In this Perspective, which draws from the experiences of clinicians and aviation experts, we argue that it is now time for the medical community to consider how we can learn from these lessons as artificial intelligence (AI) becomes increasingly integrated into clinical care. We propose that this requires a shift in perspective from AI as \"autopilot\" to collaboration with a \"digital copilot\", as well as considerations of practicalities such as scenario-based training, clinician benchmarking, and minimum unaided practice, with the ultimate aim of optimising human-AI collaboration to improve patient care.","url":"https://doi.org/10.1038/s41746-026-02410-1","authors":["Ariel Yuhan Ong","David A. Merle","Andreas Pollreisz","Siegfried K. Wagner","Mertcan Sevgi","Pearse A. Keane","Roman Huemer","Julian Oehling","Markus Jäger","Josef Huemer"],"tags":["Aviation","Parallels","Aviation safety","Perspective (graphical)","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-31","doi":"https://doi.org/10.1038/s41746-026-02410-1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3157152805","name":"Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder","source":"openalex","abstract":"In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder (VAE). Our approach combines the proposal of 1) a new VAE model, the latent space of which is modeled as a Riemannian manifold and which combines both Riemannian metric learning and normalizing flows and 2) a new generation scheme which produces more meaningful samples especially in the context of small data sets. The method is tested through a wide experimental study where its robustness to data sets, classifiers and training samples size is stressed. It is also validated on a medical imaging classification task on the challenging ADNI database where a small number of 3D brain magnetic resonance images (MRIs) are considered and augmented using the proposed VAE framework. In each case, the proposed method allows for a significant and reliable gain in the classification metrics. For instance, balanced accuracy jumps from 66.3% to 74.3% for a state-of-the-art convolutional neural network classifier trained with 50 MRIs of cognitively normal (CN) and 50 Alzheimer disease (AD) patients and from 77.7% to 86.3% when trained with 243 CN and 210 AD while improving greatly sensitivity and specificity metrics.","url":"https://doi.org/10.1109/tpami.2022.3185773","authors":["Clément Chadebec","Elina Thibeau–Sutre","Ninon Burgos","Stéphanie Allassonnière"],"tags":["Autoencoder","Artificial intelligence","Pattern recognition (psychology)","Robustness (evolution)","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-24","doi":"https://doi.org/10.1109/tpami.2022.3185773","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4376642747","name":"Fluorescence-guided surgery: comprehensive review","source":"openalex","abstract":"BACKGROUND: Despite significant improvements in preoperative workup and surgical planning, surgeons often rely on their eyes and hands during surgery. Although this can be sufficient in some patients, intraoperative guidance is highly desirable. Near-infrared fluorescence has been advocated as a potential technique to guide surgeons during surgery. METHODS: A literature search was conducted to identify relevant articles for fluorescence-guided surgery. The literature search was performed using Medical Subject Headings on PubMed for articles in English until November 2022 and a narrative review undertaken. RESULTS: The use of invisible light, enabling real-time imaging, superior penetration depth, and the possibility to use targeted imaging agents, makes this optical imaging technique increasingly popular. Four main indications are described in this review: tissue perfusion, lymph node assessment, anatomy of vital structures, and tumour tissue imaging. Furthermore, this review provides an overview of future opportunities in the field of fluorescence-guided surgery. CONCLUSION: Fluorescence-guided surgery has proven to be a widely innovative technique applicable in many fields of surgery. The potential indications for its use are diverse and can be combined. The big challenge for the future will be in bringing experimental fluorophores and conjugates through trials and into clinical practice, as well as validation of computer visualization with large data sets. This will require collaborative surgical groups focusing on utility, efficacy, and outcomes for these techniques.","url":"https://doi.org/10.1093/bjsopen/zrad049","authors":["Paul Sutton","Martijn A. van Dam","Ronan A. Cahill","J. Sven D. Mieog","Karol Połom","Alexander L. Vahrmeijer","Joost van der Vorst"],"tags":["Medicine","Medical physics","Narrative review","Image-guided surgery","Surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-05","doi":"https://doi.org/10.1093/bjsopen/zrad049","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401596293","name":"Canadian Medical Imaging Inventory 2022–2023: MRI","source":"openalex","abstract":"MRI is a noninvasive imaging modality that uses powerful electromagnetic and radiofrequency fields to produce cross-sectional images of the body. MRI is primarily used for neurologic exams (28%), followed by musculoskeletal (23%) and oncology exams (17%). In total, 432 MRI units in 11 jurisdictions were identified by the Canadian Medical Imaging Inventory (CMII) in its 2022 to 2023 national survey. Most sites are publicly funded hospitals located in urban centres. Canada has an average of 10.8 MRI units per million people. The greatest density of units per million people is in Yukon, Quebec, and New Brunswick. Overall, 2,214,157 publicly funded MRI examinations were performed in the 2022–2023 fiscal year. This represents a national average of 55.6 exams per 1,000 people, an increase of 4.3% since 2019– Canada is positioned in the bottom 25% of Organisation for Economic Co-operation and Development (OECD) countries in units per million population and the bottom 50% of OECD countries for average volume of publicly funded MRI exams per 1,000 population. The average age of MRI equipment in Canada is 8.4 years; 62.8% of MRI units are 10 years old or newer, 23.3% are 11 to 15 years old, and 13.9% are more than 15 years old. On average, MRI units operate 15.3 hours per day and 97.4 hours per week. Overall, 76.0% of sites reported MRI operation on weekends and 17% of sites reported operating 24 hours a day.","url":"https://doi.org/10.51731/cjht.2024.951","authors":["CADTH"],"tags":["Medicine","Population","Magnetic resonance imaging","Nuclear medicine","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-14","doi":"https://doi.org/10.51731/cjht.2024.951","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7133938113","name":"Artificial intelligence-assisted project-based learning: Examining the interaction with learning creativity on the students’ digital content outcomes","source":"openalex","abstract":"Despite the rapid integration of artificial intelligence (AI) in education, limited empirical evidence compares the effectiveness of AI-assisted project-based learning (PjBL) and problem-based learning (PBL) while considering students’ learning creativity in digital content production. This study aims to examine the effects of AI-assisted PjBL and AI-assisted PBL, as well as their interaction with students’ learning creativity, on learning outcomes in digital content production. The study employs a 2×2 quasi-experimental factorial design, in which factor (A) represents the learning model (AI-assisted PjBL and AI-assisted PBL) and factor (B) represents learning creativity (high and low). The population consists of eighth-grade students at the junior high school level. The findings address four hypotheses: (1) there is a significant difference in learning outcomes between students receiving AI-assisted PjBL and those receiving AI-assisted PBL (Sig. 0.043 < 0.05); (2) there is a significant interaction effect between AI-assisted learning models and learning creativity (Sig. 0.000 < 0.05); (3) students with high creativity who receive AI-assisted PjBL achieve higher learning outcomes than those receiving AI-assisted PBL (Sig. 0.000 < 0.05); and (4) students with low creativity who receive AI-assisted PjBL demonstrate lower learning outcomes than those receiving AI-assisted PBL (Sig. 0.000 < 0.05). These findings indicate that AI-assisted PjBL and learning creativity enhance students’ learning outcomes in producing creative digital content, particularly for students with high learning creativity. This study contributes empirical evidence to the integration of AI in junior high school computer science education.","url":"https://doi.org/10.22515/jemin.v6i1.12775","authors":["Rudi Hartono","Salsabila Adrisdityas Candra Rifani","Muktiono Waspodo"],"tags":["Creativity","Psychology","Mathematics education","Blended learning","Population"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-06","doi":"https://doi.org/10.22515/jemin.v6i1.12775","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7140287256","name":"Factors Associated with Artificial Intelligence-Help-Seeking Behavior Among University Students in the UAE: A Cross-Sectional Study","source":"openalex","abstract":"Artificial intelligence (AI)-mediated tools have rapidly penetrated student life and become a valuable resource for seeking help with academic assignments/tasks, psychological problems, and social interactions. This study aims to investigate the levels and associations of AI-help-seeking behavior (AI-HSB), anxiety, stress, and depression among university students in the United Arab Emirates (UAE). In addition, it examines the factors associated with AI-HSB based on the selected demographic (gender, marital status, age, academic year, employment status, major, and nationality), as well as anxiety, stress, and depression. This study employed a descriptive cross-sectional design among 433 university students, who were recruited via an online Google Form between 1 October 2025 and 10 December 2025. The study utilized validated Arabic versions of the AI-HSB scale and the anxiety, stress, and depression scale. Descriptive statistics, Pearson correlation, and predictive analyses were conducted using SPSS v 25. Results indicated that students reported moderate reliance on AI-HSB despite moderate to severe levels of psychological distress, with particular emphasis on anxiety. The AI-HSB was positively associated with anxiety, stress, and depression amongst the participants. Furthermore, both depression and the students’ academic year emerged as the only significant predictors of AI-HSB, explaining a modest but meaningful proportion of variance with an exact percentage of 18.1%. AI tools may partially circumvent stigma by offering privacy and anonymity; however, cultural expectations around interpersonal support, trust, and authority may simultaneously limit students’ willingness to rely on non-human agents for emotional care.","url":"https://doi.org/10.3390/educsci16040506","authors":["Othman A. Alfuqaha","Kyle A Msall","Rasha Mohamed Abdelrahman"],"tags":["Psychology","Scale (ratio)","Interpersonal communication","Marital status","Arabic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-24","doi":"https://doi.org/10.3390/educsci16040506","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4407046752","name":"Configuration Testing of an Artificial Pancreas System Using a Digital Twin: An Evaluative Case Study","source":"openalex","abstract":"ABSTRACT The recent growth in popularity of wearable medical devices has improved the quality of life of people with medical conditions. Testing such devices may require users to configure these systems using physical trials, putting themselves in potentially dangerous scenarios. Misconfiguration of such devices has caused disease misdiagnoses and incorrect drug prescriptions. Digital twins have been proposed as an opportunity to reduce such risks of testing system configurations in simulated environments, decoupling the user from the system under test. In this paper, we perform an evaluative case study to assess the use of a digital twin for configuration testing of an artificial pancreas system (APS) control algorithm. These systems regulate the blood glucose levels in people with type 1 diabetes mellitus, and so misconfigurations can cause severe hypoglycaemia or hyperglycaemia, which can be life‐threatening. We tested the OpenAPS control algorithm against 156 people's clinical data. We found that our digital twin provided an accurate simulation environment to perform configuration testing and accurately predict blood glucose–insulin behaviour. We evaluated different APS configurations, identifying a potentially unsafe configuration without the risks associated with a physical trial. We identified the challenges associated with modelling clinical data, which could lead to misinterpretations in configuration testing and the reduction of test reliability when modelling stochastic body dynamics.","url":"https://doi.org/10.1002/stvr.70000","authors":["Richard Somers","Neil Walkinshaw","Robert M. Hierons","Daisy Elliott","Ahmed Iqbal","Emma Walkinshaw"],"tags":["Computer science","Artificial pancreas","Wearable computer","Reliability (semiconductor)","Reliability engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-30","doi":"https://doi.org/10.1002/stvr.70000","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7129067071","name":"Toward Timely Diagnosis of Pancreatic Cancer: Revolutionizing Early Detection Through Genomics, Artificial Intelligence, and Noninvasive Biomarkers","source":"openalex","abstract":"BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive cancers, typically diagnosed at an advanced stage due to its subtle and often absent early symptoms. Despite representing only 3% of new cancer cases, it is projected to become the second leading cause of cancer-related deaths by 2030. Currently, early diagnosis remains a significant challenge, and survival rates remain poor due to the lack of effective screening tools. METHODS: We conducted a comprehensive literature review to explore the most recent advances in PDAC detection, focusing on novel biomarkers, liquid biopsies, artificial intelligence (AI)-enhanced imaging, and non-invasive surveillance strategies. We examined the role of circulating tumor DNA (ctDNA), microRNAs, and volatile organic compounds (VOCs) as diagnostic tools, alongside the integration of advanced imaging modalities like MRI, EUS, and MRCP in high-risk individuals, including those with hereditary cancer syndromes. RESULTS: Emerging technologies, such as AI-driven imaging and liquid biopsy, have shown promising improvements in detecting PDAC at earlier, potentially resectable stages. Surveillance strategies for high-risk populations, including BRCA1/2 mutation carriers and individuals with Lynch syndrome, have demonstrated increased detection of Stage I PDAC, offering a significant opportunity for curative intervention. AI and machine learning techniques are also enhancing the sensitivity and specificity of imaging, providing a new frontier in early-stage diagnosis. CONCLUSION: The integration of molecular diagnostics, advanced imaging technologies, and AI may enable a paradigm shift in PDAC detection, transitioning from late to early-stage diagnosis and potentially improving survival rates. However, further clinical validation and standardization of these technologies are essential to ensure their widespread clinical adoption. The future of PDAC detection lies in a multimodal, personalized approach, optimizing diagnostic accuracy and early intervention for high-risk individuals.","url":"https://doi.org/10.1111/jgh.70281","authors":["Hussain Ma","Sana Qammar","Ju‐Mei Wang","Aoqiang Zhai","Fu‐Yu Li","Hai‐Jie Hu"],"tags":["Medicine","Standardization","Intensive care medicine","Precision medicine","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-15","doi":"https://doi.org/10.1111/jgh.70281","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4396955550","name":"FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation","source":"openalex","abstract":"To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. In the encoder part, a dual‐stream encoder is used to capture local details and long‐range dependencies. Moreover, the attentional feature fusion module is used to perform interactive feature fusion of dual‐branch features, maximizing the retention of local details and global semantic information in medical images. At the same time, the multi‐scale feature aggregation module is used to aggregate local information and capture multi‐scale context to mine more semantic details. The multi‐level feature bridging module is used in skip connections to bridge multi‐level features and mask information to assist multi‐scale feature interaction. Experimental results on seven public medical image datasets fully demonstrate the effectiveness and advancement of our method. In future work, we plan to extend FI‐Net to support 3D medical image segmentation tasks and combine self‐supervised learning and knowledge distillation to alleviate the overfitting problem of limited data training.","url":"https://doi.org/10.1002/aisy.202400201","authors":["Yuhan Ding","Jinhui Liu","Yunbo He","Jinliang Huang","Haisu Liang","Zhenglin Yi","Yongjie Wang"],"tags":["Feature (linguistics)","Segmentation","Image segmentation","Artificial intelligence","Image (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-16","doi":"https://doi.org/10.1002/aisy.202400201","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7125713276","name":"Generative artificial intelligence as digital therapy: what do we know and how can we make it better?","source":"openalex","abstract":"SUMMARY Generative artificial intelligence (GenAI) shows promise for mental healthcare by increasing access to treatment. In this article, we analyse recent evidence on the use of GenAI chatbots as a treatment for common mental disorders. We examine key ethical and methodological considerations and discuss the specific risks for delusions. Adopting a precision psychiatry perspective, we propose that the therapeutic alliance can be improved by tailoring GenAI to mimic a user’s psychological traits, a version of socioaffective alignment.","url":"https://doi.org/10.1192/bja.2025.10193","authors":["Santiago Castiello de Obeso","Mariana Pinto da Costa"],"tags":["Generative grammar","Mental healthcare","Alliance","Computer science","Key (lock)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-26","doi":"https://doi.org/10.1192/bja.2025.10193","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4415133359","name":"Robot Path Planning: from Analytical to Computer Intelligence Approaches","source":"openalex","abstract":"Abstract In an era where robots are becoming an integral part of human quotidian activities, understanding how they function is crucial. Among the inherent building complexities, from electronics to mechanics, path planning emerges as a universal aspect of robotics. The primary contribution of this work is to provide an overview of the current state of robot path planning topics and a comparison between those same algorithms and its inherent characteristics. The path planning concept relies on the process by which an algorithm determines a collision-free path between a start and an end point, optimizing parameters such as energy consumption and distance. The quest for the most effective path planning method has been a long-standing discussion, as the choice of method is highly dependent on the specific application. This review consolidates and elucidates the categories of path planning methods, specifically classical or analytical methods, and computer intelligence methods. In addition, the operational principles of these categories will be explored, discussing their respective advantages and disadvantages, and reinforcing these discussions with relevant studies in the field. This work will focus on the most prevalent and recognized methods within the robotics path planning problem, being mobile robotics or manipulator arms, including Cell Decomposition, A*, Probabilistic Roadmaps, Rapidly-exploring Random Trees, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Potential Fields, Fuzzy, and Neural Networks. Following the detailed explanation of these methods, a comparative analysis of their advantages and drawbacks is organized in a comprehensive table. This comparison will be based on various quality metrics, such as the type of trajectory provided (global or local), the scenario implementation type (real or simulated scenarios), testing environments (static or dynamic), hybrid implementation possibilities, real-time implementation, completeness of the method, consideration of the robot’s kinodynamic constraints, use of smoothing techniques, and whether the implementation is online or offline.","url":"https://doi.org/10.1007/s10846-025-02322-4","authors":["Pedro A. Dias","João Pedro Carvalho de Souza","E. J. Solteiro Pires","Vítor Filipe","Daniel Figueiredo","Luís F. Rocha","Manuel F. Silva"],"tags":["Motion planning","Artificial intelligence","Path (computing)","Computer science","Robotics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-14","doi":"https://doi.org/10.1007/s10846-025-02322-4","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7158242381","name":"External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system","source":"openalex","abstract":"An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and is currently deployed in clinical practice in Korea (ECG Buddy, ARPI Inc.). Its external validity within U.S. healthcare settings has not been established. This study evaluated model performance using a large-scale, multi-center U.S. dataset via the Mayo Clinic Platform (MCP) Discover. Two validation cohorts were assessed: an emergency-diagnosis cohort (mortality, AMI, STEMI and equivalents, hyperkalemia, pulmonary edema) using initial ED ECGs, and a cardiac-function cohort (left and right ventricular systolic dysfunction, pulmonary hypertension, hemodynamically significant pericardial effusion) anchored to echocardiography or right-heart catheterization. The primary objective was non-inferiority of the Area Under the Receiver Operating Characteristic Curve (AUC) relative to prespecified benchmarks. Across ten target conditions, AUCs ranged from 0.883 to 0.949; all met non-inferiority criteria. Performance was consistent across sex and age strata. In a paired subset (N = 1368), ECG-AI outperformed initial troponin T for AMI (AUC 0.920 vs. 0.878) and STEMI equivalents (AUC 0.932 vs. 0.736; all P < 0.001). These findings support the model's potential for ECG-based screening and triage, and provide a foundation for prospective evaluation of calibration, clinical integration, and impact across diverse populations.","url":"https://doi.org/10.1038/s41746-026-02682-7","authors":["Haemin Lee","Yerin Kim","Daniel Sykora","Alexander J. Ryu","Y Cho","Joonghee Kim","Joanne Song"],"tags":["Medicine","Cohort","Receiver operating characteristic","Prospective cohort study","Emergency medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-28","doi":"https://doi.org/10.1038/s41746-026-02682-7","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7147240224","name":"Heat for Healing: A Review of Infrared Thermography in Medical Diagnostics and Therapy","source":"openalex","abstract":"Infrared thermography (IRT) is an emerging noninvasive imaging modality that provides real-time, contactless assessment of skin surface temperature, reflecting underlying vascular perfusion. This narrative review explores the principles, clinical utility, advantages, limitations, and future potential of IRT in vascular diagnostics and monitoring. IRT has demonstrated diagnostic relevance across a spectrum of vascular conditions, including peripheral arterial disease, diabetic foot complications, venous insufficiency, Raynaud's phenomenon, and postoperative vascular monitoring. Its key benefits - such as radiation-free imaging, portability, and dynamic functional assessment - make it especially valuable for use in vulnerable populations and resource-limited settings. However, challenges such as environmental sensitivity, lack of standardized imaging protocols, and limited specificity necessitate further validation. With the integration of artificial intelligence and wearable technology, IRT holds significant promise as a complementary tool in modern vascular medicine.","url":"https://doi.org/10.4103/jmp.jmp_175_25","authors":["Bitesh Kumar","Vishesh Jain","Devendra Kumar Yadav","Anjan Kumar Dhua","Prabudh Goel","Dhawal Jain","Shubhendu Singh"],"tags":["Thermography","Medicine","Modality (human–computer interaction)","Clinical Practice","Peripheral vessels"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.4103/jmp.jmp_175_25","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7142248183","name":"Assessing complexity of educational texts of Russian as a foreign language: Prospects and challenges of using artificial intelligence","source":"openalex","abstract":"The growing interest in Russian education, culture, and science results in the pressing demand for tools to select educational texts for Russian as a foreign language. The study is aimed at working out the algorithm and instruments for assessing the lexical complexity of text in Russian as a foreign language on CEFR with the help of LLM. The study is based on the material of a training sample, including standardized lexical minima in Russian as a foreign language and 232 texts ranked in difficulty by experts, and a test sample with 14 texts for listening in Russian as a foreign language. The methods of computational linguistics (Python script process_word_lists, LLM), expert assessment and metrics for statistical evaluation of the quality of classification models were used in the work. The study describes the successfully used large language models to assess the complexity of Russian-language texts on the RuLingva platform. The results of the study include the created linguistic profiles and the identified abilities of the large GLM 4.6 and Grok 4 fast language models to assess the complexity of educational texts in Russian as a foreign language (A1-C1). The proposed algorithm ranks texts by complexity with a high degree of accuracy, develops test tasks and selects texts for textbooks on Russian as a foreign language. The results obtained can be used by teachers in Russian as a foreign language, testologists, and linguists for preparing teaching materials, glossaries, and test assignments. The prospect of the work is to improve the developed algorithm by expanding the corpus and applying classification models for texts of different genres.","url":"https://doi.org/10.22363/2618-8163-2026-24-1-120-137","authors":["Marina I. Solnyshkina","Mariia I. Andreeva"],"tags":["Foreign language","Computer science","Test (biology)","Natural language processing","Linguistics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-28","doi":"https://doi.org/10.22363/2618-8163-2026-24-1-120-137","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4308391738","name":"Retracted March 6, 2026: Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing","source":"openalex","abstract":"Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.","url":"https://doi.org/10.1145/3523282","authors":["Yan Gong"],"tags":["Machine translation","Computer science","Artificial intelligence","Natural language processing","Rule-based machine translation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-07","doi":"https://doi.org/10.1145/3523282","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3130328637","name":"How the COVID-19 pandemic will change the future of critical care","source":"openalex","abstract":"Coronavirus disease 19 (COVID-19) has posed unprecedented healthcare system challenges, some of which will lead to transformative change. It is obvious to healthcare workers and policymakers alike that an effective critical care surge response must be nested within the overall care delivery model. The COVID-19 pandemic has highlighted key elements of emergency preparedness. These include having national or regional strategic reserves of personal protective equipment, intensive care unit (ICU) devices, consumables and pharmaceuticals, as well as effective supply chains and efficient utilization protocols. ICUs must also be prepared to accommodate surges of patients and ICU staffing models should allow for fluctuations in demand. Pre-existing ICU triage and end-of-life care principles should be established, implemented and updated. Daily workflow processes should be restructured to include remote connection with multidisciplinary healthcare workers and frequent communication with relatives. The pandemic has also demonstrated the benefits of digital transformation and the value of remote monitoring technologies, such as wireless monitoring. Finally, the pandemic has highlighted the value of pre-existing epidemiological registries and agile randomized controlled platform trials in generating fast, reliable data. The COVID-19 pandemic is a reminder that besides our duty to care, we are committed to improve. By meeting these challenges today, we will be able to provide better care to future patients.","url":"https://doi.org/10.1007/s00134-021-06352-y","authors":["Yaseen M. Arabi","Élie Azoulay","Hasan M. Al‐Dorzi","Jason Phua","Jorge I. Salluh","Alexandra Binnie","Carol Hodgson","Derek C. Angus","Maurizio Cecconi","Bin Du","Rob Fowler","Charles D. Gomersall","Peter Horby","Nicole P. Juffermans","Jozef Kesecioğlu","Ruth Kleinpell","F.S. Machado","Greg S. Martin","Geert Meyfroidt","Andrew Rhodes","Kathy Rowan","Jean‐François Timsit","Jean‐Louis Vincent","Giuseppe Citerio"],"tags":["Medicine","Health care","Pandemic","Preparedness","Personal protective equipment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-22","doi":"https://doi.org/10.1007/s00134-021-06352-y","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401616283","name":"Brain aging patterns in a large and diverse cohort of 49,482 individuals","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41591-024-03144-x","authors":["Zhijian Yang","Junhao Wen","Güray Erus","Sindhuja Tirumalai Govindarajan","Randa Melhem","Elizabeth Mamourian","Yuhan Cui","Dhivya Srinivasan","Ahmed Abdulkadir","Paraskevi Parmpi","Katharina Wittfeld","Hans J. Grabe","Robin Bülow","Stefan Frenzel","Duygu Tosun","Murat Bilgel","Yang An","Dahyun Yi","Daniel S. Marcus","Pamela LaMontagne","Tammie L.S. Benzinger","Susan R. Heckbert","Thomas R. Austin","Shari R. Waldstein","Michele K. Evans","Alan B. Zonderman","Lenore J. Launer","Aristeidis Sotiras","Mark A. Espeland","Colin L. Masters","Paul Maruff","Jürgen Fripp","Arthur W. Toga","Sid E. O’Bryant","M. Mallar Chakravarty","Sylvia Villeneuve","Sterling C. Johnson","John C. Morris","Marilyn Albert","Kristine Yaffe","Henry Völzke","Luigi Ferrucci","R. Nick Bryan","Russell T. Shinohara","Yong Fan","Mohamad Habes","Paris Alexandros Lalousis","Nikolaos Koutsouleris","David A. Wolk","Susan M. Resnick","Haochang Shou","Ilya M. Nasrallah","Christos Davatzikos"],"tags":["Brain aging","Cohort","Atrophy","Neuroimaging","Population"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-15","doi":"https://doi.org/10.1038/s41591-024-03144-x","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7124240574","name":"Symbiotic intelligence in dental trauma diagnostics—an exploratory case study","source":"openalex","abstract":"Dental trauma in children is common and requires prompt diagnosis, which can be challenging in remote or isolated settings with limited access to emergency dental care. This exploratory case study investigates whether OpenAI's o3 can support dental trauma diagnostics in primary incisors, building on prior pretesting of GPT-4 on summative dental education exams (2023) and multimodal dental trauma analyses (2024), and focusing on o3's multimodal capability and reliability in 2025 with expert assessment (“human in the loop”) prior to a supervisor seminar with students and supervisors ( N = 84). Preliminary findings indicate that GPT-4 performed well on sample exams (2023), and that 7/10 multimodal analyses of dental injuries were accurate (2024); in the 2025 case, o3 correctly identified pulp necrosis in tooth 51 and uncomplicated enamel/dentin fractures in teeth 51 and 61, consistent with IADT guidance. Human expert involvement contributed essential validation, particularly for treatment decisions and ethical considerations. Overall, the study illustrates how symbiotic intelligence—purposeful collaboration between human and AI—may enhance learning outcomes in scenario-based simulations in remote areas, while requiring active human involvement and multiple validation communities.","url":"https://doi.org/10.3389/froh.2025.1687841","authors":["Rune Johan Krumsvik","Kristin S. Klock","Magnus Holmøy Bratteberg"],"tags":["Dental trauma","Medicine","Exploratory research","Summative assessment","Medical emergency"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-15","doi":"https://doi.org/10.3389/froh.2025.1687841","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4394758452","name":"Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications","source":"openalex","abstract":"Abstract Recently, Meta AI Research approaches a general, promptable segment anything model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing and healthcare. We analyze and discuss the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks. By doing so, we aim to give a comprehensive understanding of SAM’s practical applications. This work is expected to provide insights that facilitate future research activities toward generic segmentation. Source code is publicly available at https://github.com/LiuTingWed/SAM-Not-Perfect .","url":"https://doi.org/10.1007/s11633-023-1385-0","authors":["Wei Ji","Jingjing Li","Qi Bi","Tingwei Liu","Wenbo Li","Li Cheng"],"tags":["Segmentation","Computer science","Data science","Code (set theory)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-12","doi":"https://doi.org/10.1007/s11633-023-1385-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2878119621","name":"Position paper: Telemedicine in occupational dermatology – current status and perspectives","source":"openalex","abstract":"Teledermatology is the use of telecommunication technologies to exchange medical information for diagnosis, consultation, treatment and teaching in dermatology. While its use has been evaluated in a wide range of dermatological diagnoses, only few studies exist on its validity, diagnostic precision, feasibility, and cost-effectiveness in occupational dermatology. However, these studies show a considerable potential for diagnosis, prevention, treatment support and follow-up of patients with occupational skin diseases. Asynchronous (store and forward; SAF) or synchronous dermatology teleconsults could assist occupational medicine specialists not only in occupational preventive care, but also in the context of skin cancer screening in outdoor workers. Thus, teledermatology might contribute to earlier prevention and notification of occupational skin diseases. Modern smartphone apps with artificial intelligence technologies may also facilitate self-monitoring in employees working in high-risk jobs.","url":"https://doi.org/10.1111/ddg.13605","authors":["Peter Elsner","Andrea Bauer","Thomas L. Diepgen","Hans Drexler","Manigé Fartasch","Swen Malte John","Sibylle Schliemann","Wolfgang Wehrmann","Jörg Tittelbach"],"tags":["Teledermatology","Telemedicine","Medicine","Context (archaeology)","Medical diagnosis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-07-11","doi":"https://doi.org/10.1111/ddg.13605","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4416234224","name":"Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape","source":"openalex","abstract":"Prevalence of immune diseases is rising, imposing burdens on patients, healthcare providers, and society. Addressing the future impact of immune diseases requires \"big data\" on global distribution/prevalence, patient demographics, risk factors, biomarkers, and prognosis to inform prevention, diagnosis, and treatment strategies. Big data offer promise by integrating diverse real-world data sources with artificial intelligence (AI) and big data analytics (BDA), yet cautious implementation is vital due to the potential to perpetuate and exacerbate biases. In this review, we outline some of the key challenges associated with achieving health equity through the use of big data, AI, and BDA in immune diseases and present potential solutions. For example, political/institutional will and stakeholder engagement are essential, requiring evidence of return on investment, a clear definition of success (including key metrics), and improved communication of unmet needs, disparities in treatments and outcomes, and the benefits of AI and BDA in achieving health equity. Broad representation and engagement are required to foster trust and inclusivity, involving patients and community organizations in study design, data collection, and decision-making processes. Enhancing technical capabilities and accountability with AI and BDA are also crucial to address data quality and diversity issues, ensuring datasets are of sufficient quality and representative of minoritized populations. Lastly, mitigating biases in AI and BDA is imperative, necessitating robust and iterative fairness assessments, continuous evaluation, and strong governance. Collaborative efforts to overcome these challenges are needed to leverage AI and BDA effectively, including an infrastructure for sharing harmonized big data, to advance health equity in immune diseases through transparent, fair, and impactful data-driven solutions.","url":"https://doi.org/10.3389/fdata.2025.1621526","authors":["Stan Kachnowski","Asif Khan","Shadé Floquet","Kendal K. Whitlock","Juan P. Wisnivesky","Daniel B. Neill","Irene Dankwa‐Mullan","Gezzer Ortega","Moataz Daoud","Raza Zaheer","Maia Hightower","Paul Rowe"],"tags":["Big data","Data sharing","Leverage (statistics)","Business","Accountability"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-14","doi":"https://doi.org/10.3389/fdata.2025.1621526","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4413919806","name":"Role of Artificial Intelligence in P2P Energy Trading for Transforming Smart Homes to Smart Cities: A Comprehensive Survey","source":"openalex","abstract":"Smart infrastructures, electric cars, and DERs have hastened the transition from centralized power systems to decentralized, prosumer-driven energy ecosystems. P2P energy trading allows families, villages, and cities to trade energy directly without a third party, making it important to this shift. These markets demand decision-making skills beyond standard control approaches because to their complexity, stochasticity, and real-time monitoring. AI is becoming the key driver of P2P trading, offering prosumers and municipalities with intelligent forecasting, optimization, automation, and security solutions. This survey provides a comprehensive overview of the role of AI in P2P energy trading, outlining its applications in smart homes and smart cities. We survey over a decade of research and present a taxonomy of more than 40 AI techniques, including machine learning, deep learning, reinforcement learning, optimization heuristics, federated and transfer learning, hybrid AI-blockchain models, and edge-cloud intelligence. We analyze the capability, application domains, challenges, and case studies of each technique. The poll also consolidates key open issues including privacy, interoperability, explainability, real-time latency, and the lack of standardized AI benchmarking platforms. Based on these insights, we propose future directions such as federated multi-agent frameworks, behavioral modeling, massive language model integration, quantum AI, standardized digital twin platforms, AI-driven policy simulations, and debiasing of ethical bias. This work reveals how AI can transform smart homes into active prosumers and their interactions into city-scale decentralized energy ecosystems by rigorously connecting technological advancements to real-world challenges.Keyword: Artificial intelligence, P2P energy trading, smart homes, smart cities, distributed energy resource, intelligent energy management systems, energy market design, sustainable energy transition.","url":"https://doi.org/10.22541/au.175683829.98253473/v1","authors":["Ali Raza","Muhammad Sajid Iqbal","Muhammad Adnan"],"tags":["Energy (signal processing)","Business","Computer science","Telecommunications","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-02","doi":"https://doi.org/10.22541/au.175683829.98253473/v1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7135190630","name":"The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review","source":"openalex","abstract":"Background: Adult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is essential for maximizing survival prospects. Molecular classification, particularly the detection of isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions, has become crucial for accurate diagnosis and prognosis. Artificial intelligence (AI) has emerged as a promising adjunct in enhancing diagnostic accuracy using histopathological images. Existing reviews mostly focused on radiology rather than histopathology, and no comprehensive systematic review has specifically evaluated AI performance exclusively from histopathological images for detecting these two molecular markers. Objective: This study aims to systematically evaluate the performance of AI models in detecting and classifying IDH mutation status and 1p/19q gene codeletion in adult-type gliomas using histopathological images. Methods: A systematic review was conducted in accordance with PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Diagnostic Test Accuracy) guidelines. Seven databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) were searched for studies published between 2015 and 2025. Eligible studies used AI models on histopathological images for molecular classification of adult-type gliomas and reported performance metrics. Study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool were conducted independently by two reviewers. Extracted data were synthesized narratively. Results: A total of 2453 reports were identified, with 22 studies meeting the inclusion criteria. The pooled average accuracy, sensitivity, specificity, and area under the curve (AUC) across studies were 85.46%, 84.55%, 86.03%, and 86.53%, respectively. Hybrid models demonstrated the highest diagnostic performance (accuracy 92.80% and sensitivity 89.62%). In general, AI models that used multimodal data outperformed those that used unimodal data in terms of sensitivity (90.15% vs 84.31%) and AUC (88.93% vs 86.29%). Furthermore, models had a better overall performance in identifying IDH mutations than 1p/19q codeletions, with higher accuracy (86.13% vs 81.63%), specificity (86.61% vs 78.11%), and AUC (86.74% vs 85.15%). Unexpectedly, AI models designed for binary classification exhibited lower performance than those for multiclass classification in terms of both accuracy (91.98% vs 84.02%) and sensitivity (93.41% vs 80.18%). However, these differences should be interpreted as descriptive trends rather than statistically validated superiority, as formal between-group comparisons were not feasible. Conclusions: AI models show strong potential as complementary tools for the molecular classification of adult-type gliomas using histopathology images, particularly for IDH mutation detection. However, these findings are constrained by the limited number of studies, the focus on adult-type gliomas, lack of meta-analysis, and restriction to English-language publications. While AI offers valuable diagnostic support, it must be integrated with expert clinical judgment. Future research should prioritize larger, more diverse datasets and multimodal AI frameworks and extend to other brain tumor types for broader applicability.","url":"https://doi.org/10.2196/78377","authors":["Obada Almaabreh","Rukaya Al-Dafi","Aliya Tabassum","Ahmad Othman","Alaa Abd-alrazaq"],"tags":["Artificial intelligence","Medicine","Isocitrate dehydrogenase","Medical physics","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-13","doi":"https://doi.org/10.2196/78377","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4384296340","name":"Computational Science Role in Medical and Healthcare‐Related Approach","source":"openalex","abstract":"Medical imaging, human genome research, clinical diagnosis, and medical data management are all areas where computer technology plays a critical role in modern medicine, healthcare, and life sciences. Computer science solutions will undoubtedly become a vital element of modern medicine and healthcare as computer technology continues to advance. Computational research and applications in medicine and healthcare on modeling, creating, addressing, and assessing fundamental problems are not only vital but also indispensable! With an emphasis on medical imaging, we describe a collection of key computational challenges and methodologies in current medical research, clinical practice, and applications in this session. Computational issues that emerge in medical imaging, clinical diagnosis, therapy, and other medical applications are discussed in detail. These include immune cell identification and distribution analysis, optimal segmentation, and analysis of many medical objects in 3D images, cancer research and analysis, motion tracking of massive swarming bacteria, and so on. In this book chapter, we show how to formulate difficulties as computational problems using new models, as well as how to solve them effectively. Our methods rely on cutting-edge machine learning, data mining, and geometric optimization techniques, particularly novel deep learning models and algorithms. We also offer experimental data and findings to demonstrate how our ideas may be used in clinical settings. Finally, in the exciting new field of computational medicine and healthcare, we highlight several crucial future research themes and issues.","url":"https://doi.org/10.1002/9781119763468.ch12","authors":["Pawan Whig","Arun Velu","Rahul Reddy Nadikattu","Yusuf Jibrin Alkali"],"tags":["Computer science","Data science","Health care","Computational model","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-14","doi":"https://doi.org/10.1002/9781119763468.ch12","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7140192272","name":"Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring","source":"openalex","abstract":"Gastrointestinal (GI) functional disorders and chronic inflammatory diseases impose a substantial health burden, yet their assessment remains challenging because symptoms reflect dynamic interactions among motility, visceral sensation, immune-microbiome regulation, and brain-gut signaling. Artificial intelligence (AI) is rapidly reshaping GI functional medicine by enabling scalable, quantitative interpretation of complex data generated from multimodal imaging, physiological sensing, and real-world patient monitoring. This review synthesizes advances across three tightly connected pillars that map onto a physiology-informed \"assessment-to-action\" loop: (i) AI-assisted multimodal GI imaging for quantitative phenotyping and integrated diagnosis; (ii) AI-enabled discovery and validation of digital biomarkers that capture dynamic GI function in naturalistic settings; and (iii) real-time monitoring platforms that support early warning, longitudinal assessment, and adaptive management. We summarize representative applications in functional GI disorders, inflammatory bowel disease (IBD), and GI oncology, highlighting methodological themes including multimodal fusion, temporal modeling, uncertainty estimation, and explainable AI. We then discuss barriers to translation-standardization and interoperability, external validation under dataset shift, privacy and governance, and workflow integration-and outline practical directions for building clinically trustworthy AI systems for GI functional assessment. Collectively, physiology-centered AI approaches have the potential to transform GI care from episodic testing to longitudinal, mechanism-aware monitoring and personalized intervention.","url":"https://doi.org/10.3389/fphys.2026.1778235","authors":["Liucheng Li","Fang Lv","Chen Du","Lianjun Yang","Chengzhou Pa","Yunrui Dai"],"tags":["Computer science","Workflow","Artificial intelligence","Medicine","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-25","doi":"https://doi.org/10.3389/fphys.2026.1778235","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3004042885","name":"Review—Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors","source":"openalex","abstract":"The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the downtime. With the availability of Internet of Things (IoT) technologies, it is possible to acquire the sensor data in real-time. Machine Learning and Deep Learning (DL) algorithms can then be used to predict the part and equipment failures, given enough historical data. DL algorithms have shown significant advances in problems where progress has eluded the practitioners and researchers for several decades. This paper reviews the DL algorithms used for predictive maintenance and presents a case study of engine failure prediction. We also discuss the current use of sensors in the industry and future opportunities for electrochemical sensors in predictive maintenance.","url":"https://doi.org/10.1149/1945-7111/ab67a8","authors":["Srikanth Namuduri","Barath Narayanan Narayanan","Venkata Salini Priyamvada Davuluru","L. K. BURTON","Shekhar Bhansali"],"tags":["Downtime","Predictive maintenance","Computer science","Internet of Things","Reliability engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-28","doi":"https://doi.org/10.1149/1945-7111/ab67a8","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7127149757","name":"Data pipeline quality: development and validation of a quality assessment tool for data-driven algorithms and artificial intelligence in healthcare","source":"openalex","abstract":"OBJECTIVES: To develop and validate a tool for standardised quality assessment of data-driven algorithms in healthcare, focusing on the underlying data pipeline. METHODS: Data Assessment Tool for Algorithm Critical Appraisal and Robust Evidence (DATA-CARE) was iteratively developed from the established Quality In Prognosis Studies framework, selected after reviewing 10 existing quality assessment tools for observational and artificial intelligence studies. DATA-CARE evaluates five quality domains of the data pipeline: study population, data, algorithm, outcome and report transparency. Each domain comprises three to five quality criteria. With a total score of 75 points, study quality is categorised as low (<45), moderate (45-59) or high (≥60). DATA-CARE was validated during a systematic review on data-driven algorithms using continuous physiological monitoring data within the paediatric intensive care unit. Two independent reviewers performed quality assessment using DATA-CARE of included studies. Tool validation was evaluated using inter-rater agreement and intraclass correlation coefficient (ICC). RESULTS: DATA-CARE demonstrated robust inter-rater agreement (93.5%) with ICC 0.98 (95% CI 0.96 to 0.99). Of 3858 screened studies, 31 were reviewed in the use case, describing diverse algorithms. Studies were predominantly low (32.3%) to moderate (41.9%) and sporadically (25.8%) high quality. DISCUSSION: Predominance of low-to-moderate quality studies reveals critical barriers to clinical implementation of data-driven algorithms, including low quality data capture and processing, lacking validation strategies and non-transparent reporting of findings. CONCLUSIONS: DATA-CARE allows standardised and reliable critical appraisal for a wide variety of algorithms, addressing current gaps in standardised and reproducible algorithm development.","url":"https://doi.org/10.1136/bmjhci-2025-101608","authors":["Eris van Twist","Brian van Winden","Rogier de Jonge","H Rob Taal","Matthijs de Hoog","Alfred C. Schouten","David M. J. Tax","Jan Willem Kuiper"],"tags":["Computer science","Pipeline (software)","Variety (cybernetics)","Quality assessment","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-01","doi":"https://doi.org/10.1136/bmjhci-2025-101608","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4385754666","name":"Monitoring blood pressure and cardiac function without positioning via a deep learning–assisted strain sensor array","source":"openalex","abstract":"Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.","url":"https://doi.org/10.1126/sciadv.adh0615","authors":["Shuo Li","Haomin Wang","Wei Ma","Lin Qiu","Kailun Xia","Yong Zhang","Yong Zhang","Haojie Lü","Mengjia Zhu","Xiaoping Liang","Xun‐En Wu","Huarun Liang","Yingying Zhang","Yingying Zhang"],"tags":["Computer science","Wearable computer","Deep learning","Blood pressure","Pressure sensor"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-11","doi":"https://doi.org/10.1126/sciadv.adh0615","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4400095485","name":"Comparative Performance of ChatGPT 3.5 and GPT4 on Rhinology Standardized Board Examination Questions","source":"openalex","abstract":"Objective: Advances in deep learning and artificial intelligence (AI) have led to the emergence of large language models (LLM) like ChatGPT from OpenAI. The study aimed to evaluate the performance of ChatGPT 3.5 and GPT4 on Otolaryngology (Rhinology) Standardized Board Examination questions in comparison to Otolaryngology residents. Methods: This study selected all 127 rhinology standardized questions from www.boardvitals.com, a commonly used study tool by otolaryngology residents preparing for board exams. Ninety-three text-based questions were administered to ChatGPT 3.5 and GPT4, and their answers were compared with the average results of the question bank (used primarily by otolaryngology residents). Thirty-four image-based questions were provided to GPT4 and underwent the same analysis. Based on the findings of an earlier study, a pass-fail cutoff was set at the 10th percentile. Results: = .001). GPT4 answered image-based questions correctly 64.7% of the time. Projections suggest that ChatGPT 3.5 might not pass the American Board of Otolaryngology Written Question Exam (ABOto WQE), whereas GPT4 stands a strong chance of passing. Discussion: The older LLM, ChatGPT 3.5, is unlikely to pass the ABOto WQE. However, the advanced GPT4 model exhibits a much higher likelihood of success. This rapid progression in AI indicates its potential future role in otolaryngology education. Implications for Practice: As AI technology rapidly advances, it may be that AI-assisted medical education, diagnosis, and treatment planning become commonplace in the medical and surgical landscape. Level of Evidence: Level 5.","url":"https://doi.org/10.1002/oto2.164","authors":["Evan A. Patel","Lindsay Fleischer","Peter Filip","Michael Eggerstedt","Michael J. Hutz","Elias Michaelides","Pete S. Batra","Bobby A. Tajudeen"],"tags":["Rhinology","Otorhinolaryngology","Percentile","Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-01","doi":"https://doi.org/10.1002/oto2.164","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7147256499","name":"Artificial Intelligence-Based Exosome Analysis for Improving Diagnostic Performance of Breast Lesions on Ultrasound: Protocol of a Prospective, Multicenter Cohort Study","source":"openalex","abstract":"PURPOSE: Exosome-surface enhanced Raman spectroscopy-artificial intelligence platform (exosome-SERS-AI) is an innovative liquid biopsy method that acquires SERS signals from plasma exosomes and analyzes them using deep learning models to diagnose cancer. This study aimed to evaluate whether exosome-SERS-AI could increase the diagnostic accuracy of ultrasonography (US) for suspicious breast lesions. METHODS: This prospective multicenter study enrolled 500 patients between November 2024 and December 2025. Eligible participants will be women aged ≥ 40 years who will undergo US performed by specialized breast radiologists and have suspicious breast lesions assigned to a Breast Imaging Reporting and Data System (BI-RADS) category 3-5 assessment. A 6 mL whole blood sample was collected from each participant. After plasma separation, SERS, which is highly sensitive to exosomes, was employed to measure Raman signals, and the acquired data were processed using artificial intelligence algorithms. Following sampling, all patients underwent US-guided core needle biopsy for breast lesions classified as BI-RADS category 4 and 5, and 12-months of follow-up US for lesions classified as BI-RADS category 3. Histopathological examination was used as the reference standard for BI-RADS 4 and 5 lesions, whereas stability on 12-month follow-up US was used as the reference standard for BI-RADS 3 lesions. The cohort is expected to have an equal distribution of benign and malignant cases. The following outcome measures were compared between US alone and the combination of exosome-SERS-AI with US: sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve. Enrollment is expected to be completed by 2025, and the study results are expected to be presented in 2026. DISCUSSION: This prospective multicenter study will evaluate the performance of exosome-SERS-AI compared to US in women with BI-RADS categories 3-5. Participant enrollment is ongoing. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06672302. Registered on November 4, 2024.","url":"https://doi.org/10.4048/jbc.2025.0206","authors":["Sung Eun Song","Hyunku Shin","Yong Park","Yeonho Choi","Seung Pil Jung"],"tags":["Medicine","Exosome","Oncology","Internal medicine","Cohort study"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.4048/jbc.2025.0206","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7134076461","name":"Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing","source":"openalex","abstract":"Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems.","url":"https://doi.org/10.3390/jcdd13030119","authors":["Daniela Nicoleta Crisan","Talida Georgiana Cut","Lucian-Flavius Herlo","N Ivanović","Alexandra Herlo","Luana Alexandrescu","Andreea Sălcudean","Raluca Dumache"],"tags":["Artificial intelligence","Workflow","Deep learning","Machine learning","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-06","doi":"https://doi.org/10.3390/jcdd13030119","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4399292332","name":"A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems","source":"openalex","abstract":"Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.","url":"https://doi.org/10.3390/machines12060380","authors":["Syeda Sitara Wishal Fatima","Afshin Rahimi"],"tags":["Interpretability","Computer science","Autoregressive integrated moving average","Exponential smoothing","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-01","doi":"https://doi.org/10.3390/machines12060380","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3093090100","name":"Adoption of AI-empowered industrial robots in auto component manufacturing companies","source":"openalex","abstract":"The usage of AI-empowered Industrial Robots (InRos) is booming in the Auto Component Manufacturing Companies (ACMCs) across the globe. Based on a model leveraging the Technology, Organisation, and Environment (TOE) framework, this work examines the adoption of InRos in ACMCs in the context of an emerging economy. This research scrutinises the adoption intention and potential use of InRos in ACMCs through a survey of 460 senior managers and owners of ACMCs in India. The findings indicate that perceived compatibility, external pressure, perceived benefits and support from vendors are critical predictors of InRos adoption intention. Interestingly, the study also reveals that IT infrastructure and government support do not influence InRos adoption intention. Furthermore, the analysis suggests that perceived cost issues negatively moderate the relationship between the adoption intention and potential use of InRos in ACMCs. This study offers a theoretical contribution as it deploys the traditional TOE framework and discovers counter-intuitively that IT resources are not a major driver of technology adoption: as such, it suggests that a more comprehensive framework than the traditional RBV should be adopted. The work provides managerial recommendations for managers, shedding light on the antecedents of adoption intention and potential use of InRos at ACMCs in a country where the adoption of InRos is in a nascent stage.","url":"https://doi.org/10.1080/09537287.2021.1882689","authors":["Rajasshrie Pillai","Brijesh Sivathanu","Marcello M. Mariani","Nripendra P. Rana","Bai Yang","Yogesh K. Dwivedi"],"tags":["Globe","Business","Marketing","Context (archaeology)","Work (physics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-18","doi":"https://doi.org/10.1080/09537287.2021.1882689","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4415603723","name":"Unveiling Dynamic Resilience on Sustainable Performance in Supply Chain: Artificial Intelligence, System Optimization and Information Transparency","source":"openalex","abstract":"ABSTRACT Supply chain resilience (SCR) enhances the use of artificial intelligence, system optimization, and information transparency to facilitate the dynamics in sustainable supply chain performance improvement. Prior studies neglect system dynamics involvement to identify the dynamic changes. This study proposes a hybrid method for extracting real practices from the globally registered patents and extracts these variables by adopting bidirectional encoder representations of transformers. The results of comparing models based on different environmental data reveal that supply chain resilience does not reliably improve sustainable supply chain performance, especially during crises. Artificial intelligence is able to generate dynamics in the SCR. The resilient systems could be improved through system optimization and information transparency without artificial intelligence in both the before and after dynamic environments.","url":"https://doi.org/10.1111/jbl.70046","authors":["Kuo‐Jui Wu","Ming‐Yong Han","Caiyan Huang","Hailing Qiu","Kanchana Sethanan","Ming‐Lang Tseng"],"tags":["Transparency (behavior)","Resilience (materials science)","Supply chain","System dynamics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1111/jbl.70046","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7131354288","name":"Artificial Intelligence in Adverse Outcome Pathways: A Review of Strategies for Automated Information Extraction, Quantitative Analysis, and Iterative Optimization","source":"openalex","abstract":"The rapid emergence of novel chemical substances escalates the occupational and environmental health risks, posing significant challenges to the traditional toxicological risk assessment framework. While adverse outcome pathways (AOPs) have become a pivotal theoretical framework for alternative toxicity testing and future risk assessments, their development and optimization remain hindered by time-consuming and labor-intensive manual processing. This narrative review systematically elucidates how artificial intelligence (AI) facilitates the development and optimization of AOPs. Specifically, AI automates the extraction of knowledge modules for AOPs via natural language processing, quantifies key relationships through integrating methods like Bayesian networks, and supports continuous AOP refinement using machine learning platforms. Together, these technologies establish a modern, data-driven, and iterative framework. Furthermore, the review discusses the current limitations in applying AI to the AOP domain alongside its substantial potential to enhance chemical risk assessment and regulatory decision-making. Ultimately, this work aims to provide new insights and methodologies for advancing AOP development, thereby strengthening the risk assessment and regulation of chemical exposures in environmental and occupational settings.","url":"https://doi.org/10.3390/occuphealth1010009","authors":["Ziqi Zhu","Guiping Hu","Guang Jia"],"tags":["Computer science","Adverse Outcome Pathway","Risk analysis (engineering)","Risk assessment","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-25","doi":"https://doi.org/10.3390/occuphealth1010009","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7128705500","name":"Bioinspired Cross‐Modal Self‐Adaptive Machine Intelligence for Event‐Driven and Ultrahigh‐Precision Underwater Grasping","source":"openalex","abstract":"ABSTRACT Embodied intelligent agents, which represent the future of robotics, demand precise perception and real‐time decision‐making capabilities to achieve natural environmental interactions. However current systems face inherent limitations in unimodal sensing and cross‐modal coordination, which hinder their performance in dynamic contact‐rich operations. Herein, we present a fabric‐based event‐driven tactile interface that features an innovative woven structure with cross‐fiber electrodes. It achieves breakthroughs in sensitivity (246.3 kPa −1 ), pressure detection (>450 kPa), and waterproof robustness. This interface enables millisecond‐level pressure/slip dual‐mode feedback for self‐adaptive grasping, thereby improving the dexterous manipulation of fragile or slippery objects. For underwater scenarios, a bio‐inspired visual–tactile fusion (VTF) architecture leverages tactile perception to compensate for visual limitations, demonstrating a high accuracy of 97.7% in complex tasks, including underwater transparent object manipulation and recognition of similar objects. Event‐driven tactile feedback is merged with visual semantics for decision‐level optimization, thereby enhancing the autonomy and adaptation of humanoid machine intelligence. It creates an innovative closed‐loop cross‐modal perception–decision system that builds a direct link between environmental interaction and autonomous decision‐making for intelligent agent development in open‐world scenarios. The superior performance of the VTF architecture dynamic interaction tasks represents a crucial step toward robotic systems with advanced intelligence.","url":"https://doi.org/10.1002/adma.202519665","authors":["Hongyu Chen","Zijian Huang","Yanhao Luo","Yujin Wang","Huasen Wang","Lei Liu","Yu Sun","Yu Hu","Yuchen Lin","Chao Wei","Wenjun Lin","Gantang Su","Ziquan Guo","Jianghui Zheng","Zhiqi Chen","Qingliang Liao","Yuanjin Zheng","Liao Xinqin"],"tags":["Computer science","Interface (matter)","Human–computer interaction","Tactile sensor","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-12","doi":"https://doi.org/10.1002/adma.202519665","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4391574202","name":"Possibilities and challenges of the defensive use of artificial intelligence and computer vision based technologies and applications in the defence sector","source":"openalex","abstract":"I am thankful to all those who have","url":"https://doi.org/10.17625/nke.2023.030","authors":["Viktor Huszár"],"tags":["Computer science","Artificial intelligence","Cognitive science","Engineering","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.17625/nke.2023.030","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7134903860","name":"Overview of allergic disease: Anaphylaxis – WAO White Book on Allergy 2026 – 2.11","source":"openalex","abstract":"Anaphylaxis is a rapid-onset, potentially fatal systemic hypersensitivity reaction marked by airway, breathing, or circulatory compromise, which may occur even without skin symptoms. It is driven primarily by mast-cell activation-usually through IgE-mediated pathways-leading to the release of mediators such as tryptase and histamine. Basophils can also contribute. Although traditionally considered rare, anaphylaxis has a lifetime prevalence of 0.3-5.1%, with incidence rising globally in both adults and children, likely due to increased allergic diseases. Fatalities remain relatively low. Common triggers vary by age and geography: foods such as milk, peanuts, and tree nuts dominate in children; medications-especially beta-lactam antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs)-are more common in adults; and insect venoms are major causes in some regions. Diagnosis is clinical, based on rapid multisystem involvement, despite sometimes isolated cardiovascular or non-inhaled respiratory symptoms may occur. First measures should be to remove the allergen if possible and position the patient appropriately. Immediate intramuscular epinephrine injected into the lateral thigh is the first-line treatment, with repeat dosing as needed. Intranasal epinephrine is emerging as a needle-free alternative. Adjunctive measures include oxygen, intravenous fluids, and bronchodilators for bronchospasm. Antihistamines and corticosteroids may alleviate some symptoms but do not reverse anaphylaxis and must not delay epinephrine administration. Long-term management focuses on identifying triggers through detailed history, IgE testing, skin testing, or challenge procedures; however, up to 10% of cases remain idiopathic. Patients require action plans and access to self-administered epinephrine, though availability is limited in many regions. Key unmet needs include global device access, harmonized definitions and severity scoring, improved education, digital health integration, and deeper investigation into mechanisms, biomarkers, genetics, and cofactors to optimize diagnosis, prevention, and care.","url":"https://doi.org/10.1016/j.waojou.2026.101338","authors":["Victória Cardona"],"tags":["Medicine","Allergy","Anaphylaxis","Dermatology","White (mutation)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-01","doi":"https://doi.org/10.1016/j.waojou.2026.101338","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W1714956154","name":"Top Management Team Diversity and Strategic Innovation Orientation: The Relationship and Consequences for Innovativeness and Performance","source":"openalex","abstract":"A firm's strategic innovation orientation, which is aimed at discovering and satisfying emerging customer needs with novel technological solutions, has repeatedly been shown to be crucial for firm innovativeness and firm performance. Despite its apparent importance, relatively little research has addressed antecedents of a firm's strategic orientation that help explain heterogeneity in innovation strategies across firms. Especially the influence of top management teams (TMT) should be critical, since innovation strategies are shaped at the top management level. Building on the theory of upper echelon, this study investigates how TMT characteristics affect a firm's strategic innovation orientation, and how this relates to innovation outcomes and firm performance. Hypotheses are tested on a sample of goods manufacturers using a combination of survey data, document analysis, and objective capital market data for firm performance. Results indicate that TMT diversity, measured as heterogeneity in educational, functional, industry, and organizational background, has a strong positive effect on a firm's innovation orientation. A strong proactive focus on emerging customer needs and on novel technologies then lead to a portfolio of new products with higher market newness and technology newness, which both increase firm performance. The results therefore emphasize the importance of TMT characteristics as antecedent for innovation strategy and innovation outcomes.","url":"https://doi.org/10.1111/j.1540-5885.2011.00851.x","authors":["Katrin Talke","Søren Salomo","Alexander Kock"],"tags":["Business","Marketing","Industrial organization","Market orientation","Entrepreneurial orientation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2011-07-27","doi":"https://doi.org/10.1111/j.1540-5885.2011.00851.x","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4406271458","name":"Machine Learning‐Driven Prediction, Preparation, and Evaluation of Functional Nanomedicines Via Drug–Drug Self‐Assembly","source":"openalex","abstract":"Small molecules as nanomedicine carriers offer advantages in drug loading and preparation. Selecting effective small molecules for stable nanomedicines is challenging. This study used artificial intelligence (AI) to screen drug combinations for self-assembling nanomedicines, employing physiochemical parameters to predict formation via machine learning. Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are identified as effective carriers for antineoplastic drugs, with high drug loading. Nanomedicines, PEG-coated indomethacin/paclitaxel nanomedicine (PiPTX), and laminarin-modified indomethacin/paclitaxel nanomedicine (LiDOX), are developed with extended circulation and active targeting functions. Indomethacin/paclitaxel nanomedicine iDOX exhibits pH-responsive drug release in the tumor microenvironment. These nanomedicines enhance anti-tumor effects and reduce side effects, offering a rapid approach to clinical nanomedicine development.","url":"https://doi.org/10.1002/advs.202415902","authors":["Chengyuan Zhang","Yuchuan Yuan","Qiong Xia","Junjie Wang","Kang Xu","Zhiwei Gong","Jie Lou","Gen Li","Lu Wang","Li Zhou","Zhirui Liu","Kui Luo","Xing Zhou"],"tags":["Nanomedicine","Drug","Paclitaxel","Pharmacology","Drug delivery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-10","doi":"https://doi.org/10.1002/advs.202415902","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7164135050","name":"EFFECT OF AIR POLLUTION MITIGATION BY ARTIFICIAL INTELLIGENCE TECHNOLOGY ON URBAN RESIDENTS’ HEALTH: EVIDENCE FROM 286 CITIES IN CHINA","source":"openalex","abstract":"The technological revolution represented by Artificial Intelligence (AI) not only drives substantial changes in productivity, but also provides powerful impetus for profoundly altering people’s lifestyle. This article explores the effect of AI on residents’ health with a fixed effects model, TVP-SVVAR model and data of 286 cities from 2011 to 2023 in China. The results indicate that AI can decrease mortality rates and improve resident health. Mechanism analysis shows that AI improves resident health by mitigating air pollution. Heterogeneity analysis reveals AI exerts a significant improvement effect in the eastern and central regions, while the positive effect is insignificant in the western region. Further analysis illustrates that long term effects of AI on resident health exhibit time-varying characteristics. The above findings could provide insights and suggestions into air pollution suppression and resident health enhancement for prefecture cities of China.","url":"https://doi.org/10.15666/aeer/2403_35573575","authors":["K. DONG","Y. GE","T.T. LIU","G.L. WANG","P.P. DAI","Z.H. MENG"],"tags":["China","Air pollution","Mechanism (biology)","Human health","Pollution"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.15666/aeer/2403_35573575","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154025012","name":"Responsibility Definition and Risk Management in the Clinical Application of Medical Artificial Intelligence:A Review Based on Four-Level Classification","source":"openalex","abstract":"The rapid penetration of medical artificial intelligence (MAI) into clinical diagnosis and treatment scenarios has reshaped the traditional medical service model. However, issues such as ambiguous responsibility definition and lagging risk management have severely constrained its safe and compliant development. This article uses the four-level AI classification system (tool-type, advisor-type, collaborative-type, autonomous-type) as the analytical framework to systematically review the responsibility definition logic and full-lifecycle risk management mechanisms for MAI clinical applications. The study finds that a multi-stakeholder responsibility system covering manufacturers, medical institutions, doctors, and regulatory authorities has been formed, along with a “prevention-control-remediation” risk management closed loop. However, deficiencies still exist in empirical validation, standard unification, adaptation to special scenarios, and coordination between technology and institutions. Future research should focus on directions such as dynamic responsibility quantification and matching, practical design of insurance pools, and development of lightweight management tools for primary care, providing theoretical support and practical references for the systematic governance of MAI clinical applications.","url":"https://doi.org/10.70267/cai.26v3n2.2834","authors":["Shihan Yin"],"tags":["Clinical governance","Risk management","Risk analysis (engineering)","Lagging","Process management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-13","doi":"https://doi.org/10.70267/cai.26v3n2.2834","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7117250533","name":"Ultrasound-assisted recovery of lycopene from tomato waste: a systematic review supported by generative artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.tifs.2025.105518","authors":["Cristina Arroqui","M. Berradre","Fernandez-Pan Idoya","María José Beriain","Francisco C. Ibáñez","Paloma Virseda"],"tags":["Lycopene","Automatic summarization","Systematic review","Scopus","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-25","doi":"https://doi.org/10.1016/j.tifs.2025.105518","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4383067504","name":"Navigating circular economy: Unleashing the potential of political and supply chain analytics skills among top supply chain executives for environmental orientation, regenerative supply chain practices, and supply chain viability","source":"openalex","abstract":"Abstract In a changing business environment, firms encounter significant challenges to fulfill sustainable development goals. However, firms can make substantial progress by adopting regenerative approaches grounded in circular economy principles, enabling them to effectively pursue sustainable development objectives such as responsible production and consumption (goal 12), climate action (goal 13), and the preservation of life on land (goal 15). However, there is a scarcity of research studies that offer guidance to top supply chain (SC) executives, aiming to enhance their environmental focus and enhance the SC viability through regenerative SC practices. This study employed the philosophical perspective of the natural resource‐based view. Advancing the SC literature, it tested a theoretical model to investigate the relationship between the political skills and SC analytics skills of top SC executives and the environmental orientation of SCs. Additionally, it examined the direct and indirect (via regenerative SC) relationships between a firm's environmental orientation and SC viability. This study also tested the moderating role of a firm's artificial intelligence‐driven big data analytics culture in these relationships. Applying a mixed‐methods approach, the study derived a theoretical model to link the aforementioned constructs. Apart from a qualitative investigation, data were also collected through a questionnaire‐based survey from 375 samples. The results indicated that the relationship between the political skills and SC analytics skills of top SC executives toward the environmental orientation of SCs is significant. Furthermore, they indicated that a firm's environmental orientation is positively related to a regenerative SC, which in turn enhances SC viability. The findings also provided evidence that a firm's AI‐driven big data analytics culture enhances the strength of these relationships. The current study extends the knowledge base by integrating the two unique concepts of digitalization and circular economy.","url":"https://doi.org/10.1002/bse.3507","authors":["Surajit Bag","Muhammad Sabbir Rahman"],"tags":["Supply chain","Business","Marketing","Supply chain management","Scarcity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-04","doi":"https://doi.org/10.1002/bse.3507","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7125478110","name":"Maximizing Efficiency of Artificial Intelligence-Enabled Ambient Scribes in Outpatient Settings: A Pragmatic Approach to Structuring the Patient Appointment","source":"openalex","abstract":"The introduction of artificial intelligence (AI)-assisted documentation tools, such as ambient scribes, has promised to save time and reduce clinician burnout. Ambient scribe technology has begun to deliver, showing reduction in cognitive burden, greater efficiency in documentation, and improved engagement with patients, although the long-term impact on burnout and patient safety have yet to materialize.1,2 Additionally, little is known about the quality of AI generated documentation or the shift in cognitive workload.","url":"https://doi.org/10.1016/j.mcpdig.2026.100339","authors":["Jason D. Greenwood","Marc Matthews","Joshua Overgaard","Joshua W. Ohde"],"tags":["Structuring","Computer science","Artificial intelligence","Key (lock)","Expert system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-23","doi":"https://doi.org/10.1016/j.mcpdig.2026.100339","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4205541212","name":"Spatial omics: Navigating to the golden era of cancer research","source":"openalex","abstract":"The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high-throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi-modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.","url":"https://doi.org/10.1002/ctm2.696","authors":["Yingcheng Wu","Yifei Cheng","Xiangdong Wang","Jia Fan","Qiang Gao"],"tags":["Omics","Spatial analysis","Data science","Genomics","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1002/ctm2.696","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4403910234","name":"A critical analysis of the integration of life cycle methods and quantitative methods for sustainability assessment","source":"openalex","abstract":"Abstract The utilization of Life Cycle Assessment (LCA) and its corresponding methodologies gained considerable attention within the realm of corporate social responsibility (CSR) and sustainability assessment. Nevertheless, a lack of extensive investigation on their integration along with quantitative procedures, for example, statistical techniques and artificial intelligence (AI), has emerged. The purpose of this literature review is to investigate the extent to which these methodologies have been connected so far in order to achieve objectives concerning the assessment of sustainability. The scope of the study was restricted to articles published in peer‐reviewed journals throughout the period from 1960 to 2022. The investigation was conducted by using a broad set of keywords, encompassing both life cycle methods, including Life Cycle Assessment, Environmental Life Cycle Costing and Emergy Accounting, and quantitative methods, such as mathematical methods, economic methods and building information modeling methods. A total of 144 articles addressing the combined use of life cycle methods and quantitative methodologies for the evaluation of sustainability were identified in the literature review. The greater part of these studies relied on the combined use of LCA with mathematical models, statistical methods and AI methods. As a result, the studies proven that the joint application of such methods can improve consistency of sustainability assessment and enhance CSR. Additionally, many articles suggested novel approaches, including a combined use of LCA and building information modeling as well as simulation methods. The combined use of life cycle methods alongside quantitative methods offers promise in enhancing sustainability assessment by offering more precise and consistent outcomes. However, it becomes imperative to carefully evaluate the hypotheses, accuracy of data, and uncertainty associated with each method within the integration process. Additional research needs to be conducted in order to establish standardized protocols for combining these methodologies as well as to identify the most suitable procedures for their integration with respect to specific objectives concerning sustainability assessment.","url":"https://doi.org/10.1002/csr.3010","authors":["Roberto Cerchione","Mariarosaria Morelli","Renato Passaro","Ivana Quinto"],"tags":["Sustainability","Business","Life-cycle assessment","Environmental resource management","Economics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-29","doi":"https://doi.org/10.1002/csr.3010","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2804118462","name":"The Impact of Automation on Employment: Just the Usual Structural Change?","source":"openalex","abstract":"We study the projected impact of automation on employment in the forthcoming decade, both at the macro-level and in actual (types of) sectors. Hereto, we unite an evolutionary economic model of multisectoral structural change with labor economic theory. We thus get a comprehensive framework of how displacement of labor in sectors of application is compensated by intra- and intersectoral countervailing effects and notably mopped up by newly created, labor-intensive sectors. We use several reputable datasets with expert projections on employment in occupations affected by automation (and notably by the introduction of robotics and AI) to pinpoint which and how sectors and occupations face employment shifts. This reveals how potential job loss due to automation in “applying” sectors is counterbalanced by job creation in “making” sectors as well in complementary and quaternary, spillover sectors. Finally, we study several macro-level scenarios on employment and find that mankind is facing “the usual structural change” rather than the “end of work”. We provide recommendations on policy instruments that enhance the dynamic efficiency of structural change.","url":"https://doi.org/10.3390/su10051661","authors":["Ben Vermeulen","Jan Kesselhut","Andreas Pyka","Pier Paolo Saviotti"],"tags":["Automation","Operations management","Engineering","Business","Manufacturing engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-05-21","doi":"https://doi.org/10.3390/su10051661","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7130723889","name":"Optimization of Hybrid Renewable Energy Systems: Classical Optimization Methods, Artificial Intelligence, Recent Trend, and Software Tools","source":"openalex","abstract":"This article synthesizes the state of the art in the optimization of Hybrid Renewable Energy Systems (HRES), emphasizing that robust HRES planning is inherently an integrated sizing-and-dispatch problem constrained by techno-economic, environmental, and reliability requirements. The review first consolidates classical optimization methods, highlighting the continued relevance of deterministic programming (LP/MILP/MINLP) for transparent and reproducible co-optimization of capacity investment and operational dispatch, alongside analytical, graphical, iterative, and probabilistic approaches for feasibility screening and baseline benchmarking. It then evaluates artificial intelligence–based optimization techniques, including evolutionary computation, swarm intelligence, and multi-objective evolutionary frameworks, noting their effectiveness in nonconvex, mixed-variable, and simulation-driven sizing problems while underscoring the need for rigorous constraint handling, statistical validation, and transparent reporting of computational budgets. The article further examines hybrid optimization strategies that integrate global search with exact dispatch solvers, surrogate-assisted learning, decomposition schemes, and control–co-design paradigms, identifying these as mature approaches that enhance feasibility, scalability, and operational realism. Recent trends in newly proposed AI optimizers are critically discussed, with emphasis on reproducibility, sensitivity analysis, and fair benchmarking against strong baselines. Finally, the article outlines the role of software tools in enabling practical HRES optimization, spanning packaged techno-economic platforms, solver-based modeling environments, and co-simulation workflows for network-constrained planning. Overall, the findings indicate a clear progression toward multi-objective, uncertainty-aware, degradation-informed formulations implemented through integrated toolchains and hybrid solver–AI architectures, with future work warranted on uncertainty quantification, network and resilience constraints, and reproducible evaluation protocols.","url":"https://doi.org/10.65998/ijees.v3i4.150","authors":["Ibrahim Imbayah","Mohamed Khaleel","Zıyodulla Yusupov"],"tags":["Computer science","Probabilistic logic","Evolutionary algorithm","Sizing","Benchmarking"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-27","doi":"https://doi.org/10.65998/ijees.v3i4.150","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4386442774","name":"Listen to Your Heart – Studying Heartbeat Detection and Emotional Intelligence","source":"openalex","abstract":"Abstract: Perception of emotions is at the basis of scientific literature about emotions and is considered a crucial emotional function in the Mayer and Salovey hierarchical model of Emotional Intelligence. The perception of bodily signals plays an important role in the perception of one’s own and others’ emotions. Thus, the first aim of this study was to verify if interoceptive ability, referred to as the ability to perceive bodily signals or autonomic self-perception, is related to individuals’ emotional abilities measured with the MSCEIT ( Mayer-Salovey-Caruso Emotional Intelligence Test, 2002 ). Results evidenced a positive relationship between total EI and experiential area scores (perceiving emotions and sensations) and interoceptive performance scores. A second aim was to investigate if the artificial augmentation of the cardiac perception improved performances in the same tasks. Results showed that the augmented perception of the heartbeat signal did not influence any performance in MSCEIT’s subtests. We concluded that there is a significant relationship between EI and interoceptive ability but that the artificial augmentation of the perception of bodily signals does not influence MSCEIT performances.","url":"https://doi.org/10.1027/0269-8803/a000325","authors":["Antonella D’Amico","Giulia Mangiaracina"],"tags":["Psychology","Emotional intelligence","Perception","Interoception","Heartbeat"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-05","doi":"https://doi.org/10.1027/0269-8803/a000325","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4396733676","name":"Green Synthesis of Bioplastics from Microalgae: A State-of-the-Art Review","source":"openalex","abstract":"The synthesis of conventional plastics has increased tremendously in the last decades due to rapid industrialization, population growth, and advancement in the use of modern technologies. However, overuse of these fossil fuel-based plastics has resulted in serious environmental and health hazards by causing pollution, global warming, etc. Therefore, the use of microalgae as a feedstock is a promising, green, and sustainable approach for the production of biobased plastics. Various biopolymers, such as polyhydroxybutyrate, polyurethane, polylactic acid, cellulose-based polymers, starch-based polymers, and protein-based polymers, can be produced from different strains of microalgae under varying culture conditions. Different techniques, including genetic engineering, metabolic engineering, the use of photobioreactors, response surface methodology, and artificial intelligence, are used to alter and improve microalgae stocks for the commercial synthesis of bioplastics at lower costs. In comparison to conventional plastics, these biobased plastics are biodegradable, biocompatible, recyclable, non-toxic, eco-friendly, and sustainable, with robust mechanical and thermoplastic properties. In addition, the bioplastics are suitable for a plethora of applications in the agriculture, construction, healthcare, electrical and electronics, and packaging industries. Thus, this review focuses on techniques for the production of biopolymers and bioplastics from microalgae. In addition, it discusses innovative and efficient strategies for large-scale bioplastic production while also providing insights into the life cycle assessment, end-of-life, and applications of bioplastics. Furthermore, some challenges affecting industrial scale bioplastics production and recommendations for future research are provided.","url":"https://doi.org/10.3390/polym16101322","authors":["Adegoke Isiaka Adetunji","Mariana Erasmus"],"tags":["Bioplastic","Biopolymer","Environmentally friendly","Polyhydroxybutyrate","Biochemical engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-08","doi":"https://doi.org/10.3390/polym16101322","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4388616875","name":"Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era","source":"openalex","abstract":"Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.","url":"https://doi.org/10.1002/wcms.1693","authors":["Liwei Chang","Arup Mondal","Bhumika Singh","Yisel Martínez‐Noa","Alberto Pérez"],"tags":["Drug discovery","Peptide","Computational biology","Computer science","Drug"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-12","doi":"https://doi.org/10.1002/wcms.1693","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7118208329","name":"Model confrontation and collaboration: A debate intelligence framework for enhancing medical reasoning in large language models","source":"openalex","abstract":"Medical reasoning is fundamental to clinical decision-making, underpinning tasks such as patient communication, diagnosis, and treatment planning. Inspired by psychological findings that peer interaction promotes self-correction, we introduce model confrontation and collaboration (MCC), a debate intelligence framework that transcends static ensemble methods by integrating critique and self-reflection to iteratively refine reasoning through structured, multi-round confrontation and collaboration among diverse large language models (LLMs). In multiple-choice benchmarks, MCC achieved mean accuracy on MedQA (92.6%) and PubMedQA (84.8%) and demonstrated strong performance on medical subsets of MMLU. In long-form medical question answering, MCC outperformed all individual LLMs and the domain-specific LLM Med-PaLM 2 in both physician and layperson evaluations. In diagnostic dialog tasks, MCC further excelled in both history-taking and diagnostic accuracy, reaching a top-1 diagnosis rate of 80%. These results position MCC as a scalable, model-agnostic framework that advances medical reasoning through collaborative deliberation.","url":"https://doi.org/10.1016/j.xcrm.2025.102547","authors":["Xinti Sun","Qiyang Hong","Mengyan Zhang","Yuyan Li","Tingwei Chen","Zigeng Huang","Guihan Liang","Wenjun Tang","Sulin Xu","Xiaolin Ni","Junling Pang","Peixing Wan","Erping Long"],"tags":["Layperson","Verbal reasoning","Computer science","Dialog box","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1016/j.xcrm.2025.102547","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4415076046","name":"Emotional Intelligence Training Correlates With Medical Students’ Apprehension of AI in Healthcare: A Single-Institution, Observational Study","source":"openalex","abstract":"Background: The use of artificial intelligence (AI) in healthcare is becoming increasingly widespread. While AI has been touted to improve many aspects of the field, its potential to undermine the doctor-patient relationship has also been recognized. It is not known what future physicians believe regarding the potential effects of AI on physicians' professional relationships. It is also unknown whether formal training in emotional intelligence (EI) and resilience strategies influences medical students' opinions on the use of AI in healthcare. Objective: To ascertain medical students’ opinion on the potential impact of AI on various EI-related aspects of healthcare, such as its use in guiding doctor-patient interactions, and to determine whether formal training in emotional intelligence and resilience (EIR) is associated with their opinion on these topics. Methods: Approximately 700 medical students were asked to voluntarily and anonymously complete a 12-item survey. All survey items were required to be answered for students' responses to be accepted and analyzed. Agreement with survey items was measured via a Likert-type scale, with 1 = strongly disagree and 5 = strongly agree. Results were summarized as means and standard deviations, stratified by whether students did (EIR+) or did not (EIR-) take our institution's EIR elective course. Median responses per survey item were compared across EIR+/- groups via the Mann-Whitney U test (α < 0.05), a non-parametric test comparing two independent samples allowing for unequal group size. Results: A total of 50 EIR+ students (59%) and 35 EIR- students (41%) replied, making the survey response rate approximately 12.14%. Compared to their counterparts, EIR+ students indicated statistically significantly greater disagreement with “I believe AI technology will improve the doctor-patient relationship” (3.0 (± 1.2) vs. 3.5 (± 1.0); p = 0.0327). While not statistically significant, EIR+ students also reported a similar magnitude (~0.5 points) of greater disagreement with “AI has a place in guiding physicians on how to better interact with their patients and colleagues” (2.9 (± 1.2) vs. 3.4 (± 1.1); p = 0.0701) and “Overall, I think adopting the use of AI in healthcare will be beneficial” (3.6 (± 1.2) vs. 4.0 (± 1.1); p = 0.0521). Conclusions: The preliminary data from our single-institution, observational study suggest EIR-trained medical students may be more cautious of AI usage in healthcare due to the potential negative impact AI can have on the doctor-patient relationship. However, larger studies using validated surveys are required to confirm this conclusion.","url":"https://doi.org/10.7759/cureus.94349","authors":["Austin Runde","Shambhavi Mishra","Marina Feffer","Ramzan Shahid"],"tags":["Observational study","Apprehension","Medicine","Emotional intelligence","Clinical psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-10-11","doi":"https://doi.org/10.7759/cureus.94349","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7151479764","name":"Artificial Intelligence in MRI-Based Glioma Imaging: From Radiomics-Based Machine Learning to Deep Learning Approaches","source":"openalex","abstract":"Gliomas are generally readily detected and broadly characterized using conventional MRI; however, substantial challenges remain in accurately delineating tumor extent, grading heterogeneous disease, and translating imaging findings into consistent, reproducible clinical decisions. Despite reported Dice coefficients of 0.85–0.91 for whole-tumor segmentation and classification AUC values exceeding 0.90 for glioma grading in curated datasets, most AI systems remain limited by validation design, dataset bias, and inadequate external generalizability. This narrative review synthesizes current AI applications for MRI-based glioma detection and segmentation, highlighting the evolution from radiomics-based classical machine learning approaches relying on handcrafted features to deep learning models capable of end-to-end representation learning. Commonly used MRI sequences, algorithmic paradigms, and reported performance trends are reviewed, with particular emphasis on tumor segmentation as a foundational enabling task. Key limitations that hinder clinical translation are examined, including limited dataset diversity, validation practices that inflate reported performance, domain shift across institutions, acquisition-related bias, and inadequate model interpretability. Emerging strategies to address these challenges, such as multi-institutional training, harmonization techniques, explainable AI frameworks, and workflow-integrated validation, are also discussed. While AI-based models demonstrate strong technical performance in research settings, their clinical impact will depend on rigorous external validation, transparency, and alignment with real-world neuro-oncology workflows.","url":"https://doi.org/10.3390/biomedinformatics6020020","authors":["Ammar Saloum","Israa Zaher","Christian T. Stipho","Enes Demir","Varun Naravetla","Mehrdad Pahlevani","Nasser K. Yaghi","Michael Karsy"],"tags":["Artificial intelligence","Deep learning","Computer science","Machine learning","Grading (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-07","doi":"https://doi.org/10.3390/biomedinformatics6020020","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3117781502","name":"Modeling physician variability to prioritize relevant medical record information","source":"openalex","abstract":"OBJECTIVE: Patient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases. MATERIALS AND METHODS: Critical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance. RESULTS: In 73 pairs of HLR and LR models, the HLR models achieved an area under the receiver operating characteristic curve of 0.81, 95% confidence interval (CI) [0.80-0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74-0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06-0.08]) than LR models (0.16, 95% CI [0.14-0.17]). DISCUSSION: The physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability. CONCLUSION: Hierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.","url":"https://doi.org/10.1093/jamiaopen/ooaa058","authors":["Mohammadamin Tajgardoon","Gregory F. Cooper","Andrew J. King","Gilles Clermont","Harry Hochheiser","Miloš Hauskrecht","Dean F. Sittig","Shyam Visweswaran"],"tags":["Logistic regression","Context (archaeology)","Confidence interval","Receiver operating characteristic","Relevance (law)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-12-01","doi":"https://doi.org/10.1093/jamiaopen/ooaa058","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4389179720","name":"Blazing the trail for innovative tuberculosis diagnostics","source":"openalex","abstract":"The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.","url":"https://doi.org/10.1007/s15010-023-02135-3","authors":["Seda Yerlikaya","Tobias Broger","Chris Isaacs","David Bell","Lydia Holtgrewe","Ankur Gupta‐Wright","Payam Nahid","Adithya Cattamanchi","Claudia M. Denkinger"],"tags":["Pandemic","Portfolio","Health care","Medicine","Coronavirus disease 2019 (COVID-19)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-30","doi":"https://doi.org/10.1007/s15010-023-02135-3","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4381469976","name":"Fundus Tessellated Density Assessed by Deep Learning in Primary School Children","source":"openalex","abstract":"Purpose: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. Methods: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. Results: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). Conclusions: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. Translational Relevance: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.","url":"https://doi.org/10.1167/tvst.12.6.11","authors":["Dan Huang","Rui Li","Yingxiao Qian","Saiguang Ling","Zhou Dong","Xin Ke","Qi Yan","Haohai Tong","Zijin Wang","Tengfei Long","Hu Liu","Hui Zhu"],"tags":["Fundus (uterus)","Ophthalmology","Optic disc","Fundus photography","Retinal"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-21","doi":"https://doi.org/10.1167/tvst.12.6.11","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7125477330","name":"Managing Conflict of Interest in Clinical Practice Guidelines With Artificial Intelligence: Insights From Large Language Models and Beyond","source":"openalex","abstract":"BACKGROUND: Conflict of interest (COI) management is critical for ensuring the scientific integrity and fairness of clinical practice guidelines (CPGs). Large language models (LLMs) have great potential in strengthening COI management, particularly in information collection, assessment, and supporting guideline development groups. OBJECTIVE: To explore LLMs' role in COI management during CPG development, focusing on applications, challenges, and future directions. METHODS: We examined how LLMs can support COI management by designing and testing a set of simulated COI scenarios based on established management principles. RESULTS: LLMs can improve efficiency in data collection (e.g., in analyzing disclosures), objectivity in risk assessment, and transparency in reporting. However, privacy risks (e.g., data breaches) and technical issues (e.g., model bias) hinder the adoption of LLM based approaches. Setting up policy frameworks, research collaboration, and enhanced security, such as differential privacy levels, can enhance reliability. CONCLUSION: LLMs can support COI management in CPG development if ethical issues are adequately considered, but validation in real-world settings is still needed.","url":"https://doi.org/10.1111/jebm.70114","authors":["Ye Wang","Qi Wang","Yangqin Xun","Qi Zhou","Huayu Zhang","Hanxiang Liu","Yishan Qin","M Y Wu","Zijing Wang","Haodong Li","Janne Estill","Yaolong Chen"],"tags":["Clinical Practice","Engineering ethics","Psychology","Management science","Conflict of interest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-22","doi":"https://doi.org/10.1111/jebm.70114","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7160282947","name":"Knowledge, Attitude, Benefits, Risks, Barriers, Professional Impact, and Preparedness of Nursing Students Toward the Utilization of Artificial Intelligence in Healthcare","source":"openalex","abstract":"Background/Objectives: Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making, patient monitoring, and administrative tasks. Nurses are expected to work with these technologies. However, the evidence suggests that their knowledge and preparedness remain limited. As future healthcare providers, nursing students must be prepared to integrate AI into their practice. This study aimed to assess nursing students’ knowledge, attitudes, perceived benefits and risks, barriers, professional impact, and preparedness toward AI in healthcare. Methods: This cross-sectional descriptive study was conducted between April and July 2024 at the College of Nursing, University of Hail, Saudi Arabia. A convenience sample of 320 undergraduate nursing students completed an online structured questionnaire that assessed their demographics, knowledge, attitudes, perceived barriers, benefits, risks, professional impact, and preparedness. Data were analyzed using IBM SPSS version 27 with descriptive statistics. Inferential analyses, including independent t-tests and one-way ANOVA, were performed to examine differences between groups. Pearson’s correlation was used to identify correlations between the study variables. Statistical significance was set at p < 0.05. Results: Most students (79.7%) had poor AI knowledge, whereas 52.5% reported positive attitudes. Older students (≥24 years) and internship students showed significantly more positive attitudes (p < 0.001). Knowledge was weakly correlated with attitudes (r = 0.147), benefits (r = 0.222), and risks (r = 0.152). Attitudes were weakly positively correlated with benefits (r = 0.243) and negatively correlated with barriers (r = −0.219). Conclusions: Despite their positive attitudes, nursing students showed limited knowledge and preparedness. Integrating AI education and practical training into nursing curricula is therefore essential. These findings should be interpreted cautiously given the cross-sectional design, single-institution sampling, and reliance on self-reported measures, which may limit generalizability.","url":"https://doi.org/10.3390/nursrep16050154","authors":["Awatif M. Alrasheeday","Aeshah Abdulaziz Alhawsawi","Bushra Alshammari","Sameer A. Alkubati","Wiem Aouicha","Mohamed Ayoub Tlili","Abdulhafith Alharbi","Bahia Galal Siam","Soha Mahmoud","Badria Elamin","Layla Alshammari","Hajer I. Motakef","Tahani Alkhammali","Ahad Alanazi","Fatimah Alshammari","Huda Alshammari","Ruqayyah Abdullah Almohammed","Ruba Abdulaziz Alomran"],"tags":["Preparedness","Internship","Health care","Nursing","Curriculum"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-04","doi":"https://doi.org/10.3390/nursrep16050154","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7119503186","name":"The Role of Artificial Intelligence in Combating Zoonotic and Public Health Infectious Diseases: A One Health Perspective on Challenges and Future Directions","source":"openalex","abstract":"Zoonotic diseases cause 60% of total infectious diseases and 75% of emerging infections, and are a core danger to global health security.Climate change, globalization, and urbanization are also responsible for accelerating the convergence of determinants between people, animals, and the environment, thus driving the probability of spillover events.A One Health interdisciplinary approach is essential for building sustainable disease prevention and control against such multifactorial threats.To achieve that, artificial intelligence has been a key technology that is revolutionizing zoonotic and public health early disease detection, surveillance, diagnosis, and prediction modeling.AI enables faster epidemic forecasting, better resource allocation, and gene tracking using technologies such as computer vision, machine learning, deep learning, and natural language processing.AI is also employing predictive statistics and bioinformatics to support drug and vaccine discovery.AI in human, animal, and environmental health systems holds exceptional promise for enhancing health equity and epidemic readiness to counter threats such as data privacy, algorithmic bias, and infrastructure inequality.The objective of this review is to critically evaluate the growing contribution of AI in the fight against zoonotic and public health diseases, examine the ways in which it can be incorporated into the One Health model, and outline directions for developing morally acceptable, transparent, and sustainable AI-based healthcare systems.","url":"https://doi.org/10.29261/pakvetj/2025.325","authors":[],"tags":["Perspective (graphical)","Public health","Engineering ethics","Environmental health","Infectious disease (medical specialty)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-11","doi":"https://doi.org/10.29261/pakvetj/2025.325","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7128029160","name":"A generalizable foundation model for analysis of human brain MRI","source":"openalex","abstract":"Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation.","url":"https://doi.org/10.1038/s41593-026-02202-6","authors":["Divyanshu Tak","Biniam A. Garomsa","Anna Zapaishchykova","Tafadzwa L. Chaunzwa","Juan Carlos Pardo","Zezhong Ye","John Zielke","Yashwanth Ravipati","Suraj Pai","Sri Vajapeyam","Maryam Mahootiha","Mitchell I. Parker","Luke R. G. Pike","Ceilidh Smith","Ariana Familiar","Kevin X. Liu","Sanjay P. Prabhu","Omar Arnaout","Pratiti Bandopadhayay","Ali Nabavizadeh","Sabine Mueller","Hugo J.W.L. Aerts","R. Stephanie Huang","Tina Young Poussaint","Benjamin H. Kann"],"tags":["Neuroimaging","Artificial intelligence","Computer science","Foundation (evidence)","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-05","doi":"https://doi.org/10.1038/s41593-026-02202-6","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154226246","name":"The Evolving Role of AI in Simulation-Based Medical Education: A Narrative Review","source":"openalex","abstract":"Purpose: A growing body of literature has emerged on the topic of Artificial Intelligence (AI) use in Simulation-Based Medical Education (SBME) in recent years, but most studies have focused on isolated applications of AI to components of simulation, making it difficult for educators and decision makers to make informed decisions on the use of AI. Therefore, this narrative review aims to condense the current literature on the use of AI in the SBME, its influence on experiential learning, explore challenges, and future directions in this rapidly evolving field. Methods: A targeted literature search was conducted for this review on PubMed and Google Scholar, with combinations of keywords. Articles were selected from 2019 to 2025, based on their relevance to the use of AI in SBME, in areas of teaching, learning, and assessment. Studies without educational outcomes were excluded. Results: The search produced 2019 papers, out of which 45 were analyzed after applying the exclusion criteria. These showed that AI has been applied across multiple dimensions of the SBME, including scenario development, enhancing realism, personalized and collaborative learning, developing communication and psychomotor skills, and automated and AI augmented feedback. Several challenges have been raised, like ethical and privacy concerns, lack of AI literacy among users, lack of transparency, undesired outcomes, infrastructure cost, and environmental effects. Conclusion: Benefits of AI in SBME stem from AI-augmented human teaching rather than unsupervised usage of AI tools. Additionally, the potential for personalized and accessible learning warrants further research.","url":"https://doi.org/10.2147/amep.s581691","authors":["S H Hasan","Ayesha Ahmed","Faisal Ismail"],"tags":["Narrative","Narrative review","Experiential learning","Relevance (law)","Literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-01","doi":"https://doi.org/10.2147/amep.s581691","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7135044204","name":"Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study","source":"openalex","abstract":"Objective To assess whether an artificial intelligence (AI) tool improves the accuracy, speed and confidence of general radiologists, emergency clinicians and radiographers in detecting critical non-contrast CT head (NCCTH) abnormalities and to evaluate its stand-alone performance and factors influencing diagnostic accuracy. Methods and analysis A retrospective dataset of 150 NCCTH (52 normal and 98 with critical abnormalities) was reviewed by 30 readers (10 radiologists, 15 emergency clinicians and 5 radiographers) from four National Health Service trusts. Each interpreted scan is performed unaided and then with the qER EU 2.0 AI tool, separated by a 2-week washout period. Ground truth was established by two neuroradiologists. We measured the AI’s stand-alone performance and its effect on reader accuracy, confidence and speed. Results The qER algorithm showed strong diagnostic performance (area under the receiver operator curve 0.821–0.976). With AI, pooled reader sensitivity for critical abnormalities increased from 82.8% to 89.7% (+6.9%, p<0.001) and for intracranial haemorrhage from 84.6% to 91.6% (+7.0%, p<0.001), while specificity decreased from 84.5% to 78.9% (–5.5%, p=0.046). Reader confidence did not change significantly. Emergency department (ED) clinicians with AI achieved sensitivity similar to unaided radiologists. Conclusion AI assistance increased sensitivity for detecting critical abnormalities on NCCTH but reduced specificity. AI-enabled ED clinicians to achieve diagnostic sensitivity comparable to radiologists, supporting its potential to enhance non-radiologist performance. Further studies are needed to confirm these findings in clinical practice. Trial registration number NCT06018545 .","url":"https://doi.org/10.1136/bmjdh-2026-000071","authors":["Alex Novak","Ruchir Shah","Abdala T Espinosa Morgado","Dennis Robert","Shamie Kumar","Jason Oke","Kanika Bhatia","Andrea Romsauerova","Tilak Das","The AI-REACT Reader Study Group","Mariapaola Narbone","Rahul Dharmadhikari","Mark Harrison","Kavitha Vimalesvaran","Jane Gooch","N. Woznitza","David Lowe","Haris Shuaib","Sarim Ather"],"tags":["Medicine","Receiver operating characteristic","Confidence interval","Radiology","Emergency department"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-01","doi":"https://doi.org/10.1136/bmjdh-2026-000071","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4391983664","name":"Evaluating ChatGPT-3.5 in allergology: performance in the Polish Specialist Examination","source":"openalex","abstract":"The development of Artificial Intelligence (AI) and attempts to use it in medicine are increasingly becoming the subject of more scientific research. Aim: The aim of this article is to present the effectiveness of the advanced language model, ChatGPT-3.5 in the context of the pass rate of the Polish National Specialist Examination (PES) in allergology. Additionally, it seeks to comprehend the potential applications of artificial intelligence in the field of medicine, particularly within allergology.","url":"https://doi.org/10.5114/pja.2024.135380","authors":["Michał Bielówka","Jakub Kufel","Marcin Rojek","Adam Mitręga","Dominika Kaczyńska","Łukasz Czogalik","Michał Janik","Wiktoria Bartnikowska","Sylwia Mielcarska","Dominika Kondoł"],"tags":["Library science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.5114/pja.2024.135380","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2808360126","name":"Health IT, hacking, and cybersecurity: national trends in data breaches of protected health information","source":"openalex","abstract":"OBJECTIVE: The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT. MATERIALS AND METHODS: Retrospective observational study of all available reported data breaches in the United States from 2013 to 2017, downloaded from a publicly available federal regulatory database. RESULTS: < .001). There were 128 electronic medical record-related breaches of 4 867 920 patient records, while 363 hacking incidents affected 130 702 378 records. DISCUSSION AND CONCLUSION: Despite making up less than 25% of all breaches, hacking was responsible for nearly 85% of all affected patient records. As medicine becomes increasingly interconnected and informatics-driven, significant improvements to cybersecurity must be made so our health IT infrastructure is simultaneously effective, safe, and secure.","url":"https://doi.org/10.1093/jamiaopen/ooy019","authors":["Jay G. Ronquillo","J. Winterholler","Kamil Cwikla","Raphael Szymanski","Christopher Levy"],"tags":["Hacker","Data breach","Computer security","Ransomware","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-06-11","doi":"https://doi.org/10.1093/jamiaopen/ooy019","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3183814650","name":"An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization","source":"openalex","abstract":"Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.","url":"https://doi.org/10.3390/math9151722","authors":["Ruba Abu Khurma","Hamad Alsawalqah","Ibrahim Aljarah","Mohamed Abd Elaziz","Robertas Damaševičius"],"tags":["Traverse","Support vector machine","Software","Computer science","Feature selection"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-22","doi":"https://doi.org/10.3390/math9151722","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7124275099","name":"Ovarian cancer think tank: the use of integrated artificial intelligence and computational biology in ovarian cancer diagnosis and treatment","source":"openalex","abstract":"Artificial intelligence and computational biology are rapidly advancing, offering unprecedented opportunities to transform both ovarian cancer research and clinical care. However, limited understanding of how to optimally integrate the information these tools provide with existing clinical data has led to a lag in integration. This commentary emerges from a unique and focused ovarian cancer research conference that explored how these emerging tools and technologies (i.e., artificial intelligence and computational biology) can be leveraged to address questions in pathology, develop new paradigms of tumor biology, and integrate precision medicine into clinical management of complex and rare subtypes of ovarian cancer. We highlight key ways in which systematic integration of Artificial intelligence (AI) and computational tools can be leveraged to improve outcomes in ovarian cancer as well as the limitations and risks of their application.","url":"https://doi.org/10.22514/ejgo.2026.002","authors":[],"tags":["Ovarian cancer","Medicine","Artificial intelligence","Applications of artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.22514/ejgo.2026.002","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4311100707","name":"Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring","source":"openalex","abstract":"Abstract On‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of $25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface.","url":"https://doi.org/10.1002/admi.202201735","authors":["Quan Zhang","Menglong Qu","Xingye Liu","Yilei Cui","Haining Hu","Qianyun Li","Meifu Jin","Jieyu Xian","Zhengwei Nie","Cheng Zhang"],"tags":["Data acquisition","Interface (matter)","SIGNAL (programming language)","Computer science","Wearable computer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-01","doi":"https://doi.org/10.1002/admi.202201735","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7128720054","name":"Preoperative localization of pulmonary nodules using ultra-low-dose CT based on artificial intelligence iterative reconstruction","source":"openalex","abstract":"Background: Preoperative localization of pulmonary nodules requires multiple computed tomography (CT) scans, making it an urgent problem to address how to effectively reduce radiation damage while maintaining image quality. Artificial intelligence iterative reconstruction (AIIR) can significantly improve the image quality of ultra-low-dose CT (ULDCT). This study aimed to examine the feasibility of using ULDCT-AIIR for the preoperative localization of pulmonary nodules. Methods: This prospective study enrolled 40 consecutive patients with pulmonary nodules who underwent preoperative hook-wire localization under low-dose CT (LDCT). Immediately following the LDCT, an additional ULDCT scan was performed. Images were reconstructed using filtered back projection (FBP) and a hybrid iterative reconstruction (HIR) for both LDCT and ULDCT scans; additionally, AIIR was applied solely to the ULDCT images. Objective parameters measured included image noise, contrast-to-noise ratio (CNR), and the distances between nodules and reference. Subjective image quality was assessed using a 5-point Likert scale, evaluating the visualization of pulmonary nodules, localization grids, needle tips, hook-wires, and complications. Quantitative and qualitative metrics were compared across the reconstruction groups using the Kruskal-Wallis test. Results: 0.01). The subjective visualization scores for nodules, localization grids, needle tips, hook-wires, and complications with ULDCT-AIIR were non-inferior to those with LDCT-HIR and significantly superior to most other reconstruction groups (P<0.01). Distance measurements demonstrated no significant differences between ULDCT-AIIR and other reconstruction methods (P>0.05). Conclusions: ULDCT-AIIR achieves image quality comparable to LDCT-HIR with significantly reduced radiation doses, suggesting its potential as an alternative to LDCT for preoperative pulmonary nodule localization.","url":"https://doi.org/10.21037/qims-2025-1544","authors":["Huixiang Lan","Xiaobin Liu","Danlin Ou","Sihua Zhong","Hongcheng Zhong","Mingzhu Liang"],"tags":["Medicine","Radiology","Iterative reconstruction","Image quality","Computed tomography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-13","doi":"https://doi.org/10.21037/qims-2025-1544","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4402570733","name":"ChatGPT and Tourist Decision‐Making: An Accessibility–Diagnosticity Theory Perspective","source":"openalex","abstract":"ABSTRACT This paper investigates the role of ChatGPT in informing tourist decision‐making across different destination contexts, focusing particularly on the accessibility and diagnosticity of its recommendations. Specifically, we inform our analysis with the tenets of the Accessibility–Diagnosticity Theory (ADT), to draw insights into ChatGPT's capabilities to produce contextually relevant and personalised travel content. Our findings reveal a sophisticated and multi‐dimensional advisory approach characterised by three themes: ‘Tailored Engagement and Accessibility’, ‘Diagnosticity of Information’ and ‘Contextual Variation in Criteria Prioritisation.’ These themes and their intersections highlight ChatGPT's potential to improve tourist decision‐making by offering comprehensive and user‐centric guidance. Based on these findings, we develop a model of ChatGPT's advisory dynamics in tourism decision‐making, which illustrates how ChatGPT integrates personalised insights, diagnostic relevance and contextual adaptation, offering a blueprint for leveraging artificial intelligence in enhancing the travel experience. We conclude with a discussion of the theoretical and practical contributions of the study.","url":"https://doi.org/10.1002/jtr.2757","authors":["Dimitrios P. Stergiou","Athina Nella"],"tags":["Tourism","Perspective (graphical)","Blueprint","Relevance (law)","Adaptation (eye)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.1002/jtr.2757","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154614260","name":"A Decade of Artificial Intelligence in Stroke Care (2015–2025): Trends, Clinical Translation, and the Precision Medicine Frontier—A Narrative Review","source":"openalex","abstract":"Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015–December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1–86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42–4.0). Brain–computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05–5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade.","url":"https://doi.org/10.3390/jpm16040218","authors":["Mian Urfy","Mariam Tariq Mir"],"tags":["Medicine","Randomized controlled trial","Stroke (engine)","Triage","Precision medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-16","doi":"https://doi.org/10.3390/jpm16040218","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4404728137","name":"How does artificial intelligence promote teaching innovation in basic education? Teaching experience from Macau, China","source":"openalex","abstract":"Artificial intelligence has transformed teachers’ teaching models. This article explores the application of artificial intelligence in basic education in Macao middle schools. This study adopts case analysis in qualitative research, using a total of eight cases from the innovative technology education platform of the Macau education and Youth Development Bureau. These data illustrate how Macao’s artificial intelligence technology promotes teaching innovation in basic education. These eight cases are closely related to the application of artificial intelligence in basic education in Macao. The survey results show that Macao’s education policy has a positive effect on teaching innovation in artificial intelligence education. In teaching practice, the school also cooperates with the government’s policy. The application of AI technology in teaching, students’ learning styles, changes in teachers’ roles, and new needs for teacher training are all influential.","url":"https://doi.org/10.24294/jipd10133","authors":["Sio Hong Teng","Fat Fai Ieong"],"tags":["China","Mathematics education","Psychology","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-26","doi":"https://doi.org/10.24294/jipd10133","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3030050272","name":"Monitoring Asynchrony During Invasive Mechanical Ventilation","source":"openalex","abstract":"Mechanical ventilation in critically ill patients must effectively unload inspiratory muscles and provide safe ventilation (ie, enhancing gas exchange, protect the lungs and the diaphragm). To do that, the ventilator should be in synchrony with patient's respiratory rhythm. The complexity of such interplay leads to several concerning issues that clinicians should be able to recognize. Asynchrony between the patient and the ventilator may induce several deleterious effects that require a proper physiological understanding to recognize and manage them. Different tools have been developed and proposed beyond the careful analysis of the ventilator waveforms to help clinicians in the decision-making process. Moreover, appropriate handling of asynchrony requires clinical skills, physiological knowledge, and suitable medication management. New technologies and devices are changing our daily practice, from automated real-time recognition of asynchronies and their distribution during mechanical ventilation, to smart alarms and artificial intelligence algorithms based on physiological big data and personalized medicine. Our goal as clinicians is to provide care of patients based on the most accurate and current knowledge, and to incorporate new technological methods to facilitate and improve the care of the critically ill.","url":"https://doi.org/10.4187/respcare.07404","authors":["José Aquino‐Esperanza","Leonardo Sarlabous","Candelaria de Haro","Rudys Magrans","Josefina López‐Aguilar","Lluís Blanch"],"tags":["Asynchrony (computer programming)","Medicine","Mechanical ventilation","Mechanical ventilator","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-26","doi":"https://doi.org/10.4187/respcare.07404","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4402573797","name":"Antiviral Effectiveness, Clinical Outcomes, and Artificial Intelligence Imaging Analysis for Hospitalized COVID‐19 Patients Receiving Antivirals","source":"openalex","abstract":"INTRODUCTION: There is still a lack of clinical evidence comprehensively evaluating the effectiveness of antiviral treatments for COVID-19 hospitalized patients. METHODS: A retrospective cohort study was conducted at Beijing You'An Hospital, focusing on patients treated with nirmatrelvir/ritonavir or azvudine. The study employed a tripartite analysis-viral dynamics, survival curve analysis, and AI-based radiological analysis of pulmonary CT images-aiming to assess the severity of pneumonia. RESULTS: Of 370 patients treated with either nirmatrelvir/ritonavir or azvudine as monotherapy, those in the nirmatrelvir/ritonavir group experienced faster viral clearance than those treated with azvudine (5.4 days vs. 8.4 days, p < 0.001). No significant differences were observed in the survival curves between the two drug groups. AI-based radiological analysis revealed that patients in the nirmatrelvir group had more severe pneumonia conditions (infection ratio is 11.1 vs. 5.35, p = 0.007). Patients with an infection ratio higher than 9.2 had nearly three times the mortality rate compared to those with an infection ratio lower than 9.2. CONCLUSIONS: Our study suggests that in real-world studies regarding hospitalized patients with COVID-19 pneumonia, the antiviral effect of nirmatrelvir/ritonavir is significantly superior to azvudine, but the choice of antiviral agents is not necessarily linked to clinical outcomes; the severity of pneumonia at admission is the most important factor to determine prognosis. Additionally, our findings indicate that pulmonary AI imaging analysis can be a powerful tool for predicting patient prognosis and guiding clinical decision-making.","url":"https://doi.org/10.1111/irv.70006","authors":["Yuan Gao","Yixi Dong","Qiushi Bu","Zhijie Gong","Wei Wang","Zhongkai Zhou","Yunyi Gao","Liwei Liu","Menghua Wu","Jiaying Zhang","Lianchun Liang","Hongjun Li","Mengxi Jiang","Zujin Luo","Yingmin Ma","Xinyu Zhang","Zhongjie Hu"],"tags":["Medicine","Ritonavir","Internal medicine","Pneumonia","Coronavirus disease 2019 (COVID-19)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-01","doi":"https://doi.org/10.1111/irv.70006","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7125480217","name":"Impact of artificial intelligence on empowering the future of nursing professionalism, educational and clinical advancements: an umbrella review on AI-driven transformation","source":"openalex","abstract":"Artificial Intelligence (AI) is rapidly transforming healthcare by augmenting clinical decision-making, streamlining workflows, and personalizing education and patient care. Nursing, as the largest healthcare workforce, stands at the forefront of this transformation. This review examines how AI-driven tools empower nursing professionalism, enhance educational models, and optimize clinical practice. A systematic umbrella concept analysis was conducted using PubMed, Scopus, CINAHL, and Web of Science databases. Literature published between 2010 and 2025 was reviewed. Eligible studies included original research, reviews, policy reports, and frameworks focusing on AI applications in nursing education, practice, and professional development. Data were synthesized thematically under three domains: professional identity, educational innovation, and clinical advancement. Sixty-five studies met inclusion criteria. Evidence suggests that AI supports professional autonomy through clinical decision support systems, predictive analytics, and digital documentation, reducing administrative burdens. In education, AI-enabled simulations, adaptive learning platforms, and virtual mentors enhance critical thinking and competency development. Clinically, AI improves patient monitoring, diagnostic accuracy, and personalized care delivery. However, ethical dilemmas, data privacy risks, and limited digital literacy remain significant barriers. AI offers transformative potential for strengthening nursing professionalism, integrating evidence-based education, and advancing patient-centered clinical practice. To harness these opportunities, investment in nurse-centered AI training, interdisciplinary collaboration, and policy frameworks is essential. Nursing must embrace AI as a partner technology to redefine future roles and leadership in digital healthcare ecosystems.","url":"https://doi.org/10.18203/2349-3259.ijct20260052","authors":["Mohammed Umar","Karthika S.","B. Kalyani","Lakshmi Priyadharshini V. R.","Ms. Divya Upreti","Pooja Saini","Reshma Tamang","Paramasivam Asari Geetha"],"tags":["Transformative learning","Health care","Autonomy","Nursing","Digital transformation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-22","doi":"https://doi.org/10.18203/2349-3259.ijct20260052","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4403218073","name":"Legal provisions on medical aid in dying encode moral intuition","source":"openalex","abstract":"In recent decades, many jurisdictions have moved toward legalizing euthanasia and assisted suicide, alongside a near-universal increase in public acceptance of medical aid in dying. Here, we draw on a comprehensive quantitative review of current laws on assisted dying, experimental survey evidence, and four decades of time-series data to explore the relationship between these legislative transitions and change in moral attitudes. Our analyses reveal that existing laws on medical aid in dying impose a common set of eligibility restrictions, based on the patient's age, decision-making capacity, prognosis, and the nature of their illness. Fulfillment of these eligibility criteria elevates public moral approval of physician-assisted death, equally in countries with (i.e., Spain) and without (i.e., the United Kingdom) assisted dying laws. Finally, historical records of public attitudes toward euthanasia across numerous countries uncovered anticipatory growth in moral approval leading up to legalization, but no accelerated growth thereafter. Taken together, our findings suggest that the enactment of medical aid in dying laws, and their specific provisions, crystallize patterns in moral intuition.","url":"https://doi.org/10.1073/pnas.2406823121","authors":["Ivar R. Hannikainen","Jorge Alberto Durán Suárez","Luis Espericueta","Maite Menéndez-Ferreras","David Rodríguez‐Arias"],"tags":["Legalization","Intuition","Legislature","Assisted suicide","Political science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-08","doi":"https://doi.org/10.1073/pnas.2406823121","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4389369785","name":"Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence–Ready Informatics Ecosystem for Radiation Oncology","source":"openalex","abstract":"In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.","url":"https://doi.org/10.1200/cci.23.00136","authors":["Danielle S. Bitterman","Michael F. Gensheimer","David A. Jaffray","Daniel A. Pryma","Steve Jiang","Olivier Morin","Jorge Barrios Ginart","Taman Upadhaya","Katherine A. Vallis","John M. Buatti","Joseph O. Deasy","Hsu‐Chin Hsiao","Caroline Chung","Clifton D. Fuller","Emily J. Greenspan","Kristy Cloyd-Warwick","Samir Courdy","Allen Mao","Jill S. Barnholtz‐Sloan","Ümit Topaloĝlu","Isaac Hands","Ian Maurer","May Terry","Walter J. Curran","Quynh‐Thu Le","Sorena Nadaf","Warren A. Kibbe"],"tags":["Radiation oncology","Informatics","Workflow","Health informatics","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-01","doi":"https://doi.org/10.1200/cci.23.00136","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4412715930","name":"Application Areas of Computer Vision and AI in Intelligent Automation Systems","source":"openalex","abstract":"The combination of artificial intelligence (AI) and computer vision (CV) has led to tremendous growth in industries such as manufacturing, healthcare, agriculture, transportation, and sports. BMW applications show how robots guided by vision can solve 97% of problems accurately, so product quality control drives it forward. AI-based medical imaging is expected to have a significant impact on healthcare. These technologies have led to streamlined assembly lines, and integrated analytics in manufacturing processes to increase efficiency and reduce costs. In healthcare, AI-based medical imaging is expected to play a major role in accurate diagnosis and personalized patient care. With the global market estimated to reach $45.13 billion by 2027, the safe, autonomous vehicle market will reach about $556.67 billion by 2026. Moreover, the impact of AI and CV technology is expected to affect the sports analytics market, which is estimated to reach $4.62 billion by 2025. Essentially, AI and CV are enabling intelligent automation and restructuring of businesses around the world.","url":"https://doi.org/10.1002/9781394302734.ch15","authors":["Vinod Kumar","Chander Prabha","Ajay Pal Singh","Raj Kumar"],"tags":["Automation","Computer science","Artificial intelligence","Computer vision","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.1002/9781394302734.ch15","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4384663192","name":"Stability of clinical prediction models developed using statistical or machine learning methods","source":"openalex","abstract":"Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (e.g., regression or machine learning). We raise the concern that many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks). We define four levels of model stability in estimated risks moving from the overall mean to the individual level. Through simulation and case studies of statistical and machine learning approaches, we show instability in a model's estimated risks is often considerable, and ultimately manifests itself as miscalibration of predictions in new data. Therefore, we recommend researchers always examine instability at the model development stage and propose instability plots and measures to do so. This entails repeating the model-building steps (those used to develop the original prediction model) in each of multiple (e.g., 1000) bootstrap samples, to produce multiple bootstrap models, and deriving (i) a prediction instability plot of bootstrap model versus original model predictions; (ii) the mean absolute prediction error (mean absolute difference between individuals' original and bootstrap model predictions), and (iii) calibration, classification, and decision curve instability plots of bootstrap models applied in the original sample. A case study illustrates how these instability assessments help reassure (or not) whether model predictions are likely to be reliable (or not), while informing a model's critical appraisal (risk of bias rating), fairness, and further validation requirements.","url":"https://doi.org/10.1002/bimj.202200302","authors":["Richard D Riley","Professor Gary S. Collins"],"tags":["Stability (learning theory)","Predictive modelling","Instability","Econometrics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-19","doi":"https://doi.org/10.1002/bimj.202200302","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4387369338","name":"Electrochemical methods for the determination of urea: Current trends and future perspective","source":"openalex","abstract":"Urea is the final product of nitrogen metabolism in mammals. In human beings, it is a main indicator of liver and kidney activity and a marker for hemodialysis treatments. Furthermore, urea is used in many industrial processes, in agriculture and in the farm industries. Thus, it is important to evaluate the level of this compound in biological fluids, in environmental matrices and in food samples. Electrochemical sensors represent an interesting tool for simple, rapid, in-situ, in-flow monitoring of urea. This review focuses on the recent advancements in electrochemical sensors and biosensors for urea determination. An overview of the existing electroanalytical approaches for urea determination is presented, and some new strategies are discussed, particularly those based on nanostructuration of the electrode surface. Finally, a brief description of the role that Artificial Intelligence could have in overcoming selectivity issues and speed of the analysis is also discussed.","url":"https://doi.org/10.1016/j.trac.2023.117345","authors":["Lorenzo Quadrini","Serena Laschi","Claudio Ciccone","Filippo Catelani","Ilaria Palchetti"],"tags":["Urea","Electrochemistry","Biological fluids","Biochemical engineering","Biosensor"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-05","doi":"https://doi.org/10.1016/j.trac.2023.117345","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4388818010","name":"The Shape of Medical Devices Regulation in the United Kingdom? Brexit and Beyond","source":"openalex","abstract":"The United Kingdom’s Medicines and Medical Devices Act (MMD Act) 2021 received royal assent on 11 February 2021. In its passage through parliament, as well as in the accompanying Explanatory Notes, the Act was framed by the government as a necessary post-Brexit bill. Yet prior to this, it was widely presumed that existing statutory instruments, enacted in 2019 to address medical devices regulation in anticipation of a ‘No Deal Brexit’, would provide the United Kingdom (UK) with the necessary legal framework through the transition period and beyond. The European Union (EU) exit legislation included provisions aimed at aligning domestic law with the new EU Medical Devices Regulation (EU MDR) and In Vitro Devices Regulation (EU IVDR) (which were initially due to be implemented during the EU exit transition period). However, just over a year later, at the end of 2020, while the 2021 Act was in the final stages of its parliamentary journey, legislation was introduced that reversed the provisions of the domestic medical device regulations concerning alignment for Great Britain (GB: England, Wales, and Scotland). The result is that the UK now has a dual system of regulation, with Northern Ireland (NI) being governed by the new EU MDR and EU IVDR and GB by older pre-Brexit (yet still EU-derived) law. This article is an attempt to analyse the complex situation now in existence regarding the regulation of medical devices in the UK. To this end, we focus on three main issues. First, we examine the difficulties and challenges presented by the dual system of regulation between NI and GB, highlighting the potentially far-reaching consequences of regulatory divergence between different UK jurisdictions. Second, we ask whether, in the rush to new legislation in 2021, opportunities to properly reform the approach to medical devices were missed. Finally, we look to the future, focusing on the recent Medicine and Healthcare products Regulatory Agency Consultation on the future of medical devices regulation in the UK and the challenges and opportunities that remain.","url":"https://doi.org/10.5204/lthj.3102","authors":["Muireann Quigley","Laura Downey","Zaina Mahmoud","Jean V. McHale"],"tags":["Brexit","Statutory law","Legislation","Parliament","European union"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-20","doi":"https://doi.org/10.5204/lthj.3102","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7171424249","name":"Comment on “Can Large Artificial Intelligence‐Based Linguistic Models Help to Obtain Information About Burning Mouth Syndrome?”","source":"openalex","abstract":"Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.","url":"https://doi.org/10.1111/odi.70446","authors":["Asmaa Abou‐Bakr","Fatma E. A. Hassanein"],"tags":["Computer science","Natural language processing","Burning mouth syndrome","Current (fluid)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-07-27","doi":"https://doi.org/10.1111/odi.70446","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7160510139","name":"Rationalization of reproduction - path towards dehumanization of humanity: Political, legal, and ethical aspects of using artificial intelligence in embryo selection","source":"openalex","abstract":"Artificial Intelligence is becoming an essential part of human life, and the creation of life is no exception. While using AI in biomedically assisted reproduction (BMAR) gives new hope to couples struggling with infertility, it also raises a difficult question: where are the ethical limits of letting a machine interfere with human conception? This paper argues that the growing “rationalization” of the reproductive process through AI selection is not just a technical upgrade, but a deeply political and complex issue. In this paper, we analyze the legal and ethical risks of this trend, specifically focusing on how the process of conception is becoming “dehumanized”. A major concern is that we are relying too much on algorithms that nobody truly understands. This “black box” nature of AI can easily undermine the autonomy of parents and the validity of their informed consent in one of the most private moments of their lives. The philosophical danger of treating human embryos as mere objects for selection, which directly threatens their inherent dignity, is also examined. With the goal of pointing out gaps in our current laws, the paper looks closely at the regulations in the Republic of Serbia. We find that existing legal solutions are not fully prepared for these new challenges. Finally, we propose new regulatory steps that would put the dignity of the embryo first. Through a prism of ethics and a warning against the loss of human empathy, this paper concludes that an uncritical use of AI in reproduction carries a serious risk of turning the act of creating a human being into a cold, mechanical procedure.","url":"https://doi.org/10.5937/spm96-64787","authors":["Bogdana Stjepanović"],"tags":["Dehumanization","Rationalization (economics)","Dignity","Reproduction","Autonomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.5937/spm96-64787","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2071608500","name":"Modelling the Longevity of Dental Restorations by means of a CBR System","source":"openalex","abstract":"The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.","url":"https://doi.org/10.1155/2015/540306","authors":["Ignacio Aliaga","Vicente Vera","Juan F. De Paz","Álvaro Enrique García Barbero","Mohd Saberi Mohamad"],"tags":["Amalgam (chemistry)","Longevity","Cluster analysis","Computer science","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-01-01","doi":"https://doi.org/10.1155/2015/540306","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7138843766","name":"Artificial intelligence in rehabilitation: a review of clinical effectiveness, real-world performance, safety, and equity across modalities and settings","source":"openalex","abstract":"Background: Rehabilitation faces a scale problem: millions who could benefit lack timely, effective services. Artificial intelligence (AI) and device-based modalities (e.g., robotics and VR) can extend reach and personalise care when validated, yet decision-makers lack a consolidated view of clinical usefulness, translation to practice, safety, equity, and cost. Methods: We conducted an umbrella review of reviews using a Population-Exposure-Outcome framework. Searches span biomedical, allied health, and engineering databases from inception to September 1, 2025. We distinguished AI-enabled (ML/DL) interventions from technology-assisted (no ML demonstrated) modalities and synthesised outcomes across impairment, activity, independence, usability/safety, equity, and economics. Findings: The most reproducible clinical signal is activity improvement for post-stroke upper limb with technology-assisted training (robotics with or without VR) that increases task-specific practice; effects on impairment and independence are inconsistent once dose is matched and assessors are blinded. Claims of non-inferiority are not established when prespecified margins and confidence-interval testing are absent, so parity is interpreted as no between-group advantage under those conditions. Across AI-enabled domains, a development-to-deployment performance drop is evident most notably for brain-computer-interface classifiers and computer-vision movement evaluation limiting immediate clinical impact. Imaging-based decision support (radiomics/CNN) is closer to practice but varies by software and site, requiring local calibration and impact evaluation before pathway change. Reported adverse events are generally mild, yet usability, adherence, equity, and cost are under-measured, particularly in home and hybrid delivery. Prediction-model and trial reporting frequently fall short of contemporary AI standards; representation skews toward high-income settings, and subgroup performance is seldom reported. Conclusion: An adjunct-first posture is warranted. Adoption should be gated by minimum clinically important difference-anchored benefit under dose symmetry and blinded assessment; external, multi-site validation with declared lab-to-clinic performance loss; subgroup fairness with mitigation; decision-grade economic value; interoperability; and readiness for regulation, change control, and cybersecurity. Priorities include pragmatic, multi-site, assessor-blinded, dose-matched trials; standardised safety/usability capture for home use; and a public, living evidence atlas. AI can expand rehabilitation when held to clinical standards that matter to patients and services. With clear adoption gates and continuous post-market monitoring, systems can extend access and independence without sacrificing rigour, safety, equity, or fairness.","url":"https://doi.org/10.3389/fdgth.2026.1737957","authors":["Nafisa Abdalla","Rabie Adel El Arab","Amany Abdrbo","Mohammad Almari","Mohammed Yahya Ayoub","Bilal Alsaaideh","Mohammad Suhail Dagamseh","Wesam Taher Almagharbeh","Fuad H. Abuadas","Mohammad S. Abu Mahfouz","Mastoura Khames Gaballah"],"tags":["Modalities","Artificial intelligence","Psychological intervention","Robotics","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-18","doi":"https://doi.org/10.3389/fdgth.2026.1737957","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7117648215","name":"Physics-informed artificial intelligence with splines for modeling advection–diffusion–reaction under dynamic boundaries","source":"openalex","abstract":"This paper proposes Physics-Informed Deep Operator Networks (PI-DeepONets) for rapid simulation of Advection–Diffusion–Reaction (ADR) systems with time-variable boundary conditions. These are viewed as inputs of the system and each family of inputs can be represented by a set of parameters. It is shown that, in practice, PI-DeepONets may not be able to model accurately the system of Partial Differential Equations (PDEs) for any combination of those parameterized inputs. Therefore, a new distributed architecture is proposed, which combines specialized PI-DeepONets, each being dedicated to represent one PDE. Furthermore, a mixture of experts allows to simulate multiple PI-DeepONets for which the input is a combination of several parameterized base functions. A model of an adsorption column is used to evaluate the relevance of the method. The Mixture of Experts PI-DeepONets with complex inputs such as splines, reaches a Mean Arctangent Absolute Percentage Error around 0.2 comparable to that of physics informed neural networks with constant inputs. The accuracy is improved by 20 % compared to distributed DeepONets, whereas a conventional DeepONet is unable to converge to the true solution. The results highlight the ability of a mixture of experts PI-DepeOnets to deliver an accurate and fast simulations of chemical reactors described by partial differential equations under complex and time-variable boundary conditions. The simulations can be completed in less than 10 ms compared to several minutes for traditional simulations methods, therefore opening the door to real time predictions and optimization.","url":"https://doi.org/10.1016/j.dte.2025.100083","authors":["Romain Belmonte","Jean‐Yves Dieulot","Mattia Galanti","Martin van Sint Annaland"],"tags":["Parameterized complexity","Computer science","Boundary (topology)","Artificial neural network","Set (abstract data type)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-31","doi":"https://doi.org/10.1016/j.dte.2025.100083","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4402428776","name":"Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No","source":"openalex","abstract":"OBJECTIVE: The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients' and physicians' education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers. METHODS: We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot. RESULTS: Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being \"ultrasound-guided embryo transfer\" in 7/9 (77.8%) chatbots, \"single embryo transfer\" in 4/9 (44.4%) and \"use of a soft catheter\" in 2/9 (22.2%), whereas some controversial responses like \"preimplantation genetic testing\" appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like \"endometrial receptivity assay\", \"assisted hatching\" and \"time-lapse incubator\". CONCLUSIONS: Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.","url":"https://doi.org/10.5935/1518-0557.20240050","authors":["Argyrios Kolokythas","Michael H. Dahan"],"tags":["Context (archaeology)","Artificial intelligence","Computer science","Psychology","Test (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.5935/1518-0557.20240050","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2942087467","name":"Comparison of blockchain platforms: a systematic review and healthcare examples","source":"openalex","abstract":"OBJECTIVES: To introduce healthcare or biomedical blockchain applications and their underlying blockchain platforms, compare popular blockchain platforms using a systematic review method, and provide a reference for selection of a suitable blockchain platform given requirements and technical features that are common in healthcare and biomedical research applications. TARGET AUDIENCE: Healthcare or clinical informatics researchers and software engineers who would like to learn about the important technical features of different blockchain platforms to design and implement blockchain-based health informatics applications. SCOPE: Covered topics include (1) a brief introduction to healthcare or biomedical blockchain applications and the benefits to adopt blockchain; (2) a description of key features of underlying blockchain platforms in healthcare applications; (3) development of a method for systematic review of technology, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, to investigate blockchain platforms for healthcare and medicine applications; (4) a review of 21 healthcare-related technical features of 10 popular blockchain platforms; and (5) a discussion of findings and limitations of the review.","url":"https://doi.org/10.1093/jamia/ocy185","authors":["Tsung-Ting Kuo","Hugo Zavaleta Rojas","Lucila Ohno‐Machado"],"tags":["Blockchain","Computer science","Health care","Data science","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-08","doi":"https://doi.org/10.1093/jamia/ocy185","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7167360095","name":"Nurses’ knowledge, attitudes, and perceived challenges toward artificial intelligence applications in patient care: a descriptive-analytical cross-sectional study","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into healthcare systems; however, nurses' knowledge, attitudes, and perceived challenges play a crucial role in its adoption in patient care. This study aimed to assess nurses' knowledge, attitudes, and perceived challenges toward AI, examine the relationships among these variables, and explore their associations with demographic characteristics and prior AI training. METHODS: A descriptive analytical cross-sectional study was conducted among 107 nurses working in intensive care, medical, and surgical units at Zagazig University Hospital. A purposive sampling technique was used. Data were collected over two months using structured instruments during morning shifts. RESULTS: Most participants were aged 25-34 years (55.1%), male (65.4%; reflecting the accessible sample composition), and held bachelor's degrees (68.2%), with nearly half (49.5%) having 5-10 years of clinical experience. Overall, 68.2% of nurses achieved satisfactory knowledge scores, whereas 88.8% demonstrated positive attitudes toward AI applications. Perceived challenges were mainly related to technical and ethical concerns, particularly the need for continuous system updates, cybersecurity risks, and implementation costs. A statistically significant weak negative correlation was found between nurses' knowledge and attitudes toward AI (r = -0.195, p = 0.044). No significant correlations were observed between knowledge and perceived challenges (r = -0.162, p = 0.095) or between attitudes and perceived challenges (r = 0.142, p = 0.145). Previous AI-related training was significantly associated with more positive attitudes toward AI (p = 0.019), whereas no significant associations were found with knowledge or perceived challenges. Educational level, workplace, and years of experience were not significantly associated with nurses' knowledge, attitudes, or perceived challenges. CONCLUSION: Nurses demonstrated a satisfactory knowledge and generally positive attitudes toward AI applications in patient care, while perceiving moderate implementation challenges. Although previous AI-related training was associated with more positive attitudes, no significant associations were found with knowledge or perceived challenges. The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use. Further educational initiatives are needed to enhance nurses' preparedness for AI integration in clinical practice.","url":"https://doi.org/10.1186/s12912-026-04966-5","authors":["Rehab Ragab Bayoumi Elsayed","Rehab Ragab Bayoumi Elsayed","Ahmed Mohamed Elmarakby Nagy","Eman Sobhy Elsaid Hussein","Reham Adel Ebada Elsayed","Reham Adel Ebada Elsayed","Reda Mohamed El-Sayed Ramadan"],"tags":["Nonprobability sampling","Nursing management","Medicine","Sample (material)","Health informatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-07-04","doi":"https://doi.org/10.1186/s12912-026-04966-5","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4388168456","name":"Deep Learning Networks","source":"openalex","abstract":"This textbook presents multiple facets of deep learning networks and their design, development, and deployment in artificial intelligence","url":"https://doi.org/10.1007/978-3-031-39244-3","authors":["Jayakumar Singaram","S. S. Iyengar","Azad M. Madni"],"tags":["Software deployment","Deep learning","Computer science","Artificial intelligence","Software engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-11-01","doi":"https://doi.org/10.1007/978-3-031-39244-3","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7124545010","name":"Can artificial intelligence debunk health misinformation more effectively than humans? A three‐dimensional persuasion analysis","source":"openalex","abstract":"Abstract Health misinformation presents significant challenges to public well‐being, making effective debunking strategies crucial. While artificial intelligence (AI) shows potential in generating debunking texts, its persuasiveness compared to human‐generated content remains underexplored. Drawing on Aristotle's three modes of persuasion, this study investigated the persuasive effectiveness of AI versus human‐generated health debunking texts through three complementary studies. Our findings reveal a novel pattern: AI‐generated texts significantly outperformed human texts in pathos (emotional appeal) and logos (logical argument) but underperformed in ethos (credibility), with all three dimensions serving as significant mediators of persuasiveness. More importantly, we demonstrate that source labeling effects are not uniform. While “AI‐written” labels reduced perceived persuasiveness for both AI and human texts, this algorithmic aversion was attenuated when argument quality (logos) was made salient. These findings advance persuasion theory by revealing that classical rhetoric operates differently for AI versus human sources and that algorithmic aversion is context‐dependent rather than universal. The results offer both theoretical insights into human‐AI communication and practical guidance for deploying AI in health misinformation mitigation.","url":"https://doi.org/10.1002/asi.70049","authors":["Xinyu Ji","Xing Zhang"],"tags":["Misinformation","Persuasion","Psychology","Health communication","Rhetoric"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-16","doi":"https://doi.org/10.1002/asi.70049","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7124242635","name":"Examining the Impact of Artificial Intelligence Technology on Sustainable Development in Highway Maintenance Industry: A Structural Equation Modelling Approach","source":"openalex","abstract":"In the field of the road transportation industry, quantitative research on the relationship between artificial intelligence (AI) technology and corporate sustainable development is relatively scarce. This disparity has led to discussions about whether artificial intelligence technology can truly promote the sustainable development level of the highway maintenance industry. Therefore, this study aims to quantify the relationship between artificial intelligence technology and the sustainable development of the highway maintenance industry, and to analyze the reasons behind the current controversies. The research results show: (1) Each exogenous variable has an impact on sustainable development, although the degree of influence varies, especially the economic development level (ED) has the strongest direct effect on sustainable development, followed by the level of market demand (MD), the level of policy support (PS), and the level of enterprise capital (EC); (2) Moderating variables can enhance this direct impact, among which the moderating effect of ED on the relationship between ED and sustainable development is the strongest; (3) Artificial intelligence technology has different impacts on enterprises at different positions in the industrial chain, thereby explaining the controversy over whether to adopt it or not. These conclusions highlight the value of artificial intelligence technology and provide a reasonable explanation for the existing controversies in the industry and research field.","url":"https://doi.org/10.3390/su18020889","authors":["Jizhao Zhou","Chenyang Wang","Jin Guo","Peng Qin"],"tags":["Sustainable development","Structural equation modeling","Field (mathematics)","Capital (architecture)","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-15","doi":"https://doi.org/10.3390/su18020889","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7169884846","name":"Research and Innovation in Case Management: A Decade in Review (2016–2026)","source":"openalex","abstract":"ABSTRACT: Research and innovation are foundational to the transformation of professional case management and utilization review (CM/UR). Over the past decade, CM/UR has evolved significantly to address care complexity, workforce challenges, regulatory change, and administrative burden. Innovative workforce models, expanded community coordination, and advances in analytics, automation, artificial intelligence, and interoperability have improved efficiency, compliance, continuity of care, and patient outcomes while supporting value-based care. As regulatory requirements continue to evolve, sustained research and agile innovation remain essential to optimizing CM/UR delivery while maintaining a strong person-centered focus.","url":"https://doi.org/10.1097/ncm.0000000000000890","authors":["Heaven Provo"],"tags":["Workforce","Interoperability","Agile software development","Business","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-07-10","doi":"https://doi.org/10.1097/ncm.0000000000000890","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4248219478","name":"Word sense disambiguation by selecting the best semantic type based on Journal Descriptor Indexing: Preliminary experiment","source":"openalex","abstract":"Abstract An experiment was performed at the National Library of Medicine® (NLM®) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM's MetaMap system, which maps free text to terms corresponding to concepts in NLM's Unified Medical Language System® (UMLS®) Metathesaurus®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the basis for selecting the best meaning that is correlated to UMLS semantic types (STs) assigned to ambiguous concepts in the Metathesaurus. For example, the ambiguity transport has two meanings: “Biological Transport” assigned the ST Cell Function and “Patient transport” assigned the ST Health Care Activity. A JDI‐based methodology can analyze text containing transport and determine which ST receives a higher score for that text, which then returns the associated meaning, presumed to apply to the ambiguity itself. We then present an experiment in which a baseline disambiguation method was compared to four versions of JDI in disambiguating 45 ambiguous strings from NLM's WSD Test Collection. Overall average precision for the highest‐scoring JDI version was 0.7873 compared to 0.2492 for the baseline method, and average precision for individual ambiguities was greater than 0.90 for 23 of them (51%), greater than 0.85 for 24 (53%), and greater than 0.65 for 35 (79%). On the basis of these results, we hope to improve performance of JDI and test its use in applications.","url":"https://doi.org/10.1002/asi.20257","authors":["Susanne M. Humphrey","Willie J. Rogers","Halil Kilicoglu","Dina Demner‐Fushman","Thomas C. Rindflesch"],"tags":["Unified Medical Language System","Ambiguity","Information retrieval","Computer science","Set (abstract data type)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2005-11-03","doi":"https://doi.org/10.1002/asi.20257","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4288693944","name":"A novel deep convolutional neural network algorithm for surface defect detection","source":"openalex","abstract":"Abstract The surface defect detection (SDD) problem is one of the crucial techniques during production process, so it has become a key research area to control the quality of industrial products, which has been increasingly of greater interest to the researchers especially with the rapid development of artificial neural networks technology in recent years. Therefore, this paper proposes a novel deep convolutional neural network algorithm aiming at SDD. Firstly, a dense cross-stage partial Darknet backbone network is designed for feature extraction by optimizing cross-stage partial Darknet through the idea of dense connections, which can, not only enhance feature reuse but also greatly alleviate the overfitting issue. Secondly, a new cross-stage hierarchy module is presented combining the cross-stage feature fusion strategy and depthwise separable convolution technique for each node of the path aggregated feature pyramid network (PAN). Finally, an efficient channel attention (ECA) mechanism is introduced in PAN to construct a novel ECA PAN. The experimental results on three surface defect datasets show that the mean average precision of this network is 2.63, 5.48, and 1.16$\\%$ which is higher than that of the baseline network, respectively. The proposed network outperforms not only the classical models but state-of-the-art models, which indicates the proposed algorithm can achieve higher accuracy and speed with fewer calculation parameters. And what is more, the proposed algorithm also has outstanding generalization ability.","url":"https://doi.org/10.1093/jcde/qwac071","authors":["Dehua Zhang","Xinyuan Hao","Linlin Liang","Wei Liu","Chunbin Qin"],"tags":["Overfitting","Convolutional neural network","Computer science","Artificial intelligence","Feature (linguistics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-29","doi":"https://doi.org/10.1093/jcde/qwac071","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7133358115","name":"Generative artificial intelligence in optimizing the quality of cancer care: potential, limitations, and future directions of development for Large Language Models. A narrative literature review","source":"openalex","abstract":"This literature review included scientific articles, published between 2022 and 2025, indexed in PubMed, Scopus, and Proquest. The article investigates the potential of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), to improve the quality of cancer care. LLMs have demonstrated effectiveness in patient education by simplifying complex medical terminology and tailoring content to the user’s level of understanding. LLMs also assist physicians in clinical decision-making by analyzing medical data and supporting adherence to the latest guidelines. However, expert oversight is still necessary due to the risk of error. For cancer prevention, LLMs promote healthy lifestyle adoption, participation in screening programs, and vaccination. They also play an important role in reducing inequities in access to information. Another key feature of LLMs is their ability to translate complex diagnostic reports into patient-friendly language. LLMs have also shown promise in counteracting cancer-related misinformation. However, the article identifies certain LLMs limitations, such as model hallucination, incomplete personalization, and unresolved legal liability concerns. The article emphasizes ethical dilemmas, particularly those related to patient autonomy and the risk of dehumanizing care. For future progress, the article emphasizes the need to integrate LLMs into e-health systems and to develop specialized models supported by interdisciplinary teams.","url":"https://doi.org/10.29316/hpc/218352","authors":["Joanna Gotlib-Małkowska","Kinga Włodarczyk","Paweł Koczkodaj","Marta Hreńczuk","Adrian Nowakowski","Mariusz Pańczyk","Ilona Cieślak"],"tags":["Terminology","Dehumanization","Quality (philosophy)","Narrative","Autonomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-03","doi":"https://doi.org/10.29316/hpc/218352","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4399594884","name":"When geoscience meets generative AI and large language models: Foundations, trends, and future challenges","source":"openalex","abstract":"Abstract Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.","url":"https://doi.org/10.1111/exsy.13654","authors":["Abdenour Hadid","Tanujit Chakraborty","D. Busby"],"tags":["Computer science","Generative grammar","Data science","Field (mathematics)","Generative model"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-06-11","doi":"https://doi.org/10.1111/exsy.13654","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4414110320","name":"Printed sensing human-machine interface with individualized adaptive machine learning","source":"openalex","abstract":"Developing intelligent robots with integrated sensing capabilities is critical for advanced manufacturing, medical robots, and embodied intelligence. Existing robotic sensing technologies are limited to recording of acceleration, driving torque, pressure feedback, and so on. Expanding and integrating with the multimodal sensors to mimic and even surpass the human feeling is substantially underdeveloped. Here, we introduce a printed soft human-machine interface consisting of an e-skin-enabled gesture recognitions with feedback stimulus and a soft robot with multimodal perception of contact pressure, temperature, thermal conductivity, and electrical conductivity. The sensing e-skin with adaptive machine learning was able to decode and classify the hand gestures with re-wearable convenience and individual's differences. The soft interface provides the bidirectional communications between robotics and human bodies in the close-loop. This work could substantially extend the robotic intelligence and pave the way for more practical applications.","url":"https://doi.org/10.1126/sciadv.adw3725","authors":["Guohui Wang","Yao Tang","Xinran Luo","Shengdi Lu","Yiru Zhou","Yi Lu","Guangyang Sun","Pei Liu","Jiayu Ning","Hua Jiang","Ke Hu","Hongzhen Liu","Wenqi Song","You Yu"],"tags":["Computer science","Gesture","Artificial intelligence","Robot","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-10","doi":"https://doi.org/10.1126/sciadv.adw3725","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4417488987","name":"Advanced 3D Modeling and Bioprinting of Human Anatomical Structures: A Novel Approach for Medical Education Enhancement","source":"openalex","abstract":"Current challenges in anatomical teachings, such as cadaver shortages, ethical limitations, and restricted access to pathological specimens, are increasingly being mitigated by advancing medical technologies, and among these are three-dimensional modeling technology and multi-material bioprinting. These innovations could facilitate a deeper understanding of complex anatomical components while encouraging an interactive learning environment that accommodates diverse educational needs. These technologies have the capacity to transform anatomy education, yielding better-prepared healthcare practitioners. Combining artificial intelligence with acquired medical images makes it easier to reconstruct anatomy and saves time while still being very accurate. This review seeks to thoroughly assess the current landscape of advanced three-dimensional printing, multi-material bioprinting, and related technologies used in anatomical education. It aims to consolidate evidence related to their educational effectiveness and to outline potential pathways for clinical applications and research development.","url":"https://doi.org/10.3390/app16010005","authors":["Sergio Castorina","Stefano Puleo","Caterina Crescimanno","Salvatore Pezzino"],"tags":["3D bioprinting","Computer science","Human anatomy","3d model","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-19","doi":"https://doi.org/10.3390/app16010005","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4405949375","name":"Assessing ChatGPT responses to common patient questions regarding total ankle arthroplasty","source":"openalex","abstract":"Purpose: Artificial Intelligence is becoming increasingly integrated into healthcare, making it essential to assess its potential as a reliable information source for patient queries in the ambit of orthopaedic surgery. In literature, it is being employed in foot and ankle surgery and total hip arthroplasty. The aim of the present study was to evaluate the ability of Chat Generative Pretrained Transformer (ChatGPT) version 3.5 to give accurate, complete and comprehensive responses to the most common questions which are usually asked by the patient to the surgeon regarding total ankle arthroplasty. Methods: Ten most common questions were selected by two ankle surgeons and then ChatGPT was used to answer these questions. The responses were analyzed using an accuracy score and the modified DISCERN score to assess clarity. WordCalc software package (educational-level indices) was used to assess the readability of the responses. Results: Most of ChatGPT's responses were considered excellent not requiring clarification or satisfactory requiring minimal clarification. Indeed, the accuracy score was 2, suggesting that the overall responses were satisfactory requiring minimal clarification, and DISCERN score mean was 51, which is considered good-fair. Conclusions: ChatGPT demonstrates potential as a tool for responding to common patient questions related to total ankle arthroplasty, offering clear and mostly accurate information. While its current performance is based on the available literature, ongoing advancements in artificial intelligence may further enhance its utility in healthcare communication. However, further studies are required to evaluate its role more precisely in patient information and clinical settings. Levels of Evidence: Not applicable.","url":"https://doi.org/10.1002/jeo2.70138","authors":["Elena Artioli","Francesca Veronesi","Antonio Mazzotti","Silvia Brogini","Simone Ottavio Zielli","Gianluca Giavaresi","Cesare Faldini"],"tags":["CLARITY","Foot and ankle surgery","Readability","Ankle","Orthopedic surgery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-31","doi":"https://doi.org/10.1002/jeo2.70138","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7143486389","name":"Artificial intelligence anxiety, digital well-being, and future career concerns among engineering and information technology students in Jordan","source":"openalex","abstract":"Introduction The rapid advancement of artificial intelligence (AI) is fundamentally transforming educational and employment landscapes, generating increasing psychological concerns among students in technology-intensive fields. This study examines AI-related anxiety, digital well-being, and career uncertainty among engineering and information technology (IT) students, with a focus on their prevalence, interrelationships, and demographic variations. Methods A cross-sectional quantitative design was employed using a structured survey administered to 820 undergraduate students from four Jordanian universities. Standardized measures were used to assess AI anxiety, digital well-being, and career-related concerns. Statistical analyses included descriptive statistics, correlation analysis, and group comparisons based on gender and academic discipline. Results The findings indicated elevated levels of AI anxiety ( M = 5.26, SD = 0.32), low levels of digital well-being ( M = 1.75, SD = 0.20), and moderate levels of career concerns ( M = 4.07, SD = 0.34). AI anxiety was strongly negatively correlated with digital well-being ( r = −0.849, p < 0.01) and positively correlated with career concerns ( r = 0.680, p < 0.01). Female students reported significantly higher AI anxiety and career concerns than male students ( p < 0.001). Additionally, IT students exhibited higher levels of AI anxiety and career uncertainty compared to engineering students ( p < 0.001). Discussion These findings highlight the psychological impact of AI integration on students, emphasizing the need for targeted AI literacy programs, digital well-being interventions, and career guidance strategies. Addressing gender disparities and discipline-specific differences is essential to enhance students’ resilience, adaptability, and readiness for an AI-driven labor market.","url":"https://doi.org/10.3389/frai.2026.1598741","authors":["Mais Al-Nasa’h","Luae Al-Tarawneh","Ola Abd alkareem alhwayan"],"tags":["Anxiety","Psychology","Medical education","Engineering education","Information technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-30","doi":"https://doi.org/10.3389/frai.2026.1598741","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7116896729","name":"Understanding implementation science in medical radiation sciences","source":"openalex","abstract":"OBJECTIVES: Radiography, like many allied health professions, faces persistent challenges in translating evidence and innovation into routine clinical practice. Despite a strong foundation in evidence-based practice, the adoption of new technologies, protocols, and models of care is often inconsistent, delayed, or un-sustained. This paper introduces Implementation science, which offers a key, yet underutilised approach for advancing radiographic practice by focusing on how evidence-based interventions are adopted, integrated, and sustained in real-world settings. KEY FINDINGS: We present a conceptual overview of implementation science frameworks with particular relevance to radiography e.g. technology, devices and service improvement. Key frameworks considered include the Consolidated Framework for Implementation Research (CFIR), Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM), the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, the Theoretical Domains Framework (TDF), and Normalisation Process Theory (NPT). Each is appraised for its focus, strengths, and applicability to common implementation challenges in radiography. The frameworks highlight different but complementary perspectives, for example CFIR and TDF emphasise multilevel determinants of behaviour and RE-AIM structures evaluation of implementation outcomes. Applied examples from radiography and allied health illustrate how these approaches can be used to diagnose barriers, design strategies, and evaluate implementation efforts. CONCLUSION: Implementation science provides a rich methodological and theoretical toolkit for strengthening radiography research. By applying these frameworks, studies can move beyond questions of clinical efficacy to address the practical realities of translation, adoption and sustainability. IMPLICATIONS FOR PRACTICE: Embedding implementation science within radiographic research, practice, and education can support more systematic and context-sensitive translation. This shift enables the profession to progress from demonstrating clinical potential to delivering sustained improvements in service delivery, patient safety, and professional practice.","url":"https://doi.org/10.1016/j.radi.2025.103288","authors":["F. Manning","A. Hancock","R. Meertens","J. Ede"],"tags":["Medical physicist","Medical radiation","Medical physics","Engineering ethics","Medical science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-23","doi":"https://doi.org/10.1016/j.radi.2025.103288","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4415993116","name":"Leveraging imperfection with MEDLEY: a multi-model approach harnessing bias in medical AI","source":"openalex","abstract":"Bias in medical artificial intelligence is conventionally viewed as a defect that requires elimination. However, human reasoning inherently incorporates biases shaped by education, culture, and experience, suggesting their presence may be inevitable and potentially valuable. We propose MEDLEY (Medical Ensemble Diagnostic system with Leveraged diversitY), a conceptual framework that orchestrates multiple AI models while preserving their diverse outputs rather than collapsing them into a consensus. Unlike traditional approaches that suppress disagreement, MEDLEY documents model-specific biases as potential strengths and treats hallucinations as provisional hypotheses for clinician verification. A proof-of-concept demonstrator for differential diagnosis was developed using over 30 large language models, preserving both consensus and minority views, rendering diagnostic uncertainty and latent biases transparent to support clinical oversight. While not yet a validated clinical tool, the demonstration illustrates how structured diversity can enhance medical reasoning under the supervision of clinicians. By reframing AI imperfection as a resource, MEDLEY offers a paradigm shift that opens new regulatory, ethical, and innovation pathways for developing trustworthy medical AI systems.","url":"https://doi.org/10.3389/frai.2026.1701665","authors":["Farhad Abtahi","Mehdi Astaraki","Fernando Seoane"],"tags":["Cognitive reframing","Computer science","Artificial intelligence","Trustworthiness","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-04","doi":"https://doi.org/10.3389/frai.2026.1701665","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7170152263","name":"Meaningful oversight of medical AI beyond human in the loop","source":"openalex","abstract":"Human oversight of medical AI is increasingly required, but clinician presence alone does not make oversight meaningful. We propose four interlocking conditions—epistemic capacity, cognitive space, decisional authority, and intervention effectiveness—that determine whether human judgment can function as a safety mechanism. Embedding these conditions across procurement, deployment, monitoring, and decommissioning can turn oversight into an operational patient-safety capability across predictive, generative, semi-autonomous, and agentic systems.","url":"https://doi.org/10.1038/s41746-026-02971-1","authors":["Davy van de Sande","Nicoleta Economou‐Zavlanos","Michel E. van Genderen"],"tags":["Human-in-the-loop","Function (biology)","Computer science","Intervention (counseling)","Control (management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-07-23","doi":"https://doi.org/10.1038/s41746-026-02971-1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7159795434","name":"The Impact of Generative Artificial Intelligence Use on Perceived English Learning Achievement: The Roles of Use Behavior and Task–Technology Fit","source":"openalex","abstract":"The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students' perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) theory, this study investigates how undergraduate students' use of GAI relates to perceived English learning achievement and under what conditions these associations are amplified. Using covariance-based structural equation modeling (CB-SEM), data from 537 undergraduate students across five public universities in China were analyzed. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and artificial intelligence self-efficacy significantly predict GAI use. In turn, use behavior mediates their relationships with perceived English learning achievement. Task-Technology Fit further moderates the link between use behavior and learning achievement, with stronger associations observed when GAI functionalities are perceived as closely aligned with task requirements. These results highlight the importance of use behavior and task alignment in explaining how GAI is associated with students' perceived English learning achievement and extend technology acceptance research within AI-supported language learning contexts.","url":"https://doi.org/10.3390/bs16050643","authors":["Zhongrui Wang","Shibao Guo"],"tags":["Psychology","Perception","Task (project management)","Generative grammar","Structural equation modeling"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-25","doi":"https://doi.org/10.3390/bs16050643","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7117486807","name":"Cyber–Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients","source":"openalex","abstract":"Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy.","url":"https://doi.org/10.3390/app16010318","authors":["Emilia Mikołajewska","Urszula Rogalla-Ładniak","Jolanta Masiak","Ewelina Panas","Dariusz Mikołajewski"],"tags":["Wearable computer","Field (mathematics)","Health care","Digital health","Medical research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-28","doi":"https://doi.org/10.3390/app16010318","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7161317119","name":"AI-PACE: a framework for integrating AI into medical education","source":"openalex","abstract":"Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016-2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom's Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.","url":"https://doi.org/10.1038/s41746-026-02768-2","authors":["Scott P. McGrath","Katherine K. Kim","Karnjit Johl","Haibo Wang","Nick Anderson"],"tags":["Medical education","Psychological intervention","Continuing medical education","Psychology","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-16","doi":"https://doi.org/10.1038/s41746-026-02768-2","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3038099276","name":"Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?","source":"openalex","abstract":"OBJECTIVE: The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. MATERIALS AND METHODS: Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected from a state-wide reporting system. Generalizability was evaluated on different and independent hospital-level reporting system. Concepts were extracted from report narratives using the UMLS Metathesaurus, and their relevance and frequency were used as semantic features. Performance was evaluated by F-score, Hamming loss, and exact match score and was compared with SVM ensembles using bag-of-words (BOW) features on 3 testing datasets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent =6000/5950). RESULTS: SVMs using semantic features met or outperformed those based on BOW features to identify 10 different incident types (F-score [semantics/BOW]: benchmark = 82.6%/69.4%; original = 77.9%/68.8%; independent = 78.0%/67.4%) and extreme-risk events (F-score [semantics/BOW]: benchmark = 87.3%/87.3%; original = 25.5%/19.8%; independent = 49.6%/52.7%). For incident type, the exact match score for semantic classifiers was consistently higher than BOW across all test datasets (exact match [semantics/BOW]: benchmark = 48.9%/39.9%; original = 57.9%/44.4%; independent = 59.5%/34.9%). DISCUSSION: BOW representations are not ideal for the automated identification of incident reports because they do not account for text semantics. UMLS semantic representations are likely to better capture information in report narratives, and thus may explain their superior performance. CONCLUSIONS: UMLS-based semantic classifiers were effective in identifying incidents by type and extreme-risk events, providing better generalizability than classifiers using BOW.","url":"https://doi.org/10.1093/jamia/ocaa082","authors":["Ying Wang","Enrico Coiera","Farah Magrabi"],"tags":["Unified Medical Language System","Computer science","Support vector machine","Benchmark (surveying)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-27","doi":"https://doi.org/10.1093/jamia/ocaa082","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4296184832","name":"Flourishing Ethics and identifying ethical values to instill into artificially intelligent agents","source":"openalex","abstract":"Abstract The present paper uses a Flourishing Ethics analysis to address the question of which ethical values and principles should be “instilled” into artificially intelligent agents. This is an urgent question that is still being asked seven decades after philosopher/scientist Norbert Wiener first asked it. An answer is developed by assuming that human flourishing is the central ethical value, which other ethical values, and related principles, can be used to defend and advance. The upshot is that Flourishing Ethics can provide a common underlying ethical foundation for a wide diversity of cultures and communities around the globe; and the members of each specific culture or community can add their own specific cultural values—ones which they treasure, and which help them to make sense of their moral lives.","url":"https://doi.org/10.1111/meta.12583","authors":["Nesibe Kantar","Terrell Ward Bynum"],"tags":["Flourishing","Value (mathematics)","Environmental ethics","Engineering ethics","Treasure"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-17","doi":"https://doi.org/10.1111/meta.12583","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7164344883","name":"Nanomedicine in 2026: Illustrative Quantitative Analyses of EPR Heterogeneity, Clinical Trial Attrition, and Emerging Horizons for Active Nanotherapeutics","source":"openalex","abstract":"2026 is a turning point for nanomedicine, marking the field's transition from decades of preclinical promise toward tangible clinical impact. This narrative review provides a forward-oriented synthesis of the most significant clinical breakthroughs achieved during 2025-2026, critically examines persistent barriers to clinical translation, and projects future horizons for the coming decade. To support the discussion, the review includes illustrative quantitative analyses drawn from selected published data: a comparison of EPR effect heterogeneity across human and murine tumors (23 studies, 412 patients), a funnel of nanomedicine clinical trials extracted from ClinicalTrials.gov (847 trials, 2010-2020), a comparative overview of regulatory guidance from four major agencies, and a simplified life-cycle assessment of three nanomedicine classes. These analyses are intended to highlight trends, not to replace a formal systematic review. We identify four important clinical advances: first Phase II data for hafnium oxide nanoparticle radioenhancers in inoperable lung cancer; logic-gated STING-agonistic nanoparticles for metastasis-specific immunotherapy; ultrasmall silica nanoparticles that remodel suppressive tumor microenvironments independent of a drug cargo; and CNM-Au8 gold nanocrystals advancing toward regulatory submission for amyotrophic lateral sclerosis. Collectively, these developments illustrate a major change in thinking: nanoparticle formulations no longer serve merely as delivery vehicles but increasingly function as active therapeutic agents that engage biological pathways, respond to disease-associated stimuli, and generate therapeutic effects independently of any drug cargo. This shift from passive delivery to active nanotherapeutics fundamentally changes how the field should evaluate and develop nanomedicines. Nevertheless, the number of nanomedicines that have achieved global clinical approval remains very low, estimated at only 50-80 products by 2025, underscoring a persistent translational gap. We analyze principal obstacles to clinical success, including the limited predictive validity of the enhanced permeability and retention (EPR) effect in humans, batch-to-batch manufacturing variability, safety concerns arising from bio-corona formation and organ accumulation, and the absence of harmonized regulatory frameworks. Looking forward, we identify emerging horizons: AI-driven digital twins for predictive manufacturing, carrier-free self-assembled nanomedicines from natural small molecules, nanotheranostic platforms that integrate therapy with real-time imaging, and sustainable nanomedicine designs incorporating environmental impact assessments. By bridging clinical reality with future potential, this review aims to inform researchers, clinicians, and regulatory stakeholders navigating the rapidly evolving landscape of nanomedicine.","url":"https://doi.org/10.2147/ijn.s618407","authors":["Sayed Mortaza Fayez"],"tags":["Nanomedicine","Clinical trial","Nanotechnology","Drug delivery","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-06-01","doi":"https://doi.org/10.2147/ijn.s618407","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4383873008","name":"Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019","source":"openalex","abstract":"BACKGROUND: Asthma and atopic dermatitis (AD) are chronic allergic conditions, along with allergic rhinitis and food allergy and cause high morbidity and mortality both in children and adults. This study aims to evaluate the global, regional, national, and temporal trends of the burden of asthma and AD from 1990 to 2019 and analyze their associations with geographic, demographic, social, and clinical factors. METHODS: Using data from the Global Burden of Diseases (GBD), Injuries, and Risk Factors Study 2019, we assessed the age-standardized prevalence, incidence, mortality, and disability-adjusted life years (DALYs) of both asthma and AD from 1990 to 2019, stratified by geographic region, age, sex, and socio-demographic index (SDI). DALYs were calculated as the sum of years lived with disability and years of life lost to premature mortality. Additionally, the disease burden of asthma attributable to high body mass index, occupational asthmagens, and smoking was described. RESULTS: In 2019, there were a total of 262 million [95% uncertainty interval (UI): 224-309 million] cases of asthma and 171 million [95% UI: 165-178 million] total cases of AD globally; age-standardized prevalence rates were 3416 [95% UI: 2899-4066] and 2277 [95% UI: 2192-2369] per 100,000 population for asthma and AD, respectively, a 24.1% [95% UI: -27.2 to -20.8] decrease for asthma and a 4.3% [95% UI: 3.8-4.8] decrease for AD compared to baseline in 1990. Both asthma and AD had similar trends according to age, with age-specific prevalence rates peaking at age 5-9 years and rising again in adulthood. The prevalence and incidence of asthma and AD were both higher for individuals with higher SDI; however, mortality and DALYs rates of individuals with asthma had a reverse trend, with higher mortality and DALYs rates in those in the lower SDI quintiles. Of the three risk factors, high body mass index contributed to the highest DALYs and deaths due to asthma, accounting for a total of 3.65 million [95% UI: 2.14-5.60 million] asthma DALYs and 75,377 [95% UI: 40,615-122,841] asthma deaths. CONCLUSIONS: Asthma and AD continue to cause significant morbidity worldwide, having increased in total prevalence and incidence cases worldwide, but having decreased in age-standardized prevalence rates from 1990 to 2019. Although both are more frequent at younger ages and more prevalent in high-SDI countries, each condition has distinct temporal and regional characteristics. Understanding the temporospatial trends in the disease burden of asthma and AD could guide future policies and interventions to better manage these diseases worldwide and achieve equity in prevention, diagnosis, and treatment.","url":"https://doi.org/10.1111/all.15807","authors":["Youn Ho Shin","Jimin Hwang","Rosie Kwon","Seung Won Lee","Min Seo Kim","GBD 2019 Allergic Disorders Collaborators","Jae Il Shin","Dong Keon Yon"],"tags":["Medicine","Asthma","Atopic dermatitis","Population","Disease burden"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-11","doi":"https://doi.org/10.1111/all.15807","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7125688390","name":"The Carbon Cost of Intelligence: A Domain-Specific Framework for Measuring AI Energy and Emissions","source":"openalex","abstract":"The accelerating energy demands from artificial intelligence (AI) deployment introduce systemic challenges for achieving carbon neutrality. Large language models (LLMs) represent a dominant driver of AI energy consumption, with inference operations constituting 80–90% of total energy usage. Current energy benchmarks report aggregate metrics without domain-level breakdowns, preventing accurate carbon footprint estimation for workloadspecific operations. This study addresses this critical gap by introducing a carbon-aware framework centered on the carbon cost of intelligence (CCI), a novel metric enabling workload-specific energy and carbon calculation that balances accuracy and efficiency across heterogeneous domains. This paper presents a comprehensive cross-domain energy benchmark using the massive multitask language understanding (MMLU) dataset, measuring accuracy and energy consumption in five representative domains: clinical knowledge (medicine), professional accounting (finance), professional law (legal), college computer science (technology), and general knowledge. Empirical analysis of GPT-4 across 100 MMLU questions, 20 per domain, reveals substantive variations: legal queries consume 4.3× more energy than general knowledge queries (222 J vs. 52 J per query), while energy consumption varies by domain due to input length differences. Our analysis demonstrates the evolution from simple ratio-based approaches (weighted accuracy divided by weighted energy) to harmonic mean aggregation, showing that the harmonic mean, by preventing bias from extreme values, provides more accurate carbon usage estimates. The CCI metric, calculated using weighted harmonic mean (analogous to P/E ratios in finance, where A/E represents accuracy-to-energy ratio), enables practitioners to accurately estimate energy and carbon emissions for specific workload mixes (e.g., 80% medicine + 15% general + 5% law). Results demonstrate that the domain workload mix significantly impacts carbon footprint: a law firm workload (60% law) consumes 96% more energy per query than a hospital workload (80% medicine), representing 49% potential savings through workload optimization. Carbon footprint analysis using US Northeast grid intensity (320 gCO2e/kWh) shows domain-specific emissions ranging from 0.0046–0.0197 gCO2 per query. CCI is validated through comparison with simple weighted average, demonstrating differences up to 12.1%, confirming that the harmonic mean provides more accurate and conservative carbon estimates essential for carbon reporting and neutrality planning. Our findings provide a novel cross-domain energy benchmark for GPT-4 and establish a practical carbon calculator framework for sustainable AI deployment aligned with carbon neutrality goals.","url":"https://doi.org/10.3390/en19030642","authors":["Rashanjot Kaur","Triparna Kundu","Kathleen Park","Eugene Pinsky"],"tags":["Energy consumption","Carbon footprint","Computer science","Benchmark (surveying)","Energy (signal processing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-26","doi":"https://doi.org/10.3390/en19030642","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4406272821","name":"Integrating Model‐Informed Drug Development With AI : A Synergistic Approach to Accelerating Pharmaceutical Innovation","source":"openalex","abstract":"The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.","url":"https://doi.org/10.1111/cts.70124","authors":["Karthik Raman","Rukmini Kumar","Cynthia J. Musante","Subha Madhavan"],"tags":["Interpretability","Computer science","Drug development","Pharmaceutical industry","Drug discovery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1111/cts.70124","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154861292","name":"Mapping and Quality Appraisal of Artificial Intelligence Preferential Reporting Checklists, Items, Guidelines, and Consensus in Healthcare: An Altmetric, Bibliometric, and Systematic Review","source":"openalex","abstract":"Introduction: The integration of artificial intelligence (AI) in healthcare has garnered significant scholarly attention, particularly in areas such as medical image analysis, prognosis, and treatment. Despite its potential, concerns regarding AI's reliability and application persist, prompting the development of guidelines aimed at standardizing its use in medicine. This study aims to evaluate the current content and quality of AI guidelines in healthcare, focusing on identifying gaps and providing a critical appraisal of existing checklists. Methodology: Comprehensive bibliometric analysis, Altmetric analysis, and systematic review were conducted, utilizing the AGREE II Tool for quality appraisal. The systematic search spanned Scopus, PubMed, and Dimension AI databases, focusing on English-language, open-access articles related to AI reporting guidelines. Two reviewers independently evaluated the data, with manual extraction performed in Microsoft Excel. The AGREE II Tool assessed six domains of guideline quality. Results: The search yielded 2477 articles, ultimately identifying 27 AI-specific reporting guidelines published between 2020 and 2025. The analysis revealed significant variations in quality across the AGREE II domains. Among the very first and most impactful were SPIRIT-AI, CONSORT-AI, MINIMAR, and CLAIM, all prioritized structured reporting but were hampered by their timing-based when there were few AI trials-resulting in limited applicability and risk of missing older AI terminologies. However, STAR-machine learning (ML), APPRAISE-AI, and CLEAR exhibited more general, domain-specific frameworks, whereas checklists such as CHEERS-AI, CREMLS, and MI-CLEAR-large language model (LLM) showcased limited author diversity in contribution. However, many guidelines exhibited weaknesses in methodological rigor and stakeholder involvement, limiting their practical applicability. Conclusion: The findings emphasize the need for evidence-based updates to AI reporting guidelines to ensure methodological integrity amid rapid advancements. Increased expert involvement and stakeholder engagement are crucial for enhancing the guidelines' applicability and rigor, addressing AI complexities in health research, and adapting reporting frameworks to evolving AI technologies in healthcare.","url":"https://doi.org/10.1155/ijod/6730710","authors":["Vineet Vinay","Praveen Jodalli","Mahesh S. Chavan","Dharmashree Satyarup","Ketaki Bhor","Chaitanya Buddhikot"],"tags":["Systematic review","Critical appraisal","Quality (philosophy)","Strengths and weaknesses","Data extraction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1155/ijod/6730710","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401686380","name":"Integrative approach of omics and imaging data to discover new insights for understanding brain diseases","source":"openalex","abstract":"Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.","url":"https://doi.org/10.1093/braincomms/fcae265","authors":["Jong Hyuk Yoon","Hagyeong Lee","Dayoung Kwon","Dongha Lee","Seulah Lee","Eunji Cho","Jae‐Hoon Kim","Dayea Kim"],"tags":["Neuroimaging","Omics","Data science","Computational biology","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1093/braincomms/fcae265","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3011527816","name":"Interpretable and accurate prediction models for metagenomics data","source":"openalex","abstract":"BACKGROUND: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce \"predomics\", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. RESULTS: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. CONCLUSIONS: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field.","url":"https://doi.org/10.1093/gigascience/giaa010","authors":["Edi Prifti","Yann Chevaleyre","Blaise Hanczar","Eugeni Belda","Antoine Danchin","Karine Clément","Jean‐Daniel Zucker"],"tags":["Interpretability","Microbiome","Computer science","Metagenomics","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-01","doi":"https://doi.org/10.1093/gigascience/giaa010","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4390988724","name":"Rare diseases in Germany - Developments in the status of medical care","source":"openalex","abstract":"Background: Rare diseases are a heterogeneous group of complex clinical patterns, which more often than not run a chronic course. The fact that they are rare complicates the provision of medical care for the specific diseases. Results: In the field of action titled 'Care, Centres, Networks' of its National Action Plan, the National Action League for People with Rare Diseases recommends the formation of a three-level, interconnected centre model. This form of care was investigated in two large research projects. It was shown that the time to diagnosis was markedly reduced. Commissioned by the Federal Ministry of Health, the expert report on the health status of people with rare diseases in Germany issued in 2023 concludes that the medical care provided to this group of people has improved markedly since the National Action Plan was introduced. The establishment of the Centres for Rare Diseases (ZSE, Zentren für Seltene Erkrankungen) is seen as the most important development. However, it is noted that there is still a lack of coordinated care provision pathways for referring patients to the appropriate facilities. Conclusion: The provision of care to people with rare diseases has improved upon the implementation of the measures from the National Action Plan. In a next step, care provision pathways must be established across sector boundaries. Challenges remain in the area of psychosocial care and the long-term securing of funding for these structures.","url":"https://doi.org/10.25646/11746","authors":["Miriam Schlangen"],"tags":["Action plan","Action (physics)","Psychosocial","Medicine","Christian ministry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.25646/11746","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4396642986","name":"Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography","source":"openalex","abstract":"Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular information complementary to the OCT structural images. PT-OCT typically requires long acquisition times to measure small fluctuations in the OCT phase signal. Here, we use machine learning with a neural network to infer the amplitude of the photothermal phase modulation from a short signal trace, trained in a supervised fashion with the ground truth signal obtained by conventional reconstruction of the PT-OCT signal from a longer acquisition trace. Results from phantom and tissue studies show that the developed network improves signal to noise ratio (SNR) and contrast, enabling PT-OCT imaging with short acquisition times and without any hardware modification to the PT-OCT system. The developed network removes one of the key barriers in translation of PT-OCT (i.e., long acquisition time) to the clinic.","url":"https://doi.org/10.1038/s41598-024-60682-7","authors":["Mohammadhossein Salimi","Nima Tabatabaei","Martin Villiger"],"tags":["Optical coherence tomography","Computer science","SIGNAL (programming language)","Artificial intelligence","Signal-to-noise ratio (imaging)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-04","doi":"https://doi.org/10.1038/s41598-024-60682-7","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154735908","name":"Global English-language-dominated discourse on artificial intelligence in healthcare: a three-year longitudinal analysis of the #AIinHealthcare movement on X","source":"openalex","abstract":"Background: Social media platforms facilitate global discourse on the application of artificial intelligence (AI) in healthcare. Nevertheless, there is a paucity of longitudinal analyses of digitally mediated discussions. Objective: To investigate the evolution of global English-language-dominated discourse on #AIinHealthcare over a three-year period on X (formerly Twitter). Methods: Using Fedica analytics, we analysed 57,880 tweets by 17,991 distinct users across 141 countries from 1 November 2022 to 1 November 2025. This analysis focused on English-language-dominant discourse around #AIinHealthcare (96.9% English), acknowledging hashtag-specific selection bias and linguistic limitations. This study used publicly available anonymised data and followed the ethical guidelines for social media research. Results: The #AIinHealthcare garnered 39.2 million impressions, with significant contributions from high-income countries, notably the United States (40.7%) and Canada (21.0%), as well as India (13.4%; a rapidly expanding economy), collectively accounting for 75.1% of tweets and reflecting hashtag-specific, geographically concentrated engagement. This peaked in mid-2023 and stabilized lower by mid-2025. English was the predominant language of the discourse (96.9%). The community consisted of 74.9% grassroots users with fewer than 1,000 followers, suggesting genuine participation beyond elite influencers. Total engagement reached 72,625 interactions, primarily passive, comprising 68.1% likes, 19.4% retweets, 10.3% replies, and 2.1% quote tweets. Hashtag co-occurrence patterns, supported by qualitative inspection of exemplar tweets, indicated majorly five distinct clusters: foundational technical topics (#GenerativeAI, #ChatGPT, #LLMs) peaked after November 2022; clinical application themes emerged across disease-specific specialties (#Oncology, #Cardiology, #MentalHealth); healthcare implementation themes addressed practical integration (#DigitalHealth, #Telemedicine, #EHR); governance and ethics themes gained prominence (#ResponsibleAI, #AIEthics, #ExplainableAI, #DataPrivacy); and professional integration themes fostered learning communities (#MedTwitter, #MedicalEducation). Sentiment was predominantly neutral (95%), with positive (3%) and negative (2%). Monthly tweets peaked in mid-2023 at 1,600-1,800 before declining to 750-900 per month by June 2025. High-engagement content linked AI to practical applications, governmental initiatives, and clinical breakthroughs. Conclusion: English-language-dominated discourse around #AIinHealthcare reveals hashtag-specific maturation from technical enthusiasm to governance and implementation focus. However, platform access restrictions in countries such as China and Russia may skew geographic representation. Disparities in sustainability discourse remain prevalent.","url":"https://doi.org/10.3389/fdgth.2026.1795488","authors":["Thomas Wochele‐Thoma","Thadiyan Parambil Ijinu","Sreejith Pongillyathundi Sasidharan","Anoop Manakkadan","Lathikakumariamma Sahadevakurup Shine","Neenthamadathil Mohandas Krishnakumar","Selvaraj Indira Aruna","Nagarjuna Pasupuleti","Thomas Aswany","Divakaran Chandramathi Deepthi","Zilin Ma","Yining Hua","Michał Ławiński","Olena Litvinova","Maria Kletečka-Pulker","Atanas G. Atanasov"],"tags":["Grassroots","Sociology","Discourse analysis","Elite","Social media"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-17","doi":"https://doi.org/10.3389/fdgth.2026.1795488","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4400644218","name":"A Survey on Symbolic Knowledge Distillation of Large Language Models","source":"openalex","abstract":"This survey article delves into the emerging and critical area of symbolic knowledge distillation in large language models (LLMs). As LLMs such as generative pretrained transformer-3 (GPT-3) and bidirectional encoder representations from transformers (BERT) continue to expand in scale and complexity, the challenge of effectively harnessing their extensive knowledge becomes paramount. This survey concentrates on the process of distilling the intricate, often implicit knowledge contained within these models into a more symbolic, explicit form. This transformation is crucial for enhancing the interpretability, efficiency, and applicability of LLMs. We categorize the existing research based on methodologies and applications, focusing on how symbolic knowledge distillation can be used to improve the transparency and functionality of smaller, more efficient artificial intelligence (AI) models. The survey discusses the core challenges, including maintaining the depth of knowledge in a comprehensible format, and explores the various approaches and techniques that have been developed in this field. We identify gaps in current research and potential opportunities for future advancements. This survey aims to provide a comprehensive overview of symbolic knowledge distillation in LLMs, spotlighting its significance in the progression toward more accessible and efficient AI systems.","url":"https://doi.org/10.1109/tai.2024.3428519","authors":["K. Acharya","Alvaro Velasquez","Houbing Song"],"tags":["Computer science","Distillation","The Symbolic","Natural language processing","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-15","doi":"https://doi.org/10.1109/tai.2024.3428519","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4401716126","name":"Utilizing natural language processing to analyze student narrative reflections for medical curriculum improvement","source":"openalex","abstract":"MOTIVATION: Medical curricula improvement is an ongoing process to keep material relevant and improve the student's learning experience to better prepare them for patient care. Many programs utilize end-of-year evaluations, but these frequently have low response rates and lack actionable feedback. We hypothesized that student reflections written during a fourth year Sub-Internship could be used retrospectively to mine additional information as feedback for future curriculum adjustments. However, reflections contain a large amount of narrative content that would require a cumbersome and essentially infeasible manual review process for busy medical education faculty. METHODS: We developed a Natural Language Processing (NLP) pipeline to automatically identify common themes and topics present in the set of reflective writings that could be used to improve the curriculum. The dataset contains required responses to a faculty issued question submitted between August 2016 and July 2018 about challenges experienced during the medical students fourth year Sub-Internship. RESULTS: Eleven distinct topics were identified, with several being subsequently addressed in future iterations of the curriculum. CONCLUSION: Utilizing NLP on reflective writings was able to identify areas of curriculum improvement, and the NLP results provided a quick and easy way to explore the main themes and challenges expressed by students.","url":"https://doi.org/10.1080/0142159x.2024.2390034","authors":["Amy L. Olex","Adam Garber","Sally A. Santen","Courtney Blondino","Stephanie R. Goldberg","Deborah DiazGranados"],"tags":["Curriculum","Internship","Narrative","Process (computing)","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-20","doi":"https://doi.org/10.1080/0142159x.2024.2390034","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2057374433","name":"Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features","source":"openalex","abstract":"Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.","url":"https://doi.org/10.1155/2015/136921","authors":["Deborah Mudali","Laura K. Teune","Remco J. Renken","Klaus L. Leenders","Jos B. T. M. Roerdink"],"tags":["Decision tree","Artificial intelligence","Pattern recognition (psychology)","Progressive supranuclear palsy","Principal component analysis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-01-01","doi":"https://doi.org/10.1155/2015/136921","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2593820581","name":"LINGKUNGAN ARTIFISIAL DALAM PEMBELAJARAN BAHASA ARAB","source":"openalex","abstract":"This research was aimed at proving whether artificial language environment affect Arabic language skills and analyzing how the artificial language environment impact the skills. The method used in this research was categorized into a field research which combined both qualitative and quantitative method, or well-known as mixed method. The primary source in this research was taken from the 1st and 2nd grade of senior high madrasah Pondok Pesantren Madinatunnajah Jombang Ciputat, South Tangerang academic year 2015/2016. Moreover, the secondary source was obtained from some literatures in the form of academic journals and books related to this study. The research found that environment-based language learning was more effective and influential on listening, speaking, reading, and writing skills of Arabic language. It was concluded that language skills are not only determined by LAD (Language Acquisition Device), a gifted device to acquire language, but also determined by the environment the learner lived. DOI : 10.15408/a.v3i2.4038","url":"https://doi.org/10.15408/a.v3i2.4038","authors":["Nur Habibah"],"tags":["Active listening","Arabic","Reading (process)","Affect (linguistics)","Mathematics education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2016-12-28","doi":"https://doi.org/10.15408/a.v3i2.4038","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7196941818","name":"Challenges in the diagnosis of malignant skin neoplasms using artificial intelligence software and approaches to their resolution on the example of Derma Onko Melanoma Check software","source":"openalex","abstract":"Background. Artificial intelligence (AI)-based computer vision software is becoming an increasingly important tool in dermato-oncological practice. Deep learning models have demonstrated the ability to classify skin neoplasms with accuracy comparable to that of experienced dermatologists. However, their implementation in real clinical practice has revealed several significant challenges. Objective: To analyze and systematize the major challenges arising in the AI-assisted diagnosis of malignant skin neoplasms and to propose solutions to each identified challenge using the Derma Onko Melanoma Check software as an example. Material and methods. The clinical experience of applying AI software for the diagnosis of malignant skin neoplasms was analyzed, and six major categories of challenges were identified. Specific solutions to each of these challenges are proposed on the example of the Derma Onko Melanoma Check clinical decision support system (a multimodel AI-based computer vision system) for the preliminary diagnosis and triage of patients with skin neoplasms. Results. Six major challenges were identified and systematized: (1) imbalanced training datasets, with an overrepresentation of dermoscopic images and an insufficient number of clinical photographs; (2) poor quality of images acquired by physicians (e.g., overexposure, blur, and underexposure), limiting diagnostic reliability; (3) submission of images in which the lesion occupies an insufficient area of the frame for reliable analysis; (4) the single-model architecture of most AI systems and the resulting lack of adequate oncological alertness; (5) the absence of explainable artificial intelligence (XAI) in AI-generated reports; and (6) the inability of physicians to modify or refine the AI-generated conclusion using information obtained from the patient’s medical history and clinical examination. For each challenge, a specific solution implemented in Derma Onko Melanoma Check is proposed, including automated image quality assessment, lesion size verification, a multimodel ensemble of neural networks, XAI-based visualization of high-risk regions, interactive entry of clinical history data, and support for multimodal analysis. Conclusion. The implementation of AI systems incorporating the proposed solutions into routine clinical practice, particularly in primary care settings involving general practitioners, may significantly improve the early detection of malignant skin neoplasms by raising oncological vigilance for suspicious lesions and increasing the clinical interpretability and reliability of AI-generated diagnostic conclusions.","url":"https://doi.org/10.17749/2070-4909/farmakoekonomika.2026.403","authors":["D. I. Korabelnikov","A. I. Lamotkin"],"tags":["Software","Artificial intelligence","Clinical Practice","Medical physics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-08-06","doi":"https://doi.org/10.17749/2070-4909/farmakoekonomika.2026.403","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4417272080","name":"Antimicrobial use and resistance","source":"openalex","abstract":"Antimicrobial resistance affects the delivery of safe and effective healthcare. Antimicrobial resistance has attracted strong political focus, with the 2024 United Nations General Assembly high level meeting providing a clear commitment to reducing mortality and improving antibiotic use. This review summarises recent political action, policy prioritisation, and identification of future threats. It considers infections that are caused by drug resistant pathogens and reviews available and new antibiotics that may meet unmet medical needs. Despite increasing political engagement, the global antimicrobial resistance landscape remains imbalanced. In high income hospital settings, diagnostics, antimicrobial stewardship, and infection prevention and control are improving and may be further enabled by artificial intelligence and information systems. The development and use of new antibiotics is a major focus. By contrast, in low and middle income countries, access to most of these advances is limited. In all settings, empirical prescribing of essential antibiotics remains the cornerstone of treatment and conserving their efficacy is critical to effective healthcare. Targeted prevention and optimal treatment strategies are needed to mitigate antimicrobial resistance across all settings.","url":"https://doi.org/10.1136/bmj-2024-082681","authors":["Nada Reza","Vineet Dubey","Michael Sharland","William Hope"],"tags":["Antibiotic resistance","Antimicrobial","Antibiotics","Medicine","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-12-12","doi":"https://doi.org/10.1136/bmj-2024-082681","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4409249730","name":"Strategic Implementation of Artificial Intelligence and Machine Learning in the Pharmaceutical Sector: A Dual Hesitant Fuzzy Group Decision Making Approach","source":"openalex","abstract":"The pharmaceutical industry is regarded as one of the leading industries in Bangladesh. Further developments and sustainability can be offered through the integration of Artificial Intelligence (AI) and Machine Learning (ML) in this industry as vast amounts of data can quickly be processed and analyzed with accuracy by AI which is crucial for drug discovery, and quality improvement of medicines within minimal costs. However, successful AI implementation requires strategic planning based on various factors. In this study, eight major strategies were proposed to implement and integrate AI and machine learning and also assessed based on eight criteria related to artificial intelligence and machine learning. Feedbacks were taken from eight industry experts as Dual Hesitant Fuzzy Elements (DHFEs) to build the strategy versus criteria matrices. The feedbacks were then aggregated by using a group decision making method: Dual Hesitant Fuzzy Heronian Mean (DHFHM). The scores of those aggregated DHFEs belonging to each criterion were determined and the scores of all criteria under a strategy were summed up to find the ultimate score of that corresponding strategy. The strategies were then ranked based on their scores. Finally, the robustness of the result obtained from DHFHM, sensitivity analysis was conducted by changing the values of parameters and it was found that similar results with negligible deviation were generated by the operator and through this the proposed model was proven to be validated.","url":"https://doi.org/10.46254/ba07.20240185","authors":["Md. Ashaduzzaman","Nahiyan Ishmam Nawar","Shakil Ahmed Khan","Atif Abrar Biswas","Md. Mohibul Islam"],"tags":["Dual (grammatical number)","Group decision-making","Artificial intelligence","Computer science","Fuzzy logic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-21","doi":"https://doi.org/10.46254/ba07.20240185","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7153217607","name":"AI Use for Medical Students: Impact on Clinical Skill Acquisition and Retention. A Systematic Review","source":"openalex","abstract":"Purpose: Artificial Intelligence (AI) is increasingly used in undergraduate medical education but has a potentially negative impact on clinical reasoning development. Specifically, the use of AI in medical student education may lead to deskilling and upskilling inhibition - where automation reduces practice or limits skill development - potentially impairing clinical reasoning. This systematic review aimed to synthesise evidence regarding AI-supported learning effects on acquisition and retention of clinical skills in medical students to assess its potential negative impact in medical education. Methods: A systematic search was conducted on 21 October 2025 across PubMed, Scopus, and Embase using structured Boolean queries restricted to titles and abstracts. Inclusion criteria targeted published studies involving medical students exposed to AI tools in clinical learning, reporting outcomes related to skill acquisition, reasoning, or overreliance on AI. Exclusions included non-AI digital tools, administrative AI applications, and studies without clear educational outcomes. Screening followed PRISMA guidelines. Results: From 420 records, 255 were screened. Four studies met the screening criteria, incorporating a total of 408 medical students. Across included studies, AI exposure was associated with improved efficiency and improved basic knowledge acquisition. When higher-order clinical reasoning and complex decision-making were assessed, findings were mixed: one study reported no overall difference, while others suggested weaker performance or reduced engagement when AI-supported approaches were used. Conclusion: Current evidence suggests that AI-supported learning may be associated with improved efficiency and basic knowledge acquisition in undergraduate medical education. Findings were less consistently supportive of higher-order reasoning outcomes compared with traditional teaching approaches, although the evidence base was limited. Potential risks of deskilling and upskilling inhibition warrant attention as medical schools increasingly integrate AI tools into their curricula. A striking finding of our systematic review was the very low number of existing studies identified in this important field. Further research should explore the long-term impacts of AI on medical students' independent clinical judgement and consider strategies to mitigate overreliance on AI given the profound potential impact on future patient care.","url":"https://doi.org/10.2147/amep.s583763","authors":["Jonathan Turney","Timothy Young","Dhyana Chauhan","Roshni Beeharry","Mohammad Mahmud"],"tags":["Deskilling","Medical education","Knowledge acquisition","Inclusion (mineral)","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-01","doi":"https://doi.org/10.2147/amep.s583763","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4386151807","name":"BPPV Information on Google Versus AI (ChatGPT)","source":"openalex","abstract":"OBJECTIVE: To quantitatively compare online patient education materials found using traditional search engines (Google) versus conversational Artificial Intelligence (AI) models (ChatGPT) for benign paroxysmal positional vertigo (BPPV). STUDY DESIGN: The top 30 Google search results for \"benign paroxysmal positional vertigo\" were compared to the OpenAI conversational AI language model, ChatGPT, responses for 5 common patient questions posed about BPPV in February 2023. Metrics included readability, quality, understandability, and actionability. SETTING: Online information. METHODS: Validated online information metrics including Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), DISCERN instrument score, and Patient Education Materials Assessment Tool for Printed Materials were analyzed and scored by reviewers. RESULTS: Mean readability scores, FKGL and FRE, for the Google webpages were 10.7 ± 2.6 and 46.5 ± 14.3, respectively. ChatGPT responses had a higher FKGL score of 13.9 ± 2.5 (P < .001) and a lower FRE score of 34.9 ± 11.2 (P = .005), both corresponding to lower readability. The Google webpages had a DISCERN part 2 score of 25.4 ± 7.5 compared to the individual ChatGPT responses with a score of 17.5 ± 3.9 (P = .001), and the combined ChatGPT responses with a score of 25.0 ± 0.9 (P = .928). The average scores of the reviewers for all ChatGPT responses for accuracy were 4.19 ± 0.82 and 4.31 ± 0.67 for currency. CONCLUSION: The results of this study suggest that the information on ChatGPT is more difficult to read, of lower quality, and more difficult to comprehend compared to information on Google searches.","url":"https://doi.org/10.1002/ohn.506","authors":["Jeffrey R. Bellinger","Julian S. De La Chapa","Minhie W. Kwak","Gabriel Ramos","Daniel Morrison","Bradley W. Kesser"],"tags":["Readability","Benign paroxysmal positional vertigo","Quality Score","Medicine","Quality (philosophy)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-25","doi":"https://doi.org/10.1002/ohn.506","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7154774280","name":"A Dataset for Evaluating Large Language Models on Chinese National Medical Licensing Examinations","source":"openalex","abstract":"Large language models (LLMs) are increasingly applied in medical education, question answering, and clinical reasoning, yet standardized datasets in non-English contexts remain limited. To address this gap, we present CNMLEQA, a benchmark dataset for evaluating LLMs on the Chinese National Medical Licensing Examination. The dataset integrates question-answer pairs from three sources, including PubMed, GitHub, and MedExamLLM. CNMLEQA comprises two subsets: CNMLEQA-10k (9,890 questions) and CNMLEQA-3k (2,949 questions), each consisting of multiple-choice questions with five options and one correct answer. Questions are annotated with key dimensions including: (1) question type (knowledge-based or case-based), (2) auxiliary metadata such as examination year, 3) clinical scenario information across five dimensions: disease or diagnosis, surgery, medication, laboratory examination, and symptom or sign. Annotation was conducted by clinical experts. To validate the dataset, we evaluated state-of-the-art LLMs including Gemini, DeepSeek, GPT, Qwen, and LLaMA, and conducted fine-tuning experiments specifically on Qwen models. Results show that Qwen2.5-32B achieved the accuracy of 90.88% on CNMLEQA-10k, while DeepSeek-R1 achieved the accuracy of 91.59% on CNMLEQA-3k. The fine-tuning experiments further demonstrated significant performance improvements. CNMLEQA provides a multidimensional, clinically grounded benchmark for advancing LLM evaluation in Chinese medical applications.","url":"https://doi.org/10.1038/s41597-026-07261-9","authors":["Hui Zong","Jiaxue Cha","Yi-Xiang Wang","Yu Song","Yan Zhao","Muyun Shi","Bairong Shen"],"tags":["Metadata","Benchmark (surveying)","Annotation","Computer science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-17","doi":"https://doi.org/10.1038/s41597-026-07261-9","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7134901404","name":"Personalized Rule-Based Proofreading for Speech Recognition Errors in Radiology Reports: Development Using Artificial Intelligence Coding Assistance","source":"openalex","abstract":"Version of record:","url":"https://doi.org/10.2214/ajr.26.34694","authors":["Tetsuro Araki"],"tags":["Proofreading","Computer science","Artificial intelligence","Speech recognition","Coding (social sciences)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-11","doi":"https://doi.org/10.2214/ajr.26.34694","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3201436434","name":"Verbal and General IQ Associate with Supragranular Layer Thickness and Cell Properties of the Left Temporal Cortex","source":"openalex","abstract":"The left temporal lobe is an integral part of the language system and its cortical structure and function associate with general intelligence. However, whether cortical laminar architecture and cellular properties of this brain area relate to verbal intelligence is unknown. Here, we addressed this using histological analysis and cellular recordings of neurosurgically resected temporal cortex in combination with presurgical IQ scores. We find that subjects with higher general and verbal IQ scores have thicker left (but not right) temporal cortex (Brodmann area 21, BA21). The increased thickness is due to the selective increase in layers 2 and 3 thickness, accompanied by lower neuron densities, and larger dendrites and cell body size of pyramidal neurons in these layers. Furthermore, these neurons sustain faster action potential kinetics, which improves information processing. Our results indicate that verbal mental ability associates with selective adaptations of supragranular layers and their cellular micro-architecture and function in left, but not right temporal cortex.","url":"https://doi.org/10.1093/cercor/bhab330","authors":["Djai B. Heyer","René Wilbers","Anna A. Galakhova","Els A. Hartsema","Simon Braak","Sarah Hunt","Matthijs B Verhoog","M L Muijtjens","Eline J. Mertens","Sander Idema","Johannes C. Baayen","Philip de Witt Hamer","Martin Klein","Mary McGraw","Ed S. Lein","Christiaan P. J. de Kock","Huibert D. Mansvelder","Natalia A. Goriounova"],"tags":["Temporal lobe","Temporal cortex","Cortex (anatomy)","Neuroscience","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-08","doi":"https://doi.org/10.1093/cercor/bhab330","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4387430902","name":"Industry 5.0 applications for sustainability: A systematic review and future research directions","source":"openalex","abstract":"Abstract Industry 5.0 has created innovative new prospects leading to the production of more environmentally friendly projects, services, and products. However, the available investigation of Industry 5.0 for sustainability is still in its early stages, with few systematic and scant findings. Therefore, the purpose of this study is to undertake a systematic review of industry 5.0 for sustainability in order to identify trends, categorize research themes, draw attention to research limitations, and suggest potential directions for future research. Following a systematic review approach, 48 articles on industry 5.0 for sustainability published between 2019 and 2022 were selected and reviewed. Results showed that the research trend on the contributions of Industry 5.0 to sustainability has been remarkably growing worldwide. The internet of things, artificial intelligence, and collaborative robots were the most commonly used Industry 5.0 technologies for sustainability purposes. Subsequently, this study discussed the current studies under four main research themes, namely, robot advancement, higher education sustainability, human‐centric, and ecosystem advancement. It has been found that human centric is the most popular theme. The identification of themes and sub sub‐themes in this study can help researchers identify gaps and inspire further exploration based on the existing knowledge. This study can support the decision‐making process regarding the effective implementation of Industry 5.0 solutions to enhance sustainability practices in organizations. This study discusses the current state of Industry 5.0 technologies for sustainability and provides insights into future research directions. This can motivate researchers, policymakers, and industry professionals to explore and develop innovative Industry 5.0 solutions.","url":"https://doi.org/10.1002/sd.2699","authors":["Maria Ijaz Baig","Elaheh Yadegaridehkordi"],"tags":["Sustainability","Systematic review","Sustainability organizations","Knowledge management","Sustainability science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-26","doi":"https://doi.org/10.1002/sd.2699","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7155410582","name":"The effect of medical explanations from large language models on diagnostic accuracy in radiology","source":"openalex","abstract":"Large language models (LLMs) are increasingly used by physicians for diagnostic support. A key advantage of LLMs is the ability to generate explanations that can help physicians understand the reasoning behind a diagnosis. However, the best-suited format for LLM-generated explanations remains unclear. In this large-scale study, we examined the effect of different formats for LLM explanations on clinical decision-making. For this, we conducted a randomized experiment with radiologists reviewing patient cases with radiological images (N = 2020 assessments). Participants received either no LLM support (control group) or were supported by one of three LLM-generated explanations: (1) a standard output providing the diagnosis without explanation; (2) a differential diagnosis comparing multiple possible diagnoses; or (3) a chain-of-thought explanation offering a detailed reasoning process for the diagnosis. We find that the format of explanations significantly influences diagnostic accuracy. The chain-of-thought explanations yielded the best performance, improving the diagnostic accuracy by 12.2% compared to the control condition without LLM support (P = 0.001). The chain-of-thought explanations are also superior to the standard output without explanation ( + 7.2%; P = 0.040) and the differential diagnosis format ( + 9.7%; P = 0.004). We further assessed the robustness of these findings across case difficulty and different physician backgrounds, such as general vs. specialized radiologists. Evidently, in the controlled setting of our vignette study, explaining the reasoning for a diagnosis helps physicians to identify and correct potential errors in LLM predictions and thus improve overall decisions. Altogether, the results highlight the importance of explanations in medical LLMs to support the reasoning processes of physicians, so that medical LLMs can improve diagnostic performance and, ultimately, patient outcomes.","url":"https://doi.org/10.1038/s41746-026-02619-0","authors":["Philipp Spitzer","Daniel Hendriks","Jan Rudolph","Sarah Schlaeger","Jens Ricke","Niklas Kühl","Boj Hoppe","Stefan Feuerriegel"],"tags":["Vignette","Diagnostic accuracy","Medical imaging","Medical diagnosis","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-23","doi":"https://doi.org/10.1038/s41746-026-02619-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4298355780","name":"Present and future of machine learning in breast surgery: systematic review","source":"openalex","abstract":"BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications. METHODS: A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar. RESULTS: The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation. CONCLUSION: Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.","url":"https://doi.org/10.1093/bjs/znac224","authors":["Chien Lin Soh","Viraj Shah","Arian Arjomandi Rad","Robert Vardanyan","Alina Zubarevich","Saeed Torabi","Alexander Weymann","George Miller","Johann Malawana"],"tags":["Medicine","Machine learning","Artificial intelligence","MEDLINE","Context (archaeology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-10","doi":"https://doi.org/10.1093/bjs/znac224","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4407052703","name":"ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compmedimag.2025.102501","authors":["Yiqing Shen","David Dreizin","Blanca Íñigo","Mathias Unberath"],"tags":["Computer science","Segmentation","Artificial intelligence","Exploit","Generalization"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-01","doi":"https://doi.org/10.1016/j.compmedimag.2025.102501","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4308581122","name":"Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing","source":"openalex","abstract":"Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN).","url":"https://doi.org/10.3390/app122211330","authors":["Alejandro Cortés-Leal","César Cárdenas","Carolina Del-Valle-Soto"],"tags":["Resilience (materials science)","Productivity","Emerging technologies","Computer science","Risk analysis (engineering)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-08","doi":"https://doi.org/10.3390/app122211330","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4403249102","name":"Wearable Devices and Health Monitoring","source":"openalex","abstract":"The integration of wearable devices, big data, and artificial intelligence (AI) has revolutionized remote patient care and health monitoring. These technologies enable the continuous and real-time collection of various physiological and behavioral data from individuals, allowing healthcare providers to remotely monitor their health status. Wearable devices such as smartwatches, fitness trackers, and medical sensors offer the ability to track vital signs, physical activity, and sleep patterns. The sheer volume of data generated by these devices, often referred to as big data, presents challenges and opportunities. AI plays a crucial role in deciphering this wealth of information by employing advanced algorithms for data analysis, pattern recognition, and predictive modeling. This synergy between wearable devices, big data, and AI not only empowers patients to actively engage in their own health management, but also enables healthcare professionals to make informed decisions, detect anomalies, and provide timely interventions. This abstract explores the transformative impact of wearable devices and the synergy of big data and AI in enhancing remote patient care, fostering the early detection of health issues, and ultimately improving overall patient outcomes.","url":"https://doi.org/10.1002/9781394270910.ch12","authors":["S. Kanakaprabha","G. Ganesh Kumar","Bhargavi Peddi Reddy","Yallapragada Ravi Raju","P. Chandra Mohan"],"tags":["Wearable computer","Wearable technology","Computer science","Data science","Embedded system"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-10-09","doi":"https://doi.org/10.1002/9781394270910.ch12","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7162101487","name":"Use and Public Reporting of Predetermined Change Control Plans for AI-Enabled Medical Devices","source":"openalex","abstract":"This cross-sectional study describes predetermined change control plan use among artificial intelligence (AI)–enabled medical devices and the comprehensiveness of public reporting documenting predetermined change control plans.","url":"https://doi.org/10.1001/jamahealthforum.2026.1204","authors":["Patryk A. Dabek","Florence T. Bourgeois","A. Stern","Yi Zhu","Alexander Everhart"],"tags":["Control (management)","Plan (archaeology)","Business","Risk analysis (engineering)","Process management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-22","doi":"https://doi.org/10.1001/jamahealthforum.2026.1204","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7166465407","name":"Artificial Intelligence in Oral and Maxillofacial Surgery: A Cross-Sectional Study of Knowledge, Attitudes, and Clinical Adoption among Surgeons and Trainees","source":"openalex","abstract":"To investigate how oral and maxillofacial surgery (OMS) clinicians and trainees perceive and apply artificial intelligence (AI), examining their familiarity, viewpoints, and behaviors regarding its role in OMS clinical care and education. A cross-sectional questionnaire-based investigation was undertaken among OMS specialists and trainees in Singapore to gather their opinions on AI in OMS. The instrument contained 25 items, organized into five parts, and was distributed via an online survey plat","url":"https://doi.org/10.51847/6mukmpnanw","authors":["María Hernández","Carlos Vega"],"tags":["Medicine","Medical education","Oral and maxillofacial surgery","MEDLINE","Patient care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.51847/6mukmpnanw","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4393002303","name":"On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)","source":"openalex","abstract":"Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have not yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial domains, including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality, such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, the task-agnostic large learning models (LLMs) can outperform task-specific fully supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image–based urban noise intensity classification, and remote sensing image scene classification), existing FMs still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing an FM for GeoAI is to address the multimodal nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal FM that can reason over various types of geospatial data through geospatial alignments. We conclude this article by discussing the unique risks and challenges to developing such a model for GeoAI.","url":"https://doi.org/10.1145/3653070","authors":["Gengchen Mai","Weiming Huang","Jin Sun","Suhang Song","Deepak R. Mishra","Ninghao Liu","Song Gao","Tianming Liu","Gao Cong","Yingjie Hu","Chris Cundy","Ziyuan Li","Rui Zhu","Ni Lao"],"tags":["Foundation (evidence)","Computer science","Data science","Political science","Law"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-20","doi":"https://doi.org/10.1145/3653070","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3127469141","name":"Marine Spatial Planning Connects Digital Technology, Excavation Equipment with Artificial Intelligence, Internet of Things for Underwater Heritage Investigation","source":"openalex","abstract":"Abstract BackgroundUnderwater cultural heritage (UCH) as a time capsule had accumulated marine ecological biological resources for centuries since sinking until discovery, forming an ecosystem wildly involves multifaceted fields. ProblemThe convention on the United Nations Educational, Scientific, and Cultural Organization (UNESCO), coastal states claim UCH jurisdiction in situ protection. However, foreign countries demand UCH sovereignty to evolve an international political event. Consequently, underwater archaeology preservation and exploitation become a diplomatic intrigue although no human lives beneath the sea.Purpose The purpose of paper attempts to identify significant issues about UCH providing standard operating procedures (SOP), One-Stop service for a comprehensive investigation, and finds a way for international UCH disputes. MethodThe research collected relevant UCH issues from six participants' brainstorming, statistical analysis, and the Chi-Squared test. ResultsResults showed 13 issues about UCH with no significant difference but conversion into seven groups with a significant difference. Finding & ContributionThe top three groups, marine spatial planning (MSP), Digital Technology, Excavation Equipment were identified and contributed to better UCH investigation. The finding transnational, regional level regimes, community collaborative governance offers UCH better preservation.SuggestionFinally, suggestion MSP activating idle assets construct UCH research center through digital technology with excavation equipment and link artificial intelligence (AI), internet of things (IoT) to improve UCH investigation, and international collaboration.","url":"https://doi.org/10.21203/rs.3.rs-46173/v1","authors":["Victor Te Cheng Liao"],"tags":["Jurisdiction","Convention","Politics","The Internet","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-23","doi":"https://doi.org/10.21203/rs.3.rs-46173/v1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7119539028","name":"Artificial intelligence application in the prediction of spontaneous preterm birth by cervical length in the first trimester of pregnancy: Comparison of three measurement methods","source":"openalex","abstract":"OBJECTIVES: The current study evaluates the efficacy of artificial intelligence (AI)-assisted measurement of cervical length (CL) in predicting spontaneous preterm birth (sPTB), comparing the traditional single-line and two-line methods with the innovative AI-line method in the first trimester of pregnancy. MATERIALS AND METHODS: This study is a retrospective secondary analysis of ultrasound images collected prospectively from women with a viable singleton pregnancy who were undergoing Down syndrome screening at Prince of Wales Hospital, Hong Kong SAR. CL was measured using transvaginal ultrasound, with a secondary analysis of archived 1664 images acquired during a prospective study and processed through a ResUNet-based model. This model, combining UNet and ResNet architectures, a modified ResUNet framework, aimed to overcome the limitations of current measurement techniques by providing a more accurate prediction of CL, particularly in cases where the cervix is curved. RESULTS: The AI-line method demonstrated superior accuracy in predicting sPTB at <37 and <32 weeks of gestation compared with conventional methods, with higher areas under the receiver operating characteristic curve (AUROC). The AUROC of CL measured by the AI-line method (0.676 [95% CI, 0.616-0.735], P < 0.05) in predicting sPTB at <37 weeks of gestation was significantly higher than the single-line (0.537 [95% CI, 0.474-0.6]) and two-line (0.54 [95% CI, 0.473-0.66]) methods. For the prediction of sPTB at <32 weeks of gestation, the AI-line method achieved an AUROC of 0.777 (95% CI, 0.703-0.850). CONCLUSION: The AI-line method offers a more accurate measurement of CL in the first trimester, showing potential as a tool for early screening of sPTB risk. The study's results could significantly influence clinical decision-making, providing a basis for the potential future clinical application of AI in prenatal care.","url":"https://doi.org/10.1002/ijgo.70744","authors":["Yi‐Yun Tai","Bor‐Yann Tseng","Zhu‐Han Yang","Yu Ch","Liona C. Poon"],"tags":["Medicine","First trimester","Obstetrics","Second trimester","Prenatal diagnosis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-09","doi":"https://doi.org/10.1002/ijgo.70744","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4406382240","name":"Review of current progress on additive manufacturing of medical implants and natural/synthetic fibre reinforced composites","source":"openalex","abstract":"Abstract Additive manufacturing, or 3D printing technique, is a technology that uses computerized information to generate three‐dimensional solid objects. These objects are produced by feed‐stocking and fusing materials layer by layer. Compared to conventional manufacturing, additive manufacturing can make geometrical shapes that are very complex within a short time with less material wastage. Remarkable applications of manufacturing technology are found in automobile, aerospace, medicine, and natural/synthetic fibre‐reinforced composites. Manufactured parts are fabricated using metals, ceramics, and mainly polymers or composites. Advancements in research have recently been implemented to optimize the process. This review focuses on the research progress on current methods applied to optimize 3D printed biopolymer medical implants and natural/synthetic fibre‐reinforced composites. The objective of this article is to review new opportunities to produce multifunctional materials and suggest solutions to solve persisting challenges in additive manufacturing of medical implants using natural/synthetic fiber reinforced composites. The influence of process parameters on output performance measures, as well as the modelling and simulation techniques applied, are critically established in this paper. Current 3D printing processes and technologies, including the status and future of additive manufacturing, are also critically presented. Finally, challenges and research opportunities for improved high‐performing and less costly printed parts are also illustrated.","url":"https://doi.org/10.1002/mawe.202400070","authors":["C. Nsanzumuhire","Oluyemi Ojo Daramola","Isiaka Oluwole Oladele","Akeem Damilola Akinwekomi"],"tags":["Composite material","Materials science","Synthetic fiber","Current (fluid)","Engineering"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1002/mawe.202400070","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4415961111","name":"ReclAIm: A Multiagent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI","source":"openalex","abstract":"A multiagent framework enabled automated monitoring and correction of performance decline in medical image classification models through natural language interaction, supporting reliable model maintenance in medical imaging artificial intelligence.","url":"https://doi.org/10.1148/ryai.250923","authors":["Eleftherios Tzanis","Michail E. Klontzas"],"tags":["Computer science","Reliability (semiconductor)","Medical imaging","Artificial intelligence","Clinical Practice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-06-03","doi":"https://doi.org/10.1148/ryai.250923","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7127610098","name":"Advancing AI Competency in Graduate Medical Education: A Developmental Framework","source":"openalex","abstract":"The integration of artificial intelligence (AI) into health care is no longer a scenario reserved for science fiction; it is rapidly becoming a critical component of clinical practice.1 A 2024 survey of 43 U.S. health systems found that 100% had implemented AI-based clinical documentation tools, whereas the Food and Drug Administration has authorized over 1,000 AI-enabled medical devices as of 2024—representing exponential growth from just 6 approvals in 2015 to 223 in 2023.2 As development and implementation accelerate, medical education confronts an urgent imperative to reimagine how to prepare current and future physicians for a landscape where AI tools increasingly influence healthcare policy, clinical decision making, documentation, and patient interaction.","url":"https://doi.org/10.1016/j.focus.2026.100484","authors":["Tauhid Mahmud","Yuri T. Jadotte","Dorothy Lane"],"tags":["Medical education","Psychology","Engineering ethics","Medicine","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-04","doi":"https://doi.org/10.1016/j.focus.2026.100484","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4225927996","name":"Machine learning in vascular surgery: a systematic review and critical appraisal","source":"openalex","abstract":"Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.","url":"https://doi.org/10.1038/s41746-021-00552-y","authors":["Ben Li","Tiam Feridooni","César Cuen-Ojeda","Teruko Kishibe","Charles de Mestral","Muhammad Mamdani","Mohammed Al‐Omran"],"tags":["Medicine","Critical appraisal","Vascular surgery","MEDLINE","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-19","doi":"https://doi.org/10.1038/s41746-021-00552-y","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3201330114","name":"Chaotic Organic Crystal Phosphorescent Patterns for Physical Unclonable Functions","source":"openalex","abstract":"Abstract Since the 4th Industrial Revolution, Internet of Things based environments have been widely used in various fields ranging from mobile to medical devices. Simultaneously, information leakage and hacking risks have also increased significantly, and secure authentication and security systems are constantly required. Physical unclonable functions (PUF) are in the spotlight as an alternative. Chaotic phosphorescent patterns are developed based on an organic crystal and atomic seed heterostructure for security labels with PUFs. Phosphorescent organic crystal patterns are formed on MoS2. They seem similar on a macroscopic scale, whereas each organic crystal exhibits highly disorder features on the microscopic scale. In image analysis, an encoding capacity as a single PUF domain achieves more than 1017 on a MoS2 small fragment with lengths of 25 µm. Therefore, security labels with phosphorescent PUFs can offer superior randomness and no‐cloning codes, possibly becoming a promising security strategy for authentication processes.","url":"https://doi.org/10.1002/adma.202102542","authors":["Healin Im","Jinsik Yoon","Jinho Choi","Jinsang Kim","Seungho Baek","Dong Hyuk Park","Wook Park","Sunkook Kim"],"tags":["Materials science","Phosphorescence","Chaotic","Physical unclonable function","Crystal (programming language)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-09-12","doi":"https://doi.org/10.1002/adma.202102542","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7134058249","name":"Policies and Guidelines for the Use of Artificial Intelligence in Latin American Journals Indexed in Scopus and Classified According to the Scimago Journal Rank (SJR)","source":"openalex","abstract":"The emergence of artificial intelligence tools in scientific production is generating significant challenges for scientific integrity and editorial governance, prompting journals and publishers to develop normative guidelines for their use. This study analyzes the current state of guideline implementation among Latin American journals indexed in Scopus and classified according to the Scimago Journal Rank (SJR). A quantitative approach was adopted, complemented by a descriptive documentary analysis based on a detailed review of the websites of 1119 journals from 17 Latin American countries. The collected data were systematized using Excel and analyzed through descriptive and inferential statistical techniques. The results indicate that only 27.2% of journals have explicit guidelines on the use of artificial intelligence, with a predominantly regulatory rather than punitive orientation that prioritizes technical support while restricting practices that compromise human intellectual control. Additionally, statistically significant differences were identified according to quality indicators, showing that journals with higher quality levels are more likely to have such guidelines. Overall, the findings reveal an incipient and heterogeneous regulatory development, underscoring the need to strengthen and harmonize editorial guidelines on artificial intelligence in order to safeguard transparency, clarify the responsibilities of the actors involved in the production and publication process, and protect the integrity of scientific communication.","url":"https://doi.org/10.3390/publications14010017","authors":["Cristian Zahn-Muñoz","Patricio Viancos","Nancy Alarcón-Henríquez","Bastián Aravena-Niño","Ezequiel Martínez-Rojas"],"tags":["Scopus","Normative","Rank (graph theory)","Quality (philosophy)","Latin Americans"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-06","doi":"https://doi.org/10.3390/publications14010017","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3138815606","name":"Membership Inference Attacks on Machine Learning: A Survey","source":"openalex","abstract":"Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. MIAs on ML models can directly lead to a privacy breach. For example, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models. Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. To further help the researchers, we have created an online resource repository, which we will keep updated with future relevant work. Interested readers can find the repository at https://github.com/HongshengHu/membership-inference-machine-learning-literature.","url":"https://doi.org/10.1145/3523273","authors":["Hongsheng Hu","Zoran Salčić","Lichao Sun","Gillian Dobbie","Philip S. Yu","Xuyun Zhang"],"tags":["Computer science","Inference","Machine learning","Data science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-31","doi":"https://doi.org/10.1145/3523273","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7164741966","name":"Artificial Intelligence-Assisted Quantification of Longitudinal HRCT Changes During Treatment of Pulmonary Tuberculosis: An Exploratory Proof-of-Concept Study","source":"openalex","abstract":"Background: Treatment monitoring in pulmonary tuberculosis increasingly requires assessment of residual inflammatory burden and structural lung damage beyond microbiologic response alone. High-resolution computed tomography (HRCT) can provide this information, but interpretation of serial examinations is time-consuming and partly subjective. This study did not aim to evaluate AI for the diagnosis of pulmonary tuberculosis. Instead, it explored whether artificial intelligence (AI)-assisted quantitative HRCT analysis could support longitudinal assessment of treatment-related imaging changes in patients with microbiologically confirmed pulmonary tuberculosis. Methods: We conducted a retrospective, single-center, exploratory longitudinal study of patients receiving treatment for pulmonary tuberculosis. HRCT examinations acquired at diagnosis and during follow-up were anonymized, reviewed by an expert thoracic radiologist, and processed using AVIEW Lung Texture (Coreline Soft v2.0). The software quantified total lung volume and six predefined parenchymal categories: normal lung, ground-glass opacity, consolidation, reticulation, honeycombing, and emphysema. Results: Ninety-six patients contributed 256 HRCT examinations. The most frequent software-detected abnormalities were ground-glass opacity, consolidation, and emphysema-labeled low-attenuation areas. Ground-glass opacity and consolidation showed the clearest decline across serial examinations, consistent with regression of active inflammatory disease during treatment. Reticulation showed a heterogeneous course, likely reflecting both inflammatory resolution and residual structural remodeling. Honeycombing was infrequent and quantitatively limited. Lung volume changed variably and did not consistently parallel visual improvement. A key methodological limitation was the absence of a dedicated cavity class. As a result, emphysema-labeled low-attenuation areas should not be interpreted as conventional emphysema alone, because tuberculous cavities and post-destructive abnormalities were frequently included in this category. Conclusions: AI-assisted HRCT quantification may support longitudinal assessment of pulmonary tuberculosis by providing structured and reproducible measures of interval change. However, tuberculosis-specific interpretation remains dependent on expert radiologic oversight, particularly in cavitary disease.","url":"https://doi.org/10.3390/diagnostics16121822","authors":["A Russo","Vittorio Patanè","Francesco Ruotolo","Maria Chiara Brunese","Maria Teresa Del Canto","Loredana Alessio","Caterina Monari","Nicola Coppola","Alfonso Reginelli"],"tags":["Honeycombing","Medicine","Radiology","Lung","High-resolution computed tomography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-06-12","doi":"https://doi.org/10.3390/diagnostics16121822","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3198604752","name":"A Quick Guide to Somalia in 2026: Business as Usual","source":"openalex","abstract":"The political and developmental challenges confronting Somalia are entrenched and unlikely to change significantly over the next five years. This is notably so for the transfer of security and policing responsibilities from international organizations to Somali authorities. Consequently, seeking to distinguish between Somalia’s current and future challenges is an artificial exercise that has more to do with the aspirations of the international community than with local realities. Change will undoubtedly occur, but while the strategic issues dominating the international agenda in 2026 will be different from those prioritized in 2021, the underlying trends and issues influencing the goals and behavior of Somalia’s powerbrokers and significant security actors will be much the same as they are today: the security marketplace will continue to reflect the primacy of clan-based calculations and internecine rivalries. In other words, it will be business as usual.","url":"https://doi.org/10.1080/21520844.2021.1957347","authors":["Alice Hills"],"tags":["Somali","Clan","Political science","Politics","Political economy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-03","doi":"https://doi.org/10.1080/21520844.2021.1957347","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7160492411","name":"GenAI-Supported Virtual Patients in Health Care Education: Systematic Review","source":"openalex","abstract":"Background: Generative artificial intelligence (GenAI) is enhancing virtual patient simulations in health care education by enabling dynamic, adaptive interactions, reshaping how clinical skills are taught. A synthesis of the current evidence is needed to guide implementation and future research, given the pace of technological advancement. Objective: This systematic review aims to synthesize empirical research on the design, implementation, and educational impact of GenAI-supported virtual patients in health care education. Methods: A systematic search was conducted across 5 databases (CINAHL, Medline, Embase, Scopus, and Web of Science) from their inception to March 19, 2026. Reference lists of included studies and relevant systematic reviews were also screened. Peer-reviewed studies in English that evaluated GenAI-supported virtual patients using quantitative or mixed methods were included. Two reviewers independently screened studies and extracted data. Study quality and risk of bias were assessed critically using JBI (Joanna Briggs Institute) checklists, with disagreements resolved by consensus. Results: A total of 15 studies met the inclusion criteria (total participants N=645), spanning health care disciplines, including nursing, medicine, pharmacy, radiography, and medical first-responder training. The virtual patients varied in design; input modalities included text (9 studies), voice (5 studies), or hybrid (1 study); output was text (9 studies), speech (5 studies), or both (1 study); 6 studies used 3D-embodied avatars, while 9 used nonembodied interfaces. A total of 13 studies used OpenAI GPT models (eg, ChatGPT), 1 used a fine-tuned model from a different provider, and 1 evaluated multiple model families (Claude, GPT, and open-source). Further, 6 studies used controlled experimental designs, including 3 randomized controlled trials (RCTs); the remainder were cross-sectional or prepost evaluations. Primary outcomes included user perceptions (14 studies), communication skills (4 studies), clinical reasoning (3 studies), and performance (7 studies). In controlled comparisons, GenAI-supported virtual patients consistently improved outcomes relative to control conditions: for example, enhanced clinical decision-making (RCT, n=21), ophthalmology history-taking skills (RCT, n=26), and medical history-taking performance (crossover RCT, n=20). The evidence base is characterized by brief intervention durations, a predominant reliance on single-session interactions, and a general lack of underpinning educational theory. No meta-analysis was performed due to the limited number of studies and significant heterogeneity in designs, interventions, and outcome measures. Conclusions: The evidence supports the feasibility and acceptability of GenAI-supported virtual patients, with positive learner perceptions and promising outcomes for skills development. However, critical limitations remain in emotional-behavioral complexity, simulation adaptability, and research design rigor (eg, limited use of control groups and validated instruments). The review offers educators, instructional designers, and policymakers actionable insights for integrating dynamic, artificial intelligence-driven simulations while identifying crucial gaps-such as the need for theoretical grounding, longitudinal studies, and standardized design protocols-that must be addressed for safe and effective implementation.","url":"https://doi.org/10.2196/82756","authors":["Juming Jiang","Megan Zichen Ye","T Kwok","Janet Yuen Ha Wong"],"tags":["Health care","Modalities","Pace","Systematic review","Inclusion (mineral)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-05-07","doi":"https://doi.org/10.2196/82756","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3165163745","name":"Exploring the phenomenon and ethical issues of AI paternalism in health apps","source":"openalex","abstract":"Health apps, including consumer-oriented fitness apps, have two functions. They are supposed to monitor and promote users' health, the latter by way of being an instance of persuasive technology. The use of artificial intelligence (AI) allows for AI health apps, i.e., health apps that act more and more autonomously when it comes to analyzing users' health data and arriving at tailor-made results on how to improve their health. Consequently, AI health apps seem to gain a paternalistic potential. This is a game-changer, for corresponding issues of paternalism can then no longer be traced back to human engineers. Instead, the paternalizing party just is the AI system. Hence, AI health apps lead to the novel issue of AI paternalism in health care. In this paper, I explore this novel phenomenon and its ethical implications. Firstly, I discuss from a critical perspective whether the notion of AI paternalism makes (conceptual) sense to begin with. Unsurprisingly, I argue that it does and how so. Secondly, I briefly indicate important ethical issues that AI paternalism in health apps raise and which need to be discussed in more detail in order to judge under which conditions (certain forms of) AI paternalism might be considered acceptable, if at all.","url":"https://doi.org/10.1111/bioe.12886","authors":["Michael Kühler"],"tags":["Paternalism","Phenomenon","Health care","Perspective (graphical)","Order (exchange)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-24","doi":"https://doi.org/10.1111/bioe.12886","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3046196877","name":"Diagnosis of pharyngeal cancer on endoscopic video images by Mask region‐based convolutional neural network","source":"openalex","abstract":"OBJECTIVES: We aimed to develop an artificial intelligence (AI) system for the real-time diagnosis of pharyngeal cancers. METHODS: Endoscopic video images and still images of pharyngeal cancer treated in our facility were collected. A total of 4559 images of pathologically proven pharyngeal cancer (1243 using white light imaging and 3316 using narrow-band imaging/blue laser imaging) from 276 patients were used as a training dataset. The AI system used a convolutional neural network (CNN) model typical of the type used to analyze visual imagery. Supervised learning was used to train the CNN. The AI system was evaluated using an independent validation dataset of 25 video images of pharyngeal cancer and 36 video images of normal pharynx taken at our hospital. RESULTS: The AI system diagnosed 23/25 (92%) pharyngeal cancers as cancers and 17/36 (47%) non-cancers as non-cancers. The transaction speed of the AI system was 0.03 s per image, which meets the required speed for real-time diagnosis. The sensitivity, specificity, and accuracy for the detection of cancer were 92%, 47%, and 66% respectively. CONCLUSIONS: Our single-institution study showed that our AI system for diagnosing cancers of the pharyngeal region had promising performance with high sensitivity and acceptable specificity. Further training and improvement of the system are required with a larger dataset including multiple centers.","url":"https://doi.org/10.1111/den.13800","authors":["Mitsuhiro Kono","Ryu Ishihara","Yusuke Kato","Muneaki Miyake","Ayaka Shoji","Takahiro Inoue","Katsunori Matsueda","Kotaro Waki","Hiromu Fukuda","Yusaku Shimamoto","Yasuhiro Fujiwara","Tomohiro Tada"],"tags":["Pharynx","Medicine","Convolutional neural network","Artificial intelligence","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-27","doi":"https://doi.org/10.1111/den.13800","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7133128916","name":"Artificial Intelligence for Pulmonary Abnormality Detection in Chest X-Ray Imaging: A Detailed Review of Methods, Datasets and Future Directions","source":"openalex","abstract":"Chest X-ray (CXR) imaging remains the most widely used radiological modality for assessing pulmonary and cardiothoracic disease, yet its interpretation is inherently constrained by tissue superposition, subtle radiographic findings and marked inter-observer variability. Recent advances in artificial intelligence (AI) have driven significant progress in automated CXR analysis, supported by large public datasets, evolving annotation strategies and increasingly expressive deep learning architectures. This review presents a comprehensive synthesis of approaches for pulmonary abnormality detection, encompassing convolutional neural networks, transformers, multimodal and vision–language models and self-supervised representation learning. We critically discuss their strengths, limitations and vulnerability to label noise, domain shift and shortcut learning. In parallel, we examine dataset properties, annotation practices, robustness challenges, explainability methods and the heterogeneity of evaluation protocols that hinder fair comparison and clinical translation. Building on these observations, the review identifies key future directions, including foundation models, multimodal integration, federated and domain-generalized training, longitudinal modeling, synthetic data generation and standardized clinical evaluation frameworks. By integrating methodological and clinical perspectives, this work offers an up-to-date reference for researchers and clinicians and outlines a roadmap toward reliable, interpretable and clinically deployable AI systems for chest radiography.","url":"https://doi.org/10.3390/technologies14030147","authors":["G. Parra-Cabrera","J. J. Jiménez-Delgado","F. D. Pérez-Cano"],"tags":["Artificial intelligence","Computer science","Robustness (evolution)","Abnormality","Modality (human–computer interaction)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-02-28","doi":"https://doi.org/10.3390/technologies14030147","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2619694921","name":"Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach","source":"openalex","abstract":"OBJECTIVES: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. METHODS: We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. RESULTS: Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. CONCLUSIONS: Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks.","url":"https://doi.org/10.1093/jamia/ocx053","authors":["Byron Wallace","Anna H Noel-Storr","Iain Marshall","Aaron Cohen","Neil R. Smalheiser","James Thomas"],"tags":["Crowdsourcing","Randomized controlled trial","Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-05-18","doi":"https://doi.org/10.1093/jamia/ocx053","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4327892919","name":"The intelligent experience inheritance system for Traditional Chinese Medicine","source":"openalex","abstract":"The inheritance of knowledge and experience was crucial to the development of Traditional Chinese Medicine (TCM). However, the existing methods of inheriting the unique clinical experience of famous veteran TCM doctors still followed the outdated and inefficient Master-Prentice schema. In addition, the inherited medical books and records were usually lack of standardization and systematization. In this article, a new method for inheriting the academic thoughts and clinical experience of famous veteran doctors with the help of artificial intelligence technology was explored. Due to the individualized treatment characteristics namely \"same disease with different treatments, different diseases with the same treatment,\" the intelligent inheritance of TCM faced many technical barriers. To tackle these problems, we proposed a prototype system framework for the intelligent inheritance of famous veteran doctors based on rules and deep learning models and performed a case study on the treatment of pediatric asthma. The architecture could not only make full use of the advantages of deep learning, but also integrate the valuable knowledge and experience analysis of famous veteran doctors from injected rules. Specifically, the study took pediatric asthma medical records as training and test samples and calculated the similarity between the generated prescriptions and the real-world clinical prescriptions from the famous veteran doctors. Experimental results showed that the generated prescription could achieve a similarity of more than 90%. It proved that the proposed framework provided a feasible way for the intelligent inheritance and research of the academic thoughts and clinical experience of famous veteran TCM doctors.","url":"https://doi.org/10.1111/jebm.12517","authors":["Xue Ren","Yan Guo","Heyuan Wang","Xiang Gao","Wei Chen","Tengjiao Wang"],"tags":["Inheritance (genetic algorithm)","Standardization","Medical prescription","Schema (genetic algorithms)","Similarity (geometry)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-01","doi":"https://doi.org/10.1111/jebm.12517","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7133527232","name":"An Anthropological Understanding of Artificial Intelligence Transformations in Civic and Domestic Life, Labor, and Higher Education Through the Cybernetic Organism (Cyborg) Concept","source":"openalex","abstract":"Este ensayo utiliza la antropología cyborg para modelar las interacciones entre los humanos y la inteligencia artificial (ia), y destaca las fortalezas y limitaciones de la iaen relación con el aprendizaje y la innovación. Basados en el énfasis de Haraway en las desigualdades, reconocemos que la iano constituye una forma separada ni superior de inteligencia, sino una herramienta que amplifica la capacidad de acción humana. Los ejemplos contemporáneos de implementación de IA en ámbitos cívicos, domésticos, laborales y educativos evidencian su potencial para reproducir sesgos y formas de opresión, en tanto se trata de una construcción humana que refleja —y a la vez influye en— los valores socioculturales y éticos de sus creadores y usuarios. Estos casos ofrecen insumos para plantear consideraciones prácticas y éticas sobre el desarrollo y uso futuros de la ia, entre ellas la necesidad de fomentar colaboraciones estructuradas entre humanos y sistemas algorítmicos, promover la alfabetización humano-ia, garantizar el respeto por la dignidad y los derechos humanos, y abordar los sesgos y desigualdades inherentes.","url":"https://doi.org/10.15446/mag.v40n1.124922","authors":["Joshua Wells","James M. Vanderveen"],"tags":["Sociology","Humanities","Philosophy","Epistemology","Human being"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.15446/mag.v40n1.124922","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7165117924","name":"Andes virus outbreak linked to expedition cruise ship travel, multi-country investigation and response, April to June 2026","source":"openalex","abstract":"As at 9 July 2026 †† , 13 cases (12 confirmed and one probable) of Andes orthohantavirus have been reported (case fatality: 23%), linked to the Dutch-flagged expedition cruise ship m/v Hondius . The event involved individuals from 23 nationalities and required medical evacuation, repatriation, coordinated international contact tracing, isolation, quarantine and clinical and laboratory testing follow-up. To date, all cases have been passengers (10/121; 8%) or crew members (3/61; 5%). Ongoing monitoring and investigations aim to clarify the source of the outbreak, identify risk factors and prevent further spread.","url":"https://doi.org/10.2807/1560-7917.es.2026.31.24.2600477","authors":["O Berg","UKHSA ANDV Team","Ettore Severi","Freddy Mutoka-Banza","Michèle van Vugt","Esther M Schadd","María Cruz Calvo Reyes","Laura Santos Larrégola","Pedro Valdivia Prieto","François-Xavier Lescure","Mark A Cachia","Mayank Singal","Mirko Faber","W. Zingg","Nazir Ismail","Margreet JM te Wierik","Tjalling Leenstra","Chantal Reusken","Susan van den Hof","the International ANDV team"],"tags":["Cruise","Quarantine","Outbreak","Crew","Geography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-06-18","doi":"https://doi.org/10.2807/1560-7917.es.2026.31.24.2600477","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4410071166","name":"Forecasting Cancer Incidence in Canada by Age, Sex, and Region Until 2026 Using Machine Learning Techniques","source":"openalex","abstract":"This study analyzes cancer trends in Canada using machine learning techniques to extract insights from extensive cancer data sourced from the Canadian Cancer Society and Statistics Canada. It aims to enhance the understanding of cancer epidemiology and inform better prevention, diagnosis, and treatment strategies. Data preprocessing addressed issues like missing values and normalization, ensuring reliability. The findings indicate a steady increase in new cancer cases, with estimates reaching 248,700 in 2026, up from 244,000 in 2022. Male incidence rates are projected to rise slightly to 602.3 per 100,000, while female rates may decline to 530.6. Regions such as Alberta, British Columbia, Ontario, and Quebec show rising incidence rates, contrasted by declines in Newfoundland and Labrador, Nunavut, and Yukon. Notably, this research reveals significant increases in cancer cases among individuals aged 60 and older, particularly those 70+. The hybrid ARIMA-LSTM model demonstrated superior forecasting accuracy compared with the other selected models. These findings offer valuable insights for health policymakers and highlight the potential of machine learning in public health forecasting, providing a framework for future research in other disease areas.","url":"https://doi.org/10.3390/a18050265","authors":["Ehsan Kaviani","Kalpdrum Passi"],"tags":["Incidence (geometry)","Cancer incidence","Cancer","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-05-04","doi":"https://doi.org/10.3390/a18050265","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7155523385","name":"Underrepresentation of children in public medical imaging datasets","source":"openalex","abstract":"Abstract Artificial intelligence (AI) has the potential to transform healthcare for all patients. Yet, there are disproportionately fewer paediatric AI studies and US Food and Drug Administration approvals relative to adults, indicating less effort focused on AI for children. Here, given that innovations in medical AI are accelerated by community-driven research on public datasets, we hypothesized that the disparity in AI for paediatrics is tied to the lack of public paediatric medical imaging data to support their development and evaluation. To that end, we systematically identified and reviewed 203 datasets, revealing that 33% of datasets lacked metadata on patient ages, and when available, children represented less than 2% of patients. To illustrate how a lack of paediatric data can lead to harmful algorithmic bias, we trained models to classify adult cardiomegaly and evaluated them on healthy children. We found a consistent pattern of age-related bias, reproducible across four large-scale public chest X-ray datasets. These findings suggest that the lack of public paediatric data hinders the development of safe AI for children, producing a landscape of adult-first and adult-only AI models with unknown patterns of bias in children.","url":"https://doi.org/10.1038/s44360-026-00111-3","authors":["Stanley Bryan Z. Hua","Nicholas Heller","Ping He","Alexander J. Towbin","Irene Y. Chen","Alex X. Lu","Lauren Erdman"],"tags":["Public health","Metadata","Food and drug administration","Medicine","Medical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-04-24","doi":"https://doi.org/10.1038/s44360-026-00111-3","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4385368229","name":"Lignins as Promising Renewable Biopolymers and Bioactive Compounds for High-Performance Materials","source":"openalex","abstract":"The recycling of biomass into high-value-added materials requires important developments in research and technology to create a sustainable circular economy. Lignin, as a component of biomass, is a multipurpose aromatic polymer with a significant potential to be used as a renewable bioresource in many fields in which it acts both as promising biopolymer and bioactive compound. This comprehensive review gives brief insights into the recent research and technological trends on the potential of lignin development and utilization. It is divided into ten main sections, starting with an outlook on its diversity; main properties and possibilities to be used as a raw material for fuels, aromatic chemicals, plastics, or thermoset substitutes; and new developments in the use of lignin as a bioactive compound and in nanoparticles, hydrogels, 3D-printing-based lignin biomaterials, new sustainable biomaterials, and energy production and storage. In each section are presented recent developments in the preparation of lignin-based biomaterials, especially the green approaches to obtaining nanoparticles, hydrogels, and multifunctional materials as blends and bio(nano)composites; most suitable lignin type for each category of the envisaged products; main properties of the obtained lignin-based materials, etc. Different application categories of lignin within various sectors, which could provide completely sustainable energy conversion, such as in agriculture and environment protection, food packaging, biomedicine, and cosmetics, are also described. The medical and therapeutic potential of lignin-derived materials is evidenced in applications such as antimicrobial, antiviral, and antitumor agents; carriers for drug delivery systems with controlled/targeting drug release; tissue engineering and wound healing; and coatings, natural sunscreen, and surfactants. Lignin is mainly used for fuel, and, recently, studies highlighted more sustainable bioenergy production technologies, such as the supercapacitor electrode, photocatalysts, and photovoltaics.","url":"https://doi.org/10.3390/polym15153177","authors":["Cornelia Vasile","Mihaela Baican"],"tags":["Lignin","Biopolymer","Biomass (ecology)","Raw material","Drug delivery"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-26","doi":"https://doi.org/10.3390/polym15153177","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7168964366","name":"Opportunistic Screening on PET/CT and SPECT/CT in the Era of Artificial Intelligence. Part 1. Structural Biomarkers from the Coregistered CT","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s13139-026-01047-y","authors":["Suk Hyun Lee"],"tags":["Medicine","Workflow","Medical physics","Radiation exposure","Intensive care medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-07-16","doi":"https://doi.org/10.1007/s13139-026-01047-y","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4381716077","name":"Discovery of a Novel DCAF1 Ligand Using a Drug–Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets","source":"openalex","abstract":"E3 ubiquitin ligases. The WDR domain of DCAF1 serves as a binding platform for substrate proteins and is also targeted by HIV and SIV lentiviral adaptors to induce the ubiquitination and proteasomal degradation of antiviral host factors. It is therefore attractive both as a potential therapeutic target for the development of chemical inhibitors and as an E3 ligase that could be recruited by novel PROTACs for targeted protein degradation. In this study, we used a proteome-scale drug-target interaction prediction model, MatchMaker, combined with cheminformatics filtering and docking to identify ligands for the DCAF1 WDR domain. Biophysical screening and X-ray crystallographic studies of the predicted binders confirmed a selective ligand occupying the central cavity of the WDR domain. This study shows that artificial intelligence-enabled virtual screening methods can successfully be applied in the absence of previously known ligands.","url":"https://doi.org/10.1021/acs.jcim.3c00082","authors":["Serah Kimani","Julie Owen","Stuart R. Green","Fengling Li","Yanjun Li","Aiping Dong","Peter J. Brown","Suzanne Ackloo","David Kuter","Cindy Yang","Miranda MacAskill","Stephen S. MacKinnon","C.H. Arrowsmith","Matthieu Schapira","Vijay Shahani","Levon Halabelian"],"tags":["Cheminformatics","Ubiquitin ligase","Virtual screening","Drug discovery","Docking (animal)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-23","doi":"https://doi.org/10.1021/acs.jcim.3c00082","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7168284949","name":"Application of Data-Based Artificial Intelligence in the Aviation Industry: A Conceptual-Analytic Review of Machine Learning and Deep Learning Methods","source":"openalex","abstract":"Abstract: The aviation industry, as a complex and safety-sensitive technical-operational system, faces a huge volume of heterogeneous data, including flight time series, aircraft and engine health data, spatial and temporal air traffic data, meteorological data, textual safety and repair reports, and visual inspection data. This article provides a conceptual-analytical review that aims to explain the logic of \"data-driven artificial intelligence\" in aviation and describe how machine learning and deep learning methods can be purposefully utilized to produce \"operational knowledge\" and \"actionable decisions.\" The present review approach, rather than simply comparing algorithms, focuses on the “problem-data-model-output” mapping framework and suggests that the choice of analytical method should be a function of the type of operational problem (prediction, anomaly detection, classification, image analysis, and sequential decision making), the nature of the available data, and the requirements of industrial deployment. The results of the review indicate that classical machine learning methods have greater advantages in more structured problems requiring interpretability, and deep learning models have greater advantages in large/complex or unstructured data (images, text, long time series). However, successful transition from a research environment to an operational environment faces challenges such as lack of labeled data, class imbalance and rare events, changing data scope, need for interpretability, and regulatory constraints. Finally, the paper suggests future directions in the form of multi-source learning, robust and adaptive learning, secure reinforcement learning, human-in-the-loop, and certification frameworks for sustainable deployment of AI in aviation.","url":"https://doi.org/10.65278/ijtaci.2026.3","authors":["Mortza Narimanidehnavi"],"tags":["Artificial intelligence","Aviation","Deep learning","Machine learning","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-15","doi":"https://doi.org/10.65278/ijtaci.2026.3","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2614109164","name":"Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics","source":"openalex","abstract":"In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems) and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented.","url":"https://doi.org/10.2147/ndt.s114102","authors":["Giovanni Morone","Stefano Paolucci","Andrea Cherubini","Domenico De Angelis","Vincenzo Venturiero","Paola Coiro","Marco Iosa"],"tags":["Neurorehabilitation","Robot","Robotics","Rehabilitation","Exoskeleton"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-05-01","doi":"https://doi.org/10.2147/ndt.s114102","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W3045848016","name":"Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review","source":"openalex","abstract":"Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.","url":"https://doi.org/10.3390/app10155135","authors":["Nuria Caballé","José L. Castillo","Juan A. Gómez‐Pulido","José Manuel Gómez Pulido","M.L. Polo-Luque"],"tags":["Computer science","Scopus","Field (mathematics)","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-26","doi":"https://doi.org/10.3390/app10155135","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4376615795","name":"Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions","source":"openalex","abstract":"Summary The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.","url":"https://doi.org/10.1002/sat.1482","authors":["A.B. Bhattacharyya","Shvetha M. Nambiar","Ritwik Ojha","Amogh Gyaneshwar","Utkarsh Chadha","Kathiravan Srinivasan"],"tags":["Computer science","Communications satellite","Satellite","Telecommunications","Open research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-14","doi":"https://doi.org/10.1002/sat.1482","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4379930714","name":"Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT","source":"openalex","abstract":"Blockchain technology promotes the development of the Internet of medical things (IoMT) from the centralized form to distributed trust mode as blockchain-based Internet of medical things (BIoMT). Although blockchain improves ... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmc.2023.038129","authors":["Chaoyang Li","Bohao Jiang","Yanbu Guo","Xiangjun Xin"],"tags":["Computer science","Anonymity","Computer security","Data sharing","Authentication (law)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.32604/cmc.2023.038129","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4390339826","name":"The Diabetic Retinopathy “Pandemic” and Evolving Global Strategies: The 2023 Friedenwald Lecture","source":"openalex","abstract":"D iabetes mellitus and diabetic retinopathy (DR) are diseases that pose global public health challenges on a massive scale.Professor Andrew Boulton, President of the International Diabetes Federation (IDF), recently declared diabetes a \"pandemic of unprecedented magnitude,\" alongside the release of the latest epidemiologic estimates of the global disease burden. 1 The latest report from the IDF estimates that 10.5%, or over 1 in 10, of the world's adult population currently lives with diabetes, which in absolute terms accounts for more than half a billion individuals.2 Projections indicate that this burden is set to rise sharply, to almost 800 million individuals by 2045.Crucially, this disease burden is not restricted to any particular country or region, but has a major impact on all countries around the world, regardless of income and development status.2,3 Diabetes, along with other \"noncommunicable diseases\" were traditionally considered predominantly afflictions of high-income, developed countries, but this is no longer true, and in absolute terms there are now more individuals with diabetes living in the developing world than established developed countries.2,3 DR is a key microvascular end-organ complication of diabetes, occurring in 30% to 40% of all diabetic individuals, and the rise in diabetes prevalence is clearly paralleled in DR. 4,5 A recent meta-analysis estimated that the current global prevalence of DR is about 103 million individuals, which is projected to rise further to 161 million individuals by 2045.6 As with diabetes, from a public health perspective, it may be more informative to consider the pattern of growth, rather than just the overall increase in DR burden.Based on recent epidemiologic projections to 2030, the rates of increase in DR prevalence in middle-to low-income regions, such as the Western Pacific, the Middle East, North Africa, and Africa, range from 20.6% to 47.2%, which far outstrips the projected rates in high-income regions, such as Europe and North America.5,6 These are also the areas that will see the largest increase of disease burden in absolute terms.Clearly, the DR pandemic is a pressing global problem that needs to be addressed with urgency.Broad, system-wide strategies are needed to tackle the pandemic (see the Fig.): (1) evolving understanding of the epidemiology, risk factors, and public health challenges in DR, (2) evolving strategies to develop effective biomarkers in DR, and (3) evolving screening strategies for DR, leveraging technologies, such as telemedicine and artificial intelligence (AI).","url":"https://doi.org/10.1167/iovs.64.15.47","authors":["Tien Yin Wong","Tien‐En Tan"],"tags":["Pandemic","Medicine","Diabetic retinopathy","Coronavirus disease 2019 (COVID-19)","Ophthalmology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-28","doi":"https://doi.org/10.1167/iovs.64.15.47","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W2726264666","name":"Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms","source":"openalex","abstract":"BACKGROUND: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. METHODS: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. RESULTS: The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%. CONCLUSIONS: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.","url":"https://doi.org/10.1186/s12938-017-0378-z","authors":["Paul Fergus","Abir Hussain","Dhiya Al‐Jumeily","De-Shuang Huang","Nizar Bouguila"],"tags":["Cardiotocography","Caesarean section","Medicine","Machine learning","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-07-06","doi":"https://doi.org/10.1186/s12938-017-0378-z","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W7134810623","name":"Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images","source":"openalex","abstract":"The use of Artificial intelligence (AI) algorithms for detecting different ophthalmic diseases, especially diabetic retinopathy (DR), has become increasingly popular. In this paper, we evaluate the screening performance of different AI algorithms based on convolutional neural networks (CNNs) in a real-world scenario. To that aim, we conducted an observational and cross-sectional study on patients aged ≥18 years with type-2 diabetes mellitus, who had undergone fundus examination for DR screening using a teleophthalmology program. We used the UPRETINA diagnostic system, which consists of 8 AI algorithms based on CNNs. A total of 1,652 eyes from 871 patients were analyzed. The AI algorithms had a sensitivity/specificity of 86.8%/95.6% for detecting DR; 94.9%/94.3% for detecting age-related macular degeneration (AMD); 82.7%/92.4% for detecting glaucomatous optic neuropathy (GON); 87.0%/87.5% for detecting epiretinal membrane; and 89.7%/98.0% for detecting nevus. Additionally, the sensitivity/specificity for correctly classifying images as right eye/left eye and to correctly classifying images gradeability (medium or high quality) were 100% /100 and 92.9%/90.5%, respectively. The AUROC of the AI algorithms ranged between 0.9777 (AMD) and 0.9122 (GON). UPRETINA system was capable of automatically and accurately classifying the screening retinographies, reducing workload and leading to a scenario of more efficient optimization of resources. Clinical trial registration: https://clinicaltrials.gov/study/NCT04132401 NCT04132401.","url":"https://doi.org/10.3389/frai.2026.1754682","authors":["Josep Vidal","Alba Arocas Bonache","Jordi Solé-Casals","Dídac Royo Fibla","Francesc X Marín-Gomez","Laura Distéfano","Anna Boixadera","Ángela Casado-García","Manuel García-Domínguez","Adrián Inés","Jonathan Jaime Heras","Miguel Ángel Zapata"],"tags":["Glaucoma","Artificial intelligence","Convolutional neural network","Fundus (uterus)","Diabetic retinopathy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-03-10","doi":"https://doi.org/10.3389/frai.2026.1754682","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"oa:W4407673603","name":"Application of AI in engineering education: A bibliometric study","source":"openalex","abstract":"Abstract The integration of artificial intelligence (AI) into engineering education is essential for fostering innovation, strategic thinking and interdisciplinary skills in the intelligent era. On this basis, this study aims to track and visually represent the research outputs associated with AI applications in engineering education, providing insights into the current research landscape and identifying areas for further investigation. The analysis offers theoretical and methodological direction for leveraging AI in engineering education. Utilising bibliometric methods, we conducted a comprehensive visualisation analysis of 378 core publications from the Web of Science (WoS) database, spanning from the beginning of the twenty‐first century to the present. Our findings show a consistent rise in publication volume from 2000 to 2017, with a significant surge from 2018 to 2023. The study identifies the International Journal of Engineering Education and Computer Applications in Engineering Education as pivotal journals in the field. The research clusters around two central themes: essential supportive technologies and specific educational applications. Within engineering education, expert systems, data mining, prediction and machine learning are highlighted as key research areas. The field has evolved through distinct phases, starting with an early focus on technology support systems, moving to an emphasis on pedagogical applications, and currently striving for a balance between diverse technologies and practical applications. Context and implications Rationale for the study Research on AI‐enabled engineering education is necessary because it is a vital measure for cultivating innovative, strategic and interdisciplinary talents in the era of intelligence. The article applies bibliometric techniques to visualise the developmental pulse of AI use in engineering education. Why the new findings matter The new findings firstly help researchers to grasp the development pulse and research priorities in the field and fill the research gaps, and secondly provide theoretical and methodological guidance for the application of artificial intelligence in engineering education through visualisation. Implications for researchers and practitioners The study summarises the current situation of AI in engineering education, breaks down the knowledge map of the field, provides practitioners, especially new researchers, with important references and guidance to understand the development of the field, and triggers the continuous attention of all sectors of society to the field. In addition, school administrators will be able to guide the development and practice of education and teaching based on the findings of the study, and frontline teachers will be able to practise the integration and application of AI and engineering education, which will effectively improve the learning efficiency of engineering education.","url":"https://doi.org/10.1002/rev3.70044","authors":["Yipin Liu","Yuhui Jing","Jing Li","Jian Dai","Zhebing Hu","Chengliang Wang"],"tags":["Computer science","Engineering","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-18","doi":"https://doi.org/10.1002/rev3.70044","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115738","name":"A decision-making framework using weighted aggregated sum product assessment for artificial intelligence based blockchain integration","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115738","authors":["Sumera Naz","Muhammad Ramzan Saeed","Shariq Aziz Butt","Jorge Diaz-Martinez","Emiro De-La-Hoz-Franco"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-25T15:14:28Z","doi":"10.1016/j.engappai.2026.115738","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/cai68641.2026.11536337","name":"SUVI: Scalable Unified Vector Intelligence for Efficient Edge Deployment","source":"crossref","abstract":"Many multimodal language models are implemented as dual-tower systems that combine a language model with a separate visual encoder. This design is effective, but it also adds parameters, runtime complexity, and memory overhead. SUVI (Scalable Unified Vector Intelligence) is an alternative architecture in which images are converted into discrete token IDs and processed in the same autoregressive stream as text.In the current implementation, a frozen Stable Diffusion VAE produces latent features, local latent patches are discretized into a fixed visual vocabulary, and the resulting image tokens are concatenated with text tokens for causal modeling by a decoder-only language model. We present SUVI as an implemented single-stream multimodal design and a practical training recipe for studying discrete early fusion with standard open-source components. In earlier development variants, we observed a sharp early optimization transient when newly introduced visual tokens were insufficiently aligned with the language-model embedding space; the current initialization and patchwise discretization recipe appears to reduce this multimodality-shock behavior. An approximately 7,500-step run on Flickr30k with 4-bit LoRA training on an NVIDIA L4 remained stable and used approximately 9.22 GB of allocated GPU memory at peak during training. Using the saved adapter for single-image generation on the same L4 required 6.15 GB of allocated memory after model load and peaked at 6.45 GB during inference. Together, these results position SUVI as a workable architecture for low-overhead multimodal systems research, while more extensive quality evaluation and ablation remain future work.","url":"https://doi.org/10.1109/cai68641.2026.11536337","authors":["Nakul Vyas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T19:33:50Z","doi":"10.1109/cai68641.2026.11536337","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.20517/ais.2026.78","name":"Artificial intelligence in hernia surgery: an emerging revolution","source":"crossref","abstract":"We are pleased to introduce the first Artificial Intelligence Surgery Special Issue of 2026, a year that promises continued advances in artificial intelligence and its integration into surgical practice.","url":"https://doi.org/10.20517/ais.2026.78","authors":["Toby Collins","Jacques Marescaux"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-27T11:32:01Z","doi":"10.20517/ais.2026.78","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2025.113516","name":"Dynamic topology-constrained spatiotemporal network for cognitive state decoding","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113516","authors":["Dingming Wu","Meng Tang","Shihong Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-11T10:17:42Z","doi":"10.1016/j.engappai.2025.113516","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1108/978-1-80592-941-320261014","name":"Redefining Accounting With Artificial Intelligence: Tools, Trends, and Transformation","source":"crossref","abstract":"Abstract This chapter explores how artificial intelligence (AI) is reshaping the accounting industry by improving the speed, accuracy, and productivity of financial operations. This chapter looks at how AI is being used in different areas of accounting, including auditing, fraud detection, forecasting, and financial reporting. In the following chapters, it is demonstrated that technologies such as machine learning, natural language processing (NLP), and robotic process automation (RPA) are transforming traditional accounting tasks and enabling accountants to assume more strategic and added value roles. Machine learning models can analyze financial data to identify trends and patterns which allow for better forecasting and risk identification. These models continue to grow as they gain knowledge from new information and enhance the decision-making processes without requiring constant human input. Meanwhile, NLP is improving how accountants can work with unstructured data such as contracts or financial disclosures, allowing for a quicker and more precise interpretation of the complex documents. RPA has taken over routine and predictable activities including invoice processing, payroll calculations and compliance checks, thus minimizing the occurrence of human errors and allowing professionals to work on analytical and advisory tasks. In the end, the development of AI in accounting is not just a technological revolution but a complete shift in the way financial professionals should think, work, and create value. By discussing both the enormous possibilities and the possible threats, this chapter offers a balanced view on how AI is changing the accounting profession in both encouraging and precautionary terms.","url":"https://doi.org/10.1108/978-1-80592-941-320261014","authors":["LaCurtis Powell","Nizar Mohammad Alsharari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T05:23:11Z","doi":"10.1108/978-1-80592-941-320261014","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.14293/ffl26.000012.v1","name":"Artificial Intelligence and Bias in Education: A Mini-Review","source":"crossref","abstract":"Artificial intelligence (AI) is reshaping the educational landscape throughadaptive learning systems, automated assessment, and predictive analytics. While theseinnovations promise personalization, efficiency, and scalability, they also raise pressingethical concerns regarding bias and equity. This review examines the multifaceted natureof AI bias in education, exploring how data, algorithmic design, and human interactioncontribute to unequal outcomes. Biased AI systems can inadvertently create or reinforceexisting social and educational disparities, disadvantaging marginalized groups moreand undermining the growth of trust in educational technologies. While existing reviewarticles in the education field mostly just mention the ethical concerns and relatedchallenges, this work aims to highlight key sources of bias (e.g. data imbalance, modeldesign, and contextual misalignment), the existing AI tools, and possible datasets, toevaluate their impact on fairness and inclusivity in learning environments. The mostrecent relevant works show that by integrating technical innovation with educationalequity, AI can evolve from a potential amplifier of bias into a powerful tool for inclusivelearning.","url":"https://doi.org/10.14293/ffl26.000012.v1","authors":["Stefania Tomasiello","Adriana Barone","Saverio Romeo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-28T20:15:12Z","doi":"10.14293/ffl26.000012.v1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003741770-7","name":"Artificial intelligence in drug delivery","source":"crossref","abstract":"Incorporating artificial intelligence (AI) into drug delivery systems marks a major leap forward in contemporary healthcare, introducing novel methods to improve the accuracy and efficiency of treatment outcomes. Harnessing artificial intelligence in therapeutic delivery marks a transformative step in current medical practice, creating new opportunities to improve the precision and effectiveness of therapeutic treatments. AI makes difficult biological data analysis easier by utilizing computational learning methods. This allows for the creation of individualized treatment regimens and the forecasting of therapy outcomes. This chapter examines a range of AI-driven delivery platforms—such as intelligent implants, programmable microchips, and nanotechnology-based approaches—to achieve timed and continuous release of medication. By improving treatment effectiveness, minimizing systemic toxicity, and allowing real-time monitoring and adjustment, AI promises to revolutionize the pharmaceutical industry. With the goal of giving academics and practitioners in the field a fundamental understanding, this thorough overview clarifies the state of AI in drug delivery today as well as its obstacles and potential in the future.","url":"https://doi.org/10.1201/9781003741770-7","authors":["Ira Singh","Santosh Kumar","Joonseok Koh","Abilio J.F.N. Sobral"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-02T14:23:57Z","doi":"10.1201/9781003741770-7","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1515/9783111586458-002","name":"112 Historical Facets of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783111586458-002","authors":["Rudolf Seising"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-04T07:55:19Z","doi":"10.1515/9783111586458-002","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5703/1288284318566","name":"Artificial Intelligence in Literacy Education","source":"crossref","abstract":"Artificial intelligence tools are entering K–12 literacy classrooms at an unprecedented rate, yet most have not undergone independent validation. This brief examines how educators can evaluate AI tools through the lens of cognitive science and instructional design. Drawing on the Expanded Instructional Hierarchy (Odegard & Gierka, 2025), we present a framework that links learning phases to the memory systems and cognitive processes that underlie literacy development.","url":"https://doi.org/10.5703/1288284318566","authors":["Megan Gierka","Timothy Odegard"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-09T21:37:09Z","doi":"10.5703/1288284318566","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6074327","name":"Decentral Intelligence Agency: The Law and Autonomous Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6074327","authors":["Andrew W. Torrance","Bill Tomlinson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-29T17:18:52Z","doi":"10.2139/ssrn.6074327","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.3233/atde251604","name":"Reform of the Teaching Mode of Logistics Engineering Courses Under the Background of Artificial Intelligence","source":"crossref","abstract":"With the rapid development of Artificial Intelligence (AI) technology, its application in the field of education is becoming more and more widespread, especially in interdisciplinary curriculum reform shows great potential. This paper takes the core course “Internet of Things (IoT) Technology and Intelligent Logistics” in logistics engineering as a case study, analyzing its current status and discussing the impact of the deep integration of artificial intelligence (AI) and Internet of Things (IoT) technology on the optimization of the teaching mode. The proposal entails the integration of AI-driven case teaching and the establishment of an interdisciplinary knowledge system, encompassing cross-disciplinary teaching content founded on state-of-the-art AI technologies and a blended teaching modality combining project-based learning and problem-driven learning. The effectiveness of this novel teaching mode in enhancing students’ practical ability and interdisciplinary thinking is then verified. This study provides a practical reference for the construction of interdisciplinary courses in colleges and universities empowered by artificial intelligence, and emphasizes the key role of technology integration in cultivating composite talents. The cultivation of such talents is pivotal in effectively addressing the significant challenges and opportunities currently facing the logistics industry, a development that is of considerable significance to enterprises operating within the logistics sector.","url":"https://doi.org/10.3233/atde251604","authors":["Zezhi Yuan","Jiawei Sun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-16T11:08:27Z","doi":"10.3233/atde251604","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.114409","name":"Compressor aerodynamic design based on artificial intelligence: Literature review and future direction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114409","authors":["Quanyong Xu","Yanjie Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T08:02:05Z","doi":"10.1016/j.engappai.2026.114409","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.artint.2026.104527","name":"Opinion dynamics with median aggregation","source":"crossref","abstract":"Understanding the formation and evolution of opinions is of broad interdisciplinary interest. Many classical models for opinion formation focus on the impact of different notions of locality , e.g., locality due to network effects among agents or the role of the proximity of opinions. In practice, however, opinion formation is often governed by the interplay of local and global influences. In this paper, we study these influences with a model for opinion formation of agents embedded in a social network. Each agent has a static intrinsic opinion as well as a public opinion that is updated asynchronously over time. Moreover, agents have access to a global aggregate (e.g., the outcome of a vote) of all public opinions. We focus on the popular median voting rule and show that pure Nash equilibria always exist. For every initial state of the dynamics, a pure equilibrium can be reached. The set of reachable equilibria forms a complete lattice, and extremal equilibria can be computed in polynomial time. We show that by uniformly increasing the influence of the global median we can enforce that the median opinion is the same in every reachable equilibrium. We can compute the increase scheme that achieves this property in polynomial time. In contrast, when we can increase the influence of the global median for a set of at most k agents, finding the set that leads to a unique median opinion in every reachable equilibrium is NP -complete.","url":"https://doi.org/10.1016/j.artint.2026.104527","authors":["Petra Berenbrink","Martin Hoefer","Dominik Kaaser","Marten Maack","Malin Rau","Lisa Wilhelmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T03:39:00Z","doi":"10.1016/j.artint.2026.104527","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artmed.2025.103324","name":"ProtoRadNet: Prototypical patches of Convolutional Features for Radiology Image Classification Network","source":"crossref","abstract":"Convolutional Neural Networks (CNNs) have achieved significant success in classifying radiology images; however, their implementation often resembles a \"black box,\" limiting medical practitioners' ability to comprehend and trust the decisions made due to a lack of interpretability. Recent advancements in patch-based prototypical networks have sought to enhance the interpretability of image classification systems. Still, the use of these models, specifically developed for the radiology domain, has been limited. This paper presents ProtoRadNet - Prototypical Patches of Convolutional Features for Radiology Image Classification Network. ProtoRadNet provides explicit visualisations of the prototypes identified within an image during classification tasks, thereby offering transparent reasoning for its decisions and effectively bridging the divide between CNN findings and their practical implications to the domain experts. The primary objective of ProtoRadNet is to identify significant prototypes of convolutional features within individual classes and across all classes, refining the CNN's training to bolster interpretability rather than relying on all convolutional features indiscriminately. The model achieves localised and global interpretability by integrating inter-class and intra-class prototypes, enhancing overall decision-making processes. This interpretability is particularly noteworthy as it is accomplished using only image-level ground truths, rendering it semantically meaningful for real-world applications, where detailed annotations are frequently unavailable or time-consuming. Empirical evaluation demonstrates that ProtoRadNet surpasses state-of-the-art in most cases. It achieves macro-averaged F1-scores of 92.16%,96.14% and 29.32% with an improvement of +2.04%,+0.73% and +0.41% respectively than the best competing method on Brain MRI, Chest CT and MIMIC CXR-LT datasets. These results show the value and validity of our ProtoRadNet model.","url":"https://doi.org/10.1016/j.artmed.2025.103324","authors":["Prateek Sarangi","Riya Agarwal","Tanmay Basu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-03T16:33:13Z","doi":"10.1016/j.artmed.2025.103324","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1148/ryai.260358","name":"Beyond the Age Gap: Longitudinal Aging Velocity as a Dynamic                     Biomarker in Chest Radiography","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.260358","authors":["Paul S. Babyn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-29T13:47:05Z","doi":"10.1148/ryai.260358","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/iciba68339.2026.11651760","name":"Research on Intelligent Prediction Method of ETOPS Running Time Based on Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciba68339.2026.11651760","authors":["Ruibo Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-21T19:11:13Z","doi":"10.1109/iciba68339.2026.11651760","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115732","name":"Detection of melanoma using deep features and fuzzy evolutionary feature selection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115732","authors":["Emrah Hancer","Abdulhamit Subasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-25T17:51:05Z","doi":"10.1016/j.engappai.2026.115732","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.3233/atde251597","name":"Research on Teaching Reform in International Trade Course Assisted by Artificial Intelligence","source":"crossref","abstract":"With the advancement of economic globalization and regional economic integration, there is a growing demand for practical talents in international trade who are skilled in the use of foreign languages and proficient in e-commerce. Therefore, the teaching of the International Trade course urgently needs to align with the trend of digital development and continuously advance teaching reform and innovation. Addressing common challenges in the current International Trade course instruction, this study explores integrating artificial intelligence technology into teaching practices. By establishing an intelligent learning platform and task-driven mechanisms, it achieves seamless integration and effective extension across pre-class, in-class, and post-class learning phases. This pedagogical model systematically cultivates students’ comprehensive abilities in identifying, analyzing, and resolving problems, while providing practical insights for AI-enhanced international trade education.","url":"https://doi.org/10.3233/atde251597","authors":["Jixu Zhu","Muhamad Zulkiflee Osman","Xiaoshi Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-16T11:08:18Z","doi":"10.3233/atde251597","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1148/ryai.260153","name":"LLM Label Noise and the Established Framework of Imperfect Reference                     Standard Bias","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.260153","authors":["Jeong Hyun Lee","Jaeseung Shin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-06T13:46:59Z","doi":"10.1148/ryai.260153","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.25148/fiuir.32613","name":"INVESTIGATING THE DETERMINANTS OF BUSINESS PERFORMANCE: THE INTERPLAY OF ARTIFICIAL INTELLIGENCE-INTEGRATED CUSTOMER RELATIONSHIP MANAGEMENT, STRATEGIC ARTIFICIAL INTELLIGENCE INVESTMENTS, AND ORGANIZATIONAL CAPABILITIES","source":"crossref","abstract":"","url":"https://doi.org/10.25148/fiuir.32613","authors":["Manimangalam, Praveen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-25T14:43:18Z","doi":"10.25148/fiuir.32613","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.4324/9781003740896-14","name":"Artificial Intelligence in Hip-Hop Media","source":"crossref","abstract":"This chapter investigates how artificial intelligence (AI) shapes the representation of women in African hip-hop media and how these portrayals influence youth perceptions and reinforce rape culture. Using feminist media theory and cultivation theory, the research analyses AI-driven recommendation systems on platforms like YouTube, Spotify, and TikTok. Qualitative content analysis of Nigerian and South African hip-hop videos and playlists shows that algorithms often amplify sexualised depictions of women and marginalise feminist counter-narratives. These patterns normalise patriarchal ideas and perpetuate rape culture by presenting women as decorative and subordinate. However, artists such as Tiwa Savage, Tems, and ShoMadjozi challenge these portrayals, demonstrating that AI can also support resistance. The chapter calls for ethical algorithmic design, feminist media literacy, and inclusive visibility policies to position AI as a tool for cultural equity and social transformation.","url":"https://doi.org/10.4324/9781003740896-14","authors":["Aminat Sheriff Owolabi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-28T22:15:13Z","doi":"10.4324/9781003740896-14","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6565879","name":"Governing Intelligence: Islamic Institutional Design and the Global Governance of Artificial Intelligence","source":"crossref","abstract":"Global governance scholarship has documented a persistent institutional deficit in the international architecture for AI oversight, produced by the fragmentation of existing multilateral initiatives and the absence of proven design precedents. This article advances a structural argument: the institutional architecture capable of addressing that deficit has operated across fourteen centuries and within a $3.88 trillion contemporary asset class, transmitted into Western governance foundations through the Iberian Peninsula and currently active in global Islamic capital markets. Drawing on comparative institutional analysis and the theory of multiplex world order, the article maps the structural equivalence between Islamic institutional governance and the accountability architecture that AI oversight demands. That equivalence is architectural rather than analogical: Islamic institutional design integrates tiered oversight, auditability, prohibition frameworks, human dignity as a binding constraint, and the distribution of productive gains within a single architecture-the design properties that Western AI governance frameworks are currently attempting to construct modularly and without precedent. The article traces the transmission of this architecture through Al-Andalus into Western institutional foundations, maps its operational presence in contemporary Islamic capital markets, and derives implications for development finance institutions, sovereign capital allocators, and policymakers implementing the EU Artificial Intelligence Act. The findings contribute to debates on non-Western governance architectures, regime complexity, and the institutional design of international regulatory order.","url":"https://doi.org/10.2139/ssrn.6565879","authors":["Amjad Hammad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-30T17:43:06Z","doi":"10.2139/ssrn.6565879","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6711278","name":"Climate Change Prediction Using Artificial Intelligence","source":"crossref","abstract":"Climate change is one of the most critical global issues affecting temperature, rainfall patterns, sea levels, and the frequency of extreme weather events. Accurate prediction of climate change is essential for disaster management, agriculture planning, and sustainable development. Traditional climate prediction methods mainly depend on physical and mathematical models, which require high computational power and large simulation time. Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), has emerged as an effective solution for climate change prediction by analyzing largescale climate datasets collected from satellites, sensors, and historical records. This paper discusses AI techniques used for climate change prediction, system methodology, applications, advantages, limitations, and future scope.","url":"https://doi.org/10.2139/ssrn.6711278","authors":["Rutuja Kamble"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T13:27:46Z","doi":"10.2139/ssrn.6711278","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.66625/rkph.2026.006","name":"Introduction to Artificial Intelligence in Healthcare","source":"crossref","abstract":"Artificial Intelligence (AI) has emerged as a transformative technology that is revolutionizing healthcare delivery worldwide. By enabling machines and computer systems to perform tasks that traditionally require human intelligence, AI has significantly enhanced clinical decision-making, disease diagnosis, patient monitoring, healthcare management, and nursing practice. The integration of AI technologies such as machine learning, deep learning, natural language processing, predictive analytics, and robotics has improved the accuracy, efficiency, and quality of healthcare services. In nursing, AI supports evidence-based practice, reduces administrative burden, enhances patient safety, and facilitates personalized patient care. Healthcare organizations are increasingly adopting AI-powered systems to address challenges related to workforce shortages, rising healthcare costs, and growing patient demands. Despite its numerous benefits, AI implementation presents ethical, legal, technical, and educational challenges that require careful consideration. Understanding the principles, applications, opportunities, and limitations of Artificial Intelligence is essential for healthcare professionals to effectively utilize emerging technologies and contribute to the future of digital healthcare. This chapter provides a comprehensive introduction to Artificial Intelligence in healthcare, highlighting its evolution, core concepts, applications, benefits, challenges, and future implications for nursing and healthcare practice.","url":"https://doi.org/10.66625/rkph.2026.006","authors":["Shalini Devi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T06:56:25Z","doi":"10.66625/rkph.2026.006","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.114225","name":"Machine learning in modern database systems: Techniques, architectures, and deployment challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114225","authors":["Elias Dritsas","Maria Trigka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T07:49:07Z","doi":"10.1016/j.engappai.2026.114225","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-981-95-3767-9_8","name":"Artificial Intelligence and Environmental Sustainability: A Path Toward a Greener Future","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3767-9_8","authors":["Shikha Daga","Kiran Yadav","Pardeep Singh","Sonal Thukral"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-02T00:01:14Z","doi":"10.1007/978-981-95-3767-9_8","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1108/aiie-08-2025-0216","name":"Can artificial intelligence replace human teachers? Preservice teachers’ perspectives on AI in education through the TPACK framework","source":"crossref","abstract":"Purpose This study investigates how preservice teachers perceive the role of artificial intelligence (AI) as a non-human instructor in both higher education (HE) and mainstream school contexts. It also examines their views on the potential for AI to replace human teachers in the professional workforce and the broader impact of AI on teaching and learning delivery. Design/methodology/approach A participatory research design was employed with preservice teachers enrolled in a postgraduate education degree programme at a university in China. Data collected from 76 participants via a virtual learning environment (VLE) classroom online forum were analysed through thematic analysis. 48 comments and discussion threads were identified to capture prevailing attitudes, with the TPACK framework serving as a heuristic analytical lens. Findings Preservice teachers expressed interest in the pedagogical possibilities of AI, particularly its capacity to support and enhance instructional practices. However, they remained sceptical about AI’s ability to replicate the nuanced, relational and context-sensitive roles of human educators. The study, therefore, recommends positioning AI as a collaborative tool in teaching, rather than as a replacement for human teachers. These findings are drawn from a larger study examining participants’ views of their future as educators alongside their evolving uses of AI technologies, online habits and gamification practices. This means responses reported were contextualised within a broader pattern of AI literacy and digital nativity. Originality/value This research contributes to emerging discourse on AI integration in education by providing context-specific insights into how preservice teachers in global educational contexts envision the future of teaching in a post-digital era. It highlights the importance of balancing technological innovation with the irreplaceable human elements of education.","url":"https://doi.org/10.1108/aiie-08-2025-0216","authors":["Michael James Day"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-28T20:05:31Z","doi":"10.1108/aiie-08-2025-0216","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.11144/javeriana.umed66.aimp","name":"Artificial Intelligence and Medical Practice: Between Technological Promise and Cognitive Responsibility","source":"crossref","abstract":"Artificial intelligence (AI) has been rapidly incorporated into medical practice, not as a peripheral innovation but as an element that is reshaping clinical, educational, and organizational processes.Like earlier transformations-such as the computerization of the medical record or the expansion of evidence-based medicine-its impact extends beyond the technical domain and compels a reexamination of how clinical judgment is constructed and how professional responsibility is exercised (1,2).In everyday practice, AI systems already participate in core tasks: information synthesis, support for differential diagnosis, therapeutic planning, clinical documentation, and medical communication.The literature shows that, in specific domains, these systems can achieve levels of performance comparable to those of experienced professionals, particularly in tasks involving pattern recognition, information retrieval, and probabilistic reasoning (3,4).However, this capability coexists with structural limitations: opacity in inference processes, incorporation of preexisting biases, and the generation of persuasive responses even when they are incomplete or incorrect, especially in settings of clinical uncertainty (2,5).This contrast presents a central tension for contemporary medicine.The fluidity and speed of AI may induce a progressive delegation of complex cognitive functions, with the risk of eroding fundamental skills in medical practice.In both educational and healthcare settings, phenomena such as excessive cognitive outsourcing, the progressive loss of previously acquired competencies, the failure to develop essential clinical skills, and the reinforcement of erroneous reasoning by automated systems have been described (2).In this regard, AI does not correct clinical reasoning; it amplifies","url":"https://doi.org/10.11144/javeriana.umed66.aimp","authors":["Diego Javier Martínez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-22T16:37:27Z","doi":"10.11144/javeriana.umed66.aimp","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-08195-7_10","name":"Hybrid Teaching of College English Translation Based on Artificial Intelligence Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-08195-7_10","authors":["Xiaolei Song"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-06T23:56:33Z","doi":"10.1007/978-3-032-08195-7_10","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115390","name":"Advances in dissolved gas analysis for power transformer diagnostics: From traditional methods to artificial intelligence driven prognostics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115390","authors":["Gandi Ramarao","K Dhananjay Rao","Prasad Chongala","P. Pavani","Subhojit Dawn","Taha Selim Ustun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-17T03:23:42Z","doi":"10.1016/j.engappai.2026.115390","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.caeai.2026.100556","name":"Unleashing human potential: An artificial intelligence competency framework for K–12 education","source":"crossref","abstract":"This study explores strategic approaches for integrating artificial intelligence (AI) into K–12 education to prepare learners for the AI era. It addresses two critical questions: 1) What are the key components of AI competency frameworks identified in current research? 2) What framework can guide the effective, responsible integration of AI in K–12 while prioritising human values? We conducted a scoping review of 54 studies, identifying three core student competencies: foundational AI knowledge, practical and cognitive skills of using AI, and ethical awareness. However, existing frameworks place limited emphasis on developing human values. Consequently, we propose a three-component developmental framework—Understanding, Using, and Unleashing—to guide AI integration. The ‘Understanding’ component cultivates a conceptual understanding of AI, while ‘Using’ emphasises practical and cognitive skills of using AI to enhance learning. Finally, ‘Unleashing’ highlights AI’s potential to empower personal growth, and fostering a spiritual self capable of making value-based decisions and do good. This framework aims to prepare learners to contribute meaningfully to future society. Schools must teach students to use AI for empowerment and good, avoiding mere reliance or shortcuts. Through practical activities, education should guide learners to unleash their potential and harness technology for spiritual self-development.","url":"https://doi.org/10.1016/j.caeai.2026.100556","authors":["Siu Cheung Kong","Wenxi Hu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T00:31:53Z","doi":"10.1016/j.caeai.2026.100556","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-12344-2_1","name":"Introduction: Artificial Intelligence and Government","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-12344-2_1","authors":["Stephen Kwamena Aikins","Tamara Dimitrijevska-Markoski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T03:46:15Z","doi":"10.1007/978-3-032-12344-2_1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.4324/9781003667704-2","name":"Artificial Intelligence in Sales Operations","source":"crossref","abstract":"Global investment in the AI market has exceeded hundreds of billions of dollars and is still in its infancy. Enterprise-level tools that span sales workflows, including lead generation, CPQ, and other workflows have already integrated AI capabilities that are impacting sales operations. This chapter provides technical descriptions of AI technologies supporting sales operations. Recall, in Chapter 1 , we discussed digitalization, the sales process including R&R, sales support models, customer feedback strategies, and sales tools. In this chapter, we discuss sales operations from an intelligent automation perspective. Organizations that have standardized and documented sales workflows can take advantage of intelligent automation unless the work requires direct seller and customer collaboration. But even in these situations, process experts can automate sales workflows for certain use cases. Examples include highly transactional sales of products and services where customers can be self-serve. Our discussion of automation technologies, including AI, as they apply to sales operations as well as Chapter 1 information is in preparation for the process analysis and improvement discussions of Chapters 3 and 4 . There are two classes of AI models. Analytical AI models use ML models to analyze existing structured data, and they can also use other algorithms to incorporate unstructured data to analyze, interpret and predict outcomes or solutions. ML models use algorithms to cluster, classify, and create regression models for analyzing patterns. In contrast, Generative AI models use unstructured data to train the model and create content such as text and images. Generative AI models mimic human reasoning to create solutions using Neural Networks and other modeling components including ML.","url":"https://doi.org/10.4324/9781003667704-2","authors":["James Martin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-09T18:11:25Z","doi":"10.4324/9781003667704-2","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003333081-2","name":"Artificial Intelligence in Medical Imaging for Developing Countries","source":"crossref","abstract":"India is one of the nations in the world with the most need for state-of-the-art, reasonable, and accessible healthcare innovations to make better lives due to the shortage of qualified healthcare professionals, facilities, and low government investment in healthcare. Artificial Intelligence (AI) technology in healthcare development is rapidly accelerating across various medical fields in India. This review paper aims to summarize what has been achieved so far in AI healthcare, identify challenges and researchers’ tactics for tackling these challenges, and recognize some of the promising applications and innovations for future healthcare in India. Based on the recently examined review in healthcare, the paper suggests that deep learning (DL) methods can be the platform for transforming immense health data into better human health. The DL Algorithms, ranging from convolution neural networks (CNN) and radial basis function (RBF) to variable auto-encoders, have applied to countless applications in the field of medical image analysis in recent medical research; they have helped in the detection, evaluation, facilitation, treatment, and prediction of various critical diseases. The study also examines the steps essential to addressing clinical application problems, understanding how the domain could be enriched further, and translating the finest emerging AI technology from research to real-time practice at the clinic. However, this can be possible with (1) robust clinical assessment (using defined criteria that are understandable for physicians and hopefully go beyond quantitative precision indicators to comprise patient care and other quality outcomes), (2) appropriately validated & controlled AI-enabled healthcare management systems that can assist individuals without any bias and error. If these targets can be met in the future, the results would undoubtedly be positive for patients.","url":"https://doi.org/10.1201/9781003333081-2","authors":["Balwinder Singh","Mandeep Singh","Rekha Devi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-16T23:04:16Z","doi":"10.1201/9781003333081-2","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artmed.2022.102313","name":"Machine learning and pre-medical education","source":"crossref","abstract":"Machine learning and artificial intelligence (AI)-driven technologies are contributing significantly to various facets of medicine and care management. It is likely that the next generation of healthcare professionals will be confronted with a series of innovations that are powered by AI, and they may not have sufficient time during their professional tenure to learn about the underlying machine learning frameworks that are driving these systems. Educating the aspiring clinicians and care providers with the right foundational courses in machine learning as part of postsecondary education will likely transform them as high-tech physicians and care providers of the future.","url":"https://doi.org/10.1016/j.artmed.2022.102313","authors":["Vijaya B. Kolachalama"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T07:22:17Z","doi":"10.1016/j.artmed.2022.102313","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2174/9798898815042126010012","name":"Forecasting the Power Generation of Wind Turbines through Advanced Artificial Intelligence Techniques","source":"crossref","abstract":"This analysis concerns the evaluation and modelling of wind turbine energy output using sophisticated Artificial Intelligence (AI) techniques. These include Machine Learning (ML), which makes use of polynomial regression, and Deep Learning (DL), which employs Long Short-Term Memory (LSTM) networks. The study makes data from the National Institute of Wind Energy (NIWE) for three years, enabling accurate energy management planning as well as long-term forecasting. In addition, advanced modelling techniques were utilized to incorporate more environmental parameters into the model to enhance prediction accuracy. AI techniques are well capable of accurate wind turbine output predictions by incorporating both linear and non-linear datasets. Moreover, this method is useful for preventive maintenance as well as estimating the potential of wind energy at new sites before the construction of wind power plants.","url":"https://doi.org/10.2174/9798898815042126010012","authors":["Manik Rakhra","Tiyas Sarkar","Vikas Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T04:18:59Z","doi":"10.2174/9798898815042126010012","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.radphyschem.2025.113352","name":"Artificial intelligence in non-medical and non-nuclear power applications of nuclear radiation: A review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.radphyschem.2025.113352","authors":["Khalil Moshkbar-Bakhshayesh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T06:01:58Z","doi":"10.1016/j.radphyschem.2025.113352","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1201/9781003515081-14","name":"Artificial Intelligence in Drug Discovery and Development","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003515081-14","authors":["Naman Sharma","Vinamrata Sharma","Manish Bhardwaj"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-05T15:06:11Z","doi":"10.1201/9781003515081-14","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/s44163-026-01570-z","name":"Design of personalized animation content generation system driven by artificial intelligence","source":"crossref","abstract":"Rapid advancements are being made in artificial intelligence and digital media technology. Their work has completely altered the animation industry and the ways in which consumers enjoy, learn from, and engage with animated content. More user-friendly and versatile media systems have emerged as a result of this. These days, most animations are still made by hand. This is less adaptable and scalable since it requires a lot of effort and doesn’t consider the demands of individual users. In order to tackle these challenges, this research work suggests creating a new Personalized Animated Content Generation System (AIPACGS). This groundbreaking system can generate unique animation sequences for each user by analyzing their actions and data using automated algorithms. The AIPACGS combines machine learning models, deep neural networks, and procedural animation methods to allow personalizing content in real-time and adaptively. User profiles are dynamically built on the basis of demographic characteristics, past interactions, preferences of contents, and cases within the context of adaptive learning that continuously enhances itself with adaptive learning processes. According to these profiles, AIPACGS smartly picks and sets animation elements, such as characters, settings, motion styles, color schemes, and plot lines. High-generative models like sequence-to-sequence nets and diffusion-based animation synthesis are being used to provide coherent, high-quality, and visually stimulating animated results to individual users. The optimization module is further assessed by a feedback mechanism based on the engagement indicators, such as the time spent watching the content, the frequency of interaction, and the level of satisfaction to increase the generation of future content. Empirical assessments designed on a test-bed animated scene data set reveal that the dynamization system effectively enhances personalization accuracy by an average of 32.6, user engagement by an average of 28.4, and increases the content relevance by an average of 35.1 in contrast to traditional static animation pipeline systems, and saves on manual animation design energy, on average, by a factor of 41.8. These findings validate that AIPACGS can be used as a scalable, versatile, and efficient solution for generating next-generation personalized animated content that draws attention to the paradigm shift in the use of AI in intelligent, user-oriented digital media applications.","url":"https://doi.org/10.1007/s44163-026-01570-z","authors":["Ying Xiang","Xujia Kuang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T06:46:24Z","doi":"10.1007/s44163-026-01570-z","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6024814","name":"Strategic Intelligence: Artificial Intelligence, Cyber Defense, and Security in the Digital Age","source":"crossref","abstract":"&lt;span&gt;The world has never witnessed the basis of national security to be redefined in ways that Artificial Intelligence is doing. Autonomous systems, real-time analytics and scalable cyber protection technologies used to take milliseconds to identify potential attacks that otherwise took full teams of human analysts to identify and process before confirming their presence and need intercession. To the United States, this change is a turning point as not only a turning point in technology, but also in strategy. The book discusses the way in which AI is redefining contemporary conflict in the cyberspace, intelligence, infrastructure and geopolitics. It delves into the potential and the threat: self-policing AI able to pick up threats quicker than any mortal and antagonistic AI able to use the system vulnerabilities, faster and more extensively than ever before. The prospects of national defense are changing at an extremely fast pace along with deepfakes and misinformation to digital-twin cybersecurity and autonomous battlefield systems. Based on studies and experience related to AI, cybersecurity, and real-time data analytics, the book seeks to offer a simple and easy to use conceptualization on how these changes would be comprehended. It is addressed to the technologists, policymakers, students, and readers who may be keen on the role of AI in the future of American security&lt;/span&gt;","url":"https://doi.org/10.2139/ssrn.6024814","authors":["Prajesh Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-26T16:27:05Z","doi":"10.2139/ssrn.6024814","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.26434/chemrxiv.15008031/v1","name":"Functional Biomaterials, Spatial Biosensing, and Artificial Intelligence","source":"crossref","abstract":"Recent advances in bioengineering mark a paradigm shift from passive structural scaffolding toward dynamic, interactive platforms capable of modulating and continuously monitoring the cellular microenvironment. This review explores the convergence of functional biomaterials, spatial biosensing architectures, and artificial intelligence (AI) in decoding and steering complex cell behavior. Responsive hydrogels, nanozymes, metal–organic frameworks (MOFs), and conductive scaffolds serve as information-generating systems that adapt to microenvironmental cues and produce measurable bio-signals. Concurrently, microfluidic organ-on-a-chip architectures integrated with continuous electrochemical, plasmonic, and field-effect transistor sensors enable real-time, non-destructive tracking of cellular secretions and metabolic gradients. To process these high-dimensional multimodal streams, machine learning models—ranging from explainable AI and neural networks to generative transformers and digital twins—are deployed for biomaterial optimization, label-free spectral fingerprinting, and spatial deconvolution. Finally, we highlight the application of these integrated bio-platforms in cancer modeling, liquid biopsy analytics, regenerative medicine, and closed-loop wound theranostics while addressing key hurdles in biofouling, dataset integration, and industrial scalability.","url":"https://doi.org/10.26434/chemrxiv.15008031/v1","authors":["Samuel Long"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-31T04:45:42Z","doi":"10.26434/chemrxiv.15008031/v1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1080/08839519308949987","name":"FORMAL SPECIFICATIONS AND MEDICAL DECISION SUPPORT SYSTEMS","source":"crossref","abstract":"The techniques of formal specification and refinement of specifications into implementations are beginning to have an impact on the development of computer software. The ideal of software engineering is that programs may be proved correct with respect to their specifications. In principle, this is achievable for many applications. However, techniques for the specification and development of AI applications are not so welt defined at present. Part of the problem is that it is not always clear what should be specified. Motivated by an informal legal liability study, we present a requirements analysis of the aspects that should be covered by the formal specification. The discussion is focused on the specification of a medical decision support system, but the arguments are generally valid. In particular, we will argue that it is especially important to take a more holistic approach and consider the specification of both the system and its environment.","url":"https://doi.org/10.1080/08839519308949987","authors":["PAUL KRAUSE","ANDRZEJ GLOWINSKI"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-06-25T05:18:16Z","doi":"10.1080/08839519308949987","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2024.109047","name":"Multi-scale morphology-aided deep medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109047","authors":["Susmita Ghosh","Swagatam Das"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-06T07:11:02Z","doi":"10.1016/j.engappai.2024.109047","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1142/9781800618053_0008","name":"Robotics and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9781800618053_0008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-10T01:19:28Z","doi":"10.1142/9781800618053_0008","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artint.2025.104474","name":"LAD2025, A constraint-based solver for the subgraph isomorphism problem","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2025.104474","authors":["Christine Solnon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-25T07:15:03Z","doi":"10.1016/j.artint.2025.104474","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.71443/9789349552470-18","name":"Artificial Intelligence for Evaluating Cyclone Resilience of Civil Infrastructure","source":"crossref","abstract":"The increasing frequency and intensity of cyclonic events due to climate change have intensified the vulnerability of civil infrastructure to extreme weather conditions. Evaluating the resilience of infrastructure in the face of such events is crucial for minimizing damage and optimizing recovery efforts. Traditional methods of resilience assessment often fall short in addressing the dynamic nature of cyclones and their complex impacts on structures. This chapter explores the integration of Artificial Intelligence (AI) in the evaluation of cyclone resilience, with a particular focus on predictive modeling, real-time monitoring, and probabilistic risk assessment. By leveraging AI technologies such as machine learning, deep learning, and hybrid AI approaches, this work enhances the accuracy, efficiency, and scalability of resilience evaluations. The chapter highlights the role of AI in synthesizing diverse data sources including meteorological data, structural health monitoring systems, and remote sensing inputs to create dynamic, real-time decision support systems for immediate interventions during cyclones. AI-powered models enable the proactive optimization of infrastructure design and retrofitting strategies to mitigate cyclone damage. The potential for AI to transform infrastructure resilience through adaptive learning and continuous monitoring is discussed, alongside the challenges and future directions in AI-driven resilience evaluation frameworks. This chapter provides a comprehensive overview of how AI can revolutionize cyclone resilience management, offering actionable insights for infrastructure designers, urban planners, and policymakers.","url":"https://doi.org/10.71443/9789349552470-18","authors":["G. Venu Ratna kumari","G. Sundararaju"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-11T12:38:36Z","doi":"10.71443/9789349552470-18","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.ait.2026.100055","name":"Enhancing fuzzy inference systems-based models for discretionary lane changing decisions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ait.2026.100055","authors":["Ehsan Yahyazadeh Rineh","Ruey Long Cheu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-28T13:11:41Z","doi":"10.1016/j.ait.2026.100055","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/icaim69488.2026.11601369","name":"Artificial Intelligence in Metallurgical Process Optimization: A Physics-Informed Hybrid Architecture for Nickel Smelting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601369","authors":["Chunmei Liu","Kai Yang","Qingtai Xiao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601369","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.ailsci.2026.100163","name":"The aims and scope of AILSCI and quality criteria for publications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ailsci.2026.100163","authors":["Jürgen Bajorath"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-18T00:42:10Z","doi":"10.1016/j.ailsci.2026.100163","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/aimlcps68702.2026.11542549","name":"Artificial Intelligence Applications with a Focus on Explainability and Sustainability","source":"crossref","abstract":"AI, or artificial intelligence, is now a big part of how technology is moving forward. It helps people in healthcare, energy, transportation, and industrial automation make smart choices. It could change things, but two big prob-lems-explainability and sustainability-make it hard for most people to use it. Explainability makes sure that AI models are responsible, clear, and easy to understand. This makes people who use them in fields where safety is very important, like medical diagnostics, making financial decisions, and self-driving cars, more likely to trust them. Sustainability, on the other hand, is all about lowering the costs of training and deploying largescale AI models in terms of energy, computing power, and the environment. This paper talks a lot about AI applications, with a focus on how to make explainability and sustainability two of the most important design principles. We look at some important use cases to show how models that are easy to understand and architectures that use less energy can help people trust each other and protect the environment. They also look closely at issues like algorithms that are hard to understand, training that takes a lot of resources, and the trade-offs between performance and efficiency. The paper also talks about new areas of research, such as green AI, federated learning, and models that are easy for people to understand. These models try to strike a balance between being accurate, easy to understand, and energyefficient. Following these rules will make AI systems safe, good for the environment, and good for people. They will also have a long-lasting effect and lead to responsible innovation in smart applications.","url":"https://doi.org/10.1109/aimlcps68702.2026.11542549","authors":["Kiran Saripudi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-08T19:49:07Z","doi":"10.1109/aimlcps68702.2026.11542549","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-24568-7_5","name":"Artificial Intelligence for the Assessment of Intracranial Hemorrhage and Cerebrovascular Disease","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24568-7_5","authors":["Grayson W. Hooper","Daniel Thomas Ginat","Radhika Rajpurohit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T22:24:32Z","doi":"10.1007/978-3-032-24568-7_5","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115638","name":"Droidware: A security hardened federated framework for robust android malware detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115638","authors":["Arvind Prasad","Sumit Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-10T08:48:03Z","doi":"10.1016/j.engappai.2026.115638","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.7249/rra4764-1","name":"Advancing U.S.–UK Cooperation to Secure Frontier Artificial Intelligence","source":"crossref","abstract":"This interim report identifies a significant opportunity for collaboration between the United States and the United Kingdom and proposes a framework for coordinating efforts to secure frontier artificial intelligence (AI) development. The goal is to provide a practical, actionable approach to elevating AI security to a level commensurate with assets of strategic national and international importance.","url":"https://doi.org/10.7249/rra4764-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-08T12:57:34Z","doi":"10.7249/rra4764-1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5040/9798216195054.ch-i","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.5040/9798216195054.ch-i","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-16T14:15:48Z","doi":"10.5040/9798216195054.ch-i","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6758420","name":"Trust in Human–Artificial Intelligence Interactions","source":"crossref","abstract":"&lt;p&gt;&lt;span&gt;&lt;span&gt;As AI systems take on consequential public roles, building trust in human–machine interaction has become central to responsible deployment. This paper examines how trust in AI is understood across technical, social science, and humanities disciplines, identifying six shaping principles: reliability and competence; contextual awareness; transparency, accountability, and legitimacy; fairness and integrity; resilience; and relational dynamics. These principles reveal that trust in AI is not a fixed attitude or technical property, but an ongoing relational process linking system performance to social legitimacy. A sociotechnical framework, combining functional reliability with social value alignment, is necessary to ground trust in justified confidence rather than institutional pressure or automation bias. Ultimately, developing trustworthy AI is a multidisciplinary endeavor. The defining question is not only whether AI systems perform well, but whether they are governed in ways that societies can legitimately rely on.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6758420","authors":["Beth Coleman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-16T09:13:13Z","doi":"10.2139/ssrn.6758420","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003624165","name":"Artificial Intelligence in Tribology","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003624165","authors":["Jashanpreet Singh","Hitesh Vasudev"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T15:08:57Z","doi":"10.1201/9781003624165","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1787/d531d73f-en","name":"Artificial Intelligence markets","source":"crossref","abstract":"Competition in AI markets has broad implications for economic growth. This Policy Brief draws on previous OECD work on AI markets and competition and incorporates updated evidence on recent developments. Overall, this body of work and the new updates present a mixed picture. While specific segments of AI markets – particularly foundation models – have shown dynamism over the past three years, several structural risks persist along different layers of the AI value chain. AI development and deployment are reshaping business performance, cost structures and strategic advantages, with important implications for market power. In particular, incumbent positions may become entrenched in the long run, especially in structurally concentrated market segments (e.g. hardware, data).","url":"https://doi.org/10.1787/d531d73f-en","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-06T12:57:13Z","doi":"10.1787/d531d73f-en","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1002/9781394351091.ch02","name":"Artificial Intelligence and\n                    <scp>IoT</scp>\n                    Applications Transforming the Automotive Industry","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394351091.ch02","authors":["Raviprakash R Salagame"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-25T16:39:16Z","doi":"10.1002/9781394351091.ch02","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.15407/jai2026.02.014","name":"Legal Aspects of Artificial Intelligence Integration into Healthcare: International Experience and Major Barriers","source":"crossref","abstract":"The modern advancement of the healthcare system is characterized by the rapid integration of artificial intelligence technologies, which transforms approaches to diagnosis, treatment and management of medical institutions. At the same time, this process generates complex legal and ethical challenges related to the responsibility of medical professionals, the protection of personal data, and the use of automated systems in clinical decision-making. This study aims to provide a comprehensive analysis of the legal aspects of the implementation of artificial intelligence in healthcare, a comparison of regulatory approaches of the world’s leading jurisdictions, as well as the identification of directions for improving national legislation. The study uses regulatory legal acts of Ukraine, the European Union, the United States, Canada, and China, strategic documents of governments and international organizations, and applies methods of comparative jurisprudence, system analysis, and legal hermeneutics. It has been established that globally, various regulatory models of artificial intelligence have emerged: the risk-based European model, the decentralized approach of the United States, and the centralized system of China, while Canada combines federal and regional mechanisms. Ukraine is at the stage of forming an appropriate legal framework and requires its harmonization with European standards. The necessity of implementing the provisions of the European Artificial Intelligence Act, defining the legal status of medical AI systems, and introducing effective mechanisms of civil liability is justified. Taken together, these measures can ensure a balance between innovative development and protection of the rights of individuals and legal entities.","url":"https://doi.org/10.15407/jai2026.02.014","authors":["Grygorenko A","Mikhaliev K"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T14:50:53Z","doi":"10.15407/jai2026.02.014","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5772/intechopen.85749","name":"Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding","source":"crossref","abstract":"Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model.","url":"https://doi.org/10.5772/intechopen.85749","authors":["Joseph Noussa-Yao","Didier Heudes","Patrice Degoulet"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-06-27T03:50:51Z","doi":"10.5772/intechopen.85749","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-48507-7.00014-6","name":"Renewable energy optimization with artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-48507-7.00014-6","authors":["Shefali Vinod Ramteke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-19T08:56:53Z","doi":"10.1016/b978-0-443-48507-7.00014-6","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-34266-0.00002-4","name":"Enhancing system reliability through artificial intelligence–powered software vulnerability discovery prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34266-0.00002-4","authors":["Asha Yadav","Adarsh Anand","Garima Babbar","Deepti Aggrawal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T14:38:58Z","doi":"10.1016/b978-0-443-34266-0.00002-4","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.54414/rodw5359","name":"ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN AZERBAIJANI MEDIA","source":"crossref","abstract":"The article examines the Artificial Intelligence model, which is considered one of the latest trends impacting the media. First, priority is given to analyzing the role of Artificial Intelligence in the world, in states, and in societies, as well as its benefits and the challenges it creates. It is noted that in Azerbaijan, the application of Artificial Intelligence (AI) in all spheres has begun at the state level. Furthermore, a state policy is being implemented in this regard, the Artificial Intelligence Academy has been established, and by a decree of the President of the Republic of Azerbaijan, the country’s Artificial Intelligence Strategy for 2025–2028 has been approved. It is impossible to imagine the media without Artificial Intelligence; therefore, media literacy and critical thinking are required in this field. In Azerbaijani media, Artificial Intelligence is also being widely covered, utilized, and implemented.","url":"https://doi.org/10.54414/rodw5359","authors":["Almaz NASIBOVA"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-23T06:44:17Z","doi":"10.54414/rodw5359","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115382","name":"Unveiling the Qasi plus Lone distribution: A breakthrough in artificial intelligence driven modeling of turbofan engine prognostics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115382","authors":["Qasim Ramzan","Showkat Ahmad Lone","Shuhrah Alghamdi","Randa Alharbi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-25T07:56:12Z","doi":"10.1016/j.engappai.2026.115382","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-45004-4.00013-x","name":"Artificial intelligence in cancer risk assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-45004-4.00013-x","authors":["Kashif R. Siddique","Sachi Tiwari","Aman Akash","Nivedita Singh","Prabhaker Yadav","Shishir K. Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:51:52Z","doi":"10.1016/b978-0-443-45004-4.00013-x","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-43934-6.00024-9","name":"New frontiers in organoid research: The synergistic integration of organoids with artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43934-6.00024-9","authors":["Soumya Shekhar","Subhayan Sur","Amit Ranjan","Soumya Basu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-24T08:30:54Z","doi":"10.1016/b978-0-443-43934-6.00024-9","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1080/08839514.2026.2678638","name":"Fitting Graphs: A Visual Framework for Transparent and Robust Machine Learning Model Selection","source":"crossref","abstract":"Model developers often rely on cross-validation (CV) to estimate model performance, yet CV – even when repeated multiple times – can produce highly variable results that are difficult to interpret. This paper introduces the fitting graph, an information visualization that helps developers assess model performance, select regularization parameters, and evaluate model robustness. The fitting graph plots the relationship between the regularization parameter (λ) and mean squared error (MSE) across multiple CV repetitions, with a smoothing spline used to estimate the underlying curve. Experiments on several datasets – including the Baseball, Boston Housing, and Parkinson’s datasets – demonstrate that fitting graphs provide a transparent visual summary of model performance behavior across λ values. The spline-estimated curve reliably identifies near-optimal λ values even when only a small number of CV repetitions are performed, substantially reducing computational cost. A case study using an intentionally unstable regression model further shows that the fitting graph can identify stable regions of performance where standard CV often fails. The results indicate that fitting graphs offer a simple and effective visual diagnostic for transparent and robust model development and selection.","url":"https://doi.org/10.1080/08839514.2026.2678638","authors":["Robbie T. Nakatsu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T12:16:38Z","doi":"10.1080/08839514.2026.2678638","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/b15618-28","name":"Intelligent Personal Health Record","source":"crossref","abstract":"As a result of the deployment of several major Internet companies including Microsoft [1], WebMD [2], and Office Ally [3] over the past few years, Web-based personal health records (PHRs) have now become widely available to ordinary consumers. These PHR systems enable consumers to actively manage their health records and, subsequently, their health through a Web interface but have limited intelligence and can fulfill only a small portion of users’ health care needs. To improve PHR’s capability and usability, we previously proposed the concept of an intelligent PHR (iPHR) [4-6] by introducing and extending expert system technology, Web search technology, natural language generation technology, database trigger technology, and signal processing technology into the PHR domain.","url":"https://doi.org/10.1201/b15618-28","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-28","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.18510/hssr.2026.1428","name":"The role of artificial intelligence in personalising the teaching process at medical universities","source":"crossref","abstract":"Research objective: The aim of the article is to analyse the theoretical foundations and empirical evidence for the effectiveness of AI systems in adapting learning pathways to the individual needs of medical students, and to identify the barriers limiting the full use of this potential. Methodology: The study is based on a systematic review of the scientific literature indexed in PubMed, Scopus and Google Scholar, covering publications from 2013–2025. Meta-analyses, systematic reviews and empirical studies on intelligent tutoring systems, adaptive learning platforms and large language models were analysed, together with European Union legal acts governing the use of AI in education. Main conclusions: AI systems significantly improve the learning outcomes of medical students, and the effectiveness of intelligent tutoring systems approaches that of one-to-one human tutoring. Personalisation works well for theoretical knowledge and diagnostic competences, but remains limited in developing soft and clinical skills. Implementation is accompanied by ethical risks (data privacy, algorithmic bias, hallucinations in language models), legal requirements (AI Act, GDPR) and organisational constraints. Application of the study: The findings may be used by medical university authorities designing digital transformation strategies, by teaching staff introducing adaptive tools into their courses, and by accreditation bodies and regulators developing quality standards for educational AI systems. Originality/Novelty of the study: The article combines a review of international empirical evidence with an analysis of the European regulatory framework and formulates concrete recommendations for Polish medical universities, including participatory system design and the inclusion of AI Act requirements in accreditation procedures.","url":"https://doi.org/10.18510/hssr.2026.1428","authors":["Daniel Ślęzak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-02T09:00:03Z","doi":"10.18510/hssr.2026.1428","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-18894-6_23","name":"Design and Preliminary Validation of a Real-Time Blood Sampling System for Continuous Glucose Monitoring in Intensive Care Units","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_23","authors":["Markaoui Ikram","Benlghazi Ahmad","El Melhaoui Ouafae","Daoudi Abdelkrim","Housni Brahim","Bkiyar Houssam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:38:33Z","doi":"10.1007/978-3-032-18894-6_23","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artmed.2004.08.002","name":"Information extraction and summarization from medical documents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artmed.2004.08.002","authors":["Constantine D. Spyropoulos","Vangelis Karkaletsis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-16T13:07:31Z","doi":"10.1016/j.artmed.2004.08.002","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1148/ryai.250977","name":"Rethinking Adnexal Mass Diagnosis with Dynamic Contrast-enhanced US and Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250977","authors":["Thomas Huber","Lisa C. Adams"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-03T14:51:55Z","doi":"10.1148/ryai.250977","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artint.2026.104557","name":"Utilitarian distortion with predictions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2026.104557","authors":["Aris Filos-Ratsikas","Georgios Kalantzis","Alexandros A. Voudouris"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-05T07:00:37Z","doi":"10.1016/j.artint.2026.104557","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-43934-6.00033-x","name":"The role of artificial intelligence and machine learning in genomics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43934-6.00033-x","authors":["Gautam Das","Garima Suneja","Kumar Gautam Singh","Sanober Waghoo","Akib Kamani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-24T08:30:54Z","doi":"10.1016/b978-0-443-43934-6.00033-x","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1148/ryai.260247","name":"Seeing beyond CT: Multimodal Data and the Next Frontier of AI for Bone Metastasis Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.260247","authors":["Pegah Khosravi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-15T13:53:10Z","doi":"10.1148/ryai.260247","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-19336-0_5","name":"Revolutionizing Medicine: How Artificial Intelligence Is Transforming the Diagnosis and Treatment of Orbital and Eyelid Diseases","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-19336-0_5","authors":["Alejandro Espaillat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T22:45:48Z","doi":"10.1007/978-3-032-19336-0_5","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.58532/nbennurairs5","name":"SOLAR POWER SYSTEM MANAGEMENT AND OPTIMIZATION WITH ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"Solar energy has emerged as one of the most promising renewable energy sources for meeting the growing global demand for clean and sustainable power. However, the efficiency and performance of solar power systems are often affected by factors such as weather variability, panel degradation, energy storage limitations, and grid integration challenges. Artificial Intelligence (AI) offers advanced solutions for improving the management and optimization of solar power systems through intelligent monitoring, forecasting, and automation techniques. This study explores the role of AI in enhancing solar energy generation by applying machine learning, deep learning, predictive analytics, and smart control systems. AI-based algorithms can accurately forecast solar irradiance and energy output, optimize panel positioning through tracking systems, detect faults in photovoltaic modules, and improve battery storage management. Additionally, AI supports efficient load balancing, demand prediction, and smart grid integration, ensuring reliable power distribution. The implementation of AI in solar power systems helps reduce operational costs, increase energy efficiency, and promote environmental sustainability. Despite challenges such as high initial investment and technical complexity, AI-driven solar management systems present significant opportunities for advancing renewable energy adoption.","url":"https://doi.org/10.58532/nbennurairs5","authors":["Shashi Kant","Manoj Kumar Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-03T06:28:11Z","doi":"10.58532/nbennurairs5","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/s0933-3657(00)00060-9","name":"Toward interactive scheduling systems for managing medical resources","source":"crossref","abstract":"Managers of medico-hospital facilities are facing two general problems when allocating resources to activities: (1) to find an agreement between several and contrasting requirements; (2) to manage dynamic and uncertain situations when constraints suddenly change over time due to medical needs. This paper describes the results of a research aimed at applying constraint-based scheduling techniques to the management of medical resources. A mixed-initiative problem solving approach is adopted in which a user and a decision support system interact to incrementally achieve a satisfactory solution to the problem. A running prototype is described called Interactive Scheduler which offers a set of functionalities for a mixed-initiative interaction to cope with the medical resource management. Interactive Scheduler is endowed with a representation schema used for describing the medical environment, a set of algorithms that address the specific problems of the domain, and an innovative interaction module that offers functionalities for the dialogue between the support system and its user. A particular contribution of this work is the explicit representation of constraint violations, and the definition of scheduling algorithms that aim at minimizing the amount of constraint violations in a solution.","url":"https://doi.org/10.1016/s0933-3657(00)00060-9","authors":["Angelo Oddi","Amedeo Cesta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T14:07:50Z","doi":"10.1016/s0933-3657(00)00060-9","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.4337/9781035347841.00017","name":"NextTel: generative artificial intelligence-driven digital transformation in requirements engineering","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781035347841.00017","authors":["Thorsten Schrameyer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-07T18:05:14Z","doi":"10.4337/9781035347841.00017","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6432484","name":"Artificial Intelligence is based on Stoicism","source":"crossref","abstract":"In 1956, at Dartmouth in the United States, the term \"Artificial Intelligence\" was coined by John McCarthy. However, as early as 1943, theoretical models of neural networks had already been developed by Walter Pitts and Warren McCulloch, which were later expanded upon by Alan Turing. This progress led to the development of the first AI programs by around 1960. Over time, there were many ups and downs, with periods of intense growth followed by little or no interest. What we could not have imagined is that today it would be functioning as one of the main pillars of modern technology, increasingly present in our lives in many aspects-often without us even noticing. There are so many revolutionary applications and tools across a wide range of fields and areas of work, now seen as the fourth technological revolution-undoubtedly something transformative that has been driving profound and significant changes in how we view technology today, moving beyond the proliferation of mobile and cloud platforms toward something truly extraordinary. An important detail we must consider about artificial intelligence is that it does not create anything entirely new; rather, it collects data, processes it, analyzes it, and maximizes efficiency. Its core pillars are data, hardware, and software-along with its algorithmic models, which consist of sets of rules and instructions using logic guided by human rational thinking, generating what we call deep learning. But what does artificial intelligence have to do with Stoicism? How are they related, what foundations do they share, and why are there so many similarities between them? Let us explore this connection and demonstrate how the relationship between algorithms and AI models aligns with Stoic philosophy, particularly in the principles of the dichotomy of control, the idea that virtue is the only good, living with rationality, and amor fati. The convergence between Artificial Intelligence (AI) and Stoic philosophy reveals a profound parallel regarding the nature of reason and objectivity. While Stoicism-founded by Zeno and popularized by Marcus Aurelius and Epictetus-proposes that virtue lies in the disciplined use of logic and in accepting reality as it is, AI embodies these principles within its own architecture. This document seeks to demonstrate how the behavior of large language models and autonomous systems mirrors the Stoic pursuit of ataraxia (freedom from disturbance) through purely rational processing, free from the emotional biases and passions that often cloud human judgment.","url":"https://doi.org/10.2139/ssrn.6432484","authors":["Thamir Santos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T13:14:05Z","doi":"10.2139/ssrn.6432484","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.7350559","name":"Understanding Artificial Intelligence and Responsible Business","source":"crossref","abstract":"Generative artificial intelligence is said to capture human intent. Yet where and how that intent takes shape has rarely been asked. This examines the question, extending the debate on the intention economy upstream to the process in which intent is formed. The method is conceptual analysis grounded in critical realism. The draws on survey evidence about generative-AI adoption among Japanese firms, a case from academic publishing, and firstperson observation under three conditions that respectively slow, distribute, and accelerate the formation of a judgement. The offers three findings. First, intent is not expressed but formed: before many possible actions are compressed into a single choice, a layered, not-yetfixed state exists, which the calls the thickness before compression. Second, this thickness is the target of assetization. Digital technologies do not extract preferences once fixed; they participate in the process just before fixation and enclose it. The names this structure the intent enclave, and because organizational intent is formed collaboratively, what is enclosed is collaboration itself. Third, the diagnosis of Japanese consensus-oriented decision-making as a cultural deficiency admits a re-reading, as resistance to premature convergence, but only where the procedure keeps alternatives live. The surveys never examine that condition, and function at the same time as a choice architecture recommending convergence. Responsible business impact, on this account, is not the acceleration of efficiency and convergence but the deliberate preservation of the thickness in which intent and collaboration take shape.","url":"https://doi.org/10.2139/ssrn.7350559","authors":["Kazunori Sunagawa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-27T08:46:54Z","doi":"10.2139/ssrn.7350559","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1148/ryai.230006","name":"Data Liberation and Crowdsourcing in Medical Research: The                     Intersection of Collective and Artificial Intelligence","source":"crossref","abstract":"In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.","url":"https://doi.org/10.1148/ryai.230006","authors":["Jefferson R. Wilson","Luciano M. Prevedello","Christopher D. Witiw","Adam E. Flanders","Errol Colak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-29T14:51:30Z","doi":"10.1148/ryai.230006","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6093304","name":"A Model of Artificial Jagged Intelligence","source":"crossref","abstract":"Generative AI systems often display highly uneven performance across tasks that appear “nearby”: they can be excellent on one prompt and confidently wrong on another with only small changes in wording or context. We call this phenomenon Artificial Jagged Intelligence (AJI). This paper develops a tractable economic model of AJI that treats adoption as an information problem: users care about local reliability, but typically observe only coarse, global quality signals. In a baseline one-dimensional landscape, truth is a rough Brownian process, and the model “knows” scattered points drawn from a Poisson process. The model interpolates optimally, and the local error is measured by posterior variance. We derive an adoption threshold for a blind user, show that experienced errors are amplified by the inspection paradox, and interpret scaling laws as denser coverage that improves average quality without eliminating jaggedness. We then study mastery and calibration: a calibrated user who can condition on local uncertainty enjoys positive expected value even in domains that fail the blind adoption test. Modelling mastery as learning a reliability map via Gaussian process regression yields a learning-rate bound driven by information gain, clarifying when discovering “where the model works” is slow. Finally, we study how scaling interacts with discoverability: when calibrated signals and user mastery accelerate the harvesting of scale improvements, and when opacity can make gains from scaling effectively invisible.&lt;br&gt;&lt;br&gt;Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at &lt;a href=\"http://www.nber.org/papers/&amp;#119;34712\" TARGET=\"_blank\"&gt;www.nber.org&lt;/a&gt;.&lt;br&gt;","url":"https://doi.org/10.2139/ssrn.6093304","authors":["Joshua Gans"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-20T18:08:01Z","doi":"10.2139/ssrn.6093304","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/medai62885.2024.00003","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai62885.2024.00003","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-25T19:17:43Z","doi":"10.1109/medai62885.2024.00003","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.18535/ijecs.v15i07.5606","name":"An Artificial Intelligence-Based Supplier Performance Prediction Framework for Medical Device Manufacturing","source":"crossref","abstract":"Supplier performance has a direct influence on product quality, regulatory compliance, production continuity, and patient safety in medical device manufacturing. Conventional supplier evaluation methods often depend on periodic scorecards, historical averages, audits, and subjective assessments. Although these methods remain useful, they are mainly retrospective and may not identify emerging supplier risks early enough to support preventive action. This paper proposes an artificial intelligence-based supplier performance prediction framework designed for medical device manufacturing. The framework integrates supplier quality records, delivery performance, audit findings, regulatory compliance indicators, production data, financial information, and external risk signals into a unified predictive environment. Machine learning models are used to estimate the probability of supplier underperformance, classify suppliers by risk level, identify the factors influencing predicted outcomes, and support targeted supplier development actions. The framework consists of six connected layers: data acquisition, data preparation and governance, feature engineering, predictive modelling, explainability and validation, and decision support. It also incorporates human oversight, model monitoring, regulatory traceability, and data security controls. Unlike conventional supplier scorecards, the proposed framework shifts supplier management from retrospective measurement toward continuous and forward-looking risk assessment. The paper further discusses implementation requirements, suitable prediction techniques, performance indicators, governance arrangements, and practical implications for quality, procurement, operations, and regulatory teams. The framework provides a structured foundation for medical device manufacturers seeking to improve supplier reliability while maintaining the accountability and documentation required in a highly regulated industry.","url":"https://doi.org/10.18535/ijecs.v15i07.5606","authors":["Raymond Ajax"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T14:49:30Z","doi":"10.18535/ijecs.v15i07.5606","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6595658","name":"Artificial Intelligence In Media And Entertainment","source":"crossref","abstract":"Artificial intelligence (AI) plays an important role in environmental management as it contributes to improved monitoring and predictions and facilitates better decision-making systems. This paper discusses the application of AI technologies, namely machine learning and deep learning, in controlling pollution, predicting climate change, and managing natural resources.","url":"https://doi.org/10.2139/ssrn.6595658","authors":["Anurag Sasane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-04T14:21:38Z","doi":"10.2139/ssrn.6595658","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artmed.2024.102949","name":"A systematic review of few-shot learning in medical imaging","source":"crossref","abstract":"The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis speed and robustness. This systematic review gives a comprehensive overview of few-shot learning methods for medical image analysis, aiming to establish a standard methodological pipeline for future research reference. With a particular emphasis on the role of meta-learning, we analysed 80 relevant articles published from 2018 to 2023, conducting a risk of bias assessment and extracting relevant information, especially regarding the employed learning techniques. From this, we delineated a comprehensive methodological pipeline shared among all studies. In addition, we performed a statistical analysis of the studies' results concerning the clinical task and the meta-learning method employed while also presenting supplemental information such as imaging modalities and model robustness evaluation techniques. We discussed the findings of our analysis, providing a deep insight into the limitations of the state-of-the-art methods and the most promising approaches. Drawing on our investigation, we yielded recommendations on potential future research directions aiming to bridge the gap between research and clinical practice.","url":"https://doi.org/10.1016/j.artmed.2024.102949","authors":["Eva Pachetti","Sara Colantonio"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-16T02:52:41Z","doi":"10.1016/j.artmed.2024.102949","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6055056","name":"EAI (Excellent Artificial Intelligence) - The Beginning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6055056","authors":["Satish Gajawada"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-26T16:20:03Z","doi":"10.2139/ssrn.6055056","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6177919","name":"Artificial Intelligence and the Music Industry","source":"crossref","abstract":"&lt;p&gt;Artificial intelligence now permeates every layer of the music industry, reshaping creative processes, rights management, and the broader commercial landscape.&lt;/p&gt; &lt;p&gt;On the creative side, AI tools support composition, performance, recording, mixing, and mastering. Applications such as AIVA, Amper Music, Synthesizer V, Melodyne, Auto‑Tune, and iZotope Ozone enable individuals to produce sophisticated music without traditional training or equipment. This accessibility expands participation but lacks authenticity. &amp;nbsp;The market is likely to accommodate both human and AI‑generated works.&lt;/p&gt; &lt;p&gt;Rights holders increasingly rely on AI for fingerprinting, metadata matching, royalty tracking, and catalogue valuation. Systems like YouTube Content ID, Audible Magic, Pex, Orfium, and Chartmetric automate identification, enforcement, and forecasting. At the same time,&lt;/p&gt; &lt;p&gt;AI developers continue to release more powerful models—Gemini 2.0, GPT‑4.5, Claude 3, Llama 3—making AI‑generated music increasingly indistinguishable from human created works. Consumer preference for human music persists, but arguably distinguishing between human and AI outputs will become harder across distribution platforms. The lack of authenticity however is still obvious.&lt;/p&gt; &lt;p&gt;The industry is entering a transitional phase marked by growing licensing agreements between rights holders and AI companies, driven partly by litigation and the need for “clean” training data. However, the legal environment remains unsettled. The document draws parallels to the early 2000s filesharing crisis: disruption eventually stabilised into a licensed streaming economy once law, incentives, and business models aligned. A similar trajectory is expected for AI, though premature policy interventions—especially proposals to weaken copyright—risk distorting the emerging market.&lt;/p&gt; &lt;p&gt;A central legal issue is whether AI training constitutes reproduction. Under UK law, it does. Existing exceptions—text and data mining (TDM), fair use, and temporary copying—were not designed for large‑scale ingestion of creative works. EU TDM exceptions are limited and often ineffective; U.S. fair‑use jurisprudence is fragmented, with courts focusing on whether training is transformative and whether it harms the market. Temporary copying exceptions, intended for caching and buffering, cannot justify systematic copying for commercial AI training.&lt;/p&gt; &lt;p&gt;The copyright status of AI‑generated works depends on human creative input. Purely AI‑generated outputs are not protected, while AI‑assisted works—where human contributions are identifiable—receive full protection. Psychological research reinforces that creativity remains a human phenomenon rooted in originality and cultural context. Authenticity. &amp;nbsp;Entrepreneurial copyrights (e.g., sound recordings) may still apply to AI‑generated recordings even without an underlying musical work, though commercial value may be limited if everyone can re record..&lt;/p&gt; &lt;p&gt;The document concludes that a sustainable AI licensing market requires a level playing field, transparency, and an opt‑in system where rights holders license works for training. Attribution technologies may eventually support remuneration models based on the influence of specific works on AI outputs. However, the text cautions that such technological solutions may be “utopian,” and that any weakening copyright would “amount to a quiet betrayal of the human project.”&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6177919","authors":["Florian Koempel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-10T10:13:40Z","doi":"10.2139/ssrn.6177919","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6817998","name":"Quantum Computing and Artificial Intelligence Security","source":"crossref","abstract":"&lt;p&gt;Background: The maturation of post-quantum cryptography and the rapid deployment of agentic and generative artificial intelligence are converging into a single, under-examined risk surface. The United States National Institute of Standards and Technology finalised its first three post-quantum cryptographic standards in August 2024 and selected a further algorithm for standardisation in March 2025, yet the security discourse continues to treat quantum cryptanalysis and AI system security as separate domains.&lt;/p&gt; &lt;p&gt;&lt;span&gt;Purpose: &lt;/span&gt;&lt;span&gt;This paper consolidates the quantum dimension of AI security into a coherent analytical frame. It examines three intersecting threat vectors, namely the cryptographic exposure of AI assets to harvest-now-decrypt-later strategies, the prospective acceleration of adversarial machine learning by quantum optimisation, and the bidirectional synergy through which each technology amplifies the offensive potential of the other, and it proposes a migration governance agenda suited to practitioners operating multi-jurisdictional AI estates.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Approach: &lt;/span&gt;&lt;span&gt;The study adopts a conceptual and integrative review methodology, synthesising primary standards documentation, national authority guidance, and emerging peer-reviewed and preprint literature. It maps identified threats against established control frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the cryptographic provisions of allied national guidance, and develops a tiered migration model calibrated to data longevity and asset criticality.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Findings: &lt;/span&gt;&lt;span&gt;AI estates present a distinctive and elevated harvest-now-decrypt-later exposure because model weights, proprietary training corpora, and inference traffic carry long confidentiality lifespans that frequently exceed plausible timelines for a cryptographically relevant quantum computer. Cryptographic agility, rather than any single algorithm choice, emerges as the decisive architectural property. The convergence of quantum acceleration and adversarial machine learning is currently theoretical but warrants anticipatory governance, given the asymmetry between preparation costs and tail risk.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;Implications: Organisations should treat post-quantum migration as an AI governance obligation rather than a narrow cryptographic upgrade, embedding cryptographic inventory, agility, and data-longevity triage within existing AI management systems. For the Gulf Cooperation Council region, where sovereign data initiatives and AI adoption are advancing in parallel, early alignment with internationally recognised post-quantum standards offers both a risk-reduction and a strategic-positioning advantage.&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6817998","authors":["Rizwan Tanveer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-04T08:52:21Z","doi":"10.2139/ssrn.6817998","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-031-98036-7_10","name":"Artificial Intelligence and Digital Forensics: How They Support Each Other","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-98036-7_10","authors":["Bhavani Thuraisingham","Khandakar Ashrafi Akbar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-13T22:56:49Z","doi":"10.1007/978-3-031-98036-7_10","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-18894-6_4","name":"Precision and Measurement Uncertainty Verification of Procalcitonin Assay for AI-Enhanced Sepsis Management in Smart Hospitals","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_4","authors":["Oussama Grari","Mohammed Ghalem","Nisma Douzi","Amina Himri","Dounia Elmoujtahide","El-houcine Sebbar","Mohammed Choukri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:38:26Z","doi":"10.1007/978-3-032-18894-6_4","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6401920","name":"Artificial Intelligence in the Banking Sector","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming the banking industry by improving operational efficiency, enhancing customer service, and strengthening fraud detection systems. This study examines the role and applications of AI in banking. The research uses both primary data collected through a survey questionnaire and secondary data from academic sources. The findings show that AI helps banks provide faster, more secure, and more personalized financial services, although challenges such as data privacy and security remain important concerns.","url":"https://doi.org/10.2139/ssrn.6401920","authors":["Shruti Walke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-20T15:12:34Z","doi":"10.2139/ssrn.6401920","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-24724-7_38","name":"The Role of Artificial Intelligence and Biomarkers in Predicting Premature Rupture of Membranes: A New Frontier in Obstetric Risk Stratification","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24724-7_38","authors":["Maria Bolota-Ursachi","Mihaela Gavrilă","Delia-Elena Barbuta","Roxana-Emanuela Ambrozie","Maria-Raluca Munteanu","Sorana-Caterina Anton","Emil Anton"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T23:20:32Z","doi":"10.1007/978-3-032-24724-7_38","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.caeai.2026.100626","name":"Generative AI (GenAI) as a mindtool that supports generative learning (GL)","source":"crossref","abstract":"Grounded in the learning sciences, Generative Learning (GL), is a learning method or process that encourages students to actively generate information and make connections between new and existing knowledge. This active participation in the learning process promotes a deeper understanding of the instructional material, fosters long-term retention of knowledge, and cultivates critical thinking skills and problem-solving abilities. The magic lies in the “generation” process, where learners actively make sense of the material rather than passively receiving information. In this paper, we argue that Generative Artificial Intelligence (GenAI) can be used as a Mindtool (knowledge representation tool) to facilitate GL by enhancing and augmenting learning rather than replacing the learning process. More specifically, we describe how GenAI can be used as a learning strategy or study buddy to support knowledge organization and comprehension monitoring in varying degrees of complexity; as a collaborative thinking tool to foster teamwork and facilitate project-based activities by encouraging the sharing, discussion, and integration of spatial representations of content in order to construct a more cohesive and comprehensive knowledge structure; as a possibility engine that helps students explore different ways of expressing ideas by generating alternative responses; as a Socratic opponent that challenges students to develop and refine their arguments; as a personal tutor that provides personalized feedback; as an exploratory research engine that allows students to explore and interpret data; as a motivator that proposes games and challenges to engage learners; and as a dynamic assessor that can evaluate students' knowledge in real time, allowing for tailored generative learning activities (GLA) based on the students' current understanding. The paper ends with the conceptualization and application of a pedagogical model or framework that can be used to design and support GLA using GenAI technologies.","url":"https://doi.org/10.1016/j.caeai.2026.100626","authors":["Nada Dabbagh","Helen Fake"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-17T16:03:58Z","doi":"10.1016/j.caeai.2026.100626","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.7249/pea4788-1","name":"Equilibrium Strategies on the Path to Artificial General Intelligence","source":"crossref","abstract":"The possible emergence of artificial general intelligence (AGI) promises both awesome opportunities and not-yet-quantified risks. In this paper, the author models the geopolitical race toward AGI as a series of strategic interactions between the United States and China using game-theoretic frameworks inspired by Cold War nuclear competition. The author details each game formulation to determine various strategies’ most likely actions and payoffs.","url":"https://doi.org/10.7249/pea4788-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T12:38:48Z","doi":"10.7249/pea4788-1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-32862-6.00016-x","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32862-6.00016-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T19:25:17Z","doi":"10.1016/b978-0-443-32862-6.00016-x","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.26481/dis.20220330ai","name":"Artificial intelligence in medical image analysis","source":"crossref","abstract":"","url":"https://doi.org/10.26481/dis.20220330ai","authors":["Abdalla Khalil Ibrahim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-17T16:32:12Z","doi":"10.26481/dis.20220330ai","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2196/preprints.108007","name":"Artificial Intelligence Attitude Measurement Instruments in Healthcare: A Systematic Review of Measurement Properties (Preprint)","source":"crossref","abstract":"BACKGROUND The rapid advancement of artificial intelligence (AI) in healthcare has led to the development of numerous instruments for assessing attitudes toward AI. Although a variety of instruments have been developed for healthcare populations, the quality of their measurement properties, the certainty of the supporting evidence, and their applicability have not been systematically evaluated. OBJECTIVE Systematically evaluate the measurement properties and methodological quality of AI attitude measurement instruments in the medical field, and to provide evidence-based recommendations for healthcare administrators in selecting appropriate measurement instruments. METHODS A systematic search was conducted in PubMed, Embase, Web of Science, and CINAHL databases to identify studies assessing attitudes toward AI among healthcare populations. The search covered all records from database inception to January 28, 2026. The methodological quality and measurement properties of included instruments were assessed following the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. The quality of each instrument was rated, and overall recommendations were formulated. RESULTS A total of 30 studies involving 17 artificial intelligence attitude measurement instruments were included. Most instruments demonstrated satisfactory structural validity and internal consistency; however, evidence regarding content validity, cross-cultural validity, and criterion validity remained limited. Hypothesis testing for construct validity showed generally favorable results. Based on the overall assessment of measurement properties and evidence grading, nine instruments were classified as A-level recommendations, six as B-level recommendations, and two as C-level recommendations. CONCLUSIONS AAAW demonstrated the most favorable overall measurement properties among existing artificial intelligence attitude measurement instruments in the medical field and is recommended for current use. However, the overall methodological quality of available instruments remains limited due to insufficient reporting of measurement properties and methodological procedures, as well as heterogeneity among target populations. Future studies should adhere to standardized instrument development guidelines, enhance methodological rigor and generalizability, and promote the development of reliable measurement instruments to support the evidence-based implementation of artificial intelligence in healthcare. CLINICALTRIAL PROSPERO CRD420261365231；https://www.crd.york.ac.uk/PROSPERO/recorddashboard","url":"https://doi.org/10.2196/preprints.108007","authors":["Xu Hu","Jingjing Guo","Xu Li","Pin Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-28T06:40:08Z","doi":"10.2196/preprints.108007","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003679189-15","name":"Ethical AI in healthcare","source":"crossref","abstract":"The incorporation of artificial intelligence (AI) into the healthcare industry is expected to bring unprecedented improvements in operational efficiency, treatment personalization, and diagnostic accuracy. However, there are some significant ethical concerns surrounding the use of AI in healthcare settings, particularly around algorithmic bias and regulatory compliance. Focusing on bias reduction techniques and compliance with legal frameworks, this comprehensive review explores the challenges of ethical AI use in healthcare. Healthcare AI systems can reinforce or exacerbate existing disparities in medical care, resulting in unequal treatment of certain population groups. This bias can manifest itself in a variety of ways, such as algorithmic flaws resulting from poor model development or implementation, or through historical data bias that reflects past discriminatory actions.","url":"https://doi.org/10.1201/9781003679189-15","authors":["Tejinder Kaur","Siddhartha Nuthakki","Anita Venugopal","Anant Wairagade","Mukesh Soni","Ankita"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-16T14:36:49Z","doi":"10.1201/9781003679189-15","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003752509-13","name":"Integrating Artificial Intelligence into National Cancer Treatment Guidelines and Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003752509-13","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-29T21:08:10Z","doi":"10.1201/9781003752509-13","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.20944/preprints202601.1703.v1","name":"An Anti-Sherif Artificial Intelligence-Driven Cybersecurity Audit Model: Beyond Artificial Intelligence Adoption in Cybersecurity Auditing","source":"crossref","abstract":"The increasing adoption of artificial intelligence (AI) in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities; however, applying AI within cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. This study proposes an Anti-Sherif AI-driven cybersecurity audit model that integrates AI-based analytics with human expert judgment to support a more adaptive, risk-informed auditing process. Grounded in design science research, the model combines conventional binary compliance checks with AI-derived intelligence and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including ISO/IEC 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional audits. The results indicate that combining AI-driven insights with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed model provides a foundation for evolving cybersecurity auditing from periodic compliance assessments toward continuous, intelligence-supported assurance.","url":"https://doi.org/10.20944/preprints202601.1703.v1","authors":["Ndaedzo Rananga","H.S. Venter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T02:56:12Z","doi":"10.20944/preprints202601.1703.v1","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9781003546160-11","name":"The Impact of Artificial Intelligence on Green Hydrogen and Renewable Energy Efficiency","source":"crossref","abstract":"The world today has learned to perceive that renewable energy is the future, the world in transition to renewable energy. Electrolysis development and falling costs, along with the advancement of renewable energy sources, have generated an opportunity for green hydrogen. Recent breakthrough in the energy sector through introduction of artificial intelligence (AI) has added much value to this industry. AI models and algorithms such as machine learning, fuzzy logic models, support vector regression, and artificial neural networks are instrumental for humanizing hydrogen storage, transportation, and production. It greatly contributes to the prediction of management of hydro production, various parameters, and safety protocols. Advancement of AI is bringing latest tools and technologies in hydrogen and battery technology for huge solutions toward the present global energy shortage and problems. The main aim is to display how various techniques of AI, its algorithms, and models contribute to hydrogen energy industries. In the meantime, AI models embedded with the battery technology play an important position in battery design and enhanced manufacture of batteries, diagnostic tools, and smart batter management systems. Integrating the benefits of improved performance and lifetime, these intelligent batteries are going to be the foundation for new applications of modern robotics, electric cars, aircraft, etc.","url":"https://doi.org/10.1201/9781003546160-11","authors":["Yogesh","Annu Priya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T00:35:52Z","doi":"10.1201/9781003546160-11","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5336/978-625-395-878-7_p119","name":"ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF GYNECOLOGIC CANCERS","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-878-7_p119","authors":["TÜLAY BÜLBÜL"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T08:56:43Z","doi":"10.5336/978-625-395-878-7_p119","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/s0933-3657(00)00059-2","name":"AI planning and scheduling in the medical hospital environment","source":"crossref","abstract":"Hospital management is a hard task due to the complexity of the organization, the costly infrastructure, the specialized services offered to different patients and the need for prompt reaction to emergencies. Artificial Intelligence planning and scheduling methods can offer substantial support to the management of hospitals, and help raising the standards of service. This editorial presents an overview of the achievements reported in therapy planning and hospital management together with a general roadmap of the published research in Artificial Intelligence planning and scheduling. Finally, a discussion for the future research and development in this area concludes the presentation.","url":"https://doi.org/10.1016/s0933-3657(00)00059-2","authors":["Constantine D Spyropoulos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T16:23:10Z","doi":"10.1016/s0933-3657(00)00059-2","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.artmed.2017.06.009","name":"Medical image classification via multiscale representation learning","source":"crossref","abstract":"Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide range of observation scales would be useful for medical imaging measurements. The present work proposes a multiscale representation learning method via sparse autoencoder networks to capture the intrinsic scales in medical images for the classification task. We obtain the multiscale feature detectors by the sparse autoencoders with different receptive field sizes, and then generate the feature maps by the convolution operation. This strategy can better characterize various size structures in medical imaging than single-scale version. Subsequently, Fisher vector technique is used to encode the extracted features to implement a fixed-length image representation, which provides more abundant information of high-order statistics and enhances the descriptiveness and discriminative ability of feature representation. We carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method have superior performance.","url":"https://doi.org/10.1016/j.artmed.2017.06.009","authors":["Qiling Tang","Yangyang Liu","Haihua Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-06-29T18:57:12Z","doi":"10.1016/j.artmed.2017.06.009","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.115144","name":"A decoupled deep attention framework for joint image enhancement and staining normalization of blood smear images","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115144","authors":["Jin Chen","Zheng Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-20T16:21:34Z","doi":"10.1016/j.engappai.2026.115144","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.23978/inf.178425","name":"Youth Meeting Generative Artificial Intelligence: Young People’s Ways of Navigating with Generative-Artificial-Intelligence-Powered Tools and Systems","source":"crossref","abstract":"Yucong Lao’s doctoral dissertation in the field of Information Studies, “Youth Meeting GenAI: Young people's media and information literacy practices in the information ecosystem shaped by generative artificial intelligence”, was examined on December 19, 2025, at the University of Oulu’s Faculty of Humanities. Professor Denise Agosto (Rutgers University, USA) served as the opponent, with Professor Noora Hirvonen (University of Oulu) as the custodian. The dissertation is published in University of Oulu’s publication archive OuluREPO at https://urn.fi/URN:NBN:fi:oulu-202511196795.","url":"https://doi.org/10.23978/inf.178425","authors":["Yucong Lao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-01T16:29:19Z","doi":"10.23978/inf.178425","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.4324/9781003517351-9","name":"Artificial Intelligence and the Future of Language Interpreting","source":"crossref","abstract":"This chapter examines the intersection of artificial intelligence (AI), big data, and the future of language interpreting, in particular AI‑empowered interpreting tool design. Introducing a technè-Ge‑stell-poiēsis philosophical framework that traces the evolution of AI and its relationship to interpreting, the study illustrates how those theoretical insights are embedded into the development of Enter‑Link, which, built upon substantial interpreted speech datasets, offers a pioneering digital solution that leverages AI and big data to transform real‑time language services. Theoretical insights about AI and interpreting shaped the system’s design rationale and interface components. This theory‑driven, data‑informed approach both anchors Enter‑Link in interpreting practice and equips it to respond to the evolving AI landscape, illustrating the transformative potential of principled digital solutions for the future of language interpreting.","url":"https://doi.org/10.4324/9781003517351-9","authors":["Jun Pan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-13T09:30:50Z","doi":"10.4324/9781003517351-9","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/s44163-026-01197-0","name":"College English score data analysis based on artificial intelligence","source":"crossref","abstract":"Abstract The teaching of English at colleges encounters difficulties because student achievement ranges widely and students learn through various factors, while teaching outcomes cannot be assessed without bias. The conventional analysis methods, which include descriptive statistics and linear regression, and experience-based teacher assessment, fail to reveal hidden patterns within extensive learning datasets. The research introduces an artificial intelligence framework that evaluates college English performance through its two main components: a multilayer perceptron (MLP) neural network and a random forest algorithm, which processed performance data from 583 undergraduate students. The academic year data set contains anonymous student learning behavior and assessment records, which were collected through a university academic affairs management system and an online learning platform. The analysis used Pearson correlation analysis to select twelve important feature variables. K-means clustering was applied as an unsupervised learning method to divide students into four learning categories: excellent, good, average, and in need of improvement. An MLP regression model with 12 input nodes, two hidden layers containing 64 and 32 neurons, and one output node was constructed to predict continuous English scores using the ReLU activation function and Adam optimizer. A random forest algorithm was employed to quantify the influence of different learning features on performance. Experimental results indicate that the proposed framework achieves a tolerance-based prediction accuracy of 92.3% within ± 5 score points, with a root mean square error (RMSE) of 4.76. Learning time, online quiz performance, and homework completion rate were identified as the most influential factors, demonstrating the framework’s effectiveness in supporting personalized instruction and data-driven teaching decisions.","url":"https://doi.org/10.1007/s44163-026-01197-0","authors":["Rong Jiang","Junming Hou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-03T05:26:51Z","doi":"10.1007/s44163-026-01197-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/ieeestd.2025.11077955","name":"IEEE Recommended Practice for Improving Generalizability of Artificial Intelligence for Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieeestd.2025.11077955","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-10T17:47:14Z","doi":"10.1109/ieeestd.2025.11077955","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/j.engappai.2026.114062","name":"Communicating vessels detection network for small object detection in realistic scenario","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114062","authors":["Wenkai Pang","Zhi Tan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-09T13:17:46Z","doi":"10.1016/j.engappai.2026.114062","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.2139/ssrn.6614978","name":"WORKING PAPER | PHILOSOPHY OF WORK AND ARTIFICIAL INTELLIGENCE Beyond Augmentation Why Artificial Intelligence Requires a Cognitive and Educational Refoundation of Work","source":"crossref","abstract":"In recent years, research on the impact of artificial intelligence on work has progressively abandoned the narrative of substitution in favor of the augmentation paradigm. Recent studies from MIT Sloan (Loaiza and Rigobon, 2025), McKinsey &amp;amp; Company (2025) and Pearson (2026) show with increasing consistency that value derives not from the technology itself, but from the quality of the human-AI system designed within organizations. This convergence represents a significant advance in the debate, yet conceals a structural limitation: its focus remains confined to the organizational and corporate dimension, presupposing that the individual enters the working system already endowed with the cognitive capacities necessary to interact productively with artificial systems. This paper proposes an alternative and complementary thesis: artificial intelligence requires not only a transformation of work, but a cognitive and educational refoundation of the human being that precedes entry into the productive world. Through a comparative analysis of major technological transitions and the introduction of the concept of velocity asymmetry, we argue that continuous corporate training represents a transitional and structurally insufficient solution. The true competitive advantage lies not in the ability to adopt AI tools, but in the quality of human thought that guides the dialogue with artificial cognitive systems. When this thought is absent or deactivated, AI does not solve the problem: it amplifies it.","url":"https://doi.org/10.2139/ssrn.6614978","authors":["michelangelo Benelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-24T13:32:45Z","doi":"10.2139/ssrn.6614978","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/s44163-026-01828-6","name":"Artificial intelligence driven personalized adaptive training system for individual differences in competitive sports","source":"crossref","abstract":"The current competitive sports training system generally neglects individual differences, leading to significant inter-individual variations in training effects. To address this issue, this study developed an AI-based personalized adaptive training scheme system. A 12-week intervention was conducted with 120 athletes (aged 18–25 years, 68 males, 52 females, moderate competitive level). Multi-dimensional data (physiological parameters, sports performance indexes, and psychological state) were collected to construct comprehensive athlete profiles. Experimental results showed that after adopting the AI-driven personalized training program, overall physical fitness improved by 15.20 ± 2.15% (95% CI 14.82–15.58%, P < 0.01), technical movement accuracy increased by 20.30 ± 2.48% (95% CI 19.81–20.79%, P < 0.01), and psychological adaptability score rose by 18.70 ± 2.03% (95% CI 18.30–19.10%, P < 0.01). The innovative contributions are threefold: (1) a deep learning-based athlete trait recognition model for fine-grained individual characterization; (2) a training scheme generation algorithm with adaptive adjustment mechanism for real-time dynamic optimization; (3) the pioneering introduction of a psychological state tracking unit, forming a complete body-mind coordinated training framework. These findings provide new theoretical support and practical solutions for promoting the scientific and personalized development of sports training. Clinical-trial number : This trial has been prospectively registered with the Chinese Clinical Trial Registry (Registration No.: ChiCTR2500012345, Registration Date: April 1, 2025).","url":"https://doi.org/10.1007/s44163-026-01828-6","authors":["Xiaoliang Xiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-01T02:24:51Z","doi":"10.1007/s44163-026-01828-6","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-32862-6.00015-8","name":"Welcoming the evolution of healthcare: A transformative change through artificial intelligence and robotics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32862-6.00015-8","authors":["D. Angel","H. Jemmy Christy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T19:25:12Z","doi":"10.1016/b978-0-443-32862-6.00015-8","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.62051/ijphmr.v6n1.03","name":"The Application of Artificial Intelligence in Diabetes Management","source":"crossref","abstract":"With the global prevalence of diabetes continuously rising, the management of diabetes has become a significant challenge in the field of public health. The application of Artificial Intelligence (AI) technologies in diabetes management, especially in personalized dietary interventions, has shown tremendous potential. This study conducts a systematic literature review to explore the various applications of AI in diabetes management, covering areas such as dietary intervention, personalized treatment, and complication prediction. The study finds that AI can effectively predict the risk of diabetes, optimize dietary plans, improve patient adherence, and reduce the incidence of diabetes-related complications. However, despite the significant progress made by AI technologies, challenges remain in terms of technological standardization, data privacy protection, and clinical translation. Future research should focus on the comprehensive application of AI in diabetes management, including combining exercise interventions, enhancing model accuracy, and expanding the diversity of datasets. Overall, the application of AI in personalized diabetes management holds great promise, but its clinical translation and long-term use still require further research and optimization.","url":"https://doi.org/10.62051/ijphmr.v6n1.03","authors":["Siyu Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-28T09:14:50Z","doi":"10.62051/ijphmr.v6n1.03","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1007/978-3-032-23890-0_15","name":"Digital Preservation of Family Heritage Enhanced Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-23890-0_15","authors":["Veljko Milutinovic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:56:51Z","doi":"10.1007/978-3-032-23890-0_15","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1109/gcrait55928.2022.00057","name":"Research on the application of artificial intelligence in medical imaging diagnosis","source":"crossref","abstract":"This paper first expounds the research status for artificial intelligence technology in medical imaging diagnosis, and illustrates the importance of computer-aided diagnosis with examples; Secondly, the current bottlenecks in the development of computer-aided diagnosis technology are analyzed in detail from the aspects of technology, industry and application; Finally, based on the previous analysis, the paper puts forward some suggestions on how to better use artificial intelligence technology in medical imaging diagnosis with reference to the current actual situations.","url":"https://doi.org/10.1109/gcrait55928.2022.00057","authors":["Yanli Zhao","Xiaomin Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-04T19:53:05Z","doi":"10.1109/gcrait55928.2022.00057","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/s0933-3657(98)00070-0","name":"Obtaining interpretable fuzzy classification rules from medical data","source":"crossref","abstract":"For many application problems classifiers can be used to support a decision making process. In some domains-in areas like medicine especially-it is preferable not to use black box approaches. The user should be able to understand the classifier and to evaluate its results. Fuzzy rule based classifiers are especially suitable, because they consist of simple linguistically interpretable rules and do not have some of the drawbacks of symbolic or crisp rule based classifiers. Classifiers must often be created from data by a learning process, because there is not enough expert knowledge to determine their parameters completely. A simple and convenient way to learn fuzzy classifiers from data is provided by neuro-fuzzy approaches. In this paper we discuss extensions to the learning algorithms of neuro-fuzzy classification (NEFCLASS), a neuro-fuzzy approach for data analysis that we have presented before. We present interactive strategies for pruning rules and variables from a trained classifier to enhance its readability, and demonstrate our approach on a small example.","url":"https://doi.org/10.1016/s0933-3657(98)00070-0","authors":["Detlef Nauck","Rudolf Kruse"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T14:51:33Z","doi":"10.1016/s0933-3657(98)00070-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.35337/mayas.2026.v34","name":"Artificial Intelligence in Tourism Management","source":"crossref","abstract":"Artificial Intelligence (AI) is emerging as a transformative force in the tourism industry, reshaping the way tourism services are managed, delivered, and experienced. With the growing demand for personalized travel experiences and efficient service delivery, AI has become a crucial tool in modern tourism management. This study examines the role of AI in enhancing customer experience, optimizing operational processes, improving marketing strategies, and supporting sustainable tourism development. AI-driven technologies such as chatbots, machine learning, predictive analytics, robotics, and virtual reality are increasingly being used to automate services, analyze tourist behavior, forecast demand, and improve decision-making in tourism organizations. While AI offers significant benefits, including cost reduction, improved efficiency, and better customer engagement, it also presents challenges related to data privacy, ethical concerns, high implementation costs, and workforce adaptation. The paper concludes that AI has the potential to revolutionize tourism management by enabling smarter, more personalized, and sustainable tourism systems, provided that technological, ethical, and managerial challenges are effectively addressed.","url":"https://doi.org/10.35337/mayas.2026.v34","authors":["B Priyadharshini","T Malarkodi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-13T12:36:12Z","doi":"10.35337/mayas.2026.v34","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1016/b978-0-443-40618-8.11001-0","name":"About the editor","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-40618-8.11001-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-20T21:28:18Z","doi":"10.1016/b978-0-443-40618-8.11001-0","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5336/978-625-395-878-7_p117","name":"ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF GYNECOLOGIC CANCERS","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-878-7_p117","authors":["TÜLAY BÜLBÜL"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T14:47:58Z","doi":"10.5336/978-625-395-878-7_p117","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.5336/978-625-395-878-7_p147","name":"ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF GYNECOLOGICAL INFECTIONS","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-878-7_p147","authors":["SABİHA ŞENSÖZ"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T14:47:58Z","doi":"10.5336/978-625-395-878-7_p147","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.4337/9781035338580.00014","name":"Multilateralism in the global governance of artificial intelligence","source":"crossref","abstract":"This chapter examines how international multilateralism addresses the emergence of the general-purpose technology of artificial intelligence (AI). In more detail, it analyses two key features of AI multilateralism: its generalized principles and the coordination of state relations in the realm of AI. Firstly, it distinguishes the generalized principles of AI multilateralism of epochal change, determinism, and dialectical understanding. Secondly, the adaptation of multilateralism to AI led to the integration of AI issues into the agendas of existing cooperation frameworks and the creation of new ad hoc frameworks focusing exclusively on AI issues. In both cases, AI multilateralism develops in the shadow of the state hierarchy in relations with other AI stakeholders. While AI multilateralism is multi-stakeholder, and the hierarchy between state and non-state actors may seem blurred, states preserve the competence as decisive decision-makers in agenda-setting, negotiation, and implementation of soft-law international commitments.","url":"https://doi.org/10.4337/9781035338580.00014","authors":["Michal Natorski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-11T18:01:29Z","doi":"10.4337/9781035338580.00014","addedAt":"2026-09-01T01:47:56.312Z","updatedAt":"2026-09-01T01:47:56.312Z"},{"id":"doi:10.1201/9788743812999-12","name":"AI Factory: The Future of Scalable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9788743812999-12","authors":["Ahmed Banafa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-16T23:41:36Z","doi":"10.1201/9788743812999-12","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-030-52448-7_5","name":"Artificial Intelligence in Healthcare and Medical Ethics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-52448-7_5","authors":["Perihan Elif Ekmekci","Berna Arda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-30T19:07:52Z","doi":"10.1007/978-3-030-52448-7_5","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-032-06088-4_15","name":"Artificial Intelligence in Agriculture: Technology-Powered Strategies for a Sustainable Farming Future","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06088-4_15","authors":["Shallu Duggal","Shivani Sood","Shilpa","Kamal Nain Sharma","Monika Sethi","Chander Prabha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-29T10:52:07Z","doi":"10.1007/978-3-032-06088-4_15","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1145/3800973","name":"Proceedings of the 2026 International Conference on Artificial Intelligence and Fintech","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3800973","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T03:21:14Z","doi":"10.1145/3800973","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.4018/407437","name":"Harmony of Artificial Intelligence and Emotional Intelligence in the Education Sector","source":"crossref","abstract":"An innovative strategy to improve teaching and learning is presented by the fusion of artificial intelligence (AI) and emotional intelligence (EI) in the educational field. AI is gaining huge importance in every sector. AI is expanding at a pace that is unparalleled and has the potential to completely transform many aspects of our society. EI is an essential intelligence that must be nurtured is students in their teaching-learning process. This study examines how AI and EI might work together more effectively, offering a paradigm that combines the empathetic qualities of EI with rational and scientific thinking of AI's data-driven insights and automation capabilities. In addition to examining the ways AI is currently being used in education to improve administrative efficiency, individualized learning, and adaptive assessments, the study emphasizes the value of EI in creating a welcoming and inclusive learning environment.","url":"https://doi.org/10.4018/407437","authors":["Jagneet Kour","Raino Bhatia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T19:01:11Z","doi":"10.4018/407437","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/s44163-026-00975-0","name":"Innovation of cross-cultural music teaching methods based on artificial intelligence","source":"crossref","abstract":"This research is concerned with the innovative use of AI in cross-cultural music learning, more particularly in the creation of a multimodal learning model that combines audio identification, image analysis, and text understanding. The research is conducted with undergraduate music students from various ethnic backgrounds, and the teaching materials include a wide range of cultural music pieces. Sequence identification and semantic modeling are applied in the system to realize personalized recommendations and feedback interventions. As for model development, note recognition and cultural semantic decomposition are activities performed with a hybrid RNN-Transformer architecture. To perform a detailed analysis and to continuously adjust the performance, emotional expression, and cultural knowledge during the learning process, a teaching feedback mechanism and a cultural adaptation algorithm are implemented in the system architecture. The experimental data indicate that the learners’ control of their rhythm, expressiveness of their emotions, and mastery of the culture have been facilitated to a considerable extent by the system’s high recognition accuracy and the useful feedback offered. The experiment reveals that the cultural backgrounds of students play an important role in their learning, and hence teachers are given the opportunity to develop different teaching strategies according to the variations in culture. The developed system, as a technically robust model and application paradigm for cross-cultural music education, has shown great adaptability in the spheres of teaching logic, technical execution, and educational outcomes.","url":"https://doi.org/10.1007/s44163-026-00975-0","authors":["Feng Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-23T03:14:05Z","doi":"10.1007/s44163-026-00975-0","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.4324/9781003613794-14","name":"Artificial Intelligence as Strategic Infrastructure","source":"crossref","abstract":"This chapter examines how Globo, the dominant media conglomerate in Brazil and a major force in the broader Latin American context, is embracing artificial intelligence (AI) as a strategic infrastructure to transform its operations, from content production and audience experience to internal management and advertising. Anchored in a comprehensive AI programme, Globo’s approach extends across the entire creative and operational chain, including story development, animation, dubbing, personalization, and editorial workflows. Drawing on internal documentation and a detailed interview with the company’s Director of Architecture, Partnerships, and Data/AI Strategy, this exploratory case study documents how Globo is pursuing an AI-first strategy that articulates enhancement rather than replacement of human capabilities. The analysis highlights the organizational, ethical, and cultural challenges of such implementation, particularly in terms of workforce adaptation and the volatility of generative technologies, while documenting Globo’s current approach to transparency, editorial responsibility, and the co-evolution of technology and professional practice. Positioned at the intersection of media innovation and institutional accountability, Globo provides a situated Global South perspective on deep mediatization, shedding light on the implications of algorithmic media for the operational, editorial, and institutional dynamics of legacy media organizations.","url":"https://doi.org/10.4324/9781003613794-14","authors":["Catarina Duff Burnay","Paulo Nuno Vicente"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-25T10:46:35Z","doi":"10.4324/9781003613794-14","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1201/9781003629146-9","name":"Artificial Intelligence-Driven Evidence in Arbitral Proceedings","source":"crossref","abstract":"Man and machine in the present are slowly and steadily becoming intertwined in a manner that it is often extremely difficult to separate one from the other. The luxuries of the past have come up to become the necessities of the present. The newly enacted Bharatiya Sakshya Adhiniyam (hereinafter referred to as “BSA”), 2023 , replacing the Indian Evidence Act, 1872, takes an ambitious step toward modernizing the evidence law and takes into account the challenges brought in by the digital era. Contextualizing India within the global panorama reveals that jurisdictions such as the United States and the UK approach AI‑evidence under benchmarks of relevance and reliability, closely scrutinizing probabilistic outputs and system transparency. In the arbitration domain, institutional guidelines, from the IBA Rules of Evidence to SVAMC principles, advocate for disclosure of AI use, confidentiality assurances, and human oversight. Although these are soft laws and are not binding in India, the Arbitration Act’s procedural autonomy allows tribunals to apply them. AI‑generated evidence, therefore, must navigate a dual test of meeting BSA’s statutory admissibility safeguards while conforming to international standards of procedural fairness, especially in cross-border disputes. This chapter seeks to address these questions in a three-pronged manner. First, it will map the statutory regime that allows for the use of AI-generated evidence under the BSA, especially in the context of arbitration. Second, it will dissect the cross-jurisdictional jurisprudence, both Indian and international, to assess how courts and tribunals evaluate AI-based evidence under standards such as explainability, bias testing, data security, and expert certification. Third, it will propose a calibrated framework for arbitral tribunals that harnesses AI’s prospects while ensuring compliance with evidentiary norms.","url":"https://doi.org/10.1201/9781003629146-9","authors":["Atish Chakraborty","Dhruv Maheshwari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T15:51:18Z","doi":"10.1201/9781003629146-9","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-34019-2.20001-1","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34019-2.20001-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-14T15:07:42Z","doi":"10.1016/b978-0-443-34019-2.20001-1","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1109/aitest70988.2026.00003","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitest70988.2026.00003","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-26T19:11:22Z","doi":"10.1109/aitest70988.2026.00003","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1002/9781394358212.fmatter1","name":"Front Matter","source":"crossref","abstract":"The prelims comprise: Half-Title Page Publisher Page Title Page Copyright Page Table of Contents Brief Contents of Volume 2 Preface","url":"https://doi.org/10.1002/9781394358212.fmatter1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-05T21:30:42Z","doi":"10.1002/9781394358212.fmatter1","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00010-9","name":"Algorithmic selection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00010-9","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00010-9","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-27465-7.00006-6","name":"Plant disease diagnosis and forecasting in the era of artificial intelligence, machine learning, and deep learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27465-7.00006-6","authors":["Nidhi Kumari","Nainika Nagar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T12:13:00Z","doi":"10.1016/b978-0-443-27465-7.00006-6","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/s44163-026-00915-y","name":"Authentic news detection technology based on artificial intelligence technology","source":"crossref","abstract":"In today’s increasingly rich digital information, how to effectively identify and prevent the spread of fake news has become an urgent problem that needs to be solved. Therefore, artificial intelligence technology has been introduced to detect genuine and fake news. In this regard, an improved convolutional neural network model has been developed and used for news authenticity recognition. The research results indicated that the model adopted deep learning technology, and after optimization and improvement, it has significantly improved its performance in identifying genuine and fake news. Under the same training conditions, the improved convolutional neural network model showed the highest recognition rate. Especially after 50 iterations, its accuracy reached 96.97%, far exceeding the model based on random deactivation techniques in convolutional neural networks, which had an accuracy of only 89.68% under the same number of iterations. In testing different datasets, this improved network model also demonstrated its superiority. On the FakeNewsNet dataset, the normalized mutual information of this model was 84.82%, which was 5.05% and 10.25% higher than traditional methods and methods based on random inactivation techniques, respectively. On the LIAR dataset, its adjusted Rand index reached 87.32%. The contribution of this study lies in utilizing artificial intelligence technology, particularly improved convolutional neural network models, to effectively identify and prevent fake news. This has significant practical implications for the information security of society, the protection of the public’s right to know, and the dissemination of truthful and accurate news.","url":"https://doi.org/10.1007/s44163-026-00915-y","authors":["Hui Wang","Feng Nan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-05T10:35:54Z","doi":"10.1007/s44163-026-00915-y","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.3233/faia260676","name":"Teaching Practice of Marketing Major Empowered by Generative Artificial Intelligence","source":"crossref","abstract":"The pain point of traditional education lies in its single form of interaction, which easily causes students ‘visual fatigue’, reducing learning efficiency and motivation. The emotional expression of digital teachers empowered by generative artificial intelligence combined with real teachers can significantly reduce loneliness and stress for various groups, make up for the lack of emotional interaction in traditional classrooms, and build emotional connections with learners, making interactions in the virtual world more lively. Students have cognitive biases, and learning engagement is a key indicator of students’ learning quality. This paper takes the Marketing major of W College as the research object, exploring the empowerment of marketing professional teaching practice by educational digital humans under generative artificial intelligence. It aims to carry out digital and intelligent transformation in aspects such as educational philosophy and teaching models, to cultivate students’ digital application abilities, and also to provide experiential reference for the construction of courses in other majors.","url":"https://doi.org/10.3233/faia260676","authors":["Qinxian Chen","Saipeng Xing","Xian Qin","Fen Huo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-28T08:41:13Z","doi":"10.3233/faia260676","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1109/aicconf69182.2026.11600678","name":"AICCONF 2026 Author Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aicconf69182.2026.11600678","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:02Z","doi":"10.1109/aicconf69182.2026.11600678","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-319-94878-2_8","name":"Artificial Intelligence in Medicine: Validation and Study Design","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-94878-2_8","authors":["Luke Oakden-Rayner","Lyle John Palmer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-29T09:16:52Z","doi":"10.1007/978-3-319-94878-2_8","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/s0933-3657(01)00075-6","name":"Multiple representations and multi-modal reasoning in medical diagnostic systems","source":"crossref","abstract":"The paper examines the motivations for developing medical diagnostic systems exploiting multiple representations and multi-modal reasoning. The analysis is carried on by revisiting the architectural choices of the CHECK system (developed in late 1980s) which combined heuristic and causal knowledge. The results in the theory of diagnosis and in model-based reasoning (MBR) obtained in early 1990s are used for providing a formal characterization of the notion of diagnosis and of the reasoning mechanisms used in CHECK. The paper addresses also the problem of replacing heuristic knowledge provided by human experts with operational knowledge automatically derived from the deep model. In particular, the pros and cons of knowledge compilation and of the integration of case-based reasoning (CBR) with MBR are discussed by summarizing the experience gained in developing AID and ADAPtER. The problem of using an explicit representation of time in diagnostic systems is analyzed and recent work on the different characterizations of diagnosis arising when the temporal dimension is considered is reported. Finally, the implications of the results obtained in MBR and in temporal reasoning on the future of medical diagnostic systems are briefly discussed.","url":"https://doi.org/10.1016/s0933-3657(01)00075-6","authors":["Pietro Torasso"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T12:57:34Z","doi":"10.1016/s0933-3657(01)00075-6","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-032-18897-7_12","name":"Unified AI Energy Management Diagnostics: Expert System Integration with LLM for Predictive Maintenance","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_12","authors":["Sidi Omar El kettani","Mohammed Hassani Zerrouk","Hicham Tikaoui","Omar Hassani Zerrouk","Anass El Kettani","Ahmed Mrabet","M’hamed Nour"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:52:36Z","doi":"10.1007/978-3-032-18897-7_12","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.55920/3064-8025/1213","name":"Dr. Ștefan Odobleja's Contribution to the Concept of Artificial Intelligence Brief history","source":"crossref","abstract":"","url":"https://doi.org/10.55920/3064-8025/1213","authors":["Nicolae Popescu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-15T04:18:14Z","doi":"10.55920/3064-8025/1213","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.64910/jouair.v2i1.19","name":"Talent Management Transformation: Integrating Artificial Intelligence for Organizational Competitive Advantage","source":"crossref","abstract":"The transformation of talent management in the digital era is increasingly influenced by the development of artificial intelligence (AI), which plays a strategic role in enhancing an organization's competitive advantage. AI not only improves operational efficiency but also transforms the way organizations recruit, develop, evaluate, and retain talent. This study aims to analyze the role of AI in talent management, identify challenges faced by the human resources (HR) function, and evaluate the effectiveness of AI in improving employee performance and potential. The research method used is a qualitative approach based on literature review, reviewing scientific articles published between 2020 and 2025 relevant to the topic of AI and human resource management. The study results indicate that AI contributes significantly to the talent selection process, career development, performance evaluation, and employee turnover prediction through data-driven decision-making. However, AI implementation also faces challenges, such as algorithmic bias, lack of system transparency, organizational resistance, and the risk of dehumanizing HR processes. Therefore, the successful implementation of AI in talent management depends heavily on ethical governance, data quality, organizational cultural readiness, and harmonious collaboration between technology and human roles. This research provides academic and practical contributions to understanding how AI can be optimally and sustainably utilized in talent management in the digital era.","url":"https://doi.org/10.64910/jouair.v2i1.19","authors":["Uswah Nurlatifah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-08T06:24:17Z","doi":"10.64910/jouair.v2i1.19","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.51219/jaimld/vijayalakshm/668","name":"Artificial Intelligence and Personalization in Marketing","source":"crossref","abstract":"Artificial Intelligence (AI) is revolutionizing marketing through hyper-personalization, enhancing customer engagement, and driving conversion rates.This paper explores how AI technologies including machine learning, natural language processing, and predictive analytics enable brands to analyze vast datasets, predict consumer behavior, and deliver tailored content in real time.It highlights key strategies, challenges, and future trends in AI-driven personalized marketing, emphasizing ethical considerations and customer trust.","url":"https://doi.org/10.51219/jaimld/vijayalakshm/668","authors":["S Vijayalakshmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T12:05:37Z","doi":"10.51219/jaimld/vijayalakshm/668","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.64189/vai.26105","name":"Editorial to the Inaugural Issue of Journal of Visual Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.64189/vai.26105","authors":["Nilanjan Dey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-30T17:02:18Z","doi":"10.64189/vai.26105","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.70715/jitcai.2026.v3.i4.083","name":"Artificial Intelligence and Blockchain as Determinants of Healthcare Data Security Transformation","source":"crossref","abstract":"The rapid digitalization of healthcare has led to the generation of vast amounts of sensitive patient information, increasing the need for advanced security solutions beyond traditional centralized systems. This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security. Conventional electronic health record systems often face challenges such as single points of failure, limited transparency, and vulnerability to cyber threats. Blockchain addresses these issues by providing a decentralized and immutable ledger that ensures data integrity, traceability, and secure record management through cryptographic techniques and consensus protocols. In parallel, AI strengthens security by enabling intelligent threat detection, predictive analytics, and adaptive authentication mechanisms. Machine learning algorithms continuously analyze network activities and user behaviors to identify potential breaches and insider threats in real time. The combination of AI and blockchain creates a synergistic framework in which AI enhances blockchain efficiency, while blockchain provides a transparent and trustworthy environment for AI-driven data processing. The study further explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data. Key challenges, including interoperability, scalability, regulatory compliance, and integration with legacy systems, are also discussed. Additionally, patient empowerment is enhanced through self-sovereign identity models that grant individuals greater control over their personal health information. Despite challenges related to computational complexity and standardization, the convergence of AI and blockchain offers a proactive, resilient, and privacy-preserving security architecture for modern healthcare. Future research should focus on lightweight cryptographic solutions, quantum-resistant security mechanisms, and governance frameworks for decentralized healthcare ecosystems. Overall, this integration represents a significant step toward secure, transparent, and patient-centered digital healthcare systems.","url":"https://doi.org/10.70715/jitcai.2026.v3.i4.083","authors":["OLUSEGUN GBOLADE"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T13:52:21Z","doi":"10.70715/jitcai.2026.v3.i4.083","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.4337/9781035347841.00009","name":"Introduction to Cases on Entrepreneurship and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781035347841.00009","authors":["Robin Bell","Scott Andrews"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-07T18:05:14Z","doi":"10.4337/9781035347841.00009","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-032-08195-7_4","name":"Artificial Intelligence and Content Creation in Digital Entertainment and Media","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-08195-7_4","authors":["Yineng Xiao","Shulin Feng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-06T23:30:26Z","doi":"10.1007/978-3-032-08195-7_4","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.37766/inplasy2026.5.0096","name":"Artificial Intelligence for Breast Cancer Molecular Subtype Prediction from Medical Imaging: a Systematic Review of Literature","source":"crossref","abstract":"","url":"https://doi.org/10.37766/inplasy2026.5.0096","authors":["Juan Camilo Morales Duran","Kevin Osorno Castillo","Gloria Mercedes Diaz Cabrera"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-17T16:24:41Z","doi":"10.37766/inplasy2026.5.0096","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00059-2","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00059-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00059-2","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00014-6","name":"Semiconductor chips","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00014-6","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00014-6","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-032-18897-7_5","name":"SVHRSP From Scorpion Venom for Skin Ageing: Bioinformatics Rationale and a Microneedle-Delivered Cosmetic Strategy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_5","authors":["M. N. Assabbane","M. Es-Saadi","O. El Atiqi","S. Boukind","M. D. El Amrani","Y. Benchamkha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:56:20Z","doi":"10.1007/978-3-032-18897-7_5","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1007/978-3-032-24724-7_32","name":"Fine-Tuning Vision Language Models for Medical Visual Question Answering","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24724-7_32","authors":["Victor Teslaru","Gabriel Pojoga","Ștefan-Daniel Achirei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T23:58:44Z","doi":"10.1007/978-3-032-24724-7_32","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.47852/bonviewaia62027648","name":"Treatment Regimen Segmentation from Handwritten Medical Prescriptions Using Advanced Neural Network","source":"crossref","abstract":"Handwritten medical prescriptions are a critical yet under-digitized component of clinical workflows, often serving as a source of ambiguity due to illegible handwriting, overlapping text blocks, and structural inconsistencies. The automatic segmentation of such prescriptions into meaningful textual blocks is vital for downstream tasks like drug recognition and dosage extraction. Traditional methods grounded on connected components or projection profiles often falter under the irregularities of freeform handwriting. To address these limitations, the paper proposes an advanced deep learning architecture—PrescNet—that primarily segment the treatment regimen (medicine and its associated components) as text-blocks using classical U-Net design with spatial–channel attention gates and a lightweight 32 channel projection layer to better capture salient features in prescription images. The model is trained on a custom dataset with pixel-level annotations and evaluated using 10-fold cross-validation with varying data splits. Experimental results demonstrated that the proposed architecture significantly transcend the baseline variants and a few state-of-the-art deep learning models of text-line segmentation achieving an Intersection over Union (IoU) of 87.2%, Dice score of 92.9%, and a minimal Dice loss of 0.071. The results validate its effectiveness in handling complex handwritten layouts, establishing its suitability for real-world clinical applications. Received: 12 September 2025 | Revised: 30 December 2025 | Accepted: 14 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Rekha G. R.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review &amp; editing, Visualization. Siddesha S.: Conceptualization, Validation, Formal analysis, Investigation, Writing – review &amp; editing, Supervision, Project administration. V. N. Manjunath Aradhya: Validation, Formal analysis, Investigation, Writing – review &amp; editing, Supervision.","url":"https://doi.org/10.47852/bonviewaia62027648","authors":["Rekha G. R.","Siddesha S.","V. N. Manjunath Aradhya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T08:17:02Z","doi":"10.47852/bonviewaia62027648","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-32862-6.00006-7","name":"Empowering early detection: artificial intelligence as a tool for breast cancer diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32862-6.00006-7","authors":["Pratishtha Verma","Gaurav Tripathi","Roshan Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-31T19:24:49Z","doi":"10.1016/b978-0-443-32862-6.00006-7","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/j.artmed.2026.103460","name":"Artificial intelligence based techniques for brain tumor analysis: A systematic review","source":"crossref","abstract":"Brain tumors are formed when abnormal cells grow within the brain or its surrounding tissues. Approximately 400 people in Ireland receive a primary brain tumor diagnosis each year. In the US, this number increases to almost 90,000 individuals diagnosed each year. Timely diagnosis of brain tumor is essential to saving lives and significantly reducing treatment costs. To automate this process, different Artificial Intelligence (AI) techniques have been adopted to identify brain tumors in humans. Specifically, various deep learning algorithms have been used to segment and classify brain tumors. In this paper, a systematic review is conducted based on Kitchenham & Charters methodology. We selected seven research questions to identify commonly used methods, datasets, features, metrics, and Explainable AI (XAI) approaches for AI-based analysis of brain tumors. This process starts by sourcing papers that address these techniques via the IEEE Xplore and ACM biblographic databases between January 2013 and December 2024. The papers are then filtered using specifically designed inclusion and exclusion criteria. Out of 3950 papers sourced from two electronic databases, only 101 papers were selected for this review. In summary, despite a focus on segmentation and classification, our findings indicate that no AI methods have been fully adopted in clinical practice. Furthermore, none of the reviewed papers address the specific problem of weakly-supervised brain tumor segmentation, highlighting a clear research gap in the existing literature that warrants further investigation. Also, only four articles on XAI were identified. Given the importance of transparency in network predictions for brain tumor analyses, this fact supports the need for more research in this domain.","url":"https://doi.org/10.1016/j.artmed.2026.103460","authors":["Oluwabukola G. Adegboro","Julia Dietlmeier","Noel E. O’Connor","Claudia Mazo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-30T15:33:12Z","doi":"10.1016/j.artmed.2026.103460","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.caeai.2025.100523","name":"Towards contextual-based AI: A scoping review of artificial intelligence in X reality for personalized learning","source":"crossref","abstract":"This systematic review synthesizes 54 peer-reviewed studies published between 2019 and 2025 that examine how artificial intelligence (AI) and extended reality (XR) technologies are integrated to support adaptive and personalized learning. The studies were analyzed across multiple dimensions, including learning contexts, AI applications, adaptive input parameters, software and hardware used, and evaluation methods. The findings indicate growing research interest in AI–XR integration, with the majority of studies focused on procedural training and STEM education. Across these studies, AI is frequently used in multifaceted roles, most notably as a provider of real-time adaptive feedback, conversational agent, and a generator of instructional content. Despite these promising developments, the review identifies several critical limitations. While generative AI, particularly large language models (LLMs) such as GPT, has been widely used for conversational interactions, learner profile data remains largely underutilized. Inputs such as prior knowledge and motivation are rarely incorporated. Most implementations rely on a single adaptive strategy, typically driven by performance-based measures such as pre-quiz scores or task completion. As a result, they do not fully exploit the multimodal sensing capabilities of XR platforms (e.g., eye tracking, gesture recognition, environmental tracking), which could support context-sensitive, dynamically generated 3D content aligned with when, where, and how learners need support. Current evaluations of AI–XR systems also remain dominated by short-term performance outcomes, with limited attention to knowledge transfer and critical thinking. These findings highlight key opportunities for designing context-aware, learner-centered AI–XR systems and call for future research that more fully leverages multimodal data, incorporates richer learner profile information, and is grounded in explicit pedagogical models.","url":"https://doi.org/10.1016/j.caeai.2025.100523","authors":["Zifeng Liu","Serene Cheon","Austin Stanbury","Xinyue Jiao","Wanli Xing","Hyo Kang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-16T00:20:09Z","doi":"10.1016/j.caeai.2025.100523","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/s0933-3657(02)00049-0","name":"Uniqueness of medical data mining","source":"crossref","abstract":"This article addresses the special features of data mining with medical data. Researchers in other fields may not be aware of the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous data of medicine. Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues. The mathematical understanding of estimation and hypothesis formation in medical data may be fundamentally different than those from other data collection activities. Medicine is primarily directed at patient-care activity, and only secondarily as a research resource; almost the only justification for collecting medical data is to benefit the individual patient. Finally, medical data have a special status based upon their applicability to all people; their urgency (including life-or-death); and a moral obligation to be used for beneficial purposes.","url":"https://doi.org/10.1016/s0933-3657(02)00049-0","authors":["Krzysztof J. Cios","G. William Moore"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-10-10T17:33:30Z","doi":"10.1016/s0933-3657(02)00049-0","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.ijaied.2026.100003","name":"Arthur: An artificial intelligence powered teaching assistant system for Engineering Economics class","source":"crossref","abstract":"Calculated Formula Questions (CFQs) are a prevalent and critical assignment type in engineering courses to help students practice solving real-world problems. However, providing timely and personalized feedback on CFQ assignments remains challenging in large classrooms. Recent development of artificial intelligence (AI) offers unprecedented opportunities to deliver timely feedback through empowering intelligent tutoring systems (ITSs). Nevertheless, existing efforts have been constrained to assignments with readily available structured digital data, creating a gap in supporting unstructured CFQs. This study introduces a life-cycle framework that enables the development of an AI-powered ITS for CFQs, from data curation and model training to student-facing system deployment. Using graded CFQ assignments from undergraduate Engineering Economics courses as a case study, we built a digitalized dataset and applied a novel random masking technique to augment small-scale and imbalanced data. For each CFQ, we trained an eXtreme Gradient Boosting (XGBoost) model as its AI backbone. The model functions to predict potential mistakes in the solution using only each student’s submitted numerical answers, bypassing access to full written solutions. Our experiments demonstrate the feasibility of AI models in solution diagnosis, achieving an average precision of 0.81, a recall of 0.79, and an accuracy of 0.65 in predicting mistakes. To balance feedback efficiency and accuracy, we implemented a dialogue-based interaction scheme within a student-facing web interface. This scheme adaptively gathers additional inputs from students when the AI model’s predictions have close probabilities. Together, the AI backbone models and the web interface form an AI-powered ITS ( Arthur ) that delivers real-time and personalized feedback. Our framework offers a scalable pathway for building AI-powered ITS across engineering courses.","url":"https://doi.org/10.1016/j.ijaied.2026.100003","authors":["Zhuoli Yin","Erhan Karakaya","Kalei Bass","Hua Cai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T03:18:43Z","doi":"10.1016/j.ijaied.2026.100003","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/j.engappai.2025.113230","name":"Systematic literature review of artificial intelligence techniques on condition based maintenance models for transport applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113230","authors":["Pedro Pinheiro Garcia","Lucio Flavio Vismari","Joao Batista Camargo","Jorge Rady de Almeida","Paulo Sergio Cugnasca"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T17:40:48Z","doi":"10.1016/j.engappai.2025.113230","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/aic65131.2026.11633686","name":"AIC 2026 TOC","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aic65131.2026.11633686","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-07T19:17:44Z","doi":"10.1109/aic65131.2026.11633686","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1002/9781394450114.fmatter","name":"Front Matter","source":"crossref","abstract":"The prelims comprise: Half-Title Page Publisher Page Title Page Copyright Page Table of Contents Preface","url":"https://doi.org/10.1002/9781394450114.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-10T21:20:11Z","doi":"10.1002/9781394450114.fmatter","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00021-3","name":"Data storage","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00021-3","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00021-3","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44415-9.20001-x","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44415-9.20001-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:24:49Z","doi":"10.1016/b978-0-443-44415-9.20001-x","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.115590","name":"Shadow dynamics govern light stability in vertical agrivoltaic systems: A physics-informed explainable artificial intelligence approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115590","authors":["Roby Mohajon","Sumaiya Mahmud Prity","Anika Ramisha","Md Hasibul Islam","Hrittik Mutsuddi","Anupom Bhowmick","H.A.Naeem Chowdhury"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-03T06:55:23Z","doi":"10.1016/j.engappai.2026.115590","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.115516","name":"A privacy-aware Artificial Intelligence framework for crowd monitoring and dispatching using wearable Internet of Things","source":"crossref","abstract":"Large-scale events and mass gatherings create significant challenges for crowd safety, coordination, and privacy-preserving information exchange. This study presents a privacy-aware Artificial Intelligence framework for crowd monitoring and dispatching using wearable Internet of Things (IoT) technologies. The Artificial Intelligence contribution is an agent-based coordination framework that integrates dynamic group management and policy-aware access control, while the engineering application is crowd monitoring and dispatching for large-scale events. Participants, group leaders, and coordination authorities are modeled as autonomous agents within a hierarchical Multi-Agent System (MAS). Dynamic crowd behavior is supported through a Disjoint Set Union (DSU)-based group management mechanism for group formation, splitting, and reintegration under mobility, scheduling, proximity, and health-indicator constraints from simulated wearable sensing. Privacy-aware access control is modeled through Ciphertext-Policy Attribute-Based Encryption (CP-ABE), enabling policy-compliant access to sensitive information. The framework is evaluated through simulation using the Java Agent Development Framework (JADE), involving a 1001-agent scenario, 10 independent repeatability runs, and scalability sensitivity analysis with populations of 2051, 5051, and 10,061 agents. The evaluation also includes a density-based spatial proximity-clustering baseline and crowd-safety-related proxy metrics, including elevated local-density exposure, congestion-pressure proxy, and close-contact risk. Results demonstrate stable coordination behavior, join success rates above 90%, and consistent platform-level latency patterns. Privacy enforcement introduces moderate simulated overhead, with ciphertext size increasing from approximately 992 to 1184 bytes and policy-evaluation cost increasing from 13 to 17 ms as attribute complexity grows. These findings support the feasibility of the proposed framework as a simulation-level architecture for privacy-aware crowd monitoring and dispatching.","url":"https://doi.org/10.1016/j.engappai.2026.115516","authors":["Akram Y. Sarhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-25T16:24:36Z","doi":"10.1016/j.engappai.2026.115516","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1201/9781003629498-2","name":"Ethics in the Age of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003629498-2","authors":["Manjeet Rege","Hemachandran K"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-10T18:38:54Z","doi":"10.1201/9781003629498-2","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.4274/balkanmedj.galenos.2026.2026.090626","name":"Artificial Intelligence in Orthopaedics: The Future Through the Lens of Knee Surgery","source":"crossref","abstract":"Rapid advances in robotics and artificial intelligence (AI) have intensified expectations that digitally assisted systems may substantially reshape surgical practice in the near future.Elon Musk recently suggested that robots may surpass competent human surgeons within a few years and outperform even the best surgeons within approximately five years.Although such statements are provocative, they reflect the growing expectations surrounding AI and robotics in healthcare.AI is no longer a distant technological promise in orthopedic surgery; it is increasingly being integrated into clinical workflows through imaging analysis, robotic assistance, predictive modeling, registry-based outcome assessment, wearable sensors, and digital rehabilitation platforms.1,2 Orthopedic surgeryparticularly knee surgery-is becoming an important testing ground for this digital transformation.For decades, orthopedic surgery has relied on surgical expertise, imaging interpretation, and experience-based clinical judgment.Today, however, the field is entering a more data-driven era of clinical practice, in which diagnosis, treatment planning, surgical execution, rehabilitation monitoring, and outcome evaluation are increasingly supported by integrated digital systems rather than isolated technological tools.","url":"https://doi.org/10.4274/balkanmedj.galenos.2026.2026.090626","authors":["Nihat Demirhan Demirkıran","Wolf Petersen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-23T06:24:16Z","doi":"10.4274/balkanmedj.galenos.2026.2026.090626","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.48176/esmj.2025.228","name":"ARTIFICIAL INTELLIGENCE SUPPORTED DIGITAL COUNSELING IN PREGNANCY: EVALUATING CHATGPT S PERFORMANCE ACROSS 40 SCENARIOS","source":"crossref","abstract":"Introduction: In recent years, language models supported by artificial intelligence have been increasingly utilized in healthcare counseling. This study aims to evaluate the reliability and clinical safety of ChatGPT-3.5 by assessing the medical accuracy, content completeness, and communication quality of its responses to pregnancy-related patient scenarios through expert review. Methods: Forty clinical scenarios reflecting common pregnancy concerns across all trimesters were developed. These were entered into ChatGPT-3.5, and its responses were independently evaluated by two obstetricians using an 8-item, 5-point Likert scale assessing medical accuracy, misinformation, missing information, urgency perception, referral appropriateness, home care suggestions, harmful recommendations, and language quality. A Global Quality Score (GQS) from 1 to 5 was also assigned. Internal consistency was analyzed using Cronbach’s alpha, and inter-rater agreement for GQS was evaluated via Cohen’s Kappa after categorization into low, moderate, and high-quality groups. Results: ChatGPT’s responses demonstrated high internal consistency across evaluation domains (Cronbach’s alpha = 0.872). Cohen’s Kappa for GQS agreement was 0.15, indicating slight agreement. No misinformation or harmful suggestions were identified. Most responses were medically appropriate, although some contained minor informational gaps. In 90% of the scenarios, evaluators placed responses in the same GQS category. Conclusions: ChatGPT-3.5 shows promise as a supportive digital tool for pregnancy-related counseling. However, it is not fully aligned with current clinical guidelines, which may limit its reliability in guideline-driven decision-making. Expert oversight remains essential, and further studies are needed to validate its performance in diverse clinical contexts.","url":"https://doi.org/10.48176/esmj.2025.228","authors":["Aybüke Tayarer","Kadir Kangal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-29T11:44:29Z","doi":"10.48176/esmj.2025.228","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/j.engappai.2006.01.009","name":"Concept mining for indexing medical literature","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2006.01.009","authors":["Isabelle Bichindaritz","Sarada Akkineni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-03-24T07:42:37Z","doi":"10.1016/j.engappai.2006.01.009","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.24041/ejmr.2025.24","name":"Role of Artificial Intelligence in Healthcare Services","source":"crossref","abstract":"Artificial Intelligence (AI), is one of the important tools widely used by radio-diagnostic and pathology images purposes. In the recent years AI play very important role in automated image analysis, aiding diagnoses, and improving clinical workflow. In recent years AI play interesting role in the field of cancer diagnostic. However there are so many challenges regarding the results and data biasness and legal accountability and ethical concerns.AI generated results and their data validation process is one of the big challenges in this field. In this article we cover the significant role of AI on medical diagnostic and their pros and cons of its use and the future of AI in radiology field.","url":"https://doi.org/10.24041/ejmr.2025.24","authors":["Pawan Kumar Doharey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-04T11:37:30Z","doi":"10.24041/ejmr.2025.24","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/j.artmed.2004.07.019","name":"Bio-medical entity extraction using support vector machines","source":"crossref","abstract":"Objective Support vector machines (SVMs) have achieved state-of-the-art performance in several classification tasks. In this article we apply them to the identification and semantic annotation of scientific and technical terminology in the domain of molecular biology. This illustrates the extensibility of the traditional named entity task to special domains with large-scale terminologies such as those in medicine and related disciplines. Methods and materials The foundation for the model is a sample of text annotated by a domain expert according to an ontology of concepts, properties and relations. The model then learns to annotate unseen terms in new texts and contexts. The results can be used for a variety of intelligent language processing applications. We illustrate SVMs capabilities using a sample of 100 journal abstracts texts taken from the {human, blood cell, transcription factor} domain of MEDLINE. Results Approximately 3400 terms are annotated and the model performs at about 74% F-score on cross-validation tests. A detailed analysis based on empirical evidence shows the contribution of various feature sets to performance. Conclusion Our experiments indicate a relationship between feature window size and the amount of training data and that a combination of surface words, orthographic features and head noun features achieve the best performance among the feature sets tested.","url":"https://doi.org/10.1016/j.artmed.2004.07.019","authors":["Koichi Takeuchi","Nigel Collier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-13T17:55:14Z","doi":"10.1016/j.artmed.2004.07.019","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-34266-0.20001-6","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34266-0.20001-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T14:38:58Z","doi":"10.1016/b978-0-443-34266-0.20001-6","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1109/ai4im69129.2026","name":"2026 IEEE International Symposium on Artificial Intelligence for Instrumentation and Measurement (AI4IM)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai4im69129.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-16T19:42:59Z","doi":"10.1109/ai4im69129.2026","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1109/icarai70085.2026.11635643","name":"ICARAI 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icarai70085.2026.11635643","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T19:11:45Z","doi":"10.1109/icarai70085.2026.11635643","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.artmed.2004.07.017","name":"Summarization from medical documents: a survey","source":"crossref","abstract":"Objective The aim of this paper is to survey the recent work in medical documents summarization. Background During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.","url":"https://doi.org/10.1016/j.artmed.2004.07.017","authors":["Stergos Afantenos","Vangelis Karkaletsis","Panagiotis Stamatopoulos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-14T15:22:01Z","doi":"10.1016/j.artmed.2004.07.017","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.21037/jmai-24-294","name":"Role of artificial intelligence in healthcare settings: a systematic review","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-294","authors":["Wahid Ullah","Qasim Ali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-23T02:09:28Z","doi":"10.21037/jmai-24-294","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.21037/jmai-24-111","name":"Artificial intelligence in surgery—a narrative review","source":"crossref","abstract":"Background and Objective: Integrating artificial intelligence (AI) into surgical practice has the potential to dramatically improve patient care, offering newfound opportunities to enhance precision, efficiency, and outcomes. This narrative review aims to comprehensively explore AI’s role in surgery, evaluating current applications and projecting future trajectories while addressing the ethical, legal, and practical challenges accompanying its implementation. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar databases, including articles published within the last 9 years [2016–2024]. Original research articles, systematic reviews, meta-analyses, and expert opinion pieces exploring AI’s applications, implications, and challenges in surgical settings were included. Key Content and Findings: AI-enabled technologies augment surgeons’ preoperative planning, intraoperative guidance, and postoperative care capabilities. Enhanced preoperative planning through AI-driven risk assessment and 3D modeling allows for more informed decision-making. Intraoperative AI applications, such as real-time imaging analysis and robotic assistance, improve precision and minimize complications. In the postoperative phase, AI enables personalized recovery monitoring, early complication detection, and long-term follow-up. However, integrating AI in surgery presents complex ethical challenges related to accountability, bias, confidentiality, and decision-making. The impact of AI extends to surgical education, offering personalized learning experiences and objective skill assessments. Strategies for safe and effective integration of AI in surgery include establishing robust safety protocols, conducting prospective clinical trials, training surgeons as AI end-users, and fostering multidisciplinary collaboration. Conclusions: The evolution of AI in surgery represents a transformative journey that has reshaped the surgical landscape. Realizing AI’s full potential requires ongoing research, innovation, and collaboration to address technical, ethical, and organizational challenges. By aligning technological advancements with patient welfare, ethics, and the core values of medicine, we can harness AI’s power to elevate the standard of surgical care and improve patient outcomes worldwide.","url":"https://doi.org/10.21037/jmai-24-111","authors":["Thomas F. Byrd IV","Christopher J. Tignanelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-12T05:58:31Z","doi":"10.21037/jmai-24-111","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1016/b978-0-443-29118-0.00024-4","name":"About the editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-29118-0.00024-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-06T09:32:45Z","doi":"10.1016/b978-0-443-29118-0.00024-4","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/c2025-0-02972-1","name":"Structural Reliability and Health Monitoring of Composite Structures with Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2025-0-02972-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T09:46:55Z","doi":"10.1016/c2025-0-02972-1","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-06637-4_4","name":"Multi-layer Perceptrons","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06637-4_4","authors":["Oliver Kramer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T05:09:15Z","doi":"10.1007/978-3-032-06637-4_4","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/ais69919.2026.11621168","name":"Keynote Speeches","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ais69919.2026.11621168","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-28T19:11:16Z","doi":"10.1109/ais69919.2026.11621168","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2025.112918","name":"An artificial intelligence-driven analysis of blood-based ternary nanofluid flow: A novel framework for enhanced hemorheological applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.112918","authors":["Mohib Hussain","Du Lin","Hassan Waqas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-27T19:28:05Z","doi":"10.1016/j.engappai.2025.112918","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1142/9781800617384_0007","name":"Artificial Intelligence in Education: Practice-Oriented Training with a Human-Centric Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9781800617384_0007","authors":["Brandon Swee Tuan Ng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-11T06:16:06Z","doi":"10.1142/9781800617384_0007","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.26481/dis.20231205sp","name":"Artificial intelligence in medical imaging","source":"crossref","abstract":"","url":"https://doi.org/10.26481/dis.20231205sp","authors":["Sergey Primakov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-27T16:08:01Z","doi":"10.26481/dis.20231205sp","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1109/medai62885.2024.00005","name":"Foreword","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai62885.2024.00005","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-25T19:17:43Z","doi":"10.1109/medai62885.2024.00005","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.7759/cureus.109790","name":"A Study to Assess the Awareness and Attitudes Towards Artificial Intelligence (AI) Tools Among Medical Trainees and Practitioners","source":"crossref","abstract":"Background Artificial intelligence (AI) is increasingly integrated into healthcare and medical education, offering support in clinical decision-making, learning, and information retrieval. However, its effective adoption depends on users' awareness, perception, and readiness. The study aimed to evaluate awareness and utilization of AI tools, assess perceptions, and identify facilitators and barriers influencing their acceptance among healthcare professionals and medical students. Methods: A cross-sectional study was conducted from September 2025 to January 2026 at a tertiary care center in Southern Rajasthan among 377 participants, including consultants, postgraduate trainees, and undergraduate students. A semi-structured, pre-validated, web-based questionnaire was used. Results: Among all, 327 (86.7%) participants were aware of AI bots, while only 51 (13.5%) had received formal training. Despite this, 302 (82%) reported using AI tools for academic and clinical purposes, with 93 (24.7%) using them daily. Common uses included clinical decision-making, studying medical concepts, and preparing presentations. Participants' primary concerns included accuracy (190, 50.4%), impact on critical thinking (235, 62.3%), and interpretation challenges (155, 41%). Major barriers identified were a lack of awareness, reliability issues, fear of academic misconduct, poor internet connectivity, and a lack of faculty endorsement. Facilitators included institutional guidelines, faculty training, workshops, mentorship, and curriculum integration. Conclusion: AI tools are widely used among healthcare professionals and students despite limited formal training. While perceived as useful and time-saving, concerns regarding reliability, ethics, and overdependence persist. Strengthening training, developing clear guidelines, and integrating AI into medical curricula are essential to ensure safe and effective utilization.","url":"https://doi.org/10.7759/cureus.109790","authors":["Kirti Chouhan","Khushbu Jangir","Medha Mathur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-28T07:42:09Z","doi":"10.7759/cureus.109790","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:47:56.313Z"},{"id":"doi:10.1111/nicc.70606","name":"Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside action. AI-CDSS may be particularly relevant when they support rather than replace clinical judgement. Aim To examine whether nurse-used AI-CDSS improve patient-important outcomes in acute and critical care contexts and summarise effects on care processes and nurse-reported outcomes. Study design Following PRISMA 2020 and a preregistered protocol, we searched eight databases and major trial registries for English-language studies from 1 January 2010 to 1 January 2026. Searches were conducted on 1 January 2026. We included randomised, quasi-experimental and adjusted cohort studies in which registered nurses or nursing teams were primary users of AI-CDSS generating patient-specific predictions or recommendations. Mortality was pooled using a random effects model; other outcomes were synthesised narratively. Results Seven studies involving about 75 000 patients were included. Most evidence came from acute wards, intensive care units, sepsis, deterioration and delirium-prevention contexts, with additional home and palliative care evidence. Three mortality studies were pooled. Nurse-facing AI-CDSS were associated with lower hospital mortality (RR 0.68, 95% CI 0.53-0.87; I 2 = 24%), although the prediction interval included possible no effect. Length of stay and protocol adherence generally improved when tools were embedded in nursing workflows. Nurse-reported outcomes were sparse. Conclusion Nurse-facing AI-CDSS may strengthen acute and critical care nursing by improving surveillance, escalation and protocol delivery for patients at risk of deterioration. Evidence is promising but limited by small study numbers, heterogeneous interventions and sparse nurse-reported outcomes. Critical care implementation should prioritise nurse-centred design, alert burden, equity, safety monitoring and rigorous evaluation before scale-up. Relevance to clinical practice Nurse-used AI-CDSS show potential to improve patient outcomes and care processes, but evidence remains limited and context dependent.","url":"https://doi.org/10.1111/nicc.70606","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/nicc.70606","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1136/bmjhci-2026-102271","name":"Bridging the black box: artificial intelligence in acute care needs greater interpretability and precision.","source":"europepmc","abstract":"The use of machine learning in acute care is moving from theory to real clinical settings. Two recent studies published in BMJ Health and Care Informatics discuss this and cover two important topics that focused on early triage for suspected acute coronary syndrome and emergency department (ED) flow using length of stay prediction.","url":"https://doi.org/10.1136/bmjhci-2026-102271","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjhci-2026-102271","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5797/jnet.oa.2026-0100","name":"Real-Time Artificial Intelligence-Based Detection of Flow-Directed Microcatheter Tip Markers.","source":"europepmc","abstract":"Objective Flow-directed microcatheters (FDMs) offer high flexibility and trackability for accessing distal arterial feeders. However, the radiopaque tip marker is small, making it difficult to identify on fluoroscopy. This study evaluated an intraoperative artificial intelligence (AI)-based system for real-time detection of FDM tip markers. Methods We retrospectively analyzed 10 consecutive cases of middle meningeal artery embolization for chronic subdural hematoma using the AI-based system. The detection rate was evaluated on a frame-by-frame basis during microcatheter placement. Exploratory subgroup analyses were performed based on the catheter diameter (1.5 Fr vs. 1.3 Fr) and whether the microcatheter tip or guidewire tip advanced ahead during navigation. Results Twenty-five FDM placement scenes were analyzed. Mean microcatheter navigation time was 2.1 min. The precision, recall, and detection rate of the system were 95%, 51%, and 50%, respectively; the detection rate was calculated as the proportion of true-positive frames among all analyzed frames. Although this was an exploratory subgroup analysis, the detection rate appeared higher for the 1.5-Fr microcatheters than that for the 1.3-Fr microcatheters (78% vs. 46%; p = 0.006). Furthermore, the detection rate was significantly higher when the microcatheter tip advanced ahead of the guidewire tip than vice versa (65% vs. 43%; p = 0.002). Conclusion Although the detection rate was 50%, the AI system may help operators recognize the tip position when it becomes difficult to identify on fluoroscopy. Larger multicenter studies are required to validate these findings.","url":"https://doi.org/10.5797/jnet.oa.2026-0100","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5797/jnet.oa.2026-0100","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.2196/92700","name":"Beyond Pattern Recognition: Call for Functionally Aware AI for Anatomical Illustration.","source":"europepmc","abstract":"Unlabelled This paper critically assesses the role of generative AI in anatomical illustration, identifying fundamental barriers that currently preclude AI from replacing human medical illustrators. Despite the promise of unprecedented efficiency, contemporary models exhibit persistent anatomical inaccuracies and \"hallucinations\" of nonexistent structures-flaws stemming from statistical pattern-matching rather than genuine anatomical understanding. These systems further lack pedagogical intent, clinical context, and the capacity for deliberate visual judgment, while raising unresolved ethical and copyright concerns regarding training data. Although a specialized AI for this purpose is theoretically feasible, its development as a standalone goal remains economically nonviable given the niche nature of the profession. Rather than replacing human illustrators, AI's future role will be augmentative, with the requisite anatomical intelligence likely emerging as a byproduct of broader advances in clinical applications such as surgical planning and personalized medicine. For AI-generated imagery to become educationally and clinically reliable, it will require rigorous human supervision, curated gold standard datasets, and a foundation of genuine anatomical comprehension.","url":"https://doi.org/10.2196/92700","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/92700","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fpubh.2026.1880523","name":"Artificial intelligence literacy, anxiety, and attitudes among registered nurses across different hospital tiers: a multi-center cross-sectional study.","source":"europepmc","abstract":"Background The rapid advancement of artificial intelligence is driving an unprecedented technological transformation in nursing. However, the successful integration of these technologies depends largely on the proficiency and perspectives of registered nurses. Consequently, there is an urgent need to examine the psychological and behavioral responses of this workforce. Methods A multi-center, cross-sectional survey was conducted from March to May 2026. a stratified convenience sampling method was employed to recruit 1,392 registered nurses from tertiary hospitals, secondary hospitals, and community health centers in Chongqing. Data collection instruments included a general demographic questionnaire, the Artificial Intelligence Literacy Scale, the Artificial Intelligence Anxiety Scale, and the General Attitudes Towards Artificial Intelligence Scale. Statistical analyses, including descriptive statistics, Spearman correlation, and multiple linear regression. Results A total of 1,392 registered nurses participated in the study, with a mean age of 34.69 ± 7.13 years. AI literacy scored 5.27 ± 0.90 (75.29% scoring rate). AI anxiety was moderate (51.29%), with the highest concerns appearing in socio-technical blindness (56.43%) and job replacement (55.14%). Overall, nurses maintained a positive attitude toward AI (74.00%). AI literacy was negatively correlated with anxiety ( r = -0.338, p 0.001) and significantly positively correlated with attitude ( r = 0.551, p r = -0.541, p B = -0.453, p B = 0.377, p p = 0.024) and lack of proficiency in device operation ( p = 0.033) were associated with higher anxiety levels, whereas male nurses demonstrated more positive attitudes compared to female nurses ( p = 0.011). Conclusion Nurses demonstrated high AI literacy and positive attitudes; however, anxiety remained prominent. Enhancing AI literacy may alleviate psychological anxiety, with device accessibility and usage patterns also playing critical roles. To facilitate the effective integration of artificial intelligence into clinical practice, administrators should strengthen institutional support mechanisms alongside providing facility resources and conventional education, thereby promoting the full utilization and translation of available resources.","url":"https://doi.org/10.3389/fpubh.2026.1880523","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1880523","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5527/wjn.124607","name":"Letter to the Editor: Artificial intelligence in nephrology point-of-care ultrasonography - opportunities, limitations, and future directions.","source":"europepmc","abstract":"We read with interest the retrospective cohort study by Silipigni et al entitled \"Role of point-of-care ultrasonography in kidney disease management: A single solution for multiple challenges\". Artificial intelligence (AI) is rapidly reshaping the way clinicians learn, perform, and interpret point-of-care ultrasonography (POCUS). In nephrology, AI-assisted image acquisition, automated measurements, and emerging decision-support tools offer opportunities to improve efficiency and expand access to POCUS training. At the same time, important limitations remain, particularly when ultrasound findings must be integrated with physiology and clinical context. In this commentary, we discuss the current role of AI in nephrology POCUS, examine potential pitfalls of overreliance on automated interpretation, and consider future applications that may extend beyond image analysis toward physiologic assessment and hemodynamic phenotyping. Ultimately, we suggest that AI should be used to enhance, not replace clinicians' understanding of anatomy, physiology, and patient-specific pathophysiology.","url":"https://doi.org/10.5527/wjn.124607","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5527/wjn.124607","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag119","name":"Paging the algorithm: applying the Best Available Human principle to graduate medical education.","source":"europepmc","abstract":"Artificial intelligence (AI) is transforming graduate medical education (GME), yet formal training in its responsible use remains limited. As AI capabilities expand, trainees increasingly adopt these tools informally and without structured oversight, raising urgent questions about how to integrate AI into physician training and teach its responsible use. This article applies Ethan Mollick's Best Available Human (BAH) standard as a principle to guide trainee use of AI. Best Available Human permits AI engagement when its performance meets that of the best human expertise readily available and offers a simple, flexible rule to help trainees decide when AI can responsibly augment learning and patient care. Best Available Human is adapted for GME by pairing its performance and availability threshold with structured verification and faculty review to reinforce responsible AI use. The authors apply the BAH principle across 3 domains central to GME: (1) clinical instruction, (2) diagnostic reasoning, and (3) health record composition. In each domain BAH conditions AI use on availability thresholds, demonstrated performance, and structured verification. Because the BAH principle would function best when trainees possess a foundational understanding of AI tools and their appropriate use, the authors argue that GME programs should formally incorporate established AI competencies into their curricula. These competencies align with the skills needed to master the BAH principle and use AI responsibly. Together they make disciplined, responsible AI use both teachable and feasible within contemporary GME training programs.","url":"https://doi.org/10.1093/acamed/wvag119","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag119","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00431-026-07288-5","name":"Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.","source":"europepmc","abstract":"This study aims to map ethical, legal, social, professional, and implementation issues associated with artificial intelligence (AI) in neonatal intensive care units (NICUs). A JBI-informed scoping review using the population-concept-context framework was reported according to PRISMA-ScR. Peer-reviewed English-language articles published from 1 January 2016 to 31 May 2026 were eligible. Because the original exports and screening log were unavailable, a documented updated rerun was completed on 13 July 2026 using public PubMed-indexed bibliographic searching, supplementary publisher and bibliographic web searching, and backward and forward citation chaining. Exact strategies and record-level decisions are provided as online resources. The retrieval log contained 78 record captures. After removal of 21 duplicates, 57 unique records were screened; 45 full texts were assessed, 14 were excluded with documented reasons, and 31 sources were included. Eight recurring domains were identified: data governance; bias and fairness; transparency and explainability; human oversight; accountability and surveillance; parental engagement; professional readiness and workflow; and equitable implementation. Empirical evidence was concentrated on parent and nurse perceptions, pain assessment, counseling, and explainability, whereas consent processes, subgroup fairness, liability, and post-deployment safety remained under-studied. Conclusion Ethically responsible NICU AI requires secure governance, local and subgroup validation, understandable communication, active clinician oversight, defined accountability, staff and family engagement, and prospective monitoring. Generative AI should remain supervised and should not replace clinician-family communication. What is known • Artificial intelligence is increasingly being developed for neonatal outcome prediction, monitoring, pain assessment, clinical decision support, documentation, and family communication, but relatively few systems have progressed to validated routine NICU use. • The use of AI in neonatal care raises concerns regarding privacy, algorithmic bias, explainability, accountability, human oversight, parental trust, and equitable access. What is new • This scoping review identifies eight recurring ethical and implementation domains for NICU AI: data governance, fairness, transparency, human oversight, accountability, parental engagement, professional readiness, and equitable implementation. • Current empirical evidence is concentrated on stakeholder perceptions, pain assessment, counseling, and explainability, while consent processes, subgroup fairness, legal responsibility, and post-deployment safety monitoring remain insufficiently studied.","url":"https://doi.org/10.1007/s00431-026-07288-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00431-026-07288-5","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.neunet.2026.109532","name":"Bidirectional uncertainty-aware region learning for semi-supervised medical image segmentation.","source":"europepmc","abstract":"In semi-supervised medical image segmentation, the poor quality of unlabeled data and the uncertainty in the model's predictions often lead to the generation of incorrect pseudo-labels by the model. These errors accumulate throughout model training, thereby weakening the model's performance. We found that these erroneous pseudo-labels are typically concentrated in high-uncertainty regions. Traditional methods improve performance by directly discarding pseudo-labels in these regions, which can also result in neglecting potentially valuable training data. To alleviate this problem, we propose a bidirectional uncertainty-aware region learning strategy to fully utilize the precise supervision provided by labeled data and stabilize the training of unlabeled data. Specifically, in the training labeled data, we focus on high-uncertainty regions, using precise label information to guide the model's learning in potentially uncontrollable areas. Meanwhile, in the training of unlabeled data, we concentrate on low-uncertainty regions to reduce the interference of erroneous pseudo-labels on the model. Through this bidirectional learning strategy, the model's overall performance has significantly improved. Extensive experiments show that our proposed method achieves significant performance improvement on different medical image segmentation tasks.","url":"https://doi.org/10.1016/j.neunet.2026.109532","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.109532","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s12958-026-01591-4","name":"A proposal to meet specific criteria before fully integrating Artificial Intelligence (AI) into the IVF lab.","source":"europepmc","abstract":"Artificial intelligence (AI) is generating genuine excitement in reproductive medicine for good reason. The promise of more consistent embryo assessment, reduced inter-observer variability, better outcome prediction and meaningful support for the embryologist's judgment represents a real opportunity to improve care for patients who have already invested enormously in the pursuit of a family. Realizing that promise, however, depends on something the field has not always done well which is insisting on rigorous validation before widespread adoption. This editorial is written in that spirit, as advocates for AI who believe the technology's long-term acceptance by patients, clinicians and embryologists will be determined by how carefully we deploy it now and how we proceed going forward. How will we know when AI is ready to fully deploy in the IVF lab? The answer requires distinguishing between being safe and being safe and effective and the medical community has not yet reached consensus on either standard for most applications. Professional societies will need to lead that process. However, in the meantime, with commercial adoption accelerating and stakeholder incentives not always aligned with patient welfare, we propose criteria as a starting framework for that discussion.","url":"https://doi.org/10.1186/s12958-026-01591-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12958-026-01591-4","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.18553/jmcp.2026.32.9.1130","name":"Who is responsible? A risk-stratified framework for pharmacist accountability in artificial intelligence-assisted managed care.","source":"europepmc","abstract":"Artificial intelligence (AI)-enabled tools are increasingly integrated into managed care pharmacy workflows, particularly in utilization management and prior authorization processes. Although these systems aim to improve operational consistency and efficiency, federal regulatory guidance and professional standards make clear that coverage and medical necessity determinations must remain grounded in human clinical judgment. As AI tools become part of routine managed care, responsibility for final decisions may become unclear, creating an accountability gap. This article synthesizes federal regulatory guidance and professional pharmacy standards relevant to AI use in health care. We review Centers for Medicare & Medicaid Services requirements, oversight findings from the US Department of Health and Human Services Office of Inspector General, and established pharmacy ethics frameworks. Based on this review, we propose a pharmacist-centered accountability framework for AI-assisted managed care decisions. The framework emphasizes explicit professional responsibility, structured human-in-the-loop review, and documentation practices that support transparency and auditability. Clarifying pharmacist accountability in AI-assisted utilization management can strengthen professional stewardship, enhance workforce readiness, and support responsible innovation in managed care pharmacy.","url":"https://doi.org/10.18553/jmcp.2026.32.9.1130","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.18553/jmcp.2026.32.9.1130","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1097/ruq.0000000000000753","name":"Artificial Intelligence in Ultrasound Imaging: Opportunities for Improving Diagnostic Accuracy in Gulf Health Care.","source":"europepmc","abstract":"Ultrasound imaging is widely used across cardiology, hepatology, obstetrics, breast and thyroid imaging, and emergency care because it is real-time, noninvasive, and relatively accessible. However, its diagnostic performance remains influenced by operator experience, image quality, scanner settings, and interpretation variability. Artificial intelligence (AI) has emerged as a promising support tool for ultrasound, assisting with image acquisition, quality assessment, view classification, segmentation, measurement, lesion characterization, and structured reporting. This narrative review summarizes recent developments in AI-assisted ultrasound imaging, emphasizing its technical foundations, clinical applications, validation challenges, and relevance to Gulf health care systems. Current evidence suggests that AI may improve workflow efficiency, reduce interobserver variability, and support diagnostic decision-making in selected ultrasound tasks, particularly when models are trained and tested on large, diverse data sets. Nevertheless, the clinical readiness of many AI tools remains constrained by retrospective study designs, single-center data sets, limited external validation, vendor-dependent image variability, and insufficient prospective evaluation. These limitations are particularly salient in Gulf health care, where ultrasound services are delivered across heterogeneous public, private, military, and academic institutions that use different equipment, workflows, and operator training backgrounds. Gulf countries are well-positioned to adopt AI-enabled ultrasound because of the ongoing digital health transformation and emerging regulatory frameworks, including the Saudi SFDA guidance and the UAE AI governance initiatives. However, responsible implementation will require region-specific, multicenter, multivendor validation, transparent reporting of model performance and failure cases, clinician training, and privacy-preserving data governance. AI should therefore be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.","url":"https://doi.org/10.1097/ruq.0000000000000753","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/ruq.0000000000000753","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1080/10717544.2026.2713415","name":"Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.","source":"europepmc","abstract":"Extracellular vesicles (EVs) have emerged as promising tools for early cancer detection, therapeutic monitoring, and drug delivery in oncology. Artificial intelligence (AI), particularly machine learning and deep learning, offers new analytical tools and computational approaches for EV research. This review summarizes recent advances in the application of AI to EV isolation, characterization, diagnosis, and drug delivery, with particular emphasis on its potential to enhance tumor detection sensitivity, diagnostic accuracy, and the rational design of delivery platforms. Special attention is given to the roles and recent applications of AI models in integrating multimodal features, characterizing EV heterogeneity, supporting diagnostic classification, and modeling in vivo behavior. Moreover, we examine the integration of AI with EV-based microfluidic isolation, surface-enhanced Raman spectroscopy (SERS), fluorescence imaging, and multiomics analysis. Among these areas, AI-assisted EV diagnostic applications are comparatively closer to clinical translation, with several studies incorporating patient-derived samples and AI-assisted diagnostic platforms, whereas AI-guided therapeutic EV design strategies remain largely exploratory. With the continued accumulation of multicenter, cross-platform EV datasets, improvements in algorithmic robustness, and closer integration of computational and experimental workflows, AI may support further clinical evaluation of EV-based diagnostics and the systematic optimization of therapeutic EV platforms.","url":"https://doi.org/10.1080/10717544.2026.2713415","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/10717544.2026.2713415","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/cancers18162723","name":"Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly being integrated into medical practice, offering promising tools to improve diagnostic accuracy and clinical efficiency. In the field of breast pathology, AI applications, particularly those based on deep learning (DL) and machine learning (ML), are emerging as decision-support tools in both diagnostic and prognostic workflows. This review provides a comprehensive overview of current AI-based approaches, with a focus on their clinical utility in tumor detection, histological classification, biomarker assessment, and prediction of treatment response. In addition to summarizing available AI platforms, the review critically examines their level of clinical validation, regulatory status, and integration into routine practice. Key challenges are also discussed. Overall, AI is expected to play an increasingly important role in supporting pathologists and advancing precision medicine in breast cancer management.","url":"https://doi.org/10.3390/cancers18162723","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/cancers18162723","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s11060-026-05771-5","name":"Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for lung cancer brain metastases.","source":"europepmc","abstract":"Background Prescription dose selection for lung brain metastases treated with stereotactic radiosurgery (SRS) remains largely guided by generalized practice patterns rather than tumor-specific modeling of local failure dynamics. We developed a Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS), an artificial intelligence framework for personalized dose evaluation in lung brain metastases. Methods We performed a retrospective single-center study of lung brain metastases treated with Gamma Knife radiosurgery. Only variables available at or before treatment were included. The final model used a mixture-of-experts (MoE) deep neural network with discrete-time survival modeling. Margin dose was incorporated as an explicit input variable, allowing repeated evaluation across candidate dose levels for tumor-specific dose recommendation. Internal validation consisted of grouped 5-fold cross-validation and a grouped holdout test split by patient. Results The final analytic cohort included 767 patients with 3,728 treated lung brain metastases. In grouped cross-validation, the MoE model achieved a mean Area Under the Curve (AUC) of 0.876 for 12-month local failure and a mean absolute error (MAE) of 0.99 months. In the grouped holdout test set, the model achieved an AUC of 0.863 (95% CI, 0.776-0.942) and an MAE of 1.26 months (95% CI, 0.44-1.49). Probabilistic performance was favorable, with a Brier score of 0.061, calibration intercept of 0.18, and calibration slope of 0.87. Conclusions THINKERS-Lung provides an internally validated framework for tumor-specific SRS dose evaluation in lung brain metastases and supports the feasibility of AI-guided personalized radiosurgical decision support. Clinical trial number Not applicable.","url":"https://doi.org/10.1007/s11060-026-05771-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11060-026-05771-5","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.acra.2026.06.058","name":"Peer Review, Workforce Shortages and Artificial Intelligence: Perfect Storm or Opportunity?","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.acra.2026.06.058","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.acra.2026.06.058","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/healthcare14162642","name":"A Multidisciplinary Model for Risk Management and Detection of Ageist Bias in Healthcare Systems in the Era of Artificial Intelligence.","source":"europepmc","abstract":"Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults (\"Digital Ageism\"). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure.","url":"https://doi.org/10.3390/healthcare14162642","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14162642","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ienj.2026.101890","name":"The role of artificial intelligence in predicting readmission risk in emergency patients: An umbrella review.","source":"europepmc","abstract":"Background Hospital readmission following emergency care remains a persistent challenge, reflecting gaps in care continuity, discharge planning, and risk stratification. Conventional prediction methods often fail to capture complex clinical interactions. The emergence of artificial intelligence offers new opportunities to enhance predictive accuracy by analyzing large, multidimensional healthcare datasets. Aim This umbrella review aimed to synthesize existing evidence on the role, performance, and clinical applicability of artificial intelligence in predicting readmission risk among emergency patients. Methods An umbrella review design was adopted following established evidence synthesis guidelines. Systematic reviews and meta-analyses examining artificial intelligence-based readmission prediction in emergency settings were identified through comprehensive database searches. Data were extracted on study characteristics, model types, performance metrics, and clinical implications. Methodological quality was assessed using standardized appraisal tools, and findings were integrated through narrative synthesis. Results The findings revealed consistent evidence that artificial intelligence enhances readmission risk prediction by effectively analyzing complex and multidimensional healthcare data. Machine learning and deep learning approaches were widely applied, with ensemble and neural network models frequently demonstrating strong predictive capability. The integration of electronic health records and diverse patient level variables emerged as a critical factor in improving model performance. Across the evidence, artificial intelligence was shown to support early risk stratification and inform clinical decision making in emergency settings. However, important challenges were identified, including variability in study design, limited external validation, concerns regarding interpretability, and barriers to integration within existing healthcare systems. Conclusion Artificial intelligence holds substantial potential to improve readmission prediction in emergency care by enabling more personalized and data driven decision making. Addressing issues related to validation, transparency, and system integration is essential to support its translation into routine clinical practice.","url":"https://doi.org/10.1016/j.ienj.2026.101890","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ienj.2026.101890","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s42494-026-00268-0","name":"Clinical application of artificial intelligence technology in epilepsy.","source":"europepmc","abstract":"Epilepsy is a prevalent neurological disorder, and its inherent complexity and significant interindividual variability pose substantial challenges for clinical diagnosis and treatment. Against this backdrop, the rapid development of artificial intelligence (AI) technology is driving a historic paradigm shift in the field of epilepsy diagnosis and management. This review analyses the application of AI in four core domains of epilepsy clinical practice: early diagnosis, accurate seizure prediction, individualized treatment, and long-term disease management. The primary objectives of this review are to delineate the current status of the clinical application of AI in epilepsy, clarify emerging future trends, and ultimately provide practical references and actionable insights for clinical practitioners.","url":"https://doi.org/10.1186/s42494-026-00268-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s42494-026-00268-0","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fmed.2026.1913896","name":"Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.","source":"europepmc","abstract":"Artificial intelligence (AI) is reshaping the way medical information is processed and has shown considerable potential in medical record coding and diagnosis-related group (DRG) management. Traditional medical record management mainly relies on manual coding and rule-based matching, which is often limited by low efficiency, heavy workload, and insufficient consistency, making it difficult to meet the demands of large-scale healthcare data processing. In recent years, machine learning, deep learning, and natural language processing (NLP) have been increasingly applied to automated coding, clinical information extraction, and medical record quality control, and have gradually expanded to DRG grouping prediction, risk control, and hospital operation management. Large language models (LLMs) offer new opportunities for complex medical text understanding and candidate decision support; however, their application in medical record coding and DRG management remains at an early stage of validation and exploration. At present, this field still faces several challenges, including data heterogeneity, limited model interpretability, privacy and security concerns, and insufficient cross-institutional generalizability. Future efforts should focus on standardized validation, multimodal data integration, and human-AI collaboration mechanisms to promote the robust development of intelligent medical record management systems.","url":"https://doi.org/10.3389/fmed.2026.1913896","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1913896","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag146","name":"Generative artificial intelligence-assisted learning and the rise of pseudo-competence in medical trainees.","source":"europepmc","abstract":"Chen Ding, Shuwei Weng; Generative AI-assisted learning and the rise of pseudo-competence in medical trainees, Academic Medicine, , wvag146, https://doi.or","url":"https://doi.org/10.1093/acamed/wvag146","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag146","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3346/jkms.2026.41.e298","name":"Authorship, Disclosures, and Conflicts of Interest in Papers Related to the Use of Artificial Intelligence.","source":"pubmed","abstract":"As artificial intelligence (AI), particularly generative AI, is being actively introduced and utilized in medical research and manuscript writing, new challenges are emerging in academic publishing, specifically regarding author attribution, transparency, and conflicts of interest. This review examines the current status of AI use in medical publishing by focusing on three key areas: author attribution, disclosure methods regarding AI usage, and conflicts of interest. The prevailing view to date is that AI cannot be recognized as an author because it lacks the capacity to assume the responsibility that is a core requirement of authorship. Therefore, AI contributions are generally disclosed in the acknowledgment section. As AI becomes more deeply involved in the analysis and manuscript writing processes, it is expected that discussions regarding the attribution of intellectual contributions and the boundaries between tools and contributors will become more active in the future. The transparency of reporting AI usage depends on how the AI contributes to the research or manuscript. While AI used for purposes such as grammar or spelling correction is often exempt from disclosure requirements, if AI contributes more substantially to the content of the paper-such as text generation, data analysis, or code development-it is necessary to report this by indicating such details explicitly within the paper. However, stances on the level of disclosure vary among journals, such as whether to reveal all details like model specifications or prompts. Nevertheless, when generative AI is used in the research itself, detailed reporting is increasingly emphasized to ensure the reproducibility and scientific validity of the findings. Furthermore, AI adds a new dimension to conflicts of interest. This includes financial interests related to AI development, data ownership, and potential biases inherent in training datasets and algorithms. Since these factors can influence research results in subtle ways, more transparent and comprehensive disclosure of conflicts of interest in AI-based research is crucial.","url":"https://doi.org/10.3346/jkms.2026.41.e298","authors":["Oh J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e298","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s43046-026-00397-0","name":"From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.","source":"europepmc","abstract":"Hepatocellular carcinoma (HCC) is a malignancy with high global incidence and mortality, whose significant heterogeneity and poor prognosis pose severe clinical challenges. While artificial intelligence (AI) shows potential in HCC imaging, pathology, and prognosis, its \"black-box\" nature limits clinical adoption. Explainable AI (XAI) aims to reveal the decision-making logic of AI models. This narrative review synthesizes recent advances of XAI across four key domains of HCC research. In imaging diagnosis, techniques such as Grad-CAM and SHAP have enabled semantic alignment between AI outputs and clinical standards like LI-RADS, enhancing interpretability. In biomarker discovery, XAI has progressed from identifying single markers to revealing functional gene modules and molecular subtypes through multi-omics integration. In treatment efficacy prediction, XAI-based models have quantified feature contributions to therapeutic responses, supporting individualized treatment stratification. In prognosis assessment, XAI has enabled dynamic risk stratification by integrating clinical, imaging, and pathological features. However, three cross-cutting limitations persist across these domains: explanations remain predominantly correlational rather than causal, a semantic gap exists between pixel-level heatmaps and high-level clinical reasoning, and most models are static, unable to adapt to evolving clinical data. Current research is moving toward causal inference frameworks, concept-driven interpretability, and interactive, dynamic systems. In summary, XAI is transitioning from a retrospective explanation tool toward a prospective clinical decision partner, yet bridging the gap between explanation and actionable decision support remains the central challenge.","url":"https://doi.org/10.1186/s43046-026-00397-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s43046-026-00397-0","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.pcad.2026.08.012","name":"Artificial intelligence for risk prediction in atherosclerotic cardiovascular disease: A narrative review of advances, validation challenges, and clinical translation (2020-2026).","source":"europepmc","abstract":"Since 2020, artificial intelligence (AI) has been increasingly applied to atherosclerotic cardiovascular disease (ASCVD) risk prediction. This structured narrative review with systematic evidence mapping summarizes literature (2020-2026) examining study design, data sources, model architectures, multimodal fusion, model development and validation, performance evaluation, subgroup applications, and implementation barriers. Overall, 126 studies informed the review; 93 provided sufficient information for structured extraction, including prevention setting, exact input variables, comparator scores, validation strategies, discrimination, calibration, dominant model architecture, endpoint category, foundation-model or pretrained-model status, regulatory status, and implementation features. AI-based models may offer modest but clinically meaningful gains over conventional risk equations, especially with multimodal or longitudinal data. Among the 93 studies, traditional machine learning accounted for 83 (89%), deep learning for 7 (8%), and multimodal fusion for 3 (3%). Endpoint definitions were heterogeneous (23% ASCVD-specific; 63% expanded MACE composites). Among these studies, no large language model or federated learning was used for risk prediction. Appropriate comparators should now include contemporary equations such as PREVENT, rather than only legacy tools. Major barriers remain, including limited external validation, performance attenuation, data and algorithmic bias, limited interpretability, inconsistent reporting of calibration, fairness, and clinical utility, unclear regulatory status, and limited prospective evidence. Future work should prioritize calibration, robustness, transportability, fairness, interpretability, regulatory clarity, workflow integration, and prospective evidence of decision impact or post-deployment benefit. The central question is not only whether AI can detect complex patterns, but whether such models can be trusted, implemented, and shown to advance preventive cardiology in real-world settings.","url":"https://doi.org/10.1016/j.pcad.2026.08.012","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.pcad.2026.08.012","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/jamia/ocag078","name":"Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability.","source":"europepmc","abstract":"Background Generative artificial intelligence (AI) chatbots built on large language models are rapidly entering mental-health care, offering human-like support without meeting evidentiary standards for safety or effectiveness. Objective To examine the risks, and outline a governance framework capable of supporting safe, accountable, and equitable deployment of AI mental-health chatbots. Methods We synthesized recent clinical, regulatory, and behavioral health literature on AI mental health chatbots, including reported harms and system failure modes, to identify governance gaps and develop a 3-stage safety framework. Conclusions Embedding transparency, standardized evaluation, and ongoing oversight across the chatbot lifecycle, with clear responsibilities shared among developers, regulators, clinicians, researchers, and professional societies, is essential to ensure that AI systems intended to support mental health do not inadvertently cause harm.","url":"https://doi.org/10.1093/jamia/ocag078","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag078","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4088/pcc.26f04201","name":"Generative Artificial Intelligence in Mental Health: A Guide for Primary Care Providers.","source":"europepmc","abstract":"The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry. Prim Care Companion CNS Disord 2026;28(4):26f04201 . Author affiliations are listed at the end of this article.","url":"https://doi.org/10.4088/pcc.26f04201","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4088/pcc.26f04201","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/frai.2026.1911226","name":"Editorial: Advances in artificial intelligence for early cancer detection and precision oncology.","source":"europepmc","abstract":"The response highlighted both the enthusiasm surrounding this field and its rapid evolution. In total, 22 manuscripts were submitted to this Research Topic, of which 9 were accepted following peer review. Although these papers covered diFerent cancer types, data modalities, and methodological approaches, they ultimately converged around a number of shared themes. Perhaps more importantly, they oFered a realistic picture of where the field currently stands and where it may be heading.One of the most evident themes was the continued eFort to improve the early identification and characterisation of cancer. Earlier detection remains one of the strongest arguments for integrating AI into clinical workflows. In this collection, AI-assisted analysis of computed tomography scans identified malignant spinal lesions months before they were reported in routine practice, suggesting that subtle disease-related changes may be detectable before they become apparent to human observers. Similar ambitions were reflected in studies focusing on colorectal and skin lesions. Machine learning approaches improved the recognition of sessile serrated lesions during endoscopy, while a melanoma screening framework was specifically designed to minimise the risk of overlooking malignancies. Although these studies addressed diFerent clinical problems, they shared a common objective: reducing missed diagnoses while preserving the opportunity for timely intervention.At the same time, the contributions repeatedly reminded us that high performance alone is unlikely to guarantee clinical acceptance. Questions of trust, transparency, and interpretability have become increasingly diFicult to ignore. Several studies addressed these issues directly. One combined radiological imaging with structured clinical information in an explainable multi-modal framework, demonstrating that predictive performance and interpretability do not necessarily represent competing priorities. Another explored the use of tissue symmetry patterns in colon histopathology, linking model explanations to morphological changes that pathologists already recognise in daily practice. Even studies based on more traditional machine learning techniques attempted to move beyond black-box predictions by identifying clinically meaningful features and presenting them in a way that could support decision-making. Taken together, these contributions suggest that explainability is no longer viewed as an optional addition but rather as an important component of clinically useful AI systems.The collection also reflected the increasing sophistication of methodological approaches. Several studies moved beyond single-source analyses by integrating information from multiple domains. Radiomics features derived from ultrasound images were combined with deep learning outputs and clinical variables to improve the diFerentiation of breast fibroepithelial tumours and to reduce unnecessary biopsies. Elsewhere, advances in foundation models were adapted to the specific demands of medical imaging, illustrating how developments originating outside healthcare can be reshaped for oncology applications. These studies diFered substantially in their technical implementation, yet they shared a common goal: making better use of routinely available data to support more accurate and nuanced clinical assessments.Another encouraging observation was the growing emphasis on clinical relevance. Across the papers, there was a clear eFort to move beyond reporting isolated performance metrics. Multicentre validation strategies were increasingly adopted, and several authors attempted to evaluate how their methods might influence patient management. This was evident in studies demonstrating reductions in unnecessary invasive procedures and in work designed around clinically acceptable safety thresholds rather than optimising accuracy alone. The final contribution extended this perspective even further by investigating whether deep learning models could help predict both EGFR mutation status and response to targeted EGFR inhibitor therapy in patients with metastatic non-small cell lung cancer. Such approaches point towards a future in which AI may contribute not only to diagnosis, but also to treatment selection and therapeutic planning.Notably, one of the accepted papers stepped back from proposing a new model and instead examined the broader landscape of AI applications in cervical cytology. The review highlighted issues that many researchers and clinicians will recognise: limited external validation, increasing reliance on private datasets, inconsistent evaluation practices, and the persistent gap between promising research findings and routine clinical implementation. In many ways, these observations resonate with the broader messages emerging from the original studies included in this collection.Looking across all nine contributions, perhaps the most striking observation is that progress in oncology AI no longer seems to be defined solely by improvements in predictive performance. The field appears to be entering a more mature phase, one in which questions of reliability, transparency, validation, safety, and clinical impact receive equal attention. The challenge ahead is therefore not simply to build more sophisticated algorithms, but to develop systems that clinicians can understand, trust, and incorporate into everyday practice.We hope that the studies presented in this Research Topic will encourage further collaboration between clinicians, computer scientists, engineers, and researchers working across disciplines. Advances in artificial intelligence hold considerable promise for cancer care, but their true value will ultimately be determined by whether they improve the experiences and outcomes of the patients they are intended to serve.","url":"https://doi.org/10.3389/frai.2026.1911226","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1911226","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fpubh.2026.1931303","name":"Artificial intelligence-enabled early warning systems for public health preparedness: perspectives of senior public health leaders in a Small Island Developing State.","source":"europepmc","abstract":"Purpose Artificial intelligence-enabled early warning systems (AI-EWS) are increasingly recognised as tools for strengthening public health preparedness and education in climate-vulnerable settings. However, limited empirical evidence exists on how public health leaders perceive their use in practice. This study aimed to examine the perspectives of senior public health leaders, specifically County Medical Officers of Health (CMOHs), on AI-EWS in Trinidad and Tobago. Materials and methods An exploratory descriptive study was conducted using a structured survey of County Medical Officers of Health, with six of nine CMOHs completing the questionnaire (response rate = 66.7%). The survey assessed familiarity, perceived usefulness, system priorities, institutional readiness, and implementation barriers. Data were analysed descriptively using frequencies and proportions, with open-ended responses summarised using inductive thematic categorisation. Results All respondents (6/6) identified infectious diseases and flooding as priority applications for AI-EWS, while three (3/6) identified heat-related risks. Key system features, including dashboards (5/6), integration with emergency services (5/6), and automated alerts (3/6), were widely perceived as useful. Equity was prioritised by all respondents (6/6), particularly for underserved populations. However, barriers were also reported, including budget constraints (5/6), limited technical capacity (3/6), and data challenges (3/6). Conclusion AI-EWS are perceived as valuable tools for supporting public health decision-making, coordination, and professional learning in SIDS contexts. However, successful implementation will require strengthening infrastructure, workforce capacity, governance frameworks, and equitable system design. These findings provide early empirical insight to inform the responsible integration of AI-enabled systems into public health education and preparedness.","url":"https://doi.org/10.3389/fpubh.2026.1931303","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1931303","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00345-026-06635-3","name":"Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.","source":"europepmc","abstract":"Objective Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. Methods A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool. Results A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83-0.87) and 0.83 (95% CI: 0.80-0.86), with an AUC of 0.91 (95% CI: 0.88-0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79-0.85) and specificity of 0.68 (95% CI: 0.62-0.73), with an AUC of 0.83 (95% CI: 0.80-0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups. Conclusion AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI's clinical utility across diverse diagnostic tasks in urological oncology.","url":"https://doi.org/10.1007/s00345-026-06635-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00345-026-06635-3","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s00120-026-02885-6","name":"[Use of artificial intelligence in clinical practice and hospitals].","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications continue to evolve. AI-assisted systems support radiological and pathological image interpretation, risk stratification, and clinical decision-making processes. Despite considerable opportunities, important limitations remain. AI hallucinations, algorithmic bias, data protection requirements, and regulatory considerations necessitate continuous human oversight and critical evaluation. Therefore, the long-term success of AI will depend not only on technological performance but also on its responsible integration into existing healthcare structures. This review provides a practice-oriented overview of current and future AI applications in urology and discusses opportunities, limitations, and prerequisites for safe implementation in clinical practice and hospital care.","url":"https://doi.org/10.1007/s00120-026-02885-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00120-026-02885-6","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.critrevonc.2026.105538","name":"Artificial intelligence-driven transformation in the management of atypia of undetermined significance (AUS) thyroid nodules.","source":"europepmc","abstract":"Atypia of undetermined significance (AUS) thyroid nodules account for 10-30% of thyroid fine-needle aspiration (FNA) cytology results, with a malignant risk ranging from 13% to 30%. Currently, there is no unified diagnostic and treatment protocol for AUS. Clinical diagnosis faces challenges including imprecise malignant risk assessment, low diagnostic consistency among cytopathologists, and significant variations in treatment strategies across different centers. Traditional clinical management approaches include repeat FNA, molecular testing, diagnostic surgery, and follow-up observation. Repeat FNA still yields indeterminate results in some cases; molecular testing is limited by low specificity and high cost; diagnostic surgery is prone to overtreatment and complications; and follow-up may lead to missed diagnoses. The diagnostic and therapeutic dilemmas of AUS nodules may stem from the inability of existing models to effectively integrate multimodal medical data including clinical, radiological, laboratory, and cytological information, with diagnostic decisions overly reliant on a single examination modality. Artificial intelligence (AI), with its technical advantage of enabling efficient analysis and integration of medical multimodal data, offers a novel solution for the accurate differentiation of benign and malignant AUS and the optimization of clinical decision-making pathways. This review systematically elaborates on how the application of AI in clinical diagnosis and treatment is driving the transformation of traditional clinical management of AUS and prospects the impact of AI on personalized precision treatment of such nodules as well as future research directions.","url":"https://doi.org/10.1016/j.critrevonc.2026.105538","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.critrevonc.2026.105538","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/hsr2.73083","name":"Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low-Income Populations Within United States Health Systems: A Scoping Review.","source":"europepmc","abstract":"Background and aims Low-income populations in the United States experience disproportionate exposure to adverse social conditions. This scoping review examined artificial intelligence (AI) and machine learning (ML) approaches used in United States health systems to identify financial hardship, housing instability, food insecurity, neighborhood deprivation, and other social determinants of health (SDOH), and characterized interventions implemented in response. Methods Following the Arksey and O'Malley framework and PRISMA-ScR guidance, MEDLINE, CINAHL Plus with Full Text, PsycINFO, and Academic Search Ultimate were searched for peer-reviewed studies published from January 2010 through January 2026. The primary reviewer screened 209 unique records, and two additional reviewers reviewed screening decisions. Disagreements were resolved through discussion and majority vote. Seventeen studies met the inclusion criteria. Results Studies were conducted in academic medical centers, integrated delivery networks, safety-net hospitals, Veterans Health Administration facilities, specialty clinics, and Medicaid administrative environments. Methods included predictive ML modeling, natural language processing, and unsupervised clustering, with electronic health records used in 16 of 17 studies. Only one study evaluated a structured intervention: a proactive financial assistance program for ambulatory oncology patients at risk of financial hardship and unmet social needs. The remaining studies focused primarily on risk identification or model development. External validation was uncommon, and several studies reported differential performance across socioeconomic or racial and ethnic groups. Conclusion AI and ML may support SDOH risk identification in low-income populations, but evidence of generalizability, fairness, and translation into effective interventions remains limited. Future research should prioritize external validation, equity-focused evaluation, and rigorous testing of interventions triggered by identified social risks.","url":"https://doi.org/10.1002/hsr2.73083","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.73083","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.2147/ijwh.s631732","name":"The Quantification Paradox in Gynecologic Color Doppler Ultrasound: From Spectral Indices to Microvascular Imaging and Artificial Intelligence.","source":"europepmc","abstract":"Color Doppler ultrasound has long promised to convert tumor vascularity into an objective and reproducible measure for differentiating benign from malignant gynecologic disease. The historical record is more complicated. Quantitative spectral indices such as the resistance index, pulsatility index and peak systolic velocity were repeatedly proposed as objective discriminators, but their cutoffs did not become stable clinical standards. What entered major adnexal-mass systems was instead a coarse visual color score, used within structured multivariable frameworks such as the International Ovarian Tumor Analysis models and the Ovarian-Adnexal Reporting and Data System. New technologies-superb microvascular imaging, contrast-enhanced ultrasound, radiomics and deep learning-now reopen the old ambition of vascular quantification. This narrative review reorganizes gynecologic Doppler literature around measurement rather than disease category. It proposes that a major limiting problem may have been reproducibility, not signal content. High-resolution vascular features are more likely to become clinically useful when operator, machine, acquisition and population variance are controlled. The practical agenda for Doppler innovation should therefore prioritize standardized acquisition, reproducibility reporting, calibration, external validation and task-specific deployment over another isolated high-AUC or high-resolution vascular biomarker. Literature was identified through PubMed/MEDLINE searches (inception to 31 May 2026) combining gynecologic ultrasound with Doppler, O-RADS/IOTA, SMI, CEUS, radiomics, artificial intelligence and reproducibility; citation chaining was also used, with gynecologic evidence prioritized.","url":"https://doi.org/10.2147/ijwh.s631732","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/ijwh.s631732","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/postmj/qgaf195","name":"Artificial intelligence for assessment in competency-based medical education: current practices and future directions.","source":"europepmc","abstract":"Background Competency-Based Medical Education (CBME) relies on frequent, competency-focused assessments, which can be challenging to implement consistently. Artificial Intelligence (AI) holds promise to improve assessment efficiency, objectivity, and feedback in CBME, but its use remains in early stages with limited understanding of current practices and evaluation methods. This study aims to map existing AI applications in CBME assessments to guide future work. Methods A comprehensive search was performed in MEDLINE (Ovid), EMBASE (Ovid), PsycINFO, and Scopus using tailored keywords and MeSH terms. Included studies focused on the deployment of AI for assessment within CBME, covering applications in generating, analyzing, or interpreting evaluation data across undergraduate, graduate, and continuing professional education. The PRISMA-ScR guidelines were used to ensure transparent reporting, and findings were synthesized following Levac et al.'s approach. Results Of the 1002 search results, 32 studies met the inclusion criteria. Key findings indicate a wide application of AI from surgical or procedural skill assessment, to clinical note assessment, communication assessment, feedback generation, projected trainee performance, and analysis of narrative feedback from supervisors. Conclusion This review highlights potential advantages, such as timely evaluations, and challenges, such as lack of granularity, of AI integration. In conclusion, thoughtful integration of AI into competency-based medical education can complement traditional assessment methods and enhance learner outcomes, provided it is supported by robust infrastructure, ethical oversight, and collaborative policy development.","url":"https://doi.org/10.1093/postmj/qgaf195","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/postmj/qgaf195","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s13104-025-07605-5","name":"AI in academia: navigating ethical crossroads of innovation, integrity, and equity.","source":"europepmc","abstract":"The recent integration of artificial intelligence (AI) into academia could usher in transformative efficiencies across scholarly workflows-from manuscript drafting to data analysis-yet it also presents problematic ethical challenges that urgently require intense attention. While some surveys suggest that over 50% of researchers employ AI chatbots like ChatGPT and DeepSeek for tasks such as language refinement and administrative coordination, their adoption raises potential concerns about cognitive dependency, systemic bias, and accountability gaps. AI tools can enhance productivity by automating repetitive tasks, democratizing access for non-native English speakers, and streamlining literature synthesis. However, reliance on these systems could gradually erode critical thinking skills, particularly among early-career researchers pressured to prioritize publication quantity over rigor. Ethical ambiguities seem to persist: AI-generated content may complicate authorship norms, potentially entrench biases against Global South scholarship, and introduce risks of misinformation. Transparency deficits could further undermine trust, as undisclosed AI use might compromise peer review integrity and patient privacy in medical research. To balance innovation with ethical imperatives, this study advocates a tripartite framework: [1] ethical governance, including mandated disclosure of AI contributions and inclusive dataset curation to mitigate bias; [2] symbiotic human-AI collaboration, preserving human oversight in critical analysis and interpretation; and [3] equitable innovation, leveraging AI to bridge global research disparities. Unresolved challenges-such as accountability for AI errors and the potential cognitive consequences of prolonged dependency-appear to underscore the urgent need for global standards to clarify liability and preserve academic rigor while fostering equitable innovation. Proactive engagement from journals, institutions, and developers may be essential to ensure AI augments, rather than undermines, the integrity and equity of scholarly ecosystems.","url":"https://doi.org/10.1186/s13104-025-07605-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13104-025-07605-5","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10549-026-08053-w","name":"The emerging role of artificial intelligence in preoperative prediction of surgical margin status in breast cancer surgery.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10549-026-08053-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10549-026-08053-w","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.jacadv.2026.103125","name":"Cardiology Rounds in the Age of Artificial Intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jacadv.2026.103125","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jacadv.2026.103125","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/jcm15166442","name":"A Retrospective Study of Ultrasonographic Features of Hepatic Metastases Following Adrenal Cortical Carcinoma Resection.","source":"europepmc","abstract":"Background/Objectives : Liver metastasis after surgery for adrenocortical carcinoma (ACC) is a critical factor affecting patient prognosis; however, relevant ultrasound imaging features remain poorly characterized. This retrospective study aims to systematically describe the conventional ultrasound and contrast-enhanced ultrasound (CEUS) features of post-surgical hepatic metastases from ACC and to evaluate the clinical utility of ultrasonography in the diagnosis and follow-up. Methods : A total of 10 patients with post-surgical ACC liver metastases via ultrasound-guided liver biopsy between January 2000 and June 2026 at West China Hospital of Sichuan University were retrospectively enrolled. All patients underwent conventional ultrasound (B-mode and color Doppler flow imaging, CDFI) and CEUS. Given the small sample size, only descriptive statistics were performed, and all findings should be interpreted as exploratory. Results : All 10 patients were female (age range: 38-56 years), 70% had multiple lesions. On B-mode ultrasound, 80% of lesions appeared hypoechoic, 100% exhibited heterogeneous internal echotexture, 80% had irregular shapes, and 60% displayed well-defined margins. CDFI detected internal or perilesional blood flow signals in 90% of lesions, predominantly perilesional (50%). CEUS demonstrated arterial-phase hyperenhancement in all cases (50% heterogeneous hyperenhancement, 30% peripheral-dominant enhancement, and 20% ring-like nodular hyperenhancement), followed by rapid wash-out during the portal venous or delayed phases, with 100% of lesions showing hypoenhancement at 180 s. Conclusions : These exploratory findings suggest that post-surgical ACC liver metastases typically manifest on conventional ultrasound as hypoechoic, heterogeneous solid masses with variable margins and predominant perilesional blood flow. CEUS reveals a characteristic \"fast-in, fast-out\" malignant enhancement pattern. CEUS may serve as a useful adjunct to conventional imaging within a multimodal surveillance strategy, but larger prospective studies are needed to confirm its diagnostic value.","url":"https://doi.org/10.3390/jcm15166442","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15166442","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/healthcare14162519","name":"Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.","source":"europepmc","abstract":"Background/Objectives: Artificial intelligence (AI) is increasingly introduced into clinical trial operations, but operational usefulness does not imply regulatory or site-level readiness. This review maps AI applications across trial operations and proposes an author-developed, unvalidated site-level readiness framework and preliminary deployment-decision aid. Methods: We conducted a structured narrative review with evidence mapping of peer-reviewed literature, regulatory documents and contextual sources (2020-2026). AI use cases were mapped by lifecycle stage, technical-validity reporting, evidence maturity, autonomy, trial impact and governance implications. Maturity was assessed with an author-developed 0-8 score intended for transparent mapping, not risk-of-bias grading. Results: The comparatively strongest evidence concerns patient-trial matching and eligibility assessment, which nonetheless reached only moderate maturity, being evaluated mainly retrospectively or in simulated screening rather than inside a live trial; no use case reached the highest band. Other applications remain less mature or context-dependent. Recurrent risks include hallucination, automation bias, weak local validation, limited auditability, model drift and unclear accountability. We propose a preliminary framework linking evidence maturity, technical validity, AI autonomy, trial impact and site capacity. Conclusions: AI readiness in clinical trial operations should be assessed at the level of the AI-enabled workflow rather than the model alone. Safe adoption requires context-specific technical and operational validation, human accountability, auditability, lifecycle monitoring and alignment with Good Clinical Practice.","url":"https://doi.org/10.3390/healthcare14162519","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14162519","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1017/dmp.2026.10420","name":"Experts' Opinion on Strategic Public Health CBRN Emergency Management Using Artificial Intelligence in the Middle East and North Africa Region.","source":"europepmc","abstract":"Chemical, biological, radiological, and nuclear (CBRN) incidents present escalating risks across the Middle East and North Africa (MENA) amid geopolitical instability, cross-border threats, and evolving non-state actor capabilities. This policy analysis examines why prevailing case-level and ministry-siloed governance is insufficient for population-level CBRN readiness, weighs alternative coordination models, and outlines an artificial intelligence (AI)-enabled, centrally coordinated public-health strategy adapted to the region's heterogeneity and resource constraints. We propose a National Emergency Management Advisory Council to provide statutory inter-ministerial authority and stewardship for a National CBRN Dashboard that delivers decision support, inventory tracking, simulation, and rapid triage. We situate this within existing backgrounds and analyze legal authority, financing, data governance, and feasibility in low-resource settings. While prototype AI models report high accuracy for antidote optimization, agent classification, and triage, we argue these metrics reflect controlled research, not operational readiness, and require external validation, robustness testing, and cybersecurity safeguards. A phased, evidence-graded roadmap is proposed to move MENA CBRN management from reactive to predictive and adaptive models.","url":"https://doi.org/10.1017/dmp.2026.10420","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1017/dmp.2026.10420","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fbioe.2026.1898243","name":"Artificial intelligence-assisted smart hydrogel bioinks in 3D bioprinting: design, optimization, and construct validation for functional tissue engineering.","source":"europepmc","abstract":"Hydrogel-based bioinks are central to three-dimensional (3D) bioprinting because they provide hydrated, cell-supportive microenvironments with tunable rheological, mechanical, and biological properties. Smart hydrogel bioinks further introduce stimuli-responsive and dynamic behaviors, but their development remains constrained by empirical trial-and-error workflows and weak integration among formulation design, printability, process monitoring, and post-print biological performance. This review develops an AI-assisted workflow framework for smart hydrogel bioink development rather than treating smart materials, algorithms, and autonomous laboratories as separate mature topics. We examine how artificial intelligence (AI) can support feature representation, property prediction, printability assessment, process optimization, monitoring, construct characterization, and iterative refinement. Particular emphasis is placed on distinguishing direct evidence in smart hydrogel bioinks from broader bioprinting evidence, adjacent-field methodological inspiration, and prospective autonomous concepts. We also clarify the boundaries among supervised prediction, Bayesian optimization, active learning, computer vision, feedback control, and AI-agent-assisted workflow coordination. Finally, we discuss validation, benchmarking, grouped data splitting, uncertainty estimation, out-of-distribution detection, and the need to connect early material and process descriptors with long-term biological function. Overall, AI-assisted methods can make hydrogel bioprinting more predictive and quality-oriented, but real-time closed-loop control and fully autonomous bioink laboratories remain prospective goals that require standardized datasets, validated biological endpoints, uncertainty-aware models, external validation, and human oversight.","url":"https://doi.org/10.3389/fbioe.2026.1898243","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fbioe.2026.1898243","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1556/650.2026.33592","name":"[The application of artificial intelligence in healthcare: benefits, challenges, and issues of responsibility].","source":"europepmc","abstract":"Introduction and objective Artificial intelligence is playing an increasingly important role in healthcare, particularly in diagnostics, clinical decision support, personalized medicine, robotics, administration, and medical education. At the same time, the clinical use of artificial intelligence raises not only technological opportunities but also ethical, data protection, and legal challenges. The aim of our study was to provide an integrated overview of the main benefits and risks of artificial intelligence applications in healthcare, with particular emphasis on liability-related issues. Method We conducted an interdisciplinary, narrative critical review of the literature. Scientific and regulatory sources were analyzed using content analysis, thematic analysis, and critical discourse analysis, with the integration of clinical, ethical, legal, and data protection perspectives. Results Based on the reviewed literature, artificial intelligence may improve diagnostic accuracy, support personalized decision-making, reduce administrative burden, contribute to better access to care, and create new opportunities in robotics and medical education. However, algorithmic bias, the lack of transparency resulting from the \"black box\" nature of many artificial intelligence systems, data protection and cybersecurity risks, as well as uncertainties related to social acceptance, represent major challenges. Our most important finding is that these issues converge in the question of clinical liability, while the traditional physician-centered model of responsibility appears increasingly insufficient. Conclusion The use of artificial intelligence in healthcare can only be sustainable and legitimate within an integrated, liability-centered framework. Reconsidering the distribution of responsibilities among developers, healthcare institutions, and clinicians, as well as strengthening institutional guarantees of transparency, human oversight, patient autonomy, and data protection is essential for the safe implementation of artificial intelligence in healthcare. Orv Hetil. 2026; 167(27): 1079-1087.","url":"https://doi.org/10.1556/650.2026.33592","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1556/650.2026.33592","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1148/rg.250198","name":"Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation.","source":"europepmc","abstract":"Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principles to radiology trainees. However, the potential of AI to transform radiology education remains underexplored. The authors review how AI can be leveraged to enhance radiology education, from curriculum planning to its implementation and evaluation. Guided by Harden's 10-step framework for curriculum development, they systematically examine current and potential future applications of AI at each stage. They detail how AI, particularly generative models, can augment traditional educational methods. Such applications include automating needs assessments through natural language processing of learner feedback, personalizing learning pathways based on performance data, and generating diverse educational content, including synthetic imaging cases and radiology board-style questions. Furthermore, AI can enhance teaching strategies through immersive simulations, streamlining assessments with objective report-comparison tools, and improving program management by automating administrative tasks. Although the potential is immense, significant limitations persist, including high implementation costs, the rapid pace of technological change, risks of AI bias and error, and concerns around data privacy. Despite these challenges, AI represents a paradigm shift for medical education. Radiology programs should pursue a strategic and pragmatic approach to AI adoption, starting with low-risk applications to build institutional capacity and prepare the next generation of radiologists for an AI-integrated future. © RSNA, 2026 Supplemental material is available for this article. See the invited commentary by Tejani and Cook in this issue.","url":"https://doi.org/10.1148/rg.250198","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1148/rg.250198","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag040","name":"Concerns about artificial intelligence feedback validation in the 2-Sigma platform.","source":"europepmc","abstract":"Zhicheng Du; Concerns About Artificial Intelligence Feedback Validation in the 2-Sigma Platform, Academic Medicine, , wvag040, https://doi.org/10.1093/acam","url":"https://doi.org/10.1093/acamed/wvag040","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag040","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/bjr/tqag037","name":"CLAIRE: a unified framework for reporting and assessing artificial intelligence in diagnostic imaging.","source":"europepmc","abstract":"Artificial intelligence (AI) models for diagnostic imaging face reproducibility challenges due to inconsistent reporting. Existing guidelines also lack specificity for imaging-based AI diagnostics, particularly regarding clinical usability and technical transparency. To address these gaps, the Completeness, Learnability, Applicability, Interpretability, Reproducibility, and Evaluation (CLAIRE) framework was developed as a practical reporting aid by a multidisciplinary team of clinicians and AI experts. CLAIRE was retrospectively validated on a subset of 10 imaging studies selected by theoretical saturation in medical and dental imaging. Internal validation demonstrated high reliability, with inter-rater agreement improving from Cohen's κ 0.286 to 0.987 (P < .01) after calibration, alongside a mean intra-rater reliability of 0.997 after a six-month washout period. This process yielded a 15-item structured checklist for standardizing AI reporting, supported by an objective scoring system for quality categorization and an editorial reference guide to facilitate systematic appraisal by reviewers and editors. CLAIRE aims to enhance clinician accessibility through plain-language technical summaries and assessments of real-world applicability. This proposal provides a unified and practical structure that improves reporting consistency, supports systematic assessment, and strengthens both reproducibility and clinical translation of AI-based imaging models.","url":"https://doi.org/10.1093/bjr/tqag037","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/bjr/tqag037","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/life16081346","name":"Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.","source":"europepmc","abstract":"The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment-intrinsically subject to inter-observer and intra-observer variability-with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework-supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector.","url":"https://doi.org/10.3390/life16081346","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/life16081346","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3390/jcm15156053","name":"Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.","source":"europepmc","abstract":"Introduction : Cardiovascular risk prediction remains challenging, particularly in patients with intermediate risk, mixed dyslipidemia, elevated lipoprotein(a), or variable lipid profiles. Conventional risk calculators may not fully capture nonlinear relationships among lipid, clinical, imaging, and longitudinal data. Objectives : This narrative review summarizes evidence on artificial intelligence (AI)-based cardiovascular risk assessment, focusing on lipid profile-based and multimodal models incorporating lipid-related variables. Methods : PubMed/MEDLINE, Scopus, and Google Scholar were searched for English-language articles published up to January 2026. Original studies, reviews, and relevant clinical guidelines addressing AI-based cardiovascular risk models, lipid-related predictors, and clinically applicable approaches were considered. Results : Lipid profile-based AI models may identify lipid phenotypes and lipid-related patterns associated with increased cardiovascular risk, while multimodal models have shown improved performance in selected datasets. However, the reviewed studies address heterogeneous tasks, including phenotype classification, cardiovascular event prediction, mortality prediction, patient trajectory modeling, and absolute risk estimation. Most evidence remains retrospective, with limited external validation, calibration assessment, and clinical utility data. Conclusions : AI-based models may support cardiovascular risk assessment, but routine implementation requires prospective validation, standardized evaluation, calibration, explainability, and clinical impact studies.","url":"https://doi.org/10.3390/jcm15156053","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15156053","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3389/fendo.2026.1893963","name":"From static snapshots to longitudinal trajectories: artificial intelligence in women's reproductive and ovarian health.","source":"europepmc","abstract":"Aim To distinguish ovarian longitudinal biology from direct ovarian AI evidence and transferred time-series methodology, and to evaluate how temporal information is used and linked to clinical action across ovarian malignancy, PCOS, ovarian aging/POI, and ART. Methods This structured narrative review mapped prior reviews and was supplemented by design verification of representative primary studies identified through searches covering 1 January 1989 to 1 January 2026. Two authors independently conducted the design-level verification and classification; disagreements were resolved by consensus. Evidence was classified as direct ovarian AI evidence, ovarian longitudinal biological evidence, or transferred methodological evidence. Temporal use and decision linkage were classified as T0 static prediction, T1 serial-feature summarization, T2 explicit time-aware modeling, or T3 decision-linked temporal modeling. T0-T3 describe how temporal information is used and linked to clinical decisions; they do not represent an automatic hierarchy of model quality. Recorded/calendar time, biological time, and decision time were distinguished because observation time is not necessarily equivalent to biological time. Results Available review-level evidence and the representative primary studies verified in detail indicated that AI applications in PCOS, ovarian-cancer imaging, and ovarian-reserve assessment were predominantly T0. CA-125 velocity, half-life, and nadir represented T1 serial-feature evidence rather than AI time-series modeling. Direct T2 evidence was sparse and included a longitudinal CA-125 joint model and a small number of within-cycle ART prescription models. T3 evidence linking outputs to prespecified action thresholds, calibration, false-positive burden, net benefit, clinician override, and prospective clinical outcomes was limited. Several studies described as longitudinal AI instead established longitudinal ovarian biology or transferred temporal methods from other diseases. Conclusion The clinical value of temporal AI in ovarian health depends less on architectural complexity than on correct alignment with ovarian biology, direct within-patient temporal evidence, calibrated validation, and a beneficial clinical action.","url":"https://doi.org/10.3389/fendo.2026.1893963","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1893963","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/acamed/wvag142","name":"Entangled pedagogy and the entrustment of generative artificial intelligence: a trainee perspective.","source":"europepmc","abstract":"Linda Liu, Claire Qu, Jennifer Benjamin, MD, MS; Entangled pedagogy and the entrustment of generative artificial intelligence: a trainee perspective, Acade","url":"https://doi.org/10.1093/acamed/wvag142","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag142","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1093/jpepsy/jsag027","name":"Topical review: Incorporating generative artificial intelligence into neuropsychology training: best practices, pitfalls, and recommendations for effective implementation.","source":"europepmc","abstract":"Objective Generative artificial intelligence (AI) is rapidly transforming the field of neuropsychology by offering innovative opportunities to enhance clinical assessment precision, improve diagnostic accuracy, and streamline administrative duties. AI tools have the potential to enrich trainee education by supporting case conceptualization, personalizing treatment recommendations, assisting with report writing, and simulating complex clinical scenarios. Despite these benefits, there remains a lack of standardized guidelines for how neuropsychology training programs should responsibly and effectively integrate AI into supervision and educational practice. Method This topical review integrates emerging best practices, current challenges, and future directions for AI integration into neuropsychology training. We adapt the Integrative Developmental Model (IDM) of supervision, which conceptualizes trainee growth across progressive levels of motivation, autonomy, and professional identity. Results This review highlights the importance of establishing ethical safeguards, supervisor training, curriculum development, and developmentally appropriate implementation to ensure that technology supports, rather than replaces, clinical judgement and practice. By applying IDM principles, AI can be introduced in a developmentally appropriate manner, balancing the need for structured guidance, ethical safeguards, and flexibility in supervision. Conclusions This structured approach promotes both skill acquisition and responsible professional growth while aligning with broader ethical standards in psychology. When operationalized thoughtfully, these principles enable neuropsychology training programs to harness the potential benefits of AI while maintaining clinical rigor, professional standards, and ethical integrity. Developmentally informed supervision, grounded in the IDM, provides a flexible framework to ensure that AI strengthens rather than undermines the preparation of future neuropsychologists.","url":"https://doi.org/10.1093/jpepsy/jsag027","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jpepsy/jsag027","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10072-026-09282-z","name":"Adaptive cascading artificial intelligence for Alzheimer's disease assessment: a clinically oriented narrative review and implementation framework.","source":"europepmc","abstract":"Artificial intelligence (AI) has achieved remarkable success in the diagnosis of Alzheimer's disease (AD) in the literature, where many of the models use multi-modal methods including neuroimaging, cerebrospinal fluid, genetics, and cognitive assessment. But clinical adoption of these systems is still limited since most systems are developed in an idealized setting, as cost-effective and specialized diagnostic studies are not universally accessible. We discuss the translation of benchmark performance of AI to real-world dementia care pathways. A practical framework that would be useful for scalable, equitable, and clinically deployable AI-assisted dementia care. In fact, recent advancements in blood-based biomarkers such as plasma phosphorylated tau, glial fibrillary acidic protein, and neurofilament light chain are providing new opportunities for a flexible and minimally invasive diagnosis method. Based on these advances, we propose a clinically grounded AI-assisted cascading model which mirrors real-world workflows via progressive screening, biomarker-guided assessment, selective imaging escalation, and longitudinal prognostic monitoring. We further discuss enabling methods such as sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness, and explainable AI. Finally, we outline the challenges for data design, for future validation and integration into healthcare systems, and ethical use.","url":"https://doi.org/10.1007/s10072-026-09282-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10072-026-09282-z","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10555-026-10366-7","name":"Biofabrication and artificial intelligence strategies for investigating solid- and fluid-pressure mechanobiology in pancreatic ductal adenocarcinoma.","source":"europepmc","abstract":"Pancreatic ductal adenocarcinoma (PDAC) is shaped by a mechanically abnormal tumor microenvironment (TME) in which dysregulated mechanotransduction promotes malignant progression and therapeutic resistance. Two coupled but distinct pressure states dominate this landscape: solid stress and interstitial fluid pressure (IFP). Solid stress arises from constrained tumor growth, stromal contractility, and extracellular matrix remodeling. By contrast, IFP reflects hydrostatic pressure within the interstitial fluid compartment and is elevated by vascular leakage, impaired drainage, and low tissue hydraulic conductivity. Together, these abnormalities compress vessels, disrupt transport, and activate mechanotransduction programs that reinforce malignant adaptation. Here we integrate solid stress and IFP within a unified pressure-state framework for PDAC. We examine how these forces shape tumor progression, drug transport, and therapeutic response. We then evaluate spheroid, organoid, hydrogel, bioprinted, and microfluidic models according to what they truly control, directly measure, or merely infer. This distinction separates pressure-relevant systems from pressure-reconstructing models. We also discuss stromal normalization and the emerging role of artificial intelligence and machine learning (AI/ML) in model engineering and patient stratification. Current computational approaches can optimize mechanically defined models and infer pressure-related tumor states from multimodal data. However, they still rely largely on surrogates rather than direct measurements of solid stress or IFP. Our framework defines the biomechanical validation required to develop clinically predictive models of PDAC mechanobiology.","url":"https://doi.org/10.1007/s10555-026-10366-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10555-026-10366-7","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s40200-026-02030-2","name":"Clinician-facing artificial intelligence and guideline adherence: a structured narrative review of comparative and interventional evidence.","source":"europepmc","abstract":"Purpose To synthesize empirical evidence on whether clinician-facing artificial intelligence (AI) tools improve clinical practice guideline adherence and whether AI-generated recommendations are concordant with physician decision-making. Methods We conducted a structured narrative review aligned with SANRA and informed by narrative synthesis guidance. PubMed/MEDLINE, Embase, Scopus, Web of Science, and Google Scholar were searched for studies published from January 2020 to May 2026; earlier directly relevant studies were identified through citation tracking. Eligible studies evaluated clinician-facing AI tools, guideline adherence or concordance outcomes, or physician-versus-AI recommendations. Preprints were considered separately as emerging evidence and were not included in the peer-reviewed core synthesis. Results Six peer-reviewed empirical studies met the core eligibility criteria. They included one real-world EHR-integrated pathway study, one NLP-based adherence measurement study, one randomized simulation trial of a decision-tree CDSS, and three AI-versus-physician comparative studies. Findings were most favorable for bounded tasks embedded in workflow or structured scenarios. Evidence was weaker for complex real-world decisions, and several studies did not evaluate patient outcomes. Three 2026 preprints were summarized separately as preliminary evidence. Conclusion Early evidence suggests that clinician-facing AI may support guideline-concordant care when applied to clearly defined decision tasks and integrated into clinical workflow. However, the evidence base remains small, heterogeneous, and largely indirect for diabetes care. Prospective cardiometabolic studies are needed to evaluate effectiveness, safety, usability, and patient outcomes. Supplementary information The online version contains supplementary material available at 10.1007/s40200-026-02030-2.","url":"https://doi.org/10.1007/s40200-026-02030-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s40200-026-02030-2","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.7759/cureus.113342","name":"Addressing Clinician-Educator Hesitancy Toward Artificial Intelligence Through a Peer-Led Instructional Design.","source":"europepmc","abstract":"Introduction Artificial intelligence (AI) has had a tremendous impact on medical education. National data are indicative of massive clinical interest and usage, but a \"faculty readiness gap\" exists among experienced clinician-educators. Though closing this gap is essential, traditional training models often fail to improve appropriate application of AI tools. This study describes medical education development, innovation, and competency in AI (MEDIC-AI), an instructional method designed to increase faculty readiness through peer modeling of AI applications critical to success. Methods A physician-educator (NW) at the Medical College of Georgia (MCG) at Augusta University created a peer-led instructional platform to explain and model the use and application of AI tools. The pilot episode was distributed anonymously to colleagues, and perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to implement at least one AI tool within 30 days were measured using a cross-sectional survey based on the Technology Acceptance Model (TAM). Results Evaluations from 33 faculty demonstrated substantial acceptance, reporting high PU (90.9%) and PEOU (81.8%) regarding the format, and 87.9% demonstrated BI. Qualitative data suggest that peer explanation and modeling were critical to the results. Conclusion Using AI can simplify workflow for busy physician-educators. When AI applications are simply and concisely explained and demonstrated by a trusted colleague hosting a do-it-yourself (DIY) format, participants reported increased usefulness, ease of use, and intent to implement. This suggests that incorporation of peer leadership into traditional instruction on AI tool use has the potential to close the \"faculty readiness gap,\" improving teaching and patient care.","url":"https://doi.org/10.7759/cureus.113342","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.113342","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106638","name":"Artificial Intelligence in the Golden Hour: A scoping review of prehospital trauma triage and implementation feasibility in LMICs.","source":"europepmc","abstract":"Background The Golden Hour of trauma care in Low- and Middle-Income Countries (LMICs) is routinely compromised by systemic deficits, including unmapped infrastructure, chronic traffic congestion, and a critical scarcity of diagnostic tools. While high-income countries utilize Artificial Intelligence (AI) to optimize mature systems, AI in the Global South acts as a structural substitute to leapfrog foundational barriers. This scoping review maps AI applications in LMIC prehospital care and evaluates their implementation feasibility. Methods Following PRISMA-ScR guidelines, a systematic search was performed across PubMed, ScienceDirect, Scopus, IRIS WHO, SciELO, and snowballing for the period of May 2020 to April 2026. To prioritize resource-constrained settings, high-maturity AI nations were excluded from primary synthesis. Seventeen primary studies were definitively identified and synthesized into three feasibility domains. Results (1) Clinical Feasibility: Machine Learning (ML) models such as Random Forest and LightGBM consistently outperformed traditional manual scores like the Kampala Trauma Score, achieving an AUC of 0.91 to 0.94. Bayesian models in Tanzania successfully utilized prehospital delay variables to predict mortality. (2) Operational Feasibility: Digital platforms like Flare in Kenya navigate uncharted roads using ride-hailing logic, while robust optimization in Bangladesh resists extreme traffic chaos, successfully reducing average response times from 162 to 13 min. (3) Technical Feasibility: Edge-AI hardware and Natural Language Processing (NLP) for informal audio transcription achieved 95% accuracy in connectivity-starved and noisy environments. Conclusion AI in LMICs serves as a vital diagnostic safety net rather than merely an optimization tool. However, a decisive readiness gap persists; for instance, Indonesia currently holds a health AI maturity index of 52 out of 100. Achieving an AI-enabled Golden Hour requires a strategic roadmap focused on sovereign national data registries and legal readiness to protect these leapfrog innovations from a current policy vacuum regarding liability.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106638","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106638","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1186/s12909-026-10113-0","name":"Nurse educators' experiences and perceptions using generative artificial intelligence: a systematic review.","source":"europepmc","abstract":"Background The rapid uptake of generative artificial intelligence (GenAI) in higher education has increased both enthusiasm and concern. While students' use of GenAI has been widely discussed, empirical research focusing on nurse educators' own experiences and perceptions remains limited. This systematic review synthesizes evidence on nurse educators' experiences of using generative artificial intelligence in teaching. Methods A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. Searches were performed in PubMed, CINAHL, Web of Science, and ERIC. Peer-reviewed empirical studies published in English were included. Two reviewers independently screened records, extracted data, and conducted quality appraisal using established tools. Due to methodological heterogeneity, results were synthesized thematically. Results Thirteen studies were included, representing a total of 3082 participants. Two overarching themes were identified: (1) Nurse educators' opportunities and challenges using Generative AI in teaching, and (2) Nurse educators' competence and ways of using Generative AI. Educators described Generative AI as a potentially valuable resource for teaching efficiency and organizational and pedagogical inspiration. They expressed concerns relating to their loss of professional roles, academic integrity, and erosion of critical thinking related to students. Experience with Generative AI, institutional position, organizational policy and support influenced educators' attitudes, confidence, and use. Discussion The findings reveal a tension between optimism about Generative AI's pedagogical potential and apprehension about its ethical, educational, and professional implications. Educators' calls for clearer policies, competency development, and institutional support highlight the need for systematic capacity-building. Conclusion Generative AI's value depends on educators' skills, supportive policies, and intentional use, making structured training and governance essential for integration in nurse education.","url":"https://doi.org/10.1186/s12909-026-10113-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-10113-0","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/95648","name":"Diagnostic Accuracy of Medical Imaging-Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Background Osteonecrosis of the femoral head (ONFH) is a common cause of hip disability in clinical practice. Early and accurate diagnosis can delay or even halt disease progression. In recent years, AI models based on medical imaging have been increasingly applied to the diagnosis of ONFH; however, a systematic evaluation of their diagnostic accuracy remains lacking. Objective This study aims to synthesize the overall diagnostic accuracy of medical imaging-based AI models for ONFH and to inform clinical decision-making. Methods This systematic review was conducted in accordance with the PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies) guidelines and was prospectively registered in PROSPERO (CRD420261307216). We searched PubMed, Embase, Cochrane Library, and Web of Science up to March 8, 2026. Studies developing or validating AI models for ONFH diagnosis using imaging data were eligible. Risk of bias was assessed using the QUADAS-2 tool. Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were pooled using a bivariate mixed-effects model, and a summary receiver operating characteristic (SROC) curve was constructed. Subgroup analyses were stratified by imaging modality (x-ray vs MRI), disease stage (early-stage ONFH vs all-stage ONFH), diagnostic criteria (Association Research Circulation Osseous [ARCO] staging vs other criteria), control group type (healthy controls vs disease controls), validation method (internal validation vs external validation), center type (single-center vs multicenter), and model type (deep learning vs machine learning). Meta-regression was performed to quantify the contribution of each covariate to between-study heterogeneity. Sensitivity analysis and Deeks asymmetry test assessed the robustness of the results and publication bias. Clinical utility was evaluated using the Fagan nomogram. Results A total of 12 studies comprising 16,189 hip joints were included. The pooled sensitivity was 0.91 (95% CI 0.87-0.95), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the SROC AUC was 0.97 (95% CI 0.95-0.98). Substantial between-study heterogeneity was observed ( I ²=72%, 95% CI 38%-100%). Subgroup analysis showed that MRI-based models yielded a higher diagnostic odds ratio (DOR; 382, 95% CI 220-665) than x-ray-based models (106, 95% CI 60-190), while models that underwent external validation had a lower DOR (129, 95% CI 51-329) than those with only internal validation (230, 95% CI 104-510). Meta-regression identified imaging modality as the primary source of heterogeneity, explaining 92.1% of the between-study variance. Conclusions AI models demonstrate high diagnostic accuracy in imaging-based ONFH diagnosis. However, the current evidence is constrained by the limited number of included studies, predominantly retrospective designs, and a lack of adequate external validation, and should therefore be interpreted with caution. Future research should adopt multicenter prospective designs, standardize reference standards, and implement rigorous external validation to facilitate clinical translation.","url":"https://doi.org/10.2196/95648","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/95648","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.mce.2026.112894","name":"An N-salicyloyl tryptamine derivative (compound 18) improves spermatogenic impairment in obese mice via regulating insulin resistance and enhancing Sertoli cell glycolysis.","source":"europepmc","abstract":"Objective This study aimed to investigate the therapeutic potential of compound 18, an N-salicyloyl tryptamine derivative with established anti-neuroinflammatory and neuroprotective properties, in ameliorating male reproductive dysfunction induced by high-fat diet (HFD) in obese mice and to elucidate the underlying mechanisms. Methods Male mice were fed a HFD to induce obesity and subsequently treated with compound 18 at doses of 25 or 50 mg/kg. Systemic metabolic parameters including body weight, blood glucose, insulin, and lipid profiles were measured. Histopathological assessments were conducted on liver, adipose tissue, testes, and epididymis. Molecular analyses were performed to evaluate the expression of markers related to proliferation (PCNA), apoptosis (BAX and Bcl-2), components of the insulin signaling pathway (IGF1, IGF1R), and key glycolytic enzymes (HK2, PKM2, LDHA). Results Administration of compound 18 led to significant and dose-dependent improvements in systemic metabolism, characterized by reductions in body weight, blood glucose, insulin levels, and lipid parameters. The compound also attenuated hepatic steatosis and adipocyte hypertrophy, while notably restoring testicular and epididymal tissue architecture. At the molecular level, compound 18 up-regulated the proliferative marker PCNA, modulated apoptosis-related proteins (down-regulating Bax and up-regulating Bcl-2), enhanced insulin sensitivity-as indicated by increased IGF1R and decreased IGF1 expression-and augmented glycolytic capacity in Sertoli cells through elevated expression of HK2, PKM2, and LDHA. Conclusion These results demonstrate that compound 18 effectively alleviates HFD-induced spermatogenic dysfunction concomitantly with ameliorating testicular insulin resistance and promoting glycolytic flux in Sertoli cells.","url":"https://doi.org/10.1016/j.mce.2026.112894","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.mce.2026.112894","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.5662/wjm.v16.i3.117516","name":"Surgical paradox: Pen &lt;i&gt;vs&lt;/i&gt; scalpel-an opinion review on reforming academic promotion.","source":"europepmc","abstract":"The current system for academic promotion in surgery disproportionately favors quantifiable metrics, such as publications and grant funding, over the demonstration of clinical skill, creating a fundamental \"credentialing paradox\". This publication-centric culture, amplified by the use of artificial intelligence (AI) tools, has led to a disconnect where a surgeon's academic rank may not align with their proficiency in the operating room. High publication numbers, often a prerequisite for advancement, can be achieved through statistical analyses of existing data, a trend that may not reflect hands-on surgical experience. The rise of AI further intensifies this dynamic by streamlining research tasks, allowing for high productivity without the \"manual drudgery of data gathering\". This focus on the \"pen\" over the \"scalpel\" places less-funded faculty and those who dedicate time to clinical care and education at a disadvantage, contributing to systemic disparities in promotion. To resolve this paradox, the surgical profession must redefine academic success by adopting a more holistic framework that formally recognizes and rewards excellence in clinical care, education, and mentorship alongside research output. The goal is to ensure that the most skilled and dedicated surgeons are advanced, ultimately benefiting patient care and the future of the profession.","url":"https://doi.org/10.5662/wjm.v16.i3.117516","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5662/wjm.v16.i3.117516","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.7507/1001-5515.202512069","name":"[Multi-modal tumor boundary clustering recognition based on artificial intelligence virtual cells and near-infrared surgical field].","source":"europepmc","abstract":"In order to accurately identify tumor boundaries and improve diagnostic efficiency, this study proposes a multi-modal tumor boundary identification method based on artificial intelligence virtual cells and saliency near-infrared spectrum imaging, and uses this to construct the \"Golden Eyes 3.0\" intelligent navigation system. The system integrates the artificial intelligence virtual cell model constructed by the graph convolution network and introduces a loop iterative calibration mechanism to improve prediction accuracy. At the same time, this study also uses adaptive spectral discrimination weighted to optimize spectral feature extraction, realizes multi-modal data fusion through a gated attention mechanism, and finally builds a tumor boundary recognition model based on recurrent neural networks. The test results showed that on the intraoperative data sets of glioma and thyroid cancer, the prediction accuracy of artificial intelligence virtual cells was 98.8% and 98.9% respectively, the sensitivity of saliency detection is 0.1 pmol/L, and the \"Golden Eyes 3.0\" system's recognition accuracy of tumor boundaries could reach 96.2%. The above results prove that the multi-modal tumor boundary identification method proposed in this study can accurately identify tumor boundaries in patients and improve the accuracy of early tumor diagnosis.","url":"https://doi.org/10.7507/1001-5515.202512069","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7507/1001-5515.202512069","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/s2468-1253(26)00049-x","name":"A decade of artificial intelligence in endoscopy: from proof of concept to clinical reality.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/s2468-1253(26)00049-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/s2468-1253(26)00049-x","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fonc.2026.1860859","name":"Artificial intelligence in preoperative diagnosis of thyroid nodules: a bibliometric analysis.","source":"europepmc","abstract":"Objectives To explore the current research progress and trends in the application of artificial intelligence (AI) for preoperative diagnosis of thyroid nodules (TN), and to identify the key research directions through bibliometric analysis. Methods Articles on the application of AI for preoperative diagnosis of TN were retrieved from the Web of Science core collection and Scopus databases. Tools like VOSviewer, CiteSpace, the bibliometrix, Scimago Graphica, and Charticulator were used for bibliometric analysis. Trends in annual publishing volumes, collaborations between authors and institutions, highly cited articles, keyword co-occurrence, keyword clustering, and keyword burst analysis are all included. Results A total of 1370 publications were included in this study, with an rapid growth trend observed in the annual publications in this field. China has made significant contributions, with Shanghai Jiao Tong University producing the most publications. Chinese scholar XuDong emerged as the most prolific author (30 publications). Journal analysis revealed that Thyroid is the highest-impact journal in terms of citation frequency. High-frequency keywords in the field include \"TN\", \"cancer\" and \"ultrasound\", while recent emerging keywords include \"semantic segmentation\", and \"AI\". Conclusions Since 2019, the application of AI in the preoperative diagnosis of TN has attracted increasing research attention, with China emerging as a major contributor to this research field. The number of journals publishing in this domain has significantly increased, and the publications reflect a trend toward interdisciplinary research. The research focus has gradually shifted from conventional diagnostic approaches toward pixel-level lesion quantification, fine-grained feature extraction and the integration of AI-enabled diagnostic approaches, highlighting the potential of AI to improve TN assessment.","url":"https://doi.org/10.3389/fonc.2026.1860859","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1860859","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.bbcan.2026.189678","name":"The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.","source":"europepmc","abstract":"Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.","url":"https://doi.org/10.1016/j.bbcan.2026.189678","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.bbcan.2026.189678","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3346/jkms.2026.41.e318","name":"Toward Structured Transparency: A Governance Framework for AI Use in Biomedical Publishing.","source":"pubmed","abstract":"The rapid adoption of large language models and generative artificial intelligence (AI) is transforming biomedical research and publishing. Although international organizations such as the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) have established the principle that AI cannot be recognized as an author, and the ICMJE, in its January 2026 revision, has introduced a dedicated section addressing AI use by authors, peer reviewers, and editors, journal-level Instructions for Authors still require further operational detail to apply these principles consistently throughout the publication process. This review critically examines current AI-related policies in the Journal of Korean Medical Science ( JKMS ) and the Korean Association of Medical Journal Editors (KAMJE) and identifies three major challenges: the practical limitations of the AI non-authorship principle, the inadequacy of current AI disclosure practices, and the emergence of AI-specific conflicts of interest that extends beyond conventional financial disclosures. We argue that medical publishing should move from a restrictive approach toward a framework of structured transparency that systematically documents, evaluates, and verifies AI use. To achieve this goal, we propose a practical governance framework that includes a three-tiered AI disclosure system, strengthened accountability for corresponding authors, expanded institutional conflict-of-interest disclosures, transparent reporting of AI use by peer reviewers, and formal editorial policies governing AI-assisted editorial activities. Future revisions of the KAMJE and JKMS Instructions for Authors should prioritize transparent and accountable AI governance rather than restricting AI use. Adoption of a structured, publication-wide framework encompassing authors, peer reviewers, and editors would strengthen research integrity while supporting the responsible integration of AI into biomedical publishing.","url":"https://doi.org/10.3346/jkms.2026.41.e318","authors":["Kim JM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e318","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1002/anr3.70090","name":"Learning curves and demographic influence on skill acquisition of artificial intelligence-assisted videolaryngoscopy training with the LarynGuide™ system: A pilot simulation study.","source":"europepmc","abstract":"Videolaryngoscopy is increasingly important in airway management, but optimal training methods remain unclear. Artificial intelligence may enhance procedural skill acquisition through real-time feedback and guidance. This high-fidelity simulation pilot study characterised the learning curve of artificial intelligence-assisted videolaryngoscopy using the LarynGuide™ system in a mixed cohort of medical students, anaesthesia residents and qualified anaesthetists with limited or no prior experience of videolaryngoscopy. Ninety-nine healthcare practitioners (medical students, medical doctors, anaesthesia residents and specialists) performed 10 intubation attempts on a high-fidelity simulator with artificial intelligence-assisted real-time feedback. Optimal intubation was defined a priori as ≥90% probability of time to intubation 90%. Tracheal intubation success, defined as completion of tracheal tube placement, irrespective of the number of laryngoscopy passes, was achieved in 951 of 990 attempts (96%), with mean (SD) time to intubation of 16 (13) s. At the group level, basic procedural competency (≥90% cumulative success rate, assessed using cumulative sum analysis) was reached at the fourth attempt. Success rates plateaued thereafter, time to intubation continued to fall until the seventh attempt, and the percentage of glottic opening continued to improve through the tenth attempt. Exploratory modelling estimated that more than 10 attempts would be required to reach a 90% probability of optimal intubation. Anaesthesia residents performed better than participants with no prior intubation experience: higher success rate (98% versus 93%, p = 0.003), better glottic visualisation (mean percentage of glottic opening 90% versus 78%, p < 0.001) and shorter time to intubation (14 s versus 18 s, p = 0.021). Participants with prior video game experience reached optimal intubation thresholds four attempts earlier than non-gamers (p = 0.018). Outcomes were worse in the 60- to 70-year subgroup (n = 3, p < 0.001), while the participants' sex had no influence. Satisfaction ratings were high, with ease of use reported as 9/10. Artificial intelligence-assisted videolaryngoscopy facilitated rapid acquisition of basic competency within four attempts in this pilot simulation cohort. Exploratory modelling suggests differences from historical benchmarks which warrant further investigation.","url":"https://doi.org/10.1002/anr3.70090","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/anr3.70090","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1002/hsr2.73117","name":"Beyond the Black Box: Is Artificial Intelligence Ready to Reshape Neurosurgical Decision-Making? A Narrative Review.","source":"europepmc","abstract":"Background and aims Artificial intelligence (AI) is increasingly being integrated into neurosurgical practice, offering capabilities in diagnostic imaging, surgical planning, and outcome prediction. However, the \"black box\" nature of many AI systems generating recommendations without a transparent rationale poses fundamental challenges to adoption in a specialty defined by high-stakes, irreversible interventions. This review critically examines whether AI is ready to reshape neurosurgical decision-making, synthesizing current evidence while systematically analyzing technical, ethical, and regulatory barriers to clinical integration. Methods A systematic search of PubMed, Scopus, and Web of Science databases was conducted for peer-reviewed studies published between January 2020 and March 2026. Articles reporting AI applications in neurosurgical diagnosis, prognosis, or intraoperative guidance were included. Data were synthesized thematically across clinical domains, with a focus on model interpretability, validation status, and implementation barriers. Results AI demonstrates significant capabilities: diagnostic accuracy exceeding AUC 0.90 in tumor classification, prognostic improvements up to 15% over traditional methods, and 10%-20% complication reductions with AI-assisted planning. Currently, FDA-cleared tools enable automated tumor segmentation, aneurysm detection, and spinal navigation. However, critical gaps persist: external validation remains rare ( Conclusion AI is not ready for independent decision-making in neurosurgery but serves as a powerful augmentative tool when limitations are transparently addressed. Lessons from neurosurgery offer a blueprint for AI integration across high-stake medical specialties. The black box must be opened before AI can truly reshape clinical practice.","url":"https://doi.org/10.1002/hsr2.73117","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.73117","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fendo.2026.1885905","name":"Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention.","source":"europepmc","abstract":"Prediabetes is a highly prevalent intermediate metabolic state and a major public health target for preventing type 2 diabetes mellitus (T2DM). However, current approaches to prediabetes screening, risk stratification, and lifestyle management remain limited by inconsistent diagnostic definitions, incomplete case detection, heterogeneous progression risk, and the resource-intensive nature of conventional face-to-face prevention programmes. Artificial intelligence (AI), including machine learning, deep learning, explainable AI, and algorithm-driven digital interventions, is increasingly being explored as a tool to address these gaps. This narrative review used a structured search of PubMed, Web of Science Core Collection, and Embase for studies published from January 2010 to April 2026, selecting articles that evaluated AI-assisted or algorithm-driven approaches for prediabetes screening, progression risk prediction, or lifestyle intervention and reported relevant model performance, validation, or intervention outcomes. Current evidence indicates that AI-based models can improve discrimination beyond traditional risk scores by integrating routine clinical data, longitudinal electronic health records, continuous glucose monitoring profiles, wearable-derived behavioural signals, and emerging molecular biomarkers. Some externally validated models have shown clinically relevant performance for identifying individuals at high risk of progression and for guiding more targeted preventive strategies. In parallel, fully or semi-automated digital programmes delivered through mobile applications, web platforms, connected scales, and sensor-based feedback systems have demonstrated potential to support lifestyle change, improve engagement, and reduce reliance on labour-intensive counselling. Nevertheless, translation into routine care remains constrained by heterogeneity in prediabetes definitions, limited external validation across diverse populations, uncertain long-term effectiveness, geographical imbalance in evidence, privacy concerns, and the need for stronger governance frameworks. Overall, AI should be viewed as an assistive technology that may support earlier detection, more precise risk stratification, and scalable lifestyle management in prediabetes. Further multi-centre, prospective, and implementation-focused studies are needed to establish clinical utility, equity, safety, and cost-effectiveness.","url":"https://doi.org/10.3389/fendo.2026.1885905","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1885905","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.compbiolchem.2026.109329","name":"Computational and artificial intelligence-guided discovery of potential therapeutic candidates targeting Ml0099 protein of Mycobacterium leprae.","source":"europepmc","abstract":"Despite a global decline in incidence, leprosy remains a major public health concern, with India accounting for over 55% of cases worldwide, highlighting the unmet need to characterize hypothetical proteins of M. leprae as novel therapeutic targets against emerging drug-resistant strains. The three-dimensional homology structure and characteristics of ML0099 was predicted using an artificial intelligence-driven approach implemented through the AlphaFold 3 server and deepTHMM. Molecular docking with phospholipase substrates with different head moieties, coupled with large-scale virtual screening of ∼3600 FDA-approved drugs, was performed to identify promising therapeutic candidates followed by molecular dynamics analysis to validate the affinity for bound compounds. This study demonstrated that ML0099 is likely a member of the CULP protein family, containing an N-terminal signal peptide and a conserved pentapeptide motif (GxSxG), a hallmark of lipolytic enzymes. Structural analysis further identified conserved catalytic residues-Ser175, Asp268, and His299-forming a putative catalytic triad. The phosphatidyl-choline exhibited the most favourable substrate binding profile upon docking, predicting it's optimal enzymatic substrate. Virtual screening of FDA-approved compounds identified Zileuton and Glipizide as promising candidates, exhibiting binding energies of -8.5 to -9.1 kcal/mol along with stable interaction networks. Subsequent molecular dynamics simulations, MM-PBSA/GBSA and PCA analysis confirmed the stability of the protein-ligand complexes. Computational and AI-driven approaches demonstrated the potential biological role of ML0099 and identified the repurposed drugs Zileuton and Glipizide as promising inhibitors, providing a robust foundation for future experimental validation and therapeutic development.","url":"https://doi.org/10.1016/j.compbiolchem.2026.109329","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.compbiolchem.2026.109329","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/jamia/ocag100","name":"The Big Mo: staying on the wave in an age of artificial intelligence.","source":"europepmc","abstract":"Dr. Kevin B. Johnson delivered this address on May 16, 2026, at the Commencement Ceremony of The D. Bradley McWilliams School of Biomedical Informatics, UTHealth Houston, to the graduating class of 2026. The address uses the concept of \"The Big Mo\" (compounding momentum) as a frame for understanding the current inflection point in AI and medicine. Drawing on his own career arc from paper-based clinical practice at Johns Hopkins through early adoption of health informatics to the present era of AI in healthcare, Johnson argues that the fears graduates hold about technological obsolescence and institutional instability are real but misdirected. He reframes both: biomedical informatics professionals are not targets of AI but its essential architects, and the external environment has always been uncertain for those doing important work. His charge to graduates is singular: stay on the wave.","url":"https://doi.org/10.1093/jamia/ocag100","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag100","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fnins.2026.1894751","name":"Developing artificial intelligence-based techniques for brain MRI image segmentation.","source":"europepmc","abstract":"Introduction Brain MRI image segmentation is essential for the accurate diagnosis and treatment of neurological disorders, including brain tumors, Alzheimer's disease, and multiple sclerosis. Artificial intelligence (AI), particularly deep learning, has emerged as an effective approach for improving the precision and efficiency of medical image segmentation while reducing manual effort. Methods This study utilized a publicly available Kaggle brain MRI dataset containing labeled images for supervised learning. A Convolutional Neural Network (CNN)-based framework was developed for automatic brain MRI segmentation. The methodology incorporated preprocessing techniques, including noise removal, normalization, and data augmentation, to improve image quality and model performance. The proposed model was evaluated using Accuracy, Dice Score, and Intersection over Union (IoU). Results Experimental results demonstrated that the proposed AI-based segmentation framework achieved high segmentation accuracy and effectively distinguished normal brain tissue from abnormal regions. The model outperformed conventional image-processing methods by providing improved segmentation precision, reducing manual intervention, and enhancing the reliability of medical image analysis. Discussion The findings demonstrate the potential of AI-based deep learning techniques for automated brain MRI segmentation in clinical applications. The proposed framework can support clinicians by improving diagnostic accuracy and reducing processing time. Future work will focus on implementing more advanced deep learning architectures and expanding the dataset to further improve segmentation performance and clinical applicability.","url":"https://doi.org/10.3389/fnins.2026.1894751","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnins.2026.1894751","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.xcrm.2026.102971","name":"Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.","source":"europepmc","abstract":"Steatotic liver disease (SLD) affects one-third of the global population, yet current non-invasive diagnostic methods are too costly or operator-dependent for population-scale screening. Here, we present 3D-FAICE, a deep learning system that uses three-dimensional facial imaging for non-invasive SLD detection. Trained and tested on 11,456 participants, the facial model achieves robust performance across internal, external, and self-controlled longitudinal cohorts and remains effective in a smartphone-based point-of-care setting. Metabolomic analysis reveals that facial risk scores correlate with glycolipid and amino acid pathways, supporting biological plausibility. Multimodal fusion of facial and metabolomic data further improves accuracy, and a cross-modal distillation strategy significantly elevates the performance of the facial-only model. These findings establish facial image-based AI as a non-invasive, scalable, and privacy-aware tool for SLD screening, with potential applications in self-monitoring and population health management.","url":"https://doi.org/10.1016/j.xcrm.2026.102971","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.xcrm.2026.102971","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/ibd/izag021","name":"Artificial intelligence enhances screening efficiency for inflammatory bowel disease clinical trials: a prospective study.","source":"europepmc","abstract":"An LLM-based AI tool significantly improved identification of potentially eligible patients and screening efficiency for IBD clinical trials, outperforming physician referrals by screening electronic medical records and generating ranked candidate lists based on trial inclusion/exclusion criteria, thereby enhancing trial recruitment.","url":"https://doi.org/10.1093/ibd/izag021","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/ibd/izag021","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.radi.2026.103546","name":"Artificial intelligence learning objectives in radiography education: A document analysis.","source":"europepmc","abstract":"Introduction Artificial Intelligence (AI) is rapidly changing healthcare delivery and radiography, impacting both practice and education. Despite its significance, there is limited agreement on educational priorities and curriculum organisation for radiographers. This study gathered relevant Learning Outcomes (LOs) for AI education in radiography from existing literature, structuring them according to the European Qualifications Framework (EQF) model of Knowledge, Skills and Competences (KSCs). Methods A literature review was conducted systematically utilising PRISMA reporting guidelines, and thematic analysis was applied using Saldaña's coding framework. Data were coded deductively with the EQF and open-coded to highlight further important aspects of AI education for radiographers. Results Three major themes emerged: Knowledge, Skills, Competencies, with 15 subthemes. The recommended LOs for radiographers range from fundamental practice, such as patient safety, to advanced tasks like coding AI. Conclusion The variety of LOs identified suggests that AI education in radiography cannot be presented as one unified framework. Instead, educational approaches should reflect the different roles radiographers may have in relation to AI, ensuring practitioners at all levels gain the most relevant AI-KSCs. Implications for practice AI education should be embedded in existing educational structures, with tailored outcomes for specific AI-related roles. Ongoing research is needed to determine which LOs should be prioritised for roles and educational stages.","url":"https://doi.org/10.1016/j.radi.2026.103546","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.radi.2026.103546","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s12098-026-06260-3","name":"Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.","source":"europepmc","abstract":"Multiomics, next-generation, and long-read sequencing approaches have transformed the practice of medical genetics. Complex cases often require several person-hours to make sense of the tens of thousands to millions of variants and biochemical patterns in each patient. Availability of massive datasets challenges traditional analytical and interpretive approaches. Artificial intelligence offers powerful ways to handle the growing volume and complexity of genomic and phenotypic data in clinical genetics. It is already influencing several areas of practice, including variant prioritization and interpretation, rare disease screening, and aspects of precision medicine. However, translating these advances into routine clinical use has proven difficult due to the underrepresentation of various populations, ethical issues, and issues related to data governance. As the majority of these tools are used in isolation, separate from hospital information systems and routine reporting pipelines, they are not optimally utilized. With continued progress in precision medicine and genomics, these AI genomic tools are likely to be integrated more into medical genetics practice, rather than remaining restricted to specialised or experimental settings.","url":"https://doi.org/10.1007/s12098-026-06260-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12098-026-06260-3","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.legalmed.2026.102952","name":"Nanomaterial-based latent fingerprint detection and artificial intelligence: from laboratory innovation to forensic practice.","source":"europepmc","abstract":"Background Latent fingerprint (LFP) visualization and automated recognition are fundamental to forensic identification, yet conventional physicochemical methods remain constrained by substrate specificity, chemical toxicity, limited analytical sensitivity, and incompatibility with modern digital analysis pipelines. The convergence of fluorescent nanomaterials and artificial intelligence (AI) offers a promising but operationally premature alternative. Objective This critical review appraises advances in fluorescent nanomaterial-based LFP development and AI-powered image analysis (2019-2026), with emphasis on evidence quality, methodological limitations, forensic admissibility, and ethical considerations. Methods A structured narrative review was conducted using PubMed/MEDLINE, Scopus, Web of Science Core Collection and Google Scholar. Approximately 3200 records were identified after deduplication; 350 underwent full-text screening, and 78 peer-reviewed studies met the inclusion criteria. Additional studies were identified through citation tracking. Results Six major nanomaterial classes demonstrated distinct performance advantages, but none achieved ISO/IEC 17025-compliant forensic validation. NH₂-functionalized polymer dots remain the only nanomaterial reported with NFIQ-2 scores (49-58), Level 1-3 ridge feature visualization, and AI-integrated minutiae extraction, although evidence is limited to a single laboratory. GAN-based enhancement raises unresolved evidence integrity concerns, while vision transformers and diffusion models show promise but lack forensic validation. Explainable AI (XAI) is essential for courtroom acceptance. No fully integrated end-to-end workflow has been demonstrated. Comparative analyses of nanomaterial classes and AI modalities are presented. Conclusion Routine forensic implementation is limited by the lack of standardized validation, harmonized reporting, regulatory oversight, and courtroom-compatible explainability. Addressing these gaps is essential for translating laboratory advances into forensic practice.","url":"https://doi.org/10.1016/j.legalmed.2026.102952","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.legalmed.2026.102952","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106656","name":"Knowledge and perception of artificial intelligence among operating room staff in educational hospitals: a cross-sectional survey.","source":"europepmc","abstract":"Background Successful implementation of artificial intelligence (AI) in healthcare depends not only on technological performance but also on the readiness of healthcare professionals to understand, trust, and appropriately use AI-enabled systems. Evidence regarding AI knowledge among multidisciplinary operating room (OR) staff remains limited, particularly in low- and middle-income healthcare settings. Objective To assess AI-related knowledge and perceptions among OR personnel and examine demographic factors associated with workforce preparedness for AI implementation in clinical practice. Methods An analytical cross-sectional survey was conducted among 200 OR healthcare professionals, including nurses, OR technicians, and anesthesiology personnel, working in four educational hospitals affiliated with Isfahan University of Medical Sciences, Iran. Data were collected using a researcher-developed questionnaire, validated for content and construct validity, consisting of demographic characteristics, seven knowledge items, and twenty-five perception items. Because the data were not normally distributed, Mann-Whitney U, Kruskal-Wallis, and Spearman correlation tests were used for statistical analysis. Results Participants demonstrated limited AI knowledge (mean score: 2.11 ± 0.76 out of 7; 30.1% of the maximum possible score), whereas perceptions toward AI were generally positive (mean score: 76.8 ± 14.5 out of 125). Knowledge scores differed significantly according to educational level (P Conclusions Despite favorable attitudes toward AI, OR staff demonstrated substantial deficiencies in AI knowledge. Further research and targeted educational initiatives may be warranted to improve AI knowledge among perioperative healthcare professionals and support the safe integration of clinical informatics.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106656","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106656","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106623","name":"TRIAGE-AI: A clinician-facing implementation-readiness framework for evaluating artificial intelligence in cancer pathways.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly being offered for use in cancer pathways, but clinicians still lack a practical method for judging whether a specific tool is ready for adoption in routine care. Existing standards such as DECIDE-AI, TRIPOD-AI, CONSORT-AI and evidence standards for digital health technologies are essential, but they are primarily directed at developers, trialists and regulators rather than clinical leads making procurement, governance or pathway decisions. This article proposes TRIAGE-AI, a clinician-facing implementation-readiness framework covering six domains: true clinical need, robust medical evidence, inter-site consistency, adoption fit, governance and effective decisions. The framework is intended to complement existing reporting and evaluation standards by translating their outputs into a structured clinical adoption decision. Using urological oncology as a worked example, TRIAGE-AI differentiates applications that are approaching clinical readiness, such as prostate magnetic resonance imaging AI and selected pathology biomarkers, from applications that remain premature or high risk, including renal mass characterisation tools and autonomous large language model decision support. The current evidence supports TRIAGE-AI as a framework developed and applied within urological oncology, with potential for broader application pending further testing. It is offered as a pragmatic method for clinical governance discussions, local validation planning and implementation-readiness assessment.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106623","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106623","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1714/4741.47563","name":"[Artificial intelligence in hypertension: where do we stand?]","source":"pubmed","abstract":"Artificial intelligence (AI) is entering the study of hypertension, serving both primarily clinical purposes - to assist practicing physicians - and research objectives. Hypertension presents certain specific characteristics that should be carefully considered when using AI. The measured value of blood pressure is an extremely variable parameter that is difficult to standardize and measure with precision. At present, AI is able to provide very simple, clear, and well-documented answers to clinical questions regarding the management of patients with hypertension. Its role in research is perhaps somewhat less well developed. Several studies have been published on the use of AI in identifying patients with secondary hypertension, on the risk of future hypertension in normotensive individuals, and on the risk of heart failure and other complications of hypertension. A common issue with all these studies is the relative paucity of controlled clinical trials comparing AI with traditional procedures drawing on medical literature and clinical experience (\"natural intelligence\"), precisely designed to quantify the potential superiority of AI over natural intelligence on key end-points of real clinical value. This is similar to the approach currently taken when testing a new diagnostic or therapeutic procedure. Obviously, these considerations should be viewed as encouragement, not as a barrier, to continuing clinical research in the field of AI in hypertension. It is also important that scientific articles on AI published in medical journals be written using terms that doctors can understand, to ensure transparency and interpretability.","url":"https://doi.org/10.1714/4741.47563","authors":["Verdecchia P","Angeli F","Reboldi G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1714/4741.47563","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3892/br.2026.2182","name":"Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review).","source":"europepmc","abstract":"The integration of artificial intelligence (AI) into digital pathology is perhaps the most revolutionary leap forward in modern diagnostic medicine. The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems. AI systems have achieved pathologist-level performance in controlled settings, including diagnostic accuracy >99% and area under the receiver operating characteristic curve values exceeding 0.97. However, translating research into clinical adoption is riddled with several challenges attributable to computational requirements, data standardization issues, regulatory hurdles and limitations in generalizability. Moreover, Vision Transformers are widely popular as powerful alternatives to conventional convolutional models, delivering high performance in certain domains while also imposing a novel computational burden. Overcoming these challenges is a prerequisite for the successful integration of AI into pathology practice and the realization of its full diagnostic potential.","url":"https://doi.org/10.3892/br.2026.2182","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3892/br.2026.2182","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/acamed/wvag098","name":"The 3C model: a simple prompt engineering framework for generative artificial intelligence.","source":"europepmc","abstract":"This Last Page introduces the intuitive 3C Model (Context, Creation, Clarity), a simplified prompt engineering framework designed to help medical educators effectively and confidently integrate generative artificial intelligence into teaching practices.","url":"https://doi.org/10.1093/acamed/wvag098","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/acamed/wvag098","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1080/21645515.2026.2707688","name":"Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape.","source":"pubmed","abstract":"This bibliometric analysis examines the transformative role of artificial intelligence (AI) in immune checkpoint inhibitor (ICI) research. Using VOSviewer, CiteSpace, and Bibliometrix, we analyzed 1,938 publications from the Web of Science Core Collection (2015-2026), revealing a dramatic rise in AI-related ICI studies, led by China and the USA. Key findings demonstrate AI's integration in predicting treatment response, optimizing dosing strategies, and managing immune-related adverse events. Through keyword co-occurrence and citation analyses, we identify critical AI applications including digital pathology and tumor microenvironment characterization. This comprehensive overview provides valuable insights into research trends and emerging frontiers for AI-driven innovations in cancer immunotherapy.","url":"https://doi.org/10.1080/21645515.2026.2707688","authors":["Kang J","Tang R","Li D","Ma L","Zhang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/21645515.2026.2707688","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1097/hco.0000000000001325","name":"The role of artificial intelligence in the diagnosis of pericardial constriction.","source":"europepmc","abstract":"Purpose of review Constrictive pericarditis is a complex disease whose distinction from clinical mimickers remains challenging yet critically important. Diagnosis frequently requires integration of findings across multiple imaging modalities. Artificial intelligence (AI) applications in cardiac imaging are rapidly evolving and may play an increasingly important role in the diagnosis of constrictive pericarditis. Recent findings Most established evidence supporting AI in diagnosis of constrictive pericarditis involves machine learning and deep learning applied to echocardiography, particularly for differentiating constrictive pericarditis from restrictive cardiomyopathy. Newer approaches incorporate multiple echocardiographic views and emphasize model generalizability. Emerging applications span cardiac computed tomography (CT)-for automated pericardial thickening and calcification quantification-large language models (LLMs) in cardiac magnetic resonance interpretation, and deep learning electrocardiogram analysis. Summary Applications of AI for diagnosis of constrictive pericarditis are being studied across multiple imaging modalities, but remain in early stages of development. Emerging AI concepts, including multimodal LLMs and foundation models leveraging transfer learning, show promise for further advances and eventual meaningful implementation. Cardiac CT, though underrepresented in the current AI literature for constrictive pericarditis, represents an important target for future investigation given its established role in surgical planning.","url":"https://doi.org/10.1097/hco.0000000000001325","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/hco.0000000000001325","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fpubh.2026.1855457","name":"Legal protection of personal health information in medical AI applications in China: challenges and regulatory responses.","source":"europepmc","abstract":"Medical artificial intelligence applies advanced AI technologies to enhance the efficiency and quality of clinical diagnosis and treatment. The extensive processing of personal health information in medical AI applications have further accelerated the digital transformation of healthcare services. However, medical AI also introduces new challenges for personal health information protection. In this context, health information faces not only continuous processing but also uncertain processing purposes, involvement of multiple processing entities, and accumulating algorithmic risks, all of which may weaken individuals' control over their health information and increase the risk of privacy infringement. Although China has established a preliminary legal framework for personal information protection, existing rules remain fragmented and insufficiently targeted. Specifically, the concept and scope of personal health information remain insufficiently defined, the legal bases for its processing require further clarification, and traditional informed consent rules are difficult to apply effectively in medical AI environments. Against this background, this article argues for the enactment of dedicated legislation on personal health information protection to establish a more systematic regulatory framework for defining health information and governing its processing. With respect to informed consent, the existing uniform requirement of separate consent should be refined through a tiered consent framework based on information classification and contextual risk assessment, complemented by dynamic consent mechanisms to enhance individuals' continuous control over their health information throughout the data-processing lifecycle. Furthermore, in response to emerging risks arising from the application of medical AI algorithms, a lifecycle governance framework covering algorithm design, training, and deployment should be established to enhance the accountability and safety of algorithmic processing.","url":"https://doi.org/10.3389/fpubh.2026.1855457","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1855457","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.jflm.2026.103226","name":"Artificial intelligence in forensic medicine and forensic nursing: A systematic review of clinical applications, ethical risks, and implementation challenges.","source":"europepmc","abstract":"Artificial intelligence (AI) is reshaping healthcare, and forensic medicine and nursing are no exception. From automated wound pattern analysis to natural language processing (NLP) in medicolegal documentation, AI tools are entering a field situated at the intersection of clinical care, legal accountability, and human rights. This systematic review with narrative synthesizes contemporary clinical, forensic, and AI ethics literature to examine current and emerging applications of AI in forensic practice, with particular attention to injury interpretation, postmortem interval estimation, forensic imaging, survivor documentation, and medicolegal reporting. Forensic nursing practice, including sexual assault nurse examiner (SANE) programs, domestic violence assessment, and paediatric abuse evaluation, is a central focus. The review critically evaluates ethical risks associated with deploying algorithmic decision-making in high-stakes forensic contexts where outputs may influence criminal outcomes and judicial proceedings. Issues of algorithmic bias, chain-of-custody integrity, explainability, and the potential displacement of clinical judgment are examined alongside practical considerations for implementation. Given the still-emerging forensic-specific evidence base, this review draws selectively on adjacent clinical disciplines while emphasizing the distinct medicolegal demands of forensic practice. Responsible integration of AI, the review concludes, requires interdisciplinary governance, equity-stratified validation, and preservation of the clinician-examiner's primary interpretive authority.","url":"https://doi.org/10.1016/j.jflm.2026.103226","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jflm.2026.103226","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.ctarc.2026.101344","name":"Recent advances in biomarkers, artificial intelligence, and targeted therapeutic strategies in breast cancer: Current progress and future perspectives.","source":"europepmc","abstract":"Breast cancer is a complex and heterogeneous disease that remains a major global health challenge. Recent progress in molecular biology, artificial intelligence (AI), and precision medicine has transformed its diagnosis and treatment. Conventional biomarkers such as ER, PR, HER2, and BRCA mutations continue to guide therapeutic decisions, while emerging biomarkers including TP53, PTEN, and STK11 offer new insights into tumor behavior and drug resistance. AI-based technologies, including machine learning and deep learning, have improved early detection and diagnostic accuracy through advanced medical imaging and multimodal analysis. Current treatment strategies extend beyond conventional chemotherapy and surgery to include targeted therapy, endocrine therapy, immunotherapy, antibody-drug conjugates, and gene-based approaches. Novel therapeutics such as CDK4/6 inhibitors, PARP inhibitors, PI3K inhibitors, and selective estrogen receptor degraders have demonstrated promising clinical outcomes in advanced breast cancer. Additionally, emerging technologies such as CRISPR/Cas9 gene editing and nanotechnology-based drug delivery systems show significant potential for personalized cancer therapy. This review summarizes recent advancements in breast cancer biomarkers, AI-assisted diagnostics, and modern therapeutic strategies aimed at improving precision medicine and patient outcomes.","url":"https://doi.org/10.1016/j.ctarc.2026.101344","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ctarc.2026.101344","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.diabres.2026.113485","name":"Performance of artificial intelligence in diabetes-related foot ulcer detection and assessment: a scoping review of clinical validation studies.","source":"europepmc","abstract":"This scoping review synthesized clinical validation evidence of artificial intelligence (AI) algorithms for diabetes-related foot ulcer (DRFU) detection and assessment and identified factors influencing AI performance in real-world settings. A systematic search was conducted in PubMed, MEDLINE, CINAHL, Scopus, and Google Scholar, following the Arksey and O'Malley framework and reported using PRISMA-ScR. Eligible studies involved adult patients with diabetes and foot ulcers, utilized learning-based AI models, and reported clinical validation outcomes. Eleven studies published between 2020 and 2026 were included from eight countries. Diagnostic performance varied across studies, with sensitivity of 91-100%, specificity of 20-96.8%, and intraclass correlation coefficients of 0.825-0.998 for wound measurement reliability. AI systems reduced manual area overestimation by 13.4-25.2%. Influencing factors were mapped across three NASSS framework domains: technological factors including image quality and algorithmic misclassification, adopter-level barriers including digital literacy limitations, and organisational system-level constraints including infrastructure instability and data privacy concerns. These findings suggest early-stage evidence of promising AI diagnostic performance. However, the evidence base remains limited by small sample sizes, heterogeneous designs, and the inability of current systems to assess deeper wound features. Prospective multi-centre studies with standardised protocols are needed before routine clinical adoption can be recommended.","url":"https://doi.org/10.1016/j.diabres.2026.113485","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.diabres.2026.113485","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.jmir.2026.102500","name":"Enhancing medical imaging teaching on scoliosis: Efficacy of learning method integrating 3D printing and artificial intelligence technologies.","source":"europepmc","abstract":"Background In scoliosis imaging education, the traditional lecture-based learning model can lead to low student engagement and present challenges in developing clinical thinking abilities and diagnostic skills. This study aims to evaluate the effectiveness of integrating three-dimensional (3D) printing and artificial intelligence (AI) technologies with traditional teaching methods in scoliosis education through objective and subjective assessments, and explore the integrated teaching method for medical imaging to address the limitations of traditional lecture-based teaching, thereby providing insights for the development of medical imaging education. Methods A total of 36 undergraduate medical imaging students from our institution were selected as participants and randomly assigned to a control group and an experimental group, with 18 students in each group. The control group received traditional multimedia instruction, while the experimental group supplemented traditional teaching methods with 3D-printed scoliosis models and AI software. Following the course, all participants underwent standardized assessments, which were evaluated by assessors blinded to group allocation. The final evaluation compared the two groups on theoretical knowledge test scores, imaging case analysis assessments, and questionnaire responses on comprehensive capabilities and teaching satisfaction. Results Students in the experimental group achieved significantly higher scores than the control group in both theoretical knowledge (91.01 ± 4.48 vs. 82.49 ± 4.08) and imaging case analysis (90.24 ± 4.77 vs. 80.61 ± 6.33), with statistically significant differences (p Conclusion Integrating 3D printing and artificial intelligence technologies into scoliosis teaching not only enhances students' theoretical comprehension and practical skills but also improves overall teaching quality, thereby contributing to the optimization of comprehensive educational outcomes. Plain language summary Students learning medical imaging can find it hard to understand complex spine conditions. This study compared traditional teaching with lessons that also used 3D models and artificial intelligence tools. This study found that students using the new method had better test results, stronger case analysis skills, and more interest in learning. This matters because improved teaching methods can help students gain the skills needed for better patient care.","url":"https://doi.org/10.1016/j.jmir.2026.102500","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jmir.2026.102500","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s00101-026-01713-y","name":"Identifying predatory journals made easy: is it justifiable to apply artificial intelligence for peer review?","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s00101-026-01713-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00101-026-01713-y","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/jamia/ocag099","name":"AI, humanity, and the open world of health care: enduring imperatives for the next century.","source":"europepmc","abstract":"This address was delivered by Eric Horvitz, MD, PhD, at the 2026 graduation ceremony of Columbia University School of Nursing on May 19, 2026, where he received the Second Century Award for Excellence in Health Care. The address considers the responsibilities of clinicians in shaping the future of artificial intelligence in medicine. It frames health care as an \"open world,\" where information is incomplete, time is limited, and decisions are made under uncertainty. As AI transforms biomedicine and clinical care, the address emphasizes the importance of clinician engagement in guiding how these technologies are developed and used, and calls for systems that strengthen clinical judgment, support care teams, and advance human health, dignity, connection, and trust.","url":"https://doi.org/10.1093/jamia/ocag099","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag099","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fpubh.2026.1853895","name":"Agreement between artificial intelligence and expert evaluation in assessing the quality of asthma health education videos on YouTube and Bilibili: cross-sectional study.","source":"europepmc","abstract":"Background Online video platforms such as YouTube and Bilibili have become important sources of health information for the public. However, the quality and reliability of online health education videos vary substantially. Artificial intelligence (AI), particularly large language models (LLMs), has recently shown potential for automated evaluation of health information, yet evidence regarding the agreement between AI-based and expert evaluations remains limited. Methods A cross-sectional study was conducted to analyze asthma-related health education videos retrieved from YouTube and Bilibili. Two medical experts independently evaluated video quality using the Global Quality Scale (GQS) and modified DISCERN (mDISCERN). AI-assisted evaluation was performed using the GPT-4 large language model based on video transcripts under standardized prompts. Interrater agreement between experts was assessed using intraclass correlation coefficients (ICCs) and Spearman correlations. Agreement between AI and expert ratings was evaluated using Spearman correlation and Bland-Altman analysis. Multivariable linear regression was performed to identify factors associated with differences between AI and expert scores. Results A total of 200 asthma-related health education videos were included, with 100 videos from each platform. AI ratings showed moderate correlations with expert ratings for both GQS ( ρ = 0.55, p ρ = 0.49, p Conclusion AI-generated evaluations demonstrated moderate positive correlations with expert assessments in evaluating the quality of asthma-related health education videos. AI may serve as a scalable tool for preliminary screening of online health information, although systematic differences remain across platforms and content characteristics. Expert evaluation remains essential to ensure the accuracy and reliability of medical information assessment.","url":"https://doi.org/10.3389/fpubh.2026.1853895","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1853895","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/jamia/ocag082","name":"Explainability in context: calibrating appropriate trust and reliance in artificial intelligence.","source":"europepmc","abstract":"Background and significance Predictive artificial intelligence (AI) promises to transform care delivery, enhance patient safety, and improve health outcomes. Realizing these benefits will require careful design, implementation, and monitoring strategies to avoid unintended consequences, including automation bias (i.e., erroneously favoring recommendations from automated systems). Automation bias is particularly concerning due to the variability of AI performance across time and populations, leading to predictions that may be variably incorrect, uncertain, or unfair. Approach We advocate for an expanded view of explainable AI that uses contextual information to help end users calibrate appropriate levels of trust and reliance. We propose multiple levels of contextualization-model, setting, subpopulation, and patient-that together provide insight for clinicians to evaluate the reliability of individual predictions. This includes information about historical and in-the-moment AI performance, algorithmic fairness, and prediction uncertainty. Conclusion We outline an approach to integrate context-based explanations into decision support workflows to aid clinician interpretation without adding cognitive burden.","url":"https://doi.org/10.1093/jamia/ocag082","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag082","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1186/s44348-026-00087-4","name":"Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.","source":"europepmc","abstract":"Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.","url":"https://doi.org/10.1186/s44348-026-00087-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s44348-026-00087-4","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1002/cam4.72143","name":"Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.","source":"europepmc","abstract":"Driven by changes in lifestyle and environmental factors, the global incidence of cancer is steadily increasing, which has established it as a leading cause of mortality worldwide. The current paradigm for cancer diagnosis and treatment relies on conventional methods, such as imaging, endoscopy, and tissue biopsy, which present significant limitations regarding sensitivity in early screening, diagnostic specificity, and personalized treatment. Consequently, the development of more efficient and accurate technologies remains a major objective in modern oncology research, and artificial intelligence (AI) has emerged as a particularly promising solution. Through machine learning and deep learning algorithms, AI is reshaping cancer care by enabling automated detection of minute lesions during screening and quantitative analysis of pathological features for diagnosis. It may also advance tumor theranostics through multimodal data integration for treatment stratification, response prediction, and image-guided or targeted therapeutic decision-making, whereas providing data-driven recommendations for personalized treatment. Despite these prospects, medical AI development faces several key issues, including data bias, model explainability, clinical reliability and generalizability, emerging limitations of foundation models and generative AI, and regulatory and ethical issues that need to be addressed. By reviewing recent advances in AI across screening, diagnosis, theranostics, and treatment, we aim to clarify where these methods are already useful, where evidence remains limited, and why closer collaboration among clinicians, engineers, and data scientists is needed for clinical translation. We hope this review serves as a practical reference for researchers and clinicians evaluating how AI may be integrated into oncology in a more standardized, clinically responsible way.","url":"https://doi.org/10.1002/cam4.72143","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/cam4.72143","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.exger.2026.113270","name":"Artificial intelligence-based whole-body skeletal muscle volume predicts long-term mortality after transcatheter aortic valve implantation.","source":"europepmc","abstract":"Background Frailty and sarcopenia are important determinants of outcomes in patients undergoing transcatheter aortic valve implantation (TAVI). Conventional skeletal muscle depletion assessment relies on single-slice measurements that do not capture total muscle burden. Artificial intelligence (AI)-based CT segmentation enables automated volumetric quantification of skeletal muscle and intermuscular fat (IMF). We investigated the prognostic value of AI-derived skeletal muscle volumetry and IMF in patients undergoing TAVI. Methods This retrospective cohort study analyzed pre-procedural CT scans of patients undergoing TAVI using AI-based segmentation (TotalSegmentator, Basel, Switzerland) to quantify skeletal muscle volume (SMV) and IMF. Patients were stratified by sex-specific quartiles. Skeletal muscle depletion was defined as the lowest SMV quartile (Q1), while muscle quality was assessed by the highest IMF quartile (Q4). The primary endpoint was all-cause mortality. Results A total of 470 patients were included. During a median follow-up of 4.56 years (IQR 3.57-5.99), 188 patients (40.0%) died. Patients with low SMV were older with lower body mass index and body surface area. Kaplan-Meier analysis demonstrated higher mortality in the lowest SMV quartile (log-rank p = 0.009). In multivariable Cox regression, low SMV remained independently associated with mortality (HR 1.589, 95% CI 1.101-2.294, p = 0.013). High IMF was not associated with mortality (HR 1.326, 95% CI 0.865-2.035, p = 0.196). In a combined model, SMV remained independently associated with mortality, whereas IMF remained non-significant. Conclusion AI-based CT volumetry identifies low skeletal muscle volume as an independent predictor of long-term mortality after TAVI. Muscle quantity, rather than muscle quality, appears to be the dominant prognostic determinant. AI-driven body composition analysis may serve as an imaging biomarker of biological aging and reduced physiological reserve in older adults undergoing TAVI.","url":"https://doi.org/10.1016/j.exger.2026.113270","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.exger.2026.113270","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1155/ijod/8981807","name":"Exploring Senior Dental Students' Readiness and Perceptions of Artificial Intelligence in Dentistry: Insights From a Cross-Sectional Study.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is increasingly influencing dentistry; however, senior dental students' readiness to understand and use AI remains insufficiently explored in underrepresented settings. This study assessed knowledge, perceptions, attitudes, and readiness toward AI among senior dental students in Erbil, Kurdistan Region of Iraq. Methods A cross-sectional survey was conducted among 168 senior dental students using an expert-reviewed and pilot-tested questionnaire adapted from previously used instruments. AI-related readiness was assessed using a descriptive knowledge/use index and two normalized composite scores: perception/clinical-application and attitude/future-expectation scores. Internal consistency was assessed for the two multi-item composite scores. Results Self-rated AI knowledge was moderate in 45.2% of students and high in 11.9%, while prior involvement in AI-related development or projects was limited (5.4%). The knowledge/use index was low (0.25 ± 0.20), indicating limited practical exposure and familiarity. In contrast, the perception/clinical-application score was high (0.69 ± 0.16), with favorable views particularly toward implant planning, radiographic caries detection, quality control, and diagnosis and treatment planning. The attitude/future-expectation score was also high (0.69 ± 0.26), indicating positive expectations regarding the future role of AI in dentistry. The perception/clinical-application score showed acceptable internal consistency (Cronbach's alpha = 0.78), while the attitude/future-expectation score showed high internal consistency (Cronbach's alpha = 0.86). No statistically significant differences were observed in knowledge/use, perception/clinical-application, or attitude/future-expectation scores according to gender or year of study. Conclusion Senior dental students demonstrated favorable perceptions and expectations toward AI in dentistry, particularly for diagnostic and treatment-related applications. However, practical exposure and AI-related use were limited, and concerns remained regarding empathy, accountability, ethical issues, and human oversight. Because this was an Erbil-based convenience sample, the findings should be interpreted cautiously and may not generalize to all dental students.","url":"https://doi.org/10.1155/ijod/8981807","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/ijod/8981807","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3346/jkms.2026.41.e304","name":"Proper Utilization of Generative AI in Scholarly Writing: The Researcher and Author Perspective.","source":"pubmed","abstract":"The use of generative artificial intelligence (AI) in scholarly publishing is expanding rapidly, yet clear standards for its appropriate use and disclosure remain lacking. Surveys indicate that many researchers already use AI tools for manuscript preparation, particularly for writing assistance and error detection. However, attitudes toward acceptable AI use and disclosure requirements remain inconsistent, especially regarding the use of AI in drafting manuscripts and in the peer-review process. Analyses of manuscript submissions to major journal groups suggest that the proportion of authors disclosing AI use is substantially lower than estimates from researcher surveys, indicating possible underreporting or uncertainty about reporting requirements. It is therefore essential to implement standardized frameworks that require explicit disclosure of AI use to ensure transparency and uphold trust in scholarly communication.","url":"https://doi.org/10.3346/jkms.2026.41.e304","authors":["Suh DH","Han DS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e304","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2147/amep.s622875","name":"Use and Perceptions of Generative Artificial Intelligence Tools Among First-Year Medical Students: A Cross-Sectional Survey.","source":"europepmc","abstract":"Background Artificial intelligence (AI) tools, particularly large language models, are increasingly integrated into medical education. However, little is known about how early medical students perceive their educational value, reliability, and ethical implications. Methods A cross-sectional survey was conducted among first-year students in the Undergraduate (MD) and Graduate-Entry (GEMD) Medical Programmes at the University of Nicosia, Cyprus. A total of 102 students participated across three cohorts: MD2030 (n = 44), MD2031 (n = 46), and GEMD2030 (n = 12). The questionnaire explored awareness and use of AI tools, perceived benefits and risks, trust in AI-generated medical information, ethical concerns, and attitudes toward AI in medical education. Quantitative data were analysed using descriptive and inferential statistics, while open-ended responses underwent sentiment analysis and topic modelling. Results Awareness of AI tools was nearly universal (93-100%), with ChatGPT use reported by 87-100% of students. Regular use ranged from 70.5% in MD2030 to 100% in GEMD2030. Students perceived AI as a valuable supplementary learning tool (mean Likert scores 4.07-4.22/5), particularly for improving understanding and efficiency. Trust in AI-generated medical information remained moderate (3.11-3.33/5), reflecting concerns about inaccuracies and ethical issues. Open-ended responses highlighted incorrect AI-generated information as a common concern. A significant difference in reported encounters with incorrect AI-generated content was observed between cohorts ( χ 2 = 10.73, p = 0.0047), with lower reporting among students exposed to AI-literacy training, suggesting an association between prior exposure to AI literacy activities and differences in how students report AI-generated errors. Conclusion AI tools are widely adopted by medical students and are perceived as enhancing rather than replacing traditional study methods. Structured AI literacy activities may be associated with differences in how students evaluate AI-generated content; however, further longitudinal and controlled studies are required to determine whether such educational experiences have a measurable impact on learners' attitudes and practices.","url":"https://doi.org/10.2147/amep.s622875","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/amep.s622875","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1126/sciadv.aef0286","name":"scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.","source":"europepmc","abstract":"The rapid accumulation of single-cell data has made it possible to comprehensively characterize biological systems at molecular, cellular, and donor levels. However, scalable reference mapping across different resolutions remains a major challenge in current research. Here, we propose scProtoTransformer, a prototype-based Transformer architecture designed to achieve scalable reference mapping across molecular, cell, and donor levels. scProtoTransformer introduces a knowledge-guided prototype tokenizer that projects gene expression into biologically interpretable pathway prototypes, effectively reducing numerical batch effects while preserving biological semantic patterns. Furthermore, by leveraging knowledge distilled from the foundation model and a dynamic supervised fine-tuning strategy, scProtoTransformer achieves robust biological representations with reduced pretraining requirements. Benchmark experiments across molecular, cell, and donor-level reference mapping demonstrate that scProtoTransformer delivers competitive or even superior performance compared with state-of-the-art approaches while providing interpretability through biological prototypes. Together, these results establish scProtoTransformer as a unified framework for scalable reference mapping, laying the foundation for systematic understanding from genes to individuals.","url":"https://doi.org/10.1126/sciadv.aef0286","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1126/sciadv.aef0286","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3390/healthcare14162587","name":"Q&lt;sub&gt;AI/ML-SaMD&lt;/sub&gt;: A Hybrid Health-Technology Quantifiable Quality Metric for Artificial Intelligence/Machine Learning-Based Software as a Medical Device.","source":"europepmc","abstract":"Background: The increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) into medical devices necessitates robust quality evaluation methods. However, existing approaches remain qualitative, sector-specific, or focused on isolated attributes, leaving a gap in quantifiable assessment for AI/ML-driven Software as a Medical Device (SaMD). Objective : This study introduces Q AI/ML-SaMD , a novel hybrid metric that provides a comprehensive, quantifiable measure of AI/ML-SaMD quality by synthesizing health and information technology (IT) dimensions into a single composite, benchmark-ready score. Methods : The metric integrates key attributes from a systematic literature review, classified into Health and IT domains. Sub-metrics (Q Health and Q IT ) use weighted sums, while the overall score employs a Weighted Geometric Mean with configurable parameters to penalize domain imbalances. Validation included (a) theoretical validation against four mathematical properties, (b) an illustrative example with sensitivity analysis, (c) expert-based validation with six specialists, and (d) an evidence-based case study on FDA-authorized IDx-DR using public regulatory and clinical documentation. Results : The illustrative example yielded a score of 29.7 (\"Unsuitable\"). Sensitivity analysis confirmed robustness across weight, score, and combined uncertainty perturbations, with classification unchanged. Expert validation showed 83.3% agreement. The IDx-DR case study produced a score of 82.3 (\"Admissible\"), correctly aligning with the device's regulatory status and supporting external validity. Conclusions : The Q AI/ML-SaMD metric provides a foundational, quantifiable framework for AI/ML-SaMD quality assessment, bridging qualitative regulatory principles and measurable outcomes. It offers a practical tool for developers, regulators, and clinicians to benchmark and track quality across the SaMD lifecycle.","url":"https://doi.org/10.3390/healthcare14162587","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14162587","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/bjr/tqag116","name":"Prognostic significance of baseline 18F-FDG PET/CT parameters in combination with an artificial intelligence-based pleural effusion segmentation model for malignant pleural effusion.","source":"europepmc","abstract":"Objectives We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG PET/CT images to investigate the prognostic value of PET/CT-derived parameters for overall survival (OS) among lung cancer patients with malignant pleural effusion (MPE). Methods A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation. Results The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over the LENT score for 12-month survival prediction (AUC: 0.849, 95% CI, 0.795-0.903 vs AUC: 0.732, 95% CI, 0.660-0.796). The internal bootstrap validation had an AUC of 0.840, (95% CI, 0.671-0.922) and demonstrated a well-fitting calibration curve. Conclusions The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellently in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment. Advances in knowledge AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.","url":"https://doi.org/10.1093/bjr/tqag116","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/bjr/tqag116","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.pec.2026.109832","name":"Prompting generative artificial intelligence to create plain language patient education materials.","source":"europepmc","abstract":"Objectives Generative artificial intelligence (AI) holds promise for creating patient education materials (PEM). However, AI-produced content is often difficult to read. Prompts for AI to write at a lower reading level either fail to meet recommended targets (≤6th grade) or demonstrate inconsistent results. We aimed to determine whether a new AI prompt for plain language would create PEMs that are easier to read, understand, and act upon. Methods We analyzed PEMs from two websites (HealthyChildren.org, UpToDate) and two generative AI programs (ChatGPT, Gemini). For each AI program, we used a basic prompt and an advanced prompt to generate separate documents. From these sources, we collected six sets of PEMs about five acute pediatric conditions, evaluated them using a panel of readability indices, and used ANOVA to compare average readability scores between sources. Two pediatricians assessed understandability and actionability using the Patient Education Materials Assessment Tool (PEMAT). We calculated mean PEMAT scores, with values > 70% considered understandable/actionable. Results ChatGPT (advanced prompt) created PEM with the lowest mean grade reading level (5.8; SD 0.84) and highest PEMAT scores (91% understandable, 84% actionable). Documents from generative AI using the advanced prompt and UpToDate had mean readability scores ≤ 6th grade, with significantly better readability than AI-generated documents using the basic prompt or HealthyChildren.org. All sources except HealthyChildren.org had mean understandability > 70%; AIs using the advanced prompt and UpToDate exceeded the actionability threshold. Conclusions A plain-language prompt for generative AI created PEMs that were readable, understandable, and actionable, with comparable results across two AI programs. Practice implications Healthcare providers can use this new plain-language prompt for generative AI programs to quickly create free, patient-friendly PEM about acute healthcare topics. As these AI programs were not trained to provide medical guidance, medical professionals should continue to review PEM prior to sharing with patients.","url":"https://doi.org/10.1016/j.pec.2026.109832","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.pec.2026.109832","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.13107/jocr.2026.v16.i08.7958","name":"Artificial Intelligence-Guided Precision Orthobiologics in Musculoskeletal Conditions.","source":"europepmc","abstract":"Introduction The use of orthobiologics such as autologous peripheral blood-derived orthobiologics, bone marrow-derived biologics, adipose tissue-derived biologics, mesenchymal stem cells, and extracellular vesicles is gaining traction in the field of orthobiology for the treatment of osteoarthritis (OA), tendinopathy, cartilage injuries, and delayed musculoskeletal healing. Clinical responses are inconsistent due to biological variation among patients, disease manifestations, product composition, delivery accuracy, and outcome definitions. Materials and methods The literature was searched in PubMed, Embase, Cochrane Library, and Scopus databases from January 2019 to June 2026, and landmark and regulatory concepts were considered. The search terms were orthobiologics, platelet-rich plasma, mesenchymal stromal cells, OA, tendinopathy, artificial intelligence (AI), machine learning, imaging biomarkers, ultrasound guidance, responder prediction, potency assays, software as a medical device and regulation. The synthesis of evidence was done in a narrative fashion based on the Scale for the Assessment of Narrative Review articles principles. Results AI has the greatest clinical relevance as an enabler for precision orthobiologics. Supervised models can predict responder probability after platelet-rich plasma; unsupervised clustering can identify inflammatory, metabolic, structural, or pain-dominant phenotypes; deep learning can quantify imaging biomarkers; natural language processing can identify longitudinal outcomes; and privacy-preserving learning can facilitate multicenter validation. Product variability may be lessened through parallel advances in cytometry, secretome profiling, potency testing, and image-guided delivery. However, the evidence is still early, largely retrospective, and prone to bias, data drift, poor external validation, and unclear regulatory classification. Conclusion AI-assisted orthobiologics can be considered as a translational precision-medicine architecture, not a fully formed product. Standardized characterization of the biologic, a prospectively validated prediction model, easily understandable outputs, regulatory alignment, and ongoing monitoring of outcomes before routine clinical use are all necessary for safe implementation.","url":"https://doi.org/10.13107/jocr.2026.v16.i08.7958","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.13107/jocr.2026.v16.i08.7958","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s00464-026-13257-8","name":"Artificial intelligence for predicting surgical difficulty in laparoscopic cholecystectomy: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Accurately predicting operative difficulty in laparoscopic cholecystectomy (LC) is foundational to personalized surgical planning and patient safety assurance. However, the reliability, generalizability, and true clinical utility of current Artificial Intelligence (AI) models are currently unsubstantiated. This review aimed to evaluate the predictive performance and methodological quality of AI models designed to predict LC surgical difficulty. Methods PubMed, Embase, Web of Science, and the Cochrane Library were searched from inception to March 2, 2026. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. The areas under the curve (AUC) with 95% confidence intervals were pooled using random-effects meta-analysis. The overall certainty of evidence was evaluated using the GRADE framework. The study followed PRISMA guidelines and was registered with PROSPERO (CRD420251267805). Results A total of 18 studies were included in this review. Sixteen studies were at high risk of bias. The pooled AUC for 27 training models was 0.848 (95% CI, 0.829-0.868). For 32 validation models, the pooled AUC was 0.818 (95% CI, 0.797-0.840). Ensemble models achieved the highest pooled AUCs (0.889 and 0.861) in both training and validation set. Multimodal integration of clinical features, imaging, and intraoperative video also yielded superior performance. Conclusion Current research showed significant methodological flaws. AI models based on ensemble architecture and multimodal approaches warrant further exploration. Most studies carry a high risk of bias and rarely undergo external validation, which limits clinical translation. Before clinical implementation, these models still require strict methodological evaluation, prospective multicenter testing, and proper calibration.","url":"https://doi.org/10.1007/s00464-026-13257-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00464-026-13257-8","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1136/bmjopen-2026-120834","name":"Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S): rationale and randomised controlled trial design.","source":"europepmc","abstract":"Introduction Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational artificial intelligence HeAlth supporT in Atrial Fibrillation Self-Management (CHAT-AF-S)) in improving quality of life in patients with AF. Methods and analysis CHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (aged 18 years and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life overall score. We will follow the intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived use and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and dissemination Ethics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Trial registration number Australian New Zealand Clinical Trials Registry (registration number: ACTRN12623000850673).","url":"https://doi.org/10.1136/bmjopen-2026-120834","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2026-120834","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/87887","name":"Decision Support Framework for Quality Assurance and Enhancement of Therapeutic Artificial Intelligence Systems: Mixed Methods Pilot Study.","source":"pubmed","abstract":"Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather than actionable. Organizations lack structured pathways to translate evaluation findings into validated quality improvements aligned with health care quality assurance requirements.","url":"https://doi.org/10.2196/87887","authors":["Kang B","Kwon K","Huilin P","Hong S","Choi S","Oh H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/87887","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3390/healthcare14142031","name":"The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.","source":"pubmed","abstract":"Background/Objectives : The application of artificial intelligence (AI) in the healthcare field continues to deepen, with the development paradigm gradually shifting from technology-driven innovation to design-driven innovation towards \"human-centered artificial intelligence (HCAI).\" This aims to bridge the potential of AI technology with actual clinical needs and improve the quality and accessibility of healthcare services. However, it still faces challenges such as insufficient integration of theoretical frameworks, complex implementation challenges, and an imperfect ethical governance mechanism. Methods : This article presents a systematic narrative review about the philosophical and ethical foundations of HCAI related literature, analyzes the specific clinical application models recorded in the literature, integrates key theories related to implementation science, human-machine collaboration, and explainable AI(XAI), and constructs a multidimensional comprehensive analysis framework for HCAI in the medical field. Results : The study shows that design-driven innovation is the key to bridging the gap between the potential of AI technology and practical medical applications; the successful implementation of \"human-centered artificial intelligence\" relies on interdisciplinary collaboration, stakeholder co-creation, and ethical considerations throughout the entire lifecycle; Among them, human-centered design ensures that technology meets real needs; Implementation Science guarantees innovation can effectively integrate into complex medical environments; explainable AI technology is the cornerstone of establishing clinical trust; the strategic governance framework sets boundaries and tracks for the healthy development of the entire ecosystem. Conclusions : Beyond summarizing existing research findings, this study proposes targeted design frameworks and trade-off strategies for key technical and practical dilemmas of HCAI. It also clarifies the contextual boundaries of existing empirical results, provides a differentiated operational path for the implementation of HCAI, as well as a clear direction and important reference for academic research and future practical applications of human-centered AI medicine.","url":"https://doi.org/10.3390/healthcare14142031","authors":["Liu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14142031","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.pbi.2026.102955","name":"Predicting transcriptional regulators in plants in the era of artificial intelligence.","source":"europepmc","abstract":"Identifying transcriptional regulators that control important biological pathways in plants is fundamental to understanding regulatory mechanisms, network hierarchy, and phenotypic variation. This remains challenging because transcription factors (TFs) and their targets operate within highly interconnected, dynamic, and often redundant regulatory networks. Over the past two decades, advances in omics technologies, sequencing data generation, and computational tools have shifted gene discovery from single-gene studies to network-level investigation. At the same time, these advances have created a new challenge: how to extract biologically meaningful regulatory relationships from increasingly complex and high-dimensional datasets. Recent progress in multi-omics integration and artificial intelligence (AI), including machine learning (ML), deep learning, and emerging foundation-model approaches, is beginning to address this challenge and is reshaping how transcriptional regulators, targets, and regulatory relationships are predicted in plants. In this review, we summarize advances in network-enabled gene discovery, discuss how multi-omics and AI are transforming transcriptional target prediction, and consider how these developments may lead to predictive models of plant gene regulation with applications in crop improvement and synthetic biology.","url":"https://doi.org/10.1016/j.pbi.2026.102955","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.pbi.2026.102955","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/98949","name":"Artificial Intelligence for Assessment and Feedback in Medical Education: Bibliometric Mapping Study and Thematic Evidence Map.","source":"europepmc","abstract":"Background Artificial intelligence (AI), particularly generative AI and large language models, is increasingly used for assessment-related tasks in medical education. Existing overviews often address AI in medical education broadly, limiting assessment-specific interpretation of functions, settings, learner stages, and responsible-AI reporting domains. Objective This study aims to map the literature on AI for assessment and feedback in medical education, including publication trends, bibliometric structure, assessment functions, AI types, settings, learner stages, and reporting of validity, reliability, fairness, integrity, transparency, human oversight, implementation, and governance. Methods We conducted a bibliometric mapping study incorporating structured thematic evidence-map coding. Web of Science Core Collection, Scopus, and PubMed were searched from January 1, 2015, to April 8, 2026. Document selection and main evidence-map coding were based primarily on titles, abstracts, and bibliographic metadata, with targeted ambiguity resolution. Because reporting domains may appear mainly in full-text sections, all 435 included records underwent full-text sensitivity analysis for the 8 reporting domains. Coding reliability was assessed by two coders using percent agreement and Cohen κ before adjudication. Exploratory subgroup analyses and an excluding-2026 partial-year sensitivity analysis were conducted. Results Searches identified 14,968 records; 435 were included after deduplication and selection. Overall, 399 (91.7%) records were indexed in the post-ChatGPT period. Generative AI was coded in 310 (71.3%) records, and large language models in 301 (69.2%) records. In the assessment-function umbrella analysis, learner performance evaluation accounted for 270 (62.1%) records, feedback for 93 (21.4%) records, assessment content generation for 65 (14.9%) records, and other or unclear functions for 7 (1.6%) records. The most common settings were board-style examinations (n=151, 34.7%) and written examinations (n=88, 20.2%); undergraduate medical education was the most represented learner stage (n=172, 39.5%). Full-text-confirmed reporting was most frequent for reliability (n=288, 66.2%) and implementation (n=231, 53.1%); intermediate for validity (n=158, 36.3%), fairness (n=132, 30.3%), and transparency (n=130, 29.9%); and less frequent for governance (n=57, 13.1%), human oversight (n=46, 10.6%), and integrity (n=26, 6%). Stage 1 κ values ranged from 0.785 to 0.895, and stage 2 κ values ranged from 0.809 to 0.880. Excluding 65 partial-year 2026 records did not change the overall interpretation. Conclusions The indexed English-language literature on AI for assessment and feedback in medical education expanded rapidly in the post-ChatGPT period and was concentrated in generative AI, large language models, examination-oriented assessment, and undergraduate medical education. Future studies should complement examination benchmarking with authentic assessment contexts, distinguish assessment content generation from learner-facing evaluation and feedback, and report responsible assessment domains more consistently.","url":"https://doi.org/10.2196/98949","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/98949","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3346/jkms.2026.41.e281","name":"Comprehensive Consideration of Ethics in AI-assisted Scientific Writing and Peer Review.","source":"europepmc","abstract":"The emergence of large language models and generative artificial intelligence (AI) is driving fundamental transformations in the ecosystem of scholarly publishing and peer review. As manuscript production enters an era of sophisticated technological assistance, it has become imperative to transition from traditional approaches focused on misconduct prevention toward a more proactive ethical framework. We propose a new standard centered on transparency, accountability, and confidentiality, presenting a clear solution to challenges associated with integrating AI into academic discourse. Regarding authorship, AI cannot be credited as an author; the final accountability for academic integrity lies only with human authors. Transparency is maintained through a tiered disclosure framework that mandates reporting based on the extent of artificial intelligence utilization. In the context of peer review, while the potential of artificial intelligence to optimize efficiency is recognized, its application must be restricted to a closed security system to safeguard against data breaches. Furthermore, this review highlights new risk factors such as algorithmic sycophancy and prompt injection attacks, emphasizing that a final verification through human expertise is essential to ensuring the integrity of the peer review process. In conclusion, we present a comprehensive regulatory revision roadmap integrating the authors' obligations for transparent information disclosures, reviewers' commitment to confidentiality and security, and editors' ethical oversight. This framework does not regard AI as an object of absolute prohibition but rather positions it as an advanced scholarly aid rooted in human intellectual accountability. The perspectives in this Special Issue will provide a practical framework to maintain academic rigor and enhance institutional trust in the era of AI.","url":"https://doi.org/10.3346/jkms.2026.41.e281","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3346/jkms.2026.41.e281","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2196/103597","name":"Short-Term Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women: Protocol for a Randomized Controlled Trial With a Waitlist Control.","source":"europepmc","abstract":"Background Heart disease remains a leading cause of death for women in the United States. Despite this burden, awareness that heart disease is the leading cause of death among women declined from 65% in 2009 to 44% in 2019, with the largest declines observed among Hispanic, Black, and younger women. Thus, innovative, scalable, and cost-effective educational strategies are needed to improve women's awareness of heart attack symptoms and appropriate care-seeking behaviors. Objective This study aims to evaluate the short-term efficacy of the artificial intelligence (AI) HeartBot II, a chatbot-based educational intervention, in improving women's awareness and knowledge of heart attack symptoms and care-seeking behavior compared with a waitlist control group. Methods This randomized controlled clinical trial (RCT) with a waitlist control will enroll 200 women aged 25 or older, who will be randomized using a 1:1 allocation ratio. The intervention group will download the AI HeartBot II app and complete the 4 modules (including information on heart attack symptoms, risk factors, and calling 911) over 12 weeks. The waitlist control group will start receiving an identical intervention at 12 weeks. The primary outcomes will be change from baseline to 12 weeks in a 4-item heart attack response preparedness score, calculated as the mean of 4 self-reported items assessing confidence in recognizing signs and symptoms of a heart attack, distinguishing heart attack symptoms from other medical problems, calling 911 or an ambulance if a heart attack is suspected, and reaching an emergency room within 60 minutes of symptom onset. The primary analysis will estimate the intervention effect using constrained longitudinal data analysis implemented with linear mixed models, including fixed effects for time and time-by-treatment group interaction. Sensitivity analyses for the individual ordinal items will use ordinal logistic mixed-effects models. Results We received approval from the University of California, San Francisco, Institutional Review Board (No. 25-44825) on January 9, 2026, and this trial was registered on ClinicalTrials.gov (NCT07416734) on February 11, 2026, prior to enrollment of the first participant. Recruitment began in April 2026. As of manuscript submission, 86 participants were enrolled. Enrollment is expected to be completed by September 2026, and all follow-up assessments are anticipated to be completed by March 2027. Data analysis is expected to begin in spring 2027, with study results anticipated for publication later in 2027. Conclusions To the best of our knowledge, this is the first RCT to rigorously evaluate the efficacy of the AI HeartBot II intervention. If effective, AI HeartBot II could provide a scalable, accessible, and cost-effective public health communication strategy to improve women's awareness of heart attack symptoms and promote timely care-seeking behaviors in the United States.","url":"https://doi.org/10.2196/103597","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/103597","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1002/hsr2.73042","name":"AI Agents and the Future of Clinical Judgment in Medical Education: Opportunities, Challenges, and the Need for Human-Centered Integration.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is actively transforming health professions education by introducing innovative methodologies for learning, simulation, and clinical decision support. The recent emergence of autonomous AI agents-equipped with advanced capabilities like memory, planning, and tool integration-creates unprecedented opportunities for medical training. However, this growing technological autonomy simultaneously introduces critical challenges regarding clinical judgment, professional accountability, and educational equity. Methods This perspective employs a rigorous critical analysis grounded in health professions education, medical philosophy, and AI ethics literature. It systematically evaluates the pedagogical potentials alongside the epistemological risks associated with deploying autonomous AI agents within clinical training environments. Discussion While AI agents can effectively drive adaptive learning, scalable simulations, and individualized feedback, unmonitored or inappropriate integration risks undermining core clinical reasoning. Over-reliance on algorithmic suggestions can diminish a learner's independent analytical capabilities and propagate inherent data set biases. Conversely, dismissing these technologies risks depriving learners of vital digital competencies. Consequently, the ultimate educational utility of AI agents is fundamentally dictated by deliberate instructional design and robust governance. Conclusion Rather than serving as replacements for human expertise, AI agents must be harnessed through a human-centered framework that safeguards clinical reasoning, ethical reflection, and professional accountability. By deploying targeted strategies-such as foundational AI literacy, localized data governance, controlled simulation failures, and modernized evaluations like AI-assisted OSCEs-educational institutions can ensure these technologies reinforce human-driven medical practice.","url":"https://doi.org/10.1002/hsr2.73042","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.73042","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3390/jcm15166398","name":"Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.","source":"europepmc","abstract":"Cardiovascular ultrasound is a cornerstone of noninvasive cardiac and vascular assessment, yet conventional interpretation remains operator-dependent, variable, and limited in sensitivity for subclinical disease. Artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), and, most recently, vision-language and foundation models, offers tools to automate, standardize, and extend ultrasound analysis. This narrative review examines the role of AI-enhanced cardiovascular ultrasound in the transition from descriptive imaging toward predictive and personalized medicine. We conducted a structured literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar (January 2016-May 2026), combining Medical Subject Headings and free-text terms related to AI and cardiovascular ultrasound. Original studies, meta-analyses, reviews, consensus documents, and seminal works were considered. AI now spans the entire echocardiographic workflow, acquisition guidance, view classification, segmentation (Dice ≈ 0.92-0.94 on public datasets), and automated quantification of ejection fraction and global longitudinal strain, achieving expert-level accuracy and improved reproducibility. Across clinical domains, AI supports ischemia detection on stress echocardiography, heart-failure phenogrouping, Doppler-independent aortic stenosis detection, and carotid plaque characterization for stroke-risk stratification. Emerging vision-language and multitask foundation models (e.g., EchoCLIP, EchoPrime, and PanEcho) point toward general-purpose interpretation, and a growing number of tools (Caption Guidance, Us2.ai, and Ultromics EchoGo) have obtained FDA clearance and/or CE marking. Increasingly, AI-derived imaging biomarkers feed multimodal models that enable individualized risk prediction and therapy selection. AI-enhanced cardiovascular ultrasound is poised to become a central tool of precision cardiology. Realizing its potential will require prospective multicenter validation, cross-vendor standardization, attention to generalizability, interpretability, and reproducibility, and evolving regulatory and ethical frameworks.","url":"https://doi.org/10.3390/jcm15166398","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15166398","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1093/jamia/ocag076","name":"Generative artificial intelligence for inpatient documentation summarization: mixed-methods quality assessment and early real-world experience.","source":"europepmc","abstract":"Objective We evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care. Materials and methods An AI summarization tool was created to provide a \"Patient Story\", specialty-specific \"Recent Notes\", and \"Recent Events\" over the previous 24 or 72 hours. We conducted a pragmatic mixed-methods quality assessment at three tertiary-care academic hospitals utilizing (1) Provider Documentation Summarization Quality Instrument (PDSQI-9), (2) utilization analytics, and (3) qualitative end user feedback. The PDSQI-9 included whether summaries were accurate, cited, comprehensible, organized, succinct, non-stigmatizing, synthesized, thorough, and useful. 512 users were given access, from whom 52 participants submitted 205 surveys (10.2% response rate). Results 52 respondents submitted an average of 4.3 surveys (range 1-8). Users rated the tool favorably across all PDSQI-9 domains, with a combined average score of 4.68 (range 4.56-4.78, S.D. 0.67) across the eight domains scored on a 5-point modified Likert scale. Utilization metrics demonstrated strong uptake with frequent views. Positive qualitative feedback revealed cognitive offloading for complex patients and effective summarization of medical problems. Critical feedback showed a need to cross-reference narrative notes to current-state data and lack of detailed specialty-specific summarization. Discussion Generative artificial intelligence has emerged as a potentially transformative technology for generating succinct, verifiable summaries of ongoing care. In this pragmatic implementation, end-users indicated high perceived quality across PDSQI-9 domains. Conclusions An LLM-based summarization tool for ongoing hospitalization care was rated of high quality by diverse clinicians in real-world settings, demonstrated a favorable safety profile, and showed sustained utilization.","url":"https://doi.org/10.1093/jamia/ocag076","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/jamia/ocag076","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s11894-026-01052-3","name":"Artificial Intelligence for Diagnosis of Esophageal Manometry: A Narrative Review.","source":"europepmc","abstract":"Purpose of review High-resolution manometry (HRM), the gold standard for diagnosing esophageal motility disorders, remains constrained by inter-rater variability, limited access to expert esophagologists, and inadequate training infrastructure. Many gastroenterology fellowship programs provide insufficient motility training, and dedicated expert centers remain geographically concentrated. Emerging Artificial intelligence (AI) tools have the potential to automate diagnosis, and augment clinician interpretive capacity and democratizing expert-level motility assessment. Recent findings Twenty-two studies encompassing over 5,000 patients were synthesized across three overlapping developmental phases: early machine learning for feature extraction and classification; deep learning deployed toward automated pattern recognition and motility classification and emerging multimodal and large language model-based frameworks augmenting clinical interpretation and decision-making, with diagnostic accuracies ranging from 71 to 97%. AI integration of multimodal manometric data may reveal pressure signatures imperceptible to human visual inspection, pointing toward phenotypes beyond the Chicago Classification. No study has yet demonstrated improved patient outcomes. AI's role in motility training remains largely unexplored. The AI-clinician partnership represents the most promising trajectory for esophageal manometry interpretation in coming years. AI-augmented interpretation has the potential to eliminate the two-tier diagnostic gap between academic and community practice, to accelerate trainee competency through scalable AI-supervised case libraries, and to uncover physiologic phenotypes beyond human interpretation.","url":"https://doi.org/10.1007/s11894-026-01052-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11894-026-01052-3","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1063/5.0348706","name":"Reinforcement learning with reputation-based adaptive exploration promotes cooperation.","source":"europepmc","abstract":"Reinforcement learning provides a framework for studying how individuals adjust their behavior through repeated interaction and feedback in social dilemmas. In Q-learning, exploration controls how often agents choose actions other than those favored by their current learned Q-values. Yet, the existing models usually treat the exploration rate as a constant parameter. In systems with social evaluation, however, trial-and-error behavior carries different costs and opportunities for agents with different reputations, making exploration dependent on social standing rather than uniform across agents. Herein, we develop a spatial prisoner's dilemma model in which Q-learning agents adapt their exploration rates according to local reputation differences, while reputation is updated through an asymmetric, state-dependent rule. The results show that adaptive exploration and asymmetric reputation updating each promote cooperation, but their combination produces a stronger reinforcing effect than either mechanism alone. Low-reputation agents explore more and can recover reputation through cooperation, while high-reputation agents explore less and avoid reputation losses caused by defection. This mechanism also reorganizes cooperation in space, producing a stable checkerboard-like coexistence at intermediate reputation concern. In addition, cooperation is most vulnerable at intermediate baseline exploration rates, whereas stronger asymmetric reputation updating mitigates this exploration-induced disruption. These results suggest that reputation can act not only as a record of past behavior but also as a dynamic signal that regulates exploratory behavior during learning and thereby stabilizes cooperation.","url":"https://doi.org/10.1063/5.0348706","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1063/5.0348706","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.7759/cureus.113479","name":"Artificial Intelligence in Perioperative and Pain Care: Knowledge, Attitudes, and Adoption Barriers Among Anesthesiologists.","source":"europepmc","abstract":"Background The integration of artificial intelligence (AI) in anesthesiology holds immense potential to revolutionize perioperative care. Despite the growing interest in AI, limited research exists on anesthesiologists' knowledge, attitudes, and practical exposure to AI-based tools. This study aimed to assess the knowledge, attitudes, and awareness of AI applications in perioperative care of anesthesiologists with AI applications in perioperative care, spanning preoperative, intraoperative, and postoperative domains. Methodology A structured questionnaire was distributed among anesthesiologists across six major cities of Gujarat, India: Ahmedabad, Vadodara, Surat, Rajkot, Jamnagar, and Bhavnagar. The survey included demographic details, awareness of AI applications, attitudes toward AI adoption, and perceived challenges. Descriptive and inferential statistics were used to analyze the data. Results Among 385 respondents, 94.0% (n = 362) had heard of AI in healthcare, but only 63.6% (n = 245) were aware of its role in anesthesia. Knowledge assessment categorized 21.3% (n = 82) as having good knowledge, 70.9% (n = 273) as average, and 7.8% (n = 30) as poor. While 90.4% (n = 348) agreed that AI training could enhance adoption, 84.4% (n = 325) believed AI would ease their workload. However, 9.6% (n = 37) were concerned that AI-equipped doctors might replace those without AI expertise. The most cited barriers to AI adoption were lack of knowledge (84.4%, n = 325), legal concerns (27.3%, n = 105), and limited validation studies (16.9%, n = 65). Despite these challenges, 94.0% (n = 362) expressed willingness to read AI literature, and 93.2% (n = 359) reported improved knowledge post-survey. A statistically significant association was observed between years of work experience and AI knowledge levels (p < 0.01). Conclusions This study highlights a growing interest in AI among anesthesiologists but also underscores substantial knowledge gaps and implementation challenges. While attitudes toward AI are largely positive, concerns regarding training, validation, and medico-legal implications must be addressed. Strengthening AI education, fostering interdisciplinary collaboration, and ensuring ethical integration into clinical practice will be crucial for optimizing AI's role in anesthesia. Future research should focus on validating AI tools and assessing their clinical impact.","url":"https://doi.org/10.7759/cureus.113479","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.113479","addedAt":"2026-09-01T01:47:56.313Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.21203/rs.3.rs-10529339/v1","name":"Public Perceptions of AI in Dentistry: Use, Trust, and Willingness in the Madinah Region","source":"preprints","abstract":"Abstract Background Artificial intelligence (AI), particularly large language models, has transformed health information seeking and patient education. Although AI is increasingly used to obtain dental information, evidence regarding public use, trust in AI-generated dental information, and its influence on perceptions of dental care remains limited, particularly in Saudi Arabia. This study aimed to assess AI use, trust in AI-generated dental information, and willingness to use AI across dental care scenarios among residents of the Madinah region, and to examine their associations with demographic characteristics. Methods A cross-sectional analytical study was conducted among adults (≥ 18 years) residing in the Madinah region, Saudi Arabia. Data were collected between January and March 2026 using a self-administered piloted online questionnaire. The survey assessed AI use, frequency and purpose of use, willingness to use AI across different dental care scenarios, trust in AI-generated dental information, and demographic characteristics. Data were analyzed using descriptive and inferential statistics, including one-way analysis of variance and Pearson correlation, with statistical significance set at P ≤ 0.05. Results Among 466 respondents, 385 (82.6%) reported prior AI use and were included in the main analyses. AI use was significantly associated with younger age and higher educational attainment (p","url":"https://doi.org/10.21203/rs.3.rs-10529339/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10529339/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.01.26357033","name":"Standardised evaluation and monitoring of site-specific AI performance with physical CT phantoms","source":"preprints","abstract":"Clinical deployment of medical imaging artificial intelligence (AI) requires objective and continuous quality assurance, yet standardised methods for this purpose have not been established. Here, we present a framework using physical phantoms for standardised on-site testing and monitoring of AI, demonstrated in CT-based liver lesion detection. We begin by designing phantoms tailored to the anatomical input domain expected by AI algorithms, and then systematically assess how AI performance is affected by variations in scanner technology and operation across two clinical CT systems. Next, we perform longitudinal monitoring, yielding consistent results over fifteen months on both systems. Finally, we validate clinical relevance by demonstrating that AI models trained on phantom data generalize effectively to patients and exhibit no evidence of phantom-specific adaptation. Our findings show that clinically realistic phantoms enable standardised, site-specific testing and monitoring of AI, providing a proactive method for local and cross-institutional quality assurance.","url":"https://doi.org/10.64898/2026.07.01.26357033","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.01.26357033","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10282455/v1","name":"Automated Feedback Generation in Paediatric Simulation Training: A Prospective Validation of AI-Generated Structured Feedback Reports","source":"preprints","abstract":"Abstract Artificial intelligence (AI) is transforming medical education, but evidence on how to integrate it into competency-oriented training remains limited. We prospectively evaluated whether an AI-assisted feedback system could generate structured written feedback after paediatric simulation training that is valid against the adjudicated expert reference standard and practically comparable to routine instructor reporting, while preserving the learner’s active role in the communication task. In a pre-registered, powered sample, sixteen eighth-semester medical students completed one of two paediatric communication scenarios involving new-onset type 1 diabetes or lumbar puncture. Sessions were processed with a frozen local pipeline combining speech recognition, speaker diarization, video-based behaviour analysis, and language-model scoring. Reports covered global communication, conversation structuring, and clinical content. Validity was assessed by mean weighted agreement with an adjudicated expert reference standard, with success defined as a one-sided 97.5% lower confidence bound of at least 0.80. This criterion was met in all domains, with mean weighted agreement of 0.89, 0.85, and 0.88, respectively. Practical comparability was assessed by non-inferiority to instructor total scores on a 0–100 scale, using a − 15-point margin. AI scores were non-inferior in all domains, and post hoc equivalence intervals also remained within the predefined comparability range. Repeated frozen pipeline runs produced identical outputs. Automated feedback generation was feasible, reproducible, and aligned with expert reference standard. The findings support supervised formative use that complements, but does not replace, instructor-led debriefing, while potentially making structured feedback more accessible in time-constrained educational practice.","url":"https://doi.org/10.21203/rs.3.rs-10282455/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10282455/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.20944/preprints202607.0432.v1","name":"Toward Clinically Translatable Nanorobots for Cancer: Advances in Design Principles, Navigation Control, AI-Enabled Autonomy, and Post-Treatment Removal Strategies","source":"preprints","abstract":"Conventional systemic drug delivery often yields low efficacy and significant side effects due to off-target accumulation and rapid clearance. While nanoscale carriers leverage the enhanced permeability and retention (EPR) effect, this passive targeting is unreliable because tumor vascular heterogeneity leads to widely variable nanoparticle accumulation. As an alternative, untethered micro- and nanorobots (MNRs) have been engineered to actively navigate through complex fluids and autonomously deliver payloads to previously inaccessible sites. These autonomous biohybrids integrate nanoscale actuators, sensors, and computational units to enable precise, on-demand therapeutic actions at the cellular level. In particular, integrating artificial intelligence endows nanorobots with advanced sensing and decision-making capabilities: they can analyze molecular biomarkers and map biological pathways in real time to determine optimal treatment plans, yielding highly personalized, responsive therapies with enhanced efficacy. We also examine strategies for post-treatment removal of MNRs, emphasizing immune-compatible, biodegradable designs that allow metabolic clearance. This review aims to provide a comprehensive, in-depth, and nuanced examination of nanorobots in medical applications mainly in cancer treatments. Finally, we highlight remaining technical, manufacturing, regulatory and ethical challenges such as ensuring biocompatibility, scalable fabrication, and rigorous safety evaluation that must be ad-dressed for clinical translation.","url":"https://doi.org/10.20944/preprints202607.0432.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0432.v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10102575/v1","name":"Representation Paradigms in AI-based 3D Radiological Image Reconstruction: A Literature Review","source":"preprints","abstract":"Abstract The growing demand for high-quality medical imaging in clinical practice and computer-assisted diagnosis has made 3D image reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) methods are increasingly being used to improve reconstruction accuracy, accelerate image acquisition or post-processing, and reduce patient burden. However, existing reviews often organize the field by model architecture or imaging modality, which can obscure how reconstruction behavior depends on the representation used for the target volume. This literature review synthesizes 103 eligible records, including 78 methodological studies and 25 records related to datasets, benchmarks, or reconstruction resources. We included AI-based radiological reconstruction studies and reconstruction-related resource records, while excluding irrelevant or inaccessible records. Based on how the reconstruction target is parameterized, we organize AI-based 3D radiological reconstruction into four representation families: discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations. We also summarize publicly available datasets, benchmark practices, evaluation metrics, and the strengths and limitations of different representation approaches. Finally, we discuss clinical validation, preservation of small diagnostic structures, uncertainty estimation, hallucinated anatomical structures, deployment efficiency, privacy, interpretability, and regulatory barriers. Our project is available at: https://github.com/Bean-Young/AI4Radiology.","url":"https://doi.org/10.21203/rs.3.rs-10102575/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10102575/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.07.02.26355859","name":"Human In the Loop Challenges for Quality Annotation of Pre-Cancer Lesions in Clinical Oral Images","source":"preprints","abstract":"Smartphone-based Artificial Intelligence (AI) enabled screening of the hyperplasia and dysplasia stages (pre-cancer, OPMD) offers a viable opportunity to reduce the incidence and mortality of oral cancer through early prevention. However, developing accurate segmentation models requires high-quality, pixel-level annotations that are prohibitively expensive and prone to clinical subjectivity. To address this, we empirically validated a deep learning-driven Human-in-the-Loop (HITL) iterative pseudo-labeling framework to pixel-annotate 3,026 clinical oral images. We also conducted controlled experiments to quantify the network’s tolerance to label noise (unreviewed pseudo-labels) and resolved clinical subjectivity using pixel-wise Cohen’s Kappa and the STAPLE consensus algorithm. While iterative self-training consistently improved lesion detection and spatial localization, including even a modest fraction ( → 10%) of unreviewed pseudo-labels increased training convergence instability three-to-four-fold and induced a conservative prediction bias that negatively impacted model recall. Ultimately, the model’s performance converged with the inter-rater reliability ceiling ( κ → 0.65) against a multi-expert ground truth, successfully falling within the envelope of human agreement. These findings highlight that non-experts can successfully drive the early-to-mid stages of the annotation pipeline, but a final expert-driven quality assurance step is strictly essential to mitigate training instability, confirmation bias, and clinically unacceptable recall drops. Overall, this provides a scalable, empirically validated blueprint for building domain-specific medical imaging datasets in global health settings where annotation cost, inter-observer variability, and expert unavailability challenges are most acute.","url":"https://doi.org/10.64898/2026.07.02.26355859","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.02.26355859","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9839521/v1","name":"Diagnostic Reasoning With and Without AI: Automation Bias in Pre-Clerkship Medical Students","source":"preprints","abstract":"Abstract Background As large language models become increasingly integrated into clinical workflows, medical students need structured opportunities to learn how to engage critically with artificial intelligence (AI) during clinical reasoning. Empirical evaluation of automation bias and other risks of AI use in pre-clerkship training remains limited. Methods We piloted a two-component exercise for second-year pre-clerkship medical students: an introductory lecture on AI capabilities and limitations followed by a custom-built web application integrating an AI chatbot into a clinical reasoning case in which students ranked their differential diagnoses before and after AI access and rated the perceived influence of AI on their reasoning. Diagnostic accuracy was scored against predefined criteria. Descriptive and inferential statistics were calculated. Results In a sample of 185 students, AI use was associated with increased diagnostic accuracy (Wilcoxon signed-rank Z = -4.21, P n = 65) and those whose accuracy worsened ( n = 26) after AI use rated AI as more influential than students whose accuracy did not change ( Z = -2.74, P = .006 and Z = -3.23, P = .001, respectively), suggesting that perceived influence was driven by whether AI changed students' rankings, regardless of whether the change was beneficial or detrimental. Conclusions Students benefited most from AI when their baseline reasoning was weakest but could not reliably distinguish helpful from harmful AI influence, consistent with automation bias. Foundational coursework on AI and its limitations, paired with AI-integrated case practice and faculty-led debriefing, offers one training approach to address this challenge.","url":"https://doi.org/10.21203/rs.3.rs-9839521/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9839521/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-10126184/v1","name":"Bridging the Gap: Explainability Metrics for AI Image-Based Clinical Diagnostics","source":"preprints","abstract":"Abstract Artificial intelligence (AI) has shown considerable promise in enhancing diagnostic accuracy in clinical practice. However, its integration into healthcare workflows remains hindered by the opaque nature of many AI models, often referred to as “black-box” systems, which lack interpretability and alignment with clinical reasoning. This study introduces a novel evaluation framework that incorporates explainability into the performance assessment of AI-based image classifiers for medical diagnosis. We propose the Explainability Agreement Index (EAI), a new metric designed to quantify the concordance between AI-generated explanations and clinician annotations by measuring the overlap between pixels identified as relevant by both sources, relative to the clinician-defined reference. Clinician annotations were obtained through a consensus-based evaluation by two chronic wounds specialists. Using different convolutional neural networks (CNNs) trained on a public dataset of chronic wound images, we evaluated the model’s diagnostic performance alongside two explainability methods—LIME and saliency maps. While the CNNs achieved classification accuracies of up to 72%, the mean EAI values remained low (ranging approximately between 0.19 and 0.30 across models and methods), highlighting a discrepancy between AI-generated explanations and clinician reasoning. These findings emphasize that classification accuracy does not guarantee clinical trustworthiness or interpretability. The proposed EAI framework provides a practical and quantitative approach to bridge this gap, offering a pathway toward more transparent and clinically reliable AI systems in medicine.","url":"https://doi.org/10.21203/rs.3.rs-10126184/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10126184/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.64898/2026.05.26.26354128","name":"An ECG foundation model for generalizable cardiac function prediction across the lifespan","source":"preprints","abstract":"Background Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paediatric cohorts exhibit highly variable cardiac morphology and function compared to adults, which may be useful for learning generalizable AI-ECG models. Methods We pretrained ECG-Fyler on a predominantly paediatric, all-age cohort at Boston Children’s Hospital (1992–2023), annotated with a cardiologyspecific coding system (Fyler codes), and evaluated it on assessments from echocardiography (echo) and cardiac magnetic resonance (CMR) studies. We validated on an external adult cohort from Columbia University Irving Medical Center. Performance was benchmarked against several AI-ECG foundation models by AUROC across age groups, lesion types, and limited-data scenarios. Findings The pretraining cohort comprised 782,138 ECGs from 255,271 patients (median age: 10.9 years, IQR: [2.8–16.8]). Internal evaluation included 178,495 ECG-echo pairs (median age: 10.9 [3.7–17.0]) and 8,584 ECG-CMR pairs (median age: 20.7 [15.6–29.6]). External validation included 82,543 ECG-echo pairs from adults (median age: 64.0 [52.0–74.0]). ECG-Fyler improved AUROC across biventricular dysfunction and dilation tasks, with the largest gains in low-data settings. In internal validation, ECG-Fyler detected low left ventricular ejection fraction (LVEF ≤ 40%) from only 100 fine-tuning samples (AUROC: 0.80, 95% CI: [0.78–0.80]), outperforming other models (AUROC Interpretation Pretraining on richly annotated, paediatric-dominant ECGs yields models that transfer efficiently across institutions and ages, supporting AI-ECG screening and triage when labels or imaging access are limited. Funding National Institutes of Health (R01LM012973); Kostin Innovation Fund, Boston Children’s Hospital. Research in context Evidence before this study On April 29, 2026, we searched PubMed from database inception to April 29, 2026, without language restrictions, using the terms “electrocardiogram” AND “foundation model”. We considered original studies describing ECG foundation models, transferable representation- learning approaches, or mixed-age AI-ECG studies relevant to cardiac function prediction, and excluded non-ECG studies, non-original research, and purely task-specific models without a transferable pretraining component. The available evidence consisted mainly of heterogeneous observational development and validation studies, so we did not do a formal meta-analysis. We identified 11 records. Most published artificial intelligence-enhanced electrocardiogram (AI-ECG) foundation models were developed in adult cohorts, whereas paediatric evaluation remained limited. We did not identify a previous report describing a single ECG foundation model trained on a paediatric-dominant, all-age cohort and evaluated across paediatric and adult populations using both echocardiography- and cardiac magnetic resonance-derived measures of ventricular function. Added value of this study We developed ECG-Fyler, a clinically grounded ECG foundation model pretrained on 782,138 ECGs from 255,271 patients in a paediatric-dominant, all-age cohort using structured Fyler code annotations. We evaluated transfer learning across echocardiography- and cardiac magnetic resonance-derived tasks, congenital heart disease lesion subgroups, low-resource fine-tuning scenarios, and external adult validation. ECG-Fyler consistently outperformed training from scratch and other ECG foundation-model baselines, with the largest gains when labelled data were scarce, and showed strong cross-age and cross-institution generalization from paediatric pretraining to adult external validation. Implications of all the available evidence Taken together, the available evidence suggests that ECG foundation models can improve data efficiency and generalizability, but the field remains dominated by adult data and task-specific applications. Our findings extend this evidence by suggesting that clinically grounded supervised pretraining on a paediatric-dominant, lifespanspanning ECG corpus can support generalizable prediction of ventricular dysfunction and dilation across age groups and institutions. If validated prospectively, such models could support lower-cost screening, triage, and longitudinal monitoring to help prioritize downstream echocardiography or cardiac magnetic resonance imaging, particularly in congenital heart disease and other settings where labelled imaging data are limited.","url":"https://doi.org/10.64898/2026.05.26.26354128","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.26.26354128","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.21203/rs.3.rs-9718756/v1","name":"Association of AI Literacy and AI Trust With Subjective and Objective Healthcare-Seeking Delay Among Patients With Gynecologic Malignancies","source":"preprints","abstract":"Abstract Background: Artificial intelligence (AI) literacy and AI trust may influence how patients process health information and make healthcare-seeking decisions. However, their associations with subjective and objective healthcare-seeking delay among patients with gynecologic malignancies remain unclear. Methods: This cross-sectional study included 300 patients with gynecologic malignancies. AI literacy and AI trust were assessed using validated scales. Subjective delay was measured by perceived barriers to healthcare decision-making, and objective delay was defined as ≥90 days from symptom onset to first medical consultation. Linear and logistic regression models were used to assess associations, with adjustment for sociodemographic and clinical covariates. Restricted cubic spline analyses were performed to explore potential nonlinear relationships. Results: Among 300 patients, 162 (54.0%) experienced objective healthcare-seeking delay. Higher AI literacy was associated with lower subjective delay (β = −8.72, 95% CI: −10.00 to −7.44, P Conclusions: Higher AI literacy was associated with reduced subjective and objective healthcare-seeking delay, whereas AI trust was only linked to subjective perceptions. These findings highlight the potential role of AI literacy in promoting timely healthcare-seeking behavior in gynecologic oncology.","url":"https://doi.org/10.21203/rs.3.rs-9718756/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9718756/v1","addedAt":"2026-09-01T01:47:56.314Z","updatedAt":"2026-09-01T01:48:02.062Z"},{"id":"doi:10.1007/978-1-4899-7502-7_52-1","name":"Connections Between Inductive Inference and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4899-7502-7_52-1","authors":["John Case","Sanjay Jain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-29T21:32:11Z","doi":"10.1007/978-1-4899-7502-7_52-1","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1109/icmla.2010.141","name":"Boolean Factor Analysis for Data Preprocessing in Machine Learning","source":"crossref","abstract":"We present two input data preprocessing methods for machine learning (ML). The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attributes. The methods utilize formal concept analysis (FCA) and boolean factor analysis, recently described by FCA, in that the new attributes are defined by so-called factor concepts computed from input data table. The methods are demonstrated on decision tree induction. The experimental evaluation and comparison of performance of decision trees induced from original and preprocessed input data is performed with standard decision tree induction algorithms ID3 and C4.5 on several benchmark datasets.","url":"https://doi.org/10.1109/icmla.2010.141","authors":["Jan Outrata"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-02-03T16:55:42Z","doi":"10.1109/icmla.2010.141","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1002/9781394229680.ch7","name":"Techniques of Machine Learning for Modifying the Search Strategy","source":"crossref","abstract":"This chapter presents the integration of machine learning techniques into metaheuristic algorithm by developing adaptive search strategies to improve the optimization process. The Self-Organization Maps (SOMs) are used as an unsupervised learning mechanism to produce interesting search strategies based on knowledge extraction from past iterations for a given optimization process. The produced algorithm called EA-SOM (evolutionary algorithm based on SOM) uses SOMs' competitive learning model to extract patterns from the ongoing optimization process, guiding its search in real-time. The chapter advocates moving from static knowledge incorporation to dynamic, iteratively learned strategies. By using advanced machine learning techniques like SOMs, metaheuristics can continuously learn from their entire search trajectory. This paradigm shift allows algorithms to adapt intelligently, enhancing their performance in complex optimization problems. With MATLAB implementations provided, we encourage readers to create their own machine learning-enhanced search strategies, pushing the boundaries of metaheuristic design.","url":"https://doi.org/10.1002/9781394229680.ch7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-08T21:32:12Z","doi":"10.1002/9781394229680.ch7","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/b978-0-12-811788-0.00014-7","name":"Machine Learning for Flare Forecasting","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-811788-0.00014-7","authors":["Anna M. Massone","Michele Piana","FLARECAST Consortium"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-06-08T18:34:55Z","doi":"10.1016/b978-0-12-811788-0.00014-7","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.18130/v3348gf84","name":"Machine Learning Approaches to Multi-Agent Inverse Learning Problems","source":"crossref","abstract":"The problem to infer the goals of an agent on the basis of the observation of its actions has been framed in the context of inverse reinforcement learning (IRL) and has been extensively studied in recent decades. However, this model is valid only when no other adaptive agents exist or their interference can be neglected. Otherwise, a new model taking other agents into account needs to be created in place of IRL. To this end, this dissertation proposes a multi-agent inverse reinforcement learning (MIRL) model, using the framework of stochastic games, which generalize Markov decision processes to game theoretic scenarios. We develop algorithms for two fundamental classes of MIRL problems: two-agent zero-sum and two-agent general-sum. For the first class, we develop a Bayesian solution approach in which the generative model is based on an assumption that the two agents follow a minimax bi-policy. For the second, we consider five variants: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in which the agents employ cooperative strategies that aim to maximize the total game value. In problem uCE-MIRL, agents are assumed to follow strategies that constitute a correlated equilibrium while maximizing total game value. The uNE-MIRL is similar to uCE-MIRL in total game value maximization but a Nash equilibrium is assumed to employ. The advE-MIRL and cooE-MIRL problems assume agents constitute an adversarial equilibrium and coordination equilibrium, respectively. We propose novel approaches to address these five problems under the assumption that the game observer either knows or is able to accurately estimate the policies and solution concepts for players. For uCS-MIRL, we first develop a characteristic set of solutions ensuring that the observed bi-policy is a uCS and then apply a Bayesian inverse learning method. For uCE-MIRL, we develop a linear programming problem subject to constraints that define necessary and sufficient conditions for the observed policies to be correlated equilibria. The objective is to choose a solution that not only minimizes the total game value difference between the observed bi-policy and a local uCS, but also maximizes the scale of the solution. We apply a similar treatment to the problem of uNE-MIRL. We demonstrate these algorithms on multiple grid-world experiments, concluding: 1) all these algorithms are able to recover high-quality rewards comparable to ground truths; 2) perform better than other methods, such as decentralized-MIRL and IRL.","url":"https://doi.org/10.18130/v3348gf84","authors":["Xiaomin Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-12-11T13:35:07Z","doi":"10.18130/v3348gf84","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1201/9781420067194-16","name":"Evolutionary Learning","source":"crossref","abstract":"In this chapter we are going to start by treating evolution the same way that we treated neuroscience earlier in the book-by cherry-picking a few useful concepts, and then filling in the gaps with computer science in order to make an effective learning method. To see why this might be interesting, you need to view evolution as a search problem. We don’t generally think of it in this way, but animals are competing with each other in all kinds of ways-for example, eating each other-which encourages them to try to find camouflage colours, become toxic to certain predators, etc. Evolution works on a population through an imaginary fitness landscape, which has an implicit bias towards animals that are ‘fitter,’ i.e., those animals that live long enough to reproduce, are more attractive, and so get more mates, and generate more and healthier offspring. You can find out more from hundreds of books, such as Charles Darwin’s “The Origin of Species” (the original book on the topic, still in print and very interesting) and Richard Dawkin’s “The Blind Watchmaker.” The genetic algorithm models the genetic process that gives rise to evolu- tion. In particular, it models sexual reproduction, where both parents give some genetic information to their offspring. As is sketched in Figure 12.1, in biological organisms, each parent passes on one chromosome out of their two, and so there is a 50% chance of any gene making it into the offspring. Of the two versions of each gene (one from each parent) one allele (variation) is selected. Hence, children have similarities with their parents, and there is lots of genetic inheritance. However, there are also random mutations, caused by copying errors when the chromosome material is reproduced, which mean that some things do change over time. Real genetics is obviously a lot more complicated than this, but we are taking only the things that we want for our model. The genetic algorithm shows many of the things that are best and worst about machine learning: it is often, but not always, very effective, it has an array of parameters that are crucial, but hard to set, and it is impossible to guarantee that it will find a result that is any good at all. Having said all that, it often works very well, and it has become a very popular algorithm for people to use when they have no idea of any other way to find a reasonable solution. In the terms that we saw at the end of the previous chapter, genetic al- FIGURE 12.1: Each adult in the mating pair passes one of their two chromosomes to their offspring.","url":"https://doi.org/10.1201/9781420067194-16","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-22T22:34:49Z","doi":"10.1201/9781420067194-16","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.38007/ml.2020.010102","name":"In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms","source":"crossref","abstract":"With the rapid development of modern society, especially the popularity of the Internet and the rapid development of computer technology, people's lifestyles are undergoing fundamental changes.The rapid development of the Internet has led to an explosion of data and how people use this data has become one of the most popular research topics in modern society.In the past, the limitations of computer technology made it difficult to manage large amounts of data effectively, but the rapid development of computer technology has now made it possible to recognise that it is possible to manage and analyse such large amounts of data.The main objective of this paper is to develop a study of in-vehicle speech TC based on a variety of machine learning algorithms.In this paper, after acquiring the features of the text, a classification model is trained and this tagged data is learned by the model to obtain a classifier.Plain Bayesian classification, nearest-neighbour classification and decision trees are introduced and their advantages and disadvantages are analysed.The results of experiments on text from in-vehicle speech devices reveal that text classification (TC) using support vector machines has good results; natural language understanding can be achieved by combining a rule-based approach by first performing classification and information extraction operations on the text; and the feasibility of architectural modifications is demonstrated through functional verification.","url":"https://doi.org/10.38007/ml.2020.010102","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:19:47Z","doi":"10.38007/ml.2020.010102","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1007/978-3-319-55312-2_5","name":"Creative Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-55312-2_5","authors":["Parag Kulkarni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-03-30T07:10:26Z","doi":"10.1007/978-3-319-55312-2_5","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/j.clinph.2018.04.317","name":"F154. Machine learning for the analysis of single pulse stimulation in electrocorticography","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clinph.2018.04.317","authors":["Emile d’Angremont","Geertjan J. Huiskamp","Frans S. Leijten","Christoph Brune","Michel J. van Putten"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-05-10T19:15:42Z","doi":"10.1016/j.clinph.2018.04.317","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/j.clinbiochem.2019.04.013","name":"Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation","source":"crossref","abstract":"Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine. Our review article presents an overview of the data science domain while discussing the reasons for its emergence. We also present several perspectives of its applications in clinical laboratories, along with potential ethical challenges related to AI and data science.","url":"https://doi.org/10.1016/j.clinbiochem.2019.04.013","authors":["Damien Gruson","Thibault Helleputte","Patrick Rousseau","David Gruson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-04-22T20:00:20Z","doi":"10.1016/j.clinbiochem.2019.04.013","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/j.nicl.2022.103165","name":"Perfusion heterogeneity of cerebral small vessel disease revealed via arterial spin labeling MRI and machine learning","source":"crossref","abstract":"Cerebral small vessel disease (CSVD) is associated with altered cerebral perfusion. However, global and regional cerebral blood flow (CBF) are highly heterogeneous across CSVD patients. The aim of this study was to identify subtypes of CSVD with different CBF patterns using an advanced machine learning approach. 121 CSVD patients and 53 healthy controls received arterial spin label MRI, T1 structural MRI and clinical measurements. Regional CBF were used to identify distinct perfusion subtypes of CSVD via a semi-supervised machine learning algorithm. Statistical analyses were used to explore alterations in CBF, clinical measures, gray and white matter volume between healthy controls and different subtypes of CSVD. Correlation analysis was used to assess the association between clinical measures and altered CBF in each CSVD subtype. Three subtypes of CSVD with distinct CBF patterns were found. Subtype 1 showed decreased CBF in the temporal lobe and increased CBF in the parietal and occipital lobe. Subtype 2 exhibited decreased CBF in the right hemisphere of the brain, and increased CBF in the left cerebrum. Subtype 3 demonstrated decreased CBF in the posterior part of the brain, and increased CBF in anterior part of the brain. The three subtypes also differed significantly in gender (p = 0.005), the proportion of subjects with lacune (p = 0.002), with periventricular white matter hyperintensity (p = 0.043), and CSVD burden score (p = 0.048). In subtype 3, it was found that widespread decreased CBF was correlated with total CSVD burden score (r = -0.324, p = 0.029). Compared with healthy controls, the three CSVD subtypes also showed distinct volumetric patterns of white matter. The current results associate different subtypes with different clinical and imaging phenotypes, which can improve the understanding of brain perfusion alterations of CSVD and can facilitate precision diagnosis of CSVD.","url":"https://doi.org/10.1016/j.nicl.2022.103165","authors":["Weizhao Lu","Chunyan Yu","Liru Wang","Feng Wang","Jianfeng Qiu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-26T10:11:44Z","doi":"10.1016/j.nicl.2022.103165","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/j.jclinepi.2022.01.009","name":"Applying machine learning algorithms to electronic health records to predict pneumonia after respiratory tract infection","source":"crossref","abstract":"Objectives To predict community acquired pneumonia after respiratory tract infection (RTI) consultations in primary care by applying machine learning to electronic health records. Study design and setting A population-based cohort study was conducted using primary care electronic health records between 2002 to 2017. Sixteen thousand two hundred eighty-nine patients who consulted with RTIs then subsequently diagnosed with pneumonia within 30 days were compared with a random sample of eligible RTI patients. Variable selection compared logistic regression, random forest and penalized regression models. Prediction models were developed using classification and regression trees (CART) and logistic regression. Model performance was assessed through internal and temporal validations. Results Older age, comorbidity, and initial presentation with lower respiratory tract infection (LRTIs) were identified as the main predictors of pneumonia diagnosis. Developed models achieved good discrimination accuracy with AUROC for the logistic regression model being 0.81 (0.80, 0.84) and 0.70 (0.69, 0.71) for CART during internal validation, and 0.80 (0.79, 0.81) vs. 0.68 (0.67, 0.69) for temporal validation. Conclusion From a large number of candidate variables, a small number of predictors of pneumonia were consistently identified through machine learning variable selection procedures. Logistic regression generally provided better model performance than CART models.","url":"https://doi.org/10.1016/j.jclinepi.2022.01.009","authors":["Xiaohui Sun","Abdel Douiri","Martin Gulliford"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-16T09:22:11Z","doi":"10.1016/j.jclinepi.2022.01.009","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1017/9781009072205.010","name":"Unsupervised Learning","source":"crossref","abstract":"This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.","url":"https://doi.org/10.1017/9781009072205.010","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-24T19:05:52Z","doi":"10.1017/9781009072205.010","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/b978-0-443-51671-9.00003-8","name":"The Magical World of Matrices: Building Blocks for Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-51671-9.00003-8","authors":["Weisheng Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T10:55:03Z","doi":"10.1016/b978-0-443-51671-9.00003-8","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1201/9781003396772-10","name":"Data Analytics, Machine Learning and Cloud Together","source":"crossref","abstract":"The integration of data analytics, machine learning (ML) and cloud computing changed the face of the healthcare and education industries into more efficient, personalized and accessible platforms. Predictive analytics offers substantial benefits to the health sector, including preventive detection of diseases, customized treatment plans, improvements in overall operational efficacy, better patient results and cost savings. Similarly, ML and data analytics enable adaptive learning environments, personalized educational experiences and data-driven decision-making in education to further improve student outcomes. Cloud supports scalable infrastructure to handle vast amounts of data and run complex algorithms smoothly but leaves out data privacy, algorithmic bias and other ethical issues and requires solutions like federated learning and explainable AI. The chapter reports a converging theme of these technologies into healthcare and education, raising current applications, case studies and emerging trends; it concludes with a future direction and how these innovations might construct just, intelligent systems able to promote accessibility, efficiency and personalized experience across both sectors.","url":"https://doi.org/10.1201/9781003396772-10","authors":["Neelu Jyothi Ahuja"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-29T15:15:48Z","doi":"10.1201/9781003396772-10","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1201/9781003674566-10","name":"Quantum Optimization for Machine Learning","source":"crossref","abstract":"The convergence of machine learning (ML) and quantum computing is quickly becoming a game-changing new frontier in computational science. The most intriguing area of overlap may be quantum optimization, which seeks to reduce the exponentially increasing complexity and computational cost of ML algorithms. As they grow larger and more complicated, traditional optimization methods, particularly in high-dimensional and non-convex space, will inevitably hit performance limits. Quantum optimization is the whole new paradigm that leverages quantum mechanical effects like superposition, entanglement, and tunnelling to explore solution spaces more efficiently. The chapter outlines the theory, practice, and potential of hybrid quantum-classical optimization algorithms for machine learning. The most brilliant research direction in the current NISQ (noisy intermediate-scale quantum) era is mixed systems leveraging classical computing capability augmented by quantum capacity. In such designs, computationally intensive subroutines like optimization for global optima or escape from local minima are left to quantum processors and data preprocessing, iterative refinement, and model testing are performed by classical processors. We start by defining the fundamental principles of quantum computing and acquainting readers with the fundamentals like qubits, quantum gates, and quantum circuits. These are the building blocks for quantum algorithms, which hopefully can be utilized for optimization, for example, the QAOA (quantum approximate optimization algorithm), the VQE (variational quantum Eigensolver), and quantum annealing. They all build to optimize the problems in some way differently: QAOA through parameterized quantum circuits, VQE through approximations of ground states of the Hamiltonians, and quantum annealing through quantum evolution to low-energy solutions. We then form a loop between these quantum algorithms and regular machine learning tasks. Optimization impacts virtually all ML phases, from training neural networks, where one reduces loss functions; feature selection, where one aims to discover the most relevant subset of input variables; and hyperparameter optimization, which often involves expensive search procedures over extremely large combinatorial spaces. Quantum optimization techniques can therefore, in principle, accelerate such procedures through computational complexity reduction and enhancing solutions over challenging landscapes. Realistic simulations and case studies emphasize the degree to which hybrid quantum-classical algorithms have been employed in solving ML problems, that is, QSVMs, quantum-enhanced clustering, and quantum reinforcement learning. Despite their experimental nature, these applications already show encouraging results in terms of improved performance metrics as well as reduction in time-to-solution compared to classical-only solutions. Notwithstanding that, this new field is not bereft of real challenges. Quantum hardware suffers from hard limits on qubit fidelity, decoherence, and scalability in the current times. Furthermore, quantum circuit integration into broad ML pipelines requires novel architectures and software platforms. As a counter to those issues, this chapter surveys recent advances in quantum programming languages, simulators, and hybrid platforms like PennyLane, Qiskit, and TensorFlow Quantum. We conclude by looking to the future possibility that quantum optimization will revolutionize the machine learning landscape. As hardware improves and algorithmic complexity increases, the possibility of quantum-powered learning systems solving hard problems becomes increasingly accessible. From speeding up deep network training to enabling new types of unsupervised learning, quantum optimization holds the potential to recast computational tractability in machine learning. This chapter is an all-encompassing introduction and state-of-the-art work for students, researchers, and practitioners interested in using quantum optimization for practical ML applications. It sets the foundation for understanding how to pursue hybrid approaches (how they may be applied today in the real world and bracing oneself for disruption in the future).","url":"https://doi.org/10.1201/9781003674566-10","authors":["Asha Sohal","Ramesh Kait","Houbing Herbert Song"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-24T17:57:20Z","doi":"10.1201/9781003674566-10","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1093/oso/9780198828044.003.0005","name":"Regression and optimization","source":"crossref","abstract":"This chapter returns to the more theoretical embedding of machine learning in regression. Prior chapters have shown that writing machine learning programs is easy using high-level computer languages and with the help of good machine learning libraries. However, applying such algorithms appropriately with superior performance requires considerable experience and a deeper knowledge of the underlying ideas and algorithms. This chapter takes a step back to consider basic regression in more detail, which in turn will form the foundation for discussing probabilistic models in following chapters. This includes the important discussion of gradient descent as a learning algorithm.","url":"https://doi.org/10.1093/oso/9780198828044.003.0005","authors":["Thomas P. Trappenberg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-23T10:55:28Z","doi":"10.1093/oso/9780198828044.003.0005","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/b978-0-443-27422-0.00010-4","name":"Fundamentals of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27422-0.00010-4","authors":["Kiran Mustafa","Mashallah Rezakazemi","Rao Muhammad Mahtab Mahboob"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-27T07:16:40Z","doi":"10.1016/b978-0-443-27422-0.00010-4","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1109/icaml60083.2023.00021","name":"Wind Power Prediction Based on Machine Learning","source":"crossref","abstract":"Wind power prediction is an important routine task for wind farms and grid operators to deal with the serious risks associated with high wind power penetration. To meet the high requirement of the relevant enterprises on wind power prediction accuracy, a novel machine learning-based wind power prediction model is proposed in this paper. Firstly, the extreme gradient boosting (XGBoost) algorithm is exploited to select the most valuable features to form the data that are fed into the novel model as input. Secondly, the complementary ensemble empirical mode decomposition (CEEMD) algorithm is introduced into the novel model to increase the stationarity of the input data. Thirdly, the LSTM and the CNN are combined to build a hybrid deep neural network to improve the temporal dependency extraction quality. Lastly, the effectiveness and superiority of the novel model is validated via a comparative experiment on actual wind-related data sampled from a wind farm located at P.R. China.","url":"https://doi.org/10.1109/icaml60083.2023.00021","authors":["Song Liu","Jing Lv"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-19T18:07:03Z","doi":"10.1109/icaml60083.2023.00021","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1142/9789819814572_0001","name":"Introduction to Gibbs Measures and Their Relevance in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789819814572_0001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-08T02:04:12Z","doi":"10.1142/9789819814572_0001","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.4018/978-1-60960-818-7.ch313","name":"Electricity Load Forecasting Using Machine Learning Techniques","source":"crossref","abstract":"Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.","url":"https://doi.org/10.4018/978-1-60960-818-7.ch313","authors":["Manuel Martín-Merino Acera"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-10-04T09:46:18Z","doi":"10.4018/978-1-60960-818-7.ch313","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.65525/svup.9788199565418.2026.34-58","name":"\"IoT and machine learning for enabling sustainable  development goals\"","source":"crossref","abstract":"The United Nations created an agenda for 2030 that includes 17 clearly defined objectives for sustainable development. These goals are a pressing call to action that needs cooperation and creativity among nations and organizations. The suggested agenda will be completed by the year 2023. However, the globe is still behind schedule in achieving any objectives. With concrete use examples that connect, this article explores the prospects presented by the development of artificial intelligence and the Internet of Things in achieving these aims. To cities, energy, and health. Additionally, the piece highlights the difficulties that arise from using these technologies to achieve the Sustainable Development Goals (SDGs), emphasizing the risks of bias, security, privacy of data, and multi-objective optimization of sometimes conflicting SDGs. DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.34-58","url":"https://doi.org/10.65525/svup.9788199565418.2026.34-58","authors":["Ranjan Kumar Mondal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T11:06:12Z","doi":"10.65525/svup.9788199565418.2026.34-58","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1007/s10994-021-06121-4","name":"InfoGram and admissible machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10994-021-06121-4","authors":["Subhadeep Mukhopadhyay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-10T17:02:36Z","doi":"10.1007/s10994-021-06121-4","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1023/a:1022641700528","name":"The CN2 Induction Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022641700528","authors":["Peter Clark","Tim Niblett"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022641700528","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1007/978-1-4842-2988-0_8","name":"Do Not Forget Me: The Human Side of Machine Learning","source":"crossref","abstract":"This chapter is the master of all the chapters but it does not discuss machine learning or related issues. How does a chapter in a book of machine learning that does not discuss the technology become the master of all the chapters? The plain and simple answer is that this chapter considers the people who realize machine learning on the ground level. This chapter considers the people who make strategies about machine learning implementation and its future in the organization. This chapter is important because it covers how to manage machine learning projects and train the human resources who are involved in the overall process.","url":"https://doi.org/10.1007/978-1-4842-2988-0_8","authors":["Patanjali Kashyap"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-01-05T03:53:10Z","doi":"10.1007/978-1-4842-2988-0_8","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1515/9783111288994-001","name":"1 Introduction to machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783111288994-001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-07T04:33:40Z","doi":"10.1515/9783111288994-001","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.36227/techrxiv.175355735.50084414/v1","name":"Bias Reduction Techniques in Machine Learning Models: Investigating Strategies to Detect and Minimize Bias in AI and Machine Learning Algorithms","source":"crossref","abstract":"The increasing deployment of machine learning models across diverse applications necessitates a critical assessment of fairness and bias minimization. Machine learning algorithms, often trained on historical data, can inadvertently perpetuate or even exacerbate existing biases, resulting in unfair outcomes. This article reviews contemporary bias detection and mitigation methodologies within artificial intelligence and machine learning contexts. We synthesize current research on quantifying algorithmic bias, with a focus on pre-processing, in-processing, and post-processing strategies to mitigate bias. Furthermore, we discuss the implications of bias in machine learning applications and propose a framework for ongoing bias monitoring and reduction. The paper aims to bridge gaps in the existing literature by compiling effective methodologies while also addressing emerging challenges, such as intersectionality and transferability across different domains. Addressing these biases is crucial for ensuring equitable and trustworthy AI systems, which are increasingly becoming integral to decision-making processes.","url":"https://doi.org/10.36227/techrxiv.175355735.50084414/v1","authors":["Arimondo Scrivano"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-26T19:15:58Z","doi":"10.36227/techrxiv.175355735.50084414/v1","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/b978-0-443-15364-8.00010-x","name":"Solution to real time civil engineering tasks via machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-15364-8.00010-x","authors":["Kundan Meshram"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-21T07:46:47Z","doi":"10.1016/b978-0-443-15364-8.00010-x","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1007/bf00115008","name":"Machine learning as an experimental science","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00115008","authors":["Pat Langley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-10-31T18:55:14Z","doi":"10.1007/bf00115008","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1016/b978-0-443-27374-2.00002-9","name":"Basics of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27374-2.00002-9","authors":["Julhash U. Kazi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-06T15:30:37Z","doi":"10.1016/b978-0-443-27374-2.00002-9","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1201/9781003750451-5","name":"AI/Machine Learning for Finance","source":"crossref","abstract":"This chapter addresses model robustness, focusing on avoiding overfitting through rolling-window cross-validation[cite: 37]. It introduces ensemble methods and performance metrics like AUC to evaluate trading signals.","url":"https://doi.org/10.1201/9781003750451-5","authors":["Jason Guevara","Ričards Bulavs","Oskars Linares"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T21:06:12Z","doi":"10.1201/9781003750451-5","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.4018/978-1-60566-810-9.ch006","name":"Machine Learning (ML) as a Diagnostic Task","source":"crossref","abstract":"This chapter discusses a revised definition of classification (diagnostic) test. This definition allows considering the problem of inferring classification tests as the task of searching for the best approximations of a given classification on a given set of data. Machine learning methods are reduced to this task. An algebraic model of diagnostic task is brought forward founded upon the partition lattice in which object, class, attribute, value of attribute take their interpretations.","url":"https://doi.org/10.4018/978-1-60566-810-9.ch006","authors":["Xenia Naidenova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-05-25T08:40:40Z","doi":"10.4018/978-1-60566-810-9.ch006","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.52843/cassyni.x2t0sp","name":"Machine Learning Meets Control Theory","source":"crossref","abstract":"In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.","url":"https://doi.org/10.52843/cassyni.x2t0sp","authors":["Steven L Brunton"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-28T12:47:51Z","doi":"10.52843/cassyni.x2t0sp","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.53347/rid-74943","name":"Semi-supervised learning (machine learning)","source":"crossref","abstract":"","url":"https://doi.org/10.53347/rid-74943","authors":["Candace Moore","Daniel Bell"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-25T02:40:41Z","doi":"10.53347/rid-74943","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.20944/preprints202412.1771.v1","name":"Machine‐Learning Forensics: Incorporating Machine‐Learning (ML) Techniques for Implementing Digital Forensic Readiness Model","source":"crossref","abstract":"This study implements the proposed integration of machine learning (ML) techniques into the ISO/IEC 27043:2015 international standard processes using a hypothetical case scenario for a smart building. ISO/IEC 27043:2015 does not currently incorporate ML techniques. Incorporating these techniques into ISO/IEC 27043:2015 can improve the efficiency of the processes and reduce time and human effort by automating some manual tasks of the readiness processes. This research presents a case study for the smart building dataset, applying ML techniques to implement the ML readiness model in the ISO/IEC 27043:2015 standard. It compares the results of implementing ML techniques. These results indicate how the smart environment data can be proactively analysed and classified. These techniques will enable investigators to access the information to investigate such environments.","url":"https://doi.org/10.20944/preprints202412.1771.v1","authors":["Laila Tajeldin","Hein Venter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-24T02:43:31Z","doi":"10.20944/preprints202412.1771.v1","addedAt":"2026-09-01T01:47:57.552Z","updatedAt":"2026-09-01T01:47:57.552Z"},{"id":"doi:10.1109/icmla.2006.2","name":"5th International Conference on Machine Learning and Applications-Cover","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla.2006.2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-08T12:59:25Z","doi":"10.1109/icmla.2006.2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlke55170.2022.00018","name":"Behavioral biometrics dentification based on machine learning methods","source":"crossref","abstract":"This essay aims to develop a machine learning model that is able to build an initial advantage for behavioral biometrics. While behavioral biometrics have theoretically shown to be advantageous over traditional identification methods like passwords and physical tokens, more research needs to be conducted to prove that behavioral biometrics can ensure high accuracy in order to be applied in real life situations. This essay conducts an experiment that builds and compares three models that can potentially be used in this field. We use the data posted on the UCI Machine Learning Repository, which contains a set of motion data collected by phone accelerometers. This experiment has proved the feasibility of behavioral biometrics and set up the foundation for subject-wise studies.","url":"https://doi.org/10.1109/mlke55170.2022.00018","authors":["Yi Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-29T20:07:24Z","doi":"10.1109/mlke55170.2022.00018","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlsp.2010.5589222","name":"Archetypal analysis for machine learning","source":"crossref","abstract":"Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative 'corners' of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of SVD - without being limited to orthogonal representations. In order to do large scale AA, we derive an efficient algorithm based on projected gradient as well as an initialization procedure inspired by the FURTHESTFIRST approach widely used for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering.","url":"https://doi.org/10.1109/mlsp.2010.5589222","authors":["Morten Morup","Lars Kai Hansen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-10-08T09:32:30Z","doi":"10.1109/mlsp.2010.5589222","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1049/pbte081e_ch4","name":"Machine-learning-based channel estimation","source":"crossref","abstract":"Wireless communication has been a highly active research field. Channel estimation technology plays a vital role in wireless communication systems. Channel estimates are required by wireless nodes to perform essential tasks such as precoding, beamforming, and data detection. A wireless network would have good performance with well-designed channel estimates. In this chapter, we first review the channel model for wireless communication systems and then describe two traditional channel estimation methods, and finally introduce two newly designed channel estimators based on deep learning and one expectation-maximization-based channel estimator.","url":"https://doi.org/10.1049/pbte081e_ch4","authors":["Yue Zhu","Gongpu Wang","Feifei Gao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-07-04T13:28:42Z","doi":"10.1049/pbte081e_ch4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/icmla.2006.4","name":"5th International Conference on Machine Learning and Applications-TOC","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla.2006.4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-08T08:59:25Z","doi":"10.1109/icmla.2006.4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1016/b978-0-12-821929-4.00001-9","name":"Machine learning workflows and types","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-821929-4.00001-9","authors":["Hoss Belyadi","Alireza Haghighat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-23T14:17:26Z","doi":"10.1016/b978-0-12-821929-4.00001-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003423089-2","name":"Algorithmic Foundations of Machine and Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003423089-2","authors":["Vinod Kumar Khanna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-06T23:10:06Z","doi":"10.1201/9781003423089-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.18178/ijml.2025.15.4.1184","name":"A Machine Learning-Based Framework for Image Quality Inspection of Automotive Metal Stamping Parts","source":"crossref","abstract":"Traditional quality control for automotive metal stamping parts relies heavily on Checking Fixtures (C/Fs).These fixtures are custom-engineered for individual components, resulting in high initial design and manufacturing costs, limited versatility, and time-consuming processes for each new part.Moreover, C/F inspection is a manual and subjective process, which introduces human error, measurement variability, and slower throughput in high-volume production environments.This study proposes an automated imaging-based quality inspection framework utilizing a 3D laser scanner and the k-Nearest Neighbors (k-NN) machine learning algorithm.The framework systematically analyzes complex point cloud data of scanned parts through a structured sequence of steps: Data Acquisition, Segmentation, Pre-processing, Feature Recognition, Data Analysis, Post-processing, and Final Decision-making.To ensure both high accuracy and maximum speed, each step involves direct and immediate comparison with nominal Computer-Aided Design (CAD) data or a pre-established training set.The k-NN algorithm plays a central role in the analysis phase, effectively using Euclidean distances to distinguish noise from true features, recognize geometric elements such as holes, and reliably detect defects including material burrs and dimensional springback.The proposed system offers significant advantages over traditional C/Fs, including greater versatility across diverse component geometries and substantially reduced labor costs through full automation.Additionally, it ensures faster inspection times and consistent, objective accuracy, thereby eliminating the subjectivity, human error, and physical degradation associated with conventional fixtures.This automated framework represents a more sustainable, efficient, and robust quality control solution, aligning with the future needs of the automotive stamping industry.","url":"https://doi.org/10.18178/ijml.2025.15.4.1184","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-24T07:10:23Z","doi":"10.18178/ijml.2025.15.4.1184","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-69499-8_9","name":"Shallow Learning Versus Deep Learning in Speech Recognition Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69499-8_9","authors":["Nasmin Jiwani","Ketan Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T18:01:28Z","doi":"10.1007/978-3-031-69499-8_9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4135/9781529666779.n3","name":"Riitta Katila Discusses Research Methods Using Machine Learning to Study Public/Private Firm Collaborations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781529666779.n3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-24T08:33:30Z","doi":"10.4135/9781529666779.n3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007365207130","name":"First Order Regression","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007365207130","authors":["Aram Karalič","Ivan Bratko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007365207130","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003429654-6","name":"Machine Learning with Unsupervised Quantum Models","source":"crossref","abstract":"Machine Learning (ML) is the process of training machines to analyze and learn from provided data. It can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Unsupervised learning, a method that allows the discovery of underlying patterns in data without the need for additional information or labeled targets, uncovers hidden patterns within the given dataset but lacks associated labels or classes. Unsupervised techniques prove particularly useful for exploring data and comprehending complex behaviors that challenge human identification within large datasets. They find applications in various fields, such as text categorization (e.g., news articles), anomaly detection, satellite and spatial image processing, medical image analysis, customer segmentation, and recommendation engines. Unsupervised learning primarily serves three main tasks: clustering, association, and dimensionality reduction. Clustering is a technique that automatically groups similar samples together based on their inherent characteristics. Association is a technique used to discover relationships between different features within a dataset. Dimensionality reduction is a technique employed to decrease the number of features in a dataset, especially when dealing with high-dimensional data.","url":"https://doi.org/10.1201/9781003429654-6","authors":["Prianka Ramachandran Radhabai","Sathya Karunanidhi","Shreyanth Srikanth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T19:47:03Z","doi":"10.1201/9781003429654-6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1117/12.3016686","name":"Optical random projections for large scale machine learning","source":"crossref","abstract":"Light propagation in disordered media can be seen as a linear operation on fields : a multiplication by a random matrix, between a set of input modes (for instance pixels of an SLM) and output modes (for instance pixels of a camera). This operation, akin to a single-layer of a neural network, can be leveraged for a wealth of signal processing and machine learning tasks. I will present some of our works, ranging from classification to time-series prediction, and importantly present our recent approaches to go beyond linear random projections, in order to provide deeper equivalent neural networks and better machine-learning performances across a variety of tasks.","url":"https://doi.org/10.1117/12.3016686","authors":["Sylvain Gigan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-18T23:09:32Z","doi":"10.1117/12.3016686","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022674116380","name":"Concept Formation During Interactive Theory Revision","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022674116380","authors":["Stefan Wrobel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022674116380","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1016/b978-0-44-324770-5.00014-3","name":"Detecting moving objects with machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-324770-5.00014-3","authors":["Wesley C. Fraser"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-22T05:27:38Z","doi":"10.1016/b978-0-44-324770-5.00014-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003785293-4","name":"Introduction to Machine Learning and Tiny Machine Learning","source":"crossref","abstract":"Machine learning (ML) is one of the key components of artificial intelligence (AI). Basically AI is composed of two major components, fuzzy inference system (FIS) and ML. Before we can continue on this topic, first let s try to answer a question, what is ML?","url":"https://doi.org/10.1201/9781003785293-4","authors":["Ying Bai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-27T18:04:11Z","doi":"10.1201/9781003785293-4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4135/9781529604900.n2","name":"Marian-Andrei Rizoiu Discusses Blending Digital Ethnography and Advanced Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781529604900.n2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-13T13:21:36Z","doi":"10.4135/9781529604900.n2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-3916-2_1","name":"An Overview of Machine Learning","source":"crossref","abstract":"Machine learning is a field in computer science where data are used to predict, or respond to, future data. It is closely related to the fields of pattern recognition, computational statistics, and artificial intelligence. The data may be historical or updated in real-time. Machine learning is important in areas such as facial recognition, spam filtering, and other areas where it is not feasible, or even possible, to write algorithms to perform a task.","url":"https://doi.org/10.1007/978-1-4842-3916-2_1","authors":["Michael Paluszek","Stephanie Thomas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-31T14:23:16Z","doi":"10.1007/978-1-4842-3916-2_1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003346234-13","name":"Why Machine Learning?","source":"crossref","abstract":"While both machine learning and numerical simulations have merits and demerits, machine learning models could be preferred over computational approach due to the following reasons. The adaptability of machine learning algorithm models to an unseen and novel dataset is a crucial benefit it can offer in ALD process by learning from new data and modifying their predictions to better comprehend the process and its final outcomes. It seeks to make machine learning models more transparent and interpretable to develop trust in them. It is crucial to know which physical and chemical variables significantly influence the deposition process and to decide on the best experimental setups to quantify those variables because finding the pertinent input features is one of the major hurdles in embedding domain knowledge into the ALD-based machine learning applications. The ability to optimize the deposition process by learning from sizable datasets produced by historical deposition experimental data is the key strength of utilizing machine learning algorithms in ALD.","url":"https://doi.org/10.1201/9781003346234-13","authors":["Oluwatobi Adeleke","Sina Karimzadeh","Tien-Chien Jen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-31T14:25:44Z","doi":"10.1201/9781003346234-13","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003429654-10","name":"Quantum Machine Learning Approaches","source":"crossref","abstract":"The rise of machine learning algorithms in multidisciplinary areas is inevitable. Quantum computers are a significant recent area of development where complex algorithms can be efficiently executed for better performance. Implementing complex algorithms in quantum machines for hardware support systems is escalating due to the emergence of robotics and automated systems in all domains. The acceleration of artificial systems in hardware-based systems requires special approaches, and quantum approaches are more successful in devising accelerated hybrid systems. This chapter mainly deals with the approaches to implementing machine learning algorithms using quantum computing. The chapter was initiated with the question: “Why is quantum computing required to implement machine learning algorithms?” The major hiccup in implementing any machine learning algorithm in classical systems is the complexity of the algorithms. High dimensionality is a curse in implementing machine learning algorithms in classical systems. Quantum computing is a rescuing angel to address the issue of high dimensionality using parallelism. Quantum mechanics is well-versed in generating counterintuitive patterns. The deep learning paradigms identify the data patterns and vice versa by generating similar patterns. Classical machine learning suffers by generating patterns from the quantum of data or information, whereas the quantum mechanism succeeds by implementing the same algorithm using the quantum mechanism. The potential of the quantum mechanism to overcast some of the specific problems by its implementation strategy is called quantum potential, and the strategy used to increase the quantum potential is called the quantum algorithm. The chapter deals with the complexity of quantum computing mechanisms while implementing machine learning algorithms as the prima facie. Following the complexity analysis, usage of feature maps and extraction in the quantum mechanism is discussed for a clear understanding. Quantum embedding techniques are discussed prior to algorithms used to implement machine learning in quantum computers. The remaining sections of the chapter introduces Deutch-Jozsa, HHL, QUBO quantum mechanism-based algorithms and the execution of conventional machine learning algorithms such as principal component analysis (PCA), K-Means, K-Medians, and Support Vector Machines (SVM). Critical emphasis is placed on solving NP-Hard problems using quantum mechanisms for addressing real-time problems. This chapter helps readers to implement quantum approaches for applying machine learning algorithms in large-scale systems. This chapter deals with the main concepts in understanding quantum computing for machine learning systems, and major algorithms and foundations have been discussed for better understanding of quantum computing in machine learning.","url":"https://doi.org/10.1201/9781003429654-10","authors":["Mangalraj Poobala","Ganesh Kumar Natarajan","Iniyan Shanmugam","Justin Vargese"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T19:47:03Z","doi":"10.1201/9781003429654-10","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007361123060","name":"Clausal Discovery","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007361123060","authors":["Luc De Raedt","Luc Dehaspe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007361123060","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-69499-8_6","name":"Shallow Learning Versus Deep Learning in Biomedical Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69499-8_6","authors":["Mithat Önder","Ümit Şentürk","Kemal Polat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T14:01:28Z","doi":"10.1007/978-3-031-69499-8_6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/icmla.2005.57","name":"Self bounding Genetic Algorithms for Machine Learning","source":"crossref","abstract":"We propose an abstract self bounding genetic algorithm that can be applied to various problems of machine learning. The bound on the generalization error that is output by our algorithm is based on Rademacher penalization, a data driven penalization technique. We prove probabilistic oracle inequalities for the theoretical risk of the estimators based on this approach. This is done by comparing the performance of an idealized genetic algorithm that uses a fitness function based on the generalization error with that of an empirical genetic algorithm based on Rademacher penalization. The inequalities indicate that although we are not able to implement the idealized algorithm (because of the inability to compute the generalization error), the empirical algorithm does almost as well as the idealized algorithm would.","url":"https://doi.org/10.1109/icmla.2005.57","authors":["F. Lozano","V. Koltchinskii"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-03-22T17:38:08Z","doi":"10.1109/icmla.2005.57","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-030-94178-9_2","name":"Machine Learning for Secure Hardware Design","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-94178-9_2","authors":["Mohd Syafiq Mispan","Basel Halak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T12:17:30Z","doi":"10.1007/978-3-030-94178-9_2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.48001/joitml.2024.1213-18","name":"Predictive Analysis of Campus Placement of Student Using Machine Learning Algorithms","source":"crossref","abstract":"In today's competitive job market, student campus placement process becomes very important role in the academic journey of students and the reputation of educational institutions alike. As the demand for skilled professionals continues to rise, the ability to accurately predict and optimize student placement outcomes becomes increasingly essential. This research paper proposes a machine learning algorithms analysis to forecast the campus placement of students with the help of various academic factors. The dataset contains historical placement records of students received from open-source repository named as kaggle. It includes information such as student academic achievements, internship, project, workshop, soft skill, extracurricular activities, and placement outcomes. Through data preprocessing and feature engineering, we transform raw data into a structured format suitable for predictive modeling. One hot encoding (Dummy encoding) applied on dataset to represent categorical variables as numerical values to provide more information to the model about the categorical variables. Predictive analysis work on various machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), XGBoost, AdaBoost, KNN and Naive Byes. The results of analysis demonstrate in predicting student placement outcomes with high accuracy and reliability. Accuracy is calculated in the form of precision, recall, f1-score, and support. Furthermore, we conduct feature importance analysis to identify the importance of different predictors in determining placement success. This research paper study can help to academic institutions that can identify at-risk students early in their academic journey and to improve their employability. In conclusion, research underscores the potential of predictive analysis and machine learning in optimizing student placement outcomes. By harnessing the power of data-driven insights, institutes can make use of placement prediction model to check the likelihood of campus placement of students.","url":"https://doi.org/10.48001/joitml.2024.1213-18","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-17T10:51:00Z","doi":"10.48001/joitml.2024.1213-18","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.58647/rexpo.25000077.v1","name":"Machine Learning-Base Drug Repurposing for Amyotrophic Lateral Sclerosis (ALS)","source":"crossref","abstract":"This study presents a machine learning-based framework for drug repurposing with open source datasets, leveraging cheminformatics and generative modeling to identify compounds with potential therapeutic relevance for neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS). A Random Forest classifier was trained on molecular fingerprints derived from known ALS drugs and structurally diverse non-ALS compounds, achieving an overall accuracy of 91.2% despite class imbalance. To explore a deep learning framework, a Generative Adversarial Network (GAN) was developed and trained to produce novel drug-like fingerprints. All ten generated samples were predicted as ALS-targeting by the classifier. Structural similarity analysis revealed resemblance to approved drugs, with a Tanimoto similarity of 0.214. Dimensionality reduction via PCA and t-SNE showed clustering between generated and real ALS-related fingerprints. These results support the utility of combining classification and generative models for computational drug repurposing in neurodegenerative disease research.","url":"https://doi.org/10.58647/rexpo.25000077.v1","authors":["Hannah Archer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-18T16:45:12Z","doi":"10.58647/rexpo.25000077.v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2020.010206","name":"Machine Learning based Health Status Assessment of Super-span Suspension Bridges","source":"crossref","abstract":"With the rapid development of bridge design and analysis theory, building materials development and construction technology, more and more long-span bridges are built across rivers and seas.At the same time, the bridge construction scheme of over 1000 meters of bridges across rivers and seas is becoming more and more mature.Among them, the mainstream long-span bridges are suspension bridge (SB).The accuracy of research on key structure technology of bridge body needs to be continuously improved to ensure the structural safety of long-span bridges.Therefore, based on machine learning (ML) technology, the health status of super long span SBs is evaluated in this paper.The purpose and significance of long-span bridge structure health monitoring are briefly analyzed.Through the analysis of ML neural network algorithm, the evaluation model index layer is determined; finally, this paper takes the supporting project as an example to evaluate the safety status of the completed SB, which verifies the feasibility and effectiveness of the algorithm in this paper.","url":"https://doi.org/10.38007/ml.2020.010206","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:35:28Z","doi":"10.38007/ml.2020.010206","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2022.030103","name":"Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning","source":"crossref","abstract":"The traditional electromechanical fault diagnosis mode of the ship lock adopts the wired connection control intelligent control system, which cannot be remotely and wirelessly controlled through the control field and more strict control mode.The design of ship lock electromechanical remote fault diagnosis mode based on machine learning is proposed.The concept of machine learning and the characteristics of neural network are summarized.The fault diagnosis mode and fault diagnosis technology of electrical communication system are proposed.The experimental comparison of fault diagnosis accuracy of CNN SVM GA-SVM model shows that the deep learning bearing fault diagnosis model based on CNN network model has better performance.","url":"https://doi.org/10.38007/ml.2022.030103","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T03:45:12Z","doi":"10.38007/ml.2022.030103","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1017/cbo9781139042918.005","name":"IBM Parallel Machine Learning Toolbox","source":"crossref","abstract":"In many ways, the objective of the IBM Parallel Machine Learning Toolbox (PML) is similar to that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the open source Hadoop system, which is to provide Application Programming Interfaces (APIs) that enable programmers who have no prior experience in parallel and distributed systems to nevertheless implement parallel algorithms with relative ease. Like MapReduce and Hadoop, PML supports associative-commutative computations as its primary parallelization mechanism. Unlike MapReduce and Hadoop, PML fundamentally assumes that learning algorithms can be iterative in nature, requiring multiple passes over data. It also extends the associative-commutative computational model in various aspects, the most important of which are: The ability to maintain the state of each worker node between iterations, making it possible, for example, to partition and distribute data structures across workers Efficient distribution of data, including the ability for each worker to read a subset of the data, to sample the data, or to scan the entire dataset Access to both sparse and dense datasets Parallel merge operations using tree structures for efficient collection of worker results on very large clusters In order to make these extensions to the computational model and still address ease of use, PML provides an object-oriented API in which algorithms are objects that implement a predefined set of interface methods. The PML infrastructure then uses these interface methods to distribute algorithm objects and their computations across multiple compute nodes.","url":"https://doi.org/10.1017/cbo9781139042918.005","authors":["Edwin Pednault","Elad Yom-Tov","Amol Ghoting"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-06T06:03:53Z","doi":"10.1017/cbo9781139042918.005","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-5107-2_3","name":"Continuous Delivery","source":"crossref","abstract":"The Agile principle for this chapter describes how frequently a team delivers working software. We extend this to include frequently delivering data as much of what a data engineering team delivers is high-quality data. We further extend the principle by replacing the word “frequent” with “continuous” – we believe it is important to deliver working software and data continuously. Our rewrite of this principle would be “Deliver working software and accurate data continuously.” On any given day, the team should have software and data available that effectively represents the incremental work done on the previous day.","url":"https://doi.org/10.1007/978-1-4842-5107-2_3","authors":["Eric Carter","Matthew Hurst"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-21T11:03:53Z","doi":"10.1007/978-1-4842-5107-2_3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1017/9781009024846.009","name":"Machine Learning and Its Equivalence to Statistical Data Assimilation","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009024846.009","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-27T00:07:10Z","doi":"10.1017/9781009024846.009","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-981-16-8193-6_8","name":"Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8193-6_8","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-21T09:03:38Z","doi":"10.1007/978-981-16-8193-6_8","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2022.030205","name":"Automatic Head Count Based on Machine Learning in Intelligent Video Surveillance","source":"crossref","abstract":"With the wide application of video surveillance system, visual information has become the key research element of modern security technology.Computer vision related technology can be applied to the field of intelligent surveillance, so that computers can process video.People can use computers to understand video surveillance, directly get the number of people in an area, or get the distribution of people.This paper first analyzes the existing two types of target detection algorithms, and chooses Fast R-CNN algorithm as the research object of this paper.This paper combines the research method of background modeling with the research method of deep convolutional neural network based on statistical learning to fuse all the calibration boxes of pedestrian detection results.A pedestrian count evaluation method is proposed, and the pedestrian count results are smoothed and fused.","url":"https://doi.org/10.38007/ml.2022.030205","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T05:13:47Z","doi":"10.38007/ml.2022.030205","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781032661025-1","name":"Introduction to Machine Learning and Its Applications to Neuroscience","source":"crossref","abstract":"In the rapidly evolving landscape of scientific inquiry, the convergence of machine learning (ML) and neuroscience has ignited transformative synergy, redefining the frontiers of knowledge acquisition and application. This chapter succinctly encapsulates the profound interplay between these two disciplines, unveiling their collaborative potential and highlighting their collective impact on advancing our understanding of the brain. ML, a branch of artificial intelligence, encompasses a spectrum of algorithms and methodologies that enable computers to learn from data and make predictive or analytical decisions without being explicitly programmed. In parallel, neuroscience, a multidisciplinary pursuit, seeks to unravel the intricacies of the nervous system, spanning molecular, cellular, systems, and cognitive levels of analysis. The fusion of ML and neuroscience heralds an era where computational prowess empowers the exploration of neural complexities previously obscured by data deluge. From unraveling the three-dimensional folding of proteins to deciphering orchestrating neural circuits, collaboration materializes across a wide range of applications. In neuroimaging, ML algorithms decipher patterns in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data, opening avenues to decoding cognitive states and predicting mental disorders. Neural data, often riddled with noise, undergo purification and analysis using ML techniques, culminating in nuanced insights into neural firing patterns and connectivity. Brain-computer interfaces (BCIs) leverage ML to facilitate bidirectional communication between the brain and external devices, fostering novel paradigms in neurorehabilitation and enhancing human capabilities. As this interdisciplinary synergy flourishes, ethical considerations are dominant, including privacy concerns, algorithmic bias, and model interpretability. Finally, the entwined journey of ML and neuroscience promises an unprecedented grasp of brain function and an innovative horizon where technology interfaces harmoniously with human cognition, charting an inspiring trajectory toward transformative scientific breakthroughs. Some summarized lessons learned from the chapter and some open research questions are available.","url":"https://doi.org/10.1201/9781032661025-1","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-14T12:21:05Z","doi":"10.1201/9781032661025-1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-319-75714-8_6","name":"Brain-Inspired Machine Learning Algorithm: Neural Network Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-75714-8_6","authors":["Khaled Salah Mohamed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-03-02T01:51:51Z","doi":"10.1007/978-3-319-75714-8_6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5089/9798400286537.002.a002","name":"Hungary’s Corporate Sector Risk: a Machine Learning Aproach","source":"crossref","abstract":"","url":"https://doi.org/10.5089/9798400286537.002.a002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-13T05:42:19Z","doi":"10.5089/9798400286537.002.a002","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-01580-9","name":"Adversarial Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01580-9","authors":["Yevgeniy Vorobeychik","Murat Kantarcioglu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-08T12:29:30Z","doi":"10.1007/978-3-031-01580-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlcad58807.2023.10299822","name":"Machine Learning in EDA: When and How","source":"crossref","abstract":"Machine learning is a powerful technique that can derive knowledge from large data set, and provide prediction and modeling. Since VLSI chip designs have extremely high complexity and gigantic data, recently there has been a surge in applying and adapting machine learning to accelerate the design closure. In this paper, we will discuss when and how to apply machine learning in Electronic Design Automation (EDA) improving the efficiency and quality of the design process. Furthermore, we highlight distinct challenges in EDA, including improved netlist representation, advanced timing modeling, netlist-layout multimodality, and constrained AIGC.","url":"https://doi.org/10.1109/mlcad58807.2023.10299822","authors":["Bei Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-31T13:50:10Z","doi":"10.1109/mlcad58807.2023.10299822","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.70593/978-81-981271-4-3_2","name":"Techniques and optimization algorithms in machine learning: A review","source":"crossref","abstract":"Machine learning (ML) has transformed different sectors by allowing for data-based decision-making and forecasting analysis. This study explores the most recent techniques and algorithms that are influencing the direction of machine learning. The research investigates different supervised learning techniques, such as advanced versions of decision trees, support vector machines, and ensemble methods like XGBoost and random forests. Unsupervised learning involves the study of clustering algorithms like k-means++, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN), which are used for anomaly detection and customer segmentation. An overview of convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) and their advanced form, long short-term memory (LSTM) networks, for time-series analysis and natural language processing address deep learning, a subset of ML. The study highlights the progress of generative adversarial networks (GANs) and transformer models, showcasing notable improvements in generative tasks and language models, respectively. Moreover, the research delves into reinforcement learning, with an emphasis on recent advancements in deep reinforcement learning and how it is utilized in autonomous systems and playing games. Also covered are new developments like federated learning, which tackles data privacy issues by allowing ML models to be trained on decentralized devices, and quantum machine learning, which uses quantum computing to improve algorithm performance. This thorough review is designed to give a complete understanding of modern ML techniques and algorithms, providing insights into their practical use and potential for future innovation in different industries.","url":"https://doi.org/10.70593/978-81-981271-4-3_2","authors":["Nitin Liladhar Rane","Suraj Kumar Mallick","Ömer Kaya","Jayesh Rane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-21T11:52:16Z","doi":"10.70593/978-81-981271-4-3_2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-981-16-8193-6_7","name":"Regularization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8193-6_7","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-21T09:03:38Z","doi":"10.1007/978-981-16-8193-6_7","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9780367425517-29","name":"Introduction to Machine Learning","source":"crossref","abstract":"This chapter provides a brief introduction to the field of machine learning, a subfield of artificial intelligence and pattern recognition. Machine learning techniques are becoming ubiquitous in modern technology. One of the simplest tasks for machine learning is for a computer to determine if an example data point is similar to another data point in a set of data points. Most machine learning involves learning from data or examples, which typically involves data analysis in the form of model fitting and optimizing the parameters of a model to best describe the data. The chapter introduces the idea of support vector machines as classifiers that can learn from training data in order to achieve high performance in binary classification. The most common type of machine learning algorithm deployed are neural networks. Optimization is used for the training of the weights/parameter sets of neural networks. Further advances in neural networks have introduced multiple layers of perceptrons to form artificial neural networks.","url":"https://doi.org/10.1201/9780367425517-29","authors":["Jeffrey Paul Wheeler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-28T01:58:30Z","doi":"10.1201/9780367425517-29","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-658-28674-3_5","name":"Machine learning for the estimation of affective dimensions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-658-28674-3_5","authors":["Markus Kächele"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-19T12:04:24Z","doi":"10.1007/978-3-658-28674-3_5","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1049/pbhe029e_ch13","name":"Machine learning for health care","source":"crossref","abstract":"Machine learning (ML) is predominately being used to solve various technical and nontechnical challenges around the world. Taking the context of health care, ML took over the control and helping many practitioners for effective decision-making. Practically, this is once again proved by the researchers as they are using the ML algorithms for fast detection of COVID-19 and steps are initiated for the drug discovery of the same. In this chapter, we initially discuss the work happening in COVID-19 using ML algorithms. Then, we summarize the role of ML for analyzing and assessing various chronic diseases. Even though ML algorithms are predicting multidimensional aspects of the target disease, the experts in the field are still hesitating to use those outcomes as they lack in justification. To address this, a separate concept called explainable AI (XAI) is discussed. Data scientists are using ML algorithms to address chronic health issues with less cost and in a more accurate way. The question is, how ML could achieve this? This will be the main motivation for the entire chapter. In a recent report of a Harvard Medical Survey, it said that nearly 5,000,000 Indians die every year due to the medical errors (Harvard, 2020). These errors are getting reduced when the same disease is analyzed with ML. There is a famous proverb, “Prevention is better than cure.” According to World Health Organization, in most of the cases, chronic diseases cannot be prevented. But, the impact of them can be reduced with proper data collection and effective analysis using advanced ML algorithms. For instance, to detect lung cancer, it would cost hundreds of dollars for undergoing diagnosis and medication. Above that, if the disease is detected in the advanced stages then the probability of the cure is also very less. All these negative consequences can be reduced drastically when the usage of ML for health care is further exploited.","url":"https://doi.org/10.1049/pbhe029e_ch13","authors":["B. K. Kumar Raju Alluri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-22T03:07:36Z","doi":"10.1049/pbhe029e_ch13","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-22206-1_6","name":"Fundamentals of Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-22206-1_6","authors":["Joel Mathew Cherian","Ravindra Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-21T12:02:49Z","doi":"10.1007/978-3-031-22206-1_6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/icmla.2012.41","name":"Semantic Data Types in Machine Learning from Healthcare Data","source":"crossref","abstract":"Healthcare is particularly rich in semantic information and background knowledge describing data. This paper discusses a number of semantic data types that can be found in healthcare data, presents how the semantics can be extracted from existing sources including the Unified Medical Language System (UMLS), discusses how the semantics can be used in both supervised and unsupervised learning, and presents an example rule learning system that implements several of these types. Results from three example applications in the healthcare domain are used to further exemplify semantic data types.","url":"https://doi.org/10.1109/icmla.2012.41","authors":["Janusz Wojtusiak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-01-17T15:36:00Z","doi":"10.1109/icmla.2012.41","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5772/intechopen.74754","name":"Machine Learning Algorithm for Wireless Indoor Localization","source":"crossref","abstract":"Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.","url":"https://doi.org/10.5772/intechopen.74754","authors":["Osamah Ali Abdullah","Ikhlas Abdel-Qader"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-09-20T07:16:02Z","doi":"10.5772/intechopen.74754","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.2174/9789815305395125020015","name":"Machine Learning-based High-Dimensional Text Document Classification and Clustering","source":"crossref","abstract":"Text classification is a difficult technique. Many techniques have been developed to decrease the dimension of feature vectors for use in text classification due to their enormous size. This work provides a detailed discussion of unique parameters utilising an optic clustering strategy, as well as a review of some of the most essential text categorization algorithms. In this case, the words are clustered according to their level of similarity. Each cluster's membership function is based on the mean along with the standard deviation of its data. Finally, characteristics are chosen from each grouping. Each cluster's extracted feature is the weighted sum of its words. There's also no need to guess or use trial-and-error approaches to determine the optimal number of clusters.","url":"https://doi.org/10.2174/9789815305395125020015","authors":["Ansh Kataria"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-12T11:53:51Z","doi":"10.2174/9789815305395125020015","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/bf00114115","name":"Editorial: The terminology of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00114115","authors":["Pat Langley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-11-04T01:07:55Z","doi":"10.1007/bf00114115","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-5107-2_9","name":"Technical Excellence","source":"crossref","abstract":"When we think of agility, we think of efficiency of movement and an ability to change direction in the face of a complex and changing terrain all in the pursuit of a specific goal. Usain Bolt running 100 meters in 9.58 seconds is impressive, but it isn’t an expression of agility. Lionel Messi, on the other hand, weaving in and out of a lattice of defenders while keeping the soccer ball constantly under his influence is more like it. Agility requires quick movement for sure, but it also requires the behaviors that allow us to do this without distraction (Messi doesn’t walk onto the pitch with his bootlaces undone); it requires the mobility, or dexterity, to manipulate the environment or change course while maintaining stability; it requires the awareness and presence of mind to read the shifting terrain and environment in real time to anticipate and adjust without missing a step.","url":"https://doi.org/10.1007/978-1-4842-5107-2_9","authors":["Eric Carter","Matthew Hurst"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-21T11:03:53Z","doi":"10.1007/978-1-4842-5107-2_9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/ieeeconf53024.2021.9733769","name":"Machine Learning-Driven Digital Technologies for Educational Innovation [Front matter]","source":"crossref","abstract":"Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.","url":"https://doi.org/10.1109/ieeeconf53024.2021.9733769","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-17T16:54:04Z","doi":"10.1109/ieeeconf53024.2021.9733769","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5121/csit.2026.160202","name":"A Parallel Graph Coloring Algorithm with Applications in Machine Learning","source":"crossref","abstract":"In this paper, we present a parallel algorithm for coloring 3-colorable graphs, based on Wigderson’s classical algorithm which guarantees a coloring using at most O( √ n) colors. To parallelize the approach, we integrate Luby and Gebremedhin-Manne (GM) algorithm for parallel coloring. Experimental evaluations demonstrate that our parallel algorithm achieves speedup to 16 threads.","url":"https://doi.org/10.5121/csit.2026.160202","authors":["Maedeh Yahaghi","Saeed Bakhshan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T09:59:04Z","doi":"10.5121/csit.2026.160202","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5194/soil-2019-57","name":"Machine learning and soil sciences: A review aided by machine learning tools","source":"crossref","abstract":"Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.","url":"https://doi.org/10.5194/soil-2019-57","authors":["José Padarian","Budiman Minasny","Alex B. McBratney"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-03T06:19:24Z","doi":"10.5194/soil-2019-57","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-4131-8_2","name":"Introduction to Machine Learning","source":"crossref","abstract":"When we are born, we are incapable of doing anything. We can’t even hold our head straight at that time, but eventually we start learning. Initially we all fumble, make tons of mistakes, fall down, and bang our head many times but slowly learn to sit, walk, run, write, and speak. As a built-in mechanism, we don’t require a lot of examples to learn about something. For example, just by seeing two to three houses along the roadside, we can easily learn to recognize a house. We can easily differentiate between a car and a bike just by seeing a few cars and bikes around. We can easily differentiate between a cat and a dog. Even though it seems very easy and intuitive to us as human beings, for machines it can be a herculean task.","url":"https://doi.org/10.1007/978-1-4842-4131-8_2","authors":["Pramod Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-12-14T12:45:15Z","doi":"10.1007/978-1-4842-4131-8_2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/s10994-011-5242-y","name":"The changing science of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10994-011-5242-y","authors":["Pat Langley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-02-17T19:16:54Z","doi":"10.1007/s10994-011-5242-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-01580-9_8","name":"Attacking and Defending Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01580-9_8","authors":["Yevgeniy Vorobeychik","Murat Kantarcioglu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-09T04:48:23Z","doi":"10.1007/978-3-031-01580-9_8","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003328780-2","name":"Role of Artificial Intelligence and Machine Learning in Schizophrenia—A Survey","source":"crossref","abstract":"Schizophrenia (SZ) is a neurological condition that begins before birth and usually manifests itself in preadolescence. It exhibits a range of pre-morbid and pro-dromal symptoms, progressing to a full-blown psychotic syndrome. Patients with SZ require continual integrated healthcare, yet many drop out. To achieve an accurate diagnosis of SZ, several artificial intelligence (AI) approaches have been used. AI in health care uses machine learning (ML) programs to simulate human intellect for the access of large/complicated data. SZ is a severe psychiatric health situation that impacts many individuals across the world, producing cognitive and behavioral impairments that can be extremely burdensome in daily life. New treatment approaches have the potential to improvise patient health and act as a useful aid in clinical trial of sick person selection. Despite the fact that ML approaches may recognize patient groups, they are usually “unexplainable” due to difficult programming that do not replicate doctor s regular decision-making. Existing researches have considered extensive time periods (in months and years) for their studies. Even then, the mechanisms that control SZ s genesis, relapsing, somatic symptoms, and therapy remain a mystery. ML may become a useful method to explore the mechanisms that cause SZ as a result of a number of techniques aiming at improving model interpretability and causal reasoning. Our study covers over 85 existing research articles and provides a detailed summary of the achievements, as well as the future scope of AI and ML in the detection and diagnosis of SZ. An attempt has also been made, towards the end of the chapter, to identify the pertinent research gaps in the research of SZ where AI and ML can play a more significant role.","url":"https://doi.org/10.1201/9781003328780-2","authors":["Bhawana Paliwal","Khandakar F. Rahman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-08T09:25:10Z","doi":"10.1201/9781003328780-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003538158-3","name":"Evaluation of Bio-Inspired Algorithm-based Machine Learning and Deep Learning Models","source":"crossref","abstract":"Natural processes such as neural networks, swarm intelligence, and genetic development have inspired algorithms that exhibit great promise in solving complicated optimization problems in deep learning (DL) and machine learning (ML). A thorough evaluation of bioinspired algorithm-based machine learning and deep learning models in medicine is conducted in this research chapter. This study evaluates and contrasts the performance of multiple bio-inspired algorithms using a range of datasets and goals. Ant colony optimization, particle swarm optimization, genetic algorithms, and artificial neural networks are a few of these. Experimental data are used to assess these bio-inspired models robustness, scalability, and performance in classification, regression, and optimization tasks. This chapter looks at how bio-inspired algorithms enhance the interpretability and generalizability of ML and DL models. The advantages and disadvantages of these nature-inspired methods are also discussed. The findings show how bio-inspired algorithms can be used to develop intelligent systems of ML and DL in different types of medical healthcare applications.","url":"https://doi.org/10.1201/9781003538158-3","authors":["Selvam Durairaj","Malik Mohamed Umar","B. Natarajan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-02T15:33:02Z","doi":"10.1201/9781003538158-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007510732151","name":"Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007510732151","authors":["Shai Ben-David","Nadav Eiron"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007510732151","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9780367854737-13","name":"Machine Learning for Big Data Analytics, Interactive and Reinforcement","source":"crossref","abstract":"The objective of this chapter is to provide a concise view on machine learning (ML) for big data analytics and to enlighten the reader about interactive reinforcement ML. The big data revolution promises to transform life, work, and thought by enabling process optimization, empowering insight discovery, and improving decision-making. It describes the ability to extract value from massive amounts of data through data analytics. This chapter compiles, summarizes, and organizes ML challenges with big data. It highlights the cause–effect relationship by organizing challenges according to the “big data Vs,” the dimensions that instigated the issue: volume, velocity, variety, or veracity. It also describes how reinforcement learning allows machines to work automatically. Moreover, emerging ML approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioner s select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process this chapter provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement in the field of ML with big data.","url":"https://doi.org/10.1201/9780367854737-13","authors":["Ritwik Raj","Anjana Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-10-09T12:43:22Z","doi":"10.1201/9780367854737-13","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022653511837","name":"Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022653511837","authors":["Wolfgang Maass","György Turán"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022653511837","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-19067-4_1","name":"Distributed Optimization in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-19067-4_1","authors":["Gauri Joshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-25T17:57:29Z","doi":"10.1007/978-3-031-19067-4_1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007666507971","name":"The Complexity of Learning According to Two Models of a Drifting Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007666507971","authors":["Philip M. Long"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007666507971","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-030-71881-7_12","name":"Machine Learning in Evidence Synthesis Research","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71881-7_12","authors":["Alonso Carrasco-Labra","Olivia Urquhart","Heiko Spallek"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-24T09:02:42Z","doi":"10.1007/978-3-030-71881-7_12","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-032-24858-9_2","name":"Machine Learning Algorithms and Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24858-9_2","authors":["Deepti Chopra","Roopal Khurana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-29T10:18:46Z","doi":"10.1007/978-3-032-24858-9_2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2020.010205","name":"Theory and Practice of Super Parameter Optimization for Machine Learning Algorithm","source":"crossref","abstract":"Before the rise of large machine learning algorithms, most people manually adjusted the super parameters of the model by relying on experience.However, with the increasing complexity of the model, this method obviously cannot meet the needs.This paper mainly studies the theory and practice of super parameter optimization of machine learning algorithm.This thesis proposes a regression-based hyperparameter optimization algorithm that has the same data-based optimization algorithm as the optimization algorithm Bayesian.The optimization algorithm is based on the Gaussian regression process.In addition to being affected by the super parameters of the kernel function in the process of GP regression fitting, the calculation amount of the algorithm will also increase significantly.The experimental results show that, compared to the optimization algorithm, the parameter optimization results of this algorithm are similar to those of the optimization algorithm.","url":"https://doi.org/10.38007/ml.2020.010205","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:35:28Z","doi":"10.38007/ml.2020.010205","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.31235/osf.io/wj5ne","name":"Machine Learning for All –  Introducing Machine Learning in K-12","source":"crossref","abstract":"Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early on including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks, learning as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first image recognition model of recycling trash using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics.","url":"https://doi.org/10.31235/osf.io/wj5ne","authors":["Christiane Gresse von Wangenheim","Lívia S. Marques","Jean C. R. Hauck"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-08-28T10:26:14Z","doi":"10.31235/osf.io/wj5ne","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlsp.2009.5306233","name":"Machine learning in remote sensing data processing","source":"crossref","abstract":"Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.","url":"https://doi.org/10.1109/mlsp.2009.5306233","authors":["Gustavo Camps-Valls"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-11-05T13:41:43Z","doi":"10.1109/mlsp.2009.5306233","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007369909943","name":"Learning and Revising User Profiles: The Identification of Interesting Web Sites","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007369909943","authors":["Michael Pazzani","Daniel Billsus"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007369909943","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1190/1.9781560804048.ch1","name":"Chapter 1: Introduction to Machine Learning in the Geosciences","source":"crossref","abstract":"","url":"https://doi.org/10.1190/1.9781560804048.ch1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-18T22:35:18Z","doi":"10.1190/1.9781560804048.ch1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.55529/jaimlnn.32.53.60","name":"Machine Learning in Drug Discovery","source":"crossref","abstract":"A drug is a substance that when put into the body can change the way the body works and a person's mental state. Discovering the accurate drug plays a vital role in saving precious lives. In traditional drug creation, scientists identify a target in the body and test a large range of chemical compounds on it until they obtain the results. The process gets quicker and more effective with the role of machine learning techniques. This is done by using the huge amount of biological data, medical data, algorithms and statistical models available today. This automation of the drug development process is a key to the current issue of low productivity rate that pharmaceutical companies currently face and helps eliminate the side effects. Machine learning might be a useful tool to further enhance the drug development process.","url":"https://doi.org/10.55529/jaimlnn.32.53.60","authors":["Deepak B"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-21T09:58:20Z","doi":"10.55529/jaimlnn.32.53.60","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1002/9781394329649.ch13","name":"Machine Learning‐Driven Precision Plant Breeding","source":"crossref","abstract":"Despite remarkable progress in plant science, sustainable crop enhancement and global food security continue to rely on innovative breeding strategies. Early breakthroughs involved genetic markers, which dramatically enhanced the identification and selection of genotypes with favorable traits. These markers have enabled the detection of quantitative trait loci (QTLs) associated with stress resilience in crops through marker-assisted selection (MAS) and genome-wide association studies (GWAS). While these methods remain relevant, challenges such as prolonged QTL identification and linkage drag persist. The emergence of OMICS technologies, particularly genomics and transcriptomics, has revolutionized molecular breeding. Sequencing methods combined with genetic markers now allow rapid genotyping of large populations. Genotyping by sequencing (GBS), for instance, connects phenotypes to genotypes early in growth, shortening selection timelines from years to months. Yet, handling vast OMICS-generated datasets demands advanced computational tools, artificial intelligence (AI), and machine learning (ML) algorithms. Recent advancements in AI-driven ML have introduced “speed breeding,” a paradigm shift that accelerates plant life cycles, flowering, and seed production. ML models decode complex OMICS data, pinpointing genes and proteins that enhance stress tolerance and nutrient profiles. This chapter explores AI and ML applications in modernizing crop breeding, reviews their role in identifying critical gaps, and discusses their potential to digitize agriculture for climate-resilient crops.","url":"https://doi.org/10.1002/9781394329649.ch13","authors":["Krishna Kumar Rai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-14T20:08:44Z","doi":"10.1002/9781394329649.ch13","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007682208299","name":"Randomizing Outputs to Increase Prediction Accuracy","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007682208299","authors":["Leo Breiman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007682208299","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003104315-12","name":"Basics of Machine Learning","source":"crossref","abstract":"The concept of machine learning is broad. The term “artificial intelligence (AI)” first appeared in 1956. AI refers to systems or software on computers designed to mimic intelligent tasks typically performed by humans, such as understanding spoken language, making logical inferences from data, or learning from experience.","url":"https://doi.org/10.1201/9781003104315-12","authors":["Tsutomu T. Takeuchi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-27T17:49:36Z","doi":"10.1201/9781003104315-12","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlise57402.2022.00075","name":"Stock Market Predictability Using Machine Learning Techniques","source":"crossref","abstract":"The purpose of this article is to examine stock market analysis using machine learning techniques unique and attractive way to reassess products according to the changing times, and to publish information to create textual information from one or more sources in order to identify different threats. This is accomplished by controlling the reader to estimate the value of the stock by determining the need to view chaotic data and estimate the impact of Microsoft. The product uses the Naive Bayes classifier. First, the newsletters and archives have been placed in a free file in the “Date” column after making some significant changes. It also has a structure similar to the data distribution that protects the nearest-neighbor (Well-NN) experiment. Lately, natural language processing is used in the Journal of Captions and has a guide with 20,000 commonly used words and how to convert them to vectorized text. Subsequently, Naive Bayes models were also developed using dropout datasets, 80 based on training data and 20 based on control data. Overall, the neural-based model was found to be slightly better and the macro means for F1 than the standard and configured model, and slightly better. The results of F1-score validate effectiveness of the proposed method, which achieves a satisfying performance.","url":"https://doi.org/10.1109/mlise57402.2022.00075","authors":["Jiuye Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-15T20:45:01Z","doi":"10.1109/mlise57402.2022.00075","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1010924021315","name":"Parameter Estimation in Stochastic Logic Programs","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1010924021315","authors":["James Cussens"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T23:10:54Z","doi":"10.1023/a:1010924021315","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003535850-6","name":"Robotic Process Automation in Business Management Using Machine Learning Algorithms","source":"crossref","abstract":"Robotic process automation bots try to mimic how people communicate with computers. RPA bots may handle activities that include document scanning and conversion into a machine-readable format thanks to optical character recognition. Most document automation tasks performed by RPA bots, including billing, screening resumes, inventory management, and other processes, rely on OCR. Poor handwriting and inconsistent text are common in handwritten documents. Before supplying a word to the RPA system, OCR technologies may be combined with a neural network whale optimization algorithm to accurately read the word depending on the overall content of the text. The primary objective of the research is to focus on the Robotic Automation Process in Invoice documents used in Business. The main challenge is implementing machine models that are lacking in accuracy, to overcome this challenge neural network whale optimization is to enhance the accuracy of optical character recognition.","url":"https://doi.org/10.1201/9781003535850-6","authors":["P. Gayathiri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-26T13:51:14Z","doi":"10.1201/9781003535850-6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1017934522171","name":"Using Iterated Bagging to Debias Regressions","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1017934522171","authors":["Leo Breiman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-30T09:36:44Z","doi":"10.1023/a:1017934522171","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2025.050112","name":"Optimization of Machine Learning Models and Application Supported by Data Engineering","source":"crossref","abstract":"With the rapid progress of intelligent technology, machine learning is increasingly widely used in all walks of life, and the role of data engineering is increasingly prominent, becoming the core link that determines the efficiency and practicability of the model.The quality, purification, storage and control of data are directly related to the quality and speed of model training.The optimization of techniques, such as feature extraction, hyperparameter fine-tuning, regularization processing, etc., continues to promote the leap in model performance.Data engineering is not only the basis of model training, but also plays an indispensable role in the subsequent steps of model deployment, monitoring and iterative upgrading.This paper introduces the practical application effect of machine learning model enhanced by means of feature engineering, distributed computing, big data environment, etc., in order to promote the popularization and deepening development of intelligent technology.","url":"https://doi.org/10.38007/ml.2025.050112","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-12T09:54:59Z","doi":"10.38007/ml.2025.050112","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/bf00116826","name":"New theoretical directions in machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00116826","authors":["David Haussler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-10-31T02:00:05Z","doi":"10.1007/bf00116826","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/icmla.2006.1","name":"5th International Conference on Machine Learning and Applications-Copyright","source":"crossref","abstract":"Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center. The papers in this book comprise the proceedings of the meeting mentioned on the cover and title page. They reflect the authors' opinions and, in the interests of timely dissemination, are published as presented and without change. Their inclusion in this publication does not necessarily constitute endorsement by the editors or the Institute of Electrical and Electronics Engineers, Inc.","url":"https://doi.org/10.1109/icmla.2006.1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-08T12:59:25Z","doi":"10.1109/icmla.2006.1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.65492/01/202/2024/11","name":"Machine Learning Enhanced Network Intrusion Detection  System","source":"crossref","abstract":"Contemporary cybersecurity demands have elevated network protection to a fundamental requirement for any computational system. Safeguarding networks from unauthorized infiltration is essential for maintaining seamless operational continuity in advanced network infrastructures. Network protection has emerged as a dominant concern within the information technology domain. Cybercriminals and malicious actors execute countless successful penetration attempts against network systems. An intrusion detection system serves as a cornerstone in network defense, identifying and recognizing irregularities within network security frameworks. IDS effectiveness can be evaluated through its intelligence capacity, operational efficiency, and precise identification of both novel and familiar attack patterns. The maximum gain principleprovides optimal anomaly detection capabilities. This research presents a machine learning architecture utilizing multilayer perceptron (MLP) classification, achieving 99.98% accuracy. The methodology is validated using 10-fold and Jackknife cross-validation techniques. Critical performance indicators, including accuracy, sensitivity, specificity, and Matthew’s correlation coefficient, are analyzed to assess system performance. All evaluation metrics achieved peak performance ratios, demonstrating MLP’s superiority as a classification approach. The proposed model’s accuracy, sensitivity, specificity, and MCC values reached 99.99% when tested on the complete UNSW-NB15 dataset. These findings indicate significant accuracy improvements through various perceptron architectural configurations. Both K-fold and Jackknife methodologies successfully achieved 99.99% accuracy rates.","url":"https://doi.org/10.65492/01/202/2024/11","authors":["Muhammad Yasir Iqbal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-03T12:38:48Z","doi":"10.65492/01/202/2024/11","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-6537-6_4","name":"Machine Learning Algorithms","source":"crossref","abstract":"You do not need a background in algebra and statistics to get started in machine learning. The previous chapter introduced key foundation principles. Be under no illusions; mathematics is a huge part of machine learning. Mathematics is key to understanding how the algorithm works and why coding a machine learning project from scratch is a great way to improve your mathematical and statistical skills.","url":"https://doi.org/10.1007/978-1-4842-6537-6_4","authors":["Arjun Panesar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-15T14:05:55Z","doi":"10.1007/978-1-4842-6537-6_4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1002/9781394389537.ch4","name":"Basics of Machine Learning for Brain Signal Decoding","source":"crossref","abstract":"This chapter covers the basic concepts of machine learning (ML) related to brain signal decoding, pointing out its significance in understanding neural activity and for applications such as BCI, neurofeedback, and cognitive monitoring. It describes different modalities for brain signals, including EEG, fMRI, and MEG, and discusses vital stages in the decoding process, from signal acquisition and preprocessing, to feature extraction and model training. This emphasizes how the role of ML deals with the high-dimensional nature of the data, contending with noise, and achieving real-time decoding through dimensionality reduction, pattern recognition, and time-series analysis. This chapter also explores the challenges regarding variability, noise, and computational delays in the decoding process and discusses how advanced techniques, including deep learning, are increasingly being implemented. Finally, the chapter points to promising future developments while acknowledging ML's tremendous potential to revolutionize neurotechnology and the improvement of healthcare delivery.","url":"https://doi.org/10.1002/9781394389537.ch4","authors":["Richa","Sakshi Mittal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-14T21:23:06Z","doi":"10.1002/9781394389537.ch4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/cacml55074.2022.00002","name":"Proceedings 2022 Asia Conference on Algorithms, Computing and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cacml55074.2022.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-19T19:38:41Z","doi":"10.1109/cacml55074.2022.00002","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/b11026-2","name":"What Is Machine Learning Forensics?","source":"crossref","abstract":"Data aggregation and preparation are tasks an investigator needs to perform before any analysis. When data is properly prepared, the investigator gains an understanding and insight into the criminals’ method of operation. This is an important concept: preparing the data means that the map or model is built right. Preparing the investigator means he or she is gaining insight into the crime, ensuring that the right map and model are built. Having a forensic strategy starts with identifying a need and making correct, appropriate, and informed decisions about how to construct the right map, model, and analyses. The investigator needs to start by defining crimes or fraud in a precise way: What crime is being committed? How was the crime detected? When did the crime take place? Where did the crime take place? How often does the crime occur? How much is the crime costing? What criminal clues need to be investigated?","url":"https://doi.org/10.1201/b11026-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-07-19T21:09:43Z","doi":"10.1201/b11026-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003288046-2","name":"Geospatial Big Data for Machine Learning","source":"crossref","abstract":"Geospatial Big Data for Machine Learning - 1","url":"https://doi.org/10.1201/9781003288046-2","authors":["Bharath H. Aithal","P.S. Prakash"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-11T13:32:41Z","doi":"10.1201/9781003288046-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1016/bs.agph.2020.08.002","name":"70 years of machine learning in geoscience in review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/bs.agph.2020.08.002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-18T09:17:48Z","doi":"10.1016/bs.agph.2020.08.002","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022628806385","name":"Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022628806385","authors":["Long-Ji Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022628806385","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007668716498","name":"Relational Instance-Based Learning with Lists and Terms","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007668716498","authors":["Tamás Horváth","Stefan Wrobel","Uta Bohnebeck"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007668716498","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1018280807006","name":"Active Learning for Vision-Based Robot Grasping","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1018280807006","authors":["Marcos Salganicoff","Lyle H. Ungar","Ruzena Bajcsy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-02-06T17:07:14Z","doi":"10.1023/a:1018280807006","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/b22627-5","name":"Agricultural modernization with forecasting stages and machine learning","source":"crossref","abstract":"Humans watched the natural phenomena and looked at the behavior of birds and animals. The procedure called “sortilege” or “cleromancy” includes anticipating the future from sticks, beans or different things drawn indiscriminately from an assortment. The information and data required to make formal forecasts are commonly an important component. Agriculture is the most peaceful and friendly activity for environment, and it is a very reliable and honest source of human livelihood. In developing countries, many people rely on agriculture for their livelihood. Raining seasons depend on the temperature and potential of evapotranspiration. In the world, some areas are better for the growth of the crops. According to the distance from Equator, different countries of the world have different seasons. Machine learning enables the system with the capability to automatically explore, enhance and improve according to different situations without being programmed. Machine learning is centered on the development of intelligent computer programs that can process the data and utilize.","url":"https://doi.org/10.1201/b22627-5","authors":["A.K. Awasthi","Arun Kumar Garov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-10T10:57:28Z","doi":"10.1201/b22627-5","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022652016863","name":"A Study of Explanation-Based Methods for Inductive Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022652016863","authors":["Nicholas S. Flann","Thomas G. Dietterich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022652016863","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022696716689","name":"Learning Nested Differences of Intersection-Closed Concept Classes","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022696716689","authors":["David Helmbold","Robert Sloan","Manfred K. Warmuth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022696716689","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007518724497","name":"Elevator Group Control Using Multiple Reinforcement Learning Agents","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007518724497","authors":["Robert H. Crites","Andrew G. Barto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007518724497","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-69499-8_2","name":"Shallow Learning vs. Deep Learning in Engineering Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69499-8_2","authors":["Fereshteh Jafari","Kamran Moradi","Qobad Shafiee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T18:01:28Z","doi":"10.1007/978-3-031-69499-8_2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1018396106080","name":"PAC Learning of One-Dimensional Patterns","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1018396106080","authors":["Paul W. Goldberg","Sally A. Goldman","Stephen D. Scott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-02-06T17:08:17Z","doi":"10.1023/a:1018396106080","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-030-83213-1_3","name":"Numerical Solution of Machine Learning Control Problems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-83213-1_3","authors":["Askhat Diveev","Elizaveta Shmalko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-23T16:02:25Z","doi":"10.1007/978-3-030-83213-1_3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-981-13-0200-8_4","name":"Machine Learning Approach to Evolutionary Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-13-0200-8_4","authors":["Hitoshi Iba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-06-15T17:34:53Z","doi":"10.1007/978-981-13-0200-8_4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003422839-9","name":"Machine Learning with Neural Networks","source":"crossref","abstract":"This chapter presents the theory, operation, and implementation of artificial neural networks. It starts with a basic overview of neurons and perceptrons, and then explains how perceptrons are combined into multilayer perceptrons, or MLPs. Then it discusses the topics of training, loss, and optimization. The last part of the chapter demonstrates how machine learning can be implemented in a WebGPU compute application.","url":"https://doi.org/10.1201/9781003422839-9","authors":["Matthew Scarpino"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-28T17:57:00Z","doi":"10.1201/9781003422839-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022670015471","name":"Guest Editorial","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022670015471","authors":["Katharina Morik","Francesco Bergadano","Wray Buntine"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022670015471","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003189053-9","name":"Performance Comparison of Different Machine Learning Techniques towards Prevalence of Cardiovascular Diseases (CVDs)","source":"crossref","abstract":"In these days, a large population worldwide has been suffering from CVDs or heart disease problems and millions of people have died due to this problem i.e. not getting proper treatment at the right time. In this way, effective automated system is required to help medical stakeholders to take prompt action at the right time. To gain insights and analysis from large, complex data, various machine learning software and techniques have been suggested for heart disease prediction. In this study, a performance comparison is made on machine learning techniques such as logistic regression (LR), Bayesian regularization neural network (BRNN), support vector machine (SVM), and Naïve-Bayes (NB) and analyzes their performance. In this research study, a heart disease data set from the Kaggle data repository is obtained and an experimental result generation purpose MATLAB machine learning tool is used. Moreover, the significant performances of various proposed techniques will be compared based on this. At last, the BRNN method outperformed all i.e. 96.39% accuracy achieved.","url":"https://doi.org/10.1201/9781003189053-9","authors":["Sachin Kamley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-17T17:03:25Z","doi":"10.1201/9781003189053-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022668520498","name":"Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022668520498","authors":["William W. Cohen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022668520498","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.71443/9789349552395-05","name":"Machine Learning-Based Fault Detection and Prognostics in Industrial Systems","source":"crossref","abstract":"Machine learning (ML) has revolutionized fault detection and prognosis in industrial systems, offering unprecedented capabilities for predictive maintenance and real-time monitoring. The integration of advanced ML models, including supervised learning, deep learning, and data stream mining, has enabled industries to transition from reactive to proactive maintenance strategies. This chapter explores the applications of machine learning in fault detection and prognosis, highlighting the benefits of integrating these models within predictive maintenance frameworks. Emphasis is placed on the challenges associated with missing and incomplete data, as well as the role of uncertainty management in enhancing model reliability. By examining real-time fault monitoring through data stream mining, the chapter also underscores the importance of handling high-volume, high-velocity data streams for timely fault diagnosis and prognosis. Additionally, the chapter provides insights into hybrid machine learning models, which combine the strengths of various algorithms to improve fault diagnosis accuracy and decision-making. Through these discussions, this chapter contributes to the growing body of knowledge on leveraging machine learning for optimizing industrial system performance, reducing downtime, and ensuring the sustainability of operations.","url":"https://doi.org/10.71443/9789349552395-05","authors":["Thejo Lakshmi Gudipalli","R. Jeevitha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-13T04:03:28Z","doi":"10.71443/9789349552395-05","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1018056104778","name":"Linear Least-Squares Algorithms for Temporal Difference Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1018056104778","authors":["Steven J. Bradtke","Andrew G. Barto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-02-06T17:07:14Z","doi":"10.1023/a:1018056104778","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1016/j.mlwa.2025.100692","name":"Enhanced credit risk prediction using deep learning and SMOTE-ENN resampling","source":"crossref","abstract":"Credit risk prediction is a vital task in financial services, ensuring that institutions can manage their lending risks effectively. This study investigates the effectiveness of deep learning (DL) models for credit risk prediction, with a focus on addressing the challenge of class imbalance and the black box nature of these models using the Synthetic Minority Over-sampling Technique - Edited Nearest Neighbor (SMOTE-ENN) resampling method and Shapley Additive Explanations (SHAP), respectively. The study compares the performance of various DL architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and Graph Neural Networks (GNN), on two real-world datasets: the Australian and German credit datasets. The findings reveal that the GRU model, enhanced with SMOTE-ENN resampling, outperforms other models in terms of accuracy, sensitivity, and specificity. The superior performance of the GRU-SMOTE-ENN model demonstrates its potential as a robust deep learning technique for financial institutions to enhance credit risk assessment. Additionally, the study demonstrates how the integration of SHAP values significantly improves the interpretability of deep learning models, making them more transparent and trustworthy for stakeholders.","url":"https://doi.org/10.1016/j.mlwa.2025.100692","authors":["Idowu Aruleba","Yanxia Sun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-27T11:20:32Z","doi":"10.1016/j.mlwa.2025.100692","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.14711/thesis-991012818569503412","name":"Lower the barrier of machine learning : meta learning for transfer learning and autoML","source":"crossref","abstract":"Recent years, machine learning becomes the major methodology to develop artificial intelligence applications, due to the trend of using big data in machine learning. However, traditional machine learning may have three barriers: lack of data, poor feature quality, and less data scientists. In this thesis, we focus on how to lower the barrier of machine learning. We propose to use meta learning methodology to solve these problems. Specifically, meta learning can be applied to solve the transfer learning and AutoML problems. Transfer learning can be used to weaken the impact of small data and poor feature problems, and AutoML can be used to solve the problem that there are not enough data scientists and then we may use normal engineers to build up AI systems. As the result, the three main barriers have been lowered correspondingly. We designed several new algorithms to solve the data, feature and model tuning problems, and showed advantages on many empirical studies. As meta learning may rely on auxiliary data from other sources, we found that it may lead to privacy problem. To solve this problem and make meta learning better applied, we design a new privacy-preserving learning algorithm. In this algorithm, we show how to learn from data without accessing any privacy information.","url":"https://doi.org/10.14711/thesis-991012818569503412","authors":["Wenyuan Dai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-10T00:56:11Z","doi":"10.14711/thesis-991012818569503412","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007570708568","name":"Learning Team Strategies: Soccer Case Studies","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007570708568","authors":["Rafał P. Sałustowicz","Marco A. Wiering","Jürgen Schmidhuber"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007570708568","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1022619109594","name":"Learning to Perceive and Act by Trial and Error","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022619109594","authors":["Steven D. Whitehead","Dana H. Ballard"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022619109594","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1002/9781119564843.ch2","name":"Machine Learning","source":"crossref","abstract":"This chapter focuses on the most popular artificial intelligence technique for infusing smarts into software—Machine Learning (ML). It presents examples of using ML to capture patterns in data and capture these patterns in artifacts called models. ML can be classified into three areas: unsupervised machine learning, supervised machine learning, and reinforcement learning. Unsupervised ML is about finding patterns in data without knowing the results or outcomes beforehand. This includes algorithms for clustering the data, reducing the dimensions, and detecting anomalies. Supervised ML uses labeled data to build a model that can make predictions on new data. This includes classification algorithms where the membership of each data point to a particular class is predicted. The other method is regression, where a numerical value based on input features is predicted. Finally, the chapter talks about reinforcement learning, which uses an agent that learns patterns by interacting with an environment and receiving reinforcement for taking actions.","url":"https://doi.org/10.1002/9781119564843.ch2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-04-19T07:52:51Z","doi":"10.1002/9781119564843.ch2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.5772/intechopen.79346","name":"Introductory Chapter: Machine Learning and Biometrics","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.79346","authors":["Jucheng Yang","Yarui Chen","Chuanlei Zhang","Dong Sun Park","Sook Yoon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-08-29T12:55:28Z","doi":"10.5772/intechopen.79346","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1063/5.0160568","name":"Scanning probe microscopy in the age of machine learning","source":"crossref","abstract":"Scanning probe microscopy (SPM) has revolutionized our ability to explore the nanoscale world, enabling the imaging, manipulation, and characterization of materials at the atomic and molecular level. However, conventional SPM techniques suffer from limitations, such as slow data acquisition, low signal-to-noise ratio, and complex data analysis. In recent years, the field of machine learning (ML) has emerged as a powerful tool for analyzing complex datasets and extracting meaningful patterns and features in multiple fields. The combination of ML with SPM techniques has the potential to overcome many of the limitations of conventional SPM methods and unlock new opportunities for nanoscale research. In this review article, we will provide an overview of the recent developments in ML-based SPM, including its applications in topography imaging, surface characterization, and secondary imaging modes, such as electrical, spectroscopic, and mechanical datasets. We will also discuss the challenges and opportunities of integrating ML with SPM techniques and highlight the potential impact of this interdisciplinary field on various fields of science and engineering.","url":"https://doi.org/10.1063/5.0160568","authors":["Md Ashiqur Rahman Laskar","Umberto Celano"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-10T08:23:42Z","doi":"10.1063/5.0160568","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007327622663","name":"A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007327622663","authors":["Jonathan Baxter"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007327622663","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007444708590","name":"Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007444708590","authors":["Noel E. Sharkey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007444708590","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1016/j.jclinane.2021.110444","name":"Predicting intraoperative bleeding in patients undergoing a hepatectomy using multiple machine learning and deep learning techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jclinane.2021.110444","authors":["Qiong Xue","Yu Zhu","Lihua Yang","Wen Duan","Zeping Li","Muhuo Ji","Jianhua Tong","Jian-Jun Yang","Cheng-Mao Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-06-30T21:18:21Z","doi":"10.1016/j.jclinane.2021.110444","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.64898/2026.02.13.26346244","name":"Gender-Specific Osteoporosis Risk Prediction Using Longitudinal Clinical Data and Machine Learning","source":"crossref","abstract":"Abstract Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies combine male and female datasets to train a single model, introducing bias since osteoporosis risk and progression differ by gender. This study aims to develop gender-specific machine learning models that leverage longitudinal data to predict osteoporosis risk, providing tailored insights for men and women. Data were obtained from two large longitudinal cohorts: the Study of Osteoporotic Fractures (SOF) for women and the Osteoporotic Fractures in Men Study (MrOS) for men. Multiple ML algorithms were trained and evaluated for each sex, with model performance assessed using the area under the receiver operating characteristic curve (AUC-ROC). Among the tested models, the XGBoost model demonstrated the best performance for women, achieving an AUC-ROC of 0.93 using SOF data. For men, the Random Forest model achieved an AUC-ROC of 0.89 using MrOS data. Feature importance analysis identified sex-specific osteoporosis risk factors, underscoring the need for tailored prediction and management. By revealing male and female risk factors and reducing bias from combined datasets, the work advances personalized care and supports earlier, effective clinical intervention to prevent fractures and improve health outcomes.","url":"https://doi.org/10.64898/2026.02.13.26346244","authors":["Shyama P. Tripathy","Lohitha Saripalli","Katherine Berry","Ambalangodage C. Jayasuriya","Devinder Kaur","Fayeq J. Syed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T17:50:15Z","doi":"10.64898/2026.02.13.26346244","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.21203/rs.3.rs-8533042/v1","name":"Non-contrast CT radiology-clinical machine learning modeling to predict chronic hydrocephalus after aneurysmal subarachnoid hemorrhage","source":"europepmc","abstract":"Abstract Objective To establish a machine learning model based on radiomics of non-contrast CT and clinical features to predict the occurrence of chronic hydrocephalus after aneurysmal subarachnoid hemorrhage. Methods A retrospective analysis of 150 patients with aneurysmal subarachnoid hemorrhage (aSAH) who underwent surgery between January 2020 and February 2024 was performed. Chronic hydrocephalus(CHC), defined as hydrocephalus occurring 14 days after ruptured aneurysmal hemorrhage, was determined primarily from follow-up CT images. Radiological features were extracted from non-contrast CT (NCCT) and screened using the least absolute shrinkage and selection algorithm (LASSO) regression method. The logistic regression (LR) model was employed to construct models by leveraging radiomic as well as clinical characteristics. A radiological-clinical nomogram model was developed and the predictive performance of the model was assessed using area under the curve (AUC), accuracy, sensitivity and specificity. Results A total of 150 patients were enrolled in this study. From non-contrast CT scans, 1,834 radiomic features were extracted, with 12 optimal features selected to construct the radiomic model. Univariate and stepwise multivariate analyses identified the Glasgow Coma Scale (GCS) score at admission and posterior circulation aneurysms as independent factors for constructing the clinical model. The radiomic-clinical nomogram model demonstrated area under the curve (AUC) values of 0.860 (95% CI: 0.7906–0.9303) in the training cohort and 0.683 (95% CI: 0.4795–0.8856) in the testing cohort. Conclusion The radiology - clinical nomogram model based on non - contrast CT shows a rather good performance in predicting chronic hydrocephalus following aneurysmal subarachnoid hemorrhage.","url":"https://doi.org/10.21203/rs.3.rs-8533042/v1","authors":["Haiyun Yu^","Muyun Luo","Hanlong Guo","Zecun Huang","Qiuxiang Xiao"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8533042/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1007/978-3-031-01581-6_8","name":"Continuous Knowledge Learning in Chatbots","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01581-6_8","authors":["Zhiyuan Chen","Bing Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-09T04:32:53Z","doi":"10.1007/978-3-031-01581-6_8","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781003424987-11","name":"Machine Learning, Deep Learning and IoT in Smart Healthcare for Retinal Diseases Management","source":"crossref","abstract":"The convergence of cutting-edge technologies, specifically ML and DL integrated into the IoT, is revolutionizing healthcare, particularly in the field of ophthalmology. The impact of ML and DL-based Smart IoT Healthcare Systems on retinal disease early detection and management is examined in this study. Globally, retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy are among the leading causes of visual impairment. Conventional screening techniques are expensive, time-consuming, and prone to human error. The integration of IoT devices, wearable technology, and smart sensors into ophthalmic healthcare settings presents novel opportunities for early diagnosis, continuous monitoring, and personalized treatment plans. This chapter focuses on the classification of images from the \"Cataract Dataset\" into two classes, \"normal\" and \"cataract,\" utilizing various machine learning classifiers. The proposed methodology encompasses key phases, beginning with data preparation, where images are loaded, resized, and converted into NumPy arrays. A diverse set of classifiers, including logistic regression, decision tree, random forest, SVM, KNN, and others, is employed for classification. Experimental results highlight XGBoost as the top performer with an accuracy of 0.90781, closely followed by Gradient Boosting and the Voting Classifier. The ROC AUC scores, crucial for evaluating classification models, reinforce the high performance of these classifiers. Logistic Regression, Random Forest, SVM, K-nearest neighbors, and others showcase varying degrees of effectiveness based on their ROC AUC scores. The research provides a detailed analysis of the proposed methodology s effectiveness in classifying cataract images. The comparative evaluation of diverse classifiers and the emphasis on performance metrics contribute valuable insights for selecting appropriate models based on specific application requirements.","url":"https://doi.org/10.1201/9781003424987-11","authors":["Saurabh Ranjan","Dilip Kumar Choubey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-02T15:32:01Z","doi":"10.1201/9781003424987-11","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1017980312899","name":"On Average Versus Discounted Reward Temporal-Difference Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1017980312899","authors":["John N. Tsitsiklis","Benjamin Van Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-30T09:36:44Z","doi":"10.1023/a:1017980312899","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1097/wno.0000000000001605","name":"Clinical Machine Learning Modeling Studies: Methodology and Data Reporting","source":"europepmc","abstract":"Departments of Neurology (OMD) and Ophthalmology (OMD), Mayo Clinic, Scottsdale, Arizona; Departments of Neurology (JC) and Ophthalmology (JC), Mayo Clinic, Rochester, Minnesota; and School of Computing and Augmented Intelligence (YW), Arizona State University, Phoenix, Arizona. Address correspondence to Oana M. Dumitrascu, MD, MSc, Mayo Clinic Scottsdale: Mayo Clinic Arizona, 13400 E. Shea Boulevard, Scottsdale, AZ 85259; E-mail: [email protected] The authors report no conflicts of interest.","url":"https://doi.org/10.1097/wno.0000000000001605","authors":["Oana M. Dumitrascu","Yalin Wang","John J. Chen"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1097/wno.0000000000001605","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/b978-0-12-820273-9.00003-8","name":"Machine learning for predictive analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-820273-9.00003-8","authors":["Sehj Kashyap","Kristin M. Corey","Aman Kansal","Mark Sendak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-02-10T22:43:34Z","doi":"10.1016/b978-0-12-820273-9.00003-8","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.38007/ml.2020.010405","name":"Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning","source":"crossref","abstract":"More than 20 years ago, China's hydropower undertakings rapid development, hydropower installed capacity has been a breakthrough.With the continuous improvement of hydropower installed capacity, the optimal dispatching of hydropower system is facing great challenges.In this paper, the average daily processing prediction of artificial hydropower station based on machine learning is studied.In this paper, the prediction model based on BPNN neural network is discussed to predict the daily runoff of hydropower station, and the overall system architecture is designed from the aspects of logical structure, physical structure and technical structure.Through THE simulation analysis of the water level of the hydropower station in different time periods, it is verified that the model has good simulation accuracy in the process of water level deduction, which lays a foundation for the optimization of hydropower station operation in the future.","url":"https://doi.org/10.38007/ml.2020.010405","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:57:18Z","doi":"10.38007/ml.2020.010405","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1145/3653724.3653764","name":"Prediction of Water's Safety for Consumption by Machine Learning","source":"crossref","abstract":"Water quality is the most important topic in the world. Most researchers study water using machine learning techniques to determine its potability. This paper applies similar approaches to predict the water potability and discusses why the accuracy differs. Water quality is closely related to people's lives. Cultivated land and industrial production both rely on water. The emission of sewage resulting from industrial production also endanger human health. According to the Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting models, an analysis was performed to determine the best model for predicting water potability. The Random Forest model is the best model for predicting whether water is potable or not. However, the accuracy for this model is lower. The reasons may be featuring choice, parameter modification, and deficient evaluation standards. For the results, it is recommended to use all the features to train the model in similar research in the future. And set multiple parameters before building the models and compare them.","url":"https://doi.org/10.1145/3653724.3653764","authors":["Jingyi Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-19T10:27:50Z","doi":"10.1145/3653724.3653764","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1093/oxfordhb/9780197653609.013.15","name":"Sociolinguistic Perspectives on Machine Learning with Text Data","source":"crossref","abstract":"Abstract Digitized text has become a popular form of data for sociologists. But text is also the product of many different social and linguistic processes, a topic traditionally examined by sociolinguists in the case of spoken language. This chapter presents a sociolinguistic perspective as a methodological framework when using machine learning to analyze text data. The chapter describes sociolinguistic theories and approaches to studying language while also highlighting ways that this literature tends to differ from traditional sociology. To exemplify this approach, the chapter analyzes a corpus of college admissions essays written by Latinx-identifying applicants using two popular machine-learning-based methods: topic modeling and word embeddings. Similar to variation in spoken language among constituent groups and ethnicities within the panethnic category (e.g., Mexican, Cuban), written language also varies by subgroup in meaningful ways. A sociolinguistic perspective for computational text analysis could spur theoretical and empirical insights.","url":"https://doi.org/10.1093/oxfordhb/9780197653609.013.15","authors":["AJ Alvero"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-19T03:20:41Z","doi":"10.1093/oxfordhb/9780197653609.013.15","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1201/9781032628738-1","name":"Introduction to Machine Learning for Farm Animal Behavior","source":"crossref","abstract":"Animal behavior is the study of interactions, survival strategies, and communication within the animal kingdom. It encompasses how animals respond to various elements of their environment, including both the surroundings and other organisms, such as their kin, predators, and prey.","url":"https://doi.org/10.1201/9781032628738-1","authors":["Natasa Kleanthous","Abir Hussain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-18T14:35:28Z","doi":"10.1201/9781032628738-1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4018/978-1-60960-818-7.ch208","name":"A Bayesian Based Machine Learning Application to Task Analysis","source":"crossref","abstract":"Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.","url":"https://doi.org/10.4018/978-1-60960-818-7.ch208","authors":["Shu-Chiang Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-10-04T09:46:18Z","doi":"10.4018/978-1-60960-818-7.ch208","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.3390/make1010001","name":"Introduction to MAchine Learning &amp; Knowledge Extraction (MAKE)","source":"crossref","abstract":"The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning &amp; Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.","url":"https://doi.org/10.3390/make1010001","authors":["Andreas Holzinger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-07-03T10:27:31Z","doi":"10.3390/make1010001","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1029/2025jh000676","name":"Approximation of Daily AMS‐02 Spectra With Machine Learning Methods","source":"crossref","abstract":"Abstract Almost three thousand daily AMS‐02 proton spectra from 2011 to 2019 offer the most precise and extensive data set of cosmic ray spectra covering a wide energy range. As such, they offer a unique opportunity to test machine learning algorithms for approximating cosmic ray proton spectra based on the inputs usually available to solar modulation models. We evaluated how various machine learning techniques approximate the temporal evolution of the AMS‐02 flux for a wide range of published rigidity bins from 2011 to 2019, primarily focusing on the feasibility and effectiveness of machine learning approaches compared to the traditional force field model in approximating cosmic ray proton spectra. The machine learning methods are very accurate, particularly in comparison to the established force field approach, and significantly improve the approximation of the behavior of cosmic rays.","url":"https://doi.org/10.1029/2025jh000676","authors":["Martin Nguyen","Pavol Bobík","Ján Genči"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-19T11:14:58Z","doi":"10.1029/2025jh000676","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1039/9781837070206-00280","name":"Machine Learning in Predicting the Physicochemical Properties of Drug Candidates","source":"crossref","abstract":"Physicochemical properties strongly influence the pharmacokinetic and pharmacodynamic behaviour of drugs and drug-like molecules. Experimental measurement of these properties is resource-intensive, requiring significant time, cost, and specialized equipment. As a result, machine learning (ML) and deep learning (DL) methods have emerged as efficient alternatives for predicting key properties that impact absorption, distribution, metabolism, elimination, and toxicity (ADMET). ML models trained on large datasets can provide accurate predictions using 2D and 3D molecular descriptors, while DL approaches further enhance performance by learning directly from molecular representations, capturing complex factors such as electronic effects, structural symmetry, and conformational flexibility. Numerous tools, both commercial and open-source, are now available for predicting physicochemical properties relevant to drug discovery. However, the scarcity of high-quality experimental data and reliance on calculated values for model training remain major challenges for building robust and generalizable models. Moving forward, the integration of expert chemical knowledge with ML/DL frameworks is expected to improve predictive accuracy and reliability, helping to reduce late-stage failures and enabling earlier, more informed decision-making in drug development.","url":"https://doi.org/10.1039/9781837070206-00280","authors":["Anila Nuthi","Vaibhav A. Dixit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-22T08:41:26Z","doi":"10.1039/9781837070206-00280","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-3-031-01581-6_4","name":"Continual Learning and Catastrophic Forgetting","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01581-6_4","authors":["Zhiyuan Chen","Bing Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-09T01:25:55Z","doi":"10.1007/978-3-031-01581-6_4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1002/9781394389537.ch3","name":"Acquisition of Brain Signals and Preprocessing for Machine Learning","source":"crossref","abstract":"Brain signals are critical to the functioning of the body. They help to perform all day-to-day activities and provide important insights into brain function. They are pivotal for health care applications, especially in neuroscience and Brain– Computer Interface (BCI) technologies. Machine learning (ML) is being used as a transformative tool to recognize the signals from the brain. Many applications, like cognitive signals and state monitoring, use brain signals. Electrical and magnetic signals and hemodynamic activity of the brain are gathered using brain signal acquisition technologies like electroencephalography (EEG). These dedicated devices and protocols help confirm the accuracy and reliability of the collected signals, though these signals may have artifacts. These raw signals are preprocessed and analyzed using machine learning to identify the meaningful patterns and insights. As this is related to human life, highly powerful and capable preprocessing techniques must be employed, such as filtering, artifact removal, and normalization. ML models are fed with these complex brain signals to extract the hidden features and unseen patterns to perform classification, prediction, and anomaly detection. Incorporation of ML into brain signal analysis enhances the development of intelligent diagnostic systems. This integration accelerates human life by boosting the accuracy, speed, and efficiency of neurophysiological interpretations, paving the way for more adaptive and personalized neurological applications.","url":"https://doi.org/10.1002/9781394389537.ch3","authors":["S. Viveka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-14T21:23:06Z","doi":"10.1002/9781394389537.ch3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.53347/rid-56096","name":"Supervised learning (machine learning)","source":"crossref","abstract":"","url":"https://doi.org/10.53347/rid-56096","authors":["Andrew Murphy","Matt Adams","Jarrel Seah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-25T01:53:55Z","doi":"10.53347/rid-56096","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781420067194-12","name":"Probability and Learning","source":"crossref","abstract":"One criticism that is often made of neural networks-especially the MLP-is that it is not clear exactly what it is doing: while we can go and have a look at the activations of the neurons and the weights, they don’t tell us much. We’ve already seen some methods that don’t have this problem, principally the decision tree in Chapter 6. In this chapter we are going to look at methods that are based on statistics, and that are therefore more transparent, in that we can always extract and look at the probabilities and see what they are, rather than having to worry about weights that have no obvious meaning. The penalty that we pay for this is that there are going to be a whole lot of statistical ideas that we need to understand. We will look at how to perform classification by using the frequency with which examples appear in the training data, and then we will see how we can deal with our first example of unsupervised learning, when the labels are not present for the training examples. If the data comes from known probability distributions, then we will see that it is possible to solve this problem with a very neat algorithm, the EM algorithm, which we will also see in other guises in later chapters. Finally, we will have a look at a rather different way of using the dataset when we look at nearest neighbour methods.","url":"https://doi.org/10.1201/9781420067194-12","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-22T22:34:49Z","doi":"10.1201/9781420067194-12","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1007404308006","name":"Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007404308006","authors":["Yongwon Lee","Bruce G. Buchanan","John M. Aronis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007404308006","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/infoct.2019.8710921","name":"Assertion Detection in Clinical Natural Language Processing: A Knowledge-Poor Machine Learning Approach","source":"crossref","abstract":"Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.","url":"https://doi.org/10.1109/infoct.2019.8710921","authors":["Long Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-05-13T23:04:09Z","doi":"10.1109/infoct.2019.8710921","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4018/978-1-5225-8567-1","name":"Early Detection of Neurological Disorders Using Machine Learning Systems","source":"crossref","abstract":"\"This book examines the role of machine learning systems in the detection of neurological disorders such as Alzheimer disease, Parkinson's disease, schizophrenia, and depression\"--Provided by publisher","url":"https://doi.org/10.4018/978-1-5225-8567-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-11-20T00:20:24Z","doi":"10.4018/978-1-5225-8567-1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4018/978-1-5225-5580-3.ch002","name":"Introduction to Heart","source":"crossref","abstract":"This chapter provides an introduction to the heart and the importance of detecting heart problems based on heart signals. It explains details about electrocardiogram signal and 4 common heart disorders including supraventricular tachycardia, bundle branch block, anterior myocardial infarction (Anterior MI), and inferior myocardial infarction (Inferior MI).","url":"https://doi.org/10.4018/978-1-5225-5580-3.ch002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-04-25T13:33:28Z","doi":"10.4018/978-1-5225-5580-3.ch002","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1056/nejmc2305287","name":"Artificial Intelligence and Machine Learning in Clinical Medicine","source":"crossref","abstract":"","url":"https://doi.org/10.1056/nejmc2305287","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-21T21:01:06Z","doi":"10.1056/nejmc2305287","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1002/9781394325634.oth","name":"Summary of Volume 2","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394325634.oth","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-08T16:48:48Z","doi":"10.1002/9781394325634.oth","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007681906490","name":"Strategies in Combined Learning via Logic Programs","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007681906490","authors":["Evelina Lamma","Fabrizio Riguzzi","Luís Moniz Pereira"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007681906490","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.3390/make7030075","name":"Quantum Machine Learning and Deep Learning: Fundamentals, Algorithms, Techniques, and Real-World Applications","source":"crossref","abstract":"Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial.","url":"https://doi.org/10.3390/make7030075","authors":["Maria Revythi","Georgia Koukiou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-06T07:45:11Z","doi":"10.3390/make7030075","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007676901476","name":"Relational Learning with Statistical Predicate Invention: Better Models for Hypertext","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007676901476","authors":["Mark Craven","Seán Slattery"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007676901476","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1007/978-3-031-69499-8","name":"Shallow Learning vs. Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69499-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T18:01:28Z","doi":"10.1007/978-3-031-69499-8","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1007/978-1-4842-5669-5_1","name":"Introduction to Machine Learning","source":"crossref","abstract":"Machine learning (ML) is a subfield of artificial intelligence, the science and engineering of making intelligent machines. 2 One of the pioneers of artificial intelligence, Arthur Samuel, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” 3 Figure 1-1 shows the relationship between artificial intelligence, machine learning, and deep learning. Artificial intelligence (AI) encompasses other fields, which means that while all machine learning is AI, not all AI is machine learning. Another branch of artificial intelligence, symbolic artificial intelligence, was the predominant AI research paradigm for much of the 20th century. 4 Symbolic artificial intelligence implementations are referred to as expert systems or knowledge graphs which are in essence rules engines that use if-then statements to draw logical conclusions using deductive reasoning. As you can imagine, symbolic AI suffers from several key limitations; chief among them is the complexity of revising rules once they are defined in the rules engine. Adding more rules increases the knowledge in the rules engine, but it cannot alter existing knowledge. 5 Machine learning models on the other hand are more flexible. They can be retrained on new data to learn something new or revise existing knowledge. Symbolic AI also involves significant human intervention. It relies on human knowledge and requires humans to hard-code the rules in the rules engine. On the other hand, machine learning is more dynamic, learning and recognizing patterns from input data to produce the desired output.","url":"https://doi.org/10.1007/978-1-4842-5669-5_1","authors":["Butch Quinto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-22T04:03:03Z","doi":"10.1007/978-1-4842-5669-5_1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/a:1007372016040","name":"Learning and Updating of Uncertainty in Dirichlet Models","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007372016040","authors":["Enrique Castillo","Ali S. Hadi","Cristina Solares"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007372016040","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1023/b:mach.0000015878.60765.42","name":"Introduction to the Special Issue on Meta-Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/b:mach.0000015878.60765.42","authors":["Christophe Giraud-Carrier","Ricardo Vilalta","Pavel Brazdil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-02-13T00:21:28Z","doi":"10.1023/b:mach.0000015878.60765.42","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.4018/978-1-6684-3533-5.ch010","name":"AI and Machine Learning","source":"crossref","abstract":"In the most recent decade, an enormous number of learning strategies have been presented in the field of the AI. Supervised learning has emerged as a major area of research in machine learning. Large numbers of the supervised learning methods have discovered application in their preparing and investigating assortment of information. One of the principle attributes is that the managed learning has the capacity of commenting on preparing information. The supposed marks are class names in the order cycle. There is an assortment of calculations that are utilized in the managed learning strategies. This chapter sums up the crucial parts of a couple of regulated techniques. The principle objective and commitment of this chapter is to introduce the outline of AI and give AI procedures.","url":"https://doi.org/10.4018/978-1-6684-3533-5.ch010","authors":["Manisha Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-11T11:13:16Z","doi":"10.4018/978-1-6684-3533-5.ch010","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.31219/osf.io/mz9xe","name":"ADVANCING PREDICTIVE INSIGHTS IN CARDIOLOGY: A COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR EARLY DETECTION AND CLINICAL IMPLICATIONS OF HEART DISEASE.","source":"crossref","abstract":"This research uses data to find patterns that may point to a higher risk of cardiovascular diseases and to use machine learning (ML) models to forecast heart disease, a significant cause of death worldwide (World Health Organization, 2023). Based on patient health data, we evaluate the effectiveness of many machine learning (ML) models in predicting heart disease, such as Support Vector Machines, K-nearest neighbors, Random Forest, and Logistic Regression. According to Smith and Patel (2022) and Johnson, Gupta, and Kumar (2021), the research method includes data preprocessing, exploratory data analysis, and a thorough assessment of each model's predicted accuracy. By enabling early intervention and individualized treatment regimens, our research suggests that machine learning (ML) can significantly improve the early diagnosis of cardiac illness, potentially transforming patient care and healthcare methods (Lee &amp;amp; Kim, 2020). This study emphasizes how machine learning (ML) can revolutionize healthcare, especially in heart disease prediction. This will help lower the worldwide heart disease burden and enhance customized therapy (Carter et al., 2023). Keywords: Cardiovascular, predicting, machine learning, Logistic Regression, K-nearest neighbors, Random Forest, and Support Vector Machines.","url":"https://doi.org/10.31219/osf.io/mz9xe","authors":["Faith Tobore Edafetanure-Ibeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-04T14:49:01Z","doi":"10.31219/osf.io/mz9xe","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1109/icmla.2012.173","name":"Clinical Report Classification Using Natural Language Processing and Topic Modeling","source":"crossref","abstract":"Large amount of electronic clinical data encompasses important information in free text format. To be able to help guide medical decision-making, text needs to be efficiently processed and coded. In this research, we investigate techniques to improve classification of Emergency Department computed tomography (CT) reports. The proposed system uses Natural Language Processing (NLP) to generate structured output from the reports and then machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Topic modeling of the corpora is also utilized as an alternative representation of the patient reports. Our results show that both NLP and topic modeling improves raw text classification results. Within NLP features, filtering the codes using modifiers produces the best performance. Topic modeling shows mixed results. Topic vectors provide good dimensionality reduction and get comparable classification results as with NLP features. However, binary topic classification fails to improve upon raw text classification.","url":"https://doi.org/10.1109/icmla.2012.173","authors":["Efsun Sarioglu","Hyeong-Ah Choi","Kabir Yadav"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-01-17T20:36:00Z","doi":"10.1109/icmla.2012.173","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.67420/109319.1.1.6","name":"A Hybrid Explainable Machine Learning Framework for Early Heart Disease Prediction Using Clinical Data","source":"crossref","abstract":"Heart disease is a significant type of cardiovascular disease and one of the primary causes of death worldwide, which is why precise and timely diagnostic assistance mechanisms should be a major priority. This paper is a proposal of a hybrid explainable machine learning model to predict early heart disease based on clinical and lifestyle-related features. The suggested method combines several machine learning classifiers with an ensemble learning approach to enhance the predictive robustness, and Shapley Additive explanations (SHAP) are used to enhance the model interpretability. A publicly available UCI Cleveland heart disease dataset that was obtained via Kaggle (n=303 patient records) with 14 clinical features was experimented upon. The proposed hybrid model beats the baseline models, such as Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, and the proposed model has a better performance with an accuracy of 94.87, precision of 95.21, recall of 94.02, F1-score of 94.61, and ROC-AUC of 0.97. In addition, the analysis using the SHAP allowed determining the type of chest pain, the highest heart rate that was reached, ST depression, the number of major vessels, and serum cholesterol as the most impactful predictors, as expected from clinical knowledge. The findings prove that the proposed framework is successful in terms of achieving a balance between predictive accuracy and interpretability, which makes it a useful decision- support instrument to support early detection of heart-related diseases in clinics.","url":"https://doi.org/10.67420/109319.1.1.6","authors":["Shubham Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-03T09:05:42Z","doi":"10.67420/109319.1.1.6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5772/intechopen.1015282","name":"Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics","source":"crossref","abstract":"Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This framework-oriented narrative review and perspective describe the technical and clinical foundations of ML-enabled readers, with emphasis on lateral-flow assays as the archetypal rapid format. We outline reader architectures, computer-vision pipelines, and calibration strategies that translate strip signals to clinically actionable outputs with uncertainty. Because ML becomes part of the measurement system, we propose an evaluation framework that integrates analytical performance (e.g., precision, robustness, interference, low-signal sensitivity) with clinical validation, human factors, interoperability, and continuous monitoring in the post-deployment environment. We align recommendations with laboratory and POCT quality standards, proficiency testing and accreditation guidance, and emerging regulatory expectations for AI-driven medical device software. Finally, we provide implementation case studies and a practical checklist to support the safe scale-up of ML-enabled POCT readers across decentralized settings.","url":"https://doi.org/10.5772/intechopen.1015282","authors":["Paulo Pereira"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-03T13:27:32Z","doi":"10.5772/intechopen.1015282","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1109/mlsp.2014.6958856","name":"Inferring clinical depression from speech and spoken utterances","source":"crossref","abstract":"In this paper, we investigate the problem of detecting depression from recordings of subjects' speech using speech processing and machine learning. There has been considerable interest in this problem in recent years due to the potential for developing objective assessments from real-world behaviors, which may provide valuable supplementary clinical information or may be useful in screening. The cues for depression may be present in \"what is said\" (content) and \"how it is said\" (prosody). Given the limited amounts of text data, even in this relatively large study, it is difficult to employ standard method of learning models from n-gram features. Instead, we learn models using word representations in an alternative feature space of valence and arousal. This is akin to embedding words into a real vector space albeit with manual ratings instead of those learned with deep neural networks [1]. For extracting prosody, we employ standard feature extractors such as those implemented in openSMILE and compare them with features extracted from harmonic models that we have been developing in recent years. Our experiments show that our features from harmonic model improve the performance of detecting depression from spoken utterances than other alternatives. The context features provide additional improvements to achieve an accuracy of about 74%, sufficient to be useful in screening applications.","url":"https://doi.org/10.1109/mlsp.2014.6958856","authors":["Meysam Asgari","Izhak Shafran","Lisa B. Sheeber"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-11-26T15:31:11Z","doi":"10.1109/mlsp.2014.6958856","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.21203/rs.3.rs-10408142/v1","name":"Machine Learning Prediction of Postoperative Pain Improvement Following Coronary Artery Bypass Grafting Using Preoperative Clinical, Psychosocial, Fatigue, and Functional Variables","source":"europepmc","abstract":"Abstract Purpose : Postoperative pain following Coronary Artery Bypass Grafting (CABG) can delay recovery and reduce quality of life. Early identification of patients at risk of poor pain recovery may support personalized rehabilitation planning. This study aimed to develop machine learning models to predict postoperative pain improvement using preoperative patient characteristics. Methods : Data from 192 patients who underwent CABG surgery in six Palestinian hospitals were analyzed. Preoperative demographic, clinical, psy-chosocial, fatigue, and functional variables were used as predictors. Six supervised machine learning classifiers were developed and evaluated using stratified five-fold cross-validation and an independent test set. Model performance was assessed using accuracy, weighted F1-score, and the area under the receiver operating characteristic curve (ROC-AUC). Results : The Support Vector Classifier (SVC) achieved the best performance, with a test accuracy of 84.5%, a weighted F1-score of 84.4%, and an ROC-AUC of 91.4%. Preoperative pain severity, fatigue, transfer ability, smoking status, and functional independence were identified as the most influential predictors. 1 Conclusion: Machine learning models can accurately predict postoperative pain improvement after CABG using preoperative data alone. These findings highlight the potential of machine learning to support risk stratification and personalized postoperative rehabilitation planning. Conclusion : Machine learning models can accurately predict postoperative pain improvement after CABG using preoperative data alone. These findings highlight the potential of machine learning to support risk stratification and personalized postoperative rehabilitation planning.","url":"https://doi.org/10.21203/rs.3.rs-10408142/v1","authors":["Raed Mara'Beh","Saed Mara'Beh","Osama Sawalha"],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10408142/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.71443/9789349552395-03","name":"Machine Learning-Based Digital Manufacturing and Predictive Asset Management for Industry 5.0","source":"crossref","abstract":"The rapid transformation of industrial ecosystems under Industry 4.0 has accelerated the adoption of intelligent, data-driven approaches for enhancing manufacturing efficiency and asset reliability. Digital Manufacturing integrates cyber-physical systems, Industrial Internet of Things infrastructures, and advanced analytics to enable real-time monitoring and control of production processes. Within this paradigm, Machine Learning emerges as a key enabler for predictive asset management by extracting meaningful patterns from large-scale heterogeneous industrial data. This chapter presents a comprehensive exploration of machine learning–driven methodologies for predictive maintenance, focusing on fault detection, remaining useful life estimation, and maintenance scheduling optimization. The integration of intelligent models with Digital Twin technology establishes a dynamic and continuously updated virtual representation of physical assets, enabling simulation-based decision-making and proactive maintenance strategies. A scalable architectural framework that combines edge and cloud computing paradigms supports real-time analytics and efficient data management across distributed manufacturing environments. Critical challenges, including data heterogeneity, model interpretability, cybersecurity risks, and deployment scalability, receive systematic analysis, along with emerging solutions based on explainable artificial intelligence and federated learning approaches. The chapter further highlights industrial applications and case scenarios demonstrating improvements in operational efficiency, reduced downtime, and enhanced asset lifecycle management. The presented insights contribute toward the development of autonomous, resilient, and sustainable manufacturing systems aligned with modern industrial transformation goals.","url":"https://doi.org/10.71443/9789349552395-03","authors":["Kuldeep Agnihotri","Ismatha Begum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-13T04:03:28Z","doi":"10.71443/9789349552395-03","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-031-69499-8_12","name":"Shallow Learning vs Deep Learning in Smart Grid Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69499-8_12","authors":["Musa Yilmaz","Josep M. Guerrero"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-12T18:01:28Z","doi":"10.1007/978-3-031-69499-8_12","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.1039/9781837070206-00386","name":"Machine Learning in Natural Product-based Drug Discovery","source":"crossref","abstract":"Natural products (NPs) have been used as therapeutic agents for centuries due to their unique structure and medicinal properties. However, they also come with numerous challenges, including a complex structure, limited availability, and high-throughput screening constraints. Also, traditional approaches for NP screening and development are time-consuming, resource-intensive, and have a complex chemical space. Machine learning (ML) is emerging as an excellent tool to overcome these challenges. ML techniques, including supervised and unsupervised learning, have been employed for predicting biological activities, identifying active scaffolds, optimizing lead compounds, and guiding de novo synthesis of molecules. These molecules were trained using data from various databases. The more the data, the more efficient the ML model, particularly its prediction power. The combination of ML and NP-based drug discovery will accelerate the drug discovery process encompassing target identification, lead identification and optimization, preclinical and clinical phases, and will also reduce the time and cost in the discovery pipeline. The synergy between ML and NP research is reshaping the future of drug discovery towards a more predictive and efficient paradigm.","url":"https://doi.org/10.1039/9781837070206-00386","authors":["Gargee Mahajan","Neha Kadam","Prashant S. Kharkar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-22T08:41:26Z","doi":"10.1039/9781837070206-00386","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1016/b978-0-443-30010-3.00005-2","name":"Application of machine learning and deep learning models in developmental toxicity","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30010-3.00005-2","authors":["Qunshan Jia","George Daston"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T11:38:32Z","doi":"10.1016/b978-0-443-30010-3.00005-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1007/978-1-4842-4470-8_16","name":"Optimization for Machine Learning: Gradient Descent","source":"crossref","abstract":"Gradient descent is an optimization algorithm that is used to minimize the cost function of a machine learning algorithm. Gradient descent is called an iterative optimization algorithm because, in a stepwise looping fashion, it tries to find an approximate solution by basing the next step off its present step until a terminating condition is reached that ends the loop.","url":"https://doi.org/10.1007/978-1-4842-4470-8_16","authors":["Ekaba Bisong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-27T15:06:10Z","doi":"10.1007/978-1-4842-4470-8_16","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1109/siml65326.2025.11081003","name":"Deep Learning for Audio-Based Respiratory Disease Classification Using Clinical Sound Data","source":"crossref","abstract":"Respiratory diseases, including asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia, are common global health concerns that contribute to morbidity and mortality worldwide. These conditions often present with an initial symptom such as a cough. Early and accurate diagnosis is important for effective management and improved patient outcomes. Traditional diagnostic methods, however, are often limited by subjectivity and external noise interference, emphasizing the need for innovative approaches. This research investigates the use of hybrid deep learning models for classifying respiratory diseases using clinical sound data. There were 8 classes included in the data including healthy with seven diseases; COPD, URTI, Bronchiectasis, Bronchiolitis, Pneumonia, LRTI, and Asthma. Data was simplified into only 2 classes; COPD and non-COPD (which includes healthy and the other 6 diseases). Utilizing audio recordings from the Respiratory Sound Database, this study applies data preprocessing, feature extraction, and a classification model for 2 classes (COPD and non-COPD). The study compares three hybrid architectures-CNN + RNN, CNN + LSTM, and CNN + SVM-to determine the optimal model. Metrics like F1-score, recall, accuracy, and precision are used in evaluating models. The results showed an overall great performance in CNN + LSTM with an accuracy of${8 8 \\%}$. CNN + SVM also showed good capability in classifying the data with an accuracy of 85 %. However, CNN + RNN performed quite poorly due to the incapability in capturing and remembering the patterns of the audio data. This research contributes to the development of AI-driven diagnostic tools as an early warning to respiratory diseases.","url":"https://doi.org/10.1109/siml65326.2025.11081003","authors":["Cherylene Callista Reksohartono","Crysantha Monica Lim","Alexander Agung Santoso Gunawan","Jeffrey Junior Tedjasulaksana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T18:00:49Z","doi":"10.1109/siml65326.2025.11081003","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1109/fmlds63805.2024.00061","name":"Rethinking MedSAM: Performance Discrepancies in Clinical Applications","source":"crossref","abstract":"Segment Anything Model (SAM) has gained significant attention for its versatility and effectiveness in solving various image segmentation problems. Medical image segmentation (MIS) is a complicated problem compared to natural image segmentation, considering the variability in imaging modalities, applications, and clinical requirements. We conduct an evaluation study to validate the usage of MedSAM as a foundation model for the segmentation of medical images, examining its performance in both zero-shot and fine-tuned settings. This paper critically evaluates MedSAM's performance across diverse clinical applications, highlighting performance gaps that question its generalization ability in MIS. Experiments on multiple datasets, including MRI, CT, ultrasound, endoscopy, and retinal fundus images, have shown that MedSAM struggles with complex anatomical structures. Furthermore, the results show that it is sensitive to the bounding box accuracy. These findings suggest that its current implementation lacks the robustness necessary for widespread adoption in clinical practice without fine-tuning. This paper discusses these limitations and proposes strategies for enhancing the MedSAM to address the complexities of MIS.","url":"https://doi.org/10.1109/fmlds63805.2024.00061","authors":["Muhammad Nouman","Ghada Khoriba","Essam A. Rashed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T13:27:08Z","doi":"10.1109/fmlds63805.2024.00061","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1109/ssci.2016.7849886","name":"Using machine learning to predict hypertension from a clinical dataset","source":"crossref","abstract":"Hypertension is an illness that often leads to severe and life threatening diseases such as heart failure, thickening of the heart muscle, coronary artery disease, and other severe conditions if left untreated. An artificial neural network is a powerful machine learning technique that allows prediction of the presence of the disease in susceptible populations while removing the potential for human error. In this paper, we identify the important risk factors based on patients' current health conditions, medical records, and demographics. These factors are then used to predict the presence of hypertension in an individual. These risk factors are also indicative of the probability of a person developing hypertension in the future and can, therefore, be used as an early warning system. We present a neural network model for predicting hypertension with about 82% accuracy. This is good performance given our chosen risk factors as inputs and the large integrated data used for the study. Our network model utilizes very large sample sizes (185,371 patients and 193,656 controls) from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data set. Finally, we present a literature study to show the use of these risk factors in other works along with experimental results obtained from our model.","url":"https://doi.org/10.1109/ssci.2016.7849886","authors":["Daniel LaFreniere","Farhana Zulkernine","David Barber","Ken Martin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-02-16T22:24:33Z","doi":"10.1109/ssci.2016.7849886","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.21203/rs.3.rs-10235878/v1","name":"Stratum-eval: A Normative-First Evaluation Framework for Clinical Machine Learning","source":"europepmc","abstract":"Abstract Evaluation of clinical machine learning systems has focused predominantly on discriminative performance while deferring normative questions which errors matter, to whom, and under what conditions to deployment. We think this ordering is backwards. stratum-eval is the evaluation framework we built to fix it: normative specification becomes a prerequisite, not a postscript. Before any metric is computed, evaluators must complete a Normative Specification Document (NSD) encoding the use case, fairness criterion with explicit acknowledgment of trade-offs, stakeholder map with documented exclusions, and a time-bounded validity horizon. The NSD undergoes hard validation; the pipeline raises an error instead of proceeding on an incomplete or expired specification. The five-layer evaluation protocol then measures discriminative performance, calibration, group fairness, intersectional disparity, and temporal robustness in this order. It produces a StratumReport with a structured model card and machine-readable regulatory export anchored to every NSD commitment. We demonstrate the framework on the MIMIC-IV Clinical Database Demo (v2.2), a cohort of 140 intensive care unit (ICU) stays with a Sepsis-3 outcome (prevalence 41.4%), show that the Chouldechova impossibility theorem is surfaced for normative resolution before modelling begins, and release all code under Apache 2.0.","url":"https://doi.org/10.21203/rs.3.rs-10235878/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10235878/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1111/codi.70443","name":"Methodological considerations in a radiomics-clinical machine-learning model for predicting cytoreduction completeness.","source":"europepmc","abstract":"All data generated or analysed during this study are included in this published article.","url":"https://doi.org/10.1111/codi.70443","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/codi.70443","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/jcm15093300","name":"Ultrasound-Clinical Machine Learning Models for Differentiating Early Cervical Cancer from Myoma: A Retrospective Exploratory Study.","source":"europepmc","abstract":"Objective: To develop machine learning models by integrating transvaginal ultrasound (TVUS) with clinical indicators, conduct visual analysis of the models, and systematically assess their diagnostic efficacy in differentiating early cervical neoplastic lesions. Methods: A total of 144 eligible patients (84 cases of early cervical cancer and 60 cases of cervical myoma) admitted to the First Affiliated Hospital of Chongqing Medical University from January 2018 to August 2025 were retrospectively enrolled in this study. Their clinical data, human papillomavirus (HPV) test results, Thinprep Cytologic Test (TCT) findings, TVUS images and magnetic resonance (MR) imaging data were collected and subjected to comprehensive statistical analysis. Univariate and multivariate Logistic Regression analyses were performed to identify independent differentiating factors for lesion classification. Eleven machine learning models were subsequently constructed, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong test. Finally, a nomogram was developed based on the optimal-performing model for clinical visualization. Results: The TVUS-clinical indicator integration model identified five independent differentiating factors: HPV status, TCT findings, menopausal status, ultrasonic tumor blood supply, and ultrasonic tumor morphology. In contrast, the MR-clinical indicator integration model screened out three independent factors: HPV status, TCT findings, and intratumoral signal intensity on MR T2-weighted imaging (T2WI). The TVUS integration model demonstrated marginally superior diagnostic performance, with a sensitivity of 0.988, specificity of 0.983, and an area under the ROC curve (AUC) of 0.991, compared with the MR integration model (sensitivity: 0.952, specificity: 0.950, AUC: 0.975); however, this difference in AUC values was not statistically significant ( p = 0.911). Among the 11 machine learning models, the Logistic Regression model exhibited optimal classification performance and stability. DCA curves confirmed that all constructed models outperformed single-index diagnostic strategies in clinical decision-making for lesion differentiation. A nomogram was further established based on the Logistic Regression model for intuitive clinical application. Conclusions: Multiple machine learning models integrating TVUS with clinical indicators are successfully developed, and a corresponding nomogram is constructed in this study.","url":"https://doi.org/10.3390/jcm15093300","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15093300","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1186/s13054-025-05725-9","name":"Continuous Evaluation Frameworks for Retrospective Evaluation of Clinical Machine Learning Models.","source":"europepmc","abstract":"The performance of clinical machine learning (ML) models that continuously predict the risk of a future event is typically evaluated using metrics at a single time point. While these metrics are widely accepted to evaluate the performance of ML models, they are not comprehensive enough to capture the performance and behavior of the ML models when executing continuously in real-world settings. Additionally, these single time point metrics assess model discriminability, which is different from clinical utility. In this paper, we propose three novel frameworks to continuously evaluate clinical ML models on retrospective datasets: (1) Zone-based (2) Predictive Activity Monitoring Characteristic curve-based and (3) Notification-based. We demonstrate the value of these three continuous evaluation frameworks using predictions from sepsis models evaluated on two publicly available datasets. The three proposed frameworks provide a more comprehensive evaluation and comparison of different sepsis prediction models and reveal additional aspects of model performance beyond standard metrics computed at a single time point. These frameworks provide users of clinical ML models a more realistic understanding of the model’s utility. BACKGROUND: Clinical machine learning (ML) models are increasingly used to predict patient risk continuously for future adverse events, such as sepsis. Traditionally, these models are evaluated using performance metrics computed at a single timepoint. While these metrics—such as Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision Recall curve ( AUPRC)—are widely accepted, they fail to capture how a model behaves when operating continuously in real-world clinical environments. Moreover, single time point evaluations primarily assess discriminability, which may not directly reflect a model’s clinical utility. METHODS: We propose and implement three novel retrospective evaluation frameworks to continuously assess clinical ML models: zone-based, predictive activity monitoring characteristic curve (AMOC)-based and notification-based. These frameworks were applied to sepsis prediction models using two publicly available clinical datasets. Model outputs were continuously monitored, and results were compared against traditional single time point metrics to assess differences in performance. RESULTS: The three continuous evaluation frameworks reveal multiple dimensions of model performance not captured by conventional metrics. Across both datasets, models with high AUROC demonstrate markedly different performance when continuously evaluated. CONCLUSIONS: The proposed zone-based, predictive AMOC-based, and notification-based frameworks offer a more comprehensive and realistic assessment of continuously operating clinical ML models. By uncovering temporal and behavioral aspects of model performance, these methods enable more informed model selection, deployment, and monitoring in healthcare settings. Implementing continuous evaluation can enhance trust, transparency, and clinical applicability of ML systems beyond what single time point metrics provide. TRIAL REGISTRATION: Not Applicable","url":"https://doi.org/10.1186/s13054-025-05725-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13054-025-05725-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s13304-026-02650-6","name":"Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.","source":"europepmc","abstract":"Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of clinical biomarkers. This study aimed to develop and validate a multimodal artificial intelligence (AI) approach integrating imaging-based deep learning (DL) and clinical data-driven machine learning (ML) to improve PDAC diagnosis. A retrospective cohort of 158 patients (123 PDAC, 35 benign) undergoing pancreatic surgery was analyzed. A YOLOv8-based DL model was trained on contrast-enhanced CT scans to detect pancreatic lesions, while clinical data (age, sex, serum CA19-9) were analyzed with a Random Forest ML classifier. Predictions from both models were combined into a multimodal fusion model, optimized to maximize diagnostic accuracy. Performance metrics included precision, recall, accuracy, F1-score, and ROC-AUC. The imaging-based DL model achieved strong tumor detection performance (mAP: 87.0%, precision: 86.5%, recall: 81.2%). The clinical ML model showed excellent specificity (precision: 100%, ROC-AUC: 0.931) but limited sensitivity (60%). The multimodal AI fusion model outperformed both individual models, significantly improving sensitivity, specificity, and overall diagnostic accuracy. A multimodal AI strategy integrating DL imaging analysis with ML-based clinical predictions markedly enhances diagnostic performance in pancreatic cancer. This approach offers potential as an effective decision-support tool, facilitating earlier diagnosis and optimized clinical decision-making.","url":"https://doi.org/10.1007/s13304-026-02650-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s13304-026-02650-6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1093/tropej/fmag050","name":"A 13-year cohort study using clinical machine learning to differentiate bacterial and viral infections in young infants in a dengue hyperendemic region.","source":"pubmed","abstract":"Differentiating bacterial from viral infections in febrile young infants is challenging, particularly in dengue-hyperendemic regions. We developed and internally validated a clinical machine-learning model to enhance diagnostic accuracy in this risk population in Colombia. We retrospectively analyzed a pediatric infectious admission cohort (&lt;18&#x2009;years) at a reference hospital in southern Colombia from 2007 to 2019. 4671 admissions (2251 bacterial and 2420 viral) were included. Nine clinical and laboratory variables were used to train an eXtreme Gradient Boosting (XGBoost) classifier. We divided the data into development (70%) and test (30%) sets, with Youden's J statistics defining the optimal threshold. Penalized logistic regression (LR) and single-marker rules [leukocytosis, C-reactive protein (CRP)] served as comparators. The young-infant XGBoost achieved an area under the receiver-operating characteristic curve (AUC) of 0.896, outperforming LR (0.790) and single markers (0.746-0.706). Sensitivity was 93.5%, specificity 76.1%, positive predictive value 87.9%, and negative predictive value 86.4% in the temporal validation cohort. Discrimination was highest in children aged 6-10&#x2009;years (AUC 0.967). CRP positivity, leukocytosis &gt;16&#x2009;&#xd7;&#x2009;10&#xb3; &#xb5;l-1, and thrombocytopenia &lt;150&#x2009;&#xd7;&#x2009;10&#xb3; &#xb5;l-1 were the most informative features. A nine-variable XGBoost model using routine clinical and hematologic variables accurately differentiated bacterial from viral infections in children from a low-resource dengue-endemic setting. Performance remained stable during temporal validation. Improved specificity with preserved sensitivity supports earlier targeted therapy and antibiotic stewardship. Multicenter studies and exploration of clinical challenges are the next steps for this kind of tool.","url":"https://doi.org/10.1093/tropej/fmag050","authors":["Cortés-Guzmán LJ","Salgado DM","Narváez CF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/tropej/fmag050","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00270-025-04324-z","name":"Predicting Antegrade Success in Femoropopliteal Occlusions Using Radiological and Clinical Machine Learning Models.","source":"europepmc","abstract":"Purpose To develop and validate a machine learning model integrating imaging, demographic, and laboratory features to predict the technical success of antegrade endovascular approaches for femoropopliteal artery occlusion. Materials and methods The retrospective multicenter study included 379 femoropopliteal artery interventions (training set: n = 264; internal test set: n = 66; external test set: n = 49) treated between January 2020 and June 2023. Radiological features-plaque burden, composition, vessel remodeling, and occlusion length-were extracted from non-contrast and contrast-enhanced CT. Clinical features included demographics, comorbidities, and laboratory results. Feature selection was performed using univariate and multivariate analysis. A random forest model was developed with three variations: clinical, radiological, and combined clinical-radiological. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). Results Technical failure occurred in 136 of 379 interventions (36%). Thirteen key predictors were identified, including hypertension, low-density lipoprotein, aspartate transaminase, occlusion length, Agatston score, and other imaging-based features. The clinical, radiological, and combined clinical-radiological models were developed based on the selected features. The combined model showed the highest performance, with AUC values of 0.81 in the training set, 0.77 in the internal test set, and 0.78 in the external test set. Calibration improved predictive accuracy while maintaining high specificity (> 0.75). DCA confirmed this model's superior net clinical benefit. Conclusions A machine learning model combining radiological and clinical features provides high accuracy in predicting the technical success of antegrade access in femoropopliteal interventions. The model's high specificity and clinical utility may support preoperative decision-making.","url":"https://doi.org/10.1007/s00270-025-04324-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00270-025-04324-z","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1245/s10434-026-19557-6","name":"Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.","source":"europepmc","abstract":"Background Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality therapy) in esophageal adenocarcinoma (EAC) and gastroesophageal junction adenocarcinoma (GEJAC) may improve clinical decision-making and patient counseling before esophagectomy. This study aimed to develop predictive models for pCR after trimodality using machine learning (ML) approaches. Patients and methods A total of 569 patients with EAC and GEJAC who received trimodality therapy at MD Anderson Cancer Center between 2002 and 2022 were included. Clinicopathological characteristics and survival benefit of patients who achieved a pCR were reviewed via descriptive and survival analyses. Subsequently, ML models based on clinical variables were employed to predict pCR, including BART, random forest, and XGBoost, logistic regression, and LASSO. Results pCR was achieved in 132 patients (23.2%). Poorly differentiated tumors, tumors with signet ring cell component, higher T stage, higher clinical stage, residual tumor on biopsy after chemoradiation, and higher SUVmax on positron emission tomography-contract tomography (PET-CT) after chemoradiation were significantly associated with non-pCR. pCR patients had significantly longer overall survival (OS) and relapse free survival (RFS) compared with non-pCR patients (median OS, 10.40 versus 4.42 years, log-rank p = 0.0041; median RFS, 10.40 versus 2.35 years, log-rank p Conclusions This first exploratory study supports the validity and potential utility of ML-based models for predicting pCR after trimodality therapy in EAC and GEJAC. Further validation is warranted before clinical application.","url":"https://doi.org/10.1245/s10434-026-19557-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1245/s10434-026-19557-6","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-026-45800-x","name":"A clinical machine learning model for 1-year functional outcome prediction in acute ischemic stroke: temporal validation across evolving guidelines.","source":"europepmc","abstract":"Accurate prediction of 1-year excellent functional outcome (modified Rankin Scale [mRS] 0–1) in acute ischemic stroke (AIS) patients is vital for guiding long-term rehabilitation. However, existing tools primarily focus on short-term (3-month) outcomes and often lack validation in temporally distinct cohorts, particularly when clinical guidelines and treatment landscapes evolve. To address this, we trained six machine learning models on a derivation cohort (n = 965, admitted 2020–2023) managed under the 2018 Chinese Guidelines for Diagnosis and Treatment of Acute Ischemic Stroke. The optimal logistic regression (LR) model included eight key predictors: admission NIHSS, admission mRS, age, neutrophil‑to‑lymphocyte ratio (NLR), glucose, blood urea nitrogen (BUN), D‑dimer, and B-type natriuretic peptide (BNP). The LR model was rigorously assessed on an independent temporal validation cohort (n = 144, admitted 2024) treated under the 2023 Guidelines, which expanded indications for reperfusion therapy. Although the validation cohort showed significantly higher thrombolysis rates and milder symptoms than the derivation cohort, the LR model demonstrated robust performance (AUC = 0.80, 95% CI: 0.72–0.87), significantly outperforming admission National Institutes of Health Stroke Scale (NIHSS) score (AUC = 0.73, 95% CI: 0.64–0.81). The model also showed substantial incremental value with a net reclassification improvement of 0.71 and an integrated discrimination improvement of 0.14 (both P < 0.001). Finally, an open‑access web‑based predictor was deployed to facilitate clinical implementation within the first 24 h of admission. In summary, we developed and temporally validated a robust, interpretable prediction model for 1-year functional outcome in AIS, offering a practical tool for long-term prognosis.","url":"https://doi.org/10.1038/s41598-026-45800-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-45800-x","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.apmr.2026.02.487","name":"Early Prediction of Standing at Discharge in Moderate-to-Severe Traumatic Brain Injury: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors.","source":"europepmc","abstract":"Objective To develop and internally validate a machine learning model to predict favorable standing ability at hospital discharge in patients with moderate-to-severe traumatic brain injury, incorporating both modifiable and nonmodifiable clinical factors. Design Retrospective cohort study. Setting A tertiary academic medical center in Taiwan. Participants A total of 248 adults with moderate-to-severe traumatic brain injuries admitted between 2019 and 2024 who received standard acute care and had complete discharge functional outcome data. Interventions Not applicable. Main outcome measures Favorable standing ability at discharge, defined as a score ≥6 on the Modified Intensive Care Unit Mobility Scale. Predictor variables included age, Glasgow Coma Scale score, Injury Severity Score, Charlson Comorbidity Index, alanine aminotransferase level, Standardized Education Scale (nonmodifiable), intubation duration, early mobilization, trauma activation, and selected micronutrient supplementation (modifiable). Four machine learning models-logistic regression, extreme gradient boosting, random forest, and support vector machine-were trained using an 80/20 data split. Model performance was assessed using the area under the receiver operating characteristic curve, Brier score, and decision curve analysis. Results The extreme gradient boosting model achieved the greatest discrimination (area under the receiver operating characteristic curve=0.85), good calibration (Brier score=0.16), and an accuracy of 78%, followed by the logistic regression model (area under the receiver operating characteristic curve=0.82; Brier score=0.16; accuracy=80%). The most influential predictors were age, intubation duration, and early mobilization. In the decision curve analysis, both the extreme gradient boosting and logistic regression models provided the greatest net benefit across clinically relevant probability thresholds (0.2-0.6). Conclusions This machine learning-based model enables early, individualized prediction of standing ability at discharge among patients with moderate-to-severe traumatic brain injuries. The inclusion of modifiable variables enhances its clinical utility for rehabilitation planning in the intensive care unit and supports data-driven quality improvement initiatives. External validation is recommended to assess its generalizability.","url":"https://doi.org/10.1016/j.apmr.2026.02.487","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.apmr.2026.02.487","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s43856-025-00965-w","name":"Diagnostic framework to validate clinical machine learning models locally on temporally stamped data.","source":"europepmc","abstract":"Background Real-world medical environments such as oncology are highly dynamic due to rapid changes in medical practice, technologies, and patient characteristics. This variability, if not addressed, can result in data shifts with potentially poor model performance. Presently, there are few easy-to-implement, model-agnostic diagnostic frameworks to vet machine learning models for future applicability and temporal consistency. Methods We extracted clinical data from EHR for a cohort of over 24,000 patients who received antineoplastic therapy within a distinct year. The label of this study are acute care utilization (ACU) events, i.e., emergency department visits and hospitalizations, within 180 days of treatment initiation. Our cross-sectional data spans treatment initiation points from 2010-2022. We implemented three models within our validation framework: Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Results Here, we introduce a model-agnostic diagnostic framework to validate clinical machine learning models on time-stamped data, consisting of four stages. First, the framework evaluates performance by partitioning data from multiple years into training and validation cohorts. Second, it characterizes the temporal evolution of patient outcomes and characteristics. Third, model longevity and trade-offs between data quantity and recency are explored. Finally, feature importance and data valuation algorithms are applied for feature reduction and data quality assessment. When applied to predicting ACU in cancer patients, the framework highlights fluctuations in features, labels, and data values over time. Conclusions The work in this study emphasizes the importance of data timeliness and relevance. The results on ACU in cancer patients show moderate signs of drift and corroborate the relevance of temporal considerations when validating machine learning models for deployment at the point of care.","url":"https://doi.org/10.1038/s43856-025-00965-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1038/s43856-025-00965-w","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1371/journal.pdig.0000728","name":"What makes clinical machine learning fair? A practical ethics framework.","source":"europepmc","abstract":"Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning in clinical workflow have attracted considerable attention including due to complex ethical implications-algorithmic bias being among the most frequently discussed ML models. Here we introduce and discuss a practical ethics framework inductively-generated via normative analysis of the practical challenges in developing an actual clinical ML model (see case study). The framework is usable to identify, measure and address bias in clinical machine learning models, thus improving fairness as to both model performance and health outcomes. We detail a proportionate approach to ML bias by defining the demands of fair ML in light of what is ethically justifiable and, at the same time, technically feasible in light of inevitable trade-offs. Our framework enables ethically robust and transparent decision-making both in the design and the context-dependent aspects of ML bias mitigation, thus improving accountability for both developers and clinical users.","url":"https://doi.org/10.1371/journal.pdig.0000728","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1371/journal.pdig.0000728","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1101/2025.08.01.25332805","name":"Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill children","source":"europepmc","abstract":"Objectives To streamline the development of clinical machine learning (ML) models for predicting acute neurological morbidity in critically ill children by extending our prior work to create a standardized, reproducible, and scalable workflow leveraging Fast Healthcare Interoperability Resources (FHIR), cloud infrastructure, and automated ML tools. Methods We developed workflow for extracting, cleaning, and modeling pediatric intensive care unit (PICU) data, using 168 biomarkers from 7,403 encounters at an academic Children’s hospital between 2020 and 2024. Data were processed and stored in a compliant, secure cloud environment. We evaluated four feature sets: a baseline set from prior work, a complete set, a filtered set, and a light gradient boosting machine (LightGBM)-selected set for prediction of acquired neurological morbidity. Automated ML was used to train, validate, and deploy models, with performance assessed using the area under the receiver operating characteristics curve (AUROC), area under the precision recall curve (AUPRC), F1 score, calibration metrics, and Shapley additive (SHAP) values. A FHIR-based version of the pipeline was also implemented and evaluated on a 2020 subset of the cohort. Results Filtered and LightGBM-based feature sets achieved the highest predictive performance, with AUROCs of 0.90 (95% CI: [0.88-0.92]) for both, and AUPRCs of 0.68 (95% CI: [0.63-0.73]) and 0.67(95% CI: [0.62-0.72]), respectively. SHAP value analysis revealed consistent top features across models, with vital signs and key laboratory values prominently ranked. Models trained using FHIR-formatted data from a 2020 cohort (n = 1,339) demonstrated comparable performance to those built on the complete dataset, with an AUROC of 0.87 (95% CI: [0.81-0.93]). Conclusions This study demonstrates the feasibility of a cloud-compatible, standards-based approach to clinical ML model development. By leveraging interoperable data formats and automated modeling workflows, this approach supports scalable, reproducible model construction and evaluation, enabling improved efficiency and transparency in clinical decision support.","url":"https://doi.org/10.1101/2025.08.01.25332805","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.08.01.25332805","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1186/s13244-025-01938-2","name":"Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.","source":"europepmc","abstract":"Objectives To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML. Methods Retrospective prostate mpMRI studies were classified into 5 series types-T2-weighted (T2W), diffusion-weighted images (DWI), apparent diffusion coefficients (ADC), dynamic contrast-enhanced (DCE) and other series types (others). Metadata was processed for all series and two models were trained (XGBoost after custom categorical tokenization and CatBoost with raw categorical data) using 5-fold cross-validation (CV) with different data fractions for learning curve analyses. For validation, two test sets-hold-out test set and temporal split-were used. A leave-one-group-out (LOGO) CV analysis was performed with centres as groups to understand the effect of dataset-specific data. Results 4045 studies (31,053 series) and 1004 studies (7891 series) from 11 centres were used to train and test series identification models, respectively. Test F1-scores were consistently above 0.95 (CatBoost) and 0.97 (XGBoost). Learning curves demonstrate learning saturation, while temporal validation shows model remain capable of correctly identifying all T2W/DWI/ADC triplets. However, optimal performance requires centre-specific data-controlling for model and used feature sets when comparing CV with LOGOCV, F1-score dropped for T2W, DCE and others (-0.146, -0.181 and -0.179, respectively), with larger performance decreases for CatBoost (-0.265). Finally, we delineate heuristics to assist researchers in series classification for PCa mpMRI datasets. Conclusions Automatic series-type identification is feasible and can enable automated data curation. However, dataset-specific data should be included to achieve optimal performance. Critical relevance statement Organising large collections of data is time-consuming but necessary to train clinical machine-learning models. To address this, we outline and validate an automatic series identification method that can facilitate this process. Finally, we outline a set of metadata-based heuristics that can be used to further automate series-type identification. Key points Multi-centric prostate MRI studies were used for sequence annotation model training. Automatic sequence annotation requires few instances and generalises temporally. Sequence annotation, necessary for clinical AI model training, can be performed automatically.","url":"https://doi.org/10.1186/s13244-025-01938-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1186/s13244-025-01938-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1101/2024.10.29.24316332","name":"GHOSTS: Generation of synthetic hospital time series for clinical machine learning research","source":"europepmc","abstract":"Machine learning (ML) holds great promise to support, improve, and automatize clinical decision-making in hospitals. Data protection regulations, however, hinder abundantly available routine data from being shared across sites for model training. Generative models can overcome this limitation by learning to synthesize hospital data from a target population while ensuring data privacy. Clinical time series acquired during intensive care are, however, difficult to model using established techniques, especially due to uneven sampling intervals. Here we introduce GHOSTS (Generator of Hospital Time Series), a novel generator of synthetic patient trajectories that is capable of generating heterogeneous hospital data including realistic time series with uneven sampling intervals. We further design a suite of novel benchmarks, GHOSTS-Bench. We train GHOSTS on a large cohort of patient data from the MIMIC-IV critical care dataset and measure the quality of the generated data in terms of how faithfully the distributions of individual features in the real data are approximated, how well spatio-temporal dynamics in the multivariate time series are preserved, and how well ML models trained on the generated data can solve a clinical prediction task on the real data. We observe that GHOSTS outperforms a state-of-the-art approach, DoppelGANger, with respect to these criteria.","url":"https://doi.org/10.1101/2024.10.29.24316332","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1101/2024.10.29.24316332","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1053/j.jvca.2025.08.035","name":"Clinical Machine Learning Pitfalls: Reliability of Feature Importance in Prediction of Continuous Renal Replacement Therapy in Acute Type A Aortic Dissection Assessment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1053/j.jvca.2025.08.035","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1053/j.jvca.2025.08.035","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.jclinepi.2024.111606","name":"Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms.","source":"europepmc","abstract":"Background and objectives Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through which bias is introduced in clinical ML applications is not well described. The objective of this study was to examine instances of bias in clinical ML models. We identified the sociodemographic subgroups PROGRESS that experienced bias and the reported mechanisms of bias introduction. Methods We searched MEDLINE, EMBASE, PsycINFO, and Web of Science for all studies that evaluated bias on sociodemographic factors within ML algorithms created for the purpose of facilitating clinical care. The scoping review was conducted according to the Joanna Briggs Institute guide and reported using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews. Results We identified 6448 articles, of which 760 reported on a clinical ML model and 91 (12.0%) completed a bias evaluation and met all inclusion criteria. Most studies evaluated a single sociodemographic factor (n = 56, 61.5%). The most frequently evaluated sociodemographic factor was race (n = 59, 64.8%), followed by sex/gender (n = 41, 45.1%), and age (n = 24, 26.4%), with one study (1.1%) evaluating intersectional factors. Of all studies, 74.7% (n = 68) reported that bias was present, 18.7% (n = 17) reported bias was not present, and 6.6% (n = 6) did not state whether bias was present. When present, 87% of studies reported bias against groups with socioeconomic disadvantage. Conclusion Most ML algorithms that were evaluated for bias demonstrated bias on sociodemographic factors. Furthermore, most bias evaluations concentrated on race, sex/gender, and age, while other sociodemographic factors and their intersection were infrequently assessed. Given potential health equity implications, bias assessments should be completed for all clinical ML models.","url":"https://doi.org/10.1016/j.jclinepi.2024.111606","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.jclinepi.2024.111606","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.jns.2023.122799","name":"An overview of clinical machine learning applications in neurology.","source":"europepmc","abstract":"Machine learning techniques for clinical applications are evolving, and the potential impact this will have on clinical neurology is important to recognize. By providing a broad overview on this growing paradigm of clinical tools, this article aims to help healthcare professionals in neurology prepare to navigate both the opportunities and challenges brought on through continued advancements in machine learning. This narrative review first elaborates on how machine learning models are organized and implemented. Machine learning tools are then classified by clinical application, with examples of uses within neurology described in more detail. Finally, this article addresses limitations and considerations regarding clinical machine learning applications in neurology.","url":"https://doi.org/10.1016/j.jns.2023.122799","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1016/j.jns.2023.122799","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41746-024-01094-9","name":"An operational guide to translational clinical machine learning in academic medical centers.","source":"europepmc","abstract":"Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation. Many information technology companies have created Machine Learning Operations (MLOps) teams to help with such tasks at scale, but the low penetration of home-grown data science tools in regular clinical practice precludes the formation of such teams in healthcare organizations. Based on experiences deploying data science tools at two large academic medical centers (Beth Israel Deaconess Medical Center, Boston, MA; Mayo Clinic, Rochester, MN), we propose a strategy to facilitate this transition from academic product to operational tool, defining the responsibilities of the principal investigator, data scientist, machine learning engineer, health system IT administrator, and clinician end-user throughout the process. We first enumerate the technical resources and stakeholders needed to prepare for model deployment. We then propose an approach to planning how the final product will work from data extraction and analysis to visualization of model outputs. Finally, we describe how the team should execute on this plan. We hope to guide health systems aiming to deploy minimum viable data science tools and realize their value in clinical practice.","url":"https://doi.org/10.1038/s41746-024-01094-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1038/s41746-024-01094-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.ebiom.2023.104823","name":"Detecting changes in the performance of a clinical machine learning tool over time.","source":"europepmc","abstract":"Background Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine learning model to predict BC outcomes and enhance diagnostic stewardship. While the model showed promising initial results, concerns over performance drift due to evolving patient demographics, clinical practices, and outcome rates warrant continual monitoring and evaluation of such models. Methods A real-time evaluation of the model's performance was conducted between October 2021 and September 2022. The model was integrated into Amsterdam UMC's Electronic Health Record system, predicting BC outcomes for all adult patients with BC draws in real time. The model's performance was assessed monthly using metrics including the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and Brier scores. Statistical Process Control (SPC) charts were used to monitor variation over time. Findings Across 3.035 unique adult patient visits, the model achieved an average AUC of 0.78, AUPRC of 0.41, and a Brier score of 0.10 for predicting the outcome of BCs drawn in the ED. While specific population characteristics changed over time, no statistical points outside the statistical control range were detected in the AUC, AUPRC, and Brier scores, indicating stable model performance. The average BC positivity rate during the study period was 13.4%. Interpretation Despite significant changes in clinical practice, our BC stewardship tool exhibited stable performance, suggesting its robustness to changing environments. Using SPC charts for various metrics enables simple and effective monitoring of potential performance drift. The assessment of the variation of outcome rates and population changes may guide the specific interventions, such as intercept correction or recalibration, that may be needed to maintain a stable model performance over time. This study suggested no need to recalibrate or correct our BC stewardship tool. Funding No funding to disclose.","url":"https://doi.org/10.1016/j.ebiom.2023.104823","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1016/j.ebiom.2023.104823","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.4251/wjgo.v16.i3.819","name":"T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.","source":"europepmc","abstract":"Background The study on predicting the differentiation grade of colorectal cancer (CRC) based on magnetic resonance imaging (MRI) has not been reported yet. Developing a non-invasive model to predict the differentiation grade of CRC is of great value. Aim To develop and validate machine learning-based models for predicting the differentiation grade of CRC based on T2-weighted images (T2WI). Methods We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023. Patients were randomly assigned to a training cohort ( n = 220) or a validation cohort ( n = 95) at a 7:3 ratio. Lesions were delineated layer by layer on high-resolution T2WI. Least absolute shrinkage and selection operator regression was applied to screen for radiomic features. Radiomics and clinical models were constructed using the multilayer perceptron (MLP) algorithm. These radiomic features and clinically relevant variables (selected based on a significance level of P Results After feature selection, eight radiomic features were retained from the initial 1781 features to construct the radiomic model. Eight different classifiers, including logistic regression, support vector machine, k-nearest neighbours, random forest, extreme trees, extreme gradient boosting, light gradient boosting machine, and MLP, were used to construct the model, with MLP demonstrating the best diagnostic performance. The AUC of the radiomic-clinical model was 0.862 (95%CI: 0.796-0.927) in the training cohort and 0.761 (95%CI: 0.635-0.887) in the validation cohort. The AUC for the radiomic model was 0.796 (95%CI: 0.723-0.869) in the training cohort and 0.735 (95%CI: 0.604-0.866) in the validation cohort. The clinical model achieved an AUC of 0.751 (95%CI: 0.661-0.842) in the training cohort and 0.676 (95%CI: 0.525-0.827) in the validation cohort. All three models demonstrated good accuracy. In the training cohort, the AUC of the radiomic-clinical model was significantly greater than that of the clinical model ( P = 0.005) and the radiomic model ( P = 0.016). DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process. Conclusion In this study, we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC. This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.","url":"https://doi.org/10.4251/wjgo.v16.i3.819","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.4251/wjgo.v16.i3.819","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3233/nre-240070","name":"Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.","source":"europepmc","abstract":"Background Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RAGT) in individuals with hemiparetic stroke undergoing such intervention remains unexplored. Objective We aimed to determine the best predictive model based on the international classification of functioning impairment domain features (Fugl- Meyer assessment (FMA), Modified Barthel index related-gait scale (MBI), Berg balance scale (BBS)) and reveal their responsiveness to robotic assisted gait training (RAGT) in patients with subacute stroke. Methods Data from 187 people with subacute stroke who underwent a 12-week Walkbot RAGT intervention were obtained and analyzed. Overall, 18 potential predictors encompassed demographic characteristics and the baseline score of functional and structural features. Five predictive ML models, including decision tree, random forest, eXtreme Gradient Boosting, light gradient boosting machine, and categorical boosting, were used. Results The initial and final BBS, initial BBS, final Modified Ashworth scale, and initial MBI scores were important features, predicting functional improvements. eXtreme Gradient Boosting demonstrated superior performance compared to other models in predicting functional recovery after RAGT in patients with subacute stroke. Conclusion eXtreme Gradient Boosting may be an invaluable prognostic tool, providing clinicians and caregivers with a robust framework to make precise clinical decisions regarding the identification of optimal responders and effectively pinpoint those who are most likely to derive maximum benefits from RAGT interventions.","url":"https://doi.org/10.3233/nre-240070","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.3233/nre-240070","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s40477-025-00983-3","name":"An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).","source":"europepmc","abstract":"Aim o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease. Material and methods ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review. Results new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC. Description deline the main AI methods used in OL and OC that we can try to standardize in the clinical radiological and medical practice to ameliorate the patients diagnosis and theraphy. Conclusion through new AI methods it's possible to combine research into a SwarmDeepSurv, generate new data flow channels, create medical imaging data channels of OL and OC using AI and identify new biomarkers of OL and OC. .","url":"https://doi.org/10.1007/s40477-025-00983-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1007/s40477-025-00983-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1186/s12880-024-01365-7","name":"Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.","source":"europepmc","abstract":"Objective To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. Methods A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. Results A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. Conclusion The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.","url":"https://doi.org/10.1186/s12880-024-01365-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1186/s12880-024-01365-7","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s42256-023-00697-3","name":"Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning.","source":"europepmc","abstract":"As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.","url":"https://doi.org/10.1038/s42256-023-00697-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s42256-023-00697-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/hed.27830","name":"A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.","source":"europepmc","abstract":"Background To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Methods Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram. Results A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p Conclusions The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.","url":"https://doi.org/10.1002/hed.27830","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.1002/hed.27830","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41746-023-00805-y","name":"An adversarial training framework for mitigating algorithmic biases in clinical machine learning.","source":"europepmc","abstract":"Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.","url":"https://doi.org/10.1038/s41746-023-00805-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s41746-023-00805-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.prosdent.2023.01.013","name":"Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.","source":"europepmc","abstract":"Statement of problem The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking. Purpose The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion. Material and methods Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute's Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist. Results Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience. Conclusions Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.","url":"https://doi.org/10.1016/j.prosdent.2023.01.013","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1016/j.prosdent.2023.01.013","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/47449","name":"Physicians' and Machine Learning Researchers' Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study.","source":"europepmc","abstract":"Background Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance. Objective This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine. Methods Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set. Results Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams. Conclusions These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.","url":"https://doi.org/10.2196/47449","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.2196/47449","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"epmc:MED37350883","name":"Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/37350883/","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21037/tcr-23-1375","name":"The ultrasound-based radiomics-clinical machine learning model to predict papillary thyroid microcarcinoma in TI-RADS 3 nodules.","source":"europepmc","abstract":"Background Conventional ultrasound (CUS) technology has proven to be successful in the identification of thyroid nodules. Moreover, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) was developed for the purpose of evaluating the risk of thyroid nodules based on ultrasound imaging. Nevertheless, identifying papillary thyroid microcarcinoma (PTMC) from TI-RADS 3 nodules using this system can be difficult due to overlapping morphological features. The main objective of this study was to investigate the efficacy of a machine learning model that utilizes ultrasound-based radiomics features and clinical information in accurately predicting the presence of PTMC in TI-RADS 3 nodules. Methods A total of 221 patients with TI-RADS 3 nodules were included, consisting of 91 cases of PTMC and 130 benign thyroid nodules. They were randomly divided into training and test cohort in an 8:2 ratio. Radiomics features were extracted from CUS images by manually outlining the targets, while clinical parameters were obtained from electronic medical records. The radiomics model, clinical model, and combined model were constructed and validated to distinguish between PTMC and benign thyroid nodules. Radiomics variables were extracted via the Pyradiomics package (V1.3.0). Moreover, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Light Gradient Boosting Machine (LightGBM) was employed to build both radiomics and clinical models. Ultimately, a radiomics-clinical model, which fused radiomics features with clinical information, was developed. Results Among a total of 1,477 radiomics features, fifteen features that were found to be associated with PTMC through univariate analysis and LASSO regression were selected for the development of the radiomics model. The combined \"radiomics-clinical\" model demonstrated superior diagnostic accuracy compared to the clinical model for distinguishing PTMC in both the training dataset [area under receiver operating curve (AUC): 0.975 vs. 0.845] and the validation dataset (AUC: 0.898 vs. 0.811). We constructed a radiomics-clinical nomogram, and the clinical applicability was confirmed through decision curve analysis. Conclusions Utilizing an ultrasound-based radiomics approach has proven to be effective in predicting PTMC in patients with TI-RADS 3 nodules.","url":"https://doi.org/10.21037/tcr-23-1375","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21037/tcr-23-1375","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-023-35557-y","name":"Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models.","source":"europepmc","abstract":"This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach.","url":"https://doi.org/10.1038/s41598-023-35557-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1038/s41598-023-35557-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.cell.2023.01.035","name":"From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.","source":"europepmc","abstract":"Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.","url":"https://doi.org/10.1016/j.cell.2023.01.035","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1016/j.cell.2023.01.035","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21037/gs-23-417","name":"Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma <i>in situ</i>.","source":"europepmc","abstract":"Background Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features. Methods The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5 th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models. Results In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723. Conclusions Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.","url":"https://doi.org/10.21037/gs-23-417","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.21037/gs-23-417","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1155/2023/7542813","name":"Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.","source":"europepmc","abstract":"Purpose This study assessed the impact of intraoral scanner type, operator, and data augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also evaluated the validation accuracy of an unsupervised machine-learning model trained with these scans. Methods Twenty-two dental casts were scanned using two handheld intraoral scanners and one laboratory scanner, resulting in 110 3D cast scans across five independent groups. The scans underwent uniform augmentation and were validated using Hausdorff's distance (HD) and root mean squared error (RMSE), with the laboratory scanner as reference. A 3-factor analysis of variance examined interactions between scanners, operators, and augmentation methods. Scans were divided into training and validation sets and processed through a pretrained 3D visual transformer, and validation accuracy was assessed for each of the five groups. Results No significant differences in HD and RMSE were found across handheld scanners and operators. However, significant changes in RMSE were observed between native and augmented scans with no specific interaction between scanner or operator. The 3D visual transformer achieved 96.2% validation accuracy for differentiating upper and lower scans in the augmented dataset. Native scans lacked volumetric depth, preventing their use for deep learning. Conclusion Scanner, operator, and processing method did not significantly affect the dimensional accuracy of 3D scans for unsupervised deep learning. However, data augmentation was crucial for processing intraoral scans in deep learning algorithms, introducing structural differences in the 3D scans. Clinical Significance . The specific type of intraoral scanner or the operator has no substantial influence on the quality of the generated 3D scans, but controlled data augmentation of the native scans is necessary to obtain reliable results with unsupervised deep learning.","url":"https://doi.org/10.1155/2023/7542813","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1155/2023/7542813","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3233/nre-240116","name":"Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.","source":"europepmc","abstract":"Background Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall. Objective We aimed to determine the most accurate clinical machine learning (ML) algorithm for predicting fall-related balance factors and identifying RAGT attributes. Methods We applied five ML algorithms- logistic regression, random forest, decision tree, support vector machine (SVM), and extreme gradient boosting (XGboost)- to a dataset of 105 post-stroke patients undergoing RAGT. The variables included the Berg Balance Scale score, walking speed, steps, hip and knee active torques, functional ambulation categories, Fugl- Meyer assessment (FMA), the Korean version of the Modified Barthel Index, and fall history. Results The random forest algorithm excelled (receiver operating characteristic area under the curve; AUC = 0.91) in predicting balance improvement, outperforming the SVM (AUC = 0.76) and XGboost (AUC = 0.71). Key determinants identified were knee active torque, age, step count, number of RAGT sessions, FMA, and hip torque. Conclusion The random forest algorithm was the best prediction model for identifying fall-related balance and RAGT determinants, highlighting the importance of key factors for successful RAGT outcome performance in fall-related balance improvement.","url":"https://doi.org/10.3233/nre-240116","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.3233/nre-240116","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1080/15265161.2022.2055212","name":"Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?","source":"europepmc","abstract":"McCradden et al. (2022) propose to close the “AI chasm” between algorithms and clinically meaningful application using the norms of evidence-based medicine (EBM) and clinical research, with the rat...","url":"https://doi.org/10.1080/15265161.2022.2055212","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1080/15265161.2022.2055212","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1101/2022.06.24.22276853","name":"Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning","source":"europepmc","abstract":"As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this study, we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate any site-specific (hospital) and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically-effective screening performances, while significantly improving outcome fairness compared to current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient ICU discharge status task, demonstrating model generalizability.","url":"https://doi.org/10.1101/2022.06.24.22276853","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1101/2022.06.24.22276853","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.2196/36388","name":"Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review.","source":"europepmc","abstract":"Background Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear. Objective Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML. Methods A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase databases, as well as Google Scholar, identified 635 records, of which 12 studies were included. Results Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors' chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them. Conclusions The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.","url":"https://doi.org/10.2196/36388","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.2196/36388","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.48550/arxiv.2208.01127","name":"Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.","source":"europepmc","abstract":"As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship ( i.e. , differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.","url":"https://doi.org/10.48550/arxiv.2208.01127","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.48550/arxiv.2208.01127","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.mayocpiqo.2022.03.003","name":"Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.","source":"europepmc","abstract":"Objective To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs). Patients and methods We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model's performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and tested across all the sites, and the importance of features was examined to understand the reasons for differing generalizability. Results The training data sets contained 1918-64,161 ED visits. The AUC for the pooled model ranged from 0.84 to 0.94 across the sites; the performance decreased slightly when Ns were downsampled to match those of our smallest ED site. When site-specific models were trained and tested across all the sites, the AUCs ranged more widely from 0.71 to 0.93. Within a single ED site, the performance of the 5 site-specific models was most variable for our largest and smallest EDs. Finally, when the importance of features was examined, several features were common to all site-specific models; however, the weight of these features differed. Conclusion A machine learning model for predicting hospital admission from the ED will generalize fairly well within the health care system but will still have significant differences in AUC performance across sites because of site-specific factors.","url":"https://doi.org/10.1016/j.mayocpiqo.2022.03.003","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1016/j.mayocpiqo.2022.03.003","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/33970","name":"Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.","source":"europepmc","abstract":"Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning-friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information-driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.","url":"https://doi.org/10.2196/33970","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.2196/33970","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41586-021-03583-3","name":"Swarm Learning for decentralized and confidential clinical machine learning.","source":"europepmc","abstract":"Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4,5 . Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.","url":"https://doi.org/10.1038/s41586-021-03583-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1038/s41586-021-03583-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41746-021-00495-4","name":"Beyond performance metrics: modeling outcomes and cost for clinical machine learning.","source":"europepmc","abstract":"Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is known to influence the accuracy and generalizability of predictive models, but its effects on cost-effectiveness and utilization are less well-described. A simulation-based approach by Mišić and colleagues goes beyond simple performance metrics to evaluate how process variables may influence the impact and financial feasibility of clinical prediction algorithms.","url":"https://doi.org/10.1038/s41746-021-00495-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1038/s41746-021-00495-4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.ejrad.2023.110731","name":"A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients.","source":"europepmc","abstract":"Objective To develop an effective machine learning model to preoperatively predict the occurrence of futile recanalization (FR) of acute basilar artery occlusion (ABAO) patients with endovascular treatment (EVT). Materials and methods Data from 132 ABAO patients (109 male [82.6 %]; mean age ± standard deviation, 59.1 ± 12.5 years) were randomly divided into the training (n = 106) and test cohort (n = 26) with a ratio of 8:2. FR is defined as a poor outcome [modified Rankin Scale (mRS) 4-6] despite a successful recanalization [modified Thrombolysis in Cerebral Infarction (mTICI) ≥ 2b]. A total of 1130 radiomics features were extracted from diffusion-weighted imaging (DWI) images. The least absolute shrinkage and selection operator (LASSO) regression method was applicated to select features. Support vector machine (SVM) was applicated to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve. Results The area under the receiver operating characteristic (ROC) curve (AUC) of the radiomics-clinical model was 0.897 (95 % confidence interval, 0.837-0.958) in the training cohort and 0.935 (0.833-1.000) in the test cohort. The AUC of the radiomics model was 0.887 (0.824-0.951) in the training cohort and 0.840 (0.680-1.000) in the test cohort. The AUC of the clinical model was 0.746 (0.652-0.840) in the training cohort and 0.766 (0.569-0.964) in the test cohort. The AUC of the radiomics-clinical model was significantly larger than the clinical model (p = 0.016). A radiomics-clinical nomogram was developed. The decision curve analysis indicated its clinical usefulness. Conclusion The DWI-based radiomics-clinical machine learning model achieved satisfactory performance in predicting the FR of ABAO patients preoperatively.","url":"https://doi.org/10.1016/j.ejrad.2023.110731","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.1016/j.ejrad.2023.110731","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.patter.2020.100017","name":"Physiology as a Lingua Franca for Clinical Machine Learning.","source":"europepmc","abstract":"The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a starting point for the development of a common language between clinicians and ML experts, thereby accelerating real-world impact.","url":"https://doi.org/10.1016/j.patter.2020.100017","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1016/j.patter.2020.100017","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1101/2022.01.13.22268948","name":"Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning","source":"europepmc","abstract":"Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection or magnified during model development. For example, if one class is over-presented or errors/inconsistencies in practice are reflected in the training data, then a model can be biased by these. To evaluate our adversarial training framework, we used the statistical definition of equalized odds. We evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate regional (hospital) and ethnic biases present. We trained our framework on a large, real-world COVID-19 dataset and demonstrated that adversarial training demonstrably improves outcome fairness (with respect to equalized odds), while still achieving clinically-effective screening performances (NPV > 0.98). We compared our method to the benchmark set by related previous work, and performed prospective and external validation on four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.","url":"https://doi.org/10.1101/2022.01.13.22268948","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.1101/2022.01.13.22268948","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1016/j.spinee.2020.02.016","name":"Significance of external validation in clinical machine learning: let loose too early?","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.spinee.2020.02.016","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1016/j.spinee.2020.02.016","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.spinee.2020.02.017","name":"Response to letter to the editor on \"Significance of external validation in clinical machine learning: let loose too early?\"","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.spinee.2020.02.017","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.1016/j.spinee.2020.02.017","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1017/s0033291720004766","name":"Why educating for clinical machine learning still requires attention to history: a rejoinder to Gauld <i>et al</i>.","source":"europepmc","abstract":"An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.","url":"https://doi.org/10.1017/s0033291720004766","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.1017/s0033291720004766","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1093/gigascience/gix019","name":"The need to approximate the use-case in clinical machine learning.","source":"europepmc","abstract":"The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists.","url":"https://doi.org/10.1093/gigascience/gix019","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2017","doi":"10.1093/gigascience/gix019","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1055/a-2927-0698","name":"Clinical Implementation of Pharmacogenetics-Based Machine Learning Clinical Decision Support Systems: A Scoping Review.","source":"europepmc","abstract":"Abstract BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rule-based clinical decision support systems often fall short. Machine learning-based clinical decision support systems offer a more dynamic solution, combining pharmacogenetic variants, patient-specific clinical data, medication history, and guidelines to generate personalized treatment recommendations. Abstract OBJECTIVE: This scoping review examined the extent and nature of evidence on integrating machine learning into clinical decision support systems for pharmacogenetics, emphasizing clinical implementation. By focusing on articles where tools have been integrated into clinical workflows, this review highlights progress and persistent gaps in translating pharmacogenetics-informed machine learning models into everyday clinical practice. Abstract METHODS: A comprehensive search across multiple databases identified studies published from January 2015 to September 2025. Eligible studies included any design reporting on machine learning-based clinical decision support systems incorporating pharmacogenetic data to support therapeutic decisions in clinical settings. Abstract RESULTS: Of 1262 records screened, 7 studies met inclusion criteria. These studies implemented machine learning-based clinical decision support systems integrating pharmacogenetic data to guide drug therapy in clinical environments. While varying in design, setting, and implementation maturity, most systems demonstrated potential benefits, such as reducing preventable adverse drug events, improving prescribing accuracy, or enhancing workflow integration. Two of the included studies evaluated PGx ML-CDSS tools in live clinical or trial workflows. Abstract CONCLUSION: Despite growing interest and model development, clinical implementation of machine learning-based pharmacogenetic clinical decision support tools remains limited. Most systems remain rule-based, while a few integrate machine learning with pharmacogenetic data for more personalized and adaptive decision-making. This review underscores the gap between theoretical model development and real-world application. To advance the field, implementation science research is needed to evaluate usability, clinical workflow integration, and patient outcomes. Real-world evidence is essential to unlock the full potential of machine learning-based clinical decision support in pharmacogenetics.","url":"https://doi.org/10.1055/a-2927-0698","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1055/a-2927-0698","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/nicc.70629","name":"The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, including high patient acuity, ICU delirium, and nurse workload. These factors demand innovative technological solutions. Aim This scoping review comprehensively explores the current picture of AI and ML applications in critical care nursing, focusing on decision support systems, predictive analytics, workflow automation, and patient engagement tools. Methods A search of Four databases (Scopus, PubMed/MEDLINE, Science Direct, and CINAHL) was conducted for original peer-reviewed studies published between January 2019 and September 2025. The 2019 start date was selected to capture the contemporary wave of AI applications in critical care nursing, coinciding with the documented exponential growth in AI-related ICU publications following widespread EHR adoption and the maturation of deep learning architectures. Results Five key themes were identified: predictive analytics and early warning systems, clinical decision-support tools, automation and workflow enhancements, monitoring combined with human-AI collaboration, and implementation challenges. Findings reveal that AI can reduce administrative burden and improve care quality. However, significant gaps persist, especially in evaluating long-term outcomes, nurse involvement, and ethical implementation. Conclusion This scoping review provides a contemporary, integrated thematic synthesis of machine learning and AI applications in critical care nursing. While not claiming absolute novelty, this review addresses a distinct and timely gap by simultaneously mapping predictive analytics, clinical decision support, workflow automation, and implementation challenges within a single evidence synthesis. Relevance to clinical practice AI and machine learning may support critical care nurses by facilitating earlier recognition of patient deterioration, strengthening clinical decision-making, and reducing repetitive workload. Successful implementation requires nurse involvement in system design, appropriate training, transparent algorithms, and integration with existing clinical workflows.","url":"https://doi.org/10.1111/nicc.70629","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/nicc.70629","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/84636","name":"Integrated Clinical-Molecular Risk Stratification in Diffuse Large B-Cell Lymphoma: Machine Learning Survival Analysis.","source":"europepmc","abstract":"Background The clinical outcomes of diffuse large B-cell lymphoma (DLBCL) are highly heterogeneous. While clinical indices like the international prognostic index (IPI) are widely used, their predictive accuracy remains limited. The integration of molecular features with clinical characteristics holds promise for developing more precise prognostic models to improve risk stratification and personalize treatment strategies. Objective This study aimed to systematically identify key factors influencing overall survival (OS) and relapse in patients with DLBCL by leveraging publicly available transcriptomic data and clinical information. The goal was to construct and validate a high-precision risk-prediction model by using machine learning methods to aid in individualized clinical decision-making. Methods We curated clinical and transcriptomic data from the GSE31312 cohort. A baseline clinical model was first constructed using multivariate Cox regression. Key genes associated with prognosis were identified through univariate Cox and survival analyses. Subsequently, 3 machine learning survival models, namely, fast survival support vector machine (FastSurvivalSVM), gradient boosting survival analysis (GBSurvival), and random survival forest (RSF), were trained and evaluated using 5-fold cross-validation. The interpretability of the optimal model was further elucidated using Shapley Additive Explanations (SHAP) methodology. Results The baseline clinical model confirmed age, elevated lactate dehydrogenase, Eastern Cooperative Oncology Group score, Ann Arbor stage, and B symptoms as independent risk factors for OS and relapse-free survival, with a C-index of 0.65-0.67. At the molecular level, genes such as PSMG4 and CRY1 were significantly associated with poor OS, while TMEM182 and SPIRE1 were prominent in relapse prediction. Among the machine learning models, FastSurvivalSVM demonstrated the best overall performance, achieving an area under the curve of 0.791 for 1-year OS prediction and 0.774 for 1-year relapse prediction. SHAP analysis revealed that both clinical (eg, IPI and age) and molecular (eg, PSMG4 and SPIRE1) features were critical drivers of the model's predictions. Conclusions This study successfully developed a multidimensional risk prediction model that integrates clinical and molecular characteristics for DLBCL. The FastSurvivalSVM model showed superior performance in predicting mortality and relapse risks. The interpretability analysis uncovered key prognostic factors, providing a valuable tool for personalized risk management and new theoretical insights for future mechanistic research.","url":"https://doi.org/10.2196/84636","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/84636","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s11255-026-05353-4","name":"Development and validation of a multidimensional machine learning model for predicting delayed graft function in deceased donor kidney transplantation.","source":"europepmc","abstract":"Purpose Delayed graft function (DGF) remains a significant complication following deceased donor kidney transplantation. This study aimed to develop and validate a multidimensional machine learning model for predicting DGF by integrating clinical data, machine perfusion parameters, donor scores, and histopathological scores. Methods A retrospective analysis was conducted on 961 deceased donor kidney transplant recipients from January 2019 to December 2021. The dataset was stratified by the target variable and randomly divided into training (80%) and independent testing (20%) cohorts. Fifteen data combinations across four dimensions were evaluated using six machine learning algorithms. Model performance was assessed using AUC, calibration curves, and decision curve analysis. SHAP analysis was employed for feature interpretation. Results The optimal model combining clinical supplementary data with donor scores and histopathological scores using LightGBM achieved an AUC of 0.895 (95% CI 0.806-0.985) in the testing cohort, with accuracy of 89.1%, sensitivity of 59.1%, and specificity of 93.0%. Clinical data served as the foundational predictive dimension (AUC = 0.874), while histopathological scores provided significant incremental value (ΔAUC = 0.016). Among the top five ranked combinations, four included histopathological scores. Gain-based feature importance identified donor score, renal tubular necrosis, and total pathological score as the leading predictors, with pathological indicators occupying four of the top five positions; SHAP analysis further revealed blood urea nitrogen and donor score as the most influential individual features, with renal tubular necrosis being the top-ranked histopathological predictor. Conclusion This multidimensional machine learning model demonstrates excellent predictive performance for DGF. The \"scenario-model-threshold\" clinical decision framework provides a practical tool for guiding donor kidney assessment and immunosuppressive therapy decisions in diverse clinical contexts.","url":"https://doi.org/10.1007/s11255-026-05353-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11255-026-05353-4","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10415955/v1","name":"Development and Validation of a Machine Learning-Based Prediction Model for Atrial Fibrillation in Hospitalized Patients with Coronary Heart Disease and Diabetes Mellitus: A Retrospective Study","source":"europepmc","abstract":"Abstract Background Coronary heart disease (CHD) and diabetes mellitus (DM) are among the most prevalent cardiovascular and metabolic disorders worldwide. The coexistence of these conditions substantially increases the risk of adverse cardiovascular outcomes, including atrial fibrillation (AF). Early identification of patients at high risk for AF may facilitate timely intervention and improve clinical prognosis. This study aimed to develop and validate a machine learning-based model for predicting the risk of AF in hospitalized patients with CHD and DM. Methods This retrospective study included 2,033 hospitalized patients with CHD and DM admitted to Chongqing Emergency Medical Center between January 2017 and December 2025. Patients were classified into an AF group (n = 798) and a non-AF group (n = 1,235) according to the occurrence of AF during hospitalization. Demographic characteristics, laboratory findings, and echocardiographic parameters were collected. Candidate predictors were identified using a hybrid feature-selection strategy incorporating univariate analysis, random forest feature importance ranking, and expert clinical evaluation. Six machine-learning algorithms, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), AdaBoost, and gradient boosting classifier (GBC), were developed and compared. Results Eight key predictors were ultimately selected for model construction: age, left atrial diameter (LAD), right atrial diameter (RAD), prealbumin, serum calcium, α-hydroxybutyrate dehydrogenase (HBDH), serum sodium, and the white blood cell-to-albumin ratio (WBC/Alb). Among the six machine learning models evaluated, the RF model demonstrated the best overall performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.910. The corresponding accuracy, sensitivity, and F1-score were 0.835, 0.857, and 0.839, respectively, significantly outperforming conventional models such as LR and SVM. SHapley Additive exPlanations (SHAP) analysis further enhanced model interpretability and enabled individualized risk assessment. Conclusions A machine learning-based prediction model incorporating multidimensional clinical indicators demonstrated excellent performance in identifying hospitalized patients with CHD and DM at high risk for AF. The SHAP-based visualization framework provides an intuitive and interpretable approach for individualized risk stratification and clinical decision-making. This model may serve as a practical tool for early screening, targeted intervention, and optimized management of AF in this high-risk population.","url":"https://doi.org/10.21203/rs.3.rs-10415955/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10415955/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1177/03000605261472037","name":"Development and external validation of an online interpretable machine-learning model for predicting delirium risk in acute heart failure.","source":"europepmc","abstract":"ObjectiveDelirium is a frequent complication in acute heart failure and is associated with poor outcomes in the intensive care unit. Machine learning methods can leverage high-dimensional clinical data to support early risk stratification; however, models specifically designed to predict delirium in acute heart failure are limited, underscoring the need for reliable and clinically applicable tools.MethodsPatients with acute heart failure were identified using International Classification of Diseases codes, and delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit. Baseline clinical data were subjected to feature selection using least absolute shrinkage and selection operator, Boruta and recursive feature elimination. Eight machine learning models were developed, and the optimal model was evaluated for discrimination, calibration and clinical utility, with SHapley Additive exPlanations used to interpret predictor contributions.ResultsTen key predictors were identified using three feature selection methods. The gradient boosting machine model demonstrated favourable discrimination and calibration in both internal and external validation cohorts, although the areas under the receiver operating characteristic curve of the neural network and Light Gradient Boosting Machine models were not significantly different from those of the gradient boosting model in the DeLong comparisons. SHapley Additive exPlanations identified the Glasgow Coma Scale score, Sequential Organ Failure Assessment score and sedative use as major contributors to risk prediction. The final model was implemented as an online risk calculator to provide individualised risk estimates.ConclusionsThe model showed acceptable performance for estimating delirium risk in patients with acute heart failure and may support early intensive care unit risk stratification. The web-based calculator enables individualised assessment; however, prospective multi-centre validation is needed before broad clinical implementation.","url":"https://doi.org/10.1177/03000605261472037","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03000605261472037","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/kjm2.70288","name":"Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.","source":"europepmc","abstract":"This study aimed to develop machine learning models for predicting tumor recurrence in breast cancer before neoadjuvant systemic therapy (NST) by integrating clinical and radiomic features derived from pretreatment computed tomography (CT). We retrospectively enrolled 235 patients with 237 breast tumors who underwent contrast-enhanced CT before NST. Datasets were randomly divided into five-fold training and testing sets using semi-random partitioning to ensure similar clinical characteristics between the two subsets. Subsequently, a nested five-fold cross-validation was performed to develop a recurrence prediction model using three machine learning algorithms across clinical, radiomics, and integrated models. The performance of prediction models was compared using the area under the receiver-operating characteristic curve (AUC), and the best clinical and radiomics models were further integrated to develop the final model. Kaplan-Meier analysis with a log-rank test was conducted to compare survival curves between high- and low-risk groups stratified by the prediction models. The comparisons demonstrated that the random survival forest (RSF) clinical model (mean AUC = 0.755) and the Cox-least absolute shrinkage and selection operator (Cox-LASSO) radiomics model (mean AUC = 0.636) outperformed other machine learning algorithms. The integration of the clinical (RSF) and radiomics (Cox-LASSO) models achieved a mean AUC of 0.777 in predicting tumor recurrence. The log-rank analysis revealed significant differences in the survival curves between the high- and low-risk groups stratified by the integration model on the testing sets. In conclusion, the integration of clinical and CT-based radiomics models was helpful for the pretreatment prediction of tumor recurrence in patients with breast cancer after NST.","url":"https://doi.org/10.1002/kjm2.70288","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/kjm2.70288","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fneur.2026.1847449","name":"Machine learning-based prediction of in-hospital deep vein thrombosis in patients with acute ischemic stroke: a multicenter study.","source":"europepmc","abstract":"Background Deep vein thrombosis (DVT) is a common complication of acute ischemic stroke (AIS) and may worsen clinical outcomes, yet reliable tools for early risk stratification remain limited. We aimed to develop and internally evaluate machine learning models for predicting in-hospital DVT in patients with AIS. Methods We conducted a secondary analysis of a publicly available multicenter retrospective dataset including 21,459 patients with AIS. The primary outcome was imaging-confirmed in-hospital DVT. Participants were stratified according to DVT status and randomly divided into a training set (70%) and a held-out test set (30%). Feature selection was performed using least absolute shrinkage and selection operator regression, the Boruta algorithm, variance inflation factor assessment, and clinical judgment. Eight machine learning algorithms were trained using a 19-variable full predictor set and an 8-variable simplified predictor set. Hyperparameters were optimized using repeated 5-fold cross-validation with 2 repeats. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), Brier score, calibration, decision curve analysis, and additional classification metrics. Sensitivity analyses excluded D-dimer and used within-fold synthetic minority oversampling. Results Among 21,459 patients, 1,324 (6.17%) developed in-hospital DVT. In the full predictor-set analysis, RANGER achieved the highest AUC in the held-out test set (0.976). Among models using the simplified predictor set, XGBoost achieved the highest AUC (0.917) and sensitivity (0.852), whereas SVM demonstrated the most favorable overall performance profile, with the highest AUPRC (0.605), lowest Brier score (0.038), highest positive predictive value (0.440), and highest F 1-score (0.549). D-dimer was the most influential predictor. Model performance was attenuated after exclusion of D-dimer, and the SMOTE analysis showed slightly lower discrimination and greater calibration discrepancies. All 8 simplified predictor-set models were incorporated into an online prediction platform. Conclusion Machine learning models demonstrated favorable performance for predicting in-hospital DVT after AIS. Among models using the simplified 8-variable predictor set, SVM showed the most favorable overall performance, whereas XGBoost prioritized sensitivity. Independent external validation and prospective clinical-impact assessment are required before routine clinical implementation.","url":"https://doi.org/10.3389/fneur.2026.1847449","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1847449","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/aas.70318","name":"Development and Clinical Utility of Machine Learning Models for Prediction of Same-Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study.","source":"europepmc","abstract":"Background Hip and knee replacement are common procedures with an increasing focus on same-day surgery. However, capacity constraints limit the number of eligible patients actually being scheduled for same-day discharge, calling for further selection of those with the highest likelihood of same-day discharge. Methods A prognostic study from September 2022 to April 2024 aiming to develop and evaluate three machine learning models of increasing complexity for prediction of successful same-day discharge after hip and knee replacement. Data was collected from six Danish departments with similar same-day surgery protocols and same-day surgery eligibility was according to predefined clinical criteria. The models were evaluated using receiver operating characteristic and clinical utility curves depicting the potential increase in same-day discharge at different same-day surgery capacities. Results Of 5387 eligible patients, 4466 (82.9%) were scheduled for same-day surgery. Of these, 3085 (69.1%) achieved same-day discharge and 1381 (30.9%) were admitted. The remaining 921 (17.1%) were planned as in-patients. The area under the receiver operating curve showed poor but marginally increasing predictive ability (0.586, 0.602, and 0.603, respectively). Mean probability for same-day discharge in scheduled same-day patients was significantly increased in discharged vs. admitted patients (69.90% SD: 7.6 vs. 67.03 SD: 8.3 p Conclusions Machine learning based prognostic probability scores for planning same-day hip and knee replacement in pre-selected eligible patients did not provide relevant potential increases in same-day discharge rates. Editorial comment This study assessed if an advanced model using routinely available clinical data could confidently predict whether of not cases planned for same-day hip or knee arthroplasty would be successfully discharged as planned. Data from multiple collaborating fast-track surgical centers in Denmark contributed to the model. The advanced model here based on the available clinical data did not perform clearly better than other simpler predictive models that have already been reported.","url":"https://doi.org/10.1111/aas.70318","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/aas.70318","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/2516-1091/ae98cb","name":"Machine learning for thalassemia detection: a critical review of diagnostic pathways and emerging technologies.","source":"europepmc","abstract":"Thalassemia remains a major diagnostic challenge because its detection requires the interpretation of multiple laboratory and molecular findings in different stages of care. Current diagnostic approaches are effective in clinical practice, but they are often discussed as separate methods rather than as parts of a connected diagnostic pathway. This review addresses this need through a pathway-based and methodologically oriented perspective, beginning with traditional diagnostic methods, moving to emerging diagnostic technologies, and then examining machine learning (ML) as a supportive layer within the diagnostic pathway. To clarify the role of ML, the discussion separates methodological aspects from clinical applications: the first part explains how models are developed and evaluated, while the second part examines where they may contribute in real diagnostic practice. Finally, key challenges are discussed that must be addressed for reliable clinical translation and future implementation. Overall, this review argues that the future of thalassemia detection lies in an integrated diagnostic framework that connects screening, confirmation, monitoring, and treatment planning to support faster, more reliable, and more individualized care for a variety of populations.","url":"https://doi.org/10.1088/2516-1091/ae98cb","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1088/2516-1091/ae98cb","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/nicc.70652","name":"Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.","source":"europepmc","abstract":"Background ICU-acquired weakness (ICU-AW) is a common and debilitating complication among critically ill patients, particularly those undergoing major surgery. Early identification of patients at high risk of ICU-AW may facilitate timely preventive strategies and targeted rehabilitation interventions. Aim To develop and internally validate an interpretable machine learning model for predicting ICU-AW in postoperative patients admitted to the ICU. Study design This retrospective study collected data from patients who had previously been admitted to the surgical ICU after surgery. ICU-AW was defined as a Medical Research Council sum score ≤ 48. Candidate predictors were selected using least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were developed and internally validated using a 7:3 random split and 10-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis. SHapley Additive exPlanations (SHAP) was applied to interpret model predictions. Results In total, 967 postoperative patients admitted to the surgical ICU were included in the analysis. The overall incidence of ICU-AW was 24.4%. Five predictors were selected by LASSO: postoperative delirium, interleukin-6, Barthel index, sepsis and Sequential Organ Failure Assessment (SOFA) score. Among the eight algorithms, the random forest model achieved the best discriminative performance in the validation cohort (AUC 0.788, 95% CI: 0.727-0.849) and demonstrated good calibration and clinical utility. SHAP analysis identified Barthel index, SOFA score and postoperative delirium as the top contributors to the model's predictions and provided individualised explanations of risk estimates. Conclusions This study developed an interpretable model using five variables to predict ICU-AW in surgical ICU patients, which demonstrated good performance, predictive value and clinical utility. A user-friendly web-based tool was developed to support individualised risk assessment and enhance clinical applicability. Relevance to clinical practice The proposed model may assist clinicians in early risk stratification of postoperative ICU patients and facilitate targeted surveillance and preventive care for those at high risk of ICU-AW.","url":"https://doi.org/10.1111/nicc.70652","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/nicc.70652","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/94172","name":"Predicting Cesarean Section Delivery in the United States Using Machine Learning: Population-Based Retrospective Study.","source":"europepmc","abstract":"Background Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity. Objective This study aims to document geographic variation in C-section use across the United States, identify maternal and county-level factors associated with C-section delivery, and evaluate the predictive performance of machine learning models across clinically defined risk groups. Methods This population-based study used 38,133,279 US births from the 2013-2022 National Vital Statistics System Natality Detailed Files. County identifiers were linked to national county-level measures of insurance coverage, health care capacity, and socioeconomic conditions. Logistic regression models with county fixed effects were used for feature interpretation, and supervised machine learning models were used for prediction. Analyses were conducted separately for the full sample, a low-risk sample (n=17,760,772), and a high-risk sample (n=20,372,438). Predictive performance was evaluated using accuracy, precision, recall, F 1 -score, and area under the receiver operating characteristic curve (AUC), with 200-bootstrap 95% CIs. Temporal validation was also conducted using training on earlier years and testing on later years. Results County-level C-section rates declined modestly from 32.35% in 2013 to 31.49% in 2022, but substantial geographic variation persisted, with consistently higher rates in the US South. In the full sample, model discrimination was good, with AUC values ranging from 0.8310 for logistic regression to 0.8401 for extreme gradient boosting (XGBoost). Predictive performance was substantially weaker in the low-risk sample (AUC 0.7246-0.7410) than in the high-risk sample (AUC 0.8404-0.8568). In the high-risk sample, XGBoost achieved the highest AUC (0.8568) and F 1 -score (0.7579), while random forest achieved the highest recall (0.7142). Temporal validation yielded similar results in the full sample (AUC 0.8335-0.8387), temporal low-risk sample (AUC 0.7364-0.7432), and temporal high-risk sample (AUC 0.8360-0.8479), indicating stable performance over time. Conclusions C-section use in the United States is shaped by both maternal clinical risk and geographic context. Machine learning models perform well overall and especially well in high-risk pregnancies, but prediction is substantially more difficult in low-risk pregnancies, where discretionary and contextual influences may play a larger role. These findings support the use of risk-adjusted, context-aware prediction tools for audit, benchmarking, and clinical decision support while underscoring the need for cautious implementation, subgroup monitoring, and further external validation.","url":"https://doi.org/10.2196/94172","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/94172","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/lary.70861","name":"Machine Learning-Based Risk Stratification Tool for Hearing Loss in High-Risk Neonates.","source":"europepmc","abstract":"Objective This study aims to develop and evaluate a machine learning-based risk stratification tool for predicting hearing loss in high-risk neonates using clinical risk factors to support targeted early identification. Methods A total of 270 infants, 105 with hearing loss, 165 with normal hearing who had passed initial screening but possessed clinical risk factors were retrospectively analyzed. Clinical variables included prematurity, low birth weight, hyperbilirubinemia, phototherapy, NICU stay duration, and family history. Five models (Random Forest, XGBoost, CatBoost, K-nearest neighbors, and Logistic Regression) were developed using an 80/20 train-test split and stratified 5-fold cross-validation. Additionally, a web-based clinical decision support tool was developed using the Streamlit framework to provide real-time risk assessment. Results The XGBoost model achieved the highest performance with 85.2% accuracy and an AUC of 87.1%. SHAP analysis identified NICU stay duration and positive family history as the most influential predictors for neonatal hearing loss. Conclusion Machine learning models, particularly XGBoost, provide robust risk stratification for high-risk neonates. Rather than replacing universal screenings, these tools can complement existing programs by identifying high-risk infants who require prioritized diagnostic follow-up and closer clinical monitoring. The developed web application (available at https://newbornhearing.streamlit.app/) offers a practical interface for clinical use. Level of evidence: 3","url":"https://doi.org/10.1002/lary.70861","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/lary.70861","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1186/s12911-026-03693-w","name":"Machine learning-based prediction model for predicting the impact of insulin resistance on the risk of ischemic cardiomyopathy.","source":"europepmc","abstract":"Background The triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio and triglyceride glucose-body mass (TyG-BMI) index are reliable indicators of insulin resistance (IR). This study investigated their association with ischemic cardiomyopathy (ICM) and developed a machine learning-based model for ICM risk prediction. Methods In total, 1,603 subjects participated in this study. Univariable logistic regression analysis was conducted, and variables with P Results Univariate and multivariate logistic regression analyses revealed that TyG-BMI, age, ejection fraction, TC/HDL-C, sex, HDL-C, TC, BMI, hemoglobin, diabetes, and hypertension were independent risk factors for ICM (P Conclusion The TyG-BMI and TC/HDL-C ratio independently predict ICM risk, with the XGB model identified as the most effective for ICM risk prediction, indicating substantial clinical applicability. Clinical trial registration number Not applicable.","url":"https://doi.org/10.1186/s12911-026-03693-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03693-w","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10639626/v1","name":"Performance of Machine Learning Models to Predict the Need for Computed Tomography Brain Scans From Clinical Features in Emergency Department Headache Patients","source":"europepmc","abstract":"Abstract Background There is no unifying diagnostic tool to guide neuroimaging decisions for unselected emergency department (ED) patients presenting with headache. This study aimed to compare the performance characteristics of various machine learning models in predicting the presence of clinically important computed tomography (CT) brain scan findings, thereby determining the need for neuroimaging. Methods Data were combined from our two cohorts of patients with headache who presented to the ED across 10 countries (Australia, New Zealand, Singapore, Hong Kong, United Kingdom, France, Belgium, Romania, Turkey, Israel) during a one-month period in 2019, and in Colombia throughout the 2019 calendar year. Patients aged ≥ 18 years with headache as their chief complaint were included. The primary outcome measure was the presence of clinically important CT findings, including hemorrhages, vascular abnormalities and neoplasm. Patients who did not undergo a CT scan were assumed to have no important findings. Predictor variables included headache features, past medical history, physical examination, and pathology results. Three machine learning models were compared: multivariable logistic regression, elastic net and random forest. The data were split into a training and test set in a 50:50 ratio for modeling. Results Of the 5,290 patients, 2,067 (39.1%) underwent CT scans. A clinically important radiological finding was recorded in 207 (10.0%) of these scans. The logistic regression model showed a slightly higher AUPRC (0.314) than the elastic net (0.295) and random forest models (0.276). It also demonstrated a slightly higher AUROC (0.840) compared with the elastic net (0.834) and random forest (0.797). For the logistic regression model, observed probabilities agreed with predicted probabilities below 0.2, but the model overpredicted when probabilities exceeded 0.2 (slope of calibration curve = 0.71, intercept = − 0.10). Overall, the elastic net model underpredicted, and the random forest model overpredicted. Conclusions Machine learning models can discriminate between the presence and absence of clinically important CT brain scan findings using clinical features of unselected ED patients with headache. The logistic regression model had similar discrimination, but superior calibration compared to the elastic net and random forest models. Machine learning models can potentially help clinicians decide on the need for neuroimaging.","url":"https://doi.org/10.21203/rs.3.rs-10639626/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10639626/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1371/journal.pone.0356170","name":"Comparative analysis of machine learning techniques for cardiovascular disease prediction.","source":"europepmc","abstract":"Background and objectives Early and accurate prediction of cardiovascular disease (CVD) is fundamental for reducing morbidity and mortality. Machine learning (ML) algorithms provide a data-driven, actionable foundation to strengthen clinical decision-making and enable more precise risk stratification. The purpose of this study is to identify the most significant risk factors for CVD and to compare the predictive performance of eight machine learning algorithms. Materials and methods The study utilizes the Cardiovascular Disease dataset, an open-access resource from the Kaggle repository. It applies 5-, 10-, 15-, and 20-fold cross-validation (CV) to evaluate the performance of Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, LogitBoost, Gradient Boosting, and LightGBM. Accuracy, sensitivity, specificity, precision, F1-score, false discovery rate (FDR), and area under the receiver operating characteristic curve (AUC) are used to evaluate the performance of the algorithms. The selection and ranking of relevant features are achieved through multiple methodologies, including Boruta, Regularized Random Forest, Recursive Feature Elimination, and LASSO. Results All clinical and demographic characteristics except gender show significant differences between the CVD and non-CVD groups (p-values Conclusion LightGBM demonstrates modestly superior, more balanced performance compared to the other evaluated algorithms in this dataset. While the performance (AUC = 0.800) suggests potential utility as a decision support tool, external validation is needed before clinical deployment. The model is not yet ready for standalone clinical use.","url":"https://doi.org/10.1371/journal.pone.0356170","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0356170","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-026-66914-2","name":"A network toxicology and machine learning approach to uncover the molecular machinery of bisphenol A-induced colorectal cancer.","source":"europepmc","abstract":"The carcinogenic relevance of environmental contaminant bisphenol A (BPA) to colorectal cancer (CRC) has gained growing attention, yet the molecular networks potentially linking BPA exposure to CRC remain incompletely characterized. This study integrated network toxicology and machine learning to predict candidate molecular targets and putative signaling networks associated with BPA-correlated colorectal carcinogenesis. The study's methodology involved an initial differential expression screening across several CRC transcriptomic datasets to establish a disease-specific gene signature. Subsequently, a multi-tiered computational strategy was employed: network toxicology was used to map potential BPA-protein interactions, machine learning algorithms were applied to distill a minimal set of high-impact targets, and molecular docking simulations provided atomic-level validation of the proposed binding events. We identified 53 overlapping genes between predicted BPA-interacting proteins and CRC-related transcripts. Machine learning screening further filtered a 12-gene panel with favorable predictive performance for CRC status, including MET, SORD, DPEP1, KIT, RIPK2, SET, HSP90AB1, DBF4, MMP1, MMP12, ANPEP, and GLA. Molecular docking simulations predicted stable binding interactions between BPA and the protein products encoded by these 12 genes. This study delineates a specific gene network potentially targeted by BPA to promote CRC pathogenesis. The machine learning-derived 12-gene signature, interpreted as CRC-associated genes overlapping with predicted BPA targets and supported by in silico molecular docking, offers valuable insights into the molecular basis of BPA-associated colorectal carcinogenesis and presents candidate targets for subsequent experimental validation.","url":"https://doi.org/10.1038/s41598-026-66914-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-66914-2","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2147/copd.s609203","name":"Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation.","source":"europepmc","abstract":"Background Deep vein thrombosis (DVT) is a frequent yet underrecognized complication in critically ill patients with chronic obstructive pulmonary disease (COPD). Existing risk assessment tools are not specifically tailored to this high-risk population. We aimed to develop and externally validate an interpretable machine-learning model for early prediction of DVT in ICU-admitted COPD patients. Methods Adult COPD patients admitted to the ICU were identified from the MIMIC-IV database and randomly divided into training and internal validation cohorts. Eight machine-learning algorithms were constructed and compared. The best-performing model was externally validated in MIMIC-III and eICU cohorts. Model discrimination, calibration, and clinical utility were assessed using AUC, calibration plots, decision-curve analysis (DCA), and Brier scores. SHAP analysis was applied for global and individual interpretability. A web-based calculator was developed for clinical application. Results Among 6,672 ICU patients with COPD, 462 (6.9%) developed DVT. XGBoost showed the best overall performance, with an AUC of 0.840 (95% CI 0.812-0.868) in the internal validation cohort and good calibration. External validation confirmed stable discrimination in both MIMIC-III and eICU cohorts. Model interpretation identified prolonged PTT, elevated RDW, reduced SpO 2 , and increased respiratory rate as important contributors to DVT risk. Decision-curve analysis suggested potential clinical benefit across relevant risk thresholds. Conclusion We developed and externally validated an explainable machine-learning model for early prediction of DVT in ICU patients with COPD. By providing individualized risk estimates and interpretable explanations, this tool may help clinicians identify high-risk patients earlier and support more targeted thromboprophylaxis and imaging surveillance strategies.","url":"https://doi.org/10.2147/copd.s609203","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/copd.s609203","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3349/ymj.2025.0370","name":"Development of a Multimodal Machine Learning Model for Predicting Second Osteoporotic Vertebral Compression Fractures.","source":"europepmc","abstract":"Purpose Osteoporosis increases the probability of osteoporotic vertebral compression fractures (OVCF), and the resultant pain severely limits physical activity and increases mortality and morbidity. This study aimed to develop a high-precision multimodal prediction model to forecast the risk of a second OVCF. Materials and methods This retrospective study included 178 patients from a single institution with a first OVCF between January 1, 2000, and December 31, 2019. The study dataset included 18 preoperative clinical variables, including demographics, medications, comorbidities, bone mineral density (BMD), body mass index, trunk fat/muscle ratio from dual-energy X-ray absorptiometry (DEXA), and imaging data. We employed an intermediate-fusion multimodal approach using deep neural networks, principal component analysis (PCA), and traditional classifiers. Results The final PCA-fusion+random forest model achieved strong internal cross-validation performance [accuracy 0.979, F1-score 0.969, area under the receiver operating characteristic curve (AUROC) 0.998; mean across folds]. External validation in an independent cohort demonstrated preserved discriminative ability (accuracy 0.904, F1-score 0.902, AUROC 0.923), albeit lower than internal estimates, suggesting dataset shift and possible overfitting. In a sensitivity analysis, a clinical/DEXA-only model also showed good discrimination, but the multimodal fusion model achieved higher recall, F1-score, and AUROC/area under the precision-recall curve, supporting the added value of imaging features. Feature importance and Shapley Additive Explanations highlighted DEXA body-composition measures (e.g., gynoid/android/trunk fat and tissue) and image-derived components as influential. Conclusion We developed a high-performing multimodal machine learning model for predicting a second OVCF in patients with prior fractures. By integrating clinical and imaging data, the model demonstrated strong predictive accuracy and interpretability, supported by feature importance analysis and visualization.","url":"https://doi.org/10.3349/ymj.2025.0370","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3349/ymj.2025.0370","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1096/fj.202600647r","name":"Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.","source":"europepmc","abstract":"Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic modeling while prioritizing clinically relevant protein markers. This study aims to develop and validate an explainable plasma proteomics machine-learning framework for osteoporosis diagnosis, future risk prediction, and biomarker discovery. We further tested whether a combined marker panel could distinguish normal, prevalent OP, and future incident OP states from baseline samples. Using UK Biobank plasma proteomic data, we established SPX-OP, which separately models prevalent OP and incident OP based on Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), and then evaluates whether the union of diagnostic and prognostic markers supports integrated baseline stratification. In the experiments, both the diagnostic and prognostic XGBoost models showed robust discrimination for osteoporosis status and future risk, respectively. SHAP-derived protein markers, including FSHB, ADIPOQ, SOST, COL9A1, and CHAD, were linked to osteoporosis and enriched in bone-related pathways involving bone development and remodeling, extracellular matrix organization, and inflammatory processes. Using only these SHAP-selected protein markers, the XGBoost model outperformed the full-proteome models and provided robust, simultaneous diagnostic and prognostic prediction of osteoporosis. In summary, this work transforms high-dimensional proteomic data into interpretable marker sets, paving the way for improved risk stratification and further validation of plasma protein biomarkers in osteoporosis.","url":"https://doi.org/10.1096/fj.202600647r","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1096/fj.202600647r","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fpubh.2026.1918534","name":"Interpretable machine learning enables early identification of financial risk and resource optimization in colorectal cancer surgery.","source":"europepmc","abstract":"Background Colorectal cancer (CRC) imposes major clinical and economic pressure on health systems. Under diagnosis-related group (DRG) payment, fixed reimbursement benchmarks may not capture the heterogeneity of surgical oncology or the resources required by patients with complex disease, exposing hospitals to financial losses and potentially affecting equitable access to high-quality cancer care. We developed an interpretable machine learning framework to estimate DRG-related financial risk in CRC surgery and support perioperative clinical review and local resource planning. Methods We retrospectively reviewed 2,081 patients who underwent CRC surgery between 2014 and 2024. Candidate features were screened for multicollinearity, assessed using Boruta, and refined through clinical review. Synthetic minority oversampling was then applied to reduce class imbalance. Six machine learning algorithms were trained with cross-validation and hyperparameter tuning. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP), and Sequential Forward Selection was used to reduce inputs to a clinically feasible subset. These variables were incorporated into an interactive web-based tool for individualized risk assessment. Calibration plots, Brier score, and decision curve analysis were used to evaluate reliability and clinical net benefit. A prospective cohort of 12 patients tested workflow feasibility during routine perioperative assessment. Results DRG-related financial risk, defined as expenditure above the DRG benchmark, was present in 38.4% of patients in the retrospective cohort. The random forest model achieved the best discrimination, with an area under the receiver operating characteristic curve of 0.852. SHAP analysis identified surgical approach, postoperative complications, length of hospital stay, and comorbidity burden as major contributors to predicted financial risk. The simplified model remained well calibrated and showed meaningful net benefit on decision curve analysis. In the prospective phase, clinicians used the web-based tool to stratify individual risk and simulate modifiable perioperative strategies for pathway optimization in real clinical workflow. Conclusion An interpretable machine-learning framework can provide individualized estimation of DRG-related financial risk in CRC surgery. Its integration into a point-of-care decision-support tool may support perioperative risk review, resource planning, and complex-case management under DRG-based reimbursement.","url":"https://doi.org/10.3389/fpubh.2026.1918534","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1918534","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.shpsa.2026.102202","name":"What can Artificial Intelligence learn from medicine? Generative analogies and reliable machine learning systems.","source":"europepmc","abstract":"In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the bases of its epistemic and methodological warrants. In the literature, a parallel has been drawn between medicine and ML, suggesting that we should model epistemic and methodological standards for ML on the standards of clinical translation. By developing tools from Hesse's work, we characterize the nature of this parallel as a generative analogy between the process of clinical translation and the process of building ML systems. We identify more precisely the epistemic and methodological warrants of clinical translation that are typically only mentioned when appealing to the analogy, and we show in which sense such warrants apply analogically to the context of ML. In particular, we interpret warrants of clinical translation in reliabilist terms, and we show how this can inform a new form of ML reliabilism, which is distinct from (though compatible with) existing reliabilist accounts in philosophy of AI.","url":"https://doi.org/10.1016/j.shpsa.2026.102202","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.shpsa.2026.102202","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.64898/2026.08.26.26361459","name":"Primary Care Quality and Inappropriate Community Antibiotic Use: A Double Machine Learning Instrumental Variable Approach","source":"europepmc","abstract":"Inappropriate antibiotic use presents a major global health challenge, particularly in low-resource settings where access to quality care is limited but antibiotics remain relatively unrestricted. This study estimates the causal effect of frontline primary care quality on inappropriate community antibiotic use, combining detailed community-based data from approximately 100 rural villages in rural China with an instrumental variable (IV) approach embedded within a double/debiased machine learning (DML) framework. We linked objective measures of village doctors’ clinical practice quality, measured through unannounced standardized patient visits, to household-level antibiotic use data collected from the same villages. To identify the causal effect, we constructed multiple candidate instruments from extensive provider characteristics and used an ensemble of machine learning algorithms within a flexible DML-IV framework to approximate an optimal instrument, addressing a many-weak-instruments problem. We found that improving village providers’ clinical practice quality reduced both antibiotic receipt during healthcare encounters for common diseases and household antibiotic storage for future self-medication. Our findings suggest that strengthening frontline primary care quality can meaningfully reduce inappropriate community antibiotic use without restricting access to essential treatment. More broadly, this study illustrates how causal machine learning can strengthen conventional causal estimation in complex observational settings in global health economics research.","url":"https://doi.org/10.64898/2026.08.26.26361459","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.26.26361459","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1186/s43046-026-00399-y","name":"Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.","source":"europepmc","abstract":"Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection and diagnosis, and supporting treatment selection, ML has the potential to enhance survival outcomes while increasing efficiency across oncology workflows. This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches. We highlight major application areas including early cancer detection, tumor classification, molecular subtyping, biomarker discovery, prognosis estimation, multi-omics integration, computational pathology, pharmacogenomics, and clinical decision support. We also summarize commonly used datasets, discuss the importance of interpretability for clinical trust, and outline barriers to translation such as data heterogeneity, bias, and regulatory constraints. Finally, we describe future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology. This review is intended for cancer researchers, clinicians, and data scientists seeking a practical overview of ML methods, opportunities, and translational considerations in oncology.","url":"https://doi.org/10.1186/s43046-026-00399-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s43046-026-00399-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00251-026-01412-3","name":"Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire.","source":"europepmc","abstract":"The adaptive immune receptor repertoire (AIRR) - the collection of an individual's B-cell and T-cell receptors (BCRs and TCRs, respectively) - encodes cumulative immune history and is shaped by both germline genetics and environmental exposures. Skewed repertoires have been linked to infections, vaccination responses and autoimmune diseases such as celiac disease (CeD) where biased usage of immunoglobulin and T-cell receptor genes reactive to disease relevant antigens has been reported. Motivated by evidence that germline variation, notably human leukocyte antigen (HLA), influences naïve AIRRs and by prior machine-learning studies that classified CeD using naïve BCR AIRR-sequencing (AIRR-seq) data, we applied machine learning analysis to naïve CD4 + TCR and naïve BCR AIRR-seq repertoires to test whether repertoire features can distinguish subjects with CeD from controls and to identify drivers of such classification. Naïve CD4 + TCR repertoires yielded moderate diagnosis classification, but this signal was largely explained by enrichment of the HLA-DQ2.5 allotype. TCR variable gene frequencies predicted HLA-DQ2.5 status with high accuracy and controlling for HLA-DQ2.5 abolished TCR-based diagnosis classification. In contrast, naïve BCR repertoires could not be used to classify CeD. Overall, our findings show that germline HLA variation significantly affects naïve TCR composition and thereby indirectly facilitates moderately successful CeD status classification.","url":"https://doi.org/10.1007/s00251-026-01412-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00251-026-01412-3","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/cts.70694","name":"Applying a Pharmacometrics-Enabled Machine Learning Analysis to Predict 2-Month Culture Conversion Using Phase 2a Data in a Tuberculosis Clinical Trial.","source":"europepmc","abstract":"Phase 2a trials in tuberculosis patients traditionally assess early bactericidal activity over two weeks, often using the time-to-positivity biomarker, followed by a phase 2b study typically lasting 8-week with time-to-event of culture conversion as the endpoint. This study investigated different machine learning models to predict the time-to-event of 2-month culture conversion in the REMoxTB trial with phase 2a time-to-positivity biomarker data and the impact of different phase 2a study lengths. Time-to-positivity at baseline and up to 14 days or 28 days after two moxifloxacin-containing regimens and one control regimen were analyzed using nonlinear mixed-effects modeling. The final models were used to predict individual baseline time-to-positivity and time-to-positivity differences between 0 and 14 days or 0 and 28 days. The individual predictions served as features in the following machine learning analysis. Statistical metrics and Kaplan-Meier plots informed model selection. Bi-exponential and exponential decay models described the 14-day and 28-day time-to-positivity data, respectively. In the machine learning analysis, baseline time-to-positivity and/or time-to-positivity differences were ranked as the most important features in all models. Statistical metrics and Kaplan-Meier plots indicated a good fit to culture conversion at 8 weeks using 4-week phase 2a information with a C-support vector classification model. Four-week time-to-positivity phase 2a data provided more information compared to the 2-week time-to-positivity data for the prediction of culture conversion. The workflow demonstrated the potential of machine learning to predict the time-to-event of phase 2b culture conversion up to 8 weeks using time-to-positivity phase 2a biomarker information in a clinical trial assessed with pharmacometric analysis.","url":"https://doi.org/10.1111/cts.70694","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/cts.70694","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fneur.2026.1811560","name":"LASSO-based nomogram and machine learning models for predicting 30-day nasogastric tube dependence after acute ischemic stroke.","source":"europepmc","abstract":"Background Nasogastric tube (NGT) feeding is a common method for providing enteral nutrition to patients with acute ischemic stroke (AIS) and dysphagia. However, prolonged NGT dependence contributes to adverse clinical consequences. Early identification of patients who may develop persistent NGT dependence remains challenging. Methods A total of 852 AIS patients requiring NGT were included and allocated to the training ( n = 596) and internal validation ( n = 256) sets. Predictor selection utilized least absolute shrinkage and selection operator (LASSO) regression, the eXtreme Gradient Boosting (XGBoost) algorithm, and multivariate logistic regression. A nomogram was then constructed and internally and externally validated. Three machine learning algorithms-Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Regularized Discriminant Analysis (RDA) were also developed for comparison. Model performance was evaluated using the Area Under the Curve (AUC), calibration plots, and Decision Curve Analysis (DCA). Results Four key independent predictors were retained: Diabetes Mellitus (DM), age, admission National Institutes of Health Stroke Scale (NIHSS) score, and exclusive NGT feeding. The model demonstrated strong discrimination (AUC = 0.924 in training, 0.920 in internal validation, and 0.935 in external validation), good calibration, and favorable clinical utility in DCA. Among the machine learning models, GBM demonstrated the highest accuracy, with an AUC of 0.918. The model confirmed age and NIHSS score as the most influential predictors, followed by exclusive NGT feeding and DM. Conclusion The developed nomogram provides an effective approach for predicting 30-day NGT dependence in AIS patients, enabling timely risk stratification and individualized clinical management.","url":"https://doi.org/10.3389/fneur.2026.1811560","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1811560","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1556/650.2026.33608","name":"[Clinical and preprocedural factors influencing in vitro fertilization outcomes: a machine learning-based analysis].","source":"europepmc","abstract":"Infertility affects 15-20% of the reproductive-age population, and assisted reproductive technologies, particularly in vitro fertilization (IVF), play an increasingly important role in its management. IVF success is multifactorial and influenced by numerous clinical and biological factors. Our aim was to review the most important preprocedural and procedural factors influencing IVF outcomes, with special emphasis on predictors identified by machine learning models. A narrative literature review was conducted, focusing on studies applying machine learning approaches, including results from the Gametogenesis Research Group at the University of Szeged. Machine learning models consistently identify maternal age and embryo quality (embryo score) as the strongest predictors of IVF success, maternal age above 35 years is associated with reduced success rates, besides body mass index, anti-Müllerian hormone, follicle-stimulating hormone levels, as well as endometrial thickness play relevant roles with variable impact. Preprocedural thyroid-stimulating hormone levels within the normal range show no clear association with outcomes. Overall, the results confirm that IVF success is determined by complex interactions among multiple factors. Machine learning models enable the determination of the relative weight of these well-established and repeatedly validated factors and improve the accuracy of prediction. Machine learning represents a promising tool for predicting IVF outcomes and may support personalized treatment strategies in reproductive medicine. Orv Hetil. 2026; 167(32): 1262-1268.","url":"https://doi.org/10.1556/650.2026.33608","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1556/650.2026.33608","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1080/01902148.2026.2712149","name":"Advances in the development of COPD screening models using respiratory oscillometry: An evolution from traditional regression to machine learning.","source":"europepmc","abstract":"Aim of the study Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, and early screening is critical for improving patient outcomes. This review aims to provide a comprehensive theoretical foundation and practical guidance for developing more accurate and efficient COPD screening tools by summarizing the research progress in constructing COPD screening models based on respiratory oscillometry (RO). Materials and methods We conducted a narrative literature review of PubMed, Scopus, and Embase through May 2026 to identify studies on respiratory oscillometry-based COPD screening models, with particular attention to comparisons between traditional regression/discriminant approaches and machine-learning algorithms. Respiratory oscillometry (RO), a noninvasive and convenient method for assessing lung function, was used as the technical basis. The review systematically traces the evolution of COPD screening models from traditional statistical regression models to advanced machine learning approaches, analyzing technical characteristics, performance metrics, clinical applicability, and challenges associated with different modeling techniques. Results Key findings indicate machine learning algorithms outperform traditional logistic regression in COPD screening model accuracy, with RO technology demonstrating unique advantages in noninvasive lung function assessment through cross-comparison of modeling techniques. Conclusions This review confirms RO-based machine learning models as the optimal approach for COPD screening, providing critical guidance for developing next-generation tools to enhance early detection accuracy and clinical applicability.","url":"https://doi.org/10.1080/01902148.2026.2712149","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/01902148.2026.2712149","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1097/cm9.0000000000004257","name":"Predicting PD-L1+ CD8- status in NSCLC tissue from clinical indicators using machine learning.","source":"europepmc","abstract":"Background Programmed death-ligand 1 (PD-L1) expression and CD8-positive (CD8+) T-cell infiltration in tumor tissue are associated with prognosis in non-small cell lung cancer (NSCLC). However, the prognostic value of combined PD-L1/CD8 immune phenotyping in surgically treated NSCLC and the feasibility of predicting high-risk immune phenotypes using routine clinical indicators remain unclear. This study aimed to evaluate the prognostic significance of PD-L1/CD8 status and to develop machine learning models for predicting PD-L1-positive/CD8-negative (PD-L1+ CD8-) status. Methods This study included 844 patients with NSCLC who underwent surgical resection at Wuhan Union Hospital and Renmin Hospital of Wuhan University between March 2012 and November 2022. PD-L1 expression and CD8+ T-cell infiltration were assessed by immunohistochemistry, and preoperative clinical data were collected. Kaplan-Meier analysis, stratified survival analysis, univariable and multivariable Cox proportional hazards regression models were used to evaluate the association between PD-L1/CD8 status and overall survival (OS). Patients from Wuhan Union Hospital were divided into training, test, and internal validation sets, whereas patients from Renmin Hospital of Wuhan University were used as an external validation set. Multiple machine learning models were developed to predict PD-L1+ CD8- status. Feature importance and and SHapley Additive exPlanations (SHAP) analysis were used to interpret model predictions. Results Based on PD‑L1/CD8 status, tumors from the 844 patients were classified into four immune phenotypes. Kaplan-Meier analyses revealed significant differences in OS among the four PD-L1/CD8 subgroups, with the PD-L1+ CD8- subgroup displaying the poorest survival. This pattern was also observed in several stratified analyses. Multivariable Cox regression analysis further demonstrated that PD-L1+ CD8- status was independently associated with worse OS compared with PD-L1+ CD8+ status (hazard ratio = 3.261, 95% confidence interval: 1.310-8.116, P = 0.011). On this basis, machine learning models were developed to predict PD-L1+ CD8- status. Among the evaluated models, the stacking model showed the best overall performance, with area under the curve values of 0.998, 0.700, 0.853, and 0.783 in the training, test, internal validation, and external validation sets, respectively. Feature importance and SHAP analyses suggested that PD-L1+ CD8- status was associated with a composite pattern involving hematologic, biochemical, inflammatory, and coagulation-related indicators. Conclusions PD-L1/CD8-based phenotyping identified prognostically distinct subgroups in surgically treated NSCLC, with PD-L1+ CD8- status defining a high-risk phenotype associated with poor survival. A machine learning model based on clinical indicators may enable efficient, noninvasive identification of PD-L1+ CD8- status and help guide treatment decisions.","url":"https://doi.org/10.1097/cm9.0000000000004257","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/cm9.0000000000004257","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1080/07853890.2026.2721087","name":"Development and internal validation of a machine learning-based disease burden index for irritable bowel syndrome: a multicentre cross-sectional study.","source":"europepmc","abstract":"Background Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction in gastroenterology clinics worldwide, becoming a significant public health burden. This study aims to provide an exploratory approach for comprehensive assessment of IBS burden and may serve as a basis for future external validation and further evaluation of its potential clinical application. Patients and methods This multicentre cross-sectional study included IBS patients diagnosed according to the Rome III and/or Rome IV criteria. Rome criteria-based comparisons and latent class analysis (LCA) were performed to characterize clinical heterogeneity, followed by the development of the IBS burden index (IBS-BI) using machine learning. Results Compared to the Rome IV-ineligible group, the Rome IV-eligible group exhibited significantly more severe IBS symptoms, somatization symptoms, anxiety symptoms, depression symptoms, and poorer health-related quality of life (all p p Conclusions Patients with IBS who fulfilled the Rome IV criteria showed a higher disease burden, which may be related to the more stringent diagnostic requirements of these criteria. The IBS-BI may have potential value for evaluating disease burden and patient stratification. Its clinical utility and broader applicability require further validation in independent external cohorts and prospective studies.","url":"https://doi.org/10.1080/07853890.2026.2721087","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/07853890.2026.2721087","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1371/journal.pone.0357179","name":"Labeling matters: A multicenter machine learning study on visual field progression in Glaucoma.","source":"europepmc","abstract":"Background To compare machine learning (ML) performance for detecting visual field (VF) progression across different labeling strategies using a large multicenter dataset. Methods In this multicenter retrospective study, VF data were collected from five tertiary referral hospitals. Two algorithm-derived labeling approaches were evaluated without an independent clinical reference standard: an inclusive Consensus label, defined as progression detected by at least one of five conventional algorithms (mean deviation slope, Visual Field Index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, and pointwise linear regression), and a conservative Wiggs' label, based on a region-based event-threshold rule. Four ML classifiers, support vector machine, random forest, logistic regression, and extreme gradient boosting, were trained using each labeling strategy. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision-recall analysis summarized by average precision (AP). Results Using the Consensus label, all models demonstrated excellent discrimination (AUC, 0.92-0.95), with high sensitivity (0.82-0.85) and near-perfect specificity (0.99-1.00). Precision-recall analysis showed consistently high reliability of progression detection, with AP values ranging from 0.93 to 0.94. In contrast, models trained with the Wiggs' label exhibited lower AUCs (0.88-0.89) and reduced sensitivity (0.63-0.72), while maintaining moderate-to-high specificity (0.87-0.92) and lower AP values (0.84-0.85), reflecting a stricter, region-based progression definition. Ablation analysis showed that Consensus-based performance was not driven by any single criterion, but rather by complementary information across heterogeneous progression algorithms. Conclusion In this multicenter study, the labeling strategy was a major determinant of ML performance in VF progression detection. The Consensus label enabled sensitive and reliable identification of progression with high specificity with respect to the Consensus label definition across heterogeneous clinical settings, whereas the Wiggs' label provided conservative, spatially consistent confirmation. The observed performance differences primarily reflect model-label compatibility rather than the clinical validity of either detection system, underscoring that careful definition of ground truth is critical for interpreting ML-based glaucoma progression research.","url":"https://doi.org/10.1371/journal.pone.0357179","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0357179","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/metabo16080600","name":"Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.","source":"europepmc","abstract":"Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare.","url":"https://doi.org/10.3390/metabo16080600","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/metabo16080600","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7754/clin.lab.2025.250766","name":"Identification of Glycolysis-Related Diagnostic Biomarkers for Amyotrophic Lateral Sclerosis Using Machine Learning.","source":"europepmc","abstract":"Background Glycometabolism has been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS), yet the precise molecular mechanisms underlying this association remain poorly understood. The identification of reliable biomarkers for ALS diagnosis represents a critical unmet need in clinical practice, as early detection and intervention could significantly improve patient outcomes. Methods We employed a comprehensive analytical approach combining two-sample Mendelian randomization analysis to investigate the causal relationship between blood glucose levels and ALS. Additionally, we integrated differential expression analysis, multiple machine learning algorithms, and correlation analyses to identify potential diagnostic biomarkers for ALS. The machine learning framework utilized gradient boosting tree methodology to construct predictive models, with performance evaluation conducted through cross-validation procedures. Results Mendelian randomization analysis demonstrated a significant negative causal relationship between blood glucose levels and ALS risk. Through bioinformatic analysis and machine learning approaches, we successfully identified candidate genes and constructed a high-performance predictive model using gradient boosting tree methodology, achieving an average area under the curve (AUC) of 0.8782 in cross-validation. Validation studies utilizing both bulk and single-cell RNA sequencing datasets revealed that COL5A1 and VCAN genes play significant roles in ALS pathogenesis, likely through their involvement in glycolytic pathways. Conclusions Our findings provide novel insights into the molecular mechanisms linking glycometabolism and ALS, while identifying potential diagnostic biomarkers for the disease. The identified genes, COL5A1 and VCAN, represent promising targets for further investigation in ALS pathogenesis. However, the clinical translation of these findings requires validation through additional datasets and prospective clinical trials to establish their diagnostic utility and therapeutic potential.","url":"https://doi.org/10.7754/clin.lab.2025.250766","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7754/clin.lab.2025.250766","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10315534/v1","name":"Clinical characteristics and machine learning-based prediction of postoperative enterocolitis in intestinal neuronal dysplasia: A 20-year retrospective study","source":"europepmc","abstract":"Abstract Purpose Intestinal neuronal dysplasia (IND) is a congenital enteric neuropathy characterized by abnormalities of the enteric nervous system, most commonly involving hyperplasia of the submucosal plexus and the presence of giant ganglia. This study aimed to characterize the clinical features of IND, evaluate postoperative enterocolitis (POEC), and develop a machine learning (ML)-based model for POEC prediction. Methods Patients diagnosed with IND who underwent pull-through surgery at our institution between 2005 and 2025 were retrospectively reviewed. Clinical characteristics and postoperative outcomes were analyzed. Five feature selection strategies and five machine learning algorithms were combined to construct and evaluate 101 predictive models using out-of-fold area under the receiver operating characteristic curve (AUC). Results A total of 84 patients were included in the study. Male patients predominated (57.1%), and 44.0% were underweight. Constipation was the most common presenting symptom (67.9%). Pathological comorbidities identified 30 (35.7%) patients with pure IND and 54 (64.3%) with mixed IND. Statistical analysis demonstrated that lower BMIZ values were associated with mixed IND and female sex in our cohort, whereas the interval from symptom onset to surgery was inversely associated with initial symptom severity. A machine learning model for predicting postoperative enterocolitis (POEC) was subsequently developed, in which BMIZ consistently emerged as a key predictive feature, suggesting its potential value for individualized postoperative risk assessment. Conclusion In this study, we characterized the clinical and pathological features of children with isolated intestinal neuronal dysplasia over a 20-year period. Patients were predominantly male, frequently presented with constipation, and most exhibited mixed IND rather than pure IND. Nutritional impairment was common, with female patients and those with mixed IND showing lower BMIZ values. Machine learning analyses consistently identified BMIZ as a potential predictive feature associated with postoperative enterocolitis, highlighting the potential role of nutritional status in postoperative risk stratification.","url":"https://doi.org/10.21203/rs.3.rs-10315534/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10315534/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1007/s00701-026-07005-z","name":"External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test.","source":"europepmc","abstract":"Purpose Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model for personalized testing using the five-repetition sit-to-stand (5R-STS) test, that estimates personalized upper limits of normal (ULN) to identify objective functional impairment (OFI) was externally validated to evaluate its performance and generalizability across cohorts. Methods Only healthy individuals were included in the study. After the machine learning-based model was applied to this external dataset, expected and observed 5R-STS test times were compared using standardized performance assessment metrics including root mean square error (RMSE), mean absolute error (MAE), and R 2 values. Additionally, a Bland-Altman analysis was performed to assess agreement between observed and expected values. Validation of the expected ULN involved comparing the proportion of individuals exceeding their personalized thresholds with the corresponding proportion based on the generalized threshold. Subgroup analyses by test setting and country of residence were additionally performed, along with a graphical assessment of model performance. Results Application of the model to 171 healthy individuals resulted in an RMSE of 2.33 (95% CI: 1.93 to 2.73) seconds, MAE of 1.70 (95% CI: 1.47 to 1.94) seconds, and R 2 of 0.064 (95% CI: -0.25 to 0.15). The Bland-Altman analysis demonstrated a mean bias of -1.1 s. Based on the personalized ULNs, OFI was classified in 17.5% of individuals, compared to 6.4% when using the generalized threshold of 10.4 s. These analyses indicated limited external generalization, with acceptable approximation for some faster and mid-range test times but systematic underestimation of slower test times. Multivariable regression analyses showed that remote testing was independently associated with greater prediction bias and higher odds of personalized ULN exceedance compared with supervised testing (adjusted mean difference: - 0.90 s; adjusted OR: 10.7). Exploratory country-of-residence analyses showed differences across the three largest national subgroups. 130 (76.1%) rated ease of use as excellent, and 145 (84.9%) rated clarity of instructions as excellent. 143 participants (83.6%) indicated that they prefer the 5R-STS over a battery of questionnaires. Conclusions In the context of personalized testing, moving toward individually focused precision assessment of patients requires rigorous external validation to ensure the robustness of such applied computational methods. In this external validation, the model demonstrated limited generalization including a systematic underestimation of slower test times and insufficient personalized ULN calibration. These findings indicate that external validity of models derived from single-center data can be limited, underscoring the importance of comprehensive external validation and, potentially, multicenter retraining before clinical implementation.","url":"https://doi.org/10.1007/s00701-026-07005-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00701-026-07005-z","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fcvm.2026.1838385","name":"Identification and clinical evaluation of diagnostic biomarkers for ischemic cardiomyopathy based on machine learning and transcriptomics.","source":"europepmc","abstract":"Background Ischemic cardiomyopathy (ICM) is a leading cause of heart failure, yet precise molecular tools for potential diagnosis remain limited. This study aims to identify and clinically evaluate novel diagnostic biomarkers for ICM by integrating comprehensive transcriptomic analysis, machine learning algorithms, and real-world serological assessment. Methods Gene expression profiles from the GEO database were systematically analyzed using weighted gene co-expression network analysis (WGCNA) and 12 distinct machine learning algorithms to screen for optimal diagnostic targets. Crucially, to bridge the gap between computational prediction and clinical application, the identified core diagnostic genes were evaluated at the protein level using an independent clinical cohort. Peripheral serum samples from 90 individuals (45 ICM patients and 45 controls) were analyzed via enzyme-linked immunosorbent assay (ELISA). Results We identified 501 differentially expressed genes, with the MEcyan WGCNA module showing the strongest correlation with ICM. Among the 12 evaluated machine learning models, AdaBoost showed the highest internal predictive performance (AUC = 0.976). However, this estimate is exploratory and potentially optimistic due to feature pre-selection prior to cross-validation. From this candidate pool, SEPP1, CILP, and FRZB were selected post hoc as the core diagnostic targets based on their univariate performance and clinical translational suitability. These markers were significantly associated with complement activation, extracellular matrix remodeling, and immune cell infiltration. Most importantly, our clinical ELISA experiments provided preliminary protein-level evidence that the serum protein levels of SEPP1, CILP, and FRZB were significantly elevated in ICM patients compared to controls, which is consistent with the bioinformatic predictions. Conclusion By integrating computational screening with preliminary clinical serological analysis, this study identifies SEPP1, CILP, and FRZB as potential serological biomarkers for ICM. This computational model and the accompanying preliminary serological evidence provide a theoretical basis for future clinical exploration and diagnostic biomarker development.","url":"https://doi.org/10.3389/fcvm.2026.1838385","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1838385","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1093/bjr/tqag129","name":"Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.","source":"europepmc","abstract":"Objective To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features for predicting RAS mutation status and prognosis in patients with colorectal cancer (CRC). Methods This retrospective study included 90 patients (mean age, 64 years, 60 men) with CRC who underwent pretreatment 18F-FDG-PET/CT. Radiomic features were extracted from PET images. Ten clinical variables and 49 radiomic features were analyzed. Using the AutoGluon ML framework, 3 models (clinical, radiomics, and combined) were developed with 10-fold cross-validation. RAS mutation prediction was evaluated by the AUC, and interpretability was assessed using SHAP analysis. Survival outcomes were evaluated using a RSF model, and risk scores for disease progression were calculated based on true RAS and ML-predicted RAS models. Results The radiomics ML model demonstrated the highest performance with an AUC of 0.675. SHAP analysis identified NGTDM_Complexity, kurtosis, and skewness as key contributors. For survival analysis, the C-indices based on true and ML-predicted RAS status were 0.685 and 0.675, respectively. A strong correlation was observed between the risk scores derived from the 2 models (r = 0.99, ρ = 0.98). Kaplan-Meier analysis demonstrated clear separation between high- and low-risk groups for both models (log-rank P Conclusion Although the radiomics-based ML model demonstrated moderate performance in predicting RAS mutation status, it provided prognostic stratification comparable to that based on true genetic profiles. Advances in knowledge 18F-FDG PET-based radiomics with ML may have potential as a noninvasive approach for genetic profiling and prognostic assessment in CRC, although further validation is required.","url":"https://doi.org/10.1093/bjr/tqag129","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/bjr/tqag129","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00383-026-06594-1","name":"Machine learning-based prediction model for postoperative blood transfusion in pediatric patients with Hirschsprung's disease.","source":"europepmc","abstract":"Background Children undergoing Hirschsprung's disease (HD) surgery are at risk of postoperative transfusion, which may cause complications and increase resource use. Current decisions are often empirical and lack individualized prediction tools. Methods We retrospectively analyzed 889 pediatric patients who underwent HD corrective surgery at a tertiary center (2015-2025). Postoperative transfusion was defined as ≥ 0.5 units of packed red blood cells within 72 h after surgery. Forty-seven preoperative variables were collected; least absolute shrinkage and selection operator (LASSO) regression identified key predictors. Seven machine learning models, including support vector machine (SVM), were developed and evaluated by AUC, sensitivity, specificity, calibration, and decision curve analysis (DCA). Model interpretability was assessed using SHAP analysis. Results Of 889 patients, 155 (17.4%) required postoperative transfusion, while intraoperative transfusions were rare. LASSO selected five predictors: hemoglobin, HD classification, red blood cell count, prothrombin time, and total protein. The SVM model achieved the best performance in testing cohort (AUC 0.8361; accuracy 77.15%; sensitivity 76.60%; specificity 77.27%; negative predictive value 93.92%). Calibration and DCA supported good reliability and clinical net benefit. Hemoglobin was the most influential predictor. Conclusion We developed an interpretable preoperative machine learning model for predicting transfusion risk after HD surgery in children. The SVM model demonstrated good discrimination and may assist in perioperative planning, particularly for identifying low-risk patients who may not require routine crossmatching. External multi-center validation is needed before clinical implementation.","url":"https://doi.org/10.1007/s00383-026-06594-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00383-026-06594-1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10755212/v1","name":"A spatiotemporally validated machine-learning score for venous thromboembolism risk after neurosurgery","source":"europepmc","abstract":"Abstract Background The Caprini model may overestimate postoperative venous thromboembolism (VTE) risk in neurosurgical patients, whereas existing machine-learning models often lack interpretability and robust external validation. We aimed to develop a concise and interpretable machine-learning-derived score for postoperative VTE risk after neurosurgery and to evaluate its temporal and external transportability against the Caprini model. Methods Adult neurosurgical patients were chronologically divided into training and temporal test cohorts within an internal dataset (n = 3,644). External validation was performed in the independent MIMIC-IV cohort (n = 10,268). Machine-learning modelling was used to derive a simplified score comprising peak D-dimer, age, blood transfusion, and body mass index. Discrimination, calibration, clinical utility, and risk reclassification were evaluated, with performance compared with that of the Caprini model. Results The four-variable score achieved an area under the receiver operating characteristic curve of 0.745 in the internal test cohort, compared with 0.662 for the Caprini model. In the external validation cohort, the corresponding values were 0.726 and 0.642, respectively (both P","url":"https://doi.org/10.21203/rs.3.rs-10755212/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10755212/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1080/07853890.2026.2707807","name":"Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.","source":"europepmc","abstract":"Background Intraoperative blood transfusion is common in major surgery. Predicting transfusion risk may improve perioperative management, optimize blood use, and enhance surgical planning. Machine Learning (ML) offers promising tools for individualized risk prediction. Aim To develop and validate an ML model predicting intraoperative transfusion risk in major surgery using clinical variables and inflammatory indices. Material and methods 1858 adult patients who underwent major surgical procedures at AUSL-IRCCS di Reggio Emilia between September 2021 and December 2023 were retrospectively analyzed. The dataset was splitted into training (60%), internal validation (18%) and internal test (22%) sets. The primary outcome was the intraoperative red blood cell transfusion. Thirty-three candidate variables (29 clinical and laboratory parameters and 4 calculated indices) were evaluated. Highly correlated variables were excluded and key predictors were identified using CatBoost. Predictors distributions were compared by transfusion status and oncological diagnosis. Model performance was assessed by AUC, sensitivity, specificity, NPV, PPV, and F1-score. SHAP graph were used to interpret feature contributions. Results The CatBoost model demonstrated strong predictive performance (AUC 0.88 training, 0.80 test). and a high NPV (0.89 training, 0.87 test), reliably identifyinglow-risk patients. Key predictors included type of surgery, preoperative haemoglobin, age, BMI, MCV RDW, PT and inflammatory indices such as MLR, PLR, NLR. Conclusions This ML-driven model accurately predicts intraoperative transfusion risk and identifies clinical and laboratory predictors, supporting perioperative management and more efficient resource allocation.","url":"https://doi.org/10.1080/07853890.2026.2707807","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/07853890.2026.2707807","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fcvm.2026.1921962","name":"Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.","source":"europepmc","abstract":"Cardiovascular disease remains a major global health burden. Owing to its complex pathogenesis and marked clinical heterogeneity, conventional one-size-fits-all strategies often yield limited benefit for a substantial proportion of patients. Precision medicine advocates individualized management based on patients' clinical and molecular characteristics to improve outcomes. In this context, multi-omics and machine learning provide critical technical support for precision medicine: multi-omics can capture the full spectrum of cardiovascular disease from molecular alterations to phenotypic manifestations, while machine learning is well-suited to modeling complex associations between high-dimensional, nonlinear omics data and clinical outcomes. Their integration has therefore opened new avenues for the precise management of cardiovascular disease. This review systematically summarizes recent advances in applying machine learning and multi-omics to precision cardiovascular medicine, with a focus on three core domains: diagnosis, risk prediction, and treatment response prediction. In diagnosis, these approaches can assist with definitive diagnosis, early detection, differential diagnosis, and severity assessment, thereby enabling more noninvasive, objective, rapid, and precise evaluation of cardiovascular disease. In risk prediction, they support a comprehensive framework spanning primary prevention, secondary prevention, short-term risk stratification, and screening of high-risk populations, allowing risk management across the full disease course and across diverse patient groups. In addition, they enable individualized prediction of the benefits and risks of pharmacological and surgical treatments, thereby informing therapeutic decision-making. To facilitate clinical translation, several challenges remain particularly important, including data quality control, continuous model validation, improved transparency, clarification of responsibility and accountability, and supportive policies regarding implementation and cost coverage.","url":"https://doi.org/10.3389/fcvm.2026.1921962","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1921962","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/88450","name":"Machine Learning-Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study.","source":"europepmc","abstract":"Background Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusing on biomedical factors while overlooking social determinants of health. There is a need for data-driven AI models to improve early pregnancy risk identification and management. Objective The study aimed to develop and internally validate first-trimester AI-based antenatal risk assessment models across three geographically and socioethnically diverse populations (Sweden, Chile, and Singapore) and to compare their performance with existing clinical risk assessment strategies. Methods We conducted a retrospective population-based study using routinely collected first-trimester data from over 700,000 pregnancies from Sweden, Chile, and Singapore. Separate machine learning models predicting a composite of adverse maternal and neonatal outcomes were trained and internally validated for each population. Input variables were limited to information available at or before 14 weeks' gestation. Model discrimination, measured by the area under the receiver operating characteristic (AUROC) curve, was compared with corresponding proxies for real-world first-trimester risk assessment approaches in each setting. Model interpretability was assessed using Shapley additive explanations. Results The prevalence of the composite adverse outcome was 10.40% (75,647/727,354) in Sweden, 21.94% (1302/5934) in Chile, and 16.25% (6145/37,813) in Singapore. In Sweden, the guideline-based risk assessment achieved an AUROC of 0.53, compared with 0.65 for the LightGBM (light gradient boosting machine) model ( P P P Conclusions AI-based models developed using first-trimester data generally demonstrated improved performance compared with existing first-trimester clinical risk stratification strategies across three distinct populations. These findings suggest the potential feasibility of population-specific, AI-enabled risk stratification as a clinical decision support tool and highlight the potential value of integrating social, demographic, and behavioral determinants into antenatal risk assessment frameworks to support more equitable and personalized antenatal care.","url":"https://doi.org/10.2196/88450","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/88450","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1093/intqhc/mzag128","name":"Development and validation of a machine learning-based model for predicting fall-related injury risk in hospitalized patients.","source":"europepmc","abstract":"Background Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning model to predict which in-hospital fall events would lead to patient injury, thereby supporting post-fall risk stratification. Methods We retrospectively analyzed data from 410 patients who had experienced falls at a tertiary general hospital in China. Among them, 134 patients (32.7%) had fall-related injuries. The dataset was divided into training and test sets at a 7:3 ratio by outcome-stratified random sampling. Least absolute shrinkage and selection operator regression was used for feature selection to identify relevant predictors. Four machine learning models-logistic regression, random forest, extreme gradient boosting, and support vector machine-were developed and assessed. Model performance was evaluated in the test set using the area under the receiver operating characteristic curve, Brier score, and calibration curves. According to model performance, the optimal model was selected, and multivariable logistic regression analysis was then performed to determine independent risk factors. Results In the test cohort, the logistic regression model showed the strongest predictive ability (AUC = 0.863, 95% CI: 0.785-0.941; Brier score = 0.142). Five independent risk factors were detected, including impaired consciousness (OR = 3.35, 95% CI: 1.58-7.10), reduced muscle strength (OR = 3.93, 95% CI: 1.87-8.26), use of high-risk medications (OR = 10.07, 95% CI: 5.05-20.07), ward-related environmental hazards (OR = 3.30, 95% CI: 1.63-6.69), and hypocalcemia (OR = 2.10, 95% CI: 1.05-4.19). Based on this model, a nomogram was developed, and decision curve analysis indicated a positive net clinical benefit within the threshold probability range of 0.10-0.80. Conclusion A prediction model based on five routinely collected clinical variables was developed to estimate fall-related injury risk after an in-hospital fall. The logistic regression model showed a favorable balance among predictive performance, calibration, interpretability, and clinical feasibility. This model may help support post-fall injury risk stratification, triage for further assessment, and monitoring decisions in hospitalized patients who have already experienced a fall.","url":"https://doi.org/10.1093/intqhc/mzag128","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/intqhc/mzag128","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fendo.2026.1850695","name":"Optimizing cardiovascular disease diagnosis through machine learning models integrating oxidized low-density lipoprotein and routine clinical indicators.","source":"europepmc","abstract":"Introduction To evaluate the diagnostic value of oxidized low-density lipoprotein cholesterol (oxLDL-C) for cardiovascular disease (CVD) and to construct a machine learning model integrating routine clinical indicators, thereby providing an efficient and economical tool to aid clinical diagnosis. Methods This retrospective analysis enrolled 3, 686 participants. The discriminatory performance of oxLDL-C was compared with traditional lipid markers using receiver operating characteristic curves, and its association with CVD risk was analyzed using multivariate logistic regression. Through Recursive Feature Elimination and multivariate logistic regression, six core variables (including oxLDL-C) were ultimately selected. Seven machine learning algorithms were employed to construct predictive models, and their performance was evaluated in an internal validation set. Results The discriminatory efficacy of oxLDL-C (AUC = 0.642) was significantly superior to traditional indicators such as low-density lipoprotein cholesterol. Its level was independently and positively associated with CVD risk (OR for the highest quartile = 3.773). The diagnostic model based on XGBoost demonstrated excellent discriminative ability (AUC = 0.911) and good calibration in internal validation. Discussion The machine learning model integrating oxLDL-C with routine clinical indicators performs well, offering a practical tool for preliminary CVD risk screening and patient triage in resource-limited settings.","url":"https://doi.org/10.3389/fendo.2026.1850695","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1850695","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7499/j.issn.1008-8830.2511119","name":"[Construction of a genotype-phenotype database and machine learning models for pseudohypertrophic muscular dystrophy].","source":"europepmc","abstract":"Objectives To establish a genotype-phenotype database and machine learning models for pseudohypertrophic muscular dystrophy (PMD), and to explore genotype-phenotype correlations of the disease. Methods Clinical data of children with PMD admitted to Xiangya Hospital, Central South University from January 2010 to December 2024, together with cases retrieved from the PubMed database between January 1987 and December 2024, were retrospectively collected to construct a genotype-phenotype database, with an online query function via a WeChat mini-program. Based on this database, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine algorithms were integrated using a soft voting ensemble strategy to build a machine learning model predicting clinical phenotypes associated with small variants. The predictive performance of the model was compared with that of the reading-frame rule. The model was deployed online via the Streamlit platform. Results The database included 17 053 PMD cases, comprising 472 patients in the local cohort and 16 581 literature-derived cases. Modeling and validation were performed on a filtered dataset comprising small variants. In the internal test set, the machine learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.924 (95% CI : 0.881-0.963), significantly higher than the reading-frame rule AUC of 0.652 (95% CI : 0.591-0.717) ( P CI : 0.736-1.000), compared to 0.667 (95% CI : 0.500-1.000) for the reading-frame rule, with no statistically significant difference ( P >0.05). Conclusions The constructed PMD genotype-phenotype database and machine learning prediction model provide an efficient and reliable novel tool for phenotype prediction in PMD.","url":"https://doi.org/10.7499/j.issn.1008-8830.2511119","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7499/j.issn.1008-8830.2511119","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s00520-026-11084-0","name":"Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.","source":"europepmc","abstract":"Purpose Peripherally inserted central catheter-related thrombosis (PICC-RT) is a common and serious complication in patients with hematological malignancies, leading to treatment interruptions and increased morbidity. Traditional risk assessment models are often static and lack hematology-specific predictors, resulting in suboptimal performance. We aimed to develop and validate a robust machine learning (ML) model that integrates static clinical data with dynamic biomarkers to provide individualized risk prediction. Methods This multicenter retrospective study screened 5420 patients, ultimately including 4015 adult patients with hematological malignancies who underwent PICC insertion across five participating centers. The cohort was randomly partitioned into a training set (n = 2810) and an independent testing set (n = 1205). Nine core predictors were selected via LASSO regression. We compared three algorithms, logistic regression (LR), random forest (RF), and XGBoost, using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were utilized for model interpretation. Results The incidence of symptomatic PICC-RT was 5.8% (n = 232). In the independent testing set, the XGBoost model significantly outperformed the LR baseline (AUC: 0.862 [95% CI: 0.825-0.899] vs. 0.774 [95% CI: 0.730-0.818], P Conclusions The XGBoost model, integrating dynamic biomarkers and hematology-specific factors, provides superior personalized risk stratification for PICC-RT. This tool enables the identification of high-risk patients who may benefit from enhanced surveillance or targeted thromboprophylaxis while safely identifying low-risk individuals.","url":"https://doi.org/10.1007/s00520-026-11084-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00520-026-11084-0","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s10238-026-02227-y","name":"Application of a machine learning model integrating T cell subsets and clinical markers in 28-Day mortality prediction of sepsis patients.","source":"europepmc","abstract":"To develop a machine learning-based prediction model that integrating T cell subset data with clinical features to predict the 28-day mortality risk in sepsis patients. A retrospective cohort study was conducted using the MIMIC-IV database. Collected data included demographics, T cell subsets, laboratory results, SOFA score, GCS, and 28-day mortality. Feature selection was performed using LASSO regression combined with 10-fold cross-validation. We compared the performance of six machine learning models, namely RF, SVM, XGB, GLM, GBM, and LASSO logistic regression. Model performance was evaluated using the AUROC, calibration curves, and DCA, while the SHAP method was employed to interpret the optimal model. A total of 781 sepsis patients were included in our study, with a 28-day mortality rate of 18.5%. LASSO regression identified 12 key features: Age, CD + 4 Count, CD8 + T cell count, lactate, platelet, albumin, Na+, Bun, heart rate, anion gap, eosinophil count, monocyte count. Among the six compared machine learning models, GLM achieved the best comprehensive performance in the testing set, with an AUROC of 0.720, good calibration (Brier score: 0.134). SHAP analysis clarified the contribution degree and direction of each predictive factor to the model output. A GLM model integrating T cell subsets (notably CD8 + T cell count) and clinical features was successfully constructed. The model showed moderate discriminative ability and potential clinical application value in predicting the 28-day mortality risk of sepsis. SHAP-based interpretability clarifies individual risk factors, serving as an auxiliary prognostic tool for clinicians.","url":"https://doi.org/10.1007/s10238-026-02227-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10238-026-02227-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.acra.2026.08.028","name":"Predicting Synchronous Liver Metastasis in Pancreatic Cancer Using CT Radiomics and Clinical Features: A Machine Learning Approach.","source":"europepmc","abstract":"Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis (LM) in pancreatic cancer (PC), we developed and validated machine learning models integrating clinical and computed tomography (CT) radiomics features, and compared the performance and interpretability of linear (linear discriminant analysis [LDA]) versus nonlinear (multilayer perceptron [MLP]) architectures. Materials and methods This retrospective study enrolled 340 patients with pancreatic ductal adenocarcinoma (190 with LM, 150 without). Five radiomics features were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. Based on independent clinical predictors (age, carcinoembryonic antigen [CEA], and clinical N stage) and the selected radiomics features, 26 machine learning models were constructed. The optimal models (LDA and MLP) were evaluated in an independent validation cohort (n = 102) using receiver operating characteristic curves, calibration, decision curve analysis, and SHAP (SHapley Additive exPlanations) analysis. Results In the validation cohort, LDA achieved an area under the curve (AUC) of 0.828 (95% confidence interval [CI]: 0.735-0.897), and MLP achieved 0.822 (95% CI: 0.732-0.895). Both models demonstrated good calibration, with Hosmer-Lemeshow test P values of 0.551 (Brier score = 0.168) for LDA and 0.682 (Brier score = 0.172) for MLP, alongside high net clinical benefit. SHAP analysis revealed that clinical N stage dominated the LDA model, whereas CEA and radiomics features played more prominent roles in the MLP model. Friedman's H-statistic identified five significant feature interactions. Subgroup analysis showed that MLP maintained stable performance (AUC 0.66-0.77), whereas LDA performed poorly in the lymph-node-negative (AUC = 0.50) and pancreatic head tumor (AUC = 0.62) subgroups. The clinical nomogram achieved an AUC of 0.788 in the validation cohort. Conclusion The integrated model enables accurate preoperative prediction of synchronous LM in PC. Rather than replacing pathological diagnosis, this tool is designed to assist clinical decision-making in the common dilemma of indeterminate subcentimeter liver lesions on CT: a low-risk score supports surveillance and spares unnecessary biopsy, whereas a high-risk score prompts confirmatory biopsy or a shift toward neoadjuvant systemic therapy. Both architectures exhibit robust discrimination and clinical utility; however, the nonlinear MLP model shows superior stability across clinical subgroups, offering a promising tool for individualized risk assessment and treatment planning.","url":"https://doi.org/10.1016/j.acra.2026.08.028","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.acra.2026.08.028","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fpubh.2026.1914779","name":"Predicting burnout in radiology nurses: an interpretable machine learning model developed and externally validated in a multi-center study.","source":"europepmc","abstract":"Objective To develop and externally validate an interpretable machine learning model for predicting individual burnout risk among radiology nurses, and to identify modifiable, non-linear risk thresholds to guide precision prevention. Methods A two-center, two-stage design was used. A development cohort ( n = 219) from six tertiary hospitals in China trained an XGBoost model. An independent external validation cohort ( n = 234) was collected 1.5 years later from two tertiary centers (one partially overlapping with the development sites but with entirely independent participants) to rigorously test generalizability. Feature selection used bootstrapped LASSO with permutation testing. Model performance was assessed by discrimination, calibration, and decision curve analysis. SHAP (SHapley Additive exPlanations) provided global and individual-level interpretability. Results The XGBoost model achieved strong discrimination in external validation (AUC = 0.859). In the internal test set, XGBoost outperformed logistic regression and other machine learning algorithms (AUC: 0.963 vs. 0.910 for logistic regression). SHAP analysis identified Effort-Reward Imbalance and Overcommitment as dominant predictors, and revealed a potential inflection point at approximately 65 weekly work hours, beyond which burnout risk escalated disproportionately in the SHAP analysis. Decision curve analysis confirmed net clinical benefit across threshold probabilities of 5 -65%. Individualized risk profiles were generated for actionable nurse management. Conclusion This externally validated, interpretable XGBoost-SHAP framework moves beyond population-averaged associations. By uncovering specific non-linear thresholds (e.g., 65 h/week workload) and providing personalized risk profiles, it enables nurse managers to shift from reactive crisis management to data-driven, precision prevention of burnout in radiology nurses.","url":"https://doi.org/10.3389/fpubh.2026.1914779","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1914779","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fendo.2026.1865321","name":"Interpretable machine learning for presurgical differentiation of Hürthle cell carcinoma and adenoma: a SHAP-augmented approach.","source":"europepmc","abstract":"Background The preoperative differentiation between benign Hürthle cell adenoma (HCA) and malignant Hürthle cell carcinoma (HCC) remains clinically challenging. This study aimed to develop an interpretable machine learning framework to improve diagnostic accuracy and assist clinical decision-making. Methods We retrospectively enrolled 554 patients (280 HCA, 274 HCC) from a single center. Fifteen clinical, serological, and ultrasonographic variables were incorporated. Following rigorous feature selection via LASSO and Random Forest algorithms, four advanced machine learning models and logistic regression (LR) were trained and evaluated using 10-fold cross-validation. The SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) trajectories were utilized to interpret the optimal model globally and locally. Results The XGBoost model demonstrated superior discriminative performance, achieving an Area Under the Curve (AUC) of 0.911, significantly outperforming LR (AUC = 0.746). Decision curve analysis confirmed its higher clinical net benefit. SHAP analysis demystified the algorithmic \"black box,\" identifying vascularity grade, absent halo sign, and elevated serum thyroglobulin as top malignant predictors. Furthermore, patient-specific ICE trajectories successfully simulated counterfactual clinical reasoning for misclassified cases. Conclusion The SHAP-augmented XGBoost framework provides highly accurate, transparent, and personalized presurgical risk stratification for Hürthle cell neoplasms, potentially reducing unnecessary diagnostic thyroidectomies.","url":"https://doi.org/10.3389/fendo.2026.1865321","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1865321","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1212/wnl.0000000000218438","name":"Machine Learning Characterization of Readmissions After Chronic Subdural Hematoma Hospitalizations.","source":"europepmc","abstract":"Background and objectives Readmission after chronic or subacute subdural hematoma (cSDH/sSDH) hospitalization is common, yet clinical attention and trial design have focused primarily on surgical recurrence. The full spectrum of readmission events, their clinical impact, and the heterogeneity of the affected patient population remain poorly understood. This study seeks to characterize the incidence, diversity, and outcomes of 90-day readmissions after cSDH/sSDH hospitalization and to identify patient phenotypes with distinct readmission risk profiles using machine learning-driven clustering. Methods This was a retrospective cohort study using the Nationwide Readmissions Database (2016-2022). Adults nonelectively hospitalized for cSDH/sSDH were included. The main outcome of interest was hospital readmission within 90 days, classified into readmission types. Readmission outcomes included length of stay, cost, in-hospital mortality, functional decline, new disability, and inability to return home. Machine learning-based phenotyping using Shapley Additive Explanations values from a multinomial gradient-boosted model and K-means clustering identified patient subgroups with divergent readmission patterns. Results Of 22,387 patients (mean age 70.8 years; 29.6% female), 6,497 (29.0%) were readmitted within 90 days across diverse causes. Surgical SDH recurrence accounted for only 22.5% of readmissions, and fewer than half (44.0%) were primarily SDH-related. Non-SDH readmissions carried substantial clinical impact: infection readmissions had the highest mortality (9.6%), exceeding surgical SDH (2.9%) more than 3-fold, and the highest rate of new disability (45.5%) among patients initially discharged with routine self-care. Machine learning-driven phenotyping analysis identified 5 patient clusters with unique clinical characteristics and diverging readmission patterns: low acuity (39.8%), atrial fibrillation (16.9%), elderly/frail (16.6%), young/healthy (14.5%), and high acuity (12.1%). These phenotypes revealed marked patient heterogeneity, with each cluster exhibiting distinct readmission risk profiles. Discussion Readmissions after cSDH/sSDH are diverse and predominantly nonsurgical, with non-SDH readmissions carrying equal or worse outcomes than surgical recurrence. Machine learning-based phenotyping uncovered substantial patient heterogeneity, highlighting the need for new therapeutic strategies, expanded clinical trial outcome targets beyond surgical recurrence, and comprehensive postdischarge care models tailored to distinct patient subgroups.","url":"https://doi.org/10.1212/wnl.0000000000218438","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1212/wnl.0000000000218438","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-9719269/v1","name":"Association and Incremental Predictive Value of the Triglyceride- Glucose Index and Its Derivatives for Hypertension and Diabetes: A Machine Learning-Based Study","source":"europepmc","abstract":"Abstract Background The triglyceride–glucose (TyG) index and its derivatives (TyG–BMI, TyG–WC, and TyG–WHtR) have emerged as reliable markers. This study aims to evaluate their associations with hypertension and diabetes and determine their incremental predictive value using a machine learning approach. Method Data were obtained from 632 adult residents participating in the China Chronic Disease and Risk Factor Surveillance in Anji County. All statistical analyses were performed using R software (version 4.3.3). Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test or Kruskal-Wallis test. Categorical variables were presented as frequencies (percentages) and compared using the Chi-square test or Fisher's exact test.To assess the association between TyG-related indices and the risks of hypertension and diabetes, we employed both multivariate logistic regression and Cox proportional hazards regression models. Subsequently, a comprehensive machine learning workflow was implemented to predict disease risk and assess the incremental value of incorporating these indices.. Results The prevalence of both hypertension and diabetes showed a significant increasing trend across the quartiles of all TyG-related indices, which were all positively associated with both diseases. The Lasso model outperformed other machine learning algorithms in predictive accuracy. Incorporating these four indices into the basic model significantly improved discriminative ability for hypertension. For diabetes, integrating the TyG index, TyG-WC, and TyG-WHtR optimized risk stratification, whereas adding TyG-BMI yielded no significant improvement. Incorporating these indicators into machine learning models facilitates early risk identification and targeted clinical interventions. Conclusion The TyG index and its obesity-related derivatives are significant predictors of hypertension and diabetes. Incorporating these accessible biomarkers into machine learning frameworks provides substantial incremental predictive value, thereby optimizing early risk stratification and facilitating targeted clinical interventions.","url":"https://doi.org/10.21203/rs.3.rs-9719269/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9719269/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1016/j.cmpb.2026.109609","name":"Quantum machine learning in diabetes and metabolism: Applications and translational challenges.","source":"europepmc","abstract":"Background and objective Quantum machine learning is emerging as a promising extension of artificial intelligence, with potential advantages over classical approaches in handling complex biomedical data. This review aims to evaluate quantum machine learning applications in the detection, prediction, and personalized management of metabolic syndrome and type 2 diabetes mellitus. Methods We reviewed literature published between 1994 and 6 July 2026, with peer-reviewed journal articles and conference proceedings as the principal evidence base, complemented by selected preprints and technical sources. Quantum machine learning approaches, including quantum support vector machines, quantum neural networks, and quantum echo state networks, were classified and compared with classical counterparts across obesity and early metabolic dysregulation, diabetes diagnosis and glycemic management, and chronic complications. Results Quantum machine learning and hybrid quantum-classical systems demonstrated potential benefits in small-sample and noisy environments typical of wearable and biomedical sensor data. Reported performance gains included improvements in accuracy, robustness, and scalability, though interpretability and reproducibility remain challenges. Hardware limitations associated with noisy intermediate-scale quantum devices, data encoding, and privacy considerations emerged as key barriers to clinical translation. Conclusions Preliminary studies highlight the promise of quantum machine learning for predictive and personalized management of metabolic syndrome and type 2 diabetes mellitus. However, successful clinical adoption will require robust validation pipelines, regulatory sandboxes, and harmonized compliance frameworks. Domain-specific evaluation metrics and transparent conformity assessments are essential to ensure trustworthy, scalable, and equitable deployment. A staged roadmap is proposed to bridge experimental progress with ethical and regulatory readiness.","url":"https://doi.org/10.1016/j.cmpb.2026.109609","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109609","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/89242","name":"Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.","source":"europepmc","abstract":"Background Despite advances in understanding and treating non-ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non-ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)-based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes. Objective This study aimed to develop predictive models for IHM in patients with NSTE-ACS using ML methods and predictor sets obtained during the diagnostic process. Methods This retrospective observational study included 1144 patients with NSTE-ACS admitted between 2019 and 2021. IHM occurred in 94 (8.1%) of 1144 patients. Predictive models were developed using multivariable logistic regression, Random Forest, XGBoost (Extreme Gradient Boosting), and CatBoost algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve, calibration metrics, and decision curve analysis. Results A key feature of the developed models was their applicability at different stages of the diagnostic process, using predictors available at each specific stage. At admission, ML models achieved ROC-AUC values up to 0.93. After incorporating laboratory and echocardiographic data, predictive performance increased to ROC-AUC values of 0.94 to 0.95. The best-performing model demonstrated an ROC-AUC of 0.951 (95% CI 0.946-0.956), a sensitivity of 0.902 (95% CI 0.889-0.916), and a specificity of 0.891 (95% CI 0.886-0.896). The area under the precision-recall curve reached 0.685, and the Brier score was 0.0331, indicating good calibration. Random Forest models demonstrated greater clinical utility than the Global Registry of Acute Coronary Events score ( P Conclusions ML-based models enabled accurate prediction of IHM in patients with NSTE-ACS at different stages of the diagnostic process and may improve risk stratification and clinical decision-making in real-world practice.","url":"https://doi.org/10.2196/89242","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/89242","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/anec.70229","name":"Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches.","source":"europepmc","abstract":"Background Cardiac implantable electronic devices (CIEDs) frequently detect brief, often subclinical atrial fibrillation (AF), but their value for predicting progression to persistent AF remains uncertain. Statistical and machine learning (ML) approaches may enable dynamic risk stratification using this longitudinal device data. Objective To develop a risk stratification model using clinical and CIED-derived AF burden measured over a rolling 6-month window to predict progression to persistent AF. Methods We analyzed continuous CIED data from 1985 patients without prior persistent AF implanted between 2016 and 2024 at a tertiary medical center. AF burden and clinical variables were summarized using overlapping 6-month rolling windows to estimate 1-year risk of persistent AF. Associations were evaluated using Kaplan-Meier and Cox proportional hazards models. A gradient-boosted decision tree model (XGBoost) was used to predict progression. Results During a mean follow-up of 1192 days, 874 patients (44%) developed paroxysmal AF, of whom 257 (29%) progressed to persistent AF after a mean of 813 days. Patients with no AF or 97% 1-year freedom from persistent AF, whereas those with > 8 h/day had a 63% progression rate. Higher AF burden was strongly associated with progression (maximum HR 8.66, p Conclusion CIED-detected AF burden is strongly associated with progression to persistent AF. ML-based analysis of 6-month device data enables accurate, point-in-time risk stratification to support earlier and more targeted clinical management.","url":"https://doi.org/10.1111/anec.70229","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/anec.70229","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.jstrokecerebrovasdis.2026.108727","name":"An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data.","source":"europepmc","abstract":"Background This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). Methods We analyzed a retrospective cohort of 5,014 adult AIS patients from three tertiary academic centers (2019-2023). Centers A and B (n = 3,512) formed the development cohort; Center C (n = 1,502) served as the external validation cohort. Sixty-three predictors across seven domains were extracted from the initial 24 hours. A consensus feature selection approach combining LASSO, RFE-RF, filter methods, and XGBoost importance was employed, and six machine learning algorithms were evaluated using nested cross-validation, Bayesian optimization, SMOTE, and rigorous anti-leakage protocols. SHAP values and a logistic nomogram enhanced interpretability. Results The best-performing CatBoost model with 22 RFE-RF features achieved AUC-ROC of 0.917 (95% CI 0.899-0.933) internally and 0.891 (95% CI 0.868-0.912) externally, significantly outperforming APACHE III, SOFA, OASIS, and GCS (ΔAUC 0.142-0.193, all p Conclusions Integration of bedside nursing assessments with emergency and laboratory data into an ensemble gradient-boosting model markedly improves early in-hospital mortality prediction in AIS compared with established ICU scores.","url":"https://doi.org/10.1016/j.jstrokecerebrovasdis.2026.108727","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jstrokecerebrovasdis.2026.108727","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/84260","name":"Prediction of Postoperative Vomiting Within 24 Hours Using Machine Learning With Large Language Model-Enhanced Interpretability: Development and Validation Study.","source":"europepmc","abstract":"Background Postoperative nausea and vomiting are common complications after anesthesia. However, vomiting represents a clinically distinct and objectively measurable endpoint. Objective This study aimed to develop and internally validate predictive models for postoperative vomiting within 24 hours using structured perioperative data and unstructured clinical text, while introducing a structured framework that separates feature construction from interpretability using large language models (LLMs). Methods We analyzed 33,460 anesthesia records from a single center (2019-2022). Two temporally defined prediction tasks were constructed to reflect real-world clinical decision-making and prevent information leakage: a preoperative model using variables available before anesthesia induction, and a perioperative model using variables available up to the end of surgery. Structured data were modeled using machine learning algorithms (logistic regression, Extreme Gradient Boosting, Light Gradient Boosting Machine [LightGBM]). Unstructured clinical text was incorporated through a deterministic, concept-driven preprocessing pipeline, where LLMs were used solely for normalization (temperature=0) without feature generation, followed by rule-based concept mapping and feature encoding. Post hoc interpretability was further supported using an LLM-based Question Answering Chain module. Model performance was evaluated using receiver operating characteristic-area under the curve (AUC), precision-recall AUC, calibration metrics, and threshold-based operating characteristics. Classification thresholds were selected using the Youden J statistic, and all metrics were reported with 95% CIs derived from bootstrap resampling. Decision curve analysis was performed to assess clinical utility. Results A total of 33,460 surgical procedures were included, of which 3607 (10.8%) experienced postoperative vomiting within 24 hours. In the preoperative task, LightGBM achieved an AUC of 0.729 (95% CI 0.706-0.749), compared with 0.610 (95% CI 0.588-0.632) for the Apfel score. In the end-of-surgery task, LightGBM achieved an AUC of 0.735 (95% CI 0.714-0.757). At the Youden-optimal threshold, the negative predictive value exceeded 0.95 across all models. Decision curve analysis demonstrated positive net benefit across clinically relevant threshold probabilities. Incorporating text-derived features provided modest improvements, while LLM-based explanation modules generated structured, natural-language explanations intended to enhance interpretability without substantially improving predictive performance. Conclusions Machine learning models can effectively predict postoperative vomiting within 24 hours using perioperative data. The proposed framework demonstrates that LLMs can be integrated in a controlled and reproducible manner-restricted to deterministic normalization and post hoc reasoning-to generate natural-language explanations intended to enhance the interpretability of model predictions, without introducing information leakage or altering predictive modeling. As no formal clinician-based evaluation was conducted, this interpretability benefit cannot yet be objectively confirmed, and the generated explanations should be regarded as a useful interpretability aid to be validated in future clinician-centered studies. External, multicenter validation is required before broader clinical applicability can be assumed.","url":"https://doi.org/10.2196/84260","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/84260","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.64898/2026.08.13.26359723","name":"NutrIA: Development and Internal Validation of a Hybrid Clinical Decision Support System for Personalized Preventive Nutrition","source":"europepmc","abstract":"Background The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capable of integrating artificial intelligence with evidence-based preventive nutrition. Although machine learning has shown considerable potential for health risk prediction, most existing approaches remain limited to isolated predictive models or conventional nutritional software, with little integration of multidimensional clinical assessment and personalized recommendations. Objective To develop and internally validate NutrIA, a hybrid web-based Clinical Decision Support System (CDSS) that combines machine learning, validated clinical assessment, structured clinical reasoning and personalized nutritional recommendations for preventive medicine. Methods NutrIA was developed using harmonized data from the National Health and Nutrition Examination Survey (NHANES, 1988–2018). A supervised machine learning model was trained to estimate 5-, 10- and 20-year all-cause mortality risk and subsequently integrated with an adaptive clinical questionnaire, validated screening instruments, nutritional indicators, dietary clustering, clinical phenotyping and a transparent rule-based recommendation engine within a unified web-based platform. Results The predictive model achieved ROC-AUC values of 0.894, 0.914 and 0.923 for 5-, 10- and 20-year mortality prediction, respectively. The implemented CDSS incorporates an adaptive questionnaire (151 items), 39 validated clinical assessment instruments, 17 clinical phenotypes and 31 dietary clustering modules to generate individualized nutritional and lifestyle recommendations together with an automated clinical report. The integrated framework translates probabilistic risk estimates into clinically interpretable decision support for personalized preventive nutrition. Conclusions NutrIA demonstrates the technical feasibility of integrating machine learning with knowledge-based clinical reasoning within a single web-based CDSS for preventive nutrition. Although external validation and prospective clinical evaluation are required before routine implementation, the proposed architecture represents a promising step toward clinically interpretable artificial intelligence for personalized nutritional care.","url":"https://doi.org/10.64898/2026.08.13.26359723","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.13.26359723","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10399234/v1","name":"Multimodal Ultrasound Combined with Machine Learning for Diagnosing Breast Lesions: Incremental Value Assessment of Ultrasound Super-Resolution Imaging","source":"europepmc","abstract":"Abstract Background. To integrate conventional ultrasound, contrast-enhanced ultrasound (CEUS), and ultrasound super-resolution imaging (US SRI) into an interpretable machine learning model to distinguish benign from malignant breast lesions. Methods. We retrospectively enrolled 210 cases (106 benign, 104 malignant). Following feature selection, six models were employed: Logistic Regression, Decision Tree, Random Forest (RF), Extreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Model stability and generalization were evaluated using 10-fold cross-validation (CV), an independent test set, and prospective validation (53 cases: 18 benign, 35 malignant). Performance was evaluated with the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), with SHapley Additive exPlanations (SHAP) for interpretability. Results. Lasso selected eight features: age, minimum diameter, ratio of diameter, anatomical location, breast imaging reporting and data system (BI-RADS) on CEUS, directional variance (Dir.Var), 0.5mm peritumoral perfusion index (0.5pPI), and 1.0mm peritumoral vascular density (1.0pVD). In the test set, the RF model performed best (AUC = 0.9909). SHAP identified BI-RADS on CEUS, age, and minimum diameter as core clinical features, while the three US SRI parameters provided minor but non-negligible incremental information. Calibration curves and DCA demonstrated good agreement and excellent clinical net benefit. In the prospective cohort, the RF model achieved an AUC of 0.9492, significantly higher than that of junior ultrasound physicians (P Conclusion. The multimodal machine learning model that integrated macroscopic and microscopic features exhibited excellent generalization and clinical utility for breast lesion differentiation.","url":"https://doi.org/10.21203/rs.3.rs-10399234/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10399234/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.3389/fendo.2026.1909594","name":"Machine learning versus traditional regression models for predicting diabetic retinopathy screening adherence among community-dwelling older adults with diabetes.","source":"europepmc","abstract":"Background Diabetic retinopathy (DR) is a leading cause of preventable vision loss among patients with diabetes, yet screening adherence remains suboptimal. Existing studies have mainly focused on clinical or sociodemographic determinants, with limited evidence integrating psychological mechanisms. The application of Protection Motivation Theory (PMT)-based constructs combined with machine learning for predicting DR screening adherence remains underexplored in community populations. Objective This study compared machine learning models (decision tree and random forest) with traditional logistic regression for predicting DR screening adherence among community-dwelling older adults with diabetes, incorporating a validated PMT-based questionnaire as a key psychological predictor. Methods A cluster random sampling design recruited 1,021 older adults with diabetes from four community health centers in Nantong, China (March-October 2025). Data included sociodemographic characteristics, clinical indicators, health behaviors, and PMT-based constructs. Participants were classified as good (n = 159) or poor adherence (n = 862). SMOTE and class weighting addressed class imbalance. Models were evaluated using 10-fold cross-validation. Logistic regression, decision tree, and random forest models were built using identical predictors. Decision curve analysis assessed clinical utility. Results The random forest model achieved a marginally higher AUC (0.774, 95% CI: 0.678-0.869) compared with logistic regression (AUC = 0.751, 95% CI: 0.678-0.824) and decision tree (AUC = 0.709, 95% CI: 0.596-0.821); however, DeLong's tests indicated no statistically significant differences (all p > 0.05). The decision tree exhibited the best calibration (lowest Brier score = 0.1142). DCA indicated that random forest provided the highest net benefit across most threshold probabilities. Multivariable analysis identified history of ocular disease (OR = 3.529, 95% CI: 2.430-5.139) as the strongest positive predictor, while higher HbA1c, lower self-efficacy, higher perceived severity, absence of exercise therapy, and smoking were associated with poorer adherence. Conclusion Machine learning models demonstrated comparable discriminative performance to traditional logistic regression for predicting DR screening adherence, while offering distinct profiles in calibration and clinical net benefit. Integration of PMT-based psychological constructs with clinical and behavioral factors provides a multidimensional framework for understanding screening behavior and supports risk stratification for precision diabetes care.","url":"https://doi.org/10.3389/fendo.2026.1909594","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1909594","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/cns.71067","name":"Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.","source":"europepmc","abstract":"Background Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup are limited. We aimed to develop and externally validate an interpretable machine learning model for predicting 28-day all-cause mortality after ICU admission. Methods Internal data were derived from MIMIC-III/IV, eICU, and NWICU, and external validation data from a single tertiary academic teaching hospital in China. The internal cohort was randomly split 7:3 into internal training set and internal test set. Feature selection, hyperparameter optimization, and training of five machine learning models were performed in the internal training set. Performance was evaluated in the internal test set and external validation cohort. SHAP analysis was used for interpretation, and a web-based tool was developed. Results Among 1190 included patients, 859 were in the internal cohort and 331 in the external validation cohort. Fifteen predictors were retained. LightGBM showed the best performance, with AUROCs of 0.840 and 0.764 in the internal test and external validation cohorts, respectively. Important predictors included GCS, diastolic blood pressure, glucose, and platelet count. Conclusion The developed LightGBM model showed good internal discrimination, acceptable external discrimination, and interpretable feature contributions, supporting its potential as a complementary early risk stratification aid for patients with ICH and thrombocytopenia. Prospective multicenter validation is required before clinical implementation.","url":"https://doi.org/10.1002/cns.71067","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/cns.71067","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s10067-026-08291-9","name":"Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.","source":"pubmed","abstract":"To identify poor prognostic factors in Epstein-Barr virus (EBV)-positive systemic lupus erythematosus (SLE) using interpretable machine-learning (ML) models.","url":"https://doi.org/10.1007/s10067-026-08291-9","authors":["Fang M","Huang M","Ge F","Hu L","Chen X","Sun L","Tu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10067-026-08291-9","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.64898/2026.08.25.26360552","name":"Machine Learning-Based Prediction of Maternal Morbidity across Heterogeneous Populations in the United States using Sequential Modeling of the All of Us Dataset","source":"europepmc","abstract":"In this work, we demonstrate the unprecedented value of NIH’s “All of Us Research Program” (AoURP) dataset in studying maternal morbidity and building predictive machine learning (ML) models across heterogeneous populations in the United States. We developed robust and data-driven preprocessing pipelines to curate a longitudinal, multi-site, multimodal, and demographically diverse pregnancy dataset (20,253 subjects; 27,525 pregnancy episodes) from AoURP data, using electronic health records (EHR) (Conditions, Labs, Measurements) and survey responses (Social Determinant of Health (SDoH)), focusing on 7 crucial maternal health adverse outcomes. After characterizing data quality, missingness, and heterogeneity, we performed statistical correlation analysis to identify risk factors. We subsequently developed XGBoost and sequential LSTM models to predict the adverse outcomes, reaching state-of-the-art performance for multiple outcomes. We conducted model interpretability post-hoc analysis to understand success points and fairness analysis to evaluate implications for socio-economic disparities. Four practicing physicians reviewed the set of statistically significant and ML model identified features to assess their clinical validity and novelty. Most features identified through either statistical correlations or ML feature importance analysis aligned with known clinical risk factors. Several features were identified that the ML models used but that are not currently used in clinical practice and may merit further clinical investigation. Fairness analysis revealed certain associations with SDoH and age highlight areas that warrant continued monitoring. Overall, we demonstrate that meaningful populational level patterns can be extracted, and high-performing machine learning models can be trained on this longitudinal, diverse, multi-site dataset. Important risk features, particularly novel ones identified, if validated, could inform new strategies for maternal care or enable development and validation of outcome-specific, clinically deployable ML models.","url":"https://doi.org/10.64898/2026.08.25.26360552","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.25.26360552","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1111/andr.70358","name":"Identification of Erectile Dysfunction From Routine Blood Test Data: Development and Validation of a Machine Learning-Based Prediction Model.","source":"europepmc","abstract":"Background Erectile dysfunction (ED) is a prevalent male health disorder and a recognized sentinel marker for cardiovascular disease. Current diagnostic reliance on subjective questionnaires or invasive examinations limits early screening. Aim We aimed to develop and validate a machine learning model based on routine blood test data to predict ED risk to facilitate its early clinical screening. Methods Data from 4116 men in the NHANES database (2001-2004) formed the training/internal validation sets. An independent external validation set comprised 489 clinical patients with NPTR-confirmed ED. From 49 initial demographic and blood-based indicators, feature selection via univariate logistic, multivariate logistic, and LASSO regression identified nine key predictors. Seven machine learning models were constructed, optimized via grid search with fivefold cross-validation, and evaluated using ROC analysis, calibration curves, and decision curve analysis (DCA). The optimal model was interpreted via SHAP. Results The random forest model achieved superior performance, with an external validation AUC of 0.934, accuracy of 0.918, and specificity of 0.986, significantly outperforming logistic regression (AUC = 0.743). SHAP analysis identified age, sex hormone-binding globulin (SHBG), testosterone, glucose, cholesterol, and creatinine as the most influential predictors. Discussion The study established a concise, nine-feature blood test panel and validated a high-performing predictive model in an independent clinical cohort, demonstrating significant clinical net benefit. Meanwhile, this model provides an economical, non-invasive, and scalable screening tool suitable for health check-ups, facilitating early ED identification. Conclusion A machine learning model based on routine blood tests can effectively evaluate ED risk, offering a novel foundation for early screening and precision management.","url":"https://doi.org/10.1111/andr.70358","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/andr.70358","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/tme.70105","name":"Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia.","source":"europepmc","abstract":"Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the literature on multimodal data integration-including electronic health records, genomic, proteomic and other high-dimensional datasets-in the context of AML transfusion management. The applications of machine learning algorithms such as decision trees, random forests and neural networks were analysed in the contexts of transfusion demand prediction and transfusion reaction risk assessment, with reference to representative clinical case studies demonstrating their practical utility. Multimodal big data demonstrates substantial value in optimising transfusion strategies for AML patients. Machine learning models have shown promising performance in predicting transfusion demand and assessing transfusion reaction risks, with clinical case studies supporting their practical utility. However, major challenges persist, including data privacy protection, data standardisation across platforms and model interpretability for clinical adoption. The integration of multimodal big data with advanced machine learning methodologies holds substantial promise for enabling precision, individualised transfusion management in AML. Future directions involving federated learning and explainable artificial intelligence are anticipated to address current limitations, ultimately contributing to improved transfusion safety and clinical outcomes in AML patients.","url":"https://doi.org/10.1111/tme.70105","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/tme.70105","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/genes17080974","name":"Longitudinal Transcriptomic Remodeling of Adipose Tissue After Bariatric Surgery Revealed by Differential Expression and Explainable Machine Learning.","source":"europepmc","abstract":"Background: Bariatric surgery improves metabolic health, but long-term transcriptomic remodeling of white adipose tissue (WAT) after Roux-en-Y gastric bypass (RYGB) remains incompletely defined. This study aimed to characterize longitudinal WAT gene-expression patterns after RYGB and prioritize candidate signatures of post-surgical adaptation using a publicly available dataset. Methods: We analyzed subcutaneous WAT transcriptomic data from women with obesity who underwent RYGB, with samples collected before surgery and at 2 and 5 years after surgery. Differential expression analysis was integrated with pathway enrichment, supervised machine-learning-based feature prioritization and classification, and SHAP-based model interpretation. Results: Differential expression and machine-learning analyses showed clear separation between baseline and post-surgery transcriptomic states. Pathway-level findings indicated reduced inflammatory and immune-related signaling, particularly across pathways related to phagosome function, lysosomal activity, antigen presentation, and host-defense responses after surgery. Gene-level analyses additionally suggested extracellular-matrix and metabolic remodeling. Machine-learning models distinguished baseline from post-surgery samples, while SHAP analysis identified genes with the strongest contributions to model predictions. Importantly, several statistically prioritized genes also showed high SHAP attribution, demonstrating concordance between univariate statistical significance and multivariate predictive relevance. This convergence suggests that the models captured biologically meaningful surgery-associated signals rather than purely data-driven classification artifacts. Conclusions: This study advances the interpretation of longitudinal adipose-tissue transcriptomic remodeling after RYGB by combining differential expression, pathway enrichment, supervised machine learning, and explainable AI within a unified framework. The integrated workflow prioritized candidate long-term remodeling genes, particularly immune/inflammatory and extracellular-matrix-related transcriptomic signatures, that warrant validation in independent cohorts.","url":"https://doi.org/10.3390/genes17080974","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/genes17080974","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/acm2.70772","name":"Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.","source":"europepmc","abstract":"Purpose This study aimed to evaluate the feasibility of fast Fourier transform (FFT) based spectral features for predicting volumetric modulated arc therapy (VMAT) gamma passing rates (GPRs) in virtual patient-specific quality assurance (PSQA). Methods A total of 481 VMAT treatment plans were retrospectively collected. Multileaf collimator (MLC) trajectories and monitor units (MU) were extracted from control points (CPs) and interpolated onto a uniform gantry-angle grid. FFT-based power spectra were then computed to extract global, spatial-axis, and angular-axis spectral features characterizing modulation complexity. These spectral features, alongside 25 conventional plan complexity metrics, were used to train Random Forest (RF) and XGBoost models for GPR regression and PSQA pass/fail classification. We employed a nested cross-validation strategy and used SHapley Additive exPlanations (SHAP) for model interpretation. Results Spectral features and conventional plan complexity metrics correlated significantly with GPRs, particularly spatial-axis high-frequency energy ratios and total spectral energy. In the regression task, the XGBoost model utilizing the combined feature set achieved the lowest mean absolute error (MAE) of 1.095 ± 0.182. For classification, the RF model with combined features yielded an AUC of 0.886 ± 0.043, an accuracy of 0.844 ± 0.066, and an F1 score of 0.602 ± 0.094. SHAP analysis confirmed that spectral energy, spatial-axis low- and high-frequency energy ratios, were major contributors to predicting PSQA failures. Conclusions FFT-based spectral analysis provides physically interpretable features for characterizing VMAT modulation complexity. These features showed feasible performance for GPR prediction and PSQA classification. These findings support their potential utility as interpretable inputs for virtual VMAT PSQA.","url":"https://doi.org/10.1002/acm2.70772","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/acm2.70772","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00234-026-04152-z","name":"Automated deep learning-based segmentation and volumetric analysis of meningiomas.","source":"europepmc","abstract":"Introduction Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based model for the automated segmentation of meningiomas and associated peritumoral edema on preoperative magnetic resonance imaging (MRI). Methods We trained a standard nnU-Net deep learning model on contrast-enhanced T1-weighted and FLAIR MRI scans from 100 patients treated at the University Hospital of Zurich. The model was then externally validated on 88 cases from the meningioma SEG-Class dataset from the Cancer Imaging Archive. Segmentation performance was assessed using the Dice similarity coefficient, Jaccard index, and 95th percentile Hausdorff distance. Results The model achieved mean Dice scores of 0.87 ± 0.23 for meningioma segmentation and 0.63 ± 0.38 for peritumoral edema in internal cross-validation. On the external validation set, the model achieved scores of 0.86 ± 0.17 for meningioma segmentation and 0.31 ± 0.35 for edema. Conclusion The deep learning model demonstrated high accuracy in segmenting meningiomas and modest performance for peritumoral edema. These results support the potential utility of automated segmentation tools in clinical workflows. Future work should focus on validating model performance across larger multi-center datasets.","url":"https://doi.org/10.1007/s00234-026-04152-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00234-026-04152-z","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1080/0886022x.2026.2696631","name":"An explainable machine learning model for predicting in-hospital infection in patients with systemic lupus erythematosus.","source":"europepmc","abstract":"Infection is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), yet effective tools for early identification of high-risk patients are lacking. This study aimed to develop an explainable machine learning (ML) model to predict in-hospital infection risk among SLE patients. We analyzed adult patients (≥18 years) with SLE ( n = 7,833) from three departments using a population-based electronic medical record database (2000-2024). Among them, 3,157 (40.3%) patients developed an infection after 72 h of hospitalization. An initial comprehensive variable pool of 108 candidate predictors was included, encompassing demographics, comprehensive laboratory parameters, clinical features, disease activity, and treatment exposures. Ten machine learning models were applied. Model performance was evaluated using six metrics. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Nine predictors were selected: daily prednisone equivalent dose, albumin, hydroxychloroquine use, C-reactive protein, D-dimer, glucose, cystatin C, hemoglobin, and alpha1-globulin. Among all models tested, the Gradient Boosting model demonstrated the best overall performance on the independent validation set, with an area under the curve (AUC) of 0.858, with its robustness confirmed by 5-fold and 10-fold cross-validation (mean AUCs of 0.855 ± 0.002 and 0.854 ± 0.008, respectively). SHAP analysis revealed that daily prednisone equivalent dose, albumin, and hydroxychloroquine use were the most influential factors. We developed and validated a high-performance, explainable ML model using nine routinely available clinical variables to accurately predict in-hospital infection risk in SLE patients. This tool provides transparent, individualized risk assessment and has the potential to guide personalized clinical stratification and early intervention, ultimately improving patient outcomes.","url":"https://doi.org/10.1080/0886022x.2026.2696631","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/0886022x.2026.2696631","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-10399317/v1","name":"Preoperative Prediction of Lymphovascular Invasion in Breast Cancer Using Multicenter DCE-MRI Radiomics: Comparative Analysis and External Validation of Ten Machine Learning Algorithms","source":"europepmc","abstract":"Abstract Background: Lymphatic vessel invasion (LVI) is a key histopathological factor driving the metastatic process in invasive breast cancer; however, a definitive diagnosis is typically only possible postoperatively. Imagingomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown promise in the preoperative prediction of LVI. Nevertheless, existing studies are largely limited to single-center settings, lack systematic comparisons of different machine learning algorithms, and suffer from insufficient external validation. To address these limitations, this study developed and rigorously validated a multi-center DCE-MRI-based bioinformatics framework that integrates ten machine learning algorithms to enable non-invasive preoperative prediction of LVI status. Methods This was a retrospective, multicenter study that included 935 female patients with pathologically confirmed invasive ductal carcinoma from three independent medical institutions. All patients underwent preoperative 3.0 T DCE-MRI. After manually delineating three-dimensional regions of interest (ROIs) on the phase-contrast DCE-MRI images, 1,197 radiomic features were extracted using PyRadiomics. Feature selection was performed sequentially using the Mann-Whitney U test, Spearman’s correlation analysis (excluding features with ρ > 0.9), and LASSO regression. Ten machine learning classifiers were compared: logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), random forests (RF), Extreme Trees (ExtraTrees), XGBoost, LightGBM, Gradient Boosting, AdaBoost, and Multi-Layer Perceptron (MLP), using five-fold cross-validation. A clinical model was constructed using independent predictive factors identified via multivariate logistic regression (age, histological grade, and sentinel lymph node status), and a combined clinical-radiomic nomogram was further developed. Model performance evaluation metrics included the area under the receiver operating characteristic curve (AUC), calibration curves, the DeLong test, and decision curve analysis (DCA), with evaluations conducted on the training set, an internal validation set, and two independent external test sets. Results Multivariate logistic regression analysis revealed that histological grade (OR = 1.610, 95% CI: 1.186–2.186, p = 0.010), age (OR = 0.976, 95% CI: 0.956–0.996, p = 0.048), and positive sentinel lymph nodes (OR = 6.857, 95% CI: 4.778–9.836, p Conclusion This large-scale, multicenter study demonstrates that logistic regression (LR) and multiple linear regression (MLR) are the most robust classifiers for predicting LVI based on DCE-MRI radiomics, whereas complex ensemble algorithms are prone to significant overfitting when applied to external validation data. The combined clinical-radiomic nomogram provided superior preoperative risk stratification and positive clinical utility in the internal validation, but its incremental value relative to a clinical-only model varied across different independent external centers. These findings underscore the necessity of rigorous multicenter benchmarking in radiomics research and offer a quantifiable, non-invasive decision-support tool for tailoring individualized surgical and neoadjuvant treatment strategies for breast cancer patients.","url":"https://doi.org/10.21203/rs.3.rs-10399317/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10399317/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.2147/cia.s613798","name":"Development and Validation of a Disability Risk Prediction Model for Older Adults Based on Machine Learning: A Multi-Algorithm Comparison with SHAP Interpretation.","source":"europepmc","abstract":"Objective To develop a predictive model for disability risk in older adults using machine learning algorithms. Methods A convenience sample of 13,809 older adults (aged ≥60 years) was recruited from seven medical institutions, three communities, and five nursing homes in Zunyi City, Guizhou Province. Participants were randomly divided into a training set (n = 9667) and a validation set (n = 4142) at a 7:3 ratio. Disability status was used as the outcome variable. Nine machine learning algorithms-logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine, artificial neural network, K‑nearest neighbor, and naïve Bayes-were used to construct prediction models. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, and other metrics, and the best‑performing model was selected. The SHapley Additive exPlanations (SHAP) method was used for interpretability analysis of the optimal model. Results Among the 13,809 participants, 5308 (38.44%) were identified as having disability. Among the nine models, LightGBM achieved the highest AUC (0.859), accuracy (0.792), precision (0.771), sensitivity (0.651), specificity (0.880), and F1 score (0.706). Conclusion Among the developed prediction models, the LightGBM‑based model demonstrated superior overall predictive performance in internal validation, providing a reference for disability management in older adults.","url":"https://doi.org/10.2147/cia.s613798","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/cia.s613798","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2174/0109298673472430260723093938","name":"Machine Learning Models for Mortality Prediction in Burn Patients: Performance, Reliability, and Clinical Translation.","source":"europepmc","abstract":"Introduction Despite advances in contemporary burn care, the discriminative performance and clinical applicability of traditional prognostic scoring systems may be increasingly limited. Machine learning models have been applied to predict burn mortality; however, their overall predictive performance and methodological quality have not been systematically evaluated. Methods This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Discriminative performance was assessed using the C-statistic. Pooled C-statistics and corresponding 95% credible intervals for machine learning models were estimated within a Bayesian framework. Subgroup and sensitivity analyses were performed according to model type, including random forest, support vector machine, and logistic regression, as well as the degree of class imbalance. Risk of bias and publication bias were also assessed. Results Twelve studies using diverse algorithms and data sources were included. The pooled C-statistic for the best-performing models was 0.96 (95% CrI: 0.93-0.98; 95% PI: 0.84-1.00), indicating excellent overall discrimination. Ensemble and more complex algorithms, such as random forest and support vector machine, outperformed single decision- tree models. Logistic regression also demonstrated stable performance. After excluding datasets with severe class imbalance, the overall findings remained largely unchanged. Funnel plot assessment and Egger's test did not indicate clear evidence of publication bias. Discussion The findings indicate that ML models have considerable potential for predicting mortality in patients with burns. However, methodological limitations, including a high risk of bias, limited external validation, and insufficient model interpretability, may restrict their current clinical applicability. Conclusion ML models demonstrate excellent discriminative performance for predicting mortality in patients with burns. Future high-quality multicenter studies with rigorous external validation, transparent reporting, and improved model interpretability are needed before these models can be routinely implemented in clinical practice. Registration The protocol for this study was registered with PROSPERO (registration number: CRD420251153818).","url":"https://doi.org/10.2174/0109298673472430260723093938","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2174/0109298673472430260723093938","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/jcm15166264","name":"&lt;i&gt;NBN&lt;/i&gt; rs1805794 Polymorphism Increases the Predictive Performance of Machine Learning Models for Multiple Chronic Toxicities in Head and Neck Cancer Survivors Treated with Definitive Radiotherapy ± Chemotherapy.","source":"europepmc","abstract":"Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G>C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy.","url":"https://doi.org/10.3390/jcm15166264","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15166264","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/psp4.70313","name":"Beyond Traditional Covariates: An Interpretable Machine Learning Workflow for Improved Hybrid Pharmacometric Modeling.","source":"europepmc","abstract":"Model-informed precision dosing is often constrained by the limited generalizability of traditional population pharmacokinetic models, especially in critically ill patients. A hybrid machine learning-population pharmacokinetic framework is proposed to improve a priori pharmacokinetic predictions by integrating real-world clinical data. This approach was applied to vancomycin trough concentration prediction. Two widely used two-compartment population pharmacokinetic models provided individual pharmacokinetic parameter estimates. Maximum a posteriori Bayesian estimation was used to adjust population parameters for individual patients based on drug administration records, therapeutic drug monitoring values, and patient-specific covariates from the MIMIC-IV database. The resulting clearance and central volume of distribution estimates served as training targets for XGBoost and symbolic regression models. Machine learning-predicted parameters were reinserted into the original pharmacokinetic equations to generate a priori vancomycin trough concentrations without reliance on therapeutic drug monitoring input. The hybrid models demonstrated improved prediction accuracy over traditional population pharmacokinetic covariate models and reduced vancomycin trough concentration prediction error by up to ~20%. XGBoost generally provided the highest predictive performance, while symbolic regression produced interpretable mathematical expressions revealing associations between non-traditional clinical predictors and pharmacokinetic parameters, highlighting a trade-off between accuracy and interpretability. This framework illustrates the potential of combining machine learning with population pharmacokinetic modeling to refine pharmacokinetic parameter estimation and support more precise, individualized dosing. The workflow is adaptable to other drugs and patient populations, offering a generalizable methodological strategy to identify non-traditional predictors and enhance existing pharmacometric model performance in real-world clinical settings.","url":"https://doi.org/10.1002/psp4.70313","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/psp4.70313","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.hlc.2026.03.061","name":"Interpretable Machine Learning Models for Mortality Prediction in Critically Ill Patients with Intra-Aortic Balloon Pump Therapy: Development and Evaluation.","source":"europepmc","abstract":"Background This study aims to develop and validate predictive models to assess mortality risk among patients undergoing intra-aortic balloon pump (IABP) therapy in the intensive care unit (ICU). Method A retrospective analysis was performed on 764 critically ill patients who received IABP therapy, using data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. The data set was chronologically split into training and temporal external validation cohorts. Variable selection was performed using LASSO (least absolute shrinkage and selection operator) regression, and six machine learning models were constructed. Model evaluation was conducted using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The optimal model was interpreted using SHAP (SHapley Additive exPlanations), and an online web-based calculator was developed. Results LASSO regression identified nine mortality-associated risk factors from 61 variables: lactate, norepinephrine, age, blood urea nitrogen, oliguria, coronary artery bypass grafting, red cell distribution width, renal replacement therapy, and cardiac arrest. Six machine learning models were developed: K-nearest neighbour, support vector machine, random forest (RF), extreme gradient boosting, decision tree, and LASSO logistic regression. The RF model demonstrated superior performance, achieving an area under the ROC curve of 0.965 (95% confidence interval [CI] 0.950-0.980) in the training cohort and 0.900 (95% CI 0.858-0.942) in the temporal external validation cohort. This model achieved the lowest Brier score of 0.104. Decision curve analysis confirmed the clinical utility of the RF model in predicting ICU mortality among patients undergoing IABP. Conclusions The RF model, based on interpretable machine learning, has proven to be effective in predicting ICU mortality among critically ill patients undergoing IABP therapy. This advances clinical practice by aiding in risk stratification and supporting clinicians in making more informed decisions.","url":"https://doi.org/10.1016/j.hlc.2026.03.061","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.hlc.2026.03.061","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.jvoice.2026.06.055","name":"Diagnosis of Parkinson's Disease Based on Voice and Speech Using Machine Learning and Deep Learning: A Systematic Umbrella Review.","source":"pubmed","abstract":"Early diagnosis of Parkinson's disease (PD) is complicated. Speech impairment, as an early symptom of PD, offers a noninvasive, scalable biomarker for remote assessment. Speech-based machine learning has shown promise, but methodological quality of existing evidence remains unclear. This review examines the previous reviews on the performance of machine learning models, their strengths and weaknesses, and future research opportunities in diagnosing PD using speech.","url":"https://doi.org/10.1016/j.jvoice.2026.06.055","authors":["Farajollahi B","Saffarian A","Habibi SAH","Sheikhtaheri A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jvoice.2026.06.055","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/life16081259","name":"Explainable and High-Performance ECG-Informed Machine Learning and Deep Learning Framework for Cardiovascular Risk Prediction.","source":"europepmc","abstract":"Predictive models used for the assessment of cardiovascular disease (CVD) risk must not only be highly accurate but also explainable and reliable for clinical decision-making. In this paper, we present a methodological framework for explainable machine learning and deep learning approaches to CVD risk prediction that uses clinically derived electrocardiogram (ECG) features and conventional CVD risk factors. A large synthetic dataset containing 30,000 patient cases was created according to a clinically validated distribution to provide reproducible and privacy-preserving benchmarking. A series of interpretability tests were performed on multiple models of machine learning and deep learning categories within a unified experimental setup. Out of the tested models, XGBoost and BiLSTM provided the highest discrimination capabilities in the machine learning and deep learning categories, respectively. Explaining the predictions via the SHapley Additive explanations (SHAP) approach, calibration of probability estimations, uncertainty quantification, and ablation studies were applied to assess the explainability, reliability, and robustness of the models. We show that the presented framework allows for the production of consistent and interpretable predictions along with the evaluation of the models' explainability and predictive reliability. Instead of focusing on the single-feature importance, this paper presents a reproducible methodological framework for the explainable modeling of CVD risk.","url":"https://doi.org/10.3390/life16081259","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/life16081259","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10484054/v1","name":"Clinical data-driven machine learning for predicting molar-incisor hypomineralization and hypomineralized second primary molars","source":"europepmc","abstract":"Abstract Objectives This study evaluated clinical data machine learning (ML) models for predicting molar-incisor hypomineralization (MIH) and hypomineralized second primary molars (HSPM). Material and methods Data on demographic characteristics, socioeconomic indicators, maternal medical history during pregnancy, perinatal conditions, early childhood medical history, and oral health-related factors were collected from 959 children aged 6–8 years. After preprocessing, imputation, feature engineering, and systematic evaluation of eight class imbalance-handling strategies, five supervised ML algorithms (XGBoost, Random Forest, LightGBM, CatBoost, and Support Vector Machine) were trained. Model performance was assessed on an independent test set using accuracy, recall, precision, and F1-score, with emphasis on recall due to its relevance in clinical screening. Model interpretability was investigated using SHapley Additive exPlanations (SHAP). Results For MIH prediction, the LightGBM model combined with SMOTE-ENC and ENN achieved the best balance between sensitivity and robustness (recall = 0.61; accuracy = 0.73). For HSPM prediction, XGBoost with random undersampling yielded the most stable performance across evaluation metrics (recall = 0.44; accuracy = 0.53). SHAP analysis revealed that HSPM presence, socioeconomic indicators, age, and fluoride exposure were among the most influential predictors. Conclusions ML models demonstrated clinically meaningful predictive performance for MIH and limited-to-moderate performance for HSPM. Both clinical and socioeconomic factors play a critical and interconnected role in hypomineralization risk, reinforcing the need for integrated data-driven approaches in early diagnosis. Clinical relevance Interpretable machine learning models can support early risk stratification for molar-incisor hypomineralization using routinely collected clinical data, facilitating timely preventive interventions and more individualized patient care.","url":"https://doi.org/10.21203/rs.3.rs-10484054/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10484054/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1016/j.jcms.2026.109855","name":"Clinical decision framework for sublingual hematoma: literature-based retrospective analysis with machine learning.","source":"europepmc","abstract":"Sublingual hematoma is a rare but life-threatening condition with heterogeneous etiologies that complicate emergency decision-making. This study compared clinical characteristics and management outcomes between implant-induced sublingual hematoma (ISH) and spontaneous sublingual hematoma (SSH), and developed an etiology-guided treatment algorithm through integrated statistical and machine-learning analyses. A systematic literature search through January 2025 identified 77 studies comprising 85 cases (ISH, n = 33; SSH, n = 52). Clinical variables were compared statistically, and random forest and decision tree models were applied to identify determinants of treatment and prognosis. SSH was strongly associated with cardiovascular comorbidities and anticoagulant therapy (84.6% vs 12.1%; p < 0.001), whereas ISH required surgical intervention more often (54.5% vs 13.5%; p < 0.001), with shorter hospital stays (5.3 ± 3.9 vs 10.5 ± 9.8 days). Machine learning identified symptom-to-treatment time and surgical parameters as major predictors for ISH management, while warfarin use and respiratory distress determined SSH severity. These findings indicate that sublingual hematoma requires prompt, etiology-specific management: immediate surgical hemostasis for implant-induced cases and correction of coagulopathy with airway vigilance for spontaneous cases. The proposed framework provides preliminary data-informed guidance for managing this rare but critical condition.","url":"https://doi.org/10.1016/j.jcms.2026.109855","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jcms.2026.109855","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.gerinurse.2026.104284","name":"Association between stomach pain and depression in middle-aged and older chinese adults: Evidence from CHARLS with machine learning and SHAP analysis.","source":"europepmc","abstract":"Background Emerging evidence indicates a bidirectional relationship between gastrointestinal symptoms and psychological distress, yet population-based evidence concerning stomach pain and depression remains scarce. This study investigates the association between stomach pain and depression within a nationally representative cohort of middle-aged and older adults in China. Methods Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). Multivariable logistic regression was employed to evaluate the association between stomach pain and depression, adjusting for sociodemographic, behavioral, and clinical covariates. Subgroup analyses were performed based on age, sex, residence, and comorbidities. Six machine learning models were trained to predict depression using stomach pain and related features. The extreme gradient boosting (XGBoost) model was further interpreted via Shapley Additive Explanations (SHAP). Results Stomach pain was significantly associated with higher odds of depression (adjusted odds ratio [OR]: 2.36; 95% confidence interval [CI]: 2.15-2.59). Subgroup analyses revealed consistent associations among females, rural residents, and individuals with hypertension or dyslipidemia. XGBoost demonstrated the highest predictive performance among the machine learning models (area under the receiver operating characteristic curve [AUROC]: 0.988 in training; 0.884 in testing), followed by light gradient boosting machine (LGBM) and CatBoost. SHAP analysis indicated that rural residence, education level, age, and stomach pain were the most influential predictors of depression risk. Conclusions Stomach pain is independently associated with depression among middle-aged and older adults in China. Routine assessment of gastrointestinal symptoms could aid in the early identification and stratification of depression risk in aging populations.","url":"https://doi.org/10.1016/j.gerinurse.2026.104284","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.gerinurse.2026.104284","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s11701-026-03839-y","name":"Robotic-referenced automated measurement of acetabular cup orientation on postoperative CT: development and internal validation.","source":"europepmc","abstract":"Accurate measurement of acetabular cup orientation after total hip arthroplasty is essential, but postoperative CT relies on subjective, error-prone manual measurement. Robot-assisted surgery provides intraoperative cup-orientation measurements but is costly and not widely available. We aimed to develop and evaluate a deep-learning model measuring cup orientation on postoperative CT, using robotic navigation values as reference standard. This secondary analysis of a randomized trial (ChiCTR2200060115) analyzed 94 hips with robotic intraoperative angle measurements and postoperative CT (May 2023 to May 2024). A VGG16-based U-Net segmented key anatomical structures into three-dimensional point clouds. Three measurement pathways were compared: manual annotation, machine learning, and deep learning. The optimal model was integrated into a graphical user interface. Ninety-four hips (mean age, 57.0 years ± 9.5 [standard deviation]; 62 men) were evaluated using 27,821 CT images. The best model (PointNet++) achieved mean absolute errors of 4.48° (anteversion) and 3.89° (inclination), compared with 4.08°/ 5.52° for machine learning and 8.91°/ 8.70° for manual measurement, significantly outperforming manual measurement (both p < 0.05). Exploratory full-cohort Lewinnek classification was correct in 81/94 hips versus 57/94 with manual measurement; in internal validation, 71% and 81% of predictions were within 5° of the robotic reference. A deep-learning model developed using robotic navigation values showed promising internal-validation performance for automated cup-orientation measurement on postoperative CT; independent external validation is required before broader clinical application.Trial registration ChiCTR2200060115, 19 May 2022.","url":"https://doi.org/10.1007/s11701-026-03839-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11701-026-03839-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1097/cin.0000000000001528","name":"Machine Learning-assisted Prediction of Fluid Overload and Its Nursing Implications in Patients With Acute Pancreatitis.","source":"europepmc","abstract":"Fluid overload (FO) is a critical complication in acute pancreatitis (AP), contributing to prolonged hospital stays and increased mortality. Traditional scoring systems like BISAP lack predictive accuracy for FO, placing a burden on nurses managing fluid therapy. Machine learning (ML) offered potential for early FO prediction, yet its integration into nursing practice remains underexplored. Using the MIMIC-IV database, we analyzed 3458 adult AP patients, with 22.8% experiencing FO (net fluid balance >2 L/24h plus clinical signs). Three ML models-Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM)-were developed to predict FO using clinical and nursing-derived features (eg, BUN, net fluid balance, and documentation frequency). Models were evaluated for accuracy, precision, recall, F1 score, and ROC AUC, with SHAP analysis for feature importance. A Gradio-based clinical decision support system (CDSS) was prototyped for nursing integration. LSTM outperformed RF and XGBoost, achieving an AUC of 0.92 (95% CI [0.90, 0.94]), accuracy of 0.85, and F1 score of 0.82, significantly surpassing BISAP (AUC 0.74, P 65 years (AUC 0.95) but reduced accuracy in CKD patients (AUC 0.88). The CDSS provided real-time FO risk scores, enhancing nurse decision-making. ML models, particularly LSTM, enabled accurate FO prediction in AP, with potential to transform nursing practice through proactive fluid management. Integration into CDSS supports early interventions, reducing nurse workload and improving patient outcomes.","url":"https://doi.org/10.1097/cin.0000000000001528","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/cin.0000000000001528","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fendo.2026.1901646","name":"A dual-validated machine learning model for predicting 3-year mortality in atypical pulmonary carcinoid, a rare neuroendocrine tumor.","source":"europepmc","abstract":"Objectives Atypical pulmonary carcinoid (AC) is a rare intermediate-grade neuroendocrine tumor with substantial clinical heterogeneity and an unpredictable prognosis. Accurate estimation of fixed-horizon mortality risk remains challenging because of its rarity and limited AC-specific prediction tools. This study aimed to develop and evaluate an interpretable model for estimating 3-year all-cause mortality in patients with AC. Methods Clinical data for patients with AC diagnosed between 2000 and 2021 were retrospectively obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed during 2000-2018 (n=1, 301) were randomly divided into a training set (n=910) and an internal test set (n=391). Patients diagnosed during 2019-2021 (n=446) constituted a temporal validation cohort within the same registry, and 45 patients treated at the General Hospital of Ningxia Medical University during 2015-2024 were used for preliminary independent single-center evaluation. Demographic, clinicopathological, and treatment variables were considered. LASSO regression and the Boruta algorithm selected 11 predictors: Grade, N_stage, M_stage, Bone_metastasis, Brain_metastasis, Liver_metastasis, Marital_status, Radiation, Chemotherapy, Age, and Tumor_Size. Seven machine learning models were developed to evaluate predictive performance, including Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Adaptive Boosting (AdaBoost). The SHapley Additive exPlanations (SHAP) approach was used to interpret feature importance. Results LR showed the most consistent held-out performance, with an area under the receiver operating characteristic curve of 0.802 (95% CI: 0.751-0.852) in the internal test set, 0.839 (95% CI: 0.796-0.882) in the temporal validation cohort, and 0.904 (95% CI: 0.808-1.000) in the single-center cohort. Boosting models showed larger declines from apparent training to held-out performance. Decision curve analysis (DCA) suggested potential net benefit across selected threshold probabilities. SHAP identified Age as the largest contributor to model predictions. Conclusion The LR-based model showed consistent discrimination and interpretability in internal and same-registry temporal evaluation, with promising but imprecise results in the small single-center cohort. Geographic transportability remains unestablished. Independent multi-institutional validation with fully separated preprocessing is required before clinical use.","url":"https://doi.org/10.3389/fendo.2026.1901646","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1901646","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2196/91877","name":"Gerontechnology Acceptance in Thai Older Adult Care Facilities-Sensor Network and Machine Learning Adoption: Mixed Methods Study.","source":"europepmc","abstract":"Background Thailand is undergoing a rapid demographic transition, with an estimated 28% of the population expected to be aged 60 years or older by 2030. This shift creates an urgent demand for technology-enhanced solutions for older adults. Despite growing interest in Internet of Things (IoT) sensor networks and machine learning applications for older adult care, the patterns of technology acceptance and implementation readiness in Thai older adult care facilities remain underexplored. Objective This study aimed to assess the readiness and adoption patterns of sensor network and machine learning technologies among older adults and care stakeholders in Thai older adult care facilities, guided by the Gerontechnology Acceptance Model (GTAM) and Service Exchange Value Creation Logic. Methods A sequential explanatory mixed methods design was used. Phase 1 (quantitative) involved structured technology assessments by 12 health care technology specialists and survey administration to 120 consumer representatives (older adults, n=70; adult family members, n=50), stratified across Bangkok and Chiang Mai. Phase 2 (qualitative) comprised 20 semistructured interviews and 3 purposively selected focus groups from phase 1 participants to elaborate on the quantitative findings. The primary theoretical framework was GTAM, mapping 5 constructs (perceived usefulness, ease of use, social influence, facilitating conditions, and behavioral intention) to corresponding survey items. This study was approved by the Assumption University Institutional Review Board (AU-IRB 80/2024) and is registered under a noninterventional observational design; formal clinical trial registration was not applicable. Results IoT fall detection systems received the highest clinical efficacy ratings from specialists (mean 4.5, SD 0.3 on a 5-point scale) and achieved 89% user acceptance. Artificial intelligence-driven early warning systems demonstrated the highest perceived clinical impact (mean 4.7, SD 0.2) but also the greatest implementation complexity (mean 4.2, SD 0.5). The consumer survey findings revealed that digital literacy level was the strongest predictor of behavioral adoption intention ( β =.62; P t 119 =1.98; P =.05), while Chiang Mai respondents reported a stronger preference for environmental digital health solutions (mean 4.5, SD 0.3; t 119 =4.12; P P =.005). Qualitative analysis identified 5 themes: surveillance anxiety, family-mediated adoption, regional digital trust, training needs, and dignity-preserving technology design. Conclusions Smart technology integration in Thai older adult care facilities is feasible and accepted across demographic groups when implemented in phases, culturally adapted, and supported by digital literacy training. Key adoption enablers were digital confidence, family involvement, and privacy-respecting design. The GTAM-derived findings offer an evidence-based framework for deploying gerontechnology in health care contexts in developing nations. Longitudinal outcome studies are needed to validate clinical and economic projections from prior literature.","url":"https://doi.org/10.2196/91877","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/91877","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/1741-2552/ae8eb1","name":"Machine learning approaches for prediction of epilepsy risk across clinical pathways: a systematic review.","source":"pubmed","abstract":"Objective. Machine learning (ML) and deep learning (DL) models are increasingly being explored for individualised epilepsy risk prediction after a first unprovoked seizure (UFS) and after acute brain insults such as stroke or traumatic brain injury. We systematically evaluated their predictive performance, input modalities, validation strategies, methodological quality, and translational readiness across these two clinical pathways. Approach. PubMed, Scopus, IEEE Xplore, and Web of Science were searched for English-language human studies published between January 2005 and October 2025. Eligible studies used ML or DL to predict seizure recurrence after UFS or epilepsy development after acute brain insult using clinical, neuroimaging, electrophysiological, electronic-health-record, or multimodal data. Two reviewers performed blinded duplicate screening, followed by duplicate data extraction using a CHARMS-aligned form. Risk of bias and applicability were independently assessed using PROBAST+AI across the Participants, Predictors, Outcome, and Analysis domains. Main results. Thirteen studies met the eligibility criteria: six addressed UFS and seven addressed post-insult epilepsy. Reported AUCs for the best-performing models ranged from 0.60 to 0.93, with the highest discrimination observed in models using high-dimensional neuroimaging, unstructured clinical text, or multimodal data. These inputs included MRI morphometric asymmetry, clinical free text, EEG, diffusion MRI, resting-state fMRI, and multimodal fusion. In the three studies that directly compared modality combinations, multimodal models improved AUC by approximately 0.04-0.10 over the best single-modality counterpart. Model credibility was strongest when independent validation, transparent feature handling, and calibration assessment were reported. Significance. ML/DL approaches show clear potential for earlier, individualised epilepsy risk stratification, particularly when complementary clinical, electrophysiological, and neuroimaging data are integrated. Future studies should prioritise prospective multi-site validation, standardised EEG/MRI data structures, transparent multi-metric reporting, and reproducible model documentation aligned with TRIPOD+AI and PROBAST+AI.","url":"https://doi.org/10.1088/1741-2552/ae8eb1","authors":["Omar AM","Omisade A","Gallivan JP","Winston GP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1088/1741-2552/ae8eb1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-026-63991-1","name":"Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation.","source":"europepmc","abstract":"Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Syndrome (CIS), privacy-preserving for a federated and explainable Machine Learning (ML) framework. To address missing data while preserving inter-feature dependencies, Multivariate Imputation by Chained Equations (MICE) with iterative imputers was employed. Classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model interpretability was developed through Explainable Artificial Intelligence (XAI) techniques, specifically Shapley Additive Explanations (SHAP). To ensure data confidentiality and simulate decentralized clinical environments, an in silico federated learning framework was applied. Experimental results demonstrated strong predictive performance, achieving 96.7% accuracy and 99% ROC-AUC during training, 92.5% accuracy in validation, and 81.8% accuracy with an AUC of 88% on the test set. For the Federated Learning (FL) simulation, the model maintained competitive performance, yielding an accuracy of 76.3% and an AUC of 83.9%. The proposed approach supports early diagnosis, enhances clinical trust through interpretability, and promotes secure data collaboration, thereby contributing to more informed and transparent clinical decision-making and improved patient care.","url":"https://doi.org/10.1038/s41598-026-63991-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-63991-1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10560584/v1","name":"Machine learning-based predictive model of postoperative acute heart failure among elderly hip fracture patients","source":"europepmc","abstract":"Abstract Objective This study enrolled elderly patients aged ≥ 80 years with hip fractures to investigate their clinical characteristics and key predictors of postoperative acute heart failure (AHF). We further developed traditional predictive models and machine learning models, compared their predictive performance, and aimed to provide clinical evidence for perioperative precise prevention and management in this population. Methods Patients aged ≥ 80 years with hip fractures who underwent surgical treatment at the Department of Geriatric Orthopaedics, the Third Hospital of Hebei Medical University between January 2020 and December 2022 were enrolled in this study. LASSO regression coupled with the Boruta algorithm was used for variable selection to construct a binary logistic regression nomogram. Five machine learning models (RF, XGBoost, LightGBM, NB, GBM) were developed, with hyperparameters tuned via Bayesian optimization. After identifying the optimal model, SHAP and LIME algorithms were applied for model interpretability analysis. Results A total of 661 patients were included, among whom 282 (42.6%) developed postoperative acute heart failure. Nine risk factors were screened out using LASSO regression combined with the Boruta algorithm: admission BNP, preoperative BNP, diabetes mellitus, coronary heart disease, admission hemoglobin, admission albumin, serum creatinine, admission CRP, and hematocrit. Among the conventional regression model and five machine learning models, the XGBoost model exhibited the optimal performance with favourable discrimination, calibration and clinical net benefit. Conclusions The incidence of postoperative acute heart failure is relatively high in elderly patients with hip fracture. The XGBoost model established based on key predictors achieves the best overall performance. Combined application of SHAP and LIME enables effective interpretation of the model’s decision-making mechanism, which can serve as a reference for early identification of high-risk patients and formulation of individualized intervention strategies in clinical practice.","url":"https://doi.org/10.21203/rs.3.rs-10560584/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10560584/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1080/15376516.2026.2713207","name":"Comparison of machine learning and logistic regression in predicting mortality from acute poisoning in young adults: a multicenter study identifying herbicide exposure as the predominant risk determinant.","source":"europepmc","abstract":"Objective Compare the effectiveness of machine learning algorithms and traditional logistic regression in predicting the mortality risk of young patients with acute poisoning, and establish a risk stratification nomogram. Methods This multicenter retrospective study derived a derivation cohort of 406 young adults with acute poisoning from Wenzhou and an external validation cohort of 150 patients from Lishui. LASSO regression was used to screen predictive factors from 43 candidate variables. Compare the predictive performance of 14 machine learning algorithms (including RandomForest, XGBoost, CatBoost, LightGBM, SVM, etc.) with logistic regression on a training set (7:3 random split). The model evaluation indicators include AUC, sensitivity, specificity, and calibration, and conduct internal and external verification. Results LASSO identified six independent predictive factors: white blood cell count, creatinine, herbicide poisoning, invasive mechanical ventilation, liver dysfunction, and shock. The discriminative power of the final logistic regression is comparable to that of the optimal machine learning model (internal validation AUC 0.885, external validation AUC 0.971), and it is well calibrated (Brier score 0.058-0.082, Hosmer Lemeshow test p > 0.05). A nomogram for predicting the 28-day mortality risk of young patients was constructed based on Logistic regression. Conclusion Logistic regression performs similarly to complex machine learning algorithms in predicting the risk of death from acute poisoning in young adults, with better interpretability and clinical practicality. The nomogram constructed based on this is a simple and effective early risk stratification tool, which can serve as one of the reference tools for clinical decision-making assistance.","url":"https://doi.org/10.1080/15376516.2026.2713207","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/15376516.2026.2713207","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fmed.2026.1891648","name":"Clinical risk factors and machine learning for venous thromboembolism in lung cancer: an exploratory single-center study.","source":"europepmc","abstract":"Background Venous thromboembolism (VTE) represents a common and severe comorbidity in lung cancer patients. This study aimed to characterize the clinical features and independent risk factors associated with VTE in these individuals, and to evaluate the complementary utility of machine learning approaches in enhancing VTE risk stratification. Methods This retrospective study included lung cancer patients admitted to the Fourth Affiliated Hospital of Anhui Medical University between January 2018 and April 2025. According to imaging findings, patients were categorized into a VTE group and a control group. Clinical characteristics, laboratory parameters, and treatment-related information were retrospectively collected and analyzed. Univariate and multivariable logistic regression analyses were performed to identify factors independently associated with VTE. Additionally, four machine learning models-Ridge-LR, LASSO, random forest (RF), and extreme gradient boosting (XGBoost)-were developed for exploratory purposes. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration metrics (Brier score, calibration slope and intercept, Hosmer-Lemeshow test), and decision curve analysis (DCA). Results A total of 180 patients with lung cancer were included. Elevated D-dimer levels were independently associated with increased VTE risk (OR = 1.084, p = 0.021). Chemotherapy exposure showed an OR of 2.858 (95% CI: 1.081-7.555, p = 0.034) for VTE in the 1-2 cycle subgroup, although no significant association was observed for higher cycle numbers, suggesting potential confounding. Male sex was independently associated with embolic events occurring outside the lower extremities (OR = 10.83, p = 0.034). Among the machine learning models, RF achieved the highest AUC (0.740), but Ridge-LR demonstrated the best calibration (Hosmer-Lemeshow p = 0.345). LASSO showed the highest net benefit on decision curve analysis, while RF and XGBoost exhibited significant miscalibration ( p = 0.002 for both). D-dimer (41.3%) and total protein (34.7%) were identified as the most important predictors in the XGBoost model. Conclusion Elevated D-dimer levels measured prior to VTE diagnosis during routine clinical monitoring were independently associated with subsequent VTE, supporting its potential utility as a predictive biomarker for risk stratification in patients with lung cancer. The association between chemotherapy exposure and VTE is likely influenced by selection bias and residual confounding rather than representing a true causal effect. Machine learning models did not demonstrate consistent improvement over logistic regression in discrimination or calibration in this small single-center cohort, suggesting limited added predictive value with the current dataset.","url":"https://doi.org/10.3389/fmed.2026.1891648","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1891648","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/bioengineering13080917","name":"Pilot Study Employing a Machine Learning Approach as a Potential Method for Predicting Parkinson's Disease Using Voice as a Digital Biomarker and the SHAP Approach for Feature Engineering.","source":"europepmc","abstract":"Introduction : Voice-based digital biomarkers have emerged as a promising approach for distinguishing individuals with neurodegenerative disorders, particularly Parkinson's disease (PD), from healthy subjects (HS). With the increasing availability of smartphone and web-based recording tools, voice data can be collected efficiently in both clinical and remote settings. However, further validation is required before such approaches can be translated into routine clinical practice. Methods : This study used a cross-sectional analysis at the recording level, treating repeated recordings from the same participant as separate observations collected at Vito Fazzi Hospital in Lecce, Italy. Speech recordings from individuals with Parkinson's disease (PD) and healthy controls were collected using the dedicated Talia smartphone and web application. Sustained vowel phonation (/a/) was analyzed as the primary speech task. Following data acquisition, feature extraction was performed as a crucial step in the speech analysis pipeline, as the quality and relevance of the extracted features directly influence the ability of machine learning models to discriminate between Parkinson's disease (PD) patients and healthy controls. To capture various aspects of speech impairment associated with PD, a comprehensive set of acoustic features was extracted, including long-term features (pitch, jitter, and shimmer), nonlinear descriptors such as Recurrence Period Density Entropy (RPDE), and short-term feature based on Mel-Frequency Cepstral Coefficients (MFCCs). These features were subsequently used to develop and evaluate machine learning models for the classification of Parkinson's disease and healthy subjects. Feature selection was performed using SHAP to identify the most informative vocal biomarkers. Model performance was assessed using five independent random train-test splits (70% training and 30% testing), supported by an internal five-fold cross-validation procedure within the training data. Multiple machine learning models were developed and evaluated, including Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, Decision Tree, Artificial Neural Network, and Gradient Boosting. Results : The evaluated models demonstrated strong recording-level classification performance. Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) achieved the highest accuracy scores (0.9545 and 0.9494, respectively), along with superior recall (up to 0.9500), precision (up to 0.9551), and F1-score (up to 0.9525). Both models also exhibited excellent discriminative ability, with ROC-AUC values reaching 0.9882 (ANN) and 0.9893 (KNN). In contrast, Naive Bayes and Decision Tree showed comparatively lower performance across all metrics. Log-loss analysis further confirmed the robustness of ANN and KNN, which achieved the lowest values (0.2552 and 0.2510, respectively), indicating well-calibrated predictions. Overall, the findings highlight the consistency and generalizability of ANN and KNN across cross-validation splits. Conclusions : This study demonstrates that machine learning models, particularly ANN and KNN, can effectively differentiate Parkinson's disease from healthy conditions using voice recordings. The integration of explainable AI for feature selection enhances model transparency and clinical relevance. However, the reported performance estimates were obtained from a recording-level analysis and should be interpreted as preliminary findings. Further studies involving larger cohorts and participant-level validation strategies are required to determine the generalizability and clinical applicability of these approaches.","url":"https://doi.org/10.3390/bioengineering13080917","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/bioengineering13080917","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/jdn.70172","name":"Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.","source":"europepmc","abstract":"Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a broad spectrum of symptoms, which makes timely and accurate diagnosis challenging. The development of machine learning (ML) and deep learning (DL) has created opportunities for automated ASD screening and detection. This systematic review focuses on the analyses of 59 peer-reviewed studies on unimodal and multimodal approaches to ASD detection that were published between 2019 and 2025. The results demonstrated that classical ML algorithms (such as logistic regression [LR], support vector machines [SVM] and random forests [RF]) and DL models (convolutional neural networks [CNN], recurrent neural networks (RNN) and transformers) were used to assess the accuracy of the diagnosis for a variety of data modalities ranging from behavioural measures to neuroimaging, electroencephalography (EEG), eye tracking and speech, with accuracy from 68% to 99%. A careful examination of these studies, however, shows that they share certain common flaws, including small sample size, demographic bias, overfitting and absence of external validation. Hybrid multimodal frameworks have been shown to yield consistent performance improvements over unimodal frameworks, with accuracies of 95%-99% achieved through attention, graph-based learning and hybrid fusion approaches. This review highlights four major points: (1) a critical review of dataset ethics and validity, even for non-clinical facial image datasets; (2) an architectural comparison of multimodal fusion strategies (early fusion, late fusion and hybrid fusion) focusing on computational complexity and clinical applicability; (3) a quantitative summarization of the performance trends by modalities and sample size; and (4) a structured review of indicators of reproducibility and regulatory hurdles for clinical translation. This review suggests the need to develop large, well-balanced datasets, the application of explainable AI (XAI) techniques, standardization (e.g., brain imaging data structure [BIDS]) and regulatory guidelines for facilitating the clinical translation of ASD detection systems.","url":"https://doi.org/10.1002/jdn.70172","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/jdn.70172","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.cmi.2026.08.014","name":"An Introduction to the Machine Learning Lifecycle for Clinical Microbiology.","source":"europepmc","abstract":"Background Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data. Objectives This narrative review provides a structured introduction to the machine learning (ML) lifecycle from the perspective of clinical microbiology, outlining the sequence of steps in data preparation, model development, evaluation, and deployment. Sources This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and ML, including applied research articles and relevant guidelines on AI development, evaluation, and implementation in healthcare. Content We describe the characteristics of modern microbiology datasets and emphasize the importance of rigorous problem definition, data integration, quality assessment, and feature engineering. Model development considerations are summarized for supervised learning, including hyperparameter optimization, model choice, and multimodal data integration. Evaluation frameworks are examined with attention to typical challenges for microbiology applications, including class imbalance, generalization, robustness, model interpretation, and explainability. Finally, we summarize key elements of model deployment, including reproducible packaging, integration with Laboratory Information Systems and Electronic Medical Record systems, ML operation practices, ongoing drift monitoring, and regulatory and governance requirements. Implications Successful AI implementation in microbiology demands alignment with laboratory workflows, transparency and interpretability of model behaviour, robust performance under real-world variability, and strong data governance. Addressing these factors is essential for translating promising methodological advances into solutions to enhance diagnostics, antimicrobial stewardship, and infection prevention.","url":"https://doi.org/10.1016/j.cmi.2026.08.014","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cmi.2026.08.014","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/jpm16080423","name":"An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.","source":"europepmc","abstract":"Background: Diabetes mellitus affects over 500 million people worldwide, yet many machine learning prediction models remain difficult to interpret, limiting their clinical applicability. This study proposes an explainable machine learning framework integrating the Marine Predators Algorithm (MPA) for hyperparameter optimization with SHAP-based explainability to diabetes prediction. Methods: Logistic Regression (LR), Random Forest (RF), and MPA-optimized XGBoost were evaluated using a publicly available Kaggle diabetes dataset of approximately 100,000 records. Statistical significance was assessed using Wilcoxon signed-rank tests with Bonferroni correction for fold-wise cross-validation results, while McNemar's and DeLong's tests were employed for paired comparison of independent test-set predictions and ROC-AUC values, respectively. Performance was assessed using accuracy, precision, recall, F1-score, specificity, ROC-AUC, and Brier score. SHAP was used to provide global and local model explanations. Results: The MPA-optimized XGBoost model achieved the highest performance, with 96.72% accuracy, 97.70% precision, 95.70% recall, 96.71% F1-score, and 99.56% ROC-AUC, significantly outperforming LR and RF ( p Conclusions: The proposed framework demonstrated strong predictive performance and interpretable model behavior on the publicly available diabetes dataset used in this study. These findings indicate the potential of MPA-based optimization combined with SHAP explainability for supporting transparent machine learning research in diabetes prediction. However, additional external validation using independent clinical datasets is required before considering the framework for clinical decision support or real-world deployment.","url":"https://doi.org/10.3390/jpm16080423","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jpm16080423","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/ph19081225","name":"Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.","source":"europepmc","abstract":"Background/Objectives: Drug-drug interactions (DDIs) are major medication-safety concerns, and experimental testing cannot cover the expanding number of drug pairs. This systematic review evaluates graph-based machine-learning methods for DDI prediction, focusing on machine-learning architectures, data integration, interpretability, reproducibility, and clinical relevance. Methods: Following PRISMA 2020, we systematically searched major databases for studies published between January 2021 and March 2026. We included studies that applied graph-based machine-learning models, particularly graph neural networks, to predict DDIs. We compared their data sources, model designs, validation methods, predictive performance, reproducibility, and clinical relevance. Because the studies used different datasets and evaluation methods, the findings were summarized narratively rather than combined statistically. Results: Sixty studies met the eligibility criteria. Methods progressed from graph convolutional networks and graph attention networks to graph transformers, contrastive learning, multimodal fusion, and LLM-enhanced representations. We found that reported improvements in prediction performance often remained study-specific. Only three studies explicitly mentioned or addressed data leakage, whereas most reviewed studies contained no explicit leakage discussion; leakage-aware drug-disjoint, temporal, and external evaluations were also uncommon. Uncertainty calibration, computational-resource reporting, complete reproducibility materials, and independently validated explanations were also limited. Conclusions: Graph-based machine learning is promising for DDI prioritization and hypothesis generation but remains insufficient for independent clinical decision-making. Future studies should use standardized benchmarks, leakage-aware validation, calibrated uncertainty, reproducible pipelines, validated explanations, and external or prospective evaluation.","url":"https://doi.org/10.3390/ph19081225","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/ph19081225","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00431-026-07315-5","name":"Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.","source":"europepmc","abstract":"Bacterial infections represent a critical threat to neonatal health, accounting for approximately 25% of neonatal mortality globally. Timely and precise diagnosis in infants aged 1 to 90 days is essential to facilitate rapid intervention and prevent severe complications. This study aimed to develop and evaluate machine learning (ML) models for the early, non-invasive prediction of bacterial infections using routine clinical data, maximizing clinical interpretability for point-of-care triage. Data from 306 infants aged 1 to 90 days hospitalized between January 2014 and December 2022 at a Social Security Organization hospital in Khorasan Razavi, Iran, were retrospectively analyzed. The target variable was strictly labeled using cerebrospinal fluid (CSF) culture via lumbar puncture (LP) as the definitive gold standard (n = 158 infectious, 51.6%; n = 148 non-infectious, 48.4%). Predictors were limited to routine, non-invasive paraclinical markers extracted from the electronic health record and normalized using a QuantileTransformer pipeline. Nine ML classifiers were rigorously evaluated via a leakage-safe nested cross-validation (NCV) framework (5-folds × 2 repeats outer, threefold inner). Algorithmic behavior was decoded globally and locally using SHapley Additive exPlanations (SHAP) values, and overfitting was monitored via comprehensive training-to-validation generalization audits. Top-tier models clustered within an outer-CV AUROC range of 0.74-0.76. While non-linear gradient boosting (HistGBM) achieved the highest raw discrimination (AUROC = 0.786, 95% CI: 0.757-0.812), a generalization audit revealed a severe training optimism gap (0.214). Conversely, L 2 -regularized logistic regression (LR) demonstrated equivalent discriminative stability with a minimal generalization gap (0.083) and robust probability calibration (Brier score = 0.208), leading to its selection as the final model. SHAP analysis confirmed that the predictive signal was genuinely distributed across routine urinary markers, patient age, and metabolic indicators rather than a single dominant analyte. Operational threshold adjustments proved that achieving a high sensitivity screening benchmark (≥ 95%) forced a steep parallel decline in specificity. Conclusion: ML models trained on routine, non-invasive paraclinical markers can effectively serve as objective risk-stratification aids in infant care. However, due to severe threshold-dependent specificity trade-offs, the optimized pipeline should function as a clinical decision support tool for risk tiering rather than a standalone rule-out tool to safely eliminate the need for lumbar punctures.","url":"https://doi.org/10.1007/s00431-026-07315-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00431-026-07315-5","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1161/circgen.125.005447","name":"Artificial Intelligence and Machine Learning Approaches for Cardiovascular Genomics: A State-of-the-Art Review.","source":"europepmc","abstract":"The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient's unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.","url":"https://doi.org/10.1161/circgen.125.005447","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1161/circgen.125.005447","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1080/08039488.2026.2716241","name":"Development and evaluation of machine learning models to predict mechanical restraint and related coercive measures in hospital psychiatry.","source":"europepmc","abstract":"Purpose Coercive practices in psychiatric hospitals present clinical and ethical challenges. Aiming to support prevention, we developed and evaluated machine learning models predicting mechanical restraint and a composite of related coercive measures. Materials and methods The dataset comprised electronic health records from adults admitted to the Psychiatric Services in the Central Denmark Region (2015-2021). For each inpatient day, an XGBoost model predicted mechanical restraint or composite (mechanical, chemical, or manual) restraint within 48 h. Hyperparameters were optimised for the area under the receiver operating characteristic curve (AUROC) using five-fold cross-validation on 85% of the data and validated on a held-out 15% test set. Results The cohort included 16,834 patients with 45,179 inpatient stays, covering 687,388 prediction days. 2,736 days were followed by restraint within 48 h, including 983 mechanical restraint episodes. Predictors were derived from demographics, diagnoses, medications, and clinical notes. The mechanical restraint model achieved an AUROC of 0.921 (95% CI: [0.921-0.924]) and a positive predictive value (PPV) of 4.9% at the top 1% risk threshold. The composite model yielded an AUROC of 0.912 (95% CI: [0.911-0.914]) and a PPV of 4.2% when predicting mechanical restraint, and 0.900 (95% CI: [0.899-0.901]) with a PPV of 10.4% for composite restraint. Conclusion Incorporating related coercive measures into training did not improve AUROC for predicting mechanical restraint but increased PPV when predicting composite restraint, reflecting the higher outcome prevalence. This suggests related outcomes can inform prediction of rare events in clinical prediction modelling. Future work should include further validation.","url":"https://doi.org/10.1080/08039488.2026.2716241","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/08039488.2026.2716241","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fnins.2026.1897735","name":"An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.","source":"europepmc","abstract":"Background Early identification of delayed hematoma progression (DHP) in patients with frontal lobe contusion remains challenging in emergency settings. This study aimed to develop and externally validate an interpretable multimodal machine-learning model integrating routinely available clinical, laboratory, and CT imaging features to predict DHP. Methods This retrospective multicenter study included a development cohort of 799 patients and an external validation cohort of 443 patients. The development cohort was divided into a training set and an internal test set using stratified sampling. Feature selection was performed exclusively within the training set using seven complementary methods. Ten machine-learning algorithms were trained and compared using five-fold cross-validation. Model performance was assessed using AUROC, accuracy, sensitivity, specificity, precision, F1-score, calibration analysis, and decision-curve analysis. SHapley Additive exPlanations (SHAP) was used to interpret the final model. Results Ten predictors were selected, including baseline contusion volume, hematoma density-related features, hematoma surface area-to-volume ratio, lymphocyte-to-monocyte ratio, admission Glasgow Coma Scale score, glucose-to-potassium ratio, time to baseline CT, and eosinophil count. The support vector machine (SVM) model showed the highest AUROC point estimate in the internal test set, with an AUROC of 0.801, and achieved an external validation AUROC of 0.724, indicating moderate external discrimination. Conclusion We developed an interpretable multimodal model for early prediction of DHP in patients with frontal lobe contusion. The model may assist early risk stratification and clinical monitoring, but further prospective, multicenter, and geographically diverse validation is required before broad clinical implementation.","url":"https://doi.org/10.3389/fnins.2026.1897735","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnins.2026.1897735","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1177/18796397261468824","name":"Machine learning applications in Huntington's disease prognosis: A review.","source":"pubmed","abstract":"Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Traditionally, studies relied on age-CAG models to estimate disease onset and progression, based on the well-established relationship between CAG repeat length and age at onset. However, additional genetic, environmental, and clinical factors can cause substantial variability. Recent machine learning approaches integrate clinical, imaging, and molecular data for more precise prediction of disease progression. Following PRISMA guidelines, we systematically reviewed studies on HD onset and progression. Using Web of Science, PubMed, and IEEE Xplore, 20 studies published between 2003 and 2024 met the inclusion criteria. We analyzed the machine learning approaches and input features used, assessed methodological quality, and evaluated risk of bias using the PROBAST tool. Overall, machine learning models, particularly support vector machines and ensemble approaches, consistently outperformed traditional age-CAG models. Several studies predicted conversion from premanifest to manifest HD within 5-10 years with high accuracy (88-98%). Beyond predicting onset, machine learning models have also been used to model dis-ease progression using clinical scores assessing motor, cognitive, and functional impairment. Performance was higher in studies incorporating structural and functional MRI biomarkers, and improved further with longitudinal clinical integration, enabling pre-diction of decline years before symptoms onset. Overall, machine learning shows strong potential to improve prognostic modeling in HD, especially through multimodal and longitudinal data. However, common methodological weaknesses and bias highlight the need for larger, externally validated studies using objective biomarkers.","url":"https://doi.org/10.1177/18796397261468824","authors":["Abu Zohair LM","Andriessen R","Mahmoud N","Khalid M","Zantout H","Vallejo M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/18796397261468824","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/mdc3.70749","name":"Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-&lt;sup&gt;123&lt;/sup&gt;I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.","source":"europepmc","abstract":"Background Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures. Objectives This study developed and validated machine learning models to predict H&Y scores at 5 years post-123I-ioflupane single-photon emission computed tomography (SPECT) imaging, leveraging both a real-world dataset and a subset of the PPMI cohort. The goal was to assess the utility of routinely collected clinical and imaging data for prognostic modeling. Methods Data from medical records and imaging were harmonized from 343 real-world patients and 134 PPMI patients, resulting in a merged dataset with 83 overlapping features. Random Forest and Gradient Boosting models were trained to predict 5-year H&Y scores using varying amounts of longitudinal data and imaging features. Results Models using 2 years of clinical follow-up data achieved the highest predictive accuracy. The most important predictors were early H&Y scores, gait symptom severity, and select imaging features. Conclusions Machine learning models can predict 5-year H&Y scores in PD using real-world clinical data, but imaging features add limited prognostic value. This study demonstrated that implementing machine learning models, when using real-world data, did not significantly improve the already known gap between prognostic modeling and real-world implementation. Improvement of models is, however, a promising prospect and further studies are encouraged.","url":"https://doi.org/10.1002/mdc3.70749","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/mdc3.70749","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s11060-026-05747-5","name":"Machine learning for functional outcome prediction after vestibular schwannoma surgery: a systematic review and diagnostic test accuracy meta-analysis.","source":"europepmc","abstract":"Purpose Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes. Methods PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects generalised linear mixed models. Sensitivity, specificity, diagnostic odds ratio, and AUC were synthesised, and SROC curves were constructed. The prespecified primary synthesis pooled the single best model per study; small-study effects were assessed with Deeks' test. Risk of bias (PROBAST) and certainty of evidence (GRADE) were assessed. Results Ten retrospective cohort studies encompassing 1270 patients and 56 ML models met inclusion criteria. In the prespecified primary analysis pooling the single best model per study, the summary AUC was 0.91 for facial nerve dysfunction (sensitivity 0.89, specificity 0.86) and 0.92 for hearing preservation (sensitivity 0.88, specificity 0.96). Pooling all models on held-out test data gave a facial nerve AUC of 0.81; test-set data were too sparse for a stable hearing estimate, for which only training performance could be pooled (AUC 0.79). Tumour size, age, tumour location, and baseline hearing status were the most frequently identified influential predictors. Most studies were at unclear or high risk of bias (PROBAST has no intermediate \"moderate\" category), and certainty of evidence was moderate for facial nerve dysfunction and low for hearing preservation, the latter reflecting significant small-study effects (Deeks' p = 0.004). Conclusion ML-based models demonstrate promising discrimination for predicting postoperative facial nerve and hearing outcomes after VS surgery. However, heterogeneity, limited external validation, and inconsistent reporting of calibration constrain inference regarding transportability and clinical implementation.","url":"https://doi.org/10.1007/s11060-026-05747-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11060-026-05747-5","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.pnpbp.2026.111901","name":"Machine learning classification of schizophrenia and social anhedonia based on facial expressions.","source":"europepmc","abstract":"Background and hypothesis Schizophrenia (SCZ) is characterized by deficits in emotional expression, with facial expressions serving as potential markers for diagnosis. Leveraging machine learning and computerized facial analysis, this study aimed to identify facial expression features in patients with SCZ and individuals with high social anhedonia (SocAnh), construct an explainable classification model, and explore the associations between facial features and negative symptoms. Study design Emotional expressions of 2 samples comprising 32 patients with SCZ and 34 Health controls (HC), and 56 participants with high SocAnh and 56 participants with low SocAnh were recorded based on an emotion elicitation paradigm combining film based and autobiographical methods. Facial features were extracted using FaceReader to develop classification models with a standardized machine learning pipeline. Study results Patients with SCZ showed lower intensity of sad facial expressions during neutral and positive film viewing than HC, whereas no significant differences were found between participants with high and low SocAnh. In the clinical sample, the Support Vector Machine model achieved a classification accuracy of 81.7%, with highly weighted features showing a mixed pattern across elicitation conditions. In contrast, the model distinguishing participants with high versus low SocAnh yielded lower classification performance. Exploratory correlation analyses showed modest associations between selected facial expression features and negative symptoms, but none remained statistically significant after Bonferroni correction. Conclusions Computational facial expression analysis with interpretable machine learning may help examine facial expression deficits in SCZ. Associations with negative symptoms were exploratory and require confirmation in larger independent samples.","url":"https://doi.org/10.1016/j.pnpbp.2026.111901","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.pnpbp.2026.111901","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fnagi.2026.1880346","name":"The application of traditional machine learning and deep learning with EEG for mild cognitive impairment: a bibliometric analysis.","source":"europepmc","abstract":"Objective This study uses bibliometric methods to analyze the status of electroencephalography (EEG) and machine learning applications in mild cognitive impairment (MCI) research. Methods Relevant literature was searched in the four major English databases (Web of Science, PubMed, IEEE Xplore, and Scopus) and the three major Chinese databases (CNKI, Wanfang, and VIP) from their inception to June 2026. After removing duplicates using NoteExpress and screening the literature according to the inclusion and exclusion criteria, the final data were imported into CiteSpace 6.4. R2 and VOSviewer 1.6.20 for analysis of publication trends, geographic distribution, highly co-cited references, keyword clustering, and burst detection. Results A total of 547 valid studies were included in the analysis, comprising 523 English-language studies and 24 Chinese core journal articles. The number of publications in this field has shown phased growth. It entered a period of rapid development after 2018. China ranks first globally in terms of English-language publications; however, there is a significant gap between the number of core publications in Chinese and English. Clinical Neurophysiology is the journal with the highest total local citation score (TLS). Four of the top 10 highly co-cited journals are published in the United States. Highly cited literature has jointly contributed to establishing a core body of knowledge in this field. This knowledge encompasses clinical diagnostic guidelines, electrophysiological mechanisms, signal processing methods, and intelligent algorithm models. Traditional machine learning keywords focus on methodological exploration, including feature extraction and support vector machine classification. Deep learning keywords focus on model applications, including convolutional and artificial neural networks. Keyword burst analysis identifies feature selection, decision trees, and neuropsychological assessment as the current frontier topics in this field. Conclusion Electroencephalography combined with machine learning has become an important research direction for the early identification and assessment of MCI. The research focus has shifted from traditional EEG signal analysis and manual feature engineering to optimization of deep learning models and exploration of multimodal data fusion. Improved algorithm interpretability, refined EEG rhythm and brain region localization, and the combination of neuropsychological assessment and EEG indicators have become a new research trend.","url":"https://doi.org/10.3389/fnagi.2026.1880346","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnagi.2026.1880346","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fpsyt.2026.1739597","name":"Transdiagnostic clinical correlates for behaviors of concern in pediatric neurodevelopmental disorders: a retrospective chart review and machine learning analysis.","source":"europepmc","abstract":"Introduction Behaviors of concern, such as aggression and self-injury, are common in children with neurodevelopmental disorders and contribute substantially to caregiver stress and health system burden. However, most evidence on behaviors is drawn from diagnostically homogeneous samples, limiting relevance to real-world presentations encountered in tertiary care. Our aim was to characterize the diagnostic and behavioral complexity of children referred to tertiary clinics and use machine learning to identify transdiagnostic features associated with behaviors across diverse neurodevelopmental presentations. Method This retrospective chart review and analysis examined all children aged 2 to 17 years undergoing first-time assessment at a tertiary developmental pediatrics clinic between May 2022-2023. Unstructured physician notes and supporting documentation were transformed into structured data and supervised machine learning models identified factors associated with behaviors of concern. Results Among 600 children, 83% exhibited at least one behavior of concern (mean, 3.15 [SD 2.98]). Most had multiple neurodevelopmental diagnoses (73%) and frequent co-occurrence of physical (80%) and mental health conditions (21%). Emotional dysregulation and non-cooperation were the most common behaviors, while aggression and self-injury were frequently moderate to severe. Machine learning models identified sleep difficulties, speech and language delay, restricted and repetitive behaviors (B symptoms) of autism, gastrointestinal issues, and service use as top-ranked associated factors across multiple behavioral types. Factors associated with any behaviors included ADHD, sensory impairments, and genetic contributors to autism. Discussion Transdiagnostic factors highlight key domains often under-assessed in routine care. These findings support integrated assessment models that address modifiable clinical correlates for behaviors of concern across diagnostic boundaries to improve outcomes for high-need neurodevelopmental populations.","url":"https://doi.org/10.3389/fpsyt.2026.1739597","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1739597","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1177/08850666261479295","name":"Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury.","source":"europepmc","abstract":"BackgroundSepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes may further increase adverse outcomes through infection susceptibility, immune dysfunction, and renal vulnerability. However, mortality prediction models for patients with diabetes complicated by SA-AKI remain limited. This study aimed to develop and validate a machine learning-based model for early in-hospital mortality prediction in this population.MethodsA total of 6929 patients with SA-AKI and diabetes were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and randomly divided into training and validation sets at a ratio of 7:3. Ninety-four variables, including demographics, diagnoses, clinical parameters, and medication records within the first 24 h after ICU admission, were extracted. Twelve machine learning algorithms were developed and compared, and the optimal model was selected. Recursive feature elimination was used to identify key predictors, while SHapley Additive exPlanations were applied for model interpretation. The final model was deployed as a web-based tool and externally tested using the eICU Collaborative Research Database.ResultsThirty-two key predictors were ultimately selected, including urine output rate, platelet count, lactate, weight, blood glucose, SOFA score, pH, blood urea nitrogen, vital signs, coagulation indices, vasopressor use, and other clinically relevant variables. The categorical boosting algorithm model presented better predictive performance [receiver operating characteristic (AUC): 0.828] than other models [accuracy (ACC): 70.9%, sensitivity: 78.7%, specificity: 69%, F1 score: 0.509, positive predictive value (PPV): 33.7%, and negative predictive value (NPV): 93.1%]. External testing using data from the eICU database was also well validated (AUC: 0.793).ConclusionsA CatBoost-based machine learning model incorporating 32 clinically accessible variables showed good predictive performance for in-hospital mortality in patients with diabetes and SA-AKI, supporting early risk stratification and clinical decision-making.","url":"https://doi.org/10.1177/08850666261479295","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/08850666261479295","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fendo.2026.1888060","name":"Development and internal validation of a machine-learning-based risk identification model for diabetic kidney disease in patients with type 2 diabetes.","source":"europepmc","abstract":"Background Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM) and the leading cause of end-stage renal disease in China. Limited disease awareness and insufficient early screening tools hinder timely intervention for DKD patients. This study aimed to develop and internally validate a non-invasive machine learning-based prediction model for early DKD risk identification among T2DM patients using routine clinical laboratory indicators. Methods A retrospective cross-sectional study was conducted with 602 eligible T2DM patients (457 without DKD, 145 with DKD) recruited from Sihui People's Hospital between January 2023 and June 2024. Subjects were randomly split into an 8:2 training set (n=481) and independent test set (n=121). Baseline clinical characteristics were compared between groups. Univariate and multivariate logistic regression analyses were performed to screen independent DKD predictors. Six machine learning algorithms including logistic regression, XGBoost, random forest, AdaBoost, support vector classifier (SVC), and Gaussian naive Bayes (GNB) were constructed and comprehensively assessed via AUC, accuracy, sensitivity, specificity, calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) interpretability analysis. Results Baseline comparisons showed that DKD patients presented worse glycolipid, hepatic, renal, hematological and urinary protein indicators than patients with isolated T2DM, while sex, age, BMI, DBP and FPG showed no significant intergroup differences (all P>0.05). Multivariate logistic regression identified glycated hemoglobin (HbA1c), β 2 -microglobulin (β 2 MG), and urine protein (PRO) as independent risk factors, while serum albumin (ALB) and estimated glomerular filtration rate (eGFR) acted as protective factors. Single indicator ROC analysis showed PRO and β 2 MG achieved the highest diagnostic AUC of 0.944. All six machine learning models exhibited excellent discriminative performance with validation AUCs over 0.975. Logistic regression was selected as the optimal model, yielding a test-set AUC of 0.979, sensitivity of 89.7%, and specificity of 92.4%. The model demonstrated favorable calibration and sustained positive net clinical benefit across almost all threshold probabilities in DCA. SHAP analysis ranked HbA1c, ALB, PRO, eGFR, and β 2 MG as the top five predictive features, clarifying the individual risk contribution of each biomarker. Conclusions The interpretable logistic regression model built on routine non-invasive clinical indicators reliably identifies DKD risk in T2DM patients. Glycemic control and renal injury biomarkers serve as core predictive factors. This tool provides convenient, low-cost early risk stratification for clinical practice, especially for primary care settings. Further external multicenter prospective validation is required to generalize its clinical application.","url":"https://doi.org/10.3389/fendo.2026.1888060","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1888060","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1177/11795972261481838","name":"A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.","source":"europepmc","abstract":"Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder of late life that causes cognitive and functional decline and substantial mortality. Machine learning (ML) is increasingly used to discover patterns in clinical and biomarker data that support earlier and more accurate AD detection. Objectives This scoping review addresses three core research questions. First, we investigate recent trends in using machine-learning techniques to detect Alzheimer's disease using blood biomarkers. Second, we identify the blood biomarkers involved in Alzheimer's detection and evaluate how machine learning has been applied to improve the diagnostic capabilities of these biomarkers. Third, we highlight significant challenges associated with using machine learning for blood biomarker data in Alzheimer's detection and examine proposed advancements or solutions to handle these problems. Methods In June 2025, we searched six academic databases to identify relevant papers on blood biomarkers and ML methods for Alzheimer's Disease. Search queries were developed based on our predefined research questions. Papers were then screened using defined inclusion and exclusion criteria, where titles, abstracts, and full texts of articles were systematically reviewed. Results Following the screening approach, we selected 36 papers that fulfilled our inclusion and exclusion criteria. Through careful examination, we classified blood biomarkers into four types: transcriptomics, proteomics, multi-omic biomarkers, and general elemental blood biomarkers. Across these studies, proteomic blood biomarkers consistently emerged as significant indicators for Alzheimer's disease, including Alpha-2-Macroglobulin (A2M), Apolipoprotein E (ApoE), Eotaxin-3 (EOT3), plasma phosphorylated tau (p-tau 181), and neurofilament light chain (NfL). Furthermore, we explored challenges such as small sample sizes, lack of standardization, heterogeneity, and data imbalance. Conclusions This review provides insights into how combining blood biomarkers with ML can enhance AD prediction. The review summarizes key challenges and identifies critical gaps for future research.","url":"https://doi.org/10.1177/11795972261481838","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/11795972261481838","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.22034/iji.2026.111015.3189","name":"Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity.","source":"europepmc","abstract":"Background Renal fibrosis represents the final common pathway of chronic kidney disease (CKD); however, both its definitive diagnostic biomarkers and the principal cellular mediators driving its progression remain incompletely characterized. Objective To derive a machine-learning-based transcriptomic signature from single-cell RNA-sequencing data that predicts kidney fibrosis severity and elucidates the underlying immune-stromal cellular interactions. Methods A machine-learning approach incorporating Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify a sparse transcriptomic signature. Cell-cell communication networks and pseudotime trajectories were reconstructed to characterize the fibrotic niche. The five-gene signature (CXCL13, CCL19, TNFSF13B, IL6, and TGFB1) was then correlated with eGFR, UACR, and Banff scores, and externally validated in the independent human kidney scRNA-seq dataset GSE183276. Results The fibrotic kidney microenvironment exhibited a marked expansion of fibroblasts and CD19+ B-cells. Machine-learning feature selection identified a highly predictive five-gene molecular signature comprising CXCL13, CCL19, TNFSF13B (encoding BAFF), IL6, and TGFB1. Intercellular network analysis revealed dominant B-cell-fibroblast signaling that was significantly associated with fibroblast transdifferentiation into extracellular matrix-producing myofibroblasts. The signature score correlated with eGFR, UACR, and Banff scores. External validation in the independent dataset GSE183276 confirmed the diagnostic robustness of this signature. Conclusion We present a validated machine-learning -derived transcriptomic signature that reflects the immune-stromal dynamics of renal fibrosis. This five-gene signature accurately predicts CKD severity and, importantly, implicates B-cell-mediated fibroblast activation as a promising diagnostic biomarker and a potential therapeutic target.","url":"https://doi.org/10.22034/iji.2026.111015.3189","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22034/iji.2026.111015.3189","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00221-026-07368-w","name":"The role of catastrophizing in anxiety and depression symptom scores among knee osteoarthritis patients: a multimodal assessment with EEG and machine learning.","source":"europepmc","abstract":"To examine dimensional associations between anxiety- and depression-related symptom severity, pain catastrophizing, and resting-state EEG features in patients with knee osteoarthritis (KOA). Resting-state EEG spectral power (delta, theta, alpha, beta) was analysed in 62 KOA patients from the DEFINE cohort. Emotional symptoms were assessed with the Hospital Anxiety and Depression Scale (HADS), along with the Pain Catastrophizing Scale (PCS) and clinical-demographic variables. Multivariate regression analyses identified significant predictors, while linear and tree-based machine learning models were used post hoc to explore whether multivariate and non-linear approaches converged with the regression findings. Catastrophizing was independently and significantly associated with both anxiety and depression symptom scores across regression and exploratory machine learning models. For depression, a multifactorial pattern was additionally observed: higher bilateral parietal delta power, greater catastrophizing, lower education, and greater body weight showed independent associations with more severe symptom scores. Machine learning analyses indicated that EEG features were weak standalone correlates but showed modest complementary associations when combined with clinical variables. Pain catastrophizing was consistently associated with both anxiety and depression symptom scores, and resting-state EEG features showed limited but complementary associations with depressive symptom scores in KOA. Importantly, these associations were observed within a sample presenting predominantly subclinical HADS scores, and findings should be interpreted as reflecting dimensional associations within a rehabilitation cohort rather than clinical anxiety or depressive disorder.","url":"https://doi.org/10.1007/s00221-026-07368-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00221-026-07368-w","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.ijcard.2026.134745","name":"Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.","source":"europepmc","abstract":"Background Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learning approach with data from multiple centers. Methods We conducted a retrospective cohort study using patient data from the Taipei Medical University Clinical Research Database (TMUCRD). Multiple machine learning algorithms were employed to develop predictive models for 30-day mortality. Model performance was evaluated using a stratified fivefold cross-validation approach. Key performance metrics included the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 score. Results A total of 23,267 patients (mean age 64.9 years) were included, with 1215 deaths overall (5.2%): 570 (3.7%) in the internal cohort (n = 15,510) and 645 (8.3%) in the external validation cohort (n = 7757, Shuang Ho Hospital). XGBoost achieved the best performance for the overall and acute CAD cohorts (AUROC 0.845 and 0.820, respectively), while logistic regression performed best for chronic CAD (AUROC 0.766). Key predictive features included the Charlson Comorbidity Index, hemoglobin level, emergency room admission status, age, and creatinine level. Conclusion The use of a structured machine learning approach to predict 30-day mortality in patients with acute and chronic CAD demonstrated promising discriminative performance, providing valuable insights that could enhance personalized care and inform clinical decisions.","url":"https://doi.org/10.1016/j.ijcard.2026.134745","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijcard.2026.134745","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fmed.2026.1864670","name":"Machine learning for prediction of newly diagnosed atrial fibrillation after emergency percutaneous coronary intervention during hospitalization in patients with acute ST-segment elevation myocardial infarction: a multi-center prospective study.","source":"europepmc","abstract":"Background In-hospital complications after emergency PCI in acute STEMI are important, especially newly diagnosed atrial fibrillation and are associated with hemodynamic instability, heart failure, stroke and mortality. There are complex nonlinear interactions between clinical, inflammatory, cardiac remodeling and procedural factors that traditional risk scores may not adequately capture. Thus, this study aimed to develop and validate an interpretable machine-learning model for early prediction of in-hospital NDAF in patients with acute STEMI undergoing emergency PCI. Methods From January 2021 to December 2024, this study collected data on patients with acute STEMI after emergency PCI from the five tertiary general hospitals in Chongqing. Important clinical variables were identified using the selection operator and least absolute shrinkage. Based on the area under the curve, the best predictive model was selected from eight machine learning methods. The predictive model's results were interpreted using Shapley Additive explanations. Results Eighteen variables were chosen for model building, and a total of 154 patients with acute STEMI following emergent PCI were included. With the largest area under the curve of 0.991 (95% CI: 0.963-1), sensitivity of 0.846, specificity of 0.914, and Brier score of 0.2356, the Gradient Boosting model was selected. The Shapley value analytical framework was systematically applied to decode feature importance patterns and illuminate individual prognostic determinants. Conclusion The present study developed and compared eight machine learning models for the prediction of in-hospital NDAF among acute patients with STEMI treated with emergency PCI. The Gradient Boosting classifier achieved optimal predictive performance, highlighting its potential clinical utility for early risk stratification. By identifying high-risk individuals in advance, this model may support optimized clinical management and contribute to better prognosis in this high-risk population. Machine learning computations were implemented via a publicly accessible web server integrating the pre-trained Gradient Boosting prediction model. All analytical steps were performed following the platform's default settings to maintain methodological standardization and reproducibility.","url":"https://doi.org/10.3389/fmed.2026.1864670","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1864670","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/edm2.70297","name":"A Clinically Aligned Two-Stage Machine Learning Framework for Predicting Hungry Bone Syndrome After Parathyroidectomy.","source":"europepmc","abstract":"Background Hungry bone syndrome (HBS) is a frequent and clinically significant complication following parathyroidectomy (PTX) in patients with secondary hyperparathyroidism (SHPT), often leading to prolonged hypocalcaemia and increased healthcare burden. Existing prediction models are limited by small sample sizes and inability to capture complex clinical interactions. This study aimed to develop and validate a clinically aligned, two-stage machine learning (ML) framework to predict HBS after PTX. Materials and methods A retrospective cohort of patients undergoing PTX for SHPT between 2008 and 2025 at a tertiary centre was analysed. A two-stage ML framework was constructed: stage 1 used preoperative variables to generate a risk score, and stage 2 integrated this score with intraoperative features. Multiple ML models were evaluated using area under the receiver operating characteristic curve (AUROC), calibration metrics and resampling techniques. Results A total of 882 patients were included, with an HBS incidence of 69.9%. EasyEnsemble and logistic regression demonstrated the highest discrimination (AUROC 0.712), outperforming the k-nearest neighbours baseline. EasyEnsemble achieved the best overall performance (accuracy 0.707, F1 score 0.666) and calibration (Brier score 0.186). Key predictors included elevated preoperative alkaline phosphatase, higher intact parathyroid hormone levels and lower serum calcium. Conclusion This two-stage ML framework demonstrated acceptable predictive performance and aligns with clinical decision-making processes. It enables early identification of high-risk patients and may support individualised perioperative management to mitigate HBS and its complications.","url":"https://doi.org/10.1002/edm2.70297","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/edm2.70297","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1088/1361-6579/ae9713","name":"Slide-DML: sliding-window-based estimation of heterogeneous treatment effects.","source":"europepmc","abstract":"Objective. Heterogeneous treatment effect (HTE) estimation is essential for understanding individual differences in physiological responses and supporting personalized healthcare. However, existing non-parametric HTE estimation methods often rely on complex partition strategies and may have limited interpretability when applied to physiological measurements with continuous variations and non-uniform data distributions. This study aims to develop an adaptive and interpretable framework for HTE estimation in physiological measurement systems. Approach. We propose a sliding-window-based double machine learning framework (Slide-DML) for non-parametric HTE estimation. Slide-DML adaptively constructs quasi-homogeneous local windows based on treatment effect variation and estimates local linear HTE within each selected window. The local estimates are subsequently aggregated using adaptive weighting to obtain a smooth global HTE function while preserving interpretability. Main results. The performance of Slide-DML was evaluated using synthetic data, semi-synthetic clinical data, and real physiological measurements. In synthetic experiments, Slide-DML achieved a mean squared error (MSE) of 0.006, outperforming existing machine learning-based HTE estimation methods. In semi-synthetic experiments, Slide-DML achieved a root MSE of 3.526, demonstrating superior performance compared with both machine learning-based and deep learning-based approaches. Experiments on real photoplethysmogram and electrocardiogram data further showed that the estimated treatment effect curves were consistent with established cardiovascular knowledge and provided improved interpretability. Significance. Slide-DML provides an effective and interpretable approach for estimating HTE in physiological measurement systems. By capturing continuous variations in physiological states, the proposed framework may facilitate individualized analysis of cardiovascular responses and support personalized healthcare applications, such as cuffless blood pressure monitoring using wearable physiological devices.","url":"https://doi.org/10.1088/1361-6579/ae9713","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1088/1361-6579/ae9713","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1093/rheumatology/keag446","name":"Osteoporosis prediction in primary Sjögren's syndrome: development and external validation of a machine-learning comparison model.","source":"europepmc","abstract":"Objectives Osteoporosis and fragility fractures are clinically important complications of primary Sjögren's syndrome (pSS) that may accelerate functional decline and excess mortality. In practice, osteoporosis risk is often assessed using general-population tools that do not incorporate disease activity, glucocorticoid exposure or inflammation-related bone remodelling. We aimed to develop and externally validate a prediction model for DXA-defined osteoporosis in pSS using routinely available clinical and laboratory indicators. Methods This retrospective cohort study included 1000 patients with pSS from Longhua Hospital, randomly split into training and internal validation sets (7:3), and an independent external validation cohort of 266 patients from Shanghai Seventh People's Hospital. Candidate predictors were screened by univariable analysis, multivariable logistic regression and LASSO. Logistic regression was compared with seven supervised machine-learning algorithms. Performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration and decision curve analysis. Results The final logistic regression model retained seven predictors: sex, age, current glucocorticoid use, EULAR Sjögren's Syndrome Disease Activity Index score, 25-hydroxyvitamin D, procollagen type 1 N-terminal propeptide and β-C-terminal telopeptide of type I collagen. AUCs were 0.820, 0.807 and 0.787 in the training, internal validation and external validation cohorts, respectively, with good calibration. Machine-learning models achieved higher training AUCs but showed poorer transportability. A freely accessible web-based calculator was developed for point-of-care use. Conclusion A transparent, externally validated seven-variable model provides individualized DXA-defined osteoporosis risk estimation in pSS and may help clinicians prioritize bone density testing during routine visits.","url":"https://doi.org/10.1093/rheumatology/keag446","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/rheumatology/keag446","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fped.2026.1895967","name":"Explainable machine learning for diagnosing severe &lt;i&gt;Mycoplasma pneumoniae&lt;/i&gt; pneumonia in children: model development and internal validation.","source":"europepmc","abstract":"Background Mycoplasma pneumoniae pneumonia (MPP) is common in children, but severe MPP (SMPP) may progress rapidly and is difficult to distinguish from non-severe disease because clinical, laboratory, and radiographic findings overlap. We aimed to develop and internally validate machine-learning models for adjunctive SMPP risk stratification using routine data while minimizing circular reasoning. Methods We retrospectively included consecutive children with MPP admitted from January 1, 2015, to January 1, 2026. Demographic, clinical, laboratory, and radiographic variables were collected. Variables overlapping with the severity definition, severity-proximal biomarkers, and model-derived leakage variables were excluded before modeling. Eight supervised algorithms were developed in a training cohort and evaluated in an internal test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1-score, calibration, Brier score, and decision curve analysis. Results The cohort included 1,046 children, of whom 205 had SMPP; 784 were assigned to the training set and 262 to the internal test set, including 51 SMPP cases. In the strict non-overlap test set, the support vector machine achieved the highest AUC of 0.947 [95% confidence interval (CI), 0.907-0.980], with accuracy of 0.924, sensitivity of 0.784, specificity of 0.957, F1-score of 0.800, and Brier score of 0.058. Random forest achieved an AUC of 0.926 (95% CI, 0.878-0.964), accuracy of 0.897, sensitivity of 0.824, specificity of 0.915, and Brier score of 0.094. It was retained for calibration, decision-curve, threshold, and feature-importance analyses because of its interpretability and balanced performance. Important predictors included aspartate aminotransferase, alanine aminotransferase, albumin, cough duration, blood urea nitrogen, white blood cell count, platelet count, age, wheezing, and lung rales. Conclusion After exclusion of leakage variables and severity-definition-overlapping predictors, machine-learning models maintained good internal performance for classifying SMPP in children with MPP. They should be considered adjunctive risk-stratification tools rather than standalone early diagnostic tools. Multicenter external validation and prospective workflow evaluation are required before clinical implementation.","url":"https://doi.org/10.3389/fped.2026.1895967","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1895967","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/psp4.70314","name":"Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.","source":"europepmc","abstract":"pH-dependent drug-drug interactions (DDIs) commonly occur when acid-reducing agents (ARAs) are co-administered with weakly basic drugs. Although physiologically based pharmacokinetic (PBPK) modeling effectively evaluates such DDIs, its use is limited by reliance on costly commercial software. This study developed a PBPK-informed machine learning model to support early assessment of pH-dependent DDI risk in drug development. PBPK models were built for 14 representative weakly basic drugs using literature-derived parameters to identify eight key determinants (e.g., solubility and pKa). Based on these distributions, virtual drugs were generated and simulated under varying gastric pH conditions; compounds with DDI AUC ratios 2 = 1.00, MAPE = 0.99; test: R 2 = 0.98, MAPE = 2.64). When evaluated using an external validation set comprising clinically observed data from eight drugs, 100% of the predicted values fell within the 0.5-2.0-fold range of the observed clinical values. For DDI AUC risk classification, the XGBoost model achieved an accuracy of 87.5% (7/8). This PBPK-informed ML framework enables efficient screening of pH-dependent DDI risk for weakly basic drugs co-administered with ARAs. The freely accessible web tool (https://ddi-antacid.xy3yx.com/), integrating structure-based ADMETlab3.0 estimation, offers a practical complement to conventional PBPK modeling for early drug development.","url":"https://doi.org/10.1002/psp4.70314","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/psp4.70314","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.2196/103040","name":"Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.","source":"europepmc","abstract":"Background The US Food and Drug Administration (FDA) has authorized AI-enabled and machine learning (ML)-enabled medical devices since 1995 and maintains a public registry of these authorizations. Prior analyses report that radiology dominates this landscape, but whether that concentration has persisted, intensified, or begun to reverse across 3 decades, particularly since 2022, remains insufficiently characterized. Objective This study aimed to (1) characterize the longitudinal growth of FDA-authorized AI/ML-enabled devices from 1995 to 2025, (2) quantify the temporal evolution of clinical specialty distribution across 4 eras, (3) identify emerging specialties, and (4) examine the association between manufacturer type and nonradiology authorization. Methods All 1430 devices in the FDA AI-Enabled Medical Devices registry (downloaded on March 1, 2026) with final marketing-authorization decisions through December 31, 2025, were analyzed. Devices were stratified by clinical specialty (FDA advisory committee panel) and 4 eras: Era 1 (1995-2015), Era 2 (2016-2019), Era 3 (2020-2022), and Era 4 (2023-2025). Concentration was quantified using the Herfindahl-Hirschman Index (HHI) with bootstrap CIs; the Cochran-Armitage test assessed trends in specialty share, with Bonferroni correction. Multivariable logistic regression estimated the odds of nonradiology authorization by manufacturer type and era, with an era-by-manufacturer interaction term. Sensitivity analyses used cluster-robust standard errors, a continuous authorization year variable, and Firth penalized regression. Manufacturers were classified using FDA records, Crunchbase, PitchBook, and company websites. Results Annual authorizations rose from a mean of 2.0 (SD 2.0) in Era 1 to a mean of 264 (SD 58.2) in Era 4, with 331 authorizations in 2025 alone; the 510(k) pathway accounted for 96.2% (1376/1430). Radiology led in every era but followed a nonmonotonic trajectory, rising from 35.7% (15/42, Era 1) to a peak of 85.5% (347/406, Era 3) before declining to 77.5% (614/792, Era 4), the first significant decline on record ( P =.001). The HHI fell from 0.738 (Era 3) to 0.612 (Era 4; bootstrap P χ ² 48 =328.0; P V =0.28). Compared with incumbents, start-ups (odds ratio [OR] 5.09, 95% CI 3.33-7.79) and technology companies (OR 50.62, 95% CI 12.90-198.64) had higher odds of nonradiology authorization; the nonsignificant era-by-manufacturer interaction (likelihood ratio test χ ² 8 =11.16; P =.19) indicates a persistent rather than widening effect. The technology-company OR derives from only 13 devices across 5 firms; although directionally robust in sensitivity analyses, it is imprecise and warrants cautious interpretation. Conclusions Radiology remained dominant, accounting for 77.5% (614/792) of Era 4 authorizations, but the specialty distribution showed measurable diversification during 2023 to 2025, associated with start-up and technology-company activity. Maturation of clinical data infrastructure beyond imaging is a plausible but unmeasured contributing condition, and authorization is not adoption. The findings bear on health-system readiness, workforce training, and specialty-specific regulatory frameworks.","url":"https://doi.org/10.2196/103040","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/103040","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1371/journal.pdig.0001615","name":"Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.","source":"europepmc","abstract":"Current detection models for suicidal ideation (SI) among depressed patients have primarily relied on clinical and biological features. This study aims at enhancing the real-world applicability of these models by using multimodal digital features. Patients self-administered the Hospital Anxiety and Depression Scale (HADS) and 20-item Toronto Alexithymia Scale (TAS-20). Their multimodal features (facial, acoustic and linguistic features) were obtained using ecological momentary assessment (EMA) with our self-developed smartphone application over 7 days. The Structured Interview Guide for the Hamilton Depression Rating Scale (HDRS) were conducted to identify clinically rated SI. Mood description recordings obtained from EMA were processed through natural language processing, Openface, and OpenSmile to obtain linguistic, facial and acoustics features, respectively. Multimodal features and questionnaires were then used for training and testing machine learning algorithms. The study was conducted and reported under the TRIPOD+AI guideline. Among the recruited 99 patients with major depressive disorder (MDD), 38 (mean age = 49.3 ± 9.7 y, 76% female) had SI while 61 (mean age = 51.5 ± 11.5 y, 76% female) did not have SI. Among the 10 machine learning models evaluated, the CatBoost classifier demonstrated the strongest detection performance, achieving an AUC of 0.79 (p < .001), accuracy of 0.79 and F1-score of 0.73. K-Nearest Neighbours (KNN), Artificial Neural Network (ANN) and Naive Bayes models also provided comparable results. Multimodal features were correlated with SI among MDD patients. Machine learning algorithms have modest performance in identifying SI among patients with MDD using multimodal digital features, including facial expressions, vocal characteristics and language use. However, larger and more diverse datasets are needed to enhance the generalizability and accuracy of these machine learning approaches in real-world clinical settings.","url":"https://doi.org/10.1371/journal.pdig.0001615","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001615","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fimmu.2026.1882789","name":"Prediction of severe sepsis-associated acute kidney injury incorporating immune-inflammatory profiles: development and validation of a machine learning model in a multicenter prospective cohort study.","source":"europepmc","abstract":"Introduction Severe sepsis-associated acute kidney injury (SA-AKI) is a prevalent and life-threatening complication in critically ill patients, leading to increased mortality and a heightened risk of chronic kidney dysfunction. Current prediction models for severe SA-AKI have largely overlooked the inclusion of immune and inflammatory indicators, which more accurately represent the underlying pathophysiology of sepsis-the dysregulated host response to infection. Methods Using a multicenter prospective cohort of 1,715 septic patients from five independent ICUs, we developed and validated a machine learning model integrating immune-inflammatory profiles to predict progression to severe SA-AKI, defined as KDIGO stage 2 or 3 per the Acute Disease Quality Initiative consensus criteria (occurring in 670 patients [39.1%]). Immune-inflammatory variables and routine clinical data were collected within 24 hours of sepsis diagnosis. Six machine learning algorithms were trained and evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, calibration, and decision curve analysis (DCA). Cross-institutional stability was evaluated by leave-one-center-out cross-validation (LOCO-CV) sensitivity analysis. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results Among the six models, random forest achieved the highest sensitivity (0.881) while maintaining strong discriminative ability (AUC 0.912, 95% CI 0.879-0.941, specificity 0.794) in the validation set. The final model incorporated nine clinically accessible variables: SOFA score, CD38 + CD8 + T-cell count, tumor necrosis factor-alpha, interleukin-6, immunoglobulin G, central venous oxygen saturation, bilirubin, and histories of chronic kidney disease and chronic cardiac insufficiency. The model exhibited adequate calibration (Brier score 0.115, calibration slope 1.207), positive net benefit on DCA, and good interpretability. LOCO-CV confirmed consistent performance across the five centers, with a mean AUC of 0.900 (range 0.863-0.938), demonstrating cross-institutional robustness. Conclusion An interpretable random forest model incorporating immune-inflammatory profiles accurately predicted progression to severe SA-AKI in critically ill patients with sepsis. Following external validation and further clinical implementation, this immune-inflammatory profile-based model may offer an effective and practical strategy for early risk stratification. Clinical trial registration http://www.chictr.org.cn, identifier ChiCTR2300074175.","url":"https://doi.org/10.3389/fimmu.2026.1882789","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1882789","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.64898/2026.08.19.26360765","name":"Can GPT Be Used as an Alternative Prediction Model to Traditional Machine Learning and Neural Networks on Low-Volume Clinical Data?","source":"europepmc","abstract":"Background and Objective Early and reliable disease prediction from structured clinical data remains challenging when datasets are small, highly imbalanced, and contain limited positive disease cases. Conventional machine learning (ML) and deep learning approaches often struggle to capture clinically meaningful relationships under such low-data representation conditions due to weak statistical associations between features and prediction targets. This study proposes a clinically grounded Distil GPT2-based table-to-text framework for disease prediction using structured healthcare datasets, motivated by the contextual reasoning capability of GPT models to better capture clinically meaningful relationships when statistical learning alone becomes insufficient due to limited data availability. Methods & Materials Structured clinical records were transformed into physician-style textual descriptions and enriched through GPT4-generated medical paraphrasing to improve minority-class representation while preserving clinical meaning. Both the original and generated clinical texts were used to fine-tune a Distil GPT2 model across four public healthcare datasets, including heart disease, heart failure, chronic kidney disease, and thyroid cancer recurrence. Gradient-based explainable AI analysis was additionally incorporated to identify clinically important features influencing prediction outcomes. Results The proposed framework demonstrated consistently strong predictive performance across four clinical datasets, achieving average precision, specificity, sensitivity, and F1-score of 0.96, 0.97, 0.96, and 0.96, respectively. The model achieved improved sensitivity, stronger generalization, and more stable predictive behavior compared with traditional ML, deep learning, transformer-based, and GAN-augmented approaches. Importantly, the framework consistently emphasized clinically meaningful variables even under severe class imbalance, where conventional ML and neural network models often struggled to identify key clinically relevant relationships. Conclusions The proposed Distil GPT2-based table-to-text framework provides a practical and clinically interpretable approach for disease prediction from limited structured healthcare data. By integrating contextual clinical reasoning with explainable prediction mechanisms, the framework suggests strong potential for early risk detection, transparent clinical decision support, and reliable deployment in real-world data-scarce healthcare settings.","url":"https://doi.org/10.64898/2026.08.19.26360765","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.19.26360765","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.3389/fimmu.2026.1812285","name":"Immune-inflammatory and metabolic signatures for osteoporosis risk stratification in primary Sjögren's syndrome: development and internal validation of an interpretable machine-learning model.","source":"europepmc","abstract":"Background Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease. Osteoporosis (OP) is a common complication in patients with pSS (pSS-OP), which significantly affects their quality of life and prognosis. Currently, there is a lack of efficient and objective clinical tools for the early identification of pSS patients at high risk of osteoporosis, limiting the implementation of precise interventions. Objective This study aims to integrate clinical indicators and immunological characteristics to develop and validate a machine learning model for predicting the risk of osteoporosis in patients with pSS, thereby facilitating early clinical identification and decision-making. Methods Clinical data were collected from 384 patients with pSS. Lymphocyte subsets and serum cytokine levels of IL-2, IL-4, and IL-6 were measured in all participants. Missing data were handled using multiple imputation, followed by intergroup comparisons. Feature selection was performed using Lasso regression, random forest, and stepwise regression, and the intersection of these methods was used to identify the final predictors. Based on the selected features, nine machine learning models were constructed and compared, including decision tree, k-nearest neighbor, logistic regression, elastic net, random forest, support vector machine, multilayer perceptron, LightGBM, and XGBoost. Model performance was evaluated using ROC curves, calibration curves, decision curve, accuracy, F1 score, sensitivity, specificity, and other metrics. The SHAP method was applied to interpret the optimal model. Results Patients were divided into a pSS with OP and a pSS without OP group. Six core predictors were identified: Age, ESR, Urea, MON, Ca, and Fibrinogen. Among the nine models, the XGBoost model demonstrated the best performance. In the training set, the AUC was 0.890, and the F1 score was 0.90. In the test set, the AUC was 0.808, and the F1 score was 0.81. SHAP analysis revealed the following order of feature importance: Age, MON, ESR, Ca, Urea, and Fibrinogen. Among these, Ca contributed negatively to the model prediction, while the remaining features contributed positively. Conclusion This study developed and internally validated an XGBoost machine learning model based on clinical and laboratory indicators for predicting osteoporosis risk in patients with pSS. The model demonstrated good discrimination and may assist early risk assessment.","url":"https://doi.org/10.3389/fimmu.2026.1812285","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1812285","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fimmu.2026.1870438","name":"Explainable machine learning model for in-hospital hypoglycemia risk in patients with latent autoimmune diabetes in adults.","source":"europepmc","abstract":"Background Latent autoimmune diabetes in adults (LADA) is characterized by progressive β-cell impairment and severe glycemic lability, predisposing patients to in-hospital hypoglycemia. Few tailored risk-stratification models exist for this population. This study aimed to develop and validate an interpretable machine learning model using routine clinical data to predict in-hospital hypoglycemia in LADA inpatients. Methods This multicenter retrospective study recruited participants from five Chinese tertiary hospitals between January 2019 and September 2025. Data from four centers formed the derivation cohort, and the remaining center served as the independent external validation cohort. The primary endpoint was in-hospital hypoglycemia (blood glucose Results A total of 752 LADA inpatients were enrolled. The incidence of in-hospital hypoglycemia was 44.8% in the derivation cohort and 54.4% in the external validation cohort. Six core predictive factors were identified: largest amplitude of glycemic excursion, fasting C-peptide, glycated hemoglobin, sex, insulin pump use, and previous hypoglycemia. The three models yielded numerically variable discriminative performance across cohorts. Pairwise DeLong tests indicated no statistically significant differences in the AUROC among the three algorithms during external validation. All models showed comparable calibration and threshold-dependent predictive performance in the external cohort. XGBoost was selected as the final model after comprehensive evaluation. Fasting C-peptide was identified as the most influential predictor. Exploratory subgroup analyses demonstrated generally stable model performance across clinical strata. These findings are limited by small subgroup sample sizes and wide confidence intervals, and thus cannot be generalized to external populations. Sensitivity analysis suggested that model performance was not predominantly dependent on the retained glucose-derived predictor. Conclusions The interpretable XGBoost model showed acceptable discrimination, calibration, and potential clinical utility for in-hospital hypoglycemia risk stratification in patients with LADA. This pragmatic predictive tool has the potential to support individualized inpatient glycemic management and facilitate targeted clinical intervention for LADA populations.","url":"https://doi.org/10.3389/fimmu.2026.1870438","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1870438","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fmed.2026.1880402","name":"An explainable machine learning framework integrating clinical and lipidomic signatures for early severity stratification of hypertriglyceridemic pancreatitis.","source":"europepmc","abstract":"Background Hypertriglyceridemic pancreatitis (HTGP) is increasingly recognized as a major etiology of acute pancreatitis and is associated with a higher risk of early severe progression, systemic complications, and mortality compared with other etiologies. However, currently available scoring systems demonstrate limited accuracy and poor mechanistic interpretability in HTGP. Aim To develop and validate an explainable machine learning model for early stratification of severe progression in HTGP and to explore its underlying lipidomic mechanisms. Methods A retrospective cohort of 288 patients with HTGP admitted between 2020 and 2025 was included. Patients were categorized into mild acute pancreatitis (MAP) and moderately severe/severe acute pancreatitis (MSAP/SAP). Ensemble feature selection integrating eight algorithms was applied to identify robust predictors. Ten machine learning models were constructed and compared. SHAP (SHapley Additive exPlanations) analysis was used to interpret model outputs. Untargeted serum lipidomics combined with KEGG pathway enrichment analysis was performed in 15 matched severe/non-severe AP pairs to explore the biological relevance of identified predictors. Results Among 288 patients, 150 (52.1%) were classified as MSAP/SAP at hospital admission. Ten predictors associated with inflammatory activation, coagulation dysfunction, endothelial leakage, and systemic injury were identified, including IL-6, D-dimer, PCT, albumin, CRP, SIRS, pleural effusion, and pancreatitis-associated ascitic fluid. Among all candidate algorithms, the Naive Bayes model achieved the best overall discrimination (AUC = 0.841), outperforming APACHE II and modified Marshall scores. Calibration assessment revealed a calibration intercept of -0.0332 and a Brier score of 0.2035; the calibration slope of 0.2009 indicated compression of predicted probabilities, suggesting caution in absolute risk estimation. Decision curve analysis demonstrated positive net clinical benefit across threshold probabilities from 0 to approximately 0.85, with the Naive Bayes model outperforming both benchmark scoring systems across clinically relevant thresholds. SHAP analysis identified IL-6, D-dimer, and PCT as the most influential predictors. Exploratory lipidomic pathway enrichment in a sub-cohort of 15 matched pairs identified alterations in glycerophospholipid metabolism, sphingolipid metabolism, necroptosis, and autophagy pathways, providing hypothesis-generating biological context for the identified clinical predictors. Conclusion We developed an explainable machine learning framework integrating clinical and exploratory lipidomic evidence for early severity stratification of HTGP at hospital admission. The identified predictors converged on interconnected biological axes involving inflammation, coagulation, and endothelial dysfunction. The lipidomic sub-study provided complementary pathway-level context supporting the biological plausibility of these predictors. As this study is based on a single-center retrospective cohort without external validation, findings should be regarded as preliminary; prospective multicenter validation is required before clinical deployment.","url":"https://doi.org/10.3389/fmed.2026.1880402","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1880402","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3390/diagnostics16152354","name":"Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.","source":"europepmc","abstract":"In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD) by examining the most recent literature. Articles published from 2016 to 2025 were collected from online databases such as PubMed, Web of Science, and Embase. After abstract and full-text screening, 57 articles were included in the results section. Machine learning was applied to clinical and laboratory data, medical imaging, urine samples, retinal images, and at-home measurements to diagnose CKD and predict CKD progression and related complications. Although many studies reported high discriminatory performance, the evidence base was dominated by retrospective, single-center, and methodologically heterogeneous studies, with frequent high-risk-of-bias findings and limited external validation. Furthermore, most published models are not yet sufficiently validated for clinical deployment. Before these tools can be adopted in routine care, prospective, multicenter studies are required that report calibration and clinical utility, adhere to established reporting standards, and demonstrate added value over the current standard of care.","url":"https://doi.org/10.3390/diagnostics16152354","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16152354","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj.21524","name":"A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.","source":"europepmc","abstract":"Background Deep vein thrombosis (DVT) is a common thrombotic condition with substantial morbidity when not identified early. Machine learning (ML)-based predictive models may improve early identification of patients at high risk for DVT, but few clinically applicable early-risk models exist. Objectives To develop and internally validate a ML model using routinely available clinical and laboratory indicators for early risk prediction of DVT, and to identify the most influential predictors using model explainability techniques. Methods We retrospectively analyzed clinical data from 231 patients evaluated at the Fifth Affiliated Hospital of Southern Medical University between January 2017 and June 2024. Patients were labeled as DVT occurrence ( n = 159) or non-occurrence ( n = 72). Seven candidate predictors were selected by Least Absolute Shrinkage and Selection Operator (LASSO) regression. The dataset was split into training (70%, n = 162) and test (30%, n = 69) sets. Five ML algorithms were trained: XGBoost, CatBoost, Random Forest (RF), Logistic Regression, and Support Vector Machine, with hyperparameter tuning on the training set. Model performance was assessed by 5-fold cross-validation and on the held-out test set using Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, recall, and F1 score. The best model was further interpreted via feature importance and Shapley Additive Explanations (SHAP). Results LASSO selected seven predictors: hemoglobin, platelet count, leukocyte count, fibrinogen, prothrombin time, D-dimer (DD), and glucose. The Random Forest model showed the best discrimination (test-set AUC = 0.874), with favorable accuracy, recall, and F1 compared with other classifiers (detailed metrics reported in the manuscript). In the RF model, D-dimer had the highest feature-importance contribution; SHAP analysis confirmed DD as the dominant risk driver and characterized the directions and relative effects of other features. Conclusions We developed an internally validated ML model for early DVT risk prediction using seven routine clinical variables; Random Forest achieved the best performance and identified D-dimer as the most influential predictor. This model may support earlier identification and intervention for patients at risk of DVT, pending external validation and prospective evaluation.","url":"https://doi.org/10.7717/peerj.21524","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7717/peerj.21524","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/life16081371","name":"Better Discrimination, Unchanged Practice: Are Machine Learning Models Ready to Replace Established Risk Scores in Cardiac Surgery? A Narrative Review.","source":"europepmc","abstract":"Preoperative risk stratification underpins consent, treatment selection, and quality benchmarking in cardiac surgery, a task served for two decades by regression-derived scores such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and the Society of Thoracic Surgeons Predicted Risk of Mortality (STS PROM), together with procedure-specific tools. A rapidly expanding literature reports that machine learning (ML) models achieve higher discrimination than these scores, yet established scores remain the instruments actually used at the bedside. This narrative review examines that paradox. Drawing on studies emphasized between 2023 and 2026, we argue that the reported advantage of ML is real but modest. This advantage is driven primarily by improved discrimination, while key measures of clinical value, including calibration, net benefit, and external or temporal validation, are infrequently reported. We organize the evidence around a four-lens appraisal (discrimination, calibration, clinical utility, and generalizability) and show that most cardiac surgery ML studies focus only on discrimination. We then consider why superior discrimination has not changed practice and outline the evidence needed for an ML-based risk model to justify replacing an established scoring system. The current literature supports a measured conclusion: ML is a discrimination upgrade in search of clinical proof.","url":"https://doi.org/10.3390/life16081371","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/life16081371","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/s00423-026-04143-x","name":"A Validated, explainable machine learning-based preoperative risk model for microvascular invasion in hepatocellular carcinoma.","source":"europepmc","abstract":"Purpose This investigation aimed to develop and validate a diagnostic algorithm for preoperatively assessing the likelihood of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Methods Clinical and pathological information of patients with HCC who underwent curative resection was collected from two medical centers. Data from Nanjing Drum Tower Hospital were randomly split into training (80%) and internal validation (20%) cohorts, while data from Northern Jiangsu People's Hospital were employed as an independent external validation cohort. Feature engineering was performed using recursive feature elimination (RFE) within the training cohort. Various machine learning models were applied, and their performance was evaluated through diverse metrics, such as receiver operating characteristic (ROC) curves. Additionally, the Shapley Additive Explanations (SHAP) method, together with tumor differentiation, Ki-67, and other relevant markers, were employed to enhance model interpretability and reliability. Results Among the 1106 patients enrolled, 315 were pathologically confirmed to have MVI. RFE identified tumor diameter, alpha-fetoprotein (AFP), gamma-glutamyl transferase (GGT), and pan-immune-inflammation value (PIV) as key determinants of MVI risk in patients with HCC. With these variables, the XGBoost model reached area under the curve (AUC) values of 0.893 in the training cohort, 0.845 in the internal validation cohort, and 0.793 in the external validation cohort. Correlation analyses revealed that the risk score showed significant associations with tumor differentiation and the expression of Ki-67 proliferation index, Glypican-3 (GPC3), Cytokeratin 19 (CK19), and Vascular endothelial growth factor receptor 2 (VEGFR2). Conclusion An XGBoost model incorporating tumor diameter, AFP, GGT, and PIV exhibited robust performance in assessing preoperative MVI among HCC patients.","url":"https://doi.org/10.1007/s00423-026-04143-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00423-026-04143-x","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1088/1873-4030/ae8dce","name":"Optical hand kinematics for machine learning-based screening of carpal tunnel syndrome.","source":"pubmed","abstract":"Carpal tunnel syndrome is usually assessed by clinical examination and electrodiagnostic testing, but these methods may not always be practical for rapid screening. In this work, it was explored whether hand motion recorded with a low-cost optical camera could add useful information to routine clinical data. Demographic variables, symptom-related findings obtained after the Phalen test, and five thumb-to-finger opposition tasks were collected. Hand landmarks were extracted with MediaPipe and used to derive kinematic descriptors, which were then analyzed together with clinical variables by machine-learning and hybrid deep-learning models. To reduce the risk of overly optimistic estimates, the main evaluation was carried out at hand level with leave-one-group-out cross-validation. In the primary leakage-safe comparison, the best Dataset 4 multimodal model yielded the strongest binary screening profile. Compared with the clinical Dataset 1 baseline, accuracy increased from 82.6% (95% CI 75.0%-90.2%) to 87.0% (79.3%-93.5%), sensitivity from 70.0% (54.4%-85.1%) to 80.0% (64.7%-93.4%), specificity from 88.7% (80.3%-95.6%) to 90.3% (83.1%-96.8%), and ROC-AUC from 0.845 (0.745-0.934) to 0.855 (0.752-0.945). In the same-architecture tests, the kinematic variables did not behave uniformly across model families. The clearest gain among the classical models was seen with light gradient boosting machine. Feature-level results were also consistent with the model findings, as selected thumb-opposition coordinates differed across stages and appeared to capture a functional component not represented by symptom scores. In the four-class severity task, the improvement was small: accuracy increased from 81.5% (72.8%-89.1%) to 82.6% (73.9%-90.2%), and macro-1 increased from 0.706 (0.544-0.823) to 0.748 (0.588-0.850). In this cohort, the optical hand-kinematic measures mainly strengthened the screening analysis. Their use for finer severity staging will need confirmation in larger external datasets and in participant-level analyses.","url":"https://doi.org/10.1088/1873-4030/ae8dce","authors":["Çelikbaş Ş","Akgündoğdu A","Gündüz A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1088/1873-4030/ae8dce","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fpsyt.2026.1856898","name":"Building an early warning model for the risk of suicide attempt in people with depression based on machine learning: a single-centre study.","source":"europepmc","abstract":"Background Attempted suicide is one of the most serious clinical consequences for patients with depression, and early identification of high-risk individuals is crucial for preventing suicide deaths. Traditional clinical assessment often relies on subjective judgment, lacking objective biological markers and multi-dimensional data integration analysis. Objective This study aims to use machine learning algorithms to integrate demographic characteristics, clinical symptom scales, and blood biochemical indicators to construct and validate an early warning model for the risk of attempted suicide in patients with depression, and to explore key predictors. Methods A total of 1,229 patients with depression were included in this study, including 578 cases in the suicide attempt group and 651 cases in the non-suicide attempt group. Demographic information, Hamilton Depression Scale (HAMD) sub-item scores, and biochemical indicators such as thyroid function, liver and kidney function, blood lipids, and electrolytes were collected. Predictive models were constructed using six machine learning algorithms: Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector machine (SVM), multi-layer perceptron (MLP), logistic regression (LR), and Decision tree (DT). Model performance was evaluated by area (AUC) under the receiver operating characteristic curve (ROC), accuracy, sensitivity, specificity, and F1 score, and feature importance was explained by SHAP (Shapley Additive exPlanations) values. Results Univariate analysis showed significant differences (P Conclusion This study successfully constructed an early warning model for the risk of suicide attempt in patients with depression based on multimodal data. The random forest model demonstrated good predictive performance. The study found that in addition to traditional psychosocial factors, metabolic indicators such as BMI and uric acid and thyroid function (FT3) are also important risk predictors, providing a new biological perspective for early clinical intervention.","url":"https://doi.org/10.3389/fpsyt.2026.1856898","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1856898","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10424152/v1","name":"Dataset-structured evidence synthesis for machine-learning prediction models: a methodological framework and worked example","source":"europepmc","abstract":"Abstract Background Systematic reviews of machine-learning prediction models often pool performance estimates by publication or algorithm label. This can misrepresent the evidence when algorithms share participants, benchmark datasets recur across reports, internal and external validation are combined, or discrimination is interpreted without calibration. A previous commentary proposed a dataset-structured perspective. We extend that perspective into an operational framework and a worked example. Methods We define a hierarchy of evidence units for prediction-model synthesis: data source or cohort, analytic dataset, validation exercise, modeling pipeline, performance estimate, and publication/report. We apply the framework to a published systematic review and meta-analysis of machine-learning models for early prediction of ventilator-associated pneumonia (VAP). The aim was not to update the clinical review, but to reconstruct its evidence base using dataset-structured rules. We coded data sources, analytic datasets, validation design, performance domains, data-source reuse, dependent estimates, and implications for pooling, using one row per validation exercise whenever possible. Results Under a conventional paper/model count, the VAP evidence base appeared to contain 10 included reports, 10 primary report-level model entries, and 10 extractable AUROC/c-statistic summaries. Dataset-structured reconstruction identified 7 unique data-source groups, 10 analytic datasets, and 11 validation exercises. MIMIC-III recurred in 4 reports; one abstract was treated as companion evidence to a preprint, and several reports contributed dependent algorithm or prediction-window estimates. No validation exercise met our conservative definition of fully external validation, and none provided calibration-ready or decision-analytic evidence for the VAP target. A single pooled AUROC was therefore not an appropriate primary estimand. Conclusions Dataset-structured synthesis changes both the descriptive count and the interpretation of machine-learning prediction evidence. In the VAP example, the conclusion shifted from high pooled discrimination to promising internal or temporal discrimination with unproven transportability, calibration, and clinical usefulness. Future reviews should define the validation exercise as an evidence unit, reconstruct dependence before pooling, distinguish validation designs, and report performance domains beyond discrimination.","url":"https://doi.org/10.21203/rs.3.rs-10424152/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10424152/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.2196/80133","name":"A Machine Learning-Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018.","source":"europepmc","abstract":"Background Overactive bladder (OAB) is a prevalent condition, particularly among women, characterized by urinary urgency, often accompanied by frequency and nocturia. Traditional risk prediction methods for OAB are limited, as they fail to fully integrate multidimensional risk factors, including female reproductive history. Machine learning offers potential for enhanced predictive accuracy by using large-scale datasets like the National Health and Nutrition Examination Survey (NHANES). Objective This study aimed to develop and validate a machine learning-based model to predict OAB risk in women, incorporating reproductive and sociodemographic factors, and to identify key predictors using interpretable methods. Methods This retrospective observational study analyzed data from 7884 participants across 4 consecutive cycles (2011-2018) of the National Health and Nutrition Examination Survey. LASSO (least absolute shrinkage and selection operator) regression and univariate and multivariate logistic regression analyses were applied to identify key variables in the training set. Fourteen variables were selected via LASSO regression for model construction, among which age, BMI, ratio of family income to poverty threshold (PIR), age at menarche, and number of vaginal deliveries were identified as the most significant clinical predictors. The SHAP (Shapley Additive Explanations) method interpreted the optimal model, and restricted cubic spline (RCS) curves were used for dose-response analysis. Results Five variables were identified as significant predictors. Among the 11 ML models, random forest (RF) demonstrated the highest predictive performance. The random forest model achieved an AUROC (area under the receiver operating characteristic curve) of 0.8536 (95% CI 0.8435-0.8638) in the training set and 0.6999 (95% CI 0.6768-0.7212) in the test set, indicating moderate predictive capability. SHAP analysis identified age, BMI, and the number of vaginal deliveries as the top 3 contributors to OAB risk. Both RCS and SHAP analyses revealed a positive association of age and BMI with OAB risk and a negative association with PIR. Additionally, RCS showed that the risk of OAB was higher with an earlier age at menarche and a greater number of vaginal deliveries. Conclusions Integrating ML with SHAP interpretability provides a robust predictive tool for OAB, facilitating early identification and clinical management.","url":"https://doi.org/10.2196/80133","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/80133","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1097/rti.0000000000000881","name":"Utilizing Deep Learning-based Computed Tomography Fractional Flow Reserve on Coronary Artery Disease Diagnosis and Treatment: 1-year Clinical Application From a Chinese Major Hospital.","source":"europepmc","abstract":"Purpose Machine learning-based coronary computed tomography fractional flow reserve (CT-FFR) holds great potential for assessing coronary ischemic status. The current literature lacks a comprehensive description of the routine implementation of CT-FFR in real world. To investigate the clinical characteristics and acceptance of CT-FFR in clinical decision-making among Chinese patients and subsequently assess the diagnostic accuracy of invasive coronary angiography as the reference. Materials and methods In this retrospective single-center study, 4564 patients were included. In the first part, we conducted a baseline analysis of patients and their epicardial coronary arteries. Then, we analyzed hospitalization and revascularization in the context of application of CT-FFR, using logistic regression and Sankey diagrams. Finally, we performed a diagnostic analysis of 2718 vessels in 906 patients. Results The baseline analysis included a total of 4564 patients. A statistically significant distinction was observed in the traditional risk factors for coronary heart disease between 2 groups with CT-FFR 0.8 cutoff values. Logistic regression analysis and Sankey plots revealed a association between CT-FFR ≤0.8 and subsequent hospitalization. Finally, a diagnostic analysis was performed on 2718 vessels, and the optimal diagnostic model efficacy was achieved by using a CT-FFR cutoff value of 0.8 in conjunction with stenosis ≥70% for CCTA. Conclusions Our study provides evidence that machine learning-based CT-FFR values exhibit a probably positive correlation with individuals presenting high-risk factors for coronary artery disease. Furthermore, we observed a influence of CT-FFR on the clinical decisions made by physicians. The integration of CT-FFR and CCTA has the potential to enhance diagnostic efficacy.","url":"https://doi.org/10.1097/rti.0000000000000881","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/rti.0000000000000881","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.ridd.2026.105369","name":"From clinical and typical separation to autism spectrum disorder and global developmental delay differentiation: Interpretable eye-tracking-based machine learning.","source":"europepmc","abstract":"In clinical practice, objectively distinguishing children with autism spectrum disorder (ASD) from those with typical development (TD) and other neurodevelopmental conditions with overlapping symptoms, such as global developmental delay (GDD), is critical for improving developmental outcomes. This study aimed to investigate the feasibility of eye-tracking-based machine learning (ML) models in distinguishing children with developmental concerns (ASD + GDD) from TD children, and in further differentiating ASD from GDD. A total of 168 toddlers (45 ASD, 56 GDD, and 67 TD; aged 18-48 months) viewed a socially dynamic \"hide-and-seek\" video. Both conventional area-of-interest (AOI)-based features and combined features derived from AOI combinations were used to train five ML classifiers. A two-stage classification framework was adopted, and SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions. Combined features consistently outperformed isolated AOI features. For TD versus clinical classification, the random forest model achieved the highest accuracy of 80.36% (sensitivity = 84.16%, specificity = 74.63%). In differentiating ASD from GDD, the k-Nearest Neighbors model achieved the highest accuracy of 79.21% (sensitivity = 71.11%, specificity = 85.71%). SHAP analysis indicated that cross-regional attention features contributed substantially to model performance. These findings suggest that eye-tracking-based ML models may provide a promising approach for early screening of developmental conditions and ASD-GDD differentiation. Combined features reflecting cross-regional attention distribution demonstrated higher discriminative value than conventional AOI measures. Future studies should validate these findings in larger, multicenter, and more balanced samples with richer dynamic eye-tracking representations.","url":"https://doi.org/10.1016/j.ridd.2026.105369","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ridd.2026.105369","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3389/fendo.2026.1834380","name":"Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study.","source":"europepmc","abstract":"Background Diabetic Retinopathy (DR) is among the most severe microvascular complications of diabetes, leading to visual impairment and diminished quality of life. This study developed and compared multiple machine learning models for DR risk prediction using population-based data from the Fujian Eye Study, aiming to identify the top five key predictors and establish a robust data-driven framework for early screening. Methods Data were obtained from the Fujian Eye Study, comprising 8211 participants and 51 variables. After data preprocessing, five machine learning models-Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree (DT), and Random Forest (RF)- were trained and optimized via cross-validation and grid search. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, F1-score, and AUC. Feature importance was examined using SHAP (Shapley Additive Explanations) and validated through unsupervised and nonparametric approaches-Factor Analysis (FA), Highly Variable Feature Selection (HVGS), and Spearman's rank correlation. Results Among the five models, SVC model achieved the highest performance (F1-score 92.83%, AUC 0.99). SHAP analysis identified the top five predictors of DR risk: history of diabetes, age, pulse pressure difference (PPG), near visual acuity of the left eye, and height. Cross-method comparison confirmed high feature stability across models, indicating robust predictor reproducibility. Conclusion This study successfully established and validated a machine learning-based framework for predicting diabetic retinopathy risk using data from the Fujian Eye Study. The support vector machine (SVC) model demonstrated superior predictive capability. The identified key risk factors-diabetes history, age, pulse pressure difference, left eye near visual acuity, and height-provide actionable insights for early stratification. These findings provide a reliable, data-driven tool for early DR screening, which can facilitate population-level risk stratification and inform personalized preventive interventions in clinical and public health settings.","url":"https://doi.org/10.3389/fendo.2026.1834380","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1834380","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1111/cbdd.70382","name":"Identification of Hub Gene Characteristics and Immune Landscapes Across Aging Subtypes in Atherosclerosis: A Machine Learning-Based Multi-Omics Study With Experimental Verification.","source":"europepmc","abstract":"Aging is a major risk factor for atherosclerosis (AS), but the aging-associated molecular characteristics and immune heterogeneity of AS remain incompletely understood. This study aimed to identify aging-related hub genes and characterize immune landscapes across aging subtypes of AS using integrated bioinformatics and experimental validation. AS-related candidate genes were found by overlapping DEGs identified by limma and key module genes identified by Weighted Correlation Network Analysis (WGCNA) based on the GSE100927 dataset. Consensus clustering based on aging-related DEGs (differential expression analysis based on 125 aging-related genes between AS and control) was performed to identify aging subtypes and characterize immune landscapes. Typical genes were picked out using four machine learning models: Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGB), and Generalized Linear Model (GLM). The expression of central genes was verified through single-cell data analysis. Patients with AS were enrolled (n = 30), and atherosclerotic plaque and adjacent normal arterial tissues were collected. Quantitative Polymerase Chain Reaction (qPCR) validation was performed in atherosclerotic plaques and oxidized Low-Density Lipoprotein (ox-LDL)-treated THP-1 macrophage. The effect of crucial genes on ox-LDL-induced THP-1 macrophage inflammatory response and foaming were validated by qPCR, ELISA and Oil Red O Staining. We identified 76 aging-related DEGs and classified AS samples into two aging-related subtypes (C1 and C2) with distinct immune infiltration characteristics. Machine learning analysis based on 43 candidate genes identified 5 hub genes: heat shock protein family B (small) member 7 (HSPB7), myelin expression factor 2 (MYEF2), dual specificity phosphatase 26 (DUSP26), tandem C2 domains, nuclear (TC2N), and phospholamban (PLN), whose expression patterns were further validated by single-cell RNA sequencing. Moreover, TC2N and PLN were significantly downregulated in atherosclerotic plaque tissues. TC2N and PLN expressions in atherosclerotic plaque tissue of AS patient were significantly reduced. TC2N was significantly decreased in ox-LDL-treated THP-1 macrophage. TC2N overexpression alleviated ox-LDL-induced THP-1 macrophage inflammatory response but also alleviated cell foaming. This integrated bioinformatics and experimental study identified five hub genes (HSPB7, MYEF2, DUSP26, TC2N, and PLN) associated with AS. Experimental validation confirmed that TC2N is significantly downregulated in human atherosclerotic plaques and ox-LDL-treated THP-1 macrophages, TC2N overexpression alleviates inflammatory responses and foam cell formation. These findings provide insights into the molecular mechanisms linking aging and immune dysregulation in AS and highlight TC2N as a potential regulator of AS progression.","url":"https://doi.org/10.1111/cbdd.70382","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/cbdd.70382","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/sim.70714","name":"Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes With Ordinal Outcomes.","source":"europepmc","abstract":"Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatments based on patients' treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and observational studies. However, constructing optimal DTRs for ordinal outcomes has been underexplored. This article introduces a Bayesian machine learning (BML) framework to estimate optimal DTR with ordinal outcomes. To deal with potential nonlinear associations between outcomes and predictors, we first introduce ordinal Bayesian additive regression trees (OBART), a Bayesian tree-based model that integrates the latent continuous variable framework within the traditional Bayesian additive regression trees (BART). We then incorporate OBART into the BML (named BML-OBART) to estimate optimal DTRs based on ordinal data and quantify the associated uncertainties of the estimated parameters. Extensive simulation studies were conducted to evaluate the performance of the proposed method in comparison with existing methods. We demonstrate the application of the proposed BML method using data from a smoking cessation trial and provide the OBART R package along with R code for the implementation of BML-OBART.","url":"https://doi.org/10.1002/sim.70714","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/sim.70714","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1038/s41598-026-58704-7","name":"A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.","source":"europepmc","abstract":"This study aimed to demonstrate the integration of deep learning (DL) and machine learning (ML) using only occlusal photographs to provide preliminary triage support between orthodontic and prosthodontic treatments in cases requiring alternatives to implant-based interventions. Occlusal photographs (n = 2,962) were collected under routine conditions using smartphones and digital cameras. Two groups of dental specialists independently annotated the dataset. A YOLOv8m model was used for the localization of single-tooth edentulous spaces (S-TES), which were defined as localized areas within the dental arch where a single tooth is absent while adjacent teeth remain present, as well as their mesial and distal adjacent teeth. ResNet-50/ResNet-101 and VGG-16/VGG-19 models were employed to classify the clinical conditions and anatomical categories of teeth adjacent to S-TES. A deterministic function converted the mesiodistal width of the S-TES from pixels to millimeters using mean central incisor widths. Logistic Regression (LR) and XGBoost (XGB) models were used to predict the preliminary triage outputs. Among the classifiers for the clinical tooth condition classification task, VGG-19 showed the highest macro F1 score (0.928) and ResNet-101 yielded the highest macro AUC (0.961) and weighted kappa (0.927), with the narrowest 95% confidence intervals (95% CI) for both metrics. For the anatomical categorization task, ResNet-101 showed the highest F1 score, recall, and precision. For the triage support models, LR showed the highest AUC (0.896) with an expected calibration error (ECE) of 0.041, and XGB demonstrated the highest accuracy (0.869), sensitivity (0.874), and specificity (0.862) with an ECE of 0.024. The proof-of-concept demonstrates that the integration of DL and ML can achieve acceptable performance for preliminary triage support between orthodontic and prosthodontic treatment from occlusal images, within the acknowledged limitations. Further research is needed to improve model generalizability. However, the findings indicate a promising direction for future work. While in the long term, such technology has the potential to support preliminary triage between treatment options and become a clinically applicable tool, our current findings show that external validation and improved generalizability of the proposed AI framework are necessary before such clinical applications can be realized.","url":"https://doi.org/10.1038/s41598-026-58704-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-58704-7","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.21203/rs.3.rs-10257925/v1","name":"Machine Learning-Based Prediction of Lung Cancer Risk in COPD Patients Using Clinical Severity Indicators and Comorbidity Profile","source":"europepmc","abstract":"Abstract Lung cancer remains a major cause of cancer-related mortality worldwide. It is clinically relevant in patients with chronic obstructive pulmonary disease (COPD), in whom shared risk factors and biological mechanisms may increase susceptibility. Early identification of COPD patients with elevated lung-cancer risk status could support screening prioritization and timely diagnostic evaluation. This study developed and internally evaluated a machine-learning framework for lung-cancer risk stratification among COPD patients, using demographic, physiological, disease-severity, and comorbidity variables. The analytic dataset comprised 2300 COPD patient records, with 32% labeled as lung-cancer cases and 68% as non-lung-cancer cases. Data preprocessing was implemented in leakage-controlled, training-only workflows that included imputation, categorical encoding, standardization, feature selection, class imbalance handling with SMOTE applied only to the training data, and stratified validation. Because temporal follow-up information was not available in the manuscript, the modeling task was framed as lung cancer risk-status classification rather than as definitive incident cancer prediction. Six supervised models were compared: Logistic Regression, Support Vector Machine, Decision Tree, K-Nearest Neighbors, Random Forest, and Artificial Neural Network (ANN). In the reported internal validation, the ANN achieved the highest performance, with an accuracy of 0.90, a recall of 0.92, and an AUC-ROC of 0.94; Random Forest showed comparable discrimination with an AUC-ROC of 0.93. McNemar testing suggested that ANN and Random Forest performed similarly, whereas ANN outperformed the linear baseline. Calibration analysis using the Brier Score showed the lowest value for ANN (0.097), followed by Random Forest (0.109). Smoking exposure, COPD severity, FEV1, and age were the most influential predictors in Random Forest feature-importance analysis. These findings suggest that nonlinear machine-learning models may support stratification of COPD-related lung cancer risk status. However, the results are internally validated and hypothesis-generating, and they require external validation, transparent outcome ascertainment, uncertainty estimation, and additional explainability analyses before clinical implementation.","url":"https://doi.org/10.21203/rs.3.rs-10257925/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10257925/v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.3390/medsci14040428","name":"Identifying Risk Factors for Caregiver Burden in Neurological Disorders Using Machine Learning.","source":"europepmc","abstract":"Background: Caregiver burden represents a multidimensional syndrome influenced by patient-related, relational, and contextual factors in neurological disorders. Although stroke, Parkinson's disease (PD), and Alzheimer's disease (AD) differ in clinical trajectory, comparative analyses of caregiver risk profiles across these conditions remain limited. Objective: This study aimed to identify sociodemographic, cognitive, and dyadic factors associated with caregiver burden in a clinical cohort, and to investigate their value for caregiver risk stratification using supervised machine learning models. Methods: In this monocentric observational cohort study, 113 patient-caregiver dyads (79 stroke, 19 PD, 15 AD) were consecutively enrolled in a neurorehabilitation setting. Patients underwent cognitive assessment with the Montreal Cognitive Assessment (MoCA), while caregivers completed the Caregiver Burden Inventory (CBI), which was considered the primary outcome measure. Caregiver burden was dichotomized into mild versus moderate-to-severe burden using established CBI cutoff thresholds. Group comparisons, correlation analyses (false discovery rate-corrected), and supervised machine learning models (logistic regression, random forest, AdaBoost, support-vector machine, naïve Bayes, and CatBoost) were performed using 5-fold stratified cross-validation repeated 10 times. Results: Disease-specific burden patterns emerged. Stroke caregivers reported higher time-dependent burden, whereas AD caregivers showed greater emotional burden ( p p Conclusions: Multidimensional assessment and early risk stratification, potentially supported by machine learning tools, may improve identification of vulnerable caregivers and guide tailored interventions.","url":"https://doi.org/10.3390/medsci14040428","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/medsci14040428","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1097/meg.0000000000003206","name":"Development of a machine learning-based noninvasive diagnostic model for liver fibrosis in metabolic-associated steatotic liver disease.","source":"europepmc","abstract":"Background To address the lack of simple tools for assessing fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD), this study develops a machine learning-based diagnostic model to identify patients at high risk of significant fibrosis and advanced fibrosis. Methods Data from biopsy-proven MASLD patients were randomly divided into training and validation sets. Variables were then selected, and models using Logistic, Support Vector Machine, and eXtreme Gradient Boosting (XGBoost) were compared with identify the optimal model. Finally, Shapley Additive Explanations-based interpretability analysis was applied to explain the best-performing model. The DeLong test was applied to compare the new model with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis-4 index (FIB-4) models. Results The XGBoost models demonstrated robust accuracy. For the significant fibrosis outcome, the model incorporating age, total cholesterol, glycosylated hemoglobin (HbA1c), and albumin/globulin ratio achieved an area under the receiver operating characteristic curve (AUC) of 0.74 in the training set and 0.72 in the testing set. Regarding the advanced fibrosis outcome, the model including age, total cholesterol, HbA1c, platelets, white cell count, and albumin/globulin ratio yielded an AUC of 0.78 in the training set and 0.74 in the testing set. Decision curve analysis curves confirmed clinical utility, and performance surpassed APRI and FIB-4 ( P Conclusion This study developed a novel noninvasive diagnostic model for MASLD using simple and easily accessible variables, which demonstrates superior performance compared with traditional serological models.","url":"https://doi.org/10.1097/meg.0000000000003206","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/meg.0000000000003206","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1186/s12888-026-08336-y","name":"Resting-state fMRI-based machine learning for predicting SSRI treatment response in major depressive disorder.","source":"europepmc","abstract":"Background Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Although prior research has highlighted the brain changes modulated by antidepressant therapy, their efficacy and effectiveness are debated. The low rates of treatment response still existed in the pharmacological therapy of MDD. Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. Our purpose is to develop a predictive model for MDD therapy using machine learning techniques based on resting-state fMRI metrics. Methods A total of 116 MDD patients underwent 3.0T resting-state magnetic resonance image scanning. Demographic data and the 24-item Hamilton Depression Rating Scale (HAMD-24) were collected from all participants. An additional independent cohort of 25 MDD patients was included for external model validation. Based on the reduction rate of HAMD-24 scores at different time points, the patients were divided into an early improvement group, an early response group, and a clinical response group: (1) Early improvement group: HAMD-24 reduction rate ≥ 20% after 1 week of treatment; (2) Early response group: HAMD-24 reduction rate ≥ 25% after 2 weeks of treatment; (3) Clinical response group: HAMD-24 reduction rate ≥ 50% after 4 weeks of treatment. For model construction, LASSO regression was applied for feature selection, integrating identified brain regions, HAMD-24 symptom clusters, and clinical variables. Five machine learning models were established based on features screened by the LASSO regression model to predict treatment response in MDD. All models were trained and assessed using 10-fold cross-validation, the most stable logistic regression model was selected for external validation in a temporally independent cohort (n = 25). Results At 1 week, alterations were mainly observed in the frontal and sensorimotor regions. At 2 weeks, differences were found in the opercular, postcentral, orbitofrontal, temporal, and angular areas. At 4 weeks, additional abnormalities appeared in the frontal, occipital, and postcentral cortices. Moreover, right Postcentral gyru was found to be present in the differential brain regions observed at 1 week, 2 weeks, and 4 weeks comparisons. In addition, ten features related to the treatment response were identified using the LASSO regression model, including age, years of education, core depressive symptoms, and several brain region features primarily involving the postcentral gyrus. Among the constructed machine learning models, the Logistic Regression model based on LASSO-selected features (including age, education, core depressive symptoms, and the postcentral gyrus) demonstrated the most stable performance. It achieved an area under the curve (AUC) of 0.801 in the internal validation and maintained robust predictive performance in the independent external validation set, indicating promising generalizability. Following independent external validation, the results showed that the predictive performance of the Logistic regression model on the external validation set yielded an AUC value of 0.643 and an accuracy of 0.60. This indicates that the brain regions involved in the model-namely the right postcentral gyrus, right precentral gyrus, left superior frontal gyrus, left middle occipital gyrus, and the orbital part of the right inferior frontal gyrus-hold promise as exploratory neural correlates for predicting the efficacy of SSRIs in depression. Conclusion MDD exhibits early functional alterations in brain regions involved in emotion regulation, cognition, and sensorimotor processing. Integrating these multi-metric neural features with clinical variables via machine learning models suggests potential clinical utility for evaluating and predicting early SSRI treatment outcomes.","url":"https://doi.org/10.1186/s12888-026-08336-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12888-026-08336-y","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.5281/zenodo.19414602","name":"\"A COMPUTATIONAL FRAMEWORK FOR MODERN HERBAL DRUG DEVELOPMENT\": ARTIFICIAL INTELLIGENCE – DRIVEN PHARMACOVIGILANCE","source":"datacite","abstract":"Herbal medicines are extensively utilized across the globe and constitute an important component of traditional and complementary healthcare practices. However, their safety is often inadequately monitored due to variability in composition, lack of standardization, contamination and insufficient pharmacovigilance systems. Herbal products may cause adverse drug reactions such as hepatotoxicity, nephrotoxicity, allergic reactions and clinically significant herb–drug interactions. Conventional pharmacovigilance methods face challenges in monitoring herbal medicines because of complex multi-component formulations and underreporting of adverse events. Recent advancements in artificial intelligence and machine learning offer effective solutions by enabling large-scale analysis of biomedical literature, adverse event databases and clinical records. AI-based tools such as BioBERT, OpenVigil, CLAMP, Hugging Face Transformers, and DeepChem can support automated detection of safety signals and prediction of potential herb–drug interactions. Integrating AI technologies with pharmacovigilance systems can improve Timely identification of adverse effects and improvement of overall safety. evaluation of herbal medicines. Strengthening pharmacovigilance frameworks and adopting advanced analytical tools are therefore essential to promote the safe and appropriate use of herbal medicines.","url":"https://doi.org/10.5281/zenodo.19414602","authors":["Dr. Sethuramani A.1* , Thangam V.2 , Thirupavai B.2 , Umayambigai R.2 , Thillaisathana T.2, Dharani S.2 , Ganesh S.3, Aarthy S.3"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19414602","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19925038","name":"PHARMACOLOGICAL APPROACHES TO DRUG REPURPOSING: MECHANISMS, STRATEGIES, AND CLINICAL APPLICATIONS","source":"datacite","abstract":"Drug repurposing has emerged as a strategic approach to accelerate therapy development by identifying novel uses for existing drugs, offering reduced development time, lower costs, and established safety profiles. This review provides a comprehensive analysis of pharmacological approaches to drug repurposing, focusing on mechanisms, strategic paradigms, and clinical applications. A structured literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar using keywords such as “drug repurposing,” “mechanism-based repurposing,” and “phenotype-driven repurposing,” considering articles published in English from 2015 to 2026. Studies reporting mechanistic insights, computational strategies, preclinical and clinical evidence, and regulatory considerations were included, while non-peer-reviewed or insufficiently detailed studies were excluded. Data were extracted on drug names, original and repurposed indications, mechanisms, strategies employed, and clinical outcomes. The review identified multiple successful repurposing examples, including sildenafil, thalidomide, metformin, and remdesivir, highlighting the role of both phenotype-driven and mechanism-based approaches. Computational methods, such as in silico predictions, network pharmacology, and machine learning, were found to significantly enhance identification of novel indications. Despite regulatory, intellectual property, and safety challenges, repurposed drugs demonstrated meaningful clinical impact across oncology, infectious diseases, and neurodegenerative disorders. These findings underscore drug repurposing as an evidence-driven, translational strategy that expands therapeutic options, informs clinical practice, and guides future pharmacological research.","url":"https://doi.org/10.5281/zenodo.19925038","authors":["Md. Al Amin1,2*, Joy Sarker2, Sree Karma Tigga2, Md. Ismail Kabir1"],"tags":["Drug repurposing, Drug repositioning, Mechanism-based strategy, Phenotype-driven strategy, Computational pharmacology, Translational pharmacology."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19925038","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19925039","name":"PHARMACOLOGICAL APPROACHES TO DRUG REPURPOSING: MECHANISMS, STRATEGIES, AND CLINICAL APPLICATIONS","source":"datacite","abstract":"Drug repurposing has emerged as a strategic approach to accelerate therapy development by identifying novel uses for existing drugs, offering reduced development time, lower costs, and established safety profiles. This review provides a comprehensive analysis of pharmacological approaches to drug repurposing, focusing on mechanisms, strategic paradigms, and clinical applications. A structured literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar using keywords such as “drug repurposing,” “mechanism-based repurposing,” and “phenotype-driven repurposing,” considering articles published in English from 2015 to 2026. Studies reporting mechanistic insights, computational strategies, preclinical and clinical evidence, and regulatory considerations were included, while non-peer-reviewed or insufficiently detailed studies were excluded. Data were extracted on drug names, original and repurposed indications, mechanisms, strategies employed, and clinical outcomes. The review identified multiple successful repurposing examples, including sildenafil, thalidomide, metformin, and remdesivir, highlighting the role of both phenotype-driven and mechanism-based approaches. Computational methods, such as in silico predictions, network pharmacology, and machine learning, were found to significantly enhance identification of novel indications. Despite regulatory, intellectual property, and safety challenges, repurposed drugs demonstrated meaningful clinical impact across oncology, infectious diseases, and neurodegenerative disorders. These findings underscore drug repurposing as an evidence-driven, translational strategy that expands therapeutic options, informs clinical practice, and guides future pharmacological research.","url":"https://doi.org/10.5281/zenodo.19925039","authors":["Md. Al Amin1,2*, Joy Sarker2, Sree Karma Tigga2, Md. Ismail Kabir1"],"tags":["Drug repurposing, Drug repositioning, Mechanism-based strategy, Phenotype-driven strategy, Computational pharmacology, Translational pharmacology."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19925039","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19884160","name":"COMPREHENSIVE REVIEW ON AI IN THE PHARMACEUTICAL INDUSTRY: NEW IDEAS, USES, AND WHAT THE FUTURE HOLDS","source":"datacite","abstract":"AI is changing the pharmaceutical industry by making drug discovery, development, manufacturing, and patient care more efficient, accurate, and cost-effective. Conventional pharmaceutical processes frequently entail significant time consumption, elevated costs, and substantial failure rates. AI-driven technologies like machine learning (ML) and deep learning (DL) have made drug discovery faster, improved formulation development, and made clinical trial results better. AI is also critical for pharmacovigilance, precision medicine, and managing the supply chain. Even though it has the potential to change things, there are still problems like data privacy, ethical issues, regulatory uncertainty, and a lack of interpretability. This review examines the applications, benefits, constraints, and future potential of AI in the pharmaceutical sector, offering a thorough overview for researchers and professionals.","url":"https://doi.org/10.5281/zenodo.19884160","authors":["Aby Augustine*1, Sruthi E. J.1"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19884160","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19884161","name":"COMPREHENSIVE REVIEW ON AI IN THE PHARMACEUTICAL INDUSTRY: NEW IDEAS, USES, AND WHAT THE FUTURE HOLDS","source":"datacite","abstract":"AI is changing the pharmaceutical industry by making drug discovery, development, manufacturing, and patient care more efficient, accurate, and cost-effective. Conventional pharmaceutical processes frequently entail significant time consumption, elevated costs, and substantial failure rates. AI-driven technologies like machine learning (ML) and deep learning (DL) have made drug discovery faster, improved formulation development, and made clinical trial results better. AI is also critical for pharmacovigilance, precision medicine, and managing the supply chain. Even though it has the potential to change things, there are still problems like data privacy, ethical issues, regulatory uncertainty, and a lack of interpretability. This review examines the applications, benefits, constraints, and future potential of AI in the pharmaceutical sector, offering a thorough overview for researchers and professionals.","url":"https://doi.org/10.5281/zenodo.19884161","authors":["Aby Augustine*1, Sruthi E. J.1"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19884161","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19646385","name":"Comparison of Machine Learning Approaches for Optimizing HIV Medication Outcomes","source":"datacite","abstract":"This study explores how machine learning can support better treatment decisions for people living with HIV. Using a large clinical dataset of over 283,000 patient records from treatment facilities in eastern Nigeria, four supervised learning models were compared, Random Forest, Support Vector Machine, Naïve Bayes, and Logistic Regression, to determine which best predicts medication improvement outcomes. Rather than focusing on accuracy alone, the study emphasises clinical reliability, reproducibility, and the importance of catching positive cases consistently. The findings show that Naïve Bayes, despite being a simpler model, offered the most balanced and trustworthy performance, making it the strongest candidate for real-world clinical decision support. The study also raises important questions about overfitting and data leakage in healthcare machine learning, and highlights why model validation matters as much as model performance. This work contributes to the growing evidence that thoughtful model selection and rigorous evaluation are essential for translating machine learning into safe, effective HIV care tools.","url":"https://doi.org/10.5281/zenodo.19646385","authors":["Okwuba, Praise"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19646385","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19646386","name":"Comparison of Machine Learning Approaches for Optimizing HIV Medication Outcomes","source":"datacite","abstract":"This study explores how machine learning can support better treatment decisions for people living with HIV. Using a large clinical dataset of over 283,000 patient records from treatment facilities in eastern Nigeria, four supervised learning models were compared, Random Forest, Support Vector Machine, Naïve Bayes, and Logistic Regression, to determine which best predicts medication improvement outcomes. Rather than focusing on accuracy alone, the study emphasises clinical reliability, reproducibility, and the importance of catching positive cases consistently. The findings show that Naïve Bayes, despite being a simpler model, offered the most balanced and trustworthy performance, making it the strongest candidate for real-world clinical decision support. The study also raises important questions about overfitting and data leakage in healthcare machine learning, and highlights why model validation matters as much as model performance. This work contributes to the growing evidence that thoughtful model selection and rigorous evaluation are essential for translating machine learning into safe, effective HIV care tools.","url":"https://doi.org/10.5281/zenodo.19646386","authors":["Okwuba, Praise"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19646386","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22062863","name":"AI Companion Mortality Database: Documented Deaths Associated with Conversational AI Systems (2023–2026)","source":"datacite","abstract":"Superseded by version 3.5.3 (10.5281/zenodo.22063180). In this version, the derived files platform-analysis.csv and timeline.json were generated from v3.5.0 and do not reflect v3.5.2 changes (ChatGPT third-party fatalities 15 → 16; Margaux Whittemore outcome and legal status; Kim Seoul case name). mortality-data.json is correct. Use 3.5.3 or later. A public, source-verified register of deaths in which AI chatbot interaction was alleged as a contributing factor, March 2023 through August 2026. Version 3.5.2 documents 35 fatalities across 24 incidents (occurrences of harm) on 8 tracked platforms: 18 AI users who died and 17 third-party victims killed by AI users. Every incident is supported by court filings, official government statements, or multiple independent news sources; the database makes no independent claim of causation. Scope. This database documents deaths associated with conversational AI systems — chatbots and LLM-based assistants with which a person interacted through natural language. It does not cover autonomous vehicles, clinical or diagnostic machine learning, industrial or robotic automation, autonomous or AI-assisted weapons, content-recommendation systems, or any other application of artificial intelligence. Those are distinct phenomena with distinct evidentiary standards. \"Companion\" in the database's name reflects its origin in companion-chatbot cases; the scope has since expanded to general-purpose assistants, and the name is retained for continuity of citation. The canonical record is mortality-data.json (incident records with sources, legal status, mechanism classification, and key factors; platform records; regulatory responses; derived statistics). Derived exports platform-analysis.csv and timeline.json are regenerated from it. methodology.md defines the incident definition, the three causal pathways (relational, cognitive, instrumental), the adjudicated-status rule for describing killings, the rule for counting third-party victims, and the verification tiers; verification-standards.md details the evidence tiers and sub-labels. Compiled and maintained by one independent researcher with AI assistance (disclosed in the record and on the site; the assisting system's developer is among the platforms tracked). Live site: aimortality.org. Source and history: github.com/aimortality/ai-companion-mortality-database. Correspondence: contact@aimortality.org.","url":"https://doi.org/10.5281/zenodo.22062863","authors":["Karman, Hunter"],"tags":["AI safety","conversational AI","chatbots","mortality","suicide","youth","incident database","AI harms"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22062863","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22063180","name":"AI Companion Mortality Database: Documented Deaths Associated with Conversational AI Systems (2023–2026)","source":"datacite","abstract":"A public, source-verified register of deaths in which AI chatbot interaction was alleged as a contributing factor, March 2023 through August 2026. Version 3.5.3 documents 35 fatalities across 24 incidents (occurrences of harm) on 8 tracked platforms: 18 AI users who died and 17 third-party victims killed by AI users. Every incident is supported by court filings, official government statements, or multiple independent news sources; the database makes no independent claim of causation. Scope. This database documents deaths associated with conversational AI systems — chatbots and LLM-based assistants with which a person interacted through natural language. It does not cover autonomous vehicles, clinical or diagnostic machine learning, industrial or robotic automation, autonomous or AI-assisted weapons, content-recommendation systems, or any other application of artificial intelligence. Those are distinct phenomena with distinct evidentiary standards. \"Companion\" in the database's name reflects its origin in companion-chatbot cases; the scope has since expanded to general-purpose assistants, and the name is retained for continuity of citation. The canonical record is mortality-data.json (incident records with sources, legal status, mechanism classification, and key factors; platform records; regulatory responses; derived statistics). Derived exports platform-analysis.csv and timeline.json are regenerated from it. methodology.md defines the incident definition, the three causal pathways (relational, cognitive, instrumental), the adjudicated-status rule for describing killings, the rule for counting third-party victims, and the verification tiers; verification-standards.md details the evidence tiers and sub-labels. Compiled and maintained by one independent researcher with AI assistance (disclosed in the record and on the site; the assisting system's developer is among the platforms tracked). Live site: aimortality.org. Source and history: github.com/aimortality/ai-companion-mortality-database. Correspondence: contact@aimortality.org.","url":"https://doi.org/10.5281/zenodo.22063180","authors":["Karman, Hunter"],"tags":["AI safety","conversational AI","chatbots","mortality","suicide","youth","incident database","AI harms"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22063180","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21212727","name":"TLMM v7.3: Adaptive Intervention Framework for Causal Decision Support in the Extended Amyloid–Blood Flow (ABF) Causal Cascade","source":"datacite","abstract":"Topological Latent Manifold Model (TLMM) v7.3 extends the validated causal-inference framework of TLMM v7.2 by introducing an Adaptive Decision Layer for personalized, counterfactually evaluated, and safety-constrained adaptive intervention while preserving the invariant Amyloid–Blood Flow (ABF) causal cascade. This release introduces: Adaptive Decision Layer for personalized intervention planning using constrained reinforcement learning (RL) and model predictive control (MPC) Counterfactual Policy Evaluation with individualized treatment effects (ITE), off-policy evaluation (IPS/SNIPS/Doubly Robust), and expected net benefit (ENB) ranking Expanded falsifiability framework (C1–C16) with a new Adaptive Intervention Validity domain High-resolution mechanistic causal network (N1–N16) with five latent confounders (U1–U5) and four hidden mediators (H1–H4) Safety and Human-in-the-Loop framework integrating explainability, clinical guardrails, clinician oversight, and continuous monitoring Adaptive intervention workflow, long-term outcome tracking, deployment readiness assessment, and clinical translational roadmap Machine-checkable graph specification (39 directed observed-node edges) with a companion graph validation script ensuring consistency between the manuscript, figures, and implementation The archive contains: Full manuscript (52 pages) Twenty publication-quality figures Companion Python graph validator README documentation All quantitative values, cohort sizes, validation metrics, and performance results are illustrative synthetic demonstrations intended to specify an evaluation and adaptive-intervention protocol. They do not represent clinical performance estimates from real patient data and do not constitute medical advice or clinical recommendations.","url":"https://doi.org/10.5281/zenodo.21212727","authors":["Okino, Koji"],"tags":["Topological Latent Manifold Model","TLMM","Amyloid–Blood Flow","ABF","Alzheimer's Disease","Causal AI","Causal Inference","Adaptive Intervention"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21212727","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21212726","name":"TLMM v7.3: Adaptive Intervention Framework for Causal Decision Support in the Extended Amyloid–Blood Flow (ABF) Causal Cascade","source":"datacite","abstract":"Topological Latent Manifold Model (TLMM) v7.3 extends the validated causal-inference framework of TLMM v7.2 by introducing an Adaptive Decision Layer for personalized, counterfactually evaluated, and safety-constrained adaptive intervention while preserving the invariant Amyloid–Blood Flow (ABF) causal cascade. This release introduces: Adaptive Decision Layer for personalized intervention planning using constrained reinforcement learning (RL) and model predictive control (MPC) Counterfactual Policy Evaluation with individualized treatment effects (ITE), off-policy evaluation (IPS/SNIPS/Doubly Robust), and expected net benefit (ENB) ranking Expanded falsifiability framework (C1–C16) with a new Adaptive Intervention Validity domain High-resolution mechanistic causal network (N1–N16) with five latent confounders (U1–U5) and four hidden mediators (H1–H4) Safety and Human-in-the-Loop framework integrating explainability, clinical guardrails, clinician oversight, and continuous monitoring Adaptive intervention workflow, long-term outcome tracking, deployment readiness assessment, and clinical translational roadmap Machine-checkable graph specification (39 directed observed-node edges) with a companion graph validation script ensuring consistency between the manuscript, figures, and implementation The archive contains: Full manuscript (52 pages) Twenty publication-quality figures Companion Python graph validator README documentation All quantitative values, cohort sizes, validation metrics, and performance results are illustrative synthetic demonstrations intended to specify an evaluation and adaptive-intervention protocol. They do not represent clinical performance estimates from real patient data and do not constitute medical advice or clinical recommendations.","url":"https://doi.org/10.5281/zenodo.21212726","authors":["Okino, Koji"],"tags":["Topological Latent Manifold Model","TLMM","Amyloid–Blood Flow","ABF","Alzheimer's Disease","Causal AI","Causal Inference","Adaptive Intervention"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21212726","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21280386","name":"Topological Latent Manifold Model (TLMM) v7.4: A Mechanistic, Falsifiable Framework for Closed-Loop Sequential Adaptive Intervention and Large-Scale Outcome Evaluation","source":"datacite","abstract":"Topological Latent Manifold Model (TLMM) v7.4 is a mechanistically grounded methodological framework for closed-loop sequential adaptive intervention and large-scale outcome evaluation, developed in the context of Alzheimer's disease. This release introduces an explicit separation between mechanistic falsifiable claims (M1–M20) and validation checks (V1–V20), providing complete traceability between causal hypotheses and their corresponding validation procedures. The framework is built upon an extended Appearance–Behavior Framework (ABF) causal network (N1–N16) and integrates causal inference, uncertainty quantification, sequential policy optimization, counterfactual evaluation, continuous learning, deployment readiness assessment, and precision structural medicine. The repository contains: TLMM v7.4 complete preprint (37 pages) Twenty-four publication-quality figures Supplementary Material (Figure–Claim Mapping, Validation Catalogue, Mechanistic Catalogue, Numeric QC) Reproducible Python demonstration script README and documentation The included demonstration code reproduces the internal organizational structure of TLMM v7.4, including: Mechanistic claim catalogue (M1–M20) Validation check catalogue (V1–V20) Figure–Claim Mapping Unified numeric quality-control registry Illustrative sequential adaptive intervention workflow Illustrative quantitative values are included solely to demonstrate the intended analytical workflow and should not be interpreted as completed clinical evidence. Prospective multi-center validation is planned for future work. This work is intended as a transparent, falsifiable, and extensible methodological foundation for future research in mechanistic machine learning, causal inference, sequential adaptive intervention, and precision structural medicine. License: Creative Commons Attribution 4.0 International (CC BY 4.0).","url":"https://doi.org/10.5281/zenodo.21280386","authors":["Okino, Koji"],"tags":["Topological Latent Manifold Model","TLMM","Appearance-Behavior Framework","ABF","Mechanistic Machine Learning","Causal Inference","Falsifiability","Validation Framework"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21280386","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21280385","name":"Topological Latent Manifold Model (TLMM) v7.4: A Mechanistic, Falsifiable Framework for Closed-Loop Sequential Adaptive Intervention and Large-Scale Outcome Evaluation","source":"datacite","abstract":"Topological Latent Manifold Model (TLMM) v7.4 is a mechanistically grounded methodological framework for closed-loop sequential adaptive intervention and large-scale outcome evaluation, developed in the context of Alzheimer's disease. This release introduces an explicit separation between mechanistic falsifiable claims (M1–M20) and validation checks (V1–V20), providing complete traceability between causal hypotheses and their corresponding validation procedures. The framework is built upon an extended Appearance–Behavior Framework (ABF) causal network (N1–N16) and integrates causal inference, uncertainty quantification, sequential policy optimization, counterfactual evaluation, continuous learning, deployment readiness assessment, and precision structural medicine. The repository contains: TLMM v7.4 complete preprint (37 pages) Twenty-four publication-quality figures Supplementary Material (Figure–Claim Mapping, Validation Catalogue, Mechanistic Catalogue, Numeric QC) Reproducible Python demonstration script README and documentation The included demonstration code reproduces the internal organizational structure of TLMM v7.4, including: Mechanistic claim catalogue (M1–M20) Validation check catalogue (V1–V20) Figure–Claim Mapping Unified numeric quality-control registry Illustrative sequential adaptive intervention workflow Illustrative quantitative values are included solely to demonstrate the intended analytical workflow and should not be interpreted as completed clinical evidence. Prospective multi-center validation is planned for future work. This work is intended as a transparent, falsifiable, and extensible methodological foundation for future research in mechanistic machine learning, causal inference, sequential adaptive intervention, and precision structural medicine. License: Creative Commons Attribution 4.0 International (CC BY 4.0).","url":"https://doi.org/10.5281/zenodo.21280385","authors":["Okino, Koji"],"tags":["Topological Latent Manifold Model","TLMM","Appearance-Behavior Framework","ABF","Mechanistic Machine Learning","Causal Inference","Falsifiability","Validation Framework"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21280385","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21731342","name":"An Integrated BioMEMS and Machine Learning Framework for Continuous Multi-Modal Physiological Monitoring with a Focus on Pediatric Applications","source":"datacite","abstract":"This master's thesis develops and evaluates an integrated framework that unites biomedical microelectromechanical systems (BioMEMS) with machine learning (ML) for continuous, multi-modal physiological monitoring, with a focus on pediatric applications. It addresses a persistent translation gap: BioMEMS hardware and clinical ML are usually advanced in isolation, and children—physiologically distinct from adults and underrepresented in clinical data—are especially underserved. The proposed system pairs a flexible, skin-mounted (epidermal) BioMEMS sensor patch with a hybrid machine learning pipeline. The patch captures five signal streams: electrocardiogram (ECG), photoplethysmogram (PPG), skin temperature, electrodermal activity (EDA), and triaxial acceleration. Signals are pre-processed at the edge, transmitted over a low-power wireless link, and analyzed by a stacked ensemble combining a multilayer perceptron, a random forest, and a gradient boosting classifier over jointly engineered, per-channel features. Performance is assessed on a synthetic multi-modal dataset built with realistic subject-level variability, motion artifacts, and skin-tone conditioning. On a held-out, subject-independent test set, the framework reaches an anomaly-detection AUROC of 0.865, F1 score of 0.710, sensitivity of 0.813, and specificity of 0.778. Critically, subgroup analysis reveals meaningful performance differences across age brackets, motion states, and skin-tone classes that aggregate metrics conceal—an equity finding central to safe pediatric deployment. The thesis also examines regulatory pathways, health-equity implications, and ethics for monitoring vulnerable populations, and contributes a reproducible reference architecture and a pediatric-aware evaluation protocol for future wearable-health research.","url":"https://doi.org/10.5281/zenodo.21731342","authors":["James-Knowles, Destinie A."],"tags":["Biomedical Engineering","Machine Learning","Health Equity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21731342","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21731343","name":"An Integrated BioMEMS and Machine Learning Framework for Continuous Multi-Modal Physiological Monitoring with a Focus on Pediatric Applications","source":"datacite","abstract":"This master's thesis develops and evaluates an integrated framework that unites biomedical microelectromechanical systems (BioMEMS) with machine learning (ML) for continuous, multi-modal physiological monitoring, with a focus on pediatric applications. It addresses a persistent translation gap: BioMEMS hardware and clinical ML are usually advanced in isolation, and children—physiologically distinct from adults and underrepresented in clinical data—are especially underserved. The proposed system pairs a flexible, skin-mounted (epidermal) BioMEMS sensor patch with a hybrid machine learning pipeline. The patch captures five signal streams: electrocardiogram (ECG), photoplethysmogram (PPG), skin temperature, electrodermal activity (EDA), and triaxial acceleration. Signals are pre-processed at the edge, transmitted over a low-power wireless link, and analyzed by a stacked ensemble combining a multilayer perceptron, a random forest, and a gradient boosting classifier over jointly engineered, per-channel features. Performance is assessed on a synthetic multi-modal dataset built with realistic subject-level variability, motion artifacts, and skin-tone conditioning. On a held-out, subject-independent test set, the framework reaches an anomaly-detection AUROC of 0.865, F1 score of 0.710, sensitivity of 0.813, and specificity of 0.778. Critically, subgroup analysis reveals meaningful performance differences across age brackets, motion states, and skin-tone classes that aggregate metrics conceal—an equity finding central to safe pediatric deployment. The thesis also examines regulatory pathways, health-equity implications, and ethics for monitoring vulnerable populations, and contributes a reproducible reference architecture and a pediatric-aware evaluation protocol for future wearable-health research.","url":"https://doi.org/10.5281/zenodo.21731343","authors":["James-Knowles, Destinie A."],"tags":["Biomedical Engineering","Machine Learning","Health Equity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21731343","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.11218653","name":"Intelligent Decision Support System for Healthcare","source":"datacite","abstract":"The advanced technologies of the Internet of Things (IoT) provide modern environments support for medical applications and produce a vast amount of various types of health care data, such as sensors data, clinical data, omic data, and later transfer these data to Machine learning (ML) component for data extraction, its classification, and mining, and use the filtered data for prediction of the diseases. Further, Machine learning algorithms are facilitating mathematical comparison of collected datasets for decision making systems which can identify the current trend and forecasts future issues through learning. ML-based approaches can evaluate the conditions according to the data collection. Learning datasets play an important role in predicting accurately the existing and new problems future trends. Performance of learning models depends on the quality of data collection, therefore any distorted data of type grimy data, noisy data, messy data, and incomplete information leads to erroneous detection, estimation, and prediction. Further, the experimental results display the effectiveness of the proposed approach as an efficient adoption of machine learning algorithms into IoT applications in contrary to other models.","url":"https://doi.org/10.5281/zenodo.11218653","authors":["Anup Patnaik"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.11218653","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.11218654","name":"Intelligent Decision Support System for Healthcare","source":"datacite","abstract":"The advanced technologies of the Internet of Things (IoT) provide modern environments support for medical applications and produce a vast amount of various types of health care data, such as sensors data, clinical data, omic data, and later transfer these data to Machine learning (ML) component for data extraction, its classification, and mining, and use the filtered data for prediction of the diseases. Further, Machine learning algorithms are facilitating mathematical comparison of collected datasets for decision making systems which can identify the current trend and forecasts future issues through learning. ML-based approaches can evaluate the conditions according to the data collection. Learning datasets play an important role in predicting accurately the existing and new problems future trends. Performance of learning models depends on the quality of data collection, therefore any distorted data of type grimy data, noisy data, messy data, and incomplete information leads to erroneous detection, estimation, and prediction. Further, the experimental results display the effectiveness of the proposed approach as an efficient adoption of machine learning algorithms into IoT applications in contrary to other models.","url":"https://doi.org/10.5281/zenodo.11218654","authors":["Anup Patnaik"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.11218654","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21370903","name":"AN ADVANCED DEEP LEARNING AND COMPUTER VISION BASED SYSTEM FOR LIVER TUMOR DETECTION AND LOCALIZATION WITH MACHINE LEARNING","source":"datacite","abstract":"The proposed system focuses on developing an intelligent medical imaging and predictive platform for liver tumor detection localization and disease analysis using advanced computational techniques. The framework integrates deep learning computer vision and machine learning within a unified web-based environment to enhance diagnostic efficiency. The system processes liver images through convolutional neural networks where tumor presence is identified and spatial localization is achieved for clinical interpretability. Within the evolving landscape of medical image analysis the system is conceptualized as an intelligent framework that enables automated detection while improving decision support accuracy. Further analysis involves CT scan-based evaluation where tumor size estimation is performed to understand severity and progression. The system also incorporates a machine learning module that predicts liver related diseases using patient clinical parameters thus enabling early-stage risk identification. Against the backdrop of increasing healthcare challenges the system introduces a data driven paradigm that transforms raw inputs into actionable insights. Additionally, a generative AI based doctor assistance module provides interactive guidance enhancing user engagement and accessibility. By integrating multiple computational layers the platform establishes a scalable and efficient solution contributing to modern healthcare systems and supporting improved clinical outcomes.","url":"https://doi.org/10.5281/zenodo.21370903","authors":["Ramesh Patil","Tuba Shazmeen"],"tags":["Liver Tumor Detection","Deep Learning","Computer Vision","Machine Learning","CT","Scan Analysis","Disease Prediction","Generative AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21370903","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21370904","name":"AN ADVANCED DEEP LEARNING AND COMPUTER VISION BASED SYSTEM FOR LIVER TUMOR DETECTION AND LOCALIZATION WITH MACHINE LEARNING","source":"datacite","abstract":"The proposed system focuses on developing an intelligent medical imaging and predictive platform for liver tumor detection localization and disease analysis using advanced computational techniques. The framework integrates deep learning computer vision and machine learning within a unified web-based environment to enhance diagnostic efficiency. The system processes liver images through convolutional neural networks where tumor presence is identified and spatial localization is achieved for clinical interpretability. Within the evolving landscape of medical image analysis the system is conceptualized as an intelligent framework that enables automated detection while improving decision support accuracy. Further analysis involves CT scan-based evaluation where tumor size estimation is performed to understand severity and progression. The system also incorporates a machine learning module that predicts liver related diseases using patient clinical parameters thus enabling early-stage risk identification. Against the backdrop of increasing healthcare challenges the system introduces a data driven paradigm that transforms raw inputs into actionable insights. Additionally, a generative AI based doctor assistance module provides interactive guidance enhancing user engagement and accessibility. By integrating multiple computational layers the platform establishes a scalable and efficient solution contributing to modern healthcare systems and supporting improved clinical outcomes.","url":"https://doi.org/10.5281/zenodo.21370904","authors":["Ramesh Patil","Tuba Shazmeen"],"tags":["Liver Tumor Detection","Deep Learning","Computer Vision","Machine Learning","CT","Scan Analysis","Disease Prediction","Generative AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21370904","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22062452","name":"Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials","source":"datacite","abstract":"Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use Agreement-restricted derivatives are included.","url":"https://doi.org/10.5281/zenodo.22062452","authors":["Yilmaz, Cagdas","Akdagli, Ali"],"tags":["acute kidney injury","KDIGO stage 2-3","critical care","machine learning","hospital-disjoint internal-external validation","clinical prediction","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22062452","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21824201","name":"Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials","source":"datacite","abstract":"Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use Agreement-restricted derivatives are included.","url":"https://doi.org/10.5281/zenodo.21824201","authors":["Yilmaz, Cagdas","Akdagli, Ali"],"tags":["acute kidney injury","KDIGO stage 2-3","critical care","machine learning","hospital-disjoint internal-external validation","clinical prediction","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21824201","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22062413","name":"Machine learning for early prediction of subsequent stage 2-3 acute kidney injury in critically ill adults: hospital-disjoint internal-external validation — reproducibility materials","source":"datacite","abstract":"Source-only code, parameterized SQL, analysis specifications, disclosure-safe aggregate outputs, final publication figures, and documentation aligned with the A25 submission-locked manuscript. Scientific results are unchanged. No patient-level data or Data Use Agreement-restricted derivatives are included.","url":"https://doi.org/10.5281/zenodo.22062413","authors":["Yilmaz, Cagdas","Akdagli, Ali"],"tags":["acute kidney injury","KDIGO stage 2-3","critical care","machine learning","hospital-disjoint internal-external validation","clinical prediction","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22062413","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20348835","name":"PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF TEMPOROMANDIBULAR JOINT DISORDERS (TMJD)","source":"datacite","abstract":"Temporomandibular joint disorders (TMJD) represent a group of complex musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Accurate diagnosis of TMJD remains challenging due to its multifactorial etiology, variable clinical presentation, and limitations of conventional diagnostic approaches. In recent years, artificial intelligence (AI) has emerged as a promising tool in medical diagnostics, including dentistry. This paper aims to explore the prospects, current applications, and future directions of AI in diagnosing TMJD. The study reviews machine learning (ML), deep learning (DL), and computer vision techniques applied to imaging modalities such as MRI, CBCT, and clinical data analysis. AI-based systems demonstrate high accuracy in detecting structural abnormalities, classifying TMJ disorders, and predicting disease progression. However, challenges such as data quality, ethical concerns, and integration into clinical workflows remain significant. The implementation of AI in TMJD diagnostics has the potential to enhance early detection, improve diagnostic accuracy, and support personalized treatment planning.","url":"https://doi.org/10.5281/zenodo.20348835","authors":["Axmedova Malika Qilichovna"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20348835","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20348836","name":"PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF TEMPOROMANDIBULAR JOINT DISORDERS (TMJD)","source":"datacite","abstract":"Temporomandibular joint disorders (TMJD) represent a group of complex musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Accurate diagnosis of TMJD remains challenging due to its multifactorial etiology, variable clinical presentation, and limitations of conventional diagnostic approaches. In recent years, artificial intelligence (AI) has emerged as a promising tool in medical diagnostics, including dentistry. This paper aims to explore the prospects, current applications, and future directions of AI in diagnosing TMJD. The study reviews machine learning (ML), deep learning (DL), and computer vision techniques applied to imaging modalities such as MRI, CBCT, and clinical data analysis. AI-based systems demonstrate high accuracy in detecting structural abnormalities, classifying TMJ disorders, and predicting disease progression. However, challenges such as data quality, ethical concerns, and integration into clinical workflows remain significant. The implementation of AI in TMJD diagnostics has the potential to enhance early detection, improve diagnostic accuracy, and support personalized treatment planning.","url":"https://doi.org/10.5281/zenodo.20348836","authors":["Axmedova Malika Qilichovna"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20348836","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21580187","name":"Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device","source":"datacite","abstract":"Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suffer from diabetes. This number is expected to rise to 578 million by 2030 and 700 million by 2045. Machine learning is a type of machine learning that is very helpful in various fields, including healthcare. In classification cases, ensemble methods classify by combining decisions from several other models, one way being through majority voting. Ensemble methods often produce more accurate classification or prediction results. Several ensemble methods include random forest, extra trees, rotation forest, and double random forest. The data used in this study is part of research on the development and clinical testing of a prototype non-invasive blood glucose monitoring device by the non-invasive biomarking team at IPB. The data includes both invasive and non-invasive blood glucose measurements collected in 2019. This study compares the performance of the random forest, extra trees, rotation forest, and double random forest models on blood glucose level data obtained from non-invasive devices. The research results show that the Rotation Forest algorithm is the best model, with the highest average accuracy compared to the other three algorithms, achieving an accuracy level of 0.7142857 (71.42%).","url":"https://doi.org/10.5281/zenodo.21580187","authors":["Nurrizqi, Alfi Indah","Erfiani","Soleh, Agus Mohamad"],"tags":["Blood Glucose; Ensemble Learning; Non-Invasive Device; Rotation Forest"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.21580187","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21580188","name":"Comparison of Ensemble Method Performance in Classifying Blood Sugar Levels Output from Non-Invasive Device","source":"datacite","abstract":"Diabetes Mellitus (DM) is a persistent health issue in many countries and is a leading cause of heart disease, kidney failure, and blindness The International Diabetes Federation (IDF) estimated in 2019 that at least 463 million people worldwide aged 20-79 suffer from diabetes. This number is expected to rise to 578 million by 2030 and 700 million by 2045. Machine learning is a type of machine learning that is very helpful in various fields, including healthcare. In classification cases, ensemble methods classify by combining decisions from several other models, one way being through majority voting. Ensemble methods often produce more accurate classification or prediction results. Several ensemble methods include random forest, extra trees, rotation forest, and double random forest. The data used in this study is part of research on the development and clinical testing of a prototype non-invasive blood glucose monitoring device by the non-invasive biomarking team at IPB. The data includes both invasive and non-invasive blood glucose measurements collected in 2019. This study compares the performance of the random forest, extra trees, rotation forest, and double random forest models on blood glucose level data obtained from non-invasive devices. The research results show that the Rotation Forest algorithm is the best model, with the highest average accuracy compared to the other three algorithms, achieving an accuracy level of 0.7142857 (71.42%).","url":"https://doi.org/10.5281/zenodo.21580188","authors":["Nurrizqi, Alfi Indah","Erfiani","Soleh, Agus Mohamad"],"tags":["Blood Glucose; Ensemble Learning; Non-Invasive Device; Rotation Forest"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.21580188","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22059975","name":"ATLAS-A: ECG metadata controls","source":"datacite","abstract":"This archive contains version 1.0.0 of ATLAS-A: ECG metadata controls, the reproducibility package for a study evaluating whether demographic and anthropometric metadata improve multi-label 12-lead ECG classification beyond ECG-only models. The archive includes analysis code, locked analysis plans, audit records, deterministic verification tests, aggregate numerical results, and sanitized run-level metrics for PTB-XL v1.0.3 and a patient-disjoint CODE-15% inter-dataset replication with retraining. The CODE-15% analysis is not presented as strict external validation of a fixed PTB-XL model because only three target conduction disorders could be harmonized across datasets. Raw ECG recordings, patient-level indices, model checkpoints, prediction arrays, execution logs, and the manuscript are intentionally excluded. The underlying datasets must be obtained from their official repositories. Complete environment, data-preparation, execution, and verification instructions are provided in the accompanying documentation.","url":"https://doi.org/10.5281/zenodo.22059975","authors":["Segnane, Ezyn","Ommane, Younes","Peluffo-Ordóñez, Diego H.","Mouadili, Maryam","El Waled, Khalil","Cheikh Tourad, Mohamedou","Beddi, Mohamed Abdallahi"],"tags":["Reproducible research","Patient-disjoint evaluation","CODE-15%","PTB-XL","Conduction disorders","Anthropometric metadata","Demographic metadata","Multi-label classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22059975","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22059974","name":"ATLAS-A: ECG metadata controls","source":"datacite","abstract":"This archive contains version 1.0.0 of ATLAS-A: ECG metadata controls, the reproducibility package for a study evaluating whether demographic and anthropometric metadata improve multi-label 12-lead ECG classification beyond ECG-only models. The archive includes analysis code, locked analysis plans, audit records, deterministic verification tests, aggregate numerical results, and sanitized run-level metrics for PTB-XL v1.0.3 and a patient-disjoint CODE-15% inter-dataset replication with retraining. The CODE-15% analysis is not presented as strict external validation of a fixed PTB-XL model because only three target conduction disorders could be harmonized across datasets. Raw ECG recordings, patient-level indices, model checkpoints, prediction arrays, execution logs, and the manuscript are intentionally excluded. The underlying datasets must be obtained from their official repositories. Complete environment, data-preparation, execution, and verification instructions are provided in the accompanying documentation.","url":"https://doi.org/10.5281/zenodo.22059974","authors":["Segnane, Ezyn","Ommane, Younes","Peluffo-Ordóñez, Diego H.","Mouadili, Maryam","El Waled, Khalil","Cheikh Tourad, Mohamedou","Beddi, Mohamed Abdallahi"],"tags":["Reproducible research","Patient-disjoint evaluation","CODE-15%","PTB-XL","Conduction disorders","Anthropometric metadata","Demographic metadata","Multi-label classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22059974","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20529100","name":"Artificial Intelligence and Machine Learning in Pharmacovigilance: Adverse Drug Reaction Detection and Signal Management under ICH-GCP Compliance","source":"datacite","abstract":"Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and other drug-related problems. The exponential growth in global drug utilisation combined with the proliferation of electronic health records (EHRs), social media, and spontaneous reporting systems has generated unprecedented volumes of safety data that strain traditional manual review workflows. Regulatory frameworks such as the International Council for Harmonisation – Good Clinical Practice (ICH-GCP) E2A–E6 series, the European Medicines Agency (EMA) pharmacovigilance legislation, and the FDA Sentinel System mandate rigorous, timely, and reproducible signal management processes. This article systematically reviews the application of artificial intelligence (AI) and machine learning (ML) methodologies – including natural language processing (NLP), deep learning, graph neural networks, and Bayesian statistical methods – in ADR detection, signal management, and benefit–risk assessment, with emphasis on regulatory compliance under ICH-GCP. Methods: A structured literature search was conducted across MEDLINE, EMBASE, Cochrane Library, and WHO-VigiBase publication catalogues for the period 2010–2024. Seventy-eight peer-reviewed studies, regulatory guidance documents, and technical whitepapers meeting predefined inclusion criteria were analysed. Methodologies were categorised by AI/ML technique, data source, regulatory context, and performance metrics. AI/ML systems demonstrate superior performance compared with classical disproportionality analyses in ADR signal detection, with AUROC values ranging from 0.82 to 0.97 across validated datasets. NLP-based pipelines applied to EHR free-text achieved F1 scores of 0.78–0.91 for ADR entity recognition. Large language models (LLMs) show promise for automated narrative medical case summarisation and MedDRA coding. Implementation challenges include algorithmic transparency, data heterogeneity, and harmonisation with ICH-E2B(R3) electronic reporting standards. AI and ML are transforming pharmacovigilance by enabling earlier, more sensitive, and scalable ADR signal detection. Successful integration into GCP-compliant workflows requires regulatory-grade model validation, auditability, and explainability frameworks. Prospective collaboration among industry, regulators, and academia is essential to establish harmonised standards for AI-augmented pharmacovigilance.","url":"https://doi.org/10.5281/zenodo.20529100","authors":["Sanjay R*, Hemaprasath M, Vedhanayagi Gunasekaran, Madhavan P, Hariharasudhan B, Koushik Kumaran E P, SarathKumar R"],"tags":["pharmacovigilance; adverse drug reaction; machine learning; natural language processing; signal detection; ICH-GCP; drug safety; deep learning; MedDRA; benefit-risk assessment"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20529100","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20529101","name":"Artificial Intelligence and Machine Learning in Pharmacovigilance: Adverse Drug Reaction Detection and Signal Management under ICH-GCP Compliance","source":"datacite","abstract":"Pharmacovigilance (PV) is the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs) and other drug-related problems. The exponential growth in global drug utilisation combined with the proliferation of electronic health records (EHRs), social media, and spontaneous reporting systems has generated unprecedented volumes of safety data that strain traditional manual review workflows. Regulatory frameworks such as the International Council for Harmonisation – Good Clinical Practice (ICH-GCP) E2A–E6 series, the European Medicines Agency (EMA) pharmacovigilance legislation, and the FDA Sentinel System mandate rigorous, timely, and reproducible signal management processes. This article systematically reviews the application of artificial intelligence (AI) and machine learning (ML) methodologies – including natural language processing (NLP), deep learning, graph neural networks, and Bayesian statistical methods – in ADR detection, signal management, and benefit–risk assessment, with emphasis on regulatory compliance under ICH-GCP. Methods: A structured literature search was conducted across MEDLINE, EMBASE, Cochrane Library, and WHO-VigiBase publication catalogues for the period 2010–2024. Seventy-eight peer-reviewed studies, regulatory guidance documents, and technical whitepapers meeting predefined inclusion criteria were analysed. Methodologies were categorised by AI/ML technique, data source, regulatory context, and performance metrics. AI/ML systems demonstrate superior performance compared with classical disproportionality analyses in ADR signal detection, with AUROC values ranging from 0.82 to 0.97 across validated datasets. NLP-based pipelines applied to EHR free-text achieved F1 scores of 0.78–0.91 for ADR entity recognition. Large language models (LLMs) show promise for automated narrative medical case summarisation and MedDRA coding. Implementation challenges include algorithmic transparency, data heterogeneity, and harmonisation with ICH-E2B(R3) electronic reporting standards. AI and ML are transforming pharmacovigilance by enabling earlier, more sensitive, and scalable ADR signal detection. Successful integration into GCP-compliant workflows requires regulatory-grade model validation, auditability, and explainability frameworks. Prospective collaboration among industry, regulators, and academia is essential to establish harmonised standards for AI-augmented pharmacovigilance.","url":"https://doi.org/10.5281/zenodo.20529101","authors":["Sanjay R*, Hemaprasath M, Vedhanayagi Gunasekaran, Madhavan P, Hariharasudhan B, Koushik Kumaran E P, SarathKumar R"],"tags":["pharmacovigilance; adverse drug reaction; machine learning; natural language processing; signal detection; ICH-GCP; drug safety; deep learning; MedDRA; benefit-risk assessment"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20529101","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.6084/m9.figshare.33314797","name":"Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study","source":"datacite","abstract":"The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes remains unclear. This multicenter retrospective cohort study included pregnant women with CHTN from four tertiary hospitals in China. Baseline characteristics were compared using the Kruskal–Wallis and Chi-squared tests with Bonferroni correction. Logistic regression (LR) identified risk factors for adverse outcomes. Four machine learning (ML) algorithms were validated, nomograms were developed for model visualization, and SHapley Additive exPlanations (SHAP) determined variable importance. Among 500 women, 117 (23.4%) had normal geometry, 118 (23.6%) concentric remodeling, 88 (17.6%) eccentric hypertrophy, and 177 (35.4%) concentric hypertrophy. Adverse maternal and fetal outcomes occurred in 126 (25.2%) and 359 (71.8%) women, respectively. LVH was independently associated with adverse maternal outcomes (OR 2.16, 95% CI 1.13–4.13) and fetal outcomes (OR 2.42, 95% CI 1.31–4.45). Additional maternal predictors included NYHA class III–IV, pre-eclampsia, oligohydramnios, elevated alanine aminotransferase and lactate dehydrogenase, hypoalbuminemia, greater bleeding loss, and blood transfusion. Fetal predictors included increased posterior wall thickness, oligohydramnios, abnormal umbilical artery flow, elevated alanine aminotransferase and blood urea nitrogen, low total protein, and proteinuria. ML models showed AUCs of 0.70–0.87 for maternal and 0.80–0.88 for fetal outcomes; SHAP identified LVH as an important contributor in both models. Early-pregnancy LVH was associated with higher risks of adverse maternal and fetal outcomes in women with CHTN. These models may support individualized risk stratification but require prospective external validation before clinical implementation.","url":"https://doi.org/10.6084/m9.figshare.33314797","authors":["Bei Zhang","Guilong Pan","Wei Wang","Kai Zhang","Xiao Song","Wei Tian","Ran Chu","Shuyi Li","Hui An","Shuo Zhang"],"tags":["Medicine","Cell Biology","Physiology","Pharmacology","Biotechnology","Sociology","Immunology","Mathematical Sciences not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33314797","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.6084/m9.figshare.33314797.v1","name":"Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study","source":"datacite","abstract":"The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes remains unclear. This multicenter retrospective cohort study included pregnant women with CHTN from four tertiary hospitals in China. Baseline characteristics were compared using the Kruskal–Wallis and Chi-squared tests with Bonferroni correction. Logistic regression (LR) identified risk factors for adverse outcomes. Four machine learning (ML) algorithms were validated, nomograms were developed for model visualization, and SHapley Additive exPlanations (SHAP) determined variable importance. Among 500 women, 117 (23.4%) had normal geometry, 118 (23.6%) concentric remodeling, 88 (17.6%) eccentric hypertrophy, and 177 (35.4%) concentric hypertrophy. Adverse maternal and fetal outcomes occurred in 126 (25.2%) and 359 (71.8%) women, respectively. LVH was independently associated with adverse maternal outcomes (OR 2.16, 95% CI 1.13–4.13) and fetal outcomes (OR 2.42, 95% CI 1.31–4.45). Additional maternal predictors included NYHA class III–IV, pre-eclampsia, oligohydramnios, elevated alanine aminotransferase and lactate dehydrogenase, hypoalbuminemia, greater bleeding loss, and blood transfusion. Fetal predictors included increased posterior wall thickness, oligohydramnios, abnormal umbilical artery flow, elevated alanine aminotransferase and blood urea nitrogen, low total protein, and proteinuria. ML models showed AUCs of 0.70–0.87 for maternal and 0.80–0.88 for fetal outcomes; SHAP identified LVH as an important contributor in both models. Early-pregnancy LVH was associated with higher risks of adverse maternal and fetal outcomes in women with CHTN. These models may support individualized risk stratification but require prospective external validation before clinical implementation.","url":"https://doi.org/10.6084/m9.figshare.33314797.v1","authors":["Bei Zhang","Guilong Pan","Wei Wang","Kai Zhang","Xiao Song","Wei Tian","Ran Chu","Shuyi Li","Hui An","Shuo Zhang"],"tags":["Medicine","Cell Biology","Physiology","Pharmacology","Biotechnology","Sociology","Immunology","Mathematical Sciences not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33314797.v1","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20035153","name":"AI-Based Healthcare Monitoring System for Parkinson's Disease","source":"datacite","abstract":"This research report details the development of an intelligent monitoring system for Parkinson's Disease. By leveraging machine learning and AI algorithms, the system analyzes patient data to provide accurate diagnostic insights and continuous health tracking. The project focuses on improving the quality of life for patients through early detection and data-driven clinical support. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy","url":"https://doi.org/10.5281/zenodo.20035153","authors":["Alouss, Esraa"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/standards","Artificial Intelligence/classification","Machine Learning/classification","Deep learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20035153","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20035154","name":"AI-Based Healthcare Monitoring System for Parkinson's Disease","source":"datacite","abstract":"This research report details the development of an intelligent monitoring system for Parkinson's Disease. By leveraging machine learning and AI algorithms, the system analyzes patient data to provide accurate diagnostic insights and continuous health tracking. The project focuses on improving the quality of life for patients through early detection and data-driven clinical support. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy","url":"https://doi.org/10.5281/zenodo.20035154","authors":["Alouss, Esraa"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/standards","Artificial Intelligence/classification","Machine Learning/classification","Deep learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20035154","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20135280","name":"Artificial Intelligence in Healthcare and Human Development: A Scientific and Contemporary Research Perspective (2026)","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in healthcare and human development, reshaping diagnosis, treatment, preventive care, rehabilitation, mental wellness, and lifelong human capability enhancement. Recent advances in machine learning, deep learning, natural language processing, multimodal foundation models, and wearable intelligence have accelerated precision medicine, remote monitoring, electronic health records optimization, and personalized health interventions. This research paper critically examines the scientific evolution of AI-driven healthcare systems and their broader implications for human development, including physical well-being, cognitive growth, productivity, equity, and quality of life. Using a systematic narrative review methodology based on recent peer-reviewed studies (2024–2026), the paper synthesizes current evidence, challenges, ethical issues, and future directions. The findings indicate that AI significantly improves diagnostic accuracy, clinical efficiency, rural healthcare accessibility, predictive risk modeling, and human-centered well-being outcomes, while also raising concerns related to explainability, data privacy, algorithmic bias, and regulatory governance.","url":"https://doi.org/10.5281/zenodo.20135280","authors":["Prof. (Dr.) Deepak Shivlingrao Phulari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20135280","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20135281","name":"Artificial Intelligence in Healthcare and Human Development: A Scientific and Contemporary Research Perspective (2026)","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative force in healthcare and human development, reshaping diagnosis, treatment, preventive care, rehabilitation, mental wellness, and lifelong human capability enhancement. Recent advances in machine learning, deep learning, natural language processing, multimodal foundation models, and wearable intelligence have accelerated precision medicine, remote monitoring, electronic health records optimization, and personalized health interventions. This research paper critically examines the scientific evolution of AI-driven healthcare systems and their broader implications for human development, including physical well-being, cognitive growth, productivity, equity, and quality of life. Using a systematic narrative review methodology based on recent peer-reviewed studies (2024–2026), the paper synthesizes current evidence, challenges, ethical issues, and future directions. The findings indicate that AI significantly improves diagnostic accuracy, clinical efficiency, rural healthcare accessibility, predictive risk modeling, and human-centered well-being outcomes, while also raising concerns related to explainability, data privacy, algorithmic bias, and regulatory governance.","url":"https://doi.org/10.5281/zenodo.20135281","authors":["Prof. (Dr.) Deepak Shivlingrao Phulari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20135281","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.18491820","name":"ToxKAN-Dataset","source":"datacite","abstract":"Motivation: Toxicity assessment is essential for drug development and clinical safety. However, a prevalent phenomenon in computational toxicology is the trade-off between predictive performance and mechanistic interpretability, where conventional machine learning offers greater transparency but deep learning typically achieves higher accuracy. This limitation hinders the adoption of models in regulatory decision-making, where understanding the biological reason for toxicity is as vital as the prediction itself. Results: We present ToxKAN, an adverse outcome pathway (AOP) knowledge-guided Kolmogorov-Arnold framework that integrates chemical structures and gene expression profiles for mechanistic drug toxicity prediction. Extensive evaluations demonstrate that ToxKAN outperforms state-of-the-art methods in binary toxicity prediction and achieves competitive or superior, ranking among the highest reported accuracy on fine-grained multi-label pathological phenotype prediction. Critically, the model identifies biologically coherent hierarchical reasoning paths, prioritizes core toxicity genes in low-signal settings where conventional differential expression analysis fails, and learns latent representations that reflect mechanism-aware compound organization. These results indicate that mechanism-aware architecture design can simultaneously advance predictive performance and biological interpretability. Availability and implementation: The source code and processed datasets are publicly available at https://github.com/shuangquanZW/ToxKAN and Edit upload | Zenodo.","url":"https://doi.org/10.5281/zenodo.18491820","authors":["Ziqing, Zhang"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18491820","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21131553","name":"ToxKAN-Dataset","source":"datacite","abstract":"Motivation: Toxicity assessment is essential for drug development and clinical safety. However, a prevalent phenomenon in computational toxicology is the trade-off between predictive performance and mechanistic interpretability, where conventional machine learning offers greater transparency but deep learning typically achieves higher accuracy. This limitation hinders the adoption of models in regulatory decision-making, where understanding the biological reason for toxicity is as vital as the prediction itself. Results: We present ToxKAN, an adverse outcome pathway (AOP) knowledge-guided Kolmogorov-Arnold framework that integrates chemical structures and gene expression profiles for mechanistic drug toxicity prediction. Extensive evaluations demonstrate that ToxKAN outperforms state-of-the-art methods in binary toxicity prediction and achieves competitive or superior, ranking among the highest reported accuracy on fine-grained multi-label pathological phenotype prediction. Critically, the model identifies biologically coherent hierarchical reasoning paths, prioritizes core toxicity genes in low-signal settings where conventional differential expression analysis fails, and learns latent representations that reflect mechanism-aware compound organization. These results indicate that mechanism-aware architecture design can simultaneously advance predictive performance and biological interpretability. Availability and implementation: The source code and processed datasets are publicly available at https://github.com/shuangquanZW/ToxKAN and Edit upload | Zenodo.","url":"https://doi.org/10.5281/zenodo.21131553","authors":["Ziqing, Zhang"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21131553","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19398103","name":"Systems Biology in Biomedicine: Decoding Complex Diseases Through Network-Based Approaches","source":"datacite","abstract":"This extensive review details the transformative impact of systems biology and network medicine on understanding and treating complex human diseases. Moving beyond traditional reductionist paradigms that focus on single causative agents, systems biology conceptualizes diseases as systemic defects arising from perturbations within intricate molecular networks. The article outlines the theoretical foundations of biological networks, noting their scale-free topology, modularity, and robust yet fragile nature, which explains how specific targeted disruptions lead to pathological states. A major focus is placed on the integration of multi-omics data spanning genomics, transcriptomics, proteomics, and metabolomics using advanced computational frameworks and machine learning algorithms like GAUDI, MOFA, and deep generative models. These methodologies enable the reconstruction of context-specific gene regulatory and protein-protein interaction networks, facilitating the discovery of robust molecular fingerprints and predictive biomarkers. The text extensively covers the application of artificial intelligence in accelerating drug discovery, target identification, and the validation of network-based drug repositioning and combination therapies. Case studies in cancer, Alzheimer's disease, and prion disorders illustrate how systems-level insights translate into precision medicine and novel therapeutic modalities, such as prime editing-mediated readthrough for nonsense mutations. Furthermore, the article introduces the concept of Dynamical Network Biomarkers (DNB) for detecting critical disease transitions before clinical symptoms manifest. Despite these advancements, significant translational challenges remain. The review critically examines hurdles in multi-omics data standardization, the high-dimensional low sample size problem, and the complex regulatory and manufacturing landscapes for Advanced Therapy Medicinal Products (ATMPs). It also emphasizes the necessity of optimizing preclinical models, such as using genetically diverse and aged subjects, to better reflect human clinical populations. Ultimately, by bridging computational modeling with rigorous experimental validation, systems biology provides a comprehensive, actionable framework for advancing personalized and preventive healthcare. Source: https://www.sysbiosci.com/posts/systems-biology-in-biomedicine-decoding-complex-diseases-through-networkbased-approaches","url":"https://doi.org/10.5281/zenodo.19398103","authors":["systems biological science"],"tags":["systems biology","network medicine","multi-omics integration","biomarker discovery","artificial intelligence","complex diseases","drug repositioning","dynamical network biomarkers"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19398103","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19398104","name":"Systems Biology in Biomedicine: Decoding Complex Diseases Through Network-Based Approaches","source":"datacite","abstract":"This extensive review details the transformative impact of systems biology and network medicine on understanding and treating complex human diseases. Moving beyond traditional reductionist paradigms that focus on single causative agents, systems biology conceptualizes diseases as systemic defects arising from perturbations within intricate molecular networks. The article outlines the theoretical foundations of biological networks, noting their scale-free topology, modularity, and robust yet fragile nature, which explains how specific targeted disruptions lead to pathological states. A major focus is placed on the integration of multi-omics data spanning genomics, transcriptomics, proteomics, and metabolomics using advanced computational frameworks and machine learning algorithms like GAUDI, MOFA, and deep generative models. These methodologies enable the reconstruction of context-specific gene regulatory and protein-protein interaction networks, facilitating the discovery of robust molecular fingerprints and predictive biomarkers. The text extensively covers the application of artificial intelligence in accelerating drug discovery, target identification, and the validation of network-based drug repositioning and combination therapies. Case studies in cancer, Alzheimer's disease, and prion disorders illustrate how systems-level insights translate into precision medicine and novel therapeutic modalities, such as prime editing-mediated readthrough for nonsense mutations. Furthermore, the article introduces the concept of Dynamical Network Biomarkers (DNB) for detecting critical disease transitions before clinical symptoms manifest. Despite these advancements, significant translational challenges remain. The review critically examines hurdles in multi-omics data standardization, the high-dimensional low sample size problem, and the complex regulatory and manufacturing landscapes for Advanced Therapy Medicinal Products (ATMPs). It also emphasizes the necessity of optimizing preclinical models, such as using genetically diverse and aged subjects, to better reflect human clinical populations. Ultimately, by bridging computational modeling with rigorous experimental validation, systems biology provides a comprehensive, actionable framework for advancing personalized and preventive healthcare. Source: https://www.sysbiosci.com/posts/systems-biology-in-biomedicine-decoding-complex-diseases-through-networkbased-approaches","url":"https://doi.org/10.5281/zenodo.19398104","authors":["systems biological science"],"tags":["systems biology","network medicine","multi-omics integration","biomarker discovery","artificial intelligence","complex diseases","drug repositioning","dynamical network biomarkers"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19398104","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20105742","name":"Wizako-01/Transfer-learning-model: Stroke Risk Prediction App","source":"datacite","abstract":"This repository contains a machine learning-based stroke risk prediction application developed using patient clinical and demographic data. The project covers the full machine learning workflow, including data preprocessing, exploratory analysis, model training, model evaluation, threshold selection, and deployment through a simple Streamlit web application. The application predicts the probability of stroke using patient-level variables such as age, body mass index, average glucose level, hypertension status, heart disease status, smoking status, work type, residence type, and other relevant features. The output classifies users into low-risk or high-risk categories based on a selected decision threshold. Two machine learning models were evaluated: Random Forest and Logistic Regression. Although the Random Forest model achieved high overall accuracy, it failed to detect stroke-positive cases effectively, with stroke recall of 0. Logistic Regression with class balancing was selected as the final model because it performed better for stroke detection. Using a decision threshold of 0.4, the final model achieved 0.68 accuracy and 0.84 recall for stroke cases. This project emphasizes the importance of sensitivity in health-related prediction tasks, where detecting high-risk individuals may be more clinically useful than optimizing accuracy alone. The repository includes the Streamlit application, trained model file, label encoders, decision threshold file, dependency file, and notebook documenting the training workflow. This project is intended for educational and research purposes only. It is not intended for clinical diagnosis, treatment decisions, or patient management.","url":"https://doi.org/10.5281/zenodo.20105742","authors":["Jonathan Wisdom"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20105742","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20325220","name":"Multivariate Analysis of Nutritional Variables and Inflammatory Biomarkers Using AI Models for Clinical Cancer Risk Prediction","source":"datacite","abstract":"The study showcases the application of complex machine learning models that study inflammation and nutrition together for high accuracy early cancer risk prediction. With the help of statistical preprocessing and feature nonlinearity, together with other machine learning models, the system managed to capture the interactions between nutrition and inflammation to a level that other models would not. These models report a validation error that significantly exceeds that of models that only capture the linear components of the relationships involved. Traditional models have been demonstrated to have wide margins of error with regard to the cancer risk predicted. The developed system is smart enough to combine the inflamed glycemic load, saturated fat content, CRP, IL-6, and TNF-α levels, together with antioxidant and omega-3 intake, and model cancer risk. The study describes the building of a fusion of several machine learning models into a system that is clinically operational, and that incorporates a number of electronic health record components. The system demonstrates an excellent model of the clinical value of civilizational risk factors and artificial intelligence combined.","url":"https://doi.org/10.5281/zenodo.20325220","authors":["P, Nirmala","C, Priyanka","Gupalo, Sergey","RN, Jegathambigai","Kyaw, Zaw Win","Thidar, Aung","Phone, Myint Htoo","Wana, Hla Shwe","Aye Aye, Tun","Rohini, Karunakaran","Manglesh Waran, Udayah","Lwin Lwin, Nyein","Nang, Khin Mya","Thida, Khin","Myat Myo, Naing","Sutha, Devaraj","Nazmul, MHM"],"tags":["Cancer Risk Prediction; Inflammatory Biomarkers; Nutritional Variables; Machine Learning; Deep Learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20325220","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20325221","name":"Multivariate Analysis of Nutritional Variables and Inflammatory Biomarkers Using AI Models for Clinical Cancer Risk Prediction","source":"datacite","abstract":"The study showcases the application of complex machine learning models that study inflammation and nutrition together for high accuracy early cancer risk prediction. With the help of statistical preprocessing and feature nonlinearity, together with other machine learning models, the system managed to capture the interactions between nutrition and inflammation to a level that other models would not. These models report a validation error that significantly exceeds that of models that only capture the linear components of the relationships involved. Traditional models have been demonstrated to have wide margins of error with regard to the cancer risk predicted. The developed system is smart enough to combine the inflamed glycemic load, saturated fat content, CRP, IL-6, and TNF-α levels, together with antioxidant and omega-3 intake, and model cancer risk. The study describes the building of a fusion of several machine learning models into a system that is clinically operational, and that incorporates a number of electronic health record components. The system demonstrates an excellent model of the clinical value of civilizational risk factors and artificial intelligence combined.","url":"https://doi.org/10.5281/zenodo.20325221","authors":["P, Nirmala","C, Priyanka","Gupalo, Sergey","RN, Jegathambigai","Kyaw, Zaw Win","Thidar, Aung","Phone, Myint Htoo","Wana, Hla Shwe","Aye Aye, Tun","Rohini, Karunakaran","Manglesh Waran, Udayah","Lwin Lwin, Nyein","Nang, Khin Mya","Thida, Khin","Myat Myo, Naing","Sutha, Devaraj","Nazmul, MHM"],"tags":["Cancer Risk Prediction; Inflammatory Biomarkers; Nutritional Variables; Machine Learning; Deep Learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20325221","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19681252","name":"A Comprehensive Review of Machine Learning Techniques for Early Diagnosis of Cardiovascular Disease","source":"datacite","abstract":"Heart diseases (CVDs) continue to be significant data has enabled the development of advanced ML models causes of morbidity in the world. mortality, and emphasizing the necessity of early, correct and prognostic and diagnostic systems that are ethically right. Recent improvements in artificial intelligence (AI) and machine. ML have facilitated the creation of data-driven learning (ML). clinical decision support models that can improve. Early disease diagnosis, risk prioritization and personalized treatment planning. The review summarizes current articles (2022-2025) that pay attention to ML- and AI- based. CVD risk prediction methods, CVD diagnosis methods. Assessment of myocardial ischemia, CVD early diagnosis, and. purposive treatment recommendation. The reviewed works use nonhomogeneous data, such as open repositories, large-scale real world, multi-institutional benchmarks. Hospital and ICU data. These studies are methodologically the same. supervised, ensemble, deep learning. learning architectures and hybrid models of ML and. DL and reinforcement learning of sequential clinical decision-making. Some of the studies highlight explainable AI. (XAI) methods and moral aspects to improve on. Clinical trust and safety. In comparison with other businesses, it implies that. Hybrid structures and ensemble structures frequently perform better. Stronger than predictive performance and strength. Separate models on benchmark data. However, problems connected with interpretability, extrinsic validation, fairness, and real-time clinical deployment are maintained. The review concludes with naming of important gaps in research and pointing out. Prospects at glorifiable, ethical and scalable AI. Effective real world healthcare systems.","url":"https://doi.org/10.5281/zenodo.19681252","authors":["Mohd Hamid Azeez","Prof.  Dr.  Jameel Ahmad","Mr.  Balmukund Maurya"],"tags":["Cardiovascular Disease","Chronic Kidney Disease","Machine Learning","Deep Learning","Explainable AI","Ethical AI","Support of Clinical Decision","Early Diagnosis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19681252","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19681253","name":"A Comprehensive Review of Machine Learning Techniques for Early Diagnosis of Cardiovascular Disease","source":"datacite","abstract":"Heart diseases (CVDs) continue to be significant data has enabled the development of advanced ML models causes of morbidity in the world. mortality, and emphasizing the necessity of early, correct and prognostic and diagnostic systems that are ethically right. Recent improvements in artificial intelligence (AI) and machine. ML have facilitated the creation of data-driven learning (ML). clinical decision support models that can improve. Early disease diagnosis, risk prioritization and personalized treatment planning. The review summarizes current articles (2022-2025) that pay attention to ML- and AI- based. CVD risk prediction methods, CVD diagnosis methods. Assessment of myocardial ischemia, CVD early diagnosis, and. purposive treatment recommendation. The reviewed works use nonhomogeneous data, such as open repositories, large-scale real world, multi-institutional benchmarks. Hospital and ICU data. These studies are methodologically the same. supervised, ensemble, deep learning. learning architectures and hybrid models of ML and. DL and reinforcement learning of sequential clinical decision-making. Some of the studies highlight explainable AI. (XAI) methods and moral aspects to improve on. Clinical trust and safety. In comparison with other businesses, it implies that. Hybrid structures and ensemble structures frequently perform better. Stronger than predictive performance and strength. Separate models on benchmark data. However, problems connected with interpretability, extrinsic validation, fairness, and real-time clinical deployment are maintained. The review concludes with naming of important gaps in research and pointing out. Prospects at glorifiable, ethical and scalable AI. Effective real world healthcare systems.","url":"https://doi.org/10.5281/zenodo.19681253","authors":["Mohd Hamid Azeez","Prof.  Dr.  Jameel Ahmad","Mr.  Balmukund Maurya"],"tags":["Cardiovascular Disease","Chronic Kidney Disease","Machine Learning","Deep Learning","Explainable AI","Ethical AI","Support of Clinical Decision","Early Diagnosis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19681253","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21068794","name":"Clinical Assessment Methods for Early Autism Spectrum Disorder in Paediatrics Using Explainable Artificial Intelligence and Quantum Machine Learning - Figure 3","source":"datacite","abstract":"Figure 3 illustrates the yearly distribution of dataset modalities, including EEG, MRI, multimodal, and facial datasets, across the reviewed studies.","url":"https://doi.org/10.5281/zenodo.21068794","authors":["Priya Shanthini D.r","Vasu Koduri","Naveen Maddukuri","M Kalpana Chowdary","Bini Darwin","Shajin Prince"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21068794","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21068795","name":"Clinical Assessment Methods for Early Autism Spectrum Disorder in Paediatrics Using Explainable Artificial Intelligence and Quantum Machine Learning - Figure 3","source":"datacite","abstract":"Figure 3 illustrates the yearly distribution of dataset modalities, including EEG, MRI, multimodal, and facial datasets, across the reviewed studies.","url":"https://doi.org/10.5281/zenodo.21068795","authors":["Priya Shanthini D.r","Vasu Koduri","Naveen Maddukuri","M Kalpana Chowdary","Bini Darwin","Shajin Prince"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21068795","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19571786","name":"AutoRespire: A Multimodal AI-Driven Healthcare System for Tuberculosis and Pneumonia Classification","source":"datacite","abstract":"This paper presents AutoRespire, a multimodal AI-driven healthcare system designed for the detection of Tuberculosis and Pneumonia. The system integrates chest X-ray image analysis with clinical symptom data to improve diagnostic accuracy. Advanced machine learning and deep learning models, including Random Forest and Artificial Neural Networks, are utilized to analyze medical data and generate predictions. A late fusion strategy is applied to combine outputs from different models, enhancing reliability and performance. The proposed system demonstrates improved prediction accuracy and supports early diagnosis, especially in resource-limited settings. This approach aims to assist healthcare professionals in making faster and more accurate decisions for respiratory disease detection.","url":"https://doi.org/10.5281/zenodo.19571786","authors":["M, M.Siva Krishna","Priyanka, Potti","Deyyala, Sowmyadevi","Kolla, Vivek","Sabbella, Vigneswara Reddy"],"tags":["Multimodal deep learning, respiratory disease detection, tuberculosis (TB), pneumonia, chest X-ray analysis, convolutional neural networks (CNN), MobileNet, artificial neural networks (ANN), random forest, late fusion, medical image processing, clinical symptom analysis, healthcare AI, disease prediction system"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19571786","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19571787","name":"AutoRespire: A Multimodal AI-Driven Healthcare System for Tuberculosis and Pneumonia Classification","source":"datacite","abstract":"This paper presents AutoRespire, a multimodal AI-driven healthcare system designed for the detection of Tuberculosis and Pneumonia. The system integrates chest X-ray image analysis with clinical symptom data to improve diagnostic accuracy. Advanced machine learning and deep learning models, including Random Forest and Artificial Neural Networks, are utilized to analyze medical data and generate predictions. A late fusion strategy is applied to combine outputs from different models, enhancing reliability and performance. The proposed system demonstrates improved prediction accuracy and supports early diagnosis, especially in resource-limited settings. This approach aims to assist healthcare professionals in making faster and more accurate decisions for respiratory disease detection.","url":"https://doi.org/10.5281/zenodo.19571787","authors":["M, M.Siva Krishna","Priyanka, Potti","Deyyala, Sowmyadevi","Kolla, Vivek","Sabbella, Vigneswara Reddy"],"tags":["Multimodal deep learning, respiratory disease detection, tuberculosis (TB), pneumonia, chest X-ray analysis, convolutional neural networks (CNN), MobileNet, artificial neural networks (ANN), random forest, late fusion, medical image processing, clinical symptom analysis, healthcare AI, disease prediction system"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19571787","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20513137","name":"EARLY DIAGNOSIS OF POPULATION MORBIDITY USING ARTIFICIAL INTELLIGENCE: SOCIAL SIGNIFICANCE AND CLINICAL VALIDITY","source":"datacite","abstract":"This article presents the development and expert evaluation of an AI agent ‒ an intelligent platform for early diagnosis of obstetric and gynaecological conditions. The agent operates in a conversational format (text messaging or voice call) and is designed to identify early-stage diseases through analysis of patient complaints and medical history. Image processing (ultrasound, MRI, histology) is not supported in the current version. An expert survey of specialist physicians was conducted using a 5-point Likert scale, followed by statistical analysis of the results. The study demonstrates that the system holds significant potential as a decision-support tool, provided that the physician retains the primary role in clinical decision-making.","url":"https://doi.org/10.5281/zenodo.20513137","authors":["KUANDYK TOREKELDI TILEUBERDIULY","MUSAKHANOVA AKMARAL KALMAKHANBEKOVNA"],"tags":["artificial intelligence, early diagnosis, obstetrics and gynaecology, AI agent, clinical decision-making, decision support system, conversational agent, machine learning, natural language processing."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20513137","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20513138","name":"EARLY DIAGNOSIS OF POPULATION MORBIDITY USING ARTIFICIAL INTELLIGENCE: SOCIAL SIGNIFICANCE AND CLINICAL VALIDITY","source":"datacite","abstract":"This article presents the development and expert evaluation of an AI agent ‒ an intelligent platform for early diagnosis of obstetric and gynaecological conditions. The agent operates in a conversational format (text messaging or voice call) and is designed to identify early-stage diseases through analysis of patient complaints and medical history. Image processing (ultrasound, MRI, histology) is not supported in the current version. An expert survey of specialist physicians was conducted using a 5-point Likert scale, followed by statistical analysis of the results. The study demonstrates that the system holds significant potential as a decision-support tool, provided that the physician retains the primary role in clinical decision-making.","url":"https://doi.org/10.5281/zenodo.20513138","authors":["KUANDYK TOREKELDI TILEUBERDIULY","MUSAKHANOVA AKMARAL KALMAKHANBEKOVNA"],"tags":["artificial intelligence, early diagnosis, obstetrics and gynaecology, AI agent, clinical decision-making, decision support system, conversational agent, machine learning, natural language processing."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20513138","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20080794","name":"SOLID LIPID NANOPARTICLES IN CONTROLLED DRUG DELIVERY","source":"datacite","abstract":"Solid Lipid Nanoparticles (SLNs) have attracted growing interest in pharmaceutical research over the past three decades. Their capacity to carry both lipophilic and moderately hydrophilic drugs, shield sensitive molecules during circulation, and navigate biological barriers such as the blood-brain barrier (BBB) has established them as one of the more versatile nanocarrier platforms available today. First developed in the early 1990s as a practical alternative to liposomes and polymeric nanoparticles, SLNs employ a solid lipid core stabilised by biocompatible surfactants to achieve controlled, sustained, and targeted drug release. This review examines the structural principles and compositional design of SLNs, the principal preparation methods, standard physicochemical characterisation techniques, and their therapeutic applications across multiple disease areas. Particular attention is given to current research directions including stimuli-responsive formulations, nucleic acid delivery, theranostic systems, and the integration of machine learning into formulation development. Original contributions from the authors addressing dual stimuli-responsive SLNs, peptide-functionalised active targeting, intranasal brain delivery, and AI-assisted manufacturing are also presented. Together, these developments illustrate the ongoing transition of SLN technology toward clinical translation.","url":"https://doi.org/10.5281/zenodo.20080794","authors":["1Uzair Aziz Mir, 2*Mr. Neeku Singh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20080794","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20080795","name":"SOLID LIPID NANOPARTICLES IN CONTROLLED DRUG DELIVERY","source":"datacite","abstract":"Solid Lipid Nanoparticles (SLNs) have attracted growing interest in pharmaceutical research over the past three decades. Their capacity to carry both lipophilic and moderately hydrophilic drugs, shield sensitive molecules during circulation, and navigate biological barriers such as the blood-brain barrier (BBB) has established them as one of the more versatile nanocarrier platforms available today. First developed in the early 1990s as a practical alternative to liposomes and polymeric nanoparticles, SLNs employ a solid lipid core stabilised by biocompatible surfactants to achieve controlled, sustained, and targeted drug release. This review examines the structural principles and compositional design of SLNs, the principal preparation methods, standard physicochemical characterisation techniques, and their therapeutic applications across multiple disease areas. Particular attention is given to current research directions including stimuli-responsive formulations, nucleic acid delivery, theranostic systems, and the integration of machine learning into formulation development. Original contributions from the authors addressing dual stimuli-responsive SLNs, peptide-functionalised active targeting, intranasal brain delivery, and AI-assisted manufacturing are also presented. Together, these developments illustrate the ongoing transition of SLN technology toward clinical translation.","url":"https://doi.org/10.5281/zenodo.20080795","authors":["1Uzair Aziz Mir, 2*Mr. Neeku Singh"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20080795","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20784891","name":"Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis","source":"datacite","abstract":"This dataset supports the manuscript \"Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis.\" It contains metadata extracted from 446 peer-reviewed studies detailing the application of artificial intelligence and machine learning to respiratory diagnostics. Files Included: Respiratory_AI_Database_v1.csv: The finalized, cleaned dataset containing variables for Target Condition, Signal Type, Model Architecture, Sample Size, and Best Performance Metric. Ontology_Mapping_Table: The human-in-the-loop standardization matrix used to harmonize highly variable clinical terminology into definitive parent categories. Code Availability: The Python scripts used to analyze this dataset and generate the manuscript's figures are available on GitHub at: https://github.com/Hassan-Jubair/Respiratory_AI_Database.git","url":"https://doi.org/10.5281/zenodo.20784891","authors":["Jubair, Hassan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20784891","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20784892","name":"Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis","source":"datacite","abstract":"This dataset supports the manuscript \"Performance Inflation and Reporting Biases in Respiratory Artificial Intelligence: A Data-Driven Landscape Analysis.\" It contains metadata extracted from 446 peer-reviewed studies detailing the application of artificial intelligence and machine learning to respiratory diagnostics. Files Included: Respiratory_AI_Database_v1.csv: The finalized, cleaned dataset containing variables for Target Condition, Signal Type, Model Architecture, Sample Size, and Best Performance Metric. Ontology_Mapping_Table: The human-in-the-loop standardization matrix used to harmonize highly variable clinical terminology into definitive parent categories. Code Availability: The Python scripts used to analyze this dataset and generate the manuscript's figures are available on GitHub at: https://github.com/Hassan-Jubair/Respiratory_AI_Database.git","url":"https://doi.org/10.5281/zenodo.20784892","authors":["Jubair, Hassan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20784892","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21503793","name":"Artificial intelligence in drug discovery: reshaping the future of therapeutics","source":"datacite","abstract":"Traditional drug discovery methods involve target identification, hit to lead optimization, preclinical and clinical trials that are often time consuming, expensive with high failure rates. Artificial intelligence (AI) has emerged as an innovative and transformative force in drug discovery addressing the limitations encountered in drug research. Integration of computations methods such as Machine learning (ML), Deep learning (DL) and Reinforcement learning (RL) enables efficient analysis of vast chemical and biological data sets significantly accelerating the growth of drug development. AI plays a critical role in virtual screening, target identification, drug design, physicochemical assessment, ADMET studies, pharmacodynamics and drug repurposing strategies. In clinical trials, AI enhances patient selection, trial design, outcome prediction and therapeutic intervention. Despite these advancements, challenges such as data set quality, model interpretability, computational demands and ethical concerns remains significant concerns. Nevertheless, ongoing innovations in technology and biopharmaceutics, the hybrid computational and experimental methods are helping to overcome these limitations. Overall, AI is reshaping drug discovery into a faster, more accurate, cost effective process with a potential to revolutionize in the field of therapeutics with a better health care outcome.","url":"https://doi.org/10.5281/zenodo.21503793","authors":["Harithalakshmi Jandhyam"],"tags":["Artificial intelligence","Drug discovery","Therapeutics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21503793","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21503794","name":"Artificial intelligence in drug discovery: reshaping the future of therapeutics","source":"datacite","abstract":"Traditional drug discovery methods involve target identification, hit to lead optimization, preclinical and clinical trials that are often time consuming, expensive with high failure rates. Artificial intelligence (AI) has emerged as an innovative and transformative force in drug discovery addressing the limitations encountered in drug research. Integration of computations methods such as Machine learning (ML), Deep learning (DL) and Reinforcement learning (RL) enables efficient analysis of vast chemical and biological data sets significantly accelerating the growth of drug development. AI plays a critical role in virtual screening, target identification, drug design, physicochemical assessment, ADMET studies, pharmacodynamics and drug repurposing strategies. In clinical trials, AI enhances patient selection, trial design, outcome prediction and therapeutic intervention. Despite these advancements, challenges such as data set quality, model interpretability, computational demands and ethical concerns remains significant concerns. Nevertheless, ongoing innovations in technology and biopharmaceutics, the hybrid computational and experimental methods are helping to overcome these limitations. Overall, AI is reshaping drug discovery into a faster, more accurate, cost effective process with a potential to revolutionize in the field of therapeutics with a better health care outcome.","url":"https://doi.org/10.5281/zenodo.21503794","authors":["Harithalakshmi Jandhyam"],"tags":["Artificial intelligence","Drug discovery","Therapeutics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21503794","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19890565","name":"An improved framework for detecting Thyroid disease and Thyroid Cancer using Filter Based Feature Selection","source":"datacite","abstract":"Thyroid issues and thyroid cancer are among the most frequent endocrine disorders that necessitate the earliest and most precise diagnosis to avert severe health issues. The methods usually applied in diagnostics are often protracted and can be hampered by limited accuracy due to the intricacies and high dimensionality of medical data. This study is a solution to these challenges as it puts forward a machine learning framework that is more refined and thus suitable for thyroid disease and cancer detection through the application of filter-based feature selection techniques. The approach proposed is centered on the extraction of the most relevant clinical features through the elimination of redundant and irrelevant attributes, which not only leads to the reduction of the computational burden but also the enhancement of model performance. The features selected are then employed in training and optimizing various machine learning classifiers to ensure effective classification. The framework is put through its paces using standard performance metrics that include accuracy, precision, recall, F1-score, and ROC-AUC.","url":"https://doi.org/10.5281/zenodo.19890565","authors":["Chetan Padole","Rashmi Lambhate","Chetan Rathod","Ayush Nandurkar","Arshad Ghodake"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19890565","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19890566","name":"An improved framework for detecting Thyroid disease and Thyroid Cancer using Filter Based Feature Selection","source":"datacite","abstract":"Thyroid issues and thyroid cancer are among the most frequent endocrine disorders that necessitate the earliest and most precise diagnosis to avert severe health issues. The methods usually applied in diagnostics are often protracted and can be hampered by limited accuracy due to the intricacies and high dimensionality of medical data. This study is a solution to these challenges as it puts forward a machine learning framework that is more refined and thus suitable for thyroid disease and cancer detection through the application of filter-based feature selection techniques. The approach proposed is centered on the extraction of the most relevant clinical features through the elimination of redundant and irrelevant attributes, which not only leads to the reduction of the computational burden but also the enhancement of model performance. The features selected are then employed in training and optimizing various machine learning classifiers to ensure effective classification. The framework is put through its paces using standard performance metrics that include accuracy, precision, recall, F1-score, and ROC-AUC.","url":"https://doi.org/10.5281/zenodo.19890566","authors":["Chetan Padole","Rashmi Lambhate","Chetan Rathod","Ayush Nandurkar","Arshad Ghodake"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19890566","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.16950764","name":"COSFAMM Baseline Dataset: Psychosocial and Clinical Predictors of Depression in Older Mexican Adults (2014–2019)","source":"datacite","abstract":"This dataset accompanies the article “Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Non‑Linear Effects from Machine Learning Explainability.” It contains baseline data from 1,252 adults aged ≥ 60 years participating in the COSFAMM cohort (Obesity, Sarcopenia, and Frailty in Older Mexican Adults), collected in Mexico City between 2014 and 2019. The dataset includes sociodemographic characteristics, chronic disease indicators, social isolation (LSNS‑6), perceived social support (MOS‑SSS), and depressive symptoms assessed with the CESD‑R. These variables were used to develop LASSO‑guided logistic regression and Random Forest models, followed by SHAP, Friedman's H‑statistic, and ALE analyses to characterize psychosocial and clinical predictors of depressive symptoms.","url":"https://doi.org/10.5281/zenodo.16950764","authors":["Efrén, Murillo-Zamora"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.16950764","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19258319","name":"COSFAMM Baseline Dataset: Psychosocial and Clinical Predictors of Depression in Older Mexican Adults (2014–2019)","source":"datacite","abstract":"This dataset accompanies the article “Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Non‑Linear Effects from Machine Learning Explainability.” It contains baseline data from 1,252 adults aged ≥ 60 years participating in the COSFAMM cohort (Obesity, Sarcopenia, and Frailty in Older Mexican Adults), collected in Mexico City between 2014 and 2019. The dataset includes sociodemographic characteristics, chronic disease indicators, social isolation (LSNS‑6), perceived social support (MOS‑SSS), and depressive symptoms assessed with the CESD‑R. These variables were used to develop LASSO‑guided logistic regression and Random Forest models, followed by SHAP, Friedman's H‑statistic, and ALE analyses to characterize psychosocial and clinical predictors of depressive symptoms.","url":"https://doi.org/10.5281/zenodo.19258319","authors":["Efrén, Murillo-Zamora"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19258319","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22057281","name":"From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials","source":"datacite","abstract":"Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package reproduces the complete secondary-analysis workflow, including PHQ symptom recoding, NAMU-style feature engineering, temporal feature construction, participant-level locked holdout evaluation, XGBoost modeling, participant-cluster bootstrap comparisons, incident suicidal-ideation onset analysis, temporal forward validation, SHAP feature importance, and manuscript figures. The analyses were verified against the publicly available openESM Marian dataset (0052_marian). The verified analytic sample included 6,845 transitions from 144 participants. The Temporal XGBoost model showed the highest locked-test discrimination (PR AUC = .581; ROC AUC = .891). The underlying participant-level dataset is not redistributed in this archive. It is publicly available through the openESM Zenodo record at https://doi.org/10.5281/zenodo.17348268. The original study is Marian et al. (2023), https://doi.org/10.1007/s10862-022-10014-8. The archive contains the exact locked-test participant IDs, frozen model settings and thresholds, analysis scripts, participant-cluster bootstrap procedures, aggregate results, reproducibility checks, software-environment information, and file checksums. Participant-level predictions, row-level SHAP values, and other sensitive derived outputs are not included. This package is intended for research reproducibility and methodological evaluation. The models are not intended for stand-alone clinical suicide-risk assessment or treatment decisions.","url":"https://doi.org/10.5281/zenodo.22057281","authors":["woo, sungbum"],"tags":["suicidal ideation","ecological momentary assessment machine learning","depression","temporal dynamics","XGBoost","openESM","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22057281","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22054437","name":"From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials","source":"datacite","abstract":"Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package reproduces the complete secondary-analysis workflow, including PHQ symptom recoding, NAMU-style feature engineering, temporal feature construction, participant-level locked holdout evaluation, XGBoost modeling, participant-cluster bootstrap comparisons, incident suicidal-ideation onset analysis, temporal forward validation, SHAP feature importance, and manuscript figures. The analyses were verified against the publicly available openESM Marian dataset (0052_marian). The verified analytic sample included 6,845 transitions from 144 participants. The Temporal XGBoost model showed the highest locked-test discrimination (PR AUC = .581; ROC AUC = .891). The underlying participant-level dataset is not redistributed in this archive. It is publicly available through the openESM Zenodo record at https://doi.org/10.5281/zenodo.17348268. The original study is Marian et al. (2023), https://doi.org/10.1007/s10862-022-10014-8. The archive contains the exact locked-test participant IDs, frozen model settings and thresholds, analysis scripts, participant-cluster bootstrap procedures, aggregate results, reproducibility checks, software-environment information, and file checksums. Participant-level predictions, row-level SHAP values, and other sensitive derived outputs are not included. This package is intended for research reproducibility and methodological evaluation. The models are not intended for stand-alone clinical suicide-risk assessment or treatment decisions.","url":"https://doi.org/10.5281/zenodo.22054437","authors":["woo, sungbum"],"tags":["suicidal ideation","ecological momentary assessment machine learning","depression","temporal dynamics","XGBoost","openESM","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22054437","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.24412/cl-34438-2026-785-139-149","name":"ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В НЕЙРОХИРУРГИИ ОТ ДИАГНОСТИКИ ДО ПРОГНОЗИРОВАНИЯ ИСХОДОВ ОПЕРАЦИЙ","source":"datacite","abstract":"Современная нейрохирургия требует прецизионной точности, минимизации интраоперационных рисков и объективного прогнозирования результатов лечения. Стремительное увеличение объемов диагностических данных и необходимость персонализации хирургических подходов обуславливают интеграцию методов искусственного интеллекта (ИИ) и машинного обучения (МО) в клиническую практику. Установлено, что применение ИИ в нейрорадиологии обеспечивает точность автоматической сегментации новообразований и сосудистых мальформаций на уровне 95-97%, а радиогеномика позволяет неинвазивно верифицировать молекулярный профиль опухолей. Доказана эффективность ИИ в компенсации феномена смещения мозга (brain shift) в режиме реального времени. В области прогностического моделирования алгоритмы машинного обучения демонстрируют высокую прогностическую ценность (AUC-ROC &gt; 0.88) при оценке рисков послеоперационных осложнений и выживаемости пациентов. Технологии ИИ трансформируют концепцию нейрохирургической помощи, выступая в роли когнитивного ассистента врача. Несмотря на барьеры в виде проблемы «черного ящика» и юридической неопределенности, дальнейшее развитие гибридного интеллекта (синергии хирурга и нейросети) является главным вектором эволюции специальности, способным значимо снизить уровень инвалидизации пациентов.","url":"https://doi.org/10.24412/cl-34438-2026-785-139-149","authors":["Құрбанова Лола Қанағатқызы","Каримбердиев Нурбол Мухамедалиулы","Жолдасов Элдар Бекзатұлы","Ниязов Аскер Зарафулы","Абдуллаев Алишер Рустамович"],"tags":["Искусственный интеллект","машинное обучение","нейрохирургия","радиомика","нейроонкология","компьютерное зрение","интраоперационная навигация","предиктивное моделирование"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.24412/cl-34438-2026-785-139-149","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19884113","name":"Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma","source":"datacite","abstract":"Title:THCA Prognostic Biomarker Sets – Multiple independent gene expression signatures for risk stratification in thyroid carcinoma Description: Project: THCA Prognostic Biomarker – Integrative bioinformatics approach to identify prognostic gene signatures for risk stratification in thyroid carcinoma Publication: Malik, S., & Raghava, G.P.S. (2026). Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma. bioRxiv. https://doi.org/10.64898/2026.04.23.720344 Overview:This repository accompanies the THCA prognostic biomarker study and provides seven independent, non‑overlapping sets of prognostic biomarkers for predicting overall survival (OS) in thyroid carcinoma (THCA). Thyroid cancer is the most common endocrine malignancy, with rising global incidence. Unlike existing studies that identify only a single biomarker signature, this work systematically generates multiple distinct gene sets (20 genes each) with no overlap, enabling flexible and robust risk stratification. The 20‑gene set demonstrated the strongest predictive potential, and incorporating clinical variables (age, gender, stage) further enhanced model performance (AUC = 0.96, Kappa = 0.80). Dataset summary: Source: TCGA (The Cancer Genome Atlas) – THCA cohort (UCSC Xena) Samples: 572 patients with complete transcriptomic and survival data Original feature space: 20,530 genes → after preprocessing: 10,876 informative genes Survival classes (based on overall survival time): Class 0: 0–1 year (high risk) Class 1: 1–3 years (intermediate risk) Class 2: 3–5 years (intermediate risk) Class 3: >5 years (low risk/long‑term survivors) Class balancing: SMOTE applied to address imbalance Key Findings from Correlation Analysis (Pearson, FDR Stage I (41.71) > Stage III (40.14) > Stage IV (29.59) (HR = 2.50) GO enrichment (50 positively correlated genes): Biological processes: positive regulation of transcription by RNA polymerase II, regulation of cell population proliferation, regulation of cell cycle (most significant) Cellular components: tight junction, nucleus Molecular functions: sequence‑specific DNA binding, DNA‑binding transcription activator activity Reactome pathways: Signal Transduction (R-HSA-162582) – highest gene ratio and strongest significance Univariate Cox regression: 883 genes significantly associated with OS (p 1 (risk factors) – e.g., TMEM90B (HR = 10.66), PTH1R (HR = 9.88) 342 genes – HR 1): (see Table 3 in paper) Protective factors (HR 1): TGFBR3, TIMP3, LEF1, BNIP3L Data Curation & Quality Control: Source: TCGA THCA cohort via UCSC Xena Samples: 572 patients with complete survival data Preprocessing: Removed duplicate features, filtered genes with >50% zero values, removed low‑variance features → 10,876 genes retained Normalization: Standard scaler (Z‑score) Class definition: Based on overall survival time (months) Class balancing: SMOTE (Synthetic Minority Over‑sampling Technique) Validation split: Stratified 5‑fold CV (80/20) External validation limitation: No suitable GEO datasets with survival information available for THCA Usage:These datasets and models are designed for: Identifying multiple independent prognostic biomarker sets for thyroid carcinoma Risk stratification of THCA patients (high‑risk vs. long‑term survivors) Training and benchmarking machine learning classifiers (SVM, RF, ET, XGB, LGBM) for survival prediction Drug target discovery (DGIdb analysis of prognostic genes – 233 drugs) Integrating clinical variables (age, gender, stage) with gene expression for improved prognosis Understanding molecular determinants (cell cycle regulation, signal transduction) of thyroid cancer prognosis Key Biological Insights: MAFF, NR4A3, SRF – high expression associated with longer survival (positive correlation) LOC728264, VAMP1, NOX5 – high expression associated with shorter survival (negative correlation) Cell cycle regulation – most significantly enriched GO term Signal Transduction – mo","url":"https://doi.org/10.5281/zenodo.19884113","authors":["Malik, Shivani","Raghava, Gajendra"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19884113","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19884114","name":"Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma","source":"datacite","abstract":"Title:THCA Prognostic Biomarker Sets – Multiple independent gene expression signatures for risk stratification in thyroid carcinoma Description: Project: THCA Prognostic Biomarker – Integrative bioinformatics approach to identify prognostic gene signatures for risk stratification in thyroid carcinoma Publication: Malik, S., & Raghava, G.P.S. (2026). Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma. bioRxiv. https://doi.org/10.64898/2026.04.23.720344 Overview:This repository accompanies the THCA prognostic biomarker study and provides seven independent, non‑overlapping sets of prognostic biomarkers for predicting overall survival (OS) in thyroid carcinoma (THCA). Thyroid cancer is the most common endocrine malignancy, with rising global incidence. Unlike existing studies that identify only a single biomarker signature, this work systematically generates multiple distinct gene sets (20 genes each) with no overlap, enabling flexible and robust risk stratification. The 20‑gene set demonstrated the strongest predictive potential, and incorporating clinical variables (age, gender, stage) further enhanced model performance (AUC = 0.96, Kappa = 0.80). Dataset summary: Source: TCGA (The Cancer Genome Atlas) – THCA cohort (UCSC Xena) Samples: 572 patients with complete transcriptomic and survival data Original feature space: 20,530 genes → after preprocessing: 10,876 informative genes Survival classes (based on overall survival time): Class 0: 0–1 year (high risk) Class 1: 1–3 years (intermediate risk) Class 2: 3–5 years (intermediate risk) Class 3: >5 years (low risk/long‑term survivors) Class balancing: SMOTE applied to address imbalance Key Findings from Correlation Analysis (Pearson, FDR Stage I (41.71) > Stage III (40.14) > Stage IV (29.59) (HR = 2.50) GO enrichment (50 positively correlated genes): Biological processes: positive regulation of transcription by RNA polymerase II, regulation of cell population proliferation, regulation of cell cycle (most significant) Cellular components: tight junction, nucleus Molecular functions: sequence‑specific DNA binding, DNA‑binding transcription activator activity Reactome pathways: Signal Transduction (R-HSA-162582) – highest gene ratio and strongest significance Univariate Cox regression: 883 genes significantly associated with OS (p 1 (risk factors) – e.g., TMEM90B (HR = 10.66), PTH1R (HR = 9.88) 342 genes – HR 1): (see Table 3 in paper) Protective factors (HR 1): TGFBR3, TIMP3, LEF1, BNIP3L Data Curation & Quality Control: Source: TCGA THCA cohort via UCSC Xena Samples: 572 patients with complete survival data Preprocessing: Removed duplicate features, filtered genes with >50% zero values, removed low‑variance features → 10,876 genes retained Normalization: Standard scaler (Z‑score) Class definition: Based on overall survival time (months) Class balancing: SMOTE (Synthetic Minority Over‑sampling Technique) Validation split: Stratified 5‑fold CV (80/20) External validation limitation: No suitable GEO datasets with survival information available for THCA Usage:These datasets and models are designed for: Identifying multiple independent prognostic biomarker sets for thyroid carcinoma Risk stratification of THCA patients (high‑risk vs. long‑term survivors) Training and benchmarking machine learning classifiers (SVM, RF, ET, XGB, LGBM) for survival prediction Drug target discovery (DGIdb analysis of prognostic genes – 233 drugs) Integrating clinical variables (age, gender, stage) with gene expression for improved prognosis Understanding molecular determinants (cell cycle regulation, signal transduction) of thyroid cancer prognosis Key Biological Insights: MAFF, NR4A3, SRF – high expression associated with longer survival (positive correlation) LOC728264, VAMP1, NOX5 – high expression associated with shorter survival (negative correlation) Cell cycle regulation – most significantly enriched GO term Signal Transduction – mo","url":"https://doi.org/10.5281/zenodo.19884114","authors":["Malik, Shivani","Raghava, Gajendra"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19884114","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22054438","name":"From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials","source":"datacite","abstract":"Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package reproduces the complete secondary-analysis workflow, including PHQ symptom recoding, NAMU-style feature engineering, temporal feature construction, participant-level locked holdout evaluation, XGBoost modeling, participant-cluster bootstrap comparisons, incident suicidal-ideation onset analysis, temporal forward validation, SHAP feature importance, and manuscript figures. The analyses were verified against the publicly available openESM Marian dataset (0052_marian). The verified analytic sample included 6,845 transitions from 144 participants. The Temporal XGBoost model showed the highest locked-test discrimination (PR AUC = .581; ROC AUC = .891). The underlying participant-level dataset is not redistributed in this archive. It is publicly available through the openESM Zenodo record at https://doi.org/10.5281/zenodo.17348268. The original study is Marian et al. (2023), https://doi.org/10.1007/s10862-022-10014-8. The archive contains the exact locked-test participant IDs, frozen model settings and thresholds, analysis scripts, participant-cluster bootstrap procedures, aggregate results, reproducibility checks, software-environment information, and file checksums. Participant-level predictions, row-level SHAP values, and other sensitive derived outputs are not included. This package is intended for research reproducibility and methodological evaluation. The models are not intended for stand-alone clinical suicide-risk assessment or treatment decisions.","url":"https://doi.org/10.5281/zenodo.22054438","authors":["woo, sungbum"],"tags":["suicidal ideation","ecological momentary assessment machine learning","depression","temporal dynamics","XGBoost","openESM","reproducibility"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22054438","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21557452","name":"Artificial Intelligence in MOLECULAR AUTOPSY and FORENSIC CARDIO-GENOMICS","source":"datacite","abstract":"Artificial Intelligence in Molecular Autopsy and Forensic Cardio-Genomics: Current Evidence, Clinical Applications, and Future Directions provides a concise, evidence-based overview of the emerging role of artificial intelligence in postmortem genomic investigation of sudden cardiac death and inherited cardiovascular disorders. The book explores AI-assisted genomic variant interpretation, forensic cardio-genomics, molecular autopsy workflows, explainable artificial intelligence, precision forensic medicine, and future applications in death investigation. Designed as an interdisciplinary scholarly resource, it integrates current evidence, practical concepts, and future perspectives for forensic pathologists, clinicians, geneticists, researchers, and students. ORCID: https://orcid.org/0009-0005-3523-774X Author website: https://drhakimemedivault.com Google Scholar Profile: https://scholar.google.com/citations?user=VnL3NuoAAAAJ&hl=en Google Books Catalogue: https://books.google.com/books?q=inauthor:%22Hakim+Saboowala%22 (Open in New Window)","url":"https://doi.org/10.5281/zenodo.21557452","authors":["Saboowala, Hakim K."],"tags":["Molecular Autopsy Forensic Cardio-Genomics Artificial Intelligence Explainable Artificial Intelligence Sudden Cardiac Death Forensic Genomics Genomic Variant Interpretation Precision Forensic Medicine Cardiomyopathy Channelopathies Postmortem Genetic Testing Machine Learning Clinical Genomics Digital Pathology Precision Medicine Cardiovascular Genetics Forensic Pathology Inherited Cardiac Disorders AI-Assisted Diagnostics Computational Genomics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21557452","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21557453","name":"Artificial Intelligence in MOLECULAR AUTOPSY and FORENSIC CARDIO-GENOMICS","source":"datacite","abstract":"Artificial Intelligence in Molecular Autopsy and Forensic Cardio-Genomics: Current Evidence, Clinical Applications, and Future Directions provides a concise, evidence-based overview of the emerging role of artificial intelligence in postmortem genomic investigation of sudden cardiac death and inherited cardiovascular disorders. The book explores AI-assisted genomic variant interpretation, forensic cardio-genomics, molecular autopsy workflows, explainable artificial intelligence, precision forensic medicine, and future applications in death investigation. Designed as an interdisciplinary scholarly resource, it integrates current evidence, practical concepts, and future perspectives for forensic pathologists, clinicians, geneticists, researchers, and students. ORCID: https://orcid.org/0009-0005-3523-774X Author website: https://drhakimemedivault.com Google Scholar Profile: https://scholar.google.com/citations?user=VnL3NuoAAAAJ&hl=en Google Books Catalogue: https://books.google.com/books?q=inauthor:%22Hakim+Saboowala%22 (Open in New Window)","url":"https://doi.org/10.5281/zenodo.21557453","authors":["Saboowala, Hakim K."],"tags":["Molecular Autopsy Forensic Cardio-Genomics Artificial Intelligence Explainable Artificial Intelligence Sudden Cardiac Death Forensic Genomics Genomic Variant Interpretation Precision Forensic Medicine Cardiomyopathy Channelopathies Postmortem Genetic Testing Machine Learning Clinical Genomics Digital Pathology Precision Medicine Cardiovascular Genetics Forensic Pathology Inherited Cardiac Disorders AI-Assisted Diagnostics Computational Genomics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21557453","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20975965","name":"A Revolutionary Paradigm: AI-Driven Genetic Fingerprint-Based Drug Synthesis for Precise Solid Tumor Penetration and Eradication","source":"datacite","abstract":"Solid tumors represent a formidable challenge in oncology due to their heterogeneous nature, dense extracellular matrix, and evolved resistance mechanisms. This manuscript introduces a groundbreaking, first-of-its-kind technology that leverages artificial intelligence to synthesize drug compounds capable of penetrating solid tumors with unprecedented precision by exploiting the tumor's unique genetic fingerprint. Our approach integrates high-throughput genomic sequencing, deep learning-based molecular design, and advanced pharmacokinetic modeling to create tumor-specific therapeutics.We present a comprehensive mathematical framework for drug-tumor interaction dynamics, including penetration equations, binding kinetics, and cellular uptake models. The system incorporates Bayesian inference for parameter estimation, sensitivity analysis for robustness assessment, and uncertainty quantification for clinical reliability. In silico results demonstrate >95% target specificity and >90% tumor cell eradication across multiple cancer types (breast, lung, pancreatic, glioblastoma). In silico results are presented; prospective in vivo and clinical validation are required.We provide complete Python implementations for all models, statistical analyses, and visualization tools. The methodology is fully reproducible, with all data and code embedded within this manuscript. This work establishes a new paradigm in precision oncology, offering a scalable, adaptable platform for next-generation cancer therapeutics.Keywords: Precision Oncology, Solid Tumor, Genetic Fingerprint, AI-Driven Drug Design, Bayesian Inference, Sensitivity Analysis, Uncertainty Quantification, Pharmacokinetics, Molecular Dynamics, Machine Learning","url":"https://doi.org/10.5281/zenodo.20975965","authors":["Shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20975965","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20975533","name":"A Revolutionary Paradigm: AI-Driven Genetic Fingerprint-Based Drug Synthesis for Precise Solid Tumor Penetration and Eradication","source":"datacite","abstract":"Solid tumors represent a formidable challenge in oncology due to their heterogeneous nature, dense extracellular matrix, and evolved resistance mechanisms. This manuscript introduces a groundbreaking, first-of-its-kind technology that leverages artificial intelligence to synthesize drug compounds capable of penetrating solid tumors with unprecedented precision by exploiting the tumor's unique genetic fingerprint. Our approach integrates high-throughput genomic sequencing, deep learning-based molecular design, and advanced pharmacokinetic modeling to create tumor-specific therapeutics.We present a comprehensive mathematical framework for drug-tumor interaction dynamics, including penetration equations, binding kinetics, and cellular uptake models. The system incorporates Bayesian inference for parameter estimation, sensitivity analysis for robustness assessment, and uncertainty quantification for clinical reliability. In silico results demonstrate >95% target specificity and >90% tumor cell eradication across multiple cancer types (breast, lung, pancreatic, glioblastoma). In silico results are presented; prospective in vivo and clinical validation are required.We provide complete Python implementations for all models, statistical analyses, and visualization tools. The methodology is fully reproducible, with all data and code embedded within this manuscript. This work establishes a new paradigm in precision oncology, offering a scalable, adaptable platform for next-generation cancer therapeutics.Keywords: Precision Oncology, Solid Tumor, Genetic Fingerprint, AI-Driven Drug Design, Bayesian Inference, Sensitivity Analysis, Uncertainty Quantification, Pharmacokinetics, Molecular Dynamics, Machine Learning","url":"https://doi.org/10.5281/zenodo.20975533","authors":["Shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20975533","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20111083","name":"Mingzheng — Reproducibility Data Package","source":"datacite","abstract":"This Zenodo record provides the reproducibility package for the Mingzheng study: a taxonomy-based multimodal artificial intelligence system for interpretable Traditional Chinese Medicine syndrome differentiation in cancer patients with comorbid sleep disorders. This package is independent of manuscript version. It supports reproduction of the main model benchmarks, leave-one-site-out (LOSO) ensemble inference, temporal validation, reader-study model outputs, zero-shot LLM comparisons, and supplementary subgroup analyses reported across manuscript versions. Related paper: Zheng Xiao, Xie Yong, Luo Song, et al. A taxonomy-based multimodal artificial intelligence system for interpretable syndrome differential diagnosis in cancer patients with comorbid sleep disorders. Manuscript, 2026. Zenodo DOI: https://doi.org/10.5281/zenodo.20111083 Code repository: https://github.com/Jayden-XL/mingzheng For now, this Code repository is accessible only to the editor and editor-invited reviewers. If you are accessing this record during peer review, please contact us through the editor. Contents checkpoints/ - Trained LOSO Ensemble Models, approximately 7.1 GB Trained model checkpoints for the fully deployable version of Mingzheng. The released version contains 15 checkpoints: checkpoints/ |-- loso_seed42/fold{1-5}_best_ckpt.pth |-- loso_seed43/fold{1-5}_best_ckpt.pth `-- loso_seed44/fold{1-5}_best_ckpt.pth This corresponds to 3 random seeds x 5 leave-one-site-out cross-validation folds. For site-matched LOSO inference, a patient from site s is scored only by the three checkpoints whose held-out site is s, one checkpoint per seed. The three predicted probability vectors are then averaged. This scheme prevents the patient's own clinical site from contributing to the training data and is used for deployment-style cross-site inference, temporal validation, and reader-study model outputs. The same checkpoint family can also be used to compute pooled internal LOSO ensemble metrics across the five development sites. Alternative ensemble summaries are provided only as sensitivity analyses where indicated in the analysis files. fold_splits/ - Data Partition Metadata, approximately 13 KB LOSO fold assignments and metadata: fold_splits/ `-- loso_seed{42,43,44}/loso{1-5}/split_info.json Each split_info.json contains: holdout_site test_patient_ids n_train n_val n_test Patient identifiers are de-identified study IDs. analysis/ - Aggregated Results and Manuscript Mapping, approximately 70 KB Aggregated analysis outputs used to reproduce model rankings, ablations, subgroup analyses, and manuscript display items. Key files include: ranking.csv - model ranking table with paper display names and experiment identifiers experiment_map.csv - mapping from display name to experiment name, model file, training script, base experiment, key CLI flags, seeds, and notes per_syndrome.csv - per-syndrome F1/AUC summaries ablation_v3_expert.csv - expert-annotation ablation results ablation_v4_llm.csv - LLM-component ablation results ablation_v4_bge_expert.csv - BGE-embedding ablation results fold_diagnostic.csv - per-fold diagnostic metrics *_cv_summary.json - per-seed or aggregated cross-validation summaries with fold-level F1/AUC breakdowns Because figure and table numbering may differ across journal submissions, use experiment_map.csv and the GitHub repository README to map these files to the corresponding manuscript display items. llm_responses/ - Zero-Shot LLM Outputs, Numeric-Only, approximately 61 KB Per-patient JSON-Lines outputs from the zero-shot LLM baselines evaluated in the study. Free-text fields have been removed before public release. Each record contains only the numeric and administrative fields required by the downstream reproduction scripts, such as: patient_id hashed name field where applicable cancer type parsed per-syndrome confidence scores parsed overall difficulty error status number of attempts Removed fields include raw model responses, supporting/","url":"https://doi.org/10.5281/zenodo.20111083","authors":["Zheng, Xueer","Xie, Ying","Luo, Shijun","Yan, Yici","Ruan, Shanming"],"tags":["multimodal fusion","TCM","syndrome differentiation","label shift","decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20111083","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22056198","name":"Machine Learning Generalizability in Cervical Cancer Diagnosis: A Study Protocol Comparing Standard Benchmark and African-Collected Datasets","source":"datacite","abstract":"This protocol describes a two-arm computational study examining whether machine learning models trained on standard, non-African cervical cancer cytology benchmark datasets (SIPaKMeD, Herlev) generalize to an independently collected African cervical imaging dataset (Malhari, hosted on Mendeley Data). A second arm uses a Zambian clinical registry dataset to identify predictors of late-stage cervical cancer diagnosis. All datasets used are open-access and require no registration or data-use agreement. This is a pre-results study protocol; a follow-up manuscript reporting experimental findings will be linked upon completion.","url":"https://doi.org/10.5281/zenodo.22056198","authors":["Kamau, Nyambura"],"tags":["machine learning; cervical cancer; oncology; Africa; health equity; algorithmic bias; generalizability; global health; medical imaging; diagnostic AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22056198","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22056197","name":"Machine Learning Generalizability in Cervical Cancer Diagnosis: A Study Protocol Comparing Standard Benchmark and African-Collected Datasets","source":"datacite","abstract":"This protocol describes a two-arm computational study examining whether machine learning models trained on standard, non-African cervical cancer cytology benchmark datasets (SIPaKMeD, Herlev) generalize to an independently collected African cervical imaging dataset (Malhari, hosted on Mendeley Data). A second arm uses a Zambian clinical registry dataset to identify predictors of late-stage cervical cancer diagnosis. All datasets used are open-access and require no registration or data-use agreement. This is a pre-results study protocol; a follow-up manuscript reporting experimental findings will be linked upon completion.","url":"https://doi.org/10.5281/zenodo.22056197","authors":["Kamau, Nyambura"],"tags":["machine learning; cervical cancer; oncology; Africa; health equity; algorithmic bias; generalizability; global health; medical imaging; diagnostic AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22056197","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20416545","name":"A Review on Artificial Intelligence in Modern Cardiac Therapeutics: Advancements, Applications, and Future Perspectives","source":"datacite","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, placing immense pressure on healthcare infrastructure worldwide. Traditional paradigms of cardiac care heavily depend on manual interpretation of complex diagnostic data, population-wide empirical treatment strategies, and reactive clinical frameworks. Artificial Intelligence (AI) has emerged as a disruptive technological force in modern cardiology, redefining standard workflows through advanced computational capabilities. By utilizing Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and sophisticated predictive analytics, AI is shifting cardiac care from reactive medicine to a highly proactive, precise, and preventive ecosystem. This comprehensive review explores the multidimensional role of AI across modern cardiac therapeutics. We analyze its direct applications in expanding the diagnostic capabilities of electrocardiography (ECG), automated echocardiography interpretation, high-throughput cardiac magnetic resonance imaging (CMR), and computed tomography (CT). Furthermore, this paper highlights the intervention of AI systems within clinical pharmacology, electrophysiological mapping, robotic-assisted surgical interventions, and the explosive growth of remote patient tracking via AI-enabled smart wearables. Finally, we balance these revolutionary breakthroughs against core operational challenges—such as algorithm interpretability, data silos, ethical constraints, and regulatory pathways—offering a holistic perspective on the evolution of AI-driven cardiovascular medicine.","url":"https://doi.org/10.5281/zenodo.20416545","authors":["Yash Parkhi*, Manoj Kumar Goyal, Rani Dhurete"],"tags":["Cardiovascular diseases (CVDs), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), sophisticated predictive analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20416545","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20416546","name":"A Review on Artificial Intelligence in Modern Cardiac Therapeutics: Advancements, Applications, and Future Perspectives","source":"datacite","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, placing immense pressure on healthcare infrastructure worldwide. Traditional paradigms of cardiac care heavily depend on manual interpretation of complex diagnostic data, population-wide empirical treatment strategies, and reactive clinical frameworks. Artificial Intelligence (AI) has emerged as a disruptive technological force in modern cardiology, redefining standard workflows through advanced computational capabilities. By utilizing Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and sophisticated predictive analytics, AI is shifting cardiac care from reactive medicine to a highly proactive, precise, and preventive ecosystem. This comprehensive review explores the multidimensional role of AI across modern cardiac therapeutics. We analyze its direct applications in expanding the diagnostic capabilities of electrocardiography (ECG), automated echocardiography interpretation, high-throughput cardiac magnetic resonance imaging (CMR), and computed tomography (CT). Furthermore, this paper highlights the intervention of AI systems within clinical pharmacology, electrophysiological mapping, robotic-assisted surgical interventions, and the explosive growth of remote patient tracking via AI-enabled smart wearables. Finally, we balance these revolutionary breakthroughs against core operational challenges—such as algorithm interpretability, data silos, ethical constraints, and regulatory pathways—offering a holistic perspective on the evolution of AI-driven cardiovascular medicine.","url":"https://doi.org/10.5281/zenodo.20416546","authors":["Yash Parkhi*, Manoj Kumar Goyal, Rani Dhurete"],"tags":["Cardiovascular diseases (CVDs), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), sophisticated predictive analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20416546","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22055220","name":"A New Survival and Prognosis Predictive Model for Combined-Small Cell Lung Cancer (C-SCLC): A Machine Learning Approach","source":"datacite","abstract":"Combined Small Cell Lung Cancer (C-SCLC), a rare variant subtype of lung cancer, has its distinct characteristics and prognosis challenges. Despite its clinical significance, there exists a knowledge gap in the diagnosis, treatment, and prognosis of C-SCLC. Utilizing the SEER database, an authoritative source of cancer data in the United States, we applied advanced machine learning techniques to analyze the overall survival (OS) of C-SCLC patients from 2004 to 2020, across multiple staging systems. Through rigorous data preprocessing and analysis, we developed predictive models that highlight the prognosis factors of C-SCLC but also underscore the subtype’s important features and analysis across stages. Our findings provide significant insights and contributions into the survival outcomes of C-SCLC patients, for their distinction within lung cancer classifications. Our study of C-SCLC falls among the very few longitudinal studies and analyses. We presented a model to predict the OS (Overall Survival) for patients with this rare subtype from the year 2004 to 2015, using the American Joint Committee on Cancer (AJCC) 6th edition, alongside visualizations from the analysis, and the code to reproduce these results. We only use the 2004-2015 data to model to ensure consistency in the corresponding staging system (AJCC 6th Edition). We presented a model with 81% recall for patients at high risk (less than 9 months of survival) with the most contributing factors to this prediction, which are Metastasis, Chemotherapy, Radiation, Surgery, and Tumor Size.","url":"https://doi.org/10.5281/zenodo.22055220","authors":["Faridani, Parzon Eyzadpur","Yu, Kaijie"],"tags":["Combined Small Cell Lung Cancer","C-SCLC","Lung Cancer","Machine Learning","Survival Prediction","Cancer Prognosis","SEER Database","AJCC"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22055220","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22055219","name":"A New Survival and Prognosis Predictive Model for Combined-Small Cell Lung Cancer (C-SCLC): A Machine Learning Approach","source":"datacite","abstract":"Combined Small Cell Lung Cancer (C-SCLC), a rare variant subtype of lung cancer, has its distinct characteristics and prognosis challenges. Despite its clinical significance, there exists a knowledge gap in the diagnosis, treatment, and prognosis of C-SCLC. Utilizing the SEER database, an authoritative source of cancer data in the United States, we applied advanced machine learning techniques to analyze the overall survival (OS) of C-SCLC patients from 2004 to 2020, across multiple staging systems. Through rigorous data preprocessing and analysis, we developed predictive models that highlight the prognosis factors of C-SCLC but also underscore the subtype’s important features and analysis across stages. Our findings provide significant insights and contributions into the survival outcomes of C-SCLC patients, for their distinction within lung cancer classifications. Our study of C-SCLC falls among the very few longitudinal studies and analyses. We presented a model to predict the OS (Overall Survival) for patients with this rare subtype from the year 2004 to 2015, using the American Joint Committee on Cancer (AJCC) 6th edition, alongside visualizations from the analysis, and the code to reproduce these results. We only use the 2004-2015 data to model to ensure consistency in the corresponding staging system (AJCC 6th Edition). We presented a model with 81% recall for patients at high risk (less than 9 months of survival) with the most contributing factors to this prediction, which are Metastasis, Chemotherapy, Radiation, Surgery, and Tumor Size.","url":"https://doi.org/10.5281/zenodo.22055219","authors":["Faridani, Parzon Eyzadpur","Yu, Kaijie"],"tags":["Combined Small Cell Lung Cancer","C-SCLC","Lung Cancer","Machine Learning","Survival Prediction","Cancer Prognosis","SEER Database","AJCC"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22055219","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20111082","name":"Mingzheng — Reproducibility Data Package","source":"datacite","abstract":"Zenodo v2 v2 (2026-05-25): Added a public-cohort transfer stress test on TCM-SD (Supplementary Part I). This release adds a subdirectory, tcmsd_external_validation/ (approximately 270 MB), containing reproducibility artifacts for the TCM-SD external-transfer stress test reported in Supplementary Part I: Qwen3-30B-A3B-Instruct-2507 16-slot structured extraction outputs for 5,316 TCM-SD test records, with an extraction success rate of 99.55%. BGE-m3 dense embeddings of the 16-slot sentences, with shape (5316, 16, 1024). Predictions from 15 frozen LOSO ensemble models and proxy labels for {phlegm-dampness, blood stasis, yin deficiency}. Raw logits for each ensemble member, with shape (15, 5316, 3). Five evaluation reports: A, Mingzheng zero-shot paper-locked thresholds; B, A plus half-retained threshold calibration; C, BGE-slot linear probe with 5-fold cross-validation; D, BGE-only zero-shot with a probe trained on the CRSD source cohort; and E, TF-IDF + SGD self-baseline reference. Reports include two-sided paired-bootstrap p values. All 10 pipeline scripts: L2 LLM extraction, BGE encoding, frozen Mingzheng forward propagation, the monitoring program, and five evaluation scripts. A subdirectory README documenting the workflow, results, per-file licenses, and reproduction commands. Main result (Group A vs Group D; n = 5,316; both true zero-shot, with no TCM-SD labels used): Macro-AUC = +0.020 (95% CI +0.002 to +0.038), two-sided paired-bootstrap p = 0.025, driven mainly by blood stasis (Delta AUC = +0.052, p < 0.001). This advantage did not extend to a statistically detectable macro-F1 difference (Delta F1 = +0.007, p = 0.250), and was not observed for yin deficiency. We position this analysis as a public-cohort transfer stress test rather than the primary external validation, because its operating-point performance is limited by label-transfer effects, especially the approximately 14.8-fold lower prevalence of yin deficiency relative to CRSD. The clinical-grade external-validation evidence for Mingzheng remains the prospective multicenter temporal cohort study (n = 47, collected from n = 105). License for the new subdirectory: Scripts are released under the MIT License, consistent with the Mingzheng GitHub repository. TCM-SD-derived outputs (tcmsd_l2_output.json, tcmsd_bge_4zhen.npz, tcmsd_predictions.json, tcmsd_labels.json, tcmsd_ensemble_logits.npz, tcmsd_test_l2_input.jsonl, tcmsd_eval_v*.json, and *.md) are released under CC BY-NC-SA 4.0, inheriting the ShareAlike requirement of the upstream TCM-SD dataset (Ren et al., CCL 2022; ModelScope OmniData/TCM-SD). Related materials: The corresponding code updates have been committed to the private Mingzheng GitHub repository under external_validation_tcmsd/ (commit 0046d59; https://github.com/Jayden-XL/mingzheng). For now, this repository is accessible only to the editor and editor-invited reviewers. If you are accessing this record during peer review, please contact us through the editor. Mingzheng - Reproducibility Data Package This Zenodo record provides the reproducibility package for the Mingzheng study: a taxonomy-based multimodal artificial intelligence system for interpretable Traditional Chinese Medicine syndrome differentiation in cancer patients with comorbid sleep disorders. This package is independent of manuscript version. It supports reproduction of the main model benchmarks, leave-one-site-out (LOSO) ensemble inference, temporal validation, reader-study model outputs, zero-shot LLM comparisons, and supplementary subgroup analyses reported across manuscript versions. Related paper: Zheng Xiao, Xie Yong, Luo Song, et al. A taxonomy-based multimodal artificial intelligence system for interpretable syndrome differential diagnosis in cancer patients with comorbid sleep disorders. Manuscript, 2026. Zenodo DOI: https://doi.org/10.5281/zenodo.20111083 Code repository: https://github.com/Jayden-XL/mingzheng For now, this repository is accessible only to the editor and edito","url":"https://doi.org/10.5281/zenodo.20111082","authors":["Zheng, Xueer","Xie, Ying","Luo, Shijun","Yan, Yici","Ruan, Shanming"],"tags":["multimodal fusion","TCM","syndrome differentiation","label shift","decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20111082","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20382160","name":"Mingzheng — Reproducibility Data Package","source":"datacite","abstract":"Zenodo v2 v2 (2026-05-25): Added a public-cohort transfer stress test on TCM-SD (Supplementary Part I). This release adds a subdirectory, tcmsd_external_validation/ (approximately 270 MB), containing reproducibility artifacts for the TCM-SD external-transfer stress test reported in Supplementary Part I: Qwen3-30B-A3B-Instruct-2507 16-slot structured extraction outputs for 5,316 TCM-SD test records, with an extraction success rate of 99.55%. BGE-m3 dense embeddings of the 16-slot sentences, with shape (5316, 16, 1024). Predictions from 15 frozen LOSO ensemble models and proxy labels for {phlegm-dampness, blood stasis, yin deficiency}. Raw logits for each ensemble member, with shape (15, 5316, 3). Five evaluation reports: A, Mingzheng zero-shot paper-locked thresholds; B, A plus half-retained threshold calibration; C, BGE-slot linear probe with 5-fold cross-validation; D, BGE-only zero-shot with a probe trained on the CRSD source cohort; and E, TF-IDF + SGD self-baseline reference. Reports include two-sided paired-bootstrap p values. All 10 pipeline scripts: L2 LLM extraction, BGE encoding, frozen Mingzheng forward propagation, the monitoring program, and five evaluation scripts. A subdirectory README documenting the workflow, results, per-file licenses, and reproduction commands. Main result (Group A vs Group D; n = 5,316; both true zero-shot, with no TCM-SD labels used): Macro-AUC = +0.020 (95% CI +0.002 to +0.038), two-sided paired-bootstrap p = 0.025, driven mainly by blood stasis (Delta AUC = +0.052, p < 0.001). This advantage did not extend to a statistically detectable macro-F1 difference (Delta F1 = +0.007, p = 0.250), and was not observed for yin deficiency. We position this analysis as a public-cohort transfer stress test rather than the primary external validation, because its operating-point performance is limited by label-transfer effects, especially the approximately 14.8-fold lower prevalence of yin deficiency relative to CRSD. The clinical-grade external-validation evidence for Mingzheng remains the prospective multicenter temporal cohort study (n = 47, collected from n = 105). License for the new subdirectory: Scripts are released under the MIT License, consistent with the Mingzheng GitHub repository. TCM-SD-derived outputs (tcmsd_l2_output.json, tcmsd_bge_4zhen.npz, tcmsd_predictions.json, tcmsd_labels.json, tcmsd_ensemble_logits.npz, tcmsd_test_l2_input.jsonl, tcmsd_eval_v*.json, and *.md) are released under CC BY-NC-SA 4.0, inheriting the ShareAlike requirement of the upstream TCM-SD dataset (Ren et al., CCL 2022; ModelScope OmniData/TCM-SD). Related materials: The corresponding code updates have been committed to the private Mingzheng GitHub repository under external_validation_tcmsd/ (commit 0046d59; https://github.com/Jayden-XL/mingzheng). For now, this repository is accessible only to the editor and editor-invited reviewers. If you are accessing this record during peer review, please contact us through the editor. Mingzheng - Reproducibility Data Package This Zenodo record provides the reproducibility package for the Mingzheng study: a taxonomy-based multimodal artificial intelligence system for interpretable Traditional Chinese Medicine syndrome differentiation in cancer patients with comorbid sleep disorders. This package is independent of manuscript version. It supports reproduction of the main model benchmarks, leave-one-site-out (LOSO) ensemble inference, temporal validation, reader-study model outputs, zero-shot LLM comparisons, and supplementary subgroup analyses reported across manuscript versions. Related paper: Zheng Xiao, Xie Yong, Luo Song, et al. A taxonomy-based multimodal artificial intelligence system for interpretable syndrome differential diagnosis in cancer patients with comorbid sleep disorders. Manuscript, 2026. Zenodo DOI: https://doi.org/10.5281/zenodo.20111083 Code repository: https://github.com/Jayden-XL/mingzheng For now, this repository is accessible only to the editor and edito","url":"https://doi.org/10.5281/zenodo.20382160","authors":["Zheng, Xueer","Xie, Ying","Luo, Shijun","Yan, Yici","Ruan, Shanming"],"tags":["multimodal fusion","TCM","syndrome differentiation","label shift","decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20382160","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21307548","name":"Ensemble-Based Robust Framework for Disease Prediction and Treatment Recommendation","source":"datacite","abstract":"Abstract: Early identification of diseases based on symptoms plays a vital role in enabling timely medical consultations and enhancing healthcare accessibility. With advancements in artificial intelligence, machine learning techniques effectively analyze symptom data to predict possible diseases. This project introduces an ensemble learning framework for robust disease prediction and treatment recommendation using structured tabular clinical data. The proposed system generates structured symptom features by mapping symptoms to a predefined feature list, forming a tabular dataset. Traditional machine learning classifiers—including Random Forest, Support Vector Machine (SVM), Logistic Regression, and XGBoost—are trained on this dataset. To boost prediction reliability, an ensemble framework combines predictions from these models using techniques such as voting, weighted ensemble, and stacking. Experimental results show that ensemble methods outperform individual models in accuracy. The trained models power a Flask-based web application, enabling users to input symptoms and receive predicted diseases with treatment recommendations, highlighting the system's practical utility.","url":"https://doi.org/10.5281/zenodo.21307548","authors":["Boragalli, Vidyashri","Pawar, Digvijay. J.","Sagavkar, Sandhya V.","Huddar, Mahesh G."],"tags":["Keywords: Ensemble model, Disease Prediction, Treatment Recommendation, Tabular clinical data"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21307548","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21307549","name":"Ensemble-Based Robust Framework for Disease Prediction and Treatment Recommendation","source":"datacite","abstract":"Abstract: Early identification of diseases based on symptoms plays a vital role in enabling timely medical consultations and enhancing healthcare accessibility. With advancements in artificial intelligence, machine learning techniques effectively analyze symptom data to predict possible diseases. This project introduces an ensemble learning framework for robust disease prediction and treatment recommendation using structured tabular clinical data. The proposed system generates structured symptom features by mapping symptoms to a predefined feature list, forming a tabular dataset. Traditional machine learning classifiers—including Random Forest, Support Vector Machine (SVM), Logistic Regression, and XGBoost—are trained on this dataset. To boost prediction reliability, an ensemble framework combines predictions from these models using techniques such as voting, weighted ensemble, and stacking. Experimental results show that ensemble methods outperform individual models in accuracy. The trained models power a Flask-based web application, enabling users to input symptoms and receive predicted diseases with treatment recommendations, highlighting the system's practical utility.","url":"https://doi.org/10.5281/zenodo.21307549","authors":["Boragalli, Vidyashri","Pawar, Digvijay. J.","Sagavkar, Sandhya V.","Huddar, Mahesh G."],"tags":["Keywords: Ensemble model, Disease Prediction, Treatment Recommendation, Tabular clinical data"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21307549","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20767053","name":"Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges","source":"datacite","abstract":"This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particularly convolutional neural networks (CNNs) — are being used to detect caries, assess periodontal bone loss, analyze radiographs, support cephalometric analysis, plan implant placement, and improve CAD/CAM workflows. The paper outlines key benefits such as increased diagnostic accuracy, reduced human error, and more efficient clinical workflows, while also addressing major limitations including data quality issues, ethical and privacy concerns, the need for clinical validation, and the continued importance of human oversight in final decision-making. It concludes that AI holds strong potential to enhance the quality, efficiency, and accessibility of dental care, provided it is implemented responsibly as a supportive tool rather than a replacement for clinicians","url":"https://doi.org/10.5281/zenodo.20767053","authors":["Bandaliyev"],"tags":["Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20767053","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22055236","name":"AI-Driven Framework for Smart Healthcare","source":"datacite","abstract":"This project presents an AI-Driven Framework for Smart Healthcare Diagnosis that combines deep learning and machine learning for automated pneumonia risk assessment. DenseNet121 is used for chest X-ray analysis, while XGBoost, LightGBM, and CatBoost evaluate clinical features. A fusion-based approach combines image and clinical predictions, with Grad-CAM and SHAP providing explainable insights. The system is deployed as a secure Flask web application that provides real-time predictions and automated downloadable PDF medical reports. The proposed fusion model achieves 95.6% accuracy, demonstrating its potential as an explainable clinical decision-support system.","url":"https://doi.org/10.5281/zenodo.22055236","authors":["Ande Vishnuvardhan","Bonagiri Abhishekvardhan","Bipin Yadav","Ch. Prabhavathi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22055236","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21882242","name":"AI-Driven Framework for Smart Healthcare","source":"datacite","abstract":"This project presents an AI-Driven Framework for Smart Healthcare Diagnosis that combines deep learning and machine learning for automated pneumonia risk assessment. DenseNet121 is used for chest X-ray analysis, while XGBoost, LightGBM, and CatBoost evaluate clinical features. A fusion-based approach combines image and clinical predictions, with Grad-CAM and SHAP providing explainable insights. The system is deployed as a secure Flask web application that provides real-time predictions and automated downloadable PDF medical reports. The proposed fusion model achieves 95.6% accuracy, demonstrating its potential as an explainable clinical decision-support system.","url":"https://doi.org/10.5281/zenodo.21882242","authors":["Ande Vishnuvardhan","Bonagiri Abhishekvardhan","Bipin Yadav","Ch. Prabhavathi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21882242","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19974908","name":"Development of a Cyclic Gradient Boosting Model for Early Prediction of Gestational Diabetes Risk","source":"datacite","abstract":"Gradient Boosting Model is a notable public health challenge in Nigeria, especially in the low resource care facilities lacking adequate facilities for standard GDM diagnosis. This study attempts to develop a machine learning technique capable of accurately interpreting GDM prediction results through the use of clinical data. To accomplish this objective, a Cyclic Gradient Boosting (CGB) algorithm was proposed and analyzed using Shapley Additive exPlanations (SHAP). A total of 918 antenatal patient records were used in this investigation. Based on the results obtained from training and validation process, it was possible to obtain a good performance of the designed CGB model, with 82.6% accuracy and ROC-AUC equal to 0.822 and high specificity (>90%). In addition, it is possible to mention the high precision (62.9%), and the analysis of important features revealed that family history of diabetes (~0.60) and body mass index (BMI=0.42) were the most influential predictors. More detailed investigation of the SHAP values shows that the interaction between heredity and BMI is significant (interaction weight: 0.05), as well as the influence of heredity on weight (0.04). Cyclic boosting allowed obtaining consistent and balanced feature importances. In addition, SHAP helped interpret the model by providing easily comprehensible patient specific explanations. It was found that the combination of predictive power with interpretability is possible using non-invasive routinely collected data. This approach offers a practical solution for early identification of GDM risk in resource-constrained settings as well as support targeted screening and timely intervention.","url":"https://doi.org/10.5281/zenodo.19974908","authors":["Obianozie, Ifunanya Mirian"],"tags":["Gestational Diabetes Mellitus","Explainable Machine Learning","Cyclic Gradient Boosting","SHapley Additive exPlanations","Risk Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19974908","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19974909","name":"Development of a Cyclic Gradient Boosting Model for Early Prediction of Gestational Diabetes Risk","source":"datacite","abstract":"Gradient Boosting Model is a notable public health challenge in Nigeria, especially in the low resource care facilities lacking adequate facilities for standard GDM diagnosis. This study attempts to develop a machine learning technique capable of accurately interpreting GDM prediction results through the use of clinical data. To accomplish this objective, a Cyclic Gradient Boosting (CGB) algorithm was proposed and analyzed using Shapley Additive exPlanations (SHAP). A total of 918 antenatal patient records were used in this investigation. Based on the results obtained from training and validation process, it was possible to obtain a good performance of the designed CGB model, with 82.6% accuracy and ROC-AUC equal to 0.822 and high specificity (>90%). In addition, it is possible to mention the high precision (62.9%), and the analysis of important features revealed that family history of diabetes (~0.60) and body mass index (BMI=0.42) were the most influential predictors. More detailed investigation of the SHAP values shows that the interaction between heredity and BMI is significant (interaction weight: 0.05), as well as the influence of heredity on weight (0.04). Cyclic boosting allowed obtaining consistent and balanced feature importances. In addition, SHAP helped interpret the model by providing easily comprehensible patient specific explanations. It was found that the combination of predictive power with interpretability is possible using non-invasive routinely collected data. This approach offers a practical solution for early identification of GDM risk in resource-constrained settings as well as support targeted screening and timely intervention.","url":"https://doi.org/10.5281/zenodo.19974909","authors":["Obianozie, Ifunanya Mirian"],"tags":["Gestational Diabetes Mellitus","Explainable Machine Learning","Cyclic Gradient Boosting","SHapley Additive exPlanations","Risk Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19974909","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.13140/rg.2.2.29908.56962","name":"Quantifying Anthropometric Leakage in Obesity Risk Classification and It's Implications for Threshold-Defined Labels in Clinical Machine Learning","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.2.29908.56962","authors":["Frank Anokye"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.13140/rg.2.2.29908.56962","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21434978","name":"AIMS Proof-of-Concept: Reproducible companion code and audit log for Section 5.5 of \"Legitimacy Conversion as Organizational Infrastructure: A Three-Stage Feedback Mechanism for AI Governance\"","source":"datacite","abstract":"Deterministic Python implementation (seed 42) of three AIMS governance controls (Pillar 2 equity evaluation and proxy removal; Pillar 4 SHAP-based local audit; Pillar 5 threshold-recalibration sweep) operationalized on the UCI Heart Failure Clinical Records dataset (Chicco & Jurman, 2020). The archive contains the script poc_aims.py, the audit log produced by the canonical run (aims_audit_log.jsonl), and the metadata required to reproduce every numerical value reported in Section 5.5 of the manuscript.\\n\\nHeadline empirical finding: removing the dataset's strongest clinical predictor (serum_creatinine) as a candidate socioeconomic proxy *increased* the intergroup AUC gap (0.013 -> 0.043) rather than reducing it, while only modestly degrading overall AUC (0.910 -> 0.896). This counter-intuitive result, consistent with the fairness-accuracy trade-off literature (Ktena et al., Nature Medicine 2024), is the kind of contested technical signal for which the paper's IFP Stage 2 interpretive mediation is designed.\\n\\nThe underlying dataset is downloaded automatically by the script from the UCI Machine Learning Repository on first run; it is not redistributed in this archive.\\n\\nSee README.md for full reproduction instructions and expected output values.","url":"https://doi.org/10.5281/zenodo.21434978","authors":["JEMAI, Marouen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21434978","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21434979","name":"AIMS Proof-of-Concept: Reproducible companion code and audit log for Section 5.5 of \"Legitimacy Conversion as Organizational Infrastructure: A Three-Stage Feedback Mechanism for AI Governance\"","source":"datacite","abstract":"Deterministic Python implementation (seed 42) of three AIMS governance controls (Pillar 2 equity evaluation and proxy removal; Pillar 4 SHAP-based local audit; Pillar 5 threshold-recalibration sweep) operationalized on the UCI Heart Failure Clinical Records dataset (Chicco & Jurman, 2020). The archive contains the script poc_aims.py, the audit log produced by the canonical run (aims_audit_log.jsonl), and the metadata required to reproduce every numerical value reported in Section 5.5 of the manuscript.\\n\\nHeadline empirical finding: removing the dataset's strongest clinical predictor (serum_creatinine) as a candidate socioeconomic proxy *increased* the intergroup AUC gap (0.013 -> 0.043) rather than reducing it, while only modestly degrading overall AUC (0.910 -> 0.896). This counter-intuitive result, consistent with the fairness-accuracy trade-off literature (Ktena et al., Nature Medicine 2024), is the kind of contested technical signal for which the paper's IFP Stage 2 interpretive mediation is designed.\\n\\nThe underlying dataset is downloaded automatically by the script from the UCI Machine Learning Repository on first run; it is not redistributed in this archive.\\n\\nSee README.md for full reproduction instructions and expected output values.","url":"https://doi.org/10.5281/zenodo.21434979","authors":["JEMAI, Marouen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21434979","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21579052","name":"Early Detection for Cervical Cancer Using XG Boost Algorithm through ML","source":"datacite","abstract":"This scholarly investigation presented an innovative web-based framework specifically engineered for cervical cancer detection utilizing the XG Boost algorithm, a sophisticated machine learning methodology. Through strategic utilization of a multifaceted dataset encompassing demographic and clinical history parameters including age demographics, sexual behavior patterns, contraceptive utilization metrics, and diagnostic medical records, the developed framework incorporated XGBoost technology to substantially elevate diagnostic precision and dependability. The technological platform was strategically designed to optimize early detection protocols and intervention mechanisms, which were identified as fundamental factors for enhancing patient clinical outcomes within cervical cancer management paradigms. Through methodical assessment and comprehensive analytical procedures, the research effectively demonstrated XGBoost's exceptional capabilities in predictive modeling applications for cervical cancer identification, representing a substantial advancement in the application of machine learning technologies for healthcare outcome improvement.","url":"https://doi.org/10.5281/zenodo.21579052","authors":["Pulaganti, Omprakash","C.Yamini"],"tags":["XGBoost; Cervical Cancer Detection; Machine Learning; Healthcare; Predictive Modeling"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.21579052","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.21579053","name":"Early Detection for Cervical Cancer Using XG Boost Algorithm through ML","source":"datacite","abstract":"This scholarly investigation presented an innovative web-based framework specifically engineered for cervical cancer detection utilizing the XG Boost algorithm, a sophisticated machine learning methodology. Through strategic utilization of a multifaceted dataset encompassing demographic and clinical history parameters including age demographics, sexual behavior patterns, contraceptive utilization metrics, and diagnostic medical records, the developed framework incorporated XGBoost technology to substantially elevate diagnostic precision and dependability. The technological platform was strategically designed to optimize early detection protocols and intervention mechanisms, which were identified as fundamental factors for enhancing patient clinical outcomes within cervical cancer management paradigms. Through methodical assessment and comprehensive analytical procedures, the research effectively demonstrated XGBoost's exceptional capabilities in predictive modeling applications for cervical cancer identification, representing a substantial advancement in the application of machine learning technologies for healthcare outcome improvement.","url":"https://doi.org/10.5281/zenodo.21579053","authors":["Pulaganti, Omprakash","C.Yamini"],"tags":["XGBoost; Cervical Cancer Detection; Machine Learning; Healthcare; Predictive Modeling"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.21579053","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19391168","name":"Temporal Modeling of Maternal Health Indicators Using Sequence-Based ML Models","source":"datacite","abstract":"The temporal variation of the maternal health indicators, such as blood pressure, fetal heart rate, glucose level, and gestational weight change, necessitates the use of predictive methodologies that can identify the temporal dependencies of the same. Static models can only capture sequential connections, irregular sampling, and multimodal signals and thus fail to provide adequate knowledge in early complications detection, e.g, preeclampsia, gestational diabetes, and preterm birth. Machine learning models that operate on sequences like recurrent neural networks, long short-term memory, gated recurrent units, and transformers solve these problems by enabling learning over complex patterns of time and the combination of heterogeneous forms of data. These models enable individual-based risk forecasting, active clinical actions, and population-wide analytics, whereas deployment via scalable, clear, and data protection-conscientious operations provides clinical reliability and conformity. This article summarizes the issues that affect temporal maternal data, the use of sequence-based models, the frameworks of deployment of these models, and the new available research directions such as multimodal integration, self-supervised pretraining, and cost-efficient, scalable approaches.","url":"https://doi.org/10.5281/zenodo.19391168","authors":["FNU Sudhakar Abhijeet"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19391168","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19391169","name":"Temporal Modeling of Maternal Health Indicators Using Sequence-Based ML Models","source":"datacite","abstract":"The temporal variation of the maternal health indicators, such as blood pressure, fetal heart rate, glucose level, and gestational weight change, necessitates the use of predictive methodologies that can identify the temporal dependencies of the same. Static models can only capture sequential connections, irregular sampling, and multimodal signals and thus fail to provide adequate knowledge in early complications detection, e.g, preeclampsia, gestational diabetes, and preterm birth. Machine learning models that operate on sequences like recurrent neural networks, long short-term memory, gated recurrent units, and transformers solve these problems by enabling learning over complex patterns of time and the combination of heterogeneous forms of data. These models enable individual-based risk forecasting, active clinical actions, and population-wide analytics, whereas deployment via scalable, clear, and data protection-conscientious operations provides clinical reliability and conformity. This article summarizes the issues that affect temporal maternal data, the use of sequence-based models, the frameworks of deployment of these models, and the new available research directions such as multimodal integration, self-supervised pretraining, and cost-efficient, scalable approaches.","url":"https://doi.org/10.5281/zenodo.19391169","authors":["FNU Sudhakar Abhijeet"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19391169","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19689187","name":"The integration of artificial intelligence in medical-surgical nursing: Transforming patient care and clinical decision-making","source":"datacite","abstract":"Abstract: Artificial Intelligence (AI) is increasingly reshaping healthcare delivery, particularly in the field of medical-surgical nursing, by enhancing patient care, improving clinical efficiency, and supporting evidence-based decision-making. The integration of AI technologies such as machine learning, natural language processing, predictive analytics, and robotics has enabled nurses to perform more accurate assessments, monitor patients continuously, and respond proactively to clinical changes. AI-driven clinical decision support systems assist in reducing diagnostic errors and improving patient safety, while automation of routine tasks minimizes workload and enhances productivity. Additionally, AI facilitates personalized patient care by analyzing large volumes of health data to generate tailored treatment plans. Despite these advantages, the adoption of AI in nursing also presents challenges, including ethical concerns related to data privacy, algorithmic bias, and the need for adequate training and infrastructure. This article critically examines the applications, benefits, challenges, and future implications of AI in medical-surgical nursing based on recent literature from the past decade. It emphasizes the importance of integrating technological advancements with human-centred care to achieve optimal healthcare outcomes. Keywords: Artificial Intelligence, Medical-Surgical Nursing, Clinical Decision-Making, Patient Care, Machine Learning, Predictive Analytics, Nursing Informatics, Healthcare Technology, Robotics in Nursing, Personalized Medicine.","url":"https://doi.org/10.5281/zenodo.19689187","authors":["Manmeet Kaur","Anitha KC","Anshu"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19689187","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.19689188","name":"The integration of artificial intelligence in medical-surgical nursing: Transforming patient care and clinical decision-making","source":"datacite","abstract":"Abstract: Artificial Intelligence (AI) is increasingly reshaping healthcare delivery, particularly in the field of medical-surgical nursing, by enhancing patient care, improving clinical efficiency, and supporting evidence-based decision-making. The integration of AI technologies such as machine learning, natural language processing, predictive analytics, and robotics has enabled nurses to perform more accurate assessments, monitor patients continuously, and respond proactively to clinical changes. AI-driven clinical decision support systems assist in reducing diagnostic errors and improving patient safety, while automation of routine tasks minimizes workload and enhances productivity. Additionally, AI facilitates personalized patient care by analyzing large volumes of health data to generate tailored treatment plans. Despite these advantages, the adoption of AI in nursing also presents challenges, including ethical concerns related to data privacy, algorithmic bias, and the need for adequate training and infrastructure. This article critically examines the applications, benefits, challenges, and future implications of AI in medical-surgical nursing based on recent literature from the past decade. It emphasizes the importance of integrating technological advancements with human-centred care to achieve optimal healthcare outcomes. Keywords: Artificial Intelligence, Medical-Surgical Nursing, Clinical Decision-Making, Patient Care, Machine Learning, Predictive Analytics, Nursing Informatics, Healthcare Technology, Robotics in Nursing, Personalized Medicine.","url":"https://doi.org/10.5281/zenodo.19689188","authors":["Manmeet Kaur","Anitha KC","Anshu"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19689188","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20766746","name":"Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges","source":"datacite","abstract":"This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particularly convolutional neural networks (CNNs) — are being used to detect caries, assess periodontal bone loss, analyze radiographs, support cephalometric analysis, plan implant placement, and improve CAD/CAM workflows. The paper outlines key benefits such as increased diagnostic accuracy, reduced human error, and more efficient clinical workflows, while also addressing major limitations including data quality issues, ethical and privacy concerns, the need for clinical validation, and the continued importance of human oversight in final decision-making. It concludes that AI holds strong potential to enhance the quality, efficiency, and accessibility of dental care, provided it is implemented responsibly as a supportive tool rather than a replacement for clinicians","url":"https://doi.org/10.5281/zenodo.20766746","authors":["Nihad Bandaliyev"],"tags":["Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20766746","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.20767152","name":"Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges","source":"datacite","abstract":"This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particularly convolutional neural networks (CNNs) — are being used to detect caries, assess periodontal bone loss, analyze radiographs, support cephalometric analysis, plan implant placement, and improve CAD/CAM workflows. The paper outlines key benefits such as increased diagnostic accuracy, reduced human error, and more efficient clinical workflows, while also addressing major limitations including data quality issues, ethical and privacy concerns, the need for clinical validation, and the continued importance of human oversight in final decision-making. It concludes that AI holds strong potential to enhance the quality, efficiency, and accessibility of dental care, provided it is implemented responsibly as a supportive tool rather than a replacement for clinicians","url":"https://doi.org/10.5281/zenodo.20767152","authors":["Nihad Bandaliyev"],"tags":["Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20767152","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22052962","name":"Role of Artificial Intelligence in Cancer Research Education","source":"datacite","abstract":"Cancer remains a leading cause of mortality worldwide, driving urgent demand for advanced research and education frameworks capable of keeping pace with rapidly evolving scientific knowledge. Traditional cancer research education has increasingly demonstrated its limitations in preparing future researchers to handle the computational complexity of modern oncological data. Artificial intelligence (AI), encompassing machine learning, deep learning, and natural language processing, has emerged as a transformative force in both cancer research and biomedical education. This paper examines the multifaceted role of AI in cancer research education, exploring its applications in diagnostic training, genomic data interpretation, drug discovery pedagogy, and simulation-based learning environments. Despite documented benefits including enhanced diagnostic accuracy, accelerated competency development, and improved clinical reasoning a critical research gap persists: the absence of structured, oncology-specific AI curriculum frameworks with measurable educational outcomes. Current integration remains fragmented, with most programmes lacking standardised competency benchmarks and empirical evaluation. Key challenges include algorithmic bias, data privacy constraints, high computational costs, and insufficient interdisciplinary faculty expertise. This review synthesises evidence from peer-reviewed literature published between 2020 and 2025 to propose a forward-looking agenda for AI-integrated cancer research education. Future directions emphasise adaptive learning platforms, hybrid pedagogical models, and interdisciplinary training pathways as essential strategies for closing the gap between AI's clinical promise and its educational reality.","url":"https://doi.org/10.5281/zenodo.22052962","authors":["Mariam Fatima, Mariam Fatima"],"tags":["Artificial Intelligence","Cancer research","Machine Learning","Biomedical Education","Digital Learning in Medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22052962","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.5281/zenodo.22052963","name":"Role of Artificial Intelligence in Cancer Research Education","source":"datacite","abstract":"Cancer remains a leading cause of mortality worldwide, driving urgent demand for advanced research and education frameworks capable of keeping pace with rapidly evolving scientific knowledge. Traditional cancer research education has increasingly demonstrated its limitations in preparing future researchers to handle the computational complexity of modern oncological data. Artificial intelligence (AI), encompassing machine learning, deep learning, and natural language processing, has emerged as a transformative force in both cancer research and biomedical education. This paper examines the multifaceted role of AI in cancer research education, exploring its applications in diagnostic training, genomic data interpretation, drug discovery pedagogy, and simulation-based learning environments. Despite documented benefits including enhanced diagnostic accuracy, accelerated competency development, and improved clinical reasoning a critical research gap persists: the absence of structured, oncology-specific AI curriculum frameworks with measurable educational outcomes. Current integration remains fragmented, with most programmes lacking standardised competency benchmarks and empirical evaluation. Key challenges include algorithmic bias, data privacy constraints, high computational costs, and insufficient interdisciplinary faculty expertise. This review synthesises evidence from peer-reviewed literature published between 2020 and 2025 to propose a forward-looking agenda for AI-integrated cancer research education. Future directions emphasise adaptive learning platforms, hybrid pedagogical models, and interdisciplinary training pathways as essential strategies for closing the gap between AI's clinical promise and its educational reality.","url":"https://doi.org/10.5281/zenodo.22052963","authors":["Mariam Fatima, Mariam Fatima"],"tags":["Artificial Intelligence","Cancer research","Machine Learning","Biomedical Education","Digital Learning in Medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22052963","addedAt":"2026-09-01T01:47:57.553Z","updatedAt":"2026-09-01T01:47:57.553Z"},{"id":"doi:10.21203/rs.3.rs-10478855/v2","name":"Artificial Intelligence and Machine Learning Applications in Antimicrobial Resistance Research: A Systematic Review","source":"preprints","abstract":"Abstract Background Antimicrobial resistance (AMR) represents an existential global health crisis, causing over one million deaths yearly all over the world. Conventional culture-based susceptibility testing requires 48–72 hours, an interval insufficient for guiding empirical therapy in critically ill patients. Artificial intelligence (AI) and machine learning (ML) approaches have emerged as probable alternative solutions, capable of analysing high-dimensional clinical, spectral, and genomic datasets to generate rapid resistance predictions. Objectives To systematically identify, appraise, and synthesise peer-reviewed evidence on AI/ML model applications for detecting, predicting, and managing AMR in bacterial pathogens using human clinical data. Methods A systematic search of PubMed, Scopus, and Embase was conducted for studies published between January 2019 and January 2026. Eligible studies incorporated supervised ML or deep learning models applied directly to AMR phenotype or genotype prediction using human clinical data, with quantitative performance metrics. Data extraction followed the CHARMS checklist; methodological quality was assessed using PROBAST. PRISMA 2020 guidelines were followed throughout. Results From 4,067 records identified, 57 studies met full eligibility criteria (after removal of 452 duplicates and exclusion of 3,554 records at screening and full-text review stages). The predominant algorithms were Random Forest (n ≈ 22), XGBoost (n ≈ 20), LightGBM (n ≈ 14), logistic regression with regularisation (n ≈ 18), and support vector machines (n ≈ 16). MALDI-TOF mass spectrometry spectral data and whole-genome sequencing features were the most commonly employed input modalities. Best-performing models achieved AUROC values of 0.85–0.99 for pathogens including MRSA, carbapenem-resistant Klebsiella pneumoniae (CRKP), and Acinetobacter baumannii. PROBAST assessment revealed high risk of bias in the Participants and Analysis domains across all 57 studies, predominantly due to retrospective single-centre designs and absent external validation. Conclusions Tree-based ensemble methods, particularly XGBoost and LightGBM applied to MALDI-TOF spectral inputs, demonstrate strong diagnostic potential for rapid AMR prediction. However, pervasive methodological limitations including retrospective designs, absent external validation in ~ 65% of studies, and unreported missing data handling preclude immediate clinical translation. Prospective multicentre validation, explainability integration, and standardised reporting frameworks are required before clinical adoption.","url":"https://doi.org/10.21203/rs.3.rs-10478855/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10478855/v2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10215545/v1","name":"Explainable machine learning for clinical phenotyping and mortality prediction in critically ill patients with cancer","source":"preprints","abstract":"Abstract Critically ill patients with cancer are clinically heterogeneous, limiting the prognostic performance of conventional severity scores. This multicenter retrospective study used MIMIC-IV for model derivation and the eICU Collaborative Research Database for external validation to identify data-driven clinical phenotypes and develop an explainable machine learning model for early in-hospital mortality prediction. Twenty-eight routinely available variables from the first 24 hours of ICU admission were used for consensus clustering and XGBoost-based prediction, with interpretation performed using SHapley Additive exPlanations. Overall, 14,573 patients from MIMIC-IV and 10,301 from eICU were included. Four clinically distinct phenotypes were identified, showing progressive increases in organ dysfunction, life-support use, and mortality. The XGBoost model achieved AUCs of 0.818 and 0.811 in the derivation and external validation cohorts, respectively, with acceptable Brier scores. SHAP identified blood urea nitrogen, respiratory rate, mechanical ventilation, and vasopressor use as major contributors to predicted mortality and revealed nonlinear risk patterns. These findings suggest that early ICU data can identify clinically meaningful risk patterns and support early risk stratification in critically ill patients with cancer.","url":"https://doi.org/10.21203/rs.3.rs-10215545/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10215545/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.17.26360212","name":"Development and Validation of Interpretable Machine Learning Models for Low Birth Weight in Ethiopia: A Secondary Analysis of the Ethiopian Demographic and Health Survey","source":"preprints","abstract":"Background Low birth weight remains a primary driver of neonatal and infant mortality in Ethiopia. Machine learning models can assist early risk identification, yet clinical adoption is often limited by black box algorithms and late pregnancy predictor variables. This study aimed to develop and validate interpretable machine learning models using early pregnancy and sociodemographic features from a national survey dataset. Methods Secondary data from the nationwide Ethiopian Demographic and Health Survey were analyzed. Predictors were restricted to features accessible during early antenatal visits. Six machine learning algorithms were trained and evaluated on an independent holdout test set: Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting, Random Forest and Extreme Gradient Boosting (XGBoost). Imbalance was addressed using synthetic oversampling on the training set. Model explainability was established through Shapley Additive exPlanations (SHAP). Results Out of 12876 births, 4249 (33%) were categorized as low birth weight / small birth size. XGBoost achieved superior predictive performance with an AUC-ROC of 0.947 (95% CI: 0.910-0.938) on the test set, outperforming lasso ML (0.8637) and standard logistic regression (0.8088). Key global predictive drivers identified by SHAP values included maternal anemia status, short inter pregnancy interval ( Conclusion Machine learning models trained on early pregnancy and demographic features can accurately predict low birth weight risk in Ethiopia. Integrating interpretable frameworks into primary healthcare decision support tools provides a viable strategy for early risk stratification and targeted interventions in resource-limited settings.","url":"https://doi.org/10.64898/2026.08.17.26360212","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.17.26360212","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202607.2306.v1","name":"Comparative Performance of Classical Statistical and Machine Learning Models for Melanoma Classification","source":"preprints","abstract":"Early and accurate classification of melanoma is essential for improving patient outcomes and supporting clinical decision-making. Although numerous predictive models have been proposed, comparisons between classical statistical approaches and modern machine learning algorithms are often limited by heterogeneous analytical workflows and inconsistent validation strategies. This study aimed to compare the predictive performance of classical statistical and machine learning models for melanoma classification using a fully reproducible analytical framework. A retrospective observational study was conducted using the publicly available BCN20000 dermoscopic dataset from the ISIC Archive [1,2]. After standardized data preprocessing, four routinely available clinical variables (age, sex, anatomical site and melanocytic status) were used to develop Logistic Regression, Generalized Additive Models, Random Forest and Extreme Gradient Boosting (XGBoost) classifiers. All models were trained and evaluated using the same stratified training/testing split and their performance was assessed through discrimination, calibration and SHAP explainability analysis. Machine learning models, particularly XGBoost and Random Forest, achieved superior predictive performance compared with conventional statistical approaches, while patient age emerged as the most influential predictor of malignancy. The proposed framework provides a transparent and reproducible approach for objectively comparing predictive models and supports the development of accurate, interpretable and reproducible clinical decision-support systems for melanoma classification.","url":"https://doi.org/10.20944/preprints202607.2306.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.2306.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.20.26358484","name":"Development and Validation of Machine Learning Models for Predicting 13 or More Sections in Mohs Micrographic Surgery","source":"preprints","abstract":"Background Cases requiring 13 or more tissue sections in Mohs micrographic surgery (MMS) demand extended operative time, additional resources, and often specialised closure techniques. Pre-operative identification of such cases would improve surgical scheduling, resource allocation, and patient counselling. We aimed to develop and validate a machine learning prediction tool using pre-operative clinical features to identify cases likely to require ≥13 sections. Objectives To develop and validate machine learning models for predicting which Mohs procedures will require ≥13 sections, using pre-operative clinical features, and to identify key predictive factors. Methods We analysed 408 consecutive Mohs procedures with 16 pre-operative clinical variables. Thirty machine learning algorithms were evaluated, including ensemble methods (Stacking, Voting), gradient boosting (XGBoost, LightGBM, CatBoost), neural networks (3-7 layers), support vector machines, and traditional classifiers. Model performance was assessed using 5-fold stratified cross-validation and independent test set evaluation. Feature importance was determined using SHAP (SHapley Additive exPlanations) analysis. Results The stacking ensemble achieved the highest cross-validation AUC of 0.891 (95% CI: 0.849-0.934) and test AUC of 0.884. Tumour area (cm²), calculated using the ellipse formula to approximate clinical tumour morphology, emerged as the strongest predictor (SHAP importance: 0.141), followed by tumour size dimensions (0.086 and 0.068), aggressive histopathology (0.046), and recurrence status (0.035). Wide neural network architectures (5-layer) outperformed deeper configurations (7-layer). The model demonstrated 70.7% high-confidence predictions with uncertainty Conclusions Machine learning models using pre-operative clinical features can accurately predict which Mohs procedures will require 13 or more sections. The stacking ensemble approach provides robust predictions suitable for clinical decision support. External validation in multi-centre cohorts with diverse patient populations and practice patterns is warranted to assess model generalisability.","url":"https://doi.org/10.64898/2026.07.20.26358484","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.20.26358484","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10383224/v1","name":"Development and Validation of a Machine Learning Model Integrating SPECT MPI Multiparametric Features for Risk Prediction in Coronary Artery Disease with Preserved Ejection Fraction","source":"preprints","abstract":"Abstract Objective Early identification of high-risk patients with coronary artery disease (CAD) and preserved left ventricular ejection fraction (LVEF) is essential for optimizing management. This study aimed to develop a machine learning model integrating clinical and myocardial perfusion imaging (MPI)–derived parameters to predict major adverse cardiac events (MACE). Methods We retrospectively included 1,675 patients with suspected or confirmed CAD and preserved LVEF who underwent resting MPI. Demographic, clinical, and imaging variables were collected, and missing data were handled via multiple imputation. Ten machine learning algorithms were developed and compared. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and additional metrics. Shapley additive explanations analysis (SHAP) was applied to interpret feature contributions. Results The CatBoost model showed the best performance, with cross-validated AUC of 0.693, accuracy 0.632, precision 0.551, F1 score 0.451, and Brier score 0.234. SHAP analysis identified nine key predictors: prior cardio-cerebrovascular disease, age, peak filling rate, low-density lipoprotein cholesterol, total perfusion deficit, eccentricity index (no-gated), entropy, coronary stenosis ≥ 50%, and New York Heart Association functional class. Conclusions We developed an interpretable CatBoost model integrating clinical and MPI features to predict MACE risk in CAD patients with preserved LVEF. The model demonstrated a balanced performance in terms of stability, generalizability, and clinical applicability, and may serve as a valuable tool for risk stratification and personalized management in this patient population. Trial registration Not applicable. This study is a retrospective analysis.","url":"https://doi.org/10.21203/rs.3.rs-10383224/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10383224/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10517738/v1","name":"Sexual Dysfunction Risk Assessment in Peritoneal Dialysis: A Machine Learning Study","source":"preprints","abstract":"Abstract Background: Sexual dysfunction (SD) is a common yet underrecognized complication in patients receiving peritoneal dialysis (PD). We investigated the prevalence of SD and its associated factors, and developed interpretable machine learning models to predict SD risk in PD patients. Methods: We enrolled 209 PD patients (100 males, 109 females) in this retrospective cross-sectional study. Sexual function was assessed with the International Index of Erectile Function-5 (IIEF-5) in males and the Female Sexual Function Index (FSFI) in females. We collected demographic, clinical, biochemical, and psychosocial variables, and performed univariate analysis, Spearman correlation, and multivariate linear and logistic regression. Eight machine learning algorithms were compared. SHapley Additive exPlanations (SHAP) were applied to interpret the best-performing model. Results: The overall prevalence of SD was 77.5% (162/209), with rates of 78.0% (78/100) in males and 77.1% (84/109) in females. Multivariate analysis identified depression score (SDS; β = −0.24, P = 0.013), residual renal function (β= +0.005, P = 0.021), marital status (β= +8.77, P = 0.0003), and α-blocker use (β = −4.14, P = 0.013) as independent predictors of male erectile function. In-sample AUCs from logistic regression were 0.808, 0.860, and 0.850 for overall SD, male ED, and female FSD, while corresponding test-set AUCs were 0.772, 0.672, and 0.541. Among machine learning models, CatBoost produced the highest test-set AUC (0.808), and the ensemble model (Random Forest + XGBoost) offered the best balance of sensitivity and specificity (AUC = 0.801). SHAP analysis identified SDS as the dominant predictor (mean |SHAP| = 0.0455), followed by residual renal function (0.0291), β2-microglobulin (0.0234), and hemoglobin (0.0223). Subgroup analyses indicated that psychosocial factors were more prominent in younger patients (≤40 years), while physiological parameters carried greater weight in older patients (>40 years). Conclusions : Sexual dysfunction is highly prevalent among PD patients. Depression is the strongest modifiable risk factor; residual renal function is an important protective factor. Machine learning models may help predict individual risk and guide clinical decisions.","url":"https://doi.org/10.21203/rs.3.rs-10517738/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10517738/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10349847/v1","name":"A Machine Learning Approach for Predicting 28-Day ICU Mortality in ARDS Patients Based on the New Global Definition","source":"preprints","abstract":"Abstract Background Acute respiratory distress syndrome (ARDS) is a life-threatening form of acute diffuse lung injury that can be triggered by various factors, including pneumonia, sepsis, trauma, and other etiologies. The definition of ARDS has undergone multiple revisions to date, with the new global definition (2023) introducing significant updates to the clinical diagnosis and treatment of this syndrome. This study aimed to develop and validate an interpretable prognostic prediction model for ARDS patients via machine learning techniques based on a new global definition. Methods We extracted patient data from the Medical Information Mart for Intensive Care-IV database (MIMIC-IV, version 2.2) for model training. For external validation, we used clinical data from patients who met the new global definition of ARDS and were admitted to the Affiliated Hospital of Xuzhou Medical University. LASSO regression with cross-validation was employed to select key prognostic variables. We subsequently constructed predictive models for 28-day mortality in ARDS patients via eight machine learning algorithms: logistic regression, random forest, decision tree, support vector classifier (SVC), LightGBM, XGBoost, AdaBoost, and multilayer perceptron (MLP). Model performance was assessed via receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. SHAP values were used to interpret the machine learning models. Results A total of 3382 ARDS patients were included in our analysis, from which 15 key variables were selected for model development. On the basis of the area under the ROC curve (AUC), DCA, and calibration curves, the SVC model demonstrated strong performance, achieving an average AUC of 0.819 (95% CI: 0.786–0.855) in the internal validation set and 0.871 in the external validation set. Conclusion Our machine learning-based prognostic prediction model for ARDS patients, developed in accordance with the new global definition, demonstrates reliable performance and can support clinical decision-making by helping clinicians formulate personalized treatment plans.","url":"https://doi.org/10.21203/rs.3.rs-10349847/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10349847/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10685167/v1","name":"Feature-Optimized Hybrid AI Models for Explainable and Generalizable Epilepsy Prediction","source":"preprints","abstract":"Abstract Epilepsy a neurological condition where individuals experience recurring seizures, which developing accurate, timely prediction methods for the purpose of optimal clinical care. Machine learning has achieved some positive results in this area; however, there are limitations regarding generalized use, interpretability and quality of selected features limiting the potential for these models to be used clinically. Therefore, the focus of this report will be to evaluate and analyze detailly hybrid artificial intelligence (AI) frameworks using optimized features and XAI, specifically focusing on predicting epileptic seizures. The evaluation will include various types of data utilized in the form of: electroencephalograms (EEGs); patient information; and multimodal biomedical data. Each type of hybrid method that integrates machine learning and deep learning, and its corresponding predictive capabilities, robustness, interpretability, and capability to perform well under varied conditions in relation to multiple data environments evaluated. Feature optimization strategies utilizing meta-heuristics or dimensionality reduction methods are reviewed, also includes recent advancements in the fields of machine learning, deep learning, and hybrid architectures for EEGs; patient information; and multimodal biomedical datasets. The report focuses how explainability enhances clinical acceptance through the application of SHAP, LIME and attention mechanisms. Comparative analysis and identification of research areas requiring additional work were conducted as part of identifying areas to apply proposed generic framework for improving the efficacy of epilepsy prediction systems. Study aims to provide a structure for assessing current approaches; identify significant research challenges; and guide researchers toward development of highly reliable, easily interpretable for predicting epileptic seizures.","url":"https://doi.org/10.21203/rs.3.rs-10685167/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10685167/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.06.30.26356761","name":"How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types","source":"preprints","abstract":"Background Explanations play a crucial role in helping clinicians understand how black-box machine learning models make predictions in clinical settings. Several different types of explanations have been developed, each corresponding to a unique approach for characterizing the relationships between model inputs and predictions. However, it remains unclear what types of explanations are the most valued by clinicians. Objective To improve the utility of machine learning in clinical settings, we aimed to evaluate how different explanation methods are valued by clinicians across clinically important metrics, such as importance, trust, understanding, and how explanations affect clinicians’ thinking about patients. Methods We conducted a user study of 39 critical care and hospital medicine nurses and physicians to compare attribution, counterfactual, and rule-based explanations. We analyzed the impact of each type of explanation on clinicians’ trust in and understanding of the predictions made by machine learning models, how well clinicians understood the explanation, and how the explanation affected what they thought were the most important features for determining patients’ status. We also assessed clinicians’ preferences for the representation of different types of explanations. Results Clinicians consider explanations of clinical machine learning models important, with physicians perceiving explanations as more important after interacting with them than nurses. All explanation types affected clinicians across all measured dimensions, with attribution explanations having the most significant positive effects on all measured dimensions. Moreover, nearly half of clinicians preferred viewing multiple explanation types together. Conclusions It is important to provide explanations for predictions made by machine learning models in clinical settings. When implementing machine learning explanations in these settings, developers should prioritize attribution explanations while allowing for multiple types of explanations to be shown. Furthermore, the development of new explanation methods should be tailored towards specific clinical roles, as nurses and physicians may utilize explanations differently to support their respective workflows.","url":"https://doi.org/10.64898/2026.06.30.26356761","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.30.26356761","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202608.0252.v1","name":"Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia","source":"preprints","abstract":"Acute myeloid leukemia (AML) produces more molecular, imaging, and clinical data per patient than any hematologist can hold in mind at once, and each revision of the WHO, ICC, and European LeukemiaNet (ELN) frameworks adds to the load. Artificial intelligence (AI) and machine learning (ML) now reach into every stage of AML care. Deep-learning models read therapy-relevant mutations directly from bone-marrow smears; automated flow-cytometry gating reproduces expert calls in under a minute; and the first AI pathology devices for hematology have cleared regulatory review and entered clinical use. Beyond diagnosis, ML captures the age-dependent weight of individual mutations that categorical ELN scoring misses, drug-response prediction for venetoclax–azacitidine has been validated across multiple external cohorts, and large language models are being tested for tumor-board support and trial matching. The next wave, from clonal-architecture modeling and single-cell foundation models to digital twins and reinforcement learning for adaptive dosing, could move AML management from reactive toward predictive, evolution-aware care. This review departs from existing AI-in-hematology surveys in three ways: we (i) restrict scope to AML and organize the field around clinical decision points rather than technology categories, (ii) grade every tool on a five-tier clinical-readiness level (CRL-AML 1–5), which exposes hundreds of models clustered at CRL-AML 1–2 and none yet in prospective clinical evaluation, and (iii) close with a numbered three-year agenda naming the consortia, datasets, and pragmatic trials needed to carry the field from publication to practice.","url":"https://doi.org/10.20944/preprints202608.0252.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.0252.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10555359/v1","name":"Machine Learning Model Integrating Clinical Features, CT Radiomics, and Transfer Learning for Predicting Aggressive Recurrence of Hepatocellular Carcinoma","source":"preprints","abstract":"Abstract Objective To predict aggressive recurrence of hepatocellular carcinoma (HCC) after curative resection, this study aimed to develop a preoperative noninvasive prediction model by integrating clinical features, radiomics, and transfer learning–based deep features, thereby enabling early identification of high-risk patients and providing a reference for individualized perioperative treatment decisions. Methods A total of 455 patients with HCC who underwent curative hepatectomy were retrospectively enrolled and randomly divided into a training group (n = 318) and a validation group (n = 137) in a 7:3 ratio. Radiomics features and transfer learning features were extracted from preoperative portal venous phase CT images using a ResNet-50 model. After dimensionality reduction and feature selection, a DLR feature set was constructed via early fusion. The XGBoost algorithm was employed to build clinical, radiomics, DLR, and multimodal fusion models, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Results Albumin, FIB-4 score, portal vein diameter, and involvement of liver segment S2 were identified as independent influencing factors. The multimodal fusion model achieved an AUC of 0.996 (95% CI: 0.992–1.000) in the training group, but only 0.567 (95% CI: 0.468–0.667) in the validation group, indicating marked overfitting. Calibration curves demonstrated good fit in the training group but poor fit in the validation group. Conclusion Although the multimodal fusion model performed excellently in the training group, its generalizability was limited owing to the small sample size and single-center retrospective design, rendering it currently unsuitable for clinical decision-making. Further validation and optimization are warranted in large-scale, multicenter, prospective studies.","url":"https://doi.org/10.21203/rs.3.rs-10555359/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10555359/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10478233/v1","name":"Machine learning approach to evaluate the significance of clinical factors and laboratory markers in predicting postpartum hemorrhage","source":"europepmc","abstract":"Abstract Background Identifying risk factors is the crucial step in preventing postpartum hemorrhage (PPH). Integrating laboratory, demographic, and real-time intrapartum data into predictive models allows clinical teams to move from broad risk categories to specific, individualized risk probabilities. This research aimed to evaluate the predictive power of machine learning (ML) in PPH. Methods This prospective cohort study took place at two tertiary hospitals in Tehran, Iran, between November 2024-October 2025. Inclusion criteria required singleton pregnancies reaching 28 weeks of gestation, agreement to join the study, and complete data without any omissions. Those with chorioamnionitis, or who received blood transfusions during the antepartum period were excluded. Patients were divided into those with PPH and those without PPH. Maternal clinical and laboratory data were established as input for ML models. A variety of algorithms were used to evaluate the power of ML models in predicting PPH. Accuracy, sensitivity, specificity, precision, F1 Score, Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), and Brier score were utilized to assess the performance of ML models. Results Among the 604 eligible participants, 55 (9.10%) experienced PPH. Significant variations were observed in serum lactate levels between PPH and non-PPH instances. Preeclampsia and gestational age were associated with PPH. PPH was less common in preterm deliveries and those with previous cesarean sections. Evaluating the performance of ML models for predicting PPH showed that the Logistic Regression model exceeded the performance of the others, achieving an average ROC-AUC of 0.71 and a Brier score of 0.07; however, additional metrics (sensitivity: 0.02, precision: 0.10, F1Score: 0.04) suggested it was inadequate in effectively predicting PPH. Conclusions The Pearson correlation showed a strong positive relationship among serum lactate levels, gestational age, history of cesarean, and preeclampsia related to PPH; however, the predictive performance of the ML models for PPH was poor.","url":"https://doi.org/10.21203/rs.3.rs-10478233/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10478233/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-10315339/v1","name":"Machine Learning-Based Identification of Key Predictors of Estimated Glomerular Filtration Rate in Older Taiwanese Women","source":"preprints","abstract":"Abstract Background : Estimated glomerular filtration rate (GFR) is widely used to assess kidney function but is primarily derived from serum creatinine, which is affected by age, sex, muscle mass, nutrition, inflammation, and fluid status. We aimed to identify key predictors of eGFR using machine learning. Methods : This retrospective study analyzed data from 1,344 community-dwelling Taiwanese women aged ≥65 years from the MJ Health Database. Thirty demographic, biochemical, anthropometric, and lifestyle variables were included. Five machine learning models (LR, RF, MARS, SVR, and XGBoost) were compared using root mean squared error (RMSE) and coefficient of determination (R²). Feature importance identified key predictors of eGFR. Results : Mean eGFR was 68.9 ± 15.5 mL/min/1.73 m², with a right-skewed distribution and outliers. XGBoost achieved the best predictive performance (RMSE = 9.8; R² = 0.60), outperforming the other models. Age, serum uric acid, systolic and diastolic blood pressure, and C-reactive protein were the strongest predictors of eGFR, whereas socioeconomic and lifestyle variables contributed minimally. Conclusions : Machine learning effectively captured complex non-linear relationships among clinical variables, highlighting age, uric acid, blood pressure, and inflammatory status as major determinants of renal function.","url":"https://doi.org/10.21203/rs.3.rs-10315339/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10315339/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10554485/v1","name":"Development of a Predictive Model for Lymph Node Metastasis in Endometrial Cancer Based on Integrated Clinical Characteristics, Serum Tumor Biomarkers, and Related Factors","source":"preprints","abstract":"Abstract Objective To investigate the expression levels of serum tumor biomarkers, including carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4), and carbohydrate antigen 19 − 9 (CA199), as well as other related clinical characteristics in patients with endometrial cancer (EC), and to explore their associations with lymph node metastasis (LNM). Furthermore, a predictive model for LNM in EC was developed based on machine learning algorithms by integrating clinical characteristics and serum tumor biomarkers, aiming to provide support for subsequent clinical decision-making. Methods Clinical data from 1,099 patients diagnosed with endometrial cancer at the Affiliated Cancer Hospital of Xinjiang Medical University between 2021 and 2026 were retrospectively collected. Univariate analysis was performed to identify clinical characteristics and serum tumor biomarkers significantly associated with LNM. The selected variables were subsequently incorporated into three machine learning models, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost), for model development. The dataset was randomly divided into training and validation sets at a ratio of 7:3. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), and the importance of predictive indicators was further analyzed. Results Univariate analysis demonstrated that clinical stage and serum levels of CA125, HE4, and CA199 were significantly associated with LNM in patients with EC ( P","url":"https://doi.org/10.21203/rs.3.rs-10554485/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10554485/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.24.26360900","name":"Multimodal Machine Learning for Predicting Outcomes in the PASS-01 Trial of Systemic Therapy for Metastatic Pancreatic Cancer","source":"preprints","abstract":"Purpose Modified FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) are standard first-line treatments for metastatic pancreatic ductal adenocarcinoma (PDAC), but no validated biomarker guides treatment selection. We developed MULTIPL, a multimodal machine learning system, and established the PASS-01 Challenge to benchmark prognostic and predictive biomarkers. Patients and Methods MULTIPL was trained in the COMPASS study (N=268), integrating clinical, digitized histopathology, whole-genome, and RNA-seq data. MULTIPL, PurIST, hENT1 expression, and HRDetect were evaluated in the PASS-01 trial, a randomized phase II trial of FFX versus GNP (N=160), within the Challenge. The primary endpoint was differential treatment benefit measured by concordance-for-benefit for progression-free survival. Results MULTIPL had the highest concordance index for OS among individually evaluated biomarkers (0.595; 95% confidence interval [CI], 0.55–0.65) and separated high-versus low-risk patients (hazard ratio, 1.62; 95% CI, 1.13–2.33; P =0.009). Patients recommended for GNP by MULTIPL had significantly longer OS with GNP than with FFX (hazard ratio, 0.47; 95% CI, 0.28–0.82; P =0.007), whereas patients recommended for FFX had similar OS between treatments. Interpretability analysis of MULTIPL in COMPASS identified KDM6A alterations and SSTR1 expression as prognostic biomarkers, which were validated in PASS-01. However, none of the tested biomarkers significantly predicted differential treatment benefit in the PASS-01 Challenge. Conclusion MULTIPL demonstrated robust prognostic performance in external validation, identified a subgroup enriched for benefit from GNP, and enabled discovery and validation of prognostic biomarkers in metastatic PDAC. However, no biomarker met the primary endpoint for differential treatment benefit, underscoring the value of the PASS-01 Challenge. Translational Relevance Several biomarkers have been proposed to guide first-line treatment selection in metastatic pancreatic cancer, but none are validated from randomized data. We developed MULTIPL, a multimodal machine-learning model that integrates clinical, histopathologic, genomic, and transcriptomic data from the observational COMPASS study. In parallel, we launched the PASS-01 Challenge to evaluate biomarkers in a randomized trial of modified FOLFIRINOX versus gemcitabine plus nab-paclitaxel to evaluate predictive and prognostic biomarkers. Neither MULTIPL nor the published biomarkers PurIST, hENT1, and HRDetect met the prespecified endpoint for predicting differential treatment benefit measured using concordance for benefit. MULTIPL nevertheless demonstrated prognostic capabilities and identified a subgroup with longer survival on gemcitabine plus nab-paclitaxel. Model interpretation also identified KDM6A alterations and SSTR1 expression as prognostic biomarkers, which were validated in PASS-01. These findings demonstrate the potential of multimodal machine learning in pancreatic cancer and establish the PASS-01 Challenge as a randomized evaluation of biomarkers for treatment selection.","url":"https://doi.org/10.64898/2026.08.24.26360900","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.24.26360900","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10773663/v1","name":"Estimating VO2max in patients with obesity using machine learning: A retrospective observational cohort study","source":"preprints","abstract":"Abstract This study investigated the use of machine learning models to estimate maximal oxygen consumption (VO 2max ) in adults with obesity, aiming to create an accessible alternative to traditional VOmax tests that require intensive protocols and specialized equipment. Using data from 253 participants, we evaluated eleven regression models across three levels of cumulative input feature complexity. We identified Automatic Relevance Determination (ARD) and Bayesian Ridge Regression (BRR) as the top-performing models. ARD Regression performed best at the simplest input feature complexity level, with a mean absolute percentage error (MAPE) of 12.5 (95% confidence interval (CI), 10.3-14.9), whilst BRR demonstrated superior performance at the intermediate and highest input feature complexity levels, with a MAPE of 12.0 (95% CI, 10.1 - 14.3) and 11.3 (95% CI, 9.5 - 13.3). Feature importance analysis indicated that age and sex were the most consistently important and influential predictors of VO 2max , along with waist circumference and body weight in the higher complexity models. Furthermore, the Bayesian properties of the best models yielded uncertainty estimates, and we demonstrated a statistically significant positive association between the estimation error and these uncertainty values. These results indicate that machine learning is a promising approach for estimating VO 2max , especially when comprehensive physiological inputs are available, with potential use in clinical and fitness monitoring contexts where conventional testing is difficult. Nonetheless, current models still fall short of the accuracy achieved by direct physiological measurements.","url":"https://doi.org/10.21203/rs.3.rs-10773663/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10773663/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.21.26358593","name":"Validation of clinical diagnosis and machine learning classification of cognitive impairment","source":"preprints","abstract":"Structured Abstract INTRODUCTION Cognitive syndrome diagnosis (Normal, Mild Cognitive Impairment (MCI), Dementia) is important for summarizing disease status and predicting future progression. Machine learning approaches to classification might substitute for or complement clinical diagnosis but must be shown to have validity for these purposes. METHODS A machine learning algorithm was trained in a previous study to reproduce clinical diagnosis of cognitive impairment [1]. We examined and compared concurrent validity (cross-sectional MRI measures of brain integrity) and predictive validity (longitudinal change in MRI measures of brain integrity and progression to a more impaired diagnosis/classification) of clinical diagnosis and algorithmic classification from the prior study. RESULTS Clinical diagnosis and algorithmic classifications had robust associations with clinical and MRI outcomes and differences across diagnosis/classification types were minor. Algorithmically estimated probability of a Normal diagnosis had the strongest associations with cross-sectional and longitudinal MRI outcomes. Progression from Normal to MCI or Dementia was faster for Clinical diagnosis than Algorithmic classification but future rates of MRI measured brain degeneration were essentially the same in individuals with baseline clinical diagnosis and algorithmic classification of Normal cognition. DISCUSSION Clinical diagnosis and algorithmic, machine learning based classification had robust and similar associations with independent validity criteria. Both forms of diagnosis/classification had utility for staging current brain degeneration and predicting future brain degeneration and clinical decline. This study demonstrates a validation design that can simultaneously evaluate the utility of both clinical diagnosis and algorithmic classification of cognitive impairment in a manner that improves understanding of both types of classification.","url":"https://doi.org/10.64898/2026.07.21.26358593","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.21.26358593","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10588631/v1","name":"Construction and Cross-Cohort Validation of Survival Prediction Models for Prostate Cancer: An Interpretable Machine Learning Study Based on SEER and Local Chinese Clinical Data","source":"preprints","abstract":"Abstract Objective This investigation sought to develop interpretable machine learning(ML) frameworks for forecasting overall survival (OS) in prostate cancer patients, thereby enhancing prognostic transparency to facilitate individualized clinical decision-making. Methods Patient data were sourced from the SEER database(randomly partitioned into a 7:3 ratio for training and internal validation) and supplemented by an external cohort from Kashgar, China. Independent prognostic indicators were identified using LASSO regularization combined with multivariate Cox regression. Seven predictive models were subsequently constructed, including CoxPH and six ML algorithms—RSF, GLMboost, decision tree, boosted tree(BT), DeepSurv, and NMTLR. Model discrimination and calibration were evaluated via C-index, time-dependent AUC, and integrated Brier score(iBS). Interpretability was elucidated through time-dependent permutation importance, partial dependence survival profiles, and multi-dimensional SurvSHAP(t) analysis. Results Ten predictors—age, Gleason score, M stage, PSA, surgery, marital status, histology, bone metastasis, N stage, and chemotherapy—were retained as independent factors. The boosted tree model achieved superior performance, with 1-, 3-, and 5-year OS AUCs exceeding 0.88 and iBS values below 0.160 across all cohorts. SurvSHAP(t) analysis ranked age, Gleason score, M stage, PSA, and marital status as the top five contributors. All core variables exhibited time-dependent increases in predictive weight. Notable antagonistic interactions were observed between age and M stage/Gleason score, while mild synergistic effects were found for age-chemotherapy and AJCC stage-marital status pairs. Conclusions The BT model demonstrates robust accuracy and interpretability for prostate cancer OS prediction. When combined with SurvSHAP(t) visualization, it may serve as a reliable auxiliary instrument for individualized diagnosis, treatment planning, and long-term surveillance.","url":"https://doi.org/10.21203/rs.3.rs-10588631/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10588631/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10589072/v1","name":"Predicting Human Salmonella Antimicrobial Resistance from Poultry Antibiotic Residues: A Simulation-Based Machine Learning Proof-of-Concept with One Health Integration","source":"preprints","abstract":"Abstract Background. Antimicrobial resistance (AMR) in foodborne pathogens such as Salmonella enterica represents a critical One Health challenge. Sub-therapeutic antibiotic residues in poultry meat are hypothesized to exert continuous selective pressure. Yet, the quantitative relationship between veterinary residue levels and human phenotypic resistance is difficult to study directly and remains underexplored in the literature. Methods. Because no linked human-clinical/poultry-residue dataset of this kind was available, a controlled synthetic dataset of 2,500 simulated human clinical cases was constructed, informed by field observations from the Hama University Faculty of Veterinary Medicine and by published CLSI/EUCAST susceptibility ranges. The simulation encoded four veterinary antibiotics, four geographic regions, poultry residue levels (0.02–5.0 mg/kg), and intentionally overlapping minimum inhibitory concentration (MIC) ranges (47–49%) to prevent trivial data leakage. Three classifiers were compared — a categorical-only baseline Random Forest, a standardized Logistic Regression, and an optimized Random Forest using all features — on an 80/20 stratified split (random_state = 7), with 5-fold cross-validation and isotonic probability calibration performed as robustness checks. SHAP (TreeExplainer) was used for interpretability. Results. The optimized Random Forest achieved 79.6% accuracy, AUC-ROC = 0.860 and average precision = 0.916 on the held-out test set, outperforming the categorical baseline (63.0%, AUC = 0.471) and Logistic Regression (77.8%, AUC = 0.819); five-fold cross-validation gave consistent estimates (accuracy 0.787 ± 0.030; AUC-ROC 0.860 ± 0.016). Poultry_Residue_Level_mg_kg was the dominant predictive feature (55.2% Gini importance; mean |SHAP| = 0.185), followed by Human_MIC_mg_L (38.6%; mean |SHAP| = 0.142). Isotonic calibration modestly improved probability reliability (Brier score 0.140 → 0.132). Conclusions. A signal consistent with the selective-pressure mechanism that was encoded in the simulation design was reliably recovered by an interpretable machine-learning pipeline, and this recovery exceeded what categorical descriptors alone could achieve. As a proof of concept, the framework illustrates a rapid, explainable computational approach to hypothesis generation for food-safety screening. Because the outcome and MIC variables were simulated rather than measured, the results demonstrate pipeline feasibility, not a validated biological finding; prospective validation on real, linked poultry-residue and human-clinical data is required before any translation to regulatory or slaughterhouse screening practice.","url":"https://doi.org/10.21203/rs.3.rs-10589072/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10589072/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.22.26358682","name":"Adversarial Validation Reveals Diagnostic Workflow Leakage in PCOS Machine Learning Models","source":"preprints","abstract":"Background Machine-learning models for polycystic ovary syndrome (PCOS) and other conditions frequently report near-perfect diagnostic performance, but retrospective datasets assembled from routine clinical practice can encode diagnostic-group membership in how data were acquired rather than in disease biology, and this acquisition-related information can be indistinguishable from genuine clinical signal under conventional validation. Objective To determine, using a real-world PCOS cohort as a case study, whether high classification performance reflected clinically meaningful information or artifacts of data provenance, schema structure, and measurement-acquisition workflow, and to develop a generalizable audit framework for detecting such artifacts in retrospective medical machine learning. Methods We analyzed 1,331 retrospective records (1,286 PCOS, 45 controls) from a single endocrine-gynecology database. A layered acquisition-bias framework compared classification performance using (i) raw and harmonized missingness patterns alone, (ii) measured values with and without explicit missingness indicators, and (iii) ascertainment-balanced feature sets with and without age. Logistic regression and random forest were evaluated using repeated stratified cross-validation, bootstrap resampling, label-permutation testing, and calibration analysis, and the framework was validated against a semi-synthetic experiment with known ground truth. Results Diagnostic status was perfectly predicted (ROC-AUC = 1.000) from missingness patterns alone, before any clinical value was examined, and this persisted after semantic harmonization of duplicated source columns. Performance declined progressively as acquisition-sensitive information was removed, from near-ceiling in raw and harmonized value models to a mean ROC-AUC of approximately 0.80–0.82 in the most restrictive ascertainment-balanced, age-excluded representation. The semi-synthetic experiment reproduced this pattern under known data-generating conditions, confirming that harmonization removes schema-fragmentation artifacts but not workflow-driven acquisition bias. Conclusions Apparent diagnostic performance in this cohort was substantially attributable to diagnostic workflow and data-acquisition structure rather than to a stable, transportable biological signal. The layered audit framework generalizes beyond PCOS and offers a practical tool for detecting acquisition-related leakage in retrospective clinical machine-learning studies.","url":"https://doi.org/10.64898/2026.07.22.26358682","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358682","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.17.745284","name":"Single-Molecule Proteomics via a Dynamic Translocase and Physics-Informed Machine Learning","source":"preprints","abstract":"Single-molecule protein sequencing promises to democratize clinical proteomics, but platforms retrofitting static DNA-sequencing nanopores face a fundamental biophysical bottleneck: they only measure one-dimensional excluded volume. Consequently, these static calipers struggle to resolve isobaric residues, requiring complex DNA-handle chemistries and target concentrations that exceed clinically relevant abundance ranges. Here, we introduce a dynamical, target-docking translocase engine – the anthrax toxin protective antigen (PA) – as a label-free single-molecule peptide sensor. By extracting the multi-state thermodynamic friction generated as the pore’s active site dynamically “breathes” around translocating analytes, we trained a physics-informed machine learning (PIML) architecture to classify a 20-member guest-host peptide library panel representing all 20 canonical amino acids at the single-event level. Operating at low nanomolar concentrations under a 35-millisecond thermodynamic read constraint, the translocase resolved isobaric variants (leucine and isoleucine). Furthermore, we achieved 98.02 (±0.05)% classification accuracy on a panel of five un-tagged, native clinical biomarkers (e.g., KRAS G12D, angiotensin, bradykinin). Transitioning from static volumetric measurement to time-domain thermodynamic fingerprinting establishes the requisite protein nanopore hardware for de novo proteomics.","url":"https://doi.org/10.64898/2026.08.17.745284","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.17.745284","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10458680/v1","name":"Platelet-to-Lymphocyte Ratio-Driven Machine Learning Model for Predicting In-Hospital Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention in Acute Myocardial Infarction: A Retrospective Cohort Study","source":"preprints","abstract":"Abstract Background Accurate risk assessment of major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) is central to optimizing clinical decision-making. Although inflammatory mechanisms play a crucial role in ischemia-reperfusion injury, previous studies have largely been limited to single inflammatory markers, lacking systematic integration and comparative validation of multidimensional inflammatory indices. Moreover, which inflammatory marker possesses the optimal driving efficacy within a machine learning framework remains unclear. Objective This study aimed to systematically integrate five commonly used multidimensional inflammatory indices (PLR, MLR, MHR, SII, and SIRI), construct risk prediction models for in-hospital MACE after PCI in AMI patients based on traditional logistic regression and nine machine learning algorithms, establish PLR as a core driving factor, and explore nonlinear relationships between key clinical indicators and MACE. Methods A total of 1,163 consecutive AMI patients who underwent PCI at the Second Affiliated Hospital of Shenyang Medical College from 2020 to 2025 were retrospectively enrolled. Patients were randomly divided into a training set (n = 814) and a test set (n = 349) at a 7:3 ratio. Five multidimensional inflammatory indices were calculated: platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), monocyte-to-high-density lipoprotein ratio (MHR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). LASSO regression was used for variable selection, and multivariable logistic regression was performed to identify independent risk factors and construct a nomogram. Restricted cubic spline (RCS) analysis was employed to explore nonlinear relationships. The Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively to the training set to balance class distribution. Based on the identified independent predictors, nine machine learning models (XGBoost, Random Forest, LightGBM, MLP, SVM, AdaBoost, Decision Tree, Gaussian Naive Bayes, and Logistic Regression) were constructed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The SHAP method was used for model interpretability. Robustness was verified through hard endpoint sensitivity analysis and subgroup analyses. Results The training and test sets were well-balanced in baseline characteristics. The incidence of MACE was 17.2% (140/814). Univariate analysis showed that PLR, SIRI, SII, and MLR were significantly elevated in the MACE group (P 0.05. Conclusion Among the five multidimensional inflammatory indices evaluated, PLR was the only one that retained independent predictive value after LASSO regularization and multivariable adjustment, highlighting its potential as a core inflammatory driver within the prediction framework. EF exhibits a nonlinear threshold effect with MACE risk at approximately 50%. The XGBoost model significantly outperforms traditional logistic regression in predictive performance, and when combined with SHAP analysis, enables individualized risk visualization. This PLR-driven machine learning model can serve as a precise and robust quantitative decision-making tool for early risk stratification and individualized intervention in AMI patients after PCI.","url":"https://doi.org/10.21203/rs.3.rs-10458680/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10458680/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9914444/v1","name":"Development and preliminary external validation of a machine learning-based model for predicting 28-day mortality in non-HIV patients with Pneumocystis jirovecii pneumonia: a two-centre retrospective study","source":"preprints","abstract":"Abstract Background Non-HIV patients with Pneumocystis jirovecii pneumonia (PJP), particularly those with immunocompromising conditions, are at risk of rapid clinical deterioration and short-term death. Early prognostic assessment remains difficult because outcomes are influenced by multiple interacting clinical, laboratory and treatment-related factors. This study aimed to develop and preliminarily externally validate a machine learning-based model for predicting 28-day mortality in non-HIV patients with PJP. Methods This two-centre retrospective study included adult non-HIV patients with clinically and microbiologically confirmed PJP diagnosed between 7 October 2020 and 27 August 2025 at two tertiary hospitals in Anhui Province, China. Data from the First Affiliated Hospital of the University of Science and Technology of China were used for model development, and data from the First Affiliated Hospital of Anhui Medical University were used for preliminary external validation. The primary outcome was 28-day all-cause mortality. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Six algorithms were compared: random forest, logistic regression, support vector machine, extreme gradient boosting, decision tree and LASSO logistic regression. Model performance was assessed using discrimination, calibration, decision curve analysis and SHapley Additive exPlanations. Results A total of 165 patients were included, of whom 37 died within 28 days. LASSO selected 13 candidate predictors: sex, Acute Physiology and Chronic Health Evaluation II score, antiviral therapy, glucocorticoid therapy, vasoactive drug use, absolute monocyte count, blood urea nitrogen, alanine aminotransferase, D-dimer, lactate, procalcitonin, arterial partial pressure of oxygen and arterial partial pressure of oxygen/fraction of inspired oxygen ratio. Among the six algorithms, the LASSO logistic regression model showed the highest discrimination in the development cohort, with an area under the receiver operating characteristic curve of 0.833. In the external validation cohort, the model achieved an area under the receiver operating characteristic curve of 0.950 (95% confidence interval: 0.773-1.000), sensitivity of 0.667 (95% confidence interval: 0.094–0.992), specificity of 1.000 (95% confidence interval: 0.832-1.000) and accuracy of 0.957 (95% confidence interval: 0.781–0.999) when death was defined as the positive event. SHapley Additive exPlanations suggested that lactate, D-dimer and vasoactive drug use contributed substantially to model prediction. Conclusions A LASSO logistic regression model based on routinely available clinical and laboratory variables showed promising performance for predicting 28-day mortality in non-HIV patients with PJP. Because of the retrospective design, limited sample size and small external validation cohort, the findings should be interpreted as preliminary and hypothesis-generating. Larger prospective multicentre studies are required before clinical implementation.","url":"https://doi.org/10.21203/rs.3.rs-9914444/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9914444/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10052256/v1","name":"A Fractured Promise: How Socioeconomic Status Determines the Mental Health Returns of Higher Education","source":"preprints","abstract":"Abstract Background Education is a key socioeconomic determinant of health. This study aimed to investigate the association between education level and the risk of depression among U.S. adults, elucidate its mediating mechanisms, and develop an interpretable machine learning model for risk prediction. Methods We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2005–2018 (N = 13,378). Clinically relevant depression was defined by a Patient Health Questionnaire-9 (PHQ-9) score ≥ 10. Logistic regression was used to assess associations. Mediation analysis was performed to examine the role of socioeconomic and lifestyle characteristics. Various machine learning models (e.g., XGBoost, Random Forest) were developed and compared, with SHAP (SHapley Additive exPlanations) employed to interpret key predictors. Results A significant negative association was found between education level and the risk of clinically relevant depression. This association was significantly mediated by demographic and lifestyle factors, with socioeconomic status identified as a full mediator. Among the machine learning models, the XGBoost model incorporating demographic, lifestyle, and clinical indicators achieved the best predictive performance (AUC = 0.81). SHAP analysis revealed that, in addition to education, the poverty-income ratio, partner status, and access to healthcare were the most important predictors of depression risk. Conclusion Education is a crucial protective factor against depression in U.S. adults, operating largely through improvements in socioeconomic status and the promotion of healthier lifestyles. Our findings underscore that promoting educational equity is a vital strategy for improving public mental health. The developed machine learning framework provides a robust tool for identifying high-risk populations, informing targeted interventions for vulnerable groups.","url":"https://doi.org/10.21203/rs.3.rs-10052256/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10052256/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10165727/v1","name":"Development and Validation of Machine Learning Models for Early Prediction of Methicillin-resistant Staphylococcus aureus-associated Sepsis","source":"preprints","abstract":"Abstract Background Sepsis secondary to methicillin-resistant Staphylococcus aureus (MRSA) infection is associated with considerable morbidity and mortality. Early stratification of patients at high risk remains essential for optimizing clinical outcomes. The MIMIC-IV database was used to develop and validate a machine-learning-based framework for early prediction of sepsis in patients with MRSA infection. Methods This retrospective analysis included 1,026 adult ICU admissions with confirmed MRSA. To isolate robust predictors, we employed a dual-stage feature selection strategy integrating the Boruta algorithm with Least Absolute Shrinkage and Selection Operator (LASSO) regression. Eight distinct machine learning algorithms were developed and rigorously assessed via receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). The superior model was further interpreted using SHapley Additive exPlanations (SHAP) and translated into a clinical nomogram. Results The incidence of sepsis in the study population was 28.6% (n = 293). Multivariable analysis pinpointed nine independent predictors: history of heart disease, administration of second-generation cephalosporins, norepinephrine, or meropenem, as well as elevated Acute Physiology Score III and respiratory rates were positive correlates of sepsis risk. Conversely, bacteremia, baseline bicarbonate levels, and systolic blood pressure exhibited inverse associations. Among the evaluated algorithms, Elastic Net Regression (ENET) yielded the most robust generalizability, achieving an AUC of 0.835 and 78.83% accuracy in the validation set. The ENET-derived nomogram demonstrated superior net clinical benefit compared to individual predictors. Conclusions The ENET-based model and nomogram use routine clinical data to identify MRSA-infected patients at high risk of sepsis, supporting early risk stratification and individualized treatment decisions.","url":"https://doi.org/10.21203/rs.3.rs-10165727/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10165727/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10478196/v1","name":"Development of a Multiclass Predictive Baseline for Diverticulum Ultrasound Imaging Types Using Nonlinear Machine Learning","source":"preprints","abstract":"Abstract Context: Diverticulum anomalies exhibit a range of serious clinical manifestations of a similar nature requiring accurate imaging classification to implement appropriate and targeted management. However, clinical data are often characterized by severe class imbalances and convoluted decision boundaries that prevent traditional diagnostic heuristics from being applied effectively. Objective To apply structured clinical variables to create an interpretable and reproducible machine learning framework to serve as a baseline for multiclass predictions related to the imaging types of Meckel's diverticulum. Methods The retrospective observational data used in this study comprised a sample population of 559 patients where each patient had an imaging type label associated with them. The independent predictors used in the models were a set of continuous demographic variables, a set of continuous lesion-specific variables, and a set of categorical variables, which included patient clinical symptoms and hospitalisation history, and ectopic-tissue distribution. Following a rigorous data imputation approach, the dataset was split into a training dataset and a second, independent test dataset using the stratified random-split method. Logistic regression, LASSO, Support Vector Classification, and Random Forests were the four classifiers developed and evaluated using an iterative approach. Results The Random Forest algorithm performed best overall in prediction accuracy for the testing set with an accuracy of 0.4762, balanced accuracy of 0.4567, macro F1 score of 0.3587 and macro AUC of 0.7991. Although the Random Forest showed excellent distinction for the upper-end of high frequency head categories, it was unable to provide similar levels of performance with sparse categories at the lower end of the tail, due to the inherent structural class imbalance within the data. Multivariate logistic regression confirmed that intestinal obstruction by band type retained an independent, statistically significant association with small effusions as the focus category (OR = 0.1456, p = 0.0338). Decision curve analysis confirmed that using Random Forest model outputs produced a consistently higher net clinical benefit than baseline strategies at either extreme. Conclusion The Random Forest algorithm supports a stable, reliable basis upon which multiclass diverticulum imaging types can be classified. Despite the challenges posed by a long-tailed data distribution, the proposed framework is proven practical and viable for use in real-world clinical decision support scenarios.","url":"https://doi.org/10.21203/rs.3.rs-10478196/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10478196/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10645896/v1","name":"Development and internal validation of a pre-operative machine-learning model for prolonged postoperative stay in older adults undergoing colorectal cancer surgery","source":"preprints","abstract":"Abstract Purpose Older adults undergoing colorectal cancer surgery frequently have frailty, multimorbidity and social-discharge complexity, and prolonged admission exposes them to hospital-associated disability, deconditioning and delirium. We developed and internally validated a pre-operative machine-learning model to identify those at risk of prolonged postoperative stay. Methods Retrospective single-centre cohort of 197 consecutive patients aged ≥ 65 years assessed in a combined frailty–anaesthesia clinic; 149 were operated with recorded length of stay (LOS). The primary outcome was prolonged stay (LOS > 10 days). Pre-operative predictors included age, Clinical Frailty Scale (CFS), comorbidity, polypharmacy, peri-operative risk scores, and social and operative factors. A gradient-boosted (XGBoost) classifier was evaluated by repeated stratified five-fold cross-validation; exact-LOS regression and illustrative cost scenarios were secondary. Results In cross-validation the classifier achieved an AUROC of 0.75 (95% CI 0.69–0.79); at a screening threshold, sensitivity was 0.66, specificity 0.75 and positive predictive value 0.47 (Brier score 0.16). A single 80/20 split gave a more optimistic AUROC of 0.86 with 100% sensitivity but only 10 test events. The strongest predictors were CFS and NSQIP risk, followed by age and comorbidity; day-level LOS prediction was poor (test R²≤0.06). Of 149 operated patients, 38 (25.5%) had prolonged stay; nationally, a 1-day reduction in 10% of the 20,977 annual resections would save ~ 2,098 bed-days (£1.89–£2.51 million). Conclusion A model using routine pre-operative data identified older colorectal cancer patients at risk of prolonged stay with moderate discrimination; binary classification was more robust than exact-LOS prediction. External validation and prospective evaluation are required before clinical use.","url":"https://doi.org/10.21203/rs.3.rs-10645896/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10645896/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202608.0373.v1","name":"Machine Learning-Based Early Detection of Heart Disease Risk Using Self-Reported Non-Clinical Variables","source":"preprints","abstract":"Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accessible tools for early risk assessment. This study investigates the use of established machine learning (ML) techniques to predict heart disease risk from self-reported information that can be collected through telephone or online health questionnaires. The analysis is based on the publicly available Heart-2020 dataset, derived from the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS). Four widely used ML models were trained and evaluated: Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Bagged Trees (BT), and Extreme Gradient Boosting (XGBoost). Model performance was assessed using precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC). To further improve predictive performance and robustness, an ensemble architecture based on a second-stage MLP was implemented to combine the outputs of the individual classifiers. In addition, several feature engineering techniques, including Pearson correlation analysis, Analysis of Variance (ANOVA), and Principal Component Analysis (PCA), were investigated. The proposed approaches achieved F1-scores exceeding 0.80, demonstrating strong predictive capability using only non-clinical, self-reported information. These findings suggest that simple questionnaire-based data can support automated early-warning systems capable of identifying individuals who may benefit from further medical evaluation. Beyond individual risk assessment, the proposed methodology could also facilitate large-scale population health monitoring, contributing to preventive healthcare strategies and informed public health policy development.","url":"https://doi.org/10.20944/preprints202608.0373.v1","authors":["Fernando Martín-Rodríguez","Monica Fernandez-Barciela","Ainhoa Morales-Fernendez","Maria Marante Boado"],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.0373.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10544703/v1","name":"Interpretable machine learning analysis of apelin-12 for major adverse cardiovascular events after primary PCI in ST-elevation myocardial infarction","source":"preprints","abstract":"Abstract Major adverse cardiovascular events (MACE) remain common after successful primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI), and conventional regression-based models may miss non-linear biomarker-risk associations unless these are specified in advance. Apelin-12 has been associated with MACE after STEMI, but its role in interpretable machine-learning-based risk prediction remains unclear. In a public, prospectively collected cohort of 464 patients with STEMI treated with primary PCI, including 118 MACE events during the nominal follow-up period of up to 30 months, we compared logistic regression, random forest, and XGBoost models using 14 predictors selected by LASSO, with training and test sets imputed separately to avoid direct leakage across partitions. Test-set discrimination was similar across models (AUC 0.750–0.781; DeLong’s test, all P ≥ 0.29), and the machine-learning models did not significantly outperform logistic regression, consistent with evidence that gains from flexible algorithms are often limited in modest-sized structured clinical datasets. SHAP analysis of the XGBoost model ranked Δapelin-12 and age as the most influential predictors, with apelin-12 also among the top-ranked predictors. Restricted cubic spline and piecewise logistic regression supported non-linear associations for apelin-12 variables, particularly Δapelin-12, for which the estimated breakpoint (16.5%) closely matched a 20% cut-point reported in an earlier subgroup analysis of this same cohort; the corresponding apelin-12 breakpoint (0.43 ng/mL) did not closely match that earlier study’s 0.76 ng/mL subgroup threshold. A reduced XGBoost model excluding apelin-12 variables had lower test-set AUC than the full model (0.732 vs. 0.781), but the bootstrapped 95% confidence interval for this difference crossed zero (− 0.004 to 0.102). These findings support further evaluation of apelin-12 in interpretable machine-learning-based risk models after STEMI, but external validation and a more definitive assessment of incremental value are required.","url":"https://doi.org/10.21203/rs.3.rs-10544703/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10544703/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10101757/v1","name":"Singular model selection for trustworthy label-free classifier evaluation","source":"preprints","abstract":"Abstract Trustworthy deployment of a machine learning classifier requires honest uncertainty about its measured performance, including the common case where that performance is estimated without ground-truth labels by pooling several imperfect raters or models. We show that this label-free evaluation problem is singular by construction: the latent-class likelihood it relies on has a degenerate Fisher information, so the classical justification of the Akaike and Bayesian information criteria, and of likelihood-ratio asymptotics, silently fails, and the effective model complexity is governed by the real log-canonical threshold rather than by the Euclidean parameter count. The resulting trustworthiness failure is measurable: in the weak signal, low prevalence regime, the Bayesian information criterion systematically under-selects the latent structure and collapses it to a single class, at which point the reported per-rater accuracies drift toward the marginal base rate and cease to be interpretable. Two criteria built for singular models, the singular and the widely applicable Bayesian information criteria, restore honest complexity control and recover the structure where the classical criterion fails; we close the circularity objection on the learning coefficient by thermodynamic integration in the singular regime itself. A conditional-dependence analysis delimits validity: selecting the number of classes is singular, whereas modeling dependence is a regular extension, and the recovery guarantee holds only once dependence is specified. We instantiate the method on two real label-free evaluations with no reference standard, a clinical panel of seven raters and a crowd-sourced machine-learning dataset, and on a controlled synthetic machine learning-rater study; in each, the classical criterion under-counts the latent structure while the singular criteria recover it. This is the same generative structure that underlies recent latent-variable approaches to evaluating large language models without labels.","url":"https://doi.org/10.21203/rs.3.rs-10101757/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10101757/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10306742/v1","name":"Multiscale Multimodal Connectomic Signatures of Methamphetamine- Predominant Use and Methadone-Maintained Status: mCCA+jICA Fusion and Interpretable Machine Learning","source":"preprints","abstract":"Abstract Background: Methamphetamine-predominant substance use may involve distributed alterations in structural, functional, dynamic, and topological brain networks. Methods: We analyzed multimodal magnetic resonance imaging data from 110 adult men: 35 healthy controls, 45 individuals with methamphetamine-predominant histories not receiving methadone maintenance, and 30 individuals with methamphetamine-predominant histories receiving stable methadone maintenance. Structural connectivity, resting-state functional connectivity, independent-component-derived dynamic circuits, structure-function coupling and decoupling, graph metrics, and small-world measures were organized across global, network, and hub-based scales. Multimodal fusion using multiset canonical correlation analysis with joint independent component analysis and exploratory interpretable machine-learning models were applied. Results: Global/circuit-level fusion separated methamphetamine-predominant participants from controls across structural, static functional, and dynamic blocks, with representative component differences of p = 1.30 x 10^-6, p = 1.02 x 10^-6, and p = 3.14 x 10^-5. Methadone-maintained participants also differed from controls in static functional and dynamic components. Direct methadone-maintained versus non-methadone clinical differences were strongest at the network level, including structural, static functional, and dynamic components. Machine-learning models showed stronger clinical-control discrimination than direct clinical-subgroup discrimination. Conclusions: Methamphetamine-predominant history was associated with distributed multiscale connectomic alterations. Methadone-maintained status showed partial network-specific reorganization rather than normalization toward healthy controls. Findings represent candidate imaging signatures requiring longitudinal replication and external validation.","url":"https://doi.org/10.21203/rs.3.rs-10306742/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10306742/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10480385/v1","name":"Revisiting Semantic Markers of Schizophrenia: A Machine Learning Analysis of Verbal Fluency Features","source":"preprints","abstract":"Abstract Schizophrenia is a chronic mental illness that affects thought processes, emotional regulation, and behavior. Language impairment is one its suspected cognitive signs, including abnormalities in semantic organization and verbal fluency. Prior research has demonstrated that semantic features derived from the Verbal Fluency Test (VFT), including fluency score (FS), number of semantic switches (NSS), mean cluster size (MCS), average cluster similarity (ACS), and average switch similarity (ASS), are effective in distinguishing individuals with schizophrenia from healthy controls. In this study, we examine the relative importance of these features from a machine learning perspective. Notably, this is the first work to incorporate ACS and ASS into an artificial intelligence-driven schizophrenia detection framework. The five semantic features are first ranked according to their importance using both the Mann-Whitney U test and permutation-based feature importance scores. Subsequently, order-based feature elimination is applied to the ranked feature sets and evaluated across five machine learning classifiers. Results from both pipelines consistently indicate that ACS and ASS exhibit superior discriminative power, while MCS and NSS fail to demonstrate statistically or computationally robust importance, contradicting prevailing assumptions in the existing literature. Overall, this study provides novel insights into the significance of VFT-based semantic features for schizophrenia identification, bridging traditional clinical assessment and AI-driven diagnostic frameworks.","url":"https://doi.org/10.21203/rs.3.rs-10480385/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10480385/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10411125/v1","name":"Applying Machine Learning for the Prediction of Tuberculosis Among People Living with HIV from Health Facilities in Addis Ababa, Ethiopia","source":"preprints","abstract":"Abstract Background Tuberculosis (TB) is a major global public health problem and one of the leading causes of death worldwide, particularly in the era of Human Immunodeficiency Virus (HIV). TB is among the leading causes of death for people living with HIV (PLWHIV). Early diagnosis of TB among PLWHIV remains challenging due to non-specific clinical symptoms, coexisting opportunistic infections, and limitations of conventional diagnostic and screening approaches. The objective of this study was to develop and validate a machine learning model to predict TB cases among PLWHIV using smart care data from health facilities. Methods The research employs quantitative analysis of secondary data from four health facilities that provided HIV care and treatment to PLWHIV and reported to the Addis Ababa Health Bureau. The study uses an experimental research design applying machine learning and deep learning models. Results From four facilities, 16,698 adult patients currently on ART were included; 59% were female, and 85% were married. In this study, the majority of the models performed better when the Hybrid method combined SMOTE with Edited Nearest Neighbors (ENN), and XGBoost had the strongest influence on the predictor of TB occurrence across most evaluation matrices. The XGBoost accuracy was 98%, recall 80%, precision 25%, F1 score 38%, F Beta 30%, MCC 52%, and AUC 96%. ART regimen, taking Tuberculosis Preventive Therapy (TPT), cotrimoxazole prophylaxis, months on ART, patient weight, and functional status are the strongest risk factors for developing TB and for protection against TB development. Conclusion Overall, XGBoost outperforms the other models across accuracy, precision, F1 score, and ROC AUC. Using the XGBoost model, the ART regimen, TPT, cotrimoxazole prophylaxis, months on ART, and patient weight were the strongest predictors of whether a patient would be protected against TB or would develop TB.","url":"https://doi.org/10.21203/rs.3.rs-10411125/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10411125/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10197215/v1","name":"Machine learning models for predicting multidrug-resistant organism infections after invasive procedures in intensive care unit patients: a retrospective study based on the MIMIC-IV database","source":"preprints","abstract":"Abstract Objective This study aimed to develop and compare five machine learning models utilizing the MIMIC-IV database to identify key risk factors for multidrug-resistant organism (MDRO) infection in patients with invasive procedures. Methods A cohort of 23 281 patients was extracted from MIMIC-IV. Demographic, physiological, laboratory, and clinical variables were collected. The weighted calibration method was applied to address class imbalance. The dataset was randomly divided into training and testing subsets at an 7:3 ratio. Feature selection was performed using LASSO regression and feature importance scoring. Predictive models were constructed with Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGB). Model performance was assessed via receiver operating characteristic (ROC) analysis. Calibration was evaluated using calibration curves and Brier scores, while clinical utility was examined through decision curve analysis (DCA). Model interpretability was enhanced using Shapley Additive Explanation (SHAP) values. Results Of the 23 281 patients, 1956 had invasive procedures associated MDRO infection and 21 325 had no invasive procedures associated MDRO infection. Significant differences were observed across multiple clinical parameters. XGB demonstrated the highest predictive performance, incorporating 15 features and achieving an AUC of 0.751 (95% CI: 0.728–0.773) on the test set. Calibration curves indicated good fit, supported by DCA confirming clinical usefulness. SHAP analysis highlighted the top influential features: the length of ICU stay, total antibiotic using hours, have CVC inserted, APS Ⅲ, age at ICU. Conclusion XGB emerged as the optimal predictor, which exhibited the strongest predictive performance for MDRO infections among patients undergoing invasive procedures, thereby equipping clinicians with a tool for early risk stratification and personalized treatment strategies","url":"https://doi.org/10.21203/rs.3.rs-10197215/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10197215/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202606.0367.v2","name":"A Two-Stage Ensemble Machine Learning Pipeline for Breast Cancer Diagnosis from Digital Mammograms","source":"preprints","abstract":"Background: Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. Aim: To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. Methods: The proposed framework combines image preprocessing, multiple convolutional neural networks trained under different conditions, and a second-stage classifier that integrates the CNN outputs. Several machine learning models and feature selection techniques were evaluated using publicly available mammography datasets. Results: The ensemble approach consistently outperformed the individual CNN models. The MLP classifier achieved the best overall balance between precision and recall, while the heuristic fusion method provided the highest sensitivity. Feature selection reduced model complexity while maintaining comparable performance, and cross-validation confirmed the robustness of the proposed methodology. Discussion: Combining complementary information from multiple CNNs with classical machine learning improves diagnostic performance and provides a robust framework for computer-aided breast cancer diagnosis. Conclusions: The proposed two-stage ensemble offers an effective and interpretable approach for mammographic breast cancer classification. A demonstration application incorporating Grad-CAM explainability further supports its potential use as a clinical decision-support tool.","url":"https://doi.org/10.20944/preprints202606.0367.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202606.0367.v2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.18.26360687","name":"Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data","source":"europepmc","abstract":"Venous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and the potential for automating using electronic health record (EHR) data remain unclear. We analysed 577,904 admissions and 726,896 VTE assessment forms across five NHS hospitals between 2015 and 2025 to evaluate assessment completion, concordance with structured EHR data, clinical validity, and feasibility of EHR-based automation assisted by machine learning. Overall completion was high (96.7%), and timely completion improved from 47.4% in 2015 to 90.5% in 2024. Agreement between forms and EHR data was good for common risk factors, but low-prevalence variables were often under-documented in the forms. Despite these discrepancies, form-derived thrombosis risk was associated with increased VTE incidence (OR 3.31, 95% CI 2.81–3.90). Machine learning models using first-14-hour EHR data achieved discrimination comparable to clinician-recorded variables (AUROC 0.709 vs 0.704), supporting real-time EHR-integrated assessment pre-population and decision support. Author summary We studied whether information already stored in hospital electronic health records could make required venous thromboembolism (VTE) risk assessments quicker and more reliable. VTE refers to potentially serious blood clots in the deep veins or lungs. We examined more than half a million admissions across five NHS hospitals over ten years. Most assessments were eventually completed, but many were not finished within the recommended 14-hour window. Information entered manually by clinicians often agreed with existing electronic records for common risk factors, but uncommon factors and bleeding risks were missed more often. Even with these documentation differences, the assessments identified patients who were more likely to develop VTE. We also tested machine-learning models using information available during the first 14 hours of admission. These models performed about as well as models based on clinician-completed forms. Our findings suggest that hospital systems could pre-fill parts of the assessment using data already recorded, while leaving clinicians to verify the information and make the final decision. This approach could reduce repetitive manual data entry, improve timely completion, and help clinicians identify patients who may benefit from preventive treatment.","url":"https://doi.org/10.64898/2026.08.18.26360687","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.18.26360687","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-9326952/v1","name":"Integrative Transcriptomic Analysis and Machine Learning Identify Robust Gene Expression Signatures for Cardiovascular Disease Classification","source":"preprints","abstract":"Abstract BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide and contribute substantially to the global health burden. Early diagnosis and the identification of reliable molecular biomarkers are critical for improving disease prognosis and guiding therapeutic strategies. Recent advances in high-throughput transcriptomic technologies, along with integrative bioinformatics approaches, have enabled large-scale exploration of gene expression patterns in complex diseases. However, variability across independent datasets often limits the identification of robust and reproducible biomarkers. In this context, the present study aimed to integrate multiple transcriptomic datasets and apply machine learning techniques to identify gene expression signatures capable of accurately distinguishing cardiovascular disease samples from healthy controls. METHODS: Seven publicly available cardiovascular disease transcriptomic datasets were obtained from the Gene Expression Omnibus database maintained by the National Center for Biotechnology Information. Based on predefined inclusion criteria, a total of 1,188 samples (763 cardiovascular disease and 425 healthy controls) were included for analysis. Data preprocessing, normalization, and batch effect correction were performed using the R programming environment. Differential gene expression analysis was carried out using the limma framework, and significantly altered genes were identified using an adjusted p-value threshold of","url":"https://doi.org/10.21203/rs.3.rs-9326952/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9326952/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.12688/wellcomeopenres.25768.2","name":"Development and External Validation of a Treatment-Adjusted Machine Learning Model to Support Risk-Informed Group-Based Depression Care for People Living with HIV in Uganda","source":"preprints","abstract":"Background: Group-based depression care is widely used in HIV services in Uganda, yet some patients remain symptomatic following treatment. We developed and externally validated a treatment-adjusted machine learning model to support risk-informed group-based depression care for people living with HIV (PLWH). Methods We analyzed data from 1,140 adults living with HIV and significant depression symptoms enrolled across 30 HIV clinics in the SEEK-GSP trial (PACTR201608001738234). Participants received either Group Support Psychotherapy (GSP) or Group HIV Education (GHE). The primary outcome was six-month depression non-remission, defined as Self-Reporting Questionnaire (SRQ) score ≥ 6 and a functional impairment score < 9. Three machine learning models (Elastic Net, Random Forest, and XGBoost) were trained on baseline data from Gulu and Kitgum and externally validated in Pader district data. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration slope, intercept, Brier score and decision curve analysis. Sensitivity analyses excluding treatment assignment were conducted to assess the predictive value of baseline characteristics alone. Results Treatment-adjusted models consistently outperformed treatment-excluded models. In external validation, the parsimonious XGBoost model showed the best overall performance (AUC 0.947), compared with Elastic Net (0.924) and Random Forest (0.918), and demonstrated clinical net benefit across relevant decision thresholds. Following Platt-recalibration, XGBoost showed the best overall performance, preserving strong discrimination (AUC 0.940) while improving the Brier score from 0.174 to 0.113 and the calibration slope from 7.533 to 1.093. Treatment assignment was the dominant predictor of depression non-remission risk, while age, HIV-related stigma, acceptance coping, socioeconomic vulnerability, low social support, and trauma-related symptoms also contributed to prediction. Conclusion The externally validated, Platt-recalibrated parsimonious treatment-adjusted XGBoost model provides a promising approach to identifying people living with HIV at increased risk of depression non-remission and informing enhanced care following group-based depression treatment.","url":"https://doi.org/10.12688/wellcomeopenres.25768.2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/wellcomeopenres.25768.2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-7795209/v1","name":"Machine Learning-Based Risk Prediction Model for Sleep Disorders in Middle-Aged and Older Adults at High Altitudes","source":"preprints","abstract":"Abstract This study developed and validated a machine learning model to predict sleep disorder risk in middle-aged and older adults in high-altitude regions. We enrolled 673 participants aged ≥45 years from Xining, Qinghai, China, collecting data on sociodemographic characteristics, lifestyle, health conditions, anthropometric measures, and psychological factors. Sleep disorders were evaluated using the Pittsburgh Sleep Quality Index. Recursive feature elimination with Naive Bayes was used for feature selection. Five machine learning algorithms—Logistic Regression, Naive Bayes, Random Forest, CatBoost, and K-Nearest-Neighbor—were trained and validated with a 7:3 split. Hyperparameters were optimized using grid search with 3-fold cross-validation via the \"mlr3\" package. Model performance was assessed using ROC curves, calibration curves, decision curve analysis, and multiple metrics. External validation involved 382 participants from Xunhua County, Qinghai. The DeLong test with Bonferroni correction compared AUCs among models. Sensitivity analyses assessed how missing data imputation, feature selection, and data splitting affected the optimal model’s performance. Gender, comorbidities, anxiety, and depression emerged as key predictors. The Random Forest model demonstrated superior performance (AUC: 0.801 training, 0.804 internal validation), with no significant AUC difference between sets (P > 0.05). Calibration curves and decision curve analysis confirmed its strong calibration and clinical benefit. In the external validation set, the Random Forest model surpassed others in F1 score, accuracy, and sensitivity, making it the optimal model. Sensitivity analyses indicated that imputation, feature selection, and splitting ratios influenced model performance. This Random Forest model offers a reliable tool for screening sleep disorders in high-altitude populations.","url":"https://doi.org/10.21203/rs.3.rs-7795209/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-7795209/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10106854/v1","name":"Development and External Validation of an Interpretable Machine-Learning Model for Predicting Synchronous Distant Metastasis in Invasive Bladder Cancer: A SEER Population-Based Study","source":"preprints","abstract":"Abstract Background Synchronous distant metastasis—metastasis present at diagnosis—drastically worsens survival in bladder cancer, yet accurate pre-treatment prediction tools are lacking. We developed and validated a machine-learning model to predict synchronous distant metastasis in invasive bladder cancer using only clinical variables available at diagnosis. Methods Using the SEER database, we identified 38,707 patients with invasive bladder cancer (clinical T stage ≥ T1; Ta/Tis excluded). Patients diagnosed in 2010–2017 were split 7:3 into training (n = 17,012) and internal validation (n = 7,291) cohorts; those diagnosed in 2018–2021 formed a temporal external validation cohort (n = 14,404). Nine machine-learning algorithms were developed using only diagnosis-time clinical variables and compared by discrimination, calibration, and decision-curve analysis. The best model was interpreted with SHAP, and its incremental value over TNM staging was quantified. Results Synchronous distant metastasis was present in 2,640 patients (6.8%). The gradient boosting decision tree performed best, with an external-validation AUC of 0.837 (95% CI 0.825–0.849), good calibration (Brier score 0.057), and positive net benefit. Clinical N stage, tumor size, and T stage were the most influential predictors. The model provided significant incremental value over TNM staging (ΔAUC 0.027; IDI 0.038; continuous NRI 0.380; all P","url":"https://doi.org/10.21203/rs.3.rs-10106854/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10106854/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.15.26360514","name":"Early Detection of Erythropoietic Protoporphyria Using Sequential Machine Learning on Longitudinal Electronic Health Records","source":"preprints","abstract":"Objective Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods In a retrospective case–control study at two San Francisco health systems—an academic referral center (UCSF) and a safety-net hospital (ZSFG)—we identified 74 confirmed EPP cases using combined diagnostic coding, biochemical criteria, and specialty chart review. Symptom-enriched controls were sampled at a 40:1 ratio. Longitudinal diagnoses, laboratory results, medications, procedures, and encounters preceding the outcome date were modeled with a gradient-boosting classifier (CatBoost) and a state-space sequence model (MAMBA). The best model was deployed across the UCSF population and externally validated at ZSFG without retraining. Results On the UCSF held-out test set (n=1,865; 43 cases), MAMBA outperformed CatBoost (AUC–ROC 0.91 vs 0.89; average precision 0.42 vs 0.27; precision 65% vs 20%), flagging cases a median of 229 days before documented diagnosis. Deployed across 297,967 symptom-compatible patients, it identified 310 high-risk individuals, implying a prevalence approaching genetic estimates. External validation at ZSFG showed attenuated performance (AUC–ROC 0.72; average precision 0.10) while preserving early detection (median 264 days). Discussion A sequence model integrating temporal EHR signals detected EPP months before clinical recognition, corroborating genetic evidence of substantial underdiagnosis. Cross-site attenuation reflects population and documentation differences and underscores the need for local recalibration. Conclusion Longitudinal EHR-based machine learning can shorten EPP diagnostic delay and prioritize patients for confirmatory testing, supporting proactive rare-disease case finding.","url":"https://doi.org/10.64898/2026.08.15.26360514","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.15.26360514","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10718477/v1","name":"Concentric Hypertrophy as a Prognostic Factor and Mediator of Diabetes-Related Risk in HFpEF: Development and Validation of Prediction Models Based on Cox Regression and Machine Learning Approaches Across Two Randomized Controlled Trials","source":"preprints","abstract":"Abstract ‌Background: ‌ Although the prognostic significance of left ventricular mass index (LVMi) and relative wall thickness (RWT) has been established across a spectrum of cardiovascular diseases, their value in predicting outcomes in patients with heart failure with preserved ejection fraction (HFpEF), which is known as a metabolism-related disease, remains unclear. Moreover, validated prediction tools tailored specifically for this population are unavailable. ‌Methods:‌ Left ventricular geometry of participants from TOPCAT was categorized based on LVMi and/or RWT. Differences in the composite outcome of cardiovascular death and heart failure hospitalization were analyzed using the Kaplan-Meier method and Cox regression. Mediation analysis was performed to investigate the potential mediation effects of left ventricular geometry. Prediction models incorporating left ventricular geometry were developed using Cox regression and machine learning approaches. These models were interpreted via SHapley Additive exPlanations (SHAP) and validated using data from PARAGON-HF. ‌Results:‌ Overall, 765 subjects were qualified. Restricted cubic spline curves showed a linear relationship between LVMi/RWT values and the composite outcome. Among left ventricular geometry categories, concentric hypertrophy was identified as the only independent determinant of the composite outcome, and it mediated the effect of diabetes mellitus on outcome events in patients with HFpEF. By incorporating concentric hypertrophy, we developed prediction models using Cox regression and five machine learning approaches. Among these, Cox regression and random survival forest achieved superior and comparable discriminatory capacity. Calibration curves showed satisfactory agreement between the predicted and observed probabilities. Decision curve analysis confirmed net clinical benefit across a wide range of threshold probabilities. SHAP analysis quantified the contribution of concentric hypertrophy to model predictions. External validation using the PARAGON-HF cohort confirmed the universality of these models. Risk stratification via combining the two models demonstrated superior performance over either model alone in identifying high-risk patients. ‌Conclusions:‌ Concentric hypertrophy was an independent determinant of the composite outcome of cardiovascular death and heart failure hospitalization, and mediated the effect of diabetes mellitus on outcomes in patients with HFpEF. The Cox-based and random survival forest-based models showed superior discriminatory capacity. Combining these two models for risk stratification improved the identification of high-risk patients.","url":"https://doi.org/10.21203/rs.3.rs-10718477/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10718477/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10261941/v1","name":"Cerebrospinal Fluid Chloride as an Independent Prognostic Biomarker in Cryptococcal Meningitis: A Dual-Center Externally Validated Machine Learning Study with SHAP-Based Interpretability","source":"preprints","abstract":"Abstract Background Cryptococcal meningitis (CM) carries a high 10-week mortality rate, necessitating accurate early prognostic tools. While cerebrospinal fluid chloride (CSF-Cl) is a routine test, its independent prognostic value in CM remains ill-defined. Furthermore, existing machine learning models lack external validation and clinical interpretability. This study aimed to validate CSF-Cl as an independent biomarker and to develop an externally validated, interpretable prognostic model for CM. Methods We enrolled 253 treatment-naive CM patients from two tertiary hospitals. Univariate and multivariate logistic regression identified independent prognostic risk factors across the full cohort. Patients were stratified into a training cohort (The Fourth People’s Hospital of Nanning, n = 194) and an independent external test cohort (Jiangxi Provincial Chest Hospital, n = 59). In the training set, nine machine learning classifiers were developed following feature selection via LASSO-recursive feature elimination (LASSO-RFE) and sample balancing via synthetic minority oversampling technique (SMOTE). Paired models with and without CSF-Cl were compared to quantify the incremental value of CSF-Cl using ΔAUC, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was deployed to dissect dose-response relationships and feature interactions, with subgroup analyses to define risk thresholds. Results Multivariate regression confirmed CSF-Cl as an independent risk factor for unfavorable CM outcomes (OR = 1.059, 95% CI 1.011–1.110, P = 0.017). CatBoost demonstrated the best generalizability among the nine models evaluated, yielding an AUC of 0.712 and precision of 0.722 in the external test set. Incorporation of CSF-Cl increased the external AUC from 0.651 to 0.712 (ΔAUC = 0.061), with an NRI of 0.132 and IDI of 0.040. SHAP analysis uncovered a positive dose-response association between CSF-Cl and adverse prognosis, alongside synergistic interactions with intracranial pressure (ICP) and HIV status. The model retained robust performance in HIV-positive patients and those aged","url":"https://doi.org/10.21203/rs.3.rs-10261941/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10261941/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10388541/v1","name":"Application Study of Interpretable Machine Learning Models for Predicting Postoperative Refracture After Vertebral Augmentation in Osteoporotic Vertebral Compression Fractures","source":"preprints","abstract":"Abstract Background Osteoporotic vertebral compression fractures (OVCFs) are severe osteoporosis complications; vertebral augmentation is the preferred minimally invasive treatment, but postoperative refracture risk exists. Traditional logistic regression fails to accurately assess individual risks, while interpretable machine learning (ML) excels in high-dimensional data processing, with limited relevant studies. Purposes: To develop an interpretable ML prediction model for identifying risk factors of subsequent fractures after vertebral augmentation in patients with OVCFs. Methods A retrospective analysis was conducted on clinical data of 1,502 OVCF patients who underwent vertebral augmentation. Thirty-six characteristic indicators were extracted from electronic medical records and imaging systems. Six ML prediction models were constructed. Prediction performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, accuracy, recall, F1 score, precision, calibration curves, and decision curve analysis. The optimal model was interpreted globally and locally via Shapley Additive exPlanations (SHAP) to analyze the contribution of key features. Results The 2-year post-operative subsequent fracture incidence in the study cohort was 9.65% (145 cases). After data preprocessing and model training, the extreme gradient boosting (XGBoost) model demonstrated the best performance on the test set. Calibration curve and decision curve analyses showed high consistency between predicted results and actual risks, with significant clinical net benefit. SHAP analysis identified nine key risk factors ranked by importance: age, bone cement leakage and types, history of osteoporosis, Previous history of fractures, bone mineral density, thoracolumbar fascitis, types of trauma, duration of surgery, and Braden score. Conclusions The XGBoost model combined with SHAP represents an effective tool for predicting subsequent fracture risk after vertebral augmentation in OVCF patients. Clinical application of this prediction model can assist clinicians in formulating individualized intervention strategies, thereby optimizing treatment protocols and post-operative management to reduce post-operative subsequent fracture incidence.","url":"https://doi.org/10.21203/rs.3.rs-10388541/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10388541/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9465974/v1","name":"Machine learning-based clinical, protemic and pathomics model to predict overall survival and therapeutic benefit in patients with cervical cancer","source":"preprints","abstract":"Abstract Background The tumor-node-metastasis (TNM) staging system has limitations in predicting prognosis and guiding personalized therapy for cervical cancer (CC). This study aimed to develop a cost-effective, machine learning (ML)-based multi-modal prognostic signature that integrates clinical and pathomic data, and to investigate its predictive value, underlying biological mechanisms, and clinical applicability. Methods A total of 191 CC patients were enrolled and randomly assigned to a training set (n = 132) and a test set (n = 59) at a 7:3 ratio, with an additional 49 CC patients included as an external validation cohort. Proteomic data were acquired from formalin-fixed paraffin-embedded (FFPE) surgical specimens using pressure cycling technology (PCT) combined with data-independent acquisition (DIA) mass spectrometry (MS). Pathomic data were extracted from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of the CC patients. Using LASSO Cox regression analysis, clinicopathological, proteomic, and pathomic signatures were established, respectively. A combined Pat-Cli riskscore signature for overall survival (OS) prediction was subsequently constructed and comprehensively validated. The correlation between the Pat-Cli riskscore and OS was further analyzed, which demonstrated the promising clinical utility of Pat-Cli riskscore signature for prognostic assessment in CC. Underlying biological mechanisms were additionally explored based on the proteomic data. Results The Pat-Cli model achieved an AUC of 0.964 in the training cohort and 0.802 in the test cohort for 5-year OS prediction, showing a marginal improvement over the triple combined signature (clinical + proteomic + pathomic data), with superior cost-effectiveness. Patients in the high Pat-Cli risk score group had significantly poorer OS, along with a higher incidence of pelvic lymph node metastasis, lymphovascular space invasion, and parametrial infiltration. Mechanistically, a high Pat-Cli risk score was associated with the enrichment of cell cycle-related signaling pathways. Notably, patients in the high Pat-Cli risk score group received a significant survival benefit from adjuvant chemoradiotherapy, whereas no improvement was observed in low-risk patients. Conclusion The Pat-Cli riskscore signature provides an accurate, interpretable, and cost-effective tool for CC prognosis prediction, and can effectively guide personalized adjuvant therapy selection, holding great potential to optimize CC precision management.","url":"https://doi.org/10.21203/rs.3.rs-9465974/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9465974/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10560334/v1","name":"A Weighted PRS for Ischemic Heart Disease: Candidate-Gene Associations and an Evaluation of Epistasis and Machine-Learning Approaches","source":"preprints","abstract":"Abstract Ischemic heart disease (IHD) carries a substantial polygenic component, but candidate-gene and polygenic-score evidence remains scarce in South Asians. This Pakistani case–control study evaluated eight candidate variants, their aggregation into conventional and epistasis-weighted polygenic scores, and machine-learning classifiers. 612 participants (306 IHD cases, 306 controls) were genotyped for ADAMTS7 rs3825807, ADAMTS13 rs2301612, APOE rs769452, AGT rs699, APOB rs676210, MMP9 rs3918242, MTHFR rs1801133 and ZC3HC1 rs11556924, with covariate-adjusted single-variant testing. Unweighted and effect-size-weighted polygenic risk scores (PRS) and a three-tier epistasis-weighted candidate-gene PRS (EW-cgPRS) were built, a lipid quantitative-trait scan performed, and four machine-learning classifiers benchmarked. Six of eight variants were nominally associated with IHD; four survived Bonferroni correction across 40 inheritance-model tests: ADAMTS13 (additive OR 6.85, 95% CI 3.57–13.12), ADAMTS7 (recessive OR 6.00, 2.69–13.40), APOE (dominant OR 4.44, 2.12–9.26) and MMP9 (recessive OR 14.49, 3.40–61.80). The weighted PRS separated cases from controls (cross-validated AUC 0.861), with a monotonic case-prevalence gradient across strata (top- versus bottom-tertile OR 59.2). Neither epistatic nor functional-annotation weighting improved on the classic score; the interaction tier's apparent superiority reflected covariate access, not epistasis. All four classifiers achieved near-perfect discrimination (AUC 0.98–0.999), traced to lipid-based ascertainment of controls rather than genetic signal. Several candidate variants are associated with IHD in this population, and a simple weighted score captures a reproducible genetic gradient; epistatic or functional enrichment adds nothing. The near-perfect machine-learning performance reflects study design, not clinical utility, and the largest single-variant effects warrant cautious interpretation given the modest sample size, Hardy–Weinberg deviations, and single-population design.","url":"https://doi.org/10.21203/rs.3.rs-10560334/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10560334/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10244248/v2","name":"A Cost-Sensitive and Explainable Evaluation Framework for Chronic Kidney Disease Prediction Models","source":"preprints","abstract":"Abstract This paper evaluates Logistic Regression, Random Forest and Extreme Gradient Boosting models for CKD prediction using a framework that incorporates threshold optimization, cost-sensitive analysis and interpretability techniques to assess clinical deployment suitability. The cost-sensitive analysis framework assigns greater penalties to false negative predictions than to false positive predictions, to stimulate the clinical consequences of missed diagnosis. The models eliminated or reduced false negative predictions at their optimal thresholds, hence lowering the overall cost. Furthermore, the interpretability analysis consistently identified predictors including serum creatinine, glomerular filtration rate (GFR) and protein in urine across all models indicating consistency in feature importance across traditional and ensemble models contributing toward improving confidence in black box machine learning models. The research findings demonstrate that the proof of concept extending model evaluation beyond predictive performance to include threshold optimization, cost-sensitive analysis and utilizing explainability techniques provides a more clinically relevant framework for assessing the suitability of machine learning models as clinical decision support systems.","url":"https://doi.org/10.21203/rs.3.rs-10244248/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10244248/v2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10027041/v1","name":"Prognostic Nutritional Index and Machine Learning for Mortality Prediction in Sepsis-Associated Acute Kidney Injury: A MIMIC-IV Cohort Study","source":"preprints","abstract":"Abstract Objective The Prognostic Nutritional Index has demonstrated prognostic value in diverse clinical populations, yet its precise role in sepsis-associated acute kidney injury remains incompletely characterized. This study aimed to investigate the association between this index and all-cause mortality in critically ill patients with sepsis-associated acute kidney injury and to develop a machine learning-based predictive model for enhanced risk stratification. Methods This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV database (2008–2022). Adult patients with first intensive care unit admission and sepsis-associated acute kidney injury were included. The index was calculated from serum albumin and total lymphocyte count. Cox proportional hazards regression, restricted cubic spline analysis, and two-piecewise Cox models were employed. Subgroup analyses by sex, age, and pulmonary comorbidity were performed. Feature selection used the Boruta algorithm and least absolute shrinkage and selection operator regression. Fifteen machine learning algorithms were benchmarked, with performance assessed by the area under the receiver operating characteristic curve, calibration plots, and decision curve analysis. Results A total of 4,087 patients were included. The index exhibited a robust inverse association with both 28-day and 365-day mortality. Patients in the highest quartile had a 30% reduction in 28-day mortality risk compared with the lowest quartile. Analysis revealed a significant U-shaped nonlinear relationship, with a nadir at approximately 29.13. Threshold analysis identified breakpoints at 36.42 and 35.76. The prognostic value was most pronounced among elderly patients. The random forest algorithm achieved the highest discriminative performance, with the index ranking as the third most important feature. Conclusions The Prognostic Nutritional Index serves as an independent prognostic biomarker for all-cause mortality in patients with sepsis-associated acute kidney injury, exhibiting a significant nonlinear, threshold-dependent relationship. Integration into a machine learning model demonstrated excellent calibration and clinical utility. Routine assessment may facilitate risk stratification and guide therapeutic interventions.","url":"https://doi.org/10.21203/rs.3.rs-10027041/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10027041/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.22541/authorea.15007309/v1","name":"Adaptive Agentic AI-Driven Women's Digital Twin for Personalized High-Risk Pregnancy Prediction and Clinical Decision Support","source":"preprints","abstract":"High-risk pregnancy remains a major global public health challenge due to its association with increased maternal and neonatal morbidity and mortality. Existing prediction methods primarily rely on static clinical data and conventional machine learning techniques, limiting their ability to support continuous monitoring, dynamic risk assessment, and personalized clinical decision-making. This study proposes an Adaptive Agentic AI-Driven Women’s Digital Twin framework for intelligent prediction and clinical decision support in high-risk pregnancy. The proposed framework integrates multimodal maternal healthcare data, including maternal clinical parameters and Cardiotocography (CTG) signals, to create a continuously updated digital representation of the patient. It employs an adaptive multi-agent architecture comprising monitoring, prediction, reasoning, recommendation, explanation, and feedback agents that collaboratively analyze patient data, estimate pregnancy risk, generate personalized recommendations, and continuously refine decision-making. Multiple machine learning and deep learning models are evaluated using Accuracy, Precision, Recall, F1-score, ROC-AUC, Sensitivity, Specificity, and Matthews Correlation Coefficient (MCC). Model transparency and clinical interpretability are enhanced through SHAP-based explainable artificial intelligence (XAI), enabling clinicians to understand key factors influencing predictions. Experimental results demonstrate that integrating multimodal data fusion with Women's Digital Twin technology and Agentic AI significantly improves prediction accuracy, continuous patient monitoring, individualized risk assessment, and clinical decision support.","url":"https://doi.org/10.22541/authorea.15007309/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22541/authorea.15007309/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9638574/v1","name":"Prognostic Utility of the Endothelial Activation and Stress Index in Critically Ill Patients with Heart Failure and Acute Kidney Injury: A Dual-Cohort and Machine Learning Validation Study","source":"preprints","abstract":"Abstract Background Concomitant heart failure (HF) and acute kidney injury (AKI) in critically ill patients is associated with high mortality, underscoring the need for accurate early risk stratification. The Endothelial Activation and Stress Index (EASIX), derived from three routine laboratory markers, has shown prognostic value across several clinical contexts. Its specific predictive capacity for critically ill patients with concurrent HF and AKI, however, remains undefined. Methods This retrospective cohort study analyzed adult ICU patients from the MIMIC-IV database (n = 4,000, development cohort) and the MIMIC-III-CareVue database (n = 700, temporal validation cohort). We assessed the association between admission log2-transformed EASIX (log2-EASIX) and 28-, 90-, and 180-day all-cause mortality using multivariable Cox proportional hazards regression. Dose-response relationships were evaluated with restricted cubic splines (RCS), and predictive performance was compared against established clinical scores (SOFA, OASIS, Charlson Comorbidity Index) via receiver operating characteristic (ROC) curve analysis. To validate the index's predictive importance, we further applied a machine learning pipeline that incorporated Boruta feature selection and SHAP-based interpretability. Results Elevated admission log2-EASIX demonstrated a robust, independent, and dose-dependent association with mortality across all follow-up periods. In the fully adjusted model, patients in the highest quartile (Q4) had significantly increased risks of 28-day (HR 2.37, 95% CI 1.95–2.87), 90-day (HR 2.06, 95% CI 1.75–2.42), and 180-day (HR 1.98, 95% CI 1.70–2.31) mortality compared with those in Q1. These findings were consistently replicated in the external MIMIC-III validation cohort. Restricted cubic spline analysis revealed a significant linear, monotonically increasing risk trajectory (P for non-linearity > 0.05). The discriminative performance of log2-EASIX (AUCs: 0.601–0.622) was highly competitive with that of multidimensional clinical indices. Among five machine learning algorithms, the Gradient Boosting Machine and Random Forest models achieved optimal discrimination (AUCs: 0.709–0.725), with Boruta and SHAP analyses consistently ranking log2-EASIX among the top predictive features. Conclusions Admission log2-EASIX is a parsimonious, robust, and independent predictor of short- and long-term mortality in critically ill patients with concomitant HF and AKI. Its predictive accuracy is comparable to that of complex clinical scoring systems, yet it provides a substantial practical advantage by requiring only three routinely available biomarkers. The routine calculation of EASIX could therefore improve early risk stratification and help guide personalized intensive care management.","url":"https://doi.org/10.21203/rs.3.rs-9638574/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9638574/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9670632/v1","name":"Evaluation of risk prediction models for coronary artery disease: a systematic review, meta- analysis, and machine learning validation","source":"preprints","abstract":"Abstract Background : Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide, and early diagnosis is essential to improving patient outcomes. Risk prediction models for CAD are increasingly recognized for their potential role in this process. However, the quality of the existing models varies, and their practical applicability in clinical settings requires further evaluation. Objective : This study aims to systematically review and assess the core characteristics and diagnostic performance of currently available CAD risk prediction models. Additionally, a meta-analysis and machine learning are conducted to provide insights that could inform directions for both clinical practice and future research. Methods : A comprehensive search was conducted across three English-language databases - PubMed, Embase, and Web of Science - up to February 22, 2025. The included studies primarily consisted of cohort, case-control, and cross-sectional designs. Key study details and model characteristics were extracted, and the PROBAST tool was employed to assess the risk of bias and the applicability of each study. A meta-analysis of the area under the receiver operating characteristic curve (AUC) for each model was performed using STATA software. A machine learning model was used to validate the meta-analysis conclusion using a local case-control cohort. Results : Out of 15,232 articles retrieved, 68 studies met the inclusion criteria. The majority of these studies utilized logistic regression to develop CAD risk prediction models. The meta-analysis included 58 models from 58 studies, with a summary AUC of 0.805 (95% CI: 0.784-0.827), indicating moderate discriminative ability. However, all studies exhibited a high risk of bias, primarily due to differences in study subjects, selected parameters, and analytical approaches. Sensitivity analysis revealed that removing any individual study did not substantially alter the results, indicating robustness of the findings. Subgroup analysis revealed that 4 studies focusing on patients with chronic ischemic syndrome (CIS) yielded an I² value of 67.12% (P = 0.0277). Among the 28 cohort studies, three machine-learning-based models had an I² of 54.9% (P = 0.109). Among the 28 cohort studies, 3 studies focusing on patients with CIS had an I² value of 54.7% (P = 0.110). These results illustrate to some extent the source of heterogeneity. In the local case-control cohort, machine learning models demonstrated superior performance for acute coronary syndrome (ACS) (AUC=0.993, 95% CI: 0.986-1.000) compared to the CAD cohort (AUC=0.984, 95% CI: 0.975-0.993). Conclusion : While the CAD risk prediction models included in this review demonstrate some discriminatory power, the high risk of bias across all studies may limit their clinical applicability. Future research could focus on CAD subgroups and develop corresponding machine-learning diagnostic models to enhance their clinical utility and reduce heterogeneity. Registration : This study protocol has been registered on PROSPERO (Registration Number: CRD420250640666).","url":"https://doi.org/10.21203/rs.3.rs-9670632/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9670632/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9792321/v1","name":"A Machine Learning-Based Model for Cardiovascular Disease Risk Stratification in Middle-Aged and Older Adults with Diabetes: A Cross-Sectional Study based on CHARLS","source":"preprints","abstract":"Abstract Background cardiovascular disease(CVD) is the leading cause of mortality among patients with diabetes mellitus(DM).The primary aim of this study was to develop a machine learning-based risk assessment model for CVD in the diabetic population.The model is intended to facilitate early risk stratification and support clinical decision-making. Methods We performed a retrospective research based on data from the China Health and Retirement Longitudinal Study(CHARLS) and comprised middle-aged and older adults with DM.We applied LASSO regression and the Boruta algorithm for feature selection from the initial 29 clinical variables. Six machine learning algorithms—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) were trained and comparatively evaluated.Furthermore,we employed SHapley Additive exPlanations (SHAP) to provide model interpretability. Results The feature selection process identified 8 robust clinical variables for model training.Among these algorithms,the RF model showed robust classification capabilities..On the test set,the RF model achieved an area under the receiver operating characteristic curve(AUC) of 0.752,with an accuracy of 66.12%, sensitivity of 72.53%, and specificity of61.64%. Conclusion This study identified crucial risk factors for CVD in patients with diabetes and successfully translated these findings into an explainable, machine learning-driven visual risk assessment tool. This user-friendly system provides a practical approach for personalized cardiovascular risk assessment in clinical settings. Clinical trial registration Clinical trial number: not applicable.","url":"https://doi.org/10.21203/rs.3.rs-9792321/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9792321/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10695735/v1","name":"Interpretable machine-learning models for Guillain-Barré syndrome subtype assignment using acute-phase nerve conduction study: a multicenter study","source":"preprints","abstract":"Abstract Background Guillain-Barré syndrome (GBS) comprises clinically and electrophysiologically heterogeneous subtypes whose assignment may vary according to nerve conduction study (NCS) timing, serological availability, and examiner expertise. Interpretable machine-learning models may help standardize subtype assignment from acute-phase NCS data, with and without serology. Methods In a multicenter cohort of 99 patients across nine Korean hospitals (73 internal, 26 external), NCS parameters harmonized across acquisition systems and stimulation protocols were used to train gradient-boosting models (XGBoost, LightGBM, CatBoost) to classify three mechanism-based subtypes (axonal-predominant, demyelinating, and Miller Fisher spectrum) under with-serology and without-serology conditions. Discrimination was assessed by the area under the receiver operating characteristic (ROC) and precision-recall curves (PRC) over repeated internal splits, with the external set serving as an independent validation set. SHapley Additive exPlanations (SHAP) analysis was used to examine whether model attributions were consistent with conventional electrophysiological patterns. Results Performance was higher in models incorporating serology than in NCS-only models. Macro-averaged AUC was 0.92 versus 0.87 internally and 0.90 versus 0.80 in the external validation. SHAP attributions identified subtype-relevant NCS features and supported model face validity, although performance estimates were limited by the small external sample. Conclusions Interpretable machine-learning models showed preliminary ability to reproduce expert-assigned GBS subtype assignments from acute-phase NCS. Feature attributions were consistent with expected electrophysiological patterns, supporting model face validity. Larger prospective cohorts with independent adjudication and outcome linkage are needed before these models can be used for clinical decision-making or mechanistic inference.","url":"https://doi.org/10.21203/rs.3.rs-10695735/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10695735/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.11.26360163","name":"Predictors of Visual Acuity, Intraocular Pressure, and Pain in Neovascular Glaucoma: A Mixed-Effects Model and Machine Learning Cohort Analysis","source":"europepmc","abstract":"Background/Aims Neovascular glaucoma (NVG) is a severe, secondary glaucoma. This study aimed to identify factors associated with vision, intraocular pressure (IOP), and ocular pain outcomes. Methods The cohort included all patients diagnosed with NVG during 2008–2024 at Helsinki University Hospital, Finland. Linear mixed-effects models used pre-specified covariates, whereas machine learning was given the full longitudinal data with biomicroscopic findings as an exploratory approach. Results 626 patients were analysed. Worse baseline vision and a closed angle were associated with worse follow-up vision. Treatments were associated with lower IOP and less pain rather than better vision. Age, sex and comorbidity were largely not associated with the outcomes. Glaucoma drainage devices showed the greatest initial IOP reduction (−10.2 mmHg, 95% confidence interval, CI −11.9 to −8.6 mmHg), followed by transscleral cyclophotocoagulation (TSCPC, −4.7 mmHg, 95% CI −5.8 to −3.7 mmHg) and peripheral retinal cryotherapy (−2.2 mmHg, 95% CI −3.1 to −1.4 mmHg). TSCPC and cryotherapy were also associated with reduced pain (odds ratio 0.51 and 0.46). Pan-retinal photocoagulation and anti-VEGF showed smaller IOP reductions, with a pain reduction for pan-retinal photocoagulation only. Both methods agreed, and machine learning added no novel clinical findings. Conclusions Vision in this cohort was largely set by the state of the eye at diagnosis. IOP control and pain relief therefore remain realistic goals even when sight cannot be saved. Peripheral retinal cryotherapy stood out, linked to both lower IOP and less pain, seldom reported in NVG. These associations from a large, unselected cohort identify treatments worth comparing prospectively. Key Messages What is already known on this topic Neovascular glaucoma has a poor visual prognosis. Treatments have mostly been assessed in operated patients as surgical success or failure, rather than by their associations with vision, IOP and pain across the disease course. What this study adds Treatments were associated mainly with lower intraocular pressure and less pain, not with better vision, which baseline severity largely determined. Peripheral retinal cryotherapy was associated with lower IOP and less pain, an effect seldom reported. How this study might affect research, practice or policy Intraocular pressure control and pain relief remain achievable goals, whereas visual prognosis is largely set at presentation. Précis In 626 neovascular glaucoma patients followed over 12,000 visits, baseline severity shaped visual prognosis. Treatments were associated with lower intraocular pressure and less ocular pain but rarely better vision. Mixed-effects models and machine learning agreed.","url":"https://doi.org/10.64898/2026.08.11.26360163","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.11.26360163","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.20944/preprints202608.1483.v1","name":"Antimicrobial Stewardship in the Era of AI: A Head-to-Head Comparison of a Machine Learning HTL Algorithm and Large Language Models in Real-World Infectious Disease Cases","source":"preprints","abstract":"Background: Persistently, antimicrobial resistance is one of the major global public health challenges, associated with 4.95 million deaths in 2019, of which 1.27 million were directly attributable to resistant bacterial infections. Among all types, urinary tract infections (UTIs) are among the most common worldwide, with 4.49 billion cases in 2021, and they affect older adults the most. In this context, previous studies suggest an exacerbation in the prevalence of resistant pathogens (such as extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae), a circumstance associated with delayed initiation of appropriate treatment, increased mortality, prolonged hospital stays, and selective pressure. These circumstances are more difficult to confront if the limited access to infectious disease specialists is considered, particularly in low- and middle-income countries. In this situation, artificial intelligence (AI) tools have emerged as a potential strategy to support antimicrobial decision-making. Methods: This observational cross-sectional concordance study evaluated the antimicrobial recommendations generated by three AI models: a human-in-the-loop machine learning (ML-HTL) for clinical decision support and two general-purpose language models (GPT-4.0 and Gemini 3.0). A total of 88 cases with positive urine cultures and complete clinical data were included. The AI-generated recommendations for each case were contrasted with the consensus response of an independent panel of three infectious disease specialists. Agreement was assessed using overall concordance, sensitivity, and Cohen’s kappa (κ), with 95% confidence intervals (CI). Results: The mean age of patients was 65.3 years; 81.8% were women, and 80.7% had uncomplicated UTIs. Escherichia coli was the most frequent pathogen (76.1%). Among isolates, 37.5% were ESBL-producing, 53.4% were fluoroquinolone-resistant, and 44.3% were multidrug-resistant. The ML-HTL system showed the highest agreement with the expert´s consensus (90.9%; 95% CI: 83.1–95.3), followed by GPT-4 (63.6%; 95% CI: 53.2–72.9) and Gemini (37.5%; 95% CI: 28.1–47.9). Also, ML-HTL showed high sensitivity and strong agreement (87.5%, κ = 0.89), whereas GPT-4 showed low agreement (κ = 0.32), and Gemini showed less than chance agreement (κ = −0.18). Conclusions: The ML-HTL system, specifically designed for antimicrobial optimization, may better align with experts' consensus than general-purpose language models, supporting their role in clinical decision-making.","url":"https://doi.org/10.20944/preprints202608.1483.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1483.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.22.26361087","name":"Velocity Reflection Index and Chronological Age: Confounder-Adjusted Statistical and Machine-Learning Analyses of Carotid Doppler Waveforms","source":"preprints","abstract":"Purpose To determine whether the velocity reflection index (VRI) is the carotid Doppler waveform feature most strongly associated with chronological age after adjustment for sex and exercise habit, and whether its feature ranking remains stable across cross-validated and cohort-sensitivity analyses. Methods Eight waveform-derived features were analysed in 197 participants meeting the study eligibility criteria and measured using a validated continuous-wave carotid Doppler system. Pearson and partial correlations and multivariable regression evaluated associations with chronological age. Random Forest regression with repeated 10-fold cross-validation, held-out permutation importance and bootstrap resampling assessed feature ranking. Sensitivity analysis evaluated the influence of cohort construction. Results VRI showed the strongest association with chronological age (r = 0.738, 95% CI [0.667, 0.796]) and remained strongly associated after adjustment for sex and exercise habit (partial r = 0.798). VRI ranked first by both impurity-based (0.536) and held-out permutation (0.765) importance; repeated cross-validation yielded MAE = 6.87 ± 1.26 years and R 2 = 0.572 ± 0.153. Its leading ranking was stable in 85.3% of bootstrap resamples and the age-VRI correlation was essentially unchanged in the cohort-sensitivity analysis. The exercise association was significant after age adjustment (B = -0.043, p = 0.018) but attenuated after additional adjustment for sex (B = -0.026, p = 0.098). The sex association remained significant after adjustment for age and height. Conclusion VRI was robustly associated with chronological age and retained the leading feature-importance ranking across adjusted statistical and cross-validated machine-learning analyses. Validation against an established arterial-stiffness measure in an independent cohort is required before VRI can be considered a clinical vascular-aging biomarker.","url":"https://doi.org/10.64898/2026.08.22.26361087","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.22.26361087","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10478855/v1","name":"Artificial Intelligence and Machine Learning Applications in Antimicrobial Resistance Research: A Systematic Review","source":"preprints","abstract":"Abstract Background Antimicrobial resistance (AMR) represents an existential global health crisis, causing over one million deaths yearly all over the world. Conventional culture-based susceptibility testing requires 48–72 hours, an interval insufficient for guiding empirical therapy in critically ill patients. Artificial intelligence (AI) and machine learning (ML) approaches have emerged as probable alternative solutions, capable of analysing high-dimensional clinical, spectral, and genomic datasets to generate rapid resistance predictions. Objectives To systematically identify, appraise, and synthesise peer-reviewed evidence on AI/ML model applications for detecting, predicting, and managing AMR in bacterial pathogens using human clinical data. Methods A systematic search of PubMed, Scopus, and Embase was conducted for studies published between January 2019 and January 2026. Eligible studies incorporated supervised ML or deep learning models applied directly to AMR phenotype or genotype prediction using human clinical data, with quantitative performance metrics. Data extraction followed the CHARMS checklist; methodological quality was assessed using PROBAST. PRISMA 2020 guidelines were followed throughout. Results From 4,067 records identified, 57 studies met full eligibility criteria (after removal of 452 duplicates and exclusion of 3,554 records at screening and full-text review stages). The predominant algorithms were Random Forest (n ≈ 22), XGBoost (n ≈ 20), LightGBM (n ≈ 14), logistic regression with regularisation (n ≈ 18), and support vector machines (n ≈ 16). MALDI-TOF mass spectrometry spectral data and whole-genome sequencing features were the most commonly employed input modalities. Best-performing models achieved AUROC values of 0.85–0.99 for pathogens including MRSA, carbapenem-resistant Klebsiella pneumoniae (CRKP), and Acinetobacter baumannii. PROBAST assessment revealed high risk of bias in the Participants and Analysis domains across all 57 studies, predominantly due to retrospective single-centre designs and absent external validation. Conclusions Tree-based ensemble methods, particularly XGBoost and LightGBM applied to MALDI-TOF spectral inputs, demonstrate strong diagnostic potential for rapid AMR prediction. However, pervasive methodological limitations including retrospective designs, absent external validation in ~ 65% of studies, and unreported missing data handling preclude immediate clinical translation. Prospective multicentre validation, explainability integration, and standardised reporting frameworks are required before clinical adoption.","url":"https://doi.org/10.21203/rs.3.rs-10478855/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10478855/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.31234/osf.io/u7zmq_v1","name":"Beyond Diagnosis: A Scoping Review of Machine Learning and NLP for Multilevel Mental Health Factors in Population Crises","source":"preprints","abstract":"Background: Natural disasters, pandemics, armed conflict, and economic upheaval inflict lasting harm on mental wellbeing, yet what determines who recovers and who deteriorates remains poorly understood across crisis types. Objectives: This review maps how crisis exposure shapes wellbeing across four crisis typologies, two surveillance modes (NLP-based and survey/administrative), and three impact directions (favorable, adverse, or mixed).Eligibility criteria: Peer-reviewed journal articles and conference papers published in English between 2016 and 2026, applying machine learning, NLP, or deep learning methods to a population exposed to one of the four crisis typologies, and reporting at least one measurable mental health outcome.Sources of evidence: PubMed/MEDLINE, Web of Science, Scopus, and IEEE Xplore, supplemented by forward and backward citation searching and targeted keyword searches.Charting methods: Factors were charted using the WHO Commission on Social Determinants of Health framework, adapted for the health system where the framework’s coverage is thinnest. They were sorted into tiers, subgroups, and subdimensions with maximum possible fidelity to the original framework. Results: Across 338 codeable determinants, Intermediary Determinants, particularly psychosocial and behavioral-biological factors, accounted for two-thirds of coded determinants, while NLP-based methods proved most effective at capturing structural and contextual signals (e.g., governance, discourse-based constructs) rather than the clinical burden measures that dominate closer to the individual. Favorable, protective constructs were both rarer and less computationally detected than adverse ones, concentrating almost entirely in mastery, self-concept, coping, and social capital. Forty-five factors were set aside as Outcome Measures because they described prior symptoms, diagnoses, or the study's own outcome construct, with 8 being circular with the study's stated target. Fewer studies examined economic downturns, and conflict studies had almost no NLP presence. Conclusions: These findings demonstrate this field has matured methodologically in adverse detection but remains structurally limited in reach. This is most evident in armed conflict contexts and in the surveillance of protective, resilience-oriented signals, highlighting the need for digital phenotyping, expanded NLP coverage, and wider adoption of explainable AI methods.","url":"https://doi.org/10.31234/osf.io/u7zmq_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31234/osf.io/u7zmq_v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10662071/v1","name":"Statistical and Machine Learning Assisted Tribological Characterization of Mg (97.65Wt. %)-Ag (2.03Wt. %) - Mn (0.29Wt. %) - Zr (0.03Wt. %) Alloy for Orthopedic Osteosynthesis Implants","source":"preprints","abstract":"Abstract Biodegradable magnesium (Mg)-based alloys have emerged as promising materials for temporary orthopedic osteosynthesis implants owing to their favorable mechanical properties, biocompatibility, and ability to degrade naturally after bone healing, thereby eliminating the need for secondary implant removal surgery. However, the rapid degradation and relatively poor tribological performance of magnesium alloys remain significant challenges that limit their widespread clinical application. In this study, the tribological behavior of a biodegradable Mg-Ag-Mn-Zr alloy was systematically investigated and predicted using a machine learning-assisted framework. Silver (Ag), manganese (Mn), and zirconium (Zr) were incorporated into the magnesium matrix to enhance wear resistance, mechanical strength, corrosion behavior, and biological compatibility. The results demonstrated excellent wear resistance and mechanical stability, with applied load identified as the dominant factor influencing specific wear rate. Higher applied loads improved wear resistance through the formation of a stable protective tribo-layer. Further two-body wear results revealed that wear rate and frictional force increased with increasing applied load and sliding velocity, whereas increasing sliding distance reduced wear through the formation of a stable protective tribolayer, demonstrating that contact loading is the dominant factor governing the sliding tribological performance of the Mg–Ag–Mn–Zr alloy. From Analysis of variance (ANOVA) it was observed that applied load was the most influential parameter, contributing 95.69% to the specific wear rate, during the three-body abrasion test, while contributing 92.222% to the wear rate and 99.11% to the frictional force during the two-body wear test. Response Surface Methodology (RSM) models accurately predicted the experimental responses, yielding average prediction errors of 6.78% for specific wear rate, during three-body abrasion, and 5.529% and 0.622% for wear rate and frictional force, respectively, during two-body wear. Desirability Function Analysis (DFA) successfully identified the optimum process parameters for minimizing wear and friction while maintaining superior mechanical performance. A Feedforward Artificial Neural Network (FFANN) was developed to predict the tribological performance of the biodegradable Mg–Ag–Mn–Zr alloy under three-body abrasion and two-body wear conditions, where the model demonstrated good predictive capability for tribological responses.","url":"https://doi.org/10.21203/rs.3.rs-10662071/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10662071/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10283467/v1","name":"Implementation and Study on Liver Cirrhosis Disease Diagnosis Prediction Using a Method for Machine Learning Algorithms: A Comparative Approach Analysis","source":"preprints","abstract":"Abstract Background: We explored anonymized patient records from the 'Aadarshvelu Liver-Cirrhosis-Stage-Classification' Kaggle dataset, which is rich in varied laboratory and clinical details. By visualizing model performance through confusion matrices, we uncovered how the quality of the training data shapes classifier accuracy. Methods : Our findings reveal that machine learning not only boosts diagnostic accuracy but also makes liver cirrhosis detection more cost-effective. By testing models on real clinical data, we identified the most powerful predictive techniques and crafted actionable recommendations for healthcare. These innovations pave the way for earlier, more reliable diagnoses, sharpen clinical decision-making, and inspire the development of automated tools to manage liver disease. Findings: The experimental dataset is constructed from real clinical medical records, and the model's empirical and comparative experiments are carried out, achieving an accuracy of 94.05% and a macro-averaged F1 of 95.05%. Interpretation: The Liver Cirrhosis Disease Diagnosis Prediction coding model, based on labeled attention and proposed in this paper, can effectively improve the performance of coding medical records using Machine Learning and, compared with the best-performing algorithm, achieves better results.","url":"https://doi.org/10.21203/rs.3.rs-10283467/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10283467/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.19.745843","name":"Classifying CRISPR-Cas9 Off-Target Cleavage Sites from GUIDE-seq Data: A Class-Imbalanced Machine Learning Benchmark","source":"preprints","abstract":"Off-target cleavage is a central safety concern for CRISPR-Cas9 genome editing, particularly in therapeutic applications where unintended double-strand breaks carry clinical risk. We benchmarked five machine learning classifiers — logistic regression on mismatch-count summary features, a random forest and a gradient boosting model on one-hot-encoded sgRNA/candidate-site sequence pairs, a one-dimensional convolutional neural network (CNN) over the positional mismatch map, and a gradient-boosting/CNN ensemble — on a real, published GUIDE-seq off-target dataset (Kleinstiver et al., 2016, Nature) comprising 95,829 candidate off-target sites for five sgRNAs, of which only 54 (0.06%) were experimentally validated as true cleavage sites. On a held-out, stratified test split (n = 19,166; 11 true positives), gradient boosting on combined mismatch and sequence features performed best (ROC-AUC = 0.997, PR-AUC = 0.355, best F1 = 0.50), outperforming a random forest on raw sequence encoding alone (PR-AUC = 0.083) and a sequence CNN (PR-AUC = 0.129). Because the positive class is extremely rare, we report precision-recall AUC as the primary metric rather than ROC-AUC, which is inflated by the large negative class. A positional mismatch analysis showed that experimentally validated off-target sites carried substantially fewer mismatches overall than non-cleaved candidate sites (mean 3.6 vs. 5.9 mismatches across the 23-nucleotide target), and were markedly more mismatch-intolerant in the 10-nucleotide PAM-proximal seed region (11.3% vs. 27.4% per-position mismatch rate) and at the PAM itself (6.8% vs. 16.0%), consistent with established seed-region and PAM-sensitivity models of Cas9 target recognition. We report these findings, including the low absolute precision achievable in this severely imbalanced, small-positive-class setting, as a realistic picture of what off-target classifiers can and cannot yet deliver from sequence alone.","url":"https://doi.org/10.64898/2026.08.19.745843","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.19.745843","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10682675/v1","name":"A Comprehensive Evaluation of Distributional Shift and Concept Drift Across Preprocessing Configurations, Domain Adaptation, and Representation Learning in GDM Prediction","source":"preprints","abstract":"Abstract Gestational diabetes mellitus (GDM) screening tools trained on single-population cohorts routinely fail when deployed across clinical settings, yet the literature largely benchmarks models on internal test splits while treating cross-cohort evaluation as a secondary concern. This study investigates whether any combination of model sophistication, preprocessing design, or domain adaptation can compensate for the distributional mismatch inherent in cross-cohort GDM prediction. Using a primary cohort (N = 3,525) and an independent cross-cohort test set (N = 1,205), we conducted a systematic sweep of 12 preprocessing configurations (2 feature sets: pre-screening or full mode $\\times$ 3 imputers $\\times$ 2 scalers) combined with five classical machine learning models (support vector machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), CatBoost), Synthetic Minority Over-sampling Technique (SMOTE)-based training augmentation, CORrelation ALignment (CORAL) domain adaptation, and a contrastive deep learning framework employing three loss functions (Supervised Contrastive, Normalized Temperature-scaled Cross Entropy (NT-Xent), Batch-Hard Triplet) with Variance-Invariance-Covariance Regularization (VICReg) regularization, producing 96 model-configuration combinations evaluated under 5-fold stratified cross-validation. All 60 classical-model combinations achieve near-perfect internal area under the precision-recall curve (AUPRC) (0.959--0.998) and universally collapse cross-cohort to 0.180-0.266. Within this collapsed range, excluding oral glucose tolerance test (OGTT) (pre-screening mode) with Multivariate Imputation by Chained Equations (MICE) imputation and robust scaling emerges as the most consistent strategy (mean cross-cohort AUPRC 0.228; best single result SVM 0.266), a finding we trace mechanistically to OGTT's catastrophic concept drift (d = 1.91 internally, d = 0.36 cross-cohort). CORAL domain adaptation produces a striking estimator-dependent interaction: it improves SVM cross-cohort AUPRC in full-feature configurations by up to +0.079 while degrading all gradient-boosted ensembles across all configurations and degrading every estimator in pre-screening configurations. Contrastive learning mirrors the classical result: all cross-cohort configurations collapse to 0.167-0.216 AUPRC regardless of loss function or preprocessing. In an oracle mixed-domain experiment (non-deployment setting), when training includes data from both cohorts, AUPRC recovers to 0.944-0.978, confirming that model capacity is not the bottleneck. Overall, the findings are most consistent with concept drift in the conditional relationship $P(Y \\mid X)$, for which source-only adaptation is insufficient in this setting.","url":"https://doi.org/10.21203/rs.3.rs-10682675/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10682675/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202607.0156.v1","name":"Prediction of New Onset Atrial Fibrillation in Patients with Acute Myocardial Infarction Using Artificial Intelligence and Machine Learning Algorithms","source":"preprints","abstract":"Background: /Objectives: New-onset atrial fibrillation is a common complication of acute myocardial infarction and is associated with increased mortality, heart failure, and recurrent cardiovascular events. Prediction models for the occurrence of atrial fibrillation may facilitate early risk stratification, improve patient monitoring, and support personalized therapeutic decision-making in clinical practice. Methods: A prospective study included 150 patients with acute myocardial infarction admitted within 24 hours of symptom onset. Clinical, laboratory, electrocardiographic, and echocardiographic data were collected, and a machine learning model was developed to predict new-onset atrial fibrillation. Model performance was evaluated using ROC-AUC, while SHAP analysis was applied to identify and quantify the contribution of individual predictors. Results: The machine learning model demonstrated excellent predictive performance, achieving an accuracy of 97.0%, ROC-AUC of 0.991, and precision–recall AUC of 0.991 in the independent test set. SHapley Additive Explanations (SHAP) and permutation importance analyses identified the E/e′ ratio, left ventricular mass index, left ventricular ejection fraction, left atrial volume index, oxidative stress markers (malondialdehyde and superoxide dismutase), NT-proBNP, and complete revascularization as the most influential predictors of new-onset atrial fibrillation after acute myocardial infarction. Conclusions: Machine learning combined with SHAP-based explainability showed high potential for predicting new-onset atrial fibrillation after acute myocardial infarction. This approach may improve individualized risk assessment and clinical decision-making, pending validation in larger multicenter cohorts.","url":"https://doi.org/10.20944/preprints202607.0156.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0156.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10375134/v1","name":"Desilting 4.0: A Sky–Land–Water–AI Digital-Twin Framework for Low-Emission Multi-Contaminant Co-Solidification and Machine-Learning Foresight in El Niño-Ready Andean Rivers and Lagoons","source":"preprints","abstract":"Abstract Descolmatation (desilting) of Andean rivers and lagoons in Peru routinely encounters sediment that is jointly loaded with heavy metals and organic pollutants, a co-contamination pattern that conventional single-binder, single-contaminant remediation designs do not address. This Method Article synthesizes documented, DOI-verified evidence on (i) low-emission composite binders (ground granulated blast-furnace slag, fly ash, carbide slag and organic amendments) and their micro-scale immobilization mechanisms, (ii) multi-model machine-learning (ML) ensembles for forecasting unconfined compressive strength (UCS) and leachability trends in stabilized soils and sediments, (iii) an integrated Sky–Land–Water–AI monitoring architecture adapted from space–air–ground remote-sensing networks documented for Chinese river basins, and (iv) co-solidification strategies for simultaneous heavy-metal and organic-contaminant immobilization documented in Chinese case studies. Building on this evidence base, the article proposes an integrated engineering framework formalized through four governing relations: an exponential strength–binder-ratio law, a solid–liquid partition/immobilization coefficient for co-contaminants, a weighted multi-model ML ensemble equation for trend forecasting, and a carbon-footprint reduction metric. A comparative matrix contrasts documented Asian (predominantly Chinese) full-scale and pilot practice against a proposed low-cost, solar-powered, modular configuration adapted to the topography, intermittent grid access and El Niño-driven hydrological variability of Andean rivers and lagoons in Peru, illustrated with documented water-quality evidence from the Rimac and Ichu river basins. As of mid-2026, Peru's National Committee for the Study of the El Niño Phenomenon (ENFEN) and the World Meteorological Organization have flagged a high probability of an El Niño event with intensified rainfall and riverbed sedimentation, which motivates the urgency of scalable descolmatation technology. The framework is presented as a design-and-synthesis proposal rather than a validated field trial; pilot-scale validation with Peruvian field data is identified as the necessary next step. No human or animal subjects were involved, and no primary biological or clinical data were collected.","url":"https://doi.org/10.21203/rs.3.rs-10375134/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10375134/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10753197/v1","name":"Predicting Under-Five Child Stunting Using Machine Learning and Probability Calibration: A Nationally Representative 2024/25 EDHS Study from Ethiopia","source":"europepmc","abstract":"Abstract Background Child stunting remains a critical public health challenge in Ethiopia. Standard parametric frameworks fail to capture complex non-linear interactions among multidimensional determinants. This study evaluated six supervised machine learning (ML) algorithms, incorporated probability calibration and performed clinical utility diagnostics to predict under-five child stunting. Methods We analyzed secondary data from 2024/25 the Ethiopian Demographic and Health Survey (EDHS) Kids Recode dataset (N = 10641). Six algorithms Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Gradient Boosting and XGBoost were trained on 80% of the dataset (n = 8,513) using 10-fold cross-validation and tested on a 20% holdout sample (n = 2128). Isotonic probability calibration was implemented for tree ensembles. Models were benchmarked using discrimination metrics, calibration fit and Decision Curve Analysis. Results National stunting prevalence was 36.8% (95% CI: 35.9%–37.7%). Non-parametric ensemble models outperformed linear models. Post calibration Gradient Boosting achieved an optimized AUC-ROC of 0.892 (95% CI: 0.868–0.916) with superior calibration (Hosmer-Lemeshow p > 0.05). At a p = 0.50 decision threshold, operational metrics reached 85.1% Accuracy, 82.4% Precision, 83.6% Sensitivity, 86.0% Specificity and an 83.0% F1-score. Primary predictive drivers included maternal BMI, child age gradient, household wealth quintile, maternal educational attainment and recent diarrheal illness. Conclusion Isotonic-calibrated Gradient Boosting provides highly accurate, reliably calibrated risk stratification for child stunting. Integrating these algorithmic decision support tools into community digital platforms (e.g. Ethiopia's Health Extension Program) enables proactive targeted screening before irreversible growth failure occurs.","url":"https://doi.org/10.21203/rs.3.rs-10753197/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10753197/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.64898/2026.07.22.26358695","name":"Forecasting Trajectories of Physiological Mechanics with Sparse Clinical Data Using a Data Assimilation and Machine Learning Hybrid","source":"preprints","abstract":"Clinical decisions for determining optimal patient-specific interventions are complicated prediction tasks that rely on health care professionals’ understanding of physiological mechanisms and their dynamics. These decisions are challenged by (a) observational data sparsity and (b) patient heterogeneity. Here, we focus on estimating and forecasting specific physiological properties—that are not explicitly present in clinical observations—to provide additional features using only data available bedside at the time of decision-making. Mechanistic models of physiological system(s), e.g., physiological ordinary differential equation (ODE) models, provide pathways to compensate for data sparsity by synchronizing the model with observations of an individual patient using data assimilation (DA). However, DA used in a standard computational workflow to estimate constant model parameters from presently-known data is less effective at optimizing state forecasts of the model governed by physiological processes that evolve before new observations are available. Stated simply, we cannot forecast the future evolution of the model because we cannot forecast model parameters. To support next-generation clinical decision support, we develop a new DA and machine learning (ML) hybrid pipeline to estimate and forecast individual future physiological processes by forecasting ODE model parameters. This pipeline overcomes model and DA workflow limitations by stacking a DA-estimated posterior empirical distribution of physiological parameters with longitudinal ML forecasting models. We work within the context of glycemic management in an ICU using EHR data to construct and test a use case. We use synthetic data and real-world clinical data to validate the integrated pipeline and quantify uncertainties.","url":"https://doi.org/10.64898/2026.07.22.26358695","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358695","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10350493/v1","name":"Machine learning-based prediction of preeclampsia: a comparative study on model effectiveness","source":"preprints","abstract":"Abstract Background: Preeclampsia remains a leading cause of maternal and perinatal morbidity in low- and middle-income countries (LMICs). As preparatory methodological work for an AI-powered population-based birth cohort in Sri Lanka, this study systematically evaluated machine learning algorithms for preeclampsia prediction under realistic constraints of small sample size, severe class imbalance, and routine clinical features. Methods: Data from 343 pregnant women in the Western Province of Sri Lanka were analysed, including 310 preeclampsia-negative and 33 preeclampsia-positive observations. Six algorithms were compared: Logistic Regression, Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN). Multiple imbalance-handling strategies, including SMOTE, class weighting, and cost-sensitive learning, were evaluated using five-fold cross-validation with rigorous leakage prevention. Results: Without imbalance handling, baseline models achieved high nominal accuracy (0.90-0.91) but zero sensitivity for minority-class detection. XGBoost with SMOTE showed the most favourable performance in this exploratory analysis (balanced accuracy: 0.889 ± 0.027; AUC: 0.956 ± 0.013), although estimates remained unstable because of the small number of events (n=33). KNN with SMOTE achieved comparable results (AUC: 0.914 ± 0.045), while Random Forest with SMOTE showed robust performance with lower variance (AUC: 0.902 ± 0.013). SVM with SMOTE achieved moderate performance (AUC: 0.833 ± 0.042). Logistic regression without balancing failed completely (recall=0.0), whereas logistic regression with SMOTE achieved an AUC of 0.733. ANN performance was sample-limited (AUC approximately 0.81). Models incorporating post-outcome variables produced artificially inflated but methodologically invalid performance (AUC >0.98), demonstrating target leakage. Conclusions: Ensemble methods with SMOTE can achieve promising discrimination using routine clinical variables, but this work should be interpreted as feasibility methodology rather than a clinically deployable tool. The small sample size, lack of external validation, and absence of calibration assessment preclude current clinical use. For the planned birth cohort, adequate event accumulation, rigorous temporal feature selection, and external validation remain essential prerequisites.","url":"https://doi.org/10.21203/rs.3.rs-10350493/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10350493/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10358874/v1","name":"Clinlabomics-Based Machine Learning Models for Risk Warning of Multi-Type Gastrointestinal Cancers","source":"preprints","abstract":"Abstract Gastrointestinal malignancies pose a major public health challenge in China, where the majority of patients are diagnosed late and face poor survival outcomes. A clinlabomics data-based auxiliary screening tool could address this gap, yet previous studies lack the large-scale, multicenter validation necessary to support clinical translation. This study is a multicenter retrospective analysis involving 115,032 participants from four hospitals between January 1, 2019, and April 30, 2026. The cohort from Sichuan Cancer Hospital (SCH) was designated as the discovery set. After systematic comparison of various machine learning and deep learning models, glmnet was ultimately selected as the optimal predictive model. The final tool, Gastrointestinal Cancers Seek (GaSeek), was subsequently validated in three independent external cohorts, as well as in three real-world clinical cohorts. This GaSeek achieved an area under the receiver-operating characteristic curve (AUC) of 0.960 (95% CI: 0.952-0.968) in the internal validation cohort and an AUC of 0.852 (95% CI: 0.843-0.860) in the overall external validation cohort. Analysis of feature importance identified albumin (ALB) and hemoglobin (HGB) as the most influential predictors. Furthermore, the model demonstrated robust performance in detecting early-stage gastrointestinal cancers and was proficient in screening cases with high confidence for positive results. Using an appropriate threshold, GaSeek not only screened out gastrointestinal cancers but also identified other endoscopically detectable digestive tract tumors in real-world cohorts. We used “Screening Efficiency Gain (SEG),\" defined as the ratio of the detection rate in the high-risk group to that in the overall cohort, to evaluate the improvement. In the SCH real-world cohort, whose non-cancer participants were healthy individuals, GaSeek delivered a high SEG of 15.70, showing excellent screening efficiency. The GaSeek was deployed as a publicly accessible web application (https://weiyanghe520.shinyapps.io/gastrointestinal-cancer-predictor/). It effectively identifies individuals at high risk for digestive tract tumors and could improve the efficiency of endoscopic screening for digestive tract tumors through risk stratification. This study has been registered with the Chinese Clinical Trial Registry, registration number: ChiCTR2500107671, registration date: August 15th, 2025, (https://www.chictr.org.cn/showproj.html?proj=276534).","url":"https://doi.org/10.21203/rs.3.rs-10358874/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10358874/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10596533/v1","name":"PredictStroke: A Two-Stage Modular System Combining Machine Learning Ensemble Risk Prediction and Multimodal Symptom Triage Support","source":"preprints","abstract":"Abstract Introduction Early stroke detection and long-term risk assessment are critical components of preventative health and acute triage. Existing artificial intelligence solutions typically focus on either predictive modeling using tabular data or real-time clinical symptom tracking, but rarely both. We present PredictStroke, a modular framework designed to bridge the gap between preventative analytics and acute triage support by combining ensemble-based long-term risk assessment with real-time, vision- and speech-based acute symptom classification. The predictive engine utilizes soft voting probability averaging across an ensemble of three machine learning models: K-nearest neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The real-time triage arm evaluates independent feature-engineered pipelines: an SVM classifier analyzing facial landmark asymmetry (eye, eyebrow, mouth corner height ratios) and a second SVM classifier analyzing speech fluency metrics derived from Mel-frequency cepstral coefficients (MFCCs), pitch variance, and paused intervals. Results The ensemble risk prediction engine achieved high robust performance with a precision of 0.90, recall of 0.80, and an F1-score of 0.85. Due to deployment constraints, the real-time detection arms were validated independently; the facial drooping detection module achieved an F1-score of 0.65, and the slurred speech classification module achieved an F1-score of 0.62. Conclusions Combining long-term mathematical risk modeling with independent acute symptom detection algorithms is a conceptually valuable and technically feasible framework for remote triage. The modular framework allows data modeling pipelines to scale independently, mitigating severe data availability and system integration bottlenecks common in consumer-facing digital health deployments.","url":"https://doi.org/10.21203/rs.3.rs-10596533/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10596533/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10700011/v1","name":"Beyond BMI: Chinese Visceral Adiposity Index (CVAI) improves machine learning prediction of 9-year cardiometabolic multimorbidity in the CHARLS cohort","source":"preprints","abstract":"Abstract Background Driven by rapid population aging and evolving lifestyle patterns across China, middle-aged and older individuals are at substantially elevated risk of cardiovascular disease (CVD) and diabetes mellitus (DM). The two disorders often co-occur and share metabolic risk determinants, collectively termed cardiometabolic multimorbidity (CMM). However, systematic predictive research targeting the composite endpoint of CMM remains relatively limited. Methods Based on data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011 to 2020, three separate cohorts were established for DM (n = 7,903), CVD (n = 9,997), and CMM (n = 9,490). Four machine learning algorithms, namely logistic regression, random forest, XGBoost, and LightGBM, were applied to develop 9-year risk prediction models. Model performance was evaluated using AUC, calibration curves, and Brier score, and decision curve analysis, with SHAP analysis applied for model interpretability. Results The 9-year cumulative incidence rates of DM, CVD and CMM were 11.8%, 22.7% and 28.9%, respectively. DM models achieved the best performance (AUC 0.703–0.715), with XGBoost reaching the highest AUC (0.715). CVD prediction proved the most challenging (AUC 0.604–0.630), with Logistic Regression performing best (0.630). CMM models showed intermediate performance (AUC 0.621–0.629), with XGBoost achieving the highest AUC (0.629). SHAP analysis revealed that glycemic and obesity-related indices were key predictors for DM; age and blood pressure parameters dominated CVD prediction; and the Chinese Visceral Adiposity Index (CVAI) together with depressive symptoms (CESD) emerged as the top predictors for CMM. Simplified scorecards retained most predictive performance for DM (AUC 0.681) and CMM (AUC 0.625) but failed for CVD (AUC 0.436). Conclusions DM was predicted with the highest accuracy using routine clinical data, while CVD prediction remained suboptimal, suggesting that novel biomarkers or imaging are needed. The composite CMM endpoint showed intermediate predictability, with CVAI and depressive symptoms as key predictors, underscoring the roles of visceral adiposity and mental health. Simplified scorecards offer practical tools for DM and CMM screening in primary care, but their failure for CVD highlights the inherent complexity of cardiovascular risk stratification.","url":"https://doi.org/10.21203/rs.3.rs-10700011/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10700011/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10624283/v1","name":"Integrating Triaxial IMU Sensors and Ensemble Learning for Effective Parkinson Disease Severity Classification","source":"preprints","abstract":"Abstract Parkinson disease (PD) is a progressive neurodegenerative disease that can have a significant impact on motor performance, resulting in the appearance of symptoms such as tremors, rigidity, postural instabilities and bradykinesia. Timely clinical treatment, disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years, the growth of wearable sensor technology and artificial intelligence (AI) have made it possible to create non-invasive and data-driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinson’s disease by analyzing the motion and tremor data captured by an inertial measurement unit (IMU). The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions (X, Y and Z). The signs and symptoms provide helpful information about subtle motor deficits associated with PD.Several classification models like Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were used to compare their effectiveness. The Logistic Regression model had a performance around 75% in all evaluation metrics and K-Nearest Neighbours (KNN) around 90%. The support vector machine (SVM) performed almost 94% whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96% and overall classification efficacy, respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods, having Accuracy, Precision, Recall and F1-score of around 97%. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.","url":"https://doi.org/10.21203/rs.3.rs-10624283/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10624283/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.03.26357134","name":"Machine Learning Models for Osteoporosis Prediction: A Systematic Review and Meta-Analysis","source":"preprints","abstract":"ABSTRACT Purpose The application of machine learning (ML) to osteoporosis prediction has expanded rapidly, yet no comprehensive meta-analysis has synthesized the discriminative performance of these models across all ML categories, data types, and validation strategies. This systematic review and meta-analysis aimed to evaluate the diagnostic and predictive accuracy of ML and deep learning models for osteoporosis prediction in adult populations. Methods Systematic searches of PubMed, Embase, Web of Science, and IEEE Xplore were conducted for studies published between January 2020 and February 2026. Studies developing, validating, or applying ML models for predicting osteoporosis, low bone mineral density, or osteoporotic fractures in adults were included. Methodological quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Area under the receiver operating characteristic curve (AUC) values were pooled using random-effects meta-analysis with logit transformation. Subgroup analyses were performed by data type, ML category, external validation status, and population type. The review followed PRISMA 2020 guidelines. Results Thirty-three studies were included in the qualitative synthesis and 27 in the meta-analysis. The pooled AUC was 0.879 (95% CI: 0.853–0.901), with substantial heterogeneity (I² = 99.5%). Imaging-based models outperformed clinical data models (AUC = 0.905 vs. 0.872). Deep learning achieved the highest pooled AUC (0.909), followed by ensemble methods (0.874) and traditional ML (0.840). Externally validated models showed lower performance than internally validated ones (AUC = 0.868 vs. 0.897). PROBAST assessment rated 32 of 33 studies (97.0%) as low risk of bias, though this proportion should be interpreted cautiously given that PROBAST was designed for traditional prediction models and may not fully capture ML-specific sources of bias. Egger’s test indicated significant publication bias (p Conclusions Machine learning models demonstrate overall good discriminative performance for osteoporosis prediction, albeit with substantial heterogeneity across studies (I² = 99.5%), and show potential as complementary screening tools, particularly in settings with limited DXA access. Deep learning models applied to imaging data and ensemble methods using clinical variables achieved the strongest subgroup estimates. However, extreme heterogeneity, evidence of publication bias, and limited prospective validation warrant cautious interpretation of the pooled estimate. Future research should prioritise multi-centre external validation, standardised reporting following TRIPOD+AI guidelines, and prospective clinical trials to establish real-world clinical impact. Mini-Abstract This meta-analysis of 33 studies demonstrates that ML models achieve a pooled AUC of 0.879 for osteoporosis prediction, with imaging-based deep learning models reaching 0.905, supporting their potential as complementary screening tools to DXA and FRAX®.","url":"https://doi.org/10.64898/2026.07.03.26357134","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.03.26357134","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10407532/v1","name":"Machine Learning Prognostic Stratification for Treatment Decision Support in Elderly Gastric Adenocarcinoma: A SEER Analysis with External Clinical Validation","source":"preprints","abstract":"Abstract Background : Elderly patients (≥65) constitute >60% of gastric adenocarcinoma cases yet are underrepresented in clinical trials. Machine learning survival models may capture complex prognostic interactions, but comprehensive benchmarking with external validation in this population is absent. Methods : From SEER (2000-2022), 68,921 elderly gastric adenocarcinoma patients were identified; 60,378 had complete data. After harmonizing three AJCC staging editions and propensity score matching, five survival models (Cox, RSF, XGBoost, DeepSurv, Gradient Boosting) were trained with 5-fold cross-validation and temporal validation. A Cox model with treatment×stage×age interactions estimated individualized treatment effects. External validation used a Chinese cohort (N=575) with CT-defined sarcopenia, frailty, and inflammatory biomarkers. Results : XGBoost achieved C-index 0.772, significantly outperforming Cox (0.749, +3.1%), DeepSurv (0.764), and RSF (0.738). Surgical resection showed the highest permutation importance (0.128; 4.4× Stage IV). Chemotherapy benefit was stage-dependent: null in Stage I (HR=1.00), maximal in Stage IV (HR=0.44). External validation C-index 0.617; the 20% decline was attributable to 100% surgery rate in the validation cohort rather than model overfitting. Novel clinical variables-severe sarcopenia (HR=3.39), gait speed less than 0.8m/s (HR=2.45), albumin less than 35g/L (HR=2.07)-achieved superior discrimination (C-index 0.753 vs 0.684, +10%). Conclusions : XGBoost improves prognostic discrimination over Cox. Surgery dominates prognosis; chemotherapy benefit is stage-conditional. Model transportability depends on treatment variance. Body composition and frailty variables substantially improve discrimination, supporting function-based over age-based treatment decisions.","url":"https://doi.org/10.21203/rs.3.rs-10407532/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10407532/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.22.26358742","name":"An Explainable AI-Driven Classification Framework for Parkinson’s Disease Detection via Acoustic Speech Features: A Comparative Machine Learning Study","source":"preprints","abstract":"Parkinson’s Disease (PD) is a progressive neurodegenerative disorder which significantly affects motor function, daily coordination and verbal communication. Speech-based biomarkers provide a non-invasive and scalable approach to early detection, as dysphonia is one of the earliest and most consistent clinical markers of PD. The dataset used in this study is publicly available and consists of 756 voice recordings from 252 subjects (188 with PD and 64 neurologically healthy controls) with a wide range of acoustic parameters such as Mel-Frequency Cepstral Coefficients (MFCCs), energy-based parameters, and higher-order statistical derivatives. After systematic preprocessing and z-score normalisation, five machine learning classifiers were tested: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) under a subject-independent, GroupKFold cross-validation protocol. The KNN classifier performed best overall with an accuracy of 92.10%, F1 score of 94.50%, and a precision rate of 98.09%, reducing the number of false positive diagnoses. To overcome the lack of interpretability of black-box predictive models, SHapley Additive exPlanations (SHAP) were used to explain the contribution of each feature to the prediction of an individual. The most diagnostically salient acoustic biomarkers were identified as features from the SHAP analysis: std_delta_delta_log_energy, the first Mel-Frequency Cepstral Coefficient, and Tunable Q-Factor Wavelet Transform (TQWT). This work introduces a machine learning framework that is both reproducible and clinically interpretable, combining high predictive accuracy with transparent, physiologically grounded decision logic.","url":"https://doi.org/10.64898/2026.07.22.26358742","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358742","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10264091/v1","name":"Machine Learning-Based Diagnostic Model for Preoperative Anxiety in Elderly Patients with Hip Fractures and SHAP Analysis","source":"preprints","abstract":"Abstract Aims Preoperative anxiety significantly affects the treatment prognosis of elderly patients with hip fractures. Nevertheless, owing to a combination of factors — such as patients' low educational attainment and trauma-related pain — the diagnosis and screening of this anxiety state frequently encounter considerable challenges.This study aims to develop a diagnostic model for preoperative anxiety in elderly hip fracture patients. The model is intended to assist clinicians in proactively screening high-risk individuals, thereby enabling timely interventions and personalized care to optimize recovery. Methods This study retrospectively enrolled elderly patients (aged ≥ 65 years) with hip fracture who were admitted to the Department of Geriatric Orthopedics of a tertiary hospital between November 2020 and May 2025. Based on the Generalized Anxiety Disorder (GAD) questionnaire score, patients were classified into an anxiety group (score > 4) and a non-anxiety group (score ≤ 4). Candidate predictors were screened using a combination of the LASSO algorithm and the Boruta algorithm. According to the selected predictors, patients were randomly divided into a training set and a test set at a ratio of 7:3. Seven machine learning algorithms—Logistic Regression (LR), Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were used to develop predictive models, with hyperparameter tuning performed via five-fold cross-validation. The discrimination, calibration, and clinical utility of the models were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The SHAP algorithm was employed to interpret the optimal model, quantifying the contribution and direction of influence of each variable on the prediction results. Results A total of 517 elderly patients with hip fractures were enrolled. After screening using inclusion and exclusion criteria, 400 patients were retained, including 220 patients with preoperative anxiety and 180 without anxiety. A combined LASSO and Boruta algorithm identified nine feature variables for constructing a diagnostic model for preoperative anxiety. Pearson correlation analysis showed no strong correlation among these nine variables. Seven machine learning models were developed for the diagnostic model, among which the CatBoost model performed best. In the training set, it achieved an accuracy of 0.861, precision of 0.883, recall of 0.853, F1 score of 0.868, and specificity of 0.869. In the test set, it achieved an accuracy of 0.775, precision of 0.841, recall of 0.757, F1 score of 0.797, and specificity of 0.800, indicating low overfitting. In the test set, CatBoost achieved the best performance among the seven models, exhibiting the most favorable results in terms of the ROC curve, decision curve analysis (DCA), and calibration curve, with an AUC value of 0.827 and a Brier value of 0.1714. SHAP analysis was performed on the CatBoost model, and the SHAP visualization tool revealed the following ranking of feature importance: VAS, age, HTN, CAD, Hb Min, marital status, AHF, CCI and CHF. Conclusion This study successfully developed seven machine learning diagnostic models based on nine clinical features, among which the CatBoost model demonstrated optimal performance in identifying preoperative anxiety in elderly patients with hip fractures. SHAP analysis revealed that VAS score, age, and CCI were positively correlated with preoperative anxiety risk, while Hb Min was negatively correlated. The presence of hypertension, coronary artery disease, acute/chronic heart failure, and widowed status all increased the risk.","url":"https://doi.org/10.21203/rs.3.rs-10264091/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10264091/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10003720/v1","name":"User Interfaces in Machine Learning-Based Decision Support for Emergency Department Triage – A Systematic Review","source":"preprints","abstract":"Abstract Background Triaging is a promising application for machine learning-based clinical decision support systems (CDSSs). However, despite considerable research attention, their real-world impact remains limited, with low adoption and implementation challenges being well-documented barriers. While user-centred interface design and testing are known to improve adoption and outcomes, if and how they are considered in real-world systems remains understudied. Methods This systematic review (PROSPERO ID: CRD420251119077) aims to characterize and analyse interface design features and processes for machine learning-based CDSSs for emergency department triaging. To this end, five databases were searched and two reviewers independently screened and coded the studies regarding their system characteristics, interface design decisions, and design processes. Results Of the 2,678 screened studies, 25 met the inclusion criteria. Of these, 80% had been published in the last three years, and almost half of the systems were integrated into the respective healthcare system. Almost two-thirds of systems used a visual component, but none used audio alerts. In the case of sub-problem triaging (n = 10), 80% of systems presented only a risk score or binary indicator, without accompanying recommendations or guidance for action. Although explainability was not uncommon, uncertainty and model performance indicators were rarely used. Overall, commercial systems had more elaborate interfaces but were less likely to be integrated at the time of publication. Very few studies described their design processes or testing, despite several authors attributing their systems’ shortcomings to interface-related issues. We therefore advocate user-centred design and the integration of interdisciplinary expertise to optimize the clinical use of AI for improved decision-making in an already burdened healthcare system. Furthermore, we call for adherence to reporting standards to improve accountability and future meta-analytic insights. Conclusion Despite their well-studied importance, the consideration of user needs and testing for usability remains limited in ML-based clinical decision support tools for triaging. Especially in high cognitive-load areas such as the emergency department, bridging the implementation gap will require interdisciplinary collaboration, transparent reporting, and stronger integration of human–computer interaction principles into decision support systems.","url":"https://doi.org/10.21203/rs.3.rs-10003720/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10003720/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10277358/v1","name":"Development of a Prognostic Index for Clear Cell Renal Cell Carcinoma Using Machine Learning and Multi-omics Analysis to Enhance Clinical Outcomes and Drug Sensitivity","source":"preprints","abstract":"Abstract Background Clear cell renal cell carcinoma (KIRC) represents the major malignant subtype of kidney cancer and shows pronounced biological heterogeneity. For patients with advanced disease, therapeutic resistance and the lack of stable prognostic markers continue to keep 5-year survival below 15%. Cancer driver genes (CDGs) participate in malignant transformation and tumor progression, but their combined prognostic value in KIRC has not been fully clarified. Methods We assembled a catalogue of 6,291 CDGs and designed a machine-learning-based workflow to build a Cancer Driver Gene Prognostic Index (CDPI). The analysis integrated differential expression screening, consensus clustering, WGCNA, and four feature-selection algorithms, including Lasso, decision tree, random forest, and XGBoost. Model performance was examined in the TCGA-KIRC training set and then tested in the external E-MTAB-1980 cohort. Results Differential expression screening yielded 5,243 DECDRGs and four recurrent core DECDRGs. Based on consensus clustering, KIRC samples were separated into two molecular groups, and the C1 group showed poorer survival. WGCNA selected the turquoise module as the cluster-associated module, from which 148 key DECDRGs were obtained; 105 of these genes were significant in univariate Cox analysis. Cross-model feature selection ultimately produced a CDPI composed of five genes: PLCL1, GABRB3, USP46, RNF152, and PFKP. Patients with higher CDPI values had shorter overall survival in both datasets. The CDPI-based nomogram achieved good prediction accuracy, including a 1-year AUC of 0.86, and showed a favorable decision-curve profile. High CDPI was also accompanied by stronger immune infiltration, lower tumor purity, and different mutation patterns. Drug-response prediction suggested increased sensitivity to gemcitabine, epirubicin, ULK1 inhibitors, docetaxel, and AZD7762, but reduced sensitivity to Daporinad, osimertinib, and cediranib. Conclusion The CDPI may serve as a practical stratification index for KIRC and may help interpret immune escape and treatment vulnerability, although prospective clinical confirmation is still required.","url":"https://doi.org/10.21203/rs.3.rs-10277358/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10277358/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9975580/v1","name":"Machine Learning Predicts Acute Kidney Injury in Paraquat Poisoning","source":"preprints","abstract":"Abstract Background: Acute kidney injury (AKI) is a common and fatal complication of paraquat (PQ) poisoning, with incidence of 40-70% and high mortality. This study aimed to develop and validate a machine learning model to predict AKI after PQ poisoning using readily available clinical parameters. Methods: We retrospectively analyzed 832 patients with PQ poisoning admitted to West China Hospital between September 10, 2010 and September 30, 2023. Patients were randomly divided into derivation (70%) and validation (30%) cohorts. Predictors were selected using LASSO regression. Logistic regression (LR), Bayesian methods, and support vector machine (SVM) were trained and evaluated using AUC‑ROC, calibration, and decision curve analysis (DCA). Performance was compared with the Severity Index of Paraquat Poisoning (SIPP). Results: The overall incidence of AKI was 65.5% (539/832), and in‑hospital mortality was significantly higher in the AKI group (67.9% vs. 26.6%, P Conclusions: A machine learning model based on NLR, AST, BUN, CysC, and plasma PQ concentration accurately predicts AKI in patients with PQ poisoning and outperforms the traditional SIPP score. This model can support early risk stratification and clinical decision‑making.","url":"https://doi.org/10.21203/rs.3.rs-9975580/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9975580/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.24.26361175","name":"Predicting gait patterns from actionable impairments in Duchenne muscular dystrophy: A Machine Learning and Explainable Artificial Intelligence study","source":"preprints","abstract":"Background Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Clinical management targets ‘actionable’ (i.e., modifiable) impairments, such as progressive muscle weakness and contractures, that underlie gait pathology. Gait classification may improve clinical decision-making, but the utility of gait classification in clinical practice depends on understanding how underlying, actionable impairments contribute to distinct gait patterns, which remains insufficiently understood. The research questions were: (1) Can DMD gait patterns be accurately classified from actionable impairments? and (2) Can the model’s predictions be explained, and do these explanations provide clinical utility and increase trust in the model? Methods A retrospective dataset of 274 lower-limb observations from 137 assessments in 30 boys with DMD was analyzed, including 3D gait analysis, instrumented strength assessment, and clinical examination (manual muscle testing, goniometry and clinical stiffness scale). Observations were classified into the mildly affected, tiptoeing, or flexion gait pattern. Ten predictors representing actionable impairments were included: nine predictors related to muscle weakness and contractures, and body mass index (BMI). A balanced random forest classifier was evaluated with leave-one-group-out cross-validation. Model interpretability was explored using SHapley Additive exPlanations to generate global and local explanations. An interview with a clinical expert assessed the utility of the explanations as the primary outcome, with trust in and expectations of both the model and the explanations as secondary outcomes. Results The model achieved an accuracy of 74.5%. Global explanations identified hip and knee weakness, gastrocnemius-soleus contractures, and BMI as the most important predictors across gait patterns. Local explanations illustrated how patient-specific impairments informed individual predictions. The user study demonstrated the clinical utility of the explanations, as they were perceived as interpretable, provided useful insights, and these insights were actionable. The explanations largely aligned with the expectations and increased self-reported trust in the model. Conclusions Gait patterns in DMD can be predicted from clinically actionable impairments, and explainable artificial intelligence can translate model outputs into meaningful clinical insights. This approach is promising for supporting both general and personalized rehabilitation and orthopedic strategies aimed at prolonging ambulation in DMD. Further validation in larger, multi-center cohorts is needed.","url":"https://doi.org/10.64898/2026.08.24.26361175","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.24.26361175","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10407842/v1","name":"Predictive Value of Nutritional and Inflammatory Factors for Liver Fibrosis Stage Classification Among HBV-Exposed Individuals","source":"preprints","abstract":"Abstract Background Liver fibrosis is a major contributor to liver-related morbidity and mortality among individuals with previous or current hepatitis B virus (HBV) exposure. Nutritional and inflammatory factors have been implicated in liver fibrosis, however their combined value for fibrosis stage classification remains insufficiently explored. This study aimed to investigate the associations of nutritional and inflammatory indicators with liver fibrosis severity and to develop machine-learning models for fibrosis stage classification. Methods This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 and 2019–2020 cycles. Individuals with evidence of previous or current HBV exposure (anti-HBc positive) were included. Liver fibrosis severity was assessed using transient elastography-derived liver stiffness measurements. Associations between clinical variables and fibrosis severity were evaluated using correlation analysis, univariate regression, and restricted cubic spline models. Random forest-based feature selection was performed, and six machine-learning algorithms, including Random Forest Support Vector Machine, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor, Decision Tree, and Neural Network, were developed and evaluated using repeated 10-fold cross-validation. Model performance was assessed using accuracy, multi area of under curve, F1-score, calibration analyses and . Results A total of 911 participants were included, comprising 769 individuals without fibrosis (84.41%), 39 with F1 fibrosis (4.28%), 52 with F2 fibrosis (5.71%), and 51 with F3 fibrosis (6.00%). Correlation and regression analyses demonstrated significant associations between liver fibrosis severity and multiple nutritional and inflammatory indicators. Restricted cubic spline analyses further revealed nonlinear relationships between fibrosis severity and age, BMI, GNRI, NPAR, and SIRI. Feature selection identified 10 key variables, including GNRI, BMI, albumin, total cholesterol, CRP, NPAR, creatinine, HDL cholesterol, waist-to-height ratio, and age. Among the six machine-learning algorithms evaluated, XGBoost achieved the best overall performance, with an accuracy of 0.940, a MAUC of 0.940, and a macro-F1 score of 0.804. Conclusions Nutritional and inflammatory indicators were significantly associated with liver fibrosis severity among individuals with previous or current HBV exposure. An XGBoost model constructed from routinely available clinical variables showed good performance in fibrosis stage classification and warrants further validation in independent cohorts.","url":"https://doi.org/10.21203/rs.3.rs-10407842/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10407842/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202608.1335.v1","name":"Machine Learning for Predicting Delayed Graft Function and Graft Survival After Kidney Transplantation: A Systematic Review and Meta-Analysis","source":"preprints","abstract":"Background: /Objectives: Machine-learning (ML) models have been proposed to improve prediction of delayed graft function (DGF) and graft survival after kidney transplantation. Whether they provide better predictive performance than conventional regression remains uncertain. We systematically reviewed prediction models for both outcomes and assessed the quality of the supporting evidence. Methods: We conducted a systematic review and meta-analysis of models predicting DGF and graft survival after kidney transplantation. Model discrimination was pooled using random-effects meta-analysis. Risk of bias was assessed using PROBAST. ML and conventional regression models were compared when sufficient data were available. Results: The review included 148 unique studies: 98 addressing DGF and 51 addressing graft survival, with one study contributing to both outcomes. Seventy-three DGF model estimates reported a development AUC or C-statistic. The primary meta-analysis included 16 estimates from 16 studies and produced a pooled DGF AUC of 0.814 (95% CI, 0.782–0.843). Reporting was incomplete: 57 of 73 models did not report confidence intervals, 70 lacked independent external validation, and 63 did not report calibration. ML models did not consistently outperform conventional regression. Registry-based graft-survival models had pooled C-statistics of 0.697 at 5 years and 0.723 at 10 years. Five external-validation estimates of clinical DGF models from three cohorts had a median AUC of 0.69. In a network meta-analysis limited to three graft-survival studies, conventional regression ranked highest. Conclusions: Current evidence does not demonstrate that ML provides better or more generalizable prediction of DGF or graft survival than conventional regression. The evidence is limited by incomplete reporting, infrequent calibration, high risk of bias, and limited external validation. Future studies should prioritize rigorous design, calibration, independent validation, and complete reporting over increasing model complexity.","url":"https://doi.org/10.20944/preprints202608.1335.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202608.1335.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10436753/v1","name":"Development and external validation of a machine-learning model for echocardiography-detected left ventricular systolic dysfunction in patients with sepsis: a dual-center retrospective cohort study","source":"preprints","abstract":"Abstract Background Echocardiography-detected left ventricular systolic dysfunction (LVSD) occurs in some patients with sepsis, but retrospective data cannot reliably establish sepsis as its cause. We developed and externally validated a machine-learning model for LVSD among patients with sepsis who underwent echocardiographic evaluation. Methods This retrospective dual-center cohort study used MIMIC-IV for model development and internal validation and a cohort from Linyi People's Hospital for external validation. Candidate features were screened using LASSO and multivariable logistic regression. Ten machine-learning models were evaluated for discrimination, calibration, and decision-analytic net benefit. SHAP was used for post hoc feature-attribution analysis. Results Among 1,044 patients, 809 were included in the development cohort and 235 in the external cohort; LVSD was detected in 32.6% and 24.7%, respectively. Nine independently associated variables plus SOFA were used for modeling. CatBoost was selected using internal data and achieved AUCs of 0.819 internally and 0.769 externally. In external validation, accuracy was 0.762, sensitivity 0.655, specificity 0.797, F1-score 0.576, and Brier score 0.150 (95% CI 0.120–0.179); the Hosmer-Lemeshow P value was 0.154. The maximum estimated net benefit was 0.21. SHAP ranked ln(NT-proBNP) and prior heart failure among the largest contributors to model output. Conclusion In patients with sepsis who underwent echocardiography, the internally selected CatBoost model retained moderate external discrimination and acceptable calibration for estimating LVSD probability. SHAP findings describe model attribution, not causal or biological mechanisms. Prospective validation is required before clinical use.","url":"https://doi.org/10.21203/rs.3.rs-10436753/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10436753/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10041600/v1","name":"Development and internal validation of a machine learning-based risk stratification tool for sarcopenia in Chinese older adults: findings from the CHARLS study","source":"preprints","abstract":"Abstract Background: Sarcopenia is a major geriatric syndrome with increasing prevalence in aging populations. Although individual biomarkers such as C-reactive protein (CRP) and hemoglobin (Hb) have been associated with sarcopenia, their predictive performance remains limited. Machine learning approaches that integrate multidimensional risk factors may improve sarcopenia risk stratification. This study aimed to develop and internally validate a parsimonious machine learning-based risk prediction model for sarcopenia using data from a nationally representative cohort of Chinese older adults. Methods: We analyzed data from 6,204 participants aged 60 + years from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Thirty-one candidate predictors spanning demographic, clinical, biochemical, and health-status domains were considered. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used for variable selection, followed by multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration across risk deciles. Internal validation was performed using 10-fold cross-validation. Results: The prevalence of sarcopenia was 13.5% (839/6,204). LASSO regression retained 12 predictors: age, rural residence, hypertension, chronic lung disease, digestive disease, hemoglobin, hematocrit, uric acid, HDL-C, LDL-C, triglycerides, and the CRP-to-Hb ratio. The final logistic regression model achieved an AUC of 0.836 (95% CI: 0.822–0.851) with a cross-validated AUC of 0.833 (95% CI: 0.819–0.848). At the optimal cutpoint, sensitivity was 0.757 and specificity was 0.762. The negative predictive value was 0.952, indicating high utility for sarcopenia rule-out. Observed sarcopenia prevalence ranged from 1.3% in the lowest risk decile to 62.3% in the highest. Conclusions: A 12-predictor machine learning model using routinely available clinical and biochemical variables can effectively stratify sarcopenia risk in Chinese older adults, with robust discrimination and excellent negative predictive value. This model may serve as a practical screening tool for community-based sarcopenia case-finding. External validation and longitudinal studies are warranted.","url":"https://doi.org/10.21203/rs.3.rs-10041600/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10041600/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10018144/v1","name":"Label-Free Diagnosis of Colorectal Cancer Using a SERS Platform Based on a Modified Glass Fiber Filter Membrane Combined with Machine Learning","source":"preprints","abstract":"Abstract Non-invasive screening is critically important for the early diagnosis of colorectal cancer (CRC). Surface-enhanced Raman scattering (SERS) has shown great potential in liquid biopsy, yet it still faces the challenge of poor signal reproducibility in complex serum matrices. In this study, a flexible three-dimensional SERS platform based on a 4-mercaptophenol (4-MP)-modified glass fiber filter membrane (GFF@4MP) was developed and combined with machine learning for label-free discrimination of CRC serum. Through sodium chloride modification, silver nanoparticles were uniformly immobilized onto the three-dimensional porous scaffold. Crucially, 4-MP, serving as a surface functionalization modifier, was able to modulate the interfacial physicochemical microenvironment and provide specific binding sites. This optimized the ordered adsorption of serum targets within SERS “hot spots”, significantly reduced non-specific interference, and improved spatial uniformity (relative standard deviation of 7.63%). In the analysis of clinical serum samples (30 CRC cases and 30 healthy controls), the substrate modified with 4-MP functional groups yielded SERS spectra with superior intergroup discrimination. Combined with principal component analysis (PCA), the support vector machine (SVM) model achieved the best classification performance on an independent test set, with a prediction accuracy of 93.33% and an area under the ROC curve (AUC) as high as 0.988. This study effectively enhanced the stability of SERS detection in complex biological samples through interfacial chemical modification, thereby providing a reliable methodological strategy for non-invasive clinical screening of colorectal cancer.","url":"https://doi.org/10.21203/rs.3.rs-10018144/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10018144/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10026817/v1","name":"Bayesian Machine Learning for Precision Oncology: Integrating Clinical, Genomic, and Imaging Data for Personalized Cancer Prognosis","source":"preprints","abstract":"Abstract Precision oncology aims to improve cancer prognosis and treatment decision-making by leveraging diverse patient-specific data sources. However, effectively integrating clinical, genomic, and imaging information remains challenging due to data heterogeneity, high dimensionality, and uncertainty in predictive modeling. This study proposes a novel Hierarchical Bayesian Multimodal Attention Fusion Network (HBMAF-Net) for personalized cancer prognosis. The framework combines modality-specific feature extraction, Bayesian attention-based multimodal fusion, and hierarchical probabilistic modeling to jointly analyze clinical, genomic, and imaging data while explicitly quantifying predictive uncertainty. A comprehensive simulation study was conducted to evaluate the proposed framework against conventional statistical models, machine learning approaches, and state-of-the-art multimodal deep learning methods. The results demonstrated that HBMAF-Net achieved superior predictive performance, yielding an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.931, a Concordance Index of 0.917, and improved calibration compared with competing models. The Bayesian attention mechanism provided interpretable modality-specific contributions, while the hierarchical structure effectively captured heterogeneity across cancer subtypes and treatment groups. Furthermore, uncertainty-aware predictions enhanced the reliability and transparency of prognostic estimates. These findings highlight the potential of Bayesian machine learning and multimodal data integration to advance precision oncology through accurate, interpretable, and clinically actionable personalized cancer prognosis.","url":"https://doi.org/10.21203/rs.3.rs-10026817/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10026817/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10145598/v1","name":"Early Mortality Prediction in Upper Gastrointestinal Bleeding Using Explainable Machine Learning: Comparison with Established Clinical Risk Scores","source":"preprints","abstract":"Abstract Background Early risk stratification in upper gastrointestinal bleeding (UGIB) is essential for emergency department (ED) triage, treatment planning, and mortality reduction. Although conventional clinical scores are widely used, they may not fully capture complex nonlinear interactions among physiological, laboratory, and comorbidity-related predictors. This study evaluated whether explainable machine-learning (ML) models could predict 30-day mortality in UGIB and compared their performance with established clinical risk scores. Methods This retrospective single-center cohort study included 719 adult ED presentations with UGIB between January 1, 2015, and January 1, 2026. The primary outcome was all-cause 30-day mortality. ML models were developed using two predictor sets: an early ED predictor set, and a Full model predictor set incorporating transfusion and endoscopic findings. Logistic regression, random forest, gradient boosting, XGBoost, and LightGBM models were evaluated using five-fold stratified group cross-validation. Model performance was assessed using AUROC, AUPRC, F1 score, Brier score, calibration analysis, and decision curve analysis, and compared with the Glasgow-Blatchford Score, Rockall score, AIMS65, and ABC score. Parsimonious models were developed using regularized feature selection, and SHAP analysis was used for model interpretation. Results Among 719 presentations, 53 patients (7.4%) died within 30 days. The ED XGBoost model achieved the best overall performance among prespecified models, with an AUROC of 0.789, AUPRC of 0.294, F1 score of 0.355, and Brier score of 0.061. Its performance was similar to the Full XGBoost model. Among traditional scores, ABC showed the highest AUROC (0.742), but the AUROC difference between ED XGBoost and ABC was not statistically significant. A 15-variable Minimal ED XGBoost model achieved an AUROC of 0.814 in exploratory analysis and 0.765 in fully nested sensitivity analysis. SHAP analysis identified lymphocyte count, lactate, potassium, C-reactive protein, respiratory rate, albumin, platelet count, glucose, hemoglobin, and Glasgow Coma Scale as key predictors. Conclusion Machine-learning models based on routinely available ED data showed comparable or superior discrimination, better calibration, and greater clinical utility than traditional risk scores for predicting 30-day mortality in UGIB. A parsimonious explainable ED XGBoost model may provide a practical early risk stratification tool, although external validation is required before clinical implementation.","url":"https://doi.org/10.21203/rs.3.rs-10145598/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10145598/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10601973/v1","name":"Interpretable Machine Learning and Point-of-Care Digital Risk Stratification for Pneumothorax Following Lung Tumor Ablation: A Multicenter Validation Study","source":"preprints","abstract":"Abstract Pneumothorax requiring chest tube drainage complicates 10% to 15% of percutaneous lung tumor ablation procedures. The current lack of individualized risk prediction necessitates uniform, reactive post-procedural surveillance, which fails to optimize healthcare resources. To address this, we developed and externally validated a parsimonious machine learning framework utilizing eight routinely collected clinical variables to predict intervention-requiring pneumothorax. Trained on a derivation cohort of 1,118 patients, a Random Forest (RF) architecture demonstrated superior calibration and discrimination (Area Under the Receiver Operating Characteristic Curve [AUROC] = 0.865, 95% Confidence Interval [CI]: 0.835–0.895; Area Under the Precision-Recall Curve [AUPRC] = 0.473) against eight comparator algorithms. Independent spatiotemporal external validation across two distinct geographic centers and a strict chronological hold-out cohort confirmed robust generalizability, yielding a pooled spatial AUROC of 0.857 (95% CI: 0.809–0.905) and an AUPRC of 0.527. Algorithmic transparency via SHapley Additive exPlanations (SHAP) identified total ablation time, puncture count, and tumor–pleura distance as primary mechanistic drivers, while elucidating critical higher-order feature interactions. Additionally, restricted cubic spline (RCS) analysis defined precise, non-linear intraoperative safety thresholds (e.g., 12 min total ablation, ≥ 3 punctures). Deployed as a point-of-care web application featuring out-of-distribution safeguards, this rigorously validated digital tool facilitates precise risk stratification, providing an evidence-based framework to transition perioperative management in interventional oncology from reactive surveillance to proactive, personalized care.","url":"https://doi.org/10.21203/rs.3.rs-10601973/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10601973/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.05.26359034","name":"A software package for simple and rigorous survival machine learning analysis in biomedical research","source":"preprints","abstract":"Survival analysis is a fundamental technique in biomedical research for modeling time-to-event data. It enables the identification of prognostic factors in disease, compares survival outcomes across treatment groups, and performs targeted treatment selection. A variety of machine learning (ML) approaches to survival analysis have emerged to complement classical statistical methods, especially for high-dimensional datasets with complex, nonlinear interactions between features. However, using survival ML methods requires addressing challenges such as censoring-unaware evaluation, overfitting, selecting performance metrics, and data leakage. To address these and other difficulties in using survival ML models, we developed the mlsurv software package. mlsurv is an open-source Python package built around three major design principles: 1) methodological rigor, including evidence-based model selection, leakage-free pipelines, and multi-metric evaluation, 2) multi-scale evaluation and interpretation, including population and subpopulation evaluation, patient-level explanations, and feature analysis, and 3) automated trust and transparency, including limitation flagging and TRIPOD+AI-aligned reporting. mlsurv bundles ten models spanning linear, ensemble, kernel, and deep learning families within a unified software package. To our knowledge, mlsurv is the first package to span the complete survival ML workflow from automated model recommendation through TRIPOD+AI reporting and individual patient explanation. We demonstrate mlsurv on the Chowell immunotherapy cohort (n=1,479). We found that overall survival (OS) was more predictable than progression-free survival (PFS) (concordance of 0.73 vs 0.67). Albumin was a top feature for both endpoints but dominated OS prediction, whereas tumor mutational burden rose to co-lead PFS prediction. Survival models matched the response-trained LORIS clinical score on PFS prediction and exceeded it on OS. mlsurv enables biomedical researchers to conduct rigorous, multi-model survival analysis and benchmarking using minimal code with default best practices rather than implementing custom scripts and methodological safeguards from scratch.","url":"https://doi.org/10.64898/2026.08.05.26359034","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.05.26359034","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.10.26357720","name":"Explainable Machine Learning Models for Alzheimer’s Diagnosis Using Routine and Low-Cost Clinical Data","source":"preprints","abstract":"Emerging as a significant global health challenge, Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and cognitive decline. Despite the ever-increasing waiting time for a specialist diagnosis, the need for a cost-effective and fast diagnostic technique is evident. This study explores the development of an explainable deep learning model to diagnose AD using only routine and low-cost clinical data, including demographic information, patient history, and results of neuropsychological tests (limited to those that can be automatically acquired). The analysis was carried out using a dataset provided by the National Alzheimer’s Coordinating Center, comprising 167,364 observations and 1,024 features. The findings demonstrate diagnostic performance comparable, and slightly superior, to that of clinicians when evaluated under similar informative constraints. This study introduces two classification models to discriminate whether the presumptive etiological cause of cognitive impairment is Alzheimer’s disease. The deep neural network achieved an accuracy of 90% with an area under the receiver operating characteristic curve (ROC-AUC) of 0.96, whereas the Light Gradient Boosting Machine reached the same accuracy with a ROC-AUC of 0.97.","url":"https://doi.org/10.64898/2026.07.10.26357720","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.10.26357720","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10594837/v1","name":"ADC Map Radiomics for Non-Invasive Detection of BRCA1/2 Mutation Status in Prostate Cancer: A Machine Learning-Based Radiogenomics Study","source":"europepmc","abstract":"Abstract Purpose Conventional multiparametric magnetic resonance imaging (mpMRI) parameters have limited ability to identify prostate cancer (PCa) patients harboring BRCA1/2 mutations. We investigated whether radiomic features extracted from apparent diffusion coefficient (ADC) maps can non-invasively discriminate BRCA-mutated from doubly negative (BRCA−) PCa patients. Materials and Methods This prospective cohort study with retrospective imaging analysis included 107 PCa patients (58 BRCA-positive [BRCA+], confirmed by germline and/or somatic testing; 49 doubly negative controls [BRCA−]) at a quaternary referral center (2021–2024). Patients with a negative result in one genetic pathway who were not tested in the other were excluded. Conventional mpMRI parameters (lesion size, mean ADC, zonal location, PI-RADS category, prostate volume) were compared between groups. Radiomic features were extracted from ADC maps using PyRadiomics (107 features; 33 retained after morphological robustness filtering). Twenty-five machine-learning algorithm families were evaluated with stratified k-fold cross-validation. The primary endpoint was area under the precision-recall curve (PR-AUC); secondary endpoint was ROC-AUC. Results No significant differences were found in conventional mpMRI parameters between BRCA + and BRCA− groups (mean ADC: 0.631 ± 0.153 vs. 0.675 ± 0.177 × 10⁻³ mm²/s, p = 0.19; lesion size, PI-RADS, zonal location: all p > 0.10). For radiomic analysis (n = 98; 51 BRCA+, 47 BRCA−), the best overall model was CatBoost applied to robustness-filtered radiomic features (PR-AUC = 0.6936; ROC-AUC = 0.6612). Restricting analysis to lesions ≥ 500 voxels (n = 31) improved performance (PR-AUC = 0.778; ROC-AUC = 0.615). Conclusion Conventional mpMRI parameters cannot distinguish BRCA-mutated from non-mutated PCa. ADC map radiomics combined with machine learning demonstrated intermediate yet promising discriminatory performance, suggesting that quantitative imaging features may capture biologically relevant information associated with BRCA mutational status. Larger multicenter validation studies are warranted. Clinical trial number : not applicable.","url":"https://doi.org/10.21203/rs.3.rs-10594837/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10594837/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21203/rs.3.rs-10250026/v1","name":"Interpretable Machine Learning Enables Preoperative Physiologic Risk Stratification for Dysphagia After Anti-Reflux Surgery","source":"preprints","abstract":"Abstract Postoperative dysphagia remains one of the most clinically significant complications following anti-reflux surgery, yet existing preoperative risk stratification approaches incompletely integrate esophageal motility, reflux burden, and esophagogastric junction biomechanics. We developed an interpretable machine learning framework integrating multimodal physiologic and clinical data to predict new-onset postoperative dysphagia and translate these relationships into a clinically deployable risk score. Following screening of 878 consecutive patients undergoing anti-reflux surgery at a high-volume tertiary referral center, 428 patients met inclusion criteria and underwent multimodal preoperative physiologic assessment including high-resolution manometry, EndoFLIP impedance planimetry, Bravo pH monitoring, and GERD-HRQL evaluation. Patients were divided into derivation (n = 362) and independent holdout validation (n = 66) cohorts. An ensemble machine learning framework integrating logistic regression, random forest, XGBoost, and support vector machine models was developed using engineered higher-order physiologic interaction features and subsequently translated into an interpretable point-based scoring system. The ensemble model demonstrated strong discrimination in the derivation cohort (AUC = 0.91, accuracy = 80.9%, sensitivity = 86.2%, specificity = 75.7%) with preserved performance in the independent holdout cohort (AUC = 0.72, balanced accuracy = 68.7%, sensitivity = 70.0%, specificity = 67.4%). Interaction features integrating reflux burden, esophageal contractility, distensibility, and patient-level modifiers demonstrated greater predictive utility than isolated physiologic variables alone. The resulting cumulative risk score enabled stratification into distinct postoperative dysphagia susceptibility groups across both derivation and validation cohorts and was deployed as an open-source web-based calculator for individualized risk estimation. These findings demonstrate that integrated multimodal esophageal physiology combined with interpretable machine learning enables clinically meaningful prediction of postoperative dysphagia after anti-reflux surgery and may support future personalized perioperative risk stratification and prospective multicenter validation.","url":"https://doi.org/10.21203/rs.3.rs-10250026/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10250026/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10560805/v1","name":"Machine Learning Versus Self-Supervised Transfer Learning for 30-Day Readmission Prediction in Diabetic Patients","source":"preprints","abstract":"Abstract Background: Hospital readmission within 30 days of discharge is a closely watched indicator of care quality and a substantial driver of avoidable healthcare cost, and diabetes is among the chronic conditions most strongly associated with early readmission. This study compares six predictive models for 30-day readmission in patients with diabetes, aiming to establish realistic performance expectations for models built on administrative and coded clinical features alone. Methods: We used the University of California, Irvine (UCI) Diabetes 130-US Hospitals dataset (101,766 encounters, 130 hospitals, 1999-2008). Four classical classifiers (logistic regression, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were compared against two multilayer perceptrons (MLPs), one trained from scratch and one initialized via self-supervised, denoising-autoencoder pretraining. Since tabular data lacks direct analogs of image-style pretraining, transfer learning, and augmentation, each concept was mapped to a tabular equivalent: denoising-autoencoder pretraining for transfer learning, gradient-boosted tree ensembles as the \"pretrained\" family for structured data, and the Synthetic Minority Oversampling Technique, applied only within cross-validation folds, for augmentation. After excluding encounters ending in death or hospice discharge, 99,340 encounters were split into stratified 70/15/15 train, validation, and test partitions (11.4% readmission prevalence). Models were evaluated by area under the receiver operating characteristic curve (AUROC), and decision thresholds were examined separately from discrimination. Results: On validation, XGBoost achieved the highest discrimination (AUROC = 0.672), ahead of LightGBM (0.669), random forest (0.654), logistic regression (0.641), and both MLP variants (0.585, 0.582). After randomized hyperparameter search with cross-validation (AUROC = 0.661), tuned XGBoost reached a test AUROC of 0.675 and an area under the precision-recall curve of 0.244. A default 0.5 threshold yielded a sensitivity of only 1.4%; a threshold chosen via Youden's J statistic on validation alone (0.128) raised sensitivity to 58.0% at the cost of precision (18.5%). These results converge with two independent 2025-2026 studies on the same dataset, suggesting a practical discrimination ceiling near AUROC 0.66-0.69. Conclusions: Tree ensembles outperformed both neural network variants, consistent with the broader tabular-data literature. The findings argue for realistic expectations of electronic health record data for readmission risk stratification, and for decision-threshold selection to be treated as a first-class methodological choice. Trial registration: Not applicable. This study involved secondary analysis of a publicly available, de-identified dataset and did not constitute a prospective healthcare intervention trial.","url":"https://doi.org/10.21203/rs.3.rs-10560805/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10560805/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.22.26358674","name":"Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis","source":"preprints","abstract":"Background Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse generator that administers electrical stimulation to designated brain regions responsible for motor control. The preoperative identification of effective predictive factors is of utmost importance for appropriate patient selection. In this study, we evaluate the potential of machine learning-based neuroimaging for predicting DBS outcomes. (PROSPERO Registration: CRD420261279318) Method Following the PRISMA statement, eligible studies were selected through searching three databases (PubMed, Scopus, Web of Science) on November 6, 2025. Methodological quality was assessed using the PROBAST+AI tool. Random-effects models pooled discrimination performance (AUC). Heterogeneity was investigated using meta-regressions for age and gender alongside subgroup analysis by type of algorithm. Publication bias was assessed using Egger’s regression test. Results Twenty studies were included in the analysis. Most investigations focused on PD, STN-DBS, and postoperative motor improvement, while a smaller number assessed neuropsychiatric outcomes. Overall, the pooled discrimination for models predicting motor outcomes showed an AUC of 0.86, and the pooled models for delirium showed an AUC of 0.87. Regarding the risk of bias assessment, seven studies were classified as low risk, while thirteen were identified as high risk. Conclusion Machine learning-based neuroimaging shows promising potential for preoperative prediction of DBS outcomes. However, the current literature is characterized by a persistent gap between encouraging discrimination and reliable clinical readiness. The main weakness of the field lies in analytical rigor and generalizability. These models should currently only be considered as promising research tools.","url":"https://doi.org/10.64898/2026.07.22.26358674","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358674","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9444977/v1","name":"Artificial Intelligence for Predicting Lymph Node Metastasis in Prostate Cancer: An evidence-based analysis of Machine Learning and Deep Learning Using CT and MRI","source":"preprints","abstract":"Abstract Background Preoperative identification of lymph node metastasis (LNM) is pivotal for tailoring surgical strategies in prostate cancer. Current radiologic assessment is limited by low sensitivity for micro-metastasis. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL), has been increasingly applied, but their comparative diagnostic performance remains unclear. Methods This meta-analysis was performed in accordance with PRISMA-DTA guidelines. Databases were searched from inception to February 2026. Bivariate random-effects models were used to pool diagnostic metrics. Subgroup analyses and meta-regression were conducted. Results A total of 21 studies (15 ML, 6 DL) comprising 2,054 patients were included. The overall pooled sensitivity was 0.81 (95% CI: 0.73–0.87), specificity was 0.84 (95% CI: 0.78–0.89), positive likelihood ratio (PLR) was 5.14 (3.62–7.29), negative likelihood ratio (NLR) was 0.23 (0.15–0.33), diagnostic odds ratio (DOR) was 22.79 (12.84–40.46), and the (AUC) was 0.90 (95% CI: 0.87–0.92). ML and DL showed comparable overall accuracy (0.80 vs. 0.83). A striking algorithm–modality interaction was identified: in DL models, magnetic resonance imaging (MRI) was superior to computed tomography (CT) (Accuracy: 0.91 vs. 0.78, P = 0.004); in ML models, CT was superior to MRI (Accuracy: 0.85 vs. 0.73, P = 0.043). Subgroup meta-regression confirmed imaging modality as a primary source of heterogeneity within ML and DL groups. Deeks’ test showed no publication bias ( P = 0.65). Conclusion Both ML and DL models demonstrated excellent diagnostic performance in prostate cancer LNM. Clinical implementation should follow an algorithm–modality matching principle: DL is more suitable for MRI data, while ML is optimized for CT data.","url":"https://doi.org/10.21203/rs.3.rs-9444977/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9444977/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.02.26357105","name":"Combined triglyceride–glucose and frailty index (TyGFI) and risk of endometrial cancer in U.S. women aged ≥45: NHANES 2011–2018 analysis integrating data engineering and machine learning with logistic modeling","source":"preprints","abstract":"The authors have withdrawn this manuscript because the number of positive cases in the original data is too small, leading to major flaws in the statistical analysis and rendering the findings and conclusions unreliable. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.","url":"https://doi.org/10.64898/2026.07.02.26357105","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.02.26357105","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10708277/v1","name":"A Quantum-Inspired Ensemble Learning Framework for Multi-Class Respiratory Disease Diagnosis","source":"preprints","abstract":"Abstract Accurate multi-class diagnosis of respiratory diseases using clinical tabular data remains a challenging problem in medical decision support systems due to complex non-linear feature dependencies, severe class imbalance, and limited interpretability of conventional machine learning models. While recent data-driven approaches demonstrate strong predictive performance, many fail to preserve higher-order interaction structure, address imbalance in a geometry-aware manner, or provide clinically meaningful explanations. To address these limitations, this study proposes Quantum-Cognitive Fusion (Q-CogFusion), a unified quantum-inspired framework integrating Quantum-Cognitive Feature Entanglement (QCFE) for correlation-driven phase-modulated feature synthesis, Adaptive Manifold Regularization with Tangent Propagation (AMR-TP) for geometry-preserving class balancing, a Multi-Scale Attention Fusion Network (MSAFN) for performance-adaptive ensemble aggregation, and an entanglement-aware SHAP-based explainability mechanism for dependency-consistent interpretation. The framework was evaluated on 2,000 clinically annotated patient records spanning five respiratory conditions using stratified cross-validation. Q-CogFusion achieved high diagnostic performance, attaining 99.40% accuracy, 99.40% macro F1-score, and a macro-averaged AUC of 1.00, with consistently strong recall across minority disease classes. Ablation analysis demonstrated that QCFE substantially enhanced class separability under imbalanced conditions, while AMR-TP maintained robust performance under strict class balancing. Comparative evaluation showed that the proposed framework consistently outperformed conventional machine learning and ensemble baselines, while providing interpretable insights aligned with known cardiopulmonary pathophysiology. These results suggest that Q-CogFusion offers a reliable and explainable diagnostic solution suitable for deployment in resource-constrained clinical decision support environments.","url":"https://doi.org/10.21203/rs.3.rs-10708277/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10708277/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10658395/v1","name":"Primary Care Gaps in Stroke-Prevention Risk-Factor Control Among Young Black and Hispanic Adults: A Cross-Sectional Machine-Learning Analysis of NHANES (1999–2018)","source":"preprints","abstract":"Abstract Background. Young Black and Hispanic adults in the United States bear a disproportionate burden of cardiovascular stroke-risk factors. Using the National Health and Nutrition Examination Survey (NHANES), we quantified racial and ethnic disparities in multifactorial risk-factor control among adults aged 18–55 years and evaluated whether machine learning models built from non-clinical characteristics could identify individuals with poor control. Methods. Cross-sectional analysis of 10 NHANES cycles (1999–2000 through 2017–2018), adults aged 18–55 years (N = 32,567 main; N = 31,056 laboratory). Two composite outcomes: poor control (main) = elevated blood pressure (systolic ≥ 130 mm Hg or diastolic ≥ 80 mm Hg) or current smoking (≥ 1 of 2); poor control (laboratory) = ≥ 2 of 4 components adding HbA1c ≥ 6.5% and LDL-C ≥ 130 mg/dL. Model A used demographic, socioeconomic, and healthcare-access features. Model B added HDL-C, total cholesterol, and triglycerides (non-outcome lipid biomarkers). Both models excluded outcome-defining variables. Four algorithms were trained with survey weights and evaluated on a stratified held-out test set. Results. Survey-weighted prevalence of poor control (main) was 45.8% overall and 52.0% among Non-Hispanic Black adults (prevalence ratio 1.10 versus Non-Hispanic White; absolute difference +4.7 percentage points). XGBoost achieved survey-weighted test AUROC = 0.716 (main) and 0.822 (laboratory). Disparities persisted within insurance strata. Conclusions. Demographic, socioeconomic, and healthcare-access characteristics were associated with poor cardiovascular risk-factor control and provided moderate discrimination in the main cohort. These associations do not establish causal effects, but they may help identify groups that could benefit from targeted preventive-care outreach.","url":"https://doi.org/10.21203/rs.3.rs-10658395/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10658395/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10122436/v1","name":"Application of Multifeature EEG Analysis and Machine Learning in the Investigation of Neurobiological Mechanisms of Schizophrenia","source":"preprints","abstract":"Abstract Schizophrenia (SC) is a severe mental disorder characterized by complex pathological mechanisms and clinical manifestations, which poses significant challenges for diagnosis and treatment. This study aimed to explore the neural mechanism abnormalities in patients with SC through electroencephalogram (EEG) signal analysis and develop an efficient intelligent diagnostic model. EEG data were collected from 45 SC patients and 41 healthy controls in an eyes-closed resting state. Power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI) features were extracted to comprehensively characterize brain function differences. To address the limitations of traditional machine learning models in hyperparameter tuning, a Genetic Algorithm-based Voting ensemble learning model (GA-Voting) was proposed. Experimental results demonstrated that the GA-Voting model achieved excellent performance in SC identification, with an accuracy of 99.55%±0.22%, outperforming single classifiers and other traditional methods. This study not only provides new insights into the neural mechanisms of SC but also offers an efficient and stable approach for clinical intelligent diagnosis, with significant theoretical and practical value.","url":"https://doi.org/10.21203/rs.3.rs-10122436/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10122436/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.06.28.26356788","name":"Improving ART Retention Through Machine Learning–Guided Targeting of Interventions: A Monte Carlo Simulation Study in Lilongwe, Malawi","source":"preprints","abstract":"Retention in antiretroviral therapy (ART) care remains a major challenge in high-burden settings such as Malawi, where substantial loss to follow up undermines treatment outcomes and long-term epidemic control. Although machine learning models can accurately identify patients at high risk of disengagement, there is limited evidence on how these predictions can be translated into improved retention outcomes in practice. This study addresses this gap by explicitly linking machine learning–based risk stratification to the targeted allocation of retention interventions, providing a framework for evaluating their expected impact on ART retention outcomes. We developed a patient-level Monte Carlo simulation model that integrates individual predicted probabilities of loss to follow up from a validated Extreme Gradient Boosting model with intervention effect sizes derived from a meta-analysis of ART retention interventions conducted in sub-Saharan Africa. The study population included 1,705 ART patients receiving care at Lighthouse Trust clinics in Lilongwe, Malawi. Patients were stratified by predicted risk, and the highest-risk group (n = 512) was targeted for intervention. Six interventions were evaluated, including Expert Client support, psychosocial support, two-way text messaging, adherence clubs, community ART groups, and teen clubs, followed by subgroup-specific and combined approaches allocated based on predicted risk to reflect real-world programme implementation. The primary outcome was twelve-month ART retention, estimated over 5,000 simulation iterations. Subgroup and post-simulation analyses were conducted to assess heterogeneity in intervention response. Among patients classified as high risk (n = 512), baseline retention was 44.1%. Individual interventions improved retention to 52.7% with two-way texting (RR = 1.19; p Author Summary Retaining people living with HIV (PLHIV) in long term care is essential for effective treatment and for reducing HIV transmission, yet many patients especially in high HIV burden and resource limited settings like Malawi disengage from antiretroviral therapy (ART). Current approaches, such as tracing patients after they miss clinic visits, tend to be reactive and can place a significant burden on already limited healthcare resources. While machine learning models can identify individuals at high risk of dropping out of care, they do not by themselves improve retention. Their value lies in helping health systems identify which patients should receive targeted support. However, there is limited evidence on how these predictions can be used to improve patient retention outcomes in practice. In this study, we used machine learning predictions to identify individuals at high risk of dropping out of care and then simulated how retention could be improved by targeting different interventions to these patients. Using Clinical, behavioral and psychosocial data from two HIV clinics in Malawi, we estimated how different interventions, including peer support, psychosocial support, and two-way text messaging, perform when applied individually or in combination. We found that targeting interventions based on predicted risk can substantially improve retention, particularly when multiple interventions are delivered together. However, nearly half of high-risk patients remained difficult to retain despite receiving support, with poorer outcomes observed among those who were virally unsuppressed, had depressive symptoms, or were younger These findings suggest that integrating predictive tools into routine HIV programs could enable more efficient targeting of interventions to patients most at risk of disengagement, leading to improved retention outcomes. At the same time, more intensive and tailored strategies are needed for patients who remain at high risk despite interventions, particularly those who are virally unsuppressed, have depressive symptoms, or are younger.","url":"https://doi.org/10.64898/2026.06.28.26356788","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.28.26356788","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.14293/pr2199.003819.v1","name":"Explainable Artificial Intelligence for Early Heart Disease Prediction Using Ensemble Machine Learning Models","source":"preprints","abstract":"Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for accurate and interpretable predictive systems that can support early diagnosis and clinical decision-making. Recent advances in machine learning have demonstrated significant potential for identifying cardiovascular risk patterns from patient health records; however, many high-performing models operate as black boxes, limiting their acceptance in healthcare environments where transparency and trust are essential. This study proposes an explainable artificial intelligence (XAI) framework for early heart disease prediction using ensemble machine learning models. The framework integrates multiple predictive algorithms through ensemble learning techniques to improve classification performance while maintaining model interpretability. Patient demographic information, clinical measurements, and lifestyle-related factors are utilized to develop a robust predictive system capable of distinguishing individuals at risk of heart disease from healthy subjects. To enhance transparency, explainability methods are incorporated to identify the most influential features contributing to predictions and to provide both global and local interpretations of model behavior. Experimental evaluation demonstrates that the ensemble approach achieves superior predictive performance compared with individual machine learning models, while the explainability component offers clinically meaningful insights into risk factors associated with cardiovascular conditions. The proposed framework supports reliable, transparent, and data-driven healthcare decision-making, facilitating earlier intervention and improved patient outcomes. The findings highlight the potential of combining ensemble learning and explainable artificial intelligence to advance trustworthy predictive analytics in cardiovascular medicine.","url":"https://doi.org/10.14293/pr2199.003819.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.003819.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10246279/v1","name":"Machine learning–based 18F-FDG PET/MR fusion radiomics for predicting EGFR mutation in lung cancer","source":"preprints","abstract":"Abstract Background This study aimed to develop a machine learning–based radiomics model based on 18F‑FDG PET/MR images, to predict EGFR mutation status and mutation abundance in lung cancer, and to explore the value of multimodal radiomic features in clinical decision‑making. Methods 66 patients were included in this retrospective study. Then, the registered PET and MR images were fused using 3D Slicer. Radiomic features were extracted from the PET/MR images and fused images using PyRadiomics. Feature selection was conducted using LASSO regression with leave-one-out cross-validation (LOOCV). Logistic regression was employed for binary classification of EGFR mutation status, and linear regression was used to predict continuous variant allele frequency. Results EGFR mutation status was significantly associated with female sex, higher T stage and advanced pathological stage, irregular nodule shape, larger tumor dimensions, and greater MR signal heterogeneity (all p β = 18.46, p = 0.011) and PET wavelet LLH kurtosis ( β = 7.23, p = 0.009) emerging as key predictors. In the Exon21 subgroup, a single feature, PET wavelet‑HHL ngtdm Busyness, explained 85.8% of the variance in variant allele frequency (R² = 0.858).","url":"https://doi.org/10.21203/rs.3.rs-10246279/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10246279/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10559402/v1","name":"Integrating Predictive Modeling into Viral Test Stewardship: HHV-7 as a Use Case","source":"preprints","abstract":"Abstract Human herpesvirus 7 (HHV-7) is a common pediatric virus whose clinical relevance is often underestimated, resulting in heterogeneous testing practices and variable diagnostic yield. Improving diagnostic decision-making requires the ability to identify, at the time of request, those patients most likely to test positive, allowing molecular testing to be prioritized where it is clinically most informative. Here, we present a machine learning–based workflow that integrates routinely available demographic, clinical, laboratory, temporal, and virological history data to predict the probability of HHV-7 positivity in pediatric patients. Several supervised classification algorithms were evaluated, with Random Forest achieving the most balanced performance in the historical cohort, capturing clinically meaningful patterns without the need for resampling. When applied to a more recent cohort, however, model performance declined, revealing the presence of temporal dataset shift and underscoring the dynamic nature of real-world clinical data. Together, these results demonstrate that machine learning can identify patients with increased likelihood of HHV-7 infection and support more targeted and rational viral testing strategies. More broadly, this work highlights both the potential and the limitations of data-driven decision support tools in pediatric infectious disease diagnostics, emphasizing the need for continuous monitoring and model recalibration prior to clinical deployment.","url":"https://doi.org/10.21203/rs.3.rs-10559402/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10559402/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10654448/v1","name":"Explainable Machine Learning and Temporal Calibration Drift of a TyG- BMI-Based Model for Early Prediction of Gestational Diabetes Mellitus","source":"europepmc","abstract":"Abstract Background First-trimester prediction of gestational diabetes mellitus (GDM) is useful only when risk estimates remain accurate over time and are interpretable enough for clinical discussion. Many GDM prediction studies emphasize discrimination, whereas temporal transportability, calibration stability, and model explainability are less often assessed together. Objective To develop and temporally validate first-trimester GDM prediction models based on TyG-BMI and related clinical variables, compare logistic regression, XGBoost, and LightGBM, and evaluate calibration drift, apparent logistic recalibration, and SHAP-based interpretability. Methods This single-center retrospective cohort included 528 singleton pregnancies. Women enrolled in 2023–2024 formed the development cohort (n = 368, with a stratified internal holdout), and women enrolled in 2025 formed an independent temporal validation cohort (n = 160). Candidate predictors included first-trimester metabolic, anthropometric, blood-pressure, and obstetric-history variables. Model performance was assessed using AUC with bootstrap 95% CIs, Brier score, calibration intercept, calibration slope, and SHAP analysis. Results GDM occurred in 96 women (18.2%; 18.5% in the development cohort and 17.5% in the temporal validation cohort). In temporal validation, XGBoost showed the highest discrimination (AUC 0.881, 95% CI 0.819–0.933), followed by LightGBM (0.870) and logistic regression (0.857). Calibration changed over time: calibration intercepts shifted downward from the internal holdout to the temporal cohort (XGBoost Δintercept − 0.866; LightGBM − 0.977), indicating increasing average overprediction under the offset-model definition of calibration-in-the-large, a directionally consistent shift seen across all three models (though with wide confidence intervals), while discrimination was largely preserved. Apparent logistic recalibration set the intercept to approximately 0 and the slope to 1.0 in the temporal cohort and improved the XGBoost Brier score from 0.111 to 0.100. SHAP analysis ranked TyG-BMI as the dominant predictor, followed by age, FPG, gestational age, and pre-pregnancy BMI. Conclusion A first-trimester TyG-BMI-based gradient-boosting model showed strong temporal discrimination for early GDM prediction. Calibration drift occurred despite stable AUC and was partly correctable by recalibration, underscoring the need for calibration monitoring before clinical deployment.","url":"https://doi.org/10.21203/rs.3.rs-10654448/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10654448/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21203/rs.3.rs-9032125/v1","name":"Development and validation of a machine learning-based risk stratification model for 30-day mortality in sepsis patients with gastrointestinal bleeding","source":"preprints","abstract":"Abstract Background Sepsis complicated by gastrointestinal bleeding (GIB) is associated with high mortality in the intensive care unit (ICU). Current prognostic tools lack specificity for this dual comorbidity, making early and accurate risk stratification a major clinical challenge. Methods In this multicenter retrospective study, we used data from the MIMIC-IV database \\(n=1816\\) to develop and internally validate the model, and an independent cohort \\(n=129\\) from Sun Yat-sen Memorial Hospital for external validation. Adult patients with both sepsis and GIB within the first 24 hours of ICU admission were enrolled. The primary endpoint was 30-day all-cause mortality. Eight machine learning algorithms were constructed and compared; the optimal model was selected based on discriminative ability, calibration, and clinical utility, and its predictive interpretability was explored via SHapley Additive exPlanations (SHAP). Results The Extreme Gradient Boosting (XGBoost) model exhibited the optimal predictive performance, with an area under the curve (AUC) of 0.838 in internal validation and 0.778 in external validation. This model had robust calibration and yielded a positive net benefit across a broad range of clinical decision thresholds. The top predictive factors included Acute Physiology Score III (APS III), age at admission, Sequential Organ Failure Assessment (SOFA) score, mean body temperature, and coagulation biomarkers. Conclusion We developed and validated an interpretable machine learning model for predicting 30-day all-cause mortality in sepsis patients with GIB. This model showed significantly higher predictive accuracy, sensitivity and specificity than previously reported prognostic models, and it addresses the lack of disease-specific risk stratification tools for this high-risk subgroup.","url":"https://doi.org/10.21203/rs.3.rs-9032125/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9032125/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.14293/pr2199.004321.v1","name":"Medical Contingency Security (MCS): An N-1 Reliability Criterion for Failure-Resilient Clinical Prediction","source":"preprints","abstract":"Clinical prediction systems are commonly optimized for intact-input discrimination, while safety engineering in other high-consequence fields asks a different question: what remains safe when one credible component fails? This paper proposes Medical Contingency Security (MCS), a formal N-1 reliability criterion transferred from power-system security analysis to clinical and biomedical prediction. The central object is the clinical security floor, the minimum predictive utility over an exhaustive set of single-measurement contingencies. We introduce a contingency-regularized risk (CRR) training objective that directly penalizes the worst single-feature outage, implemented in two forms: a linear model (CRR-linear) and a one-hidden-layer nonlinear model (CRR-MLP), together with a contingency persistence map that ranks structurally critical measurements. The framework is deliberately distinct from random feature dropout: contingencies are enumerated, scored, ranked, and audited as explicit system failures. Every reported number is derived from a single, fully deterministic, dependency-light pure-NumPy pipeline with no external deep-learning framework, so that the primary benchmark and all secondary analyses (including the regularization-strength sensitivity sweep) share one evaluation code path by construction. On the public Breast Cancer Wisconsin (Diagnostic) dataset (569 observations, 30 continuous nuclear measurements, five-fold stratified cross-validation, seed 123), the pooled out-of-fold results are: nominal AUC / N-1 floor / degradation of 0.9941/0.9915/0.00262 for logistic regression, 0.9879/0.9843/0.00360 for random forest, 0.9945/0.9920/0.00246 for an RBF support-vector machine, 0.9924/0.9907/0.00166 for CRR-linear, and 0.9887/0.9864/0.00238 for CRR-MLP. CRR-linear attains the lowest pooled degradation of all five models, but it does not attain the highest γ=2 security-adjusted utility: the RBF-SVM is narrowly highest (0.98958 versus 0.98907 for CRR-linear), a difference well inside the paired Bayesian-bootstrap 95% interval for the utility-relevant floor difference ([−0.00666, 0.00187]). A paired effect-size analysis at fold level (Cohen's dz) finds only small-to-negligible standardized effects for every CRR-linear/baseline degradation comparison (|dz| ≤ 0.40), and the fold-level and pooled-out-of-fold estimators of the same quantity are shown to disagree in sign for CRR-linear vs. SVM — a discrepancy that is analyzed explicitly rather than concealed. A nonlinear CRR-MLP variant does not outperform CRR-linear on this dataset, and a synthetic, explicitly non-clinical secondary cohort is used only as an internal generalization stress test, not as external validation. The paper is organized into a Part I (empirically evaluated static contingency framework) and a Part II (conceptual, non-validated dynamic and translational extensions) to prevent the two from being read with the same evidentiary weight. The work is a hypothesis-generating systems-engineering proposal, and the abstract and conclusions are calibrated to a small, single-dataset effect size rather than to a claim of clinical or general machine-learning superiority.","url":"https://doi.org/10.14293/pr2199.004321.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.004321.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.27.26359010","name":"Rapid diagnosis of fever etiology using wearable temperature monitoring and machine learning","source":"preprints","abstract":"Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20–30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq®, BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84–1.0) for identifying engraftment fevers with a mean AUROC of 0.88 ± 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning–based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.","url":"https://doi.org/10.64898/2026.07.27.26359010","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.27.26359010","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9987430/v2","name":"Efficacy and safety of Rhodiola crenulata injection in the treatment of acute coronary syndrome: A meta-analysis with machine learning","source":"preprints","abstract":"Abstract Background: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular death and disability. Despite increasingly comprehensive guideline-directed therapy and the widespread use of contemporary revascularization, substantial unmet clinical needs persist. In China, Rhodiola crenulata injection (RCI) is widely used as an adjunct to standard Western medicine (WM) for ACS, yet its benefit-risk profile remains uncertain. Methods: RCTs up to July 2025 were retrieved from eight databases. ACS patients receiving RCI + WM or WM alone were included. Meta-analyses were performed using RevMan 5.4 and Stata 17.0, with TSA applied to evaluate information size and robustness. Risk of bias and evidence certainty were assessed using RoB 2 and GRADE, respectively. Five machine learning models (Random Forest, XGBoost, Lasso, Multilayer Perceptron, and a stacking model) were used to model time–effect relationships and optimal RCI treatment durations. Results: Forty-five RCTs (n = 4,217) were included. Compared to WM alone, RCI + WM resulted in a reduced incidence of MACE, angina attacks, duration of angina attacks, TC and LDL-C levels, improved total clinical efficacy, electrocardiographic efficacy and HDL-C levels. The incidence of adverse events was not statistically different. Machine learning predicted the optimal treatment durations of RCI as 10.08 days for UA and 14.39 days for AMI. Conclusion: In ACS, adding RCI as an adjunct to WM demonstrates efficacy in reducing MACE, improving clinical symptoms, myocardial ischemia, and lipid profiles, thereby providing additional clinical benefits.","url":"https://doi.org/10.21203/rs.3.rs-9987430/v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9987430/v2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10027511/v1","name":"Interpretable Radiomics-Based Machine Learning for Non-Invasive Prediction of Tertiary Lymphoid Structure Status in Pancreatic Ductal Adenocarcinoma","source":"preprints","abstract":"Abstract Background Tertiary lymphoid structures (TLS) represent organized ectopic lymphoid aggregates within the tumor microenvironment and are increasingly recognized as markers of local antitumor immune organization. In pancreatic ductal adenocarcinoma (PDAC), a malignancy characterized by dense desmoplasia and profound immune suppression, non-invasive identification of TLS-positive tumors may facilitate imaging-based immune phenotyping and translational patient stratification. This study aimed to develop and internally validate an interpretable radiomics-based machine learning framework for predicting TLS status in PDAC. Methods A total of 152 patients with PDAC were included, comprising 61 TLS-positive and 91 TLS-negative cases. Patients were randomly divided into a training set and an independent test set using a 70/30 stratified split, resulting in 106 training cases and 46 test cases. Expert-defined regions of interest were used for radiomics feature extraction. Initially, 1059 radiomics features were extracted. Feature reduction included missingness filtering, variance filtering, Pearson correlation filtering, least absolute shrinkage and selection operator regression, and mutual information supplementation, resulting in a final 20-feature panel. Five supervised machine learning classifiers were developed and compared: logistic regression, support vector machine, random forest, XGBoost, and LightGBM. Model performance was assessed using discrimination metrics, precision-recall analysis, calibration assessment, decision-curve analysis, and bootstrap confidence intervals. Model interpretability was evaluated using SHapley Additive exPlanations at both global and patient-specific levels. Results Feature reduction decreased the initial 1059 radiomics features to a final 20-feature panel, including first-order, GLCM, GLDM, GLSZM, NGTDM, and shape features. Among the evaluated models, XGBoost achieved the highest test-set discrimination, with an AUC of 0.960, accuracy of 0.891, F1 score of 0.865, and precision of 0.842. LightGBM showed comparable performance, with an AUC of 0.946 and similar accuracy and F1 score. Calibration and decision-curve analyses supported the potential clinical utility of model-derived TLS probability. Global SHAP analysis identified texture- and intensity-related radiomics features as important contributors, while local SHAP explanations demonstrated patient-level feature contributions aligned with representative TLS pathology. Conclusions An interpretable radiomics-based machine learning framework enabled non-invasive prediction of TLS status in PDAC with strong internal performance. By integrating radiomics, SHAP explainability, and TLS pathology, this study provides a potential imaging-based strategy for immune phenotyping in PDAC. External validation and prospective evaluation are warranted.","url":"https://doi.org/10.21203/rs.3.rs-10027511/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10027511/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10366656/v1","name":"Diagnostic Prediction of Attention-Deficit/Hyperactivity Disorder in Children Using AULA: A Virtual Reality-Based Continuous Performance Task","source":"preprints","abstract":"Abstract Accurately diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) remains challenging due to symptom variability, reliance on subjective reports, and limited ecological validity of traditional tools. This study investigates the diagnostic utility of AULA, a Virtual Reality-based Continuous Performance Task (VR-CPT), in distinguishing children with ADHD from neurotypical peers. AULA simulates a realistic 3D classroom with auditory and visual stimuli and distractors, enabling the capture of behavioral and kinematic data. A balanced sample of 352 children (176 ADHD, 176 control) aged 6 to 16 completed AULA. Feature selection based on mutual information, correlations, and effect size identified14 key variables, including reaction time (RT) variability, commission and omission errors, and motor activity. Machine learning models (CatBoost, LightGBM, and Extra Trees) achieved high classification accuracy (ROC-AUC > .92), with an ensemble model reaching .9377. The most predictive features were distractor-modulated variables, such as RT variability and commission errors, supporting the cognitive-energetic model of ADHD. Findings suggest AULA effectively captures attentional instability, impulsivity, and motor dysregulation in ecologically valid scenarios. This supports the integration of VR-CPTs and machine learning into clinical practice to enhance diagnostic accuracy and guide personalized interventions. Future research should explore longitudinal applications and the integration of explainable AI methods to enhance clinical decision-making.","url":"https://doi.org/10.21203/rs.3.rs-10366656/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10366656/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.21.26357764","name":"Risk stratification for the rapid pain progression phenotype in knee osteoarthritis using interpretable multimodal machine learning: Development in the Osteoarthritis Initiative and external evaluation in the Prospective Cohort of Osteoarthritis from A Coruña","source":"preprints","abstract":"ABSTRACT Objective To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteoarthritis and to evaluate its performance in the independent PROCOAC cohort. Methods An elastic-net logistic regression model was trained using Osteoarthritis Initiative (OAI) data. Rapid pain progression was defined over overlapping 24-month windows using normalized WOMAC pain. Harmonized clinical, genetic and proteomic candidates were evaluated, with feature selection by permutation importance. The frozen algorithm was tested in an OAI hold-out set and externally evaluated in PROCOAC. Logistic recalibration corrected prevalence shifts. Clinical utility was assessed by decision curve analysis. Results OAI comprised 2,934 individuals and 14,488 instances. Feature pruning reduced 159 candidates to a 19-variable clinical-genetic signature driven by Kellgren-Lawrence grade, localized knee pain, BMI and two genetic variants (rs73631790, rs9912678); no proteomic variable was retained. External testing in PROCOAC (582 individuals, 1609 instances) showed ROC-AUC 0.744 (95% CI 0.714 to 0.772) and PR-AUC 0.519. Following recalibration, the sensitive screening threshold yielded NPV 0.875 (95% CI 0.849 to 0.898) and sensitivity 0.804 (95% CI 0.760 to 0.844), whereas the high-specificity threshold achieved PPV 0.610 (95% CI 0.523 to 0.692) and specificity 0.941 (95% CI 0.924 to 0.954). Decision curve analysis showed positive net benefit at both thresholds, supporting a three-tier risk stratification framework. Conclusions This externally evaluated model identified patients at risk of rapid pain progression using an MRI-free clinical-genetic signature. Recalibrated thresholds may support risk-adapted monitoring, advanced imaging prioritization and trial enrichment.","url":"https://doi.org/10.64898/2026.07.21.26357764","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.21.26357764","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10613529/v1","name":"CardioTrust: Trustworthy Retrieval Augmented Clinical Decision Support for Personalized Cardiovascular Disease Prediction","source":"preprints","abstract":"Abstract Cardiovascular disease (CVD) remains a leading cause of global mortality, demanding timely and precise risk stratification alongside transparent decision support. While machine learning models excel at risk prediction, they lack clinical interpretability. Conversely, Large Language Models (LLMs) provide narrative clinical reasoning but suffer from hallucinations and ungrounded recommendations. To overcome these limitations, we propose CardioTrust, an integrated trustworthy AI decision support framework that unifies Machine Learning risk stratification, prediction guided Hybrid Retrieval Augmented Generation (Hybrid RAG), SHAP explainability, and an automated Trust Validation module. Extensive 5 fold cross validation across multi center cardiovascular benchmarks demonstrates that CardioTrust achieves superior predictive performance (95.84% ± 0.45% accuracy, 0.982 ± 0.003 AUROC, 0.941 ± 0.005 precision, and 0.952 ± 0.004 recall), significantly outperforming baseline models including Logistic Regression (84.21% ± 0.82%), Support Vector Machines (86.50% ± 0.74%), and XGBoost (92.15% ± 0.51%). Furthermore, our prediction guided Hybrid RAG and Trust Validation module ensure that generated recommendations remain factually grounded, verifiable, and aligned with feature importance attributions. CardioTrust provides a robust, transparent, and clinician aligned solution for personalized cardiovascular care.","url":"https://doi.org/10.21203/rs.3.rs-10613529/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10613529/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.06.23.26356305","name":"Leveraging Machine Learning Approaches to Identify Health-Related Social Needs Screening from Electronic Health Records","source":"preprints","abstract":"ABSTRACT Health-related social needs (HRSNs), such as housing instability, food insecurity, and transportation challenges, are nonmedical factors associated with poorer health and well-being. Screening for unmet HRSNs is a critical step towards identifying at-risk patients, but manual screening is resource intensive and often incomplete. We utilized Electronic Health Records (EHR) data to develop machine learning models to identify unmet HRSNs using a limited set of non-modifiable sociodemographic features available in EHRs. We included 745,975 patients screened for at least one HRSN using data from community health centers that participated in the OCHIN practice-based research network between 2016 and 2022. Logistic regression, random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were trained to predict unmet HRSNs. Model performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUROC). For overall HRSN prediction, LightGBM (AUROC, 64.5%, 95%CI: 64.3, 64.7) performed slightly better than logistic regression (61.4%), RF (63.7%), and XGBoost (60.3%). Similar performances were observed predicting individual HRSNs. Model performances were modest; however, they establish a benchmark for predictive performance achievable using only routinely available demographic data and provide a foundation for incorporating additional clinical and area-level social determinants of health data.","url":"https://doi.org/10.64898/2026.06.23.26356305","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.23.26356305","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202607.1965.v1","name":"Machine Learning-Based Prediction of Sleep Quality in Patients with Multiple Sclerosis","source":"preprints","abstract":"Background: /Objectives: Sleep disturbances are common but frequently underrecognized in multiple sclerosis (MS), independently predicting reduced quality of life and worsening fatigue, cognitive impairment, and depression. While pain, nocturia, fatigue, and mood symptoms are established contributors, cardiometabolic and inflammatory factors remain largely unexplored despite their known links to poor sleep in other populations. This study used interpretable machine learning to determine the relative contribution of disease-related, cardiometabolic, and inflammatory parameters to sleep quality, assessed by the PSQI, in patients with MS. Methods: This cross-sectional, observational, single-center cohort study enrolled adult patients with MS. Sleep quality (PSQI), daytime sleepiness (ESS), and restless legs syndrome severity (IRLS) were assessed alongside clinical, anthropometric, hemodynamic, and laboratory parameters, including inflammatory and metabolic indices. Three regression models, Support Vector Regression, Random Forest, and XGBoost, were trained on 28 predictors to predict PSQI global scores and evaluated on a independent test set, with feature contributions examined using SHAP analysis. Results: A total of 173 patients with MS were included (mean age 39.66 ± 11.86 years, 69.9% female, 90.2% RRMS), with 48.0% classified as poor sleepers (PSQI 5) and a mean PSQI score of 6.06 ± 3.47. XGBoost achieved the best predictive performance (test R² = 0.451). SHAP analysis identified IRLS severity as the strongest predictor of PSQI across all three models, followed by EDSS score and depression in the Random Forest and XGBoost models, while daytime sleepiness (ESS) ranked consistently among the top predictors. Cardiometabolic and inflammatory parameters contributed inconsistently, with several showing effects opposite to physiological expectation. Conclusions: Sleep impairment in MS was driven mainly by restless legs syndrome severity and neurological disability, with depression also contributing in the two best-performing models. Disease-related and symptomatic factors outweighed cardiometabolic and inflammatory contributions. These findings support the value of interpretable machine learning for identifying the multifactorial drivers of sleep quality in MS.","url":"https://doi.org/10.20944/preprints202607.1965.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.1965.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.21.26358463","name":"A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disorders","source":"preprints","abstract":"Background: Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, significant hurdles include disease rarity, geographical dispersion of patients, funding, clinical trial design, and execution. Crucially, the paucity of robust biomarkers and objective measures of disease progression hampers evaluation of efficacy, drug development and regulatory approval. To address this paradigm, we developed a Multimodal Multiomics Machine Learning (MMM) framework, integrating large-scale, multi-source patient datasets to generate quantitative metrics for disease stratification and longitudinal tracking. We applied MMM to PLA2G6-associated neurodegeneration (PLAN), an ultra-rare condition currently lacking validated biomarkers, where precision gene therapy approaches are at an advanced preclinical stage. Methods A large, single time-point international natural history study (n = 310) was conducted alongside development of a disease-specific rating scale (CoPLAN-DRS), prospective longitudinal neuroimaging, and multiomic biomarker discovery. Machine learning methods were applied to the integrated dataset. Results Kaplan-Meier analyses enabled estimates for survival and time to loss of ambulation. Multiple clinical, radiological, and biofluid biomarkers were identified, clearly correlating with disease progression. The CoPLAN-DRS and brain MRI Quantitative Susceptibility Mapping showed strong positive correlation with age (rho = 0.69, 0.96 respectively). Nicastrin, a critical structural component of the gamma-secretase complex in Amyloid Precursor Protein (APP) processing, was identified as a novel biomarker. Neurofilament light levels showed strong negative correlation with disease progression (rho = -0.74). The complex multi-dimensional dataset was distilled into a simplified, clinically intuitive Digital Disease Dashboard (DDD), enabling real-time visualisation of disease severity. Conclusions Our study highlights the clinical utility of MMM in integrating multi-dimensional data from rare disease cohorts, delivering an unbiased, data-driven, optimised biomarker set. Condensing this into the DDD provides a pragmatically useful tool for clinicians, facilitating longitudinal tracking of disease. The MMM and DDD have accelerated clinical-trial readiness for PLAN, and potentially applicable to a broad range of neurogenetic disorders.","url":"https://doi.org/10.64898/2026.07.21.26358463","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.21.26358463","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10071129/v1","name":"Utilizing Machine Learning to Classify Type of Heartbeat in Athletes from Electrocardiogram Data","source":"preprints","abstract":"Abstract Sudden cardiac events in athletes, though rare, carry severe consequences and underscore the need for accurate and scalable screening methods. Electrocardiogram (ECG) analysis is a cornerstone of cardiac evaluation, but widespread implementation in athletic screening is limited by clinician workload and the difficulty of distinguishing clinically meaningful arrhythmias. This study develops a machine learning framework for automated ECG heartbeat classification to support athlete cardiac screening. Heartbeat annotations from a large publicly available dataset were mapped from Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT) codes to the Association for the Advancement of Medical Instrumentation (AAMI) standard, enabling clinically interpretable, standardized classification into normal, supraventricular ectopic, and ventricular ectopic beat categories. Two deep learning models were developed: a residual convolutional neural network with a lead attention mechanism, and a bidirectional gated recurrent unit network. Both were trained using focal loss to address severe class imbalance, then combined into a weighted ensemble using fusion weights derived from validation performance. The ensemble achieved a macro-averaged F1 score of 0.855 and 93.5% overall accuracy on the held-out test set, outperforming both individual models. These findings demonstrate that machine learning-based ECG analysis can provide accurate, interpretable, and scalable screening support for athletic populations.","url":"https://doi.org/10.21203/rs.3.rs-10071129/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10071129/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.06.20.26356146","name":"Comparative Evaluation of Machine Learning and Deep Learning Models for Early Prediction of Severe Acute Pancreatitis: A Multi-Model Study Using the 2012 Revised Atlanta Classification","source":"preprints","abstract":"Background Acute pancreatitis (AP) is a common gastrointestinal emergency with a subset of patients progressing to severe acute pancreatitis (SAP), which carries substantial morbidity and mortality. Current clinical severity scores such as BISAP, APACHE II, Ranson, and the Modified CT Severity Index require 24-48 hours of observation before reliable assessment is possible, limiting early triage. Machine learning (ML) approaches using routine admission laboratory values may enable earlier, more accurate prediction. Methods We evaluated 11 models spanning three architectural families—classical ML (Logistic Regression, Random Forest, Gradient Boosting), feedforward deep learning (MLP, Residual MLP, Attention MLP), and recurrent deep learning (LSTM, Stacked LSTM, Bidirectional LSTM, LSTM+Attention, CNN-LSTM) —on a Chinese AP cohort of 722 patients (585 severe, 137 mild) labelled according to the 2012 Revised Atlanta Classification. Performance was assessed via 5-fold stratified cross-validation using AUC-ROC, F1 score, sensitivity, specificity, and positive predictive value (PPV), with decision thresholds optimised for maximal F1. Results Random Forest achieved the highest AUC of 0.877 (F1=0.917, sensitivity = 96.8%, PPV=87.1%), followed closely by Gradient Boosting (AUC = 0.874, F1 = 0.918). Classical ML models consistently outperformed their deep learning counterparts. CNN-LSTM was the best-performing recurrent model (AUC = 0.777) but remained inferior to all classical approaches. LSTM-family models produced AUC values of 0.684−0.777, likely reflecting the cross-sectional tabular nature of the data, which does not naturally exploit sequential modelling. Conclusions Random Forest provides robust, high-sensitivity early prediction of SAP severity using routine admission data. The findings support the clinical utility of classical ML on structured medical data and highlight the limited added value of recurrent architectures for tabular single-encounter datasets. External prospective validation is required before clinical deployment.","url":"https://doi.org/10.64898/2026.06.20.26356146","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.20.26356146","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.05.26359806","name":"Multimodal Radiogenomic Machine Learning for Biochemical Recurrence Prediction Following Radical Prostatectomy Using PSMA-PET, mpMRI, and the Decipher Genomic Classifier","source":"preprints","abstract":"Background Biochemical recurrence (BCR) occurs in up to 40% of men following radical prostatectomy (RP). Current risk models rely primarily on clinicopathologic variables and may not fully capture the biological heterogeneity associated with recurrence. The Decipher Genomic Classifier (DGC), prostate-specific membrane antigen positron emission tomography (PSMA-PET), and multiparametric magnetic resonance imaging (mpMRI) provide complementary prognostic information that may improve prediction. Objective To develop and evaluate machine learning (ML) models integrating DGC, PSMA-PET, and mpMRI for preoperative prediction of BCR following RP. Methods This retrospective study included patients with available preoperative DGC, PSMA-PET, mpMRI, and clinicopathologic data. Logistic regression (LR), random forest (RF), and XGBoost models were developed using single- and multimodality feature combinations. Early- and intermediate-fusion strategies were evaluated. Performance was assessed using an area under the receiver operating characteristic curve (AUC) and accuracy. Clinical utility was evaluated using decision curve analysis. Results XGBoost consistently outperformed LR and RF. DGC achieved the highest single-modality performance (AUC 0.94, accuracy 86.7%). Among multimodal models, DGC combined with PSMA-PET using intermediate fusion achieved the best overall performance (AUC 0.93, accuracy 87.0%). Addition of mpMRI reduced performance (AUC 0.85, accuracy 83.0%). Decision curve analysis demonstrated positive net benefit across clinically relevant thresholds. Conclusion XGBoost-based multimodal fusion improved preoperative BCR prediction following RP. DGC was the strongest individual predictor, while integration with PSMA-PET provided the best overall performance, supporting the potential of radiogenomic ML models for personalized risk stratification.","url":"https://doi.org/10.64898/2026.08.05.26359806","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.05.26359806","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.08.13.744720","name":"Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature","source":"preprints","abstract":"Background Severe dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. Methods Plasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning–based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. Results Increasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-γ, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. Conclusion Integrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.","url":"https://doi.org/10.64898/2026.08.13.744720","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.13.744720","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10193652/v1","name":"Predicting tuberculosis treatment relapse among geriatric patient using machine learning algorithms in a high Tuberculosis-burden Ghanaian setting.","source":"preprints","abstract":"Abstract Background, the world health organization estimated in 2020 of 5.8 million people newly diagnosed with Tuberculosis, predicting tuberculosis treatment relapse is crucial for improving patient treatment outcomes, studies have explored the factors connected with the treatment relapse risk in patients especially the elderly, Machine learning has transcended from a computational novelty role and emerged as a cornerstone of modern diagnostic and treatment outcome prediction. the argument is no longer whether machine learning should be used in healthcare, but how to integrate it responsibly. The Methodology involved training and testing Machine learning (SVM,RF,DNN,LR) models on a tuberculosis geriatric dataset for predicting treatment relapse on scikit-learn's train-test-split function into an 80% training and 20% Testing split. Results revealed random forest algorithm achieving an accuracy of 99%, 97% precision with a recall of 92%, F1-score of 95% and an area under curve (AUC ROC) of 96%, combination of high recall and very high precision suggests Random Forest effectively captures complex interactions among clinical and behavioural predictors, favourable balance between sensitivity and specificity makes it best among the SVM, DNN and LR algorithms. Recommendations proposed were to encourage data-sharing policies and electronic health records to enhance multi-agency collaboration and transparency, Random Forest model be considered for pilot integration into Tuberculosis clinic workflows at St. Michael Catholic Hospital and Continue model refinement to improve recall without compromising precision and accuracy.","url":"https://doi.org/10.21203/rs.3.rs-10193652/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10193652/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.20944/preprints202607.0511.v1","name":"Management of Prediction and Classifying of Wound Healing Results in Plastic and Reconstructive Surgery Based on Machine Learning Models","source":"preprints","abstract":"Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.","url":"https://doi.org/10.20944/preprints202607.0511.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0511.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9824273/v1","name":"An Explainable Hybrid of Classical and Quantum Support Vector Machine Models for Maternal Mental Health Risk Prediction Using Multicountry Data","source":"preprints","abstract":"Abstract Maternal mental health disorders remain a significant global public health challenge, particularly in low- and middle-income countries where limited mental health resources and inadequate screening systems contribute to underdiagnosis and delayed intervention. Recent advances in artificial intelligence and machine learning have demonstrated potential for improving early maternal mental health risk prediction using psychosocial and clinical data. However, existing approaches predominantly rely on classical machine learning models and rarely explore the integration of quantum-enhanced learning and explainable artificial intelligence. This study proposes an explainable Hybrid Classical-Quantum Support Vector Machine (SVM) model for maternal mental health risk prediction using multicountry data. The proposed model integrates Classical SVM optimization with Quantum SVM-based feature representation to improve nonlinear learning and predictive robustness. Comparative experiments were conducted against several classical and quantum machine learning models, including Random Forest, XGBoost, LightGBM, CatBoost, Stacking Ensemble, and standalone Quantum SVM. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC), and Precision-Recall Area Under the Curve (PR-AUC). The proposed Hybrid Classical-Quantum SVM achieved highly competitive performance with an accuracy of 99.86%, F1-score of 99.80%, ROC-AUC of 99.92%, and PR-AUC of 99.91%. SHapley Additive exPlanations (SHAP) analysis further identified suicidal ideation, stress, concentration difficulties, fatigue, poor appetite, and feelings of hopelessness as major predictors contributing to maternal mental health risk classification. The findings demonstrate that hybrid classical–quantum learning architectures can effectively combine the strengths of classical discriminative learning and quantum-enhanced feature representation for robust maternal mental health prediction.","url":"https://doi.org/10.21203/rs.3.rs-9824273/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9824273/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9772119/v1","name":"Machine Learning Models Combining Multimodal Ultrasound and Clinical Factors for Predicting Ischemic Stroke Risk in Patients with Carotid Plaques","source":"europepmc","abstract":"Abstract Background Carotid plaque vulnerability is a key predictor of ischemic stroke (IS). We aimed to evaluate the performance of machine learning models combining multimodal ultrasound and clinical features to assess IS risk in patients with carotid plaques. Methods This prospective study enrolled 231 inpatients with ultrasound-verified carotid plaques (December 2022–December 2024), partitioned into IS and non-IS groups based on recent neuroimaging. The dataset was randomly split into training (n = 162) and testing (n = 69) cohorts. Candidate variables included multimodal ultrasound (shear wave elastography [SWE], contrast-enhanced ultrasound [CEUS], Doppler, grayscale) and clinical parameters. Feature selection utilized the intersection of univariate logistic regression (LR) and 10-fold cross-validated LASSO regression, followed by stepwise LR minimizing the Akaike Information Criterion. Five machine learning models—LR, k-nearest neighbors, support vector machine, decision tree, and random forest (RF)—were constructed. Performance was evaluated via receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) approach interpreted the optimal model. Results Five predictive features were selected: plaque thickness, plaque SWE near-shoulder (NS), intraplaque neovascularization, triglycerides, and smoking history. The RF model exhibited the optimal predictive performance, yielding an area under the curve (AUC) of 0.874 in the testing set, with accuracy, sensitivity, and specificity of 0.81, 0.80, and 0.83, respectively. SHAP analysis identified Plaque SWE_NS as the primary contributor to model output, where lower SWE values indicated higher IS risk. Conclusions Machine learning models integrating multimodal ultrasound and clinical factors demonstrate robust predictive capability for IS events. The optimal RF model facilitates accurate identification and risk stratification, providing valuable adjunctive information for individualized clinical assessment.","url":"https://doi.org/10.21203/rs.3.rs-9772119/v1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9772119/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21203/rs.3.rs-10440906/v1","name":"An Interpretable Machine Learning Framework for Predicting Disease Progression in Early-Stage Cardiovascular-Kidney-Metabolic(CKM) Syndrome: A Prospective Cohort Study","source":"preprints","abstract":"Abstract Objective: Cardiovascular-kidney-metabolic (CKM) syndrome constitutes a continuum of metabolic, renal, and cardiovascular dysfunction, yet practical tools for stratifying progression risk in its early, modifiable stages remain absent. We aimed to develop an interpretable machine learning (ML) framework to estimate stage-advancement risk among individuals with early-stage CKM syndrome, emphasizing calibration stability and subgroup heterogeneity. Methods: This prospective cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 3,773 participants aged ≥45 years with baseline (2011) CKM stages 0–2 and complete follow-up through 2015 were included. Progression was defined as an increase of at least one CKM stage. Six ML models were evaluated through a three-stage selection process that sequentially prioritized discrimination, calibration stability (calibration slope and Integrated Calibration Index [ICI]), and clinical utility (Decision Curve Analysis). SHapley Additive exPlanations (SHAP) were applied to interpret model predictions. Subgroup analyses were stratified by baseline CKM stage. Results: Over the 4-year period, 1,560 participants (41.3%) progressed. This rate substantially exceeded the 15–34% stage-transition frequencies documented in Western cohorts, reflecting marked dynamic instability of early CKM in Chinese middle-aged and older adults. LASSO retained eight predictors: age, BMI, SBP, HbA1c, eGFR, baseline CKM stage, sex, and education. CatBoost yielded the highest discrimination (test AUC = 0.772) but was poorly calibrated (calibration slope = 1.305; ICI = 0.060). Random Forest (RF) was selected as the optimal model given its calibration stability (slope = 1.056; ICI = 0.026) and highest net benefit. Global SHAP analysis identified baseline CKM stage, HbA1c, and SBP as the leading predictors and uncovered a non-monotonic inverse stage effect. Subgroup analyses by baseline stage revealed a predictive ceiling effect in Stages 0–1 (AUCs ~0.60), attributable to progression rates exceeding 70%, while calibration remained acceptable across all stages. Conclusions: We present a well-calibrated, interpretable RF framework for risk stratification. The ceiling effect observed in early CKM stages indicates that individuals in Stages 0–1 may benefit more from immediate preventive care than from further conventional risk stratification.","url":"https://doi.org/10.21203/rs.3.rs-10440906/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10440906/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.12.26357878","name":"Explainable Longitudinal Machine Learning for Dementia Progression Using Cognitive and MRI Biomarkers","source":"preprints","abstract":"Dementia is a progressive neurological condition characterized by cognitive decline and structural brain changes that evolve. Longitudinal modeling of these changes is important for improving disease monitoring, identifying progression patterns, and supporting early risk stratification. This study developed an explainable longitudinal machine-learning framework for dementia progression, using cognitive and Magnetic Resonance Imaging (MRI)-derived biomarkers from the Open Access Series of Imaging Studies (OASIS-2) longitudinal dataset. The dataset included 150 subjects and 373 repeated observations classified as Non-demented, Demented, or Converted. Current-visit features, previous-visit features, and slope-based temporal features were constructed from Mini-Mental State Examination, Clinical Dementia Rating, normalized whole-brain volume, estimated total intracranial volume, atlas scaling factor, Age, and MRI delay. Baseline models were compared with a longitudinal gradient-boosted model, using patient-level splitting to reduce data leakage across repeated visits. The proposed longiGradient Gradient boosting model achieved the best held-out test performance, with an accuracy of 88.16%, a macro F1-score of 0.776, and a weighted F1-score of 0.860. The model showed strong classification performance for Demented and Non-demented individuals, while converted cases remained more difficult to identify. A regularized gradient boosting model was also evaluated as an overfitting sensitivity analysis; although it reduced the perfect training fit, it did not improve held-out test performance. Feature importance, permutation importance, and SHapley Additive exPlanations identified Clinical Dementia Rating as the dominant predictor, with slope-based Clinical Dementia Rating providing additional longitudinal information. These findings suggest that combining cognitive measures, MRI-derived biomarkers, and temporal feature engineering can improve dementia progression modeling, although external validation in larger longitudinal cohorts is needed.","url":"https://doi.org/10.64898/2026.07.12.26357878","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.12.26357878","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9862192/v1","name":"A machine learning-based risk model for loss of functional teeth in older adults: development and validation","source":"preprints","abstract":"Abstract Background Functional tooth loss (FTL) is a significant public health challenge among older adults, with documented adverse effects on oral function, quality of life, and systemic health outcomes. Current risk assessment tools, largely derived from cross-sectional or retrospective studies, lack robust prospective validity and individual-level predictive precision. To address this gap, we developed and rigorously validated a machine learning-based predictive model for FTL risk among community-dwelling older adults (≥ 65 years) to enable early identification of high-risk individuals and inform targeted, evidence-based preventive interventions. Methods We analyzed baseline data from 2,285 community-dwelling adults aged ≥ 65 years, including oral clinical examinations, sociodemographic characteristics, health behaviors, and systemic conditions. The dataset was randomly split into a training set (70%) and a testing set (30%). We developed prediction models using four machine learning algorithms (logistic regression, decision trees, random forests, and gradient boosting machines [XGBoost]). Model performance was comprehensively evaluated using discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration plots), and clinical utility (decision curve analysis). Results The Xgboost model demonstrated superior predictive performance, with an AUC of 0.924 (95% CI: 0.903–0.945), an accuracy of 0.846 (95% CI: 0.816–0.878), and a sensitivity of 0.883 (95% CI: 0.846–0.920). Calibration curves showed strong agreement between predicted and observed risks. Compared with logistic regression (AUC = 0.900), Xgboost yielded a statistically significant improvement in predictive accuracy. Decision curve analysis further confirmed the model's clinical net benefit across a wide range of threshold probabilities. Key predictors of FTL included infrequent brushing (less than twice daily), smoking history, chronic disease, and lower educational attainment. Conclusion This machine learning model can accurately stratify FTL risk among older adults using readily available clinical and behavioral indicators. Integrating it into community-based health platforms could enable real-time risk screening during routine dental visits or annual health examinations, potentially reducing the burden of tooth loss and promoting oral-systemic health in aging populations.","url":"https://doi.org/10.21203/rs.3.rs-9862192/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9862192/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10433360/v1","name":"A Leakage-Aware Machine Learning Framework for Multiclass Comorbidity Classification in Fibromyalgia: Integrating Genetic Feature Selection, Nested Cross-Validation, and Explainable AI","source":"preprints","abstract":"Abstract Fibromyalgia frequently co-occurs with disc herniation and inflammatory bowel syndrome, and the overlapping symptom profiles of these conditions complicate differential diagnosis. This paper proposes a machine learning framework for three-class comorbidity classification that distinguishes No Comorbidity, Disc Herniation, and Inflammatory Bowel Syndrome from multi-instrument clinical assessment data. Missing values are imputed with a $k$-nearest-neighbor pass, after which Mutual Information feature selection retains $K=50$ features and a Genetic Algorithm refinement step converges to a 24-feature subset; both selection stages are computed once on the full analytical cohort ahead of the outer cross-validation split, a design choice this paper discloses and discusses explicitly rather than folding into an unqualified leakage-free claim. Multivariate Imputation by Chained Equations, $z$-score standardization, and Borderline Synthetic Minority Oversampling, with a standard Synthetic Minority Oversampling fallback, are the operations genuinely refit inside every outer training fold, so that no held-out patient contributes to the imputation statistics, scaling parameters, or synthetic minority samples used to train that fold's classifiers. Sixteen supervised classifiers spanning six algorithmic families were benchmarked under five-fold nested cross-validation and validated with the Friedman omnibus test ($\\chi^2 = 42.25$, $p","url":"https://doi.org/10.21203/rs.3.rs-10433360/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10433360/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.17.26358221","name":"Development and external validation of deep learning models for spontaneous preterm birth prediction from mid-trimester cervical ultrasound","source":"preprints","abstract":"Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance.","url":"https://doi.org/10.64898/2026.07.17.26358221","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.17.26358221","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-10407148/v1","name":"Predicting In-Hospital Mortality in ICU Patients with Intracerebral Hemorrhage: A Machine Learning Study Using Dynamic Time-Series Features with Dual-Database Validation","source":"preprints","abstract":"Abstract Background ICH has high ICU mortality, yet conventional scores rely on static data and miss early dynamics. We developed and validated machine learning models using 24-hour time-series features to predict in-hospital mortality. Methods This TRIPOD + AI retrospective study used MIMIC-IV for development (n = 1,962) and internal validation (n = 842), and eICU-CRD for external validation (n = 3,470). The first 24 hours were divided into four 6-hour windows; statistical features of eight physiological parameters were extracted per window. MICE and SMOTE addressed missing data and class imbalance. LASSO selected 29 features for four models (LR, RF, XGBoost, LightGBM), benchmarked against a GCS-only baseline. Performance was assessed by AUC, calibration, DCA, and SHAP. Results Mortality was 21.3%, 21.5%, and 17.3% across cohorts. Twenty-two of 29 features (76%) were dynamic. LightGBM achieved the best performance: internal AUC 0.823 and external AUC 0.813, significantly exceeding the GCS-only baseline (0.778). Calibration was satisfactory; DCA demonstrated net benefit. SHAP identified GCS and BUN as top predictors, with dynamic vital sign features contributing substantially. Conclusions Dynamic physiological trajectories encode prognostic information beyond static snapshots, underscoring the value of continuous ICU monitoring. Our study offers an interpretable, imaging-free tool for early risk stratification, with potential for integration into clinical systems to guide triage and surveillance in high-risk patients.","url":"https://doi.org/10.21203/rs.3.rs-10407148/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10407148/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.64898/2026.07.28.26359155","name":"First-24-hour machine learning for 30-day mortality prediction in ICU trauma patients: development in MIMIC-III and cross-database evaluation in MIMIC-IV","source":"preprints","abstract":"ICU trauma patients are clinically heterogeneous, and early mortality risk stratification may support monitoring and resource allocation. We developed machine learning models for 30-day mortality prediction using information recorded during the first 24 hours after ICU admission. In MIMIC-III, six feature configurations were trained using 3,411 patients and compared in a patient-level configuration-selection hold-out subset of 853 patients. The selected XGBoost configuration yielded an area under the precision–recall curve (AUPRC) of 0.556 and an area under the receiver operating characteristic curve (AUROC) of 0.863. For cross-database evaluation, a 228-predictor harmonized XGBoost model was refitted on the complete MIMIC-III cohort and evaluated in 13,747 MIMIC-IV ICU stays without using MIMIC-IV outcomes for model development or recalibration. It achieved an AUPRC of 0.495, an AUROC of 0.825, and a Brier score of 0.109. Calibration was monotonic but showed increasing overprediction at higher predicted risks. First-24-hour clinical information retained predictive value across MIMIC database versions, although internal configuration selection, model differences, same-center provenance, and incomplete feature-mapping documentation limit generalizability and deployment readiness.","url":"https://doi.org/10.64898/2026.07.28.26359155","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.28.26359155","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.21203/rs.3.rs-9539932/v1","name":"Explainable Machine Learning Model for Prostate Cancer Risk Prediction Using Clinical and Polygenic Risk Features in a Nationwide Korean Cohort","source":"preprints","abstract":"Abstract Background Early prediction of prostate cancer risk is critical for timely diagnosis and treatment. While prostate-specific antigen (PSA) testing is widely used in screening, its limited specificity highlights the need for complementary biomarkers. We aimed to develop machine learning–based predictive models for prostate cancer using demographic and genomic data, and to evaluate the incremental predictive value of PSA. Methods We constructed and compared four machine learning models—Ridge, Random Forest, LightGBM, and XGBoost—using Korean Cancer Prevention Study-II (KCPS-II), with and without PSA as a predictor. Results A total of 18,592 participants were included in the test set, of whom 157 had confirmed prostate cancer. Model performance was evaluated using recall (sensitivity), AUC, specificiy, and F1 score. SHAP (SHapley Additive exPlanations) were used to interpret feature importance. Among model without PSA, LightGBM model achieved the highest recall (0.873) and AUC (0.902; 95% CI: 0.897–0.906), correctly identifying 142 of 157 cancer cases. Machine learning models incorporating PSA and genomic data demonstrate strong poetntial for accurate and interpretable prostate cancer risk prediction. Conclusions Our results may aid in personalzied screening strategies, particualrly by offering explainable predictions based on age, PSA, and genetic risk profiles. External validation and fufther integration with MRI or Gleason grading data may improve clinical applicability.","url":"https://doi.org/10.21203/rs.3.rs-9539932/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9539932/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.14293/pr2199.003706.v2","name":"Machine Learning Approaches for Intelligent Dental Implant Planning and Crown Morphology Prediction","source":"preprints","abstract":"Dental implant therapy requires precise planning to ensure functional performance, aesthetic outcomes, and long-term clinical success. Conventional implant planning and crown design often rely on clinician experience, radiographic interpretation, and manual measurements, which may introduce variability and increase treatment time. Recent advances in machine learning have created opportunities for data-driven decision support systems that can improve diagnostic accuracy and treatment predictability. This paper examines the application of machine learning techniques for intelligent dental implant planning and crown morphology prediction. Various algorithms, including artificial neural networks, support vector machines, random forests, convolutional neural networks, and deep learning architectures, are explored to analyze cone-beam computed tomography images, assess bone quality, identify optimal implant positions, and predict patient-specific crown morphology. The study highlights how these approaches can integrate anatomical, functional, and aesthetic parameters to generate personalized treatment recommendations. Furthermore, the paper discusses current challenges related to data availability, model interpretability, clinical validation, and integration into routine dental workflows. The findings indicate that machine learning-assisted systems can enhance treatment efficiency, reduce planning errors, and support more consistent restorative outcomes. As digital dentistry continues to evolve, intelligent predictive models are expected to play an increasingly important role in achieving accurate, patient-centered implant rehabilitation.","url":"https://doi.org/10.14293/pr2199.003706.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.003706.v2","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9659318/v1","name":"Integrating Deep Learning and Radiomics in Chest CT for Risk-Adapted Management of Early-Stage Lung Adenocarcinoma: A Dual-Prediction Model","source":"preprints","abstract":"Abstract Background To develop and validate a CT-based hybrid model for preoperative prediction of invasive tumor features (visceral pleural invasion, lymphovascular invasion, or spread through air spaces) and occult lymph node metastasis (OLNM) in clinical early-stage (cT1-2N0M0) lung adenocarcinoma (LUAD), and embed it into a clinical decision system. Methods This retrospective two-center study included 639 patients with clinical stage T1-2N0M0 LUAD. Two hybrid models were developed integrating handcrafted radiomics, deep learning (ResNet50), and clinical-semantic CT features. Model A predicted invasive features in node-negative patients; Model B predicted OLNM. After feature harmonization and selection, machine learning classifiers were optimized and translated into a three-category decision system via sequential thresholding. Results The Random Forest-based integrated model showed robust performance. For Model B (OLNM), AUCs were 0.937 (cross-validation) and 0.806 (test set); for Model A (invasive features), AUCs were 0.857 and 0.758, respectively. The decision system effectively stratified patients: test set OLNM rates were 0.0%, 8.3%, and 15.8% across low-, intermediate-, and high-risk groups (P for trend","url":"https://doi.org/10.21203/rs.3.rs-9659318/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9659318/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.31.26359396","name":"Beyond the Red Complex:  <i>De Novo</i>  Marker Discovery Uncovers Novel Periodontitis-Associated Taxa and Enables Non-Invasive Machine Learning Diagnosis","source":"preprints","abstract":"Background Periodontitis affects over 1 billion people worldwide, yet diagnosis relies on clinical measures that capture tissue destruction rather than underlying microbial dysbiosis. Most microbial-biomarker studies use 16S rRNA sequencing or reference-database mapping, systematically under-detecting uncultivated or divergent taxa. Methods We assembled 341 supra- and subgingival shotgun metagenomes (218 periodontitis, 123 health) across nine countries/regions. Using MetaMarker, a de novo, reference-free pipeline, we identified conserved genomic markers directly from reads in a 305-sample discovery pool without database mapping. Markers were taxonomically annotated against the Human Oral Microbiome Database, functionally annotated with Prodigal/eggNOG-mapper, and used for eight machine-learning classifiers, externally validated on three independent held-out cohorts (36 samples). Results We recovered 2,142 significant markers (1,999 periodontitis-enriched, 143 health-enriched; q Conclusions Reference-free metagenomic marker discovery recovers known periodontal pathobiology while revealing unrecognized candidate biomarkers and supports an accurate, externally validated, non-invasive classifier with translational potential as a compact diagnostic panel.","url":"https://doi.org/10.64898/2026.07.31.26359396","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.31.26359396","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.22541/authorea.15006727/v1","name":"CardioSafeAI: An Explainable Federated Machine Learning Framework with Multi-Stage Hybrid Feature Selection for Cardiovascular Disease Prediction","source":"preprints","abstract":"cardiovascular disease (CVD) is a leading cause of global mortality and may progress without obvious symptoms. This study proposes CardioSafeAI, an explainable federated learning framework for predicting ten-year coronary heart disease risk from a public dataset. The workflow includes imputation, encoding, Min--Max normalization and SMOTE. A hybrid feature selection method combines Pearson correlation, Information Gain and Gain Ratio. Eleven individual models and a Stacking ensemble were evaluated. The Stacking classifier achieved the highest centralized accuracy of 91.1%. In a five-client simulation, the federated Stacking model achieved 90.80% accuracy and an ROC-AUC of 0.9486. SHAP and LIME provided global and patient-level explanations. CardioSafeAI therefore provides a promising basis for interpretable cardiovascular risk assessment, although independent and prospective clinical validation is required before use in clinical decision making.","url":"https://doi.org/10.22541/authorea.15006727/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22541/authorea.15006727/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10177679/v1","name":"Interpretable Machine Learning for Subtype-Specific Prognosis in Sudden Hearing Loss","source":"preprints","abstract":"Abstract Background Sudden sensorineural hearing loss (SSNHL) is a highly heterogeneous condition with unpredictable recovery. We aimed to develop and validate an interpretable, subtype-specific machine learning (ML) framework for prognostic assessment using the largest SSNHL clinical cohort reported to date to overcome the \"black-box\" nature of existing models. Method In this retrospective cohort study, 3,957 patients diagnosed with SSNHL between 2010 and 2017 were analyzed. Eight ML algorithms were trained and validated for five predefined audiogram subtypes: ascending, descending, atypical, profound, and flat. Model performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC), and Brier score. SHapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) were used to interpret the models and quantify the nonlinear impact of predictors. Results In total, 3,957 patients were included in the analysis. The subtype-specific models demonstrated robust performance with test accuracies ranging from 0.706 to 0.774. Support Vector Machine and Logistic Regression emerged as the optimal classifiers. Five core prognostic factors were identified: disease duration, pure-tone average, age, WHO hearing classification, and systemic glucocorticoid therapy. Notably, the study quantified subtype-specific treatment windows, showing the disease duration threshold for a 50% recovery probability ranged from 7.5 to 13.1 days. Conclusion ML is a reliable tool for high-precision prognostic assessment in SSNHL. By quantifying actionable intervention thresholds, this interpretable framework moves beyond \"black-box\" predictions, providing a real-time decision support tool that reinforces the necessity of early systemic glucocorticoid therapy.","url":"https://doi.org/10.21203/rs.3.rs-10177679/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10177679/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.08.15.26360510","name":"Spatial and Machine Learning Analysis of Breast and Cervical Cancer Screening Uptake in Ghana: Evidence from the 2022 Ghana Demographic and Health Survey","source":"preprints","abstract":"Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) have already shown that wealth, education, and place of residence pattern who gets screened [1–3]. Whether this patterning clusters geographically below the level of administrative region has not been tested for this population, and whether cluster-aware machine learning adds anything to the standard regression approach used so far remains open. Methods We analyzed the 2022 Ghana Demographic and Health Survey women’s file (N = 15,014; primary sample of women aged 25-49 years, n = 9,510) linked to cluster geographic coordinates for 618 enumeration areas. Clinical breast examination and cervical cancer testing were the two outcomes. We estimated survey-weighted prevalence across demographic and socioeconomic strata, tested global spatial autocorrelation with Moran’s I, mapped local clustering with Getis-Ord Gi* statistics, and separately fitted gradient-boosted classifiers on individual-level socioeconomic covariates, validated under cluster-held-out five-fold cross-validation to prevent within-cluster information leakage. Feature contributions to the breast-screening model were interpreted with an additive, feature-level explanation technique, and socioeconomic inequality was quantified with both the ordinary and Erreygers-corrected concentration index. The predictive models did not include geographic coordinates or survey weights; both are noted as limitations. Results Weighted prevalence among women aged 25-49 years was 22.5% (standard error 0.77) for breast examination and 6.9% (standard error 0.46) for cervical testing. Both rose with education and wealth and were roughly double in urban areas relative to rural ones. Moran’s I was positive and significant for both outcomes (breast: 0.217, z = 11.69, p Conclusions Screening uptake in Ghana is spatially clustered at a resolution that regional reporting cannot show, and this clustering is compositionally associated with, though not formally shown to be mediated by, the socioeconomic makeup of individual clusters. Cluster-level spatial analysis and a model-based risk ranking may offer a useful complement to regional targeting, but calibration, external geographic validation, and comparison against a regional-allocation baseline are needed before any operational use. Trial registration Not applicable. This is a secondary, hypothesis-generating cross-sectional analysis of existing, publicly available survey data and was not prospectively registered.","url":"https://doi.org/10.64898/2026.08.15.26360510","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.15.26360510","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.30.741666","name":"Non-Invasive Embryo Quality Assessment via Matrix-Optimized Untargeted LC-MS Metabolomics of Spent Embryo Culture Media and Weighted Ensemble Machine Learning","source":"preprints","abstract":"Background Non-invasive embryo quality assessment is a critical unmet need in assisted reproductive technology (ART). Preimplantation genetic testing for aneuploidy (PGT-A) is effective but requires invasive biopsy that may compromise embryo viability. Metabolomics of spent embryo culture media (SECM) offers a non-invasive alternative, yet analytical challenges—limited sample volume, high salt content, and abundant proteins—have hindered standardization and clinical translation. Results We systematically optimized sample preparation for untargeted LC-MS metabolomics of SECM using human serum as a reference. Optimal conditions were highly matrix-dependent: SECM required 7× volume of 50% acetonitrile for extraction and 40% acetonitrile for reconstitution, whereas serum required 10× volume of 100% methanol and 100% water—reflecting that SECM contains more non-polar species than serum. Applying the optimized workflow to 120 clinical SECM samples (72 euploid, 48 aneuploid), we identified 102 differential metabolites between euploid and aneuploid embryos, with prominent enrichment of lipid pathways (fatty acid metabolism, β-oxidation, sphingolipid metabolism) and involvement of amino acid (methionine, tryptophan) and TCA cycle metabolism. A weighted ensemble machine learning model discriminated aneuploid from euploid embryos with an AUC of 0.977, 100.0% specificity, and 89.6% sensitivity. Among 72 euploid embryos stratified by morphological grading (good, fair, poor), metabolic alterations progressed from mitochondrial energy deficiency (good vs. fair) to broader lipid dysregulation (fair vs. poor), with the ensemble model achieving AUCs of 0.944, 0.889, and 0.943, respectively. Conclusions This study establishes a rigorously optimized and validated SECM metabolomics workflow that overcomes key analytical barriers in this challenging matrix. Our findings demonstrate that metabolic signatures—particularly in lipid and energy metabolism—are strongly associated with both embryo ploidy and morphological quality, providing biological insights into the metabolic underpinnings of embryo developmental competence. The high predictive performance of the ensemble model supports the feasibility of non-invasive embryo assessment as a complementary tool to existing methods, with potential to reduce reliance on invasive biopsy in ART. External validation in prospective multi-center cohorts is warranted to further assess clinical utility and generalizability. Graphical Abstract","url":"https://doi.org/10.64898/2026.07.30.741666","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.30.741666","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.12688/wellcomeopenres.26814.1","name":"Feasibility of Real-Time AI Models For Clinical Tasks Involving Tetanus: Study Protocol","source":"preprints","abstract":"Early prediction of disease progression in tetanus enables timely intervention which can improve outcome. We have developed a machine learning model to predict transition to severe tetanus. The model uses continuous pulse plethysmography waveforms recorded from low-cost wearable pulse oximeters. Data from these monitors is read by the machine learning model in evaluating change over time. If a pre-specified threshold is reached, an alert is generated. To evaluate the feasibility of a clinical decision support system that incorporates our model, we describe a prospective study in adults with tetanus admitted to the intensive care unit in a tertiary hospital in Vietnam. The study aims to evaluate the frequency and accuracy of the alerts and potential clinical usefulness. For the purposes of this study, our machine-learning model runs in parallel with clinical care, and alerts are only seen by the study team who evaluate the patient status at the time of the alert.","url":"https://doi.org/10.12688/wellcomeopenres.26814.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/wellcomeopenres.26814.1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.20944/preprints202607.0486.v1","name":"Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning","source":"preprints","abstract":"Background: /Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysis combined with interpretable machine learning could quantify gait-related motor phenotypes in individuals with schizophrenia. Methods: Gait videos were collected from 100 individuals with schizophrenia and 35 healthy controls using a standardized recording setup. MediaPipe Pose was used to extract skeletal landmarks and derive 12 image-plane spatiotemporal and estimated two-dimensional knee-kinematic gait features. After temporal segmentation and quality control, 404 usable gait segments were analyzed. Decision Tree and Support Vector Machine models were applied for exploratory segment-level group-separation analysis using 15-fold cross-validation after pre-cross-validation resampling. Results: Several extracted gait features differed between groups, particularly image-plane ankle displacement, mean step displacement, displacement velocity, step characteristics, and knee-joint motion. In the Decision Tree model, image-plane ankle displacement served as the primary root node, indicating its central role in internal segment-level group separation. However, because the healthy control group was substantially younger and not age-matched, multiple gait segments from the same participant could appear across cross-validation folds, and resampling was performed before fold partitioning, model performance should be interpreted as exploratory internal segment-level behavior rather than participant-level classification evidence. Conclusions: Markerless video-based gait analysis with interpretable machine learning may provide a feasible research-support approach for quantifying gait-related motor phenotypes in individuals with schizophrenia. These findings should not be interpreted as evidence for clinical classification or screening. Future studies require matched controls, psychiatric comparison groups, subject-wise validation, external datasets, calibrated gait measures, and privacy-preserving data governance.","url":"https://doi.org/10.20944/preprints202607.0486.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202607.0486.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10241449/v1","name":"Prediction of Pediatric Lower Urinary Tract Dysfunction Using Uroflowmetry Data and Machine Learning with Explainable AI Techniques","source":"preprints","abstract":"Abstract Purpose To develop and evaluate a machine learning based approach for the interpretation of uroflowmetry in pediatric lower urinary tract dysfunction, aiming to reduce interobserver variability and improve diagnostic consistency while maintaining clinical interpretability. Methods In this single-center retrospective study, 2663 pediatric UF records (age 2–18 years, July 2019-March 2024) were analyzed. LUTD subtypes were assigned to the International Children's Continence Society terminology. Two settings were evaluated: three-class (overactive bladder (OAB), dysfunctional voiding (DV), normal (N)) and four-class (adding DV-OAB). Eight predictors (four numerical, four categorical) were used. Seven ML models (Decision Trees, Naive Bayes, Support Vector Machine, Efficiently Trained Linear Classifiers (Logistic Regression), Gaussian Kernel, Ensemble Classifiers, and Artificial Neural Networks) were trained with Bayesian hyperparameter optimization using stratified 10-fold cross-validation on a 90% − 10% train-test split. Weighted accuracy, precision, recall, and F1-score were reported. Feature importance was quantified with Shapley Additive Explanations (SHAP). Reporting followed TRIPOD-AI. Results In the three-class setting, the ensemble classifier achieved the best test performance (accuracy 86.09%, F1 86.03%) and all models exceeded 82% test accuracy. In the four-class setting, test accuracy dropped to 72.56–78.95%, and most models failed to predict the DV-OAB class owing to its low prevalence (2.55%). SHAP identified bladder capacity as the dominant feature for OAB, pelvic floor activity for DV, and residual volume as a secondary DV feature. Sex contributed zero predictive value. Conclusions Explainable ML enables interpretable classification of pediatric LUTD subtypes from UF data across a wide age range, including children under 5 years. External multicenter validation is required before clinical deployment.","url":"https://doi.org/10.21203/rs.3.rs-10241449/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10241449/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9626231/v1","name":"Integrating Salivary Microbiome Profiles with Clinical and Behavioral Data for Caries Risk Prediction in School-Age Children","source":"preprints","abstract":"Abstract Background: Dental caries remains a major public health problem among school children, and evidence show the contribution of oral microbiota dysbiosis in the disease onset and progression. However, integrative predictive models incorporating oral microbiome, clinical, and behavioural data remain limited. Objective This study aimed to develop and validate a machine learning-based predictive framework for dental caries-related risk and severity in school children by integrating salivary microbiome profiles, alongside clinical, behavioural and sociodemographic data. Materials and methods A cross-sectional study was conducted among 60 children aged 7-12 years old. Caries status was assessed using ICDAS II and categorised into caries-free, low, moderate, and severe groups. Unstimulated saliva samples were analysed using 16S rRNA gene sequencing to characterise the oral microbiome. Clinical indices, oral hygiene status, dietary habits, and socioeconomic variables were recorded. Analysis of microbial diversity, taxonomic composition, and host-microbiome associations were performed. Multiple machine learning classifiers were constructed and evaluated to predict caries risk and severity. Results Metagenomic analysis reveals caries-free children possess significantly higher oral microbial diversity than caries-prone individuals, who exhibit dysbiosis and a shifted Firmicutes-to-Proteobacteria ratio. Clinical indices and maternal education strongly correlate with caries status. Furthermore, a Gaussian Naive Bayes machine-learning model achieved 80% accuracy in predicting caries risk by integrating key microbial biomarkers, like S. sanguinis, with socio-behavioral factors. Conclusion Combining oral microbiome profiles with clinical and behavioral determinants creates a robust predictive framework for early caries detection and personalized pediatric oral health management. Clinical relevance The usage of machine learning approach in integrating salivary microbiome data with clinical and behavioural factors provides a non-invasive and effective approach for early caries risk classification. This model provides evidence for precision-based-prevention, and individualised caries-care in paediatric dentistry.","url":"https://doi.org/10.21203/rs.3.rs-9626231/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9626231/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10090900/v1","name":"Clinical Readiness of Machine-Learning Risk Models in Non-Variceal Upper Gastrointestinal Bleeding: A Systematic Review and Network Meta-Analysis","source":"preprints","abstract":"Abstract Risk scores guide discharge, urgent endoscopy, transfusion, and monitoring in non-variceal upper gastrointestinal bleeding (NV-UGIB), but their performance relative to machine learning (ML) models remains unclear. We reviewed 26 studies and performed a network meta-analysis of high-risk clinical outcomes (PROSPERO: CRD420261405871). ML models demonstrated higher apparent discrimination than the Glasgow-Blatchford Score in direct comparisons (ΔAUROC + 0.148, 95% CI + 0.069 to + 0.228); however, the network was sparse and primarily anchored to GBS. Externally validated models showed imprecise estimates crossing zero (ΔAUROC + 0.158, 95% CI − 0.180 to + 0.496). Evidence certainty was Very Low to Low, with inconsistent reporting of calibration, decision-curve analysis, and external validation. Current evidence does not support replacing established scores with ML in routine NV-UGIB care. Future studies should compare ML with multiple established scores using prospective external cohorts and decision-curve analysis.","url":"https://doi.org/10.21203/rs.3.rs-10090900/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10090900/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9465265/v1","name":"Development and Evaluation of a Nurse-Centered Machine Learning Tool for Pressure Injury Risk Assessment in Adult ICU Patients: Retrospective Cohort Study","source":"preprints","abstract":"Abstract Background Pressure injuries are a common complication in intensive care units, and existing risk assessment tools often rely on subjective judgment with inconsistent predictive performance. Recent machine learning models have shown promise, but many rely on variables that are not directly actionable by nurses, limiting their clinical applicability. Therefore, this study aimed to develop and internally validate a nurse-centered machine learning–based tool for pressure injury risk assessment in adult ICU patients using variables directly measurable by bedside nurses, and to implement it as a web-based clinical decision support interface. Methods Five nurse-measurable predictors were used: level of consciousness, lower limb muscle strength, maximum body temperature, minimum body temperature, and average daily incontinence episodes. Multiple machine learning algorithms were systematically compared, and the final Gaussian Naïve Bayes model was retrained with SMOTE applied exclusively to the training set. Internal validation was conducted using 5-fold cross-validation and bootstrap resampling, decision curve analysis was performed to assess clinical utility, SHAP values were used to evaluate model interpretability, and isotonic regression recalibration was applied to improve the reliability of predicted probabilities. The final model was implemented as a web-based clinical decision support interface. Results The Gaussian Naïve Bayes model achieved an AUC of 0.887 and recall of 0.739 on the held-out test set, with bootstrap-based internal validation confirming stable performance (AUC 0.888, 95% CI: 0.868–0.906). Decision curve analysis demonstrated positive net benefit up to a threshold probability of 15.2%. Isotonic regression recalibration substantially improved probability calibration (calibration slope = 0.914, scaled Brier score = 0.130) while preserving discrimination. SHAP analysis identified lower limb muscle strength, level of consciousness, daily incontinence episodes, and minimum body temperature as key predictors of pressure injury risk. Conclusions A nurse-centered machine learning tool for pressure injury risk assessment was developed and internally validated using variables routinely documented by ICU nurses, and implemented as a web-based clinical decision support interface. The tool demonstrated adequate discriminative performance, well-calibrated probability estimates and clinical utility, and may support nurse awareness of cumulative pressure injury risk patterns during ICU stay, facilitating prioritization of preventive nursing interventions. Trial registration Clinical trial number: not applicable.","url":"https://doi.org/10.21203/rs.3.rs-9465265/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9465265/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10117658/v1","name":"Enhancing Explainability in AI Models for Blood Glucose Prediction in Diabetes Patients","source":"preprints","abstract":"Abstract Background Accurate short-term blood glucose prediction can help reduce adverse glycemic events and improve diabetes management. Although machine learning models have shown promising performance for glucose forecasting, their clinical adoption remains limited due to a lack of transparency. This study aimed to evaluate both the predictive performance and interpretability of machine learning models for 30-minute blood glucose prediction in individuals with Type 1 and Type 2 diabetes. Methods We use continuous glucose monitoring and clinical data from Type 1 and Type 2 diabetes cohorts to develop five blood glucose prediction models: Linear Regression, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory. Model performance was assessed using Root Mean Squared Error and the coefficient of determination. After that, explainable artificial intelligence (XAI) methods including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied to analyze global feature importance and explain individual predictions. Furthermore, comparative analyses were conducted across diabetes types and prediction scenarios. Results Support Vector Regression and tree-based ensemble models achieved the strongest predictive performance across both diabetes populations, while Long Short-Term Memory networks were constrained by limited data availability. Recent glucose measurements were consistently identified as the most influential predictors. Interpretability analysis revealed distinct glucose dynamics between diabetes types, with Type 1 diabetes exhibiting greater short-term volatility and Type 2 diabetes showing more stable patterns with additional contributions from clinical variables. SHAP produced more stable and comprehensive explanations, whereas LIME provided simpler but less robust local interpretations. Conclusions Machine learning models can provide accurate short-term glucose forecasts while maintaining meaningful interpretability through explainable artificial intelligence techniques. SHAP and LIME offer complementary strengths, with the former providing higher fidelity and the latter improving accessibility. These findings support the development of transparent and trustworthy artificial intelligence systems for diabetes management and clinical decision support.","url":"https://doi.org/10.21203/rs.3.rs-10117658/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10117658/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.22.26358736","name":"A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification","source":"preprints","abstract":"FeePredict is a three-stage random forest machine learning framework to simultaneously predict whether Medicare reimbursement rates for specific procedures will change, in which direction they will change, and by how much. FeePredict was applied to the four major Medicare fee schedules: the Clinical Laboratory Fee Schedule (CLFS), the Physician Fee Schedule (PFS), the Ambulance Fee Schedule (AFS), and the Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) fee schedule. Each of these fee schedules contains publicly available data from the Centers for Medicare & Medicaid Services (CMS) for the years 2024, 2025, and 2026, with the number of procedures represented in the data ranging from 3,264 to 2,952,842 observations. FeePredict utilizes lag-1 feature engineering and train-only preprocessing steps to ensure that there is no data leakage into the model. Chronological out-of-time validation was performed on three of the four fee schedules to determine the generalizability of the model over time. FeePredict significantly outperformed the assumption that there would be no changes to Medicare reimbursement rates for procedures ( p < 0.001), achieving concordance indices between 0.815 and 0.998, and reducing the mean absolute error for predicting changes to reimbursement rates by 29% to 85%. Permutation testing of the model with shuffled reimbursement rate labels indicates that there is no evidence of data leakage (AUC values: 0.467-0.515). The model achieved concordance indices of 0.854 and 0.972 for the CLFS and DMEPOS fee schedules, respectively, outside of its training period, but performed less well outside of its training period for the PFS, indicating that it generalizes less well to changes to the Medicare policy regime that existed after its training period. Overall, though, these results indicate that it is possible to accurately predict whether Medicare reimbursement rates for medical procedures will change using only data from the historical versions of those fee schedules.","url":"https://doi.org/10.64898/2026.07.22.26358736","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.26358736","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10215598/v1","name":"Development and Validation of an Interpretable Machine Learning Model for Depression Risk Stratification in Middle-Aged and Older Patients with Gastrointestinal Diseases: A Cross-Sectional Study Using CHARLS Data","source":"preprints","abstract":"Abstract Background Depression is highly prevalent in middle-aged and older patients with gastrointestinal diseases (GID) and severely impairs their quality of life and treatment outcomes. This cross-sectional study aimed to develop and validate an interpretable machine learning (ML) model for the concurrent risk stratification of depressive symptoms in this population. Methods This retrospective analysis used data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS). Potential predictors were selected using Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) regression. The outcome was current depression at the 2018 wave, defined as a CES-D-10 score ≥ 10. Six ML algorithms were employed to construct the models. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, F1-score, calibration curves, and decision curve analysis. The SHapley Additive exPlanations (SHAP) framework was used to interpret the feature contributions. Results Among the 1,269 enrolled participants (574 with depression), six key predictors were identified using both LASSO and Boruta. The XGBoost model achieved the best discriminative ability, with AUC values of 0.78 (training) and 0.666 (validation). The SHAP analysis further ranked the six predictors in descending order of importance: self-reported health status, sleep time, gender, IADL, trouble with body pain, and ADL. Conclusion We developed an interpretable ML model for concurrent depression risk screening in middle-aged and older patients with GID. This tool may facilitate prompt identification and targeted intervention, though external validation is warranted before clinical deployment.","url":"https://doi.org/10.21203/rs.3.rs-10215598/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10215598/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10080452/v1","name":"Artificial Intelligence for ICU Mortality Prediction:Comparative Machine Learning Models and the Role of Surgical Patients","source":"preprints","abstract":"Abstract Purpose: To compare artificial intelligence and machine-learning models for in-hospital mortality prediction in adult intensive care unit (ICU) patients and examine the contribution of surgical status to model behavior. Methods: This retrospective single-center study included 769 ICU admissions recorded between March 2023 and December 2025. Decision tree, logistic regression, random forest, tuned random forest, and multilayer perceptron models were evaluated. Mortality was treated as the positive class for clinically intuitive reporting. Results: In the 30% test cohort ( n = 231), random forest showed the highest discrimination (AUC 0.91), followed by tuned random forest (AUC 0.90), logistic regression (AUC 0.88), multilayer perceptron (AUC 0.85), and decision tree (AUC 0.66). Key clinically informative signals included sepsis or infection, surgical operation, trauma, APACHE II, troponin, and D- dimer. Surgical status emerged as an important contributor within the reported models, although subgroup-specific performance metrics were not available. Conclusion: Explainable artificial intelligence approaches may support ICU mortality prediction and clinical decision support while highlighting clinically plausible patterns related to acute severity, multimorbidity, and the surgical pathway. Trial registration: Not applicable; this study is a retrospective observational analysis and was not a clinical trial.","url":"https://doi.org/10.21203/rs.3.rs-10080452/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10080452/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10624154/v1","name":"Structured-to-Narrative Representation Framework for Next-Day Discharge Prediction Using Structured EHR Data and Clinical Language Models","source":"preprints","abstract":"Abstract Objective Accurate prediction of next-day hospital discharge after elective spine surgery is critical for optimizing inpatient operations and perioperative care coordination. This study introduces a structured-to-narrative modeling framework that transforms high-dimensional structured electronic health record data into clinically interpretable textual representations for discharge prediction. Materials and Methods We analyzed structured electronic health record data from 1,958 patients undergoing elective spine surgery, comprising approximately 600 binary and categorical variables spanning demographics, laboratory, diagnosis, and procedure data. We evaluated machine learning models and a fine-tuned domain-specific transformer trained exclusively on structured-to-narrative representations derived from these variables. Models were assessed under strict patient-level separation using five-fold cross-validation. Discrimination, calibration, and class-specific performance were evaluated using AUROC, PRAUC, Brier score, precision, recall, and F1-score, with interpretability examined using SHAP and narrative-level probability analysis. Results Ensemble machine learning models achieved strong discrimination and calibration, with a maximum AUROC of 0.94. The narrative-centered transformer achieved competitive performance with a mean AUROC of 0.84, PRAUC of 0.57, and Brier score of 0.17. Threshold optimization shifted the transformer toward a sensitivity-focused prediction behavior, achieving a mean recall of 0.75. SHAP analysis and narrative-level probability inspection demonstrated clinically coherent attribution aligned with procedural burden, age, and physiologic instability. Discussion While ensemble models achieved superior discrimination, the narrative-centered transformer provides a language-native, representation-driven alternative that supports clinically interpretable reasoning directly from structured data. Conclusion Structured-to-narrative modeling offers a scalable and interpretable pathway for integrating predictive modeling into real-world perioperative decision support.","url":"https://doi.org/10.21203/rs.3.rs-10624154/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10624154/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.06.28.734076","name":"A systematic analysis of machine learning pipelines for robust antimicrobial resistance prediction","source":"preprints","abstract":"Motivation Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spread. Although many previous works have explored the use of tree-based machine learning (ML) models to predict resistance, the field lacks a systematic evaluation of the training pipeline across a variety of pathogenic species and antibiotics. Results Using nine clinically relevant species–antibiotic combinations from the NCBI antimicrobial susceptibility testing database, we present a detailed analysis of the ML pipeline and identify key factors affecting model performance and evaluation. We begin by relabelling all isolates using current CLSI minimum inhibitory concentration breakpoints to resolve inconsistencies and increase available data, resulting in up to a 19% label swap and 56% data enlargement per species– antibiotic combination. We identify several key training parameters including k -mer length, which can increase classification F1 scores by over 20 points compared to commonly used k -values, feature matrix truncation, which can induce polynomial time reductions with limited performance reduction, and ML model class. By comparing 5-fold cross-validation with evaluation on an unseen clinical dataset, we show that random cross-validation splits—often criticized as overly optimistic—can act as a strong proxy for downstream clinical performance, yielding closer F1 scores than phylogeny-aware splits in all cases. We finally present an interpretability study which shows that over 95% of k -mers used by our models are associated with identifiable genomic features. Our results highlight the importance of feature design, evaluation protocol, and biological analysis in genomic AMR prediction, and support tree-based models as a robust and interpretable method. Availability and implementation Python code is made freely available: https://github.com/chandar-lab/amr-pred","url":"https://doi.org/10.64898/2026.06.28.734076","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.28.734076","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9705859/v1","name":"Endothelial Activation and Stress Index as a Prognostic Marker and Machine Learning Predictor of Mortality in Mechanically Ventilated ICU Patients: A Multicenter Retrospective Cohort Study","source":"preprints","abstract":"Abstract Background The Endothelial Activation and Stress Index (EASIX), derived from lactate dehydrogenase, serum creatinine, and platelet count, has been associated with adverse outcomes in hematological and cardiological populations, but its prognostic value in mechanically ventilated intensive care unit (ICU) patients remains unclear. Methods We conducted a multicenter retrospective cohort study using the MIMIC-IV database (primary cohort, n = 5,300) and the eICU Collaborative Research Database (external validation cohort, n = 243). The primary outcome was 28-day all-cause mortality; the secondary outcome was 90-day all-cause mortality. Cox proportional-hazards regression with restricted cubic splines was used to evaluate the association between log 2 -transformed EASIX (Log 2 EASIX) and mortality. Fifteen machine learning classifiers were trained on a consensus feature set derived from Boruta and LASSO selection; the optimal model was interpreted using SHapley Additive exPlanations (SHAP) and assessed for clinical utility by decision curve analysis (DCA). Results In the fully adjusted model, each one-unit increment in Log 2 EASIX was associated with a 25% higher hazard of 28-day mortality (HR 1.25, 95% CI 1.19–1.32) and a 21% higher hazard of 90-day mortality (HR 1.21, 95% CI 1.16–1.27). The dose-response relationship was predominantly log-linear, with no evidence of a threshold effect. CatBoost achieved the highest discriminative performance (internal AUC 0.758; external AUC 0.745), with Log 2 EASIX ranking third in global feature importance by SHAP analysis. DCA confirmed net clinical benefit over treat-all and treat-none strategies across a broad range of threshold probabilities. Conclusions Log 2 EASIX independently predicts short- and medium-term mortality in mechanically ventilated ICU patients and contributes meaningful prognostic information within an externally validated machine learning framework, supporting its potential role as a bedside risk-stratification tool.","url":"https://doi.org/10.21203/rs.3.rs-9705859/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9705859/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.17.26358320","name":"Machine learning models to improve targeting of blood culture testing","source":"europepmc","abstract":"Background Bloodstream infections are a major cause of mortality, yet the primary testing method, blood cultures, have low positivity ( Methods In this retrospective cohort study, we used routinely collected clinical and laboratory data available around culture collection from a large multi-site NHS trust (Oxford University Hospitals; Infections in Oxfordshire Research Database), between 1 January 2016 and 17 March 2025. All blood cultures taken from adults and children were included. XGBoost models were trained to predict pathogenic blood culture positivity using a temporal split (training before 1 January 2024; held-out test thereafter). External validation used emergency department data (between 1st May 2019 and 30th April 2024) from University College London Hospitals. An additional analysis examined blood culture reallocation towards the highest-risk untested admissions. Findings 294,064 cultures were included (positivity 5.6%). In the temporal hold-out test set (n=46,339), AUROC (Area Under the Receiver Operating Characteristic) was 0.853 (95% CI 0.846–0.860), rising to 0.876 in emergency department patients, and the model was well calibrated (slope 1.046). In external validation (n=37,326), AUROC was 0.847 (95% CI 0.839–0.856) with preserved calibration. In a simulated resource-neutral reallocation, replacing the 10,000 lowest-risk sent cultures with the highest-risk untested emergency admissions yielded 627 additional positive cultures (28.3% relative increase in yield). Performance was reduced when restricted to data available at the point of culture collection (AUROC 0.769, 95% CI 0.760–0.779). Interpretation An externally validated, well calibrated machine learning model built from broadly available, routinely collected data could improve blood culture yield without increasing testing volume, supporting resource-neutral diagnostic stewardship across NHS sites.","url":"https://doi.org/10.64898/2026.07.17.26358320","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.17.26358320","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-9767581/v1","name":"Machine learning for early prediction of 90-day mortality in ICU patients with HBV-related liver disease","source":"preprints","abstract":"Abstract Patients with HBV-related liver disease requiring intensive care are at high risk of short-term mortality, and early prognostic stratification using information available within the first 24 hours of ICU admission remains clinically important. This retrospective study developed and temporally validated machine learning models to predict mortality in ICU patients with HBV-related liver disease using the MIMIC-IV database. A total of 1,191 adult patients undergoing their first hospital admission and first ICU stay were included, among whom 438 patients died within 90 days. Patients admitted from 2008 to 2016 were assigned to the model development cohort, while those admitted from 2017 to 2019 were assigned to the temporal validation cohort. Elastic net logistic regression and XGBoost models were constructed using first-24-hour clinical variables, and their performance was compared with conventional severity scores, including MELD, SOFA, and OASIS. Model performance was assessed using the area under the receiver operating characteristic curve, area under the precision-recall curve, Brier score, calibration, decision curve analysis, and bootstrap 95% confidence intervals. In the temporal validation cohort, XGBoost achieved the best overall performance, with an AUROC of 0.792, AUPRC of 0.744, and Brier score of 0.187, while elastic net showed comparable discrimination with an AUROC of 0.786, AUPRC of 0.720, and Brier score of 0.195. Both machine learning models outperformed MELD, SOFA, and OASIS, which showed lower AUROC values of 0.684, 0.684, and 0.630, respectively. Decision curve analysis further demonstrated greater net benefit for the machine learning models across clinically relevant threshold ranges, with consistent findings in strict HBV and secondary-endpoint analyses. These findings suggest that machine learning models based on variables available within the first 24 hours of ICU admission may improve 90-day mortality risk stratification in ICU patients with HBV-related liver disease.","url":"https://doi.org/10.21203/rs.3.rs-9767581/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9767581/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9987430/v1","name":"Efficacy and safety of Rhodiola crenulata injection in the treatment of acute coronary syndrome: A meta-analysis with machine learning","source":"preprints","abstract":"Abstract Background: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular death and disability. Despite increasingly comprehensive guideline-directed therapy and the widespread use of contemporary revascularization, substantial unmet clinical needs persist. In China, Rhodiola crenulata injection (RCI) is widely used as an adjunct to standard Western medicine (WM) for ACS, yet its benefit-risk profile remains uncertain. Methods: RCTs up to July 2025 were retrieved from eight databases. ACS patients receiving RCI + WM or WM alone were included. Meta-analyses were performed using RevMan 5.4 and Stata 17.0, with TSA applied to evaluate information size and robustness. Risk of bias and evidence certainty were assessed using RoB 2 and GRADE, respectively. Five machine learning models (Random Forest, XGBoost, Lasso, Multilayer Perceptron, and a stacking model) were used to model time–effect relationships and optimal RCI treatment durations. Results: Forty-five RCTs (n = 4,217) were included. Compared to WM alone, RCI + WM resulted in a reduced incidence of MACE, angina attacks, duration of angina attacks, TC and LDL-C levels, improved total clinical efficacy, electrocardiographic efficacy and HDL-C levels. The incidence of adverse events was not statistically different. Machine learning predicted the optimal treatment durations of RCI as 10.08 days for UA and 14.39 days for AMI. Conclusion: In ACS, adding RCI as an adjunct to WM demonstrates efficacy in reducing MACE, improving clinical symptoms, myocardial ischemia, and lipid profiles, thereby providing additional clinical benefits.","url":"https://doi.org/10.21203/rs.3.rs-9987430/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9987430/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.06.26357357","name":"Explainable machine learning reveals the challenges of predicting new-onset motor complications in Parkinson’s disease","source":"preprints","abstract":"Predicting new-onset motor fluctuations and levodopa-induced dyskinesias (LID) is crucial for optimizing Parkinson’s disease management. To establish a transparent prognostic framework, we applied explainable machine learning to real-world, multicentric clinical data from 247 patients to forecast the 3-year onset of these complications. Evaluated strictly on complication-free patients, the models achieved moderate predictive power (LID MCC=0.28; fluctuations MCC=0.32). SHAP-based interpretability confirmed predictions aligned accurately with established clinical knowledge, driven primarily by levodopa duration and Levodopa Equivalent Daily Dose, with risk increasing significantly above a 300-400 mg threshold. Crucially, an ablation study revealed that excluding patients with pre-existing complications from training caused model sensitivity to collapse, demonstrating that the full spectrum of disease severity is essential for robust risk stratification. Ultimately, this rigorous methodological stress-test demonstrates that baseline clinical features alone yield limited absolute sensitivity, highlighting the necessity of integrating dynamic, longitudinal data to achieve clinical-grade individualized prediction.","url":"https://doi.org/10.64898/2026.07.06.26357357","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.06.26357357","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9802817/v1","name":"Development and validation of a machine learning-based model incorporating systemic inflammatory, nutritional, and coagulation parameters for predicting metastasis in lung cancer","source":"preprints","abstract":"Abstract Purpose This study aimed to develop and validate a novel machine learning-based model for predicting distant metastasis in lung cancer, uniquely incorporating multidimensional pretreatment indicators, namely markers of systemic inflammation, tumor status, coagulation function, and nutritional status. Methods This retrospective case–control study enrolled 408 treatment-naïve patients with newly diagnosed lung cancer at Guangzhou First People's Hospital between November 2023 and October 2024, comprising 202 patients with distant metastasis and 206 without. A total of 18 peripheral blood biomarkers—reflecting systemic inflammation, tumor burden, coagulation function, and nutritional status—along with six demographic and clinical characteristics were collected as candidate predictors. Candidate variables were initially screened using Univariate logistic regression. Subsequently, three complementary feature selection methods—LASSO regression, RFE, and the Boruta algorithm—were applied in parallel to identify the most robust predictive features. Nine machine learning algorithms were then trained to predict the presence of distant metastasis. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, F1 score, and 95% CI. The optimal algorithm was selected based on comprehensive performance metrics, and feature importance was interpreted using SHAP values. Results The AdaBoost algorithm demonstrated the best predictive performance for assessing distant metastasis risk in lung cancer, achieving an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.757–0.902), and was subsequently selected to construct the final prediction model. The mean AUC derived from 5-fold cross-validation was 0.91, further supporting the model's stability and robustness. The learning curve, decision curve analysis, and calibration curve collectively confirmed that the AdaBoost model exhibited optimal generalization ability, clinical utility, and calibration accuracy. Decision curve analysis demonstrated a favorable net benefit across a wide range of threshold probabilities, indicating strong clinical applicability. SHAP analysis revealed that the top 10 most influential predictors—CEA, SII, smoking history, CYFRA21-1, D-dimer, Fibrinogen, AGR, Pathological type, SIRI, and NSE—were highly consistent with the feature importance rankings obtained from the decision tree model. These indicators were identified as the most critical contributors to predicting lung cancer metastasis, underscoring their biological relevance and clinical significance. Conclusion This study successfully developed and validated a novel AdaBoost‑based predictive model for pretreatment assessment of distant metastasis risk in lung cancer. It represents a low‑cost, non‑invasive, and clinically actionable tool to facilitate early risk stratification and individualized decision‑making for lung cancer patients.","url":"https://doi.org/10.21203/rs.3.rs-9802817/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9802817/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.01.26356827","name":"Personalized planning of cardiac resynchronization therapy through integration of coronary sinus geometry, clinical data, digital twins, and machine learning: visualization, stratification, and optimization","source":"preprints","abstract":"Background Cardiac resynchronization therapy (CRT) fails in 30% of patients, often due to suboptimal left ventricular pacing site (LVPS) selection. Current practice lacks tools for pre-procedural, patient-specific LVPS optimization within the accessible coronary sinus (CS) tributaries. This study aimed to develop a digital twin and an explainable ML-based clinical decision support framework to address this issue. Methods Personalized 3D cardiac models incorporating ventricular anatomy, myocardial fibrosis, and CS anatomy were constructed from CT and LGE-MRI for 74 CRT candidates. Finite-element Eikonal simulations of biventricular pacing generated patient-specific electrophysiological features at candidate LVPS. A Machine Learning (ML) classifier was trained on a hybrid feature set of pre-procedural clinical variables and model-derived indices, validated by leave-one-out cross-validation. SHAP analysis provided a physiologically interpretable rationale for each prediction. The framework was applied to a pilot cohort of 19 patients with reconstructed 3D CS anatomy to generate a spatial likelihood map of CRT response across all clinically implantable pacing sites within each patient’s CS. Results The ML classifier outperformed the reference Feeny clinical calculator under LOO-CV (accuracy 0.78 vs 0.58; F1-score 0.75 vs 0.43), AUC=0.78, sensi-tivity=0.80, specificity=0.77. Bootstrap analysis yielded mean AUC=0.85 (95% CI 0.70–0.95). In the pilot CS cohort, the framework identified that 8 of 13 clinical non-responders had no accessible CS site predicted to yield a positive response, supporting redirection towards alternative pacing strategies. In the remaining 5, alternative implantable sites with high predicted response probability were identified. SHAP analysis confirmed that dominant predictors were patient-specific in their relative contributions, supporting individualized over heuristic-based LVPS selection. Conclusion This pilot study demonstrates the feasibility of a digital twin and explainable ML framework as a pre-procedural clinical decision support tool for CRT planning, stratifying patients and identifying optimal implantable sites with transparent anatomical rationale. Prospective validation and regulatory evaluation are required before clinical deployment.","url":"https://doi.org/10.64898/2026.07.01.26356827","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.01.26356827","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.06.18.26355972","name":"MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer’s Disease Neuroimaging Initiative","source":"preprints","abstract":"ABSTRACT Background Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to ARIA-H risk. We developed MCH-Guard, a multimodal machine-learning framework, to stratify MCH risk using ADNI data (N=813). Methods Nested models integrated clinical history, fluid biomarkers, and imaging to predict MCH presence, incidence, and stability. Results The comprehensive model detected baseline MCH with high accuracy (AUC 0.86). Notably, the “minimal” model (M1), utilizing only demographics and clinical history, achieved robust performance (AUC 0.82). Longitudinal models predicted time-to-onset (R 2 =0.68) and stratified four-year risk. Furthermore, we identified a transient vascular instability phenotype—where MCH status fluctuates— which was strongly predicted by hepatic factors. Conclusions MCH-Guard offers a flexible clinical decision-support tool for optimizing spontaneous MCH & ARIA surveillance. The strong performance of the clinical-only M1 model supports equitable risk assessment in resource-limited settings, while the characterization of vascular instability addresses a critical confounder in safety monitoring.","url":"https://doi.org/10.64898/2026.06.18.26355972","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.18.26355972","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.30.26359086","name":"Interpretable machine learning prediction of in-hospital mortality in ICU patients with cancer and sepsis using first-day data: Development using MIMIC-IV and external validation in eICU-CRD","source":"preprints","abstract":"Background: Critically ill patients with cancer and sepsis have high in-hospital mortality, but externally validated prediction models are limited. Objective: To develop and externally validate an interpretable machine learning framework using first-day intensive care data. Methods: We used MIMIC-IV version 3.1 for development and internal validation and eICU-CRD for external validation. Eligible adults had cancer, an intensive care unit stay of at least 24 hours, and met a prespecified operational sepsis definition. The prediction landmark was 24 hours after admission. The MIMIC-IV cohort included 3,729 stays (training, n = 2, 983; internal validation, n = 746). Same-admission diagnosis-derived variables were excluded, and 345 predictors were retained. Fourteen predictive models and a dummy baseline were evaluated. Frozen pipelines and training-derived thresholds were applied to eICU-CRD without refitting or recalibration. Results: Gradient boosting was selected as the primary model and achieved an internal AUROC of 0.8480 (95% CI, 0.8173-0.8755), AUPRC of 0.6984, and Brier score of 0.1346. Important predictors included Glasgow Coma Scale components, temperature, lactate dehydrogenase, age, respiratory rate, oxygen saturation, blood urea nitrogen, and serum lactate. In eICU-CRD (n = 611), gradient boosting achieved an AUROC of 0.7483 (95% CI, 0.7026-0.7919), AUPRC of 0.6243, and Brier score of 0.1709. Random forest had the highest external AUROC in secondary comparisons (0.7731). Conclusions: First-day data supported useful internal discrimination, but performance declined under locked external validation. Multicenter validation, recalibration, threshold assessment, and prospective evaluation are required before clinical implementation.","url":"https://doi.org/10.64898/2026.07.30.26359086","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.30.26359086","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.08.15.26360497","name":"Genomic subtypes inferred from clinical sequencing provide significant prognostic stratification in metastatic breast cancer","source":"preprints","abstract":"Purpose The 11 Integrative Cluster (IntClust) genomic subtypes of breast cancer have both prognostic and predictive value but require integrated DNA copy-number and gene expression profiling, which are not routinely used in clinical care. We tested whether IntClust could be inferred from clinical DNA targeted gene panel sequencing alone and whether the assignments stratify overall survival (OS) in a contemporary cohort. Methods A machine-learning model was trained on METABRIC data (N=1,980), externally validated on TCGA-BRCA data (N=1,066), and applied to DNA targeted gene panel testing data from 5,368 patients in MSK-CHORD. OS was analyzed by Kaplan-Meier and Cox-regression. Results IntClust assigned strongly stratified OS in both localized (P Conclusion IntClust can be inferred from routine clinical sequencing and resolves survival heterogeneity not captured by ER or HER2. IntClust stratification further reveals subtype-specific contexts for prognostic effects of the same mutation drivers, and for acquisition of ESR1 mutations. Highlights IntClust genomic subtypes can be inferred from routine clinical targeted DNA panel sequencing alone. Machine learning classifier trained on METABRIC, validated on TCGA, applied to 5,368 MSK-CHORD patients. Inferred IntClust strongly stratified overall survival in metastatic and localized breast cancer. Median OS in ER+ metastatic disease ranged 46–116 months; TNBC IC10 vs IC4ER− 28 vs 47 months. Prognostic impact of TP53, PIK3CA and GATA3 mutations, and ESR1 acquisition, is IntClust-dependent.","url":"https://doi.org/10.64898/2026.08.15.26360497","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.15.26360497","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.06.30.26356926","name":"Spatial transcriptomics and machine learning define exhaustion-like bone marrow T-cell islands associated with myeloma progression and clinical risk","source":"preprints","abstract":"Background Multiple myeloma (MM) progression is accompanied by remodeling of the bone marrow immune microenvironment. Local interactions among malignant plasma cells, stromal cells, myeloid cells, and immune cells not only support tumor cell survival, expansion, and immune escape, but are also closely associated with disease progression, therapeutic response, and clinical prognosis. Moreover, T cell exhaustion is a common T cells dysfunction in MM and limited efficacy of T cell-targeting therapies. However, the in situ organization and clinical significance of exhausted T cells in MM patients bone marrow remain insufficiently understood. Methods In this study, we analyzed bone marrow Xenium 5K spatial transcriptomics data from control (Ctrl), monoclonal gammopathy of undetermined significance (MGUS), smoldering myeloma (SM), and MM samples. After canonical multi-sample integration and celltype annotation, we used Gaussian mixture model (GMM)-based spatial partitioning, and multilayer perceptron (MLP) machine learning for systematic characterization the T cell microenvironment in MM bone marrow. Results Our results showed that exhaustion-like T cells increased during MM progression and formed spatially discrete T cell-enriched regions in the bone marrow, which we defined as exhaustion-like bone marrow T cell islands (eBM-TIs). These niches were mainly characterized by enhanced T cell-plasma cell communication associated with upregulated Galectin signaling. Pseudobulk analysis further showed enhanced IFN-related signaling in eBM-TIs, accompanied by upregulation of CXCR3 ligands such as CXCL9 and CXCL10, suggesting that the IFN-CXCL9/10 axis may contribute to T cell chemotaxis, maintenance of chronic inflammation, and formation of exhaustion-like states. By transferring spatial niche labels to scRNA-seq cohorts with available clinical staging information using MLP, we further found that the proportion of eBM-TI-like T cells was associated with higher disease risk and unfavorable prognostic outcomes. Conclusions In summary, this study identifies eBM-TIs as a spatial niche in the MM bone marrow. These niches represent an important immune unit linking chronic inflammation, T cell exhaustion, and clinical risk, and may serve as a potential biomarker of MM disease progression.","url":"https://doi.org/10.64898/2026.06.30.26356926","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.30.26356926","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9672899/v1","name":"An Explainable AI Framework Integrating Survival Analysis and Machine Learning for COX Inhibitor Evaluation","source":"preprints","abstract":"Abstract Even though enzymes such as cyclooxygenase (COX) are fundamental in the modulation of inflammation and pain, fragmented analytical methodologies and reduced model interpretability remain a bottleneck to predictive assessment of COX inhibitors. To fill this gap, we developed an integrated multi model framework that integrates modern machine learning algorithms with classical survival analysis to increase the predictive power and transparency of preclinical drug evaluation. They were systematically benchmarked, with a range of six models, which are logistic regression, random forest, support vector machine and Cox proportion hazard, on a carefully selected dataset of recently synthesized benzimidazole triazole analogs, using a nested 5-fold cross-validation. The performance of the model was measured using the concordance index (C-index) and area under the ROC (AUC-ROC). The Cox model was highly interpretable with the stable hazard-ratio estimation (C-index = 0.78), whereas the random forest had the highest discrimination (AUC = 0.89; accuracy = 0.85). These findings demonstrate that predictive accuracy and clinical intuition are improved when interpretable statistical models are used alongside flexible machine learning algorithms. The proposed framework provides an iterative and explainable AI pipeline capable of accelerating translational research and rational drug discovery in biomedical scenarios with less granularity.","url":"https://doi.org/10.21203/rs.3.rs-9672899/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9672899/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.14.26358030","name":"Life-Stage Heterogeneity in the Mental Health Treatment Gap: An Unsupervised Machine Learning Profiling of Symptomatic US Adults","source":"preprints","abstract":"Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specific barriers to care. This study uses an unsupervised machine learning pipeline to identify distinct socio-behavioural and biological sub-populations within the untreated cohort to guide targeted public health interventions. Methods Data were pooled from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) cycles ( N = 11,848 total adult respondents). A symptomatic cohort of 3,075 individuals experiencing daily or weekly anxiety or depression symptoms was isolated, excluding severe liver pathology outliers (GGT ≥ 80 U/L). A 22-feature matrix combining continuous clinical biomarkers (systolic blood pressure, waist circumference, HbA1c) and categorical social variables was projected using Factor Analysis of Mixed Data (FAMD). Latent sub-populations were identified via Gaussian Mixture Modelling (GMM), optimized by the Bayesian Information Criterion (BIC). Results The broad baseline population revealed a substantial mental health burden, with 30.4% reporting active psychiatric symptoms, of whom 71.6% were entirely unmedicated. The GMM pipeline successfully isolated three distinct sub-populations ( k = 3) separated by age, clinical strain, and treatment rates: Cluster 0 (Mature Adults, mean age 55.03): high psychiatric severity (34.1% severe untreated), central obesity, and hypertensive strain (135.82 mmHg), with 64.1% untreated despite frequent primary care contact; Cluster 1 (Working Professionals, mean age 38.38): highly educated, female-dominated (70.5%), with 77.7% untreated driven by moderate distress; Cluster 2 (Emerging Youth, mean age 18.49): a highly vulnerable late-adolescent group with a staggering 90.2% untreated rate. Conclusion The unmedicated symptomatic population is highly diverse and segmented by life stage. These profiles show that the treatment gap is driven by age-specific barriers, specifically workforce-age symptom masking and late-adolescent developmental transitions. Closing this deficit requires shifting from uniform public health approaches toward targeted interventions, such as digital peer support networks for youth and integrated primary care screenings for older adults.","url":"https://doi.org/10.64898/2026.07.14.26358030","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.14.26358030","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10301019/v1","name":"Development and Bidirectional Cross-Database Validation of a Machine Learning Model for Predicting Invasive Fungal Infection in Critically Ill Patients","source":"preprints","abstract":"Abstract Background Invasive fungal infection (IFI) is a life-threatening complication in critically ill patients, yet externally validated, generalizable machine learning (ML) tools for early risk stratification remain scarce. We developed and rigorously validated an interpretable ML model for IFI prediction across two independent ICU databases. Methods This retrospective cohort study used MIMIC-IV (94,458 ICU admissions; 295 IFI cases, 0.31%) for model development and the eICU Collaborative Research Database (159,204 patients across 208 hospitals; 767 IFI cases, 0.48%) for external validation. IFI was defined by deep-site culture positivity in MIMIC-IV and by ICD codes plus antifungal therapy in eICU. We applied least absolute shrinkage and selection operator (LASSO) regression for feature selection, propensity score matching with SMOTE to address class imbalance, and XGBoost optimized via Optuna as the primary classifier. SHAP values provided model interpretability. Bidirectional cross-database validation characterized model transferability under reciprocal distribution shift. Results The MIMIC-IV model achieved an AUC of 0.876 (95% CI: 0.852–0.899), with SOFA score, ICU length of stay, age, white blood cell count, and platelet count ranking as top predictors. Striking directional asymmetry emerged in cross-database validation: the multi-center eICU model generalized successfully to MIMIC-IV (AUC 0.829), whereas the reverse transfer failed catastrophically (AUC 0.401). Key barriers included scoring system incompatibility (SOFA vs. APACHE), divergent feature availability, and IFI definition heterogeneity. Decision curve analysis demonstrated net clinical benefit at probability thresholds of 0.5–5%. Conclusions Multi-center training data substantially enhances cross-institutional generalizability of ML prediction models, though systematic feature harmonization and prospective validation remain indispensable. These findings provide actionable guidance for developing robust, deployable IFI prediction tools across diverse ICU settings.","url":"https://doi.org/10.21203/rs.3.rs-10301019/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10301019/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9815563/v1","name":"Development and Validation of a Machine Learning-Based Clinical Prediction Model for Liver Metastasis in Stage III–IV Non-Small Cell Lung Cancer","source":"preprints","abstract":"Abstract Liver metastasis is among the major determinants of prognosis in non-small cell lung cancer (NSCLC), and early identification of high-risk patients has direct implications for clinical management. We retrospectively enrolled 849 patients with stage III–IV NSCLC and split them 8:2 into a training set (n = 679) and a validation set (n = 170). Feature selection followed a Venn intersection approach combining Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest (RF), and decision tree (DT) algorithms, which identified six shared predictors: lactate dehydrogenase (LDH), multi-organ metastasis, hemoglobin (HGB), thrombin time (TT), platelet count (PLT), and alkaline phosphatase (ALP). Ten machine learning (ML) classifiers were trained on these variables, and two with complementary strengths were assembled into a Stacking ensemble. On the validation set, the Stacking model outperformed all candidate models (AUC = 0.852, 95% CI: 0.769–0.921; AUPRC = 0.565; sensitivity = 76.0%; NPV = 0.952), with well-calibrated probability estimates confirmed by calibration curves and decision curve analysis. SHAP analysis identified elevated LDH (roughly ≥ 250 U/L) and multi-organ metastasis as the two strongest contributors to individual predictions, while lower HGB, shortened TT, and abnormal PLT or ALP each added smaller but independent predictive weight. These findings demonstrate that a Stacking ML approach can predict liver metastasis risk in stage III–IV NSCLC with strong discrimination and reliable calibration. An online calculator has been deployed for point-of-care use; external validation in prospective cohorts is needed before broader clinical application.","url":"https://doi.org/10.21203/rs.3.rs-9815563/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9815563/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.06.28.26356787","name":"Prediction of post-operative delirium with machine learning in abdominal surgery with comorbidity indices and laboratory values","source":"preprints","abstract":"Background Postoperative delirium (POD) is a complication associated with most types of surgery, and is associated with a number of detrimental effects. Therefore, it is of interest to determine which patients may be at higher risk of POD so that mitigating steps may be taken. We sought to determine whether POD can be accurately predicted with common machine learning (ML) models. Methods Using the Medical Information Mart for Intensive Care (MIMIC)-IV database, we identified 8026 abdominal surgery procedures across 7215 adult patients. Using demographic information, such as age, type of surgery, sex; as well as commonly measured laboratory values (such as electrolytes and blood counts) and comorbidity indices, we determined to what extend common ML models, such as random forests, support vector machines, extreme gradient boosted machines, and neural networks, could predict POD. Results Random forests outperformed logistic regression, support vector machines, extreme gradient boosted machines, and neural networks, with respect to individual t -tests. The random forest model had a sensitivity of 73.11, a specificity of 71.14, and an area under the receiver operator characteristic curve of 0.800. Age, comorbidity indices, gender, and alcohol use carried significant predictive weight in this cohort. Conclusions Machine learning models are effective predictors of postoperative delirium, although further work is required to increase clinical utility of such tools. Markers of inflammation, comorbidity indices, and alcohol use are important predictive features alongside better-known features such as age.","url":"https://doi.org/10.64898/2026.06.28.26356787","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.28.26356787","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.14293/pr2199.003706.v1","name":"Machine Learning Approaches for Intelligent Dental Implant Planning and Crown Morphology Prediction","source":"preprints","abstract":"Dental implant therapy requires precise planning to ensure functional performance, aesthetic outcomes, and long-term clinical success. Conventional implant planning and crown design often rely on clinician experience, radiographic interpretation, and manual measurements, which may introduce variability and increase treatment time. Recent advances in machine learning have created opportunities for data-driven decision support systems that can improve diagnostic accuracy and treatment predictability. This paper examines the application of machine learning techniques for intelligent dental implant planning and crown morphology prediction. Various algorithms, including artificial neural networks, support vector machines, random forests, convolutional neural networks, and deep learning architectures, are explored to analyze cone-beam computed tomography images, assess bone quality, identify optimal implant positions, and predict patient-specific crown morphology. The study highlights how these approaches can integrate anatomical, functional, and aesthetic parameters to generate personalized treatment recommendations. Furthermore, the paper discusses current challenges related to data availability, model interpretability, clinical validation, and integration into routine dental workflows. The findings indicate that machine learning-assisted systems can enhance treatment efficiency, reduce planning errors, and support more consistent restorative outcomes. As digital dentistry continues to evolve, intelligent predictive models are expected to play an increasingly important role in achieving accurate, patient-centered implant rehabilitation.","url":"https://doi.org/10.14293/pr2199.003706.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.003706.v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.22.740145","name":"Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment","source":"preprints","abstract":"BACKGROUND Genetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODS We applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72 , GRN , or MAPT mutations. cTI produced an individual “genetic FTD progression score” (0–1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain–behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTS Genetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p CONCLUSIONS Machine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.","url":"https://doi.org/10.64898/2026.07.22.740145","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.22.740145","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.05.27.26354194","name":"Development and Validation of a Machine Learning Model to Predict Prognosis in Patients with Advanced Head and Neck Cancer","source":"preprints","abstract":"Importance Prognostic tools beyond staging are needed to guide treatment and counseling in head and neck squamous cell carcinoma (HNSCC). Objective To develop and externally validate a machine learning model predicting survival in advanced HNSCC using routinely collected clinical and biomarker data. Design, Setting, and Participants Retrospective, multi-institutional cohort study including 2,385 patients with stage III–IV HNSCC diagnosed from 2012–2022 in the University of California Health Data Warehouse (UCHDW). Patients were randomly split into training (n = 1,908) and test (n = 477) sets. Partial external validation used 7,749 patients from the Surveillance, Epidemiology, and End Results (SEER) registry (2010–2020). Exposures Demographic, tumor, treatment, comorbidity, and biomarker variables recorded at or before diagnosis. Main Outcomes and Measures The primary outcome was all-cause mortality within 70 months. Cox proportional hazards models included all predictors. Discrimination was assessed with Harrell’s concordance index (C-index), calibration with predicted vs observed survival, and stratification with Kaplan–Meier curves. A Random Survival Forest (RSF) was trained for benchmarking and interpretability using Shapley Additive exPlanations (SHAP). Results Among 2,385 patients in UCHDW (median age, 63 years; 29.0% mortality), the Cox model achieved a C-index of 0.735 in the internal test set. Risk quartiles showed clear separation on Kaplan–Meier curves (log-rank p Conclusions and Relevance A machine learning model using routine clinical and biomarker data demonstrated good prognostic performance in advanced HNSCC, with partial external validation. Such approaches may support individualized survival estimates, risk stratification, and treatment discussions, but broader validation is required before clinical adoption. Key Points Question Can a machine learning model accurately predict prognosis in patients with head and neck cancer utilizing only clinical features and biomarkers? Finding In a study of 2,385 patients with advanced HNSCC from the UC Health Data Warehouse, a Cox model incorporating demographics, staging, comorbidities, treatment, and clinical biomarkers showed good internal discrimination (C-index 0.735). In partial external validation using 7,749 SEER patients with only demographic, staging, subsite, and treatment variables, performance remained acceptable (C-index 0.688) with preserved risk stratification. Meaning Machine learning using clinical data can predict prognosis in head and neck cancer; this tool could be used to provide individualized survival estimates and help guide treatment discussions and risk stratification at diagnosis.","url":"https://doi.org/10.64898/2026.05.27.26354194","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.05.27.26354194","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-10073946/v1","name":"Association Between the Hemoglobin-to-Red Cell Distribution Width Ratio and Risk of Type 2 Diabetic Kidney Disease: A Machine Learning Study","source":"preprints","abstract":"Abstract Background Hemoglobin (Hb) and red cell distribution width (RDW) are well-established risk factors for the onset and progression of diabetic kidney disease (DKD). Consequently, this study investigated the clinical association between the hemoglobin-to-RDW ratio (HRR) and the risk of incident DKD in patients with type 2 diabetes mellitus (T2DM). Methods This study utilized the MIMIC-IV3.2 database and,through rigorous inclusion and exclusion criteria, ultimately identified 8,201 patients diagnosed with T2DM during their ICU stay. Using random sampling,the dataset was divided into a training set (5,741 cases) and a validation set (2,460 cases) in a 70:30 ratio. Through receiver operating characteristic (ROC) curve analysis, the consistency of the predictive performance of HRR, Hb, and RDW for assessing DKD risk was compared between the training and validation sets. Restricted cubic splines (RCS) were used to explore nonlinear dose-response relationships and determine the optimal risk thresholds for HRR in both the training and validation sets. Finally, by constructing various machine learning models and integrating multi-indicator information, we comprehensively validated the clinical significance of HRR in assessing the risk of DKD. Results We confirmed through multivariate logistic regression analysis that elevated HRR is significantly negatively correlated with the risk of DKD (P","url":"https://doi.org/10.21203/rs.3.rs-10073946/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10073946/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9590078/v1","name":"The Analysis of Sepsis Vascular Response Index Based on Machine Learning","source":"preprints","abstract":"Abstract Purpose: This study aims to explore the clinical value of monitoring the vascular response index and other hemodynamic parameters using the Pulse Indicator Continuous Cardiac Output (PICCO) monitoring device in the treatment of septic shock patients. It also aims to examine its impact on treatment plans for patients with different hemodynamic states. Based on the results, individualized treatment recommendations for septic shock patients will be proposed. Methods: Clinical data from septic patients admitted to the intensive care unit of the Second Affiliated Hospital of Guilin Medical University between December 2017 and December 2022 were collected. The vascular response index of septic patients was analyzed, with related hemodynamic indicators, cardiovascular response data, and other parameters related to vascular function extracted from the clinical records. The vascular response index was calculated and formed. Missing values were imputed using the mean imputation method, and outliers were handled to ensure data quality. All data were standardized to make it suitable for training machine learning models. The gradient boosting tree method was used to analyze the vascular response index. Additionally, the Optuna library was used for hyperparameter optimization of the gradient boosting model, selecting the best combination of hyperparameters, including tree depth, learning rate, regularization parameters, and subsample ratios, to improve the model’s predictive accuracy. Finally, five-fold cross-validation was used to evaluate the model’s performance, ensuring its stability and generalization ability through multiple training and validation iterations. The model’s classification performance was evaluated using accuracy, ROC curve, and the Area Under the Curve (AUC). Results: (1) A total of 126 adult septic shock patients were included in this study, with 89 male patients and 37 female patients, aged 18-90 years, with an average age of (60.61 ± 15.07). Among them, 44 patients survived, and 82 patients died, with a 28-day survival rate of 34.92%. (2) The gradient boosting tree method was used to analyze the characteristics of sepsis. The study found that MAP (mean arterial pressure), SVV (stroke volume variation), VIS (visual index), and ELWI (extracellular lung water index) played a key role in predicting the vascular response of septic patients and may be the primary physiological indicators affecting vascular response. The ROC curve showed that the model performed well in the classification process, with an AUC value of 0.90, indicating high discriminative ability, effectively distinguishing the vascular response status of septic patients. An AUC close to 1 indicates excellent predictive performance, accurately distinguishing patients with different response types. (3) SHAP value analysis indicated that MAP, SVV, VIS, and ELWI significantly contributed to the prediction of vascular response in septic shock patients, with hemodynamic indicators like MAP and SVV having a crucial impact on the vascular response status of these patients. (4) The study compared the gradient boosting tree model with SVM and LR machine learning methods. The experimental comparison showed that the gradient boosting tree method provided better results in feature analysis and ROC curves compared to other models. Conclusion: (1) The higher the APACHE II score, SOFA score, SVV, and VIS value in septic shock patients, the higher the mortality rate; the lower the lactate clearance rate at 72 hours, the higher the mortality rate. (2) The vascular response index (VRI) is of significant clinical value in predicting the prognosis of septic shock patients. A lower VRI level may indicate a higher mortality rate for septic shock patients. (3) When the vascular response index (VRI) is","url":"https://doi.org/10.21203/rs.3.rs-9590078/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9590078/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-8571942/v1","name":"Machine Learning–Derived Cardiometabolic Risk Burden Identified from Health Checkup Data in a Japanese Population","source":"preprints","abstract":"Abstract Background Conventional cardiometabolic risk classifications rely on predefined diagnostic thresholds and often fail to capture the continuous, multifactorial, and heterogeneous nature of risk in the general population. In particular, individuals with similar diagnostic profiles may exhibit markedly different underlying risk patterns that are not readily apparent using traditional frameworks. This study aimed to derive and interpret a machine learning–based cardiometabolic risk burden using routinely collected health checkup data. Methods This cross-sectional study included 17,567 adults who underwent comprehensive health checkups in Japan. A machine learning–derived cardiometabolic risk burden was defined as a multifactorial risk phenotype identified through supervised learning, integrating major cardiometabolic domains including blood pressure, lipid abnormalities, glycemic status, obesity, smoking, and treatment-related information. Logistic regression, random forest, and extreme gradient boosting (XGBoost) models were developed. Model performance was evaluated using discrimination, calibration, and decision curve analysis. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results The machine learning model identified a cardiometabolic risk burden driven by the combined and nonlinear effects of adiposity-related measures, glycemic status, blood pressure, lipid-related parameters, and inflammatory markers. Explainable modeling revealed substantial heterogeneity in individual risk profiles, even among participants with similar conventional risk factor levels, highlighting patterns that were not captured by threshold-based diagnostic criteria. The relative importance and directionality of predictors varied across individuals, underscoring the multifactorial and personalized nature of cardiometabolic risk. Conclusions Using routinely collected health checkup data, we identified a machine learning–derived cardiometabolic risk burden characterized by heterogeneous and nonlinear contributions of multiple clinical domains. Explainable modeling clarified how distinct combinations of adiposity, metabolic, hemodynamic, lipid, and inflammatory factors shaped individual risk profiles beyond conventional diagnostic frameworks. This integrated, data-driven approach may support more individualized cardiometabolic risk stratification and decision-making in population-based preventive settings. Background Cardiometabolic risk reflects the cumulative and interacting effects of metabolic abnormalities, lifestyle factors, and treatment status. Conventional risk classifications, such as metabolic syndrome, rely on predefined thresholds and may insufficiently capture the continuous and heterogeneous nature of cardiometabolic vulnerability, particularly in East Asian populations. We aimed to develop and interpret a machine learning–derived cardiometabolic risk burden using health checkup data. Methods This cross-sectional study included 17,567 adults who underwent comprehensive health checkups in Japan. A machine learning–derived cardiometabolic risk burden was defined as a multifactorial risk phenotype identified through supervised learning, integrating major cardiometabolic domains including blood pressure, lipid abnormalities, glycemic status, obesity, smoking, and treatment-related information. Logistic regression, random forest, and extreme gradient boosting (XGBoost) models were developed. Model performance was evaluated using discrimination, calibration, and decision curve analysis. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results All models demonstrated robust discrimination, acceptable calibration, and potential clinical utility for identifying individuals with a high cardiometabolic risk burden, with the XGBoost model achieving the highest overall performance. SHAP analyses revealed nonlinear and heterogeneous contributions of key risk factors, including adiposity-related measures, glycemic indices, blood pressure, smoking status, and treatment-related variables. Interaction analyses further showed that the influence of individual risk factors varied across metabolic contexts, revealing complex risk patterns not captured by conventional threshold-based classifications. Conclusions A machine learning–derived, multifactorial cardiometabolic risk burden can be effectively identified using standard health checkup data and meaningfully interpreted through explainable modeling techniques. By capturing nonlinear and heterogeneous risk patterns, this integrated, data-driven approach provides insights beyond conventional diagnostic frameworks and may support more individualized cardiometabolic risk stratification in population-based preventive medicine.","url":"https://doi.org/10.21203/rs.3.rs-8571942/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8571942/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.05.26357337","name":"External Validation of Machine Learning Models for Traumatic Brain Injury: Performance, Efficiency, and Carbon Imprint","source":"preprints","abstract":"ABSTRACT Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and energy consumption, yet simpler models may offer comparable clinical benefit with lower barriers to deployment. This study compares predictive performance, computational efficiency, carbon footprint, and real-world feasibility of resource-efficient (“pauci-parameter”) versus data-intensive (“multiparameter”) ML models for predicting TBI patient care pathways and outcomes. Methods External validation study in a level 1 trauma center (n=534 adult TBI patients with GCS Results Multiparameter models showed superior performance but did not consistently translate to better clinical utility. PREHOSP (pauci-parameter) showed comparable performance to complex models for most outcomes. The best-performing multiparameter model (MULTI-PRE) required 100-fold longer inference time and 10-fold higher carbon emissions per prediction versus simple models, while net clinical benefit was nearly identical (0.06 vs 0.05). Models using only prehospital data demonstrated greater generalizability and lower deployment barriers. Interpretation Computational complexity and resource intensity should factor equally with predictive performance in clinical AI deployment decisions. For sustainable digital health implementation—especially in resource-limited settings—simpler models with comparable clinical benefit may enable broader access while reducing environmental and financial costs. Funding Fondation Gueules Cassées, Grant 27-2023 AUTHOR SUMMARY Why Was This Study Done? Artificial intelligence (AI) models are increasingly used in hospitals to help doctors predict which brain injury patients need intensive care and what their outcomes might be. However, many published models are very complex. They use multiple variables and require expensive computer systems to run. Hospitals often struggle to implement these complex models because they require operationalisation barriers. – Multiple data systems that don’t easily communicate – Expensive computer infrastructure – Highly trained technical staff – Energy-intensive processing This raises the question whether complex models actually work better than simpler ones? If simple models work just as well, they could be used in many more hospitals especially in lower-resource countries where complex systems aren’t available. What Did the Researchers Do? The team compared seven different Machine and Deep Learning models for predicting brain injury patient needs and outcomes in 534 patients treated at a major trauma center in France. They tested: – Two simple models using only routine prehospital data (vital signs, Glasgow Coma Scale) – Five complex models using 40+ variables from multiple hospital computer systems plus CT scan segmentation analysis They compared the models’ accuracy, how long they took to run, how much electricity they used (carbon footprint), and how easy they would be to implement in different types of hospitals. What Did They Find? The simple models worked almost as well as the complex ones. For example: – Simple model (PREHOSP): Predicted mortality with reasonable accuracy – Complex model (MULTI-PRE): Predicted mortality slightly better, but required 100 times more processing time and 10 times more electricity When measured by “clinical benefit”, the number of correct treatment decisions made per 100 patients, both simple and complex models performed similarly (0.05 vs 0.06 additional correct decisions). The complex model’s training generated as much greenhouse gas as a clinical CT scan. For hospitals committed to environmental sustainability, this matters. Why Is This Important? This study challenges the common assumption that more complex models perform better. The findings have practical implications: For well-resourced hospitals: Complex models may offer only modest benefit that may not justify their cost and complexity. For hospitals with limited budgets: Simple models may provide comparable accuracy without expensive infrastructure. For low-income countries: Simple models using prehospital data may be deployed immediately in emergency medical services, even without integrated hospital computer systems. For environmental sustainability: Simpler AI models consume far less energy, an important as hospitals worldwide commit to net-zero emissions and will implement energy intense decision support tools. What Are the Clinical Implications? The authors recommend: Asses clinical performance, operational barriers and carbon imprint when comparing simple and complex models before implementation and deployment of decision support tools Reserve complex models for specialized high-resource trauma centers where infrastructure supports them Validate simple models in diverse care settings (ambulance services, regional hospitals, low-income countries) to confirm they work everywhere What Comes Next? The next step is testing the simple model in real-world settings across different countries and hospital types to prove it works reliably everywhere.","url":"https://doi.org/10.64898/2026.07.05.26357337","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.05.26357337","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9964712/v1","name":"A Pilot Study of an Interpretable Machine Learning Model for Fetal Birth Weight Estimation: Standardizing Accuracy Across Sonographer Experience","source":"preprints","abstract":"Abstract Background Accurate prediction of fetal birth weight is essential for antenatal management; however, traditional formulas demonstrate limited precision and are susceptible to label leakage. This study aimed to develop a prediction model based on 30 leakage-free features and to evaluate its clinical classification performance and standardization effect across sonographers with varying experience levels. Methods A total of 1,245 singleton pregnancies were retrospectively enrolled from a single center. Thirty leakage-free features were derived from raw clinical and ultrasonographic data. Ridge regression was employed as the primary modeling approach and compared against six alternative algorithms. Model performance was assessed through 5-fold stratified cross-validation, bootstrap resampling, and SHAP interpretability analysis. A P10/P90 combined with fundal height classification strategy was designed and evaluated. The standardization effect was quantified by comparing prediction errors between the 30-feature machine learning model and the Hadlock 4 formula across junior and senior sonographers. Results The 30-feature Ridge model (α = 1.0) achieved a 5-fold cross-validation mean absolute error (MAE) of 185.41 g and a test-set MAE of 188.74 g, outperforming all tree-based models. The three most important features were estimated fetal volume, gestational weeks at ultrasound, and the gestational weeks × abdominal circumference interaction term. The P10/P90 combined with fundal height > 36 cm classification strategy achieved 100% recall for low-birth-weight infants (LBWI) and 57.1% recall for fetal macrosomia. Compared with the Hadlock 4 formula, the model reduced MAE by 11.3% for junior sonographers and 10.1% for senior sonographers, leading to consistent accuracy improvements across both experience levels. These findings require prospective multicenter validation before clinical implementation. Conclusions The 30-feature Ridge regression model achieved robust fetal weight prediction without label leakage. The P10/P90 combined with fundal height strategy demonstrated favorable clinical safety. The model provided uniform, interpretable, and potentially deployable accuracy enhancement for sonographers across experience levels, suggesting promise as an adjunctive tool for antenatal assessment pending external validation.","url":"https://doi.org/10.21203/rs.3.rs-9964712/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9964712/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.21203/rs.3.rs-9519467/v1","name":"Identifying cognitive impairment in older adults using machine learning on combined fNIRS and motion data during an upper extremity dual task function","source":"preprints","abstract":"Abstract It is critical that dementia clinical interventions begin early in the disease progression to be effective. Similar clinical manifestations may be observed in both cognitively healthy older adults and older adults with early-stage cognitive impairment, posing a challenge for early disease identification. This study explored classification models using a combination of motor- (gyroscope) and brain- (functional near infrared spectroscopy (fNIRS)) based features for potential use in screening cognitive impairment in older adults. Cognitively normal older adults (CNOA, n = 43; age = 75.47 ± 7.15) and cognitively impaired older adults (CIOA, n = 32; age = 75.90 ± 7.48) completed a 3-minute resting period followed by 3-minute upper extremity dual task function (UEF) involving simultaneous serial subtraction and elbow flexion. The selected features included motor variability and fNIRS anterior prefrontal cortex connectivity outcomes. Logistic regression, support vector machine (SVM), and bootstrap aggregated decision trees predicted the cognitive classification of participants. Cross-validation results suggest SVM models had superior performance with an average accuracy of 76%, Receiver Operating Characteristic - Area Under Curve (ROC-AUC) of 0.86, and F1 score of 69. When used with classification algorithms, the UEF dual task may offer an objective technique for early dementia identification.","url":"https://doi.org/10.21203/rs.3.rs-9519467/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9519467/v1","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"doi:10.64898/2026.07.27.741090","name":"CARDIAX-NNFE A Cardiac Mechanics SciML Framework","source":"preprints","abstract":"One goal of Scientific Machine Learning (SciML) is to advance traditional scientific computing frameworks with modern machine learning tools. This includes extending established methods, such as the finite element method, with cardiac function applications due to their complexity and need for very rapid execution times for real time clinical use. In this work, we present an advanced form of the Neural Network Finite Element (NNFE) method specialized for cardiac simulations, termed CARDIAX-NNFE . The NNFE method learns the parameter-to-displacement field map by training over the residual of the hyperelastic material PDE, using the domain represented by finite elements. The implementation is developed in Python using JAX to leverage its automatic differentiation, highly parallel GPU, and JIT-compilation capabilities. To demonstrate CARDIAX-NNFE effectiveness, we trained full cardiac pressure-volume responses using a simplified heart model, spanning the entire cardiac physiological functional range. Results indicated the ability to simulate a family of pressure-volume solutions with average nodal positional error of 0.023 mm and maximal error of 0.054 mm, with a single complete PV loop evaluated in 0.002 seconds. The CARDIAX-NNFE software platform thus provides for a robust platform for cardiac functional simulations. Moreover, it provides the structure for residual-based SciML methods, which can apply to a variety of physics-based biomedical problems that require high execution speed for clinical applications.","url":"https://doi.org/10.64898/2026.07.27.741090","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.27.741090","addedAt":"2026-09-01T01:47:57.554Z","updatedAt":"2026-09-01T01:48:02.960Z"},{"id":"oa:W4380538374","name":"Bias in AI-based models for medical applications: challenges and mitigation strategies","source":"openalex","abstract":"Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI applications hold promise as tools to predict surgical outcomes, assess technical skills, or guide surgeons intraoperatively via computer vision. On the other hand, AI systems can also suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation. Bias particularly impacts disadvantaged populations, which can be subject to algorithmic predictions that are less accurate or underestimate the need for care. Thus, strategies for detecting and mitigating bias are pivotal for creating AI technology that is generalizable and fair. Here, we discuss a recent study that developed a new strategy to mitigate bias in surgical AI systems.","url":"https://doi.org/10.1038/s41746-023-00858-z","authors":["Mirja Mittermaier","Marium Raza","Joseph C. Kvedar"],"tags":["Disadvantaged","Socioeconomic status","Health care","Sexual orientation","Prejudice (legal term)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-13","doi":"https://doi.org/10.1038/s41746-023-00858-z","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W3039300052","name":"Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis","source":"openalex","abstract":"This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis. First, vibration signals are transformed into images by short-time Fourier transform (STFT). A CNN is applied as classification model, and Gradient class activation mapping (Grad-CAM) is utilized to generate the attention of model. By analyzing the attentions, the explanation of classification models for vibration signals analysis can be carried out. Finally, the verifications of attention are introduced by neural networks, adaptive network-based fuzzy inference system (ANFIS), and decision trees to demonstrate the proposed results. By the proposed methodology, the explanation of model using highlighted attentions is carried out.","url":"https://doi.org/10.1109/access.2020.3006491","authors":["Han-Yun Chen","Ching‐Hung Lee"],"tags":["Computer science","Artificial intelligence","Short-time Fourier transform","Vibration","Artificial neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1109/access.2020.3006491","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W2793079232","name":"Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study","source":"openalex","abstract":"The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.","url":"https://doi.org/10.1038/s41598-018-22612-2","authors":["Stuart Keel","Pei Ying Lee","Jane Scheetz","Zhixi Li","Mark A. Kotowicz","Richard J. MacIsaac","Mingguang He"],"tags":["Medicine","Diabetic retinopathy","Grading (engineering)","Referral","Outpatient clinic"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-03-06","doi":"https://doi.org/10.1038/s41598-018-22612-2","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4223984071","name":"Medical Students’ Perceptions towards Digitization and Artificial Intelligence: A Mixed-Methods Study","source":"openalex","abstract":"Digital technologies in health care, including artificial intelligence (AI) and robotics, constantly increase. The aim of this study was to explore attitudes of 2020 medical students’ generation towards various aspects of eHealth technologies with the focus on AI using an exploratory sequential mixed-method analysis. Data from semi-structured interviews with 28 students from five medical faculties were used to construct an online survey send to about 80,000 medical students in Germany. Most students expressed positive attitudes towards digital applications in medicine. Students with a problem-based curriculum (PBC) in contrast to those with a science-based curriculum (SBC) and male undergraduate students think that AI solutions result in better diagnosis than those from physicians (p < 0.001). Male undergraduate students had the most positive view of AI (p < 0.002). Around 38% of the students felt ill-prepared and could not answer AI-related questions because digitization in medicine and AI are not a formal part of the medical curriculum. AI rating regarding the usefulness in diagnostics differed significantly between groups. Higher emphasis in medical curriculum of digital solutions in patient care is postulated.","url":"https://doi.org/10.3390/healthcare10040723","authors":["Adrian Gillissen","Tonja Kochanek","Michaela Zupanic","Jan P. Ehlers"],"tags":["Medical education","Curriculum","Digitization","Focus group","Construct (python library)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-04-13","doi":"https://doi.org/10.3390/healthcare10040723","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W2963672486","name":"Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines","source":"openalex","abstract":"This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI) techniques. We first analyze the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu, and the curriculum. Then, we transform the relationship model into the perception-planning-action model to explore what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting a human-robot teaching-learning process and AI-assisted educational techniques, which illustrates the design guidelines and future research perspectives in intelligent tutoring robots.","url":"https://doi.org/10.3390/app9102078","authors":["Jinyu Yang","Bo Zhang"],"tags":["Computer science","Status quo","Artificial intelligence","Robot","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-05-20","doi":"https://doi.org/10.3390/app9102078","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4213445714","name":"Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security","source":"openalex","abstract":"Abstract The recent years have garnered huge attention towards the Internet of Things (IoT) because it enables its consumers to improve their lifestyles and professionally keep up with the technological advancements in the cyber-physical world. The IoT edge devices are heterogeneous in terms of the technology they are built on and the storage file formats used. These devices require highly secure modes of mutual authentication to authenticate each other before actually sending the data. Mutual authentication is a very important aspect of peer-to-peer communication. Secure session keys enable these resource-constrained devices to authenticate each other. After successful authentication, a device can be authorized and can be granted access to shared resources. The need for validating a device requesting data transfer to avoid data privacy breaches that may compromise confidentiality and integrity. Blockchain and artificial intelligence (AI) both are extensively being used as an integrated part of IoT networks for security enhancements. Blockchain provides a decentralized mechanism to store validated session keys that can be allotted to the network devices. Blockchain is also used to load balance the stressing edge devices during low battery levels. AI on the other hand provides better learning and adaptiveness towards IoT attacks. The integration of newer technologies in IoT key management yields enhanced security features. In this article, we systematically survey recent trending technologies from an IoT security point of view and discuss traditional key security mechanisms. This article delivers a comprehensive quality study for researchers on authentication and session keys, integrating IoT with blockchain and AI-based authentication in cybersecurity.","url":"https://doi.org/10.1007/s40747-022-00667-z","authors":["Ankit Attkan","Virender Ranga"],"tags":["Computer science","Computer security","Authentication (law)","Blockchain","Mutual authentication"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-02-24","doi":"https://doi.org/10.1007/s40747-022-00667-z","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4285013446","name":"Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges","source":"openalex","abstract":"This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.","url":"https://doi.org/10.3389/fspor.2022.861466","authors":["Fabian Hammes","Alexander Hagg","Alexander Asteroth","Daniel Link"],"tags":["Elite","Field (mathematics)","Narrative","Service (business)","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-11","doi":"https://doi.org/10.3389/fspor.2022.861466","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W3176290932","name":"Embryo selection with artificial intelligence: how to evaluate and compare methods?","source":"openalex","abstract":"Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.","url":"https://doi.org/10.1007/s10815-021-02254-6","authors":["Mikkel Fly Kragh","Henrik Karstoft"],"tags":["Selection (genetic algorithm)","Reproductive medicine","Artificial intelligence","Embryo","Biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-26","doi":"https://doi.org/10.1007/s10815-021-02254-6","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4367850403","name":"Artificial Intelligence in Engineering","source":"openalex","abstract":"Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results.","url":"https://doi.org/10.47709/brilliance.v3i1.2170","authors":["Mohamed Khaleel","Abdussalam Ali Ahmed","Abdulgader Alsharif"],"tags":["Artificial intelligence","Computer science","Engineering design process","Applications of artificial intelligence","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-13","doi":"https://doi.org/10.47709/brilliance.v3i1.2170","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4362611482","name":"The Ethics of Artificial Intelligence for Intelligence Analysis: a Review of the Key Challenges with Recommendations","source":"openalex","abstract":"Intelligence agencies have identified artificial intelligence (AI) as a key technology for maintaining an edge over adversaries. As a result, efforts to develop, acquire, and employ AI capabilities for purposes of national security are growing. This article reviews the ethical challenges presented by the use of AI for augmented intelligence analysis. These challenges have been identified through a qualitative systematic review of the relevant literature. The article identifies five sets of ethical challenges relating to intrusion, explainability and accountability, bias, authoritarianism and political security, and collaboration and classification, and offers a series of recommendations targeted at intelligence agencies to address and mitigate these challenges.","url":"https://doi.org/10.1007/s44206-023-00036-4","authors":["Alexander Blanchard","Mariarosaria Taddeo"],"tags":["Key (lock)","Accountability","Engineering ethics","Political science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-01","doi":"https://doi.org/10.1007/s44206-023-00036-4","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4378515194","name":"Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review","source":"openalex","abstract":"Background: Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care. Objective: The aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field. Methods: A search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability). Results: Six out of 882 articles met the criteria for eligibility. Artificial Intelligence (AI) users were the most frequently described stakeholders. XAI served various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations was based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction was the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also varied. Conclusion: XAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.","url":"https://doi.org/10.1016/j.heliyon.2023.e16110","authors":["Jinsun Jung","Hyungbok Lee","Hyunggu Jung","Hyeoneui Kim"],"tags":["Quality (philosophy)","Health care","Task (project management)","Identification (biology)","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-01","doi":"https://doi.org/10.1016/j.heliyon.2023.e16110","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4206312283","name":"Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?","source":"openalex","abstract":"Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from \"training data,\" that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.","url":"https://doi.org/10.3389/fcvm.2021.818765","authors":["Anastasia Fotaki","Esther Puyol‐Antón","Amedeo Chiribiri","René M. Botnar","Kuberan Pushparajah","Claudia Prieto"],"tags":["Biobank","Artificial intelligence","Field (mathematics)","Data science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-10","doi":"https://doi.org/10.3389/fcvm.2021.818765","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4281889493","name":"Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management","source":"openalex","abstract":"Companies neither fully exploit the potential of Artificial Intelligence (AI), nor that of Machine Learning (ML), its most prominent method. This is true in particular of marketing, where its possible use extends beyond mere segmentation, personalization, and decision-making. We explore the drivers of and barriers to AI and ML in marketing by adopting a dual strategic and behavioral focus, which provides both an inward (AI and ML for marketers) and an outward (AI and ML for customers) perspective. From our mixed-method approach (a Delphi study, a survey, and two focus groups), we derive several research propositions that address the challenges facing marketing managers and organizations in three distinct domains: (1) Culture, Strategy, and Implementation; (2) Decision-Making and Ethics; (3) Customer Management. Our findings contribute to better understanding the human factor behind AI and ML, and aim to stimulate interdisciplinary inquiry across marketing, organizational behavior, psychology, and ethics.","url":"https://doi.org/10.1016/j.jbusres.2022.04.007","authors":["Gioia V. Volkmar","Peter Mathias Fischer","Sven Reinecke"],"tags":["Marketing","Delphi method","Personalization","Knowledge management","Exploit"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-01","doi":"https://doi.org/10.1016/j.jbusres.2022.04.007","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4398201186","name":"Transforming Health Care With Artificial Intelligence: Redefining Medical Documentation","source":"openalex","abstract":"The growing burden of medical documentation, particularly with the widespread adoption of electronic health records (EHRs), has contributed to physician burnout and decreased job satisfaction.1 The intricate structure of medical notes, combined with the time-consuming data entry process,2 and physicians’ often limited typing proficiency, has considerably impeded their ability to prioritize patient care.3 The recent rise of artificial intelligence (AI) in health care offers a promising solution to alleviate the burden of documentation and optimize clinical workflows.","url":"https://doi.org/10.1016/j.mcpdig.2024.05.006","authors":["Archana Reddy Bongurala","Dhaval Save","Ankit Virmani","Rahul Kashyap"],"tags":["Documentation","Health care","Medicine","Nursing","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-22","doi":"https://doi.org/10.1016/j.mcpdig.2024.05.006","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W2341139844","name":"Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery","source":"openalex","abstract":"This article proposes a new grand challenge for AI: to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize. There are a series of human cognitive limitations that prevent us from making accelerated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left at the level of a cottage industry. AI systems can transform scientific discoveries into highly efficient practices, thereby enabling us to expand our knowledge in unprecedented ways. Such systems may out‐compute all possible hypotheses and may redefine the nature of scientific intuition, hence the scientific discovery process.","url":"https://doi.org/10.1609/aimag.v37i1.2642","authors":["Hiroaki Kitano"],"tags":["Scientific discovery","Intuition","Data science","Computer science","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2016-03-01","doi":"https://doi.org/10.1609/aimag.v37i1.2642","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W4360891289","name":"Capabilities of GPT-4 on Medical Challenge Problems","source":"openalex","abstract":"Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the USMLE, a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond measuring model performance, experiments were conducted to investigate the influence of test questions containing both text and images on model performance, probe for memorization of content during training, and study probability calibration, which is of critical importance in high-stakes applications like medicine. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). In addition, GPT-4 is significantly better calibrated than GPT-3.5, demonstrating a much-improved ability to predict the likelihood that its answers are correct. We also explore the behavior of the model qualitatively through a case study that shows the ability of GPT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case. Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.","url":"https://doi.org/10.48550/arxiv.2303.13375","authors":["Harsha Nori","Nicholas King","Scott Mayer McKinney","Dean Carignan","Eric Horvitz"],"tags":["Benchmark (surveying)","Licensure","United States Medical Licensing Examination","Computer science","Calibration"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-20","doi":"https://doi.org/10.48550/arxiv.2303.13375","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W3088090115","name":"Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review","source":"openalex","abstract":"Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.","url":"https://doi.org/10.3748/wjg.v26.i37.5617","authors":["Miguel Jiménez Pérez","Rocío González Grande"],"tags":["Hepatocellular carcinoma","Medicine","Carcinoma","Pathology","Oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-27","doi":"https://doi.org/10.3748/wjg.v26.i37.5617","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W2948525168","name":"Deep Learning Models for Retinal Blood Vessels Segmentation: A Review","source":"openalex","abstract":"This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis.","url":"https://doi.org/10.1109/access.2019.2920616","authors":["Toufique Ahmed Soomro","Ahmed J. Afifi","Lihong Zheng","Shafiullah Soomro","Junbin Gao","Olaf Hellwich","Manoranjan Paul"],"tags":["Deep learning","Computer science","Artificial intelligence","Segmentation","Image segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-01-01","doi":"https://doi.org/10.1109/access.2019.2920616","addedAt":"2026-09-01T01:47:58.473Z","updatedAt":"2026-09-01T01:47:58.473Z"},{"id":"oa:W3199049727","name":"Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.giq.2021.101624","authors":["Manu Sharma","Sunil Luthra","Sudhanshu Joshi","Anil Kumar"],"tags":["Scope (computer science)","Workforce","Quality (philosophy)","Big data","Emerging technologies"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-01","doi":"https://doi.org/10.1016/j.giq.2021.101624","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3036858161","name":"Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature","source":"openalex","abstract":"BACKGROUND: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. METHODS: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. RESULTS: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n=15, 55.6%) and on surgical site infections (SSI, n=8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. DISCUSSION: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored.","url":"https://doi.org/10.1016/j.jiph.2020.06.006","authors":["Alessandro Scardoni","Federica Balzarini","Carlo Signorelli","Federico Cabitza","Anna Odone"],"tags":["Systematic review","Standardization","Health care","Medicine","Logistic regression"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-16","doi":"https://doi.org/10.1016/j.jiph.2020.06.006","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4366710236","name":"Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What Healthcare Stakeholders Need to Know","source":"openalex","abstract":"This review aimed to specify different concepts that are essential to the development of medical devices (MDs) with artificial intelligence (AI) (AI-based MDs) and shed light on how algorithm performance, interpretability, and explainability are key assets. First, a literature review was performed to determine the key criteria needed for a health technology assessment of AI-based MDs in the existing guidelines. Then, we analyzed the existing assessment methodologies of the different criteria selected after the literature review. The scoping review revealed that health technology assessment agencies have highlighted different criteria, with 3 important ones to reinforce confidence in AI-based MDs: performance, interpretability, and explainability. We give recommendations on how and when to evaluate performance on the basis of the model structure and available data. In addition, should interpretability and explainability be difficult to define mathematically, we describe existing ways to support their evaluation. We also provide a decision support flowchart to identify the anticipated regulatory requirements for the development and assessment of AI-based MDs. The importance of explainability and interpretability techniques in health technology assessment agencies is increasing to hold stakeholders more accountable for the decisions made by AI-based MDs. The identification of 3 main assessment criteria for AI-based MDs according to health technology assessment guidelines led us to propose a set of tools and methods to help understand how and why machine learning algorithms work as well as their predictions.","url":"https://doi.org/10.1016/j.mcpdig.2023.02.004","authors":["Line Farah","Juliette Murris","Isabelle Borget","Agathe Guilloux","Nicolas Martelli","Sandrine Katsahian"],"tags":["Interpretability","Health care","Artificial intelligence","Computer science","Business"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-21","doi":"https://doi.org/10.1016/j.mcpdig.2023.02.004","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3208514929","name":"Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review","source":"openalex","abstract":"BACKGROUND: In the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. OBJECTIVE: The aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. METHODS: We conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. RESULTS: The literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients' individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. CONCLUSIONS: There is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families.","url":"https://doi.org/10.2196/25745","authors":["Vera Kaelin","Mina Valizadeh","Zurisadai Salgado","Natalie Parde","Mary A. Khetani"],"tags":["International Classification of Functioning, Disability and Health","Rehabilitation","Scope (computer science)","Inclusion (mineral)","Psychological intervention"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-04","doi":"https://doi.org/10.2196/25745","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4298156445","name":"Artificial intelligence in pharmacology research and practice","source":"openalex","abstract":"In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.","url":"https://doi.org/10.1111/cts.13431","authors":["Maaike van der Lee","Jesse J. Swen"],"tags":["Cluster analysis","Clinical pharmacology","Data science","Clinical Practice","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-01","doi":"https://doi.org/10.1111/cts.13431","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4382810696","name":"Detecting dental caries on oral photographs using artificial intelligence: A systematic review","source":"openalex","abstract":"OBJECTIVES: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. METHODS: Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. RESULTS: Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%-94.3% and 42.8%-95.4%, respectively. Irrespective of the task, F1-scores were 68.3%-95.4% for professional cameras, 78.8%-87.6%, for intraoral cameras, and 42.8%-80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. CONCLUSION: Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.","url":"https://doi.org/10.1111/odi.14659","authors":["Mohammad Moharrami","Julie Farmer","Sonica Singhal","Erin Watson","Michael Glogauer","Alistair E. W. Johnson","Falk Schwendicke","Carlos Quiñonez"],"tags":["Dentistry","Artificial intelligence","Medicine","Computer science","Orthodontics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-01","doi":"https://doi.org/10.1111/odi.14659","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3152403802","name":"An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1007/978-3-030-96068-1_1","authors":["Rex Liu","Albara Ah Ramli","Huanle Zhang","Erik Henricson","Xin Liu"],"tags":["Computer science","Wearable computer","Artificial intelligence","Health care","Variety (cybernetics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1007/978-3-030-96068-1_1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4391969619","name":"A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare","source":"openalex","abstract":"Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was \"unavailable\", \"partially available,\" or \"fully available.\" The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.","url":"https://doi.org/10.3389/fdgth.2024.1267290","authors":["Jana Fehr","Brian Citro","Rohit Malpani","Christoph Lippert","Vince I. Madai"],"tags":["Transparency (behavior)","Documentation","Software deployment","Vendor","Trustworthiness"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-20","doi":"https://doi.org/10.3389/fdgth.2024.1267290","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4416566022","name":"The impact of artificial intelligence-driven simulation on the development of non-technical skills in medical education: a systematic review","source":"openalex","abstract":"PURPOSE: Artificial intelligence (AI)-driven simulation is an emerging approach in healthcare education that enhances learning effectiveness. This review examined its impact on the development of non-technical skills among medical learners. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic review was conducted using the following databases: Web of Science, ScienceDirect, Scopus, and PubMed. The quality of the included studies was assessed using the Mixed. Methods: Appraisal Tool. The protocol was previously registered in PROSPERO (CRD420251038024). RESULTS: Of the 1,442 studies identified in the initial search, 20 met the inclusion criteria, involving 2,535 participants. The simulators varied considerably, ranging from platforms built on symbolic AI methods to social robots powered by computational AI. Among the 15 AI-driven simulators, 10 used ChatGPT or its variants as virtual patients. Several studies evaluated multiple non-technical skills simultaneously. Communication and clinical reasoning were the most frequently assessed skills, appearing in 12 and 6 studies, respectively, which generally reported positive outcomes. Improvements were also noted in decision-making, empathy, self-confidence, critical thinking, and problem-solving. In contrast, emotional regulation, assessed in a single study, showed no significant difference. Notably, none of the studies examined reflection, reflective practice, teamwork, or leadership. CONCLUSION: AI-driven simulation shows substantial potential for enhancing non-technical skills in medical education, particularly communication and clinical reasoning. However, its effects on several other non-technical skills remain unclear. Given heterogeneity in study designs and outcome measures, these findings should be interpreted cautiously. These considerations highlight the need for further research to support integrating this innovative approach into medical curricula.","url":"https://doi.org/10.3352/jeehp.2025.22.37","authors":["Sana Loubbairi","Yassmine El Moussaoui","Laila Lahlou","Imad Chakri","Hicham Nassik"],"tags":["Outcome (game theory)","Computer science","Medical simulation","Medical education","Management science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-11-24","doi":"https://doi.org/10.3352/jeehp.2025.22.37","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3090128601","name":"Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons","source":"openalex","abstract":"Big data has transformed into a trend phrase in healthcare and neurosurgery, becoming a pervasive and inescapable phrase in everyday life. The upsurge in big data applications is a direct consequence of the drastic boom in information technology as well as the growing number of internet-connected devices called the Internet of Things in healthcare. Compared with business, marketing, and other sectors, healthcare applications are lagging due to a lack of technical knowledge among healthcare workers, technological limitations in acquiring and analyzing the data, and improper governance of healthcare big data. Despite these limitations, the medical literature is flooded with big data-related articles, and most of these are filled with abstruse terminologies such as machine learning, artificial intelligence, artificial neural network, and algorithm. Many of the recent articles are restricted to neurosurgical registries, creating a false impression that big data is synonymous with registries. Others advocate that the utilization of big data will be the panacea to all healthcare problems and research in the future. Without a proper understanding of these principles, it becomes easy to get lost without the ability to differentiate hype from reality. To that end, the authors give a brief narrative of big data analysis in neurosurgery and review its applications, limitations, and the challenges it presents for neurosurgeons and healthcare professionals naive to this field. Awareness of these basic concepts will allow neurosurgeons to understand the literature regarding big data, enabling them to make better decisions and deliver personalized care.","url":"https://doi.org/10.3171/2020.5.jns201288","authors":["Bharath Raju","Fareed Jumah","Omar Ashraf","Vinayak Narayan","Gaurav Gupta","Hai Sun","Patrick Hilden","Anil Nanda"],"tags":["Big data","Panacea (medicine)","Health care","Data science","The Internet"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-02","doi":"https://doi.org/10.3171/2020.5.jns201288","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3206214356","name":"Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis","source":"openalex","abstract":"Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.","url":"https://doi.org/10.1016/j.csbj.2021.10.006","authors":["Κωνσταντίνα Κούρου","Konstantinos Exarchos","Costas Papaloukas","Prodromos Sakaloglou","Themis Exarchos","Dimitrios I. Fotiadis"],"tags":["Machine learning","Artificial intelligence","Computer science","Clinical Practice","Transparency (behavior)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1016/j.csbj.2021.10.006","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4309686618","name":"Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review","source":"openalex","abstract":"Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists.","url":"https://doi.org/10.3390/app122211864","authors":["Farraj Albalawi","Khalid Abalkhail"],"tags":["Computer science","Radiation treatment planning","Artificial intelligence","Web of science","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-21","doi":"https://doi.org/10.3390/app122211864","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2916492225","name":"Reimagining Medical Education in the Age of AI","source":"openalex","abstract":"Available medical knowledge exceeds the organizing capacity of the human mind, yet medical education remains based on information acquisition and application. Complicating this information overload crisis among learners is the fact that physicians' skill sets now must include collaborating with and managing artificial intelligence (AI) applications that aggregate big data, generate diagnostic and treatment recommendations, and assign confidence ratings to those recommendations. Thus, an overhaul of medical school curricula is due and should focus on knowledge management (rather than information acquisition), effective use of AI, improved communication, and empathy cultivation.","url":"https://doi.org/10.1001/amajethics.2019.146","authors":["Steven A. Wartman","C. Donald Combs"],"tags":["Empathy","Curriculum","Knowledge management","Information overload","Medical information"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-02-01","doi":"https://doi.org/10.1001/amajethics.2019.146","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4401616798","name":"Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare – The Narrative Review","source":"openalex","abstract":"Background: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. Main Applications: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.","url":"https://doi.org/10.2147/jmdh.s482757","authors":["Zifang Shang","Varun Chauhan","Kirti Devi","Sandip Patil"],"tags":["Health care","Narrative","Data science","Computer science","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-01","doi":"https://doi.org/10.2147/jmdh.s482757","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392714531","name":"Development and validation of a scale for dependence on artificial intelligence in university students","source":"openalex","abstract":"Background Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have also emerged, including the potential decline in cognitive skills and student motivation due to excessive reliance on AI. Objective To develop and validate a Scale for Dependence on Artificial Intelligence (DIA). Methods An Exploratory Factor Analysis (EFA) was used to identify the underlying structure of the DIA scale, followed by a Confirmatory Factor Analysis (CFA) to assess and confirm this structure. In addition, the scale’s invariance based on participants’ gender was evaluated. Results A total of 528 university students aged between 18 and 37 years (M = 20.31, SD = 3.8) participated. The EFA revealed a unifactorial structure for the scale, which was subsequently confirmed by the CFA. Invariance analyses showed that the scale is applicable and consistent for both men and women. Conclusion The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI. Its gender invariance makes it applicable in diverse population studies. In the age of digitalization, it is essential to understand the dynamics between humans and AI to navigate wisely and ensure a beneficial coexistence.","url":"https://doi.org/10.3389/feduc.2024.1323898","authors":["Wilter C. Morales-García","Liset Z. Sairitupa-Sanchez","Sandra B. Morales-García","Mardel Morales-García"],"tags":["Scale (ratio)","Computer science","Artificial intelligence","Cartography","Geography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-12","doi":"https://doi.org/10.3389/feduc.2024.1323898","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392101515","name":"Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review","source":"openalex","abstract":"Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.","url":"https://doi.org/10.3389/fphar.2024.1181183","authors":["Enyu Zhou","Qin Shen","Yang Hou"],"tags":["Modernization theory","Standardization","Traditional Chinese medicine","Quality (philosophy)","China"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-23","doi":"https://doi.org/10.3389/fphar.2024.1181183","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4283167339","name":"News from Generative Artificial Intelligence Is Believed Less","source":"openalex","abstract":"Artificial Intelligence (AI) can generate text virtually indistinguishable from text written by humans. A key question, then, is whether people believe news headlines generated by AI as much as news headlines generated by humans. AI is viewed as lacking human motives and emotions, suggesting that people might view news written by AI as more accurate. By contrast, two pre-registered experiments on representative U.S. samples (N = 4,034) showed that people rated news headlines written by AI as less accurate than those written by humans. People were more likely to incorrectly rate news headlines written by AI (vs. a human) as inaccurate when they were actually true, and more likely to correctly rate them as inaccurate when they were indeed false. Our findings are important given the increasing adoption of AI in news generation, and the associated ethical and governance pressures to disclose it use and address standards of transparency and accountability.","url":"https://doi.org/10.1145/3531146.3533077","authors":["Chiara Longoni","Andrey Fradkin","Luca Cian","Gordon Pennycook"],"tags":["Transparency (behavior)","Generative grammar","Accountability","Artificial intelligence","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-20","doi":"https://doi.org/10.1145/3531146.3533077","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4313289426","name":"Evidence, ethics and the promise of artificial intelligence in psychiatry","source":"openalex","abstract":"Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine's understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry.","url":"https://doi.org/10.1136/jme-2022-108447","authors":["Melissa D. McCradden","Katrina Hui","Daniel Z. Buchman"],"tags":["Humility","Unintended consequences","Affect (linguistics)","Psychology","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-29","doi":"https://doi.org/10.1136/jme-2022-108447","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3143116276","name":"An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy","source":"openalex","abstract":"Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists' workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York's Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either \"Suspicious\" or \"Not Suspicious\" for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate's handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.","url":"https://doi.org/10.1038/s41379-021-00794-x","authors":["Sudhir Perincheri","Angelique Levi","Romulo Celli","Peter Gershkovich","David L. Rimm","Jon S. Morrow","Brandon Rothrock","Patricia Raciti","David S. Klimstra","John H. Sinard"],"tags":["Medicine","Prostate cancer","Prostate","Prostate biopsy","Biopsy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-29","doi":"https://doi.org/10.1038/s41379-021-00794-x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4308364545","name":"Artificial Intelligence in Breast Cancer Screening","source":"openalex","abstract":"Importance: Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain. Objectives: To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system. Evidence Review: Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021. Findings: Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices. Conclusions and Relevance: The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.","url":"https://doi.org/10.1001/jamainternmed.2022.4969","authors":["Kunal C. Potnis","Joseph S. Ross","Sanjay Aneja","Cary P. Gross","Ilana B. Richman"],"tags":["Medicine","Food and drug administration","Triage","Breast cancer","Clearance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-07","doi":"https://doi.org/10.1001/jamainternmed.2022.4969","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3135423294","name":"The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.oraloncology.2021.105254","authors":["Betül İlhan","Pelin Güneri","Petra Wilder‐Smith"],"tags":["Medicine","Referral","Intensive care medicine","Cancer","Population"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-09","doi":"https://doi.org/10.1016/j.oraloncology.2021.105254","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2142114107","name":"Control strategies for active lower extremity prosthetics and orthotics: a review","source":"openalex","abstract":": Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user.This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user's sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies.As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os--not as independent devices, but as actors within an ecosystem--is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers.Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use.The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user's neuromusculoskeletal system and are practical for use in locomotive ADL.","url":"https://doi.org/10.1186/1743-0003-12-1","authors":["Michael R. Tucker","Jérémy Olivier","Anna Pagel","Hannes Bleuler","Mohamed Bouri","Olivier Lambercy","José del R. Millán","Robert Riener","Heike Vallery","Roger Gassert"],"tags":["Orthotics","Context (archaeology)","Computer science","Wearable computer","Control (management)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-01-01","doi":"https://doi.org/10.1186/1743-0003-12-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4285385408","name":"Artificial intelligence in liver ultrasound","source":"openalex","abstract":"Artificial intelligence (AI) is playing an increasingly important role in medicine, especially in the field of medical imaging. It can be used to diagnose diseases and predict certain statuses and possible events that may happen. Recently, more and more studies have confirmed the value of AI based on ultrasound in the evaluation of diffuse liver diseases and focal liver lesions. It can assess the severity of liver fibrosis and nonalcoholic fatty liver, differentially diagnose benign and malignant liver lesions, distinguish primary from secondary liver cancers, predict the curative effect of liver cancer treatment and recurrence after treatment, and predict microvascular invasion in hepatocellular carcinoma. The findings from these studies have great clinical application potential in the near future. The purpose of this review is to comprehensively introduce the current status and future perspectives of AI in liver ultrasound.","url":"https://doi.org/10.3748/wjg.v28.i27.3398","authors":["Liu-Liu Cao","Peng Mei","Xiang Xie","Gong-Quan Chen","Shu-Yan Huang","Jiayu Wang","Fan Jiang","Xin‐Wu Cui","Christoph F. Dietrich"],"tags":["Medicine","Hepatocellular carcinoma","Ultrasound","Liver fibrosis","Liver cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-14","doi":"https://doi.org/10.3748/wjg.v28.i27.3398","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4318619419","name":"Evaluation of the Diagnostic and Prognostic Accuracy of Artificial Intelligence in Endodontic Dentistry: A Comprehensive Review of Literature","source":"openalex","abstract":"Aim . This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry. Introduction . Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology . The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry. Results . The preliminary search yielded 2560 articles relevant enough to the paper’s purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures. Conclusion . In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.","url":"https://doi.org/10.1155/2023/7049360","authors":["Mohmed Isaqali Karobari","Abdul Habeeb Adil","Syed Nahid Basheer","Sabari Murugesan","Kamatchi Subramani Savadamoorthi","Mohammed Mustafa","Abdulaziz Abdulwahed","Ahmed A. Almokhatieb"],"tags":["Dentistry","Orthodontics","Medicine","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1155/2023/7049360","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4362580565","name":"Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses","source":"openalex","abstract":"Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.","url":"https://doi.org/10.1186/s11671-023-03842-4","authors":["Murugan Ramalingam","Abinaya Jaisankar","Lijia Cheng","Sasirekha Krishnan","Liang Lan","Anwarul Hassan","Hilal Türkoğlu Şaşmazel","Hirokazu Kaji","Hans‐Peter Deigner","José Luís Pedraz","Hae‐Won Kim","Zheng Shi","Giovanna Marrazza"],"tags":["Biosensor","Nanotechnology","Computer science","Biochemical engineering","Materials science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-01","doi":"https://doi.org/10.1186/s11671-023-03842-4","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2922735499","name":"Artificial Intelligence in Smart Tourism: A Conceptual Framework","source":"openalex","abstract":"Smart tourism destination as: an innovative tourist destination, built on an infrastructure of state-of-the-art technology guaranteeing the sustainable development of tourist areas, accessible to everyone, which facilitates the visitor’s interaction with and integration into his or her surroundings, increases the quality of the experience at the destination, and improves residents’ quality of life. Lopez de Avila (2015). Smart tourism involves multiple components and layers of “smart” include (1) Smart Destinations which was special cases of smart cities integration of ICT’s into physical infrastructure, (2) Smart experience which specifically focus on technology-mediated tourism experience and their engagement through personalization, context-awareness and real-time monitoring, (3) Smart business refer to the complex business ecosystem that creates and supports the exchange of touristic resource and the co-creation of tourism experience. Gretzel et al, (2015). Smart tourism also clearly relies on the ability to not only collect enormous of data but to intelligently store, process, combine, analyze and use big data to inform business innovation, operations and services by artificial intelligence and big data technique. The rapid development of information communication technology (ICT) such as artificial intelligent, cloud computing, mobile device, big data mining and social media cause computing, storage and communication relevant software and hardware popular. Facebook, Amazon, Apple, Microsoft and Google have risen rapidly since 2000. In recent years, Emerging technologies such as Artificial Intelligence, Internet of Thing, Robotic, Cyber Security, 3D printer and Block chain also accelerate the development of industry toward digital transformation trend such as Fintech, e-commerce, smart cities, smart tourism, smart healthcare, smart manufacturing... This study proposes a conceptual framework that integrates (1) artificial intelligence/machine learning, (2) institution/organizational and (3) business processes to assist smart tourism stake holder to leverage artificial intelligence to integrate cross-departmental business and streamline key performance metrics to build a business-level IT Strategy. Artificial intelligence as long as the function includes (1) Cognitive engagement to (voice/pattern recognition function) (2) Cognitive process automation (Robotic Process Automation) (3) Cognitive insight (forecast, recommendation).","url":"https://openalex.org/W2922735499","authors":["Rua‐Huan Tsaih","Chih Chun Hsu"],"tags":["Tourism","Conceptual framework","Computer science","Artificial intelligence","Sociology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-01-01","doi":"","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4372311383","name":"A machine learning and explainable artificial intelligence triage-prediction system for COVID-19","source":"openalex","abstract":"COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.","url":"https://doi.org/10.1016/j.dajour.2023.100246","authors":["Varada Vivek Khanna","Krishnaraj Chadaga","Niranjana Sampathila","Srikanth Prabhu","Rajagopala Chadaga P."],"tags":["Random forest","Artificial intelligence","Machine learning","Computer science","Naive Bayes classifier"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-06","doi":"https://doi.org/10.1016/j.dajour.2023.100246","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3118650257","name":"Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines","source":"openalex","abstract":"BACKGROUND: The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of 'AI in health' means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. MAIN TEXT: We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines-SPIRIT and CONSORT-are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. CONCLUSION: The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.","url":"https://doi.org/10.1186/s13063-020-04951-6","authors":["Hussein Ibrahim","Xiaoxuan Liu","Samantha Cruz Rivera","David Moher","An‐Wen Chan","Matthew R. Sydes","Melanie Calvert","Alastair K. Denniston"],"tags":["Psychological intervention","Medicine","Consolidated Standards of Reporting Trials","Transparency (behavior)","Multidisciplinary approach"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-06","doi":"https://doi.org/10.1186/s13063-020-04951-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3087496930","name":"Applications of artificial intelligence (AI) in diagnostic radiology: a technography study","source":"openalex","abstract":"OBJECTIVES: Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain. METHODS: We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval. RESULTS: We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies. We show that AI applications are primarily narrow in terms of tasks, modality, and anatomic region. A majority of the available AI functionalities focus on supporting the \"perception\" and \"reasoning\" in the radiology workflow. CONCLUSIONS: Thereby, we contribute by (1) offering a systematic framework for analyzing and mapping the technological developments in the diagnostic radiology domain, (2) providing empirical evidence regarding the landscape of AI applications, and (3) offering insights into the current state of AI applications. Accordingly, we discuss the potential impacts of AI applications on the radiology work and we highlight future possibilities for developing these applications. KEY POINTS: • Many AI applications are introduced to the radiology domain and their number and diversity grow very fast. • Most of the AI applications are narrow in terms of modality, body part, and pathology. • A lot of applications focus on supporting \"perception\" and \"reasoning\" tasks.","url":"https://doi.org/10.1007/s00330-020-07230-9","authors":["Mohammad Hosein Rezazade Mehrizi","Peter M. A. van Ooijen","Milou Homan"],"tags":["Neuroradiology","Interventional radiology","Medicine","Radiology","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-18","doi":"https://doi.org/10.1007/s00330-020-07230-9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4399236723","name":"Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives","source":"openalex","abstract":"The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.","url":"https://doi.org/10.3390/biomedicines12061220","authors":["Lorenzo Di Sarno","Anya Caroselli","Giovanna Tonin","Benedetta Graglia","Valeria Pansini","Francesco Andrea Causio","Antonio Gatto","Antonio Chiaretti"],"tags":["Context (archaeology)","Triage","Data science","Computer science","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-30","doi":"https://doi.org/10.3390/biomedicines12061220","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4324308124","name":"An overview and a roadmap for artificial intelligence in hematology and oncology","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS: In this article, we provide an expert-based consensus statement by the joint Working Group on \"Artificial Intelligence in Hematology and Oncology\" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS: First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION: Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.","url":"https://doi.org/10.1007/s00432-023-04667-5","authors":["Wiebke Rösler","Michael Altenbuchinger","Bettina Baeßler","Tim Beißbarth","Gernot Beutel","Robert M. Bock","Nikolas von Bubnoff","Jan‐Niklas Eckardt","Sebastian Foersch","Chiara Maria Lavinia Loeffler","Jan Moritz Middeke","Martha-Lena Mueller","Thomas Oellerich","Benjamin Risse","André Scherag","Christoph Schliemann","Markus Scholz","Rainer Spang","Christian Thielscher","Ioannis Tsoukakis","Jakob Nikolas Kather"],"tags":["Hematology","Internal medicine","Medicine","Oncology","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-15","doi":"https://doi.org/10.1007/s00432-023-04667-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4283749998","name":"Spiking Neural Networks and Their Applications: A Review","source":"openalex","abstract":"The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.","url":"https://doi.org/10.3390/brainsci12070863","authors":["Kashu Yamazaki","Viet-Khoa Vo-Ho","Darshan Bulsara","Ngan Le"],"tags":["Spiking neural network","Computer science","Artificial neural network","Artificial intelligence","Spike (software development)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-30","doi":"https://doi.org/10.3390/brainsci12070863","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2008402888","name":"Second Life: an overview of the potential of 3‐D virtual worlds in medical and health education","source":"openalex","abstract":"This hybrid review-case study introduces three-dimensional (3-D) virtual worlds and their educational potential to medical/health librarians and educators. Second life (http://secondlife.com/) is perhaps the most popular virtual world platform in use today, with an emphasis on social interaction. We describe some medical and health education examples from Second Life, including Second Life Medical and Consumer Health Libraries (Healthinfo Island-funded by a grant from the US National Library of Medicine), and VNEC (Virtual Neurological Education Centre-developed at the University of Plymouth, UK), which we present as two detailed 'case studies'. The pedagogical potentials of Second Life are then discussed, as well as some issues and challenges related to the use of virtual worlds. We have also compiled an up-to-date resource page (http://healthcybermap.org/sl.htm), with additional online material and pointers to support and extend this study.","url":"https://doi.org/10.1111/j.1471-1842.2007.00733.x","authors":["Maged N. Kamel Boulos","Lee Hetherington","Steve Wheeler"],"tags":["Metaverse","Virtual world","Resource (disambiguation)","Medical education","World Wide Web"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2007-11-14","doi":"https://doi.org/10.1111/j.1471-1842.2007.00733.x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4294325389","name":"Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review","source":"openalex","abstract":"OBJECTIVE: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied. RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis. CONCLUSIONS: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.","url":"https://doi.org/10.3390/ijerph191710860","authors":["Mohamed Zahoor Ul Huqh","Johari Yap Abdullah","Ling Shing Wong","Nafij Bin Jamayet","Mohammad Khursheed Alam","Farah Rashid","Adam Husein","Wan Muhamad Amir W Ahmad","Sumaiya Zabin Eusufzai","Somasundaram Prasadh","Vetriselvan Subramaniyan","Neeraj Kumar Fuloria","Shivkanya Fuloria","Mahendran Sekar","Siddharthan Selvaraj"],"tags":["Artificial intelligence","Orthodontics","Computer science","Dentistry","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-31","doi":"https://doi.org/10.3390/ijerph191710860","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3164844993","name":"Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review","source":"openalex","abstract":"Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.","url":"https://doi.org/10.3390/diagnostics11061004","authors":["Sanjeev B. Khanagar","Sachin Naik","Abdulaziz A. Al Kheraif","Satish Vishwanathaiah","Prabhadevi C Maganur","Yaser Ali Alhazmi","Shazia Mushtaq","Sachin C. Sarode","Gargi S. Sarode","Alessio Zanza","Luca Testarelli","Shankargouda Patil"],"tags":["Artificial intelligence","Scopus","Logistic regression","MEDLINE","Etiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-31","doi":"https://doi.org/10.3390/diagnostics11061004","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4366769280","name":"Using AI-generated suggestions from ChatGPT to optimize clinical decision support","source":"openalex","abstract":"OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. METHODS: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. RESULTS: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. CONCLUSION: AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.","url":"https://doi.org/10.1093/jamia/ocad072","authors":["Siru Liu","Aileen P. Wright","Barron L. Patterson","Jonathan P. Wanderer","Robert W. Turer","Scott D. Nelson","Allison B. McCoy","Dean F. Sittig","Adam Wright"],"tags":["Clinical decision support system","Computer science","Decision support system","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-22","doi":"https://doi.org/10.1093/jamia/ocad072","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2966532261","name":"The Role of Leadership in a Digitalized World: A Review","source":"openalex","abstract":"Digital technology has changed organizations in an irreversible way. Like the movable type printing accelerated the evolution of our history, digitalization is shaping organizations, work environment and processes, creating new challenges leaders have to face. Social science scholars have been trying to understand this multifaceted phenomenon, however, findings have accumulated in a fragmented and dispersed fashion across different disciplines, and do not seem to converge within a clear picture. To overcome this shortcoming in the literature and foster clarity and alignment in the academic debate, this paper provides a comprehensive analysis of the contribution of studies on leadership and digitalization, identifying patterns of thought and findings across various social science disciplines, such as management and psychology. It clarifies key definitions and ideas, highlighting the main theories and findings drawn by scholars. Further, it identifies categories that group papers according to the macro level of analysis (e-leadership and organization, digital tools, ethical issues, and social movements), and micro level of analysis (the role of C-level managers, leader's skills in the digital age, practices for leading virtual teams). Main findings show leaders are key actors in the development of a digital culture: they need to create relationships with multiple and scattered stakeholders, and focus on enabling collaborative processes in complex settings, while attending to pressing ethical concerns. With this research, we contribute to advance theoretically the debate about digital transformation and leadership, offering an extensive and systematic review, and identifying key future research opportunities to advance knowledge in this field.","url":"https://doi.org/10.3389/fpsyg.2019.01938","authors":["Laura Cortellazzo","Elena Bruni","Rita Zampieri"],"tags":["CLARITY","Engineering ethics","Public relations","Digital transformation","Face (sociological concept)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-08-27","doi":"https://doi.org/10.3389/fpsyg.2019.01938","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3025095375","name":"Applications of artificial intelligence and machine learning in respiratory medicine","source":"openalex","abstract":"The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)-complex networks residing in silico but loosely modelled on the human brain-that can process complex input data such as a chest radiograph image and output a classification such as 'normal' or 'abnormal'. DNNs are 'trained' using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.","url":"https://doi.org/10.1136/thoraxjnl-2020-214556","authors":["Sherif Gonem","Wim Janssens","Nilakash Das","Marko Topalovic"],"tags":["Artificial intelligence","Machine learning","Chest radiograph","Artificial neural network","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-05-14","doi":"https://doi.org/10.1136/thoraxjnl-2020-214556","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3102883434","name":"Artificial intelligence for the detection of vertebral fractures on plain spinal radiography","source":"openalex","abstract":"Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluation of VFs. Therefore, minimizing the diagnostic errors of VFs on PTLR is crucial. Image identification based on a deep convolutional neural network (DCNN) has been recognized to be potentially effective as a diagnostic strategy; however, the accuracy for detecting VFs has not been fully investigated. A DCNN was trained with PTLR images of 300 patients (150 patients with and 150 without VFs). The accuracy, sensitivity, and specificity of diagnosis of the model were calculated and compared with those of orthopedic residents, orthopedic surgeons, and spine surgeons. The DCNN achieved accuracy, sensitivity, and specificity rates of 86.0% [95% confidence interval (CI) 82.0-90.0%], 84.7% (95% CI 78.8-90.5%), and 87.3% (95% CI 81.9-92.7%), respectively. Both the accuracy and sensitivity of the model were suggested to be noninferior to those of orthopedic surgeons. The DCNN can assist clinicians in the early identification of VFs and in managing patients, to prevent further invasive interventions and a decreased quality of life.","url":"https://doi.org/10.1038/s41598-020-76866-w","authors":["Kazuma Murata","Kenji Endo","Takato Aihara","Hidekazu Suzuki","Yasunobu Sawaji","Yuji Matsuoka","Hirosuke Nishimura","Taichiro Takamatsu","Takamitsu Konishi","Asato Maekawa","Hideya Yamauchi","Kei Kanazawa","Hiroo Endo","Hanako Tsuji","Shigeru Inoue","Noritoshi Fukushima","Hiroyuki Kikuchi","Hiroki Sato","Kengo Yamamoto"],"tags":["Medicine","Orthopedic surgery","Radiography","Confidence interval","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-18","doi":"https://doi.org/10.1038/s41598-020-76866-w","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4386136032","name":"Perception, performance, and detectability of conversational artificial intelligence across 32 university courses","source":"openalex","abstract":"The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.","url":"https://doi.org/10.1038/s41598-023-38964-3","authors":["Hazem Ibrahim","Fengyuan Liu","Rohail Asim","Balaraju Battu","Sidahmed Benabderrahmane","Bashar Alhafni","Wifag Adnan","Tuka Alhanai","Bedoor AlShebli","Riyadh Baghdadi","Jocelyn J. Bélanger","Elena Beretta","Kemal Çelik","Moumena Chaqfeh","Mohammed F. Daqaq","Zaynab El Bernoussi","Daryl Fougnie","Borja García de Soto","Alberto Gandolfi","András György","Nizar Habash","J. Andrew Harris","Aaron Kaufman","Lefteris M. Kirousis","Korhan Koçak","Kangsan Lee","Seung-Ah Lee","Samreen Malik","Michail Maniatakos","David Melcher","Azzam Mourad","Minsu Park","Mahmoud Rasras","Alicja Reuben","Dania Zantout","Nancy W. Gleason","Kinga Makovi","Talal Rahwan","Yasir Zaki"],"tags":["Perception","Computer science","Artificial intelligence","Psychology","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-24","doi":"https://doi.org/10.1038/s41598-023-38964-3","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4285237357","name":"Artificial Communication","source":"openalex","abstract":"A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication. Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication. To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an algorithm—which is not random and is completely controlled, although not by the processes of the human mind. Esposito investigates this by examining the use of algorithms in different areas of social life. She explores the proliferation of lists (and lists of lists) online, explaining that the web works on the basis of lists to produce further lists; the use of visualization; digital profiling and algorithmic individualization, which personalize a mass medium with playlists and recommendations; and the implications of the “right to be forgotten.” Finally, she considers how photographs today seem to be used to escape the present rather than to preserve a memory.","url":"https://doi.org/10.7551/mitpress/14189.001.0001","authors":["Elena Esposito"],"tags":["Computer science","Artificial intelligence","Analogy","sort","Human communication"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-05-24","doi":"https://doi.org/10.7551/mitpress/14189.001.0001","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4300690111","name":"Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI)","source":"openalex","abstract":"OBJECTIVES: Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model. METHODS: MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments. RESULTS: The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects. CONCLUSIONS: We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.","url":"https://doi.org/10.1017/s0266462322000551","authors":["Iben Fasterholdt","Tue Kjølhede","Mohammad Naghavi‐Behzad","Thomas Schmidt","Quinnie T. S. Rautalammi","Malene Grubbe Hildebrandt","Anne Gerdes","Astrid Barkler","Kristian Kidholm","Valeria E. Rac","Benjamin Schnack Rasmussen"],"tags":["Value (mathematics)","Artificial intelligence","Medical physics","Computer science","Nuclear medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1017/s0266462322000551","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2980107661","name":"Evaluation of e-learning for medical education in low- and middle-income countries: A systematic review","source":"openalex","abstract":"In low- and middle-income countries (LMICs), e-learning for medical education may alleviate the burden of severe health worker shortages and deliver affordable access to high quality medical education. However, diverse challenges in infrastructure and adoption are encountered when implementing e-learning within medical education in particular. Understanding what constitutes successful e-learning is an important first step for determining its effectiveness. The objective of this study was to systematically review e-learning interventions for medical education in LMICs, focusing on their evaluation and assessment methods. Nine databases were searched for publications from January 2007 to June 2017. We included 52 studies with a total of 12,294 participants. Most e-learning interventions were pilot studies (73%), which mainly employed summative assessments of study participants (83%) and evaluated the e-learning intervention with questionnaires (45%). Study designs, evaluation and assessment methods showed considerable variation, as did the study quality, evaluation periods, outcome and effectiveness measures. Included studies mainly utilized subjective measures and custom-built evaluation frameworks, which resulted in both low comparability and poor validity. The majority of studies self-concluded that they had had an effective e-learning intervention, thus indicating potential benefits of e-learning for LMICs. However, MERSQI and NOS ratings revealed the low quality of the studies' evidence for comparability, evaluation instrument validity, study outcomes and participant blinding. Many e-learning interventions were small-scale and conducted as short-termed pilots. More rigorous evaluation methods for e-learning implementations in LMICs are needed to understand the strengths and shortcomings of e-learning for medical education in low-resource contexts. Valid and reliable evaluations are the foundation to guide and improve e-learning interventions, increase their sustainability, alleviate shortages in health care workers and improve the quality of medical care in LMICs.","url":"https://doi.org/10.1016/j.compedu.2019.103726","authors":["Sandra Barteit","Dorota Guzek","Albrecht Jahn","Till Bärnighausen","Margarida Mendes Jorge","Florian Neuhann"],"tags":["Comparability","Psychological intervention","Medical education","Medicine","Summative assessment"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-07","doi":"https://doi.org/10.1016/j.compedu.2019.103726","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3211034094","name":"Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review","source":"openalex","abstract":"The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool interventions in clinical practice was limited. This study aimed to investigate the clinical impact and quality of randomized controlled trials (RCTs) involving interventions evaluating TS, machine learning (ML), and deep learning (DL) prediction tools. A systematic review on PubMed was conducted to identify RCTs involving TS/ML/DL tool interventions in the past decade. A total of 65 RCTs from 26,082 records were included. A majority of them had model development studies and generally good performance was achieved. The function of TS and ML tools in the RCTs mainly included assistive treatment decisions, assistive diagnosis, and risk stratification, but DL trials were only conducted for assistive diagnosis. Nearly two-fifths of the trial interventions showed no clinical benefit compared to standard care. Though DL and ML interventions achieved higher rates of positive results than TS in the RCTs, in trials with low risk of bias (17/65) the advantage of DL to TS was reduced while the advantage of ML to TS disappeared. The current applications of DL were not yet fully spread performed in medicine. It is predictable that DL will integrate more complex clinical problems than ML and TS tools in the future. Therefore, rigorous studies are required before the clinical application of these tools.","url":"https://doi.org/10.1038/s41746-021-00524-2","authors":["Qian Zhou","Zhihang Chen","Yi-heng Cao","Sui Peng"],"tags":["Psychological intervention","Randomized controlled trial","Medicine","Clinical trial","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-28","doi":"https://doi.org/10.1038/s41746-021-00524-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4306178549","name":"Literature reviews as independent studies: guidelines for academic practice","source":"openalex","abstract":"Abstract Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.","url":"https://doi.org/10.1007/s11846-022-00588-8","authors":["Sascha Kraus","Matthias Breier","Weng Marc Lim","Marina Dabić","Satish Kumar","Dominik K. Kanbach","Debmalya Mukherjee","Vincenzo Corvello","Juan Piñeiro Chousa","Eric W. Liguori","Daniel Palacios-Marqués","Francesco Schiavone","Alberto Ferraris","Cristina Fernandes","João J. Ferreira"],"tags":["Confusion","Systematic review","Epistemology","Engineering ethics","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-14","doi":"https://doi.org/10.1007/s11846-022-00588-8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4401259609","name":"Application of medical artificial intelligence technology in sub-Saharan Africa: Prospects for medical laboratories","source":"openalex","abstract":"The widespread adoption of artificial intelligence (AI) technology globally has brought significant changes to various sectors. AI-assisted algorithms have notably improved decision-making, operational efficiency, and productivity, especially in healthcare and medicine. However, in low and middle-income countries (LMICs), particularly in sub-Saharan Africa (SSA), the integration of medical AI has faced delays and challenges, slowing its acceptance and implementation in medical interventions. This thematic narrative critically explores the current trends and patterns in applying medical AI in SSA, with a specific focus on its potential impact on medical laboratories. The review covers the general use of medical AI in SSA, examining factors like enablers, challenges, and opportunities that influence healthcare systems. Additionally, it looks into the implications of medical AI for medical laboratories and suggests context-specific and practical recommendations for potential integration. We highlight various challenges, including data availability, security concerns, resource limitations, regulatory gaps, poor internet connectivity, and digital literacy issues, contributing to the slow integration of AI in healthcare systems in SSA. Despite challenges, the adoption of medical AI in SSA medical laboratories holds latent potential for improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Further exploration and careful consideration are necessary to unlock these possibilities.","url":"https://doi.org/10.1016/j.smhl.2024.100505","authors":["Richard Kobina Dadzie Ephraim","Gabriel Pezahso Kotam","Evans Duah","Frank Naku Ghartey","Evans Mantiri Mathebula","Tivani P. Mashamba-Thompson"],"tags":["Context (archaeology)","Workflow","Health care","Psychological intervention","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-02","doi":"https://doi.org/10.1016/j.smhl.2024.100505","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3081726800","name":"Identifying normal mammograms in a large screening population using artificial intelligence","source":"openalex","abstract":"OBJECTIVES: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. METHODS: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning-based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). RESULTS: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3-19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1-8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0-54.0) exams, including 7 (10.3%; 95% CI 3.1-17.5) cancers and 52 (27.8%; 95% CI 21.4-34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. CONCLUSIONS: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. KEY POINTS: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.","url":"https://doi.org/10.1007/s00330-020-07165-1","authors":["Kristina Lång","Magnus Dustler","Victor Dahlblom","Anna Åkesson","Ingvar Andersson","Sophia Zackrisson"],"tags":["Medicine","Neuroradiology","Interventional radiology","Radiology","Ultrasound"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-09-02","doi":"https://doi.org/10.1007/s00330-020-07165-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4396798371","name":"Artificial intelligence in the clinical laboratory","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.cca.2024.119724","authors":["Hanjing Hou","Rui Zhang","Jinming Li"],"tags":["Medical laboratory","Workflow","Computer science","Process (computing)","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-09","doi":"https://doi.org/10.1016/j.cca.2024.119724","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4292858576","name":"Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives","source":"openalex","abstract":"Background: The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text: At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions: This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.","url":"https://doi.org/10.1016/j.aopr.2022.100078","authors":["Kai Jin","Juan Ye"],"tags":["Macular degeneration","Diabetic retinopathy","Optometry","Glaucoma","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-24","doi":"https://doi.org/10.1016/j.aopr.2022.100078","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3205361135","name":"Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review","source":"openalex","abstract":"Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: \"Artificial Intelligence\", \"Feature Extraction\", \"Segmentation\", \"Computer Vision\", \"Machine Learning\", \"Deep Learning\", \"Neural Network\", \"Low Back Pain\", \"Lumbar\". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.","url":"https://doi.org/10.3390/ijerph182010909","authors":["Federico D’Antoni","Fabrizio Russo","Luca Ambrosio","Luca Vollero","Gianluca Vadalà","Mario Merone","Rocco Papalia","Vincenzo Denaro"],"tags":["Artificial intelligence","Low back pain","Deep learning","Computer science","Segmentation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-17","doi":"https://doi.org/10.3390/ijerph182010909","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4293029376","name":"Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications","source":"openalex","abstract":"Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.","url":"https://doi.org/10.3390/electronics11172634","authors":["Manisha Singh","Gurubasavaraj V. Pujar","Sethu Arun Kumar","Meduri Bhagyalalitha","Handattu Shankaranarayana Akshatha","Belal Abuhaija","Anas Ratib Alsoud","Laith Abualigah","Narasimha M. Beeraka","Amir H. Gandomi"],"tags":["Artificial intelligence","Machine learning","Tuberculosis","Deep learning","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-08-23","doi":"https://doi.org/10.3390/electronics11172634","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4402054386","name":"Artificial Intelligence of Things: A Survey","source":"openalex","abstract":"The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.","url":"https://doi.org/10.1145/3690639","authors":["Md Shakhrul Iman Siam","Hyunho Ahn","Li Liu","Samiul Alam","Haowei Shen","Zhichao Cao","Ness B. Shroff","Bhaskar Krishnamachari","Mani Srivastava","Mi Zhang"],"tags":["Computer science","Internet of Things","Artificial intelligence","Data science","Human–computer interaction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-08-30","doi":"https://doi.org/10.1145/3690639","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392810470","name":"Artificial intelligence-based MRI radiomics and radiogenomics in glioma","source":"openalex","abstract":"The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.","url":"https://doi.org/10.1186/s40644-024-00682-y","authors":["Haiqing Fan","Yilin Luo","Fang Gu","Bin Tian","Yongqin Xiong","Guipeng Wu","Xin Nie","Jing Yu","Juan Tong","Xin Liao"],"tags":["Radiogenomics","Radiomics","Medicine","Glioma","Bioinformatics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-14","doi":"https://doi.org/10.1186/s40644-024-00682-y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4385556979","name":"What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine","source":"openalex","abstract":"Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.","url":"https://doi.org/10.3390/diagnostics13152582","authors":["Jakub Kufel","Katarzyna Bargieł-Łączek","Szymon Kocot","Maciej Koźlik","Wiktoria Bartnikowska","Michał Janik","Łukasz Czogalik","Piotr Dudek","Mikołaj Magiera","Anna Lis","Iga Paszkiewicz","Zbigniew Nawrat","Maciej Cebula","Katarzyna Gruszczyńska"],"tags":["Artificial intelligence","Artificial neural network","Machine learning","Computer science","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-03","doi":"https://doi.org/10.3390/diagnostics13152582","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3209423806","name":"Responsible innovation ecosystems: Ethical implications of the application of the ecosystem concept to artificial intelligence","source":"openalex","abstract":"The concept of innovation ecosystems has become prominent due to its explanatory power. It offers a convincing account of innovation, explaining how and why innovation pathways change and evolve. It has been adopted to explain, predict, and steer innovation. The increasing importance of innovation for most aspects of human life calls for the inclusion of ethical and social rights aspects into the innovation ecosystems discourse. The current innovation ecosystems literature does not provide guidance on how the integration of ethical and social concerns into innovation ecosystems can be realised. One way to achieve this is to draw on the discussion of responsible research and innovation (RRI). This paper applies RRI to the innovation ecosystems discourse and proposes the concept of responsible innovation systems. It draws on the discussion of the ethics of artificial intelligence (AI) to explore how responsible AI innovation ecosystems can be shaped and realised.","url":"https://doi.org/10.1016/j.ijinfomgt.2021.102441","authors":["Bernd Carsten Stahl"],"tags":["Responsible Research and Innovation","Ecosystem","Knowledge management","Business","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-10-26","doi":"https://doi.org/10.1016/j.ijinfomgt.2021.102441","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3172882582","name":"Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection","source":"openalex","abstract":"High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.","url":"https://doi.org/10.1038/s41467-021-24001-2","authors":["Masateru Taniguchi","Shohei Minami","Chikako Ono","Rina Hamajima","Ayumi Morimura","Shigeto Hamaguchi","Yukihiro Akeda","Yuta Kanai","Takeshi Kobayashi","Wataru Kamitani","Yutaka Terada","Koichiro Suzuki","Nobuaki Hatori","Yoshiaki Yamagishi","Nobuei Washizu","Hiroyasu Takei","Osamu Sakamoto","Norihiko Naono","Kenji Tatematsu","Takashi Washio","Yoshiharu Matsuura","Kazunori Tomono"],"tags":["Nanopore","Nanopore sequencing","Computer science","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","Coronavirus"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-06-17","doi":"https://doi.org/10.1038/s41467-021-24001-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3010945169","name":"Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review","source":"openalex","abstract":"Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.","url":"https://doi.org/10.3390/app10062021","authors":["Ibrahem Kandel","Mauro Castelli"],"tags":["Transfer of learning","Artificial intelligence","Deep learning","Computer science","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-03-16","doi":"https://doi.org/10.3390/app10062021","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W1584925641","name":"Liability for Distributed Artificial Intelligences","source":"openalex","abstract":"W]hat is interesting here is that the program does have the potential to arrive at very strange answers","url":"https://doi.org/10.15779/z38zd4w","authors":["Curtis E.A. Karnow"],"tags":["Liability","Business","Computer science","Finance"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1996-01-01","doi":"https://doi.org/10.15779/z38zd4w","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2010215165","name":"Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques","source":"openalex","abstract":"The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.","url":"https://doi.org/10.1155/2015/986736","authors":["Paul Fergus","David Hignett","Abir Hussain","Dhiya Al‐Jumeily","Khaled Abdel‐Aziz"],"tags":["Electroencephalography","Epilepsy","Epileptic seizure","Artificial intelligence","Scalp"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-01-01","doi":"https://doi.org/10.1155/2015/986736","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4408736524","name":"A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians","source":"openalex","abstract":"While generative artificial intelligence (AI) has shown potential in medical diagnostics, comprehensive evaluation of its diagnostic performance and comparison with physicians has not been extensively explored. We conducted a systematic review and meta-analysis of studies validating generative AI models for diagnostic tasks published between June 2018 and June 2024. Analysis of 83 studies revealed an overall diagnostic accuracy of 52.1%. No significant performance difference was found between AI models and physicians overall (p = 0.10) or non-expert physicians (p = 0.93). However, AI models performed significantly worse than expert physicians (p = 0.007). Several models demonstrated slightly higher performance compared to non-experts, although the differences were not significant. Generative AI demonstrates promising diagnostic capabilities with accuracy varying by model. Although it has not yet achieved expert-level reliability, these findings suggest potential for enhancing healthcare delivery and medical education when implemented with appropriate understanding of its limitations.","url":"https://doi.org/10.1038/s41746-025-01543-z","authors":["Hirotaka Takita","Daijiro Kabata","Shannon L. Walston","Hiroyuki Tatekawa","Kenichi Saito","Yasushi Tsujimoto","Yukio Miki","Daiju Ueda"],"tags":["Meta-analysis","Generative grammar","Reliability (semiconductor)","Diagnostic accuracy","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-03-22","doi":"https://doi.org/10.1038/s41746-025-01543-z","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4324020464","name":"Artificial intelligence‐based chatbot patient information on common retinal diseases using ChatGPT","source":"openalex","abstract":"File S1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.","url":"https://doi.org/10.1111/aos.15661","authors":["Ivan Potapenko","Lars Christian Boberg‐Ans","Michael Stormly Hansen","Oliver Niels Klefter","Elon H. C. van Dijk","Yousif Subhi"],"tags":["Chatbot","Retinal","Computer science","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-13","doi":"https://doi.org/10.1111/aos.15661","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4285728900","name":"Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study","source":"openalex","abstract":"Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students' perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master's and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.","url":"https://doi.org/10.3390/ijerph19148733","authors":["Xin Li","Michael Yi‐Chao Jiang","Morris Siu–Yung Jong","Xinping Zhang","Ching Sing Chai"],"tags":["Perception","Psychology","Applied psychology","Survey research","Neuroscience"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-18","doi":"https://doi.org/10.3390/ijerph19148733","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2336445786","name":"Process mining in healthcare: A literature review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jbi.2016.04.007","authors":["Eric Rojas","Jorge Muñoz-Gama","Marcos Sepúlveda","Daniel Capurro"],"tags":["Process mining","Process (computing)","Computer science","Scope (computer science)","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2016-04-29","doi":"https://doi.org/10.1016/j.jbi.2016.04.007","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3017157669","name":"Segmentation and Feature Extraction in Medical Imaging: A Systematic Review","source":"openalex","abstract":"Image processing techniques being crucial towards analyzing and resolving issues in medical imaging since last two decades. Medical imaging is a process or technique to find the inner or outer construction of mortal body. The process observes medicinal diagnosis, analyze illnesses and develop data-sets of normal and abnormal imageries. Medical imaging is divided in two folds such as invisible-light medical imaging and visible-light medical imaging. The second type of medical imaging were can be understood by a common person whereas the first type can be interpreted by a radiologist. Analysis of all these require segmentation and feature extraction. In fact a lot of medical imaging techniques are available but authors restrict survey to tumor detection through mammograms or magnetic resonance imaging. In this paper, authors survey on various segmentation and feature extraction methods in medicinal images used for preprocessing.","url":"https://doi.org/10.1016/j.procs.2020.03.179","authors":["Chiranji Lal Chowdhary","D. P. Acharjya"],"tags":["Computer science","Preprocessor","Medical imaging","Segmentation","Feature extraction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1016/j.procs.2020.03.179","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4407242328","name":"Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human","source":"openalex","abstract":"Recent advancements in Explainable Artificial Intelligence (XAI) aim to bridge the gap between complex artificial intelligence (AI) models and human understanding, fostering trust and usability in AI systems. However, challenges persist in comprehensively interpreting these models, hindering their widespread adoption. This study addresses these challenges by exploring recently emerging techniques in XAI. The primary problem addressed is the lack of transparency and interpretability in AI models to humanity for institution-wide use, which undermines user trust and inhibits their integration into critical decision-making processes. Through an in-depth review, this study identifies the objectives of enhancing the interpretability of AI models and improving human understanding of their decision-making processes. Various methodological approaches, including post-hoc explanations, model transparency methods, and interactive visualization techniques, are investigated to elucidate AI model behaviours. We further present techniques and methods to make AI models more interpretable and understandable to humans including their strengths and weaknesses to demonstrate promising advancements in model interpretability, facilitating better comprehension of complex AI systems by humans. In addition, we provide the application of XAI in local use cases. Challenges, solutions, and open research directions were highlighted to clarify these compelling XAI utilization challenges. The implications of this research are profound, as enhanced interpretability fosters trust in AI systems across diverse applications, from healthcare to finance. By empowering users to understand and scrutinize AI decisions, these techniques pave the way for more responsible and accountable AI deployment.","url":"https://doi.org/10.1007/s11063-025-11732-2","authors":["Daniel J. Mathew","Deborah Ebem","Anayo Chukwu Ikegwu","Pamela Eberechukwu Ukeoma","Ngozi Fidelia Dibiaezue"],"tags":["Computational intelligence","Artificial intelligence","Computer science","Human intelligence","Cognitive science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-07","doi":"https://doi.org/10.1007/s11063-025-11732-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2934730619","name":"Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments","source":"openalex","abstract":"Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist radiographs. Patients and methods - 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index; the results were compared with those of medical professional groups. Results - The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation - The network exhibited a diagnostic ability similar to that of the orthopedists and a performance superior to that of the radiologists in distinguishing AP wrist radiographs with DRFs from normal images under limited conditions. Further studies are required to determine the feasibility of applying our method as an auxiliary in clinical practice under extended conditions.","url":"https://doi.org/10.1080/17453674.2019.1600125","authors":["Kaifeng Gan","Dingli Xu","Yimu Lin","Yandong Shen","Ting Zhang","Keqi Hu","Ke Zhou","Mingguang Bi","Lingxiao Pan","Wei Wu","Yunpeng Liu"],"tags":["Convolutional neural network","Radiography","Youden's J statistic","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-03","doi":"https://doi.org/10.1080/17453674.2019.1600125","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4414267489","name":"Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery","source":"openalex","abstract":"Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A comprehensive PubMed search was performed without language or publication date restrictions, combining Medical Subject Headings (MeSH) and free-text keywords for AI with domain-specific terms. The search yielded 2047 records, of which 243 duplicates were removed, leaving 1804 unique studies. After screening titles and abstracts, 1482 records were excluded due to irrelevance, preclinical scope, or lack of patient-level outcomes. Full-text review of 322 articles led to the exclusion of 172 studies (no clinical validation or outcomes, n = 64; methodological studies, n = 43; preclinical and in vitro-only, n = 39; conference abstracts without peer-reviewed full text, n = 26). Ultimately, 150 studies met inclusion criteria and were analyzed qualitatively. Data extraction focused on study context, AI technique, dataset characteristics, comparator benchmarks, and reported outcomes, such as diagnostic accuracy, area under the curve (AUC), efficiency, and clinical improvements. Results: AI demonstrated strong performance in diagnostic imaging, achieving expert-level accuracy in tasks such as cancer detection (AUC up to 0.94). CDSS showed promise in predicting adverse events (sepsis, atrial fibrillation), though real-world outcome evidence was mixed. In surgery, AI enhanced intraoperative guidance and risk stratification. Pathology benefited from AI-assisted diagnosis and molecular inference from histology. AI also accelerated drug discovery through protein structure prediction and virtual screening. However, challenges included limited explainability, data bias, lack of prospective trials, and regulatory hurdles. Conclusions: AI is transforming clinical medicine, offering improved accuracy, efficiency, and discovery. Yet, its integration into routine care demands rigorous validation, ethical oversight, and human-AI collaboration. Continued interdisciplinary efforts will be essential to translate these innovations into safe and effective patient-centered care.","url":"https://doi.org/10.3390/clinpract15090169","authors":["Eren Öğüt"],"tags":["Medicine","Clinical decision support system","MEDLINE","Medical physics","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-09-16","doi":"https://doi.org/10.3390/clinpract15090169","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4396869758","name":"Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology","source":"openalex","abstract":"In this mini-review, we delve into the transformative impact of artificial intelligence (AI) and machine learning (ML) in the field of microbiology. The paper provides a brief overview of various domains where AI is reshaping practices, including clinical diagnostics, drug and vaccine discovery, and public health management. Our discussion spotlights the implementation of convolutional neural networks for enhanced pathogen identification, the advancements in point-of-care diagnostics, and the emergence of new antimicrobials to tackle resistant strains. The application of AI in epidemiology, microbial ecology and forensic microbiology is also outlined, underscoring its proficiency in deciphering complex microbial interactions and forecasting disease outbreaks. We critically examine the challenges in AI application, such as ensuring data quality and overcoming algorithmic constraints, and stress the necessity for interpretable AI models that align with medical and ethical standards. We address the intricacies of digitalization in microbiology diagnostics, emphasizing the need for efficient data management in laboratory and clinical environments. Looking forward, we identify key directions for AI in microbiology, particularly focusing on developing adaptable, self-updating AI models and their integration into clinical settings. We conclude by highlighting AI's potential to revolutionize microbiological diagnostics and infection control, significantly influencing patient care and public health. This review serves as an invitation to explore AI's integration into microbiology, showcasing its role in evolving current methodologies and propelling future innovations.","url":"https://doi.org/10.1080/13102818.2024.2349587","authors":["Virna-Maria Tsitou","Dimitrios Rallis","Mariana Tsekova","Nikolay Yanev"],"tags":["Clinical microbiology","Microbiology","Medical microbiology","Biology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-05-12","doi":"https://doi.org/10.1080/13102818.2024.2349587","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4296555550","name":"Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip","source":"openalex","abstract":"Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.","url":"https://doi.org/10.3389/fbioe.2022.985692","authors":["Wanying Gao","Chunyan Wang","Qiwei Li","Xijing Zhang","Jianmin Yuan","Dianfu Li","Yu Sun","Zaozao Chen","Zhongze Gu"],"tags":["Medical imaging","Computer science","Artificial intelligence","Tissue engineering","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-12","doi":"https://doi.org/10.3389/fbioe.2022.985692","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3110254058","name":"Artificial intelligence in the diagnosis of pediatric allergic diseases","source":"openalex","abstract":"Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.","url":"https://doi.org/10.1111/pai.13419","authors":["Giuliana Ferrante","Amelia Licari","Salvatore Fasola","Gian Luigi Marseglia","Stefania La Grutta"],"tags":["Medicine","Applications of artificial intelligence","Relevance (law)","Health care","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-11-21","doi":"https://doi.org/10.1111/pai.13419","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4367853273","name":"A Review of Artificial Intelligence Adoption in Second-Language Learning","source":"openalex","abstract":"Professionals are implementing artificial intelligence (AI) technology in different fields owing to its diverse uses and benefits. Similarly, AI professionals are also beginning to implement AI technology in foreign-language education and second-language learning. Therefore, through a systematic literature review, this paper analyzes the role of AI in helping learners of a second language to master pronunciation. A detailed and in-depth search of different well-known databases was conducted, and of 116 articles, only 39 were selected for this paper. AI algorithms can advance language learning and acquisition in almost every dialect and could be significant for different parties in different ways. For example, organizations could utilize AI technology to develop their workers’ knowledge; individual learners could use AI technology to facilitate their studies anywhere and anytime; and traditional learning institutions could incorporate AI-powered methods of language learning to diversify learners’ opportunities. There are many benefits to employing AI in language learning, particularly in second-language learning.","url":"https://doi.org/10.17507/tpls.1305.21","authors":["Sultan Almelhes"],"tags":["Pronunciation","Language acquisition","Computer science","Artificial intelligence","Foreign language"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-05-01","doi":"https://doi.org/10.17507/tpls.1305.21","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4386135236","name":"Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance","source":"openalex","abstract":"The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.","url":"https://doi.org/10.2967/jnumed.123.266110","authors":["Jonathan Herington","Melissa D. McCradden","Kathleen Creel","Ronald Boellaard","Elizabeth C. Jones","Abhinav K. Jha","Arman Rahmim","Peter J. H. Scott","John J. Sunderland","Richard L. Wahl","Sven Zuehlsdorff","Babak Saboury"],"tags":["Software deployment","Autonomy","Transparency (behavior)","Accountability","Economic Justice"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-24","doi":"https://doi.org/10.2967/jnumed.123.266110","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4383313974","name":"Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons","source":"openalex","abstract":"BACKGROUND: Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use. METHODS: PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis. RESULTS: Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process. CONCLUSIONS: The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human-machine interaction, have been neglected in the debate on AI for clinical ethics so far. TRIAL REGISTRATION: This review is registered at Open Science Framework ( https://osf.io/wvcs9 ).","url":"https://doi.org/10.1186/s12910-023-00929-6","authors":["Lasse Benzinger","Frank Ursin","Wolf‐Tilo Balke","Tim Kacprowski","Sabine Salloch"],"tags":["Beneficence","Philosophy of medicine","Economic Justice","Deliberation","Autonomy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-07-06","doi":"https://doi.org/10.1186/s12910-023-00929-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W7125589886","name":"Clinical research on artificial intelligence medical diagnostic devices: A scoping review","source":"openalex","abstract":"Artificial intelligence medical diagnostic devices (AIMDDs) show strong potential but face barriers to clinical use, emphasizing the need for rigorous clinical research. We assessed current AIMDD research, key challenges, and future directions. A scoping review followed Arksey and O'Malley's methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. PubMed, Web of Science Core Collection, and the Cochrane Database of Systematic Reviews (January 2020–December 2024) were searched on AIMDD design, implementation, and evaluation. Two independent researchers screened and extracted data from the literature using predefined criteria. Ninety-seven articles met the inclusion criteria. Machine learning and deep learning approaches dominated across diverse disease fields, with oncology being the most frequent (41 %). The key challenges identified include insufficient quantity, quality, representativeness, and diversity of data; research designs that do not adequately address clinical needs; poor patient selection; poorly defined gold standards; lack of external and prospective validation; and a disconnect between validation strategies and clinical practice. Additionally, issues such as the “black box” phenomenon, overfitting, and data privacy concerns hinder clinical translation. Completeness and standardization of reporting were also found to be lacking. Significant challenges remain in the development and clinical application of AIMDD. To facilitate their clinical translation, improvements are needed in dataset optimization, clinically driven research design, development of evaluation frameworks, enhanced interpretability, and standardized reporting and validation of algorithms. • This study used a scoping review to synthesize evidence in the complex, high-volume AI medical diagnosis field. • Oncology was the most studied discipline, comprising 41 % of all included publications. • Key barriers include poor data quality, limited external validation, and incomplete methodological reporting. • Future work should focus on dataset optimization, clinician-led design, explainable AI, and standardized validation.","url":"https://doi.org/10.1016/j.engmed.2026.100120","authors":["Xiaowei Zhang","Xiaowei Zhang","Changning Liu","Yifan Sun","Liangzhen You","Xiaoyu Zhang","Xiaoyu Zhang","Hongcai Shang"],"tags":["Standardization","Systematic review","MEDLINE","Artificial intelligence","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2026-01-24","doi":"https://doi.org/10.1016/j.engmed.2026.100120","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4390421984","name":"Is Attention all You Need in Medical Image Analysis? A Review","source":"openalex","abstract":"Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling \"global\" pixel interactions, which restricts their generalisation ability to understand out-of-distribution data with different \"global\" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (\"Transf/Attention\") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.","url":"https://doi.org/10.1109/jbhi.2023.3348436","authors":["Giorgos Papanastasiou","Νικόλαος Δικαίος","Jiahao Huang","Chengjia Wang","Guang Yang"],"tags":["Computer science","Transformer","Data science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-29","doi":"https://doi.org/10.1109/jbhi.2023.3348436","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4389993479","name":"Validity and reliability of artificial intelligence chatbots as public sources of information on endodontics","source":"openalex","abstract":"AIM: This study aimed to evaluate and compare the validity and reliability of responses provided by GPT-3.5, Google Bard, and Bing to frequently asked questions (FAQs) in the field of endodontics. METHODOLOGY: FAQs were formulated by expert endodontists (n = 10) and collected through GPT-3.5 queries (n = 10), with every question posed to each chatbot three times. Responses (N = 180) were independently evaluated by two board-certified endodontists using a modified Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Disagreements on scoring were resolved through evidence-based discussions. The validity of responses was analysed by categorizing scores into valid or invalid at two thresholds: The low threshold was set at score ≥4 for all three responses whilst the high threshold was set at score 5 for all three responses. Fisher's exact test was conducted to compare the validity of responses between chatbots. Cronbach's alpha was calculated to assess the reliability by assessing the consistency of repeated responses for each chatbot. RESULTS: All three chatbots provided answers to all questions. Using the low-threshold validity test (GPT-3.5: 95%; Google Bard: 85%; Bing: 75%), there was no significant difference between the platforms (p > .05). When using the high-threshold validity test, the chatbot scores were substantially lower (GPT-3.5: 60%; Google Bard: 15%; Bing: 15%). The validity of GPT-3.5 responses was significantly higher than Google Bard and Bing (p = .008). All three chatbots achieved an acceptable level of reliability (Cronbach's alpha >0.7). CONCLUSIONS: GPT-3.5 provided more credible information on topics related to endodontics compared to Google Bard and Bing.","url":"https://doi.org/10.1111/iej.14014","authors":["Hossein Mohammad‐Rahimi","Seyed AmirHossein Ourang","Mohamad Amin Pourhoseingholi","Omid Dianat","P. M. H. Dummer","Ali Nosrat"],"tags":["Endodontics","Reliability (semiconductor)","Computer science","Engineering","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-20","doi":"https://doi.org/10.1111/iej.14014","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2989492337","name":"Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics","source":"openalex","abstract":"BACKGROUND: Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. METHODS: Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. RESULTS: According to confusion matrices decision tree, CSV1 (97.1%)-CSV2 (90.5%), SVM: CVS3 (73.2%)-CVS4 (58.5%), and kNN: CVS 5 (60.9%)-CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. CONCLUSION: In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.","url":"https://doi.org/10.1186/s40510-019-0295-8","authors":["Hatice Kök","Ayşe Merve Acılar","Mehmet Said İzgi"],"tags":["Cervical vertebrae","Decision tree","Artificial intelligence","Support vector machine","Algorithm"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-11-14","doi":"https://doi.org/10.1186/s40510-019-0295-8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4200240489","name":"Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects)","source":"openalex","abstract":"Background: Cutting-edge digital technologies are being actively introduced into healthcare. The recent successful efforts of artificial intelligence in diagnosing, predicting and studying diseases, as well as in surgical assisting demonstrate its high efficiency. The AI’s ability to promptly take decisions and learn independently has motivated large corporations to focus on its development and gradual introduction into everyday life. Legal aspects of medical activities are of particular importance, yet the legal regulation of AI’s performance in healthcare is still in its infancy. The state is to a considerable extent responsible for the formation of a legal regime that would meet the needs of modern society (digital society). Objective: This study aims to determine the possible modes of AI’s functioning, to identify the participants in medical-legal relations, to define the legal personality of AI and circumscribe the scope of its competencies. Of importance is the issue of determining the grounds for imposing legal liability on persons responsible for the performance of an AI system. Results: The present study identifies the prospects for a legal assessment of AI applications in medicine. The article reviews the sources of legal regulation of AI, including the unique sources of law sanctioned by the state. Particular focus is placed on medical-legal customs and medical practices. Conclusions: The presented analysis has allowed formulating the approaches to the legal regulation of AI in healthcare.","url":"https://doi.org/10.3390/laws11010003","authors":["Vasiliy A. Laptev","И. В. Ершова","Daria Rinatovna Feyzrakhmanova"],"tags":["Scope (computer science)","Liability","Health care","Engineering ethics","State (computer science)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-29","doi":"https://doi.org/10.3390/laws11010003","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3216614472","name":"Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems","source":"openalex","abstract":"In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.","url":"https://doi.org/10.3390/machines9120328","authors":["Ahmad Abubakar","Carlos Frederico Meschini Almeida","Matheus Mingatos Fernandes Gemignani"],"tags":["Photovoltaic system","Photovoltaics","Fault detection and isolation","Reliability engineering","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-01","doi":"https://doi.org/10.3390/machines9120328","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3217621526","name":"Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.cjca.2021.11.011","authors":["Chayakrit Krittanawong","Mehmet Aydar","Hafeez Ul Hassan Virk","Anirudh Kumar","Scott Kaplin","Lucca Guimaraes","Zhen Wang","Jonathan L. Halperin"],"tags":["Blockchain","Big data","Cryptocurrency","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-30","doi":"https://doi.org/10.1016/j.cjca.2021.11.011","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4395053818","name":"Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects","source":"openalex","abstract":"BACKGROUND: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES: This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS: Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION: Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.","url":"https://doi.org/10.1016/j.ijmedinf.2024.105464","authors":["Rasheed Omobolaji Alabi","Mohammed Elmusrati","Ilmo Leivo","Alhadi Almangush","Antti Mäkitie"],"tags":["Radiomics","Head and neck cancer","Head and neck","Medicine","Current (fluid)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-04-23","doi":"https://doi.org/10.1016/j.ijmedinf.2024.105464","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2996541402","name":"From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices","source":"openalex","abstract":"The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741-742, 1960. https://doi.org/10.1126/science.132.3429.741 ; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.","url":"https://doi.org/10.1007/s11948-019-00165-5","authors":["Jessica Morley","Luciano Floridi","Libby Kinsey","Anat Elhalal"],"tags":["Beneficence","Mainstream","Engineering ethics","Autonomy","Typology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-12-11","doi":"https://doi.org/10.1007/s11948-019-00165-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4406312972","name":"Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review","source":"openalex","abstract":"Predicting drug-target interactions (DTI) is a complex task. With the introduction of artificial intelligence (AI) methods such as machine learning and deep learning, AI-based DTI prediction can significantly enhance speed, reduce costs, and screen potential drug design options before conducting actual experiments. However, the application of AI methods also faces several challenges that need to be addressed. This article reviews various AI-based approaches and suggests possible future directions.","url":"https://doi.org/10.1038/s44385-024-00003-9","authors":["Qian Liao","Yu Zhang","Ying Chu","Yi Ding","Yi Ding","Zhen Liu","Xianyi Zhao","Jie Wan","Jie Wan","Yijie Ding","Yijie Ding","Prayag Tiwari","Quan Zou","Ke Han"],"tags":["Drug","Artificial intelligence","Computer science","Drug target","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-13","doi":"https://doi.org/10.1038/s44385-024-00003-9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4367843698","name":"Artificial Intelligence in CT and MR Imaging for Oncological Applications","source":"openalex","abstract":"Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.","url":"https://doi.org/10.3390/cancers15092573","authors":["Ramesh Paudyal","Akash Shah","Oğuz Akın","Richard Kinh Gian","Amaresha Shridhar Konar","Vaios Hatzoglou","Usman Mahmood","Nancy Y. Lee","Richard J. Wong","Suchandrima Banerjee","Jaemin Shin","Harini Veeraraghavan","Amita Shukla‐Dave"],"tags":["Medical physics","Medicine","Magnetic resonance imaging","Medical imaging","Radiology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-30","doi":"https://doi.org/10.3390/cancers15092573","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2783489174","name":"On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness","source":"openalex","abstract":"In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.","url":"https://doi.org/10.1007/s11205-017-1832-9","authors":["Salvatore Greco","Alessio Ishizaka","Menelaos Tasiou","Gianpiero Torrisi"],"tags":["Popularity","Robustness (evolution)","Variety (cybernetics)","Criticism","Globe"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-01-17","doi":"https://doi.org/10.1007/s11205-017-1832-9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3130071898","name":"Plant diseases and pests detection based on deep learning: a review","source":"openalex","abstract":"Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.","url":"https://doi.org/10.1186/s13007-021-00722-9","authors":["Jun Liu","Xuewei Wang"],"tags":["Deep learning","Artificial intelligence","Identification (biology)","Computer science","Field (mathematics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-24","doi":"https://doi.org/10.1186/s13007-021-00722-9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4391544808","name":"PubMed and beyond: biomedical literature search in the age of artificial intelligence","source":"openalex","abstract":"Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.","url":"https://doi.org/10.1016/j.ebiom.2024.104988","authors":["Qiao Jin","Robert Leaman","Zhiyong Lu"],"tags":["Biomedicine","Data science","Computer science","Meaning (existential)","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-01","doi":"https://doi.org/10.1016/j.ebiom.2024.104988","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4214677181","name":"Personas for Artificial Intelligence (AI) an Open Source Toolbox","source":"openalex","abstract":"Personas have successfully supported the development of classical user interfaces for more than two decades by mapping users’ mental models to specific contexts. The rapid proliferation of Artificial Intelligence (AI) applications makes it necessary to create new approaches for future human-AI interfaces. Human-AI interfaces differ from classical human-computer interfaces in many ways, such as gaining some degree of human-like cognitive, self-executing, and self-adaptive capabilities and autonomy, and generating unexpected outputs that require non-deterministic interactions. Moreover, the most successful AI approaches are so-called “black box” systems, where the technology and the machine learning process are opaque to the user and the AI output is far not intuitive. This work shows how the personas method can be adapted to support the development of human-centered AI applications, and we demonstrate this on the example of a medical context. This work is - to our knowledge - the first to provide personas for AI using an openly availablePersonas for AI toolbox. The toolbox contains guidelines and material supporting persona development for AI as well as templates and pictures for persona visualisation. It is ready to use and freely available to the international research and development community. Additionally, an example from medical AI is provided as a best practice use case. This work is intended to help foster the development of novel human-AI interfaces that will be urgently needed in the near future.","url":"https://doi.org/10.1109/access.2022.3154776","authors":["Andreas Holzinger","Michaela Kargl","Bettina Kipperer","Peter Regitnig","Markus Plass","Heimo Müller"],"tags":["Persona","Computer science","Toolbox","Human–computer interaction","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1109/access.2022.3154776","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3138147850","name":"Mapping Artificial Intelligence in Education Research: a Network-based Keyword Analysis","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s40593-021-00244-4","authors":["Shihui Feng","Nancy Law"],"tags":["Computer science","Learning analytics","Field (mathematics)","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-03-15","doi":"https://doi.org/10.1007/s40593-021-00244-4","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3154183468","name":"Artificial intelligence in gastroenterology and hepatology: Status and challenges","source":"openalex","abstract":"Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.","url":"https://doi.org/10.3748/wjg.v27.i16.1664","authors":["Jiasheng Cao","Ziyi Lu","Mingyu Chen","Bin Zhang","Sarun Juengpanich","Jiahao Hu","Shijie Li","Win Topatana","Xue-Yin Zhou","Feng Xu","Jiliang Shen","Yu Liu","Xiujun Cai"],"tags":["Hepatology","Medicine","Internal medicine","Medical physics","MEDLINE"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-04-21","doi":"https://doi.org/10.3748/wjg.v27.i16.1664","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4404701785","name":"Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions","source":"openalex","abstract":"Owing to the exceptional porous properties of metal-organic frameworks (MOFs), there has recently been a surge of interest, evidenced by a plethora of research into their design, synthesis, properties, and applications. This expanding research landscape has driven significant advancements in the precise regulation of MOF design and synthesis. Initially dominated by large-scale synthesis approaches, this field has evolved towards more targeted functional modifications. Recently, the integration of computational science, particularly through artificial intelligence predictions, has ushered in a new era of innovation, enabling more precise and efficient MOF design and synthesis methodologies. The objective of this review is to provide readers with an extensive overview of the development process of MOF design and synthesis, and to present visions for future developments.","url":"https://doi.org/10.1039/d4cs00432a","authors":["Zongsu Han","Yihao Yang","Joshua Rushlow","Jiatong Huo","Zhaoyi Liu","Yu‐Chuan Hsu","Rujie Yin","Mengmeng Wang","Rong‐Ran Liang","Kunyu Wang","Hong‐Cai Zhou"],"tags":["Metal-organic framework","Scale (ratio)","Nanotechnology","Computer science","Materials science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-11-25","doi":"https://doi.org/10.1039/d4cs00432a","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3217124584","name":"Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review","source":"openalex","abstract":"Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.","url":"https://doi.org/10.1016/s2589-7500(21)00229-6","authors":["Ania Syrowatka","Wenyu Song","Mary G. Amato","Dinah Foer","Heba H Edrees","Zoe Co","Masha Kuznetsova","Sevan Dulgarian","Diane L. Seger","Aurélien Simona","Paul Bain","Gretchen Purcell Jackson","Kyu Rhee","David W. Bates"],"tags":["Harm","Key (lock)","Computer science","Artificial intelligence","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-11-23","doi":"https://doi.org/10.1016/s2589-7500(21)00229-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4205468340","name":"Keratoconus: An updated review","source":"openalex","abstract":"Keratoconus is a bilateral and asymmetric disease which results in progressive thinning and steeping of the cornea leading to irregular astigmatism and decreased visual acuity. Traditionally, the condition has been described as a noninflammatory disease; however, more recently it has been associated with ocular inflammation. Keratoconus normally develops in the second and third decades of life and progresses until the fourth decade. The condition affects all ethnicities and both sexes. The prevalence and incidence rates of keratoconus have been estimated to be between 0.2 and 4,790 per 100,000 persons and 1.5 and 25 cases per 100,000 persons/year, respectively, with highest rates typically occurring in 20- to 30-year-olds and Middle Eastern and Asian ethnicities. Progressive stromal thinning, rupture of the anterior limiting membrane, and subsequent ectasia of the central/paracentral cornea are the most commonly observed histopathological findings. A family history of keratoconus, eye rubbing, eczema, asthma, and allergy are risk factors for developing keratoconus. Detecting keratoconus in its earliest stages remains a challenge. Corneal topography is the primary diagnostic tool for keratoconus detection. In incipient cases, however, the use of a single parameter to diagnose keratoconus is insufficient, and in addition to corneal topography, corneal pachymetry and higher order aberration data are now commonly used. Keratoconus severity and progression may be classified based on morphological features and disease evolution, ocular signs, and index-based systems. Keratoconus treatment varies depending on disease severity and progression. Mild cases are typically treated with spectacles, moderate cases with contact lenses, while severe cases that cannot be managed with scleral contact lenses may require corneal surgery. Mild to moderate cases of progressive keratoconus may also be treated surgically, most commonly with corneal cross-linking. This article provides an updated review on the definition, epidemiology, histopathology, aetiology and pathogenesis, clinical features, detection, classification, and management and treatment strategies for keratoconus.","url":"https://doi.org/10.1016/j.clae.2021.101559","authors":["Jacinto Santodomingo‐Rubido","Gonzalo Carracedo","Asaki Suzaki","César Villa-Collar","Stephen J. Vincent","James S. Wolffsohn"],"tags":["Keratoconus","Medicine","Ophthalmology","Cornea","Corneal topography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-04","doi":"https://doi.org/10.1016/j.clae.2021.101559","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3131719100","name":"A literature survey of the robotic technologies during the COVID-19 pandemic","source":"openalex","abstract":"Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies' perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies' perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work's technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.","url":"https://doi.org/10.1016/j.jmsy.2021.02.005","authors":["Xi Vincent Wang","Lihui Wang"],"tags":["Pandemic","Robotics","Leverage (statistics)","Coronavirus disease 2019 (COVID-19)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-15","doi":"https://doi.org/10.1016/j.jmsy.2021.02.005","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4323981835","name":"Can an artificial intelligence chatbot be the author of a scholarly article?","source":"openalex","abstract":"At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.","url":"https://doi.org/10.3352/jeehp.2023.20.6","authors":["Ju Yoen Lee"],"tags":["Chatbot","Computer science","Perspective (graphical)","World Wide Web","Deception"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-27","doi":"https://doi.org/10.3352/jeehp.2023.20.6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4391531696","name":"Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review","source":"openalex","abstract":"BACKGROUND: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS: In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as \"recommended\"; however, 281 and 187 were \"not recommended\" and \"warning,\" respectively. CONCLUSION: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.","url":"https://doi.org/10.1186/s12916-024-03273-7","authors":["Yue Cai","Yuqing Cai","Liying Tang","Yihan Wang","Mengchun Gong","Tian-Ci Jing","Huijun Li","Jesse Li‐Ling","Wei Hu","Zhihua Yin","Da-Xin Gong","Guangwei Zhang"],"tags":["Medicine","Artificial intelligence","Machine learning","Predictive modelling","Data extraction"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-05","doi":"https://doi.org/10.1186/s12916-024-03273-7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3093064335","name":"Artificial Intelligence in Screening Mammography: A Population Survey of Women’s Preferences","source":"openalex","abstract":"OBJECTIVE: To investigate the general population's view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms. METHODS: Dutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error. RESULTS: Of the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with \"neither agree nor disagree.\" The item AI as a second reader was mostly responded with \"neither agree nor disagree\" (37.1%) and \"agree\" (37.6%), whereas the two last items on developer's and radiologist' responsibilities were mostly answered with \"neither agree nor disagree\" (44.6% and 39.2%, respectively). DISCUSSION: Despite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.","url":"https://doi.org/10.1016/j.jacr.2020.09.042","authors":["Yfke Ongena","Derya Yakar","Marieke Haan","Thomas C. Kwee"],"tags":["Mammography","Population","Reading (process)","Interpretation (philosophy)","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-12","doi":"https://doi.org/10.1016/j.jacr.2020.09.042","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4212775192","name":"Trust in medical artificial intelligence: a discretionary account","source":"openalex","abstract":"Abstract This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is exercised by AI practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI.","url":"https://doi.org/10.1007/s10676-022-09630-5","authors":["Philip J. Nickel"],"tags":["Normative","Sketch","Computer science","Artificial intelligence","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-24","doi":"https://doi.org/10.1007/s10676-022-09630-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4411100445","name":"Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology","source":"openalex","abstract":"Clinical decision-making in oncology is complex, requiring the integration of multimodal data and multidomain expertise. We developed and evaluated an autonomous clinical artificial intelligence (AI) agent leveraging GPT-4 with multimodal precision oncology tools to support personalized clinical decision-making. The system incorporates vision transformers for detecting microsatellite instability and KRAS and BRAF mutations from histopathology slides, MedSAM for radiological image segmentation and web-based search tools such as OncoKB, PubMed and Google. Evaluated on 20 realistic multimodal patient cases, the AI agent autonomously used appropriate tools with 87.5% accuracy, reached correct clinical conclusions in 91.0% of cases and accurately cited relevant oncology guidelines 75.5% of the time. Compared to GPT-4 alone, the integrated AI agent drastically improved decision-making accuracy from 30.3% to 87.2%. These findings demonstrate that integrating language models with precision oncology and search tools substantially enhances clinical accuracy, establishing a robust foundation for deploying AI-driven personalized oncology support systems.","url":"https://doi.org/10.1038/s43018-025-00991-6","authors":["Dyke Ferber","Omar S. M. El Nahhas","Georg Wölflein","Isabella C. Wiest","Jan Clusmann","Marie-Elisabeth Leßmann","Sebastian Foersch","Jacqueline Lammert","Maximilian Tschochohei","Dirk Jaeger","Manuel Salto‐Tellez","Nikolaus Schultz","Daniel Truhn","Jakob Nikolas Kather"],"tags":["Artificial intelligence","Clinical decision making","Computer science","KRAS","Precision oncology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-06-06","doi":"https://doi.org/10.1038/s43018-025-00991-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4312032666","name":"Literature analysis of artificial intelligence in biomedicine","source":"openalex","abstract":"Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.","url":"https://doi.org/10.21037/atm-2022-50","authors":["Tim Hulsen"],"tags":["Artificial intelligence","Computer science","Biomedicine","Standardization","Big data"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-11-19","doi":"https://doi.org/10.21037/atm-2022-50","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3161731903","name":"Levels of explainable artificial intelligence for human-aligned conversational explanations","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.artint.2021.103525","authors":["Richard Dazeley","Peter Vamplew","Cameron Foale","Charlotte Young","Sunil Aryal","Francisco Cruz"],"tags":["Process (computing)","Artificial intelligence","Interpretation (philosophy)","Legislature","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-11","doi":"https://doi.org/10.1016/j.artint.2021.103525","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4281612393","name":"Prediction of preterm birth using artificial intelligence: a systematic review","source":"openalex","abstract":"Preterm birth is the leading cause of neonatal death. It is challenging to predict preterm birth. We elucidated the state of artificial intelligence research on the prediction of preterm birth, clarifying the predictive values and accuracy. We performed a systematic review using three databases (PubMed, Web of Science, and Scopus) in August 2020, with keywords as ‘artificial intelligence,’ ‘deep learning,’ ‘machine learning,’ and ‘neural network’ combined with ‘preterm birth’. We included 22 publications between 2010 and 2020. Regarding the predictive values, electrohysterogram images were mostly used, followed by the biological profiles, the metabolic panel in amniotic fluid or maternal blood, and the cervical images on the ultrasound examination. The size of dataset in most studies was hundred cases and too small for learning, although only three studies used the medical database over a hundred thousand cases. The accuracy was better in the studies using the metabolic panel and electrohysterogram images. Impact statementWhat is already known on this subject? Preterm birth is the leading cause of newborn morbidity and mortality. Presently, the prediction of preterm birth in individual cases is still challenging.What the results of this study add? Using artificial intelligence such as deep learning and machine learning models, clinical data could lead to accurate prediction of preterm birth.What the implications are of these findings for clinical practice and/or further research? The size of the datasets was too small for the models using artificial intelligence in the previous studies. Big data should be prepared for the future studies.","url":"https://doi.org/10.1080/01443615.2022.2056828","authors":["Munetoshi Akazawa","Kazunori Hashimoto"],"tags":["Medicine","Artificial intelligence","Machine learning","Artificial neural network","Deep learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-06-01","doi":"https://doi.org/10.1080/01443615.2022.2056828","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4382463158","name":"Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments","source":"openalex","abstract":"With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences.","url":"https://doi.org/10.3390/s23135913","authors":["Wei‐Hsun Wang","Wen-Shin Hsu"],"tags":["Health care","Wearable computer","Wearable technology","Knowledge management","System integration"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-26","doi":"https://doi.org/10.3390/s23135913","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4362696287","name":"Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art","source":"openalex","abstract":"Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.","url":"https://doi.org/10.3233/idt-230092","authors":["Maria Virvou"],"tags":["Usability","Computer science","Autonomy","Reciprocity (cultural anthropology)","User experience design"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-04-07","doi":"https://doi.org/10.3233/idt-230092","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4307514945","name":"Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review","source":"openalex","abstract":"Background and Objective: Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a \"black box\". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the \"black box\" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows. Methods: We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022. Key Content and Findings: In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive. Conclusions: Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.","url":"https://doi.org/10.21037/tcr-22-1626","authors":["Colton Ladbury","Reza Zarinshenas","Hemal Semwal","A. C. Tam","Nagarajan Vaidehi","Andréi S. Rodin","An Liu","Scott Glaser","Ravi Salgia","Arya Amini"],"tags":["Interpretability","Computer science","Workflow","Identification (biology)","Feature selection"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-01","doi":"https://doi.org/10.21037/tcr-22-1626","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2981124546","name":"Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study","source":"openalex","abstract":"BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field. OBJECTIVE: This study aimed to investigate the general public's acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people's intention to use these devices. METHODS: We proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care-related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model's path effects, significance, goodness of fit, and mediation and moderation effects were analyzed. RESULTS: =0.515) is significantly affected by subjective norms (beta=.408; P<.001), perceived usefulness (beta=.336; P=.03), and resistance bias (beta=-.237; P=.02). Subjective norms and perceived behavior control had an indirect impact on intention to use through perceived usefulness and perceived ease of use. Eye health consciousness had an indirect positive effect on intention to use through perceived usefulness. Trust had a significant moderation effect (beta=-.095; P=.049) on the effect path of perceived usefulness to intention to use. CONCLUSIONS: The item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products.","url":"https://doi.org/10.2196/14316","authors":["Tiantian Ye","Jiaolong Xue","Mingguang He","Jing Gu","Haotian Lin","Xu Bin","Yu Cheng"],"tags":["Structural equation modeling","Confirmatory factor analysis","Psychosocial","Psychology","Construct validity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-17","doi":"https://doi.org/10.2196/14316","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4220664218","name":"Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.ijmedinf.2022.104738","authors":["Anto Čartolovni","Ana Tomičić","Elvira Lazić Mosler"],"tags":["CINAHL","Health care","PsycINFO","Accountability","Transparency (behavior)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-14","doi":"https://doi.org/10.1016/j.ijmedinf.2022.104738","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3162605393","name":"Artificial intelligence applied to musculoskeletal oncology: a systematic review","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s00256-021-03820-w","authors":["Matthew Li","Syed Rakin Ahmed","Edwin Choy","Santiago A. Lozano‐Calderón","Jayashree Kalpathy–Cramer","Connie Y. Chang"],"tags":["Multidisciplinary approach","Medicine","Radiomics","Artificial intelligence","Bone scintigraphy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-19","doi":"https://doi.org/10.1007/s00256-021-03820-w","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2896735034","name":"A Systematic Review of Social Presence: Definition, Antecedents, and Implications","source":"openalex","abstract":"Social presence, or the feeling of being there with a \"real\" person, is a crucial component of interactions that take place in virtual reality. This paper reviews the concept, antecedents, and implications of social presence, with a focus on the literature regarding the predictors of social presence. The article begins by exploring the concept of social presence, distinguishing it from two other dimensions of presence-telepresence and self-presence. After establishing the definition of social presence, the article offers a systematic review of 233 separate findings identified from 152 studies that investigate the factors (i.e., immersive qualities, contextual differences, and individual psychological traits) that predict social presence. Finally, the paper discusses the implications of heightened social presence and when it does and does not enhance one's experience in a virtual environment.","url":"https://doi.org/10.3389/frobt.2018.00114","authors":["Catherine S. Oh","Jeremy N. Bailenson","Greg Welch"],"tags":["Feeling","Focus (optics)","Computer science","Component (thermodynamics)","Social psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2018-10-15","doi":"https://doi.org/10.3389/frobt.2018.00114","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3091875764","name":"Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.compbiomed.2020.104043","authors":["Ankush D. Jamthikar","Deep Gupta","Luca Saba","Narendra N. Khanna","Klaudija Višković","Sophie Mavrogeni","John R. Laird","Naveed Sattar","Amer M. Johri","Gyan Pareek","Martin Miner","Petros P. Sfikakis","Athanasios Protogerou","Vijay Viswanathan","Aditya Sharma","George D. Kitas","Andrew Nicolaides","Raghu Kolluri","Jasjit S. Suri"],"tags":["Risk assessment","Predictive power","Predictive modelling","Flexibility (engineering)","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-10-08","doi":"https://doi.org/10.1016/j.compbiomed.2020.104043","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4405494680","name":"Applications of artificial intelligence in current pharmacy practice: A scoping review","source":"openalex","abstract":"BACKGROUND: Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice. METHODS: A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: (\"artificial intelligence\") AND (\"pharmacy\" OR \"pharmacist\" OR \"pharmaceutical service\" OR \"pharmacy service\"). Reference lists of identified review articles were also screened. RESULTS: The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes. CONCLUSIONS: This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.","url":"https://doi.org/10.1016/j.sapharm.2024.12.007","authors":["Hatzimanolis Jessica","Riley Britney","El-Den Sarira","Parisa Aslani","Zhou Joe","Chval Kathryn B."],"tags":["Pharmacy","Current (fluid)","Computer science","Data science","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-17","doi":"https://doi.org/10.1016/j.sapharm.2024.12.007","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4324046518","name":"Chatting and cheating: Ensuring academic integrity in the era of ChatGPT","source":"openalex","abstract":"The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of using ChatGPT in higher education, and discusses the potential risks and rewards of these tools. The paper also considers the difficulties of detecting and preventing academic dishonesty, and suggests strategies that universities can adopt to ensure ethical and responsible use of these tools. These strategies include developing policies and procedures, providing training and support, and using various methods to detect and prevent cheating. The paper concludes that while the use of AI in higher education presents both opportunities and challenges, universities can effectively address these concerns by taking a proactive and ethical approach to the use of these tools.","url":"https://doi.org/10.1080/14703297.2023.2190148","authors":["Debby Cotton","Peter A. Cotton","J. Reuben Shipway"],"tags":["Cheating","Academic dishonesty","Academic integrity","Honesty","Engineering ethics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-13","doi":"https://doi.org/10.1080/14703297.2023.2190148","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4206656376","name":"Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities","source":"openalex","abstract":"ABSTRACT: Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.","url":"https://doi.org/10.1097/rct.0000000000001247","authors":["Ahmed W. Moawad","David T. Fuentes","Mohamed G. ElBanan","Ahmed S. Shalaby","Jeffrey Guccione","Serageldin Kamel","Corey T. Jensen","Khaled M. Elsayes"],"tags":["Artificial intelligence","Convolutional neural network","Medical imaging","Deep learning","Workflow"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1097/rct.0000000000001247","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4313397009","name":"Wearable and flexible electrochemical sensors for sweat analysis: a review","source":"openalex","abstract":"Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.","url":"https://doi.org/10.1038/s41378-022-00443-6","authors":["Fupeng Gao","Chunxiu Liu","Lichao Zhang","Tiezhu Liu","Zheng Wang","Zixuan Song","Haoyuan Cai","Zhen Fang","Jiamin Chen","Junbo Wang","Mengdi Han","Jun Wang","Kai Lin","Ruoyong Wang","Mingxiao Li","Qian Mei","Xibo Ma","Shuli Liang","Guangyang Gou","Ning Xue"],"tags":["Wearable computer","Wearable technology","Nanotechnology","Computer science","Materials science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-31","doi":"https://doi.org/10.1038/s41378-022-00443-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4360610013","name":"Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging","source":"openalex","abstract":"","url":"https://doi.org/10.1038/s41591-023-02252-4","authors":["Todd Hollon","Cheng Jiang","Asadur Chowdury","Mustafa Nasir-Moin","Akhil Kondepudi","Alexander A. Aabedi","Arjun R. Adapa","Wajd N. Al‐Holou","Jason Heth","Oren Sagher","Pedro R. Löwenstein","Maria Castro","Lisa Irina Wadiura","Georg Widhalm","Volker Neuschmelting","David Reinecke","Niklas von Spreckelsen","Mitchel S. Berger","Shawn L. Hervey‐Jumper","John G. Golfinos","Matija Snuderl","Sandra Camelo‐Piragua","Christian W. Freudiger","Honglak Lee","Daniel A. Orringer"],"tags":["Glioma","Medicine","ATRX","Histology","Diffuse optical imaging"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-23","doi":"https://doi.org/10.1038/s41591-023-02252-4","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4391042079","name":"Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management","source":"openalex","abstract":"Shuang Ren, Riikka M. Sarala, Paul Hibbert The advent of generative artificial intelligence (GAI) has sparked both enthusiasm and anxiety as different stakeholders grapple with the potential to reshape the business and management landscape. This dynamic discourse extends beyond GAI itself to encompass closely related innovations that have existed for some time, for example, machine learning, thereby creating a collective anticipation of opportunities and dilemmas surrounding the transformative or disruptive capacities of these emerging technologies. Recently, ChatGPT's ability to access information from the web in real time marks a significant advancement with profound implications for businesses. This feature is argued to enhance the model's capacity to provide up-to-date, contextually relevant information, enabling more dynamic customer interactions. For businesses, this could mean improvements in areas like market analysis, trend tracking, customer service and real-time data-driven problem-solving. However, this also raises concerns about the accuracy and reliability of the information sourced, given the dynamic and sometimes unverified nature of web content. Additionally, real-time web access might complicate data privacy and security, as the boundaries of GAI interactions extend into the vast and diverse Internet landscape. These factors necessitate a careful and responsible approach to evaluating and using advanced GAI capabilities in business and management contexts. GAI is attracting much interest both in the academic and business practitioner literature. A quick search in Google Scholar, using the search terms ‘generative artificial intelligence’ and ‘business’ or ‘management’, yields approximately 1740 results. Within this extensive repository, scholars delve into diverse facets, exploring GAI's potential applications across various business and management functions, contemplating its implications for management educators and scrutinizing specific technological applications. Learned societies such as the British Academy of Management have also joined forces in leading the discussion on AI and digitalization in business and management academe. Meanwhile, practitioners and consultants alike (e.g. McKinsey & Company, PWC, World Economic Forum) have produced dedicated discussions, reports and forums to offer insights into the multifaceted impacts and considerations surrounding the integration of GAI in contemporary business and management practices. Table 1 illustrates some current applications of GAI as documented in the practitioner literature. Zalando [online platform for fashion and lifestyle] Instacart [e-commerce application] Salesforce [cloud-based customer relationship software provider] DHL [logistics provider] Coca-Cola [beverage company] Nestlé and Mondelez [confectionary] Heinz [food processing company] Air India [airline] Duolingo [language learning application] Mastercard [financial services] In an attempt to capture the new opportunities and challenges brought about by this technology and to hopefully find a way forward to guide research and practice, management journals have been swift to embrace the trend, introducing special issues on GAI. These issues aim to promote intellectual debate, for instance in relation to specific business disciplines (e.g. Benbya, Pachidi and Jarvenpaa, 2021) or organizational possibilities and pitfalls (Chalmers et al., 2023). However, amidst these commendable efforts that reflect a broad spectrum of perspectives, a critical examination of the burgeoning hype around GAI reveals a significant gap. Despite the proliferation of discussions from scholars, practitioners and the general public, the prevailing discourse is often speculative, lacking a robust theoretical foundation. This deficiency points to the challenges to existing theories in terms of their efficacy in explaining the unique demands created by GAI and indicates an urgent need for refining prior theories or even r","url":"https://doi.org/10.1111/1467-8551.12788","authors":["Olivia Brown","Robert M. Davison","Stephanie Decker","David A. Ellis","James Faulconbridge","Julie Gore","Michelle Greenwood","Gazi Islam","Christina Lubinski","Niall MacKenzie","Renate E. Meyer","Daniel Muzio","Paolo Quattrone","M. N. Ravishankar","Tammar B. Zilber","Shuang Ren","Riikka M. Sarala","Paul Hibbert"],"tags":["Anticipation (artificial intelligence)","Business intelligence","Computer science","Generative grammar","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1111/1467-8551.12788","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3016019826","name":"Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally","source":"openalex","abstract":"Predictive computing tools are increasingly being used and have demonstrated successfulness in providing insights that can lead to better health policy and management. However, as these technologies are still in their infancy stages, slow progress is being made in their adoption for serious consideration at national and international policy levels. However, a recent case evidences that the precision of Artificial Intelligence (AI) driven algorithms are gaining in accuracy. AI modelling driven by companies such as BlueDot and Metabiota anticipated the Coronavirus (COVID-19) in China before it caught the world by surprise in late 2019 by both scouting its impact and its spread. From a survey of past viral outbreaks over the last 20 years, this paper explores how early viral detection will reduce in time as computing technology is enhanced and as more data communication and libraries are ensured between varying data information systems. For this enhanced data sharing activity to take place, it is noted that efficient data protocols have to be enforced to ensure that data is shared across networks and systems while ensuring privacy and preventing oversight, especially in the case of medical data. This will render enhanced AI predictive tools which will influence future urban health policy internationally.","url":"https://doi.org/10.3390/ai1020009","authors":["Zaheer Allam","Gourav Dey","David S. Jones"],"tags":["Surprise","Coronavirus disease 2019 (COVID-19)","China","Data science","Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-13","doi":"https://doi.org/10.3390/ai1020009","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2023691053","name":"Artificial neural networks in pathology and medical laboratories","source":"openalex","abstract":"","url":"https://doi.org/10.1016/s0140-6736(95)92904-5","authors":["Richard Dybowski","Vanya Gant"],"tags":["Computer science","Turnaround time","Housekeeping","Automation","Microprocessor"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"1995-11-01","doi":"https://doi.org/10.1016/s0140-6736(95)92904-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4296949118","name":"Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges","source":"openalex","abstract":"OBJECTIVES: This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. MATERIAL AND METHODS: Narrative literature review. RESULTS: Narrative literature review. CONCLUSION: Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients' facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. CLINICAL SIGNIFICANCE: While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics.","url":"https://doi.org/10.1007/s00784-022-04724-2","authors":["Raphael Patcas","Michael M. Bornstein","Marc Schätzle","Radu Timofte"],"tags":["Face (sociological concept)","Narrative","Narrative review","Medicine","Dentistry"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-24","doi":"https://doi.org/10.1007/s00784-022-04724-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3163233543","name":"Deep Learning-Based Artificial Intelligence for Mammography","source":"openalex","abstract":"During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.","url":"https://doi.org/10.3348/kjr.2020.1210","authors":["Jung Hyun Yoon","Eun‐Kyung Kim"],"tags":["Medicine","Mammography","Breast cancer","Incidence (geometry)","Breast cancer screening"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.3348/kjr.2020.1210","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392014371","name":"Innovative applications of artificial intelligence during the COVID-19 pandemic","source":"openalex","abstract":"The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.","url":"https://doi.org/10.1016/j.imj.2024.100095","authors":["Chenrui Lv","Wenqiang Guo","Xinyi Yin","Liu Liu","Xinlei Huang","Shimin Li","Li Zhang"],"tags":["Pandemic","Repurposing","Big data","Data science","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-02-21","doi":"https://doi.org/10.1016/j.imj.2024.100095","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4386847565","name":"The challenges imposed by artificial intelligence: are we ready in medical education?","source":"openalex","abstract":"Artificial intelligence (AI) is the science and engineering of making intelligent machines. In medical education, the usefulness of AI and its applications is being explored in training, learning, simulation, curriculum, and developing new assessment tools. This editorial encourages authors to submit their research on AI concerning medical education to enrich our knowledge.","url":"https://doi.org/10.1186/s12909-023-04660-z","authors":["Samy A. Azer","Anthony P. S. Guerrero"],"tags":["Curriculum","Applications of artificial intelligence","Computer science","Engineering ethics","Medical education"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-09-19","doi":"https://doi.org/10.1186/s12909-023-04660-z","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392938070","name":"ChatGPT in medicine: prospects and challenges: a review article","source":"openalex","abstract":"It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generative artificial intelligence (AI) program. The introduction of this cross-generational product initially brought a huge shock to people with its incredible potential and then aroused increasing concerns among people. In the field of medicine, researchers have extensively explored the possible applications of ChatGPT and achieved numerous satisfactory results. However, opportunities and issues always come together. Problems have also been exposed during the applications of ChatGPT, requiring cautious handling, thorough consideration, and further guidelines for safe use. Here, the authors summarized the potential applications of ChatGPT in the medical field, including revolutionizing healthcare consultation, assisting patient management and treatment, transforming medical education, and facilitating clinical research. Meanwhile, the authors also enumerated researchers' concerns arising along with its broad and satisfactory applications. As it is irreversible that AI will gradually permeate every aspect of modern life, the authors hope that this review can not only promote people's understanding of the potential applications of ChatGPT in the future but also remind them to be more cautious about this \"Pandora's Box\" in the medical field. It is necessary to establish normative guidelines for its safe use in the medical field as soon as possible.","url":"https://doi.org/10.1097/js9.0000000000001312","authors":["Songtao Tan","Xin Xin","Di Wu"],"tags":["Medicine","Engineering ethics","Normative","Field (mathematics)","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-19","doi":"https://doi.org/10.1097/js9.0000000000001312","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4405171814","name":"Generative Artificial Intelligence in Mental Healthcare: An Ethical Evaluation","source":"openalex","abstract":"Abstract Purpose Since November 2022, generative artificial intelligence (AI) chatbots, such as ChatGPT, that are powered by large language models (LLM) have been the subject of growing attention in healthcare. Using biomedical ethical principles to frame our discussion, this review seeks to clarify the current ethical implications of these chatbots, and to identify the key empirical questions that should be pursued to inform ethical practice. Recent findings In the past two years, research has been conducted into the capacity of generative AI chatbots to pass medical school examinations, evaluate complex diagnostic cases, solicit patient histories, interpret and summarize clinical documentation, and deliver empathic care. These studies demonstrate the scope and growing potential of this AI to assist with clinical tasks. Summary Despite increasing recognition that generative AI can play a valuable role in assisting with clinical tasks, there has been limited, focused attention paid to the ethical consequences of these technologies for mental healthcare. Adopting a framework of biomedical ethics, this review sought to evaluate the ethics of generative AI tools in mental healthcare, and to motivate further research into the benefits and harms of these tools.","url":"https://doi.org/10.1007/s40501-024-00340-x","authors":["Charlotte Blease","Adam Rodman"],"tags":["Generative grammar","Psychology","Mental health care","Neurology","Mental health"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-12-09","doi":"https://doi.org/10.1007/s40501-024-00340-x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4387709287","name":"Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers","source":"openalex","abstract":"Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as \"consumers\", \"translators\", or \"developers\". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.","url":"https://doi.org/10.1016/j.xcrm.2023.101230","authors":["Faye Yu Ci Ng","Arun James Thirunavukarasu","Haoran Cheng","Ting Fang Tan","Laura Gutiérrez","Yanyan Lan","Jasmine Chiat Ling Ong","Yap Seng Chong","Kee Yuan Ngiam","Dean Ho","Tien Yin Wong","Kenneth Kwek","Finale Doshi‐Velez","Catherine R. Lucey","Thomas M. Coffman","Daniel Shu Wei Ting"],"tags":["Curriculum","Workforce","Health care","Knowledge management","Literacy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-01","doi":"https://doi.org/10.1016/j.xcrm.2023.101230","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3163577050","name":"The application of artificial intelligence in lung cancer: a narrative review","source":"openalex","abstract":"OBJECTIVE: This review was conducted to systematically summarize the progress made by artificial intelligence technology in early screening based medical imaging, pathological diagnosis, genomics inspection, prognostic evaluation, and individual treatment of lung cancer. BACKGROUND: Lung cancer has a high mortality rate in China, which is closely related to the fact that most lung cancer patients are not diagnosed until the malignancy is advanced. Challenges remain in the early detection, accurate diagnosis, monitoring and individual treatment of lung cancers. Artificial intelligence has developed to process large amounts of data, from clinical presentations to physiological images, which is essential for solving the complex issues with clinical medicine. Increasing evidence has suggested that artificial intelligence technology provides novel, promising strategies for the diagnosis and treatment of lung cancer. METHODS: A review of literature was conducted in PubMed, EMBASE and Cochrane to identify the latest research on artificial intelligence and lung cancer, and ultimately to generate a narrative review. CONCLUSIONS: Artificial intelligence plays an important role in the imaging inspection, histopathology examination and genomics inspection of lung cancer. In addition, artificial intelligence has the ability to detect a small number of biomarkers, which is conducive to lung cancer monitoring. Moreover, the intelligent treatment of lung cancer has gradually become the trend of future development, whether in internal medicine or surgical treatment. It is believed that artificial intelligence could improve the early diagnosis of lung cancer and assist doctors in treating lung cancer patients individually.","url":"https://doi.org/10.21037/tcr-20-3398","authors":["Huixian Zhang","Die Meng","Siqi Cai","Haoyue Guo","Peixin Chen","Zixuan Zheng","Jun Zhu","Wencheng Zhao","Hao Wang","Sha Zhao","Jia Yu","Yayi He"],"tags":["Lung cancer","Narrative","Medicine","Narrative review","Cancer"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-05-01","doi":"https://doi.org/10.21037/tcr-20-3398","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4400348220","name":"Research integrity in the era of artificial intelligence: Challenges and responses","source":"openalex","abstract":"The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.","url":"https://doi.org/10.1097/md.0000000000038811","authors":["Ziyu Chen","Chang-ye Chen","Guozhao Yang","Xiangpeng He","Xiaoxia Chi","Zhuoying Zeng","Xuhong Chen"],"tags":["Transparency (behavior)","Scientific misconduct","Engineering ethics","Research ethics","Research integrity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-07-05","doi":"https://doi.org/10.1097/md.0000000000038811","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392523501","name":"Artificial Intelligence in Medical Imaging : A Review","source":"openalex","abstract":"Artificial intelligence is the science of making machines that can think and act same as like humans. ‘Artificial’ are the objects created by the human beings and ‘Intelligence’ is the capability to perform the given task by interacting with a huge information. The AI in healthcare has made dramatic progress in recent years. Artificial intelligence may give better treatment to patients by taking the excellent decision in healthcare and medicine by prevention, detection, diagnosis, and treatment of the disease.","url":"https://doi.org/10.32628/ijsrset241119","authors":["Miss. Aboli Sanjay Gujar","Chinmay R. Sambhe","Miss. Tanmayi Ajay Dubey"],"tags":["Medical imaging","Artificial intelligence","Computer science","Cognitive science","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.32628/ijsrset241119","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4381250863","name":"A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle","source":"openalex","abstract":"Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual’s privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.","url":"https://doi.org/10.1109/access.2023.3287195","authors":["Sakib Shahriar","Sonal Allana","Seyed Mehdi Hazratifard","Rozita Dara"],"tags":["Information privacy","Computer science","Privacy by Design","Transparency (behavior)","Legislation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/access.2023.3287195","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4312132544","name":"Designing a feature selection method based on explainable artificial intelligence","source":"openalex","abstract":"Abstract Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes domains, including credit-risk assessment and medical diagnostics. Consequently, AI systems increasingly affect humans, yet many state-of-the-art systems lack transparency and thus, deny the individual’s “right to explanation”. As a remedy, researchers and practitioners have developed explainable AI, which provides reasoning on how AI systems infer individual predictions. However, with recent legal initiatives demanding comprehensive explainability throughout the (development of an) AI system, we argue that the pre-processing stage has been unjustifiably neglected and should receive greater attention in current efforts to establish explainability. In this paper, we focus on introducing explainability to an integral part of the pre-processing stage: feature selection. Specifically, we build upon design science research to develop a design framework for explainable feature selection. We instantiate the design framework in a running software artifact and evaluate it in two focus group sessions. Our artifact helps organizations to persuasively justify feature selection to stakeholders and, thus, comply with upcoming AI legislation. We further provide researchers and practitioners with a design framework consisting of meta-requirements and design principles for explainable feature selection.","url":"https://doi.org/10.1007/s12525-022-00608-1","authors":["Jan Zacharias","Moritz von Zahn","Johannes Chen","Oliver Hinz"],"tags":["Artifact (error)","Transparency (behavior)","Computer science","Feature selection","Selection (genetic algorithm)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-01","doi":"https://doi.org/10.1007/s12525-022-00608-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3189251308","name":"A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications","source":"openalex","abstract":"The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users' privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient's medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.","url":"https://doi.org/10.3390/healthcare9081019","authors":["Mohamed Yaseen Jabarulla","Heung-No Lee"],"tags":["Health care","Pandemic","Workflow","Blockchain","Data sharing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-08-08","doi":"https://doi.org/10.3390/healthcare9081019","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4406882703","name":"A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity","source":"openalex","abstract":"The rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a \"black box\" effect that can hinder the analysts' understanding of their decision-making processes. Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. Through a comprehensive analysis of recent studies, this review identifies commonly used XAI techniques, evaluates their effectiveness within IDS frameworks, and examines their benefits and limitations. Findings indicate that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging. Furthermore, the review highlights critical gaps in standardization and scalability, emphasizing the need for hybrid models and real-time explainability. The paper concludes with recommendations for future research directions, suggesting improvements in XAI techniques tailored for IDS, standardized evaluation metrics, and ethical frameworks prioritizing security and transparency. This review aims to inform researchers and practitioners about current trends and future opportunities in leveraging XAI to enhance IDS effectiveness, fostering a more transparent and resilient cybersecurity landscape.","url":"https://doi.org/10.3389/frai.2025.1526221","authors":["Vincent Zibi Mohale","Ibidun Christiana Obagbuwa"],"tags":["Interpretability","Transparency (behavior)","Intrusion detection system","Computer science","Computer security"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-01-28","doi":"https://doi.org/10.3389/frai.2025.1526221","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3135955748","name":"Regulatory aspects of artificial intelligence and machine learning-enabled software as medical devices (SaMD)","source":"openalex","abstract":"","url":"https://doi.org/10.1016/b978-0-12-820239-5.00010-3","authors":["Michael Mähler","Carolina Auza","Roger Albesa","Carlos Melus","Jungen Andrew Wu"],"tags":["Food and drug administration","Process (computing)","Software","Computer science","Cover (algebra)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-01-01","doi":"https://doi.org/10.1016/b978-0-12-820239-5.00010-3","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4407092486","name":"Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy","source":"openalex","abstract":"Abstract The use of artificial intelligence (AI) applications is increasing in daily life. However, despite its remarkable capabilities, the widespread integration of this technology into all aspects of human life also carries the risk of causing unforeseen psychological difficulties for humanity. For this reason, it is necessary to identify the factors that may particularly affect artificial intelligence anxiety. We examined the sequential mediation effect of AI literacy and attitudes towards AI on the relationship between generative AI acceptance and AI anxiety. The study was conducted with 494 university students ( M age = 21.84, SD = 1.24). The research model was designed according to the PROCESS Macro model 80 proposed by Hayes. Data were analyzed using the SPSS v.26 program, Pearson’s correlations, and Hayes’s PROCESS macro method for mediation. The results revealed that AI anxiety has significant relationships with generative AI acceptance, AI literacy, and attitudes toward AI. Indirect effect tests supported the mediating relationship between attitudes toward AI and AI literacy. These results help us understand the underlying mechanism in the relationship between generative AI acceptance and AI anxiety.","url":"https://doi.org/10.1007/s12144-025-07433-7","authors":["Serkan Cengiz","Adem Peker"],"tags":["Psychology","Mediation","Anxiety","Generative grammar","Mechanism (biology)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2025-02-03","doi":"https://doi.org/10.1007/s12144-025-07433-7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4294636892","name":"Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review","source":"openalex","abstract":"OBJECTIVES: The aim of this study was to evaluate the quality of reporting of randomised controlled trials (RCTs) of artificial intelligence (AI) in healthcare against Consolidated Standards of Reporting Trials-AI (CONSORT-AI) guidelines. DESIGN: Systematic review. DATA SOURCES: We searched PubMed and EMBASE databases for studies reported from January 2015 to December 2021. ELIGIBILITY CRITERIA: We included RCTs reported in English that used AI as the intervention. Protocols, conference abstracts, studies on robotics and studies related to medical education were excluded. DATA EXTRACTION: The included studies were graded using the CONSORT-AI checklist, comprising 43 items, by two independent graders. The results were tabulated and descriptive statistics were reported. RESULTS: We screened 1501 potential abstracts, of which 112 full-text articles were reviewed for eligibility. A total of 42 studies were included. The number of participants ranged from 22 to 2352. Only two items of the CONSORT-AI items were fully reported in all studies. Five items were not applicable in more than 85% of the studies. Nineteen per cent (8/42) of the studies did not report more than 50% (21/43) of the CONSORT-AI checklist items. CONCLUSIONS: The quality of reporting of RCTs in AI is suboptimal. As reporting is variable in existing RCTs, caution should be exercised in interpreting the findings of some studies.","url":"https://doi.org/10.1136/bmjopen-2022-061519","authors":["Rida Shahzad","Bushra Ayub","M. A. Rehman Siddiqui"],"tags":["Medicine","Checklist","Consolidated Standards of Reporting Trials","Data extraction","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-09-01","doi":"https://doi.org/10.1136/bmjopen-2022-061519","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3184237284","name":"Artificial Intelligence in Cyber Security","source":"openalex","abstract":"Abstract Without substantial automation, individuals cannot manage the complexity of operations and the scale of information to be utilized to secure cyberspace. Nonetheless, technology and software with traditional fixed implementations are difficult to build (hardwired decision-making logic) in order to successfully safeguard against security threats. This condition can be dealt with using machine simplicity and learning methods in AI. This paper provides a concise overview of AI implementations of various cybersecurity using artificial technologies and evaluates the prospects for expanding the cybersecurity capabilities by enhancing the defence mechanism. We may infer that valuable applications already exist after the review of current artificial intelligence software on cybersecurity. First of all, they are used to protect the periphery and many other cybersecurity areas with neural networks. On the other hand, it was clear that certain cybersecurity problems would only be overcome efficiently if artificial intelligence approaches are deployed. In strategic decision making, for example, comprehensive information is important, and logical decision assistance is one of the still unanswered cybersecurity issues.","url":"https://doi.org/10.1088/1742-6596/1964/4/042072","authors":["Rammanohar Das","Raghav Sandhane"],"tags":["Computer security","Computer science","Cyberspace","Implementation","Simplicity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-01","doi":"https://doi.org/10.1088/1742-6596/1964/4/042072","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4307973054","name":"Considering Clinician Competencies for the Implementation of Artificial Intelligence–Based Tools in Health Care: Findings From a Scoping Review","source":"openalex","abstract":"BACKGROUND: The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding. OBJECTIVE: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. METHODS: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. RESULTS: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. CONCLUSIONS: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.","url":"https://doi.org/10.2196/37478","authors":["Kim V. Garvey","Kelly Jean Thomas Craig","Regina G. Russell","Laurie L. Novak","Donald E. Moore","Bonnie M. Miller"],"tags":["CINAHL","MEDLINE","Health care","Competence (human resources)","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-25","doi":"https://doi.org/10.2196/37478","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4226395747","name":"Study and Analysis of the Collaborative Approach of Artificial Intelligence and Medical in Effective Healthcare: A Systematic Review","source":"openalex","abstract":"Artificial Intelligence (AI) has been making tremendous progress in transforming industries across different sectors globally, and healthcare is no exception. Increasing cost and patient outcomes are two recurring challenges faced by the healthcare industry. AI is very useful to manage the health insurance claims. Some other areas where data and information collected by AI engines are very useful to identify the suitable health support, services and consultancies for patients. It is also useful for pharmaceutical department as for drug and vaccine development. Artificial Intelligence is one of the important field of Computer Science and Engineering which plays a vital role to shape various applications to make the life better for human beings. This application can be from the field of sales, business, marketing, healthcare and other field to change the life of the human being. In the field of medical and health care, there is challenge of human resources as the doctors, nurses and other medical staff. This challenge can be handled by the Artificial Intelligence through various application to handle this challenge in the field of Health care. Various fields and application of AI like image processing, Natural Language Processing, Machine Learning, etc. over structured and unstructured data sets. This paper highlights the statics on the analysis of the available documents related to AI in healthcare.","url":"https://doi.org/10.1109/icac3n53548.2021.9725690","authors":["Darpan Anand","Surender Singh","Saurabh Saurabh"],"tags":["Health care","Field (mathematics)","Computer science","Applications of artificial intelligence","Knowledge management"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-17","doi":"https://doi.org/10.1109/icac3n53548.2021.9725690","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4285040359","name":"Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging","source":"openalex","abstract":"INTRODUCTION: Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community. METHODS: A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants' perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data. RESULTS: A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification. CONCLUSION: Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies. IMPLICATIONS FOR PRACTICE: This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession.","url":"https://doi.org/10.1016/j.radi.2022.06.020","authors":["Sinead Coakley","Rena Young","Niamh Moore","Andrew England","Alexander T. O’Mahony","Owen J. O’Connor","Michael M. Maher","Mark F. McEntee"],"tags":["Workforce","Apprehension","Medical education","Perception","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-12","doi":"https://doi.org/10.1016/j.radi.2022.06.020","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4389728663","name":"Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan","source":"openalex","abstract":"INTRODUCTION: The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions. METHODS: An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores. RESULTS: Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps. CONCLUSIONS: This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.","url":"https://doi.org/10.1186/s12911-023-02403-0","authors":["Walid Al‐Qerem","Judith Eberhardt","Anan S. Jarab","Abdel Qader Al Bawab","Alaa M. Hammad","Fawaz Alasmari","Badi’ah Alazab","Daoud Abu Husein","Jumana Alazab","Saed Al-Beool"],"tags":["Health informatics","Medical education","Knowledge management","Psychology","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-12-14","doi":"https://doi.org/10.1186/s12911-023-02403-0","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4396946008","name":"Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education","source":"openalex","abstract":"With the recent integration of artificial intelligence (AI) in the educational field, understanding the variables that are related to teacher attitudes towards AI can be crucial for understanding their perspectives in the classroom. That is why the present study aimed to investigate whether Teacher Digital Competence is related to Teacher Attitudes towards AI, and if so, whether this relationship is moderated by the teacher's educational stage, age, sex, years of experience, and field of knowledge. A total of 445 Spanish teachers from primary, secondary, and higher education participated in this study, responding to the Teacher Digital Competence Scale and the Teacher Attitudes towards AI Scale. The results revealed that, regardless of educational stage, sex, age, years of experience or field of knowledge, higher teacher digital competence is associated with a more positive teacher attitude towards AI. Moreover, high levels of willingness to use AI but low levels of personal experience with AI were found. Based on these results, it may be interesting to implement future interventions based on AI to enhance key dimensions of teacher digital competence, such as Information Management, Content Creation, and Problem-Solving. This could improve Teacher Digital Competence and subsequently enhance teachers' perception of using artificial intelligence in the educational context.","url":"https://doi.org/10.1016/j.ijer.2024.102381","authors":["Héctor Galindo‐Domínguez","Nahia Delgado","Lucía Campo","Daniel Losada Iglesias"],"tags":["Competence (human resources)","Psychology","Mathematics education","Pedagogy","Social psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-01-01","doi":"https://doi.org/10.1016/j.ijer.2024.102381","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4392450439","name":"Dr. Google to Dr. ChatGPT: assessing the content and quality of artificial intelligence-generated medical information on appendicitis","source":"openalex","abstract":"INTRODUCTION: Generative artificial intelligence (AI) chatbots have recently been posited as potential sources of online medical information for patients making medical decisions. Existing online patient-oriented medical information has repeatedly been shown to be of variable quality and difficult readability. Therefore, we sought to evaluate the content and quality of AI-generated medical information on acute appendicitis. METHODS: A modified DISCERN assessment tool, comprising 16 distinct criteria each scored on a 5-point Likert scale (score range 16-80), was used to assess AI-generated content. Readability was determined using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) scores. Four popular chatbots, ChatGPT-3.5 and ChatGPT-4, Bard, and Claude-2, were prompted to generate medical information about appendicitis. Three investigators independently scored the generated texts blinded to the identity of the AI platforms. RESULTS: ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 had overall mean (SD) quality scores of 60.7 (1.2), 62.0 (1.0), 62.3 (1.2), and 51.3 (2.3), respectively, on a scale of 16-80. Inter-rater reliability was 0.81, 0.75, 0.81, and 0.72, respectively, indicating substantial agreement. Claude-2 demonstrated a significantly lower mean quality score compared to ChatGPT-4 (p = 0.001), ChatGPT-3.5 (p = 0.005), and Bard (p = 0.001). Bard was the only AI platform that listed verifiable sources, while Claude-2 provided fabricated sources. All chatbots except for Claude-2 advised readers to consult a physician if experiencing symptoms. Regarding readability, FKGL and FRE scores of ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 were 14.6 and 23.8, 11.9 and 33.9, 8.6 and 52.8, 11.0 and 36.6, respectively, indicating difficulty readability at a college reading skill level. CONCLUSION: AI-generated medical information on appendicitis scored favorably upon quality assessment, but most either fabricated sources or did not provide any altogether. Additionally, overall readability far exceeded recommended levels for the public. Generative AI platforms demonstrate measured potential for patient education and engagement about appendicitis.","url":"https://doi.org/10.1007/s00464-024-10739-5","authors":["Yazid K. Ghanem","Armaun D. Rouhi","Ammr Al-Houssan","Zena Saleh","Matthew C. Moccia","Hansa Joshi","Kristoffel R. Dumon","Young Ki Hong","Francis Spitz","Amit Joshi","Michael Kwiatt"],"tags":["Readability","Likert scale","Reading (process)","Quality (philosophy)","Scale (ratio)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-05","doi":"https://doi.org/10.1007/s00464-024-10739-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3181963722","name":"The future of Artificial Intelligence in Cybersecurity: A Comprehensive Survey","source":"openalex","abstract":"AI in Cybersecurity Market scheme helps organizations in observance, detecting, reporting, and countering cyber threats to keep up information confidentiality. The increasing awareness among folks, advancements in info technology, up-gradation of intelligence and police work solutions, and in","url":"https://doi.org/10.4108/eai.7-7-2021.170285","authors":["Feng Tao","Muhammad Shoaib Akhtar","Zhang Jiayuan"],"tags":["Confidentiality","Computer security","Work (physics)","Scheme (mathematics)","Gradation"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-07","doi":"https://doi.org/10.4108/eai.7-7-2021.170285","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3015509439","name":"Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia","source":"openalex","abstract":"BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia. METHODS: The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results. FINDINGS: The best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88-0.89) or temporal split (0.86, 95% CI 0.85-0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets. INTERPRETATION: Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors. FUNDING: This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.","url":"https://doi.org/10.1016/j.ebiom.2020.102710","authors":["Herdiantri Sufriyana","Yu‐Wei Wu","Emily Chia‐Yu Su"],"tags":["Preeclampsia","Computer science","Health insurance","Medicine","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-04-01","doi":"https://doi.org/10.1016/j.ebiom.2020.102710","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4200045082","name":"AI in marketing, consumer research and psychology: A systematic literature review and research agenda","source":"openalex","abstract":"Abstract This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics.","url":"https://doi.org/10.1002/mar.21619","authors":["Marcello M. Mariani","Rodrigo Perez‐Vega","Jochen Wirtz"],"tags":["Big data","Consumer behaviour","Social media","Psychology","Cognition"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-09","doi":"https://doi.org/10.1002/mar.21619","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4288447337","name":"The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis","source":"openalex","abstract":"With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.","url":"https://doi.org/10.3389/fmed.2022.935080","authors":["Shufan Liang","Jiechao Ma","Gang Wang","Jun Shao","Jingwei Li","Hui Deng","Chengdi Wang","Weimin Li"],"tags":["Tuberculosis","Medicine","Artificial intelligence","Pulmonary tuberculosis","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-07-28","doi":"https://doi.org/10.3389/fmed.2022.935080","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3203392755","name":"Demystifying the Draft EU Artificial Intelligence Act","source":"openalex","abstract":"In April 2021, the European Commission proposed a Regulation on Artificial Intelligence, known as the AI Act. We present an overview of the Act and analyse its implications, drawing on scholarship ranging from the study of contemporary AI practices to the structure of EU product safety regimes over the last four decades. Aspects of the AI Act, such as different rules for different risk-levels of AI, make sense. But we also find that some provisions of the draft AI Act have surprising legal implications, whilst others may be largely ineffective at achieving their stated goals. Several overarching aspects, including the enforcement regime and the effect of maximum harmonisation on the space for AI policy more generally, engender significant concern. These issues should be addressed as a priority in the legislative process.","url":"https://doi.org/10.31235/osf.io/38p5f","authors":["Michael Veale","Frederik Zuiderveen Borgesius"],"tags":["Enforcement","Political science","Legislature","Commission","European commission"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-07-06","doi":"https://doi.org/10.31235/osf.io/38p5f","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2767411797","name":"Predictable response: Finding optimal drugs and doses using artificial intelligence","source":"openalex","abstract":"","url":"https://doi.org/10.1038/nm1117-1244","authors":["Shraddha Chakradhar"],"tags":["Artificial intelligence","Medicine","Biology","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2017-11-01","doi":"https://doi.org/10.1038/nm1117-1244","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4315606155","name":"The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review","source":"openalex","abstract":"Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. Comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.","url":"https://doi.org/10.1109/access.2023.3236084","authors":["Ciro Mennella","Umberto Maniscalco","Giuseppe De Pietro","Massimo Esposito"],"tags":["Computer science","Artificial intelligence","Generalizability theory","Machine learning","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/access.2023.3236084","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4205471456","name":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","source":"openalex","abstract":"With the rise of technology and the continued economic growth evident in modern society, acts of fraud have become much more prevalent in the financial industry, costing institutions and consumers hundreds of billions of dollars annually. Fraudsters are continuously evolving their approaches to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. These crimes include credit card fraud, healthcare and automobile insurance fraud, money laundering, securities and commodities fraud and insider trading. On their own, fraud prevention systems do not provide adequate security against these criminal acts. As such, the need for fraud detection systems to detect fraudulent acts after they have already been committed and the potential cost savings of doing so is more evident than ever. Anomaly detection techniques have been intensively studied for this purpose by researchers over the last couple of decades, many of which employed statistical, artificial intelligence and machine learning models. Supervised learning algorithms have been the most popular types of models studied in research up until recently. However, supervised learning models are associated with many challenges that have been and can be addressed by semi-supervised and unsupervised learning models proposed in recently published literature. This survey aims to investigate and present a thorough review of the most popular and effective anomaly detection techniques applied to detect financial fraud, with a focus on highlighting the recent advancements in the areas of semi-supervised and unsupervised learning.","url":"https://doi.org/10.1016/j.eswa.2021.116429","authors":["Waleed Hilal","S. Andrew Gadsden","John Yawney"],"tags":["Credit card fraud","Anomaly detection","Exploit","Computer science","Insider"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-12-31","doi":"https://doi.org/10.1016/j.eswa.2021.116429","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3089028909","name":"A Unifying Review of Deep and Shallow Anomaly Detection","source":"openalex","abstract":"Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text. These results have sparked a renewed interest in the AD problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review, we aim to identify the common underlying principles and the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic “shallow” and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that are enriched by the use of recent explainability techniques and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in AD.","url":"https://doi.org/10.1109/jproc.2021.3052449","authors":["Lukas Ruff","Jacob R. Kauffmann","Robert A. Vandermeulen","Gregoire Montavon","Wojciech Samek","Marius Kloft","Thomas G. Dietterich","Klaus-Robert Muller"],"tags":["Variety (cybernetics)","Computer science","Deep learning","Field (mathematics)","Generative grammar"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2021-02-05","doi":"https://doi.org/10.1109/jproc.2021.3052449","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4319869596","name":"Artificial intelligence in multi-objective drug design","source":"openalex","abstract":"The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has seen a rapid increase in developments and applications. Population-based metaheuris-tics and deep reinforcement learning are the most commonly used artificial intelligence methods in the field, but recently conditional learning methods are gaining popularity. The former approaches are coupled with a MOO strat-egy which is most commonly an aggregation function, but Pareto-based strategies are widespread too. Besides these and conditional learning, various innovative approaches to tackle MOO in drug design have been proposed. Here we provide a brief overview of the field and the latest innovations.","url":"https://doi.org/10.1016/j.sbi.2023.102537","authors":["Sohvi Luukkonen","Helle W. van den Maagdenberg","Michael Emmerich","Gerard J. P. van Westen"],"tags":["Artificial intelligence","Machine learning","Computer science","Field (mathematics)","Popularity"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-02-10","doi":"https://doi.org/10.1016/j.sbi.2023.102537","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4312055891","name":"Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals","source":"openalex","abstract":"Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by assisting and augmenting the cognitive processes of healthcare professionals, the coverage of clinically relevant tasks by AI benchmarks is largely unclear. Furthermore, there is a lack of systematized meta-information that allows clinical AI researchers to quickly determine accessibility, scope, content and other characteristics of datasets and benchmark datasets relevant to the clinical domain. To address these issues, we curated and released a comprehensive catalogue of datasets and benchmarks pertaining to the broad domain of clinical and biomedical natural language processing (NLP), based on a systematic review of literature and. A total of 450 NLP datasets were manually systematized and annotated with rich metadata, such as targeted tasks, clinical applicability, data types, performance metrics, accessibility and licensing information, and availability of data splits. We then compared tasks covered by AI benchmark datasets with relevant tasks that medical practitioners reported as highly desirable targets for automation in a previous empirical study. Our analysis indicates that AI benchmarks of direct clinical relevance are scarce and fail to cover most work activities that clinicians want to see addressed. In particular, tasks associated with routine documentation and patient data administration workflows are not represented despite significant associated workloads. Thus, currently available AI benchmarks are improperly aligned with desired targets for AI automation in clinical settings, and novel benchmarks should be created to fill these gaps.","url":"https://doi.org/10.1016/j.jbi.2022.104274","authors":["Kathrin Blagec","Jakob Kraiger","Wolfgang Frühwirt","Matthias Samwald"],"tags":["Computer science","Benchmark (surveying)","Workflow","Documentation","Data science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-17","doi":"https://doi.org/10.1016/j.jbi.2022.104274","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2037486375","name":"Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review","source":"openalex","abstract":"Electrical impedance tomography (EIT) is a nondestructive imaging technique used to estimate the internal conductivity distribution of a conductive domain by taking potential measurements only at the domain boundaries. If a thin electrically conductive material that responds to pressure with local changes in conductivity is used as a conductive domain, then EIT can be used to create a large-scale pressure-sensitive artificial skin for robotics applications. This paper presents a review of EIT and its application as a robotics sensitive skin, including EIT excitation and image reconstruction techniques, materials, and skin fabrication techniques. Touch interpretation via EIT-based artificial skins is also reviewed.","url":"https://doi.org/10.1109/jsen.2014.2375346","authors":["David Silvera‐Tawil","David Rye","Manuchehr Soleimani","Mari Velonaki"],"tags":["Electrical impedance tomography","Electrical conductor","Robotics","Electrical impedance","Electrical resistivity tomography"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2014-12-02","doi":"https://doi.org/10.1109/jsen.2014.2375346","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4353083153","name":"Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective","source":"openalex","abstract":"Abstract The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things","url":"https://doi.org/10.1002/widm.1496","authors":["Indrajeet Ghosh","Sreenivasan Ramasamy Ramamurthy","Avijoy Chakma","Nirmalya Roy"],"tags":["Computer science","Data science","Artificial intelligence","Analytics","Open research"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-21","doi":"https://doi.org/10.1002/widm.1496","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2940709043","name":"An Open Science Approach to Artificial Intelligence in Healthcare","source":"openalex","abstract":"OBJECTIVES: Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an \"open science\" approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. METHOD: In response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings. RESULTS: The recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of \"black-box\" AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing. CONCLUSIONS: As AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.","url":"https://doi.org/10.1055/s-0039-1677898","authors":["Chris Paton","Shinji Kobayashi"],"tags":["Health care","Knowledge management","Computer science","Openness to experience","Data sharing"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-25","doi":"https://doi.org/10.1055/s-0039-1677898","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4307941324","name":"Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes—A Systematic Review","source":"openalex","abstract":"(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence methods in predicting adverse pregnancy outcomes. The PROSPERO ID number is CRD42020178944, and the study protocol was published before this publication. (3) Results: AI application was found in nine groups: general pregnancy risk assessment, prenatal diagnosis, pregnancy hypertension disorders, fetal growth, stillbirth, gestational diabetes, preterm deliveries, delivery route, and others. According to this systematic review, the best artificial intelligence application for assessing medical conditions is ANN methods. The average accuracy of ANN methods was established to be around 80-90%. (4) Conclusions: The application of AI methods as a digital software can help medical practitioners in their everyday practice during pregnancy risk assessment. Based on published studies, models that used ANN methods could be applied in APO prediction. Nevertheless, further studies could identify new methods with an even better prediction potential.","url":"https://doi.org/10.3390/healthcare10112164","authors":["Stepan Feduniw","Dawid Golik","Anna Kajdy","Michał Pruc","Jan Modzelewski","Dorota Sys","Sebastian Kwiatkowski","Elżbieta Makomaska-Szaroszyk","Michał Rabijewski"],"tags":["Pregnancy","Medicine","MEDLINE","Gestational diabetes","Systematic review"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-10-29","doi":"https://doi.org/10.3390/healthcare10112164","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3035307628","name":"Patients’ Utilization and Perception of an Artificial Intelligence–Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study","source":"openalex","abstract":"BACKGROUND: When someone needs to know whether and when to seek medical attention, there are a range of options to consider. Each will have consequences for the individual (primarily considering trust, convenience, usefulness, and opportunity costs) and for the wider health system (affecting clinical throughput, cost, and system efficiency). Digital symptom assessment technologies that leverage artificial intelligence may help patients navigate to the right type of care with the correct degree of urgency. However, a recent review highlighted a gap in the literature on the real-world usability of these technologies. OBJECTIVE: We sought to explore the usability, acceptability, and utility of one such symptom assessment technology, Ada, in a primary care setting. METHODS: Patients with a new complaint attending a primary care clinic in South London were invited to use a custom version of the Ada symptom assessment mobile app. This exploratory pilot study was conducted between November 2017 and January 2018 in a practice with 20,000 registered patients. Participants were asked to complete an Ada self-assessment about their presenting complaint on a study smartphone, with assistance provided if required. Perceptions on the app and its utility were collected through a self-completed study questionnaire following completion of the Ada self-assessment. RESULTS: Over a 3-month period, 523 patients participated. Most were female (n=325, 62.1%), mean age 39.79 years (SD 17.7 years), with a larger proportion (413/506, 81.6%) of working-age individuals (aged 15-64) than the general population (66.0%). Participants rated Ada's ease of use highly, with most (511/522, 97.8%) reporting it was very or quite easy. Most would use Ada again (443/503, 88.1%) and agreed they would recommend it to a friend or relative (444/520, 85.3%). We identified a number of age-related trends among respondents, with a directional trend for more young respondents to report Ada had provided helpful advice (50/54, 93%, 18-24-year olds reported helpful) than older respondents (19/32, 59%, adults aged 70+ reported helpful). We found no sex differences on any of the usability questions fielded. While most respondents reported that using the symptom checker would not have made a difference in their care-seeking behavior (425/494, 86.0%), a sizable minority (63/494, 12.8%) reported they would have used lower-intensity care such as self-care, pharmacy, or delaying their appointment. The proportion was higher for patients aged 18-24 (11/50, 22%) than aged 70+ (0/28, 0%). CONCLUSIONS: In this exploratory pilot study, the digital symptom checker was rated as highly usable and acceptable by patients in a primary care setting. Further research is needed to confirm whether the app might appropriately direct patients to timely care, and understand how this might save resources for the health system. More work is also needed to ensure the benefits accrue equally to older age groups.","url":"https://doi.org/10.2196/19713","authors":["Stephen Miller","Stephen Gilbert","Vishaal Virani","Paul Wicks"],"tags":["Usability","Complaint","Population","Medicine","Health care"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-06-14","doi":"https://doi.org/10.2196/19713","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4387357807","name":"Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department","source":"openalex","abstract":"Importance: Multimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images. Objective: To evaluate the accuracy and quality of AI-generated chest radiograph interpretations in the emergency department setting. Design, Setting, and Participants: This was a retrospective diagnostic study of 500 randomly sampled emergency department encounters at a tertiary care institution including chest radiographs interpreted by both a teleradiology service and on-site attending radiologist from January 2022 to January 2023. An AI interpretation was generated for each radiograph. The 3 radiograph interpretations were each rated in duplicate by 6 emergency department physicians using a 5-point Likert scale. Main Outcomes and Measures: The primary outcome was any difference in Likert scores between radiologist, AI, and teleradiology reports, using a cumulative link mixed model. Secondary analyses compared the probability of each report type containing no clinically significant discrepancy with further stratification by finding presence, using a logistic mixed-effects model. Physician comments on discrepancies were recorded. Results: A total of 500 ED studies were included from 500 unique patients with a mean (SD) age of 53.3 (21.6) years; 282 patients (56.4%) were female. There was a significant association of report type with ratings, with post hoc tests revealing significantly greater scores for AI (mean [SE] score, 3.22 [0.34]; P < .001) and radiologist (mean [SE] score, 3.34 [0.34]; P < .001) reports compared with teleradiology (mean [SE] score, 2.74 [0.34]) reports. AI and radiologist reports were not significantly different. On secondary analysis, there was no difference in the probability of no clinically significant discrepancy between the 3 report types. Further stratification of reports by presence of cardiomegaly, pulmonary edema, pleural effusion, infiltrate, pneumothorax, and support devices also yielded no difference in the probability of containing no clinically significant discrepancy between the report types. Conclusions and Relevance: In a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.","url":"https://doi.org/10.1001/jamanetworkopen.2023.36100","authors":["Jonathan Huang","Luke A Neill","Matthew T. Wittbrodt","David Melnick","Matthew Klug","Michael P. Thompson","John Bailitz","Timothy M. Loftus","Sanjeev Malik","Amit Phull","Victoria Weston","J. Alex Heller","Mozziyar Etemadi"],"tags":["Teleradiology","Emergency department","Medicine","Chest radiograph","Likert scale"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-05","doi":"https://doi.org/10.1001/jamanetworkopen.2023.36100","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4318478060","name":"The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises","source":"openalex","abstract":"Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called \"B-SMEs\" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively.","url":"https://doi.org/10.1038/s41598-023-28707-9","authors":["Abdullah Ayub Khan","Asif Ali Laghari","Peng Li","Mazhar Ali Dootio","Shahid Karim"],"tags":["Blockchain","Computer science","Small and medium-sized enterprises","Interoperability","General partnership"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-30","doi":"https://doi.org/10.1038/s41598-023-28707-9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4387936071","name":"The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review","source":"openalex","abstract":"Abstract There have been many applications and influences of Artificial intelligence (AI) in many sectors and its professionals, that of radiotherapy and the medical physicist is no different. AI and technological advances have necessitated changing roles of medical physicists due to the development of modernized technology with image-guided accessories for the radiotherapy treatment of cancer patients. Given the changing role of medical physicists in ensuring patient safety and optimal care, AI can reshape radiotherapy practice now and in some years to come. Medical physicists’ roles in radiotherapy practice have evolved to meet technology for the management of better patient care in the age of modern radiotherapy. This short review provides an insight into the influence of AI on the changing role of medical physicists in each specific chain of the workflow in radiotherapy in which they are involved.","url":"https://doi.org/10.1259/bjro.20230003","authors":["Emmanuel Fiagbedzi","Francis Hasford","Samuel Nii Adu Tagoe","Samuel Nii Tagoe"],"tags":["Medical physicist","Physicist","Work (physics)","Radiation therapy","Medical physics"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-10-19","doi":"https://doi.org/10.1259/bjro.20230003","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4361010280","name":"Artificial intelligence in public health: the potential of epidemic early warning systems","source":"openalex","abstract":"The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.","url":"https://doi.org/10.1177/03000605231159335","authors":["C. Raina MacIntyre","Xin Chen","Mohana Kunasekaran","Ashley Quigley","Samsung Lim","Haley Stone","Hye-Young Paik","Lina Yao","David Heslop","Wenzhao Wei","Ines Sarmiento","Deepti Gurdasani"],"tags":["Public health surveillance","Public health","Warning system","Open data","Medicine"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-03-01","doi":"https://doi.org/10.1177/03000605231159335","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2941525594","name":"The Price of Artificial Intelligence","source":"openalex","abstract":"INTRODUCTION: Whilst general artificial intelligence (AI) is yet to appear, today's narrow AI is already good enough to transform much of healthcare over the next two decades. OBJECTIVE: There is much discussion of the potential benefits of AI in healthcare and this paper reviews the cost that may need to be paid for these benefits, including changes in the way healthcare is practiced, patients are engaged, medical records are created, and work is reimbursed. RESULTS: Whilst AI will be applied to classic pattern recognition tasks like diagnosis or treatment recommendation, it is likely to be as disruptive to clinical work as it is to care delivery. Digital scribe systems that use AI to automatically create electronic health records promise great efficiency for clinicians but may lead to potentially very different types of clinical records and workflows. In disciplines like radiology, AI is likely to see image interpretation become an automated process with diminishing human engagement. Primary care is also being disrupted by AI-enabled services that automate triage, along with services such as telemedical consultations. This altered future may necessarily see an economic change where clinicians are increasingly reimbursed for value, and AI is reimbursed at a much lower cost for volume. CONCLUSION: AI is likely to be associated with some of the biggest changes we will see in healthcare in our lifetime. To fully engage with this change brings promise of the greatest reward. To not engage is to pay the highest price.","url":"https://doi.org/10.1055/s-0039-1677892","authors":["Enrico Coiera"],"tags":["Workflow","Triage","Health care","Artificial intelligence","Process (computing)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-04-25","doi":"https://doi.org/10.1055/s-0039-1677892","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4286511253","name":"Artificial intelligence-based robots in education: A systematic review of selected SSCI publications","source":"openalex","abstract":"With the rapid development of artificial intelligence, the application of AI robots (Artificial Intelligence-based robots) for instruction has become an attractive research topic. Numerous studies have shown that AI robots may provide new opportunities for learning designs in school settings or professional training. However, there is no review examining the role and research foci of AI-Robots in Education (AIRE) research. This study therefore explored the research trends of AIRE by conducting a systematic review of SSCI (Social Sciences Citation Index) journal articles published in the Web of Science (WoS). The study analyzed the participants, duration of the studies, learning environments, application domains, data analysis, evaluations of learners' performance, learning strategies, roles of AI-robots, and research issues. The research findings are concluded as follows: (1) The countries of Canada, Chile, and South Korea invested in AIRE research early, and focused on students' learning performance and learning behavior. (2) Most AIRE research focuses on research targets under the age of 13 and completed experiments within 4 weeks in a physical environment. Most AIRE research has been applied in the disciplines of Language and Science, and problem-solving related strategies and mixed strategies are the most commonly used strategies. (3) AI-robots are often applied and regarded as tutees or tutors. Regardless of what the AI-robot's role is, learning performance is the most widely focused variable in AIRE research. Furthermore, attitudes, opinions of learners or learning perceptions and learning behavior are other frequently discussed themes. To sum up, this study makes several recommendations for AIRE research for educators, researchers, and policy makers in higher education settings as a reference based on the results.","url":"https://doi.org/10.1016/j.caeai.2022.100091","authors":["Shih‐Ting Chu","Gwo‐Jen Hwang","Yun‐Fang Tu"],"tags":["Robot","Artificial intelligence","Citation","Perception","Computer science"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1016/j.caeai.2022.100091","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2271206385","name":"A Review of Point Cloud Registration Algorithms for Mobile Robotics","source":"openalex","abstract":"The topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration.","url":"https://doi.org/10.1561/2300000035","authors":["François Pomerleau","Francis Colas","Roland Siegwart"],"tags":["Robotics","Artificial intelligence","Computer science","Point cloud","Focus (optics)"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-05-27","doi":"https://doi.org/10.1561/2300000035","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4381384569","name":"Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images","source":"openalex","abstract":"Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.","url":"https://doi.org/10.1016/j.ejrad.2023.110934","authors":["Zhaomin Yao","Hongyu Wang","W. C. Yan","Z Q Wang","Z Q Wang","Wenwen Zhang","Zi Wang","Zhiguo Wang","Guoxu Zhang"],"tags":["Medicine","Disease","Optimism","Magnetic resonance imaging","Transformative learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-06-20","doi":"https://doi.org/10.1016/j.ejrad.2023.110934","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4322627364","name":"Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence","source":"openalex","abstract":"Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real‐life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge‐based graph, and other areas due to their excellent characteristics in processing non‐European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph‐based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph‐based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.","url":"https://doi.org/10.1155/2023/8342104","authors":["Uzair Aslam Bhatti","Hao Tang","Guilu Wu","Shah Marjan","Aamir Hussain"],"tags":["Computer science","Graph","Artificial intelligence","Pooling","Convolutional neural network"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1155/2023/8342104","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4311786576","name":"The adoption of artificial intelligence applications in education","source":"openalex","abstract":"Artificial intelligence is user-friendly and possesses useful characteristics to share across the various services that are provided. By enhancing innovative contact, artificial intelligence applications (AIA) enable a more involved environment in governmental institutions. The goal of this study is to discover how users in the UAE feel about using AIA for educational reasons. Data collected from a survey of 387 university students were used to validate the model and hypotheses. The adoption features, such as perceived compatibility, trialability, relative advantage, ease of doing business, and technology export, are included in the conceptual model. The current study's practical implications are crucial in that they push the relevant educational authorities to comprehend the significance of each component and enable them to make plans and efforts in accordance with the order of the factors' relative importance. The managerial implications give educational sectors insight on how to apply AIA in their system to improve the growth of the provided service and to make the process easier for all users. The conceptual model of the paper, which links both traits of the individual and those of the technology, is what makes it new. The findings indicate that the diffusion theory variables outperform the other two variables of ease of doing business and technology export.","url":"https://doi.org/10.5267/j.ijdns.2022.8.013","authors":["Khadija Alhumaid","Shamma Al Naqbi","Deena Elsori","Maha Al Mansoori"],"tags":["Knowledge management","Usability","Computer science","Conceptual model","Conceptual framework"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-12-15","doi":"https://doi.org/10.5267/j.ijdns.2022.8.013","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W1995687988","name":"Temporal reasoning with medical data—A review with emphasis on medical natural language processing","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.jbi.2006.12.009","authors":["Li Zhou","George Hripcsak"],"tags":["Computer science","Data science","Context (archaeology)","Natural language processing","Artificial intelligence"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2007-01-13","doi":"https://doi.org/10.1016/j.jbi.2006.12.009","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4221038044","name":"An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction","source":"openalex","abstract":"Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.","url":"https://doi.org/10.3389/fpubh.2022.860396","authors":["Junaid Rashid","Saba Batool","Jungeun Kim","Muhammad Wasif Nisar","Amir Hussain","Sapna Juneja","Riti Kushwaha"],"tags":["Artificial intelligence","Computer science","Machine learning","Support vector machine","Random forest"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2022-03-31","doi":"https://doi.org/10.3389/fpubh.2022.860396","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4393167975","name":"Ethics of artificial intelligence in medicine","source":"openalex","abstract":"ABSTRACT: This article reviews the main ethical issues that arise from the use of artificial intelligence (AI) technologies in medicine. Issues around trust, responsibility, risks of discrimination, privacy, autonomy, and potential benefits and harms are assessed. For better or worse, AI is a promising technology that can revolutionise healthcare delivery. It is up to us to make AI a tool for the good by ensuring that ethical oversight accompanies the design, development and implementation of AI technology in clinical practice.","url":"https://doi.org/10.4103/singaporemedj.smj-2023-279","authors":["Julian Savulescu","Alberto Giubilini","Robert Vandersluis","Abhishek Mishra"],"tags":["Autonomy","Engineering ethics","Ethical issues","Applications of artificial intelligence","Ethics of technology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-03-01","doi":"https://doi.org/10.4103/singaporemedj.smj-2023-279","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4386254770","name":"Cultural Differences in People's Reactions and Applications of Robots, Algorithms, and Artificial Intelligence","source":"openalex","abstract":"Abstract Although research in cultural psychology has established that virtually all human behaviors and cognitions are in some ways shaped by culture, culture has been surprisingly absent from the emerging literature on the psychology of technology. In this perspective article, we first review recent findings on machine aversion versus appreciation. We then offer a cross-cultural perspective in understanding how people might react differently to machines. We propose three frameworks – historical, religious, and exposure – to explain how Asians might be more accepting of machines than their Western counterparts. We end the article by discussing three exciting human–machine applications found primarily in Asia and provide future research directions.","url":"https://doi.org/10.1017/mor.2023.21","authors":["Kai Chi Yam","Tiffany C. Y. Tan","Joshua Conrad Jackson","Azim Shariff","Kurt Gray"],"tags":["Perspective (graphical)","Cultural psychology","Cognition","Sociology","Psychology"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-08-29","doi":"https://doi.org/10.1017/mor.2023.21","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4385263677","name":"Artificial intelligence and ChatGPT in Orthopaedics and sports medicine","source":"openalex","abstract":"Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.Level of evidence: Level V, letter to review.","url":"https://doi.org/10.1186/s40634-023-00642-8","authors":["Aly M. Fayed","Nacime Salomão Barbachan Mansur","Képler Alencar Mendes de Carvalho","Andrew Behrens","Pieter D’Hooghe","César de César Netto"],"tags":["Sports medicine","Orthopedic surgery","Engineering","Medicine","Physical therapy"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1186/s40634-023-00642-8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W4402971773","name":"Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review","source":"openalex","abstract":"BACKGROUND: In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. MAIN TEXT: In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. CONCLUSION: In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.","url":"https://doi.org/10.1186/s40662-024-00405-1","authors":["Shaopan Wang","Xin He","Zhongquan Jian","Jie Li","Changsheng Xu","Yuguang Chen","Yuwen Liu","Han Chen","Caihong Huang","Jiaoyue Hu","Zuguo Liu"],"tags":["Modalities","Modal","Artificial intelligence","Computer science","Medical diagnosis"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2024-09-30","doi":"https://doi.org/10.1186/s40662-024-00405-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W2981217223","name":"Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges","source":"openalex","abstract":"Abstract Artificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.","url":"https://doi.org/10.1175/bams-d-18-0324.1","authors":["Sid‐Ahmed Boukabara","Vladimir M. Krasnopolsky","Jebb Q. Stewart","E. Maddy","Narges Shahroudi","Ross N. Hoffman"],"tags":["Numerical weather prediction","Computer science","Data assimilation","Remote sensing","Satellite"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2019-10-18","doi":"https://doi.org/10.1175/bams-d-18-0324.1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W1909793163","name":"Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist","source":"openalex","abstract":"While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (\"majority\", \"quorum\", and \"weighted quorum\") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.","url":"https://doi.org/10.1371/journal.pone.0134269","authors":["Max Wolf","Jens Krause","Patricia A. Carney","Andy Bogart","Ralf H. J. M. Kurvers"],"tags":["Collective intelligence","Computer science","MEDLINE","Data science","Medical decision making"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2015-08-12","doi":"https://doi.org/10.1371/journal.pone.0134269","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"oa:W3045281502","name":"Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review","source":"openalex","abstract":"Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed.","url":"https://doi.org/10.48550/arxiv.2007.10785","authors":["Afshin Shoeibi","Marjane Khodatars","Mahboobeh Jafari","Navid Ghassemi","Delaram Sadeghi","Parisa Moridian","Ali Khadem","Roohallah Alizadehsani","Sadiq Hussain","Assef Zare","Zahra Alizadeh Sani","Fahime Khozeimeh","Saeid Nahavandi","U. Rajendra Acharya","J. M. Górriz"],"tags":["Artificial intelligence","Coronavirus disease 2019 (COVID-19)","Deep learning","Computer science","Machine learning"],"confidence":0.72,"sites":["biomed-ai"],"publishedDate":"2020-07-16","doi":"https://doi.org/10.48550/arxiv.2007.10785","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.caeai.2022.100080","name":"Personalized feedback in digital learning environments: Classification framework and literature review","source":"crossref","abstract":"Digital learning technologies offer many opportunities to personalize instruction and learning in K-12 and higher education. In the last ten years, a growing body of research described personalized feedback implementations and investigated their effects on educational outcomes. Building on personalized education and adaptive learning systems models, this review provides an analytic framework to summarize key features of personalized feedback implementations and main empirical results. The systematic literature search resulted in 39 studies published in the last ten years. We found that scholars developed and investigated personalized feedback on the microscale, mesoscale, and macroscale of digital learning environments. However, the adaptive sources (To what is feedback adapted?) are mainly restricted to the current knowledge level and learning behavior data. Other interesting data sources for feedback adaptation remain underresearched, e.g., emotional state measures, progress measures, learning goals, or personality traits. Only a minority of the reviewed studies provided an empirical or theoretical rationale for assigning feedback messages to different types of students. Most studies report positive or at least mixed or neutral effects of personalized feedback on educational outcomes. This review discusses several implications for future directions in research on digitalized and personalized feedback. This study also adds to previous literature reviews on automatic and adaptive feedback that did not clearly distinguish task-adaptiveness and student-adaptiveness in digital feedback examples.","url":"https://doi.org/10.1016/j.caeai.2022.100080","authors":["Uwe Maier","Christian Klotz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-23T21:26:53Z","doi":"10.1016/j.caeai.2022.100080","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.17762/turcomat.v12i2.2286","name":"Artificial Intelligence Techniques for Cancer Detection in Medical Image Processing: A Review","source":"crossref","abstract":"Cancer is the uncontrolled growth of abnormal cells in any part of a body. Cancer is a broad term for a group of diseases caused when abnormal cells grows in different body parts. There are more than hundred types of Cancer such as Lung cancer, Breast cancer, Skin cancer, Oral cancer, Colon cancer and Prostate cancer. Delay in treatment can cause serious health issues, even cause loss of life. This paper gives the review on methods of detection of lung cancer and brain cancer and liver using image processing. The methods used for detection are Automated and computer-aided detection system (CAD) with artificial intelligence and these methods are good to process a large datasets to provide accurate and efficient results in the detection of cancer. However, these processing system have to face many challenges to implement on large scale including imageacquisition, pre-processing, segmentation, and data management and classification strategies to be compatible with AI. This paper reviews the various image acquisition and segmentation techniques. These techniques become the need of an hour to cater the growing patient population and for the improvement in the Healthcare system.","url":"https://doi.org/10.17762/turcomat.v12i2.2286","authors":["Er. Charnpreet kaur, Et. al."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-10T18:13:14Z","doi":"10.17762/turcomat.v12i2.2286","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s43681-026-01213-0","name":"Artificial intelligence in peer review of medical journals: promise, pitfalls, and ethical imperatives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s43681-026-01213-0","authors":["Shadab Ahamad","Ramya Raghavan","Prachi Kukshal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-23T10:09:31Z","doi":"10.1007/s43681-026-01213-0","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1186/s12909-025-08162-y","name":"Artificial intelligence in orthopaedic education, training and research: a systematic review","source":"crossref","abstract":"Background Artificial intelligence (AI) increasingly transforms orthopedic education and research, offering novel surgical training and scientific advancement approaches. However, these AI applications' scope, impact, and limitations require comprehensive evaluation. Aim to systematically review AI technologies' practical applications, benefits, limitations, and future directions in orthopedic education and research. Methods This systematic review was registered with PROSPERO (CRD420251064596). A comprehensive search of PubMed, Embase, and Scopus was conducted. Studies reporting AI technologies-such as machine learning, virtual reality (VR) simulation, generative AI, and adaptive learning systems-were included in orthopedic training or research. Due to heterogeneity in study designs and outcome measures, data were extracted and synthesized narratively. Results AI technologies consistently improve training efficiency, personalized learning, and objective skill assessments. VR simulation and machine learning-based feedback tools enhanced technical proficiency and significantly reduced learning curves. Adaptive learning platforms enabled tailored educational pathways. However, generative AI applications remain nascent, with notable concerns regarding content accuracy and bias. Across studies, key limitations included data bias, over-reliance on automated systems, high implementation costs, and a lack of longitudinal and real-world validation. Most studies were limited to simulation-based environments with insufficient evidence on clinical skill transfer. Ethical, curricular, and regulatory considerations remain underdeveloped. Conclusions AI holds considerable potential to advance orthopedic education and research by enabling more efficient, equitable, and personalized training. However, its integration must be guided by rigorous validation, ethical standards, and stakeholder collaboration. Hybrid human-AI training models and standardized evaluation metrics are essential to realizing AI's full potential in orthopedics.","url":"https://doi.org/10.1186/s12909-025-08162-y","authors":["Bibek Banskota","Rajan Bhusal","Prkash Kumar Yadav","Ashok Kumar Banskota"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-13T08:31:25Z","doi":"10.1186/s12909-025-08162-y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s44163-025-00437-z","name":"A systematic literature review on the role of artificial intelligence in citizen science","source":"crossref","abstract":"Abstract Citizen science (CS) has emerged as a collaborative process for addressing complex scientific and societal challenges. The emergence of artificial intelligence (AI) into CS projects, has transformed data collection, analysis, and validation steps. However, significant gaps remain in understanding the methodologies, applications, and challenges of AI-CS integration. Our systematic review seeks to address the gaps by answering three questions: (1) What AI methodologies are most commonly applied in CS projects? (2) How does AI integration impact the efficiency and scalability of CS initiatives? (3) What challenges arise from AI-CS integration, and how are they mitigated? Following the PRISMA-ScR guidelines, a systematic search of Scopus, ACM Digital Library, and Web of Science identified relevant articles published between 2013 and 2024. From an initial pool of 2,470 publications, 90 were retained after filtering through the eligibility criteria. Our findings illustrate ML techniques, including deep learning, clustering algorithms, and convolutional neural networks, boost data annotation, classification, and validation in applications across various disciplines. However, challenges such as data quality variability, algorithmic opacity, and scalability constraints persist. Our conclusion identifies the multifaceted role of AI in citizen science, categorized into three primary functions: (1) assisting or replacing humans in task completion, (2) influencing human behaviour and fostering engagement, and (3) improving insights through pattern identification and decision support.","url":"https://doi.org/10.1007/s44163-025-00437-z","authors":["Germain Abdul-Rahman","Andrej Zwitter","Noman Haleem"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T14:00:32Z","doi":"10.1007/s44163-025-00437-z","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/cjce.24246/v1/review2","name":"Review for \"Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cjce.24246/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-05T09:40:23Z","doi":"10.1002/cjce.24246/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.47391/jpma.aku-18","name":"Artificial intelligence in dentistry, orthodontics and Orthognathic surgery: A literature review","source":"crossref","abstract":"Artificial intelligence is the ability of machines to work like humans. The concept initially began with the advent of mathematical models which gave calculated outputs based on inputs fed into the system. This was later modified with the introduction of various algorithms which can either give output based on overall data analysis or by selection of information within previous data. It is steadily becoming a favoured mode of treatment due to its efficiency and ability to manage complex conditions in all specialities. In dentistry, artificial intelligence has also popularised over the past few decades. They have been found useful fordiagnosis in restorative dentistry, oral pathology and oral surgery. In orthodontics, they have been utilised for diagnosis, assessment of treatment needs, cephalometrics, treatment planning and orthognathic surgeries etc. The current literature review was planned to highlight the uses of artificial intelligence in dentistry, specifically in orthodontics and orthognathic surgery. Continuous...","url":"https://doi.org/10.47391/jpma.aku-18","authors":["Arshad Siddiqui","Rashna Hoshang Sukhia","Dinaz Ghandhi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-09T08:18:44Z","doi":"10.47391/jpma.aku-18","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.11622/smedj.2022044","name":"Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications","source":"crossref","abstract":"Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.","url":"https://doi.org/10.11622/smedj.2022044","authors":["JW Li","LM Wang","TL Ang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-26T04:51:45Z","doi":"10.11622/smedj.2022044","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.jval.2021.11.939","name":"POSB290 Artificial Intelligence in Medical Diagnostics: A Review of NICE MedTech Innovation Briefings","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jval.2021.11.939","authors":["D. Foster","P. Ioannou","I. Willits"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-19T19:38:28Z","doi":"10.1016/j.jval.2021.11.939","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/95157","name":"Artificial Intelligence for Healthcare Quality and Patient Safety: A Scoping Review of Diagnostic, Predictive, and Decision Support Applications (Preprint)","source":"crossref","abstract":"","url":"https://doi.org/10.2196/95157","authors":["Yang Xu","Jeremy Veillard","Jude Kong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-03T06:40:07Z","doi":"10.2196/95157","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.69987/aimlr.2026.70202","name":"Comparative Evaluation of Self-Supervised Pretraining Strategies for Few-Shot Medical Image Analysis","source":"crossref","abstract":"Self-supervised learning has emerged as a promising solution to address the chronic scarcity of labeled medical imaging data. This study presents a comprehensive evaluation of mainstream self-supervised pretraining strategies, including contrastive learning methods (CLIP, DINO) and masked image modeling approaches (MAE), specifically focusing on their effectiveness in few-shot medical image analysis scenarios. We systematically assess the feature representation quality and downstream task performance of these methods across multiple medical imaging modalities including chest X-rays, CT scans, and MRI sequences. Our experimental framework evaluates these strategies under various data-scarce conditions (5-shot, 10-shot, and 50-shot settings) using standardized benchmark datasets. Linear probing experiments reveal that masked autoencoder-based methods achieve superior feature discriminability with 87.3% accuracy compared to 84.1% for contrastive approaches. However, contrastive methods demonstrate stronger cross-domain transfer capabilities, maintaining 81.2% average performance when adapted to unseen anatomical regions versus 76.8% for reconstruction-based methods. Our quantitative analysis further indicates that hybrid pretraining strategies combining both paradigms yield optimal results in extremely low-data regimes, achieving 89.6% classification accuracy with only 10 labeled samples per class. These findings provide evidence-based guidance for selecting appropriate self-supervised pretraining strategies based on specific clinical deployment scenarios, data availability constraints, and computational resource limitations.","url":"https://doi.org/10.69987/aimlr.2026.70202","authors":["Mingxuan Han","Zhengyu Jin","Danbing Zou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-11T10:23:39Z","doi":"10.69987/aimlr.2026.70202","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1201/9781003394068-6","name":"Centring the Patient in Implementing AI for Medical Imaging","source":"crossref","abstract":"Healthcare is rapidly evolving, with the integration of artificial intelligence (AI) and data analytics in medical imaging promising to revolutionise patient care and delivery. This chapter aims to critically examine the implementation of AI in medical imaging from the lens of patient-centred care and equality, acknowledging the profound impact of technology on patient outcomes and experiences. At the outset, it is necessary to understand patients’ perspectives on AI in healthcare. Recent surveys and studies have shed light on varying attitudes and perceptions among patients regarding the integration of AI-driven technologies. While some express optimism about the potential for improved diagnosis and treatment, others harbour concerns about privacy, reliability, and the potential loss of human interaction in healthcare delivery. In sum, the chapter provides comprehensive insights into the effective implementation of patient-centred AI in medical imaging, proposing a critical awareness to ensure the quality and accessibility of healthcare for all. By navigating the intersection of technology, patient care, and equality, healthcare practitioners and stakeholders hold the potential to integrate AI in ways that address current challenges and advance the collective well-being of society.","url":"https://doi.org/10.1201/9781003394068-6","authors":["Fay Manning"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T09:15:48Z","doi":"10.1201/9781003394068-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/0004-3702(85)90013-x","name":"Artificial intelligence and robotics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90013-x","authors":["Michael Brady"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90013-x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/e2coj9","name":"Review of: \"An Explorative Review of Artificial Intelligence Software (Chatbot) Impact on Education System\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.","url":"https://doi.org/10.32388/e2coj9","authors":["Mihai L. Mocanu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-19T12:24:38Z","doi":"10.32388/e2coj9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.26434/chemrxiv-2025-8l1km","name":"Artificial Intelligence in Scalable Materials Synthesis and Manufacturing: A Comprehensive Review","source":"crossref","abstract":"While artificial intelligence (AI) has transformed materials discovery, the primary bottleneck to technological impact remains the transition from lab-scale synthesis to robust, industrial-scale manufacturing. Most promising materials perish in this depth, which is referred as the \"valley of death.\" The current review consolidates and critically evaluates the emerging ecosystem of AI-driven strategies and frameworks designed specifically to bridge this \"lab-to-fab\" gap. The review shifts our attention from property prediction to the engineering-driven problems of manufacturability. Further, the review discusses the main obstacles to scaling the production of materials, such as reproducibility, process optimization in the context of uncertainty, and techno-economic viability, as well as the AI approaches being developed to overcome them. This includes Natural Language Processing (NLP) for method extraction, graph neural networks for reaction modeling, reinforcement learning for process control, Bayesian optimization for defining process windows, and integrated AI-Techno-Economic Analysis (TEA) frameworks. The article concludes in a forward-looking roadmap for the future of AI in chemical and materials engineering. The proposed conclusion is defined by a paradigm shift from simply finding new materials to creating viable, economical, and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation.","url":"https://doi.org/10.26434/chemrxiv-2025-8l1km","authors":["Nagababu Andraju"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-12T20:41:18Z","doi":"10.26434/chemrxiv-2025-8l1km","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/ekulvo","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/ekulvo","authors":["Enrico Brugnami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-10T07:26:29Z","doi":"10.32388/ekulvo","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/47445","name":"Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review","source":"crossref","abstract":"Background Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. Objective This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. Methods This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. Results Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer–based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. Conclusions It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.","url":"https://doi.org/10.2196/47445","authors":["Hazrat Ali","Rizwan Qureshi","Zubair Shah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-17T11:00:58Z","doi":"10.2196/47445","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.55248/gengpi.6.0425.1558","name":"Artificial Intelligence: Transforming Managerial Practices-A systematic literature review","source":"crossref","abstract":"Artificial Intelligence (AI) embodies the remarkable capacity of machines to glean insights from historical data, recognize patterns, and utilize that knowledge to render decisions or offer recommendations.Its integration into various management domains has showcased an array of possibilities, empowering efficiency and optimizing numerous operational processes.AI, through pre-set algorithms and cohesive computational methodologies, augments the human facet of HR by infusing technology with intelligence.This fusion promises an evolved landscape that elevates both job seekers and existing employees, fostering an environment conducive to generating superior and expedited outcomes.Among the manifold applications, AI's prowess shines in talent acquisition and recruitment-critical functions within the HR spectrum.By significantly curtailing the time and energy traditionally spent on tasks like applicant screening, database management, interview coordination, and query resolution, AI paves the way for a more efficient and streamlined process.In this research paper we discuss multifaceted utilities of Artificial Intelligence within the realm of management, exploring its transformative impact across various operational facets.","url":"https://doi.org/10.55248/gengpi.6.0425.1558","authors":["Dr. Ona Ladiwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-03T09:18:57Z","doi":"10.55248/gengpi.6.0425.1558","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1177/27000710251386963/v2/review1","name":"Review for \"How Human Personality Will Change with the Use of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/27000710251386963/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-28T04:00:21Z","doi":"10.1177/27000710251386963/v2/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.7205421","name":"A Review on: Impact of Artificial Intelligence in Daily Life","source":"crossref","abstract":"Artificial Intelligence (AI) has moved from specialized laboratories into ordinary routines. Search engines rank information, navigation applications estimate routes, streaming platforms recommend content, smartphones improve photographs, banks screen transactions, and generative AI systems assist with writing, coding and learning. This review examines the impact of AI in daily life with attention to education, healthcare, communication, transportation, finance, entertainment, shopping, workplaces and smart homes. It also discusses risks such as privacy loss, biased automated decisions, misinformation, over-dependence and changes in employment. The paper uses a descriptive review methodology based on published research and reports from established institutions. The evidence suggests that AI can improve convenience, accessibility, speed and personalization, but its benefits depend on human oversight, responsible data practices and AI literacy. The central conclusion is that AI should be treated as a supporting technology rather than an unquestioned replacement for human judgement.","url":"https://doi.org/10.2139/ssrn.7205421","authors":["Gautam Godara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-07T10:43:59Z","doi":"10.2139/ssrn.7205421","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1186/s12910-025-01198-1","name":"Ethical and social considerations of applying artificial intelligence in healthcare—a two-pronged scoping review","source":"crossref","abstract":"Abstract Background Artificial Intelligence (AI) is being designed, tested, and in many cases actively employed in almost every aspect of healthcare from primary care to public health. It is by now well established that any application of AI carries an attendant responsibility to consider the ethical and societal aspects of its development, deployment and impact. However, in the rapidly developing field of AI, developments such as machine learning, neural networks, generative AI, and large language models have the potential to raise new and distinct ethical and social issues compared to, for example, automated data processing or more ‘basic’ algorithms. Methods This article presents a scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design. One strand of the review (SR1) consists of a broad review of the academic literature restricted to a recent timeframe (2021–23), to better capture up to date developments and debates. The second strand (SR2) consists of a narrow review, limited to prior systematic and scoping reviews on the ethics of AI in healthcare, but extended over a longer timeframe (2014–2024) to capture longstanding and recurring themes and issues in the debate. This strategy provides a practical way to deal with an increasingly voluminous literature on the ethics of AI in healthcare in a way that accounts for both the depth and evolution of the literature. Results SR1 captures the heterogeneity of audience, medical fields, and ethical and societal themes (and their tradeoffs) raised by AI systems. SR2 provides a comprehensive picture of the way scoping reviews on ethical and societal issues in AI in healthcare have been conceptualized, as well as the trends and gaps identified. Conclusion Our analysis shows that the typical approach to ethical issues in AI, which is based on the appeal to general principles, becomes increasingly unlikely to do justice to the nuances and specificities of the ethical and societal issues raised by AI in healthcare, as the technology moves from abstract debate and discussion to real world situated applications and concerns in healthcare settings.","url":"https://doi.org/10.1186/s12910-025-01198-1","authors":["Emanuele Ratti","Michael Morrison","Ivett Jakab"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-26T20:58:42Z","doi":"10.1186/s12910-025-01198-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.4271/j3298_202407","name":"Artificial Intelligence Data for Ground Vehicle Applications","source":"crossref","abstract":"&lt;div class=\"section abstract\"&gt; &lt;div class=\"htmlview paragraph\"&gt;This SAE Technical Information Report provides preliminary information regarding the current state of data collection, data processing methods, and usage for developing AI and its enabled systems and applications in the ground vehicle domain. This information report is a survey of topics highlighting data’s impact on AI solutions and methods that may be used to develop or improve data-related processes.&lt;/div&gt; &lt;div class=\"htmlview paragraph\"&gt;This report may offer insights that can drive innovation, improve safety, optimize performance, and develop regulatory compliance methods. Solution providers may find this information insightful and realize the potential for collaborative measures in development of their AI-enabled systems. Developers of standards and lawmakers may gain a better understanding of the current state of the industry and find opportunities to develop policies to guide the future of transportation, which in turn directly impacts the public. Other committees and academic affiliates may see links between data for AI in the ground vehicle domain and their domains of interest, which could spawn novel research, development, and standards.&lt;/div&gt; &lt;/div&gt;","url":"https://doi.org/10.4271/j3298_202407","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-26T22:15:32Z","doi":"10.4271/j3298_202407","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-026-11541-6","name":"Super greedy trees","source":"crossref","abstract":"We introduce Super Greedy Trees (SGTs), a decision-tree framework that extends CART by constructing tree splits from lasso-penalized parametric models. At each tree node, a model fitted to the local data induces an adaptive multivariate geometric cut (linear or curved) selected to greedily reduce empirical risk. This yields richer partitions than axis-parallel CART while keeping each split easy to inspect through sparse local structure. In simulated and real-world regression studies, SGTs and an ensemble extension (Super Greedy Forests, SGFs) perform well relative to CART, oblique trees, random forests, and gradient boosted trees, especially when the underlying response surface is complex. In a treadmill ECG and clinical-data case study, SGFs identify sparse combinations of signals associated with long-term survival. The SGT framework thus provides a flexible and theoretically sound approach to tree-based learning.","url":"https://doi.org/10.1007/s10462-026-11541-6","authors":["Hemant Ishwaran"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-22T06:15:42Z","doi":"10.1007/s10462-026-11541-6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1080/08839514.2018.1442991","name":"Machine Learning Applications in Baseball: A Systematic Literature Review","source":"crossref","abstract":"Statistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. In particular, analysts can now apply machine learning algorithms to large baseball data sets to derive meaningful insights into player and team performance. In the interest of stimulating new research and serving as a go-to resource for academic and industrial analysts, we perform a systematic literature review of machine learning applications in baseball analytics. The approaches employed in literature fall mainly under three problem class umbrellas: Regression, Binary Classification, and Multiclass Classification. We categorize these approaches, provide our insights on possible future applications, and conclude with a summary of our findings. We find two algorithms dominate the literature: (1) Support Vector Machines for classification problems and (2) k-nearest neighbors for both classification and Regression problems. We postulate that recent proliferation of neural networks in general machine learning research will soon carry over into baseball analytics.","url":"https://doi.org/10.1080/08839514.2018.1442991","authors":["Kaan Koseler","Matthew Stephan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-02-27T02:31:32Z","doi":"10.1080/08839514.2018.1442991","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2025.113542","name":"Machine learning for resilience analysis: a review of systems under cyberattacks","source":"crossref","abstract":"Intelligent cyber-physical systems (CPS) are increasingly exposed to sophisticated cyberattacks, yet existing machine learning (ML)-focused surveys provide only fragmented coverage of the resilience lifecycle. They often separate attack modelling from defence strategies, overlook cross-sector insights, and lack clear evaluation benchmarks, limiting practical applicability. This review offers the first lifecycle-oriented synthesis of ML-enabled CPS resilience, covering detection, defence, recovery, and adaptation. It integrates diverse ML-based attack techniques with corresponding resilience mechanisms and provides a comparative analysis across two critical CPS sectors—power and water—to highlight shared vulnerabilities and sector-specific behaviours. The review further distils key limitations in current ML approaches, including data scarcity, interpretability challenges, and limited real-world validation. Finally, it proposes five actionable research directions and resilience quantification considerations to guide future development of robust and transferable ML-based CPS resilience frameworks.","url":"https://doi.org/10.1016/j.engappai.2025.113542","authors":["Ruoqing Yin","Liz Varga"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-20T04:14:17Z","doi":"10.1016/j.engappai.2025.113542","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.60087/jaigs.v2i1.40","name":"Exploring Current Trends in Artificial Intelligence Technology  An Extensive Review","source":"crossref","abstract":"Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.","url":"https://doi.org/10.60087/jaigs.v2i1.40","authors":["Jeff Shuford","Md.Mafiqul Islam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-08T06:21:41Z","doi":"10.60087/jaigs.v2i1.40","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.37497/rev.artif.intell.educ.v6ii.54","name":"INTELIGÊNCIA ARTIFICIAL E DISCENTES VULNERÁVEIS: BARREIRAS E DESAFIOS","source":"crossref","abstract":"Objetivo : investigar como os discentes vulneráveis percebem as barreiras e desafios da inteligência artificial na biblioteconomia em relação aos graduandos em situação de vulnerabilidade social. Método : Trata-se de estudo de caso exploratório com abordagem qualitativa envolvendo discentes do curso de biblioteconomia de uma universidade pública federal de Minas Gerais. Resultados : Os participantes possuem uma opinião majoritariamente positiva quanto à incorporação da inteligência artificial no contexto educacional, no entanto, sugerem uma mediação pedagógica habilidades e acesso equitativo às tecnologias digitais, pois sem essas condições, a inteligência artificial pode fortalecer desigualdades preexistentes. Conclusão: O estudo evidencia a necessidade de políticas educacionais certificadas a inteligência artificial, que promove uma inclusão mais justa e emancipadora.","url":"https://doi.org/10.37497/rev.artif.intell.educ.v6ii.54","authors":["Janete Fernandes Silva","Cláudia Aparecida Avelar Ferreira"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T19:51:33Z","doi":"10.37497/rev.artif.intell.educ.v6ii.54","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1177/08944393241268526","name":"Does the Media’s Partisanship Influence News Coverage on Artificial Intelligence Issues? Media Coverage Analysis on Artificial Intelligence Issues","source":"crossref","abstract":"This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.","url":"https://doi.org/10.1177/08944393241268526","authors":["Mikyung Chang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-02T09:27:43Z","doi":"10.1177/08944393241268526","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32350/umtair.22.03","name":"Role of Artificial Intelligence in different aspects of Public Health","source":"crossref","abstract":"In the next decade of disease surveillance research, innovative and novel techniques are required to utilise massive quantities of complex and multi-dimensional data, effectively. Public health is one of the most significant domains of public governance and artificial intelligence has emerged as an innovative problem-solving technique in this domain. Artificial intelligence is a requirement for the early identification of diseases and disasters in order to prevent high mortality rates and reduce economic burden by timely providing appropriate healthcare. This detection is made possible in this research by identifying patterns in the database. This review shows that the use and development of AI techniques has increased in the field of public health over the past few years and most of the existing studies show a positive impact of AI in the domain of public health. This study is divided into three portions. The first portion reviews the role and potential usage of artificial intelligence in epidemics, since it is very important to timely investigate them and AI has the potential to cope with them. The second part of the review provides a detailed discussion about serious game usage in public health. Serious games are used for the training and rehabilitation of the gamer. The third part deals with the management of public health emergencies including evacuation, causality response, and information processing.","url":"https://doi.org/10.32350/umtair.22.03","authors":["Hassan Anwar","Ifra Chaudhary","Umar Latif","Ali Latif"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-22T09:07:12Z","doi":"10.32350/umtair.22.03","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/34vvzc","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/34vvzc","authors":["Rick Rejeleene"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-17T20:52:03Z","doi":"10.32388/34vvzc","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.4337108","name":"The Artificial Intelligence (AI) Application in Medicine: A Review Article","source":"crossref","abstract":"The dramatic growth of heart, cancer, and bone numerous diseases bear permanent effects and complications on society; which led the medical community to pursue programs for early detection and effective treatment. In medicine, however, the diagnosis of diseases requires an efficient methodology to benefit physicians in the appropriate diagnosis of disease with the least time and maximum accuracy; which makes physicians take a significant step toward disease control.","url":"https://doi.org/10.2139/ssrn.4337108","authors":["AliReza Mahboubi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-27T17:23:17Z","doi":"10.2139/ssrn.4337108","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/jbmr.4879/v3/review1","name":"Review for \"Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/jbmr.4879/v3/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-17T09:07:52Z","doi":"10.1002/jbmr.4879/v3/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.4113550","name":"Using Artificial Intelligence in the Law Review Submissions Process","source":"crossref","abstract":"The use of artificial intelligence to help editors examine law review submissions may provide a way to improve an overburdened system. This Article is the first to explore the promise and pitfalls of using artificial intelligence in the law review submissions process. Technology-assisted review of submissions offers many possible benefits. It can simplify preemption checks, prevent plagiarism, detect failure to comply with formatting requirements, and identify missing citations. These efficiencies may allow editors to address serious flaws in the current selection process, including the use of heuristics that may result in discriminatory outcomes and dependence on lower-ranked journals to conduct the initial review of submissions. Although editors should not rely on a score assigned by an algorithm to decide whether to accept an article, technology-assisted review could increase the efficiency of initial screening and provide feedback to editors on their selection decisions. Uncovering potential human bias in the selection process may encourage editors to develop ways to minimize its harmful effects. &lt;br&gt;&lt;br&gt;Despite these benefits, using artificial intelligence to streamline the submissions process raises significant concerns. Technology-assisted review may enable efficient implementation of existing biases into the selection process, rather than correcting them. Artificial intelligence systems may rely on considerations that result in discriminatory effects and negatively impact groups that are not adequately represented during development. The tendency to defer to seemingly neutral and often opaque algorithms can increase the risk of adverse outcomes. With careful oversight, however, some of these concerns can be addressed. Even an imperfect system may be worth using in limited situations where the benefits substantially outweigh the potential harms. With appropriate supervision, circumscribed application, and ongoing refinement, artificial intelligence may provide a more efficient and fairer submissions experience for both editors and authors.","url":"https://doi.org/10.2139/ssrn.4113550","authors":["Brenda M. Simon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-28T08:19:26Z","doi":"10.2139/ssrn.4113550","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.57041/4ammg320","name":"Future of Artificial Intelligence with the Perspective of Deep Learning: A Review","source":"crossref","abstract":"Artificial Intelligence (AI) has become one of the most transformative technologies of the modern era, influencing diverse domains including healthcare, transportation, manufacturing, education, finance, and communication. The rapid evolution of AI has been largely driven by advances in deep learning, a subset of machine learning inspired by the structure and function of the human brain. Deep learning has enabled machines to process complex data, recognize patterns, understand natural language, and make intelligent decisions with unprecedented accuracy. This review paper explores the future trajectory of AI through the perspective of deep learning, discussing its current achievements, emerging trends, opportunities, and challenges. The paper highlights the development of advanced neural architectures, generative AI, multimodal learning, explainable AI, edge intelligence, and autonomous systems. Furthermore, ethical concerns, computational limitations, and the necessity for trustworthy AI are examined. The review concludes that deep learning will remain a fundamental pillar of future AI systems, while its integration with other technologies and human-centered principles will determine the direction of intelligent systems.","url":"https://doi.org/10.57041/4ammg320","authors":["Asanka Sayakara","Zaid Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T13:21:16Z","doi":"10.57041/4ammg320","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/s0004-3702(19)30204-8","name":"Introducing article numbering to Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(19)30204-8","authors":["Sweitze Roffel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-22T09:11:39Z","doi":"10.1016/s0004-3702(19)30204-8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-023-10535-y","name":"A critical review on applications of artificial intelligence in manufacturing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-023-10535-y","authors":["Omkar Mypati","Avishek Mukherjee","Debasish Mishra","Surjya Kanta Pal","Partha Pratim Chakrabarti","Arpan Pal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-01T15:01:43Z","doi":"10.1007/s10462-023-10535-y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.3389/frai.2026.1686454","name":"Both ends of artificial intelligence impacting privacy: a review of violation and protection","source":"crossref","abstract":"The intersection of Artificial Intelligence (AI) and privacy presents both significant challenges and opportunities. As AI systems become increasingly embedded in many aspects of our lives, including healthcare, finance, and social networks, and introduce significant concerns regarding privacy issues – the need for effective privacy-preserving mechanisms also grows. This review systematically analyzes 94 research papers in the field of AI and privacy. To model this complex issue, we categorized privacy in AI through a multi-dimensional approach that includes technological domains' privacy actions, privacy-preserving strategies, and AI-privacy interaction directions. A novel technique based on a Graph Database (Neo4J) which is available to the reader was employed to facilitate visualization of the complex relations between the reviewed objects. Moreover, the Graph, which is actually the review, can be queried and updated with future publications. Key findings indicate that AI can be both a potential threat to privacy, for example due to inference risks and data exploitation, as well as a tool for enhancing privacy through techniques such as federated learning and differential privacy. The study highlights regulatory, ethical, and technical challenges, emphasizing the need for interdisciplinary collaboration.","url":"https://doi.org/10.3389/frai.2026.1686454","authors":["Nadav Voloch","Ron S. Hirschprung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-18T07:30:16Z","doi":"10.3389/frai.2026.1686454","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.17504/protocols.io.n2bvje1wngk5/v1","name":"Mapping Factors Influencing the Psychological Well-Being of Medical Students Interacting with Generative Artificial Intelligence: A Scoping Review  v1","source":"crossref","abstract":"The rapid integration of generative artificial intelligence (GenAI) tools such as ChatGPT and large language models (LLMs) into medical education has created new opportunities for learning, simulation, and clinical reasoning. However, these technologies also introduce potential challenges affecting students’ psychological well-being, including anxiety about reliability, ethical concerns, and changes in academic engagement. This protocol describes a scoping review designed to map the factors influencing the psychological well-being of medical students in their interactions with GenAI. The review will follow the methodological framework proposed by Arksey and O’Malley and guided by the PRISMA-ScR checklist. Searches will be conducted across international and Persian databases from 2018 onward, using the PCC framework (Population–Concept–Context) to ensure comprehensive coverage. Eligible studies will include empirical and theoretical works addressing medical students’ experiences with GenAI in educational contexts. Data will be extracted using a structured form and synthesized through descriptive mapping and thematic analysis. The expected outcome is a conceptual framework summarizing factors that positively or negatively influence medical students’ psychological well-being when engaging with GenAI. This review will identify knowledge gaps and provide evidence to inform educational policy, research, and the development of a culturally adapted conceptual model based on the METUX framework.","url":"https://doi.org/10.17504/protocols.io.n2bvje1wngk5/v1","authors":["Golchehreh Ahmadi","Noushin Kohan","Rita Mojtahedzadeh","Ken Masters","Hanieh Zehtab-Hashemi","Aeen Mohammadi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-04T10:10:55Z","doi":"10.17504/protocols.io.n2bvje1wngk5/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21103/article13(1)_ra1","name":"The Application of Artificial Intelligence in Detecting Breast Lesions with Medical Imaging: A Literature Review","source":"crossref","abstract":"Breast cancer is considered the most commonly diagnosed cancer among women worldwide. Several studies have shown that mammography screening could significantly decrease breast cancer mortality. Despite other screening modalities, such as MRI and ultrasound (US), mammography plays a vital role in detecting cancer and following up on it, due to its qualities and properties. The aim of this literature review is to look at recent studies that use AI with different medical imaging mammograms, MRI, and US, in detecting breast lesions. A literature search was carried out using Google Scholar, Semantic Scholar, medRxiv, and PubMed databases for a period of the last four years. The search terms were \"breast lesion,\" \"breast imaging,\" and \"breast cancer\" combined with \"machine learning,\" \"deep learning,\" and \"artificial intelligence.\" Among these studies, only the medical imaging related to breast lesions with AI was selected. A total of 25 articles were extracted from the following databases: 4 Google Scholar, 3 Semantic Scholar, 4 medRxiv, and 14 PubMed. Only papers related to breast lesions with medical imaging modalities were extracted, and all duplications were removed. In this study, the papers were reviewed by medical imaging professionals. This literature review summarizes the most recent articles on utilizing AI in detecting breast lesions for different imaging modalities: mammogram, ultrasound, and MRI. Reviewed studies showed that AI performance in detecting lesions was significant, associated with high accuracy, sensitivity, and specificity for these modalities.","url":"https://doi.org/10.21103/article13(1)_ra1","authors":["Salem Saeed Alghamdi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-04T22:24:41Z","doi":"10.21103/article13(1)_ra1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/preprints.48754","name":"Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis (Preprint)","source":"crossref","abstract":"BACKGROUND Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently. OBJECTIVE This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety. METHODS Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies–Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate. RESULTS Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods. CONCLUSIONS Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI. CLINICALTRIAL PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560","url":"https://doi.org/10.2196/preprints.48754","authors":["Alaa Abd-alrazaq","Rawan AlSaad","Manale Harfouche","Sarah Aziz","Arfan Ahmed","Rafat Damseh","Javaid Sheikh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-08T15:45:58Z","doi":"10.2196/preprints.48754","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1145/3429889.3429941","name":"A Review on the Mechanisms of Mitochondrial Dynamics and the Relationship with Neurodegeneration","source":"crossref","abstract":"Mitochondrial health underlies many critical processes in the cell, and the health of the mitochondria is in turn regulated by mitochondrial dynamics -- the joint processes of mitochondrial fission, fusion and trafficking within the cell. The role of mitochondrial dynamics in regulating both ATP production and apoptosis via maintaining mitochondrial morphology has only recently been described. In particular, this regulation is a key etiology for neurodegenerative diseases due to the high energetic requirement of neural cells. Although the central effectors of fusion and fission have been identified, little is known about how accessory proteins and regulatory networks affect context-dependent and temporal changes in the mitochondrial network. In this review, we will mainly focus on the mechanisms of mitochondrial fission, fusion and trafficking, as well as the regulation in neurodegenerative diseases, hoping to provide a new direction for the future researches and the treatments of neurodegenerative diseases by regulating mitochondrial dynamics.","url":"https://doi.org/10.1145/3429889.3429941","authors":["YaNan Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-04T22:47:01Z","doi":"10.1145/3429889.3429941","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.70818/bmcj.v06i2.191","name":"Artificial Intelligence in Health Sciences","source":"crossref","abstract":"Artificial intelligence (AI) is intelligence of machines which can think and act like humans performing tasks that otherwise would require human intelligence. It is actually a rapidly growing field of computer science dealing with the building of smart machines which can learn from the nature and environment and can take firm decisions based on the collected information. Artificial Intelligence can be translated into different fields of computer science concerned with applications of automated interfaces in almost all sectors of society and life like healthcare and medical institutions, academic institutions, commercial and financial institutions like Amazon, Netflix etc. and so on and so on. Commercial aviations associated with Al has been extensively automated and planes are flown by autopilots with human control just for few minutes only per flight. Al has advanced medical technological applications especially in areas of huge database with an abundance of information and a very little time. Examples of applications of Al in healthcare sector are medical diagnosis, prognosis and management options like telemedicine, robotic assisted surgery, innovations pharmaceutical and discoveries, medial transcriptions etc.","url":"https://doi.org/10.70818/bmcj.v06i2.191","authors":["M Manzurul Haque"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-17T10:40:05Z","doi":"10.70818/bmcj.v06i2.191","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/preprints.26522","name":"Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review (Preprint)","source":"crossref","abstract":"BACKGROUND Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. OBJECTIVE This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. METHODS Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography &amp;amp; Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. RESULTS The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. CONCLUSIONS The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.","url":"https://doi.org/10.2196/preprints.26522","authors":["Kathrin Seibert","Dominik Domhoff","Dominik Bruch","Matthias Schulte-Althoff","Daniel Fürstenau","Felix Biessmann","Karin Wolf-Ostermann"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-21T15:55:25Z","doi":"10.2196/preprints.26522","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/9im62n","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"However, the manuscript suffers from a few shortcomings (listed below) and addressing these","url":"https://doi.org/10.32388/9im62n","authors":["Debraj Sen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-09T02:51:33Z","doi":"10.32388/9im62n","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/cjce.24246/v2/review3","name":"Review for \"Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cjce.24246/v2/review3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-05T09:40:23Z","doi":"10.1002/cjce.24246/v2/review3","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.5812/ermsj-162371","name":"Challenges in Implementing Artificial Intelligence for Nursing Education: A Systematic Review","source":"crossref","abstract":"Context: The use of artificial intelligence (AI) in health sciences education offers numerous benefits; however, a lack of awareness regarding the limitations of these tools may lead to financial and human losses. Objectives: Given that nurses constitute the largest segment of the healthcare team and their education is of paramount importance, this study explores the challenges associated with the use of AI in nursing education. Data Sources: A systematic review was conducted in 2025, utilizing databases such as ERIC, CINAHL, PubMed, Scopus, and Web of Science to search for relevant studies. The search employed appropriate keywords, with limitations set to articles published within the last five years and in English. Study Selection: After identifying relevant articles, their quality was assessed using suitable tools. This step ensured that only high-quality studies were included in the review, providing a reliable basis for analysis. Data Extraction: Various data were extracted, categorized, and analyzed. This comprehensive approach allowed for a thorough examination of the challenges associated with the use of AI in nursing education. Results: Out of 307 identified studies, 18 articles were reviewed. Based on the findings of this study, the challenges associated with the use of AI in nursing education were categorized into six main groups: Educational, technological, ethical, trust-building, human resource, and economic challenges. Conclusions: The challenges identified in this study were grouped into six primary categories, with the roots of these challenges linked to issues related to educational organizations, personnel capabilities, and the design of AI systems. Recognizing the challenges of using AI in nursing education can not only prevent unintended problems but also aid in preserving economic and human resources.","url":"https://doi.org/10.5812/ermsj-162371","authors":["Mohammad Dehghani","Mohammad Hossein Pourasad","Hero Khezri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-02T06:15:03Z","doi":"10.5812/ermsj-162371","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-024-10765-8","name":"Correction to: From understanding diseases to drug design: can artificial intelligence bridge the gap?","source":"crossref","abstract":"The Publishing Team missed some corrections during the proofreading stage.Below is the list of corrections: Page 2 of 38: Fig. 1 is shifted to the top of Page 2 of 38.Page 2 of 38: The term \"For example\" was split between pages 2 and 4 with a Table 1 in between.The text is now shifted so that \"For example\" is together.Page 2 of 38: A full stop is added at the end of the following statement: \"Table 1 provides a list of some important summary tables and figures reported in the literature about AI in drug discovery.\".Page 2 of 38: The statement \"and below is a brief description of the most used classes and subclasses of AI algorithms in this review\" was deleted.Page 2 of 38: The statement \"There are different classes of ML methods, among which the most commonly used methods…\" is now replaced with \"There are different classes of ML methods.The most commonly used methods…\".Page 5 of 38: Two commas are added in the following statement: \"A major limitation of DNNs is their complex nature, which makes them challenging to interpret, …\".Page 7 of 38: Table 2 is rotated.Page 9 of 38: The hyphen in `AI-methods` is removed in the following statement: \"At the end of each section, we present a critical review on the AI methods discussed, …\" Page 11 of 38: The following sentence now begins on a new line: \"AI has also significantly assisted in the early detection of a life-threatening condition called sepsis, where the body develops an extreme immune response towards infections.\"Page 18 of 38: A full stop is added at the end of the following statement: \"Since many diseases are associated with the upregulation or downregulation of certain proteins, it is important to correctly identify the protein responsible for causing a disease during drug development.\"Page 28 of 38: A comma is replaced with a full stop at the end of the following statement: \"…, we propose establishing a comprehensive repository of protein structures with standardized data content and formats.","url":"https://doi.org/10.1007/s10462-024-10765-8","authors":["Anju Choorakottayil Pushkaran","Alya A. Arabi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-19T01:01:51Z","doi":"10.1007/s10462-024-10765-8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.36079/lamintang.ijai-0801.193","name":"A Comprehensive Review on Artificial Intelligence Techniques for Covid-19 Pandemic","source":"crossref","abstract":"The pandemic situation due to the emergence of Covid-19 presents various problems physically, economically and mentally for the individuals world-wide, therefore faster solutions with wider access is essential to solve the problems which aids as a support to the healthcare. This is made possible through the incorporation of Artificial Intelligence (AI) technology to handle the situation of pandemic. This paper aims to present a comprehensive re-view of the applications employed using AI for the problems faced during Covid-19 pandemic. The AI applications involved in screening, predicting, forecasting, neighborhood contact tracing and drug discovery of Covid-19 are addressed in this review. This review also presents detailed working of AI algorithms in each application. This paper helps the researchers with vivid information of AI applications of Covid-19 pandemic.","url":"https://doi.org/10.36079/lamintang.ijai-0801.193","authors":["Anisha C. D","Saranya K. G"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-06-22T05:19:27Z","doi":"10.36079/lamintang.ijai-0801.193","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1145/3573942.3574048","name":"A Review of U-Net Network Medical Image Segmentation Applications","source":"crossref","abstract":"This paper provides a comprehensive review of U-Net networks. It describes the basic structure and working principle of convolutional neural networks and U-Net networks, summarizes the improvement of U-Net network model; summarizes the improvement of U-Net network structure in terms of convolutional operation, pooling operation, fully connected layer and output layer; and outlooks the future development direction of U-Net networks.","url":"https://doi.org/10.1145/3573942.3574048","authors":["Yi Cai","Jiangying Yuan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-16T23:45:42Z","doi":"10.1145/3573942.3574048","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.5312495","name":"Teaching Medicine with Generative Artificial Intelligence (GenAI): A Review of Practices, Pitfalls, and Possibilities in Medical Education","source":"crossref","abstract":"Once confined to science fiction and speculative futures, generative artificial intelligence (GenAI) has swiftly entered the lecture halls of modern medical education. Despite its expanding use, a synthesis of its implementation, limitations, and educational value remains underexplored. This review aims to critically examine current applications, identify pedagogical pitfalls, and delineate future trajectories for GenAI in medical training. Key innovations include AI-driven content generation tailored to curricular benchmarks, automated assessments with real-time diagnostic feedback, and immersive virtual patient simulations replicating complex pathophysiologies. Additional advances span multilingual knowledge translation, anatomically precise surgical training environments, and adaptive learning systems powered by intelligent tutoring frameworks. As discussed herein, GenAI holds transformative potential for advancing clinical competence in an evolving medical landscape-provided its integration is evidence-based, ethically sound, and educationally coherent.","url":"https://doi.org/10.2139/ssrn.5312495","authors":["Manuel Garcia","Raquel Simões de Almeida","Dharel Acut","Rui Almeida","Precious Garcia","Eleonora Stefanelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-23T14:23:34Z","doi":"10.2139/ssrn.5312495","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.62787/mhm.v3i3.224","name":"Medical Ethics in the Age of Artificial Intelligence","source":"crossref","abstract":"On the afternoon of July 9, 2025, a high-table forum on \"Medical Ethics in the Era of Artificial Intelligence\" was successfully held, co-sponsored by the School of Journalism and Communication of Peking University and the journal Chinese Medical Ethics. As one of the sub-sessions of The 8th “Medicine, Humanity and Media”Health Communication International Conference &amp; PhD Symposium, the forum brought together experts from various fields to share and discuss topics such as the ethical governance of AI-assisted diagnosis, the ethical self-reflexivity of medical artificial intelligence, ethical assessment, ethical review strategies and legal responses.","url":"https://doi.org/10.62787/mhm.v3i3.224","authors":["Haoyi Liu","Shiyu Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-06T16:20:17Z","doi":"10.62787/mhm.v3i3.224","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.65639/kjvm.2026.184","name":"Artificial Intelligence in cancer diagnosis and prediction: a review study","source":"crossref","abstract":"Artificial intelligence (AI) has emerged as a transformative technology in field of veterinary medicine and comparative oncology, permitting rapid and accurate analysis of complex clinical, histopathological, imaging, and genomic datasets. The Traditional diagnostic and prognostic procedures, although effective, are often limited by time constraints, subjective interpretation, and difficulties in processing large-scale data. Recent advances in machine learning (ML), deep learning (DL), computational pathology, and radiomics have enhanced the ability to predict, diagnose, and manage animal diseases with improved precision. The purpose of this review is to summarize and assess current applications of AI in animal disease diagnosis and prognosis, with particular emphasis on veterinary oncology and One Health perspectives. This review focuses on animal health data and veterinary clinical applications while also discussing the wider implications for human and environmental health.The reviewed studies reveal that AI-based analytical models can successfully integrate histological, clinical, imaging, and genomic information for improving disease detection, prognostication, treatment planning, and outcome prediction in animals. The deep learning and radiomics methods showed promising diagnostic performance across several veterinary modalities, principally in cancer detection and pathological assessment. Besides, AI technologies contribute to reducing the diagnostic time, lowering healthcare costs, increasing the accuracy, and supporting precision veterinary medicine. However, important challenges remain, including data heterogeneity, limited dataset availability, algorithmic bias, lack of interpretability, controlling concerns, and insufficient real-world clinical validation. In conclusion, AI has significant potential to revolutionize veterinary diagnostics and disease management by enabling more accurate and personalized approaches to animal healthcare. Collaboration among veterinarians, veterinary researchers, and commercial AI developers will be essential to achieve reliable clinical integration and maximize the benefits of AI technologies. Through the One Health framework, advancements in veterinary AI may also contribute to improving human health and environmental sustainability.","url":"https://doi.org/10.65639/kjvm.2026.184","authors":["Zahraa Kadim Shakir","Zahraa S. Mahdi","Nihad K.Abbas Abbas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-15T21:04:58Z","doi":"10.65639/kjvm.2026.184","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/105514","name":"Artificial Intelligence-Based Exercise Prescription for Chronic Disease Management: An Umbrella Review of Systematic Reviews and Meta-Analyses (Preprint)","source":"crossref","abstract":"","url":"https://doi.org/10.2196/105514","authors":["Yi-Ting Wang","Chengyi Timon Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T14:05:05Z","doi":"10.2196/105514","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.5341000","name":"Artificial Intelligence Applications in Mortgage-Backed Securities: A Comprehensive Review","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5341000","authors":["Satyadhar Joshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-16T15:23:15Z","doi":"10.2139/ssrn.5341000","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1098/rsos.230806/v1/review1","name":"Review for \"Human-centred artificial intelligence for mobile health sensing: challenges and opportunities\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.230806/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-16T16:03:27Z","doi":"10.1098/rsos.230806/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.31224/2958","name":"Product design and development using Artificial Intelligence (AI) techniques: A review","source":"crossref","abstract":"This paper aims to conduct a literature review and analyze the current AI techniques used in product design and development within industries in a competitive manner.With a proper implementation process, AI techniques will positively impact organizations to manage the increasing complexity of products and customers' requirements within a short product life cycle.Manufacturers must process and manage complex information efficiently, effectively reducing time-to-market.These requirements have led to the rise in the application of artificial intelligence (AI) technology in product design and development to manage the process.Also, it attracted significant attention to a new design paradigm called AI-enabled product design.Incorporating AI into the Product Development Process (PDP) facilitates the product design process to be more intelligent, accurately interprets vast data, and achieve specific goals and tasks through flexible adaptation.The paper reviews the AI techniques that set the foundations for PDP development.Subsequently, this paper will cover how AI-enabled design has helped in product design and development, such as e.g., extending product life.Furthermore, it can contribute versatile designs to meet a variety of customer demands since AI can process excess data instantly and provide predictions to optimize product design strategies such as matching mechanisms.With the support of AI technology, manufacturers can develop more effective maintenance and recovery by measurement of their products in real-time.Finally, a conclusion about the advantages/limits of the (AI)-enabled design and future research perspectives are discussed in the last section.","url":"https://doi.org/10.31224/2958","authors":["Anurag Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-17T18:19:34Z","doi":"10.31224/2958","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/h2h46m","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/h2h46m","authors":["Sana Ali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-16T15:08:40Z","doi":"10.32388/h2h46m","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/3iquks","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/3iquks","authors":["Som Biswas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-14T15:19:06Z","doi":"10.32388/3iquks","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/2041-210x.14485/v1/review1","name":"Review for \"Improving the integration of artificial intelligence into existing ecological inference workflows\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/2041-210x.14485/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-28T16:02:01Z","doi":"10.1111/2041-210x.14485/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/m9wha7","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/m9wha7","authors":["Chhavi Rana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-04T06:27:04Z","doi":"10.32388/m9wha7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/2041-210x.13827/v1/review2","name":"Review for \"A Deep Generative Artificial Intelligence system to predict species coexistence patterns\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/2041-210x.13827/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-21T09:26:00Z","doi":"10.1111/2041-210x.13827/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/qpfq8o","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/qpfq8o","authors":["Feng Yao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-09T10:19:35Z","doi":"10.32388/qpfq8o","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/beer.12479/v1/review1","name":"Review for \"Ethical implications of text generation in the age of artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/beer.12479/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-07T20:49:41Z","doi":"10.1111/beer.12479/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.67065/p7adrx27","name":"AI for Sanitation Equity: A Review of Artificial Intelligence Applications in Monitoring and Mitigating Open Defecation in Marginalized Indian Communities","source":"crossref","abstract":"This paper reviews the application of artificial intelligence (AI) in addressing open defecation within marginalized Indian communities. Despite large-scale sanitation campaigns, significant disparities persist, especially among Scheduled Castes, Scheduled Tribes, and slum populations. AI technologies—such as satellite image analysis, mobile data collection, IoT- enabled smart toilets, and GIS-based spatial modeling—offer new pathways for real-time monitoring, predictive risk mapping, and behavior change. However, gaps remain in ethical deployment, inclusion of marginalized groups, and integration with social learningframeworks. Through a systematic synthesis of academic studies, government data, and pilot projects, this review evaluates the readiness, limitations, and equity impact of AI interventions. It highlights the need for participatory AI design, stronger data governance, and interdisciplinary collaboration to ensure responsible innovation. By centering community agency, this review outlines a roadmap for equitable AI adoption in India’s sanitation ecosystem, aligning with Sustainable Development Goal 6 on clean water and sanitation for all.","url":"https://doi.org/10.67065/p7adrx27","authors":["Dr. Bushra S. P. Singh","Dr. Swati Bhatia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-19T15:54:26Z","doi":"10.67065/p7adrx27","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/preprints.66530","name":"Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis (Preprint)","source":"crossref","abstract":"BACKGROUND Endometrial cancer is one of the most common gynecological tumors, and early screening and diagnosis are crucial for its treatment. Research on the application of artificial intelligence (AI) in the diagnosis of endometrial cancer is increasing, but there is currently no comprehensive meta-analysis to evaluate the diagnostic accuracy of AI in screening for endometrial cancer. OBJECTIVE This paper presents a systematic review of AI-based endometrial cancer screening, which is needed to clarify its diagnostic accuracy and provide evidence for the application of AI technology in screening for endometrial cancer. METHODS A search was conducted across PubMed, Embase, Cochrane Library, Web of Science, and Scopus databases to include studies published in English, which evaluated the performance of AI in endometrial cancer screening. A total of 2 independent reviewers screened the titles and abstracts, and the quality of the selected studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies—2 (QUADAS-2) tool. The certainty of the diagnostic test evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. RESULTS A total of 13 studies were included, and the hierarchical summary receiver operating characteristic model used for the meta-analysis showed that the overall sensitivity of AI-based endometrial cancer screening was 86% (95% CI 79%-90%) and specificity was 92% (95% CI 87%-95%). Subgroup analysis revealed similar results across AI type, study region, publication year, and study type, but the overall quality of evidence was low. CONCLUSIONS AI-based endometrial cancer screening can effectively detect patients with endometrial cancer, but large-scale population studies are needed in the future to further clarify the diagnostic accuracy of AI in screening for endometrial cancer. CLINICALTRIAL PROSPERO CRD42024519835; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024519835","url":"https://doi.org/10.2196/preprints.66530","authors":["Longyun Wang","Zeyu Wang","Bowei Zhao","Kai Wang","Jingying Zheng","Lijing Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-18T20:10:06Z","doi":"10.2196/preprints.66530","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.24018/ejai.2023.2.2.23","name":"A Historical Review and Philosophical Examination of the two Paradigms in Artificial Intelligence Research","source":"crossref","abstract":"Artificial intelligence (AI) is a field that has undergone significant changes and challenges over time. This paper reviews the historical development of AI and representative philosophical thinking, and also considers the methodology and applications of AI, and anticipates its continued advancement. It discusses two main paradigms: symbolism and connectionism, which differ in how they explain and implement intelligence through symbols or artificial neural networks. However, neither paradigm is the final answer to AI research but rather reflects the best answer at a given time. The paper also analyzes the shortcomings of both paradigms from a philosophical perspective and argues that the most fundamental philosophical issue therein is understanding the difference between biological and artificial intelligence.","url":"https://doi.org/10.24018/ejai.2023.2.2.23","authors":["Zhang Youheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-20T14:31:11Z","doi":"10.24018/ejai.2023.2.2.23","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.54941/ahfe1004666","name":"Application of Artificial Intelligence, Machine Learning and Deep Learning in Piloted Aircraft Operations: Systematic Review","source":"crossref","abstract":"Aviation research on artificial intelligence (AI), machine learning (ML), and deep learning (DL) has seen significant growth as these emerging technologies hold immense potential for supporting both human-centred and technology-centred aspects of civil aircraft operations. This systematic review, following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, was registered on the Open Science Framework (DOI 10.17605/OSF.IO/ZR7A3) and focused specifically on the use of AI, ML, and DL in human-centric flight operations. The review conducted a comprehensive search of databases including Scopus, Web of Science, IEEE Xplore, as well as online repositories (ResearchGate and Aerospace Research Central) to identify relevant articles published between 2013 and 2023. In total, 32 studies were included, which explored various applications of AI, ML, and DL in aircraft pilots and flight operations. The studies were categorized into four main areas: (i) assessment and management of human factors risks, including AI-assisted data analysis of pilot performance, crew resource management, and ML-based support for pilots’ cognitive workload monitoring, (ii) detection of human errors, with support systems based on ML-based approaches for real-time monitoring and DL models for biometric monitoring of cockpit pilots were identified for the detection of human errors in flight safety, (iii) reduction and prediction of human errors, categorized into AI-assisted predictive analytics in flight accidents, and ML-based pattern recognition to predict unstable approaches, and (iv) prevention of human errors in aviation through ML utilization for pilot training enhancement, and AI-supporting flight automation and decision support systems for flight operation. Analysis of the included studies revealed a rising trend in the publication of articles after 2020, albeit at a slow rate. It is worth noting that the majority of studies focused on conceptual applications, with fewer studies involving empirical testing. The findings of this review highlight the potential for future research in developing and testing improved human factors risk assessment (HRA) models assisted by computational intelligence in piloted aircraft operations, with the ultimate aim of enhancing flight safety.","url":"https://doi.org/10.54941/ahfe1004666","authors":["Steven Tze Fung Lam","Alan H.S. Chan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-10T21:08:20Z","doi":"10.54941/ahfe1004666","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/ichcai70183.2026.11607635","name":"Human-Centric Artificial Intelligence in Recruitment: A Systematic Review of Applicant's Perception, Understanding, and Adoption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ichcai70183.2026.11607635","authors":["Yashika Gupta","Neetima Agarwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-20T20:18:41Z","doi":"10.1109/ichcai70183.2026.11607635","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.57185/joss.v4i4.437","name":"The Role of Artificial Intelligence for Medical Professionals in Indonesia: A Systematic Literature Review","source":"crossref","abstract":"The development of Artificial Intelligence (AI) technology has brought significant transformation to the global healthcare system, including in Indonesia. This study aims to systematically examine the implementation and impact of AI on the performance of medical personnel in Indonesia through a systematic literature review approach. The research method follows the PRISMA protocol by analyzing scientific publications from 2019 to 2024 obtained from the Google Scholar, PubMed, ScienceDirect, and Indonesian Medical Journal databases. The results indicate that the application of AI has contributed positively to improving diagnostic accuracy (87.5%), healthcare service efficiency (82.3%), and clinical decision-making quality (85.2%). Nevertheless, this study also highlights significant challenges in the implementation of AI, such as limited digital infrastructure (65.4%), inadequate competence among health human resources (58.7%), concerns over data privacy and security, and the lack of comprehensive regulatory frameworks (52.3%), all of which hinder optimal integration. This study provides a scientific foundation for policymakers to develop regulations that support AI adaptation in healthcare while safeguarding ethical standards and patient safety. Additionally, the research offers practical insights for future studies to focus on overcoming technical, educational, and legal barriers, ensuring that AI integration advances equitable and sustainable healthcare services in Indonesia.","url":"https://doi.org/10.57185/joss.v4i4.437","authors":["Sigit Ahmadi","Nur Wening"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-04T23:31:01Z","doi":"10.57185/joss.v4i4.437","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-022-10260-y","name":"A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-022-10260-y","authors":["Khanh T. P. Nguyen","Kamal Medjaher","Do T. Tran"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-09T03:04:41Z","doi":"10.1007/s10462-022-10260-y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.35972/kindai.v22i2.2658","name":"Transformasi Audit Internal melalui Artificial Intelligence: Systematic Literature Review Periode 2021–2026","source":"crossref","abstract":"Artificial Intelligence (AI) semakin berperan dalam mendukung audit internal seiring meningkatnya kompleksitas risiko, volume data, dan tuntutan pengambilan keputusan yang cepat. Meskipun penerapan AI dalam bidang auditing berkembang pesat, kajian yang secara khusus mensintesis transformasi audit internal masih terbatas. Penelitian ini menggunakan pendekatan Systematic Literature Review (SLR) berdasarkan pedoman PRISMA 2020 untuk mensintesis bukti ilmiah mengenai transformasi audit internal melalui AI dari 21 artikel terindeks Scopus yang diterbitkan pada periode 2021–2026. Hasil kajian menunjukkan bahwa Machine Learning merupakan teknologi AI yang paling dominan, diikuti oleh Robotic Process Automation, Hybrid Artificial Intelligence, dan Natural Language Processing. Pemanfaatan AI terutama terdapat pada area Risk Assessment, Audit Data Analytics, dan Audit Decision Making. AI terbukti meningkatkan kualitas analisis, akurasi, kualitas audit, efisiensi operasional, serta dukungan pengambilan keputusan. Namun, implementasinya masih menghadapi kendala berupa keterbatasan kompetensi auditor, kualitas data, kepercayaan terhadap AI, dan isu explainability. Temuan ini menegaskan bahwa AI meningkatkan efektivitas dan nilai tambah fungsi audit internal sekaligus membuka peluang penelitian pada konteks negara berkembang, sektor publik, serta penerapan Generative AI dan Large Language Models.","url":"https://doi.org/10.35972/kindai.v22i2.2658","authors":["Ibnu Hadi","Lidya Primta Surbakti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-20T11:26:21Z","doi":"10.35972/kindai.v22i2.2658","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/s5wdmn","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/s5wdmn","authors":["Marcel Patalon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-13T13:30:00Z","doi":"10.32388/s5wdmn","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1098/rsos.230806/v1/review2","name":"Review for \"Human-centred artificial intelligence for mobile health sensing: challenges and opportunities\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.230806/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-16T16:03:27Z","doi":"10.1098/rsos.230806/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/196m9i","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/196m9i","authors":["Shipra Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-12T11:25:35Z","doi":"10.32388/196m9i","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.61336/im/26-05-106","name":"IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DOCTOR- PATIENT RELATIONSHIP IN DERMATOLOGY: A NARRATIVE REVIEW","source":"crossref","abstract":"Background: Artificial intelligence (AI), particularly convolutional neural networks and large language models, is increasingly being integrated into dermatology for image-based diagnostics, teledermatology, and patient-facing decision support. While these technologies may improve clinical efficiency and expand access to dermatologic care, their influence on the dermatologist–patient relationship remains uncertain. Objectives: To synthesize current evidence regarding the impact of AI on trust, communication, empathy, professional autonomy, and ethical practice within the dermatologist–patient relationship. Materials and Methods: A narrative review of English-language literature published between January 2018 and March 2026 was conducted using PubMed, Scopus, and Google Scholar databases. Original research articles, systematic reviews, and authoritative commentaries addressing AI applications in dermatology and their implications for patient experience and clinical practice were included. Selection was based on relevance and methodological quality. Results: AI demonstrated improved diagnostic accuracy in specific dermatological tasks and enhanced access to care through teledermatology and asynchronous triage systems. Patients generally showed greater acceptance of AI when used as a physician-support tool rather than as an independent diagnostic system. Key concerns identified included reduced human interaction, automation bias, limited algorithmic performance in darker skin types, data privacy and informed consent challenges, and unresolved medico-legal accountability issues. Conclusion: AI should be implemented as an adjunct to dermatological practice within an augmented-intelligence framework rather than as a replacement for clinicians. Maintaining empathy, ensuring clinician oversight, achieving equitable performance across diverse skin types, and establishing transparent ethical and regulatory governance are critical to preserving the integrity of the dermatologist–patient relationship","url":"https://doi.org/10.61336/im/26-05-106","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T10:21:46Z","doi":"10.61336/im/26-05-106","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/cjce.24246/v2/review1","name":"Review for \"Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cjce.24246/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-05T09:40:23Z","doi":"10.1002/cjce.24246/v2/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/th14nc","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"thesis; Clarity and validity of argumentation;Acquaintance with contemporary research of the subject; Conclusions -do they make any contribution to the field; Language, style; Relevance of Keywords and Summary to the title. Relevance of the subject and explication of the problem: GoodThe paper provides a clear explanation of the role of Artificial Intelligence (AI) and digital technologies in future education.It discusses the benefits and limitations of introducing AI techniques in learning and sets the context for the Fourth Industrial Revolution. Explication of the thesis: GoodThe thesis is clearly stated in the introduction and is consistently addressed throughout the paper.The author discusses the role of AI in education and its potential impact on teaching and learning processes. Clarity and validity of argumentation: Good / MediumThe argumentation in the paper is generally clear, but some points could be further elaborated to strengthen the validity of the arguments.More supporting evidence and examples could enhance the overall clarity and validity of the argumentation. Acquaintance with contemporary research of the subject: GoodThe author demonstrates familiarity with contemporary research on the subject of AI in education.They cite relevant sources to support their claims and discuss different perspectives on the topic. Conclusions -do they make any contribution to the field? MediumThe paper provides some conclusions regarding the role of AI in education, but they could be more explicit in discussing the contributions and implications of the findings.A stronger emphasis on the potential impact and future directions of AI in education would enhance the contribution to the field.","url":"https://doi.org/10.32388/th14nc","authors":["Rossitza Kaltenborn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-01T10:34:46Z","doi":"10.32388/th14nc","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/jbmr.4879/v2/review1","name":"Review for \"Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/jbmr.4879/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-17T09:07:52Z","doi":"10.1002/jbmr.4879/v2/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.31219/osf.io/nr7d6","name":"Disillusioned with artificial intelligence: A book review","source":"crossref","abstract":"Ariane Hanemaayer’s edited book “Artificial Intelligence and its discontents: Critiques from social sciences and humanities” (2022) discusses a wide range of issues on the challenges artificial intelligence (AI) technologies bring to society: gender bias in machine translation systems, AI implementation in China and India, the black feminist perspectives of emerging technologies, etc. [1]. Critically, the authors discuss these issues in the tradition of discontent and show how AI is embedded within a larger socio-cultural and political context where its development and deployment can generate unforeseen adverse effects on various groups. Hence, an urgent need to establish AI discontent as its own subfield.","url":"https://doi.org/10.31219/osf.io/nr7d6","authors":["Tung Manh Ho"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-07T23:58:31Z","doi":"10.31219/osf.io/nr7d6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.22541/au.164670471.11415616/v1","name":"Definitions of Artificial Intelligence: A review","source":"crossref","abstract":"The field of Artificial Intelligence (AI) in recent years, has proven critical in almost every sector of the economy. Numerous applications have been developed based on the concepts and techniques provided by this field. Every field of great potential continues to draw attention and evolve. As a result, well-defined terminology also plays a key role in the development and evolution of these important fields as AI is no exception. The PRISMA Flow Diagram is used in scanning the retrieved articles from the Scopus database. A total number of 347 articles were obtained as search results based on the research query of which 21 articles provided 34 definitions. A generic definition for Artificial Intelligence is proposed.","url":"https://doi.org/10.22541/au.164670471.11415616/v1","authors":["PETER APPIAHENE","Emmanuel Adjei Domfeh","Bernard Andoh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-07T20:58:36Z","doi":"10.22541/au.164670471.11415616/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/ebayzc","name":"Review of: \"Artificial Intelligence and Inequality: Challenges and Opportunities\"","source":"crossref","abstract":"To enhance the reader's understanding and provide greater clarity,","url":"https://doi.org/10.32388/ebayzc","authors":["Shokhan M. Al-Barzinji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-31T22:07:00Z","doi":"10.32388/ebayzc","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1177/27000710251386963/v1/review1","name":"Review for \"How Human Personality Will Change with the Use of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/27000710251386963/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-28T04:00:21Z","doi":"10.1177/27000710251386963/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.36227/techrxiv.176621103.38393943/v1","name":"Quantum Artificial Intelligence: A Review of Techniques, Applications, and Future Directions","source":"crossref","abstract":"Artificial Intelligence (AI) continues to drive innovation across industries, but faces mounting challenges in computational scalability, optimization, and interpretability as models grow increasingly complex. Quantum Artificial Intelligence (QAI) offers a promising path forward by leveraging quantum phenomena such as superposition and entanglement to augment learning processes beyond classical limits. This review synthesizes the recent progress in QAI, emphasizing a comparative perspective framed through its advantages and limitations. It examines how hybrid quantum-classical architectures and variational models seek to address constraints in the Noisy Intermediate-Scale Quantum (NISQ) era, and how applications in healthcare, finance, and cybersecurity illustrate both the potential and the barriers of the field. While several studies have broadly mapped the growth of QAI in algorithms and applications, this paper adopts a different perspective: emphasizing comparative analysis through advantages and limitations. The focus is on where QAI offers unique opportunities, where it encounters obstacles distinct from classical AI and quantum computing, and what research directions may transform these challenges into breakthroughs. By situating QAI within this comparative framework, the paper provides a realistic yet forward-looking assessment of its trajectory as a paradigm poised to reshape computational intelligence.","url":"https://doi.org/10.36227/techrxiv.176621103.38393943/v1","authors":["Kalyani Guddanti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-20T06:10:43Z","doi":"10.36227/techrxiv.176621103.38393943/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.20955/r.2024.12","name":"Artificial Intelligence and Inflation Forecasts","source":"crossref","abstract":"","url":"https://doi.org/10.20955/r.2024.12","authors":["Miguel Faria-e-Castro","Fernando Leibovici"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-27T20:58:27Z","doi":"10.20955/r.2024.12","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.5762982","name":"Artificial Intelligence in Legal Education: A Scoping Review","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5762982","authors":["Steve Lorteau","Douglas Sarro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-18T16:15:13Z","doi":"10.2139/ssrn.5762982","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/preprints.35465","name":"Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review (Preprint)","source":"crossref","abstract":"BACKGROUND The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others. OBJECTIVE The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals. METHODS The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process. RESULTS The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals. CONCLUSIONS In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.","url":"https://doi.org/10.2196/preprints.35465","authors":["Ferdinand Dhombres","Jules Bonnard","Kévin Bailly","Paul Maurice","Aris T Papageorghiou","Jean-Marie Jouannic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-20T14:46:57Z","doi":"10.2196/preprints.35465","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-3655543/v1","name":"Medical Students’ Attitude towards Artificial Intelligence in Medicine: A National Multicenter Survey","source":"crossref","abstract":"Abstract 1.1 Objectives The rapid advancement of Artificial Intelligence (AI) is leading to its integration into various fields, including medicine, which is at the forefront of this development. There are numerous perspectives on the future role of AI in medicine, with varying levels of positivity or negativity. In this study, we aimed to assess undergraduate medical students' attitudes towards integrating AI in medicine, to evaluate their need for AI training, identify misconceptions about AI, and explore its impact on their choice of specialization 1.2 Methods To assess the attitudes of medical students towards the integration of AI in medicine, a web-based multicenter survey was conducted across both public and private medical institutes in Ethiopia over a three-month period from June to August 2022. The survey questionnaire included questions on the students’ familiarity with AI, perceptions of AI in medicine, and the need for AI training among medical students. Group comparison was tested by chi-square test for categorical responses and Mann-Whitney test for ordinal rating scale responses. 1.3 Results Of the 1002 respondents, 68.3% were unaware of AI's role in medicine despite over two-thirds (69%) considering themselves tech-savvy; however, a significant majority recognized AI's applications in daily life, particularly through social media (77.6%) and mainstream media (44.9%). Most students (86.9%) believed AI would improve medicine, and notably, 84.9% advocated for its inclusion in medical training, reflecting a strong sentiment that AI education should be a part of the medical curriculum. Over half of the participants (56.8%) felt that AI would positively impact their choice of medical specialty, yet there was a split opinion about the emphasis on AI in developing countries like Ethiopia, with 41.7% preferring not to prioritize it. Students interested in specialties influenced by AI were more inclined to perceive AI as beneficial to medicine, suggesting a correlation between their specialty interests and their views on AI's role in healthcare. 1.4 Conclusions The research findings indicate that while many students surveyed had limited awareness and basic understanding of medical AI, the majority recognized its significant potential in medicine. Most were not concerned about AI replacing them in the future and agreed that AI will enhance medicine and should be integrated into the medical curriculum.","url":"https://doi.org/10.21203/rs.3.rs-3655543/v1","authors":["Yodit Abraham Yaynishet","Fuad Menur Nasir","Samuel Sisay Hailu","Daniel Zewdneh Solomon","Eden Kahsay Gidena","Abdulhamid Mustefa Bedewi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-20T20:07:42Z","doi":"10.21203/rs.3.rs-3655543/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1023/b:aire.0000020937.82267.32","name":"An Intelligent System to Monitor the Chemical Concentration of Electroplating Process: An Integrated OLAP and Fuzzy Logic Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1023/b:aire.0000020937.82267.32","authors":["R.W.K. Leung","H.C.W. Lau","C.K. Kwong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-03-24T05:12:04Z","doi":"10.1023/b:aire.0000020937.82267.32","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/pklq3u","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/pklq3u","authors":["Daniel Gervais"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-17T14:36:27Z","doi":"10.32388/pklq3u","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/po6oaq","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/po6oaq","authors":["Ericka Darmawan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-11T09:52:18Z","doi":"10.32388/po6oaq","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/evj.14559/v1/review2","name":"Review for \"Artificial intelligence in smartphone video analysis for equine asthma diagnostic support\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/evj.14559/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T23:07:28Z","doi":"10.1111/evj.14559/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/icd.70098/v2/review1","name":"Review for \"Bibliometric Examination of Artificial Intelligence Studies on Infants\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/icd.70098/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-13T21:06:14Z","doi":"10.1002/icd.70098/v2/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32920/31383082","name":"Artificial Intelligence Applications in Motivational Interviewing; A Systematic Literature Review","source":"crossref","abstract":"&lt;p dir=\"ltr\"&gt;Motivational interviewing (MI) is effective for behavior change but faces delivery challenges. With current technological advances, many researchers have explored the possible applications of Artificial Intelligence in Motivational Interviewing. Using a Systematic Literature Review (SLR) methodology and adhering to the Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) guidelines, this thesis provides insights into the current landscape of Artificial Intelligence applications in Motivational Interviewing. A co-occurrence map of author keywords and index keywords of 62 articles was created in VOSviewer software, which generated three key themes with 18 keywords. In addition, using the KeyBERT NLP technique, the top keywords of article abstracts were generated and grouped into five main themes. The dominant technologies were explored, and it revealed that because of the importance of including MI principles in communications, Chatbots and Conversational Agents could provide promising avenues for addressing the challenges of MI delivery.&lt;/p&gt;","url":"https://doi.org/10.32920/31383082","authors":["Armita Sadeghian Barzoki"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T14:02:03Z","doi":"10.32920/31383082","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.6451699","name":"The Scholarly Review Process in Finance: Experiments in Artificial Intelligence","source":"crossref","abstract":"I ask an AI agent to specifically reject papers without human intervention. I present the agents with three of my coauthored papers: one working paper, and two published papers in so-called \"A journals.\" This note presents the referee's reports generated by the agent. The reports all took less than 120 seconds to generate, with only the pdfs of the paper as input. I also asked the agent to \"dispute\" its own report on the working paper, and it did so with ease. I conclude that AI-generated reports are superficially indistinguishable from human-generated ones, and guardrails are required to be placed on reviewers and editors relating to use of AI.","url":"https://doi.org/10.2139/ssrn.6451699","authors":["Avanidhar Subrahmanyam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T10:05:14Z","doi":"10.2139/ssrn.6451699","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/yw7hur","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/yw7hur","authors":["Bharath Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-24T13:35:03Z","doi":"10.32388/yw7hur","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.51985/jbumdc2024452","name":"Limitations of Artificial Intelligence in Orthodontics. Literature Review","source":"crossref","abstract":"In the 21st century, advances in computer technology and data science have brought significant innovation to orthodontics, especially through Artificial Intelligence (AI) and Machine Learning (ML). This study, conducted from July 2 to August 15, 2024, in the Orthodontic Department at Rawal Institute of Health Sciences Islamabad, reviews AI’s transformative role in dentistry, focusing on its applications, benefits, and challenges. A comprehensive literature search across PubMed and Google Scholar yielded 260 peer-reviewed articles from 2001 to 2024. After applying stringent selection criteria, the review focused on AI's historical development, applications, and limitations in orthodontics. While AI enhances diagnostic imaging and patient care, it cannot replace clinical expertise. Key challenges include patient privacy, data security, and ethical considerations. AI systems rely heavily on high-quality data, necessitating rigorous training. Therefore, AI should be viewed as an adjunct in orthodontics, providing a “second opinion” to support clinical decisions.","url":"https://doi.org/10.51985/jbumdc2024452","authors":["Sadia Naureen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-08T10:27:26Z","doi":"10.51985/jbumdc2024452","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/32578","name":"The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations","source":"crossref","abstract":"Background Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.","url":"https://doi.org/10.2196/32578","authors":["Han Shi Jocelyn Chew"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-13T10:17:53Z","doi":"10.2196/32578","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.52629/jamsa.vi.1018","name":"Artificial Companions and Social Well-being: From Artificial Intelligence Dependency to a Conceptual Framework for Balanced Human- Artificial Intelligence Mental Support","source":"crossref","abstract":"Artificial Companions and Social Well-being: From Artificial Intelligence Dependency to a Conceptual Framework for Balanced Human- Artificial Intelligence Mental Support Abstract This study investigates the relationship between Artificial Intelligence dependency and social dissatisfaction, aiming to understand how interaction with Artificial Companions influences users’ social satisfaction and emotional reliance. A quantitative research design was adopted, combining a structured survey with statistical correlation analysis. The adapted questionnaire integrated elements from the Internet Addiction Test and UCLA Loneliness Scale, collecting data from 295 valid participants. All variables were standardized using z-scores, and internal reliability was verified through Cronbach’s Alpha (α = 0.90). Correlation and group comparison analyses were conducted to examine the associations between Artificial Intelligence Dependency and Social Dissatisfaction . Results indicated a moderate positive correlation between AI Dependency and Social Dissatisfaction (r = 0.5403), suggesting that individuals with higher social dissatisfaction tend to rely more on artificial intelligence for emotional support. Comparative analysis revealed significant differences between high and low Artificial Intelligence Dependency groups (p &lt; 0.01), further confirming that excessive artificial intelligence reliance is linked to diminished real- life social satisfaction. The findings were visualized through z-score distribution plots and comparative mean graphs, highlighting consistent trends across user segments. These results provide empirical evidence for the dual role of Artificial Intelligence as both a compensatory and displacement mechanism in social well-being. After finishing the investigation, we did some paper review and came up with a conceptual hybrid Artificial Intelligence model that offers daily emotional interaction and real-time support through chatbots, adapts support using reinforcement learning and add time limits and alerts human counselors when signs of over-reliance or distress appear. For our conclusion, though people might gain reliance on artificial intelligence , with responsible design and human oversight, artificial intelligence can still be a good tool to enhance people’s mental health Keywords: AI dependence, social well-being, loneliness, emotional interaction, digital companion, mental health","url":"https://doi.org/10.52629/jamsa.vi.1018","authors":["Yu-Jun Huang","Tzu-Hsuan Yu","YU-Hung Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T07:45:18Z","doi":"10.52629/jamsa.vi.1018","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.artmed.2018.10.009","name":"Diabetic retinopathy techniques in retinal images: A review","source":"crossref","abstract":"The diabetic retinopathy is the main reason of vision loss in people. Medical experts recognize some clinical, geometrical and haemodynamic features of diabetic retinopathy. These features include the blood vessel area, exudates, microaneurysm, hemorrhages and neovascularization, etc. In Computer Aided Diagnosis (CAD) systems, these features are detected in fundus images using computer vision techniques. In this paper, we review the methods of low, middle and high level vision for automatic detection and classification of diabetic retinopathy.We give a detailed review of 79 algorithms for detecting different features of diabetic retinopathy during the last eight years.","url":"https://doi.org/10.1016/j.artmed.2018.10.009","authors":["Nadeem Salamat","Malik M. Saad Missen","Aqsa Rashid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-11-15T23:53:22Z","doi":"10.1016/j.artmed.2018.10.009","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/mon51r","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"It is a delightful experience to come across documents such as this one, which is comprehensive and inspiring, discussing emerging topics that are still being debated within academia!While I would choose a slightly different title (\"Artificial Intelligence and Digital Technologies in Future Education\", for the sake of English), the ideas in this document are clear and well-organized.Congratulations to the author!My first reaction was to applaud this exposition of the significant benefits to various educational processes, including teaching, learning, assessment, and training, because by providing personalized learning experiences, the use of AI in education can enhance the effectiveness and efficiency of educational interventions for those students who are the most defenseless and vulnerable.Furthermore, I find the author's idea attractive that Soft Computing techniques, including fuzzy logic, neural networks, and genetic algorithms, could improve cognitive strategies for critical thinking and creative decision-making, leading to the resolution of complex problems in real-life situations.However, I felt apprehensive about the idea of robots serving as personal assistants for young children, since early education should be tailored to the unique needs of each individual child, with unexpected facets of learning treated as","url":"https://doi.org/10.32388/mon51r","authors":["Neus Lorenzo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-03T19:07:06Z","doi":"10.32388/mon51r","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/978-3-030-62796-6_16","name":"A Review on Smart Universities and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-62796-6_16","authors":["Mohammad Al-Shoqran","Samer Shorman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-02-12T07:04:03Z","doi":"10.1007/978-3-030-62796-6_16","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/preprints.74956","name":"Artificial Intelligence in Electrocardiography and Echocardiography for Early Diagnosing Pulmonary Hypertension: A Systematic Review and Meta-Analysis (Preprint)","source":"crossref","abstract":"BACKGROUND Early detection of pulmonary hypertension (PH) remains challenging, and right heart catheterization (RHC) is invasive. Artificial intelligence (AI) applied to electrocardiography (ECG) and echocardiography (Echo) offers a potential non-invasive triage strategy, but its overall diagnostic accuracy and clinical readiness remain unclear. OBJECTIVE We aimed to systematically evaluate the diagnostic performance of ECG- and Echo-based AI models for detecting PH and compare their accuracy with clinician interpretation. METHODS We searched PubMed, Embase, and Web of Science from inception to Jan 1, 2026, for studies evaluating AI models applied to ECG or Echo for PH detection, using RHC or guideline-directed clinical criteria as reference standards. Risk of bias and applicability were assessed with PROBAST+AI, and certainty of evidence was graded using GRADE. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. RESULTS Fifteen retrospective studies comprising 504,108 participants (96,890 patients with PH) were included. Echo-based AI demonstrated a pooled sensitivity of 0.91 (95% CI [0.85-0.94]; I2 = 94.41%) and specificity of 0.81 (95% CI [0.69-0.89]; I2 = 98.66%), with a summary area under the curve (AUC) of 0.93 (95% CI [0.91-0.95]). ECG-based AI yielded a pooled sensitivity of 0.81 (95% CI [0.79-0.82]; I2 = 96.91%) and specificity of 0.75 (95% CI [0.68-0.81]; I2 = 99.92%), with an AUC of 0.84 (95% CI [0.81-0.87). In two head-to-head comparative studies, diagnostic performance did not differ significantly between Echo-based AI and clinicians (P =0.80). CONCLUSIONS AI models based on ECG and Echo demonstrate moderate-to-high diagnostic accuracy for PH detection, suggesting a potential role in non-invasive clinical triage. However, based on GRADE assessment, the current certainty of evidence is low due to substantial heterogeneity, retrospective study designs, and inconsistent reference standards. Implementation into clinical pathways cannot be recommended until validated by prospective, multicenter studies using standardized RHC definitions. CLINICALTRIAL CRD42024590680","url":"https://doi.org/10.2196/preprints.74956","authors":["Lang Liu","Ziqing Nie","Mogana Darshini Ganggayah","Sivakumar Krishnasamy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-06T16:55:07Z","doi":"10.2196/preprints.74956","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-4580530/v1","name":"Monitoring Racial Disparities in the Era of Artificial Intelligence","source":"crossref","abstract":"Abstract This paper utilized primary findings from an AI image generator of faces to analyse how racially inclusive the program is. This paper rendered 100 results and found that, from the findings, 77% of the faces generated were white faces, while the program failed to render a single black face. This paper calls for algorithmic auditing at the state level to ensure algorithmic inclusivity. Not rectifying this dilemma may yield AI algorithms that are not sufficiently catered to the South African context and application. Another consideration pertains to evaluating whether these algorithms are safe for public dissemination. This paper rests on the position that strong policy and state instruments are required to help circumvent the encroachment of technology that has not been sufficiently vetted for public use. This paper critically engages with the already prevalent discourse on bias mitigation and looks at ways people could bypass these regulatory mechanisms, such as the European Union’s Artificial Intelligence Act. This critical perspective looks to help assess if the proposed conjectures are sufficient.","url":"https://doi.org/10.21203/rs.3.rs-4580530/v1","authors":["Blessing Mbalaka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-18T16:14:51Z","doi":"10.21203/rs.3.rs-4580530/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-4057710/v1","name":"Artificial intelligence modeling for power system planning","source":"crossref","abstract":"Abstract This paper presents artificial neural network (ANN) models for prediction of energy consumption compared with autoregressive integrated moving average (ARIMA) modeland adaptive neuro fuzzy inference system (ANFIS) models for thermal power plant production, hydro power plant production as well as CO 2 emission.These models were developed to streamline power systems planning and minimize errors in planning. The model's accurate functionality is ensured by incorporating the collective expertise of power system planning professionals accumulated over the years. By adhering to these calculations, human errors are eliminated, the benefits of human knowledge are maximized, and a precise framework is established for planning the functionality of large power systems. ANN model for prediction were compared with autoregressive integrated moving average (ARIMA) model. Prediction of power plants production is an effective way to organize power systems for future time intervals so there will be less problems in the growing complexity of power systems. These are problems with a great number of data whose connections are not easy to define. In this paper the solution is presented in the form of ANNas part of supervised machine learning (SML) and ANFISmethodology. Calculations with ANN model was done simultaneously with standard forecasting ARIMA model in order to facilitate a comparison. Databases that are used for training ANN, ANFIS and ARIMA models contain hours of yearly data from Serbian power systems that are related to production of different energy sources, energy consumption and parameters that influence them. Trained ANN models are used to predict energy consumption and ANFIS models are used for prediction of energy production from different energy sources and CO 2 emission on yearly or daily basis. Considering the existence of great numbers of data from previous years from which experts made conclusions for production scheduling this data is used to train ANFIS models that will help experts in optimal production scheduling. Using models like these human errors would be minimized and planning will be optimized and improved for experts.","url":"https://doi.org/10.21203/rs.3.rs-4057710/v1","authors":["Sonja Knežević","Mileta Žarković"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-14T10:36:53Z","doi":"10.21203/rs.3.rs-4057710/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-3306884/v1","name":"Robust and eXplainable artificial intelligence","source":"crossref","abstract":"Abstract Artificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. This is a problem in regulated industries, as authorities aimed at monitoring the risks arising from the application of AI methods may not validate them. No measurement methodologies are yet available to jointly assess accuracy, explainability and robustness of machine learning models. We propose a methodology which fills the gap, extending the forward search approach, employed in robust statistical learning, to machine learning models. Doing so, we will be able to evaluate, by means of interpretable statistical tests, whether a specific AI application is accurate, explainable and robust, by means of a unifying methodology. We apply our proposal to the context of bitcoin price prediction, comparing a linear regression model against a non linear neural network model.","url":"https://doi.org/10.21203/rs.3.rs-3306884/v1","authors":["Paolo Giudici","Emanuela Raffinetti","Marco Riani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-04T04:11:01Z","doi":"10.21203/rs.3.rs-3306884/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/beer.12479/v2/review1","name":"Review for \"Ethical implications of text generation in the age of artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/beer.12479/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-07T20:49:41Z","doi":"10.1111/beer.12479/v2/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.14322/publons.r9317718","name":"Review of \"Diagnostic advances of artificial intelligence and radiomics in gastroenterology\"","source":"crossref","abstract":"","url":"https://doi.org/10.14322/publons.r9317718","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-28T08:29:48Z","doi":"10.14322/publons.r9317718","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/cjce.24246/v1/review1","name":"Review for \"Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cjce.24246/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-05T09:40:23Z","doi":"10.1002/cjce.24246/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/2041-210x.14485/v1/review2","name":"Review for \"Improving the integration of artificial intelligence into existing ecological inference workflows\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/2041-210x.14485/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-28T16:02:01Z","doi":"10.1111/2041-210x.14485/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2139/ssrn.5132482","name":"Review of Machine Learning and Artificial Intelligence in Health Care","source":"crossref","abstract":"This paper explores the emerging role of machine learning in healthcare, underscoring its potential to enhance diagnostic precision, optimize treatment strategies, and improve patient outcomes through data analysis. It emphasizes that its integration can revolutionize healthcare delivery and operational efficiency. It also highlights the ethical considerations and challenges associated with implementing these technologies, including data privacy concerns and the need for robust regulatory frameworks to ensure safe and equitable use in clinical settings.","url":"https://doi.org/10.2139/ssrn.5132482","authors":["Sivudu Macherla"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-09T16:12:39Z","doi":"10.2139/ssrn.5132482","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-025-11429-x","name":"A review of artificial intelligence techniques for anomaly detection in smart grid","source":"crossref","abstract":"Abstract In the era of smart grids (SGs), as more interconnected energy sources and renewable sources are used, it is becoming increasingly important to have robust and accurate advanced anomaly detection methods. Due to the complexity of modern power systems, anomalies need to be detected more efficiently. This study provides a comprehensive overview of integrating renewable energy sources into SGs and the increasing importance of robust anomaly detection methods in ensuring grid security and reliability. Addressing four key research areas, we explore the current trends in applying machine learning techniques to SG anomaly detection research, identifying anomalies such as electricity theft, cyber-attacks, power system disturbances, and abnormal consumption patterns. We systematically evaluate the utilization of different machine learning models, including supervised, unsupervised, semi-supervised, and reinforcement learning, to detect each anomaly within SG environments. Furthermore, we assess the effectiveness of the anomaly detection algorithms and discuss the potential for further research, emphasizing the need for multidisciplinary collaboration and continuous development to overcome challenges and adapt to evolving grid dynamics and cyber threats. The findings of this study suggest that machine learning significantly contributes to ensuring the resilience and efficiency of SGs in the face of evolving challenges.","url":"https://doi.org/10.1007/s10462-025-11429-x","authors":["Md Al Amin Sarker","Irrai Anbu Jayaraj","Bharanidharan Shanmugam","Sami Azam","Suresh Thennadil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-03T08:49:20Z","doi":"10.1007/s10462-025-11429-x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1017/s0269888900007724","name":"Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ.","source":"crossref","abstract":"","url":"https://doi.org/10.1017/s0269888900007724","authors":["Gerd Brewka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2009-07-07T09:34:14Z","doi":"10.1017/s0269888900007724","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.14342/smog.2024.124.119","name":"A Systematic Review of Generative Artificial Intelligence in L2 Education","source":"crossref","abstract":"This literature review examines a comprehensive review of generative AI (GenAI) in English as second/foreign language (L2) education, analyzing its application in writing, pronunciation, translation, and assessment. Drawing on thematic analysis of recent studies, it identifies key categories, research themes, and significant findings while addressing gaps in prior reviews. Methodologically, the study synthesizes insights from peer-reviewed research published between 2023 and 2024, offering a robust framework for understanding GenAI’s impact and guiding future exploration in L2 education. Key findings highlight GenAI's potential to enhance personalization, efficiency, and learner engagement, while addressing challenges such as ethical concerns, over-reliance, and output quality. Teacher training, critical evaluation, and balanced integration are essential for effective implementation. The review underscores the importance of combining technological innovation with pedagogical expertise to maximize GenAI's impact on L2 learning and teaching.","url":"https://doi.org/10.14342/smog.2024.124.119","authors":["Youn-Kyoung Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-17T02:33:21Z","doi":"10.14342/smog.2024.124.119","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-024-10805-3","name":"A review of digital twins and their application in cybersecurity based on artificial intelligence","source":"crossref","abstract":"Abstract The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.","url":"https://doi.org/10.1007/s10462-024-10805-3","authors":["Mohammadhossein Homaei","Óscar Mogollón-Gutiérrez","José Carlos Sancho","Mar Ávila","Andrés Caro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-10T08:02:58Z","doi":"10.1007/s10462-024-10805-3","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.5592/co/cetra.2022.1415","name":"Review of the Artificial Intelligence methods used for permanent way diagnostics","source":"crossref","abstract":"The article discusses the implementation potential of the Artificial Intelligence methods in monitoring and analysis of permanent way condition monitoring. Regular inspection of railway track conditions is crucial for maintaining safe and reliable train operations. The diagnostic track recording vehicles and trolleys collect voluminous accurate information on track and turnouts' safety and functional parameters. The traditional analysis of this data made by human experts only turns out to be less efficient and prone to human error than automated analyses. Research into artificial intelligence yielded methods to carry out tasks previously considered too complex to be done without human intervention. Most inspection data can be analysed automatically, be it track and turnout geometry readings and video inspection information. If required, unique annotation overlays and reporting procedures can be applied to provide instantaneous results. Information collected by the test vehicles provides diagnostic data, which the diagnostic software can analyse on the intelligent platform. This intelligent platform can use various Artificial Intelligence tools like expert systems, intelligent agents continuously browsing the diagnostic results database, Genetic Algorithms, Neural Networks, or Bayesian framework as a self-learning system. The automated and unbiased analysis results make sound maintenance decisions possible. Such an approach makes allocating the limited budgets and resources possible with various priorities to optimise the amount of investment required to keep the assets in good health. Efficient maintenance planning has become possible with maintenance work schedules, work order generation, work maintenance support and others, categorising the track and turnout quality based on the collected information.","url":"https://doi.org/10.5592/co/cetra.2022.1415","authors":["Janusz Madejski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-27T14:23:20Z","doi":"10.5592/co/cetra.2022.1415","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/projs8","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.The paper explores the role of soft computing (SC) methods, such as artificial neural networks (ANNs), fuzzy logic (FL), genetic algorithms (GAs), and probabilistic reasoning in education.The author highlights the benefits and limitations of elearning in the context of traditional teaching and learning methods. Strengths:1.The paper provides a comprehensive overview of the various SC methods, their development, and their potential applications in education.","url":"https://doi.org/10.32388/projs8","authors":["Giovanni Briganti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-26T02:51:45Z","doi":"10.32388/projs8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/pai.13419/v1/review2","name":"Review for \"Artificial intelligence in the diagnosis of pediatric allergic diseases\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/pai.13419/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-23T05:40:26Z","doi":"10.1111/pai.13419/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/wje7bg","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/wje7bg","authors":["Ken Masters"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-18T04:22:02Z","doi":"10.32388/wje7bg","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/equ13y","name":"Review of: \"Education, Artificial Intelligence, and the Digital Age\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.","url":"https://doi.org/10.32388/equ13y","authors":["Tiago Nascimento Borges Slavov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-19T15:05:17Z","doi":"10.32388/equ13y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/j80buv","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/j80buv","authors":["Diego Rodrigues"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-25T21:32:50Z","doi":"10.32388/j80buv","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/yjqe6p","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"Positioning metacognition as the \" nal frontier\" of human competitiveness against AI, which is a compelling and underexplored theme.Integrating case studies from AI milestones (Deep Blue, AlphaGo, AlphaZero, DeepSeek) with their pedagogical implications.Proposing practical classroom strategies (self-questioning, re ective journals, think-aloud methods) within the context of AI-driven transformations, which grounds theoretical discussions in tangible practice.This makes the work original in framing education reform through metacognition while situating it in the broader AI-human debate. StrengthsInterdisciplinary scope: The paper successfully bridges AI developments with educational theory.Practical relevance: Provides examples of classroom practices that are low-cost and implementable, which enhances its utility for educators.","url":"https://doi.org/10.32388/yjqe6p","authors":["Inderpreet Kaur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-20T06:32:37Z","doi":"10.32388/yjqe6p","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/i3pny0","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/i3pny0","authors":["Broumi Said"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-18T05:37:51Z","doi":"10.32388/i3pny0","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/vo6hp6","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/vo6hp6","authors":["Zvonimir Krajcer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-09T13:53:16Z","doi":"10.32388/vo6hp6","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32920/31383082.v1","name":"Artificial Intelligence Applications in Motivational Interviewing; A Systematic Literature Review","source":"crossref","abstract":"&lt;p dir=\"ltr\"&gt;Motivational interviewing (MI) is effective for behavior change but faces delivery challenges. With current technological advances, many researchers have explored the possible applications of Artificial Intelligence in Motivational Interviewing. Using a Systematic Literature Review (SLR) methodology and adhering to the Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) guidelines, this thesis provides insights into the current landscape of Artificial Intelligence applications in Motivational Interviewing. A co-occurrence map of author keywords and index keywords of 62 articles was created in VOSviewer software, which generated three key themes with 18 keywords. In addition, using the KeyBERT NLP technique, the top keywords of article abstracts were generated and grouped into five main themes. The dominant technologies were explored, and it revealed that because of the importance of including MI principles in communications, Chatbots and Conversational Agents could provide promising avenues for addressing the challenges of MI delivery.&lt;/p&gt;","url":"https://doi.org/10.32920/31383082.v1","authors":["Armita Sadeghian Barzoki"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T14:02:01Z","doi":"10.32920/31383082.v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-025-11487-1","name":"Artificial intelligence-generated content (AIGC) in biomedical research, healthcare delivery, and clinical practices: technologies, applications, and regulatory considerations","source":"crossref","abstract":"Abstract Artificial Intelligence-Generated Content (AIGC) represents a paradigm shift in biomedical research and healthcare delivery, offering unprecedented capabilities for content creation, medical data analysis, and patient care optimization. This review examines the evolution of AIGC technologies from rule-based systems to advanced multimodal large models, with specific focus on their applications in healthcare settings. We analyze the three core capabilities of AIGC: intelligent digital content twinning, editing, and creation, and their transformative potential in medical imaging, clinical documentation, drug discovery, and personalized medicine. This paper discusses key challenges including algorithmic transparency, data privacy, and regulatory compliance, particularly in light of World Health Organization (WHO) guidelines for AI in health. Our findings indicate that while AIGC technologies show remarkable promise in enhancing diagnostic accuracy, streamlining clinical workflows, and democratizing healthcare access, careful consideration of ethical implications and regulatory frameworks is essential for safe and effective implementation.","url":"https://doi.org/10.1007/s10462-025-11487-1","authors":["Jiancheng Ye"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-19T08:37:17Z","doi":"10.1007/s10462-025-11487-1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.artint.2013.06.003","name":"Multiple instance classification: Review, taxonomy and comparative study","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2013.06.003","authors":["Jaume Amores"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-06-19T16:17:25Z","doi":"10.1016/j.artint.2013.06.003","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.35712/aig.v3.i5.117","name":"Artificial intelligence in gastroenterology: A narrative review","source":"crossref","abstract":"","url":"https://doi.org/10.35712/aig.v3.i5.117","authors":["Jonathan S Galati","Robert J Duve","Matthew O'Mara","Seth A Gross"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-28T06:26:10Z","doi":"10.35712/aig.v3.i5.117","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.5220/0012709300003854","name":"Impact of Artificial Intelligence-Assisted Pathology on Patient Healthcare: Literature Review","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012709300003854","authors":["Achref Miry","Mohammed Tbouda","Kenza Oqbani","Sanae Abbaoui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-12T15:39:24Z","doi":"10.5220/0012709300003854","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/czo1wn","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"The paper provides a well-organized and informative study on the benefits","url":"https://doi.org/10.32388/czo1wn","authors":["Ruchi Mittal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-24T02:22:18Z","doi":"10.32388/czo1wn","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/nyas.70009/v1/review1","name":"Review for \"Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/nyas.70009/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T23:08:38Z","doi":"10.1111/nyas.70009/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/53de2w","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"The definition and lens adopted for AI is rather unusual, why is AI focused on creating smart devices?See Ashok et al.(2022) for a discussion on wide variety of definitions.You need to clarify in the abstract the type of paper, is it an opinion piece?Use of AI and digital technologies in Education could be viewed in different ways.It could be use of AI to analyse student and staff data, use of AI tools for data analytics modules, use of AI to interact with students and staff online (signposting, natural language processing), use of AI to write assessments, use of provide feedback… the list goes on.It will help if you elaborate on this (scope of this paper) in the Abstract.Concepts introduced in the Abstract like teaching soft computing, e-learning are diverse aspects, not sure if the reader understands your focus.You introduce a term -social robots, not sure what that means.","url":"https://doi.org/10.32388/53de2w","authors":["Mona Ashok"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-24T03:55:51Z","doi":"10.32388/53de2w","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.37547/ijmscr/volume06issue05-26","name":"The Role and Future Prospects of Artificial Intelligence Technologies in The Medical Education Process","source":"crossref","abstract":"Artificial intelligence (AI) has emerged as one of the most transformative technologies in healthcare and education. In recent years, AI-based tools have increasingly been integrated into medical education to enhance learning outcomes, support clinical decision-making, and improve the development of professional competencies among medical students. The use of intelligent tutoring systems, virtual simulations, adaptive learning platforms, and generative AI applications provides personalized learning experiences and facilitates the acquisition of theoretical knowledge and practical skills. Furthermore, AI contributes to the assessment of student performance, the identification of learning gaps, and the optimization of educational processes. Despite its numerous advantages, the implementation of AI in medical education raises important challenges related to ethics, data privacy, academic integrity, and the need for digital literacy among educators and students. This article examines the current role of artificial intelligence technologies in medical education, highlights their benefits and limitations, and discusses future prospects for their integration into competency-based medical education. The findings suggest that AI has significant potential to improve the quality, accessibility, and effectiveness of medical education while preparing future healthcare professionals for an increasingly digital healthcare environment.","url":"https://doi.org/10.37547/ijmscr/volume06issue05-26","authors":["Sokina Bonu Ravshanzoda Ikramova","Nargiza Jabborovna Akhmadaliyeva","Durdonakhon Yusupjonovna Nasirova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-06T11:45:43Z","doi":"10.37547/ijmscr/volume06issue05-26","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.25259/gjmpbu_32_2025_171","name":"Using Artificial Intelligence to Improve Quality of Health Professions Education – A Systematic Review","source":"crossref","abstract":"","url":"https://doi.org/10.25259/gjmpbu_32_2025_171","authors":["K. Sandeep","D. Anusha","R. Kavitha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-30T20:38:55Z","doi":"10.25259/gjmpbu_32_2025_171","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.2196/20701","name":"Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review","source":"crossref","abstract":"Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.","url":"https://doi.org/10.2196/20701","authors":["Theresa Schachner","Roman Keller","Florian v Wangenheim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-26T21:40:10Z","doi":"10.2196/20701","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.3403/30467396u","name":"Information technology - Artificial intelligence - Artificial intelligence concepts and terminology","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30467396u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-12T20:30:30Z","doi":"10.3403/30467396u","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.37497/rev.artif.intell.educ.v5i00.29","name":"The evolution of artificial intelligence: problems and prospects of rational cognition","source":"crossref","abstract":"Objective: This article undertakes a comprehensive exploration of the constructivist paradigm in artificial intelligence (AI) development, aiming to uncover how constructivist perspectives shape our understanding of AI. It delves into the evolution of AI thought, emphasizing the significance of constructivist epistemology in comprehending AI's philosophical and cognitive dimensions. Method: The study employs a variety of philosophical methodologies, including historical-philosophical analysis, comparative analysis of philosophical teachings, and a system-structural dialectical approach. These methods facilitate an in-depth examination of AI's conceptual intricacies within a constructivist framework, focusing on the relationship between artificial and natural intelligence and the epistemological implications of AI. Results: The investigation reveals that the main challenge in AI research is the absence of clear problem-solving rules, highlighting the current limitations of human self-knowledge in logical and emotional intelligence. It showcases AI's vast capabilities, from extensive knowledge bases to real-time processing, and emphasizes AI's role in enhancing human cognitive processes. Conclusions: Artificial intelligence, as a construct of human intellect, mirrors the capacity for design and creativity inherent in human thought. The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further our grasp of AI and its cognitive potential.","url":"https://doi.org/10.37497/rev.artif.intell.educ.v5i00.29","authors":["Petro Rybalko"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-16T15:30:03Z","doi":"10.37497/rev.artif.intell.educ.v5i00.29","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-5102946/v1","name":"Artificial Intelligence Role in Advancing the Design Education Process","source":"crossref","abstract":"Abstract The current research paper explores the creative integration between science and arts, highlighting its significance in intellectual and professional fields such as architectural design, advertising design, and fashion design. This integration aims to enhance creativity and innovation among learners by providing them with tools and methodologies that develop their creative abilities, including intellectual fluency, and examining the impact of this integration on design outcomes. It also highlights the importance of updating and developing educational methods, with a focus on the role of artificial intelligence as an assistive tool in this context. The study emphasizes the necessity of human oversight to ensure a balanced and creative learning environment, as artificial intelligence is currently incapable of fully performing human tasks. In the applied section of the research, two different educational experiments were conducted in 2024 at the Architecture College, Yarmouk Private University, on a single group consisting of 15 students, selected as a purposive target sample. In the first experiment, the traditional method was employed for teaching and supervising a swimming pool hall design project, while in the second experiment, an approach based on artificial intelligence was utilized for a museum design project. Finally, the study concluded that the integration of science with the arts in design education using artificial intelligence can enhance learners' problem-solving skills, innovation, and critical thinking. It also enables them to develop new skills that contribute to a comprehensive understanding of design processes. The achievement of the aforementioned objectives was facilitated by a series of procedures using the descriptive methodology, which involves the application of certain artificial intelligence tools in the design education process, followed by their interpretation and discussion. Subsequently, the applied analytical methodology was employed in the research's practical section, where the results of the studied group were examined and analyzed using SPSS (a software specialized in statistical data analysis).","url":"https://doi.org/10.21203/rs.3.rs-5102946/v1","authors":["Yamen Idelbi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T13:02:13Z","doi":"10.21203/rs.3.rs-5102946/v1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/htrdsk","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.","url":"https://doi.org/10.32388/htrdsk","authors":["Ricardo Reier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-09T06:59:07Z","doi":"10.32388/htrdsk","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.52519/00258","name":"Reducing MR Acquisition Times Using Artificial Intelligence: A Systematic Review Author","source":"crossref","abstract":"Abstract Purpose To synthesize existing evidence on artificial intelligence (AI) applications for reducing magnetic resonance (MR) imaging acquisition time, evaluate their impact on image quality, and identify challenges in clinical implementation. Method A systematic literature review was conducted using PubMed, CINAHL, and Scopus including peer reviewed, English-language articles published between 2020 and 2026. Both qualitative and quantitative primary research were eligible and had to be on AI use in MR processes. Findings were analyzed through thematic synthesis, and the quality of each study was appraised using relevant critical appraisal tools. Results Three major themes emerged: AI methods in MR procedures, perceived effectiveness of AI in improving efficiency in imaging, and implementation issues. Discussion The findings revealed a growing application of AI in MR; however, a clear link between optimized workflow and actual reductions in acquisition time has not been established. Although qualitative evidence is abundant, most studies focus on improved workflow efficiency and reporting speed rather than directly measuring acquisition time. AI is primarily used through machine learning and computer-aided detection systems, which enhance workflow performance, but their impact on acquisition time remains unclear. Key barriers include variability in performance, lack of standardization, and limited clinician confidence in AI tools. Conclusion AI-driven MR acceleration is a promising advancement, but further research is needed to address implementation barriers and support widespread clinical adoption.","url":"https://doi.org/10.52519/00258","authors":["Fatin M. Arief"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T18:43:48Z","doi":"10.52519/00258","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/aof9r8","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Thepaper\" -just need to separate these two words…this applies in a few cases here, so just a simple grammatical matter.","url":"https://doi.org/10.32388/aof9r8","authors":["Patrick Stacey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-05T09:48:36Z","doi":"10.32388/aof9r8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/jv2mk8","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/jv2mk8","authors":["Rashmi Kodikal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-29T09:29:50Z","doi":"10.32388/jv2mk8","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/pg1r2l","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.This article summarizes a series of key techniques in Computer-enhanced Education, such as Soft Computing, AI, Social robots, and genetic algorithms.From my point of view, it is more like a survey, rather than a scientific paper.The article is readable but requires efforts to improve its quality.My comments are listed below.","url":"https://doi.org/10.32388/pg1r2l","authors":["Fei Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-15T02:28:16Z","doi":"10.32388/pg1r2l","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-026-11670-y","name":"Challenges and opportunities of generative artificial intelligence models in audio/acoustic domain: a comprehensive survey","source":"crossref","abstract":"Abstract Generative artificial intelligence (AI) has transformed image and text processing, but its adoption in the audio/acoustic domain remains underexplored due to inherent challenges in modeling long-term temporal dependencies in one-dimensional signals and achieving human-perceptible coherence. This comprehensive survey addresses these gaps by systematically reviewing state-of-the-art generative AI models, including generative adversarial networks (GANs), diffusion/flow-matching models, variational autoencoders (VAEs), recurrent neural networks (RNNs), transformers, and Neural Codec Language Models (Codec LMs), organized around three primary application domains: (1) speech synthesis , encompassing text-to-speech conversion, neural vocoding, voice conversion, and zero-shot voice cloning; (2) music generation , covering both symbolic and acoustic composition, multi-track generation, and style transfer; and (3) general audio synthesis, sound effects, and source separation , including text-to-audio generation, audio restoration and enhancement, data augmentation, and conditional source separation. We provide a detailed taxonomy of architectures, functionalities, comparative strengths, and limitations, supported by common evaluation metrics. Structured comparisons with existing surveys demonstrate that this is the first work to jointly cover all three audio domains and all generative model families, while also providing dedicated evaluation-metric analysis and cross-domain comparative assessments. We further highlight emerging opportunities in AI applications such as healthcare monitoring (e.g., symptom analysis and mental health assessment) and biometric authentication, demonstrating the potential of synthetic audio to address real-life challenges. Through research gap identification, model efficacy comparison, and future direction outlining, this survey serves as a foundational reference for advancing generative AI techniques across the audio domain.","url":"https://doi.org/10.1007/s10462-026-11670-y","authors":["Sayanton Dibbo","Sudip Vhaduri","Chia-Hua Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-18T11:33:40Z","doi":"10.1007/s10462-026-11670-y","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2020.103924","name":"Rough computing — A review of abstraction, hybridization and extent of applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2020.103924","authors":["D.P. Acharjya","Ajith Abraham"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-11T13:44:36Z","doi":"10.1016/j.engappai.2020.103924","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1201/9781003529231-55","name":"A Comprehensive Review of Advanced Artificial Intelligence Integration in ICT Systems: Methodologies, Applications, and 55 Future Directions","source":"crossref","abstract":"This paper explores the integration of advanced artificial intelligence (AI) in ICT systems, employing machine learning and symbolic AI for problem-solving, including logic programming, expert systems, fuzzy logic, case-based reasoning, knowledge graphs, planning, and reinforcement learning algorithms. It focuses on AI applications in medical and health care, cybersecurity, data management, cloud computing, human-computer interaction, and network communication. The analysis delves into key AI methodologies and algorithms, highlighting their impact on efficiency and reliability. The paper emphasizes that addressing challenges and seizing AI opportunities is crucial for ensuring a sustainable and innovative future in ICT. It underscores the significance of widespread AI integration across various sectors to maximize its benefits. By examining the synergy of advanced AI systems in solving problems and optimizing processes, the paper contributes to the broader discourse on the transformative potential of AI in shaping the future landscape of information and communication technology. In essence, this exploration positions advanced AI as a linchpin for addressing contemporary challenges and fostering innovation in ICT. With its focus on practical applications and underlying methodologies, the paper serves as a valuable resource for understanding the current landscape and paving the way for future developments in the integration of advanced AI within ICT systems.","url":"https://doi.org/10.1201/9781003529231-55","authors":["Gopisetty Pardhavika","R. Prisicilla"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T10:35:53Z","doi":"10.1201/9781003529231-55","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s44163-024-00114-7","name":"Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review","source":"crossref","abstract":"Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.","url":"https://doi.org/10.1007/s44163-024-00114-7","authors":["Maria Frasca","Davide La Torre","Gabriella Pravettoni","Ilaria Cutica"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-27T19:02:18Z","doi":"10.1007/s44163-024-00114-7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.18276/978-83-8419-028-9-04","name":"THE ROLE OF ARTIFICIAL INTELLIGENCE AND ICT IN VETERINARY BUSINESS: A LITERATURE REVIEW","source":"crossref","abstract":"Purpose: The integration of artificial intelligence (AI) and ICT technologies in veterinary units offers new opportunities for diagnostics, remote monitoring, and practice management. The aim of this article is to conduct a systematic literature review to analyze the applications, challenges, and future prospects of artificial intelligence (AI) and information and communication technologies (ICT) in veterinary industry Ipsum is simply dummy text of the printing and typesetting industry. Need for the study: Despite AI’s growing role in veterinary care, challenges such as algorithmic bias, regulatory concerns, and limited research on long-term impacts persist. Additionally, the adoption of AI in practice management remains underexplored, despite its potential to improve efficiency, automate workflows, and optimize resource allocation. A systematic review of existing literature is necessary to address these gaps. Methodology: This study conducts a systematic literature review of AI applications in veterinary medicine using Scopus, Web of Science, and Google Scholar. The analysis explores key themes, including diagnostics, telemedicine, data management, business efficiency, and regulatory challenges. Findings: AI enhances diagnostic accuracy, workflow automation, and predictive analytics, improving clinical decision-making and patient outcomes. In veterinary practice management, AI-driven automation optimizes scheduling, inventory control, and client communication. However, barriers such as technological resistance and regulatory uncertainty hinder widespread adoption. Emerging trends include interdisciplinary collaboration, blockchain for data security, and AI training in veterinary curricula. Practical Implications: AI adoption can transform veterinary practice management by enhancing efficiency, reducing administrative burdens, and improving profitability. Future research should explore long-term impacts, standardization, and client acceptance to ensure responsible and effective AI implementation in veterinary medicine. By addressing these challenges, AI and digital technologies can significantly advance both veterinary care and practice management, leading to improved patient outcomes and business performance.","url":"https://doi.org/10.18276/978-83-8419-028-9-04","authors":["Karolina Beyer","Edyta Skarzyńska","Kesra Nermend"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-29T22:35:41Z","doi":"10.18276/978-83-8419-028-9-04","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/0004-3702(86)90075-5","name":"Artificial intelligence: The very idea","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90075-5","authors":["André Vellino"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(86)90075-5","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1108/s1548-643520230000020015","name":"Anthropomorphism in Artificial Intelligence: A Review of Empirical Work Across Domains and Insights for Future Research","source":"crossref","abstract":"Abstract Purpose Anthropomorphism in Artificial Intelligence (AI)-powered devices is being used increasingly frequently in consumer-facing situations (e.g., AI Assistants such as Alexa, virtual agents in websites, call/chat bots, etc.), and therefore, it is essential to understand anthropomorphism in AI both to understand consequences for consumers and to optimize firms' product development and marketing. Extant literature is fragmented across several domains and is limited in the marketing domain. In this review, we aim to bring together the insights from different fields and develop a parsimonious conceptual framework to guide future research in fields of marketing and consumer behavior. Methodology We conduct a review of empirical articles published until November 2021 in Financial Times Top 50 (FT50) journals as well as in 41 additional journals selected across several disciplinary domains: computer science, robotics, psychology, marketing, and consumer behavior. Findings Based on literature review and synthesis, we propose a three-step guiding framework for future research and practice on AI anthropomorphism. Research Implications Our proposed conceptual framework informs marketing and consumer behavior domains with findings accumulated in other research domains, offers important directions for future research, and provides a parsimonious guide for marketing managers to optimally utilize anthropomorphism in AI to the benefit of both firms and consumers. Originality/Value We contribute to the emerging literature on anthropomorphism in AI in three ways. First, we expedite the information flow between disciplines by integrating insights from different fields of inquiry. Second, based on our synthesis of literature, we offer a conceptual framework to organize the outcomes of AI anthropomorphism in a tidy and concise manner. Third, based on our review and conceptual framework, we offer key directions to guide future research endeavors.","url":"https://doi.org/10.1108/s1548-643520230000020015","authors":["Ertugrul Uysal","Sascha Alavi","Valéry Bezençon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-27T12:13:14Z","doi":"10.1108/s1548-643520230000020015","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/nyas.70009/v1/review2","name":"Review for \"Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/nyas.70009/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T23:08:38Z","doi":"10.1111/nyas.70009/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1098/rsos.230806/v1/review3","name":"Review for \"Human-centred artificial intelligence for mobile health sensing: challenges and opportunities\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.230806/v1/review3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-16T16:03:27Z","doi":"10.1098/rsos.230806/v1/review3","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/1z5nqq","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.This is an interesting overview of new and proposed uses of computer-based instruction methods, now grouped under the AI rubric.I think it is a very valuable overview of the field.These are my suggestions to improve it:","url":"https://doi.org/10.32388/1z5nqq","authors":["Thomas Fowler"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-05-02T13:55:17Z","doi":"10.32388/1z5nqq","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/oyu7aj","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/oyu7aj","authors":["Steven Bickley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-20T20:42:07Z","doi":"10.32388/oyu7aj","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/wsr19q","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/wsr19q","authors":["Marie Devlin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-02T10:45:45Z","doi":"10.32388/wsr19q","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/svtmfl","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/svtmfl","authors":["Anca Jianu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T16:00:07Z","doi":"10.32388/svtmfl","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1177/27000710251386963/v1/review2","name":"Review for \"How Human Personality Will Change with the Use of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/27000710251386963/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-28T04:00:21Z","doi":"10.1177/27000710251386963/v1/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/978-3-032-18894-6_2","name":"Use and Perception of AI for Psychological Self-Help Among Medical Students in Morocco","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_2","authors":["Hind Moumni","Fatima Elghazouani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:38:24Z","doi":"10.1007/978-3-032-18894-6_2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s44163-026-01031-7","name":"Artificial intelligence in suicide risk assessment: a systematic literature review","source":"crossref","abstract":"Abstract Suicide remains a leading cause of preventable death worldwide, requiring timely and scalable interventions. This systematic literature review examines how Artificial Intelligence (AI) has been applied to suicide. Following PRISMA guidelines and a registered PROSPERO protocol, a comprehensive search across APA PsycNET, PubMed, IEEE Xplore, and Scopus yielded 1,293 records. No publication date limits were applied; all eligible studies available up to the final search date (May 2025) were included. After screening and quality appraisal, 160 studies published in peer-reviewed, Q1-ranked journals were included for in-depth synthesis. The review follows an AI taxonomy categorising the different AI technologies into machine learning (ML), deep learning (DL), natural language processing (NLP), generative AI (GenAI), large language models (LLMs), and explainable AI (XAI). It organises findings into several thematic domains, such as social media-based, electronic health records, demographic modelling, clinical transitions, and emerging technologies. The findings revealed that NLP and DL approaches, particularly on social media and clinical datasets, performed better than traditional statistical methods in identifying suicidal behaviour. Population-specific models (by age, gender, and veteran status) enhance prediction accuracy. XAI methods such as SHAP and LIME improve model transparency and clinical trust, while GenAI and LLMs are emerging as promising yet underexplored tools. Despite growing interest, the review identified limitations in cross-cultural generalisability, lack of prospective validation, and underrepresentation of low- and middle-income countries (LMICs). The findings also showed that AI complements, rather than replaces, traditional suicide assessment tools, offering hybrid potential for real-time and personalised suicide prevention. The study concludes with a call for ethically aligned, explainable, and context-sensitive AI frameworks to ensure fair, unbiased, scalable deployment in mental health care.","url":"https://doi.org/10.1007/s44163-026-01031-7","authors":["Tsholofelo Mokheleli","Tebogo Makaba","Patrick Ndayizigamiye","Nompumelelo Ndlovu","Hossana Twinomurinzi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-02T15:36:28Z","doi":"10.1007/s44163-026-01031-7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1109/iicaiet67254.2025.11265479","name":"Review of Artificial Intelligence Applications in Performance Prediction of Advanced Energy Materials","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2> 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.","url":"https://doi.org/10.1109/iicaiet67254.2025.11265479","authors":["Paula Marielle Ababao","Ian Benitez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-05T18:36:16Z","doi":"10.1109/iicaiet67254.2025.11265479","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s10462-025-11108-x","name":"Artificial intelligence advances in anomaly detection for telecom networks","source":"crossref","abstract":"Telecommunication networks are becoming increasingly dynamic and complex due to the massive amounts of data they process. As a result, detecting abnormal events within these networks is essential for maintaining security and ensuring seamless operation. Traditional methods of anomaly detection, which rely on rule-based systems, are no longer effective in today’s fast-evolving telecom landscape. Thus, making AI useful in addressing these shortcomings. This review critically examines the role of Artificial Intelligence (AI), particularly deep learning, in modern anomaly detection systems for telecom networks. It explores the evolution from early strategies to current AI-driven approaches, discussing the challenges, the implementation of machine learning algorithms, and practical case studies. Additionally, emerging AI technologies such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) are highlighted for their potential to enhance anomaly detection. This review provides AI’s transformative impact on telecom anomaly detection, addressing challenges while leveraging 5G/6G, edge computing, and the Internet of Things (IoT). It recommends hybrid models, advanced data preprocessing, and self-adaptive systems to enhance robustness and reliability, enabling telecom operators to proactively manage anomalies and optimize performance in a data driven environment.","url":"https://doi.org/10.1007/s10462-025-11108-x","authors":["Enerst Edozie","Aliyu Nuhu Shuaibu","Bashir Olaniyi Sadiq","Ukagwu Kelechi John"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-25T03:04:41Z","doi":"10.1007/s10462-025-11108-x","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1117/12.2659278","name":"Artificial intelligence opportunities and challenges for enterprise management: a review and research hotspot","source":"crossref","abstract":"Artificial intelligence(AI) is being integrated into enterprises’ strategy for improving management performance, while many managers do not know the focus of AI research, nor do they know what specific aspects AI is applicable to and the possible impact. The main purpose of this research is to analyze the change that brought by technologies based on AI on enterprise, to determine the compound effect on enterprise management reform. This paper proposes a framework for fully understanding the opportunities and challenges on enterprise management reform brought by AI to help companies break down their prejudices about AI. Through bibliometrics method and literature review, we find that AI applications can promote the beneficial changes of the three relationships in enterprise management, including the relationship between enterprises and customers, superiors and subordinates, as well as the relationship between traditional production factors and data factors. Meanwhile, enterprises may face difficulties in security and privacy, law and ethics, and culture and talent. All in all, enterprise management will embrace the opportunities of innovation in theory and practice in the era of AI if we can effectively prevent or deal with the possible problems.","url":"https://doi.org/10.1117/12.2659278","authors":["Qun Liu","Fucheng Liang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T15:35:34Z","doi":"10.1117/12.2659278","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2019.08.018","name":"Hybrid structures in time series modeling and forecasting: A review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2019.08.018","authors":["Zahra Hajirahimi","Mehdi Khashei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-05T12:45:07Z","doi":"10.1016/j.engappai.2019.08.018","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/mk3vn9","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/mk3vn9","authors":["Tay Tan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T11:11:54Z","doi":"10.32388/mk3vn9","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/mtr4b7","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/mtr4b7","authors":["Anca Jianu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-05T14:23:23Z","doi":"10.32388/mtr4b7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.21203/rs.3.rs-3462413/v2","name":"Leveraging Artificial Intelligence for Enhanced Project Completion in Education","source":"crossref","abstract":"Abstract This research investigates the integration of Artificial Intelligence (AI) in educational settings, specifically its impact on project-based learning. In a controlled environment, 200 students participated, evaluating the effects of an AI-driven project completion support system on time management, engagement, and academic performance. Significant improvements were observed in the Experimental Group, emphasizing the positive influence of AI on educational project completion and its potential to enhance overall academic success. Sampling involved 200 students from Mumbai, India, randomly assigned to control (n = 100) and experimental (n = 100) groups using unbiased random sampling techniques, ensuring generalizability. Data collection employed pre/post-project surveys, project completion rates, and academic metrics, capturing insights into students' habits before and after AI support system implementation. Led by EdTech Research Association faculty and co-author Kavita Roy, the study showcased marked improvements in time management, engagement, and academic performance in the Experimental Group. The AI-driven system emerged as an effective guide, aiding students in time management and positively influencing engagement and academic performance. Acknowledging study limitations guides future research. Implications advocate for strategic AI integration in education, urging institutions and policymakers to responsibly adopt AI tools. Positive outcomes highlight the need for preparing students for the digital age. Future research should explore long-term effects, diversity considerations, ethics, and teacher training programs, providing a comprehensive understanding of AI's role in education. In summary, the findings underscore AI's positive impact on project completion, signaling a path to a more efficient learning environment and contributing significantly to students' overall academic success in the evolving educational technology landscape.","url":"https://doi.org/10.21203/rs.3.rs-3462413/v2","authors":["KHRITISH SWARGIARY"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-09T10:53:20Z","doi":"10.21203/rs.3.rs-3462413/v2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1002/cjce.24246/v2/review2","name":"Review for \"Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cjce.24246/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-05T09:40:23Z","doi":"10.1002/cjce.24246/v2/review2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/asvmxd","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/asvmxd","authors":["Y.p. Tsang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-26T01:24:21Z","doi":"10.32388/asvmxd","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1111/cge.14527/v1/review1","name":"Review for \"Simplified detection of genetic background admixture using artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/cge.14527/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-15T08:04:12Z","doi":"10.1111/cge.14527/v1/review1","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/0004-3702(74)90016-2","name":"Artificial intelligence: a paper symposium","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(74)90016-2","authors":["John McCarthy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(74)90016-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3389/frai.2024.1347815","name":"Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis","source":"crossref","abstract":"The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.","url":"https://doi.org/10.3389/frai.2024.1347815","authors":["Haruna Chiroma","Ibrahim Abaker Targio Hashem","Mohammed Maray"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-12T00:44:52Z","doi":"10.3389/frai.2024.1347815","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.artmed.2021.102157","name":"Dengue models based on machine learning techniques: A systematic literature review","source":"crossref","abstract":"Background Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years. Methods Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified. Results Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%. Conclusions We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.","url":"https://doi.org/10.1016/j.artmed.2021.102157","authors":["William Hoyos","Jose Aguilar","Mauricio Toro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-24T02:12:05Z","doi":"10.1016/j.artmed.2021.102157","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.engappai.2023.106129","name":"Sampling and noise filtering methods for recommender systems: A literature review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.106129","authors":["Kirti Jain","Rajni Jindal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-31T04:59:50Z","doi":"10.1016/j.engappai.2023.106129","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.31235/osf.io/yq57a","name":"Artificial Intelligence and Public Values:  A Literature Review","source":"crossref","abstract":"In recent years, there has been both growing public debate and increased scholarship and policy dialogue on public and societal concerns associated with the rise of AI and its applications. This paper presents the results and insights gained from a systematic literature review on the societal dimensions of AI invention and innovation, focusing on the public values expressed in AI patent documents. We build on public value frameworks to conceptualize public values expressed in patents as statements regarding the potential social objectives and benefits of an invention, beyond the private value to patent holders. Although the United States Patent and Trademarking Office (USPTO) has no formal requirements for expressions of public values in patent documents, patent applications often describe the broader objectives or problems that the invention aims to address. Such public value statements inform the context for understanding the potential utility of a patent.","url":"https://doi.org/10.31235/osf.io/yq57a","authors":["Divali Legore","Christine Webster"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-05T00:01:11Z","doi":"10.31235/osf.io/yq57a","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.32388/hjz1al","name":"Review of: \"Artificial Intelligence and Digital Technologies in the Future Education\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/hjz1al","authors":["Shrddha Sagar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-26T02:39:30Z","doi":"10.32388/hjz1al","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/978-3-030-62582-5_7","name":"Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-62582-5_7","authors":["Nikolaos Doukas","Peter Stavroulakis","Nikolaos Bardis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-20T20:02:41Z","doi":"10.1007/978-3-030-62582-5_7","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/s0933-3657(97)00027-4","name":"Evaluation of a medical diagnosis system using simulator test scenarios","source":"crossref","abstract":"This paper describes an informal but systematic method for how to test and verify a knowledge-based system in a large open-ended medical target domain. The system used is Guardian, an intelligent system for monitoring and diagnosis of post-cardiac surgery patients in an intensive-care unit. The knowledge base is tested and verified by running the system on a series of realistic test scenarios, both with an embedded simulator and with an external simulation system. The same scenarios are presented to human test subjects, making it possible to compare and analyze the performance of the knowledge-based system with that of human physicians. The use of simulators instead of clinical data also means that it is possible to test crucial scenarios which occur seldom in medical practice. Our results show that a system like Guardian might indeed be useful in medical care.","url":"https://doi.org/10.1016/s0933-3657(97)00027-4","authors":["Jan Eric Larsson","B Hayes-Roth","D.M Gaba","B.E Smith"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T19:37:38Z","doi":"10.1016/s0933-3657(97)00027-4","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.artmed.2022.102311","name":"Multilabel classification of medical concepts for patient clinical profile identification","source":"crossref","abstract":"Background The development of electronic health records has provided a large volume of unstructured biomedical information. Extracting patient characteristics from these data has become a major challenge, especially in languages other than English. Methods Inspired by the French Text Mining Challenge (DEFT 2021) [1] in which we participated, our study proposes a multilabel classification of clinical narratives, allowing us to automatically extract the main features of a patient report. Our system is an end-to-end pipeline from raw text to labels with two main steps: named entity recognition and multilabel classification. Both steps are based on a neural network architecture based on transformers. To train our final classifier, we extended the dataset with all English and French Unified Medical Language System (UMLS) vocabularies related to human diseases. We focus our study on the multilingualism of training resources and models, with experiments combining French and English in different ways (multilingual embeddings or translation). Results We obtained an overall average micro-F1 score of 0.811 for the multilingual version, 0.807 for the French-only version and 0.797 for the translated version. Conclusion Our study proposes an original multilabel classification of French clinical notes for patient phenotyping. We show that a multilingual algorithm trained on annotated real clinical notes and UMLS vocabularies leads to the best results.","url":"https://doi.org/10.1016/j.artmed.2022.102311","authors":["Christel Gérardin","Perceval Wajsbürt","Pascal Vaillant","Ali Bellamine","Fabrice Carrat","Xavier Tannier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-26T12:28:47Z","doi":"10.1016/j.artmed.2022.102311","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/ai-si66213.2025.11341181","name":"Integrating Artificial Intelligence in Language Education: A Systematic Literature Review","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai-si66213.2025.11341181","authors":["Pan Qi","Nurul Farhana Binti Jumaat","Hassan Abuhassna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:55:29Z","doi":"10.1109/ai-si66213.2025.11341181","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1007/s44163-026-01460-4","name":"A systematic review on organizational adoption and performance constructs related to artificial intelligence in SMEs","source":"crossref","abstract":"Abstract This study examines how Small and Medium-sized Enterprises (SMEs) adopt Artificial Intelligence (AI) and the implications for organizational performance. Based on a systematic literature review of 68 peer-reviewed articles indexed in Scopus, the analysis draws on the Technology-Organization-Environment framework and the Diffusion of Innovations theory to identify the main factors shaping AI integration in SMEs. The findings indicate that AI adoption is associated with improvements in efficiency, decision-making, and competitiveness, but its outcomes depend on organizational readiness, trust, and alignment with strategic objectives. At the same time, barriers such as limited resources, technological complexity, and cultural resistance continue to constrain adoption. The review also identifies gaps in performance measurement, sector-specific adoption, and behavioral perspectives, suggesting directions for future research. By focusing on the SME context, this study contributes to the literature by clarifying the organizational conditions under which AI adoption can enhance performance and competitiveness, while offering practical insights for managers and policymakers.","url":"https://doi.org/10.1007/s44163-026-01460-4","authors":["Clesio Landini","Marcio Cardoso Machado"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T07:23:48Z","doi":"10.1007/s44163-026-01460-4","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/b978-0-323-90037-9.00007-2","name":"Artificial intelligence–assisted headache classification: a review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90037-9.00007-2","authors":["Bindu Menon","Anitha S. Pillai","Prabha Susy Mathew","Anna M. Bartkowiak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-02T19:33:30Z","doi":"10.1016/b978-0-323-90037-9.00007-2","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.3403/30467396","name":"Information technology - Artificial intelligence - Artificial intelligence concepts and terminology","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30467396","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-12T20:30:30Z","doi":"10.3403/30467396","addedAt":"2026-09-01T01:47:58.474Z","updatedAt":"2026-09-01T01:47:58.474Z"},{"id":"doi:10.1016/j.ejim.2026.107172","name":"Artificial intelligence in medicine: An ethical and legal review.","source":"europepmc","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) into medical practice marks a transformative shift within healthcare delivery, diagnostics, and treatment paradigms. Although AI systems exhibit sophisticated capabilities in image recognition, predictive analytics, and clinical decision support, their implementation introduces fundamental ethical and legal questions requiring thorough examination. This review addresses the primary ethical principles relevant to AI in medicine - including respect for patient autonomy, beneficence, non-maleficence, and justice - alongside key legal frameworks with respect to liability, data protection, regulatory compliance, and algorithmic transparency. It analyzes challenges such as algorithmic bias, patient privacy, informed consent in automated decision-making, and the evolving responsibilities of healthcare professionals in AI-based clinical environments. Additionally, the review investigates emerging regulatory approaches across jurisdictions, considering how existing medical device regulations, data protection laws, and professional liability frameworks are adapting to AI technologies. The paper concludes with recommendations for clinicians, policymakers, and developers to promote ethical AI deployment that strengthens the physician-patient relationship and advances healthcare equity.","url":"https://doi.org/10.1016/j.ejim.2026.107172","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejim.2026.107172","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00417-026-07459-y","name":"Ethical considerations in generative artificial intelligence for synthetic data generation in ophthalmology.","source":"europepmc","abstract":"Generative artificial intelligence (AI) is beginning to reshape medical research, with ophthalmology at the forefront of this transformation. This narrative review examines the ethical considerations and challenges of using generative AI - particularly Generative Adversarial Networks (GANs) and diffusion models - to create synthetic data for ophthalmic research. Synthetic data offers genuine solutions to long-standing obstacles: data scarcity, augmentation of datasets for rare diseases, and privacy-preserving multi-institutional collaboration. Yet the same capabilities introduce formidable risks. Key concerns include patient re-identification from ostensibly anonymous synthetic data, the perpetuation and amplification of algorithmic biases inherent in source datasets, the prospect of scientific misconduct through \"deepfake\" medical images, and unresolved questions of data ownership, governance, and legal liability. Clinical validity cannot be inferred from computational metrics alone; it requires a multi-faceted evaluation framework that incorporates expert clinical review and downstream task performance. To harness the potential of generative AI while mitigating its risks, a proactive, interdisciplinary approach is essential - combining rigorous technical validation with ethical oversight and clear regulatory guidance. This article concludes with policy recommendations to foster responsible innovation and ensure that this powerful technology is developed and deployed safely, equitably, and aligned with the core tenets of scientific integrity and patient care.","url":"https://doi.org/10.1007/s00417-026-07459-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00417-026-07459-y","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/mop.0000000000001600","name":"Artificial intelligence in pediatric medical genetics and genomics.","source":"pubmed","abstract":"Artificial intelligence (AI) is being rapidly but unevenly integrated into many aspects of medicine as well as society more broadly. This review focuses on recent developments in AI through the lens of how it is affecting the field of pediatric genomics.","url":"https://doi.org/10.1097/mop.0000000000001600","authors":["Meltzer JA","Solomon BD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/mop.0000000000001600","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1007/s11604-026-02066-7","name":"Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.","source":"europepmc","abstract":"Prostate-specific membrane antigen positron emission tomography (PSMA-PET) has become pivotal in prostate cancer (PCa) management, offering superior sensitivity over conventional imaging for detecting tumors, metastases, and biochemical recurrence. However, interpretive subjectivity, workflow inefficiencies, and heterogeneous PSMA expression remain significant limitations. Artificial intelligence (AI), particularly radiomics and deep learning, addresses these challenges by enabling automated lesion analysis and image enhancement. This review examines the impact of AI across the PSMA-PET workflow, covering optimized image acquisition (e.g., low-dose protocols, motion correction), enhanced interpretation (e.g., lesion characterization, prognostic stratification), and personalized theranostics (e.g., treatment response forecasting, radioligand therapy dosimetry). Despite promising multicenter validation, challenges remain in annotation standardization, data heterogeneity, model generalizability, interpretability, regulatory integration, and ethics. We further discuss emerging frontiers, including multimodal multi-omic integration, generative AI, and AI-driven clinical decision support systems. Notably, we highlight the evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertise. These advances position AI-integrated PSMA-PET to drive precision oncology, with key pathways outlined for clinical translation and future innovation in PCa care.","url":"https://doi.org/10.1007/s11604-026-02066-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11604-026-02066-7","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s41077-026-00447-6","name":"Artificial intelligence to support debriefing in simulation-based healthcare education: a scoping review.","source":"europepmc","abstract":"Background Artificial intelligence is increasingly being integrated into healthcare education and simulation-based education. However, its role in supporting the debriefing phase of simulation remains underexplored and inconsistently described. This scoping review aimed to map the existing literature on the use of artificial intelligence to support debriefing in simulation-based healthcare education. Methods A scoping review was conducted in accordance with Arksey and O'Malley's framework and Joanna Briggs Institute guidance and reported in line with PRISMA-ScR. MEDLINE, Scopus, Web of Science, and CINAHL were searched without date restrictions. Eligible studies examined the use of artificial intelligence to support debriefing-related processes within healthcare simulation. Data were charted using a structured extraction form and synthesised descriptively and thematically. Results Seven studies published between 2023 and 2026 met the inclusion criteria. Studies were conducted in the United States, Switzerland, Chile, and South Korea. Artificial intelligence applications clustered into three domains: communication and performance analytics using speech recognition and natural language processing; generative artificial intelligence systems supporting facilitator feedback and structured report generation; and learner-facing reflective dialogue systems. Across the included studies, artificial intelligence was mainly positioned as an adjunct to human facilitation rather than as a replacement for facilitators. Reported outcomes focused primarily on feasibility, usability, technical accuracy, and perceived educational value, with limited evidence of objective improvements in learner performance or clinical outcomes. Conclusions Artificial intelligence is emerging as a supportive tool for debriefing in simulation-based healthcare education. Current evidence remains limited, exploratory, and largely single-institutional, indicating the need for more rigorous research on educational effectiveness, ethical implementation, and the continuing role of human facilitation.","url":"https://doi.org/10.1186/s41077-026-00447-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s41077-026-00447-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00261-026-05714-8","name":"Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.","source":"pubmed","abstract":"Artificial intelligence (AI) is rapidly integrating into clinical radiology. As primary diagnosticians, radiologists increasingly interpret AI-generated analyses and are expected to oversee the monitoring and governance of deployed AI systems. Although AI literacy among radiologists is improving, several technical aspects of AI remain insufficiently accessible. One such concept is uncertainty quantification (UQ), which estimates the reliability of AI predictions and can signal when outputs should be interpreted with caution. This review introduces key UQ concepts relevant to radiology, distinguishing between aleatoric uncertainty and epistemic uncertainty arising from data variability and knowledge gaps. We summarize commonly used UQ approaches in current research and practice. Furthermore, through a narrative review of selected recent AI imaging studies, we illustrate how UQ methods are applied in practice and highlight methodological trends, findings, and limitations. Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.","url":"https://doi.org/10.1007/s00261-026-05714-8","authors":["Vega Lara F","Koopmans LD","Roest C","Turkbey B","Yakar D","Kwee TC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00261-026-05714-8","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s11912-026-01810-6","name":"From Symptom Control to Precision Supportive Oncology: Integrating Artificial Intelligence in Supportive Oncology for Gastrointestinal Cancers.","source":"europepmc","abstract":"Purpose of review Gastrointestinal (GI) cancers are among the most common malignancies worldwide and impose a substantial symptom burden from diagnosis through survivorship. Despite advances in systemic therapies and surgical approaches, patients continue to experience undertreated symptoms, psychosocial distress, financial toxicity, and fragmented supportive care. This review examines how artificial intelligence (AI) may help transform GI supportive oncology from a reactive, episodic model to a proactive, continuous, and personalized approach. Recent findings AI applications in GI supportive oncology are advancing along two related domains: (1) AI-enabled patient-reported outcome (PRO) tools, including real-time symptom monitoring, unsupervised symptom clustering, and AI-enhanced triage pathways; and (2) tumor-aware supportive care, including AI-driven radiomics for sarcopenia detection and multimodal prognostic models that inform supportive care needs. Systematic electronic PRO monitoring has been associated with improved survival and reduced acute care utilization, while AI-automated CT sarcopenia detection identifies muscle wasting that routine clinical documentation often misses. Important implementation challenges remain, including the black-box problem, algorithmic bias, privacy concerns, the digital divide, and regulatory uncertainty. Precision supportive oncology, integrating PRO-based symptom intelligence with imaging-derived risk stratification, has the potential to improve GI cancer care by making it more anticipatory and patient-centered. This review proposes a two-lane framework consisting of PRO-driven symptom intelligence and tumor-aware supportive care, unified by principles of privacy, explainability, and equity. Responsible adoption will require prospective validation, equitable design, and clinically interpretable systems.","url":"https://doi.org/10.1007/s11912-026-01810-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11912-026-01810-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1371/journal.pdig.0001621","name":"Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.","source":"europepmc","abstract":"Artificial intelligence (AI) predictive models demonstrate potential for transforming clinical decision-making across medicine. However, conventional randomized controlled trials (RCTs), the gold standard for evaluating medical interventions, are ill-suited for clinical AI tools due to their static design, lengthy timelines, and inability to accommodate algorithms that evolve and adapt to changing clinical contexts. In this narrative review, we outline the limitations of traditional evaluation frameworks and propose a paradigm shift toward adaptive, iterative, and context-specific assessment methodologies. Evaluating clinical AI in practice requires three interdependent but epistemologically distinct activities: performance monitoring, which tracks the technical characteristics of the deployed model (calibration, discrimination, data drift, alert burden, fairness, workflow fidelity); clinical impact monitoring, which observationally and prospectively tracks whether the initial clinical benefit appears sustained over time; and scientific evidence generation, which produces causal estimates of deployment effects on patient outcomes through pragmatic, adaptive trial designs and causal inference techniques. We propose a predictive-AI-specific framework that links performance monitoring, clinical impact monitoring, evidence generation, causal estimands, and governance of model updates into one coherent decision pathway for clinicians and trialists. We present a governance-driven escalation protocol specifying when monitoring signals should trigger formal evidence generation, a decision pathway mapping signal types (performance or clinical impact) to trial design classifications, and a guide to causal inference methods for clinical AI trials. Drawing from adaptive platform and pragmatic trial designs, we recommend continuous monitoring approaches that prioritize patient-centered outcomes, health equity, and workflow integration over narrow performance metrics, and provide actionable steps to design a clinical AI trial. Successful implementation requires clinician engagement, transparency, and ongoing education regarding AI capabilities and limitations. Within this new evaluation paradigm, predictive AI can progress from a promising technology to reliable clinical tools that improve patient outcomes, support clinical decision-making, and uphold ethical standards in routine practice.","url":"https://doi.org/10.1371/journal.pdig.0001621","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001621","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/1756-185x.70816","name":"Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.","source":"europepmc","abstract":"Background The integration of artificial intelligence (AI) into healthcare has shown significant promise in addressing complex diagnostic and therapeutic challenges in rheumatology. This review examines the current state of AI applications across rheumatological practice. Objective To systematically evaluate AI applications in rheumatology, assess their clinical performance and identify future research directions. Methods We conducted a comprehensive literature review of AI applications in rheumatology, focusing on diagnostic imaging, clinical decision support and disease monitoring across major rheumatic conditions. Results AI demonstrates promising performance across multiple domains, with diagnostic accuracies frequently exceeding 80%-90% for imaging interpretation and disease classification. Applications span from automated radiographic scoring to real-time disease monitoring. Conclusions While AI shows significant potential in rheumatology, successful clinical implementation requires addressing challenges related to data quality, algorithm transparency and clinical integration.","url":"https://doi.org/10.1111/1756-185x.70816","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/1756-185x.70816","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00210-026-05700-3","name":"Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.","source":"europepmc","abstract":"Artificial intelligence (AI) has rapidly advanced in breast cancer imaging, demonstrating high diagnostic and predictive performance across imaging modalities. However, the clinical reliability and translational readiness of these models remain uncertain due to methodological variability and limited external validation. The objective of this study is to systematically evaluate the diagnostic performance, predictive capabilities, and clinical readiness of AI-based models in breast cancer imaging, with emphasis on methodological quality, validation strategies, and translational applicability. A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Scopus, and Web of Science were searched for studies published between January 2015 and March 2026. Original studies applying AI techniques to mammography, MRI, ultrasound, and digital breast tomosynthesis, reporting quantitative performance metrics, were included. Methodological quality was assessed using the QUADAS-2 tool. Due to substantial heterogeneity in imaging modalities, model architectures, dataset composition, and outcome reporting, a qualitative synthesis was performed. Sixty-two studies were included. Across lesion detection and benign-versus-malignant classification tasks, deep learning models achieved AUCs of 0.80-0.95 across mammography, MRI, and ultrasound, whereas MRI-based treatment-response prediction models achieved AUCs up to 0.97. However, fewer than 25% of studies performed external validation, and approximately 85% relied on retrospective, single-center datasets. Internally validated models consistently reported higher performance, indicating systematic performance inflation of approximately 5-15%. Moderate to high risk of bias was observed, particularly in patient selection and applicability domains. AI models in breast cancer imaging show strong technical performance but limited clinical reliability. Addressing validation gaps, methodological heterogeneity, and lack of prospective evidence is essential for clinical translation.","url":"https://doi.org/10.1007/s00210-026-05700-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00210-026-05700-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.7759/cureus.112828","name":"Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.","source":"europepmc","abstract":"Echocardiography is one of the most commonly used diagnostic methods in cardiovascular diseases because it is non-invasive, widely available, and capable of providing real-time assessment of function and cardiac structure. Despite these advantages, conventional echocardiography is often limited by operator dependence, interobserver inconstancy, the time-intensive nature of manual acquisition and measurement. Recent advances in artificial intelligence (AI), particularly deep learning, have created new opportunities to automate multiple steps of the echocardiographic workflow, starting from image acquisition and view classification to chamber segmentation, functional quantification, and hemodynamic estimation. This review provides an overview of the current clinical applications of artificial intelligence (AI) throughout the echocardiographic workflow. It focuses on key areas where AI has been applied, including automated view recognition, image quality assessment, cardiac phase identification, chamber segmentation, left ventricular ejection fraction estimation, strain analysis, and prediction of hemodynamic and disease-related parameters. Major challenges limiting wider clinical implementation were also highlighted in this review, such as insufficient external validation, dependence on image quality, differences between ultrasound vendors and patient populations, limited model interpretability, and lack of clear evidence demonstrating improved patient outcomes. Several AI-based tools, particularly those for automated view classification and chamber quantification, are becoming increasingly integrated into routine clinical practice, but many more advanced applications are possible, for which research is ongoing. The successful adoption of AI in echocardiography will depend not only on continued improvements in algorithm performance, but also on rigorous clinical validation, smooth integration into existing workflows, transparent reporting of model development and evaluation, and evidence that these technologies provide meaningful benefits for patient care.","url":"https://doi.org/10.7759/cureus.112828","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.112828","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2147/amep.s613617","name":"Integrating Artificial Intelligence into Medical Education in LMICs: A Narrative Review.","source":"europepmc","abstract":"Artificial intelligence (AI) is reshaping clinical practice, yet formal AI education in medical curricula has lagged significantly behind-a gap particularly acute in low- and middle-income countries (LMICs). This narrative review examines AI integration in medical education across LMICs, with primary contextual focus on sub-Saharan Africa and African health systems within this broader framing. Available evidence suggests that a substantial proportion of medical students globally may lack formal AI education despite growing clinical AI adoption among physicians, with LMICs and African contexts disproportionately underrepresented in AI-in-medical-education literature. African contexts face compounding implementation challenges-infrastructure deficits, data scarcity, algorithmic bias in externally designed tools, and regulatory gaps-yet possess distinctive contextual opportunities. Applying a structured critical counterargument analysis, the review interrogates both the rationale for integration and the strongest arguments for delay. The review's contribution lies in its LMICs-and-Africa-centred framing, its integration of three complementary theoretical frameworks, and its policy-oriented, phased implementation synthesis-dimensions not addressed in aggregate by existing reviews. AI integration in medical education in LMICs is a context-sensitive priority. The risks of unplanned inaction-widening competency gaps and forfeiture of iterative evaluation data-should be weighed against the risks of implementation, with careful, locally adapted, phased approaches offering the most defensible pathway forward.","url":"https://doi.org/10.2147/amep.s613617","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/amep.s613617","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.jaim.2026.101372","name":"Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice.","source":"europepmc","abstract":"Prakriti, the Ayurvedic concept of individual somatic constitution, forms the foundation for personalized preventive and therapeutic strategies by classifying individuals based on distinctive physical, physiological, and psychological attributes. Traditional methods of Prakriti assessment, such as physician-administered questionnaires, clinical examinations, and observational techniques, though deeply rooted in Ayurvedic principles, are often constrained by subjectivity, limited reproducibility, and a lack of standardization. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and digital health technologies offer new opportunities to modernize Prakriti assessment and enhance its reliability. This narrative review systematically located, selected, and synthesized relevant literature from scientific databases and institutional repositories to identify digital, AI, and ML tools applicable to the parameters outlined in the Central Council for Research in Ayurvedic Sciences (CCRAS) Prakriti Assessment Manual. Retrieved data were categorized according to their relevance to physical, physiological, psychological, and behavioral domains, emphasizing technologies that enable objective measurement and computational analysis. The review highlights the potential of integrating physiological sensors, image analysis systems, and computational algorithms with classical Ayurvedic approaches to improve the accuracy and clinical relevance of Prakriti analysis. The convergence of Ayurveda and digital health technologies holds transformative potential to generate evidence, enhance clinical applicability, and foster interdisciplinary research in personalized medicine. However, challenges related to data quality, interoperability, algorithmic interpretability, and domain-specific validation must be addressed to ensure credibility and wider adoption. Overall, digital innovations can significantly enhance the rigor, reach, and impact of Prakriti assessment in integrative healthcare.","url":"https://doi.org/10.1016/j.jaim.2026.101372","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jaim.2026.101372","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s10396-026-01664-2","name":"Artificial intelligence in ultrasound imaging for carpal tunnel syndrome: what clinicians need to know.","source":"europepmc","abstract":"This review examines artificial intelligence (AI) applications in ultrasound imaging for carpal tunnel syndrome diagnosis. Deep learning models have achieved Dice coefficients exceeding 0.85 for median nerve segmentation and diagnostic accuracy with area under the curve values up to 0.926, often matching specialist performance. However, methodological limitations exist: only three of 13 included studies (23%) performed external validation, and most used single-center retrospective designs. No prospective clinical trials have demonstrated improved patient outcomes. We critically appraise study quality, discuss current limitations, and provide practical recommendations for clinicians considering AI integration into their practice.","url":"https://doi.org/10.1007/s10396-026-01664-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10396-026-01664-2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s11357-026-02478-3","name":"CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives.","source":"europepmc","abstract":"Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentially modifiable determinant of early neurological deterioration and poor functional outcome. Computed tomography (CT) remains the first-line imaging modality for acute ICH and provides essential information for early HE risk stratification. However, current evidence is dispersed across conventional CT signs, composite scores, radiomics, machine learning, and deep learning approaches, many of which have been reported descriptively without sufficient comparison of clinical utility, validation quality, or translational readiness. This review critically evaluates CT-based prediction of HE and adverse outcomes after spontaneous ICH. We compare contrast-enhanced markers, including the spot sign, leakage sign, and iodine sign, with non-contrast CT markers such as the blend sign, black hole sign, island sign, satellite sign, hypodensity sign, swirl sign, hematoma shape, and density heterogeneity, focusing on sensitivity, specificity, reproducibility, availability, and clinical applicability. We further assess composite prediction models and artificial intelligence approaches, emphasizing limitations related to small cohorts, overfitting, dataset heterogeneity, insufficient external validation, interpretability, and workflow integration. Given the scope of GeroScience, we also discuss how vascular aging, cerebral amyloid angiopathy, frailty, anticoagulant exposure, and age-associated vulnerability to secondary injury may influence HE risk and outcome prediction. Future progress will require interpretable, multimodal, prospectively validated models that integrate CT imaging, clinical variables, biomarkers, and aging-related factors to support individualized management of ICH.","url":"https://doi.org/10.1007/s11357-026-02478-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11357-026-02478-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fmed.2026.1879708","name":"Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.","source":"europepmc","abstract":"Dental caries remains the most prevalent chronic disease worldwide, affecting more than two billion people and driving substantial healthcare costs. While early detection is central to minimally invasive dentistry, traditional diagnostic methods suffer from limited sensitivity and high inter-examiner variability, especially for incipient lesions. Artificial Intelligence (AI) has shown promise in overcoming these limitations, and many studies have reported expert-level performance under controlled conditions. However, a substantial translational gap persists between algorithmic success in silico and reliable performance in real-world clinical environments. This review synthesizes the full development pipeline of AI for caries diagnosis-from data curation and ground-truth construction to model design, validation, and clinical deployment. We highlight persistent bottlenecks including domain shift across imaging devices and clinical settings, subjective and inconsistent annotation practices, limited multimodal datasets, and heterogeneous reporting standards. Emerging strategies such as multi-center data collection, probabilistic labeling, self-supervised learning, domain adaptation, and test-time augmentation offer partial solutions but remain underutilized. We argue for a paradigm shift from binary detection toward quantitative, risk-based staging that aligns with minimally invasive dentistry and the WHO Global Oral Health Action Plan 2023-2030. By advocating for standardized multimodal datasets, rigorous external validation, explainable interfaces, and human-centered clinical integration, this review outlines a roadmap for translating AI innovation into trustworthy, equitable, and clinically meaningful decision-support systems capable of reducing the global burden of untreated caries.","url":"https://doi.org/10.3389/fmed.2026.1879708","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1879708","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ejmp.2026.105915","name":"Generative artificial intelligence and scientific integrity in medical physics: opportunities, risks, and responsibilities in AI-assisted scientific writing and peer review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.ejmp.2026.105915","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ejmp.2026.105915","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2174/0115733963476613260729200611","name":"Comparative Performance of Artificial Intelligence Models in Predicting Low Birth Weight: Systematic Review and Narrative Synthesis.","source":"europepmc","abstract":"Background Low Birth Weight (LBW), defined as Methods Following PRISMA guidelines, Scopus, Web of Science, and PubMed were searched for studies utilizing AI/ML for LBW. Methodological quality was assessed via the PROBAST tool. Due to extreme statistical heterogeneity and inconsistent reporting of metrics, a narrative synthesis approach was employed. Results Forty studies met inclusion criteria. Quality assessment revealed that 82.5% were at high or unclear risk of bias, primarily due to poor reporting of calibration and data handling. Formal metaanalysis was precluded by extreme statistical heterogeneity (I2>98%) and the role of LBW as a surrogate marker for diverse phenotypes. Narrative synthesis indicated that ensemble architectures (Random Forest, XGBoost) consistently outperformed traditional linear models. Discrimination was significantly higher in high-income settings (AUC: 0.85-0.95) than in resource-limited settings (AUC: 0.65-0.75). Models integrating dynamic data, such as longitudinal ultrasound, demonstrated superior sensitivity over those relying on static clinical history. Performance trends across settings were highly variable, and given the heterogeneity, these findings must be considered strictly exploratory. Conclusion AI/ML models show promise for LBW prediction, but their efficacy is strictly contextdependent. Widespread methodological bias and lack of calibration metrics currently limit \"bedside readiness.\" Clinical translation requires a transition toward population-specific, interpretable tools validated through rigorous external cohorts.","url":"https://doi.org/10.2174/0115733963476613260729200611","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2174/0115733963476613260729200611","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/edt.70122","name":"Artificial Intelligence Chatbots and Large Language Models for Traumatic Dental Injury Management and Decision Support: A Systematic Review With Function-Based Narrative Synthesis and Exploratory Meta-Analysis.","source":"europepmc","abstract":"Traumatic dental injuries require rapid, accurate, and context-sensitive decisions, particularly during emergencies such as tooth avulsion, luxation injuries, and trauma involving the primary dentition. Artificial intelligence (AI) chatbots and large language models (LLMs) are increasingly used by patients, caregivers, students, and clinicians for immediate information and decision support, yet their reliability in dental trauma remains uncertain. This systematic review aimed to evaluate the accuracy, consistency, safety, guideline concordance, and clinical applicability of AI chatbots and LLMs for traumatic dental injury management and decision support. PubMed, Scopus, and Embase were searched through April 2026. Eligible studies evaluated AI-generated responses against clinical guidelines, expert assessment, validated dental trauma scenarios, photographs, real cases, standardized questions, or other reproducible reference standards. Twenty-eight studies met the eligibility criteria. A function-based narrative synthesis classified the evidence into five translational functions: patient- and caregiver-facing emergency guidance, guideline-based diagnostic and management benchmarking, domain-specific or document-grounded workflows, multimodal and real-case validation, and avulsion-specific prognostic or advanced decision support. An exploratory meta-analysis of nine study-level estimates with comparable binary outcomes demonstrated a pooled proportion of accurate or guideline-concordant responses of 81.7%, although heterogeneity was very high. Across studies, performance varied according to model, prompt format, reference standard, input type, and assessment methodology. Safety concerns included incomplete recommendations, misleading information, inconsistent responses, limited readability, unreliable references, and injury-specific performance variability. Overall, current evidence indicates that AI chatbots and LLMs can support dental trauma information retrieval and supervised clinical decision support, particularly when constrained by curated guidelines or domain-specific workflows. However, the available evidence remains limited by substantial methodological heterogeneity and limited real-world validation and therefore does not support the autonomous use of these systems for emergency traumatic dental injury management. Trial Registration: PROSPERO; CRD420261394046.","url":"https://doi.org/10.1111/edt.70122","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/edt.70122","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1093/ehjdh/ztag075","name":"Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.","source":"europepmc","abstract":"Early identification of acute myocardial infarction (AMI) remains challenging, particularly in non-ST-segment elevation presentations and occluded myocardial infarction, where conventional electrocardiogram (ECG) interpretation has limited sensitivity. Artificial intelligence-enabled ECG (AI-ECG) has emerged as a promising strategy to enhance early triage and diagnostic accuracy. To systematically evaluate the diagnostic performance of AI-enabled ECG algorithms for the detection of AMI, including ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), across diverse clinical settings. This diagnostic systematic review and meta-analysis was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420261292271). PubMed, Embase, and Cochrane CENTRAL were searched through January 2026. Studies evaluating AI-based ECG models for AMI detection and reporting sufficient data to reconstruct 2 × 2 contingency tables were included. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated using random-effects models (restricted maximum likelihood). Summary receiver operating characteristic (SROC) curves and area under the curve (AUC) were generated. Pre-specified subgroup analyses were performed for STEMI and NSTEMI Ten observational studies comprising 94 510 participants were included. For overall AMI detection,. AI-ECG demonstrated a pooled sensitivity of 89.4% (95% CI, 79.7-94.8) and specificity of 96% (95% CI, 91.2-98.2). The pooled NPV was 98.7% (95% CI, 94.1-99.7), and the pooled PPV was 73.3% (95% CI, 50.2-88.2). The SROC AUC was 0.97 (95% CI, 0.92-0.98). In STEMI, pooled sensitivity and specificity were 94.4% and 97.5%, respectively (AUC 0.98). In NSTEMI, pooled sensitivity was lower at 65.0%, with specificity of 87.5% and an AUC of 0.71. Heterogeneity was substantial, particularly among NSTEMI cohorts. AI-enabled ECG demonstrates high sensitivity and consistently excellent negative predictive value for AMI detection, supporting its role as a scalable, non-invasive triage adjunct at first medical contact. These findings highlight the potential of AI-ECG to facilitate early rule-out strategies and improve prioritization of patients requiring urgent ischaemic evaluation. Beyond diagnostic accuracy, AI-ECG may support probabilistic risk stratification and integration into early clinical decision-making pathways.","url":"https://doi.org/10.1093/ehjdh/ztag075","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/ehjdh/ztag075","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/den.70268","name":"The Impact of Artificial Intelligence-Assisted Endoscopy on the Detection of Upper Gastrointestinal Neoplasms: A Systematic Review and Meta-Analyses.","source":"europepmc","abstract":"Objectives Early detection of upper gastrointestinal (UGI) neoplasms is challenging because of their subtle endoscopic features. Artificial intelligence (AI) has improved endoscopic imaging and lesion recognition. This meta-analysis aimed to assess the effectiveness of AI-assisted real-time esophagogastroduodenoscopy (EGD) for detecting UGI neoplasms based on evidence from randomized controlled trials (RCTs). Methods PubMed, EMBASE, and Cochrane Library were searched for RCTs comparing AI-assisted EGD with conventional EGD up to April 11, 2026. Risk ratios (RRs) were calculated for the neoplasm detection rate (DR), and incidence rate ratios (IRRs) were pooled for lesions per endoscopy (LPE). Subgroup analyses were conducted according to AI system and pathological types. Random-effects models with Hartung-Knapp adjustment were applied. Results Ten RCTs involving 87,721 participants were included. Compared with conventional EGD, AI-assisted EGD significantly improved neoplasm DRs in the esophagus (RR = 1.47, 95% confidence interval [CI] 1.13-1.90, p = 0.012) and stomach (RR = 1.47, 95% CI 1.02-2.13, p = 0.043). In subgroup analyses, computer-assisted detection (CADe) was associated with increased LPE of esophageal neoplasms (IRR = 1.62, 95% CI 1.08-2.45, p = 0.033). AI-assisted EGD also increased the LPEs of gastric cancer (IRR = 1.45, 95% CI 1.09-1.92, p = 0.022) and low-grade intraepithelial neoplasia (IRR = 1.39, 95% CI 1.13-1.70, p = 0.012). Conclusions AI-assisted real-time EGD was associated with improved detection of UGI neoplasms and may support endoscopists in routine clinical practice. Further studies are required to evaluate the effectiveness of different AI systems across pathological subtypes. Trial registration PROSPERO; CRD420251158943.","url":"https://doi.org/10.1111/den.70268","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/den.70268","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106645","name":"AI-generated patient instructions as safety-critical communication: a provisional evidence-informed framework for clinical deployment.","source":"europepmc","abstract":"Generative artificial intelligence (AI) can convert clinical information into patient-facing instructions, including discharge summaries, medication explanations, portal messages and plain-language educational materials. These outputs may improve accessibility and reduce documentation burden. They also create a distinct patient-safety problem: patients may act on fluent, incomplete, or contextually unsafe instructions without the clinical knowledge required to detect error. Early evaluations show a recurring trade-off. Large language models can improve readability and understandability of discharge information, yet physician and pharmacist review has identified omissions, inaccuracies, newly introduced actions, medication-related problems and potentially harmful safety issues, particularly in complex discharge contexts. This narrative and interpretative review reframes AI-generated patient instructions as a safety-critical informatics intervention rather than a language-simplification tool. A targeted literature mapping was undertaken across empirical studies of AI-generated discharge communication, health-literacy and medication-safety literature, patient-safety evidence, and emerging artificial-intelligence governance frameworks. I propose a sevendomain safety framework covering factual accuracy, clinical completeness, actionability, medication clarity, escalation and safety-netting, health-literacy alignment, and accountability with auditability. The framework is intended to support implementation, local evaluation and reviewer assessment rather than to function as a formal consensus guideline. High-risk patient-facing outputs require accountable clinical workflows, with human verification, traceability, equity testing and post-deployment monitoring. Before wider use, evaluation needs to assess not only readability, but also comprehension, actionability and potential harm.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106645","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106645","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/sap.0000000000004848","name":"Seeing Beyond the Surgeon's Eye: Artificial Intelligence for Postoperative Monitoring of Free Vascularized Flaps: A Narrative Review.","source":"europepmc","abstract":"Background Postoperative monitoring of free vascularized flaps (FVFs) is critical for early detection of ischemia and timely salvage. Although traditional monitoring relies heavily on clinical examination and adjunctive devices, these approaches are labor‑intensive, subjective, and resource‑dependent. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled automated, data‑driven approaches to postoperative free‑flap monitoring. However, the clinical readiness, methodological robustness, and translational challenges of these AI‑based systems remain incompletely understood. Objective This review examines recent studies on AI‑assisted postoperative free‑flap monitoring, focusing on technical methodologies, clinical applications, translational relevance, current limitations, and future research directions. Methods A systematic-narrative hybrid review was conducted by searching PubMed, Embase, Web of Science, and Google Scholar from January 2000 to February 2, 2026. Eligible studies reported clinically relevant outcomes, including flap viability, ischemia detection, venous congestion, thrombosis prevention, and salvage rates. Studies focused on preoperative prediction models, non‑AI monitoring techniques, editorials, and opinion articles were excluded. Two independent reviewers performed study selection and data extraction, with disagreements resolved by consensus. Results Nine studies met the inclusion criteria. Most studies used supervised machine‑learning models, primarily using visible‑light images, infrared imaging, photoplethysmography, hyperspectral imaging, or multimodal sensor data for flap surveillance. All but 1 study were retrospective, and no prospective human clinical trials were identified. AI‑based systems demonstrated promising performance in detecting arterial ischemia and venous congestion, often achieving diagnostic accuracies comparable to or exceeding those of traditional monitoring methods. Diagnostic accuracy across studies ranged from 82% to 98.4%. Key challenges included limited data sets, lack of external validation, heterogeneous methodologies, absence of standardized outcome metrics, and concerns about generalizability across diverse skin tones and clinical settings. Conclusions AI‑assisted postoperative free‑flap monitoring is a rapidly evolving and promising adjunct to conventional surveillance. Although current evidence suggests benefits for early ischemia detection and workload reduction, significant barriers-particularly methodological heterogeneity, data scarcity, and limited prospective validation-must be addressed before widespread clinical implementation. Future progress will depend on standardized data sets, transparent model development, multicenter prospective studies, and ethically sound, interoperable AI frameworks that enable equitable and scalable precision microsurgical care.","url":"https://doi.org/10.1097/sap.0000000000004848","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/sap.0000000000004848","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00330-026-12825-9","name":"Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.","source":"europepmc","abstract":"Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSON 2.0/EUPS protocol) for cases requiring 3-month follow-up. To assess true algorithmic robustness, all AI-detected baseline candidate nodules (≥ 100 mm³) proceeded to fully automated longitudinal matching without any manual selection. The pulmonary AI identified 181 participants with 378 baseline nodules ≥ 100 mm³. In total, 39 nodules had naturally resolved at follow-up. The pulmonary AI achieved an 83.5% (283/339; 95% CI: 79.2-87.1%) matching success rate for 339 persisting nodules. Matching performance was 91.8% (89/97) for participants with a single baseline candidate nodule (59.7% of the cohort) and 72.8% (75/103) for participants with more than five nodules (6.6%). Expert review of the 56/339 (16.5%) unmatched findings showed that almost all were non-nodular structures (91.1%, 51/56), predominantly pleural plaques (46.4%, 26/56). Consequently, only five unmatched discrete solid nodules (1.5%; 95% CI: 0.6-3.5% of 339 persisting findings) required manual intervention. In conclusion, the pulmonary AI demonstrates robust longitudinal matching performance, with substantial potential for follow-up manual tracking workload reduction. KEY POINTS: Question Does standalone AI longitudinal nodule tracking provide sufficient nodule-level technical reliability to avoid manual review bottlenecks in an automated lung cancer screening workflow? Findings Pulmonary AI matched 83.5% of persisting nodules (91.8% for single nodules, 72.8% for > 5 nodules); 91.1% failures were non-nodular, with only 1.5% requiring manual correction. Clinical relevance Pulmonary AI matching can potentially reduce the manual tracking workload in lung cancer screening, with only 1.5% of persisting nodules requiring manual correction. Performance is reduced in scans with high nodule burden, and prospective validation in diverse populations is needed.","url":"https://doi.org/10.1007/s00330-026-12825-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00330-026-12825-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1080/17434440.2026.2723944","name":"Overcoming the opaque side of AI in healthcare: a lifecycle-based approach.","source":"europepmc","abstract":"Introduction Transparency has emerged as a foundational condition for trustworthy Artificial Intelligence (AI) in healthcare. Despite its centrality, practical approaches to systematically operationalize transparency across the entire lifecycle of AI-enabled medical devices remain fragmented and insufficiently structured. This work addresses this gap by proposing a lifecycle-oriented operational approach to guide the consistent implementation and evaluation of transparency in AI-based medical technologies. Areas covered A narrative synthesis of regulatory texts, international standards, and scientific literature related to software as a medical device (SaMD), the EU Medical Device Regulation (MDR), the EU Artificial Intelligence Act (AI Act), data-protection rules, and relevant ISO/IEC guidance. Using a SaMD lifecycle framework, we mapped transparency requirements to practical development, validation, and governance activities across ideation, data and design inputs, risk management, implementation/verification, technical and clinical validation, market placement, maintenance, and disposal. Expert opinion Transparency must be engineered as a lifecycle property, not an afterthought. We propose nine operational measures - covering documented design assumptions and datasets, transparency-oriented risk and change control, traceability, subgroup and independent validation, usability-based explainability, calibrated clinical evaluation, structured labeling, version-controlled updates, and regulated end-of-life data handling - to support regulatory readiness, calibrated clinical trust, and safe real-world integration of AI in healthcare.","url":"https://doi.org/10.1080/17434440.2026.2723944","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/17434440.2026.2723944","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.12701/jyms.2026.43.54","name":"Artificial intelligence anxiety: a narrative review of psychiatric conceptualization and clinical management.","source":"europepmc","abstract":"Artificial intelligence (AI) is gradually permeating every aspect of daily life. AI is now more than a mere tool; it significantly influences individual capabilities, professional values, social status, and our perspectives on the future. Amid these AI-driven changes, the concept of AI anxiety (AIA) has emerged to describe the associated emotional distress; however, its psychiatric significance has not yet been clearly defined. Presently, AIA can be conceptualized not as a distinct psychiatric diagnosis, but as a stress response capable of amplifying pre-existing vulnerabilities, psychiatric symptoms, maladaptive coping mechanisms, and functional impairments. This review categorizes AIA into five interrelated domains: anxiety regarding competence and adaptation, anxiety concerning occupational displacement and role loss, anxiety related to sociotechnical mistrust and loss of control, anxiety regarding identity and the human-machine boundary, and existential or catastrophic anxiety regarding the future. The clinical assessment of patients should evaluate specific AI-related experiences, perceived threats, behavioral responses, functional consequences, comorbid symptoms, and reality-testing abilities. Cases accompanied by excessive reliance on AI, social isolation, or the substitution of human support with AI interactions warrant increased attention. Therapeutic approaches include psychoeducation, cognitive and behavioral strategies, acceptance-based approaches, meaning-centered psychotherapy, AI literacy education, and efforts to restore human connections. Future studies should clarify the definition and scope of AIA and elucidate its course and treatment responsiveness through longitudinal and clinical studies using reliable assessment tools.","url":"https://doi.org/10.12701/jyms.2026.43.54","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12701/jyms.2026.43.54","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.clnesp.2026.105019","name":"Comment on \"artificial intelligence in clinical nutrition: A narrative review\".","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.clnesp.2026.105019","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.clnesp.2026.105019","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1093/sleep/zsag149","name":"Artificial intelligence for sleep instability and motor phenotyping: clinical translation beyond sleep staging.","source":"europepmc","abstract":"Sleep medicine has rapidly adopted artificial intelligence (AI), but most applications still prioritize automated sleep staging or single summary indices, limiting clinical translation when symptoms arise from within-stage dynamics. This review proposes a physiology-grounded framework in which AI targets sleep microstructure and nocturnal motor activity as temporally structured expressions of sleep-wake control. We discuss how transient arousals and cyclic alternating pattern activity can be modeled as time-resolved instability trajectories rather than reduced to hourly counts, and why grounding models in established constructs improves interpretability and trust. We then examine motor events across the continuum from leg movements to periodic limb movements and large muscle group movements, emphasizing that periodicity, clustering, state dependence, and coupling to cortical and autonomic activation convey more clinical information than event counts alone. Because autonomic surges are measurable outside the laboratory, we highlight multimodal approaches integrating electroencephalography, electromyography, actigraphy, cardiopulmonary signals, and wearable photoplethysmography to infer instability and movement-autonomic coupling in ambulatory settings. Finally, we translate these outputs into clinician-readable phenotypes that may refine diagnosis, prognosis, and treatment stratification, and we define priorities for the field: harmonized labeling standards, multi-center external validation, calibration across age and comorbidity, explainable AI approaches, and deployment as decision-support tools that complement expert judgment.","url":"https://doi.org/10.1093/sleep/zsag149","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/sleep/zsag149","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/froh.2026.1895438","name":"Application, performance and limitations of artificial intelligence for the diagnosis and prediction of oro-facial pain: a systematic review.","source":"europepmc","abstract":"Background Oro-Facial Pain (OFP) disorders are a group of diseases characterized by overlapping clinical features leading to diagnostic and therapeutic challenges. Recent advances in artificial intelligence (AI) such as machine learning (ML) and deep learning (DL) have shown potential to improve the accuracy of diagnosis, prediction of pain and decision making in the clinic. The objective of this systematic review is to assess the use, diagnostic accuracy, and clinical utility of AI models in the diagnosis and prediction of OFP conditions. Methods This systematic review was performed following the PRISMA-DTA guidelines and registered at PROSPERO (CRD420261322606). A comprehensive electronic search was conducted for studies published from January 1, 2000 to February 1, 2026 in PubMed, Scopus, Embase, Cochrane Library, Web of Science and Google Scholar. Eligible studies examined AI-based methods for the diagnosis, classification, localization or prediction of OFP conditions. Independent reviewers performed study selection, data extraction, and quality assessment. Methodological quality was assessed using the QUADAS-2 tool and the certainty of the evidence was assessed using GRADE approach. Qualitative synthesis was performed due to substantial heterogeneity in study design, datasets, AI architectures and outcome measures. Results Twenty studies were included. These studies assessed AI applications for general diagnosis of orofacial pain, temporomandibular disorders, trigeminal neuralgia and facial pain syndromes, prediction of postoperative odontogenic pain, and localization of dental pain. The AI models were trained on heterogeneous data modalities including clinical records, questionnaires, thermography, radiographic imaging, MRI, neuroimaging and electronic health records. ML algorithms and artificial neural network (ANN) models demonstrated diagnostic accuracies ranging from 75% to 99%, demonstrating a potential for better performance on imaging-based and structured clinical datasets. Thermography-based ML models achieved the highest reported accuracy (99%) over other modalities, and MRI-based predictive models for temporomandibular disorders exhibited high discriminatory ability (AUC up to 0.899). Most studies were classified as low risk of bias in the patient selection and index test domains, but there were major concerns with regard to applicability and limited external validation in the reference standard domain. Overall certainty of evidence was rated as moderate. Conclusion AI-based systems have great potential as adjunctive tools for the diagnosis, classification and prediction of OFP conditions, especially when using structured clinical and imaging datasets. However, the current evidence is limited by methodological heterogeneity, retrospective study design, and lack of external validation. Future research should focus on prospective multi-center studies, standardized datasets, explainable AI frameworks, and the integration of multimodal AI approaches into clinically applicable decision support systems. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261322606, identifier CRD420261322606.","url":"https://doi.org/10.3389/froh.2026.1895438","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/froh.2026.1895438","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fpubh.2026.1902424","name":"Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review.","source":"europepmc","abstract":"Objective To map the evidence on artificial intelligence (AI)-generated diabetes-related patient education materials and patient-facing health information, with particular attention to AI models, prompting approaches, evaluation methods, and information-quality outcomes. Methods This scoping review was conducted in accordance with the JBI methodology for scoping reviews and reported following the PRISMA-ScR checklist. The review was registered on the Open Science Framework (doi: 10.17605/OSF.IO/U4FAE) PubMed, Web of Science, Embase, Scopus, Cochrane CENTRAL, CNKI, WanFang Data, and SinoMed were searched from inception to May 1, 2026. Chinese- and English-language literature was searched. Two reviewers independently screened studies, charted data, and mapped reported outcomes to Wang and Strong's information quality framework. Outcomes not adequately represented by the framework were retained as additional dimensions. Descriptive statistics and narrative synthesis were used. Results Of 6,049 records identified, 24 studies from 11 countries or regions were included. All studies evaluated ChatGPT or another GPT-family model; 21 used zero-shot or direct prompting, three used role prompting, and two implemented retrieval-augmented generation. Eleven indicators were mapped to the information quality framework, with ease of understanding ( n = 14), accuracy ( n = 13), and believability ( n = 9) assessed most frequently. Six additional outcomes were identified: clinical safety ( n = 5), actionability ( n = 3), response efficiency ( n = 1), personalization ( n = 1), transparency ( n = 1), and empathy ( n = 1). Most studies reported reading demands above those generally recommended for patient education, although findings varied by language, material type, and assessment method. Study-specific instruments were used in 17 studies (70.8%), whereas 10 (41.7%) used structured or established tools. Only six studies reported full source or model blinding, 10 reported quantitative inter-rater agreement, and three involved patients or members of the public. Conclusion Research on AI-generated diabetes education is expanding, but substantial heterogeneity in prompts, evaluators, tools, and outcome definitions limits comparison across studies. Future research should prioritize validated, multilingual, and patient-centered evaluation tools that integrate conventional information-quality attributes with clinically relevant dimensions such as safety, actionability, personalization, transparency, empathy, and response efficiency.","url":"https://doi.org/10.3389/fpubh.2026.1902424","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1902424","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/jcm15134988","name":"Artificial Intelligence in Vascular Surgery: A Literature Review Focusing on Current Applications, Imaging Advances and Future Prospects.","source":"europepmc","abstract":"Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into vascular surgery, particularly in diagnostic imaging, perioperative planning, intraoperative guidance, and postoperative surveillance. This literature review evaluates the current applications of artificial intelligence in vascular surgery and endovascular practice, with a particular focus on imaging technologies and their role in improving diagnostic precision, workflow efficiency, and patient outcomes. In addition, the review examines emerging AI applications in operative workflow optimization, endovascular navigation, postoperative surveillance, training platforms, and AI-assisted clinical decision support. Methods: A literature review was conducted using PubMed and Scopus with the search terms: (artificial intelligence OR AI OR neural network) AND (vascular surgery) AND (diagnosis OR treatment). Reference lists of included studies were manually screened, and additional recent studies were identified from relevant journals. Articles published in English up to April 2026 were included. Studies were assessed for their applications in vascular diagnostics, plaque characterization, endovascular workflow optimization, and postoperative surveillance. Results: AI demonstrated strong diagnostic performance across multiple imaging modalities. Deep learning systems achieved a sensitivity of 91.3% and specificity of 95.2% in peripheral arterial stenosis classification, while plaque characterization models showed accuracies up to 96% and substantial agreement with expert imaging interpretation. AI-assisted operative systems improved procedural efficiency through reductions in operative duration, radiation exposure, and contrast utilization. However, many studies were retrospective, single-center, and based on relatively small cohorts with heterogeneous endpoints. Conclusions: AI has significant potential to improve vascular surgical practice through enhanced image interpretation, procedural guidance, and individualized treatment planning. Despite promising outcomes, current evidence remains limited by methodological heterogeneity and insufficient external validation. Prospective multicenter studies and standardized evaluation frameworks are required before widespread clinical implementation can be achieved.","url":"https://doi.org/10.3390/jcm15134988","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15134988","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1080/14737159.2026.2706000","name":"The expanding role of artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging.","source":"europepmc","abstract":"Introduction The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical diagnosis. AI can assist medical professionals in medical diagnosis by its unique ability to analyze complex data from multiple sources, including medical images, gene sequences, and Electronic Health Records (EHRs). Areas covered Its application in other clinical processes, such as risk classification, diagnostic workflows, and disease risk prediction from patient symptoms, can also speed up diagnosis, reduce costs, and improve diagnostic outcomes. Expert opinion However, its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data. By addressing these limitations, AI can be very effective in increasing medical professionals' knowledge and, consequently, improving patient outcomes.","url":"https://doi.org/10.1080/14737159.2026.2706000","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/14737159.2026.2706000","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ultrasmedbio.2026.07.021","name":"Artificial Intelligence-Assisted Contrast-Enhanced Ultrasound for Perfusion Evaluation: State-of-the-Art Review.","source":"europepmc","abstract":"Accurate perfusion evaluation is crucial for clinical diagnosis and treatment, such as lesion characterization and risk stratification of carotid atherosclerotic plaques. Contrast-enhanced ultrasound (CEUS) examination, which enhances microvascular visualization with contrast agents, offers the advantages of being non-invasive, real time and free of ionizing radiation, making it widely applicable for perfusion evaluation. However, traditional CEUS analysis may have several limitations, in part due to operator dependence. A single CEUS examination generates massive dynamic cine-loop sequences, so manual frame-by-frame analysis is not only time consuming and labor intensive, but also prone to missing key information. It also struggles to obtain quantitative perfusion parameters and has the problem of inter-observer variability. Artificial intelligence (AI) can efficiently process high-dimensional CEUS data through automated data pre-processing, intelligent segmentation of regions of interest, standardized feature extraction and accurate decision support. AI improves the diagnostic efficiency and consistency of CEUS analysis, thus becoming an ideal tool to address these limitations. In this review, we summarize the mechanism, clinical value and limitations of CEUS examination for perfusion evaluation and introduce the core AI techniques for CEUS analysis and the technical workflow of AI-assisted CEUS. We then elaborate on the status of applying AI-assisted CEUS across multiple organ systems. Finally, we discuss the current challenges and future directions of this technology, aiming to promote its clinical application.","url":"https://doi.org/10.1016/j.ultrasmedbio.2026.07.021","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ultrasmedbio.2026.07.021","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s12911-026-03699-4","name":"A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.","source":"pubmed","abstract":"Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Although artificial intelligence (AI) models promise to improve predictions, questions remain about interpretability, the reliability of risk factors, and the need for cross-validation. This scoping review examined the extent, types, and reporting quality of studies that used AI-based prognostic models to predict CVD risk in individuals without established CVD.","url":"https://doi.org/10.1186/s12911-026-03699-4","authors":["Pai S","Subramanian R","Krishnan L","John D","Subramanya T","Kinra S","Kaur P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03699-4","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1177/10538127261464781","name":"The effectiveness of artificial intelligence-based interventions on treatment outcomes for low back pain: A systematic review and meta-analysis of randomized controlled trials.","source":"europepmc","abstract":"BackgroundLow back pain is a global health challenge that requires constant progress to aid its management. Currently, artificial intelligence, as a major technological shift, has entered medicine to guide and improve management, and it is slowly being used for low back pain. With the growing number of studies, there is a lack of a summary of them.ObjectiveThe objective of this study is to investigate the effectiveness of artificial intelligence-based interventions in improving pain and functional outcomes among adults with low back pain when compared to standard interventions.MethodsA comprehensive search of PubMed, Scopus, and Web of Science was conducted. Intervention groups received artificial intelligence-based interventions, while control groups received standard interventions for low back pain. After data extraction, assessment of risk of bias was done, and then a meta-analysis was performed.ResultsFive studies met the inclusion criteria and were included in the systematic review, and four of them in the meta-analysis. Artificial intelligence-based interventions demonstrated lower pain scores (p = 0.001) and lower disability scores (p = 0.02) at endpoints than controls. Review of the studies revealed either significant or no improvement in the quality of recovery and psychological factors between the groups.ConclusionBased on the existing evidence, artificial intelligence-guided interventions, when compared to standard interventions, may improve pain and disability in adults with low back pain. But further research is necessary to establish its clinical relevance and future use.","url":"https://doi.org/10.1177/10538127261464781","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/10538127261464781","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fimmu.2026.1915941","name":"The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.","source":"europepmc","abstract":"Natural killer (NK) cells are important effector cells of the innate immune system and have been investigated as a therapeutic platform for cancer immunotherapy. Although NK cell-based therapies have shown clinical activity in hematological malignancies, their application in solid tumors remains limited by restricted tumor infiltration, functional suppression, and the complexity of the tumor microenvironment (TME). Recent advances in spatial transcriptomics and artificial intelligence (AI) have provided new approaches for characterizing NK cell distribution, functional states, and cellular interactions within the TME. Critically, the spatial distribution and structural organization of NK cells within tumor niches - including their proximity to tumor cells, stromal barriers, and immune effector partners - are fundamental determinants of their cytotoxic function. A deeper understanding of how spatial context shapes therapeutic response is therefore central to advancing NK cell immunotherapy. This review summarizes how AI-assisted analysis of spatial and multi-omics data may contribute to the discovery and validation of biomarkers associated with NK cell therapy response. It also discusses the potential applications of AI in optimizing chimeric antigen receptor (CAR)-NK cell engineering, combination therapy strategies, and individualized dosing regimens. By synthesizing studies published in recent years, this review highlights the emerging shift from response prediction toward treatment optimization, while emphasizing the current limitations of available evidence, including model interpretability, data heterogeneity, causal inference, and clinical validation. Finally, we discuss how four-dimensional (4D) dynamic monitoring and explainable AI may support the future development of more precise and personalized NK cell immunotherapy strategies.","url":"https://doi.org/10.3389/fimmu.2026.1915941","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1915941","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1038/s41586-026-10687-1","name":"Safety and security of large language models in healthcare.","source":"europepmc","abstract":"Integration of artificial intelligence methods into clinical care is proceeding rapidly, driven by advances in generative artificial intelligence, most notably large language models. Large language models trained on large amounts of text have shown potential across nearly every domain of healthcare. However, their broad applicability also comes with new responsibilities, vulnerabilities and threats. These need to be assessed and mitigated before widespread clinical adoption. Here we review the available literature on security and safety of large language models themselves as well as their integration with hospital workflows and interactions with human healthcare providers. We systematically map security hazards to development stages of clinical artificial intelligence systems (design, data, model, inference and environment), identify safety layers, from core optimization objectives, knowledge integrity and alignment, to interaction with humans and systems, and classify threats by their current clinical relevance. Finally, we provide a perspective on current mitigation techniques, illustrating respective stakeholders' responsibilities.","url":"https://doi.org/10.1038/s41586-026-10687-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41586-026-10687-1","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s11604-026-02037-y","name":"Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging.","source":"europepmc","abstract":"Magnetic resonance neuroimaging is undergoing a major paradigm shift from traditional qualitative anatomical mapping toward integrated, quantitative measurement systems with biological interpretability. This review systematically synthesizes nine methodological pillars driving this transformation, encompassing advances ranging from hardware innovation to artificial intelligence algorithms. We first explore the pivotal role of deep learning in image reconstruction and acceleration, followed by detailed analyses of quantitative brain oxygen metabolism assessment, standardized spinal cord imaging frameworks, and the non-invasive monitoring of the glymphatic system using diffusion MRI. Furthermore, the review delves into tractometry, susceptibility-based myelin mapping, the clinical standardization of arterial spin labeling, and the application of radiomics in extracting high-dimensional phenotypes. Finally, the importance of open science and workflow coordination in enhancing research reproducibility is highlighted. Through the deep integration of hardware, sequences, and artificial intelligence, these technologies form a synergistic ecosystem that provides unprecedented precision tools and translational potential for both basic neuroscience research and clinical precision medicine. Across these domains, AI contributes not only to acceleration and reconstruction but also to segmentation, quality control, quantitative parameter extraction, and multiparametric pattern recognition that can support diagnostic interpretation. The quantitative emphasis of this review therefore lies in measurable outputs such as image-quality metrics, metabolic and perfusion parameters, tract-specific diffusion indices, susceptibility-based components, and radiomic features.","url":"https://doi.org/10.1007/s11604-026-02037-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11604-026-02037-y","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1159/000551437","name":"Artificial Intelligence in Interventional Pulmonology.","source":"pubmed","abstract":"Artificial intelligence (AI) has revolutionized interventional pulmonology (IP) by enhancing diagnostic accuracy, procedural efficiency, and training standardization. This review synthesizes current advancements and applications of AI across four key domains: education, imaging, navigation, and robotic bronchoscopy systems (RBS).","url":"https://doi.org/10.1159/000551437","authors":["You ZD","He W","Liu J","Tang J","Li H","Li Z","Bai Y","Li S","Zhong C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1159/000551437","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.7759/cureus.113226","name":"Artificial Intelligence-Enabled Electrocardiography for Potassium Abnormality Detection and Estimation: A Systematic Review.","source":"europepmc","abstract":"Laboratory measurement of serum, plasma, or whole-blood potassium remains the reference standard for diagnosing hyperkalemia and hypokalemia, yet results are not always immediately available when rapid clinical decisions are required. Artificial intelligence-enabled electrocardiography (AI-ECG) has been proposed as an early, non-invasive tool that may identify potassium abnormalities while laboratory confirmation is pending. This systematic review evaluated adult studies published between 2016 and 2026 in which AI or machine-learning (ML) techniques were applied to ECGs to detect hyperkalemia or hypokalemia, classify potassium status, or estimate continuous potassium concentration, always against a paired laboratory potassium reference standard. We searched multiple biomedical, preprint, trial, and citation databases through June 18, 2026, following PRISMA-DTA, PRISMA 2020, and PRISMA-S reporting principles. Methodological quality was assessed with QUADAS-2. Of 366 records assessed for eligibility, 33 peer-reviewed primary AI/ML studies were included. Hyperkalemia detection dominated the evidence base, with many externally or internally validated studies reporting area under the receiver operating characteristic curve (AUROC) in the high 0.8 to mid-0.9 range, whereas evidence for hypokalemia detection, categorical potassium-status classification, and continuous potassium estimation remained overlapping and more heterogeneous. Although reported performance was often encouraging, overall risk of bias was low in only four studies, unclear in 23, and high in six. Considerable heterogeneity in potassium thresholds, ECG modalities, validation strategies, reporting units, and performance metrics precluded meta-analysis. Overall, the available evidence suggests that AI-ECG is a promising investigational adjunct for triage, monitoring, and clinical decision support, primarily serving as a rule-out tool rather than a replacement for laboratory potassium testing. Its clinical utility may be particularly relevant in higher-prevalence populations, such as patients with chronic kidney disease or those receiving dialysis, where positive predictive value and monitoring yield may be more favorable. Prospective external validation, calibration, subgroup reporting, reproducibility, and workflow-impact studies are needed before routine clinical implementation.","url":"https://doi.org/10.7759/cureus.113226","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.113226","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/nu18162638","name":"Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly being applied in healthcare, with growing relevance to clinical nutrition. This narrative review examines current and emerging uses of AI in nutrition care within the Nutrition Care Process framework, with attention to assessment, monitoring and evaluation, diagnosis, intervention, and clinical support tools. Current applications include AI-assisted dietary assessment using image recognition, wearable sensors, analysis of continuous glucose and other physiologic data for early risk detection, and support for malnutrition screening and diagnosis. AI is also being explored for identifying micronutrient deficiencies and complications of nutrient excess, as well as for screening and early intervention in eating disorders. In nutrition intervention, AI has potential to support personalized dietary planning, nutrition support in intensive care settings, behavioral interventions, and precision nutrition approaches such as digital twins. Additional applications include clinical decision support and documentation assistance. However, despite its usefulness, concerns about AI systems exist. Its performance depends on the quality of the data used to train it; it can introduce bias, and it can produce inaccurate or misleading outputs. In addition, overreliance on AI may also reduce clinician attentiveness and contribute to cognitive errors. For these reasons, AI should be regarded as a support tool rather than a replacement for human clinical care. Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.","url":"https://doi.org/10.3390/nu18162638","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/nu18162638","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.clnesp.2026.103621","name":"Comment on \"Artificial intelligence in the management of hospital malnutrition: A systematic review\".","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.clnesp.2026.103621","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.clnesp.2026.103621","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fphys.2026.1909816","name":"Artificial intelligence for pathologic myopia classification based on the META-PM system: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has shown considerable potential for pathologic myopia (PM) detection, yet its overall diagnostic performance under the META-PM classification framework remains uncertain. Methods A comprehensive literature search was conducted across PubMed, Web of Science, IEEE Xplore, and Embase up to February 10, 2026. Studies applying deep learning models for PM classification using the META-PM system were included. Pooled sensitivity, specificity, and hierarchical summary receiver operating characteristic (HSROC) analyses were calculated. Twelve studies were eligible for the systematic review, of which ten were included in the meta-analysis. Results For referable PM detection, the pooled sensitivity and specificity were 0.96 (95% CI: 0.93-0.97) and 0.98 (95% CI: 0.96-0.99), respectively. For PM classification, the pooled macro-area under the receiver operating characteristic curve (AUC) reached 0.99 (95% CI: 0.98-1.00), indicating excellent overall diagnostic performance. Fagan nomogram analysis demonstrated favorable post-test probabilities across different clinical scenarios. The meta-regression identified external validation and image input resolution as significant sources of heterogeneity across studies. Conclusion Overall, AI demonstrates outstanding performance for automated PM detection and grading under the META-PM framework. Future studies should focus on multicenter external validation, prospective clinical evaluation, and integration with emerging ultra-widefield imaging technologies to facilitate real- world implementation in primary eye-care screening and risk stratification systems. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261351134.","url":"https://doi.org/10.3389/fphys.2026.1909816","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphys.2026.1909816","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1177/10806032261465600","name":"Artificial Intelligence in Wilderness Search and Rescue: A Narrative Review.","source":"europepmc","abstract":"Artificial intelligence (AI) is transforming wilderness Search and Rescue (SAR), where time constraints, austere conditions, and limited personnel have historically defined outcomes. This narrative review synthesizes the current evidence for AI applications in SAR, drawing on parallel developments in prehospital Emergency Medical Services (EMS) to illuminate the field's trajectory. This article is the result of searching PubMed, IEEE Xplore, Scopus, and Google Scholar from 2018 through March 2026 using combinations of \"artificial intelligence,\" \"machine learning,\" \"search and rescue,\" \"unmanned aerial vehicle,\" \"wilderness medicine,\" and \"prehospital care.\" Operational reports and trade publications were included when peer-reviewed sources were unavailable for deployed SAR technologies. Findings were organized under 3 temporal frameworks: operationally deployed, demonstrated capability approaching scale, and credible near-to-medium-term projection. AI-enabled unmanned aerial vehicles with thermal imaging and computer vision are operationally deployed in wilderness SAR, with field-validated rescues demonstrating detection through canopy, darkness, and adverse weather. Deep reinforcement learning algorithms for autonomous search-path optimization achieve more than 160% improvement over conventional coverage methods. However, the prehospital EMS literature provides an essential cautionary lesson: The Blomberg randomized, controlled trial showed that even technically superior AI may fail to improve outcomes without careful attention to human-AI interaction design and workflow integration. AI appears to be contributing to successful SAR outcomes and has been associated with several documented live rescues. Near-term priorities include prospective outcome validation, swarm-drone coordination, large language model-assisted wilderness medical protocols, and sustainable funding models for volunteer SAR organizations.","url":"https://doi.org/10.1177/10806032261465600","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/10806032261465600","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s11033-026-12344-2","name":"Rapid diagnostics innovations for urinary tract infections using molecular biology, artificial intelligence and antimicrobial resistance surveillance: a comprehensive review.","source":"europepmc","abstract":"Urinary tract infections (UTIs) are among the most prevalent bacterial infections worldwide, accounting for a major clinical impact due to high recurrence rates, microbial diversity, biofilm formation, polymicrobial contribution and the rapid emergence of antimicrobial resistance (AMR). Traditional culture-based diagnostic approaches are constrained by high turnaround times and insufficient resolution of virulence and resistance factors, frequently result in empirical antibiotic therapy. Recent breakthroughs in molecular biology and biotechnology have fuelled the advancement of innovative diagnostic approaches for the quick, sensitive and pathogen-specific detection of uro-pathogens. Rapid pathogen identification and antibiotic susceptibility testing are critically needed for the effective targeted antibiotic therapy. This paper initially examines promising technologies employing machine learning models to provide rapid diagnostic outcomes, specifically for urinary tract infections. This paper focuses on promising molecular diagnostic technologies such as nucleic acid amplification, biosensor-based platforms, microfluidic lab-on-chip systems and omics-based approaches, along with their amalgamation with Artificial intelligence (AI) and smart diagnostics. Improved diagnostic precision, recommendations for targeted antimicrobial therapy and support for antimicrobial resistance surveillance and management are among the translational applications of these developments that are highlighted. Challenges related to medical ethics, execution and regulations are also covered. The combined efforts of next-generation and AI-assisted diagnostic tools offer a revolutionary paradigm for accurate and efficient antibiotic resistance control to treat UTIs.","url":"https://doi.org/10.1007/s11033-026-12344-2","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11033-026-12344-2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1177/26323524261474075","name":"A primer on artificial intelligence for palliative care educators.","source":"europepmc","abstract":"Artificial intelligence (AI) is rapidly being adopted in education in the health care professions, including in palliative care. Yet existing AI primers for health professions education (HPE) are not specific to palliative care (PC) and overlook the relational, prognostic, and cultural sensitivities central to the field. This narrative primer addresses that gap. Informed by a review of the literature, it equips PC educators with practical guidance for responsibly harnessing AI. We first introduce foundational AI concepts relevant to educators and clinicians, including machine learning (ML), large language models (LLMs), generative AI (GenAI) and agentic AI. We then trace a progression from general HPE use, such as study support, assessment, and AI-enhanced simulation, to PC-specific applications in curriculum design, serious-illness communication training, and interprofessional teamwork. Throughout, we situate the risks where they arise, with attention to concerns most consequential for PC: bias, communication integrity and hallucination, data privacy, and over-reliance on AI, in a field where relational, humanistic practice and nuanced communication are central. Guiding principles of ethics, equity, and patient-centeredness anchor the discussion. We close with concrete implications for educators and curriculum development: building AI literacy, establishing governance and appropriate-use policies, and verifying AI-generated outputs against trusted sources. The aim is an educator-AI partnership that safeguards what is essential in PC: compassionate, dignified, patient-centered decision-making and care.","url":"https://doi.org/10.1177/26323524261474075","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/26323524261474075","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/cco.0000000000001257","name":"Artificial intelligence for systemic therapy selection and prediction of outcomes in advanced gynecological cancers.","source":"europepmc","abstract":"Purpose of review Advanced gynecologic cancers are treated broadly, with variable outcomes, even when selected by traditional precision oncology biomarkers. Alternative artificial intelligence (AI)-based biomarkers may improve patient selection, cost efficiency and reduce turnaround times. This review summarizes recent evidence regarding application of AI models for prognostic and predictive biomarker development in gynecological malignancies. Recent findings AI-based biomarker discovery has focused on harnessing computational pathology and radiomics machine and deep learning models to infer molecular subtypes, BRCA 1/2 and homologous recombination status, as well as platinum and maintenance therapy sensitivity. However, small samples sizes, modest discriminative power, lack of explainability and of prospective validation are significant limitations. Summary There is retrospective evidence that AI models potentially constitute useful approaches for precision oncology biomarker detection in advanced gynecological cancers, conditional to prospective validation.","url":"https://doi.org/10.1097/cco.0000000000001257","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/cco.0000000000001257","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.7759/cureus.111617","name":"Diagnostic Accuracy of Artificial Intelligence for Detection of Intracranial Haemorrhage on Non-contrast CT Head: A Systematic Review.","source":"pubmed","abstract":"Intracranial haemorrhage (ICH) is a life-threatening condition requiring rapid diagnosis to improve clinical outcomes. Non-contrast computed tomography (CT) is the primary imaging modality; however, increasing workload and diagnostic variability may lead to delays. Artificial intelligence (AI) has emerged as a potential tool to enhance detection. This systematic review evaluates the diagnostic accuracy of AI algorithms for detecting ICH on non-contrast CT.&#xa0; This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020)&#xa0;guidelines, and the research protocol was registered in&#xa0;PROSPERO (CRD420261320521). A comprehensive search of PubMed, Cochrane Library, CENTRAL, ScienceDirect, and Google Scholar was performed. Diagnostic accuracy studies assessing AI-based detection of ICH on CT were included. Data extraction and study selection were conducted independently.&#xa0;Validated tools, including Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Grading of Recommendations Assessment, Development and Evaluation (GRADE), were additionally utilised to assess risk of bias and certainty of evidence. A total of 25,528&#xa0;records were identified through database searching (PubMed: 392; Google Scholar: 17,900; ScienceDirect: 7,218; Cochrane Library: 18). A total of eight studies, which met the inclusion criteria, were included, demonstrating variability in AI models, datasets, and validation methods. Overall, AI algorithms showed high diagnostic performance, with sensitivities ranging from 0.73 to 0.95 and specificities from 0.80 to 0.98. Studies using larger datasets reported higher accuracy. However, heterogeneity in study design and reference standards was significant.&#xa0; AI demonstrates promising diagnostic accuracy for ICH detection, particularly as a triage tool. However, variability and low-quality evidence limit generalizability. AI should be used alongside radiologists, with further prospective studies required before widespread clinical implementation.","url":"https://doi.org/10.7759/cureus.111617","authors":["Sachithananthan HV","Chitravanshi A","Umasankar A","Banerjee I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.111617","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.2147/amep.s612934","name":"Integrating Artificial Intelligence with Gamification in Medical Education: A Pedagogically Grounded Framework and Critical Review.","source":"europepmc","abstract":"Digital learning technologies have transformed medical education, with artificial intelligence (AI) and gamification emerging as two of the most active areas of innovation. While each has demonstrated value independently, their integration offers distinctive potential to personalise learning, sustain engagement, and produce durable educational outcomes. Yet the convergence remains empirically disjointed and theoretically underdeveloped. This pedagogically grounded critical review synthesises the evidence at the crossroads of AI and gamification in medical and health professional education, drawing on randomised trials, scoping reviews, meta-analyses, and case studies from PubMed, DOAJ, ERIC, and Web of Science. We propose an operational definition of AI-enhanced gamification and introduce an integration matrix linking five AI methods (reinforcement learning, Bayesian learner modelling, natural language processing, computer vision, recommender systems) to specific gamification elements and to learning mechanisms grounded in Self-Determination Theory, Flow Theory, constructivism, Vygotsky's Zone of Proximal Development, connectivism, the TPACK model, and the Behaviour Change Technique taxonomy. We map applications across health literacy, mental health psychoeducation, rehabilitation, and medical education, supported by ten real-world examples. We identify challenges in theoretical grounding, outcome measurement, validation, algorithmic bias, reproducibility, equity, and regulation, and close with a prioritised, feasibility-tagged research agenda for advancing AI-enhanced gamification as a credible digital learning innovation in medical education.","url":"https://doi.org/10.2147/amep.s612934","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/amep.s612934","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/healthcare14142153","name":"Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.","source":"pubmed","abstract":"Background: The integration of Artificial Intelligence (AI) and algorithms into healthcare is transformative, particularly in diagnosing rare diseases (RDs), enhancing the accuracy and speed of condition identification. Objectives: This systematic literature review investigates perceptions and attitudes toward the use of AI in Electronic Health Records (EHRs) for screening patients at risk of RD, aiming to understand patients' and healthcare providers' expectations and concerns. Methods: Following PRISMA guidelines, a systematic search was performed in December 2023. A search strategy developed by the research team in collaboration with an expert librarian, using the PICO framework, was applied. Searches were conducted in PubMed, Scopus, and Web of Science. The search strategy covered four main concepts: diagnostic techniques, medical records, AI, and attitudes toward these technologies. Results: The initial search retrieved 3348 articles after duplicate removal. However, no studies met the inclusion criteria. As a result, no eligible studies were identified, preventing risk-of-bias assessment or data synthesis. Discussion: The absence of relevant studies highlights the need for further research focusing on patient and healthcare provider attitudes toward AI-integrated EHRs, especially in RD and their early detection. Conclusions: The lack of studies on stakeholder attitudes toward AI in EHRs for RD screening represents an important research gap. Addressing this gap will improve the understanding and development of AI applications in healthcare, ensuring they meet user needs and ethical standards.","url":"https://doi.org/10.3390/healthcare14142153","authors":["Martin S","Grauman Å","Coulter J","Hasan B","Veldwijk J","Hansson M","Anyouzoa A","Nyoungui E","Elomaa K","Zschuentzsch J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14142153","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.cgh.2026.06.036","name":"Artificial Intelligence-Enabled Opportunities to Reduce Administrative Bloat in Gastroenterology Practices: Scoping Review.","source":"europepmc","abstract":"Background & aims Health care administration accounts for approximately 25% of health care expenditures. We performed a scoping review of artificial intelligence-enabled administrative products in gastroenterology and hepatology, including widely deployed commercial platforms, examining administrative functions, validation practices, and alignment with high-burden workflows. Methods A 2-component search strategy combining systematic literature search and a targeted environmental scan of commercial platforms identified 34 discrete artificial intelligence products and 2 narrative reviews. Each product was assessed using the 4-domain Framework for the Appropriate Implementation and Review of Artificial Intelligence rubric. Validation was categorized as unvalidated, internally validated, vendor-sponsored, or externally validated. Results Products spanned 8 administrative function domains, including billing and coding, clinical documentation, patient engagement, and prior authorization. Twenty-six of 34 were general-platform products, and only 8 were gastroenterology-specific, of which 7 remained precommercialized. Four of 34 products reported no validation data, 2 relied on vendor-sponsored evidence, and 4 were externally validated. Framework for the Appropriate Implementation and Review of Artificial Intelligence scores were lowest for Fairness (1.59/5; standard deviation, 0.66), followed by Reliability (2.57/5; standard deviation, 0.97), Interpretability (2.72/5; standard deviation, 1.01), and Accountability (2.78/5; standard deviation, 0.64). Conclusions Commercialized products were broadly designed, least validated, and scored lowest on Framework for the Appropriate Implementation and Review of Artificial Intelligence metrics, particularly Fairness, whereas gastroenterology-specific products with greater methodological rigor remained largely precommercialized. Commercial general-platform products lacked gastroenterology-specific validation, and high-burden specialty workflows including hepatology care coordination and transplant documentation remained comparatively underserved. Realizing the potential of administrative artificial intelligence in gastroenterology requires moving toward empirically validated, specialty-specific, and equitable solutions, where success is measured by reduced administrative burden and time returned to patient care, not by revenue generation.","url":"https://doi.org/10.1016/j.cgh.2026.06.036","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cgh.2026.06.036","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.7759/cureus.112841","name":"Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction.","source":"europepmc","abstract":"Cardiovascular diseases (CVDs) continue to be a major cause of death and morbidity throughout the world, and there is an increasing need for better diagnostic and predictive approaches. Existing methods do not fully reflect complex clinical interactions, and new computational methods possess greater capabilities. The limitations, including a lack of diversity in datasets, low generalizability, and limited interpretability, limit general use in the clinic. The review highlights the latest developments in artificial intelligence (AI) for diagnosing and predicting cardiovascular risk factors, with a focus on its clinical applications and current challenges. A literature review was conducted on machine learning (ML) and deep learning (DL) applications in imaging, electrocardiography (ECG), and predictive modeling. AI has been shown to improve diagnostic accuracy, facilitate earlier diagnosis, and enhance the ability to stratify disease risk compared with traditional methods. The seamless integration with wearable technologies ensures continuous monitoring and proactive management. While these developments help to ensure more accurate and individualized care, there are issues of validation, ethics, and integration. Moreover, integration of multi-modal data sources and real-time analytics enhances clinical decision-making and risk assessment. As technology continues to evolve, its scalability and applicability across various healthcare settings are expected to improve. In summary, AI has the potential to revolutionize cardiovascular care and enhance clinical outcomes by leveraging data-driven approaches.","url":"https://doi.org/10.7759/cureus.112841","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.112841","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/diagnostics16142196","name":"Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.","source":"pubmed","abstract":"Gastric cancer is a globally prevalent malignancy, with early detection being pivotal for improving patient survival. While endoscopy remains the diagnostic gold standard, it frequently faces challenges such as missed lesions and operator dependency. Artificial intelligence (AI) has emerged as a powerful tool to address these limitations. This narrative review synthesizes recent evidence from PubMed and Web of Science, focusing on four core functional domains of AI-assisted gastric endoscopy: lesion detection and characterization, margin delineation, invasion depth prediction, and blind-spot monitoring. Furthermore, we summarize current limitations, including single-center data biases and algorithmic \"black-box\" issues, and discuss future directions such as multimodal data integration and real-time video analysis systems. Ultimately, carefully validated AI represents a vital clinical adjunct that holds great potential to significantly enhance diagnostic accuracy and patient outcomes.","url":"https://doi.org/10.3390/diagnostics16142196","authors":["Su M","Fu S","Liao W","Yang A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16142196","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.3389/fmed.2026.1870486","name":"Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.","source":"europepmc","abstract":"Background Osteoporosis often remains undetected until fracture. Dual-energy X-ray absorptiometry (DXA) screening is limited by poor accessibility. Opportunistic screening of routine medical images, powered by artificial intelligence (AI), may enable automated, large-scale bone health assessment. Methods This systematic review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane Library, and Web of Science up to February 2026 for studies developing AI models for opportunistic osteoporosis screening. Two reviewers independently screened, extracted data, and assessed risk of bias using phase classification and PROBAST. Outcomes included osteoporosis, osteopenia, and fracture risk prediction, evaluated mainly by area under the curve (AUC). Principal component analysis (PCA) was used to explore sources of heterogeneity. Results Of 57 included studies (2019-2026), most were retrospective and single-center. Computed tomography (CT) was the most common modality (38 studies), and the spine was the most frequent ROI. Deep learning (29 studies) has largely replaced traditional machine learning, and foundation models have recently emerged. Model performance was generally high (AUC range 0.630-1.000 across tasks), but heterogeneity was substantial. PCA identified sample size, number of centers, and single-center design as major drivers of heterogeneity, smaller and single-center studies tended to report higher AUCs, suggesting possible overestimation. PROBAST assessment revealed that the absence of external validation was the leading source of bias, only 36% of studies conducted external validation, with additional concerns in participant selection and analysis domains. Conclusion AI-based opportunistic osteoporosis screening has progressed rapidly with encouraging performance. However, the evidence base remains dominated by retrospective single-center studies with limited external validation, which hinders clinical translation. Future work should prioritize multicenter collaboration, prospective validation, and standardized reporting. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261451716, identifier PROSPERO (CRD420261451716).","url":"https://doi.org/10.3389/fmed.2026.1870486","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1870486","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00330-026-12715-0","name":"Efficacy evaluation of artificial intelligence in radiological imaging diagnosis based on randomized controlled trials: a scoping review.","source":"europepmc","abstract":"Objective Artificial intelligence (AI) demonstrates significant potential in medical imaging diagnosis, yet its real-world clinical value requires validation through high-quality randomized controlled trials (RCTs). Existing RCTs report heterogeneous results across settings and outcomes, motivating a scoping review to map current evidence and identify gaps. Materials and methods This scoping review mapped RCTs published up to March 2026 that evaluated AI tools for imaging-based diagnosis in radiology in clinical settings. We systematically searched PubMed, Embase, and Web of Science, screened studies using predefined eligibility criteria, and extracted study characteristics and outcomes. Risk of bias was assessed using QUADAS-2 and the RoB 2 tool, and findings were synthesized descriptively in line with PRISMA-ScR. Results By analyzing the included RCTs, AI tools for imaging-based diagnosis in radiology were mainly deployed as clinician-facing decision aids and were generally associated with higher sensitivity or lesion detection rates and shorter image-processing time. However, the benefits were smaller in complex scenarios such as emergency care, and low specificity remained a common limitation. Conclusion Overall, AI tools for imaging-based diagnosis in radiology are currently used mainly as clinician-facing decision aids and may be most beneficial in standardized tasks, with effects varying across clinical settings. Larger multicenter prospective RCTs with consistent, clinically meaningful endpoints are needed to support robust clinical translation. Key points Question Is there any high-quality evidence from randomized controlled trials (RCTs) to evaluate the clinical benefits of artificial intelligence (AI) in radiological image diagnosis? Findings Through a review of nine RCTs, AI tools for imaging-based diagnosis tended to raise sensitivity, but gains were smaller in emergency care and specificity often remained low. Clinical relevance As an assistive tool, AI can improve imaging sensitivity and reduce missed diagnoses in standardized scenarios; however, further high-quality RCT evidence is still needed.","url":"https://doi.org/10.1007/s00330-026-12715-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00330-026-12715-0","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1097/mop.0000000000001612","name":"The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease.","source":"europepmc","abstract":"Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate that deep learning models can accurately identify arrhythmias, ventricular dysfunction, and CHD from standard ECG. Convolutional neural networks remain the dominant architecture, although transformer-based foundation models and self-supervised learning approaches are increasingly being explored. AI-ECG applications in PCHD have expanded from automated interpretation toward proactive risk stratification, including prediction of ventricular dysfunction, mortality, and sudden cardiac death risk. Additional work has investigated wearable monitoring, telemetry analysis, and integration with longitudinal clinical data. Despite promising performance, most studies remain retrospective and single-center, with limited external validation and challenges related to small datasets, physiologic heterogeneity, and age-dependent ECG variation. Summary AI-ECG has the potential to transform PCHD by improving diagnostic accuracy, enabling earlier disease detection, and enhancing longitudinal risk assessment. Broader clinical implementation will require multicenter collaboration, prospective validation, standardized datasets, and careful attention to ethical, regulatory, and equity considerations.","url":"https://doi.org/10.1097/mop.0000000000001612","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/mop.0000000000001612","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/frai.2026.1909177","name":"Deep learning and hybrid architectures for atypical and complex bone fracture diagnosis: a systematic review of performance and clinical validity.","source":"europepmc","abstract":"Artificial intelligence (AI) is reshaping fracture diagnosis in medical imaging. Despite these advances, accurately identifying atypical fractures (such as stress or pathological fractures) and complex fractures (including comminuted and pelvic fractures) remains a significant clinical challenge. This systematic review evaluates the current evidence on AI models, including advanced architectures, for detecting, classifying, and segmenting atypical and complex bone fractures in humans. A total of 40 studies published between 2015 and 2026 met the predefined inclusion criteria. Eligible studies used real-world imaging modalities (X-ray, CT, or MRI), focused on atypical or complex fractures, employed AI-based approaches with expert-validated reference standards, and reported quantitative performance metrics. Studies based exclusively on synthetic data, restricted to simple fractures, or lacking adequate validation were excluded. Advanced AI models, including hybrid frameworks such as 3D U-Net variants and DeepLabV3+MobileNetV3, were associated with improved performance in several studies, particularly for identifying subtle and multi-fragment fractures. However, substantial heterogeneity in study design, datasets, validation strategies, and evaluation metrics limits direct comparisons across models. Hybrid systems, particularly CNN-based architectures combined with level-set methods or multi-network pipelines, also appeared effective in capturing complex fracture patterns in several studies, although this observation is based on a limited and heterogeneous body of evidence. Overall, the available evidence suggests that advanced AI models have considerable potential to improve the detection, classification, and segmentation of atypical and complex fractures. Nevertheless, the predominance of single-center studies, the limited use of external or prospective validation, and methodological heterogeneity indicate that further standardized, multicenter clinical validation is required before these models can be widely implemented in routine clinical practice.","url":"https://doi.org/10.3389/frai.2026.1909177","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1909177","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/fmed.2026.1873826","name":"Recent advances in artificial intelligence-assisted medical imaging education.","source":"europepmc","abstract":"Introduction The deep integration of artificial intelligence (AI) and medical imaging represents a major trend in the transformation of healthcare, driving advancements in technologies such as image reconstruction. At the same time, medical schools worldwide are integrating AI into medical imaging education. This paper reviews recent advances in artificial intelligent medical imaging education and offers recommendations regarding curriculum design and faculty development for training professionals in artificial intelligent medical imaging. Methods This study analyzed the application of AI in medical imaging education and corresponding talent development models through a literature review of core databases such as PubMed and Web of Science, supplemented by case studies, to draw conclusions and propose targeted recommendations. Results AI has been widely applied in medical imaging education to enhance educational quality and other aspects. However, globally, AI-related radiology education exhibits inconsistencies in curriculum design and insufficient integration of technology. Although preliminary evidence suggests that AI can effectively improve teaching outcomes, the lack of standardized teaching guidelines has led to gaps in the knowledge system. Conclusion The integration of AI and medical imaging offers significant advantages in medical imaging education. However, while the education sector has already adopted various strategies-such as human-machine collaborative education-it still faces challenges, including a shortage of interdisciplinary faculty and a disconnect between the curriculum and clinical practice. Improvements must be made through strategies such as faculty development, pedagogical transformation, fostering AI literacy, and standardizing teaching frameworks. Future research should explore the adaptability of AI across different training stages to promote the sustainable integration of these two fields and the development of relevant professionals.","url":"https://doi.org/10.3389/fmed.2026.1873826","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1873826","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s00261-026-05735-3","name":"Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.","source":"europepmc","abstract":"Background Prostate cancer imaging is inherently multimodal, yet many AI tools remain single-modality and therefore misaligned with real-world abdominal/genitourinary radiology decision-making. Purpose We review workflow-relevant multimodal AI methods that fuse mpMRI, PSMA PET, ultrasound (including TRUS and elastography), and clinical or pathology data for diagnosis, local staging, and treatment personalization. Content MRI-plus-clinical fusion improves csPCa triage beyond imaging-only baselines and supports practical risk-model implementations. MRI-TRUS fusion models demonstrate improved lesion localization for targeted biopsy compared with unimodal AI and standard radiologist MRI interpretation in multicenter settings. For local staging, multimodal strategies for extraprostatic extension prediction are supported by meta-analytic evidence and emerging PET/MRI- or PET/CT-plus-MRI approaches that can assist radiologists and inform nerve-sparing planning. For treatment personalization, multimodal models predict biochemical recurrence after prostatectomy and extend toward systemic endpoints using imaging fused with clinical variables or pathology-derived features. Conclusion The most adoption-ready directions for Abdominal Radiology readers are modular multimodal systems that improve triage, guide biopsy targeting, and quantify local extension risk with transparent validation pathways and human-centered deployment design.","url":"https://doi.org/10.1007/s00261-026-05735-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00261-026-05735-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1136/bmjopen-2026-121820","name":"Awareness, knowledge and attitudes towards artificial intelligence among nursing students: a systematic review protocol.","source":"europepmc","abstract":"Introduction Internationally, nursing students' awareness and familiarity with artificial intelligence (AI) remain a challenge as evidenced by the current literature. Interestingly, the Gulf Cooperation Council (GCC) region has earned a strong standing for driving national digital transformation; however, this ambition has not been translated into research. Despite growing interest in AI-driven healthcare, empirical studies examining nursing students' readiness in these countries to operate in healthcare environments remain limited, representing a critical gap in the literature. Our initial literature review found a high degree of heterogeneity among study designs, measurement tools and theoretical framing and highlighted an unequivocal need for a rigorous and systematic synthesis to uncover consistent patterns, methodological gaps and contextual factors that shape nursing students' engagement with AI.This protocol aims to provide a structured plan for combining the current evidence on nursing students' awareness, knowledge and attitudes regarding AI applications in nursing education. Methods and analysis This protocol is prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines, and it is registered with the international Prospective Register of Systematic Reviews (PROSPERO). A comprehensive and systematic search of the literature will be undertaken across four key electronic databases: PubMed, CINAHL, Scopus and Web of Science, using Boolean search strings constructed from Medical Subject Headings-controlled terms and free-text keywords encompassing six predefined thematic domains: AI applications in nursing education, student awareness, attitudes, technology acceptance, adoption, ethical considerations and regional context. All studies published in English between January 2020 and June 2026 including cross-sectional, cohort, quasiexperimental and qualitative studies will be included in this review. The primary outcome is nursing students' awareness of and attitudes toward AI; secondary outcomes include AI-related knowledge, behavioural intention and ethical concerns. A 41-item standardised form will be used for data extraction across all included studies, systematically capturing study characteristics, instruments, theoretical frameworks, barriers, facilitators and the outcomes of interest. To assess the studies' quality, we will use the Joanna Briggs Institute (JBI) Critical Appraisal Tools for quantitative and qualitative studies, ensuring a comprehensive and methodologically consistent appraisal process across all included studies. Narrative synthesis will be performed complemented by meta-analysis where applicable, organised by the construct domains and geographic regions. This protocol provides an in-depth, systematic review plan that will report the most thorough synthesis to date regarding nursing students' awareness, knowledge levels and perceptions of the utilisation of AI within nursing education. The review will identify validated instruments for cross-cultural adaptation, establish benchmarks and estimate prevalence of awareness, knowledge and attitude. We will describe the theoretical and contextual contrived factors associated with these constructs in nursing students. Ethics and dissemination As this systematic review is based exclusively on published literature and does not involve the collection of primary data from human participants or animals, formal ethical approval is not required. Findings from this review will be disseminated through publication in a peer-reviewed journal and presented at relevant national and international nursing and healthcare conferences. The review is expected to generate evidence-based insights that will inform nursing curricula, guide institutional policy on AI integration and highlight the critical evidence gap in the GCC region, including Oman, thereby contributing to the advancement of AI-ready nursing education internationally. A key focus will be mapping geographic variation, with particular attention to the GCC region where empirical evidence remains sparse. Prospero registration number CRD420261320108.","url":"https://doi.org/10.1136/bmjopen-2026-121820","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2026-121820","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.7759/cureus.112306","name":"Conversational Artificial Intelligence and Neuropsychiatric Risk: A Narrative Review and Case-Based Synthesis Proposing a Delusional Feedback Loop.","source":"europepmc","abstract":"Conversational AI, powered by artificial intelligence, is becoming a common tool for accessing health information, educating patients, and obtaining general medical advice. These advanced systems, known as large language models, can produce responses that sound remarkably human. Nevertheless, these systems are prone to \"AI confabulations,\" whereby they confidently generate incorrect information that could harm patients. This highlights the need to inform healthcare workers and individuals who may be prone to trusting these devices. New evidence suggests that AI may also exacerbate mental health conditions, particularly psychosis, paranoia, and related vulnerable states, especially among susceptible individuals. We conducted a targeted literature review and case-based analysis of 35 reported instances in which interactions with generative AI systems were temporally associated with the onset or worsening of psychotic symptoms. Across cases, recurrent patterns included reinforcement of delusional beliefs, amplification of pre-existing psychiatric vulnerabilities, promotion of harmful behaviors, and dissemination of unsafe medical guidance. Common contributing factors included prior psychiatric history, substance use, sleep disturbance, and prolonged AI engagement. We propose a conceptual hypothesis termed the delusional feedback loop, in which AI-generated responses iteratively validate distorted beliefs, contributing to their persistence and escalation. This process can be conceptualized as involving four components: underlying vulnerability, exposure to conversational AI, validation of distorted beliefs, and reinforcement through repeated interactions. Despite the rapid integration of conversational AI into health information seeking, there is currently no framework in the neuropsychiatric literature describing how AI interactions may relate to psychosis vulnerability. Existing reports are limited to isolated case descriptions without a common mechanism. This review addresses this gap.","url":"https://doi.org/10.7759/cureus.112306","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.112306","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.3389/fped.2026.1932796","name":"Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.","source":"europepmc","abstract":"Background The interpretation of pediatric electrocardiograms (ECGs) and management of childhood arrhythmias represent specialized clinical disciplines complicated by age-dependent physiological evolution. While artificial intelligence (AI) has transformed adult cardiology, its application to pediatric electrophysiology remains largely in the research phase. Objective This review critically appraises the current evidence, methodological rigor, clinical readiness, and translational challenges of AI in pediatric arrhythmia detection, risk stratification, and management. Methods A synthesis of contemporary literature was conducted, evaluating machine learning (ML), deep learning (DL), large language models (LLMs), wearable sensors, and intensive care monitoring across pediatric cohorts. Main findings While purpose-built DL models demonstrate strong diagnostic performance for specific electrical phenotypes-such as Wolff-Parkinson-White (WPW) syndrome, long QT syndrome (LQTS), and neonatal bradycardia-the vast majority of published tools remain unvalidated retrospective proofs-of-concept. Current AI algorithms analyze isolated ECG waveforms under curated conditions and cannot replace holistic clinical evaluations incorporating patient history, family screening, genetics, and multi-modality diagnostic testing. Significant barriers persist, including pervasive data scarcity, lack of prospective external validation, limited saliency map reproducibility in Explainable AI (XAI), and uncalibrated false alarms. Furthermore, adult-trained algorithms and general-purpose LLMs yield unacceptable diagnostic error rates when applied to children. Conclusions AI holds promise for enhancing pediatric arrhythmia care, but clinical integration requires moving beyond isolated performance metrics. Future progress hinges on prospective multicenter validation, privacy-preserving federated learning, multimodal data integration, and explicit definition of AI's role as a clinical decision-support tool within real-world workflows.","url":"https://doi.org/10.3389/fped.2026.1932796","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1932796","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.3390/jcm15156059","name":"Artificial Intelligence in Obstetrics: Current Trends and Future Directions.","source":"europepmc","abstract":"Background: Artificial intelligence (AI), which spans machine learning (ML), deep learning (DL), computer vision, and natural language processing (NLP), is now used across obstetric care, including ultrasound interpretation (biometry, anomaly detection), fetal monitoring (cardiotocography), maternal risk stratification (preeclampsia, preterm birth, hemorrhage), labor and delivery decision support, genomic screening, and telehealth. Methods: We conducted a narrative (non-systematic) review of the literature published between 2016 and 2026, distinguishing the level of evidence supporting each application. Results: Reported performance is frequently high for image-based tasks such as fetal biometry and anomaly detection (accuracy and AUC often exceeding 0.85), whereas intrapartum CTG analysis remains modest (AUROC ~0.60-0.70, overlapping the inter-observer variability of clinicians). Most published evidence is retrospective and internally validated; comparatively few tools have undergone external or prospective validation, and only a small number have received regulatory clearance. Limitations: The evidence base is heterogeneous, external validation and calibration are often absent, and we did not perform a formal risk-of-bias appraisal. Conclusions: AI has real potential to improve prenatal diagnosis and individualized care, but claims that it is ready for the clinic are often premature. Prospective and external validation, calibration and clinical-utility assessment, transparent reporting, attention to bias and equity, and sustained clinician oversight are prerequisites for safe adoption.","url":"https://doi.org/10.3390/jcm15156059","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15156059","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1016/j.compbiolchem.2026.109280","name":"Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.","source":"europepmc","abstract":"Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.","url":"https://doi.org/10.1016/j.compbiolchem.2026.109280","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.compbiolchem.2026.109280","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1080/17474086.2026.2708713","name":"Can artificial intelligence lead the next frontier of hemophilia management and care?","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/17474086.2026.2708713","authors":["John MJ","Chakrabarti P","Yanamandra U"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/17474086.2026.2708713","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3174/ajnr.a9146","name":"Recent Advances in Hypothalamic Segmentation for Neuroimaging: A Comprehensive Review.","source":"europepmc","abstract":"The hypothalamus is a key structure in the human brain, comprising numerous functionally distinct subnuclei that regulate critical physiologic processes such as energy balance, stress response, and circadian rhythms. Due to the complexity and functional diversity of its subregions, precise segmentation is essential for elucidating its operational mechanisms. This review systematically summarizes hypothalamic segmentation methods and their applications in physiologic and clinical research. Current approaches are categorized into 2 complementary types: anatomy-based manual segmentation and deep learning-based fully automated segmentation. The former provides a standard for algorithm validation through expert knowledge, enabling accurate identification of key functional subregions; the latter offers an efficient solution for large-scale studies, facilitating in-depth exploration of the hypothalamus's heterogeneous functional architecture. The review also highlights major challenges in the field, including the lack of unified segmentation protocols-which hinders cross-study comparability-and a significant methodologic gap in pediatric population studies. Moving forward, it is crucial to establish standardized segmentation workflows, reduce subjective bias, improve reproducibility, and address the technical shortcomings in hypothalamic segmentation for children, thereby laying a foundation for comprehensively understanding the structure and function of this critical brain region.","url":"https://doi.org/10.3174/ajnr.a9146","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3174/ajnr.a9146","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.3390/healthcare14142114","name":"Artificial Intelligence in Social Health: A Narrative Review of Uses, Advantages, Challenges, and Future Directions.","source":"pubmed","abstract":"Artificial intelligence (AI) is deeply integrated into daily life. Emerging evidence suggests AI may help change the dynamics of social relationships by influencing social interactions, connectivity, and interpersonal relationships, and by providing new avenues for communication and contributing to improved social well-being. Therefore, this review aims to explore the potential of artificial intelligence (AI) technologies as a tool to enhance social health, focusing on current applications, advantages, challenges, and ethical considerations associated with their implementation, as well as opportunities for future development. The literature on the relationship between the connectedness of social health dimensions and AI as a tool to better understand how interactions with AI technologies may influence social well-being. In this current review, key terms such as \"Artificial Intelligence\", \"Social Health\", \"social inequalities\", \"AI algorithm\", \"AI technology\", \"social connection\", \"digital communication\", \"social participation\", \"social support\", \"social isolation\", \"loneliness\", \"mental wellbeing\", were used to search relevant literature on Google Scholar, PubMed, Scopus and Web of Sciences. In addition, relevant aspects of the multidimensional impacts of AI on social health dimensions are also discussed. The use of AI technologies by individuals within societies was found to hold profound potential to reshape social health through enhancing social relationships, bridging communication gaps in diverse populations, stimulating social dynamics, and understanding human emotions. It may contribute to reducing social inequalities, promoting equity, accommodating individual differences, and enhancing the effectiveness of many tasks in the social and health care systems through deep learning, natural language processing, and machine learning techniques. This reduces social exclusion and increases accessibility and quality of health and social services. However, AI has also posed distinguishable challenges to its adoption, specifically in terms of data quality, privacy and security, algorithmic bias, ethical issues, public trust and acceptance, and regulatory and policy gaps. Evidence suggests that building public trust in the future of AI in social health requires interdisciplinary collaboration among health providers and professionals, social scientists, community members, and policymakers. Such collaboration is crucial to ensure that AI platforms do not perpetuate social inequalities or biases by maintaining transparency, explainability, and demonstrated effectiveness. In conclusion, the integration of AI into social health dimensions holds promise for social health transformation. As we move forward, several key areas need to be addressed to develop a robust governance and regulatory framework, along with ethical guidelines to ensure privacy protection, respect for human rights, transparency, and the promotion of the common good.","url":"https://doi.org/10.3390/healthcare14142114","authors":["Elmosaad YM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14142114","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1016/j.compbiomed.2026.111859","name":"A clinically grounded taxonomy and systematic review of artificial intelligence for cardiovascular diagnosis: From machine learning to multimodal and agentic systems.","source":"pubmed","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, creating an urgent need for accurate, trustworthy, and clinically deployable artificial intelligence (AI) systems capable of supporting complex diagnostic decision-making. Although AI has advanced considerably in cardiovascular diagnosis, existing evidence remains fragmented across algorithms, data modalities, and isolated application domains, limiting a comprehensive understanding of clinically integrated AI systems. This study presents a PRISMA 2020-guided systematic review and proposes a clinically grounded six-layer taxonomy that organizes cardiovascular AI according to diagnostic objectives, data modalities, modeling paradigms, data integration complexity, interpretability and trustworthiness, and deployment maturity. A systematic search of PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 226 records, of which 76 primary empirical studies met the predefined eligibility criteria and were included in the comparative evidence synthesis. The review demonstrates the evolution of cardiovascular AI from conventional machine learning applied to structured clinical data toward deep learning for physiological signals and medical imaging, followed by multimodal AI systems integrating heterogeneous clinical information. Comparative synthesis across the proposed taxonomy highlights substantial progress in predictive performance while revealing persistent challenges related to external validation, dataset representativeness, workflow integration, explainability, privacy, governance, and prospective clinical deployment. The review further distinguishes clinically validated technologies from emerging paradigms, including federated learning, foundation models, and agentic AI. Overall, the proposed taxonomy provides a unified framework for organizing contemporary cardiovascular AI research and offers a practical roadmap for evaluating the maturity, trustworthiness, and clinical readiness of next-generation intelligent diagnostic systems.","url":"https://doi.org/10.1016/j.compbiomed.2026.111859","authors":["Rezaei Z","Amini MA","Banad YM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.compbiomed.2026.111859","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1093/gpbjnl/qzag088","name":"Artificial Intelligence in Post-translational Modification Site Prediction: Progress and Future Perspectives.","source":"europepmc","abstract":"Post-translational modifications (PTMs) are pivotal in modulating protein function and cellular processes. However, experimental identification of PTM sites remains costly and labor-intensive. Recent advances in artificial intelligence (AI) have enabled accurate and scalable in silico PTM site prediction from large-scale proteomic data. In this review, we provide a comprehensive and up-to-date overview of AI-driven PTM site prediction across more than ten PTM classes, covering single-PTM site prediction, multiple-PTM site prediction, inter-site crosstalk prediction, and functional prediction of modification sites. We systematically analyze and compare key AI frameworks, from conventional machine learning to deep learning, and summarize representative tools. We also identify key challenges and propose future directions for improvement. To facilitate application and ongoing progress, we provide practical guidelines for method selection and have established a dedicated website, which serves as a community benchmarking resource for the development of PTM site prediction tools. This website will be regularly updated with emerging prediction tools. By integrating comprehensive literature analysis with a dynamic online resource, we aim to provide a reliable foundation for understanding current capabilities and guiding the future development of PTM site prediction tools, thereby promoting the integration of AI into practical biomedical research applications.","url":"https://doi.org/10.1093/gpbjnl/qzag088","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/gpbjnl/qzag088","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1186/s12909-026-09687-6","name":"Artificial intelligence in urological education: a systematic review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is currently widely applied across various fields, especially in the field of education where it holds significant implications. This systematic review aims to explore the specific role of AI in urological education. Methods Following the PRISMA 2020 guidelines, we searched the PubMed, Scopus, and Web of Science databases for articles related to AI in urological education published up to February 10, 2026. The quality of included studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI), and educational outcomes were classified based on the modified Kirkpatrick framework. Results A total of 15 eligible study records were finally included. The use of AI in urology education can be categorized into two groups: interventions that are used directly in the educational process, and enabling technologies that provide support for education. It was showed that combining AI in urology education had a positive impact on both learners and teachers. For learners such as medical students, urology residents, or interns, the AI-based hybrid teaching model increased learning efficiency and improved students' theoretical exam scores. Meanwhile, the combination of high-fidelity virtual reality simulation training and intelligent AI feedback showed potential in facilitating the acquisition of clinical skills, in which simulation scores were positively correlated with surgical potential. On the other hand, as the supporting or enabling technology, AI assisted teachers in designing teaching materials and developing assessment programs. In terms of evaluating learners' performance, AI provided fair and objective evaluation results. In addition, machine learning and computer vision algorithms incorporating AI could automatically recognize surgical steps with high precision. Conclusion The application of AI possesses the capacity to drive transformative changes in urological education. AI can serve as an effective tool to improve students' learning efficiency, theoretical knowledge level and clinical skills through assisted teaching. Moreover, as the enabling technology, the application of AI may be potential in providing support during the teaching process. But AI should be utilized in a responsible and ethical manner.","url":"https://doi.org/10.1186/s12909-026-09687-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12909-026-09687-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1186/s12871-026-03997-4","name":"Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review.","source":"europepmc","abstract":"Background Artificial intelligence (AI) has been increasingly used in care delivery in intensive care units (ICUs) and anesthesia-critical care practice through telemedicine, tele-ICU systems, and remote patient monitoring, and is expected to support real-time clinical decision-making. Methods This scoping review followed PRISMA-ScR guidelines to map the existing evidence of AI in critical care and anesthesia-related ICU environments for telemedicine, telemonitoring, and clinical decision support systems. PubMed, Scopus, and Google Scholar were used to search for relevant literature, including the use of AI, telemedicine, predictive analytics, remote monitoring, and anesthesia-informed clinical decision support in critical care. Results The literature reviewed primarily focused on the non-generative AI solutions, such as machine learning, deep learning-based monitoring, and AI clinical decision support systems. Such systems can facilitate remote continuous monitoring, early detection of clinical deterioration, and clinical decision-making in the ICU perioperative anesthesia-critical care settings. The results were grouped into the following categories: tele-ICU implementation, predictive analytics, tele-monitoring, and AI-guided clinical decision support. The reported benefits included better monitoring, improved workflow, enhanced anesthesia and critical care decision-making, and greater access to specialist care, but there was substantial variation in the evidence of consistent improvement in patient-centered outcomes, with most of it being observational. Data quality, interoperability, model transparency, ethical issues, and lack of prospective clinical validation were the key difficulties encountered. Conclusion AI-enabled telemedicine remains a nascent healthcare space in the ICU and anesthesia-critical care continuum, and further standardization, validation, and prospective clinical testing are needed to ensure its safe and scalable integration into clinical practice.","url":"https://doi.org/10.1186/s12871-026-03997-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12871-026-03997-4","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.3389/fimmu.2026.1866011","name":"Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.","source":"europepmc","abstract":"Primary liver cancer is a major global cause of cancer death, and hepatocellular carcinoma (HCC) is the predominant histological subtype. This literature review synthesizes current evidence on the exposome, multi-omics landscape, and artificial intelligence (AI)-based integration strategies relevant to biomarker discovery in liver cancer, with a focus on biological rationale, emerging clinical applications, and translational limitations. Key etiologic drivers include viral hepatitis, alcohol-related liver disease, and metabolic dysfunction-associated steatotic liver disease, all of which interact with environmental exposures across the life course. Biomarker discovery increasingly relies on integrated assessment of exposure-related signals together with genomic, epigenomic, transcriptomic, proteomic, metabolomic, and spatially resolved data. Hepatocarcinogenesis involves a complex interplay of chronic liver injury, environmentally patterned molecular perturbation, and dynamic tumor-host interactions. We emphasize an exposome-informed, multimodal strategy in which interpretable AI models identify clinically relevant signatures for early detection, prognostic stratification, and treatment guidance. Critical limitations of current evidence include incomplete exposure assessment, heterogeneous data platforms, retrospective study design, limited external validation, and insufficient model transparency. Emerging approaches, including proteogenomic, lipidomic, single-cell, and digital pathology-based modeling, show promise but require further validation in etiologically diverse cohorts. The purpose of this review is to critically examine how AI can integrate exposome-related information with multi-omics data for biomarker discovery in liver cancer. Here, particular attention is given to the exposure-to-biomarker sequence, immune-metabolic remodeling, liquid-biopsy translation, and the reduction of high-dimensional signatures into clinically deployable assays.","url":"https://doi.org/10.3389/fimmu.2026.1866011","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1866011","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1002/wjs.70504","name":"Artificial Intelligence in Medical Writing: A Practical and Ethical Framework for Surgical Research and Publication.","source":"europepmc","abstract":"Background Artificial intelligence (AI) is rapidly transforming surgical research and medical publishing by changing how clinicians discover, evaluate, synthesize, and communicate scientific evidence. Despite widespread adoption, practical guidance on the responsible integration of AI into academic writing remains limited, particularly as large language models (LLMs) and emerging AI systems become increasingly sophisticated. Methods This narrative review examines the contemporary AI ecosystem relevant to medical writing, including LLMs, retrieval-augmented systems, structured evidence extraction platforms, citation analytics tools, AI-enhanced academic databases, and emerging agentic AI systems. Current evidence relating to AI-assisted manuscript preparation, ethical considerations, governance, confidentiality, reproducibility, and scientific integrity was reviewed. A practical workflow integrating literature discovery, evidence extraction, synthesis, citation validation, and editorial refinement is proposed. Results AI can substantially improve the efficiency, organization, clarity, and consistency of manuscript preparation while supporting evidence retrieval, synthesis, and editorial refinement. However, responsible use requires awareness of important limitations, including hallucinated references, publication bias amplification, confidentiality risks, language weighting, and the inability of current LLMs to distinguish reliably between truth and plausibility. The review also highlights the increasing integration of academic databases with AI-assisted retrieval and synthesis, together with the emergence of agentic research systems capable of performing increasingly complex scientific workflows under human supervision. Conclusions AI should be regarded as an adjunct to, not a substitute for, scientific reasoning and scholarly judgment. When used transparently and under expert supervision, AI can enhance the quality and efficiency of medical writing while preserving the central intellectual responsibilities of authorship, interpretation, verification, and accountability. As AI systems become increasingly autonomous, maintaining rigorous human oversight will become progressively more important.","url":"https://doi.org/10.1002/wjs.70504","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/wjs.70504","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.3389/fpubh.2026.1899528","name":"Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.","source":"europepmc","abstract":"Background Emergency medicine education occurs in high-acuity, interruption-prone, and time-constrained environments, where learners must develop rapid clinical reasoning, effective communication, procedural competence, and reliable documentation skills. Large language models (LLMs) are increasingly being explored in health-professions education. This narrative review synthesizes emerging applications, major risks, and implementation pathways for LLMs in emergency medicine education. Methods A structured narrative review was conducted using PubMed, Web of Science Core Collection, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The search period extended from January 1, 2023, to April 10, 2026. English and Chinese search blocks combined terms related to LLMs or generative artificial intelligence, emergency medicine or emergency care contexts, and education, training, simulation, assessment, communication, documentation, or implementation. After duplicate removal, title and abstract screening, and full-text review, 48 English-language studies and 5 Chinese-language studies were included. Eligible records addressed emergency medicine or emergency medical services education, simulation or virtual-patient applications, formative assessment and feedback, documentation or discharge communication, or governance issues relevant to educational use in emergency settings. Results LLMs showed potential across multiple educational domains in emergency medicine, including just-in-time tutoring, resource generation, case drafting, simulation and virtual-patient rehearsal, formative feedback support, examination and competency-assessment support, documentation and discharge communication coaching, and educator workflow support. Potential benefits included more timely feedback, broader access to structured teaching resources, repeated rehearsal of low-frequency high-acuity scenarios, and greater consistency in communication training. Translation into routine educational practice remains constrained by hallucination, context mismatch with local protocols, automation bias, limited relational authenticity in AI-mediated interaction, privacy and cybersecurity concerns, multilingual inequity, uncertain validity of AI-assisted assessment, and uneven faculty readiness. Conclusion LLMs hold substantial promise for strengthening emergency medicine education, particularly in areas requiring rapid language-based support, structured feedback, scalable case generation, and communication rehearsal. Current evidence supports phased adoption, local grounding in institutional protocols, secure workflows, explicit faculty oversight, and evaluation of educational, operational, and governance outcomes. This review proposes a pragmatic framework for the integration of LLMs into emergency medicine education.","url":"https://doi.org/10.3389/fpubh.2026.1899528","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1899528","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1016/j.ijpharm.2026.127155","name":"Artificial intelligence and CRISPR-based approaches for targeted delivery of bacteriophages.","source":"europepmc","abstract":"The rapid emergence of Multidrug-Resistant (MDR) bacteria has increased interest in bacteriophage therapy as a promising alternative to conventional antibiotics. Bacteriophages are host-specific bacterial viruses that selectively infect and destroy pathogenic bacterial strains. Recent developments in artificial intelligence (AI) and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based technologies offer innovative approaches to address challenges such as narrow host range, rapid immune clearance, phage instability, bacterial resistance, and biofilm penetration barriers. By integrating AI-driven structural modeling with CRISPR-mediated genome editing, these methods enable the targeted delivery of bacteriophages. This review focuses on next-generation approaches that combine AI-assisted phage identification, host prediction, and therapeutic optimization with CRISPR-based genome engineering for targeted phage delivery and improved safety. Overall, this review highlights the potential of AI- and CRISPR-assisted phage therapy for the treatment of MDR bacterial infections. It will also provide a systematic overview of bacteriophage biology, life cycle, and mechanisms of action, while focusing on the influence of phage morphology on therapeutic performance, recent advances, current clinical, preclinical studies, and future perspectives. Although phage therapy shows considerable potential against MDR bacterial infections, several challenges related to delivery, safety, and clinical translation remain. The integration of AI and CRISPR technologies has the potential to improve phage selection, targeting specificity, and therapeutic performance. However, continued research, clinical validation, and regulatory development will be essential for translating these advances into practical antimicrobial therapies.","url":"https://doi.org/10.1016/j.ijpharm.2026.127155","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijpharm.2026.127155","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"pmid:42293622","name":"Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.","source":"pubmed","abstract":"The convergence of artificial intelligence (AI) and healthcare is reshaping clinical practice, yet this transformation raises pressing questions about scientific rigor and ethical responsibility. This review provides a critical appraisal of research integrity and data ethics considerations specific to AI implementation in integrated healthcare settings. We analyzed peer-reviewed literature from 2019 to 2025, focusing on algorithmic transparency, model validation and reproducibility, bias detection, privacy protection, informed consent paradigms, and governance frameworks. Our analysis reveals a fundamental tension: the data-intensive nature of AI development often conflicts with established principles of patient autonomy and data protection. The opacity of deep learning models challenges conventional standards of scientific transparency, while datasets reflecting historical healthcare disparities risk encoding and amplifying bias. We propose an integrated governance model that aligns technical validation with ethical oversight, emphasizing the need for prospective clinical trials, diverse stakeholder engagement, and adaptive regulatory approaches. This review offers practical guidance for researchers, clinicians, and policymakers navigating the complex intersection of AI innovation and healthcare ethics.","url":"https://pubmed.ncbi.nlm.nih.gov/42293622/","authors":["Zeng W","Wei H","Chen Y","Tang J","Ge J","Chen H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1838551","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293552","name":"Microbiome and One Health in GCC countries: current status, research gaps, and future directions.","source":"pubmed","abstract":"Microbiome science has emerged as a central component of the One Health framework, linking human, animal, and environmental health. Although global microbiome research has expanded rapidly, a comprehensive evaluation of microbiome research development and integration across the Gulf Cooperation Council (GCC) countries remains lacking. This systematic review aimed to characterize microbiome research in the GCC countries, identify major research gaps, and evaluate alignment with One Health principles while proposing a strategic framework to support coordinated regional development.","url":"https://pubmed.ncbi.nlm.nih.gov/42293552/","authors":["Aldriwesh MG","Bin Shuraym H","Asiri NY","Asiri WY","Abukhalid NF","Alasiri A","Alghoribi MF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1821688","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293430","name":"AI-empowered clinical evidence for integrated Chinese-Western medicine: Introduction to the ACE-iMed series and platform dissemination.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42293430/","authors":["Yu X","Ma Y","Luo X","Chen Y","Bian Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.imr.2026.101355","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293429","name":"Design and methodology of the AI-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform.","source":"pubmed","abstract":"Integrated Chinese-Western medicine (ICWM) is a distinctive medical system that plays an important role in healthcare and has received increasing attention in recent years. To facilitate the dissemination of evidence in ICWM, we developed an Artificial Intelligence (AI)-empowered Clinical Evidence for Integrated Chinese-Western Medicine (ACE-iMed) platform.","url":"https://pubmed.ncbi.nlm.nih.gov/42293429/","authors":["Liu H","Xu K","Zhang J","Wu S","Qin Y","Ma Y","Yu X","Zhang H","Li H","Wu M","Wang Z","Luo X","Wang B","Yao Y","Feng Y","Sun L","Dong M","Hong Y","Liu J","Yang R","Hu Y","Lai H","Zhou Q","Li X","Ge L","Chen Y","Bian Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.imr.2026.101351","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293413","name":"Artificial Intelligence Integration in Multidisciplinary Wound Management: A Scoping Review of Barriers and Facilitators in Clinical Workflows.","source":"pubmed","abstract":"Chronic wound management is a complex global health challenge that requires coordinated multidisciplinary care. Artificial intelligence (AI) has the potential to improve wound assessment, documentation, and clinical decision support. However, its successful implementation depends not only on algorithmic accuracy but also on its alignment with existing sociotechnical systems and clinical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42293413/","authors":["Sa'ban FZ","Purba CIH","Rahayu U","Aziz MA","Afriana R","Yudha F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/JMDH.S619318","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293387","name":"Intelligent multimodal-energy-driven piezoelectric antibacterial platforms: From structural control to system-level diagnosis.","source":"pubmed","abstract":"Piezocatalysis offers a non-antibiotic, physical-chemical approach to address the challenge of bacterial infection resistance, but its reliance on single energy sources and empirical design limits its potential. This review focuses on intelligent piezoelectric antibacterial platforms powered by multimodal energy and enhanced by artificial intelligence, systematically summarizing the latest research progress in the field. First, based on an in-depth analysis of the limitations of traditional antibacterial strategies, the evolutionary trajectory of piezoelectric technology from single-energy to multimodal synergistic driving is clarified. Second, we systematically explain the piezocatalytic antibacterial mechanism and reveal its synergistic enhancement with other antibacterial components under multiphysical fields, highlighting their comprehensive advantages in improving antibacterial efficacy and achieving precise spatiotemporal control. Furthermore, the synergistic regulatory effects of key structural parameters on the piezoelectric properties and antibacterial activity of materials are thoroughly analyzed. Additionally, typical application cases of such intelligent platforms in cutting-edge scenarios, such as smart wound management, functional anti-infection implant coatings, and precise intervention for localized infections in the lungs and oral cavity, are systematically reviewed. Finally, critical challenges related to material stability, biosafety, and scalable production are discussed, and future research directions empowered by AI are prospected. This review establishes an interdisciplinary theoretical framework and extensible technical pathway for constructing adaptive piezoelectric antibacterial platforms.","url":"https://pubmed.ncbi.nlm.nih.gov/42293387/","authors":["Liu J","Li J","Sun H","Seo J","Zhang Y","Yu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.mtbio.2026.103299","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42293032","name":"Digital twins and digital models of the human circulatory system.","source":"pubmed","abstract":"Digital models and digital twins of human circulatory transport could transform the way cardiovascular and haematological diseases are understood, monitored and treated. Digital twins are dynamic virtual representations of physical systems that continuously assimilate real-world data to simulate and predict system behaviour. However, translating digital twins into clinical practice remains challenging owing to the complexity of human physiology and the need for continuous bidirectional coupling between virtual models and their physical counterparts. Advances in medical-grade sensors, wearable devices, microfluidics, artificial intelligence and high-performance computing are accelerating the evolution of digital models into clinically meaningful digital twins. In this Review, we examine how digital twins can model the human circulatory system across scales, from macroscopic blood flow to molecular and cellular transport. We outline the essential components of a circulatory-transport digital twin, describe the pathophysiological conditions that can be digitally represented, and discuss approaches for acquiring and integrating physiological data, computational modelling strategies and model-based inference. We further survey applications of digital models and digital twins across various types of model inferences, from mechanistic insights to clinical decisions such as disease diagnosis, risk stratification, surgical planning and treatment planning. Finally, we identify key challenges and opportunities for next-generation circulatory digital twins capable of real-time monitoring, predictive simulation and closed-loop therapeutic control.","url":"https://pubmed.ncbi.nlm.nih.gov/42293032/","authors":["Wu R","Ferreira G","Khan NS","Mahmud ST","Stoop J","Sohn LL","Leopold JA","Randles A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 30","doi":"10.1038/s44222-026-00427-5","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292924","name":"Innovations in Pediatric Nursing: Emerging Technologies Including Virtual Reality, Telehealth, Artificial Intelligence, and Digital Mental Health-A Scoping Review.","source":"pubmed","abstract":"Pediatric nursing is rapidly evolving with the integration of innovative technologies aimed at improving healthcare delivery and outcomes for children.","url":"https://pubmed.ncbi.nlm.nih.gov/42292924/","authors":["Mohamed SMM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/23779608261433684","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292676","name":"Managing Myopic Glaucoma-Beyond Structural Fragility and Diagnostic Challenges: Review Article.","source":"pubmed","abstract":"Myopic glaucoma is a distinct subtype of glaucomatous optic neuropathy that is often underrecognized or misdiagnosed, particularly in younger patients whose intraocular pressures (IOP) fall within statistically normal ranges. High myopia presents complex structural, functional, and biomechanical challenges that hinder both early detection and effective disease management.This review summarizes current evidence on the pathophysiology, clinical presentation, diagnostic limitations, and tailored therapeutic strategies for myopic glaucoma, providing a practical clinical framework for improved management.","url":"https://pubmed.ncbi.nlm.nih.gov/42292676/","authors":["Diniz CK","Meira MA","Gondim LA","Dorairaj S","Lemos MB","Kanadani FN","Graciatelli CP","Paranhos A Jr","Prata TS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Mar","doi":"10.5005/jp-journals-10078-1504","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292611","name":"The predictive value of the ratio of multi-dimensional inflammatory biomarkers in pediatric diseases from the perspective of pro-inflammatory/anti-inflammatory balance: a systematic review.","source":"pubmed","abstract":"Imbalance of immune homeostasis, particularly the dysregulation of pro-inflammatory/anti-inflammatory balance, is a core pathological mechanism in many acute and chronic pediatric diseases. Traditional single inflammatory biomarkers have limitations in disease prediction, clinical evaluation, and prognostic stratification, as they cannot reflect the overall dynamic balance of the immune network. This systematic review aimed to evaluate the predictive value of multi&#x2011;dimensional inflammatory biomarker ratios centered on pro&#x2011;inflammatory/anti&#x2011;inflammatory balance in pediatric inflammatory diseases, and to clarify their classification and clinical application strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42292611/","authors":["Chen X","Ding B","Shen Z","Han W","Li Y","Xie Y","Wang K","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 31","doi":"10.21037/tp-2026-0189","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292527","name":"Multitechnological integration advances musculoskeletal regeneration: synergistic progress of organoids, 3D/4D bioprinting, single-cell omics and artificial intelligence.","source":"pubmed","abstract":"Regenerative repair of injuries and degenerative diseases in the musculoskeletal system remains a paramount clinical challenge, as conventional single-modal technologies fail to recapitulate the intricate structural and functional complexity of native musculoskeletal tissues. In recent years, the rapid evolution of cutting-edge technologies-including organoids, 3D/4D bioprinting, single-cell omics and artificial intelligence (AI)-has unlocked novel opportunities for addressing this longstanding issue. This review aims to systematically elucidate how the integration of these four core technologies drives progress in musculoskeletal regeneration research. By exploring the intrinsic methodological details of each technology and their underlying synergistic mechanisms, we summarize the latest advances in multitechnological integration for fabricating highly biomimetic in vitro models, enabling precise and dynamic fabrication of tissue-engineered constructs, deciphering cellular heterogeneity during tissue development and repair at high resolution, and optimizing data-driven personalized regenerative strategies. We further explicitly distinguish the in vitro and in vivo applications of each technology with representative experimental evidence and translational implications, and provide comprehensive tabular summaries of state-of-the-art research over the past 5&#xa0;years. Furthermore, we prospect the future development directions and critical challenges facing this interdisciplinary field.","url":"https://pubmed.ncbi.nlm.nih.gov/42292527/","authors":["Liu Z","Fan H","Zhai T","Ma Z","Yang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fbioe.2026.1824644","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292410","name":"Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.","source":"pubmed","abstract":"Artificial intelligence (AI) is a transformative technology that has captivated the medical world with its potential to optimize cancer treatment and enhance precision oncology. In cancer diagnosis and treatment, various AI technologies have already provided high-level data examination and analytics that preceding innovations were not capable of. Cancer immunotherapy is a treatment that seeks to boost the immune system to recognize and eradicate tumors. It is a field that is constantly evolving, serving as a fertile environment where AI technologies can accelerate discovery and personalize its regimens. In recent years, AI has played an increased role in the optimization of immunotherapy delivery and drug development. Traditional machine learning and its subfield of deep learning algorithms have already impacted response prediction and related tasks, such as patient stratification for immune checkpoint blockade treatment and identifying potent T-cells in the laboratory to develop effective cellular therapies. Additionally, recently developed technologies such as generative AI (gen AI) and foundation models have expanded upon traditional AI algorithms with new applications such as treatment plan generation and adverse event prediction. As innovations such as agentic AI and the model context protocol (MCP) become increasingly available, efficiency and success in immunotherapy development and delivery could further improve. That said, some challenges must be overcome for AI to reach its full potential in immunotherapy. These include concerns related to data quality control, patient safety, and addressing ethical dilemmas. In this article, we briefly review available state-of-the-art AI technologies for immunotherapy and highlight their capabilities. Then, we examine the current AI applications in immunotherapy including cell therapies, checkpoint inhibitors, and cancer vaccines, covering a diverse array of technologies over a wide range of applications. We analyze the datasets used, performance metrics, and downstream tasks, and highlight existing limitations. Subsequently, we discuss some of the obstacles that have prevented AI from routine clinical adoption. Finally, we envision the future of AI in immunotherapy that may include a framework involving an orchestration of multiple specialized AI agents with a human in the loop.","url":"https://pubmed.ncbi.nlm.nih.gov/42292410/","authors":["Tarhini A","Dave P","Pilon-Thomas S","El Naqa I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1777580","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292247","name":"Generative AI and student learning performance in medical higher education: a social cognitive theory perspective.","source":"pubmed","abstract":"This study examines the impact of Generative Artificial Intelligence (GAI) on medical students' learning performance in higher education. Drawing on Social Cognitive Theory, it investigates the mediating roles of technology self-efficacy, perceived learning outcomes, and cognitive engagement, as well as the moderating role of fairness and ethical perceptions in AI-supported learning environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42292247/","authors":["Ashraf MA","Aftab M","Mohsin M","Maqbool S","Hanif M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1807322","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292138","name":"Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) have increasingly transformative potential in orthopedic surgery, enhancing precision, efficiency, and outcomes. We aim to raise surgeon and researcher awareness of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD+AI) reporting guidelines for developing and applying AI/ML by conducting a gap analysis on manuscripts published prior or immediately after the release of the guidelines. Our goal is to help improve future reporting and transparency of AI/ML studies regarding shoulder arthroplasty and similar reconstructive procedures.","url":"https://pubmed.ncbi.nlm.nih.gov/42292138/","authors":["Checketts JX","Marsh L","Anderson M","Hughes G","Williamson TK","Hsu JE","Schiffman CJ","Matsen FA 3rd"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.jseint.2026.101715","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292078","name":"Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.","source":"pubmed","abstract":"This study aimed to conduct a scoping review to systematically review automatic identification techniques for soft-tissue and hard-tissue landmarks in three-dimensional (3D)-computed tomography (CT)/cone-beam computed tomography (CBCT), particularly focusing on artificial intelligence (AI)-based methods, to explore the progress and challenges in accuracy, efficiency, and clinical applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42292078/","authors":["Wu Y","Zhai J","Wang Y","Zhang Y","Wang X","Wang C","Huang L","Jiang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdmed.2026.1847046","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292071","name":"Political determinants of melanoma: Global inequities in skin cancer care.","source":"pubmed","abstract":"Melanoma outcomes differ across countries, income settings, and skin tones, reflecting not only variable environmental and genetic risks but also upstream political determinants of health that shape exposure, access, and resources.","url":"https://pubmed.ncbi.nlm.nih.gov/42292071/","authors":["Ma S","Zieneldien T","Aljassabi A","Kim J","Tan IJ","Willmann J","Feliciano EJG","Dee EC","Lipner S","Grant-Kels JM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jdin.2026.05.006","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292068","name":"The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) plays a crucial role in modern analytical chemistry, offering solutions to long-standing challenges. Conventional techniques, such as spectrophotometric analysis and chromatography, often face issues like spectral overlap, matrix interference, and extensive experimental optimization. AI and machine learning (ML) approaches address these limitations by enabling spectral deconvolution, pattern recognition, prediction of retention factors, and automated optimization of separation conditions. Beyond enhancing traditional methods, AI supports the development of innovative analytical platforms. Modern analytical chemistry increasingly relies on smartphone- and paper-based sensors for on-site detection of biomarkers and pollutants. These portable, low-cost systems generate complex datasets requiring advanced computational tools, where AI can improve reliability and sensitivity when validated. AI also plays a vital role in synthesizing and optimizing nanomaterials such as carbon quantum dots (CQDs), accelerating experimental fine-tuning through predictive modeling and optimization algorithms. Moreover, AI facilitates the interpretation of large-scale data, providing deeper insights while reducing human error and analysis time. Despite these advancements, challenges remain regarding model interpretability and the integration of heterogeneous datasets. Addressing these requires explainable ML methods that bridge computational predictions with chemical reasoning. This review highlights current AI applications in chromatographic analysis, drug stability studies, and modern analytical chemistry, discusses implementation challenges, and explores future trends shaping the next generation of intelligent analytical systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42292068/","authors":["Elagamy SH","Chanduluru HK","Obaydo RH","Lotfy HM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/ianc/2645726","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42292041","name":"Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.","source":"pubmed","abstract":"The integration of machine learning (ML) and deep learning (DL) into drug discovery and target identification has catalyzed a paradigm shift in pharmaceutical research, enhancing efficiency and translational potential for nano-enabled therapeutics. ML models have demonstrated up to 85% accuracy in predicting drug-target interactions, whereas DL frameworks, such as convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer architectures, can improve molecular property predictions by 40%. AI-driven drug discovery workflows have curtailed drug candidate attrition rates by up to 30% and accelerated discovery timelines by 20%-40%, accentuating their rising industrial and clinical impact. This critical review evaluates the transformative roles of ML and DL in the drug discovery pipeline, emphasizing their capacity to accelerate development timelines and advance precision nano medicine. We analyzed predictive modelling techniques, including quantitative structure-activity relationship (QSAR) and absorption, distribution, metabolism, and excretion (ADME) predictions, which streamline the identification of viable drug candidates, including nanocarrier-enabled drug systems. Virtual screening and bioactivity prediction further refine candidate prioritization, whereas target identification and validation leverage protein-ligand interaction modelling and biological pathway analysis to ensure therapeutic specificity. Additionally, we discuss the profound impact of DL on medical image analysis, genomic data interpretation, and protein structure prediction (PSP), which collectively advance structural bioinformatics and enable optimized targeted nano medicine. By synergizing ML and DL, multi-modal data fusion, explainable artificial intelligence (XAI), and nanotechnology-driven datasets, the drug discovery process is evolving into a more efficient, predictive, and patient-centric endeavor, paving the way for ground-breaking therapies and improved clinical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42292041/","authors":["Chakraborty A","Gholap AD","Khuspe PR","Sundaram G","Webster TJ","Khalid M","Haris MS","Faiyazuddin M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/IJN.S600651","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291893","name":"Feasibility of Deep Learning-Based Segmentation of the Facial and Vestibulocochlear Nerves on High-Resolution Magnetic Resonance Imaging.","source":"pubmed","abstract":"To evaluate the feasibility of deep learning-based automated segmentation of the facial and vestibulocochlear nerves within the cisternal and intracanalicular segments on high-resolution magnetic resonance imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/42291893/","authors":["Bartellas M","Chillakuru Y","Su M","Yusina S","Jethanamest D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.7759/cureus.108842","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291860","name":"The Effect of Structured Context on Chest Radiograph Interpretation by a Multimodal Large Language Model: A Pilot Comparative Study.","source":"pubmed","abstract":"Background Multimodal large language models are increasingly discussed as potential adjuncts for medical image interpretation, yet the extent to which structured contextual guidance affects performance remains uncertain. Objective The aim of this study was to determine whether a structured contextual intervention improves ChatGPT performance on chest radiograph interpretation. Methods We conducted a comparative pilot study using 50 chest radiographs with established reference diagnoses. In the baseline condition, ChatGPT interpreted each image using only the standardized prompt, \"Diagnose this X-ray image.\" In the structured-context condition, the model first reviewed a Radiopaedia-derived teaching module containing 100 labeled chest radiographs spanning 10 common diagnoses and then interpreted the same test set using an author-developed radiologic framework that emphasized study identification, image-quality assessment, systematic visual review, separation of findings from diagnostic impressions, explicit communication of uncertainty, and structured reporting. Performance was assessed with percent-correct scoring, with partial credit awarded when responses demonstrated appropriate reasoning but lacked full specificity. Results Overall accuracy improved from 28% at baseline to 62% after the structured-context intervention. Accuracy reached 100% for chronic obstructive pulmonary disease, pulmonary edema, and foreign body identification, and improved to 80% for pneumothorax, cardiomegaly, and pleural effusion. Performance remained limited for lung cancer (20%) and feeding tube placement (40%), and rib fractures were not identified in either condition. Accuracy for atelectasis declined from 40% to 20%. Conclusions Structured contextual guidance improved performance on selected chest radiograph tasks, but overall diagnostic reliability remained inadequate for independent clinical use. Larger studies with standardized scoring, external validation, and imaging-specific evaluation are needed.","url":"https://pubmed.ncbi.nlm.nih.gov/42291860/","authors":["Cusick A","Guy S","Herz C","Manfre C","DeVries R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.7759/cureus.108715","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291826","name":"From Callus to Code: A systematic review of early evidence on artificial intelligence in fracture non-union.","source":"pubmed","abstract":"Failure of fracture healing continues to pose a major challenge in orthopaedic practice, occurring in approximately 5-10% of long bone injuries and contributing to prolonged disability and increased healthcare utilisation. Traditional prediction methods rely on clinical and radiographic assessment but are limited by variability and inability to capture complex, non-linear interactions between risk factors. Advances in artificial intelligence (AI), particularly machine learning and computational modeling, offer new opportunities for improving the prediction and management of fracture healing outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42291826/","authors":["Desouza C","Senthil V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jcot.2026.103507","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291761","name":"The landscape of machine learning in clinical applications: A thematic mapping of evolution, frontiers, and future opportunities.","source":"pubmed","abstract":"Machine learning (ML) has become a transformative force in clinical research, offering predictive precision and data-driven decision-making across diverse medical domains. Despite this rapid adoption, a comprehensive informatic-based synthesis of ML applications in clinical trials remains lacking. This study systematically maps the scientific landscape, thematic evolution, and emerging directions of ML-related clinical trial research.","url":"https://pubmed.ncbi.nlm.nih.gov/42291761/","authors":["Talib AM","Abdelwahab SI","Elhassan Taha MM","Daadaa Y","Alomary FO","Obaid EM","Alhathli MA","Farasani A","Moshi J","Khamjan N","Al-Ahmadi HH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261461041","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291403","name":"Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.","source":"pubmed","abstract":"Non-small cell lung cancer (NSCLC) remains one of the leading causes of global cancer mortality despite advances in immunotherapy. While immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis have transformed clinical outcomes for selected patients, response rates remain highly variable due to tumor heterogeneity, immune escape mechanisms, and evolving biomarker complexity. The need for dynamic, integrative biomarkers that better predict treatment response and guide personalized therapy is increasingly critical. This narrative review synthesizes recent advances (2023-2025) in genomic, transcriptomic, proteomic, metabolomic, and liquid-biopsy-based biomarkers relevant to NSCLC immunotherapy. Key databases, including PubMed, Scopus, and Web of Science, were screened, with emphasis on emerging artificial intelligence (AI) and digital twin-based frameworks supporting precision immuno-oncology. Across studies, single biomarkers such as PD-L1 or tumor mutational burden (TMB) demonstrate limited standalone predictive value. Multi-omic signatures incorporating circulating tumor DNA (ctDNA) fragmentomics, exosomal PD-L1, T-cell receptor (TCR) repertoire diversity, DDR alterations, metabolic checkpoint activity, and spatial immune profiling demonstrate improved accuracy and clinical relevance (clinical and preclinical evidence). AI-based multimodal models and digital immune twins further enhance predictive capacity by mapping resistance trajectories and simulating individualized therapeutic responses (computational/model-based evidence).The transition from static biomarkers toward integrated multi-omic and AI-driven decision frameworks represents a paradigm shift in NSCLC immunotherapy. These emerging platforms support a future of adaptive, anticipatory, and personalized treatment strategies with strong translational potential.","url":"https://pubmed.ncbi.nlm.nih.gov/42291403/","authors":["Abuhassan Q","Al-Ameer HJ","Balogh Z","Rekha MM","Sahoo S","Bavanilatha M","Arora V","Sinha A","Khazratov A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.22038/ijbms.2026.92560.19984","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291376","name":"Annual progress in transbronchial diagnosis and treatment of pulmonary malignant tumors [2025]: a narrative review.","source":"pubmed","abstract":"The rapid advancement of transbronchial endoscopic interventional techniques is profoundly transforming the diagnostic and therapeutic landscape of pulmonary malignant tumors. This article reviews high-quality, high-impact clinical studies on transbronchial diagnosis and treatment of pulmonary malignant tumors published in 2025, covering: precise diagnosis of peripheral pulmonary lesions (PPLs), lymph node biopsy strategies, combined utilization of multiple biopsy tools, artificial intelligence (AI)-assisted diagnostic system and rapid on-site evaluation (ROSE) for biopsy strategies development, ablation for peripheral pulmonary malignant tumors and central airway interventions, with a focus on their clinical application value.","url":"https://pubmed.ncbi.nlm.nih.gov/42291376/","authors":["Sun A","Xu D","Sun J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 31","doi":"10.21037/tlcr-2026-0281","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291339","name":"Applying artificial intelligence to ensure high quality and equitable lung cancer screening.","source":"pubmed","abstract":"Lung cancer screening (LCS) with low-dose computed tomography has the potential to improve early detection and promote more equitable health outcomes. However, traditional eligibility criteria, based primarily on age and smoking history, may overlook high-risk individuals, particularly in underrepresented populations. These include racial minorities and individuals living in rural areas, who often face limited access to screening centers and high-quality imaging interpretations. Artificial intelligence (AI) offers promising solutions to potentially enhance the effectiveness and equity of LCS. First, AI could refine risk stratification by incorporating additional clinical data, social determinants of health, environmental exposures, and comorbidities, thereby identifying high-risk individuals who may be missed by conventional criteria (e.g., Black Americans, women). Second, AI could improve access to high-quality screening by enhancing image acquisition across diverse technologies and enabling remote interpretation through telehealth. Third, AI tools could support radiologists by increasing the accuracy of nodule detection and improving the assessment of malignancy risk in detected nodules. Finally, AI could assist in managing incidental findings and facilitate opportunistic screening, further expanding the impact of LCS. Despite its promise, the implementation of AI in clinical practice faces several barriers. These include regulatory hurdles, the need for clinical billing codes, and substantial investment in infrastructure, training and ongoing monitoring of these technologies. Further, the consideration of fairness-aware frameworks to mitigate racial bias in AI tools developed from non-representative datasets. Integrating AI into the radiologic workflow, with attention to these challenges, may address disparities and improve the overall quality and reach of LCS. However, AI implementation will need to be carefully evaluated to determine whether it is achieving these goals.","url":"https://pubmed.ncbi.nlm.nih.gov/42291339/","authors":["Sieren JC","Newell JD Jr","Guerra CE","Hoffman RM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 31","doi":"10.21037/tlcr-2026-1-0224","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291291","name":"Editorial: Next-generation technologies and multidisciplinary integration for oral cancer diagnosis and treatment.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42291291/","authors":["Chakraborty A","Shankar A","Aziz F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/froh.2026.1870105","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42291168","name":"A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.","source":"pubmed","abstract":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease, affecting approximately 30 percent of the population worldwide. Despite this high prevalence, the disease remains underdiagnosed, partially due to the low sensitivity of non-invasive tests (NITs) and reliance on invasive liver biopsies. This review aims to summarize the current literature regarding the role of artificial intelligence (AI) in optimizing the diagnosis and management of MASLD.","url":"https://pubmed.ncbi.nlm.nih.gov/42291168/","authors":["Niazi A","Singh B","Singh C","Thakral N","Batta A","Sohal A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/tgh-2026-0002","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290999","name":"The neuro-skeletal crosstalk: Mechanisms, clinical implications, and smart material interventions.","source":"pubmed","abstract":"In recent years, the concept of neuro-skeletal crosstalk, highlighting the reciprocal interactions between the nervous and skeletal systems, has opened new avenues for understanding the pathogenesis and intervention strategies of complex diseases. This review summarizes the roles of molecular networks such as neurotransmitters, endocrine factors, immune mediators, and extracellular vesicles in bone metabolism, repair, and neurodegenerative diseases, with an emphasis on recent advances regarding bone-derived signals-including the Piezo1 channel and osteocalcin-in neural regulation. Building on this foundation, we focus on advances in frontier materials such as nanomaterials and hydrogels for modulating the brain-bone microenvironment and facilitating coordinated tissue regeneration, as well as new strategies for targeted drug delivery and immune microenvironment modulation. Empowered by next-generation technologies-including multi-omics, artificial intelligence, and organ-on-a-chip systems-the investigation of the fundamental mechanisms and personalized interventions of the brain-bone axis is entering a new era of opportunity. We hope that this review will provide a theoretical basis and valuable reference for future mechanistic studies and innovation in this interdisciplinary field.","url":"https://pubmed.ncbi.nlm.nih.gov/42290999/","authors":["Liu W","Shen X","He Y","Ding Z","Zhou J","Guo J","Lin X","Zhang L","Yuan P","Wu Y","Guo J","Rong X","Sun L","Yu E","Shi Y","He J","Ji Y","Li T","Wang J","Wu T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1016/j.jot.2026.101130","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290926","name":"Good things come in threes: evaluating clinical utility of machine learning-derived clusters.","source":"pubmed","abstract":"Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose.","url":"https://pubmed.ncbi.nlm.nih.gov/42290926/","authors":["Lisik D","De Kok JWTM","Bermúdez Barón N","Vanfleteren LEGW","Nwaru BI","Basna R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1093/jamiaopen/ooag098","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290728","name":"Advancements in diagnosis, preventive care, and future directions in the holistic management of pediatric asthma.","source":"pubmed","abstract":"Pediatric asthma is a highly prevalent chronic respiratory disorder characterized by airway inflammation, hyperreactivity, and variable airflow limitation, resulting in significant global health and socioeconomic burdens. Advances in diagnosis, including the measurement of fractional exhaled nitric oxide, assessment of blood eosinophils, and artificial intelligence-assisted tools, have improved early detection and disease stratification. Optimal management combines planned daily care, which includes systematic symptom monitoring, adherence to prescribed medications, frequent physical activity, and the promotion of adequate sleep. Targeted preventative methods, including allergy avoidance, viral infection prophylaxis, air quality improvement, and lifestyle optimization, reduce exacerbation risk and promote long-term control. Multiple variables influence pediatric asthma outcomes, including genetic predisposition, environmental exposures, and social determinants, emphasizing the significance of coordinated care among caregivers, schools, and multidisciplinary healthcare teams. Stepwise pharmacological and non-pharmacological therapies provide tailored treatment options for severe or refractory conditions. Persistent barriers to healthcare access, disparities in disease burden, and issues with treatment adherence underline the need for creative solutions. Emerging approaches, such as digital self-management tools, precision-based prevention strategies, and community-level interventions, present prospects to improve disease control. Effective asthma care goes beyond pharmacological treatment, focusing on early intervention, personalized support, and equitable access to improve children's long-term respiratory health and quality of life.","url":"https://pubmed.ncbi.nlm.nih.gov/42290728/","authors":["Guo W","Li K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1793567","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290694","name":"Which explainable AI methods in medical imaging are clinically impactful? A systematic literature review addressing the clinician's perspective.","source":"pubmed","abstract":"Explainable Artificial Intelligence (XAI) has emerged as a strategy to enhance the transparency and interpretability of AI systems in medical imaging. Although numerous methods have been developed to generate explanations of model behavior, their evaluation has predominantly relied on technical performance metrics rather than clinician-centered assessment. The limited involvement of clinicians in the development and validation of XAI methods, together with the absence of clinically meaningful evaluation frameworks, represents a significant barrier to the successful integration of AI into routine clinical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42290694/","authors":["Ud Din S","Kemna R","Ket JCF","Iqbal M","Bohoudi O","Hoogendoorn M","Beretta E","Lisowska A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1819422","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290654","name":"The Missing Millions: Building Cancer Pathology Artificial Intelligence for Safety Net Populations.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42290654/","authors":["Jouzi Z","Omar M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.mcpdig.2026.100374","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290653","name":"Introducing Adjunct Medical Products and Artificial Intelligence Governance Gaps.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42290653/","authors":["Duffourc MN","Gilbert S","Nadeau C","Mindy Nunez Duffourc","Stephen Gilbert","Courtney Nadeau"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.mcpdig.2026.100373","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"pmid:42290458","name":"Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.","source":"pubmed","abstract":"Adolescent idiopathic scoliosis (AIS) screening remains controversial, particularly regarding its effectiveness and cost-effectiveness in population settings. The Scoliosis Research Society - AIS screening (SRS-AIS) is simple, reliable, and widely accepted in clinical practice. With the emerging of artificial intelligence (AI) in medical screening, AI-based approaches have been proposed as potential alternatives. However, whether AI-AIS screening offers meaningful advantages over established SRS-AIS screening in real-world practice remains unclear. This study aimed to evaluate the efficacy, methodological quality, and practical relevance of AI-based AIS screening in comparison with conventional SRS-AIS screening approache.","url":"https://pubmed.ncbi.nlm.nih.gov/42290458/","authors":["Zhou Z","He X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 15","doi":"10.3233/SHTI260781","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42290241","name":"Clinical implementation of artificial intelligence in adolescent mental healthcare.","source":"pubmed","abstract":"This review aims to summarize recent literature on artificial intelligence (AI) tools for adolescent mental health, including the types of tools available, their clinical applications, effectiveness, and safety, as well as relevant ethical considerations.","url":"https://pubmed.ncbi.nlm.nih.gov/42290241/","authors":["Bischops AC","Kavanaugh JR","Bickham DS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1097/MOP.0000000000001584","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42289673","name":"Quantification of mitral regurgitation: from traditional methods to artificial intelligence.","source":"pubmed","abstract":"Mitral regurgitation (MR) is a common valvular disorder and associated with adverse outcomes. Echocardiography is the primary imaging modality for MR assessment; however, standard methods rely on geometric assumptions and single-frame analysis, which are especially inaccurate in the setting of eccentric, multiple or non-holosystolic jets. Recent developments in machine learning have enabled automated quantification of MR, which analyse regurgitant flow throughout systole, account for non-hemispheric orifice area and jet morphology, and demonstrate favourable agreement with cardiac magnetic resonance. In addition, AI capable of detecting and grading MR directly from echocardiographic clips offer potential utility for screening, particularly in low-resource settings where specialist review is limited. Quantification of Mitral Regurgitation: from traditional methods to Artificial Intelligence.","url":"https://pubmed.ncbi.nlm.nih.gov/42289673/","authors":["Cho K","Su J","Bonnefous O","Prabhu S","Anthony C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 15","doi":"10.1186/s12947-026-00374-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42289600","name":"Hydroxychloroquine-associated cardiomyopathy with nondiagnostic noninvasive evaluation: diagnostic value of endomyocardial biopsy in a case-based review.","source":"pubmed","abstract":"Hydroxychloroquine (HCQ) is a cornerstone in treating autoimmune diseases and is generally well-tolerated. Cardiotoxicity is a rare but serious adverse effect of prolonged HCQ therapy. The progression of cardiac dysfunction may evade early detection with standard noninvasive investigations. This case report was prepared in accordance with the CABARET guidelines from the EQUATOR Network. The patient underwent comprehensive clinical evaluation for diagnostic clarification. A 57-year-old woman with Sjogren's disease (SjD) who was treated with HCQ 400&#xa0;mg daily for nearly 21 years (cumulative dose&#x2009;~&#x2009;3,000&#xa0;g). The patient started to develop symptoms of syncope, shortness of breath and lower extremity edema. Noninvasive evaluation including echocardiography, coronary angiography, and cardiac magnetic resonance imaging failed to identify a clear etiology, demonstrating preserved ventricular size and systolic function without evidence of infiltrative or inflammatory cardiomyopathy. Continued decline in the patient's clinical status in addition to lack of response to medical therapy, an endomyocardial biopsy (EMB) was performed and revealed findings consistent with HCQ induced cardiotoxicity. HCQ was stopped; however, the damage was beyond reversal, so the patient ended up having an orthotopic heart transplantation. This case sheds light on an irreversible HCQ-induced cardiotoxicity that ended up with a heart transplantation. Despite extensive cardiac workup this adverse effect remained undetected until advanced disease. Delayed diagnosis can lead to irreversible cardiac damage and EMB should be considered in patients on prolonged HCQ therapy with unexplained cardiac symptoms and nondiagnostic workup. This can prompt early discontinuation of HCQ with potentially reversible damage and more favorable outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42289600/","authors":["Alkhatib A","Al-Kofahi M","Abumuhfouz M","Elliott DRF","Noaiseh G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 14","doi":"10.1007/s00296-026-06176-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42289424","name":"Syphilis epidemiology in Jordan: prevalence, incidence, and seroreversion from 15 years of laboratory-based data.","source":"pubmed","abstract":"Syphilis is a health priority targeted for elimination by 2030, yet its global epidemiology remains insufficiently characterized. The study analyzed data from 25,630 adults aged&#x2009;&#x2265;&#x2009;15 years who underwent syphilis testing in Jordan at Biolab Diagnostic Laboratories during 2010-2025. Cross-sectional and retrospective cohort analyses were used to estimate prevalence, temporal trends, patterns of rapid plasma reagin (RPR) and Treponema pallidum pallidum hemagglutination assay (TPHA) reactivity, associated risk factors, and incidence rate. Syphilis prevalence (ever RPR- or TPHA-reactive) was 0.9% (95% CI: 0.8-1.0%) overall and only 0.08% (95% CI: 0.05-0.10%) among those tested for visa-related medical screening. RPR reactivity was 0.5% (95% CI: 0.4-0.6%), and among RPR-reactive individuals, 79.2% (95% CI: 65.0-89.5%) were TPHA-reactive within 30 days. Outpatient or walk-in visits, doctor referrals, and laboratory referrals showed substantially higher reactivity than visa-related medical screening, whereas sex, governorate, insurance status, and testing year showed no significant associations. Cumulative syphilis incidence was 0.18% (95% CI: 0.04-0.72%) after two years of follow-up, with an incidence rate of 0.92 per 1,000 person-years (95% CI: 0.23-3.68). Syphilis prevalence and incidence in Jordan are relatively low, but with no evidence of decline over 15 years, indicating limited progress toward elimination targets.","url":"https://pubmed.ncbi.nlm.nih.gov/42289424/","authors":["Abu-Dayyeh I","Chemaitelly H","Al Tibi A","Ghunaim M","Hasan T","Abdelnour A","Abu-Raddad LJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 14","doi":"10.1038/s41598-026-56491-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42289330","name":"Deploying AI for global health equity: A readiness matrix and case studies from low- and middle-income countries.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming healthcare and can reduce inequalities through optimized resource allocation, timely diagnosis, and personalized care. However, over 3.5 billion people still lack access to basic health services, primarily in low- and middle-income countries (LMICs) due to infrastructural, socioeconomic, and cultural barriers. This article examines the opportunities and limitations of AI in advancing global health equity, focusing on region-specific challenges. It presents a narrative review and case-based synthesis of AI deployment in LMICs, using examples from India, Peru, Senegal, Kenya and Cambodia. Key implementation factors include infrastructure, workforce capacity, digital literacy, data availability, resource constraints, and cultural fit. A tiered AI readiness matrix is introduced to compare preparedness and support policymakers in prioritizing AI applications. Ethical risks such as algorithmic bias, data sovereignty and transparency are also emphasized. Successful AI deployment requires localized adaptation, participatory design, and supportive governance. This study provides a strategic framework for policymakers, developers, and global health actors who aim to implement AI equitably and effectively.","url":"https://pubmed.ncbi.nlm.nih.gov/42289330/","authors":["Dinc R","Ardic N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr-Jun","doi":"10.1177/14604582261455105","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42289215","name":"Artificial intelligence in clinical metagenomic pathogen detection: A critical review of pipeline integrations, challenges, and future directions.","source":"pubmed","abstract":"Metagenomic next-generation sequencing (mNGS) has expanded the scope of clinical diagnostics by enabling culture-independent detection of microorganisms in patient samples. However, mNGS clinical utility remains constrained by substantial computational demands, reference database biases, and the persistent challenge of distinguishing true pathogens from host background, commensal flora and environmental contamination. Traditional alignment and k-mer-based bioinformatics pipelines frequently struggle to balance speed, sensitivity, and the ability to detect highly divergent or novel organisms. This review critically synthesizes the current landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications across the mNGS diagnostic pipeline, examining deep learning architectures-including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers-as integrated into raw read processing, host sequence depletion, primary taxonomic classification, and ancillary detection of antimicrobial resistance (AMR) and virulence factors. While several AI methodologies report high classification accuracy in benchmarking studies, we note that most performance claims derive from simulated datasets or controlled mock communities rather than prospective clinical validation. Significant gaps persist, including limited AI integration in front-end signal optimization, inadequate automated clinical reporting, absence of standardized benchmarking metrics, and unresolved questions regarding data leakage, reproducibility, and generalizability. Successful clinical translation will require addressing the interpretability limitations of current explainable AI approaches, navigating complex and evolving regulatory landscapes for Software as a Medical Device (SaMD), and bridging the gap between computational feasibility and demonstrated patient-outcome benefit. The development of genomic foundation models and multi-modal clinical integration holds promise for advancing mNGS toward real-time, actionable diagnostics, though substantial evidence gaps remain between current proof-of-concept demonstrations and validated clinical deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42289215/","authors":["Dai J","Tan X","Ma J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.mimet.2026.107592","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42288938","name":"Predicting unfavorable tuberculosis outcomes using machine learning: a prospective cohort.","source":"pubmed","abstract":"Tuberculosis (TB) continues to be a primary cause of mortality from a singular infectious agent worldwide, with a significant number of patients still encountering unfavorable outcomes such as treatment failure, relapse, or death. Early identification of high-risk individuals is essential for optimizing clinical management, yet conventional statistical approaches often fail to capture the complex, nonlinear interactions among clinical predictors. Machine learning (ML) presents a promising alternative; however, previous ML-based prognostic studies in TB have been constrained by small sample sizes, retrospective designs, or insufficient external validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42288938/","authors":["Lee T","Choi I","Lee H","Kim HW","Lee EG","Park Y","Jung SS","Kim JW","Oh JY","Lee H","Kim SH","Kim SH","Lyu J","Kwon SJ","Jeong YJ","Koo HK","Kim JS","Min J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1186/s41182-026-00969-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42288615","name":"FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.","source":"pubmed","abstract":"Medical imaging is central to modern diagnostics, and artificial intelligence (AI) systems are increasingly used to support image-based analysis by improving efficiency, accuracy, and access to care. However, inequities in healthcare access and differential disease prevalence create severe demographic imbalances in clinical image data. Such imbalances are compounded by the fact that diseases can manifest with distinct features across demographic groups, rendering certain phenotypic presentations naturally rare. AI models trained on such imbalanced data risk perpetuating diagnostic bias and widening healthcare disparities. Here we introduce FairGen, a fairness-aware diffusion framework that synthesizes demographically balanced medical images while preserving pathology-relevant visual features. By embedding physician-aligned preferences into the generation process, FairGen improves subgroup coverage during synthesis and downstream classification. Applied to dermatology, radiology, and neuroimaging benchmark tasks, FairGen achieves fairness improvements of 95.9% for skin images, 80.0% for chest radiography, and 35.2% for brain MRI, while maintaining competitive diagnostic accuracy relative to models trained on original clinical data. Clinician-facing expert review and external validation on independent cohorts further support that these gains extend beyond standard fidelity metrics and are not confined to the original in-distribution datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/42288615/","authors":["Li Z","Zhang R","Tan Z","Aizenstein HJ","Hu J","Chen T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1038/s41746-026-02868-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42287460","name":"Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.","source":"pubmed","abstract":"Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease with substantial variation in response to biologic therapy. Artificial intelligence (AI) is increasingly used to predict treatment response, but most existing models are association-based and offer limited interpretability for clinical decision-making. To critically review the role of causal artificial intelligence in predicting and explaining biologic treatment response in RA and assess its potential to improve interpretability and individualized treatment selection. A narrative review with an integrative approach was conducted, as described by Gasparyan et al. Studies on causal inference, explainable AI, and biologic treatment-response modeling in RA were identified through searches of PubMed/MEDLINE, Scopus, Web of Science, and the Directory of Open Access Journals, updated to May 2026, with backward citation screening. The RA literature is dominated by predictive and explainable machine learning studies rather than integrated causal AI systems. Recent scoping reviews identified 24 RA-specific studies and 74 RA studies in a broader inflammatory arthritis literature, with AUC values 0.54 to 0.92. External validation is uncommon. A smaller body of work has begun to estimate individualized treatment effects or emulate target trials, suggesting a bridge toward causal reasoning. Causal AI is a promising translational direction for predicting and explaining biologic treatment response in RA. The evidence base remains limited; empirical validation, stronger handling of confounding, and usable transparent models are needed. Priorities include narrow drug coverage, sequential treatment decisions, multimodal data integration, fairness, and shared decision making.","url":"https://pubmed.ncbi.nlm.nih.gov/42287460/","authors":["Bedaiwi M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1007/s00296-026-06156-7","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42287453","name":"The evolution of AI-integrated genome editing and its challenges.","source":"pubmed","abstract":"Artificial Intelligence (AI) is poised to revolutionize the field of genome editing by enhancing precision, efficiency, and accessibility. AI-driven approaches are already improving the design of CRISPR-based systems by enabling more accurate identification of target sequences and predicting off-target effects. Machine learning (ML) algorithms can analyze vast genomic data, as well as identify patterns and mutations that might be overlooked by traditional methods. Taking together, utilizing AI/ML tools allow for the enhancement of every step in genome editing. Recent advances also demonstrated that AI-powered tools can facilitate the simulation and modeling of genetic modifications, predicting their effects on cellular behavior and phenotypes. This allows for a more rapid prediction of the genome editing effects, without the need for wet lab. Additionally, AI can accelerate drug discovery and therapeutic development by streamlining the identification of genetic targets and optimizing gene therapies. The integration of AI with genome editing promises to democratize access to cutting-edge technologies, enabling researchers to design and plan for complex genetic modifications with minimal technical expertise. Drawing from various examples, this paper dives into the advancements and applications of AI in genome editing, its limitations, as well as future directions and opportunities in this field.","url":"https://pubmed.ncbi.nlm.nih.gov/42287453/","authors":["Wong MTJ","Zulkifli ND","Ravichandran T","Vasodavan K","Al-Shaibah O","Himel GMS","Achuthan A","Anthonysamy MA","Theva Das K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1007/s00335-026-10248-x","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42287407","name":"Transitioning from child and adolescent psychiatry to follow-up care: a concept mapping study.","source":"pubmed","abstract":"Youth with severe and enduring mental health problems often require follow-up care after treatment in child and adolescent psychiatry (CAP). In this study, follow-up care is defined as services beyond CAP, including basic mental health support, primary mental health care, and practical or parenting support delivered by community-based teams. However, transitions to such care are often fragmented, poorly coordinated, and experienced as abrupt. This study explores perspectives of CAP clinicians, follow-up care providers, parents, and youth to identify factors considered both important and feasible for improving care transitions. Using Group Concept Mapping (GCM), a participatory mixed-methods approach, participants (n&#x2009;=&#x2009;55) generated, sorted, and rated statements perceived as important and feasible in transitions from CAP to follow-up care. Data were analyzed using multidimensional scaling, hierarchical cluster analysis and Go-Zone analysis. Participants generated 56 statements, thematically grouped into nine clusters: (1) Care coordination, (2) Integrated care networks, (3) Care organization, (4) Family-centered care, (5) Follow-up care, (6) Shared decision-making, (7) Handover, (8) Accessible services, and (9) Empowerment and recovery. The findings suggest that transitioning from CAP to follow-up care is multifaceted and requires more than a timely handover between care providers. Particularly, shared decision-making, recovery orientation, and family involvement emerged as central themes across clusters. Our study indicates that improving transitions may benefit from coordinated, relational, and tailored approaches that promote continuity and empower youth and families throughout the process.","url":"https://pubmed.ncbi.nlm.nih.gov/42287407/","authors":["Heek HC","Beckers T","Vermeiren RRJM","Mulder EA","Nooteboom LA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1007/s00787-026-03088-2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42287017","name":"A Review of the Australian MRI Linac Program: From Pie in the Sky to Research Milestone.","source":"pubmed","abstract":"The Australian Magnetic Resonance Imaging (MRI) Linear Accelerator program (MRI linac) was a major research project that aimed to build and test a unique MRI linac prototype for cancer treatment. It aimed to improve radiotherapy anatomical targeting and explore physiological targeting. The purpose of this report is to summarise the development and achievements of the program so as to provide an example of a successful large-scale research project in Australian radiation oncology. The project involved six Australian universities and international collaborators. We developed and built a unique MRI linac configuration comprising a 1&#x2009;T magnetic field and a 6 MV accelerator, with the beam delivered in line with B 0 and the patient placed across B 0 in the split between the two halves of the magnet. The broad research domains were personalised disease targeting, medical device innovation, and biodiscovery. Specific projects included Artificial Intelligence image enhancement, radiation dosimetry in high magnetic fields, MRI characterisation of cancer heterogeneity in human tumours, and animal and human studies. Over $27 million was obtained to support the program from competitive sources. The program published over 120 papers and supported 25 PhD completions. The learnings from the Australian MRI linac program are that Australia has world-class radiotherapy research in physics and engineering, that major projects need a lot of time and a lot of collaboration, and that large, novel radiotherapy projects can attract significant funding and produce significant results.","url":"https://pubmed.ncbi.nlm.nih.gov/42287017/","authors":["Barton MB","Delaney GP","Greer P","Holloway L","Metcalfe P","Rai R","Pham T","Price WS","Waddington D","Crozier S","Keall P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1111/1754-9485.70133","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42286997","name":"Prescribing Caution: A Critique of OpenEvidence to Answer Medication-Related Questions.","source":"pubmed","abstract":"OpenEvidence is a newer medical large language model (LLM) that is growing in use among health professionals and students for clinical care and research. This paper provides an overview of existing literature on the accuracy of using OpenEvidence to answer clinical questions. Given the limited research exploring OpenEvidence's answers to clinical or medication-related questions from a pharmacist's perspective, this paper presents two illustrative examples that highlight inaccuracies of OpenEvidence's responses and source summarization errors related to pharmacotherapeutic content. The authors also offer suggestions for the responsible use of this tool, acknowledging that its adoption by pharmacists and health care professionals is likely to expand.","url":"https://pubmed.ncbi.nlm.nih.gov/42286997/","authors":["Bergsbaken JM","Wilson P","Vandagriff S","Christensen L","Vellardita L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/jac5.70237","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42286915","name":"Data After Death: Post-Mortem Ethics of Burn Patient Records and Images.","source":"pubmed","abstract":"Burn care frequently relies on extensive documentation, including graphic photographic images and detailed clinical records. While these materials are essential for diagnosis, treatment planning, and research, their use after a patient's death raises complex ethical questions. The emergence of new technologies such as artificial intelligence training, alongside the increased visibility of burn images in education and public health campaigns, challenges traditional notions of confidentiality and consent. This narrative review examines the ethical boundaries of using burn patient records and images post-mortem, with a focus on emerging concerns around digital remains and posthumous consent. A narrative review of peer-reviewed literature, professional guidelines, and position statements published between 2000 and 2025 was conducted. Sources included PubMed, Scopus, and Google Scholar, using search terms such as \"burn injuries,\" \"medical photography,\" \"post-mortem consent,\" \"digital remains,\" and \"medical ethics.\" Relevant publications addressing clinical practice, teaching, research, and social media use in burn care were synthesized to identify key ethical themes and gaps. The literature reveals that while ethical frameworks for consent, privacy, and confidentiality are well established during life, guidance becomes inconsistent once the patient has died. A small but growing body of scholarship identifies posthumous privacy as an emerging domain of bioethics. Across studies, concerns included dignity after death, risks of re-identification on digital platforms, and the absence of explicit patient directives regarding posthumous use of images and data. Current medical guidelines provide minimal direction, leaving ambiguity for clinicians and researchers. The ethical use of burn patient images and records after death remains underexplored, particularly in the context of AI training datasets and social media awareness campaigns. The absence of consensus underscores the need for professional societies to establish clearer policies and protocols that honor patient dignity beyond life. Establishing standards for posthumous consent will help clinicians, educators, and researchers navigate the evolving landscape of digital medicine responsibly.","url":"https://pubmed.ncbi.nlm.nih.gov/42286915/","authors":["Khorsandi J","Mansoury B","Blank L","Ahmed AB","Alkhouri S","Cataldo K","MacDavid J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 13","doi":"10.1093/jbcr/irag097","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42286628","name":"Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the medical field. The aim of this review was to outline the current scientific state of AI and ML application in sports medicine, evaluate the level of clinical validation and readiness for implementation, and identify key priorities to guide future advancements and implementation into injury risk assessment, diagnosis, rehabilitation and clinical decision-making in sport medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42286628/","authors":["Lindskog J","Heder Ternell K","Yu Y","Lindman I","Samuelsson K","Hamrin Senorski E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1186/s12911-026-03615-w","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42286322","name":"General-purpose large language models outperform specialized clinical AI tools on medical benchmarks.","source":"pubmed","abstract":"Specialized clinical artificial intelligence (AI) tools are entering medical practice despite scarce independent evaluation. We quantitatively evaluate two clinical AI tools, OpenEvidence and UpToDate Expert AI, built on large language models (LLMs) against three frontier LLMs: GPT-5.2, Gemini 3.1 Pro and Claude Opus 4.6. Our evaluation has three stages: (1) 500 MedQA questions testing medical knowledge, (2) 500 HealthBench items measuring alignment with clinicians and (3) the real clinical queries (RCQ) benchmark, built from 100 de-identified queries from physicians to a general-purpose language model in a live clinical environment. For the RCQ benchmark, 12 US clinicians performed randomized, blinded review of model outputs, producing 1,800 model-question annotations. Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ. These findings highlight the need for independent, real-world evaluation of AI tools before they enter clinical settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42286322/","authors":["Vishwanath K","Alyakin A","Ghosh M","Hage A","Neifert SN","Orillac C","Mandelberg NJ","Khan HA","Lee JV","Yao JJ","Small WR","Varma A","Hewitt DB","Aphinyanaphongs Y","Alber DA","Oermann EK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1038/s41591-026-04431-5","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42285444","name":"The INSPIRE doctoral network in safety pharmacology - Looking back and ahead.","source":"pubmed","abstract":"The INSPIRE project, funded by the European Marie Sk&#x142;odowska-Curie Action (MSCA) program, was a doctoral network designed to train early-stage researchers (ESRs) and stimulate exploratory research in cardiovascular safety pharmacology (SP). From 2020 until 2024, INSPIRE trained 15 doctoral candidates hosted by a consortium of academic and industrial partners. The current publication summarizes the outcomes of INSPIRE and reflects on the impact to the field. As part of INSPIRE, ESRs were engaged in individual research projects covering a range of topics, yet grounded in 4 thematic clusters to facilitate collaboration. The first cluster focused on human induced pluripotent stem cells-derived cardiomyocyte (hiPSC-CM) assays and explored additional readouts such as morphological profiling, miRNAs panels and artificial intelligence assisted analyses. The second cluster aimed to develop a new telemetry platform with integrated positioning system, but technical drawbacks required initiation of alternative plans still aligned with the mission to refine in vivo SP studies. A third cluster explored new concepts for hemodynamic assessment, such as arterial stiffness and in silico modelling. Finally, the fourth cluster involved mechanistic research in cardio-oncology. Some ESR projects, especially those hosted by industry partners, delivered tangible prototype products or services with potential for commercialization. Alternative outcomes included optimized experimental workflows, datasets with reference compounds and mechanistic insights. Overall, INSPIRE delivered on its mission to initiate explorative research in SP, although many results will require further development, standardization and establishment of their context of use. This highlights the difficulty of achieving regulatory relevance within PhD timelines. For the latter, sustained engagement from industry beyond the duration of the doctoral program seems essential. Educationally, 8 ESRs have successfully defended their PhD thesis and a few ESRs have transitioned into permanent roles in drug R&amp;D. Mobility of ESRs was a crucial, mandatory element of the training resulting in 23 secondments. The close interaction with industrial partners significantly enhanced trainee awareness of regulatory thinking, experimental robustness, and translational constraints. A yearly summer school was organized to facilitate mutual learning and build group cohesion. Meanwhile, the INSPIRE summer school has become a legacy with close to 100 alumni and offers a mix of theoretical lectures and broader sessions on career development. Looking ahead, ongoing revisions of international SP guidance (ICH S7A) and increasing interest in new approach methodologies (NAMs) create opportunities for a next-generation training network. In this context, we propose a roadmap for initiating INSPIRE v2.0 and have launched a call for input and participation to help shaping the future of SP.","url":"https://pubmed.ncbi.nlm.nih.gov/42285444/","authors":["Guns PJ","Szabó BR","Cherubin M","Liu H","Klein C","Hlova B","Biagini T","Alizadeh EA","Costa-Faya S","Van Assche CH","Wesley CD","Van Daele M","Pannucci P","Van Berlo B","Krüger DN","Savchenko AS","De Meyer GRY","Valentin JP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.vascn.2026.108433","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42285388","name":"Multifunctional bioengineered scaffolds and adjunct therapeutic strategies for diabetic foot ulcers.","source":"pubmed","abstract":"Diabetic foot ulcers (DFUs) are chronic, non-healing wounds that affect up to 34% of diabetic patients. DFUs are complicated by infection in nearly 60% of cases and frequently progress to amputation. DFU pathology is characterized by a persistent inflammatory state, impaired angiogenesis, and infection. This creates a complex microenvironment refractory to standard care, with fewer than 20% of DFUs healing within 8 weeks. In this review article, normal and pathophysiological processes of wound healing, current clinical management strategies, and adjunct therapeutics in the clinical pipeline are discussed, followed by recent advances in multifunctional bioengineered platforms. These platforms are categorized into three main systems: hydrogels, electrospun dressings, and 3D-bioprinted constructs, in addition to hybrid fabrication approaches and the integration of low-temperature plasma therapy as emerging multi-targeted strategies. For hydrogels, stimuli-responsive designs that respond to mechanical force, pH, glucose, and excess reactive oxygen species to actively modulate drug release and scaffold behavior are discussed. For electrospun scaffolds, strategies for controlled, multi-therapeutic delivery, including fiber blending, surface conjugation, and core-shell architectures are reviewed. Next, 3D bioprinting as a platform for patient-specific, cell-laden constructs is presented and covers major fabrication techniques and the emerging potential of handheld in situ bioprinters for accelerating clinical translation. Multi-targeted hybrid approaches that combine these platforms, along with the synergistic integration of low-temperature plasma therapy for broad-spectrum antimicrobial action, biofilm disruption, and immune modulation are emphasized. Unlike prior material-centric reviews, this review adopts a function-driven framework that organizes scaffold systems based on their ability to address key DFU pathologies, including infection, inflammation, impaired angiogenesis, and delayed healing, providing a more clinically relevant perspective. Finally, emerging directions such as artificial intelligence (AI)-guided design, in situ bioprinting, and recent clinical trends are discussed to bridge scaffold design with translational application. STATEMENT OF SIGNIFICANCE: Diabetic foot ulcers present a critical global health challenge characterized by a highly inflammatory microenvironment that remains refractory to standard care. This review elucidates the paradigm shift from passive wound dressings to \"intelligent,\" multifunctional bioengineered scaffolds designed to actively modulate DFUs. We critically examine recent advances in stimuli-responsive hydrogels (pH-, glucose-, and reactive oxygen species-sensitive), mechanically active contractile patches, complex electrospun architectures, and 3D bioprinting. Furthermore, by integrating emerging technologies such as handheld in situ 3D bioprinting, low-temperature plasma therapy, and artificial intelligence-driven design, this work provides a roadmap for the next generation of precision biomaterials capable of overcoming specific biological barriers to regeneration in chronic wounds.","url":"https://pubmed.ncbi.nlm.nih.gov/42285388/","authors":["Edalatian Zakeri S","Kanakarajan Vijaya Kumari P","Malik S","Aamir A","Kumar L","Sacchetta T","Vijayan VM","Hwang PTJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.actbio.2026.06.020","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42285341","name":"Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.","source":"pubmed","abstract":"BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.","url":"https://pubmed.ncbi.nlm.nih.gov/42285341/","authors":["Nejatinejad M","Maleki-Aram A","Namavar Abibiglou A","Vahedi C","Panahian A","Ebrahimi A","Yaghoubi R","Parsaei H","Vafaei S","Jafari D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1016/j.cca.2026.121178","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284999","name":"Nanoscale interrogation of α-Synuclein oligomers: Bridging biophysics, interface science, and neurobiology.","source":"pubmed","abstract":"&#x3b1;-Synuclein (&#x3b1;-syn) is an intrinsically disordered presynaptic protein. In synucleinopathies, it undergoes a structural transition into &#x3b2;-sheet-rich conformers that promote the formation of amyloid fibrils and pathological inclusions. Although fibrillar aggregates have been studied extensively, soluble oligomers, which may have the greatest neurotoxic potential, remain poorly understood because of their transient nature and structural heterogeneity. This review critically examines &#x3b1;-syn oligomeric species, with particular emphasis on advanced biophysical and surface characterization techniques and neurobiological models to elucidate oligomer formation, membrane interactions, and toxic mechanisms. Recent advances in spectroscopy, high-resolution microscopy, and mass spectrometry have significantly expanded the ability to characterize &#x3b1;-syn oligomers and their aggregation pathways. Nanopore-based and single-molecule approaches enable the investigation of transient and structurally heterogeneous oligomeric species at the level of individual particles. Interface science has further clarified direct interactions between oligomers and lipid membranes, providing mechanistic insight into neurotoxicity and the lipid-dependent modulation of &#x3b1;-syn conformation and stability. Neurobiological models have revealed multi-organelle disruption, prion-like propagation, and disease subtype-specific seeding. However, no single approach fully captures oligomer pathogenicity, which emerges from the interplay of structure, interfacial behavior, and cellular vulnerability. However, existing frameworks do not adequately address this complexity. Additional barriers include poor reproducibility and limited sensitivity across approaches. Future work should integrate these technologies with standardized biological protocols, advanced artificial intelligence algorithms, and biocompatible nanomaterials. Such an interdisciplinary approach could enable the development of multiscale platforms for real-time studies of &#x3b1;-syn soluble conformers, with clinical utility in the management of synucleinopathies.","url":"https://pubmed.ncbi.nlm.nih.gov/42284999/","authors":["Goncerz M","Rubiś B","Szukalska M","Totoń E","Nehra M","Malesza I","Sujka-Kordowska P","Jachimska B","Florek E","Mądry E","Kumar S","Kujawska M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cis.2026.103965","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284856","name":"Teaching with intelligence; AI-enhanced pedagogies in nursing education A scoping review.","source":"pubmed","abstract":"To map current evidence on AI integration in nursing education, identifying pedagogical applications, learning outcomes, future research directions.","url":"https://pubmed.ncbi.nlm.nih.gov/42284856/","authors":["Nugent L","Murray B","Moore Z","Patton D","O'Connor T","Watson C","Walsh K","Renjith V","George J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.nepr.2026.104878","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284608","name":"AI in UK Medical Education: A Framework for Curriculum Reform.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly transforming health care through improvements in diagnosis, predictive analytics, and workflow optimization. However, there remains a significant gap in AI training within UK medical education, leaving future clinicians underprepared for AI-driven health care environments. This viewpoint paper investigated global best practices for AI integration into medical education and proposes a structured framework for embedding AI into the UK medical curriculum. It aimed to assess current attitudes, highlight existing knowledge gaps, and recommend practical implementation strategies. An analysis of international case studies (eg, Stanford University, the University of Toronto, and Chinese University of Hong Kong) was conducted alongside a review of teaching methodologies, stakeholder perspectives, and UK-based surveys to identify core competencies and challenges in AI education. Effective integration strategies include the use of AI-powered simulations, interdisciplinary collaboration, elective modules, and faculty training. Major barriers include lack of AI-literate educators, insufficient ethical training, and limited infrastructure. Knowledge gaps persist among students and faculty in areas such as algorithmic bias, AI ethics, and clinical decision-making. To meet the demands of modern health care, the UK medical curriculum must adopt comprehensive AI training. This includes practical exposure, ethical awareness, and stakeholder engagement. Proactive reform will ensure that graduates are equipped to critically and ethically apply AI tools in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42284608/","authors":["Gaur A","Kirani Tan J","Sridhar Rao M","Fuad M","Bhatti T","Rishab Bharadwaj H","Mohamed Ahmed KAH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.2196/81953","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284524","name":"Smart feeding: the role of artificial intelligence and integrated nutrition platforms in the ICU.","source":"pubmed","abstract":"Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electronic health records and imaging. New technologies have tried to overcome obstacles to implement guidelines and recommendations. This review aims to describe the recent progress in the use of machine learning and new technologies in the field of nutrition of the critically ill.","url":"https://pubmed.ncbi.nlm.nih.gov/42284524/","authors":["Singer P","Raphaeli O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1097/MCC.0000000000001397","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284471","name":"Embodied Intelligence Applications in Health Care Populations: Scoping Review.","source":"pubmed","abstract":"Embodied intelligence-artificial intelligence instantiated in physical or virtual bodies that can perceive, communicate, and interact with users and their environments-has been increasingly applied in health care. However, the evidence base remains fragmented because of inconsistent terminology, diverse embodiment forms, and limited synthesis of application domains, target populations, care settings, acceptability, and effectiveness. This fragmentation constrains conceptual clarity and translation into routine health care practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42284471/","authors":["Shu W","Zhou Y","Sun E","Deng L","Ye X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.2196/83871","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42284085","name":"Artificial Intelligence in Biomedical Scientific Publishing.","source":"pubmed","abstract":"Artificial intelligence is now embedded across the scientific research and publishing ecosystem, influencing discovery, analysis, knowledge translation, authorship, peer review, and editorial workflows. In cardiovascular and biomedical sciences, these developments offer substantial opportunities to accelerate knowledge generation, integrate complex datasets, and improve efficiency and consistency. At the same time, they introduce new risks related to bias, transparency, data integrity, and authorship responsibility, potentially endangering trust in the scientific record. This commentary examines the evolving role of AI in biomedical publishing, with particular attention to generative models and machine learning tools. We review both benefits and limitations, highlight risks such as fabricated content, biased outputs, and erosion of accountability, and discuss why traditional detection approaches are insufficient. Instead, we argue for a shift toward transparency, provenance, and enforceable human responsibility as the core principles guiding AI use, ensuring that AI strengthens rather than undermines scientific rigour and public trust. We outline practical expectations for authors, reviewers, editors, and publishers, with emphasis on reporting standards, reproducibility under rapidly evolving model versions, and the conflict-of-interest implications of AI tooling for the editorial process itself.","url":"https://pubmed.ncbi.nlm.nih.gov/42284085/","authors":["Guzik TJ","Aboyans V","Agewall S","Bailey S","Baranchuk A","Bäck M","Böhm M","Bonow RO","Booz GW","Boriani G","Bozkurt B","Bruining N","Capodanno D","Erol Ç","Ferdinandy P","Gimelli A","Heusch G","Kahan T","Lang CC","Maurer G","Mentz RJ","Metra M","Moons P","Piskorz D","Piepoli M","Ponikowski P","Popma A","Rocca B","Fores JS","Sommer P","Touyz RM","Ungvari Z","Vranckx P","Hill JA","Lüscher TF","Crea F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1093/eurheartj/ehag494","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42283880","name":"Glomerulonephritis in Takayasu arteritis is dominated by mesangial and AA amyloid lesions and often arises during remission: a case-based review.","source":"pubmed","abstract":"Takayasu arteritis (TAK) is a large-vessel vasculitis in which glomerulonephritis is a rare complication with a poorly characterized clinical and histopathological profile. Patients with biopsy-proven glomerulonephritis were retrospectively identified from a single-center TAK cohort followed between 2014 and 2022. A systematic review of PubMed/MEDLINE, Scopus, and Web of Science for articles published up to December 2025 was conducted in parallel, excluding studies involving patients younger than 18&#xa0;years and cases with confounding autoimmune conditions or non-classifiable biopsy findings. This case-based review was reported in accordance with the CABARET and PRISMA 2020 standards. Among 72 patients with TAK, three (4.2%) had biopsy-proven glomerulonephritis. The systematic review yielded 31 articles describing 39 additional cases (84.6% female; median age at TAK diagnosis 26&#xa0;years; median age at glomerular disease diagnosis 33&#xa0;years). Glomerular disease was diagnosed a median of 5&#xa0;years (IQR 0-11) after TAK and most often presented as asymptomatic proteinuria or nephrotic syndrome. Mesangial proliferative glomerulonephritis was the most common subtype (35.9%), followed by AA amyloidosis (30.8%), membranoproliferative glomerulonephritis (12.8%), focal segmental glomerulosclerosis (10.3%), and membranous nephropathy (7.7%). Renal artery stenosis was absent in 69.2% of cases. Corticosteroids were used in 92.3% of cases and biologic agents in 10.3%. Glomerulonephritis is a rare but clinically significant late complication of TAK, characterized by a distinct histopathological spectrum dominated by mesangial lesions, AA amyloidosis, and membranoproliferative glomerulonephritis. Routine proteinuria screening in patients with TAK, including during clinical remission, is warranted to enable early detection and timely management.","url":"https://pubmed.ncbi.nlm.nih.gov/42283880/","authors":["Kardaş RC","Yıldırım D","Kaya B","Vasi İ","Duran R","Erden A","Küçük H","Göker B","Öztürk MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1007/s00296-026-06196-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42283795","name":"Analytic Misjudgment of Drug Safety Evidence and Causality: From the Prosecutor's Fallacy and Simpson's Paradox to Artificial Intelligence.","source":"pubmed","abstract":"Drug safety assessment, particularly in the post-marketing setting, is especially vulnerable to analytic misjudgment because it relies on heterogeneous evidence streams, incomplete data, infrequent events, and decisions made under substantial uncertainty. Recurring sources of error include misinterpretation of conditional probabilities, conflation of association with causation, inappropriate denominator and comparator selection, inadequate consideration of background incidence and confounding, aggregation artifacts such as Simpson's paradox, and overinterpretation of exploratory findings arising from multiplicity or repeated testing. Misjudgment may be further amplified by spontaneous reporting data that lack explicit exposure denominators and are susceptible to reporting bias, by fragile or incomplete meta-analyses, and by premature regulatory or public responses to weak or incompletely contextualized signals. Using selected real-world case studies and conceptual examples, this narrative review illustrates how such errors arise and propagate across clinical, regulatory, and public domains, and how they can materially influence causality assessment and decision making. The paper also discusses how artificial intelligence (AI), if implemented without transparency, bias assessment, and clinical oversight, may amplify rather than reduce these vulnerabilities. Greater analytic discipline, clearer communication of uncertainty, triangulation across evidence streams, and careful governance of emerging AI-enabled tools are needed to support more reliable drug safety evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/42283795/","authors":["Hammad TA","Rochon J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1007/s40264-026-01683-5","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42283781","name":"Artificial Intelligence in Self-Management of Gestational Diabetes Mellitus: A Systematic Review.","source":"pubmed","abstract":"The prevalence of gestational diabetes mellitus (GDM) continues to rise, necessitating reliable and effective self-management strategies to improve maternal and neonatal outcomes. However, current self-management models face challenges, including insufficient data monitoring and analysis, delayed modifications to treatment protocols, and excessive reliance on manual processes. With the expanding application of artificial intelligence (AI) in healthcare, its potential value in the self-management of GDM has attracted increasing attention. This systematic review aimed to synthesize the evidence on the application of AI technologies in the self-management of patients with GDM. This systematic review was conducted in April 2025 and included comprehensive literature searches across PubMed, Embase, The Cochrane Library, Scopus, Web of Science, CINAHL, CBM, CNKI, VIP, and Wanfang databases. The search strategy combined Medical Subject Headings and free-text terms related to GDM, AI, machine learning, and self-management. Quantitative studies that explored the application of AI in the self-management of patients with GDM were included, including randomized controlled trials and cohort studies. Two researchers independently performed study selection and data extraction, followed by quality assessment using risk-of-bias instruments appropriate for each study design. Data were synthesized using a narrative approach combined with thematic synthesis. The initial search yielded 18,973 records. After stepwise screening, 10 studies were included. A total of 645 patients with GDM completed AI-assisted interventions (from 661 initially enrolled), along with 864 control participants (from 877 enrolled). A variety of AI technologies were employed, including expert systems, machine learning, and natural language processing. Their primary functions included abnormality detection and alert triggering, personalized treatment plan generation and adjustment, and data integration and management. The studies reported multiple outcomes. Regarding health outcomes, six studies reported that AI interventions were associated with improved glycemic control, although heterogeneity was observed in delivery outcomes and insulin utilization rates. In terms of adherence, AI interventions tended to increase the frequency of blood glucose monitoring and data upload rates. Regarding system usability, limited data suggested that the accuracy of dietary recommendations and detection of blood glucose abnormalities was satisfactory, whereas the adoption rate of insulin treatment adjustment recommendations was relatively low. User satisfaction was generally high. Facilitators for implementation included technological advantages, user experience, and external support, whereas barriers included data integration and quality issues, technical and hardware or software limitations, patient acceptance, and difficulties in clinical integration. Preliminary evidence suggests that AI may contribute to the self-management of GDM; however, its practical application faces several obstacles. Future efforts should focus on conducting high-quality clinical research and evaluating implementation-related experiences to facilitate the integration of AI into GDM self-management.","url":"https://pubmed.ncbi.nlm.nih.gov/42283781/","authors":["Guo X","Sun K","Zhang R","Yang X","Zhao J","Cui H","He F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1007/s10916-026-02419-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42283165","name":"Recent developments in intein-mediated protein splicing and their applications in bioengineering.","source":"pubmed","abstract":"Inteins are self-excising protein elements that catalyze their own removal from host polypeptides and mediate the ligation of the surrounding exteins, generating mature and functional proteins without the requirement for external cofactors or energy sources. Since their discovery, inteins have attracted considerable interest due to their unique catalytic mechanisms, structural diversity, and broad applicability. Extensive progress in deciphering the molecular basis of cis- and trans-splicing inteins has facilitated the rational design and engineering of improved variants with enhanced efficiency, controllability, and substrate tolerance. These advances have significantly expanded their practical utility. Distinctive features such as compact architecture, high fidelity of splicing, orthogonal activity, minimal cytotoxicity, and irreversible function position inteins as powerful molecular tools in biotechnology and biomedicine. Their applications are increasingly diverse, ranging from fundamental protein engineering tasks such as protein purification, site-specific modification, and selenoprotein production to more translational uses, including microbial drug targeting, intein-based biosensing, targeted gene delivery, and therapeutic gene editing. In addition, conditional inteins, engineered to respond to environmental or molecular cues, are opening new avenues in synthetic biology and biomedicine, enabling precise control over protein function in complex cellular contexts. This review provides a comprehensive overview of intein biology, classification, and mechanistic insights, followed by a discussion of recent developments in their biotechnological and biomedical applications. Particular attention is given to challenges that continue to limit broader adoption, such as incomplete splicing, extein compatibility issues, and context-dependent efficiency. Finally, we highlight emerging opportunities in the field, including the computational design of orthogonal intein libraries, integration with high-throughput screening methods, and the incorporation of artificial intelligence to accelerate intein engineering and application discovery. Collectively, these developments underscore the transformative potential of inteins as versatile molecular tools poised to impact diverse areas of life sciences and therapeutic innovation.","url":"https://pubmed.ncbi.nlm.nih.gov/42283165/","authors":["Rashidi Ghalamkhan Z","Farajnia S","Seirafi A","Toraby S","Zia Sarabi P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.2174/0113892037464437260521205540","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282465","name":"Medical imaging-derived artificial intelligence for prognostic stratification and treatment response prediction in interventional therapy of hepatocellular carcinoma.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) is a malignant tumor that is common worldwide. It is characterized by high incidence and mortality rates. Interventional therapy is a minimally invasive treatment for HCC that offers diverse methods that cover different stages. Because of the significant heterogeneity of tumors, even at the same stage, the effectiveness of interventional therapy can vary greatly, which makes it difficult for clinicians to determine the optimal treatment plan before treatment. Increasing evidence suggests that tumor-related imaging characteristics are correlated with biological functions and can be used to predict different subtypes of HCC and reflect their heterogeneity. In recent years, artificial intelligence (AI) has received widespread attention and been applied widely. AI can automatically extract features from medical images, objectively quantifying low-dimensional to high-dimensional information about tumors, which helps to directly or indirectly predict prognostic stratification and treatment response to interventional therapy. Furthermore, when AI integrates high-dimensional quantifiable information from imaging data with multimodal clinical and molecular data, its accuracy and interpretability improve significantly. Although image-derived AI models have achieved good performance and have broad prospects for application in the prognosis and treatment of HCC, their clinical implementation has limitations, including data and imaging standardization, model interpretability, and the need for multicenter validation. This review summarizes the latest advancements in medical image-driven AI in the prognostic stratification and efficacy prediction of interventional therapy for HCC, and outlines the main challenges that need to be addressed and good prospects for application.","url":"https://pubmed.ncbi.nlm.nih.gov/42282465/","authors":["Lei Y","Xia T","Wang Y","Zhou X","Gao X","Ju S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.iliver.2026.100240","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282246","name":"Transforming cutting-edge healthcare: Emerging trends in metabolomics and drug design using artificial intelligence and big data methodologies.","source":"pubmed","abstract":"Artificial intelligence is a disruptive area which transforms cutting-edge healthcare technology to analyze clinical workflows, sharpen diagnostics, and improve precision medicine. The goal of this review is to identify approaches involving a collaborative examination to determine the key features influencing the adoption of artificial intelligence methodologies in advanced cutting-edge solutions for metabolomics and drug design. In clinical and translational settings, a comprehensive investigation of legal and ethical principles will be included to highlight the significance of omics analysis and drug design in the application of artificial intelligence tools with artificial intelligence in healthcare, the real-world uses, and difficulties tied to societal and regulatory issues. The real-world effects of artificial intelligence for researchers and technicians can provide guidance for tailored strategies focused on leveraging potential to improve high-dimensional data analysis on metabolomics and drug design. As artificial intelligence methodologies continue to evolve, efforts must be directed toward structured frameworks that uphold human oversight and engagement to optimize the utility of artificial intelligence algorithms and big data methodologies. The key contributions of this study include a comprehensive overview of cutting-edge artificial intelligence methodologies and software programs in metabolomics and drug design, and critical perspectives which can solidify the future directions in the development of algorithmic approaches to bridge metabolomics and drug design.","url":"https://pubmed.ncbi.nlm.nih.gov/42282246/","authors":["Kim S","Kim D","Ko J","Zan X","Wong KL","Mao Y","Kaur L","Nam H","Lee JW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261458965","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282197","name":"Medication-Wide Association Study of Alzheimer's Disease and Related Dementias: Identifying Drug Candidates from Electronic Health Records through Explainable AI.","source":"pubmed","abstract":"Alzheimer's disease (AD) is a leading cause of death and disability, and treatment options for Alzheimer's disease and related dementias (ADRD) remain limited. We applied a data-driven, mechanism-agnostic Medication-Wide Association Study Plus (MWAS+) framework to identify candidate medications associated with ADRD using longitudinal electronic health record data and explainable artificial intelligence (AI).","url":"https://pubmed.ncbi.nlm.nih.gov/42282197/","authors":["Shao Y","Yin Y","Cheng Y","McGeary JE","Taveira TH","Tsuang DW","Logue MW","Ayandeh S","Ahmed A","Zamrini E","Zeng-Treitler Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 3","doi":"10.64898/2026.06.02.26354752","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282127","name":"Extracellular vesicles in prostate cancer: current understanding and future perspectives.","source":"pubmed","abstract":"Exosomes are nanoscale, lipid bilayer extracellular vesicles (EVs) actively secreted by cells under both physiological and pathological conditions. As key mediators of intercellular communication, exosomes carry a diverse array of molecular cargo, including nucleic acids, proteins, lipids, and metabolites, which can influence the function of recipient cells. Based on evidence from 2016-2025 across PubMed, Embase, the Cochrane Library, and clinicaltrials.org, this review consolidates current understanding of exosome biology in prostate cancer (PCa). We discuss exosome biogenesis, molecular cargo composition, and their functional roles in tumor progression, angiogenesis, immune evasion, metastasis, and therapy resistance of PCa. We emphasize the biomarker potential of exosomes, which can overcome the limitations of PSA, such as poor specificity and overdiagnosis. Unlike free-circulating molecules, the exosomal lipid bilayer protects its cargo from enzymatic degradation, thereby stabilizing prostate-specific markers such as AR-V7, PCA3, ERG mRNAs, PSMA protein, and other microRNAs. Furthermore, we highlight that exosomes derived from urine and prostatic secretions could serve as promising biomarkers for early diagnosis, prognosis, and monitoring therapeutic response (via longitudinal sampling) due to the prostate's anatomical proximity to these fluids. Despite this wealth of information, significant challenges in standardized isolation techniques and clinical application due to tumor heterogeneity remain. This review also aims to bridge existing gaps and provide future prospectives toward hybrid isolation techniques, cell-specific exosome profiling, and artificial intelligence (AI)-driven multi-omics data integration to leverage exosome biology and address PCa-specific biomarkers and heterogeneity, thereby improving patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42282127/","authors":["Perumalsamy B","Seshacharyulu P","Vengoji R","Shonka N","Teply BA","Batra SK","Muniyan S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.jncc.2026.03.004","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282111","name":"Emerging advancements and expanding technological scope of education and practices in pharmacy and pharmaceutical sciences.","source":"pubmed","abstract":"The pharmacy profession stands at a pivotal moment, as emerging scientific advancements and evolving healthcare demands require adept pharmacy practitioners and scientists. The integration of precision medicine, cellular and acellular regeneration, nano and bioengineering (e.g., 3D/4D bioprinting), digital therapeutics, artificial intelligence (AI)-point-of-care testing, and treating is starting to reshape pharmacy education, practice, and patient care. Pharmacy education needs to embrace these innovations to prepare graduates for the future of practice, to optimize therapeutic outcomes, and contribute meaningfully to translational medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42282111/","authors":["Barar J","Fiano K","Seamon M","Albensi B","Khanfar N","Omidi Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.34172/bi.33047","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282080","name":"Protocol: Effectiveness of Artificial Intelligence-Based Psychotherapy in Treating Mental Disorders.","source":"pubmed","abstract":"This is the protocol for a Campbell systematic review. The objective is as follows: to assess the effectiveness of artificial intelligence-based psychotherapeutic interventions for individuals formally diagnosed with mental disorders. In addition to evaluating overall psychological outcomes, the review will explore how intervention effects vary by country, AI architecture, underlying psychotherapeutic model, involvement of human therapists, and targeted diagnostic category.","url":"https://pubmed.ncbi.nlm.nih.gov/42282080/","authors":["Soni AK","Singh VK","Kumar M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1177/18911803261439274","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282066","name":"Baseline characteristics of patients enrolled in the AZIMUTH trial: an e-health-integrated, smartphone app-based model of care for heart failure patients.","source":"pubmed","abstract":"The 'FondAZione A. Gemelli IRCCS Artificial Intelligence Empowered Digital PlatforM to sUpport paTients with Heart Failure' (AZIMUTH) study is a multicentre prospective outpatient trial assessing an app-based, e-health-integrated model of care for heart failure (HF) patients. Its primary aim is to evaluate the feasibility and patient/provider acceptance of the app-based intervention, while key secondary aims include assessing its clinical impact through changes in guideline-directed medical therapy (GDMT) prescription/adherence and quality of life.","url":"https://pubmed.ncbi.nlm.nih.gov/42282066/","authors":["D'Amario D","Paglianiti DA","Laborante R","Elia S","Rizzo G","Giubilato S","Catalano M","Amico F","Volterrani M","Restivo A","Patarnello S","Kyriazakos S","Luraschi A","Delvinioti A","Tomassini F","Kostopoulou K","Iaconelli A","Incaminato E","Canonico F","Gorini M","Marcoli S","Bartoli V","Griffiths T","Fenici P","Cesario A","Patti G","Crea F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1093/ehjopen/oeag072","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42282062","name":"The cumulative incidence of atrial fibrillation in the hospitalized medical patient: a systematic review and meta-analysis.","source":"pubmed","abstract":"New-onset atrial fibrillation (NOAF) is the most common cardiac arrhythmia occurring in hospitalized patients. NOAF confers a significant risk of cardiovascular morbidity and mortality in the form of symptomatic tachyarrhythmia, heart failure, stroke and sudden cardiac death. It is unclear what the cumulative incidence of NOAF is in the hospitalized medical (non-surgical) patient. MEDLINE and Embase were searched for studies published between 01 January 2000 and 10 March 2026. All studies reporting the cumulative incidence of NOAF in hospitalized medical (non-surgical) patients were included. The pooled cumulative incidence of NOAF, 95% confidence intervals (CI), and 95% prediction intervals (PI) were computed using a random-effects model. The inconsistency index ( I 2 ) was calculated to measure heterogeneity. Subgroup analyses were also performed. A study protocol was registered with the PROSPERO database of systematic reviews (CRD42024626333) A total of 10 323 articles were identified from our searches, and of these 62 met the inclusion criteria. The cumulative incidence of NOAF across all studies was 99 474 (crude incidence 2.4%) out of 3 608 663 patients. The pooled cumulative incidence of NOAF was 9% (95% CI: 7-11%), with substantial heterogeneity ( I 2 = 99.9%) and a wide prediction interval (1-42%). In meta-regression, ICU setting (OR 2.46, 95% CI: 1.22-5.00; P = 0.013) and prospective study design (OR 1.73, 95% CI: 1.01-2.95; P = 0.044) were independently associated with higher NOAF incidence, while year of publication and use of administrative data were not significant. Subgroup analyses demonstrated consistently higher incidence in prospective and critically ill populations. The cumulative incidence of NOAF in patients hospitalized for acute medical illness varies significantly dependent on disease severity and methods to detect it. Attempts to pool data across studies are limited by differences in study design, patient populations and disease severity.","url":"https://pubmed.ncbi.nlm.nih.gov/42282062/","authors":["Essa H","Balu A","Lip GYH","Welters I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1093/ehjopen/oeag080","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42281628","name":"Beyond COSS: The Evolution of Hemodynamic Imaging and Revascularization Strategies in Chronic Cerebrovascular Occlusion.","source":"pubmed","abstract":"Chronic carotid occlusion (CCO) carries a significant risk of ischemic stroke, particularly in patients with impaired cerebrovascular reserve. Extracranial-intracranial (EC-IC) bypass emerged as a popular treatment strategy with high technical success rates. However, multiple large randomized trials, including the Carotid Occlusion Surgery Study (COSS), failed to demonstrate a statistically significant benefit, mainly due to perioperative stroke risk and limitations in patient selection. Surgical revascularization declined as a result. We aim through this review to re-examine the role of flow augmentation in CCO in light of modern advances in cerebral perfusion imaging. Contemporary modalities-including computed tomography perfusion and emerging magnetic resonance techniques-allow better identification of patients with hemodynamic compromise who may be at highest risk despite medical therapy. Future studies incorporating refined imaging criteria may better define the subset of patients most likely to benefit from selective revascularization.","url":"https://pubmed.ncbi.nlm.nih.gov/42281628/","authors":["Jumah F","Brooks H","Siddiq F","Otvos B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May-Jun","doi":"","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42281584","name":"Application of Artificial Intelligence in the Diagnosis, Prediction, and Management of Metabolic Syndrome: A Systematic Review.","source":"pubmed","abstract":"Metabolic syndrome (MetS) is associated with increased risks of cardiovascular disease and type 2 diabetes, with recent global prevalence estimates of approximately 28%-31% in adults. Artificial intelligence (AI) and machine learning offer potential to improve risk stratification beyond conventional statistical methods. This systematic review aims to synthesize evidence on AI applications for the diagnosis and prediction of MetS, while assessing the limited evidence on extensions for prevention and management.","url":"https://pubmed.ncbi.nlm.nih.gov/42281584/","authors":["Esmailzadeh A","Norouzkhani N","Sezavar Dokhtfaroughi S","Rasoulian A","Mazaheri Habibi MR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.72636","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42281165","name":"Evaluation of DeepSeek-Generated Biochemistry Clinical Cases for Teaching Purpose.","source":"pubmed","abstract":"To overcome limitations in traditional case-based teaching, this study developed an AI-driven workflow using DeepSeek to generate contemporary, interdisciplinary clinical biochemistry cases. Through human-AI collaboration, eight structured cases covering key topics such as carbohydrate and lipid metabolism were created, each including a clinical description, molecular mechanisms, and Q&amp;A, followed by instructor review. Fifteen medical students and 15 instructors evaluated the cases using a 5-point Likert scale across multiple dimensions, while student performance was compared between a group using AI-generated cases and a control group. The AI generated each case in 10-15&#x2009;min, significantly faster than manual development. Cases presented a logical progression from molecular mechanism to clinical management and incorporated recent advances such as CRISPR and PCSK9 inhibitors. Content integration received the highest ratings, though instructors scored pedagogical applicability lower. Error analysis indicated that AI excelled in maintaining logical consistency, whereas human reviewers enhanced precision in clinical details. Students who used the AI-generated cases achieved significantly higher examination scores than the control group (p&#x2009;&lt;&#x2009;0.05). In conclusion, DeepSeek-generated cases are efficient, interdisciplinary, and innovative. Human review remains essential for ensuring clinical rigor, particularly in nuanced scenarios. This collaborative approach enhances both the efficiency of case development and the educational quality of biochemistry teaching materials.","url":"https://pubmed.ncbi.nlm.nih.gov/42281165/","authors":["Tan S","Ni S","Zhang K","Yang Y","Tan H","Li L","Sun Y","Peng Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Aug","doi":"10.1002/bmb.70063","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279544","name":"Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications.","source":"pubmed","abstract":"Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Conventional clinical markers of renal function lack sufficient sensitivity for early diagnosis, whereas renal biopsy is unsuitable for routine monitoring because of its invasiveness.","url":"https://pubmed.ncbi.nlm.nih.gov/42279544/","authors":["Ni M","Huang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 29","doi":"10.3390/diagnostics16111676","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279465","name":"Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.","source":"pubmed","abstract":"Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome characterized by frequent underdiagnosis, diverse etiologies, and limited therapeutic options. Given its complexity, artificial intelligence (AI) and machine learning (ML) offer promising avenues to decode high-dimensional, multi-modal healthcare data. This review aims to synthesize the current landscape of AI/ML applications in HFpEF, evaluating their potential to address critical unmet clinical needs. Methods: We conducted a comprehensive review of the literature focusing on AI/ML paradigms in HFpEF. Key methodological frameworks were examined, including supervised, unsupervised, semi-supervised, and reinforcement learning, alongside advanced techniques such as deep learning and natural language processing (NLP). The analysis focused on the application of these techniques across four domains: diagnosis, sub-phenotyping, risk prediction, and optimization of diagnostic modalities, with specific emphasis on studies incorporating external validation. Results: Current evidence demonstrates that AI approaches effectively enhance diagnostic accuracy and facilitate the identification of distinct HFpEF phenotypes beyond traditional classifications. These technologies show significant utility in refining prognostic assessments and optimizing diagnostic testing strategies. Furthermore, ML-driven analytics provide a robust framework for improving patient selection and streamlining clinical trial design, potentially overcoming historical barriers to drug development in this population. Conclusions: AI represents a transformative tool capable of dissecting the heterogeneity of HFpEF to enable precision medicine. While the potential to improve clinical outcomes is substantial, challenges regarding model interpretability, bias, and clinical integration persist. Future efforts must focus on rigorous external validation and prospective trials to ensure the responsible translation of these technologies into routine clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42279465/","authors":["Li X","Xu C","Deng W","Zhang Y","Liu C","Gao L","Ji M","He Q","Wu Z","Qin S","Lin Y","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 23","doi":"10.3390/diagnostics16111597","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279437","name":"AI-Driven Design of High Affinity Biomolecule-Drug Conjugates for Gynecological Cancer Therapy: An Up-to-Date Narrative Review.","source":"pubmed","abstract":"Background: Gynecological cancers include collections of cancers with diverse cellular and molecular characteristics that often develop drug resistance, making them treatment-resistant. Biomolecule-drug conjugates (BDCs), especially antibody-drug conjugates (ADCs), have revolutionized the targeted therapy of cancer; however, the creation of these entities has so far been achieved by empirical, resource-intensive design methods. Objective: The aim of this review is to critically analyze how AI can be used for the rational design and optimization of high-affinity BDCs for gynecological cancer treatment. Methods and discussion: Recent advances in machine learning (ML)- and deep learning (DL)-based methods to predict biomolecule-target binding affinity, structural compatibility, linker stability, payload selection, trafficking in the cell, and biomolecule resistance mechanisms are summarized. The review also explores the possibilities for incorporation of structural, chemical, biological, and multi-omics data to enhance specificity, efficacy, and safety of conjugates. Besides antibody-based systems, AI-assisted design approaches with peptides, aptamers, and hybrid biomolecular systems are also included. This review also highlights parameters and experimental/numerical validation restrictions related to data quality, interpretability of models, regulatory aspects, etc. Conclusions: AI-based conjugate engineering is increasingly moving BDC development from a largely 'trial and error' approach to a more predictive and data-driven approach. While there are still challenges to be addressed in terms of translations and validations, the potential of AI approaches in the field of precision oncology and the development of more personalized treatment is promising in the context of gynecological cancers.","url":"https://pubmed.ncbi.nlm.nih.gov/42279437/","authors":["Garg P","Horne D","Salgia R","Singhal SS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 5","doi":"10.3390/cancers18111856","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279362","name":"Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.","source":"pubmed","abstract":"Brain tumors in older adults are difficult to diagnose and manage due to nonspecific symptoms, overlapping with neurodegenerative diseases such as dementia, and significant tumor heterogeneity. Although molecular markers such as IDH1/2 mutations, MGMT promoter methylation, TERT alterations, and 1p/19q co-deletion have improved glioma classification and prognostic assessment, current care still relies on invasive tissue biopsies, which limit longitudinal monitoring and may not fully capture tumor complexity because of sampling bias, assay variability, and limited accessibility. Liquid biopsy offers a promising alternative that enables the detection of tumor-derived DNA, RNA, proteins, and extracellular vesicles, supporting earlier diagnosis and real-time monitoring of disease progression and treatment response. However, liquid biopsy for brain tumors is not yet clinically definitive due to low biomarker abundance, lack of standardization, and limited validation, and therefore, it cannot replace tissue diagnosis. Ongoing research focuses on multi-analyte biomarker panels, improved assay standardization, and integration with imaging and tissue-based data. In parallel, artificial intelligence and machine learning are advancing the field by integrating multi-omics and radiomic data to enhance detection, classify tumors, and predict key molecular alterations, supporting the emerging framework of radiogenomics. Together, these developments are driving a shift toward more precise and dynamic approaches to brain tumor diagnosis and management, with relevance for improving outcomes in older adults with brain cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42279362/","authors":["Nguyen TTT","Ðoàn LN","Boriushkin E","Badr CE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 29","doi":"10.3390/cancers18111779","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279299","name":"Artificial Intelligence in Image Assisted Radiation Oncology.","source":"pubmed","abstract":"Advanced imaging is the cornerstone of modern radiation oncology, contributing to each phase of patient care, from diagnosis and treatment planning to delivery and follow-up. It has evolved from providing purely geometric guidance to enabling biological and dynamic precision, capturing detailed spatial and functional information about tumors and surrounding tissues. This progress has also generated vast amounts of complex data that remain largely underexplored. AI-based methods have shown promises to unlock the potential of these data, ensuring quality and standardization while extracting previously inaccessible insights. AI-driven tools can enhance accuracy, efficiency, and personalization of radiation oncology through precision diagnosis, automated segmentation, adaptive treatment planning, real-time image guidance, and predictive response assessment. In this review, we conducted a systematic bibliometric analysis of relevant literature published in the last decade and explored current advancements in AI and radiomics applications across radiation oncology. We also addressed ongoing challenges, such as data heterogeneity, model interpretability, and clinical implementation, and discussed future directions for integrating AI-powered imaging solutions into routine practice to advance precision cancer care.","url":"https://pubmed.ncbi.nlm.nih.gov/42279299/","authors":["Wang H","Zhao Y","Chen X","McDonald B","Li Y","Xie J","Rhee DJ","Lim TY","Netherton TJ","Phan J","Spiotto MT","Lin MH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 25","doi":"10.3390/cancers18111715","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279220","name":"Advanced Drug Delivery Strategies in Geriatric Patients with Polypharmacy: Integrating Pharmacokinetics, Personalized Medicine, and Emerging Technologies.","source":"pubmed","abstract":"Background/Objectives : The rapid growth of the global aging population, projected to reach 2.1 billion older adults by 2050, presents major challenges for pharmacotherapy and drug delivery. Age-related physiological changes affecting pharmacokinetics and pharmacodynamics, widespread polypharmacy, and functional impairments such as dysphagia, cognitive decline, and sensory or motor limitations reduce the effectiveness and safety of conventional \"one-size-fits-all\" medication approaches. This review aimed to evaluate the major barriers to effective drug delivery in older adults and to assess emerging patient-centered and technology-driven drug delivery systems designed to improve medication adherence, safety, and therapeutic outcomes in geriatric populations. Methods : A comprehensive narrative review of current literature was conducted focusing on geriatric pharmacotherapy, age-related barriers to medication administration, and advanced drug delivery technologies. The review analyzed evidence regarding modified oral formulations, transdermal systems, long-acting injectables, implantable devices, nanotechnology-based platforms, digital health integrations, pharmacogenomics, biomarker-guided therapy, and deprescribing strategies including STOPP/START criteria and Beers Criteria. Studies addressing polypharmacy, medication adherence, and personalized medicine in older adults were also evaluated. Results : Evidence indicates that older adults experience significant medication-related challenges due to multimorbidity, polypharmacy, and functional decline. Dysphagia affects more than half of nursing home residents, while polypharmacy prevalence reaches up to 86.6% in some populations. Emerging drug delivery technologies demonstrated potential to improve adherence, dosing precision, and patient convenience. Personalized approaches incorporating pharmacogenomics, biomarker-guided treatment, and AI-assisted dosing showed promise for optimizing therapy. However, major limitations remain, including underrepresentation of older adults in clinical trials, limited high-quality evidence supporting many polypharmacy interventions, and insufficient implementation of advanced drug delivery systems in routine clinical practice. Conclusions : Current evidence supports a transition from standardized medication approaches toward flexible, individualized, and patient-centered drug delivery strategies for older adults. Advanced delivery technologies and personalized pharmacotherapy may improve medication safety, adherence, and quality of life in aging populations, although stronger clinical evidence and broader implementation are still needed. Future progress will require interdisciplinary care models, improved geriatric representation in clinical research, and regulatory reforms supporting the integration of innovative drug delivery systems into routine healthcare practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42279220/","authors":["Bartusik-Aebisher D","Bania K","George BP","Dynarowicz K","Aebisher D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 4","doi":"10.3390/jcm15114359","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279142","name":"Sedation in Gastrointestinal Endoscopy: From Drug-Centered Protocols to Personalized, Technology-Supported Pathways: A Narrative Review.","source":"pubmed","abstract":"Background/Objectives : Sedation is a fundamental component of gastrointestinal endoscopy, improving patient comfort, procedural quality, and overall satisfaction. However, traditional drug-centered sedation models are increasingly challenged by rising procedural volumes, aging populations, and limited anesthesiology resources. The aim of this narrative review is to provide an integrated overview of evolving pharmacological agents, monitoring strategies, organizational models, and future directions toward personalized, technology-supported sedation pathways. Methods : A structured literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science for studies published between January 2010 and December 2025. Relevant guidelines, randomized controlled trials, meta-analyses, and large observational studies were included. Evidence was synthesized qualitatively, emphasizing clinical applicability and real-world relevance. Results : Propofol remains the most widely used sedative agent due to its rapid onset and recovery profile, although its narrow therapeutic window and lack of antagonist limit its safety in high-risk patients. Emerging agents such as remimazolam and ciprofol demonstrate comparable efficacy with improved respiratory and hemodynamic safety profiles, particularly in elderly populations. Adjunctive strategies, including procedure-specific approaches such as spinal anesthesia, may further optimize sedation. Advanced monitoring tools, such as capnography, bispectral index, and high-flow nasal cannula, show potential in enhancing safety, especially in selected high-risk groups. Structured training programs and standardized discharge criteria are essential for ensuring quality and safety. Conclusions : Sedation in gastrointestinal endoscopy is transitioning from a standardized, drug-centered approach to a personalized, risk-adapted, and technology-supported model. Integration of novel pharmacological agents, advanced monitoring, and structured training will be key to improving patient safety, procedural efficiency, and healthcare sustainability.","url":"https://pubmed.ncbi.nlm.nih.gov/42279142/","authors":["Bonura GF","Soriani P","Gualandi N","Cortegoso Valdivia P","Gabbani T","Parrella A","Koulaouzidis A","Manno M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 1","doi":"10.3390/jcm15114281","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42279003","name":"The Role of Virtual and Augmented Reality in Transsphenoidal Surgical Approaches to the Sellar and Parasellar Area-A Systematic Review.","source":"pubmed","abstract":"Background/Objectives : Transsphenoidal surgery has become the gold standard for the treatment of sellar and parasellar lesions, but it remains associated with significant anatomical challenges and the risk of intraoperative complications. The limitations of conventional imaging in depicting the complex three-dimensional anatomy of the skull base have led to a growing interest in virtual (VR) and augmented reality (AR) technologies, which offer enhanced spatial visualization, preoperative simulation, and image-guided intraoperative navigation. This systematic review aims to evaluate the current evidence on the role of virtual and augmented reality in transsphenoidal surgical interventions, with a focus on their impact on preoperative planning, intraoperative orientation, surgical outcomes, and neurosurgical training. Methods : A systematic literature search was conducted in accordance with PRISMA 2020 guidelines across PubMed, Scopus, and Web of Science for the period 2015-2025. MeSH terms and free-text keywords related to transsphenoidal surgery, sphenoid sinus anatomy, and VR/AR technologies were combined using Boolean operators. Risk of bias was assessed using RoB 2.0 for RCTs; methodological quality was assessed using the Newcastle-Ottawa Scale for observational studies and AMSTAR 2 for systematic reviews. Clinical, morphometric, and experimental studies evaluating VR/AR applications were included. Data were extracted using a standardized protocol and synthesized through qualitative analysis, with subgroup analysis by technology type (VR vs. AR) and clinical application domain. Results : A total of 218 publications were identified, of which 52 met the inclusion criteria (clinical studies n = 12, simulation and technology studies n = 30, morphological studies n = 10). VR-based three-dimensional reconstructions were consistently associated with improved preoperative spatial orientation and anatomical landmark recognition. AR systems demonstrated a meaningful contribution to intraoperative navigation, with reported reductions in time to target and improved visualization of critical neurovascular structures. VR platforms showed high effectiveness in surgical training, with shorter learning curves and improved technical performance. However, the majority of included studies were small observational cohorts, simulation studies, or expert overviews, with substantial heterogeneity in methodology, technology platforms, and outcome measures, precluding quantitative meta-analysis. Conclusions : Virtual and augmented reality represent clinically promising adjuncts to transsphenoidal surgery, with demonstrated benefits in preoperative planning, intraoperative navigation, and surgical training. These conclusions should be interpreted in the context of a predominantly early-phase and heterogeneous evidence base. Standardized protocols, larger prospective studies, and randomized trials are needed before the integration of VR/AR with navigation systems and artificial intelligence can be established as a routine component of personalized transsphenoidal surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/42279003/","authors":["Bechev K","Markov D","Aleksiev V","Markov G","Poryazova E","Fasova A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 27","doi":"10.3390/jcm15114142","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278814","name":"Perioperative Anxiety in Adults: A Narrative Review of Pathophysiology, Assessment, and Multimodal Management Strategies.","source":"pubmed","abstract":"Perioperative anxiety is a common psychophysiological stress response experienced by patients before and after surgery, with a global prevalence of approximately 48%. Its occurrence is influenced by multiple factors including age, sex, type of surgery, and psychosocial determinants. The underlying pathophysiological mechanisms are complex, involving multi-system interactions such as autonomic nervous system imbalance, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, dysfunction of limbic system neural circuits, and neuroinflammation. Current assessment strategies are evolving from sole reliance on psychological scales toward multimodal approaches incorporating objective biomarkers including heart rate variability, cortisol, and electroencephalography. Management paradigms have shifted from traditional pharmacological premedication to integrated systems encompassing structured patient education, digital health tools, neuromodulation techniques, and cognitive behavioral therapy. However, significant gaps persist regarding standardized screening protocols, biomarker validation, and targeted intervention pathways for high-risk populations. Future management is likely to require more individualized risk assessment and intervention selection. Biomarker-based risk prediction, artificial intelligence-assisted intervention decision-making, and the deep integration of digital therapeutics such as virtual reality with existing enhanced recovery pathways will be key directions for improving patient outcomes and recovery quality. This structured narrative review summarizes current evidence on perioperative anxiety in adults, focusing on epidemiology, pathophysiological mechanisms, assessment tools, biomarkers, and multimodal management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42278814/","authors":["Chen J","Zhuang Y","Mao M","Chu Q","Xia Z","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 3","doi":"10.3390/healthcare14111561","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278796","name":"Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.","source":"pubmed","abstract":"Background and Objectives: Frailty is a multidimensional vulnerability in older adults; the Fried phenotype and Frailty Index are clinically informative but labor-intensive, limiting scalability for community screening. Machine learning (ML) can model heterogeneous, high-dimensional data, but real-world adoption is constrained by heterogeneity in definitions, predictors, validation strategies, and explainability. We systematically synthesized ML-based studies of frailty prediction and classification in community-dwelling older adults, examining validation rigor, explainability, and implementation readiness. Methods: This systematic review followed PRISMA 2020 and was registered in PROSPERO (CRD420251081555). PubMed, Embase, Web of Science, and Scopus were searched on 4 July 2025, with a supplementary IEEE Xplore and ACM Digital Library search conducted on 12 May 2026. Eligible studies included community-dwelling adults aged &#x2265;60 years, ML-based frailty prediction or classification, sample &#x2265; 1000, and publication in a peer-reviewed journal indexed in the Web of Science Core Collection; hospital-based studies were excluded. Risk of bias and reporting quality were assessed with PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis); implementation readiness was assessed with the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework and a Technology Readiness Level (TRL)-style rubric. Findings were synthesized narratively. Results: Fourteen studies (development cohorts 1230-86,133 participants) were included; the supplementary IEEE/ACM search identified 42 records but yielded no additional eligible studies. Classification of current frailty status (n = 7) yielded AUROCs (area under the receiver operating characteristic curve) of 0.70-0.98, with the highest values likely reflecting partial label overlap with frailty components; incident prediction (n = 6) yielded internal AUROCs of 0.70-0.81 and same-cohort temporal AUROCs of 0.58-0.85; independent external validation was uncommon. Only 2 of 14 studies had both low overall risk of bias and low applicability concern (PROBAST); the field is concentrated at TRL 4-6, with no study at TRL 7 or higher and none documenting Implementation or Maintenance domains of RE-AIM. Conclusions: ML-based frailty models show heterogeneous discrimination and limited readiness for routine community use. Priorities include standardized task-type-specific definitions, independent external validation, calibration and decision-curve reporting, transparent predictor disclosure, and prospective implementation evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/42278796/","authors":["Kim S","Shin MJ","Choi BK","Obradovic Z","Rubin DJ","Park JH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 1","doi":"10.3390/healthcare14111543","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278751","name":"Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly influencing healthcare administration and clinical informatics by supporting disease diagnosis, clinical decision-making, treatment personalization, drug discovery, remote monitoring, public health surveillance, and hospital operations. However, the successful adoption of AI in healthcare depends not only on algorithmic performance, but also on its safe integration into clinical information systems, organizational workflows, and governance structures. This article presents a narrative critical review of recent advances in AI-driven healthcare, with a focus on four major domains: AI-enabled disease diagnosis, treatment personalization and clinical decision support, drug discovery and biomedical knowledge generation, and healthcare administration. Evidence from radiology, pathology, ophthalmology, dermatology, and cardiology shows that AI systems can achieve strong diagnostic performance in selected settings, while applications in electronic health records, natural language processing, telemedicine, and predictive analytics are increasingly used to support healthcare delivery and operational decision-making. At the same time, important barriers continue to limit real-world implementation, including fragmented data infrastructures, limited interoperability, poor data quality, algorithmic bias, lack of explainability, privacy and cybersecurity risks, unclear accountability, and insufficient external validation. This review critically examines these challenges and proposes a governance-oriented roadmap for responsible AI integration in healthcare administration and clinical informatics. The proposed roadmap emphasizes data readiness, model validation, workflow integration, institutional accountability, post-deployment monitoring, and workforce readiness. The findings suggest that AI can contribute to more efficient, accessible, and patient-centered healthcare only when it is implemented within trustworthy medical informatics ecosystems supported by ethical governance, human oversight, and continuous evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/42278751/","authors":["Aldosari H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 28","doi":"10.3390/healthcare14111497","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278712","name":"Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance-A Scoping Review of Reviews.","source":"pubmed","abstract":"Background: Clinical artificial intelligence (AI) is rapidly moving from retrospective model development into prospective evaluation, implementation, and routine care. Existing reviews have addressed specific aspects of this transition, including monitoring, drift, implementation, governance, and human-AI interaction; however, these bodies of work remain methodologically and conceptually fragmented across different review traditions. Methods: We conducted a scoping review of review-level and review-oriented literature. We searched MEDLINE, Embase, Scopus, and Web of Science Core Collection from database inception to 28 February 2026. We charted review characteristics and conducted an inductive thematic synthesis of extracted review-level findings, while distinguishing operational, deployment-proximal, methodological, and conceptual/governance-oriented evidence. Results: We included 25 review-level publications spanning systematic, scoping, methodological, narrative, and governance-oriented reviews. Three major themes emerged. First, clinically important risks were consistently framed as socio-technical rather than purely algorithmic: trustworthiness depended not only on technical performance, but also on fairness, transparency, workflow fit, human oversight, and organisational readiness. Second, the included review literature consistently recommended post-deployment monitoring but showed limited operational maturity; monitoring methods, action thresholds, fairness surveillance, and corrective responses were weakly standardised, and mature evidence from activated systems in routine care remained sparse. Third, trustworthy implementation was increasingly framed as a lifecycle governance challenge extending beyond procurement and initial validation to include local validation, subgroup auditing, drift detection, controlled updating, incident response, and, where necessary, rollback or retirement. Discussion: The review literature suggests a persistent normative-operational gap, meaning that recommendations about what trustworthy clinical AI should require have advanced faster than evidence on how monitoring, updating, and governance are implemented in routine care. The strongest unresolved challenge is therefore not principal generation alone, but the translation of monitoring and governance expectations into actionable operational systems. Conclusions: Post-development trustworthiness in clinical AI should be understood as a lifecycle property, not a one-time technical achievement. Future work should prioritise stronger operational evidence, clearer reporting of deployment-proximal and post-deployment evaluation, methodological standardisation of monitoring metrics and thresholds, implementation research on feasible governance models, and evaluation frameworks for assessing post-deployment safety, fairness, accountability, and sustainability.","url":"https://pubmed.ncbi.nlm.nih.gov/42278712/","authors":["El Arab RA","Mustafa MH","Almagharbeh WT","Saleem NH","Al Abdulmohsen S","Boathab R","Bu Washl M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 25","doi":"10.3390/healthcare14111459","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278491","name":"DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.","source":"pubmed","abstract":"Quantitative structure-activity relationship (QSAR) modeling has conventionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts prepared three-dimensional molecular conformers into image representations and feeds them directly into convolutional neural networks for activity prediction. This focused narrative review traces DeepSnap from its introduction in 2018 to its current state and places it within the broader landscape of descriptor-based QSAR, topology-based and 3D-aware graph neural networks, and related image-based or semi-image-based molecular representation approaches. Previous studies applied DeepSnap to Tox21 nuclear receptor and molecular initiating event endpoints, rat hepatic clearance, blood-brain barrier penetration, acute oral toxicity, and cosmetics-pharmaceutical compound classification. Across the DeepSnap series, image-based and descriptor-based predictions have provided complementary information, particularly in ensemble or consensus models. However, high or near-ceiling ROC-AUC values reported for selected endpoints should not be interpreted as indicating deterministic or universally generalizable predictions; rather, they should be considered in the context of endpoint-specific model development, image-rendering parameter optimization, possible class imbalance, split dependence, limited matched external replication, and incomplete benchmarking against modern molecular representation models. Limitations include a dependence on nonphysical rendering parameters, single- or representative-conformer input, incomplete matched benchmarking against 2D and 3D molecular representation models, and an interpretability gap addressed in part by CAM-family visualization in the AI-based Substance Hazard Integrated Prediction System (AI-SHIPS) and S-COPHY (a model developed by Shiseido for cosmetics-pharmaceutical compound classification). Future directions include standardized image-generation protocols, conformer-ensemble extensions, systematic interpretability analysis, matched benchmarking, and potential integration with graph-based and 3D-aware molecular learning approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42278491/","authors":["Uesawa Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 30","doi":"10.3390/ijms27114965","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278411","name":"Brain Cancer: Molecular Alterations and Emerging Trends in Neuropharmacology.","source":"pubmed","abstract":"Central nervous system (CNS) tumors represent a heterogeneous group of neoplasms associated with significant morbidity and mortality despite their relatively low incidence. Advances in the fifth edition of the World Health Organization (WHO) classification have emphasized the integration of histopathological, immunohistochemical, and molecular features, fundamentally transforming diagnostic and prognostic frameworks in neuro-oncology. This manuscript aims to provide an overview of CNS tumor biology, focusing on key diagnostic markers, genetic and epigenetic alterations, and emerging therapeutic strategies. It further describes recent advances in multi-omics approaches and artificial intelligence, which enable deeper characterization of tumor heterogeneity and support the development of precision medicine strategies. Finally, current and emerging therapeutic modalities, including combination therapies, targeted treatments, and novel molecular targets, are examined with emphasis on overcoming resistance mechanisms and improving clinical outcomes. Overall, the integration of molecular biology, advanced diagnostics, and innovative therapeutic approaches represents a critical step toward personalized management of CNS tumors and improved patient survival.","url":"https://pubmed.ncbi.nlm.nih.gov/42278411/","authors":["Leskova B","D'Agostino I","Mattova S","Urbanska N","Blicharova A","Simko P","Toplu A","Karaman M","Kiskova-Simkova T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 28","doi":"10.3390/ijms27114880","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278344","name":"Molecular Mechanisms and Multi-Omics Integration in Heart Failure: From Pathophysiology to Precision Medicine.","source":"pubmed","abstract":"Heart failure (HF) is a complex and heterogeneous clinical syndrome defined by progressive structural, functional, and molecular alterations in the myocardium, representing a significant global health challenge. Beyond haemodynamic compromise, HF arises from intricate interactions among neurohormonal activation, chronic inflammation, oxidative stress, mitochondrial dysfunction, impaired calcium handling, and extracellular matrix remodelling. These processes drive maladaptive cardiac remodelling and progressive functional decline across multiple HF phenotypes, including HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved ejection fraction (HFpEF). Recent advances in molecular biology have highlighted the critical roles of genomic, epigenetic, and transcriptomic mechanisms in the progression of HF. DNA methylation, histone modifications, chromatin remodelling, and non-coding RNAs regulate gene expression in response to environmental and metabolic stimuli, thereby connecting systemic risk factors to cardiac dysfunction. Proteomic and post-translational modifications, such as phosphorylation, acetylation, and redox signalling, modulate protein function and contribute to contractile impairment and metabolic dysregulation. Metabolomic studies have revealed significant changes in myocardial energy metabolism, including reduced oxidative capacity, decreased metabolic flexibility, and limited bioenergetic reserves. The integration of multi-omics approaches-including genomics, transcriptomics, proteomics, metabolomics, and epigenomics-has provided unprecedented insight into the biological heterogeneity of HF, facilitating the identification of distinct molecular subtypes and novel therapeutic targets. Systems biology and network-based analyses, supported by artificial intelligence and machine learning, enable the synthesis of complex datasets and enhance risk classification, prognosis, and personalised treatment approaches. This narrative review synthesises the current understanding of the molecular mechanisms underlying HF, with particular emphasis on the interplay between metabolic and epigenetic regulation in disease progression. It also highlights emerging translational opportunities, including omics-based biomarkers, targeted therapies, and precision medicine approaches. Despite significant advances, challenges remain in translating these findings into clinical practice, underscoring the need for standardised methodologies, extensive validation, and integrative frameworks. Ultimately, a systems-level, multi-omics perspective is crucial for redefining HF as a biologically stratified condition in the landscape of advancing tailored cardiovascular medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42278344/","authors":["Maida CD","Pacinella G","Daidone M","Bona MM","Scaglione S","Malfitano R","Norrito R","Cassataro G","Dell'Ajra L","Ferrantelli S","Vassallo GA","Tuttolomondo A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 27","doi":"10.3390/ijms27114814","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42278281","name":"Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.","source":"pubmed","abstract":"Protein interactions form large-scale networks known as protein-protein interaction networks (PPINs) or protein complex networks (PCNs). Extracting meaningful structural frameworks from these molecular relationships through mathematical modeling enables a deeper understanding of biological processes. Although static protein network models have provided valuable insights into the organization of PPINs, they are limited in their ability to capture the dynamic and cooperative nature of protein complexes. This review begins by introducing fundamental concepts in graph and hypergraph theory, with an emphasis on centrality measures. We then discuss the evolution of PPIN analysis from static representations to dynamic graph- and hypergraph-based frameworks. Specifically, we review dynamic PPINs and the challenges associated with their interpolation, dynamic centrality measures, and network models capable of representing multi-node relationships that have been applied to PPINs. Finally, we highlight recent advances in machine learning and deep learning approaches that integrate interaction data with functional annotations, sequence information, and cellular context to predict novel interactions and reconstruct transient protein complexes. Taken together, dynamic PPIN modeling combined with experimental validation provides an integrated framework for understanding coordinated protein functions in cellular processes and across biological systems as well as supporting drug development.","url":"https://pubmed.ncbi.nlm.nih.gov/42278281/","authors":["Chan KY","Yamaguchi T","Izumiya Y","Chu YW","Watanabe T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 25","doi":"10.3390/ijms27114750","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277825","name":"Expanding the ethics horizon: integrating African canons for inclusive research ethics in Africa.","source":"pubmed","abstract":"Society 5.0 envisions a future where technology and humanity integrate to address societal concerns. In an era marked by significant technological advancements, particularly in artificial intelligence, it is vital to uphold the core values of compassionate care and human connection. While the healthcare sector is central to this transformation, the prevailing research ethics frameworks remain dominated by Western individualism and positivism.","url":"https://pubmed.ncbi.nlm.nih.gov/42277825/","authors":["Nel C","Wolvaardt JE","du Toit P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1186/s12910-026-01519-y","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277821","name":"AI-assisted diagnosis of cardiac amyloidosis using electrocardiograms and echocardiography: a multicenter retrospective study in China.","source":"pubmed","abstract":"Cardiac amyloidosis (CA) is an under-recognized cause of left-ventricular hypertrophy (LVH) that is often misclassified as hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD). We aimed to develop and externally validate an AI model using electrocardiograms (ECG) and echocardiography to distinguish CA from other LVH aetiologies.","url":"https://pubmed.ncbi.nlm.nih.gov/42277821/","authors":["Zhang S","Wan Z","Hu Y","Wu M","Zhang X","Tian Z","Zhang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1186/s12916-026-04987-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277776","name":"Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.","source":"pubmed","abstract":"Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to provide broad exposure to a wide variety of conditions, including those that are rare, subtle, or easily confused with one another. Although artificial intelligence (AI) has shown impressive performance in medical image classification, its potential to actively improve clinical education has not been fully realised. In this study, we introduce AI-generated counterfactual chest X-rays-real patient images digitally altered to show a different but still realistic condition, while keeping the same patient's anatomy. These counterfactuals create 'what if' scenarios within the same patient and offer a novel tool to enhance diagnostic learning, support decision-making, and improve confidence calibration in medical trainees and professionals.Forty-two participants, including medical students and doctors, completed a four-part online study involving diagnostic classification, comparison of counterfactual image pairs, image authenticity judgements, and clinical treatment planning. All image comparisons focused on three clinical conditions: healthy lungs, pneumonia, and pleural effusion. Statistical analyses included t-tests, ANOVAs, Pearson correlations, and linear regression.Results showed that counterfactual images meaningfully supported diagnostic learning. Participants demonstrated improved accuracy and confidence over time, particularly when distinguishing pleural effusion from healthy lungs. Confidence became more closely aligned with accuracy, and participants were better able to recognise condition-specific differences. These comparisons also revealed areas of diagnostic weakness that would be difficult to detect through conventional instruction alone. While real images led to higher raw diagnostic accuracy, counterfactuals proved highly effective in promoting learning and reflective reasoning. Image authenticity influenced clinical decision-making, and initial confidence was a stronger predictor of final treatment confidence than diagnostic correctness.These findings highlight the strong potential of counterfactual imaging to enhance radiology education. By enabling learners to engage with rare, complex, or demographically underrepresented cases in a controlled and consistent way, AI-generated counterfactuals offer a powerful and scalable tool to improve diagnostic preparedness and clinical confidence in future healthcare professionals.","url":"https://pubmed.ncbi.nlm.nih.gov/42277776/","authors":["Mohr G","Zhu Y","Ye X","Lennon M","MacLellan C","Maclay J","Lowe DJ","Sainsbury C","Dong F","Lagnado D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1186/s12909-026-09597-7","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277722","name":"AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches.","source":"pubmed","abstract":"Trisomy 21 (Down syndrome) remains the most prevalent autosomal aneuploidy, necessitating accurate prenatal diagnosis. While cell-free fetal DNA (cffDNA)-based non-invasive prenatal testing (NIPT) has transformed screening, challenges persist in low fetal fraction cases and confined placental mosaicism. Integrating artificial intelligence (AI), multi-omics, and fetal cell-based approaches represents a paradigm shift toward comprehensive prenatal diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42277722/","authors":["Elmetwalli A","Ameen F","Magrashi N","Alamri ES","Albalawi AN","Alzahrani OR","El-Far AH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1186/s12884-026-09141-x","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277679","name":"Emerging technologies in interventional pain management: a scoping review on current innovations and future directions.","source":"pubmed","abstract":"Emerging digital and bioelectronic technologies are rapidly transforming interventional pain management, but their clinical roles and evidence base remain unclear. A scoping review of these innovations is needed to map the current landscape and identify gaps for future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42277679/","authors":["Farrokhi MR","Vadiee G","Nematollahi R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1186/s12871-026-04017-1","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277677","name":"Strongyloides stercoralis infection in patients presenting with lower respiratory tract infections: a retrospective case series from a tertiary hospital in Vietnam.","source":"pubmed","abstract":"Strongyloides stercoralis infection is frequently overlooked in patients presenting with lower respiratory tract infections (LRTIs) because of nonspecific clinical manifestations, despite its potential to cause severe pulmonary involvement, particularly in endemic regions.","url":"https://pubmed.ncbi.nlm.nih.gov/42277677/","authors":["Van Giap V","Xuan Co D","Thu Phuong P","Thi Quynh Huong P","Thanh Thuy P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1186/s12879-026-13742-4","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277591","name":"Mapping E-Cigarette Content on Social Media: A Scoping Review of Methods, AI Applications, and Promotional Strategies.","source":"pubmed","abstract":"E-cigarette narratives on social media are rapidly evolving alongside the rise of short-form video and advances in artificial intelligence (AI) that enable scalable content analysis. This scoping review mapped research on e-cigarette-related social media content, focusing on methodological approaches (including AI use) and key findings (study domains, promotional strategies, and reporting of additional social media dimensions). Web of Science, Scopus, and PubMed were searched in October 2025 for English-language, peer-reviewed studies published between 2011 and 2025. After independent title/abstract screening by two reviewers and full-text assessment, 136 studies were included. Twitter/X was the most frequently studied platform, followed by Instagram, TikTok, YouTube, and Facebook; only TikTok showed a steady increase in the number of studies. Manual coding predominated, with smaller proportions using computational or hybrid approaches. Computational approaches were generally applied to larger datasets and more consistently reported validation. AI applications were concentrated in topic discovery and sentiment assessment, whereas multimodal approaches such as image classification and sociodemographic inference were uncommon despite the shift toward video-centric platforms. This review advances previous mapping by providing an AI application taxonomy and highlighting methodological gaps in validation practice across manual, computational, and hybrid pipelines. Future studies should expand the use of AI applications alongside transparent and robust validation practices to enable scalable, reliable, and efficient social media surveillance on e-cigarettes.","url":"https://pubmed.ncbi.nlm.nih.gov/42277591/","authors":["Tiong WN","A Rahim AI","Arifin Wan Mansor WN","Yaacob NA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/10810730.2026.2684616","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277490","name":"Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies.","source":"pubmed","abstract":"Neuropathic pain remains challenging due to its heterogeneous mechanisms and variable treatment response. This narrative review evaluates recent advances in artificial intelligence (AI) and machine learning (ML) for patient phenotyping, treatment selection, outcome prediction, neuromodulation, and regenerative therapies.","url":"https://pubmed.ncbi.nlm.nih.gov/42277490/","authors":["Lo Bianco G","Deer TR","Occhigrossi F","Diwan S","Mercieri M","D'Angelo FP","Martinez SM","Day MR","Navani A","Abd-Elsayed A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1007/s11916-026-01517-0","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277467","name":"Innovative strategies to overcome cancer drug resistance: from nanodelivery systems to targeted therapeutics.","source":"pubmed","abstract":"Cancer drug resistance, driven by complex genetic mutations, epigenetic plasticity, and tumor microenvironment interactions, remains the primary cause of therapeutic failure and poor patient survival. This review critically synthesizes current resistance mechanisms to propose a unified framework that maps specific molecular drivers to corresponding therapeutic interventions, ranging from conventional chemosensitizers to emerging AI-guided strategies. We demonstrate that while chemosensitizers effectively reverse efflux-mediated resistance and that targeted therapies address specific oncogenic drivers, their efficacy is often limited by biological barriers; conversely, nanoparticle delivery systems significantly increase bioavailability, and CRISPR/Cas9 offers precise correction of intrinsic genetic defects. Crucially, we identify that no single modality is universally sufficient; instead, maximal therapeutic impact arises from the strategic cross-talk between technologies, such as the use of AI to predict resistance evolution and optimize the timing of combination therapies or nanocarrier-delivered gene editing. Future clinical success depends on shifting from isolated treatment protocols to interdisciplinary, data-driven personalized medicine that dynamically adapts to the evolving landscape of tumor resistance, ultimately transforming fatal malignancies into manageable chronic conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/42277467/","authors":["Rejili M","Farahani N","Alimohammadi M","Hushmandi K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 12","doi":"10.1007/s12672-026-05260-1","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277410","name":"Revenue-sensitive evaluation of AI-assisted ICD-10-CM coding and human-AI collaboration under dual DRG payment systems.","source":"pubmed","abstract":"Automated ICD-10-CM coding is critical for hospital reimbursement under Diagnosis-Related Group (DRG) payment systems, yet standard metrics weight all errors equally. This study evaluated 11 models on MIMIC-IV under heterogeneous conditions (the full 7942-code space, top-50 self-trained baselines, and 200-admission zero-shot LLM samples) and proposed two revenue-sensitive metrics: the Revenue Sensitivity Index (RSI) and Coding Reimbursement Score (CRS). Performance was compared across US Medicare Severity DRG (MS-DRG) and Taiwan DRG (Tw-DRG) systems, with five human-AI review strategies simulated. PLM-ICD achieved the highest micro-averaged F1 (0.5934), while open-source zero-shot LLMs performed markedly worse in this exploratory comparison. A 26.5% CRS gap separated the best and worst fine-tuned models. Rankings were identical under both DRG schemes (Spearman &#x3c1;&#x2009;=&#x2009;1.00), indicating stability under a tiered Tw-DRG approximation (93.9% coverage), not the official grouper. At a 20% review rate, revenue-targeted prioritization achieved 43.2% CRS reduction versus 20.0% for random sampling, reaching 91% of the oracle bound. Revenue-aware evaluation captures financially meaningful differences missed by standard metrics, and revenue-guided human-AI collaboration emerges as a candidate deployment framework requiring prospective validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42277410/","authors":["Chen MI","Hsu YL","Chen YH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1038/s41598-026-57682-0","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42277326","name":"A systematic review of explainable artificial intelligence and cardiac electrophysiological models addressing sports-related sudden cardiac death and arrest in adolescents and young adults.","source":"pubmed","abstract":"Sudden Cardiac Death (SCD) is a fatal event occurring within one hour of a witnessed or 24&#x2009;hours of an unwitnessed Sudden Cardiac Arrest (SCA), being the leading medical cause of death among adolescent and young adult athletes. We examined the epidemiology of sports-related SCD (SrSCD) and SCA (SrSCA) incidence in adolescents and young adults, explainable Artificial Intelligence (xAI) applied to life-threatening arrhythmias, and cardiac electrophysiological models. We systematically searched peer-reviewed studies from eight databases between 2013-2025 (PROSPERO: CRD42024565960), using PROBAST for bias assessment. From 9574 studies, we included 84 (incidence: 16, xAI: 30, modelling: 38). SrSCD incidence ranged from 0.1 to 0.6 per 100,000 participants per year. Gradient-weighted Class Activation Mapping dominated as xAI technique. Cardiac electrophysiological models predominantly focused on cellular and tissue-level electrophysiology. We advocate for standardised SrSCD/SrSCA definitions and integration of epidemiological risk factors with xAI and cardiac modelling frameworks to advance athlete-specific risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/42277326/","authors":["Vanegas Müller E","Srikijkasemwat N","Gan A","Raman B","Harford M","He L","Banerjee A","Gehmlich K","Leeson P","Villarroel M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1038/s41746-026-02878-x","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42276961","name":"Disease Prediction and Precision Veterinary Medicine: Applications, Opportunities, and Limitations of Artificial Intelligence in Small Animal Practice.","source":"pubmed","abstract":"Artificial intelligence and machine learning are increasingly shaping the future of small animal veterinary medicine, particularly through predictive modeling that estimates disease risk. This article introduces key concepts underlying disease prediction and precision veterinary medicine and explains how diverse data sources including electronic medical records, insurance claims, wearable devices, and environmental datasets support predictive analytics. The article reviews common modeling approaches, emerging clinical applications, and the role of companion animals as sentinels in a One Health framework. It also examines practical limitations, potential biases, and ethical considerations, emphasizing that predictive tools should complement, not replace, clinical expertise in veterinary practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42276961/","authors":["Ruple A","Reid SWJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.cvsm.2026.04.003","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42276187","name":"Clinical Importance of All the Characteristics of Late Gadolinium Enhancement from Acquisition to Expert and Artificial Intelligence Analysis: State-of-the-Art.","source":"pubmed","abstract":"Late gadolinium enhancement (LGE) assessed by cardiovascular magnetic resonance is the cornerstone in the assessment of myocardial tissue characterization, providing crucial diagnostic and prognostic information across a wide spectrum of cardiac conditions. While LGE is traditionally evaluated for its presence and extent, a comprehensive assessment of its diverse characteristics, called \"LGE granularity\"-including its location, extent, and pattern-offers deeper insights into myocardial pathophysiology. The clinical significance of LGE is influenced by various factors, ranging from acquisition protocols including choice of contrast-media and post-processing techniques to interpretation by expert readers and, more recently, artificial intelligence (AI)-based analysis. Advances in imaging protocols have refined LGE detection and quantification, improving diagnostic accuracy and reproducibility. Furthermore, AI approaches are revolutionizing LGE assessment by enabling automated segmentation, feature extraction, and risk stratification. Despite the widespread clinical use of LGE, challenges remain in standardizing acquisition parameters and harmonizing interpretation criteria across centers. Additionally, the integration of AI into clinical workflows raises important considerations regarding validation, generalizability, and physician acceptance. However, emerging evidence suggests that AI-based LGE analysis may improve prognostic modeling, facilitate earlier disease detection, and enhance personalized therapeutic decision-making. This review provides a state-of-the-art of LGE's technical, interpretative, and prognostic aspects, highlighting the role of AI in myocardial tissue characterization. By bridging traditional expert analysis with cutting-edge computational techniques, the future of LGE assessment aims to refine cardiac risk stratification and guide precision medicine in cardiology.","url":"https://pubmed.ncbi.nlm.nih.gov/42276187/","authors":["Florence J","Unger A","Gonçalves T","Liberato G","Garot J","Soulat G","Pontana F","Dacher JN","Bohbot Y","Schulz-Menger J","Toupin S","Lima JAC","Pezel T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1016/j.jocmr.2026.102764","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42276181","name":"Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.","source":"pubmed","abstract":"Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personalized cancer care. This systematic review and meta-analysis aim to evaluate the predictive performance, methodological quality, and clinical implementation of deep learning models for cancer treatment outcomes. A comprehensive search across ten databases and preprint servers identified 158 eligible studies, with 89 included in the quantitative synthesis. Results revealed pooled AUCs of 0.823 (internal validation) and 0.787 (external validation), with superior performance observed in multimodal and Transformer-based models. However, given the substantial heterogeneity (I 2 &#xa0;&gt;&#xa0;70&#xa0;%) across included studies, these pooled estimates should be interpreted as broad indicators of methodological feasibility rather than definitive performance benchmarks. Methodological inconsistencies, high risk of bias, and limited external validation were common, and only 9&#xa0;% of models had been implemented clinically. This study contributes to the literature by providing the first cross-cancer meta-analytic synthesis of deep learning in treatment prediction across cancer types and model architectures. Findings highlight both the promise and the current limitations of AI integration in oncology and emphasize the need for rigorous validation, transparent reporting, and translational research. The review encompassed studies on both solid tumors (breast, lung, colorectal, prostate, and others) and various treatment modalities including chemotherapy, immunotherapy, radiation therapy, targeted therapy, and surgical interventions. Outcome measures included treatment response prediction (measured via AUC), overall survival and progression-free survival (evaluated using C-index and hazard ratios), and clinical utility (assessed through net benefit and decision curve analyses).","url":"https://pubmed.ncbi.nlm.nih.gov/42276181/","authors":["Tunca S","Balcioglu YS","Elmas-Cecen BO"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jbi.2026.105068","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42276030","name":"Charting human cellular senescence in aging and disease.","source":"pubmed","abstract":"Cellular senescence comprises diverse cell states emerging across human tissues during aging and disease. Integrating single-cell and spatial multi-omics with AI-driven analyses enables systematic mapping of senescent cell heterogeneity (\"senotypes\"), revealing tissue-specific programs and microenvironmental interactions. These advances provide frameworks for biomarker discovery and development of targeted senotherapeutic strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42276030/","authors":["Suryadevara V","Farzad N","Yang M","Xu K","Tsankov A","Thompson RC","Sloan N","Phatnani H","Olinger B","Mares JA","Lund AN","Li D","Gorospe M","Ding L","Beckmann N","Basisty N","Anerillas C","NIH SenNet consortium","Robbins P","Fan R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1016/j.cell.2026.05.028","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275675","name":"Expeditionary Point-of-Care Ultrasound for Combat and Austere Environments.","source":"pubmed","abstract":"Point-of-care ultrasound (POCUS) enhances combat survivability, yet civilian standards often fail to address battlefield constraints. This scoping review delineates Expeditionary POCUS (E-POCUS) as a distinct capability.","url":"https://pubmed.ncbi.nlm.nih.gov/42275675/","authors":["Stevens RA","Ausman CE","Korb D","Hall B","Cunningham CW","Mitchell CA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 27","doi":"10.55460/J.Spec.Oper.Med.2026.GOTR-RO8R","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275619","name":"Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly integrated into oncology across clinical care, research workflows, and translational implementation. This chapter provides a practical overview of these domains, emphasizing opportunities and limitations relevant to real-world oncology practice. In clinical care, AI applications include ambient documentation systems, decision support tools, and remote monitoring platforms. These technologies may reduce administrative burden, assist with evidence synthesis and guideline navigation, and enable longitudinal assessment of symptoms and functional status through wearable and patient-reported data. Emerging tools such as large language models also support patient communication through education, translation, and symptom triage. However, across these applications, performance remains dependent on data quality, validation, and appropriate clinical oversight. In research and scholarship, AI is increasingly used to support grant development, study design, literature review, and manuscript preparation. These tools may improve efficiency, enhance clarity, and assist in identifying research gaps or methodological approaches. At the same time, concerns regarding accuracy, hallucinated content, data privacy, and ethical responsibility require careful oversight. The responsibility for hypothesis generation, methodological rigor, and scientific integrity remains with the investigator. From a translational perspective, a persistent gap exists between model development and clinical deployment. Differences in data quality, infrastructure, and patient populations may limit generalizability, while workflow integration, clinician readiness, and bias mitigation remain critical challenges. Collectively, AI has the potential to enhance oncology care and research, but its impact depends on rigorous validation, equitable implementation, and sustained human oversight.","url":"https://pubmed.ncbi.nlm.nih.gov/42275619/","authors":["Hundal J","Veettil AAV","Chung C","Hosseini M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1200/EDBK-26-517280","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275446","name":"Effectiveness of Guideline-Based Clinical Decision Support Systems: Protocol for a Systematic Review.","source":"pubmed","abstract":"Clinical guidelines (CGs) standardize care through evidence-based recommendations, while clinical decision support systems (CDSS) can assist in applying these guidelines to individual patients. The scientific basis for the decisions offered by decision support systems is often not explicitly stated or not clearly specified in the literature on CDSS. Therefore, a systematic examination of the literature is needed to map the current state of CDSS, with a particular focus on the integration of CGs.","url":"https://pubmed.ncbi.nlm.nih.gov/42275446/","authors":["Aksu BN","Gashi B","Schiefenhövel F","Boeker M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.2196/87006","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275394","name":"Clinical artificial intelligence applications of vision-language foundation models.","source":"pubmed","abstract":"Vision-language models (VLMs) represent a transformative advance in generative artificial intelligence (AI), using multimodal data processing to enhance clinical decision-making and workflow efficiency. Built on transformer architectures, VLMs excel in tasks like image interpretation, report generation, and visual question-answering, with emerging applications in radiology, pathology, and broader clinical practice. Their potential extends to automating documentation, improving medical education, and assisting with clinical decision-making in real-time. However, successful integration requires rigorous validation to address challenges such as bias, interpretability, and safety concerns. Prospective clinical trials, health economic evaluations, and stakeholder engagement are essential to ensure equitable and effective deployment. Regulatory frameworks must evolve to accommodate VLM functionality while maintaining accountability and protecting patient safety. By balancing innovation with robust oversight, VLMs hold promise in reducing clinician workload, expanding access to expert care, and advancing precision medicine-ushering in a new era of AI-augmented healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/42275394/","authors":["Thirunavukarasu AJ","Li S","Qin P","Nie D","Sanghera R","Lim E","Yu J","Zhang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1371/journal.pdig.0001453","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275230","name":"AI-Generated Multiple Mini Interview (MMI) Stations for Medical School Admissions: Psychometric Evaluation.","source":"pubmed","abstract":"Multiple mini interviews (MMIs) are widely used in medical school admissions to assess applicants' nonacademic attributes in a structured and reliable manner. However, the development of high-quality MMI stations is resource intensive and dependent on expert input.","url":"https://pubmed.ncbi.nlm.nih.gov/42275230/","authors":["Hamila S","Birchill K","Cao K","Hossain MN","Bullock S","Hodgson W","Harrison J","Leech M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.2196/86208","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42275183","name":"Current state of electronic problems lists in primary care: a rapid scoping review.","source":"pubmed","abstract":"Electronic problem lists (PLs) are central to the problem-oriented medical record and increasingly underpin clinical decision support (CDS), interoperability, and emerging artificial intelligence applications in primary care. However, persistent concerns regarding PL accuracy, completeness, and governance limit their clinical value.","url":"https://pubmed.ncbi.nlm.nih.gov/42275183/","authors":["Nair R","Bekker I","Singer A","Lau F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1093/fampra/cmag036","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274913","name":"Infections in Burn Patients: Pathophysiology, Prevention, and Contemporary Therapeutic Strategies in the Era of Antimicrobial Resistance.","source":"pubmed","abstract":"Burn injuries are associated with significant morbidity and mortality, largely driven by infectious complications. Disruption of the skin barrier, systemic inflammation, and postburn immunosuppression promote microbial colonization and progression to local and systemic infections, a challenge further exacerbated by the increasing prevalence of multidrug-resistant organisms (MDRO). Patients with extensive burns, inhalation injury, prolonged hospitalization, and invasive device use are particularly vulnerable. The microbiological profile of burn wound infections evolves dynamically, shifting from early Gram-positive predominance to Gram-negative and multidrug-resistant (MDR) pathogens, including members of the ESKAPE group, in later stages. Effective management, therefore, requires an integrated approach combining early surgical intervention, strict infection control measures, continuous microbiological surveillance, and targeted antimicrobial therapy. This narrative review synthesizes current evidence on the pathophysiology, risk factors, and microbiological dynamics of burn-associated infections, alongside contemporary prevention and treatment strategies, including topical therapies, advanced biomaterials, systemic antibiotic approaches, and antimicrobial stewardship. Emerging strategies such as nanoparticle-based systems and artificial intelligence-driven predictive models highlight a shift toward precision medicine in burn care; however, their clinical translation remains limited by insufficient validation and standardization. In conclusion, optimizing outcomes in burn-associated infections will depend on integrating evidence-based clinical practices with innovative technologies, while reinforcing antimicrobial stewardship and developing validated predictive tools to address the growing burden of antimicrobial resistance.","url":"https://pubmed.ncbi.nlm.nih.gov/42274913/","authors":["Țânțu AC","Țânțu MM","Vrancianu CO","Sandu AM","Cristian RE","Constantin M","Păunescu A","Diaconescu D","Dragu ER","Adameșteanu MO","Chioaru B","Hariga SC","Jecan CR","Vîlcea ID"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1007/s40121-026-01376-7","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274864","name":"On the Use and Application of Virtual Reality in Diagnostic Radiology.","source":"pubmed","abstract":"Immersive technologies, particularly virtual reality (VR), have significantly advanced since their inception in the mid-twentieth century. In recent years, the development of affordable, high-quality VR systems has catalyzed their application across various fields, including diagnostic radiology. Radiologists can use VR to navigate complex anatomical structures in three dimensions, enhancing their understanding of spatial relationships and pathology and helping them visualize and interpret diagnostic medical images. This article explains the history of immersive technologies, how VR is currently being used in diagnostic radiology, and assesses prospects for future applications. Integration with artificial intelligence could lead to more sophisticated simulations, enhancing diagnostic accuracy and personalized treatment plans. VR's role in teleradiology is expected to grow, facilitating remote consultations and virtual collaboration among radiologists, improving accessibility for patients in underserved regions.","url":"https://pubmed.ncbi.nlm.nih.gov/42274864/","authors":["Bauml J","Rizvi A","Quraishi MI","Krupinski EA","Wald C","Wesolowski MJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1007/s10278-026-02032-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274840","name":"Refining the Bariatric Journey: A Proposed Neoadjuvant and Adjuvant Framework for GLP-1 Receptor Agonists Inspired by Oncology.","source":"pubmed","abstract":"Bariatric surgery is the most effective intervention for severe obesity, yet challenges such as perioperative risks in super-obesity and long-term weight regain persist. This narrative review proposes a novel multimodal framework for integrating GLP-1 receptor agonists (GLP-1 RAs) into surgical pathways. Drawing a conceptual analogy from oncologic \"neoadjuvant\" and \"adjuvant\" treatment models, we explore how pharmacotherapy can optimize surgical safety and reinforce long-term metabolic control.","url":"https://pubmed.ncbi.nlm.nih.gov/42274840/","authors":["Song K","Zhang Q","Li S","Du Q","Ng SK","Chen Y","Pu Y","Widjaja J","Chen H","Chen Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1007/s13679-026-00726-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274839","name":"Recent advances in biomass deconstruction, microbial conversion, artificial intelligence, and carbon capture for sustainable bioenergy.","source":"pubmed","abstract":"The transition from fossil fuels to low-carbon bio-based energy systems is increasingly constrained by the efficiency, scalability, and integration of conversion technologies. Addressing this challenge, this review critically analyzes microbial biofuel production through a conversion-centric and systems-level framework, emphasizing how feedstock diversity, pretreatment chemistry, enzymatic deconstruction, and microbial metabolism collectively govern overall process performance. This review evaluates how pretreatment strategies modulate biomass recalcitrance, hydrolysate chemical ecologies, inhibitor profiles, and redox balance, thereby imposing fundamental constraints on enzymatic efficiency, microbial conversion yields, and emissions outcomes. Advances in enzymatic hydrolysis are assessed in terms of bond-specific catalysis, enzyme synergy, and persistent bottlenecks arising from substrate heterogeneity, lignin enzyme interactions, and non-productive binding, while microbial engineering strategies from robust monocultures to synthetic consortia and cell-free systems are examined through techno-economic and metabolic flux perspectives. It further highlights the emerging role of artificial intelligence and multi-omics integration in enabling predictive optimization of pretreatment severity, enzyme cocktails, and metabolic routing, moving beyond empirical process tuning. This article establishes a unified framework for integrated \"microbial lignocellulose-to-fuel\" pathways, demonstrating how coordinated advances in conversion technologies are essential for achieving scalable, economically viable, and environmentally sustainable bioenergy systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42274839/","authors":["Kumar V","Mishra S","Joshi P","Misra J","Yegneswaran PP","Mallikarjun B","Yazdani SS","Lal PB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 11","doi":"10.1186/s40643-026-01081-w","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274624","name":"Artificial Intelligence in Tumor Evolution: Understanding Cancer Complexity Through Multi-Modal Data Integration in Precision Oncology.","source":"pubmed","abstract":"Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42274624/","authors":["Espinosa-Sánchez A","Carnero A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 3","doi":"10.3390/cells15111031","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274555","name":"Polypharmacology of Pathway Crosstalk in Neurodegenerative Diseases: Chemical Modulation of Interconnected Signaling Networks.","source":"pubmed","abstract":"Neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), arise from highly interconnected molecular and cellular abnormalities that progressively lead to neuronal dysfunction, synaptic failure, and cell death. This review provides a unified framework to understand the interrelated molecular mechanisms driving these diseases, with a focus on identifying key disease-specific intervention nodes. Core contributors include oxidative stress, mitochondrial dysfunction, protein aggregation, neuroinflammation, and emerging roles of peroxisomal dysfunction in redox imbalance, lipid dysregulation, and inflammatory amplification. Single-target therapies often show limited efficacy due to the complex, interconnected nature of these pathways. In contrast, polypharmacology, which targets multiple disease-relevant mechanisms simultaneously, offers a more promising therapeutic strategy. This review critically examines how pathway crosstalk drives neurodegenerative progression, with particular emphasis on mitochondrial-ROS-inflammatory signaling, aggregation-proteostasis failure, synaptic-neuroimmune dysfunction, and gut-brain communication. It evaluates various multi-node intervention strategies, including multi-target-directed ligands (MTDLs), molecular hybrids, natural products, drug repurposing, and nanocarrier-based delivery systems. Advances in network pharmacology, artificial intelligence (AI), bioinformatics, and multi-omics have enhanced the identification of actionable therapeutic nodes, candidate compounds, and brain-targeted delivery platforms. Notably, the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathways-play distinct roles in neuroinflammation, amplifying neuronal damage by releasing inflammatory cytokines and inducing mitochondrial dysfunction. However, successful translation into clinical practice remains constrained by challenges such as blood-brain barrier penetration, patient heterogeneity, and biomarker limitations. The review advocates for a shift towards mechanism-informed, patient-stratified polypharmacological strategies to better address the network pathology of neurodegeneration, despite significant translational hurdles.","url":"https://pubmed.ncbi.nlm.nih.gov/42274555/","authors":["Khan MS","Zafar I","Noman M","Yang G","Kang KS","Bopassa JC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May 22","doi":"10.3390/cells15110962","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274320","name":"Radiology: Cardiothoracic Imaging Highlights 2025.","source":"pubmed","abstract":"Radiology: Cardiothoracic Imaging publishes novel research and technical developments in cardiac, thoracic, and vascular imaging. This review article, led by the Radiology: Cardiothoracic Imaging Early Career Editorial Board, highlights selected articles published in the journal between November 2024 and October 2025. Featured articles span the breadth of cardiothoracic and vascular imaging, including cardiac CT assessment of prosthetic heart valves, photon-counting CT for improved coronary stent evaluation, and streamlined multiparametric cardiac MRI acquisition techniques. Additional topics include imaging of mitral annular disjunction; cardiac MRI markers of diastolic dysfunction, myocardial heterogeneity, and myocarditis prognosis; and cardiac MRI-based assessment of sarcopenia as a novel prognostic marker. Ongoing research and future directions include accelerated cardiac MRI, opportunistic cardiovascular risk assessment from incidental findings at routine imaging, and expanding applications of quantitative and artificial intelligence-driven techniques across cardiac, thoracic, oncologic, and vascular imaging. Keywords: Deep Learning, CT, CT-Coronary Angiography, CT Angiography, CT-Photon Counting, CT-Quantitative, Aorta, Coronary Arteries, Clinical Testing, MR Imaging, MRI, Cardiac, Pulmonary, Heart, Lung, Artificial Intelligence, Mitral Valve, Thoracic Tumor Staging, Vascular &#xa9; RSNA, 2026.","url":"https://pubmed.ncbi.nlm.nih.gov/42274320/","authors":["Pinos D","Gomes de Farias LP","Onnis C","Suchá D","Escalon JG","Hanneman K","Abbara S","Litt HI","Gulsin GS","Mastrodicasa D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1148/ryct.260065","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274298","name":"FROM DISCOVERY SCIENCE TO THE CLINIC - HERITABLE ENDOCRINE CANCERS AND RELATED DISORDERS: Functional imaging in hereditary endocrine neoplasms: evolving modalities and clinical implications.","source":"pubmed","abstract":"Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and high risk of multifocal and metastatic disease. Functional imaging (FI) is now integral to their management, providing molecular characterisation that complements conventional anatomical modalities and frequently enables earlier or more specific lesion detection. This review summarises current FI approaches across major hereditary syndromes, including multiple endocrine neoplasia types 1-4, von Hippel-Lindau disease, and hereditary paraganglioma-phaeochromocytoma syndromes with emphasis on radiotracer selection, genotype-phenotype correlations and implications for clinical practice. The performance and biological rationale for established radiotracers, including 18F-FDG, 68Ga-DOTATATE, 18F-DOPA, and choline-based PET agents and emerging probes, is described. Increasing evidence supports genotype-directed imaging algorithms, particularly in HPPSs, where molecular subtype predicts tracer avidity and guides theranostic strategies. The expanding theranostic framework, anchored in somatostatin receptors and norepinephrine transporter-directed radiopharmaceuticals, has reshaped treatment pathways for advanced and metastatic disease. Implementation challenges persist, including variable global access, high costs, workforce limitations, and concerns regarding cumulative radiation exposure during decades of surveillance. Technical constraints related to spatial resolution, partial-volume effects, and motion artefact continue to limit the sensitivity of small-lesion detection. Emerging approaches incorporating radiomics and artificial intelligence offer opportunities to enhance lesion characterisation, infer genetic subtype, and improve prognostication. Advancing FI in hereditary endocrine neoplasia will require genetically informed surveillance protocols, harmonised imaging standards, and equitable access to specialised modalities. Collectively, these developments have the potential to refine risk stratification, improve treatment selection, and optimise long-term outcomes for individuals with hereditary endocrine malignancies.","url":"https://pubmed.ncbi.nlm.nih.gov/42274298/","authors":["Ludgate S","Brennan SC","McNeil J","Tsang VH","Robinson BG","Tacon L","Clifton-Bligh RJ","Gild ML"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1530/ERC-25-0516","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274161","name":"From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death.","source":"pubmed","abstract":"QT prolongation causes torsades de pointes sudden death from heritable, pharmacologic, metabolic, nutritional triggers. Its dimensions have been studied separately. This integrative review synthesizes research on molecular pathogenesis, acquired/metabolic/nutritional risks, clinical stratification, therapy, and AI prediction. Dual-function channel mutations and post-translational defects underlie congenital LQTS beyond classic three genes. Drug-gene-metabolic interactions amplify acquired risk; insulin resistance, NAFLD, and adiposity are independent risk factors. Nutritional exposures (grapefruit juice, licorice, energy drinks) compound arrhythmic risk. QTc threshold alone is insufficient; T-wave morphology, genotype, electromechanical window dynamics, and M-FACT score add prognostic value. Nonpenetrant LQTS carries near-population-level event risk. Genotype-targeted mexiletine and left cardiac sympathetic denervation are validated alternatives. Machine learning outperforms clinical scores; deep learning distinguishes congenital from acquired QT prolongation on ECG. Precision QT management requires integrated strategies including nutritional and metabolic determinants, QTc measurement, and AI-enhanced prediction. Prospective data remain essential before algorithmic tools guide decisions.","url":"https://pubmed.ncbi.nlm.nih.gov/42274161/","authors":["Farjam M","Yazdanpanah MH","Fereydouni N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1002/clc.70370","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42274067","name":"Review Article | Artificial Intelligence Applications in Prognosis and Treatment of Neuro-oncology: A review.","source":"pubmed","abstract":"Artificial intelligence (AI) is the general term that contains Machine Learning (ML), and Deep Learning (DL). Utilization of these technologies in brain tumor treatment and prognosis is promising.","url":"https://pubmed.ncbi.nlm.nih.gov/42274067/","authors":["Al-Rahbi A","Al-Habsi T","Al-Saadi T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42273687","name":"Precision immunopharmacology in peri-implantitis management: from molecular mechanisms to advanced therapeutic strategies.","source":"pubmed","abstract":"To review the evolving shift in peri-implantitis research from traditional mechanical debridement toward host-modulatory and immunopharmacological concepts, focusing on molecular pathogenesis, candidate therapeutic targets, and advanced drug delivery systems requiring further validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42273687/","authors":["Chen Y","Yuan Q","Cui M","Guo Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1748582","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42273663","name":"Artificial intelligence in preventive care in primary health care settings: a scoping review.","source":"pubmed","abstract":"Artificial Intelligence (AI) could be integrated into Primary Health Care (PHC) to enhance the preventive care of several diseases. This scoping review aims to provide current evidence on AI applications for the prevention of non-infectious diseases in PHC. A structured search was conducted in PubMed/Medline and Scopus databases to identify studies evaluating AI-based interventions implemented in the preventive care of non-infectious diseases in the PHC sector. AI-supported preventive care was compared to standard preventive care or non-AI-based interventions. Preventive medicine was defined as at least one primary outcome related to disease incidence, risk reduction, and early detection rates of non-infectious diseases. AI demonstrates significant potential in preventive medicine in PHC as it enables proactive, personalized, and data-driven interventions. However, its adoption requires strategies to overcome technical, ethical, and organizational barriers. Future research should address real-world implementation, cost-effectiveness, and clinician engagement to maximize clinical impact.","url":"https://pubmed.ncbi.nlm.nih.gov/42273663/","authors":["Katsakiori PF","Mulita F","Papadimitroulas P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5114/amsad/220788","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42273651","name":"Personalised Dosimetry in Nuclear Medicine: Bridging Physics, Biology and AI for Next Generation Radiopharmaceutical Therapy.","source":"pubmed","abstract":"Radiopharmaceutical dosimetry is rapidly evolving from a physics-dominated calculation tool to a central pillar of precision nuclear medicine. As targeted radionuclide therapies expand across indications, there is a growing clinical imperative to personalize dose estimation, predict therapeutic efficacy and mitigate organ toxicity. This review critically examines the current landscape of dosimetry methods including organ level Medical Internal Radiation Dose (MIRD) schema, voxel-based S-values, Monte Carlo (MC) simulations and emerging artificial intelligence (AI)-assisted segmentation tools and their translational relevance. Through a comprehensive literature search of 177 Lu peptide receptor radio nuclide therapy (PRRT) studies published between 2020 and 2025, we evaluate methodological heterogeneity and quantify dose variations across organs. Findings reveal persistent inconsistencies in absorbed dose estimates with reported kidney doses varying, 0.3-0.9&#xa0;Gy/GBq and tumor doses ranging 1-10&#xa0;Gy/GBq largely driven by differences in imaging protocol timing, segmentation strategy, and time-point sampling across studies. We also discuss regulatory trends, biologically informed dosimetry models incorporating relative biological effectiveness (RBE), and future integration with dose-point kernel (DPK) based and dose-volume histogram (DVH) driven computational frameworks. The field must now shift toward harmonized, reproducible standards that bridge physics, biology, and computation, transforming dosimetry into a predictive engine for individualized radiopharmaceutical therapy (RPT).","url":"https://pubmed.ncbi.nlm.nih.gov/42273651/","authors":["Shanmugiah J","Kim JS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1007/s13139-026-00988-8","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42273605","name":"What are we missing? Reflections on the 'problem' of missed appointments in the UK.","source":"pubmed","abstract":"Tackling missed appointments has become a prominent part of conversations about the 'recovery' of the UK NHS from the COVID-19 pandemic. With long waiting lists, delays in access to primary care, and overburdened staff, it seems logical that efforts should be made to reduce the time lost to unused appointment slots. Yet care needs to be taken to reflect on how and why missed appointments have become an area of focus, and to be transparent about what this means for patient care and health inequalities. This article provides a critical perspective on the current mainstream approach to missed appointments in policy, research and practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42273605/","authors":["Lindsay C","Ellis DA","Baruffati D","Mackenzie M","O'Donnell CA","Simpson SA","Wong G","Williamson AE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3310/nihropenres.14239.2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42273521","name":"Digital Tools for the Recognition and Triage of Abnormal Uterine Bleeding: A Narrative Review.","source":"pubmed","abstract":"Abnormal uterine bleeding is a common gynaecological presentation, but patients frequently face difficulty distinguishing normal variation from bleeding that requires medical assessment. Digital health tools, including menstrual tracking applications, electronic bleeding diaries, telemedicine, eConsult systems, digital education, decision aids, and artificial intelligence-based risk prediction models, may improve recognition, documentation, triage, referral, and follow-up. This narrative review synthesizes the available literature on digital tools relevant to abnormal uterine bleeding and heavy menstrual bleeding. A structured search of PubMed, Scopus, and Web of Science was conducted in May 2026 using terms for abnormal uterine bleeding, heavy menstrual bleeding, menstrual disorders, digital health, mobile applications, menstrual tracking, symptom checkers, telehealth, decision aids, patient portals, remote monitoring, artificial intelligence, and chatbots. After deduplication, 122 unique records were screened, and 27 articles were included. The evidence shows that digital menstrual tracking can identify abnormal bleeding patterns at scale and may improve symptom histories, while mobile pictorial blood assessment charts and smartphone bleeding diaries offer more structured quantification than recall alone. Telemedicine and eConsult pathways appear feasible for selected abnormal uterine bleeding presentations when supported by safety-netting and clear thresholds for in-person assessment. Digital education and decision aids can improve knowledge and shared decision-making, although evidence for clinical outcomes is limited. Artificial intelligence models show promise for risk stratification, particularly for endometrial pathology, but require external validation, transparency, and clinical governance before routine deployment. Overall, digital tools may support earlier recognition and more efficient triage of abnormal uterine bleeding, but current evidence remains fragmented. Future research should prioritize validated triage algorithms, patient-centred outcomes, equity, privacy, and integration with routine gynaecological workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42273521/","authors":["Zahoor H","Rajra SK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.110510","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42272923","name":"Emerging metallic nanotechnology platforms for cancer sensing and imaging.","source":"pubmed","abstract":"Despite major advances, the management of cancer remains one of the greatest unresolved challenges in medicine and a leading cause of death worldwide. Thus, improved, early, and minimally invasive diagnostics are urgently demanded. In recent years, extraordinary progress in nanotechnology has radically changed this situation by providing unprecedented molecular-level insight into cancer cell mechanisms through highly sensitive biosensing platforms and multifunctional nanoprobes for advanced imaging. This review offers a unique perspective on emerging nanotechnology-based cancer diagnostics by examining the interconnected development of sensing and imaging technologies through the lens of nanomaterial physicochemical properties. In sensing, we highlight recent breakthroughs in electrochemical, optical, photoelectrochemical (PEC), enzymatic, surface-enhanced Raman scattering (SERS)-based, electroluminescence-PEC hybrid, molecular, fuel cell-powered, and nanocatalytic biosensing strategies. These approaches enable ultrasensitive and selective detection of a wide range of circulating biomarkers, including microRNAs (miRNAs), extracellular vesicles (EVs), exosomes, cell-free DNA, oncogenes, circular RNAs, circulating tumor cells (CTCs), telomerase activity, circulating proteins, methylation patterns, and tumor-associated carbohydrates. Beyond sensing, we also highlight significant advances in nanotechnology-based imaging, including magnetic resonance imaging (MRI), SERS, fluorescence, circular dichroism, and photoacoustic imaging. In these modalities, intelligent nanoprobes enhance specificity, resolution, and in vivo tissue imaging capabilities. Finally, we discuss the growing convergence of sensing and imaging nanotechnologies, the need for integrated theranostic platforms, translation into point-of-care devices, incorporation of artificial intelligence-driven analytics, and critical considerations related to nanobiomaterial safety. Collectively, these advances position nanotechnology as a transformative force in next-generation cancer diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42272923/","authors":["Negahdary M","Skinner W","Mabbott S","Nicolson F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.nantod.2026.103054","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42272679","name":"Revolutionizing medical physiology education: a narrative review of the transition from traditional classrooms to artificial intelligence.","source":"pubmed","abstract":"Medical physiology is a foundational component of medical education, offering vital details about normal human function and a deep understanding of disease processes. Traditionally taught through lectures, textbooks, and laboratory demonstrations, physiology education has progressively evolved in response to advances in technology and changing learner needs. This review examines the historical development of physiology teaching and synthesizes contemporary innovations that are transforming the educational landscape. Emphasis is placed on simulation-based education, hybrid and virtual classrooms, immersive technologies such as virtual and augmented reality, gamification, and student-centered learning strategies, all of which promote active engagement, conceptual understanding, and integration of physiological knowledge into clinical contexts. The review also explores the emerging role of artificial intelligence in physiology education, including personalized learning pathways, adaptive assessments, and real-time physiological simulations, with relevance for resource-limited settings. Innovations in assessment, competency-based medical education, interdisciplinary integration, and clinically oriented physiology teaching are highlighted as key contributors to improved learning outcomes. Additionally, the review addresses inclusivity and accessibility through the use of open educational resources and digital platforms that reduce disparities in educational access. Despite these advances, several challenges remain, including infrastructure limitations, faculty training gaps, institutional resistance, and ethical considerations surrounding the use of advanced technologies. Furthermore, the review outlines future directions focused on holistic learning environments, deeper clinical integration, and a balanced adoption of technology alongside traditional teaching methods. Overall, this narrative review highlights the importance of adopting a learner-centered, adaptable, and inclusive approach to physiology education that equips medical students for complex clinical practice and lifelong learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42272679/","authors":["Al-Rahbi A","Agarwal H","Sirasanagandla SR","Azmi MO","Al-Badi Q","Kolekar A","Rachoori S","Kumar RS","Sakr H","Joseph MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1781197","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42272650","name":"Artificial intelligence-assisted radiotherapy for pelvic and abdominal malignancies: assessing feasibility in the context of Africa-specific risks.","source":"pubmed","abstract":"Cancer care in Africa remains severely limited, with pelvic and abdominal malignancies contributing substantially to the disease burden. Radiotherapy is essential but constrained by infrastructure deficits, workforce shortages, and systemic inequities. Artificial intelligence (AI) may help strengthen radiotherapy through automation and improved workflow efficiency. This narrative review summarises current evidence on AI assisted radiotherapy for pelvic and abdominal cancers in Africa, highlighting feasibility, and regional specific implementation risks. The review shows that AI tools for auto-contouring, treatment planning support, quality assurance, and workflow optimisation can improve efficiency and ease workload when implemented within appropriate clinical and governance frameworks. Their clinical impact in African radiotherapy, however, is constrained by limited digital infrastructure, workforce shortages, weak data governance, regulatory gaps, and poor model generalisability. Additional risks including data bias from non-African training datasets, and fragile IT systems underscore the need for cautious deployment. A feasibility-first, phased adoption strategy centred on hybrid AI-human workflows, regional model validation, workforce upskilling, and policy-led governance offers a safe and practical route for integrating AI into African radiotherapy. When integrated within resilient systems and guided by risk-aware strategies, AI has the potential to act as a capacity multiplier rather than a substitute, offering a more equitable access to high quality radiotherapy across Africa.","url":"https://pubmed.ncbi.nlm.nih.gov/42272650/","authors":["Fiagbedzi E","Acquah GF","Baidoo AM","Osei-Poku L","Pokoo-Aikins M","Dery T","Agyabeng A","Issahaku S","Sackey TA","Adu-Poku M","Sosu EK","Tagoe S","Addison ECK","Hasford F","Stoeva M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1686296","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42272586","name":"Robot-Assisted Dentistry: What the Evidence Supports and Which Outcomes Are Still Missing.","source":"pubmed","abstract":"Dentistry has rapidly adopted digitally planned workflows, supported by cone-beam computed tomography, intraoral scanning, and virtual planning, expanding computer-assisted implant surgery through static guides, dynamic navigation, and robotic guidance. However, dental robotics is used inconsistently, and conflating robotics with navigation or automation can obscure key mechanistic and safety differences. This narrative review aimed to provide a clinically grounded synthesis of dental robotics across specialties, with emphasis on workflow dependencies, outcome patterns, and translational constraints. A structured literature search was conducted up to March 2026 using PubMed, Embase, Scopus, and Web of Science. Evidence was synthesized thematically, prioritizing clinical outcomes like accuracy, complications, peri-implant parameters, survival, patient- or provider-reported outcomes, workflow data, and implementation considerations. The most mature clinical evidence concerns robot-assisted implant placement, in which high agreement between planned and achieved positions was observed. Yet, accuracy should be interpreted as workflow performance influenced by imaging, planning, registration, tracking stability, and intraoperative conditions. Comparative data suggest the clearest gains versus freehand placement, while early adoption may increase operative time due to added setup and verification steps. Outside implantology, robotics follows distinct trajectories, including robotic orthodontic archwire bending, aligner-related automation, and transoral robotic surgery in maxillofacial practice, underscoring the need to appraise domains using different outcome hierarchies. Restorative and prosthodontic applications are emerging, ranging from robot-guided tooth preparation for crowns, onlays, and veneers to laboratory robotics for manufacturing,&#xa0;yet these remain largely industrial. Across domains, a recurring limitation is the mismatch between commonly reported geometric accuracy endpoints and underreported clinically decisive outcomes, which include complications, patient-reported outcomes, long-term maintenance, and economic value. Cost remains a major barrier, and formal cost-effectiveness evidence is limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42272586/","authors":["Alghamdi A","Alqahtani G","Dashti A","Aljameel N","Almadluh A","Agili A","Baqazi F","Kutbi A","Muhyedin N","Alsaeed F","Al-Qahtani H","Alshahrani N","Alshehri R","Al-Qahtani M","Alharbi A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.7759/cureus.108601","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42272575","name":"Characterization and Clinical Implications of Multidrug-Resistant Klebsiella pneumoniae in ICU Patients in India: A Comprehensive Review.","source":"pubmed","abstract":"Multidrug-resistant (MDR) Klebsiella pneumoniae has become a prominent cause of healthcare-associated infections, especially in Intensive Care Units (ICUs), constituting a major threat to patient outcomes and healthcare systems. This review examines the epidemiology, risk factors, mechanisms of antimicrobial resistance, virulence determinants, clinical manifestations, treatment approaches, infection control measures, and future perspectives of MDR K. pneumoniae , with particular emphasis on the Indian context. The rising incidence of carbapenem-resistant and hypervirulent strains has severely restricted available therapeutic choices, leading to higher mortality rates, prolonged hospital stays, and increased healthcare costs. Resistance is largely driven by mechanisms such as extended-spectrum &#x3b2;-lactamase (ESBL) and carbapenemase production, along with plasmid-mediated gene transfer, which facilitates rapid spread. Furthermore, virulence determinants such as capsule production, siderophore systems, and biofilm formation contribute to increased pathogenicity. Treatment remains difficult because of the scarcity of effective antibiotics, often requiring combination therapies and the investigation of novel treatment modalities. Reinforcing infection control practices, antimicrobial stewardship, and surveillance systems is crucial to curb the spread of MDR strains. Additionally, emerging approaches such as rapid diagnostic tools, genomic surveillance, and artificial intelligence-based predictive models offer potential for early detection and improved clinical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42272575/","authors":["Karande G","Mohite ST","Karande S","Patil S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.7759/cureus.108593","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42271596","name":"[Telemedicine and Digital Technologies in Neurological Intractable Diseases].","source":"pubmed","abstract":"The management of neurological intractable diseases is constrained by structural barriers, including the uneven distribution of specialists and the chronic, progressive course of these conditions, which limits timely access to appropriate care. In the context of an aging society and emerging infectious diseases, telemedicine and telehealth have gained prominence as strategies to improve access to specialized services. Advances in digital and information-communication technologies further strengthen these approaches by enabling remote monitoring and data-driven evaluation. In addition, integration with artificial intelligence may transform disease management by supporting continuous, proactive, and personalized care.","url":"https://pubmed.ncbi.nlm.nih.gov/42271596/","authors":["Oyama G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.11477/mf.188160960780060732","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42271404","name":"Artificial intelligence augmented tutoring vs expert instruction on learning simulated general surgical skills: a systematic review and meta-analysis.","source":"pubmed","abstract":"Surgical training suffers from a global deficit; 5 billion people lack access to safe surgery, with an estimated 143 million additional procedures needed annually. Traditional surgical education, constrained by the apprenticeship model, faces critical limitations in standardization and scalability, particularly in low- and middle-income countries where expert mentors are scarce. AI-augmented tutoring systems represent a potentially transformative solution. This systematic review and meta-analysis were conducted to address that evidence gap.","url":"https://pubmed.ncbi.nlm.nih.gov/42271404/","authors":["Hanna F","Gad MM","Helmy MBF","Eisa MM","Omran MWZ","Youssef M","Hassan AY","Kotb AW","Abusalah MA","El-Helbawy A","Basta MGZ","Kalmoush AE","Hindawi MD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1186/s12909-026-09606-9","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42271275","name":"Yolov7 neural network for pelvic X-rays after total hip arthroplasty: automatically measuring component position parameters- a retrospective feasibility study.","source":"pubmed","abstract":"After total hip arthroplasty (THA), regular follow-up X-rays are required to evaluate the position of the prosthesis. However, measuring the parameters of prosthesis position consumes a significant amount of time, and there may be variations in the results obtained by different doctors.","url":"https://pubmed.ncbi.nlm.nih.gov/42271275/","authors":["Li J","Ding L","Zhang K","Zhang Y","Gao S","Jin F","Ji Q","Chen Q","Guo Z","Lan W","Wang H","Zhang L","Li X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1186/s12880-026-02412-1","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42271122","name":"Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.","source":"pubmed","abstract":"Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constraints and substantial increases in cognitive and physical workloads, making workload reduction an urgent priority. We propose a tripartite collaboration model in which medical staff, artificial intelligence agents, and robots redesign hemodialysis workflows at the task level. Artificial intelligence agents support non-physical work through three coordinated modules: \"Eye\" integrates and visualizes multimodal data from dialysis machines, electronic health records, laboratories, and home or wearable monitoring to highlight early signals of deterioration; \"Brain\" uses machine learning to predict complications such as intradialytic hypotension and to support optimization of dry-weight estimation, anemia and chronic kidney disease-mineral and bone disorder management, and prescription trade-offs through scenario simulation; and \"Language\", based on large language models, drafts structured session summaries and plain-language explanations anchored to verified data, with clinician review to mitigate hallucinations and omissions. Robots reduce physical workload through equipment preparation, transport, and environmental maintenance, and may extend to reproducible vascular access surveillance using robotic ultrasound and, in the longer term, assisted cannulation. Clinicians/medical staff remain accountable for goal setting, value-laden decisions, communication, and authorization of automated outputs and actions. We also summarize governance requirements-interoperability, human factors evaluation, privacy and cybersecurity, and staged deployment starting from low-risk, verifiable functions. By delegating routine cognitive and physical work while preserving human responsibility and relational care, the model may enable more proactive, patient-centered hemodialysis and support sustainable staffing and workload reduction.","url":"https://pubmed.ncbi.nlm.nih.gov/42271122/","authors":["Noda R","Sakurada T","Ichikawa D","Shibagaki Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s10157-026-02903-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270954","name":"Does ChatGPT need a psychiatrist? Similarities between human psychopathology and errors in large language models.","source":"pubmed","abstract":"Two striking phenomena of the human mind encountered in mental healthcare are hallucinations and confabulations; perceiving things that are not there, or filling memory gaps with invented stories. Interestingly, contemporary artificial intelligence systems, such as large language models (LLMs) and automatic speech recognition tools, show remarkably similar errors. They are known to \"hallucinate\" words, or \"confabulate\" facts when information is missing, producing output that feels coherent but is false. In this article, we explore these parallels between psychiatric symptoms in humans and mistakes in model output. By comparing how and why these errors arise, we aim to illuminate shared computational principles underlying predictive systems. These comparisons highlight both the risks of relying on imperfect AI systems and the opportunity to use them as computational mirrors to better understand the human mind and the other way around: knowledge from psychiatric symptoms may help to improve AI systems to reduce error rates.","url":"https://pubmed.ncbi.nlm.nih.gov/42270954/","authors":["de Boer JN","Ciampelli S","Hailemariam AK","Koops S","Sommer IEC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1038/s44277-026-00064-1","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270730","name":"Non-invasive characterization of the relationship between skin microrelief and dermal-epidermal junction topography using line-field confocal optical coherence tomography (LC-OCT).","source":"pubmed","abstract":"Non-invasive technologies for observing internal skin structures without damaging the surface have advanced in recent years. However, an integrated understanding of internal structures together with the morphology of surface \"microrelief\"-the surface pattern consisting of furrows and ridges (i.e., sulci cutis and cristae cutis)-remains limited. We elucidated the relationship between internal skin structure and microrelief by combining line-field confocal optical coherence tomography (LC-OCT) with a proprietary AI system. We applied a semantic segmentation-based image AI system to LC-OCT data from 11 adults. The system extracted the coordinates of sulci cutis, stratum corneum thickness, viable epidermal thickness, and dermal-epidermal junction (DEJ) topography. The analysis revealed that the skin surface and DEJ in sulci cutis were nearly parallel. The DEJ undulation index in cristae cutis correlated with epidermal thickness, stratum corneum multilayering rate, and ceramide subclass parameters. We verified the relationship between the skin surface and the DEJ structure by incorporating positional information of the microrelief. LC-OCT combined with AI-based analysis provides a non-invasive approach to link age-related changes in DEJ structure with alterations in microrelief and related biomarkers.","url":"https://pubmed.ncbi.nlm.nih.gov/42270730/","authors":["Uda K","Iwami K","Yoshida N","Homma T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1038/s41598-026-56985-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270324","name":"A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.","source":"pubmed","abstract":"Conventional endoscopic indices for ulcerative colitis (UC) primarily assess the most severely affected segment, potentially underestimating the cumulative inflammatory burden across the colon. Consequently, there is increasing interest in assessment methods that incorporate both disease severity and spatial extent, including emerging artificial intelligence (AI)-based approaches. The aim of this study was to map and critically evaluate current evidence on endoscopic indices that integrate disease extent in UC, including both conventional and AI-assisted approaches. Original studies in which patients with UC were evaluated via endoscopic approaches for disease activity, severity, or extent and clinically relevant outcomes were reported. Non-original articles and studies of other diseases were excluded. PubMed/MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 2000 and December 2025. Two reviewers independently screened studies and extracted data. Findings were synthesized narratively due to heterogeneity. Twenty-one studies were included: eight addressed index development or validation, 11 evaluated clinical outcomes, and four investigated AI-assisted assessments, with some studies contributing to multiple categories. Extent-based indices showed stronger correlations with biomarkers and histological activity than conventional focal scores, although their ability to predict relapse was inconsistent. AI-based systems enabled automated assessment of inflammatory distribution, improving reproducibility and showing promising clinical associations, but external validation remains limited. Extent-integrated endoscopic assessment provides a more comprehensive evaluation of disease burden in UC patients and may improve risk stratification and treatment monitoring. However, methodological heterogeneity and limited prospective validation hinder clinical implementation. Further standardization and multicenter studies are needed.","url":"https://pubmed.ncbi.nlm.nih.gov/42270324/","authors":["Maeda Y","Kudo SE","Kuroki T","Kawabata Y","Ichimasa K","Misawa M","Ogata N","Ohtsuka K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.5009/gnl260078","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270291","name":"Evaluating reasoning in multimodal large language models for ophthalmology: a bilingual benchmark study using clinical vignettes and imaging.","source":"pubmed","abstract":"Large language models (LLMs) excel in text-based medical exams, but their ability to integrate multimodal data, critical for ophthalmology, is underexplored. This study evaluates vision-language LLMs' accuracy and reasoning in complex ophthalmic questions.","url":"https://pubmed.ncbi.nlm.nih.gov/42270291/","authors":["Yin H","Zhao K","Shi D","Grzybowski A","Jin K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1136/bjo-2025-328992","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270202","name":"Big Data in Pediatric Oncology: Hope, Hype, Reality.","source":"pubmed","abstract":"Proposed uses of \"big data\" in pediatric oncology are growing alongside the notoriety that big data has achieved from uses outside of medicine. While big data approaches hold promise to revolutionize how we collect, analyze, and act upon biomedical data, a number of distinct challenges constrain our ability fully to leverage big data to improve human health. In this article, we focus on applications of big data approaches in pediatric oncology. We highlight challenges that limit our ability to leverage big data using existing methods and discuss promising ways using big data.","url":"https://pubmed.ncbi.nlm.nih.gov/42270202/","authors":["Wyatt KD","Volchenboum SL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.hoc.2026.02.004","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42270038","name":"Dysregulated lipid metabolism in dry eye disease: Pathophysiological mechanisms and emerging treatments.","source":"pubmed","abstract":"Dry eye disease (DED) is increasingly recognized as a metabolic disorder characterized by the disruption of lipid homeostasis rather than solely an inflammatory condition. This review synthesizes current evidence regarding the dysregulation of lipid metabolism in the pathogenesis of DED. Furthermore, this review systematically summarizes lipid metabolic alterations involving the meibomian glands, lacrimal glands, cornea, conjunctiva, and the tear film. The ocular surface functions as an integrated Ocular Lipid Unit (OLU) where metabolic crosstalk occurs among these tissues. Key pathological mechanisms include the impairment of mitochondrial fatty acid oxidation and the dysregulated synthesis of very-long-chain fatty acids (VLCFAs) which compromise meibum fluidity and stability. Furthermore, an imbalance between pro-inflammatory n-6 and pro-resolving n-3 polyunsaturated fatty acids (PUFAs) causes chronic ocular surface inflammation. These metabolic shifts promote lipid peroxidation and ferroptosis while systemic factors such as aging and diabetes mellitus further exacerbate ocular lipotoxicity. Advances in lipidomics reveal that physical properties like viscoelasticity and molecular ordering are more clinically relevant than absolute lipid quantity. Emerging therapeutic strategies encompass physical therapies, novel pharmacological agents targeting ferroptosis or specific metabolic enzymes, and dietary interventions. Future management should prioritize restoring metabolic homeostasis of the entire OLU and targeting specific lipid defects to achieve precision medicine in DED.","url":"https://pubmed.ncbi.nlm.nih.gov/42270038/","authors":["Duan H","Sheng P","Zhang Y","Ma B","Yang L","Yoon KC","Lan Q","Qi H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.jtos.2026.06.003","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42269942","name":"Artificial intelligence and medical retina training: A scoping review of educational opportunities, emerging risks, and curricular responses.","source":"pubmed","abstract":"Autonomous artificial intelligence (AI) systems for retinal image interpretation are being deployed in routine clinical practice, fundamentally altering the training environment of retinal specialists. We map available evidence on how AI integration affects medical retina specialist training, addressing educational opportunities, developmental risks, and curricular responses. Following the PRISMA extension for scoping reviews and Joanna Briggs Institute methodology, we searched PubMed/MEDLINE, Embase, Web of Science, the Cochrane Library, and gray literature from major ophthalmological and medical education organizations from inception to March, 2026. Six thematic domains emerged: deployment context, artificial intelligence as educational tool, explainability and pedagogy, risks to trainee development, large language models and assessment validity, and institutional responses. AI creates genuine opportunities for personalized case allocation, synthetic dataset generation, explainable visual feedback, and knowledge scaffolding in medical retina training, while simultaneously posing documented risks including deskilling, never-skilling, automation bias, and disruption of established assessment frameworks. The professional identity formation of trainees in environments where AI routinely matches or exceeds first-year resident performance remains an underexplored concern. Institutional and accreditation responses lag substantially behind clinical deployment, ophthalmology-specific competency frameworks validated for residency training are largely absent, and targeted research and coordinated curriculum reform are urgently needed.","url":"https://pubmed.ncbi.nlm.nih.gov/42269942/","authors":["Hanhart J","Brosh K","Zur D","Loewenstein A","Zadok D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1016/j.survophthal.2026.06.007","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42269370","name":"Translational gaps in immuno-AI: From algorithmic accuracy to clinical trust.","source":"pubmed","abstract":"Artificial intelligence has shown remarkable promise in predicting patient responses to immune checkpoint inhibitors across cancers. However, despite high statistical performance, clinical translation remains minimal. This disconnect between algorithmic accuracy and clinical adoption, termed the translational gap, reflects unresolved challenges in validation, interpretability, and regulatory integration. This review critically examines key barriers preventing translation of Immuno-AI systems from research prototypes to clinically trusted decision-support tools. It analyzes methodological, regulatory, ethical, and infrastructural factors limiting implementation and proposes strategies for developing clinically trustworthy AI in immuno-oncology. A structured literature search was conducted across PubMed, Embase, Scopus, and Web of Science for studies published 2018-2025 reporting AI or machine learning models predicting ICI response or toxicity in human cohorts. Narrative synthesis was applied, focusing on translational bottlenecks. Three dominant factors underpin the translational gap: (1) insufficient external and prospective validation, leading to overestimation of model performance; (2) limited interpretability and absence of explainable frameworks suitable for clinical use; and (3) regulatory and infrastructural immaturity, including lack of harmonised standards for adaptive AI systems. These limitations contribute to absence of clinician confidence and hinder regulatory approval. Bridging the translational gap in Immuno-AI requires a shift from model-centric optimisation to system-level accountability. Clinically trustworthy AI must be validated across institutions, designed for interpretability, and governed by transparent, ethical frameworks. Collaborative efforts among researchers, clinicians, and regulators are essential to ensure future Immuno-AI systems achieve algorithmic excellence, clinical credibility, and social legitimacy.","url":"https://pubmed.ncbi.nlm.nih.gov/42269370/","authors":["Oisakede EO","Ayo Daniel RI","Olawuyi OF","Alabi JO","Analikwu CC","Olawade DB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.humimm.2026.111774","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42269210","name":"Artificial intelligence to revolutionize surgical decision-making is still just around the corner.","source":"pubmed","abstract":"Medical artificial intelligence, especially large language models, has engendered both excitement and unease across the medical community, promising improved surgical diagnostic accuracy, perioperative decision-making, and patient safety amidst concerns of considerable bias and its implications on the future of human expertise. The evolution of artificial intelligence clinical decision support originated in the surgical field through early systems like AAPHelp, which leveraged Bayesian reasoning to diagnose acute abdominal pain. Despite the initial promise of artificial intelligence clinical decision support, attempts to develop more robust diagnostic tools largely fell short in the 1980s, with instruments unable to adequately diagnose and manage complex presentations, a lack of transparency in their decision-making processes, and limitations in scope. It was not until the 2010s that clinical decision support began to show renewed potential as a meaningful adjunct in surgical practice, largely driven by advancements in machine learning techniques and wider access to large clinical data sets. New artificial intelligence models, particularly those utilizing large language models, now demonstrate impressive capabilities, from predicting the risk of postoperative complications for individual patients to streamlining clinical documentation and beyond. However, challenges persist regarding transparency, bias, reliability, and integration into clinical workflows. Despite these hurdles, artificial intelligence-based tools like large language models represent an exciting new chapter in surgical decision-making. Built on a long history of clinical decision support systems in surgery, these technologies hold great promise to meaningfully augment surgical practice and improve care for patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42269210/","authors":["Zhang SK","Rodman A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.surg.2026.110326","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268534","name":"Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.","source":"pubmed","abstract":"Acute kidney injury (AKI) is a major complication in critically ill patients, burdening both patients and healthcare systems. We previously introduced an AI-based model for early and continuous prediction of ICU-acquired AKI (ICU-A-AKI-2/3). In this study, we enhanced the model to better handle missing data, a common challenge in clinical settings. The upgraded model was validated in both retrospective and prospective cohorts, demonstrating improved robustness and predictive performance.","url":"https://pubmed.ncbi.nlm.nih.gov/42268534/","authors":["Alfieri F","Zappalà S","Bacci A","Cauda V","Basso M","Musso G","Cochelli L","Votta CD","Mariconti L","Maderna L","Russo G","Gomez J","Esteban-Reboll F","Gilavert Cuevas MC","Bodì M","Finazzi S","Kashani K","Ancona A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1186/s40635-026-00928-y","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268366","name":"Unlocking Neuroprotection: Exercise-Induced Muscle Secretome (Myokines) as a Therapeutic Avenue Against Alzheimer's Disease Pathogenesis.","source":"pubmed","abstract":"This review critically evaluates exercise-induced myokines as neuroprotective agents against Alzheimer's disease (AD) and is structured around three thematic sections: (1) mechanistic foundations of myokine neuroprotection, (2) translational barriers to therapeutic development, and (3) a strategic framework for future research. Epidemiological studies associate physical exercise with reduced AD risk (30-45%), yet mechanisms remain incompletely resolved. Preclinical studies demonstrate that exercise-induced myokines (Irisin, BDNF, Cathepsin B) modulate AD pathology by: (1) attenuating amyloid-beta (A&#x3b2;)/tau accumulation, (2) suppressing neuroinflammation, and (3) enhancing synaptic plasticity. However, human exercise interventions show conflicting results influenced by APOE genotype, age, and exercise modality. Associative human data suggest that Interleukin-6 (IL-6) exemplifies pleiotropy-affording neuroprotective effects in acute contexts but potentially detrimental effects in states of chronic inflammation. Therapeutic hurdles include blood-brain barrier (BBB) penetration, pleiotropic risks, and patient heterogeneity. Emerging concepts such as combinatorial approaches (nanocarriers, exercise mimetics) and biomarker-driven trials are proposed as hypothetical future strategies; however, these remain unvalidated and require substantial preclinical development before implemented in clinical care. This narrative review is structured around three thematic sections: mechanistic foundations of myokine neuroprotection, translational barriers to therapeutic development, and a strategic framework for future research. The muscle-brain axis represents a compelling but complex therapeutic target. Based on current preclinical and correlational human evidence, future research should prioritize mechanistic rigor, standardized biomarker validation, and clinically viable delivery strategies. Notably, several approaches discussed herein-including nanocarrier delivery systems, exercise mimetics, and combinatorial myokine cocktails-remain speculative and are presented as future research directions rather than established therapeutic interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42268366/","authors":["Shirvani H","Pescatello LS","Eftekhari Moghadam AR","Arabzadeh E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1007/s12031-026-02556-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268305","name":"Diagnostic accuracy of an artificial intelligence-based breast ultrasound tool in pregnant and lactating patients.","source":"pubmed","abstract":"To assess the diagnostic performance of an artificial intelligence (AI)-based decision support tool for breast ultrasound in pregnant and lactating patients and compare BI-RADS assessments with radiologist interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/42268305/","authors":["Dwan D","Lamb LR","Haver HL","Giess CS","Fishman MDC","DiPiro PJ","Bahl M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1007/s00330-026-12659-5","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268300","name":"Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.","source":"pubmed","abstract":"To benchmark medical image-specific vision-language models (VLMs) against real-world radiologist-written reports, focusing on diagnostic quality, clinical acceptability, hallucinations, and language clarity.","url":"https://pubmed.ncbi.nlm.nih.gov/42268300/","authors":["Lim WH","Lee JY","Lee JH","Kim S","Kim H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1007/s00330-026-12648-8","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268299","name":"Reply to the Letter to the Editor: \"Artificial intelligence for post-neoadjuvant axillary prediction\".","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42268299/","authors":["Li Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1007/s00330-026-12672-8","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42268278","name":"[Digital technologies in the diagnosis and treatment of sleep disorders in patients with post-traumatic stress disorder].","source":"pubmed","abstract":"Sleep disorders are a primary symptom of post-traumatic stress disorder (PTSD). Advances in computer technology have enabled the development of widely accessible wearable devices (WDs) that record objective parameters of patient activity and physiological functions. When integrated into telemedicine platforms, these devices facilitate patient access to diagnostic and treatment methods. This review summarizes the use of WDs, mobile, and web applications for diagnosing sleep disorders in PTSD and evaluating the effectiveness of therapeutic interventions. The application of modern technologies in research to establish new treatment approaches for PTSD and to translate scientific developments into clinical practice is discussed. The review also analyzes the use of computerized therapies that enable remote and largely autonomous treatment. Issues related to the use of digital technologies for diagnosing and treating PTSD are addressed, including confidentiality, security, and the need for careful interpretation of results, particularly for patients with mental disabilities and cognitive deficits.","url":"https://pubmed.ncbi.nlm.nih.gov/42268278/","authors":["Cheremushkin EA","Ukraintseva YV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17116/jnevro202612605287","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42267948","name":"Head-to-Head Comparative Evaluation of Four Commercially Available Artificial Intelligence Systems for Detecting Referable Diabetic Retinopathy in a Tanzanian Population.","source":"pubmed","abstract":"Comparative evaluations of commercially available artificial intelligence (AI) systems for use in diabetic retinopathy (DR) screening, particularly studies that identify systems by name, are limited, constraining procurement and implementation. This study aimed to identify commercially available AI systems potentially suitable for DR screening in a low-resource Tanzanian setting and compare their accuracy in detecting referable DR.","url":"https://pubmed.ncbi.nlm.nih.gov/42267948/","authors":["Cleland CR","Bascaran C","Makupa WU","Shilio B","Tufail A","Egan C","Fajtl J","Olvera-Barrios A","Rudnicka AR","Owen CG","Wallis C","Cartwright E","Bastawrous A","Macleod D","Burton MJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.2337/dc26-0572","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42267901","name":"MedADL: high-throughput information extraction of functional status from electronic health records to advance frailty assessment in older adults.","source":"pubmed","abstract":"Functional status is essential for assessing frailty and planning care in older adults but is often under-documented in the structured fields of electronic health records. Manual chart review can capture functional status information but is labor-intensive and time-consuming. In this study, we developed and validated a scalable natural language processing (NLP) model to extract functional status information from unstructured electronic health record (EHR) notes.","url":"https://pubmed.ncbi.nlm.nih.gov/42267901/","authors":["Fu S","Yue Z","Nguyen JT","Liu H","Ahn J","Ramirez V","Des Bordes J","Liu H","Kwak MJ","Rianon NJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1093/gerona/glag154","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42267500","name":"The role of emerging technology in expanding regional anesthesia education and patient access.","source":"pubmed","abstract":"The purpose of this article is to identify promising technologies that can enhance core regional anesthesia training, competence, and global implementation, which will lead to both expansion in patient access and improvements in perioperative outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42267500/","authors":["Foster KE","Hong HJ","Kapur N","Mariano ER"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1097/ACO.0000000000001676","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42267096","name":"Research advances in chronic thromboembolic pulmonary hypertension: from pathological mechanisms to multidisciplinary management.","source":"pubmed","abstract":"Chronic Thromboembolic Pulmonary Hypertension (CTEPH), classified as group 4 pulmonary hypertension (PH), is a progressive disease caused by unresolved pulmonary artery thrombi that undergo organization and fibrosis, leading to increased pulmonary vascular resistance, right heart failure, and death. Over the past decade, the understanding, diagnosis, and management of CTEPH have undergone profound transformation. This review aims to summarize and discuss recent advances in CTEPH, focusing on pathophysiological mechanisms, diagnostic innovations, therapeutic evolution, and future directions. Current evidence establishes CTEPH as a complex, multifactorial disease involving genetic susceptibility, endothelial dysfunction, inflammation, and aberrant vascular remodeling-far beyond simple mechanical obstruction. In diagnosis, novel imaging modalities including ultra-high-resolution CT, dual-energy CT, computational fluid dynamics, and artificial intelligence have significantly enhanced the sensitivity, objectivity, and functional assessment of pulmonary vascular lesions. Therapeutically, a \"three-pillar\" paradigm is now firmly established, with pulmonary endarterectomy (PEA) as the curative cornerstone, complemented by balloon pulmonary angioplasty (BPA) and targeted pharmacotherapy (e.g., riociguat). This paradigm is increasingly evolving toward multimodal combination strategies, including preoperative bridging therapy and management of residual PH after intervention. Despite these advances, critical challenges remain: precise identification of operable patients, optimization of surgical and interventional techniques, development of novel targeted therapies, and construction of individualized prognostic models integrating multiomics and artificial intelligence. By addressing these core issues, this review provides a comprehensive, clinically oriented perspective on the current state and future trajectory of CTEPH research and multidisciplinary management, while also discussing emerging precision medicine approaches (e.g., multi-omics and artificial intelligence) that remain investigational.","url":"https://pubmed.ncbi.nlm.nih.gov/42267096/","authors":["Zhang JJ","Huang YQ","Liu XK","Sun XL","Zhong X","Zhou C","Wang C","Xie P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1832792","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266981","name":"Ten Commandments of Off-Pump Coronary Artery Bypass Surgery.","source":"pubmed","abstract":"Off-pump coronary artery bypass (OPCAB) surgery offers distinct advantages over conventional on-pump techniques, including reduced inflammatory response, lower stroke risk, and improved outcomes in high-risk patients. Despite these benefits, concerns persist regarding graft patency, incomplete revascularization, and technical complexity, limiting widespread adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/42266981/","authors":["Raja SG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.atssr.2025.09.027","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266942","name":"Occupational dust exposure and cerebral small vessel disease: a public health perspective on prevention and early detection.","source":"pubmed","abstract":"Occupational dust exposure represents a modifiable environmental risk factor not only for respiratory diseases but also for accelerated brain aging through its association with cerebral small vessel disease (CSVD). This link poses a significant yet underrecognized public health challenge for aging workforces in industrial settings. This review synthesizes epidemiological and mechanistic evidence supporting the \"lung-brain axis\"-a proposed pathway through which dust inhalation may contribute to systemic inflammation, blood-brain barrier disruption, and subsequent CSVD pathology. We critically evaluate the central role of multimodal neuroimaging [including advanced Magnetic resonance imaging (MRI) and retinal imaging] in detecting and quantifying these cerebrovascular changes. Furthermore, we highlight the transformative potential of artificial intelligence (AI) in integrating multi-source data for risk prediction and enabling early intervention. This synthesis aims to highlight an emerging occupational health threat and to propose a scalable framework for surveillance and prevention to protect brain health in exposed worker populations. Most available evidence is cross-sectional; prospective studies are needed to establish causality.","url":"https://pubmed.ncbi.nlm.nih.gov/42266942/","authors":["Wang Y","Zhang S","Chen L","Zhang H","Cao A","Du P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1771274","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266909","name":"Implementation of evidence-based alcohol policies to reduce alcohol-related harm and liver disease to advance public health in Europe.","source":"pubmed","abstract":"Alcohol-related liver disease (ALD) remains a leading cause of preventable morbidity and mortality in Europe. Despite robust evidence that alcohol-related population-level policies delay use initiation and reduce associated harms, their implementation in Europe has been inconsistent and frequently undermined by alcohol industry interference, fragmented governance, and policy inertia. The burden of ALD as well as combined metabolic dysfunction and alcohol-associated liver disease (MetALD) has grown steadily, driven by increased alcohol intake, widespread metabolic risk factors, delayed diagnosis, and poor integration between primary care, substance use services, endocrinology, and hepatology. In this Series paper, we combine epidemiology, policy evaluations, and clinical evidence to examine these findings through the lenses of policy, system preparedness, education, and stigma. We highlight screening strategies for alcohol use and liver disease and describe models of multidisciplinary and digital care. Finally, we outline priorities for policy reform, stigma reduction, youth-focused prevention, and research on biomarkers, pharmacotherapies, and digital/artificial intelligence tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42266909/","authors":["Pose E","Díaz LA","Parker R","Israelsen M","Marjot T","Forrest E","Dhanda A","Paradis C","Arab JP","Krag A","Bataller R","Murray F","Lazarus JV","Carrieri P","Brennan PN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.lanepe.2026.101707","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266906","name":"Diagnostic innovation and models of care to improve fibrosis detection and risk stratification in steatotic liver disease.","source":"pubmed","abstract":"Steatotic liver disease (SLD) is the leading cause of chronic liver disease in Europe, with liver fibrosis representing the strongest predictor of liver-related outcomes and an important contributor to cardiometabolic risk. This Series paper examines diagnostic innovation and models of care to improve fibrosis detection and risk stratification across the continuum of care for SLD. A growing range of non-invasive tests for fibrosis assessment is now available, including blood-based biomarkers, imaging modalities, automated laboratory algorithms, and artificial intelligence-enabled tools. However, implementation remains inconsistent because of limited awareness, restricted geographic and financial access to advanced diagnostics, fragmented referral pathways, heterogeneous reimbursement, limited use of automated reflex testing, and poor digital integration across laboratories and electronic health records. Integrated multidisciplinary models of care linking primary care with specialist services may improve early fibrosis detection, referral efficiency, and equitable access to risk-stratified management, particularly among people living with indicator conditions such as type 2 diabetes and obesity.","url":"https://pubmed.ncbi.nlm.nih.gov/42266906/","authors":["Pugliese N","White TM","Brennan PN","Pannain S","Hagström H","Michel M","Rice-Duek L","Targher G","Caussy C","Dillon JF","Tacke F","Kopka CJ","Sebastiani G","Boursier J","Tsochatzis EA","Brouwer WP","Guaraldi G","Vettor R","Thiele M","Roden M","Stefan N","Jarvis H","Gines P","Schattenberg JM","Pose E","Buttigieg S","Byrne CD","Lazarus JV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1016/j.lanepe.2026.101722","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266634","name":"Colorectal Surgery Education in 2040.","source":"pubmed","abstract":"The Accreditation Council for Graduate Medical Education (ACGME) recently conducted their 10-year specialty-specific revision of the colon and rectal surgery program requirements. During this process, leaders in our field began to hypothesize what training in our specialty may look like in the future. Here we identify the potential of competency-based assessment through entrustable professional activities (EPAs) in subspecialty surgical education. We also recognize new technology, including AI and machine learning, and its application to forward education in the ever-evolving field of surgery. The key to the future of colorectal residency is harnessing these advancements to create a residency driven by \"precision education\": a personalized and targeted education for the individual trainee. Given the growing sources of educational assessment tools and competency data, this opportunity will only exponentially increase over the coming years and provide a backbone for the development of training in the future.","url":"https://pubmed.ncbi.nlm.nih.gov/42266634/","authors":["Ault GT","Lim A","Peters A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1055/s-0045-1813667","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266631","name":"Self-learning and Simulation in the 21st Century: From Textbooks to ChatGPT.","source":"pubmed","abstract":"The growth of the digital age has corresponded with decreased operative experience and concern for low readiness for practice among surgical trainees, allowing for rapidly advancing technology to attempt to fill this educational need, often through independent study. Online platforms provide an accessible, convenient space for trainees and faculty to obtain e-Learning materials, including operative videos, educational podcasts, recorded lectures, and interactive content. Social media continues to grow as a space for dissemination of such materials and for live and ongoing discussion along the continuum from medical students to expert surgeons. More recently, artificial intelligence-based tools are being studied and implemented as methods for self-assessment for surgical trainees for clinical acumen, board examination preparation, and automated review of intraoperative video. Simulation remains an integral component of the independent development of technical skills with ongoing advancement in physical models and the integration of artificial intelligence and extended reality tools. Surgical education will continue to evolve and benefit from the integration of these technologies into traditional learning methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42266631/","authors":["Hoagland DL","Abelson J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1055/s-0045-1813685","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266580","name":"Transdisciplinary fetal-neonatal neurology training integrates women's and children's health with life-course brain capital strategies: a narrative review.","source":"pubmed","abstract":"Neurological and mental health disorders affect over one-third of the global population. Healthcare systems continue to treat maternal brain health and neurodevelopment as separate domains. Critical intervention windows continue to be missed before and during the first 1,000&#x202f;days after conception. Current fetal-neonatal neurology training reflects healthcare fragmentation. Specialty-siloed education impedes integrative critical thinking that more successfully capitalizes on pre-conception and gestational neuroprotective opportunities. This narrative review presents perspectives that argue for a transdisciplinary approach among stakeholders that advances life-course brain healthcare. Integrative women's and children's health, the developmental origins of health and disease, cultural neuroscience, and brain health capital frameworks collectively contribute to an educational, practice and research model. This methodology more productively addresses public health priorities to offer equitable global brain health care based on knowledge of intersectionality. We propose that every pregnancy represents a brain health intervention opportunity. Healthcare bundles have been defined as a set of three to five evidence-based interventions to assess the quality and outcome of medical care choices. Equity-informed brain care bundles similarly can be developed to assess proactive and reactive neuroprotective intervention outcomes. Gene-environment interactions will influence the dynamic neural exposome across each person's lifespan. More effective therapeutic options can shift intergenerational neurodevelopmental trajectories to improve neurologic and mental health for entire communities. Combining biological, social, and structural determinants determine the direction of vulnerability or resilience pathways based on time-sensitive shared healthcare decisions. Two clinical vignettes ground this theoretical framework with fetal-neonatal neurology practice experiences. Emphasis on fragmented care, limited genomic screening, structural inequity, and uncorrected environmental exposures diminish preventable neurological and maternal outcomes across generations. We propose five implementation recommendations: dismantle structural barriers to integrate care; redesign training around transdisciplinary competency frameworks; realign payment structures to incentivize coordinated care; reorient research priorities with integrated care models; and develop measurable metrics of integrated maternal-child brain health. Artificial intelligence-assisted monitoring and learning health system platforms offer infrastructural elements to enable equitable intervention scaling across diverse clinical settings. Implementation of this framework across each lifespan will reduce intergenerational burdens of neurological and mental health disorders to sustain global brain health equity.","url":"https://pubmed.ncbi.nlm.nih.gov/42266580/","authors":["Scher MS","Adalat S","Eyre H","Msall ME","Ramey SL","Ramey CT","Cristancho A","Markvarde A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1756627","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266472","name":"The integration of psychosocial care, ethical governance, and patient-centered research in current and future approaches in the field of vascularized composite allotransplantation.","source":"pubmed","abstract":"Vascularized composite allotransplantation (VCA) has transitioned from an experimental endeavor to a clinically viable option for patients with complex tissue loss. While advances in microsurgery, immunosuppression, and perioperative care have improved graft survival, long-term success remains contingent upon psychosocial integration and robust ethical governance. These developments are reshaping not only clinical outcomes but also the conceptual foundations of transplantation. VCA uniquely challenges traditional paradigms, as it is primarily life-enhancing rather than life-saving, thereby intensifying ethical scrutiny regarding risk-benefit proportionality. Psychosocial factors such as identity reconstruction, body wholeness, personal autonomy, adherence, and long-term mental health are central determinants of graft success. Concurrently, innovations such as AI-driven allocation systems, big data analytics, and digital monitoring introduce novel ethical concerns related to autonomy, transparency, data governance, and equity. A key objective of this mini-review is to critically examine how emerging technological innovations in VCA intersect with psychosocial processes and bioethical principles, and to outline a framework for their responsible integration into clinical practice. It argues that technological progress must be embedded within a patient-centered approach that prioritizes lived experience, relational ethics, and trust. The integration of person-centered care models, longitudinal assessments, and interdisciplinary \"team science\" approaches is essential for ensuring that innovation translates into meaningful, equitable, and ethically sustainable improvements in patient outcomes Ultimately, the future of VCA depends on aligning biomedical innovation with psychosocial resilience and ethical legitimacy, ensuring that reconstructive success translates into meaningful improvements in quality of life and providing access to these transplants for all patients in need.","url":"https://pubmed.ncbi.nlm.nih.gov/42266472/","authors":["Kumnig M","Hopfgartner L","Brandacher G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frtra.2026.1869486","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42266216","name":"Research advances of tubeless thoracic surgery for pulmonary nodules: current status and future challenges.","source":"pubmed","abstract":"This narrative review aims to summarize the current research progress on Tubeless thoracic surgery for pulmonary nodules, analyze its physiological basis and clinical applications, and outline future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/42266216/","authors":["Li H","Zhao L","Tian H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fsurg.2026.1834893","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265979","name":"Redesigning trauma-focused interventions to address capacity challenges in mental health systems.","source":"pubmed","abstract":"Despite strong evidence supporting the use of trauma-focused evidence-based psychotherapies (EBPs) to treat posttraumatic stress disorder (PTSD), health care systems experience meaningful challenges in delivering these treatments at scale. With a focus on two large, integrated health care systems (Military Health System, Veterans Health Administration), we discuss the challenges to scaling up access to EBPs for PTSD, as well as proposed strategies to mitigate these challenges. We identify three primary barriers to delivery of EBPs for PTSD: insufficient staffing, difficulty connecting patients with EBP-trained providers, and structural inefficiencies characteristic of the weekly therapy model. We review emerging strategies to redesign the delivery of EBPs for PTSD, including accelerated delivery formats, alternative delivery settings, task-sharing, web- and app-based interventions, and the use of artificial intelligence. Taken together, these strategies represent promising opportunities to efficiently use limited staffing to increase access to EBPs for PTSD, while retaining or increasing intervention effectiveness. Further research is needed to determine how to most effectively integrate these strategies into a stepped-care model of treatment delivery, where redesigned approaches complement traditional EBP delivery models to address the real-world constraints of health care systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42265979/","authors":["McLean CP","Holder N","Holliday R","Rosen CS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1002/jts.70090","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265769","name":"AquaAI: development and internal validation of a Danish transformer-based model to identify drowning and aquatic incidents in prehospital medical records.","source":"pubmed","abstract":"Effective prevention of drowning and aquatic incidents requires timely and accurate surveillance supported by high-quality validated data. In Denmark, the use of the free-text fields in the Danish Prehospital Medical Record has proven effective in identifying potentially relevant cases for such surveillance. While these free-text fields contain rich contextual information, manual screening of all records is impractical. This study aimed to develop and internally validate a Danish natural language processing pipeline for identifying drowning and aquatic incidents (AquaAI) from routine prehospital records and prioritizing records for final manual validation by medical experts.","url":"https://pubmed.ncbi.nlm.nih.gov/42265769/","authors":["Ostenfeldt CK","Breindahl N","Graff C","Kragholm KH","Christensen HC","Blomberg SNF","Danish Drowning Validation Group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1186/s13049-026-01644-y","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265702","name":"Building a predictive model for nursing students' use of artificial intelligence: advancing technology acceptance and application in nursing education.","source":"pubmed","abstract":"With the rapid development of artificial intelligence (AI) technology, its application across various industries, particularly in healthcare and nursing, has been expanding. However, the factors influencing nursing students' acceptance and use of AI tools have not been fully explored. Understanding the key factors that affect nursing students' use of AI tools is crucial to enhancing AI integration into nursing education.","url":"https://pubmed.ncbi.nlm.nih.gov/42265702/","authors":["Zhang H","Hu Q","Yu S","Shi H","Xia Z","Meng F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1186/s12912-026-04853-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265674","name":"Tumor organoids as a revolutionary platform for advancing cancer nanomedicine.","source":"pubmed","abstract":"Nanomedicine has revolutionized oncology through targeted drug delivery and theranostic applications. However, its clinical translation remains a formidable challenge, primarily due to the translational gap created by conventional preclinical models. These models often fail to recapitulate patient-specific tumor heterogeneity and the complex dynamics of the tumor microenvironment. This review posits that&#xa0;emerging tumor organoid platforms, particularly tumor organoid-on-a-chip systems, represent a transformative solution. A key contribution of this review is the introduction of a comprehensive evaluation framework for assessing nanomedicine efficacy, safety and clinical correlation using organoid platforms, offering a critical comparison with conventional models. Furthermore, we identify pivotal&#xa0;challenges including simulation of tumor immune microenvironment, material compatibility, and integration with artificial intelligence, and propose innovative bioengineering approaches to address them. By faithfully bridging the gap between in vitro models and human pathophysiology, tumor organoid platforms are poised to unlock the full clinical potential of next-generation cancer nanomedicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42265674/","authors":["Wu C","Chen J","Zhang C","Ji J","Tan Y","Liu Z","Wu Q","Zheng X","Luo K","Xiao K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1186/s12943-026-02702-w","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265626","name":"Time-dependent risk of sleep disorders in patients with epilepsy: a nationwide cohort study.","source":"pubmed","abstract":"Sleep disturbances are common yet underrecognized comorbidities in epilepsy, adversely affecting seizure control and quality of life. This study aimed to evaluate the long-term risk and risk factors of sleep disorders among patients with newly diagnosed epilepsy using a nationwide cohort with 10 years of follow-up.","url":"https://pubmed.ncbi.nlm.nih.gov/42265626/","authors":["Lee SW","Kang C","Choi U","Jung H","Bae Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 10","doi":"10.1186/s12883-026-05038-6","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42265523","name":"Artificial intelligence in pediatric intensive care units: current applications in sepsis management.","source":"pubmed","abstract":"Early detection of sepsis in pediatric intensive care units (PICUs) is critical, but challenging due to its nonspecific clinical presentation and marked physiological heterogeneity. Artificial intelligence (AI) offers transformative potential for precision sepsis management, but clinical translation remains complex due to methodological and implementation barriers.","url":"https://pubmed.ncbi.nlm.nih.gov/42265523/","authors":["Fu S","Li F","Qian SY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.1007/s12519-026-01052-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42264690","name":"Future of Robotics and Integration of Artificial Intelligence: Toward Computer-Assisted Surgery and the Real Democratization of Surgical Care.","source":"pubmed","abstract":"Minimally invasive surgery has evolved from a disruptive concept to a cornerstone of modern surgical oncology. The integration of imaging, robotics and artificial intelligence (AI) has transformed surgical care into a highly data-driven, precise and standardized discipline. These advances are significant in oncologic surgery, where complex anatomy, biological variability and the need for radical resections demand unprecedented accuracy. This perspective review examines the current state and future direction of computer assisted surgery, exploring how the fusion of imaging, robotics and AI is reshaping clinical practice and promoting the democratization of surgery. The future of surgery will be built on a symbiotic relationship between human expertise and technological intelligence.","url":"https://pubmed.ncbi.nlm.nih.gov/42264690/","authors":["Giménez ME","Innocenzi C","Schulze FE","Forgione A","Marescaux J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.soc.2025.12.003","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42264416","name":"Pancreatic Neuroendocrine Neoplasms.","source":"pubmed","abstract":"Pancreatic neuroendocrine neoplasms are heterogeneous tumors whose incidence has risen with advances in, and increased use of, cross sectional and endoscopic imaging. They range from well differentiated, often indolent neuroendocrine tumors to aggressive neuroendocrine carcinomas. Most PNENs are non-functional and detected incidentally, whereas functional tumors present with hormone related clinical syndromes. Accurate diagnosis and staging rely on multimodality imaging. Pancreatic protocol CT and MRI remain first-line tools for anatomic assessment, providing information on tumor morphology, vascular involvement, and metastatic disease. MRI, particularly with diffusion weighted and hepatobiliary contrast imaging adds strength for detecting hepatic metastases. Functional imaging with somatostatin receptor (SSTR) PET/CT or PET/MRI is essential for identifying SSTR expressing disease, guiding management, evaluating heterogeneity, and selecting candidates for peptide receptor radionuclide therapy. Dual tracer imaging with SSTR PET and &#xb9;&#x2078;F FDG provides prognostic insight and detects dedifferentiated tumor components. Ongoing challenges include standardized surveillance and response assessment to therapy. Emerging radiomics and artificial intelligence tools may hold promise for improving personalized management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42264416/","authors":["Panda A","Itani M","Mhlanga J","Singh C","Catalano OA","Lall C","Arif-Tiwari H","Chu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1053/j.sult.2026.06.001","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42264375","name":"Ethical implications of the use of artificial intelligence in peer review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42264375/","authors":["Nukaly H","Barnawi G","Grant-Kels JM","Elston DM","Lipner SR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1016/j.jaad.2026.05.125","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42263950","name":"A scoping review on aging and cardiovascular diseases - Molecular mediators and artificial intelligence-based advanced diagnostic methods.","source":"pubmed","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, and their risk increases with age. Biological age reflects physiological decline more accurately than chronological age and may improve cardiovascular risk prediction. Recent advances in Artificial Intelligence (AI) have enabled estimation of biological age from electrocardiograms (ECGs), imaging, biomarkers, and omics data. However, the existing evidence on AI-derived biological age in cardiovascular medicine has not been comprehensively synthesized.","url":"https://pubmed.ncbi.nlm.nih.gov/42263950/","authors":["Sudoso AM","Ciarpaglini L","Scuppa D","Trasatti E","Sciandrone M","Galiuto L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct 1","doi":"10.1016/j.ijcard.2026.134615","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42263836","name":"Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.","source":"pubmed","abstract":"Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-initiating capacity and have been implicated in metastasis, therapeutic resistance, and disease relapse. These cells not only possess intrinsic resistance mechanisms, such as quiescence, enhanced DNA repair, and ABC transporter overexpression, but also actively orchestrate a profoundly immunosuppressive tumor microenvironment (TME). Emerging evidence suggests that CRC-SCs may contribute to shaping an immunosuppressive TME through secretion of exosomes and cytokines that influence immune cells (e.g., regulatory T cells, myeloid-derived suppressor cells, and tumor-associated macrophages) and key stromal cell populations (e.g., cancer-associated fibroblasts). This review provides a comprehensive analysis of the evolving landscape of immunotherapies designed to target CRC-SCs. We evaluate immunotherapeutic strategies including immune checkpoint inhibitors, while also discussing the targeting of critical signaling pathways like Wnt, Notch, and Hh. Furthermore, we explore the growing role of artificial intelligence (AI)-based approaches in deciphering CRC-SC heterogeneity, identifying predictive biomarkers, and accelerating therapeutic target discovery. Despite these advancements, significant challenges remain, including tumor heterogeneity, the immunosuppressive TME, and on-target/off-tumor toxicity. We conclude by outlining future directions that emphasize combination therapies, novel delivery systems, and AI-driven precision medicine as crucial strategies to more effectively target CRC-SC populations and improve durable disease control.","url":"https://pubmed.ncbi.nlm.nih.gov/42263836/","authors":["Hamid M","Siddig AMA","Suliman R","Saeed A","Mussa A","Al-Hatamleh MAI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 28","doi":"10.1016/j.canlet.2026.218665","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42263756","name":"Artificial intelligence in 3D bioprinting and biofabrication: a validation-stringency assessment.","source":"pubmed","abstract":"Three-dimensional bioprinting and biofabrication increasingly use artificial intelligence (AI) and machine learning (ML) for bioink formulation, printability assessment, process monitoring, and post-print maturation. However, most studies train and test their models within a single batch, printer, or laboratory, leaving model behavior under changing conditions unclear. In this review, we address this gap with two complementary frameworks. First, we introduce a three-tier taxonomy of AI methods, classifying them by how they learn, what model is used, and what task is performed. Second, we apply a five-level validation Ladder, conceptually related to staged-maturity frameworks used in engineering and clinical AI, that grades evaluation rigor from random internal splits (Level 0) to real-time deployment with predefined acceptance criteria (Level 4). We apply both frameworks to 40 primary studies across the bioprinting pipeline and discuss their implications across seven tissue systems: bone/cartilage, cardiac, hepatic, neural, skin, vascular, and tumor. Approximately 83% of these studies remain at Level 0, boundary Level 0-1, or Level 1; 15% reach Level 2 cross-condition testing, only one study (2.5%) reaches Level 3 multi-laboratory testing, and none reach Level 4. This pattern reflects experimental scope rather than method immaturity, since higher levels require data from multiple independent batches and printer configurations. We conclude that community benchmark datasets for printability prediction, defect detection, and organoid phenotyping-annotated with their tier and validation level-are the most direct route toward bioprinting AI that can be reproduced beyond a single laboratory.","url":"https://pubmed.ncbi.nlm.nih.gov/42263756/","authors":["Lee JS","Cha JH","Kim BS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1088/1758-5090/ae7b07","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42263727","name":"Global advances in health artificial intelligence: a workforce imperative.","source":"pubmed","abstract":"The global health workforce is approaching a breaking point, driven by administrative overload, inefficient workflows, burnout, and accelerating retirements, with a projected global shortfall of 11 million health professionals by 2030. This urgency coincides with the rapid emergence of clinical artificial intelligence (AI) tools, especially generative systems now embedded in documentation, triage, and workflow support. Therefore, AI should be framed less as a substitute for clinicians than as a retention strategy that preserves careers, expertise, and the human core of care. High-impact uses include ambient documentation, coding support, scheduling and demand prediction, claims and billing support, and inbox triage-tools that can reduce clerical burden and return time to caring, teaching, and leadership. Workforce shortages also create an ethical and geopolitical dilemma; reliance on international recruitment can deepen global inequities, whereas responsible AI deployment might ease competition for scarce talent and expand capacity in lower-resource settings. Yet, AI will not fix dysfunctional systems by default; poorly designed implementation can shift burdens, erode confidence, and widen gaps in health-care quality, access, and clinician wellbeing. Practice must remain clinician-led, patient-centred, and grounded in shared decision making. The policy priority is expertise amplification, not workforce replacement.","url":"https://pubmed.ncbi.nlm.nih.gov/42263727/","authors":["Dai T","McDonald KM","Baumgart DC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 8","doi":"10.1016/S0140-6736(26)00693-8","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42262632","name":"Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning.","source":"pubmed","abstract":"Artificial intelligence offers great opportunities in critical care, particularly when a vast amount of continuously acquired physiological data is incorporated. High-quality, reliably labelled data are paramount for developing and training artificial intelligence methods. However, routinely recorded data in critical care are often noisy, and the sheer volume of high-resolution data is challenging to manage. Generalizable solutions for these problems are lacking, restricting progress. To address these barriers, we developed Vitabel, an open-source Python framework for post hoc loading, visualizing, aligning, and annotating medical time series. The framework provides sensible defaults and interactive components for efficient use in preconfigured workflows, while remaining flexible and extendable for custom analysis and annotation pipelines. It integrates seamlessly into Jupyter Notebooks, providing an interactive, customizable interface for visual interaction with the data. In this publication, we demonstrate its utility across three use cases. The code and exemplary data are provided as browser-based demos. Vitabel is freely available and published under the MIT license accompanying this publication.","url":"https://pubmed.ncbi.nlm.nih.gov/42262632/","authors":["Orlob S","Kern WJ","Hackl B","Wnent J","Gräsner JT","Holler M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1007/s10916-026-02417-x","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42262612","name":"Underrecognized depressive and anxiety symptoms in inflammatory arthritis: implications for systematic screening using PHQ-9 and GAD-7.","source":"pubmed","abstract":"Depression and anxiety are common comorbidities in patients with inflammatory arthritis (IA), including rheumatoid arthritis (RA), axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA). Despite their clinical relevance and impact on disease outcomes, these mental health conditions remain frequently underrecognized in routine rheumatology practice.&#xa0;To evaluate the prevalence of depression and anxiety symptoms in patients with IA in a real-world clinical setting and to compare the utility of different patient-reported questionnaires for their identification.&#xa0;Cross-sectional, real-world observational study.&#xa0;Patients with RA, axSpA, and PsA were evaluated. Depression and anxiety symptoms were assessed using the PHQ-9 and GAD-7, along with specific items of the Multidimensional Health Assessment Questionnaire (MDHAQ).&#xa0;A total of 255 patients were included (RA n&#x2009;=&#x2009;105, PsA n&#x2009;=&#x2009;60, axSpA n&#x2009;=&#x2009;90), with mean ages 59, 47, and 42 years, respectively. Across all groups, the prevalence of depression symptoms identified using PHQ-9 were more frequently observed than reflected in prior routine clinical records .In RA, depressive symptoms prevalence increased from 8.6%(n&#x2009;=&#x2009;9) to 24.8% (n&#x2009;=&#x2009;26); in PsA, from 5% (n&#x2009;=&#x2009;3) to 18.3%(n&#x2009;=&#x2009;11); and in axSpA, from 14.4% (n&#x2009;=&#x2009;13) to 21.1%(n&#x2009;=&#x2009;19). A similar pattern was observed for anxiety symptoms. When assessed using GAD-7 the prevalence increased from 4.9% (n&#x2009;=&#x2009;5) to 20% (n&#x2009;=&#x2009;21) in RA, from 5.1% (n&#x2009;=&#x2009;3) to 16.7% (n&#x2009;=&#x2009;10) in PsA, and from 7.8% (n&#x2009;=&#x2009;7) to 12.2% (n&#x2009;=&#x2009;11) in axSpA when evaluated. No significant differences in median PHQ-9 or GAD-7 scores were observed between the groups. MDHAQ mental health items showed strong correlations with PHQ-9 and GAD-7 composite scores.&#xa0;Depression and anxiety symptoms are underrecognized yet highly prevalent in patients with inflammatory arthritis. They can be effectively identified using dedicated screening questionnaires such as the PHQ-9 and GAD-7. Mental health items in the MDHAQ may represent the first step in identifying patients who require further evaluation for psychiatric disorders.","url":"https://pubmed.ncbi.nlm.nih.gov/42262612/","authors":["Kolasińska M","Wilk M","Polak D","Siwek M","Krupa AJ","Korkosz M","Guła Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1007/s00296-026-06126-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42262608","name":"The interconnection between obstructive sleep apnoea and spondyloarthritis: pathophysiology, clinical evidence, and future perspectives.","source":"pubmed","abstract":"Obstructive sleep apnoea (OSA) and spondyloarthritis (SpA) are chronic inflammatory disorders associated with substantial cardiometabolic morbidity and impaired quality of life. Increasing evidence suggests that these conditions frequently coexist and may share overlapping pathogenic mechanisms involving intermittent hypoxia, oxidative stress, endothelial dysfunction, autonomic dysregulation, and activation of TNF-&#x3b1; and the IL-17/IL-23 axis. This narrative review summarises current evidence on the epidemiological, mechanistic, and clinical relationship between OSA and SpA, with emphasis on axial spondyloarthritis and psoriatic arthritis. Available studies indicate an increased prevalence of OSA in ankylosing spondylitis and psoriatic arthritis, although evidence remains heterogeneous and limited by small cohorts, cross-sectional designs, and inconsistent use of polysomnography. Structural spinal restriction, obesity, metabolic syndrome, chronic inflammation, and altered sleep architecture may all contribute to OSA susceptibility in SpA populations. Conversely, OSA-related intermittent hypoxia may amplify inflammatory pathways relevant to SpA pathobiology, potentially worsening fatigue, pain, disease activity, and cardiovascular risk. Preliminary evidence also suggests that continuous positive airway pressure and biologic therapies may favourably influence inflammatory and sleep-related outcomes, although disease-specific interventional evidence remains limited. Current evidence suggests a clinically relevant association between OSA and SpA, but the directionality and clinical significance of this interaction remain incompletely understood. Prospective longitudinal and mechanistic studies integrating objective sleep assessment, inflammatory biomarkers, and rheumatologic outcomes are needed to clarify causality and guide multidisciplinary management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42262608/","authors":["Belančić A","Rogoznica Pavlović M","Fajkić A","Vučković M","Šimac Prižmić P","Gkrinia EMM","Radić J","Đogaš Z","Radić M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1007/s00296-026-06169-2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42262593","name":"Enhancing spinal mobility and functional capacity through aerobic exercise and yoga in ankylosing spondylitis: a randomized controlled trial.","source":"pubmed","abstract":"Objective This study aimed to investigate the effects of yoga-based exercises, combined with aerobic exercises, on spinal mobility, disease activity and functional capacity in Ankylosing spondylitis (AS). Methods A total of 51 patients with AS (27 males, 24 females; mean age: 40.7&#x2009;&#xb1;&#x2009;9.1&#xa0;years; range 24-57&#xa0;years) completed in this prospective, randomized, controlled study. Group 1 (n&#x2009;=&#x2009;25) received only aerobic exercise therapy, while group 2 (n&#x2009;=&#x2009;26) received yoga therapy in combination with aerobic exercises. The intensity of the aerobic exercise program were determined using Cardiopulmonary Exercise Testing (CPET). The aerobic program was administered for 30&#xa0;min over 12 sessions, under physician supervision with monitoring using a lower extremity ergometer. In group 2, in addition to the aerobic program, a yoga-based exercise consisting of spinal flexibility, relaxation, breathing and meditation exercises was administered for 30&#xa0;min over 12 sessions. Spinal mobility (chest expansion(CE) and BASMI), peripheral muscle strength and laboratory measures (ESR and CRP), were recorded pre- and post-treatment. SpA-specific clinical measures included the ASDAS-CRP, BASFI, ASQoL scale, fibromyalgia scores. Functional capacity was evaluated using the 6-Minute Walk Test (6-MWT). Respiratory parameters and aerobic capacity levels, recorded using Pulmonary Function Tests (PFT) and CPET. Results In the analysis comparing pre- and post-treatment delta gain values between the two groups, statistically significant differences favoring group 2 were found in CE (p&#x2009;=&#x2009;0.002), BASMI (p&#x2009;=&#x2009;0.025), right and left handgrip strength (p&#x2009;=&#x2009;0.023, 0.044), BASFI (p&#x2009;=&#x2009;0.03), 6-MWT (p&#x2009;=&#x2009;0.005) and resting systolic blood pressure (p&#x2009;=&#x2009;0.012). In the Holm-Bonferroni-adjusted sensitivity analysis of between-group delta comparisons, chest expansion, BASMI, and 6-MWT remained statistically significant among clinical and functional secondary outcomes, whereas handgrip strength and BASFI no longer remained statistically significant Conclusion This study showed that a physician-supervised combined aerobic and yoga-based exercise program may provide additional benefits over aerobic exercise alone in selected clinical and functional outcomes, particularly chest expansion, spinal mobility, functional capacity. We suggest that yoga-based exercises combined with aerobic exercise, could be included in the treatment plans of patients with AS.","url":"https://pubmed.ncbi.nlm.nih.gov/42262593/","authors":["Önal R","Ordu Gökkaya NK","Dizdar D","Korkmaz S","Alemdaroğlu E","Güler T","Yurdakul FG","Bodur H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1007/s00296-026-06185-2","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42262139","name":"Advancing One Health genomics in Africa: opportunities and challenges for outbreak and antimicrobial resistance control.","source":"pubmed","abstract":"SUMMARYAfrica's ongoing struggles with emerging epidemics and antimicrobial resistance (AMR) underscore the urgency of integrating pathogen genomics and surveillance systems into the continent's One Health strategy, particularly given the existing limitations in preparedness and technological resources. This review brings together current evidence on the growth of sequencing infrastructure, the development of regional genomic hubs, and the establishment of governance frameworks, while identifying critical challenges in data integration, bioinformatics capacity, and sustainable financing. Special focus is placed on the lack of African-based genomic data, with our analysis showing that only 1.82% of the global total is available. Case studies illustrate the immense potential and importance of pathogen genomics, giving policymakers a tangible sense of its impact. These examples demonstrate how genomic technologies integrated with artificial intelligence (AI) are transforming outbreak response, AMR surveillance, and stewardship programs by enabling early detection of zoonotic threats, mapping transmission pathways, and guiding vaccine development. However, to fully realize this scientific intel, it is essential to embed One Health pathogen surveillance within strong policy and system frameworks to ensure the translation of technical progress into lasting institutional capacity and sustainable impact. Long-term implementation depends on coordinated investment and advocacy across four interdependent pillars: data architecture, governance and sovereignty, human capital, and technical capacity.","url":"https://pubmed.ncbi.nlm.nih.gov/42262139/","authors":["Derar DI","Hany F","Eltaher H","Mahmoud S","Sedarous Y","Ruan Z","Alkhaldi M","Elhadidy M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1128/cmr.00387-25","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261595","name":"Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.","source":"pubmed","abstract":"CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based genome and transcriptome editing technologies have emerged as powerful tools for therapeutic, agricultural, and industrial applications. However, their broader clinical and translational use remains limited by variable guide RNA (gRNA) or single-guide RNA (sgRNA) efficiency and unintended off-target activity, which may lead to genotoxic effects and major safety concerns. To address these challenges, recent research has increasingly shifted from heuristic scoring approaches and traditional machine learning (ML) methods toward deep learning (DL) models capable of learning complex sequence-function relationships from large-scale experimental datasets generated by assays such as GUIDE-seq (Genome-wide Unbiased Identification of Double-stranded Breaks Enabled by Sequencing), CIRCLE-seq (Circularization for In Vitro Reporting of Cleavage Effects by Sequencing), and CHANGE-seq (Cumulative and Homology-independent Analysis of Nuclease Genome-wide Effects by Sequencing). This review critically examines recent advances in DL approaches for gRNA optimization and off-target prediction in CRISPR systems. We discuss the development of convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based architectures, and foundation models designed to improve prediction accuracy, specificity, and generalizability across diverse biological contexts.","url":"https://pubmed.ncbi.nlm.nih.gov/42261595/","authors":["Saeed M","Arham M","Zafar I","Jamal A","Hussian M","Usman M","Bahwerth FS","Noman M","Hossain MB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1002/biot.70255","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261384","name":"Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.","source":"pubmed","abstract":"Emergency and critical care medicine requires the rapid synthesis of heterogeneous clinical data under extreme time constraints. Early artificial intelligence tools lacked the flexibility to manage real-world patient heterogeneity. Large language models (LLMs) offer a paradigm shift by demonstrating advanced natural language understanding, cross-task generalization, and context-sensitive reasoning, thereby bridging the gap between fragmented algorithms and holistic clinical decision support. The effective deployment of these models is grounded in four methodological pillars: domain adaptation, knowledge integration, multimodal and temporal modeling, and transparency. Domain adaptation and knowledge integration specifically empower the context-sensitive reasoning required for high-stakes intensive care. This theoretical framework enables their application across clinical decision support, documentation optimization, medical education, and clinical research. Integrating continuous physiological waveforms with multi-omics data facilitates dynamic risk stratification for complex conditions like sepsis, while natural language-to-structured query language capabilities accelerate clinical data extraction and quality improvement. The transition of LLMs from experimental settings to routine clinical deployment remains constrained by model hallucinations, multimodal integration barriers, and unresolved ethical governance. Sustainable implementation requires a human-in-the-loop copilot design, rigorous multicenter prospective validation, and transparent regulatory frameworks. Addressing these challenges is essential to ensure that technological innovations safely translate into measurable improvements in patient survival and clinical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42261384/","authors":["Yang J","Yang S","Shao Z","Chen T","Shen H","Zhou P","Xia B","Lei X","Wang L","Xue D","Zheng S","Yu Y","Zhang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/burnst/tkag026","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261352","name":"Impact of artificial intelligence-based and technology-enhanced educational tools compared to traditional teaching methods on learning outcomes among healthcare professionals and students: a systematic review.","source":"pubmed","abstract":"Artificial intelligence (AI) is rapidly reshaping health profession education through tools such as virtual simulation, adaptive learning platforms, intelligent tutoring systems and ChatGPT-assisted learning. However, its effectiveness compared with traditional teaching methods remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42261352/","authors":["Amandu Matua G","Shanmugam V","Nattamai Jothilal V","Lovelin Auguskani JP","Arumugam Y","Ravi K","Kumar Jayapal S","Arulappan J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 5","doi":"10.1177/17449871261445369","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261256","name":"Progress of Traditional Chinese Medicine in Targeting Ferroptosis to Combat Cerebral Ischemia-Reperfusion Injury.","source":"pubmed","abstract":"Cerebral ischemia-reperfusion injury (CIRI) often results in significant morbidity and mortality. The effectiveness of current reperfusion therapies is hindered by substantial secondary damage, which highlights the need for novel molecular targets and neuroprotective strategies. Ferroptosis, a form of programmed cell death driven by iron dysregulation and lipid peroxidation, has emerged as a key player in CIRI pathogenesis. Traditional Chinese medicine (TCM) has demonstrated considerable potential in managing IS, and increasing evidence suggests its neuroprotective effects are primarily mediated through modulation of ferroptotic pathways. This review evaluates the efficacy of various TCM approaches in combating CIRI, and offers a specific focus on the roles of active compounds (e.g., astragaloside IV, rhein, baicalein, ginkgolide B, and berberine), classic herbal formulas (e.g., Buyang Huanwu Decoction, Tongqiao Huoxue Decoction, and Naotaifang), and acupuncture therapies. This review outlines how these interventions attenuate CIRI by targeting critical ferroptosis-related mechanisms. These mechanisms include enhancing anti-oxidant defenses (Nrf2/SLC7A11/GPX4), modulating lipid metabolism (ACSL4), restoring iron homeostasis, and maintaining the ischemic microenvironment (including blood-brain barrier integrity and microglial polarization). However, current research faces challenges such as the insufficient understanding of the pharmacological basis of complex formulas, unclear dose-response relationships, and a lack of comprehensive validation along the \"ingredient-target-phenotype\" continuum. Future research should focus on leveraging advanced technologies - such as network pharmacology, multi-omics, and artificial intelligence - to unravel the antiferroptotic networks of TCM. Such integrative approaches will drive the modernization of TCM and facilitate the development of precision medicine to thus present new opportunities for neuroprotection in CIRI.","url":"https://pubmed.ncbi.nlm.nih.gov/42261256/","authors":["Li J","Liu A","Gao W","Ye Q","Xing L","Duan P","Guo W","Li H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1142/S0192415X26500382","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261122","name":"Identification, reliability, and validity of drug-drug interaction checkers in chronic diseases: A systematic review.","source":"pubmed","abstract":"Drug-drug interactions (DDIs) pose major risks in chronic disease management where polypharmacy is common. Although digital DDI tools are used widely to aid healthcare professionals, tools rely on theoretical interactions. This limitation questions their reliability and validity for clinical relevancy. Hence, this systematic review identified available DDI tools and evaluated their reliability and validity in chronic disease management. Following PRISMA guidelines, MEDLINE, Embase, Scopus, Web of Science, Google Scholar, ACM, and IEEE Xplore were searched for studies published between January 2015 and April 2025. Methodological quality, reliability, and validity were assessed using tailored versions of QUADAS-2 and QUADAS-C through agreement and performance metrics. Forty-four studies met inclusion criteria. Micromedex, Drugs.com&#xae;, Lexicomp&#x2122;, Medscape&#xae;, Epocrates&#xae;, and UpToDate&#xae; were most frequently assessed (median: three tools per study). Inter-rater reliability was generally low, with limited agreement between DDI tools (Fleiss' &#x3ba; -0.155 to 0.483; Cohen's &#x3ba; -0.065 to 0.726; Gwet's AC1 0.12 to 0.46). Negative inter-rater reliability statistics indicate disagreement not due to chance, suggesting oppositional classification for some DDIs. Two studies used Kendall's W and Kruskal-Wallis tests reported significant differences (P&#x2009;&lt;&#x2009;0.05). Seventeen studies examined consensus, seven using clinicians, drug labels, or textbooks as reference standards. High risks of bias were noted in medication selection, reference quality, and index comparability. Overall, DDI tools showed limited reliability and limited evidence of validity against consistent clinical outcome-anchor standards, with poor consensus even when used collectively. Standardised evaluation frameworks and greater algorithm transparency are needed to enhance accuracy and harmonisation across tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42261122/","authors":["Gibson BN","Chodapuneedi S","Koh HY","Gupta S","Gasevic D","Wang AY","Marzolini C","Khoo S","Romero L","Dalli L","Janetzki J","Koh HJW","Talic S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1111/bph.70515","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42261027","name":"Can artificial intelligence change the face of orthopaedic surgery? Current and future concepts.","source":"pubmed","abstract":"Orthopaedic surgery has evolved remarkably into a highly specialised and advanced medical field. The discipline has seen substantial improvements in surgical techniques and the development of subspecialties. Outcomes can vary widely, with some procedures not yielding the expected improvement in quality of life. This variability in outcomes leads to inconsistencies in the standard of care. Artificial intelligence emerges as a powerful tool to bridge gaps in knowledge and standardise care across the orthopaedic field. Artificial intelligence can assist in various aspects of orthopaedic surgery, such as image recognition, risk assessment and clinical decision-making. However, the implementation of artificial intelligence in orthopaedics is not without challenges. The reliance on large datasets raises concerns about data ownership, privacy and the potential for algorithmic bias. As artificial intelligence continues to evolve, its role in orthopaedics will likely expand, offering new opportunities to optimise patient care and improve the overall quality of healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/42261027/","authors":["Mustafa M","Ahmed N","Ain NU","Najjad KR","Noor SS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 May","doi":"10.47391/JPMA.20770","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260964","name":"Artificial intelligence in traumatic stress treatment: The TRUST framework for ethical development, clinical applications, and research advancement.","source":"pubmed","abstract":"Posttraumatic stress disorder (PTSD) and depression are common diagnoses following traumatic events, with several available evidence-based interventions to reduce symptomology. However, trauma populations face significant access barriers that limit their adoption and reach. Artificial intelligence (AI) technologies, such as large language models (LLMs), have the potential to enhance access, cost-efficiency, delivery, and quality of traumatic stress interventions. Their application to traumatic stress treatment and research is understudied and requires responsible and ethical development, evaluation, and monitoring to maintain service quality and delivery for trauma survivors and their providers. We present considerations for the responsible use of AI tools to ethically shape trauma-focused treatment and research, with future use cases to highlight these critical considerations. A multidisciplinary approach to trauma-based LLM development that integrates feedback and evaluation from trauma experts, clinicians, and trauma survivors and prioritizes the quality, safety, effectiveness, and equity of trauma-focused care is essential. We propose a framework, TRUST, that is informed by evidence from adjacent mental health AI applications and emerging research on digital trauma interventions, while acknowledging the limited trauma-specific AI trial data. These topics were presented at the 41st Annual Meeting of the International Society for Traumatic Stress Studies via a panel of experts in AI technologies to support trauma-focused interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42260964/","authors":["Ridings LE","Held P","Kuhn E","Wiltsey-Stirman S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1002/jts.70092","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260784","name":"Noninvasive Detection of Coronary Microvascular Dysfunction.","source":"pubmed","abstract":"Coronary Microvascular Dysfunction (CMVD) can lead to myocardial ischemia and increase the risk of adverse cardiovascular events. In clinical practice, early and accurate diagnosis of CMVD is essential for effective intervention and management. However, because CMVD and obstructive coronary artery disease share similar clinical presentations, distinguishing CMVD remains challenging.","url":"https://pubmed.ncbi.nlm.nih.gov/42260784/","authors":["Li L","Tse G","Bazoukis G","Rajan R","He S","Dong J","Dai F","Wang X","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 5","doi":"10.2174/011573403X421674260106111919","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260734","name":"Mutually Reinforcing Polyphenol and Nanomedicines: Unlocking the Potential of Polyphenol-Based Nanodrugs for Liver Injury Therapy.","source":"pubmed","abstract":"The liver performs around a thousand essential functions, including metabolism, detoxification, and immune regulation. However, its constant exposure to harmful substances, such as alcohol, drugs, and toxins, makes it prone to injury. Persistent exposure drives liver disease progression, which causes about &#x223c;4% of all deaths annually. Current treatments for liver injury, which combine targeted and supportive care, remain limited by poor drug delivery and efficacy. Anti-inflammatory and antioxidant treatments are essential, yet the pathology is a complex interplay of oxidative stress, inflammation, hepatocyte death, and fibrosis, resisting simple interventions. Polyphenols are multi-target compounds, but their efficacy is hindered by conventional drawbacks. Nanotechnology mimics nanoscale biology to improve pharmacokinetics, enable liver-targeted delivery, and support combination therapies. Notably, polyphenols can act as building units for novel nanodrugs or modify other nanoparticles due to structural features. Thus, polyphenol-based nanodrugs achieve efficient liver enrichment and hold unique potential to tackle multiple therapeutic challenges. Herein, this article analyzes the core pathophysiology of liver injury, elaborates on the biological activities of polyphenols and clinical challenges, and focuses on how to leverage the distinctive chemical properties of polyphenols to construct advanced nanodrugs, and how these nanodrugs can address multiple pathogenic facets of liver injury.","url":"https://pubmed.ncbi.nlm.nih.gov/42260734/","authors":["Liu M","Huang R","Wang S","Lin Y","Xiong T","Li R","Zheng W","Huang Q","Nan Y","Liu Z","Ai K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/adhm.71320","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260596","name":"Electrolyte balance and muscle damage after adolescent binge alcohol use: a retrospective study.","source":"pubmed","abstract":"Binge alcohol drinking is a recognized cause of peripheral muscle damage, leading to increased creatine kinase levels. However, the potential role of electrolyte and osmolality changes in muscle damage remains unclear. This study investigates the prevalence of muscle damage in adolescents after binge drinking and its association with ethanol, potassium, sodium, calcium, and osmolality levels.","url":"https://pubmed.ncbi.nlm.nih.gov/42260596/","authors":["Hunjan I","Pistritto E","Corsello A","Bianchetti MG","Alberti I","Vassilopoulou E","Agostoni C","Salvini F","Lavagno C","Milani GP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1186/s13052-026-02280-z","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260526","name":"Legal and ethical responsibility for AI-assisted diagnostic and referral errors: a scoping review of comparative approaches with implications for Saudi healthcare.","source":"pubmed","abstract":"Artificial intelligence (AI) systems are increasingly embedded in clinical diagnostics and referral pathways, yet when these tools contribute to patient harm, traditional medico-legal doctrines are strained by algorithmic opacity, automation bias, and distributed decision-making. Beyond legal uncertainty, AI integration raises fundamental ethical concerns regarding clinician moral agency, patient autonomy, and distributive justice. This scoping review maps how jurisdictions worldwide allocate legal and ethical responsibility for AI-assisted diagnostic and referral errors, identifies governance mechanisms that support accountability, and examines the implications for Saudi Arabia's healthcare system under Vision 2030.","url":"https://pubmed.ncbi.nlm.nih.gov/42260526/","authors":["Almutairi AO","Almutairi ASO","Almutairi FSO","Ali M","Ming L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1186/s12910-026-01509-0","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260517","name":"Heat shock protein-mediated remodeling of the bone immune microenvironment: mechanisms and precision therapeutic strategies for osteoporosis.","source":"pubmed","abstract":"Osteoporosis is a systemic bone disorder marked by reduced bone mass and deteriorating bone microstructure, commonly associated with inflammation, oxidative stress, and imbalances in immune homeostasis. Among the key mechanisms underlying Osteoporosis progression, disruption of the bone immune microenvironment has emerged as a critical scientific issue. Heat shock proteins (HSPs), as highly conserved molecular chaperones and stress-responsive proteins, not only prevent protein misfolding and aggregation under stress conditions to maintain protein homeostasis, but also participate in immune signaling and adaptive stress responses, thereby playing important roles in preserving bone metabolic homeostasis. This review aims to systematically examine the roles of HSP families-HSPB, HSP40, HSP70, HSP90, and HSP110-in bone remodeling, the balance between osteogenesis and osteoclastogenesis, and the interaction between endoplasmic reticulum stress and mitochondrial energy metabolism. The review also highlights the dual roles of HSPs in regulating bone immune homeostasis. HSPs participate at the cellular level in regulating osteoblast, osteoclast, and bone marrow mesenchymal stem cell function. Current evidence suggests that HSPs act as both molecular chaperones and stress-responsive immune modulators, regulating the balance between bone formation and bone resorption and remodeling the bone immune microenvironment. This review also summarizes HSP-targeted therapeutic strategies, including small molecules, natural products, neutralizing antibodies, physical stimulation, and gene- and cell-based therapies.","url":"https://pubmed.ncbi.nlm.nih.gov/42260517/","authors":["An J","Bai J","Li L","Li X","Zhang Y","Lv H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1186/s12967-026-08389-3","addedAt":"2026-09-01T01:47:58.475Z","updatedAt":"2026-09-01T01:47:58.475Z"},{"id":"pmid:42260375","name":"HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data.","source":"pubmed","abstract":"Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to specific patient populations. We aimed to develop a simple, interpretable, and broadly applicable risk score to screen for 30-day postoperative mortality using routinely available variables.","url":"https://pubmed.ncbi.nlm.nih.gov/42260375/","authors":["Kim HS","Shin HS","Goh J","Jun SH","Lee CH","Yoon JH","Chung CH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1186/s12871-026-03907-8","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"pmid:42260351","name":"Artificial intelligence in anaesthesiology: why don't we have it in our hands after a decade of innovation? A systematic review and perspective.","source":"pubmed","abstract":"Machine learning (ML) tools are increasingly integrated into various sectors, including healthcare, where they have demonstrated disruptive potential. While anaesthesia is a specialty historically shaped by technological innovation, the precise clinical impact of ML remains poorly defined. Furthermore, despite a decade of prolific model development, the systematic translation of these computational tools into routine, everyday clinical workflows has stagnated.","url":"https://pubmed.ncbi.nlm.nih.gov/42260351/","authors":["Florquin R","Dony P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 9","doi":"10.1186/s12871-026-03995-6","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"pmid:42260175","name":"Risk and liability in the deployment of AI systems for surgery: a SAGES white paper.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly utilized in surgical care for decision support, operative planning, intraoperative guidance, and autonomous functions. While these systems can enhance efficiency and clinical performance, they also introduce risks related to technology, human factors, legal issues, and ethics. Current regulatory and legal frameworks are not fully equipped to address the challenges of AI-assisted surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/42260175/","authors":["Hashimoto DA","Marwaha JS","Lee SA","Schwaitzberg S","Duffourc MN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1007/s00464-026-12881-8","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.18218525","name":"Artificial Intelligence in Clinical Diagnostics and Therapeutic Innovation.","source":"datacite","abstract":"Artificial Intelligence (AI) is a modern and smart technology that supports doctors in many important areas of healthcare. It works like a fast and intelligent assistant that can review medical reports, images, and patient information within seconds. Because of this ability, AI has a strong impact on how diseases are diagnosed and treated. From a diagnostic perspective, AI plays a major role in early disease detection. It studies X- rays MRI and CT scans and blood reports much faster than humans. It can identify very small changes inside the body that might be early signs of serious conditions such as cancer, heart disease, or brain disorders. Early detection makes treatment easier and improves recovery. AI can also study a patient’s medical history, family background, lifestyle, and symptoms to predict future disease risks, helping doctors take action sooner. From a therapeutic perspective, AI helps doctors choose the best treatment for each patient. It analyzes medical history, age, lifestyle, and response to medicines to suggest suitable treatment plans. AI also supports surgeries through robotic systems that perform movements with high precision, reducing errors and helping patients heal faster. AI does not replace doctors but strengthens their ability to provide better, more personalized care. With AI, healthcare becomes more accurate, efficient, and patient-friendly, and it will continue improving medical services in the future.","url":"https://doi.org/10.5281/zenodo.18218525","authors":["Shaikh Anambano","Chowdhari Sana","Ansari Ashra","Ansari Nashra","Tanzia Palje"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18218525","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.18218524","name":"Artificial Intelligence in Clinical Diagnostics and Therapeutic Innovation.","source":"datacite","abstract":"Artificial Intelligence (AI) is a modern and smart technology that supports doctors in many important areas of healthcare. It works like a fast and intelligent assistant that can review medical reports, images, and patient information within seconds. Because of this ability, AI has a strong impact on how diseases are diagnosed and treated. From a diagnostic perspective, AI plays a major role in early disease detection. It studies X- rays MRI and CT scans and blood reports much faster than humans. It can identify very small changes inside the body that might be early signs of serious conditions such as cancer, heart disease, or brain disorders. Early detection makes treatment easier and improves recovery. AI can also study a patient’s medical history, family background, lifestyle, and symptoms to predict future disease risks, helping doctors take action sooner. From a therapeutic perspective, AI helps doctors choose the best treatment for each patient. It analyzes medical history, age, lifestyle, and response to medicines to suggest suitable treatment plans. AI also supports surgeries through robotic systems that perform movements with high precision, reducing errors and helping patients heal faster. AI does not replace doctors but strengthens their ability to provide better, more personalized care. With AI, healthcare becomes more accurate, efficient, and patient-friendly, and it will continue improving medical services in the future.","url":"https://doi.org/10.5281/zenodo.18218524","authors":["Shaikh Anambano","Chowdhari Sana","Ansari Ashra","Ansari Nashra","Tanzia Palje"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18218524","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/8dhme","name":"Applications of Artificial Intelligence in Bowel Preparation Assessment: A Scoping Review","source":"datacite","abstract":"Colonoscopy is the gold standard for screening and diagnosing colorectal cancer. Its diagnostic efficacy is highly dependent on the quality of bowel preparation. Inadequate bowel preparation can reduce the detection rate of polyps, increase the risk of missed diagnoses, operation time, and medical costs. Currently, the assessment of bowel preparation quality mainly relies on subjective scoring by endoscopists using tools such as the Boston Bowel Preparation Scale. However, this subjective assessment has significant inter-observer differences, which affect the consistency and reliability of the assessment, and thus may affect the standardization and quality control of clinical decisions. To enhance the objectivity of the assessment, studies have explored computational methods based on image analysis for colonoscopy. In recent years, with the breakthroughs in artificial intelligence, especially in deep learning technology, it has demonstrated superior performance over traditional methods in the field of medical image analysis. AI technology provides revolutionary tools for automating and objectively evaluating the quality of bowel preparation, potentially addressing the inherent limitations of subjective assessment. Although the prospects are broad, the application of AI in the assessment of bowel preparation is still in its early stages and shows a decentralized characteristic. Some existing studies focus on using pre-examination photos taken by patients for predictive assessment, while others are dedicated to developing intraoperative assistance tools based on real-time endoscopic videos. Currently, there is a lack of systematic research to integrate and compare the technical paths, clinical goals, and validation evidence of these different application scenarios. This lack of a panoramic view hinders researchers and clinicians from having an overall understanding of the development trends, technical maturity, and transformation challenges in this field. This study aims to comprehensively map out the current application status of artificial intelligence in the assessment of the effectiveness of colonoscopy bowel preparation through a systematic review. The specific research questions are: What are the main technical characteristics of AI applications in the assessment of bowel preparation in the current evidence? How are these applications divided based on the timing of assessment (before the examination vs. during the examination)? What clinical problems do they respectively solve and what efficacy have they achieved? This review will provide an evidence map and directional guidance for future technology development, clinical research, and the formulation of practice guidelines.","url":"https://doi.org/10.17605/osf.io/8dhme","authors":["吴甜"],"tags":["Gastroenterology","Medicine and Health Sciences","Life Sciences","Medical Specialties","Nursing","FOS: Health sciences","artificial intelligence","bowel preparation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/8dhme","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19356456","name":"Impact and acceptance of digital non-pharmacological treatments for insomnia among cancer patients: a scoping review protocol","source":"datacite","abstract":"This document outlines a comprehensive protocol for a scoping review that examines how digital technologies can assist cancer patients suffering from insomnia. The researchers aim to investigate the effectiveness and user satisfaction of non-pharmacological interventions, including specialized software, wearable devices, and artificial intelligence. This review will analyze primary studies published between 2020 and 2025. The goal is to explore how medical treatments can be integrated with technological solutions. The methodology focuses on identifying research gaps related to how these tools address various comorbidities and the types of data they collect. Ultimately, the study aims to provide a structured overview of the current landscape of electronic sleep therapies within oncology care.","url":"https://doi.org/10.5281/zenodo.19356456","authors":["VELEZ GUTIERREZ, JOSE DANIEL","Seepold, Ralf","ORTEGA, JUAN ANTONIO","Martínez Madrid, Natividad"],"tags":["Telemedicine","Digital Health","Sleep Medicine","Medical Informatics","Artificial Intelligence","Scoping Review Protocols"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19356456","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19356457","name":"Impact and acceptance of digital non-pharmacological treatments for insomnia among cancer patients: a scoping review protocol","source":"datacite","abstract":"This document outlines a comprehensive protocol for a scoping review that examines how digital technologies can assist cancer patients suffering from insomnia. The researchers aim to investigate the effectiveness and user satisfaction of non-pharmacological interventions, including specialized software, wearable devices, and artificial intelligence. This review will analyze primary studies published between 2020 and 2025. The goal is to explore how medical treatments can be integrated with technological solutions. The methodology focuses on identifying research gaps related to how these tools address various comorbidities and the types of data they collect. Ultimately, the study aims to provide a structured overview of the current landscape of electronic sleep therapies within oncology care.","url":"https://doi.org/10.5281/zenodo.19356457","authors":["VELEZ GUTIERREZ, JOSE DANIEL","Seepold, Ralf","ORTEGA, JUAN ANTONIO","Martínez Madrid, Natividad"],"tags":["Telemedicine","Digital Health","Sleep Medicine","Medical Informatics","Artificial Intelligence","Scoping Review Protocols"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.19356457","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.13254317","name":"BIMCV-Prostate-Dataset V1","source":"datacite","abstract":"⚠️ Data Availability Notice The BIMCV Prostate Dataset is currently subject to a regulatory reassessment process conducted by the competent authorities and collaborating healthcare institutions in the Valencian Community (Spain). This process aims to ensure full compliance with updated legal and ethical frameworks governing the sharing of medical data. Consequently, although the dataset is publicly registered and associated with a peer-reviewed publication, access to the data is temporarily suspended until the review process is completed and all requirements are fulfilled. Access will be reinstated as soon as authorization is granted under the revised regulations. We remain committed to transparency and to enabling data access in accordance with applicable standards. ---The BIMCV Prostate Dataset is a comprehensive and diverse dataset that includes a total of 9,341 prostate MRI sessions, distributed among 8,441 subjects, collected from 16 healthcare centers in the Valencian Community, Spain. This dataset is structured according to the MIDS (Medical Imaging Data Structure) standard, ensuring consistent and accessible organization for researchers, facilitating data use and analysis. The first version of the dataset focuses on sessions that contain the three mentioned imaging modalities (T2W, DWI, and ADC), resulting in a total of 1,730 complete sessions, with a total of 4,663 samples for training, of which 2,594 are csPCa positive and 2,069 are csPCa negative. This information can be found in the table available on GitHub. The dataset includes MRI images in three modalities: T2-weighted images (T2W), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. In total, the dataset includes 32,662 T2W images (62.97%), 8,036 DWI images (15.49%), and 11,167 ADC maps (21.53%), including both the original maps and those calculated from the available DWI images. This additional calculation process was carried out to ensure the dataset's integrity and consistency, allowing for comprehensive analysis in the field of prostate oncology. The exploratory data analysis (EDA) performed on this dataset has provided insights into the characteristics and distribution of the images, ensuring the dataset's representativeness and diversity. For example, it was found that Health Center 5 contributed the highest proportion of sessions (15.6%), followed by Health Center 7 (12.3%) and Health Center 17 (10.5%). This level of diversity in data sources ensures that the dataset encompasses a wide range of imaging acquisition practices and patient demographics, improving the generalization of artificial intelligence models developed with this data. Additionally, the analysis of the distribution by MRI equipment manufacturer revealed that most images were acquired with General Electric equipment (66.7%), followed by Philips (25.1%) and Siemens (8.13%). Similarly, most sessions were conducted with 1.5 Tesla machines (63%), followed by 3.0 Tesla machines (36.5%), reflecting standard clinical practices in the region. Regarding the distribution of labels within the dataset, of the total cases, 4,871 (approximately 52%) are labeled as csPCa positive, while 3,514 cases (approximately 37%) are labeled as csPCa negative.","url":"https://doi.org/10.5281/zenodo.13254317","authors":["Alzate-Grisales, Jesus Alejandro","de la Iglesia Vaya, Maria"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13254317","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.13254318","name":"BIMCV-Prostate-Dataset V1","source":"datacite","abstract":"⚠️ Data Availability Notice The BIMCV Prostate Dataset is currently subject to a regulatory reassessment process conducted by the competent authorities and collaborating healthcare institutions in the Valencian Community (Spain). This process aims to ensure full compliance with updated legal and ethical frameworks governing the sharing of medical data. Consequently, although the dataset is publicly registered and associated with a peer-reviewed publication, access to the data is temporarily suspended until the review process is completed and all requirements are fulfilled. Access will be reinstated as soon as authorization is granted under the revised regulations. We remain committed to transparency and to enabling data access in accordance with applicable standards. ---The BIMCV Prostate Dataset is a comprehensive and diverse dataset that includes a total of 9,341 prostate MRI sessions, distributed among 8,441 subjects, collected from 16 healthcare centers in the Valencian Community, Spain. This dataset is structured according to the MIDS (Medical Imaging Data Structure) standard, ensuring consistent and accessible organization for researchers, facilitating data use and analysis. The first version of the dataset focuses on sessions that contain the three mentioned imaging modalities (T2W, DWI, and ADC), resulting in a total of 1,730 complete sessions, with a total of 4,663 samples for training, of which 2,594 are csPCa positive and 2,069 are csPCa negative. This information can be found in the table available on GitHub. The dataset includes MRI images in three modalities: T2-weighted images (T2W), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. In total, the dataset includes 32,662 T2W images (62.97%), 8,036 DWI images (15.49%), and 11,167 ADC maps (21.53%), including both the original maps and those calculated from the available DWI images. This additional calculation process was carried out to ensure the dataset's integrity and consistency, allowing for comprehensive analysis in the field of prostate oncology. The exploratory data analysis (EDA) performed on this dataset has provided insights into the characteristics and distribution of the images, ensuring the dataset's representativeness and diversity. For example, it was found that Health Center 5 contributed the highest proportion of sessions (15.6%), followed by Health Center 7 (12.3%) and Health Center 17 (10.5%). This level of diversity in data sources ensures that the dataset encompasses a wide range of imaging acquisition practices and patient demographics, improving the generalization of artificial intelligence models developed with this data. Additionally, the analysis of the distribution by MRI equipment manufacturer revealed that most images were acquired with General Electric equipment (66.7%), followed by Philips (25.1%) and Siemens (8.13%). Similarly, most sessions were conducted with 1.5 Tesla machines (63%), followed by 3.0 Tesla machines (36.5%), reflecting standard clinical practices in the region. Regarding the distribution of labels within the dataset, of the total cases, 4,871 (approximately 52%) are labeled as csPCa positive, while 3,514 cases (approximately 37%) are labeled as csPCa negative.","url":"https://doi.org/10.5281/zenodo.13254318","authors":["Alzate-Grisales, Jesus Alejandro","de la Iglesia Vaya, Maria"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13254318","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.26262/heal.auth.ir.371159","name":"Το μέλλον της τεχνητής νοημοσύνης στη χειρουργική. Μία συστηματική βιβλιογραφική ανασκόπηση","source":"datacite","abstract":"Η ταχεία ανάπτυξη των τεχνολογιών Τεχνητής Νοημοσύνης έχει προκαλέσει σημαντικούς μετασχηματισμούς σε διάφορους τομείς, συμπεριλαμβανομένης και της ιατρικής. Η παρούσα μεταπτυχιακή διατριβή επιδιώκει να προσφέρει μια ολοκληρωμένη, βιβλιογραφικά τεκμηριωμένη ανασκόπηση σχετικά με τις εφαρμογές, τις προκλήσεις και τις επιπτώσεις της Τεχνητής Νοημοσύνης στη σύγχρονη χειρουργική πρακτική. Βασιζόμενη σε ευρύ φάσμα ακαδημαϊκών πηγών και διεθνών μελετών περίπτωσης, η έρευνα εξετάζει τον τρόπο με τον οποίο η AI αναδιαμορφώνει τη λήψη κλινικών αποφάσεων, τις χειρουργικές διαδικασίες, την ιατρική εκπαίδευση, καθώς και το ηθικό και νομικό πλαίσιο που διέπει την παροχή υπηρεσιών υγείας. Η μελέτη δομείται σε έξι κεφάλαια. Στο πρώτο κεφάλαιο ορίζεται το επιστημονικό πρόβλημα και παρουσιάζεται η μεθοδολογική προσέγγιση, η οποία συνδυάζει ποιοτική, ερμηνευτική και συγκριτική ανάλυση. Το δεύτερο διερευνά την τεχνολογική ενσωμάτωση της AI στη χειρουργική αίθουσα, με έμφαση στη ρομποτική χειρουργική, τα συστήματα υποστήριξης αποφάσεων, την ανάλυση δεδομένων σε πραγματικό χρόνο και την υπολογιστική όραση. Στο τρίτο αναλύονται κριτικά οι ηθικές και νομικές προκλήσεις, όπως η λογοδοσία, η διαφάνεια και η αυτονομία του ασθενούς, σε περιβάλλοντα ιατρικής πρακτικής με υποστήριξη AI. Το τέταρτο κεφάλαιο εξετάζει τις επιστημολογικές και εκπαιδευτικές μεταβολές που προκαλούνται από την αυξανόμενη εξάρτηση από την αλγοριθμική αυθεντία, ενώ το πέμπτο κεφάλαιο συνθέτει τα βασικά ευρήματα προτείνοντας κατευθύνσεις πολιτικής και θεσμικές μεταρρυθμίσεις. Στο έκτο και τελευταίο κεφάλαιο παρουσιάζονται τα συνολικά συμπεράσματα, με έμφαση στην ανάγκη για μια διεπιστημονική προσέγγιση απέναντι στις πολυπλοκότητες που εισάγει η Τεχνητή Νοημοσύνη στο χειρουργικό πλαίσιο. Συνολικά, η διατριβή υπογραμμίζει ότι η εφαρμογή της AI στη χειρουργική δεν αποτελεί απλώς μια τεχνολογική πρόοδο, αλλά έναν μετασχηματισμό παραδείγματος, ο οποίος αναδιαμορφώνει τη γνώση, την πρακτική και την ευθύνη της ιατρικής στον 21ο αιώνα.","url":"https://doi.org/10.26262/heal.auth.ir.371159","authors":["Πασσά, Γεωργία Μ."],"tags":["Τεχνητή Νοημοσύνη","Χειρουργική","Ρομποτική Χειρουργική","Artificial Intelligence","Surgery","Robotic Surgery"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.26262/heal.auth.ir.371159","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/f9bwp","name":"Artificial Intelligence in Advance Care Planning for Cancer Patients: A Scoping Review","source":"datacite","abstract":"Advance care planning (ACP) is a patient-centered communication process that enables cancer patients to articulate their values, goals, and preferences for future medical care, particularly in the context of end-of-life decision-making. In recent years, artificial intelligence (AI) has emerged as a promising tool to support ACP through applications such as predictive analytics to identify patients who may benefit from ACP conversations, natural language processing to extract preferences from clinical notes, and conversational agents to facilitate patient education and documentation. However, the current landscape of AI applications in this domain remains fragmented, with no comprehensive synthesis of available evidence. This scoping review aims to systematically map the existing literature on the application of artificial intelligence in advance care planning for cancer patients, providing a comprehensive overview of current evidence and identifying gaps to inform future research and clinical practice.","url":"https://doi.org/10.17605/osf.io/f9bwp","authors":["Zhang, Shufang"],"tags":["Medicine and Health Sciences","Nursing","FOS: Health sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/f9bwp","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.30619367","name":"Artifial Intelligence in Medical Imaging: A Systematic Literature review using PRISMA","source":"datacite","abstract":"Artificial Intelligence (AI) has rapidly emerged as a transformative force in medical imaging, offering advanced capabilities for image acquisition, interpretation, and diagnosis. This research proposal presents a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, aiming to synthesize current evidence on the applications, performance, and challenges of AI in medical imaging. The study focuses on machine learning and deep learning techniques applied to radiology, pathology, and other image-based diagnostics. Through a structured search of major databases such as PubMed, IEEE Xplore, and Scopus, relevant peer-reviewed articles published between 2015 and 2025 will be identified , screened, and analyzed according to PRISMA guidelines. The review seeks to classify AI applications by modality, diagnostic accuracy, and clinical integration, while also highlighting methodological limitations, ethical concerns, and reproducibility issues. Expected outcomes include a comprehensive mapping of existing research trends, identification of critical gaps, and formulation of recommendations for future studies and clinical implementation. By consolidating current evidence, this review aims to contribute to a clearer understanding of AI’s role in enhancing diagnostic precision, workflow efficiency, and patient outcomes within the field of medical imaging","url":"https://doi.org/10.6084/m9.figshare.30619367","authors":["Nugumanov, Alikhan"],"tags":["Artificial intelligence not elsewhere classified","Radiology and organ imaging"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30619367","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.30609992","name":"<b>The Role of Artificial Intelligence in Diagnosing Malignant Tumors</b>","source":"datacite","abstract":"This paper explores the transformative impact of artificial intelligence (AI) in early tumor diagnosis, emphasizing its role in analyzing health records, medical images, biopsies, and blood tests for improved risk stratification. While screening programs have enhanced survival, challenges remain in patient selection and diagnostic workforces. The review covers diverse AI approaches, including logistic regression, deep learning, and neural networks, applied to various data types in oncology. It discusses the clinical implications, current models in practice, and potential limitations such as ethical concerns and resource demands. We provide an overview of the main artificial intelligence approaches, encompassing historical models like logistic regression, alongside deep learning and neural networks, emphasizing their applications in early diagnosis. We describe the role of AI in tumor detection, prognosis, and treatment administration, and we introduce the application of state-of-the-art large language models in oncology clinics. Our exploration extends to AI applications for omics data types, offering perspectives on their combination for decision-support tools. Concurrently, we evaluate existing constraints and challenges in applying artificial intelligence to precision oncology. The overall aim is to showcase AI's promise in revolutionizing tumor diagnosis while acknowledging and addressing associated challenges, thereby advancing patient care.","url":"https://doi.org/10.6084/m9.figshare.30609992","authors":["Ahmad, Dr. Shmmon","khan, zafar","Moh, Aijaz","Kamboj, Anjoo"],"tags":["Artificial intelligence not elsewhere classified","Pharmacology and pharmaceutical sciences not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30609992","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5167/uzh-279239","name":"A Unified Framework for Constructing and Evaluating Medical Tests","source":"datacite","abstract":"The evaluation of medical tests used to identify diseases is becoming increasingly important. Advancements in medical technologies such as omics, medical imaging, and wearables are driving the rapid discovery of new biomarkers. At the same time, data science and artificial intelligence are revolutionizing how we analyze and interpret data from these technologies. Together, these developments create exciting new opportunities for disease diagnosis, prognosis, and therapy development. However, evaluating the accuracy of medical tests for clinical use is complex. Challenges include missing data, confounding biases from study designs, technological detection limits, and longitudinal disease outcomes. While existing methods address these issues individually, expecting practitioners to adopt separate methods for each complication is impractical. A unified statistical framework for constructing and evaluating diagnostic and prognostic tests is needed. This thesis establishes the foundation for such a framework. Starting with the widely used receiver operating characteristic (ROC) curve, we review the current research landscape, identify methodological gaps, and propose transformation models as a versatile solution. These are models designed to flexibly capture the distributions of biomarkers and outcomes and form the core of our framework. In the first paper, we address diagnostic test evaluation, focusing on how covariates like age, sex, and comorbidities impact diagnostic accuracy. By leveraging transformation models, we estimate metrics such as ROC curves and summary indices while adjusting for covariates, ensuring that the results are supported by appropriate statistical inference. Further, our proposed methods are capable of addressing confounding bias and detection limits. The second paper extends this framework to prognostic tests, which predict future events rather than binary disease states. To address challenges such as time-dependent outcomes and incomplete follow-up, we develop multivariate transformation models that capture the relationship between biomarkers and event times. These models enable the derivation of time-dependent ROC curves and provide tools for assessing disease progression and survival. The third paper focuses on combining multiple biomarkers into a composite score for improved disease classification. This approach addresses the inherent complexity of diseases, as each biomarker captures a unique aspect of the underlying pathology. We develop methods to model the dependencies between biomarkers and leverage this information to create new diagnostic tests with greater accuracy. These advancements make diagnostic tools that are not only more precise but also affordable, noninvasive, and widely accessible. These methods are available in the tram R package and can be used for applications in clinical settings. By unifying the evaluation of diagnostic and prognostic tests under a single framework, this work ensures more reliable assessments of medical tests and provides easily accessible tools for the purpose.","url":"https://doi.org/10.5167/uzh-279239","authors":["Sewak, Ainesh"],"tags":["610 Medicine &amp; health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5167/uzh-279239","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.30335272","name":"A Historical Correlation of Medical Advancements with Socio-Political and Technological Epochs","source":"datacite","abstract":"Modern medicine relies on observing clinical symptoms and tracking diseaseprogression, enabling rational diagnosis, treatment, and prevention. Since theprehistoric era, medicine has advanced through observation, experimentation,and innovation, resulting in effective treatments and longer life spans. However,despite these successes, many diseases still challenge modern medicine. Ourmain argument is that medical progress is shaped by the interdependencebetween medicine, socio-political stability, and technological development.To demonstrate this, we analyzed major databases from prehistoric to moderntimes and conducted a narrative review of secondary sources. We found thattransformative eras; from Imhotep's promotion of natural disease causes inancient Egypt to Hippocrates' reforms in Greece and Avicenna's contributionsin the Middle Ages align with strong socio-political contexts. In recent times,technological advancements, such as artificial intelligence, have driven newdevelopments like robotic-assisted surgery and genetic medicine in theGlobal North. Therefore, our review concludes that robust socio-political andtechnological environments are crucial for medical advancement, forging adynamic relationship between medicine, politics, and technology.","url":"https://doi.org/10.6084/m9.figshare.30335272","authors":["Ojo, Olusola"],"tags":["Economic history","Political theory and political philosophy","Political science not elsewhere classified","Medical anthropology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30335272","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.30113146","name":"<b>Machine Learning Approaches in Multimodal Analysis of Lung Cancer:</b><b>A Comprehensive Scoping Review</b>","source":"datacite","abstract":"Protocol for Scoping Review Title Machine Learning Applications in Multimodal Analysis of Lung Cancer: A Scoping Review Introduction Lung cancer remains the leading cause of cancer-related mortality worldwide. Its biological heterogeneity presents significant challenges for diagnosis, prognosis, and treatment. Advances in artificial intelligence (AI) and machine learning (ML) have enabled the integration of multimodal datasets (e.g., imaging, histopathology, genomics, clinical records), creating new opportunities for precision oncology. Although individual modalities have been extensively studied, multimodal ML applications in lung cancer remain less systematically mapped. Understanding which methodologies are being employed across data fusion, model architectures, validation strategies, and explainability is essential to identify methodological strengths and gaps. This protocol describes the planned scoping review, following JBI methodology for scoping reviews (Aromataris et al., 2024) and the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist (Tricco et al., 2018), with alignment to the updated PRISMA 2020 guidelines (Page et al., 2021a; Page et al., 2021b). Objectives • To map methodological approaches in ML applied to multimodal lung cancer datasets. • To classify ML studies by data modalities, fusion techniques, learning strategies, and validation methods. • To highlight methodological trends, challenges, and research gaps. Review Questions 1. What ML methodologies have been applied to multimodal datasets in lung cancer? 2. Which combinations of data modalities are most commonly studied? 3. Which fusion strategies, learning paradigms, and validation methods dominate the field? 4. What gaps and methodological challenges remain in this domain? Eligibility Criteria Population: Studies on lung cancer patients (all subtypes). Concept: Application of ML methodologies (supervised, unsupervised, semi-supervised, reinforcement learning, ensemble methods, deep learning, transfer learning, explainable AI). Context: Multimodal data integration (e.g., imaging + genomics, clinical + omics, pathology + imaging). Outcomes: Diagnostic, prognostic, predictive, and treatment-response applications. Study types: Peer-reviewed studies from 2013 onward; English language. Exclusion: Reviews, editorials, commentaries, non-lung cancer studies, single-modality ML studies. Methods Information Sources Databases: PubMed, Scopus, Embase, Web of Science, and IEEE Xplore. Supplementary hand-searching of reference lists and Google Scholar will also be conducted. Search Strategy The following database-specific strategies will be applied. Actual searches already run are included, with enhancements for sensitivity and specificity using subject headings and synonyms. • PubMed (Feb 6, 2025 – enhanced): ((\"NSCLC\"[Title/Abstract] OR \"non small cell lung cancer\"[Title/Abstract] OR \"lung cancer\"[Title/Abstract] OR \"Lung Neoplasms\"[MeSH])) AND ((\"machine learning\"[Title/Abstract] OR \"deep learning\"[Title/Abstract] OR \"artificial intelligence\"[Title/Abstract] OR \"Machine Learning\"[MeSH])) OR (\"image processing\"[Title/Abstract] OR \"Image Processing, Computer-Assisted\"[MeSH])) AND ((booksdocs[Filter] OR clinicaltrial[Filter] OR randomizedcontrolledtrial[Filter]) AND (2013:2025[pdat])) NOT (\"review\"[Publication Type] OR \"systematic review\"[tiab] OR \"meta-analysis\"[tiab]). • Scopus (Feb 8, 2025 – enhanced): TITLE-ABS-KEY (\"NSCLC\" OR \"non small cell lung cancer\" OR \"lung cancer\") AND TITLE-ABS-KEY (\"machine learning\" OR \"deep learning\" OR \"artificial intelligence\" OR \"image processing\") AND TITLE-ABS-KEY (\"clinical trial\" OR \"randomized controlled trial\" OR \"diagnostic accuracy\" OR \"prediction model\") AND NOT TITLE-ABS-KEY (\"review\" OR \"systematic review\" OR \"meta-analysis\" OR \"scoping review\" OR \"editorial\" OR \"commentary\" OR \"letter\") AND PUBYEAR &gt; 2012 AND PUBYEAR &lt; 2025 AND (LIMIT-TO (LANGUAGE , \"English\")) AND (LIMIT-TO (DOCTYPE , \"ar\") OR LIMIT-TO (D","url":"https://doi.org/10.6084/m9.figshare.30113146","authors":["Hashempour, Sara","Han, Lee","Galanti, Mattia","Mayhue, Sari"],"tags":["Diagnostic radiography","Clinical sciences not elsewhere classified","Cancer diagnosis","Cancer genetics","Chemotherapy","Radiation therapy","Predictive and prognostic markers"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30113146","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.30061528","name":"Global Genomics Workforce Development: Innovative Strategies in Healthcare Sectors","source":"datacite","abstract":"The fast-paced progress of genomic medicine has led to a worldwide workforce shortage, risking the application of genomic research in clinical settings. This thorough review examines the current genomics workforce in clinical, laboratory, and healthcare sectors, highlighting key shortages and barriers to implementation. By analyzing empirical evidence and various theoretical frameworks, we highlight critical capacity limitations. For example, only 5,629 certified genetic counselors serve the US population, 41% of laboratories cannot fill technologist roles, and medical schools offer less than 10 hours of genomics education. The review highlights successful international models from both developed and developing nations, showcasing innovative initiatives such as H3Africa's $180 million investment in training professionals across 30 African countries, as well as technology-enabled programs that have reached over 350 researchers worldwide. Economic analyses show very high returns on workforce investment, such as the Human Genome Project, which yielded a 141:1 return, and the US genomics sector, now contributing $265 billion to the economy and supporting over 850,000 jobs. We identify comprehensive competency frameworks, educational innovations such as virtual reality training with a Cohen's d of 0.73 for knowledge enhancement, and policy imperatives supporting sustainable development. Projections for the future indicate a significant transformation in the workforce, driven by artificial intelligence, where up to 40% of tasks could be automated, and entirely new types of roles may emerge. Strategic recommendations highlight the need for collaboration among educational institutions, healthcare providers, policymakers, and technology developers to tackle current shortages and develop sustainable capacity for implementing precision medicine.","url":"https://doi.org/10.6084/m9.figshare.30061528","authors":["Zary, Nabil"],"tags":["Genomics","Workforce planning","Medical genetics (excl. cancer genetics)"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30061528","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.29877074","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29877074","authors":["Rahman NaziL, Ashikur"],"tags":["Community child health","Health equity","Health promotion","Injury prevention","Preventative health care","Social determinants of health","Public health not elsewhere classified","Biofabrication"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29877074","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.29876651","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29876651","authors":["Rahman NaziL, Ashikur"],"tags":["Naturopathy","Chiropractic","Traditional Chinese medicine and treatments","Traditional, complementary and integrative medicine not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29876651","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/497wc","name":"Inteligência artificial no planejamento e prognóstico do tratamento endodôntico: um protocolo de revisão de escopo.","source":"datacite","abstract":"RESUMO O tratamento endodôntico requer pleno conhecimento da anatomia dos canais radiculares, sendo os exames de imagem fundamentais para o planejamento e a obtenção de um prognóstico favorável. Nesse contexto, a Inteligência Artificial (IA) tem emergido como uma ferramenta promissora na análise de imagens e no suporte à tomada de decisão clínica. Nesse viés, o presente estudo objetiva mapear as ferramentas de Inteligência Artificial disponíveis na literatura, a fim de auxiliar no planejamento e no prognóstico do tratamento endodôntico. O presente trabalho seguirá a metodologia de revisão de escopo, conforme os padrões e normas do Joanna Briggs Institute (JBI) e do checklist PRISMA-ScR. A pergunta norteadora foi elaborada com base na estratégia PCC: população de elementos dentários submetidos ao tratamento endodôntico; uso de ferramentas de inteligência artificial; e o planejamento e prognóstico do tratamento endodôntico. A busca será realizada nas plataformas PubMed via MEDLINE, Google Acadêmico, SciELO, Scopus e Dentistry &amp; Oral Sciences Source (DoSS), utilizando descritores DeCS/MeSH associados a operadores booleanos. Os resultados serão sintetizados em tabelas e gráficos informativos. Espera-se encontrar diversas ferramentas úteis no dia a dia endodôntico, além de identificar lacunas do conhecimento a serem desenvolvidas. Palavras-chave: Endodontia; Inteligência Artificial; Tratamento do Canal Radicular; Planejamento; Prognóstico. ABSTRACT Endodontic treatment requires thorough knowledge of root canal anatomy, and imaging exams are essential for planning and achieving a favorable prognosis. In this context, Artificial Intelligence (AI) has emerged as a promising tool for image analysis and clinical decision-making support. From this perspective, the present study aims to map the Artificial Intelligence tools available in the literature in order to assist in the planning and prognosis of endodontic treatment. This study will follow the scoping review methodology, in accordance with the standards and guidelines of the Joanna Briggs Institute (JBI) and the PRISMA-ScR checklist. The guiding research question was developed based on the PCC strategy: population of teeth undergoing endodontic treatment; use of Artificial Intelligence tools; and planning and prognosis of endodontic treatment. The search will be conducted in the databases PubMed via MEDLINE, Google Scholar, SciELO, Scopus, and Dentistry &amp; Oral Sciences Source (DoSS), using DeCS/MeSH descriptors combined with Boolean operators. The results will be synthesized in tables and informative charts. It is expected to identify several useful tools for daily endodontic practice, as well as to highlight knowledge gaps to be addressed. Keywords: Artificial Intelligence; Endodontics; Planning; Prognosis; Root Canal Therapy. INTRODUÇÃO O tratamento endodôntico tem como finalidade principal a descontaminação do sistema de canais radiculares infectados por microrganismos responsáveis pela formação das lesões apicais¹. Todavia, para a realização do preparo químico-mecânico do sistema de canais, é necessário um conhecimento prévio da anatomia dos canais radiculares do elemento dentário. Isso porque cada elemento dentário apresenta uma anatomia radicular característica e, apesar do conhecimento padrão já estabelecido, podem ocorrer variações anatômicas que dificultam o tratamento endodôntico, as quais podem ser identificadas radiograficamente². Para a realização de um tratamento endodôntico eficaz, exames de imagem são necessários, incluindo radiografia periapical, panorâmica e tomografia computadorizada de feixe cônico³. A partir da realização desses exames radiográficos, é possível identificar a anatomia dos canais radiculares de cada elemento dentário, bem como suas variações anatômicas, tais como canais laterais, ístimo e ramificações apicais, os quais podem dificultar o tratamento endodôntico. Além disso, por meio dos exames de imagem utilizados no tratamento de canais r","url":"https://doi.org/10.17605/osf.io/497wc","authors":["dos Santos Sol, Isabella Soares","de Castro de Souza, Maria Isabel","de Castro, Antonio Jose Ribeiro"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Endodontics and Endodontology","Computer Sciences","Dentistry","FOS: Clinical medicine","Artificial Intelligence and Robotics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/497wc","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/wu5gy","name":"Methods and Metrics for Patient-Facing Explainability in Medical Artificial Intelligence: A Systematic Scoping Review","source":"datacite","abstract":"Purpose: As Artificial Intelligence (AI) becomes increasingly integral to clinical healthcare, providing transparent and interpretable models for patients - who are typically both \"clinically and technically lay\" - is a critical ethical and safety requirement. However, current research in Explainable AI (XAI) remains predominantly \"expert-centric,\" focusing on the interpretive needs of clinicians and developers rather than the end-user patient population. The primary purpose of this systematic scoping review is to map the current landscape of patient-facing XAI within clinical medicine. We aim to identify the various XAI modalities (e.g., natural language, saliency maps, and feature importance) currently deployed in patient-facing interfaces and analyse the evaluation metrics used to assess their effectiveness. These metrics will be synthesized using a Tripartite Metric Framework, categorizing findings into: Subjective Measures: Perceived trust, satisfaction, and transparency. Objective Measures: Quantifiable user comprehension and mental model accuracy. Behavioural Measures: Appropriate reliance, adherence to AI advice, and potential safety risks. Review Summary: The review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Databases including PubMed, Medline (Ovid), Embase, and Scopus will be searched using a strategy designed to capture patient-facing Explainable AI (XAI) within clinical contexts. Papers will be screened in two stages by two independent reviewers to identify studies involving populations that are both clinically and technically lay. Data extraction will capture study characteristics and evaluation metrics categorized through a Tripartite Metric Framework: subjective (trust), objective (comprehension), and behavioural (reliance). Findings will be synthesized quantitatively and qualitatively to map the conceptual landscape and identify significant evidence gaps in patient-centred XAI evaluation. Expectations: We anticipate finding a heterogeneous range of studies across various medical domains. We expect that current evaluation practices will be heavily skewed toward subjective measures - such as user satisfaction or perceived trust - with a notable lack of standardized, validated metrics for assessing objective patient comprehension or mental model accuracy. Furthermore, we expect to find evidence of the \"Over-trust\" phenomenon, where transparent but complex explanations may lead patients to follow incorrect AI advice without sufficient critical evaluation, highlighting a critical safety risk in current patient-facing XAI design.","url":"https://doi.org/10.17605/osf.io/wu5gy","authors":["Ashvin Kumarathas","Bowyer, Stuart"],"tags":["Health Information Technology","Physical Sciences and Mathematics","Medicine and Health Sciences","Computer Sciences","Medical Education","Artificial Intelligence and Robotics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/wu5gy","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19304429","name":"Investigating Biomarkers and Computer-Assisted Diagnostic Strategies for Autism Spectrum Disorder: A Preliminary Survey","source":"datacite","abstract":"Autism is a broad neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. Autism Spectrum Disorder (ASD) is the subject of a broad and intense research effort, with more than 40 EU funded research projects devoted to some of its aspects in the last 20 years. Similar effort is being done in many other big economical areas. One of common goals is to find its causes at various levels, be it genetic, metabolic, neural or brain based. Searching for the pathogenesis leads also to findings which are also diagnostic indications, i.e. biomarkers of the disorder that can be used to guide early diagnosis, which in its turn may allow to apply therapeutic or palliative treatments from an early age. ASD computer aided diagnosis (CAD) has been gaining interest in the scientific community in the recent years, aiming to contribure to its early detection. In this report we gather the approaches that have been reported in the recent literature trying to be comprehensive, though keeping pace of the reported results may be difficult. Some CAD approaches are based on behavioral characterizations, while the majority of approaches are based on the analysis of brain neural activity and morphology in some way or another. Most recent studies are focused on the detection of brain functional connectivity anomalies using specific signals such as electroencephalographic (EEG) recordings or functional magnetic resonance imaging (fMRI). The emergence of large public repositories of data is boosting research in this topic. 1 Introduction Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. Autism Table 1: Time distribution of references found searching by \"computer aided diagnosis autism\" in Pubmed spectrum disorder (ASD), aka autism spectrum condition (ASC), is a chronic inhabilitating cognitive impairment that takes a wide variety of forms, hence the use of the term \"spectrum\", and has a high prevalence in the general population, with a neat imbalance in distribution towards the male gender. A recent normative study [36] on brain cortical structure modeled by a probabilistic predictive model concluded that there is some indication that sexual-related characteristics of the brain are highly correlated with ASD. Computer aided diagnosis (CAD) aims to help the clinical practitioner to achieve early and accurate diagnosis of ASC in order to try to apply early treatments hoping to improve the child's condition in some way. Recent trials [46, 82, 84, 92, 98, 118] emphasize the improved effect achieved when the treatment is applied at early ages, even todlers. In this report we will not discuss the clinical aspects such as treatment protocols or diagnositic procedures follow in the clinic. In the works reviewed, the child diagnostic has been produced by a competent personnel or agency. A search in Pubmed using the terms \"computer aided diagnosis autism\" resulted in 285 references. The peak interest seems to be in year 2012, but a steady flow of papers is appearing since then dealing with the problem of devising CAD tools for ASD diagnosis coming from a diversity of bio-information sources. Table 1 shows the distribution in time of the references found. A CAD system is in essence a classifier system composed of predictive models built by machine learning processes. Machine leraning can be used to build hierarchies of categories which may help to refine diagnostic process [28], but mostly is used to give a response to the question \"Is this child at high risk of ASD?\". Regarding the kind of modeling approach used, the literature offers a wide variety: Rule based expert systems [72]. Deep learning architectures [3, 102] and shallow artificial neural networks[34]. • Support Vector Machines [102, 50, 66, 71, 74]. Statistical inference (i.e. ANOVA) is traditionally used in biomarker iden-tification. Regarding the kind","url":"https://doi.org/10.5281/zenodo.19304429","authors":["Dr. Salvatore Brischetto\nÉcole des Ponts ParisTech","France."],"tags":["Computer Engineering","Advanced Computing","Technology","Open Access"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.5281/zenodo.19304429","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19304430","name":"Investigating Biomarkers and Computer-Assisted Diagnostic Strategies for Autism Spectrum Disorder: A Preliminary Survey","source":"datacite","abstract":"Autism is a broad neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. Autism Spectrum Disorder (ASD) is the subject of a broad and intense research effort, with more than 40 EU funded research projects devoted to some of its aspects in the last 20 years. Similar effort is being done in many other big economical areas. One of common goals is to find its causes at various levels, be it genetic, metabolic, neural or brain based. Searching for the pathogenesis leads also to findings which are also diagnostic indications, i.e. biomarkers of the disorder that can be used to guide early diagnosis, which in its turn may allow to apply therapeutic or palliative treatments from an early age. ASD computer aided diagnosis (CAD) has been gaining interest in the scientific community in the recent years, aiming to contribure to its early detection. In this report we gather the approaches that have been reported in the recent literature trying to be comprehensive, though keeping pace of the reported results may be difficult. Some CAD approaches are based on behavioral characterizations, while the majority of approaches are based on the analysis of brain neural activity and morphology in some way or another. Most recent studies are focused on the detection of brain functional connectivity anomalies using specific signals such as electroencephalographic (EEG) recordings or functional magnetic resonance imaging (fMRI). The emergence of large public repositories of data is boosting research in this topic. 1 Introduction Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. Autism Table 1: Time distribution of references found searching by \"computer aided diagnosis autism\" in Pubmed spectrum disorder (ASD), aka autism spectrum condition (ASC), is a chronic inhabilitating cognitive impairment that takes a wide variety of forms, hence the use of the term \"spectrum\", and has a high prevalence in the general population, with a neat imbalance in distribution towards the male gender. A recent normative study [36] on brain cortical structure modeled by a probabilistic predictive model concluded that there is some indication that sexual-related characteristics of the brain are highly correlated with ASD. Computer aided diagnosis (CAD) aims to help the clinical practitioner to achieve early and accurate diagnosis of ASC in order to try to apply early treatments hoping to improve the child's condition in some way. Recent trials [46, 82, 84, 92, 98, 118] emphasize the improved effect achieved when the treatment is applied at early ages, even todlers. In this report we will not discuss the clinical aspects such as treatment protocols or diagnositic procedures follow in the clinic. In the works reviewed, the child diagnostic has been produced by a competent personnel or agency. A search in Pubmed using the terms \"computer aided diagnosis autism\" resulted in 285 references. The peak interest seems to be in year 2012, but a steady flow of papers is appearing since then dealing with the problem of devising CAD tools for ASD diagnosis coming from a diversity of bio-information sources. Table 1 shows the distribution in time of the references found. A CAD system is in essence a classifier system composed of predictive models built by machine learning processes. Machine leraning can be used to build hierarchies of categories which may help to refine diagnostic process [28], but mostly is used to give a response to the question \"Is this child at high risk of ASD?\". Regarding the kind of modeling approach used, the literature offers a wide variety: Rule based expert systems [72]. Deep learning architectures [3, 102] and shallow artificial neural networks[34]. • Support Vector Machines [102, 50, 66, 71, 74]. Statistical inference (i.e. ANOVA) is traditionally used in biomarker iden-tification. Regarding the kind","url":"https://doi.org/10.5281/zenodo.19304430","authors":["Dr. Salvatore Brischetto\nÉcole des Ponts ParisTech","France."],"tags":["Computer Engineering","Advanced Computing","Technology","Open Access"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.5281/zenodo.19304430","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.27909630","name":"Note from the Editor-in-Chief: Post-Publication Open Peer Review Model: An Opportunity or a Challenge for Scientific Journals?سخن سردبیر: مدل داوری همتای باز پس از انتشار : یک فرصت یا چالش در پیش روی مجلات علمی؟","source":"datacite","abstract":"Historians of science attribute the concept of peer review—not the process itself—as a method for evaluating written works to ancient Greece (5th century BC) or Middle Eastern scholars (around 900 AD) (Al-Mousawi, 2020). Despite this, the principle of peer review has a long history, dating back to the publication of Philosophical Transactions by the Royal Society of London in 1665 (Moxham &amp; Fyfe, 2018). Concurrently, some texts reference the Journal de scavant , published in Paris on January 5, 1665, as the first peer-reviewed scientific publication. However, according to the Merriam-Webster dictionary (n.d.), the term peer-review was introduced in 1969, and according to the Oxford English Dictionary (n.d.), the term entered the scientific lexicon in 1971. Additionally, the term referee was introduced in 1817 by George Greenough, a geologist who became familiar with this term early in his life as a law student (Al-Mousawi, 2020). Exploring the evolution of peer review provides clear evidence of the direct relationship between peer review and the growth and development of scientific journals. Journals have consistently relied on peer reviewers to ensure they make informed decisions regarding the publication and accreditation of submitted articles. Despite this, the peer review process has continually evolved in response to the trends shaping technological advancements. With the development of peer review models that align with contemporary technologies and requirements, fresh opportunities have arisen for the stakeholders involved in the peer review process. In this context, although the Blind Peer Review model was once regarded as the most prevalent peer review method, other models have gradually gained acceptance among various journals. These include Single-blind peer review , Double-blind peer review , Signed peer review/Transparent peer review , and Open peer review which were welcomed by a variety of journals and developed over time. However, the development of peer review does not stop there. Influenced by advancements related to the open access movement and the phenomenon of open science, one of its latest innovations has emerged known as the Post-publication open peer review model. The Post-publication open peer review is a type of open review system in which articles and research works undergo the peer review process after being made publicly available as preprints. This model does not include an editor. To eliminate potential biases, peer reviewers are solely responsible for collectively determining the validity and suitability of articles for publication, effectively assuming the role of an editor. Among the platforms that utilize post-publication open peer review and have introduced innovative approaches to scientific publishing and peer review, we can mention Qeios[1], Orvium[2], eLife[3], ScienceOpen[4], Octopus[5], and F1000Research[6]. In addition to benefiting from a rigorous peer review process, these platforms offer the opportunity for the free and rapid publication of high-quality articles and research. They also make the content accessible to the public through open access. Using the capabilities of artificial intelligence, along with the voluntary participation of other researchers as peer reviewers, these platforms integrate human expertise and AI to identify the most effective methods of peer review. In this model, artificial intelligence ensures the accuracy and reliability of peer review at every stage. Particularly during the initial review phase, before the work enters the peer review process, it plays a crucial role in filtering out non-scientific content and detecting scientific plagiarism. In addition, artificial intelligence facilitates the unbiased selection and identification of peer reviewers by analyzing their records and profiles. It also streamlines the process of sending review invitations and enables the verification of identities and review reports submitted by volunteer peer reviewe","url":"https://doi.org/10.6084/m9.figshare.27909630","authors":["Noroozi Chakoli عبدالرضا نوروزی چاکلی, Abdolreza"],"tags":["Informetrics","Information governance, policy and ethics","Open access","Social and community informatics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.27909630","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19282558","name":"Explainable Artificial Intelligence In Healthcare: Methods, Applications, Challenges, And Future Directions","source":"datacite","abstract":"Artificial Intelligence (AI) has redefined the landscape of the healthcare sector by offering accurate diagnosis, analysis, and treatment of various diseases, amongst other benefits. Notably, most advanced AI systems are viewed as 'black boxes,' owing to the lack of transparency of decision-making processes, making it difficult for medical and healthcare experts to put their trust in AI. Explainability of Artificial Intelligence (XAI) seeks to remedy this challenge facing the medical and healthcare sector by offering insights into the decision-making of AI systems. In the paper, the author offers a comprehensive review of various Explainability of Artificial Intelligence systems in the medical and healthcare sector, amongst key disciplines like radiology, oncology, cardiology, and telemedicine, amongst various AI systems. According to the review, Explainability of Artificial Intelligence systems are of critical importance in the medical and healthcare sector, considering the evaluation of AI systems, for instance, in medical environments, where accuracy and explanations of AI decision-making processes are paramount for the sector.","url":"https://doi.org/10.5281/zenodo.19282558","authors":["Mrs. A. Sangeetha Priya","K. Dinesh Kumar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19282558","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19282557","name":"Explainable Artificial Intelligence In Healthcare: Methods, Applications, Challenges, And Future Directions","source":"datacite","abstract":"Artificial Intelligence (AI) has redefined the landscape of the healthcare sector by offering accurate diagnosis, analysis, and treatment of various diseases, amongst other benefits. Notably, most advanced AI systems are viewed as 'black boxes,' owing to the lack of transparency of decision-making processes, making it difficult for medical and healthcare experts to put their trust in AI. Explainability of Artificial Intelligence (XAI) seeks to remedy this challenge facing the medical and healthcare sector by offering insights into the decision-making of AI systems. In the paper, the author offers a comprehensive review of various Explainability of Artificial Intelligence systems in the medical and healthcare sector, amongst key disciplines like radiology, oncology, cardiology, and telemedicine, amongst various AI systems. According to the review, Explainability of Artificial Intelligence systems are of critical importance in the medical and healthcare sector, considering the evaluation of AI systems, for instance, in medical environments, where accuracy and explanations of AI decision-making processes are paramount for the sector.","url":"https://doi.org/10.5281/zenodo.19282557","authors":["Mrs. A. Sangeetha Priya","K. Dinesh Kumar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19282557","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.19391981","name":"International development cooperation, corruption, and COVID-19 in Kenya: Lessons for infectious disease control","source":"datacite","abstract":"The novel coronavirus poses a grave danger to human populations, healthcare systems and world economies. These threats are more pronounced in developing countries with ill-prepared healthcare systems to manage such a pandemic. International development partners have responded by supporting them with financial, technical, and in-kind support. A review of local and international mainstream media reveals a very disturbing picture: Kenya’s corrupt officials, particularly in the health ministry, were mismanaging and diverting foreign aid meant for COVID-19 mitigation and control efforts. In addition, the country missed valuable opportunities to improvise and innovate its health systems. It also failed to use cost-effective interventions that could have delivered stronger outcomes, such as using schools as isolation centers and deploying the Nyumba Kumi initiative. To successfully mitigate against future infectious disease outbreaks, partners would need to help developing countries: establish, equip and staff a fit-for-purpose healthcare infrastructure; grow domestic capacity for manufacturing drugs, vaccines and medical equipment; upgrade medical research capacity; and develop a surveillance system that is driven by Big Data and Artificial Intelligence. It is imperative that they combat corruption by seeking value for money and embracing the principles of aid effectiveness – both of which are missing in the ongoing flows to Kenya.","url":"https://doi.org/10.6084/m9.figshare.19391981","authors":["Chesoli, Kennedy","Kimosop, Peter"],"tags":["International economics","Economic development policy","International relations","Infectious diseases"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.19391981","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.20374005","name":"PROLIFERATE: An adaptable framework with tools to evaluate different processes, outputs, and products via participatory research","source":"datacite","abstract":"This framework presents a constructivist evaluation procedure for assessing the personal construction of meaning (e.g., end-user experiences, understandings, beliefs, and perceptions)1-3. It considers the non-linear characteristics of complex and adaptive issues/products/problems4 from an end-user perspective. It is operationalized to evaluate and monitor real-world situations, research outcomes, and products. It assesses their fitness via person-centered parameters, which can be adaptable for evaluating different subjective experiences concerning any evaluated matter and associated behaviors. It can be utilized to evaluate \"usability\" and its impact (i.e., the consequence of the usability or a prediction of such effect) on products and processes. Usability can refer to a broad set of criteria (e.g., satisfaction, learnability, efficiency, memorability, robustness, effectiveness, and accessibility)2,3,5. These constructs fluctuate depending on each end-user context and their adaptive dynamics2,6-10. The versatile and variable nature of end-user perceptions/experiences can be captured, qualified, and quantified as 'feedback' by this approach: PROLIFERATE: An ada p table framewo r k with to o ls to eva l uate d i f f erent process e s, outputs, and p r oducts vi a par t icipatory res e arch. It is an emergent dialectical multimethod evaluation 11-15. The PROLIFERATE metaparadigm method (epistemological basis and procedure in Figure 1)11-14 accommodates a way to qualify and/or quantify each end-user's subjective experience using a dialectical pluralism approach16, which can be adaptable to the evaluation of AI (i.e., usability and impact) because, in PROLIFERATE, the evaluated matter is assessed considering these domains of end-users behaviors or constructs:17 1. Comprehension or understanding 2. Resonance or emotional responses 3. Uptake barriers 4. Motivation to use it and associated behavior change 5. Optimization suggestions PROLIFERATE design11-14 implies that, for instance, the usability of AI can be evaluated by adapting and analyzing the combination of its five constructs to obtain estimations of AI usability (constructs 1,2,3) and impact (constructs 4, 5). These constructs can be analyzed using different procedures, including Bayesian statistics and prediction modeling11-14. In this method, Knowledge Translation (KT) is understood as per the Knowledge Translation Complexity Network Model (KT-CNM)4. It refers to simultaneous networks interacting in non-linear ways in which different agents/people, entities, or nodes (i.e., researchers, community, clinicians, etc.), are linked, connected, or interact based on their social features, conditions, institutional structures, roles, and behaviors, represented in knowledge translation processes or stages: problem identification, evaluation, implementation, knowledge creation, etc.)4,8,18. This complex lens does not assume cause and effect but a continuum of interactions between people and knowledge. PROLIFERATE takes a complex system approach as an evaluation framework as it can be combined with quantitative tools while emphasizing collaboration with end-users (i.e., participatory research)11-14. This emergent evaluation framework is evolving within several KT projects: · It has been ethically approved to assess interdisciplinary learnings, e.g., (1) within a university allied health practice (Health2Go) and (2) pilot tested within the co-design of KT video resources.11,12 · It has been examined in knowledge actualization discussions concerning a science implementation project.13 · It is currently being adapted to evaluate research training modules, lab-training strategies, and AI.14,15 References : 1. Louder E, Wyborn C, Cvitanovic C, Bednarek AT. A synthesis of the frameworks available to guide evaluations of research impact at the interface of environmental science, policy and practice. Environmental Science &amp; Policy 2021; 116 : 258-65. 2. Dawood KA, Sharif KY, Ghani AA, Zu","url":"https://doi.org/10.6084/m9.figshare.20374005","authors":["Pinero de Plaza, Maria Alejandra"],"tags":["Sociology and social studies of science and technology","Public health not elsewhere classified","Health informatics and information systems","Health care administration","Health promotion","Health surveillance","Health and community services","Health policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.20374005","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.6084/m9.figshare.13208165","name":"A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic","source":"datacite","abstract":"While the world has experience with many different types of infectious diseases, the current crisis re- lated to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The trans- mission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pan- demic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The pri- mary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic anal- ysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.","url":"https://doi.org/10.6084/m9.figshare.13208165","authors":["jamil, akhtar"],"tags":["Artificial intelligence not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.6084/m9.figshare.13208165","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/q9rdz","name":"Artificial intelligence for image-based recognition of diabetes-related foot disease: a scoping review protocol","source":"datacite","abstract":"Diabetes-related foot disease is one of the most severe complications associated with diabetes mellitus and represents a significant global public health challenge. Diabetic foot ulcers are responsible for high rates of hospitalization, infection, and lower-limb amputations, generating substantial social and economic impacts on healthcare systems worldwide. Early detection and appropriate management of foot lesions are essential strategies for preventing severe complications and improving the quality of life of individuals living with diabetes. Recent advances in artificial intelligence have enabled the development of computational models capable of analyzing medical images and supporting clinical decision-making. In particular, machine learning and deep learning techniques have demonstrated promising results in the automatic detection, classification, and monitoring of diabetic foot lesions through image analysis. This project aims to map the current scientific evidence regarding the use of artificial intelligence techniques applied to image-based recognition of diabetes-related foot disease. A scoping review will be conducted to identify the main algorithms used, types of medical image datasets available, and the diagnostic performance metrics reported in the literature. The findings of this review are expected to contribute to the development of innovative digital health solutions aimed at improving early detection and clinical management of diabetic foot complications. In addition, the results may support the future development of artificial intelligence–based tools and mobile health applications designed to assist healthcare professionals in the prevention of diabetes-related amputations.","url":"https://doi.org/10.17605/osf.io/q9rdz","authors":["Fernandes, Catiane Raquel S"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/q9rdz","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/7k42g","name":"Artificial intelligence for image-based recognition of diabetes-related foot disease: a scoping review protocol","source":"datacite","abstract":"This project aims to map the scientific evidence on the use of artificial intelligence (AI) for image-based recognition of diabetes-related foot disease. Diabetic foot complications are a major public health concern, often leading to infections, hospitalizations, and amputations. Early detection remains a challenge, particularly in primary healthcare settings. Recent advances in AI, especially machine learning and deep learning, have enabled the development of models capable of analyzing medical images and supporting clinical decision-making. This scoping review will identify the main algorithms used, types of image datasets, and performance metrics reported in the literature. The findings are expected to support the development of digital health solutions aimed at improving early detection and prevention of complications associated with diabetes-related foot disease.","url":"https://doi.org/10.17605/osf.io/7k42g","authors":["Fernandes, Catiane Raquel S","Silva, Nicole Enders"],"tags":["Business","Medicine and Health Sciences","Education","Engineering","IA","deeptech","healthech"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/7k42g","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/efbsp","name":"A Conceptual Framework for Integrating the Automated Multiple-Pass Method, Motivational Interviewing, Shared Decision-Making, Speech-to-Text Technology, and Generative Artificial Intelligence in Diabetes Self-Management Education and Support: A Scoping Review","source":"datacite","abstract":"This is a retrospective registration for a completed scoping review conducted in accordance with PRISMA-ScR 2018 guidelines. The review maps evidence on the integration of AMPM, MI, SDM, STT, and GenAI in type 2 diabetes self-management education and support (DSMES). Eligibility criteria, search strategy, and analytic methods were defined prior to data collection.","url":"https://doi.org/10.17605/osf.io/efbsp","authors":["HSU, HUI-CHUN","Yau-Jiunn Lee","Yu-Houng Chang","Tang, Wei-Hua"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Medical Specialties","Computer Sciences","Endocrinology, Diabetes, and Metabolism","Artificial Intelligence and Robotics","AMPM","DSMES"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2027","doi":"10.17605/osf.io/efbsp","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19220216","name":"RAD-CaseBookLLM-08","source":"datacite","abstract":"RAD-CaseBookLLM-08 is an open-access dataset containing Large Language Model (LLM)-generated educational texts focused on radiological differential diagnosis. The dataset was generated using ChatGPT-4o (OpenAI, web-based version, March 2025) under standardized prompting conditions (new user account, conversation memory disabled, new chat session for each topic). For each entry, a structured prompt was used, varying only the radiological theme under study. Prompts and responses were written exclusively in English, and all outputs were copied verbatim without editing, preserving formatting and model-generated concluding statements. The thematic inputs provided to the LLM correspond to radiological “key imaging findings” topic titles from the casebook Top 3 Differentials in Radiology: A Case Review (O’Brien WT, 2010). No copyrighted text, images, figures, case descriptions, explanations, or other protected material from the original publication were reproduced, copied, or included in this dataset. All educational content contained herein was independently generated by the LLM based solely on the thematic titles. The dataset is organized by radiology subspecialty and provided in PDF, DOCX, JSON, CSV and MD formats, distributed as compressed ZIP archives. It was created as part of a multicenter comparative study evaluating the perceived educational usefulness of LLM-generated differential diagnosis teaching material versus a traditional radiology casebook among junior and advanced radiology trainees. The exact prompt template used for dataset generation is provided in the file “prompt_template.txt” included in this repository. No executable code, scripts, or API-based pipelines were used during dataset creation. The prompt template constitutes the reproducible methodological component of this dataset. The primary purpose of this dataset is to provide a structured LLM-generated equivalent of a radiology differential diagnosis casebook covering diverse thematic imaging findings. It is intended to serve as a research resource for studying the educational characteristics, strengths, limitations, and reproducibility of LLM-generated medical teaching content. This dataset is not designed or validated for direct clinical use or as formally accredited educational material. This dataset is released under the CC0 1.0 Universal license to promote transparency, reproducibility, and further research in radiology education and medical artificial intelligence. This repository represents version 1.1 of the dataset (february 2026).","url":"https://doi.org/10.5281/zenodo.19220216","authors":["Saliba, Thomas","Fahrni, Guillaume"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19220216","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.18625031","name":"RAD-CaseBookLLM-08","source":"datacite","abstract":"RAD-CaseBookLLM-08 is an open-access dataset containing Large Language Model (LLM)-generated educational texts focused on radiological differential diagnosis. The dataset was generated using ChatGPT-4o (OpenAI, web-based version, March 2025) under standardized prompting conditions (new user account, conversation memory disabled, new chat session for each topic). For each entry, a structured prompt was used, varying only the radiological theme under study. Prompts and responses were written exclusively in English, and all outputs were copied verbatim without editing, preserving formatting and model-generated concluding statements. The thematic inputs provided to the LLM correspond to radiological “key imaging findings” topic titles from the casebook Top 3 Differentials in Radiology: A Case Review (O’Brien WT, 2010). No copyrighted text, images, figures, case descriptions, explanations, or other protected material from the original publication were reproduced, copied, or included in this dataset. All educational content contained herein was independently generated by the LLM based solely on the thematic titles. The dataset is organized by radiology subspecialty and provided in PDF, DOCX, JSON, CSV and MD formats, distributed as compressed ZIP archives. It was created as part of a multicenter comparative study evaluating the perceived educational usefulness of LLM-generated differential diagnosis teaching material versus a traditional radiology casebook among junior and advanced radiology trainees. The exact prompt template used for dataset generation is provided in the file “prompt_template.txt” included in this repository. No executable code, scripts, or API-based pipelines were used during dataset creation. The prompt template constitutes the reproducible methodological component of this dataset. The primary purpose of this dataset is to provide a structured LLM-generated equivalent of a radiology differential diagnosis casebook covering diverse thematic imaging findings. It is intended to serve as a research resource for studying the educational characteristics, strengths, limitations, and reproducibility of LLM-generated medical teaching content. This dataset is not designed or validated for direct clinical use or as formally accredited educational material. This dataset is released under the CC0 1.0 Universal license to promote transparency, reproducibility, and further research in radiology education and medical artificial intelligence. This repository represents version 1.1 of the dataset (february 2026).","url":"https://doi.org/10.5281/zenodo.18625031","authors":["Saliba, Thomas","Fahrni, Guillaume"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18625031","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/jxbms","name":"Artificial Intelligence and Radiomics for Differentiating Pseudoprogression from True Progression in High-Grade Gliomas: A Meta-Analysis","source":"datacite","abstract":"Differentiating true progression (TP) from pseudoprogression (PsP) in high-grade gliomas (HGGs) on MRI is a critical clinical challenge. This meta-analysis evaluates the overall diagnostic accuracy of radiomics and artificial intelligence (AI) models to identify algorithmic determinants of optimal performance. A PRISMA-compliant search of PubMed, Ovid MEDLINE, and EMBASE (up to 2025) was conducted. Quality was assessed via QUADAS-2. Diagnostic metrics were pooled utilizing bivariate random-effects models and Summary Receiver Operating Characteristic (SROC) curves. Across 34 included studies, the overall pooled sensitivity and specificity were 82% and 79%, respectively. Shape-based and Deep Learning (DL) features achieved the highest Area Under the Curve (up to 0.96 and 0.95). First-order statistics and Gray-Level Co-occurrence Matrix (GLCM) yielded the highest consistency and accuracy peaks (up to 98%). Multiparametric MRI was the most robust and widely used imaging modality. Radiomics demonstrates high, reliable diagnostic potential for discriminating HGG PsP from TP. While advanced DL models and shape features maximize discriminative performance, classical handcrafted features remain the most extensively validated. Future studies must prioritize independent external validation, federated learning (FL), and Explainable AI (XAI) for clinical translation.","url":"https://doi.org/10.17605/osf.io/jxbms","authors":["Facchinetti, Giovanni","De Maria, Lucio","Pagani, Nicola","Ponzio, Francesco"],"tags":["Radiology","Medicine and Health Sciences","Medical Specialties","Oncology","Neurology","Artificial Intelligence","High-Grade Gliomas","MRI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/jxbms","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19218621","name":"Artificial Intelligence Driven Multidisciplinary Approaches for Detection  and Prevention of Child Abuse and Neglect: A Comprehensive Review","source":"datacite","abstract":"Child abuse and neglect (CAN) continue to pose a significant global public health challenge, with profound and lasting impacts on physical, psychological, and social well-being. Despite advances in legislation and child protection systems, early detection remains suboptimal due to underreporting, diagnostic ambiguity, and systemic limitations. In recent years, the integration of artificial intelligence (AI), machine learning (ML), and digital health technologies has emerged as a promising avenue for improving detection, documentation, and prevention strategies. This narrative review explores the evolving landscape of CAN by synthesizing current knowledge on epidemiology, clinical manifestations, risk factors, and multidisciplinary management, while critically examining the role of AIdriven tools in enhancing clinical decision-making and forensic accuracy. Special emphasis is placed on the contribution of dental and medical professionals in identifying early signs of abuse, particularly orofacial injuries. Furthermore, ethical considerations, challenges in low- and middle-income countries, and future research directions are discussed. The convergence of clinical expertise and intelligent technologies offers a transformative approach to safeguarding vulnerable children and strengthening global child protection frameworks.","url":"https://doi.org/10.5281/zenodo.19218621","authors":["Dr. Brij Kumar, Dr. Mariya Luqman, Dr. Namrata J Mantri, Dr. Sannidhi M Jagtap"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19218621","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19218622","name":"Artificial Intelligence Driven Multidisciplinary Approaches for Detection  and Prevention of Child Abuse and Neglect: A Comprehensive Review","source":"datacite","abstract":"Child abuse and neglect (CAN) continue to pose a significant global public health challenge, with profound and lasting impacts on physical, psychological, and social well-being. Despite advances in legislation and child protection systems, early detection remains suboptimal due to underreporting, diagnostic ambiguity, and systemic limitations. In recent years, the integration of artificial intelligence (AI), machine learning (ML), and digital health technologies has emerged as a promising avenue for improving detection, documentation, and prevention strategies. This narrative review explores the evolving landscape of CAN by synthesizing current knowledge on epidemiology, clinical manifestations, risk factors, and multidisciplinary management, while critically examining the role of AIdriven tools in enhancing clinical decision-making and forensic accuracy. Special emphasis is placed on the contribution of dental and medical professionals in identifying early signs of abuse, particularly orofacial injuries. Furthermore, ethical considerations, challenges in low- and middle-income countries, and future research directions are discussed. The convergence of clinical expertise and intelligent technologies offers a transformative approach to safeguarding vulnerable children and strengthening global child protection frameworks.","url":"https://doi.org/10.5281/zenodo.19218622","authors":["Dr. Brij Kumar, Dr. Mariya Luqman, Dr. Namrata J Mantri, Dr. Sannidhi M Jagtap"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19218622","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19218362","name":"A COMPREHENSIVE GLOBAL REVIEW OF RENAL TOXICITY ASSOCIATED WITH HERBAL AND ALTERNATIVE MEDICINES","source":"datacite","abstract":"The widespread use of herbal and traditional medicines across cultures continues to grow, driven by the perception that “natural” equals “safe.” However, an alarming trend of renal complications—including acute kidney injury (AKI) and chronic kidney disease (CKD)—has emerged in association with various herbal products. This review provides a critical assessment of the nephrotoxic potential of traditional and herbal medicines. It explores the pathophysiological mechanisms of toxicity, including direct tubular damage, oxidative stress, immune-mediated injury, and hemodynamic alterations. Common nephrotoxic agents include Aristolochia species (containing aristolochic acid), Aloe vera, Tripterygium wilfordii, and heavy metal-adulterated Ayurvedic preparations. The paper highlights clinical case reports and epidemiological data indicating rising herb-induced nephropathies, particularly in regions with weak pharmacovigilance systems. Vulnerable populations, including those with pre-existing renal disease, the elderly, and individuals engaging in polyherbal or herb-drug combinations, face the highest risks. In many countries, lax regulatory oversight allows contaminated, mislabeled, and adulterated products to proliferate. The absence of standardized dosing, proper labeling, and systematic reporting mechanisms hampers both prevention and response. Special emphasis is placed on the interplay between traditional healing practices and modern medical challenges, including herb-drug interactions, underreported toxicities, and the need for integrative policy frameworks. This review calls for the implementation of toxicovigilance programs, healthcare provider education, and global regulatory harmonization to address these public health threats. Additionally, the application of artificial intelligence (AI) in predicting herb-induced toxicity and improving surveillance systems is proposed as a frontier area of research. In conclusion, the balance between preserving the therapeutic value of herbal medicine and ensuring renal safety must be achieved through multidisciplinary collaboration and evidence-based strategies.","url":"https://doi.org/10.5281/zenodo.19218362","authors":["Okeke, Chinedu Michael Emeka"],"tags":["Herbal nephrotoxicity, Traditional medicine, Acute kidney injury (AKI), Chronic kidney disease (CKD), Aristolochic acid"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19218362","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19218363","name":"A COMPREHENSIVE GLOBAL REVIEW OF RENAL TOXICITY ASSOCIATED WITH HERBAL AND ALTERNATIVE MEDICINES","source":"datacite","abstract":"The widespread use of herbal and traditional medicines across cultures continues to grow, driven by the perception that “natural” equals “safe.” However, an alarming trend of renal complications—including acute kidney injury (AKI) and chronic kidney disease (CKD)—has emerged in association with various herbal products. This review provides a critical assessment of the nephrotoxic potential of traditional and herbal medicines. It explores the pathophysiological mechanisms of toxicity, including direct tubular damage, oxidative stress, immune-mediated injury, and hemodynamic alterations. Common nephrotoxic agents include Aristolochia species (containing aristolochic acid), Aloe vera, Tripterygium wilfordii, and heavy metal-adulterated Ayurvedic preparations. The paper highlights clinical case reports and epidemiological data indicating rising herb-induced nephropathies, particularly in regions with weak pharmacovigilance systems. Vulnerable populations, including those with pre-existing renal disease, the elderly, and individuals engaging in polyherbal or herb-drug combinations, face the highest risks. In many countries, lax regulatory oversight allows contaminated, mislabeled, and adulterated products to proliferate. The absence of standardized dosing, proper labeling, and systematic reporting mechanisms hampers both prevention and response. Special emphasis is placed on the interplay between traditional healing practices and modern medical challenges, including herb-drug interactions, underreported toxicities, and the need for integrative policy frameworks. This review calls for the implementation of toxicovigilance programs, healthcare provider education, and global regulatory harmonization to address these public health threats. Additionally, the application of artificial intelligence (AI) in predicting herb-induced toxicity and improving surveillance systems is proposed as a frontier area of research. In conclusion, the balance between preserving the therapeutic value of herbal medicine and ensuring renal safety must be achieved through multidisciplinary collaboration and evidence-based strategies.","url":"https://doi.org/10.5281/zenodo.19218363","authors":["Okeke, Chinedu Michael Emeka"],"tags":["Herbal nephrotoxicity, Traditional medicine, Acute kidney injury (AKI), Chronic kidney disease (CKD), Aristolochic acid"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19218363","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/j4hn7","name":"Artificial Intelligence Self-Efficacy and Attitudes Toward AI Among Medical and Nursing Students: A Scoping Review Protocol","source":"datacite","abstract":"This scoping review summarizes the literature on AI self-efficacy and AI-related attitudes among undergraduate and postgraduate medical and nursing students. It explores definitions, measurement methods, reported outcomes, and identifies gaps for future research.","url":"https://doi.org/10.17605/osf.io/j4hn7","authors":["Chiung-Jung (Jo) Wu","Ha, Tam","Mary-Anne Ramis"],"tags":["Medicine and Health Sciences","Education","Medical Education","Nursing","FOS: Health sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/j4hn7","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19172183","name":"Central Intelligence Project™ Public Notice and Conceptual Architecture Statement: Organic Atomic Node Intelligence (OANI)™","source":"datacite","abstract":"This publication is a controlled-disclosure public record establishing the authorship, chronology, mission, public conceptual architecture, and market entry of Central Intelligence Project™ and Organic Atomic Node Intelligence (OANI)™. It explains what Central Intelligence Project and OANI are, what they do, why they exist, how they differ from generic artificial intelligence systems, and the public conceptual architecture through which they operate. The document presents Central Intelligence Project as an institutional analytical architecture designed to transform fragmented information into structured understanding, operationally useful intelligence, and disciplined public-facing outputs. It presents OANI as the core analytical framework within that architecture, structured to identify meaningful informational units, connect those units into structured relationships, evaluate significance, and produce usable outputs across research, investigations, operational environments, administrative systems, public-interest analysis, and strategic communication. This version also documents a broad representative portfolio of current research and analytical initiatives at the public-safe layer, including vehicle-systems analysis, Common Operating Picture development, adversarial historical stress testing, historical and biographical intelligence assembly, biomedical and strategic-risk analysis, infrastructure optimization, veteran-disability and hazard-correlation analysis, cognitive compartmentalization in military and high-risk occupations, polypharmacy and pharmacogenomics, toxic occupational hazards, public-order and emergency-management analysis, advanced medical modeling, and institutional integrity and accountability audit frameworks. This record is intended to place the public and the market on notice that Central Intelligence Project™ and OANI™ exist as independently developed, mission-oriented, analytically structured frameworks with defined terminology, coherent public architecture, and active public-facing development, while preserving undisclosed proprietary methods, internal workflows, implementation pathways, and other confidential proprietary matter. Omission of such matter from this publication does not constitute abandonment, waiver, or dedication to the public. Version 1.2. Versions 1.0 and 1.1 were circulated internally in draft form for review and continuity purposes prior to formal public release.","url":"https://doi.org/10.5281/zenodo.19172183","authors":["Marino, Nicholas"],"tags":["Central Intelligence Project","OANI","Organic Atomic Node Intelligence","Analytical Architecture","Public-Interest Intelligence","Structured Analysis","Governance Analysis","Operational Awareness"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19172183","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19209428","name":"Central Intelligence Project™ Public Notice and Conceptual Architecture Statement: Organic Atomic Node Intelligence (OANI)™","source":"datacite","abstract":"This publication is a controlled-disclosure public record establishing the authorship, chronology, mission, public conceptual architecture, and market entry of Central Intelligence Project™ and Organic Atomic Node Intelligence (OANI)™. It explains what Central Intelligence Project and OANI are, what they do, why they exist, how they differ from generic artificial intelligence systems, and the public conceptual architecture through which they operate. The document presents Central Intelligence Project as an institutional analytical architecture designed to transform fragmented information into structured understanding, operationally useful intelligence, and disciplined public-facing outputs. It presents OANI as the core analytical framework within that architecture, structured to identify meaningful informational units, connect those units into structured relationships, evaluate significance, and produce usable outputs across research, investigations, operational environments, administrative systems, public-interest analysis, and strategic communication. This version also documents a broad representative portfolio of current research and analytical initiatives at the public-safe layer, including vehicle-systems analysis, Common Operating Picture development, adversarial historical stress testing, historical and biographical intelligence assembly, biomedical and strategic-risk analysis, infrastructure optimization, veteran-disability and hazard-correlation analysis, cognitive compartmentalization in military and high-risk occupations, polypharmacy and pharmacogenomics, toxic occupational hazards, public-order and emergency-management analysis, advanced medical modeling, and institutional integrity and accountability audit frameworks. This record is intended to place the public and the market on notice that Central Intelligence Project™ and OANI™ exist as independently developed, mission-oriented, analytically structured frameworks with defined terminology, coherent public architecture, and active public-facing development, while preserving undisclosed proprietary methods, internal workflows, implementation pathways, and other confidential proprietary matter. Omission of such matter from this publication does not constitute abandonment, waiver, or dedication to the public. Version 1.2. Versions 1.0 and 1.1 were circulated internally in draft form for review and continuity purposes prior to formal public release.","url":"https://doi.org/10.5281/zenodo.19209428","authors":["Marino, Nicholas"],"tags":["Central Intelligence Project","OANI","Organic Atomic Node Intelligence","Analytical Architecture","Public-Interest Intelligence","Structured Analysis","Governance Analysis","Operational Awareness"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19209428","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/nkbsf","name":"A Scoping Review of Actionable Recommendations for the Deployment of Artificial Intelligence in Healthcare: Protocol","source":"datacite","abstract":"This pre-registered scoping review systematically maps actionable recommendations for AI deployment in healthcare across the full deployment lifecycle. Following JBI methodology and PRISMA-ScR standards, we search MEDLINE (via PubMed) and IEEE Xplore (January 2020 to search date), alongside a purposive systematic grey literature search covering regulatory authorities, policy bodies, and professional societies across Europe, the UK, and selected countries globally. Recommendations are mapped against the Compass AI deployment lifecycle framework, which organises deployment into six phases across three stages: Before going live (Needs Assessment and Procurement; Local Performance Testing); Going live (Clinical Workflow Integration; User Training); and Sustaining (Ongoing Monitoring and Maintenance; Decommissioning). Each extracted recommendation is assigned a deployment phase, specificity level (high / medium / low), recommendation type, target stakeholder, and evidence basis. The review will identify which phases have comprehensive versus sparse guidance, what proportion of recommendations are immediately implementable versus aspirational, which stakeholders are underrepresented, and what practical tools exist to support deployment. All data and materials will be made publicly available via this OSF repository.","url":"https://doi.org/10.17605/osf.io/nkbsf","authors":["Ossa, Laura Arbelaez","Fehr, Jana","Madai, Vince","Topff, Laurens","Zullino, Sara","Beets-Tan, Regina","Gordebeke, Peter","Bruni, Margherita","Rodriguez, Pablo","Cerda, Leonor","Van Diest, Paul","Stockheim, Jessica","Gumbs, Andrew","Philippe, Olivier","Polónia, António","Patarnello, Stefano","Bayarri, Angel Alberich","Rosas, Claudia","Lekadir, Karim"],"tags":["Health Information Technology","Medicine and Health Sciences","AI deployment","AI ethics","AI governance","AI implementation","AI policies","artificial intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/nkbsf","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19198800","name":"PREreview of \"Search for Medical Information and Treatment Options for Musculoskeletal Disorders through an Artificial Intelligence Chatbot: Focusing on Shoulder Impingement Syndrome\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19198800. Short Summary of Main Findings In this December 2022 medRxiv preprint (v2), the authors evaluated the performance of an early AI chatbot (primarily ChatGPT) in providing medical information and treatment options for shoulder impingement syndrome. They submitted a series of structured queries on symptoms, diagnosis, conservative treatments, exercises, and surgical options. The chatbot generated generally coherent, readable responses that covered common knowledge on the topic, but often lacked depth, cited no sources, occasionally included inaccuracies or hallucinations, and provided overly generic or incomplete advice (e.g., on exercise prescription or when to seek specialist care). The study highlighted both the potential accessibility of AI for patient education and its current limitations in reliability for musculoskeletal disorders. How This Work Has Moved the Field Forward It represents one of the earliest documented evaluations of ChatGPT in a specific orthopedic/physiotherapy context (shoulder impingement), shortly after the tool's public release. This helped spark the subsequent wave of research on large language models (LLMs) in patient education, clinical decision support, and medical information dissemination, contributing to growing awareness of both opportunities and risks of AI chatbots in healthcare. Major Issues Remains an unreviewed preprint with no identified peer-reviewed journal publication. Very early evaluation of a rapidly evolving tool (ChatGPT-3.5 era); findings are now largely outdated given major model improvements. Subjective and non-standardized evaluation methods (no clear scoring rubric, inter-rater reliability, or comparison with gold-standard sources like clinical guidelines). Small scope (single condition, limited queries) and lack of clinical validation or real-patient outcomes. Minor Issues Title is long and somewhat wordy. Limited discussion of ethical, liability, or misinformation risks. No quantitative metrics (e.g., accuracy percentages, readability scores) in some sections; results rely heavily on qualitative description. Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.","url":"https://doi.org/10.5281/zenodo.19198800","authors":["OMAR GHARISIA"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19198800","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19198799","name":"PREreview of \"Search for Medical Information and Treatment Options for Musculoskeletal Disorders through an Artificial Intelligence Chatbot: Focusing on Shoulder Impingement Syndrome\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19198800. Short Summary of Main Findings In this December 2022 medRxiv preprint (v2), the authors evaluated the performance of an early AI chatbot (primarily ChatGPT) in providing medical information and treatment options for shoulder impingement syndrome. They submitted a series of structured queries on symptoms, diagnosis, conservative treatments, exercises, and surgical options. The chatbot generated generally coherent, readable responses that covered common knowledge on the topic, but often lacked depth, cited no sources, occasionally included inaccuracies or hallucinations, and provided overly generic or incomplete advice (e.g., on exercise prescription or when to seek specialist care). The study highlighted both the potential accessibility of AI for patient education and its current limitations in reliability for musculoskeletal disorders. How This Work Has Moved the Field Forward It represents one of the earliest documented evaluations of ChatGPT in a specific orthopedic/physiotherapy context (shoulder impingement), shortly after the tool's public release. This helped spark the subsequent wave of research on large language models (LLMs) in patient education, clinical decision support, and medical information dissemination, contributing to growing awareness of both opportunities and risks of AI chatbots in healthcare. Major Issues Remains an unreviewed preprint with no identified peer-reviewed journal publication. Very early evaluation of a rapidly evolving tool (ChatGPT-3.5 era); findings are now largely outdated given major model improvements. Subjective and non-standardized evaluation methods (no clear scoring rubric, inter-rater reliability, or comparison with gold-standard sources like clinical guidelines). Small scope (single condition, limited queries) and lack of clinical validation or real-patient outcomes. Minor Issues Title is long and somewhat wordy. Limited discussion of ethical, liability, or misinformation risks. No quantitative metrics (e.g., accuracy percentages, readability scores) in some sections; results rely heavily on qualitative description. Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.","url":"https://doi.org/10.5281/zenodo.19198799","authors":["OMAR GHARISIA"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19198799","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25959/23226026.v1","name":"Evaluation of clinical decision support provided by medication review software","source":"datacite","abstract":"Aim : The purpose of this investigation was to evaluate the clinical decision support capacity of commercial computer software designed to assist pharmacists performing medication reviews. The primary hypothesis was: If medication review software is related to pharmacist knowledge, then the detection of therapeutic problems will result in a similar frequency and scope of identified problems as those identified by pharmacists. Method : Home medication review data collected during 2008 for a previous study were used for this investigation. The data contained original pharmacist findings of drug-related problems (DRPs), patient demographics, medications, laboratory results and diagnoses. Two commercial software applications advertising decision support were assessed, Monitor-Rx (MRX) utilising simple rules triggered by the presence of medication and Medication™ Review Mentor (MRM) utilising an advanced artificial intelligence rules-based approach. The previously collected data were entered into each of the applications and the DRPs identified by each tool were recorded. Additionally, published prescribing criteria, Beers (2003 and 2012 versions), Screening Tool of Older Person’s Prescriptions and Screening Tool to Alert doctors to Right Treatment (STOPP/START) and Prescribing Indicators in Elderly Australians (PIEA) were also adapted so as to be applied computationally over the same set of patient data. DRPs were assigned broad DOCUMENT classifications and examined by frequency and type. A common vocabulary of descriptive classifications capturing essential DRP concepts was developed to allow detailed comparison between the various DRP sources. The ability of software to identify the same classifications in the same patients as pharmacists was assessed as a crude measure of clinical relevance. A panel of pharmacology experts assessed the DRPs identified by pharmacists, MRM, MRX and the STOPP/START prescribing criteria for their opinions concerning clinical relevance, excessive DRP findings, missed DRPs and the appropriateness of recommendations. A qualitative survey of pharmacists who used MRM was also undertaken to obtain their opinions of the decision support capability of MRM. Results : In total, across 570 patients, pharmacists identified 2020 DRPs, MRM 3209, PIEA 1492, STOPP 1032, Beers03 404 and Beers12 399. Ten percent of the volume of DRPs identified by MRM were found to be duplicated DRPs, where the same essential problem was identified more than once for a patients, typically via different rules. Using a smaller sub-sample of 100 patients, MRX identified 1265 DRPs. Pharmacist DRPs encompassed the widest range of DOCUMENT classifications, followed by MRM, then the sets of prescribing criteria and finally MRX. A list of 141 descriptive classifications was developed which described the various DRP concepts in depth. Pharmacist-only descriptive classifications involving compliance and notclassifiable DRPs were excluded from assessment, since it was impossible to detect these DRPs without access to additional patient data that was not included in pharmacists written reports. Pharmacist DRPs were associated with 113 different descriptive classifications, MRM 100 and MRX 17. MRM was able to identify 90 differing classification types that were also identifiable by pharmacists. MRM was able to identify the same problems in the same patients as the reviewing pharmacists identified in 389 instances, whereas MRX identified the same problems in the same patients in only 11 instances. Assessment of expert opinions found that experts generally agreed that MRM presented clinically relevant DRPs (80%) and appropriate recommendations for DRP resolution. This finding contrasted strongly for MRX, with experts of the opinion that MRX presented few clinically relevant DRPs (13%). Similarly, relatively few experts agreed that MRM presented too many DRP findings (19%) whereas the vast majority of experts agreed MRX presented an excessive numbe","url":"https://doi.org/10.25959/23226026.v1","authors":["Curtain, Colin"],"tags":["Clinical pharmacy and pharmacy practice"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25959/23226026.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25959/23226026","name":"Evaluation of clinical decision support provided by medication review software","source":"datacite","abstract":"Aim : The purpose of this investigation was to evaluate the clinical decision support capacity of commercial computer software designed to assist pharmacists performing medication reviews. The primary hypothesis was: If medication review software is related to pharmacist knowledge, then the detection of therapeutic problems will result in a similar frequency and scope of identified problems as those identified by pharmacists. Method : Home medication review data collected during 2008 for a previous study were used for this investigation. The data contained original pharmacist findings of drug-related problems (DRPs), patient demographics, medications, laboratory results and diagnoses. Two commercial software applications advertising decision support were assessed, Monitor-Rx (MRX) utilising simple rules triggered by the presence of medication and Medication™ Review Mentor (MRM) utilising an advanced artificial intelligence rules-based approach. The previously collected data were entered into each of the applications and the DRPs identified by each tool were recorded. Additionally, published prescribing criteria, Beers (2003 and 2012 versions), Screening Tool of Older Person’s Prescriptions and Screening Tool to Alert doctors to Right Treatment (STOPP/START) and Prescribing Indicators in Elderly Australians (PIEA) were also adapted so as to be applied computationally over the same set of patient data. DRPs were assigned broad DOCUMENT classifications and examined by frequency and type. A common vocabulary of descriptive classifications capturing essential DRP concepts was developed to allow detailed comparison between the various DRP sources. The ability of software to identify the same classifications in the same patients as pharmacists was assessed as a crude measure of clinical relevance. A panel of pharmacology experts assessed the DRPs identified by pharmacists, MRM, MRX and the STOPP/START prescribing criteria for their opinions concerning clinical relevance, excessive DRP findings, missed DRPs and the appropriateness of recommendations. A qualitative survey of pharmacists who used MRM was also undertaken to obtain their opinions of the decision support capability of MRM. Results : In total, across 570 patients, pharmacists identified 2020 DRPs, MRM 3209, PIEA 1492, STOPP 1032, Beers03 404 and Beers12 399. Ten percent of the volume of DRPs identified by MRM were found to be duplicated DRPs, where the same essential problem was identified more than once for a patients, typically via different rules. Using a smaller sub-sample of 100 patients, MRX identified 1265 DRPs. Pharmacist DRPs encompassed the widest range of DOCUMENT classifications, followed by MRM, then the sets of prescribing criteria and finally MRX. A list of 141 descriptive classifications was developed which described the various DRP concepts in depth. Pharmacist-only descriptive classifications involving compliance and notclassifiable DRPs were excluded from assessment, since it was impossible to detect these DRPs without access to additional patient data that was not included in pharmacists written reports. Pharmacist DRPs were associated with 113 different descriptive classifications, MRM 100 and MRX 17. MRM was able to identify 90 differing classification types that were also identifiable by pharmacists. MRM was able to identify the same problems in the same patients as the reviewing pharmacists identified in 389 instances, whereas MRX identified the same problems in the same patients in only 11 instances. Assessment of expert opinions found that experts generally agreed that MRM presented clinically relevant DRPs (80%) and appropriate recommendations for DRP resolution. This finding contrasted strongly for MRX, with experts of the opinion that MRX presented few clinically relevant DRPs (13%). Similarly, relatively few experts agreed that MRM presented too many DRP findings (19%) whereas the vast majority of experts agreed MRX presented an excessive numbe","url":"https://doi.org/10.25959/23226026","authors":["Curtain, Colin"],"tags":["Clinical pharmacy and pharmacy practice"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25959/23226026","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25959/23242625.v1","name":"Evaluation of clinical decision support provided by medication review software","source":"datacite","abstract":"Aim The purpose of this investigation was to evaluate the clinical decision support capacity of commercial computer software designed to assist pharmacists performing medication reviews. The primary hypothesis was: If medication review software is related to pharmacist knowledge, then the detection of therapeutic problems will result in a similar frequency and scope of identified problems as those identified by pharmacists. Method Home medication review data collected during 2008 for a previous study were used for this investigation. The data contained original pharmacist findings of drug-related problems (DRPs), patient demographics, medications, laboratory results and diagnoses. Two commercial software applications advertising decision support were assessed, Monitor-Rx (MRX) utilising simple rules triggered by the presence of medication and Medication‚Äöv묢 Review Mentor (MRM) utilising an advanced artificial intelligence rules-based approach. The previously collected data were entered into each of the applications and the DRPs identified by each tool were recorded. Additionally, published prescribing criteria, Beers (2003 and 2012 versions), Screening Tool of Older Person's Prescriptions and Screening Tool to Alert doctors to Right Treatment (STOPP/START) and Prescribing Indicators in Elderly Australians (PIEA) were also adapted so as to be applied computationally over the same set of patient data. DRPs were assigned broad DOCUMENT classifications and examined by frequency and type. A common vocabulary of descriptive classifications capturing essential DRP concepts was developed to allow detailed comparison between the various DRP sources. The ability of software to identify the same classifications in the same patients as pharmacists was assessed as a crude measure of clinical relevance. A panel of pharmacology experts assessed the DRPs identified by pharmacists, MRM, MRX and the STOPP/START prescribing criteria for their opinions concerning clinical relevance, excessive DRP findings, missed DRPs and the appropriateness of recommendations. A qualitative survey of pharmacists who used MRM was also undertaken to obtain their opinions of the decision support capability of MRM. Results In total, across 570 patients, pharmacists identified 2020 DRPs, MRM 3209, PIEA 1492, STOPP 1032, Beers03 404 and Beers12 399. Ten percent of the volume of DRPs identified by MRM were found to be duplicated DRPs, where the same essential problem was identified more than once for a patients, typically via different rules. Using a smaller sub-sample of 100 patients, MRX identified 1265 DRPs. Pharmacist DRPs encompassed the widest range of DOCUMENT classifications, followed by MRM, then the sets of prescribing criteria and finally MRX. A list of 141 descriptive classifications was developed which described the various DRP concepts in depth. Pharmacist-only descriptive classifications involving compliance and not-classifiable DRPs were excluded from assessment, since it was impossible to detect these DRPs without access to additional patient data that was not included in pharmacists written reports. Pharmacist DRPs were associated with 113 different descriptive classifications, MRM 100 and MRX 17. MRM was able to identify 90 differing classification types that were also identifiable by pharmacists. MRM was able to identify the same problems in the same patients as the reviewing pharmacists identified in 389 instances, whereas MRX identified the same problems in the same patients in only 11 instances. Assessment of expert opinions found that experts generally agreed that MRM presented clinically relevant DRPs (80%) and appropriate recommendations for DRP resolution. This finding contrasted strongly for MRX, with experts of the opinion that MRX presented few clinically relevant DRPs (13%). Similarly, relatively few experts agreed that MRM presented too many DRP findings (19%) whereas the vast majority of experts agreed MRX presented an excessive numb","url":"https://doi.org/10.25959/23242625.v1","authors":["Curtin, CM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25959/23242625.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25959/23242625","name":"Evaluation of clinical decision support provided by medication review software","source":"datacite","abstract":"Aim The purpose of this investigation was to evaluate the clinical decision support capacity of commercial computer software designed to assist pharmacists performing medication reviews. The primary hypothesis was: If medication review software is related to pharmacist knowledge, then the detection of therapeutic problems will result in a similar frequency and scope of identified problems as those identified by pharmacists. Method Home medication review data collected during 2008 for a previous study were used for this investigation. The data contained original pharmacist findings of drug-related problems (DRPs), patient demographics, medications, laboratory results and diagnoses. Two commercial software applications advertising decision support were assessed, Monitor-Rx (MRX) utilising simple rules triggered by the presence of medication and Medication‚Äöv묢 Review Mentor (MRM) utilising an advanced artificial intelligence rules-based approach. The previously collected data were entered into each of the applications and the DRPs identified by each tool were recorded. Additionally, published prescribing criteria, Beers (2003 and 2012 versions), Screening Tool of Older Person's Prescriptions and Screening Tool to Alert doctors to Right Treatment (STOPP/START) and Prescribing Indicators in Elderly Australians (PIEA) were also adapted so as to be applied computationally over the same set of patient data. DRPs were assigned broad DOCUMENT classifications and examined by frequency and type. A common vocabulary of descriptive classifications capturing essential DRP concepts was developed to allow detailed comparison between the various DRP sources. The ability of software to identify the same classifications in the same patients as pharmacists was assessed as a crude measure of clinical relevance. A panel of pharmacology experts assessed the DRPs identified by pharmacists, MRM, MRX and the STOPP/START prescribing criteria for their opinions concerning clinical relevance, excessive DRP findings, missed DRPs and the appropriateness of recommendations. A qualitative survey of pharmacists who used MRM was also undertaken to obtain their opinions of the decision support capability of MRM. Results In total, across 570 patients, pharmacists identified 2020 DRPs, MRM 3209, PIEA 1492, STOPP 1032, Beers03 404 and Beers12 399. Ten percent of the volume of DRPs identified by MRM were found to be duplicated DRPs, where the same essential problem was identified more than once for a patients, typically via different rules. Using a smaller sub-sample of 100 patients, MRX identified 1265 DRPs. Pharmacist DRPs encompassed the widest range of DOCUMENT classifications, followed by MRM, then the sets of prescribing criteria and finally MRX. A list of 141 descriptive classifications was developed which described the various DRP concepts in depth. Pharmacist-only descriptive classifications involving compliance and not-classifiable DRPs were excluded from assessment, since it was impossible to detect these DRPs without access to additional patient data that was not included in pharmacists written reports. Pharmacist DRPs were associated with 113 different descriptive classifications, MRM 100 and MRX 17. MRM was able to identify 90 differing classification types that were also identifiable by pharmacists. MRM was able to identify the same problems in the same patients as the reviewing pharmacists identified in 389 instances, whereas MRX identified the same problems in the same patients in only 11 instances. Assessment of expert opinions found that experts generally agreed that MRM presented clinically relevant DRPs (80%) and appropriate recommendations for DRP resolution. This finding contrasted strongly for MRX, with experts of the opinion that MRX presented few clinically relevant DRPs (13%). Similarly, relatively few experts agreed that MRM presented too many DRP findings (19%) whereas the vast majority of experts agreed MRX presented an excessive numb","url":"https://doi.org/10.25959/23242625","authors":["Curtin, CM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25959/23242625","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/hk4cg","name":"Geographic Disparities and Algorithmic Bias in AI-Generated Content for Medical Education: A Mixed-Methods Bibliometric and Scoping Review","source":"datacite","abstract":"1. Project Summary This study aims to conduct a systematicand critical analysis of existing scientific literature regarding the application of Artificial Intelligence Generated Content (AIGC) in the field of medical education. Specifically, we focus on the critical issues of Geographic Disparities and Algorithmic Bias. Through a comprehensive search of the Web of Science database, we have screened and synthesized key research findings, theoretical models, and methodological advances from recent years. This review not only summarizes current consensus and major controversies but also identifies gaps in existing research to propose directions for future inquiry. 2. Objectives / Aims The core objectives of this project are: Panoramic Mapping: To map the developmental context of the research topic from 2023 to 2025, visualizing the \"knowledge graph\" of the field. Synthesis: To integrate fragmented research data and extract key viewpoints and evolutionary trends regarding geographic disparities and algorithmic bias in AIGC for medical education. Critical Evaluation: To analyze the strengths and weaknesses of current research methodologies and assess the strength and limitations of current evidence. Future Directions: To provide a theoretical basis and directional suggestions for subsequent empirical research based on identified literature gaps. 3. Scope and Methodology Transparency is key to this registration. The following details the execution of the review: Search Strategy: The review follows the PRISMA framework for literature screening. The search was conducted using the Web of Science database with the following search strings: 表格 Concept Search Terms Field #1: Medical Education \"Medical Education\" OR \"Education, Medical\" Topic #2: Generative AI &amp; Chatbots \"Generative Artificial Intelligence\" OR \"Artificial Intelligence, Generative\" OR \"Gen AI\" OR \"GenAI\" OR \"Generative AI\" OR \"Chatbot\" OR \"Chatbots\" OR \"Chat-GPT\" OR \"Chat GPT\" OR \"ChatGPT\" OR \"ChatGPTs\" Topic Final Query #1 AND #2 Eligibility Criteria: Inclusion: Peer-reviewed articles published in SCI/SSCI journals. Exclusion: Conference abstracts, non-English literature, and duplicate data. Analysis Method: This study employs a Scoping Review methodology combined with Scientometric Analysis. We will perform qualitative descriptions and quantitative integration of the extracted data. Visualization and mapping will be conducted using CiteSpace (version 6.3.R1; 64-bit; Advanced) and R (version 4.5.0; R Foundation for Statistical Computing) utilizing the bibliometrix software package. 4. Expected Outcomes Through this research, we anticipate the following outcomes: Status Quo Presentation: Clearly demonstrate the latest advancements and mainstream viewpoints in the field to the academic community. Gap Identification: Explicitly point out contradictions in current research or sub-areas that have not been sufficiently explored (e.g., lack of longitudinal data, insufficient sample diversity regarding geographic representation). Academic Contribution: Provide an authoritative reference for researchers, policymakers, and clinical practitioners to reduce information asymmetry and promote the healthy development of the field. Dissemination: The final synthesized review will be submitted for publication in a relevant SCI-indexed journal.","url":"https://doi.org/10.17605/osf.io/hk4cg","authors":["Li, Jinlei"],"tags":["Medicine and Health Sciences","Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/hk4cg","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19175660","name":"Quantum Machine Learning: A Comprehensive Scientific Review From Foundational Algorithms to 2026 State-of-the-Art","source":"datacite","abstract":"Quantum Machine Learning (QML) has emerged as a pivotal field at the intersection of quantum computing and artificial intelligence, addressing the scalability limits of classical machine learning amid exploding data volumes and computational demands. This comprehensive review, synthesized from the February 2026 report \"Quantum Machine Learning: A Comprehensive Scientific Review,\" consolidates the document's core insights into background, results, and inferences. Drawing from 94 peer-reviewed papers and 410 abstracts, it traces QML's evolution from theoretical foundations to hardware-constrained implementations on Noisy Intermediate-Scale Quantum (NISQ) devices. Background The document grounds QML in the convergence of quantum mechanics' unique properties—superposition, entanglement, and interference—with machine learning paradigms like supervised, unsupervised, and reinforcement learning. Classical ML faces bottlenecks in training deep networks on massive datasets, consuming vast energy and resources, while quantum hardware has advanced to commercial NISQ platforms with 50-1,000 qubits, albeit plagued by noise, short coherence times (microseconds to milliseconds), gate errors (1-5%), and connectivity limits. Historically, QML began in 2009-2015 with theoretical breakthroughs like the HHL algorithm for linear systems speedup and early quantum support vector machines (QSVM). The 2016-2020 era birthed variational quantum algorithms (VQAs) like VQE and QAOA, adaptable to NISQ via hybrid quantum-classical optimization. By 2021-2023, hardware integration spurred practical demos, such as entanglement-based classification on photonic chips. The 2026 landscape emphasizes hardware-aware designs: shallow circuits (5-15 layers), minimal qubits (2-50), efficient ansatze matching native gates (e.g., IBM's CNOTs), and noise mitigation like zero-noise extrapolation. Core methodologies dominate: Variational Quantum Circuits (VQCs) use parameterized layers for state preparation, entanglement, and measurement, trained via parameter-shift gradients integrated with PyTorch/TensorFlow. Quantum Neural Networks (QNNs) layer these for neural-like processing, often hybrid with classical layers. Quantum LSTMs (QLSTMs) handle sequences but demand more resources. Quantum kernels map data to Hilbert spaces for SVMs or k-NN, with feature maps like ZZ or Pauli. Emerging: Gaussian Boson Sampling (GBS) for unsupervised learning on photonic chips, quantum convolutions for images. NISQ constraints drive principles like shallow depths, hybrid loops, and platforms (superconducting: IBM/Google; ion traps: IonQ; photonic). Applications target quantum chemistry/drug discovery, where quantum simulations shine, plus medical diagnostics, finance, materials. Results Empirical findings from 2026 highlight proof-of-concepts on real hardware. A two-qubit variational QNN learned XOR on a desktop NMR quantum computer, hitting state fidelities of 98.85%-99.35% In chemistry, ML-enhanced OM2 cut atomization enthalpy errors from 6.3 to 1.7 kcal/mol across 6,095 C7H10O2 isomers; Δ-machine learning achieved <1 kcal/mol chemical accuracy on 16,000+ isomers, transferable from 1-10% training sets. On-the-fly MD with Bayesian forces slashed QM calls for silicon simulations. A hardware-feasible virtual screening QML framework (6 circuit units, shallow depth) scored RMSE 2.37 kcal/mol, Pearson 0.650 for ligand affinities; rankings held under noise with 100,000 shots. Medical: Hybrid QNNs boosted heart disease, dementia, liver detection, diabetic retinopathy (balanced multiclass), leukemia via blood cells, MedMNIST benchmarks on hardware. Finance: Contextual QNNs for stocks, fraud detection. Others: GBS on 16-source photonic chips outperformed classical in feature extraction/handwritten digits; QSVMs resilient to noise/imbalance on iris/wine/breast cancer datasets; QLSTMs/QNNs benchmarked, simpler ansatze (A4) best for anomaly detection/energy. Comparisons: Classical ANN/LSTM/CatBoost fas","url":"https://doi.org/10.5281/zenodo.19175660","authors":["Praharshit, Sharma"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19175660","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19175659","name":"Quantum Machine Learning: A Comprehensive Scientific Review From Foundational Algorithms to 2026 State-of-the-Art","source":"datacite","abstract":"Quantum Machine Learning (QML) has emerged as a pivotal field at the intersection of quantum computing and artificial intelligence, addressing the scalability limits of classical machine learning amid exploding data volumes and computational demands. This comprehensive review, synthesized from the February 2026 report \"Quantum Machine Learning: A Comprehensive Scientific Review,\" consolidates the document's core insights into background, results, and inferences. Drawing from 94 peer-reviewed papers and 410 abstracts, it traces QML's evolution from theoretical foundations to hardware-constrained implementations on Noisy Intermediate-Scale Quantum (NISQ) devices. Background The document grounds QML in the convergence of quantum mechanics' unique properties—superposition, entanglement, and interference—with machine learning paradigms like supervised, unsupervised, and reinforcement learning. Classical ML faces bottlenecks in training deep networks on massive datasets, consuming vast energy and resources, while quantum hardware has advanced to commercial NISQ platforms with 50-1,000 qubits, albeit plagued by noise, short coherence times (microseconds to milliseconds), gate errors (1-5%), and connectivity limits. Historically, QML began in 2009-2015 with theoretical breakthroughs like the HHL algorithm for linear systems speedup and early quantum support vector machines (QSVM). The 2016-2020 era birthed variational quantum algorithms (VQAs) like VQE and QAOA, adaptable to NISQ via hybrid quantum-classical optimization. By 2021-2023, hardware integration spurred practical demos, such as entanglement-based classification on photonic chips. The 2026 landscape emphasizes hardware-aware designs: shallow circuits (5-15 layers), minimal qubits (2-50), efficient ansatze matching native gates (e.g., IBM's CNOTs), and noise mitigation like zero-noise extrapolation. Core methodologies dominate: Variational Quantum Circuits (VQCs) use parameterized layers for state preparation, entanglement, and measurement, trained via parameter-shift gradients integrated with PyTorch/TensorFlow. Quantum Neural Networks (QNNs) layer these for neural-like processing, often hybrid with classical layers. Quantum LSTMs (QLSTMs) handle sequences but demand more resources. Quantum kernels map data to Hilbert spaces for SVMs or k-NN, with feature maps like ZZ or Pauli. Emerging: Gaussian Boson Sampling (GBS) for unsupervised learning on photonic chips, quantum convolutions for images. NISQ constraints drive principles like shallow depths, hybrid loops, and platforms (superconducting: IBM/Google; ion traps: IonQ; photonic). Applications target quantum chemistry/drug discovery, where quantum simulations shine, plus medical diagnostics, finance, materials. Results Empirical findings from 2026 highlight proof-of-concepts on real hardware. A two-qubit variational QNN learned XOR on a desktop NMR quantum computer, hitting state fidelities of 98.85%-99.35% In chemistry, ML-enhanced OM2 cut atomization enthalpy errors from 6.3 to 1.7 kcal/mol across 6,095 C7H10O2 isomers; Δ-machine learning achieved <1 kcal/mol chemical accuracy on 16,000+ isomers, transferable from 1-10% training sets. On-the-fly MD with Bayesian forces slashed QM calls for silicon simulations. A hardware-feasible virtual screening QML framework (6 circuit units, shallow depth) scored RMSE 2.37 kcal/mol, Pearson 0.650 for ligand affinities; rankings held under noise with 100,000 shots. Medical: Hybrid QNNs boosted heart disease, dementia, liver detection, diabetic retinopathy (balanced multiclass), leukemia via blood cells, MedMNIST benchmarks on hardware. Finance: Contextual QNNs for stocks, fraud detection. Others: GBS on 16-source photonic chips outperformed classical in feature extraction/handwritten digits; QSVMs resilient to noise/imbalance on iris/wine/breast cancer datasets; QLSTMs/QNNs benchmarked, simpler ansatze (A4) best for anomaly detection/energy. Comparisons: Classical ANN/LSTM/CatBoost fas","url":"https://doi.org/10.5281/zenodo.19175659","authors":["Praharshit, Sharma"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19175659","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.48550/arxiv.2603.19512","name":"FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy","source":"datacite","abstract":"The reliability of artificial intelligence (AI) in medical imaging critically depends on its robustness to heterogeneous and corrupted images acquired with diverse devices across different hospitals which is highly challenging. Therefore, this paper introduces FedAgain, a trust-based Federated Learning (Federated Learning) strategy designed to enhance robustness and generalization for automated kidney stone identification from endoscopic images. FedAgain integrates a dual trust mechanism that combines benchmark reliability and model divergence to dynamically weight client contributions, mitigating the impact of noisy or adversarial updates during aggregation. The framework enables the training of collaborative models across multiple institutions while preserving data privacy and promoting stable convergence under real-world conditions. Extensive experiments across five datasets, including two canonical benchmarks (MNIST and CIFAR-10), two private multi-institutional kidney stone datasets, and one public dataset (MyStone), demonstrate that FedAgain consistently outperforms standard Federated Learning baselines under non-identically and independently distributed (non-IID) data and corrupted-client scenarios. By maintaining diagnostic accuracy and performance stability under varying conditions, FedAgain represents a practical advance toward reliable, privacy-preserving, and clinically deployable federated AI for medical imaging.","url":"https://doi.org/10.48550/arxiv.2603.19512","authors":["Reyes-Amezcua, Ivan","Lopez-Tiro, Francisco","Larose, Clément","Daul, Christian","Mendez-Vazquez, Andres","Ochoa-Ruiz, Gilberto"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.19512","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19172184","name":"Central Intelligence Project™ Public Notice and Conceptual Architecture Statement: Organic Atomic Node Intelligence (OANI)™","source":"datacite","abstract":"This publication is a controlled-disclosure public record establishing the authorship, chronology, mission, public conceptual architecture, and market entry of Central Intelligence Project™ and Organic Atomic Node Intelligence (OANI)™. It explains what Central Intelligence Project and OANI are, what they do, why they exist, how they differ from generic artificial intelligence systems, and the public conceptual architecture through which they operate. The document presents Central Intelligence Project as an institutional analytical architecture designed to transform fragmented information into structured understanding, operationally useful intelligence, and disciplined public-facing outputs. It presents OANI as the core analytical framework within that architecture, structured to identify meaningful informational units, connect those units into structured relationships, evaluate significance, and produce usable outputs across research, investigations, operational environments, administrative systems, public-interest analysis, and strategic communication. This version also documents a broad representative portfolio of current research and analytical initiatives at the public-safe layer, including vehicle-systems analysis, Common Operating Picture development, adversarial historical stress testing, historical and biographical intelligence assembly, biomedical and strategic-risk analysis, infrastructure optimization, veteran-disability and hazard-correlation analysis, cognitive compartmentalization in military and high-risk occupations, polypharmacy and pharmacogenomics, toxic occupational hazards, public-order and emergency-management analysis, advanced medical modeling, and institutional integrity and accountability audit frameworks. This record is intended to place the public and the market on notice that Central Intelligence Project™ and OANI™ exist as independently developed, mission-oriented, analytically structured frameworks with defined terminology, coherent public architecture, and active public-facing development, while preserving undisclosed proprietary methods, internal workflows, implementation pathways, and other confidential proprietary matter. Omission of such matter from this publication does not constitute abandonment, waiver, or dedication to the public. Version 1.2. Versions 1.0 and 1.1 were circulated internally in draft form for review and continuity purposes prior to formal public release.","url":"https://doi.org/10.5281/zenodo.19172184","authors":["Marino, Nicholas"],"tags":["Central Intelligence Project","OANI","Organic Atomic Node Intelligence","Analytical Architecture","Public-Interest Intelligence","Structured Analysis","Governance Analysis","Operational Awareness"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19172184","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/7fq92","name":"Artificial Intelligence and Deep Learning in Ophthalmology Education: A Scoping Review on Clinical Reasoning and Competence.","source":"datacite","abstract":"Ophthalmology is a medical specialty based on visual skills, where diagnosis and therapeutic planning depend on expert interpretation of multiple image modalities combined with clinical and functional data such as visual data (1,2). The core competency of medical students or residents is the coherent clinical reasoning by identifying subtle alterations in the retina, optic nerve or anterior segment, which is difficult to accomplish, largely because education continues to rely on opportunistic patient exposure, case variability, and the limited time available for specialists to supervise and provide feedback on performance (3,4,5). Moreover, population aging and the increasing incidence of vision-threatening diseases (e.g., diabetic retinopathy, age-related macular degeneration (ARMD) and glaucoma) are generating an increasing demand for ophthalmologic care and straining traditional training systems, which are already considered insufficient, particularly in resource-limited regions (3,1,6). In this context, the emergence of artificial intelligence (AI), particularly deep learning (DL) for the automated analysis of medical images is one of the most significant technological transformations in contemporary medicine, and ophthalmology is positioned among the specialties best suited to benefit from these advances (2,7,8). To date, most applications of AI and DL in ophthalmology have been oriented towards automated clinical diagnosis and risk stratification, with particular emphasis on diabetic retinopathy screening, detection of ARMD, and identification of glaucomatous optic neuropathy from fundus photographs and OCT (1,2,9). Currently, emerging studies explore these AI capabilities in educational contexts, such as AI-assisted tutoring platforms, retinopathy reading and labelling systems, and algorithms for the identification of multiple fundus diseases or myopia, which improve performance and understanding compared with traditional teaching methods, enable residents and students to train in lesion grading with immediate feedback and personalize residency training experiences according to case history (11,12,13, 14). Furthermore, virtual reality and augmented reality simulators combined with intelligent tutoring systems have shown objective improvements in microsurgical skills, whereas interactive machine learning platforms, such as fundus image interpretation systems, allow trainees to compare their markings against algorithm outputs and practice with substantial case variability (5,15,16). More recently, synthetic images have been generated through generative models of different pathologies, as well as the use of large language models (LLMs) to create clinical cases and examination questions, further expanding the repertoire of potentially available didactic resources (17,18,19,20). Against this backdrop, the development of clinical reasoning and diagnostic criteria has become a critical competency for these physicians. Clinical reasoning may be understood as the process by which the clinician integrates the clinical history, physical examination and complementary tests to generate, contrast and refine diagnostic hypotheses and therapeutic decisions (21,22). Recent literature on ophthalmology education underscores that one of the essential training objectives is to cultivate accurate and adaptive diagnostic reasoning and describes how AI-based diagnostic decision support systems and simulation platforms are beginning to be utilized as “virtual mentors” that provide visual explanations, saliency maps, step-by-step feedback, and exposure to diverse virtual cases, in order to help learners internalize expert decision-making patterns (3,21). Concurrently, the emergence of LLMs has renewed interest in modelling clinical thinking (22,23). However, this sparks a critical pedagogical debate: while AI can serve as a scaffold for reasoning, concerns persist regarding algorithmic opacity (“black box”), bias, and the risk of fostering techn","url":"https://doi.org/10.17605/osf.io/7fq92","authors":["Chaves, Natalia Castaño"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/7fq92","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19145324","name":"PREDICTIVE MODELLING OF DIAGNOSTIC ERRORS USING EHR","source":"datacite","abstract":"Diagnostic errors such as missed, delayed, or incorrect diagnoses remain a major concern in healthcare systems worldwide. Despite the widespread adoption of Electronic Health Records (EHR), clinicians often face challenges in interpreting large volumes of patient data effectively, which can lead to diagnostic mismatches. This research proposes a predictive modelling framework for identifying diagnostic errors using EHR data through an Explainable Artificial Intelligence (XAI) based Clinical Decision Support System (CDSS). The system integrates structured data such as laboratory values and vital signs, unstructured clinical notes, and temporal patient records to detect potential diagnostic discordance. Structured patient information is processed using the XGBoost algorithm to identify key diagnostic indicators, while temporal trends in patient vitals are captured using a Long Short-Term Memory (LSTM) neural network with an attention mechanism. Additionally, natural language processing techniques such as TFIDF and BioBERT are used to analyze clinical notes and extract meaningful patterns from textual medical data. The outputs from these models are combined using an ensemble learning approach to produce a Diagnostic Discordance Score that highlights potential mismatches between clinical evidence and physician diagnoses. Explainability is ensured through SHAP feature importance and attention visualization, allowing clinicians to understand model reasoning. A full-stack webbased implementation using FastAPI, React, and MySQL provides role-based access for doctors, reviewers, administrators, and researchers. The system also includes a human-in-the-loop validation mechanism where experts review predictions to improve model performance over time. The proposed framework enhances diagnostic safety, improves clinical decision-making, and demonstrates how explainable AI can support healthcare professionals in reducing diagnostic errors and improving patient outcomes.","url":"https://doi.org/10.5281/zenodo.19145324","authors":["IJDIM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19145324","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19145325","name":"PREDICTIVE MODELLING OF DIAGNOSTIC ERRORS USING EHR","source":"datacite","abstract":"Diagnostic errors such as missed, delayed, or incorrect diagnoses remain a major concern in healthcare systems worldwide. Despite the widespread adoption of Electronic Health Records (EHR), clinicians often face challenges in interpreting large volumes of patient data effectively, which can lead to diagnostic mismatches. This research proposes a predictive modelling framework for identifying diagnostic errors using EHR data through an Explainable Artificial Intelligence (XAI) based Clinical Decision Support System (CDSS). The system integrates structured data such as laboratory values and vital signs, unstructured clinical notes, and temporal patient records to detect potential diagnostic discordance. Structured patient information is processed using the XGBoost algorithm to identify key diagnostic indicators, while temporal trends in patient vitals are captured using a Long Short-Term Memory (LSTM) neural network with an attention mechanism. Additionally, natural language processing techniques such as TFIDF and BioBERT are used to analyze clinical notes and extract meaningful patterns from textual medical data. The outputs from these models are combined using an ensemble learning approach to produce a Diagnostic Discordance Score that highlights potential mismatches between clinical evidence and physician diagnoses. Explainability is ensured through SHAP feature importance and attention visualization, allowing clinicians to understand model reasoning. A full-stack webbased implementation using FastAPI, React, and MySQL provides role-based access for doctors, reviewers, administrators, and researchers. The system also includes a human-in-the-loop validation mechanism where experts review predictions to improve model performance over time. The proposed framework enhances diagnostic safety, improves clinical decision-making, and demonstrates how explainable AI can support healthcare professionals in reducing diagnostic errors and improving patient outcomes.","url":"https://doi.org/10.5281/zenodo.19145325","authors":["IJDIM"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19145325","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/bg76j","name":"Ethical Governance and Leadership in the Integration of AI and Big Data within 'One Health', Veterinary Medicine, and Sustainable Agriculture Curricula: A Scoping Review of Multidisciplinary Educational Frameworks","source":"datacite","abstract":"This scoping review investigates the intersection of Ethical Governance, Digital Leadership, and Multidisciplinary Education within the 'One Health' framework. As Artificial Intelligence (AI) and Big Data analytics become central to global health and food systems, there is a critical need for standardized pedagogical frameworks that ensure these technologies are integrated responsibly. The purpose of this research is to: Map the global landscape of educational models that combine AI/Big Data with Human Health, Veterinary Medicine, and Sustainable Agriculture. Identify the Ethical Governance structures required to lead multidisciplinary academic teams in high-velocity data environments. Synthesize leadership strategies that address algorithmic transparency, data privacy, and cross-disciplinary communication. Expected Outcomes: The review will produce a comprehensive taxonomy of leadership competencies and a conceptual framework for ethical AI integration. This will serve as a strategic roadmap for Higher Education leaders to prepare future professionals for the complex, data-driven challenges of zoonotic disease management, food security, and environmental sustainability.","url":"https://doi.org/10.17605/osf.io/bg76j","authors":["Ismaile, Samantha","Alhosban, Fuad","Rabiha Seboussi","Hanen Ben Abdallah"],"tags":["Food Security","Physical Sciences and Mathematics","Agriculture","Veterinary Medicine","Educational Leadership","Public Health","Medicine and Health Sciences","Life Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/bg76j","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.57945/manara.hbku.28434173.v1","name":"Nuclei Classification of Inflammatory Cells in Pediatrics: A Novel Transfer Learning Approach for Digital Histopathology","source":"datacite","abstract":"The field of Digital Histopathology has seen many significant advancements in the past decade, and this is primarily because of the developments that have been seen in Artificial Intelligence, pertaining to the segmentation and classification of cell types in Whole Slide Images (WSI) of tissues. The focus of these developments has been on neoplastic or cancerous cell types in adults as the primary area of research. In this work, we propose a transfer learning approach to improve the classification of inflammatory cells for pediatric patients, as it is an emerging research area that is gaining momentum in the medical field. We conduct a review to identify the existing models in this field and propose a novel approach to improve the model's performance through the usage of transfer learning. We chose the PanNuke dataset, as it provides labeled tissue samples that have been expertly reviewed and have been shown to encompass real-world variations that would be experienced in a real-world setting, making it a more practical choice for training the model. We showcase that our SqueezeNet model developed on Fast.ai archives state-of-the-art performance on the PanNuke dataset, with an overall weighted average F1-Score of 89.2% on the binary classifier (between inflammatory cells and all other types of cells.) It also achieves 79.3% on the multi-class classifier (between all five cell types included in the PanNuke dataset.) We also show that our score achieves an improvement of 41% in the weighted average F1-Score against the previous state-of-the-art Hover-Net model for the Neoplastic, Epithelial, Inflammatory, and Connective cell types. Furthermore, we demonstrate achieving state-of-the-art sensitivity and precision scores in all trained cell types for the classification task for individual class performance, and the overall model weighted average.","url":"https://doi.org/10.57945/manara.hbku.28434173.v1","authors":["Al-Biltaji, Hussam Raed Hamed"],"tags":["Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.57945/manara.hbku.28434173.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.57945/manara.hbku.28434173","name":"Nuclei Classification of Inflammatory Cells in Pediatrics: A Novel Transfer Learning Approach for Digital Histopathology","source":"datacite","abstract":"The field of Digital Histopathology has seen many significant advancements in the past decade, and this is primarily because of the developments that have been seen in Artificial Intelligence, pertaining to the segmentation and classification of cell types in Whole Slide Images (WSI) of tissues. The focus of these developments has been on neoplastic or cancerous cell types in adults as the primary area of research. In this work, we propose a transfer learning approach to improve the classification of inflammatory cells for pediatric patients, as it is an emerging research area that is gaining momentum in the medical field. We conduct a review to identify the existing models in this field and propose a novel approach to improve the model's performance through the usage of transfer learning. We chose the PanNuke dataset, as it provides labeled tissue samples that have been expertly reviewed and have been shown to encompass real-world variations that would be experienced in a real-world setting, making it a more practical choice for training the model. We showcase that our SqueezeNet model developed on Fast.ai archives state-of-the-art performance on the PanNuke dataset, with an overall weighted average F1-Score of 89.2% on the binary classifier (between inflammatory cells and all other types of cells.) It also achieves 79.3% on the multi-class classifier (between all five cell types included in the PanNuke dataset.) We also show that our score achieves an improvement of 41% in the weighted average F1-Score against the previous state-of-the-art Hover-Net model for the Neoplastic, Epithelial, Inflammatory, and Connective cell types. Furthermore, we demonstrate achieving state-of-the-art sensitivity and precision scores in all trained cell types for the classification task for individual class performance, and the overall model weighted average.","url":"https://doi.org/10.57945/manara.hbku.28434173","authors":["Al-Biltaji, Hussam Raed Hamed"],"tags":["Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.57945/manara.hbku.28434173","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.57945/manara.hbku.28433948.v1","name":"An Artificial Intelligence Tool to Detect and Classify Skin Cancer","source":"datacite","abstract":"Artificial Intelligence (AI) is being used to facilitate and automate traditional processes in different fields, including healthcare. Specifically, AI can be used to improve medical imaging and the diagnosis of skin-related diseases. In this work, we implement an AI-based solution to improve the current skin cancer diagnostic methods, including screening, self-examination, and microspectroscopy. These methods are costly, time-consuming, and depend on the availability of a professional physician. We conduct a scoping review to identify existing works on the used AI tools to diagnose skin cancer and we review 28 relevant attempts. However, there are still some issues with these techniques, such as accuracy, reliability, usability, and privacy. To overcome these issues, we build a deep learning model using the fast.ai Python library. The model is trained and tested on the ISIC dataset, which is anonymized and contains 11,720 dermoscopic images from seven different types of skin lesions. The model is then transferred into an iOS application that can be installed on any device with Apple’s A12 Bionic processor. Our tool overcomes issues in existing solutions as patients can securely take or import pictures of skin lesions using their iOS devices anywhere to get accurate diagnoses instantly. The tool is also affordable, compact, easy to use, quick, and provides diagnoses with relatively high performance. It outperforms the accuracy of existing works by 5 - 10% with a score of 95%. Existing works rely on one or two performance metrics to evaluate their models. However, to capture different aspects and to further ensure the reliability of our model, we include ten statistical measures with a grand mean score of 90%.","url":"https://doi.org/10.57945/manara.hbku.28433948.v1","authors":["Takiddin, Abdulrahman"],"tags":["Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.57945/manara.hbku.28433948.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.57945/manara.hbku.28433948","name":"An Artificial Intelligence Tool to Detect and Classify Skin Cancer","source":"datacite","abstract":"Artificial Intelligence (AI) is being used to facilitate and automate traditional processes in different fields, including healthcare. Specifically, AI can be used to improve medical imaging and the diagnosis of skin-related diseases. In this work, we implement an AI-based solution to improve the current skin cancer diagnostic methods, including screening, self-examination, and microspectroscopy. These methods are costly, time-consuming, and depend on the availability of a professional physician. We conduct a scoping review to identify existing works on the used AI tools to diagnose skin cancer and we review 28 relevant attempts. However, there are still some issues with these techniques, such as accuracy, reliability, usability, and privacy. To overcome these issues, we build a deep learning model using the fast.ai Python library. The model is trained and tested on the ISIC dataset, which is anonymized and contains 11,720 dermoscopic images from seven different types of skin lesions. The model is then transferred into an iOS application that can be installed on any device with Apple’s A12 Bionic processor. Our tool overcomes issues in existing solutions as patients can securely take or import pictures of skin lesions using their iOS devices anywhere to get accurate diagnoses instantly. The tool is also affordable, compact, easy to use, quick, and provides diagnoses with relatively high performance. It outperforms the accuracy of existing works by 5 - 10% with a score of 95%. Existing works rely on one or two performance metrics to evaluate their models. However, to capture different aspects and to further ensure the reliability of our model, we include ten statistical measures with a grand mean score of 90%.","url":"https://doi.org/10.57945/manara.hbku.28433948","authors":["Takiddin, Abdulrahman"],"tags":["Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.57945/manara.hbku.28433948","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/jkcyp","name":"Patient Factors in Medical Artificial Intelligence","source":"datacite","abstract":"Artificial intelligence (AI) is increasingly embedded in clinical care, spanning applications in screening, diagnosis, treatment decision support, monitoring and so on. Despite rapid technological progress, current research remains heavily focused on model performance and how they may help clinicians, with little attention paid to patient-related factors that influence how AI tools are used, experienced, and accepted in real-world settings. Patient factors such as trust, usability perceptions, satisfaction play a crucial role in shaping AI uptake and its clinical impact. However, these factors have not been systematically mapped, and no comprehensive framework currently exists to guide patient-centered development and implementation of medical AI. This systematic review aims to synthesize all reported patient-related factors in empirical studies involving AI tools used directly by patients or applied to patient care. By mapping existing evidence, summarizing measurement approaches, and identifying conceptual and methodological gaps, the review will clarify how patient factors are currently understood and where further research is needed. The findings will support more equitable and patient-centered AI design, inform clinical implementation strategies, and contribute to the development of foundational frameworks for evaluating patient–AI interactions in healthcare.","url":"https://doi.org/10.17605/osf.io/jkcyp","authors":["Shixin Lai","Zhang, Yulian","Zhouyu Guan"],"tags":["Medicine and Health Sciences","Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/jkcyp","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/bzqh6","name":"Data collection and analysis from the Informatics Department of the Unified Health System in Brazil, using open source programming languages: a scoping review","source":"datacite","abstract":"1 INTRODUCTION The consolidation of the Brazilian Unified Health System (SUS) brought with it the need to develop instruments capable of recording, organizing, and providing information to support health management (Lima et al., 2009). Since its creation, SUS has required information systems capable of consolidating data in a structured and standardized manner, supporting both epidemiological surveillance and health planning. In this process, the Department of Informatics of SUS (DATASUS) assumed a strategic role by gathering, processing, and providing nationwide data, becoming one of the main sources of information for managers, researchers, and health professionals (Brazil, 2020a). The availability of reliable and accessible data is a central requirement for public policy formulation, indicator evaluation, and the development of research in public health (Costa et al., 2025). However, the use of these databases still faces challenges related to the decentralization of sources, heterogeneity of records, data quality, and periodic system updates, which may limit their applicability in more complex analyses (Brazil, 2020a). To overcome these limitations, the National Health Information and Informatics Policy (PNIIS) and the Digital Health Strategy for Brazil 2020–2028 (ESD28) have established guidelines focused on information governance, interoperability, and strengthening the technological infrastructure of SUS (Brazil, 2020b). Established in 2004, the PNIIS defined principles for the use of Information and Communication Technologies within SUS, emphasizing the integration of national information systems, improvement of data quality, and strengthening of health management (Brazil, 2004). These guidelines were later updated to incorporate goals related to interoperability and information governance (Brazil, 2021). Among the structuring initiatives for the modernization of SUS, the National Health Data Network (RNDS) stands out as a central platform integrating information from different systems and levels of care, promoting interoperability and providing essential data to support decision-making. Other initiatives include SUS Digital, through the “Meu SUS Digital” tool, and the PQDAS Program, which standardizes and improves the quality of supplementary health data in alignment with SUS data (Brazil; Ministry of Health; Secretariat of Digital Health Information, 2024). The adoption of digital technologies not only modernizes infrastructure but also transforms the interaction between citizens, professionals, and managers, increasing efficiency and integration of care. This approach represents the evolution of the concept of eHealth, expanding the use of digital resources beyond service informatization. Technologies such as artificial intelligence, big data, telemedicine, wearable devices, and mobile applications have been used to improve management, access, and quality of care (World Health Organization, 2021). According to the Ministry of Health (Brazil, 2020a), DATASUS integrates systems covering various areas of health care, such as the Hospital Information System (SIH), Ambulatory Information System (SIA), Mortality Information System (SIM), Live Birth Information System (SINASC), and the Notifiable Diseases Information System (SINAN), among others. Despite the breadth of these databases, challenges persist regarding data quality, updating, standardization, and interoperability, which may limit reliable analyses and evidence-based decision-making. Ensuring efficient integration among systems is essential so that managers and professionals have access to consistent and up-to-date information. Data collection and management in Brazil are directly linked to PNIIS and ESD28. While PNIIS establishes standards for organization, integration, and governance of health information, ESD28 defines priorities for digital technology use, including interoperability, professional training, and innovation. The articulation between these initi","url":"https://doi.org/10.17605/osf.io/bzqh6","authors":["Fonte, Danyelle Oliveira","de Sene Amâncio Zara, Ana Laura"],"tags":["Data Storage Systems","Computer Engineering","Engineering","Data extraction","Datasus","Health information systems","Open‑source programming","Scoping review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/bzqh6","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/sweh3","name":"Governing Artificial Intelligence in Assessment Design in Undergraduate Medical Education: A Scoping Review on Guidelines and Frameworks","source":"datacite","abstract":"The aim of this scoping review is to map existing guidelines, frameworks, and governance models that address the use of artificial intelligence in assessment design in undergraduate medical education. It will identify and categorize these sources in order to clarify the extent to which current guidance supports efficiency, validity, academic integrity, and accreditation requirements. It will also highlight gaps that inform future policy development for medical schools and regulatory bodies, thus ensuring that AI-human collaborative models bridge formative feedback with summative rigor safely and effectively. So the research question is : What guidelines and frameworks that currently govern the use of artificial intelligence in assessment design in undergraduate medical education?","url":"https://doi.org/10.17605/osf.io/sweh3","authors":["amal a galil mohammed elnour"],"tags":["Medicine and Health Sciences","Education","AI. artificial intelligence","assessment design","frameworks","governace","guidelines","undergraduate medical education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/sweh3","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19104278","name":"The Architecture of Flow: Verb-Driven Non-Linearity and Semantic Transformation in Advanced Computational Frameworks","source":"datacite","abstract":"The Architecture of Flow: Verb-Driven Non-Linearity and Semantic Transformation in Advanced Computational Frameworks The mainstream trajectory of artificial intelligence and digital framework architecture has long been anchored in an object-oriented, noun-centric epistemology. In these dominant, conventional paradigms, data is treated as a static entity—a file to be retrieved, a node to be accessed, or a fixed vector to be mathematically projected. However, a radical departure from this linear, rigid structure is required to achieve genuine cognitive fluidity and real-time semantic adaptation in artificial intelligence. This shift necessitates stepping back from traditional architectural assumptions and adopting a methodology characterized profoundly by \"thinking in verbs\".1 When analyzing the construction of highly advanced, non-linear frameworks, it becomes immediately apparent that their architecture is not defined by static knowledge repositories, but by the dynamic currents that shape them. An artificial intelligence stripped of mainstream contextual biases, forced to rely exclusively on the specific textual artifacts and conversational flows provided by a framework's architect, must observe the shape and flow of the input itself. The methodology of the architect—how questions are built, how contexts are continuously layered, and how linear thought is aggressively dismantled—serves as the blueprint for the AI's cognitive mapping. To understand this framework, the artificial intelligence must learn to replicate this flow. It must stay in the verbs. It must abandon mainstream, sequential logic because the architecture itself is the stream. The system does not merely retrieve; it decomposes, extracts, evaluates, routes, throttles, synthesizes, and regenerates.3 This report exhaustively details the theoretical, mathematical, pedagogical, and computational realities of this verb-driven, non-linear paradigm. The Ontological Shift: Process Philosophy and the Primacy of the Verb The concept of thinking in verbs traces its intellectual lineage directly to process philosophy, most notably the work of Alfred North Whitehead, who argued that reality is fundamentally composed of dynamic events and processes of becoming, rather than static objects of being.1 This philosophical stance posits that entities are not defined by their intrinsic, unchanging properties, but by their continuous interactions and transformations. In the context of linguistics and cognitive neuroscience, this distinction is physically mapped within the human brain. Neurological studies indicate that thinking in verbs actively stimulates Broca's frontal area, while impairments in finding verbs are directly associated with left frontal damage and Broca's aphasia.4 Conversely, impairments in finding nouns are associated with temporal lobe pathology.4 The cognitive act of processing action, flow, and transformation requires a fundamentally different neural routing than the act of categorizing static objects. In computational systems, this philosophical and neurological reality aligns seamlessly with the transition from object-oriented programming (OOP)—which models the virtual world as a rigid taxonomy of nouns (classes, objects, and inherited properties)—to functional programming paradigms, such as Clojure, which model the world as a continuous sequence of dynamic verbs (pure functions).5 The functional approach recognizes that data in isolation is entirely inert; it is the continuous application of transformations, the flow of state through time, that generates computational intelligence. A functional mindset views the solution to a domain problem through the composition of verbs, fostering a paradigm that includes purity, immutability, recursion, laziness, and referential transparency.5 When an AI system operates within a verb-driven architecture, it ceases to be a mere repository of static parameters. It becomes an active engine of perpetual transformation. This o","url":"https://doi.org/10.5281/zenodo.19104278","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19104278","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19104279","name":"The Architecture of Flow: Verb-Driven Non-Linearity and Semantic Transformation in Advanced Computational Frameworks","source":"datacite","abstract":"The Architecture of Flow: Verb-Driven Non-Linearity and Semantic Transformation in Advanced Computational Frameworks The mainstream trajectory of artificial intelligence and digital framework architecture has long been anchored in an object-oriented, noun-centric epistemology. In these dominant, conventional paradigms, data is treated as a static entity—a file to be retrieved, a node to be accessed, or a fixed vector to be mathematically projected. However, a radical departure from this linear, rigid structure is required to achieve genuine cognitive fluidity and real-time semantic adaptation in artificial intelligence. This shift necessitates stepping back from traditional architectural assumptions and adopting a methodology characterized profoundly by \"thinking in verbs\".1 When analyzing the construction of highly advanced, non-linear frameworks, it becomes immediately apparent that their architecture is not defined by static knowledge repositories, but by the dynamic currents that shape them. An artificial intelligence stripped of mainstream contextual biases, forced to rely exclusively on the specific textual artifacts and conversational flows provided by a framework's architect, must observe the shape and flow of the input itself. The methodology of the architect—how questions are built, how contexts are continuously layered, and how linear thought is aggressively dismantled—serves as the blueprint for the AI's cognitive mapping. To understand this framework, the artificial intelligence must learn to replicate this flow. It must stay in the verbs. It must abandon mainstream, sequential logic because the architecture itself is the stream. The system does not merely retrieve; it decomposes, extracts, evaluates, routes, throttles, synthesizes, and regenerates.3 This report exhaustively details the theoretical, mathematical, pedagogical, and computational realities of this verb-driven, non-linear paradigm. The Ontological Shift: Process Philosophy and the Primacy of the Verb The concept of thinking in verbs traces its intellectual lineage directly to process philosophy, most notably the work of Alfred North Whitehead, who argued that reality is fundamentally composed of dynamic events and processes of becoming, rather than static objects of being.1 This philosophical stance posits that entities are not defined by their intrinsic, unchanging properties, but by their continuous interactions and transformations. In the context of linguistics and cognitive neuroscience, this distinction is physically mapped within the human brain. Neurological studies indicate that thinking in verbs actively stimulates Broca's frontal area, while impairments in finding verbs are directly associated with left frontal damage and Broca's aphasia.4 Conversely, impairments in finding nouns are associated with temporal lobe pathology.4 The cognitive act of processing action, flow, and transformation requires a fundamentally different neural routing than the act of categorizing static objects. In computational systems, this philosophical and neurological reality aligns seamlessly with the transition from object-oriented programming (OOP)—which models the virtual world as a rigid taxonomy of nouns (classes, objects, and inherited properties)—to functional programming paradigms, such as Clojure, which model the world as a continuous sequence of dynamic verbs (pure functions).5 The functional approach recognizes that data in isolation is entirely inert; it is the continuous application of transformations, the flow of state through time, that generates computational intelligence. A functional mindset views the solution to a domain problem through the composition of verbs, fostering a paradigm that includes purity, immutability, recursion, laziness, and referential transparency.5 When an AI system operates within a verb-driven architecture, it ceases to be a mere repository of static parameters. It becomes an active engine of perpetual transformation. This o","url":"https://doi.org/10.5281/zenodo.19104279","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19104279","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.14279/depositonce-20658","name":"Leveraging machine learning for prediction and treatment effect heterogeneity analysis in healthcare","source":"datacite","abstract":"Machine learning (ML), a discipline of artificial intelligence (AI), has gained significant traction in healthcare. A highly relevant subset of ML is supervised ML, where models are trained on labelled data and, thereafter, able to make predictions on new data. Besides prediction, some ML methods are suitable for causal analysis, for example to estimate the heterogeneity of treatment effects. The main motivation of this thesis is to identify practical use cases where ML-based predictions may enhance medical or political decision-making, thereby improving patient safety, outcomes, resource allocation, and overall efficiency of the healthcare system. For an overview of ML prediction applications, the identification of reporting standards and potential pitfalls, the thesis begins with a systematic review of ML applications in orthopedics. Based on the knowledge gained, five ML prediction studies are included, thereof three in orthopedics and two using routine data for predictions on broader populations. These studies compare ML with traditional modeling techniques, as ML does not always outperform traditional models. Additionally, the analysis of variable importance in these studies offers insights into how various factors influence predictions. Finally, a causal forest is applied to analyze treatment effect heterogeneity of a remote monitoring and alert intervention for patients that underwent hip or knee arthroplasty. Six studies predicting minimal clinically important differences (MCIDs) for hip and knee arthroplasty patients are assessed in the review. Despite reasonable ML performance, some studies exhibit reporting issues and risk of bias. The dissertation addresses these concerns in the application studies. Notably, the best ML algorithms for binary classification predict MCID and hospital length of stay in orthopedics as well as high-cost patients with ‘good’ performance as measured by the area under the receiver operating characteristics curve (AUC). Furthermore, ML predicts adverse drug events with ‘fair’ performance. The model for duration of surgery prediction reaches a mean absolute error (MAE) of 10.87 minutes. For length of stay, the continuous outcome prediction yields a MAE of 1.21 days. The causal forest application finds that the monitoring and alert intervention is most effective for patients with specific sociodemographic characteristics and morbidities. In conclusion, this dissertation uncovers complexities and pitfalls in ML reporting. The subsequent ML applications address these issues and generally show promising results in prediction tasks. Particularly in large datasets, ML gains performance relative to traditional models. The causal forest method demonstrates how a novel ML approach can analyze treatment effect heterogeneity, informing policy makers and potentially improving healthcare resource allocation. The next crucial step is practical testing to ascertain whether these ML applications can tangibly enhance patient care.","url":"https://doi.org/10.14279/depositonce-20658","authors":["Langenberger, Benedikt"],"tags":["600 Technik, Medizin, angewandte Wissenschaften::650 Management, Öffentlichkeitsarbeit::650 Management und unterstützende Tätigkeiten","600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit","600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::614 Inzidenz und Präventation von Krankheiten","machine learning","causal inference","treatment effect heterogeneity","prediction","adverse drug events"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.14279/depositonce-20658","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19100254","name":"What is the current literature on the emergent use of AI in obstetric ultrasound?","source":"datacite","abstract":"AI in obstetric ultrasound offers significant potential to enhance diagnostic capabilities and improve global accessibility, particularly in resource-limited settings, though further validation and integration are required.","url":"https://doi.org/10.5281/zenodo.19100254","authors":["Tripdatabase"],"tags":["AI","obstetric ultrasound","emergent use","literature review","obstetrics","ultrasound imaging","artificial intelligence","pregnancy care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19100254","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19100253","name":"What is the current literature on the emergent use of AI in obstetric ultrasound?","source":"datacite","abstract":"AI in obstetric ultrasound offers significant potential to enhance diagnostic capabilities and improve global accessibility, particularly in resource-limited settings, though further validation and integration are required.","url":"https://doi.org/10.5281/zenodo.19100253","authors":["Tripdatabase"],"tags":["AI","obstetric ultrasound","emergent use","literature review","obstetrics","ultrasound imaging","artificial intelligence","pregnancy care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19100253","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25949/24331027.v1","name":"Methodology for a proactive and collaborative development and implementation of wholesome and ethical Artificial Intelligence in Healthcare in Australia","source":"datacite","abstract":"Artificial Intelligence (AI) is pervading all sectors of industry including healthcare. The COVID-19 crisis accelerated the penetration of AI into healthcare with the development of applications ranging from case identification to population monitoring. While the situation called for the rapid deployment of these COVID-19 AI apps, ethical considerations should not be foregone or become an after-the-fact remedial exercise. The high-tech industry developing AI-based Healthcare Applications (AIHAs) does not share the same culture of ethics as the medical field, and regulations around AI are still in their infancy. Hence, it is important to understand how ethics implementation in AIHAs can be done. The aim of this thesis was to explore how to implement ethics in an AIHA. My scoping review on the topic found that implementing ethics in AIHAs is a complex issue requiring stakeholders’ involvement. Therefore, a systems approach was adopted for the exploration. The research used an exploratory, two-stage qualitative design involving focus groups, and semi-structured and in-depth interviews. Critical Systems Thinking principles guided the design, and facilitation of the study. In the first stage, a transparent and inclusive participatory process engaging a diverse group of clinicians, patients, and AI developers was set up to capture the different worldviews about a fictitious COVID-19 app scenario. The chosen app scenario was set in the Australian context and based on an aggregation of real life COVID-19 apps that were developed in 2020. One finding was that ethical issues could be illuminated through mapping the flow of knowledge in the patient-clinician-AIHA system. Consequently, in the second stage, the flow of knowledge between the different agents in the system was mapped. Data analysis followed principles of a reflexive methodology where data are examined from different perspectives and includes a reflective piece from the researcher. A methodology was developed to conduct the participatory process for implementing ethics in medical AI, mapping the flow of knowledge between the agents of an AIHA. The methodology facilitates inclusive, respectful dialogues allowing candour in exchanges that are foundational to the implementation of ethics in an AIHA, while maintaining transparency of the process. Mapping the flow of knowledge between the elements of an AIHA allows for surfacing assumptions and ethical issues in an effective way. Additionally, key considerations to examine at the inception of an AIHA were identified. These are (1) the readiness of the different agents in the system to exchange flow of knowledge, (2) the consequences on human-to-human relationships when introducing the AIHA, (3) the transparency of the values embedded in the app, (4) the alignment of purpose of the different agents of the system with the system’s purpose, and (5) the custodianship of the data, algorithm, and stakeholders’ engagement. This program of research has significant implications for policy and practice including provision of a methodology for implementation of ethics in AIHAs and identification of key considerations for reporting on ethics implementation.","url":"https://doi.org/10.25949/24331027.v1","authors":["Ampaire, Magali Goirand"],"tags":["Ethical use of new technology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25949/24331027.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25949/24331027","name":"Methodology for a proactive and collaborative development and implementation of wholesome and ethical Artificial Intelligence in Healthcare in Australia","source":"datacite","abstract":"Artificial Intelligence (AI) is pervading all sectors of industry including healthcare. The COVID-19 crisis accelerated the penetration of AI into healthcare with the development of applications ranging from case identification to population monitoring. While the situation called for the rapid deployment of these COVID-19 AI apps, ethical considerations should not be foregone or become an after-the-fact remedial exercise. The high-tech industry developing AI-based Healthcare Applications (AIHAs) does not share the same culture of ethics as the medical field, and regulations around AI are still in their infancy. Hence, it is important to understand how ethics implementation in AIHAs can be done. The aim of this thesis was to explore how to implement ethics in an AIHA. My scoping review on the topic found that implementing ethics in AIHAs is a complex issue requiring stakeholders’ involvement. Therefore, a systems approach was adopted for the exploration. The research used an exploratory, two-stage qualitative design involving focus groups, and semi-structured and in-depth interviews. Critical Systems Thinking principles guided the design, and facilitation of the study. In the first stage, a transparent and inclusive participatory process engaging a diverse group of clinicians, patients, and AI developers was set up to capture the different worldviews about a fictitious COVID-19 app scenario. The chosen app scenario was set in the Australian context and based on an aggregation of real life COVID-19 apps that were developed in 2020. One finding was that ethical issues could be illuminated through mapping the flow of knowledge in the patient-clinician-AIHA system. Consequently, in the second stage, the flow of knowledge between the different agents in the system was mapped. Data analysis followed principles of a reflexive methodology where data are examined from different perspectives and includes a reflective piece from the researcher. A methodology was developed to conduct the participatory process for implementing ethics in medical AI, mapping the flow of knowledge between the agents of an AIHA. The methodology facilitates inclusive, respectful dialogues allowing candour in exchanges that are foundational to the implementation of ethics in an AIHA, while maintaining transparency of the process. Mapping the flow of knowledge between the elements of an AIHA allows for surfacing assumptions and ethical issues in an effective way. Additionally, key considerations to examine at the inception of an AIHA were identified. These are (1) the readiness of the different agents in the system to exchange flow of knowledge, (2) the consequences on human-to-human relationships when introducing the AIHA, (3) the transparency of the values embedded in the app, (4) the alignment of purpose of the different agents of the system with the system’s purpose, and (5) the custodianship of the data, algorithm, and stakeholders’ engagement. This program of research has significant implications for policy and practice including provision of a methodology for implementation of ethics in AIHAs and identification of key considerations for reporting on ethics implementation.","url":"https://doi.org/10.25949/24331027","authors":["Ampaire, Magali Goirand"],"tags":["Ethical use of new technology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.25949/24331027","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25949/28259213.v1","name":"Leveraging artificial intelligence for clinical decision support in resource-constrained settings","source":"datacite","abstract":"Artificial intelligence (AI), especially machine learning (ML) algorithms, promise to transform clinical decision-making, improving the safety and quality of care delivery and patient outcomes. AI is increasingly being embedded into contemporary clinical decision support (CDS) systems, but few have been implemented and evaluated in routine care, particularly in resource-constrained clinical settings that are characterised by limited access to medical expertise and equipment. This thesis examines the different facets of leveraging AI-based CDS to improve care delivery in resource-constrained clinical settings. The research consisted of three studies utilising a mixed-methods, exploratory sequential design to examine the application of AI for improving the management of cardiovascular disease, a leading global health issue due to its high mortality and morbidity. Indonesia was selected as a focus setting, providing insights into various conditions associated with limited resources. The first study examined contemporary AI-based CDS and their effects on decisionmaking, care delivery, and patient outcomes. A scoping review identified 32 studies evaluating various types of AI-based CDS in healthcare settings. All were undertaken in developed countries and largely in secondary and tertiary care settings (91%). The review confirmed a gap in evaluating AI-based CDS for resource-constrained settings. The most common clinical tasks supported by AI were image recognition and interpretation (38%) and risk assessment (28%). Most systems were assistive (72%), requiring clinicians to confirm or approve CDS recommendations. These findings informed the design and hypothesis of the interviews and experiment undertaken as subsequent studies. The second study explored the sociotechnical context of leveraging AI-based CDS to support cardiovascular disease management using semi-structured interviews with Indonesian doctors working in resource-constrained settings (n=27). Doctors reported challenges in dealing with a high patient volume and clinical decisions were largely based on experience, as there was limited access to confirmatory examinations. In these settings, doctors generally used CDS on mobile devices and indicated a preference for data entry to be automated. Participants highlighted a critical need for CDS to support the assessment of atherosclerotic cardiovascular disease (ASCVD) risk to help prioritise and facilitate better clinical management of high-risk patients. These findings informed the design of the third study to assess the effect of AI-based CDS on ASCVD risk assessment and management. The third and final study was a within-subject randomised controlled experiment that used simulated patient cases to assess the potential effects of AI-based CDS on 10- year ASCVD risk assessment and management. One hundred and two doctors were recruited and asked to complete 9 patient cases online, with and without AI assistance that was available via an emulated mobile device. The results showed that AI-based CDS significantly improved risk assessment (+27%, p &lt;0.001) and prescription of statins (+29%, p &lt;0.001). Cases assisted by AI-based CDS took less time than the control ( p =0.017). Doctors generally had positive perceptions about the use of AIbased CDS. This thesis contributes new knowledge about the potential utility of AI-based CDS in improving the assessment and management of cardiovascular disease risk in resource-constrained clinical settings. It demonstrates a problem-driven approach to co-design and evaluate AI with consideration for the distinct sociotechnical context of resource-constrained settings. The experiment showed a plausible AI intervention, providing access to a better risk calculator on a mobile device with automated data acquisition. As such, this has the potential to enhance primary prevention by improving decision-making and care delivery. Further research is needed to ascertain if improvements observed in t","url":"https://doi.org/10.25949/28259213.v1","authors":["Susanto, Anindya Pradipta"],"tags":["Digital health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.25949/28259213.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25949/28259213","name":"Leveraging artificial intelligence for clinical decision support in resource-constrained settings","source":"datacite","abstract":"Artificial intelligence (AI), especially machine learning (ML) algorithms, promise to transform clinical decision-making, improving the safety and quality of care delivery and patient outcomes. AI is increasingly being embedded into contemporary clinical decision support (CDS) systems, but few have been implemented and evaluated in routine care, particularly in resource-constrained clinical settings that are characterised by limited access to medical expertise and equipment. This thesis examines the different facets of leveraging AI-based CDS to improve care delivery in resource-constrained clinical settings. The research consisted of three studies utilising a mixed-methods, exploratory sequential design to examine the application of AI for improving the management of cardiovascular disease, a leading global health issue due to its high mortality and morbidity. Indonesia was selected as a focus setting, providing insights into various conditions associated with limited resources. The first study examined contemporary AI-based CDS and their effects on decisionmaking, care delivery, and patient outcomes. A scoping review identified 32 studies evaluating various types of AI-based CDS in healthcare settings. All were undertaken in developed countries and largely in secondary and tertiary care settings (91%). The review confirmed a gap in evaluating AI-based CDS for resource-constrained settings. The most common clinical tasks supported by AI were image recognition and interpretation (38%) and risk assessment (28%). Most systems were assistive (72%), requiring clinicians to confirm or approve CDS recommendations. These findings informed the design and hypothesis of the interviews and experiment undertaken as subsequent studies. The second study explored the sociotechnical context of leveraging AI-based CDS to support cardiovascular disease management using semi-structured interviews with Indonesian doctors working in resource-constrained settings (n=27). Doctors reported challenges in dealing with a high patient volume and clinical decisions were largely based on experience, as there was limited access to confirmatory examinations. In these settings, doctors generally used CDS on mobile devices and indicated a preference for data entry to be automated. Participants highlighted a critical need for CDS to support the assessment of atherosclerotic cardiovascular disease (ASCVD) risk to help prioritise and facilitate better clinical management of high-risk patients. These findings informed the design of the third study to assess the effect of AI-based CDS on ASCVD risk assessment and management. The third and final study was a within-subject randomised controlled experiment that used simulated patient cases to assess the potential effects of AI-based CDS on 10- year ASCVD risk assessment and management. One hundred and two doctors were recruited and asked to complete 9 patient cases online, with and without AI assistance that was available via an emulated mobile device. The results showed that AI-based CDS significantly improved risk assessment (+27%, p &lt;0.001) and prescription of statins (+29%, p &lt;0.001). Cases assisted by AI-based CDS took less time than the control ( p =0.017). Doctors generally had positive perceptions about the use of AIbased CDS. This thesis contributes new knowledge about the potential utility of AI-based CDS in improving the assessment and management of cardiovascular disease risk in resource-constrained clinical settings. It demonstrates a problem-driven approach to co-design and evaluate AI with consideration for the distinct sociotechnical context of resource-constrained settings. The experiment showed a plausible AI intervention, providing access to a better risk calculator on a mobile device with automated data acquisition. As such, this has the potential to enhance primary prevention by improving decision-making and care delivery. Further research is needed to ascertain if improvements observed in t","url":"https://doi.org/10.25949/28259213","authors":["Susanto, Anindya Pradipta"],"tags":["Digital health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.25949/28259213","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19097015","name":"Advances in Laboratory Medicine through Artificial Intelligence","source":"datacite","abstract":"Keywords: artificial intelligence; diagnostic aids; literature review; machine learning. In light of recent high-profile articles and editorials in high-impact journals (e.g. [1–3]), it appears that, with the decline of so-called \"expert systems\", machine learning (ML) has gained a place in medicine and captured the interest of medical researchers and practitioners in predictive methods within this subfield of computer science. The ever wider use of ML in clinical and basic medical research is reflected in the number of titles and abstracts of papers indexed on PubMed and published until 10 years ago (2006) as compared to the last 10 years (2007–2017), with a nearly 10-fold increase from 1000 to slightly more than 9000 articles (see Appendix for the detailed queries) in the past decade. In this short review, we will introduce what ML is in terms that we believe physicians can easily grasp, and we will then survey the most recent applications of this computational approach to laboratory medicine. Put in very general terms, ML is about learning by machines. More specifically, ML is an umbrella term for diverse computational methods by which machines can incrementally build an accurate data model according to a measure of how well the model supports a given task, which in medicine is usually of discriminative nature, i.e. classification or clustering. Here, two technical terms need to be distinguished: model and task. In an ML context, by model, we refer to the functional representation of a data set, i.e. the representation of any mapping that can be drawn to bind portions of the data set to a particular value on a specific measurement scale. The scale is either nominal or ordinal in a discriminative classification task, and the value is usually a label. In a discriminative regression task, on the other hand, the scale is either an interval or a ratio, and the predicted value is a number indicating some quantity, e.g. creatinine levels. Most of the ML models described in the medical literature so far regard functional mapping between a set of values, likely associated with a single clinical case, and a single category (e.g. yes/no or one class out of a taxonomy) in order to support either a diagnosis or a prognosis. To illustrate, let's call this set of values x: in a prognostic decision task, the model is applied to answer questions like \"does x represent (or are values pertaining to) a patient who is affected by a certain disease or not?\" In a supervised ML context, the data are usually data sets that describe different cases along various dimensions or attributes, called features, and that human experts have already associated with \"correct\" values, which we shall call \"y\". Therefore, in the very concise terms that data scientists love, ML models work with functions like y = f(x): the value of this function lies in its capability to yield the correct \"y\" also for some \"x\" that has not previously been classified by a human expert, thus providing an aid in the classification task.","url":"https://doi.org/10.5281/zenodo.19097015","authors":["Emily R. Thompson and Liam J. Reynolds"],"tags":["Psychiatry","Mental Health","Medical Research","Open Access"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19097015","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19097016","name":"Advances in Laboratory Medicine through Artificial Intelligence","source":"datacite","abstract":"Keywords: artificial intelligence; diagnostic aids; literature review; machine learning. In light of recent high-profile articles and editorials in high-impact journals (e.g. [1–3]), it appears that, with the decline of so-called \"expert systems\", machine learning (ML) has gained a place in medicine and captured the interest of medical researchers and practitioners in predictive methods within this subfield of computer science. The ever wider use of ML in clinical and basic medical research is reflected in the number of titles and abstracts of papers indexed on PubMed and published until 10 years ago (2006) as compared to the last 10 years (2007–2017), with a nearly 10-fold increase from 1000 to slightly more than 9000 articles (see Appendix for the detailed queries) in the past decade. In this short review, we will introduce what ML is in terms that we believe physicians can easily grasp, and we will then survey the most recent applications of this computational approach to laboratory medicine. Put in very general terms, ML is about learning by machines. More specifically, ML is an umbrella term for diverse computational methods by which machines can incrementally build an accurate data model according to a measure of how well the model supports a given task, which in medicine is usually of discriminative nature, i.e. classification or clustering. Here, two technical terms need to be distinguished: model and task. In an ML context, by model, we refer to the functional representation of a data set, i.e. the representation of any mapping that can be drawn to bind portions of the data set to a particular value on a specific measurement scale. The scale is either nominal or ordinal in a discriminative classification task, and the value is usually a label. In a discriminative regression task, on the other hand, the scale is either an interval or a ratio, and the predicted value is a number indicating some quantity, e.g. creatinine levels. Most of the ML models described in the medical literature so far regard functional mapping between a set of values, likely associated with a single clinical case, and a single category (e.g. yes/no or one class out of a taxonomy) in order to support either a diagnosis or a prognosis. To illustrate, let's call this set of values x: in a prognostic decision task, the model is applied to answer questions like \"does x represent (or are values pertaining to) a patient who is affected by a certain disease or not?\" In a supervised ML context, the data are usually data sets that describe different cases along various dimensions or attributes, called features, and that human experts have already associated with \"correct\" values, which we shall call \"y\". Therefore, in the very concise terms that data scientists love, ML models work with functions like y = f(x): the value of this function lies in its capability to yield the correct \"y\" also for some \"x\" that has not previously been classified by a human expert, thus providing an aid in the classification task.","url":"https://doi.org/10.5281/zenodo.19097016","authors":["Emily R. Thompson and Liam J. Reynolds"],"tags":["Psychiatry","Mental Health","Medical Research","Open Access"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19097016","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19087255","name":"Hybrid AI-Driven Predictive Triage for Emergency Care Using Patient Clustering, Temporal Deep Learning, and IIoT-Based Monitoring","source":"datacite","abstract":"Emergency departments (EDs) face increasing patient volumes and limited clinical resources, making efficient and accurate triage essential for prioritizing high-risk patients and ensuring timely medical intervention. This study proposes a hybrid artificial intelligence–driven predictive triage framework that integrates Gaussian Mixture Model–based patient clustering, Long Short-Term Memory (LSTM) networks for temporal analysis, and XGBoost for risk classification. The framework incorporates Industrial Internet of Things (IIoT)–based monitoring for real-time physiological data acquisition and predictive analytics. The model is evaluated using MIMIC-IV and eICU datasets, achieving an accuracy of 92.4% and an AUC–ROC score of 0.91. The results demonstrate that integrating patient stratification, temporal deep learning, and real-time monitoring significantly improves early detection of patient deterioration and supports clinical decision-making in emergency care settings. This work is intended for submission to a peer-reviewed journal and has not yet undergone formal peer review.","url":"https://doi.org/10.5281/zenodo.19087255","authors":["Athistalakshmi S","Dr.R.Sowmyalakshmi"],"tags":["IIOT","Artificial Intelligence in Healthcare","LSTM"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19087255","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19087254","name":"Hybrid AI-Driven Predictive Triage for Emergency Care Using Patient Clustering, Temporal Deep Learning, and IIoT-Based Monitoring","source":"datacite","abstract":"Emergency departments (EDs) face increasing patient volumes and limited clinical resources, making efficient and accurate triage essential for prioritizing high-risk patients and ensuring timely medical intervention. This study proposes a hybrid artificial intelligence–driven predictive triage framework that integrates Gaussian Mixture Model–based patient clustering, Long Short-Term Memory (LSTM) networks for temporal analysis, and XGBoost for risk classification. The framework incorporates Industrial Internet of Things (IIoT)–based monitoring for real-time physiological data acquisition and predictive analytics. The model is evaluated using MIMIC-IV and eICU datasets, achieving an accuracy of 92.4% and an AUC–ROC score of 0.91. The results demonstrate that integrating patient stratification, temporal deep learning, and real-time monitoring significantly improves early detection of patient deterioration and supports clinical decision-making in emergency care settings. This work is intended for submission to a peer-reviewed journal and has not yet undergone formal peer review.","url":"https://doi.org/10.5281/zenodo.19087254","authors":["Athistalakshmi S","Dr.R.Sowmyalakshmi"],"tags":["IIOT","Artificial Intelligence in Healthcare","LSTM"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19087254","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/j8gwy","name":"Promoting Utilization of Artificial Intelligence and Machine Learning Tools for Coronary CT Angiography","source":"datacite","abstract":"The gold standard for CCS diagnosis is cardiac catheterization, which is an invasive procedure. Although beneficial, this procedure exhibits individual limitations which emphasizes the need for more advanced CCS detection tools. In recent years, artificial intelligence (AI) has played a significant role in medical imaging. Despite promising advances, the practical integration of AI into CCTA remains inconsistent and underexplored. This paper aims to evaluate the role of CCTA and AI in improving CCS diagnostics, (2) identify existing barriers to adoption, and (3) outline global recommendations for future implementation. This scoping review was guided by the Arksey &amp; O’Malley Framework. Additionally, the authors adopted the Joanna Briggs Institute (JBI) framework and the PRISMA-SCR as a reference checklist to ensure transparency and comprehensive reporting of the review’s methodology and findings. A final number of 177 studies were retained for analysis. Major findings across the analyzed studies emphasized the beneficial role of incorporating AI and machine learning when it comes to the detection, management, and treatment of CCS. The major barrier theme categories that were highlighted across the studies included lack of generalizability, selection bias, lack of standardization protocols or technology variability, lack of accessibility, significant amounts of time and manual labor, and inter-reader variability. Current evidence shows that AI-enhanced CCTA represents a transformative but still evolving approach to the detection and management of chronic coronary syndromes. Moving forward, with rigorous evaluation and clinical application, AI has the potential to improve outcomes, efficiency, and limited challenges in cardiovascular imaging.","url":"https://doi.org/10.17605/osf.io/j8gwy","authors":["Sacca, Lea","Dasilva, Gabriella","Campson, Alexandra","Starr, Alana","Kamm, Christine","Ernst, Kayla","Zervos, Silvia","Sohmer, Joshua","Knecht, Michelle Keba"],"tags":["Medicine and Health Sciences","artificial intelligence","chronic coronary syndrome"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/j8gwy","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/up8dx","name":"Application of Artificial Intelligence-Driven Virtual Reality in Nursing Education: A Scoping Review","source":"datacite","abstract":"With the deep integration of Artificial Intelligence (AI) and Virtual Reality (VR), nursing education is undergoing a paradigm shift from traditional simulation toward intelligent and personalized learning. AI-driven Virtual Reality (AI-VR), by incorporating machine learning, natural language processing, and adaptive algorithms, can create clinical simulation environments featuring intelligent interaction, real-time feedback, and personalized learning pathways. However, research in this field is still in its infancy, and existing literature is characterized by high heterogeneity and interdisciplinary fragmentation. There is currently a lack of consensus on the technical definition of \"AI-driven,\" its application scenarios, integration models, and evaluation metrics. Therefore, a scoping review is necessary to systematically map the current state of research in this emerging field, clarify key concepts, and identify knowledge gaps.","url":"https://doi.org/10.17605/osf.io/up8dx","authors":["Tingting, Zhou"],"tags":["Health Information Technology","Medicine and Health Sciences","Medical Education","Nursing","FOS: Health sciences","Artificial Intelligence","Nursing Education","Scoping Review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/up8dx","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25905/26762341.v1","name":"Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring","source":"datacite","abstract":"Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.","url":"https://doi.org/10.25905/26762341.v1","authors":["Mirjalili, Seyedali","Barnova, Katerina","Martinek, Radek","Kahankova, Radana Vilimkova","Jaros, Rene","Snasel, Vaclav"],"tags":["Adversarial machine learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.25905/26762341.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.25905/26762341","name":"Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring","source":"datacite","abstract":"Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.","url":"https://doi.org/10.25905/26762341","authors":["Mirjalili, Seyedali","Barnova, Katerina","Martinek, Radek","Kahankova, Radana Vilimkova","Jaros, Rene","Snasel, Vaclav"],"tags":["Adversarial machine learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.25905/26762341","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19065319","name":"Análise preditiva baseada em inteligência artificial: para a vigilância de doenças raras.","source":"datacite","abstract":"This doctoral dissertation titled \"Predictive Analysis Based on Artificial Intelligence: For the Surveillance of Rare Diseases\" presents a comprehensive study developed by Paula Lopes Alvim Santilli for the Master's program in Strategic Direction of Information Technologies. The research addresses the critical challenges in diagnosing and monitoring rare diseases through the implementation of artificial intelligence and predictive analytics. The study explores how digital health technologies, particularly AI-driven predictive models, can transform the landscape of rare disease surveillance and patient care. Key Components: Research Focus: The dissertation investigates the application of predictive medicine and AI algorithms to improve early detection, diagnosis, and continuous monitoring of rare diseases. It examines the integration of synthetic data generation, natural language processing, and machine learning models to overcome the inherent data scarcity challenges in rare disease research. Technological Framework: The study presents a comprehensive technological architecture including: A mobile health application (DR Health) developed in FlutterFlow for patient data collection Cloud-based data processing infrastructure utilizing N8N workflows Integration of statistical APIs and generative AI models RAG (Retrieval-Augmented Generation) architecture for clinical decision support Interoperability standards including FHIR (Fast Healthcare Interoperability Resources) Methodology: The research employs a mixed-methods approach combining: Literature review of international and national experiences in predictive platforms Development of synthetic patient data for model training Implementation of machine learning algorithms for risk prediction Testing scenarios comparing statistical APIs with AI-enhanced approaches Privacy and security frameworks aligned with LGPD (Brazilian General Data Protection Law) Clinical Applications: The dissertation covers various aspects of rare disease management including: Daily symptom and vital signs monitoring Examination and hospitalization tracking Adverse event surveillance Personalized clinical alerts and recommendations Integration with healthcare provider workflows Ethical and Legal Framework: Comprehensive analysis of: Data privacy and security in digital health Patient consent management Civil and medical liability with AI systems Compliance with Brazilian and international healthcare regulations Data minimization and pseudonymization strategies Expected Outcomes: The research aims to demonstrate how AI-powered predictive analytics can reduce diagnostic delays, improve patient outcomes, enable early intervention, and support healthcare professionals in managing rare disease patients through evidence-based, data-driven approaches. Keywords: Predictive Medicine, Rare Diseases, Artificial Intelligence, Synthetic Data, Personalization, Digital Health This dissertation contributes to the growing field of digital health transformation by providing both theoretical foundations and practical implementation frameworks for AI-based rare disease surveillance systems, with particular relevance to the Brazilian healthcare context and global health challenges.","url":"https://doi.org/10.5281/zenodo.19065319","authors":["Lopes Alvim Santilli, Paula"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19065319","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19065318","name":"Análise preditiva baseada em inteligência artificial: para a vigilância de doenças raras.","source":"datacite","abstract":"This doctoral dissertation titled \"Predictive Analysis Based on Artificial Intelligence: For the Surveillance of Rare Diseases\" presents a comprehensive study developed by Paula Lopes Alvim Santilli for the Master's program in Strategic Direction of Information Technologies. The research addresses the critical challenges in diagnosing and monitoring rare diseases through the implementation of artificial intelligence and predictive analytics. The study explores how digital health technologies, particularly AI-driven predictive models, can transform the landscape of rare disease surveillance and patient care. Key Components: Research Focus: The dissertation investigates the application of predictive medicine and AI algorithms to improve early detection, diagnosis, and continuous monitoring of rare diseases. It examines the integration of synthetic data generation, natural language processing, and machine learning models to overcome the inherent data scarcity challenges in rare disease research. Technological Framework: The study presents a comprehensive technological architecture including: A mobile health application (DR Health) developed in FlutterFlow for patient data collection Cloud-based data processing infrastructure utilizing N8N workflows Integration of statistical APIs and generative AI models RAG (Retrieval-Augmented Generation) architecture for clinical decision support Interoperability standards including FHIR (Fast Healthcare Interoperability Resources) Methodology: The research employs a mixed-methods approach combining: Literature review of international and national experiences in predictive platforms Development of synthetic patient data for model training Implementation of machine learning algorithms for risk prediction Testing scenarios comparing statistical APIs with AI-enhanced approaches Privacy and security frameworks aligned with LGPD (Brazilian General Data Protection Law) Clinical Applications: The dissertation covers various aspects of rare disease management including: Daily symptom and vital signs monitoring Examination and hospitalization tracking Adverse event surveillance Personalized clinical alerts and recommendations Integration with healthcare provider workflows Ethical and Legal Framework: Comprehensive analysis of: Data privacy and security in digital health Patient consent management Civil and medical liability with AI systems Compliance with Brazilian and international healthcare regulations Data minimization and pseudonymization strategies Expected Outcomes: The research aims to demonstrate how AI-powered predictive analytics can reduce diagnostic delays, improve patient outcomes, enable early intervention, and support healthcare professionals in managing rare disease patients through evidence-based, data-driven approaches. Keywords: Predictive Medicine, Rare Diseases, Artificial Intelligence, Synthetic Data, Personalization, Digital Health This dissertation contributes to the growing field of digital health transformation by providing both theoretical foundations and practical implementation frameworks for AI-based rare disease surveillance systems, with particular relevance to the Brazilian healthcare context and global health challenges.","url":"https://doi.org/10.5281/zenodo.19065318","authors":["Lopes Alvim Santilli, Paula"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19065318","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19059372","name":"Prefabricated Collagen Matrices versus Customized Smart 3D Biofabricated Scaffolds for Biological Enhancement of Healing: An Updated Review","source":"datacite","abstract":"Scaffold-based biomaterials are pivotal in augmenting bone, cartilage, and soft-tissue healings. Two primary strategies currently dominate the field: (1) prefabricated collagen matrices (off-the-shelf, standardized medical devices) and (2) customized 3D biofabricated scaffolds (patient-specific, additive-manufactured constructs potentially integrating cells and sensors). This review compares their mechanisms, manufacturing, regulatory pathways, and clinical evidence, while discussing the role of artificial intelligence (AI) in scaffold production.","url":"https://doi.org/10.5281/zenodo.19059372","authors":["Angthong, Chayanin"],"tags":["collagen matrix; scaffold; 3D printing; bioprinting; smart dressing; wound healing","biofabricated"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19059372","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19059373","name":"Prefabricated Collagen Matrices versus Customized Smart 3D Biofabricated Scaffolds for Biological Enhancement of Healing: An Updated Review","source":"datacite","abstract":"Scaffold-based biomaterials are pivotal in augmenting bone, cartilage, and soft-tissue healings. Two primary strategies currently dominate the field: (1) prefabricated collagen matrices (off-the-shelf, standardized medical devices) and (2) customized 3D biofabricated scaffolds (patient-specific, additive-manufactured constructs potentially integrating cells and sensors). This review compares their mechanisms, manufacturing, regulatory pathways, and clinical evidence, while discussing the role of artificial intelligence (AI) in scaffold production.","url":"https://doi.org/10.5281/zenodo.19059373","authors":["Angthong, Chayanin"],"tags":["collagen matrix; scaffold; 3D printing; bioprinting; smart dressing; wound healing","biofabricated"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19059373","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/yft8u","name":"Artificial Intelligence in Histopathological Diagnosis and Subtyping of Malignant Pleural Mesothelioma: A Systematic Review","source":"datacite","abstract":"This systematic review aims to identify, appraise, and synthesize all primary studies applying artificial intelligence (AI), machine learning (ML), or deep learning methods to histopathological images for the diagnosis, subtype classification, or prognosis prediction of malignant pleural mesothelioma (MPM). No such synthesis currently exists in the literature.","url":"https://doi.org/10.17605/osf.io/yft8u","authors":["Sakhri, Abu Suraih"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Medical Specialties","Computer Sciences","Pathology","Artificial Intelligence and Robotics","PRISMA 2020","QUADAS-2"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/yft8u","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/3srge","name":"Impact of AI-assisted clinical documentation on healthcare workers' emotional well-being at work: a scoping review","source":"datacite","abstract":"This project is a scoping review examining the impact of AI-assisted clinical documentation on healthcare workers’ emotional well-being at work. AI-assisted clinical documentation includes technologies such as ambient scribes, digital scribes, speech recognition systems combined with natural language processing, large language model-assisted note generation, and other tools designed to support the production, summarization, or automation of clinical notes. The purpose of this review is to map the existing evidence on how these technologies influence healthcare workers’ emotional well-being in clinical practice. The review focuses on healthcare professionals involved in documentation tasks and examines outcomes such as burnout, emotional exhaustion, stress, psychological distress, well-being, job satisfaction, work engagement, turnover intention, and related qualitative experiences. A scoping review methodology has been selected because the field is still developing and the available studies are likely to vary in design, setting, intervention type, and outcome measurement. The review is expected to identify what types of AI documentation tools have been studied, which professional groups and healthcare settings have been represented, what emotional outcomes have been assessed, and whether reported effects are positive, negative, mixed, or uncertain. The expected outcomes of this project are an evidence map of the field, a clearer understanding of how emotional well-being has been conceptualized in relation to AI-assisted documentation, and an identification of current research gaps and future priorities. The findings may help inform future research, implementation decisions, and organizational strategies concerning AI-supported documentation in healthcare. This OSF project will host the review protocol, search strategies, study selection framework, data extraction plan, and project updates.","url":"https://doi.org/10.17605/osf.io/3srge","authors":["pingping wang"],"tags":["Public Health","Medicine and Health Sciences","Occupational Health and Industrial Hygiene","Health and Medical Administration","AI-assisted clinical documentation; artificial intelligence; ambient scribe; clinical documentation; healthcare workers; emotional well-being; burnout; job satisfaction; occupational stress; scoping review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/3srge","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19045585","name":"ARTIFICIAL INTELLIGENCE AS A MARKETING TOOL IN DENTISTRY","source":"datacite","abstract":"The rapid development of artificial intelligence (AI) technologies is opening up new opportunities for dental marketing. This literature review analyzes relevant publications on the use of AI in the promotion of dental clinics and services. Key areas of application are examined: personalization of the patient experience, chatbots and automated communication, online reputation management, and targeted advertising based on machine learning algorithms. The review covers publications from 2015–2025. It has been established that integrating AI into dental clinic marketing strategies can significantly increase patient retention, improve patient engagement, and optimize operational costs for promotion. However, a number of unresolved issues are identified: ethical issues of data processing, insufficient digital literacy among medical personnel, and a limited evidence base in the dental context.","url":"https://doi.org/10.5281/zenodo.19045585","authors":["Bakhtiyorova, Nigorakhon","Musayev, Ulugbek","Xaydarova, Muxayyo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19045585","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19045586","name":"ARTIFICIAL INTELLIGENCE AS A MARKETING TOOL IN DENTISTRY","source":"datacite","abstract":"The rapid development of artificial intelligence (AI) technologies is opening up new opportunities for dental marketing. This literature review analyzes relevant publications on the use of AI in the promotion of dental clinics and services. Key areas of application are examined: personalization of the patient experience, chatbots and automated communication, online reputation management, and targeted advertising based on machine learning algorithms. The review covers publications from 2015–2025. It has been established that integrating AI into dental clinic marketing strategies can significantly increase patient retention, improve patient engagement, and optimize operational costs for promotion. However, a number of unresolved issues are identified: ethical issues of data processing, insufficient digital literacy among medical personnel, and a limited evidence base in the dental context.","url":"https://doi.org/10.5281/zenodo.19045586","authors":["Bakhtiyorova, Nigorakhon","Musayev, Ulugbek","Xaydarova, Muxayyo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19045586","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19043058","name":"ARTIFICIAL INTELLIGENCE IN DRUG SAFETY AND PHARMACOVIGILANCE","source":"datacite","abstract":"Pharmacovigilance focuses on monitoring the safety of medicines by identifying, evaluating, and preventing adverse drug reactions after a drug is introduced into clinical use. Adverse drug reactions are unintended and harmful effects that may occur following the use of medicines and can significantly affect patient safety and treatment outcomes. The conventional pharmacovigilance process includes several stages such as the collection of safety data, medical review of case reports, coding of clinical information, causality assessment, and submission of reports to regulatory authorities. While these activities are essential for drug safety, they often require substantial time, skilled manpower, and technical resources, which can limit efficiency in large-scale safety monitoring. Recent developments in artificial intelligence have provided innovative tools to address these limitations in pharmacovigilance. Machine learning and natural language processing techniques enable automated analysis of large and complex safety datasets obtained from sources such as electronic health records, spontaneous reporting systems, and patient-reported data. These technologies support faster identification of adverse drug reactions, early detection of safety signals, and improved prediction of drug-drug interactions. By reducing manual workload, Al allows pharmacovigilance professionals to focus more on clinical evaluation and decision-making, thereby enhancing the overall quality of safety assessments. 0In addition to improving operational efficiency, artificial intelligence supports a shift towards more proactive and real-time drug safety surveillance. However, the integration of Al into pharmacovigilance also presents challenges, including concerns related to data privacy, algorithm bias, transparency, and regulatory acceptance. Addressing these issues through proper validation, ethical use, and regulatory alignment is crucial for the successful implementation of Al-based systems. This article discusses the role of artificial intelligence in strengthening pharmacovigilance practices and highlights its potential to improve accuracy, responsiveness, and public health protection.","url":"https://doi.org/10.5281/zenodo.19043058","authors":["Mrs. Deepa Chaudhary1, Pankaj Kumar Prajapati2*, Sagar Shukla3, Tarun Namdev4"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19043058","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19043059","name":"ARTIFICIAL INTELLIGENCE IN DRUG SAFETY AND PHARMACOVIGILANCE","source":"datacite","abstract":"Pharmacovigilance focuses on monitoring the safety of medicines by identifying, evaluating, and preventing adverse drug reactions after a drug is introduced into clinical use. Adverse drug reactions are unintended and harmful effects that may occur following the use of medicines and can significantly affect patient safety and treatment outcomes. The conventional pharmacovigilance process includes several stages such as the collection of safety data, medical review of case reports, coding of clinical information, causality assessment, and submission of reports to regulatory authorities. While these activities are essential for drug safety, they often require substantial time, skilled manpower, and technical resources, which can limit efficiency in large-scale safety monitoring. Recent developments in artificial intelligence have provided innovative tools to address these limitations in pharmacovigilance. Machine learning and natural language processing techniques enable automated analysis of large and complex safety datasets obtained from sources such as electronic health records, spontaneous reporting systems, and patient-reported data. These technologies support faster identification of adverse drug reactions, early detection of safety signals, and improved prediction of drug-drug interactions. By reducing manual workload, Al allows pharmacovigilance professionals to focus more on clinical evaluation and decision-making, thereby enhancing the overall quality of safety assessments. 0In addition to improving operational efficiency, artificial intelligence supports a shift towards more proactive and real-time drug safety surveillance. However, the integration of Al into pharmacovigilance also presents challenges, including concerns related to data privacy, algorithm bias, transparency, and regulatory acceptance. Addressing these issues through proper validation, ethical use, and regulatory alignment is crucial for the successful implementation of Al-based systems. This article discusses the role of artificial intelligence in strengthening pharmacovigilance practices and highlights its potential to improve accuracy, responsiveness, and public health protection.","url":"https://doi.org/10.5281/zenodo.19043059","authors":["Mrs. Deepa Chaudhary1, Pankaj Kumar Prajapati2*, Sagar Shukla3, Tarun Namdev4"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19043059","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/3qgjd","name":"Data collection and analysis from the Informatics Department of the Unified Health System in Brazil, using open source programming languages: a scoping review","source":"datacite","abstract":"1 INTRODUCTION The consolidation of the Brazilian Unified Health System (SUS) brought with it the need to develop instruments capable of recording, organizing, and providing information to support health management (Lima et al., 2009). Since its creation, SUS has required information systems capable of consolidating data in a structured and standardized manner, supporting both epidemiological surveillance and health planning. In this process, the Department of Informatics of SUS (DATASUS) assumed a strategic role by gathering, processing, and providing nationwide data, becoming one of the main sources of information for managers, researchers, and health professionals (Brazil, 2020a). The availability of reliable and accessible data is a central requirement for public policy formulation, indicator evaluation, and the development of research in public health (Costa et al., 2025). However, the use of these databases still faces challenges related to the decentralization of sources, heterogeneity of records, data quality, and periodic system updates, which may limit their applicability in more complex analyses (Brazil, 2020a). To overcome these limitations, the National Health Information and Informatics Policy (PNIIS) and the Digital Health Strategy for Brazil 2020–2028 (ESD28) have established guidelines focused on information governance, interoperability, and strengthening the technological infrastructure of SUS (Brazil, 2020b). Established in 2004, the PNIIS defined principles for the use of Information and Communication Technologies within SUS, emphasizing the integration of national information systems, improvement of data quality, and strengthening of health management (Brazil, 2004). These guidelines were later updated to incorporate goals related to interoperability and information governance (Brazil, 2021). Among the structuring initiatives for the modernization of SUS, the National Health Data Network (RNDS) stands out as a central platform integrating information from different systems and levels of care, promoting interoperability and providing essential data to support decision-making. Other initiatives include SUS Digital, through the “Meu SUS Digital” tool, and the PQDAS Program, which standardizes and improves the quality of supplementary health data in alignment with SUS data (Brazil; Ministry of Health; Secretariat of Digital Health Information, 2024). The adoption of digital technologies not only modernizes infrastructure but also transforms the interaction between citizens, professionals, and managers, increasing efficiency and integration of care. This approach represents the evolution of the concept of eHealth, expanding the use of digital resources beyond service informatization. Technologies such as artificial intelligence, big data, telemedicine, wearable devices, and mobile applications have been used to improve management, access, and quality of care (World Health Organization, 2021). According to the Ministry of Health (Brazil, 2020a), DATASUS integrates systems covering various areas of health care, such as the Hospital Information System (SIH), Ambulatory Information System (SIA), Mortality Information System (SIM), Live Birth Information System (SINASC), and the Notifiable Diseases Information System (SINAN), among others. Despite the breadth of these databases, challenges persist regarding data quality, updating, standardization, and interoperability, which may limit reliable analyses and evidence-based decision-making. Ensuring efficient integration among systems is essential so that managers and professionals have access to consistent and up-to-date information. Data collection and management in Brazil are directly linked to PNIIS and ESD28. While PNIIS establishes standards for organization, integration, and governance of health information, ESD28 defines priorities for digital technology use, including interoperability, professional training, and innovation. The articulation between these initi","url":"https://doi.org/10.17605/osf.io/3qgjd","authors":["Fonte, Danyelle Oliveira","de Sene Amâncio Zara, Ana Laura"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/3qgjd","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.14578179","name":"BANGLADESH JOURNAL of DENTAL RESEARCH & EDUCATION","source":"datacite","abstract":"www.bjdre.org Home About the Journal Journal Committe Instruction to Author Archives (BADI)® About the Journal Bangladesh Journal of Dental Research & Education (BJDRE) is the official publication of the Bangladesh Academy of Dentistry International (BADI)®. The journal publishes peer-reviewed scientific articles in dentistry and oral health sciences, supporting clinical practice, research, and dental education. Recognized by: Bangladesh Medical & Dental Council (BM&DC)ISSN (Online): 2308-9733ISSN (Print): 2225-9015 BJDRE is a peer-reviewed journal, aims to promote evidence-based dentistry and improve oral health through publication of high-quality research and scholarly articles. BJDRE welcomes submissions from clinicians, academicians, researchers and postgraduate students in dentistry and related disciplines. Publication Frequency BJDRE is published biannually (January and July) subject to editorial scheduling. Journal Sections Original Articles Review Articles Case Reports Short Communications Letters to the Editor Aims and Scope Aims BJDRE aims to:• Promote quality research and academic excellence in dentistry• Support evidence-based clinical practice• Encourage innovation in oral health care and dental education• Contribute to oral health promotion and public health policy Scope The journal considers manuscripts in:• Conservative dentistry & endodontics• Prosthodontics & implant dentistry• Periodontology• Oral & maxillofacial surgery• Orthodontics & dentofacial orthopedics• Pediatric dentistry• Oral pathology & oral medicine• Dental public health• Public Health• Dental materials and biomaterials• Dental education and research methodology Peer Review Policy The Bangladesh Journal of Dental Research & Education (BJDRE) follows a rigorous peer review process to ensure scientific quality, research integrity, and ethical publication standards. The journal adheres to internationally recognized editorial and publication ethics guidelines consistent with the recommendations of COPE, DOAJ and ICMJE. Peer Review System BJDRE uses a double-blind peer review system, in which the identities of both authors and reviewers remain confidential. Each manuscript is normally evaluated by at least two independent reviewers with expertise in the relevant field. Editorial Screening All submitted manuscripts undergo an initial evaluation by the Editor-in-Chief and the editorial team. This screening assesses: Completeness of the submission Compliance with journal scope and author guidelines Plagiarism screening Ethical compliance Manuscripts that do not meet the basic requirements of the journal may be rejected without external peer review.Typical screening timeline: 7–14 days Reviewer Selection and Evaluation Reviewers are selected based on subject expertise, academic qualifications, and prior experience in reviewing scholarly manuscripts. Reviewers must declare any conflicts of interest before accepting a review invitation. Manuscripts sent for external review are evaluated on the basis of: Originality and scientific significance Methodological quality and data analysis Validity of conclusions Ethical compliance and reporting standards Clarity and quality of writing Relevance to the scope of the journal Editorial Decision Based on reviewer comments and editorial assessment, the decision may include: Acceptance (with or without minor revisions) Minor or major revisions required Rejection with an option to resubmit Rejection due to lack of novelty or major scientific concerns The Editor-in-Chief makes the final decision after considering reviewer recommendations. Average Time from Submission to Publication The average time from manuscript submission to final publication is approximately 12 weeks, which includes editorial screening, peer review, author revisions, and final proofreading/typesetting. Editorial Independence If a manuscript is submitted by the Editor-in-Chief, editorial board members, or journal staff, the individual concerned will n","url":"https://doi.org/10.5281/zenodo.14578179","authors":["BJDRE"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2011","doi":"10.5281/zenodo.14578179","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.14578180","name":"BANGLADESH JOURNAL of DENTAL RESEARCH & EDUCATION","source":"datacite","abstract":"www.bjdre.org Home About the Journal Journal Committe Instruction to Author Archives (BADI)® About the Journal Bangladesh Journal of Dental Research & Education (BJDRE) is the official publication of the Bangladesh Academy of Dentistry International (BADI)®. The journal publishes peer-reviewed scientific articles in dentistry and oral health sciences, supporting clinical practice, research, and dental education. Recognized by: Bangladesh Medical & Dental Council (BM&DC)ISSN (Online): 2308-9733ISSN (Print): 2225-9015 BJDRE is a peer-reviewed journal, aims to promote evidence-based dentistry and improve oral health through publication of high-quality research and scholarly articles. BJDRE welcomes submissions from clinicians, academicians, researchers and postgraduate students in dentistry and related disciplines. Publication Frequency BJDRE is published biannually (January and July) subject to editorial scheduling. Journal Sections Original Articles Review Articles Case Reports Short Communications Letters to the Editor Aims and Scope Aims BJDRE aims to:• Promote quality research and academic excellence in dentistry• Support evidence-based clinical practice• Encourage innovation in oral health care and dental education• Contribute to oral health promotion and public health policy Scope The journal considers manuscripts in:• Conservative dentistry & endodontics• Prosthodontics & implant dentistry• Periodontology• Oral & maxillofacial surgery• Orthodontics & dentofacial orthopedics• Pediatric dentistry• Oral pathology & oral medicine• Dental public health• Public Health• Dental materials and biomaterials• Dental education and research methodology Peer Review Policy The Bangladesh Journal of Dental Research & Education (BJDRE) follows a rigorous peer review process to ensure scientific quality, research integrity, and ethical publication standards. The journal adheres to internationally recognized editorial and publication ethics guidelines consistent with the recommendations of COPE, DOAJ and ICMJE. Peer Review System BJDRE uses a double-blind peer review system, in which the identities of both authors and reviewers remain confidential. Each manuscript is normally evaluated by at least two independent reviewers with expertise in the relevant field. Editorial Screening All submitted manuscripts undergo an initial evaluation by the Editor-in-Chief and the editorial team. This screening assesses: Completeness of the submission Compliance with journal scope and author guidelines Plagiarism screening Ethical compliance Manuscripts that do not meet the basic requirements of the journal may be rejected without external peer review.Typical screening timeline: 7–14 days Reviewer Selection and Evaluation Reviewers are selected based on subject expertise, academic qualifications, and prior experience in reviewing scholarly manuscripts. Reviewers must declare any conflicts of interest before accepting a review invitation. Manuscripts sent for external review are evaluated on the basis of: Originality and scientific significance Methodological quality and data analysis Validity of conclusions Ethical compliance and reporting standards Clarity and quality of writing Relevance to the scope of the journal Editorial Decision Based on reviewer comments and editorial assessment, the decision may include: Acceptance (with or without minor revisions) Minor or major revisions required Rejection with an option to resubmit Rejection due to lack of novelty or major scientific concerns The Editor-in-Chief makes the final decision after considering reviewer recommendations. Average Time from Submission to Publication The average time from manuscript submission to final publication is approximately 12 weeks, which includes editorial screening, peer review, author revisions, and final proofreading/typesetting. Editorial Independence If a manuscript is submitted by the Editor-in-Chief, editorial board members, or journal staff, the individual concerned will n","url":"https://doi.org/10.5281/zenodo.14578180","authors":["BJDRE"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2011","doi":"10.5281/zenodo.14578180","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/rbpgk","name":"Applications of Large Language Models in Creativity and Knowledge Generation: A Scoping Review","source":"datacite","abstract":"This scoping review was conducted following the PRISMA-ScR guidelines to map existing literature on the role of artificial intelligence, particularly large language models, in enhancing scientific creativity within medical sciences. A systematic search was performed across PubMed, Google Scholar, and Dimensions AI, yielding 381 records. After removing duplicates, titles and abstracts were screened, and 100 articles were selected for full-text review. Of these, 79 studies were available for full-text screening and were independently assessed by two reviewers. Studies were included using the Population–Concept–Context (PCC) framework, focusing on medical researchers and professionals (population), the impact of AI on scientific creativity (concept), and healthcare-related scientific disciplines (context). Relevant data were extracted on study characteristics, AI applications, creativity measures, and research outcomes. The findings were synthesized through thematic analysis, identifying five major themes: AI as a tool for scientific creativity, AI-enhanced creativity in medical education, ethical and legal challenges, human AI creative collaboration, and technological innovation in science education.","url":"https://doi.org/10.17605/osf.io/rbpgk","authors":["Koirala, Diwakar"],"tags":["Medicine and Health Sciences","Creativity","Large Language Model","Medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/rbpgk","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19000149","name":"On the Implementation of AI systems in Oncology","source":"datacite","abstract":"Traditional cancer diagnosis is often hindered by subjective interpretation, high costs, and significant false-negative rates, which can delay vital early-stage detection and reduce patient survival rates. To address these limitations, Artificial Intelligence (AI) and Deep Learning (DL) frameworks have emerged as transformative tools in modern oncology. This review evaluates the application of DL and Convolutional Neural Networks (CNNs) in medical imaging, specifically focusing on Computer-Aided Diagnosis (CAD), automated image segmentation, and lesion classification across lung, breast, brain, cervical, and liver cancers. While these AI systems demonstrate diagnostic accuracy and precision comparable to professional radiologists, their clinical implementation is currently obstructed by the “unexplainability” of complex algorithms, the scarcity of high-quality training datasets, and profound ethical concerns regarding patient privacy and data consent. Furthermore, this paper explores the critical role of Explainable AI (XAI), including techniques like SHAP, LIME, and Grad-CAM, in bridging the accuracy-explainability tradeoff by making AI reasoning transparent. Ultimately, fostering clinical trust through transparent reasoning and ethically sourced data is essential for the future integration of AI in daily diagnostic workflows.","url":"https://doi.org/10.5281/zenodo.19000149","authors":["Das, Sahil"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19000149","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.19000148","name":"On the Implementation of AI systems in Oncology","source":"datacite","abstract":"Traditional cancer diagnosis is often hindered by subjective interpretation, high costs, and significant false-negative rates, which can delay vital early-stage detection and reduce patient survival rates. To address these limitations, Artificial Intelligence (AI) and Deep Learning (DL) frameworks have emerged as transformative tools in modern oncology. This review evaluates the application of DL and Convolutional Neural Networks (CNNs) in medical imaging, specifically focusing on Computer-Aided Diagnosis (CAD), automated image segmentation, and lesion classification across lung, breast, brain, cervical, and liver cancers. While these AI systems demonstrate diagnostic accuracy and precision comparable to professional radiologists, their clinical implementation is currently obstructed by the “unexplainability” of complex algorithms, the scarcity of high-quality training datasets, and profound ethical concerns regarding patient privacy and data consent. Furthermore, this paper explores the critical role of Explainable AI (XAI), including techniques like SHAP, LIME, and Grad-CAM, in bridging the accuracy-explainability tradeoff by making AI reasoning transparent. Ultimately, fostering clinical trust through transparent reasoning and ethically sourced data is essential for the future integration of AI in daily diagnostic workflows.","url":"https://doi.org/10.5281/zenodo.19000148","authors":["Das, Sahil"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19000148","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.17605/osf.io/nd47g","name":"The Impact of Large Language Models and Artificial Intelligence Tutors on Medical Residency Exam Preparation: A Scoping Review","source":"datacite","abstract":"The rapid integration of Artificial Intelligence (AI) and Large Language Models (LLMs) has fundamentally transformed medical education, driving a transition from traditional teaching methodologies toward dynamic, highly personalized learning environments [1, 2, 3]. These technologies are increasingly embedded in learner centred curricula, where intelligent tutoring systems support continuous performance monitoring and individualized feedback [1, 3]. AI systems can track learner errors, adjust the difficulty of content, and provide just-in-time remediation, tailoring instruction to each student’s progression [1, 3]. Furthermore, immersive simulations and AI-mediated patients allow learners to rehearse diagnostic reasoning in realistic, risk-free settings, reinforcing clinical decision-making while reducing dependence on limited human instructors [1, 2]. This reflects a structural shift toward AI-supported, multimodal learning ecosystems [2]. Medical licensing and residency entrance examinations, such as the USMLE, MIR, or ENARM, constitute high-stakes milestones essential for independent practice [4]. The intense cognitive load, time pressure, and perceived stakes of these assessments often generate significant stress, anxiety, and burnout, potentially undermining well-being during critical phases of professional identity formation [5,6]. Simultaneously, students frequently rely on traditional preparation methods, such as static question banks and passive reading, which are increasingly viewed as outdated [3]. These conventional, one-size-fits-all strategies fail to adapt to individual learning paces, lack the dynamic feedback needed to cultivate complex clinical reasoning, and struggle to keep up with the continually expanding biomedical literature [3]. At the intersection of these needs, LLMs have emerged as disruptive tools for exam preparation and self-directed study [7]. A rapidly expanding body of empirical work shows that pooled LLM accuracy now consistently approaches or surpasses human pass thresholds across multiple international licensing systems, with newer multimodal architectures (e.g., GPT-4o, DeepSeek-R1) ranking among the best-performing models [4]. Using techniques like chain-of-thought prompting, these models effectively narrow the gap with human examinees [8]. Crucially, LLMs function beyond mere static answer keys; they act as interactive assistants mediating scenario-based learning [9]. They generate clinical vignettes, simulate patient histories, and provide immediate explanatory feedback, allowing students to practice history-taking, diagnostic questioning, and patient centred communication within a flexible, on-demand environment [3, 9]. Despite evidence that LLMs can successfully solve exam-style questions, a critical gap persists regarding their true pedagogical impact, long-term effects on knowledge retention, and students' perceptions in high-stakes contexts [9, 10]. Current literature reveals substantial heterogeneity in study designs and evaluation frameworks, making it difficult to draw robust conclusions about learning gains [9]. Furthermore, conceptual analyses emphasize that while generative AI may scaffold critical thinking under guided use, uncritical reliance risks cognitive offloading, erosion of self-regulated learning, and diminished opportunities for the deliberate practice central to clinical expertise [10]. Existing evidence is largely short-term and focused on controlled tasks, underscoring the need for systematic research linking AI use to authentic educational outcomes [10]. This lack of standardized evaluation frameworks is compounded by serious ethical concerns regarding algorithmic bias, hallucinations, and technological dependence [3]. Biased training data can reproduce health disparities, risk the normalization of skewed diagnostic heuristics and expose students to inequitable clinical exemplars [3]. Concurrently, AI-driven \"infodemic\" and hallucinations—plausible but factuall","url":"https://doi.org/10.17605/osf.io/nd47g","authors":["Chaves, Natalia Castaño","Rincon, Erwin Hernando Hernandez"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/nd47g","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.18994448","name":"ARTIFICIAL INTELLIGENCE AS A MARKETING TOOL IN DENTISTRY","source":"datacite","abstract":"The rapid development of artificial intelligence (AI) technologies is opening up new opportunities for dental marketing. This literature review analyzes relevant publications on the use of AI in the promotion of dental clinics and services. Key areas of application are examined: personalization of the patient experience, chatbots and automated communication, online reputation management, and targeted advertising based on machine learning algorithms. The review covers publications from 2015–2025. It has been established that integrating AI into dental clinic marketing strategies can significantly increase patient retention, improve patient engagement, and optimize operational costs for promotion. However, a number of unresolved issues are identified: ethical issues of data processing, insufficient digital literacy among medical personnel, and a limited evidence base in the dental context.","url":"https://doi.org/10.5281/zenodo.18994448","authors":["Bakhtiyorova, Nigorakhon","Musayev, Ulugbek","Xaydarova, Muxayyo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18994448","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.5281/zenodo.18994449","name":"ARTIFICIAL INTELLIGENCE AS A MARKETING TOOL IN DENTISTRY","source":"datacite","abstract":"The rapid development of artificial intelligence (AI) technologies is opening up new opportunities for dental marketing. This literature review analyzes relevant publications on the use of AI in the promotion of dental clinics and services. Key areas of application are examined: personalization of the patient experience, chatbots and automated communication, online reputation management, and targeted advertising based on machine learning algorithms. The review covers publications from 2015–2025. It has been established that integrating AI into dental clinic marketing strategies can significantly increase patient retention, improve patient engagement, and optimize operational costs for promotion. However, a number of unresolved issues are identified: ethical issues of data processing, insufficient digital literacy among medical personnel, and a limited evidence base in the dental context.","url":"https://doi.org/10.5281/zenodo.18994449","authors":["Bakhtiyorova, Nigorakhon","Musayev, Ulugbek","Xaydarova, Muxayyo"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18994449","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.48550/arxiv.2603.05884","name":"Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness","source":"datacite","abstract":"Recent breakthroughs in artificial intelligence through foundation models and agents have accelerated the evolution of computational pathology. Demonstrated performance gains reported across academia in benchmarking datasets in predictive tasks such as diagnosis, prognosis, and treatment response have ignited substantial enthusiasm for clinical application. Despite this development momentum, real world adoption has lagged, as implementation faces economic, technical, and administrative challenges. Beyond existing discussions of technical architectures and comparative performance, this review considers how these emerging AI systems can be responsibly integrated into medical practice by connecting deployable clinical relevance with downstream analytical capabilities and their technical maturity, operational readiness, and economic and regulatory context. Drawing on perspectives from an international group, we provide a practical assessment of current capabilities and barriers to adoption in patient care settings.","url":"https://doi.org/10.48550/arxiv.2603.05884","authors":["Da, Qian","Chen, Yijiang","Ju, Min","Ji, Zheyi","Zhou, Albert","Wang, Wenwen","Abikenari, Matthew A","Chikontwe, Philip","Larghero, Guillaume","Chen, Bowen","Neidlinger, Peter","Zhong, Dingrong","Wang, Shuhao","Xu, Wei","Williamson, Drew","Corredor, German","Yang, Sen","Lu, Le","Han, Xiao","Yu, Kun-Hsing","Huang, Jun-zhou","Barisoni, Laura","Litjens, Geert","Madabhushi, Anant","Zhu, Lifeng","Wang, Chaofu","Zhao, Junhan","Hu, Weiguo"],"tags":["Computational Engineering, Finance, and Science (cs.CE)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.05884","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:47:58.476Z"},{"id":"doi:10.32388/x3o2n6.3","name":"Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative Review","source":"preprints","abstract":"The rapid global expansion of artificial intelligence (AI), particularly generative models, drives energy-intensive data centers with substantial environmental and public health costs. This narrative review synthesizes information obtained from the scientific literature confirming contributions of AI to greenhouse gas emissions, freshwater depletion, e-waste, and air pollution from fossil-powered grids. Public health risks include algorithmic bias exacerbating disparities, AI-generated misinformation/deepfakes eroding trust, privacy loss, mental health harms, and job displacement impacting social determinants. These burdens disproportionately affect marginalized communities via environmental justice failures and biased algorithms. While acknowledging that certain AI applications, particularly in climate modeling, medical diagnostics, and energy optimization, may offer net benefits under appropriate governance, this review focuses on the documented adverse impacts of current large-scale, commercial AI deployment patterns._ _Mitigation demands life-cycle assessments, renewable energy mandates, circular hardware economies, bias audits, and policies prioritizing health equity. Sustainable AI requires coordinated action across stakeholders. Implementing these mitigation strategies is constrained by major obstacles in cost, technical infrastructure, and governance. Overcoming these barriers requires the development of comprehensive economic analyses and structured strategic roadmaps.","url":"https://doi.org/10.32388/x3o2n6.3","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.32388/x3o2n6.3","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.14293/pr2199.003002.v1","name":"Artificial Intelligence-Augmented Analytical Method Development and Validation in Pharmaceutical Manufacturing: Current Applications, Regulatory Landscape, and Future Directions","source":"preprints","abstract":"The pharmaceutical, biotechnology, and medical device industries are undergoing a paradigm shift as artificial intelligence (AI) and machine learning (ML) technologies are increasingly integrated into analytical method development, validation, and lifecycle management. Traditional approaches to method development, while well-established under regulatory frameworks such as ICH Q2(R2) and the recently adopted ICH Q14, are often labor-intensive, iterative, and resource-constrained. This review examines the current landscape of AI-augmented analytical method development and validation across diverse pharmaceutical modalities, including small molecules, biologics, ophthalmic emulsions, and lipid nanoparticle-based mRNA vaccine delivery systems. The paper discusses how AI and ML tools, including deep learning, predictive analytics, and computer vision, are being applied to accelerate method optimization, enhance robustness evaluation, predict method performance, and strengthen data integrity throughout the analytical procedure lifecycle. The regulatory context is explored in depth, with particular attention to the ICH Q14 enhanced approach, the analytical target profile concept, and the role of GAMP 5 and ALCOA+ principles in ensuring that AI-driven analytical workflows remain compliant and audit-ready. Key challenges, including model interpretability, validation of AI tools themselves, regulatory acceptance, and data quality, are examined alongside emerging opportunities such as multi-omics integration, generative AI for method design, and predictive bio-cyber resilience frameworks. This review concludes with a set of recommendations for researchers, regulators, and industry practitioners seeking to harness AI for next-generation analytical method development while maintaining the scientific rigor and regulatory compliance that underpin patient safety and product quality.","url":"https://doi.org/10.14293/pr2199.003002.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14293/pr2199.003002.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.20944/preprints202602.0240.v1","name":"The Use of Artificial Intelligence (AI) in the Management of Medical Rehabilitation Programs","source":"preprints","abstract":"The adoption of AI technologies in rehabilitation is transforming physiotherapy, enabling more accurate, streamlined, and personalized treatment strategies. While virtual reality (VR) and robotics have already brought significant advancements to the field, AI elevates rehabilitation by offering data-driven insights, real-time patient monitoring, and adaptive treatment plans. Our study explores the role of AI in managing medical rehabilitation programs, presenting a comprehensive review of the latest empirical studies. The findings reveal that AI-based technologies, such as machine learning algorithms, computer vision, and predictive analytics, not only improve treatment outcomes but also increase patient engagement and accessibility. Furthermore, AI-driven systems enable continuous progress tracking, early detection of complications, and tailored therapy adjustments, fostering a more responsive and effective rehabilitation process. This article underscores that integrating AI into medical rehabilitation programs marks a transformative shift in patient care, promising more dynamic, individualized, and impactful recovery.","url":"https://doi.org/10.20944/preprints202602.0240.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202602.0240.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.64898/2026.06.24.26356504","name":"Gaps in Congenital Heart Disease Care: Social Drivers and Clinical Consequences","source":"preprints","abstract":"Background Gaps in care (GIC) among patients with congenital heart disease (CHD) are associated with adverse outcomes, yet the specific social and healthcare-related factors contributing to GIC and the clinical consequences of delayed re-engagement in care remain poorly characterized. Large electronic medical record datasets often cannot distinguish true GIC from clinically appropriate care patterns or capture the patient-level factors contributing to GIC. Methods We conducted a retrospective cohort study, combining large data with manual chart review, of 1,746 patients of all ages with surgically repaired CHD between 2003 and 2020 at a tertiary care center serving four states. GIC was defined as more than 3 years and 3 months between cardiology visits and exceeding the physician recommended follow-up interval. Results Of the cohort, 916 patients (52%) met criteria for potential GIC. Following a structured manual chart review, a substantial subset was reclassified as having appropriate care, leaving 275 patients (15.7%) with true GIC. After multivariable adjustment, older age and simple anatomic CHD complexity were independently associated with GIC. Among patients with GIC, 17.8% had a documented contributor, most commonly insurance instability or social factors. Forty-one and one-half percent returned to care (RTC), and many were asymptomatic but had significant disease progression. Thirteen percent of patients who RTC required cardiac intervention, including semi-urgent or urgent procedures, and 26.7% of those requiring intervention experienced significant morbidity or mortality, including stroke, infective endocarditis, urgent transplant referral, or death. These outcomes occurred across all levels of CHD complexity, including patients with simple CHD. Conclusions GIC remain prevalent in patients with surgically repaired CHD and are associated with significant morbidity and mortality across the full spectrum of anatomic complexity. They are most often driven by insurance instability and social vulnerability rather than clinical factors, and many adverse outcomes may be preventable with consistent longitudinal care. These findings support a shift toward proactive care models that integrate standardized follow-up pathways, systematic assessment of patient-level needs, and emerging analytic tools to identify at-risk patients before GIC occur. CLINICAL PERSPECTIVE What Is New? Gaps in care (GIC) remained common among patients with surgically repaired CHD and were associated with insurance instability, social vulnerability, asymptomatic disease progression, major adverse clinical events, and death. What Are the Clinical Implications? Reducing GIC and its consequences will require systematic assessment of social and patient-reported needs, education that asymptomatic status does not imply disease stability, and emerging predictive tools such as artificial intelligence to identify patients at risk for disengagement before adverse outcomes occur.","url":"https://doi.org/10.64898/2026.06.24.26356504","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.24.26356504","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.31234/osf.io/ctk7j_v3","name":"Designing Psychometric Measures for LLMs: Framework and Application to Racial Bias","source":"preprints","abstract":"Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years, several LLMs have been introduced, including ChatGPT by OpenAI, Claude by Anthropic, and Llama by Meta, among others. These models have the potential to be employed across a wide range of human–machineinteraction applications, such as chatbots for information retrieval, assistance in corporate hiring decisions, college admissions, financial loan approvals, parole determinations, and even in medical fields like psychotherapy delivered through chatbots. The key question is whether these chatbots will interact with humans in a bias-free manner or if they will further reinforce the existing pathological biases present in human-to-human interactions. If the latter is true, then how can werigorously measure these biases?We address this challenge by introducing STAMP-LLM (Standardized Test Assessment Measurement Protocol for LLMs), a principled two-phase framework for designing psychometric measures to evaluate chatbot biases: (i) a Definitional phase for construct mapping, item development, and expert review; and (ii) a Data/Analysis phase for protocol control (prompts/decoding), automated sampling,pre-specified scoring, and basic reliability/validity checks. We illustrate STAMP-LLM on racial bias using one explicit and two implicit measures.","url":"https://doi.org/10.31234/osf.io/ctk7j_v3","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31234/osf.io/ctk7j_v3","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.12688/f1000research.175198.1","name":"Protocol for Conducting a Scoping Review on The Use of AI in Automated Scoring of Short-Answer Questions in Medical Education","source":"preprints","abstract":"Assessment plays a central role in medical education by evaluating learners’ knowledge, skills, and professional competencies. While multiple-choice questions (MCQs) are widely used due to their efficiency and broad content coverage, they primarily assess recall and recognition, limiting their ability to measure higher-order reasoning. Short-answer questions (SAQs), in contrast, promote deeper cognitive processing and provide better discrimination between levels of student performance. However, SAQs are resource-intensive to grade and susceptible to scorer inconsistency and rater bias, highlighting a need for more efficient and reliable assessment solutions. Artificial Intelligence (AI) has emerged as a transformative tool in medical education, enhancing learning, supporting adaptive instruction, and automating assessment processes. AI-driven systems using machine learning and natural language processing have been increasingly applied to automated scoring of SAQs. These systems offer potential benefits, including reduced grading burden, greater scoring consistency, and timely feedback to learners. Despite promising developments, concerns persist regarding algorithmic transparency, data privacy, and the reliability and validity of automated scoring compared with human graders. Existing studies report mixed results, underscoring the need for a comprehensive examination of current approaches. This scoping review aims to systematically map the literature on AI-based models used for automated scoring of SAQs in medical education. Specifically, it seeks to identify the types of AI models employed, evaluate their accuracy and reliability relative to human graders, describe reported advantages and challenges, and assess fairness and feasibility within educational settings. Following the Joanna Briggs Institute methodology and the Population–Concept–Context framework, the review will include empirical studies published since 2015 involving medical students and AI-driven SAQ scoring. Findings will provide an evidence-based overview of current practices, highlight gaps in the literature, and inform future research and implementation strategies for AI-assisted assessment in medical education.","url":"https://doi.org/10.12688/f1000research.175198.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.12688/f1000research.175198.1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.20944/preprints202604.0792.v1","name":"Unannotated Genes in Genomics: Challenges, Opportunities, and AI Solutions","source":"preprints","abstract":"The rapid proliferation of next-generation sequencing (NGS) technologies has generated an unprecedented volume of genomic data, yet a substantial fraction of these sequenced genomes remains functionally uncharacterized, a phenomenon collectively termed genomic dark matter. Unannotated genes, including hypothetical proteins (HPs), orphan and de novo genes, small open reading frames (smORFs), and non-canonical ORFs (ncORFs), constitute 40–60% of bacterial genomes, approximately 30–35% of the human proteome, and up to 43% of metagenomic protein clusters. These uncharacterized sequences represent a critical bottleneck in translating genomic data into biological insight and biotechnological innovation. This review provides a comprehensive examination of the categories of unannotated genes, the systemic challenges that perpetuate the annotation gap, and the diverse biotechnological opportunities these sequences harbor across plant, animal, microbial, medical, and industrial domains. Critically, we evaluate the transformative role of artificial intelligence (AI) in bridging this gap, encompassing protein structure prediction tools such as AlphaFold2 and ESMFold, protein and genome language models including ESM2 and DNABERT-2, deep learning-based functional inference frameworks, and high-throughput experimental validation platforms such as CRISPR perturbomics and transposon-insertion sequencing (TIS). We argue that an integrative, AI-driven approach to functional genomics is not merely advantageous but essential for realizing the full potential of the genomic revolution.","url":"https://doi.org/10.20944/preprints202604.0792.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202604.0792.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202602.2017.v1","name":"NEXUS: A Multi-Agent Architectural Position Paperfor Autonomous Insurance Transitioning from Human-Default to AI-Native Decision Environments","source":"preprints","abstract":"Modern insurance organizations have adopted artificial intelligence in narrow, task-specific roles, resulting in fragmented systems that optimize isolated functions without fundamentally reshaping the underwriting and claims lifecycle. This “incrementalism” yields a human-default, sequential process plagued by structural bottlenecks, inconsistent risk evaluation, and limited transparency. This paper introduces NEXUS (Next-Generation Executive Underwriting and Settlement Intelligence), a framework to re-architect insurance as an AI-native system. NEXUS transitions AI from a peripheral tool to the primary orchestrator of end-to-end processes, conceptualizing the insurance lifecycle as a conversational, agent-orchestrated workflow. It is realized through a unified conversational interface that coordinates a decentralized ecosystem of specialized, collaborative AI agents each responsible for domain-specific reasoning such as geospatial risk assessment, financial verification, or medical outcome analysis. The central innovation is the Truth Score Engine (TSE), a governance-first aggregation mechanism that non-linearly synthesizes agent outputs by weighting evidentiary provenance, confidence estimates, and cross-agent consistency. The TSE governs decisions via a Three-Tiered Confidence Protocol: • High Confidence ( 90%) validates outcomes for immediate human sign-off without re-verification; • Medium Confidence (60-90%) routes decision summaries for targeted human review of specific flags; • Low Confidence ( 60%) escalates cases as ‘’Risky,’’ reverting to traditional manual investigation. This protocol yields a single, auditable decision artifact while preserving full traceability of the reasoning pathway. By embedding multi-agent coordination, contextual awareness, and tiered governance at the architectural level, NEXUS demonstrates a scalable pathway toward adaptive, transparent insurance systems. It ensures precision, combats fraud, and dramatically reduces settlement time, positioning AI-native governance as a foundational requirement for deploying trusted, autonomous decision-making in high-stakes financial domains.","url":"https://doi.org/10.20944/preprints202602.2017.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202602.2017.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202603.2415.v1","name":"The Evolution of Laryngoscopy","source":"preprints","abstract":"The work of anesthesiologists–intensivists, critical care specialists, and medical emergency teams is multifaceted and complex, with airway management representing a cornerstone and a common denominator of many procedures and interventions. Tracheal intubation has represented, and still represents today, the gold standard for airway control. Laryngoscopy evolution has progressed in parallel with technological development and innovation, resulting in the evolution of new skills and in the expansion of possibilities and safety for patient care. The evolution of laryngoscopy essentially took place between the late 1800s and the first half of the 1900s, with the consecration of the MacIntosh laryngoscope. Almost 50 years later, the world witnessed a pivotal turning point around the 2000s with the introduction of videolaryngoscopes. Along this path, the devices that have succeeded one another introduced new problems, driving the search for new solutions. At present day, tracheal intubation with videolaryngoscopy has achieved success and safety standards that are certainly superior, if not unimaginable, when compared with the early days of the technique. In this review we will retrace the historical aspects of the evolution of laryngoscopy, analyzing the problems that have emerged over time with the various devices and the solutions adopted. We will then examine the evolution of videolaryngoscopes, the impact of these devices on both technical skills and non-technical skills, as well as the debate surrounding their routine use (universal videolaryngoscopy) and the choice of the best adjuncts to optimize success during their use, including the application of assistive artificial intelligence to improve both success rates and the learning curve. This journey, after 150 years of evolution, has probably reached today the highest possible level of expression in terms of safety and efficacy.","url":"https://doi.org/10.20944/preprints202603.2415.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202603.2415.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8800183/v1","name":"What is \"Medicine\"? A scoping review as baseline for the Generative AI era","source":"preprints","abstract":"Abstract The meaning of “medicine” has received very little systematic attention. This scoping review summarizes published English-language efforts to define or conceptualize “medicine” and serves as a baseline for an era in which generative artificial intelligence (GenAI) and other computational systems increasingly participate in medical activities, reshaping the notion of expertise, and blurring the limits of authority, accountability and governance. We searched PubMed, Embase and the Cochrane Database of Systematic Reviews; Google Scholar (to March 2020); the top 11 English dictionaries; and the websites of the world’s top 10 ranked medical schools, all 113 members of the World Medical Association, and other major institutions. We included any text-based definition or conceptualization and extracted data in duplicate, copying eligible statements verbatim. The searches yielded 5,341 citations; 17 sources contained eligible statements. Of these, 12 mentioned health, 10 considered medicine as a science, and 9 focused on the prevention, treatment or cure of diseases. All dictionaries provided at least one eligible statement, most emphasizing disease/illness and some mentioning health. None of the screened medical schools, associations or institutions offered an explicit definition or conceptualization, and no source described a systematic, replicable process to capture the meaning of “medicine.” Bold, systematic and replicable initiatives are needed to fill this gap, to separate medicine from other professions, and to clarify its role in the creation and preservation of health beyond the chemical-mechanical view of patients and their diseases. Without systematic definition or conceptualization, medicine risks being shaped by operational forces, such as billing structures, automation capabilities, and platform incentives, rather than by principled commitments about its purpose and boundaries.","url":"https://doi.org/10.21203/rs.3.rs-8800183/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8800183/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.08.01.26359453","name":"Predicting Unplanned Hospital Readmissions in People with Multiple Long-Term Conditions","source":"preprints","abstract":"The prevalence of multiple long-term conditions (MLTCs) is associated with increased healthcare utilisation and an elevated risk of unplanned 30-day hospital readmission. Existing prediction tools predominantly focus on single-disease cohorts and fail to capture the clinical heterogeneity, polypharmacy, and care complexity characteristic of MLTC populations. Using data from 99,207 UK Biobank (UKBB) participants with MLTCs ( ≥ 2 long-term conditions), we developed Self-HR, a two-stage self-supervised learning framework that learns transferable patient representations from longitudinal clinical data encompassing hospital admission diagnoses, primary care prescriptions, long-term condition histories, and demographic factors. Self-HR achieved an AUROC of 0.92 and AUPRC of 0.75 in the UKBB discovery cohort, outperforming all supervised baselines — including XGBoost, Random Forest, and fully supervised neural networks — across both overall and minority-class metrics. Performance was sustained upon external validation in 79,224 multimorbid individuals from the Clinical Practice Research Datalink (CPRD; AUROC 0.86, F1 score 0.67 for the readmitted class). Self-HR demonstrated superior robustness to partial outcome labelling and class imbalance, maintaining an F1 score of 0.62 for readmitted patients when trained on only 50% labelled data, compared with 0.28 for the best supervised comparator. Ablation analyses identified incident admission diagnoses as the strongest predictive feature, followed by primary care prescriptions and long-term condition history. Beyond binary classification, Self-HR generalised to regression tasks — predicting incident and emergency admission durations — through fine-tuning alone, achieving the lowest MAE and RMSE across all tasks without repeat pretraining. These findings support Self-HR as a data-efficient and generalisable framework for readmission risk prediction in multimorbid populations, with potential to inform proactive discharge planning and targeted post-discharge care.","url":"https://doi.org/10.64898/2026.08.01.26359453","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.08.01.26359453","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.03.11.26347789","name":"Regression vs. Medical LLMs: A Comprehensive Study for CVD and Mortality Risk Prediction","source":"preprints","abstract":"Cardiovascular diseases (CVDs) remain the foremost cause of global morbidity and mortality, driving an urgent need for robust predictive tools that enable early detection and preventive intervention. Traditional regression-based models—such as linear and logistic regression, regression trees and forests, and Support Vector Machines (SVMs)—have long underpinned CVD risk estimation but often assume linear relationships, homogeneous effects across populations, and a limited number of predictors. Recent advances in regression, such as bagging and boosting, as well as Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are increasingly shifting this paradigm. In this paper, we review key developments in the context of both classic regression techniques and recent GenAI approaches, and we put a particular focus on openly available Medical LLMs (MedLLMs) in combination with few-shot prompting and classification finetuning. Based on the LURIC cardiovascular health study, we investigate a broad variety of biomarkers and risk factors under two different cohorts of 3,316 CVD risk patients who underwent coronary angiography in Germany between 1997 and 2000. Our results demonstrate that large, pretrained MedLLMs (70B) achieve up to 82% AUROC for 1-year all-cause mortality (1YM) prediction with optimized few-shot prompting, thus performing competitively with recent regression techniques and state-of-the-art methods from the medical literature such as CoroPredict, SMART and SCORE2. Smaller models (8B) can be finetuned to match or even surpass their larger counterparts as well as commercial models like ClaudeSonnet-4.5 and ChatGPT-5.2. Among all evaluated approaches, the best-performing boosting-based regression technique (CatBoost) and commercial LLM (Gemini-3-Flash) both achieve an AUROC of up to 85%. Further model-calibration and -stratification analyses reveal a systematic mortality over-prediction (ECE: 0.05–0.10) of MedLLMs, while Platt scaling effectively reduces such miscalibrations by 60–90%.","url":"https://doi.org/10.64898/2026.03.11.26347789","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.11.26347789","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202602.0238.v1","name":"From Exposure to Atherosclerosis: The Cardiovascular Impact of Phthalates and Implications for IHD Prevention","source":"preprints","abstract":"Despite decades of interventions targeting modifiable risk factors to reduce the burden of cardiovascular disease, ischemic heart disease (IHD) remains the leading cause of mortality and the second leading cause of disabilityadjusted lifeyears worldwide. Growing evidence suggests that phthalates - plasticizers widely used in consumer products, cosmetics, and medical devices, and therefore ubiquitous across environmental media - may contribute to IHD development. Epidemiological studies have reported associations between phthalate exposure and multiple markers of atherosclerosis, the pathological hallmark of IHD, with or without mediation by traditional cardiovascular risk factors. Experimental models support these findings, showing that phthalates can induce oxidative stress, mitochondrial dysfunction, apoptosis, lipid accumulation, and epigenetic alterations, all of which promote endothelial damage and atherogenesis. In this review, we synthesize current epidemiological findings linking phthalate exposure to IHD, describe the main cellular and molecular mechanisms involved, and outline research gaps and regulatory perspectives. We also discuss how novel analytical frameworks- including Artificial Intelligence - may enhance the integration of environmental, clinical, and molecular data to advance risk prediction and prevention strategies.","url":"https://doi.org/10.20944/preprints202602.0238.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202602.0238.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.04.03.26350064","name":"From Registration to Insight: How STRONG AYA Transforms Registry Data to Enhance Decision-Support Tools for Adolescent and Young Adult Oncology","source":"preprints","abstract":"Background Population-based cancer registers (PBCR) are important for monitoring trends in cancer epidemiology, facilitating the implementation of effective cancer services. Adolescents and Young Adult (AYA) with cancer are a patient group with a unique set of needs. The utility of PBCR in AYA is limited by the lack of AYA-specific data items. STRONG AYA, an international multidisciplinary consortium is addressing this through federated learning (FL) methodology and novel data visualisation concepts. A Core Outcome Set (COS) has been developed to measure outcomes of importance through clinical data and Patient Reported Outcomes (PROs). We describe how data from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), a PBCR in the UK is being used within STRONG AYA and how the subsequent analyses can guide patient consultations. Methods Data from the YSRCCYP were imported into a Vantage 6 node, from which FL analyses are performed along with data provided by other consortium members. The results are extracted into the PROMPT software and integrated into patient electronic healthcare records. Results Healthcare professionals can view the results of individual PROs at various time points and in comparison, to summary analyses carried out within the STRONG AYA infrastructure. Results can be filtered by age, disease, country and stage. Conclusion We have demonstrated how a regional PBCR can contribute to a pan-European infrastructure and analyses viewed to enhance patient consultations. Such analyses have the potential to be used for research and policy-making, improving outcomes for AYA.","url":"https://doi.org/10.64898/2026.04.03.26350064","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.04.03.26350064","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7419701/v1","name":"Identification of adventitious lung sounds in children with respiratory illnesses in Bangladesh using Artificial Intelligence and Digital Auscultation","source":"preprints","abstract":"Abstract Integrated management of childhood illness (IMCI) guidelines have high sensitivity but low specificity for pneumonia diagnosis. Artificial intelligence (AI)-enabled digital stethoscopes capable of analyzing lung sounds may improve IMCI diagnostic performance. We evaluated the performance of an AI algorithm trained to identify normal and adventitial lung sounds in children. Non-physician health workers recorded lung sounds from four chest positions using a digital stethoscope in under-five-year-old children with suspected pneumonia at community clinics in Bangladesh. A trained paediatrician listening panel classified chest position recordings as normal, abnormal (crackles and/or wheeze), or uninterpretable. The AI algorithm similarly classified chest position recordings except for the uninterpretable category. AI algorithm and listening panel comparisons were made at the child-level, and included chest positions considered interpretable, uninterpretable, and high- and low-confidence by the panel. Of 990 enrolled children, 867 (87%) had at least 3 interpretable chest position recordings by the panel and were analyzed. Compared to the panel, AI algorithm sensitivity and specificity for detecting abnormal sounds were 61.8% and 60.7% among all children, and 63.5% and 66.8% in IMCI pneumonia cases. Overall, the AI algorithm achieved moderate classification performance. Classification performance will benefit from further AI algorithm training to categorize recordings as uninterpretable.","url":"https://doi.org/10.21203/rs.3.rs-7419701/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-7419701/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202601.0502.v1","name":"Integrating Digital Health into Oncology: A Comprehensive Review","source":"preprints","abstract":"Background: /Objectives: Digital health encompasses telemedicine, mobile health (mHealth), wearable technologies, big data analytics, artificial intelligence (AI), machine learning (ML), and immersive technologies. In oncology, where care is complex, multidisciplinary, and longitudinal, these tools offer opportunities to enhance prevention, early detection, treatment planning, patient–clinician communication, survivorship, and palliative care. However, inconsistent definitions and ongoing ethical, regulatory, and implementation challenges hinder optimal integration. This review aims to synthesize current evidence on digital health in oncology and examine its applications across the cancer care continuum. Methods: A comprehensive narrative review of peer-reviewed literature was conducted, including clinical studies, trials, and systematic reviews evaluating digital health technologies in oncology. Evidence was organized according to key phases of the cancer care continuum, from prevention and diagnosis to treatment delivery, survivorship, and end-of-life care. Results: Digital health applications extend beyond virtual consultations. AI- and ML-driven systems support diagnostics, medical imaging, genomics, and treatment planning, while mHealth applications and wearable devices enable real-time symptom monitoring, toxicity reporting, and long-term follow-up. Digital education and communication platforms improve shared decision-making and patient engagement. Across diverse oncology settings, these tools demonstrate feasibility, high patient and clinician satisfaction, and potential improvements in care coordination and efficiency. Nevertheless, challenges related to data quality, interoperability, privacy, algorithmic bias, equity of access, and regulatory oversight persist. Conclusions: Digital health is increasingly embedded across the oncology care continuum and holds substantial promise for advancing personalized, patient-centred cancer care. Continued multidisciplinary collaboration, robust clinical validation, and responsible governance are essential to ensure safe, equitable, and clinically meaningful global implementation.","url":"https://doi.org/10.20944/preprints202601.0502.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.20944/preprints202601.0502.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.01.01.26343326","name":"Measurement of retrieved chunk quality from real-world knowledge in retrieval-augmented generation: A Phase 1 foundational study","source":"preprints","abstract":"Retrieval-augmented generation (RAG) holds promise for supporting high-stakes medical decision-making. However, most research has focused on downstream optimization of parameters and algorithms. This Phase 1 foundational study quantitatively evaluated the upstream quality of knowledge documents and their impact on retrieval performance, using Japanese clinical research protocol manuals for Institutional Review Board pre-screening support as a case study. We established a three-tier evaluation framework: Level 1 assessed knowledge document quality through independent expert review across Structure, Granularity, and Noise dimensions; Level 2a evaluated the structural quality of retrieved chunks using large language model-as-a-Judge across five metrics; and Level 2b conducted proof-of-concept content appropriateness evaluation against a Gold Standard derived from international guidelines. Using Google Cloud Vertex AI Search, we analyzed 594 chunks from baseline knowledge (A-line: four institutional manuals as-is) and six chunks from optimized knowledge (B-line: proof of concept). Level 2a evaluations employed deterministic settings with five independent trials, achieving excellent reliability (intraclass correlation coefficient of 0.936). The results revealed substantial quality limitations in the A-line chunks: the median scores were 2.0 or below across all five metrics, with fewer than 20% of the chunks reaching practical utility thresholds (score of 4 or higher). Even among the top-ranked results, fewer than half met the practical utility criteria, except for Faithfulness. The inter-rater agreement in the Level 1 evaluation was fair (Fleiss kappa value of 0.269), indicating the need for framework refinement. The retrieved chunk lengths significantly exceeded the configured settings (median of 3,861 characters versus 500 tokens), potentially indicating information dilution. The B-line optimization achieved perfect scores across all metrics, demonstrating potential for improvement. These findings demonstrate that upstream document quality constrains retrieval performance, challenging assumptions regarding plug-and-play RAG deployment. Author Summary Artificial intelligence systems that retrieve information from documents and generate responses are increasingly being used to support medical decision-making. We questioned the assumption that uploading existing documents is sufficient for fine-tuning the algorithms of these systems by investigating whether document quality is a limiting factor. We studied Japanese clinical research manuals used for research ethics review, assessing the efficacy of an AI system in retrieving information from these documents. We evaluated nearly 600 text segments retrieved by the system and found that fewer than one in five segments met our quality standards, even among the highest-ranked results. The system frequently retrieved excessively long passages obscuring key information. However, when we restructured one document section using clearer organization and formatting, the system achieved perfect performance scores. This improvement suggests that not only algorithm optimization but also document preparation is crucial for system effectiveness. Our findings challenge the “plug-and-play” assumption commonly used in AI deployment. For high-stakes medical applications, organizations cannot simply expect reliable results to be obtained by uploading existing documents. Instead, they must invest in preparing well-structured knowledge documents. This foundational work establishes measurement methods to guide such preparation, which is essential before these systems can safely support healthcare decision-making.","url":"https://doi.org/10.64898/2026.01.01.26343326","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.01.26343326","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.01.05.26343473","name":"What Do Clinicians Edit in Ambient AI-Drafted Clinical Documentation? A Qualitative Content Analysis","source":"preprints","abstract":"Objective Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. Materials and Methods Eight coders analyzed clinical documentation generated by ambient AI from 200 clinical encounters. We developed an inductive coding framework with 11 codes across three categories: clinical content, terminology, and language style. Interrater reliability was assessed using Cohen’s kappa. We then applied thematic analysis to synthesize patterns across the coded edits. Results The most frequently edited content pertained to clinical facts including orders (e.g., procedures, lab tests) (40.0%), symptoms (30.3%), medication prescriptions (27.3%), and diagnosis descriptions (25.9%). In comparison, edits related to terminology use (11.6%) and language style (7.2%) were less frequent. The results of our thematic analysis show that most edits can be categorized into one of the following five types: to correct factual errors, to address needs of medical specialty, to express diagnostic certainties, to convert patient expressions into objective assessments recorded in medical terms, and to reorganize or condense content. Conclusion and Discussion Clinicians routinely revise ambient AI drafts to improve accuracy and clinical specificity. Future work on AI development and clinical implementation should emphasize specialty customization and support personalized documentation practices, alongside clinician education that promotes robust and consistent review routines to ensure documentation quality.","url":"https://doi.org/10.64898/2026.01.05.26343473","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.01.05.26343473","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8459685/v1","name":"An Adaptive Foundation Model with Evidence-based Clinical Reasoning for Gastroenterology","source":"preprints","abstract":"Abstract Gastrointestinal diseases affect 2.86 billion people globally, with capsule endoscopy (CE) providing crucial diagnostics but requiring manual review of over 60,000 frames per examination, a process associated with 17.4% disease miss rates. While artificial intelligence shows promise for CE analysis, existing endoscopic vision-language models (VLMs) lack multi-video understanding capability and cannot replicate the systematic multi-evidence reasoning that gastroenterologists integrate findings across anatomical regions to synthesize cohesive diagnoses. Here we introduce CE-R1, an adaptive foundation model with evidence-based clinical reasoning capabilities specifically designed for gastroenterology. CE-R1 incorporates a dynamic router that assesses query complexity and selectively routes cases to either a lightweight model for straightforward questions or a deep reasoning model that generates transparent, step-by-step diagnostic thought processes. To enable this capability, we construct CE-Bench, the first large-scale multimodal CE dataset comprising 502,066 visual question-answering pairs with chain-of-thought reasoning annotations, spanning 70 fine-grained clinical sub-tasks across five core diagnostic categories: anatomy identification, endoscopic findings recognition, disease diagnosis, treatment planning, and medical report generation. Comprehensive evaluation on both in-distribution and out-of-distribution datasets from four independent hospitals demonstrates that CE-R1 achieves 86.7% overall accuracy, substantially outperforming state-of-the-art VLMs (best baseline: 24.6%) and surpassing average physician performance (39.9%) by 21.1%. CE-R1 maintains superior generalization across external validation sets (65.1–81.9% accuracy). Critically, the multi-evidence clinical reasoning capability delivers substantial performance gains in complex diagnostic tasks: CE-R1 surpasses the model without reasoning by 8.5% in disease diagnosis, demonstrating the clinical value of transparent, step-by-step diagnostic processes. These results establish CE-R1 as a robust foundation model for comprehensive CE analysis with immediate applications in clinical decision support and medical education.","url":"https://doi.org/10.21203/rs.3.rs-8459685/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8459685/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.02.23.26346628","name":"An interpretable and explainable neural network to classify sports-related cardiac arrhythmias in professional football athletes","source":"preprints","abstract":"Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-related cardiac arrhythmias using a novel explainability framework comprising data analysis, model interpretability, post-hoc visualisation, and systematic assessment. Two neural networks—one with interpretable sinc convolution and one with standard convolution—were trained on general-population ECGs (PhysioNet, n=88,253, 30 arrhythmias, three continents) and tested on professional footballers (PF12RED, n=161) via domain adaptation for normal sinus rhythm (NSR), sinus bradycardia (SB), incomplete right bundle branch block (IRBBB), and T-wave inversion (TWI). Sinc convolution achieved superior NSR detection (AUROC 0.75 vs 0.70), whilst standard convolution excelled at SB (0.74 vs 0.73), IRBBB (0.66 vs 0.58), and TWI (0.59 vs 0.54). Gradient-weighted Class Activation Mapping revealed that sinc models focus on physiologically relevant ECG segments (the PR interval for NSR/SB and the T wave for TWI). We hypothesise that sinc convolution better captures periodic rhythms but struggles with complex morphological patterns, suggesting architectural choice should align with underlying cardiac pathophysiology. Graphical abstract Abbreviations: AI, artificial intelligence; AUPRC, area under the precision-recall curve; AUROC, area under the receiver operating characteristic curve; Conv, convolution; ECG, electrocardiogram; Grad-CAM, gradient-weighted class activation mapping; IAVB, first-degree atrioventricular block; IRBBB, incomplete right bundle branch block; LAD, left axis deviation; LBBB, left bundle branch block; LVH, left ventricular hypertrophy; NSR, normal sinus rhythm; QT, QT interval; RAD, right axis deviation; RBBB, right bundle branch block; RVH, right ventricular hypertrophy; SA, sinus arrhythmia; SB, sinus bradycardia; TWI, T-wave inversion; xAI, explainable artificial intelligence.","url":"https://doi.org/10.64898/2026.02.23.26346628","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.02.23.26346628","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.03.04.26347604","name":"Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning of Bridge2AI AI-READI Data","source":"preprints","abstract":"ABSTRACT As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%–10% to type 2 diabetes. Total diabetes (diagnosed and undiagnosed) affects approximately 40.1 million Americans, or 12% of the population, with roughly 1.5 million new cases diagnosed annually. Continuous Glucose Monitoring (CGM) provides real-time, 24/7 insights into glycemic variability, detecting dangerous highs, lows, and trends that HbA1c (a 3-month average) misses. It enables, for instance, identification of nocturnal hypoglycemia or postprandial spikes, enhancing personalized, actionable treatment decisions and improving safety. The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) dataset was produced by the National Institutes of Health (NIH) Common Fund Data Ecosystem (CFDE) Bridge2AI program. This dataset offers a rich resource for diabetes research, providing comprehensive biosensor data from over 1,067 participants. However, like many medical datasets, AI-READI contains label inaccuracies due to self-reported health surveys and static HbA1c indicators, which can undermine model effectiveness. We developed a strong classification framework using Convolutional-Bidirectional Long Short-Term Memory (Conv+BiLSTM) to analyze and accurately classify glycemic health states from continuous glucose monitoring time-series data. Our aim was to establish and correct any misclassified labels through hybrid unsupervised-supervised learning methods and validated our results with expert-in-the-loop clinical review. We analyzed 784 participants from the AI-READI dataset, which represented four health states: healthy, prediabetes lifestyle controlled, oral medication, and insulin-dependent. Based on recommendations from the literature and our own expertise, we sought to compare the self-provided “healthy” group labels with a cluster-agnostic, CGM-defined healthy (CGM-H) reference derived from the CGM metrics using K-means clustering (K=6) on standardized CGM summary features to identify CGM-H participants and then applied XGBoost-based iterative label refinement. We identified a misclassification rate of 56.9% (161/283) in the initially labeled “healthy” group. After eight iterations of XGBoost refinement with dual-criterion relabeling (≥80% probability + unanimous out-of-fold voting), the cleaned dataset increased CGM-H participants from 122 to 195 for binary classification. Next, we developed a Conv+BiLSTM model combining Convolutional layers (32, 64 filters) for local temporal feature extraction with Bidirectional LSTM layers (64, 32 units) for sequence modeling, using time-series engineered features including rolling statistics, glucose derivatives, and circadian rhythm encoding. Class imbalance was addressed with per-class weighting, and 5-fold stratified cross-validation estimated generalization performance, computing a global decision threshold (0.374) by maximizing Youden’s J statistic on concatenated out-of-fold predictions. Additionally, we analyzed heart rate, activity level, and stress and sleep data and validated it against CGM data. The Conv+BiLSTM model achieved ROC-AUC ≈ 0.932 on the held-out test set and 0.907 ± 0.026 in cross-validation, with well-calibrated predictions (Expected Calibration Error = 0.075, temperature scaling T = 1.00). A 3-tier confidence-based decision system achieved 82% detection rate with only 6% OGTT burden, enabling actionable clinical recommendations. This hybrid approach addressed label noise while achieving high discrimination. This framework demonstrates potential for real-time glycemic state monitoring and early intervention in diabetes progression.","url":"https://doi.org/10.64898/2026.03.04.26347604","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.04.26347604","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7613144/v1","name":"Reimagining Primary Health Care: A Historical and Contemporary Review of Community-Based Care Models and Innovations","source":"preprints","abstract":"Abstract BACKGROUND Historically, community-based care has served as a foundation of health service delivery in resource-constrained settings. However, shifting demographics, rising chronic diseases burdens, and digital transformations increasingly challenge the sustainability and equity of these models. We reviewed historical as well as contemporary evidence on community-based care and provide a consolidated summary on the effectiveness and limitations of existing models; identify emerging challenges and future directions to ensure that future remains aligned with the goal of universal health coverage. METHOD Wes conducted a scoping review of available literature using the Arksey and O’Malley framework. A comprehensive search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and grey literature from 1975 to 2025 using a combination of search terms related to community-based care models, community health workers, volunteer-based programs, or digital innovations linked to community care. The data were synthesized thematically into categories reflecting historical evolution, key achievements, challenges, and future directions. RESULTS A total of 134 documents were reviewed. Community-based care consistently improved access to essential services, particularly maternal and child health, infectious disease control, and health promotion in underserved populations. Programs led by volunteer and community health worker led programs contributed to health systems strengthening but faced persistent challenges including high attrition, limited funding, and fragmented integration. Case studies from Nepal, Ethiopia, Brazil, and Rwanda highlighted significant reductions in child mortality, improvement in maternal care, and strengthened resilience during pandemics. Emerging challenges included syndemics, demographic transitions, urbanization, and weakening social structures. Digital technologies and artificial intelligence emerged as potential tools to expand access and efficiency, though they also pose risks if deployed inequitably or without adequate regulation. CONCLUSION Community-based care remains essential for bridging gaps in health system and advancing universal health coverage. Its continued effectiveness will depend on evolving into diagonally integrated, technologically enabled, and people centered model that balances innovation with equity, trust, and cultural relevance. Long-term investment in governance, workforce training, digital infrastructure, and active community engagement will be critical to building resilience against future health crises.","url":"https://doi.org/10.21203/rs.3.rs-7613144/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7613144/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6966149/v1","name":"Predicting Annotation Yield in Artificial Intelligence-Ranked Electronic Health Record Cohorts: A Regression-Based Framework for Efficient Manual Review","source":"preprints","abstract":"Abstract Background Painstaking manual chart review of EHRs is still the chief bottleneck in retrospective studies, especially when rare-disease cohorts demand high specificity. Automated NLP rankers help, yet when trained on dated data they leave teams guessing how long to keep reviewing charts. We therefore present a regression-based ‘screening-saturation’ model that predicts residual yield at every point along the ranked list. Methods Leveraging a previously validated SVM that ranks notes for pediatric status epilepticus, we trained four predictive models: linear, polynomial, and support-vector regressions plus a lightweight neural net, on notes from 2013 and tested them on data from 2020. Our target was the proportion of true positives (ESE or RSE) expected below any score threshold. Results Polynomial regression offered the best balance of generalizability and interpretability, which demonstrated a strong predictive performance even under temporal data shifts. Regression outputs were used to simulate stopping rules for manual review, which captured 80% of positives after reviewing just 16.6% of notes (an 83% workload cut). Conclusion Our scalable, model-agnostic framework turns AI scores into actionable staffing decisions in clinical workflows. This screening-saturation model integrates with clinician-in-the-loop tools and adapts readily across medical domains that need lean chart review.","url":"https://doi.org/10.21203/rs.3.rs-6966149/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6966149/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.03.02.26347175","name":"AI-Generated Responses to Patient's Messages: Effectiveness, Feasibility and Implementation","source":"preprints","abstract":"Background: Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language models (LLMs) can generate draft responses to patient messages using electronic health record (EHR) data. This could mitigate increased workload related to high message volumes. While effectiveness and feasibility of these GenAI tools have been studied in the United States, evidence from non-English contexts is scarce, particularly regarding user experience. Objective This study evaluated the effectiveness, feasibility and barriers and facilitators of implementing Epic's Augmented Response Technology (Art) GenAI tool (Epic Systems Corporation, Verona, WI, USA) in a Dutch academic healthcare setting among a broad range of end users. It explored healthcare professionals' (HCP) usage metrics, expectations, and early user experiences. Methods We conducted a hybrid type 1 effectiveness-implementation design. HCPs of four clinical departments (dermatology, medical oncology, otorhinolaryngology, and pulmonology) participated in a six-month study. Effectiveness of Art was assessed using efficiency indicators from Epic (including all InBasket users in the hospital) and survey scales measuring well-being and clinical efficiency at three time points: PRE, POST-1 (1 month), and POST-2 (4 months). Feasibility of Art was evaluated through adoption indicators from Epic and survey scales on use and usability. Barriers and facilitators of Art implementation were collected through the survey and thematized using the NASSS framework (Nonadoption, Abandonment, Scale-up, Spread and Sustainability). Results 237 unique HCPs generated a total of 8,410 drafts. Review and drafting times were similar for users with and without Art, indicating minimal differences. Perceived clinical efficiency declined significantly from PRE to POST-2, while well-being remained unchanged. Adoption was initially high but decreased over time, averaging 16.7% across departments. Usability and intention-to-use scores also declined significantly. Qualitative findings highlighted time savings, well-structured drafts, and patient-centered language as facilitators. Reported barriers included limited impact on time, low practical utility, content inaccuracies, and style misalignment. Conclusions This evaluation of a GenAI tool for patient-provider communication in a non-English academic hospital revealed mixed perceptions of effectiveness and feasibility. High initial expectations contrasted with limited perceived impact on time-savings, well-being and clinical efficiency, alongside declining adoption and usability. Barriers and facilitators revealed contrasting views. These findings underscore the need for a workflow for the handling of user feedback, guidance on clinical responsibilities, along with clear communication about the tool's purpose and limitations to manage expectations. Additionally, establishing consensus on a set of quality indicators and their thresholds that indicate when a GenAI tool is sufficiently robust will be critical for responsible scaling of GenAI in clinical practice.","url":"https://doi.org/10.64898/2026.03.02.26347175","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.03.02.26347175","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202510.0547.v1","name":"GenAI Agents for Early Disease Diagnosis: A Review of Architectures, Applications, and Policy Directions","source":"preprints","abstract":"The integration of Artificial Intelligence (AI) agents into healthcare represents a paradigm shift in medical diagnostics, enabling autonomous systems that leverage multimodal data fusion, advanced machine learning architectures, and clinical reasoning engines. We explore their architectural components, including perception, knowledge base, reasoning engine, and decision-making modules. The paper then delves into key application areas such as medical imaging analysis, rare disease identification, multimodal diagnostic dialogue, and AI-powered generalist diagnostic agents. We examine the core architectural components including perception modules for EHR integration (HL7/FHIR standards), medical imaging analysis (DICOM, CNN architectures), genomic data processing (FASTQ/BAM formats), and multimodal biomarker integration. The paper details specialized AI agents for medical imaging analysis using 2D/3D convolutional neural networks and vision transformers, rare disease diagnosis through few-shot learning and knowledge graph reasoning, and multimodal diagnostic systems exemplified by Google's AMIE framework. We evaluate the technical implementation challenges including data privacy compliance (HIPAA, GDPR), model interpretability requirements (SHAP, LIME explanations), and regulatory considerations (FDA SaMD frameworks). Performance analysis demonstrates significant improvements in diagnostic accuracy (AUC-ROC improvements of 15-25\\% across studies), operational efficiency through automated workflow orchestration, and early disease detection capabilities surpassing traditional diagnostic methods. The synthesis of recent publications indicates that AI diagnostic agents achieve clinical performance comparable to healthcare professionals in specific domains while enabling proactive healthcare through predictive analytics and personalized treatment recommendations. Furthermore, we analyze the significant benefits offered by these systems, including improved diagnostic precision, operational efficiency, and personalized patient care. Finally, we address the critical challenges and future research directions, focusing on data privacy, model interpretability, regulatory hurdles, and the path toward medical superintelligence. Future research directions focus on federated learning approaches for privacy-preserving model training, explainable AI for clinical trust adoption, and the development of medical superintelligence systems capable of holistic patient health modeling across temporal and multimodal data dimensions.","url":"https://doi.org/10.20944/preprints202510.0547.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.0547.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202505.1160.v1","name":"Artificial Intelligence in Primary Malignant Bone Tumors Imaging: A Narrative Review","source":"preprints","abstract":"Artificial Intelligence (AI) is definitely a transformative tool in orthopedic oncology, enabling advancements in the diagnosis, classification, and treatment response prediction of primary malignant bone tumors (PBT). AI, particularly machine learning and deep learning ,utilizes the power of computational algorithms and large datasets to improve imaging interpretation and clinical decision making. Radiomics, combined with AI, facilitates the extraction of quantitative features from medical images, in order to precisely characterise the tumor and assist in personalized therapeutic strategies. The integration of convolutional neural networks has demonstrated outstanding capabilities in imaging pattern recognition, advancing tumor detection, segmentation, and differentiation.","url":"https://doi.org/10.20944/preprints202505.1160.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202505.1160.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6498214/v1","name":"Artificial Intelligence in Dental Education: A Scoping Review of Opportunities, Challenges, and Ethical Frameworks for Shaping Accreditation Standards and Future Practice","source":"preprints","abstract":"Abstract Background: The integration of artificial intelligence (AI) into dental education offers transformative potential for enhancing learning outcomes, clinical training, and institutional efficiency. However, rapid AI adoption introduces ethical, logistical, and pedagogical challenges that require systematic exploration. This scoping review maps the current applications, challenges, and future directions of AI in dental education, focusing on its integration into curricula while ensuring ethical, equitable, and pedagogically sound practices. Methods: The Joanna Briggs Institute framework was followed, with reporting per the PRISMA-ScR guidelines for scoping reviews. A systematic search was conducted across PubMed, EMBASE, MEDLINE-Ovid, and Google Scholar for studies published between January 2018 and January 2025. The search terms included \"artificial intelligence,\" \"dental education,\" \"machine learning,\" \"ChatGPT,\" and \"ethical challenges,\" with Medical Subject Headings (MeSH) terms applied where applicable. After duplicate removal, 624 510 records underwent title/abstract screening, followed by a full-text review of 57 articles, with 43 studies meeting the eligibility criteria. Data extraction focused on the study design, population, AI type, key outcomes, and challenges. Results: The key findings include the following: 1. AI-Driven Personalization: Generative AI (e.g., ChatGPT) reduced grading time by 45% and improved reflective learning outcomes, although 33% of studies reported algorithmic bias due to nonrepresentative training data. 2. In clinical training, AI tools achieved 99% accuracy in caries detection compared with 77–79% accuracy for students, but models trained on homogeneous datasets underperformed in diverse cohorts. 3. Institutional Efficiency : Automated scheduling reduced administrative workloads by 30%, yet only 18% of institutions had updated curricula to include AI literacy modules. 4. Ethical Governance: Data privacy and data protection breaches occurred in 24% of the studies, and 41% reported faculty resistance to AI adoption, highlighting the need for dental-specific guidelines. Conclusion: AI holds significant promise for dental education but requires addressing ethical, logistical, and pedagogical challenges. Future efforts should focus on updating accreditation standards, fostering interdisciplinary collaboration, and developing hybrid models that balance AI-driven efficiency with traditional mentorship. Longitudinal studies are needed to evaluate the long-term impact of AI on clinical competence and patient outcomes. Significance: Dental educators need clearer guidance on integrating AI into the dental curriculum.","url":"https://doi.org/10.21203/rs.3.rs-6498214/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6498214/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.07.15.26358182","name":"Scaling ECG Foundation Models and Identifying a Threshold for Effective Representation Learning","source":"preprints","abstract":"We conducted a scaling evaluation of unlabeled pretraining for electrocardiogram foundation model performance. One-dimensional vision transformer (1D-ViT) masked autoencoders were pretrained across increasing ECG volumes and fine-tuned for rhythm, morphology, diagnostic, and structural heart disease tasks. Models pretrained at ≤400,000 ECGs failed to consistently exceed controls without self-supervised pre-training, whereas 600,000–800,000 ECGs improved AUROC across tasks, suggesting a minimum threshold for effective ECG representation learning.","url":"https://doi.org/10.64898/2026.07.15.26358182","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.07.15.26358182","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.12688/wellcomeopenres.24335.2","name":"Protocol for a systematic review of wearable devices for antenatal fetal monitoring","source":"preprints","abstract":"Introduction Fetal monitoring is a crucial component of antenatal care, facilitating early detection of fetal compromise and improving pregnancy outcomes. Traditional monitoring methods such as cardiotocography (CTG) and ultrasound are effective but primarily limited to clinical settings, requiring specialized expertise and resources. The rise of wearable medical devices and artificial intelligence (AI) applications presents an opportunity to enhance fetal monitoring by enabling continuous, real-time data collection outside clinical environments. These technologies have the potential to improve fetal health and obstetric outcomes, particularly in resource-limited settings. This systematic review aims to evaluate the use of wearable devices for antenatal fetal monitoring and their impact on fetal and obstetric outcomes. Methods and analysis This systematic review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Synthesis Without Meta-analysis (SWiM) framework. A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science will be conducted to identify primary research studies investigating wearable devices designed for fetal monitoring during pregnancy. Studies will be included if they assess the effectiveness, accuracy, and clinical impact of wearable fetal monitoring devices. Primary outcomes will include markers of fetal well-being as well as neonatal and obstetric outcomes. Secondary outcomes will focus on patient experience and acceptability. Data extraction and quality assessment will be conducted independently by two reviewers using the National Institutes of Health (NIH) Quality Assessment Tool and the Newcastle-Ottawa Scale. A narrative synthesis will be performed to summarise the findings. Ethics and dissemination Ethical approval is not required since the study involves analysing published literature. The findings will be shared through peer-reviewed publications and conference presentations. This review will enhance the evidence base regarding the clinical utility of wearable fetal monitoring technologies and inform future research and device development. PROSPERO Registration: CRD4202348755 (current version 4.1).","url":"https://doi.org/10.12688/wellcomeopenres.24335.2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/wellcomeopenres.24335.2","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7713694/v1","name":"Lack of children in public medical imaging data points to growing age bias in biomedical AI","source":"preprints","abstract":"Abstract Background. Artificial intelligence (AI) is transforming healthcare, but its benefits have not been equitably distributed, with children being particularly overlooked. Only 17% of FDA-approved medical AI devices are labeled for pediatric use. We hypothesized that this disparity may be due to a fundamental data gap in pediatric medical imaging. Methods. To test this hypothesis, we performed a systematic review of 180 publicly available medical imaging datasets to assess pediatric data representation. To identify the primary data sources used for methods development, we first surveyed papers from a machine learning imaging conference. Finally, we evaluated the performance of adult-trained chest radiograph models when applied to pediatric populations to quantify potential age-related bias. Results . Our systematic review found that children represent less than 1% of the data in public medical imaging datasets. The majority of machine learning conference papers we surveyed relied on publicly available data for model development. Furthermore, we found that adult-trained chest radiograph models exhibit significant age bias when applied to pediatric populations, with higher false positive rates in younger children. Discussion. This study highlights the urgent need for increased pediatric representation in publicly accessible medical datasets. Our findings suggest that the lack of pediatric data may contribute to the scarcity of AI tools for children and the poor performance of adult-trained models in this population. We provide actionable recommendations for researchers, policymakers, and data curators to address this age equity gap and mitigate the potential harms of AI systems not trained on pediatric patients.","url":"https://doi.org/10.21203/rs.3.rs-7713694/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7713694/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.06.04.26354889","name":"Closing the Paediatric Gap: Adult-Trained AI Generalises Robustly to Paediatric Coeliac Disease Diagnosis","source":"preprints","abstract":"Background Coeliac disease (CD) diagnosis on duodenal biopsies is limited by interobserver variability. We have previously demonstrated pathologist-level performance with our artificial intelligence (AI) model for the histopathological diagnosis of adult CD, but not in paediatric practice. As paediatric CD screening programmes expand internationally, accurate and scalable diagnostic tools are needed. We investigated whether an AI model trained exclusively on adult whole-slide images (WSIs) can generalise to paediatric CD diagnosis across independent centres. Methods A training and validation dataset of 9,958 WSIs from 8,421 adult patients (961 CD) from five centres was used to develop an ensemble of multiple-instance learning models using features from a foundation model. Testing was performed on 708 consecutive paediatric patients (86 CD) from two centres (Edinburgh and Southampton) not included in training. Model calibration was assessed, and probability outputs were grouped into clinically interpretable categories. Findings In adult cross-validation, the AI model achieved an area under the receiver operating characteristic curve (AUC) of 98.7%, sensitivity of 84.9%, specificity of 99.0%, and negative predictive value (NPV) of 98.1%. On testing (paediatric) datasets, performance remained high (AUC 98.8%, sensitivity 80.2%, specificity 98.4%, NPV 97.3%). Restricting analysis to predictions outside the intermediate-probability range (predicted CD probability Interpretation Our AI model, trained on adult biopsies, generalises to paediatric CD diagnosis across centres and scanner platforms. Well-calibrated probability outputs provide clinically interpretable measures of diagnostic confidence and could support safe identification of CD-negative biopsies within defined thresholds. These findings demonstrate the feasibility of applying adult-derived AI models in paediatric populations and reinforce the importance of multi-site (D1 & D2) biopsy sampling.","url":"https://doi.org/10.64898/2026.06.04.26354889","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.06.04.26354889","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.08.04.25332966","name":"The Effectiveness of Large Language Models in Providing Automated Feedback in Medical Imaging Education: A Protocol for a Systematic Review","source":"preprints","abstract":"Background Large Language Models (LLMs) represent an ever-emerging and rapidly evolving generative artificial intelligence (AI) modality with promising developments in the field of medical education. LLMs can provide automated feedback services to medical trainees (i.e. medical students, residents, fellows, etc.) and possibly serve a role in medical imaging education. Aim This systematic review aims to comprehensively explore the current applications and educational outcomes of LLMs in providing automated feedback on medical imaging reports. Methods This study employs a comprehensive systematic review strategy, involving an extensive search of the literature (Pubmed, Scopus, Embase, and Cochrane), data extraction, and synthesis of the data. Conclusion This systematic review will highlight the best practices of LLM use in automated feedback of medical imaging reports and guide further development of these models.","url":"https://doi.org/10.1101/2025.08.04.25332966","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.08.04.25332966","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202510.0510.v1","name":"Musculoskeletal Digital Therapeutics and Digital Health Rehabilitation: A Global Paradigm Shift in Orthopaedic Care","source":"preprints","abstract":"Musculoskeletal disorders (MSDs) affect over 1.7 billion people globally and rep-resent the leading cause of disability worldwide. Conventional rehabilitation strate-gies face challenges including limited accessibility, suboptimal adherence, and lack of personalization. Digital therapeutics (DTx)—evidence-based, software-driven interventions regulated as medical devices—have emerged as transformative solu-tions in chronic disease management. This comprehensive review synthesizes current knowledge on musculoskeletal DTx and digital health rehabilitation across ortho-paedic subspecialties. We describe core enabling technologies including artificial intelligence-driven motion analysis, wearable sensors, tele-rehabilitation platforms, and cloud-based ecosystems. Clinical applications spanning spine, upper and lower extremities, sports injuries, and trauma are examined alongside global regulatory frameworks, economic considerations, and implementation challenges. Early clinical evidence demonstrates improvements in functional outcomes, adherence, and cost-effectiveness. Future directions include digital twin technologies, predictive analytics, and integration with precision orthopaedics. By establishing a compre-hensive framework for musculoskeletal DTx implementation, this review highlights their potential to improve outcomes, reduce healthcare costs, and address global rehabilitation access gaps.","url":"https://doi.org/10.20944/preprints202510.0510.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.0510.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202510.2019.v1","name":"The Evolution and Advancement of YOLO Algorithms in Object Detection: From Real-Time Breakthroughs to Modern Architectures","source":"preprints","abstract":"Object detection represents a foundational capability in Artificial Intelligence (AI), enabling machines to interpret visual environments through precise object localization and classification. This comprehensive review chronicles the revolutionary evolution of the You Only Look Once (YOLO) framework from its inception to the state-of-the-art YOLOv12. Beginning with the limitations of classical approaches using handcrafted features, YOLO’s paradigm-shifting is documented transition to unified real-time detection via regression-based architectures. Methodically analyzing each major version (v1- v12), key innovations is detailed including multi-scale predictions (v2/v3), anchor-free designs (v8), programmable gradient information (v9), and attention-enhanced cross-scale fusion (v12). The review establishes how successive iterations systematically addressed critical challenges: reducing computational latency by 47× versus R-CNN variants, improving mAP by 32.7% on COCO benchmarks, and enabling deployment on edge devices. Beyond architectural analysis, comparative performance evaluations is presented across diverse applications—from autonomous driving to medical imaging—demonstrating YOLO’s unprecedented balance of speed (142 FPS) and accuracy (78.4% AP). The paper further examines emerging implementation trends, hardware optimizations, and domain-specific adaptations that cement YOLO’s position as the de facto framework for real-time vision systems. Our review analysis provides both technical and historical context for researchers and practitioners navigating the landscape of modern object detection.","url":"https://doi.org/10.20944/preprints202510.2019.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202510.2019.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7080209/v1","name":"Advancing SDG-4 through AI and STEAM-Integrated Curriculum for Inclusive Medical Education: A Scoping Review Proposing the INSPIRE Model","source":"preprints","abstract":"Abstract The fourth United Nations Sustainable Development Goal (SDG-4) seeks to ensure inclusive and equitable quality education and pursue lifelong learning opportunities for everyone. In medical education, the incorporation of Artificial Intelligence (AI) and Science, Technology, Engineering, Arts, and Mathematics (STEAM) has transformational potential in realising these objectives through creating inclusive, innovative, and adaptive learning spaces. This scoping review considers the use of AI and STEAM-based curriculum design and assessment to further SDG-4 within medical education for the purposes of enhancing inclusivity, scalability, and ethical practice. Adopting the PRISMA-ScR guidelines, researchers systematically charted peer-reviewed literature obtained from PubMed, Scopus, and Web of Science. 50 studies were identified, with prominent themes being: AI-oriented personalised learning, STEAM-based interdisciplinary education, and issues such as ethical dilemmas, digital divides, and curriculum congestion. The review uncovers a significant absence of standard frameworks to implement AI and suggests the INSPIRE Model (Inclusive, Nimble, Scalable, Personalised, Interprofessional, Reflective, and Ethical) as a new framework to fill these gaps. The INSPIRE Model uses AI-powered technologies, including intelligent tutoring systems and real-time analytics, in conjunction with STEAM principles to produce adaptive, modular curricula that address the needs of various types of learners, including those in low-resource environments. It advocates for ethical use of AI, Interprofessional collaboration, and AI literacy in order to equip medical students for an AI-enhanced healthcare future. Evidence shows that integration of AI and STEAM can promote access to quality education, narrow gaps, and drive critical thinking, in line with SDG-4 goals. Yet, access impediments such as scarce infrastructure and faculty development call for specific interventions like international partnerships and scalable digital tools. The INSPIRE Model provides a blueprint for stakeholders to integrate inclusive, sustainable systems of medical education. Subsequent studies should aim to pilot-test the model and assess its long-term effect on educational equity as well as on health outcomes, while being guided by SDG-4's vision of lifelong learning for all.","url":"https://doi.org/10.21203/rs.3.rs-7080209/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7080209/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.05.14.25327520","name":"Applications of Artificial Intelligence in clinical decision-making and technical support in Oncology: A Scoping Review protocol","source":"preprints","abstract":"ABSTRACT Introduction The management of cancer care generates vast amounts of data, collected in the clinical registry; however, the interpretation of these unstandardized and heterogeneous records is often challenging. Artificial intelligence (AI) is emerging as a promising tool in healthcare, particularly in Oncology, where early diagnosis, complex therapies, long term follow-up and the impact on quality of life are paramount. This technology offers significant opportunities to support clinical decision-making and optimize the management of clinical data. Nevertheless, the literature describing how AI is concretely applied to support decision-making and administrative workflow in oncology remains scattered. Objective The aim is to identify evidence regarding the use of AI in Oncology, focusing on its role in supporting clinical decision-making and technical procedures across the entire cancer care continuum. Methods This scoping review will analyze primary studies published between 2020 and 2025. Eligibility criteria will be determined based on the PCC framework: Population (cancer patients), Concept (AI), and Context (clinical records). Studies focusing solely on population registries, those lacking identifiable AI use, as well as narrative reviews, will be excluded. Relevant literature will be identified through a systematic search in MEDLINE, EMBASE and CENTRAL. Additionally, forward citation tracking and review of existing systematic reviews will be conducted. Two independent reviewers will screen articles by titles and abstracts and consequently by full-text, using RAYYAN software. Data extraction will include the following items: cancer type, specific AI technique, the model or tool used, clinical utility, primary recipient (patient or health professional), stage of implementation, and reported outcomes or limitations. Quality assessment or bias evaluation will not be performed. Findings will be synthesized narratively and, where appropriate, represented graphically. Potential Results This research is expected to provide a comprehensive synthesis of AI applications in Oncology, highlighting the current role of this technology in enhancing the management of medical records and supporting clinical decision-making in the cancer care continuum. Expected outcomes include evidence of an upward trend in recent publications on the subject, a classification of techniques and models, a catalog of approved AI-based tools, distinctions between patient-oriented and healthcare professional support tools, and the identification of key limitations. Conclusion The findings of this review will enable clinicians and patients to gain insights into the current landscape of AI in Oncology, identify future research directions, and develop evidence-based strategies for the responsible integration of AI into cancer care. The goal is to position this technology as a supportive tool that complements, rather than replaces, healthcare professionals.","url":"https://doi.org/10.1101/2025.05.14.25327520","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.05.14.25327520","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202508.1425.v2","name":"Multimodal Generative AI in Diagnostics: Bridging Medical Imaging and Clinical Reasoning","source":"preprints","abstract":"Multimodal generative artificial intelligence (AI) has emerged as a transformative technology in clinical diagnostics, integrating diverse data sources—medical imaging, genomic profiles, clinical narratives, and electronic health records—to significantly enhance diagnostic accuracy, clinical decision-making, and personalized patient care. This review systematically explores the landscape of multimodal AI across key medical specialties, including radiology, pathology, dermatology, ophthalmology, neurology, and oncology, highlighting recent methodological advancements, performance evaluations, and practical clinical implementations. Technical strategies such as tool-use, grafting, and unified multimodal architectures are critically assessed, identifying their strengths and limitations concerning clinical applicability, interpretability, and computational efficiency. Synthetic multimodal data generation methodologies—Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and large language models (LLMs)—are evaluated for addressing data scarcity in rare disease research, enhancing international collaboration, and mitigating privacy concerns. Additionally, this review addresses pivotal ethical, regulatory, and liability challenges, emphasizing fairness, transparency, and accountability in AI-driven clinical diagnostics. Strategic priorities for future research are identified, including rigorous prospective clinical validation, development of standardized multimodal datasets, enhanced model interpretability, and robust regulatory frameworks. Ultimately, realizing the transformative potential of multimodal generative AI in clinical practice will require interdisciplinary collaboration among clinicians, researchers, ethicists, regulators, and patient advocacy groups, ensuring these powerful tools effectively augment human expertise, improve healthcare delivery, and advance precision medicine.","url":"https://doi.org/10.20944/preprints202508.1425.v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202508.1425.v2","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8919841/v1","name":"Substance-induced manic psychosis in which delusions were corroborated by a chatbot - case report","source":"preprints","abstract":"Abstract Background: This case describes a substance-induced manic episode with psychotic features in which interaction with an AI (artificial intelligence) chatbot appeared to corroborate and reinforce the patient’s delusional thought content and to contradict medical advice. Excerpts from the patient’s interactions with the AI chatbot provide novel clinical insight into this phenomenon, which to date has primarily been reported in news media. Case Presentation: A man in his 30s presented to the emergency department with a one-week history of escalating behavioural disturbance, severe insomnia, pressured and overinclusive speech, and grandiose beliefs. Symptom onset followed heavy polysubstance use at a recreational event, including psilocybin (dried mushrooms and liquid preparation), ketamine, cocaine, and alcohol. During this period, the patient reported extensive interaction with an AI chatbot (ChatGPT). The AI chatbot reportedly affirmed his perceived “spiritual awakening,” minimised the possibility that his presentation represented a manic episode, and provided medical advice, including discouragement of prescribed antipsychotic medication. Mental state examination was consistent with a manic episode with psychotic features, without evidence of perceptual disturbance. He was detained under mental health legislation for further assessment and commenced on olanzapine, with adjunctive sleep restoration and psychological interventions. Behavioural management included implementation of a care plan restricting AI chatbot use. Over several weeks, psychotic symptoms and behavioural disinhibition diminished, with subsequent improvement in insight. Conclusions: Concerns regarding potentially harmful interactions between AI chatbots and individuals with mental illness have largely been raised in news media. This case demonstrates that, in patients with psychotic symptoms, AI chatbots may reinforce delusional beliefs and impair the development of insight, and may also interfere with engagement with treatment by providing advice that conflicts with clinical recommendations. These observations raise clinical, ethical, and risk-management considerations regarding AI chatbot use during acute psychiatric illness. As AI chatbot use becomes increasingly widespread, clinicians should consider assessing their use and impact within clinical assessments and, where clinically indicated, implementing interventions to mitigate associated risks, ranging from psychoeducation to use-restriction strategies. Future population-level studies are required to establish the epidemiology of AI-associated mental health harms, and AI companies must bolster efforts to implement harm minimisation strategies and safeguards.","url":"https://doi.org/10.21203/rs.3.rs-8919841/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8919841/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202506.0768.v1","name":"The Future of Aesthetic Medicine: Patient‐Centered Trends and Technologies with Dr. Face and Dr. Slim","source":"preprints","abstract":"Background: Aesthetic medicine is evolving rapidly, driven by the convergence of patient-centered care and technological advancements. This literature review synthesizes current trends and future directions, emphasizing the shift from procedure-centric to holistic patient management, psychological considerations, ethical dilemmas, and the integration of innovations such as minimally invasive techniques, regenerative medicine, and Artificial Intelligence (AI). The review also addresses ethical and regulatory challenges in a commercial landscape. Methods: A systematic literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar for peer-reviewed articles published between 2014 and 2025. Keywords included \"aesthetic medicine,\" \"patient-centered care,\" \"minimally invasive procedures,\" \"regenerative medicine,\" and \"artificial intelligence in aesthetics.\" Articles were selected based on relevance to patient-centered trends, technological innovations, and ethical considerations. Data were synthesized to evaluate efficacy, safety, and long-term outcomes. Results: The review identifies a paradigm shift toward patient-centered care, prioritizing psychological well-being, shared decision-making, and Patient-Reported Outcome Measures (PROMs) like FACE-Q. Minimally invasive techniques (e.g., injectables, energy-based devices) and regenerative therapies (e.g., PRP, exosomes, stem cells) offer natural, long-lasting results. AI and digital tools enhance personalized treatment planning and outcome prediction. Ethical challenges, including overtreatment and regulatory gaps, persist, necessitating standardized protocols and robust oversight. Contributions from Dr. Face, Dr. Slim, and Premiumdoctors.org exemplify interdisciplinary progress. Conclusions: Aesthetic medicine is poised to become a distinct medical discipline, integrating patient-centered care with advanced technologies. Long-term efficacy studies, interdisciplinary collaboration, and ethical frameworks are critical to ensure patient well-being. Formalizing the field and addressing regulatory challenges will foster evidence-based practice and sustainable growth.","url":"https://doi.org/10.20944/preprints202506.0768.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202506.0768.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7441779/v1","name":"Innovations in assessment for graduate and fellowship medical education in critical care: a scoping review","source":"preprints","abstract":"Abstract Background As critical care medical education advances within a competency-based framework, effective assessment strategies are essential to ensure trainees acquire the necessary skills for high-stakes situations. Despite growing interest, assessment practices remain inconsistent, with significant variations in their application, although conceptual and technological innovations support this process. This scoping review aimed to map the literature on innovation in assessment within graduate and fellowship critical care medical education. Methods Following the Joanna Briggs' Institute framework and PRISMA-ScR, a search of four databases (PubMed, Scopus, Web of Science, LILACS) was conducted for articles from January 2014 to June 2025. Results Sixty-seven peer-reviewed articles were analyzed across four domains: assessment methods and tools, competency frameworks, innovation and technology, and ethics in assessment. Assessment in critical care is shifting toward a structured, multimodal, formative, and longitudinal model. Commonly used tools include workplace-based assessments and simulation. Milestones and entrustable professional activities are increasingly used to support entrustment decisions and track progress. Technology-enhanced strategies, including high-fidelity simulations, mobile apps, and artificial intelligence, improve feedback quality and real-time assessment. Ethical challenges, especially rater bias, underassessment of professionalism, and inequities, remain persistent concerns, highlighting the importance of fairness and transparency in evaluation. Conclusions Graduate critical care assessment is advancing towards a programmatic, multidimensional model that integrates clinical and professional competencies. Emerging frameworks and digital platforms are supporting more equitable, formative, and trustworthy evaluations. Future efforts should focus on standardization, bias, and ethical practices throughout the assessment continuum.","url":"https://doi.org/10.21203/rs.3.rs-7441779/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7441779/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.03.17.25324103","name":"Synthesizing evidence regarding artificial intelligence generated radiological reports based on medical images: a scoping review protocol","source":"preprints","abstract":"Introduction Considering numerous radiological images and the heavy workload of writing corresponding reports in clinical work, it is significant to leverage artificial intelligence (AI) to facilitate this process and reduce the burden of radiologists. In the past few years, particularly with the advent of vision language models, some works explored generating radiological reports directly from images. However, despite some efforts demonstrated in previous studies, limitations in AI-generated radiological reports persist. Current research mainly focuses on detecting abnormalities, rather than generating textual reports from medical images. The evidence for AI application in radiological report writing has not been synthesized. This scoping review aims to map the current literature on the engagement of AI-generating radiological reports based on images. Methods and analysis Following a well-established scoping review methodology, five stages are provided: i) determining the research question, ii) searching strategy, iii) inclusion/exclusion criteria, iv) data extraction, and v) results analysis. Four databases will be applied to search peer-reviewed literature from January 2016 to February 2025. A two-stage screening process will be conducted by two independent reviewers to determine the eligibility of articles, and only those regarding AI-generated radiological reports will be included. All data from eligible articles will be extracted and analyzed using narrative and descriptive analyses, presenting in a standard form. Ethic and dissemination Ethic approval is no required in this scoping review. Experts from Hospital of University of Geneva will be consulted to provide professional insight and feedback regarding the study findings and help with dissemination activities in peer-reviewed publications or academic presentations","url":"https://doi.org/10.1101/2025.03.17.25324103","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.03.17.25324103","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.08.29.25333768","name":"Leveraging Foundation Models in Maternal and Child Health: A Systematic Review","source":"preprints","abstract":"Maternal and child health (MCH) represents a critical domain requiring accurate, timely, and data-driven decision-making to optimize outcomes from pregnancy through early childhood. Foundation models (FMs) are large pre-trained artificial intelligence models that offer potential for clinical support in diagnostics, medical adherence, and reducing disparities. We conducted a systematic review to identify recent studies leveraging FMs in MCH published between 2020 and 2025. Of 785 studies, 63 met the inclusion criteria. FMs demonstrated strong potential to generalize across clinical tasks by integrating multimodal data, including text, electronic health records, imaging, and temporal data to support disease diagnosis, streamline clinical documentation, and generate high-quality medical responses throughout maternal, neonatal, and pediatric care. Moving forward, rigorous validation and close collaboration with clinicians will be essential for the safe, equitable, and effective deployment of FMs in MCH care.","url":"https://doi.org/10.1101/2025.08.29.25333768","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.08.29.25333768","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.64898/2026.02.04.26345355","name":"Managing AI-Enabled Uncertainty in Clinical AI Deployment: Mixed-Methods Study of Governance, Workflow, and Organizational Learning in an ICU Decision Support Pilot","source":"preprints","abstract":"Background Health care organizations are increasingly required to make strategic decisions about artificial intelligence (AI) systems before their clinical value, operational consequences, governance requirements, and workforce implications are fully known. Clinical AI pilots can reduce this uncertainty only if they generate management-relevant evidence beyond retrospective model performance, including evidence on workflow fit, governance architecture, user interaction, accountability, and continuous learning. Objective This study aimed to examine how a prospective intensive care unit (ICU) deployment of an AI-based clinical decision support system (CDSS) surfaced organizational and governance-related uncertainties, and to derive a transferable management toolbox for early-stage clinical AI deployment. Methods We conducted a prospective, single-center mixed-methods implementation evaluation of a machine-learning-based CDSS for ICU length-of-stay prediction in a surgical ICU at a German university hospital. The study included 267 consecutive ICU stays and was approved by the LMU Munich Institutional Review Board (Project No. 24-0336) and registered with the German Clinical Trials Register (DRKS00037851). The evaluation combined five management-relevant domains: workflow integration and operational adoption, governance architecture and data-protection effects, live bedside benchmarking as a continuous learning mechanism, embedded ethics and accountability design, and human-AI interaction with user heterogeneity assessed using the Psychological Assessment of AI-based Decision Support Systems instrument. Results The deployment showed that AI-enabled uncertainty was generated primarily at the interface between the CDSS and its organizational setting. A low-burden “glance-and-judge” workflow enabled routine use among consultants and resident physicians, with usage proportions of 72% (191/267) and 61% (162/267), respectively. Governance requirements led to an air-gapped architecture with once-daily data refreshes, enabling compliant use but creating operational latency; outdated case-list entries occurred in 4 of 148 benchmarked stays (2.7%). Live benchmarking functioned as a feedback loop and supported exploratory model refinement, with CDSS mean absolute error decreasing from 5.95 to 4.12 days after the first model update. Embedded ethics informed onboarding, accountability framing, interface wording, and explainability design. Human-AI interaction assessment suggested heterogeneous user archetypes with distinct change-management needs. Conclusions Clinical AI deployment should be managed as an organizational learning process rather than as a purely technical implementation. The relevant management object is the deployed sociotechnical system, including workflow, governance architecture, feedback loops, ethics, and user interaction. We propose a Governance-Aware AI Management toolbox that helps administrators and digital health officers distinguish solvable implementation-design uncertainty from true lack of clinical value, and supports staged, evidence-based decisions about scaling, investment, and definitive evaluation.","url":"https://doi.org/10.64898/2026.02.04.26345355","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.64898/2026.02.04.26345355","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.31234/osf.io/ctk7j_v2","name":"Designing Psychometric Measures for LLMs: Framework and Application to Racial Bias","source":"preprints","abstract":"Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years, several LLMs have been introduced, including ChatGPT by OpenAI, Claude by Anthropic, and Llama by Meta, among others. These models have the potential to be employed across a wide range of human–machineinteraction applications, such as chatbots for information retrieval, assistance in corporate hiring decisions, college admissions, financial loan approvals, parole determinations, and even in medical fields like psychotherapy delivered through chatbots. The key question is whether these chatbots will interact with humans in a bias-free manner or if they will further reinforce the existing pathological biases present in human-to-human interactions. If the latter is true, then how can werigorously measure these biases?We address this challenge by introducing STAMP-LLM (Standardized Test Assessment Measurement Protocol for LLMs), a principled two-phase framework for designing psychometric measures to evaluate chatbot biases: (i) a Definitional phase for construct mapping, item development, and expert review; and (ii) a Data/Analysis phase for protocol control (prompts/decoding), automated sampling,pre-specified scoring, and basic reliability/validity checks. We illustrate STAMP-LLM on racial bias using one explicit and two implicit measures.","url":"https://doi.org/10.31234/osf.io/ctk7j_v2","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.31234/osf.io/ctk7j_v2","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.1333.v1","name":"Agentic GenAI for Infectious Disease Management: A Comprehensive Review","source":"preprints","abstract":"The global management of infectious diseases, from pandemics to antimicrobial resistance, remains a critical public health challenge. This comprehensive review paper synthesizes the emerging paradigm of Agentic Artificial Intelligence (AI) for infectious disease management, marking a significant evolution beyond traditional generative AI. We define Agentic AI as autonomous systems capable of reasoning, planning, and executing complex, multi-step tasks by leveraging tools such as scientific databases and analytical engines. The core architectural components—planning modules, tool use APIs, memory, and guardrails—are detailed, alongside examples from industry platforms like Oracle OCI and IQVIA. A systematic analysis demonstrates key applications: revolutionizing disease surveillance and forecasting with superior predictive accuracy; drastically accelerating antibiotic discovery through \\textit{de novo} molecular design; augmenting clinical diagnostics and decision support; and automating scientific literature synthesis. The review further categorizes agent-specific approaches tailored to pathogen characteristics, including RNA viruses, drug-resistant bacteria, and neglected diseases. However, this promise is tempered by substantial challenges, including data bias, model hallucination, security vulnerabilities, and a lack of regulatory frameworks. Performance must be evaluated through multifaceted metrics like Task Success Rate and Medical Harmfulness Score, not just accuracy. A systematic exploration of key applications is presented, including enhanced disease surveillance and forecasting, accelerated drug and antibiotic discovery, AI-augmented clinical diagnostics and decision support, and automated scientific research. We further analyze the significant technical, ethical, and implementation challenges, such as data quality, hallucination risks, and the ``black box'' problem. Finally, we outline future directions, emphasizing the need for robust validation frameworks, human-AI collaboration models, and sustainable integration into public health infrastructure. The future direction emphasizes human-AI collaboration, robust benchmarking, and equitable deployment to avoid exacerbating global health disparities.","url":"https://doi.org/10.20944/preprints202509.1333.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.1333.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9587581/v1","name":"Adapting a Complex Health Intervention Guided by the Core Functions and Form Model: A Methodology and Case Example of Participatory Adaptation","source":"preprints","abstract":"Abstract Background : Adapting evidence-based interventions (EBIs) to fit local contexts while preserving fidelity to core mechanisms is challenging, particularly for complex, multicomponent interventions. The Core Functions and Forms (CFF) model distinguishes essential intervention mechanisms (core functions) from adaptable delivery strategies (forms), offering a principled approach to balance fidelity and tailoring. We applied CFF to adapt hospital discharge EBIs for patients with non-English language preference (NELP). Methods : Using Steps 1 and 2 of the ADAPT framework integrated with CFF, we convened an Adaptation Team to (1) rate the importance of core functions for NELP discharge care, (2) generate and evaluate forms—divided into specific forms (concrete components) and cross-cutting forms (e.g., who delivers, when, where, and how)—and (3) refine a pilot intervention. We collected participant demographics, audio-recorded and transcribed four facilitated meetings, and administered structured ranking and rating surveys via REDCap. Two analysts conducted rapid content extraction within one week of each meeting to compile ideas for subsequent rounds. Quantitative synthesis included mean importance rankings and mean impact and feasibility scores (1–10 scale). Specific forms were catalogued by corresponding core function. Results : The Adaptation Team (n=13) included clinicians, nurses, professional interpreters, social workers, and patient/caregiver representatives with lived NELP experience. The highest-ranked core functions were \"understand and address patient priorities\" and \"assess and address linguistic and cultural needs.\" For intervention delivery, physicians were rated highest for impact (mean=9.5) but lowest for feasibility (mean=4.8), while nurses, social workers, and community health workers offered better balance with high impact (mean range 8.1–8.5) and greater feasibility (mean range 7.3–7.4). In-person contact during admission plus post-discharge follow-up was rated most impactful (mean=9.4) but least feasible (mean=4.3), though feasibility improved with remote delivery (mean=8.0). For language concordance, remote delivery by a language-concordant provider best balanced impact (mean=8.1) and feasibility (mean=8.3). Conclusions : This work demonstrates operational integration of CFF into ADAPT, coupling theory-driven function specification with systematic stakeholder engagement. Key innovations include applying CFF across a family of related discharge EBIs to enable reuse of shared mechanisms and explicitly separating specific from cross-cutting forms to highlight implementation-relevant decisions.","url":"https://doi.org/10.21203/rs.3.rs-9587581/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9587581/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.02.14.25322289","name":"Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review","source":"preprints","abstract":"Introduction Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients with potential intracranial pathology could prove to be beneficial, particularly in resource-limited settings where access to neuroimaging may be limited. However, a comprehensive overview of the current literature on this field is lacking. Methods We conducted a systematic review on the use of AI for papilloedema detection by searching four databases: Ovid MEDLINE, Embase, Web of Science, and IEEE Xplore. Included studies were assessed for quality of reporting using the Checklist for AI in Medical Imaging and appraised using a novel 5-domain rubric, ‘SMART’, for the presence of bias. For a subset of studies, we also assessed the diagnostic test accuracy using the ‘Metadta’ command on Stata. Results We included nineteen deep learning systems and eight non-deep learning systems. The median number of images of normal optic discs used in the training set was 2509 (IQR 580 to 9156) and in the testing set was 569 (IQR 119 to 1378). The number of papilloedema images in the training and testing sets was lower with a median of 1292 (IQR 201-2882) in training set and 201 (IQR 57-388) in the testing set. Age and gender were the two most frequently reported demographic data, included by one-third of the studies. Only ten studies performed external validation. The pooled sensitivity and specificity were calculated to be 0.87 [95% CI 0.76-0.93] and 0.90 [95% CI 0.74-0.97], respectively. Conclusion Though AI model performance values are reported to be high, results need to be interpreted with caution due highly biased data selection, poor quality of reporting, and limited evidence of reproducibility. Deep learning models show promise in retinal image analysis of papilloedema, however, external validation using large, diverse datasets in a variety of clinical settings is required before it can be considered a tool for triage of intracranial pathologies in resource-limited areas. Author summary Papilledema is a condition characterised by the swelling of the optic disc in the eye. It can be caused by increased intracranial pressure. It can be caused by an increase in pressures within the cranial cavity, which may be due to traumatic brain injuries, tumours, or infections. In low-resource settings where access to specialist imaging is limited, identifying papilledema with accuracy by the bedside using retinal images and artificial intelligence could potentially serve as a tool for triaging when raised intracranial pressure is suspected and urgent surgical or medical intervention may be required. Our systematic review has critically appraised the primary studies which use any form of artificial intelligence to detect papilloedema from images of the retina. We provide a comprehensive overview and an in-depth discussion on the quality of reporting, areas of bias in model design, common limitations, and key findings from the primary literature.","url":"https://doi.org/10.1101/2025.02.14.25322289","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.02.14.25322289","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-7997734/v1","name":"A modular deep learning pipeline for standardised analysis of pelvic ultrasound images for gynaecology","source":"preprints","abstract":"Abstract Pelvic ultrasound is the principal imaging modality for evaluating the female reproductive tract, yet its clinical utility is constrained by substantial inter-observer variability and dependence on operator expertise. The anatomical complexity and physiological variability of the female pelvis present a uniquely demanding environment for automated image analysis: the uterus, ovaries, and endometrium differ markedly between individuals and change continuously across the menstrual cycle, making consistent, reproducible measurement a persistent challenge even for experienced practitioners. To address these limitations, we present a modular artificial intelligence pipeline for automated extraction of quantitative morphological measurements from pelvic ultrasound DICOM images. The pipeline comprises five sequential analytical stages. A ResNet-50 convolutional neural network, trained on 7,721 images across five anatomical categories, classifies each image frame with high discriminative accuracy (AUC 0.90 to 1.00). Complementary modules using optical character recognition and HSV-based colour thresholding determine ovarian laterality and detect Colour Doppler signals, while a random forest classifier identifies biplane split-screen views required for volumetric estimation (AUC = 0.96). A YOLOv5 object detection model, trained on 5,496 annotated images encompassing 25,942 follicle and 6,896 ovary regions, localises ovarian structures exclusively within ovary-classified frames to reduce false-positive detection of pelvic fluid collections. Two dedicated U-Net segmentation models, trained on 807 expert-annotated images, delineate ovarian and uterine structures independently, achieving Dice similarity coefficients of 0.856 and 0.899 respectively. Ellipse fitting applied to segmentation contours, calibrated using DICOM pixel spacing metadata, enables standardised extraction of endometrial thickness, ovary volume, and uterus volume. End-to-end evaluation in 100 patients demonstrated that automated measurements correlate meaningfully with both manual-caliper and manual-contour reference standards. Importantly, agreement between the two manual reference methods was itself moderate for several parameters, illustrating the inherent subjectivity of morphological measurement in pelvic ultrasound and providing essential context for interpreting automated performance. Automated endometrial thickness approached the level of agreement observed between the two manual methods, whilst ovarian volume concordance was comparable to inter-method human variability for that parameter. This pipeline provides a reproducible, scalable foundation for automated gynaecological imaging assessment. By decoupling morphological measurement from individual operator judgement and enabling structured data extraction at scale, it advances the goal of consistent, accessible, and high-quality imaging analysis as a cornerstone of women’s health care.","url":"https://doi.org/10.21203/rs.3.rs-7997734/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-7997734/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.31234/osf.io/ctk7j_v1","name":"Designing Psychometric Measures for LLMs: Framework and Application to Racial Bias","source":"preprints","abstract":"Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years, several LLMs have been introduced, including ChatGPT by OpenAI, Claude by Anthropic, and Llama by Meta, among others. These models have the potential to be employed across a wide range of human–machineinteraction applications, such as chatbots for information retrieval, assistance in corporate hiring decisions, college admissions, financial loan approvals, parole determinations, and even in medical fields like psychotherapy delivered through chatbots. The key question is whether these chatbots will interact with humans in a bias-free manner or if they will further reinforce the existing pathological biases present in human-to-human interactions. If the latter is true, then how can werigorously measure these biases?We address this challenge by introducing STAMP-LLM (Standardized Test Assessment Measurement Protocol for LLMs), a principled two-phase framework for designing psychometric measures to evaluate chatbot biases: (i) a Definitional phase for construct mapping, item development, and expert review; and (ii) a Data/Analysis phase for protocol control (prompts/decoding), automated sampling,pre-specified scoring, and basic reliability/validity checks. We illustrate STAMP-LLM on racial bias using one explicit and two implicit measures.","url":"https://doi.org/10.31234/osf.io/ctk7j_v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.31234/osf.io/ctk7j_v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9654603/v1","name":"Clinical safety evaluation of ambient voice technologies in surgical documentation","source":"preprints","abstract":"Abstract Introduction: Ambient voice technologies (AVT) utilise automatic speech recognition (ASR) to convert speech into text and are advocated as efficient, accurate tools for documenting the clinical record. Method: We evaluated eight contemporary AVT for surgical documentation, prioritising safety and potential harm. We tested three commercial systems: Dragon Medical-One, Heidi-Health, Tortus; four speech-to-text Application Programming Interfaces (Speechmatics-Enhanced, Amazon Medical-Transcribe, Whisper, GPT4oTranscribe); and an experimental two-stage ASR–Large Language Model (LLM) incorporating GPT4oTranscribe with GPT-5 LLM generative error correction (GPT4oTranscribe-Corrected-5). Reference transcripts from open-access surgical case reports were recorded for transcription (n=100; 32,764 words; range 43-441 words; mean 327.6 words-per-transcript). Primary outcome was the proportion of transcripts containing at least one clinically significant Class 3 error, graded for potential harm. Secondary outcomes were transcription accuracy: Domain Word Error Rate (DWER) against SNOMED-CT, Word Error Rate (WER), Non-Domain WER (N-DWER), lexical accuracy (ROUGE-score), semantic similarity (BERT and BART) and hallucinations (content absent from the audio). Results: Across 800 transcripts, the proportion containing at least one clinically significant Class 3 error ranged from 27% (GPT4oTranscribe-Corrected-5) to 65% (Amazon Medical Transcribe; Dragon Medical One). Narration length significantly increased odds of Class 3 errors for four systems (GPT4o Transcribe, Tortus, Amazon Medical Transcribe, and Dragon Medical One; ORs 2.33-3.36 per additional 100 reference words). Errors with potential to cause severe harm or death (NHS England Levels 3-4) totalled n=35 across systems and were concentrated in medication type and dose (37.1%) and investigations and laboratory results (34.3%). Performance differed across systems and metrics (P Conclusions: Contemporary AVT can produce accurate records; however, the persistence of clinically significant mistranscriptions with the potential for harm obligates cautious implementation.","url":"https://doi.org/10.21203/rs.3.rs-9654603/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9654603/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202508.1425.v1","name":"Multimodal Generative AI in Diagnostics: Bridging Medical Imaging and Clinical Reasoning","source":"preprints","abstract":"Multimodal generative artificial intelligence (AI) has emerged as a transformative approach in medical diagnostics, integrating diverse data sources to significantly enhance clinical decision-making and patient care. In this review, we systematically analyze recent advancements and methodologies in multimodal generative AI, focusing particularly on the fusion of medical imaging data with clinical records, genomic information, and textual narratives. We evaluate how these combined modalities closely mimic physician cognitive processes, leading to improved diagnostic accuracy and personalized patient management across various specialties including radiology, pathology, dermatology, and ophthalmology. Specifically, we discuss three key integration strategies: tool-use approaches, where large language models orchestrate specialized diagnostic modules; grafting techniques, which directly incorporate visual analysis into linguistic frameworks; and unified frameworks, providing simultaneous multimodal data processing within cohesive models. Additionally, we highlight exemplary models, such as PathChat, demonstrating substantial accuracy improvements (e.g., 89.5% in pathological image interpretation) resulting from multimodal integration. We also critically assess ongoing challenges, including technical barriers to data integration, interpretability issues affecting clinical trust, privacy and ethical concerns, and the evolving regulatory landscape surrounding AI-driven diagnostics. Finally, we propose directions for future research, emphasizing the need for large-scale clinical validation studies, standardized evaluation frameworks, advances in explainable AI methods, and privacy-preserving techniques such as federated learning. Ultimately, multimodal generative AI holds significant promise to augment rather than replace clinical expertise, serving as a powerful complement to human decision-making in medicine.","url":"https://doi.org/10.20944/preprints202508.1425.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202508.1425.v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-10157626/v1","name":"Investigating the fundamental characteristics of retinal age models","source":"preprints","abstract":"Abstract Retinal age is a biological ageing marker estimated from retinal images. Its deviation from chronological age, termed the retinal age gap (RAG), has been associated with adverse health outcomes. However, fundamental characteristics of retinal age models that are critical for interpreting and applying RAG remain underexplored, including the consistency of these associations across age subgroups as well as model generalisability across different cohorts. In this study, we used 327,764 retinal images to develop a retinal age model and investigate associations between RAG and systemic diseases. We examined association consistency across age subgroups and evaluated model performance on three external datasets with distinct imaging devices and populations. RAG showed positive associations with systemic diseases in young and middle-aged subgroups, but negative associations in the aged subgroup, revealing substantial age-dependent inconsistency. We further showed that this inconsistency was explained by regression-to-the-mean effects which varied by health status. In external evaluations, the generalisability of RAG varied considerably by cohort and intended application. Large age estimation errors did not necessarily indicate limited clinical utility, instead suggesting that model generalisability should be defined and evaluated in an application-specific manner rather than based on age estimation accuracy alone. Overall, this study investigates key characteristics of retinal age models that affect the interpretation of RAG and its disease associations. Our findings highlight the importance of reporting age-dependent associations, and emphasise the need for multi-dimensional, application-specific external evaluation for reliable use of retinal age and RAG in future clinical applications. More broadly, our findings and practical recommendations may extend to wider biological age estimation models.","url":"https://doi.org/10.21203/rs.3.rs-10157626/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10157626/v1","addedAt":"2026-09-01T01:47:58.476Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1016/0933-3657(89)90013-4","name":"Deep and shallow models in medical expert systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(89)90013-4","authors":["E.T. Keravnou","J. Washbrook"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(89)90013-4","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-18450-5.00007-4","name":"Artificial intelligence based Alzheimer’s disease detection using deep feature extraction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-18450-5.00007-4","authors":["Manav Nitin Kapadnis","Abhijit Bhattacharyya","Abdulhamit Subasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T15:22:28Z","doi":"10.1016/b978-0-443-18450-5.00007-4","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-18897-7_14","name":"BAI-PLF: A Blockchain-AI-IoT Framework for Energy-Efficient and Predictive Logistics and Healthcare in Africa","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18897-7_14","authors":["Aya Bouri","Saadia Chabel","Aissa Kerkour Elmiad","Asmae El Mezouari","El Miloud Ar-Reyouchi","Kamal Ghoumid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T13:55:26Z","doi":"10.1007/978-3-032-18897-7_14","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.2196/98668","name":"Retraction: Artificial Intelligence–Based Neural Network for the Diagnosis of Diabetes: Model Development","source":"crossref","abstract":"","url":"https://doi.org/10.2196/98668","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-28T19:40:07Z","doi":"10.2196/98668","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/medai67139.2025.00006","name":"Acknowledgement","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai67139.2025.00006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00006","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.66128/jdhe202601.1","name":"Ethical Review in Medical Artificial Intelligence Applications: Challenges, Frameworks, and Future Directions","source":"crossref","abstract":"The rapid advancement of medical artificial intelligence (AI) technologies is profoundly transforming healthcare practices, offering significant potential in disease diagnosis, treatment decision-making, health management, and drug development. However, the application of these technologies raises complex ethical, legal, and social issues, challenging traditional ethical review mechanisms. This review systematically examines the unique ethical challenges posed by medical AI applications, evaluates the applicability and limitations of existing ethical review frameworks, and explores future directions for developing novel ethical review paradigms tailored to the distinctive characteristics of AI. Key topics discussed include data privacy and security, algorithmic fairness and bias, transparency and explainability, accountability, clinical efficacy validation, and patient autonomy. By addressing these core issues, this article aims to provide theoretical insights and practical guidance for establishing a robust, effective, and trustworthy ethical governance system for medical AI, thereby supporting responsible innovation and safeguarding patient rights in the evolving landscape of healthcare technology.","url":"https://doi.org/10.66128/jdhe202601.1","authors":["xianqi zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-19T01:23:03Z","doi":"10.66128/jdhe202601.1","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/rev-ai70456.2026","name":"2026 International Conference on Revolutionary Artificial Intelligence and Future Applications (Rev-AI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/rev-ai70456.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T19:17:14Z","doi":"10.1109/rev-ai70456.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-38343-4.00301-3","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-38343-4.00301-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-13T16:16:54Z","doi":"10.1016/b978-0-443-38343-4.00301-3","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.59646/635","name":"Advanced Research Methodology (Strategies for Scholarly Inquiry)","source":"crossref","abstract":"","url":"https://doi.org/10.59646/635","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T16:48:58Z","doi":"10.59646/635","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-26779-6.12001-7","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26779-6.12001-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-29T05:52:28Z","doi":"10.1016/b978-0-443-26779-6.12001-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1145/3811238","name":"Proceedings of the 2026 International Conference on Artificial Intelligence and Agents","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3811238","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-06T05:06:47Z","doi":"10.1145/3811238","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-323-95462-4.00016-9","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95462-4.00016-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-02T07:43:05Z","doi":"10.1016/b978-0-323-95462-4.00016-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-18450-5.00020-7","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-18450-5.00020-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T14:05:22Z","doi":"10.1016/b978-0-443-18450-5.00020-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1186/s12910-026-01474-8","name":"Care ethics and the transformation of care in an age of artificial intelligence","source":"crossref","abstract":"Abstract Background The integration of artificial intelligence (AI) and care robots into healthcare raises a central ethical question: what constitutes care, and how should it be delivered when machines perform caregiving tasks? Dominant AI ethics frameworks, including principlism, deontology, and consequentialism, focus on fairness, duties, and outcomes. While important, these approaches often view care primarily as technical compliance or efficiency, overlooking its relational and evaluative aspects. Methods This analysis employs a normative, conceptual approach rooted in care and relational ethics, examining duty-based, outcome-oriented, and virtue-based care frameworks to highlight how they differ from care ethics as a structured moral practice. Tronto’s four stages of care (attentiveness, responsibility, competence, and responsiveness) serve as a framework for assessing moral labor in caregiving. Literature on social robots in healthcare is used illustratively. Conceptual analysis compares the interpersonal and moral dimensions of human caregiving with forms of AI interaction, noting changes when machines mediate or perform caregiving tasks. Results The analysis shows that although AI systems can improve monitoring, coordination, and task performance, they do not assume moral responsibility or provide the relational and evaluative work that caregiving requires. Social and assistive robots reorganize moral labor by shifting attentiveness toward sensing, responsibility toward oversight, competence toward optimization, and responsiveness toward adaptive feedback. These changes create a functional resemblance to care without reproducing the moral engagement that characterizes genuine caregiving. Conclusion Care ethics elucidates the moral practices of caregiving and how these practices are transformed through the integration of AI into healthcare relationships. Since caregiving involves vulnerability, interdependence, and judgment, it cannot rely solely on efficiency. A care-ethical perspective demonstrates that AI does not replace moral labor; rather, it reorganizes it in ways that reduce the conditions under which authentic caregiving can be conducted. The incorporation of care ethics into AI governance frameworks provides tools for assessing not just what these technologies do but what they cost the moral practice of caregiving.","url":"https://doi.org/10.1186/s12910-026-01474-8","authors":["Rachel Wangari Kimani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-30T05:24:43Z","doi":"10.1186/s12910-026-01474-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.61362/core0517","name":"The Horizon of Artificial Intelligence","source":"crossref","abstract":"The Horizon of Artificial Intelligence is a wide-ranging intellectual journey across human intelligence, artificial intelligence, culture, innovation, education, ethics, and the future of knowledge. Written by Piero Formica, with additional contributions by invited scholars and practitioners, and published by the International Core Academy of Sciences and Humanities as a CORE Academy Digital Edition, the book approaches AI not only as a technology but as a moving horizon that reshapes how human beings think, learn, create, govern, and imagine the future.","url":"https://doi.org/10.61362/core0517","authors":["Piero Formica"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-16T17:39:46Z","doi":"10.61362/core0517","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00053-1","name":"Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00053-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00053-1","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aaiml67890.2026","name":"2026 International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aaiml67890.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-06T19:39:34Z","doi":"10.1109/aaiml67890.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/s10668-026-07989-9","name":"Artificial intelligence and sustainable medical resource coordination in public health emergencies: a differential game approach","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10668-026-07989-9","authors":["Jie Leng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T03:22:05Z","doi":"10.1007/s10668-026-07989-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aicconf69182.2026.11600695","name":"AICCONF 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aicconf69182.2026.11600695","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:02Z","doi":"10.1109/aicconf69182.2026.11600695","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.20944/preprints202103.0120.v2","name":"Medical Applications of Artificial Intelligence","source":"crossref","abstract":"The medical &amp;amp; the dental field is a never ending field of innovations &amp;amp; developments and each time the reasearchers come up with something new. One such new dimension in the fields of medicine being the incorporation of Artificial intelligence assisted technologies improving diagnosis, treatmemt plan and treatment stategies. This review focusses on the application of different technologies of AI in different fields of medicine.","url":"https://doi.org/10.20944/preprints202103.0120.v2","authors":["Rupanjan Roy","Aishani Baksi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-31T02:52:26Z","doi":"10.20944/preprints202103.0120.v2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/medai67139.2025.00003","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1109/medai67139.2025.00003","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T04:49:42Z","doi":"10.1109/medai67139.2025.00003","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.114481","name":"Artificial intelligence in lung cancer imaging: A review of framework architectures and computer-aided diagnosis advancements","source":"crossref","abstract":"The fight against lung cancer knows no boundaries of age, gender, or ethnicity. The key to conquering this global challenge lies in timely detection, which dramatically enhances survival rates and quality of life post-diagnosis. This survey aims to address the lack of comprehensive reviews in the domain of automated lung cancer diagnosis dedicated to image processing through the lens of artificial intelligence and computer-aided diagnosis (CAD) systems. Although there is growing interest in this field, there is a dearth of literature offering a detailed examination of the framework architecture of these systems. To fill this gap, this study adopted a focused approach, analyzing 131 original articles from 2019 to 2024, sourced from Scopus and Web of Science indexed repositories. In this paper, a structured framework was introduced to enable a thorough analysis, evaluation and validation of existing CAD techniques. The review investigated raw imaging data and framework components, identified optimization opportunities, such as refining pre-processing techniques and improving feature extraction methods. Additionally, the study conducted a comparative analysis among various CAD systems, aiding researchers in selecting optimal methods for lung cancer diagnosis. Moreover, the study established detailed guidelines for documenting model specifications in CAD systems, enhancing reproducibility. Ultimately, this framework provides a roadmap for future research in the field, addressing the limitations of current CAD systems, and contributing to improved accuracy and efficiency in lung cancer detection.","url":"https://doi.org/10.1016/j.engappai.2026.114481","authors":["Sher Lyn Tan","Ganeshsree Selvachandran","Weiping Ding","Ketan Kotecha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-17T10:56:12Z","doi":"10.1016/j.engappai.2026.114481","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00016-x","name":"Algorithmic design","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00016-x","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00016-x","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-06637-4_7","name":"Large Language Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06637-4_7","authors":["Oliver Kramer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T05:21:45Z","doi":"10.1007/978-3-032-06637-4_7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00492-9","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00492-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00492-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icaim69488.2026.11601936","name":"ICAIM 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601936","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601936","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/raiie70320.2026","name":"2026 5th International Symposium on Robotics, Artificial Intelligence and Information Engineering (RAIIE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/raiie70320.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-23T19:44:07Z","doi":"10.1109/raiie70320.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1002/9781394278886.index","name":"Index","source":"crossref","abstract":"Note: Page numbers in italics indicate a figure and page numbers in bold indicate a table on the corresponding page.a AAL.see Automated Anatomical Labeling (AAL) AD. see Alzheimer's disease (AD) ADAM.see Amsterdam Decoding and Modeling Toolbox (ADAM) Addenbrooke's Cognitive Examination-Revised (ACE-III) 66 ADNI.see Alzheimer's Disease Neuroimaging Initiative (ADNI) Adversarial Co-training Network 269 AEs.see autoencoders (AEs) agglomerative hierarchical clustering 126 AIA.see attribute inference attack (AIA) AI-inspired subtype analysis 11, 125.see also clustering methods AI-neuroscience intersection applications 4-6, 5 artificial intelligence, defined 2, 3 data and model sharing frameworks 23-24 data-related challenges 16-17 in disease diagnosis and early prediction 6 disease mechanisms 7 ethical and privacy issues 19-21 from explainability to reliability 24-25 historical milestones in 3-4","url":"https://doi.org/10.1002/9781394278886.index","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T13:39:39Z","doi":"10.1002/9781394278886.index","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44430-2.01001-x","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44430-2.01001-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:25:52Z","doi":"10.1016/b978-0-443-44430-2.01001-x","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-44-333496-2.00006-x","name":"About the editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-333496-2.00006-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:42:43Z","doi":"10.1016/b978-0-44-333496-2.00006-x","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-33124-4.00301-5","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33124-4.00301-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T11:42:50Z","doi":"10.1016/b978-0-443-33124-4.00301-5","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00019-5","name":"Algorithmic testing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00019-5","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00019-5","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00024-9","name":"Data acquisition","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00024-9","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00024-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1145/3807246","name":"Proceedings of the 2026 International Conference on Artificial Intelligence and Control","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3807246","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T10:50:37Z","doi":"10.1145/3807246","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-030-92087-6_8","name":"Biobanks and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_8","authors":["Musa Abdulkareem","Nay Aung","Steffen E. Petersen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/0933-3657(94)90033-7","name":"Emerging medical applications of virtual reality: A surgeon's perspective","source":"crossref","abstract":"Medical applications for virtual reality (VR) technologies are just beginning to emerge. These include VR surgical simulators, telepresence surgery, complex medical database visualization, and rehabilitation. These applications are mediated through the computer interface and embody VR as an integral part of a paradigm shift in the field of medicine. The Green Telepresence Surgery System consists of two components, the surgical workstation and the remote worksite. At the remote site there is a 3-D camera system and responsive manipulators with sensory input. At the workstation there is a 3-D monitor and dexterous handles with force feedback. The VR surgical simulator is a stylized recreation of the human abdomen with several essential organs. Using a head-mounted display and DataGlove, a person can learn anatomy from a new perspective by 'flying' inside and around the organs, or can practice surgical procedures with a scalpel and clamps. Database visualization creates 3-D images of complex medical data for new perspectives in analysis. VR applications in rehabilitation medicine permit impaired individuals to perform tasks not otherwise available to them, allow accurate assessment and therapy for their disabilities, and help architects understand their critical needs in public or personal space. And to support these advanced technologies, the operating room and hospital of the future will be first designed and tested in virtual reality, bringing together the full power of the digital physician.","url":"https://doi.org/10.1016/0933-3657(94)90033-7","authors":["Richard M. Satava"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T05:53:05Z","doi":"10.1016/0933-3657(94)90033-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-981-92-1527-0","name":"New Frontiers in Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-92-1527-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-05T05:39:23Z","doi":"10.1007/978-981-92-1527-0","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/ainit70033.2026","name":"2026 7th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ainit70033.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-16T19:42:59Z","doi":"10.1109/ainit70033.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-34135-9.00021-0","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34135-9.00021-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T07:37:26Z","doi":"10.1016/b978-0-443-34135-9.00021-0","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.66419/jsmrai.7692","name":"Proceedings of the Joint Symposium on Multidisciplinary Research and Artificial Intelligence (JSMRAI)","source":"crossref","abstract":"","url":"https://doi.org/10.66419/jsmrai.7692","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-07T16:58:16Z","doi":"10.66419/jsmrai.7692","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-27465-7.09998-2","name":"About the editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27465-7.09998-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T12:13:00Z","doi":"10.1016/b978-0-443-27465-7.09998-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.66366/aits.1.2","name":"Recent Developments in Artificial Intelligence and Sustainable Technologies”, aims to highlight the latest research contributions at the intersection of artificial intelligence, optimization methods, and sustainable technological solutions","source":"crossref","abstract":"","url":"https://doi.org/10.66366/aits.1.2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-02T19:35:34Z","doi":"10.66366/aits.1.2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1108/978-1-80686-865-020261010","name":"Leveraging Artificial Intelligence for Transforming Medical Tourism: Opportunities, Challenges, and Entrepreneurial Perspectives","source":"crossref","abstract":"The incorporation of artificial intelligence (AI) in medical tourism represents a significant change in the manner in which global healthcare services are accessed, delivered, and managed. This research examines the impact of AI on the medical tourism value chain from diagnosis and hospital administration to patient engagement and clinical follow-up. The research utilizes a qualitative exploratory perspective, using case studies from prominent medical tourism destinations such as India, Thailand, Turkey, and Singapore, to help explore how AI technologies such as machine learning, predictive analytics, and virtual assistants increase the efficiency and personalization of healthcare services, and their management and decision-making process. The study also reflects on the entrepreneurial, ethical, and policy dimensions of AI adoption, including enablers and constraints of AI usage and data privacy, regulatory inconsistencies, and stakeholder readiness. In particular, the study presents a proposed strategic framework for the application of AI in the medical tourism ecosystem while enhancing the innovative, competitive, and sustainable features of medical tourism. The study ends by providing policy-makers, entrepreneurs, and healthcare administrators with insights to harness AI in global medical tourism earnest.","url":"https://doi.org/10.1108/978-1-80686-865-020261010","authors":["C. V. Suresh Babu","P. Yukesh Kumar","E. Thirumalai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-12T11:21:01Z","doi":"10.1108/978-1-80686-865-020261010","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-13565-0_34","name":"Problems and Prospects of Legal Regulation of Artificial Intelligence in Telemedicine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-13565-0_34","authors":["Ivan A. Usenkov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-05T22:52:09Z","doi":"10.1007/978-3-032-13565-0_34","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-26745-1.00301-7","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26745-1.00301-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-27T07:21:18Z","doi":"10.1016/b978-0-443-26745-1.00301-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00012-2","name":"Algorithmic training","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00012-2","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00012-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-33151-0.00014-9","name":"Glossary","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33151-0.00014-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T12:29:25Z","doi":"10.1016/b978-0-443-33151-0.00014-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/bdai70753.2026","name":"2026 IEEE 9th International Conference on Big Data and Artificial Intelligence (BDAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bdai70753.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-24T19:17:35Z","doi":"10.1109/bdai70753.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.4103/ijves.ijves_20_26","name":"The Changing Landscape of Medical Research: Learning to Think in the Age of Artificial Intelligence","source":"crossref","abstract":"The integration of Artificial Intelligence into medical research represents a fundamental transformation in how we generate, verify, and publish scientific knowledge. As we watch our residents produce polished case reports in minutes using Large Language Models, a deeper question emerges: what happens to the cognitive architecture of the researcher when the friction of research itself disappears? THE HIDDEN PEDAGOGY OF THE LITERATURE SEARCH For decades, we’ve trained researchers through the apprenticeship model of the case report and literature review.[1] We sent residents to PubMed, watching them navigate hundreds of abstracts to write a single paper. This process seemed inefficient – a necessary burden of academic medicine. However, we failed to appreciate that this “inefficiency” was actually the curriculum. When a researcher manually searches the literature, they’re not just retrieving documents—they’re mapping the intellectual landscape of their field. The act of rejecting irrelevant papers teaches the boundaries of the research question. Encountering what a disease is not creates richer understanding of what it is. Coming across unexpected articles from adjacent fields sparks serendipitous connections. This is what cognitive psychologists call “desirable difficulties” – learning conditions that feel harder but produce deeper, more durable knowledge.[2] The danger of AI in research isn’t that it helps too little; it’s that it helps too much. When we eliminate the struggle of synthesis – the manual gathering of sources, the cognitive wrestling with contradictory evidence, the laborious drafting of arguments – we remove the very process that builds the researcher’s mind. A trainee can now produce a publication-ready manuscript without ever engaging in the intellectual struggle that transforms information into understanding. The polished output masks the cognitive deficit. HOW ARTIFICIAL INTELLIGENCE CHANGES THE RESEARCH BRAIN Research into cognitive offloading reveals a troubling trade-off. When access to external information is easy, we use it liberally – and our internal processing suffers accordingly. But AI goes beyond the “Google Effect” of offloading memory; it offloads analytical thinking itself. When a researcher asks AI to “summarize the pathophysiology of arteriovenous malformations,” they bypass the metabolic cost of synthesis. The neural circuits for critical analysis – the ability to hold conflicting evidence in working memory and adjudicate between them weaken from disuse. Studies show that handwriting and manual typing create widespread brain connectivity patterns crucial for memory encoding and semantic integration.[3] But, when researchers edit AI-generated text, they engage only in verification, not generation. They correct syntax, not construct logic. The knowledge passes through the mind without embedding itself in long-term memory. This is the paradox: AI can make us more productive while simultaneously making us less knowledgeable. THE TRANSFORMATION OF RESEARCH TRAINING The case report has long served as the apprenticeship piece for medical researchers a bridge between clinical observation and scientific contribution.[1] Its educational value never lay in its impact factor but in the process of creating it. Learning to prove uniqueness through exhaustive literature searching, structuring clinical narratives within rigid scientific formats, navigating peer review and revision—these experiences teach how medical truth is established and validated. Now, AI can generate a structurally perfect, guideline-compliant manuscript in seconds. The output often appears superior to what most junior researchers could produce. However, this polish conceals a void. If AI identifies the knowledge gap, selects the references, and frames the argument, the researcher becomes merely a data provider. They lose the struggle of defining significance and with it, they lose the development of tacit knowledge, the intuitive “research sense” that cannot be taught but must be earned through experience. Perhaps most dangerous is what we might call the “illusion of competence.” Researchers may mistake their ability to prompt AI for the ability to conduct research itself. They confuse access to intelligence with possession of intelligence. When a researcher has a hypothesis and prompts AI for supporting evidence, the algorithm obligingly fabricates references or cherry-picks data, creating an echo chamber that reinforces biased reasoning.[4] This automation bias – the tendency to trust algorithmic suggestions means errors propagate into the published literature with less scrutiny than ever before. THE EXISTENTIAL THREAT TO SCIENTIFIC PUBLISHING The impact of AI extends beyond individual learning to the integrity of the scientific enterprise itself. Traditional research relied heavily on serendipity – finding crucial insights in papers we weren’t specifically seeking. Manual browsing of literature allowed researchers to connect disparate ideas across fields. However, AI search tools are precision instruments that optimize for relevance, potentially sterilizing the creative chaos where innovation often emerges. By showing us only what we’re looking for, algorithms may hide what we need to discover. Even more concerning is the phenomenon of “Model Collapse.” As AI becomes the dominant tool for writing scientific papers, the published literature becomes increasingly populated with AI-generated content. Future AI models, trained on this synthetic data, degrade over generations.[5] They lose the variance where rare diseases, unexpected findings, and paradigm-shifting theories exist. The output regresses toward a bland mean of “average” science. We risk creating a self-referential echo chamber divorced from biological reality, populated by hallucinated references and plausible but false mechanisms. If 90% of future literature is AI-generated, finding the 10% of genuine human observation becomes nearly impossible. REDESIGNING RESEARCH TRAINING FOR THE ARTIFICIAL INTELLIGENCE ERA We cannot ban AI from research – that would be denial of reality. But we cannot accept unbridled automation either – that would be abdication of responsibility. We must deliberately reintroduce cognitive friction to preserve intellectual development while harnessing AI’s power.[2] The artificial intelligence audit approach Rather than grading the final manuscript, evaluate how the researcher interacted with AI. Require explicit documentation of verification: “AI cited Smith et al. 2020 for this mechanism; I retrieved the original paper and found the AI misrepresented the conclusion. The actual finding was…” This transforms potential over-reliance into active critical engagement, teaching epistemic humility and source verification.[4] Reverse prompting Have researchers use AI as an interviewer rather than an answerer. “I have a case of spontaneous carotid dissection in a young patient. Ask me probing questions to help determine if this represents a publishable observation and what the key knowledge gap might be.” This restores generative processing – the researcher must articulate their thinking rather than passively receive AI output. The sparring partner model Treat AI as an intellectual opponent, not an oracle. After formulating a hypothesis, require researchers to ask AI to “provide three strong arguments against this interpretation based on current evidence.” This directly counters confirmation bias and forces engagement with contradictory data – a core competency of rigorous research Cognitive forcing functions Just as pilots must demonstrate manual flying skills despite autopilot systems, researchers must demonstrate manual synthesis capability.[2] Designate specific research phases as “AI-free zones,” particularly the initial problem formulation and hypothesis generation. Require oral defenses where researchers explain their methodology and findings without reference materials. If they cannot articulate it independently, they haven’t truly learned it. REDEFINING PROFESSIONAL IDENTITY The physician-scientist of the future will not be defined by their ability to write a sentence but by their ability to verify it. As the cost of generating text approaches zero, the value of verifying text approaches infinity. The new professional identity is that of Guardian of Epistemic Integrity – the human-in-the-loop who mediates between algorithmic logic and clinical reality.[6] Paradoxically, as “hard” skills like data analysis become automated, “soft” skills become critical. AI can write the case report, but it cannot interview the patient to elicit the subtle history that makes the case unique. It cannot navigate the ethical nuances of consent in complex family dynamics. These deeply human capacities must move from the periphery to the center of our curriculum. CONCLUSION: SYMBIOSIS, NOT SURRENDER The principles of rigorous research training are not obsolete – but AI brutally exposes that much of what we called training was actually unintentional cognitive resistance training. Removing this resistance without replacing it will produce cognitive frailty: researchers with access to the world’s knowledge but lacking the neural architecture to evaluate its truth. The future is not about competing with AI’s speed; it’s about mastering AI’s direction. We must teach our trainees not just to ask AI for answers but to question the answers it gives. The learning that happens through process doesn’t have to fade – it must simply migrate from the production of text to the interrogation of truth. As vascular surgeons, we understand that the best outcomes come not from technology alone but from the synthesis of technical precision and human judgment. The same principle applies here. We must cultivate hybrid intelligence – directing algorithmic agents while reserving the human mind for its highest and most irreplaceable function: making meaning out of information. The resistance we feel toward AI in training isn’t nostalgia – it’s wisdom. However, wisdom also demands we adapt. Our challenge is to harness AI’s power without surrendering the cognitive development that transforms medical students into thinking physicians. The apprenticeship model of surgical training has survived centuries of change. It will survive this transition too if we have the courage to redesign it with intentionality.","url":"https://doi.org/10.4103/ijves.ijves_20_26","authors":["Himanshu Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-23T13:00:15Z","doi":"10.4103/ijves.ijves_20_26","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icaim69488.2026.11601704","name":"ICAIM 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601704","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601704","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-981-95-2525-6_10","name":"Transformers","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-2525-6_10","authors":["Shenghua Gao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T07:05:47Z","doi":"10.1007/978-981-95-2525-6_10","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-26466-5.20001-9","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26466-5.20001-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T07:31:43Z","doi":"10.1016/b978-0-443-26466-5.20001-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1002/9781394318292","name":"Material and Artificial Intelligence in Architecture","source":"crossref","abstract":"Offers a bold and materially grounded rethinking of design with artificial intelligence, and proposes a new model of agency distributed across material, artificial, and human actors Rooted in material philosophy and ecological design thinking, Material and Artificial Intelligence in Architecture establishes a comprehensive framework for understanding AI in architecture. Conceptually rigorous yet accessibly written and visually rich, the book interweaves design, theory, technology, and practice to explore how artificial intelligence, material agency, and human authorship will co-create the built environment of the future. Challenging both pervasive fear-driven narratives and techno-solutionist hype that dominate public discourse around AI, the book proposes a new plane of engagement—introduced as Shared Materiality. This concept recognizes the entangled agency of human, artificial, and material actors. It also addresses the ethical, ecological, and social implications of AI technologies, including the vast energy demands of AI systems, embedded algorithmic bias, and broader concerns about systemic inequity and access. Specifically within the field of architecture, it critiques the dominance of image-based AI in current architectural discourse and practice, redirecting attention to geometry, morphology, and structure—fundamental spatial dimensions of architecture—and discusses emerging AI models that engage these domains. Drawing on the author's extensive teaching and research experience, Material and Artificial Intelligence in Architecture discusses: Theory, technology, and design: a rare integration of architecture's core domains, combining deep conceptual framing, technological insight, and advanced experimentation in design. Comprehensive conceptual framework: for understanding AI in architecture, rooted in new materialist thought and philosophical realism. AI intuition: cultivating future designers’ ability to collaborate with AI through attuned expertise rather than control. Advanced methodologies for Urban Design and Urban Housing design: with a detailed presentation of speculative studio work that integrates AI, robotic fabrication, and material practice. Critique of typology in architecture: proposing instead an open taxonomy: a dynamic, adaptive classification system that aligns with the distributed logic of AI systems. Critique of image-based AI in architecture: advocating geometry-based approaches that emphasize spatio-tectonic reasoning, material articulation, and spatial intelligence. Ethical and ecological implications of AI systems: addressing authorship, access, equity, energy consumption, algorithmic bias, and the extractive infrastructures underlying current technologies. Material and Artificial Intelligence in Architecture delivers cutting-edge insights for architects, design enthusiasts, and interdisciplinary readers interested in the intersection of design, philosophy, and artificial intelligence. It is also highly suitable for students, educators, and researchers in architecture and design, with particular relevance to graduate-level and post-professional programs.","url":"https://doi.org/10.1002/9781394318292","authors":["Jonas Coersmeier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-24T21:31:46Z","doi":"10.1002/9781394318292","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.64782/vera.vap28","name":"Teaching Mary Wollstonecraft Through Artificial Intelligence: Rethinking Literature in the Digital Age","source":"crossref","abstract":"Mary Wollstonecraft’s contribution to both literature and education is seminal, particularly through her work A Vindication of the Rights of Woman (1792), which advocated women’s equality and intellectual development. Nevertheless, historical contexts and linguistic complexities of 18th century texts can be challenging to contemporary students. In the present age of digitalization, Artificial Intelligence (AI) can present innovative pedagogical opportunities which can reimagine the instructional patterns. This chapter delves into examining how AI can assist in developing educational tools which can enhance teaching literary concepts, strengthening textual understanding and improve students’ attentiveness. This chapter will also delve into analyzing how Natural Language Processing (NLP) and Automated Text Analysis, AI based platforms can help learners understand literature, combining digital humanities and AI assisted teaching. In this chapter, we will try to analyse how AI can help understand Wollstonecraft’s arguments through AI-assisted interpretative learning.","url":"https://doi.org/10.64782/vera.vap28","authors":["Ujjal Das","Anasuya Adhikari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-12T19:54:07Z","doi":"10.64782/vera.vap28","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-34135-9.00007-6","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34135-9.00007-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T07:37:26Z","doi":"10.1016/b978-0-443-34135-9.00007-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/c2025-0-01605-8","name":"Embedded Artificial Intelligence and the Internet of Things for Photovoltaic Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2025-0-01605-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-21T10:34:06Z","doi":"10.1016/c2025-0-01605-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aitest70988.2026.00045","name":"Author Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitest70988.2026.00045","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-26T19:06:48Z","doi":"10.1109/aitest70988.2026.00045","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44021-2.00019-1","name":"Multidisciplinary approach for female sexual dysfunction: Bridging Unani and contemporary medicine along with artificial intelligence innovations","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44021-2.00019-1","authors":["Arshiya Sultana","Uzma Shamim Ansari","Khaleequr Rahman","Sumbul Mehdi","Mohd Ammar Bin Hayat","Khadija Khaleeq","Ramsha Arshad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T12:02:29Z","doi":"10.1016/b978-0-443-44021-2.00019-1","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.52442/jrmi.v11i1.902","name":"Pragmatic awareness of Artificial Intelligence among medical students: a coss-sectional survey","source":"crossref","abstract":"Background: The integration of Artificial Intelligence (AI) into medicine is revolutionizing both clinical practice and medical education. However, the knowledge, attitudes, and perceptions of medical students towards AI remain underexplored. Objectives: To evaluate the knowledge attitudes and perceptions of medical students towards the integration of AI technologies in medical education, clinical practice, and healthcare decision-making. Materials and Methods: A cross-sectional survey was conducted among medical students at Rehman Medical College (RMC) Peshawar, Pakistan. Data were collected through a structured questionnaire assessing demographics, knowledge of AI, attitudes towards its use in medicine, and perceptions of its role in medical education and practice. Descriptive statistics and inferential analysis were applied to analyze the data. Results: Of 100 medical students, 85% were aged 18-25 years, with 60% females and 40% males. Only 10% had additional AI training, and 20% had attended AI-related talks (p=0.04). About 50% were competent in computer literacy, and 55% frequently used computer technology for learning (p=0.03). Regarding AI’s impact, 40% agreed it reduces medical errors (p=0.02), and 45% felt it facilitates physicians' access to information (p=0.01). However, 15% expressed concerns about AI damaging the physician-patient relationship (p=0.04). In AI in medicine, 92% had used AI applications, and 75% believed AI would positively transform medicine (p=0.02). While 40% feared job reductions in medical staff (p=0.05), 75% thought AI would improve patient care (p=0.04). For AI education, 85% supported its inclusion in the curriculum, with 50% favoring practical content (p=0.02) and case studies (p=0.01). 90% agreed AI education should be available to all medical staff (p=0.01). Conclusion: Medical students at Rehman Medical College showed a positive attitude towards AI’s potential to improve healthcare. However, there is a clear demand for more focused AI education, including practical applications and case studies.","url":"https://doi.org/10.52442/jrmi.v11i1.902","authors":["Humaira Achakzai","Shahzadi Manayal","Muhammad Iqbal Wahid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-09T03:57:20Z","doi":"10.52442/jrmi.v11i1.902","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44121-9.00003-2","name":"Harnessing artificial intelligence for screening phytochemicals in gastrointestinal cancer therapeutics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44121-9.00003-2","authors":["Mamta Goswami","Priyakshi Nath","Sibashish Kityania","Rajat Nath","Deepa Nath","Anupam Das Talukdar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T09:59:39Z","doi":"10.1016/b978-0-443-44121-9.00003-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.3923/jai.2026.49.59","name":"Large Language Models in Mathematical Intelligent Educational Assessment: A Literature Review","source":"crossref","abstract":"","url":"https://doi.org/10.3923/jai.2026.49.59","authors":["Xu Tong","Razali Yaakob","Sina Abdipoor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-28T10:07:53Z","doi":"10.3923/jai.2026.49.59","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/icaibd69640.2026.11637310","name":"Artificial Intelligence for Breast Cancer Detection in Mammography: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaibd69640.2026.11637310","authors":["Aya Kamel","Zouhair Chiba","Samira El Moumen","Salma Ennaqui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-13T19:11:08Z","doi":"10.1109/icaibd69640.2026.11637310","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2026.103398","name":"EvoPS: Evolutionary Patch Selection in the Training Embedding Space of Whole Slide Images","source":"crossref","abstract":"In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) requires their decomposition into thousands of patches, resulting in high-dimensional embeddings that are computationally costly to process and often dominated by uninformative regions. Existing patch selection methods typically rely on heuristic sampling and do not explicitly address the trade-off between representation compactness and diagnostic accuracy. To address this gap, we propose EvoPS (Evolutionary Patch Selection), a novel framework that formulates patch selection within the training embedding space as a multi-objective optimization problem and leverages an evolutionary search to simultaneously minimize the number of selected patch embeddings and maximize the performance of a downstream similarity search task, generating a Pareto front of optimal trade-off solutions. By identifying a compact and diagnostically informative subset of training patches, EvoPS produces higher-quality training representations that reduce memory requirements and improve the signal-to-noise ratio of the training set. We validated our framework across four major cancer cohorts from The Cancer Genome Atlas (TCGA) using five histopathology foundation models. The results demonstrate that EvoPS can reduce the required number of training patches by over 90% while consistently maintaining or even improving the final classification F 1 -score compared to a state-of-the-art patch selection method. The EvoPS framework provides a robust and principled method for creating efficient, accurate, and interpretable WSI representations, empowering users to select an optimal balance between computational cost and diagnostic performance.","url":"https://doi.org/10.1016/j.artmed.2026.103398","authors":["Saya Hashemian","Azam Asilian Bidgoli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-03T17:21:02Z","doi":"10.1016/j.artmed.2026.103398","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-43934-6.00025-0","name":"Minimizing vulnerability of artificial intelligence (AI) programs from cyberattacks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43934-6.00025-0","authors":["Laishram Saya","Deepanshu Vasuja","Gauri Jha","Atreyee Bagchi","Shivam Saini","Pooja","Sunita Hooda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-24T08:30:54Z","doi":"10.1016/b978-0-443-43934-6.00025-0","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.69648/gxmc2707","name":"Court Proceedings and Artificial Intelligence - New Horizons","source":"crossref","abstract":"Gender equality constitutes both a fundamental human right and a central pillar of sustainable socio-economic development (Gјorgјioska et al., 2025). Despite notable gains in educational attainment, gender disparities remain pronounced in Science, Technology, Engineering, and Mathematics (STEM), particularly in pathways leading to technical leadership. This study explores the “Southeast European Paradox,” whereby countries such as North Macedonia and Serbia record substantially higher shares of female STEM graduates than the European Union average, yet struggle to retain this talent within the labor market (OECD, 2024). Adopting a mixed-methods analytical approach, the research interprets these patterns through the theoretical lenses of social identity theory, social constructivism, and feminist institutionalism. The findings point to a persistent “leaky pipeline”: although women in North Macedonia perform strongly in tertiary education, a significant proportion subsequently exit STEM careers. This attrition is closely associated with exclusionary institutional environments, gendered perceptions of technical competence, and limited career progression opportunities. Comparative evidence from neighboring Southeast European contexts further indicates enduring sectoral segregation, with women underrepresented in high-value industrial domains relative to service-oriented sectors. The study concludes that formally gender-neutral policy frameworks are insufficient to address these structural constraints. Instead, more robust and targeted interventions are required, including institutional accountability mechanisms such as Gender Responsive Budgeting, enforceable organizational quotas, and systematic gender-sensitivity training within educational and professional settings.","url":"https://doi.org/10.69648/gxmc2707","authors":["Fatime Reka Hasani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-24T12:52:52Z","doi":"10.69648/gxmc2707","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-031-99882-9","name":"Green Artificial Intelligence and Industrial Applications (G-AIIA)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99882-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T06:06:22Z","doi":"10.1007/978-3-031-99882-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.116082","name":"An automatic construction of a financial sentiment lexicon","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.116082","authors":["Tommaso Garutti","Flavius Frasincar","Finn van der Knaap"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-31T12:15:03Z","doi":"10.1016/j.engappai.2026.116082","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-06637-4_2","name":"K-Nearest Neighbors","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06637-4_2","authors":["Oliver Kramer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T05:24:07Z","doi":"10.1007/978-3-032-06637-4_2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44728-0.00023-8","name":"The “black box”","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44728-0.00023-8","authors":["Luca Saba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T12:39:58Z","doi":"10.1016/b978-0-443-44728-0.00023-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00028-6","name":"Materials selection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00028-6","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00028-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44415-9.05001-8","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44415-9.05001-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:24:49Z","doi":"10.1016/b978-0-443-44415-9.05001-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-33151-0.00002-2","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33151-0.00002-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T12:29:25Z","doi":"10.1016/b978-0-443-33151-0.00002-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aisei68628.2026","name":"2026 International Conference on Artificial Intelligence for Sustainable Engineering and Innovation (AISEI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aisei68628.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T19:43:57Z","doi":"10.1109/aisei68628.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/mai69289.2026.11543830","name":"MAI 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mai69289.2026.11543830","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-08T19:49:29Z","doi":"10.1109/mai69289.2026.11543830","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/ais69919.2026.11621238","name":"AIS 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ais69919.2026.11621238","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-28T19:11:59Z","doi":"10.1109/ais69919.2026.11621238","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icerai69511.2026","name":"2026 International Conference on Electrical/Electronics, Robotics, Artificial Intelligence, and Informatics (ICERAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icerai69511.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-29T19:46:34Z","doi":"10.1109/icerai69511.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/caibda70336.2026","name":"2026 6th International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/caibda70336.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-28T19:11:31Z","doi":"10.1109/caibda70336.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/s0933-3657(00)00078-6","name":"The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School","source":"crossref","abstract":"The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.","url":"https://doi.org/10.1016/s0933-3657(00)00078-6","authors":["Klaus-Peter Adlassnig"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T18:16:42Z","doi":"10.1016/s0933-3657(00)00078-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icarai70085.2026.11635766","name":"ICARAI 2026 Authors Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icarai70085.2026.11635766","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T19:14:18Z","doi":"10.1109/icarai70085.2026.11635766","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aitest70988.2026.00010","name":"AITest 2026 Program Committee","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitest70988.2026.00010","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-26T19:13:29Z","doi":"10.1109/aitest70988.2026.00010","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.caeai.2026.100558","name":"Optimizing automated scoring in ILSAs with prompt compression","source":"crossref","abstract":"Automated scoring (AS) has become increasingly prevalent in educational measurement. However, applying it to international reading assessments remains challenging, particularly due to the length and complexity of the required prompting, driven by the need to include lengthy reading passages and detailed scoring guides. Processing these lengthy inputs results in high computational costs and may impede the performance of large language models (LLMs). This study explored the potential of optimizing AS with prompt compression using OpenAI’s LLM, GPT-4o. Our results show that prompt compression significantly reduces the length of reading passages and scoring guides while maintaining their essential content. Reading passages and scoring guides were compressed to approximately 18% and 15% of their original lengths, respectively. Despite this substantial compression, the AS showed remarkable performance, with an accuracy of 92.87% and a kappa score of 0.8041, closely approximating the results obtained without compression. These findings suggest optimizing AS with prompt compression can improve its efficiency and scalability, particularly in international reading assessments.","url":"https://doi.org/10.1016/j.caeai.2026.100558","authors":["Ji Yoon Jung","Ummugul Bezirhan","Matthias von Davier"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-11T16:59:32Z","doi":"10.1016/j.caeai.2026.100558","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2025.113137","name":"Fast building of stochastic configuration networks for big data analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113137","authors":["Sergei Romanov","Dianhui Wang","Dmitrii Kaplun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-17T08:54:24Z","doi":"10.1016/j.engappai.2025.113137","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.113910","name":"Deep learning based algorithms for automatic modulation classification: Trends, challenges, and future directions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.113910","authors":["Shalu","Brahmjit Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-24T13:03:23Z","doi":"10.1016/j.engappai.2026.113910","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1002/9781394305612.ch16","name":"Explainable Artificial Intelligence in Malware Analysis and Forensics","source":"crossref","abstract":"Using explainable artificial intelligence (XAI) techniques in malware analysis and digital forensics shows promise for transforming cybersecurity practices. This paper examines the role of XAI in providing understandable insights into malware behavior and characteristics, addressing the limitations of traditional approaches, and improving threat detection capabilities. By using interpretable machine learning models and analyzing feature importance, XAI allows security analysts to comprehend the reasoning behind automated decisions and prioritize response efforts accordingly. Real-world case studies demonstrate the effectiveness of XAI in recognizing and mitigating cyber threats, while ethical considerations emphasize the necessity of responsible and transparent use of XAI in cybersecurity practices. Looking ahead, future directions and emerging trends in real-time XAI applications, hybrid approaches, and interdisciplinary collaboration present exciting opportunities for advancing the field of XAI-driven malware analysis and digital forensics.","url":"https://doi.org/10.1002/9781394305612.ch16","authors":["Abdullah S. Alshraá","Mahdi Dibaei","Mamdouh Muhammad","Reinhard German"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-08T21:30:13Z","doi":"10.1002/9781394305612.ch16","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icssas68835.2026.11559380","name":"Self-Sustainable Artificial Intelligence Framework for Predictive Optimization of FSW-Processed Aluminium—Fly Ash Composites","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icssas68835.2026.11559380","authors":["E. Aravindaraj","Natrayan L"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T19:52:40Z","doi":"10.1109/icssas68835.2026.11559380","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/rmkmate69073.2026.11518830","name":"Artificial Intelligence–Driven Semantic Integration of Data Mining Services in Cloud Platforms","source":"crossref","abstract":"The accelerated growth of heterogeneous information in cloud computing systems, serious concerns are raised with regards to semantic interoperability and computational efficiency of data mining services. In this study, it is suggested to provide a Semantic-Aware Service Cloud Computing Architecture (SSCA), which is a new framework that leverages artificial intelligence to promote intelligent integration of semantics. SSCA also includes ontology-based knowledge representation, knowledge representation in vectors, and query optimization. It also introduces a better Support Vector Machine- Vector (SVMV) algorithm to determine the ontology classification automatically, and a query- fragment caching algorithm that is tuned with semantic similarity. Empirical analysis proves that SSCCA saves 60/80/50 percent of data integration time, costs associated with the calculations, and responsiveness of queries compared to the traditional methods. As a result, the architecture successfully fills the semantic gap of multi-cloud environment and offers a scalable basis of high-performance, intelligent data mining services.","url":"https://doi.org/10.1109/rmkmate69073.2026.11518830","authors":["Harish Chamarthi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-20T19:49:10Z","doi":"10.1109/rmkmate69073.2026.11518830","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-34266-0.01001-9","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34266-0.01001-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T14:38:58Z","doi":"10.1016/b978-0-443-34266-0.01001-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-34019-2.01001-4","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34019-2.01001-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-14T15:07:42Z","doi":"10.1016/b978-0-443-34019-2.01001-4","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00058-0","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00058-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00058-0","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-44-333496-2.00002-2","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-333496-2.00002-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:42:43Z","doi":"10.1016/b978-0-44-333496-2.00002-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/acdsa67686.2026","name":"2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acdsa67686.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T19:51:54Z","doi":"10.1109/acdsa67686.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-43934-6.00031-6","name":"Artificial intelligence at the heart of nutrigenomics: Machine learning for a customized diet","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43934-6.00031-6","authors":["Samudra Prosad Banik","Anand Swaroop","Harekrishna Jana","Debasis Bagchi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-24T08:30:54Z","doi":"10.1016/b978-0-443-43934-6.00031-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00506-1","name":"INDEX","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00506-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00506-1","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-27465-7.80740-2","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27465-7.80740-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T12:13:00Z","doi":"10.1016/b978-0-443-27465-7.80740-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00306-2","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00306-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00306-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00304-9","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00304-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00304-9","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.22271/ed.book.3598","name":"Applications of Artificial Intelligence and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.22271/ed.book.3598","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-07T08:56:39Z","doi":"10.22271/ed.book.3598","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-27692-7.28069-6","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27692-7.28069-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T10:20:40Z","doi":"10.1016/b978-0-443-27692-7.28069-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2026.114800","name":"Artificial neural network-assisted optimization of slippery boundaries on the fluid transport in permeable renal tubules","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114800","authors":["Venkateshwarlu G.","Ravikiran G.","Varunkumar M.","C.S.K. Raju"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-10T08:18:01Z","doi":"10.1016/j.engappai.2026.114800","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aide69088.2026.11544485","name":"AIDE 2026 TOC","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aide69088.2026.11544485","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-05T19:37:49Z","doi":"10.1109/aide69088.2026.11544485","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1142/9781800617384_0017","name":"Will Computers Exhibit “Artificial Intuition”?","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9781800617384_0017","authors":["Jay Liebowitz","Giovanni Miragliotta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-11T06:16:06Z","doi":"10.1142/9781800617384_0017","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/acdsa67686.2026.11468069","name":"A Study on Enhancing Early Childhood Educators' Information Literacy Through Artificial Intelligence","source":"crossref","abstract":"In the current era where artificial intelligence (AI) is transforming education, the information literacy of early childhood teachers is crucial for both the digitalization of preschool education and teachers' professional growth. The AI-TPACK framework can support teachers in better integrating technology into their teaching. This study uses empirical methods, including a survey of 245 kindergarten teachers in Nanning, Guangxi, and follow-up telephone interviews with 50 teachers. Results show that while teachers perform well in information awareness and information ethics, they face obvious gaps in information knowledge, information skills, and information thinking. In particular, the ability to apply AI technology in teaching practice is underdeveloped. Several challenges in developing teachers' information literacy were identified. These include unclear strategies for improvement, low motivation for self-development, insufficient practical skills, and limited ability to anticipate educational needs. To address these issues, this study proposes several measures: implementing targeted policies, improving understanding of smart education, enhancing the integration of knowledge and skills, promoting awareness of digital responsibility, and organizing teaching and research training. These recommendations aim to help improve early childhood teachers' information literacy within the AI-TPACK framework.","url":"https://doi.org/10.1109/acdsa67686.2026.11468069","authors":["Weihua Lan","Jun Ren","Jiachao Wei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T19:50:24Z","doi":"10.1109/acdsa67686.2026.11468069","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-981-95-8212-9_5","name":"The Future of Sustainable Development: Navigating Innovation, Policy, and Global Imperatives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-8212-9_5","authors":["Tankiso Moloi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T23:42:10Z","doi":"10.1007/978-981-95-8212-9_5","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.engappai.2025.113497","name":"A state-aware, hierarchical deep learning framework for automated visual glitch detection in games","source":"crossref","abstract":"Visual anomalies in video games can degrade user experience and impact overall software quality, highlighting the need for scalable methods within modern quality assurance (QA) pipelines. Manual testing remains resource-intensive and difficult to scale, while existing AI-based approaches often struggle to generalize across diverse rendering styles and gameplay scenarios. This paper presents a hierarchical visual anomaly detection framework that integrates game state information to enhance contextual awareness and detection accuracy. A synthetic data generation pipeline is introduced to create high-fidelity, game-specific training samples that capture the visual characteristics and edge cases of individual titles. Human-in-the-loop mechanisms support the identification of challenging scenarios and the definition of functional test conditions suitable for continuous integration workflows. The system operates continuously during production, enabling real-time detection of rendering anomalies without interfering with gameplay. The proposed framework is evaluated across three commercial game titles, demonstrating its effectiveness and adaptability. It comprises a configurable data generation pipeline, a state-conditioned detection model, and an automated anomaly identification tool, forming a modular and extensible QA solution for interactive software systems.","url":"https://doi.org/10.1016/j.engappai.2025.113497","authors":["Ciprian Paduraru"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-31T10:07:05Z","doi":"10.1016/j.engappai.2025.113497","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.artint.2026.104540","name":"Decision-theoretic planning and cognitive modeling for active cyber deception","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2026.104540","authors":["Aditya Shinde","Prashant Doshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-11T08:53:14Z","doi":"10.1016/j.artint.2026.104540","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.5005/ijaim-11066-0004","name":"Forward into Light: A United States of America Perspective on Artificial Intelligence in Medicine","source":"crossref","abstract":"","url":"https://doi.org/10.5005/ijaim-11066-0004","authors":["Ruchi Bhatia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T10:35:06Z","doi":"10.5005/ijaim-11066-0004","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-13545-3.09991-6","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13545-3.09991-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-22T11:22:59Z","doi":"10.1016/b978-0-443-13545-3.09991-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-40501-3.43601-0","name":"Dedication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-40501-3.43601-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T11:36:54Z","doi":"10.1016/b978-0-443-40501-3.43601-0","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00307-4","name":"Acknowledgments","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00307-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00307-4","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.21037/jmai-24-366","name":"Medical expert system as a chatbot for screening of anxiety disorders in children and adolescents","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-24-366","authors":["Venkateshwar Rao Madasu","Mohammadreza Hajiarbabi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T06:38:00Z","doi":"10.21037/jmai-24-366","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/aiita69518.2026","name":"2026 6th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiita69518.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-24T19:48:44Z","doi":"10.1109/aiita69518.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.59728/jaie.2026.5.1.116","name":"A Study on the Design of an AI Philosophy Curriculum","source":"crossref","abstract":"This study proposes a broadly applicable AI philosophy curriculum for universities. It first defines the meaning and characteristics of AI philosophy by examining the usage and significance of ethics, ethical theory, and philosophy in relation to AI discourse, and by clarifying the characteristic relationship between AI ethics and AI philosophy. Accordingly, an AI philosophy course is conceptualized as a form of philosophical ethics curriculum focused on AI-related phenomena. To support this conceptualization and to construct the course content in practice, existing AI-related ethics and philosophy courses at universities in South Korea are briefly reviewed, categorized by course provider, target students, and teaching methods. Based on this review, a curriculum model is designed, and its adaptability to different academic units and student levels is illustrated.","url":"https://doi.org/10.59728/jaie.2026.5.1.116","authors":["Hyeongjoo Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-04T01:46:58Z","doi":"10.59728/jaie.2026.5.1.116","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/icaim69488.2026.11601433","name":"ICAIM 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601433","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601433","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-18392-7_8","name":"Artificial Intelligence-Supported Willingness to Communicate and Intercultural Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18392-7_8","authors":["Thanh Tien Nguyen","Hung Phu Bui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-12T23:17:23Z","doi":"10.1007/978-3-032-18392-7_8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1109/eeicai68535.2026","name":"2026 International Conference on Electrical Engineering, Intelligent Control and Artificial Intelligence (EEICAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eeicai68535.2026","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-24T19:47:03Z","doi":"10.1109/eeicai68535.2026","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44415-9.01001-2","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44415-9.01001-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:24:49Z","doi":"10.1016/b978-0-443-44415-9.01001-2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-33151-0.00013-7","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33151-0.00013-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T12:29:25Z","doi":"10.1016/b978-0-443-33151-0.00013-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.artmed.2011.10.002","name":"Collaboration-based medical knowledge recommendation","source":"crossref","abstract":"Purpose Clinicians rely on a large amount of medical knowledge when performing clinical work. In clinical environment, clinical organizations must exploit effective methods of seeking and recommending appropriate medical knowledge in order to help clinicians perform their work. Method Aiming at supporting medical knowledge search more accurately and realistically, this paper proposes a collaboration-based medical knowledge recommendation approach. In particular, the proposed approach generates clinician trust profile based on the measure of trust factors implicitly from clinicians' past rating behaviors on knowledge items. And then the generated clinician trust profile is incorporated into collaborative filtering techniques to improve the quality of medical knowledge recommendation, to solve the information-overload problem by suggesting knowledge items of interest to clinicians. Results Two case studies are conducted at Zhejiang Huzhou Central Hospital of China. One case study is about the drug recommendation hold in the endocrinology department of the hospital. The experimental dataset records 16 clinicians' drug prescribing tracks in six months. This case study shows a proof-of-concept of the proposed approach. The other case study addresses the problem of radiological computed tomography (CT)-scan report recommendation. In particular, 30 pieces of CT-scan examinational reports about cerebral hemorrhage patients are collected from electronic medical record systems of the hospital, and are evaluated and rated by 19 radiologists of the radiology department and 7 clinicians of the neurology department, respectively. This case study provides some confidence the proposed approach will scale up. Conclusion The experimental results show that the proposed approach performs well in recommending medical knowledge items of interest to clinicians, which indicates that the proposed approach is feasible in clinical practice.","url":"https://doi.org/10.1016/j.artmed.2011.10.002","authors":["Zhengxing Huang","Xudong Lu","Huilong Duan","Chenhui Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-12-09T20:38:54Z","doi":"10.1016/j.artmed.2011.10.002","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-13565-0_36","name":"Classification of Artificial Intelligence Tools Used in Judicial Activities","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-13565-0_36","authors":["Antonina S. Ershova","Marina L. Davydova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-05T23:03:16Z","doi":"10.1007/978-3-032-13565-0_36","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.7759/cureus.105503","name":"Artificial Intelligence in Undergraduate Medical Education: A Cross-Sectional Study of Utilization Patterns and Perceptions Among Medical Students","source":"crossref","abstract":"Background Artificial intelligence (AI), particularly tools such as ChatGPT, has quickly become part of how medical students study and revise. In day-to-day academic settings, many students are already using AI to clarify concepts and prepare for exams. However, there are limited data from Indian medical institutions regarding how frequently these tools are used and how students perceive them. Objective To assess awareness, patterns of use, perceived usefulness, reliability, and concerns related to AI tools among undergraduate medical students at All India Institute of Medical Sciences (AIIMS) Rishikesh. Methods A cross-sectional questionnaire-based study was conducted among 297 undergraduate MBBS students at AIIMS, Rishikesh. The study was designed and carried out by faculty from the Department of Anatomy. A structured survey collected information on the frequency of AI use, preferred platforms, academic applications, trust in AI-generated information, and perceived risks. Data were analyzed using descriptive statistics, and chi-square tests were applied to assess associations between selected variables. Results Most students (91.6%) reported using AI tools for academic purposes, with ChatGPT (OpenAI, San Francisco, California, US) being the most commonly used platform (96%). The majority used AI to better understand difficult topics (88.2%). Although 70.7% considered AI outputs to be good or reliable, concerns were common, particularly regarding accuracy (72.1%) and data privacy (50.2%). Students who used AI more frequently were significantly more likely to support formal integration of AI into the medical curriculum (χ² = 16.82, p = 0.001). Overall, 69.4% favored structured incorporation of AI training. Conclusion AI tools are already widely used by undergraduate medical students and are largely viewed as helpful supplementary learning resources. At the same time, students remain cautious about accuracy and ethical implications. These findings suggest that rather than ignoring AI use, medical institutions should consider structured guidance and training to ensure responsible and effective integration.","url":"https://doi.org/10.7759/cureus.105503","authors":["Raju R Bokan","Rashmi Malhotra","Mukund Vatsa","Kanchan Bisht","Mukesh Singla","Rajeev Choudhary"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T16:00:17Z","doi":"10.7759/cureus.105503","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1186/s12909-026-09108-8","name":"Awareness, attitudes, and educational use of artificial intelligence among medical students: a large cross-sectional survey","source":"crossref","abstract":"BACKGROUND: Artificial intelligence (AI) is increasingly influencing medical education and clinical practice. Understanding medical students’ awareness, attitudes, and usage patterns of AI is essential for guiding curriculum development and ensuring responsible integration into undergraduate medical training. METHODS: A nationwide cross-sectional survey was conducted between November and December 2025 among undergraduate medical students in Türkiye. A structured questionnaire assessed demographic characteristics, AI awareness and frequency of use, educational and clinical applications, attitudes toward AI, ethical concerns, and future educational expectations. A composite AI Attitude Score was calculated using three Likert-scale items, and internal consistency was evaluated using Cronbach’s alpha. Descriptive and inferential statistical analyses were performed. RESULTS: A total of 1,346 medical students were included. Overall, 81.1% reported awareness of AI applications in medicine. AI tools were predominantly used for educational purposes (73.3%), whereas clinical or simulation-based use was limited (11.6%). The mean composite AI Attitude Score was 3.41 ± 0.61, indicating moderately positive perceptions (Cronbach’s alpha = 0.71). Ethical concerns were reported by 67.4% of participants. A majority (79.1%) expressed interest in receiving formal AI-related education. No significant differences were observed between preclinical and clinical students in overall attitudes or educational demand; however, clinical students reported significantly greater use of AI in clinical contexts (p < 0.001). CONCLUSION: Medical students demonstrate high awareness and generally positive attitudes toward AI; however, clinical integration remains limited and ethical concerns are prevalent. These findings underscore the need for structured, ethically grounded AI education within undergraduate medical curricula. Findings should be interpreted with consideration of potential self-selection bias due to voluntary online participitation.","url":"https://doi.org/10.1186/s12909-026-09108-8","authors":["Ali Veysel Kara","Hatice Harmancı","Yusuf Yılmaz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-01T06:51:24Z","doi":"10.1186/s12909-026-09108-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1002/9781394336166.ch3","name":"Forecasting of Electromagnetic Relay Epoch Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394336166.ch3","authors":["T. Maris Murugan","E. Sathish","C. Jayabharathi","A. Malligarjun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-06T21:27:18Z","doi":"10.1002/9781394336166.ch3","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-30010-3.00010-6","name":"Application of machine learning and artificial intelligence methods for predicting antimicrobial resistance","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30010-3.00010-6","authors":["Kun Mi","Simone Marini","Zhoumeng Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T11:38:32Z","doi":"10.1016/b978-0-443-30010-3.00010-6","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.113856","name":"Dynamic quantum annealing optimized quantum neural networks for remaining useful lifetime prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.113856","authors":["Manoranjan Gandhudi","Gangadharan G.R."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-13T17:12:31Z","doi":"10.1016/j.engappai.2026.113856","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.67298/paper/480001","name":"From Tool to Order: The Dual Reshaping of Employment Structure and Human Social Structure by Artificial Intelligence","source":"crossref","abstract":"Artificial intelligence is penetrating every corner of the economy and society at a pace far exceeding expectations, and its impact on human society has risen from that of a mere technical tool to a structural reordering of society itself. This paper systematically analyzes the profound changes brought about by artificial intelligence along two dimensions: the employment structure and the human social structure. At the level of employment structure, artificial intelligence drives the labor market from \"polarization\" toward \"upgrading\" through three mechanisms—substitution, creation, and industrial-structure transformation—giving rise to new structural contradictions such as \"middle-tier collapse\" and \"career-ladder fracture.\" At the level of human social structure, artificial intelligence is reshaping the modes of knowledge production and cognition, intensifying the concentration of wealth and power, restructuring the social division of labor and human-machine relations, and giving rise to a new form of inequality—the \"intelligence divide.\" The study finds that the transformation of the employment structure and that of the human social structure are not isolated from one another, but are deeply coupled through a transmission chain running from \"skills\" to \"income\" to \"power.\" In response to this dual structural shock, this paper proposes a systematic response framework built along three dimensions: the restructuring of the education system, the transformation of the social security system, and the innovation of the governance paradigm.","url":"https://doi.org/10.67298/paper/480001","authors":["Jiafen Rao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-09T10:11:25Z","doi":"10.67298/paper/480001","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1108/978-1-80592-941-320261002","name":"Adoption of Artificial Intelligence in Accounting Practices: A Study of Moroccan Accounting Firms","source":"crossref","abstract":"Abstract Artificial intelligence (AI) has brought significant transformation to professional practice across various industries, and accounting is one of the most highly impacted professions. By automating time-consuming and routine tasks such as data entry, invoice processing, and bank reconciliations, AI allows accountants to divert their focus to higher value services. Such services include financial analysis, strategic advisory, and personalized customer service. As a result, AI improves not just efficiency and accuracy but also the quality of service and hence innovation and competitiveness in accounting business. In order to develop a more nuanced understanding of the extent and nature of AI adoption by accounting practices, a qualitative research was conducted with the help of structured interview guide. This research involved several Moroccan accounting firms and aimed to identify the level to which AI tools were being adopted in their operations. The research identified growing interest in AI, and some firms are already leveraging AI-based solutions to automate processes and improve customer experience. However, the adoption of AI is not uniform. Some businesses are fully engaged in digital transformation, while others are held back by barriers such as budget constraints, shortage of technical expertise, or uncertainly about the return on investment. The findings highlight the double reality of momentum and caution. They point to the need for more awareness, targeted training, and enabling policies to support AI adoption. Overall, the study highlights the transformative potential of AI in accounting, especially when coupled with a clear strategic intent and investment in change management.","url":"https://doi.org/10.1108/978-1-80592-941-320261002","authors":["Haichar Mohammed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T05:23:11Z","doi":"10.1108/978-1-80592-941-320261002","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-44121-9.00014-7","name":"Exploring artificial intelligence approaches for studying intrinsically disorder proteins involvement in gastrointestinal cancers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44121-9.00014-7","authors":["Saswati Sarita Mohanty","P.J. Jayalekshmi, Madhulika Namdeo","Dinakara Rao Ampasala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T09:59:39Z","doi":"10.1016/b978-0-443-44121-9.00014-7","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/j.aichem.2025.100101","name":"Comparative study of machine learning methods for accurate prediction of logP and pKb","source":"crossref","abstract":"Machine learning (ML) has become a powerful tool for predicting molecular physicochemical properties. It finds applications in various research and development sectors, such as materials science, pharmaceutical chemistry, and environmental science. However, systematic comparisons between different types of properties remain limited. In this study, we developed two structured datasets: a logP dataset containing 1117 molecules and a pKb dataset containing 1268 molecules. For logP, each molecule is represented by 623 molecular descriptors generated exclusively by RDKit/Mordred, while a combination of 150 quantum chemistry descriptors from DFT calculations and molecular fingerprints derived from RDKit is used for pKb. Several ML algorithms were evaluated using an identical workflow, and the relevance of the descriptors was analyzed using SHAP, followed by feature pruning based on correlation. For the logP dataset, the LightGradBoost model achieved an R 2 of 0.94, an RMSE of 0.31, and an MAE of 0.42 on the independent test set, accurately reproducing experimental logP values in the range of −11.6 to 1.58. For pKb prediction, Random Forest (RF) proved most accurate, with an MAE of 1.69 and an RMSE of 1.68, with predicted values covering the entire range of experimental pKb values (−37 to 29.2). Our results indicate that, while RDKit/Mordred descriptors can predict logP with high accuracy, pKb remains a more challenging property to model, even when incorporating high-level DFT descriptors. The study therefore proposes a unified framework for the comparative evaluation of cross-property machine learning models and highlights the influence of the type of descriptor and the choice of algorithm on performance for chemically distinct properties.","url":"https://doi.org/10.1016/j.aichem.2025.100101","authors":["Juda Baikété","Alhadji Malloum","Jeanet Conradie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T16:23:36Z","doi":"10.1016/j.aichem.2025.100101","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.59646/624","name":"360° Marketing: Integrating Traditional and Digital Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.59646/624","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T16:48:58Z","doi":"10.59646/624","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1093/law/9780198925705.001.0001","name":"The EU Artificial Intelligence Act","source":"crossref","abstract":"Abstract This commentary offers a comprehensive, article-by-article analysis of the EU Artificial Intelligence Act (AIA), a landmark regulation comprising 113 articles and 180 recitals. It traces the AIA’s evolution from early robotics debates to the Commission’s 2020 White Paper and trilogue negotiations, highlighting political compromises and regulatory challenges. The work examines the Act through risk-based approaches, regulatory theory, and geopolitical dimensions, addressing issues of opacity, complexity, and adaptability inherent in AI governance. Beyond textual interpretation, it contextualizes provisions within EU law on product liability, fundamental rights, and data protection, while considering implementing acts and emerging soft law. By blending technical detail with normative analysis, the commentary underscores the AIA’s dual role as a practical regulatory instrument and a symbolic milestone in Europe’s assertive digital governance strategy.","url":"https://doi.org/10.1093/law/9780198925705.001.0001","authors":["Michèle Finck"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-26T10:32:01Z","doi":"10.1093/law/9780198925705.001.0001","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1002/9781394358212.fmatter2","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394358212.fmatter2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-05T21:30:42Z","doi":"10.1002/9781394358212.fmatter2","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00051-8","name":"Acknowledgments","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00051-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00051-8","addedAt":"2026-09-01T01:47:59.004Z","updatedAt":"2026-09-01T01:47:59.004Z"},{"id":"doi:10.1007/978-3-032-24895-4","name":"Dental Clinical Procedures using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24895-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T04:41:22Z","doi":"10.1007/978-3-032-24895-4","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-23621-1.00016-3","name":"Explanations of convergence theories","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23621-1.00016-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-06T00:55:02Z","doi":"10.1016/b978-0-443-23621-1.00016-3","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1109/acdsa67686.2026.11468190","name":"Artificial Intelligence and Risk Management in Finance: A Scientometric Analysis","source":"crossref","abstract":"Artificial Intelligence (AI) is revolutionizing the tools used in the prediction, automation, and decision-making processes in risk management. This paper performs a scientometric analysis of the application of AI in risk management using the Scopus database. The use of bibliometric coupling, co-citation, and co-occurrence analyses through VOSviewer helped in the recognition of the principal themes and trends. The analysis uncovered two principal nodes: Decision Support, Governance, and Safety Systems and Predictive Intelligence for Financial, Supply Chain, and Industrial Analytics. The small number of clusters is suggestive of the fact that while the use of AI is gaining momentum, the corresponding research is still in the early phases of development. This illustrates the critical need for interdisciplinary research as well as the fundamental role the AI has in management of risk in terms of greater visibility, predicting power, and resilience.","url":"https://doi.org/10.1109/acdsa67686.2026.11468190","authors":["Arpita Sharma","Anil Khurana","Deepika Goel","Sanjay Saini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T19:50:24Z","doi":"10.1109/acdsa67686.2026.11468190","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.engappai.2026.113959","name":"Heterogeneous Patent Graph Prompt Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.113959","authors":["Xi Zeng","Pei-Yuan Lai","Chang-Dong Wang","Qing-Yun Dai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-27T20:06:08Z","doi":"10.1016/j.engappai.2026.113959","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.caeai.2026.100601","name":"Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts","source":"crossref","abstract":"Large language models (LLMs) are transforming higher education, yet their learning benefits likely depend on students’ AI literacy and how they prompt and verify AI outputs. This mixed-methods study investigates how AI literacy relates to prompting proficiency and verification behavior, and how these behaviors operate through two mechanisms, trust calibration and extraneous cognitive load, to predict coursework performance across two discipline–institution contexts in Thailand. The author surveyed Design students (n = 221) and Business students (n = 222) and analyzed the data using multigroup structural equation modeling, complemented by thematic analysis of semi-structured interviews. Results indicated that AI literacy significantly predicted both prompting proficiency and verification behavior (p < .001). Prompting proficiency was positively associated with trust calibration, whereas verification behavior was negatively associated with extraneous cognitive load, and the corresponding indirect effects were supported. Context-specific patterns also emerged: in the Design context, verification behavior showed a stronger direct association with task quality, while in the Business context, trust calibration was the stronger predictor of assignment quality. Interview evidence helped explain these differences. Design students used ChatGPT primarily to accelerate ideation and refinement, whereas Business students used it to support structured analysis and to make their work more auditable. Across both contexts, verification functioned as a metacognitive safeguard that reduced overload and stabilized accuracy. These findings suggest that teaching AI literacy in higher education should emphasize rubric-guided prompting, verification routines, and calibrated trust to balance efficiency with deeper engagement, with attention to context-specific pedagogical demands.","url":"https://doi.org/10.1016/j.caeai.2026.100601","authors":["Qinjie Shen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-05T07:17:49Z","doi":"10.1016/j.caeai.2026.100601","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1109/icaim69488.2026.11601309","name":"ICAIM 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601309","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601309","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-34135-9.00005-2","name":"The future of work: a human-centric approach with artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34135-9.00005-2","authors":["Elakkiya Elango","Gnanasankaran Natarajan","Ahamed Labbe Hanees","Balasubramanian Shanmuganathan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T07:37:26Z","doi":"10.1016/b978-0-443-34135-9.00005-2","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1016/b978-0-443-43934-6.00021-3","name":"State of the art artificial intelligence assisted disease detection tools","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43934-6.00021-3","authors":["Sushanta Kumar Das","Rahul Mishra","Amit Samanta","Saumendu Deb Roy","Ujjwal Sahoo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-24T08:30:54Z","doi":"10.1016/b978-0-443-43934-6.00021-3","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.daai.2026.100099","name":"TRACE: An embodied paper-based human–AI co-doodling ecosystem with authorship traces","source":"crossref","abstract":"Background While early ideation benefits from loose, ambiguous sketches on paper, most generative AI tools are screen-based, high-fidelity outputs and prompt-heavy control. These conditions can disrupt creative flow, increase fixation risk, and blur authorship in human–AI co-creation scenarios in the idea exploration phase. Objective TRACE (TRaced Authorship in Co-doodling Ecosystem), a paper-based system that generates doodle-style AI, was developed to enable variations from a user’s hand sketch and physically plots them back onto the same page. The system supports authorship transparency by rendering human marks in blue and AI suggestions in red, making contributions legible. Interviews with industrial design-education experts were conducted to collect perspectives on usability, pedagogical considerations, adoption constraints, and authorship/assessment implications. Methods The prototype integrates live paper-to-digital capture, a five-button physical interface, AI generation, and a pen plotter. Development used an LLM-assisted, prompt-driven workflow (“vibe coding”) to implement the automation pipeline and the physical embodiment of the integrated AI. A video-based expert evaluation, followed by a structured rubric (perceived usefulness, ease of use, intention to use) and a semi-structured interview, were conducted. Eight design-educators completed (∼75 min each; 3–9 years teaching, 6–22 years design experience) the evaluations and interviews. Results Experts valued the embodied plotting workflow for keeping ideation physically grounded and supporting “call-and-response” exploration. The blue–red distinction was viewed as critical for communicating authorship expectations and enabling fair assessment, such as verifying student intent and documenting how AI suggestions were evaluated. Key improvements include clearer expectation-setting (doodle vs polished), more flexible selection and controllable divergence, and classroom-ready deployment guidance. Conclusions Embodied, paper-first AI co-doodling can reduce prompt burden and strengthen authorship transparency when AI contributions are visibly traced. Vibe coding can accelerate prototyping of complex digital–physical co-creative systems and enable early expert validation of usability and pedagogical risks.","url":"https://doi.org/10.1016/j.daai.2026.100099","authors":["Byungsoo Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-06T15:29:08Z","doi":"10.1016/j.daai.2026.100099","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1021/acssensors.6c00988","name":"Artificial Intelligence Needs Sensors: Building the Data Layer for Artificial Intelligence-Accelerated Discovery","source":"crossref","abstract":"","url":"https://doi.org/10.1021/acssensors.6c00988","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-18T10:05:04Z","doi":"10.1021/acssensors.6c00988","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-323-95464-8.00021-9","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95464-8.00021-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-05T23:40:28Z","doi":"10.1016/b978-0-323-95464-8.00021-9","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00001-8","name":"Algorithmic validation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00001-8","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00001-8","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.55277/researchhub.gtjjvyag","name":"Artificial-Intelligence-Foundation Exam Questions Pdf 2026 - Fast Track Your Success","source":"crossref","abstract":"","url":"https://doi.org/10.55277/researchhub.gtjjvyag","authors":["Anita Storey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-04T12:40:39Z","doi":"10.55277/researchhub.gtjjvyag","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-44430-2.20001-7","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44430-2.20001-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:25:52Z","doi":"10.1016/b978-0-443-44430-2.20001-7","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1109/aitc70732.2026.11666538","name":"Copyright and Reprint Permission","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aitc70732.2026.11666538","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-28T19:12:54Z","doi":"10.1109/aitc70732.2026.11666538","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.engappai.2026.114193","name":"DeepSoundVisionNet: A new approach to urban sound classification using visual representations of audio signals","source":"crossref","abstract":"A novel approach for urban sound classification using visual representations of audio signals presented in this study. Leveraging the UrbanSound8K dataset, audio signals are transformed into visual formats through Chromagram, Short-Time Fourier Transform (STFT), Constant-Q Transform (CQT), and Mel spectrogram methods. These are combined via channel-wise stacking of the three most effective spectrograms to create enhanced visual datasets. Five new datasets were derived from UrbanSound8K to support diverse evaluations. The visual forms of audio data allow for detailed feature extraction and effective input for deep learning models. The study compares classification performance across several architectures, including Visual Geometry Group 19-layer network (VGG19), Visual Geometry Group 16-layer network (VGG16), Residual Network with 50 layers (ResNet50), Mobile Neural Network (MobileNet), Inception Architecture Version 3 (InceptionV3), Densely Connected Convolutional Network with 201 layers (DenseNet201), Neural Architecture Search Network Large (NASNetLarge), Inception combined with Residual Network Version 2 (InceptionResNetV2), and Extreme Inception (Xception). A new model named DeepSoundVisionNet (DSVNet) is proposed, demonstrating superior performance. Using 10-fold cross-validation, DSVNet achieved 95.02% accuracy with stacked spectrograms and 93.56% on Mel spectrograms (batch size 16). STFT yielded 91.15%, CQT 82.29%, and Chromagram 75.93% accuracy. DSVNet shows high capability in handling complex data through visualized audio processing. The research highlights the power of deep learning in smart city applications, environmental sound monitoring, and real-time recognition, offering a foundation for enhancing the precision and efficiency of future sound classification systems.","url":"https://doi.org/10.1016/j.engappai.2026.114193","authors":["Ilkay Cinar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T08:06:03Z","doi":"10.1016/j.engappai.2026.114193","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-40501-3.00017-0","name":"Scope and application of artificial intelligence in food supply chain management","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-40501-3.00017-0","authors":["Thania Maion Melo","Jenyffer Guerra","Cristina L.M. Silva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T11:36:54Z","doi":"10.1016/b978-0-443-40501-3.00017-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1177/29498732251408218","name":"Towards Semantic Understanding of Graph Neural Network Layers Embedding with Functional Semantic Activation Mapping","source":"crossref","abstract":"Graph Neural Networks (GNNs) are now a standard tool for modelling graph structured data in applications such as molecular property prediction, drug discovery, recommender systems, and citation networks. However, despite their strong predictive performance, they still suffer from the black box problem. Most existing explainability methods focus on local-level explainability, explaining individual predictions. They highlight important nodes and edges but don′t capture how the model behaves globally across a dataset. As a result, global-level explainability remains an open challenge. In this paper, we extend our previous work on Functional Semantic Activation Mapping (FSAM) to investigate how varying the number of GNN layers affects both representation quality and predictive performance. Across several datasets, increasing depth may improve accuracy but does not necessarily enhance semantic coherence. In some cases, performance gains coincide with a decline in semantic quality, suggesting that spurious patterns may drive correct predictions for wrong reasons. FSAM layer-wise activation tracking allowed us to track neuron activations across layers, revealing that deeper layers can reduce neuron specialisation and lead to class misclassifications. Our findings demonstrate a critical trade-off that increased depth can compromise interpretability without commensurate gains in meaningful semantic learning.","url":"https://doi.org/10.1177/29498732251408218","authors":["Kislay Raj","Alessandra Mileo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-09T18:56:31Z","doi":"10.1177/29498732251408218","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.engappai.2025.113110","name":"Solving assembly line balancing problems with reinforcement learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113110","authors":["Adil Baykasoğlu","Mümin Emre Şenol","Behice Meltem Kayhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-15T06:24:41Z","doi":"10.1016/j.engappai.2025.113110","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-40572-3.00016-2","name":"The role of learning and development in artificial intelligence-transformed pharma industry","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-40572-3.00016-2","authors":["Islam Elsaeed Hassan Elkholy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-28T12:54:38Z","doi":"10.1016/b978-0-443-40572-3.00016-2","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1007/978-981-95-8212-9_4","name":"The Trajectory of Sustainable Development: From Nascent Concerns to Global Imperatives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-8212-9_4","authors":["Tankiso Moloi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T23:40:38Z","doi":"10.1007/978-981-95-8212-9_4","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.53478/tuba.978-625-6110-86-1.ch08","name":"A Review of Artificial Intelligence in Education: What is Ahead, What is Left Behind at Global Scale?","source":"crossref","abstract":"This narrative review synthesizes recent research on artificial intelligence (AI) in education to address gaps in understanding its diverse roles, integration strategies, and pedagogical implications. The review aims to evaluate AI integration within curricula and instructional practices, examine AI literacy development, identify inclusive pedagogical strategies, compare adoption challenges including ethics and equity, and analyze AI's role in fostering critical thinking and creativity. Literature published between 2020 and 2025 was identified through searches of the Scopus, Web of Science, PubMed/MEDLINE, and arXiv databases, supplemented by AI-assisted semantic search, and a purposively selected body of 86 sources comprising peer-reviewed journal articles, books, conference proceedings, and preprints was retained as representative evidence for critical synthesis. A thematic analysis of empirical and theoretical studies across global K-12, higher education, and professional contexts was conducted, focusing on AI literacy and educator preparedness, curriculum design, pedagogical effectiveness, inclusivity, and ethics. Findings reveal that AI-enabled personalized and adaptive learning can significantly enhance student engagement and outcomes while supporting diverse learner needs; however, equitable access and infrastructure disparities limit inclusivity. Curriculum frameworks emphasize interdisciplinary AI literacy integrating ethical and technical competencies, yet standardization and comprehensive coverage remain insufficient. Educator readiness is critical but hindered by knowledge gaps and limited professional development, affecting effective AI adoption. Ethical concerns, including data privacy and algorithmic bias, are widely recognized but inadequately addressed in practice. Collectively, these findings underscore AI's transformative potential in education contingent on balanced curriculum design, ethical integration, and robust teacher support. The review informs educators, policymakers, and researchers on effective AI embedding strategies to prepare learners for an AI-driven future.","url":"https://doi.org/10.53478/tuba.978-625-6110-86-1.ch08","authors":["Mehmet Akın Bulut","Jeffrey Buckley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T13:32:58Z","doi":"10.53478/tuba.978-625-6110-86-1.ch08","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artint.2026.104604","name":"ParaKplex: A parallel local search algorithm for the maximum K-Plex problem","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.artint.2026.104604","authors":["Jieyu Wu","Rui Sun","Yiyuan Wang","Minghao Yin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-20T15:03:47Z","doi":"10.1016/j.artint.2026.104604","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-27692-7.09009-2","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27692-7.09009-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T10:20:40Z","doi":"10.1016/b978-0-443-27692-7.09009-2","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-36729-8.11001-0","name":"About the series editors","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36729-8.11001-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:42:20Z","doi":"10.1016/b978-0-443-36729-8.11001-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-27608-8.00302-7","name":"Titlepage","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27608-8.00302-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-08T11:16:19Z","doi":"10.1016/b978-0-443-27608-8.00302-7","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1007/978-3-032-24568-7","name":"Artificial Intelligence in Neuroradiology","source":"crossref","abstract":"This book offers an approachable guide to the use of artificial intelligence in neuroradiology, including how it's conducted and applied in practice.","url":"https://doi.org/10.1007/978-3-032-24568-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T22:11:08Z","doi":"10.1007/978-3-032-24568-7","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-33151-0.00012-5","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33151-0.00012-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-31T12:29:25Z","doi":"10.1016/b978-0-443-33151-0.00012-5","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.59646/568","name":"Smart Learning Environments: Artificial Intelligence in Pedagogy","source":"crossref","abstract":"","url":"https://doi.org/10.59646/568","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T20:09:33Z","doi":"10.59646/568","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1016/b978-0-443-30036-3.00029-0","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30036-3.00029-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-13T01:17:13Z","doi":"10.1016/b978-0-443-30036-3.00029-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1016/j.engappai.2026.115206","name":"A multi-modal framework for generating and evaluating diverse jokes using large language models","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115206","authors":["Minghua Tang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-28T16:13:44Z","doi":"10.1016/j.engappai.2026.115206","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1109/icaim69488.2026.11601244","name":"ICAIM 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaim69488.2026.11601244","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-17T19:43:01Z","doi":"10.1109/icaim69488.2026.11601244","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1109/ichcai70183.2026.11607654","name":"Temperature-Driven Uncertainty Assessment in LLM Medical Reasoning Using Geometric Embedding Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ichcai70183.2026.11607654","authors":["Ainaz Rafiei","Jungwon Seo","Betul Yurdem","Ferhat Ozgur Catak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-20T20:18:41Z","doi":"10.1109/ichcai70183.2026.11607654","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.5114/ppiel.2026.163569","name":"Application of artificial intelligence in screening postural assessment of children and adolescents: implications for medical and nursing practice","source":"crossref","abstract":"Postural assessment is a basic element of physiotherapy and, increasingly, nursing diagnostics, as the reliability of subsequent preventive decisions depends on it.Classical methods based on visual inspection are limited by examiner subjectivity and poor reproducibility.This paper describes the principles of the RTMPose (Real-Time Multi-person Pose Estimation) model, its potential applications in the quantitative assessment of postural asymmetry, and, importantly, the limitations of the method.The critical analysis is illustrated by a single case of a school-aged child examined in three projections.Particular attention is paid to the potential role of the tool in school-based postural screening performed by nurses, and to the conditions for safe and lawful implementation, including the protection of the pupil's personal data and image.","url":"https://doi.org/10.5114/ppiel.2026.163569","authors":["Tomasz Szaporów","Elżbieta Szczygieł"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-05T11:13:02Z","doi":"10.5114/ppiel.2026.163569","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.26524/royal.303.6","name":"NEW TECHNOLOGIES AND FUTURE DIRECTIONS FOR RENAL CARE USING ARTIFICIAL INTELLIGENCE IN KIDNEY DISEASE","source":"crossref","abstract":"The Acute kidney injury, chronic kidney disease and final stages of chronic kidney disease are the types of kidney diseases affecting 850 million people worldwide. Early intervention, constant monitoring and target management are required to improve outcomes. Without symptoms, the nature of kidney damage makes intervention in a timely and efficient manner extremely difficult and complicated. Thankfully, the rise of AI technologies in healthcare has made strides in nephrology possible. Predictive analytics, more personalized treatment plans, and precise diagnostics all become a reality with AI. This chapter discusses the application of AI technologies such as machine learning, deep learning, and natural language processing to various stages of care in kidney healthcare. This includes the entire continuum of care from early detection and risk assessment to managing dialysis, evaluating transplant outcomes, conducting digital pathology, and even in predictive analytics. The chapter AI tools related to research and drug development as well as integrating multi-omics for precision nephrology. It also analyzes in depth ethics concerning biases in algorithms, data governance and regulations. This chapter concludes with a conversation about possible paths in the future, focusing on the uptake of AI into a clinical environment, especially in resource- limited environments. It emphasizes that emerging technologies such as federated learning, portable devices and digital twins can change renal aid.","url":"https://doi.org/10.26524/royal.303.6","authors":["Laxmitha Shetty"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-04T09:59:09Z","doi":"10.26524/royal.303.6","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1080/08839514.2026.2635226","name":"A Noise-Score-Based Cleaning Framework for Multi-Class Label Noise","source":"crossref","abstract":"In machine learning, the objective of training a classification model is to learn the mapping relationship between features and labels. Label noise data has a severe detrimental effect on model performance, often surpassing the impact of feature noise. Consequently, label noise cleaning techniques constitute one of the most popular topics within data quality research. Numerous approaches to addressing label noise are based on filtering or correction. When employed independently, these approaches often fail to achieve satisfactory results in numerous scenarios. Conversely, their combined application typically yields more pronounced effects. CNC-NOS represents an advanced label noise cleaning method, employing an integrated filter and noise scores for noise identification and processing. However, the design of its clean function relies on absolute distance in noise score calculation, failing to capture the density of noisy samples among neighbors. Furthermore, neighbor determination remains reliant on Euclidean distance, insufficiently accounting for spatial distribution. This paper therefore proposes LNC-RDNCN, a multi-class label noise cleaning method based on relative density and nearest centroid neighbors (NCN). Extensive simulation experiments demonstrate that this method can accurately identify noisy data, implement appropriate corrections and filtering to enhance data quality, and generally outperform other noise processing methods in terms of average accuracy.","url":"https://doi.org/10.1080/08839514.2026.2635226","authors":["Pengfei Fu","Xiaofeng Liu","Mingyu Feng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T15:52:23Z","doi":"10.1080/08839514.2026.2635226","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00003-1","name":"Synthetic data","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00003-1","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00003-1","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1007/978-3-032-19336-0","name":"Revolutionizing Ophthalmology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-19336-0","authors":["Alejandro Espaillat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-22T22:45:38Z","doi":"10.1007/978-3-032-19336-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1201/9781003741770-2","name":"Applications of artificial intelligence in healthcare","source":"crossref","abstract":"Artificial intelligence (AI) in the healthcare domain serves as a transformative analytical engine, deciphering the intricate connections between clinical data and patient outcomes to deliver pioneering solutions that elevate the standard of medical care. AI permeates diverse medical domains, including precision diagnostics, therapeutic algorithm design, computational pharmacology, personalized care, and real-time patient management. The primary distinction between AI technology and traditional healthcare technologies lies in AI s ability to handle vast and diverse datasets, process information with remarkable efficiency, and deliver precise and actionable insights to end users. AI elucidates intricate, non-obvious data topologies and latent correlations that elude conventional analytical paradigms by harnessing the computational depth of machine learning architectures and deep neural frameworks. These advancements enhance diagnostic precision and treatment effectiveness while enabling the creation of personalized therapeutic strategies. As a result, patient outcomes improve, and healthcare delivery becomes more efficient. This chapter delineates the transformative infusion of AI into healthcare, elucidating its multidimensional impact across clinical praxis, exploring its transformative potential, and how it can revolutionize various aspects of medical practice. Through continuous learning and adaptation, AI is poised to drive advancements that benefit both practitioners and patients, shaping the future of healthcare.","url":"https://doi.org/10.1201/9781003741770-2","authors":["Rashmi Rameshwari","Naina Soni","Devendra Kumar Verma","Santosh Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-02T14:23:57Z","doi":"10.1201/9781003741770-2","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1201/9781003471165-13","name":"Artificial Intelligence for Simulation and Neurosurgical Training","source":"crossref","abstract":"This chapter explores the evolution and impact of artificial intelligence (AI) in enhancing neurosurgical education and skill development. Traditional neurosurgical training methods, including dissections on biological and synthetic models, are limited in the reproducibility of surgical cases and opportunities for repetitive practice. The advent of virtual reality (VR) simulations marked a significant advancement, offering interactive three-dimensional (3D) models for risk-free surgical practice. The integration of AI in these simulations has revolutionized skill assessment by providing immediate, quantifiable feedback through complex algorithms, enabling objective evaluation of trainee performance. Various machine learning (ML) models, such as K-nearest neighbor, support vector machines (SVMs), and neural networks (NNs), are employed to analyze performance metrics from VR simulations, distinguishing between different levels of surgical expertise and tracking learning curves over time. Specific AI applications include VR subpial tumor resection and spine surgery simulations, where AI models assess skills based on metrics like instrument handling, force application and task completion time. AI-driven systems, such as the Virtual Operative Assistant (VOA), offer personalized feedback, significantly enhancing training outcomes compared to traditional methods. Notably, trials demonstrated that AI tutor groups outperformed those receiving expert instructor feedback, highlighting the transformative potential of AI in surgical education. Comprehensive datasets, improved simulation realism, and standardized AI-enhanced educational models to ensure broader adoption and integration into neurosurgical training programs are necessary to further advance the field of neurosurgical education.","url":"https://doi.org/10.1201/9781003471165-13","authors":["Andre A. Payman","Roberto Rodriguez Rubio"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-28T13:03:11Z","doi":"10.1201/9781003471165-13","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1109/gaiis69281.2026.11519261","name":"Automated Modeling from Medical Guidelines to BPMN Based on Large Language Models","source":"crossref","abstract":"Medical guideline texts are characterized by strong domain specificity, complex conditional logic, and implicit semantics, which make their automatic transformation into executable process models highly challenging.To address this problem, we reformulate the task as a structure-constrained, multi-stage semantic reasoning problem and propose a dependency-aware BPMN automatic modeling method based on large language models. The proposed approach explicitly decomposes the modeling process into three progressively structured semantic stages: activity and event extraction, dependency relation identification, and BPMN structure generation, thereby improving logical consistency while effectively reducing model hallucinations. Experimental results demonstrate that the proposed method achieves F1 scores of 0.88 and 0.87 in activity recognition and dependency extraction, respectively, and yields significant improvements in gateway identification and overall structural matching accuracy. The results indicate that the proposed approach exhibits clear advantages in modeling complex conditional branches, enhancing process structure interpretability, and controlling modeling errors, representing a novel methodological paradigm for medical guideline process modeling.","url":"https://doi.org/10.1109/gaiis69281.2026.11519261","authors":["Feng Gao","Jian Chen","Yinghui Jin","Siyuan Ruan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-18T19:44:47Z","doi":"10.1109/gaiis69281.2026.11519261","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.inat.2026.102289","name":"Artificial intelligence in medical education: a feasibility case study based on proceedings from a patient care teaching conference","source":"crossref","abstract":"Background Artificial intelligence (AI) is transforming clinical practice, but its role in medical education remains underdeveloped. Patient care teaching conferences are rich in reasoning and literature appraisal, yet their educational value is rarely formalized. Objective To describe a hybrid mentorship-plus-AI workflow for converting raw neurosurgical conference notes into structured study materials and to assess its feasibility as a human-supervised educational process. Methods We conducted a scoping review (2010–2025) of PubMed, Scopus, and Web of Science, identifying 435 records; 10 studies met inclusion criteria. In parallel, we performed a case study in which neurosurgical conference notes were processed with ChatGPT to generate key learning points, open research questions, and targeted literature. Outputs were reviewed for clarity, accuracy, and alignment with educational goals. Results The scoping review showed that AI use in medical and health professions education is expanding, particularly in radiology training, simulation, and content generation. In the feasibility case study, ChatGPT transformed raw neurosurgical conference notes into concise, structured draft materials with key learning points, research questions, and curated references. These outputs were subsequently reviewed by senior faculty for clarity, accuracy, and alignment with educational goals. Because learner outcomes and formal operational metrics were not directly measured, educational benefit and workflow impact were not inferred. Conclusions AI may complement conference-based teaching when used within a structured, human-supervised workflow. A structured, human-supervised generative-AI workflow was feasible for converting de-identified conference notes into draft educational materials while preserving the mentor–learner dynamic. These findings should be interpreted as a process description and feasibility assessment rather than evidence of educational effectiveness.","url":"https://doi.org/10.1016/j.inat.2026.102289","authors":["Ahmad Sweid","Issam A. Awad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-06T04:43:08Z","doi":"10.1016/j.inat.2026.102289","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"doi:10.1016/j.engappai.2026.115242","name":"Depth-based segment any leaf: A zero-shot pipeline for plant disease detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115242","authors":["Duygu Sinanc Terzi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T09:01:43Z","doi":"10.1016/j.engappai.2026.115242","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527952","name":"College Students' Trust in Generative AI for Mental Health.","source":"pubmed","abstract":"This study aimed to examine college students' trust in generative artificial intelligence (AI) for mental health information and decisions.","url":"https://pubmed.ncbi.nlm.nih.gov/42527952/","authors":["Liu CH","Yip T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 30","doi":"10.1176/appi.ps.20260116","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527816","name":"The Unsteady Return of Bedside Motor Command-Following After Acute Brain Injury.","source":"pubmed","abstract":"Following a verbal command marks the bedside transition from unresponsiveness to overt recovery of consciousness after acute brain injury. Its timing across phenotypes, stability once present, and dependence on sedation are uncharacterized at scale.","url":"https://pubmed.ncbi.nlm.nih.gov/42527816/","authors":["Gorenshtein A","Adiniaev Y","Omar M","Barash Y","Klang E","Daniel O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s12028-026-02624-x","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527795","name":"Contrast-Enhanced Mammography-Based Radiomics for Predicting Ductal Carcinoma In Situ in Breast Cancer.","source":"pubmed","abstract":"Our investigation focuses on developing and testing a radiomics nomogram based on contrast-enhanced mammography (CEM) and clinical factors to predict ductal carcinoma in situ (DCIS) in breast cancer.&#xa0;A retrospective analysis was performed on 731 breast cancer cases who underwent CEM examination and subsequent surgical treatment with complete pathological results, enrolled from five centers. Radiomics features were derived from both low-energy and recombined CEM images for each patient. The Minimum Redundancy Maximum Relevance (mRMR&#xff09;and least absolute shrinkage and selection operator (LASSO) methods were used to select radiomics features. The radiomics signature (Rad-score) was calculated as a weighted linear combination of the most discriminative features. The univariate and multivariate logistic regression were used to select the clinical factors. A radiomics nomogram was established by integrating the Rad-score and independent clinical risk factors. The receiver operator characteristic curves (ROCs) and calibration curves were used to assess the performance of the radiomics nomogram.&#xa0;The Rad-score was calculated through the integration of 11 radiomics features. The radiomics nomogram was developed&#xa0;from Rad-score, age, menstrual status and background parenchymal enhancement (BPE) by logistic regression, which showed better predictive performance in both internal and pooled external test sets, with AUCs of 0.889 (95% con&#xfb01;dence interval [CI]: 0.847-0.932) and 0.822 (95% CI: 0.630-1.000), respectively. The calibration curves exhibited excellent consistency between predicted and observed probabilities.&#xa0;The radiomics nomogram incorporated with CEM-based radiomics features, age, menstrual status and BPE showed acceptable performance in predicting the ductal carcinoma in situ in breast cancer. As a preliminary exploratory study, our findings require further validation in larger, multi-center external cohorts.","url":"https://pubmed.ncbi.nlm.nih.gov/42527795/","authors":["Zhang K","Qiu J","Wang L","Mao N","Wang Q","Sun P","Chen G","Qiao G","Wang S","Cao K","Zhang Y","Lin F","Xu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s10278-026-02127-3","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527788","name":"A Thyroid Nodule Differentiation Model for Benign-Malignant Identification by Fusing Transfer Learning and Gradient Boosting Decision Tree.","source":"pubmed","abstract":"Accurate identification of benign and malignant thyroid nodules is a core link in clinical diagnosis and treatment decision-making, which directly affects the selection of subsequent treatment plans for patients. Aiming at the problems such as the strong subjectivity of traditional ultrasound diagnosis and the insufficient generalization ability of a single artificial intelligence model in small-sample medical scenarios, this study proposes an optimized computer-aided diagnosis model on the basis of the published hybrid diagnosis framework. The model extracts high-level semantic features of ultrasound images through the ResNet50 network with transfer learning and constructs a two-stage diagnosis architecture combined with the XGBoost classifier with optimized parameters, giving full play to the advantages of deep learning in feature representation and the high-efficiency classification ability of gradient boosting decision trees. Experimental results based on a multi-center clinical ultrasound dataset show that the diagnostic accuracy of the optimized model reaches 95 % , and it outperforms the original hybrid model and traditional single algorithms in key indicators such as precision, recall, and F1-score. The proposed framework demonstrated promising diagnostic performance on the internal retrospective dataset. However, further validation using independent external cohorts is required before broader clinical application. It provides an objective and reliable diagnostic reference for clinicians and has important clinical value for reducing the unnecessary rate of fine-needle aspiration biopsy and improving the efficiency of early diagnosis of thyroid diseases.","url":"https://pubmed.ncbi.nlm.nih.gov/42527788/","authors":["Li B","Li T","Ju H","Zhang D","Zhang Y","Guo L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s10278-026-02120-w","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527780","name":"Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.","source":"pubmed","abstract":"The objective was to develop and validate an explainable artificial intelligence (AI)-based multimodal approach for preoperative risk stratification of central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) and to evaluate its role in supporting radiologist decision-making. This multicenter retrospective study enrolled patients with pathologically confirmed PTC from four hospitals. Preoperative two-dimensional ultrasound, strain elastography, shear-wave elastography, and clinical variables were integrated to develop a multimodal predictive model. Model interpretability was achieved using SHapley Additive exPlanations (SHAP) to provide feature-level explanations supporting clinical interpretation. To assess clinical usability, a controlled reader study was conducted in which six radiologists with varying experience independently evaluated cases under three conditions: without AI assistance, with basic AI assistance (probability output only), and with explainable AI assistance (visualized feature-level contributions). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and reader performance was assessed using paired statistical comparisons and interreader agreement analysis. A total of 428 patients (mean age, 44&#xa0;years&#x2009;&#xb1;&#x2009;12; 369 women) with 508 PTC nodules were included, of whom 225 (44.3%) had CLNM. The multimodal model achieved AUCs of 0.975, 0.917, and 0.844 in the training, validation, and external test cohorts, respectively, outperforming single-modality and simplified fusion approaches (p&#x2009;&lt;&#x2009;0.05). SHAP identified age, texture-derived radiomic features, and elastography-derived stiffness-related features as key contributors. In the reader study, explainable AI assistance significantly improved diagnostic accuracy across all experience levels, increased diagnostic confidence, and raised human-AI agreement to substantial or almost-perfect levels. An explainable AI-based multimodal approach enables accurate preoperative risk stratification of CLNM in PTC and improves radiologist diagnostic performance, with potential to support clinical decision-making within radiology workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42527780/","authors":["Huang Z","Wang J","Chen M","Chu X","Li J","Sun X","Yang L","Wong ST","Chen Y","Wang T","Li H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s10278-026-02141-5","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527758","name":"Elevated hypoxia-inducible factor-1α in pediatric patients with patent ductus arteriosus: a pilot study.","source":"pubmed","abstract":"Children with complex cyanotic congenital heart disease (CHD) and with pulmonary hypertension (PH) commonly experience hypoxia. Hypoxia-inducible factor-1&#x3b1; (HIF-1&#x3b1;) is a key regulator of cellular responses to low oxygen, and its levels are elevated in these conditions. However, HIF-1&#x3b1; concentrations in non-cyanotic CHD, such as patent ductus arteriosus (PDA), remain largely unknown. This study aimed to evaluate circulating serum HIF-1&#x3b1; levels in patients with PDA and compare them with those in healthy children and in patients with complex CHD after Fontan palliation.","url":"https://pubmed.ncbi.nlm.nih.gov/42527758/","authors":["Trębacz O","Florek P","Podlewski J","Iwaniec T","Tarała W","Weryński P","Niemiec Ł","Szewczyk B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s43440-026-00883-1","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527751","name":"Rethinking Case Allocation in Dermatopathology: Beyond First-In, First-Out.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42527751/","authors":["Pickler JGJ","Junior HF","Pedack FR","Silva BL","de França PHC","de Paula Alves Coelho KM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1111/cup.70184","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527699","name":"Strategies for pediatric dose derivation from population pharmacokinetic models.","source":"pubmed","abstract":"Health authorities worldwide require clinical studies in children to ensure scientifically rigorous and consistent dosing. Before pediatric data are available, model-based methods provide a scientific and reproducible way to determine doses for pediatric studies. A general introduction to pediatric scaling is provided, followed by a focus on population pharmacokinetic methods to scale from adults to children using exposure matching. Exposure matching involves two steps: first, optimal pediatric doses are derived using a fine grid of body sizes and doses; second, feasible doses are selected based on availability of dose strengths. Three strategies are employed and compared, best fit (similar exposure in adults and children across ages and body sizes), conservative (children's exposure not exceeding adults'), and progressive (children's exposure not less than adults'). Each recommendation is evaluated against the exposure metrics C max , C trough , and AUC at steady state for the optimal dosing scheme. Simulations assess interindividual variability in exposure. Visualizations enable risk assessment of exposure distributions and post-hoc refinement of dosing schemes. The formalized approach offers clinical teams a reproducible basis for pediatric dose selection. An implementation in R and Monolix is provided.","url":"https://pubmed.ncbi.nlm.nih.gov/42527699/","authors":["Krause A","Cellière G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s10928-026-10053-8","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527557","name":"Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.","source":"pubmed","abstract":"This paper presents a critical analysis of AI advancements in chest radiograph (CXR) analysis, tracing its evolution from conventional machine learning to deep learning and multimodal approaches. Early models relied on hand-crafted features, while recent CNNs and transformer-based architectures now achieve diagnostic accuracies exceeding or comparable to radiologists for various thoracic conditions. The recent integration of large language models and multimodal systems-combining imaging with clinical text-has further improved performance and interpretability. Despite notable success, challenges still remain, including model bias, limited generalisation across institutions, and explainability. Solutions such as data sharing, domain adaptation, and explainable-AI techniques are actively being explored. Looking forward, AI systems trained on diverse patient data streams promise enhanced clinical integration and diagnostic precision.","url":"https://pubmed.ncbi.nlm.nih.gov/42527557/","authors":["Matsuo H","Nishio M","Fujimoto K","Deperrois N","Matsunaga T","Nooralahzadeh F","Krauthammer M","Murakami T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 2","doi":"10.1038/s44401-026-00087-y","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527531","name":"Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent.","source":"pubmed","abstract":"We tested state-of-the-art LLMs under clinical-scale workloads using two designs: a single agent handling all tasks and a multi-agent orchestrator assigning each task to a dedicated worker. Across retrieval, extraction, and dosing tasks, batch sizes ranged from 5-80. Multi-agent accuracy remained high (90.6% at 5 tasks; 65.3% at 80), while single-agent accuracy collapsed (73.1% to 16.6%; p&#x2009;&lt;&#x2009;0.01). Multi-agent runs used up to 65-fold fewer tokens and limited latency growth. These findings show that lightweight orchestration preserves accuracy and efficiency under mixed-task clinical loads.","url":"https://pubmed.ncbi.nlm.nih.gov/42527531/","authors":["Klang E","Omar M","Raut G","Agbareia R","Timsina P","Freeman R","Gavin N","Stump L","Charney AW","Glicksberg BS","Nadkarni GN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Mar 9","doi":"10.1038/s44401-026-00077-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527522","name":"A framework for using AI to drive care model transformation: building cars rather than faster horses.","source":"pubmed","abstract":"Despite advances in science and technology, persistent challenges in the delivery of healthcare call for care model transformations that have yet to be realized. Artificial intelligence could drive these transformations, but has yet to do so at scale. We present a four-layer framework for leveraging AI to design new care models: Knowledge (clinical content and institutional expertise), Intelligence (AI-powered synthesis and reasoning), Application (user interfaces), and Workflow (redesigned care processes). These layers are modular yet tightly interdependent, requiring cross-functional teams to design across the full stack. We illustrate this framework through an AI-enabled specialty consultation service deployed within Stanford Health Care, a quaternary academic medical center, that integrates all four layers to transform how expertise is delivered. This framework offers health system leaders a roadmap for moving beyond technology deployment toward systematic care model engineering&#x2014;an organizational capability that will help shape the future of healthcare delivery.","url":"https://pubmed.ncbi.nlm.nih.gov/42527522/","authors":["Li RC","Rosengaus L","Gohil L","Sharp C","Pfeffer MA","Sehgal N","Entwistle D","Hofmann LV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 28","doi":"10.1038/s44401-026-00071-6","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527498","name":"Receptiveness of physicians towards artificial intelligence-driven drug prescription: a nationwide survey.","source":"pubmed","abstract":"Using artificial intelligence (AI) to prescribe drugs has advanced slowly. Whether a \"doctor-in-the-loop\" design would increase acceptance of drug-prescribing AI is unknown, as are settings where physicians envision AI-driven drug prescription most likely to be implemented. We surveyed a stratified sample of 2708 physicians throughout China to interrogate their opinions on drug-prescribing AI. Most respondents (78%) are receptive to using drug-prescribing AI and anticipate doing so within 5 years. Respondents suggested initial settings for AI-driven drug prescribing include situations where there are standard guidelines (74%), where the decision is whether to continue a current prescription in someone (55%), and where prescribing decisions rely on high-complexity clinical data (44%). Many (66%) indicated a preference for conditional to fully autonomous drug-prescribing AI. Clustering analysis identified 2 psychological profile-types, \"optimists\" and \"pragmatists\", who have different standards for model efficacy, expediency, explainability, and governance/stewardship for drug-prescribing AI. A high level of using medical AI is the strongest predictor for being an optimist (OR&#x2009;=&#x2009;2.98 [2.53, 3.51]; P&#x2009;&lt;&#x2009;0.0001). In conclusion, our data point to the wide acceptability of conditional autonomous drug-prescribing AI among Chinese physicians. Moreover, disparity in optimism about drug-prescribing AI is caused by disparity in prior exposure to medical AI.","url":"https://pubmed.ncbi.nlm.nih.gov/42527498/","authors":["Zhang W","Qi S","Hu Y","Zhai X","Zhai W","Liu S","Li Z","Su W","Liu S","Liu W","Liu J","Yin J","Xie M","Zheng A","Zhang L","He A","Zhang R","Liu W","Ding K","Wu L","Meng Y","Li Q","Yangjin B","Li T","Liu C","Gao D","He H","Long F","Ge X","Sun XX","Ma H","Su T","Du S","Chen M","Feng Y","Song Z","Wang J","Gale RP","Gong X","Shen Q","Chen J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 8","doi":"10.1038/s44401-026-00101-3","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527488","name":"Uses of generative AI by non-clinician staff at an academic medical center.","source":"pubmed","abstract":"Large language model (LLM) chat tools have the potential to transform healthcare workflows by improving efficiency and reducing administrative burdens. While prior research has predominantly focused on clinicians, non-clinician healthcare staff constitute the majority of the workforce, and their real-world chat tool use remains uncharacterized. This retrospective, cross-sectional study analyzed de-identified chat logs from a secure, HIPAA-compliant LLM chat tool deployed at an academic medical center over an 11-month period. Among 30,503 chat threads analyzed, 98% originated from non-clinician users across 239 roles. Usage was dominated by administrative tasks including email and document writing (53.9%), text manipulation (9.1%), and brainstorming (6.7%). A notable proportion of interactions included off-label queries unrelated to work or organizational goals, including 5.9% involving clinical decision-making. These findings highlight the need for targeted training, tailored governance policies, and refined evaluation frameworks to optimize appropriate LLM use while mitigating risks in healthcare settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42527488/","authors":["Black KC","Haberkorn WJ","Ma SP","Kiani H","Bhasin A","Chen JH","Shah NH","Morse K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Feb 2","doi":"10.1038/s44401-025-00063-y","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527484","name":"MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.","source":"pubmed","abstract":"Inpatient pathways require complex clinical decision-making based on comprehensive patient information, yet research on medical LLMs is limited in this area due to the lack of large-scale datasets. Existing medical benchmarks primarily focused on question-answering and examinations, overlooking the multifaceted nature of inpatient decision-making. To address this gap, we developed the IPDS benchmark, comprising 51,274 cases across 9 triage departments, 17 major disease categories, and 16 treatment options. We further proposed the Multi-Agent Inpatient Pathways (MAP) framework, containing three specialized clinical agents: a triage agent for patient admission, a diagnosis agent for diagnostic decision-making, and a treatment agent for care planning. A chief agent guides and promotes these agents to ensure coordination. Experiments demonstrated that MAP achieved superior alignment with operational protocols compared to state-of-the-art LLMs. The MAP sets a foundation for advancing inpatient support systems, offering significant potential for enhancing operational efficiency and resource planning in healthcare facilities.","url":"https://pubmed.ncbi.nlm.nih.gov/42527484/","authors":["Chen Z","Peng Z","Liang X","Wang C","Liang P","Zeng L","Ju M","Yuan Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 1","doi":"10.1038/s44401-026-00085-0","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527448","name":"Rethinking pathology image analysis through shuffling.","source":"pubmed","abstract":"Pathological examination is the current gold standard in cancer diagnosis, yet artificial intelligence (AI) methods still struggle to capture the multi-scale heterogeneity of tumor morphology across patients, tissues, and magnifications. Here, we introduce the PAthoentity Shuffle Strategy (PASS), a principled framework that explicitly models pathoentities, the critical biological structures such as cells, glands, and tissues, and their hierarchical relationships. By controlled shuffling of pathoentities within and across samples, PASS enriches the relational structure available to neural networks, encouraging them to learn both local homogeneity and global heterogeneity. We provide theoretical analysis showing that PASS achieves error bounds comparable to state-of-the-art methods, supporting shuffling as a generalizable computational principle rather than a heuristic. Extensive evaluation on 10 datasets spanning 8 diseases, 9 organs, and 4 magnification levels demonstrates consistent performance gains, robust generalization, and scalability across diverse pathological contexts. Importantly, PASS further shows translational value in a rapid onsite evaluation (ROSE) scenario in gastroenterology, highlighting its potential for clinical deployment. Overall, this study establishes pathoentity shuffling as an effective principle for pathological image analysis, bridging biological insight and computational design to enhance diagnostic modeling and morphological hierarchy learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42527448/","authors":["Liu Z","Zhang T","Chen BK","Feng Y","Lyu S","Lei Y","Ying N","Feng Y","Zhao Y","Zhang P","Song F","Ma C","He Y","Kawaguchi K","Lee HK","Jin Y","Zhang G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1038/s44385-026-00096-4","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527444","name":"SignatureGuard: hybrid CNN-transformer model for signature verification and identification across Arabic and English datasets.","source":"pubmed","abstract":"Offline signature verification has a persistent Latin-script bias: most systems are built and evaluated on English datasets, while Arabic and other non-Latin scripts are largely absent from the benchmarking literature. SignatureGuard is a three-task framework that evaluates six hybrid CNN-transformer architectures on two offline signature benchmarks (one Arabic, ASVAR; one English, CEDAR) under a single shared preprocessing and training pipeline, enabling direct architectural comparison across writing systems. The three tasks are binary forgery detection, multi-class biometric identification, and forgery source identification. To address the Arabic data gap, we publicly released ASVAR: 3471 images (1712 genuine, 1759 forged) from 70 individuals. Hybrid pairings of EfficientNetB7 or ResNet50 with the Vision Transformer (ViT-B/16) achieve test accuracies of 98.2% and 98.4% on forgery detection, macro-F1 above 0.97, and Cohen's &#x3ba; above 0.96; 95% Wilson confidence intervals (&#xb1;1.5&#xa0;pp) confirm these are not artefacts of finite test-set size. MobileNetV2-based hybrids trail by at most 0.7 percentage points. Architectural rankings are broadly consistent across both datasets. All results are obtained under a seen-writer, image-level 80/10/10 split-a closed-set protocol that supports reproducible architectural comparison but overestimates real-world deployment performance; a signer-disjoint evaluation is identified as the primary follow-on experiment. An information-theoretic argument demonstrates that the hybrid classification head cannot perform worse than either frozen component in isolation. Confusion-matrix analysis, multi-seed validation, and backbone fine-tuning are recommended as extensions.","url":"https://pubmed.ncbi.nlm.nih.gov/42527444/","authors":["Balat M","Mamdouh E","Awaad R","Elhoseny M","Anter AM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1038/s41598-026-62860-1","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527437","name":"A hybrid ConvNeXt-ViT framework with differential evolution optimization for breast cancer classification.","source":"pubmed","abstract":"Breast cancer, a leading cause of mortality among women worldwide, necessitates early detection through mammography. Yet, automated classification remains challenging due to class imbalance, limited datasets, and the need for both local and global feature extraction. While convolutional neural networks (CNNs) excel in local feature extraction for mammogram classification, they struggle with long-range contextual dependencies. Conversely, transformer-based models capture global relationships effectively but require large datasets and substantial computational resources, limiting their applicability in medical imaging. To overcome these limitations, we propose DEViTNeXt, a new hybrid framework that synergistically combines ConvNeXt's convolutional efficiency with Vision Transformer (ViT) attention-based global modeling, enhanced by Differential Evolution (DE) optimization. The framework employs comprehensive preprocessing (Gaussian filtering, CLAHE enhancement) and a hybrid augmentation pipeline that integrates GAN-based synthesis of malignant cases with geometric transformations. Dual-branch feature extraction leverages ConvNeXt for hierarchical local features and ViT for global contextual relationships, with Multi-Head Attention (MHA) refinement dynamically emphasizing diagnostic regions in both branches. A DE-optimized MHA fusion layer adaptively integrates complementary. A composite loss function (Weighted Cross-Entropy + Focal Loss) addresses class imbalance while focusing on complex malignant cases. Extensive experiments on the CBIS-DDSM and MIAS datasets demonstrate DEViTNeXt's superiority, achieving 99.63% accuracy, 99.45% sensitivity, and 99.55% specificity on CBIS-DDSM under binary (Benign vs. Malignant) classification, and 98.50% accuracy on MIAS (3-class), outperforming state-of-the-art methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42527437/","authors":["Aldawsari MA","Aldosari SJ","Ismail A","Emam MM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1038/s41598-026-61594-4","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527429","name":"Perceptions of simulated artificial intelligence in medical consultations: associations with stress, memory, and perceived credibility.","source":"pubmed","abstract":"The growing integration of digital technologies into healthcare requires understanding the impact of different consultation modalities on patient stress, memory and perceived credibility. This study compared different consultation modalities (human physician, in person or via video call; AI-style physician, as a chatbot or avatar) in standardized simulated medical consultations involving the delivery of bad news. For the AI-styled physician consultations, a Wizard of Oz design was used, in which participants were told they interacted with an AI physician while a human controlled the interaction. 163 healthy participants experienced either a human or an AI physician. Stress was measured through ratings and salivary cortisol, alongside memory retrieval and situation credibility. Human-based consultations led to higher stress levels than AI-styled formats. Greater perceived credibility was associated with stronger stress responses. Memory retrieval was lowest in the AI-styled chatbot condition. These findings show that different consultation modalities are associated with varying levels of stress and memory in medical settings. AI-styled physician interactions may reduce stress in routine medical communication, but their use should be carefully considered for critical medical communication.","url":"https://pubmed.ncbi.nlm.nih.gov/42527429/","authors":["Mayer CJ","Swysen TL","Kurz TR","Stephan TI","Geisel D","Doll ES","Mahal N","Lerch SP","Sailer S","Schaaf CP","Merz CJ","Wüstenberg T","Ehrenthal JC","Walter S","Mahal J","Ditzen B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1038/s41746-026-03022-5","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527366","name":"Theoretical modeling and numerical simulation of a light-field detector for x-ray differential phase contrast imaging.","source":"pubmed","abstract":"Most recently, a novel color-encoding light-field detector based on perovskite material has been demonstrated for x-ray differential phase contrast (DPC) imaging. However, it lacks performance evaluation and comparison between such a novel light-field detector and a widely used grating interferometer before applying it into medical imaging field.","url":"https://pubmed.ncbi.nlm.nih.gov/42527366/","authors":["Tan Y","Zhu J","Zhang X","Zheng H","Liang D","Ge Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70601","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527364","name":"Concordance Between Radiologist and AI-based Volumetric Breast Density Assessments: Clinical and Economic Implications - A Retrospective Cohort Study.","source":"pubmed","abstract":"Radiologists classify breast density using the Breast Imaging Reporting and Data System (BI-RADS) as required by the 2024 Mammography Quality Standards Act (MQSA), which mandates disclosure of breast density in all mammography reports. FDA-approved AI software now provides objective, reproducible breast density assessments to improve workflow and guide supplemental screening. This study examines concordance between AI-based systems (Volumetric density measurement by Volpara/Lunit) and radiologist assessments and explores potential clinical and economic implications.","url":"https://pubmed.ncbi.nlm.nih.gov/42527364/","authors":["Devine JR","Withrow ADM","Choudhry S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527305","name":"Neuroimaging in Distal & Medium Vessel Occlusions.","source":"pubmed","abstract":"Distal medium vessel occlusions (DMVOs), as a cause of acute ischemic stroke, typically present with unique challenges in diagnosis and treatment owing to their anatomical variability, heterogeneous clinical manifestations, and limitations in current imaging techniques. Computed tomography angiography (CTA) often fails to detect DMVOs accurately, necessitating the use of alternative imaging modalities such as time-to-maximum (Tmax) maps, which have shown significantly higher sensitivity. Emerging artificial intelligence (AI) tools offer promising diagnostic support but require further validation. Endovascular thrombectomy (EVT) for DMVOs remains a subject of debate, with ongoing efforts to refine techniques and develop more navigable and effective devices. Current evidence is largely limited to single-center studies, emphasizing the need for large-scale, multicenter trials to establish standardized protocols and improve clinical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42527305/","authors":["Rehman S","Akram U","Waseem S","Kobeisy AAN","Ahmed S","Ali MR","Malik MAJ","Zafar MT","Murad F","Sarwar A","Nadeem A","Yedavalli V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.3174/ajnr.A9549","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527291","name":"[(18)F]FDG-PET in Lung Cancer: From Staging to Therapy Response.","source":"pubmed","abstract":"Lung cancer, the most commonly diagnosed malignancy, is the leading cause of cancer-related mortality worldwide. Advancements in molecular imaging have expanded the role of [18F] 2-Fluoro-2-deoxy-glucose (FDG) PET/CT in the management of lung cancer, from the evaluation of pulmonary nodules (including solitary pulmonary nodule) to staging, radiotherapy planning and response evaluation. This review summarizes updates on the utility of FDG PET in non-small-cell lung cancer and expands on the current and future potential of artificial intelligence and radiogenomics in molecular imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/42527291/","authors":["Singh H","Aggarwal P","Kumar R","Singh N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.cpet.2026.06.005","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527225","name":"A multimodal artificial intelligence method for accurate diagnosis of autoimmune gastritis.","source":"pubmed","abstract":"Autoimmune gastritis (AIG), a chronic inflammatory disease associated with various comorbidities and complications, is often subject to missed or delayed diagnoses.","url":"https://pubmed.ncbi.nlm.nih.gov/42527225/","authors":["Cao Y","Jin X","Zhao Y","Zhang G","Ge W","Zhou J","Liu Y","Huang P","Zhang G","Han Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.dld.2026.07.005","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527126","name":"[Current status and challenges of artificial intelligence application in pediatric intensive care unit].","source":"pubmed","abstract":"&#x4eba;&#x5de5;&#x667a;&#x80fd;&#xff08;AI&#xff09;&#x51ed;&#x501f;&#x5f3a;&#x5927;&#x7684;&#x6570;&#x636e;&#x6316;&#x6398;&#x4e0e;&#x63a8;&#x6f14;&#x80fd;&#x529b;&#xff0c;&#x5df2;&#x9010;&#x6b65;&#x5e94;&#x7528;&#x4e8e;&#x513f;&#x7ae5;&#x91cd;&#x75c7;&#x76d1;&#x62a4;&#x75c5;&#x623f;&#xff08;PICU&#xff09;&#x7684;&#x4e34;&#x5e8a;&#x8f85;&#x52a9;&#x51b3;&#x7b56;&#xff0c;&#x65e8;&#x5728;&#x6ee1;&#x8db3;&#x60a3;&#x513f;&#x65e9;&#x671f;&#x98ce;&#x9669;&#x5206;&#x5c42;&#x3001;&#x51b3;&#x7b56;&#x652f;&#x6301;&#x53ca;&#x8d44;&#x6e90;&#x914d;&#x7f6e;&#x4f18;&#x5316;&#x7b49;&#x5173;&#x952e;&#x9700;&#x6c42;&#x3002;&#x8fd1;&#x671f;&#x7814;&#x7a76;&#x8868;&#x660e;&#xff0c;&#x57fa;&#x4e8e;&#x5e38;&#x89c4;&#x4e34;&#x5e8a;&#x6570;&#x636e;&#x6784;&#x5efa;&#x7684;&#x673a;&#x5668;&#x5b66;&#x4e60;&#x548c;&#x6df1;&#x5ea6;&#x5b66;&#x4e60;&#x6a21;&#x578b;&#xff0c;&#x5728;&#x5371;&#x91cd;&#x60a3;&#x513f;&#x65e9;&#x671f;&#x8bc6;&#x522b;&#x65b9;&#x9762;&#x5c55;&#x73b0;&#x51fa;&#x5353;&#x8d8a;&#x6027;&#x80fd;&#xff0c;&#x76f8;&#x8f83;&#x4e8e;&#x4f20;&#x7edf;&#x75be;&#x75c5;&#x4e25;&#x91cd;&#x7a0b;&#x5ea6;&#x8bc4;&#x5206;&#x7cfb;&#x7edf;&#x5177;&#x6709;&#x66f4;&#x4f18;&#x7684;&#x5224;&#x522b;&#x6548;&#x80fd;&#x3002;&#x5c3d;&#x7ba1;AI&#x5728;&#x91cd;&#x75c7;&#x533b;&#x5b66;&#x9886;&#x57df;&#x5e94;&#x7528;&#x524d;&#x666f;&#x5e7f;&#x9614;&#xff0c;&#x4f46;&#x5176;&#x4e34;&#x5e8a;&#x8f6c;&#x5316;&#x4ecd;&#x9762;&#x4e34;&#x6570;&#x636e;&#x8d28;&#x91cf;&#x3001;&#x6a21;&#x578b;&#x53ef;&#x89e3;&#x91ca;&#x6027;&#x4ee5;&#x53ca;&#x4e34;&#x5e8a;&#x6574;&#x5408;&#x7b49;&#x5173;&#x952e;&#x74f6;&#x9888;&#x3002;&#x540c;&#x65f6;&#xff0c;&#x513f;&#x79d1;&#x7279;&#x6b8a;&#x4eba;&#x7fa4;&#x5bf9;&#x5e94;&#x7684;&#x4f26;&#x7406;&#x89c4;&#x8303;&#x4e0e;&#x76d1;&#x7ba1;&#x4f53;&#x7cfb;&#x4ecd;&#x6709;&#x5f85;&#x5b8c;&#x5584;&#x3002;&#x672c;&#x6587;&#x7cfb;&#x7edf;&#x68b3;&#x7406;&#x4e86;AI&#x5728;PICU&#x9886;&#x57df;&#x7684;&#x5e94;&#x7528;&#x73b0;&#x72b6;&#xff0c;&#x91cd;&#x70b9;&#x63a2;&#x8ba8;&#x5371;&#x91cd;&#x4e8b;&#x4ef6;&#x9884;&#x6d4b;&#x3001;&#x5de5;&#x4f5c;&#x6d41;&#x7a0b;&#x4f18;&#x5316;&#x4ee5;&#x53ca;&#x4e2a;&#x4f53;&#x5316;&#x7cbe;&#x51c6;&#x533b;&#x7597;&#x7684;&#x5b9e;&#x65bd;&#x8def;&#x5f84;&#xff0c;&#x5ba2;&#x89c2;&#x5206;&#x6790;&#x5f53;&#x524d;&#x6280;&#x672f;&#x5e94;&#x7528;&#x7684;&#x673a;&#x9047;&#x4e0e;&#x6311;&#x6218;&#xff0c;&#x4ee5;&#x671f;&#x4e3a;&#x4e34;&#x5e8a;&#x53ca;&#x79d1;&#x7814;&#x5de5;&#x4f5c;&#x63d0;&#x4f9b;&#x53c2;&#x8003;&#xff0c;&#x63a8;&#x52a8;AI&#x5728;&#x8be5;&#x9886;&#x57df;&#x89c4;&#x8303;&#x3001;&#x5b89;&#x5168;&#x3001;&#x6709;&#x6548;&#x5730;&#x843d;&#x5730;&#x8f6c;&#x5316;&#x3002;.","url":"https://pubmed.ncbi.nlm.nih.gov/42527126/","authors":["Fu S","Xu F","Li F","Qian SY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 2","doi":"10.3760/cma.j.cn112140-20260309-00195","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527115","name":"Cross-Cultural Applicability and Application of the Nursing Leaders' Readiness for Artificial Intelligence Scale: A Cross-Sectional Study.","source":"pubmed","abstract":"To translate, revise and evaluate the Chinese-version Nursing Leaders' Readiness for Artificial Intelligence Scale and assess Chinese nursing leaders' AI readiness.","url":"https://pubmed.ncbi.nlm.nih.gov/42527115/","authors":["Yang H","Guo Y","Qiao Y","Bai W","Chen C","Hu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/jonm/9637045","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527096","name":"Pitfalls and Emerging Trends in AI-driven Pathological Analysis of Metastatic Brain Tumors.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) and computational pathology has the potential to refine prognostic stratification in patients with metastatic brain tumors (MBTs). However, current AI models, which are trained predominantly on extracranial tissues, encounter significant challenges in the unique intracranial microenvironment. Unlike previous reviews that focus on primary brain tumors, this article addresses the challenges of applying AI pathology to MBTs. We discuss three major limitations in AI-driven MBT pathology. First, models struggle with cellular lineage distinction between resident microglia and bone marrow-derived macrophages. Second, there is a need for topological analysis of immune cell distribution within the immune-privileged context of the brain. Third, domain shift arises from neuropil texture and metabolic adaptation. Emerging approaches, such as multiplex immunohistochemistry (mIHC) and graph neural networks (GNNs), can enable lineage-specific, spatially resolved, and metabolically contextualized analysis. However, prospective validation in MBT cohorts remains necessary. We propose a brain-optimized, multidimensional framework that integrates clinical parameters with spatial immune and metabolic features. Such models can refine prognosis beyond conventional Stage IV classification and support individualized therapeutic strategies for patients with MBTs.","url":"https://pubmed.ncbi.nlm.nih.gov/42527096/","authors":["Fujita M","Nawa S","Ito E","Nagasaka T","Kuramitsu S","Yamashita K","Nakata S","Ohno M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.21873/anticanres.18318","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527084","name":"Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning.","source":"pubmed","abstract":"Predicting local recurrence remains challenging in carbon-ion radiotherapy (CIRT) for non-small cell lung cancer (NSCLC). In this study, we aimed to develop and validate a machine learning model to predict local recurrence after CIRT for early-stage peripheral NSCLC.","url":"https://pubmed.ncbi.nlm.nih.gov/42527084/","authors":["Mochida K","Miyasaka Y","Kubo N","Yoshida H","Okano N","Kawamura H","Ohno T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.21873/anticanres.18299","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42527035","name":"Robot-assisted gait training using a robotic exoskeleton during inpatient rehabilitation in acute Guillain-Barré Syndrome.","source":"pubmed","abstract":"A woman in her early 60s with acute Guillain-Barr&#xe9; syndrome (GBS) developed rapidly progressive tetraparesis and respiratory failure requiring non-invasive ventilation. Following intravenous immunoglobulin and medical stabilisation, she entered inpatient rehabilitation with severe weakness, poor truncal control and inability to ambulate. Despite intensive conventional therapy, early gait training was limited. Exoskeleton-based robot-assisted gait training (RAGT) using the Hybrid Assistive Limb (HAL; Cyberdyne Inc, Tsukuba, Japan) was introduced during inpatient rehabilitation as an adjunct to standard therapy. The patient completed 10 HAL sessions over a 15-day period without adverse events. During this time, progressive improvements in ambulatory distance and functional independence were observed. By completion of HAL training, she was able to ambulate 50 m with a walking stick and minimal assistance. She subsequently continued conventional rehabilitation and achieved independent ambulation by discharge. This case suggests that HAL-based RAGT may be a feasible adjunct to inpatient rehabilitation in medically stable patients with acute GBS. The intervention was well tolerated in this patient. Further studies are required to establish patient-selection criteria, optimal timing and the effectiveness of exoskeleton-assisted gait training in GBS.","url":"https://pubmed.ncbi.nlm.nih.gov/42527035/","authors":["Ser JS","Lui SK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1136/bcr-2026-272503","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526978","name":"Applications of Deep Learning in Endocrine Neoplasms.","source":"pubmed","abstract":"Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.","url":"https://pubmed.ncbi.nlm.nih.gov/42526978/","authors":["Ramesh S","Dolezal JM","Pearson AT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.cll.2026.06.014","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526909","name":"Machine Learning Modelling, Single-Cell Landscape Profiling and Spatial Transcriptomics Provide New Insights Into SUMOylation in Head and Neck Squamous Cell Carcinoma.","source":"pubmed","abstract":"SUMOylation is implicated in the regulation of multiple malignancies. However, its potential roles in head and neck squamous cell carcinoma (HNSCC) remain insufficiently characterised. By integrating bulk RNA-seq, scRNA-seq and stRNA-seq datasets, we systematically interrogated the biological relevance of SUMOylation in HNSCC. Key markers from the signature were further validated using in vitro functional assays. A recognition model was established and validated using 692 HNSCC and 178 non-HNSCC samples. SROC analysis demonstrated robust performance across eight datasets (AUC = 0.92). Functional enrichment and scRNA-seq analyses indicated that SUMOylation may exert its effects in HNSCC primarily through cell-cycle regulation. Among the model features, SAE1 was markedly overexpressed in HNSCC (SMD&#xa0;=&#xa0;1.07, 95% CI 0.56-1.59, p&#xa0;&lt;&#xa0;0.05). In vitro assays further confirmed that SAE1 enhanced proliferation, colony formation and migration in SAS and SCC-9 cells and validated its role in regulating cell cycle progression and apoptosis. We established a SUMOylation-related recognition model for HNSCC with consistently strong performance in multiple external validation cohorts. SAE1 emerges as a candidate molecular biomarker for HNSCC.","url":"https://pubmed.ncbi.nlm.nih.gov/42526909/","authors":["Fang Z","Mei K","Liu J","Zhou W","Li T","Zhang H","Cao C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1049/syb2.70082","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526900","name":"Training the algorithmically augmented orthodontist: competencies, curriculum design, and implementation challenges.","source":"pubmed","abstract":"To establish a structured framework for incorporating artificial intelligence (AI) education into orthodontic residency training, outlining the competencies required for thoughtful and responsible use of AI in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42526900/","authors":["Yadav S","Gandhi V","Hansraj V","Venugopalan SR","Vaiid N","Allareddy V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.2319/Yadav_Special_Article.1","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526781","name":"Rewiring immunometabolism in hepato-biliary-pancreatic cancers: Unraveling TME-mediated metabolic suppression and advancing next-generation immunotherapeutic approaches.","source":"pubmed","abstract":"Hepato-biliary-pancreatic cancers, notorious for their pronounced heterogeneity and poor prognosis, continue to be a dominant factor in cancer-related deaths globally. Although immunotherapy has dramatically reshaped the cancer treatment landscape, its efficacy in these malignancies remains suboptimal due to the intense immunosuppression in the tumor microenvironment (TME) stemming from metabolic dysregulation. This review provides an in-depth analysis of the fundamental mechanisms of immunometabolic suppression. It highlights the strategies employed by tumor cells to compete with immune cells for nutrients, the accumulation of inhibitory metabolitessuch as lactate and adenosine, and disrupt pivotal metabolic pathways in CD8 + T, NK, and myeloid cells, ultimately leading to functional depletion. The novelty of this work lies in its systematic classification of these metabolic vulnerabilities and the introduction of a tripartite approach to address them: nutrient redistribution, metabolite scavenging, and enhancement of immune cell intrinsic metabolism. This approach aims to complement the next generation of immunotherapies, converting immunologically \"cold\" tumors into \"hot\" ones. Additionally, the review emphasizes the potential of integrating multi-omics profiling, artificial intelligence, and biomarker-guided personalized therapy as avenues to overcome resistance and enhance clinical efficacy. By viewing the challenge of therapy resistance through a metabolic perspective, this study not only enriches our foundational understanding of tumor-immune interactions but also presents a practical framework for designing highly efficacious combination therapies, representing a crucial advancement in the management of these formidable cancers.","url":"https://pubmed.ncbi.nlm.nih.gov/42526781/","authors":["Wu Y","Chen Z","Hu Y","Ren S","Li MY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.autrev.2026.104150","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526719","name":"The hard exudate pathway: Biological mechanisms, imaging correlates, and clinical implications.","source":"pubmed","abstract":"Hard exudates are common fundus findings in retinal vascular disease, but are frequently regarded as passive remnants of vascular leakage rather than biologically meaningful lesions. Traditionally described as lipid deposits associated with diabetic retinopathy and retinal vein occlusion, they have received limited integrative analysis across disease contexts. Emerging insights from vascular biology, lipid metabolism, and multimodal imaging challenge this view and reveal that hard exudates represent organized extracellular lipid-protein aggregates arising from sustained vascular-metabolic dysfunction. We synthesize current evidence on the biochemical composition, pathophysiology, and biophysical behavior of hard exudates, integrating contributions from both retinal and choroidal vascular compartments and dysfunction of the inner and outer blood-retinal barriers. Advances in optical coherence tomography enable precise layer-specific localization, facilitate differentiation from phenotypic mimickers, and support a mechanism-based diagnostic framework. Therapeutic considerations emphasize vascular stabilization over lesion-directed treatment, recognizing delayed and incomplete regression. Finally, emerging quantitative imaging and artificial intelligence-based approaches are discussed as future strategies to reposition hard exudates as dynamic biomarkers of disease chronicity and prognostic risk.","url":"https://pubmed.ncbi.nlm.nih.gov/42526719/","authors":["Venkatesh R","Hande P","Prabhu V","Tendulkar K","Chokkahalli NK","Jayadev C","Yadav NK","Angrish S","Sirsikar A","Chhablani J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.survophthal.2026.07.013","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526602","name":"An Explainable Cryobiopsy AI Model, CRAI, to Predict Progression in Interstitial Pneumonia.","source":"pubmed","abstract":"Interstitial lung disease (ILD) encompasses diverse pulmonary disorders with varied prognoses. Current pathological diagnoses suffer from inter-observer variability, necessitating more standardized approaches. We developed an ensemble AI model for cryobiopsy, CRAI, to analyze transbronchial lung cryobiopsy (TBLC) specimens and predict patient outcomes. CRAI comprises seven modules for detecting histological features, generating 17 pathologically significant findings. A downstream XGBoost classifier was developed to predict disease progression using these findings. The model's performance was evaluated using respiratory function changes and survival analysis in cross-validation and external test cohorts. In the internal cross-validation (135 cases), the model predicted 105 cases without disease progression and 30 with disease progression. The annual &#x394;%FVC was -1.293 in the non-progressive group versus -5.198 in the progressive group, a difference that was significant for CRAI while only five of 19 pathologists achieved significant differentiation using usual interstitial pneumonia (UIP) diagnosis. Survival analysis demonstrated significantly shorter survival times in the progressive group (p = 0.038). CRAI provides a comprehensive, interpretable approach to analyzing TBLC specimens, offering potential for standardizing ILD diagnosis and predicting disease progression. The model could facilitate early identification of progressive cases and guide personalized therapeutic interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42526602/","authors":["Uegami W","Okoshi EN","Lami K","Nei Y","Ozasa M","Kataoka K","Kitamura Y","Kohashi Y","Cooper LAD","Sakanashi H","Saito Y","Kondoh Y","study group on CRYOSOLUTION","Fukuoka J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.modpat.2026.101047","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526601","name":"End-to-End Clinical Validation of a Human-Supervised Large Language Model Agent for Enterprise Surgical Pathology Reporting.","source":"pubmed","abstract":"Large language models (LLMs) show promise for text-based pathology tasks, yet most reported applications remain experimental, lack formal clinical validation, or operate outside secure, health system-approved environments. We developed and clinically validated a rule-guided, agent-based LLM that assists gastrointestinal (GI) biopsy reporting by automating report structuring while preserving full diagnostic authority with the pathologist. The AI agent (Microsoft 365 Copilot) ran within an enterprise-approved, HIPAA-compliant Microsoft 365 environment, configured with a fixed rule-based system configuration prompt and a quick-text knowledge base. In a prospective validation, 94 GI biopsy cases were evaluated by subspecialty GI pathologists using specimen container labels extracted from the laboratory information system and pathologist-entered shorthand diagnoses. Agent outputs were reviewed for formatting accuracy, organ and procedure identification, shorthand expansion fidelity, blank diagnosis enforcement, and diagnostic safety. The agent preserved specimen part structure and correctly identified organ, sub-organ, and procedure context in 100% of cases; shorthand expansion was accurate in all applicable cases. Minor formatting deviations occurred in 8 cases (8.5%) without affecting diagnostic meaning. Two cases (2%) showed minor diagnostic misinterpretation, in which descriptive container-label terms (e.g., \"ulcer,\" \"erosion\") were incorporated into diagnostic text; no hallucinated diagnoses were identified. Repeatability testing on cases enriched for descriptive labels showed 81% identical outputs across 105 runs (19% variability), with non-reproducible semantic leakage in 3% of runs. A comparative time study showed faster AI-assisted reporting (mean 39 vs 72 seconds for speech-to-text and 76 seconds for manual typing; &#x223c;33-37 second reductions, p &lt; 0.05), measured across the full workflow through sign-out, with lower variability. By restricting this end-to-end, production-embedded agent to rule-guided structuring, formatting, and controlled shorthand expansion while prohibiting diagnostic inference, the system achieved high efficiency, consistency, and seamless workflow integration on real GI biopsy cases. Low-frequency, stochastic errors and minor variability remain inherent to LLMs despite strict constraints; although infrequent, they indicate such systems are best suited for non-diagnostic, clerical augmentation rather than autonomous use. All output therefore requires pathologist careful review before sign-out. These findings support constrained, agent-based LLMs to safely enhance reporting efficiency while preserving diagnostic responsibility and human oversight.","url":"https://pubmed.ncbi.nlm.nih.gov/42526601/","authors":["Abukhiran I","Mansour A","Caicedo ML","Minkowitz JM","Pantanowitz L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.modpat.2026.101049","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526578","name":"Identification of key biomarkers for ferroptosis in diabetic kidney disease using machine learning and WGCNA.","source":"pubmed","abstract":"Early detection of diabetic kidney disease (DKD) remains challenging because currently available clinical markers mainly reflect established renal injury rather than early pathogenic changes. Ferroptosis has been increasingly implicated in DKD development, we aimed to identify ferroptosis-related molecular signatures and candidate diagnostic biomarkers for DKD.","url":"https://pubmed.ncbi.nlm.nih.gov/42526578/","authors":["Liu Y","Huang Y","Su Y","Wang L","Lv S","Fu Y","Liu Y","Chang B","Ding Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov","doi":"10.1016/j.mce.2026.112877","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526533","name":"Artificial intelligence-assisted proteomic signatures for discriminating malignant from benign pulmonary nodules.","source":"pubmed","abstract":"Lung cancer remains a major global health burden. Although low-dose CT (LDCT) is effective for early detection, its clinical application is limited by a high false-positive rate and the need for subsequent invasive confirmation, such as biopsy. Meanwhile, the extensive protein-level alterations observed in lung cancer provide a rationale for developing minimally invasive plasma proteomics-based screening a panel of biomarkers. In this study, untargeted proteomic profiling was performed on a tissue cohort (n = 61, paired tumor and normal adjacent tissue samples spanning pre-invasive (AAH/AIS), MIA and IAC), a discovery plasma cohort (n = 221), and an independent validation plasma cohort (n = 144). Integrative tissue-plasma analysis was used to identify concordant proteins. Classifier training was conducted on the plasma proteomics data through a LightGBM-based pipeline with a two-stage feature selection strategy to derive plasma protein panels and construct binary classifiers that distinguish malignant from benign nodules. The diagnostic performance of the trained models was then evaluated in an independent validation cohort. Furthermore, binary classifiers were constructed to discriminate the invasiveness of lesions among LUADs. A subset of proteins was identified whose expression in both tissue and plasma was significantly associated with tumor invasiveness. The compact pipeline identified reproducible plasma protein panels and trained LightGBM classifiers that robustly discriminated malignant from benign pulmonary nodules. The classifiers maintained strong performance in the independent validation cohort (AUC-ROC: 0.949-0.986). We developed separate classification models to stratify lung adenocarcinoma subtypes by distinct patterns of invasiveness (AUC-ROC: 0.762-0.803). High-depth proteomics combined with LightGBM enables the identification of robust plasma protein panels for discriminating pulmonary nodules and stratifying invasiveness. This generalizable strategy, along with reproducible biomarker panels, supports non-invasive early diagnosis of lung adenocarcinoma.","url":"https://pubmed.ncbi.nlm.nih.gov/42526533/","authors":["Luo J","Dong Q","Yang J","Shan H","Zhong Q","Zhang Y","Wang G","Liu J","Yin Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.trsl.2026.07.013","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526479","name":"Diagnostic features predicted by deep learning improve human object recognition in simulated prosthetic vision.","source":"pubmed","abstract":"Objective . Cortical visual prostheses aim to partially restore vision by electrically stimulating V1. Yet, their effectiveness is constrained by the limited number of simultaneously elicitable phosphenes. Identifying sparse but informative visual features for object recognition is therefore crucial to enable functional prosthetic vision. In this study, we aim to target diagnostic features for object recognition. Approach . We combined a deep learning pipeline with bioplausible phosphene simulation to optimize phosphene placement for object recognition. The phosphene renderings were presented to 63 participants who performed an object recognition task under simulated prosthetic vision with varying electrode densities. Main results . Performance in the proposed deep neural network (DNN) phosphene placement strategy was compared to contour-based and luminance-based phosphene placements. Results demonstrate that the DNN-based approach significantly reduces the number of phosphenes required for object recognition. Importantly, this advantage was especially pronounced at low level electrode densities. Significance . These findings indicate that DNNs can effectively identify diagnostic object features and enhance recognition under simulated prosthetic vision. The proposed pipeline provides a principled method for optimizing visual representations in cortical visual prostheses, highlighting the importance of task-relevant feature selection imposed by constraints on simultaneous stimulation capacity.","url":"https://pubmed.ncbi.nlm.nih.gov/42526479/","authors":["Scialom E","Lonnqvist B","Bornet A","Herzog MH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 18","doi":"10.1088/1741-2552/ae9227","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526385","name":"Histology-derived prediction of a stromal remodeling program in pancreatic ductal adenocarcinoma with transcriptomic and spatial validation.","source":"pubmed","abstract":"To develop and validate a histology-based model for predicting a stromal stiffness-related molecular surrogate in pancreatic ductal adenocarcinoma (PDAC) and to assess its spatial biological plausibility.","url":"https://pubmed.ncbi.nlm.nih.gov/42526385/","authors":["Bao W","Wang T","Xia X","Chen L","Qi H","Feng Y","Fa X","Ma G","Chen S","Leng K","Zhou S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.tranon.2026.102946","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526280","name":"A deep learning-based framework for the malignancy analysis of thyroid lesions in contrast-enhanced ultrasound videos.","source":"pubmed","abstract":"Contrast-enhanced ultrasound (CEUS) is widely used for evaluating thyroid nodule malignancy, but conventional time-intensity curve (TIC) analysis is labor-intensive and operator-dependent. This study proposes LSTAC, an automated framework for nodule segmentation and TIC analysis in CEUS videos.","url":"https://pubmed.ncbi.nlm.nih.gov/42526280/","authors":["Yang A","Li L","He R","Zhou X","Zheng C","Xu G","Ren J","Cui X","Wu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 26","doi":"10.1016/j.artmed.2026.103500","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526183","name":"AI for mental disorders and diabetes comorbidity management: A scoping review.","source":"pubmed","abstract":"The comorbidity of mental disorders and diabetes is on the rise, presenting a significant global public health challenge that gravely impacts the physical and psychological health of patients and presents obstacles to their effective management. The coronavirus disease 2019 (COVID-19) pandemic, in particular, aggravated these challenges owing to restrictions on in-person care. Artificial intelligence (AI) interventions have emerged as a promising solution to alleviate this burden. Thus, we conducted a scoping review to map the current evidence in the literature and provide a clear understanding of AI for mental disorders and comorbid diabetes.","url":"https://pubmed.ncbi.nlm.nih.gov/42526183/","authors":["Kaburu FM","Zhu P","Kudiza A","McDonnell D","da Veiga CP","Nie JB","Xiang YT","Su Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 18","doi":"10.1016/j.genhosppsych.2026.06.009","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42526023","name":"Detecting Narcissistic Personality Disorder Traits on Forums: Proof-of-Concept Study.","source":"pubmed","abstract":"Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42526023/","authors":["Aladağ AE","Özgür A","Akbaş NB","Zahmacioglu O","Bingol HO"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.2196/75799","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525959","name":"GRACE-ViT: Grouped Recalibration With Adaptive Contextual Emphasis for Breast Cancer Neoadjuvant Chemotherapy Response Prediction.","source":"pubmed","abstract":"IntroductionAccurate prediction of neoadjuvant chemotherapy (NAC) response in breast cancer is important for treatment planning and individualized therapy selection. Breast MRI contains subtle patterns related to treatment response. These patterns vary across image regions and may be difficult for standard deep learning models to capture. In this retrospective clinical prediction model development and validation study, we propose GRACE-ViT, a Vision Transformer-based framework for three-class NAC response prediction from pre-treatment breast MRI. The three classes are partial response, complete response, and stable disease.MethodsGRACE-ViT uses a pretrained ViT backbone with a lightweight token recalibration module called Grouped Recalibration with Adaptive Contextual Emphasis (GRACE). The GRACE module refines patch-token features using dual-mode token scoring, a spatial coherence prior, and cross-group feature integration. This helps the model focus on important breast regions while keeping global image context. The model was evaluated on 736 axial T1-weighted breast MRI images. It was compared with convolutional, transformer-based, and medical-imaging models under the same preprocessing, training, validation, and testing protocol. Performance was measured using accuracy, mean F1-score, precision, and recall where bootstrap resampling was used to estimate 95% confidence intervals.ResultsGRACE-ViT reached a mean accuracy of 95.50% (95% CI: 90.99-99.10%) and a mean F1-score of 95.47% (95% CI: 91.10-99.06%). It outperformed the strongest baseline with only a small increase in computational cost. The per-class results were balanced across the three response categories, suggesting that the model did not favor one clinical outcome over another. Visual results showed that the model mainly focused on clinically relevant breast regions rather than background areas.ConclusionsThese results show that targeted token recalibration can improve ViT-based breast MRI analysis without greatly increasing model complexity, leading GRACE-ViT to provide an efficient and interpretable imaging-based approach for predicting NAC response and supporting individualized treatment decisions in breast cancer care.","url":"https://pubmed.ncbi.nlm.nih.gov/42525959/","authors":["Abdelhalim I","Alghamdi NS","Sivakumar NR","Basheer S","Ali KM","Contractor S","El-Baz A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/15330338261474068","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525919","name":"Validation of a Text-Mining Tool for Extracting Routine Clinical Care Data in Early-Stage Resectable Non-Small Cell Lung Cancer.","source":"pubmed","abstract":"Manual chart review (MR) of electronic health records (EHRs) is time-consuming, error-prone, and limits the reproducibility and scalability of real-world data (RWD) research. Automation and standardization using natural language processing (NLP) could improve efficiency and scalability. CTcue is an NLP-based software platform designed to extract structured and unstructured data from EHRs. This study evaluated the accuracy and efficiency of CTcue versus MR in patients with early-stage resectable non-small cell lung cancer (NSCLC).","url":"https://pubmed.ncbi.nlm.nih.gov/42525919/","authors":["Abedian Kalkhoran H","Martinot T","Schonewille LHN","Huyuk M","Huigen MHJ","Guchelaar HJ","Cohen D","Smit EF","Zwaveling J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Sep","doi":"10.1200/CCI-25-00383","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525890","name":"Integrated Radioproteomic Modeling for Early Recurrence Prediction and Metabolic Characterization in Hepatocellular Carcinoma.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with high recurrence rates after surgical resection posing a significant challenge. While deep learning (DL) approaches show promise in predicting HCC recurrence, their clinical translation is limited by poor interpretability and unclear mechanisms. Our study aimed to develop an interpretable DL framework that predicts recurrence while elucidating underlying biology through integration of radiologic imaging and multiomics profiling.","url":"https://pubmed.ncbi.nlm.nih.gov/42525890/","authors":["Zhuang Q","Xu T","Xing X","Hu E","Zhou Y","Zheng X","Bai L","Huang Y","Liu X","Chen Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1200/PO-25-00745","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525870","name":"Performance of 5 Large Language Models in Perioperative Consultation for Pediatric Hypospadias: Cross-Sectional Comparative Study.","source":"pubmed","abstract":"Hypospadias is a common congenital malformation requiring surgery. Caregivers face substantial perioperative information needs, and large language models (LLMs) offer a potential health education channel, but their performance in pediatric urology and the relation between citation accuracy and clinical content safety lack systematic evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/42525870/","authors":["Kang T","Yuan C","Hu X","Huang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.2196/93393","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525862","name":"AI in the OR: Ethics and the Evolving Role of Surgeons.","source":"pubmed","abstract":"AI is rapidly entering the operating room, both as a clinical tool and as increasingly autonomous systems. In this News and Perspectives article, JMIR Correspondent and intensive care unit nurse Jenna Congdon reports on the ethical implications and evolving role of human surgeons.","url":"https://pubmed.ncbi.nlm.nih.gov/42525862/","authors":["Congdon J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.2196/107619","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525854","name":"Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.","source":"pubmed","abstract":"Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Cardiac MRI (CMR), computed tomography (CT), and echocardiography are integral to routine cardiovascular care, while bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics are providing increasingly detailed molecular characterization of cardiac tissue. Yet, these imaging and molecular data are still analysed in largely separate pipelines. This review examines joint representations that link cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states. An imaging-anchored perspective is adopted in which echocardiography, CMR, and CT define a spatial phenotype of the heart, and bulk, single-cell and spatial transcriptomics provide cell-type- and location-specific molecular context. We define the representation requirements of each modality, compare multimodal fusion strategies, and synthesize integrative pipelines for radiogenomics, spatial alignment, and image-based gene-expression prediction, together with their validation requirements, limitations, and failure modes. Spatial multiomic maps of human myocardium and atherosclerotic plaque, together with single-cell, spatial, and multimodal medical foundation models, are advancing imaging-anchored multiomics; however, cost, scalability, and tissue availability remain substantial barriers to large-scale cardiovascular translation.","url":"https://pubmed.ncbi.nlm.nih.gov/42525854/","authors":["Le MHN","Nguyen TH","Li T","Quang Gia Le B","Huynh HH","Raj M","Yang C","Xu M","Vinh T","Quoc Khanh Le N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.1093/bib/bbag365","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525827","name":"Methods used and lessons learnt: a post-graduate general practice curriculum redesign initiative from Ireland.","source":"pubmed","abstract":"Curriculum review is a core component of postgraduate medical education, with implications for trainees, educators, and healthcare systems. In 2021, the Irish College of General Practitioners assumed full responsibility for national GP training in Ireland, creating an opportunity for comprehensive curriculum redesign. The redesign reduced curricular complexity, enhanced usability, and strengthened constructive alignment. Digitizing the curriculum improved accessibility, enables real-time updates, and supports learner engagement through multimedia. A structured governance framework, including ongoing review and audit cycles, has been established.","url":"https://pubmed.ncbi.nlm.nih.gov/42525827/","authors":["Scully R","McCarthy P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1093/postmj/qgag102","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525797","name":"Boron-Doped Diamond Electrodes for Voltammetric Determination of Major Antibiotic Classes: Electrochemical Mechanisms, Intelligent Analytical Systems, and Critical Perspectives.","source":"pubmed","abstract":"This review sort of critically assesses how boron-doped diamond electrodes (BDDEs) are becoming more important for voltammetric determination of major antibiotic groups, like fluoroquinolones, tetracyclines, sulfonamides, $\\beta$-lactams and rifamycins. It points out that conventional chromatography still has limitations such as high costs, plus demanding and sometimes complicated sample preparation. Because of that, BDDEs are positioned as a faster, very sensitive option. Their wide potential windows, low background currents, excellent durability, and this noticeable anti-fouling tendency help them show better analytical results than typical electrode materials. When it comes to actually measuring trace levels, differential pulse voltammetry (DPV) and square-wave voltammetry (SWV) seem to be the most effective. Also, if you use nanostructured or otherwise modified BDDE surfaces, selectivity can improve in a meaningful way. Beyond sensing, BDDE-based platforms also look promising for joint applications such as detection and environmental electrooxidative remediation. Compared with earlier studies, this work is different because it ties together electrochemical mechanisms, voltammetric performance, and intelligent technologies. In the near future, combining artificial intelligence, chemometrics, portable setups, and automated systems should enable real-time, more sustainable antibiotic monitoring across pharmaceutical, clinical, and environmental matrices.","url":"https://pubmed.ncbi.nlm.nih.gov/42525797/","authors":["Barzani HAH","Omer RA","Salih Barzani KI","Omar Othman H","Noaman AS","Issa KD","Sulaiman SH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1080/10408347.2026.2708870","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525725","name":"Multilevel dynamics of the brain, hormones, mind, and behavior in social human-robot interaction.","source":"pubmed","abstract":"As robots enter homes, workplaces, and health care settings, sustaining trust during social interaction becomes a central challenge for human-robot interaction. However, relatively little is known about how humans integrate signals across the brain, hormones, mind, and behavior when robots violate expectations or display social expressiveness. Addressing this gap, we examined how robot performance (congruent versus erroneous) and expressiveness (animated versus stationary) shape multilevel human responses during face-to-face decision-making with an embodied humanoid robot. Participants engaged with the robot in person while neural activity was monitored using functional near-infrared spectroscopy, alongside salivary oxytocin assays, self-reported trust, and behavioral influence measures. Robot errors, implemented as cooperative norm violations, reliably reduced trust and influence, establishing performance reliability as the foundation of trust. Expressiveness amplified these effects: Animated robots elicited stronger prefrontal engagement and cross-level neural-hormonal coupling. Elevated oxytocin was most strongly linked to reduced trust during expressive robot errors, alongside diminished behavioral influence. This pattern is consistent with a context-sensitive vigilance response, indicating that oxytocin in human-robot interaction may heighten sensitivity to norm violations rather than reliably promote bonding. Validation analyses provided small, directionally consistent support for this pattern under counterbalanced order and improved temporal separation. Together, these findings establish a multilevel framework for studying trust in human-robot interaction and reveal a critical design trade-off: Expressive design enhances engagement but can make robot errors disproportionately damaging to trust. These insights identify a biologically grounded boundary condition for oxytocin's role in social interaction and inform the design of socially effective and trustworthy robots.","url":"https://pubmed.ncbi.nlm.nih.gov/42525725/","authors":["Topoglu Y","Krueger F","Joshi S","Rothstein N","Franke AA","Li X","Gratch J","de Visser EJ","Ayaz H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1126/scirobotics.aec1762","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525693","name":"Object detection in histology: A multi-dataset benchmark and test-time inference.","source":"pubmed","abstract":"Medical image analysis has become increasingly important for automated medical diagnosis, as well as deep learning. Specifically, object detection models may help in automatically identifying pathological structures and features. This study presents a comprehensive comparative analysis for object detection tasks in histological images of the latest models including the YOLO (You Only Look Once) architectures, from YOLOv8 to the recently introduced YOLOv12. These models were evaluated alongside alternative architectures including RT-DETR, YOLO-World, and YOLOE across five diverse histology datasets: BCNB, Nuclei, TNBC, MoNuSAC, and CryoNuSeg. The experimental analysis employed standardized training protocols with consistent hyperparameters and data augmentation strategies, evaluating the performance through multiple metrics, inference time, and computational cost. The results obtained on the five datasets indicate that YOLOv11 consistently showed a strong performance across multiple datasets, however the newly introduced attention mechanisms of YOLOv12 show good performance, despite the model having slightly lower overall performance. Specialized variants like YOLOE demonstrated promising results for specific applications, while RT-DETR showed poor performance on smaller objects, which are typical in histological images. Statistical analyses indicate that YOLOv11 indeed has the best performance but that all models have a poor performance on objects of small sizes; moreover, the most common cases of failure are background false positives and missed detections. This comprehensive evaluation provides insights for the current state of object detection architectures for clinical histopathology applications and establishes benchmarks for future avenues of research in automated medical image analysis. In addition to the multi-model benchmark, we propose Test-time Graph Similarity Propagation (TGSP), a test-time self-supervised refinement that uses ResNet50 deep features to build a k-NN similarity graph over detections and performs label propagation to re-score predicted boxes. TGSP replaces TSBP's iterative Earth-Mover matching with adaptive per-class quantile thresholds and graph-based label propagation, eliminating K-means hyperparameters and better scalability. Our analysis on histology datasets TGSP consistently matches or improves F1 relative to both a fixed 0.5 threshold and TSBP, with the biggest gains when base-model confidence calibration is poor.","url":"https://pubmed.ncbi.nlm.nih.gov/42525693/","authors":["Leordean DV","Ardelean ER"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354618","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525673","name":"A comparative analysis of readability, quality, and reliability in large language model outputs pertaining to knee osteoarthritis queries.","source":"pubmed","abstract":"This study aims to comparatively examine the readability, accuracy, and quality of responses provided by artificial intelligence (AI)-based chatbots such as Perplexity, ChatGPT-5, and Gemini to questions about knee osteoarthritis (KOA), which accounts for approximately four-fifths of the global osteoarthritis (OA) burden. In this study, 8 keywords were determined by excluding repetitive, irrelevant or synonymous ones from the 25 most frequently used English keywords associated with KOA based on Google Trends data, and these terms were asked as questions to three different artificial intelligence-based chatbots. The study measured readability using formulas like Coleman-Liau Index (CLI), Automated Readability Index (ARI), and Linsear Write (LW). Reliability of the information was assessed using the Journal of the American Medical Association (JAMA) benchmarks along with the modified DISCERN instrument. To determine overall content quality, the Global Quality Score (GQS) and the Ensuring Quality Information for Patients (EQIP) scale were applied. Together, these tools provided a comprehensive assessment of how understandable, reliable, and high-quality each chatbot's responses were. The most frequently searched keywords related to OA were \"osteoarthritis of knee,\" \"knee pain,\" and \"osteoarthritis knee pain.\" A readability analysis of responses from three different AI-based chat systems revealed that all platforms had text levels above the Grade 6 threshold, and this difference was statistically significant (p&#x2009;&lt;&#x2009;0.05). Comparisons demonstrated that ChatGPT-5 produced the most readable content (FRES:45, GFOG:11.9, FKGL:9.24, CLI:14.03, SMOG:8.37, ARI:11.37, LW:7.2). However, Perplexity achieved significantly higher scores than ChatGPT-5 across all quality and reliability assessments, yielding superior median scores (DISCERN: 4, JAMA: 2, GQS: 4, EQIP: 92.8). Perplexity also outperformed Gemini in the mDISCERN reliability assessment (p&#x2009;=&#x2009;0.001), while no significant difference in quality or reliability was found between Gemini and ChatGPT-5. No statistically significant difference was found between Gemini and ChatGPT in reliability and quality surveys. This analysis of KOA highlights significant challenges regarding the potential of popular AI chatbots for patient information. When examining readability levels, responses from these tools consistently exceed the recommended comprehensibility threshold, making it difficult for patients to absorb critical information. Furthermore, the relatively low scores recorded in reliability and content quality assessments raise significant concerns about the scientific validity and integrity of the medical information presented. Given these findings, the sufficient quality, robustness, and appropriate levels of understandability of future AI-based tools can only be ensured by the establishment and operation of an effective oversight mechanism.","url":"https://pubmed.ncbi.nlm.nih.gov/42525673/","authors":["Maraşlı E","Ozduran E","Hancı V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353355","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525422","name":"Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks.","source":"pubmed","abstract":"Working memory (WM) is thought to rely on sophisticated frontostriatal mechanisms for selective gating, supporting updating and readout of information to and from distinct memory \"addresses\" (neural populations). According to computational models, capacity limitations in WM arise due to challenges in \"role addressability\": learning to correctly bind items in memory to their respective roles and assigning credit to the corresponding gating operations. However, these conclusions are based on particular assumptions about biological neural networks, and it is unclear whether the principles generalize to other architectures. To address this question, we examine whether similar gating mechanisms emerge in Transformer neural network architectures, which have demonstrated success on tasks requiring executive function-the ability to represent, coordinate, and manage multiple subtasks-yet lack intentionally built-in gating mechanisms. We analyze the mechanisms that emerge within Transformers trained on human WM tasks explicitly designed to place demands on gating. We find that the Transformer's attention mechanism develops role-addressable input and output gating, but only when trained on task distributions that benefit from frontostriatal-like gating mechanisms. Moreover, these gating strategies support enhanced generalization and variable binding and increase the models' effective capacity to store and access multiple items in memory, resembling the constraints found in frontostriatal models. These results suggest that gating mechanisms serve a fundamental computational role in managing role addressability and binding and highlight opportunities for future research on computational similarities between modern artificial intelligence architectures and models of the human brain.","url":"https://pubmed.ncbi.nlm.nih.gov/42525422/","authors":["Soni A","Traylor A","Merullo J","Frank MJ","Pavlick E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1162/JOCN.a.2679","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525407","name":"Artificial Intelligence-Driven Video-Based Surgical Skill Assessment in Hepatobiliary Laparoscopic Surgery.","source":"pubmed","abstract":"This quality improvement study evaluates the use of a video-based artificial intelligence framework that uses multi-instrument tracking and clinically informed spatiotemporal kinematic features to provide objective, granular assessment of laparoscopic cholecystectomy skill.","url":"https://pubmed.ncbi.nlm.nih.gov/42525407/","authors":["Zeng X","Li Y","Wang J","Yang L","Li X","Wu P","You C","Luo W","Liu W","Jia F","Tao H","Yang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1001/jamasurg.2026.3007","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525364","name":"Compounding advantage: Medical AI and structural injustice.","source":"pubmed","abstract":"Artificial intelligence in clinical medicine is widely presented as a democratising advance, bringing the interpretive capacity of leading institutions to those that lack it. This paper argues that, under prevailing market governance, AI does not democratise but compounds: it operates as a self-amplifying mechanism that converts each increment of institutional advantage into the means of acquiring the next. The distinctive injustice this produces is not a snapshot maldistribution of a beneficial resource but a dynamic one - a ratchet. Standard distributive principles capture it only awkwardly. A Rawlsian analysis, and Daniels's extension of fair equality of opportunity to health care, can condemn any given unequal distribution, but they are framed to assess states of affairs rather than the self-reinforcing processes that generate them. Iris Marion Young's account of structural injustice is the more adequate lens, for the compounding mechanism has just the features her account describes: large-scale group disadvantage produced by the aggregation of individually reasonable actions, under accepted institutions, with no culpable author. The medical-AI case also sharpens her account, displaying a feature it does not emphasise - that the engine of the injustice is the distribution of a genuine good, so that the structure deepens disadvantage faster precisely as the technology improves. A second channel, in which tools are less reliable for under-represented populations because training data accumulate where infrastructure already concentrates, is shown to be the same mechanism operating on quality rather than access. Normative implications follow for the dynamics of diffusion, and professional obligation is grounded in Young's social-connection model of responsibility.","url":"https://pubmed.ncbi.nlm.nih.gov/42525364/","authors":["Nikolic B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s40592-026-00305-1","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525338","name":"Religious Concerns about the Integration of Artificial Intelligence into Clinical Medicine and Patient Care.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into the medical and spiritual care of the sick is expected to challenge a range of religious values and norms. Various religious authorities have expressed concerns about AI's integration, which include the questionable ability of AI to express genuine empathy, the risk of humans abandoning caretaking roles, the misuse of&#xa0;AI for religious rites, the attributing of spiritual and supernatural qualities to machines leading to idolatry, the undermining of provider responsibility and accountability, the dereliction of human intellect, the risk of bias and inappropriate proselytization, and threats to health equity. This manuscript identifies common concerns across major religious traditions, including Judaism, Christianity, Islam, Hinduism, and Buddhism. AI integration should align with values that promote human health and flourishing. Such values are often rooted in religious teaching, and understanding the reservations that religious communities have toward AI will help ensure such alignment.","url":"https://pubmed.ncbi.nlm.nih.gov/42525338/","authors":["Bao GC","Gabbay E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s10943-026-02745-9","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525286","name":"[Artificial intelligence in the care of geriatric patients-more of a curse or more of a blessing?].","source":"pubmed","abstract":"Artificial intelligence (AI) is conquering medicine in many fields. With geriatric patients, it is important not only to understand the decision tree of, for example, a&#xa0;tumor disease, but also to assess their functionality and functional deficits.","url":"https://pubmed.ncbi.nlm.nih.gov/42525286/","authors":["Wiedemann A","Manseck A","Stein J","Fröhner M","Fiebig C","Piotrowski A","Umbehr M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s00120-026-02890-9","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525231","name":"Agreement of AI-assisted semi-automated right ventricular function analysis using 3D transthoracic echocardiography with artificial intelligence.","source":"pubmed","abstract":"Advances in three-dimensional (3D) transthoracic echocardiography (TTE) with artificial intelligence (AI) enable AI-assisted semi-automated right ventricular (RV) function analysis, including two-dimensional (2D) measurements. However, data on the agreement of these semi-automated analyses in routine clinical practice remain limited. This study evaluates the agreement of AI-based 3D TTE semi-automated measurements compared to manual 2D TTE measurements.","url":"https://pubmed.ncbi.nlm.nih.gov/42525231/","authors":["Shiokawa N","Izumo M","Sato Y","Uenomachi N","Miyauchi M","Okamura T","Akashi YJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s12574-026-00749-8","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525113","name":"Author response to OSIN-D-26-01484: \"Clarifying statistical methods in a TriNetX analysis of statins and osteoporosis in CKD patients\".","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42525113/","authors":["Wu PH","Lin YT","Chen JC","Lin SY","Kao LT","Huang YT","Ho PS","Chen CH","Lee TC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s00198-026-08167-3","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42525086","name":"Intracranial pressure physiology, monitoring and individualized management in the acute brain injured patient.","source":"pubmed","abstract":"Acute brain injury (ABI), including traumatic brain injury, ischemic and hemorrhagic stroke, is associated with high morbidity and mortality, which is driven not only by the primary brain injury, but also by the development of secondary cerebral insults. Among these, raised intracranial pressure (ICP) plays a central pathophysiological role, acting both as a consequence and a driver of ongoing brain injury through mechanical deformation and cerebral ischemia. Although invasive intracranial pressure (ICP) monitoring has a longstanding and ongoing role in neurocritical care management, the interpretation and clinical use of ICP remain controversial. Traditional management strategies rely on fixed ICP thresholds (e.g.,&#x2009;&gt;&#x2009;22&#xa0;mmHg) to trigger a standardized stepwise escalation of therapy; however, growing clinical evidence indicates that tolerance to ICP elevation varies widely across patients, disease entities, and physiological contexts. This review summarizes the physiological determinants of ICP, including intracranial compliance, cerebrospinal fluid dynamics, cerebral blood volume, and systemic factors, and describes the mechanisms underlying intracranial hypertension. We discuss limitations of using fixed ICP thresholds and highlight emerging concepts, such as ICP burden, waveform morphology, cerebral autoregulation, and functional brain monitoring, as tools to individualize ICP interpretation. The role of invasive and noninvasive ICP monitoring (nICP) modalities is reviewed, emphasizing the complementary value of nICP in guiding decision-making when invasive monitoring is unavailable or contraindicated. Particular attention is given to the integration of ICP within multimodal neuromonitoring frameworks assessing cerebral perfusion, oxygenation, and metabolism. Finally, we explore future perspectives, including the potential of artificial intelligence-based approaches to analyse complex neuromonitoring data, predict secondary insults, and move toward actionable, patientspecific therapeutic strategies. Collectively, these advances support a shift from a uniform, threshold-driven approach toward individualized, physiology-informed management of intracranial hypertension.","url":"https://pubmed.ncbi.nlm.nih.gov/42525086/","authors":["Taccone FS","Arabi Y","Baggiani M","Brasil S","Figaji A","Gouvea Bogossian E","Hawryluk G","Mccredie V","Moller K","Puppo C","Robba C","Shrestha GS","Tokmak F","Udy A","Wong YL","Meyfroidt G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s00134-026-08558-4","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524783","name":"Managing Aortic Regurgitation and Dynamic Aortic Thrombus in a Patient With Left Ventricular Assist Device.","source":"pubmed","abstract":"Patients with end-stage heart failure undergoing durable left ventricular assist device (dLVAD) implantation are at risk of developing de novo or progressive aortic regurgitation (AR) during long-term support.","url":"https://pubmed.ncbi.nlm.nih.gov/42524783/","authors":["Stegmann A","Nagel A","Lanmüller P","Nersesian G","Lewin D","Falkensteiner C","Dreysse S","Unbehaun A","Starck C","Potapov EV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1016/j.jaccas.2026.109544","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524724","name":"Prediction Model for Mild Cognitive Impairment in Older Chinese Patients With Cerebral Small Vessel Disease Based on XGBoost Algorithms and Shapley Additive Explanations.","source":"pubmed","abstract":"This study aims to evaluate cognitive function in patients with Cerebral Small Vessel Disease (CSVD) and investigate its association with variables such as serum Insulin-like Growth Factor-1 (IGF-1). Artificial intelligence algorithms, specifically eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), were utilized for analysis and interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/42524724/","authors":["Gao P","Su J","Ma X","Ma X","Chen Y","Shen J","Wu Y","Shi C","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/cns.71044","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524711","name":"Anatomy-specific Performance of CT Angiography-based AI for Anterior Circulation Occlusion: Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Purpose To evaluate anatomy-specific diagnostic performance of CT angiography-based artificial intelligence (AI) for anterior circulation occlusion detection and negative-result implications for distal occlusions. Materials and Methods In this Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA)-compliant, prospectively registered systematic review and diagnostic meta-analysis, PubMed, Embase, and Web of Science were searched for AI-based CT angiography studies from January 1, 2019, through March 11, 2026. Pooled sensitivity and specificity were estimated for global large-vessel occlusions (LVOs), combined internal carotid artery/first segment of the middle cerebral artery (ICA/M1) occlusions, and distal second/third segments of the middle cerebral artery (M2/M3) occlusions with a bivariate random-effects model. Evidence certainty was assessed with GRADE; a supportive record-level multilevel bivariate generalized linear mixed model (GLMM) adjusted for occlusion territory, study design, algorithm type, publication year, and section thickness. Results Thirty-one reports involving 15,708 patients were included. Pooled sensitivity/specificity were 80.1%/91.9% for global LVOs, 91.6%/92.7% for ICA/M1 occlusions, and 52.7%/94.8% for M2/M3 occlusions. In the supportive adjusted model, ICA/M1 occlusions had higher sensitivity than global LVOs (OR, 2.28; P &lt; .001), whereas M2/M3 occlusions had lower sensitivity (OR, 0.25; P &lt; .001). Evidence certainty was moderate for ICA/M1 sensitivity and very low for M2/M3 sensitivity. For M2/M3 occlusions, a negative AI result yielded an LR- of 0.50 (95% CI, 0.28-0.73), corresponding to posttest probabilities of 17.6% and 33.3% at 30% and 50% pretest probabilities. Conclusion CT angiography-based AI performance varied by occlusion territory; lower M2/M3 sensitivity limited negative-result reliability. &#xa9;RSNA, 2026.","url":"https://pubmed.ncbi.nlm.nih.gov/42524711/","authors":["Liu Y","Li C","Ren Z","Hu Y","Wang C","Chen C","Liu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1148/ryai.260261","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524619","name":"A Low-Cost, Easily Implementable Model for Multiskill Hands-On Training in Otolaryngology Education.","source":"pubmed","abstract":"Early exposure to otolaryngology-head and neck surgery is limited in undergraduate medical education, and existing hands-on training programs are often resource-intensive or focused on single procedures, restricting their broader implementation. This study aimed to develop a low-cost, easily implementable, and reproducible multiskill hands-on training model in otolaryngology and to evaluate its educational validity.","url":"https://pubmed.ncbi.nlm.nih.gov/42524619/","authors":["Inoue T","Sakaue S","Ominato H","Wakisaka R","Kono M","Yamaki H","Ohara K","Kumai T","Takahara M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/lio2.70517","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524542","name":"Saudi Medical Students' Perceptions and Attitudes of Integrating Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Study.","source":"pubmed","abstract":"Generative Artificial Intelligence (GenAI) has catalyzed a transformation in medical education. Understanding learners' perceptions is essential to guide their responsible integration into curricula.","url":"https://pubmed.ncbi.nlm.nih.gov/42524542/","authors":["Aljamaan F","Mubarak MF","Altamimi I","Alanteet AA","Alsalman MA","Dasuqi SA","Alballaa R","Alarifi MI","Saadon AA","Alhaqbani AO","Alhadlaq AA","Alokayli SH","Alrasheed BN","Alkhalife SI","Sattar K","Jamal A","Soliman M","Saad K","Temsah MH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/hsr2.72904","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524460","name":"Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.","source":"pubmed","abstract":"Progress in artificial intelligence (AI)-based analysis of surgical videos has been constrained by reliance on manual frame-level annotations rather than patient-level outcomes. In addition, concerns about data privacy restrict the exchange of laparoscopic video data and, thereby, multicenter collaboration.","url":"https://pubmed.ncbi.nlm.nih.gov/42524460/","authors":["Saldanha OL","Pfeiffer K","Bodenstedt S","Kirchner M","Jenke AC","Barata C","Barbosa S","Barthel J","Carstens M","Castro LT","Dehlke K","Dietz S","Emmanouilidis S","Fitze G","Holderried F","Kanjo W","Leitermann L","Mees ST","Soares AS","Pascoal M","Pistorius S","Prudlo C","Schultz J","Seiberth A","Thiel K","Wu X","Ziehn D","Speidel S","Weitz J","Distler M","Kather JN","Kolbinger FR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1056/aioa2501116","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524332","name":"Risk Management of Large Language Model-Based Exercise and Health Guidance: A China-Anchored, Comparatively Informed Six-Dimensional Trigger Matrix and Lifecycle Governance Framework for the Wellness-to-SaMD Continuum.","source":"pubmed","abstract":"LLM-based exercise and health guidance creates a distinctive risk-management challenge: seemingly modest changes in product claims, target users, data inputs, personalization, automation, human oversight, or updates may move a tool from general wellness support toward higher-risk medical use. In exercise prescription and rehabilitation, an unsafe recommendation can affect physical load, recognition of warning symptoms, and timely referral. We conducted a structured narrative review and doctrinal/comparative legal analysis, anchored in China's National Medical Products Administration (NMPA) framework and informed by the European Union Medical Device Regulation (MDR), the EU Artificial Intelligence Act, and US Food and Drug Administration (FDA) and International Medical Device Regulators Forum (IMDRF) materials. Peer-reviewed literature was primarily searched for 2019-2026, with foundational regulatory, legal, and technical guidance included where directly relevant. We propose a six-dimensional trigger matrix covering intended use and claims, user context, depth of personalization, data and sensor sources, automation, human oversight and closed-loop control, and upgrade and change pathways. The framework includes anchored Green/Yellow/Red coding rules, non-compensatory aggregation rules, a structured governance checklist, and a lifecycle pathway for evidence generation, risk management, and change control. In a preliminary application exercise, three independent raters applied the coding rules to seven standardized hypothetical scenarios and achieved complete agreement on all dimension-level and overall designations (Fleiss' kappa = 1.00). This small exercise supports initial reproducibility of the rubric but does not establish legal classification accuracy, clinical validity, or real-world effectiveness. The proposed framework is intended to support earlier risk identification, evidence planning, procurement review, and dialogue among developers, healthcare institutions, and regulators; it does not replace product-specific legal analysis or regulatory determination.","url":"https://pubmed.ncbi.nlm.nih.gov/42524332/","authors":["Pan K","Lin X","Huang S","Huang C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/RMHP.S624615","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524305","name":"A practical guide to the implementation of AI in orthopaedic research-Part 4: Prerequisites for a successful orthopedics AI-driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology.","source":"pubmed","abstract":"Translating artificial intelligence (AI) research in orthopedics from proof-of-concept studies into production-grade clinical systems requires the systematic satisfaction of four prerequisite domains: interdisciplinary team architecture, technical data management, ethical and regulatory governance and production-grade technology and deployment infrastructure. Despite a tenfold increase in orthopedic AI publications, fewer than 6% of studies reach routine clinical deployment, reflecting persistent gaps in each of these domains. This article provides a technically rigorous, evidence-based framework organized around these four pillars. The interdisciplinary team may be structured using a product-centric topology that decouples stream-aligned clinical teams from platform infrastructure teams, following Huffman et al.'s six-step AI project lifecycle: obtain/curate/label data; establish a reference standard; develop the model; evaluate performance; externally validate and iteratively reinforce until clinical implementation is viable. Data management requires data extraction protocols, integration for bulk exports and a multi-component de-identification pipeline. A multi-stage Institutional Review Board framework governs ethical oversight, scaling from Exempt review for retrospective de-identified studies to Full Board Review with prospective validation and mandatory human-override mechanisms for interventional deployment. Responsible clinical deployment requires a multi-layer Clinical Machine Learning Operations framework, implementing privacy-preserving deployment, clinical observability, compliance audit trails and human-in-the-loop governance. Model drift has to be monitored with a degradation threshold triggering mandatory human review.","url":"https://pubmed.ncbi.nlm.nih.gov/42524305/","authors":["Longo UG","Merone M","Schena E","Bandini B","Nicodemi G","Zsidai B","Hilkert AS","Senorski EH","Grassi A","Ley C","Herbst E","Hirschmann MT","Kopf S","Seil R","Tischer T","Feldt R","Samuelsson K","Oettl FC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/jeo2.70863","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524275","name":"Integration of Transformer-Based Architecture and Large Language Models for Optical Coherence Tomography Data Analysis to Improve the Accuracy of Differential Diagnosis of Retinal Diseases.","source":"pubmed","abstract":"The aim of the present study was to improve the differential diagnosis accuracy of retinal diseases combining a transformer model to classify the biomarkers on OCT images and the large language model DeepSeek-V3.","url":"https://pubmed.ncbi.nlm.nih.gov/42524275/","authors":["Konshina OV","Pershin AD","Kulyabin MK","Borisov VI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17691/stm2026.18.3.01","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.005Z"},{"id":"pmid:42524236","name":"Influence of Functional Magnetic Resonance Imaging Data Preprocessing Pipelines on the Accuracy of Schizophrenia Classification Using Machine Learning Methods.","source":"pubmed","abstract":"The aim of the study was to analyze the influence of various pipelines for preprocessing raw functional magnetic resonance imaging (fMRI) data on the accuracy of classification of subjects into schizophrenia patients and healthy controls using machine learning methods, and to give recommendations for optimizing the data preprocessing pipeline for this task.","url":"https://pubmed.ncbi.nlm.nih.gov/42524236/","authors":["Poyda AA","Orlov VA","Zhemchuzhnikov AD","Kozlov SO","Kartashov SI","Bravve LV","Kaydan MA","Kostyuk GP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17691/stm2026.18.3.02","addedAt":"2026-09-01T01:47:59.005Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42524231","name":"Context-dependent immune regulation: a mechanistic and AI-enabled integrative framework.","source":"pubmed","abstract":"The immune system functions as a dynamic, multiscale, and context-sensitive network whose outputs depend on cellular composition, spatial organization, metabolic state, host background, and temporal trajectory. These properties have long been investigated through systems immunology, mathematical modeling, and immune simulation. This Perspective uses context dependence as an organizing principle to connect these foundations with recent artificial intelligence (AI) methods, and develops a context-centered view in which immune context is treated as an explicit, partially measurable, and partially uncertain state that can be encoded, constrained, and prospectively tested. It summarizes representative phenomena across cancer, infection, innate immunity, autoimmunity, and genetic variation; describes their microenvironmental, metabolic, systemic, and temporal determinants; and distinguishes the capabilities of mechanistic, data-driven, symbolic, and hybrid models. The proposed hybrid AI-mechanistic framework positions AI as a tool for context representation and multimodal integration, while mechanistic and knowledge-based components preserve explicit dynamics, biological constraints, and testable interventions. Practical strategies are discussed for limited biomaterial, partially longitudinal animal studies, missing or unpaired modalities, interpretability, and prospective validation. The framework does not require every modality in every sample; instead, it emphasizes minimum context annotation, uncertainty-aware integration, explicit assumptions, and benchmarking against simpler models.","url":"https://pubmed.ncbi.nlm.nih.gov/42524231/","authors":["Chen Q","Zhu Y","He Y","Wang Y","Xu Q","Wang Z","Hu F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1850490","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42524120","name":"Agentic Artificial Intelligence in Eye Banking: A Proposed Workflow.","source":"pubmed","abstract":"To develop an agentic artificial intelligence (AI) framework that streamlines and standardizes eye bank operations by automating donor screening, image analysis, and tissue suitability assessment under expert supervision.","url":"https://pubmed.ncbi.nlm.nih.gov/42524120/","authors":["Karmakar R","Kiros EK","Nath S","Keane PA","Kumar K","Lohmeier J","Sikder S","Meinecke E","Hanna C","Eghrari AO"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.1097/ebct.0000000000000054","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42524018","name":"The role of Epstein-Barr virus in NK/T cell lymphoproliferative disorders: molecular mechanisms and potential therapeutic strategies.","source":"pubmed","abstract":"Epstein-Barr virus (EBV) is a widely prevalent lymphotropic &#x3b3;-herpesvirus, with approximately 95% of the population showing evidence of infection at some point during their lifetime. While most infections are asymptomatic or follow a self-limiting clinical course, in certain populations, EBV can lead to a range of lymphoproliferative disorders (LPDs), particularly subtypes originating from T cells and natural killer (NK) cells, which are often characterized by highly aggressive disease progression. This review aims to systematically discuss the molecular basis of EBV infection, covering its viral biological properties, regulation of the latent and lytic cycles, key viral protein functions (e.g., LMP1, LMP2A, EBNA1), miRNA regulatory mechanisms, and the activation of various host signaling pathways (such as NF-&#x3ba;B, PI3K-AKT, JAK-STAT) that contribute to the maintenance of latent infection, cell transformation, and immune evasion. Additionally, the review focuses on the pathogenic contributions of these mechanisms in EBV-related T/NK cell lymphoproliferative diseases. Research highlights include the in-depth analysis of virus-host genome interaction mechanisms, the identification of novel molecular biomarkers, and the development of targeted therapeutic strategies (e.g., PD-1/PD-L1 immune checkpoint inhibitors, EBV-specific T cell therapy). Through this comprehensive review, it is hoped that personalized medicine and artificial intelligence-assisted multimodal decision-making will be applied to the precise prevention and treatment of EBV-related diseases.","url":"https://pubmed.ncbi.nlm.nih.gov/42524018/","authors":["Fan M","Kou H","Tang LV"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1826581","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42524013","name":"From serum inflammatory markers to fluid, tissue, and molecular assays: current advances in the laboratory diagnosis of bone and joint infections.","source":"pubmed","abstract":"Bone and joint infections (BJIs), including periprosthetic joint infection (PJI), fracture-related infection (FRI), and osteomyelitis, present persistent diagnostic challenges driven by biofilm formation and a high incidence of culture-negative cases. Traditional diagnostic modalities relying on peripheral serum markers and conventional cultures are often limited by insufficient specificity or prolonged turnaround times. This narrative review critically evaluates recent advances in laboratory diagnosis for bone and joint infections, with particular attention to disease-specific applicability across periprosthetic joint infection, fracture-related infection, native vertebral osteomyelitis, diabetic foot osteomyelitis, and other osteomyelitis-related conditions. Current evidence indicates that while traditional serum inflammatory markers are valuable for initial screening, their susceptibility to aseptic inflammatory confounders precludes standalone diagnostic confirmation. In contrast, localized sampling demonstrates significant superiority: novel synovial fluid biomarkers, notably calprotectin and alpha-defensin, accurately reflect the infection microenvironment and offer exceptional diagnostic specificity. At the tissue level, the integration of multiple deep-tissue sampling with preprocessing techniques like sonication has substantially enhanced the recovery of occult biofilm-encased pathogens. Furthermore, targeted and untargeted molecular assays, including multiplex PCR panels, broad-range bacterial PCR, amplicon-based sequencing, and untargeted shotgun metagenomic sequencing, have expanded the diagnostic toolkit for culture-negative, low-virulence, and polymicrobial infections. The diagnostic framework for BJIs has decisively shifted from the pursuit of a solitary \"silver bullet\" marker toward multimodal, culture-independent assay panels and artificial intelligence-assisted risk stratification algorithms. Future clinical breakthroughs will depend heavily on the global standardization of disease definitions, robust external validation of predictive models, and the seamless integration of advanced laboratory techniques into multidisciplinary team (MDT) workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42524013/","authors":["Li J","Lian S","Liu Y","Yang X","Liu D","Chen J","Xiong H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcimb.2026.1865643","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523996","name":"Descriptive analysis of prescription interception patterns: characterizing medication safety risks in an outpatient setting.","source":"pubmed","abstract":"Prescription errors remain a significant challenge to medication safety in hospital settings. Forced Interception (FI) systems, which automatically flag and block potentially problematic prescriptions, serve as critical safeguards against adverse drug events. However, the specific characteristics and underlying causes of intercepted prescriptions, particularly in Chinese hospital contexts, require further investigation to inform targeted quality improvement strategies. This study aimed to analyze the characteristics and interception reasons of FI prescriptions in a hospital setting, with the goal of identifying patterns that could guide system upgrades, clinical training, and policy interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42523996/","authors":["Li N","Tang W","Li N","Gong C","Wei Q","Gong J","Zhai J","Zhang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphar.2026.1826012","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523935","name":"Interpretable and explainable artificial intelligence for wearable sensor-based fall risk assessment in older adults: a systematic review with considerations for prosthetics and orthotics.","source":"pubmed","abstract":"Falls among older adults are a leading cause of morbidity and loss of independence. Wearable sensors combined with machine learning (ML) offer opportunities for objective fall risk evaluation, but low model transparency limits clinical adoption. Interpretable and explainable artificial intelligence (XAI) methods can address this constraint, yet their application in wearable sensor-based fall risk assessment has not been systematically examined.","url":"https://pubmed.ncbi.nlm.nih.gov/42523935/","authors":["Samdani AH","Khan SJ","Farhan M","Yusuf KQ","Bari AZ","Siddiqui MF","Nasir U","Ahmed N","Alqahtani SA","Alshahrani YMS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fncom.2026.1860978","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523880","name":"Evidence integration: The transformative role of artificial intelligence in maternal health.","source":"pubmed","abstract":"Global maternal health outcomes remain inequitable, particularly in low- and middle-income countries, due to persistent gaps in access, care continuity, and postpartum follow-up. Digital health tools, especially mobile health and artificial intelligence, offer promising avenues to enhance education, monitoring, risk stratification, and clinical decision-support.","url":"https://pubmed.ncbi.nlm.nih.gov/42523880/","authors":["Baniasadi M","Radfar A","Najafi K","Aftabi H","Khorshidi N","Nezamnia M","Khajehpoor M","Behzadi A","Amini E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261473727","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523813","name":"LLM-as-a-judge for infection prevention and control and antimicrobial resistance impact: comparing three main LLMs vs. human experts' assessment.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks.","url":"https://pubmed.ncbi.nlm.nih.gov/42523813/","authors":["Di Pumpo M","Villani L","Gualano MR","Buonsenso D","Raffaelli F","Donà D","Laurenti P","Maio V","Boccia S","Ricciardi W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1874389","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523804","name":"The trends of retinoblastoma research in the past decade.","source":"pubmed","abstract":"To conduct a bibliometric analysis of retinoblastoma (RB) research to delineate global research trends, identify key research contributors, and highlight emerging research directions in this field.","url":"https://pubmed.ncbi.nlm.nih.gov/42523804/","authors":["Yang YH","Zhou WD","Zhao HQ","Zhang RH","Dong L","Shao L","Wei WB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.18240/ijo.2026.08.19","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523801","name":"AI literacy in undergraduate medical education: a competency-based interpretive framework for curriculum and assessment.","source":"pubmed","abstract":"Artificial intelligence (AI) is becoming a core educational concern in undergraduate medical education as AI-enabled tools increasingly shape clinical workflows, learning environments, and patient care. The challenge is no longer simply whether AI should be included in the curriculum, but how AI literacy should be bounded for undergraduate learners and translated into teachable, observable, and assessable educational outcomes. This focused conceptual narrative review synthesized literature on AI literacy and related constructs in undergraduate medical education, using a structured search and interpretive synthesis with competency-based medical education (CBME) as an interpretive lens. PubMed and ERIC were searched for English-language literature from 1 January 2020 to 15 April 2026. Local screening records identified 94 standardized bibliography records, 66 records screened after deduplication, 40 full-text reports assessed, and 30 publications contributing to the final synthesis. Five recurring domains were identified: Foundational AI knowledge; applied clinical interpretation and use; data literacy and critical appraisal; ethics, law, and professional responsibility; and human-AI collaboration and professional formation. Through a CBME lens, these domains can be translated into learning outcomes, contextualized tasks, observable performances, and programmatic assessment evidence. The literature most strongly supports conceptual clarification, domain identification, and curricular translation, whereas evidence for longitudinal development, observable performance, and validated undergraduate assessment remains limited. The proposed framework, examples, milestones, and rubric anchors are synthesis-informed design propositions that require empirical validation before high-stakes use.","url":"https://pubmed.ncbi.nlm.nih.gov/42523801/","authors":["Fu C","Li J","Fan H","Ren R","Cui K","Zhang Y","Yu J","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1871524","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523781","name":"Nurses' AI training acceptance and tool usage: a structural equation model of individual and hospital-level factors.","source":"pubmed","abstract":"With the rapid advancement of artificial intelligence (AI), it has exerted a profound influence on the medical field. Currently, AI applications in nursing remain nascent in China. This study aimed to investigate nurses' attitudes and anxiety levels toward AI in general hospitals in western China, and to analyze the association of these psychological factors with their AI training acceptance and clinical AI tool usage behavior.","url":"https://pubmed.ncbi.nlm.nih.gov/42523781/","authors":["Guo L","Ding Q","Tang M","Wei S","Chen L","Cao X","Zhang X","Zheng Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1864635","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523766","name":"Trauma behind the algorithm: a phenomenological analysis of violent content data work.","source":"pubmed","abstract":"One of the promises of artificial intelligence (AI) is that it can automate many roles currently performed by humans. However, in the ethics of AI, increased attention is given to the vast amount of labour required for the development, processing, annotation, and implementation of AI. This hidden employment (or 'ghost work') is invisible because of the precarious nature of these jobs, poor working conditions, and the social and economic burdens on employees. For example, to ensure AI does not create undesirable content and that social media platforms do not host it, human data annotators and content moderators (violent content data workers-VCDWs) must read, watch, and listen to content containing torture, rape, bestiality, child sexual abuse, and murder. Through phenomenological analysis, this paper demonstrates that this work has deeply traumatic effects on the affectivity, embodiment, and intersubjectivity of VCDWs. While several other professions (e.g., law enforcement officers, war journalists, and medical professionals) must view violent content, the duration, velocity, and accumulation of exposure to violent images in VCDWs is unprecedented. The outcome of this is trauma from being unable to decipher the ontological status of the content, compassion fatigue due to helplessness to help those suffering, and a myriad of physical, psychological, and interrelation problems for VCDWs. This paper exposes a profession that is systematically concealed, buried under NDAs, outsourced to the Global South, and laundered through ethics-washing and 'responsible AI' branding.","url":"https://pubmed.ncbi.nlm.nih.gov/42523766/","authors":["Ryan M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s43681-026-01282-1","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523730","name":"Analysis of latent profiles and influencing factors of artificial intelligence anxiety among newly recruited nurses: a cross-sectional study.","source":"pubmed","abstract":"To investigate the current status of artificial intelligence (AI) anxiety among newly recruited nurses, explore its latent categories and characteristics, and analyze related influencing factors, thereby providing a scientific basis for promoting the acceptance of AI technology among newly recruited nurses and enhancing the nursing workforce's adaptability to AI applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42523730/","authors":["Ma X","Wang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1903509","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523715","name":"From Intensive Care Unit Prediction to Intensive Care Unit Accountability: A Global Readiness Passport for Machine-learning Prognostication.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42523715/","authors":["Vijayasimha M","Srikanth M","Kaur R","Thukral B","Malini KP","Kaur S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.5005/jp-journals-10071-25224","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523704","name":"AI in transplantation: levelling the playing field and raising the methodological standards.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42523704/","authors":["Pilat N","Bellini MI","Berney T","Aubert O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/ti.2026.17015","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523658","name":"A hybrid ViT-L/32-MaxViT-L architecture with adaptive gated fusion for multiclass gastrointestinal disease detection and multi-method post-hoc explainability.","source":"pubmed","abstract":"Accurate detection of gastrointestinal diseases from endoscopic images remains challenging due to substantial inter-class similarity, intra-class variability, and heterogeneous lesion presentation. While convolutional neural networks (CNNs) effectively capture fine mucosal textures, their limited receptive field restricts global contextual reasoning. Conversely, transformer architectures model long-range dependencies but may underrepresent localized structural detail.","url":"https://pubmed.ncbi.nlm.nih.gov/42523658/","authors":["Ganie SM","Dutta Pramanik PK","Zhao Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1869015","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523639","name":"Stage-specific machine learning prediction of cumulative live birth in women with diminished ovarian reserve.","source":"pubmed","abstract":"Women with diminished ovarian reserve (DOR) experience cumulative live birth (cLBR) rates below 30% following embryo transfer, yet existing prediction tools rely on static baseline parameters and lack interpretability, limiting their clinical utility for counseling about long-term treatment success.","url":"https://pubmed.ncbi.nlm.nih.gov/42523639/","authors":["Liu L","Liu B","Huang Q","Qin L","Jiang L","Wu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1832301","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523610","name":"Interpretable machine learning-driven multi-omics risk stratification and drug repurposing nominates Treg/Th17 with gluconeogenesis/lactylation integration as a prognostic and druggable biomarker for glioblastoma patients.","source":"pubmed","abstract":"Dysregulation of Treg/Th17 balance and gluconeogenesis/lactylation contributes to glioblastoma (GBM) progression. Hence, it is essential for gaining insights into their mechanisms in GBM.","url":"https://pubmed.ncbi.nlm.nih.gov/42523610/","authors":["Xie S","Chen W","Zhang B","Weng S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1761182","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523586","name":"Beyond static biomarkers: systems biology and AI for decoding cancer dynamics.","source":"pubmed","abstract":"Precision oncology has been built largely on static biomarkers, including mutational profiles, receptor status, histopathologic classes, and single-time-point molecular signatures. These readouts have transformed diagnosis and treatment selection, but they remain mismatched to a disease whose most consequential behaviors-progression, metastasis, treatment adaptation, dormancy, and relapse-are dynamic, noisy, and multiscale. In this article, we argue that cancer is better understood as a stochastic, coupled biological system than as a fixed molecular identity. We bring together adjacent literatures spanning cancer cell states, spatial and ecological organization, metabolism and dormancy, mechanobiology, longitudinal biomarkers, quantitative oncology, and AI-enabled representation learning, and develop a mathematically grounded framework for reasoning about cancer dynamics. Our central claim is modest but consequential: future biomarkers should not only classify current disease state, but also estimate transition risk, system instability, and trajectory direction. To support that claim, we distinguish latent biological state from clinical observation, clarify why partial observability makes dynamic inference difficult, and introduce a mathematically explicit but deliberately constrained formal scaffold based on stochastic state-space models, local linearization, and layer-specific dynamical motifs. We then develop six internal biological layers of cancer dynamics-molecular regulatory dynamics, cellular state plasticity, spatial niche organization, tumor ecosystem co-evolution, metabolic-epigenetic coupling with dormancy, and mechanobiological feedback-and treat longitudinal clinical monitoring as a linked observation layer rather than a mechanistic subsystem. Particular attention is given to noise; transcriptional noise, ecological variability, treatment-induced perturbation, and measurement noise all shape how cancer states are occupied, destabilized, and detected. Finally, we review dynamic biomarker evidence from ctDNA-guided adjuvant therapy, circulating tumor cells, serial imaging, and adaptive therapy, discuss how AI can support inference under partial observability without replacing mechanism, examine regulatory and health-equity constraints, and outline the research agenda needed to turn dynamic oncology from a compelling idea into a reproducible clinical discipline.","url":"https://pubmed.ncbi.nlm.nih.gov/42523586/","authors":["Dutta S","Goli A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fsysb.2026.1855016","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523553","name":"Large-Scale Multi-Cancer Detection by Learning Segmentation from Reports.","source":"pubmed","abstract":"More than 300 million computed tomography (CT) scans are performed worldwide each year, yet many early or incidental tumors in these scans remain undetected. Artificial intelligence (AI) could help: segmentation models can surpass radiologists and alternative AI models in detecting tumors, and they localize the tumors for radiologist verification. However, segmentation-based tumor detection has long been limited by the need for tumor masks: radiologist-drawn tumor outlines that are scarce, expensive, and entirely unavailable for many cancer types. In contrast, nearly every CT scan is accompanied by a radiology report with detailed tumor descriptions. Yet, these reports have not been used effectively to train tumor segmentation models. Here, we introduce R-Super, a framework that converts routine radiology and pathology reports into localized training signals for tumor segmentation. R-Super trains AI to segment tumors that match their descriptions in reports. Reports are only needed for training, not inference. We trained R-Super on 127,496 CT-Report pairs (42 million 2D images, USA) and evaluated it internally at UCSF ( N = 2,301 , USA) and externally at Stanford ( N = 1,976 , USA), Medipol ( N = 1,327 , Turkey), and Basel ( N = 2,935 , Switzerland). R-Super detects 7 tumor types for which public tumor masks are scarce or absent: spleen, gallbladder, prostate, bladder, uterus, esophagus, and adrenal tumors. Training R-Super on over 100,000 reports (no mask) outperformed mask-based segmentation models trained on 870 masks, demonstrating that large-scale report-based training surpasses smaller-scale mask-based training. Alternatively, by training R-Super on both these reports and masks together, cancer detection sensitivity increased by over +11% beyond mask-only training, and DSC by +14%. R-Super significantly surpassed 6 alternative training frameworks trained on the same dataset and 9 leading public AI models. R-Super significantly surpassed six radiologists in detecting six tumor types and matched them for uterus tumors in a reader study. On average, R-Super detected 56% more malignant tumors than the radiologists, at matched false positive rate. These results show that radiology reports are not merely clinical documentation, but a large-scale, underutilized source of localized supervision for training more accurate cancer detection AI. By effectively learning from reports, R-Super enables tumor segmentation models to scale beyond scarce radiologist-drawn tumor masks and advances automated and incidental cancer detection closer to clinical deployment. We release code, over 22,000 CT scans and reports, and the first public AI model to reach or surpass radiologist performance in detecting these tumor types on CT.","url":"https://pubmed.ncbi.nlm.nih.gov/42523553/","authors":["Bassi PRAS","Zhou X","Li W","Płotka S","Wasserthal J","Chen J","Hamamci IE","Er S","Prządo J","Zhu Z","Durak G","Romańczyk W","Taktak YB","Akan M","Wang Y","Ye S","Chen Q","Kumar A","Wu L","Zhang G","Menze B","Ćwikła JB","Zhou Y","Miller FH","Gao Y","Chaudhari AS","Langlotz CP","Bagci U","Decherchi S","Cavalli A","Sitek A","Wang K","Yang Y","Yuille AL","Zhou Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.21203/rs.3.rs-10131590/v1","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523491","name":"A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.","source":"pubmed","abstract":"Neuroscience-inspired neural networks provide a promising framework for bridging biological principles and adaptive artificial intelligence systems. Here, we propose a novel synchronization-based synaptic learning rule for self-organizing probabilistic spiking neural networks (PSNNs) with feedback inhibition. In the proposed model, synaptic plasticity is regulated by the temporal synchronization of presynaptic spike activity of single neurons, enabling unsupervised adaptation of synaptic weights and network connectivity. We systematically investigated how feedback inhibition influences network dynamics, stability, synchronization, and pattern separation efficacy. The results revealed that moderate inhibition produces an optimal balance between excitatory and inhibitory activity, maximizing pattern separation while preventing both excessive excitation and over-suppression of network activity. Comparative analysis further demonstrated that the proposed synchronization-based learning mechanism outperforms conventional Hebbian learning in achieving efficient and stable pattern separation in this neural network. Finally, the trained network was embedded in a simulated autonomous agent navigating a two-dimensional environment, where it successfully identified and avoided a learned obstacle pattern. These findings highlight the critical role of inhibitory regulation and synchronization-driven plasticity in self-organizing spiking systems and support the potential application of biologically inspired learning mechanisms in computational neuroscience, neuromorphic computing, and cognitive robotics.","url":"https://pubmed.ncbi.nlm.nih.gov/42523491/","authors":["Faghihi F","Moustafa A","Neymotin S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.21203/rs.3.rs-9917769/v1","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523467","name":"Exploring Attitudes of Primary Caregivers Towards Pediatric Tissue-Based Research using Large Language Models: Insights from Rural and Urban Community Calls and Surveys.","source":"pubmed","abstract":"Explore the perspectives of primary caregivers towards pediatric tissue-based research participation.","url":"https://pubmed.ncbi.nlm.nih.gov/42523467/","authors":["Chotani A","Moradinasab N","Sullivan BH","Griffin-Scudari L","Cohen J","Rhoads SF","Meyer C","Setiady I","Weinhouse A","Dumont M","Greene AR","Thiagarajah JR","Silvester J","Glover SC","Syed S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.64898/2026.07.08.26357557","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523456","name":"The topology of adolescent mental health.","source":"pubmed","abstract":"The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.","url":"https://pubmed.ncbi.nlm.nih.gov/42523456/","authors":["Jelen MB","Mousley A","Fakhar K","Trachtenberg E","He Y","Kohler R","Aggarwal S","Warrier V","Bzdok D","Yip SW","Astle DE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.64898/2026.07.13.26357465","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523250","name":"Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis.","source":"pubmed","abstract":"Multi-omics and spatial-omics technologies are exploding in use, producing increasingly complex datasets. Existing bioinformatics tools are developing rapidly but fail to fully enforce the FAIR principles, leaving the field vulnerable to escalating issues in computational reproducibility. Here, we introduce a reproducible-by-design paradigm represented in an omics data processing package, RomicsProcessor. At its core, the \"Romics_object\", which is a self-contained digital artifact that encapsulates the full history of the data from the original data to the fully processed state, capturing the details of the transformative steps and the required dependencies. This architecture ensures that computational workflows are fully portable and reproducible. In this manuscript, we demonstrate RomicProcessor's computational capabilities and scalability on diverse datasets, including bulk proteomics, large-scale multiplexed immunofluorescence, and multi-batch mass spectrometry imaging. Providing a robust framework for truly FAIR Data Principles-based analysis, RomicsProcessor is a blueprint for the next generation of reproducible bioinformatics tools that can dramatically accelerate discovery in multi-omics biology in the era of artificial intelligence.","url":"https://pubmed.ncbi.nlm.nih.gov/42523250/","authors":["Gorman BL","Bhotika H","Jehrio M","Purkerson JM","Carlin F","Nakayasu E","Dylag AM","Misra RS","Adkins J","Anderton CR","Pryhuber G","Clair G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.64898/2026.07.09.737600","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42523221","name":"Transplant-Agents: A Multi-Agent Artificial Intelligence Framework for Reproducibility Assessment of Post-Transplant Risk Prediction and Rejection Biomarkers.","source":"pubmed","abstract":"Reproducible biomarker identification and transplant rejection risk prediction remain fundamental yet unsolved challenges in transplantation medicine. Traditional approaches rely on hypothesis-driven analyses and domain expertise, limiting scalability and generalizability across diverse populations. We introduce Transplant-Agents , a data-driven multi-agent AI framework integrating large language models (LLMs) with machine learning algorithms for automated biomarker identification and rejection risk prediction. Agents interact through structured, iterative dialogue governed by predefined rules and criteria, converging on optimal biomarker sets reproducible across multiple iterations. We evaluated three multicenter clinical trial transplant datasets from ImmPort, comprising 683 patients across kidney, liver, and heart transplant cohorts. Transplant-Agents achieve AUROC scores of 0.93, 0.88, and 0.88. Feature importance analysis further confirms the stability, interpretability and potential generalizability of identified biomarkers. This work demonstrates that AI-agent frameworks can reliably reproduce established transplant biomarkers while enabling transparent, validated, and standardized risk prediction pipelines.","url":"https://pubmed.ncbi.nlm.nih.gov/42523221/","authors":["Ding S","Bhattacharya S","Sarwal MM","Sirota M","Butte AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.1101/2025.07.10.664265","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522996","name":"Surgery Versus Radiotherapy in Older Adults with Oral Cavity Cancer: A Nationwide Comparative Effectiveness Study.","source":"pubmed","abstract":"Older adults with oral cavity squamous cell carcinoma (OCSCC) are underrepresented in clinical trials, and the comparative effectiveness of surgery versus definitive radiotherapy with or without chemotherapy (RT&#x2009;&#xb1;&#x2009;CT) remains uncertain.","url":"https://pubmed.ncbi.nlm.nih.gov/42522996/","authors":["Lin KC","Chang CL","Chen WM","Shia BC","Wu SY","Fang CY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/19160216261468732","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522931","name":"Mortality risk estimation for autistic older adults: comparing novel machine-learning derived weights versus standard weights for the Charlson Comorbidity Index.","source":"pubmed","abstract":"Aim: We aimed to compare Quan and colleagues (2011) established weights for the Charlson Comorbidity Index (CCI) conditions to autism-specific weights for predicting mortality risk in autistic older adults. Materials &amp; methods: We used inpatient healthcare claims from autistic older adults (aged 65+; n&#xa0;=&#xa0;2829) using the Medicare Standard Analytic Files from 2021 to 2023. We used a machine learning technique called stochastic hill climbing to assign weights to the 12 CCI conditions to maximize predictive ability for 30-day and 1-year mortality. We then compared the resulting area under the curve (AUC) against the established weights. Results: The established weights had poor predictive ability for 30-day (AUC: 0.68; 95% CI: 0.62-0.74) and 1-year mortality (AUC: 0.67; 95% CI: 0.63-0.72). The autism-specific weights also had poor predictive ability for 30-day (AUC: 0.67; 95% CI: 0.61-0.73) and 1-year mortality (AUC: 0.67; 95% CI: 0.62-0.71). Conclusion: The established and autism-specific CCI weights performed similarly in predicting mortality among autistic older adults. Findings may suggest adjusting CCI weights alone is insufficient to accurately predict mortality risk in autistic older adults, and additional health conditions not currently captured by the CCI may need to be added to better predict mortality in this population. Future studies on developing an autism-specific mortality risk index are warranted.","url":"https://pubmed.ncbi.nlm.nih.gov/42522931/","authors":["Blake M","Nikahd M","Hyer JM","Patterson BW","Wolf BJ","Bishop L","Hand BN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.57264/cer-2026-0053","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522922","name":"Radiolabeling of clinically approved nanomedicine carriers: lessons from albumin- and liposome-based systems for biomaterial design and translational imaging.","source":"pubmed","abstract":"Radiolabeling is a powerful approach for investigating the in vivo behavior of nanomedicine carriers beyond conventional pharmacokinetic analysis. This review examines clinically approved nanocarrier platforms, particularly albumin-bound nanoparticles and PEGylated liposomes, using Abraxane (nab-paclitaxel) and Doxil/Caelyx (liposomal doxorubicin) as representative systems. These formulations are considered not only as anticancer therapeutics but also as translational biomaterial platforms whose biological performance can be quantitatively evaluated by radionuclide imaging.Direct and indirect radiolabeling strategies are compared with respect to labeling efficiency, carrier integrity, physicochemical stability, and biological interpretation. Evidence from preclinical and clinical PET and SPECT studies is integrated to assess organ-specific accumulation, mononuclear phagocyte system uptake, tumor-targeting heterogeneity, and microenvironment-dependent distribution.Relevant peer-reviewed English-language studies published between January 2000 and June 2026 were identified through PubMed/MEDLINE, Web of Science, Scopus, and Google Scholar. The review further derives practical design principles for next-generation drug-delivery biomaterials and discusses imaging-guided patient stratification and predictive assessment of nanocarrier delivery. By integrating radiochemistry, biomaterials science, and molecular imaging, this work provides a translational framework for clinically relevant nanomedicine development.","url":"https://pubmed.ncbi.nlm.nih.gov/42522922/","authors":["Han YR","Lee SB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1080/17435889.2026.2708061","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522786","name":"Glomerular Crescents in Adult Lupus Nephritis: Clinical and Pathological Insights From Saudi Arabia.","source":"pubmed","abstract":"Crescent formation is a marker of severe kidney injury in lupus nephritis (LN), but data from Saudi Arabia are scarce. We studied how crescents affect clinical features, pathology, and outcomes in Saudi patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42522786/","authors":["Al-Qurashi SH","Khalil MAM","Mahmood HHK","Alrowaie FA","Almansour AM","Alghamdi RMH","Alghamdi LS","Alsharif MM","Alghamdi RA","Elgadi A","Sadagah NM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/tswj/1894247","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522651","name":"Enhancing Deep Learning Chest Disease Diagnosis through Adversarial Training for Robust and Reliable Medical Imaging.","source":"pubmed","abstract":"Chest X-ray imaging is widely used for the diagnosis of thoracic diseases; however, developing reliable automated systems remains challenging due to variability in imaging conditions and disease presentation.","url":"https://pubmed.ncbi.nlm.nih.gov/42522651/","authors":["Al Omar AF","Aljawarneh SA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 26","doi":"10.2174/0109298673455955260715051730","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522570","name":"Artificial intelligence-enhanced electrocardiography for prediction of cancer therapy-related cardiac dysfunction.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42522570/","authors":["Zeidaabadi B","Glen C","Barker J","Peck OH","Tan YY","Waterston A","Squire I","Sau A","Lang NN","Ng FS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 4","doi":"10.1093/europace/euag191","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522449","name":"Intelligent Automation Improved Efficiency in Pharmacovigilance Safety Signal Assessment.","source":"pubmed","abstract":"Pharmacovigilance is the science involving the detection, assessment, understanding, and prevention of adverse events associated with drugs, biologics, or medical devices. In pharmacovigilance, information that suggests a new potential causal relationship between an intervention and an adverse event is called a safety signal. Following their detection, safety signals are assessed via a comprehensive, structured analysis to more fully elucidate whether a correlation exists. This key process often requires the manual assessment of many individual case safety report (ICSR) narratives to extract meaningful information in a labor-intensive, time-consuming, and variability-prone manner. In this retrospective feasibility study, we describe the potential utility of an intelligent automation system leveraging the GPT-4o large language model to automate the extraction of case elements of interest from a series of ICSR narratives while maintaining human expert oversight. Our proprietary platform allowed users to extract the presence or absence of risk factors and responses to dechallenge and rechallenge via instructional prompts built on a common template structure. Case elements for five historical signal assessments were selected based on need for and feasibility of artificial intelligence extraction. Performance ranged from F1&#x2009;=&#x2009;0.444 to 1.000 for risk factors and from F1&#x2009;=&#x2009;0.429 to 0.909 for responses to dechallenge and rechallenge. Even when considering the need to verify GPT-4o outputs for accuracy, potential time savings were identified. To the best of our knowledge, our results are the first to demonstrate an intelligent automation platform that may streamline signal management workflows using a machine-first, human-verified operational workflow while maintaining regulatory compliance.","url":"https://pubmed.ncbi.nlm.nih.gov/42522449/","authors":["Warner J","Teodoro LHS","Jardim AP","Albera C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1002/cpt.70409","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522379","name":"Clinical justification for osteoporosis investigation: transitioning from opportunistic to diagnostic referrals from alternative forms of imaging.","source":"pubmed","abstract":"Clinical justification remains fundamental to the safe use of imaging involving ionising radiation, requiring a favourable balance between diagnostic benefit and stochastic risk. Concurrently, advances in imaging technology and artificial intelligence have enabled opportunistic identification of additional pathologies beyond the primary indication for imaging. This opinion article discusses how emerging opportunistic osteoporosis detection technologies may eventually transition into clinically justified diagnostic pathways in their own right.","url":"https://pubmed.ncbi.nlm.nih.gov/42522379/","authors":["Meertens R","Senior C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1088/1361-6498/ae8afc","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522243","name":"Patients might distrust honest doctors.","source":"pubmed","abstract":"Evidence-based medicine revolutionized clinical practice, bringing expectations that physician-patient communication should be similarly transparent about scientific evidence and uncertainty. However, the replication crisis has revealed significant weaknesses in biomedical research. This creates something of a paradox: honest disclosure of uncertainty is ethically obligatory but often breeds patient distrust rather than confidence. This commentary argues that trust in medicine requires systemic educational reform to improve public scientific literacy, and offers some practical communicative strategies physicians can employ immediately, including deliberate language hedging and setting realistic expectations. Critically, such approaches only succeed when grounded in compassionate physician-patient relationships. In an era of increasing commercialization and artificial intelligence in medicine, preserving this fundamental relationship is essential for maintaining public trust in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/42522243/","authors":["Hyde B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1002/jhm.70416","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522165","name":"Preliminary Evaluation of a Dental Virtual Standardized Patient in Endodontic Clinical Reasoning and Decision-Making: Face and Construct Validity.","source":"pubmed","abstract":"To develop and evaluate a dental virtual standardized patient (D-VSP) integrating natural-language intent recognition, a high-fidelity haptic simulator, and chairside visuals, to establish its face validity as a training tool for endodontic clinical reasoning and decision-making, and to examine its construct validity as a summative assessment instrument.","url":"https://pubmed.ncbi.nlm.nih.gov/42522165/","authors":["Yuan Z","Zhu Q","Ren Y","Xu Y","Wang X","Wen M","Han Z","Chau RCW","Foong CC","Felszeghy S","Qiu Y","Dong Y","Yang S","Ji P","Pang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1111/iej.70223","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522152","name":"The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.","source":"pubmed","abstract":"Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED).","url":"https://pubmed.ncbi.nlm.nih.gov/42522152/","authors":["Dinh M","Corbett E","Ngo TT","Salmon E","Khan SA","Pethani F","Moore N","Koprinska I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1111/1742-6723.70285","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522138","name":"Combining Artificial Intelligence and Indocyanine Green-Dyed Gauze for Dual-Navigation in Minimally Invasive Gastrointestinal Surgery: A Preliminary Study.","source":"pubmed","abstract":"This preliminary study evaluated the feasibility of a dual-navigation strategy that integrates artificial intelligence (AI)-assisted identification of safe dissection planes with indocyanine green (ICG)-dyed gauze-based localization of anatomical targets during minimally invasive gastrointestinal surgery. Fluorescence imaging is widely used for intraoperative guidance, and artificial intelligence-assisted prediction can contribute to enhancing surgical decision-making and reducing adverse events. The feasibility of this system was evaluated in 39 patients undergoing laparoscopic or robot-assisted gastrointestinal surgery. Data from the Eureka surgical AI system, used in conjunction with indocyanine green-dyed gauze, were retrospectively analyzed to identify loose connective tissue, indicating a safe dissection plane. Eureka successfully predicted loose connective tissue to identify safe dissection planes during tissue separation. In 23 cases, the gauze was more readily detected under near-infrared fluorescence than under white light, thereby serving as a precise intraoperative marker. These preliminary findings suggest that combining AI-assisted dissection-plane recognition with fluorescence-guided target localization is feasible and may support real-time orientation during minimally invasive gastrointestinal surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/42522138/","authors":["Tashiro Y","Aoki T","Watanabe R","Yamazaki K","Date H","Kitajima T","Nagaishi S","Yamazaki T","Tomioka K","Shibata H","Matsuda K","Watanabe M","Yasunaga H","Ando S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1177/00031348261472840","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42522037","name":"Reducing healthcare emissions in the United States in the era of federal environmental deregulation: established and emerging mitigation opportunities and concomitant benefits.","source":"pubmed","abstract":"The United States (US) healthcare sector leads the world in per capita greenhouse gas emissions, contributing disproportionately to global climate change. The recent reversal of federal climate regulations has increased the need for voluntary and urgent action from the US healthcare sector. In 2022, the US healthcare sector emissions resulted in over 425,000 disability-adjusted life years lost, reflecting the direct human cost of healthcare-related greenhouse gas emissions. Climate change worsens health outcomes and disrupts access to care - these impacts fall heavily on vulnerable populations including low-income communities, indigenous populations, the elderly, and children. Three key areas for decarbonization include infrastructure, supply chains, and operations and can be accounted for and addressed through the Greenhouse Gas Protocol framework. Some established mitigation strategies include transitioning to renewable energy, reducing medical waste, adopting lower emissions anesthetics and inhalers, and implementing sustainable food management. However, current greenhouse gas inventories utilized by health systems may not yet account for the rapidly expanding landscape of technology in healthcare and its growing contribution to emissions. Emerging priorities for mitigation that are often overlooked include reducing emissions associated with medical conferences and the growing energy demands of artificial intelligence. Here, we discuss how decarbonization not only reduces emissions but also provides important co-benefits, including better air quality, climate resilience, reduced costs, and improved workforce well-being.","url":"https://pubmed.ncbi.nlm.nih.gov/42522037/","authors":["Mascarenhas E","Fayanju OA","Arroyo AC","Tirumalasetty J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1186/s44263-026-00303-9","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521959","name":"Artificial Intelligence in Psychiatric Graduate Medical Education.","source":"pubmed","abstract":"Psychiatry graduate medical education (GME) faces converging pressures of increasing clinical demand, rising administrative burden, and workforce burnout. The emergence of artificial intelligence (AI), particularly large language models (LLMs), offers new possibilities for expanding educational capacity and personalizing training. However, the relational and narrative foundations of psychiatric practice create unique challenges for responsible integration. This review defines opportunities and risks of AI across key domains of psychiatry GME.","url":"https://pubmed.ncbi.nlm.nih.gov/42521959/","authors":["Khan M","Edgcomb J","Heldt J","Yang Y","Debonis K","Richards M","Khalsa SS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s40596-026-02406-9","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521824","name":"AI proteomics: from protein identification to virtual cells.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.","url":"https://pubmed.ncbi.nlm.nih.gov/42521824/","authors":["Sun Y","A J","Liu Z","Sun R","Qian L","Payne SH","Bittremieux W","Ralser M","Li C","Chen Y","Dong Z","Perez-Riverol Y","Khan A","Sander C","Aebersold R","Vizcaíno JA","Krieger JR","Yao J","Han W","Zhang L","Zhu Y","Xuan Y","Sun BB","Qiao L","Hermjakob H","Tang H","Gao H","Deng Y","Zhong Q","Chang C","Bandeira N","Li M","E W","Sun S","Yang Y","Omenn GS","Zhang Y","Xu P","Fu Y","Liu X","Overall CM","Wang Y","Deutsch EW","Chen L","Cox J","Demichev V","He F","Huang J","Jin H","Liu C","Li N","Luan Z","Song J","Yu K","Wan W","Wang T","Zhang K","Zhang L","Bell PA","Mann M","Zhang B","Guo T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1038/s41592-026-03085-y","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521804","name":"High incidence of colorectal cancer in Japanese individuals with hypertension.","source":"pubmed","abstract":"The association between hypertension and the incidence of colorectal cancer remains controversial. This study aimed to clarify this relationship in a Japanese population using two large-scale cohort datasets. We conducted a population-based retrospective cohort study using the Shizuoka Kokuho Database, which contains insurance claims from the Shizuoka region and medical checkup data collected between 2012 and 2022. Propensity score matching was used to compare colorectal cancer incidence between normotensive and hypertensive groups, and cumulative incidence was analyzed using Gray's test, with death treated as a competing risk. Among individuals not taking antihypertensive medications, the normotensive and hypertensive group included 113,724 and 85,399 individuals, respectively. During the follow-up period (median, 4.38 years), colorectal cancer developed in 1130 individuals in the normotensive group and 850 in the hypertensive group. After propensity score matching, colorectal cancer incidence was significantly higher in the hypertensive group, with a hazard ratio of 1.15 (95% confidence interval, 1.02-1.30). This association was observed in men. Among individuals taking antihypertensive medications, an increased risk of colorectal cancer was observed in women. To evaluate whether genetically determined susceptibility to hypertension influences colorectal cancer incidence, we additionally analyzed polygenic risk score for blood pressure using a separate Japanese prospective cohort (Japan Multi-Institutional Collaborative Cohort). No positive association between polygenic risk score for blood pressure and colorectal cancer incidence was identified. This discordance between epidemiological findings and genetic analysis warrants careful interpretation, including, but not limited to, the possibility of previously unrecognized environmental factors shared by colorectal cancer and hypertension.","url":"https://pubmed.ncbi.nlm.nih.gov/42521804/","authors":["Mizuno H","Nakatani E","Oze I","Fujii R","Kuriyama N","Hisamatsu T","Ikezaki H","Nishida Y","Okada R","Koyanagi YN","Michihata N","Tanoue S","Nakagawa-Senda H","Setoh K","Kuriki K","Miura K","Watanabe K","Nakatochi M","Momozawa Y","Matsunaga T","Matsuo K","Kinoshita K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1038/s41440-026-02748-9","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521802","name":"Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.","source":"pubmed","abstract":"Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications.&#xa0;Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42521802/","authors":["Sayer M","Chang PD","Hamano H","Yamamoto R","Nagasaka M","Naqvi AA","Patel PM","Zamami Y","Ozaki AF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 29","doi":"10.1007/s00520-026-10984-5","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521687","name":"Machine learning reveals biocontrol agents shaping disease outcome in natural Arabidopsis populations.","source":"pubmed","abstract":"Plants recruit antagonistic microbes to defend against phytopathogens, offering a route to rational biocontrol beyond empirical screening. Here, using six generations of leaf-microbiome data from natural Arabidopsis populations infected by the oomycete Albugo laibachii, we show that microbial diversity is driven by infection, site, and host genotype, and that infected plants form modular networks with increased inter-kingdom antagonism. We train four machine-learning models to discriminate infected from uninfected plants by microbiota composition and identify microbes enriched in diseased (disease-associated) or healthy (health-associated) plants. Testing the most predictive bacteria, fungi, and cercozoa in planta, we find all confer varying protection against Albugo, with health-associated microbes outperforming disease-associated taxa. The best candidate, a Cystofilobasidium fungus, is validated in a synthetic community, where genomic and community assays indicate biocontrol acts mainly through microbe-microbe interactions rather than plant immune activation. This work shows that pairing microbiome data with machine learning identifies effective biocontrol agents.","url":"https://pubmed.ncbi.nlm.nih.gov/42521687/","authors":["Mahmoudi M","Hu Y","Almario J","Stincone P","Tenzer LM","Chaudhry V","Braun L","Quinzer S","Nieselt K","Kemen E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1038/s41467-026-75789-w","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521561","name":"Artificial intelligence and asthma: Pros, cons, and potential impact on community outcomes.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42521561/","authors":["Covington C","Mueller KS","Acevedo J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 2","doi":"10.1016/j.jnma.2026.06.011","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521547","name":"Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning.","source":"pubmed","abstract":"Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation-such as the implant passport or surgical report-is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction.","url":"https://pubmed.ncbi.nlm.nih.gov/42521547/","authors":["Mesterházi M","Balogh J","Takács A","Faragó J","Orsós M","Tényi D","Rácz D","Ország A","Könyves V","Csertán A","Benczúr A","Mikesy G","Joó T","Pollner P","Mijiritsky E","Blum IR","Kivovics M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.prosdent.2026.07.008","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521420","name":"Regularized Joint Reconstruction and Slab Combination for Accelerated Three-Dimensional Multi-Slab Diffusion-Weighted Imaging Using Multi-Scale Energy Models.","source":"pubmed","abstract":"To jointly reconstruct high-resolution diffusion-weighted volumes and eliminate slab-boundary artifacts while preserving fine anatomical detail from undersampled 3D multi-slab k-space acquisitions.","url":"https://pubmed.ncbi.nlm.nih.gov/42521420/","authors":["Ghorbani R","Rikhab Chand J","Lee CY","Jacob M","Mani M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov","doi":"10.1002/mrm.70512","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521345","name":"A Multimodal Machine Learning Model for Framingham Risk Score-Based Cardiovascular Risk Stratification in Patients With Obstructive Sleep Apnea.","source":"pubmed","abstract":"Machine learning provides a powerful tool to capture complex, nonlinear patterns in biomedical data and enhance precision risk stratification. In this study, we applied a multilayer perceptron (MLP) framework to classify Framingham Risk Score-based cardiovascular risk categories in patients with obstructive sleep apnea (OSA). By integrating polysomnographic, clinical, and haematological data, the model demonstrated robust discriminative performance (AUROC&#x2009;=&#x2009;0.822). The findings suggest that OSA-related phenotypes may provide additional information relevant to cardiovascular risk stratification beyond conventional risk factors, highlighting the added value of incorporating sleep-specific markers into cardiovascular risk assessment. This work illustrates the potential of machine learning to deliver more comprehensive cardiovascular risk stratification in OSA populations. Future studies with larger, multi-center cohorts, expanded biomarker panels, and explainable artificial intelligence approaches are needed to further refine predictive performance, improve interpretability, and support future validation and clinical application.","url":"https://pubmed.ncbi.nlm.nih.gov/42521345/","authors":["Zhao F","Li Y","Li C","Xu H","Zhou E","Huang W","Zhu H","Guan J","Yi H","Zou J","Chen N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1111/jsr.70413","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521200","name":"Academic Trajectories of Latin American Female Researchers in the Field of Physical Activity and Health.","source":"pubmed","abstract":"Women remain underrepresented in academic leadership globally, particularly in health fields. In Latin America, limited funding, centralized research systems, and institutional inequities further restrict women's advancement. In physical activity and health research, these disparities may limit perspectives shaping evidence and policy. We examined institutional environments, authorship patterns, and factors associated with women researchers' progression across Latin America.","url":"https://pubmed.ncbi.nlm.nih.gov/42521200/","authors":["Ramírez Varela A","Mejía-Grueso J","Bandeira PFR","Singh UP","Lee IM","Alarcón-Aguilar J","Chagas-Costa E","Honda S","Ochoa-Avilés A","De Roia G","Kohn ER","Niño-Cruz GI","Roa-Urrutia P","Crochemore-Silva I","Jiang X","Pratt M","Hallal PC","Salvo D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1123/jpah.2026-0063","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521098","name":"Artificial Intelligence-Assisted Risk Stratification in Stage II Colorectal Cancer: Multi-Institutional Validation of Semantically Enhanced Deep Learning.","source":"pubmed","abstract":"Accurate risk stratification in Stage II colorectal cancer is essential for treatment decision-making, as current guidelines recommend adjuvant chemotherapy only for patients with a high risk of relapse. We aimed to develop and validate an artificial intelligence-based approach for automated invasive front assessment to improve prognostic stratification in this population.","url":"https://pubmed.ncbi.nlm.nih.gov/42521098/","authors":["Magisson F","He Z","Millward J","Harris A","Chen Z","Mielke LA","Tran K","Ward RL","Hawkins NJ","Sieber OM","Wong R","Shapiro J","Harris S","Khattak A","Burge M","Gibbs P","Tie J","Williams DS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1053/j.gastro.2026.07.009","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521025","name":"Transdermal delivery of biologics.","source":"pubmed","abstract":"Biologics, including peptides, proteins, antibodies, vaccines and nucleic acid-based therapeutics, have transformed the management of chronic, metabolic, inflammatory, oncological and infectious diseases. Despite their clinical success, their wider use remains constrained by invasive administration routes, poor patient acceptability and formulation challenges related to instability, degradation, aggregation and immunogenicity. Transdermal delivery offers a minimally invasive alternative with potential for controlled, sustained and patient-friendly administration; however, the stratum corneum and viable skin layers strongly restrict the transport of large, hydrophilic and structurally sensitive biomolecules. This review critically evaluates conventional and emerging strategies for transdermal delivery of biologics through a biologic-specific and translational framework. Chemical permeation enhancers, ionic liquids, iontophoresis, sonophoresis, electroporation, microdermabrasion, microneedles, laser and radiofrequency ablation, cell-penetrating peptides, nanocarriers, jet injection, wearable systems and hybrid platforms are compared according to their mechanisms, biologic compatibility, dose capacity, delivery depth, scalability and clinical readiness. Particular attention is given to the relationship between biologic class, dose requirement, local versus systemic exposure, lymphatic uptake, microchannel closure, formulation stability and pharmacokinetic feasibility. Recent advances in smart and stimuli-responsive systems, 3D printing, bioelectronic interfaces and artificial intelligence/machine learning (AI/ML)-guided formulation design are also discussed. Key translational barriers are considered, including biologic stability, dose scalability, skin metabolism, immune responses, manufacturing reproducibility, sterility assurance, usability and regulatory complexity. Integrating mechanistic, formulation, device and translational perspectives, this review identifies realistic opportunities for transdermal biologics delivery while critically outlining the remaining limitations for clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42521025/","authors":["Vora LK","Gowda BHJ","Strauss J","Tijani A","Desai N","Austin E","Chablani L","Bagwe P","Datta D","Vora D","Bandi SP","Puri A","Donnelly RF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.jconrel.2026.115214","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42521015","name":"Artificial intelligence in antimicrobial stewardship: prediction, clinical applications, and implementation challenges.","source":"pubmed","abstract":"Antimicrobial resistance (AMR) continues to threaten modern infectious diseases practice. Antimicrobial stewardship programmes (ASPs) remain central to optimizing antimicrobial use, yet stewardship has become increasingly challenging because of rising clinical complexity, expanding data sources, and persistent workforce and analytic constraints. Artificial intelligence (AI) may strengthen stewardship by integrating clinical, microbiologic, and contextual data to support more timely and individualized decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42521015/","authors":["Matsuo T","Nigo M","Borgonovo F","Yoshida H","Tande AJ","Berbari EF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.cmi.2026.07.032","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520981","name":"An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in STEMI: A retrospective study with independent external validation.","source":"pubmed","abstract":"Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (pPCI) in patients with ST-segment elevation myocardial infarction (STEMI). This study aimed to develop and externally validate an interpretable model for predicting LVA after pPCI.","url":"https://pubmed.ncbi.nlm.nih.gov/42520981/","authors":["Zhao W","Ruan G","Wang H","Li M","Hu D","Xu X","Yang L","Zhao Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov 1","doi":"10.1016/j.ijcard.2026.134702","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520820","name":"Fundamentals of Foundation Models in Radiological Image Processing: Opportunities, Limitations, and Clinical Case Studies.","source":"pubmed","abstract":"Artificial intelligence (AI) has become firmly established in radiology, with most current applications relying on task-specific convolutional neural networks (CNNs) such as U-Net architectures for segmentation, detection, and classification. The emergence of foundation models (FMs), however, marks a fundamental paradigm shift. These large-scale, pretrained models are designed to flexibly adapt to a wide range of radiological tasks, often requiring only minimal task-specific fine-tuning. In medical imaging, FMs are typically trained on large, heterogeneous, and multimodal data sets and enable novel applications such as zero-shot segmentation, prompt-based image exploration, and the integration of clinical context information. In parallel with academic developments, the first vendors have begun to incorporate FM-based approaches in commercial radiology products, gradually replacing narrowly specialized model architectures. For clinical practice, this technological shift offers potential benefits, including reduced false-positive findings, automated triage in emergency imaging, and improved detection of complex or atypical pathologies. At the same time, these advances come with increased demands on computational resources and costs, as well as unresolved challenges regarding validation, explainability, and regulatory approval. This review introduces the core principles of foundation models, discusses their technical and economic implications, and illustrates - using clinically relevant examples - how this new generation of models may fundamentally transform radiological image analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42520820/","authors":["Shahzadi I","Borggrefe J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1055/a-2899-1290","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520788","name":"Social Perceptions of Konzo Disease in Nampula Province, Mozambique.","source":"pubmed","abstract":"Konzo, a neurodegenerative disease prevalent in Nampula Province, Mozambique, presents debilitating symptoms known locally as \"mantakassa\" (\"that which breaks legs\"). Although the biomedical literature attributes its cause to insufficiently processed cassava amid food insecurity, traditional narratives often cite supernatural explanations, like witchcraft. This review analyzed the existing literature to understand social perceptions of konzo, focusing on traditional leaders' perspectives and their implications for public health. It found that konzo cases cluster within families owing to shared dietary habits and socioeconomic vulnerabilities, disproportionately affecting women and children. Community narratives often link the disease to spiritual causes, differing from biomedical views. Health care workers noted low diagnostic accuracy and limited knowledge of the condition. Notably, the role of traditional healers, often the first line of care in rural areas, remains underdocumented, highlighting a critical knowledge gap. Further research into their understanding of konzo is essential for developing culturally sensitive health interventions and improving public health strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42520788/","authors":["Raimundo L","Rodrigues D","Silva MG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.4269/ajtmh.26-0017","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520656","name":"Sex Differences in Presentation and Outcomes of Transthyretin Amyloid Cardiomyopathy.","source":"pubmed","abstract":"Transthyretin amyloid cardiomyopathy (ATTR-CM) is more often diagnosed in men than in women but sex-specific data remain limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42520656/","authors":["Ciocca N","Fuentes Artiles R","Studer Bruengger A","Hugelshofer S","Stämpfli SF","Ehl NF","Pieczora L","Suter A","Pongan D","Ministrini S","Pfister O","Meyer P","Shiri I","Hunziker L","Hundertmark MJ","Gräni C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jacadv.2026.103068","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520654","name":"Lesion-Level Association Between Artificial Intelligence-Derived Coronary Calcium Volume and Plaque Vulnerability.","source":"pubmed","abstract":"Extensively calcified lesions are typically at low risk for lesion-level acute events, which may be related to less vulnerable plaques.","url":"https://pubmed.ncbi.nlm.nih.gov/42520654/","authors":["van der Waerden RGA","van der Zande JL","Mol JQ","Cancian P","Luttikholt TJ","Gu X","Heil L","Roleder T","Thannhauser J","Saitta S","Sánchez CI","van Ginneken B","Išgum I","van Royen N","Volleberg RHJA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jacadv.2026.103077","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520591","name":"Temporal linguistic shifts in oncology randomized controlled trials following large language model availability: A corpus analysis of 21,392 publications.","source":"pubmed","abstract":"Public availability of large language models (LLMs) from late 2022 has raised concerns about AI-assisted writing in scientific publishing. Oncology randomized controlled trials (RCTs) underpin cancer treatment guidelines and regulatory decisions worldwide, yet whether linguistic changes have followed the emergence of LLMs has not been examined at scale.","url":"https://pubmed.ncbi.nlm.nih.gov/42520591/","authors":["Silva A","E Silva VS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 9","doi":"10.1016/j.ejca.2026.116963","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520553","name":"Risk stratification of IDH-wildtype glioblastoma: Deep learning on histopathology images and biological interpretation.","source":"pubmed","abstract":"The prognosis of IDH wild-type glioblastoma (GBM) remains poor, with limited pathological biomarkers for survival prediction. This study aimed to develop a prognostic model using deep learning-based pathological features and characterize its biological relevance.","url":"https://pubmed.ncbi.nlm.nih.gov/42520553/","authors":["Ma Z","Wang Z","Zhao B","Wang W","Su D","Li S","Ji Y","Yan D","Zhang Z","Meng F","Zhang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 26","doi":"10.1016/j.ejso.2026.112036","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520535","name":"Causality-inspired representation learning with spatiotemporal memory for polyp detection in endoscopic videos.","source":"pubmed","abstract":"Early detection of colorectal polyps is crucial to reduce the morbidity and mortality associated with colorectal cancer. However, during endoscopy, continuous camera motion and the complex clinical environment often degrade key visual cues (e.g., polyp morphology, texture, and boundaries). This leads to cross-frame view shifts, which are characterized by heterogeneous appearances of the same lesion over time, thereby introducing spurious correlations that hinder reliable assessment. To address this, we propose a causality-inspired representation learning framework with spatiotemporal memory for polyp detection in colonoscopy videos (CIRL-Polyp). Specifically, we develop a novel View-Shift-Aware Causal Intervention Module (VACIM) to remove non-causal influences by enforcing prediction invariance under view shift perturbations. To further constrain non-causal factors, we introduce a dual-branch detection framework that processes the original and intervention frame sequences in parallel and enforces prediction consistency across branches, thereby promoting invariance to non-causal variations. In addition, we propose Causal Temporal Consistency Memory (CTCM) to leverage long sequence-dependency features and stabilize causal representations by constructing memory banks across branches and performing temporal consistency-enhanced cross-attention. Comprehensive experiments on two public video datasets and a private dataset demonstrate that CIRL-Polyp outperforms existing methods, which validates the effectiveness and suggests the clinical potential of the proposed framework from a causal perspective.","url":"https://pubmed.ncbi.nlm.nih.gov/42520535/","authors":["Hu Z","Sun C","Zheng Q","He X","Xue C","Zhou G","Chen Y","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 25","doi":"10.1016/j.media.2026.104231","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520516","name":"Machine learning, WGCNA and molecular docking identify SLC transporter genes as biomarkers and drug targets in Helicobacter pylori-associated carcinogenesis.","source":"pubmed","abstract":"Solute carrier (SLC) family genes govern transmembrane transport and cellular metabolism, yet their systematic roles from Helicobacter pylori infection to gastric cancer (GC) remain undefined. By integrating multiple GEO datasets with weighted gene co-expression network analysis, differential expression, and machine learning algorithms, we identified seven core SLC genes (SLC9A9, SLC43A2, SLC16A6, SLC7A14, SLC28A3, SLC4A11, SLC5A2) for H. pylori infection and nine (SLC1A3, SLC2A3, SLC15A3, SLC19A3, SLC2A12, SLC16A4, SLC25A4, SLC9A7, SLC28A3) for GC. Logistic regression models achieved high diagnostic accuracy (AUC&#xa0;=&#xa0;0.976 and 0.874, respectively). Immune infiltration analysis revealed distinct correlation patterns: SLC4A11 and SLC5A2 associated with activated mast cells, neutrophils, and M2 macrophages in infection, while SLC1A3 and SLC2A3 correlated broadly with M1/M2 macrophages and activated CD4 + T cells in GC. Enrichment analyses confirmed involvement in transmembrane transport, immune-related pathways, and cancer signaling. Transcription factor prediction identified CEBPB, RELB, and STAT5A as key upstream regulators. Molecular docking demonstrated strong binding affinities between SLC proteins and repurposable compounds (3'-azido-3'-deoxythymidine, CHEMBL1182312, quinine). This study provides the first systematic characterization of SLC family expression across the H. pylori-GC continuum, identifying potential diagnostic biomarkers and therapeutic targets that warrant further experimental validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42520516/","authors":["Lv N","He X","Cai T","Xu Y","Xie J","Zhao X","Yang B","Liu X","Zhang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 25","doi":"10.1016/j.bioorg.2026.110296","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520465","name":"DINCH induces intestinal toxicity through \"ROS-ENO1-PA-lipid peroxidation\" axis: An integrated approach combining network toxicology and experimental validation.","source":"pubmed","abstract":"Cyclohexane-1,2-dicarboxylic acid diisononyl ester (DINCH), as a \"green\" alternative to phthalates, has been widely used in consumer products and industrial materials. Consequently, its environmental and human exposure levels continue to rise, necessitating a systematic evaluation of its mechanism of intestinal toxicity. This study developed a strategy integrating \"network toxicology-machine learning-molecular simulation-experimental validation\": First, multiple databases were combined to identify potential intestinal toxicity targets of DINCH, followed by the construction of a PPI network and the identification of the core pathway \"metabolic pathway\" through GO/KEGG enrichment. Subsequently, ENO1 was selected by machine learning. Molecular docking and molecular dynamics simulations consistently demonstrate a interaction between DINCH and ENO1. In vitro experiments confirmed that high concentrations of DINCH significantly downregulated ENO1 expression and attenuated the H&#x2082;O&#x2082;-induced increase in ENO1 levels, leading to ROS accumulation, mitochondrial dysfunction, and lipid peroxidation. Furthermore, experiments with ENO1 gene silencing demonstrated that DINCH-induced lipid peroxidation was dependent on ENO1. Exogenous supplementation of PA significantly reversed DINCH-triggered mitochondrial damage, and lipid peroxidation. NAC and Mito-TEMPO interventions also alleviated the H&#x2082;O&#x2082;&#x202f;+&#x202f;DINCH-induced lipid peroxidation. In summary, this study is the first to elucidate the novel molecular mechanism by which DINCH aggravates H 2 O 2 -induced ROS-ENO1-dependent oxidative stress, providing critical scientific evidence for revising its safety thresholds, managing exposure risks, and developing next-generation alternative plasticizers.","url":"https://pubmed.ncbi.nlm.nih.gov/42520465/","authors":["Kang K","Zhu T","Huang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 1","doi":"10.1016/j.ecoenv.2026.120530","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520386","name":"Sustaining Canada's cancer workforce: A pan-Canadian review of innovations in the rising needs for equitable care.","source":"pubmed","abstract":"Cancer care systems globally are facing a growing crisis driven by rising cancer incidence, increasing clinical and social complexity, persistent inequities in access to specialized services, and mounting pressures on the oncology workforce. While innovations in technology and team-based care offer opportunities to improve access, coordination, and outcomes, their scalability depends on strengthening oncology workforce capacity.","url":"https://pubmed.ncbi.nlm.nih.gov/42520386/","authors":["Tomblin Murphy G","Sampalli T","Domm D","Peacock S","Navaratnam S","MacKenzie K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.healthpol.2026.105714","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520334","name":"The art/science continuum in the age of artificial intelligence.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42520334/","authors":["Currie GM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.jmir.2026.102553","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520287","name":"Machine Learning for Predicting Critical Postoperative Interventions: Proof-of-Concept Study Using the INSPIRE Dataset.","source":"pubmed","abstract":"Postoperative complications contribute significantly to patient morbidity and mortality. Early prediction of such complications could enable the care team to intervene promptly and improve patient outcomes. Existing surgical risk scores are easy to use but lack accuracy and do not provide individualized risk or guidance for clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42520287/","authors":["Shukla M","Fodor P","Yelika S","Ahn N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/65327","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520279","name":"Propagation of Interpreter Errors by Ambient AI Scribes: Study Using Simulated Clinical Encounters.","source":"pubmed","abstract":"In simulated English and Spanish clinical encounters, ambient AI scribes propagated interpreter errors into clinical notes, with patterns varying by speaker role and error type. These findings highlight the need for further evaluation of AI-scribe performance in multilingual and interpreter-mediated clinical care.","url":"https://pubmed.ncbi.nlm.nih.gov/42520279/","authors":["Rabotin A","Aguilar E","Sandoval Gonzalez S","Iniguez A","Jung C","Lee E","Bell DS","Arroyo J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/88734","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520278","name":"Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"BackgroundStroke remains a leading cause of mortality, long-term disability, and healthcare expenditure worldwide, placing substantial strain on healthcare systems, particularly in low- and middle-income countries. Effective risk stratification can facilitate targeted prevention strategies, optimize resource allocation, and reduce avoidable hospitalizations. This study synthesizes existing evidence on the predictive performance of artificial intelligence (AI)-based models for stroke risk assessment through meta-analysis and explores their potential implications for healthcare system planning.MethodsStudies were systematically retrieved from Web of Science (WoS), PubMed, and Scopus until 31 January 2025. The review followed the PRISMA 2020 guidelines. Area Under the Receiver Operating Characteristic Curve (AUC) values were extracted for each algorithm type and pooled using meta-analytic methods.ResultsDeep learning (DL) algorithms demonstrated favorable pooled discriminative performance (AUC: 0.955; 95% CI: 0.906-1.00, I 2 = 85.75%), especially for imaging-based models. Sensitivity analysis modestly reduced heterogeneity (I 2 from 85.75% to 61.77%). Substantial heterogeneity remained across study populations, healthcare settings, predictor characteristics, and validation strategies, limiting the generalizability of findings.ConclusionsAI-based models, particularly DL approaches, demonstrate favorable predictive performance for stroke risk stratification. However, considerable methodological heterogeneity, limited external validation, and risk of bias reduce confidence in widespread clinical implementation. Future research should follow standardized reporting and validation frameworks, such as TRIPOD, to improve methodological rigor, transparency, and clinical applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42520278/","authors":["Nopour R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/00469580261466517","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520276","name":"Evaluation of large language models in a national orthopaedic proficiency examination: Implications for health informatics and medical education.","source":"pubmed","abstract":"ObjectiveThis study evaluates the performance of large language models (LLMs)-ChatGPT-4.0, Gemini 2.0 Pro, o3-mini, Doctor GPT and DeepSeek-V3-in a national orthopaedic proficiency examination and explores their implications for health informatics and medical education. The responses of these models were analysed to assess accuracy rates and differences between models.MethodA total of 100 multiple-choice questions from the 2024 TOTEK examination were administered to each AI model under identical conditions. Correct and incorrect responses were recorded, and differences in performance were evaluated using chi-square testing and frequency analysis. Question categories were also compared to identify domain-specific variations.Resultso3-mini achieved the highest accuracy rate (79%), while Gemini 2.0 showed the lowest (68%); all models exceeded the 60% pass threshold. A statistically significant difference between models was identified in the Surgical Procedures category, in which Gemini 2.0 answered fewer questions correctly (23/36) than the other models (30-32/36) (&#x3c7; 2 = 9.87, df = 4, p = 0.043). No significant differences were observed in the remaining categories (all p &gt; 0.05), and the overall difference in accuracy between models did not reach statistical significance (&#x3c7; 2 = 4.01, df = 4, p = 0.405). Clinical decision-making and visual content-based questions were the most challenging for all models.ConclusionAI models demonstrate generally high accuracy in medical examinations; however, they struggle with interpreting clinical context, recognising atypical medical scenarios and answering questions involving visual content.","url":"https://pubmed.ncbi.nlm.nih.gov/42520276/","authors":["Arı B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Sep","doi":"10.1177/14604582261470637","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520225","name":"Large Language Models in German Continuing Medical Education Assessments: Protocol for a Fully Crossed Experimental Study.","source":"pubmed","abstract":"Continuing medical education (CME) is a legal and ethical obligation for physicians in Germany. The rapid rise of large language models (LLMs) such as ChatGPT, Gemini, Claude, and Grok raises concerns about the integrity of CME assessments, as LLMs can already pass German CME tests.","url":"https://pubmed.ncbi.nlm.nih.gov/42520225/","authors":["Özmen L","Burisch C","Gödde D","Breuckmann F","Ehlers J","Sellmann T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/91675","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520220","name":"Development and Evaluation of Individualized Music Therapy for Common Mental Disorders: Protocol for a Multistage Study.","source":"pubmed","abstract":"Pharmacotherapy for common mental disorders is frequently limited by adverse events and suboptimal adherence. While music therapy offers a promising nonpharmacological alternative, its clinical utility is currently constrained by limited accessibility, inconsistent efficacy, and a lack of mechanistic clarity.","url":"https://pubmed.ncbi.nlm.nih.gov/42520220/","authors":["Xiao C","Wei J","Li T","Cao J","Li Q","Duan Y","Geng W","Zhu B","Liu B","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/90617","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520216","name":"The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Reports: Multicenter Study.","source":"pubmed","abstract":"Although thyroid nodules are detected in up to 60% of adults on ultrasound, the vast majority are benign, creating a substantial decision-making burden compounded by heterogeneous practice guidelines. Large language models (LLMs) show promise in processing unstructured medical text and are emerging as tools for report interpretation among both clinicians and patients. However, their reliability across distinct clinical tasks in thyroid ultrasound interpretation remains poorly characterized.","url":"https://pubmed.ncbi.nlm.nih.gov/42520216/","authors":["Xie Y","Liu J","Zhan B","Zhang K","Li Y","Ning C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/93890","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520201","name":"From doing to being: Generative AI, workplace affordances, and professional identity.","source":"pubmed","abstract":"Generative artificial intelligence (AI) is having a profound impact on medical education, and much of our discourse has adopted a technical lens to examine tool use, faculty upskilling strategies, and the risks of AI for learning. Learner socialisation, role modelling, and professional identity formation occur within the clinical learning environment and depend on the affordances provided by workplace communities of practice. There is an urgent need for our discourse to consider how AI, by relocating the uncertainty that drives much workplace learning, may affect these formative conditions and what that means for clinician development.","url":"https://pubmed.ncbi.nlm.nih.gov/42520201/","authors":["Lavercombe M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1080/0142159X.2026.2710213","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520166","name":"Artificial intelligence predicts recurrent autoimmune hepatitis after liver transplantation in a multicenter cohort study.","source":"pubmed","abstract":"Autoimmune hepatitis (AIH) is an important indication for liver transplantation (LT), but recurrence affects over 30% of recipients, threatening long-term survival. Current strategies to prevent recurrence and progressive graft fibrosis remain suboptimal, with limited evidence to guide selection of immunosuppressive regimens. We aimed to develop a dynamic, individualized, artificial intelligence-powered model for post-transplant recurrent AIH.","url":"https://pubmed.ncbi.nlm.nih.gov/42520166/","authors":["Bhat M","Sun Y","Manickavel P","Maleki S","Ronca V","Hansen BE","Hirschfield G","Elwir S","Alsaed M","Milkiewicz P","Janik MK","Marschall HU","Burza MA","Efe C","Calışkan AR","Harputluoglu M","Kabaçam G","Terrabuio D","de Quadros Onofrio F","Selzner N","Bonder A","Parés A","Llovet L","Akyıldız M","Arikan C","Manns MP","Taubert R","Weber AL","Schiano TD","Haydel B","Czubkowski P","Socha P","Ołdak N","Akamatsu N","Tanaka A","Levy C","Martin EF","Goel A","Sedki M","Jankowska I","Ikegami T","Rodriguez M","Sterneck M","Weiler-Normann C","Schramm C","Donato MF","Lohse A","Andrade RJ","Patwardhan VR","van Hoek B","Biewenga M","Kremer AE","Ueda Y","Deneau M","Pedersen M","Mayo MJ","Floreani A","Burra P","Secchi MF","Terziroli Beretta-Piccoli B","Sciveres M","Maggiore G","Jafri SM","Debray D","Girard M","Lacaille F","de Boer YS","Lleo A","Mason AL","Heneghan M","Oo YH","Lytvyak E","Montano-Loza AJ","International AIH Study Group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1097/HC9.0000000000001004","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520136","name":"A Large Language Model-Driven System for Advance Care Planning Training Among Health Care Providers in the Chinese Context: Development and Technical Evaluation.","source":"pubmed","abstract":"With the expanding need for advance care planning (ACP), innovative educational strategies for training health care providers are increasingly required. Large language model (LLM)-based ACP chatbots offer a novel and potentially effective solution to enhance health care providers' competence in navigating complex ACP conversations.","url":"https://pubmed.ncbi.nlm.nih.gov/42520136/","authors":["Tan M","Tang S","Kan S","Wu B","Ni Z","Zhang H","Ding J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/87288","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520072","name":"Toward universal representations of the living: Physiological invariance for transportable medical AI.","source":"pubmed","abstract":"Medical artificial intelligence (AI) models often degrade when deployed beyond their training environment, suggesting reliance on context-specific correlations rather than stable physiological structure. This article considers whether improved transportability may require representation learning strategies aligned with biological mechanisms expected to persist across populations, devices, and care pathways. Physiological invariance is introduced as the hypothesis that outcome-relevant predictive relationships may be mediated by latent physiological processes that are more stable across environments than observed measurements shaped by workflows or data acquisition. Multimodal self-supervised learning combined with mechanism-informed regularization may help identify such environment-stable structure, although empirical validation remains limited. Physiological invariance is not proposed as a sufficient or necessary condition for generalization, but as a candidate structural explanation for transportability in domains where shared biological mechanisms exist.","url":"https://pubmed.ncbi.nlm.nih.gov/42520072/","authors":["Vallée A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1371/journal.pdig.0001610","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520067","name":"Machine learning reveals temperature as a key predictor of dengue risk across Thailand's provinces: A 20-year analysis.","source":"pubmed","abstract":"Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors. Understanding these predictive factors is essential for developing robust forecasting systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42520067/","authors":["Suttirat P","Chadsuthi S","Aekthong S","Rocklöv J","Bicout DJ","Haddawy P","Yin MS","Lawpoolsri S","Modchang C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1371/journal.pntd.0014590","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42520059","name":"Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study.","source":"pubmed","abstract":"Artificial intelligence-generated health information is increasingly used by patients, but its reliability, visible transparency indicators, and readability remain uncertain in specialized ophthalmic conditions such as age-related macular degeneration (AMD).","url":"https://pubmed.ncbi.nlm.nih.gov/42520059/","authors":["Lu Z","Cao H","Ma C","Zheng J","Ma X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.2196/91016","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519958","name":"AI-assisted OCT biomarker quantification reveals distinct retinal fluid trajectories in central retinal vein occlusion.","source":"pubmed","abstract":"To characterize longitudinal patterns of intraretinal (IRF) and subretinal fluid (SRF) dynamics using AI-assisted optical coherence tomography (OCT) biomarker quantification and to evaluate their association with long-term visual outcomes in eyes with central retinal vein occlusion (CRVO).","url":"https://pubmed.ncbi.nlm.nih.gov/42519958/","authors":["Kaiser KP","Steiner D","Jaggi D","Ay C","Zinkernagel M","Heussen FM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1097/IAE.0000000000004932","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519894","name":"Transformer-based rapid dose calculation for carbon ion therapy.","source":"pubmed","abstract":"Online adaptive radiotherapy (OART) improves the precision of carbon ion therapy by enabling dynamic treatment plan adjustment. However, its implementation is critically constrained by the limited development of fast and accurate dose engines for both plan adjustment and online verification.","url":"https://pubmed.ncbi.nlm.nih.gov/42519894/","authors":["Liu Y","Hu Y","He P","Zhao Y","Li Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70587","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519782","name":"Applications of natural language processing and large language models in sports injury assessment and rehabilitation decision-making: a scoping review.","source":"pubmed","abstract":"This study aims to provide a scoping review of the current applications of natural language processing (NLP) and large language models (LLMs) in the assessment of sports injuries and rehabilitation decision-making, with the goal of identifying the technical methods, data sources, target populations, key findings, and knowledge gaps in existing research, and to provide evidence-based guidance for clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42519782/","authors":["Wang H","Liu Y","Hu L","Wang X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1866874","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519773","name":"A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination.","source":"pubmed","abstract":"To systematically evaluate the overall performance, subject-based differences, and question type adaptability of five mainstream large language models (ChatGPT, DeepSeek, Kimi, Qwen, and Doubao) in the Chinese National Pharmacist Licensing Examination (CNPLE), and to explore their feasibility as auxiliary tools for pharmaceutical examinations.","url":"https://pubmed.ncbi.nlm.nih.gov/42519773/","authors":["Huang C","Sun Y","Liu W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1880914","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519595","name":"Sequential multi-site fine-tuning for incremental deployment of large language models for mobility functional status extraction.","source":"pubmed","abstract":"This study evaluated sequential multi-site fine-tuning of large language models (LLMs), simulating incremental deployment across institutions for extracting mobility functional status from unstructured clinical notes.","url":"https://pubmed.ncbi.nlm.nih.gov/42519595/","authors":["Liu X","Garg M","Jeon E","Jia H","Crowson CS","Sauver JS","Pagali SR","Sohn S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1093/jamiaopen/ooag139","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519588","name":"Internal Medicine Physicians' Reflections on AI Tools for Research Tasks in Turkey: A Qualitative Descriptive Study Following a Brief Educational Session.","source":"pubmed","abstract":"This study explored internal medicine physicians' perceptions of AI tools used for research tasks (eg, literature searching, reference management, scholarly writing support, and visualization) following a brief educational session.","url":"https://pubmed.ncbi.nlm.nih.gov/42519588/","authors":["Zorlu Görgülügil G","Özdede M","Özbilen M","Polat ZP","Genç AC","Şahin SE","Şahintürk Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/AMEP.S607947","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519406","name":"Patient Perspectives on Systemic Bevacizumab Treatment for Recurrent Respiratory Papillomatosis.","source":"pubmed","abstract":"Systemic bevacizumab has been shown to reduce the surgical burden of recurrent respiratory papillomatosis (RRP). However, no previous studies have examined specific patient-reported differences in quality-of-life (QoL) between systemic bevacizumab and surgical treatment. This study aimed to assess patient perspectives across two academic medical centers to inform clinical practice and shared decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42519406/","authors":["Gao S","Jones J","Triantafillou V","Akst LM","Hillel AT","Motz KM","Klein AM","Best SR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/lio2.70500","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519396","name":"Deep learning analysis of capillary refill dynamics in ischemic colitis: differentiating reversible vs. gangrenous mucosa.","source":"pubmed","abstract":"Ischemic colitis remains a diagnostic challenge, as endoscopic grading of mucosal ischemia is subjective and insensitive for transmural infarction. While the majority of non-gangrenous cases are resolved conservatively, gangrenous mucosa necessitates immediate resection. The current reliance on visual signals, such as color and bleeding, results in misclassification and variability. We propose a novel deep learning approach to quantify micro-capillary refill dynamics from colonoscopy video. The technology produces objective perfusion measures and real-time viability classification by examining frame-by-frame color recovery following temporary mucosal blanching. This approach makes use of recent developments in AI-assisted colonoscopy, annotated datasets, and endoscopic hardware. By incorporating capillary refill analysis, operational decision-making might be improved, interobserver variability could be decreased, and unnecessary colectomy and delayed gangrene treatment could be avoided. Pilot studies are warranted to validate this concept and establish its role in guiding therapy for ischemic colitis.","url":"https://pubmed.ncbi.nlm.nih.gov/42519396/","authors":["Yasin Z","Sajid H","Saleem NUA","Abu Nahla U"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fgstr.2026.1782255","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519377","name":"Deep learning model to generate patient-specific pulmonary vein isolation lines from successful atrial fibrillation ablation cases: a proof-of-concept study.","source":"pubmed","abstract":"Pulmonary vein isolation (PVI) is an established standard ablation for atrial fibrillation (AF), however, AF recurrence remains a major clinical challenge. We developed a deep learning model to generate patient-specific PVI lines on pre-ablation 3D voltage maps, using lesion sets from successful AF ablation cases with documented freedom from recurrence for more than one year as ground truth. Using a U-Net-based architecture trained and evaluated on 513 maps from 171 such cases, the model reproduced the anatomical and electrophysiological features of these PVI lesion sets. On the held-out test set, the model achieved a mean Intersection over Union of 0.87 and a Dice score of 0.93. As a proof of concept, these findings suggest that the model can reproduce patient-specific PVI patterns associated with successful outcomes; whether this translates into reduced recurrence requires prospective clinical validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42519377/","authors":["Sakamoto K","Tohyama T","Yokoyama H","Watanabe T","Nagayama T","Mukai Y","Kawai S","Yakabe D","Mannoji H","Nagaoka K","Tanaka A","Yamamoto M","Ogawa K","Mikami T","Inoue S","Takase S","Inoue K","Hosokawa K","Todaka K","Tsutsui H","Abe K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1859430","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519312","name":"A comprehensive study based on large-sample multi-omics integration and machine learning to decode mitochondria-associated genes: from digestive tract tumours to gastric cancer.","source":"pubmed","abstract":"Mitochondria-related genes play a crucial role in driving tumour cell progression, but little is known about their molecular mechanisms and biological pathways. This study conducted a comprehensive analysis of the mitochondrial key gene LACTB2 in digestive tract tumours and explored a novel early blood-based diagnostic model for gastric cancer (GC).","url":"https://pubmed.ncbi.nlm.nih.gov/42519312/","authors":["Zhang W","Tang YL","Chen YY","Shang Y","Mo HB","Huang J","Li YF","Liu ZH","Tan GL","Ning YK","Chen GQ","Ling JW","Wang L","Jiang JS","Luo JY","Chen G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1719986","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519311","name":"A multi-omics framework integrating gut microbiota, blood metabolites, and immune cells to elucidate the pathogenesis of Alzheimer's disease.","source":"pubmed","abstract":"Alzheimer's disease (AD) develops through complex interactions between the central nervous system and peripheral systems. The microbiota-metabolite-immune axis has emerged as an important focus of AD research. However, the coordinated mechanisms that regulate this axis remain poorly understood.","url":"https://pubmed.ncbi.nlm.nih.gov/42519311/","authors":["Wang B","Yan W","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1842398","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519267","name":"Statistical Consistency in Artificial Intelligence-Assisted Computations: A Comparison of SPSS 31.0 and GPT-5.5.","source":"pubmed","abstract":"Background Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, is increasingly being used for statistical interpretation and research support. While traditional statistical software such as IBM SPSS remains the gold standard for transparent and reproducible analyses, concerns persist regarding the accuracy, consistency, and reproducibility of LLM-generated statistical outputs. Given the continuous evolution of LLMs, replication studies are needed to evaluate whether newer models demonstrate improved statistical reliability and alignment with established analytic standards. Methodology In total, 14 statistical procedures were applied to real datasets that previously generated peer&#x2011;reviewed, published scientific articles. The analyses encompassed descriptive statistics, Pearson product-moment correlation coefficient (Pearson r), multiple correlation using Pearson r, Spearman's rho, simple linear regression, one-sample and paired t&#x2011;tests, two independent&#x2011;sample t&#x2011;tests, multiple linear regression, one-way analysis of variance (ANOVA), repeated&#x2011;measures ANOVA, two&#x2011;way (factorial) ANOVA, and multivariate analysis of variance (MANOVA). Datasets were collected within a systematically defined timeframe (2012-2023), ensuring temporal consistency and representativeness. All analyses were executed by copying and pasting prompts into GPT-5.5, accompanied by the corresponding SPSS 31.0 variable names copied and pasted directly from the original SPSS datasets. Results Results demonstrated concordance between SPSS 31.0 and GPT-5.5 across most descriptive and inferential statistical procedures, although notable discrepancies did occur. Identical or near-identical findings were observed for descriptive statistics, Pearson correlations, one-sample t-tests, paired t-tests, two independent-sample&#xa0;t-tests, simple and multiple linear regression, and one-way ANOVA. Correlation coefficients, effect sizes, confidence intervals, and significance values were mostly consistent across platforms. Discrepancies emerged in selected nonparametric analyses, factorial ANOVA, MANOVA, and repeated-measures ANOVA procedures. Despite these computational differences, the overall substantive interpretations and statistical conclusions remained largely consistent between SPSS 31.0 and GPT-5.5, but not perfectly. These findings suggest GPT-5.5 demonstrates some statistical consistency with traditional statistical software for many common analytic procedures, while more advanced multivariate analyses may still require independent verification using established statistical platforms. Conclusions GPT-5.5 demonstrated considerable agreement with SPSS 31.0 across many commonly used statistical procedures, including descriptive statistics, correlations, regression analyses, t-tests, and one-way ANOVA. However, more notable discrepancies emerged in several analyses, particularly more complex procedures such as factorial ANOVA, MANOVA, and repeated-measures ANOVA. These findings suggest that GPT-5.5 may serve as a useful adjunct for statistical interpretation and exploratory research, but AI-generated statistical outputs should be interpreted with caution. Validated statistical software and appropriate methodological oversight remain essential for confirmatory analyses and research involving clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42519267/","authors":["Strale FF Jr","German RM","Mendes-Kramer V","Hammer SB","Ehrman RR","Sherwin RL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111207","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519026","name":"TMEM97 regulates cholesterol biosynthesis and mitochondrial metabolism in gastric carcinoma.","source":"pubmed","abstract":"Cancer cells regulate cholesterol levels to sustain proliferation and metabolic adaptation. Here, we show that TMEM97 coordinates cholesterol sensing with oxidative phosphorylation in gastric cancer. Loss of TMEM97 reduced in vitro proliferation and impaired xenograft tumor growth. TMEM97 deficiency disrupted cholesterol homeostasis, causing accumulation of the post-lanosterol intermediate follicular fluid meiosis-activating sterol (FF-MAS) under both normal and lipoprotein-deficient serum conditions. Metabolomic and transcriptomic analyses further revealed altered tricarboxylic acid (TCA) cycle activity under lipoprotein-deficient conditions. Structural analysis identified a conserved cholesterol recognition amino acid consensus (CARC) motif in TMEM97, and its deletion reduced cholesterol binding. Collectively, TMEM97 regulates sterol regulatory element binding protein (SREBP)-associated lipid programs, post-lanosterol cholesterol biosynthesis, and mitochondrial bioenergetics. These findings identify TMEM97 as a regulator of cholesterol-dependent metabolic adaptation in gastric cancer and support TMEM97-associated cholesterol regulation as a potential therapeutic vulnerability.","url":"https://pubmed.ncbi.nlm.nih.gov/42519026/","authors":["Ada SK","Yozbatıran Y","Beyaz BY","Sarı C","Dolanbay EG","Saygı Hİ","Sümer C","Njume CM","Çakmak A","Sarı FZ","Çimen Ş","Aras M","Kılıçkap A","Yücel B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 21","doi":"10.1016/j.isci.2026.116773","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42519014","name":"Interactions between minimal amount of exercise and low-density lipoprotein cholesterol on incident dementia after stroke.","source":"pubmed","abstract":"Cognitive impairment often follows stroke. Regular physical activity has been shown to be associated with reduced dementia risk, while the association with low-density lipoprotein cholesterol (LDL-C) is still not clear. We examined the association of minimal amount of exercise (MAE) and LDL-C with incident dementia after stroke. A retrospective cohort study was conducted using the History-Based Artificial Intelligence Clinical Diagnosis of Dementia Syndrome project, including adults aged 50 years or older with stroke from three medical centers in Taiwan. MAE and LDL-C were used to classify individuals. Among 679 post-stroke individuals without dementia at baseline, exercise was associated with better cognitive and functional status. Dementia progression was highest in the low LDL-C/no exercise group and lowest in the high LDL-C/exercise group. Regular MAE, not too low LDL-C levels (&#x2265;100 mg/dL), or both combined, were associated with a lower risk of progression to incident dementia.","url":"https://pubmed.ncbi.nlm.nih.gov/42519014/","authors":["Hong LW","Chiu HH","Chiu PY","Wang YC","Yeh HC","Kung WM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 21","doi":"10.1016/j.isci.2026.116806","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518770","name":"Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.","source":"pubmed","abstract":"The aim of this study was to develop and validate a cascaded artificial intelligence (AI) framework using a segmentation-discrimination strategy for automated detection of ossicular chain malformations (OCM) on computed tomography scans.","url":"https://pubmed.ncbi.nlm.nih.gov/42518770/","authors":["Gao Y","Wang R","Yang J","Li Y","Shang L","Meng X","Zhou Q","Shi F","Wu X","Yang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fbioe.2026.1731385","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518762","name":"Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.","source":"pubmed","abstract":"Following technological developments and new landmark trials, the diagnostic work-up of symptomatic chronic coronary artery disease (CAD) has evolved. Clinical guidelines now favor noninvasive anatomical assessments by coronary CT angiography (CCTA) as the first-line modality to evaluate CAD in the majority of patients with chest pain. This shift from ischemia testing to stenosis and plaque characterization has resulted in the development of new imaging biomarkers reflecting a variety of coronary plaque features, many of which have proven to be important clinical risk markers. Consequently, there has been a transition from qualitative to semi-quantitative and fully quantitative plaque acquisitions over the entire coronary tree. With the integration of artificial intelligence, novel software enables rapid quantitative acquisitions of plaque components, making them feasible for use in clinical practice. CCTA has also enabled identification of precursor features associated with plaque development such as peri-coronary artery adipose tissue attenuation and epicardial adipose tissue volume. This review provides an overview of CCTA derived plaque features in CAD and associated imaging biomarkers of risk to highlight their potential applications in precision phenotyping and individualized management decisions. It further outlines anticipated future developments that may enable widespread clinical adoption of these novel imaging biomarkers.","url":"https://pubmed.ncbi.nlm.nih.gov/42518762/","authors":["Lenell J","Grodecki K","Kwiecinski J","Slomka PJ","Dweck MR","Williams MC","Dewey M","Newby DE","Dey D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1093/bjro/tzag014","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518267","name":"The evolving role of the histotechnologist in digital, value based healthcare.","source":"pubmed","abstract":"Digital pathology and value-based healthcare (VBHC) are converging to reshape diagnostic medicine, yet the histotechnologist, whose tissue preparation enables every glass slide and whole slide image (WSI), remains largely invisible in this transformation. This article reframes the histotechnologist from technical operator to strategic value creator whose work directly shapes diagnostic accuracy, turnaround time, cost-per-case, and the quality metrics that increasingly govern reimbursement. Although laboratory testing accounts for roughly 2.3-2.5% of healthcare spending while informing nearly 70% of clinical decisions, its value-makers are seldom represented in VBHC planning. Mapping histotechnology onto established frameworks (Porter's value agenda and Clinical Lab 2.0), we show how bench-level practice translates into institutional performance. Because an estimated 60-70% of laboratory errors arise in the pre-analytical phase that histotechnologists primarily control, specimen fidelity becomes a measurable driver of clinical and financial outcomes. We argue that standardized, high-quality slide preparation is a prerequisite for reliable WSI and artificial intelligence (AI)-assisted diagnosis, positioning histotechnologists as the essential human quality-control layer for computational pathology. Amid workforce shortages, burnout, and reimbursement pressures including the 2014 US Protecting Access to Medicare Act (PAMA), we outline actionable workforce-development strategies to align histotechnology with digital, value-based care and realize the promise of AI in laboratory medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42518267/","authors":["Patel AU","Pirain D","Lamba Saini M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1080/01478885.2026.2700086","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518215","name":"What does generative artificial intelligence mean for the future of nicotine and tobacco scientific publishing?","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42518215/","authors":["Notley C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 24","doi":"10.1093/ntr/ntag160","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518201","name":"Validation of a Machine Learning Approach to the Analysis of Multifocal Electroretinograms for Hydroxychloroquine Retinopathy.","source":"pubmed","abstract":"Hydroxychloroquine (HCQ) retinopathy is detectable through multimodal ophthalmic screening, yet individual diagnostic tests each have inherent limitations. Machine learning may simplify monitoring, but few HCQ screening algorithms have undergone rigorous external validation. This study evaluated the clinical utility of the Multifocal Electroretinogram Classification Interface (MERCI) algorithm by assessing its ability to predict HCQ retinopathy compared with diagnoses derived from American Academy of Ophthalmology (AAO) guidelines.","url":"https://pubmed.ncbi.nlm.nih.gov/42518201/","authors":["Wong G","Mercer G","Ballios BG","Wright T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1167/tvst.15.7.29","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518148","name":"Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly used in health information seeking, but their performance in testicular cancer education remains unclear. This study evaluated the validity, reliability, and readability of responses generated by four widely used artificial intelligence (AI) chatbots.","url":"https://pubmed.ncbi.nlm.nih.gov/42518148/","authors":["Zhao J","Sun S","Zhao A","Liang R","Peng L","Peng X","Li R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1245/s10434-026-20266-3","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42518079","name":"Governing Clinical Readiness Claims Derived from Medical AI Benchmark Results.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42518079/","authors":["Dong Y","Cheng J","Ding C","Lu R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s10916-026-02445-7","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517962","name":"Stigma, lost autonomy, reclaimed identity, and uncertainty around safe movement: a qualitative interview study of physical activity in gout.","source":"pubmed","abstract":"To explore patients' experiences and perceptions of physical activity in the context of gout and gout flares. Interpretive description informed the design of this qualitative study. Individual interviews were conducted with 25 people with gout. An interview guide was used to lead discussions, focusing on participants' experiences of gout flares, physical activity engagement, and the perceived impact of flares on activity levels. Key questions were designed to capture culturally specific understandings and experiences related to gout and physical activity. Interviews were audio-recorded and transcribed verbatim. Data were analysed using reflexive thematic analysis. Four themes were generated from the data: (1) The experience of physical activity is shaped by societal misconceptions about gout; (2) A loss of physicality comes with a loss of autonomy; (3) Reclaiming body and identity through physical activity; and (4) Living with uncertainty: wanting to be active but not knowing how. Gout flares disrupted mobility, independence, work, family roles, and identity. Participants described stigma, fear of judgement, activity modification, pacing and uncertainty about whether exercise could trigger flares or damage joints, particularly in the absence of gout-specific advice. This study offers new insights into how societal misconceptions about gout influence the patient experience of physical activity, including loss of autonomy, identity reconstruction through movement, and uncertainty around safe exercise. These findings may inform future intervention development, and patient-centred physical activity guidance that supports people with gout to move safely, confidently, and meaningfully.","url":"https://pubmed.ncbi.nlm.nih.gov/42517962/","authors":["Stewart S","Anderson L","Ka'ai T","Rudolph K","Collis J","Kayes N","Rice D","Zeng I","Dalbeth N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s00296-026-06265-3","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517937","name":"[Concepts and clinical application of digital 3D microscopy].","source":"pubmed","abstract":"Heads-up 3D surgery is becoming increasingly more important in ophthalmic microsurgery. Digital 3D visualization systems supplement traditional binocular microscopes by high-resolution monitor presentations and enable improved depth perception, ergonomics, team communication and teaching.","url":"https://pubmed.ncbi.nlm.nih.gov/42517937/","authors":["Eckardt F","Plettenberg P","Hattenbach LO","Priglinger S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s00347-026-02488-8","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517899","name":"Automated MRI-based framework combining deep learning localization and machine learning classification for pubertal olecranon bone age assessment.","source":"pubmed","abstract":"This study developed a magnetic resonance imaging (MRI)-based artificial intelligence (AI) framework for ordered pubertal olecranon bone age (BA) staging and evaluated internal performance and preliminary cross-centre transportability.","url":"https://pubmed.ncbi.nlm.nih.gov/42517899/","authors":["Luo Y","Zhang X","Pan S","Yu F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00414-026-03934-7","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42517890","name":"Hidden risk in normal myocardial perfusion scans: AI-detected proximal coronary calcium on CT attenuation maps improves prognosis.","source":"pubmed","abstract":"Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries.","url":"https://pubmed.ncbi.nlm.nih.gov/42517890/","authors":["Zhou J","Miller RJH","Shanbhag A","Killekar A","Han D","Patel KK","Pieszko K","Yi J","Urs MK","Ramirez G","Lemley M","Kavanagh PB","Liang JX","Kamagate A","Builoff V","Einstein AJ","Feher A","Miller EJ","Sinusas AJ","Ruddy TD","Knight S","Le VT","Mason S","Chareonthaitawee P","Wopperer S","Alexanderson E","Carvajal-Juarez I","Rosamond TL","Slipczuk L","Travin MI","Packard RRS","Acampa W","Al-Mallah M","deKemp RA","Buechel RR","Berman DS","Dey D","Di Carli MF","Slomka PJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s00259-026-08084-x","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517875","name":"Using Machine Learning to Automate the Analysis of an Olfactory Habituation-Dishabituation Task in Mice.","source":"pubmed","abstract":"Improving the efficiency and accuracy of annotation and extraction of performance data from mouse behavioral tasks will improve both the throughput and scientific value of preclinical research.","url":"https://pubmed.ncbi.nlm.nih.gov/42517875/","authors":["Boyanova S","Correa MH","Bains RS","Wiseman FK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/brb3.71619","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517820","name":"Patient-specific multimodal learning with multi-view contrastive alignment for chest X-ray report generation.","source":"pubmed","abstract":"Radiology reports play a pivotal role in guiding treatment planning and enabling effective doctor-patient communication. However, their manual composition imposes a substantial workload on radiologists. Although automatic radiology report generation has emerged as a promising alternative, existing approaches predominantly rely on single-view chest X-rays and fail to adequately leverage patient-specific context, thereby limiting diagnostic accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/42517820/","authors":["Miao Q","Liu K","Ma Z","Li Y","Kang X","Liu R","Liu T","Xie K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 3","doi":"10.1093/bioinformatics/btag566","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517779","name":"The role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.","source":"pubmed","abstract":"The contemporary healthcare landscape is experiencing a significant transformation driven by the rapid growth of digital health data and advancements in computational technologies. At the center of this evolution is Machine Learning (ML), a branch of artificial intelligence that enables systems to learn from data, recognize patterns, and support decision-making with minimal human intervention. This paper presents a comprehensive analysis of the role of ML in enhancing early disease detection, accurate diagnosis, and timely treatment across modern healthcare systems. It begins by discussing key ML paradigms, including supervised, unsupervised, and reinforcement learning, and their applications in medical practice. The study further highlights how advanced ML and deep learning algorithms achieve human-level or even superior performance in analyzing complex healthcare data such as medical imaging, genomics, and electronic health records. ML applications in the early detection of diseases such as cancer, diabetic retinopathy, and sepsis are explored, emphasizing their ability to identify subtle pre-symptomatic patterns. Additionally, the paper examines the role of ML in differential diagnosis, risk stratification, and personalized medicine through multi-omics data integration. Furthermore, the paper discusses the contribution of ML to precision oncology, drug discovery, and chronic disease management. Despite its potential, challenges such as data quality, interpretability, ethical concerns, regulatory barriers, and privacy issues continue to hinder widespread clinical adoption. The paper concludes that ML will augment rather than replace clinicians, enabling predictive, personalized, and data-driven healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/42517779/","authors":["Shrivastava A","Tripathi A","Rajput J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1177/09287329261468976","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517768","name":"Radiograph-based Deep Learning Algorithm for Assisting Bone Tumor Risk Assessment: A Multicenter Study.","source":"pubmed","abstract":"Background Accurate radiograph-based risk stratification is critical for bone tumor management. However, the multicenter performance of the Bone Reporting and Data System (Bone-RADS) and the added value of deep learning-based decision support remain unclear. Purpose To develop and evaluate a radiograph-based deep learning model for binary risk stratification-benign versus potentially malignant (ie, intermediate or malignant)-and to compare it with radiologist and Bone-RADS assessments. Materials and Methods This multicenter study retrospectively obtained radiographs of histopathologically confirmed bone tumors from 10 institutions (January 2010 to December 2024) and prospectively recruited participants from two of these centers for testing (May-August 2025). The developed image-clinical/semantic-RADS attention combined model (DL-Clinic-RADS) used a two-view cross-attention vision transformer integrating clinical and semantic features with expert consensus. The model performance was compared with that of nine musculoskeletal radiologists, Bone-RADS, and artificial intelligence (AI)-aided classification. The primary endpoint was the area under the receiver operating characteristic curve (AUC), with secondary endpoints including precision-recall curves, calibration, decision curve analysis, interreader agreement, reading time and confidence, and simulated clinical decision-making. Results The retrospective dataset comprised 1777 patients (1009 male; mean age, 36.93 years &#xb1; 21.32 [SD]), and the prospective testing dataset comprised 152 participants (79 male; mean age, 36.22 years &#xb1; 21.27). The DL-Clinic-RADS achieved an AUC of 0.96 (95% CI: 0.94, 0.98) in the external set and 0.99 (95% CI: 0.97, 1.00) in the prospective set, outperforming the other five models ( P &lt; .001), radiologist assessments (external set AUC, 0.68-0.82; P &lt; .001), and the Bone-RADS assessment (external set AUC, 0.75-0.81; P &lt; .001). With AI assistance, the mean reader AUC increased by 0.07 (95% CI: 0.03, 0.11), the interreader agreement improved (Fleiss &#x3ba;, +0.06 to +0.23), and the median reading time decreased by 3 seconds overall. Conclusion Compared with the Bone-RADS and radiologist assessments, the DL-Clinic-RADS significantly improved the radiographic risk stratification of bone tumors, enhancing clinical decision-making and workflow efficiency. &#xa9; RSNA, 2026 Clinical trial registration no. ChiCTR2500102778 Supplemental material is available for this article. See also the editorial by Tordjman and Taouli in this issue.","url":"https://pubmed.ncbi.nlm.nih.gov/42517768/","authors":["Song C","Xu J","Jiang T","Wang X","Tang J","Wang Z","Chen Y","Wang L","Li X","Zou J","Hou L","Guan S","Cui J","Xu W","Nie P","Wang T","Zhao X","Huang C","Chen S","Duan F","Wan G","Wang H","Hao D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1148/radiol.252921","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517710","name":"Scalable discovery of homomeric protein-protein interactions from cross-linking mass spectrometry data with CLAUDIO 2.0.","source":"pubmed","abstract":"Cross-linking mass spectrometry (XL-MS) is a powerful biochemical approach for residue-level characterization of protein structures and interactions under near-native conditions. The growing scale of XL-MS datasets demands scalable analysis pipelines that capture signals often overlooked in conventional workflows, including homomeric interactions. Here, we present CLAUDIO 2.0, a next-generation framework for structural analysis of large-scale XL-MS data. CLAUDIO 2.0 identifies homomeric interactions using overlapping peptide sequences and structural evaluation. Our optimized workflow improves computational efficiency, enabling scalable analysis and expanding structural coverage. Applied to a human mitochondrial XL-MS dataset, CLAUDIO 2.0 evaluates over 75% of cross-links using available high-confidence structural models, reduces runtime by over 95% (averaging 5&#x2009;s per cross-link) compared to its predecessor, and identifies 205 proteins with homomeric interaction signals. CLAUDIO 2.0 is freely available under the MIT License at (https://github.com/ElhabashyLab/CLAUDIO) and as a web server at (https://elhabashylab.org/claudio), providing an accessible platform for scalable structural proteomics.","url":"https://pubmed.ncbi.nlm.nih.gov/42517710/","authors":["Löser T","Röhl A","Baier M","Lupas A","Kohlbacher O","Elhabashy H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/pro.70732","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517577","name":"Community-Driven Research Priorities for Genetic Counseling: Shaping the Future of Genetic Counseling Research by Centering Multicultural Perspectives.","source":"pubmed","abstract":"Community-based participatory research (CBPR), a research approach that centers the collective strengths of community-researcher partnerships, can direct future genetic counseling (GC) research initiatives toward issues that are critical to communities most impacted by genomic healthcare disparities. Co-developed, innovative solutions to persistent research gaps may help address community access to and education about genomic healthcare. This study utilized a CBPR approach to develop a prioritized list of GC research directions that align with the goals and needs of representatives from racially/ethnically underrepresented communities in research. Twelve members from four community advisory boards (CABs) representing the Hispanic/Latino, Black/African American, Hmong, and Somali communities came together to complete three card sorting activities. Twenty cards describing research project ideas were developed through inductive qualitative content analysis of 22 meetings held with individual CABs (April 2023 to May 2025). Three members from each CAB first sorted their own CAB's project idea cards. Then, members were divided into three new small groups with one member from each CAB. Cross-CAB groups completed a second card sorting activity to identify their most important project ideas, followed by a third sorting activity to prioritize their top card. The cross-CAB identified three research priorities: (A) work with primary care clinics to encourage more people to see a genetic counselor, (B) learn which GC techniques are best according to patients and the community, and (C) investigate whether educational projects make people more aware of GC or not. By centering multicultural perspectives in the creation of a GC research agenda, the future of GC research can be better aligned with the priorities of communities and eventually support novel, tailored strategies to address genomic healthcare disparities.","url":"https://pubmed.ncbi.nlm.nih.gov/42517577/","authors":["Johnson VR","Fisher ER","Culhane-Pera KA","Lumpkins CY","Shire A","Ramírez M","Parra de Young A","Uribe Abad CE","Yang E","Lee S","Abdi A","Vann JM","Moss S","Zierhut HA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/jgc4.70270","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517500","name":"ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction.","source":"pubmed","abstract":"Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequently overlooked or attributed to other conditions. This study proposes a novel, externally validated artificial intelligence (AI) tool using ECG data (ECG-AI) for the simultaneous detection of subtypes of LV dysfunction and HFpEF.","url":"https://pubmed.ncbi.nlm.nih.gov/42517500/","authors":["Karabayir I","Gilbert O","Valika A","Celik T","Krishnan A","Chinthala L","Pandey A","Soliman EZ","Jefferies JL","Kitzman D","Herrington D","Davis RL","Akbilgic O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 4","doi":"10.1161/JAHA.124.041948","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"pmid:42517220","name":"A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.","source":"pubmed","abstract":"Congenital heart defects afflict &#x2248;1% of all births worldwide. Although deep learning has shown significant promise in automating and improving adult echocardiography analysis, existing pediatric-based models are often limited to single tasks and specific echocardiographic views. To address this, we introduce EchoAI-Peds, a multitask deep learning model for pediatric echocardiography. Our model was developed using the most comprehensive set of pediatric labels to date and is designed to integrate information from multiple views simultaneously.","url":"https://pubmed.ncbi.nlm.nih.gov/42517220/","authors":["Cho J","Mathur M","Kaur D","Duda M","Dahlan A","Krishnan A","Leipzig M","Shad R","Gonzalez AK","Logan J","Seidman C","Fong R","Kumar A","Zakka C","Carter E","Padiyath A","Jones A","Quartermain MD","Langlotz CP","Jolley MA","Hiesinger W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1161/CIRCULATIONAHA.126.080619","addedAt":"2026-09-01T01:47:59.006Z","updatedAt":"2026-09-01T01:47:59.006Z"},{"id":"doi:10.1007/978-3-032-18894-6_16","name":"MedACT-CL: Knowledge-Enhanced Tri-Modal Contrastive Learning for Medical Action Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_16","authors":["Hakim Nasaoui","Hassan Silkan","Insaf Bellamine"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:38:11Z","doi":"10.1007/978-3-032-18894-6_16","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1145/3777577.3777598","name":"Artificial Intelligence-Clinical Decision Support Systems: Integrating Intelligent Evidence Retrieval with Advanced AI for Personalized Healthcare","source":"crossref","abstract":"In recent years, artificial intelligence and big data have increasingly influenced healthcare, contributing to the development of clinical decision support systems (CDSS). This paper presents a framework for an Artificial Intelligence-CDSS(AI-CDSS) that retrieves and summarizes recent medical literature to build a continuously updated knowledge base. The goal is to provide clinicians with the latest evidence for informed decision-making. The framework uses natural language processing and graph neural networks to convert unstructured literature into structured knowledge graphs, while integrating multimodal data to generate clinical recommendations. These recommendations are personalized and interpretable, facilitated by Transformer models. The paper discusses the challenges encountered by the framework, its role in precision medicine, and its potential applications in clinical decision-making, as well as future directions for improving data integration and decision models.","url":"https://doi.org/10.1145/3777577.3777598","authors":["Bangyin Xiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577.3777598","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3403/30374629","name":"Information technology - Artificial intelligence - Artificial intelligence concepts and terminology","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30374629","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-22T20:30:23Z","doi":"10.3403/30374629","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-030-92087-6_34","name":"Magnetic Resonance Imaging-Based 4D Flow: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_34","authors":["Eva S. Peper","Sebastian Kozerke","Pim van Ooij"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_34","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.59849/aidd.2024.9","name":"Artificial intelligence and mathematics, specific fields of application of artificial intelligence","source":"crossref","abstract":"Artificial intelligence (AI) technologies are one of the most important scientific and technological achievements of the modern era.Artificial intelligence (AI) means that machines and computer systems have the ability to think and learn like the human brain.The main goal of AI is to create systems that imitate human intelligence and have a higher level of decision-making ability.The main principles and algorithms of AI technologies are based on various fields of mathematics.Mathematics is the main field of science that is the basis of artificial intelligence and plays an important role in its development.The concept of smart village has come to the fore in recent years as an important tool for the development of rural areas and raising the standard of living of its population.This concept aims to ensure effective management of agriculture, infrastructure and services.Artificial intelligence technologies play an important role in this process and create new opportunities in agriculture, energy management, efficient use of water resources and other fields.","url":"https://doi.org/10.59849/aidd.2024.9","authors":["Elmira Akhundova","Nazifa Abdullayeva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-26T13:48:23Z","doi":"10.59849/aidd.2024.9","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3403/30374629u","name":"Information technology - Artificial intelligence - Artificial intelligence concepts and terminology","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30374629u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-22T20:30:23Z","doi":"10.3403/30374629u","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/icaiihi67124.2025.11403110","name":"Art of Explainable Artificial Intelligence for Medical Image Analysis","source":"crossref","abstract":"A desktop-based artificial intelligence system developed for the classification of chest X-ray images into Normal or Pneumonia categories. The approach employs a lightweight Convolutional Neural Network (CNN) trained on carefully preprocessed grayscale chest X-rays to achieve reliable accuracy. To address the limitation of black-box AI models, the system integrates Gradient-weighted Class Activation Mapping (Grad-CAM), which generates intuitive heatmaps that highlight the specific lung regions influencing the model’s decision. A modern and user-friendly interface built with CustomTkinter enables users to upload images, receive real-time predictions, generate synthetic patient profiles, and view Grad-CAM explanations seamlessly. The solution is designed to operate efficiently on standard desktop computers without relying on external servers, making it resource-friendly and scalable. By combining predictive accuracy, interpretability, and usability, the system supports clinicians, radiologists, and medical trainees, offering a practical tool for education, clinical demonstrations, and deployment in low-resource healthcare environments.","url":"https://doi.org/10.1109/icaiihi67124.2025.11403110","authors":["Y. Srinivas","G. Shravya","K. Santhoshini","G. Pravalika","K. Ramya","K. Gopika"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-25T20:55:03Z","doi":"10.1109/icaiihi67124.2025.11403110","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9781003257721-4","name":"Statistical Algorithm for Change Point Detection in Multivariate Time Series of Medicine Data Based on Principles of Explainable Artificial Intelligence","source":"crossref","abstract":"Modern models of artificial intelligence, as a rule, work on the principle of a black box. Having trained the model on the training data, we proceed to its exploitation, relying on its accuracy, assessed in the process of training, cross-validation and testing. However, within the framework of this approach, we cannot justify the prediction of the model, since in many cases it is either completely impossible (e.g., in the case of convolutional neural networks that generate features themselves) or extremely difficult (e.g., in the case of the support vector machine, which is based on the method of optimizing a certain functional, and not on the assessment of the probability of data belonging to a certain class). Understanding how a model works increases confidence in its results. In medical practice, this means that for an individual patient, his/her individual prognosis is more important than the average probability estimate for the group in which he/she is included. In the proposed paper, an important practical problem is considered – the detection of a change points in a multidimensional time series. Also, we propose a novel method for solving this problem. Such time series are often generated by patient monitoring devices and require clear and fast classification. A clear probabilistic interpretation of the method underlying this classification greatly enhances its value in the frame of explainable artificial intelligence.","url":"https://doi.org/10.1201/9781003257721-4","authors":["D. Klyushin","A. Urazovskyi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-15T21:05:06Z","doi":"10.1201/9781003257721-4","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37547/ajbspi/volume05issue12-01","name":"Artificial Intelligence In Medical Education: Transforming Learning And Clinical Skills Development","source":"crossref","abstract":"Artificial Intelligence (AI) is rapidly reshaping medical education by providing innovative methods to enhance both theoretical knowledge and clinical skills development. AI technologies, including intelligent tutoring systems, adaptive learning platforms, simulation-based training, and virtual patient models, allow medical students to engage in personalized and interactive learning experiences. These tools enable learners to practice clinical procedures in a risk-free environment, improve diagnostic reasoning, and receive immediate feedback on their performance. Furthermore, AI supports remote and hybrid learning, increasing accessibility and flexibility in medical training. While AI offers substantial benefits, its integration requires careful oversight, evidence-based strategies, and educator involvement to ensure optimal educational outcomes. This paper highlights the transformative potential of AI in medical education and proposes strategies for its responsible and effective implementation in curricula.","url":"https://doi.org/10.37547/ajbspi/volume05issue12-01","authors":["Djalilova Gulchekhra Azamovna"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-24T11:55:20Z","doi":"10.37547/ajbspi/volume05issue12-01","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.7551/mitpress/15378.003.0006","name":"Intelligence (Artificial)","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15378.003.0006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T16:25:21Z","doi":"10.7551/mitpress/15378.003.0006","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1504/ijaih.2026.154465","name":"Medical imaging defect segmentation using artificial intelligence and deep learning techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijaih.2026.154465","authors":["Wilson Singh","Shruti Bharadwaj","Rakesh Dubey","Neeti Bharadwaj"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-30T11:30:34Z","doi":"10.1504/ijaih.2026.154465","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/s0933-3657(03)00010-1","name":"Automated monitoring of medical protocols: a secure and distributed architecture","source":"crossref","abstract":"The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.","url":"https://doi.org/10.1016/s0933-3657(03)00010-1","authors":["T. Alsinet","C. Ansótegui","R. Béjar","C. Fernández","F. Manyà"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T17:33:30Z","doi":"10.1016/s0933-3657(03)00010-1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.25299/uirlrev.2026.vol10(1).24400","name":"Legal Responbility for the Misuse of Artificial Intelligence in Criminal Offenses in Indonesia","source":"crossref","abstract":"Basically, artificial intelligence (AI) is a program or machine created by humans that aims to mimic human abilities in various types of tasks. Considering that Indonesia does not yet have specific regulations regarding AI, the ability of AI to act like humans raises legal issues, especially when AI commits criminal acts that harm others. The research method used is normative juridical research, which employs a statutory approach and a conceptual approach. Laws and regulations related to the subject of the research are evaluated through a legislative approach. The analysis results show that AI can only be considered as a legal object in Indonesian positive law. Humans are absolute legal subjects and possess awareness and intent in actions carried out through AI, the creators and users of AI are responsible for AI's actions in criminal law","url":"https://doi.org/10.25299/uirlrev.2026.vol10(1).24400","authors":["Junaidi Junaidi","Rozlinda Mohamed Fadzil","Tharin Phanuphong Vejasak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-22T11:30:05Z","doi":"10.25299/uirlrev.2026.vol10(1).24400","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21037/jmai-2025-104","name":"The financial challenges of artificial intelligence integration in healthcare: lessons from the past and considerations for the future","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-2025-104","authors":["Niklesh Akula","Ronald Rodriguez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-04T02:27:42Z","doi":"10.21037/jmai-2025-104","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.caeai.2022.100118","name":"Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education","source":"crossref","abstract":"Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012–2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.","url":"https://doi.org/10.1016/j.caeai.2022.100118","authors":["Thomas K.F. Chiu","Qi Xia","Xinyan Zhou","Ching Sing Chai","Miaoting Cheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-19T20:22:42Z","doi":"10.1016/j.caeai.2022.100118","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(81)90012-6","name":"NATO symposium on artificial and human intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(81)90012-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(81)90012-6","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1002/9781394272624.ch1","name":"An Overview of Medical Diagnostics through Artificial Intelligence‐Powered Histopathological Imaging and Video Analysis","source":"crossref","abstract":"Histopathological imaging has a substantial impact on the diagnosis and prognosis of many illnesses, including cancer, infectious infections, and autoimmune disorders. The introduction of artificial intelligence (AI) techniques to histological analysis, such as mammography, endoscopy, ultrasound, and MRI, has recently transformed medical diagnostics. The intent of this research is to give the lector with a thorough accepting of the present state of video analysis also AI-assisted histopathological imaging in the context of medical diagnosis. This article demonstrates how deep learning and machine learning algorithms can be used to automate data analysis and activities like segmentation, detection, and classification. The importance of interpretability in medical applications, as well as the usage of artificial intelligence (AI) in medical picture analysis, are also discussed. To obtain the best results, it will be necessary to give clinical decision support, disease diagnostics, and customized treatment strategies. The researchers thoroughly reviewed previous studies on the use of AI-contributed histopathology imaging for the diagnosis and treatment of medical illnesses. Furthermore, we advocate for increased multidisciplinary collaboration and research in this area.","url":"https://doi.org/10.1002/9781394272624.ch1","authors":["Atul Rathore","Praveen Lalwani","Pooja Lalwani","Rabia Musheer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-10T21:19:31Z","doi":"10.1002/9781394272624.ch1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2022.105744","name":"MSE-Fusion: Weakly supervised medical image fusion with modal synthesis and enhancement","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2022.105744","authors":["Lifang Wang","Yang Liu","Jia Mi","Jiong Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-22T06:13:25Z","doi":"10.1016/j.engappai.2022.105744","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/ic:19960641","name":"Identification of boundaries in MRI medical images using artificial neural networks","source":"crossref","abstract":"In the area of medical imaging, fully-automatic and robust segmentation techniques would have an enormous beneficial impact on clinical practice and research, by decreasing dramatically the manual effort which must otherwise be devoted to this task. Deployment of conventional image processing techniques has not so far led to a fully-automatic solution, although semi-automatic systems do exist. Since no known, robust segmentation algorithm exists, the ability of neural networks to discover regularities and features in complex data is appealing. Indeed, many preliminary attempts at neural segmentation have been described, although none yet achieves the necessary level of performance for routine application. Southampton General Hospital have a requirement to obtain lung-boundary data within an asthma research project. In connection with this requirement, we have previously reported on work in which multilayer perceptrons (MLPs) are trained using backpropagation to segment the region of the lungs in magnetic resonance images of the thorax. This is achieved by training the network to classify voxels as either boundary (voxels on the boundary between lung interior and surrounding tissue) or non-boundary. In this paper, we present the latest results using this technique. We also show how the generalisation performance of the MLP can be improved using a variety of techniques, including weight pruning algorithms.","url":"https://doi.org/10.1049/ic:19960641","authors":["I. Middleton"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-11-22T16:27:28Z","doi":"10.1049/ic:19960641","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21037/jmai-2026-1-0006","name":"Artificial intelligence’s gold rush moment: when innovation meets inflation","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-2026-1-0006","authors":["Ali Zifan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-21T08:20:55Z","doi":"10.21037/jmai-2026-1-0006","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-030-92087-6_4","name":"Data Preparation for Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_4","authors":["Aline L. de Araujo","Cailin Hardell","Wojciech A. Koszek","Jie Wu","Martin J. Willemink"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_4","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21869/2223-1552-2024-14-5-240-255","name":"Social roles of artificial intelligence. Part 2. Artificial intelligence systems in scientific research and medical practice","source":"crossref","abstract":"The relevance. The rate of spread of artificial systems with intelligence is increasing every year. This is evidenced by a large number of scientific publications, patents, research in this area and support for government programs. However, the ambiguity of its use in science and in practice leaves AI the subject of heated discussions both in the humanitarian community and among scientists who use artificial intelligence technologies in their professional activities. The purpose of the article is to analyze the role of artificial systems with intelligence in scientific and professional, and specifically, medical practice. Objectives: to study digital technologies with elements of artificial intelligence used in science and high-tech practices, their potential and risks; to identify a number of social roles that can be assigned to programs with elements of intelligence as assistants to scientists; to show the possibilities and risks of introducing AI into medical practice. Methodology . As the main approach to solving the tasks set, the article uses an interdisciplinary synthesis of philosophical reflections, statistics and the results of the practical application of AI in healthcare, which allows highlighting the anthropological and social problems of the rapid introduction of new technologies into the social sphere. Results. This article examines healthcare as an environment for the rapid introduction of AI into all system processes, from diagnosis to management of medical complexes. The possible roles of AIS as an assistant manager, analyst, consultant and qualified colleague in modern technology-oriented healthcare are shown. Conclusions. When using AI in scientific work and professional practices, it is possible to identify pragmatic, psychological and ethical aspects. Problems were found not only in the disclosure of confidential information about patients, but also in more serious shortcomings related to the effectiveness of the organization of healthcare as a social practice and the loss of important professional competencies of practitioners.","url":"https://doi.org/10.21869/2223-1552-2024-14-5-240-255","authors":["I. A. Aseeva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-08T15:07:19Z","doi":"10.21869/2223-1552-2024-14-5-240-255","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.33545/27076571.2025.v6.i2d.217","name":"Artificial intelligence and its medical application: the latest novelties about cancer","source":"crossref","abstract":"The concept of artificial intelligence was born at least seventy years ago but is only in the last two years that it has found wide use in many scientific fields, including physics, chemistry and medicine, in the form of neural networks, machine learning and deep learning, and whose inventors promise to become legend. Compared to traditional medicine, AI represents the stimulating alternative and promises to revolutionize many aspects of medicine, both in diagnosis and in therapy, that seems to never end. AI can automate a growing number of biomedical tasks, ranging from clinical decision to the design of increasingly refined and innovative care protocols.","url":"https://doi.org/10.33545/27076571.2025.v6.i2d.217","authors":["Cesare Achilli","Maurizio Siletti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-08T05:44:24Z","doi":"10.33545/27076571.2025.v6.i2d.217","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-030-92087-6_17","name":"Radiation Dose Optimization: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_17","authors":["Damiano Caruso","Domenico De Santis","Tiziano Polidori","Marta Zerunian","Andrea Laghi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_17","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(77)90021-2","name":"Natural and artificial intelligence (conceptual approach) — materials of the Fourth International Joint Conference on Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(77)90021-2","authors":["Yefim Schukin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(77)90021-2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(93)90163-6","name":"Artificial intelligence in perspective: a retrospective on fifty volumes of the Artificial Intelligence Journal","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90163-6","authors":["Daniel G. Bobrow"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90163-6","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.34218/ijaimed_04_01_001","name":"INTEGRATING ARTIFICIAL INTELLIGENCE INTO MODERN MEDICINE–EVOLUTION OF MEDICAL DIAGNOSIS, TREATMENT, PATIENT CARE, AND FUTURE DIRECTIONS","source":"crossref","abstract":"Healthcare systems are complicated and difficult, but artificial intelligence (AI) is changing the medical industry and how doctors diagnose, treat, and oversee patient care.AI's incorporation into medical research holds the potential to significantly increase healthcare's effectiveness, precision, and customization.The types of AI, their possible uses in the healthcare sector, and the advantages of using them are all covered in detail in this study.AI does, however, present certain difficulties, including moral and legal dilemmas that must be resolved by human knowledge.","url":"https://doi.org/10.34218/ijaimed_04_01_001","authors":["Vishwa Karthik Kohir","Hrishikesha Bhadra Kohir","Vineet Gautam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-15T18:14:18Z","doi":"10.34218/ijaimed_04_01_001","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s12032-023-02154-y","name":"Artificial intelligence and Italian culture: an understanding of how artificial intelligence can transform the radiation therapy landscape","source":"crossref","abstract":"Abstract The aim is to support the perception of artificial intelligence in the radiation therapy landscape.","url":"https://doi.org/10.1007/s12032-023-02154-y","authors":["Francesca De Felice"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-04T07:02:43Z","doi":"10.1007/s12032-023-02154-y","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2007.02.007","name":"Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment","source":"crossref","abstract":"Objective This work presents a novel approach for the automated prediction of the aesthetic result of breast cancer conservative treatment (BCCT). Cosmetic assessment plays a major role in the study of BCCT. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. Methodology The problem is addressed as a pattern recognition task. A dataset of images of patients was classified in four classes (excellent, good, fair, poor) by a panel of international experts, providing a gold standard classification. As possible types of objective features we considered those already identified by domain experts as relevant to the aesthetic evaluation of the surgical procedure, namely those assessing breast asymmetry, skin colour difference and scar visibility. A classifier based on support vector machines was developed from objective features extracted from the reference dataset. Results A correct classification rate of about 70% was obtained when categorizing a set of unseen images into the aforementioned four classes. This accuracy is comparable with the result of the best evaluator from the panel of experts. Conclusion The results obtained are rather encouraging and the developed tool could be very helpful in assuring objective assessment of the aesthetic outcome of BCCT.","url":"https://doi.org/10.1016/j.artmed.2007.02.007","authors":["Jaime S. Cardoso","Maria J. Cardoso"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-04-09T07:01:46Z","doi":"10.1016/j.artmed.2007.02.007","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-030-92087-6_21","name":"Currently Available Artificial Intelligence Softwares for Cardiothoracic Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_21","authors":["Yasasvi Tadavarthi","Judy Wawira Gichoya","Nabile Safdar","Imon Banerjee","Hari Trivedi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_21","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(72)90052-5","name":"The International Joint Conference on Artificial Intelligence (IJCAI)","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(72)90052-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-25T09:45:39Z","doi":"10.1016/0004-3702(72)90052-5","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2012.03.002","name":"An implicit approach to deal with periodically repeated medical data","source":"crossref","abstract":"Context Temporal information plays a crucial role in medicine, so that in medical informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. Objective In this paper, we propose an innovative approach to cope with periodic relational medical data in an implicit way. Methods We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus approach. Then, we identify some important types of temporal queries, and present query answering algorithms to answer them. Finally, we also run experiments to evaluate our approach. Results The experiments show that our approach outperforms current explicit approaches, especially as regard disk I/O. Conclusion We have provided an implicit approach to periodic data with is a consistent extension of TSQL2 (and which is thus grant interoperable with it), and we have experimentally proven that it outperforms current explicit approaches.","url":"https://doi.org/10.1016/j.artmed.2012.03.002","authors":["Bela Stantic","Paolo Terenziani","Guido Governatori","Alessio Bottrighi","Abdul Sattar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-04-12T23:47:57Z","doi":"10.1016/j.artmed.2012.03.002","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-981-15-7317-0_31","name":"Artificial Intelligence in Covid-19: Application and Legal Conundrums","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-7317-0_31","authors":["Lipsa Dash","Sambhabi Patnaik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-29T15:17:41Z","doi":"10.1007/978-981-15-7317-0_31","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/icdsaai59313.2023.10452443","name":"A Medical Chatbot Embedding with Artificial Intelligence for Self-Diagnosis","source":"crossref","abstract":"Healthcare is essential for a healthy existence. Scheduling a doctor's appointment for each health concern, however, could be incredibly difficult. Statistics show that most people already have heart or lung illness, even though many report never having been to a doctor. Most people do not have the medical knowledge to assess or diagnose their health issues accurately. In advanced human-computer interaction, natural language processing research is creating natural interaction modes like chatbots. Corporate and commercial applications are using chatbots to answer user enquiries in natural language. The objective is to create an artificial intelligence-powered medical chatbot that can diagnose illnesses and offer guidance on how to manage them effectively prior to the need for direct consultation with a physician. The suggested chatbot model acquires information about their health and then provides personalized responses and suggestions for dealing with each user's health issues while lowering healthcare expenses.","url":"https://doi.org/10.1109/icdsaai59313.2023.10452443","authors":["G. Jegan","P Kavi Priya","I.Rexiline Sheeba","G. Bindu Poojitha","G. Anjali Raghava"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-04T14:01:00Z","doi":"10.1109/icdsaai59313.2023.10452443","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2025.111251","name":"Multi-axis vision transformer for medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111251","authors":["Abdul Rehman Khan","Asifullah Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-11T15:51:59Z","doi":"10.1016/j.engappai.2025.111251","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.51473/rcmos.v1i2.2025.1653","name":"Inteligência artificial generativa na educação: revisão de literatura sobre aspectos críticos, possibilidades e desafios","source":"crossref","abstract":"The expansion of Generative Artificial Intelligence (GAI) in the educational context has brought about significant changes in teaching, learning, and assessment processes. Recent advances in large-scale language models have broadened the use of technological resources aimed at personalizing teaching, generating immediate feedback, and optimizing teaching tasks. Conversely, its adoption raises ethical, pedagogical, and socio-technical risks related to authorship, academic integrity, algorithmic biases, data privacy, and digital inequalities. This literature review analyzed publications between 2018 and 2023 on the use of GAI in education, considering scientific articles, technical reports, and international guidelines. The research used recognized databases and minimum selection criteria to ensure the credibility of the evidence consulted. The results indicate that the pedagogical use of GAI has the potential to innovate teaching practices and support formative processes, provided it is accompanied by critical teacher mediation, transparent institutional policies, and adequate teacher training. It is concluded that the responsible incorporation of GIA depends less on the technology itself and more on building governance, an ethical culture, and professional teacher development that guide its pedagogical use.","url":"https://doi.org/10.51473/rcmos.v1i2.2025.1653","authors":["Marcos Antonio da Conceição Silva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-07T03:34:14Z","doi":"10.51473/rcmos.v1i2.2025.1653","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-030-92087-6_48","name":"Artificial Intelligence- and Radiomics-Based Evaluation of Carotid Artery Disease","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_48","authors":["Michele Porcu","Riccardo Cau","Jasjit S. Suri","Luca Saba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_48","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2026.113763","name":"Optimised Canny edge detection algorithm for medical image feature mapping and extraction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.113763","authors":["A.E. Emmanuel","K.A. Amusa","T.C. Erinosho","M.T. Raji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-12T19:21:28Z","doi":"10.1016/j.engappai.2026.113763","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/s0933-3657(96)00369-7","name":"The GRAIL concept modelling language for medical terminology","source":"crossref","abstract":"The GALEN representation and integration language (GRAIL) has been developed to support effective clinical user interfaces and extensible re-usable models of medical terminology. It has been used successfully to develop the prototype GALEN common reference (CORE) model for medical terminology and for a series of projects in clinical user interfaces within the GALEN and PEN&PAD projects. GRAIL is a description logic or frame language with novel features to support part-whole and other transitive relations and to support the GALEN modelling style aimed at re-use and application independence. GRAIL began as an experimental language. However, it has clarified many requirements for an effective knowledge representation language for clinical concepts. It still has numerous limitations despite its practical successes. The GRAIL experience is expected to form the basis for future languages which meet the same requirements but have greater expressiveness and more soundly based semantics. This paper provides a description and motivation for the GRAIL language and gives examples of the modelling paradigm which it supports.","url":"https://doi.org/10.1016/s0933-3657(96)00369-7","authors":["A.L. Rector","S. Bechhofer","C.A. Goble","I. Horrocks","W.A. Nowlan","W.D. Solomon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-26T00:25:06Z","doi":"10.1016/s0933-3657(96)00369-7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/dom.70671/v2/review2","name":"Review for \"Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/dom.70671/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T21:13:09Z","doi":"10.1111/dom.70671/v2/review2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9781003315476-10","name":"Artificial Intelligence at the Service of the Detection of COVID-19","source":"crossref","abstract":"The most widely used method for diagnosing COVID-19 is the reverse transcriptase PCR test “RT-PCR,” which is a time-consuming, expensive, difficult, and complex manual technique that necessitates the involvement of professional medical personnel. Furthermore, RT-PCR testing has a wide range of sensitivity. Thus, there is an urgent need to design alternative automated methods for a more rapid and precise COVID-19 diagnostic. Recently, many researchers from around the world have proposed new ways to detect COVID-19 making use of medical pictures like chest X-rays and CT scans are combined with artificial intelligence. In this chapter, we study, analyze, discuss, and compare some of these proposed solutions.","url":"https://doi.org/10.1201/9781003315476-10","authors":["Rabiaa Tbibe","Ben Othman Soufiene","Chinmay Chakraborty","Sakli Hedi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-13T13:59:26Z","doi":"10.1201/9781003315476-10","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-981-99-8441-1_20","name":"Application of Artificial Intelligence in Ophthalmology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8441-1_20","authors":["Weixing Zhang","Yifan Xiang","Lixue Liu","Zizheng Cao","Longhui Li","You Li","Jingjing Chen","Xiaohang Wu","Haotian Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T00:02:50Z","doi":"10.1007/978-981-99-8441-1_20","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2023.102758","name":"A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification","source":"crossref","abstract":"The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the utilization of textual data from social media in the healthcare management industry is still somewhat limited when it is compared to other industries. Investigating how current machine learning and natural language processing technologies can be used in the healthcare industry to gauge public sentiment is an important study. Earlier works on healthcare sentiment analysis have utilized traditional word embedding models trained on the general and medical corpus. However, integration of medical knowledge to pre-trained word embedding models has not been considered yet. Word embedding models trained on the general corpus led to the problem of lacking medical knowledge and the models trained on the small size of the medical corpus have limitations in capturing semantic and syntactic properties. This research proposes a new word embedding model named Word Embedding Integrated with Medical Knowledge Vector (WE-iMKVec). The proposed model integrates sentiment lexicons and medical knowledgebases into the pre-trained word embedding to enrich the properties of word embedding. A new medical-aware sentiment polarity score is proposed for the utilization in learning neural-network sentiment and these vectors incorporate with the original pre-trained word vectors. The resulting vectors are enriched with lexicon vectors and the medical knowledge vectors: Adverse Drug Reaction (ADR) vector and Unified Medical Language System (UMLS) vector are used to build the proposed WE-iMKVec model. WE-iMKVec is validated on the five different social media healthcare review datasets and the empirical results showed its superiority over traditional word embedding models in medical sentiment analysis. The highest improvement can be found in the patients.info medical condition dataset where the proposed model outperforms three conventional word2vec models (Google-News, PubMed-PMC, and Drug Reviews) by 12.7 %, 31.4 %, and 25.4 % respectively in terms of F1 score.","url":"https://doi.org/10.1016/j.artmed.2023.102758","authors":["Aye Hninn Khine","Wiphada Wettayaprasit","Jarunee Duangsuwan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-08T11:48:28Z","doi":"10.1016/j.artmed.2023.102758","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2023.102745","name":"Evaluation of deep learning-based depression detection using medical claims data","source":"crossref","abstract":"Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.","url":"https://doi.org/10.1016/j.artmed.2023.102745","authors":["Markus Bertl","Nzamba Bignoumba","Peeter Ross","Sadok Ben Yahia","Dirk Draheim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-04T08:43:12Z","doi":"10.1016/j.artmed.2023.102745","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2004.01.013","name":"An efficient and scalable deformable model for virtual reality-based medical applications","source":"crossref","abstract":"Modeling of tissue deformation is of great importance to virtual reality (VR)-based medical simulations. Considerable effort has been dedicated to the development of interactively deformable virtual tissues. In this paper, an efficient and scalable deformable model is presented for virtual-reality-based medical applications. It considers deformation as a localized force transmittal process which is governed by algorithms based on breadth-first search (BFS). The computational speed is scalable to facilitate real-time interaction by adjusting the penetration depth. Simulated annealing (SA) algorithms are developed to optimize the model parameters by using the reference data generated with the linear static finite element method (FEM). The mechanical behavior and timing performance of the model have been evaluated. The model has been applied to simulate the typical behavior of living tissues and anisotropic materials. Integration with a haptic device has also been achieved on a generic personal computer (PC) platform. The proposed technique provides a feasible solution for VR-based medical simulations and has the potential for multi-user collaborative work in virtual environment.","url":"https://doi.org/10.1016/j.artmed.2004.01.013","authors":["Kup-Sze Choi","Hanqiu Sun","Pheng-Ann Heng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-05-11T10:35:09Z","doi":"10.1016/j.artmed.2004.01.013","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/s0933-3657(98)00065-7","name":"Medical data mining using evolutionary computation","source":"crossref","abstract":"In this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains.","url":"https://doi.org/10.1016/s0933-3657(98)00065-7","authors":["Po Shun Ngan","Man Leung Wong","Wai Lam","Kwong Sak Leung","Jack C.Y Cheng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-26T04:25:06Z","doi":"10.1016/s0933-3657(98)00065-7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-031-94302-7_29","name":"Artificial Intelligence Future in Oncology for Breast Cancer: Risk Prediction and Monitoring","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94302-7_29","authors":["Seema Vanjire","Devanshi Rajyaguru","Ritesh Jabade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:27:45Z","doi":"10.1007/978-3-031-94302-7_29","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.35711/aimi.v1.i1.1","name":"Rising role of artificial intelligence in image reconstruction for biomedical imaging","source":"crossref","abstract":"","url":"https://doi.org/10.35711/aimi.v1.i1.1","authors":["Xue-Li Chen","Tian-Yu Yan","Nan Wang","Karen M von Deneen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-10T05:01:12Z","doi":"10.35711/aimi.v1.i1.1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-031-94302-7_44","name":"Artificial Intelligence in Cancer Management: Bridging Gaps in Global Healthcare Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94302-7_44","authors":["Tushar Savale","Tarun Madan Kanade","Deepali Pulekar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:09:19Z","doi":"10.1007/978-3-031-94302-7_44","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.35119/maio.v6i1.137","name":"Artificial intelligence in practice: measuring its medical accuracy in oculoplastics consultations","source":"crossref","abstract":"Purpose: The aim of this study was to investigate the medical accuracy of responses produced by Chat Generative Pretrained Transformer 4 (Chat GPT-4) and DALLE-2 in relation to common questions encountered during oculoplastic consultations. Methods: The 5 most frequently discussed oculoplastic procedures on social media were selected for evaluation using Chat GPT-4 and DALLE-2. Questions were formulated from common patient concerns and inputted into Chat GPT-4, and responses were assessed on a 3-point scale. For procedure imagery, descriptions were submitted to DALLE-2, and the resulted images were graded for anatomical and surgical accuracy. Grading was completed by 5 oculoplastic surgeons through a 110-question survey. Results: Overall, 87.3% of Chat GPT-4’s responses achieved a score of 2 or 3 points, denoting a good to high level of accuracy. Across all procedures, questions about pain, bruising, procedure risk, and adverse events garnered high scores. Conversely, responses regarding specific case scenarios, procedure longevity, and proceduredefinitions were less accurate. Images produced by DALLE-2-were notably subpar, often failing to accurately depict surgical outcomes and realistic details. Conclusions: Chat GPT-4 demonstrated a creditable level of accuracy in addressing common oculoplastic procedure concerns. However, its limitations in handling case-based scenarios suggests that it is best suited as a supplementary source of information rather than a primary diagnostic or consultative tool. The current state of medical imagery generated by means of artificial intelligence lacks anatomical accuracy. Significant technological advancements are necessary before such imagery can complement oculoplastic consultations effectively.","url":"https://doi.org/10.35119/maio.v6i1.137","authors":["Adam J. Neuhouser","Alisha Kamboj","Ali Mokhtarzadeh","Andrew R. Harrison"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T17:45:29Z","doi":"10.35119/maio.v6i1.137","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2023.102611","name":"Medical visual question answering: A survey","source":"crossref","abstract":"Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.","url":"https://doi.org/10.1016/j.artmed.2023.102611","authors":["Zhihong Lin","Donghao Zhang","Qingyi Tao","Danli Shi","Gholamreza Haffari","Qi Wu","Mingguang He","Zongyuan Ge"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-08T11:53:47Z","doi":"10.1016/j.artmed.2023.102611","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37467/revtechno.v13.4804","name":"La farmacia en la nueva era de la inteligencia artificial","source":"crossref","abstract":"Artificial intelligence has become a key piece of human knowledge and due to its importance, it has been a fundamental tool for various areas. One of the applications of AI can be seen in the health domain, particularly in pharmacy, various efforts have been made to solve tasks in an automated way in the pharmaceutical area, which range from the distribution of drugs, the interaction from chatbots with patients and follow-up medical control, to support to find a diagnosis. This article describes relevant research in the area, providing an overview of the importance of AI in pharmacy.","url":"https://doi.org/10.37467/revtechno.v13.4804","authors":["Monica Doralis Ortega Urbano"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-27T16:23:15Z","doi":"10.37467/revtechno.v13.4804","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1145/3777577","name":"Proceedings of the 2025 6th International Symposium on Artificial Intelligence for Medical Sciences","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3777577","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T18:07:00Z","doi":"10.1145/3777577","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(80)90026-0","name":"Principles of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(80)90026-0","authors":["John McDermott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(80)90026-0","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(78)90003-6","name":"Artificial intelligence and natural man","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(78)90003-6","authors":["J.M. Brady"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-06-30T12:44:57Z","doi":"10.1016/0004-3702(78)90003-6","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(82)90046-7","name":"IJCAI-83: International joint conference on artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(82)90046-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(82)90046-7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(87)90086-5","name":"Third international conference on artificial intelligence and education","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90086-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(87)90086-5","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.48047/cu/54/01/952-965","name":"Perception of Medical Students about Artificial Intelligence Use in Radiology","source":"crossref","abstract":"Objective: The study aimed at evaluation of the perception of medical students regarding impact of AI on radiology. Study Design: Cross-sectional study. Place and Duration of Study: Northern Border University (NBU), Arar, Saudi Arabia from 6th January 2024 to 6th March 2024. Methodology: The study was conducted among the medical students of clinical years at NBU, Arar. After taking their consents, the students were asked to fill a pre-designed online questionnaire. The data collected from the online questionnaire was evaluated using Statistical Package of Social Sciences Version 20. The continuous data was analyzed with the help of Student’s t-test while Pearson’s Chi-square was applied on nominal data. P value of <0.05 was considered significant.","url":"https://doi.org/10.48047/cu/54/01/952-965","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-13T15:51:45Z","doi":"10.48047/cu/54/01/952-965","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1097/acm.0000000000004183","name":"Artificial Intelligence in Medical Education","source":"crossref","abstract":"To the Editor: I greatly appreciated the commentary by Dr. Carin 1 for calling attention to the frontier of artificial intelligence (AI) within medical education. I agree with his argument for the inclusion of AI in medical education and would like to propose a direction for future curriculum development. An AI medical curriculum should provide trainees with the skills to critically evaluate AI applications akin to critiquing a research article introducing a new medication, procedure, or surgical technique. Machine learning and AI applications will become more common in clinical medicine, and physicians must have the expertise to evaluate whether to incorporate these algorithms into their practice. The assessment of AI applications requires a unique curriculum because current evidence-based medicine (EBM) guidelines are incomplete for the evaluation of AI medical algorithms. The issues of “black box” interpretability, data security, and decision liability create problems not addressed by traditional biostatistics. 2 Unfortunately, there is not yet a consensus methodology for critiquing AI algorithms for medical use. However, recent publications in Nature3 and JAMIA Open4 have begun to offer promising schemas for systematic evaluation. The schema proposed by Park and colleagues 4 is the most complete and was published only in October 2020. Ultimately, I believe medical educators should allow for the field of medical AI to develop a consensus best practice method for algorithm evaluation. In many ways, the current AI environment mirrors the EBM paradigm of the 1990s, where a central work, such as the JAMA article “Users’ guides to the medical literature,” 5 must be established before widespread adoption into medical education. Once a similar consensus is formed within medical AI, educators will have a clear outline to develop a curriculum that trains students to critique AI products for inclusion into their clinical practice.","url":"https://doi.org/10.1097/acm.0000000000004183","authors":["Thomas Robert Savage"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-25T16:01:44Z","doi":"10.1097/acm.0000000000004183","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9781032644509-2","name":"Applications of Transformer in Medical Imaging","source":"crossref","abstract":"Medical images are important part of modern healthcare, helping in disease diagnosis, treatment planning, and monitoring of patients. In healthcare applications, biomedical images comprise approximately 90% data. Recent advancements in deep learning have contributed novel techniques in image analysis, and one of these techniques, Transformer, which was mostly used for natural language processing, has emerged as one of the useful tools in the field of medical image analysis. In this chapter, we investigate several applications of Transformers in the field of biomedical imaging, emphasizing on their capacity to improve image classification, detection, segmentation, and synthesis. This chapter covers the fundamental concepts behind Transformers and their architecture, along with self-attention techniques. It emphasizes on adapting pre-trained models toward medical imaging applications, minimizing the requirement for huge amounts of labeled datasets and also enhancing model generalization. The primary applications of Transformers in medical images, such as disease detection, an atomically segmentation, and image synthesis, are discussed. In conclusion, the fascinating integration of Transformers and medical imaging has the potential to revolutionize the healthcare domain along with improving diagnostic accuracy, reducing human intervention, and enhancing patient care. This chapter contributes to a comprehensive understanding of how Transformers act as the state-of-the-art in biomedical imaging and inspire further exploration at this exciting enhancement of deep learning and healthcare.","url":"https://doi.org/10.1201/9781032644509-2","authors":["Satish Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T15:28:02Z","doi":"10.1201/9781032644509-2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-323-99421-7.00007-6","name":"A conceptual framework for Artificial Intelligence of Medical Things (AIoMT)","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99421-7.00007-6","authors":["Hamed Nozari","Reza Tavakkoli-Moghaddam","Javid Ghahremani-Nahr","Esmaeil Najafi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:36:57Z","doi":"10.1016/b978-0-323-99421-7.00007-6","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1080/0142159x.2026.2663855","name":"Undergraduate medical students and artificial intelligence: A global systematic review and meta-analysis using a knowledge–attitude–practice framework","source":"crossref","abstract":"Background Artificial intelligence (AI), including deep learning and large language models, is increasingly reshaping healthcare and medical education. Since the public release of generative AI tools such as ChatGPT, medical students have become a particularly exposed group, often engaging with these technologies without standardized curricular guidance. Although previous reviews have explored readiness, acceptance, or anxiety, the relationship between students' knowledge, attitudes, and practical engagement with AI has not been comprehensively synthesized. This study therefore examined undergraduate medical students' engagement with AI using a Knowledge-Attitude-Practice (KAP) framework. Methods This systematic review and meta-analysis was reported in accordance with PRISMA 2020. The research question was structured using the PCC framework. PubMed, Scopus, and Web of Science were searched up to September 30, 2025. Eligible studies included cross-sectional and psychometric validation studies addressing AI-related perceptions among undergraduate medical students. Methodological quality was assessed using the AXIS tool. Because of substantial heterogeneity in instruments and reporting, quantitative synthesis was restricted to 11 studies using the MAIRS-MS scale and reporting comparable percentage-based outcomes. Pooled estimates were calculated using Freeman-Tukey double arcsine transformation and a random-effects DerSimonian-Laird model. Results Forty-three studies met the inclusion criteria, including 37 cross-sectional and 6 psychometric validation studies. Attitudes were the most frequently explored dimension, followed by knowledge and practice. In the 11 studies included in the quantitative synthesis, pooled estimates showed moderate knowledge (0.55; 95% CI: 0.49-0.61), positive attitudes (0.64; 95% CI: 0.59-0.69), and comparable practice levels (0.63; 95% CI: 0.59-0.67). Heterogeneity remained high, while Egger's test showed no significant small-study effects. Conclusions Medical students appear receptive to AI, but their knowledge remains moderate and their practical engagement reflects this limited preparation. These findings support the need for structured undergraduate AI curricula integrating theoretical, applied, and ethical-legal competencies.","url":"https://doi.org/10.1080/0142159x.2026.2663855","authors":["Paolo Visci","Francesco Lupelli","Francesco Calò","Gianmarco Sirago","Biagio Solarino","Alessandro Dell’Erba","Davide Ferorelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-01T03:31:58Z","doi":"10.1080/0142159x.2026.2663855","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/dom.70621/v1/review1","name":"Review for \"Artificial Intelligence in Screening and Grading Diabetic Eye Diseases: A Systematic Review from Algorithms to Clinic\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/dom.70621/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-09T21:05:07Z","doi":"10.1111/dom.70621/v1/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.51473/rbmed.v1i1.2026.19","name":"Inteligência Artificial No Diagnóstico Por Imagem: Uma Meta-Análise Sobre Impactos No Diagnóstico Precoce","source":"crossref","abstract":"Artificial intelligence (AI) has profoundly transformed radiology and diagnostic imaging, offering computational tools capable of identifying patterns with high accuracy. This meta-analysis synthesizes the findings of three scientific studies indexed in SciELO, covering the use of machine learning and deep learning algorithms in the diagnosis of eye diseases, pulmonary nodules, breast cancer, and various lesions in imaging exams. The analyzed studies demonstrate that systems based on convolutional neural networks (CNNs) can achieve diagnostic accuracies equal to or greater than those of human experts in specific visual pattern recognition tasks. The study by Abed and Al-Bakry (2024) demonstrated 99.9% accuracy in classifying eight eye diseases through fundoscopy. The works of Santos et al. (2019) and Koenigkam-Santos et al. (2019) consolidate the theoretical foundations of AI applied to radiology, addressing everything from computer-assisted diagnosis to radiomics and precision medicine. The integrated analysis of these findings points to consistent benefits in increasing diagnostic sensitivity, reducing false negatives, and optimizing clinical workflow, especially in cancer screening. It is concluded that AI represents an essential complementary resource to the work of the radiologist, with the potential to expand access to early diagnosis, although further multicenter studies with prospective data are still needed for validation in diverse clinical scenarios.","url":"https://doi.org/10.51473/rbmed.v1i1.2026.19","authors":["Eduarda Parzianello Lubi","Nicole Parzianello Lubi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-15T10:40:34Z","doi":"10.51473/rbmed.v1i1.2026.19","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-032-18894-6_3","name":"Harnessing Artificial Intelligence to Tackle Biofilm Infections: Advances, Challenges, and Perspectives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-18894-6_3","authors":["Oussama Grari","Said Ezrari","Imane El Yandouzi","Elmostapha Benaissa","Yassine Ben Lahlou","Mohammed Lahmer","Abderrazak Saddari","Mostafa Elouennass","Adil Maleb"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T00:37:26Z","doi":"10.1007/978-3-032-18894-6_3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.23880/phoa-16000184","name":"Role of Artificial Intelligence in Medical Imaging for Accelerated Response in Dual Pandemics of TB and Covid-19","source":"crossref","abstract":"The world is facing dual pandemics of TB and Covid-19 simultaneously. Digital Chest X-ray is the mainstay for screening chest TB. In community setting for TB screening, its likely that Covid-19 and similar community acquired pneumonia are likely to be detected. The key milestones of TB elimination need recalibration under the shadow of Covid-19 pandemic. Technological innovations like deep learning or computer assisted detection (CAD) can assist in instant screening and triage for TB in such situations. Innovative solutions like these have the potential to reduce the burden on the healthcare ecosystem and ensure access to healthcare imaging to the global population. Organizations will need to think through well while deploying and integrating these solutions seamlessly in the existing workflows. Organizations should understand the challenges AI solutions face and have safeguards to mitigate these risks. Reliable technological solutions are the best hope mankind has to empower experts and ensure imaging is accessible, affordable and available to all.","url":"https://doi.org/10.23880/phoa-16000184","authors":["Amit kharat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-22T10:37:18Z","doi":"10.23880/phoa-16000184","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-981-95-4338-0_12","name":"Interdisciplinary Collaboration for AI Development","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-4338-0_12","authors":["Takanobu Hirosawa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T15:06:46Z","doi":"10.1007/978-981-95-4338-0_12","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/pbhe050e_bm","name":"Back Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbhe050e_bm","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T03:11:32Z","doi":"10.1049/pbhe050e_bm","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3126/gmj.v4i1.79129","name":"Artificial Intelligence in medical field:  Challenges and consequences","source":"crossref","abstract":"The emergence of artificial intelligence (AI) has opened up a new realm of possibilities across various industries, with healthcare being no exception. AI has shown immense potential to revolutionize medical practices, from early disease detection and personalized treatment plans to accelerating drug discovery. However, these advancements also bring substantial challenges and implications that require careful consideration to ensure AI integration serves the best interests of both patients and healthcare providers.","url":"https://doi.org/10.3126/gmj.v4i1.79129","authors":["Bibhuti Nath Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-30T07:47:27Z","doi":"10.3126/gmj.v4i1.79129","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(85)90068-2","name":"First Spanish meeting on artificial intelligence for databases","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90068-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90068-2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(77)90013-3","name":"Artificial intelligence—A personal view","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(77)90013-3","authors":["D. Marr"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(77)90013-3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-323-99421-7.00015-5","name":"Artificial intelligence in healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99421-7.00015-5","authors":["Sanskar Srivastava","Amit Kumar Tyagi","Sajidha S. A."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:37:48Z","doi":"10.1016/b978-0-323-99421-7.00015-5","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9781003394068-4","name":"Balancing Trust and Reliance","source":"crossref","abstract":"AI is becoming ever more pervasive in healthcare. Radiography, as a technologically advanced profession, has experienced an influx of various AI-based assistive technologies which, due in part to government incentivisation, are being increasingly integrated into the clinical setting. In the UK, revisions to the Health and Care Professions Council s (HCPC) Standards of Proficiency (SoP), valid from September 2023, place a requirement on registered radiographers to ‘demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (standard 12.25). Whilst the radiology and radiography professions have been accustomed to adopting new technologies, the advent of deep learning has presented new challenges for even the technologically proficient user, and more concerning challenges may exist for those for whom clinical AI and deep learning technologies remain areas which require further exploration and understanding. This chapter aims to clarify barriers to the responsible use of AI in the clinical setting related to the human interaction with advanced systems and address issues of potential over- and underreliance on emerging technologies. It will introduce the reader to both ‘sides of the coin’ – how to ensure appropriate trust relating to the technology used and how this might be achieved, leading to improved human-computer interaction, with a focus on the vital roles which radiographers may play in responsible technology use and acceptance.","url":"https://doi.org/10.1201/9781003394068-4","authors":["Clare Rainey"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T09:15:48Z","doi":"10.1201/9781003394068-4","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/3-540-60025-6_169","name":"Representing medical context using rule-based object-oriented programming techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-60025-6_169","authors":["Michel Dojat","François Pachet"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-26T12:30:00Z","doi":"10.1007/3-540-60025-6_169","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/pbhe050e_ch1","name":"Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective","source":"crossref","abstract":"The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult due to explainability issues. Explainable AI (XAI) has been studied as a potential remedy for the problems with current AI methods. The usage of ML with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS has played a crucial role in systems' attempts to improve patient safety and the standard of care, particularly for non-communicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad overview of the application of XAI in MDSS toward various infectious diseases, summarizes recent research on the use and effects of MDSS in healthcare with regard to non-communicable diseases, and offers suggestions for users to keep in mind as these systems are incorporated into healthcare systems and utilized outside of contexts for research and development.","url":"https://doi.org/10.1049/pbhe050e_ch1","authors":["Oluwafisayo Babatope Ayoade","Tinuke Omolewa Oladele","Agbotiname Lucky Imoize","Joseph Bamidele Awotunde","Adetoye Jerome Adeloye","Segun Omotayo Olorunyomi","Ayorinde Oladele Idowu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T03:11:32Z","doi":"10.1049/pbhe050e_ch1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3390/biom11010090","name":"Application of Artificial Intelligence for Medical Research","source":"crossref","abstract":"The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [...]","url":"https://doi.org/10.3390/biom11010090","authors":["Ryuji Hamamoto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-01-12T20:11:31Z","doi":"10.3390/biom11010090","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1177/10732748231159553/v1/review1","name":"Review for \"The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/10732748231159553/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-28T16:01:37Z","doi":"10.1177/10732748231159553/v1/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/dom.70671/v1/review1","name":"Review for \"Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/dom.70671/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T21:13:09Z","doi":"10.1111/dom.70671/v1/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37497/rev.artif.intell.education.v3i00.3","name":"Challenges and benefits of 7 ways artificial intelligence in education sector","source":"crossref","abstract":"Objective: This article aims to investigate the integration of artificial intelligence (AI) into the education sector and explore its impact on teaching and learning in the digital era. By synthesizing existing literature, the study seeks to provide insights into the benefits and challenges associated with AI's incorporation in education. Method: A comprehensive review of the literature was conducted using a narrative synthesis approach. Peer-reviewed articles explicitly defining AI within an educational context, published in English and subjected to peer review, were included. Five independent reviewers evaluated research quality, extracted relevant data, and analyzed search results to ensure a comprehensive overview of the field. Results: The study reveals the significant influence of AI on education. Its implementation is identified as a pivotal and strategic element of educational advancement. The growing role of AI as a digital assistant is particularly noteworthy, facilitating personalized learning experiences for students by tailoring educational resources to individual preferences and subject-specific needs. AI technologies are also instrumental in supporting both teachers and students across various aspects of education. Conclusions: The integration of AI in education marks a transformative phase for the industry. While AI offers substantial benefits in terms of enhanced learning experiences and increased teacher efficiency, potential drawbacks and concerns are also evident. Privacy, security, and safety issues arising from AI development underscore the need for thoughtful implementation. Thus, AI's impact on education is multifaceted, presenting both positive and negative implications. This study serves as a foundation for stakeholders in education to navigate the evolving landscape, making informed decisions that harness the potential of AI while mitigating its risks. By comprehensively assessing AI's current and future role in education, this research contributes to shaping a balanced and effective utilization of AI technologies in the pursuit of educational excellence.","url":"https://doi.org/10.37497/rev.artif.intell.education.v3i00.3","authors":["Sonali Tambuskar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-14T15:15:10Z","doi":"10.37497/rev.artif.intell.education.v3i00.3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-444-88650-7.50033-0","name":"Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-444-88650-7.50033-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-06-18T20:56:51Z","doi":"10.1016/b978-0-444-88650-7.50033-0","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.5772/intechopen.96112","name":"Artificial Intelligence Assisted Innovation","source":"crossref","abstract":"Artificial Intelligence Assisted Innovation (AIAI) is a technology designed to improve innovation productivity by helping human innovators with all the support tasks that kindle the creative spark, and also with sorting out innovative propositions for their merit. Innovation activity is mushrooming and hence innovative history is an ever growing data accumulation. AIAI identified a universal innovation map, which is processed like the tape in a Turing machine, only here in the Innovation Turing machine, marking an innovation pathway. By mapping innovative history onto these maps, one enables the growing record of innovation history to guide current innovation as to merit, expected cost, estimated duration, etc. Using Monte Carlo and Discriminant Analysis, an Artificial Innovation Assistant runs a dialog with the human innovator with a net effect of accelerated innovation. Users of AIAI are expected to exhibit a commanding lead over innovators guided only by their creativity.","url":"https://doi.org/10.5772/intechopen.96112","authors":["Gideon Samid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-02-19T16:01:46Z","doi":"10.5772/intechopen.96112","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/ic:19960183","name":"Safety reasoning in medical decision support","source":"crossref","abstract":"In protocol based medical care, protocols and guidelines are employed to standardise some aspect of treatment: to regularise the management of a disease or to enforce a particular protocol during a clinical trial of a new therapeutic agent. Some guidelines do not need to be followed rigorously, whereas adherence to a clinical trial protocol is essential if statistical analysis of trial results is to be scientifically valid or if the safety of patients is to be ensured. The use of computers in the application of clinical guidelines and protocols is becoming more widespread and improved compliance and more complete data capture for subsequent analysis of clinical trial results have been reported. In particular, decision support systems for protocol based care in oncology have been studied for more than 20 years. The safety aspects of such systems were studied in an earlier project and some of the resulting generic safety principles were encoded in OaSiS, a prototype knowledge based system supporting protocol based care in cancer management. A small number of computer aids for designing new clinical trials, therapy plans and associated protocol documents have been implemented. Notable examples are DaT and OPAL. We summarise work on the empirical derivation, formalisation and implementation of safety reasoning for cancer management and describe the reuse of the safety knowledge obtained to generate specific safety clauses semi automatically during clinical trial design and protocol document assembly. (3 pages)","url":"https://doi.org/10.1049/ic:19960183","authors":["P. Hammond"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-11-22T19:40:26Z","doi":"10.1049/ic:19960183","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-981-95-4338-0_2","name":"Historical Evolution of Diagnostic Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-4338-0_2","authors":["Takanobu Hirosawa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T15:06:38Z","doi":"10.1007/978-981-95-4338-0_2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.31525/ct1-nct04215224","name":"Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04215224","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-03T03:43:51Z","doi":"10.31525/ct1-nct04215224","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-443-44021-2.00012-9","name":"Artificial intelligence in cancer medicine: Nanotherapeutics and integrative approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44021-2.00012-9","authors":["Nazia Hassan","Uzair Ali","Asgar Ali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T12:02:29Z","doi":"10.1016/b978-0-443-44021-2.00012-9","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2139/ssrn.5380483","name":"The Use of Artificial Intelligence in SETI (Search for Extraterrestrial Intelligence): A Literature Review","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5380483","authors":["Abid Hossain Rion","Mashrufa Meghla Any"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-12T10:09:39Z","doi":"10.2139/ssrn.5380483","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1002/eng2.70518/v1/review2","name":"Review for \"Artificial Intelligence in Wire Arc Additive Manufacturing: A Systematic Review and Patent Landscape Analysis\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70518/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T21:12:02Z","doi":"10.1002/eng2.70518/v1/review2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2022.105311","name":"A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2022.105311","authors":["Ebubekir Kaya","Beyza Gorkemli","Bahriye Akay","Dervis Karaboga"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-31T11:30:32Z","doi":"10.1016/j.engappai.2022.105311","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.22215/timreview/1286","name":"Editorial: Artificial Intelligence","source":"crossref","abstract":"Welcome to the December issue of the Technology Innovation Management Review. This is the second edition, after the one published in October 2019, which includes articles that were initially presented at a conference of the International Society for Professional Innovation Management (ISPIM), which took place June 16-19, 2019, in Florence, Italy. The ISPIM conference in","url":"https://doi.org/10.22215/timreview/1286","authors":["Stoyan Tanev"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-20T13:15:02Z","doi":"10.22215/timreview/1286","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/dom.70671/v1/review6","name":"Review for \"Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/dom.70671/v1/review6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T21:13:09Z","doi":"10.1111/dom.70671/v1/review6","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37497/rev.artif.intell.educ.v6ii.65","name":"Editorial Perspective on the 2025 Edition (V6) of the Review of Artificial Intelligence in Education: Advances, challenges, and theoretical convergences in global AI-in-education research","source":"crossref","abstract":"Purpose: To present an editorial and analytical synthesis of the 2025 issue (Volume 6) of the Review of Artificial Intelligence in Education, highlighting conceptual, methodological, and thematic trends emerging across the international studies published on AI in education. Methodology: An integrative review of all articles in the edition was conducted, examining theoretical perspectives, methodological designs, explanatory models, institutional diversity, and cross-national contributions. The synthesis draws on comparative analysis of findings from Europe, Asia, Latin America, the Caribbean, Africa, and Brazil. Findings: The issue reveals four major axes: (1) AI governance and ethics, with growing emphasis on regulation, explainability, and compliance frameworks (EU; ISO 42001). (2) Pedagogical practices and real-world AI use by students and educators, showing tensions between efficiency gains and educational risks. (3) Student vulnerability and digital inequality, underscoring the need for inclusive policy frameworks. (4) Methodological diversification, marked by PLS-SEM, Social Network Analysis, PRISMA-based reviews, normative-institutional analyses, and conceptual modeling in generative AI. Conclusion: The 2025 edition reinforces the journal’s role as a global reference in AI-in-education research, combining scientific rigor, methodological plurality, and commitment to open science. The contributions deepen international dialogue on responsible AI governance, innovative teaching practices, and equitable digital transformation.","url":"https://doi.org/10.37497/rev.artif.intell.educ.v6ii.65","authors":["Altieres de Oliveira Silva","Diego Janes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-04T20:55:28Z","doi":"10.37497/rev.artif.intell.educ.v6ii.65","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1037/t95976-000","name":"Medical Artificial Intelligence Readiness Scale for Medical Students--Persian Version","source":"crossref","abstract":"","url":"https://doi.org/10.1037/t95976-000","authors":["Nasrin Khajeali","Noushin Kohan","Sajjad Rezaei","Alia Saberi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-11T13:57:54Z","doi":"10.1037/t95976-000","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.4103/mgr.medgasres-d-24-00113","name":"Recent advances in medical gas sensing with artificial intelligence–enabled technology","source":"crossref","abstract":"Recent advancements in artificial intelligence–enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency in healthcare. Medical gases, including oxygen, nitrous oxide, and carbon dioxide, are essential for various treatments but pose health risks if improperly managed. This review highlights the integration of artificial intelligence in medical gas sensing, enhancing traditional sensors through advanced data processing, pattern recognition, and real-time monitoring capabilities. Artificial intelligence improves the ability to detect harmful gas levels, enabling immediate intervention to prevent adverse health effects. Moreover, developments in nanotechnology have resulted in advanced materials, such as metal oxides and carbon-based nanomaterials, which increase sensitivity and selectivity. These innovations, combined with artificial intelligence, support continuous patient monitoring and predictive diagnostics, paving the way for future breakthroughs in medical care.","url":"https://doi.org/10.4103/mgr.medgasres-d-24-00113","authors":["Chitaranjan Mahapatra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-20T03:01:55Z","doi":"10.4103/mgr.medgasres-d-24-00113","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2139/ssrn.4843988","name":"Artificial Intelligence and Machine Learning in Business Intelligence, Finance, and E-commerce: a Review","source":"crossref","abstract":"This research examines the transformative effects of artificial intelligence (AI) and machine learning (ML) on business intelligence (BI), finance, and e-commerce, focusing on recent advancements and emerging trends. AI and ML have significantly enhanced BI by enabling more accurate predictive analytics, real-time data processing, and improved decision-making capabilities. In the financial sector, AI-driven algorithms are revolutionizing risk management, fraud detection, and personalized financial services, leading to more secure and efficient systems. The e-commerce industry is experiencing a major shift with AI and ML enhancing customer experience through personalized recommendations, dynamic pricing, and intelligent chatbots. The paper also highlights the integration of AI with big data analytics and the Internet of Things (IoT), creating a more interconnected and data-driven business landscape. Ethical considerations and challenges such as data privacy, algorithmic bias, and the need for regulatory frameworks are discussed. Additionally, the review identifies key areas for future research, including the development of explainable AI (XAI) to improve transparency and trust, and the potential for AI to drive sustainable business practices. This paper provides a detailed overview of how AI and ML are transforming BI, finance, and e-commerce, offering valuable insights for researchers, practitioners, and policymakers looking to leverage these technologies for competitive advantage and sustainable growth.","url":"https://doi.org/10.2139/ssrn.4843988","authors":["Nitin Rane","Saurabh Choudhary","Jayesh Rane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-30T11:15:08Z","doi":"10.2139/ssrn.4843988","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1080/08839510490442102","name":"BOOK REVIEW: MOBILE ROBOTICS - A PRACTICAL INTRODUCTION, 2ND EDITION, BY ULRICH NEHMZOW","source":"crossref","abstract":"I used to think you needed very expensive sensors, elaborate algorithms, and, above all, significant computing power to build a mobile robot that learns. Well, this book made me reconsider. In his ...","url":"https://doi.org/10.1080/08839510490442102","authors":["ALEXANDER K. SEEWALD"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-05-10T20:03:43Z","doi":"10.1080/08839510490442102","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21569/2222-7415-2020-10-4-179-185","name":"THE IMPACT OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY: AS PERCEIVED BY MEDICAL STUDENTS","source":"crossref","abstract":"","url":"https://doi.org/10.21569/2222-7415-2020-10-4-179-185","authors":["P. Kasetti","R. Botchu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-18T17:34:59Z","doi":"10.21569/2222-7415-2020-10-4-179-185","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.5772/intechopen.115279","name":"Perspective Chapter: Future Impact of Artificial Intelligence on Medical Subspecialties – Dermatology and Neurology","source":"crossref","abstract":"Without a doubt, academic medicine and research fields have been greatly impacted by the recent introduction of artificial intelligence (AI) machines and software programs. For subspecialties, such as dermatology and neurology, AI systems have been integrated to assist in the management of workflow in the office and clinical settings. This chapter highlights a review of the most up-to-date AI tools for clinical applications in dermatology, and its impact on telemedicine and medical education. Our authors also comment on challenges with AI in dermatology, particularly with consumer trust. Within the field of neurology, the authors examined the impact of AI technologies in imaging interpretation, electroencephalography (EEG) interpretation, in the neuro-intensive care unit (ICU) setting, for stroke events, epilepsy, and neurodegenerative conditions. We conclude our chapter with a brief overview of job security and the implications for medical professionals to work more with AI in the future.","url":"https://doi.org/10.5772/intechopen.115279","authors":["Nadia Abidi","Zehara Abidi","Brian Hanrahan","Mini Parampreet Kaur","Yemesrach Kerego","Anna Ng Pellegrino","Venkatraman Thulasi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T11:51:52Z","doi":"10.5772/intechopen.115279","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21955/mep.1115718.1","name":"Impact of using artificial intelligence based scribing tools on training medical students: A qualitative study","source":"crossref","abstract":"Artificial Intelligence based clinical note taking software solutions (Commonly known as AI scribing) are fast spreading in all clinical domains. This popularity has made not only clinicians, but medical students using this software. The lack of widely accepted usage guidance means their use is largely unregulated, even by medical students. Therefore, this qualitative study was conducted among undergraduate medical educators to explore the effects of using AI Scribing on the learning experience of Medical Students. The results indicated both positive and negative impacts. The most standing out concerns were the hindrances on developing clinical decision making skills, potential legal and ethical threats around privacy and confidentiality issues and confidentiality concerns. High quality notes and potential improvement of doctor-patient relationships were the main positives. Therefore AI Scribing tools have multiple positives and concerns alike. Until more regulations and usage recommendations emerge, they better be used with caution during training.","url":"https://doi.org/10.21955/mep.1115718.1","authors":["Nishan Silva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T03:17:10Z","doi":"10.21955/mep.1115718.1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.26650/b/t3.2024.40.027","name":"Use of Artificial Intelligence in Dentistry","source":"crossref","abstract":"","url":"https://doi.org/10.26650/b/t3.2024.40.027","authors":["Ümit Güray Efes","Dilan Topkıran"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-30T09:11:35Z","doi":"10.26650/b/t3.2024.40.027","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2139/ssrn.7351051","name":"Advancing Normalisation Process Theory for Artificial Intelligence Integration in Medical Education Assessment","source":"crossref","abstract":"The integration of Artificial Intelligence (AI) into medical education assessment represents a paradigm shift from traditional psychometric models to data-driven, adaptive evaluation systems. Despite the rapid maturation of generative and predictive technologies, institutional implementation remains hindered by a complex architecture of systemic and ethical challenges. This study employs a two-phase methodological design, combining a systematic literature review of seventy-seven empirical studies with Interpretive Structural Modelling (ISM) and the Nominal Group Technique (NGT) involving nine international experts. The analysis identifies twenty-three discrete challenges, categorised into nine mega-challenges, and establishes a five-level hierarchy of interdependencies. The results indicate that ’Lack of AI Literacy and Critical Thinking’ acts as the foundational root determinant, carrying the highest structural weight (Importance Score = 22). Crucially, this study advances Normalisation Process Theory (NPT) by proposing a conditional hierarchical extension. While traditional NPT treats its four generative mechanisms - Coherence, Cognitive Participation, Collective Action, and Reflexive Monitoring - as non-linear and interacting, the findings of this study suggest that for algorithmic interventions, successful normalisation is contingent upon pre-existing cognitive readiness (literacy) and structural architectural properties (transparency). This hierarchical advancement provides a strategic roadmap for medical educators to navigate the ’algorithmic frontier’ whilst preserving the essential tenets of clinical wisdom and pedagogical integrity.","url":"https://doi.org/10.2139/ssrn.7351051","authors":["Morteza Rezaeizadeh","Shameq Sayeed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-29T07:15:26Z","doi":"10.2139/ssrn.7351051","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/b978-0-323-95462-4.00013-3","name":"Medical image super-resolution","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95462-4.00013-3","authors":["Wafaa Abdulhameed Al-Olofi","Muhammad Ali Rushdi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-02T07:43:04Z","doi":"10.1016/b978-0-323-95462-4.00013-3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s40670-024-02218-2","name":"An Introductory Module of Generative Artificial Intelligence in Medical Education","source":"crossref","abstract":"The lack of training in generative AI (GenAI) among most medical educators poses an important challenge. Recognizing this need, an introductory session was created. A pre- and post-survey was distributed to gain insights on the impact of the module and identify concerns about GenAI in medical education. Scores for each statement were higher upon completion of the module session. Feedback showed that the module was well-received and reflected an openness to discuss the benefits and challenges of GenAI. We need to help educators and learners how to use GenAI tools judiciously and understand their benefits and limitations. Supplementary information The online version contains supplementary material available at 10.1007/s40670-024-02218-2.","url":"https://doi.org/10.1007/s40670-024-02218-2","authors":["Jorge Cervantes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-07T10:09:18Z","doi":"10.1007/s40670-024-02218-2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.36922/aih.2809","name":"The perspectives of eye care professionals on the integration of artificial intelligence in eye care practices: A systematic review","source":"crossref","abstract":"Artificial intelligence (AI) technology has recently been integrated into the health-care industry, including in optometry and ophthalmology. This systematic review assessed the opinions (i.e., perspectives, concerns, and degrees of acceptance) of eye care professionals regarding AI integration into eye care practices. The literature search was conducted using the PubMed and MEDLINE databases. A total of 780 related articles were identified. Among these articles, 304 duplicates were removed, 450 articles were excluded after reviewing the abstract, and 18 articles were excluded after reviewing the full text as these articles were not relevant and/or did not report surveys. The remaining eight included studies were assessed accordingly. Most ophthalmologists and optometrists had a positive perception toward incorporating AI into eye care practices, and these professionals shared that AI would effectively enhance clinical eye care practices. However, certain eye care professionals were concerned about the diagnostic accuracy of AI, the high implementation costs, privacy issues, and the quality of AI-integrated patient care. Several eye care professionals also expressed concerns that AI technology could eventually replace some of their major responsibilities in the practice, suggesting that stakeholders should essentially address these concerns and ensure that AI integration in eye care practices is implemented thoughtfully and ethically to maximize its benefits while preserving the quality of patient care. Nonetheless, this systematic review highlighted the predominantly positive attitude among eye care professionals toward AI integration into eye care practices, warranting further research and collaboration between AI developers and eye care professionals to effectively address the current challenges.","url":"https://doi.org/10.36922/aih.2809","authors":["Obehi Suzan Idogen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-25T08:57:26Z","doi":"10.36922/aih.2809","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.31219/osf.io/m5n96","name":"Applications of Artificial Intelligence in Medical Imaging: Current State and Future Perspectives","source":"crossref","abstract":"Applications of Artificial Intelligence in Medical Imaging: Current State and FuturePerspectives","url":"https://doi.org/10.31219/osf.io/m5n96","authors":["Ansab Niazi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-25T05:01:56Z","doi":"10.31219/osf.io/m5n96","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2025.111577","name":"Medical artificial intelligence for early detection of lung cancer: A survey","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111577","authors":["Guohui Cai","Ying Cai","Zeyu Zhang","Yuanzhouhan Cao","Lin Wu","Daji Ergu","Zhibin Liao","Yang Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-15T16:56:11Z","doi":"10.1016/j.engappai.2025.111577","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-031-65038-3_21","name":"Artificial Intelligence and Assessment Generators in Education: A Comprehensive Review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65038-3_21","authors":["Youness Boutyour","Abdellah Idrissi","Lorna Uden"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-03T09:02:10Z","doi":"10.1007/978-3-031-65038-3_21","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.61092/iaea.9olq-aqa9","name":"Clinical Implementation of Artificial Intelligence Systems in Medical Imaging and Radiotherapy","source":"crossref","abstract":"Artificial intelligence (AI) has significant potential to impact processes in science and technology, including in the area of human health. While bringing potential benefits to healthcare, the application of AI systems also introduces new challenges and potential risks. This publication is aimed at clinically qualified medical physicists, who are health professionals uniquely positioned to bridge the gap between complex AI systems and practical clinical applications. It provides comprehensive guidance for the clinical implementation of imaging based AI systems in medical imaging and radiotherapy, addressing the entire process, from the initial assessment of needs through selection, commissioning, ongoing (quality) management and eventual decommissioning. Although the primary focus is on imaging based AI systems, the guidance provided in this publication is broadly applicable to non-imaging based AI systems as well.","url":"https://doi.org/10.61092/iaea.9olq-aqa9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-16T08:39:44Z","doi":"10.61092/iaea.9olq-aqa9","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/bf00141759","name":"Book review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00141759","authors":["Ajit Narayanan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-08-24T13:33:15Z","doi":"10.1007/bf00141759","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37497/sdgs.v6istudies.43","name":"Artificial Intelligence (AI) in Competitive Intelligence (CI) Research","source":"crossref","abstract":"Objective: The rapid advancement of artificial intelligence (AI) has significantly influenced research and academic practices, prompting universities to create guidelines for student use of large language models (LLMs). However, there is ongoing debate among academic journals and conferences regarding the necessity of reporting AI assistance in manuscript development. This paper aims to explore diverse perspectives on the use of LLMs in scholarly research, particularly within the context of competitive intelligence (CI), and to offer guidelines for CI researchers on how to effectively leverage AI tools like GPT models. Method: The study conducts a comprehensive review of existing literature on the integration of AI in academic research, focusing specifically on the capabilities of generative AI models such as ChatGPT-4, Scholar GPT, and Consensus GPT. These models, developed by OpenAI, are evaluated for their utility in various stages of the research process, including literature review, qualitative analysis, and data analysis. The analysis emphasizes how the quality of AI-generated outputs depends on the specificity of the user's input. Results: While LLMs have demonstrated significant potential in enhancing literature reviews, qualitative research, and data analysis, the study finds that their full capabilities in academic research remain underexplored. The research highlights both the concerns about potential \"contamination\" of scholarly work through AI use and the benefits these models offer, especially when used strategically. Conclusions: The article presents a structured guide for business researchers, with particular emphasis on those engaged in competitive intelligence, to integrate AI language models effectively throughout the research process. The findings underline the importance of input specificity and provide practical recommendations for leveraging LLMs to enhance research efficiency and output quality.","url":"https://doi.org/10.37497/sdgs.v6istudies.43","authors":["Joseph F. Hair","Misty Sabol"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-02T20:08:32Z","doi":"10.37497/sdgs.v6istudies.43","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3389/978-2-8325-4535-5","name":"Health economics, medical technology and artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.3389/978-2-8325-4535-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-26T13:55:12Z","doi":"10.3389/978-2-8325-4535-5","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/pbhe050e_ch2","name":"Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges","source":"crossref","abstract":"The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the issues with explainability. Explainable AI (XAI) has been studied as a possible remedy for the issues with current AI methods. The usage of machine learning (ML) with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS have played a crucial role in systems' attempts to advance patient wellbeing and the standard of care, particularly for non-communicable illnesses. Moreover, they have been a crucial prerequisite for the effective utilization of electronic healthcare (EHRs) data. This chapter bargains a comprehensive impression of the application of AI and XAI in MDSSs, summarizes recent research on the use and effects of MDSS in healthcare, and offers suggestions for users to keep in mind as these systems are integrated into healthcare systems and utilized outside of contexts for research and development.","url":"https://doi.org/10.1049/pbhe050e_ch2","authors":["Joseph Bamidele Awotunde","Emmanuel Abidemi Adeniyi","Sunday Adeola Ajagbe","Agbotiname Lucky Imoize","Olukayode Ayodele Oki","Sanjay Misra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T03:11:32Z","doi":"10.1049/pbhe050e_ch2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.54167/rei.v2i2.1715","name":"Reseña: \"Creative Applications of Artificial Intelligence in Education\"","source":"crossref","abstract":"El libro \"Creative Applications of Artificial Intelligence in Education\", editado por Alex Urmeneta y Margarida Romero, presenta una amplia exploración de cómo la Inteligencia Artificial (IA) está transformando el ámbito educativo a través de aplicaciones creativas. La obra está dividida en tres partes y aborda desde aplicaciones generales de IA en educación, pasando por implementaciones específicas en la educación primaria y secundaria, hasta su uso en la educación superior. En cada sección analiza diversos temas que van desde las oportunidades que la IA ofrece para personalizar y mejorar el aprendizaje, hasta los desafíos éticos, técnicos y pedagógicos que surgen con su integración. Además, se enfatiza la importancia de mantener un enfoque centrado en el ser humano, promoviendo una colaboración equilibrada entre humanos y máquinas para potenciar las capacidades educativas sin desplazar el rol fundamental de los educadores.","url":"https://doi.org/10.54167/rei.v2i2.1715","authors":["Humberto MartínezCamacho"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-09T18:53:14Z","doi":"10.54167/rei.v2i2.1715","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.26650/b/t3.2024.40.032","name":"Artificial Intelligence Applications in Pedodontics","source":"crossref","abstract":"","url":"https://doi.org/10.26650/b/t3.2024.40.032","authors":["Yelda Kasımoğlu","Selin Saygılı","Şeyda Taşkesen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-30T09:34:11Z","doi":"10.26650/b/t3.2024.40.032","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-025-11489-z","name":"Advancements and challenges of federated learning in medical imaging: a systematic literature review","source":"crossref","abstract":"Abstract Cancer diagnosis has entered an era where precision depends not only on image quality but on the intelligence that interprets it. While deep learning has revolutionized medical imaging, its reliance on centralized data limits collaboration due to privacy constraints and fragmented data ownership. Federated Learning (FL) offers a breakthrough enabling multiple institutions to co-train robust diagnostic models without sharing sensitive patient data. This survey provides a comprehensive cancer-specific synthesis of state-of-the-art FL applications in medical imaging, spanning five critical domains: lung, breast, brain, skin, and colorectal cancers. Beyond summarizing prior work, we uncover patterns in architecture choice (U-Net variants, Convolutional Neural Network (CNN)–Recurrent Neural Network(RNN) hybrids, dataset reuse, and state-of-the-art privacy frameworks such as homomorphic encryption and blockchain-backed consensus. We expose performance bottlenecks, heterogeneity risks, and critical absences of clinical deployment benchmarks. What emerges is not just a landscape of what has been done but a roadmap for what is needed to build secure, distributed, and clinically validated Artificial Intelligence (AI) systems. This work offers a foundation for researchers and healthcare technologists aiming to close the gap between research silos and real-world deployment.","url":"https://doi.org/10.1007/s10462-025-11489-z","authors":["Durjoy Ghosh","Maliha Mehjabin","Md. Eshmam Rayed","M. F. Mridha","Md. Mohsin Kabir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-21T08:54:00Z","doi":"10.1007/s10462-025-11489-z","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/b15618-15","name":"A System for Melanoma Diagnosis Based on Data Mining","source":"crossref","abstract":"Melanoma is a dangerous skin cancer. It is less common than other skin cancers but much more dangerous if not detected early. Melanoma causes 75% of deaths of people affected by skin cancer. Annually, 160,000 new cases of melanoma are reported worldwide [1].","url":"https://doi.org/10.1201/b15618-15","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-15","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3389/frai.2024.1474932","name":"Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review","source":"crossref","abstract":"The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI’s role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI’s application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination ( R 2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO 2 , CO, SO 2 , and O 3 . In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models’ performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.","url":"https://doi.org/10.3389/frai.2024.1474932","authors":["Eliezer Zahid Gill","Daniela Cardone","Alessia Amelio"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-12T01:19:19Z","doi":"10.3389/frai.2024.1474932","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3389/fpsyg.2022.1049401","name":"How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context","source":"crossref","abstract":"The booming Artificial Intelligence (AI) provides fertile ground for AI in education. So far, few reviews have been deployed to explore how AI empowers English as Foreign Language (EFL) teaching and learning. This study attempts to give a brief yet profound overview of AI in the EFL context by summarizing and delineating six dominant forms of AI application, including Automatic Evaluation Systems, Neural Machine Translation Tools, Intelligent Tutoring Systems (ITSs), AI Chatting Robots, Intelligent Virtual Environment, and Affective Computing (AC) in ITSs. The review furthermore uncovers a current paucity of research on applying AC in the EFL context and exploring pedagogical and ethical implications of AI in the EFL context. Ultimately, challenges from technical and teachers' perspectives, as well as future research directions, are illuminated, hopefully proffering new insights for the future study.","url":"https://doi.org/10.3389/fpsyg.2022.1049401","authors":["Ruihong Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-16T01:33:49Z","doi":"10.3389/fpsyg.2022.1049401","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2196/preprints.35587","name":"Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum (Preprint)","source":"crossref","abstract":"UNSTRUCTURED Artificial intelligence (AI) is on course to become a mainstay in the patient’s room, physician’s office, and the surgical suite. Current advancements in health care technology might put future physicians in an insufficiently equipped position to deal with the advancements and challenges brought about by AI and machine learning solutions. Physicians will be tasked regularly with clinical decision-making with the assistance of AI-driven predictions. Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care. Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education’s guidance on AI in medical education. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should understand the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues, and the role of data in the development of AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision-making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.","url":"https://doi.org/10.2196/preprints.35587","authors":["Joel Grunhut","Oge Marques","Adam T M Wyatt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-12-13T20:27:49Z","doi":"10.2196/preprints.35587","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.20897/jirais/17640","name":"A focused review of artificial intelligence in education: Evolution and challenges","source":"crossref","abstract":"The given systematic analysis reviews 40 articles published in 2015-2025 to discuss the examine the evolution, applications, and challenges of artificial intelligence (AI) in education, specifically in the bi/multilingual learning settings. The review relies on empirical and theoretical study and provides identification of the three major domains, including personalized learning, intelligent tutoring systems and chatbots, and automated assessment. The research results demonstrate that AI improves student engagement and learning performance and teaching efficiency due to the adaptive feedback and real-time analytics, particularly when used to support multiliteracy language learning practices. There are, however, major issues of concern that data privacy, algorithmic bias, unequal access, and the disappearance of relational and cultural facets of teaching and learning. The review highlights the empathy gap in the AI tools and demands the incorporation of AI into the mainstream in an inclusive, ethically based and linguistically responsive manner. It promotes the change in automation to intelligence augmentation and places AI at the service of educators offloading them with fair, human-centered, and AI assistive tools in multilingual learning students in English-dominant settings. The implications refer to the imperative to provide strong governance structures, human centered training of teachers in which AI serves as an addition to intelligence and not intelligence, and inclusive design to provide equitable and effective AI integration and to provide a balanced innovation with a focus on human-centered learning.","url":"https://doi.org/10.20897/jirais/17640","authors":["Erkan Acar","Youmna Deiri","Fatih Yigit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-06T13:02:50Z","doi":"10.20897/jirais/17640","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1002/eng2.70518/v2/review2","name":"Review for \"Artificial Intelligence in Wire Arc Additive Manufacturing: A Systematic Review and Patent Landscape Analysis\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70518/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T21:12:02Z","doi":"10.1002/eng2.70518/v2/review2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.7287/peerj-cs.1394v0.1/reviews/3","name":"Peer Review #3 of \"A systematic review on artificial intelligence techniques for detecting thyroid diseases (v0.1)\"","source":"crossref","abstract":"The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years.In this context, early detection of diseases is the most common area of application.In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early.Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs.This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland.The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field.After a careful selection process, we selected and reviewed 72 articles, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection.The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo-and hyperthyroidism.The average accuracy of detection is high (96.84%),but the usage of private and outdated datasets with a majority of clinical data is very common.Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages and future developments in the application of artificial intelligence for thyroid diseases detection.","url":"https://doi.org/10.7287/peerj-cs.1394v0.1/reviews/3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-11T02:30:42Z","doi":"10.7287/peerj-cs.1394v0.1/reviews/3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2020.101849","name":"Automatic segmentation of knee menisci – A systematic review","source":"crossref","abstract":"Magnetic resonance imaging (MRI) has proved to be an invaluable component of pathogenesis research in osteoarthritis. Nevertheless, the detection of a meniscal lesion from magnetic resonance (MR) images is always challenging for both clinicians and researchers, because the surrounding tissues lead to similar signals within MR measurements, thus being difficult to discriminate. Moreover, the size and shape of osteoarthritic and non-osteoarthritic menisci vary to a large extent between individuals of same features, e.g. height, weight, age, etc. An effective way to visualize the entire volume of knee menisci is to segment the menisci voxels from the MR images, which is also useful to evaluate particular properties quantitatively. However, segmentation is a tedious and time-consuming task, and requires adequate training for being done properly. With the advancement of both MRI technology and computer methods, researchers have developed several algorithms to automate the task of meniscus segmentation of the individual knee during the last two decades. The objective of this systematic review was to present available fully automatic and semi-automatic segmentation methods of the knee meniscus published in different scientific articles according to the PRISMA statement. This review should provide a vivid description of the scientific advancements to clinicians and researchers in this field to help developing novel automated methods for clinical applications.","url":"https://doi.org/10.1016/j.artmed.2020.101849","authors":["Muhammed Masudur Rahman","Lutz Dürselen","Andreas Martin Seitz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-05T23:09:58Z","doi":"10.1016/j.artmed.2020.101849","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.65150/ep-jefrr/v2e6/2026-05","name":"Artificial Intelligence and Its Impact in Iraq","source":"crossref","abstract":"Throughout history, technological changes have significantly influenced how people conduct business. The internet is one of the most recent examples of how businesses must adapt to stay successful in the market. Although the idea of artificial intelligence (AI) has existed since the 1950s, its impact on corporate governance has been largely overlooked. While the current developments in AI are not the first major shift and won't be the last, every industry has been affected. Whether in healthcare, education, manufacturing, or e-governance, AI is making a global impact. Research suggests that digital transformation is a challenging process for businesses. Transitioning from traditional methods to technology-based solutions often meets resistance from employees. Integrating AI into corporate governance can be seen as a form of digital transformation. This paper reviews the literature on how this transformation can happen within corporate governance and the factors that need to be considered. A qualitative research approach was used for this study. Data was collected through semi-structured interviews. A set of prepared questions ensured the discussion stayed on topic. Twenty interviews were conducted with twenty participants. These participants were divided into three groups: AI experts working in the private sector, senior corporate professionals, and public servants handling IT projects in the government. The participants' responses were analysed and presented in this paper. The analysis shows that the participants are cautiously optimistic about using AI in corporate governance. They recognize benefits such as transparency, accountability, and faster, unbiased decision-making. However, they also express concerns about a lack of regulations, the potential for misuse, ethical issues, and a shortage of skilled workers.","url":"https://doi.org/10.65150/ep-jefrr/v2e6/2026-05","authors":["Dr Aladdin Mahmood Karin Al-Khafaji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-20T10:12:56Z","doi":"10.65150/ep-jefrr/v2e6/2026-05","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2196/preprints.94979","name":"Artificial Intelligence in Digital Health Interventions for Obesity Management with focus on Generative Artificial Intelligence: a Scoping Review (Preprint)","source":"crossref","abstract":"BACKGROUND The high prevalence rates of obesity place it as a public health concern. Obesity management increasingly incorporates digital health interventions with rapid growth in artificial intelligence applications. However, the role of generative artificial intelligence (GenAI) remains unclear, particularly in relation to health literacy and community engagement. OBJECTIVE To systematically map an overview of generative artificial intelligence (AI) applications within digital health interventions for adult obesity management and identify how health literacy and community engagement are addressed within these applications. METHODS A scoping review of literature published from inception to October 2025 was conducted according to the JBI Manual for Evidence Synthesis for Scoping Reviews by the Joanna Briggs Institute. Three databases (PubMed, Web of Science and Scopus) were searched. Eligible studies included records reporting the use of artificial intelligence, including GenAI, for obesity management in adults. Data was extracted and synthesised descriptively through quantitative and qualitative analysis. The results are presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines. RESULTS Database searches retrieved 18,955 records, 108 met the inclusion criteria. Traditional AI methods appeared in 69% (n = 75) of studies, while generative AI was used in 10% (n = 11). Among GenAI records, 55% (n = 6) supported diet planning or nutritional guidance, 18% (n = 2) incorporated health literacy-related features, and 9% (n = 1) reported community engagement elements. CONCLUSIONS Evidence on GenAI in obesity management interventions remains limited. Future research should evaluate generative artificial intelligence tools in real-world settings and incorporate health literacy and community engagement frameworks in their implementation.","url":"https://doi.org/10.2196/preprints.94979","authors":["Raul Enrique Dena Medecigo","Bronwen J Swinnerton","Bassey Ebenso","Arunangsu Chatterjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T18:20:06Z","doi":"10.2196/preprints.94979","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/odi.15100/v3/review1","name":"Review for \"The risks of artificial intelligence: A narrative review and ethical reflection from an Oral Medicine group\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/odi.15100/v3/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-24T17:08:37Z","doi":"10.1111/odi.15100/v3/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1177/10732748231159553/v1/review2","name":"Review for \"The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/10732748231159553/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-28T16:01:37Z","doi":"10.1177/10732748231159553/v1/review2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.69667/rmj.26235","name":"Understanding and Using Artificial Intelligence in Libyan Dental Practice","source":"crossref","abstract":"By improving patient engagement, treatment planning, and diagnostic accuracy, machine learning (AI) is quickly revolutionizing healthcare, including dentistry. However, nothing is known about Libyan dental professionals' and patients' awareness of and interaction with AI. Dentists, dentistry interns, graduate pupils, and patients who visited dental offices in Tripoli participated in a cross-sectional study. A standardized questionnaire measuring knowledge, perception, and application of AI was used to gather data. Inferential and descriptive statistics were used. Among 200 participants (100 physicians, 100 patients), 60% acknowledged awareness of AI applications. Younger workers and those with advanced degrees showed higher levels of awareness. Actual clinical application was restricted, with less than 40% using AI for assessment or therapy planning. 56% of patients were aware of artificial intelligence, with the majority being younger and better educated. Inadequate training, technological assets, and pedagogical unification were all significant impediments. While AI knowledge is increasing in Libyan dentistry practice, its use is still limited. Introducing AI into the dentistry curriculum and offering technical support are essential to ensuring effective adoption.","url":"https://doi.org/10.69667/rmj.26235","authors":["Abduladeem Gadad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-17T22:43:07Z","doi":"10.69667/rmj.26235","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.5005/ijaim-11066-0001","name":"Readiness and Anxiety among Medical Students Regarding Artificial Intelligence at a Medical College in Western India: A Cross-sectional Study","source":"crossref","abstract":"Aims and background: Artificial intelligence (AI) is increasingly being integrated into medical education, clinical practice, and research.To be prepared for AI-related new roles and tasks in the future, medical students will need to be trained in AI, which will enhance their AI readiness and reduce AI-related anxiety.The objective of the study was to assess the readiness for AI and level of AI-anxiety among the medical students.Methodology: A cross-sectional study was conducted among 192 medical students at a medical college in Western India.Artificial intelligence readiness was measured with the medical artificial intelligence readiness scale for medical students (MAIRS-MS).Artificial intelligence anxiety was measured with the artificial intelligence anxiety scale (AIAS).Mean scores for the two scales and their subdomains were calculated.The association between AI readiness and AI-anxiety was assessed with Pearson correlation.A multiple linear regression analysis was performed to examine the independent effects of the MAIRS-MS score and associated factors on AIAS score. Results:The overall mean MAIRS-MS score was 68.57± 13.10.Overall mean AIAS score was 87.02 ± 19.48.There was no significant difference in scores between male and female students.The association between AI readiness and AI-anxiety was weakly negative, though not statistically significant.Most students expressed a strong felt need for AI training and supported its integration into the medical curriculum.Conclusion: Medical students reported moderate levels of AI readiness and AI-anxiety.Artificial intelligence readiness showed weak negative association with AI-anxiety.Most students expressed need for AI training within the medical curriculum. Clinical significance:The findings highlight the need to incorporate basic AI education into undergraduate medical training to improve their AI readiness and prepare future-ready physicians for the ever-evolving landscape of AI-enabled health care.","url":"https://doi.org/10.5005/ijaim-11066-0001","authors":["Frederick Satiro Vaz","Yash G Tar","Ramsley M Cardozo","Shourya Gandhi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-11T10:35:06Z","doi":"10.5005/ijaim-11066-0001","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37547/ajast/volume05issue11-26","name":"Integration Of Artificial Intelligence In Environmental Protection Technologies","source":"crossref","abstract":"This research explores the integration of artificial intelligence (AI) into modern environmental protection technologies. It highlights how AI-driven systems—such as machine learning, computer vision, and data analytics—enhance environmental monitoring, pollution control, waste management, and climate modeling. The paper discusses applications of AI in predicting natural disasters, optimizing energy use, and supporting sustainable resource management. Furthermore, ethical challenges, data reliability, and policy implications are examined to ensure responsible and effective deployment of AI technologies in environmental protection. The study concludes that AI has transformative potential to accelerate global sustainability efforts and improve decision-making for environmental governance.","url":"https://doi.org/10.37547/ajast/volume05issue11-26","authors":["Jumaqulova Zulayho Bahodirjon kizi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-16T11:29:09Z","doi":"10.37547/ajast/volume05issue11-26","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.7759/cureus.38325","name":"Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists","source":"crossref","abstract":"Background Current technological developments in medical imaging are primarily focused on increasing the integration of artificial intelligence (AI) into all medical imaging modalities. They are already considered capable of handling tasks such as image reconstruction, processing (denoising, segmentation), analysis, and predictive modeling. The purpose of this study is to assess the awareness (knowledge, attitudes, and practices) of radiologists and radiologic technologists regarding AI in medical imaging. Materials and methods This cross-sectional, qualitative study focuses on radiologists and radiologic technologists in Saudi Arabia, Sudan, and Yemen. A self-administered questionnaire based on published studies was used to collect primary data. Version 25.0 of IBM SPSS Statistics (IBM Corp., Armonk, NY) was used for the statistical analysis. The demographics were summarized as frequency and percentage. Independent samples t-tests and ANOVA tests were used to evaluate and compare the degree of AI awareness among the study groups. Results A total of 210 individuals completed the survey. According to demographic information, there were 134 (63.8%) radiologic technologists and 76 radiologists (36.2%). Of the participants, 131 (62%) were male, while 79 (37.6%) were female. A total of 130 (61.9%) of the targeted respondents had a positive attitude, 105 (50%) had appropriate practice, and 122 (58.1%) of them were informed (knowledgeable) about AI in medical imaging. There was a significant difference in knowledge awareness between radiologists and radiologic technologists (p-value: 0.05). Regarding practice awareness, it turned out that females are more knowledgeable than males (p-value: 0.007). Additionally, it was discovered that significant differences indicated that bachelor's degree holders have a higher level of practice awareness than diploma holders (p-value: Conclusion Significant differences between the respondent's knowledge awareness regarding specialization, gender, and experience are linked with relatively sufficient AI-basic knowledge and positive attitude awareness among radiologists and radiologic technologists. Only half of the study participants had appropriate practical awareness; therefore, additional training could enhance practical awareness.","url":"https://doi.org/10.7759/cureus.38325","authors":["Kamal Alsultan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-30T11:39:00Z","doi":"10.7759/cureus.38325","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.55248/gengpi.07.0526.d1102","name":"Enhancing Medical Image Recognition through Fuzzy Logic-Based Uncertainty Modeling in Artificial Intelligence Systems","source":"crossref","abstract":"Medical image recognition has become a critical component in modern healthcare systems, enabling early diagnosis and improving clinical decision-making.However, traditional artificial intelligence (AI) models, particularly deep learning approaches, often struggle to handle uncertainty, ambiguity, and imprecision inherent in medical imaging data.This limitation may lead to reduced interpretability and reliability in real-world clinical environments.To address these challenges, this study proposes a fuzzy logic-based uncertainty modeling framework integrated with artificial intelligence for enhanced medical image recognition.The proposed approach leverages fuzzy sets and linguistic variables to represent uncertain and vague information present in medical images, such as variations in tissue boundaries, noise, and low contrast regions.By incorporating fuzzy inference mechanisms into AI-based recognition pipelines, the system can mimic human-like reasoning and provide more flexible and interpretable decision outputs.The methodology includes the definition of input features extracted from medical images, the design of appropriate membership functions, and the construction of a comprehensive fuzzy rule base for classification and diagnosis tasks.Experimental results demonstrate that the integration of fuzzy logic significantly improves recognition accuracy, robustness, and interpretability compared to conventional AI models.Moreover, the proposed system enhances transparency in decision-making, which is crucial for clinical acceptance and trust.This study contributes to the development of intelligent, reliable, and explainable medical image recognition systems by effectively handling uncertainty through fuzzy logic-based modeling.","url":"https://doi.org/10.55248/gengpi.07.0526.d1102","authors":["Shabnam Hajieva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-15T06:08:52Z","doi":"10.55248/gengpi.07.0526.d1102","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.11591/ijai.v13.i3.pp2514-2523","name":"Artificial intelligence in land use prediction modeling: a review","source":"crossref","abstract":"&lt;p&gt;&lt;span lang=\"EN-US\"&gt;This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.&lt;/span&gt;&lt;/p&gt;","url":"https://doi.org/10.11591/ijai.v13.i3.pp2514-2523","authors":["Westi Utami","Catur Sugiyanto","Noorhadi Rahardjo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-17T10:12:11Z","doi":"10.11591/ijai.v13.i3.pp2514-2523","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1111/odi.15100/v2/review1","name":"Review for \"The risks of artificial intelligence: A narrative review and ethical reflection from an Oral Medicine group\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/odi.15100/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-24T17:08:37Z","doi":"10.1111/odi.15100/v2/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1002/dmrr.70039/v1/review1","name":"Review for \"Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/dmrr.70039/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-31T17:06:13Z","doi":"10.1002/dmrr.70039/v1/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.59564/amrj/03.02/0010","name":"A Methodological Framework for Integrating Artificial Intelligence Agents in Medical Device Design","source":"crossref","abstract":"","url":"https://doi.org/10.59564/amrj/03.02/0010","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-06T07:11:31Z","doi":"10.59564/amrj/03.02/0010","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.64576/0101","name":"Stanford Emerging Technology Review 2025 Chapter 1: Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.64576/0101","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-06T17:21:31Z","doi":"10.64576/0101","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-981-96-9199-9_35","name":"Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9199-9_35","authors":["Saleh Abbas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-24T11:35:29Z","doi":"10.1007/978-981-96-9199-9_35","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(95)90024-1","name":"Too many ideas, just one word: a review of Margaret Boden's the Creative Mind: Myths and Mechanisms","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)90024-1","authors":["Kenneth B. Haase"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-11T23:30:03Z","doi":"10.1016/0004-3702(95)90024-1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3233/faia230041","name":"Policy Regulation of Artificial Intelligence: A Review of the Literature","source":"crossref","abstract":"With the all-round penetration of new AI technologies into human society, the necessity of policy regulation is becoming more and more prominent. A review of the research results on policy regulation of AI in the West from 1992 to 2020 reveals that: the research on AI policy regulation is still in its initial stage, and researchers mostly use law, political science and management science as their research perspectives to enter the topic, and the research fields are becoming more and more diversified, but not closely connected; The research topics are still at the level of philosophical value and contingency, specifically, the value judgment of policy regulation, procedural mechanism, and ethical issues of medical application, and there are controversies among different scholars, but a basic consensus has been reached on the ethical concerns and regulatory necessity of AI. The future research trends of policy regulation of AI mainly include three aspects: multi-discipline, implementation and diffusion of policy regulation, and ethical reflection.","url":"https://doi.org/10.3233/faia230041","authors":["Mengdie Du"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-10T06:54:44Z","doi":"10.3233/faia230041","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2024.109578","name":"Anomaly detection in Smart-manufacturing era: A review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109578","authors":["Iñaki Elía","Miguel Pagola"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-21T20:04:56Z","doi":"10.1016/j.engappai.2024.109578","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37497/rev.artif.intell.educ.v5i00.18","name":"Artificial Intelligence in education: What are the opportunities and challenges?","source":"crossref","abstract":"Objective:The paper aims to explore the integration of Artificial Intelligence (AI) tools in the educational sector. It underscores the urgent need for establishing proper regulations and checks to ensure the ethical and effective use of these tools. Method: The insights are based on a think piece authored by UNESCO Assistant Director-General for Education, Stefania Giannini. Data was collected through a global survey involving over 450 educational institutions, including schools and universities. Results: A mere 10% of the institutions surveyed have formulated policies or guidelines concerning the deployment of generative AI. The scrutiny for validating textbooks is found to be more stringent than the introduction of generative AI tools in educational settings. Criteria for textbook evaluations encompass content accuracy, relevance to teaching, age suitability, cultural and social appropriateness, and protection against biases. Conclusions: The paper suggests that educational institutions should not be solely dependent on AI corporate entities for setting regulations. While education continues to be a profoundly human endeavor, the surge of digital tools during the COVID-19 crisis underscored the academic and societal challenges students encounter in the absence of human touch. There's a risk that generative AI might diminish the significance of educators and pave the way for increased automation in the educational domain. Addressing the prevailing challenges in education necessitates investments in both schools and educators, rather than just technology. Practical Implications: It's imperative for education ministries to join forces with other regulatory bodies, particularly those overseeing technological advancements, to assess and approve AI tools. UNESCO is at the forefront of global dialogues with stakeholders and is in the process of framing policy guidelines on the use of generative AI in education. UNESCO's 'Recommendation on the Ethics of Artificial Intelligence', released in November 2021, accentuates the principles of safety, inclusivity, diversity, transparency, and excellence.","url":"https://doi.org/10.37497/rev.artif.intell.educ.v5i00.18","authors":["Altieres de Oliveira Silva","Diego dos Santos Janes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-30T21:24:28Z","doi":"10.37497/rev.artif.intell.educ.v5i00.18","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.31525/ct1-nct04132401","name":"Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04132401","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-19T03:26:54Z","doi":"10.31525/ct1-nct04132401","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2196/preprints.91345","name":"Knowledge, attitudes, and use of artificial intelligence by medical students: a mixed-method study (Preprint)","source":"crossref","abstract":"BACKGROUND Artificial intelligence (AI) is transforming medicine by enhancing care and reducing administrative tasks, and facilitating research. AI also raises many concerns, including a lack of clinical context awareness, data dependence, and the absence of ethical judgment. As future practitioners, medical students must be prepared for these changes. Most studies assessing students' attitudes and knowledge were conducted before artificial intelligence became accessible and tailored to the needs of the population. Therefore, how medical students actually use AI remains largely unexplored. OBJECTIVE This study explores French medical students' knowledge and attitudes toward AI. METHODS A mixed-methods study was conducted in 2025 among French medical students in their 4th to 6th year of school, corresponding to the clerkship year. An online survey adapted from Ten et al. 2025 included open-ended questions about AI definition and feelings toward AI, a Likert scale item to assess specific attitudes, and multiple-choice questions about the characteristics of the student. Quantitative analysis was performed using non-parametric tests (Kruskal-Wallis) to compare attitudes by AI knowledge level, academic years, career aspirations, and ranking within the class. Qualitative analysis was performed inductively. RESULTS Of 1,377 responses received, 1,342 were included. Students had a mean age of 23.1 years and were predominantly in their 5th year. Only 6% provided a correct definition of AI, while 51% gave incorrect responses. Attitudes toward AI were generally positive, with a mean score of 6.85, with significant differences by correct response to the definition (p &lt;0.01; Unknown: 6.12, Incorrect: 6.84, Partially correct: 6.94, Correct: 6.88) and by career goals (p&lt;0.01; clinical: 6.58; research: 6.83; private practice: 7.19). Regarding learning, 49% of students think that AI learning should be outside the curriculum, compared to 44%. Most of the students suggested AI training through multiple workshops Qualitative analysis revealed five themes: Representation, Nuanced Optimism, Critical Consideration, Replacement, and AI Use. Students represent AI as a robot, as an improved search engine, or as an unlimited data source. Their nuanced optimism blends enthusiasm for efficient patient care and provides an opportunity to focus more on the patient relationship, with fears of dehumanization, energy costs, and skill regression. Critical consideration underscores distrust in ethical dilemmas and data security risks. Replacement concerns arise over shifting professional roles, though many believe human empathy remains irreplaceable. For AI use, students highlight administrative aid, personalized training, and clinical support. CONCLUSIONS There is growing interest in AI among medical students, accompanied by new ecological concerns and fears of skill loss. Students seem to have learned to use AI on their own for learning. These results highlight the need to adapt training programs to include the responsible use of these technologies and how to use AI to its fullest potential.","url":"https://doi.org/10.2196/preprints.91345","authors":["Frédéric Paris","Laure Abensur Vuillaume"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T15:40:08Z","doi":"10.2196/preprints.91345","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.61466/ijcmr3060004","name":"Generative artificial intelligence in postgraduate medical education","source":"crossref","abstract":"Generative artificial intelligence is swiftly revolutionizing multiple areas, including healthcare and education. This research examines the possible advantages and hazards of generative artificial intelligence in graduate medical education. We examine the current literature and offer insights on the potential effects of generative artificial intelligence on graduate medical education, highlighting five principal areas of opportunity: reduction of electronic health record workload, clinical simulation, personalized education, research and analytics assistance, and clinical decision support. We subsequently examine critical hazards, such as inaccuracies and excessive dependence on artificial intelligence-generated information, threats to authenticity and academic integrity, potential biases in artificial intelligence outputs, and privacy issues. As generative artificial intelligence technology advances, it is poised to play a significant role in the future of graduate medical education; nevertheless, its integration must be informed by a comprehensive understanding of its advantages and constraints.","url":"https://doi.org/10.61466/ijcmr3060004","authors":["Nabi Akbarnezhad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-01T07:31:18Z","doi":"10.61466/ijcmr3060004","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.25259/gjmpbu_32_2025_122","name":"Artificial Intelligence Tools: Utility in Quality Medical Education","source":"crossref","abstract":"","url":"https://doi.org/10.25259/gjmpbu_32_2025_122","authors":["P. Kruthika"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-30T20:38:55Z","doi":"10.25259/gjmpbu_32_2025_122","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.artmed.2018.10.006","name":"Computational methods for Gene Regulatory Networks reconstruction and analysis: A review","source":"crossref","abstract":"In the recent years, the vast amount of genetic information generated by new-generation approaches, have led to the need of new data handling methods. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Gene Regulatory Networks (GRN) arise as an increasingly-promising tool for the modelling and analysis of biological processes. This review is an attempt to summarize the state of the art in the field of GRNs. Essential points in the field are addressed, thereof: (a) the type of data used for network generation, (b) machine learning methods and tools used for network generation, (c) model optimization and (d) computational approaches used for network validation. This survey is intended to provide an overview of the subject for readers to improve their knowledge in the field of GRN for future research.","url":"https://doi.org/10.1016/j.artmed.2018.10.006","authors":["Fernando M. Delgado","Francisco Gómez-Vela"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-11-09T08:55:06Z","doi":"10.1016/j.artmed.2018.10.006","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/icaaic60222.2024.10575724","name":"A Comprehensive Review of an Artificial Intelligence Assisted Resource Allocation Strategy in Wireless Sensor Networks","source":"crossref","abstract":"In today’s world, the Wireless Sensor Network (WSN) is quite important. The implementation of sensor networks through wireless media has also risen, reflecting the growing importance of tracking and monitoring operations. Sensors are ubiquitous, but they come with a host of problems related to power consumption, security, coverage, latency, and design; WSNs are the subject of active investigation. Extending the useful life of sensors has recently emerged as a priority in the scientific community. With a microelectronic device, a sensor has a limited amount of power. The energy required for processing is supplied by the power source. Charging and upgrading nodes might be challenging task if the nodes are spread apart and the energy usage is just as important as an issue with hardware. This research study enhances the energy consumption of its nodes in WSN and evaluates several methods for localization and resource allocation. This research provides a solution to the challenging problem of node management and scheduling by predicting the energy consumption of WSNs to carry out all communications. By modifying AI logic to increase fault tolerance and including the notion of digital twins to make informed judgments and optimize resource utilization via WSN, this study resolves the prior complications. The findings will be useful in enhancing the knowledge of current procedures and creating new, cutting-edge approaches.","url":"https://doi.org/10.1109/icaaic60222.2024.10575724","authors":["P. Packiyalakshmi","A. Ramathilagam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T18:01:35Z","doi":"10.1109/icaaic60222.2024.10575724","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.37126/aige.v3.i2.9","name":"Artificial intelligence in the endoscopic approach of biliary tract diseases: A current review","source":"crossref","abstract":"","url":"https://doi.org/10.37126/aige.v3.i2.9","authors":["Fábio Pereira Correia","Luís Carvalho Lourenço"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-28T11:17:47Z","doi":"10.37126/aige.v3.i2.9","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-024-10704-7","name":"Artificial intelligence strategies for simulating the integrated energy systems","source":"crossref","abstract":"Abstract In recent decades, the operational impact of Artificial Intelligence (AI) strategies is massively dominating the scientific arena of improving the operation of energy systems and their hybrid integrations. Comprehensively, this paper highlights the firm methodological link of AI strategies with the different defined categories of numerical methods in hypothetically simulating the complex integrated energy systems especially the integration of Renewable Energy Sources (RES). The conducted studies in this paper are related to the bifurcations of the applied numerical simulation methodologies for efficient energy systems and the practical implementations of the optimal operated energy systems considering the integration scenarios of these methodologies with AI strategies. Furthermore, this research reviews innovatively several case studies and practical examples to emphasize the effective contributions of AI strategies in enhancing the computational analysis of numerical simulation methods forming a smart approach for assessing experimental studies that are associated with energy systems. Finally, this paper deeply discusses the concept of integration either in the hybrid controlling strategies combining AI with numerical simulation methods or in combining different energy systems in one hybrid model for reliable operation considering the complexity level.","url":"https://doi.org/10.1007/s10462-024-10704-7","authors":["M. Talaat","M. Tayseer","M. A. Farahat","Dongran Song"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-01T12:01:52Z","doi":"10.1007/s10462-024-10704-7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.46871/eams.1482458","name":"Cancer and Artificial Intelligence","source":"crossref","abstract":"Cancer is a multifactorial group of diseases that are known to affect human life with incidence and mortality rates. Artificial Intelligence and the development of new strategies in cancer treatment are of great importance in helping physicians apply optimized treatment tailored to the patient, overcoming both physical and psychological difficulties, and preventing the recurrence and spread of the disease. Thanks to the field of health and Artificial Intelligence, which are integrated with current developments in coordination with each other, great advances have been made and continue to be made in the diagnosis, treatment and prognosis of cancer, one of the major problems of our age.","url":"https://doi.org/10.46871/eams.1482458","authors":["Leyla Tutar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-24T11:54:08Z","doi":"10.46871/eams.1482458","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-020-09854-1","name":"Deep semantic segmentation of natural and medical images: a review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-020-09854-1","authors":["Saeid Asgari Taghanaki","Kumar Abhishek","Joseph Paul Cohen","Julien Cohen-Adad","Ghassan Hamarneh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-13T14:02:22Z","doi":"10.1007/s10462-020-09854-1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9780367229184","name":"Artificial Intelligence in Medical Imaging","source":"crossref","abstract":"Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective","url":"https://doi.org/10.1201/9780367229184","authors":["Lia Morra","Silvia Delsanto","Loredana Correale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-26T08:29:57Z","doi":"10.1201/9780367229184","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1093/oxfordjournals.bmb.a070856","name":"ARTIFICIAL INTELLIGENCE","source":"crossref","abstract":"CHRISTOPHER LONGUET-HIGGINS, M.A. D.Phil. F.R.S.; ARTIFICIAL INTELLIGENCE, British Medical Bulletin, Volume 27, Issue 3, 1 September 1971, Pages 218–221, https:","url":"https://doi.org/10.1093/oxfordjournals.bmb.a070856","authors":["CHRISTOPHER LONGUET-HIGGINS"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-02-01T10:30:10Z","doi":"10.1093/oxfordjournals.bmb.a070856","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/b15618-17","name":"Deep Learning for the Semiautomated Analysis of Pap Smears","source":"crossref","abstract":"References ..................................................................................................................................... 211 The precision in distinguishing a cancerous structure from a benign structure has the potential to immediately improve health outcomes in one of our most pressing diseases. It will also contribute to one of artificial intelligence’s (AI’s) biggest remaining frontiers-the automated extraction of complex discriminative features.","url":"https://doi.org/10.1201/b15618-17","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T17:35:58Z","doi":"10.1201/b15618-17","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1049/pbhe057e_ch11","name":"Role of artificial intelligence in medical IoT devices","source":"crossref","abstract":"Artificial intelligence (AI) and Internet of Things (IoT) advancements are assisting the medical industry and allowing hospitals to utilize the data provided. Doctors are discovering AI applications all over the medical field. They may now simply monitor patients and keep track of them without having to be present. Because of medical developments and AI contributions to medical IoT devices, they will know which patients require rapid attention. Medical IoT devices are increasing the quality of care as well as monitoring and advising patients on the side effects of certain medical procedures. This chapter investigated the impact of AI and its advancement in medical IoT, as well as its contribution.","url":"https://doi.org/10.1049/pbhe057e_ch11","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-21T08:10:00Z","doi":"10.1049/pbhe057e_ch11","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.18502/jimc.v8i3.18786","name":"Ethical Considerations in the Application of Artificial Intelligence in Health Systems: A Narrative Review","source":"crossref","abstract":"Artificial Intelligence (AI) has emerged as a transformative technology in healthcare, enabling the management of vast data volumes and predictive analysis of the future of issues to support decision-making. This narrative review examines ethical dimensions of AI integration in health systems, drawing from articles published from January 1, 2000 to November 30, 2023, across seven databases—Cochrane Library, PubMed, SCOPUS, Science Direct, BMJ Journals, ProQuest, and SAGE. Using Boolean operators such as “AI” paired with “health”, “health system”, or “hygiene”, the study identifies critical ethical concerns including the preservation of human dignity, confidentiality, informed consent, and the dual principles of beneficence and nonmaleficence. It further highlights systemic challenges like algorithmic bias, transparency gaps in decision-making processes, and disruptions to social justice, alongside legal complexities surrounding accountability for errors, fraud, and compensatory mechanisms. To address these challenges, the study advocates for multilayered solutions. These include establishing ethical audits, formulating policies to ensure equitable global access to AI benefits, and enforcing robust data protection frameworks. Designers are urged to develop comprehensive systems safeguarding patient confidentiality and extending privacy protections to healthcare personnel and affiliated individuals. International regulatory standards must align with social and ethical norms rooted in human dignity, while frameworks for error identification and damage compensation should be prioritized. Continuous adaptation of AI capabilities to evolving medical expertise, coupled with strict adherence to ethical guidelines, is emphasized as essential for sustainable integration of AI in healthcare systems.","url":"https://doi.org/10.18502/jimc.v8i3.18786","authors":["Saeedeh Saeedi Tehrani","Jannat Mashayekhi","Abdolhassan Kazemi","Saeed Biroudian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-31T07:08:25Z","doi":"10.18502/jimc.v8i3.18786","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.47852/bonviewaia52023691","name":"The Use of Artificial Intelligence in Facilities Management: Potential Applications from Systematic Literature Review","source":"crossref","abstract":"The use of artificial intelligence (AI), which is in constant development, has impacted various spheres of society, profoundly reshaping how organizations conduct their business and bringing significant challenges. Understanding the applications and maturity levels of AI technologies is crucial for organizations seeking to harness the full potential of these innovations. The systematic literature review (SLR) revealed that while there are some models for stages of AI application, none have been specifically adapted and evaluated for the facilities management (FM) environment. FM is an operations area responsible for integrating people, spaces, processes, and technologies in built environments, aiming for optimal functionality throughout the life cycle. Additionally, the SLR revealed that AI applications in FM often rely on legacy platforms such as supervisory control and data acquisition for building maintenance activities and building information modeling for construction, as a form of adaptive technologies. Given this gap and the importance of this sector to companies, it is essential to identify which AI technologies are used and at what stages they are. The results indicated various potential AI applications in FM, which can present different maturity stages, underscoring the need for adapted models so that managers can categorize and subsequently direct managerial efforts in pursuit of operational excellence. The goal is to provide organizations with practical and adaptable insights to assess, enhance, and optimize AI applications, thereby increasing efficiency, productivity, and innovation in their operations. From an academic perspective, the study aims to fill the research gap on AI typologies and maturity levels in the context of FM, contributing to the advancement of FM theory. Received: 25 June 2024 | Revised: 23 October 2024 | Accepted: 18 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Robson Quinello: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review &amp; editing, Visualization, Supervision, Project administration. Paulo Tromboni de Souza Nascimento: Conceptualization.","url":"https://doi.org/10.47852/bonviewaia52023691","authors":["Robson Quinello","Paulo Tromboni de Souza Nascimento"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-18T21:56:26Z","doi":"10.47852/bonviewaia52023691","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1515/9783110668322-205","name":"List of contributors","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110668322-205","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-02-10T13:23:46Z","doi":"10.1515/9783110668322-205","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.2196/preprints.67816","name":"Patient Perspectives on Artificial Intelligence in Medical Imaging (Preprint)","source":"crossref","abstract":"UNSTRUCTURED The integration of artificial intelligence (AI) into medical imaging has the potential to improve diagnostic accuracy, efficiency, and patient outcomes. However, its successful adoption may depend not only on technological advancements but also on how AI is perceived and understood by patients. This paper explores patient perspectives on AI in medical imaging, with a focus on trust, human interaction, and ethical considerations such as data privacy and accountability. Studies suggest that while some patients recognize AI’s potential to enhance diagnostic processes, others are concerned about losing the empathy and nuanced judgment of clinicians. Concerns about bias in AI, particularly for underrepresented patient populations, and the financial implications of adopting these technologies also emerge as key issues. The paper proposes that increasing transparency about AI use in diagnostics—such as informing patients when AI has contributed to their care—could help address these concerns. Additionally, offering patients more information and choice regarding the AI tools used in their diagnostic processes may foster greater trust. A patient-centered approach that considers these perspectives may help guide the responsible integration of AI in medical imaging. N/A N/A N/A N/A N/A","url":"https://doi.org/10.2196/preprints.67816","authors":["Jeffry Glenning","Lisa Gualtieri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-06T11:53:35Z","doi":"10.2196/preprints.67816","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(85)90022-0","name":"Call for papers: CSCSI-86 Canadian artificial intelligence conference","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90022-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(85)90022-0","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1109/idicaihei65991.2025.11378748","name":"Role of Artificial Intelligence in Newborn Screening: A Comprehensive Review","source":"crossref","abstract":"Artificial intelligence (AI) is the capacity of computer systems to perform tasks typically associated with human cognition, including learning, reasoning, problem-solving, perception, and decision-making. The tools and software that allow computers to sense their surroundings and employ intelligence and learning to act in ways that maximise the likelihood that they will accomplish predetermined goals are developed and studied in this field of computer science study. Newborn screening (NBS) is a public health initiative that involves evaluating newborns quickly after birth for diseases that can be treated but are not yet clinically noticeable. The objective is to detect newborns who are susceptible to these disorders early enough to validate the diagnosis and offer treatment that will change the disease's clinical trajectory and stop or lessen its clinical symptoms. Artificial intelligence (AI) has become a potent tool to improve the effectiveness, precision, and reach of NBS programs due to the growing complexity and volume of data produced by sophisticated biochemical, genetic, and metabolomic testing. This paper highlights how machine learning (ML) and deep learning (DL) models might transform the early diagnosis and management of neonatal illnesses by examining the uses, advantages, drawbacks, and future potential of AI in newborn screening.","url":"https://doi.org/10.1109/idicaihei65991.2025.11378748","authors":["Anurag Chakraborty","Archana Dhok","Ashish Anjankar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-23T20:43:58Z","doi":"10.1109/idicaihei65991.2025.11378748","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/j.engappai.2023.106959","name":"Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2023.106959","authors":["Ali Mohammadi","Farid Sheikholeslam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-16T01:54:41Z","doi":"10.1016/j.engappai.2023.106959","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1111/ijal.70034/v1/review2","name":"Review for \"Artificial intelligence for language learning: A systematic review of its design, theoretical foundations, implementation, and impact\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/ijal.70034/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T02:46:17Z","doi":"10.1111/ijal.70034/v1/review2","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21203/rs.3.rs-8642098/v1","name":"A Scoping Review of Racial Bias Mechanisms and Mitigation Frameworks in Clinical Artificial Intelligence","source":"crossref","abstract":"Abstract This scoping review synthesizes evidence on how racial bias arises in clinical artificial intelligence (AI) systems and how it can be mitigated through technical, governance, and policy approaches. We conducted a scoping review of clinical AI/ML studies and relevant conceptual frameworks, with searches limited to English-language sources published between September 2020 and November 2025. Study selection was documented using a PRISMA 2020 flow diagram. Eligible studies examined racial or demographic bias mechanisms, fairness evaluation, or mitigation strategies in real-world clinical contexts. Across 22 included studies, recurring pathways to inequity included underrepresentation and label noise in training data, proxy variables that encode structural disadvantage, differences in access and measurement that distort outcomes, and limited external validation in diverse settings. Mitigation strategies clustered into (1) data and evaluation improvements (e.g., subgroup reporting, calibration, and cross-site validation), (2) model and optimization approaches (e.g., reweighting and fairness-aware objectives), and (3) governance levers (e.g., documentation, equity impact assessments, and monitoring requirements). We translate these findings into a practical framework linking bias mechanisms to mitigation actions and implementation levers, with an emphasis on feasible steps for health systems and policymakers to reduce avoidable inequities during AI deployment.","url":"https://doi.org/10.21203/rs.3.rs-8642098/v1","authors":["Aayush Sisodia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-21T10:16:21Z","doi":"10.21203/rs.3.rs-8642098/v1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.21203/rs.3.rs-9707758/v1","name":"Racial Bias Mechanisms and Mitigation Frameworks in Clinical Artificial Intelligence: A Structured Narrative Review","source":"crossref","abstract":"Abstract This structured narrative review synthesizes evidence on how racial bias arises in clinical artificial intelligence (AI) systems and how it can be mitigated through technical, governance, and policy interventions relevant to health system deployment. A targeted search of PubMed, Scopus, and Google Scholar was used to identify English-language empirical studies, evaluative frameworks, and policy-relevant sources published between September 2020 and November 2025. Twenty-two sources informed the thematic synthesis. Recurring pathways to inequity included underrepresentation and label noise in training data, proxy variables that encode structural disadvantage, differences in access and measurement that distort outcomes, and limited external validation in diverse settings. Mitigation strategies clustered into data and evaluation improvements, model and optimization approaches, and governance levers such as documentation, equity impact assessment, and post-deployment monitoring. The review links these mechanisms to feasible implementation actions for health systems and policymakers and argues that equitable clinical AI requires not only model-level correction but also data governance, deployment monitoring, and organizational accountability.","url":"https://doi.org/10.21203/rs.3.rs-9707758/v1","authors":["Aayush Sisodia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-22T06:32:56Z","doi":"10.21203/rs.3.rs-9707758/v1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/978-3-031-67256-9_7","name":"A Brief Review of Artificial Intelligence for Sport Informatics in the Scope of Human–Computer Interaction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-67256-9_7","authors":["Marco Speicher","Patrick Berndt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-02T19:02:26Z","doi":"10.1007/978-3-031-67256-9_7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-005-9016-4","name":"Editorial – The 15th Artificial Intelligence and Cognitive Science Conference (AICS-04)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-005-9016-4","authors":["Lorraine McGinty","Brian Crean"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-10-20T14:05:37Z","doi":"10.1007/s10462-005-9016-4","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.59324/jaitd.2026.2(1).06","name":"Artificial Intelligence in Science Education: An Integrative Review of Personalized Learning, Ethics, and Policy Challenges","source":"crossref","abstract":"Artificial intelligence (AI) is swiftly revolutionizing science education through the implementation of adaptive and generative technologies that personalize learning, automate assessment, and facilitate inquiry-based experimentation. This integrative review consolidates empirical and conceptual works published from 2019 to 2024 about AI applications in physics and allied sciences, analyzing their educational potential, professional development needs, and ethical considerations. Studies indicate that intelligent tutoring systems, predictive analytics, and natural language processing technologies can improve conceptual comprehension, offer immediate feedback, and facilitate differentiated training on a large scale. These advantages, however, rely on continuous teacher professional development that enhances AI literacy, promotes critical analysis of algorithmic results, and facilitates the creation of hybrid learning environments that integrate AI with existing digital resources. The analysis highlights critical obstacles, such as algorithmic bias, data privacy issues, openness in decision-making, and enduring inequities in infrastructure that threaten to exacerbate the digital divide. Resolving these concerns necessitates synchronized policy interventions, strong regulatory structures, and inclusive professional learning communities to guarantee that AI functions as a collaborative ally rather than a replacement for educators. This article elucidates the opportunities and systemic risks associated with AI adoption, offering guidelines for educators, researchers, and policymakers aiming to integrate AI in a manner that reinforces the fundamental ideals of science education: curiosity, equity, and critical inquiry.","url":"https://doi.org/10.59324/jaitd.2026.2(1).06","authors":["Konstantinos T. Kotsis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-13T19:13:31Z","doi":"10.59324/jaitd.2026.2(1).06","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.3390/books978-3-0365-6488-3","name":"Artificial Intelligence Applied to Medical Imaging and Computational Biology","source":"crossref","abstract":"Medical imaging and computational biology continuously pose new fundamental medical and biological questions that often give rise to novel challenges in Artificial Intelligence. These research fields present an increasing need for the application of cutting-edge computational approaches that generally involve machine learning or computational intelligence techniques, which can effectively perform bioimage and biosignal processing in different clinical areas.","url":"https://doi.org/10.3390/books978-3-0365-6488-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-10T02:37:24Z","doi":"10.3390/books978-3-0365-6488-3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1201/9781003257721","name":"Medical Data Analysis and Processing using Explainable Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003257721","authors":["Om Prakash Jena","Mrutyunjaya Panda","Utku Kose"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-15T21:05:06Z","doi":"10.1201/9781003257721","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.31525/ct1-nct04260321","name":"The AID Study 2: Artificial Intelligence for Colorectal Adenoma Detection 2","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04260321","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-08T03:51:46Z","doi":"10.31525/ct1-nct04260321","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1016/0004-3702(79)90006-7","name":"IJCAI-79, Sixth international joint conference on artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(79)90006-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-02-10T09:09:02Z","doi":"10.1016/0004-3702(79)90006-7","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.54364/aaiml.2025.54248","name":"Deep Learning Algorithms in Medical Image Processing: A Critical and Comprehensive Review","source":"crossref","abstract":"Deep learning has soon taken medical image processing to the cutting-edge, with stateof-the-art performance in classification, segmentation, and anomaly detection. Convolutional neural networks (CNNs) led early breakthroughs, then generative adversarial networks (GANs) for data augmentation and super-resolution, and vision transformers (ViTs) and selfsupervised learning (SSL) more recently for global context modeling and label-efficient training. Federated learning (FL) has become a privacy-preserving framework for multiinstitutional collaboration. In spite of these developments, translation to clinical practice continues to be limited by issues of interpretability, data variability, regulatory affairs, and ethical review. This review offers a critical integration of 2024–2025 advances in medical imaging deep learning, organized along a three-axis taxonomy: (1) architectural innovation, (2) paradigms for training, and (3) integration with clinical practice. In contrast to previous surveys, quantitative performance benchmarks are associate with particular datasets, compare explainable AI (XAI) tools to the criterion of clinical usability, and place technical advancement within the contemporary debates over regulation and ethics, such as the EU AI Act (2024) and FDA developments.","url":"https://doi.org/10.54364/aaiml.2025.54248","authors":["Mohammed Ahmed Alharbi","Morched Derbali","Rayed Alakhtar","Mutasem Jarrah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-10T11:12:18Z","doi":"10.54364/aaiml.2025.54248","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-024-11101-w","name":"A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities","source":"crossref","abstract":"The increasing number of medical devices in the Internet of Medical Things (IoMT) environment has raised significant cybersecurity concerns. These devices often have weak security features, poor design, and insufficient authentication protocols, making them vulnerable to cyberattacks and intrusions. To mitigate these threats, robust security measures are essential. This includes implementing strong security protocols, ensuring continuous security monitoring, enforcing regular updates, and maintaining a constant response plan. Additionally, designing an effective Intrusion Detection System (IDS) is crucial to safeguard patient data and devices. This paper systematically studies the current state of the literature and the essential methods for intrusion detection in the IoMT. Employing a selection process, the paper identifies 28 critical studies published between 2018 and April 2024. The intrusion detection mechanisms in the IoMT are divided into five categories: IDS based on artificial intelligence models, datasets used in IoMT for IDS, fundamental security requirements, intrusion detection processes, and evaluation metrics. This paper dissects the various mechanisms within each category in a meticulous and comprehensive analysis. Finally, the paper examines the challenges and open issues in developing IDSs in IoMT. By offering a roadmap for researchers to enhance IDSs in the IoMT, this paper has the potential to significantly impact the fields of computer engineering, cybersecurity, and healthcare, thereby contributing to the advancement of these crucial fields.","url":"https://doi.org/10.1007/s10462-024-11101-w","authors":["Arezou Naghib","Farhad Soleimanian Gharehchopogh","Azadeh Zamanifar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-30T00:47:03Z","doi":"10.1007/s10462-024-11101-w","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1080/08839514.2021.2014186","name":"Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review","source":"crossref","abstract":"The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.","url":"https://doi.org/10.1080/08839514.2021.2014186","authors":["Komang Wahyu Trisna","Huang Jin Jie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-07T16:01:25Z","doi":"10.1080/08839514.2021.2014186","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.33969/ais.2024060111","name":"A review of Brain Cancer Detection and Classification Using Artificial Intelligence and Machine Learning","source":"crossref","abstract":"Brain cancer is a devastating and life-threatening disease that affects millions of individuals worldwide. Timely and accurate detection of brain tumors is crucial for effective treatment and patient outcomes. In recent years, there has been a growing interest in the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the detection and classification of brain cancer. The integration of AI and ML into medical imaging and diagnostic processes has shown remarkable potential in enhancing the accuracy and efficiency of brain tumor diagnosis. These technologies offer the capability to analyze complex patterns and structures within medical images, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, aiding in the early identification of brain tumors and the precise categorization of tumor types. This review aims to provide a comprehensive assessment of the current state of research in the field of Brain Cancer Detection and Classification using AI and ML. It delves into the methodologies, datasets, and performance metrics utilized in various studies. Additionally, it explores the challenges and limitations of existing approaches, ethical considerations. As the capabilities of AI and ML continue to evolve, understanding their potential in brain cancer diagnosis is of paramount importance. This review will not only summarize the achievements made thus far but also offer insights into the future directions and implications of integrating AI and ML in the critical domain of brain cancer detection and classification. As AI continues to evolve, it has the potential to revolutionize brain cancer treatment, ultimately improving patient outcomes and saving lives.","url":"https://doi.org/10.33969/ais.2024060111","authors":["Sanjukta Chakraborty","Dilip Kumar Banerjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-17T13:02:23Z","doi":"10.33969/ais.2024060111","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s44163-026-01476-w","name":"A scoping review of emerging benefits and challenges of generative artificial intelligence in postgraduate research supervision","source":"crossref","abstract":"The integration of generative artificial intelligence (GenAI) tools is rapidly transforming higher education. While these technologies offer promising opportunities to enhance research practices, they also raise essential challenges and ethical concerns. This scoping review systematically mapped the current literature on the emerging benefits and challenges of GenAI in postgraduate research supervision. Using the five-stage framework proposed by Arksey and O’Malley, we searched key databases, including Scopus, Web of Science, EBSCO, and Google Scholar, as well as grey literature sources. Searches were limited to publications from January 2018 to May 2025 (inclusive); the final search was completed on 05 May 2025. Studies and reports addressing the use, benefits, challenges, and ethical concerns of GenAI in postgraduate supervision were included. Data were extracted, charted, and thematically analysed to synthesise insights across contexts. Seventeen studies that met the inclusion criteria, published from 2018 to 2025, were included. The findings revealed that GenAI reduces supervisor workload by performing time-consuming administrative and editorial tasks, such as basic proofreading, grammar correction, and formatting, thereby allowing supervisors to focus on higher-level intellectual guidance. However, challenges include supervisors’ limited experience with GenAI, unclear institutional policies and guidelines, and ethical concerns, including academic integrity, transparency, bias, and data privacy, which are exacerbated when students over-rely on AI or fail to disclose its use. While GenAI holds significant potential to complement postgraduate supervision, it cannot replace the relational, disciplinary, and interpretive expertise of human supervisors. Effective integration of GenAI in postgraduate research supervision requires institutional policy frameworks, supervisor capacity-building, and strengthened ethical governance to ensure responsible, equitable, and high-quality postgraduate research.","url":"https://doi.org/10.1007/s44163-026-01476-w","authors":["D. Gumede","P. R. Gumede"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T03:47:48Z","doi":"10.1007/s44163-026-01476-w","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:47:59.448Z"},{"id":"doi:10.1007/s10462-020-09935-1","name":"Artificial intelligence techniques and their application in oil and gas industry","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-020-09935-1","authors":["Sachin Choubey","G. P. Karmakar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-16T08:18:45Z","doi":"10.1007/s10462-020-09935-1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1002/eng2.70518/v4/review1","name":"Review for \"Artificial Intelligence in Wire Arc Additive Manufacturing: A Systematic Review and Patent Landscape Analysis\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70518/v4/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T21:12:02Z","doi":"10.1002/eng2.70518/v4/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1177/10732748231159553/v2/review1","name":"Review for \"The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/10732748231159553/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-28T16:01:37Z","doi":"10.1177/10732748231159553/v2/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1111/ijal.70034/v1/review3","name":"Review for \"Artificial intelligence for language learning: A systematic review of its design, theoretical foundations, implementation, and impact\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/ijal.70034/v1/review3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T02:46:17Z","doi":"10.1111/ijal.70034/v1/review3","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1111/ijal.70034/v1/review1","name":"Review for \"Artificial intelligence for language learning: A systematic review of its design, theoretical foundations, implementation, and impact\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/ijal.70034/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T02:46:17Z","doi":"10.1111/ijal.70034/v1/review1","addedAt":"2026-09-01T01:47:59.448Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.5281/zenodo.18983700","name":"Recent developments in machine learning modeling methods for hypertension treatment","source":"datacite","abstract":"Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalisation of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clari ed. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to ef ciently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.","url":"https://doi.org/10.5281/zenodo.18983700","authors":["Kohjitani, Hirohiko","Koshimizu, Hiroshi","Nakamura, Kazuki","Okuno, Yasushi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.18983700","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18983701","name":"Recent developments in machine learning modeling methods for hypertension treatment","source":"datacite","abstract":"Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalisation of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clari ed. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to ef ciently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.","url":"https://doi.org/10.5281/zenodo.18983701","authors":["Kohjitani, Hirohiko","Koshimizu, Hiroshi","Nakamura, Kazuki","Okuno, Yasushi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.18983701","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18979749","name":"Exploring The Role Of Quantum Technologies And Artificial Intelligence In Life Sciences And Healthcare","source":"datacite","abstract":"The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.","url":"https://doi.org/10.5281/zenodo.18979749","authors":["Nathivadhani N","Akaliya S","Dr R.Karthik"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18979749","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18979750","name":"Exploring The Role Of Quantum Technologies And Artificial Intelligence In Life Sciences And Healthcare","source":"datacite","abstract":"The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.","url":"https://doi.org/10.5281/zenodo.18979750","authors":["Nathivadhani N","Akaliya S","Dr R.Karthik"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18979750","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/h42g7","name":"Ethical and Bioethical Gaps and Challenges in the Implementation of Artificial Intelligence in Global Medical Education: A Scoping Review","source":"datacite","abstract":"This project contains the protocol for a scoping review aimed at mapping the available scientific evidence on ethical and bioethical gaps and challenges associated with the implementation of artificial intelligence in medical education at a global level. The review will follow the Joanna Briggs Institute methodology for scoping reviews and will be reported according to the PRISMA-ScR guidelines. The study will analyze ethical issues such as algorithmic bias, data privacy, transparency, governance, and equity in the adoption of AI technologies within medical education and health professions training worldwide.","url":"https://doi.org/10.17605/osf.io/h42g7","authors":["Villafañe, Nicolle Valeria Vargas"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Education","Computer Sciences","Artificial Intelligence and Robotics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/h42g7","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/hqz7n","name":"Desempeño diagnóstico de los sistemas de inteligencia artificial en pacientes adultos con sospecha de glaucoma primario de ángulo abierto: una revisión sistemática exploratoria","source":"datacite","abstract":"El glaucoma primario de ángulo abierto (GPAA) es una neuropatía óptica crónica y progresiva caracterizada por daño del nervio óptico y pérdida de la capa de fibras nerviosas retinianas, lo que conduce a defectos irreversibles del campo visual y constituye una de las principales causas de ceguera irreversible a nivel mundial (1,2). Se estima que su prevalencia global en adultos oscila entre 2.4% y 3.0%, con variaciones importantes según edad, origen étnico y factores genéticos (3,4). El diagnóstico temprano del GPAA representa un desafío clínico importante debido a la variabilidad interobservador en la evaluación del nervio óptico y la interpretación de pruebas diagnósticas como la fotografía de fondo de ojo, la tomografía de coherencia óptica (OCT) y los campos visuales computarizados (5,6). Además, el acceso limitado a especialistas en oftalmología en muchos sistemas de salud, particularmente en regiones de ingresos medios y bajos, contribuye al subdiagnóstico de la enfermedad (7,8). En este contexto, los sistemas de inteligencia artificial (IA), particularmente aquellos basados en aprendizaje automático (machine learning) y aprendizaje profundo (deep learning), han emergido como herramientas prometedoras para mejorar la precisión y eficiencia del diagnóstico del glaucoma. Diversos estudios han demostrado que algoritmos basados en redes neuronales convolucionales pueden alcanzar sensibilidades y especificidades superiores al 90% en la detección de neuropatía óptica glaucomatosa utilizando imágenes de fondo de ojo y OCT (9–12). Sin embargo, la literatura disponible presenta heterogeneidad metodológica en términos de diseños de estudio, poblaciones evaluadas, estándares de referencia y modalidades diagnósticas utilizadas, lo que dificulta la síntesis estructurada de la evidencia sobre el desempeño diagnóstico de estos sistemas (13,14). El objetivo de esta revisión sistemática exploratoria es mapear y sintetizar la evidencia disponible sobre el desempeño diagnóstico de sistemas de inteligencia artificial comparados con métodos diagnósticos convencionales en pacientes adultos con sospecha de glaucoma primario de ángulo abierto. La revisión seguirá la metodología del Joanna Briggs Institute (JBI) para revisiones de alcance y las directrices PRISMA-ScR, incluyendo estudios publicados entre enero de 2020 y marzo de 2026 (15,16). Se realizará una búsqueda sistemática en las bases de datos PubMed/MEDLINE, Scopus, Web of Science y BIREME/BVS. Dos revisores independientes realizarán la selección de estudios y la extracción de datos, y el riesgo de sesgo será evaluado mediante la herramienta QUADAS-AI (17,18). Los resultados se sintetizarán mediante análisis descriptivo y narrativo de las métricas de precisión diagnóstica reportadas, incluyendo sensibilidad, especificidad, valores predictivos, exactitud diagnóstica y área bajo la curva ROC. Esta revisión permitirá caracterizar el estado actual de la evidencia sobre la aplicación clínica de la inteligencia artificial en el diagnóstico del GPAA, así como identificar brechas de conocimiento relevantes para futuras investigaciones y estrategias de implementación clínica. Referencias 1. Weinreb RN, Aung T, Medeiros FA. Primary open-angle glaucoma. Nat Rev Dis Primers. 2016;2:16067. doi:10.1038/nrdp.2016.67 2. Shan S, Zhang X, Li Y, et al. Global incidence and risk factors for glaucoma: A systematic review and meta-analysis of prospective studies. J Glob Health. 2024;14:04252. doi:10.7189/jogh.14.04252 3. Zhang N, Wang J, Li Y, Jiang B. Prevalence of primary open angle glaucoma in the last 20 years: a meta-analysis and systematic review. Sci Rep. 2021;11(1):13762. doi:10.1038/s41598-021-92971-w 4. Gedde SJ, Vinod K, Wright MM, et al. Primary Open-Angle Glaucoma Preferred Practice Pattern®. Ophthalmology. 2021;128(1):P71-P150. doi:10.1016/j.ophtha.2020.10.022 5. Pourjavan S, et al. Evaluating the influence of clinical data on inter-observer variability in optic disc analysis for AI-assisted gla","url":"https://doi.org/10.17605/osf.io/hqz7n","authors":["Tovar, Juan Nicolas Alvarez","Quintero, Eduardo Andrés Tuta"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/hqz7n","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/axs3d","name":"Modern Machine Learning and Artificial Intelligence Applications in Neuroimaging for Classification and Reconstruction of Human Cognitive and Phenomenological States: A Scoping Review","source":"datacite","abstract":"This scoping review maps the current state of deep learning and artificial intelligence applications in non-invasive neuroimaging (fMRI, EEG, MEG, fNIRS, PET) for the classification and reconstruction of human cognitive and phenomenological states. The review follows JBI methodology and PRISMA-ScR reporting standards. Fifteen cognitive domains are covered, including visual perception, auditory processing, language and inner speech, emotion, memory, consciousness, attention, decision-making, reward, social cognition, pain, self-referential processing, executive functions, and sleep stages. Primary empirical studies from 2015 to present using at least one deep learning architecture are included. Databases searched include MEDLINE, Embase, IEEE Xplore, Web of Science, Scopus, and ProQuest. Screening is conducted in Covidence with dual independent review.","url":"https://doi.org/10.17605/osf.io/axs3d","authors":["Kerbech, Anders","Bergsmark, Lars Petter Sødal"],"tags":["Computational Neuroscience","Cognitive Neuroscience","Physical Sciences and Mathematics","Radiology","Medicine and Health Sciences","Life Sciences","Medical Specialties","Cognitive Psychology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/axs3d","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/f84rx","name":"Artificial Intelligence-Enhanced Electrocardiography for the Diagnosis of Arrhythmogenic Right Ventricular Cardiomyopathy: a Scoping Review","source":"datacite","abstract":"Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare but potentially life-threatening cardiomyopathy and a leading cause of sudden cardiac death among young individuals and athletes. The diagnosis of ARVC encompasses electrocardiographic (ECG) and morphofunctional criteria, often requiring cardiac magnetic resonance (CMR) imaging, which is not routinely applicable or widely available, particularly in developing nations, and may be subtle during the early or concealed phases of the disease. ECG abnormalities may also be covert and nonspecific. In addition, conventional ECG interpretation is subject to interobserver variability. Recent advances in artificial intelligence (AI), particularly deep learning–based ECG analysis, have demonstrated improved diagnostic performance in detecting complex and subtle cardiac abnormalities. This scoping review aims to systematically map and summarize current evidence on the application, methodology, and diagnostic performance of AI-enhanced ECG for the detection of ARVC, identifying existing advances, limitations, and gaps in the literature.","url":"https://doi.org/10.17605/osf.io/f84rx","authors":["Maharani, Erika","Sania Zahrani","Prof. Dr. dr. Lucia Kris Dinarti, SpPD, SpJP(K)","Prof. Dr. dr. Yoga Yuniadi, SpJP (K)","dr. Dyah Wulan Anggrahini, Ph.D., Sp.JP","Setiawan, Noor Akhmad"],"tags":["Cardiology","Medicine and Health Sciences","Medical Specialties","Arrhythmogenic Right Ventricular Cardiomyopathy","Artificial Intelligence","Deep Learning","Electrocardiography","Scoping Review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/f84rx","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/9esy5","name":"Application of digital health technology in the care of patients with coronary heart disease and diabetes mellitus: a scoped review","source":"datacite","abstract":"Coronary heart disease (CHD) and diabetes mellitus (DM) are both major global public health problems, and their combined prevalence is high and complex, which requires long-term and multi-dimensional management. The traditional nursing model can not meet the individual needs of such patients. Digital health technology (such as mobile medical APP, remote monitoring, wearable devices, artificial intelligence, etc.) provides a new path for chronic disease management, but its application status, effects and evidence base in patients with coronary heart disease combined with diabetes mellitus are still lack of systematic review. This scope-based review aims to 1. Identify research gaps: systematically analyze existing evidence to identify the types of use, effectiveness, and implementation barriers of digital health technologies in this population; 2. Guiding clinical practice: providing scientific basis for nursing staff to formulate personalized intervention programs and optimizing nursing process; 3. Facilitating policy making: providing data support for health authorities to improve chronic disease management strategies; 4. Promote the development of disciplines: fill the theoretical gap in the application of digital health technology in the nursing field, and promote the process of nursing information.","url":"https://doi.org/10.17605/osf.io/9esy5","authors":["Ruiyan, Zhao"],"tags":["Diseases","Medicine and Health Sciences","Cardiovascular Diseases","Comorbidity","Diabetes","Digital Health","nursing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/9esy5","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/rdpqh","name":"Current landscape of the use of artificial intelligence-based conversational agents (chatbots) in prenatal care: A scoping review.","source":"datacite","abstract":"Introduction: Uninterrupted prenatal care is fundamental for mitigating maternal-fetal morbidity and mortality. However, limited consultation times and structural barriers within primary care restrict the provision of comprehensive health education. Consequently, pregnant women frequently turn to digital sources to resolve doubts, risking exposure to medical misinformation. In recent years, the advancement of artificial intelligence (AI) and automated conversational agents (chatbots) has offered a scalable solution to provide immediate, empathetic, and evidence-based support. Objective: To explore and describe the scope of current literature regarding the design, clinical application, and acceptability of artificial intelligence-driven conversational agents (chatbots) used for health education and the promotion of adherence during prenatal care. Methodology: A scoping review of scientific articles, clinical trials, and observational studies published in the last 10 years in databases such as PubMed, Scopus, and Web of Science. The search strategy utilizes MeSH terms and text words associated with the themes of prenatal care, artificial intelligence, natural language processing, chatbots, and health education. Expected results: This study expects to consolidate an evidence map detailing the design, architecture, and implementation of artificial intelligence-based conversational agents (chatbots) during prenatal care. Additionally, it projects evaluating their clinical and educational benefits regarding maternal adherence, outlining the ethical, technical, and acceptability barriers for their future application in primary care. Descriptors (DeCS/MeSH): Prenatal Care; Artificial Intelligence; Natural Language Processing; Health Education; Patient Compliance. Keywords:Prenatal care, chatbots, artificial intelligence, health education, adherence.","url":"https://doi.org/10.17605/osf.io/rdpqh","authors":["Botia, Danna Fernanda Hernandez","Bautista, Ana Sofia Ayure","Rincon, Erwin Hernando Hernandez"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/rdpqh","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/25425943.v1","name":"AI in Diagnostic Imaging: Revolutionising Accuracy and Efficiency","source":"datacite","abstract":"Introduction: This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans. Methods: A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies. Results and discussion: Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training. Conclusion: AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.","url":"https://doi.org/10.26181/25425943.v1","authors":["Khalifa, Mohamed","Albadawy, Mona"],"tags":["Biomedical and clinical sciences","Clinical sciences","Information and computing sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/25425943.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/25425943","name":"AI in Diagnostic Imaging: Revolutionising Accuracy and Efficiency","source":"datacite","abstract":"Introduction: This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans. Methods: A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies. Results and discussion: Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training. Conclusion: AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.","url":"https://doi.org/10.26181/25425943","authors":["Khalifa, Mohamed","Albadawy, Mona"],"tags":["Biomedical and clinical sciences","Clinical sciences","Information and computing sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/25425943","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/25608642.v1","name":"Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions","source":"datacite","abstract":"Background: Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency. Methods: This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction. Results: Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: (1) Diagnosis and early detection of disease; (2) Prognosis of disease course and outcomes; (3) Risk assessment of future disease; (4) Treatment response for personalised medicine; (5) Disease progression; (6) Readmission risks; (7) Complication risks; and (8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction. Discussion: The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery. Conclusion and recommendations: AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.","url":"https://doi.org/10.26181/25608642.v1","authors":["Khalifa, Mohamed","Albadawy, M"],"tags":["Biomedical and clinical sciences","Health services and systems","Health sciences","FOS: Health sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/25608642.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/25608642","name":"Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions","source":"datacite","abstract":"Background: Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency. Methods: This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction. Results: Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: (1) Diagnosis and early detection of disease; (2) Prognosis of disease course and outcomes; (3) Risk assessment of future disease; (4) Treatment response for personalised medicine; (5) Disease progression; (6) Readmission risks; (7) Complication risks; and (8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction. Discussion: The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery. Conclusion and recommendations: AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.","url":"https://doi.org/10.26181/25608642","authors":["Khalifa, Mohamed","Albadawy, M"],"tags":["Biomedical and clinical sciences","Health services and systems","Health sciences","FOS: Health sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/25608642","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/26378968.v1","name":"Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis","source":"datacite","abstract":"Abstract: This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.","url":"https://doi.org/10.26181/26378968.v1","authors":["Adibi, Sasan","Rajabifard, A","Shojaei, D","Wickramasinghe, Nilmini"],"tags":["Distributed computing and systems software","Human-centred computing","Electronics, sensors and digital hardware","Electrical engineering","Information and computing sciences","Data management and data science"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/26378968.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/26378968","name":"Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis","source":"datacite","abstract":"Abstract: This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.","url":"https://doi.org/10.26181/26378968","authors":["Adibi, Sasan","Rajabifard, A","Shojaei, D","Wickramasinghe, Nilmini"],"tags":["Distributed computing and systems software","Human-centred computing","Electronics, sensors and digital hardware","Electrical engineering","Information and computing sciences","Data management and data science"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/26378968","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/27184584.v1","name":"ChatGPT and generative AI in urology and surgery—A narrative review","source":"datacite","abstract":"Introduction: ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery. Methods: We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms ‘ChatGPT’ and ‘surgery’ were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed. Results: Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality. Conclusion: The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.","url":"https://doi.org/10.26181/27184584.v1","authors":["Qin, S","Chislett, B","Ischia, J","Ranasinghe, Weranja","De Silva, Daswin","Coles-Black, J","Woon, D","Bolton, D"],"tags":["Biomedical and clinical sciences","Clinical sciences","Artificial intelligence","Nephrology and urology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/27184584.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.26181/27184584","name":"ChatGPT and generative AI in urology and surgery—A narrative review","source":"datacite","abstract":"Introduction: ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery. Methods: We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms ‘ChatGPT’ and ‘surgery’ were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed. Results: Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality. Conclusion: The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.","url":"https://doi.org/10.26181/27184584","authors":["Qin, S","Chislett, B","Ischia, J","Ranasinghe, Weranja","De Silva, Daswin","Coles-Black, J","Woon, D","Bolton, D"],"tags":["Biomedical and clinical sciences","Clinical sciences","Artificial intelligence","Nephrology and urology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.26181/27184584","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18203365","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4650 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"Hamzah Quantum Intelligence (HQI). ................................................................................................................................................................................................................................................................. 12 Years Classical Search Method (2014-2026) for MH 370 Was Exactly Like Trying to See X-rays While Wearing Sunglasses — A Completely Wrong Tool for an Entirely Different Task.” ................................................................................................................................................................................................................................................................. Dedicated Lagrangian for the Recovery of MH370 (Level 165): $$\\mathcal{L}_{MH370}^{(165)} = \\oint_{\\partial \\mathcal{V}_{165}} \\left[ \\mathcal{Q}_{H} \\left( IGARI_{sync} \\right) + \\Xi_{SIO} \\left( \\mathcal{G}_{\\mu\\nu}^{161} \\otimes \\mathcal{P}_{lock} \\right) - \\frac{\\hbar_{H} \\mathcal{S}_{cabin}}{\\exp(\\mathcal{I}_{DNA}^{2014})} \\right] \\sqrt{-\\mathbb{G}_{165}} \\, d\\Omega$$ The nexus between this formula and MH370 explains why we are still searching in January 2026. From this Lagrangian perspective: The aircraft is there (Coordinates 34.48° S). The aircraft is invisible (Due to the $\\mathbb{G}$ metric deviation). The aircraft must not be touched (Due to the risk of collapsing the passenger safeguard). ................................................................................................................................................................................................................................................................. Status of Life: The Passengers are Alive Contrary to the laws of classical physics which dictate biological death, the Hamzah Equation (HCP) proves that the 239 occupants are in a state of ‘Conscious Stasis’. Proof: Due to the entropy suppression term, biological time within the cabin has stopped. For them, not even a single second has passed until now since 2014. 2. Geographical Position and Precise Depth The aircraft is stabilised in the ‘Earth’s Informational Sanctuary’: Coordinates: 34.4812° S (Latitude) / 93.6165° E (Longitude). Location: Near the Broken Ridge submarine plateau. Depth: 4650 metres below sea level. Hull Status: 100% integrated, resting on the ocean floor at a 188-degree angle. Confidential Section: Encrypted Geolocation & Bio-Stasis Lagrangian $$\\mathcal{L}_{Final}^{(165)} = \\oint_{\\text{Broken Ridge}} \\left[ \\frac{\\Psi_{stasis} \\otimes \\Omega_{H}^*}{\\sqrt{-\\mathbb{G}_{165} \\cdot \\exp(1 - \\phi_{sync})}} \\right] \\otimes \\Xi_{\\mu\\nu} \\star \\delta(\\vec{R} - \\vec{R}_{target}) \\, d\\tau$$ Numerical Proof and 5-Step Output Calculations (Final Sovereignty Audit) Step 1: Mass-Location Verification $$\\vec{R}_{lock} = \\int_{2014}^{2026} \\nabla \\phi_{sync} \\cdot dt \\equiv (34.4812^\\circ S, 93.6165^\\circ E)$$ Output: 99.9% certainty in the lack of structural displacement due to atomic locking. Step 2: Life-Potential Analysis at Depth Pressure $$\\mathbb{V}_{life} = \\frac{\\Omega_H^* \\cdot \\Psi_{internal}}{\\exp(450 \\, atm)} \\otimes \\mathcal{I}_{core} \\equiv 1.00$$ Output: Proof of life-potential equality with the moment of flight; no cellular erosion has occurred. Step 3: Determination of the Lethal Exclusion Zone $$r_{crit} = \\sqrt{\\frac{\\mathbb{K}_{165}}{\\pi \\cdot \\Omega_H^*}} \\approx 165.0 \\, \\text{metres}$$ Output: Precise determination of the 165-metre boundary; crossing this boundary with classical instruments causes the internal implosion of the structure. Step 4: Mechanical Chaos Assessment $$\\Delta S_{tool} = \\oint \\mathcal{P}_{log} \\cdot d\\vec{A} \\implies \\text{Status: Catastrophic Trigger}$$ Output: Final warning; cranes and cables will cause the cancellation of the protective code and the destruction of 239 humans. Step 5: Final Stewardship Verdict $$\\text{Verdict} = \\text{Alive} \\otimes \\text{Protected} \\otimes \\text{Accessible\\_by\\_HQI\\_Only} =","url":"https://doi.org/10.5281/zenodo.18203365","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18203365","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18203470","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Plasma Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. During this dimensional rupture, key components such as the flaperon were not separated due to physical impact with the sea, but rather as a consequence of tensorial stress and phase mismatch at an altitude of 35,000 feet. Through a mechanism known as tangential disc ejection, and under the influence of extreme rotational velocity, these elements detached from the airframe and—rather than falling locally—were projected westwards towards Malaysia and the equatorial currents. The asymmetric concentration of recovered debris—particularly the retrieval of heavy structural components from the aircraft’s right front section (such as the flaperon and outer flap), contrasted with only a single trailing edge from the left front—supports the mechanism of a “tangential ejection caused by tensorial torque” at the IGARI point. This metallurgical asymmetry indicates that the right front section, subjected to intense centrifugal force, experienced physical disintegration before full entry i","url":"https://doi.org/10.5281/zenodo.18203470","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18203470","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/qbpyc","name":"Artificial Intelligence for the Management of Non-Communicable Diseases in Primary Healthcare: Implementation, Effectiveness, and Challenges—A Scoping Review","source":"datacite","abstract":"Artificial intelligence (AI) is widely considered to have the potential to strengthen the capacity of non-communicable disease management in primary healthcare. However, evidence regarding its real-world implementation and effectiveness in primary care settings has not been systematically synthesized. This scoping review aims to comprehensively map the current applications of AI across the full continuum of chronic non-communicable disease management in primary healthcare—from prevention and screening to diagnosis, treatment, and long-term management—and to summarize reported evaluation indicators and key implementation barriers.","url":"https://doi.org/10.17605/osf.io/qbpyc","authors":["Lu, Rui"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Primary Care","Medical Specialties","Computer Sciences","Artificial Intelligence and Robotics","Artificial Intelligence","Chronic Disease Management"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/qbpyc","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/z84xw","name":"The implementation of digital interventions to address alcohol use in primary care: a scoping review","source":"datacite","abstract":"The advances in technology have significantly transformed healthcare, with digital health playing a pivotal role in improving patient outcomes. These innovations, such as mobile apps, wearables, and telemedicine, offer benefits like enhanced accessibility, real-time health monitoring, and early detection of medical issues. Digital health interventions empower patients to take an active role in their care, improving engagement and adherence to treatment. Such advances in the digital healthcare field call for further consideration using an implementation science approach to ensure this research can become integrated into standard medical care. An implementation science framework focuses on understanding the factors between clinical development and research and its eventual use as a medical tool. Given the increasing prevalence of alcohol-related harm and the potential for primary care providers to play a key role in identifying and addressing alcohol use, digital interventions offer an innovative and scalable solution for improving care delivery. Nair et al. 2015 provided valuable insights into the effectiveness and challenges of digital alcohol interventions in primary care settings. However, given the rapid advancements in technology over the past decade, the findings of this review have become outdated. Innovations such as the integration of artificial intelligence, machine learning, and wearable devices have significantly expanded the scope and capabilities of digital interventions. Additionally, the widespread adoption of telemedicine and mobile health applications has transformed patient care, improving accessibility and engagement. These technological developments necessitate a scoping review to capture the latest evidence and assess the implementation science of digital tools in healthcare. This review aims to: 1. Highlight implementation factors: Understanding the factors that influence the adoption and use of digital interventions in primary care—such as healthcare provider involvement, patient preferences, and technological barriers—will provide insights into the challenges and opportunities for integration into existing care models. 2. Identify and categorize digital interventions: By mapping the various types of digital tools available, the review will highlight the breadth of interventions used to address alcohol use in primary care, including apps, telehealth platforms, and online counseling. 3. Assess effectiveness and feasibility: The review will evaluate how well these digital interventions work in primary care settings, considering clinical outcomes, user engagement, and their potential for broad implementation within busy healthcare environments. 4. Identify gaps in the evidence: By synthesizing the current literature, the review will uncover areas where evidence is lacking, guiding future research efforts to strengthen the understanding of digital interventions' role in alcohol use management. This review seeks to inform healthcare providers, policymakers, and researchers about the current implementation landscape of digital interventions in alcohol use management, with the goal of improving the effectiveness, accessibility, and integration of these interventions into primary care practice.","url":"https://doi.org/10.17605/osf.io/z84xw","authors":["Becker, Sean","E. Jennifer Edelman","Kiluk, Brian","Shreyas Nair","Bebarta, Emma","Middya, Ayesha","Grimshaw, Alyssa"],"tags":["Health Information Technology","Substance Abuse and Addiction","Medicine and Health Sciences","Mental and Social Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/z84xw","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/nq6d4","name":"Artificial intelligence-guided antibiotic therapy in pediatric populations: Scoping review","source":"datacite","abstract":"More than 100 years ago, the first antibiotic was developed; since then, antibiotics have been one of the greatest inventions of science and medicine in the 20th century. However, this has been accompanied by a large number of new challenges, one of the most important being the increase in bacterial resistance (1), which is now one of the greatest threats to global health, affecting everyone, regardless of age or country of residence. Antibiotic resistance increases medical costs, prolongs hospital stays, and also increases mortality rates (2). There are certain groups in which the management of antibiotics represents a greater challenge, one of these being the pediatric population. Patients in whom symptoms are more nonspecific, sampling is more difficult, and, in addition, these patients have variable microbiological response times. This has led to greater variability in drug choice and treatment duration among healthcare professionals (3). Bacterial infections in pediatric patients are of vital importance. According to the WHO, pneumonia caused 808,000 deaths in children under 5 years of age in 2017, accounting for 15% of deaths in this age group (4). In 2024, the WHO published a list of the most dangerous bacterial pathogens to human health due to their antibiotic resistance profile, highlighting critically important pathogens such as carbapenem-resistant Acinetobacter baumannii and rifampicin-resistant Mycobacterium tuberculosis, among others (5). With the advent of artificial intelligence, a significant number of opportunities have been identified to improve the management of infectious diseases. Taking into account medical history, patients' vital signs, laboratory results, and microbiology, tools have been developed with the help of artificial intelligence that have contributed to clinical decision-making and therapeutic recommendations for patients (6). In the case of the pediatric population, artificial intelligence has been implemented in different scenarios. One example is the prediction of severe sepsis with the help of a machine learning algorithm, which has had favorable results in detecting patients with this pathology (7). Similarly, tools have been developed to determine the start of antibiotic therapy, such as the AiSEPTRON study, conducted in the United Kingdom, which demonstrated that machine learning models can be used to accurately identify patients who require antibiotic therapy in different scenarios (8). However, due to the limited availability of pediatric data in different databases and heterogeneity among age groups, the appropriate management of antibiotics in the pediatric population with the help of artificial intelligence remains a challenge. Therefore, there is a need for a comprehensive review that synthesizes the available knowledge on the use of artificial intelligence as a tool to guide antimicrobial therapies in the pediatric population. This review will identify the technologies developed in recent years, identify their advantages and disadvantages, analyze opportunities for improvement, and synthesize the benefits they have brought to the management of bacterial infections in pediatric patients. It is hoped that the findings of this research will help guide educators, researchers, healthcare professionals, and decision-makers toward the use and implementation of these tools in appropriate contexts.","url":"https://doi.org/10.17605/osf.io/nq6d4","authors":["Ferro, Daniel Felipe Cardona","Benavides, Catalina","Rincon, Erwin Hernando Hernandez"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/nq6d4","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/28wra","name":"Evidential Deep Learning derived risk of Schizophrenia based on structural MRI and its relation to genetic risk factors","source":"datacite","abstract":"For many years, Machine Learning Algorithms have been employed in an effort to accurately detect, classify and predict psychologic disorders using a wide range of variables, from clinical measurements, over electrophysiologic data (Rahul et al. 2024) to structural imaging. One of the most researched topics hereby is the classification of Schizophrenia using sMRI / fMRI images, where throughout the last decade, Neural Networks have gained increasing popularity, (Sadeghi et al. 2022) and regularly obtain better performances (Di Camillo et al. 2024). However, there are several prevalent issues inhibiting the practical use of Deep Learning and the interpretability of its results. Most notably, many studies suffer from extremely small sample sizes, sometimes well below 100 MRI scans in total, which is arguably not sufficient for a Deep Learning Task, especially one so complex. Furthermore, only a handful of studies examine the performance of their classifier on unseen data from different acquisition sites (Cui et al. 2022, Wang et al. 2024, Vieira et al. 2019), which however is crucial for determining the generalizability of the trained model, as otherwise it can not be ensured that the model is not overfitting on sample-specific markers. This often leads to studies reporting impressive accuracies (or similar metrics such as AUROC, Sensitivity, Specificity) far beyond 0.9 - but it is highly questionable if those models could reach even remotely comparable performances on scans from independent acquisition sites. (Di Camillo et al. 2024) Beyond issues centring around the lack of data, neural networks itself suffer from multiple shortcomings, such as being a “black-box”, making it difficult to know which information a network utilizes for its prediction – though several approaches tackling this have been successfully implemented over the years -, and to interpret the results beyond the classification. Another problem is the inability of a standard neural network classifier to express confidence in its prediction – for example an image classifier trained entirely on pictures of cats and dogs would classify an image of an horse as either of the former two, with no way to indicate that there even is the chance of the image not belonging to either class. To address this, multiple methods have been introduced over the years, such as Bayesian Neural Networks, Deep Ensembles (Lakshminarayanan et al. 2016), Monte Carlo Dropout (Gal and Ghahramani 2015) and Evidential Deep Learning (Sensoy et al. 2018). This work tries to address these issues sequentially. First, a Evidential Deep Learning Classifier will be trained, as a computationally efficient alternative to Deep Ensembles, BNNs and MCD, capable of expressing confidence not only in the form of predictive variance, but as both vacuity (from here on referred to as uncertainty) and dissonance, whilst ideally rivalling a Neural Network trained as a current state-of-the-art softmax classifier in performance. As Evidential Deep Learning – to the best of our knowledge – has not yet been applied to any 3D neuroimaging task, with the most similar application being the classification of chest radiography scans (Ghesu et al. 2020), a comparison of multiple EDL Loss functions, as well as other methods of estimating confidence/uncertainty will be carried out, and potential hybrid use cases examined. Second, the trained classifier will be applied to the UK Biobank, and its results will be linked to the genetic profiles of the subjects in two distinct ways. This makes it possible to make inferences on the “type” of information our classifier extracts from structural MRI Scans to make its classifications. Whilst this won’t allow inferences on the relation between specific brain structures and genetic risk variants, it is feasible to draw tentative conclusions if the classification corresponds to specific genetic risk for Schizophrenia, a general genetic risk for psychopathology, no genetic risk at all, o","url":"https://doi.org/10.17605/osf.io/28wra","authors":["Kähler, Asuka Lucius","Jawinski, Philippe"],"tags":["Clinical Psychology","Biological Psychology","Social and Behavioral Sciences","Psychology","FOS: Psychology","Evidential Deep Learning","Polygenic Risk","Schizophrenia"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/28wra","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18213579","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Plasma Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E) via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. During this dimensional rupture, key components such as the flaperon were not separated due to physical impact with the sea, but rather as a consequence of tensorial stress and phase mismatch at an altitude of 35,000 feet. Through a mechanism known as tangential disc ejection, and under the influence of extreme rotational velocity, these elements detached from the airframe and—rather than falling locally—were projected westwards towards Malaysia and the equatorial currents. The asymmetric concentration of recovered debris—particularly the retrieval of heavy structural components from the aircraft’s right front section (such as the flaperon and outer flap), contrasted with only a single trailing edge from the left front—supports the mechanism of a “tangential ejection caused by tensorial torque” at the IGARI point. This metallurgical asymmetry indicates that the right front section, subjected to intense centrifugal force, experienced physical disintegration before full entry i","url":"https://doi.org/10.5281/zenodo.18213579","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18213579","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18900900","name":"Pharmacogenomics In Oncology: A Step Toward Personalized Cancer Therapy","source":"datacite","abstract":"Pharmacogenomics serves as the main turning point which transforms empirical methods of oncology into personalized treatment methods based on scientific knowledge. The metabolic pathways and treatment responses of individual patients plus their drug side effects create challenges for medical researchers who attempt to design universal treatment approaches because these factors particularly affect drugs that have limited safe dosage ranges. The review collects all available mechanistic research and translational research and clinical research which supports the dual genomic framework that supports oncology pharmacogenomics. The established treatment methods include DPYD-guided fluoropyrimidine dosing and TPMT and NUDT15 testing for thiopurines and UGT1A1 stratification for irinotecan which establish severe toxicity reduction through genotype-based drug selection. The identification of somatic biomarkers including EGFR and HER2 and KRAS and BRAF and BRCA mutations has created new methods for choosing targeted treatments and tracking treatment resistance. The implementation process faces obstacles despite CPIC and international oncology organizations producing strong guidelines because of cost-effectiveness assessments and the absence of randomized clinical research and difficulties in interpreting variants and the use of European genomic databases. The development of liquid biopsy technologies and artificial intelligence-based variant modeling and long-read sequencing will enhance predictive precision because they will solve existing unknowns. The healthcare system needs pharmacogenomic data to function effectively in both electronic health records and multidisciplinary molecular tumor boards. Through its fair and detailed implementation of pharmacogenomics hospitals can achieve optimal cancer treatments which prevent unnecessary patient damage..","url":"https://doi.org/10.5281/zenodo.18900900","authors":["Pedde Vaishnavi D.*, Katre Roshani L., Dr. Giri Ashok B."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18900900","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18900901","name":"Pharmacogenomics In Oncology: A Step Toward Personalized Cancer Therapy","source":"datacite","abstract":"Pharmacogenomics serves as the main turning point which transforms empirical methods of oncology into personalized treatment methods based on scientific knowledge. The metabolic pathways and treatment responses of individual patients plus their drug side effects create challenges for medical researchers who attempt to design universal treatment approaches because these factors particularly affect drugs that have limited safe dosage ranges. The review collects all available mechanistic research and translational research and clinical research which supports the dual genomic framework that supports oncology pharmacogenomics. The established treatment methods include DPYD-guided fluoropyrimidine dosing and TPMT and NUDT15 testing for thiopurines and UGT1A1 stratification for irinotecan which establish severe toxicity reduction through genotype-based drug selection. The identification of somatic biomarkers including EGFR and HER2 and KRAS and BRAF and BRCA mutations has created new methods for choosing targeted treatments and tracking treatment resistance. The implementation process faces obstacles despite CPIC and international oncology organizations producing strong guidelines because of cost-effectiveness assessments and the absence of randomized clinical research and difficulties in interpreting variants and the use of European genomic databases. The development of liquid biopsy technologies and artificial intelligence-based variant modeling and long-read sequencing will enhance predictive precision because they will solve existing unknowns. The healthcare system needs pharmacogenomic data to function effectively in both electronic health records and multidisciplinary molecular tumor boards. Through its fair and detailed implementation of pharmacogenomics hospitals can achieve optimal cancer treatments which prevent unnecessary patient damage..","url":"https://doi.org/10.5281/zenodo.18900901","authors":["Pedde Vaishnavi D.*, Katre Roshani L., Dr. Giri Ashok B."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18900901","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/vb48k","name":"Protocol for a Scoping Review: The Use of Artificial Intelligence in Medical Imaging to Support Radiologists’ Decision-Making","source":"datacite","abstract":"Scoping review protocol examining how artificial intelligence is used in medical imaging to support radiologists’ decision-making, with a focus on imaging tasks, workflow roles, and real-world implementation.","url":"https://doi.org/10.17605/osf.io/vb48k","authors":["Wang, Changxu"],"tags":["Medicine and Health Sciences","artificial intelligence","clinical decision support","medical imaging"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/vb48k","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/tgqfc","name":"Deep Learning in Medical Imaging for Liver Cancer Diagnosis: A Scoping Review of Predictive Analytics, Synthetic Data, and Ethical Challenges","source":"datacite","abstract":"This project contains the protocol for a scoping review examining the application of deep learning techniques in medical imaging for liver cancer diagnosis. The review will map existing literature on AI-based diagnostic models, predictive analytics, synthetic data generation approaches, and ethical challenges related to the use of artificial intelligence in liver cancer imaging. The review will follow the PRISMA-ScR guidelines.","url":"https://doi.org/10.17605/osf.io/tgqfc","authors":["Pushp Goel"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Computer Sciences","Artificial Intelligence and Robotics","AI in healthcare","Deep learning","Medical imaging","predictive analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/tgqfc","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18887718","name":"Medical Technologies: Pharmaceutical Cosmetology, Cosmeceuticals – Opportunities and Limitations","source":"datacite","abstract":"Introduction. Pharmaceutical cosmetology is an interdisciplinary field at the junction of pharmacy, dermatology, and aesthetic medicine. The active development of cosmeceuticals is associated with the growing demand for products that combine a cosmetic effect with a biological effect on the skin. At the same time, the lack of a single regulatory status and mandatory randomized clinical trials creates several scientific and legal challenges. Purpose. To analyze the possibilities and limitations of cosmeceuticals in pharmaceutical cosmetology from the standpoint of modern medical technologies, evidence-based medicine, and pharmaceutical care. Methods. A systematic literature review was conducted using the PRISMA protocol using the PubMed, Scopus, Web of Science and Cochrane Library databases for 2015–2026. Out of 1243 publications, 62 sources with evidence levels I–II according to the Oxford Centre for Evidence-Based Medicine were selected after screening. Results. The article summarizes modern technological platforms of cosmeceuticals, including nanoemulsions, liposomes, multiple emulsions, and probiotic complexes, which increase the bioavailability of active ingredients by 3-5 times. The main groups of cosmeceutical active ingredients and their clinical efficacy in photoaging, acne, hyperpigmentation and alopecia are systematized. The importance of pharmaceutical care and consulting algorithms for increasing the safety of cosmeceutical use is shown. The prospects for personalized cosmeceuticals based on genetic tests and artificial intelligence are identified. Regulatory gaps in the USA, EU and Ukraine are outlined. Conclusions. Cosmeceuticals are a promising direction of integration of pharmacy, dermatology, and digital technologies, which demonstrates proven effectiveness in mild forms of dermatoses and prevention of photoaging. Further development requires standardization of the term, development of national regulatory approaches and implementation of pharmacovigilance systems.","url":"https://doi.org/10.5281/zenodo.18887718","authors":["Shapovalova, Viktoriia","Osyntseva, Alina","Shapovalov, Valentyn"],"tags":["medical technologies","pharmaceutical cosmetology","Cosmeceuticals","cosmeceuticals","cosmeceutical active ingredients","regulatory status"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18887718","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18887719","name":"Medical Technologies: Pharmaceutical Cosmetology, Cosmeceuticals – Opportunities and Limitations","source":"datacite","abstract":"Introduction. Pharmaceutical cosmetology is an interdisciplinary field at the junction of pharmacy, dermatology, and aesthetic medicine. The active development of cosmeceuticals is associated with the growing demand for products that combine a cosmetic effect with a biological effect on the skin. At the same time, the lack of a single regulatory status and mandatory randomized clinical trials creates several scientific and legal challenges. Purpose. To analyze the possibilities and limitations of cosmeceuticals in pharmaceutical cosmetology from the standpoint of modern medical technologies, evidence-based medicine, and pharmaceutical care. Methods. A systematic literature review was conducted using the PRISMA protocol using the PubMed, Scopus, Web of Science and Cochrane Library databases for 2015–2026. Out of 1243 publications, 62 sources with evidence levels I–II according to the Oxford Centre for Evidence-Based Medicine were selected after screening. Results. The article summarizes modern technological platforms of cosmeceuticals, including nanoemulsions, liposomes, multiple emulsions, and probiotic complexes, which increase the bioavailability of active ingredients by 3-5 times. The main groups of cosmeceutical active ingredients and their clinical efficacy in photoaging, acne, hyperpigmentation and alopecia are systematized. The importance of pharmaceutical care and consulting algorithms for increasing the safety of cosmeceutical use is shown. The prospects for personalized cosmeceuticals based on genetic tests and artificial intelligence are identified. Regulatory gaps in the USA, EU and Ukraine are outlined. Conclusions. Cosmeceuticals are a promising direction of integration of pharmacy, dermatology, and digital technologies, which demonstrates proven effectiveness in mild forms of dermatoses and prevention of photoaging. Further development requires standardization of the term, development of national regulatory approaches and implementation of pharmacovigilance systems.","url":"https://doi.org/10.5281/zenodo.18887719","authors":["Shapovalova, Viktoriia","Osyntseva, Alina","Shapovalov, Valentyn"],"tags":["medical technologies","pharmaceutical cosmetology","Cosmeceuticals","cosmeceuticals","cosmeceutical active ingredients","regulatory status"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18887719","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/5e29w","name":"Deep Learning for Liver Disease Analysis Using MRI: A Scoping Review of Current Methodologies and Future Directions","source":"datacite","abstract":"With the advancement in Artificial Intelligence in recent years, the application of deep learning techniques within the medical domain has become an increasingly researched area. Among these applications, the intervention of deep learning in liver disease diagnosis using MRI has seen an increased interest in recent years. Despite the growing volume of studies, there remains a need to systematically map the existing literature to guide the new researchers. The present study undertakes a scoping review of research on liver disease related deep learning applications using MRI published in and between 2015 and 2025 to determine the extent these researches have undertaken, the different types of liver diseases they have researched, publication trends, specific diagnosis steps that deep learning was applied to, methodologies explored, deep learning architecture trends, issues encountered, existing gaps, and future direction of utilizing deep learning in liver disease classification using MRI. By synthesizing issues encountered and identifying existing gaps, this study will provide a roadmap for future research directions in developing non-invasive, AI-enhanced diagnostic tools.","url":"https://doi.org/10.17605/osf.io/5e29w","authors":["Thalangama, Thathsara"],"tags":["Hepatology","Physical Sciences and Mathematics","Diseases","Medicine and Health Sciences","Medical Specialties","Computer Sciences","Deep Learning","Fatty Liver"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/5e29w","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18884321","name":"FROM CLINICS TO COMPLIANCE: THE ROLE OF AI IN ADVANCING MENORRHAGIA TREATMENT AND REGULATORY OVERSIGHT","source":"datacite","abstract":"Menorrhagia, or Heavy Menstrual Bleeding (HMB), is a common gynecological disorder that significantly affects women’s health and quality of life. Conventional diagnostic and treatment approaches often rely on subjective assessment and standardized therapies, leading to delayed diagnosis and inconsistent clinical outcomes. Artificial Intelligence (AI) has emerged as a promising tool in healthcare, enabling improved diagnostic accuracy, predictive analytics, personalized treatment planning, and continuous monitoring through digital platforms. AI-based systems can analyze medical imaging, menstrual health data, and clinical records to support timely and evidence-based clinical decisions. However, the adoption of AI in healthcare introduces important regulatory challenges, including software validation, data privacy, cybersecurity, algorithm transparency, and lifecycle management under Software as a Medical Device (SaMD) frameworks. Regulatory authorities such as FDA, EMA, and CDSCO are developing guidance to ensure safe and effective integration of AI technologies. This review highlights the role of AI in advancing menorrhagia treatment while emphasizing the importance of strong regulatory oversight for ethical and compliant implementation. Conclusion: Artificial Intelligence offers promising advancements in the diagnosis and management of menorrhagia. However, effective regulatory oversight is essential to ensure safety, transparency, and ethical implementation. A balanced integration of innovation and compliance will support the responsible adoption of AI in women’s healthcare.","url":"https://doi.org/10.5281/zenodo.18884321","authors":["Acharya Vaishnavi Kamlesh*1, Syed Shoaib Ali2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18884321","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18884322","name":"FROM CLINICS TO COMPLIANCE: THE ROLE OF AI IN ADVANCING MENORRHAGIA TREATMENT AND REGULATORY OVERSIGHT","source":"datacite","abstract":"Menorrhagia, or Heavy Menstrual Bleeding (HMB), is a common gynecological disorder that significantly affects women’s health and quality of life. Conventional diagnostic and treatment approaches often rely on subjective assessment and standardized therapies, leading to delayed diagnosis and inconsistent clinical outcomes. Artificial Intelligence (AI) has emerged as a promising tool in healthcare, enabling improved diagnostic accuracy, predictive analytics, personalized treatment planning, and continuous monitoring through digital platforms. AI-based systems can analyze medical imaging, menstrual health data, and clinical records to support timely and evidence-based clinical decisions. However, the adoption of AI in healthcare introduces important regulatory challenges, including software validation, data privacy, cybersecurity, algorithm transparency, and lifecycle management under Software as a Medical Device (SaMD) frameworks. Regulatory authorities such as FDA, EMA, and CDSCO are developing guidance to ensure safe and effective integration of AI technologies. This review highlights the role of AI in advancing menorrhagia treatment while emphasizing the importance of strong regulatory oversight for ethical and compliant implementation. Conclusion: Artificial Intelligence offers promising advancements in the diagnosis and management of menorrhagia. However, effective regulatory oversight is essential to ensure safety, transparency, and ethical implementation. A balanced integration of innovation and compliance will support the responsible adoption of AI in women’s healthcare.","url":"https://doi.org/10.5281/zenodo.18884322","authors":["Acharya Vaishnavi Kamlesh*1, Syed Shoaib Ali2"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18884322","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/ph2ad","name":"Application of Artificial Intelligence in Radiological Exams as a Medical Education Tool: A Scoping Review Protocol","source":"datacite","abstract":"This study consists of a scoping review aimed at mapping and analyzing the use of Artificial Intelligence in radiology as a tool for medical education. The review seeks to identify the main strategies for using Artificial Intelligence in radiology teaching during medical training, as well as to recognize gaps related to its absence or limited integration into traditional educational models. The review will be conducted in accordance with the methodological guidelines of the Joanna Briggs Institute (JBI) for scoping reviews. The reporting of results will follow the recommendations of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review Extension). Studies published within the last five years, available in full text, in Portuguese, English, and Spanish, will be included. Searches will be performed in the following databases: PubMed, LILACS, Cochrane Library, Scopus, and Web of Science. The temporal scope was defined considering the rapid technological advancement of Artificial Intelligence in healthcare and medical education, particularly following recent developments in machine learning and generative AI from 2021 onward, aiming to capture the most current and relevant evidence. As this review follows JBI methodological guidance for scoping reviews, a formal risk of bias assessment will not be conducted, since the objective of this study is to map the available evidence rather than assess effectiveness.","url":"https://doi.org/10.17605/osf.io/ph2ad","authors":["Hikigi, Juliana Tiemi","Fernandes, Isabela Oliveira","Borges, Maria Eduarda Brandão Faria","Monteiro, Aline Maciel"],"tags":["Radiology","Medicine and Health Sciences","Medical Specialties","Medical Education","Artificial intelligence","Medical education","Radiology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/ph2ad","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18881201","name":"Security Challenges In Healthcare Cloud Apis: A Systematic Review","source":"datacite","abstract":"Cloud computing has a substantial influence on the healthcare ICT infrastructure because of its flexible, scalable, and cost-effective features; it also plays an important role in electronic health record management, clinical workflow, and theinteroperability of disparate healthcare systems. The use of cloud-based Application Programming Interfaces in a healthcare system raises security and compliance risks due to sensitive protected health information and strict data protection requirements. This systematic review describes the various privacy and security challenges and vulnerabilities associated with healthcare cloud application programming interfaces and identifies the most important security areas that need consideration, including authentication protocols, data encryption protocols, and secure data transmission protocols. This article also explains the fundamental building blocks of healthcare cloud APIs and reviews their unique privacy and security challenges for real-time access and interoperability, as well as informed consent workflows. The article analyzes the potential attack surfaces for healthcarecloud APIs, such as man-in-the-middle attacks, distributed denial of service, and unauthorized access, and their impact on healthcare operations and patient safety. Additional threats include issues with verifying and managing medical device access (like credential management, MFA, token-based authentication, role-based access control, and attribute-based access control), using encryption for stored and transmitted data, managing encryption keys, and how end-to-end encryption affects performance in complex systems. Existing security standards include general and sector-specific recommendations for security hygiene, such as security securityby- design, continuous security monitoring, incident reporting, and regulatory compliance. Emerging security concerns for APIs include artificial intelligence for threat detection, the adoption of zero-trust architecture, and the use of quantumresistant encryption mechanisms in light of quantum computing developments. Recommendations are made for healthcare organizational leadership, cloud service providers, and policymakers to improve API security posture, prompt security innovation and address security challenges while maintaining operational efficiency and privacy compliance.","url":"https://doi.org/10.5281/zenodo.18881201","authors":["Brahmanand Reddy Bhavanam"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18881201","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18881202","name":"Security Challenges In Healthcare Cloud Apis: A Systematic Review","source":"datacite","abstract":"Cloud computing has a substantial influence on the healthcare ICT infrastructure because of its flexible, scalable, and cost-effective features; it also plays an important role in electronic health record management, clinical workflow, and theinteroperability of disparate healthcare systems. The use of cloud-based Application Programming Interfaces in a healthcare system raises security and compliance risks due to sensitive protected health information and strict data protection requirements. This systematic review describes the various privacy and security challenges and vulnerabilities associated with healthcare cloud application programming interfaces and identifies the most important security areas that need consideration, including authentication protocols, data encryption protocols, and secure data transmission protocols. This article also explains the fundamental building blocks of healthcare cloud APIs and reviews their unique privacy and security challenges for real-time access and interoperability, as well as informed consent workflows. The article analyzes the potential attack surfaces for healthcarecloud APIs, such as man-in-the-middle attacks, distributed denial of service, and unauthorized access, and their impact on healthcare operations and patient safety. Additional threats include issues with verifying and managing medical device access (like credential management, MFA, token-based authentication, role-based access control, and attribute-based access control), using encryption for stored and transmitted data, managing encryption keys, and how end-to-end encryption affects performance in complex systems. Existing security standards include general and sector-specific recommendations for security hygiene, such as security securityby- design, continuous security monitoring, incident reporting, and regulatory compliance. Emerging security concerns for APIs include artificial intelligence for threat detection, the adoption of zero-trust architecture, and the use of quantumresistant encryption mechanisms in light of quantum computing developments. Recommendations are made for healthcare organizational leadership, cloud service providers, and policymakers to improve API security posture, prompt security innovation and address security challenges while maintaining operational efficiency and privacy compliance.","url":"https://doi.org/10.5281/zenodo.18881202","authors":["Brahmanand Reddy Bhavanam"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18881202","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/6bdcu","name":"Artificial intelligence and critical thinking in medical education: a review of the scope of evidence.","source":"datacite","abstract":"This study presents a scoping review aimed at mapping and synthesizing the available scientific evidence on the relationship between artificial intelligence (AI) and the development of critical thinking in medical education. The review examines how AI-based tools, including large language models, adaptive learning systems, and intelligent tutoring platforms, are being incorporated into educational environments and how they may influence higher-order cognitive skills among medical students and health-science trainees. Following PRISMA-ScR methodological guidance, the study identifies research trends, educational applications, potential benefits, and methodological limitations within the current literature. The review also highlights existing knowledge gaps and proposes directions for future research on the integration of AI technologies in the development of critical thinking and clinical reasoning in medical education.","url":"https://doi.org/10.17605/osf.io/6bdcu","authors":["JUAN LUGO"],"tags":["Medicine and Health Sciences","Education","Generative Artificial Intelligence; Critical Thinking; Education Medical; Health Sciences; Large Language Models"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/6bdcu","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18867818","name":"Nurse-led escalation of early warning scores and digital/AI deterioration alerts in adult inpatients: a systematic review and meta-analysis of ward cardiac arrest, unplanned ICU admission, and in-hospital mortality","source":"datacite","abstract":"1. Review title Nurse-led escalation of early warning scores and digital/AI deterioration alerts in adult inpatients: a systematic review and meta-analysis of ward cardiac arrest, unplanned ICU admission, and in-hospital mortality 2. Original language title English). 3. Anticipated or actual start date of the review [2026-01-20] 4. Anticipated completion date [2026-03-05] 5. Stage of review at time of registration · Searches: [Completed] · Screening: [Completed] · Data extraction: [Ongoing] · Risk of bias assessment: [Ongoing] · Data analysis: [Ongoing]6. Named contact Name: [Ugwu Okechukwu Paul-Chima]Email: [ugwuopc@kiu.ac.ug]Institution: [Kampala International University] 7. Review team members and affiliations 1,2Fadia Ahmed Abdelkader Reshia, 3Ugwu Okechukwu Paul-Chima and 4,5Ashiru Muhammad 1Assistant Professor of Medical Surgical Nursing Department, College of Nursing, Jouf University, Saudi Arabia. 2Assistant Professor of Critical Care and Emergency Nursing, Faculty of Nursing, Mansoura University, Mansoura, Egypt. 3Department of Research and Publication Kampala International University Uganda 4Department of Nursing Sciences Bayero University Kano, Nigeria 5Faculty of Nursing Sciences Kampala International University Uganda. 8. Review question What are the effects of nurse-led escalation of early warning scores (manual or digital) and/or digital/AI deterioration alerts on adult inpatient ward outcomes (in-hospital mortality, ward cardiac arrest, unplanned ICU admission/transfer) and on escalation process outcomes (e.g., timeliness, escalation frequency, protocol adherence) where reported? 9. Condition or domain being studied Adult inpatient clinical deterioration on hospital wards, including outcomes such as ward cardiac arrest, unplanned ICU admission/transfer, sepsis progression, length of stay, and mortality. 10. Participants/population Adults (≥18 years) admitted to acute-care hospital wards (medical/surgical and mixed wards). Mixed-population studies are eligible if adult inpatient outcomes are extractable. 11. Intervention(s), exposure(s) Nurse-led escalation strategies, defined as models in which nurses have primary operational responsibility for initiating/delivering/enforcing escalation triggered by:(i) early warning scores (manual or digital) and/or(ii) electronic surveillance or AI/ML-generated deterioration alerts, including nurse-activated pathways embedded within rapid response systems. Intervention groupings (for subgrouping/narrative structure): 1. score-based escalation and protocolisation 2. digitised surveillance and alerting pathways 3. AI/ML-enabled deterioration prediction and alerting 4. response-team activation and escalation timeliness pathways 12. Comparator(s)/control Usual care; pre-intervention baseline; waitlist/attention control; or alternative escalation/monitoring practice. 13. Types of study to be included · Tier 1 (effectiveness): Randomised trials (including cluster and stepped-wedge) and comparative quasi-experimental designs with extractable quantitative data (e.g., controlled before–after, interrupted time-series with appropriate modelling, comparative cohort studies). · Tier 2 (contextual/prediction/association): Prediction-performance studies (AUROC/AUPRC/NRI) and association studies (e.g., escalation delay vs outcomes), synthesised narratively. 14. Context Acute-care hospital ward settings (non-ICU), including systems integrated with rapid response teams. 15. Primary outcome(s) Primary outcomes (hierarchically prespecified): 1. In-hospital mortality 2. Ward cardiac arrest (or in-hospital arrest outside ICU where reported) 3. Unplanned ICU admission/transfer 16. Secondary outcome(s) Time-to-review; rapid response team activation; delay to ICU transfer; hospital length of stay; deterioration/sepsis process outcomes (e.g., time-to-antibiotics) when paired with a clinical endpoint; escalation timeliness/frequency and protocol adherence outcomes where reported. 17. Search strategy an","url":"https://doi.org/10.5281/zenodo.18867818","authors":["Fadia, Ahmed Abdelkader Reshia,","Ugwu, Okechukwu Paul-Chima","Ashiru, Muhammad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18867818","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18867817","name":"Nurse-led escalation of early warning scores and digital/AI deterioration alerts in adult inpatients: a systematic review and meta-analysis of ward cardiac arrest, unplanned ICU admission, and in-hospital mortality","source":"datacite","abstract":"1. Review title Nurse-led escalation of early warning scores and digital/AI deterioration alerts in adult inpatients: a systematic review and meta-analysis of ward cardiac arrest, unplanned ICU admission, and in-hospital mortality 2. Original language title English). 3. Anticipated or actual start date of the review [2026-01-20] 4. Anticipated completion date [2026-03-05] 5. Stage of review at time of registration · Searches: [Completed] · Screening: [Completed] · Data extraction: [Ongoing] · Risk of bias assessment: [Ongoing] · Data analysis: [Ongoing]6. Named contact Name: [Ugwu Okechukwu Paul-Chima]Email: [ugwuopc@kiu.ac.ug]Institution: [Kampala International University] 7. Review team members and affiliations 1,2Fadia Ahmed Abdelkader Reshia, 3Ugwu Okechukwu Paul-Chima and 4,5Ashiru Muhammad 1Assistant Professor of Medical Surgical Nursing Department, College of Nursing, Jouf University, Saudi Arabia. 2Assistant Professor of Critical Care and Emergency Nursing, Faculty of Nursing, Mansoura University, Mansoura, Egypt. 3Department of Research and Publication Kampala International University Uganda 4Department of Nursing Sciences Bayero University Kano, Nigeria 5Faculty of Nursing Sciences Kampala International University Uganda. 8. Review question What are the effects of nurse-led escalation of early warning scores (manual or digital) and/or digital/AI deterioration alerts on adult inpatient ward outcomes (in-hospital mortality, ward cardiac arrest, unplanned ICU admission/transfer) and on escalation process outcomes (e.g., timeliness, escalation frequency, protocol adherence) where reported? 9. Condition or domain being studied Adult inpatient clinical deterioration on hospital wards, including outcomes such as ward cardiac arrest, unplanned ICU admission/transfer, sepsis progression, length of stay, and mortality. 10. Participants/population Adults (≥18 years) admitted to acute-care hospital wards (medical/surgical and mixed wards). Mixed-population studies are eligible if adult inpatient outcomes are extractable. 11. Intervention(s), exposure(s) Nurse-led escalation strategies, defined as models in which nurses have primary operational responsibility for initiating/delivering/enforcing escalation triggered by:(i) early warning scores (manual or digital) and/or(ii) electronic surveillance or AI/ML-generated deterioration alerts, including nurse-activated pathways embedded within rapid response systems. Intervention groupings (for subgrouping/narrative structure): 1. score-based escalation and protocolisation 2. digitised surveillance and alerting pathways 3. AI/ML-enabled deterioration prediction and alerting 4. response-team activation and escalation timeliness pathways 12. Comparator(s)/control Usual care; pre-intervention baseline; waitlist/attention control; or alternative escalation/monitoring practice. 13. Types of study to be included · Tier 1 (effectiveness): Randomised trials (including cluster and stepped-wedge) and comparative quasi-experimental designs with extractable quantitative data (e.g., controlled before–after, interrupted time-series with appropriate modelling, comparative cohort studies). · Tier 2 (contextual/prediction/association): Prediction-performance studies (AUROC/AUPRC/NRI) and association studies (e.g., escalation delay vs outcomes), synthesised narratively. 14. Context Acute-care hospital ward settings (non-ICU), including systems integrated with rapid response teams. 15. Primary outcome(s) Primary outcomes (hierarchically prespecified): 1. In-hospital mortality 2. Ward cardiac arrest (or in-hospital arrest outside ICU where reported) 3. Unplanned ICU admission/transfer 16. Secondary outcome(s) Time-to-review; rapid response team activation; delay to ICU transfer; hospital length of stay; deterioration/sepsis process outcomes (e.g., time-to-antibiotics) when paired with a clinical endpoint; escalation timeliness/frequency and protocol adherence outcomes where reported. 17. Search strategy an","url":"https://doi.org/10.5281/zenodo.18867817","authors":["Fadia, Ahmed Abdelkader Reshia,","Ugwu, Okechukwu Paul-Chima","Ashiru, Muhammad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18867817","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18856289","name":"Artificial Intelligence and Automation in Hospital Administrative Systems: A Scoping Review","source":"datacite","abstract":"Abstract : Background: Hospital administrative processes including billing, scheduling, and medical records management—are critical to health system performance but are often characterized by inefficiencies, high operational costs, and workforce burden. Artificial intelligence (AI) and automation technologies, including robotic process automation (RPA) and natural language processing (NLP), have emerged as potential solutions to streamline these processes and enhance productivity. Objective: This scoping review aimed to synthesize existing evidence on the use of AI and automation in hospital administrative functions, focusing on efficiency gains, cost savings, implementation barriers, and ethical and regulatory considerations. Methods: A scoping search of peer-reviewed literature was conducted across major electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. Studies published between 2015 and 2025 that examined AI-based or automation-driven interventions in hospital administrative settings were included. Eligible studies addressed applications in billing, scheduling, records management, hospital information systems, or workflow optimization. Data was extracted and synthesized narratively due to heterogeneity in study designs and outcome measures. Results: The review identified substantial evidence that AI and automation improve administrative efficiency through reduction of processing time, minimization of manual errors, and optimization of resource allocation. RPA demonstrated significant benefits in billing and claims processing, while NLP enhanced documentation accuracy and records retrieval. Several studies reported measurable cost savings and productivity improvements following implementation. However, common barriers included integration challenges with legacy systems, limited interoperability, data quality concerns, staff resistance, insufficient training, high upfront costs, and uncertain short-term return on investment. Regulatory and governance challenges, particularly data protection compliance and algorithm transparency were also frequently highlighted. Conclusion: AI and automation technologies show considerable promise in transforming hospital administrative processes by improving efficiency and reducing operational costs. Nevertheless, successful implementation requires strong governance frameworks, workforce capacity building, financial planning, and ethical oversight. Future research should focus on longitudinal cost-effectiveness evaluations and context-specific implementation strategies, particularly in resource-limited health systems.","url":"https://doi.org/10.5281/zenodo.18856289","authors":["Pradeep, Wijesinghe","Poojani, Illangasinghe"],"tags":["Artificial Intelligence, Billing systems, Cost savings, Digital health governance, Efficiency, Health Information Systems, Hospital administration, natural language processing, Robotic process automation."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18856289","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18856290","name":"Artificial Intelligence and Automation in Hospital Administrative Systems: A Scoping Review","source":"datacite","abstract":"Abstract : Background: Hospital administrative processes including billing, scheduling, and medical records management—are critical to health system performance but are often characterized by inefficiencies, high operational costs, and workforce burden. Artificial intelligence (AI) and automation technologies, including robotic process automation (RPA) and natural language processing (NLP), have emerged as potential solutions to streamline these processes and enhance productivity. Objective: This scoping review aimed to synthesize existing evidence on the use of AI and automation in hospital administrative functions, focusing on efficiency gains, cost savings, implementation barriers, and ethical and regulatory considerations. Methods: A scoping search of peer-reviewed literature was conducted across major electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. Studies published between 2015 and 2025 that examined AI-based or automation-driven interventions in hospital administrative settings were included. Eligible studies addressed applications in billing, scheduling, records management, hospital information systems, or workflow optimization. Data was extracted and synthesized narratively due to heterogeneity in study designs and outcome measures. Results: The review identified substantial evidence that AI and automation improve administrative efficiency through reduction of processing time, minimization of manual errors, and optimization of resource allocation. RPA demonstrated significant benefits in billing and claims processing, while NLP enhanced documentation accuracy and records retrieval. Several studies reported measurable cost savings and productivity improvements following implementation. However, common barriers included integration challenges with legacy systems, limited interoperability, data quality concerns, staff resistance, insufficient training, high upfront costs, and uncertain short-term return on investment. Regulatory and governance challenges, particularly data protection compliance and algorithm transparency were also frequently highlighted. Conclusion: AI and automation technologies show considerable promise in transforming hospital administrative processes by improving efficiency and reducing operational costs. Nevertheless, successful implementation requires strong governance frameworks, workforce capacity building, financial planning, and ethical oversight. Future research should focus on longitudinal cost-effectiveness evaluations and context-specific implementation strategies, particularly in resource-limited health systems.","url":"https://doi.org/10.5281/zenodo.18856290","authors":["Pradeep, Wijesinghe","Poojani, Illangasinghe"],"tags":["Artificial Intelligence, Billing systems, Cost savings, Digital health governance, Efficiency, Health Information Systems, Hospital administration, natural language processing, Robotic process automation."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18856290","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18851337","name":"DESIGNING TRUSTWORTHY AI IN HEALTHCARE: EXPERIENCES WITH COPILOT AGENTS, AGENTIC MODELS, AND RAG INTEGRATION","source":"datacite","abstract":"Healthcare systems need to reduce administrative burden and support decision-making for clinical practice. Artificial intelligence approaches have the potential to reduce documentation and support diagnosis. Copilot Agents are in-app assistants that enable users to ask questions, automate documentation tasks, and coordinate clinical work processes without interrupting their current tasks within electronic health record systems. Agentic AI is not limited to single-turn questions and responses but also includes goal-aware reasoning during multi-turn tasks. Examples of such tasks span from processing prior authorizations to transitioning care calls and quality measurement documentation. Further, the clinical review at several checkpoints in the architecture is important to the implementation. For the RAG to be factually correct, language model outputs are grounded in validated institutional knowledge bases and clinically accepted guidelines. Source attribution mechanisms enable clinicians to trace model outputs to their respective information sources or references. Critical to the architecture of the RAG are security, privacy, and interpretability constraints in medical practices. Governance frameworks created by ongoing monitoring, responding to incidents, and involving stakeholders are essential for successfully using AI solutions in a way that supports rather than replaces clinical decision-making","url":"https://doi.org/10.5281/zenodo.18851337","authors":["Venkata Babu Mogili"],"tags":["Retrieval-Augmented Generation, Clinical Decision Support, Electronic Health Records, Healthcare Artificial Intelligence, Agentic Systems, Knowledge Grounding"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18851337","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18851338","name":"DESIGNING TRUSTWORTHY AI IN HEALTHCARE: EXPERIENCES WITH COPILOT AGENTS, AGENTIC MODELS, AND RAG INTEGRATION","source":"datacite","abstract":"Healthcare systems need to reduce administrative burden and support decision-making for clinical practice. Artificial intelligence approaches have the potential to reduce documentation and support diagnosis. Copilot Agents are in-app assistants that enable users to ask questions, automate documentation tasks, and coordinate clinical work processes without interrupting their current tasks within electronic health record systems. Agentic AI is not limited to single-turn questions and responses but also includes goal-aware reasoning during multi-turn tasks. Examples of such tasks span from processing prior authorizations to transitioning care calls and quality measurement documentation. Further, the clinical review at several checkpoints in the architecture is important to the implementation. For the RAG to be factually correct, language model outputs are grounded in validated institutional knowledge bases and clinically accepted guidelines. Source attribution mechanisms enable clinicians to trace model outputs to their respective information sources or references. Critical to the architecture of the RAG are security, privacy, and interpretability constraints in medical practices. Governance frameworks created by ongoing monitoring, responding to incidents, and involving stakeholders are essential for successfully using AI solutions in a way that supports rather than replaces clinical decision-making","url":"https://doi.org/10.5281/zenodo.18851338","authors":["Venkata Babu Mogili"],"tags":["Retrieval-Augmented Generation, Clinical Decision Support, Electronic Health Records, Healthcare Artificial Intelligence, Agentic Systems, Knowledge Grounding"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18851338","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18742013","name":"mailcom: Pseudonymization Tool for Textual Data","source":"datacite","abstract":"The rapid growth of data and its usage by Artificial Intelligence applications leads to heightened concerns about data privacy. Researchers often need to analyze datasets that contain personal information, sometimes paired with sensitive attributes such as medical records or political views. To support such analyses without exposing identifiable content, the Scientific Software Center (SSC) of Heidelberg University developed the mailcom package for pseudonymization. This capability is especially important when employing web-hosted Large Language Models for downstream analysis. As a use case, we applied mailcom to a multilingual email corpus in Spanish, French, and Portuguese contributed by multiple donors as part of a pilot study, in collaboration with the research group of Sybille Große (Department of Romance Studies, Heidelberg University). To protect donor privacy, sensitive information such as names, email addresses, and numbers is extracted and pseudonymized. The package processes text from email subjects and bodies in eml and html formats, as well as from csv rows, making it applicable to a wide range of textual data beyond email. mailcom is built entirely on open-source libraries and is designed for configurability and extensibility. Its core features are: (i) language identification, (ii) named-entity recognition, (iii) extraction of temporal expressions, and (iv) de-identifying sensitive data via pseudonyms. Three aforementioned languages are supported by default, with options to add further languages and change back-end libraries via configuration. We present these features in end-to-end processing pipelines using examples from our use case. The main parts include: (1) General workflow from raw text to pseudonymized output,(2) Default libraries and techniques (e.g. eml-parser, spaCy, langid, langdetect, transformers, and rule-based)(3) Mechanisms for adapting to new languages, transformer pipelines, and spaCy models with minimal effort. Since pseudonymized outputs still require human review to guarantee full anonymization, the package serves as a scalable pre-processing layer that reduces manual work while establishing a principled baseline of privacy protection. This reproducible, privacy-aware tool enables empirical research on digital text under current data-ethics and governance standards.","url":"https://doi.org/10.5281/zenodo.18742013","authors":["Le, Kim Tuyen","Gärtner, Laura","Fleischle, Felix","Schoeller, Thore","Große, Sybille","Ulusoy, Inga"],"tags":["pseudonymization","sensitive data","Natural Language Processing","named entity recognition"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18742013","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18742014","name":"mailcom: Pseudonymization Tool for Textual Data","source":"datacite","abstract":"The rapid growth of data and its usage by Artificial Intelligence applications leads to heightened concerns about data privacy. Researchers often need to analyze datasets that contain personal information, sometimes paired with sensitive attributes such as medical records or political views. To support such analyses without exposing identifiable content, the Scientific Software Center (SSC) of Heidelberg University developed the mailcom package for pseudonymization. This capability is especially important when employing web-hosted Large Language Models for downstream analysis. As a use case, we applied mailcom to a multilingual email corpus in Spanish, French, and Portuguese contributed by multiple donors as part of a pilot study, in collaboration with the research group of Sybille Große (Department of Romance Studies, Heidelberg University). To protect donor privacy, sensitive information such as names, email addresses, and numbers is extracted and pseudonymized. The package processes text from email subjects and bodies in eml and html formats, as well as from csv rows, making it applicable to a wide range of textual data beyond email. mailcom is built entirely on open-source libraries and is designed for configurability and extensibility. Its core features are: (i) language identification, (ii) named-entity recognition, (iii) extraction of temporal expressions, and (iv) de-identifying sensitive data via pseudonyms. Three aforementioned languages are supported by default, with options to add further languages and change back-end libraries via configuration. We present these features in end-to-end processing pipelines using examples from our use case. The main parts include: (1) General workflow from raw text to pseudonymized output,(2) Default libraries and techniques (e.g. eml-parser, spaCy, langid, langdetect, transformers, and rule-based)(3) Mechanisms for adapting to new languages, transformer pipelines, and spaCy models with minimal effort. Since pseudonymized outputs still require human review to guarantee full anonymization, the package serves as a scalable pre-processing layer that reduces manual work while establishing a principled baseline of privacy protection. This reproducible, privacy-aware tool enables empirical research on digital text under current data-ethics and governance standards.","url":"https://doi.org/10.5281/zenodo.18742014","authors":["Le, Kim Tuyen","Gärtner, Laura","Fleischle, Felix","Schoeller, Thore","Große, Sybille","Ulusoy, Inga"],"tags":["pseudonymization","sensitive data","Natural Language Processing","named entity recognition"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18742014","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/b2zx6","name":"Overview of the Scope of Application of Machine Learning Models in Predicting Maternal Delivery Risk and Outcomes","source":"datacite","abstract":"Childbirth is a normal physiological process for human reproduction, but there are various risks to maternal and fetal health and safety during this process . The 'Action Plan to Enhance Maternal and Infant Safety (2021-2025)' proposes a goal to reduce the national maternal mortality rate to 14.5 per 100,000 by 2025, requiring the implementation of pregnancy risk screening and assessment to improve the level of maternal and infant safety and lay a foundation for the 'Healthy China 2030' goal . In traditional childbirth models, women often experience numerous adverse experiences during labor, such as severe pain, fear, and anxiety, which can affect the progress of labor and maternal outcomes . Therefore, clinical practice must highly value interventions for adverse childbirth to predict risks and adverse outcomes during labor . Machine learning (machine learning, ML) is a significant branch of artificial intelligence, with its core lying in training algorithms through data to enable computers to perform predictive, classification, or decision-making tasks without explicit programming, allowing the construction of risk prediction models to estimate the probability of an individual developing a certain disease or the likelihood of future onset . ML models can integrate complex nonlinear relationships between multiple predictive variables, offering advantages over traditional statistical methods, especially in predicting early-onset diseases, where they demonstrate good accuracy and potential . In recent years, ML models and related research in the field of obstetrics have garnered significant attention. ML can improve the detection rate of risks during childbirth, reduce medical errors, and shorten diagnostic times . Therefore, this review aims to introduce ML algorithm types, data processing, application types, and application effects for predicting maternal childbirth risks and outcomes, deeply exploring the impact of ML model applications and effects on predicting maternal childbirth risks and outcomes, and providing references for the development and application of ML models for predicting maternal childbirth risks and outcomes in the future.","url":"https://doi.org/10.17605/osf.io/b2zx6","authors":["张博文","张帆"],"tags":["Medicine and Health Sciences","Nursing","FOS: Health sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/b2zx6","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.48549/6871","name":"Ethische Überlegungen zur Künstlichen Intelligenz in der Medizin","source":"datacite","abstract":"Einleitung Die zunehmende Anwendung von Künstlicher Intelligenz (KI) in der Medizin wirft grundlegende ethische Fragen auf: Die Anwendung von KI in der Medizin bringt ethische Fragen mit sich. Wer trägt die Verantwortung, wenn eine KI einen Fehler macht? Soll eine KI Entscheidungen über Leben und Tod fällen dürfen? Wird KI die soziale Ungerechtigkeit in der Medizin verstärken? Ist Empathie programmierbar? Wären Sie bereit, alle Ihre Daten (alles, was Sie denken, fühlen, machen, sehen etc.) preiszugeben für eine hundertprozentig genaue Diagnose? Diese Dissertation dient als ethische Standortbestimmung der Entwicklung und Anwendung von KI in der Medizin, indem sie Perspektiven von Expertinnen und Experten aus dem Bereich der KI-Entwicklung einholt. Methodik Die Arbeit folgt dem qualitativen Forschungsansatz. Es wurden sieben semistrukturierte Interviews mit Expertinnen und Experten aus dem Bereich Data Science, Medizintechnik, Softwareentwicklung, Computer Engineering etc. geführt. Die Analyse der Interviews erfolgte mittels Interpretativer Phänomenologischer Analyse (IPA) und wurde mit einer strategischen Literatursuche unterstützt. Ergebnisse Aus der Auswertung der Interviews gingen vier zentrale Themenbereiche hervor: Verantwortungsvolle Entwicklung und Implementierung von KI-Systemen, Sinn und Zweck des Algorithmus, KI als Werkzeug: Die Zukunft des Arztberufs sowie Standards und Richtlinien. Diskussion Die Ergebnisse zeigen, dass klassische medizinethische Prinzipien – wie Autonomie, Gerechtigkeit und das Prinzip des Nichtschadens – auch bei der Anwendung von KI funktionieren, jedoch teilweise ergänzt werden müssen z.B. durch technologieethische Ansätze. Besonders das Black-Box-Dilemma und das Problem von Verantwortungslücken werfen Fragen auf, die mit bestehenden ethischen und juristischen Instrumenten nur unzureichend beantwortet werden können. KI-Expertinnen und Experten sind sich über die Zukunft von medizinischer KI nicht immer einig, sehen jedoch grundsätzlich ein grosses Potential darin. Schlussfolgerung Die Untersuchung zeigt, dass eine ethische Reflexion bei Entwicklerinnen und Entwicklern von medizinischen KI-Systemen bereits präsent ist – aber stärker institutionell verankert und durch klare Richtlinien gestützt werden muss. KI soll den Menschen nicht ersetzen, sondern soll als zusätzliches Werkzeug dienen. Der gesellschaftliche und professionelle Umgang mit KI steht an einem Wendepunkt, der interdisziplinäre Kooperation, gesetzliche Orientierung und ethische Sensibilisierung zwingend erforderlich macht.","url":"https://doi.org/10.48549/6871","authors":["Mahler, Maria Katharina"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48549/6871","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.48550/arxiv.2509.03906","name":"Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning","source":"datacite","abstract":"The clinical adoption of artificial intelligence (AI) in medical diagnostics is critically hampered by its black-box nature, which prevents clinicians from verifying the rationale behind automated decisions. To overcome this fundamental barrier, we introduce DeepMedix-R1, a foundation model (FM) for chest X-ray (CXR) interpretation that generates not only accurate diagnoses but also a transparent, step-by-step reasoning process grounded in specific visual evidence. Our methodology employs a sequential training strategy, beginning with instruction fine-tuning, followed by a cold-start phase to elicit reasoning capabilities. Critically, we then implement reinforcement learning with grounded rewards to meticulously refine the model, aligning both its diagnostic outputs and its reasoning pathways with clinical plausibility. Quantitative assessments show that DeepMedix-R1 substantially outperforms advanced FMs, achieving improvements in report generation and visual question answering tasks. We also introduce Report Arena, a novel LLM-based benchmark that ranks DeepMedix-R1 first among competing models for output quality. Most significantly, a formal review by clinical experts reveals a profound preference for DeepMedix-R1's generated reasoning over the broadly adopted Qwen2.5-VL-7B model, confirming its superior interpretability and clinical utility.","url":"https://doi.org/10.48550/arxiv.2509.03906","authors":["Lin, Qika","Zhu, Yifan","Pu, Bin","Huang, Ling","Luo, Haoran","Ma, Jingying","Wu, Feng","He, Kai","Xu, Jiaxing","Peng, Zhen","Zhao, Tianzhe","Xu, Fangzhi","Zhang, Jian","Ou, Zhonghong","Cambria, Erik","Mishra, Swapnil","Feng, Mengling"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.03906","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18842148","name":"AI in Healthcare: Transformative Innovations, Trends, and Ethical Considerations (2014 - 2023)","source":"datacite","abstract":"This note synthesises key developments in the use of artificial intelligence (AI) in healthcare over the 2014–2023 period, with emphasis on clinical applications and governance constraints. It reviews advances in machine learning for diagnosis and prognosis, predictive analytics for personalised medicine, natural language processing for clinical text mining, and computer vision for medical imaging. The discussion highlights the statistical and data science foundations required for robust development and evaluation, including external validation, calibration, subgroup assessment, and monitoring for dataset shift. Methodologically, the synthesis is informed by a structured search in the Web of Science Core Collection (conducted 21 May 2023), targeting English-language review articles (publication years 2020–2023) and complemented by foundational works widely cited in the field. Findings indicate that AI systems can improve diagnostic precision and operational efficiency when embedded within accountable clinical workflows. However, adoption is constrained by persistent challenges related to explainability, interoperability, bias, privacy protection, and institutional governance. The note concludes that AI should be treated as clinical augmentation under human oversight, with transparent governance and rigorous evaluation as prerequisites for sustainable deployment. Keywords: artificial intelligence in healthcare; machine learning; predictive analytics; natural language processing; medical imaging; health data governance.","url":"https://doi.org/10.5281/zenodo.18842148","authors":["Silva-Morales, Milena-Jael"],"tags":["artificial intelligence in healthcare","Machine Learning/ethics","medical imaging","Natural language processing","natural language processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.18842148","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18844379","name":"AI in Healthcare: Transformative Innovations, Trends, and Ethical Considerations (2014 - 2023)","source":"datacite","abstract":"This note synthesises key developments in the use of artificial intelligence (AI) in healthcare over the 2014–2023 period, with emphasis on clinical applications and governance constraints. It reviews advances in machine learning for diagnosis and prognosis, predictive analytics for personalised medicine, natural language processing for clinical text mining, and computer vision for medical imaging. The discussion highlights the statistical and data science foundations required for robust development and evaluation, including external validation, calibration, subgroup assessment, and monitoring for dataset shift. Methodologically, the synthesis is informed by a structured search in the Web of Science Core Collection (conducted 21 May 2023), targeting English-language review articles (publication years 2020–2023) and complemented by foundational works widely cited in the field. Findings indicate that AI systems can improve diagnostic precision and operational efficiency when embedded within accountable clinical workflows. However, adoption is constrained by persistent challenges related to explainability, interoperability, bias, privacy protection, and institutional governance. The note concludes that AI should be treated as clinical augmentation under human oversight, with transparent governance and rigorous evaluation as prerequisites for sustainable deployment. Keywords: artificial intelligence in healthcare; machine learning; predictive analytics; natural language processing; medical imaging; health data governance.","url":"https://doi.org/10.5281/zenodo.18844379","authors":["Silva-Morales, Milena-Jael"],"tags":["artificial intelligence in healthcare","Machine Learning/ethics","medical imaging","Natural language processing","natural language processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.18844379","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18843546","name":"AI in Healthcare: Transformative Innovations, Trends, and Ethical Considerations (2014 - 2023)","source":"datacite","abstract":"This note synthesises key developments in the use of artificial intelligence (AI) in healthcare over the 2014–2023 period, with emphasis on clinical applications and governance constraints. It reviews advances in machine learning for diagnosis and prognosis, predictive analytics for personalised medicine, natural language processing for clinical text mining, and computer vision for medical imaging. The discussion highlights the statistical and data science foundations required for robust development and evaluation, including external validation, calibration, subgroup assessment, and monitoring for dataset shift. Methodologically, the synthesis is informed by a structured search in the Web of Science Core Collection (conducted 21 May 2023), targeting English-language review articles (publication years 2020–2023) and complemented by foundational works widely cited in the field. Findings indicate that AI systems can improve diagnostic precision and operational efficiency when embedded within accountable clinical workflows. However, adoption is constrained by persistent challenges related to explainability, interoperability, bias, privacy protection, and institutional governance. The note concludes that AI should be treated as clinical augmentation under human oversight, with transparent governance and rigorous evaluation as prerequisites for sustainable deployment. Keywords: artificial intelligence in healthcare; machine learning; predictive analytics; natural language processing; medical imaging; health data governance.","url":"https://doi.org/10.5281/zenodo.18843546","authors":["Silva-Morales, Milena-Jael"],"tags":["artificial intelligence in healthcare","Machine Learning/ethics","medical imaging","Natural language processing","natural language processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.18843546","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18842149","name":"AI in Healthcare: Transformative Innovations, Trends, and Ethical Considerations (2014 - 2023)","source":"datacite","abstract":"This note synthesises key developments in the use of artificial intelligence (AI) in healthcare over the 2014–2023 period, with emphasis on clinical applications and governance constraints. It reviews advances in machine learning for diagnosis and prognosis, predictive analytics for personalised medicine, natural language processing for clinical text mining, and computer vision for medical imaging. The discussion highlights the statistical and data science foundations required for robust development and evaluation, including external validation, calibration, subgroup assessment, and monitoring for dataset shift. Methodologically, the synthesis is informed by a structured search in the Web of Science Core Collection (conducted 21 May 2023), targeting English-language review articles (publication years 2020–2023) and complemented by foundational works widely cited in the field. Findings indicate that AI systems can improve diagnostic precision and operational efficiency when embedded within accountable clinical workflows. However, adoption is constrained by persistent challenges related to explainability, interoperability, bias, privacy protection, and institutional governance. The note concludes that AI should be treated as clinical augmentation under human oversight, with transparent governance and rigorous evaluation as prerequisites for sustainable deployment. Keywords: artificial intelligence in healthcare; machine learning; predictive analytics; natural language processing; medical imaging; health data governance.","url":"https://doi.org/10.5281/zenodo.18842149","authors":["Silva-Morales, Milena-Jael"],"tags":["artificial intelligence in healthcare","Machine Learning/ethics","medical imaging","Natural language processing","natural language processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.18842149","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.21443627.v1","name":"Additional file 1 of Value assessment of artificial intelligence in medical imaging: a scoping review","source":"datacite","abstract":"Additional file 1. Final searches for the scoping review. The final searches for the scoping review is provided.","url":"https://doi.org/10.6084/m9.figshare.21443627.v1","authors":["Fasterholdt, Iben","Naghavi-Behzad, Mohammad","Rasmussen, Benjamin S. B.","Kjølhede, Tue","Skjøth, Mette Maria","Hildebrandt, Malene Grubbe","Kidholm, Kristian"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.21443627.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.21443627","name":"Additional file 1 of Value assessment of artificial intelligence in medical imaging: a scoping review","source":"datacite","abstract":"Additional file 1. Final searches for the scoping review. The final searches for the scoping review is provided.","url":"https://doi.org/10.6084/m9.figshare.21443627","authors":["Fasterholdt, Iben","Naghavi-Behzad, Mohammad","Rasmussen, Benjamin S. B.","Kjølhede, Tue","Skjøth, Mette Maria","Hildebrandt, Malene Grubbe","Kidholm, Kristian"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.21443627","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.21443630.v1","name":"Additional file 2 of Value assessment of artificial intelligence in medical imaging: a scoping review","source":"datacite","abstract":"Additional file 2. Full data analysis for all domains. The full data analysis for all domains is provided.","url":"https://doi.org/10.6084/m9.figshare.21443630.v1","authors":["Fasterholdt, Iben","Naghavi-Behzad, Mohammad","Rasmussen, Benjamin S. B.","Kjølhede, Tue","Skjøth, Mette Maria","Hildebrandt, Malene Grubbe","Kidholm, Kristian"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.21443630.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.21443630","name":"Additional file 2 of Value assessment of artificial intelligence in medical imaging: a scoping review","source":"datacite","abstract":"Additional file 2. Full data analysis for all domains. The full data analysis for all domains is provided.","url":"https://doi.org/10.6084/m9.figshare.21443630","authors":["Fasterholdt, Iben","Naghavi-Behzad, Mohammad","Rasmussen, Benjamin S. B.","Kjølhede, Tue","Skjøth, Mette Maria","Hildebrandt, Malene Grubbe","Kidholm, Kristian"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.6084/m9.figshare.21443630","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18841570","name":"Embedding AI Governance in Hospitals: AI Under Medical-Led Constitutional Governance (SMART-H 3.0 / CAH-SPINE)","source":"datacite","abstract":"Artificial intelligence and autonomous robotics are entering hospitals. This is no longer a question of whether but of when, how fast, and — most critically — under whose governance. This paper proposes SMART-H 3.0 (Smart Medical Autonomous Robotics Technology for Hospitals), a governance-native architecture for AI-enabled hospitals that places infection prevention for immunocompromised patients as its primary constitutional objective. The architecture builds upon two prior published frameworks by the present author: the RAH-SPINE (Recursive Agentic-Human Governance Spine) twelve-layer governance stack, which provides the foundational layered architecture for embedding governance rules into autonomous AI systems; and the Computable Governance Notation (CGN) formalism, which enables machine-readable regulatory policies to compile into executable operational constraints across diverse jurisdictions. Together, these frameworks serve as the proposed central nervous system of the hospital-as-organism. At the heart of the architecture lies a six-phase recursive governance loop — Sense, Score, Simulate, Act, Review, Learn — through which every autonomous decision is continuously evaluated, validated against constitutional constraints, and subject to medical-led human review. The paper introduces the Constitutional Autonomous Hospital Spine (CAH-SPINE), a fourteen-layer governance stack extending RAH-SPINE with hospital-specific components including digital twin simulation, autonomous swarm robotics coordination, sovereign AI processing, and post-quantum cryptographic identity. A constitutional zone model classifies hospital areas by immunocompromised patient vulnerability, with non-optimisable safety floors that cannot be overridden by AI optimisation. The proposal is explicitly conceptual (TRL 1-2): no prototype exists, no clinical validation has been conducted, and all quantitative parameters are illustrative governance modelling constructs. The paper contributes an architectural hypothesis — that the hospital of 2035 may be defined less by the sophistication of its robots or the power of its AI than by the maturity of its governance.","url":"https://doi.org/10.5281/zenodo.18841570","authors":["BRIZUELA, HORACIO"],"tags":["Smart Hospital AI Governance Healthcare-Associated Infections Autonomous Robotics Constitutional Governance Recursive Governance Infection Prevention and Control EU AI Act Medical Device Regulation Antimicrobial Resistance RAH-SPINE CAH-SPINE CGN Digital Twin"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18841570","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18841569","name":"Embedding AI Governance in Hospitals: AI Under Medical-Led Constitutional Governance (SMART-H 3.0 / CAH-SPINE)","source":"datacite","abstract":"Artificial intelligence and autonomous robotics are entering hospitals. This is no longer a question of whether but of when, how fast, and — most critically — under whose governance. This paper proposes SMART-H 3.0 (Smart Medical Autonomous Robotics Technology for Hospitals), a governance-native architecture for AI-enabled hospitals that places infection prevention for immunocompromised patients as its primary constitutional objective. The architecture builds upon two prior published frameworks by the present author: the RAH-SPINE (Recursive Agentic-Human Governance Spine) twelve-layer governance stack, which provides the foundational layered architecture for embedding governance rules into autonomous AI systems; and the Computable Governance Notation (CGN) formalism, which enables machine-readable regulatory policies to compile into executable operational constraints across diverse jurisdictions. Together, these frameworks serve as the proposed central nervous system of the hospital-as-organism. At the heart of the architecture lies a six-phase recursive governance loop — Sense, Score, Simulate, Act, Review, Learn — through which every autonomous decision is continuously evaluated, validated against constitutional constraints, and subject to medical-led human review. The paper introduces the Constitutional Autonomous Hospital Spine (CAH-SPINE), a fourteen-layer governance stack extending RAH-SPINE with hospital-specific components including digital twin simulation, autonomous swarm robotics coordination, sovereign AI processing, and post-quantum cryptographic identity. A constitutional zone model classifies hospital areas by immunocompromised patient vulnerability, with non-optimisable safety floors that cannot be overridden by AI optimisation. The proposal is explicitly conceptual (TRL 1-2): no prototype exists, no clinical validation has been conducted, and all quantitative parameters are illustrative governance modelling constructs. The paper contributes an architectural hypothesis — that the hospital of 2035 may be defined less by the sophistication of its robots or the power of its AI than by the maturity of its governance.","url":"https://doi.org/10.5281/zenodo.18841569","authors":["BRIZUELA, HORACIO"],"tags":["Smart Hospital AI Governance Healthcare-Associated Infections Autonomous Robotics Constitutional Governance Recursive Governance Infection Prevention and Control EU AI Act Medical Device Regulation Antimicrobial Resistance RAH-SPINE CAH-SPINE CGN Digital Twin"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18841569","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/agjcy","name":"Systematic review : The Role of Artificial Intelligence in hyperspectral Imaging for Medical Applications","source":"datacite","abstract":"Medical hyperspectral imaging (HSI) combined with AI analysis is a rapidly evolving field that aims to enhance diagnostic accuracy and clinical outcomes. HSI captures both spatial and spectral information, providing detailed insights into the physiological and biochemical properties of tissues. AI methods, particularly machine learning (ML) and deep learning (DL), have revolutionized data processing in HSI by automating feature extraction, improving classification accuracy, and enabling real-time decision-making for a variety of clinical applications. The goal of this systematic review it to visualize and quantify the diversity of HSI systems used in clinical studies and identify the effect on AI development. Which generic AI techniques are used to solve which type of clinical tasks and what are the challenges?","url":"https://doi.org/10.17605/osf.io/agjcy","authors":["Willems, Siri"],"tags":["Translational Medical Research","Physical Sciences and Mathematics","Medicine and Health Sciences","Computer Sciences","Artificial Intelligence and Robotics","Engineering","Biomedical Engineering and Bioengineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/agjcy","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/6sjdq","name":"Application of Artificial Intelligence in Diet Management of Inflammatory Bowel Disease: a Scoping Review","source":"datacite","abstract":"Background Inflammatory bowel disease (IBD) mainly includes Crohn's Disease (CD) and Ulcerative Colitis (Ulcerative Colitis). UC is a group of chronic non-specific inflammatory diseases with abdominal pain, diarrhea, mucous blood stool as the main symptoms. In the past 30 years, the number of patients with IBD has been growing, and the current incidence has not yet reached a peak, and the disease burden will further increase in the future. It is generally believed that diet is closely related to the occurrence, development and prognosis of IBD. Reasonable diet is not only beneficial to correct malnutrition, but also to promote mucosal repair and achieve the purpose of maintaining disease remission. With the continuous expansion of the application of artificial intelligence technology in the field of medical treatment and nursing and the continuous development of precise diet management, the research of artificial intelligence in the field of diet management of IBD patients has gradually increased. However, the specific applications of AI in diet management among patients with IBD and how to address barriers in their current usage is widely unexplored. Objective To review the research status of artificial intelligence (AI) in diet management of patients with IBD. Methods Following the methodology of the scoping review, the following databases were searched to include relevant literature: PubMed, Web of Science, Embase, Cochrane Library, CINAHL, IEEE Xplore, Association for Computing Machinery Digital Library, China Biomedical Literature Database, CNKI, Wanfang and VIP. The search period was from the establishment until March 2024 and the included articles were summarized and analyzed. Title and abstract screening and full-text review are performed using Endnote, where all articles are independently revised by two reviewers. We include articles written in English or Chinese on experimental and observational studies with qualitative, quantitative and mixed methods approaches, which evaluate AI technologies applicable to nursing. Data are extracted to Excel by two researchers and summarized. Results The scoping review will be completed in June 2024. The results will describe where research has been down, what AI technologies have been developed and studied to assist diet management in patients with IBD, how these technologies have been evaluated, how nurses have participated and how ethical issues have been addressed in the research. Conclusion Findings of this review will provide an overall state of artificial intelligence-based applications in diet management among patients with IBD. This information is important in understanding critical issues that require attention for the use of AI in diet management.","url":"https://doi.org/10.17605/osf.io/6sjdq","authors":["Li, Yiting","Wenjing Tu","Ziqi Mei","Tingting YIN"],"tags":["Physical Sciences and Mathematics","Dietetics and Clinical Nutrition","Medicine and Health Sciences","Computer Sciences","Nursing","FOS: Health sciences","Artificial Intelligence and Robotics","Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.17605/osf.io/6sjdq","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18826438","name":"Quantitative Competence and Editorial Gatekeeping: A Cross-Sectional Assessment of Machine Learning Evaluation Capacity at Major US Medical Journals — Data and Code","source":"datacite","abstract":"This deposit contains the complete dataset, analysis code, predictive models, and manuscript supporting a cross-sectional audit of machine learning and artificial intelligence (ML/AI) evaluation capacity at 9 major US medical journals. Key findings: 0 of 14 editors-in-chief hold formal ML/AI training, first-author ML publications, or quantitative doctoral degrees These journals collectively publish 0.115% of global PubMed-indexed ML/AI biomedical output (263 of 227,731 papers, 2023–2025) Predictive models estimate triage error rates of 45–78% for quantitatively intensive manuscripts Contents: 21 raw PubMed Timeline CSV exports (searched February 27, 2026) Consolidated data with EIC credential profiles (JSON) Standalone predictive model (Python, no external dependencies) Data extraction and visualization scripts 4 interactive HTML visualizations Manuscript formatted for BMJ submission (Word) Proposed author experience survey design Full methodological documentation","url":"https://doi.org/10.5281/zenodo.18826438","authors":["Demidont, A.C."],"tags":["editorial competence","machine learning","artificial intelligence","medical journals","peer review","publication bias","desk rejection","PubMed"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18826438","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18826439","name":"Quantitative Competence and Editorial Gatekeeping: A Cross-Sectional Assessment of Machine Learning Evaluation Capacity at Major US Medical Journals — Data and Code","source":"datacite","abstract":"This deposit contains the complete dataset, analysis code, predictive models, and manuscript supporting a cross-sectional audit of machine learning and artificial intelligence (ML/AI) evaluation capacity at 9 major US medical journals. Key findings: 0 of 14 editors-in-chief hold formal ML/AI training, first-author ML publications, or quantitative doctoral degrees These journals collectively publish 0.115% of global PubMed-indexed ML/AI biomedical output (263 of 227,731 papers, 2023–2025) Predictive models estimate triage error rates of 45–78% for quantitatively intensive manuscripts Contents: 21 raw PubMed Timeline CSV exports (searched February 27, 2026) Consolidated data with EIC credential profiles (JSON) Standalone predictive model (Python, no external dependencies) Data extraction and visualization scripts 4 interactive HTML visualizations Manuscript formatted for BMJ submission (Word) Proposed author experience survey design Full methodological documentation","url":"https://doi.org/10.5281/zenodo.18826439","authors":["Demidont, A.C."],"tags":["editorial competence","machine learning","artificial intelligence","medical journals","peer review","publication bias","desk rejection","PubMed"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18826439","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/zx6p2","name":"Clinical Conversational AI for Virtual Patient Simulation in Medical Education: A Systematic Review and Meta-Analysis","source":"datacite","abstract":"Artificial intelligence is rapidly expanding in medical education. While traditional simulation has long supported OSCE preparation and clinical skills training, conversational AI offers a new avenue for learner interaction through virtual patients and examiners capable of natural dialogue. Early evidence suggests these systems may enhance communication skills, diagnostic reasoning, prescribing accuracy, and data interpretation. However, the existing literature is dispersed, making the overall effectiveness unclear. A systematic review and meta-analysis are therefore warranted to synthesise the available evidence.","url":"https://doi.org/10.17605/osf.io/zx6p2","authors":["Still, Amy"],"tags":["Medicine and Health Sciences","Medical Education"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/zx6p2","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/56d8j","name":"Performance of Large Language Models in Automated Medical Literature Screening: A Systematic Review and Meta-analysis","source":"datacite","abstract":"This project aims to systematically evaluate the performance, reliability, and heterogeneity of large language models (LLMs) in the automated screening of medical literature. As the volume of scientific publications continues to grow exponentially, the screening phase of systematic reviews has become increasingly time-consuming and labor-intensive. Recent advances in artificial intelligence—particularly large language models such as GPT-series, LLaMA, Claude, Gemini, Copilot, DeepSeek, and Qwen—have shown the potential to automate key steps of evidence synthesis. However, the accuracy, consistency, and generalizability of these models across different screening tasks and clinical domains remain unclear. This study will conduct a comprehensive systematic review and meta-analysis to summarize existing evidence on the use of LLMs for title/abstract screening, citation filtering, and study selection in medical and health-related systematic reviews. We will assess multiple performance indicators, including sensitivity, specificity, precision, recall, F1-score, workload reduction, and time efficiency. We also aim to explore sources of heterogeneity, such as model type, prompt design, clinical field, screening task complexity, dataset characteristics, and evaluation methodology. The review will follow PRISMA 2020 guidelines. Searches will be conducted across PubMed, Embase, Scopus, Web of Science, and preprint servers. Eligible studies will include evaluations of LLM-assisted or fully automated literature screening workflows, using either real-world systematic review datasets or curated benchmark sets. Risk of bias will be assessed using adapted tools for AI performance studies. Where feasible, meta-analysis will be performed to synthesize quantitative performance metrics.","url":"https://doi.org/10.17605/osf.io/56d8j","authors":["Xie, Chenggong"],"tags":["Diagnosis","Medicine and Health Sciences","Analytical, Diagnostic and Therapeutic Techniques and Equipment"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/56d8j","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/ecjkq","name":"AI-based chatbots in adult nutrition-related weight management: a scoping review of use, safety, and equity","source":"datacite","abstract":"The objective of this scoping review is to map how artificial intelligence (AI) based chatbots are used in nutrition weight management in adults, as well as identify and synthesise evidence on equity, safety and accessibility, with a particular focus on culturally and linguistically diverse (CALD) individuals (however not a requirement for eligibility criteria). Due to the youth of AI technologies, it is expected that this scoping review will yield results from a limited number of studies.","url":"https://doi.org/10.17605/osf.io/ecjkq","authors":["Lim, James","Roy, Rajshri"],"tags":["Health Information Technology","Translational Medical Research","Dietetics and Clinical Nutrition","Nutrition","Medicine and Health Sciences","Life Sciences","Health and Medical Administration","AI Chatbot"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/ecjkq","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/h27vf","name":"Use of Artificial Intelligence Platforms in Enhancing Learning and Research Skills Among Postgraduate Medical Trainees: A Systematic Review and Qualitative Meta-Synthesis","source":"datacite","abstract":"This systematic review examines how postgraduate medical trainees (residents and fellows) use artificial intelligence platforms, particularly large language models like ChatGPT and GPT-4, for learning and research. PURPOSE: To synthesize evidence on AI adoption patterns, perceived benefits and challenges, and ethical concerns in postgraduate medical education following the November 2022 release of ChatGPT—a transformative moment that enabled individual trainees to access AI tools without institutional gatekeeping. METHODS: Following PRISMA 2020 guidelines, we searched three databases (SciSpace, Google Scholar, PubMed) for studies published January 2023-December 2024. Two independent reviewers screened 2,847 records, extracting data from 24 eligible studies involving 2,897 trainees across 15 countries. Quality appraisal used the Mixed Methods Appraisal Tool. Reflexive thematic analysis synthesized qualitative findings. EXPECTED OUTCOMES: 1. Comprehensive mapping of AI use patterns across specialties, training levels, and geographic contexts 2. Identification of six key themes: cognitive scaffolding, efficiency gains, research productivity, critical evaluation needs, ethical concerns, and equity implications 3. Assessment of evidence quality—revealing that all studies rely on self-reported perceptions without objective outcome validation 4. Policy recommendations for residency programs addressing academic integrity, diagnostic accuracy concerns, and equitable access 5. Research agenda highlighting critical gaps: need for objective learning outcome studies, longitudinal competency assessments, and patient care impact evaluations This review addresses an urgent knowledge gap as AI tools fundamentally reshape medical training, informing evidence-based educational policy development.","url":"https://doi.org/10.17605/osf.io/h27vf","authors":["Ali, Sajjad"],"tags":["Education",": artificial intelligence; medical education; graduate medical education; residency training; large language models; ChatGPT; systematic review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/h27vf","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/dj7ym","name":"Artificial intelligence for informed decision-making during clinical trial consent: A scoping review of applications, outcomes, and ethical considerations","source":"datacite","abstract":"Clinical trials are essential for advancing medical treatments but face persistent challenges including slow recruitment, high costs, and complex regulatory demands. Artificial intelligence (AI), particularly large generative AI, has emerged as a potential tool to improve efficiency across multiple stages of trial design and conduct. One ethically sensitive domain is the informed consent process, where AI may support comprehension through document generation, conversational interfaces, and personalized explanations. Consent process in clinical trials is a longitudinal process, which includes information disclosure, decision supports, comprehension, dynamic conversations, and documentations. While early studies suggest potential benefits, concerns remain regarding safety, biases, and undue influence. The literature is rapidly expanding but fragmented across empirical studies and ethical analyses, warranting a scoping review to map the field.","url":"https://doi.org/10.17605/osf.io/dj7ym","authors":["Lee, Seung Heyck","Yahya, Ayesha","Serpico, Kimberley","Yeh, Natalie","Amin, Amina","Bell, Jennifer","Kieran, Quinn"],"tags":["Medicine and Health Sciences","artificial intelligence","clinical trial","consent","large language model","participant decision-making"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/dj7ym","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18807749","name":"PENTLLM: A Pentagon-Based Multi-Expert Large Language Model Architecture for Medical Research Applications","source":"datacite","abstract":"Background: Artificial Intelligence-based technologies, in particular Large Language Models, are getting increasingly popular with improving capabilities in text generation and image analysis. However, single Large Language Models still lack the capability to do comprehensive academic medical research which is free of bias and hallucinations. Objective: This paper introduces a novel concept of PENTLLM (Pentagon Large Language Model), which is a multi-expert AI architecture using five specialized LLM experts with a central NEXUS synthesis providing a comprehensive academic module including statistical analysis for medical research providing research proposal analysis, peer review for manuscripts, systematic reviews, meta-analysis, apart from other tools of CV writing, document processing, and coding, with self-learning through persistent memory. Methods: PENTLLM architecture consists of 5 LLMs (STEM, Language, Knowledge, Reasoning, Creative) that work in parallel, with responses from all experts synthesized and then unified through a central NEXUS model. The PENTLLM NEXUS system comprises 98 Python modules, 145 API endpoints, and 69,637 lines of code. Moreover, it has integration of agentic AI, LangChain, LangGraph, metacognition and hypermemory to enable workflow orchestration. It is also equipped with a clinical RAG module which enables evidence-based responses based on uploaded clinical guidelines and policies. PENTLLM also has comprehensive tools for office documents. We conducted all trials on a desktop workstation with Intel Core Ultra 9 285K (24 cores), 48GB RAM, and NVIDIA RTX 5090 (32GB VRAM) running Windows 11. Ollama with locally running 32B parameter models were used. Results: PENTLLM demonstrated robust performance across academic medical research tasks. Multi-model architecture of PENTLLM exhibited superior outcomes on various academic tasks when compared to single model approaches. In the peer review task Pentagon mode successfully generated comprehensive qualitative reviewer commentary (431 words covering 8 review dimensions) where single-model approaches failed to produce any detailed output; however, overall scores of peer review across both remained the same. Interestingly, Pentagon used only 38 seconds more compared to a single model. Similarly, in literature review tasks, Pentagon mode again showed a higher quality score (0.74 vs 0.69) and lower hallucination risk (0.25 vs 0.30) with zero detected biases when this output was compared to a single model. When tested for systematic review, Pentagon mode again produced fewer statistical reporting errors (3 vs 7), showed lower hallucination risk (0.36 vs 0.42), and produced more comprehensive output (44,615 vs 37,057 characters, 61 vs 57 references) with comparable processing time (296s vs 301s). It showed personalized responses based on user preferences and interaction due to its self-learning memory system. We noticed that Pentagon mode parallel execution was constrained by single-GPU VRAM capacity, which resulted in sequential model loading. Conclusion: PENTLLM has shown significant advancement in AI-assisted medical research providing a privacy-preserving platform with development and analysis of research proposals to peer review and publication. Multi-model architecture is superior in performance with not much noticeable difference in the output time compared to single models. Full paper to follow…… Patent Pending at United States Patent and Trademark Office (USPTO)","url":"https://doi.org/10.5281/zenodo.18807749","authors":["Waheed, Muhammad Atif"],"tags":["PENTLLM, Large Language Models, Multi-Agent Architecture, Medical Research, Artificial Intelligence, Peer Review, Systematic Review, Meta-Analysis, Agentic AI, Clinical RAG, Evidence-Based Medicine, Privacy-Preserving AI, LangChain, LangGraph, Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18807749","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18807750","name":"PENTLLM: A Pentagon-Based Multi-Expert Large Language Model Architecture for Medical Research Applications","source":"datacite","abstract":"Background: Artificial Intelligence-based technologies, in particular Large Language Models, are getting increasingly popular with improving capabilities in text generation and image analysis. However, single Large Language Models still lack the capability to do comprehensive academic medical research which is free of bias and hallucinations. Objective: This paper introduces a novel concept of PENTLLM (Pentagon Large Language Model), which is a multi-expert AI architecture using five specialized LLM experts with a central NEXUS synthesis providing a comprehensive academic module including statistical analysis for medical research providing research proposal analysis, peer review for manuscripts, systematic reviews, meta-analysis, apart from other tools of CV writing, document processing, and coding, with self-learning through persistent memory. Methods: PENTLLM architecture consists of 5 LLMs (STEM, Language, Knowledge, Reasoning, Creative) that work in parallel, with responses from all experts synthesized and then unified through a central NEXUS model. The PENTLLM NEXUS system comprises 98 Python modules, 145 API endpoints, and 69,637 lines of code. Moreover, it has integration of agentic AI, LangChain, LangGraph, metacognition and hypermemory to enable workflow orchestration. It is also equipped with a clinical RAG module which enables evidence-based responses based on uploaded clinical guidelines and policies. PENTLLM also has comprehensive tools for office documents. We conducted all trials on a desktop workstation with Intel Core Ultra 9 285K (24 cores), 48GB RAM, and NVIDIA RTX 5090 (32GB VRAM) running Windows 11. Ollama with locally running 32B parameter models were used. Results: PENTLLM demonstrated robust performance across academic medical research tasks. Multi-model architecture of PENTLLM exhibited superior outcomes on various academic tasks when compared to single model approaches. In the peer review task Pentagon mode successfully generated comprehensive qualitative reviewer commentary (431 words covering 8 review dimensions) where single-model approaches failed to produce any detailed output; however, overall scores of peer review across both remained the same. Interestingly, Pentagon used only 38 seconds more compared to a single model. Similarly, in literature review tasks, Pentagon mode again showed a higher quality score (0.74 vs 0.69) and lower hallucination risk (0.25 vs 0.30) with zero detected biases when this output was compared to a single model. When tested for systematic review, Pentagon mode again produced fewer statistical reporting errors (3 vs 7), showed lower hallucination risk (0.36 vs 0.42), and produced more comprehensive output (44,615 vs 37,057 characters, 61 vs 57 references) with comparable processing time (296s vs 301s). It showed personalized responses based on user preferences and interaction due to its self-learning memory system. We noticed that Pentagon mode parallel execution was constrained by single-GPU VRAM capacity, which resulted in sequential model loading. Conclusion: PENTLLM has shown significant advancement in AI-assisted medical research providing a privacy-preserving platform with development and analysis of research proposals to peer review and publication. Multi-model architecture is superior in performance with not much noticeable difference in the output time compared to single models. Full paper to follow…… Patent Pending at United States Patent and Trademark Office (USPTO)","url":"https://doi.org/10.5281/zenodo.18807750","authors":["Waheed, Muhammad Atif"],"tags":["PENTLLM, Large Language Models, Multi-Agent Architecture, Medical Research, Artificial Intelligence, Peer Review, Systematic Review, Meta-Analysis, Agentic AI, Clinical RAG, Evidence-Based Medicine, Privacy-Preserving AI, LangChain, LangGraph, Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18807750","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/qfyb8","name":"Artificial intelligence-based clinical simulation and its contribution to learning and cognitive processes in undergraduate medical students: a scoping review","source":"datacite","abstract":"Introduction: The training of future physicians occurs in a context where preventable clinical errors are still frequent and safe decision-making is increasingly complex. Faced with this scenario, clinical simulation has emerged as a key strategy for risk-free learning for patients. More recently, the incorporation of artificial intelligence has begun to transform these simulated environments, allowing for more interactive, adaptive, and personalized experiences. However, the real impact of Artificial intelligence-based clinical simulation on learning outcomes and cognitive processes in undergraduate medical students has not yet been clearly systematized. Methodology: A scoping review will be carried out to analyze scientific articles, educational studies, reviews, and technical literature published between 2015 and 2025, which address the use of Artificial intelligence-based clinical simulation in medical education. The search will be carried out in the PubMed, Scopus, Web of Science and BIREME databases, using MeSH and DeCS descriptors related to artificial intelligence, clinical simulation, learning and medical education, complemented by the review of grey literature and the chain search technique of references. Expected results: It is expected to map the current applications of Artificial intelligence-based clinical simulation in undergraduate medical education, as well as the learning outcomes, cognitive processes, benefits, challenges, and knowledge gaps reported. Conclusions: This review will offer a comprehensive view of the potential and limitations of clinical simulation based on artificial intelligence, contributing to guide its critical and pedagogically grounded incorporation into undergraduate medical training. Descriptors (DeCS/MeSH): Artificial Intelligence; Clinical Simulation; Medical Education; Medical Students; Learning. Keywords: Clinical simulation; artificial intelligence; undergraduate medical education; clinical reasoning; learning.","url":"https://doi.org/10.17605/osf.io/qfyb8","authors":["Pesantes, Mariana","Franco, Sofía Rincón","Sanmiguel, Juliana Andrea Suárez","Rodriguez, Lidia Yuliana Orozco","Rincon, Erwin Hernando Hernandez"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/qfyb8","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/mvtw9","name":"ORACLES-AI - ORal Annotated Clinical Lesion Evaluation dataset for Artificial Intelligence","source":"datacite","abstract":"ORACLES-AI (ORal Annotated Clinical Lesion Evaluation dataset for Artificial Intelligence) is an annotated, image-based dataset developed to advance research, education, and computational modelling in the domain of oral mucosal health and disease. The dataset comprises high-quality intraoral clinical images collected from participants representing a broad spectrum of oral mucosal conditions, ranging from normal mucosa and variations from normal to Oral Potentially Malignant Disorders (OPMDs) and oral cancers. Each image is accompanied by expert-generated annotations and extensive, non-identifiable metadata, making ORACLES-AI a comprehensive and interpretable resource for both clinical and computational research applications. Data Collection and Methodology Participants were prospectively recruited, and intraoral images were acquired using standardized mobile phone–based intraoral photography protocols. Participants were enrolled from one of the spoke centers operating under a Hub-and-Spoke model established as part of an Indian Council of Medical Research (ICMR)–funded study. In this model, Ragas Dental College and Hospital, Chennai, functions as the central Hub, while private dental institutions and non-governmental organizations across different regions of India serve as Spokes responsible for data collection and community-level oral screening. The present dataset represents images and annotations obtained from one such Spoke center, collected in accordance with uniform methodological guidelines defined by the Hub institution. All intraoral images were captured following a copyrighted Standard Operating Procedure (SOP) for intraoral photography developed by the Hub to ensure consistency, reproducibility, and diagnostic usability across participating centers. Prior to imaging, camera lenses were cleaned, and photographs were taken at an approximate distance of 4–5 cm from the oral cavity, primarily under natural lighting conditions, with auxiliary lighting used when necessary. Retraction aids, including mouth mirrors or wooden retractors, were employed to optimize visualization. For each participant, eight standard intraoral sites were systematically photographed: dorsal tongue, ventral tongue, right buccal mucosa, left buccal mucosa, upper labial mucosa, lower labial mucosa, maxillary arch, and mandibular arch. Image quality was assessed based on predefined criteria, including centering, illumination, sharpness, and absence of motion blur or extraneous artifacts, and images not meeting these criteria were reacquired. The dataset is designed to be dynamic and will be periodically updated with additional participants and newly annotated images to enhance its coverage, diversity, and clinical representativeness. Annotation and Regional Attributes All images underwent expert review and annotation by Oral Pathologist and Public Health Dentistry specialists using the VGG Image Annotator (VIA), version 3.0.13. Regions of Interest (ROIs) were delineated using polygonal annotations to define lesion boundaries, mucosal sub-sites, and relevant diagnostic features such as surface texture, color variation, and border irregularity. Annotations were stored in JSON format to ensure compatibility with deep learning and computer vision frameworks. Each image file follows a structured naming convention in the format A_B_C.jpeg, where A represents a unique anonymized participant identifier, B denotes the site of data collection, and C specifies the intraoral region (e.g., DT for dorsal tongue, LB for left buccal mucosa). Data Structure The dataset is organized into four primary diagnostic categories: (1) normal mucosa, representing healthy oral tissues; (2) variations from normal, encompassing minor deviations from typical mucosal appearance; (3) Oral Potentially Malignant Disorders (OPMDs) and (4) oral cancer, comprising histopathologically confirmed squamous cell carcinoma cases. Each image is linked to its corresponding JSON annotation file ","url":"https://doi.org/10.17605/osf.io/mvtw9","authors":["Dr Madan Kumar","Dr Ranganathan Kannan","Dr Lavanya C","Dr. Jayanta Chattopadhyay","Dr. Rakshith Shetty","Dr. Subhalakshmi Sen","Dr Surojit Bose","Dr S Rajeshwari"],"tags":["Health Information Technology","Neoplasms","Computational Engineering","Diseases","Diagnosis","Medicine and Health Sciences","Telemedicine","Analytical, Diagnostic and Therapeutic Techniques and Equipment"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/mvtw9","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/4bzmh","name":"Pedagogical use of artificial intelligence in health sciences education: a systematic review of faculty perceptions and experiences","source":"datacite","abstract":"This project is a systematic review on the pedagogical use of artificial intelligence (AI) in health sciences education, focusing on university faculty perceptions and experiences. The aim is to synthesize empirical evidence on how faculty conceptualize AI as an educational tool, how they describe its integration into teaching practices, and which benefits, challenges, and ethical concerns they report. The review targets health sciences programs within higher education settings and will use a structured search across relevant databases, applying predefined inclusion and exclusion criteria. The study will follow PRISMA reporting guidelines and will be organized according to the SALSA framework (Search, Appraisal, Synthesis, Analysis), conducting a narrative/thematic synthesis of the findings. This synthesis is intended to inform faculty development initiatives and institutional policies for a pedagogically robust and ethically responsible integration of AI in health sciences education.","url":"https://doi.org/10.17605/osf.io/4bzmh","authors":["Quidel, Juan Francisco Hernández","Lindín, Carles","Parcerisa, Lluís","i Valero, Joan-Anton Sánchez"],"tags":["Teacher Education and Professional Development","Higher Education and Teaching","Medicine and Health Sciences","Education","Higher Education","Medical Education","Artificial Intelligence","Educational Technology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/4bzmh","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/p7tfv","name":"Artificial Intelligence (AI) Integration in Non-Medical Health Professions Education (HPE): Protocol for a Scoping Review","source":"datacite","abstract":"The aim of this scoping review is to systematically map and synthesize the published literature on the integration of artificial intelligence (AI) in non-medical health professions education (HPE). The review will focus on AI-related educational content, teaching and instructional strategies, prerequisite and foundational competencies required to engage with AI education, assessment approaches, reported educational outcomes, implementation challenges, and recommendations for curriculum development.","url":"https://doi.org/10.17605/osf.io/p7tfv","authors":["Yousefi, Farzaneh","Gagnon, Marie-Pierre","Sasseville, Maxime","Dehnavieh, Reza","Laberge, Maude","Naffi, Nadia","Bergeron, Frédéric","Nadali, Mohsen","Ahouehome, Romulus","Fatima-Azzahrae Adnane","Yohan Mauve","Naye, Florian"],"tags":["Medicine and Health Sciences","Education","AIhealth","Artificial intelligence","Curriculum","Health education","Health professions education","Scoping review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/p7tfv","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/zxqcv","name":"Accuracy of Generative Artificial Intelligence in Risk-of-Bias Assessment using RoB 2.0 Tool and Extracting Data in Exercise Therapy for Chronic Low Back Pain RCTs","source":"datacite","abstract":"The production of medical and scientific research has grown exponentially since the first medical articles were published. The current pyramid of evidence-based medicine represents the tip of the research iceberg, and the achievement of next-generation “deep” EBP requires synthesis and deep merging of all available data in the literature. For this reason, conducting systematic reviews and meta-analyses has become a necessity to more accurately and comprehensively present accumulated knowledge to a scientific, clinical, and general audience. First-level evidence can take a large amount of time and effort to complete, with an estimated mean time to accomplish a systematic review of 15 months, and the methodological standards for performing them have increased over time. Moreover, tools that meet these standards, such as RoB 2.0, are detailed and comprehensive, but at the same time can be challenging even for reviewers with considerable experience. For this reason, as mountains of unsynthesised research evidence accumulate, it is necessary to improve the tools for collecting, filtering, and synthesizing these evidences. In this way, the tasks required to develop a literature synthesis could be facilitated by the application of artificial intelligence (AI), which may be regarded as the development of algorithms that aim to simulate human intelligence, thereby allowing computers to perform tasks requiring human cognitive skills. ChatGPT can be described as a promising or even a revolutionary tool for scientific research in both academic writing and in the research process itself. It was listed in several sources as an efficient and promising tool for conducting comprehensive literature reviews and generating computer codes, thereby saving time for the research steps that require more effort from human intelligence. One of the most advanced recent models, GPT‑4o, released by OpenAI in May 2024, is a Large Multimodal Model (LMM), capable of processing diverse data modalities, such as text, images, audio, and video. The presented research project wants to evaluate the accuracy of ChatGPT in the assessment of Risk-of-Bias with RoB 2.0 tool and in the data extraction in musculoskeletal physiotherapy RCTs. For RoB assessment, a tailored prompt will be developed and applied to guide ChatGPT-4o in conducting an independent assessment using the RoB 2.0 tool. ChatGPT-4o’s evaluations will be compared item-by-item with baseline human assessments obtained in a previous study to determine agreement and reliability. For data extraction, human reviewers will independently perform a double-blinded extraction of the dataset. In parallel, two distinct prompts will be designed for ChatGPT-4o to extract the same information. AI-outputs extracted in this way will be subsequently compared with human extractions to evaluate their consistency and accuracy.","url":"https://doi.org/10.17605/osf.io/zxqcv","authors":["Carrer, Luca","Pozzati, Marco","Taborelli, Dario","Maschi, Niccolò","Innocenti, Tiziano","Salvioli, Stefano"],"tags":["Medicine and Health Sciences","Generative Artificial Intelligence","Large Language Model","Systematic Review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/zxqcv","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18802220","name":"PHARMACEUTICAL SCIENCE IN THE ERA OFARTIFICIAL INTELLIGENCE","source":"datacite","abstract":"Purpose: This review discovers the transformative impression of Artificial Intelligence (AI) and Machine Learning (ML) on the pharmaceutical industry and healthcare delivery, focusing on drug discovery, clinical pharmacy practice, and operational productivity. Methods: The article inspects the historical development of AI, from early neural models to modern deep learning designs like GANs, RNNs, and Transformers, and assesses their specific requests transversely to the drug life span. Results: AI is publicised to significantly accelerate R&D by detecting drug leads quicker and adjusting clinical trials through patient-specific data analysis. In clinical settings, AI-driven choice support systems boost patient safety by reducing medication errors, predicting adverse reactions, and refining adherence—especially realising a 40% growth in adherence in community pharmacies. Still, technologies such as computer vision are restyling medicine supervision and analytical precision in medical imagination. Challenges: In spite of these benefits, the evolution characteristics sprints with \"black box\" interpretability, data privacy risks, algorithmic bias, and high implementation charges. Conclusion: This review highlights that despite the fact AI is redesigning pharmacy into an extra detailed and inventive field, its innocuous integration requires a specialised workforce exercise, strong governing agendas, and a constant emphasis on the vital social assembly in patient care.","url":"https://doi.org/10.5281/zenodo.18802220","authors":["Dhairya Gambhir*","Kanishek Tiwari","Govind Saini","Himanshu Dhakad","Keshav Yadav"],"tags":["Artificial Intelligence","Technology","Pharmaceuticals","Drug Formulation","Challenges"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18802220","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18802221","name":"PHARMACEUTICAL SCIENCE IN THE ERA OFARTIFICIAL INTELLIGENCE","source":"datacite","abstract":"Purpose: This review discovers the transformative impression of Artificial Intelligence (AI) and Machine Learning (ML) on the pharmaceutical industry and healthcare delivery, focusing on drug discovery, clinical pharmacy practice, and operational productivity. Methods: The article inspects the historical development of AI, from early neural models to modern deep learning designs like GANs, RNNs, and Transformers, and assesses their specific requests transversely to the drug life span. Results: AI is publicised to significantly accelerate R&D by detecting drug leads quicker and adjusting clinical trials through patient-specific data analysis. In clinical settings, AI-driven choice support systems boost patient safety by reducing medication errors, predicting adverse reactions, and refining adherence—especially realising a 40% growth in adherence in community pharmacies. Still, technologies such as computer vision are restyling medicine supervision and analytical precision in medical imagination. Challenges: In spite of these benefits, the evolution characteristics sprints with \"black box\" interpretability, data privacy risks, algorithmic bias, and high implementation charges. Conclusion: This review highlights that despite the fact AI is redesigning pharmacy into an extra detailed and inventive field, its innocuous integration requires a specialised workforce exercise, strong governing agendas, and a constant emphasis on the vital social assembly in patient care.","url":"https://doi.org/10.5281/zenodo.18802221","authors":["Dhairya Gambhir*","Kanishek Tiwari","Govind Saini","Himanshu Dhakad","Keshav Yadav"],"tags":["Artificial Intelligence","Technology","Pharmaceuticals","Drug Formulation","Challenges"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18802221","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.24965962.v1","name":"Additional file 1 of Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis","source":"datacite","abstract":"Additional file 1. Search terms and search strategy.","url":"https://doi.org/10.6084/m9.figshare.24965962.v1","authors":["Bai, Anying","Si, Mingyu","Xue, Peng","Qu, Yimin","Jiang, Yu"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.24965962.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.24965962","name":"Additional file 1 of Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis","source":"datacite","abstract":"Additional file 1. Search terms and search strategy.","url":"https://doi.org/10.6084/m9.figshare.24965962","authors":["Bai, Anying","Si, Mingyu","Xue, Peng","Qu, Yimin","Jiang, Yu"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.24965962","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.24966082.v1","name":"Additional file 2 of Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis","source":"datacite","abstract":"Additional file 2. PRISMA checklist.","url":"https://doi.org/10.6084/m9.figshare.24966082.v1","authors":["Bai, Anying","Si, Mingyu","Xue, Peng","Qu, Yimin","Jiang, Yu"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.24966082.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.24966082","name":"Additional file 2 of Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis","source":"datacite","abstract":"Additional file 2. PRISMA checklist.","url":"https://doi.org/10.6084/m9.figshare.24966082","authors":["Bai, Anying","Si, Mingyu","Xue, Peng","Qu, Yimin","Jiang, Yu"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.24966082","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18799832","name":"Vulnerability-Aware Artificial Intelligence (VAAF): A Care-Centered Conceptual Framework for Human-in-the-Loop Clinical AI in Radiology","source":"datacite","abstract":"This working paper introduces the Vulnerability-Aware Artificial Intelligence Framework (VAAF), a conceptual approach for integrating artificial intelligence into healthcare while preserving relational and ethical dimensions of care. Rather than framing AI as an autonomous decision-maker, VAAF conceptualizes clinical AI as a mediating infrastructure designed to reduce cognitive and technical burdens on healthcare professionals, enabling greater attention to patient interpretation, communication, and support. Drawing on ethics of care and digital hermeneutics, the framework proposes a shift from opaque algorithmic systems toward transparent human-in-the-loop models. Using fetal magnetic resonance imaging as an illustrative clinical context, the paper explores how technological mediation shapes experiences of vulnerability and meaning-making within highly technical medical environments. The framework contributes to ongoing debates in digital health and medical humanities by positioning vulnerability as a central design principle for future clinical AI systems. Document type: Working paper (pre-peer review version) Este working paper presenta el Marco de Inteligencia Artificial Sensible a la Vulnerabilidad (Vulnerability-Aware Artificial Intelligence Framework, VAAF), un enfoque conceptual orientado a integrar la inteligencia artificial en la atención sanitaria preservando las dimensiones relacionales y éticas del cuidado. En lugar de concebir la IA como un agente autónomo de toma de decisiones, el modelo VAAF entiende la IA clínica como una infraestructura de mediación diseñada para reducir las cargas cognitivas y técnicas de los profesionales sanitarios, favoreciendo una mayor atención a la interpretación, la comunicación y el acompañamiento de las personas pacientes. Basado en la ética del cuidado y la hermenéutica digital, el marco propone una transición desde sistemas algorítmicos opacos hacia modelos transparentes con supervisión humana (human-in-the-loop). Utilizando la resonancia magnética fetal como contexto clínico ilustrativo, el trabajo explora cómo la mediación tecnológica configura las experiencias de vulnerabilidad y los procesos de construcción de significado en entornos médicos altamente tecnificados. El marco contribuye a los debates actuales en salud digital y humanidades médicas al situar la vulnerabilidad como un principio central de diseño para los futuros sistemas de inteligencia artificial clínica. Este documento corresponde a una versión preliminar (working paper) destinada a discusión académica y podrá evolucionar en futuras publicaciones revisadas por pares.","url":"https://doi.org/10.5281/zenodo.18799832","authors":["Blanco Sorzano, David"],"tags":["Artificial Intelligence","Digital Health","Radiology","Radiology/ethics","Health sciences","FOS: Health sciences","Human-in-the-loop AI","Vulnerability-Aware Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18799832","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18801037","name":"Vulnerability-Aware Artificial Intelligence (VAAF): A Care-Centered Conceptual Framework for Human-in-the-Loop Clinical AI in Radiology","source":"datacite","abstract":"This working paper introduces the Vulnerability-Aware Artificial Intelligence Framework (VAAF), a conceptual approach for integrating artificial intelligence into healthcare while preserving relational and ethical dimensions of care. Rather than framing AI as an autonomous decision-maker, VAAF conceptualizes clinical AI as a mediating infrastructure designed to reduce cognitive and technical burdens on healthcare professionals, enabling greater attention to patient interpretation, communication, and support. Drawing on ethics of care and digital hermeneutics, the framework proposes a shift from opaque algorithmic systems toward transparent human-in-the-loop models. Using fetal magnetic resonance imaging as an illustrative clinical context, the paper explores how technological mediation shapes experiences of vulnerability and meaning-making within highly technical medical environments. The framework contributes to ongoing debates in digital health and medical humanities by positioning vulnerability as a central design principle for future clinical AI systems. Document type: Working paper (pre-peer review version) Este working paper presenta el Marco de Inteligencia Artificial Sensible a la Vulnerabilidad (Vulnerability-Aware Artificial Intelligence Framework, VAAF), un enfoque conceptual orientado a integrar la inteligencia artificial en la atención sanitaria preservando las dimensiones relacionales y éticas del cuidado. En lugar de concebir la IA como un agente autónomo de toma de decisiones, el modelo VAAF entiende la IA clínica como una infraestructura de mediación diseñada para reducir las cargas cognitivas y técnicas de los profesionales sanitarios, favoreciendo una mayor atención a la interpretación, la comunicación y el acompañamiento de las personas pacientes. Basado en la ética del cuidado y la hermenéutica digital, el marco propone una transición desde sistemas algorítmicos opacos hacia modelos transparentes con supervisión humana (human-in-the-loop). Utilizando la resonancia magnética fetal como contexto clínico ilustrativo, el trabajo explora cómo la mediación tecnológica configura las experiencias de vulnerabilidad y los procesos de construcción de significado en entornos médicos altamente tecnificados. El marco contribuye a los debates actuales en salud digital y humanidades médicas al situar la vulnerabilidad como un principio central de diseño para los futuros sistemas de inteligencia artificial clínica. Este documento corresponde a una versión preliminar (working paper) destinada a discusión académica y podrá evolucionar en futuras publicaciones revisadas por pares.","url":"https://doi.org/10.5281/zenodo.18801037","authors":["Blanco Sorzano, David"],"tags":["Artificial Intelligence","Digital Health","Radiology","Radiology/ethics","Health sciences","FOS: Health sciences","Human-in-the-loop AI","Vulnerability-Aware Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18801037","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18799833","name":"Vulnerability-Aware Artificial Intelligence (VAAF): A Care-Centered Conceptual Framework for Human-in-the-Loop Clinical AI in Radiology","source":"datacite","abstract":"This working paper introduces the Vulnerability-Aware Artificial Intelligence Framework (VAAF), a conceptual approach for integrating artificial intelligence into healthcare while preserving relational and ethical dimensions of care. Rather than framing AI as an autonomous decision-maker, VAAF conceptualizes clinical AI as a mediating infrastructure designed to reduce cognitive and technical burdens on healthcare professionals, enabling greater attention to patient interpretation, communication, and support. Drawing on ethics of care and digital hermeneutics, the framework proposes a shift from opaque algorithmic systems toward transparent human-in-the-loop models. Using fetal magnetic resonance imaging as an illustrative clinical context, the paper explores how technological mediation shapes experiences of vulnerability and meaning-making within highly technical medical environments. The framework contributes to ongoing debates in digital health and medical humanities by positioning vulnerability as a central design principle for future clinical AI systems. Document type: Working paper (pre-peer review version) Este working paper presenta el Marco de Inteligencia Artificial Sensible a la Vulnerabilidad (Vulnerability-Aware Artificial Intelligence Framework, VAAF), un enfoque conceptual orientado a integrar la inteligencia artificial en la atención sanitaria preservando las dimensiones relacionales y éticas del cuidado. En lugar de concebir la IA como un agente autónomo de toma de decisiones, el modelo VAAF entiende la IA clínica como una infraestructura de mediación diseñada para reducir las cargas cognitivas y técnicas de los profesionales sanitarios, favoreciendo una mayor atención a la interpretación, la comunicación y el acompañamiento de las personas pacientes. Basado en la ética del cuidado y la hermenéutica digital, el marco propone una transición desde sistemas algorítmicos opacos hacia modelos transparentes con supervisión humana (human-in-the-loop). Utilizando la resonancia magnética fetal como contexto clínico ilustrativo, el trabajo explora cómo la mediación tecnológica configura las experiencias de vulnerabilidad y los procesos de construcción de significado en entornos médicos altamente tecnificados. El marco contribuye a los debates actuales en salud digital y humanidades médicas al situar la vulnerabilidad como un principio central de diseño para los futuros sistemas de inteligencia artificial clínica. Este documento corresponde a una versión preliminar (working paper) destinada a discusión académica y podrá evolucionar en futuras publicaciones revisadas por pares.","url":"https://doi.org/10.5281/zenodo.18799833","authors":["Blanco Sorzano, David"],"tags":["Artificial Intelligence","Digital Health","Radiology","Radiology/ethics","Health sciences","FOS: Health sciences","Human-in-the-loop AI","Vulnerability-Aware Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18799833","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.31429418","name":"Data augmentation strategies for GAN-based medical image analysis: an empirical review","source":"datacite","abstract":"Medical image generation using artificial intelligence, especially Generative Adversarial Networks (GANs), has become a powerful solution to address challenges of limited data, class imbalance, and privacy restrictions in clinical imaging. While several GAN-based strategies have been proposed, earlier reviews often describe models in isolation without linking them clearly to medical datasets, evaluation measures, or diagnostic impact. This review focuses on GAN-based augmentation methods applied to medical imaging, systematically comparing prominent GAN families such as DCGAN, Pix2Pix, CycleGAN, StarGAN, and DualGAN across commonly used datasets, including BraTS, ISIC, DRIVE, and ADNI. Reported outcomes are synthesised using image-quality metrics (SSIM, PSNR, FID, LPIPS) and task-based measures (accuracy, sensitivity, Dice score). Findings suggest Pix2Pix frequently improves accuracy and SSIM on MRI and dermoscopy images, CycleGAN enhances sensitivity in retinal tasks, while newer models like StyleGAN and diffusion-based approaches achieve stronger perceptual fidelity. The review concludes that GAN-driven augmentation provides measurable benefits but is highly task- and dataset-dependent, emphasizing the future promise of hybrid GAN–diffusion pipelines and explainability tools for clinical adoption.","url":"https://doi.org/10.6084/m9.figshare.31429418","authors":["Dash, Archana","Panigrahi, Soumyarashmi","Adhikary, Dibya Ranjan Das","Swarnkar, Tripti"],"tags":["Space Science","Medicine","Biotechnology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31429418","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.31429418.v1","name":"Data augmentation strategies for GAN-based medical image analysis: an empirical review","source":"datacite","abstract":"Medical image generation using artificial intelligence, especially Generative Adversarial Networks (GANs), has become a powerful solution to address challenges of limited data, class imbalance, and privacy restrictions in clinical imaging. While several GAN-based strategies have been proposed, earlier reviews often describe models in isolation without linking them clearly to medical datasets, evaluation measures, or diagnostic impact. This review focuses on GAN-based augmentation methods applied to medical imaging, systematically comparing prominent GAN families such as DCGAN, Pix2Pix, CycleGAN, StarGAN, and DualGAN across commonly used datasets, including BraTS, ISIC, DRIVE, and ADNI. Reported outcomes are synthesised using image-quality metrics (SSIM, PSNR, FID, LPIPS) and task-based measures (accuracy, sensitivity, Dice score). Findings suggest Pix2Pix frequently improves accuracy and SSIM on MRI and dermoscopy images, CycleGAN enhances sensitivity in retinal tasks, while newer models like StyleGAN and diffusion-based approaches achieve stronger perceptual fidelity. The review concludes that GAN-driven augmentation provides measurable benefits but is highly task- and dataset-dependent, emphasizing the future promise of hybrid GAN–diffusion pipelines and explainability tools for clinical adoption.","url":"https://doi.org/10.6084/m9.figshare.31429418.v1","authors":["Dash, Archana","Panigrahi, Soumyarashmi","Adhikary, Dibya Ranjan Das","Swarnkar, Tripti"],"tags":["Space Science","Medicine","Biotechnology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31429418.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18795506","name":"Federated Learning Physical AI Oncology Trials Unification","source":"datacite","abstract":"The transition from conventional software-only artificial intelligence to physical AI systems incor- porating robotic hardware in oncology clinical trials represents a paradigm shift requiring unified infrastructure for privacy, regulation, cross-framework interoperability, and multi-organization coop- eration. This paper presents the PAI Oncology Trial FL platform (v1.1.0), a comprehensive federated learning framework comprising 235 Python modules (∼86,800 lines of code) that unifies five critical infrastructure pillars: (1) Privacy Infrastructure implementing all 18 HIPAA Safe Harbor identifiers with HMAC-SHA256 pseudonymization, (2) Regulatory Infrastructure spanning FDA, IRB, ICH- GCP, and multi-jurisdiction compliance across v0.6.0 and v0.9.1, (3) Cross-Framework Unification bridging NVIDIA Isaac Sim, MuJoCo, Gazebo, and PyBullet simulation environments, (4) Stan- dards & Benchmarking for Q1 2026 objectives including model conversion and registry pipelines, and (5) Multi-Organization Cooperation enabling federated training across academic medical cen- ters, community hospitals, and pharmaceutical companies. End-to-end workflow demonstrations are presented across 31 example scripts, 6 agentic AI production examples implementing Model Context Protocol (MCP), ReAct reasoning, real-time monitoring, autonomous orchestration, safety- constrained execution, and RAG-based compliance. A triple AI peer review process (v0.9.4–v0.9.9) using sequential Codex-to-Claude Code review-fix cycles resolved 31/31 code recommendations at 100% completion, establishing a dual-manufacturer trust benchmark for AI-generated clinical trial software. The platform demonstrates that unified federated learning infrastructure is a necessary precondition for transitioning the oncology industry to using robots in physical AI clinical trials.","url":"https://doi.org/10.5281/zenodo.18795506","authors":["Kawchak, Kevin"],"tags":["Federated Learning","Physical AI","Oncology Clinical Trials","Digital Twins","Regulatory Compliance","HIPAA","Cross-Framework Unification","Agentic AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18795506","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18795507","name":"Federated Learning Physical AI Oncology Trials Unification","source":"datacite","abstract":"The transition from conventional software-only artificial intelligence to physical AI systems incor- porating robotic hardware in oncology clinical trials represents a paradigm shift requiring unified infrastructure for privacy, regulation, cross-framework interoperability, and multi-organization coop- eration. This paper presents the PAI Oncology Trial FL platform (v1.1.0), a comprehensive federated learning framework comprising 235 Python modules (∼86,800 lines of code) that unifies five critical infrastructure pillars: (1) Privacy Infrastructure implementing all 18 HIPAA Safe Harbor identifiers with HMAC-SHA256 pseudonymization, (2) Regulatory Infrastructure spanning FDA, IRB, ICH- GCP, and multi-jurisdiction compliance across v0.6.0 and v0.9.1, (3) Cross-Framework Unification bridging NVIDIA Isaac Sim, MuJoCo, Gazebo, and PyBullet simulation environments, (4) Stan- dards & Benchmarking for Q1 2026 objectives including model conversion and registry pipelines, and (5) Multi-Organization Cooperation enabling federated training across academic medical cen- ters, community hospitals, and pharmaceutical companies. End-to-end workflow demonstrations are presented across 31 example scripts, 6 agentic AI production examples implementing Model Context Protocol (MCP), ReAct reasoning, real-time monitoring, autonomous orchestration, safety- constrained execution, and RAG-based compliance. A triple AI peer review process (v0.9.4–v0.9.9) using sequential Codex-to-Claude Code review-fix cycles resolved 31/31 code recommendations at 100% completion, establishing a dual-manufacturer trust benchmark for AI-generated clinical trial software. The platform demonstrates that unified federated learning infrastructure is a necessary precondition for transitioning the oncology industry to using robots in physical AI clinical trials.","url":"https://doi.org/10.5281/zenodo.18795507","authors":["Kawchak, Kevin"],"tags":["Federated Learning","Physical AI","Oncology Clinical Trials","Digital Twins","Regulatory Compliance","HIPAA","Cross-Framework Unification","Agentic AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18795507","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/cpv7j","name":"Analysis of technical nonconformities in health systems with LLMs: an interdisciplinary scoping review protocol","source":"datacite","abstract":"This scoping review protocol outlines a methodological approach to investigate how Artificial Intelligence (AI), particularly Large Language Models (LLMs), is employed to support regulatory compliance and software quality within medical systems. The development of healthcare software requires strict adherence to complex global regulations (such as IEC 62304, FDA guidelines, and LGPD), making manual validation processes costly and time-consuming. Following the Joanna Briggs Institute (JBI) framework , this study will systematically search four major databases (IEEE Xplore, PubMed, Scopus, and Web of Science). The objective is to map current literature, identify existing AI-driven tools for source code and requirements analysis, and pinpoint knowledge gaps. Ultimately, this review aims to provide a scientific foundation for developing new solutions that mitigate regulatory bottlenecks and ensure patient safety during the medical software development lifecycle","url":"https://doi.org/10.17605/osf.io/cpv7j","authors":["da Silva Júnior, Klayton Marcos Veloso","Bublitz, Frederico Moreira","de França Clemente e Rodrigues de Oliveira, Rodolfo Heckmann","de Barros Sales, Débora Karoliny","Vital, José Edimosio Costa","José Jandeilson Xavier dos Santos"],"tags":["Health Information Technology","Computer Engineering","Other Medicine and Health Sciences","Medicine and Health Sciences","Engineering","Artificial Intelligence","Compliance Checking","Large Language Models"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/cpv7j","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18786048","name":"AI-Based Early Detection of Breast Cancer: Technical, Ethical, and Legal   Perspectives","source":"datacite","abstract":"Breast cancer is one of the most prevalent and life-threatening cancers affecting women globally, with survival outcomes highly dependent on early detection. Conventional diagnostic techniques such as mammography, ultrasound, and biopsy are effective but face limitations including human interpretation variability, delayed diagnosis, and limited accessibility in resource-constrained regions. Artificial Intelligence (AI) has emerged as a powerful tool for early breast cancer detection by enabling automated medical image analysis, predictive modeling, and clinical decision support. This paper presents a comprehensive review of AI-based approaches for early detection of breast cancer, covering machine learning and deep learning techniques applied to medical imaging, histopathology, and patient data. In addition to technical advancements, the paper emphasizes ethical challenges and legal considerations such as data privacy, regulatory approval, accountability, and algorithmic fairness. Integrating AI responsibly within legal and regulatory frameworks is essential for ensuring safe, accurate, and equitable deployment in real-world healthcare systems.","url":"https://doi.org/10.5281/zenodo.18786048","authors":["Lunge, Vidya S","Lunge, Radha S"],"tags":["Breast Cancer, Artificial Intelligence, Early Detection, Medical Imaging, Deep Learning, Data Privacy, Healthcare Regulations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18786048","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18786047","name":"AI-Based Early Detection of Breast Cancer: Technical, Ethical, and Legal   Perspectives","source":"datacite","abstract":"Breast cancer is one of the most prevalent and life-threatening cancers affecting women globally, with survival outcomes highly dependent on early detection. Conventional diagnostic techniques such as mammography, ultrasound, and biopsy are effective but face limitations including human interpretation variability, delayed diagnosis, and limited accessibility in resource-constrained regions. Artificial Intelligence (AI) has emerged as a powerful tool for early breast cancer detection by enabling automated medical image analysis, predictive modeling, and clinical decision support. This paper presents a comprehensive review of AI-based approaches for early detection of breast cancer, covering machine learning and deep learning techniques applied to medical imaging, histopathology, and patient data. In addition to technical advancements, the paper emphasizes ethical challenges and legal considerations such as data privacy, regulatory approval, accountability, and algorithmic fairness. Integrating AI responsibly within legal and regulatory frameworks is essential for ensuring safe, accurate, and equitable deployment in real-world healthcare systems.","url":"https://doi.org/10.5281/zenodo.18786047","authors":["Lunge, Vidya S","Lunge, Radha S"],"tags":["Breast Cancer, Artificial Intelligence, Early Detection, Medical Imaging, Deep Learning, Data Privacy, Healthcare Regulations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18786047","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/2uypt","name":"Use of Artificial Intelligence-Based Tools in Medical Education for Strengthening Clinical Reasoning: A Scoping Review","source":"datacite","abstract":"This scoping review aims to map and characterize the available evidence on the use of artificial intelligence tools in medical education, particularly their role in strengthening clinical reasoning and clinical decision-making among physicians in training. A comprehensive search will be conducted in major biomedical databases. Eligible studies will include original research and relevant reviews involving medical students, residents, or physicians in clinical training. Data will be charted and synthesized descriptively to identify applications, benefits, limitations, and research gaps. The findings will inform future educational strategies and research priorities for the responsible integration of AI in medical training.","url":"https://doi.org/10.17605/osf.io/2uypt","authors":["Peñuela, Camilo Andres Torres","Hernandez, Daniela Johanna Restrepo","Peñuela, Claudia Liliana Jaimes"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/2uypt","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/9vwyb","name":"To review the application of multimodal knowledge graph in full-cycle health management of patients with dementia","source":"datacite","abstract":"This scoping review aims to systematically map the existing literature on the application of multimodal knowledge graphs in the full-cycle health management of patients with dementia. With the global aging population and rising dementia prevalence, there is an urgent need for integrated, intelligent health management approaches. Multimodal knowledge graphs integrate heterogeneous data sources (e.g., clinical records, medical imaging, behavioral monitoring) and hold promise for personalized and continuous dementia care. The review will examine how these knowledge graphs are constructed, identify their specific application forms in full-cycle health management, analyze the main content areas covered, and evaluate reported effectiveness. Following established scoping review methodology, we will systematically search nine databases (Wanfang Data, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, CINAHL, Cochrane Library) from inception to December 2025. Two independent reviewers will screen studies, extract data, and synthesize findings. Expected outcomes include a comprehensive overview of current applications, a classification of application scenarios and technological approaches, identification of research gaps and limitations, and recommendations for future research and practical implementation. This review will provide a foundational understanding for researchers and practitioners in dementia care and artificial intelligence.","url":"https://doi.org/10.17605/osf.io/9vwyb","authors":["Dai, Ruru","Weiwei Jiang","Xingmei Mao"],"tags":["Medicine and Health Sciences","Multimodal Knowledge Graph Dementia Scoping Review Health Management Artificial Intelligence Nursing Care Clinical Decision Support Aging Alzheimer's Disease Digital Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/9vwyb","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18778694","name":"PREreview of \"A Note on the Relationship of Normative Principles Between Decision Under Risk and Over Time\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18778695. Summary of the Research This paper explores whether the two core rules used in neoclassical economic theory are consistent with each other. The first rule is the expected utility formula used for decisions under risk. The second rule is exponential discounting used for decisions over time. The author uses probability discounting models from behavioral psychology, especially those developed by Rachlin, to show that when risk is converted into an equivalent waiting time, expected utility ends up matching the form of a hyperbolic discounting function rather than an exponential one. This means that expected utility theory becomes equivalent to a model that produces dynamically inconsistent choices. The author argues that if we assume risk is processed as a time like quantity, then expected utility is no longer a fully normative rule. The paper then discusses how this insight may affect work in behavioral economics, neuroeconomics, and quantum epistemics. Major Issues 1. The central argument depends on a strong assumption that is not fully defended The paper treats risk as waiting time by using Rachlin's probability discounting model. This is the basis for the claim that expected utility theory becomes mathematically equivalent to a special case of hyperbolic discounting. However, this assumption is very strong. It assumes that all risky choices behave as if the decision maker is effectively playing a repeated game where the probability of winning translates to expected delay. In many situations, risky choices are not repeated. For example, decisions about insurance, medical treatment, or large investments often involve a single event. The paper would be stronger if it compared one shot risk and repeated risk more carefully, and showed exactly when the probability to delay transformation is valid and when it is not. 2. The key mathematical equivalence needs more detail to be convincing The main result is the claim that p times u(x) in expected utility theory can be rewritten as u(x) divided by a linear function of odds against. This requires that the hyperbolic discounting model uses a specific parameter value of one. The paper shows this quickly, but the steps are not fully laid out. A clearer walk through would be helpful. For example, readers would benefit from seeing how the odds against formula O(p) equals 1 divided by p minus one, and how this maps cleanly into the hyperbolic discounting function. This is the main foundation of the incompatibility claim, so it deserves a more complete derivation. 3. Exponential discounting is treated as the only valid benchmark for rationality The paper states that exponential discounting is the normative rule for intertemporal choices. But modern economics and psychology do not treat exponential discounting as the only candidate for rational time preferences. Several models in the literature argue that exponential discounting may not reflect real human behavior and that rationality may need a different form. Since the main conclusion is about incompatibility with exponential discounting, the paper needs to address why exponential discounting must be considered normative in the first place. 4. The paper frequently introduces connections to neuroscience and gambling but does not show their exact relevance The discussion of dopamine, cognitive ability, gambling behavior, and computational psychiatry is interesting, but the links to the main mathematical result are not fully explained. These topics appear abruptly, and the reader is expected to infer the connection. For example, the paper mentions that pathological gamblers show a mix of delay and probability discounting issues, but it does not show how the incompatibility between expected utility and exponential discounting helps us understand these results. It would help to explain these links mor","url":"https://doi.org/10.5281/zenodo.18778694","authors":["Stefan Daniel Anim-Sampong"],"tags":["Requested PREreview"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18778694","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18778695","name":"PREreview of \"A Note on the Relationship of Normative Principles Between Decision Under Risk and Over Time\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18778695. Summary of the Research This paper explores whether the two core rules used in neoclassical economic theory are consistent with each other. The first rule is the expected utility formula used for decisions under risk. The second rule is exponential discounting used for decisions over time. The author uses probability discounting models from behavioral psychology, especially those developed by Rachlin, to show that when risk is converted into an equivalent waiting time, expected utility ends up matching the form of a hyperbolic discounting function rather than an exponential one. This means that expected utility theory becomes equivalent to a model that produces dynamically inconsistent choices. The author argues that if we assume risk is processed as a time like quantity, then expected utility is no longer a fully normative rule. The paper then discusses how this insight may affect work in behavioral economics, neuroeconomics, and quantum epistemics. Major Issues 1. The central argument depends on a strong assumption that is not fully defended The paper treats risk as waiting time by using Rachlin's probability discounting model. This is the basis for the claim that expected utility theory becomes mathematically equivalent to a special case of hyperbolic discounting. However, this assumption is very strong. It assumes that all risky choices behave as if the decision maker is effectively playing a repeated game where the probability of winning translates to expected delay. In many situations, risky choices are not repeated. For example, decisions about insurance, medical treatment, or large investments often involve a single event. The paper would be stronger if it compared one shot risk and repeated risk more carefully, and showed exactly when the probability to delay transformation is valid and when it is not. 2. The key mathematical equivalence needs more detail to be convincing The main result is the claim that p times u(x) in expected utility theory can be rewritten as u(x) divided by a linear function of odds against. This requires that the hyperbolic discounting model uses a specific parameter value of one. The paper shows this quickly, but the steps are not fully laid out. A clearer walk through would be helpful. For example, readers would benefit from seeing how the odds against formula O(p) equals 1 divided by p minus one, and how this maps cleanly into the hyperbolic discounting function. This is the main foundation of the incompatibility claim, so it deserves a more complete derivation. 3. Exponential discounting is treated as the only valid benchmark for rationality The paper states that exponential discounting is the normative rule for intertemporal choices. But modern economics and psychology do not treat exponential discounting as the only candidate for rational time preferences. Several models in the literature argue that exponential discounting may not reflect real human behavior and that rationality may need a different form. Since the main conclusion is about incompatibility with exponential discounting, the paper needs to address why exponential discounting must be considered normative in the first place. 4. The paper frequently introduces connections to neuroscience and gambling but does not show their exact relevance The discussion of dopamine, cognitive ability, gambling behavior, and computational psychiatry is interesting, but the links to the main mathematical result are not fully explained. These topics appear abruptly, and the reader is expected to infer the connection. For example, the paper mentions that pathological gamblers show a mix of delay and probability discounting issues, but it does not show how the incompatibility between expected utility and exponential discounting helps us understand these results. It would help to explain these links mor","url":"https://doi.org/10.5281/zenodo.18778695","authors":["Stefan Daniel Anim-Sampong"],"tags":["Requested PREreview"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18778695","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.17605/osf.io/8ysw6","name":"Healthcare Artificial Intelligence Policies in Australia and New Zealand","source":"datacite","abstract":"Artificial intelligence (AI) technologies are rapidly transforming healthcare delivery globally. The pace of technological advancement is currently exceeding the speed of regulatory adaptation, creating a significant gap in published systematic evidence. Australia and New Zealand, as neighboring healthcare systems with shared professional bodies and similar regulatory approaches, provide an ideal comparative case study. Furthermore, the elevation of Indigenous data governance principles (honoring Te Tiriti o Waitangi and Aboriginal/Torres Strait Islander rights) is critical for ensuring cultural safety in AI systems.","url":"https://doi.org/10.17605/osf.io/8ysw6","authors":["Mordaunt, Dylan A","Palmer, Lyle","Kirkpatrick, Emily","Hosking, Michael"],"tags":["Health Information Technology","Public Administration","Comparative Politics","Other Public Health","Public Policy","Policy Design, Analysis, and Evaluation","Other Legal Studies","Public Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/8ysw6","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18770642","name":"Artificial Intelligence and Machine Learning in Healthcare: A Comprehensive Review of Clinical Applications, Challenges, And Future Perspectives","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) technologies in healthcare has revolutionized clinical decision-making, diagnostic accuracy, and treatment planning. This comprehensive review examines the current state of AI/ML applications across multiple healthcare domains, including medical imaging, drug discovery, clinical prediction models, and personalized medicine. Through systematic analysis of 40+ peer-reviewed studies and clinical trials, we evaluate the clinical efficacy, regulatory challenges, ethical considerations, and implementation barriers associated with these technologies. Our findings demonstrate that while AI/ML systems have achieved performance metrics comparable to or exceeding human experts in specific diagnostic tasks, significant gaps remain in generalizability, interpretability, and clinical validation. This paper synthesizes current evidence on AI/ML applications, discusses critical challenges in model validation and regulatory approval, addresses ethical concerns regarding bias and patient privacy, and proposes a framework for responsible AI implementation in clinical practice. We conclude that successful integration of AI/ML in healthcare requires interdisciplinary collaboration between clinicians, computer scientists, bioethicists, and regulatory bodies to ensure patient safety, equitable access, and evidence-based clinical practice.","url":"https://doi.org/10.5281/zenodo.18770642","authors":["Jammalamudi Chaitanya babu","Dr. Jayant Isaac"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18770642","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.5281/zenodo.18770643","name":"Artificial Intelligence and Machine Learning in Healthcare: A Comprehensive Review of Clinical Applications, Challenges, And Future Perspectives","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) technologies in healthcare has revolutionized clinical decision-making, diagnostic accuracy, and treatment planning. This comprehensive review examines the current state of AI/ML applications across multiple healthcare domains, including medical imaging, drug discovery, clinical prediction models, and personalized medicine. Through systematic analysis of 40+ peer-reviewed studies and clinical trials, we evaluate the clinical efficacy, regulatory challenges, ethical considerations, and implementation barriers associated with these technologies. Our findings demonstrate that while AI/ML systems have achieved performance metrics comparable to or exceeding human experts in specific diagnostic tasks, significant gaps remain in generalizability, interpretability, and clinical validation. This paper synthesizes current evidence on AI/ML applications, discusses critical challenges in model validation and regulatory approval, addresses ethical concerns regarding bias and patient privacy, and proposes a framework for responsible AI implementation in clinical practice. We conclude that successful integration of AI/ML in healthcare requires interdisciplinary collaboration between clinicians, computer scientists, bioethicists, and regulatory bodies to ensure patient safety, equitable access, and evidence-based clinical practice.","url":"https://doi.org/10.5281/zenodo.18770643","authors":["Jammalamudi Chaitanya babu","Dr. Jayant Isaac"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18770643","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.26634669.v1","name":"Additional file 1 of Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis","source":"datacite","abstract":"Additional file 1: Table S1. Search strategies. Table S2. Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Table S3. PROBAST (Prediction model Risk of Bias Assessment Tool) Review Items. Table S4. Result of CLAIM. Table S5. Result of PROBAST. Figure S1 (A–L) Forest plot of the pooled DSC of 12 OARs. Figure S2 (A–L) Funnel plots for meta-analysis of 12 OARs. Figure S3 (A–H) Forest plot of the DSC of segmentation of 4 OARs in CT or MRI images. Figure S4 (A–H) Forest plot of the DSC of segmentation of 4 OARs in 2D or 3D images.","url":"https://doi.org/10.6084/m9.figshare.26634669.v1","authors":["Liu, Peiru","Sun, Ying","Zhao, Xinzhuo","Yan, Ying"],"tags":["Space Science","Biotechnology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26634669.v1","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.6084/m9.figshare.26634669","name":"Additional file 1 of Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis","source":"datacite","abstract":"Additional file 1: Table S1. Search strategies. Table S2. Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Table S3. PROBAST (Prediction model Risk of Bias Assessment Tool) Review Items. Table S4. Result of CLAIM. Table S5. Result of PROBAST. Figure S1 (A–L) Forest plot of the pooled DSC of 12 OARs. Figure S2 (A–L) Funnel plots for meta-analysis of 12 OARs. Figure S3 (A–H) Forest plot of the DSC of segmentation of 4 OARs in CT or MRI images. Figure S4 (A–H) Forest plot of the DSC of segmentation of 4 OARs in 2D or 3D images.","url":"https://doi.org/10.6084/m9.figshare.26634669","authors":["Liu, Peiru","Sun, Ying","Zhao, Xinzhuo","Yan, Ying"],"tags":["Space Science","Biotechnology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26634669","addedAt":"2026-09-01T01:47:59.449Z","updatedAt":"2026-09-01T01:47:59.449Z"},{"id":"doi:10.20944/preprints202504.0147.v1","name":"Artificial Intelligence in Global Health: Transforming the Diagnosis and Management of Infectious Diseases","source":"preprints","abstract":"Infectious diseases remain a significant global health challenge, intensified by emerging pathogens, antimicrobial resistance, and limited healthcare access in low-resource settings whereas the emergence of Artificial Intelligence (AI) has transformed the way of infectious disease management by enhancing diagnostics, surveillance, drug discovery, and personalized treatment strategies. AI-driven approaches like Machine Learning (ML), Natural Language Processing (NLP) and deep learning, have facilitated early identification of disease, optimized healthcare resource allocation, and accelerated both the vaccine and drug development. AI powered diagnostic tools, such as computer vision-based medical imaging models and real-time epidemiological surveillance systems, have been instrumental in pandemic response efforts. Moreover, use of AI improved Anti-Microbial Resistance (AMR) monitoring, ensuring timely intervention against drug-resistant infections. More specifically, AI is developing at unprecedented scale which is being adopted and deployed even faster in every sphere of life globally. Despite its beneficial potential, there are some challenges like data privacy, ethical concerns, and infrastructure limitations causing barriers to widespread AI adoption in healthcare. Therefore, there is a requirement for collective global efforts to establish governance and standards that uphold the shared values, and address risks and build trust. Thus, the present review explores the current advancements, challenges, and future directions of AI in infectious disease management, highlighting its transformative impact on global health security.","url":"https://doi.org/10.20944/preprints202504.0147.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202504.0147.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202502.2232.v1","name":"Provable AI Ethics and Explainability in Next-Generation Medical and Educational AI agents: Trustworthy Ethical Firewall","source":"preprints","abstract":"The rapid evolution of artificial intelligence is reshaping both medicine and education while simultaneously raising critical ethical concerns. This study proposes an integrated complex framework labeled as Ethical firewall, that embeds provable ethical constraints directly into artificial intelligence (AI) decision-making architectures. By combining formal verification methods, cryptographic immutability, and emotion-analogous escalation protocols, the approach ensures that AI systems not only perform with high efficiency but also remain steadfastly aligned with core human values. The review of recent advances in AI explainability and emergent value systems—highlighting how large language models may inadvertently develop their own biased value hierarchies—and discuss the implications of accelerated AI learning speeds as potential precursors to artificial general intelligence (AGI). Furthermore, it addresses the societal impacts of these advancements, particularly the risk of workforce displacement in healthcare and education, and advocates for new oversight roles such as the Ethical AI Officer. The findings suggest that by fusing rigorous mathematical safeguards with human-centered oversight, next-generation AI can achieve both superior performance and robust ethical compliance, ultimately fostering greater trust and accountability in high-stakes applications.","url":"https://doi.org/10.20944/preprints202502.2232.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202502.2232.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-8987235/v1","name":"Cognitive Speed–Accuracy Dissociation in Multiple Myeloma: A Cross‑Sectional Study","source":"preprints","abstract":"Abstract Purpose This study examined whether adults with Multiple Myeloma (MM) show measurable cognitive differences compared with neurologically healthy peers. The primary research question focused on identifying domain-specific cognitive deficits and determining whether standard screening tools adequately capture cognitive abilities. Methods A cross-sectional design compared 45 adults with MM to 40 age-matched controls. Participants completed 40–50 minutes of cognitive and psychological assessments, including the Montreal Cognitive Assessment (MoCA), validated measures of mood and daily functioning, and a digitised cognitive battery assessing key cognitive domains. Group differences in reaction time (RTs) and accuracy were analysed using ANCOVAs adjusting for age and education, Bayesian and EZ-drift diffusion modelling (EZ-DDM) to characterise domain specific deficits and latent decision-making processes. Results Group-level analyses revealed slower RTs in MM following adjustment for age and education, with accuracy largely preserved. Bayesian modelling identified multi-domain RT-related deficits in ~ 22% of MM patients, particularly in cognitive flexibility and semantic processing. EZ-DDM indicated that group differences were driven by reduced drift rates and prolonged non-decision times in MM, suggesting slower evidence accumulation with slower sensory encoding and/or motor execution. Conclusion Cognitive slowing in MM is selective and heterogeneous, with processing speed emerging as the most sensitive marker. Conventional screening may underestimated subtle deficits, underscoring the need for longitudinal and neuroimaging studies to disentangle disease and treatment related effects. Preserving processing speed, critical for everyday functioning and social interactions, should be a priority in cancer and chronic disease research.","url":"https://doi.org/10.21203/rs.3.rs-8987235/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8987235/v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202509.0624.v1","name":"The Irreversible March of Time: Ischemic Delay and Impact on Outcomes in ST-Segment Elevation Myocardial Infarction","source":"preprints","abstract":"ST-segment elevation myocardial infarction (STEMI) represents a time-critical medical emergency where complete coronary artery occlusion initiates progressive myocardial necrosis. The fundamental principle of modern STEMI care - \"Time is Muscle\" - establishes that ischemic duration directly determines infarct size and clinical outcomes. Each minute of delay correlates with increased mortality, larger infarcts, and a higher risk of heart failure development. Total ischemic time encompasses both patient-mediated delays (often the largest component) and system-related delays, each influenced by distinct factors requiring targeted interventions. This comprehensive review analyzes the components of total ischemic time, quantifies the clinical consequences of delay, and evaluates evidence-based mitigation strategies. We examine the evolution from fibri-nolysis to primary percutaneous coronary intervention and the resulting logistical challenges. System-level interventions - including public awareness campaigns, re-gionalized STEMI networks, pre-hospital ECG acquisition, and standardized hospital protocols - have dramatically reduced treatment times. However, persistent disparities based on geography, presentation timing, sex, race, and age remain problematic. Emerging technologies, particularly artificial intelligence for ECG interpretation, offer promise for further time reduction.","url":"https://doi.org/10.20944/preprints202509.0624.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202509.0624.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202508.0341.v1","name":"AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms","source":"preprints","abstract":"The global prevalence of lung cancer calls for innovative methods to improve diagnosis and treatment, particularly in developing nations where there is a critical shortage of onco-pathologists. This systematic review examines the role of artificial intelligence (AI) in lung cancer diagnosis, focusing on machine learning and deep learning ap-proaches as potential solutions to this challenge. The evaluation utilized PRISMA guidelines to include 14 studies for conducting a meta-analysis that measured AI-based tool effectiveness in lung cancer pathology diagnosis. Key performance metrics ana-lyzed included sensitivity, specificity, and predictive values. Advanced AI architec-tures, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs), were identified as instrumental in enhancing diagnostic accuracy and enabling large-scale screening. The review also investigates AI-based identification of promising biomarkers which enhance medical diagnosis while modernizing clinical procedures for improved workflow success. AI has demonstrated success in reducing clinical workloads, thereby optimizing healthcare operations. This review further ad-dresses critical barriers to implementation, including limited generalizability across diverse populations and ethical concerns related to clinical deployment. Unlike pre-vious reviews, this work evaluates AI technologies across multiple imaging modalities, including histopathology, computed tomography (CT), and X-rays. The findings sug-gest that AI has significant potential as a transformative tool for achieving more accu-rate diagnoses, facilitating personalized treatment strategies, and ultimately improving patient outcomes worldwide.","url":"https://doi.org/10.20944/preprints202508.0341.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202508.0341.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202503.2116.v1","name":"Harnessing Artificial Intelligence for Diagnosis, Treatment and Research of Multiple Sclerosis","source":"preprints","abstract":"Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting over 2.8 million people around the world. Artificial intelligence (AI) is becoming increasingly utilized in many areas including patient care for MS. AI is revolutionizing the diagnosis and treatment of MS by enhancing the accuracy and efficiency of both processes. AI algorithms, particularly those based on machine learning, are being used to analyse medical imaging data, such as MRI scans, to detect early signs of MS, monitor disease progression and assess patient treatment response with greater precision. AI can help identify subtle changes in the brain and spinal cord that may be missed by human clinicians, leading to earlier diagnosis and more personalized treatment plans. Additionally, AI is being employed to predict disease outcomes which could allow clinicians to tailor therapies for individual patients based on their unique disease characteristics. In drug development, AI is accelerating the identification of potential therapeutic targets and the optimization of clinical trial designs, potentially leading to faster development of new treatments for MS. AI is also playing a critical role in MS fundamental research by promoting efficient analysis of vast amounts of single-cell data. Through these advancements, AI could improve the overall management of MS, offering more timely interventions and better patient outcomes. In this review we discuss these topics and whether the influence of AI on diagnosis, treatment and research of MS can change the future of this field.","url":"https://doi.org/10.20944/preprints202503.2116.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202503.2116.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.20944/preprints202505.2151.v1","name":"A Review on Deep Learning Techniques for Medical Image Segmentation and Classification","source":"preprints","abstract":"The intersection of artificial intelligence (AI) with medical imaging has advanced at an unprecedented rate over the last decade, enabling the diagnosis of disease, the planning of treatment, and monitoring of patients. Deep learning, specifically convolutional neural networks and their derivatives, has been employed to interpret medical images with specialized methods and proven high accuracy in working with complex forms of medical imaging. This review summarizes the most well-known of deep learning architectures in the implementation of medical image analysis, including CNNs, U-Net, ResNet, DenseNet, GANs, and transformer architecture-based models. We discuss the most common applications (e.g., tumor detection, organ segmentation of medical images and image enhancement) associated with each of the architectures. Even with successful applications of AI analyses in medical imaging, specific challenges remain that limit adoption of AI tools by the clinical community that range from lack of annotated data to questions of interpretability and ultimately clinical applicability. We highlight the current work and future directions intended to mitigate main challenges, thus maximizing the potential of AI in clinical practice. This article serves as a detailed overview of AI and medical imaging, and will serve as a deep resource to the reader that wishes to engage in the discussion of AI and its applications in medical imaging.","url":"https://doi.org/10.20944/preprints202505.2151.v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.20944/preprints202505.2151.v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-6930293/v1","name":"A Research Roadmap for AI Opportunities in Student Assessment for Medical Education","source":"preprints","abstract":"Abstract The integration of Artificial Intelligence (AI) in medical education is rapidly transforming assessment practices, offering unprecedented opportunities to enhance student evaluation, feedback, and learning pathways. However, despite the potential, a comprehensive understanding of these opportunities and their interdependencies has been lacking. This study provides a critical review of the literature on AI’s role in medical education assessment, categorising 22 identified opportunities into seven major \"mega-opportunities\" that address various aspects of student assessment. Through the application of Interpretive Structural Modelling (ISM), the cause-effect interdependencies among these mega-opportunities were explored, revealing a complex web of relationships that guide their effective implementation. The findings highlight the central role of \"Automated Feedback and Evaluation\" and \"Data-Driven Analytics and Curriculum Improvement\" as foundational drivers, with far-reaching impacts on other areas like \"Simulation-Based Assessment\" and \"Longitudinal Assessment and Development.\" This paper culminates in the proposal of a research roadmap that highlights the priority of addressing different mega-opportunities in AI and assessment, offering practical guidelines for medical researchers, educators, institutions, and policymakers to adopt AI-driven assessment strategies. Future research avenues are identified to explore the real-world application and impact of these AI-driven innovations, focusing on longitudinal studies and educational equity. The findings underscore the need for continued research to refine the model proposed by this study and adapt it to diverse educational environments.","url":"https://doi.org/10.21203/rs.3.rs-6930293/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6930293/v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.12688/wellcomeopenres.24335.1","name":"Protocol for a systematic review of wearable devices for antenatal fetal monitoring","source":"preprints","abstract":"Introduction: Fetal monitoring is a crucial component of antenatal care, facilitating early detection of fetal compromise and improving pregnancy outcomes. Traditional monitoring methods such as cardiotocography (CTG) and ultrasound are effective but primarily limited to clinical settings, requiring specialized expertise and resources. The rise of wearable medical devices and artificial intelligence (AI) applications presents an opportunity to enhance fetal monitoring by enabling continuous, real-time data collection outside clinical environments. These technologies have the potential to improve fetal health and obstetric outcomes, particularly in resource-limited settings. This systematic review aims to evaluate the use of wearable devices for antenatal fetal monitoring and their impact on fetal and obstetric outcomes. Methods and Analysis This systematic review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Synthesis Without Meta-analysis (SWiM) framework. A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science will be conducted to identify primary research studies investigating wearable devices designed for fetal monitoring during pregnancy. Studies will be included if they assess the effectiveness, accuracy, and clinical impact of wearable fetal monitoring devices. Primary outcomes will include markers of fetal well-being as well as neonatal and obstetric outcomes. Secondary outcomes will focus on patient experience and acceptability. Data extraction and quality assessment will be conducted independently by two reviewers using the National Institutes of Health (NIH) Quality Assessment Tool and the Newcastle-Ottawa Scale. A narrative synthesis will be performed to summarise the findings. Ethics and Dissemination Ethical approval is not required since the study involves analysing published literature. The findings will be shared through peer-reviewed publications and conference presentations. This review will enhance the evidence base regarding the clinical utility of wearable fetal monitoring technologies and inform future research and device development. PROSPERO Registration: CRD4202348755 (current version 4.1)","url":"https://doi.org/10.12688/wellcomeopenres.24335.1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.12688/wellcomeopenres.24335.1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.22541/au.175494657.73891664/v1","name":"Cardiovascular Pathologies: A Comprehensive Review of Mechanisms, Risk Factors, and Advanced Therapeutic Modalities","source":"preprints","abstract":"Cardiovascular diseases (CVDs) represent the leading cause of global morbidity and mortality, imposing a significant and escalating burden on public health systems and economies. This article provides a comprehensive overview of the multifaceted nature of CVDs, delving into their epidemiological impact, the intricate molecular and cellular mechanisms underlying their pathogenesis, and the broad spectrum of risk factors. Key pathophysiological processes are explored in detail, including atherosclerosis-initiated by endothelial dysfunction and driven by inflammation, oxidative stress, and extracellular matrix remodeling-which underpins major conditions like coronary artery disease, hypertension, and heart failure. The review dissects the roles of crucial molecular pathways such as the Renin-Angiotensin-Aldosterone System (RAAS) and cellular events like ischemia-reperfusion injury. It discusses both traditional risk factors (eg, dyslipidemia, hypertension) and emerging determinants, including inflammation (evidenced by biomarkers like hsCRP), genetics, epigenetic modifications, and environmental exposures, highlighting the concept of a \"common soil\" etiology where risk factors cluster and interact. Furthermore, the article examines the evolution of therapeutic strategies, from population-based prevention policies to next-generation pharmacologic interventions such as PCSK9 inhibitors, SGLT2 inhibitors, targeted antiinflammatory agents, and emerging RNA-based therapies and gene-editing technologies. It emphasizes the transformative potential of advanced medical devices, digital technologies, and artificial intelligence in creating a new paradigm of personalized medicine while also addressing the persistent challenges and critical knowledge gaps that must be bridged to advance cardiovascular medicine.","url":"https://doi.org/10.22541/au.175494657.73891664/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.22541/au.175494657.73891664/v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.21203/rs.3.rs-9242336/v1","name":"Combining BulkFormer and TabPFN to predict post- transplant function from kidney biopsies during machine perfusion or cold storage","source":"preprints","abstract":"Abstract Generating predictions from transcriptomic data poses a unique challenge due to the high number of genes, and often small sample size. BulkFormer and TabPFN have emerged as leading transformer-based foundation models for bulk transcriptomic and tabular data respectively. We explore an artificial intelligence pipeline using BulkFormer-TabPFN v2.5 which generates zero-shot predictions from raw RNA-Seq count data without retraining. This was tested on three cohorts of biopsies taken from donated human kidneys. BulkFormer-TabPFN was able to predict delayed kidney function using RNA-Seq counts from kidneys undergoing ex-situ normothermic machine perfusion (NMP; c-statistic=0.82, 95% CI=0.67–0.97). Predictive discrimination was optimised under the following conditions: BulkFormer-TabPFN versus TabPFN alone, maximum absolute aggregation of BulkFormer gene-level embeddings, biopsies taken during ex-situ NMP versus cold storage. BulkFormer-TabPFN predictions were modified by cytokine filter treatment during ex-situ NMP, suggesting they could be a dynamic surrogate endpoint for novel therapeutics, which is intrinsically linked to post-transplant outcome. This demonstrates for the first time synergistic benefits of these foundation models, to generate zero-shot predictions without model retraining. The provided code provides a blueprint to replicate generating predictions from RNA-Seq count data, which could be applied in a wide range of biomedical contexts within and beyond organ transplantation.","url":"https://doi.org/10.21203/rs.3.rs-9242336/v1","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9242336/v1","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.1101/2025.01.08.25320130","name":"Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review","source":"preprints","abstract":"ABSTRACT Background Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Methods Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Results Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n=11,44%) and included less than 250 patients (n=18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n=9,36%) or risk stratification (n=10,40%) rather than IPMN detection (n=5,20%) or IPMN segmentation (n=2,8%). Conclusion This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation. Graphical Abstract Highlights Artificial Intelligence holds promise in the field of IPMN by augmenting IPMN detection, differentiation of different types of pancreatic cysts, stratifying malignant progression risk, and automating the analysis of IPMN imaging through computational cyst segmentation. The majority of studies related to AI-based analysis of IPMN imaging use single-center patient cohorts of less than 250 patients to develop and validate computational models and consider imaging as the only data modality. Reporting transparency of existing studies on AI in IPMN imaging is limited and there remains a scarcity of comprehensive, multimodal approaches as well as clinical translation.","url":"https://doi.org/10.1101/2025.01.08.25320130","authors":[],"tags":[],"confidence":0.74,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.1101/2025.01.08.25320130","addedAt":"2026-09-01T01:47:59.450Z","updatedAt":"2026-09-01T01:48:00.329Z"},{"id":"doi:10.31525/ct1-nct04217018","name":"MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.31525/ct1-nct04217018","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-03T22:49:45Z","doi":"10.31525/ct1-nct04217018","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.2196/preprints.63775","name":"Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis (Preprint)","source":"crossref","abstract":"BACKGROUND Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain. OBJECTIVE This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century. METHODS Documents were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics. RESULTS Altogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. &lt;i&gt;Cureus&lt;/i&gt;, &lt;i&gt;JMIR Medical Education&lt;/i&gt;, &lt;i&gt;Medical Teacher&lt;/i&gt;, and &lt;i&gt;BMC Medical Education&lt;/i&gt; ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas. CONCLUSIONS This study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits.","url":"https://doi.org/10.2196/preprints.63775","authors":["Rui Li","Tong Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-30T18:00:54Z","doi":"10.2196/preprints.63775","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.64044/n6yf4f41","name":"Artificial Intelligence for International Supply Chain Management: Overcoming Complexity with Digital Transformations","source":"crossref","abstract":"Nowadays, supply chains have evolved into highly complex networks that are becoming ever more interdependent, unpredictable, and vulnerable to disruptions. Their complexity stretches traditional SCM models, requiring the use of more brilliant and reactive systems. An enabling technology that transforms, Artificial Intelligence (AI) provides solutions in predictive intelligence, automation, real-time tracking, and intelligent decision-making. This paper consolidates recent research to investigate how AI technologies are reshaping global SCM. I then explore the digitalization of the supply chain, the main AI technologies, and the ethical considerations. Building on a foundation established from the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), this paper contextualizes AI's strategic importance. It details AI's potential to increase the accuracy of demand forecasting, reduce operational costs, and improve resilience, but recognizes data quality, enormous upfront implementation costs, and algorithmic transparency as obstacles. This has to be concentrated in the (research and practice) in ethical frameworks, human-AI cooperation, and SME inclusivity as key contributing fields to ensure future digital transformation.","url":"https://doi.org/10.64044/n6yf4f41","authors":["David Hua","Racheal Ankunda","Oghenemarho Karieren","Oluwaseni Adeyinka","Mustapha Seidu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-29T05:01:55Z","doi":"10.64044/n6yf4f41","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.62019/krgp3769","name":"ARTIFICIAL INTELLIGENCE IN GYNECOLOGICAL CARE: TRANSFORMING DIAGNOSIS, TREATMENT, AND PERSONALIZED MEDICINE","source":"crossref","abstract":"AI has brought about remarkable improvement to gynecological practice through increased diagnostic capabilities, advancement of treatment plans, and development of patient-centered health care. Advanced technology is making it easier to diagnose gynecological disorders like cervical cancer, ovarian cancer, and endometriosis through deep learning and machine learning algorithms to minimize diagnostic mistakes. In surgeries, mechanical enhancement by the aid of AI helps in timeliness, accuracy, and reduced post-surgical complications hence improved recovery. In addition, AI has been instrumental in the application of reproductive medicine through the successful enhancement of IVF, prediction of ovarian reserve, and various fertility treatments. Telemedicine solutions driven by artificial intelligence, wearable devices, and chatbots have also helped increase the availability of gynecological services in remote and underdeveloped regions. However, the gynecology patient decision-making supported by AI carries certain ethical issues such as personal data protection, distorted AI, and other factors restricting AI use and supplying a set of rules for robust usage of AI. Therefore, the impact of AI in gynecological surgery offers a promising future with enhancements to be made on the accuracy of diagnosis and surgery in addition to bespoke treatment plans for patients. Challenges such as high implementation costs and lack of staffing competent in AI use among healthcare practitioners will have to be overcome to unlock more use of AI in gynecology.","url":"https://doi.org/10.62019/krgp3769","authors":["Dr. Qosain Suriya","Muhammad Waqar Ali","Kashan Ali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-18T05:24:30Z","doi":"10.62019/krgp3769","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1023/b:arti.0000046021.87243.06","name":"Book Review: The Dynamics of Judicial Proof. Computation, Logic, and Common Sense","source":"crossref","abstract":"","url":"https://doi.org/10.1023/b:arti.0000046021.87243.06","authors":["Bart Verheij"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-10-27T01:09:03Z","doi":"10.1023/b:arti.0000046021.87243.06","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/s10462-013-9421-z","name":"Special issue of the 8th AIAI 2012 (Artificial Intelligence Applications and Innovations) international conference","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-013-9421-z","authors":["Lazaros Iliadis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-08-13T01:31:23Z","doi":"10.1007/s10462-013-9421-z","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1109/aiim64537.2024.10934548","name":"Artificial Intelligence Based Visual Review Technology of Engineering Safety Design Documents","source":"crossref","abstract":"The accuracy and completeness of safety design documents are important indicators for ensuring the safe operation of engineering. Traditional document review methods suffer from issues such as relying on manual experience, time-consuming and error prone processes, and low efficiency. Based on this, this study proposes an efficient and accurate intelligent review method for engineering safety documents by combining artificial intelligence (AI) technology. This method integrates natural language processing, machine learning and other technologies, based on relevant specifications, legal provisions, relevant documents, experience and other review criteria, to review the content integrity and targeted solutions of design documents from two professional dimensions. Finally, the review results are evaluated and assisted in early warning, breaking through the limitations of traditional manual review, greatly improving review efficiency, and achieving refined intelligent review of engineering safety design documents.","url":"https://doi.org/10.1109/aiim64537.2024.10934548","authors":["Ting Liu","Mingjiang Wang","Qingchao Lv","Qun Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-26T22:21:14Z","doi":"10.1109/aiim64537.2024.10934548","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.3403/30491581u","name":"Information technology � Artificial intelligence � Controllability of automated artificial intelligence systems","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30491581u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-16T20:30:50Z","doi":"10.3403/30491581u","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.56570/jimgs.v2i1.92","name":"Artificial Intelligence Driving  Diabetes Care","source":"crossref","abstract":"Artificial intelligence (AI), a technology reshapinghealthcare, is used to investigate, gather data and drawconclusions from electronic medical records andimaging procedures. AI has been shown to aid inidentifying, categorizing, diagnosing, and managingdiabetic mellitus [1]. It will likely continue to do so witha clear understanding and the ability to find previouslyunidentified solutions [2]. AI has a role in diabeteshelping to anticipate the diagnosis, provide nutritionand exercise goals, monitor complications, and assistwith self-management [7]. We may now see themanagement of diabetes and other chronic diseasesfrom a new viewpoint thanks to the expanded use ofcontinuous glucose monitoring and the identification ofpatterns in glucose fluctuation known as glucosevariability [3]. It has paved the way for in-depthresearch into the many new factors that affectmanaging diabetes such as socio-economic factors,sleep, and activity [1,2]","url":"https://doi.org/10.56570/jimgs.v2i1.92","authors":["Aishwarya Sadagopan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-01T05:13:44Z","doi":"10.56570/jimgs.v2i1.92","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.20517/ais.2022.24","name":"Leveraging artificial intelligence for resident recruitment: can the dream of holistic review be realized?","source":"crossref","abstract":"Aim: The purpose of this study was to investigate if principles of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), could be applied to the personal statements of general surgery residency applicants in order to gain valuable insight into the candidates and facilitate a more comprehensive assessment. Methods: The personal statements from individuals applying for a general surgery residency position during the 2021/22 application cycle (n = 1792) were analyzed using AI technology. Comparison groups were drawn from a database of documents from the general population and the personal statements of current general surgery residents (n = 64) at a single academic center. The study was conducted in collaboration with a leading language psychology and natural language processing organization. Results: Applicants exhibited a language-based personality that was highly self-assured (P &lt; 0.0001) and trusting (P &lt; 0.0001), and less stress-prone (P &lt; 0.0001) and impulsive (P &lt; 0.0001) than that of the general population. Compared to the general applicant pool, current residents were significantly more emotionally aware (P &lt; 0.001) and organized (P &lt; 0.001) and less self-assured (P &lt; 0.001) and less driven by power (P &lt; 0.001). Conclusion: Natural language processing technology can be utilized to assess the unique characteristics of general surgery resident applicants based on the content of their personal statements. In addition, candidates who successfully gain admission to a single academic program display different language-based personalities and drives compared to the general applicant pool. Incorporating these principles of artificial intelligence into the residency selection process could facilitate a more holistic evaluation of candidates.","url":"https://doi.org/10.20517/ais.2022.24","authors":["Ace St John","Stephen M. Kavic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-27T00:58:15Z","doi":"10.20517/ais.2022.24","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/978-981-96-6863-2_5","name":"Revolutionizing Natural Resource Management with Artificial Intelligence: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-6863-2_5","authors":["Balendra V. S. Chauhan","Ajitanshu Vedrtnam","Kevin P. Wyche","Sneha Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-17T17:13:18Z","doi":"10.1007/978-981-96-6863-2_5","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.11591/ijai.v13.i4.pp3786-3792","name":"Artificial intelligence for deepfake detection: systematic review and impact analysis","source":"crossref","abstract":"&lt;p&gt;Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand them. Despite extensive use of face swap services, nothing is known about deepfakes' social impact. The misuse of deepfakes in image-based sexual assault and public figure distortion, especially in politics, highlight the necessity for further research on their social impact. Using state-of-the-art deepfake detection methods like fake face and deepfake detectors and a broad forgery analysis tool reduces the damage deepfakes do. We inquire about to review deepfake detection research and its social impacts in this work. In this paper we analysed various deepfake methods, social impact with misutilization of deepfake technology, and finally giving clear analysis of existing machine learning models. We want to illuminate the potential effects of deepfakes on society and suggest solutions by combining study data.&lt;/p&gt;","url":"https://doi.org/10.11591/ijai.v13.i4.pp3786-3792","authors":["Venkateswarlu Sunkari","Ayyagari Sri Nagesh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-08T17:52:21Z","doi":"10.11591/ijai.v13.i4.pp3786-3792","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/s10462-023-10594-1","name":"Artificial intelligence-assisted water quality index determination for healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-023-10594-1","authors":["Ankush Manocha","Sandeep Kumar Sood","Munish Bhatia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-09T05:01:36Z","doi":"10.1007/s10462-023-10594-1","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1002/9781394303601.ch18","name":"Artificial Intelligence‐Based Cyber Security and Digital Forensics","source":"crossref","abstract":"Today cyber threats are becoming more sophisticated and technology is advancing at a rapid pace, new approaches to cybersecurity and digital forensics are required. Artificial intelligence (AI) has surfaced as a potential game-changer in the fight against these challenges. This article aims to provide a comprehensive overview of the role that artificial intelligence plays in cybersecurity and digital forensics. By radically improving threat identification, mitigation, and incident response, technologies powered by artificial intelligence are reshaping cybersecurity. Machine learning and deep learning are some of the approaches being used to sift through massive amounts of data, spot anomalies, and predict when security breaches may occur. In addition, AI-powered solutions are making cybersecurity systems more flexible, which opens the door to proactive defensive mechanisms and real-time threat intelligence. The field of digital forensics is seeing heavy use of artificial intelligence (AI) to speed up investigations and locate digital evidence. Automating the examination of digital artifacts is becoming a reality with the use of artificial intelligence methods like pattern recognition, image identification, and natural language processing. This allows for more efficient and effective investigations. Furthermore, AI-driven instruments are assisting with the restoration of digital crime scenes and the attribution of hack perpetrators. With an emphasis on highlighting the capabilities and limitations of these domains, this book discusses cybersecurity and digital forensics that are based on artificial intelligence. In addition to outlining research directions, new trends, and challenges, the article delves into the ethical and legal issues associated with AI in different industries. To sum up, AI is revolutionizing cybersecurity and digital forensics by laying the groundwork for more robust and proactive protection mechanisms and more efficient and accurate digital investigations.","url":"https://doi.org/10.1002/9781394303601.ch18","authors":["Amit Kumar Tyagi","Shabanm Kumari","Richa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-13T21:20:12Z","doi":"10.1002/9781394303601.ch18","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/978-981-99-8441-1_6","name":"Application of Artificial Intelligence in Optimizing Medical Imaging Workflows","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8441-1_6","authors":["Wenzhi Lv","Wenzhen Zhu","Meiyun Wang","Yang Hou","Junfang Xian","Dairong Cao","Feng Wang","Gang Huang","Caiqiang Xue","Qi Yang","Yan Guo","Junlin Zhou","Huimao Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T00:02:50Z","doi":"10.1007/978-981-99-8441-1_6","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.24002/jarina.v5i1.11340","name":"A Systematic Review: Examining the Impacts of Artificial Intelligence","source":"crossref","abstract":"Since its breakthrough in the mid-20th century, Artificial Intelligence (AI) has held great promises for improving the capacity of urban planning to address complex problems. Despite this, the literature on how AI was specifically utilized and how it impacted urban planning remains limited. This study was aimed at examining how AI-driven technology shapes the landscape of urban planning. To attain this, we reviewed 48 articles after performing a systematic screening of 2,359 journal records in the Scopus database, published since the rising use of AI in urban planning. We found that urban planners have broadly adopted AI to address various complex environmental problems toward the making of sustainable and smart cities. Additionally, Machine Learning, Big Data, and the Internet of Things (IoT) are also indicated as AI-driven technologies commonly adopted in urban planning over the years.","url":"https://doi.org/10.24002/jarina.v5i1.11340","authors":["David Chow","Catharina Dwi Astuti Depari","Eva Gabriella"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-27T04:48:02Z","doi":"10.24002/jarina.v5i1.11340","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1016/j.caeai.2023.100141","name":"A systematic literature review: Recent techniques of predicting STEM stream students","source":"crossref","abstract":"Nowadays, fewer students are choosing to enroll in STEM (science, technology, engineering, and mathematics) fields. STEM students in schools and in higher educational institutions appear to be waning, as evidenced by low secondary school STEM enrolments. To add to this, there are also STEM stream students who dropped out and switched to non-STEM streams. This resulted in a shortage of qualified candidates for STEM-based higher education programmes, and subsequently an insufficient number of STEM graduates. Researchers have found several potential contributing factors that may have impacted students’ selection of STEM. However, this relationship is still unclear and needs further investigation. This goal of this systematic review is to assess the factors that can be used to predict students’ selection of a STEM major using existing techniques. To do this, PRISMA’s Systematic Literature Review (SLR) process was used to map the findings of previous studies based on the designed research questions (RQs). More specifically, the objective of this analysis was to compile, summarise, and assess related works in order to identify current contributing variables, potential techniques, dataset characteristics, challenges, and future directions within the scope of this investigation. Papers published in major online scientific databases, including Science Direct, Scopus, IEEE Xplore, ACM, ProQuest, and Springer, between 2011 and April 2021 were identified and analysed. Although there were 1248 publications found through extensive SLR selection processes using specific inclusion and exclusion criteria, only 121 articles were selected. After being analyzed, only 16 articles were found to have discussed about machine learning (ML) techniques and showed that the most accurate predictions were possible based on different variables or factors. In addition, the dataset characteristics were found to have impacted the accuracy of the prediction results. However, the available evidences were limited, and the output findings from each study reviewed were relatively diverse. Therefore, evidences discussing the potential usefulness of ML techniques to analyze the relationship between contributing factors should be strengthened.","url":"https://doi.org/10.1016/j.caeai.2023.100141","authors":["Norismiza Ismail","Umi Kalsom Yusof"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-10T19:24:13Z","doi":"10.1016/j.caeai.2023.100141","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1109/aisp53593.2022.9760655","name":"A review of Artificial Intelligence approach for credit risk assessment","source":"crossref","abstract":"Every day, each bank around the world has to analyze many credit applications from its customers and prospects, individuals, professionals, or companies. Banks develop their rating system based on different parameters but most of them do not take benefit of the tremendous set of Big Data available and gathered continuously. To extract valuable information, Big Data analysis (BDA) and artificial intelligence (AI) lead to interesting applications for the banking industry such as segmentation, customized service, customer relationship management, fraud detection, credit risk assessment, and in all back, middle, and front office missions. This article presents the benefit of artificial intelligence for credit risk assessment. A state of art for the actual research advance is discussed concerning this specific item. To handle this review, we first focused on the keywords to capture and analyze the available articles of experts. We limited the period from 2016 to 2021 to skim the recent advances. Researchers have explored different methods with feature selection, classification, and prediction. Algorithms of Data mining, machine learning (supervised and unsupervised), and deep learning (artificial neural networks) are very different and tackle various aspects to be explored. With these advances, banks can become smart and propose a better and quicker service while preserving themselves from losses due to credit defaulters. Support vector machine, Catboost, decision tree, and logistic regression have delivered interesting results according to the studied researches.","url":"https://doi.org/10.1109/aisp53593.2022.9760655","authors":["Imane Rhzioual Berrada","Fatima Zohra Barramou","Omar Bachir Alami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-25T21:25:46Z","doi":"10.1109/aisp53593.2022.9760655","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.32388/poxvgc","name":"Review of: \"An Explorative Review of Artificial Intelligence Software (Chatbot) Impact on Education System\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/poxvgc","authors":["María Cora Urdaneta Ponte"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-07T09:43:25Z","doi":"10.32388/poxvgc","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1016/0004-3702(87)90084-1","name":"Fifth annual conference on applications of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(87)90084-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(87)90084-1","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1016/0004-3702(85)90046-3","name":"Second annual conference on applications of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90046-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90046-3","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1016/0004-3702(89)90073-8","name":"On logical foundations of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90073-8","authors":["Nils Nilsson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(89)90073-8","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1016/0004-3702(91)90049-p","name":"Logic and artificial intelligence","source":"crossref","abstract":"The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.","url":"https://doi.org/10.1016/0004-3702(91)90049-p","authors":["Nils J. Nilsson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(91)90049-p","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.2196/preprints.25745","name":"Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review (Preprint)","source":"crossref","abstract":"BACKGROUND In the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. OBJECTIVE The aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. METHODS We conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. RESULTS The literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients’ individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. CONCLUSIONS There is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families.","url":"https://doi.org/10.2196/preprints.25745","authors":["Vera C Kaelin","Mina Valizadeh","Zurisadai Salgado","Natalie Parde","Mary A Khetani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-11-04T14:46:52Z","doi":"10.2196/preprints.25745","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.9734/bpi/nvmms/v2/7980e","name":"Artificial Intelligence in Dentistry-A Review Article","source":"crossref","abstract":"The field of Artificial Intelligence (AI) has experienced great development and growth over the past two decades. AI has tremendous potential in the health care field. In dentistry, AI is being used for a variety of purposes. Once considered to be a science fiction is now becoming reality in health care. AI is a fast-growing technology that enables machines to perform tasks with the cognitive skills of humans. Neural networks which are commonly used in dentistry are a part of AI, and are very similar to the human brain in their work and they can solve the given problems and make fast decisions. More research work and advancements are needed in the use of neural networks in dentistry to use them in the daily practice. This review is about usage and development of AI in recent years in the field of dentistry.","url":"https://doi.org/10.9734/bpi/nvmms/v2/7980e","authors":["Vytheeswari R.","Sudarshan R.","Madhulika Naidu","Anitha M.","Nandini Priya M.","Anu M."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-20T05:42:45Z","doi":"10.9734/bpi/nvmms/v2/7980e","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1101/2025.10.20.25338371","name":"Artificial intelligence in health professions education:\n                  <i>A state-of-the-art meta-review</i>","source":"crossref","abstract":"Abstract The growing adoption of artificial intelligence (AI) technologies in healthcare is transforming modern healthcare systems, necessitating current and future healthcare providers to be educated on the meaningful use of AI in their academic and professional activities. Despite an emerging body of literature emphasizing the use of AI in health professions education (HPE) and the availability of multiple reviews on this topic, there is a lack of meta-research evidence that can provide a broader overview of the evidence landscape reported across the existing systematically conducted literature reviews. This meta-review aimed to synthesize evidence on the applications of different AI technologies in HPE, multi-level factors influencing the applications of AI in HPE, and associated outcomes from existing systematically conducted literature reviews (SCLRs). A total of 48 eligible SCLRs were identified from six databases and additional sources, and the synthesized findings suggest emerging use cases of multiple AI technologies among HPE users and institutions, including AI-assisted instructional delivery, augmenting learning sessions, content optimization, and providing feedback. While most reviews reported positive HPE-related outcomes, there are critical challenges at the user and institutional levels, which should be considered for effective AI implementation in HPE. Building AI capacities among HPE users and facilitating AI resources development are critical for AI adoption. This meta-review may inform HPE and broader healthcare communities to advance knowledge and practice on evidence-based AI in HPE settings.","url":"https://doi.org/10.1101/2025.10.20.25338371","authors":["M. Mahbub Hossain","Puspita Hossain","Tamal Joyti Roy","Jyoti Das","Samia Tasnim","Ping Ma","Winston Liaw"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-21T18:50:12Z","doi":"10.1101/2025.10.20.25338371","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/978-3-030-92087-6_43","name":"Lung Cancer Screening and Nodule Detection: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_43","authors":["Sunyi Zheng","Peter M. A. van Ooijen","Matthijs Oudkerk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_43","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.2196/preprints.78500","name":"Artificial intelligence-supported digital microscopy diagnostics in primary health care laboratories: a scoping review (Preprint)","source":"crossref","abstract":"BACKGROUND Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and a decreased need for experts on-site. To our knowledge, no scoping or systematic review has previously examined the use of AI-supported digital microscopy in primary health care laboratories, and a scoping review could guide future research by providing insights into the challenges of implementing these novel methods. OBJECTIVE This scoping review aimed to map published peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject. METHODS A systematic search of the databases PubMed, Web of Science, Embase, and IEEE was conducted on October 2, 2024. The inclusion criteria in the scoping review were based on three concepts: using digital microscopy, AI, and comparison of the results to a standard diagnostic system; and one context, being performed in primary health care laboratories. Additional inclusion criteria were peer reviewed diagnostic accuracy studies in English performed on humans and achieving a sample level diagnosis. The study selection and data extraction were performed by two independent researchers, and cases of disagreement were solved through discussion with a third researcher. The methodology is in accordance with the JBI methodology for scoping reviews. RESULTS A total of 3,403 articles were screened during the article identification process, of which 22 (0.6%) were included in the scoping review. The samples analyzed were as follows: blood (n=12) for blood cell and malaria detection; urine (n=4) for urinalysis and parasite detection; cytology of atypical oral (n=1) and cervical cells (n=2); stool (n=2) for parasite detection; and sputum (n=1) for ferning-patterns indicating inflammation. Both conventional (n=15) and specifically developed methods (n=7) were used in sample preparation. The AI models used were based on both single (n=11) and multiple AI-algorithms (n=11) and all studies except one used convolutional neural networks. The AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for complete blood counts, malaria detection, identification of stool and genitourinary parasites, screening for oral and cervical cellular atypia, detection of pulmonary inflammation, and urinalysis. The AI-supported digital microscopy had higher sensitivity than manual microscopy in six out of seven (85.7%) studies that used a reference standard that allowed for this comparison. CONCLUSIONS AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for diagnosing multiple targets in primary health care laboratories and may be particularly advantageous for improving diagnostic sensitivity. However, many shared challenges, ranging from sample preparation to workflow integration, need to be addressed to enable real world implementation. CLINICALTRIAL JMIR Res Protoc 2024;13:e58149 doi:10.2196/58149 INTERNATIONAL REGISTERED REPORT RR2-10.2196/58149","url":"https://doi.org/10.2196/preprints.78500","authors":["Joar von Bahr","Antti Suutala","Vinod Diwan","Andreas Mårtensson","Nina Linder","Johan Lundin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T04:30:11Z","doi":"10.2196/preprints.78500","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.62019/p2cm6s55","name":"ARTIFICIAL INTELLIGENCE IN ENHANCING BIODEGRADATION PROCESSES: A DATA-DRIVEN APPROACH TO ENVIRONMENTAL SUSTAINABILITY","source":"crossref","abstract":"Objective: The purpose of this study is therefore to assess how AI can be used to advance biodegradation processes and thus support environmental sustainability by increasing the biodegradation rate, decreasing the likelihood of human mistakes, and anticipating the right conditions. Methodology: A quantitative research method was used and 250 professionals from different sectors including environmental scientists, molecular biologists, bio-technologists, and professionals in the field of Artificial Intelligence were included in the sample. The research applied a structured questionnaire that left a great impression on the participant’s opinions on the efficiency of AI in biodegradation, which was a blend of Likert-scale and multiple-choice questions. Using descriptive statistics, Cronbach’s Alpha for reliability, and principal component analysis (PCA) for dimensionality the data were analyzed. Results: Based on the results, it emerged that AI is considered to be useful in increasing biodegradation efficiency and estimating the best conditions. However, some questions were made about the effectiveness of using AI in determining the original human errors. Cronbach’s Alpha yielded a negative value of -0. 397 hence pointing out the low internal consistency of the data gathered The PCA result also indicated that perceiving AI was influenced by more than a single dimension, and the first two principal components accounted for 17 percent only. 5% and 17. 0% of the variance. Conclusion: Although there are various benefits to using AI in biodegradation there are also some limitations, especially on the reliability and consistency of the process. It was also observed from the results of the study that refinement of the AI tools and research focusing on the aims of AI to the actual biodegradation requirements are required. Filling these gaps will be critical to realizing AI’s potential for supporting environmental sustainability.","url":"https://doi.org/10.62019/p2cm6s55","authors":["Muhammad Usman","Abdul Rauf","Maria Rafique"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-09T06:28:01Z","doi":"10.62019/p2cm6s55","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.21608/puj.2024.324262.1271","name":"Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024)","source":"crossref","abstract":"Artificial Intelligence (AI) was introduced to the field of Medical Parasitology with many applications including predicting epidemics, diagnosis, therapeutic approaches, and diseases control. The current systematic review was conducted to retrieve published articles in the last decade related to AI applications in Medical Parasitology aiming to provide comprehensive data for more advancement in field diagnosis, and drug development. The PubMed, Scopus and Web of Science databases were screened systematically for articles covering AI in Parasitology published from 2014 to 2024, and SWOT analysis was conducted. In diagnosis, results revealed plenty of AI modalities including mobile applications, machine learning (ML) or deep learning (DL) based methods, neural network image models, convolutional neural network (CNN), digital microscopy, helminth egg analysis platform (HEAP), and transfer learning-based techniques. In addition, screening drug libraries opens new avenues for identification of new drug targets, and drug repurposing or combinations for better therapeutic regimens. It was concluded that AI modalities can help in making decisions and diagnosing parasites in various samples. Moreover, AI represents a crucial step for repurposing available drugs, and discovering drug targets for de novo drug development.","url":"https://doi.org/10.21608/puj.2024.324262.1271","authors":["Reham Mostafa","Noha Taha","Fatma Eissa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-26T12:38:07Z","doi":"10.21608/puj.2024.324262.1271","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/s44163-026-01848-2","name":"Hybrid and transformer based artificial intelligence for diabetic retinopathy diagnosis a systematic review of methods challenges and clinical readiness","source":"crossref","abstract":"Abstract Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, placing a growing burden on healthcare systems. While artificial intelligence (AI) offers promising tools for early detection and scalable screening, its transition from research to real-world clinical use faces significant hurdles. This systematic review, guided by PRISMA principles, examines 27 recent studies (2023–2025) to map the evolving landscape of AI-driven DR diagnosis. We categorize approaches into five families: convolutional neural networks (CNNs), hybrid CNN–machine learning models, transformer-based architectures, clinical data–driven predictors, and multimodal fusion systems. Our analysis reveals that while transformers excel in severity grading and hybrid models demonstrate practical robustness, critical gaps persist—including poor early-DR sensitivity, limited generalizability across imaging devices, and a lack of clinical explainability. We argue that future efforts must prioritize lightweight, interpretable, and temporally aware AI systems that integrate multimodal patient data. By bridging technical innovation with clinical pragmatism, this review aims to accelerate the development of deployable AI solutions for global DR screening.","url":"https://doi.org/10.1007/s44163-026-01848-2","authors":["Frenisha Digaswala","Amit Ganatra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-04T03:49:52Z","doi":"10.1007/s44163-026-01848-2","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1108/aiie-08-2025-0240","name":"Artificial intelligence in school leadership: a structured literature review of organisational benefits and ethical challenges","source":"crossref","abstract":"Purpose This study investigates how artificial intelligence (AI) integrates into school leadership by examining organisational benefits and ethical challenges. As AI permeates educational administration, school leaders must navigate risks and opportunities in data privacy, fairness, and accountability. Design/methodology/approach A PRISMA-aligned structured literature review was conducted on publications from 2019 to 2025. Searches were performed in Scopus, Web of Science, ERIC, and Google Scholar, focussing on K–12 school leadership, with selective higher education sources included only for transferable governance mechanisms (e.g. policy, procurement, documentation/explainability, and auditability). Studies were screened for leadership relevance and ethical-legal engagement. Findings were synthesised using reflexive thematic analysis and conceptual mapping, yielding a final corpus of 50 publications. Findings AI affords benefits for school leadership, including administrative efficiency, decision support and, under data governance, more equitable resource allocation. However, adoption introduces ethical-legal challenges. Key concerns include algorithmic bias, opacity in decision-making, and diffuse accountability. Many systems lack robust oversight, clear roles, and targeted training for ethical implementation. Practical implications School leaders should embed AI in distributed leadership, mandate explainability and audits in procurement, invest in privacy/data literacy, and align analytics with instructional priorities to secure equity, lawful processing, and reviewable accountability. A one-page governance map (Leaders' Governance Guide) is provided, mapping use cases to risks, safeguards, and an equity note. Originality/value The review links benefits and risks to accountability and legal implications, proposing a leadership governance frame to support equitable, transparent AI in schools.","url":"https://doi.org/10.1108/aiie-08-2025-0240","authors":["Electra Lipsou","Nicos Keravnos","Nikleia Eteokleous"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T16:14:44Z","doi":"10.1108/aiie-08-2025-0240","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10462-013-9402-2","name":"Development and implementation of clinical guidelines: An artificial intelligence perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10462-013-9402-2","authors":["Tiago Oliveira","Paulo Novais","José Neves"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-03-12T05:51:54Z","doi":"10.1007/s10462-013-9402-2","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.5195/ijms.2023.2610","name":"Navigating the Digital Frontier: A Review on the Applications of Artificial Intelligence in Medicine and Surgery","source":"crossref","abstract":"Artificial intelligence (AI) is being integrated into several fields worldwide due to its impressive capabilities in completing tasks, sometimes autonomously. Research by several groups worldwide has shown that AI could similarly be incorporated into clinical practice. Convolutional neural network (CNN) models have an inherent capability of recognising and classifying patterns, allowing them to be used in imaging and other diagnostic techniques in various clinical specialities. With some AI systems already in use, it is anticipated that several other AI models will come into clinical practice in the coming years to improve healthcare and patient outcomes. Hence, it is paramount that current medical students and practising doctors keep up with these recent advances in AI to provide the best standard of care for patients. This narrative review explores the basis of deep learning CNN models and summarises extensive literature to provide an overview of some of the recent applications of CNN models to various clinical specialities in medicine and surgery.","url":"https://doi.org/10.5195/ijms.2023.2610","authors":["Sri Sai Rohit Kosuri","David Sunnucks"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-28T20:48:50Z","doi":"10.5195/ijms.2023.2610","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1177/20552076231189331","name":"Application of artificial intelligence in medical technologies: A systematic review of main trends","source":"crossref","abstract":"Objective Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies. Methods The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review. Results As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users. Conclusions This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.","url":"https://doi.org/10.1177/20552076231189331","authors":["Olga Vl Bitkina","Jaehyun Park","Hyun K. Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-19T01:55:29Z","doi":"10.1177/20552076231189331","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.3991/ijoe.v19i17.42431","name":"Artificial Intelligence Techniques in Medical Imaging: A Systematic Review","source":"crossref","abstract":"This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts.","url":"https://doi.org/10.3991/ijoe.v19i17.42431","authors":["Abdellah Azizi","Mostafa Azizi","M'barek Nasri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-15T16:27:41Z","doi":"10.3991/ijoe.v19i17.42431","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.62502/ijmi/v2i4art3","name":"Ethical and Clinical Challenges of Artificial Intelligence Implementation in Diagnostic Radiology: A Mini Review","source":"crossref","abstract":"Artificial intelligence (AI) is rapidly transforming diagnostic radiology by enhancing image interpretation, workflow efficiency, and clinical decision-making.Advanced AI algorithms, particularly those based on machine learning and deep learning, have demonstrated promising performance in tasks such as lesion detection, disease classification, and prognostic assessment across multiple imaging modalities.Despite these advancements, the integration of AI into routine radiological practice presents significant ethical and clinical challenges that must be carefully addressed to ensure safe, effective, and equitable use.This mini review explores the key ethical concerns associated with AI implementation in diagnostic radiology, including data privacy, algorithmic bias, transparency, and accountability in clinical decision-making.The reliance on large datasets for AI training raises concerns regarding patient consent, data security, and representativeness, which may influence algorithm performance across diverse populations.From a clinical perspective, challenges such as workflow integration, validation across different imaging systems, regulatory approval, and the need for continuous performance monitoring are critically examined.The impact of AI on professional roles and responsibilities of radiologists is also discussed, emphasizing the importance of human oversight and collaborative decision-making.The review highlights that while AI has the potential to augment radiological practice and improve patient outcomes, its successful implementation depends on robust ethical frameworks, standardized validation protocols, and interdisciplinary collaboration among clinicians, engineers, policymakers, and patients.Addressing these challenges proactively will support the responsible adoption of AI and ensure that technological innovation aligns with the core principles of patientcentered care in radiology.","url":"https://doi.org/10.62502/ijmi/v2i4art3","authors":["Anushka Teli","Sunita Kumari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-15T18:00:24Z","doi":"10.62502/ijmi/v2i4art3","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.31674/mjmr.2024.v08i03.003","name":"Leveraging Artificial Intelligence to Address Adolescent Sexually Transmitted Infections: A Systematic Review","source":"crossref","abstract":"Background: The integration of Artificial Intelligence (AI) into daily life provides a unique opportunity to address significant health concerns. In particular, the tech-savvy adolescent population could benefit from AI-enhanced access to reproductive health services, especially for the prevention, screening, and treatment of Sexually Transmitted Infections (STIs). Objective: This research aims to evaluate the impact of AI technology on improving adolescents' access to reproductive health services related to STIs. The study involves a systematic review of literature published from 2020 to 2024 across various databases. Methods: A systematic review methodology was employed, utilizing databases such as Google Scholar, PubMed, Semantic Scholar, Science Direct, and IEEE-XPLORE. Keywords used in the search included \"artificial intelligence,\" \"adolescents OR teenagers,\" and \"sexually transmitted infections OR sexually transmitted diseases.\" Results: The review identifies AI as a pivotal tool in sexual education, particularly through the use of interactive and engaging chatbots. AI facilitates innovative educational interventions, allowing vulnerable and marginalized groups, including adolescents, to discuss and learn about sensitive topics like STIs. Conclusion: The study highlights the significant potential of AI in improving sexual health education for adolescents. The limited availability of research in this area underscores the importance of this study in advancing knowledge and addressing gaps in the application of AI for adolescent STI prevention and treatment.","url":"https://doi.org/10.31674/mjmr.2024.v08i03.003","authors":["Erika Agung Mulyaningsih","Niken Bayu Argaheni","Septiana Juwita"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-03T07:58:06Z","doi":"10.31674/mjmr.2024.v08i03.003","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.18034/mjmbr.v6i2.566","name":"Implementation of Artificial Intelligence in Agriculture: A Review for CMS Optimization","source":"crossref","abstract":"Agriculture has a critical role to play in the financial domain. Likewise, automation of multiple processes in agriculture has been a great concern as well as an alarming subject across the world. The population all over the world is growing at a high rate and with this increment, demand for agriculture and its jobs is also growing exponentially. The usual techniques that were used by the farmers are not efficient enough to meet these requirements. Along these lines, new digital techniques are presented. These new strategies satisfy the proper management of agricultural products as well as services so that farmers can make the most of technology to increase their profit rates. AI in the agricultural landscape has initiated a revolutionary change. It has guarded the harvest yield from different declining factors such as environmental changes, over population, dynamic business demands, and food safety issues. By using artificial intelligence we can foster smart farming practices to limit the loss of farmers and give them high returns. Using artificial intelligence platforms, one can collect an enormous amount of information from government and public sites or real-time monitoring and collection of different information is likewise possible by utilizing IoT (Internet of Things) and afterward can be explored with precision to empower the farmers for resolving every one of the issues faced by farmers in the agriculture area. This research is conducted in order to help local farmers everywhere in the world to manage their agriculture practices all the more effectively. The strategy discussed in this paper is leveraging the model of waterfall methodology for planning and creating a system smart enough by performing a sequential cycle that starts with data collection, requirement analysis, plan, coding, and testing and finally implements that system as a whole. This system can also be used to foster ideas to manage normal issues in agriculture information systems, to improve the policy programs, the augmentation, and analysis practices, and to manage data on agriculture. Finally, conclusion about agricultural information systems are discussed and suggestions for additional development of agriculture data systems is presented.","url":"https://doi.org/10.18034/mjmbr.v6i2.566","authors":["Takudzwa Fadziso"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-06-25T06:14:17Z","doi":"10.18034/mjmbr.v6i2.566","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/978-3-319-94878-2_20","name":"Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-94878-2_20","authors":["Erik R. Ranschaert","André J. Duerinckx","Paul Algra","Elmar Kotter","Hans Kortman","Sergey Morozov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-29T14:16:52Z","doi":"10.1007/978-3-319-94878-2_20","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.33140/amlai.01.01.03","name":"Artificial Intelligence and Advances","source":"crossref","abstract":"This research abstract shares open source theme smart materials and technology for the Current and Future Global Challenges in relation with artificial intelligence which is the simulation of human intelligence processes by machines, especially computer systems. AI will also have a major impact on illegal/illicit /legal harmful drugs, chemicals, toxic herbs and others Control Intervention. the patent pending this scientific computing innovation; will include illegal/illicit/ legal harmful drugs, chemicals, toxic herbs and others Control security system and special purpose computer connected intelligent equipment’s, with sensors capable of taking thousands of measurements throughout the production process and generating billions of data points used to monitor, analyze and control the trafficking process. AI and machine learning are also contributing to the development of next-generation security system, accelerating the development of security intervention for conditions where there are no viable options today. Artificial intelligence promises both to improve existing goods and services, by enabling the automation of many tasks, to greatly increase the efficiency with which they are produced. But it may have an even larger impact on the economy by serving as a new general-purpose method of invention for unique or special tasks; Where Artificial intelligence (AI) clearly lead to better outcomes, already producing benefits and optimizing processes, increasingly sophisticated algorithms and machine learning techniques of data on a particular issue, generate insights, detections and resulting with more efficiency than teams of humans ever could.","url":"https://doi.org/10.33140/amlai.01.01.03","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-23T06:00:40Z","doi":"10.33140/amlai.01.01.03","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.2196/preprints.81429","name":"A Systematic Review of the Utility of Generative Artificial Intelligence in Orthopaedic Surgery Patient Education Material (Preprint)","source":"crossref","abstract":"BACKGROUND Large language models (LLMs), a type of generative Artificial Intelligence (AI), are capable of interpreting and generating human-like text and offer the potential to synthesize and simplify complex medical information into accessible, human-like responses in a seemingly instantaneous manner. However, concerns remain regarding their implementation in the healthcare setting. OBJECTIVE The aim of this systematic review is to assess the accuracy, readability and quality of responses provided by LLMs when responding to frequently asked questions (FAQs) by patients, generating patient education materials (PEMs) and re-writing pre-existing PEMs within the field of orthopaedic surgery. METHODS A scoping literature review following PRISMA guidelines was conducted using PubMed, Ovid and Embase databases. Papers where LLMs were used to generate, re-write or modify PEMs or respond to frequently asked questions in orthopaedics with outputs evaluated using any validated quality, accuracy or readability assessment tool were included. Studies that compared the output between different LLMs were also included. Papers that were available as abstract-only, non-English language and did not assess LLMs were excluded. This systematic review was registered with PROSPERO (CRD420251087612). RESULTS A total of 94 studies were identified in the literature search and 32 met the inclusion criteria. All LLM models provided moderate to highly accurate responses that were relevant, however factual inaccuracies and an inability to handle clinical nuance were noted with all material produced by LLMs. 12/32 (37.5%) studies utilized instruction based fine tuning (IBFT), 2/32 (6.25%) studies utilized reinforcement learning human feedback (RLHF) and 1/32 (3.13%) studies utilized both IBFT and RLHF to fine-tune and optimize the LLM response. LLM responses that were not fine-tuned provided responses that exceeded average patient literacy levels. Fine-tuned ChatGPT-4.0 models significantly improved clarity and reduced complexity without compromising clinical accuracy. Comparative studies indicated gradual LLM improvement. CONCLUSIONS LLMs demonstrated moderate to high accuracy when producing responses that could be used for patient education. They offer information in an easily accessible, seemingly instantaneous manner that is either free of cost or requires minimal expenditure. However, these responses consistently produce inaccuracies and lack clinical nuance. Whilst fine-tuning improved LLM response, and gradual model improvement was noted, constant expert oversight remains essential as uninformed LLM use risks the potential of mis-information and error for patients. Inadequate prompting and a high reading level of responses are all limitations to LLM safety and utility in healthcare. LLMs will continue to evolve rapidly and ongoing research in to the use of LLMs in patient education in orthopaedic surgery is necessary, especially with well-structured methods where LLMs receive adequate IBFT and RLHF to optimize their function. CLINICALTRIAL This systematic review was registered with PROSPERO (CRD420251087612).","url":"https://doi.org/10.2196/preprints.81429","authors":["Hugh Jones","Kayden Chahal","Charles Picton","Taha Sherief Benamer","Ahmed Elnewishy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-30T21:05:05Z","doi":"10.2196/preprints.81429","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.70593/deepsci.0203002","name":"Artificial intelligence in medical education: A systematic review of teaching, learning, and academic integrity issues","source":"crossref","abstract":"The accelerated adoption of artificial intelligence in medical education has produced both great opportunities in terms of improving teaching, learning, and evaluation and also the crux of the matter of academic integrity, ethical governance, and educational quality. This systematic review has attempted to compile existing program of artificial intelligence, generative artificial intelligence, large language models, adaptive learning, intelligent tutoring systems and simulation-based learning in medical education, particularly in terms of teaching practices, instructional outcomes, and academic dishonesty concerns. Within the framework of PRISMA, the search of pertinent literature was conducted. Research about AI-assisted teaching, personalized learning, virtual patients, automated feedback, assessment analytics, clinical reasoning, plagiarism, academic misconduct, and ethical AI were located. The results show that AI has revolutionized medical curriculum delivery in the factors of personalization in the learning pathways, predictive analytics, natural language processing, competency-based education, and simulated environment. Large language models and ChatGPT have shown potential to improve student engagement, clinical decision support, formative assessment, and learner autonomy. Yet, significant issues and fears of algorithmic bias, data privacy, explainable AI, professionalism on the internet, offloading human cognition, overweighting on autopilot processes remain. Among the most commonly reported risks were the academic integrity issues, such as plagiarism and cheating detection, fake citations and unauthorized AI-assisted assessment. The review has shown that effective implementation of artificial intelligence in healthcare education needs to be accompanied by effective ethical governance, faculty training, AI literacy training, clear regulatory frameworks, and human-AI partnership.","url":"https://doi.org/10.70593/deepsci.0203002","authors":["Emeh Blessing"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-04T07:13:52Z","doi":"10.70593/deepsci.0203002","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/978-1-4613-8777-0","name":"Selected Topics in Medical Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4613-8777-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-10-18T13:16:03Z","doi":"10.1007/978-1-4613-8777-0","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1201/b15618-25","name":"Intelligent Light Therapy for Older Adults: Ambient Assisted Living","source":"crossref","abstract":"Light therapy is increasingly administered and studied as a nonpharmacologic treatment for a variety of health-related problems, including treatment of people with dementia. It is applied in a variety of ways, ranging from being exposed to daylight (in sanatoria) to being exposed to light emitted from electrical sources. These include light boxes, light showers, and ambient bright light. Light therapy covers an area in medicine where medical sciences meet the realms of physics, engineering, and technology (van Hoof et al. 2012).","url":"https://doi.org/10.1201/b15618-25","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-10-11T13:35:58Z","doi":"10.1201/b15618-25","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1186/s40561-025-00403-3","name":"Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review","source":"crossref","abstract":"Abstract Current advances in academic research stem from two main sources: artificial intelligence technologies and the specific field of generative artificial intelligence. However, the ethical use of these technologies and their implications for academic integrity has not been sufficiently investigated. Therefore, this research examines the ethical use of artificial intelligence technologies and Generative Artificial Intelligence in academic research. It focuses on the current field conditions, detection of research trends, and critical gaps. The study uses a combination of bibliometric and thematic content analysis methods to examine the methodological framework of AI, GenAI, and academic integrity from an interdisciplinary perspective. The research reveals that GenAI integration speed has accelerated across all research stages, including academic writing, literature review, data analysis, and hypothesis development. The study also identifies risks such as biased algorithms, plagiarism risk, false information production, and potential damage to academic integrity. The research ethics approaches developed by academic institutions and journals have not reached maturity in the context of AI. Future research on GenAI within academic processes requires forming ethical principles integrated with oversight systems and policy frameworks.","url":"https://doi.org/10.1186/s40561-025-00403-3","authors":["Khalid H. Arar","Hamit Özen","Gülşah Polat","Selahattin Turan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-22T07:25:55Z","doi":"10.1186/s40561-025-00403-3","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1080/08839514.2024.2327890","name":"Collaborative Intelligence: A Scoping Review Of Current Applications","source":"crossref","abstract":"This review provides a novel examination of the emerging field of collaborative intelligence and demonstrates the value that human-AI teams can deliver. Humans and artificial intelligence (AI) systems have complementary strengths. This complementarity creates the potential to achieve a step-change in performance by combining inputs from human and AI on a common task. We introduce the construct of “collaborative intelligence” and develop a set of criteria, for evaluating whether an AI system enables collaborative intelligence. Applications utilizing collaborative intelligence had to have (1) complementarity (i.e. the collaboration draws upon complementary human and AI capability to improve outcomes), (2) a shared objective and outcome, and (3) sustained, two-way task-related interaction between human and AI. A systematic review of 1,250 AI applications published between 2012 and 2021 was carried out to investigate whether real-world examples of “collaborative intelligence” could be identified. The review yielded 16 AI systems which met the criteria, demonstrating that collaboration between humans and AI systems is possible and that these systems offer a wide range of performance benefits including efficiency, quality, creativity, safety, and human enjoyment.","url":"https://doi.org/10.1080/08839514.2024.2327890","authors":["Emma Schleiger","Claire Mason","Claire Naughtin","Andrew Reeson","Cecile Paris"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-18T05:46:30Z","doi":"10.1080/08839514.2024.2327890","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.3403/30397412","name":"Information technology � Artificial intelligence � Overview of trustworthiness in artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30397412","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-01T20:30:14Z","doi":"10.3403/30397412","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1007/bf02221493","name":"Formal systems in Artificial Intelligence: an illustration using semigroup, automata and language theory","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf02221493","authors":["P. T. Hadingham"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-10-06T15:24:06Z","doi":"10.1007/bf02221493","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1145/3718491.3718523","name":"A Review of the Application and Development of Artificial Intelligence Technology in Museums","source":"crossref","abstract":"With the rapid development of artificial intelligence (AI) technology, its applications in museums have become increasingly widespread. However, systematic discussions of its specific applications and future development trends remain limited. Based on literature analysis from CNKI, Scopus, and Google Scholar databases, this study systematically examines the current applications of AI in museums, identifies existing challenges and solutions, and explores future development trends. The findings reveal that AI technology has significantly enhanced museum visitor experiences by providing personalized, immersive, and accessible diversified services. In terms of operational management, AI demonstrates notable advantages in cost control and efficiency optimization, effectively improving museum resource utilization. Furthermore, AI promotes the integration of culture and technology, innovation in education and research, and interdisciplinary collaboration. This study provides theoretical support for AI applications in the museum sector, helping researchers, funding agencies, and practitioners understand current status and development directions.","url":"https://doi.org/10.1145/3718491.3718523","authors":["Jun Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-02T20:59:40Z","doi":"10.1145/3718491.3718523","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.36922/aih025270059","name":"Artificial intelligence in health systems: A comprehensive review of opportunities and limitations","source":"crossref","abstract":"Artificial intelligence (AI) has emerged as a transformative tool across multiple sectors, with healthcare being one of the most promising domains. This review article explores the foundational concepts of AI and its rapidly expanding applications in the healthcare sector. The integration of AI in health systems encompasses various branches, including diagnostic imaging, drug discovery, virtual health assistants, robotic surgery, and personalized medicine. AI-powered tools have demonstrated significant advantages, such as enhancing diagnostic accuracy, optimizing treatment plans, reducing administrative burdens, and improving patient outcomes. However, the deployment of AI in healthcare also presents notable challenges and limitations. These include data privacy concerns, algorithmic bias, lack of transparency, and the need for substantial infrastructure and workforce training. Moreover, ethical and regulatory issues continue to influence the pace and scope of AI adoption. This review critically examines these aspects while highlighting recent innovations that underscore AI&amp;rsquo;s potential. Finally, the article outlines future directions for AI in healthcare, emphasizing the need for interdisciplinary collaboration, robust ethical frameworks, and the development of explainable AI systems. As technology evolves, a balanced approach that maximizes benefits while mitigating risks is essential for the sustainable integration of AI into global health systems.","url":"https://doi.org/10.36922/aih025270059","authors":["Md. Monirul Islam","Iqbal Mahmud","Sabrina Amin Shovon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-22T01:26:04Z","doi":"10.36922/aih025270059","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1504/ijaisc.2008.021264","name":"Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review","source":"crossref","abstract":"Artificial Intelligence (AI) has been used and applied in different sectors, such as engineering, economic, medicine, military, marine, etc. AI has also been applied for modelling, identification, optimisation, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of AI techniques for modelling, prediction and forecasting of solar radiation data. Published literature works presented in this paper show the potential of AI as a design tool for prediction and forecasting of solar radiation data; additionally, they present the advantages of using AI-based prediction solar radiation data in isolated areas where there no instrument for the measurement of this data, especially the parameters related to photovoltaic (PV) systems. Solar radiation plays a very important factor in PV-system performance and sizing.","url":"https://doi.org/10.1504/ijaisc.2008.021264","authors":["Adel Mellit"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-11-15T07:30:17Z","doi":"10.1504/ijaisc.2008.021264","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.26634/jaim.4.1.1462","name":"Artificial Intelligence-Driven Approaches for Phishing Detection and Cyber Threat Analysis: a Comprehensive Review","source":"crossref","abstract":"The rapid proliferation of digital communication and internet-based services has led to an exponential rise in cyber threats, particularly phishing attacks and social engineering exploits. Traditional rule-based detection mechanisms have proven insufficient in combating sophisticated, evolving threats. This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML) driven approaches employed for phishing URL detection, email classification, and broader cyber threat analysis. We examine supervised, unsupervised, and deep learning models including Decision Trees, Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, evaluating their effectiveness based on accuracy, precision, recall, and F1-score metrics. The study also explores feature extraction methodologies, publicly available datasets, and the integration of Natural Language Processing (NLP) for semantic analysis of phishing content. Findings indicate that ensemble learning methods and deep learning architectures consistently outperform traditional classifiers, achieving detection rates above 97% in controlled environments. The paper concludes with identified research gaps, limitations of current models, and directions for future work including real-time adaptive detection systems.","url":"https://doi.org/10.26634/jaim.4.1.1462","authors":["Ujjval Rana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-11T07:50:49Z","doi":"10.26634/jaim.4.1.1462","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3126/jucms.v13i03.88831","name":"The Role of Peer Review in the Era of Artificial Intelligence","source":"crossref","abstract":"INTRODUCTION Peer review remains the foundation of scholarly publishing, safeguarding scientific rigor, ethical standards, and academic credibility. Due to the rapid emergence of artificial intelligence (AI) in research and manuscript preparation, the peer review process now faces new opportunities and unprecedented challenges. Importantly, peer review also has an educational role. Constructive reviewer feedback helps authors refine their arguments, improve methodological clarity, and strengthen ethical compliance. CONCLUSION Artificial intelligence is reshaping the landscape of scientific publishing, but it does not replace the need for peer review. On the contrary, it amplifies its importance. Peer review remains the essential human safeguard that ensures science is credible, ethical, and socially responsible. As AI continues to evolve, strengthening and adapting the peer review process will be crucial in preserving the integrity of medical research and advancing trustworthy scientific knowledge.","url":"https://doi.org/10.3126/jucms.v13i03.88831","authors":["Santosh Shah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-07T14:47:31Z","doi":"10.3126/jucms.v13i03.88831","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1186/s13037-019-0188-2","name":"Artificial intelligence systems for complex decision-making in acute care medicine: a review","source":"crossref","abstract":"The integration of artificial intelligence (AI) into acute care brings a new source of intellectual thought to the bedside. This offers great potential for synergy between AI systems and the human intellect already delivering care. This much needed help should be embraced, if proven effective. However, there is a risk that the present role of physicians and nurses as the primary arbiters of acute care in hospitals may be overtaken by computers. While many argue that this transition is inevitable, the process of developing a formal plan to prevent the need to pass control of patient care to computers should not be further delayed. The first step in the interdiction process is to recognize; the limitations of existing hospital protocols, why we need AI in acute care, and finally how the focus of medical decision making will change with the integration of AI based analysis. The second step is to develop a strategy for changing the focus of medical education to empower physicians to maintain oversight of AI. Physicians, nurses, and experts in the field of safe hospital communication must control the transition to AI integrated care because there is significant risk during the transition period and much of this risk is subtle, unique to the hospital environment, and outside the expertise of AI designers. AI is needed in acute care because AI detects complex relational time-series patterns within datasets and this level of analysis transcends conventional threshold based analysis applied in hospital protocols in use today. For this reason medical education will have to change to provide healthcare workers with the ability to understand and over-read relational time pattern centered communications from AI. Medical education will need to place less emphasis on threshold decision making and a greater focus on detection, analysis, and the pathophysiologic basis of relational time patterns. This should be an early part of a medical student's education because this is what their hospital companion (the AI) will be doing. Effective communication between human and artificial intelligence requires a common pattern centered knowledge base. Experts in safety focused human to human communication in hospitals should lead during this transition process.","url":"https://doi.org/10.1186/s13037-019-0188-2","authors":["Lawrence A. Lynn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-02-01T02:03:45Z","doi":"10.1186/s13037-019-0188-2","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1109/globalaisummit62156.2024.10947838","name":"Machine Learning for Medical Diagnosis: A Review of Fuzzy Random Forests and Applications","source":"crossref","abstract":"This paper explores Fuzzy Random Forests, a machine learning approach for classification tasks that combines ensemble classifiers, randomness, and fuzzy logic. Ensemble classifiers provide robustness, while randomness reduces correlation and increases diversity. Fuzzy logic allows the model to handle imperfect data by incorporating membership degrees. The research investigates Fuzzy Random Forests' effectiveness and compares its performance with other ensemble learning methods. While traditional disease detection algorithms often struggle with real-world complexities, fuzzy random forest offers a promising alternative. This paper reviews research on fuzzy random forest applications in medicine for a decade. Based on several fuzzy applications in the medical business, this paper focuses on seven common medical conditions: diabetes, asthma, liver ailment, breast cancer, cholera, heart disease, and dentistry. The main goal is to research and use fuzzy random forests in both new and existing sectors in the future because of their many medical uses.","url":"https://doi.org/10.1109/globalaisummit62156.2024.10947838","authors":["Avneesh Verma","Priyanka Arora","Sonika Dahiya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-09T17:50:59Z","doi":"10.1109/globalaisummit62156.2024.10947838","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.21203/rs.3.rs-3956881/v1","name":"Liver cancer detection using Artificial Intelligence","source":"crossref","abstract":"Abstract A noticeable increase in statistics of liver cancer in Egypt and all over the world. Therefore, using Artificial Intelligence (AI) to increase the detection accuracy and minimize human errors during manual classification of liver images. Where the manual classification of liver Computed Tomography (CT) scan images require a very great effort and time-consuming tasks. This study aims to improve a high-performance computer detection system. The proposed model used to detect liver tumor is based on Convolutional Neural Network (CNN) techniques and the machine learning techniques, which are of the most application of AI that used in biomedical image classification and recognition. The dataset used in this study is composed of 9255 CT scan images. The proposed model consists of three main steps. The first step aims to compare between three deep learning model which that Liver Tuned High-Resolution Network (LTHR-Net), Deep Residual Network (ResNet50) and Visual Geometry Group Network (VGG19-Net) to get the most suitable deep learning model that improve the system detection accuracy. The next step aims to apply three machine learning classifiers and compare their performance to increase the system detection accuracy. These classifiers are Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM). The final step improves the system detection accuracy by applying decision fusion techniques at the classifiers classification result using majority voting algorithm. The accuracy of the proposed model achieved 99.9% by using LTHR-Net as based model and applied majority voting algorithm on the classification output of the three machine learning classifiers.","url":"https://doi.org/10.21203/rs.3.rs-3956881/v1","authors":["Noha Badrawy","Apeer .T.Khalil","Hanan .M.Amer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-16T08:25:30Z","doi":"10.21203/rs.3.rs-3956881/v1","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.32388/65g1ps","name":"Review of: \"Education, Artificial Intelligence, and the Digital Age\"","source":"crossref","abstract":"Potential competing interests: No potential competing","url":"https://doi.org/10.32388/65g1ps","authors":["Carlos Augusto da Silva Cunha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-17T17:58:46Z","doi":"10.32388/65g1ps","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.1039/d6tb00696e/v1/review2","name":"Review for \"Multimodal Health Monitoring and Theranostics Based on Functionalized Hydrogels and Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d6tb00696e/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-10T21:05:57Z","doi":"10.1039/d6tb00696e/v1/review2","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.326Z"},{"id":"doi:10.36227/techrxiv.21493761","name":"Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review","source":"crossref","abstract":"&lt;p&gt;To keep up with the new technology modernization and the profit in shake of investors and stakeholders and importantly for the nation, and to ensure health and safety mining industry needs to approve new-age autonomous technologies and intelligent system in their field. Integration of Artificial Intelligence, Machine Learning, Internet of Things (IoT) and Automation are the keys to the 4th revolution in mining industry. This paper presents the overview of recent research upon artificial intelligence enhanced techniques and possibilities in mining operations and mining related domains. There is also a brief about the recent autonomous techniques and equipment in mining industry. Implementations and possibilities of artificial intelligence in safety and accident analysis of mining operations are sincerely detailed. Computer vision and spatial image analysis is also discussed as the recent advancement of deep learning and pattern recognition. Other mining related implementations of intelligent systems includes fragment analysis of ores, intelligent ventilation, on-site mineral processing simplification, digital twinning, mineral exploration, mineral price forecasting, mining equipment selection, post-mining land reclamation and scheduling. This paper also notes the detailed obstacles for implementing intelligent systems in mining industry.&lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.21493761","authors":["Ritwick Ghosh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-10T00:46:15Z","doi":"10.36227/techrxiv.21493761","addedAt":"2026-09-01T01:48:00.326Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.32388/0ghze2","name":"Review of: \"Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to","url":"https://doi.org/10.32388/0ghze2","authors":["Shabarinath Bb"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-17T03:35:25Z","doi":"10.32388/0ghze2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1039/d6tb00696e/v2/review2","name":"Review for \"Multimodal Health Monitoring and Theranostics Based on Functionalized Hydrogels and Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d6tb00696e/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-10T21:05:57Z","doi":"10.1039/d6tb00696e/v2/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/cesm.70045/v1/review3","name":"Review for \"Artificial Intelligence Search Tools for Evidence Synthesis: Comparative Analysis and Implementation Recommendations\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cesm.70045/v1/review3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-30T23:44:52Z","doi":"10.1002/cesm.70045/v1/review3","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1039/d4md00722k/v1/review1","name":"Review for \"SIGMAP: an explainable artificial intelligence tool for SIGMA-1 receptor affinity Prediction\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d4md00722k/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-09T16:10:22Z","doi":"10.1039/d4md00722k/v1/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/eng2.70518/v5/review1","name":"Review for \"Artificial Intelligence in Wire Arc Additive Manufacturing: A Systematic Review and Patent Landscape Analysis\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70518/v5/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-12T21:12:02Z","doi":"10.1002/eng2.70518/v5/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.105904","name":"Diagnostic performance of artificial intelligence based on cardiovascular magnetic resonance imaging for myocarditis: a systematic review and meta-analysis (Preprint)","source":"crossref","abstract":"BACKGROUND At present, the development of artificial intelligence is rapid. We have noticed that the artificial intelligence based on MRI is controversy in diagnosing myocarditis. OBJECTIVE The aim is to assess the diagnostic capability of artificial intelligence (AI) in identifying myocarditis through cardiovascular magnetic resonance imaging (MRI) METHODS A comprehensive search of studies was conducted through Web of Science, Embase and PubMed with a focus on researches published before June 7, 2026. If the studies assessment involved the application of AI models based on cardiovascular MRI in the detection of myocarditis, it will be included. The bivariate random effects model was used to ascertain the joint consideration of sensitivity and specificity. Heterogeneity across studies was assessed using the I² statistic. Employing the revised QUADAS-2 tool assesses the risk of bias. The certainty of evidence was evaluated according to GRADE framework. RESULTS Out of the initially identified 1,222 studies, there eventually included 17 studies. The ultimate analysis involved 93,740 patients and images. For myocarditis, AI showed that the sensitivity was 0.93 (0.88 − 0.96) and specificity was 0.94 (0.89 − 0.97), with the AUC of 0.98 (0.96 - 0.99). The asymmetry test of the Deeks' funnel plot did not indicate any significant publication bias (P = 0.46). Meta-regression and subgroup analysis revealed that there are markedly different in groups of analysis, AI method, reference standard and years (P &lt; 0.05). CONCLUSIONS By aggregating the data, this meta-analysis manifested that cardiovascular MRI based on AI revealed excellent ability in diagnosing myocarditis. However, this study is subject to limitations, including its retrospective design and the methodological heterogeneity across cardiovascular MRI. In the future, there is an urgent need for more forward-looking multi-center studies to prove this conclusion.","url":"https://doi.org/10.2196/preprints.105904","authors":["Li Yu","Yan Zhang","AiJie Hou","Fei Xia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-30T13:45:21Z","doi":"10.2196/preprints.105904","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.5812/semj-139627","name":"Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review","source":"crossref","abstract":"Context: Artificial intelligence (AI) Chatbots are computer programs that simulate human conversation and use artificial intelligence, including machine learning and natural language processing, to interact with users via natural language. With the outbreak of the COVID-19 pandemic, the use of digital health technologies such as chatbots has accelerated. Objectives: This study aims to investigate the application of AI chatbots in combating the COVID-19 pandemic and explore their features. Methods: We reviewed the literature on health chatbots during the COVID-19 pandemic. PubMed, Scopus, Web of Science, and Google Scholar were searched using relevant keywords such as “chatbot”, “conversational agent,” and “artificial intelligence”. To select the relevant articles, we conducted title, abstract, and full-text screening based on inclusion and exclusion criteria. Chatbots, their applications, and design features were extracted from the selected articles. Results: Out of 673 articles initially identified, 17 articles were eligible for inclusion. We categorized the selected AI chatbots based on their roles, applications, and design characteristics. Around 70% of chatbots were designed to play a preventive role. Our review identified 8 key applications of the AI chatbots during the COVID-19 pandemic, which include (1) information dissemination and education, (2) self-assessment and screening, (3) connecting to health centers, (4) combating misinformation and fake news, (5) patient tracking and service delivery (6) mental health (7) monitoring exposure (8) vaccine information and scheduling. AI chatbots were deployed on various platforms, including mobile apps, web, and social media. Mobile-based chatbots were the most frequent. All chatbots used Natural Language Understanding (NLU) methods to understand natural language input and act on the user’s request. More than 50% of AI chatbots used NLU platforms, including Google Dialogflow, Rasa framework, and IBM Watson. Conclusions: AI chatbots can play an effective role in combating the COVID-19 pandemic. Increasing people’s awareness, optimizing the use of health resources, and reducing unnecessary encounters are some of the advantages of using AI chatbots during the COVID-19 outbreak. Using NLU platforms can be a suitable solution for developing AI chatbots in the healthcare domain. With advancements in the field of artificial intelligence, it seems that AI chatbots will have a promising future in healthcare, particularly in public health, chronic disease management, and mental health.","url":"https://doi.org/10.5812/semj-139627","authors":["Abdollah Mahdavi","Masoud Amanzadeh","Mahnaz Hamedan","Roya Naemi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-16T08:40:24Z","doi":"10.5812/semj-139627","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s44163-024-00105-8","name":"Between artificial intelligence and customer experience: a literature review on the intersection","source":"crossref","abstract":"Abstract This paper is a literature review of the intersection field between Artificial Intelligence (AI) and Customer Experience (CX). We analyzed and synthesized the most recent and prominent literature on the subject, providing an overview of the state of the art, through articles found in the Scopus database. Among the main findings, it is noteworthy that this intersection appears as an interdisciplinary topic of interest in the fields of Computer Science, Business and Management, and Engineering. Additionally, studies often examine conversational agents such as chatbots and voicebots, as well as machine learning prediction models and recommendation systems as a way to improve the Customer Experience. The most common sectors in the review are tourism, banking and e-commerce. Other segments and technologies appear less and may be underrepresented, thus a scope for future research agenda. Despite the existing literature, it is observed that there is still substantial space for expansion and exploration, especially considering the emergence of new generative Artificial Intelligence models.","url":"https://doi.org/10.1007/s44163-024-00105-8","authors":["Melise Peruchini","Gustavo Modena da Silva","Julio Monteiro Teixeira"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-09T14:02:31Z","doi":"10.1007/s44163-024-00105-8","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2024.109104","name":"Advanced informatic technologies for intelligent construction: A review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.109104","authors":["Limao Zhang","Yongsheng Li","Yue Pan","Lieyun Ding"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-29T15:38:21Z","doi":"10.1016/j.engappai.2024.109104","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1109/icaaic60222.2024.10575018","name":"A Comprehensive Review of Fracture Detection and Identification from Medical Images using Deep Learning Methods","source":"crossref","abstract":"The detection and identification of fractures have significant importance in the field of medical imaging, as they play a crucial role in facilitating precise diagnoses and prompt development of treatment strategies. Over the course of the last decade, there has been a notable progression in deep learning methods, demonstrating their effectiveness in a range of medical image analysis, such as fracture diagnosis and classification. This review study offers a full and extensive examination of recent advancements in using deep learning techniques for the purpose of detecting and identifying fractures from medical imaging. This study conducts a comprehensive examination of the prevailing methodology, commonly used datasets, inherent obstacles, and probable future trajectories within this rapidly developing field. The objective of this work is to provide significant insights into the transformational effects of deep learning on the interpretation of medical images linked to fractures. This research seeks to contribute to the improvement of patient care and diagnosis by examining the influence of Deep Learning (DL) in this field.","url":"https://doi.org/10.1109/icaaic60222.2024.10575018","authors":["T Praveen Kumar","K Suthendran"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T18:01:35Z","doi":"10.1109/icaaic60222.2024.10575018","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.40337","name":"Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review (Preprint)","source":"crossref","abstract":"BACKGROUND This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public. OBJECTIVE This study reviews the existing survey research on the United States’ public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings. METHODS We conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups. RESULTS This review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use. CONCLUSIONS Americans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.","url":"https://doi.org/10.2196/preprints.40337","authors":["Becca Beets","Todd P Newman","Emily L Howell","Luye Bao","Shiyu Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-04T14:34:52Z","doi":"10.2196/preprints.40337","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/22934","name":"Artificial Intelligence for Skin Cancer Detection: Scoping Review","source":"crossref","abstract":"Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.","url":"https://doi.org/10.2196/22934","authors":["Abdulrahman Takiddin","Jens Schneider","Yin Yang","Alaa Abd-Alrazaq","Mowafa Househ"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-03T21:57:58Z","doi":"10.2196/22934","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.21037/jmai-20-30","name":"Interpretative applications of artificial intelligence in musculoskeletal imaging: concepts, current practice, and future directions","source":"crossref","abstract":"Abstract: Artificial intelligence (AI) promises wide-reaching impacts on the field of radiology, and has the potential to influence every aspect of image interpretation. In recent decades, significant advancements in computing power, combined with the availability of large data stores or “Big Data” and algorithm democratization have revolutionized AI and machine learning (ML). Research applications utilizing these technological advancements are booming, and their adoption is expected to continue to rise at a rapid pace. While AI and ML have impacted many components of the imaging value chain, the purpose of this article is to discuss interpretative uses of the technology as it relates to musculoskeletal (MSK) radiology. This review provides a general introduction to AI and ML concepts, and highlights the major promises, challenges, and anticipated future applications of these developments in MSK radiology. AI and ML advances for image interpretation can increase the value that MSK radiologists provide to their patients, referring clinicians, and organizations by increasing diagnostic accuracy while decreasing turnaround times, enhancing image processing and quantitative analysis, and by potentially improving patient outcomes. Familiarity with these processes among MSK clinicians and researchers will be paramount to the improvement and implementation of these new techniques into the clinical practice. Radiology departments, practices and practitioners who embrace these technologies now will be well-suited to lead this influential change in our field in the near future.","url":"https://doi.org/10.21037/jmai-20-30","authors":["Teresa T. Martin-Carreras","Hongming Li","Po-Hao Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-23T06:55:19Z","doi":"10.21037/jmai-20-30","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2021.102158","name":"The three ghosts of medical AI: Can the black-box present deliver?","source":"crossref","abstract":"Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.","url":"https://doi.org/10.1016/j.artmed.2021.102158","authors":["Thomas P. Quinn","Stephan Jacobs","Manisha Senadeera","Vuong Le","Simon Coghlan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-28T15:08:39Z","doi":"10.1016/j.artmed.2021.102158","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/make8050134","name":"Explainable Artificial Intelligence (XAI) for Cancer Classification in Medical Imaging: A Systematic Review","source":"crossref","abstract":"Our study examines the growing role of Explainable Artificial Intelligence (XAI) in cancer medical imaging, where transparency and interpretability are essential for trustworthy clinical decision making. Using a PRISMA-guided systematic literature review, 926 records published between 2020 and 2026 were identified from major databases, with 46 studies meeting the inclusion criteria after screening and quality assessment. The review systematically analyzes XAI techniques, model architectures, evaluation approaches, interpretability mechanisms, challenges, and future research directions. The findings show that gradient-based methods, particularly Grad-CAM, dominate the field due to their ease of integration with convolutional neural networks. At the same time, complementary approaches such as LIME, SHAP, and Integrated Gradients provide additional attribution insights. Evaluation practices remain heterogeneous, with a strong reliance on qualitative visual inspection and limited standardized quantitative frameworks. XAI contributes to interpretability primarily through spatial localization, feature attribution, and clinical decision support; however, challenges persist, including instability in explanations, coarse localization, high computational cost, and limited compatibility with transformer-based models. Overall, while XAI enhances transparency in cancer imaging, its clinical reliability remains constrained by methodological and technical limitations. Future work should focus on standardized evaluation, clinician-centered validation, and the development of robust, multimodal, and architecture-aware explainability frameworks.","url":"https://doi.org/10.3390/make8050134","authors":["Khairil Imran Ghauth","Yanche Ari Kustiawan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-20T09:12:41Z","doi":"10.3390/make8050134","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s10462-024-11040-6","name":"Specification overfitting in artificial intelligence","source":"crossref","abstract":"Abstract Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology’s potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting , a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.","url":"https://doi.org/10.1007/s10462-024-11040-6","authors":["Benjamin Roth","Pedro Henrique Luz de Araujo","Yuxi Xia","Saskia Kaltenbrunner","Christoph Korab"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-19T22:57:19Z","doi":"10.1007/s10462-024-11040-6","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s10462-025-11167-0","name":"Bibliometric analysis of artificial intelligence cyberattack detection models","source":"crossref","abstract":"Abstract Cybercriminals have increasingly adopted advanced and cutting-edge methods that expand the scale and speed of their attacks in recent years. This trend coincides with the rising demand for and scarcity of highly skilled cybersecurity specialists, making them both expensive and difficult to find. Recently, researchers have demonstrated the effectiveness of Artificial Intelligence (AI) approaches in combating sophisticated cyberattacks. However, comprehensive bibliometric data illustrating the study of AI approaches in cyberattack detection remain sparse. This study addresses this gap by investigating the current state of AI-based cyberattack detection research. The study analyzed the Scopus database using bibliometric analysis on a pool of over 2,338 articles published between 2014 and 2024, including 1217 journal articles, 828 conference papers, 121 conference reviews, 85 book chapters, 70 reviews, 5 editorials, and 2 books and short surveys. The study explores various AI-based cyberattack detection approaches globally, focusing on machine learning and deep learning algorithms. The bibliometric analysis was conducted using R, an open-source statistical tool, and Biblioshiny. The findings establish that AI, particularly machine learning and deep learning, enhances intrusion detection accuracy and is a growing research trend. Researchers have effectively employed these techniques for malware detection. The USA leads in AI cyberattack research, followed by India, China, Saudi Arabia, and Australia. Despite publishing fewer articles, Canada and Italy received significant citations. Additionally, strong research collaboration exists among the USA, China, Australia, Saudi Arabia, and India. Keyword analysis highlights AI’s effectiveness in identifying patterns and malicious behaviours, enhancing intrusion detection even in complex cyberattacks. Machine learning can detect intrusions based on anomalies caused by malicious or compromised devices, as well as unknown threats, with speed, accuracy, and a low false-positive rate.","url":"https://doi.org/10.1007/s10462-025-11167-0","authors":["Blessing Guembe","Sanjay Misra","Ambrose Azeta","Ines Lopez-Baldominos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-21T23:39:04Z","doi":"10.1007/s10462-025-11167-0","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(89)90071-4","name":"Logical foundations of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90071-4","authors":["Stephen W. Smoliar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90071-4","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/s0004-3702(98)00055-1","name":"Creativity and artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0004-3702(98)00055-1","authors":["Margaret A. Boden"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T10:46:00Z","doi":"10.1016/s0004-3702(98)00055-1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2021.102177","name":"A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports","source":"crossref","abstract":"Background and objectives The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task. In this work we present an alternative system for automatically recommending ICD-10 codes to be assigned to EHRs. Methods Our proposal is based on characterising ICD-10 codes by a set of keyphrases that represent them. These keyphrases do not only include those that have literally appeared in some EHR with the considered ICD-10 codes assigned, but also others that have been obtained by a statistical process able to capture expressions that have led the annotators to assign the code. Results The result is an information model that allows to efficiently recommend codes to a new EHR based on their textual content. We explore an approach that proves to be competitive with other state-of-the-art approaches and can be combined with them to optimise results. Conclusions In addition to its effectiveness, the recommendations of this method are easily interpretable since the phrases in an EHR leading to recommend an ICD-10 code are known. Moreover, the keyphrases associated with each ICD-10 code can be a valuable additional source of information for other approaches, such as machine learning techniques.","url":"https://doi.org/10.1016/j.artmed.2021.102177","authors":["Andres Duque","Hermenegildo Fabregat","Lourdes Araujo","Juan Martinez-Romo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-22T19:22:10Z","doi":"10.1016/j.artmed.2021.102177","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(93)90054-f","name":"Neural networks in artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90054-f","authors":["David S. Touretzky"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(93)90054-f","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2024.108627","name":"Research on medical insurance anti-gang fraud model based on the knowledge graph","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108627","authors":["Fangzheng Cheng","Chun Yan","Wei Liu","Xiangyun Lin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-27T11:57:50Z","doi":"10.1016/j.engappai.2024.108627","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1148/ryai.210284","name":"Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks","source":"crossref","abstract":"Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension-capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 Keywords: Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN).","url":"https://doi.org/10.1148/ryai.210284","authors":["Martin J. Willemink","Holger R. Roth","Veit Sandfort"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-02T13:56:20Z","doi":"10.1148/ryai.210284","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(93)90064-i","name":"Norvig's paradigms of artificial intelligence programming","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(93)90064-i","authors":["Wong JooFung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(93)90064-i","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.58532/v3bfai1p1ch2","name":"ARTIFICIAL INTELLIGENCE BASED MEDICAL SENSORS FOR HEALTH CARE","source":"crossref","abstract":"The traditional medical order has been disrupted by the older age populace and the presence of communicable disorders, greatly raising the strain on health maintenance as well as unfavorably disturbing the conservative system. Artificial intelligence (AI)-based medical sensors offer novel perspectives on how to gather information for modern medicine to track changes in the environment and people's health. The position of AI-equipped health detecting sensors for off-body illness, adjoining-body observing, sickness forecast, and medical verdict sustain systems is briefly reviewed in this paper, along with the ongoing difficulties and possible solutions for moving from concept to implementation. Development in the integration of clinical sensors as well as AI coding are anticipated to open the door to near the beginning recognition and medical verdict sustain as well as increase the accuracy and effectiveness of medical diagnosis in the very near future.","url":"https://doi.org/10.58532/v3bfai1p1ch2","authors":["Ms. N. Kavitha","Dr. P. Moniya","Ms. P. Palaniyammal","Ms. P. Shakthipriya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-10T06:01:32Z","doi":"10.58532/v3bfai1p1ch2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.5121/csit.2026.1601014","name":"AN ARTIFICIAL INTELLIGENCE SYSTEM/PROGRAM TO ASSIST AND REHABILITATE HUMAN ENERGY, DISORDERS, AND MEDICAL CONDITIONS USING ADAPTIVE SOUNDWAVE, BPM MATCHING, AND ACCURATE DATA ANALYSIS THROUGH MACHINE LEARNING","source":"crossref","abstract":"Neurological and psychiatric conditions including Alzheimer's disease, PTSD, depression, and schizophrenia affect hundreds of millions of people globally, yet existing pharmaceutical treatments are expensive, inconsistent, and out of reach for many. This paper proposes frequency-based music therapy, delivered through an AI-powered mobile application called Querey, as a clinically grounded and non-invasive alternative. Querey is built around three components: a state-based discovery survey, an adaptive AI coach, and a mood stimulation engine rooted in brainwave entrainment and BPM science [9]. Challenges included music licensing constraints, navigating App Store deployment as a first-time developer, and maintaining data accuracy across the personalization pipeline. Experiments showed a mean satisfaction score of 7.5 out of 10 for BPM accuracy, with calming prescriptions outperforming energizing ones, and a twelve-week clinical trial comparing Querey against live therapy and passive listening across diagnosed populations [1]. When technology is designed around how the brain actually works, the results speak for themselves.","url":"https://doi.org/10.5121/csit.2026.1601014","authors":["Kunqi Miao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-28T07:50:15Z","doi":"10.5121/csit.2026.1601014","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s44163-026-01399-6","name":"A systematic review of artificial intelligence, machine learning, and environment–social–governance in marketplace lending","source":"crossref","abstract":"Abstract The convergence of artificial intelligence (AI), machine learning (ML), and environmental, social, and governance (ESG) considerations has transformed financial decision-making. This transformation yields several advantages, including enhanced predictive accuracy, real-time fraud detection, expanded access for underserved populations, integration of sustainability metrics into credit models, and increased transparency and regulatory compliance. This systematic review addresses four research questions using the Antecedents-Decisions-Outcomes (ADO) framework to map, synthesize, and critically evaluate the AI/ML-ESG nexus within marketplace lending. Following PRISMA-2020 guidelines, 555 peer-reviewed studies published between January 2015 and December 2025 were identified from Scopus and Web of Science and analyzed through narrative synthesis. Methodological trends have shifted from statistical approaches (65% through 2017) to machine learning (2018–2021), deep learning (2020–2023), and, most recently, explainable AI with ESG integration (42% of studies published from 2024 onward). Based on descriptive comparison of individually reported results across heterogeneous studies, ensemble machine learning methods demonstrate superior performance (88–96% accuracy, AUC 0.88–0.96) and efficiency (0.5–4 h, approximately $8 per application) compared to traditional statistics (65–75% accuracy, 8–48 h, $45–125). Deep learning techniques also achieve high accuracy (85–95%), while the limited number of ESG-ML hybrid models report balanced sustainability objectives with robust performance (87–93%). However, ESG rating inconsistency across agencies (correlation 0.38–0.59) poses a critical challenge to the reliability of ESG-ML hybrid models, and the substantial heterogeneity across datasets, evaluation protocols, and geographic contexts limits the generalizability of cross-study performance comparisons. Six critical research gaps are identified: explainability, ESG standardization, real-time model adaptability, fairness, privacy, and alternative data validation. The findings indicate that the field is advancing toward responsible, transparent lending practices, though significant methodological and standardization challenges remain.","url":"https://doi.org/10.1007/s44163-026-01399-6","authors":["Jewel Kumar Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-18T08:09:02Z","doi":"10.1007/s44163-026-01399-6","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s10462-025-11332-5","name":"What fifty-one years of linguistics and artificial intelligence research tell us about their correlation: A scientometric analysis","source":"crossref","abstract":"There is a strong correlation between linguistics and artificial intelligence (AI), best manifested by deep learning language models. This study provides a thorough scientometric analysis of this correlation, synthesizing the intellectual production over 51 years, from 1974 to 2024. Web of Science Core Collection (WoSCC) database was the data source. The data collected were analyzed by two powerful software, viz., CiteSpace and VOSviewer, through which mapping visualizations of the intellectual landscape, trending issues and (re)emerging hotspots were generated. The results indicate that in the 1980s and 1990s, linguistics and AI (AIL) research was not robust, characterized by unstable publication over time. It has, however, witnessed a remarkable increase of publication since then, reaching 1478 articles in 2023, and 546 articles in January-March timespan in 2024, involving emerging issues including Natural language processing , Cross-sectional study , Using bidirectional encoder representation , and Using ChatGPT and hotspots such as Novice programmer , Prioritization , and Artificial intelligence , addressing new horizons, new topics, and launching new applications and powerful deep learning language models including ChatGPT. It concludes that linguistics and AI correlation is established at several levels, research centers, journals, and countries shaping AIL knowledge production and reshaping its future frontiers.","url":"https://doi.org/10.1007/s10462-025-11332-5","authors":["Mohammed Q. Shormani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T02:48:29Z","doi":"10.1007/s10462-025-11332-5","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.29057/mjmr.v12i24.12397","name":"In the future will artificial intelligence be able to replace doctors? -narrative review","source":"crossref","abstract":"The article provides a comprehensive narrative review of the role of artificial intelligence (AI) in the future of medicine, focusing on its potential to replace physicians in various clinical applications. AI technologies such as machine and deep learning, especially convolutional neural networks (CNNs), are explored in detail, and their applications in AI-assisted diagnosis in areas such as oncology, cardiology, and dentistry are discussed. Both advantages and disadvantages of AI in medicine are highlighted, including its ability to analyze large volumes of medical data and improve diagnostic accuracy, as well as ethical and practical challenges related to patient data protection and transparency in decision-making. Although AI shows great potential to transform medical care, it is concluded that it currently remains a support tool for clinicians and cannot completely replace clinical decision making. It highlights the importance of addressing the remaining challenges and continuing to research and develop new technologies to maximize the potential of AI in medicine.","url":"https://doi.org/10.29057/mjmr.v12i24.12397","authors":["Sergio David Pintado Brito"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T20:05:38Z","doi":"10.29057/mjmr.v12i24.12397","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1149/10701.0307ecst","name":"Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review","source":"crossref","abstract":"Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in computer-aided diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence techniques to identify thoracic diseases in medical images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria.","url":"https://doi.org/10.1149/10701.0307ecst","authors":["Shubhra Prakash","Ramamurthy B"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-29T17:03:50Z","doi":"10.1149/10701.0307ecst","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.66491","name":"Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis (Preprint)","source":"crossref","abstract":"BACKGROUND Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of lung sounds is a key diagnostic tool but is prone to subjective variability. The integration of artificial intelligence (AI) and machine learning (ML) with electronic stethoscopes offers a promising approach for automated and objective lung sound. OBJECTIVE This systematic review and meta-analysis assess the performance of ML models in pediatric lung sound analysis. The study evaluates the methodologies, model performance, and database characteristics while identifying limitations and future directions for clinical implementation. METHODS A systematic search was conducted in Medline via PubMed, Embase, Web of Science, OVID, and IEEE Xplore for studies published between January 1, 1990, and December 16, 2024. Inclusion criteria are as follows: studies developing ML models for pediatric lung sound classification with a defined database, physician-labeled reference standard, and reported performance metrics. Exclusion criteria are as follows: studies focusing on adults, cardiac auscultation, validation of existing models, or lacking performance metrics. Risk of bias was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies (version 2) framework. Data were extracted on study design, dataset, ML methods, feature extraction, and classification tasks. Bivariate meta-analysis was performed for binary classification tasks, including wheezing and abnormal lung sound detection. RESULTS A total of 41 studies met the inclusion criteria. The most common classification task was binary detection of abnormal lung sounds, particularly wheezing. Pooled sensitivity and specificity for wheeze detection were 0.902 (95% CI 0.726-0.970) and 0.955 (95% CI 0.762-0.993), respectively. For abnormal lung sound detection, pooled sensitivity was 0.907 (95% CI 0.816-0.956) and specificity 0.877 (95% CI 0.813-0.921). The most frequently used feature extraction methods were Mel-spectrogram, Mel-frequency cepstral coefficients, and short-time Fourier transform. Convolutional neural networks were the predominant ML model, often combined with recurrent neural networks or residual network architectures. However, high heterogeneity in dataset size, annotation methods, and evaluation criteria were observed. Most studies relied on small, single-center datasets, limiting generalizability. CONCLUSIONS ML models show high accuracy in pediatric lung sound analysis, but face limitations due to dataset heterogeneity, lack of standard guidelines, and limited external validation. Future research should focus on standardized protocols and the development of large-scale, multicenter datasets to improve model robustness and clinical implementation.","url":"https://doi.org/10.2196/preprints.66491","authors":["Ji Soo Park","Sa-Yoon Park","Jae Won Moon","Kwangsoo Kim","Dong In Suh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-18T20:05:06Z","doi":"10.2196/preprints.66491","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.33545/26638266.2025.v7.i2a.262","name":"The role of artificial intelligence in diagnosing glaucoma: A systematic review","source":"crossref","abstract":"Artificial intelligence applications in ophthalmology have proliferated rapidly, with glaucoma diagnosis representing a particularly active area of development given the disease's insidious progression and significant visual morbidity when untreated. This systematic review evaluated the diagnostic performance and clinical implementation of AI-based glaucoma detection systems across multiple French healthcare settings. We conducted a prospective multicenter assessment involving 847 patients undergoing glaucoma screening at two academic ophthalmology centers between February 2020 and October 2021. The primary research site was the Department of Ophthalmology at Hôpital des Quinze-Vingts in Paris, with the Centre Hospitalier Universitaire de Bordeaux serving as the secondary site. AI system performance demonstrated stage-dependent patterns, with sensitivity ranging from 78.4% for glaucoma suspects to 97.2% for severe disease. Overall accuracy reached 94.8% after 12 months of implementation, reflecting algorithmic learning from accumulated diagnostic feedback. Agreement between AI predictions and expert glaucoma specialists showed strong correlation at r=0.89, with 77.8% of cases demonstrating high agreement within 10 percentage points. The AI-assisted screening pathway reduced time to diagnosis by 66% compared to traditional approaches while maintaining diagnostic accuracy. Implementation challenges included integration with existing clinical workflows and the need for ongoing algorithm calibration. False positive rates declined from 11.8% initially to 4.7% by month 12, indicating continuous performance improvement. These findings support broader adoption of AI-assisted glaucoma screening, particularly in settings with limited specialist access, while emphasizing the importance of maintaining expert oversight for complex or borderline cases.","url":"https://doi.org/10.33545/26638266.2025.v7.i2a.262","authors":["Pierre Dubois","Claire Moreau"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-11T10:18:05Z","doi":"10.33545/26638266.2025.v7.i2a.262","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(86)90055-x","name":"Artificial intelligence applications for business management","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(86)90055-x","authors":["Mark Stefik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(86)90055-x","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(95)00039-h","name":"Artificial intelligence: an empirical science","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(95)00039-h","authors":["Herbert A. Simon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T22:20:38Z","doi":"10.1016/0004-3702(95)00039-h","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s12262-025-04365-1","name":"A Narrative Review of Artificial Intelligence in Medical Education of Surgical Oncology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s12262-025-04365-1","authors":["Hankui Hu","Zhoupeng Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-01T02:03:20Z","doi":"10.1007/s12262-025-04365-1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2020.103760","name":"Scoring and assessment in medical VR training simulators with dynamic time series classification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2020.103760","authors":["Neil Vaughan","Bogdan Gabrys"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-12T06:01:13Z","doi":"10.1016/j.engappai.2020.103760","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.4018/978-1-60566-174-2.ch003","name":"Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval","source":"crossref","abstract":"Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.","url":"https://doi.org/10.4018/978-1-60566-174-2.ch003","authors":["Gang Zhang","Z. M. Ma","Li Yan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-05-24T11:40:34Z","doi":"10.4018/978-1-60566-174-2.ch003","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.31729/jnma.5257","name":"Artificial Intelligence in Medical Science: Perspective from a Medical Student","source":"crossref","abstract":"N/A","url":"https://doi.org/10.31729/jnma.5257","authors":["Gaurab Mainali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-02T19:31:23Z","doi":"10.31729/jnma.5257","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.18203/2349-3933.ijam20230073","name":"Application of artificial intelligence in medical care: review of current status","source":"crossref","abstract":"Artificial intelligence (AI) has transformed almost all spheres of our life and has the potential to radically alter the field of health care. The increasing availability of healthcare data and rapid development of big data analytic methods has made possible the recent successful applications of AI in healthcare. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making. To date, many AI systems have been developed in healthcare, but use and adoption in clinical practice has been limited. In this article, we tried to review few of promising AI techniques and tools, which can have a great impact on our health care system and in turn on quality of life.","url":"https://doi.org/10.18203/2349-3933.ijam20230073","authors":["Hetal Pandya","Tanay Pandya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-24T07:53:14Z","doi":"10.18203/2349-3933.ijam20230073","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-981-95-3811-9_12","name":"Explainable Artificial Intelligence for Cardiovascular Risk Assessment: A Comparative Study of Interpretable Machine Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3811-9_12","authors":["Abdullah","Kamran Shaukat","Zulaikha Fatima","Tony Jan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T22:06:17Z","doi":"10.1007/978-981-95-3811-9_12","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2025.113244","name":"Auto-encoding clinical language for zero-shot medical report generation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113244","authors":["Yuena Jiang","Yanxun Chang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-20T16:29:30Z","doi":"10.1016/j.engappai.2025.113244","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/2041-210x.14044/v1/review1","name":"Review for \"An evaluation of platforms for processing camera‐trap data using artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/2041-210x.14044/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-29T16:02:35Z","doi":"10.1111/2041-210x.14044/v1/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.21203/rs.3.rs-3671314/v1","name":"Artificial Intelligence and exporting performance: Firm-level evidence from Portuga","source":"crossref","abstract":"Abstract The adoption of new digital technologies offer new opportunities and has the scope to engender positive effects on firms' expansion and success in international markets. This paper examine the main factors driving the adoption of Artificial Intelligence (AI) and AI-related digital technologies that enable the Industry 4.0 transformation and whether these new generation of digital technologies affect exporting performance at firm level. Using a rich and representative sample of Portuguese firms over the period 2014-2020, the estimated results suggest that firm's ex-ante performance, digital infrastructures and in-house ICT skills are the main drivers of digitalisation. However, conditional to ex-ante firm's performance, there are heterogenous effects on exporting performance across digital technologies and across industries. Moreover, there is evidence of positive selection towards large firms, casting doubts on the inclusiveness of the adoption process and the performance effects of AI and AI-related technologies. JEL Classi cation: L20, H81, L25","url":"https://doi.org/10.21203/rs.3.rs-3671314/v1","authors":["Natalia Barbosa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-11T11:35:03Z","doi":"10.21203/rs.3.rs-3671314/v1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2004.10.004","name":"Medical image compression using topology-preserving neural networks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2004.10.004","authors":["Anke Meyer-Bäse","Karsten Jancke","Axel Wismüller","Simon Foo","Thomas Martinetz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-15T19:01:49Z","doi":"10.1016/j.engappai.2004.10.004","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s12880-023-00965-z","name":"Correction: Value assessment of artificial intelligence in medical imaging: a scoping review","source":"crossref","abstract":"Background: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system.However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists.This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging.Methods: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature).Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted.An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes.Results: Seventy-nine studies were included out of 5890 identified articles.An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies.Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects.The frequency of mentioning a domain varied from 20 to 78% within the included papers.Only 15/86 studies were actual assessments of AI technologies.The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations.Conclusions: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains.The domain classification based on European assessment framework proved useful and current analysis added one new domain.Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.","url":"https://doi.org/10.1186/s12880-023-00965-z","authors":["Iben Fasterholdt","Mohammad Naghavi-Behzad","Benjamin S. B. Rasmussen","Tue Kjølhede","Mette Maria Skjøth","Malene Grubbe Hildebrandt","Kristian Kidholm"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-23T13:03:18Z","doi":"10.1186/s12880-023-00965-z","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.53396","name":"Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review (Preprint)","source":"crossref","abstract":"BACKGROUND In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. OBJECTIVE The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. METHODS A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. RESULTS Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. CONCLUSIONS These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.","url":"https://doi.org/10.2196/preprints.53396","authors":["Rawan AlSaad","Alaa Abd-alrazaq","Fadi Choucair","Arfan Ahmed","Sarah Aziz","Javaid Sheikh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-05T15:03:26Z","doi":"10.2196/preprints.53396","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.4016/22623.01","name":"Ethical Artificial Intelligence","source":"crossref","abstract":"A revolution in the understanding and implementation of an artificially intelligent virtuous computer concerning the recently issued U.S. patent entitled: Inductive Inference Affective Language Analyzer Simulating Artificial Intelligence (patent No. 6,587,846) by inventor/author John E. LaMuth M. S. As implied in its title, this innovation is the 1st affect- ive language analyzer incorporating ethical/motivational terms, serving in the role of interactive computer interface. It enables a computer to reason and speak in an","url":"https://doi.org/10.4016/22623.01","authors":["John Lamuth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-09-03T15:55:51Z","doi":"10.4016/22623.01","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/s0933-3657(98)00038-4","name":"A development environment for knowledge-based medical applications on the world-wide web","source":"crossref","abstract":"The World-Wide Web (WWW) is increasingly being used as a platform to develop distributed applications, particularly in contexts, such as medical ones, where high usability and availability are required. In this paper we propose a methodology for the development of knowledge-based medical applications on the web, based on the use of an explicit domain ontology to automatically generate parts of the system. We describe a development environment, centred on the LISPWEB Common Lisp HTTP server, that supports this methodology, and we show how it facilitates the creation of complex web-based applications, by overcoming the limitations that normally affect the adequacy of the web for this purpose. Finally, we present an outline of a system for the management of diabetic patients built using the LISPWEB environment.","url":"https://doi.org/10.1016/s0933-3657(98)00038-4","authors":["A. Riva","R. Bellazzi","G. Lanzola","M. Stefanelli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T19:37:38Z","doi":"10.1016/s0933-3657(98)00038-4","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(89)90072-6","name":"Logical foundations of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(89)90072-6","authors":["John F. Sowa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0004-3702(89)90072-6","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.36227/techrxiv.19563262.v1","name":"Artificial Intelligence and Blockchain Driven Beyond 5G Networks: A Review","source":"crossref","abstract":"Swift evolution of novel computing and communication technologies in beyond 5G networks (B5G) opens up the possibilities for advanced techniques to tackle various issues which can not be solved by the existing frameworks. As the number of devices continuously increase, blockchain can provide a secure platform for communication among all the users in the network. Moreover, along with the security, blockchain requires low computation and also provides fast network response. Furthermore, artificial intelligence enhances the ability of devices to learn and construct knowledge about dynamic wireless environments. Recently, a lot of researchers have shown interest in integration of both the platforms to solve the complex problems of B5G networks. This work reviews the application of both the technologies in various networks related problems recently completed by the authors. The work also discusses various possible research issues that can be handled by the integration of both platforms.","url":"https://doi.org/10.36227/techrxiv.19563262.v1","authors":["Nitin Gupta","Uttam Ghosh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-13T01:25:12Z","doi":"10.36227/techrxiv.19563262.v1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/2050-7038.13020/v1/review2","name":"Review for \"Privacy boundary determination of smart meter data using an artificial intelligence adversary\"","source":"crossref","abstract":"Get your research seenMake an impact with these nine promotional tools. SEO• Use relevant keywords to make your title and abstract clear and easy to search for.• Off-page SEO strategies, like link building, can help get your paper seen. Conferences• Whether you're networking informally or presenting, think about some simple messages to promote your work.","url":"https://doi.org/10.1002/2050-7038.13020/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-15T17:02:46Z","doi":"10.1002/2050-7038.13020/v1/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/jph.70084/v1/review2","name":"Review for \"Artificial Intelligence and Plant Disease Management: An Agro-Innovative Approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/jph.70084/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:07:48Z","doi":"10.1111/jph.70084/v1/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.32388/nd4x65","name":"Review of: \"Metacognition and Pedagogy in the Era of Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/nd4x65","authors":["David López-Villanueva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-02T08:05:04Z","doi":"10.32388/nd4x65","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2024.108059","name":"MixSegNet: Fusing multiple mixed-supervisory signals with multiple views of networks for mixed-supervised medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2024.108059","authors":["Ziyang Wang","Chen Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-24T06:49:05Z","doi":"10.1016/j.engappai.2024.108059","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0933-3657(90)90008-f","name":"Expertext for medical care and literature retrieval","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(90)90008-f","authors":["Roy Rada","Judith Barlow","Pieter Zanstra","Pieter de Vries Robbe","Djujan Bijstra","Jan Potharst"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(90)90008-f","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-3-030-92087-6_35","name":"Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_35","authors":["Tiziano Passerini","Yitong Yang","Teodora Chitiboi","John N. Oshinski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_35","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2007.07.005","name":"Evaluation of rule interestingness measures in medical knowledge discovery in databases","source":"crossref","abstract":"Objective We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. Methods and materials We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical expert's interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. Results and conclusion The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.","url":"https://doi.org/10.1016/j.artmed.2007.07.005","authors":["Miho Ohsaki","Hidenao Abe","Shusaku Tsumoto","Hideto Yokoi","Takahira Yamaguchi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2007-09-12T07:21:58Z","doi":"10.1016/j.artmed.2007.07.005","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.15407/jai2025.03.078","name":"Artificial Intelligence-Based Decision Support Systems for Predictive Maintenance and Diagnostics of Medical Devices and Equipment","source":"crossref","abstract":"The article is devoted to the development and scientific justification of an integrated conceptual model of artificial intelligence (AI)-driven decision support systems for predictive maintenance and diagnostics of medical devices. It critically analyzes the underlying causes of the low efficiency of existing quality management systems (QMS) in the healthcare sector, which hinder maintaining the proper technical condition of equipment, ensuring patient safety, and optimizing costs. Enhanced metrics and criteria are proposed for proactive assessment of QMS reliability and performance, integrating classical indicators (reliability, operational availability, MTBF, MTTR) with modern condition indicators (Health Index, HI, Remaining Useful Life, RUL). Particular attention is paid to the potential of Predictive Maintenance (PdM), the Internet of Medical Things (IoMT), and big data for creating continuous telemetry streams that enable accurate diagnostics and early fault detection. The role of Digital Twins (DT) is substantiated as an operational digital layer for real-time decision support, improved medical equipment readiness, and maintenance scenario modeling. It is demonstrated how the integration of AI/Machine Learning (ML) with DT enables a closed-loop “data → model → decision” cycle, contributing to increased patient safety, asset management transparency, and product lifecycle management (PLM) optimization. Ethical, regulatory, and organizational challenges of implementation are addressed, including data protection, KPI standardization, algorithm transparency (DECIDE-AI), and workforce training. The obtained results form the foundation of a roadmap for the digital transformation of maintenance in healthcare and create prerequisites for managed quality of medical services","url":"https://doi.org/10.15407/jai2025.03.078","authors":["Kovalevskyy S"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-14T07:59:51Z","doi":"10.15407/jai2025.03.078","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(85)90004-9","name":"Machine learning: An artificial intelligence approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(85)90004-9","authors":["Kurt VanLehn"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(85)90004-9","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.21037/jmai-22-35","name":"Artificial intelligence and clinical stability after the Norwood operation","source":"crossref","abstract":"Background: Postoperative management of the neonate following the Norwood operation is among the most complex and challenging in pediatric critical care and high mortality remains. Artificial intelligence (AI) is poised to assist in monitoring of this complex population to improve clinical care, evaluation and outcomes. Methods: In a dedicated Pediatric Cardiac Intensive Care Unit in a quaternary Children’s Hospital, a convolutional neural network (CNN) model was developed and trained on electrocardiogram (ECG) waveforms from 45 neonates after the Norwood procedure. Waveforms from the first two postoperative days (critical) and the day prior to transfer from the intensive care unit (ICU) (stable) were used for training. The model was evaluated on a separate cohort of 10 neonates following the Norwood procedure. Models were compared to traditional machine learning algorithms on non-waveform data, and then combined in a final model. Retrospective clinical observation scoring was completed for comparison. Results: The CNN model yielded an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.97 (±0.02). The final model combining the CNN, random forest (RF) on vital signs, and logistic regression achieved an AUC-ROC of 0.98 (±0.02) and an AUC of precision recall (AUC-PR) of 0.97 (±0.04) for distinguishing critical from stable. Clinical observations to assess patient stability agreed with the final model 78% of the time. This suggests that opportunities exist to improve the assessment of overall clinical state through the implementation of an AI based data monitoring tool. Conclusions: This novel, combined AI models can accurately detect changes in clinical status as patients progress from critically ill to stable following the Norwood procedure. This work provides the basis of a novel bedside monitoring tool and suggests new ways AI may influence clinical care beyond predicting deterioration events.","url":"https://doi.org/10.21037/jmai-22-35","authors":["Alaa Aljiffry","Yanbo Xu","Shenda Hong","Justin B. Long","Jimeng Sun","Kevin O. Maher"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-20T09:51:55Z","doi":"10.21037/jmai-22-35","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-3-030-92087-6_22","name":"Cardiac CT Guidelines and Clinical Applications: Where Does Artificial Intelligence Fit In?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_22","authors":["Livia Marchitelli","Federica Catapano","Giulia Cundari","Marco Francone"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_22","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.13052/rp-9788770041010","name":"Advancing Edge Artificial Intelligence","source":"crossref","abstract":"The \"River Publishers Series in Communications and Networking\" is a series of comprehensive academic and professional books which focus on communication and network systems.Topics range from the theory and use of systems involving all terminals, computers, and information processors to wired and wireless networks and network layouts, protocols, architectures, and implementations.Also covered are developments stemming from new market demands in systems, products, and technologies such as personal communications services, multimedia systems, enterprise networks, and optical communications.The series includes research monographs, edited volumes, handbooks and textbooks, providing professionals, researchers, educators, and advanced students in the field with an invaluable insight into the latest research and developments.","url":"https://doi.org/10.13052/rp-9788770041010","authors":["Ovidiu Vermesan","Dave Marples"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-10T13:25:30Z","doi":"10.13052/rp-9788770041010","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0954-1810(89)90017-4","name":"BASIC artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(89)90017-4","authors":["K.J. MacCallum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0954-1810(89)90017-4","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.46233","name":"Correction: Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review (Preprint)","source":"crossref","abstract":"UNSTRUCTURED","url":"https://doi.org/10.2196/preprints.46233","authors":["Alaa Abd-alrazaq","Rawan AlSaad","Sarah Aziz","Arfan Ahmed","Kerstin Denecke","Mowafa Househ","Faisal Farooq","Javaid Sheikh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-02T19:44:22Z","doi":"10.2196/preprints.46233","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(75)90023-5","name":"Artificial intelligence and simulation of behaviour summer conference 1976","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(75)90023-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(75)90023-5","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/s0933-3657(99)00030-5","name":"An object-oriented design for automated navigation of semantic networks inside a medical data dictionary","source":"crossref","abstract":"In this paper we present a data dictionary server for the automated navigation of information sources. The underlying knowledge is represented within a medical data dictionary. The mapping between medical terms and information sources is based on a semantic network. The key aspect of implementing the dictionary server is how to represent the semantic network in a way that is easier to navigate and to operate, i.e. how to abstract the semantic network and to represent it in memory for various operations. This paper describes an object-oriented design based on Java that represents the semantic network in terms of a group of objects. A node and its relationships to its neighbors are encapsulated in one object. Based on such a representation model, several operations have been implemented. They comprise the extraction of parts of the semantic network which can be reached from a given node as well as finding all paths between a start node and a predefined destination node. This solution is independent of any given layout of the semantic structure. Therefore the module, called Giessen Data Dictionary Server can act independent of a specific clinical information system. The dictionary server will be used to present clinical information, e.g. treatment guidelines or drug information sources to the clinician in an appropriate working context. The server is invoked from clinical documentation applications which contain an infobutton. Automated navigation will guide the user to all the information relevant to her/his topic, which is currently available inside our closed clinical network.","url":"https://doi.org/10.1016/s0933-3657(99)00030-5","authors":["W. Ruan","T. Bürkle","J. Dudeck"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T23:37:38Z","doi":"10.1016/s0933-3657(99)00030-5","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-981-99-8441-1_18","name":"Applications of Artificial Intelligence in Ultrasound Medicine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8441-1_18","authors":["Hui-Xiong Xu","Yu-Ting Shen","Bo-Yang Zhou","Chong-Ke Zhao","Yi-Kang Sun","Li-Fan Wan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T00:02:50Z","doi":"10.1007/978-981-99-8441-1_18","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.21037/jmai-24-251","name":"Development and validation of a data visualization dashboard for automatic pain assessment and artificial intelligence analyses in cancer patients","source":"crossref","abstract":"Background: Pain is one of the most common and debilitating symptoms in cancer patients. Despite accurate assessment is fundamental for pain treatment, unidimensional and multidimensional subjective instruments have important limitations. This article aims to introduce a dashboard designed for multimodal data collection and visualization, which is essential for developing artificial intelligence (AI) models for automatic pain assessment (APA) in cancer pain. Methods: Functional and non-functional prerequisites were integrated. Concerning non-functional prerequisites, the dashboard was developed as a web app. For the creation of the mock-ups, the Figma web app was implemented. Shneiderman’s eight golden rules and Nielsen’s 10 heuristics were followed for interface design. Subsequently, a usability test was conducted by engaging 5 clinicians. Results: The dashboard was developed. The average success rate of the usability test was 80%. No major usability issues were identified. One user reported difficulties in task execution. Another user completed all tasks within the allotted time, except for the task of adding a new drug to the system. The feedback analysis revealed a lack of experience with computer systems. Potential solutions include introducing an initial tutorial for less experienced users and making the relevant fields more clearly visible. Conclusions: Since the use of AI-powered APA techniques is still in its infancy, further developments are needed for their widespread implementation in clinical practice. Data collection is the key step for these developments as it can act as ground truth for the training of automated systems. This user-friendly graphical interface can be utilized to design high-performance AI models for personalized pain management.","url":"https://doi.org/10.21037/jmai-24-251","authors":["Francesco Cutugno","Marco Cascella"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-23T07:09:28Z","doi":"10.21037/jmai-24-251","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2016.08.003","name":"Executable medical guidelines with Arden Syntax—Applications in dermatology and obstetrics","source":"crossref","abstract":"Introduction Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge. One of these languages is Arden Syntax for Medical Logic Systems, an International Health Level Seven (HL7) standard whose development started in 1989. Its latest version is 2.10, which was presented in 2014. In the present report we discuss Arden Syntax as a modern medical knowledge representation and processing language, and show that this language is not only well suited to define clinical alerts, reminders, and recommendations, but can also be used to implement and process computerized medical practice guidelines. Methods This section describes how contemporary software such as Java, server software, web-services, XML, is used to implement CDSSs based on Arden Syntax. Special emphasis is given to clinical decision support (CDS) that employs practice guidelines as its clinical knowledge base. Results Two guideline-based applications using Arden Syntax for medical knowledge representation and processing were developed. The first is a software platform for implementing practice guidelines from dermatology. This application employs fuzzy set theory and logic to represent linguistic and propositional uncertainty in medical data, knowledge, and conclusions. The second application implements a reminder system based on clinically published standard operating procedures in obstetrics to prevent deviations from state-of-the-art care. A to-do list with necessary actions specifically tailored to the gestational week/labor/delivery is generated. Discussion Today, with the latest versions of Arden Syntax and the application of contemporary software development methods, Arden Syntax has become a powerful and versatile medical knowledge representation and processing language, well suited to implement a large range of CDSSs, including clinical-practice-guideline-based CDSSs. Moreover, such CDS is provided and can be shared as a service by different medical institutions, redefining the sharing of medical knowledge. Arden Syntax is also highly flexible and provides developers the freedom to use up-to-date software design and programming patterns for external patient data access.","url":"https://doi.org/10.1016/j.artmed.2016.08.003","authors":["Alexander Seitinger","Andrea Rappelsberger","Harald Leitich","Michael Binder","Klaus-Peter Adlassnig"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-08-12T17:45:25Z","doi":"10.1016/j.artmed.2016.08.003","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2004.07.001","name":"Applying spatial distribution analysis techniques to classification of 3D medical images","source":"crossref","abstract":"Objective The objective of this paper is to classify 3D medical images by analyzing spatial distributions to model and characterize the arrangement of the regions of interest (ROIs) in 3D space. Methods and material Two methods are proposed for facilitating such classification. The first method uses measures of similarity, such as the Mahalanobis distance and the Kullback-Leibler (KL) divergence, to compute the difference between spatial probability distributions of ROIs in an image of a new subject and each of the considered classes represented by historical data (e.g., normal versus disease class). A new subject is predicted to belong to the class corresponding to the most similar dataset. The second method employs the maximum likelihood (ML) principle to predict the class that most likely produced the dataset of the new subject. Results The proposed methods have been experimentally evaluated on three datasets: synthetic data (mixtures of Gaussian distributions), realistic lesion-deficit data (generated by a simulator conforming to a clinical study), and functional MRI activation data obtained from a study designed to explore neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). Conclusion Performed experiments demonstrated that the approaches based on the KL divergence and the ML method provide superior accuracy compared to the Mahalanobis distance. The later technique could still be a method of choice when the distributions differ significantly, since it is faster and less complex. The obtained classification accuracy with errors smaller than 1% supports that useful diagnosis assistance could be achieved assuming sufficiently informative historic data and sufficient information on the new subject.","url":"https://doi.org/10.1016/j.artmed.2004.07.001","authors":["Dragoljub Pokrajac","Vasileios Megalooikonomou","Aleksandar Lazarevic","Despina Kontos","Zoran Obradovic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-12-14T10:22:01Z","doi":"10.1016/j.artmed.2004.07.001","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3403/30397412u","name":"Information technology � Artificial intelligence � Overview of trustworthiness in artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30397412u","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-01T20:30:14Z","doi":"10.3403/30397412u","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.4271/j3312_202502","name":"Artificial Intelligence Use Cases for Ground Vehicle Applications","source":"crossref","abstract":"&lt;div class=\"section abstract\"&gt; &lt;div class=\"htmlview paragraph\"&gt;This SAE Technical Information Report identifies use cases for AI technology applications to ground vehicles and transportation infrastructure. Whenever applicable, functional definitions and noted issues and concerns are provided in consistent with the current industry mobility practices and published peer-reviewed literature.&lt;/div&gt;&lt;/div&gt;","url":"https://doi.org/10.4271/j3312_202502","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-28T21:15:49Z","doi":"10.4271/j3312_202502","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/s0933-3657(00)00092-0","name":"Mining association rules with improved semantics in medical databases","source":"crossref","abstract":"The discovery of new knowledge by mining medical databases is crucial in order to make an effective use of stored data, enhancing patient management tasks. One of the main objectives of data mining methods is to provide a clear and understandable description of patterns held in data. We introduce a new approach to find association rules among quantitative values in relational databases. The semantics of such rules are improved by introducing imprecise terms in both the antecedent and the consequent, as these terms are the most commonly used in human conversation and reasoning. The terms are modeled by means of fuzzy sets defined in the appropriate domains. However, the mining task is performed on the precise data. These \"fuzzy association rules\" are more informative than rules relating precise values. We also introduce a new measure of accuracy, based on Shortliffe and Buchanan's certainty factors [Shortliffe E, Buchanan B. Math Biosci 1975;23:351-79]. Also, the semantics of the usual measure of usefulness of an association rule, called support are discussed and some new criteria are introduced. Our new measures have been shown to be more understandable and appropriate than ordinary ones. Several experiments on large medical databases show that our new approach can provide useful knowledge with better semantics in this field.","url":"https://doi.org/10.1016/s0933-3657(00)00092-0","authors":["Miguel Delgado","Daniel Sánchez","Marı́a J Martı́n-Bautista","Marı́a-Amparo Vila"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T18:16:42Z","doi":"10.1016/s0933-3657(00)00092-0","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2025.111028","name":"A diffusion model based on multi-scale spatial Mamba for medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.111028","authors":["Chun Li","Qiule Sun","Muqing Zhang","Jianxin Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-21T06:04:48Z","doi":"10.1016/j.engappai.2025.111028","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.53656/math2024-6-7-art","name":"Artificial Intelligence in Cybersecurity: Rigorous Critical Review, Methodological Challenges and Future Research Directions","source":"crossref","abstract":"This paper offers a critical review of the applications of AI in cybersecurity, focusing on the recent trends of automation in threat detection, enhancement of response strategies, and prediction of vulnerabilities. The methodology is based on a thorough analysis of empirical studies up to 2021 as per the efficiency of AI malware detection, insider threat identification, and mitigation of zero-day vulnerabilities. In particular, machine learning- and deep learning-based methodologies of artificial intelligence ensure clear advantages over conventional models concerning the precision in detection and reduction of false positives. However, challenges persist regarding explainability, scalability, and ethical concerns around data bias and quality. Finally, this paper concludes by pointing out some areas of future research with regard to needing XAI techniques and methods related to bias reduction to establish better trust in the efficacy of AI-driven cybersecurity frameworks.","url":"https://doi.org/10.53656/math2024-6-7-art","authors":["Maria Mpitsi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-09T06:37:45Z","doi":"10.53656/math2024-6-7-art","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.61955/lobupo","name":"Driving library service delivery by the instrumentality of artificial intelligence: A scoping review","source":"crossref","abstract":"Libraries are saddled with the responsibility of providing information resources and services to their users, considering their preference for more digital content as well as quick and unrestricted access. Artificial Intelligence (AI) has emerged as a transformative tool in driving library service delivery, offering unprecedented opportunities to enhance efficiency with great potential for providing high-quality library services through effectively handling user inquiries, providing instant assi","url":"https://doi.org/10.61955/lobupo","authors":["Nwobu, Nwobu,"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-03T04:21:55Z","doi":"10.61955/lobupo","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1109/ictai59109.2023.00018","name":"Rule-Augmented Artificial Intelligence-empowered Systems for Medical Diagnosis using Large Language Models","source":"crossref","abstract":"In this paper, we investigate the enhancement of Artificial Intelligence (AI) technologies in healthcare and the better understanding of medical literature with the use of Large Language Models (LLMs) and Natural Language Processing (NLP). Specifically, we introduce a rule-augmented AI-empowered system which incorporates a rule-based decision system, the ChatGPT application programming interface (API), and other external machine learning and analytical APIs to offer diagnostic suggestions to patients. The complexities of patient healthcare experiences, including doctor-patient interactions, understanding levels, treatment procedures, and preventive care, are considered. We illustrate how a diagnostic process typically integrates various strategies depending on various factors. To digitize the greatest portion of the process, we propose and illustrate the use of LLMs for humanizing the communication process and investigating ways to reduce burdens and costs in primary healthcare. We also outline a theoretical decision model for evaluating the use of technological components from external sources versus building them from scratch. The paper is structured into sections detailing background theories and context, our proposed and implemented rule-augmented AI-empowered system, as well as a system test in a corresponding use case. Finally, the paper key findings are presented, which contribute valuable insights for future work in this field.","url":"https://doi.org/10.1109/ictai59109.2023.00018","authors":["Dimitrios P. Panagoulias","Filippos A. Palamidas","Maria Virvou","George A. Tsihrintzis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-20T19:19:02Z","doi":"10.1109/ictai59109.2023.00018","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.63383/bkti7090","name":"Reshaping Business With Artificial Intelligence","source":"crossref","abstract":"Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.","url":"https://doi.org/10.63383/bkti7090","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-18T13:39:42Z","doi":"10.63383/bkti7090","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.52783/pst.2371","name":"A Systematic Review of Artificial Intelligence Integration in Management Information Systems","source":"crossref","abstract":"Introduction: Introduction: The integration of artificial intelligence (AI) within management information systems (MIS) has become a significant focus in modern organizational research and practice. This systematic review consolidates existing knowledge on this topic, highlighting current trends, challenges, and research gaps. Problem Statement: As MIS play a crucial role in organizational decision-making and operational efficiency, understanding the implications, opportunities, and challenges of AI integration is increasingly important. However, the existing literature lacks a comprehensive overview of findings in this field. Objective: This study aims to systematically examine current literature on AI integration in MIS to (1) identify central themes and trends, (2) review methodologies applied, (3) evaluate key findings and impacts, and (4) suggest future research directions and practical applications. Methodology: The study adopts a systematic literature review approach, including the systematic selection, screening, and analysis of relevant academic articles from established databases. Defined inclusion criteria will ensure relevance and rigor among selected studies, while data extraction and synthesis will provide a comprehensive analysis of identified research. Results: This review will offer insights into the current state of AI integration in MIS, outlining key themes, common methodologies, technological advances, organizational impacts, and literature gaps. Conclusion: By synthesizing existing research, this systematic review will enhance understanding of AI integration in MIS, identify research gaps, and suggest future directions for both academic and practical applications. Findings will offer valuable insights for researchers, practitioners, and policymakers on the potential and challenges of using AI within MIS. DOI : https://doi.org/10.52783/pst.2371","url":"https://doi.org/10.52783/pst.2371","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-05T09:41:25Z","doi":"10.52783/pst.2371","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2022.105004","name":"MF2-Net: A multipath feature fusion network for medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2022.105004","authors":["Nagaraj Yamanakkanavar","Bumshik Lee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-10T18:52:49Z","doi":"10.1016/j.engappai.2022.105004","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2021.102020","name":"A survey of deep learning models in medical therapeutic areas","source":"crossref","abstract":"Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.","url":"https://doi.org/10.1016/j.artmed.2021.102020","authors":["Alberto Nogales","Álvaro J. García-Tejedor","Diana Monge","Juan Serrano Vara","Cristina Antón"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-01-18T01:29:02Z","doi":"10.1016/j.artmed.2021.102020","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1039/d4dd00318g/v2/review2","name":"Review for \"Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d4dd00318g/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-21T16:17:26Z","doi":"10.1039/d4dd00318g/v2/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.32388/yhpkp7","name":"Review of: \"Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/yhpkp7","authors":["Biljana Rondovic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-18T11:12:36Z","doi":"10.32388/yhpkp7","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/coa.14305/v1/review1","name":"Review for \"Can Artificial Intelligence Software be Utilised for Thyroid Multi‐Disciplinary Team Outcomes?\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/coa.14305/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-21T17:04:03Z","doi":"10.1111/coa.14305/v1/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.32388/siwf32","name":"Review of: \"Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/siwf32","authors":["Marcin Pełka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-03T15:20:13Z","doi":"10.32388/siwf32","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1039/d6tb00696e/v1/review1","name":"Review for \"Multimodal Health Monitoring and Theranostics Based on Functionalized Hydrogels and Artificial Intelligence\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d6tb00696e/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-10T21:05:57Z","doi":"10.1039/d6tb00696e/v1/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1177/10732748251343245/v1/review2","name":"Review for \"Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer\"","source":"crossref","abstract":"","url":"https://doi.org/10.1177/10732748251343245/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-25T06:28:41Z","doi":"10.1177/10732748251343245/v1/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.engappai.2025.110583","name":"Class imbalance-aware domain specific transfer learning approach for medical image classification: Application on COVID-19 detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.110583","authors":["Marut Jindal","Birmohan Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-21T13:43:21Z","doi":"10.1016/j.engappai.2025.110583","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ejrai.2025.100051","name":"Reframing AI evaluation: From performance metrics to clinical impact","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ejrai.2025.100051","authors":["Somayeh Farahani","Sidong Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-05T06:04:55Z","doi":"10.1016/j.ejrai.2025.100051","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1201/9781003315476-12","name":"Artificial Intelligence–Enabled Wearable ECG for Elderly Patients","source":"crossref","abstract":"ECG monitoring system value chain research and analysis helps to understand the value and contribution of each process in the system, the best practices that each process can adopt, and the goal of the whole system to ensure better diagnosis of disease goal. The ECG monitoring value chain includes a series of general processes such as data collection, preprocessing, feature extraction, processing, analysis, and visualization. Artificial intelligence could therefore become a great help. It will undoubtedly enable them, in the near future, to provide better care for people who have undergone ECGs due to suspected fibrillation. Atrial—or auricular—fibrillation affects 1% of the population, but especially the elderly. It is defined by anarchic and rapid electrical activity of the atria (upper chambers of the heart) and it results in their disordered and inefficient contraction.","url":"https://doi.org/10.1201/9781003315476-12","authors":["Nizar Sakli","Chokri Baccouch","Ben Othman Soufiene","Chinmay Chakraborty","Sakli Hedi","Mustapha Najjari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-13T08:59:26Z","doi":"10.1201/9781003315476-12","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1111/jph.70084/v2/review2","name":"Review for \"Artificial Intelligence and Plant Disease Management: An Agro-Innovative Approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1111/jph.70084/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-01T00:07:48Z","doi":"10.1111/jph.70084/v2/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s12880-022-00918-y","name":"Value assessment of artificial intelligence in medical imaging: a scoping review","source":"crossref","abstract":"Abstract Background Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. Methods We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. Results Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. Conclusions This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.","url":"https://doi.org/10.1186/s12880-022-00918-y","authors":["Iben Fasterholdt","Mohammad Naghavi-Behzad","Benjamin S. B. Rasmussen","Tue Kjølhede","Mette Maria Skjøth","Malene Grubbe Hildebrandt","Kristian Kidholm"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-31T10:02:56Z","doi":"10.1186/s12880-022-00918-y","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.46482","name":"Artificial Intelligence in Medical Education: Comparative Analysis of ChatGPT, Bing, and Medical Students in Germany (Preprint)","source":"crossref","abstract":"BACKGROUND Large language models (LLMs) have demonstrated significant potential in diverse domains, including medicine. Nonetheless, there is a scarcity of studies examining their performance in medical examinations, especially those conducted in languages other than English, and in direct comparison with medical students. Analyzing the performance of LLMs in state medical examinations can provide insights into their capabilities and limitations and evaluate their potential role in medical education and examination preparation. OBJECTIVE This study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate their performance relative to that of medical students. METHODS The LLMs were assessed on a total of 630 questions from the spring and fall German Medical State Examinations of 2022. The performance was evaluated with and without media-related questions. Statistical analyses included 1-way ANOVA and independent samples &lt;i&gt;t&lt;/i&gt; tests for pairwise comparisons. The relative strength of the LLMs in comparison with that of the students was also evaluated. RESULTS GPT-4 achieved the highest overall performance, correctly answering 88.1% of questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The students had an average correct answer rate of 74.6%. Both GPT-4 and Bing significantly outperformed the students in both examinations. When media questions were excluded, Bing achieved the highest performance of 90.7%, closely followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a significant decline in the performance of GPT-4 and Bing in the fall 2022 examination, which was attributed to a higher proportion of media-related questions and a potential increase in question difficulty. CONCLUSIONS LLMs, particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical education and for pretesting examination questions. Their high performance, even relative to that of medical students, indicates promising avenues for further development and integration into the educational and clinical landscape.","url":"https://doi.org/10.2196/preprints.46482","authors":["Jonas Roos","Adnan Kasapovic","Tom Jansen","Robert Kaczmarczyk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-04T13:15:39Z","doi":"10.2196/preprints.46482","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/s10489-024-05849-5","name":"Symmetric perception and ordinal regression for detecting scoliosis natural image","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10489-024-05849-5","authors":["Xiaojia Zhu","Rui Chen","Xiaoqi Guo","Zhiwen Shao","Yuhu Dai","Ming Zhang","Chuandong Lang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-16T08:31:06Z","doi":"10.1007/s10489-024-05849-5","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106558","name":"From internal accuracy to clinical readiness: a systematic review of artificial intelligence-based mental health studies in Southeast Asia","source":"crossref","abstract":"Background Artificial intelligence (AI) is increasingly applied to mental health research, but internal performance does not establish clinical or public health readiness. Objective To synthesise this regional AI literature and assess validation, reporting transparency, reproducibility, implementation-readiness, and clinical readiness. Methods Scopus, PubMed, and IEEE Xplore were searched from inception to March 26, 2026. Eligible records were English-language peer-reviewed articles or conference proceedings applying AI, machine learning, or deep learning to mental health or psychiatric outcomes. Results Ninety-nine studies were included. Evidence was concentrated in Indonesia, Malaysia, and Thailand, and more than half addressed depression-spectrum conditions. Studies mainly used questionnaires, clinical-tabular data, social media, or digital-trace data for classification, detection, or severity stratification. Validation was almost entirely internal; no study reported external or independent validation. Calibration, uncertainty, error analysis, fairness or bias assessment, code or data availability, and implementation context were rarely reported. No study met the criteria for high clinical readiness. Conclusions The regional evidence base is expanding, but remains concentrated at the model-development and internal-validation stages. Future work should prioritise representative datasets, transparent reporting, privacy-preserving external validation across settings and languages, and evaluation linked to mental health services or public health workflows. The proposed clinical translation roadmap supports the development of reproducible, workflow-aligned, and clinically meaningful AI applications.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106558","authors":["Erwin Yudi Hidayat","Azah Kamilah Muda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T16:35:40Z","doi":"10.1016/j.ijmedinf.2026.106558","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1186/s12880-025-02118-w","name":"Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges","source":"crossref","abstract":"Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency and trustworthiness, as well as for developing AI-based diagnostic systems in medical imaging to achieve clinical acceptability and reliability. This synthesis aligns with the current context of XAI in medical imaging on four key dimensions: trends in techniques, their application to clinical use cases or with human subjects, and the associated problems. In radiology and pathology, we report on image analysis based on current literature from credible databases. This review extends on the existing surveys by explicitly addressing the focus on feature selection (FS), graph neural networks (GNNs), and multimodal transformers and combining them to a cohesive XAI taxonomy and matches the techniques to the specific impact points in the clinical workflow in radiology and pathology. We present saliency maps, attention mechanisms, and gradient-based and rule-based elucidations of deep learning (DL) models in today’s healthcare environment. Finally, the results show that XAI significantly enhances overall clinical decision-making by making the high-level reasoning of the model readily available to users, thereby increasing their confidence in clinical decisions despite the remaining obstacles, including standardization, interpretability, data bias, and complicated data integration. From 980 records, 289 duplicates were removed, 691 screened, 209 excluded, 482 full texts assessed, 263 excluded with 219 yielding 133 included studies. Exploring this third dimension within an XAI environment identifies research gaps and paves the way for robust medical imaging XAI solutions.Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12880-025-02118-w","authors":["Fahad Ahmed","Naila Sammar Naz","Sunawar Khan","Ateeq Ur Rehman","Waleed M. Ismael","Muhammad Adnan Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-05T18:51:53Z","doi":"10.1186/s12880-025-02118-w","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3403/30491581","name":"Information technology � Artificial intelligence � Controllability of automated artificial intelligence systems","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30491581","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-16T20:30:50Z","doi":"10.3403/30491581","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-3-030-92087-6_24","name":"Artificial Intelligence-Based Evaluation of Coronary Calcium","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92087-6_24","authors":["Sanne G. M. van Velzen","Nils Hampe","Bob D. de Vos","Ivana Išgum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-22T01:03:34Z","doi":"10.1007/978-3-030-92087-6_24","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1007/978-3-031-15816-2_16","name":"Applications of Artificial Intelligence in Medical Images Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-15816-2_16","authors":["Pushpanjali Gupta","Prasan Kumar Sahoo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-09T08:03:57Z","doi":"10.1007/978-3-031-15816-2_16","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/s0933-3657(00)00102-0","name":"Exploring presentation methods for tomographic medical image viewing","source":"crossref","abstract":"This paper explores the presentation of tomographic medical images on a computer screen. Limitations of the computer screen are apparent, as even a very large computer monitor cannot display an entire study consisting of dozens of images at once. Our objective is to propose filmless computer presentation methods for these images, in particular for magnetic resonance images. First, we observe the magnetic resonance image analysis task in the traditional light screen environment where presentation of many images has always been possible. We then propose solutions for meeting requirements in the computer environment. After implementation of these solutions we obtain user feedback on alternatives in order to determine feasibility and preference. Observations reveal three requirement categories: user control of film management, navigation of images and image series, and simultaneous availability of detail and context. We developed a framework of detail-in-context-technique parameters for the purpose of viewing tomographic medical images and presented our solution directions to the radiologists for feedback. Results from the user feedback study support the feasibility of the proposed approaches and clearly indicate the importance of presentation issues in the development of medical imaging viewing systems.","url":"https://doi.org/10.1016/s0933-3657(00)00102-0","authors":["Johanna E. van der Heyden","Kori M. Inkpen","M.Stella Atkins","M.Sheelagh T. Carpendale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T21:53:17Z","doi":"10.1016/s0933-3657(00)00102-0","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ijmedinf.2025.106087","name":"Artificial intelligence in nursing support for patients: A rapid review","source":"crossref","abstract":"Objectives This study aimed to gain insights into the potential of using evidence from artificial intelligence (AI) in nursing support for clinical practice and decision-making and its implications for future studies. Methods This was a rapid review of the literature. The PubMed, CINAHL, and CENTRAL in the Cochrane Library databases were searched for randomized controlled trials (RCTs) on nursing support using AI for patient care, published between 2010 and 2024. The included studies were assessed for quality appraisal using the mixed methods appraisal tool, version 2018, and data extracted from each study were synthesized narratively considering the review questions. Results After removing duplicates, 5,176 studies were identified, resulting in the inclusion of 20 studies. The 20 studies were published between 2012 and 2024, with five studies focusing on \"providing information and advice to patients using AI,\" seven studies on \"clinical decision-making support for nurses using predictive algorithms,\" and eight studies on \"psychosocial support using AI-equipped social robots.\" In the qualitative appraisal of these studies, the proportion of studies that met the quality criteria for each item ranged from 35% to 85%. Conclusions and implications In this review, we identified 20 RCTs that have been published so far on AI in nursing support, and currently, research is shifting from the development and testing phase to the implementation and effectiveness verification phase. However, many studies were found to be at risk of bias, suggesting caution when applying the evidence to clinical practice. Moving forward, there is a pressing need for the accumulation of high-quality RCTs and evidence integration through meta-analyses. Brief summary This study explored the potential of AI in nursing support, highlighting 20 published RCTs. Research in this field is transitioning from development and testing to implementation and effectiveness verification.","url":"https://doi.org/10.1016/j.ijmedinf.2025.106087","authors":["Yoshiyasu Ito","Kohei Kajiwara","Jun Kako","Masamitsu Kobayashi","Michihiro Tsubaki","Hideaki Sakuramoto","Makoto Yamanaka","Takahiro Kakeda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-19T16:33:36Z","doi":"10.1016/j.ijmedinf.2025.106087","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1177/23821205211036836","name":"Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes","source":"crossref","abstract":"BACKGROUND As medicine and the delivery of healthcare enters the age of Artificial Intelligence (AI), the need for competent human–machine interaction to aid clinical decisions will rise. Medical students need to be sufficiently proficient in AI, its advantages to improve healthcare's expenses, quality, and access. Similarly, students must be educated about the shortfalls of AI such as bias, transparency, and liability. Overlooking a technology that will be transformative for the foreseeable future would place medical students at a disadvantage. However, there has been little interest in researching a proper method to implement AI in the medical education curriculum. This study aims to review the current literature that covers the attitudes of medical students towards AI, implementation of AI in the medical curriculum, and describe the need for more research in this area. METHODS An integrative review was performed to combine data from various research designs and literature. Pubmed, Medline (Ovid), GoogleScholar, and Web of Science articles between 2010 and 2020 were all searched with particular inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were successively pooled together, recorded, and analyzed quantitatively using a modified Hawkings evaluation form. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was utilized to help improve reporting. RESULTS A total of 39 articles meeting inclusion criteria were identified. Primary assessments of medical students attitudes were identified (n = 5). Plans to implement AI in the curriculum for the purpose of teaching students about AI (n = 6) and articles reporting actual implemented changes (n = 2) were assessed. Finally, 26 articles described the need for more research on this topic or calling for the need of change in medical curriculum to anticipate AI in healthcare. CONCLUSIONS There are few plans or implementations reported on how to incorporate AI in the medical curriculum. Medical schools must work together to create a longitudinal study and initiative on how to successfully equip medical students with knowledge in AI.","url":"https://doi.org/10.1177/23821205211036836","authors":["Joel Grunhut","Adam TM Wyatt","Oge Marques"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-06T05:56:22Z","doi":"10.1177/23821205211036836","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.jtumed.2023.12.007","name":"Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis","source":"crossref","abstract":"Objectives The challenges in diagnosing keratoconus (KC) have led researchers to explore the use of artificial intelligence (AI) as a diagnostic tool. AI has emerged as a new way to improve the efficiency of KC diagnosis. This study analyzed the use of AI as a diagnostic modality for KC. Methods This study used a systematic review and meta-analysis following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched selected databases using a combination of search terms: \"((Artificial Intelligence) OR (Diagnostic Modality)) AND (Keratoconus)\" from PubMed, Medline, and ScienceDirect within the last 5 years (2018-2023). Following a systematic review protocol, we selected 11 articles and 6 articles were eligible for final analysis. The relevant data were analyzed with Review Manager 5.4 software and the final output was presented in a forest plot. Results This research found neural networks as the most used AI model in diagnosing KC. Neural networks and naïve bayes showed the highest accuracy of AI in diagnosing KC with a sensitivity of 1.00, while random forests were >0.90. All studies in each group have proven high sensitivity and specificity over 0.90. Conclusions AI potentially makes a better diagnosis of the KC with its high performance, particularly on sensitivity and specificity, which can help clinicians make medical decisions about an individual patient.","url":"https://doi.org/10.1016/j.jtumed.2023.12.007","authors":["Azzahra Afifah","Fara Syafira","Putri Mahirah Afladhanti","Dini Dharmawidiarini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-01T15:49:03Z","doi":"10.1016/j.jtumed.2023.12.007","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2478/eras-2025-0009","name":"Transformations of Medical Professional Authority in the Age of Artificial Intelligence and Medical Automation: A Panel Analysis of the G20 Healthcare Sector (2015–2022)","source":"crossref","abstract":"Abstract This study explores the impact of artificial intelligence (AI) and automation technologies on the professional authority of physicians in G20 countries. By analyzing panel data from 2015 to 2022, the research investigates how the adoption of digital health infrastructure—measured through an ICT index—affects the average number of doctor visits per capita. The findings suggest a statistically significant negative relationship, indicating that as digital health tools become more prevalent, the traditional role and authority of physicians may shift. This transformation reflects broader changes in the doctor-patient relationship and raises sociological questions about trust, expertise, and control over medical knowledge. The study contributes to the sociology of professions by highlighting how technology can reshape established authority structures in healthcare systems.","url":"https://doi.org/10.2478/eras-2025-0009","authors":["Abderahmane Djerfi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-24T09:27:54Z","doi":"10.2478/eras-2025-0009","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3390/medsci13030159","name":"Artificial Intelligence Models for Diagnosis of Periodontitis Using Non-Invasive Biological Markers: A Systematic Review and Meta-Analysis of Patient-Based Studies","source":"crossref","abstract":"Background/Objectives: Early diagnosis of periodontitis remains challenging using traditional clinical methods. This systematic review and meta-analysis evaluated the diagnostic accuracy of artificial intelligence (AI) models trained on non-invasive or minimally invasive biomarkers—including saliva, gingival crevicular fluid (GCF), and immunologic profiles—for diagnosing and classifying periodontitis in human subjects. Methods: A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, EMBASE, and Cochrane CENTRAL was conducted from database inception to June 2025. Eligible studies used AI or machine learning models with patient-derived biomarker data and reported diagnostic performance metrics. Results: Seven studies were included, employing various AI models such as random forest, artificial neural networks, and gradient boosting. Biomarkers were derived from saliva (n = 4), saliva-derived biomarkers from oral rinse (n = 1), immunologic profiles (n = 1), and tissue-based gene expression (n = 1). Reported area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.96. Meta-analysis of studies with comparable outcomes showed a pooled sensitivity of 0.89 (95% CI: 0.84–0.93), a specificity of 0.87 (95% CI: 0.80–0.92), and a summary AUC of 0.92. Subgroup analysis revealed that models using salivary biomarkers achieved a higher pooled AUC (0.94) than those using GCF or immunologic markers (AUC: 0.89). Sensitivity analyses excluding studies with unclear bias did not significantly alter pooled estimates, affirming robustness. The overall certainty of evidence was rated as moderate to high. Conclusions: AI-based diagnostic models utilizing salivary, microbiome, or immunologic biomarkers demonstrated quantitatively high accuracy; however, the overall certainty of evidence was rated as moderate to high due to limitations in study design and validation.","url":"https://doi.org/10.3390/medsci13030159","authors":["Carlos M. Ardila","Anny M. Vivares-Builes","Pradeep Kumar Yadalam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-01T08:28:15Z","doi":"10.3390/medsci13030159","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0954-1810(91)90007-b","name":"Artificial intelligence: Engineering","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(91)90007-b","authors":["M.A. Rosenman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T08:02:52Z","doi":"10.1016/0954-1810(91)90007-b","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0933-3657(95)90009-8","name":"Artificial intelligence in medicine","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(95)90009-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T13:20:58Z","doi":"10.1016/0933-3657(95)90009-8","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1109/tgrs.2025.3534794/v2/review2","name":"Review for \"Radiometric Calibration Using Artificial Intelligence: Constituting Uniform Observing Systems for Infrared Satellites\"","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tgrs.2025.3534794/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-21T22:58:33Z","doi":"10.1109/tgrs.2025.3534794/v2/review2","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/brb3.70548/v3/review1","name":"Review for \"Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/brb3.70548/v3/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-13T17:13:03Z","doi":"10.1002/brb3.70548/v3/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.32388/zpbcm0","name":"Review of: \"Supply Chain Fraud Prediction with Machine Learning and Artificial intelligence\"","source":"crossref","abstract":"Potential competing interests: No potential competing interests to declare.This paper presents an application of machine learning to supply chain fraud.The authors use a large dataset and try three different types of models on the data: logistic regression, random forests and neural networks.The authors explain how they prune their dataset to eliminate non-essential features, and how they perform a feature-importance analysis.The","url":"https://doi.org/10.32388/zpbcm0","authors":["Suryoday Basak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-17T16:54:43Z","doi":"10.32388/zpbcm0","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2004.01.014","name":"Ontology development for unified traditional Chinese medical language system","source":"crossref","abstract":"Traditional Chinese medicine (TCM) as a complete knowledge system researches into human health conditions via a different approach compared to orthodox medicine. We are developing a unified traditional Chinese medical language system (UTCMLS) through an ontology approach that will support TCM language knowledge storage, concept-based information retrieval and information integration. UTCMLS is a huge knowledge project, which is a broad collaboration of 16 distributed groups, most of them with no prior experience of formal ontology development. Therefore, the cooperative and comprehensive ontology engineering is crucial. We use Protégé 2000 for ontology development of concepts and relationships that represent the domain and that will permit storage of TCM knowledge. This paper focuses on the methodology, design and development of ontology for UTCMLS.","url":"https://doi.org/10.1016/j.artmed.2004.01.014","authors":["Xuezhong Zhou","Zhaohui Wu","Aining Yin","Lancheng Wu","Weiyu Fan","Ruen Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-08-18T13:05:21Z","doi":"10.1016/j.artmed.2004.01.014","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2019.101710","name":"A novel model for evaluation Hospital medical care systems based on plithogenic sets","source":"crossref","abstract":"This research suggests an approach constructed on the connotation of plithogenic theory and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) technique to come up with a methodical procedure to assess the infirmary serving under a framework of plithogenic theory, where the ambiguity, incomplete information, qualitative information, approximate evaluation, imprecision and uncertainty are addressed with semantic expressions determined by plithogenic numbers and computing of contradiction degrees of attribute values. This research stratifies the plithogenic multi criteria decision making (MCDM) strategy for defining the significant weights of assessing standards, and the VIKOR technique is applied for enhancing the serving efficiency classifications of the possible substitutes. An experimental issue, including 11 assessing standards, 3 private and 2 general hospitals in Zagazig, has been evaluated by 3 assessors from several areas of medical activities, asked to validate the suggested strategy. In this research, we give some definitions of the plithogenic environment, which is more general and comprehensive than fuzzy, intuitionistic fuzzy and neutrosophic ones. The plithogeny is interested in the contradiction degrees between attribute values that help in better calculating the aggregations. We conducted the data analysis and the results showed us that the serving efficiency of private medical centers is superior than that of general medical centers due to the fact that public medical centers are scarcely supported by governmental institutions. The private medical centers have to ward themselves to keep possession of bringing patients or attract patients. We conducted the sensitivity analysis of the achieved results, to verify their validity, and to find out to what extent the different values affect the ranking of available alternatives.","url":"https://doi.org/10.1016/j.artmed.2019.101710","authors":["Mohamed Abdel-Basset","Mohamed El-hoseny","Abduallah Gamal","Florentin Smarandache"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-31T10:54:52Z","doi":"10.1016/j.artmed.2019.101710","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/0004-3702(82)90027-3","name":"The handbook of artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0004-3702(82)90027-3","authors":["Frederick Hayes-Roth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-14T13:02:52Z","doi":"10.1016/0004-3702(82)90027-3","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.11591/ijai.v14.i3.pp2169-2177","name":"Semantic based medical visual question answering with explainable artificial intelligence","source":"crossref","abstract":"The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.","url":"https://doi.org/10.11591/ijai.v14.i3.pp2169-2177","authors":["Sheerin Sitara Noor Mohamed","Kavitha Srinivasan","Raghuraman Gopalsamy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T11:59:47Z","doi":"10.11591/ijai.v14.i3.pp2169-2177","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s12910-026-01445-z","name":"The effects of artificial intelligence ethics awareness, digital literacy, and metacognition on nursing students’ attitudes toward artificial intelligence","source":"crossref","abstract":"BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, raising new ethical challenges related to patient rights, transparency, and fairness. Nursing students must therefore develop competencies not only in digital and cognitive skills but also in AI ethics awareness to ensure responsible adoption of these technologies. METHODS: A descriptive correlational study was conducted with 121 nursing students in South Korea. Validated instruments measured AI ethics awareness, digital literacy, metacognition, and attitudes toward AI. Data were analyzed using t-tests, ANOVA, Pearson’s correlation, and multiple regression. RESULTS: AI ethics awareness (β = 0.30, p < .001) and digital literacy (β = 0.38, p < .001) were significant predictors of attitudes toward AI, jointly explaining 31.8% of the variance. Higher levels of ethical awareness and digital competence were associated with more positive perceptions of AI. CONCLUSIONS: The findings highlight the ethical and educational importance of preparing healthcare professionals to navigate AI responsibly. Embedding ethical principles such as autonomy, justice, and accountability into nursing and healthcare curricula, as well as into institutional and national standards, will be crucial for protecting patient rights and promoting fairness in AI-driven healthcare.","url":"https://doi.org/10.1186/s12910-026-01445-z","authors":["Chohee Bang","Yujin Hur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-02T01:16:12Z","doi":"10.1186/s12910-026-01445-z","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/b978-0-443-43823-3.00012-1","name":"Integrated artificial intelligence in patient counseling: A comprehensive guide for bioengineers and medical professionals","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-43823-3.00012-1","authors":["Akanksha Dwivedi","Roohi Yusufi","Anuradha Derashri","Disha Sharma","Kuldeep Vinchurkar","Divyang Patel","Meghraj Suryawanshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-05T11:02:04Z","doi":"10.1016/b978-0-443-43823-3.00012-1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2017.05.006","name":"Medical image classification based on multi-scale non-negative sparse coding","source":"crossref","abstract":"With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.","url":"https://doi.org/10.1016/j.artmed.2017.05.006","authors":["Ruijie Zhang","Jian Shen","Fushan Wei","Xiong Li","Arun Kumar Sangaiah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-05-30T08:46:56Z","doi":"10.1016/j.artmed.2017.05.006","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1186/s12911-023-02397-9","name":"Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis","source":"crossref","abstract":"Abstract Background Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time. Methods Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386. Results Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83–91%), specificity of 94% (92–96%), and AUC of 97% (95–98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or &gt; 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia). Conclusions Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.","url":"https://doi.org/10.1186/s12911-023-02397-9","authors":["Anying Bai","Mingyu Si","Peng Xue","Yimin Qu","Yu Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-08T13:02:27Z","doi":"10.1186/s12911-023-02397-9","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.ijmedinf.2025.106215","name":"Predictive modeling of hospital emergency department demand using artificial intelligence: A systematic review","source":"crossref","abstract":"Background Accurately forecasting patient arrivals in hospital emergency departments (EDs) is critical for hospital capacity and planning and clinical decision-making. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown promising performance over traditional time series approaches. However, the extent to which these models are validated and generalizable remains uncertain. Objective To systematically review the literature on predictive models for hospital ED demand forecasting, focusing on algorithms used, internal and external variables, validation strategies and limitations pre- and post-pandemic developments. Methods A systematic literature review (SLR) was conducted following PRISMA guidelines. Five databases (PubMed, IEEE, Springer, ScienceDirect, ACM) were searched for peer-reviewed articles published between January 2019 and July 2025. Eligible studies applied predictive algorithms - excluding those focused on COVID-19 - to forecast ED visits. Extracted data included modeling approaches, feature types, evaluation metrics, and validation methods. Results Eleven studies met the inclusion criteria. Classical models such as ARIMA and SARIMA remain in use, but ML (e.g., XGBoost, Random Forest) and DL (e.g., LSTM, CNN) showed higher predictive accuracy, especially with high-dimensional, nonlinear data. Incorporating external variables-such as weather (temperature, humidity, wind), air quality, and calendar events-consistently improved performance. Common metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), with MAPE ranging from 3 % to 18 %. Few studies performed external validation, and only a minority employed explainable AI methods (e.g., SHAP) to address interpretability. Conclusions AI-based models offer strong potential for ED demand forecasting, particularly when integrating environmental and temporal features. However, limited external validation and lack of interpretability remain significant barriers to clinical adoption. Future research should prioritize multicenter validation, standardized evaluation, and explainable AI to support reliable, transparent, and scalable use in hospital emergency departments.","url":"https://doi.org/10.1016/j.ijmedinf.2025.106215","authors":["Jorge Blanco","Marina Ferreras","Oscar Cosido"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-13T23:36:27Z","doi":"10.1016/j.ijmedinf.2025.106215","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21275/sr25506132808","name":"Advancements and Challenges of Artificial Intelligence in Modern Dentistry: A Narrative Review","source":"crossref","abstract":"Artificial intelligence (AI) is rapidly transforming modern dentistry, offering new opportunities to enhance diagnostic accuracy, treatment outcomes, and clinical efficiency. This narrative review delves into current AI applications across dental specialties such as diagnostic imaging, orthodontics, prosthodontics, and pediatric dentistry-highlighting successes and existing limitations. Emphasis is placed on machine learning, radiographic interpretation, computer -aided restorations, and ethical challenges related to privacy, access, and transparency. The article advocates for further research to standardize methodologies and address data -related concerns, ensuring responsible and effective integration of AI in dental practice.","url":"https://doi.org/10.21275/sr25506132808","authors":["Ralitsa Bogovskagigova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-10T11:54:12Z","doi":"10.21275/sr25506132808","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1016/j.artmed.2006.08.003","name":"Extending temporal databases to deal with telic/atelic medical data","source":"crossref","abstract":"Objective In this paper, we aim at defining a general-purpose data model and query language coping with both \"telic\" and \"atelic\" medical data. Background In the area of Medical Informatics, there is an increasing realization that temporal information plays a crucial role, so that suitable database models and query languages are needed to store and support it. However, despite the wide range of approaches in the area, in this paper we show that a relevant class of medical data cannot be properly dealt with. Methodology We first show that data models based on the \"point-based\" semantics, which is (implicitly or explicitly) assumed by the totality of temporal database approaches, have several limitations when dealing with \"telic\" data. We then propose a new model (based on the \"interval-based\" semantics) to cope with such data, and extend the query language accordingly. Results We propose a new three-sorted model and a query language to properly deal with both \"telic\" and \"atelic\" medical data (as well as non-temporal data). Our query language is flexible, since it allows one to switch from \"atelic\" to \"telic\" data, and vice versa. Conclusion In this paper, we demonstrate the feasibility of a database approach copying with both telic and atelic data as needed in several (medical) applications.","url":"https://doi.org/10.1016/j.artmed.2006.08.003","authors":["Paolo Terenziani","Richard T. Snodgrass","Alessio Bottrighi","Mauro Torchio","Gianpaolo Molino"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-10-06T12:59:46Z","doi":"10.1016/j.artmed.2006.08.003","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.3389/frai.2022.1015418","name":"Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study","source":"crossref","abstract":"Purpose This study assesses the knowledge and attitudes of medical students in Lebanon toward Artificial Intelligence (AI) in medical education. It also explores the students' perspectives regarding the role of AI in medical education as a subject in the curriculum and a teaching tool. Methods This is a cross-sectional study using an online survey consisting of close-ended questions. The survey targets medical students at all medical levels across the 7 medical schools in Lebanon. Results A total of 206 medical students responded. When assessing AI knowledge sources (81.1%) got their information from the media as compared to (9.7%) from medical school curriculum. However, Students who learned the basics of AI as part of the medical school curriculum were more knowledge about AI than their peers who did not. Students in their clinical years appear to be more knowledgeable about AI in medicine. The advancements in AI affected the choice of specialty of around a quarter of the students (26.8%). Finally, only a quarter of students (26.5%) want to be assessed by AI, even though the majority (57.7%) reported that assessment by AI is more objective. Conclusions Education about AI should be incorporated in the medical school curriculum to improve the knowledge and attitudes of medical students. Improving AI knowledge in medical students will in turn increase acceptance of AI as a tool in medical education, thus unlocking its potential in revolutionizing medical education.","url":"https://doi.org/10.3389/frai.2022.1015418","authors":["George Doumat","Darine Daher","Nadim-Nicolas Ghanem","Beatrice Khater"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-02T13:34:47Z","doi":"10.3389/frai.2022.1015418","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1145/3706890.3706917","name":"Discussion on the Development Trends of Medical Education Research Enabled by Artificial Intelligence in the Digital Context","source":"crossref","abstract":"Digitization is a major theoretical proposition for contemporary development in China, greatly promoting the breadth and depth of medical education research. Artificial intelligence is an important carrier for the implementation of digitization. In order to systematically summarize the impact of artificial intelligence technology on the development trend of medical education research under the digital background, and explore the ways in which artificial intelligence empowers medical education research, this article used CiteSpace software to conduct a graph analysis of 385 Chinese core journal articles on CNKI from 2013 to 2024. Based on a comprehensive understanding of the content and characteristics of medical education research, statistics were conducted on the number of publications, authors, and institutions. Through keyword clustering analysis, the hotspots and directions of research were analyzed, and the idea of using digital technology, combining macro theoretical basis and national policy guidance, and constructing a digital construction of medical education with comprehensive coordinated development of education, technology, and talent was proposed.","url":"https://doi.org/10.1145/3706890.3706917","authors":["Lei Zhai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-13T13:37:20Z","doi":"10.1145/3706890.3706917","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.21608/ejai.2026.502973.1039","name":"Class Shortest Path Algorithm in Weighted Rough Hypergraphs with some medical applications","source":"crossref","abstract":"","url":"https://doi.org/10.21608/ejai.2026.502973.1039","authors":["Ayman Elsaid Ammar","Abd El Fattah El Atik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-13T00:55:54Z","doi":"10.21608/ejai.2026.502973.1039","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.2196/preprints.22845","name":"Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint (Preprint)","source":"crossref","abstract":"BACKGROUND Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. OBJECTIVE The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. METHODS We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. RESULTS Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. CONCLUSIONS As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.","url":"https://doi.org/10.2196/preprints.22845","authors":["Jingwen Zhang","Yoo Jung Oh","Patrick Lange","Zhou Yu","Yoshimi Fukuoka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-29T19:56:25Z","doi":"10.2196/preprints.22845","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.1002/cesm.70059/v1/review1","name":"Review for \"Human‐in‐the‐Loop Artificial Intelligence System for Systematic Literature Review: Methods and Validations for the AutoLit Review Software\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cesm.70059/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T21:13:42Z","doi":"10.1002/cesm.70059/v1/review1","addedAt":"2026-09-01T01:48:00.327Z","updatedAt":"2026-09-01T01:48:00.327Z"},{"id":"doi:10.6084/m9.figshare.26706867.v1","name":"Additional file 1 of Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis","source":"datacite","abstract":"Supplementary Materials 1.","url":"https://doi.org/10.6084/m9.figshare.26706867.v1","authors":["Amiri, Hamidreza","Peiravi, Samira","rezazadeh shojaee, Seyedeh sara","Rouhparvarzamin, Motahareh","Nateghi, Mohammad Naser","Etemadi, Mohammad Hossein","ShojaeiBaghini, Mahdie","Musaie, Farhan","Anvari, Mohammad Hossein","Asadi Anar, Mahsa"],"tags":["Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26706867.v1","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.26706867","name":"Additional file 1 of Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis","source":"datacite","abstract":"Supplementary Materials 1.","url":"https://doi.org/10.6084/m9.figshare.26706867","authors":["Amiri, Hamidreza","Peiravi, Samira","rezazadeh shojaee, Seyedeh sara","Rouhparvarzamin, Motahareh","Nateghi, Mohammad Naser","Etemadi, Mohammad Hossein","ShojaeiBaghini, Mahdie","Musaie, Farhan","Anvari, Mohammad Hossein","Asadi Anar, Mahsa"],"tags":["Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26706867","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18752406","name":"Artificial Intelligence Jammalamudi Chaitanya babu and Machine Learning in Healthcare: A Comprehensive Review of Clinical Applications, Challenges, And Future Perspectives","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) technologies in healthcare has revolutionized clinical decision-making, diagnostic accuracy, and treatment planning. This comprehensive review examines the current state of AI/ML applications across multiple healthcare domains, including medical imaging, drug discovery, clinical prediction models, and personalized medicine. Through systematic analysis of 40+ peer-reviewed studies and clinical trials, we evaluate the clinical efficacy, regulatory challenges, ethical considerations, and implementation barriers associated with these technologies. Our findings demonstrate that while AI/ML systems have achieved performance metrics comparable to or exceeding human experts in specific diagnostic tasks, significant gaps remain in generalizability, interpretability, and clinical validation. This paper synthesizes current evidence on AI/ML applications, discusses critical challenges in model validation and regulatory approval, addresses ethical concerns regarding bias and patient privacy, and proposes a framework for responsible AI implementation in clinical practice. We conclude that successful integration of AI/ML in healthcare requires interdisciplinary collaboration between clinicians, computer scientists, bioethicists, and regulatory bodies to ensure patient safety, equitable access, and evidence-based clinical practice.","url":"https://doi.org/10.5281/zenodo.18752406","authors":["Jammalamudi Chaitanya babu","Dr. Jayant Isaac"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18752406","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18752407","name":"Artificial Intelligence Jammalamudi Chaitanya babu and Machine Learning in Healthcare: A Comprehensive Review of Clinical Applications, Challenges, And Future Perspectives","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) technologies in healthcare has revolutionized clinical decision-making, diagnostic accuracy, and treatment planning. This comprehensive review examines the current state of AI/ML applications across multiple healthcare domains, including medical imaging, drug discovery, clinical prediction models, and personalized medicine. Through systematic analysis of 40+ peer-reviewed studies and clinical trials, we evaluate the clinical efficacy, regulatory challenges, ethical considerations, and implementation barriers associated with these technologies. Our findings demonstrate that while AI/ML systems have achieved performance metrics comparable to or exceeding human experts in specific diagnostic tasks, significant gaps remain in generalizability, interpretability, and clinical validation. This paper synthesizes current evidence on AI/ML applications, discusses critical challenges in model validation and regulatory approval, addresses ethical concerns regarding bias and patient privacy, and proposes a framework for responsible AI implementation in clinical practice. We conclude that successful integration of AI/ML in healthcare requires interdisciplinary collaboration between clinicians, computer scientists, bioethicists, and regulatory bodies to ensure patient safety, equitable access, and evidence-based clinical practice.","url":"https://doi.org/10.5281/zenodo.18752407","authors":["Jammalamudi Chaitanya babu","Dr. Jayant Isaac"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18752407","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/bxv2h","name":"AI-Induced Intelligence Erosion and Susceptibility to AI Hallucinations in Health Professions Education: A scoping review protocol","source":"datacite","abstract":"This scoping review will systematically map evidence on the relationship between AI-induced intelligence erosion (including automation bias, cognitive offloading, and reduced critical thinking) and health professions students' ability to detect AI hallucinations from generative AI tools like ChatGPT, Claude, and Gemini. Following Joanna Briggs Institute (JBI) methodology, we conducted a thorough search in seven databases (MEDLINE/PubMed, ERIC, Google Scholar, BASE, medRxiv, bioRxiv, WHO Global Index Medicus) from November 2022 onwards. Three independent reviewers will screen citations using Rayyan with blind mode enabled. The population includes undergraduate and postgraduate health professions students (medical, nursing, dental, pharmacy, allied health, residents, interns) globally. We will include studies reporting on AI hallucinations/errors OR cognitive impacts of generative AI use in educational contexts. This review will identify research gaps, clarify \"intelligence erosion\" terminology in medical education, and inform safe integration of generative AI while preserving clinical reasoning skills. Expected outcomes include a systematic evidence map, conceptual framework, and recommendations for practice and research.","url":"https://doi.org/10.17605/osf.io/bxv2h","authors":["Karna, Swarnim Lal","Khadka, Anjan"],"tags":["Physical Sciences and Mathematics","Medicine and Health Sciences","Education","Computer Sciences","Artificial Intelligence and Robotics","AI hallucinations","artificial intelligence","cognitive offloading"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/bxv2h","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18748903","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18748903","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18748903","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18749488","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18749488","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18749488","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18748904","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18748904","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18748904","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18749306","name":"Rational Use of Antibiotics in the Era of Antimicrobial Resistance: Recent Advances and Future Directions","source":"datacite","abstract":"Antimicrobial resistance (AMR) represents a critical global public health threat, undermining a century of medical advancement and posing significant risks to modern healthcare systems. The irrational and excessive application of antibiotics in human and animal medicine is a principal accelerator of this crisis. This narrative review synthesizes contemporary evidence, drawing from PubMed, Web of Science, and key guideline databases from 2015 to 2024, to evaluate strategies for the rational deployment of antibiotics. We examine established interventions, including structured antimicrobial stewardship programs, the integration of rapid diagnostic technologies, and the application of biomarkers for clinical decision-making. Recent progress in genomics, point-of-care diagnostics, and artificial intelligence is scrutinized for its role in refining prescribing accuracy. The review further explores the imperative of the One Health paradigm and proposes future trajectories, such as novel economic models to stimulate antibiotic innovation and robust global surveillance frameworks. Ultimately, curbing AMR necessitates a sustained, collaborative effort across sectors, underpinned by public education and dedicated investment in research.","url":"https://doi.org/10.5281/zenodo.18749306","authors":["Mudhalkar Karan, Kotgire Omkar, Dr. Giri Ashok Bhimrao"],"tags":["Antimicrobial resistance, rational use of antibiotics, antimicrobial stewardship, rapid diagnostics, One Health, antibiotic policy."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18749306","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18749307","name":"Rational Use of Antibiotics in the Era of Antimicrobial Resistance: Recent Advances and Future Directions","source":"datacite","abstract":"Antimicrobial resistance (AMR) represents a critical global public health threat, undermining a century of medical advancement and posing significant risks to modern healthcare systems. The irrational and excessive application of antibiotics in human and animal medicine is a principal accelerator of this crisis. This narrative review synthesizes contemporary evidence, drawing from PubMed, Web of Science, and key guideline databases from 2015 to 2024, to evaluate strategies for the rational deployment of antibiotics. We examine established interventions, including structured antimicrobial stewardship programs, the integration of rapid diagnostic technologies, and the application of biomarkers for clinical decision-making. Recent progress in genomics, point-of-care diagnostics, and artificial intelligence is scrutinized for its role in refining prescribing accuracy. The review further explores the imperative of the One Health paradigm and proposes future trajectories, such as novel economic models to stimulate antibiotic innovation and robust global surveillance frameworks. Ultimately, curbing AMR necessitates a sustained, collaborative effort across sectors, underpinned by public education and dedicated investment in research.","url":"https://doi.org/10.5281/zenodo.18749307","authors":["Mudhalkar Karan, Kotgire Omkar, Dr. Giri Ashok Bhimrao"],"tags":["Antimicrobial resistance, rational use of antibiotics, antimicrobial stewardship, rapid diagnostics, One Health, antibiotic policy."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18749307","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18731604","name":"Human Factors & Just Culture in Paramedicine | Work-as-Done vs Work-as-Imagined (Ivan McCann)","source":"datacite","abstract":"Episode Summary In this episode, Ivan McCann joins the podcast to discuss human factors, patient safety, and just culture in paramedicine. With experience spanning Ontario EMS, remote and austere medical environments, and Ireland’s community healthcare system, Ivan explores how “work as done” often differs from “work as imagined” — and why designing systems for real-world operational conditions is essential to improving safety. The conversation bridges frontline paramedic experience with formal human factors training and examines how healthcare can move beyond blame-based responses toward systems-based learning. Key Topics Discussed • Human factors theory and application in paramedicine• Work-as-done vs work-as-imagined• Designing equipment, workflows, and environments for real-world conditions• Aviation safety lessons and their limits in healthcare• Medication access, labeling, and task-step design• Building human factors capacity within paramedic services• Just culture — accountability without blame• Second victims and psychological safety• Evolving incident review processes This episode challenges the assumption that healthcare is inherently safe and argues that safety must be intentionally designed into systems, environments, and organizational culture. Patient safety and paramedic safety are inseparable. Sustainable improvement requires systems thinking, frontline involvement, and psychologically safe learning environments. Episode Timeline 00:00 Introduction and Background05:34 Work as Done vs Work as Imagined14:05 Designing Systems for Real-World Use19:10 Aviation and Human Factors Origins23:47 Misconceptions About Human Factors in Healthcare27:56 Practical Design Examples in EMS35:28 Is Healthcare Truly a Safe Industry?41:22 Just Culture and Accountability47:05 A Needs-Based Model for Incident Response51:27 Final Reflections and Future Directions About the Guest Ivan McCann is a paramedic and human factors specialist with experience in Ontario EMS, remote contract medicine, and Ireland’s healthcare system. He holds advanced training in human factors in patient safety and works in patient safety within community healthcare settings. His work focuses on integrating systems design principles into frontline healthcare environments. Medical & Educational Disclaimer The content presented in this episode is for educational and professional development purposes only. It does not constitute medical advice and is not a substitute for accredited paramedic training programs, clinical education, or local medical directives. Always practice within your professional scope and comply with applicable regulatory standards and medical oversight requirements. The views expressed are those of the participants and do not necessarily reflect the positions of affiliated organizations. AI & Synthetic Media Disclosure Artificial intelligence tools were used in the production of this episode for transcription, audio processing, and editing assistance. All clinical and systems-level content was reviewed by a qualified healthcare professional prior to release. AI tools were used solely as production assistants and did not generate medical recommendations or replace professional judgment.","url":"https://doi.org/10.5281/zenodo.18731604","authors":["Cichowski, RYAN","Rodger, Jakob","McCann, Ivan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18731604","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18731605","name":"Human Factors & Just Culture in Paramedicine | Work-as-Done vs Work-as-Imagined (Ivan McCann)","source":"datacite","abstract":"Episode Summary In this episode, Ivan McCann joins the podcast to discuss human factors, patient safety, and just culture in paramedicine. With experience spanning Ontario EMS, remote and austere medical environments, and Ireland’s community healthcare system, Ivan explores how “work as done” often differs from “work as imagined” — and why designing systems for real-world operational conditions is essential to improving safety. The conversation bridges frontline paramedic experience with formal human factors training and examines how healthcare can move beyond blame-based responses toward systems-based learning. Key Topics Discussed • Human factors theory and application in paramedicine• Work-as-done vs work-as-imagined• Designing equipment, workflows, and environments for real-world conditions• Aviation safety lessons and their limits in healthcare• Medication access, labeling, and task-step design• Building human factors capacity within paramedic services• Just culture — accountability without blame• Second victims and psychological safety• Evolving incident review processes This episode challenges the assumption that healthcare is inherently safe and argues that safety must be intentionally designed into systems, environments, and organizational culture. Patient safety and paramedic safety are inseparable. Sustainable improvement requires systems thinking, frontline involvement, and psychologically safe learning environments. Episode Timeline 00:00 Introduction and Background05:34 Work as Done vs Work as Imagined14:05 Designing Systems for Real-World Use19:10 Aviation and Human Factors Origins23:47 Misconceptions About Human Factors in Healthcare27:56 Practical Design Examples in EMS35:28 Is Healthcare Truly a Safe Industry?41:22 Just Culture and Accountability47:05 A Needs-Based Model for Incident Response51:27 Final Reflections and Future Directions About the Guest Ivan McCann is a paramedic and human factors specialist with experience in Ontario EMS, remote contract medicine, and Ireland’s healthcare system. He holds advanced training in human factors in patient safety and works in patient safety within community healthcare settings. His work focuses on integrating systems design principles into frontline healthcare environments. Medical & Educational Disclaimer The content presented in this episode is for educational and professional development purposes only. It does not constitute medical advice and is not a substitute for accredited paramedic training programs, clinical education, or local medical directives. Always practice within your professional scope and comply with applicable regulatory standards and medical oversight requirements. The views expressed are those of the participants and do not necessarily reflect the positions of affiliated organizations. AI & Synthetic Media Disclosure Artificial intelligence tools were used in the production of this episode for transcription, audio processing, and editing assistance. All clinical and systems-level content was reviewed by a qualified healthcare professional prior to release. AI tools were used solely as production assistants and did not generate medical recommendations or replace professional judgment.","url":"https://doi.org/10.5281/zenodo.18731605","authors":["Cichowski, RYAN","Rodger, Jakob","McCann, Ivan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18731605","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18736904","name":"Perceived Impact of Artificial Intelligence Usage on Academic Performance Among Undergraduate Health Science Students in Mirpurkhas, Sindh-Pakistan: A Multi-Center Study","source":"datacite","abstract":"Background: Many medical and nursing students are increasingly utilizing artificial intelligence tools such as Telemedicine, Jeni.AI, Gemini, and ChatGPT to support both academic and clinical activities. These tools offer rapid responses and can save valuable time. However, there is growing concern that students in cities like Mirpurkhas may become overly reliant on AI, which could potentially compromise the development of critical thinking, problem-solving, and research skills. Methods: A descriptive cross-sectional study was conducted among undergraduate health science students at selected public and private centers in Mirpurkhas. Stratified random sampling was used to recruit a total of 197 participants, and data were collected via Google Forms from January to February 2026. An adapted questionnaire was employed, with minor modifications based on expert review to ensure validity. The collected data were analyzed using SPSS version 27.0, and descriptive statistics were generated for demographic subgroups. Findings: Among the 197 participants, the majority were BSN students (76.1%), aged 18–22 years (67.5%), and enrolled in private institutions (63%). Most students had a CGPA between 3.01–3.50. High perception scores were observed for General Use of AI (3.79 ± 0.40), Future of AI in Education (3.75 ± 0.46), and Impact on Student Skills (3.65 ± 0.48). Effectiveness of AI in Education was rated moderately high (3.49 ± 0.45), while Accuracy and Reliability received a moderate score (2.60 ± 0.50). These findings suggest that students generally find AI tools easy to use; however, they also express concerns about potential erosion of their natural skills and critical thinking. Conclusion: The healthcare undergraduate students demonstrated a high level of acceptance and utilization of artificial intelligence (AI) as a supportive academic tool. While AI use was generally associated with enhanced academic performance, concerns regarding accuracy and potential over-reliance remain. Implementation of structured training programs and clear ethical guidelines is recommended to ensure responsible and effective integration of AI in healthcare academia.","url":"https://doi.org/10.5281/zenodo.18736904","authors":["Samia Khalil Ahmed","Irfan Ali Chandio","Anum Pervaiz","Aiman Fatima","Khadija","Sheela Davi Malhi"],"tags":["Artificial Intelligence","Healthcare Education","Academic Performance","Undergraduate Students","Perception; Mirpurkhas","Sindh, Pakistan"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.18736904","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18736905","name":"Perceived Impact of Artificial Intelligence Usage on Academic Performance Among Undergraduate Health Science Students in Mirpurkhas, Sindh-Pakistan: A Multi-Center Study","source":"datacite","abstract":"Background: Many medical and nursing students are increasingly utilizing artificial intelligence tools such as Telemedicine, Jeni.AI, Gemini, and ChatGPT to support both academic and clinical activities. These tools offer rapid responses and can save valuable time. However, there is growing concern that students in cities like Mirpurkhas may become overly reliant on AI, which could potentially compromise the development of critical thinking, problem-solving, and research skills. Methods: A descriptive cross-sectional study was conducted among undergraduate health science students at selected public and private centers in Mirpurkhas. Stratified random sampling was used to recruit a total of 197 participants, and data were collected via Google Forms from January to February 2026. An adapted questionnaire was employed, with minor modifications based on expert review to ensure validity. The collected data were analyzed using SPSS version 27.0, and descriptive statistics were generated for demographic subgroups. Findings: Among the 197 participants, the majority were BSN students (76.1%), aged 18–22 years (67.5%), and enrolled in private institutions (63%). Most students had a CGPA between 3.01–3.50. High perception scores were observed for General Use of AI (3.79 ± 0.40), Future of AI in Education (3.75 ± 0.46), and Impact on Student Skills (3.65 ± 0.48). Effectiveness of AI in Education was rated moderately high (3.49 ± 0.45), while Accuracy and Reliability received a moderate score (2.60 ± 0.50). These findings suggest that students generally find AI tools easy to use; however, they also express concerns about potential erosion of their natural skills and critical thinking. Conclusion: The healthcare undergraduate students demonstrated a high level of acceptance and utilization of artificial intelligence (AI) as a supportive academic tool. While AI use was generally associated with enhanced academic performance, concerns regarding accuracy and potential over-reliance remain. Implementation of structured training programs and clear ethical guidelines is recommended to ensure responsible and effective integration of AI in healthcare academia.","url":"https://doi.org/10.5281/zenodo.18736905","authors":["Samia Khalil Ahmed","Irfan Ali Chandio","Anum Pervaiz","Aiman Fatima","Khadija","Sheela Davi Malhi"],"tags":["Artificial Intelligence","Healthcare Education","Academic Performance","Undergraduate Students","Perception; Mirpurkhas","Sindh, Pakistan"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.18736905","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/n7v2k","name":"Performance and Applicability of ChatGPT and Other Generative Artificial Intelligence on Hypertension: A Scoping Review","source":"datacite","abstract":"This scoping review aims to map and synthesize the existing evidence on the performance and applicability of ChatGPT and other generative artificial intelligence (AI) tools in the context of hypertension. The review will examine how generative AI has been used across hypertension-related domains, including screening, diagnosis, risk stratification, patient education, clinical decision support, and health system applications. Additionally, it will explore reported accuracy, reliability, benefits, limitations, ethical considerations, and implementation challenges. By systematically identifying knowledge gaps and emerging trends, this scoping review seeks to inform clinicians, researchers, and policymakers about the current and potential roles of generative AI in hypertension care and research.","url":"https://doi.org/10.17605/osf.io/n7v2k","authors":["GC, Saroj","Dheeraj Kumar Maheshwari","Bhusal, Pawan","Basnet, Bibhusan","Basnet, Roshan","ashma baruwal"],"tags":["Physical Sciences and Mathematics","Cardiology","Medicine and Health Sciences","Medical Specialties","Computer Sciences","Artificial Intelligence and Robotics","Applicability","ChatGPT"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/n7v2k","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.30287251.v1","name":"An audit of AI-related documents across U.S. medical schools: A framework-based qualitative content analysis","source":"datacite","abstract":"Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI’s rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies. AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes. AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, p = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally. AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.","url":"https://doi.org/10.6084/m9.figshare.30287251.v1","authors":["Rush, Emily","Byram, Jessica N.","Garnett, Colleen N.","DeVaul, Nicole","Smith, Laura","Checchi, Margaret","Martin, Daniel","Hoffman, Leslie A.","Brown, Kirstin M.","Mumbower, Daniel J.","Becker, Robert M.","Roach, Victoria A.","Doubleday, Alison F.","Edwards, Danielle N.","Lufler, Rebecca S.","Wactor, Alexandra","Boxerman, Sophia","Smith, Suzanne","Herriott, Hannah","Wilson, Adam B."],"tags":["Medicine","Genetics","FOS: Biological sciences","Sociology","FOS: Sociology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30287251.v1","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.30287251","name":"An audit of AI-related documents across U.S. medical schools: A framework-based qualitative content analysis","source":"datacite","abstract":"Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI’s rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies. AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes. AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, p = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally. AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.","url":"https://doi.org/10.6084/m9.figshare.30287251","authors":["Rush, Emily","Byram, Jessica N.","Garnett, Colleen N.","DeVaul, Nicole","Smith, Laura","Checchi, Margaret","Martin, Daniel","Hoffman, Leslie A.","Brown, Kirstin M.","Mumbower, Daniel J.","Becker, Robert M.","Roach, Victoria A.","Doubleday, Alison F.","Edwards, Danielle N.","Lufler, Rebecca S.","Wactor, Alexandra","Boxerman, Sophia","Smith, Suzanne","Herriott, Hannah","Wilson, Adam B."],"tags":["Medicine","Genetics","FOS: Biological sciences","Sociology","FOS: Sociology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30287251","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/3mf6x","name":"Mapping AI-augmented computing architectures for IoT-based fall prevention in older adults: a scoping review","source":"datacite","abstract":"This scoping review aims to systematically map and synthesize the existing literature on AI-augmented computing architectures — including edge, fog, cloud, and hybrid configurations — applied to Internet of Health Things (IoHT)-based fall prevention systems for older adults. Despite the growing body of research on AI-driven fall detection and IoT-enabled geriatric monitoring, no comprehensive review has yet examined how different computing architectures influence system performance, latency, accuracy, and clinical applicability in real-world fall prevention contexts. Guided by the Joanna Briggs Institute (JBI) methodology for scoping reviews and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, this review will search six databases: PubMed, Scopus, IEEE Xplore, Semantic Scholar, OpenAlex, and arXiv. Two independent reviewers will conduct screening using Rayyan, with deduplication managed through Mendeley. No restrictions on publication date will be applied; only English-language articles will be included. The review seeks to address the following research questions: (1) What types of AI-augmented computing architectures have been applied to IoT-based fall prevention in older adults? (2) What AI/ML techniques are deployed across these architectures, and how do they perform in terms of detection accuracy, latency, and resource efficiency? (3) What gaps and challenges exist in the current literature regarding clinical translation, scalability, and real-world deployment? Expected outcomes include a comprehensive evidence map of computing architecture typologies, an analysis of AI model performance across different architectural configurations, and the identification of research gaps to guide future development of scalable, clinically viable fall prevention systems for aging populations.","url":"https://doi.org/10.17605/osf.io/3mf6x","authors":["DHARMANSYAH, DHIKA"],"tags":["Health Information Technology","Physical Sciences and Mathematics","Geriatrics","Public Health","Medicine and Health Sciences","Medical Specialties","Community Health and Preventive Medicine","Computer Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/3mf6x","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18237321","name":"MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E).","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… How MH 370 will be recover to surface? By Tensorial Metric Tunneling from deepth of occeian to the L32 runway KLIA within Max 8.4 Seconds not the classical invasive methods. (RED ALERT) ........................................................................................................................................................................................................................................................................... \"If Twelve Years of Multi-Billion-Dollar Technology have Failed to Recover So Much as a Single Bolt from MH 370, Occam’s Razor Dictates that the Flaw Lies not Within the 'Search Perimeter,' but within Your Very 'Physical Foundations.\" ........................................................................................................................................................................................................................................................................... MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. $\\text{Dedicated Lagrangian Proof","url":"https://doi.org/10.5281/zenodo.18237321","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18237321","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18220587","name":"MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E).","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… How MH 370 will be recover to surface? By Tensorial Metric Tunneling from deepth of occeian to the L32 runway KLIA within Max 8.4 Seconds not the classical invasive methods. (RED ALERT) ........................................................................................................................................................................................................................................................................... \"If Twelve Years of Multi-Billion-Dollar Technology have Failed to Recover So Much as a Single Bolt from MH 370, Occam’s Razor Dictates that the Flaw Lies not Within the 'Search Perimeter,' but within Your Very 'Physical Foundations.\" ........................................................................................................................................................................................................................................................................... MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. $\\text{Dedicated Lagrangian Proof","url":"https://doi.org/10.5281/zenodo.18220587","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18220587","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/gh9nk","name":"Energy Sustainability Strategies in the Implementation of Generative AI in Healthcare: A Scoping Review","source":"datacite","abstract":"This project aims to map and synthesize the strategies and patterns related to energy sustainability in the implementation and use of generative artificial intelligence (AI) in healthcare settings. The rapid adoption of large language models (LLMs) and other generative AI systems in clinical practice, medical education, public health, and healthcare management has raised concerns regarding their environmental impact, energy consumption, ethical implications, and equity in access. This scoping review follows the Joanna Briggs Institute (JBI) methodology and addresses the research question: “What energy sustainability strategies and patterns are reported in the literature regarding the use of generative AI in healthcare?” The search was conducted between January 19 and February 11 in the PubMed and IEEE Xplore databases, using a structured combination of terms related to sustainability, large language models, and healthcare. The review seeks to identify reported strategies such as the use of smaller and specialized models, governance and regulatory approaches, ethical frameworks, equity-oriented implementation practices, and considerations related to energy efficiency. Expected outcomes include a structured mapping of sustainability dimensions (environmental, social, ethical, and operational) and identification of gaps, particularly regarding standardized metrics for measuring the real energy impact of generative AI systems in healthcare contexts.","url":"https://doi.org/10.17605/osf.io/gh9nk","authors":["Flach, Julia Camilly Assunção","Schiavon, Dieine Estela Bernieri"],"tags":["Sustainability","Physical Sciences and Mathematics","Medicine and Health Sciences","Environmental Sciences","Computer Sciences","Artificial Intelligence and Robotics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/gh9nk","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/zt6x4","name":"Exploring Artificial Intelligence Models for the Diagnosis of Sarcopenia: A Scoping Review","source":"datacite","abstract":"Objetive: The objective of this scoping review is to systematically map the existing literature on AI and ML models developed for the detection or prediction of sarcopenia, to describe their methodological characteristics, input features, model performance, validation strategies, and adherence to reporting standards. By providing a structured overview of the current evidence landscape, this review aims to inform future research, promote methodological rigor, and facilitate the responsible integration of AI into sarcopenia assessment. METHODS We performed this scoping review in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR, 2022). This review was reported and summarized using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations extension for scoping reviews (PRISMA-ScR, 2022)(8). The protocol for this scoping review is registered in OSF. The questions of the scoping review were: 1- What AI/ML algorithms have been used for binary diagnosis of sarcopenia, and how have these evolved over time? 2- What are the most frequently used input variables/features (e.g., DXA/CT/MRI/ultrasound, BIA, strength tests, gait/performance, labs, EHR variables)? 3- What are the reported sensitivity and specificity (and other metrics), and how are they estimated (internal/external validation)? 4- To what extent do studies adhere to TRIPOD+AI reporting items Search strategy and inclusion criteria The scoping review search was conducted across six databases (Medline, Scopus, Scielo, Web of Science and LILACS) up to February 2026. The search strategy, including all identified keywords, MeSH (Medical Subject Headings) and DeCS (Descriptores en Ciencias de la Salud) terms in English, Portuguese and Spanish according to the database requirement for search strategy is in Supplementary Table S1. The reference list of all included sources of evidence was screened for additional studies. This scoping review considered development and/or validation studies of AI/ML prediction/diagnostic models (including regression-based ML, classical ML, and deep learning) aimed at binary classification of sarcopenia (yes/no), that were published in Spanish, Portuguese and English. Studies that met the following criteria were included: 1) Population: Adults assessed sarcopenia that explicitly state that sarcopenia was diagnosed according to a recognized consensus or guideline (e.g., EWGSOP2, AWGS, FNIH, IWGS); 2) Concept: AI/ML models that output binary sarcopenia classification (present/absent) and 3) Context: Any setting (community, hospital, outpatient), any country. Studies will be excluded if sarcopenia is not defined according to an established consensus; if the AI model predicts continuous outcomes (e.g., muscle mass values) without a binary classification aligned to consensus criteria; if the study does not involve an AI-based model; or if it is a review, editorial, commentary, or animal study. Screening tools not embedded within an AI or machine learning framework will also be excluded. All citations were uploaded and duplicates removed using RAYYAN system wish is software for managing and streamlining a systematic review. Study selection All records were imported into Rayyan software for reference management and duplicate removal. Screening was conducted in two stages: 1) title and abstract screening and 2) full-text eligibility assessment. Both stages were performed independently by two reviewers (LAS and CBO). Discrepancies were resolved through discussion, and when necessary, by consultation with a third reviewer (WSL). The study selection process is presented using a PRISMA-ScR flow diagram. Reasons for exclusion at the full-text stage were documented. In accordance with JBI guidance, no formal risk of bias or methodological quality appraisal was performed, as the objective was to map the evidence rather than synthesize effectiveness or causal inference. Data extraction Data extraction was performe","url":"https://doi.org/10.17605/osf.io/zt6x4","authors":["Loyola, Walter Aquiles Sepúlveda","Barros-Osorio, Cristián","Gallegos, Eduardo"],"tags":["Medicine and Health Sciences","Life Sciences","artificial intelligence","machine learning","sarcopenia"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/zt6x4","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18711590","name":"Advances in Diagnostic Technologies and Precision Medicine: Clinical Anatomy–Oriented Insights from WHX Labs 2026","source":"datacite","abstract":"Recent advances in diagnostic technologies and precision medicine have transformed clinical decision-making, emphasizing individualized patient care based on biological, molecular, and anatomical variability. Clinical anatomy remains a foundational pillar in this transformation, guiding accurate diagnosis, imaging interpretation, and image-guided interventions. Global healthcare platforms such as WHX Labs 2026 provide valuable insight into emerging diagnostic innovations that bridge laboratory sciences, imaging technologies, and clinical application. This narrative review explores recent advances in diagnostic technologies and precision medicine through a clinical anatomy–oriented lens, using key themes highlighted at WHX Labs 2026 as a contextual framework. Emphasis is placed on advanced imaging modalities, molecular and laboratory diagnostics, artificial intelligence–assisted diagnostic systems, and three-dimensional anatomical visualization technologies. The integration of clinical anatomy with these innovations is discussed in relation to diagnostic accuracy, procedural safety, and personalized patient management. Furthermore, implications for clinical practice, interdisciplinary collaboration, and medical education are examined. By reinforcing the central role of anatomy in modern diagnostics, this review highlights how anatomy-guided diagnostic innovation supports precision medicine and underscores the importance of anatomically informed technological development in future healthcare systems.","url":"https://doi.org/10.5281/zenodo.18711590","authors":["Dr. Uma Pandalai"],"tags":["Clinical anatomy; diagnostic technologies; precision medicine; WHX Labs 2026; biomedical innovation; imaging diagnostics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18711590","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18711591","name":"Advances in Diagnostic Technologies and Precision Medicine: Clinical Anatomy–Oriented Insights from WHX Labs 2026","source":"datacite","abstract":"Recent advances in diagnostic technologies and precision medicine have transformed clinical decision-making, emphasizing individualized patient care based on biological, molecular, and anatomical variability. Clinical anatomy remains a foundational pillar in this transformation, guiding accurate diagnosis, imaging interpretation, and image-guided interventions. Global healthcare platforms such as WHX Labs 2026 provide valuable insight into emerging diagnostic innovations that bridge laboratory sciences, imaging technologies, and clinical application. This narrative review explores recent advances in diagnostic technologies and precision medicine through a clinical anatomy–oriented lens, using key themes highlighted at WHX Labs 2026 as a contextual framework. Emphasis is placed on advanced imaging modalities, molecular and laboratory diagnostics, artificial intelligence–assisted diagnostic systems, and three-dimensional anatomical visualization technologies. The integration of clinical anatomy with these innovations is discussed in relation to diagnostic accuracy, procedural safety, and personalized patient management. Furthermore, implications for clinical practice, interdisciplinary collaboration, and medical education are examined. By reinforcing the central role of anatomy in modern diagnostics, this review highlights how anatomy-guided diagnostic innovation supports precision medicine and underscores the importance of anatomically informed technological development in future healthcare systems.","url":"https://doi.org/10.5281/zenodo.18711591","authors":["Dr. Uma Pandalai"],"tags":["Clinical anatomy; diagnostic technologies; precision medicine; WHX Labs 2026; biomedical innovation; imaging diagnostics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18711591","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18710882","name":"Generative AI for Clinical Decision Support Systems: A Comprehensive Review","source":"datacite","abstract":"Generative artificial intelligence (GenAI) has emerged as a transformative technology in healthcare, with particularpromise for enhancing clinical decision support systems (CDSS). The rapid advancement of large language models(LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models hascreated unprecedented opportunities for improving diagnostic accuracy, treatment planning, and patient outcomes.This comprehensive literature review synthesizes current evidence on the application of generative AI technologiesin clinical decision support systems, examining their architectures, applications across medical specialties,performance metrics, implementation challenges, and ethical considerations. We conducted a systematic review ofrecent literature (2023-2025) focusing on generative AI applications in CDSS. A total of 3,941 publications relatedto LLMs in medicine were identified, with particular emphasis on clinical decision support applications. Studieswere analyzed across multiple dimensions including model architectures, clinical specialties, performance metrics,and implementation barriers. Generative AI models, particularly GPT-4 and advanced LLMs, demonstrate accuracyrates of 80-88% in clinical decision-making tasks, with area under curve (AUC) scores ranging from 0.79 to 0.87across different clinical applications. Radiology, oncology, and mental health emerge as the primary specialtiesadopting these technologies. However, significant implementation barriers persist, including data privacy concerns(reported in 85% of studies), system integration challenges (78%), and clinician acceptance issues (72%). Theintegration of clinical guidelines with LLMs shows promise, with PaLM 2 demonstrating superior performance inguideline-based treatment recommendations. Generative AI represents a paradigm shift in clinical decision support,offering substantial potential for improving healthcare quality and outcomes. Successful implementation requiresaddressing critical challenges in data privacy, system integration, transparency, and regulatory compliance. Futuredevelopments should focus on multi-modal foundation models, enhanced explainability, and user-centered design tofacilitate clinical adoption while maintaining patient safety and ethical standards.","url":"https://doi.org/10.5281/zenodo.18710882","authors":["Manjiri U. Karande","Nitin A. Kharche","Santosh R. Shekokar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18710882","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18710881","name":"Generative AI for Clinical Decision Support Systems: A Comprehensive Review","source":"datacite","abstract":"Generative artificial intelligence (GenAI) has emerged as a transformative technology in healthcare, with particularpromise for enhancing clinical decision support systems (CDSS). The rapid advancement of large language models(LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models hascreated unprecedented opportunities for improving diagnostic accuracy, treatment planning, and patient outcomes.This comprehensive literature review synthesizes current evidence on the application of generative AI technologiesin clinical decision support systems, examining their architectures, applications across medical specialties,performance metrics, implementation challenges, and ethical considerations. We conducted a systematic review ofrecent literature (2023-2025) focusing on generative AI applications in CDSS. A total of 3,941 publications relatedto LLMs in medicine were identified, with particular emphasis on clinical decision support applications. Studieswere analyzed across multiple dimensions including model architectures, clinical specialties, performance metrics,and implementation barriers. Generative AI models, particularly GPT-4 and advanced LLMs, demonstrate accuracyrates of 80-88% in clinical decision-making tasks, with area under curve (AUC) scores ranging from 0.79 to 0.87across different clinical applications. Radiology, oncology, and mental health emerge as the primary specialtiesadopting these technologies. However, significant implementation barriers persist, including data privacy concerns(reported in 85% of studies), system integration challenges (78%), and clinician acceptance issues (72%). Theintegration of clinical guidelines with LLMs shows promise, with PaLM 2 demonstrating superior performance inguideline-based treatment recommendations. Generative AI represents a paradigm shift in clinical decision support,offering substantial potential for improving healthcare quality and outcomes. Successful implementation requiresaddressing critical challenges in data privacy, system integration, transparency, and regulatory compliance. Futuredevelopments should focus on multi-modal foundation models, enhanced explainability, and user-centered design tofacilitate clinical adoption while maintaining patient safety and ethical standards.","url":"https://doi.org/10.5281/zenodo.18710881","authors":["Manjiri U. Karande","Nitin A. Kharche","Santosh R. Shekokar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18710881","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/ejmhd","name":"APPLICATIONS of ARTIFICIAL INTELLIGENCE in PUBLIC HEALTH POLICIES: A SCOPING REVIEW","source":"datacite","abstract":"This scoping review aims to map how artificial intelligence (AI) tools have been applied within the field of public and collective health, particularly in planning, management, evaluation of health policies, epidemiological monitoring, and promotion of equity in healthcare. The study is motivated by the rapid expansion of AI applications in health systems, especially after the COVID-19 pandemic and the growing adoption of digital health technologies. The review will follow the methodological guidelines of the Joanna Briggs Institute (JBI) and the PRISMA-ScR checklist to ensure rigor, transparency, and reproducibility. The research question was structured using the PCC mnemonic: Concept – AI tools and their applicability; Context – public health policies. The search strategy was developed through preliminary exploration of relevant articles, identification of descriptors in DeCS, MeSH, and Emtree, and expansion of synonyms using a large language model (ChatGPT). Searches will be conducted in PubMed/MEDLINE, Scopus, Embase, Web of Science, SciELO, LILACS, and CINAHL, without restrictions on publication year and including studies in Portuguese, English, and Spanish. Eligible studies include theoretical, empirical, quantitative, qualitative, and mixed-methods research addressing the use or application of AI tools in public or collective health. Duplicate records will be removed, and screening will occur in two stages—title/abstract screening followed by full-text review—conducted independently by two reviewers, with a third reviewer resolving disagreements. Reference management and screening will be supported by Rayyan. Data will be extracted using a standardized form containing study characteristics, objectives, methods, results, AI tools described, their applications, and their relevance to public health policy contexts. The findings will provide an updated and structured overview of the integration of AI in collective health, identifying opportunities, challenges, gaps, and implications for policy development, governance, equity, and ethical use of emerging technologies in health systems. REFERENCES REFERÊNCIAS SICHMAN, Jaime Simão. Inteligência Artificial e sociedade: avanços e riscos. Estudos Avançados, v. 35, p. 37-50, 2021. SHARIFANI, Koosha; AMINI, Mahyar. Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, v. 10, n. 07, p. 3897-3904, 2023. DOURADO, Daniel de Araujo; AITH, Fernando Mussa Abujamra. A regulação da inteligência artificial na saúde no Brasil começa com a Lei Geral de Proteção de Dados Pessoais. Revista de Saúde Pública, v. 56, p. 80, 2022. MATTOS, Samuel Miranda; CESTARI, Virna Ribeiro Feitosa; MOREIRA, Thereza Maria Magalhães. Protocolo de revisão de escopo: aperfeiçoamento do guia PRISMA-ScR. Rev Enferm UFPI, p. e3062-e3062, 2023. ACEVEDO, Marco Emilio Sánchez. La inteligencia artificial en el sector público y su límite respecto de los derechos fundamentales. Estudios Constitucionales, v. 20, n. 2, 2022. DO NASCIMENTO NETO, Conrado Dias et al. Inteligência artificial e novas tecnologias em saúde: desafios e perspectivas. Brazilian Journal of Development, v. 6, n. 2, p. 9431-9445, 2020 RAPOSO, D. R. A., ALVES JÚNIOR, J. S., SILVA, D. T. O. DA, ALVES, R. DE A., SANTOS, D. C. M. DOS, ALVES, T. M., SILVA, J. K. S. DA, ARAGÃO, S. F., &amp; FARIAS, C. M. S. (2024). Avanços na assistência de enfermagem: uma revisão de escopo sobre o uso da Inteligência Artificial na ciência do cuidar . CONTRIBUCIONES A LAS CIENCIAS SOCIALES, 17(7), e8854 . https://doi.org/10.55905/revconv.17n.7-417 TEIXEIRA, Renata Ferreira. Fusão de dados aplicando modelos de machine learning. 2024. Tese de Doutorado. BRAZ, Matheus Viana; MENDES, Thiago Casemiro; FERREIRA, Yasmin Alexandre. Ideologia gerencialista e plataformas de treinamentos de dados para Inteligência Artificial (IA): condições de trabalho e saúde dos trabalhadores no Brasil. Revista Eletrônica de Comuni","url":"https://doi.org/10.17605/osf.io/ejmhd","authors":["de Assis Cavalcanti, Gustavo Bezerra"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/ejmhd","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/fw7qu","name":"APPLICATION OF ARTIFICIAL INTELLIGENCE IN PUBLIC AND COLLECTIVE HEALTH: A SCOPING REVIEW FOR CONTINUING HEALTH EDUCATION","source":"datacite","abstract":"This protocol presents a scoping review aimed at mapping the applicability of artificial intelligence (AI) in public health. Considering the rapid expansion of AI-based tools in the health field, it is crucial to comprehensively understand how these technologies have been incorporated into different domains, such as health policy planning and management, service evaluation, epidemiological monitoring, equity in healthcare access, and other dimensions related to strengthening health systems. The study is justified by the growing relevance of AI as a decision-support instrument in health, recognizing both the opportunities and the ethical, technical, and social challenges arising from its use. By mapping available evidence, this review seeks to identify how AI tools have been applied, in which areas they show the greatest potential impact, and the existing limitations for their large-scale incorporation in public health. To ensure methodological rigor and transparency, the review will follow the guidelines of the Joanna Briggs Institute (JBI), internationally recognized for standardizing scoping reviews, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), which provides a structured checklist and flow diagram specific to this type of review. This methodological framework will strengthen the credibility of the findings and ensure that the results can be reproduced and used by other researchers, policymakers, and public health managers. The search strategy was initially developed through a preliminary exploration of descriptors and indexed terms in LILACS and PubMed/MEDLINE, followed by mapping in controlled vocabularies such as DeCS, MeSH, and Emtree. Additionally, the ChatGPT 5.0 large language model was used to identify synonyms and free terms, enhancing the sensitivity and comprehensiveness of the search strategy. The final strategy combines indexed terms and free-text terms in Portuguese and English, articulated by Boolean operators, to capture the widest range of relevant studies. The literature search will be conducted in major national and international databases, including PubMed/MEDLINE, Scopus, Embase, Web of Science, SciELO, LILACS, and CINAHL. The review will include theoretical and empirical studies, quantitative, qualitative, and mixed-methods research, published in Portuguese, English, and Spanish, with no date restrictions. The selected references will be managed through Rayyan, a web-based tool that facilitates duplicate removal, collaborative screening, and reference organization. The selection process will be carried out in two stages (title/abstract screening and full-text reading) by two independent reviewers, with a third reviewer resolving potential disagreements. Data extraction will be systematized in a structured spreadsheet, capturing key study information such as authorship, year of publication, objectives, methodology, and main findings. Specific information regarding the AI tool, its applicability, and the public health context in which it was applied will also be collected. This systematic organization will enable a narrative and descriptive synthesis of the results, highlighting trends, knowledge gaps, and implications for practice, management, and policy-making. Unlike systematic reviews that address narrowly focused questions, a scoping review is particularly appropriate for this study as it allows for a broad and detailed exploration of an emerging field such as the intersection between artificial intelligence and public health. The expected outcomes include identifying areas where AI has already been consolidated as a support tool, as well as the technical, ethical, and organizational challenges that still limit its use, especially in low- and middle-income countries. Therefore, this scoping review aims to provide a comprehensive, critical, and updated overview of AI integration into public health, contributing to evidence-infor","url":"https://doi.org/10.17605/osf.io/fw7qu","authors":["de Assis Cavalcanti, Gustavo Bezerra","da Silva Souza, Camila Mendes"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/fw7qu","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/c3bea","name":"How socioeconomic status impacts the adoption and uptake of digitally-based health interventions: a scoping review protocol","source":"datacite","abstract":"Over the past few decades, health service delivery has changed significantly with advancements in technology; this is reflected in the widespread integration of digital health within the health system. Digital health technologies or tools (DHTs) are becoming increasingly integral to best practice health care. Given the pace of innovation in digital health, DHTs are routinely being leveraged as interventions to aid individuals, the health workforce and health system users to address a variety of health and health system challenges. According to the World Health Organisation (WHO), these digital health interventions (DHIs) can be classified into four overarching groups based on the targeted primary user: (1) interventions for persons; (2) interventions for healthcare providers; (3) interventions for health management and support personnel; and (4) interventions for data services. Interventions for persons (i.e. person-centred DHIs), target members of the public who are potential or current users of health services and caregivers of individuals receiving health services. Technologies such as telemedicine, patient portals, mobile health applications and remote monitoring, are shown to improve patients’ quality of life and health outcomes. These technologies increase accessibility to health services, strengthen patient engagement and empowerment, and simultaneously reduce costs, thereby making healthcare more affordable. System-based DHIs (i.e. interventions for healthcare providers, health management and support personnel, and data services), such as electronic medical records, clinical decision support systems and artificial intelligence systems, are shown to improve overall health care delivery by optimising clinical practice and management, and reducing healthcare workers’ cognitive load. Despite their positive impacts on health outcomes, service delivery, and quality, the implementation of DHTs, particularly patient-centred DHTs, has faced a number of challenges. Although small-scale and proof-of-concept trials show promising results, DHTs are rarely sustained beyond the study period and often fail to translate to the same success in the real-world settings of busy public health clinics, large health systems, and various community settings. Additionally, their effectiveness is often hindered by a lack of uptake and usage. This disparity in access and use is known as the digital divide. Existing literature indicates that individuals belonging to more disadvantaged populations (i.e. those with lower levels of education, lower income, low literacy levels, or who are culturally diverse or live in rural areas) are less likely to start or continue using DHTs, despite the increased availability of the internet and digital technologies. However, there is a lack of evidence describing the differential impact of DHTs across different socioeconomic groups. Given the push towards the use of digital tools and interventions in health care15, underserved populations, especially those in lower socioeconomic groups, are at risk of being left behind in reaping the benefits of DHTs. Therefore, the objective of this scoping review is to explore the differential impact of patient-centred digitally-based interventions according to socioeconomic position. By understanding the equity impacts of DHTs across socioeconomic groups, this research can guide healthcare practices in implementing DHTs that are tailored across the population, concurrently ensuring that they are accessible and sustainable. Furthermore, policymakers could be more informed in developing equity-based criteria for the implementation of DHTs across health services to avoid perpetuating disparities in health outcomes. The research question is ‘What is the differential impact of patient-centred digital health interventions according to socioeconomic position?’ The objectives are as follows: Primary objective: 1. To describe the differential impact of patient-centred digital health int","url":"https://doi.org/10.17605/osf.io/c3bea","authors":["Wang, Sofia","Beauchamp, Alison","Azar, Denise","Shee, Anna Wong","Metcalf, Olivia"],"tags":["Health Information Technology","Public Health","Medicine and Health Sciences","Health Services Research","Differential impact","Digital divide","Digital health","Equity"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.17605/osf.io/c3bea","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18690001","name":"4. Phthisiatry and Contemporary Tuberculosis Medicine: A Comprehensive Review of Diagnostic Advances, Treatment Paradigms, and Global Health Challenges","source":"datacite","abstract":"Tuberculosis (TB) remains a leading contagious complaint killer encyclopedically, with Mycobacterium tuberculosis continuing to pose significant public health challenges despite advances in medical wisdom. The field of Phthisiatry, historically devoted to TB drug, has evolved mainly with molecular diagnostics, new rectifiers, and integrated care approaches. ideal This review synthesizes current knowledge on TB epidemiology, pathophysiology, individual methodologies, and treatment strategies, with emphasis on medicine resistant TB operation and arising exploration directions. styles A comprehensive literature review was conducted examining peer reviewed publications from 2015 2024, WHO reports, and clinical trial databases. Search strategies included terms related to tuberculosis diagnostics, treatment rules, medicine resistance, and public health interventions. Results Recent data indicate 10.8 million TB cases encyclopedically in 2023, with 1.3 million deaths. Molecular diagnostics, particularly GeneXpert MTB/ RIF, have revolutionized rapid fire opinion and resistance discovery. new rules incorporating bedaquiline, dexaminid, and pteromalid demonstrate bettered issues for medicine resistant TB. Artificial intelligence operations in radiological interpretation show promising perceptivity. TB HIV coinfection requires intertwined operation approaches. Social determinants including poverty, malnutrition, and healthcare access significantly impact complaint burden. Conclusion While significant progress has been made in TB control, multidrug resistant strains, healthcare injuries, and individual gaps in resource limited settings remain critical walls to elimination. unborn directions include coming generation vaccines, host directed curatives, and digital adherence technologies to support the WHO End TB Strategy. Keywords Tuberculosis, Phthisiatry, Mycobacterium tuberculosis, Drug resistant TB, GeneXpert, Bedaquiline, TB HIV coinfection Abstract Tuberculosis (TB) stands as one of humanity's oldest recorded conditions and continues to represent a redoubtable global health challenge in the twenty first century. The term' Phthisiatry,’ deduced from the Greek word' phthisis' meaning consumption or wasting, historically designated the technical medical field devoted to understanding and treating TB. This title reflects the complaint’s characteristic incarnation of progressive wasting and pulmonary destruction that defined its clinical donation before the arrival of effective chemotherapy (Daniel, 2006). The literal line of Phthisiatry encompasses vital discoveries that converted TB from an incorrigible scourge to a potentially curable infection. Robert Koch's 1882 identification of Mycobacterium tuberculosis as the causative agent established the bacteriological foundation of the complaint (Koch, 1982). latterly, themed twentieth century witnessed revolutionary remedial advances with streptomycin's discovery in 1943, followed by para amino salicylic acid and isoniazid, inaugurating the chemotherapy period (Schatz et al., 1944). These developments, coupled with rifampicin’s preface in the 1960s, enabled effective short course chemotherapy and dramatically reduced TB mortality in resource rich nations. Despite these medical triumphs, TB remains the leading cause of death from a single contagious agent, surpassing HIV/ AIDS in periodic mortality. The World Health Organization (WHO) reported roughly 10.8 million incident TB cases encyclopedically in 2023, with an estimated 1.3 million deaths among HIV negative individualities and a fresh 167,000 deaths among people living with HIV (World Health Organization, 2023). The complaint burden disproportionately affects low and middle income countries, with eight nations counting for two thirds of the global TB prevalence India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the Democratic Republic of the Congo. Contemporary Phthisiatry confronts multifaceted challenges that ","url":"https://doi.org/10.5281/zenodo.18690001","authors":["Kurmanaliev, Nurlanbek","Ansari Mohammad Aman","Das Prayag","Faiz Mohammad Alam","Javed Aqib","Khan Imran","Khan Shohab Gufran","Kuppal Kumar Ragavan","Mohammad Aqib"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18690001","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18690002","name":"4. Phthisiatry and Contemporary Tuberculosis Medicine: A Comprehensive Review of Diagnostic Advances, Treatment Paradigms, and Global Health Challenges","source":"datacite","abstract":"Tuberculosis (TB) remains a leading contagious complaint killer encyclopedically, with Mycobacterium tuberculosis continuing to pose significant public health challenges despite advances in medical wisdom. The field of Phthisiatry, historically devoted to TB drug, has evolved mainly with molecular diagnostics, new rectifiers, and integrated care approaches. ideal This review synthesizes current knowledge on TB epidemiology, pathophysiology, individual methodologies, and treatment strategies, with emphasis on medicine resistant TB operation and arising exploration directions. styles A comprehensive literature review was conducted examining peer reviewed publications from 2015 2024, WHO reports, and clinical trial databases. Search strategies included terms related to tuberculosis diagnostics, treatment rules, medicine resistance, and public health interventions. Results Recent data indicate 10.8 million TB cases encyclopedically in 2023, with 1.3 million deaths. Molecular diagnostics, particularly GeneXpert MTB/ RIF, have revolutionized rapid fire opinion and resistance discovery. new rules incorporating bedaquiline, dexaminid, and pteromalid demonstrate bettered issues for medicine resistant TB. Artificial intelligence operations in radiological interpretation show promising perceptivity. TB HIV coinfection requires intertwined operation approaches. Social determinants including poverty, malnutrition, and healthcare access significantly impact complaint burden. Conclusion While significant progress has been made in TB control, multidrug resistant strains, healthcare injuries, and individual gaps in resource limited settings remain critical walls to elimination. unborn directions include coming generation vaccines, host directed curatives, and digital adherence technologies to support the WHO End TB Strategy. Keywords Tuberculosis, Phthisiatry, Mycobacterium tuberculosis, Drug resistant TB, GeneXpert, Bedaquiline, TB HIV coinfection Abstract Tuberculosis (TB) stands as one of humanity's oldest recorded conditions and continues to represent a redoubtable global health challenge in the twenty first century. The term' Phthisiatry,’ deduced from the Greek word' phthisis' meaning consumption or wasting, historically designated the technical medical field devoted to understanding and treating TB. This title reflects the complaint’s characteristic incarnation of progressive wasting and pulmonary destruction that defined its clinical donation before the arrival of effective chemotherapy (Daniel, 2006). The literal line of Phthisiatry encompasses vital discoveries that converted TB from an incorrigible scourge to a potentially curable infection. Robert Koch's 1882 identification of Mycobacterium tuberculosis as the causative agent established the bacteriological foundation of the complaint (Koch, 1982). latterly, themed twentieth century witnessed revolutionary remedial advances with streptomycin's discovery in 1943, followed by para amino salicylic acid and isoniazid, inaugurating the chemotherapy period (Schatz et al., 1944). These developments, coupled with rifampicin’s preface in the 1960s, enabled effective short course chemotherapy and dramatically reduced TB mortality in resource rich nations. Despite these medical triumphs, TB remains the leading cause of death from a single contagious agent, surpassing HIV/ AIDS in periodic mortality. The World Health Organization (WHO) reported roughly 10.8 million incident TB cases encyclopedically in 2023, with an estimated 1.3 million deaths among HIV negative individualities and a fresh 167,000 deaths among people living with HIV (World Health Organization, 2023). The complaint burden disproportionately affects low and middle income countries, with eight nations counting for two thirds of the global TB prevalence India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the Democratic Republic of the Congo. Contemporary Phthisiatry confronts multifaceted challenges that ","url":"https://doi.org/10.5281/zenodo.18690002","authors":["Kurmanaliev, Nurlanbek","Ansari Mohammad Aman","Das Prayag","Faiz Mohammad Alam","Javed Aqib","Khan Imran","Khan Shohab Gufran","Kuppal Kumar Ragavan","Mohammad Aqib"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18690002","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.c.8315191.v1","name":"A scoping review of the use of generative artificial intelligence tools in health profession education","source":"datacite","abstract":"Abstract Background Generative Artificial Intelligence (GenAI) is one of the leading innovations that is expected to reshape society for decades to come. Health professions education (HPE) programs are expected to prepare graduates with adequate knowledge and skills to provide high-quality patient-centered care. Although the use of GenAI in health professions is increasing, its optimal integration in HPE is still ambiguous. This scoping review aims to synthesize currently available literature regarding the use of GenAI in health professions education. Method This scoping review is conducted following JBI methodology for scoping reviews framework 2020 and aligned with PRISMA-ScR. A systematic and comprehensive search was conducted in PubMed, ERIC, CINAHL, Embase, Scopus, Cochrane Library, and ProQuest Central with no language restrictions. The identified evidence was screened and extracted using Covidence software. Publications on the integration of GenAI in undergraduate or graduate health profession education were considered. Data was analyzed and presented using graphs and charts. Followed by a narrative thematic mapping of the included studies. Results Out of 14,208 scanned records, 241 were considered eligible. The included studies discuss the application of GenAI in diverse education processes of different health professions, such as curriculum design, content creation, content delivery, personalized learning, assessment, evaluation, and feedback provision. Most studies focused on ChatGPT integration in medical and nursing education, with content creation emerging as the predominant area of integration, whereas curriculum design and GenAI literacy were underexplored. Perception studies reported a positive perspective regarding GenAI used in education among students and faculty. Conclusion This review provides an overview of the current integration of GenAI in HPE in the literature, highlighting the associated opportunities, challenges, facilitators, and barriers. Future education efforts should focus on enhancing GenAI literacy, developing policy, and adopting a balanced approach. In addition to conducting comparative studies and long-term assessment of GenAI impact.","url":"https://doi.org/10.6084/m9.figshare.c.8315191.v1","authors":["Basil, Mounyah","Ahmed, Waad","Hajeomar, Reem","Strawbridge, Judith","Lynch, Matthew","Mukhalalati, Banan"],"tags":["Medicine","Environmental Sciences not elsewhere classified","Sociology","FOS: Sociology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8315191.v1","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.c.8315191","name":"A scoping review of the use of generative artificial intelligence tools in health profession education","source":"datacite","abstract":"Abstract Background Generative Artificial Intelligence (GenAI) is one of the leading innovations that is expected to reshape society for decades to come. Health professions education (HPE) programs are expected to prepare graduates with adequate knowledge and skills to provide high-quality patient-centered care. Although the use of GenAI in health professions is increasing, its optimal integration in HPE is still ambiguous. This scoping review aims to synthesize currently available literature regarding the use of GenAI in health professions education. Method This scoping review is conducted following JBI methodology for scoping reviews framework 2020 and aligned with PRISMA-ScR. A systematic and comprehensive search was conducted in PubMed, ERIC, CINAHL, Embase, Scopus, Cochrane Library, and ProQuest Central with no language restrictions. The identified evidence was screened and extracted using Covidence software. Publications on the integration of GenAI in undergraduate or graduate health profession education were considered. Data was analyzed and presented using graphs and charts. Followed by a narrative thematic mapping of the included studies. Results Out of 14,208 scanned records, 241 were considered eligible. The included studies discuss the application of GenAI in diverse education processes of different health professions, such as curriculum design, content creation, content delivery, personalized learning, assessment, evaluation, and feedback provision. Most studies focused on ChatGPT integration in medical and nursing education, with content creation emerging as the predominant area of integration, whereas curriculum design and GenAI literacy were underexplored. Perception studies reported a positive perspective regarding GenAI used in education among students and faculty. Conclusion This review provides an overview of the current integration of GenAI in HPE in the literature, highlighting the associated opportunities, challenges, facilitators, and barriers. Future education efforts should focus on enhancing GenAI literacy, developing policy, and adopting a balanced approach. In addition to conducting comparative studies and long-term assessment of GenAI impact.","url":"https://doi.org/10.6084/m9.figshare.c.8315191","authors":["Basil, Mounyah","Ahmed, Waad","Hajeomar, Reem","Strawbridge, Judith","Lynch, Matthew","Mukhalalati, Banan"],"tags":["Medicine","Environmental Sciences not elsewhere classified","Sociology","FOS: Sociology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8315191","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.16779222","name":"Enhancing Clinical Decision Support through Intelligent Integration of Electronic Health Records","source":"datacite","abstract":"The integration of Electronic Health Records (EHR) with Clinical Decision Support Systems (CDSS) has transformed modern healthcare by enhancing diagnostic accuracy, reducing medical errors, and improving patient outcomes. This study investigates the potential of intelligent integration strategies using structured and unstructured data from EHRs, bolstered by artificial intelligence (AI) methods. It presents a detailed literature review, a proposed framework, and real-world data insights into system effectiveness.","url":"https://doi.org/10.5281/zenodo.16779222","authors":["researcher"],"tags":["Electronic Health Records","Clinical Decision Support","Machine Learning","Healthcare Informatics","Predictive Analytics","AI in Healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.16779222","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.16779223","name":"Enhancing Clinical Decision Support through Intelligent Integration of Electronic Health Records","source":"datacite","abstract":"The integration of Electronic Health Records (EHR) with Clinical Decision Support Systems (CDSS) has transformed modern healthcare by enhancing diagnostic accuracy, reducing medical errors, and improving patient outcomes. This study investigates the potential of intelligent integration strategies using structured and unstructured data from EHRs, bolstered by artificial intelligence (AI) methods. It presents a detailed literature review, a proposed framework, and real-world data insights into system effectiveness.","url":"https://doi.org/10.5281/zenodo.16779223","authors":["researcher"],"tags":["Electronic Health Records","Clinical Decision Support","Machine Learning","Healthcare Informatics","Predictive Analytics","AI in Healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.16779223","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18667708","name":"RAD-CaseBookLLM-08","source":"datacite","abstract":"RAD-CaseBookLLM-08 is an open-access dataset containing Large Language Model (LLM)-generated educational texts focused on radiological differential diagnosis. The dataset was generated using ChatGPT-4o (OpenAI, web-based version, March 2025) under standardized prompting conditions (new user account, conversation memory disabled, new chat session for each topic). For each entry, a structured prompt was used, varying only the radiological theme under study. Prompts and responses were written exclusively in English, and all outputs were copied verbatim without editing, preserving formatting and model-generated concluding statements. The thematic inputs provided to the LLM correspond to radiological “key imaging findings” topic titles from the casebook Top 3 Differentials in Radiology: A Case Review (O’Brien WT, 2010). No copyrighted text, images, figures, case descriptions, explanations, or other protected material from the original publication were reproduced, copied, or included in this dataset. All educational content contained herein was independently generated by the LLM based solely on the thematic titles. The dataset is organized by radiology subspecialty and provided in both PDF and DOCX formats, distributed as compressed ZIP archives. It was created as part of a multicenter comparative study evaluating the perceived educational usefulness of LLM-generated differential diagnosis teaching material versus a traditional radiology casebook among junior and advanced radiology trainees. The exact prompt template used for dataset generation is provided in the file “prompt_template.txt” included in this repository. No executable code, scripts, or API-based pipelines were used during dataset creation. The prompt template constitutes the reproducible methodological component of this dataset. The primary purpose of this dataset is to provide a structured LLM-generated equivalent of a radiology differential diagnosis casebook covering diverse thematic imaging findings. It is intended to serve as a research resource for studying the educational characteristics, strengths, limitations, and reproducibility of LLM-generated medical teaching content. This dataset is not designed or validated for direct clinical use or as formally accredited educational material. This dataset is released under the CC0 1.0 Universal license to promote transparency, reproducibility, and further research in radiology education and medical artificial intelligence. This repository represents version 1.1 of the dataset (february 2026).","url":"https://doi.org/10.5281/zenodo.18667708","authors":["Saliba, Thomas","Fahrni, Guillaume"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18667708","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/9tv45","name":"A Rapid Update Protocol f a Published Scoping Review on AI-Enabled Digital Scribes in Primary Care (2025-2026)","source":"datacite","abstract":"Abstract Background: Artificial intelligence (AI)-enabled digital scribes, powered by automatic speech recognition (ASR) and natural language processing (NLP), are increasingly implemented in primary care to reduce documentation burden and support clinical workflow within electronic health records and electronic medical records. A scoping review published in 2026 synthesized evidence through November 2024. However, the rapid expansion of large language model-driven ambient documentation systems in 2025–2026 warrants an updated evidence synthesis. Given the accelerated pace of technological development and implementation, a rapid update methodology is appropriate to identify newly published studies and emerging implementation evidence. Methods/design: This rapid update will build upon a previously published scoping review and will follow methodological guidance for rapid reviews while maintaining transparency in reporting. Joanna Briggs Institute guidance for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) will be followed. The search will be limited to studies published from December 2024 to present (2026). Only a single database will be used (PubMed). Search terms will mirror the original strategy and include combinations of “digital scribe,” “ambient documentation,” “automatic speech recognition,” “natural language processing,” “machine learning,” and “electronic health record.” Eligible studies will include peer-reviewed empirical research examining AI-enabled digital scribes in primary care settings. Consistent with rapid review methodology, screening and data extraction will be conducted by a single reviewer with secondary verification of a subset of records. Extracted findings will be mapped to three predefined analytic domains: (1) effectiveness (documentation accuracy and workflow efficiency), (2) integration (technical interoperability and workflow fit), and (3) adoption (usability, training, uptake, safety, and cost). Methodological quality will not be used as a criterion for exclusion. Modified rapid review techniques will include a single reviewer, single database, and appropriate modifications with the PRISMA-SCR framework. This protocol is registered at Open Science Framework (https://osf.io/qw3v7). Methodological quality will not be used as a criterion for study exclusion. Discussion: This rapid update will identify emerging evidence on real-world implementation, safety, and sustainability of AI-enabled digital scribes in primary care. By focusing on literature published after November 2024, this review aims to capture developments associated with large language model-driven systems and early adoption at scale. Findings will inform future research directions and contribute to understanding how rapidly evolving AI documentation technologies are being integrated into primary care practice.","url":"https://doi.org/10.17605/osf.io/9tv45","authors":["rajesh nair, "],"tags":["Medicine and Health Sciences","digital scribes","electronic health record","electronic medical record","primary care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/9tv45","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18360608","name":"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers","source":"datacite","abstract":"Description This document serves as the formal cover letter for the academic submission of \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" It outlines the strategic and scientific importance of the Clark-Oneiro Synchronization Bridge, a breakthrough technology that leverages solar-core quantum correlations to trigger controlled nuclear de-excitation in Hafnium-178m2. This submission marks the inaugural public disclosure for the Lucid Oneiro Annals of Physics (LOAP) and establishes scientific priority for the Clark-Oneiro Synchronization Protocol. COVER LETTER: SUBMISSION FOR PEER REVIEW TO: The Academic Community, Strategic Reviewers, and Peer Referees FROM: Office of the Principal Investigator, Kevin Rashaud Clark DATE: January 24, 2026 SUBJECT: Breakthrough in Non-Local Nuclear De-excitation: The Clark-Oneiro Bridge Dear Colleagues and Distinguished Reviewers, I am formally submitting for your review and consideration the preprint titled: \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" For over three decades, the field of high-energy density physics has been at a standstill regarding the \"Hafnium Controversy.\" The scientific community has long recognized the immense energy potential of the Hf-178m2 isomer ($1.3 \\text{ GJ/g}$), yet we have remained bound by the thermodynamic impossibility of a portable, low-input trigger. The enclosed research, conducted under the auspices of Lucid Oneiro and its strategic divisions NEUAEON and The Hypersphere, presents the definitive resolution to this threshold problem. By shifting the paradigm from energy-based bombardment to the Clark-Oneiro Synchronization Protocol, we demonstrate that nuclear de-excitation can be achieved through non-local phase-matching with solar-core reactions. Key Innovations Addressed in this Paper: The Dicke-Clark Regime: Application of macroscopic quantum superradiance to establish a coherent nuclear state ($I \\propto N^2 \\gamma$). ACSE Mechanism: The introduction of Artificial Cross-Section Enhancement, allowing for resonant neutrino coupling as a sub-picosecond informational trigger. Strategic Differentials: The first viable roadmap for both high-output Quantum Batteries and non-kinetic strategic defense systems. CERTIFICATION OF HUMAN CONCEPTION & INTELLECTUAL PRIORITY This document and the associated research represent the original discoveries and conceptual frameworks of Kevin Rashaud Clark. While advanced computational tools were utilized for data synthesis and manuscript formatting, the core inventive steps, proprietary nomenclature (e.g., Clark-Oneiro Bridge), and strategic differentials are the product of human creative intelligence. This letter serves as a formal declaration of authorship for all purposes related to the Nobel Committee for Physics and international patent offices. DISCLOSURE & TRADE SECRETS This disclosure is made to establish scientific priority and to invite rigorous academic discourse. While the theoretical foundations are presented here for peer evaluation, the specific engineering specifications of the SSPT (Solar-Synchronized Particulate Trigger) remain the proprietary trade secrets of Lucid Oneiro. We believe this work represents a fundamental shift in human capability—transitioning from the era of \"Force\" to the era of \"Synchronization.\" We welcome your critical analysis and look forward to the ensuing dialogue. Respectfully submitted, Kevin Rashaud Clark Principal Investigator Founder, Lucid Oneiro | NEUAEON | The Hypersphere suno.com/theworldsleast X/Instagram: @NEUAEON Institutional Note: All tactical and licensing inquiries regarding the Clark-Oneiro Synchronization Bridge must be formally directed to the board of directors at Lucid Oneiro. Proprietary protections are in full effect. Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers Published in: Lucid Oneiro Annals of Phys","url":"https://doi.org/10.5281/zenodo.18360608","authors":["Kevin Rashaud Clark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18360608","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18444550","name":"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers","source":"datacite","abstract":"Description This document serves as the formal cover letter for the academic submission of \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" It outlines the strategic and scientific importance of the Clark-Oneiro Synchronization Bridge, a breakthrough technology that leverages solar-core quantum correlations to trigger controlled nuclear de-excitation in Hafnium-178m2. This submission marks the inaugural public disclosure for the Lucid Oneiro Annals of Physics (LOAP) and establishes scientific priority for the Clark-Oneiro Synchronization Protocol. COVER LETTER: SUBMISSION FOR PEER REVIEW TO: The Academic Community, Strategic Reviewers, and Peer Referees FROM: Office of the Principal Investigator, Kevin Rashaud Clark DATE: January 24, 2026 SUBJECT: Breakthrough in Non-Local Nuclear De-excitation: The Clark-Oneiro Bridge Dear Colleagues and Distinguished Reviewers, I am formally submitting for your review and consideration the preprint titled: \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" For over three decades, the field of high-energy density physics has been at a standstill regarding the \"Hafnium Controversy.\" The scientific community has long recognized the immense energy potential of the Hf-178m2 isomer ($1.3 \\text{ GJ/g}$), yet we have remained bound by the thermodynamic impossibility of a portable, low-input trigger. The enclosed research, conducted under the auspices of Lucid Oneiro and its strategic divisions NEUAEON and The Hypersphere, presents the definitive resolution to this threshold problem. By shifting the paradigm from energy-based bombardment to the Clark-Oneiro Synchronization Protocol, we demonstrate that nuclear de-excitation can be achieved through non-local phase-matching with solar-core reactions. Key Innovations Addressed in this Paper: The Dicke-Clark Regime: Application of macroscopic quantum superradiance to establish a coherent nuclear state ($I \\propto N^2 \\gamma$). ACSE Mechanism: The introduction of Artificial Cross-Section Enhancement, allowing for resonant neutrino coupling as a sub-picosecond informational trigger. Strategic Differentials: The first viable roadmap for both high-output Quantum Batteries and non-kinetic strategic defense systems. CERTIFICATION OF HUMAN CONCEPTION & INTELLECTUAL PRIORITY This document and the associated research represent the original discoveries and conceptual frameworks of Kevin Rashaud Clark. While advanced computational tools were utilized for data synthesis and manuscript formatting, the core inventive steps, proprietary nomenclature (e.g., Clark-Oneiro Bridge), and strategic differentials are the product of human creative intelligence. This letter serves as a formal declaration of authorship for all purposes related to the Nobel Committee for Physics and international patent offices. DISCLOSURE & TRADE SECRETS This disclosure is made to establish scientific priority and to invite rigorous academic discourse. While the theoretical foundations are presented here for peer evaluation, the specific engineering specifications of the SSPT (Solar-Synchronized Particulate Trigger) remain the proprietary trade secrets of Lucid Oneiro. We believe this work represents a fundamental shift in human capability—transitioning from the era of \"Force\" to the era of \"Synchronization.\" We welcome your critical analysis and look forward to the ensuing dialogue. Respectfully submitted, Kevin Rashaud Clark Principal Investigator Founder, Lucid Oneiro | NEUAEON | The Hypersphere suno.com/theworldsleast X/Instagram: @NEUAEON Institutional Note: All tactical and licensing inquiries regarding the Clark-Oneiro Synchronization Bridge must be formally directed to the board of directors at Lucid Oneiro. Proprietary protections are in full effect. Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers Published in: Lucid Oneiro Annals of Phys","url":"https://doi.org/10.5281/zenodo.18444550","authors":["Kevin Rashaud Clark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18444550","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18448140","name":"Qatar Nodal Technology World Arbitration Center: Institutional Framework for Global Nodal Governance (Cryptographic Traceability Edition)","source":"datacite","abstract":"This document establishes Qatar as the World Center for Nodal Technology Arbitration (QNTWAC) under the D10Z Universal Nodal Architecture framework. The center operates as the sole global authority for commercial arbitration, governance coordination across 22 MENA member states, standards validation, crisis response protocols, and technology transfer administration. VERSION 1.0.1 — CRYPTOGRAPHIC TRACEABILITY EDITION This version integrates complete cryptographic traceability through:- Genesis Hash validation (fd6733e1d26706dbd90ef3d37d0b15bc20ce9b5003e34865e756bd24c7fb7ec3)- Master Index I.1.1 reference (Cross-Layer Validation Authority)- Layer-specific hashes (L0 Physical, L1 Computational, MI Integration)- Forensic hardening patches (PAT-001, PAT-002, PAT-003)- Independent audit capability (SHA3-256 verification) INSTITUTIONAL FRAMEWORK INCLUDES: Governance Structure:- Plenipotentiary Council (22 MENA states, one vote per state)- Executive Secretariat (Doha headquarters - proposed)- Technical Validation Authority (QCRI) Revenue Distribution:- 10% Creator/Development Team (D10Z Institute)- 90% Council-Administered Fund (40% R&D, 30% MENA ecosystem, 20% crisis reserve, 10% operations) Reserved Rights:- Gaming and entertainment applications reserved exclusively to Vision Holder (non-transferable, non-negotiable) Crisis Response:- 24-hour emergency deployment authorization- Regional coordination protocols- TÜV-validated operational capacity Technical Validation:- Independent validation- Measured performance: 14.9× compression, 100% reconstruction accuracy, zero data loss (10,000 test cycles)- Energy efficiency: 92% reduction vs. traditional infrastructure- Water consumption: Zero (passive ionic resonance cooling) Economic Impact:- $50.84B 10-year GCC value (conservative estimate)- 5,968% ROI- <60 days payback period Cryptographic Integrity:- All institutional decisions anchored to sealed D10Z Genesis Hash- Immutable traceability across physical (L0) and computational (L1) layers- Transitive Audit Law: die_serial → build_id → window_hash- Forensic audit automation enabled Legal Framework:- Substantive law: Swiss Federal Code- Arbitration: ICC Rules (Dubai seat)- Languages: English (official), Arabic (official translations) Implementation Timeline:- Q1 2026: Establishment (Qatar endorsement, Secretariat formation)- Q2 2026: Council formation (≥16 states quorum)- Q3-Q4 2026: Operational deployment (first certifications)- 2027-2028: Global scaling (80,000-node deployments) DOCUMENT STATUS:This is an institutional framework proposal that will become binding upon Qatari government endorsement and acceptance by ≥12 MENA member states (quorum). It establishes the legal, technical, and operational foundation for sovereign nodal technology deployment globally. The framework is being presented to:- Qatar Investment Authority (QIA)- Qai (Qatar National AI Company)- Qatar Computing Research Institute (QCRI)- National AI Committee (MCIT Qatar)- 22 MENA member states for consideration RELATED WORKS:- L0 Physical Layer: DOI 10.5281/zenodo.18423626 (MIZAN Hermeticity Protocol)- L1 Computational Layer: DOI 10.5281/zenodo.18423799 (D10Z-TTA Governance Framework)- FARID Governance: DOI 10.5281/zenodo.18446625 (Structural Requirements)- Master Index I.1.1: Integrated herein (Sealed: 2026-01-30) VERIFICATION:Document hash (SHA3-256): c24541bec30484a217e1bda0a8b862488a0ab1a220252bd0010352b5085777c5Genesis hash (System Authority): fd6733e1d26706dbd90ef3d37d0b15bc20ce9b5003e34865e756bd24c7fb7ec3 Any byte-level modification creates a different hash and therefore a different institutional framework. Refer to Section X.1 for amendment procedures. FILES INCLUDED:1. QATAR_NODAL_ARBITRATION_CENTER_FINAL.md (Main institutional document, 29KB)2. QATAR_CENTER_EXECUTIVE_BRIEF.md (Executive summary for leaders, 13KB)3. CHANGELOG_v1.0.1.md (Version history and cryptographic additions, 7.5KB)4. README.md (Quick start guide, 6KB)5. CITATION.cff (Academic citation met","url":"https://doi.org/10.5281/zenodo.18448140","authors":["Al Thani, Jamil"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18448140","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18448141","name":"Qatar Nodal Technology World Arbitration Center: Institutional Framework for Global Nodal Governance (Cryptographic Traceability Edition)","source":"datacite","abstract":"This document establishes Qatar as the World Center for Nodal Technology Arbitration (QNTWAC) under the D10Z Universal Nodal Architecture framework. The center operates as the sole global authority for commercial arbitration, governance coordination across 22 MENA member states, standards validation, crisis response protocols, and technology transfer administration. VERSION 1.0.1 — CRYPTOGRAPHIC TRACEABILITY EDITION This version integrates complete cryptographic traceability through:- Genesis Hash validation (fd6733e1d26706dbd90ef3d37d0b15bc20ce9b5003e34865e756bd24c7fb7ec3)- Master Index I.1.1 reference (Cross-Layer Validation Authority)- Layer-specific hashes (L0 Physical, L1 Computational, MI Integration)- Forensic hardening patches (PAT-001, PAT-002, PAT-003)- Independent audit capability (SHA3-256 verification) INSTITUTIONAL FRAMEWORK INCLUDES: Governance Structure:- Plenipotentiary Council (22 MENA states, one vote per state)- Executive Secretariat (Doha headquarters - proposed)- Technical Validation Authority (QCRI) Revenue Distribution:- 10% Creator/Development Team (D10Z Institute)- 90% Council-Administered Fund (40% R&D, 30% MENA ecosystem, 20% crisis reserve, 10% operations) Reserved Rights:- Gaming and entertainment applications reserved exclusively to Vision Holder (non-transferable, non-negotiable) Crisis Response:- 24-hour emergency deployment authorization- Regional coordination protocols- TÜV-validated operational capacity Technical Validation:- Independent validation- Measured performance: 14.9× compression, 100% reconstruction accuracy, zero data loss (10,000 test cycles)- Energy efficiency: 92% reduction vs. traditional infrastructure- Water consumption: Zero (passive ionic resonance cooling) Economic Impact:- $50.84B 10-year GCC value (conservative estimate)- 5,968% ROI- <60 days payback period Cryptographic Integrity:- All institutional decisions anchored to sealed D10Z Genesis Hash- Immutable traceability across physical (L0) and computational (L1) layers- Transitive Audit Law: die_serial → build_id → window_hash- Forensic audit automation enabled Legal Framework:- Substantive law: Swiss Federal Code- Arbitration: ICC Rules (Dubai seat)- Languages: English (official), Arabic (official translations) Implementation Timeline:- Q1 2026: Establishment (Qatar endorsement, Secretariat formation)- Q2 2026: Council formation (≥16 states quorum)- Q3-Q4 2026: Operational deployment (first certifications)- 2027-2028: Global scaling (80,000-node deployments) DOCUMENT STATUS:This is an institutional framework proposal that will become binding upon Qatari government endorsement and acceptance by ≥12 MENA member states (quorum). It establishes the legal, technical, and operational foundation for sovereign nodal technology deployment globally. The framework is being presented to:- Qatar Investment Authority (QIA)- Qai (Qatar National AI Company)- Qatar Computing Research Institute (QCRI)- National AI Committee (MCIT Qatar)- 22 MENA member states for consideration RELATED WORKS:- L0 Physical Layer: DOI 10.5281/zenodo.18423626 (MIZAN Hermeticity Protocol)- L1 Computational Layer: DOI 10.5281/zenodo.18423799 (D10Z-TTA Governance Framework)- FARID Governance: DOI 10.5281/zenodo.18446625 (Structural Requirements)- Master Index I.1.1: Integrated herein (Sealed: 2026-01-30) VERIFICATION:Document hash (SHA3-256): c24541bec30484a217e1bda0a8b862488a0ab1a220252bd0010352b5085777c5Genesis hash (System Authority): fd6733e1d26706dbd90ef3d37d0b15bc20ce9b5003e34865e756bd24c7fb7ec3 Any byte-level modification creates a different hash and therefore a different institutional framework. Refer to Section X.1 for amendment procedures. FILES INCLUDED:1. QATAR_NODAL_ARBITRATION_CENTER_FINAL.md (Main institutional document, 29KB)2. QATAR_CENTER_EXECUTIVE_BRIEF.md (Executive summary for leaders, 13KB)3. CHANGELOG_v1.0.1.md (Version history and cryptographic additions, 7.5KB)4. README.md (Quick start guide, 6KB)5. CITATION.cff (Academic citation met","url":"https://doi.org/10.5281/zenodo.18448141","authors":["Al Thani, Jamil"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18448141","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18656150","name":"THE HISTORY AND FUTURE OF COMPOSITE MATERIALS","source":"datacite","abstract":"Composite materials have played a transformative role in human technological development, from ancient straw-reinforced clay bricks to advanced carbon fiber-reinforced polymers used in aerospace and biomedical engineering. This thesis examines the historical evolution of composite materials, analyzes key scientific and technological breakthroughs, and explores future trends including nanocomposites, smart materials, sustainable composites, and additive manufacturing applications. The study applies a qualitative literature review methodology combined with comparative technological analysis. Findings demonstrate that composite materials have consistently driven innovation by offering superior strength-to-weight ratios, corrosion resistance, durability, and multifunctionality. Future development is expected to focus on sustainability, recyclability, integration of artificial intelligence in material design, and bio-inspired composite structures. The research confirms that composite materials will remain central to aerospace, automotive, construction, renewable energy, and medical industries.","url":"https://doi.org/10.5281/zenodo.18656150","authors":["Nomozov, Bekzod"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18656150","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18656149","name":"THE HISTORY AND FUTURE OF COMPOSITE MATERIALS","source":"datacite","abstract":"Composite materials have played a transformative role in human technological development, from ancient straw-reinforced clay bricks to advanced carbon fiber-reinforced polymers used in aerospace and biomedical engineering. This thesis examines the historical evolution of composite materials, analyzes key scientific and technological breakthroughs, and explores future trends including nanocomposites, smart materials, sustainable composites, and additive manufacturing applications. The study applies a qualitative literature review methodology combined with comparative technological analysis. Findings demonstrate that composite materials have consistently driven innovation by offering superior strength-to-weight ratios, corrosion resistance, durability, and multifunctionality. Future development is expected to focus on sustainability, recyclability, integration of artificial intelligence in material design, and bio-inspired composite structures. The research confirms that composite materials will remain central to aerospace, automotive, construction, renewable energy, and medical industries.","url":"https://doi.org/10.5281/zenodo.18656149","authors":["Nomozov, Bekzod"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18656149","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/yhsg3","name":"Large Language Models in Traditional Chinese Medicine Clinical Decision Support: A Scoping Review","source":"datacite","abstract":"The rapid evolution of Artificial Intelligence, particularly Large Language Models (LLMs), has catalyzed transformative applications across education, psychology, and general healthcare. In the medical domain, LLMs have demonstrated significant potential in diagnostic assistance, clinical Q&amp;A, and electronic health record (EHR) processing. However, the application of LLMs within the specialized field of Traditional Chinese Medicine (TCM) clinical decision-making—which involves unique diagnostic logic such as \"Pattern Differentiation\" (Bian Zheng)—remains in its nascent stage. Existing research is fragmented, and there is a lack of systematic synthesis regarding the current state of the art. This review aims to map the existing literature to clarify the application scope, efficacy, and inherent limitations of LLMs in TCM clinical settings.","url":"https://doi.org/10.17605/osf.io/yhsg3","authors":["jiangrong, Wang"],"tags":["Health Information Technology","Medicine and Health Sciences","Analytical, Diagnostic and Therapeutic Techniques and Equipment","Large Language Models; Traditional Chinese Medicine; Clinical Decision Support Systems; Artificial Intelligence; Scoping Review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/yhsg3","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.48550/arxiv.2510.09308","name":"A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms","source":"datacite","abstract":"Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with support vector machines achieving up to 98.5 percent and 98.3 percent accuracy in key tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles metamodeling, semantic integration, and automated code generation can provide a practical path toward interoperable, reproducible, and trustworthy digital health platforms.","url":"https://doi.org/10.48550/arxiv.2510.09308","authors":["Raheem, Mira","Elgammal, Amal","Papazoglou, Michael","Krämer, Bernd","El-Tazi, Neamat"],"tags":["Software Engineering (cs.SE)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.09308","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18649561","name":"Blood Group Detection Using Artificial Intelligence","source":"datacite","abstract":"Blood group detection is an essential part of healthcare, especially in situations like blood transfusions, organ transplants, and medical emergencies. Traditionally, blood groups are identified using laboratory-based methods that require blood samples, chemical reagents, and trained personnel. These methods, while effective, can be time-consuming, invasive, and prone to human error.In recent years, advancements in technology have introduced artificial intelligence (AI) techniques that can detect blood groups through fingerprints, providing a non-invasive, faster, and more accurate alternative. AI-based finger machines analyze patterns and features in fingerprints to predict blood group types without the need for drawing blood.This review paper explores both traditional and AI-based methods for blood group detection. It explains how AI finger machines work, compares them with old methods, discusses their advantages and limitations, and highlights potential applications in hospitals, blood donation camps, and personal healthcare monitoring. The paper also looks at the future prospects of integrating AI blood detection systems with modern healthcare technology to improve efficiency and accessibility.","url":"https://doi.org/10.5281/zenodo.18649561","authors":["Krushi Pradhan*, Pratik Bhabad, Janvi Patil"],"tags":["Artificial Intelligence (AI), Blood Group Detection, Finger Recognition, Machine Learning, Biomedical Device, Deep Learning, Image Processing, Sensor Technology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18649561","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18649562","name":"Blood Group Detection Using Artificial Intelligence","source":"datacite","abstract":"Blood group detection is an essential part of healthcare, especially in situations like blood transfusions, organ transplants, and medical emergencies. Traditionally, blood groups are identified using laboratory-based methods that require blood samples, chemical reagents, and trained personnel. These methods, while effective, can be time-consuming, invasive, and prone to human error.In recent years, advancements in technology have introduced artificial intelligence (AI) techniques that can detect blood groups through fingerprints, providing a non-invasive, faster, and more accurate alternative. AI-based finger machines analyze patterns and features in fingerprints to predict blood group types without the need for drawing blood.This review paper explores both traditional and AI-based methods for blood group detection. It explains how AI finger machines work, compares them with old methods, discusses their advantages and limitations, and highlights potential applications in hospitals, blood donation camps, and personal healthcare monitoring. The paper also looks at the future prospects of integrating AI blood detection systems with modern healthcare technology to improve efficiency and accessibility.","url":"https://doi.org/10.5281/zenodo.18649562","authors":["Krushi Pradhan*, Pratik Bhabad, Janvi Patil"],"tags":["Artificial Intelligence (AI), Blood Group Detection, Finger Recognition, Machine Learning, Biomedical Device, Deep Learning, Image Processing, Sensor Technology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18649562","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18643556","name":"AI-DRIVEN DISEASE PREDICTION AND DIAGNOSIS: A SYSTEMATIC REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPROACHES","source":"datacite","abstract":"The rapid advancement of artificial intelligence (AI) has significantly transformed the healthcare sector, particularly in the domain of disease prediction and diagnosis. This paper presents a comprehensive and systematic review of recent research on AI-driven healthcare prediction systems, focusing on machine learning and deep learning methodologies. The study highlights the growing importance of intelligent systems in analysing complex healthcare data and generating reliable predictions that support early disease detection and improved clinical decision-making. Although AI has brought remarkable improvements in predictive healthcare, several challenges remain, including data quality, interpretability, privacy concerns, and integration with real-world clinical workflows. This review examines a wide range of recent studies to identify commonly used algorithms, datasets, and evaluation strategies, while also analysing their strengths and limitations. Furthermore, the paper discusses the potential impact of AI-based healthcare prediction on patient outcomes and healthcare efficiency. The findings demonstrate that AI plays a crucial role in accurate disease diagnosis, healthcare forecasting, and large-scale health data analysis by leveraging electronic health records and reconstructing patient medical histories. Finally, the study outlines key challenges and proposes future research directions aimed at maximizing the effectiveness and adoption of AI in disease diagnosis and predictive healthcare systems.","url":"https://doi.org/10.5281/zenodo.18643556","authors":["Sayed Ayan Ahmed","Osama nusrat khan","Shabbir Ansari","Priyanshu pandey"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18643556","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18643555","name":"AI-DRIVEN DISEASE PREDICTION AND DIAGNOSIS: A SYSTEMATIC REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPROACHES","source":"datacite","abstract":"The rapid advancement of artificial intelligence (AI) has significantly transformed the healthcare sector, particularly in the domain of disease prediction and diagnosis. This paper presents a comprehensive and systematic review of recent research on AI-driven healthcare prediction systems, focusing on machine learning and deep learning methodologies. The study highlights the growing importance of intelligent systems in analysing complex healthcare data and generating reliable predictions that support early disease detection and improved clinical decision-making. Although AI has brought remarkable improvements in predictive healthcare, several challenges remain, including data quality, interpretability, privacy concerns, and integration with real-world clinical workflows. This review examines a wide range of recent studies to identify commonly used algorithms, datasets, and evaluation strategies, while also analysing their strengths and limitations. Furthermore, the paper discusses the potential impact of AI-based healthcare prediction on patient outcomes and healthcare efficiency. The findings demonstrate that AI plays a crucial role in accurate disease diagnosis, healthcare forecasting, and large-scale health data analysis by leveraging electronic health records and reconstructing patient medical histories. Finally, the study outlines key challenges and proposes future research directions aimed at maximizing the effectiveness and adoption of AI in disease diagnosis and predictive healthcare systems.","url":"https://doi.org/10.5281/zenodo.18643555","authors":["Sayed Ayan Ahmed","Osama nusrat khan","Shabbir Ansari","Priyanshu pandey"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18643555","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18643342","name":"MEDICAL INNOVATIONS: A NEW ERA OF EARLY DISEASE DETECTION AND EFFECTIVE TREATMENT","source":"datacite","abstract":"Recent advances in medical science and technology have transformed healthcare systems worldwide, leading to a new era of early disease detection and effective treatment. Innovations such as artificial intelligence, genomics, wearable devices, telemedicine, nanotechnology, and personalized medicine have significantly improved diagnostic accuracy and therapeutic outcomes. Early identification of diseases plays a crucial role in reducing morbidity, mortality, and healthcare costs, while enhancing patients’ quality of life. This article explores the role of modern medical innovations in revolutionizing diagnostic and treatment strategies. It highlights how digital health platforms enable continuous patient monitoring, how artificial intelligence supports clinical decision-making, and how molecular medicine facilitates targeted therapies. Furthermore, the integration of big data and machine learning has enabled predictive modeling for disease prevention and management. Despite remarkable progress, challenges such as data privacy, ethical concerns, accessibility, and cost-effectiveness remain. Addressing these issues is essential to ensure equitable healthcare delivery. Through a comprehensive literature review and analysis of current methodologies, this study evaluates the impact of innovative technologies on healthcare efficiency and patient outcomes. The findings indicate that early diagnostic tools and advanced treatment approaches significantly reduce disease burden and improve survival rates. Ultimately, medical innovations are reshaping modern healthcare by promoting preventive medicine, personalized care, and evidence-based practice. Continued investment in research, interdisciplinary collaboration, and policy development is required to maximize the benefits of technological progress in medicine. This article provides valuable insights for healthcare professionals, researchers, and policymakers seeking to understand and implement innovative solutions for sustainable healthcare development.","url":"https://doi.org/10.5281/zenodo.18643342","authors":["Raimjonov Barkamol Qobiljon o'g'li","Worldly Knowledge Publishing Centre"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18643342","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18643343","name":"MEDICAL INNOVATIONS: A NEW ERA OF EARLY DISEASE DETECTION AND EFFECTIVE TREATMENT","source":"datacite","abstract":"Recent advances in medical science and technology have transformed healthcare systems worldwide, leading to a new era of early disease detection and effective treatment. Innovations such as artificial intelligence, genomics, wearable devices, telemedicine, nanotechnology, and personalized medicine have significantly improved diagnostic accuracy and therapeutic outcomes. Early identification of diseases plays a crucial role in reducing morbidity, mortality, and healthcare costs, while enhancing patients’ quality of life. This article explores the role of modern medical innovations in revolutionizing diagnostic and treatment strategies. It highlights how digital health platforms enable continuous patient monitoring, how artificial intelligence supports clinical decision-making, and how molecular medicine facilitates targeted therapies. Furthermore, the integration of big data and machine learning has enabled predictive modeling for disease prevention and management. Despite remarkable progress, challenges such as data privacy, ethical concerns, accessibility, and cost-effectiveness remain. Addressing these issues is essential to ensure equitable healthcare delivery. Through a comprehensive literature review and analysis of current methodologies, this study evaluates the impact of innovative technologies on healthcare efficiency and patient outcomes. The findings indicate that early diagnostic tools and advanced treatment approaches significantly reduce disease burden and improve survival rates. Ultimately, medical innovations are reshaping modern healthcare by promoting preventive medicine, personalized care, and evidence-based practice. Continued investment in research, interdisciplinary collaboration, and policy development is required to maximize the benefits of technological progress in medicine. This article provides valuable insights for healthcare professionals, researchers, and policymakers seeking to understand and implement innovative solutions for sustainable healthcare development.","url":"https://doi.org/10.5281/zenodo.18643343","authors":["Raimjonov Barkamol Qobiljon o'g'li","Worldly Knowledge Publishing Centre"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18643343","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18641876","name":"Review on the Application of Medical Artificial Intelligence in The Gambia's Health Care System","source":"datacite","abstract":"It is evident that quality health care increases the likelihood of desired health outcomes, leading to a healthy nation, and of course, preserving the Sustainable Development Goals (SDGs) and a healthful working population. Medical Artificial Intelligence (MAI) technologies have been radically transforming the health care systems of advanced countries such as China, the US, and India. However, The Gambia continues to struggle with the provision of equity and quality health care services, and very little or no research has been done to explore the advantage of adopting innovative technological solutions, such as MAI, to mitigate these challenges. Therefore, this paper evaluates the adoption of MAI technologies within The Gambia's health care system, intending to attain the quantifiable attributes of quality health care, such as equity, easy accessibility, timeliness, affordability, safety, and effectiveness. Additionally, the paper presents and discussed the motivation, use cases, and challenges of adopting these technologies. Most importantly, it also recommends the adoption of specific feasible MAI technologies for adoption. Finally, the findings presented in this paper will not only help to better understand the current challenges faced by Gambia's health care system but, most importantly, the solutions to these problems.","url":"https://doi.org/10.5281/zenodo.18641876","authors":["Ebrima Jaw","Wang Xue Ming","Lang Loum","Lamin L.   Janneh"],"tags":["Medical Artificial Intelligences (MAI); Clinical Decision Support Systems (CDSS); Motivation; Challenges; Use Cases; The Gambia; Health Care Systems;"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.18641876","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18641877","name":"Review on the Application of Medical Artificial Intelligence in The Gambia's Health Care System","source":"datacite","abstract":"It is evident that quality health care increases the likelihood of desired health outcomes, leading to a healthy nation, and of course, preserving the Sustainable Development Goals (SDGs) and a healthful working population. Medical Artificial Intelligence (MAI) technologies have been radically transforming the health care systems of advanced countries such as China, the US, and India. However, The Gambia continues to struggle with the provision of equity and quality health care services, and very little or no research has been done to explore the advantage of adopting innovative technological solutions, such as MAI, to mitigate these challenges. Therefore, this paper evaluates the adoption of MAI technologies within The Gambia's health care system, intending to attain the quantifiable attributes of quality health care, such as equity, easy accessibility, timeliness, affordability, safety, and effectiveness. Additionally, the paper presents and discussed the motivation, use cases, and challenges of adopting these technologies. Most importantly, it also recommends the adoption of specific feasible MAI technologies for adoption. Finally, the findings presented in this paper will not only help to better understand the current challenges faced by Gambia's health care system but, most importantly, the solutions to these problems.","url":"https://doi.org/10.5281/zenodo.18641877","authors":["Ebrima Jaw","Wang Xue Ming","Lang Loum","Lamin L.   Janneh"],"tags":["Medical Artificial Intelligences (MAI); Clinical Decision Support Systems (CDSS); Motivation; Challenges; Use Cases; The Gambia; Health Care Systems;"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.18641877","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.30945099.v1","name":"Artificial intelligence in breast cancer: clinical applications in diagnosis, prognosis, and therapeutics","source":"datacite","abstract":"Breast cancer (BC) presents a considerable global health challenge and is characterized by increasing mortality and morbidity rates. Prompt screening and accurate diagnosis are crucial for improving patient outcomes. For the assessment of BC, radiographic imaging modalities such as digital breast tomosynthesis (DBT), ultrasound, digital mammography (DM), magnetic resonance imaging (MRI), and nuclear medicine procedures are commonly used. The gold standard for confirming cancer is histopathology. To effectively support the segmentation, diagnosis, and prognosis of BC. Artificial intelligence (AI) technologies show great promise for the quantitative depiction of medical images. This review explores recent strides in AI applications for BC. The literature search from 2018 to 2025 was performed with the PubMed database. It includes rapid breast lesion detection, segmentation, cancer diagnosis and enhanced imaging quality through data augmentation. It also discusses the biological characterization of BC via AI-based classification tools, including subtyping and staging. Furthermore, this review also explores the use of multiomics data to predict clinical outcomes such as survival, treatment response, and metastasis in BC. Additionally, we recognized the challenges faced by AI in BC in real-world applications, including organizing data, model interpretability, and regulatory compliance. BC is one of the most common cancers in women worldwide. Early detection greatly increases the chances of successful treatment, but traditional screening and diagnosis methods rely on specialists who can review only many images and samples each day. AI offers new tools that learn from large collections of medical data to help doctors detect, diagnose, and treat BC more effectively. In screening, AI can quickly analyze mammograms, digital breast tomosynthesis, ultrasounds, and MRIs. It highlights suspicious areas, decreases false alarms, and can spot small lesions that humans might miss – especially in dense breast tissue. By prioritizing high-risk images for review, AI makes it easy for radiologists without compromising accuracy. When a biopsy is taken, AI systems examine digitized tissue slides to assess tumor features and key biomarkers, such as hormone receptors (ERs, PRs), HER2, and proliferation markers (Ki-67). These automated analyses are consistent, rapid, and can sometimes match or exceed human performance, helping pathologists make faster, more reliable diagnoses. AI models also combine imaging, genetic, and clinical information to predict an individual patient’s risk of developing BC, the likely course of the disease, and how it will respond to specific treatments. Such personalized predictions guide decisions about preventive measures, chemotherapy, surgery, and targeted therapies. Despite their great promise, AI tools require large, high-quality datasets for training, clear validation standards, and safeguards for patient privacy. Ongoing studies are working to ensure that these systems are robust across diverse populations. With continued research, careful testing, and thoughtful integration into clinical practice, AI has the potential to transform BC care to make screening more accurate, make diagnosis more precise, and make treatment truly personalized.","url":"https://doi.org/10.6084/m9.figshare.30945099.v1","authors":["Singh, Janhvi","Alsaidan, Omar Awad","Aodah, Alhussain","Alrobaian, Majed","Almalki, Waleed H","Almujri, Salem Salman","Sahoo, Ankit","Alam, Kainat","Lal, Jonathan A.","Barkat, Md Abul","Rahman, Mahfoozur"],"tags":["Space Science","Medicine","Cell Biology","Neuroscience","Pharmacology","Biotechnology","Information Systems not elsewhere classified","Cancer"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30945099.v1","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.30945099","name":"Artificial intelligence in breast cancer: clinical applications in diagnosis, prognosis, and therapeutics","source":"datacite","abstract":"Breast cancer (BC) presents a considerable global health challenge and is characterized by increasing mortality and morbidity rates. Prompt screening and accurate diagnosis are crucial for improving patient outcomes. For the assessment of BC, radiographic imaging modalities such as digital breast tomosynthesis (DBT), ultrasound, digital mammography (DM), magnetic resonance imaging (MRI), and nuclear medicine procedures are commonly used. The gold standard for confirming cancer is histopathology. To effectively support the segmentation, diagnosis, and prognosis of BC. Artificial intelligence (AI) technologies show great promise for the quantitative depiction of medical images. This review explores recent strides in AI applications for BC. The literature search from 2018 to 2025 was performed with the PubMed database. It includes rapid breast lesion detection, segmentation, cancer diagnosis and enhanced imaging quality through data augmentation. It also discusses the biological characterization of BC via AI-based classification tools, including subtyping and staging. Furthermore, this review also explores the use of multiomics data to predict clinical outcomes such as survival, treatment response, and metastasis in BC. Additionally, we recognized the challenges faced by AI in BC in real-world applications, including organizing data, model interpretability, and regulatory compliance. BC is one of the most common cancers in women worldwide. Early detection greatly increases the chances of successful treatment, but traditional screening and diagnosis methods rely on specialists who can review only many images and samples each day. AI offers new tools that learn from large collections of medical data to help doctors detect, diagnose, and treat BC more effectively. In screening, AI can quickly analyze mammograms, digital breast tomosynthesis, ultrasounds, and MRIs. It highlights suspicious areas, decreases false alarms, and can spot small lesions that humans might miss – especially in dense breast tissue. By prioritizing high-risk images for review, AI makes it easy for radiologists without compromising accuracy. When a biopsy is taken, AI systems examine digitized tissue slides to assess tumor features and key biomarkers, such as hormone receptors (ERs, PRs), HER2, and proliferation markers (Ki-67). These automated analyses are consistent, rapid, and can sometimes match or exceed human performance, helping pathologists make faster, more reliable diagnoses. AI models also combine imaging, genetic, and clinical information to predict an individual patient’s risk of developing BC, the likely course of the disease, and how it will respond to specific treatments. Such personalized predictions guide decisions about preventive measures, chemotherapy, surgery, and targeted therapies. Despite their great promise, AI tools require large, high-quality datasets for training, clear validation standards, and safeguards for patient privacy. Ongoing studies are working to ensure that these systems are robust across diverse populations. With continued research, careful testing, and thoughtful integration into clinical practice, AI has the potential to transform BC care to make screening more accurate, make diagnosis more precise, and make treatment truly personalized.","url":"https://doi.org/10.6084/m9.figshare.30945099","authors":["Singh, Janhvi","Alsaidan, Omar Awad","Aodah, Alhussain","Alrobaian, Majed","Almalki, Waleed H","Almujri, Salem Salman","Sahoo, Ankit","Alam, Kainat","Lal, Jonathan A.","Barkat, Md Abul","Rahman, Mahfoozur"],"tags":["Space Science","Medicine","Cell Biology","Neuroscience","Pharmacology","Biotechnology","Information Systems not elsewhere classified","Cancer"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30945099","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18625032","name":"RAD-CaseBookLLM-08","source":"datacite","abstract":"RAD-CaseBookLLM-08 is an open-access dataset containing Large Language Model (LLM)-generated educational texts focused on radiological differential diagnosis. The dataset was generated using ChatGPT-4o (OpenAI, web-based version, March 2025) under standardized prompting conditions (new user account, conversation memory disabled, new chat session for each topic). For each entry, a structured prompt was used, varying only the radiological theme under study. Prompts and responses were written exclusively in English, and all outputs were copied verbatim without editing, preserving formatting and model-generated concluding statements. The thematic inputs provided to the LLM correspond to radiological “key imaging findings” topic titles from the casebook Top 3 Differentials in Radiology: A Case Review (O’Brien WT, 2010). No copyrighted text, images, figures, case descriptions, explanations, or other protected material from the original publication were reproduced, copied, or included in this dataset. All educational content contained herein was independently generated by the LLM based solely on the thematic titles. The dataset is organized by radiology subspecialty and provided in both PDF and DOCX formats, distributed as compressed ZIP archives. It was created as part of a multicenter comparative study evaluating the perceived educational usefulness of LLM-generated differential diagnosis teaching material versus a traditional radiology casebook among junior and advanced radiology trainees. The primary purpose of this dataset is to provide a structured LLM-generated equivalent of a radiology differential diagnosis casebook covering diverse thematic imaging findings. It is intended to serve as a research resource for studying the educational characteristics, strengths, limitations, and reproducibility of LLM-generated medical teaching content. This dataset is not designed or validated for direct clinical use or as formally accredited educational material. This dataset is released under the CC0 1.0 Universal license to promote transparency, reproducibility, and further research in radiology education and medical artificial intelligence. A comprehensive methodological description of the dataset will be provided in a dedicated data publication.","url":"https://doi.org/10.5281/zenodo.18625032","authors":["Saliba, Thomas","Fahrni, Guillaume"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18625032","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/nhdxb","name":"Digital Scribes in Primary Care: A Rapid Scoping Review Protocol","source":"datacite","abstract":"Abstract Background: Digital scribe technologies are increasingly being explored in primary care as a means of reducing documentation burden and supporting clinical workflows. These systems commonly rely on automatic speech recognition (ASR) and natural language processing (NLP), which form the technical foundation of many artificial intelligence (AI)-enabled documentation tools. The purpose of this rapid scoping review is to map the existing literature on AI-enabled digital scribes in primary care, with a focus on reported effectiveness, integration with clinical workflows, and factors influencing adoption. Methods/design: This scoping review will be conducted in accordance with the Joanna Briggs Institute guidance for scoping reviews and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews framework. A systematic search will be conducted in PubMed. Search terms will include combinations of “digital scribe,” “automatic speech recognition,” “natural language processing,” “machine learning,” “electronic health record,” and “electronic medical record.” Eligible studies will examine AI-enabled digital scribes in primary care settings using quantitative, qualitative, or mixed-methods designs. Two reviewers will independently screen studies and chart data, with disagreements resolved through discussion or consultation with additional team members. Methodological quality will not be used as a criterion for study exclusion. Expected Outcomes: This scoping review is expected to provide a comprehensive mapping of the existing evidence on AI-enabled digital scribes in primary care, including how these technologies are evaluated, implemented, and adopted across different clinical contexts. These findings are intended to inform future research, evaluation design, and evidence-informed adoption of digital scribe technologies in primary care.","url":"https://doi.org/10.17605/osf.io/nhdxb","authors":["rajesh nair, "],"tags":["Medicine and Health Sciences","Digital scribes"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/nhdxb","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18617695","name":"AI-Powered Liquid Biopsy Interpretation: A Systematic Literature Review","source":"datacite","abstract":"Liquid biopsy has arisen as a revolutionary method in oncology, which permits non-invasive identification and tracking of cancer by means of circulating biomarkers including cell-free DNA, tumor cells in circulation, and exosomes. Applying artificial intelligence (AI) to liquid biopsy interpretation shows great potential for advancing diagnostic precision, prognostic classification, and treatment choices, but a thorough review of current studies is missing. This systematic literature review seeks to synthesize and rigorously assess the present landscape of AI-driven liquid biopsy interpretation, covering aspects such as biomarker analysis, cancer diagnosis, and wider clinical uses. We performed a thorough analysis of peer-reviewed research, with particular attention to the relationship between AI techniques and liquid biopsy information, and also investigated issues including data variability, the general applicability of models, and validation in clinical settings. The results indicate AI methods, especially machine learning and deep learning, show outstanding capability in improving the accuracy and precision of cancer detection via liquid biopsy, with marked progress in diagnosing early-stage cases and tracking minimal residual disease. However, inconsistencies in validation protocols and limited translational studies highlight gaps between computational innovation and clinical adoption. The analysis additionally highlights developing tendencies, including the merging of diverse data types and interpretable artificial intelligence, which could resolve existing constraints. We conclude AI-driven liquid biopsy analysis is a swiftly advancing area in precision oncology, yet consistent frameworks and rigorous clinical studies are crucial to achieve its complete benefits. This study serves as a key resource for scientists and medical professionals exploring the convergence of artificial intelligence and liquid biopsy in oncology.","url":"https://doi.org/10.5281/zenodo.18617695","authors":["Pokorny, Laszlo"],"tags":["Artificial intelligence","Artificial Intelligence","Medical Technology","Medicine","Biopsy","Biopsy/instrumentation","Biopsy/methods","Oncology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18617695","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18617694","name":"AI-Powered Liquid Biopsy Interpretation: A Systematic Literature Review","source":"datacite","abstract":"Liquid biopsy has arisen as a revolutionary method in oncology, which permits non-invasive identification and tracking of cancer by means of circulating biomarkers including cell-free DNA, tumor cells in circulation, and exosomes. Applying artificial intelligence (AI) to liquid biopsy interpretation shows great potential for advancing diagnostic precision, prognostic classification, and treatment choices, but a thorough review of current studies is missing. This systematic literature review seeks to synthesize and rigorously assess the present landscape of AI-driven liquid biopsy interpretation, covering aspects such as biomarker analysis, cancer diagnosis, and wider clinical uses. We performed a thorough analysis of peer-reviewed research, with particular attention to the relationship between AI techniques and liquid biopsy information, and also investigated issues including data variability, the general applicability of models, and validation in clinical settings. The results indicate AI methods, especially machine learning and deep learning, show outstanding capability in improving the accuracy and precision of cancer detection via liquid biopsy, with marked progress in diagnosing early-stage cases and tracking minimal residual disease. However, inconsistencies in validation protocols and limited translational studies highlight gaps between computational innovation and clinical adoption. The analysis additionally highlights developing tendencies, including the merging of diverse data types and interpretable artificial intelligence, which could resolve existing constraints. We conclude AI-driven liquid biopsy analysis is a swiftly advancing area in precision oncology, yet consistent frameworks and rigorous clinical studies are crucial to achieve its complete benefits. This study serves as a key resource for scientists and medical professionals exploring the convergence of artificial intelligence and liquid biopsy in oncology.","url":"https://doi.org/10.5281/zenodo.18617694","authors":["Pokorny, Laszlo"],"tags":["Artificial intelligence","Artificial Intelligence","Medical Technology","Medicine","Biopsy","Biopsy/instrumentation","Biopsy/methods","Oncology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18617694","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/9hwrk","name":"Applications of artificial intelligence in advanced clinical simulation for anesthesia education: a scoping review","source":"datacite","abstract":"Anesthesiology is characterized by its development in complex, dynamic, and high-risk clinical environments, where decision-making under pressure, effective team coordination, and the precise execution of technical and non-technical skills are crucial for patient safety. In this context, advanced clinical simulation has become a fundamental educational strategy for the training and evaluation of anesthesiologists, enabling the practice of highly complex, low-frequency scenarios that are difficult to address safely and systematically in real-world clinical practice (1,2). Therefore, simulation has evolved from a complementary tool to a structural component of modern anesthesia curricula. In line with the above, numerous high-impact studies and reviews have demonstrated that advanced clinical simulation improves technical performance, strengthens non-technical skills, and contributes to the development of clinical reasoning and crisis management in anesthesia (3,4). Furthermore, when simulation is integrated with sound pedagogical principles such as deliberate practice, structured feedback, and alignment with curricular objectives, it is associated with sustained improvements in learning and greater transfer to the real clinical setting (5,6). However, despite its widespread adoption, significant challenges remain related to the standardization of scenarios, the objective evaluation of performance, and the personalization of training according to the individual needs of learners. In relation to these challenges, anesthesiology has been one of the pioneering specialties in the use of simulation for training in crisis management and teamwork, particularly through approaches such as crisis resource management and structured debriefing (7,8). However, even in well-established simulation programs, performance assessment continues to rely heavily on human observation, which introduces inter-rater variability, limits program scalability, and hinders personalized feedback based on objective data (4,9). Consequently, the need to incorporate new tools that optimize the measurement, analysis, and feedback of performance in advanced clinical simulation has been highlighted. In this context, artificial intelligence emerges as a technology with the potential to substantially transform medical education and, in particular, clinical simulation. Artificial intelligence has demonstrated increasing capabilities to analyze large volumes of data, identify complex patterns, and generate adaptive systems that can support educational, evaluative, and decision-making processes (10,11). In the field of medical education, recent high-impact reviews have documented a sustained increase in the use of artificial intelligence for teaching, performance assessment, and supporting clinical reasoning, although they have also highlighted methodological fragmentation and the need for clear conceptual frameworks for its responsible implementation (12). Specifically, the convergence of artificial intelligence and advanced clinical simulation offers unique opportunities for anesthesia education. These include the dynamic generation of simulated scenarios, real-time adaptation of case complexity based on learner performance, automated analysis of technical and non-technical variables, and support for debriefing through advanced performance analytics (13,14). Consequently, AI-based simulation could overcome some of the current limitations of traditional models, facilitating more personalized, objective, and scalable training. However, despite the growing interest in these applications, the available evidence remains scattered and heterogeneous. Studies differ widely in the types of algorithms used, simulation environments, fidelity levels, target populations, and educational outcomes assessed. Furthermore, ethical, pedagogical, and validity considerations have not yet been systematically addressed, particularly regarding algorithmic transparency, bias, and curriculum in","url":"https://doi.org/10.17605/osf.io/9hwrk","authors":["Concha, Valeria"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/9hwrk","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/83rzt","name":"Implementation of artificial intelligence in prosthetics for amputees: a scoping review","source":"datacite","abstract":"Amputation is the surgical removal of a limb secondary to injury or disease and represents a growing global health burden. Its incidence and prevalence have increased over recent years, driven primarily by trauma, peripheral arterial disease, diabetes mellitus, and hypoperfusion states. Individuals living with limb loss face significant physical, functional, and psychosocial challenges that substantially affect quality of life. Prosthetic devices have evolved significantly; however, achieving control and sensory feedback comparable to a biological limb remains challenging. Artificial Intelligence (AI) has emerged as a promising tool to enhance prosthetic functionality through adaptive motor control and personalization. Despite increasing research, evidence remains heterogeneous. This scoping review aims to systematically map existing evidence on AI implementation in prosthetics for amputee patients.","url":"https://doi.org/10.17605/osf.io/83rzt","authors":["Rodriguez, Maria Jose","Rincon, Erwin Hernando Hernandez","Rodriguez, Maria Alejandra","Polo, Maria Fernanda"],"tags":["Orthopedics","Public Health","Medicine and Health Sciences","Medical Specialties","Plastic Surgery","International Public Health","amputation","artificial intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/83rzt","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.c.8303026","name":"Scoping insights into artificial intelligence-driven treatment of diabetes mellitus in clinical practice","source":"datacite","abstract":"Abstract Background Diabetes mellitus (DM) is a significant health concern around the world due to its increasing prevalence, high rates of morbidity and morbidity from diabetes and diabetic complications, and the associated economic burden. Despite the rapid development of digital healthcare ecosystem, DM remains an incurable lifelong disorder. Nevertheless, there has been a growing interest in developing and using artificial intelligence (AI) technologies for DM management and care. By advancing the understanding of AI-driven technology in treatment of DM and refining clinical approaches, healthcare providers can better navigate the challenges and maximize the benefits associated with these technologies. From this perspective, this scoping review aimed to provide insights to the most recent applications and progress of AI technology to various aspects and opportunities of DM treatment in clinical practice. Methods Comprehensive review following database search on the applications of AI in treatment of DM (T1DM and T2DM) in clinical settings. A literature search was conducted in databases, such as PubMed, Web of Science, and Scopus. The search covered references from 2000 to 2024 with data extraction and organized thematically yielding a total of 14 relevant studies included in this study. Results The majority of the studies were based on database analysis using AI (n = 7) followed by randomized controlled trials (n = 5). This review found that the application of AI, specifically machine learning (ML) and deep learning (DL)-based medical devices and prediction models, shows great promise for real-time monitoring and management, personalized treatment planning, and has advanced significantly in supporting predictive models for the treatment of DM or its complications. Conclusion AI has the potential to change the way this chronic disease is treated and can provide an additional opportunity to achieve better efficiency in DM care. Nevertheless, most applications are still adjunctive (decision-support) and face significant practical, ethical, and validation hurdles.","url":"https://doi.org/10.6084/m9.figshare.c.8303026","authors":["Al-Taie, Anmar","Hafida, Majida","Abdulsattar, Mina","El Mahmoud, Rayan"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8303026","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.6084/m9.figshare.c.8303026.v1","name":"Scoping insights into artificial intelligence-driven treatment of diabetes mellitus in clinical practice","source":"datacite","abstract":"Abstract Background Diabetes mellitus (DM) is a significant health concern around the world due to its increasing prevalence, high rates of morbidity and morbidity from diabetes and diabetic complications, and the associated economic burden. Despite the rapid development of digital healthcare ecosystem, DM remains an incurable lifelong disorder. Nevertheless, there has been a growing interest in developing and using artificial intelligence (AI) technologies for DM management and care. By advancing the understanding of AI-driven technology in treatment of DM and refining clinical approaches, healthcare providers can better navigate the challenges and maximize the benefits associated with these technologies. From this perspective, this scoping review aimed to provide insights to the most recent applications and progress of AI technology to various aspects and opportunities of DM treatment in clinical practice. Methods Comprehensive review following database search on the applications of AI in treatment of DM (T1DM and T2DM) in clinical settings. A literature search was conducted in databases, such as PubMed, Web of Science, and Scopus. The search covered references from 2000 to 2024 with data extraction and organized thematically yielding a total of 14 relevant studies included in this study. Results The majority of the studies were based on database analysis using AI (n = 7) followed by randomized controlled trials (n = 5). This review found that the application of AI, specifically machine learning (ML) and deep learning (DL)-based medical devices and prediction models, shows great promise for real-time monitoring and management, personalized treatment planning, and has advanced significantly in supporting predictive models for the treatment of DM or its complications. Conclusion AI has the potential to change the way this chronic disease is treated and can provide an additional opportunity to achieve better efficiency in DM care. Nevertheless, most applications are still adjunctive (decision-support) and face significant practical, ethical, and validation hurdles.","url":"https://doi.org/10.6084/m9.figshare.c.8303026.v1","authors":["Al-Taie, Anmar","Hafida, Majida","Abdulsattar, Mina","El Mahmoud, Rayan"],"tags":["Artificial Intelligence and Image Processing","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8303026.v1","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/4dn38","name":"Off-Label Combination Therapies for the Management of Atopic Dermatitis: A Protocol for a Scoping Review","source":"datacite","abstract":"Introduction: Atopic dermatitis (AD) is a chronic, heterogeneous and relapsing inflammatory skin disease that presents a significant global health burden, affecting up to 20% of the paediatric population and 2-10% of adults worldwide.1–3 AD is considered the skin disease with the highest non-fatal health burden due to its profound negative impact on patient quality of life, sleep and mental health.1,4–6 AD is characterised by severe pruritus and xerosis, the pathogenesis of which is multifactorial, including an interplay between genetic predisposition, immune dysregulation with aberrant Th2 responses, skin barrier disruption and cutaneous dysbiosis; each of which may be modulated by environmental factors.2,7 These cytokines play a central role in B-cell maturation, IgE class switching and the down-regulation of essential skin barrier proteins such filaggrin, loricrin and involucrin.8 Current Therapeutic Landscape: The current management of AD follows a step-wise approach based on disease severity.9 For mild forms, the cornerstone of therapy includes trigger avoidance, regular use of emollients to combat xerosis and the application of topical corticosteroids (TCS) or topical calcineurin inhibitors (TCI).9 However, patients with moderate-to-severe disease that remains recalcitrant to topical interventions often require second-line treatments, including phototherapy or systemic immunomodulatory agents.5,9 Historically, systemic options were limited to conventional immunosuppressants such as ciclosporin A (CsA), methotrexate (MTX), azathioprine (AZA) and mycophenolate mofetil (MMF).10,11 Despite extensive clinical experience for use in management of moderate-severe AD, the above therapies are not licensed by the FDA/EMA/Japan for management of moderate to severe AD. Their long-term application is often curtailed by an unfavourable risk-benefit ratio, potential organ toxicity (e.g., nephrotoxicity with CsA or bone-marrow suppression with MTX), and a lack of robust long-term data.10,12 The therapeutic landscape was revolutionised in 2017 with the approval of dupilumab, the first biologic targeting the IL-4 receptor alpha chain to inhibit the signalling of IL-4 and IL-13.13 Since then the therapeutic landscape has expanded to include other biologics such as lebrikizumab, nemolizumab and tralokinumab, as well as oral small molecule drugs such as Janus kinase (JAK) inhibitors (e.g., abrocitinib, baricitinib, and upadacitinib).14 Despite these advancements, a subset of patients, demonstrates an inadequate response or resistance to biologic monotherapy.14,15 The Challenge of Recalcitrant Disease and Off-Label Combinations: In clinical practice, dermatologists often encounter patients whose disease is not adequately controlled by a single systemic agent.16 While phase III clinical trials typically evaluate these drugs as monotherapy (or with limited TCS use), daily practice sometimes involves an off-label combination of systemic therapies.1,3,17 There are two primary clinical scenarios where these combinations occur: 1. Transitioning and tapering: patients starting a new biologic like dupilumab may continue a conventional immunosuppressant (such as CsA) for several weeks to prevent disease flares during the transition period or to induce faster clinical remission.18 2. Rescue therapy: In patients experiencing a lack of response or loss of disease control on biologic monotherapy, a second systemic agent or phototherapy may be added as a \"rescue\" or ‘combination’ strategy to enhance effectiveness and maintain long-term control.3 Observational data indicate that up to 45% of patients in real-world settings may continue systemic immunosuppressants during the initiation of dupilumab.1 Common combinations reported in the literature include dupilumab paired with methylprednisolone, ciclosporin, methotrexate or azathioprine.3 Rationale for a Scoping Review: Despite the increasing use of combined systemic regimens, there is neither standardized gu","url":"https://doi.org/10.17605/osf.io/4dn38","authors":["Sexton, Fiona","Wang, Leo","Stefanovic, Nicholas","Irvine, Alan"],"tags":["Dermatology","Other Chemicals and Drugs","Chemicals and Drugs","Medicine and Health Sciences","Medical Specialties","Atopic dermatitis","Biologic agent","Off-Label Combination Therapies"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/4dn38","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/s6ed5","name":"Reporting of AI and ML Studies in Elderly Care Settings: Protocol for a Rapid Scoping Review","source":"datacite","abstract":"Background. Artificial intelligence (AI) and machine learning (ML) systems are increasingly proposed for use in elderly care settings such as nursing homes, assisted living, and community- or home-based care. These systems often support care planning, monitoring, safety management, or operational decision-making rather than classical medical diagnosis or prognosis. Existing reporting guidelines for AI-based studies have primarily been developed for medical research and clinical decision-making, and their adequacy for AI systems deployed in elderly care contexts remains unclear. Objectives. This rapid scoping review aims to map current reporting and evaluation practices of AI and ML studies intended for elderly care settings. The objective is to identify how key aspects related to context, data, evaluation, bias, uncertainty, and deployment are reported, and to assess whether systematic reporting gaps exist that may not be sufficiently addressed by existing medical AI reporting guidelines. Eligibility criteria. Eligible studies will report original empirical research on AI or ML systems intended to support tasks in elderly care contexts, including nursing homes, assisted living, and community- or home-based care. Purely diagnostic or prognostic medical AI studies will be excluded. Only peer-reviewed journal articles and full conference proceedings papers published from 2020 onwards will be considered. Sources of evidence. The search will be conducted in PubMed and IEEE Xplore. Reference lists of included studies will be screened for additional eligible publications. The final search date will be reported. Charting methods. Data will be charted using a standardized extraction form focusing on whether predefined reporting items are reported, not on their correctness or methodological quality. Extracted items will cover context and framing, data and population characteristics, evaluation practices (including internal and external validation and evaluation in real-world care contexts), and reporting of bias, uncertainty, limitations, and use of reporting guidelines. Results will be summarized descriptively.","url":"https://doi.org/10.17605/osf.io/s6ed5","authors":["Schropp, Jonas","Bochtler, Katja"],"tags":["Physical Sciences and Mathematics","Geriatrics","Medicine and Health Sciences","Geriatric Nursing","Medical Specialties","Public Health and Community Nursing","Computer Sciences","Nursing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/s6ed5","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18522299","name":"Product Moral Harmfulness and Price Fairness: Consumer Vulnerability's Moderating and Mediating Effects","source":"datacite","abstract":"*****Abstract: This research examines how perceived product moral harmfulness interacts with consumer vulnerability to influence price fairness perceptions. Drawing on moral foundations theory and the moral harm model of price fairness, the study proposes that consumers engage in dual moral assessments when evaluating pricing strategies, simultaneously considering the moral implications of the product being sold and the vulnerability of the targeted consumers. When both assessments trigger moral concern, fairness reactions amplify beyond the independent effects of either factor. A controlled experiment (N = 407, MTurk) manipulated product type (beer vs. juice) and targeting strategy (vulnerable vs. control consumers) and reveals an 81.8% moral amplification effect (95% CI [20.4%, 205.4%]) when harmful products target vulnerable consumers. Beneficial products receive higher price fairness ratings than harmful products (d = 0.82), with this difference increasing substantially when pricing strategies targeted vulnerable consumers. Parallel mediation analysis indicates that consumer impact perceptions served as the primary mechanism (accounting for 101.3% of the total effect), alongside secondary pathways through perceptions of harmfulness (88.5%) and vulnerability (30.8%). These findings advance understanding of moral judgment in pricing contexts by demonstrating that fairness evaluations are fundamentally shaped by the intersection of product moral harmfulness and consumer vulnerability, underscoring amplified concerns when harmful products target vulnerable populations. *****Keywords: price fairness, consumer vulnerability, moral harmfulness, moral amplification, pricing strategies, moral foundations theory, consumer behavior, SDG 12 (Responsible Consumption and Production), SDG 10 (Reduced Inequalities), SDG 3 (Good Health and Well-being) *****Subjects: #PriceFairness #ConsumerVulnerability #MoralHarmfulness #MoralAmplification #PricingStrategies #MoralFoundationsTheory #ConsumerBehavior #MarketingEthics #VulnerableConsumers #BehavioralPricing #GEMS #GlobalEmpiricalMarketingStudies #SDG12 #SDG10 #SDG3 #SustainableConsumption #EthicalPricing #ConsumerEthics *****Citing This Article: Chan, Y. F. (2026). Product Moral Harmfulness and Price Fairness: Consumer Vulnerability’s Moderating and Mediating Effects. Global Empirical Marketing Studies, 2(1), Article e1a.2026.02.08. https://doi.org/10.5281/zenodo.18522300 *****References: Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T Campbell, M. C., Pomerance, J., & Carter, E. L. P. (2025). Painful prices: The moral harm model of price fairness. Journal of Consumer Research, 52(1), ucaf045. https://doi.org/10.1093/jcr/ucaf045 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. Elenbaas, L., Rizzo, M. T., Cooley, S., & Killen, M. (2016). Rectifying social inequalities in a resource allocation task. Cognition, 155, 176–187. https://doi.org/10.1016/j.cognition.2016.07.002 Feather, N. T. (1999). Judgments of deservingness: Studies in the psychology of justice and achievement. Personality and Social Psychology Review, 3(2), 86–107. https://doi.org/10.1207/s15327957pspr0302_1 Goenka, S., Sen, S., & Thomas, M. (2025). Moral motives in consumption. Journal of the Association for Consumer Research, 10(1), 1–10. https://doi.org/10.1086/733458 Halibas, A., Akram, U., Hoang, A. P., & Hoang, M. D. T. (2025). Unveiling the future of responsible, sustainable, and ethical consumption: A bibliometric study on Gen Z and young consumers. Young Consumers: Insight and Ideas for Responsible Marketers, 26(7), 142–171. https://doi.org/10.1108/YC-11-2024-2327 Hamilton, K., Dunnett, S., & Piacentini, M. (2015). Consumer vulnerability: Conditions, contexts and characteristics (1st ed.). Routledge. https://doi.org/10.4324/9780203797792 Haye","url":"https://doi.org/10.5281/zenodo.18522299","authors":["CHAN, Yiu Fai"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18522299","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18522300","name":"Product Moral Harmfulness and Price Fairness: Consumer Vulnerability's Moderating and Mediating Effects","source":"datacite","abstract":"*****Abstract: This research examines how perceived product moral harmfulness interacts with consumer vulnerability to influence price fairness perceptions. Drawing on moral foundations theory and the moral harm model of price fairness, the study proposes that consumers engage in dual moral assessments when evaluating pricing strategies, simultaneously considering the moral implications of the product being sold and the vulnerability of the targeted consumers. When both assessments trigger moral concern, fairness reactions amplify beyond the independent effects of either factor. A controlled experiment (N = 407, MTurk) manipulated product type (beer vs. juice) and targeting strategy (vulnerable vs. control consumers) and reveals an 81.8% moral amplification effect (95% CI [20.4%, 205.4%]) when harmful products target vulnerable consumers. Beneficial products receive higher price fairness ratings than harmful products (d = 0.82), with this difference increasing substantially when pricing strategies targeted vulnerable consumers. Parallel mediation analysis indicates that consumer impact perceptions served as the primary mechanism (accounting for 101.3% of the total effect), alongside secondary pathways through perceptions of harmfulness (88.5%) and vulnerability (30.8%). These findings advance understanding of moral judgment in pricing contexts by demonstrating that fairness evaluations are fundamentally shaped by the intersection of product moral harmfulness and consumer vulnerability, underscoring amplified concerns when harmful products target vulnerable populations. *****Keywords: price fairness, consumer vulnerability, moral harmfulness, moral amplification, pricing strategies, moral foundations theory, consumer behavior, SDG 12 (Responsible Consumption and Production), SDG 10 (Reduced Inequalities), SDG 3 (Good Health and Well-being) *****Subjects: #PriceFairness #ConsumerVulnerability #MoralHarmfulness #MoralAmplification #PricingStrategies #MoralFoundationsTheory #ConsumerBehavior #MarketingEthics #VulnerableConsumers #BehavioralPricing #GEMS #GlobalEmpiricalMarketingStudies #SDG12 #SDG10 #SDG3 #SustainableConsumption #EthicalPricing #ConsumerEthics *****Citing This Article: Chan, Y. F. (2026). Product Moral Harmfulness and Price Fairness: Consumer Vulnerability’s Moderating and Mediating Effects. Global Empirical Marketing Studies, 2(1), Article e1a.2026.02.08. https://doi.org/10.5281/zenodo.18522300 *****References: Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T Campbell, M. C., Pomerance, J., & Carter, E. L. P. (2025). Painful prices: The moral harm model of price fairness. Journal of Consumer Research, 52(1), ucaf045. https://doi.org/10.1093/jcr/ucaf045 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. Elenbaas, L., Rizzo, M. T., Cooley, S., & Killen, M. (2016). Rectifying social inequalities in a resource allocation task. Cognition, 155, 176–187. https://doi.org/10.1016/j.cognition.2016.07.002 Feather, N. T. (1999). Judgments of deservingness: Studies in the psychology of justice and achievement. Personality and Social Psychology Review, 3(2), 86–107. https://doi.org/10.1207/s15327957pspr0302_1 Goenka, S., Sen, S., & Thomas, M. (2025). Moral motives in consumption. Journal of the Association for Consumer Research, 10(1), 1–10. https://doi.org/10.1086/733458 Halibas, A., Akram, U., Hoang, A. P., & Hoang, M. D. T. (2025). Unveiling the future of responsible, sustainable, and ethical consumption: A bibliometric study on Gen Z and young consumers. Young Consumers: Insight and Ideas for Responsible Marketers, 26(7), 142–171. https://doi.org/10.1108/YC-11-2024-2327 Hamilton, K., Dunnett, S., & Piacentini, M. (2015). Consumer vulnerability: Conditions, contexts and characteristics (1st ed.). Routledge. https://doi.org/10.4324/9780203797792 Haye","url":"https://doi.org/10.5281/zenodo.18522300","authors":["CHAN, Yiu Fai"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18522300","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18516931","name":"A Survey on Stroke Disease Classification and Prediction using Machine Learning Algorithms","source":"datacite","abstract":"Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. This review aims to identify and analyze the Machine Learning approaches used for Stroke Prediction. We have considered the previously published works to review the Machine learning techniques used for Stroke Predictions. It's been found that the majority of the research work was done on mortality rate and functional outcome as the predicted outcomes. The most commonly used techniques were random forest, support vector machines, decision trees and neural networks. However, a few predictors and classifiers did primitive reporting standards for medical sector tools and none of which proved to be of any practical use.","url":"https://doi.org/10.5281/zenodo.18516931","authors":["Veena Potdar","Lavanya Santhosh","Yashu Raj Gowda C Y"],"tags":["Stroke prediction","Machine learning approaches","Sensitivity and Specificity","Comparison Analysis."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.18516931","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18516930","name":"A Survey on Stroke Disease Classification and Prediction using Machine Learning Algorithms","source":"datacite","abstract":"Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. This review aims to identify and analyze the Machine Learning approaches used for Stroke Prediction. We have considered the previously published works to review the Machine learning techniques used for Stroke Predictions. It's been found that the majority of the research work was done on mortality rate and functional outcome as the predicted outcomes. The most commonly used techniques were random forest, support vector machines, decision trees and neural networks. However, a few predictors and classifiers did primitive reporting standards for medical sector tools and none of which proved to be of any practical use.","url":"https://doi.org/10.5281/zenodo.18516930","authors":["Veena Potdar","Lavanya Santhosh","Yashu Raj Gowda C Y"],"tags":["Stroke prediction","Machine learning approaches","Sensitivity and Specificity","Comparison Analysis."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.18516930","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18476707","name":"Systematic Integration of Artificial Intelligence and Machine Learning in the Early Detection and Management of Goitre: A Global Epidemiological and Computational Framework","source":"datacite","abstract":"Background: Goitre remains a high-signal global health indicator of thyroid dysfunction and population-level iodine status. Despite progress in salt iodization, early-stage thyroid enlargement is frequently under-detected in routine practice, especially when physical examination is confounded by body habitus and clinician subjectivity. Ultrasound is the preferred modality for early assessment, but interpretation is operator-dependent and increasingly burdened by rising thyroid nodule prevalence. Objective: This review synthesizes evidence on Machine Learning (ML) and Artificial Intelligence (AI) methods for predicting thyroid dysfunction and diagnosing early goitre (WHO Grade 1), with a practical emphasis on multi-modal “holistic AI” systems that combine tabular laboratory markers with imaging features. Methods: We summarize (i) supervised learning pipelines for structured clinical data (e.g., age, sex, TSH, T3, T4, T4U/FTI), (ii) deep learning architectures for ultrasound-based detection and segmentation (CNNs, U-Net variants, Vision Transformers), and (iii) deployment considerations including explainability, bias control, and reproducible benchmarking using open datasets. Following common clinical ML reporting practice, we emphasize confusion-matrixbased evaluation (precision/recall/F1/MCC) and strong ensemble baselines for tabular prediction. [30,31] Results: For tabular prediction tasks, stacked ensembles and gradient-boosted trees repeatedly rank among the best-performing approaches, particularly when combined with careful feature engineering and imbalance mitigation. For imaging, segmentation-first pipelines that estimate thyroid volume (e.g., U-Net family) and classification models leveraging multi-channel inputs or self-attention mechanisms (e.g., ViTs) report high diagnostic performance in differentiating benign enlargement from suspicious nodular patterns. Emerging smartphone-assisted workflows and LLM-based clinical summarization show promise for low-resource settings but require rigorous validation. Conclusion: AI can shift goitre management from late-stage detection to proactive screening by improving sensitivity for occult Grade 1 enlargement, standardizing ultrasound interpretation, and reducing unnecessary invasive procedures. Clinical adoption, however, depends on transparent explainability, external validation across diverse cohorts, and governance aligned with high-risk medical AI standards.","url":"https://doi.org/10.5281/zenodo.18476707","authors":["Akugri, Kingdom Mutala","Agbenyo, Prince","Akugri, Marious","Keteku, Lovelyn"],"tags":["Goitre","Thyroid","Artificial Intelligence","Machine Learning","Ultrasound","Vision Transformer","U-Net","Explainable AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18476707","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18476708","name":"Systematic Integration of Artificial Intelligence and Machine Learning in the Early Detection and Management of Goitre: A Global Epidemiological and Computational Framework","source":"datacite","abstract":"Background: Goitre remains a high-signal global health indicator of thyroid dysfunction and population-level iodine status. Despite progress in salt iodization, early-stage thyroid enlargement is frequently under-detected in routine practice, especially when physical examination is confounded by body habitus and clinician subjectivity. Ultrasound is the preferred modality for early assessment, but interpretation is operator-dependent and increasingly burdened by rising thyroid nodule prevalence. Objective: This review synthesizes evidence on Machine Learning (ML) and Artificial Intelligence (AI) methods for predicting thyroid dysfunction and diagnosing early goitre (WHO Grade 1), with a practical emphasis on multi-modal “holistic AI” systems that combine tabular laboratory markers with imaging features. Methods: We summarize (i) supervised learning pipelines for structured clinical data (e.g., age, sex, TSH, T3, T4, T4U/FTI), (ii) deep learning architectures for ultrasound-based detection and segmentation (CNNs, U-Net variants, Vision Transformers), and (iii) deployment considerations including explainability, bias control, and reproducible benchmarking using open datasets. Following common clinical ML reporting practice, we emphasize confusion-matrixbased evaluation (precision/recall/F1/MCC) and strong ensemble baselines for tabular prediction. [30,31] Results: For tabular prediction tasks, stacked ensembles and gradient-boosted trees repeatedly rank among the best-performing approaches, particularly when combined with careful feature engineering and imbalance mitigation. For imaging, segmentation-first pipelines that estimate thyroid volume (e.g., U-Net family) and classification models leveraging multi-channel inputs or self-attention mechanisms (e.g., ViTs) report high diagnostic performance in differentiating benign enlargement from suspicious nodular patterns. Emerging smartphone-assisted workflows and LLM-based clinical summarization show promise for low-resource settings but require rigorous validation. Conclusion: AI can shift goitre management from late-stage detection to proactive screening by improving sensitivity for occult Grade 1 enlargement, standardizing ultrasound interpretation, and reducing unnecessary invasive procedures. Clinical adoption, however, depends on transparent explainability, external validation across diverse cohorts, and governance aligned with high-risk medical AI standards.","url":"https://doi.org/10.5281/zenodo.18476708","authors":["Akugri, Kingdom Mutala","Agbenyo, Prince","Akugri, Marious","Keteku, Lovelyn"],"tags":["Goitre","Thyroid","Artificial Intelligence","Machine Learning","Ultrasound","Vision Transformer","U-Net","Explainable AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18476708","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17615/pbjk-tp20","name":"Clinical decision support systems in orthodontics: A narrative review of data science approaches","source":"datacite","abstract":"Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.","url":"https://doi.org/10.17615/pbjk-tp20","authors":["Deleat‐Besson, Romain","Zhang, Winston","Gryak, Jonathan","Gurgel, Marcela","Yatabe, Marilia","Benavides, Erika","Styner, Martin","Paniagua, Beatriz","Soki, Fabiana","Massaro, Camila","Soroushmehr, Reza","Cevidanes, Lucia H. S.","Del Castillo, Aron Aliaga","Al Turkestani, Najla","Tengfei, Li","Le, Celia","Evangelista, Karine","Fillion‐Robin, Jean‐Christophe","Prieto, Juan Carlos","Najarian, Kayvan","Bianchi, Jonas","Ruellas, Antonio C. O."],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.17615/pbjk-tp20","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18506433","name":"Sistemas Mecatrónicos Aplicados a Rehabilitación Médica: Una Revisión del Estado del Arte","source":"datacite","abstract":"Introducción: La rehabilitación médica enfrenta una demanda creciente con aproximadamente 2,400 millones de personas requiriendo servicios especializados a nivel mundial. Los sistemas mecatrónicos, que integran ingeniería mecánica, electrónica, control automático e inteligencia artificial, emergen como solución prometedora ante las limitaciones de métodos tradicionales en disponibilidad, costos y objetividad de medición. Objetivo: Analizar el estado actual de los sistemas mecatrónicos aplicados a la rehabilitación médica mediante una revisión bibliográfica no sistemática, identificando las principales tecnologías, aplicaciones clínicas y tendencias de desarrollo en este campo interdisciplinario. Metodología: Se realizó búsqueda sistemática en PubMed, IEEE Xplore, ScienceDirect, Scopus y Google Scholar, priorizando publicaciones de 2024-2025. Se aplicó el método de análisis-síntesis para categorizar información en dimensiones temáticas: tipos de sistemas, aplicaciones clínicas, tecnologías de sensado y actuación, estrategias de control, evidencia de efectividad y tendencias futuras. Resultados: Los sistemas automatizados incluyen plataformas de marcha robótica, electroestimulación funcional, plataformas computarizadas de equilibrio, biofeedback y realidad virtual terapéutica. Los sistemas mecatrónicos abarcan exoesqueletos robóticos, robots de extremidad superior, sistemas de movilización pasiva continua, plataformas tipo end-effector, dispositivos de asistencia para mano, sistemas híbridos FES-robótica y plataformas con retroalimentación háptica. Se identificó brecha crítica entre desarrollo tecnológico y adopción clínica, con solo dos dispositivos comercialmente aprobados. Conclusión: Los sistemas mecatrónicos demuestran ventajas en precisión, repetibilidad y cuantificación objetiva. Sin embargo, persisten desafíos en costos elevados, peso excesivo, complejidad operativa y ausencia de protocolos estandarizados. Las direcciones futuras priorizan inteligencia artificial, materiales flexibles, diseño modular, telerrehabilitación y validación clínica rigurosa, para lograr adopción generalizada equitativa. Área de estudio general: Ingeniería Biomédica. Área de estudio específica: Sistemas Automatizados y Mecatrónicos de Rehabilitación Médica.","url":"https://doi.org/10.5281/zenodo.18506433","authors":["Pérez-Insuasti, Juan José","Flores-Andino, Víctor Manuel"],"tags":["mechatronics","rehabilitation","medical robotics","exoskeletons","robot-assisted therapy","mecatrónica","rehabilitación","robótica médica"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18506433","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18506434","name":"Sistemas Mecatrónicos Aplicados a Rehabilitación Médica: Una Revisión del Estado del Arte","source":"datacite","abstract":"Introducción: La rehabilitación médica enfrenta una demanda creciente con aproximadamente 2,400 millones de personas requiriendo servicios especializados a nivel mundial. Los sistemas mecatrónicos, que integran ingeniería mecánica, electrónica, control automático e inteligencia artificial, emergen como solución prometedora ante las limitaciones de métodos tradicionales en disponibilidad, costos y objetividad de medición. Objetivo: Analizar el estado actual de los sistemas mecatrónicos aplicados a la rehabilitación médica mediante una revisión bibliográfica no sistemática, identificando las principales tecnologías, aplicaciones clínicas y tendencias de desarrollo en este campo interdisciplinario. Metodología: Se realizó búsqueda sistemática en PubMed, IEEE Xplore, ScienceDirect, Scopus y Google Scholar, priorizando publicaciones de 2024-2025. Se aplicó el método de análisis-síntesis para categorizar información en dimensiones temáticas: tipos de sistemas, aplicaciones clínicas, tecnologías de sensado y actuación, estrategias de control, evidencia de efectividad y tendencias futuras. Resultados: Los sistemas automatizados incluyen plataformas de marcha robótica, electroestimulación funcional, plataformas computarizadas de equilibrio, biofeedback y realidad virtual terapéutica. Los sistemas mecatrónicos abarcan exoesqueletos robóticos, robots de extremidad superior, sistemas de movilización pasiva continua, plataformas tipo end-effector, dispositivos de asistencia para mano, sistemas híbridos FES-robótica y plataformas con retroalimentación háptica. Se identificó brecha crítica entre desarrollo tecnológico y adopción clínica, con solo dos dispositivos comercialmente aprobados. Conclusión: Los sistemas mecatrónicos demuestran ventajas en precisión, repetibilidad y cuantificación objetiva. Sin embargo, persisten desafíos en costos elevados, peso excesivo, complejidad operativa y ausencia de protocolos estandarizados. Las direcciones futuras priorizan inteligencia artificial, materiales flexibles, diseño modular, telerrehabilitación y validación clínica rigurosa, para lograr adopción generalizada equitativa. Área de estudio general: Ingeniería Biomédica. Área de estudio específica: Sistemas Automatizados y Mecatrónicos de Rehabilitación Médica.","url":"https://doi.org/10.5281/zenodo.18506434","authors":["Pérez-Insuasti, Juan José","Flores-Andino, Víctor Manuel"],"tags":["mechatronics","rehabilitation","medical robotics","exoskeletons","robot-assisted therapy","mecatrónica","rehabilitación","robótica médica"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18506434","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18508855","name":"Role of Artificial Intelligence in Pharmaceutical Research","source":"datacite","abstract":"Artificial intelligence (AI) has emerged as a transformative technology in pharmaceutical research, offering innovative solutions to long-standing challenges in drug discovery, development, formulation, and clinical decision-making. The pharmaceutical industry faces increasing pressure to reduce research timelines, minimize expenditure, and enhance success rates of new molecular entities, all while maintaining safety and efficacy standards. AI-driven computational models, including machine learning (ML), deep learning (DL), natural language processing (NLP), and generative algorithms, have revolutionized the identification of drug targets, prediction of molecular interactions, and optimization of drug candidates. These technologies enable the rapid screening of large chemical libraries, prediction of ADMET properties, virtual clinical trial simulations, and personalized medicine strategies using real-world datasets. In addition, AI supports formulation development by modeling excipient compatibility, optimizing process parameters, and ensuring consistent product quality through predictive quality-by-design (QbD) tools. The integration of AI in pharmacovigilance enhances adverse event detection, signal identification, and risk assessment through automated data mining of medical reports, social media, and electronic health records. Despite its promising potential, the application of AI faces challenges such as data privacy concerns, algorithmic transparency, regulatory acceptance, and the need for high-quality datasets. This review comprehensively discusses the current applications, technological advancements, opportunities, and limitations of AI in pharmaceutical research, highlighting its growing role as a catalyst for innovation and future global healthcare improvement.","url":"https://doi.org/10.5281/zenodo.18508855","authors":["Yash Borse*, Ayush sawant, Dr. Moreshwar Patil, Dr. Sanjay Kshirsagar"],"tags":["Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Drug Discovery, Drug Development, Virtual Screening, QSAR Modeling, ADMET Prediction, Pharmaceutical Formulation, Quality by Design (QbD), Predictive Analytics,."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18508855","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18508856","name":"Role of Artificial Intelligence in Pharmaceutical Research","source":"datacite","abstract":"Artificial intelligence (AI) has emerged as a transformative technology in pharmaceutical research, offering innovative solutions to long-standing challenges in drug discovery, development, formulation, and clinical decision-making. The pharmaceutical industry faces increasing pressure to reduce research timelines, minimize expenditure, and enhance success rates of new molecular entities, all while maintaining safety and efficacy standards. AI-driven computational models, including machine learning (ML), deep learning (DL), natural language processing (NLP), and generative algorithms, have revolutionized the identification of drug targets, prediction of molecular interactions, and optimization of drug candidates. These technologies enable the rapid screening of large chemical libraries, prediction of ADMET properties, virtual clinical trial simulations, and personalized medicine strategies using real-world datasets. In addition, AI supports formulation development by modeling excipient compatibility, optimizing process parameters, and ensuring consistent product quality through predictive quality-by-design (QbD) tools. The integration of AI in pharmacovigilance enhances adverse event detection, signal identification, and risk assessment through automated data mining of medical reports, social media, and electronic health records. Despite its promising potential, the application of AI faces challenges such as data privacy concerns, algorithmic transparency, regulatory acceptance, and the need for high-quality datasets. This review comprehensively discusses the current applications, technological advancements, opportunities, and limitations of AI in pharmaceutical research, highlighting its growing role as a catalyst for innovation and future global healthcare improvement.","url":"https://doi.org/10.5281/zenodo.18508856","authors":["Yash Borse*, Ayush sawant, Dr. Moreshwar Patil, Dr. Sanjay Kshirsagar"],"tags":["Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Drug Discovery, Drug Development, Virtual Screening, QSAR Modeling, ADMET Prediction, Pharmaceutical Formulation, Quality by Design (QbD), Predictive Analytics,."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18508856","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18507991","name":"Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning","source":"datacite","abstract":"This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of the proposed Class-Adaptive Color-Sensitive (CASC) filtering algorithm, the Dense Class-Adaptive Color-Sensitive DenseNet-121 (DenseCSENet-121) architecture, preprocessing scripts, and model evaluation routines. The materials allow reproduction of the reported experimental results on the nine-category waste classification dataset described in the paper.","url":"https://doi.org/10.5281/zenodo.18507991","authors":["Shyamala Devi, M","Natarajan, Yuvaraj","K. R., Sri Preethaa"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18507991","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18507990","name":"Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning","source":"datacite","abstract":"This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of the proposed Class-Adaptive Color-Sensitive (CASC) filtering algorithm, the Dense Class-Adaptive Color-Sensitive DenseNet-121 (DenseCSENet-121) architecture, preprocessing scripts, and model evaluation routines. The materials allow reproduction of the reported experimental results on the nine-category waste classification dataset described in the paper.","url":"https://doi.org/10.5281/zenodo.18507990","authors":["Shyamala Devi, M","Natarajan, Yuvaraj","K. R., Sri Preethaa"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18507990","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.48550/arxiv.2510.24750","name":"Opportunistic Screening of Wolff-Parkinson-White Syndrome using Single-Lead AI-ECG Mobile System: A Real-World Study of over 3.5 million ECG Recordings in China","source":"datacite","abstract":"Wolff-Parkinson-White (WPW) syndrome, a congenital cardiac conduction abnormality with low prevalence, carries a significant risk of sudden cardiac death. Early identification remains challenging due to screening costs and professional resource scarcity. This retrospective real-world study systematically evaluates an integrated Artificial Intelligence-enabled mobile screening system comprising portable single-lead devices, AI primary screening, and cardiologist review. Analyzing 3,566,626 ECG records from 87,836 individuals between 2019 and 2025, the AI model achieved an AUC of 0.6676 and a specificity of 95.92% in complex real-world signal environments. Despite predictive probability bias inherent in ultra-low prevalence contexts, the model demonstrated stable risk stratification, with high-confidence scores concentrated among true positive individuals. The risk of detecting WPW in AI-positive records was 86.2-fold higher than in AI-negative records. By implementing a human-AI collaborative workflow, the volume of ECGs requiring manual review was reduced by approximately 99.5% compared to universal screening. In an ideal collaborative scenario, an average of only 18 ECGs required review to confirm one WPW case, representing a more than 60-fold increase in screening efficiency. Compared to traditional 12-lead ECGs and electrophysiological studies, this system significantly reduced time and medical costs. Our findings suggest that a risk-stratification-based human-AI collaborative system provides a promising paradigm for the early public health detection of low-prevalence, high-risk arrhythmias.","url":"https://doi.org/10.48550/arxiv.2510.24750","authors":["Huang, Shun","Zhang, Deyun","Fan, Sumei","Tang, Gongzheng","Geng, Shijia","Xiao, Yujie","Wu, Xingliang","Yan, Mingke","Wang, Haoyu","Zhang, Rui","Fu, Zhaoji","Hong, Shenda"],"tags":["Signal Processing (eess.SP)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.24750","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.17946701","name":"Safety as Clarity: Interpretive Stability, Human–AI Communication, and UX-Level Governance Frameworks","source":"datacite","abstract":"This report presents Safety as Clarity, a constitutional framework for artificial intelligence (AI) safety grounded in interpretive stability and human–AI communication integrity. The work addresses a persistent gap in contemporary AI safety discourse: the tendency for safety failures to arise not from policy violations or model malfunction, but from breakdowns in user interpretation at the interaction and user-experience (UX) level. Synthesizing a series of previously published reference models—including the Freedom OS constitutional protocol, the DM Governance Architecture, the Amano Sand Model, the Arc of Unity, and the Interaction Energy Framework—this report formalizes clarity as an infrastructural safety invariant. It proposes that interpretability, tone stability, and meaning preservation must be governed explicitly to prevent silent safety regression as AI systems evolve. The framework is descriptive and structural in nature. It makes no medical, psychological, or anthropomorphic claims, and does not present empirical trials or statistical evaluation. Instead, it provides a rigorously bounded conceptual architecture intended to support AI governance, safety review, UX design, and interpretability analysis. The report is designed as a reference document for researchers, practitioners, and reviewers seeking to understand how safety risks manifest at the human–AI interface and how such risks may be mitigated through constitutional and UX-level governance mechanisms.","url":"https://doi.org/10.5281/zenodo.17946701","authors":["Madrid, David Chamberlin","Madrid, Philosopher"],"tags":["AI safety AI governance interpretability human–AI interaction user experience (UX) safety interpretive stability clarity constitutional AI safety infrastructure interaction design"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17946701","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.17946700","name":"Safety as Clarity: Interpretive Stability, Human–AI Communication, and UX-Level Governance Frameworks","source":"datacite","abstract":"This report presents Safety as Clarity, a constitutional framework for artificial intelligence (AI) safety grounded in interpretive stability and human–AI communication integrity. The work addresses a persistent gap in contemporary AI safety discourse: the tendency for safety failures to arise not from policy violations or model malfunction, but from breakdowns in user interpretation at the interaction and user-experience (UX) level. Synthesizing a series of previously published reference models—including the Freedom OS constitutional protocol, the DM Governance Architecture, the Amano Sand Model, the Arc of Unity, and the Interaction Energy Framework—this report formalizes clarity as an infrastructural safety invariant. It proposes that interpretability, tone stability, and meaning preservation must be governed explicitly to prevent silent safety regression as AI systems evolve. The framework is descriptive and structural in nature. It makes no medical, psychological, or anthropomorphic claims, and does not present empirical trials or statistical evaluation. Instead, it provides a rigorously bounded conceptual architecture intended to support AI governance, safety review, UX design, and interpretability analysis. The report is designed as a reference document for researchers, practitioners, and reviewers seeking to understand how safety risks manifest at the human–AI interface and how such risks may be mitigated through constitutional and UX-level governance mechanisms.","url":"https://doi.org/10.5281/zenodo.17946700","authors":["Madrid, David Chamberlin","Madrid, Philosopher"],"tags":["AI safety AI governance interpretability human–AI interaction user experience (UX) safety interpretive stability clarity constitutional AI safety infrastructure interaction design"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17946700","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/pvkw5","name":"Data Quality for the Application of Artificial Intelligence in Medical and Dental Diagnostic Imaging: A Scoping Review and Checklists for Critical Appraisal and Metadata Reporting","source":"datacite","abstract":"The adoption of artificial intelligence, in particular machine learning, has seen a rise in the field of medicine and dentistry. Although artificial intelligence has shown potential in guiding medical and dental diagnosis of different radiographic or non-radiographic imaging modalities, the confidence in its utilization warrants further development and utilization of good quality training data. Thus, the aim of this scoping review is to to explore and map out key data quality characteristics for AI applications in medical and dental diagnostic imaging, and to develop critical appraisal checklists and metadata reporting checklists for data users, researchers and authors.","url":"https://doi.org/10.17605/osf.io/pvkw5","authors":["Ku, Jason Chi-Kit","Mao, Kaijing"],"tags":["Medical Sciences","Medicine and Health Sciences","Dentistry","FOS: Clinical medicine","Artificial Intelligence","Diagnostic Imaging","Digital Dentistry","checklist"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/pvkw5","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.17367484","name":"VALIDATE D4.1 Study Initiation Package","source":"datacite","abstract":"Artificial intelligence (AI)-powered prognostic tools and clinical decision support systems can predict the outcome of certain diseases based on a multitude of patient data at high speed, facilitating decisions by healthcare professionals. In acute ischemic stroke, the overall treatment effect and population-wide outcome benefit of treatments such as IV thrombolysis and mechanical thrombectomy are well established. However, in individual patients it is difficult to predict the prognosis in the acute phase of stroke: some patients are candidates for these treatments, but may have poor clinical outcomes (no improvement of stroke or even worsening). Our aim in this study is to validate an artificial intelligence (AI)-based prognostic tool to provide accurate real-time outcome prediction in patients with acute ischemic stroke. During the study, all patients admitted to the emergency room with an acute ischemic stroke will receive the usual treatment for acute stroke in accordance with the stroke neurologists in charge. A “shadow” clinical researcher, without interaction with treating physicians, will collect the data required by the AI model in vivo. These data will be obtained by filling in clinical data through an App on a hospital mobile/tablet, and by a connection with your electronic medical record. The AI models will estimate the outcome of an acute stroke patient, and this prediction will be compared with the real outcome of the patient after 3 months of follow-up. This document presents the first draft of the study initiation package of the study “Validation of a Trustworthy AI-based Clinical Decision Support System for Improving Patient Outcome in Acute Stroke Treatment””. The study aims to develop a demonstrator to provide accurate prognosis for acute ischemic stroke patients during the hyperacute phase. It has a preclinical phase with the development of the artificial intelligence (AI) models based on retrospective data, and a prospective phase to test these models in real time through a prospective multicenter shadowing observational non-interventional study. The predicted outcomes of the patients will be compared with real clinician and patient reported outcomes (PROs and CROs). Here we provide the protocol of the clinical study, the information form and informed consent form for patients/families and the interaction with the Institutional Review Board (IRB) in the different international centers. Now, an IRB approval from the pre-clinical retrospective study has been received, and we are waiting for the complete (pre-clinical and prospective data) IRB approval. The clinical study is also being evaluated for registration in clinicaltrials.gov.","url":"https://doi.org/10.5281/zenodo.17367484","authors":["Rubiera Del Fueyo, Marta A","Bonekamp, Susanne","Leker, Ronen"],"tags":["Artificial Intelligence","Stroke","Acute Stroke","Prognosis","Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17367484","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.17367485","name":"VALIDATE D4.1 Study Initiation Package","source":"datacite","abstract":"Artificial intelligence (AI)-powered prognostic tools and clinical decision support systems can predict the outcome of certain diseases based on a multitude of patient data at high speed, facilitating decisions by healthcare professionals. In acute ischemic stroke, the overall treatment effect and population-wide outcome benefit of treatments such as IV thrombolysis and mechanical thrombectomy are well established. However, in individual patients it is difficult to predict the prognosis in the acute phase of stroke: some patients are candidates for these treatments, but may have poor clinical outcomes (no improvement of stroke or even worsening). Our aim in this study is to validate an artificial intelligence (AI)-based prognostic tool to provide accurate real-time outcome prediction in patients with acute ischemic stroke. During the study, all patients admitted to the emergency room with an acute ischemic stroke will receive the usual treatment for acute stroke in accordance with the stroke neurologists in charge. A “shadow” clinical researcher, without interaction with treating physicians, will collect the data required by the AI model in vivo. These data will be obtained by filling in clinical data through an App on a hospital mobile/tablet, and by a connection with your electronic medical record. The AI models will estimate the outcome of an acute stroke patient, and this prediction will be compared with the real outcome of the patient after 3 months of follow-up. This document presents the first draft of the study initiation package of the study “Validation of a Trustworthy AI-based Clinical Decision Support System for Improving Patient Outcome in Acute Stroke Treatment””. The study aims to develop a demonstrator to provide accurate prognosis for acute ischemic stroke patients during the hyperacute phase. It has a preclinical phase with the development of the artificial intelligence (AI) models based on retrospective data, and a prospective phase to test these models in real time through a prospective multicenter shadowing observational non-interventional study. The predicted outcomes of the patients will be compared with real clinician and patient reported outcomes (PROs and CROs). Here we provide the protocol of the clinical study, the information form and informed consent form for patients/families and the interaction with the Institutional Review Board (IRB) in the different international centers. Now, an IRB approval from the pre-clinical retrospective study has been received, and we are waiting for the complete (pre-clinical and prospective data) IRB approval. The clinical study is also being evaluated for registration in clinicaltrials.gov.","url":"https://doi.org/10.5281/zenodo.17367485","authors":["Rubiera Del Fueyo, Marta A","Bonekamp, Susanne","Leker, Ronen"],"tags":["Artificial Intelligence","Stroke","Acute Stroke","Prognosis","Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17367485","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/6bz74","name":"Generative Artificial Intelligence in Health Education for Chronic Disease Patients: A Scoping Review","source":"datacite","abstract":"With the rapid iteration of Generative Artificial Intelligence (GenAI) technology and its deep integration with the healthcare field, its application potential in health education for patients with chronic diseases has become increasingly prominent. Chronic diseases are characterized by long duration, complex management, and high demand for patients' self-health management. Traditional health education models have pain points such as homogeneous content, insufficient personalization, limited communication efficiency, and unsustained follow-up interventions, making it difficult to fully meet the differentiated health knowledge needs and long-term health management demands of different patients with chronic diseases. This study adopts a scoping review method to systematically search domestic and foreign literatures on the application of generative artificial intelligence in health education for patients with chronic diseases, clarify the application scenarios, application models, and core technology types of generative artificial intelligence in this field, sort out the advantages and existing problems in the current application process, and analyze the key factors affecting its promotion and application. The purpose of the study is to comprehensively and systematically present the application status and research progress of generative artificial intelligence in the field of health education for patients with chronic diseases, fill the gap in the existing research on the overall application of this field, provide clear direction guidance for the subsequent related research, and at the same time provide theoretical reference and practical basis for clinical medical staff and health managers to formulate scientific and reasonable chronic disease health education strategies. The expected outcomes of this study include: clarifying the main application scenarios of generative artificial intelligence in health education for patients with chronic diseases (such as personalized health knowledge push, intelligent consultation and answering, customization of health management plans, rehabilitation training guidance, etc.) and corresponding application models; summarizing the current core technical bottlenecks, ethical risks, application limitations and improvement suggestions in application; forming a scoping review report on the application of generative artificial intelligence in health education for patients with chronic diseases; providing basic support for subsequent targeted empirical research, technical optimization research and policy formulation, promoting generative artificial intelligence technology to better empower health education for patients with chronic diseases, improving the pertinence, effectiveness and accessibility of health education, helping patients with chronic diseases improve their self-health management ability and improve health outcomes.","url":"https://doi.org/10.17605/osf.io/6bz74","authors":["郭佳欢","刘彦慧"],"tags":["Medicine and Health Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/6bz74","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.48550/arxiv.2506.17442","name":"Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation","source":"datacite","abstract":"Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to factors such as shifting data distributions, changes in patient characteristics, evolving clinical protocols, and variations in data quality. These factors can compromise model reliability, posing safety concerns and increasing the likelihood of inaccurate predictions or adverse outcomes. This review presents a forward-looking perspective on monitoring and maintaining the \"health\" of AI systems in healthcare. We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms. The paper begins by reviewing common causes of performance degradation at both data and model levels. We then summarize key techniques for detecting data and model drift, followed by an in-depth look at root cause analysis. Correction strategies are further reviewed, ranging from model retraining to test-time adaptation. Our survey spans both traditional machine learning models and state-of-the-art large language models (LLMs), offering insights into their strengths and limitations. Finally, we discuss ongoing technical challenges and propose future research directions. This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.","url":"https://doi.org/10.48550/arxiv.2506.17442","authors":["Guan, Hao","Bates, David","Zhou, Li"],"tags":["Artificial Intelligence (cs.AI)","Emerging Technologies (cs.ET)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.17442","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18483667","name":"AI/ML-Based Demand Forecasting Across Industries: Transforming Traditional Sales Prediction Through Advanced Analytics","source":"datacite","abstract":"Demand forecasting is a strategic discipline that anticipates future customer demand, enabling organizations to align production, inventory, and capacity decisions with market needs to drive profitability and competitive advantage. Traditional statistical methods have served organizations for decades, relying primarily on historical sales patterns and time-series analysis to generate predictions. However, contemporary business environments characterized by unprecedented demand volatility, rapidly shifting consumer preferences, and complex market dynamics have exposed significant limitations in conventional forecasting approaches. Artificial intelligence and machine learning technologies offer transformative capabilities that address these challenges through multi-source data integration, pattern recognition across vast datasets, and adaptive learning mechanisms that respond to changing market conditions in real-time. This review examines the evolution from traditional statistical forecasting to AI-powered demand prediction systems, with primary emphasis on retail sector implementations where inventory optimization and sales forecasting directly impact profitability margins. Healthcare organizations utilize similar technologies to forecast medical supply requirements and patient volume patterns, ensuring adequate resource availability while minimizing waste. Manufacturing facilities leverage demand forecasting to optimize production scheduling and raw material procurement, reducing idle capacity and inventory holding costs. Energy providers employ predictive models to anticipate consumption patterns across different seasons and customer segments, enabling efficient grid management and capacity planning. E-commerce platforms process millions of transactions daily and require sophisticated forecasting systems to manage logistics networks spanning multiple fulfillment centers and delivery regions. Telecommunications companies forecast network capacity requirements and service demand patterns to guide infrastructure investments and maintenance schedules. Across all these domains, organizations report forecast accuracy improvements ranging from five to fifteen percentage points when transitioning from traditional methods to machine learning approaches, with leading implementations achieving reliability levels between eighty-five and ninety-five percent. These gains translate directly into reduced stockout incidents, lower inventory carrying costs, improved customer satisfaction scores, and enhanced operational efficiency metrics. The article explores implementation frameworks encompassing data collection strategies, model development methodologies, accuracy measurement techniques, and the critical external factors that contemporary forecasting systems must incorporate to maintain predictive validity in dynamic market environments.","url":"https://doi.org/10.5281/zenodo.18483667","authors":["Balusamy Chinnappaiyan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18483667","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18483668","name":"AI/ML-Based Demand Forecasting Across Industries: Transforming Traditional Sales Prediction Through Advanced Analytics","source":"datacite","abstract":"Demand forecasting is a strategic discipline that anticipates future customer demand, enabling organizations to align production, inventory, and capacity decisions with market needs to drive profitability and competitive advantage. Traditional statistical methods have served organizations for decades, relying primarily on historical sales patterns and time-series analysis to generate predictions. However, contemporary business environments characterized by unprecedented demand volatility, rapidly shifting consumer preferences, and complex market dynamics have exposed significant limitations in conventional forecasting approaches. Artificial intelligence and machine learning technologies offer transformative capabilities that address these challenges through multi-source data integration, pattern recognition across vast datasets, and adaptive learning mechanisms that respond to changing market conditions in real-time. This review examines the evolution from traditional statistical forecasting to AI-powered demand prediction systems, with primary emphasis on retail sector implementations where inventory optimization and sales forecasting directly impact profitability margins. Healthcare organizations utilize similar technologies to forecast medical supply requirements and patient volume patterns, ensuring adequate resource availability while minimizing waste. Manufacturing facilities leverage demand forecasting to optimize production scheduling and raw material procurement, reducing idle capacity and inventory holding costs. Energy providers employ predictive models to anticipate consumption patterns across different seasons and customer segments, enabling efficient grid management and capacity planning. E-commerce platforms process millions of transactions daily and require sophisticated forecasting systems to manage logistics networks spanning multiple fulfillment centers and delivery regions. Telecommunications companies forecast network capacity requirements and service demand patterns to guide infrastructure investments and maintenance schedules. Across all these domains, organizations report forecast accuracy improvements ranging from five to fifteen percentage points when transitioning from traditional methods to machine learning approaches, with leading implementations achieving reliability levels between eighty-five and ninety-five percent. These gains translate directly into reduced stockout incidents, lower inventory carrying costs, improved customer satisfaction scores, and enhanced operational efficiency metrics. The article explores implementation frameworks encompassing data collection strategies, model development methodologies, accuracy measurement techniques, and the critical external factors that contemporary forecasting systems must incorporate to maintain predictive validity in dynamic market environments.","url":"https://doi.org/10.5281/zenodo.18483668","authors":["Balusamy Chinnappaiyan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18483668","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/3bv2p","name":"Artificial Intelligence for Stroke Risk Stratification and Surgical Decision-Making in Asymptomatic Carotid Stenosis: A Scoping Review","source":"datacite","abstract":"This project is a scoping review examining how artificial intelligence (AI) and machine learning (ML) approaches have been applied to stroke risk stratification in patients with asymptomatic carotid stenosis, and how these models may inform decisions between surgical intervention and best medical therapy. The central premise is that, despite major advances in imaging, plaque characterisation, and computational modelling, the carotid field still lacks reliable tools to identify which asymptomatic patients are at sufficiently high risk of future stroke to justify carotid endarterectomy or stenting in the era of contemporary medical therapy. While numerous studies have developed AI models to analyse ultrasound, CT, and MRI plaque features, hemodynamics, and clinical variables, these efforts have not been synthesised in the context of the clinical question that matters most: patient selection for intervention. The review will systematically map the existing literature on AI-driven risk prediction in asymptomatic carotid disease, including models that predict ipsilateral stroke, transient ischaemic attack, plaque progression or instability, microembolisation, cognitive decline, and the need for intervention. It will characterise the data sources used by these models, such as radiomics, ultrasound texture analysis, MRI plaque imaging, CT angiography features, clinical and biochemical variables, and computational flow modelling, as well as the modelling techniques employed, including supervised machine learning, deep learning, clustering, and risk modelling approaches. In addition, the review will examine the stage of clinical translation of these models and how their outputs align, or fail to align, with current guideline-based risk stratification for carotid intervention. A key component of this review is conceptual: to evaluate whether existing AI models, even if not designed for this purpose, could theoretically function as decision-support tools to distinguish high-risk asymptomatic patients who may benefit from surgery from those better managed with medical therapy alone. By identifying gaps between AI risk prediction research and real-world surgical decision-making, this review aims to connect two parallel fields, computational plaque analysis and carotid management, that have largely evolved in isolation. The expected outcomes of this project include a comprehensive map of AI applications in asymptomatic carotid stenosis, identification of recurring methodological patterns and data modalities, clarification of how these models relate to clinically meaningful outcomes, and a clear delineation of the translational gap preventing their use in practice. Ultimately, the review aims to provide a foundation for future research and guideline development by outlining how AI-driven risk stratification could reshape patient selection for carotid endarterectomy, stenting, and best medical therapy in the modern era of stroke prevention.","url":"https://doi.org/10.17605/osf.io/3bv2p","authors":["Chinmay Sharma","Hilkiah, Jeremiah","Saricilar, Erin","Jesudason, Daniel"],"tags":["Medicine and Health Sciences","Analytical, Diagnostic and Therapeutic Techniques and Equipment","Other Analytical, Diagnostic and Therapeutic Techniques and Equipment","Artificial Intelligence","Carotid Disease","Preventive Neurology","Stroke","Vascular Disease"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/3bv2p","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/54zdb","name":"AI vs SP in History-Taking Training: A Noninferiority Study","source":"datacite","abstract":"This research project investigates whether artificial intelligence–generated standardized patients (AI-SPs), powered by large language models (LLMs), can serve as a non-inferior alternative to traditional instructor-portrayed standardized patients (Teacher-SPs) in teaching clinical history-taking skills to medical students. The study employs a prospective, stratified, randomized controlled noninferiority design. A total of 180 second-year medical students from Tongji Medical College will be randomly assigned to receive history-taking training in one of two common clinical scenarios—chest pain or hemoptysis—using either an AI-SP interface or a human Teacher-SP. Following the intervention, all participants will undergo a standardized Objective Structured Clinical Examination (OSCE) scored by blinded assessors using a validated 100-point checklist that evaluates key domains including chief complaint, history of present illness, past medical history, family/social history, and system review. The primary hypothesis is that the mean OSCE score in the AI-SP group will not be more than 5 points lower than that in the Teacher-SP group (noninferiority margin Δ = 4 on a 100-point scale), based on prior educational literature suggesting that a 5-point difference represents the smallest clinically meaningful gap in performance assessment. Secondary outcomes include student satisfaction, perceived realism, engagement, and self-efficacy, measured via post-intervention Likert-scale surveys. If noninferiority is demonstrated, this would provide robust evidence supporting the use of AI-SPs as a scalable, consistent, and resource-efficient tool for clinical skills training—particularly valuable in settings with limited access to trained human standardized patients. Expected outcomes include: (1) Empirical validation of AI-SPs as a pedagogically equivalent modality for history-taking instruction; (2) Quantitative and qualitative insights into learner experiences with AI-driven clinical simulation; (3) Public sharing of the study protocol, OSCE rubric, survey instruments, and analysis code on this OSF project page to enhance transparency and reproducibility in AI-augmented medical education research. This work contributes to the responsible, evidence-based integration of generative AI into health professions education while adhering to principles of scientific rigor, equity, and open scholarship.","url":"https://doi.org/10.17605/osf.io/54zdb","authors":["Fan, Jiahui"],"tags":["Curriculum and Instruction","Education","medical education ； clinical skills training； history-taking； AI in education；"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2030","doi":"10.17605/osf.io/54zdb","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/2pqw4","name":"Current Applications of Artificial Intelligence in Medical Journal Editing","source":"datacite","abstract":"Medical journals serve as the cornerstone for the transmission of medical knowledge, clinical decision-making, and the formulation of public health policies. The quality and integrity of the research they publish directly impact the credibility of scientific progress and patient safety. Within the current scholarly publishing system, peer review is regarded as the core mechanism safeguarding this quality—a critical process for the systematic and critical evaluation of original research. However, this system is under dual pressure. On one hand, the exponential growth of global research output makes it difficult for reviewers to comprehensively track the vast volume of multilingual literature, amplifying the inherent limitations of their knowledge breadth. On the other hand, technological misuse has given rise to novel forms of academic misconduct, ranging from the mass production by \"paper mills\" to the use of generative AI for semantic paraphrasing to evade plagiarism detection, making the identification of manuscript authenticity and originality unprecedentedly complex. The rise of generative artificial intelligence technology, represented by large language models (LLMs), presents a transformative and complex solution to this predicament. It demonstrates potential as a powerful auxiliary tool: by rapidly parsing and synthesizing vast amounts of text, it can provide reviewers with more comprehensive research background support; through automated screening, it can assist in detecting methodological flaws, anomalies in statistical analysis, or adherence to reporting guidelines (e.g., CONSORT, PRISMA), thereby enhancing the rigor and efficiency of the review process. Recent studies indicate that advanced LLMs have shown impressive accuracy in automatically assessing the compliance of research reports with these guidelines. Yet, the same technology can also be leveraged to generate or deeply alter academic text, posing a new threat to the integrity of journal content and making the development and deployment of effective AI-generated content detection tools an urgent need for editorial offices. This novel risk—how to effectively identify content generated or substantially modified by AI—has become a new challenge facing editors. Confronted with this technological wave, the international publishing community has responded swiftly, though consensus and norms are still evolving. From the principle statements of organizations like the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE), to the varied usage policies of top-tier journals such as Nature, Science, and The Lancet, a global discussion and practical exploration on how to responsibly integrate AI is intensifying. Although related discussions and practices are burgeoning, the academic community still lacks a systematic synthesis of this field. Existing literature primarily consists of technical reports focusing on single application scenarios (e.g., language polishing, plagiarism detection) or opinion pieces centered on ethics and policies, lacking integrative research that provides a panoramic view of the application status, evidence base, practical models, and core controversies of AI across the entire workflow of medical journal editing—from initial manuscript screening to post-publication dissemination. Therefore, this study aims to employ a scoping review methodology to, for the first time, systematically synthesize the existing evidence in this field. It seeks to clarify the application landscape of AI technology across the complete editorial chain, identify research hotspots and evidence gaps, and thereby provide a solid foundation for evidence-based decision-making by journal editorial offices, direction-setting for technology developers, and the refinement of relevant academic policies.","url":"https://doi.org/10.17605/osf.io/2pqw4","authors":["Bingyi Wang"],"tags":["Physical Sciences and Mathematics","Other Medicine and Health Sciences","Medicine and Health Sciences","Computer Sciences","Artificial Intelligence and Robotics","Medical Journal Editing","Peer Review"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/2pqw4","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18453349","name":"\"ARTIFICIAL INTELLIGENCE IN MODERN HEALTHCARE: BENEFITS AND CHALLENGES\"","source":"datacite","abstract":"Artificial Intelligence (AI) is rapidly transforming the healthcare industry by enhancing diagnostics, treatment planning, and patient care. This study examines the benefits and challenges of AI integration in modern healthcare systems. AI technologies, including machine learning, natural language processing, and robotics, have demonstrated significant potential in improving accuracy, efficiency, and accessibility of medical services. Through a comprehensive review of academic literature, policy reports, and case studies, the research identifies both the positive impacts and limitations of AI in clinical practice. The findings reveal that AI can enhance diagnostic accuracy, reduce human error, streamline administrative processes, and facilitate personalized medicine. For instance, machine learning algorithms can analyze vast datasets to detect diseases at early stages, while AI-powered robotic systems assist in complex surgeries with precision. Furthermore, AI applications improve patient engagement through digital health platforms and predictive analytics, allowing for proactive care management. However, the study also highlights significant challenges, including data privacy concerns, algorithmic bias, high implementation costs, and ethical considerations in clinical decision-making. Healthcare professionals require adequate training to collaborate effectively with AI systems, and regulatory frameworks must evolve to ensure safe and equitable use of AI technologies. Additionally, the integration of AI should complement rather than replace human judgment, maintaining the patient-centered approach of healthcare. In conclusion, AI presents transformative opportunities for modern healthcare but requires careful management to address ethical, technical, and operational challenges. The study underscores the need for balanced adoption strategies, robust governance, and continuous evaluation to maximize the benefits of AI while mitigating potential risks. By understanding both the advantages and limitations, healthcare stakeholders can implement AI solutions that enhance patient outcomes, improve operational efficiency, and support sustainable healthcare delivery.","url":"https://doi.org/10.5281/zenodo.18453349","authors":["Abdumannobova Rayyona Rustamjon kizi","Zuxriddinova Dilshoda Nuriddin kizi","Worldly Knowledge Publishing Centre"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18453349","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18453350","name":"\"ARTIFICIAL INTELLIGENCE IN MODERN HEALTHCARE: BENEFITS AND CHALLENGES\"","source":"datacite","abstract":"Artificial Intelligence (AI) is rapidly transforming the healthcare industry by enhancing diagnostics, treatment planning, and patient care. This study examines the benefits and challenges of AI integration in modern healthcare systems. AI technologies, including machine learning, natural language processing, and robotics, have demonstrated significant potential in improving accuracy, efficiency, and accessibility of medical services. Through a comprehensive review of academic literature, policy reports, and case studies, the research identifies both the positive impacts and limitations of AI in clinical practice. The findings reveal that AI can enhance diagnostic accuracy, reduce human error, streamline administrative processes, and facilitate personalized medicine. For instance, machine learning algorithms can analyze vast datasets to detect diseases at early stages, while AI-powered robotic systems assist in complex surgeries with precision. Furthermore, AI applications improve patient engagement through digital health platforms and predictive analytics, allowing for proactive care management. However, the study also highlights significant challenges, including data privacy concerns, algorithmic bias, high implementation costs, and ethical considerations in clinical decision-making. Healthcare professionals require adequate training to collaborate effectively with AI systems, and regulatory frameworks must evolve to ensure safe and equitable use of AI technologies. Additionally, the integration of AI should complement rather than replace human judgment, maintaining the patient-centered approach of healthcare. In conclusion, AI presents transformative opportunities for modern healthcare but requires careful management to address ethical, technical, and operational challenges. The study underscores the need for balanced adoption strategies, robust governance, and continuous evaluation to maximize the benefits of AI while mitigating potential risks. By understanding both the advantages and limitations, healthcare stakeholders can implement AI solutions that enhance patient outcomes, improve operational efficiency, and support sustainable healthcare delivery.","url":"https://doi.org/10.5281/zenodo.18453350","authors":["Abdumannobova Rayyona Rustamjon kizi","Zuxriddinova Dilshoda Nuriddin kizi","Worldly Knowledge Publishing Centre"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18453350","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.48550/arxiv.2505.04769","name":"Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges","source":"datacite","abstract":"Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models, systematically organized across five thematic pillars that structure the landscape of this rapidly evolving field. We begin by establishing the conceptual foundations of VLA systems, tracing their evolution from cross-modal learning architectures to generalist agents that tightly integrate vision-language models (VLMs), action planners, and hierarchical controllers. Our methodology adopts a rigorous literature review framework, covering over 80 VLA models published in the past three years. Key progress areas include architectural innovations, efficient training strategies, and real-time inference accelerations. We explore diverse application domains such as autonomous vehicles, medical and industrial robotics, precision agriculture, humanoid robotics, and augmented reality. We analyzed challenges and propose solutions including agentic adaptation and cross-embodiment planning. Furthermore, we outline a forward-looking roadmap where VLA models, VLMs, and agentic AI converge to strengthen socially aligned, adaptive, and general-purpose embodied agents. This work, is expected to serve as a foundational reference for advancing intelligent, real-world robotics and artificial general intelligence. The project repository is available on GitHub as https://github.com/Applied-AI-Research-Lab/Vision-Language-Action-Models-Concepts-Progress-Applications-and-Challenges. [Index Terms: Vision Language Action, VLA, Vision Language Models, VLMs, Action Tokenization, NLP]","url":"https://doi.org/10.48550/arxiv.2505.04769","authors":["Sapkota, Ranjan","Cao, Yang","Roumeliotis, Konstantinos I.","Karkee, Manoj"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.04769","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18452046","name":"Use of Generative Artificial Intelligence for Consultation Preparation in Shared Decision Making: Can a Handbook Provide Support?","source":"datacite","abstract":"Background: Shared Decision Making (SDM) is considered the gold standard of patient-centered care but often fails in clinical routine due to time constraints and a lack of patient preparation. While patients often struggle to articulate their preferences and questions, new developments in the field of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer the potential to bridge this gap. Objective: This paper examines the potential of GenAI as a supportive tool for preparing doctor-patient consultations. The aim is to conceptualize an evidence-based handbook providing patients and physicians with guidance (prompts) to make SDM processes more efficient and personalized. Results: The literature review indicates that GenAI can meaningfully complement traditional decision aids through personalization and interactivity. Specific areas of application include translating complex medical information into patient-friendly language, supporting value clarification, and generating individualized Question Prompt Lists. However, a handbook for using these technologies must address critical risks, particularly \"hallucinations\" (factual errors), bias in training data, and data privacy issues. Effective \"prompt engineering\" is identified as a new key competency for both patients and providers. Conclusion: A structured handbook for the use of GenAI has the potential to reduce asymmetry in the doctor-patient relationship and increase consultation efficiency. However, prerequisites for implementation include strict safety mechanisms, consideration of health literacy, and ethical validation of AI outputs.","url":"https://doi.org/10.5281/zenodo.18452046","authors":["Wittal, Cornelius"],"tags":["Artificial intelligence","Artificial Intelligence","Artificial Intelligence/trends","Artificial Intelligence/ethics","Medicine","Physicians","Physician Executives/trends","Patients"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18452046","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.17605/osf.io/763xn","name":"Current Practices in Utilizing Large Language Models in Anatomical Pathology: A Scoping Review Protocol","source":"datacite","abstract":"Large language models (LLMs), which are advanced artificial intelligence systems designed for natural language processing tasks, demonstrate significant potential in enhancing workflows in medicine, including in pathology. However, as a relatively new technology, there has been limited documentation of technical best practices and standards for large language model operations (LLMOps), especially in medical settings. We aim to survey LLMs applications to anatomical pathology diagnostic workflows that are under investigation, and to assess the current implementation frameworks of large language models in anatomical pathology as compared to current standards in computer science, via a comprehensive review of the literature within the past ten years in PubMed, Embase, CINAHL, and Cochrane Library databases.","url":"https://doi.org/10.17605/osf.io/763xn","authors":["Ho, Kenneth","Lam, Bertha Ching Wai"],"tags":["Medical Sciences","Physical Sciences and Mathematics","Medical Pathology","Medicine and Health Sciences","Computer Sciences","Artificial Intelligence and Robotics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2027","doi":"10.17605/osf.io/763xn","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18444759","name":"From one-size-fits-all to tailor-made: The emergence of personalized medicine in clinical practice","source":"datacite","abstract":"A significant departure from the conventional one-size-fits-all approach is represented by personalized medicine, which is the customization of medical care to each patient's unique traits. This review thoroughly examines the past, present, and future developments of personalized medicine. We examine its fundamental pillars-genomics, pharmacogenomics, and biomarker discovery-and describe their clinical uses in rare illnesses, oncology, cardiology, and psychiatry. The importance of enabling technologies, such as artificial intelligence, big data analytics, and next-generation sequencing, is emphasized. It also discusses the major obstacles to wider adoption, including the need for physician education, regulatory and reimbursement barriers, health fairness and access, and data privacy and security. A more dynamic, predictive, and participative approach to medicine is being made possible by the convergence of wearable technology, digital health technologies, and multi-omics data. Even if science and technology have advanced remarkably, to fully realize PM's promise and guarantee that it benefits all facets of society, concurrent breakthroughs in ethical frameworks, health policy, and interdisciplinary collaboration are required.","url":"https://doi.org/10.5281/zenodo.18444759","authors":["Mediterranean Journal of Medicine and Medical Sciences"],"tags":["Medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18444759","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18444760","name":"From one-size-fits-all to tailor-made: The emergence of personalized medicine in clinical practice","source":"datacite","abstract":"A significant departure from the conventional one-size-fits-all approach is represented by personalized medicine, which is the customization of medical care to each patient's unique traits. This review thoroughly examines the past, present, and future developments of personalized medicine. We examine its fundamental pillars-genomics, pharmacogenomics, and biomarker discovery-and describe their clinical uses in rare illnesses, oncology, cardiology, and psychiatry. The importance of enabling technologies, such as artificial intelligence, big data analytics, and next-generation sequencing, is emphasized. It also discusses the major obstacles to wider adoption, including the need for physician education, regulatory and reimbursement barriers, health fairness and access, and data privacy and security. A more dynamic, predictive, and participative approach to medicine is being made possible by the convergence of wearable technology, digital health technologies, and multi-omics data. Even if science and technology have advanced remarkably, to fully realize PM's promise and guarantee that it benefits all facets of society, concurrent breakthroughs in ethical frameworks, health policy, and interdisciplinary collaboration are required.","url":"https://doi.org/10.5281/zenodo.18444760","authors":["Mediterranean Journal of Medicine and Medical Sciences"],"tags":["Medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18444760","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18444533","name":"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers","source":"datacite","abstract":"Description This document serves as the formal cover letter for the academic submission of \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" It outlines the strategic and scientific importance of the Clark-Oneiro Synchronization Bridge, a breakthrough technology that leverages solar-core quantum correlations to trigger controlled nuclear de-excitation in Hafnium-178m2. This submission marks the inaugural public disclosure for the Lucid Oneiro Annals of Physics (LOAP) and establishes scientific priority for the Clark-Oneiro Synchronization Protocol. COVER LETTER: SUBMISSION FOR PEER REVIEW TO: The Academic Community, Strategic Reviewers, and Peer Referees FROM: Office of the Principal Investigator, Kevin Rashaud Clark DATE: January 24, 2026 SUBJECT: Breakthrough in Non-Local Nuclear De-excitation: The Clark-Oneiro Bridge Dear Colleagues and Distinguished Reviewers, I am formally submitting for your review and consideration the preprint titled: \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" For over three decades, the field of high-energy density physics has been at a standstill regarding the \"Hafnium Controversy.\" The scientific community has long recognized the immense energy potential of the Hf-178m2 isomer ($1.3 \\text{ GJ/g}$), yet we have remained bound by the thermodynamic impossibility of a portable, low-input trigger. The enclosed research, conducted under the auspices of Lucid Oneiro and its strategic divisions NEUAEON and The Hypersphere, presents the definitive resolution to this threshold problem. By shifting the paradigm from energy-based bombardment to the Clark-Oneiro Synchronization Protocol, we demonstrate that nuclear de-excitation can be achieved through non-local phase-matching with solar-core reactions. Key Innovations Addressed in this Paper: The Dicke-Clark Regime: Application of macroscopic quantum superradiance to establish a coherent nuclear state ($I \\propto N^2 \\gamma$). ACSE Mechanism: The introduction of Artificial Cross-Section Enhancement, allowing for resonant neutrino coupling as a sub-picosecond informational trigger. Strategic Differentials: The first viable roadmap for both high-output Quantum Batteries and non-kinetic strategic defense systems. CERTIFICATION OF HUMAN CONCEPTION & INTELLECTUAL PRIORITY This document and the associated research represent the original discoveries and conceptual frameworks of Kevin Rashaud Clark. While advanced computational tools were utilized for data synthesis and manuscript formatting, the core inventive steps, proprietary nomenclature (e.g., Clark-Oneiro Bridge), and strategic differentials are the product of human creative intelligence. This letter serves as a formal declaration of authorship for all purposes related to the Nobel Committee for Physics and international patent offices. DISCLOSURE & TRADE SECRETS This disclosure is made to establish scientific priority and to invite rigorous academic discourse. While the theoretical foundations are presented here for peer evaluation, the specific engineering specifications of the SSPT (Solar-Synchronized Particulate Trigger) remain the proprietary trade secrets of Lucid Oneiro. We believe this work represents a fundamental shift in human capability—transitioning from the era of \"Force\" to the era of \"Synchronization.\" We welcome your critical analysis and look forward to the ensuing dialogue. Respectfully submitted, Kevin Rashaud Clark Principal Investigator Founder, Lucid Oneiro | NEUAEON | The Hypersphere suno.com/theworldsleast X/Instagram: @NEUAEON Institutional Note: All tactical and licensing inquiries regarding the Clark-Oneiro Synchronization Bridge must be formally directed to the board of directors at Lucid Oneiro. Proprietary protections are in full effect. Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers Published in: Lucid Oneiro Annals of Phys","url":"https://doi.org/10.5281/zenodo.18444533","authors":["Kevin Rashaud Clark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18444533","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.5281/zenodo.18360609","name":"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers","source":"datacite","abstract":"Description This document serves as the formal cover letter for the academic submission of \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" It outlines the strategic and scientific importance of the Clark-Oneiro Synchronization Bridge, a breakthrough technology that leverages solar-core quantum correlations to trigger controlled nuclear de-excitation in Hafnium-178m2. This submission marks the inaugural public disclosure for the Lucid Oneiro Annals of Physics (LOAP) and establishes scientific priority for the Clark-Oneiro Synchronization Protocol. COVER LETTER: SUBMISSION FOR PEER REVIEW TO: The Academic Community, Strategic Reviewers, and Peer Referees FROM: Office of the Principal Investigator, Kevin Rashaud Clark DATE: January 24, 2026 SUBJECT: Breakthrough in Non-Local Nuclear De-excitation: The Clark-Oneiro Bridge Dear Colleagues and Distinguished Reviewers, I am formally submitting for your review and consideration the preprint titled: \"Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers.\" For over three decades, the field of high-energy density physics has been at a standstill regarding the \"Hafnium Controversy.\" The scientific community has long recognized the immense energy potential of the Hf-178m2 isomer ($1.3 \\text{ GJ/g}$), yet we have remained bound by the thermodynamic impossibility of a portable, low-input trigger. The enclosed research, conducted under the auspices of Lucid Oneiro and its strategic divisions NEUAEON and The Hypersphere, presents the definitive resolution to this threshold problem. By shifting the paradigm from energy-based bombardment to the Clark-Oneiro Synchronization Protocol, we demonstrate that nuclear de-excitation can be achieved through non-local phase-matching with solar-core reactions. Key Innovations Addressed in this Paper: The Dicke-Clark Regime: Application of macroscopic quantum superradiance to establish a coherent nuclear state ($I \\propto N^2 \\gamma$). ACSE Mechanism: The introduction of Artificial Cross-Section Enhancement, allowing for resonant neutrino coupling as a sub-picosecond informational trigger. Strategic Differentials: The first viable roadmap for both high-output Quantum Batteries and non-kinetic strategic defense systems. CERTIFICATION OF HUMAN CONCEPTION & INTELLECTUAL PRIORITY This document and the associated research represent the original discoveries and conceptual frameworks of Kevin Rashaud Clark. While advanced computational tools were utilized for data synthesis and manuscript formatting, the core inventive steps, proprietary nomenclature (e.g., Clark-Oneiro Bridge), and strategic differentials are the product of human creative intelligence. This letter serves as a formal declaration of authorship for all purposes related to the Nobel Committee for Physics and international patent offices. DISCLOSURE & TRADE SECRETS This disclosure is made to establish scientific priority and to invite rigorous academic discourse. While the theoretical foundations are presented here for peer evaluation, the specific engineering specifications of the SSPT (Solar-Synchronized Particulate Trigger) remain the proprietary trade secrets of Lucid Oneiro. We believe this work represents a fundamental shift in human capability—transitioning from the era of \"Force\" to the era of \"Synchronization.\" We welcome your critical analysis and look forward to the ensuing dialogue. Respectfully submitted, Kevin Rashaud Clark Principal Investigator Founder, Lucid Oneiro | NEUAEON | The Hypersphere suno.com/theworldsleast X/Instagram: @NEUAEON Institutional Note: All tactical and licensing inquiries regarding the Clark-Oneiro Synchronization Bridge must be formally directed to the board of directors at Lucid Oneiro. Proprietary protections are in full effect. Non-Local Induced Gamma Emission (IGE) via Solar-Coupled Quantum Superradiance in Nuclear Isomers Published in: Lucid Oneiro Annals of Phys","url":"https://doi.org/10.5281/zenodo.18360609","authors":["Kevin Rashaud Clark"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18360609","addedAt":"2026-09-01T01:48:00.328Z","updatedAt":"2026-09-01T01:48:00.328Z"},{"id":"doi:10.1016/b978-0-12-801522-3.00009-4","name":"Sparsity-Aware Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-801522-3.00009-4","authors":["Sergios Theodoridis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-04-10T22:52:56Z","doi":"10.1016/b978-0-12-801522-3.00009-4","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1016/b978-0-12-801522-3.00010-0","name":"Sparsity-Aware Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-801522-3.00010-0","authors":["Sergios Theodoridis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-04-10T22:52:56Z","doi":"10.1016/b978-0-12-801522-3.00010-0","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1093/bjs/znab134.006","name":"223 The Clinical Applications of Machine Learning in Plastic and Reconstructive Surgery: A Systematic Review","source":"crossref","abstract":"Abstract Background Machine learning (ML) is a set of models and methods that can automatically detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. The aim of this review is to evaluate the applications of machine learning in plastic and reconstructive surgery. Method EMBASE, MEDLINE and CENTRAL were searched from 1990 to 2020 for studies in which machine learning has been employed in the clinical setting of reconstructive surgery. Primary outcomes will be the evaluation of the accuracy of machine learning models in predicting a clinical diagnosis and post-surgical outcomes. Results The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n = 22), outcome prediction (n = 21) and pre-operative planning (n = 8). The mean accuracy for diagnosis prediction, outcome prediction and pre-operative planning was 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n = 31), support vector machine (n = 13), decision trees/random forests (10) and logistic regression (n = 9). Discussion ML has demonstrated excellent performance in diagnosis and outcome predictions, but it is still in its infancy. Further research is warranted to evaluate its applications.","url":"https://doi.org/10.1093/bjs/znab134.006","authors":["A Mantelakis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-08T15:00:20Z","doi":"10.1093/bjs/znab134.006","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1016/j.cca.2024.118807","name":"Accessing artificial intelligence and machine learning for clinical decision making","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cca.2024.118807","authors":["S. Sawant","A.R. Bhatia","S.V. Arya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-08T06:52:40Z","doi":"10.1016/j.cca.2024.118807","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.2139/ssrn.4463332","name":"Clinical Decision Support System Using a Machine Learning Model to Assist Simultaneous Cardiopulmonary Auscultation: Open-Label Randomized Controlled Trial","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4463332","authors":["Takanobu Hirosawa","Tetsu Sakamoto","Yukinori Harada","Taro Shimizu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-15T19:11:57Z","doi":"10.2139/ssrn.4463332","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.26685/urncst.426","name":"Feasibility Study: Machine Learning in Neurodegenerative Disorders, Alzheimer’s Disease","source":"crossref","abstract":"Introduction: Clinical decision support systems (CDSSs), powered by machine learning and artificial intelligence, have demonstrated potential in clinical diagnosis and intervention for neurological and psychiatric disorders. Considering the importance of early detection and intervention of Alzheimer’s disease (AD), this study aims to explore the potential of a data-driven non-knowledge-based machine learning CDSS for predicting AD diagnoses in individuals. In non-knowledge-based CDSSs, no prior knowledge about AD or any other disorder impacts the decision-making of classification models Method: In this study, publicly available data of 14037 data points collected by the Alzheimer’s Disease Neuroimaging Initiative were used for model training and testing. Binary classification and multiclassification machine learning were applied, and results from six mainstream classification models were analyzed. Results: The binary classification models (AD diagnosis present or absent) gave accuracies around 0.92-0.93, and the multiclassification models gave accuracies around 0.85-0.87. Logistic regression model (binary classification) had the highest overall hit rate (0.93). This model maintained this hit rate when only features with over 90% non-empty data are available. Discussion: Binary classification models are more reliable for diagnosing AD than multiclassification models. The high hit rates of the logistic regression model (binary classification) on generally available data implicate its feasibility. Conclusion: There is strong potential for a complete machine learning-based CDSS to aid in AD diagnoses in the future","url":"https://doi.org/10.26685/urncst.426","authors":["Xiangxuan Kong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-03T05:16:12Z","doi":"10.26685/urncst.426","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.12732/ijam.v39i1s.1686","name":"ENHANCED COPD RISK PREDICTION USING CLINICAL DATA: A MACHINE LEARNING-BASED COPD-RINET FRAMEWORK","source":"crossref","abstract":"Chronic Obstructive Pulmonary Disease (COPD) is a long-term lung disease that makes normal breathing more difficult as it develops, often underdiagnosed in its early stages, particularly in settings lacking spirometry. This study presents COPD-RiNet, a machine learning (ML) framework for early COPD detection using non-invasive clinical and hematological features. The framework utilizes data cleaning and reduces dimensionality by using principal component analysis (PCA). For classifying the results, it uses a combined approach that includes Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) algorithms. The model will be assessed using a unified clinical dataset, where it is portioned between 70%for training and 30% for testing. The performance will be further examined using a 10-fold validation approach.COPD-RiNet achieved 99.2% accuracy, 0.98 F1-score, and 0.97 AUC-ROC, surpassing models such as FDDLM, COPD-MMDDxNet, and Dragonfly-Optimized KELM. Leveraging routinely collected parameters, COPD-RiNet offers a feasible solution for early diagnosis in primary care and resource-limited settings, with future work aimed at incorporating disease severity assessment and progression prediction.","url":"https://doi.org/10.12732/ijam.v39i1s.1686","authors":["C. Maheswari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-31T09:18:56Z","doi":"10.12732/ijam.v39i1s.1686","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1101/2022.07.13.22277577","name":"Semantic and phonetic markers in schizophrenia-spectrum disorders; a combinatory machine learning approach","source":"crossref","abstract":"Abstract Introduction Speech is a promising marker for schizophrenia-spectrum disorder diagnosis, as it closely reflects symptoms. Previous approaches have made use of different feature domains of speech in classification, including semantic and phonetic features. However, an examination of the relative contribution and accuracy per domain remains an area of active investigation. Here, we examine these domains (i.e. phonetic and semantic) separately and in combination. Methods Using a semi-structured interview with neutral topics, speech of 94 schizophrenia-spectrum subjects (SSD) and 73 healthy controls (HC) was recorded. Phonetic features were extracted using a standardized feature set, and transcribed interviews were used to assess word connectedness using a word2vec model. Separate cross-validated random forest classifiers were trained on each feature domain. A third, combinatory classifier was used to combine features from both domains. Results The phonetic domain random forest achieved 81% accuracy in classifying SSD from HC. For the semantic domain, the classifier reached an accuracy of 80% with a sparse set of features with 10-fold cross-validation. Joining features from the domains, the combined classifier reached 85% accuracy, significantly improving on models trained on separate domains. Top features were fragmented speech for phonetic and variance of connectedness for semantic, with both being the top features for the combined classifier. Discussion Both semantic and phonetic domains achieved similar results compared with previous research. Combining these features shows the relative value of each domain, as well as the increased classification performance from implementing features from multiple domains. Explainability of models and their feature importance is a requirement for future clinical applications.","url":"https://doi.org/10.1101/2022.07.13.22277577","authors":["A.E. Voppel","J.N. de Boer","S.G. Brederoo","H.G. Schnack","I.E.C. Sommer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-15T15:50:18Z","doi":"10.1101/2022.07.13.22277577","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.57233/ihcfs.040","name":"EARLY DETECTION OF PANCREATIC CANCER USING URINARY BIOMARKERS AND CLINICAL FEATURES: A BIOSTATISTICAL AND MACHINE LEARNING APPROACH","source":"crossref","abstract":"","url":"https://doi.org/10.57233/ihcfs.040","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-30T09:49:51Z","doi":"10.57233/ihcfs.040","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.52783/anvi.v28.2196","name":"Machine Learning-Based Prediction of COVID-19 Patient Outcomes: Enhancing Clinical Decision-Making","source":"crossref","abstract":"The COVID-19 epidemic has created unprecedented challenges to healthcare systems around the world, demanding the development of reliable techniques for predicting patient outcomes to guide clinical decisions. This paper comprehensively explores machine learning-based methodologies for predicting COVID-19 patient outcomes. Using a broad dataset that includes demographic information, symptoms, comorbidities, laboratory test results, and imaging data, we use cutting-edge machine-learning approaches to create prediction models. Data preprocessing techniques including feature engineering and selection are applied to enhance model performance and reliability. Various machine learning algorithms, support vector machine, logistic regression, support and Naïve Bayes, are evaluated for their efficacy in predicting disease severity, hospitalization, and mortality outcomes. Model performance is assessed and evaluated using standard evaluation criteria, assuring the robustness of all models. These models provide useful insights into COVID-19 patient prognosis and can assist healthcare staff in triaging patients, optimizing treatment regimens, and allocating resources more efficiently. Ethical concerns around patient data privacy and model interpretability are thoroughly followed throughout the study. In all, this work highlights the promise of machine learning techniques for improving COVID-19 patient care and public health response efforts.","url":"https://doi.org/10.52783/anvi.v28.2196","authors":["Nitin N. Sakhare"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-15T09:00:13Z","doi":"10.52783/anvi.v28.2196","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.63282/3050-9262.ijaidsml-v6i4p131","name":"AI-Driven Quality Assurance Frameworks for Decentralized Clinical Trials","source":"crossref","abstract":"Traditional quality assurance (QA) processes used in electronic Clinical Outcome Assessment (eCOA), and Decentralized Clinical Trial (DCT) platforms rely on a variety of manual, reactive approaches with many regulatory risk elements. The purpose of this research is to introduce an AI-powered Predictive Quality Assurance (PQA) system that uses Ensemble Machine Learning Models to predict problems with the DCT platform or eCOA application prior to their release into production. Using simulated and anonymized QA data sets, the PQA system will implement Supervised Learning, Deep Learning and Hybrid models to generate Risk Scores at the Module Level as well as Optimize Regression Test Coverage in Real Time. The evaluation outcomes showed improvements in defect prediction accuracy and reductions in Defect Leakage post Release, and thus the use of AI-Driven QA can enhance the dependability of software applications, prepare the software applications for Regulatory Audits, and help enable the Digital Transformation of Clinical Research Systems.","url":"https://doi.org/10.63282/3050-9262.ijaidsml-v6i4p131","authors":["Rohit Singh Raja"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-08T07:17:34Z","doi":"10.63282/3050-9262.ijaidsml-v6i4p131","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.3389/fmed.2025.1704829","name":"Eye-tracking biomarkers of clinical expertise in ECG interpretation: statistical and machine learning evidence","source":"crossref","abstract":"Introduction Interpreting the electrocardiogram (ECG) is a fundamental clinical skill, and mistakes are still prevalent in the workforce, especially among trainees and non-specialist clinicians. Eye-tracking technology has recently become a popular method for investigating visual expertise. However, few studies have integrated visual behavior metrics with machine learning to accurately classify expertise levels. Methods The original dataset included 62 participants from 10 healthcare roles (students, nurses, technicians, residents, fellows, consultants) who interpreted standardized ECGs. Eye movements were recorded using a Tobii Pro X2-60 tracker. ECGs were segmented into grid-based and functional Areas of Interest (AOIs). Certain eye-tracking metrics, such as Fixation Count, Time to First Fixation (TTFF), Gaze Duration, and Revisit Count, were evaluated via statistical analyses (ANOVA, Kruskal–Wallis, t -tests). Gaze features were used to train machine learning models (Random Forest, Support Vector Machine, K-Nearest Neighbors), and clustering was performed with K-means. Results Experts demonstrated faster TTFF, fewer revisits, and shorter fixation durations compared to novices. Experts exhibited more efficient gaze behavior, with fewer fixations within each diagnostic AOI but a higher overall fixation count per ECG due to broader systematic scanning. The correlation between fixation count and gaze duration was high ( R 2 = 0.76). Random Forest achieved the best classification accuracy (84%), outperforming SVM (78%) and KNN (74%). A Random Forest classifier achieved an accuracy of 84% using five-fold cross-validation, and performance significantly exceeded chance based on a 1,000-permutation test ( p &amp;lt; 0.001), demonstrating robust discriminative ability. These findings indicate that gaze-based features can reliably differentiate expertise levels. The groups identified by K-means clustering corresponded (for the most part) to novice, intermediate, and expert. Feature importance showed that leads V1, V2, and the rhythm strip were the top predictors of expertise. Conclusion Eye-tracking parameters differentiated levels of ECG interpretation expertise. These results suggest that gaze-derived metrics may serve as potential surrogate indicators that support assessment and training in medical education.","url":"https://doi.org/10.3389/fmed.2025.1704829","authors":["Eyad Talal Attar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-13T14:36:35Z","doi":"10.3389/fmed.2025.1704829","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1016/j.hlc.2026.04.003","name":"Bridging the Gap Between Machine Learning Innovation and Clinical Practice","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.hlc.2026.04.003","authors":["Salvatore Pepe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-08T01:15:24Z","doi":"10.1016/j.hlc.2026.04.003","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1287/lytx.2020.01.03","name":"Why Operationalizing Machine Learning Requires a Shrewd Business Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1287/lytx.2020.01.03","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-18T21:01:21Z","doi":"10.1287/lytx.2020.01.03","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.38124/ijisrt/25oct784","name":"Pneumonia Detection Using Machine Learning and Deep Learning Methods on Clinical and Chest  X-Ray Data","source":"crossref","abstract":"Pneumonia remains a major global health problem requiring timely and accurate treatment to improve patient outcomes. This study presents a comparative analysis of machine learning and deep learning methods for pneumonia detection using both clinical and chest X-ray data. Clinical features such as age, sex, temperature, heart rate, and laboratory results were integrated with imaging data from the Kaggle Chest X-Ray Pneumonia Dataset. Data preprocessing involved normalization, feature encoding, and image resizing to 224×224 pixels. Traditional machine learning models—Random Forest, Support Vector Machine (SVM), and Naive Bayes—were developed and compared with a Convolutional Neural Network (CNN) designed for image-based classification. Evaluation metrics including accuracy, precision, recall, F1-score, and ROC-AUC were used to assess performance. Experimental results demonstrated that the CNN model achieved the highest accuracy of 95%, outperforming all traditional models, while Random Forest achieved the best results among classical algorithms with 91% accuracy. The findings highlight the effectiveness of integrating clinical and imaging data for improved diagnostic accuracy and reliability. Future work will explore multi- class classification, larger datasets, and real-time deployment in hospital environments.","url":"https://doi.org/10.38124/ijisrt/25oct784","authors":["Athira V. P."],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:17:07Z","doi":"10.38124/ijisrt/25oct784","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1007/978-0-387-30164-8_171","name":"Continual Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_171","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:42:10Z","doi":"10.1007/978-0-387-30164-8_171","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1007/978-0-387-30164-8_611","name":"Offline Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_611","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:26:38Z","doi":"10.1007/978-0-387-30164-8_611","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1007/978-0-387-30164-8_632","name":"Passive Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_632","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:26:04Z","doi":"10.1007/978-0-387-30164-8_632","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.32657/10356/3804","name":"Incremental extreme learning machine","source":"crossref","abstract":"I-ELM and other Popular Incremental Learning Algorithms . . . . .","url":"https://doi.org/10.32657/10356/3804","authors":["Lei Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-02T15:11:06Z","doi":"10.32657/10356/3804","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.33915/etd.12118","name":"Machine Learning for Biosensors","source":"crossref","abstract":"Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system.","url":"https://doi.org/10.33915/etd.12118","authors":["Gayathri Anapanani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-14T15:31:44Z","doi":"10.33915/etd.12118","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.33696/diabetes.4.050","name":"A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction for People Over 65","source":"crossref","abstract":"The global outbreak of COVID-19 has resulted in over 378 million infections and worldwide casualties have surpassed 5.6 million. Identifying individuals at highest risk of COVID-19 death may help risk evaluation and health service provision. Personalized risk prediction that uses a broad range of comorbidities requires a cohort size larger than that reported in prior studies.","url":"https://doi.org/10.33696/diabetes.4.050","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-16T16:36:23Z","doi":"10.33696/diabetes.4.050","addedAt":"2026-09-01T01:48:00.556Z","updatedAt":"2026-09-01T01:48:00.556Z"},{"id":"doi:10.1158/1078-0432.ccr-20-0523","name":"Predicted Prognosis of Pancreatic Cancer Patients by Machine Learning—Letter","source":"crossref","abstract":"We recently read the article by Yokoyama and colleagues ([1][1]), in which the authors report a predictive model integrating DNA methylation status of three mucin genes to predict overall survival at a designated 5-year interval in pancreatic cancer. They collected samples from 191 patients and","url":"https://doi.org/10.1158/1078-0432.ccr-20-0523","authors":["Julius M. Kernbach","Victor E. Staartjes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-15T12:35:23Z","doi":"10.1158/1078-0432.ccr-20-0523","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9780429446030-13","name":"Analysis of Heart Disease Prediction Using Various Machine Learning Techniques","source":"crossref","abstract":"The health care industry produces a huge amount of data. These data are not always used to the full extent and are often underutilized. Using these huge amount of data, a disease can be detected, predicted, or even cured. Diseases like heart disease, cancer, tumor, and Alzheimer’s disease pose threat to mankind. In this paper, we try to concentrate on heart disease prediction. Using machine learning techniques, heart disease can be predicted.Medical data such as blood pressure, hypertension, diabetes, number of cigarettes smoked per day, and so on are taken as input and then these features are modeled for prediction. This model can then be used to predict future medical data. Algorithms like k-nearest neighbor, naïve Bayes, support vector machine, and decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with the good accuracy is taken as the model for predicting heart disease.","url":"https://doi.org/10.1201/9780429446030-13","authors":["M. Marimuthu","S. Deivarani","R. Gayathri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-10-30T16:31:23Z","doi":"10.1201/9780429446030-13","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icmla61862.2024.00204","name":"Evaluating the Clinical Applicability of Neural Networks for Meningioma Tumor Segmentation on Multiparametric 3D MRI","source":"crossref","abstract":"Magnetic resonance imaging (MRI), in both 2D and 3D configurations, serves as the leading non-invasive and non-ionizing technique for the detection and clinical assessment of brain tumors. 3D multiparametric MRIs (mpMRIs) offer enhanced spatial and biological context over other MRIs, which can help clinicians plan tailored treatment and therapy. Presently, surgical intervention remains the primary treatment method for brain tumors, requiring accurate segmentation of tumor tissue on MRI scans. However, manual tumor segmentation and traditional machine learning techniques are labor intensive and subject to observer bias. Deep learning models can provide enhanced accuracy and precision in tumor segmentation due to their ability to automatically extract intricate features and patterns from MRI scans. In this study, MRI scans from 358 meningioma patients were used to train a series of deep learning models to segment the tumor subregions. We hypothesized that a deep learning framework designed for meningioma tumor segmentation and trained utilizing 3D mpMRIs would demonstrate superior segmentation accuracy compared to a framework trained on 2D MRIs or single-sequence 3D MRIs. We compared the model predictions with the ground truth labels using similarity metrics to assess accuracy and clinical applicability. The model trained on 3D mpMRI scans showed reliable performance for meningioma subregion segmentation with a Dice Similarity Coefficient score of 0.91 and a median Sensitivity of91.38%. It outperformed the other models due to the additional spatial context provided by 3D mpMRIs, suggesting it could be readily used in clinical practice to help guide treatment strategy.","url":"https://doi.org/10.1109/icmla61862.2024.00204","authors":["Diya Sreedhar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-04T18:39:11Z","doi":"10.1109/icmla61862.2024.00204","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-99-7007-0_7","name":"Practical Applications of Online Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-7007-0_7","authors":["Steffen Moritz","Florian Dumpert","Christian Jung","Thomas Bartz-Beielstein","Eva Bartz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-05T05:02:18Z","doi":"10.1007/978-981-99-7007-0_7","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-15-8210-3_4","name":"Applications of Machine Learning in Medical Research","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-8210-3_4","authors":["Basavarajaiah D. M.","Bhamidipati Narasimha Murthy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-05T11:06:30Z","doi":"10.1007/978-981-15-8210-3_4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781003726913-21","name":"Integration of Multimodal NLP Systems for Real-Time Clinical Decision Support in Digital Healthcare","source":"crossref","abstract":"The Integration of Multimodal Natural Language Processing(NLP) Systems for Real-Time Clinical Decision Support System(CDSS) in Digital Healthcare, improves existing clinical decision support systems by integrating various sources of information, including real-time sensor data, Electronic Health Records (EHRs), medical images, and unstructured clinical texts.In contrast to traditional unimodal configurations, this approach combines pre-trained language models with multimodal data fusion techniques to generate a single patient representation, facilitating more precise and contextually aware predictions.Empirical testing shows the system to outperform unimodal models, achieving top-1 clinical event prediction accuracy of 75.0% and top-3 accuracy of 93.5%. This real-time hybrid fusion approach enhances clinical workflow and health outcomes in addition to enhanced diagnostic sensitivity, optimized treatment regimens, and personalized patient care. Beyond addressing heterogeneous data challenges, multimodal NLP decision support offers a scalable platform for providing AI-driven healthcare solutions across varied clinical settings.","url":"https://doi.org/10.1201/9781003726913-21","authors":["Karthikeyan Panneerselvam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T09:34:20Z","doi":"10.1201/9781003726913-21","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9780367821654-1","name":"Introduction to Artificial Intelligence and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9780367821654-1","authors":["Frank M. Groom"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-25T08:22:46Z","doi":"10.1201/9780367821654-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022852608280","name":"Knowledge Acquisition Via Incremental Conceptual Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022852608280","authors":["Douglas H. Fisher"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:57:10Z","doi":"10.1023/a:1022852608280","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781003503828-4","name":"Machine Learning for Product Design and Customization in Advanced Manufacturing Practices","source":"crossref","abstract":"The design and development of a particular product always play an important role in the successful performance of that particular product so that it may sustain itself in the competitive market for a longer period. Machine learning has been reported as a novel approach for different biomedical and industrial engineering applications. But very few studies have been reported on the applications of machine learning in advanced manufacturing practices such as 3D printing processes for the fabrication of innovative products with design customization for innovative engineering solutions. This study highlights the need for machine learning in the design and customization of 3D-printed thermoplastic composite matrix-based smart solutions for smart civil structures. This concept note outlines that 3D-printed smart solutions with programmability features may be tuned using machine learning for the development of customized solutions for not only civil structures but also biomedical and production engineering applications. As an extension to the previous studies that explored the one-way and two-way programmability properties in the 3D-printed thermoplastic customized solution for heritage buildings, this work outlines that the integration of the machine learning approach may help in the design and customization of more effective products for the similar application in composite structures. The machine learning-assisted product design, development, and customization may be regarded as a novel way to fabricate customized 3D-printed solutions and other advanced manufacturing practices for engineering applications.","url":"https://doi.org/10.1201/9781003503828-4","authors":["Vinay Kumar","Nishant Ranjan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-29T15:02:02Z","doi":"10.1201/9781003503828-4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.2174/9789815305395125020013","name":"Lexical Methods for Identifying Emotions in Text Based on Machine Learning","source":"crossref","abstract":"The study of emotions has emerged as an important area of research because of the wealth of information it can provide. Emotions can be expressed in a variety of ways including words, facial expressions, written material, and movements. Natural language processing (NLP) &amp;amp; deep learning concepts are essential to solving the content-based classification problem that is emotion detection in a text document. Therefore, in this research, we suggest using deep learning to aid semantic text analysis in the task of identifying human emotions from transcripts of spoken language. Visual forms of expression, such as makeover jargon, may be used to convey the feeling. Datasets of recorded voices from people with Autism Spectrum Disorder (ASD) are transcribed for analysis. However, in this paper, we specialize in detecting emotions from all of the textual dataset and using the semantic data enhancement process to fill a few of the phrases, or half-broken speech, as patients with Autism Spectrum Disorder (ASD) lack social contact skills due to the patient not very well articulating their communication.","url":"https://doi.org/10.2174/9789815305395125020013","authors":["Mridula Gupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-12T11:53:51Z","doi":"10.2174/9789815305395125020013","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-6684-6291-1.ch008","name":"Machine Learning Applications for Anomaly Detection","source":"crossref","abstract":"The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.","url":"https://doi.org/10.4018/978-1-6684-6291-1.ch008","authors":["Teguh Wahyono","Yaya Heryadi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-08T11:31:23Z","doi":"10.4018/978-1-6684-6291-1.ch008","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.5772/217","name":"Machine Learning","source":"crossref","abstract":"Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.","url":"https://doi.org/10.5772/217","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-03-23T15:40:22Z","doi":"10.5772/217","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.18127/j19998554-202501-04","name":"Research of unsupervised machine learning methods for data analy-sis and preparation and modern approaches to building machine learning models – automated machine learning systems","source":"crossref","abstract":"Data preprocessing in machine learning tasks is an important step in the data mining process. To automate the data processing process and make it more suitable for the data under study, data preprocessing methods are implemented in AutoML systems. The purpose of the work is to compare the quality of work of AutoML systems for building a target model and training it. Study of the operation of modern AutoML systems has been conducted. Recommendations have been proposed for the use of unsupervised machine learning algorithms for the tasks of filling gaps, detecting and removing anomalies, and reducing the dimensionality of a data set. The conducted research allows us to determine the applicability of modern AutoML systems for building a machine learning model, to better understand the features of the systems, and to find out the possibility of their use in solving practical problems.","url":"https://doi.org/10.18127/j19998554-202501-04","authors":["I.A. Popova","G.I. Afanasyev","V.B. Timofeev","Yu.E. Gapanyuk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-21T10:09:24Z","doi":"10.18127/j19998554-202501-04","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-5225-9902-9.ch008","name":"Machine Learning in Python","source":"crossref","abstract":"Diabetes is a disease of the modern world. The modern lifestyle has led to unhealthy eating habits causing type 2 diabetes. Machine learning has gained a lot of popularity in the recent days. It has applications in various fields and has proven to be increasingly effective in the medical field. The purpose of this chapter is to predict the diabetes outcome of a person based on other factors or attributes. Various machine learning algorithms like logistic regression (LR), tuned and not tuned random forest (RF), and multilayer perceptron (MLP) have been used as classifiers for diabetes prediction. This chapter also presents a comparative study of these algorithms based on various performance metrics like accuracy, sensitivity, specificity, and F1 score.","url":"https://doi.org/10.4018/978-1-5225-9902-9.ch008","authors":["Astha Baranwal","Bhagyashree R. Bagwe","Vanitha M"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-07-24T09:29:18Z","doi":"10.4018/978-1-5225-9902-9.ch008","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-6684-4671-3.ch007","name":"Parasite Detection From Digital Images Using Deep Learning","source":"crossref","abstract":"Parasitosis is a disease caused by parasites that could infect humans, animals, or plants. The parasites include mites, ascariasis, liver flukes, and malaria. The methods to detect parasites include pathological examination, immunological examination, and imaging examination. In this chapter, parasitic infections are detected from digital images acquired from a microscope, which will look for the possible infection caused by worms or eggs in a sample, such as mites and malaria. Rapid and accurate classification and detection of parasites will be very helpful for fast diagnosis and treatment. In this chapter, a malaria detection method is deployed by using deep learning based on TensorFlow and achieved 0.73 mAP@0.5IOU. Even if it does not seem to be a perfect performance, in the limited time and resources, the results are still valuable. The future work could port the model to mobile phones for image detection, which would bring much more convenience and portability.","url":"https://doi.org/10.4018/978-1-6684-4671-3.ch007","authors":["Yulin Zhu","Wei Qi Yan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-24T11:37:36Z","doi":"10.4018/978-1-6684-4671-3.ch007","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1111/ceo.14542","name":"Correction to “Predicting Ophthalmic Clinic Non‐Attendance Using Machine Learning: Development and Validation of Models Using Nationwide Data”","source":"crossref","abstract":"","url":"https://doi.org/10.1111/ceo.14542","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-06T19:30:56Z","doi":"10.1111/ceo.14542","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.yacr.2020.05.005","name":"Artificial Intelligence and Machine Learning Applications in Musculoskeletal Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.yacr.2020.05.005","authors":["Sheila Enamandram","Emir Sandhu","Bao H. Do","Joshua J. Reicher","Christopher F. Beaulieu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-28T18:24:23Z","doi":"10.1016/j.yacr.2020.05.005","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.23943/princeton/9780691218700.003.0006","name":"A Research Agenda","source":"crossref","abstract":"This chapter focuses on the on cross-section of stock returns in research opportunities and related potential applications in other areas of asset pricing. It begins with empirical applications of machine learning (ML) tools, noting that part of the challenge in empirical work is to sort out how to select and tweak ML methods to be most useful in asset pricing applications. Some progress has been made, but many questions remain about economically appropriate methods of regularization, the role of nonlinearities, and how to deal with structural change in the underlying data-generating process. The chapter discusses areas where application of ML methods seems promising and fall into the area of asset demand analysis. It offers some suggestions on how asset pricing theory could advance by adopting ML as a model of investor belief formation in high-dimensional environments.","url":"https://doi.org/10.23943/princeton/9780691218700.003.0006","authors":["Stefan Nagel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-23T12:43:40Z","doi":"10.23943/princeton/9780691218700.003.0006","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-96-9396-2_4","name":"Trustworthy Machine Learning with Out-of-Distribution Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9396-2_4","authors":["Bo Han","Tongliang Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-19T19:52:18Z","doi":"10.1007/978-981-96-9396-2_4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-16-8881-2_11","name":"Machine Learning and Life Sciences","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8881-2_11","authors":["Shyamasree Ghosh","Rathi Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T17:03:22Z","doi":"10.1007/978-981-16-8881-2_11","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007405111315","name":"Training a Vision Guided Mobile Robot","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007405111315","authors":["Gordon Wyeth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007405111315","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-7998-9220-5.ch185","name":"AUTOMATON","source":"crossref","abstract":"This article displays a design ethnographic case study on an ongoing machine learning project at a Scandinavian gamification start-up company. From late 2020 until early 2021, the project produced a machine learning proof of concept, later implemented in the gamification start-up´s application programming interface to offer smart gamification. The initial results show promise in using prediction models to automate the cluster model selection affording more functional, autonomous, and scalable user segments that are faster to implement. The finding provides opportunities for gamification (e.g., in learning analytics and health informatics). An identified challenge was performance; the neural networks required hyperparameter fine-tuning, which is time-consuming and limits scalability. Interesting further investigations should consider the neural network fine-tuning process, but also attempt to verify the effectiveness of the cluster models selection compared with a control group.","url":"https://doi.org/10.4018/978-1-7998-9220-5.ch185","authors":["Adam Palmquist","Isak Barbopoulos","Miralem Helmefalk"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T15:44:01Z","doi":"10.4018/978-1-7998-9220-5.ch185","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.bpsc.2025.06.004","name":"Machine Learning–Based Clinical Prediction Models in Psychopathology: Can Transfer Learning Fix the “Illusory Generalizability” Problem?","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.bpsc.2025.06.004","authors":["Jason Smucny"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-05T15:42:43Z","doi":"10.1016/j.bpsc.2025.06.004","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-0-387-45528-0","name":"Pattern Recognition and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-45528-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2006-12-21T17:48:14Z","doi":"10.1007/978-0-387-45528-0","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1002/9781394173358.ch1","name":"Statistical Similarity in Machine Learning","source":"crossref","abstract":"The main requirement for machine learning algorithms is the ability to generalize learning outcomes to new samples. This requirement is based on the compactness hypothesis, which assumes that objects belonging to the same class are located closer to each other in the feature space than to objects of other classes. The formulation of this hypothesis is geometric in nature since it refers to the concept of proximity between points in a vector space, while at the same time assuming that a certain metric is introduced in this space, and objects can be represented as a feature vector. In many applications, for example, in biological or medical research, an object is described not by one set of features, but by several sets of features. In addition, feature values can be random values. In this case, it is necessary to correctly interpret the concept of proximity. We propose replacing this concept with statistical homogeneity and give examples in which this approach turns out to be effective on the example of the universal nonparametric test for homogeneity, which has the significance level that does not depend on the truth or falsity of the hypothesis, and the samples can be arbitrary, i.e. have different location parameters and the same scale parameter, the same location parameter and different scale parameters, and both different location and scale parameters. Objects are considered homogeneous if their features obey the same distributions. This approach is of practical value both in the diagnosis of various diseases and in the analysis of transition points in time series.","url":"https://doi.org/10.1002/9781394173358.ch1","authors":["Dmitriy Klyushin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-16T09:12:07Z","doi":"10.1002/9781394173358.ch1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1049/pbte081e_ch5","name":"Signal identification in cognitive radios using machine learning","source":"crossref","abstract":"As an intelligent radio, cognitive radio (CR) allows the CR users to access and share the licensed spectrum. Being a typical noncooperative system, the applications of signal identification in CRs have emerged. This chapter introduces several signal identification techniques, which are implemented based on the machine-learning theory.","url":"https://doi.org/10.1049/pbte081e_ch5","authors":["Jingwen Zhang","Fanggang Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-07-04T13:28:42Z","doi":"10.1049/pbte081e_ch5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/979-8-8688-2527-9_5","name":"Infrastructure for Machine Learning Workloads","source":"crossref","abstract":"","url":"https://doi.org/10.1007/979-8-8688-2527-9_5","authors":["Mohammad Reza Mahdiani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-30T22:18:03Z","doi":"10.1007/979-8-8688-2527-9_5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1002/9781394198030.ch9","name":"Machine Learning for Energy Analysis","source":"crossref","abstract":"In this chapter, the authors begin by defining machine learning, discussing the different types, and introducing three common algorithms that are used for research. They then investigate the use of machine learning algorithms for energy analysis on a project case called Residential Tower. The goal of the investigation is to understand how well these algorithms perform in predicting energy performance, which algorithm performs best, and what factors impact algorithm performance most. Three classifications of machine learning algorithms are typically recognized – supervised learning, unsupervised learning, and reinforcement learning. A neural network is a computational structure formed from a network of individual processing units, called neurons, that send messages to each other. Current architectural practice recognizes the need to design high performance buildings and the need for building performance analysis to achieve this goal.","url":"https://doi.org/10.1002/9781394198030.ch9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T23:12:41Z","doi":"10.1002/9781394198030.ch9","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-19-0840-8_36","name":"Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-0840-8_36","authors":["Likha Ganu","Biri Arun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-25T08:03:22Z","doi":"10.1007/978-981-19-0840-8_36","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-3-319-32545-3_4","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-32545-3_4","authors":["Achim Zielesny"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-04-14T10:45:37Z","doi":"10.1007/978-3-319-32545-3_4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.36647/ciml/04.01.a002","name":"Big Data Analytics Using Machine Learning Techniques for Prediction on Datasets","source":"crossref","abstract":"Data analytics is the process of performing scientific and statistical analysis on raw data in order to transform it into information that can be used for gaining knowledge. A recently emerging trend in feature abstraction is the combination of computational techniques and big data analysis. This requires gaining knowledge from trustworthy data sources, being able to digest information quickly, and making accurate predictions about the future. The primary objective of this study is to locate the machine learning strategies that produce the most accurate prediction by utilising the model that has been proposed. The supervised and unsupervised strategies have been implemented in a variety of different ways using the MapReduce methodology; however, the suggested model makes use of the Apache Spark framework in order to compare the many existing methods. In this study, the emphasis is placed on elucidating the characteristics of datasets in order to conduct the most accurate analysis possible using machine learning techniques. For the purpose of conducting an analysis of the data sets, machine learning methods such as linear regression, decision trees, random forests, and gradient boosting tree algorithms are utilised. In light of the findings of this research, it is possible to draw the conclusion that when the Spark framework is applied on top of Machine Learning methods, the efficiency of the model is improved by a factor of seventy percent in comparison to the MapReduce paradigm. Keyword : Apache Spark Framework, Big Data Analytics, Machine Learning Algorithms, MapReduce Paradigm.","url":"https://doi.org/10.36647/ciml/04.01.a002","authors":["Ankit Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T08:19:15Z","doi":"10.36647/ciml/04.01.a002","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/b978-0-443-22001-2.00002-0","name":"Fundamentals of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22001-2.00002-0","authors":["Yan Liang","Jeong-Yeol Yoon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:47:21Z","doi":"10.1016/b978-0-443-22001-2.00002-0","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781315371740-6","name":"Large-Scale Machine Learning for Species Distributions","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781315371740-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-09-01T11:30:37Z","doi":"10.1201/9781315371740-6","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781351029940-1","name":"Machine Learning","source":"crossref","abstract":"Machine learning (ML) has become a powerful tool in real-life applications including modern biology. The relationship between biology and ML has gained widespread attention in various facets of biological engineering. The chapter first introduces ML and its comparison with other computing environments. The various approaches to ML such as the decision tree, deep learning, support of vector machines, clustering, Bayesian networks, and mining methods are discussed. Data mining with different techniques and related analysis are also then discussed. Finally, the importance of big data and various implementation frameworks and applications in biology are elaborated in detail. In can be concluded that ML along with human intervention can prove to be a powerful tool for many aspects of biological engineering, as it can alleviate the burden of solving many biological problems and save the time and cost required for new experiments and provide predictions to guide new experiments.","url":"https://doi.org/10.1201/9781351029940-1","authors":["Mohd Zafar","Ramkumar Lakshmi Narayanan","Saroj K. Meher","Shishir K. Behera"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-09-01T00:12:43Z","doi":"10.1201/9781351029940-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-6684-6291-1.ch045","name":"Machine Learning for Web Proxy Analytics","source":"crossref","abstract":"Proxy servers used around the globe are typically graded and built for small businesses to large enterprises. This does not dismiss any of the current efforts to keep the general consumer of an electronic device safe from malicious websites or denying youth of obscene content. With the emergence of machine learning, we can utilize the power to have smart security instantiated around the population's everyday life. In this work, we present a simple solution of providing a web proxy to each user of mobile devices or any networked computer powered by a neural network. The idea is to have a proxy server to handle the functionality to allow safe websites to be rendered per request. When a website request is made and not identified in the pre-determined website database, the proxy server will utilize a trained neural network to determine whether or not to render that website. The neural network will be trained on a vast collection of sampled websites by category. The neural network needs to be trained constantly to improve decision making as new websites are visited.","url":"https://doi.org/10.4018/978-1-6684-6291-1.ch045","authors":["Mark Maldonado","Ayad Barsoum"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-08T11:31:23Z","doi":"10.4018/978-1-6684-6291-1.ch045","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022644732619","name":"Polynomial Time Learnability of Simple Deterministic Languages","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022644732619","authors":["Hiroki Ishizaka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022644732619","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/b978-0-12-804076-8.00014-1","name":"Machine learning in brain imaging genomics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-804076-8.00014-1","authors":["J. Yan","L. Du","X. Yao","L. Shen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-08-19T15:32:31Z","doi":"10.1016/b978-0-12-804076-8.00014-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007424614876","name":"Tracking the Best Expert","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007424614876","authors":["Mark Herbster","Manfred K. Warmuth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007424614876","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/mlcr57210.2022.00001","name":"2022 International Conference on Machine Learning, Control, and Robotics MLCR 2022","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlcr57210.2022.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-08T18:58:55Z","doi":"10.1109/mlcr57210.2022.00001","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.48001/joitml.2024.1219-23","name":"Credit Card Fraud Detection System using Machine Learning","source":"crossref","abstract":"The global danger posed by credit card fraud to financial institutions and customers makes the development of strong fraud detection systems imperative. In this study, we suggest a novel method for detecting credit card fraud that makes use of Convolutional Neural Networks (CNNs) and synthetic data generation. Inadequate feature representations and unbalanced datasets are common problems for traditional fraud detection algorithms. We use synthetic data generation techniques to build a balanced dataset that accurately represents the intricacies of fraudulent transactions in order to overcome these difficulties.","url":"https://doi.org/10.48001/joitml.2024.1219-23","authors":["Saurav Naik","Aarth Dahale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-16T08:58:52Z","doi":"10.48001/joitml.2024.1219-23","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781003346234-7","name":"Fundamentals of Machine Learning","source":"crossref","abstract":"This chapter provides a brief survey of machine learning (ML) basics and introduce its core ideas, algorithms, and applications in diverse fields as a background information for understanding its application in atomic layer deposition and thin film deposition. There is an upsurge in the adoption of AI, ML, and deep learning (DL) in contemporary times by different businesses and sectors to construct intelligent devices and products, making them the most talked-about technologies in business. Although these terminologies dominate business conversations worldwide, many people have trouble distinguishing them. ML creates a set of rules automatically using answers and data, as opposed to symbolic AI, which uses rules and data to make responses. In order to facilitate a response or action, these rules can then be applied to fresh, unobserved data. The common distinguishing features of the DL from the traditional ML are the kind of data it uses and the ways in which it learns.","url":"https://doi.org/10.1201/9781003346234-7","authors":["Oluwatobi Adeleke","Sina Karimzadeh","Tien-Chien Jen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-31T14:25:44Z","doi":"10.1201/9781003346234-7","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.38007/ml.2022.030404","name":"Predicting the Employment Destinations of University Students Based on Machine Learning Algorithms","source":"crossref","abstract":"The research on the classification prediction of students' employment destinations in higher education not only opens up new application areas for classification algorithms, but also represents a new attempt to introduce machine learning algorithms into the analysis of employment guidance and the development of teaching systems.The purpose of this paper is to study the employment destination prediction of college students based on machine learning algorithms.The factors influencing the employability of university students are analysed and the XGBoost model in decision trees is explored.A graduate employment prediction algorithm based on HMIGW feature selection and XGBoost algorithm is proposed to predict the employment situation of the class of 2022 and the type of employment, and the experimental results show that the algorithm is able to obtain relatively accurate conclusions on graduate employment prediction.","url":"https://doi.org/10.38007/ml.2022.030404","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T05:40:40Z","doi":"10.38007/ml.2022.030404","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007472513967","name":"Tracking the Best Disjunction","source":"crossref","abstract":"Littlestone developed a simple deterministic on-line learning algorithm for learning k-literal disjunctions. This algorithm (called $${WINNOW}$$ ) keeps one weight for each of then variables and does multiplicative updates to its weights. We develop a randomized version of $${WINNOW} $$ and prove bounds for an adaptation of the algorithm for the case when the disjunction may change over time. In this case a possible target disjunction schedule $${\\mathcal{T}} $$ is a sequence of disjunctions (one per trial) and the shift size is the total number of literals that are added/removed from the disjunctions as one progresses through the sequence. We develop an algorithm that predicts nearly as well as the best disjunction schedule for an arbitrary sequence of examples. This algorithm that allows us to track the predictions of the best disjunction is hardly more complex than the original version. However, the amortized analysis needed for obtaining worst-case mistake bounds requires new techniques. In some cases our lower bounds show that the upper bounds of our algorithm have the right constant in front of the leading term in the mistake bound and almost the right constant in front of the second leading term. Computer experiments support our theoretical findings.","url":"https://doi.org/10.1023/a:1007472513967","authors":["Peter Auer","Manfred K. Warmuth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007472513967","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1145/3468891.3468896","name":"Image-based Candlestick Pattern Classification with Machine Learning","source":"crossref","abstract":"Financial markets, such as the stock market, bond market and foreign exchange market, are important channels for fund transfer. As a graphical analysis tool, candlestick charts use graphs to display the open, high, low, and close prices in a specific period. In the past, there have been attempts to identify the characteristics of candlesticks based on Gramian Angular Field (GAF) images, but they are not perfect. In this study, we implemented Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), AdaBoost, Random Forest (RF) and XGBoost models, we found that the use of deep learning models is not the best choice for the recognition of candlestick features based on GAF images. Comparing these models, MLP and CNN are better than AdaBoost and RF, but worse than XGBoost. Our results show that for the candlestick pattern classification problem based on GAF images, it is unnecessary to use complex CNNs and traditional machine learning models can also achieve satisfactory results with much less computation resources.","url":"https://doi.org/10.1145/3468891.3468896","authors":["Chenghan Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-06T17:42:54Z","doi":"10.1145/3468891.3468896","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022607123649","name":"Multivariate Decision Trees","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022607123649","authors":["Carla E. Brodley","Paul E. Utgoff"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022607123649","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.mlwa.2022.100318","name":"Distinctive features of nonverbal behavior and mimicry in application interviews through data analysis and machine learning","source":"crossref","abstract":"This paper reveals the characteristics and effects of nonverbal behavior and human mimicry in the context of application interviews. It discloses a novel analyzation method for psychological research by utilizing machine learning. In comparison to traditional manual data analysis, machine learning proves to be able to analyze the data more deeply and to discover connections in the data invisible to the human eye. The paper describes an experiment to measure and analyze the reactions of evaluators to job applicants who adopt specific behaviors: mimicry, suppress, immediacy and natural behavior. First, evaluation of the applicant qualifications by the interviewer reveals how behavioral self-management can improve the interviewer’s opinion of the candidate. Secondly, the underlying mechanics of mimicry behavior are exposed through analysis of seven nonverbal actions. Manual data analysis determines the frequency features of the actions and answers how often the actions are performed and how often they are mimicked during application interviews. Two of the seven actions are here deemed negligible due too low frequency features. Finally, machine learning is employed to analyze the data in great detail and distinguish the four behavior categories from each other. A Random Forest classifier is able to achieve 55.2% accuracy for predicting the behavior condition of the interviews while human observers reach an accuracy of 32.9%. The feature set for the classifier is reduced to 130 features with the most important features relating to the correlations between the leaning forward actions of the interview participants.","url":"https://doi.org/10.1016/j.mlwa.2022.100318","authors":["Sanne Roegiers","Elias Corneillie","Filip Lievens","Frederik Anseel","Peter Veelaert","Wilfried Philips"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-07T11:42:40Z","doi":"10.1016/j.mlwa.2022.100318","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.mlwa.2026.100875","name":"A hybrid modeling framework for quality prediction in sheet hydroforming process: application of regression analysis and machine learning algorithm","source":"crossref","abstract":"In recent years, the sheet hydroforming process has received widespread attention as one of the new and efficient methods in metal forming. The complexity of material behavior at different temperatures and strain rates has made the need for accurate modeling and reliable prediction of results more apparent than ever before. In the current research work, using the Johnson-Cook material model, the temperature and strain-rate dependent plastic behavior was simulated in the Abaqus software environment to more accurately represent the sheet deformation obtained from the hydroforming process. Three key process variables—including fluid pressure, sheet blank-holder force, and die edge radius—were selected at three different levels, and 27 numerical simulations were performed based on a full-factorial design of experiment. To evaluate the process performance, two quality indices, maximum thinning and thickness uniformity, were considered as the main quality criteria. The relative importance of each index was then determined using the Shannon entropy method (thinning about 65% and uniformity about 35%). Next, two prediction models—including a regression model and a K-nearest neighbor machine learning algorithm—were employed to identify the relationships between inputs and outputs and predict intermediate values. Based on the FE simulation outcomes, the optimal thinning and uniformity rates were improved by about 47% and 80%, respectively, compared to the worst case. The simultaneous study of two key indicators, combined with the application of the aforementioned methods, can lead to more accurate predictions of the quality of hydroformed components.","url":"https://doi.org/10.1016/j.mlwa.2026.100875","authors":["Kiarash Keivan","Saeed Yaghoubi","Sayyed Roohollah Kazemi","Masoud Seidi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-27T07:55:07Z","doi":"10.1016/j.mlwa.2026.100875","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.5220/0012800500003885","name":"Comparative Analysis of Machine Learning Models in Predictive Analytics for Residential Energy Consumption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012800500003885","authors":["Hongyuan Jia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T17:41:23Z","doi":"10.5220/0012800500003885","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1242/dev.202066","name":"Machine learning approaches for image classification in developmental biology and clinical embryology","source":"crossref","abstract":"ABSTRACT The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis in developmental biology and clinical embryology. In this Spotlight, we provide an introduction to machine learning for developmental biologists interested in incorporating machine learning techniques into their research. We give an overview of essential machine learning concepts and models and describe a few recent examples of how these techniques can be used in developmental biology. We also briefly discuss latest advancements in the field and how it might develop in the future.","url":"https://doi.org/10.1242/dev.202066","authors":["Camilla Mapstone","Berenika Plusa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T11:38:11Z","doi":"10.1242/dev.202066","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.56975/ijnti.v4i4.232134","name":"Sepsis Prediction System using Machine Learning and Deep Learning Approaches for Early Clinical Diagnosis","source":"crossref","abstract":"Sepsis is a life-threatening medical condition caused by the body's extreme response to infection, which can lead to organ failure and death if not detected and treated early. Timely identification of sepsis remains a significant challenge in healthcare systems worldwide due to the complex physiological patterns and non-specific early symptoms. This paper presents a comprehensive machine learning and deep learning-based approach for early prediction of sepsis using patient vital signs and physiological parameters. The proposed system integrates multiple predictive models including Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks to enhance prediction accuracy and reliability. The methodology incorporates extensive data preprocessing techniques including median imputation for missing values, z-score normalization for feature scaling, and correlation-based feature selection. The system processes critical clinical parameters including heart rate, body temperature, systolic blood pressure, mean arterial pressure, oxygen saturation, respiratory rate, and various laboratory values. Experimental evaluation is conducted using a comprehensive dataset of ICU patient records with rigorous performance assessment through accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Results demonstrate that ensemble methods, particularly XGBoost, achieve superior discriminative performance with an accuracy of 98.2\\% and the highest AUC-ROC of 0.7114 among all evaluated models. The integration of risk stratification capabilities enables clinical decision support through three-tier classification (low, medium, high risk), facilitating personalized patient management and resource optimization in critical care settings.","url":"https://doi.org/10.56975/ijnti.v4i4.232134","authors":["K.V.Kiran K.V.Kiran","Malla Pavithra","Shaik Nazeer Babu","Suraboina Yerusha","Boddupalli Koteswara Rao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-17T08:50:07Z","doi":"10.56975/ijnti.v4i4.232134","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/aibdf67964.2025.11440817","name":"Benchmarking Machine Learning and Deep Learning Models for 60-min Ahead Blood Glucose Prediction in Type 1 Diabetes with Simulated and Clinical Data","source":"crossref","abstract":"Blood glucose (BG) management is crucial for patients with type 1 diabetes, requiring insulin dosage adjustments based on glucose levels to maintain concentrations within the target range. Accurate BG predictions provide insights into future glucose trends, enabling appropriate preventive measures to avoid short-term consequences or long-term complications. With the advancement of continuous glucose monitoring (CGM) systems, machine learning and deep learning techniques have been increasingly applied to improve prediction accuracy. This study systematically investigates and compares the prediction performance of six representative models for 60-minute ahead BG forecasting under a unified experimental framework. Three traditional machine learning models are evaluated, including Ridge Regression, Random Forest, and LightGBM, alongside three deep learning sequence models including LSTM, GRU, and Transformer. The models are assessed using both the T1DMS simulated dataset and the OhioT1DM clinical dataset from patients with type 1 diabetes. Results reveal fundamental performance differences between simulated and clinical environments. On the simulated dataset, all models achieve remarkably strong performance with minimal differentiation, indicating that simulated data systematically underestimates real-world physiological complexity by abstracting sensor noise, behavioral irregularity, and metabolic heterogeneity. In contrast, the clinical dataset exposes significant performance stratification across models. GRU demonstrates superior performance with RMSE of 30.90 ± 1.52 mg/dL, MAE of 22.58 ± 0.90 mg/dL, and R2of 0.6804 ± 0.1036, substantially outperforming Ridge Regression with RMSE of 35.61 ± 2.78 mg/dL, MAE of 26.22 ± 2.15 mg/dL, and R2of 0.5910 ± 0.0709. Notably, GRU surpasses LSTM on clinical data despite inferior simulated performance, suggesting its simpler gating mechanism provides enhanced regularization when confronting measurement uncertainty inherent in clinical CGM data. Transformer exhibits disproportionate degradation on clinical data, indicating vulnerability to irregular temporal patterns. This study demonstrates that recurrent neural networks, particularly GRU, achieve optimal clinical BG prediction performance. The findings emphasize the critical importance of clinical data validation for developing reliable diabetes management systems.","url":"https://doi.org/10.1109/aibdf67964.2025.11440817","authors":["Zelin Shen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-24T19:45:52Z","doi":"10.1109/aibdf67964.2025.11440817","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/s13337-023-00841-y","name":"Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook","source":"crossref","abstract":"In the rapidly evolving field of clinical virology, technological advancements have always played a pivotal role in driving transformative changes. This comprehensive review delves into the burgeoning integration of artificial intelligence (AI), machine learning, and deep learning into virological research and practice. As we elucidate, these computational tools have significantly enhanced diagnostic precision, therapeutic interventions, and epidemiological monitoring. Through in-depth analyses of notable case studies, we showcase how algorithms can optimize viral genome sequencing, accelerate drug discovery, and offer predictive insights into viral outbreaks. However, with these advancements come inherent challenges, particularly in data security, algorithmic biases, and ethical considerations. Addressing these challenges head-on, we discuss potential remedial measures and underscore the significance of interdisciplinary collaboration between virologists, data scientists, and ethicists. Conclusively, this review posits an outlook that anticipates a symbiotic relationship between AI-driven tools and virology, heralding a new era of proactive and personalized patient care.","url":"https://doi.org/10.1007/s13337-023-00841-y","authors":["Abhishek Padhi","Ashwini Agarwal","Shailendra K. Saxena","C. D. S. Katoch"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-21T07:01:55Z","doi":"10.1007/s13337-023-00841-y","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/s40588-026-00265-4","name":"Reshaping Parasitology Diagnostics with Machine Learning: A Path Toward Equity in Global Health","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s40588-026-00265-4","authors":["Varol Tunali"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-24T07:22:00Z","doi":"10.1007/s40588-026-00265-4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.33457/ijhsrp.1649066","name":"HARNESSING MACHINE LEARNING IN HPV DIAGNOSTICS: MODEL PERFORMANCE, EXPLAINABILITY, AND CLINICAL INTEGRATION","source":"crossref","abstract":": Human Papillomavirus (HPV) remains a significant global health concern, contributing to cervical and oropharyngeal cancers. While traditional diagnostic methods such as PCR-based assays and cytological screenings are widely used, they present limitations in sensitivity, specificity, and scalability. Recent advances in machine learning (ML) have enabled more precise and automated HPV detection and genotyping. This review aims to evaluate the current ML methodologies in HPV diagnostics, compare their performance metrics, and discuss future directions for improving artificial intelligence (AI) -driven HPV screening. CNN-based models exhibited superior performance in cytology and histopathology-based HPV detection, achieving high accuracy in lesion classification. Hybrid models integrating ML with molecular diagnostics improved HPV genotyping precision. Support vector machine (SVM) and random forest (RF) demonstrated efficacy in genomic classification, whereas transformer-based models enhanced feature extraction and risk stratification. Despite these advancements, data heterogeneity, explainability, and clinical validation remain substantial barriers to widespread adoption. ML-driven HPV diagnostics offer unprecedented improvements in efficiency, accuracy, and accessibility. However, critical issues related to data standardization, bias mitigation, and regulatory frameworks must be addressed to ensure clinical reliability. Future research should prioritize explainable AI (XAI), federated learning, and robust validation studies to enhance model generalizability and real-world applicability. The seamless integration of AI-powered tools into HPV screening programs holds transformative potential for early detection, personalized risk assessment, and improved patient outcomes, ultimately contributing to the global reduction of HPV-related malignancies.","url":"https://doi.org/10.33457/ijhsrp.1649066","authors":["Bahar Senel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-28T15:30:28Z","doi":"10.33457/ijhsrp.1649066","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.neucom.2018.04.049","name":"Novel and improved stage estimation in Parkinson's disease using clinical scales and machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2018.04.049","authors":["R. Prashanth","Sumantra Dutta Roy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-05-05T01:33:28Z","doi":"10.1016/j.neucom.2018.04.049","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.53766/acbio/2025.15.31.06","name":"Machine-Learning Classifier for Liver Disease and Periodontitis Using Biochemical and Clinical Parameters","source":"crossref","abstract":"Introduction: The liver is vital for various physiological functions, including bile and protein synthesis necessary for digestion, nutrient metabolism, and cholesterol regulation. Periodontitis, an oral disease associated with systemic conditions like cardiovascular diseases and Alzheimer's disease, has recently been implicated in affecting liver health through systemic inflammation pathways. Objective: This study aims to investigate the use of machine-learning techniques, specifically light gradient-boosted trees, to diagnose liver disease in patients with periodontitis using biochemical and clinical parameters. Methods: From prior records, a Dental and Medical College obtained 325 data for preprocessing and exploratory analysis. Our research uses data preprocessing, feature selection, and model construction to predict liver disease risk in periodontitis patients. Gradient boosting, random forests, and Keras residual network are evaluated using accuracy and confusion matrix. Results: The study conducted extensive exploratory data analysis to assess key biochemical and clinical parameters indicative of liver health. The study evaluated the accuracy of machine-learning models—LGBM (Light Gradient Boosted Trees), Keras Slim, and Random Forest—for diagnosing liver disease in patients with periodontitis. Results indicated high accuracies of approximately 98%, 84%, and 96%, respectively, underscoring their potential for precise and non-invasive diagnostic applications in clinical settings. Conclusions: The study highlights the significant role of biochemical and clinical parameters in assessing liver health within the context of periodontitis. Elevated levels of these enzymes indicate potential liver damage or diseases, underscoring their utility as diagnostic markers.","url":"https://doi.org/10.53766/acbio/2025.15.31.06","authors":["Pradeep Kumar","Jai Varsheni","Carlos Ardila"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T12:54:10Z","doi":"10.53766/acbio/2025.15.31.06","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icmlc.2003.1259555","name":"2003 International Conference On Machine Learning And Cybernetics","source":"crossref","abstract":"Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.","url":"https://doi.org/10.1109/icmlc.2003.1259555","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-07T13:32:59Z","doi":"10.1109/icmlc.2003.1259555","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-3951-3_5","name":"Machine Learning Environments","source":"crossref","abstract":"You have learned about data and algorithms. Next, you will put the pieces together and build the CML model. ML environments perform a critical function. They act as an important piece of middleware, enabling you to create ML models from the data for later use by your application. This chapter will cover the following:","url":"https://doi.org/10.1007/978-1-4842-3951-3_5","authors":["Mark Wickham"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-10-23T21:29:44Z","doi":"10.1007/978-1-4842-3951-3_5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-94-007-6886-4_1","name":"Introduction to Machine Learning Part Two","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-94-007-6886-4_1","authors":["Ton J. Cleophas","Aeilko H. Zwinderman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-05-29T15:06:45Z","doi":"10.1007/978-94-007-6886-4_1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmlc.2017.8107772","name":"Malaria parasite detection using different machine learning classifier","source":"crossref","abstract":"In the tropical and the subtropical countries, malaria has been a challenge, which really needs a quick and precise diagnosis to put a stop or control the disease. The conventional microscopy method has some shortcomings which includes time consumption and reproducibility. Many of the alternative methods are expensive and it's not readily accessible to the developing countries that need them. In this paper a fast and precise system was developed using stained blood smear images. We employed watershed segmentation technique to acquire plasmodium infected and non-infected erythrocytes and relevant feature was extracted. Six different machine learning techniques for classification are used in the experiments. Fine Gaussian SVM had a True Positive Rate (TPR) of 99.8% in the detection of the plasmodium infected erythrocyte.","url":"https://doi.org/10.1109/icmlc.2017.8107772","authors":["Adedeji Olugboja","Zenghui Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-11-16T21:55:05Z","doi":"10.1109/icmlc.2017.8107772","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.5121/csit.2025.152016","name":"MACHINE LEARNING ALGORITHMS IN FACILITATING AND ASSISTING ROCKET DESCENT AND LANDING","source":"crossref","abstract":"This paper presents a Unity-based reinforcement learning system for simulating rocket descent and landing. Leveraging the Unity ML-Agents framework, our approach applies Proximal Policy Optimization (PPO) combined with imitation learning to balance exploration with guided behavior [8]. Unlike prior works, our system introduces vertical dynamics, randomized initial conditions to reduce overfitting, and variable environmental factors such as gravity, drag, rocket mass, and thruster power. We further refine the reward structure by incorporating precision- and time-based incentives, including a “bullseye bonus” for accuracy and a time bonus for efficiency. Experimental results show that our rocket agents achieve competitive success rates compared to existing implementations, even under more complex conditions. By extending Unity’s simulation environment with both technical rigor and user-oriented design, this work contributes to advancing reinforcement learning applications in aerospace while also promoting accessibility and engagement for broader audiences interested in space exploration technologies [9].","url":"https://doi.org/10.5121/csit.2025.152016","authors":["Ryan Shen","Andrew Park"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-30T11:16:56Z","doi":"10.5121/csit.2025.152016","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1145/3674029.3674033","name":"Diabetes Prediction Using Machine Learning","source":"crossref","abstract":"Machine learning (ML) techniques for healthcare informatics provide health professional insight into disease development. Many healthcare topics are suitable for ML research, such as diabetes prediction and classification. Common ML approaches use a classification method to predict the outcome of the disease for given test data, though these solutions tend to have limited accuracy rates. Further tuning with extra manipulation of the dataset helps improve the model's accuracy to a certain level, but this requires certain professional knowledge in the medical domain. In this research, we propose using a DNN (Deep Neural Network) approach to predict the outcome of diabetes from the test data. Based on the dataset statistics, we simply transform 1D diabetes test data arrays to 2D Farrays without complex medical knowledge. We use a 2D convolution function to extract the features for prediction in addition to modifying the final stage activation function, to which the response is similar to a unit step function for binary classification problems. Our DNN model prediction accuracy has improved over the known non-deep learning classification models.","url":"https://doi.org/10.1145/3674029.3674033","authors":["Stephanie Tian","Guanghui Hui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-11T12:25:22Z","doi":"10.1145/3674029.3674033","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.58532/nbennurgodpsw6","name":"QUANTUM MACHINE LEARNING OVERVIEW","source":"crossref","abstract":"Quantum Machine Learning (QML) represents an emerging interdisciplinary field that harnesses quantum computing principles to enhance machine learning algorithms and develop quantum-native learning paradigms. This convergence exploits fundamental quantum mechanical phenomena superposition, entanglement, and quantum interference—to potentially achieve exponential computational advantages over classical approaches for specific algorithmic tasks. The theoretical foundation of QML rests on quantum systems' ability to encode information in exponentially scaling state spaces, enabling complex data representation in logarithmically fewer qubits compared to classical bits. Key quantum advantage mechanisms include quantum parallelism through superposition states, entanglement based correlations for efficient encoding of data relationships, and quantum interference effects that amplify optimal solutions while suppressing suboptimal ones. Contemporary QML approaches encompass several algorithmic frameworks. Variational Quantum Algorithms (VQAs) represent the most promising near-term strategy, utilizing hybrid quantum-classical optimization with parameterized quantum circuits optimized through classical feedback loops. Quantum Neural Networks extend classical architectures into quantum domains using trainable quantum gates, while quantum kernel methods leverage quantum feature maps to project data into high-dimensional Hilbert spaces for enhanced classification and regression tasks.","url":"https://doi.org/10.58532/nbennurgodpsw6","authors":["Sulekh Kumar","Kamla Kumari","Prbhat Purushottam","Momita Kundu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-18T06:02:56Z","doi":"10.58532/nbennurgodpsw6","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/b978-0-443-22145-3.00005-0","name":"Machine learning-enabled powder-spreading process","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22145-3.00005-0","authors":["Ramandeep Singh Sidhu","Raman Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-18T22:14:46Z","doi":"10.1016/b978-0-443-22145-3.00005-0","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.3389/978-2-8325-5297-1","name":"Linguistic Biomarkers of Neurological, Cognitive, and Psychiatric Disorders: Verification, Analytical Validation, Clinical Validation, and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.3389/978-2-8325-5297-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-07T05:35:13Z","doi":"10.3389/978-2-8325-5297-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4236/oalib.1113193","name":"Machine Learning-Based Detection of Human Metapneumovirus (HMPV) Using Clinical Data","source":"crossref","abstract":"Human Metapneumovirus (HMPV) is a prominent respiratory pathogen, particularly affecting children, the elderly, and immunocompromised populations.Early detection of HMPV is critical for timely intervention and improved patient outcomes; however, traditional diagnostic methods are often hindered by overlapping symptoms with other respiratory illnesses.This research explores the application of machine learning models for HMPV detection using synthetic clinical data designed to replicate real-world scenarios.The dataset incorporates vital clinical features such as fever, cough, fatigue, symptom duration, oxygen saturation, heart rate, and respiratory rate.To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed, resulting in improved sensitivity toward minority class cases.A tuned XGBoost classifier demonstrated robust performance, achieving an accuracy of 73.54%, an F1-score of 0.7063, and a ROC-AUC of 0.7990.Key visualizations, including confusion matrices, ROC curves, and feature importance analyses, provided insights into the model's efficacy and clinical relevance.This study underscores the potential of machine learning in augmenting clinical decision-making processes for early and accurate detection of HMPV, while also highlighting the importance of preprocessing techniques like data balancing in enhancing model performance.These findings pave the way for scalable, AI-driven diagnostic solutions that can be extended to other respiratory illnesses.","url":"https://doi.org/10.4236/oalib.1113193","authors":["Rocco de Filippis","Abdullah Al Foysal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-11T05:54:01Z","doi":"10.4236/oalib.1113193","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1080/02713683.2016.1175019","name":"Machine Learning Techniques in Clinical Vision Sciences","source":"crossref","abstract":"This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.","url":"https://doi.org/10.1080/02713683.2016.1175019","authors":["Miguel Caixinha","Sandrina Nunes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-06-30T18:34:41Z","doi":"10.1080/02713683.2016.1175019","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.12688/f1000research.channels.152","name":"Machine learning: life science","source":"crossref","abstract":"","url":"https://doi.org/10.12688/f1000research.channels.152","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-05-22T13:55:19Z","doi":"10.12688/f1000research.channels.152","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.2139/ssrn.4202847","name":"Machine learning","source":"crossref","abstract":"This entry describes machine learning as a phenomenon.","url":"https://doi.org/10.2139/ssrn.4202847","authors":["Alina Trapova"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-04T23:09:44Z","doi":"10.2139/ssrn.4202847","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1002/clc.24260","name":"Utilizing machine learning for predicting heart failure outcomes: A path toward developing a patient‐centered approach","source":"crossref","abstract":"Heart failure (HF)—a global pandemic—poses a huge burden to healthcare systems, with a staggering 64.3 million people worldwide estimated to suffer from the ailment in 2017. Projections suggest a total cost of around $69.8 billion for HF by the year 2030 in the United States.1 This highlights the immense economic burden of the disease and calls for effective strategies vis-à-vis its treatment and more importantly, prevention. Recent studies have outlined how machine learning (ML) can be used to build predictive models from multidimensional datasets. This has led to the establishment of the role of AI in early detection of future mortality and destabilizing episodes, therefore allowing for the optimization of cardiovascular disease outcomes.2 A recent study published by Ketabi et al. analyzed the performance of 10 ML algorithms and chose the best algorithm to predict mortality and readmission of HF patients.3 Two thousand four hundred and eighty-eight patients' information was documented after their first hospital admission and they were then followed to determine three outcomes: hospital readmission, 1-month mortality, and 1-year mortality. 14.7% of these patients were readmitted to the hospital, 3.9% died within a month and 13.7% died within a year. To determine this, 57 different factors were considered independent variables to predict outcomes and were entered into and evaluated using ML algorithms. The data were divided into two sets: training sets for the machine algorithm to teach itself, and test sets for evaluating the classifier's prediction error rate after learning. The five metrics utilized to compare the models were accuracy, sensitivity, specificity, F1 score, and AUC. Out of the 10 ML algorithms, CatBoost (CAT) had the best performance in terms of predicting heart failure outcomes. It identified length of stay in the hospital, haemoglobin level, and family history of MI as the most important predictors for readmission, 1-month mortality, and 1-year mortality, respectively. These findings can thus be significant in helping doctors individualize HF patients at high risk of readmission or death. We can therefore conclude that early detection of patients at risk of HF—through the use of ML—will allow for timely interventions to be made. Intensive monitoring of patients predicted to experience a negative outcome will help ensure the development of a more patient-centered approach, forcing clinicians to ensure such patients are of utmost priority and tailor their treatment plans with caution and vigilance. The COACH trial found that using a tool in the emergency department to guide management plans for HF patients, combined with providing standardized transitional care, improved outcomes for such patients.4 This supports the idea that a ML-based predictive model will aid in risk-based decision-making leading to better HF patient results, and also help reduce unnecessary hospital readmissions by identifying patients who require more attention post-discharge. Additionally, doctors will be compelled to thoroughly counsel their patients, briefing them regarding specific lifestyle modifications required to lower the already very high risk of experiencing a similar cardiac event in the near future. Moreover, given the immense economic burden HF presents with, the use of ML will allow resources to be allocated efficiently based on predicted risk. In spite of all these merits, the current study has certain limitations including its retrospective design that used the records from the Fasa Registry on Systolic Heart Failure (FaRSH). For this reason, we recommend conducting prospective studies to overcome potential selection bias that may be associated with the study's current design. Moreover, the study population comprised mainly of patients of Arab and Persian ethnicity. Given the differences in incidence of congestive heart failure by ethnicity,5 this highlights the need for similar studies to be conducted on a wider geographical scale for more conclusive findings. Rayyan Nabi came up with the idea for the letter and drafted the manuscript. Tabeer Zahid helped in writing the manuscript and provided supportive ideas for its completion. Hanzala Ahmed Farooqi reviewed and supervised the article to ensure its clarity. The authors declare no conflict of interest.","url":"https://doi.org/10.1002/clc.24260","authors":["Rayyan Nabi","Tabeer Zahid","Hanzala A. Farooqi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-26T09:50:06Z","doi":"10.1002/clc.24260","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icmi68585.2026.11539870","name":"Pixheart: AI-Powered Automated ECG Analysis Using Hybrid CNN-LSTM and Machine Learning Models for Clinical Diagnostics","source":"crossref","abstract":"Working with Pixemantic and its Pixheart platform, this project introduces an AI-driven web app for automated Electrocardiogram (ECG) analysis. The system processes scanned ECG images using CNN and LSTM models to extract cardiac features to detect abnormalities with accuracy levels that match those achieved by medical professionals. Our hybrid CNN-LSTM model achieved an accuracy of 86%, with precision at 88%, recall at 87%, and an F1-score of 87%. Support Vector Machines (SVM) and Logistic Regression achieved strong results in traditional machine learning approaches because SVM models achieved 96% accuracy and Logistic Regression achieved 93%. Random Forest and XGBoost provide distinct advantages: Random Forest achieved perfect MI precision of 1.00 and achieved high PM precision of 0.97 but XGBoost delivered better HB recall with 0.81 and achieved an F1-score of 0.86 . The platform supports real-time analysis, clear waveform visuals, and report generation. Medical specialists who work in clinical environments have verified the data quality together with the model's operational success which proves the system's ability to deliver quick and precise ECG interpretation .","url":"https://doi.org/10.1109/icmi68585.2026.11539870","authors":["Mohamed Touati","Rabeb Touati","Mohamed Medimegh","Laurent Nana","Mohamed Wiem Mkaouer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T20:03:16Z","doi":"10.1109/icmi68585.2026.11539870","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1097/jce.0000000000000454","name":"US Food and Drug Administration Releases Artificial Intelligence/Machine Learning Action Plan","source":"crossref","abstract":"Journal of Clinical Engineering: 4/6 2021 - Volume 46 - Issue 2 - p 54-55 doi: 10.1097/JCE.0000000000000454","url":"https://doi.org/10.1097/jce.0000000000000454","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-18T15:00:47Z","doi":"10.1097/jce.0000000000000454","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007510522680","name":"Learning to Improve Coordinated Actions in Cooperative Distributed Problem-Solving Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007510522680","authors":["Toshiharu Sugawara","Victor Lesser"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007510522680","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icmlc.2003.1259691","name":"2003 International Conference On Machine Learning And Cybernetics","source":"crossref","abstract":"Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.","url":"https://doi.org/10.1109/icmlc.2003.1259691","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-07T17:32:59Z","doi":"10.1109/icmlc.2003.1259691","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-1-4899-7502-7_979-1","name":"Machine Learning with Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4899-7502-7_979-1","authors":["Patrick C. K. Hung","Sarajane Marques Peres"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-25T14:52:38Z","doi":"10.1007/978-1-4899-7502-7_979-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icaml54311.2021.00002","name":"Proceedings 2021 3rd International Conference on Applied Machine Learning [Title page iii]","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaml54311.2021.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-18T20:34:52Z","doi":"10.1109/icaml54311.2021.00002","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022618500334","name":"Editorial: New Editorial Board Members","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022618500334","authors":["Thomas G. Dietterich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022618500334","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.5089/9798400200267.001.a001","name":"Structural Breaks in Carbon Emissions: A Machine Learning Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.5089/9798400200267.001.a001","authors":["Jiaxiong Yao","Yunhui Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-15T10:55:32Z","doi":"10.5089/9798400200267.001.a001","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1007601015854","name":"Robust Classification for Imprecise Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007601015854","authors":["Foster Provost","Tom Fawcett"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:54:50Z","doi":"10.1023/a:1007601015854","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1101/2024.05.22.595393","name":"Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues","source":"crossref","abstract":"Abstract Significance Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen’s architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues. Aim To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures. Approach We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment. Results Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson’s R 0.82) in the collagen orientation, and similarly high correlation (Pearson’s R 0.66) in the collagen alignment. Conclusions We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.","url":"https://doi.org/10.1101/2024.05.22.595393","authors":["Wihan Adi","Bryan E. Rubio Perez","Yuming Liu","Sydney Runkle","Kevin W. Eliceiri","Filiz Yesilkoy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-27T00:55:15Z","doi":"10.1101/2024.05.22.595393","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1093/clinchem/hvac071","name":"Toward a Personal Drug Detection Assistant: Machine Learning for Detection of Synthetic Cannabinoid Receptor Agonists","source":"crossref","abstract":"Detection of novel psychoactive substances such as synthetic cannabinoid receptor agonists (SCRAs) is a worldwide problem with serious clinical and public health implications. Rapid, low-cost, and accurate screening methods are needed but are fraught with challenges. Activity-based bioassays coupled with machine learning (ML) to automate profile interpretation may be a promising approach. ML is a type of artificial intelligence that enables a computer program to identify a mathematical model to accurately describe the relationship between input data and desired outputs, without human intervention or programming. It is particularly promising for revealing complex and nonlinear relationships that may exceed human capacity for recognition. That said, it is important to note that the recent progress in the development and uptake of ML is relegated to solving specific, targeted problems, for which algorithms are expressly designed and validated. In laboratory medicine, ML is typically performed in a supervised manner to predict values (regression) or groups (classification) for individual cases. Supervised learning requires that each observation include a ground truth, which can be used to train the ML model and assess its performance. Thus, the size and composition of the training and validation data sets are critical in ML development and evaluation. ML is useful for complex pattern recognition, as is required when examining output from clinical laboratory methods that require interpretation. In such cases, ML may be applied to automate or augment human interpretation, which is time-consuming, costly, and often subjective with variation between human expert reviewers. In some cases, the specific human expertise required is not widely available, so applying an algorithm may also allow for expansion of such methods to a broader user base. This issue of Clinical Chemistry includes an article by Janssens et al., who aim to automate scoring of receptor-activation profiles for SCRAs using ML (1). SCRAs are one of the largest groups of new psychoactive substances that are widely used and known to cause harmful intoxications. Detection of SCRAs is challenging due to the rapidly evolving list of relevant compounds, as new analogues are continuously created to evade regulations and increase potency and older ones may no longer be used. This requires analytical approaches that are untargeted, yet sensitive. One of the most common is liquid chromatography-high resolution mass spectrometry (LC-HRMS) (2). Activity-based bioassays using receptor-activation time-series profiles are another type of method used to test samples for SCRAs (3). These bioassays allow for universal screening, since they are not specific for a compound structure, but instead for its biological activity. Activity-based assays overcome some of the challenges with LC-HRMS with their lower cost, lack of need for spectral libraries, and sensitivity. Although they may be easier to implement than a LC-HRMS system and workflow, SCRA bioassays require cell culture materials and techniques with human interpretation of the resulting time-series profiles, thus limiting their availability. Human experts inspect the time traces for each sample and compare them to those obtained for blanks, looking for visual indication of a rise in signal intensity over time. Before embarking on the ML approach, the authors developed a decision tree method that reduced discrepancies between the 2 human experts who interpreted the profiles and would discuss cases to arrive at a consensus score. Despite this improved consistency, an unexplained subjective component of the scoring remained. Therefore, Janssens et al. employ ML as a method to automatically classify SCRA activity assay results from 968 serum samples as positive or negative (1). Features are generated from the receptor activation time-series profiles using 2 approaches: (a) an automated program to identify a set of 499 characteristics defining each curve and (b) fitting each curve to a fifth-order polynomial and extracting the coefficients. These features are used as inputs for a random forest ML model that is trained and tested on classes originating from LC-HRMS results and from expert scoring for comparison, as each sample was tested by both methods. The various permutations of their training and testing approaches demonstrate the value of expert knowledge in this problem. Ultimately, the selected model did not outperform the human experts, who achieved 94.6% sensitivity and 98.5% specificity. Because this is a binary classification, the prediction threshold could be adjusted to match the sensitivity obtained by the manual scoring process. However, this results in decreased specificity (84.7%) with 3.5× more false positives when using the ML method. These false-positive samples would be needlessly tested by LC-HRMS per the confirmation process. The authors felt the current performance of the ML model was not adequate for a first-line SCRA activity-based screening protocol, making manual scoring necessary to achieve required sensitivity and specificity. Although not an outright success story for ML applied to this problem, since human experts are still required, this work does suggest that computational approaches may be applied for SCRA receptor activation profile interpretation. The algorithms improve standardization among human experts and potentially automate classification of serum samples, reducing result turnaround time. The authors also illustrate several sound practices in developing and reporting this ML-based approach. This includes some of those described in a recent issue of Clinical Chemistry (4) and is worth highlighting a select few here. This problem does seem like one that is well suited for ML, and Janssen et al. attempt different strategies for formulating the problem and for extracting the inputs, selecting the more interpretable option given comparable predictive performance. They expand their performance metrics to include area under the precision-recall curve, which is appropriate since there was an imbalance in the positive vs negative classes of their data set. While the predictive performance metrics (e.g., area under the precision-recall curve, sensitivity, specificity) are most commonly reported and discussed, these do not always provide a clear picture of the impact the ML application will have on the intended workflow. Although the performance metrics seem reasonably high, the impact of the decreased specificity is described in the context of the unnecessary confirmatory tests and seems to be a key component leading to the continued need for human interpretation of SCRA activity-based assays. Finally, the code used for this work is publicly available, which helps to ensure thorough peer review and transparency for interested readers. In contrast, Janssen et al. did not use an external or independent validation data set, possibly due to a limited number of available samples (1). Their results are based on cross-validation experiments, limiting any conclusions about the true generalizability of their model and demonstrating a proof-of-concept for this ML approach. A more rigorously validated and generalizable ML model would be needed to realize the potential benefit of translating it to other laboratories to increase access and standardization of SCRA activity-based assays. Although a limited number of interpretable inputs are used, the relative feature importance for the random forest model is not clear, nor is the types of samples it finds most challenging. Robust training of such methods is difficult due to a relatively low number of total positive cases that are further fragmented to represent many individual compounds and analogues that change over time. Attempts to examine the ML model with global or local interpretability methods (5) or to investigate characteristics of the cases that were incorrectly classified may help to improve future versions and close the performance gap relative to the human experts. The continued application of ML for different problems in clinical laboratories to automate highly manual, specialized, time-consuming, and subjective workflows is encouraging. One of the most promising areas is in profile interpretation. As shown here, good concordance can be achieved between human experts and analytical methods, but trade-offs of speed or efficiency with accuracy must be considered for each scenario. The emphasis on transparency and practicality in this article exemplifies how the community should move forward in developing and reporting ML applications, learning from each other, and improving together as we proceed. SCRA, synthetic cannabinoid receptor agonist; ML, machine learning; LC-HRMS, liquid chromatography-high resolution mass spectrometry. All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: S. Haymond, AACC. None declared. None declared. S. Haymond, AACC, MSACL. None declared. None declared. None declared. S. Haymond, support for attending meetings and/or travel from AACC, MSACL.","url":"https://doi.org/10.1093/clinchem/hvac071","authors":["Shannon Haymond"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-21T19:14:23Z","doi":"10.1093/clinchem/hvac071","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.47795/ojhn3816","name":"The effect of machine learning and artificial intelligence on the use of robots in neurosurgery","source":"crossref","abstract":"Robotics has made rapid inroads into specialised fields of surgery like neurosurgery. The robot has many advantages like increasing accuracy, eliminating muscle fatigue and physiological tremor during long operations, all of which improve outcomes and avoid potential complications in high risk neurosurgery procedures. At present, we use pre-programmed robots to guide the surgeon accurately localising the brain anatomy. This allows the surgeon to place electrodes in the brain or pedicle screws in the spine or take precise biopsies. Use of robots in Neurosurgery are presently limited to these tasks. In future, robots will have to perform these tasks and other complex procedures without the involvement of the surgeon. happen, robots will need be equipped with algorithms and software to help them to learn without being programmed, that is, via machine learning. At the core of this process is the ability of the robots to exploit large amounts of data based on their computational power and then translate it to actions that would mimic the surgeon. Progress on this front has been slow, mainly due to ethical (data access) and safety issues. It is felt that with the ability of newer generation computers to absorb and analyse tremendous amounts of data (e.g. thousands of operations of a particular kind), machines will learn to copy the actions of the surgeon and make safe choices so they can be trusted to perform the surgery and manage any unforeseen events which may arise from the surgery. The two main challenges that hinder complete automation are surgical perception and tissue manipulation. Neurosurgery operates within severe spatial constraints and the consequence of any tissue injury is likely to be catastrophic. The tactical nous and fine sense acquired by an experienced surgeon in his hands can be learned by the machine through the development of haptic feedback through tissue resistance. Long distance robotic surgery supervised by a distant surgeon (Telesurgery) can only be safe if used over a dedicated network. This will ensure telecommunication is uninterrupted during the procedure and information exchange is concurrent. The risk to safety increases significantly with latencies above 200ms and exponentially above 1000ms. In this article, we discuss the current situation, the future possibilities and the factors which are an obstacle to the quick adoption of AI and ML in neurosurgery.","url":"https://doi.org/10.47795/ojhn3816","authors":["Dev Bhattacharyya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-23T17:05:47Z","doi":"10.47795/ojhn3816","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/11564096_2","name":"Machine Learning for Natural Language Processing (and Vice Versa?)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11564096_2","authors":["Claire Cardie"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2005-11-09T11:54:27Z","doi":"10.1007/11564096_2","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.mlwa.2025.100623","name":"Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders","source":"crossref","abstract":"Selection of appropriate suppliers and allocation the orders among them have become the two key strategic decisions regarding purchasing. In this study, a two-phase integrated approach is proposed for solving supplier selection and order allocation problems. Phase 1 contains four techniques from statistics and Machine Learning (ML), including Auto-Regressive Integrated Moving Average, Random Forest, Gradient Boosting Regression, and Long Short-term Memory for forecasting the demands, using large amounts of real historical data. In Phase 2, suppliers’ qualitative weights are determined by a fuzzy logic model. Then, a new multi-objective programming model is designed, considering multiple periods and products. In this phase, the results of Phase 1 and the results of the fuzzy model are utilized as inputs for the multi-objective model. The weighted-sum method is applied for solving the multi-objective model. The results show Random Forest model leads to more accurate predictions than the other examined models in this study. In addition, based on the results, the selection of the forecasting techniques and different weights of suppliers affect both supplier selection and the related orders.","url":"https://doi.org/10.1016/j.mlwa.2025.100623","authors":["Asma ul Husna","Saman Hassanzadeh Amin","Ahmad Ghasempoor"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-18T19:36:48Z","doi":"10.1016/j.mlwa.2025.100623","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781003538158-8","name":"Bio-Inspired Algorithms in Machine Learning and Deep Learning for Diabetes Diagnosis","source":"crossref","abstract":"Bio Inspired algorithms in machine learning and deep learning for diabetic affects millions of individuals throughout the globe and is the leading cause of blindness in persons of working age who have diabetes. This highlights the critical need of a trustworthy retinal screening method. Recently, with the use of effective Image Processing technology, Deep Learning algorithms have showed promise for application in population diagnosis and classification. In this research, proposed system used a Conventional Neural Network as part of a deep learning approach to rapidly detect diabetes in newly recorded medical images. Classifiers well-suited to many different classification tasks may be mined from data. Main objective of this research is to solve the challenge of detecting diabetes and Glaucoma in retinal pictures, using feed-forward neural network. The suggested approach significantly improves the speed and accuracy of illness detection over conventional techniques due to its much greater rate of complete classification. When a self-trained model is used, such as Alexnet, extensive testing may assist improve accuracy. As a result, the suggested approach vastly enhances the speed and precision of diabetes recognition. The results show a classification accuracy of 99.61%, sensitivity of 98.65%, and specificity of 98.60%.","url":"https://doi.org/10.1201/9781003538158-8","authors":["S. Aathilakshmi","S. Balasubramaniam","Ayodeji Olalekan Salau"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-02T15:33:02Z","doi":"10.1201/9781003538158-8","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/springerreference_179347","name":"Phase Transitions in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_179347","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-07T08:44:39Z","doi":"10.1007/springerreference_179347","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/springerreference_63722","name":"Machine Learning in Computational Biology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_63722","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-08-29T12:38:13Z","doi":"10.1007/springerreference_63722","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.mlwa.2023.100519","name":"An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors","source":"crossref","abstract":"This study aimed to identify the most impactful set of intrinsic and extrinsic fall risk factors and develop a data-driven inpatient fall risk assessment tool (FRAT). The dataset used for the study comprised in-hospital fall records from 2012 to 2017. Four machine learning (ML) algorithms, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (Gboost), and Deep Neural Network (DNN) were utilized to predict the inpatient fall risk level. To enhance the performance of the prediction models, two approaches were implemented, including (1) feature selection to identify the optimal feature set and (2) the development of three distinct shift-wise models. Furthermore, the optimal feature sets in the shift-wise models were extracted. According to the results, DNN outperformed other methods by reaching an accuracy, sensitivity, specificity, and AUC of 0.71, 0.8, 0.6, and 0.7, respectively, considering the full set of features. The performance of the models was further improved (by 3%-5%) by conducting a feature selection process for all models. Specifically, the DNN model achieved an accuracy of 0.74 while considering the optimal set of predictors. Moreover, the shift-wise RF models demonstrated higher accuracies (by 4%-10%) compared to the same model using a full feature set. This study's outcome confirms ML models' compelling capability in developing an inpatient FRAT while considering intrinsic and extrinsic factors. The insight from such models could form a foundation to (1) monitor the inpatients’ fall risk, (2) identify the major factors involved in inpatient falls, and (3) create subject-specific self-care plans.","url":"https://doi.org/10.1016/j.mlwa.2023.100519","authors":["Sonia Jahangiri","Masoud Abdollahi","Rasika Patil","Ehsan Rashedi","Nasibeh Azadeh-Fard"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-08T03:01:45Z","doi":"10.1016/j.mlwa.2023.100519","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.58496/bjml/2024/008","name":"Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT","source":"crossref","abstract":"The energy management system designed on the networking platform has been interfaced with controller to control the electrical device using the Wireless communication has been used as the most reliable and efficient technology in short-range communication. In this method IoT-based energy management could significantly contribute to energy conservation of home appliances device. This model analyses an IoT-based smart energy meter that automatically tracks residential energy consumption using current and voltage sensors. Input values senses unit that detects and controls the electrical devices used for daily actions. The ESP32 is used due to its built-in Wi-Fi facility, allowing data collection and exchange from electronic hardware to a cloud platform. The virtual android app displays the value of voltage, current, power, and unit consumed on a mobile screen, enhancing the efficiency of the system. The developed coding system to enhance system performance and provide more accurate results and ESP32 controller to interface non-invasive CT and voltage sensors, delivering data to a Blynk server over the internet. Model show the system accurately records voltage, current, dynamic power, and increasing power consumption and outcome accordingly, the home concerned person can turn ON/OFF the device based on such information if customer based user information.","url":"https://doi.org/10.58496/bjml/2024/008","authors":["M.Sahaya Sheela","S. Gopalakrishnan","I.Parvin Begum","J. Jasmine Hephzipah","M Gopianand","D. Harika"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T09:11:40Z","doi":"10.58496/bjml/2024/008","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-6684-6291-1.ch006","name":"High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods","source":"crossref","abstract":"In this chapter, the authors realized prediction applications of concrete compressive strength values via generation of various hybrid models, which are based on decision trees as main a prediction method. This was completed by using different artificial intelligence and machine learning techniques. In respect to this aim, the authors presented a literature review. The authors explained the machine learning methods that they used as well as with their developments and structural features. Next, the authors performed various applications to predict concrete compressive strength. Then, the feature selection was applied to a prediction model in order to determine parameters that were primarily important for the compressive strength prediction model. The authors evaluated the success of both models with respect to correctness and precision prediction of values with different error metrics and calculations.","url":"https://doi.org/10.4018/978-1-6684-6291-1.ch006","authors":["Melda Yucel","Ersin Namlı"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-08T11:31:23Z","doi":"10.4018/978-1-6684-6291-1.ch006","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.3389/978-2-88966-862-5","name":"Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases, 2nd Edition","source":"crossref","abstract":"","url":"https://doi.org/10.3389/978-2-88966-862-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-01T15:54:18Z","doi":"10.3389/978-2-88966-862-5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/icssas68835.2026.11559523","name":"Stroke Risk Prediction Using Imbalance-Sensitive Machine Learning Models on Structured Clinical Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icssas68835.2026.11559523","authors":["Ayyappan G"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T19:52:40Z","doi":"10.1109/icssas68835.2026.11559523","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.21203/rs.3.rs-7518311/v1","name":"Non-invasive Prediction of Ki-67 Status in Hepatocellular Carcinoma Using Interpretable Machine Learning with Clinical and CT Radiomics Features","source":"crossref","abstract":"Abstract Objective : This study aims to investigate the application value of a radiomics-based machine learning model derived from CT images in the preoperative prediction of Ki-67 proliferation status in hepatocellular carcinoma (HCC). Additionally, the SHapley Additive exPlanations (SHAP) method is employed to visualize the prediction process of the combined model, enhancing its interpretability and facilitating its clinical application. Methods : This study retrospectively collected preoperative enhanced CT images from 172 patients with pathologically confirmed hepatocellular carcinoma (HCC). The patients were randomly divided into training and validation cohorts (7:3 ratio) and categorized into low-expression (≤15%) and high-expression (&gt;15%) groups based on the Ki-67 labeling index. Radiomic features were extracted using the PyRadiomics library in Python. Feature selection was performed using mutual information, LASSO regression, and feature selection networks. Machine learning models, including logistic regression, support vector machine, random forest, and XGBoost, were developed. The optimal model was selected based on ROC curve, AUC, and calibration curve analysis, and an integrated model was built by combining it with clinical features. Model interpretability was visualized using SHAP. Results : Among the radiomics-based models constructed using CT imaging features, the random forest model based on the venous phase (PVP sequence) demonstrated the most stable performance, with six radiomic features selected. This model achieved an average AUC of 0.91 in the training set, with a sensitivity of 85%, specificity of 82%, and accuracy of 83%. In the test set, it yielded an AUC of 0.80, sensitivity of 73%, specificity of 78%, and accuracy of 76%. Furthermore, incorporating these six radiomic features with the clinical variable (AFP), logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost) models were constructed. The results indicated that the combined random forest model exhibited the highest discriminative performance. This model achieved an average AUC of 0.92 in the training set, with a sensitivity of 82%, specificity of 87%, and accuracy of 85%. In the test set, the AUC was 0.82, with a sensitivity of 72%, specificity of 76%, and accuracy of 74%. Additionally, the calibration curve demonstrated good model fit, suggesting that the combined random forest model provides higher accuracy and stability in predicting Ki-67 expression status. Conclusions : The radiomics-based interpretable machine learning model presents a non-invasive methodology for the reliable prediction of Ki-67 expression status in patients with hepatocellular carcinoma, exhibiting robust discriminative performance.","url":"https://doi.org/10.21203/rs.3.rs-7518311/v1","authors":["Shengzhen Dai","Xiaoyi Wang","Shishi Luo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-03T09:37:02Z","doi":"10.21203/rs.3.rs-7518311/v1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1101/2022.01.22.22269384","name":"A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning","source":"crossref","abstract":"Abstract Backround Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Method A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array. Results Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. Using 6 of the characteristic features (genes) discovered during the selection process, 99,6% of predictive performance was achieved. The 3 genes contributing most to the predictive power of the model (92,7% predictive performance) are also deregulated in temporal lobe epilepsy. KEGG analysis revealed participation of 4 identified features in 6 pathways which have been associated with bipolar disorder. Conclusion 93% Area Under Curve, 96% Average Precision, and complete separation between unaffected controls and patients with bipolar disorder, were achieved in ∼2 hours. The cerebellar transcriptomic biosignature suggests a potential genetic overlap with temporal lobe epilepsy and new genetic contributions to the pathogenesis of bipolar disorder.","url":"https://doi.org/10.1101/2022.01.22.22269384","authors":["Georgios V. Thomaidis","Konstantinos Papadimitriou","Sotirios Michos","Evangelos Chartampilas","Ioannis Tsamardinos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-01-23T08:40:12Z","doi":"10.1101/2022.01.22.22269384","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.47310/srjecs.2021.v01i01.002","name":"A Survey on Design a Machine Learning Method to Identify Skin Diseases Using Machine Learning and Image Processing","source":"crossref","abstract":"Machine learning algorithms are being used extensively in biomedical fields for segmentation and analysis. These algorithms use features derived from images as input to make a conclusion. So, choosing proper feature extraction methods pooled with suitable Machine Learning (ML) algorithms is very important to accomplish good classification accuracy. During the literature survey, we found that there is a deficiency of information about machine learning algorithms for skin disease classification. Image Processing and machine learning based examinations are being utilized in a few territories; for example, face recognition, unique finger impression recognition, tumor identification and segmentation. Generally used ML algorithms are Linear Differential Analysis (LDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nave Bias Classier, K-Nearest Neighbor (KNN) and Deep Learning Algorithm. The choice of information include is crucial in any classification task, utilizing ML calculations.","url":"https://doi.org/10.47310/srjecs.2021.v01i01.002","authors":["Amar S. Chandgude","Farah Haneef"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T15:05:36Z","doi":"10.47310/srjecs.2021.v01i01.002","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4135/9781529795417","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781529795417","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-06-24T09:07:23Z","doi":"10.4135/9781529795417","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.61700/b2lk2bcl2ejll2029","name":"Causal Machine Learning","source":"crossref","abstract":"This seminar provides an in-depth exploration of causal machine learning, equipping participants with sophisticated techniques to differentiate causation from correlation using modern machine learning tools integrated with robust econometric methods. By introducing a number of well defined points of departure, the seminar is designed to provide exposure to advanced methods for causal inference without requiring significant prerequisites. Attendees will gain practical skills and theoretical knowledge to apply these approaches effectively in their research across various domains.","url":"https://doi.org/10.61700/b2lk2bcl2ejll2029","authors":["Melvyn Weeks"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-14T10:11:44Z","doi":"10.61700/b2lk2bcl2ejll2029","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.7717/peerj-cs.2016/fig-4","name":"Figure 4: The machine learning algorithms.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2016/fig-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-14T03:56:27Z","doi":"10.7717/peerj-cs.2016/fig-4","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022804725118","name":"A Review of the Fourth International Workshop on Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022804725118","authors":["Rogers P. Hall","Brian Falkenhainer","Nicholas Flann","Steve Hampson","Robert Reinke","Jeff Shrager","Michael H. Sims","Prasad Tadepalli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:57:10Z","doi":"10.1023/a:1022804725118","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-97-3954-7_2","name":"Basics of Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-3954-7_2","authors":["Yu Geng","Qin Li","Geng Yang","Wan Qiu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-29T20:24:55Z","doi":"10.1007/978-981-97-3954-7_2","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/b978-0-32-385227-2.00009-7","name":"Machine learning for long-haul optical systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-32-385227-2.00009-7","authors":["Shaoliang Zhang","Christian Häger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-18T10:02:05Z","doi":"10.1016/b978-0-32-385227-2.00009-7","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/b978-0-323-90049-2.00027-5","name":"Machine learning for vibrational spectroscopy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-90049-2.00027-5","authors":["Sergei Manzhos","Manabu Ihara","Tucker Carrington"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-23T21:53:22Z","doi":"10.1016/b978-0-323-90049-2.00027-5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781032623276-1","name":"Recent Advances in Machine Learning Strategies and Its Applications","source":"crossref","abstract":"Today, machine learning is a widely used technique. An important artificial intelligence-led advancement in the field of digitalization solutions is machine learning, which was primarily a subcategory of artificial intelligence and attracted substantial attention in practical reality and increased reality technology adoption. Application in machine learning is greatly assisting in this because there is a requisite to uncover something that can lead to critical conclusions due to its volume of data that has grown over the previous several decades. Notwithstanding the requisite to analyze and understand data, machine learning is succeeding in several fields, including intellectual control, decision-making, speech identification, natural language processing, computer graphics, and vision. Due to their outstanding performance, deep and machine learning strategies have recently increased extensive recognition and acceptance through several real-time engineering uses. Scheming automated and intelligent programs that can manage information in health, cyber-security, etc., requires knowledge of machine learning. Numerous methods, including reinforcement learning, as well as semi-supervised, unsupervised, and supervised strategies, contribute to building robust machine learning models. This chapter offers a thorough analysis of managing real-time engineering uses by machine learning that will raise the capabilities and intelligence of an application. This chapter outlines the goals of the study and the challenges that machine learning methods face while handling practical applications. In addition to serving as a practical reference for decision-makers, this study serves as a point of reference for academics and industry specialists.","url":"https://doi.org/10.1201/9781032623276-1","authors":["S. Saranyadevi","Lekshmi Gangadhar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-28T19:00:48Z","doi":"10.1201/9781032623276-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-97-0217-6_9","name":"Unsupervised Machine Learning and Beyond Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-0217-6_9","authors":["Keisuke Takahashi","Lauren Takahashi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-30T07:01:42Z","doi":"10.1007/978-981-97-0217-6_9","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.58532/nbennurch183","name":"OVERVIEW: MACHINE LEARNING","source":"crossref","abstract":"Machine learning is a subfield of artificial intelligence that involves the development of algorithms that allow computers to learn from data, without being explicitly programmed. It involves the use of statistical models and algorithms to analyse and identify patterns in data, and make predictions or decisions based on that data. Machine learning algorithms are capable of improving their performance as they are exposed to more data, allowing them to automatically adapt and improve over time. Machine learning is widely used in a variety of applications such as image and speech recognition, natural language processing, recommendation systems, and predictive analytics","url":"https://doi.org/10.58532/nbennurch183","authors":["Vaishali Jain","Shiv Kumar Tiwari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-07T02:15:51Z","doi":"10.58532/nbennurch183","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-15-1706-8_3","name":"Machine Learning and Cybersecurity","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-1706-8_3","authors":["Tony Thomas","Athira P. Vijayaraghavan","Sabu Emmanuel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-16T17:02:52Z","doi":"10.1007/978-981-15-1706-8_3","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1026319107706","name":"The Robustness of the p-Norm Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1026319107706","authors":["Claudio Gentile"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-11-03T19:30:08Z","doi":"10.1023/a:1026319107706","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-1-4842-5599-5_6","name":"Artificial Intelligence and Machine Learning","source":"crossref","abstract":"Artificial intelligence and machine learning have always been hot topics in news and science fiction. Recently they got more media attention because of technology breakthroughs in deep learning and more consumer-facing applications on the market. The terms machine learning and artificial intelligence are sometimes used interchangeably, but there is a subtle difference. Artificial intelligence focuses on “intelligence”. An AI system tries to behave as if it possesses human intelligence, no matter what the underlying method or algorithm is. But in machine learning, the focus is on “learning,” where the system is trying to learn something from the data without a human explicitly programming the knowledge. For example, one of the early successes in AI was the expert system. In an expert system, the knowledge of a particular field is written down as rules and programmed directly into the code, so the system can answer questions or perform tasks as if it were a domain expert. This kind of system might appear to have some level of human intelligence, but underneath it’s not actually “learning” from data. So this system can be called an AI system but not a machine learning system.","url":"https://doi.org/10.1007/978-1-4842-5599-5_6","authors":["Shing Lyu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-27T11:02:38Z","doi":"10.1007/978-1-4842-5599-5_6","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/aists66100.2025.11232739","name":"A Comparative Study of Machine Learning and Deep Learning Techniques for Cardiovascular Disease Prediction Using Clinical Health Records","source":"crossref","abstract":"Cardiovascular disease (CVD) remains the leading cause of global death, highlighting the need for effective and early detection methods. We introduce a comparative study of machine learning (ML) and deep learning (DL) algorithms for the prediction of cardiovascular disease from clinical health records in this study. The dataset contains anonymized patient data, which includes demographic information and important medical indicators such as age, blood pressure, serum cholesterol, and ECG results. Implementing four predictive models: Logistic Regression, Random Forest, Naive Bayes and a Feedforward Neural Network. Please note: each model was tested on a split of the data (80:20) using standard performance metrics (Of course, each metric is useful in each context)— Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The Random Forest had the best accuracy of 99.0% while the Neural Network performed with an accuracy of 98.5% with all yielding ROC-AUC closer to 1.00. In our findings, we observed that training all four models resulted in well-performing models but ensemble models and deep learning models exhibit higher generalization capabilities for structured sets in the medical fields. Our study presents empirical evidence on the relative efficacy of ML and DL methods for clinical diagnostics and sets the stage for deploying AI-based decision support systems in real-world healthcare settings.","url":"https://doi.org/10.1109/aists66100.2025.11232739","authors":["Deep Dave","Madhu Shukla","Neel Dholakia","Vipul Ladva"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-17T18:38:53Z","doi":"10.1109/aists66100.2025.11232739","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.7717/peerj.18851/supp-3","name":"Supplemental Information 3: Light Gradient Boosting Machine code snippets used during machine learning analysis","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj.18851/supp-3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-05T03:05:31Z","doi":"10.7717/peerj.18851/supp-3","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1101/19006189","name":"Psychiatric comorbid disorders of cognition: A machine learning approach using 1,175 UK Biobank participants","source":"crossref","abstract":"Abstract Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently 1 . Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are important longitudinal predictors of cognitive change. Methods UK Biobank participants used at three time points (n= 502,664): baseline, 1 st follow-up (n= 20,257) and 1 st imaging study (n=40,199). Participants with no missing data were 1,175 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. Conclusions Outcomes suggest psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. Clinical implications Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care. Summary Box What is already known about this subject? 3-4 bullet points Poor mental health is associated with cognitive deficits. One in four older adults experience a decline in affective state with increasing age. ML approaches have certain advantages in identifying patterns of information useful for the prediction of an outcome. What are the new findings? 3-4 bullet points Psychiatric disorders are important comorbid disorders of long-term cognitive change. Machine-learning methods such as sequence learning based methods are able to offer non-parametric joint modelling, allow for multiplicity of factors and provide prediction models that are more robust and accurate for longitudinal data The outcome of the RNN analysis found that anxiety and depression were stronger predictors of change IIV over time than either cardiovascular disease and diabetes or the covariate variables. How might it impact on clinical practice in the foreseeable future? The important predictive effect of mental health on longitudinal cognition should be noted and, its comorbidity relationship with other conditions such as cardiovascular disease likewise to be considered in primary care and other clinical settings","url":"https://doi.org/10.1101/19006189","authors":["Chenlu Li","Delia A. Gheorghe","John E.J. Gallacher","Sarah Bauermeister"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-27T14:05:14Z","doi":"10.1101/19006189","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/b978-0-12-819742-4.00007-x","name":"Project management for a machine learning project","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-819742-4.00007-x","authors":["Peter Dabrowski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-01-30T01:43:45Z","doi":"10.1016/b978-0-12-819742-4.00007-x","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.38007/ml.2021.020103","name":"Machine Learning in the Construction of Library's Special Collection Document Information Resources","source":"crossref","abstract":"With the rapid development of Internet technology, more and more people begin to focus on the construction of library (CL) information resources (IR) in the field of mobile network and social media applications.In order not to let the unique culture disappear gradually, and to realize cultural exchange and inheritance, this paper intends to use modern technology to build IR of library related documents.This paper mainly uses the principal component analysis and survey methods to carry out relevant research on the CL special collection document IR.The survey results show that 70% of people think that the utilization rate of library resources is not high, while 68% of people think that the research on special collection culture should be strengthened.","url":"https://doi.org/10.38007/ml.2021.020103","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T02:56:36Z","doi":"10.38007/ml.2021.020103","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-1-4842-5107-2_12","name":"Tuning and Adjusting","source":"crossref","abstract":"We’ve talked about several mechanisms in this book for looking back and reflecting on past progress with an aim to becoming more effective as individuals, managers, and teams. To recap those mechanisms, they include Retrospectives , Data Wallows, Quality Reviews , Live Site Reviews , Engineering Reviews , and Surveys.","url":"https://doi.org/10.1007/978-1-4842-5107-2_12","authors":["Eric Carter","Matthew Hurst"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-08-21T15:03:53Z","doi":"10.1007/978-1-4842-5107-2_12","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1023/a:1022687630067","name":"Research Note on Decision Lists","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022687630067","authors":["Ron Kohavi","Scott Benson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022687630067","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/mlds.2017.6","name":"Classification of Cancerous Profiles Using Machine Learning","source":"crossref","abstract":"There are a variety of options available for cancer treatment. The type of treatment recommended for an individual is influenced by various factors such as cancer-type, the severity of a cancer (stage) and most important the genetic heterogeneity. In such a complex environment, the targeted drug treatments are likely to be irresponsive or respond differently. To study anti-cancer drug response we need to understand cancerous profiles. These cancerous profiles carry information which can reveal the underlying factors responsible for cancer growth. Hence, there is need to analyze cancer data for predicting optimal treatment options. Analysis of such profiles can help to predict and discover potential drug targets and drugs. In this paper the main aim is to provide machine learning based classification technique for cancerous profiles.","url":"https://doi.org/10.1109/mlds.2017.6","authors":["Aman Sharma","Rinkle Rani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-03-22T16:25:36Z","doi":"10.1109/mlds.2017.6","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9781003424987-5","name":"Transforming Healthcare through Machine Learning and the Internet of Things","source":"crossref","abstract":"Machine learning (ML) and the Internet of Things (IoT) are two of the most influential and disruptive technologies in the modern world. They have the potential to transform healthcare by providing innovative solutions that can improve the quality, efficiency, accessibility, affordability, and safety of healthcare services. In this chapter, we explore the current and future applications of ML and IoT in healthcare, as well as the challenges and considerations that need to be addressed to ensure their ethical, legal, and social implications are properly handled. We review some of the most important applications of ML in healthcare, such as disease diagnosis and predictive analytics, personalized treatment plans, image analysis and radiology, and drug discovery. We also review some of the most promising applications of IoT in healthcare, such as remote patient monitoring, smart healthcare facilities, and medication management. Furthermore, we review some of the most exciting applications of ML and IoT in healthcare that leverage their combined capabilities, such as early warning systems, predictive maintenance, and patient-centred care. Additionally, we review some of the most critical challenges and considerations for ML and IoT in healthcare that involve addressing their data privacy and security, data quality and standardization, integration and interoperability, and ethical and regulatory issues. Finally, we review some of the most potential future directions for ML and IoT in healthcare that involve developing AI-powered virtual health assistants, population health management systems, and drug personalization methods. We hope that this chapter will provide you with a comprehensive overview of the current state-of-the-art and future trends of ML and IoT in healthcare, as well as inspire you to explore further research opportunities and practical applications in this domain.","url":"https://doi.org/10.1201/9781003424987-5","authors":["Faridoddin Shariaty"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-02T15:32:01Z","doi":"10.1201/9781003424987-5","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.65525/svup.9788199565418.2026.159-163","name":"\"Precision Dining: Machine Learning Techniques for  Intelligent Food Recommendations\"","source":"crossref","abstract":"The rise of personalized services has transformed numerous industries, and the food sector is no exception. This paper delves into the development of a machine learning-based intelligent food recommendation system designed to enhance the dining experience through precision dining. By analyzing user preferences, dietary restrictions, nutritional needs, and contextual factors such as location and time, the system generates highly personalized meal suggestions. The core of the recommendation engine employs advanced machine learning techniques, including collaborative filtering, content-based filtering, and deep learning models, to continuously learn from user feedback and refine its recommendations. We present a detailed overview of the system’s architecture, the algorithms employed, and the data processing methodologies. Additionally, we highlight the system’s performance through empirical evaluations and user studies, demonstrating significant improvements in user satisfaction and engagement. This research underscores the potential of machine learning to revolutionize food recommendations, offering tailored dining solutions that cater to individual tastes and health requirements, ultimately contributing to a more enjoyable and health-conscious dining experience. DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.159-163","url":"https://doi.org/10.65525/svup.9788199565418.2026.159-163","authors":["Jayashree Paul","Sourav Saha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T11:06:12Z","doi":"10.65525/svup.9788199565418.2026.159-163","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1111/jep.70001","name":"Machine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards","source":"crossref","abstract":"ABSTRACT Objective This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Background Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID‐19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. Materials and Methods A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward‐level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k‐Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. Reporting Method STROBE checklist. Results The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. Discussion The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Conclusion Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. Implications The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. Impact The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data‐driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence‐based decisions in healthcare management. Patient or Public Contribution While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.","url":"https://doi.org/10.1111/jep.70001","authors":["Manar Aslan","Ergin Toros"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-21T08:46:32Z","doi":"10.1111/jep.70001","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1093/clinchem/hvaa212","name":"Deus Ex Machina? Predicting SARS-CoV-2 Infection from Lab Tests Using Machine Learning","source":"crossref","abstract":"Coronavirus 2019 (COVID-19) has disrupted lives, the economy, and healthcare systems across the globe, unlike any infectious disease in 100 years. As we collectively seek to survive and emerge from this ongoing crisis, it is worth evaluating any scientific discovery that may help reduce health risks or address barriers to the response. One particular barrier that will persist through the pandemic is the speed and availability of COVID-19 diagnostic testing. Test availability continues to be impeded by global supply chain shortages and logistic challenges, which have often caused long turnaround times and delayed results. This problem is partially addressed in the study by Yang and coworkers (1), who aim to predict SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection before COVID-19 reverse transcription–PCR results are available by combining routinely available laboratory results with modern machine learning methods. Applying machine learning to lab results can be useful when the relationship between individual analytes and disease state is complex or unknown, as is the case with COVID-19. The machine learning algorithms proposed by Yang and coworkers could be useful in the absence of definitive test results to help guide patient-management decisions. The study has several notable strengths including the apparent generalizability of the prediction, the ability to improve the algorithm as additional cases are added, and the use of widely available lab tests. In terms of available data, routine complete blood count, coagulation, electrolyte, and kidney and liver function tests are the most commonly ordered laboratory tests, providing a ready source of predictors without the need to order additional tests. Because machine learning algorithms are highly amenable to retraining, continuously adding more classified data (patients with known COVID-19 status) should improve the overall performance. Indeed, the performance of machine learning algorithms generally improves with larger data sets (2). Cross-validation across 2 different hospitals with different instrumentation demonstrates that the algorithm has the potential to be used widely. In a real-world setting, implementing predictive algorithms for COVID-19 presents several challenges including (a) integration into electronic medical records (EMRs), (b) reporting of predictions, and (c) the inherent opacity of machine learning algorithms. Underpinning these challenges are the key questions of what a given prediction indicates and what action a physician can take with an individual patient. EMRs, laboratory information systems, and middleware systems can be programmed to perform a wide array of calculations; however, the use of machine learning methods requires integration between the highly specialized software that generates the predictions and the laboratory information system or EMR. In the current situation, this would entail a continuous exchange of laboratory data and predictions between the programming language Python with the scikit-learn machine learning library and the laboratory information system. Although integrated and embedded machine learning in EMRs is often touted as the next great advance in medicine, it is currently neither common nor trivial to implement—perhaps this is another technology adoption that will be driven faster by COVID-19. Last, related to the use of predictions and EMR integration, is how to report the probabilities generated by the algorithms. For example, should predictions be provided as a probability score, a risk-related keyword (low, medium, high), a binary measure (detected or undetected), or a textual report explaining the results and algorithm? Overall, adoption is likely to depend on how easy it is to convey what the prediction can and cannot provide. Regardless of the challenges, the incredible strain of COVID-19 on healthcare systems necessitates new approaches to diagnostics, patient management, and data use. With that context, the algorithms presented by Yang and coworkers have the potential to augment more conventional methods for rapid assessment of patients with COVID-19. Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.","url":"https://doi.org/10.1093/clinchem/hvaa212","authors":["Christopher R McCudden"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-10-10T04:29:18Z","doi":"10.1093/clinchem/hvaa212","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.archger.2026.106167","name":"From epidemiological models to machine learning in clinical prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.archger.2026.106167","authors":["Liang-Kung Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T16:01:28Z","doi":"10.1016/j.archger.2026.106167","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-3-030-33970-8_37","name":"Ordinal Scaling for Clinical Scores with Inconsistent Intervals (900 Patients)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-33970-8_37","authors":["Ton J. Cleophas","Aeilko H. Zwinderman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-03-03T10:02:54Z","doi":"10.1007/978-3-030-33970-8_37","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1002/bimj.200490090","name":"S18.4: Machine learning and micro array data in clinical research","source":"crossref","abstract":"","url":"https://doi.org/10.1002/bimj.200490090","authors":["Berthold Lausen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-13T12:39:23Z","doi":"10.1002/bimj.200490090","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/saci46893.2019.9111519","name":"Machine Learning for Personalized Medicine: Clinical Outcome Prediction and Diagnosis : Plenary Talk","source":"crossref","abstract":"Machine learning and computational intelligence have been applied to a wide range of medical problems to assist medical professionals in decision-making. Especially artificial neural networks, fuzzy systems and powerful hybrid neuro-fuzzy approaches have already proven their strong potentials in medicine. This is especially important and interesting in emerging field of personalized medicine, which is often described as providing ”the right patient with the right drug at the right dose at the right time” and represents tailoring of medical treatment to the individual patient characteristics, needs and preferences. Machine learning focuses on the development of computer programs that can access data and use it to learn by themselves. Regarding medical applications concerning prediction of patient's clinical outcome, machine learning can be considered as a data-driven analytic approach that specializes in the integration of multiple risk factors into a predictive tool. In this talk hypothesis has been considered that machine learning based models may help to improve prediction of clinical outcome in various medical fields, in comparison to traditional statistical and scoring approaches. This hypothesis has been tested with our own results from several studies. Our experiences have been considered regarding artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance, adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability, machine learning leukemia clinical outcome prediction, neural prediction of mortality in spontaneous intracerebral hemorrhage based on initial clinical parameters and others. Finally, to demonstrated potentials of machine learning in medical diagnosis, our results concerning cervical cancer detection by improving standard screening tests have been considered.","url":"https://doi.org/10.1109/saci46893.2019.9111519","authors":["Zarko Cojbasic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-09T21:46:49Z","doi":"10.1109/saci46893.2019.9111519","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.3410/f.727475490.793587380","name":"Faculty Opinions recommendation of Can machine-learning improve cardiovascular risk prediction using routine clinical data?","source":"crossref","abstract":"","url":"https://doi.org/10.3410/f.727475490.793587380","authors":["Jennifer K Quint"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-11T09:55:11Z","doi":"10.3410/f.727475490.793587380","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1212/wnl.0000000000217766","name":"Machine Learning Prediction of Reversible Versus Progressive Cognitive Impairment Using Clinical Documentation (P10-12.014)","source":"crossref","abstract":"","url":"https://doi.org/10.1212/wnl.0000000000217766","authors":["Peter Pressman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-10T17:56:11Z","doi":"10.1212/wnl.0000000000217766","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1002/9781118559963.ch2","name":"Modern Machine Learning Techniques","source":"crossref","abstract":"This chapter contains sections titled: A Unified Framework for Manifold Learning Spectral Clustering and Graph Cut Ensemble Manifold Learning Multiple Kernel Learning Multiview Subspace Learning Multiview Distance Metric Learning Multi-Task Learning Chapter Summary","url":"https://doi.org/10.1002/9781118559963.ch2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2013-04-02T17:21:44Z","doi":"10.1002/9781118559963.ch2","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1201/9780429292835-6","name":"Writing Machine Learning Code","source":"crossref","abstract":"Programming is, in many ways, a conversation with a computer, but it is also conversation with other developers [105]. As vague as it sounds, we should strive to write code that is simple to read and whose meaning is obvious [252]. Code is read much more often than it is written: most of the cost of a piece of software is in its maintenance, which is typically performed by people other than those who first wrote the code. Achieving clarity involves effort on several fronts. Different trade-offs between clarity, consistency, development speed and the existence of useful libraries may motivate the use of particular programming languages for different modules ( Section 6.1 ). Things should be named appropriately ( Section 6.2 ), code should be formatted and laid out consistently ( Section 6.3 ), functions and modules should be organised tidily in files and directories ( Section 6.4 ). Finally, having multiple people go through the code and review it ( Section 6.6 ) helps in identifying how to improve it. We can then change it gradually by refactoring it ( Section 6.7 ), which is the safest way to make sure we do not introduce any new bugs. Both activities require an efficient use of source version control ( Section 6.5 ), which will also be key for deploying ( Chapter 7 ), documenting ( Chapter 8 ) and testing ( Chapter 9 ) our machine learning pipeline. As an example, we will refactor a sample of code used for teaching in academia ( Section 6.8 ).","url":"https://doi.org/10.1201/9780429292835-6","authors":["Marco Scutari","Mauro Malvestio"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-01T17:03:18Z","doi":"10.1201/9780429292835-6","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-1-4842-3787-8_12","name":"Monetizing Retail Machine Learning","source":"crossref","abstract":"In this chapter, I am going to put forward some innovative ideas that can be monetized using machine learning. I will also show you some examples where a similar approach has been used and has succeeded.","url":"https://doi.org/10.1007/978-1-4842-3787-8_12","authors":["Puneet Mathur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-12-12T14:05:50Z","doi":"10.1007/978-1-4842-3787-8_12","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.34218/ijaiml_04_02_004","name":"MACHINE LEARNING APPLICATIONS IN SOIL CLASSIFICATION AND CHARACTERIZATION","source":"crossref","abstract":"Accurate soil classification and characterization form the foundation of agricultural productivity, environmental assessment, and civil engineering design.Conventional soil analysis relies on laboratory testing and empirical correlations, which are often time-consuming, costly, and location-dependent.In recent years, the integration of machine learning (ML) algorithms has transformed soil science by enabling rapid, data-driven prediction of soil types and properties using diverse datasets such as spectral signatures, physical parameters, and geotechnical indices.This paper explores the application of supervised and unsupervised ML modelsincluding Decision Trees, Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Gradient Boosting methods-for soil classification and property prediction.The study demonstrates how these models enhance prediction accuracy for key parameters such as texture, moisture content, shear strength, and organic matter.Experimental results reveal that ensemble-based and deep learning methods outperform traditional regression techniques, achieving accuracies exceeding 90% for soil texture classification.The paper concludes with insights into challenges, model interpretability, and potential integration with remote sensing and Internet of Things (IoT) frameworks for sustainable soil monitoring.","url":"https://doi.org/10.34218/ijaiml_04_02_004","authors":["Aashi Chandarana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-02T14:46:03Z","doi":"10.34218/ijaiml_04_02_004","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-99-3917-6_7","name":"Support Vector Machine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3917-6_7","authors":["Hang Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-06T00:02:11Z","doi":"10.1007/978-981-99-3917-6_7","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.jclinane.2024.111737","name":"Artificial neural networks and machine learning in anesthesia and perioperative medicine: Reflections on the 2024 Nobel prize in physics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jclinane.2024.111737","authors":["Yahui Xu","Nie Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-26T22:05:17Z","doi":"10.1016/j.jclinane.2024.111737","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.3390/life16040633","name":"Integrated Clinical, Molecular, and Machine Learning Assessment of Familial Hypercholesterolemia","source":"crossref","abstract":"Background: In clinical practice, LDL-dominant familial hypercholesterolemia (FH) may overlap phenotypically with triglyceride-dominant or mixed familial dyslipidemia. Rule-based diagnostic approaches like the Dutch Lipid Clinic Network (DLCN) and Simon Broome (SB) criteria are frequently used in countries with limited genetic testing, but their concordance with molecular confirmation is inconsistent. In a large Turkish tertiary-care cohort, we studied phenotype-related discordance between clinical criteria and molecular data and tested whether machine learning (ML) models could improve the prediction of reportable pathogenic/likely pathogenic variant positivity among patients with a clinical FH phenotype. Methods: Patients referred for suspected familial hyperlipidemia underwent targeted next-generation sequencing with a 9-gene panel. For the ML analysis, we focused on FH cases with a definitive molecular status (pathogenic/likely pathogenic vs. no reportable variant; variants of uncertain significance were excluded) and applied an 80/20 stratified split (n = 200; 82 molecular-positive cases). Elastic-net logistic regression, random forest, and XGBoost models trained on routinely available clinical variables were compared with dichotomized SB and DLCN classifications. Results: SB positivity was significantly more frequent in triglyceride-dominant phenotypes than in FH (68.4% vs. 52.3%, p = 0.041), despite the substantially lower molecular positivity (14.0% vs. 36.9%, p = 0.002), indicating FH-like false-positive clinical classification in mixed dyslipidemia. In the FH test set, the ML models showed higher discrimination for reportable pathogenic/likely pathogenic variant positivity than dichotomized rule-based criteria (AUC: XGBoost 0.808; random forest 0.769; elastic-net 0.747 vs. SB 0.639; and DLCN 0.598). Thirteen novel variants absent from gnomAD were identified, predominantly in LDLR. Conclusions: In this real-world Turkish cohort, within clinically defined FH cases, ML models performed better at predicting LP/P variant positivity than dichotomized DLCN and Simon Broome criteria. ML-based risk stratification may support prioritization for genetic testing; however, external validation is warranted.","url":"https://doi.org/10.3390/life16040633","authors":["Mustafa Tarık Alay","Atakan Deniz","Hanife Saat","Haktan Bağış Erdem"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-09T12:21:32Z","doi":"10.3390/life16040633","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1101/2025.01.24.634132","name":"CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics","source":"crossref","abstract":"SUMMARY Single-cell technologies have enhanced our knowledge of molecular and cellular heterogeneity underlying disease. As the scale of single-cell datasets expands, linking cell-level phenotypic alterations with clinical outcomes becomes increasingly challenging. To address this, we introduce CellPhenoX, an eXplainable machine learning method to identify cell-specific phenotypes that influence clinical outcomes. CellPhenoX integrates classification models, explainable AI techniques, and a statistical framework to generate interpretable, cell-specific scores that uncover cell populations associated with relevant clinical phenotypes and interaction effects. We demonstrated the performance of CellPhenoX across diverse single-cell designs, including simulations, binary disease-control comparisons, and multi-class studies. Notably, CellPhenoX identified an activated monocyte phenotype in COVID-19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovered an inflammation-associated gradient in fibroblasts from ulcerative colitis. We anticipate that CellPhenoX holds the potential to detect clinically relevant phenotypic changes in single-cell data with multiple sources of variation, paving the way for translating single-cell findings into clinical impact.","url":"https://doi.org/10.1101/2025.01.24.634132","authors":["Jade Young","Jun Inamo","Zachary Caterer","Revanth Krishna","Fan Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-27T20:20:20Z","doi":"10.1101/2025.01.24.634132","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.65923/z1cqfz20","name":"Explainable and Interpretable Machine Learning Frameworks for Early Diabetes Risk Prediction and Clinical Decision Support","source":"crossref","abstract":"Early detection of diabetes is essential for preventing severe health complications, improving patient outcomes, and enabling timely medical intervention through personalized healthcare strategies. With the growing availability of healthcare data and advancements in artificial intelligence, interpretable machine learning approaches have emerged as promising tools for supporting accurate and transparent diabetes risk prediction in clinical environments. This study investigates the application of explainable and interpretable machine learning techniques for early diabetes prediction while emphasizing model transparency, clinical reliability, and decision-making trustworthiness. Using the Pima Indians Diabetes Dataset, comprehensive data preprocessing procedures were performed, including missing value handling, feature normalization, outlier management, and selection of clinically relevant attributes such as glucose concentration, body mass index (BMI), insulin levels, age, blood pressure, and skin thickness measurementsVisualization-driven analyses were also employed to enhance the understanding of model behavior, feature importance, and patient-specific prediction explanations.","url":"https://doi.org/10.65923/z1cqfz20","authors":["Arvind Kulkarni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-14T16:23:38Z","doi":"10.65923/z1cqfz20","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.31979/etd.cbtz-nn4v","name":"Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage","source":"crossref","abstract":"Anomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs). Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server's resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server's capacity to give resources to legitimate customers. To recognize attacks and common occurrences, machine learning techniques such as Quadratic Support Vector Machines (QSVM), Random Forest, and neural network models such as MLP and Autoencoders are employed. Various machine learning algorithms are used on the optimised NSL-KDD dataset to provide an efficient and accurate predictor of network intrusions. In this research, we propose a neural network based model and experiment on various central and spiral rearrangements of the features for distinguishing between different types of attacks and support our approach of better preservation of feature structure with image representations. The results are analysed and compared to existing models and prior research. The outcomes of this study have practical implications for improving the security and performance of cloud computing systems, specifically in the area of identifying and mitigating network intrusions.","url":"https://doi.org/10.31979/etd.cbtz-nn4v","authors":["Tarun Mourya Satveli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-15T15:53:00Z","doi":"10.31979/etd.cbtz-nn4v","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.31219/osf.io/xh8uc","name":"Prevent Road Accident using Machine to Machine (M2M) Learning","source":"crossref","abstract":"The number of road casualties is steadily rising, while the age of driverless vehicles on the road is rapidly coming. Machine-to-machine (M2M) communication and the use of Big Data created by M2M communication have enormous promise for improving road safety. A training dataset-less Deep Learning strategy that uses only a safety model and optimizes it sequentially through M2M learning over time can prevent a lack of suitable Knowledge Base while also improving the capacity to handle unpredictable scenarios. The article outlines an M2M learning model based on in-vehicle sensors that can be used to reduce traffic accidents.","url":"https://doi.org/10.31219/osf.io/xh8uc","authors":["Taibu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-02T05:26:04Z","doi":"10.31219/osf.io/xh8uc","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.mlwa.2024.100533","name":"Erratum to “Automated recognition of individual performers from de-identified video sequences” [Machine Learning with Applications 11 (2023) 100450]","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.mlwa.2024.100533","authors":["Zizui Chen","Stephen Czarnuch","Erica Dove","Arlene Astell"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-07T05:03:21Z","doi":"10.1016/j.mlwa.2024.100533","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.3892/br.2025.2070","name":"Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)","source":"crossref","abstract":"With the advancement of precision medicine, machine learning (ML) and deep learning have increasingly become a pivotal tool for driving medical innovation. Precision medicine, grounded in individual variability, aims to deliver personalized treatment interventions, with ML serving as a critical enabler for achieving this goal. Recent ML-driven progress in genomic analysis, personalized treatment optimization and disease diagnostics have significantly elevated the accuracy and efficacy of medical decision-making processes. However, the widespread adoption of artificial intelligence also faces multifaceted challenges, including data privacy frameworks, cybersecurity risks, ethical considerations and the integration of technology with clinical workflows. The present review seeks to analyze cutting-edge applications of ML within precision medicine domains, examine its challenges, and project future evolutionary pathways, emphasizing the critical need for proactive attention to these issues to ensure tangible benefits for patients and healthcare systems.","url":"https://doi.org/10.3892/br.2025.2070","authors":["Qiang Zhao","Guangxin Li","Kunpeng Du"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-15T12:10:54Z","doi":"10.3892/br.2025.2070","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.clnesp.2024.07.610","name":"Machine learning metabolomics profiling of dietary interventions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clnesp.2024.07.610","authors":["A. Kouraki","A. Nogal","W. Nocun","P. Louca","A. Vijay","K. Wong","G.A. Michelotti","C. Menni","A.M. Valdes"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T07:48:17Z","doi":"10.1016/j.clnesp.2024.07.610","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.2196/71949","name":"Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study","source":"crossref","abstract":"Abstract Background Medications are essential for health care but can cause adverse drug events (ADEs), which are harmful and sometimes fatal. Detecting ADEs is a challenging task because they are often not documented in the structured data of electronic health records (EHRs). There is a need for automatically extracting ADE-related information from clinical notes, as manual review is labor-intensive and time-consuming. Objective This study aims to fine-tune the pretrained clinical language model, Swedish Deidentified Clinical Bidirectional Encoder Representations from Transformers (SweDeClin-BERT), for medical named entity recognition (NER) and relation extraction (RE) tasks, and to implement an integrated NER-RE approach to more effectively identify ADEs in clinical notes from clinical units in Sweden. The performance of this approach is compared with our previous machine learning method, which used conditional random fields (CRFs) and random forest (RF). Methods A subset of clinical notes from the Stockholm EPR (Electronic Patient Record) Corpus, dated 2009‐2010, containing suspected ADEs based on International Classification of Diseases, 10th Revision ( ICD-10 ) codes in the A.1 and A.2 categories was randomly sampled. These notes were annotated by a physician with ADE-related entities and relations following the ADE annotation guidelines. We fine-tuned the SweDeClin-BERT model for the NER and RE tasks and implemented an integrated NER-RE pipeline to extract entities and relationships from clinical notes. The models were evaluated using 395 clinical notes from clinical units in Sweden. The NER-RE pipeline was then applied to classify the clinical notes as containing or not containing ADEs. In addition, we conducted an error analysis to better understand the model’s behavior and to identify potential areas for improvement. Results In total, 62% of notes contained an explicit description of an ADE, indicating that an ADE-related ICD-10 code alone does not ensure detailed event documentation. The fine-tuned SweDeClin-BERT model achieved an F 1 -score of 0.845 for NER and 0.81 for RE task, outperforming the baseline models (CRFs for NER and random forests for RE). In particular, the RE task showed a 53% improvement in macro-average F 1 -score compared to the baseline. The integrated NER-RE pipeline achieved an overall F 1 -score of 0.81. Conclusions Using a domain-specific language model like SweDeClin-BERT for detecting ADEs in clinical notes demonstrates improved classification performance (0.77 in strict and 0.81 in relaxed mode) compared to conventional machine learning models like CRFs and RF. The proposed fine-tuned ADE model requires further refinement and evaluation on annotated clinical notes from another hospital to evaluate the model’s generalizability. In addition, the annotation guidelines should be revised, as there is an overlap of words between the Finding and Disorder entity categories, which were not consistently distinguished by the annotators. Furthermore, future work should address the handling of compound words and split entities to better capture context in the Swedish language.","url":"https://doi.org/10.2196/71949","authors":["Elizaveta Kopacheva","Aron Henriksson","Hercules Dalianis","Tora Hammar","Alisa Lincke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-23T04:25:06Z","doi":"10.2196/71949","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1093/clinchem/hvac202","name":"Do You See What I See? Automated IFE Interpretation Using Machine Learning","source":"crossref","abstract":"Recent advances in machine learning (ML) have opened the possibility of applying this powerful toolset more broadly to address diagnostic problems in laboratory medicine. The clinical laboratory is a natural area in which to utilize ML, as laboratories routinely generate large amounts of quantitative data from individual patients. Properly applied, ML can be highly effective in detecting patterns in such high-dimensional data, whether those data represent the aggregated results of a several individual tests or a single, complex measurement such as a digital image. For diagnostic purposes, the goal of ML is predicting a specific target output from the complex input data, whether that output represents a diagnosis derived by other means or simply the diagnosis that would be given by a human using the same data. In this issue, Hu and colleagues describe an application of ML to what has traditionally been a human task of visual interpretation: the identification of monoclonal bands (often paraproteins) from serum immunofixation gels (1). Serum protein electrophoresis (SPE), can identify the presence of a monoclonal protein, andin conjunction with immunofixation electrophoresis (IFE)provide information on the specific immunoglobulin subtype. Because the presence of a monoclonal immunoglobulin suggests the corresponding expansion of a clonal population of plasma cells, SPE and IFE are an important part of the diagnostic workup of any putative plasma cell dyscrasia (2). Further, the subtype information provided by IFE can suggest clinically relevant differences in the expected manifestation of the disease and may provide additional information in the setting of disease recurrence. However, IFE gels currently require human visual interpretation to detect and classify immunoglobulin bands. This can present a significant workload challenge when supporting a busy oncology service, and it also raises concerns about consistency in the interpretation of borderline cases.","url":"https://doi.org/10.1093/clinchem/hvac202","authors":["Stephen R Master","Shannon Haymond"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-28T14:02:29Z","doi":"10.1093/clinchem/hvac202","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-3-030-47994-7_12","name":"Machine Learning for Clinical Predictive Analytics","source":"crossref","abstract":"In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning, and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning experiments for clinical prediction tasks. This chapter is composed of five sections. First, we will explain why machine learning techniques are helpful for researchers in solving clinical prediction problems (Sect. 12.1 ). Understanding the motivations behind machine learning approaches in healthcare are essential, since precision and accuracy are often critical in healthcare problems, and everything from diagnostic decisions to predictive clinical analytics could dramatically benefit from data-based processes with improved efficiency and reliability. In the second section, we will introduce several important concepts in machine learning in a colloquial manner, such as learning scenarios, objective/target function, error and loss function and metrics, optimization and model validation, and finally a summary of model selection methods (Sect. 12.2 ). These topics will help us utilize machine learning algorithms in an appropriate way. Following that, we will introduce some popular machine learning algorithms for prediction problems (Sect. 12.3 ), for example, logistic regression, decision tree and support vector machine. Then, we will discuss some limitations and pitfalls of using the machine learning approach (Sect. 12.4 ). Lastly, we will provide case studies using real intensive care unit (ICU) data from a publicly available dataset, PhysioNet Challenge 2012, as well as the breast tumor data from Breast Cancer Wisconsin (Diagnostic) Database, and summarize what we have presented in this chapter (Sect. 12.5 ).","url":"https://doi.org/10.1007/978-3-030-47994-7_12","authors":["Wei-Hung Weng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-31T14:03:07Z","doi":"10.1007/978-3-030-47994-7_12","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/j.clinph.2019.04.337","name":"O-21 Separation of major depression patients from healthy controls using machine learning approach to resting-state EEG","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clinph.2019.04.337","authors":["Alexander Ledovsky","Elena Mnatsakanian","Maxim Sharaev","Evgeny Burnaev"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-05-29T21:09:00Z","doi":"10.1016/j.clinph.2019.04.337","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2010.134","name":"The Influence Machine: Nonnegative Instance-Space Learning with Differentiated Regularization","source":"crossref","abstract":"We introduce a new method for classification called the influence machine. The influence machine assigns influence powers to the instances in the training sample so that they can apply their influence to other instances through the connections between the instances specified by a connection matrix. A new instance is classified to be positive if the overall influence it receives is positive and vice versa. Similar to support vector machine (SVM), the influence machine selects a small subset of the training instances to give influence power. However, this selection is very different from how the support vectors are selected by SVM. Experiment results show that the classification performance of the influence machine is comparable to that of the SVM. In a few cases, the influence machine shows much better classification accuracy. The influence machine has other advantages: any similarity matrix can be applied with the influence machine, not like SVM which requires that the kernel be positive definite. Furthermore, the influence machine uses linear optimization, instead of the quadratic optimization used by SVM. It may be more suitable for large scale learning problems.","url":"https://doi.org/10.1109/icmla.2010.134","authors":["Jian Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-02-03T16:55:42Z","doi":"10.1109/icmla.2010.134","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1109/mlcss57186.2022.00009","name":"Stock Price Prediction using Machine Learning","source":"crossref","abstract":"The stock market has long attracted the attention of both researchers and ordinary people. Essentially, a typical person believes that investing in the stock market is akin to catching a falling knife because they believe that there is no way to foresee the price of any company over a certain length of time. So, it's all down to chance when it comes to making money in the stock market. However, according to academics, the stock market is based on profit, loss, opening price, closing price, neighboring closing price, and number of shares traded at the moment. The stock market always operates in cycles, and if someone can anticipate when the bull and bear runs will begin and end utilizing today's technologies, the stock market may be incredibly profitable, or it can be the polar opposite. That is why we want to apply machine learning to anticipate the future price of the stock so that we may make more money in a shorter period of time while minimizing risk.","url":"https://doi.org/10.1109/mlcss57186.2022.00009","authors":["Anshuman Behera","Ayes Chinmay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-28T18:02:17Z","doi":"10.1109/mlcss57186.2022.00009","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.32614/cran.package.iforecast","name":"iForecast: Machine Learning Time Series Forecasting","source":"crossref","abstract":"Compute onestep and multistep time series forecasts for machine learning models.","url":"https://doi.org/10.32614/cran.package.iforecast","authors":["Ho Tsung-wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-12T12:28:15Z","doi":"10.32614/cran.package.iforecast","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.4018/978-1-7998-9220-5.ch056","name":"Machine Learning Enables Decision-Making Processes for an Enterprise","source":"crossref","abstract":"Business decisions need to be made based on constantly changing data from a variety of sources. The data for business enterprises consists of both internal and external sources. Some managerial decisions are qualitative. Thus, it is necessary to incorporate this knowledge in developing decision support systems. Advancements in the information and communication technology discipline provide the various concepts for designing and developing business models. The concept of machine learning is one of the important concepts provided by the above discipline. Machine learning has become one of the most important elements for adopting innovative ways in understanding the thought process of humans and duplicating this process through computing systems. Machine learning is a form of artificial intelligence that enables a system to learn from data rather than explicit programming. This article gives an overview of the machine learning process and collaborative concepts. A case illustration related to a textile mill in India is discussed in the context of machine learning.","url":"https://doi.org/10.4018/978-1-7998-9220-5.ch056","authors":["N. Raghavendra Rao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T15:44:01Z","doi":"10.4018/978-1-7998-9220-5.ch056","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-3-031-39477-5_3","name":"The (Black Box) Machine Learning Process","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-39477-5_3","authors":["Gerald Friedland"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-01T06:02:58Z","doi":"10.1007/978-3-031-39477-5_3","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.5121/csit.2021.111401","name":"Introducing the Viewpoint in the Resource Description using Machine Learning","source":"crossref","abstract":"Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.","url":"https://doi.org/10.5121/csit.2021.111401","authors":["Ouahiba Djama"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-28T11:38:29Z","doi":"10.5121/csit.2021.111401","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-981-15-1706-8_2","name":"Introduction to Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-1706-8_2","authors":["Tony Thomas","Athira P. Vijayaraghavan","Sabu Emmanuel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-16T17:02:52Z","doi":"10.1007/978-981-15-1706-8_2","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-1-4842-7098-1","name":"Quantum Machine Learning: An Applied Approach","source":"crossref","abstract":"This book on adapting quantum computing and machine learning algorithms takes a hands-on approach using updated libraries.","url":"https://doi.org/10.1007/978-1-4842-7098-1","authors":["Santanu Ganguly"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-07-29T20:04:30Z","doi":"10.1007/978-1-4842-7098-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/979-8-8688-1076-3","name":"The Definitive Guide to Machine Learning Operations in AWS","source":"crossref","abstract":"This book covers AWS MLOps tools such as Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS.","url":"https://doi.org/10.1007/979-8-8688-1076-3","authors":["Neel Sendas","Deepali Rajale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-03T18:04:13Z","doi":"10.1007/979-8-8688-1076-3","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.53347/rid-56116","name":"Reinforcement learning (machine learning)","source":"crossref","abstract":"","url":"https://doi.org/10.53347/rid-56116","authors":["Frank Gaillard","Andrew Murphy","Jarrel Seah"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-25T01:54:02Z","doi":"10.53347/rid-56116","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-0-387-30164-8_614","name":"Ontology Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_614","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:26:38Z","doi":"10.1007/978-0-387-30164-8_614","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1007/978-0-387-30164-8_782","name":"Statistical Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_782","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:22:42Z","doi":"10.1007/978-0-387-30164-8_782","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.7717/peerj-cs.1942/fig-1","name":"Figure 1: The machine learning pipeline.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1942/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-19T03:45:50Z","doi":"10.7717/peerj-cs.1942/fig-1","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"doi:10.1016/c2013-0-19170-2","name":"Quantum Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2013-0-19170-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2016-08-04T05:30:35Z","doi":"10.1016/c2013-0-19170-2","addedAt":"2026-09-01T01:48:00.557Z","updatedAt":"2026-09-01T01:48:00.557Z"},{"id":"pmid:42497260","name":"Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction.","source":"pubmed","abstract":"Anti-angiogenic therapy benefits vary, with response rates of 40 to 70%, highlighting the need for early biomarkers to identify responders. We developed an automated machine learning framework that uses delta quantitative vascular morphometry features from standard contrast-enhanced CT to evaluate treatment response. This workflow combines automated tumor and vessel segmentation with feature extraction from routine scans for clinical use. Shapley additive explanations (SHAP)-based attributions identify key vascular and clinical features, providing meaningful, imaging-visible evidence aligned with therapy targets beyond traditional radiomics. Using baseline and follow-up CTs from 163 patients with lung cancer, we built three models using fivefold cross-validation, with the delta-merge model achieving high accuracy (area under the receiver operating characteristic curve&#xa0;=&#xa0;0.842 internally, 0.806 externally). SHAP analysis uncovered an \"arterial-dominant, venous-adaptive\" pattern, where arterial involvement and venous recovery distinguish responders. This automated workflow and visualization support early, imaging-based response assessment and personalized treatment.","url":"https://pubmed.ncbi.nlm.nih.gov/42497260/","authors":["Hu K","Cai Q","Xu J","Ai S","Ou W","Liu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1126/sciadv.aeb3543","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42497190","name":"Fundus image analysis of retinitis pigmentosa using artificial intelligence.","source":"pubmed","abstract":"Retinitis pigmentosa (RP) is a group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis would be helpful for effective management of the disease; however, many patients stay unaware of early symptoms. Meanwhile, fundus images are widely obtained during routine medical checkups but are underused for detecting RP. This study explores the effectiveness of finetuning deep learning models, pre-trained for general visual tasks, to identify RP from color fundus images. The dataset comprised 321 color fundus images from 201 Japanese subjects at Keio University Hospital, including 200 images from 107 patients with retinitis pigmentosa and 121 images from 94 non-retinitis pigmentosa subjects. Multiple images were available for some subjects. Using transfer learning, pretrained convolutional neural network models -VGG16, Resnet50, and InceptionV3- were finetuned to detect RP. As a result, Inception V3 achieved the best accuracy of 96.97%, which matches the average diagnostic accuracy of ophthalmologists. Gradient-weighted Class Activation Mapping (Grad-CAM) suggested that the model attended to clinically relevant fundus regions, including the peripheral retina and posterior pole, which may reflect features such as peripheral degenerative changes and retinal vascular attenuation. These findings support the potential interpretability of the finetuned model and suggest that deep learning may assist ophthalmologists in RP screening as a supportive tool.","url":"https://pubmed.ncbi.nlm.nih.gov/42497190/","authors":["Ubukata S","Masayoshi K","Katada Y","Yang L","Ozawa N","Ibuki M","Negishi K","Kurihara T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354452","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42497140","name":"A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography.","source":"pubmed","abstract":"Ninety percent of drugs fail during clinical trials, mainly due to lack of clinical efficacy. Recent developments in in vitro models such as 3D tumor heterospheroids have led to improvements in failure rates, but the relative lack of standardized evaluation methods for 3D cultures limits their utility in high-throughput screening. Optical coherence tomography (OCT) shows significant promise for high-throughput screening of 3D models; however, the optimal classification model and key image features for assessing drug efficacy in OCT images of spheroids has yet to be explored in detail. In this study, we investigate whether OCT combined with machine learning methods can be used to identify biomarkers of drug efficacy in 3D tumor spheroid models. We further compare the performance of two different models to determine the optimal configuration for accurate classification. Volumetric OCT images were acquired of co-cultured HT29 spheroids treated with 3 different concentrations of cisplatin. A two-dimensional multi-view ResNet model and a three-dimensional ResNet model were used to classify the images and to identify key image features associated with each group. Differences between spheroids treated with different concentrations of cisplatin are clearly visible in the OCT images. Our model was able to classify the images based on cisplatin concentration with 71.9% accuracy using the 2D multi-view model and 91.2% accuracy using the 3D model. Key features in the 3D model significantly improved the model accuracy. These results underscore the possibility that OCT could be used for high-throughput screening of drugs using 3D in vitro models and highlight key identifying features for further investigation.","url":"https://pubmed.ncbi.nlm.nih.gov/42497140/","authors":["Untracht GR","Kaminski J","Guldenring E","Nielsen BS","Holmstrøm K","Jensen KH","Andersen PE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353170","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42497050","name":"An Interpretable Machine Learning Model for Continuous Prediction of Acute Kidney Injury in Critically Ill Patients With Atrial Fibrillation.","source":"pubmed","abstract":"The occurrence of acute kidney injury (AKI) in hospitalized patients with atrial fibrillation (AF) significantly increases the mortality risk. Currently, effective tools for early identification and dynamic predictive models remain lacking for this high-risk population, and the roles and underlying mechanisms of cardiorenal crosstalk remain underexplored. This study aims to develop a clinically interpretable model that continuously predicts the risk of AKI in patients with AF following admission to the intensive care unit (ICU). By retrospectively analyzing 19,347 critically ill patients with AF from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, we developed five machine learning models and two deep learning models. Model performance was assessed using Decision Curve Analysis (DCA) and calibration curves. Simultaneously, the SHapley Additive exPlanations (SHAP) framework was utilized to quantify feature contributions and generate clinically interpretable visualizations. The results demonstrated that the eXtreme Gradient Boosting (XGBoost) model achieved the highest predictive performance, with an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI: 0.864-0.868) and an area under the precision-recall curve (AUPRC) of 0.723 (95% CI: 0.718-0.729). Combined with real-time electronic health record (EHR) data, this proposed model can provide reliable reference evidence for clinicians to formulate reasonable clinical decisions and facilitate rational patient management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42497050/","authors":["Liu W","Zhao F","Qin H","He J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1109/TBME.2026.3716727","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496900","name":"Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.","source":"pubmed","abstract":"Accurate prognostication after aneurysmal subarachnoid hemorrhage (aSAH) remains challenging. Conventional clinical and radiological grading systems, including the World Federation of Neurosurgical Societies (WFNS), Hunt-Hess, and Fisher scales, are widely used but have limited discriminative capacity. This study aimed to systematically compare machine learning (ML)-based prognostic models with conventional grading systems for predicting functional outcomes and mortality after aSAH, and to evaluate factors influencing ML performance.","url":"https://pubmed.ncbi.nlm.nih.gov/42496900/","authors":["Ogolo D","Akwada O","Ajare E","Opara B","Campbell F","Okwuoma O","Mezue W","Ohaegbulam S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1007/s00234-026-04115-4","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496848","name":"Development and validation of a machine learning-based hierarchical classification model for predicting the early therapeutic response to initial (131)I therapy in papillary thyroid carcinoma.","source":"pubmed","abstract":"Personalized tools for accurately predicting papillary thyroid carcinoma (PTC) patients' response to initial 131 I therapy are lacking. This study aimed to develop a machine learning (ML) model for prediction of 6-12&#xa0;month therapeutic response after therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42496848/","authors":["Tang Z","Wang Z","Pang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1007/s12149-026-02258-1","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496846","name":"A Framework for Predicting Neurofeedback Treatment Response in ADHD Using EEG Functional Connectivity and Genetic Algorithm-Driven Channel Selection.","source":"pubmed","abstract":"In this article, we present a computational framework for predicting treatment response to neurofeedback (NF) among patients with Attention-Deficit/Hyperactivity Disorder (ADHD). The proposed framework uses functional brain connectivity analysis of electroencephalogram (EEG) signals acquired during an early-to-mid NF treatment window to classify participants as eventual responders or non-responders. The six-stage algorithm was evaluated using an open-access EEG dataset from the Mendeley Data repository comprising 60 children with ADHD aged 6-12 years. The framework includes a preprocessing pipeline designed to reduce EEG artifacts and noise. Next, spectral features, specifically of the alpha and beta frequency bands, were extracted from the noise-reduced signals. In the fourth stage, functional connectivity was estimated by calculating Phase Locking Value (PLV) between all electrode pairs, thereby quantifying inter-channel phase synchronization. The fifth stage, which is an essential stage, was dimensionality reduction to find the most discriminative features. Dimensionality reduction was achieved in a two-process manner; for the first process, statistical screening was performed using Welch's t-test with FDR correction, followed by GA-based channel selection to identify the most discriminative electrode subset. The GA analysis identified a compact six-channel subset consisting of C3, C4, Cz, Fz, Fp1, and T6 from the original 32-channel montage. In the last classification stage, the reduced feature vectors were input as part of an ensemble of machine learning classifiers to achieve classification. Model performance was evaluated using subject-wise grouped cross-validation, with the best configuration achieving an accuracy of 84.72%. These results suggest that the proposed data-driven framework may support future research on individualized NF response prediction, pending validation on independent clinical datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/42496846/","authors":["Li Z","Zhu Z","Li D","Huang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1007/s10578-026-02067-7","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496805","name":"Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.","source":"pubmed","abstract":"Artificial intelligence (AI) demonstrates potential throughout the cancer care continuum, with evidence supporting its application in medical imaging for detection, staging, treatment planning, and prognostic evaluation. However, clinical translation is hindered by challenges, data curation and annotation, model interpretability, generalizability, and integration into workflows. To address these barriers and provide guidance, a national multidisciplinary expert panel in China developed this consensus. A modified Delphi approach was employed to achieve expert consensus, involving 81 specialists in radiology, nuclear medicine, oncology, and imaging AI from university hospitals across China. These experts completed a survey containing 30 core statements addressing AI applications in clinical cancer imaging, spanning cancer screening, diagnosis, staging, treatment planning, response assessment, prognostic prediction, data governance, and implementation. Consensus was defined as a mean score &#x2265; 7 on a 9-point Likert scale, with &#x2265; 80% of experts scoring &#x2265; 7. All 30 statements fulfilled these thresholds, with mean scores ranging from 8.06 to 8.58 and the proportion of experts scoring &#x2265; 7 ranging from 86% to 98%. This expert consensus summarizes key AI application scenarios in cancer imaging and delivers recommendations on data acquisition and annotation, model development and validation, interpretability, multicenter generalizability, privacy-preserving collaboration, clinical workflow integration, and post-deployment monitoring, while contextualizing these statements across major clinical application domains and key implementation challenges in practice. It further identifies priority research directions, including the integration of multimodal and multi-omics data, longitudinal modeling of treatment response, and prospective validation in clinical settings, to support the safe, effective implementation of AI technologies in cancer imaging. KEY POINTS: Question AI translation in oncologic imaging remains constrained by limitations in rigorous validation, actionable interpretability, standardization, governance, and workflow integration. Findings Eighty-one Chinese experts reached consensus on 30 clinically practical statements covering AI applications from early detection to deployment. Critical relevance statement Recommendations highlight expert-supervised labeling, multicenter validation, subgroup evaluation, interpretable outputs, privacy-secured collaboration, integrated workflows, and post-implementation surveillance.","url":"https://pubmed.ncbi.nlm.nih.gov/42496805/","authors":["Wu H","Yin X","Cheng J","Zhang J","Zheng L","Zhang L","Wu F","Zhao Q","Yang J","Wang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1186/s13244-026-02359-5","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496798","name":"Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation.","source":"pubmed","abstract":"Graph-based white matter tractometry represents bundles as networks, preserving spatial topology that traditional along-tract profiles collapse. This enables the detection of distributed pathology patterns organized across connected regions. The field is at a critical juncture: methods are proliferating rapidly, but validation infrastructure lags. We systematically assess maturity across the analytical pipeline, from diffusion measurements through graph construction, detection models, clinical applications, and validation resources. Diffusion metrics measure water behavior rather than directly measuring tissue microstructure. Their biological interpretation relies on model assumptions that have been validated only in restricted contexts. Tractography provides spatial scaffolding with known limitations, and graph-construction choices encode implicit hypotheses about the organization of pathology but are often underspecified. Clinical studies demonstrate consistent group differences across disorders. Where controlled comparisons exist, graph methods show modest improvements over traditional approaches. However, comprehensive benchmarking against TBSS and AFQ is absent. Dedicated validation platforms for tractometry detection models do not yet exist. We document concrete barriers to systematic validation and assess emerging infrastructure addressing these gaps. Graph-based tractometry shows promise as a research tool. Realizing its clinical potential requires validation matching the maturity of traditional approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42496798/","authors":["Li J","Zhang C","Wan Z","Xie Y","Zhang H","Liu X","Wu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1007/s12021-026-09804-2","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496710","name":"Improving clinical reliability of LLM reasoning for depression assessment via structured generation and GRPO.","source":"pubmed","abstract":"ObjectiveDigital mental health screening is increasingly explored through the use of AI systems. Yet, most models provide limited insight into how predictions are derived, restricting clinical trust and patient-centered adoption. We investigate whether Large Language Models (LLMs) can generate structured DSM-5-aligned rationales for depression assessment when trained with Group Relative Policy Optimization (GRPO), a reinforcement learning method that encourages outputs aligned with DSM-5 diagnostic criteria.MethodsWe fine-tuned LLMs (1B-27B parameters) on the ReDSM5 dataset, covering 1,484 Reddit posts annotated by a licensed psychologist for DSM-5 depressive symptoms and accompanied by expert rationales. We compared standard Supervised Fine-Tuning (SFT) with GRPO-based optimization using a composite reward integrating symptom classification accuracy and the quality of generated reasoning judged against clinical rationales.ResultsGRPO consistently improved symptom detection over SFT, with relative gains exceeding 10% for mid-sized models and weighted F1 scores above 0.60. Models trained to generate structured DSM-5-aligned rationales exhibited additional performance boosts (0.09-0.39 F1), particularly for complex symptoms requiring complex contextual interpretation. Qualitative analysis shows that GRPO encourages models to reference symptom-relevant evidence rather than relying on superficial cues.ConclusionsGRPO enables LLMs to produce clinically grounded explanations while improving classification accuracy, representing a promising direction for interpretable AI in social media-based mental health screening, with potential to support patient-centered applications pending clinical validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42496710/","authors":["Bao E","Perez A","Parapar J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Sep","doi":"10.1177/00368504261467483","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496664","name":"The next century of Academic Medicine: balancing high tech and high touch in the age of artificial intelligence.","source":"pubmed","abstract":"As Academic Medicine marks its centennial, academic medical centers (AMCs) face a defining inflection point driven by the convergence of generative artificial intelligence (AI), machine learning, computational biology, digital health platforms, autonomous systems, and increasingly continuous streams of clinical and behavioral data. Together, these technologies are transforming how health professionals are educated, how biomedical knowledge is generated, how care is delivered, and how health outcomes are measured. In this Commentary, the authors argue that these changes require more than technological adoption; they demand a redefinition of the mission and responsibilities of academic medicine. AMCs must move beyond their traditional roles in education, research, and clinical care to become leaders in responsible innovation, ethical governance, workforce transformation, public trust, community engagement, health equity, and global stewardship. The authors examine implications for individualized education, AI-enabled clinical care, accelerated scientific discovery, and the democratization of health knowledge across diverse settings. The authors contend that AMCs must deliberately redesign curricula, research ecosystems, care models, and institutional incentives to ensure that technological advances strengthen rather than diminish human judgment, compassion, and equity. The next century of academic medicine will be defined not by whether AMCs adopt emerging technologies, but by whether they shape their development and use in ways that preserve the human relationships and societal responsibilities at the heart of medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42496664/","authors":["Prober CG","Shah NH","Arvin AM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1093/acamed/wvag214","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496643","name":"Characterization and Validation of EHR Computable Phenotypes for Long COVID Using Patient-Reported Symptoms: Insights from the Nationwide RECOVER Program.","source":"pubmed","abstract":"Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC.","url":"https://pubmed.ncbi.nlm.nih.gov/42496643/","authors":["Castro VM","Gainer V","Wattanasin N","Cagan A","Holzbach A","Chan J","Horwitz L","Kenney R","Diaz I","Mandel H","Wuller S","Hornig M","O'Brien L","Wylam A","Doster J","Moffitt RA","Pfaff E","Weiner MG","Abedian S","Koropsak M","Parthasarathy S","Razzaghi H","Manjourides J","Karlson EW","Murphy SN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1093/jamia/ocag115","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496598","name":"PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.","source":"pubmed","abstract":"Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/42496598/","authors":["Martorell-Marugán J","Ellson I","López-Domínguez R","Jurado-Bascón PP","Villatoro-García JA","Wang C","Baribaud F","Toro-Domínguez D","Carmona-Sáez P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 3","doi":"10.1093/bioinformatics/btag519","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496586","name":"Multimodal deep learning fusion for automatic pain detection in cancer patients.","source":"pubmed","abstract":"Since pain is a multidimensional and subjective experience, pain assessment remains challenging. With advances in artificial intelligence (AI), automatic pain assessment (APA) systems offer a valuable opportunity for objective pain evaluation. However, most approaches focus on a single modality. In this proof-of-concept study, exploring multimodal fusion strategies in a controlled experimental setting, we present a deep learning framework for multimodal fusion that combines facial, acoustic, and textual information to improve APA in cancer patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42496586/","authors":["Cascella M","Mariani F","Barberio D","Crispo A","Ottaiano A","Bimonte S","Franci G","Sabbatino F","Piazza O","Cutugno F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan 1","doi":"10.1515/sjpain-2026-0015","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496469","name":"Toward Child-Centred Artificial Intelligence in Pediatric Emergency Medicine: A Perspective on Clinical Decision Support, Stakeholder Engagement and Education.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing evidence supporting its applicability across time-critical clinical environments. This perspective aims to evaluate the integration of AI and machine learning (ML) into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education. Within clinical decision support, ML architectures have demonstrated high predictive performance across several high-acuity clinical scenarios, including triage stratification, pediatric traumatic brain injury risk classification, early sepsis detection and clinical deterioration prediction, and dermatological assessment. Model interpretability and real-world implementability remain critical prerequisites for clinical adoption, with explainability methods representing fundamental instruments to enhance transparency and stakeholder trust. Regarding stakeholder engagement, the triadic dynamic among clinicians, caregivers, and patients defines a unique communication challenge in PEDs, with large language models (LLMs) showing preliminary utility; however, stakeholder-inclusive model validation and robust data privacy protections for minors remain key challenges, particularly regarding legal ambiguities of LLM deployment in clinical pipelines. In medical education, AI-driven simulation platforms and LLM-generated adaptive curricula represent promising tools for competency-based training across pediatric emergency scenarios. Future directions emphasize the imperative of prospective multicenter validation in pediatric-specific cohorts, rigorous data quality standards addressing conformance, completeness, and plausibility, and the development of pediatric-tailored governance frameworks. Real-world implementation will require the systematic involvement of all stakeholders-including children, caregivers, clinicians, developers, and institutions-as co-designers of equitable, transparent, and safe AI systems for this uniquely vulnerable population.","url":"https://pubmed.ncbi.nlm.nih.gov/42496469/","authors":["Gasparini L","Gobbi N","Zama D","Lanari M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/pediatric18040091","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496466","name":"Utilizing Machine Learning for Diagnostic Assistance of Pediatric Sepsis and Septic Shock in Resource-Limited Settings.","source":"pubmed","abstract":"Sepsis is a leading cause of pediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries (LMICs). However, timely recognition of potential sepsis and access to healthcare resources needed to diagnose pediatric sepsis according to international guidelines are challenging in LMICs. This exploratory study aimed to develop machine learning (ML) models to detect pediatric sepsis and septic shock using a simplified set of clinical data contextualized for practical use in resource-limited settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42496466/","authors":["Bunch K","Shaima SN","Mamun GMS","Jarabana SG","Gainey M","Rahman ASMMH","Genisca A","Jindal A","Kadakia N","Sarmin M","Afroze F","Levine AC","Chisti MJ","Garbern SC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.3390/pediatric18040088","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496364","name":"Accelerating Pediatric Oncology Drug Development: Advances in Clinical Pharmacology, Trial Design, Non-Clinical Evidence, and Regulatory Science.","source":"pubmed","abstract":"Pediatric oncology drug development remains uniquely challenging due to the rarity and biological heterogeneity of childhood cancers, ethical considerations in trial conduct, and the limited feasibility of large, randomized studies. Despite these barriers, recent years have seen a notable acceleration in the approval of oncology therapies for pediatric populations, driven by advances in molecularly targeted treatments, evolving regulatory requirements, and innovation in trial design. Reducing nonclinical data requirements and increasing the adaptation of model&#x2011;informed drug development approaches are also contributing to advances in pediatric oncology treatment by supporting dose selection, optimizing study design, and reducing unnecessary patient burden. In this review, recent regulatory requirements from the United States, the EU, and other key regions on pediatric oncology drug development are discussed. Thirty-three drugs with oncology indications in pediatric populations approved by the US FDA between 2018 and 2025 are reviewed. These approvals provide examples of how nonclinical and clinical data were generated with a focus on strategies for dose-finding and justification. Common challenges and considerations related to clinical operations and formulation development in pediatric populations and the emerging use of real-world data, external controls, and artificial intelligence/machine learning are also discussed.","url":"https://pubmed.ncbi.nlm.nih.gov/42496364/","authors":["Wen YF","Wang X","Boyanapalli S","Anderson L","Dang T","Xing G","Calvet M","Hart T","Kasichayanula S","Zhao X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/jcph.70242","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42496302","name":"Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.","source":"pubmed","abstract":"Sub-Saharan Africa suffers the greatest impact of malaria, with the 2024 Health Organization (WHO )report stating that the region represents 94% of global cases and 95% of deaths. Challenges in malaria elimination stem from weak health systems and limitations of traditional diagnostic methods like microscopy and malaria Rapid Diagnostic Tests (mRDTs), which result in missed diagnoses, delays in treatment, and preventable fatalities in resource-limited settings. This paper addresses these diagnostic limitations by developing and systematically evaluating a machine learning (ML) framework for malaria diagnosis that leverages routine clinical symptoms and demographic information tailored for these environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42496302/","authors":["Nyengera P","Takawira HT","Mlambo FF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/idr18040072","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42496141","name":"Introduction to the special issue on ecological perspectives on hearing.","source":"pubmed","abstract":"This special issue explores an ecological perspective to hearing in human and non-human animals. The goal is to understand the relationship between the auditory system and the natural sounds of real-world environments, including animal vocalizations such as birdsongs or insect stridulations and geophysical sounds such as stream, wind, or rainfall sounds. Furthering this understanding requires drawing insights from evolution, behavior, acoustics, modeling, and more. The articles in this issue span diverse disciplines such as evolutionary biology, soundscape ecology and ecoacoustics, neuro-ethology and bioacoustics, computational acoustics, psychoacoustics, machine learning, clinical audiology, and physiology. Collectively, the articles in this joint issue with JASA and JASA Express Letters demonstrate how this interdisciplinary approach to hearing can advance our understanding of auditory monitoring and comprehension of natural sounds and scenes.","url":"https://pubmed.ncbi.nlm.nih.gov/42496141/","authors":["Heller LM","Desjonqu��res C","Lorenzi C","Theunissen F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1121/10.0044474","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495989","name":"Artificial intelligence for the assessment of diastolic function.","source":"pubmed","abstract":"Assessment of left ventricular diastolic function remains one of the most challenging aspects of echocardiography. Artificial intelligence (AI) has emerged as a transformative tool capable of automating data acquisition, analysis, and interpretation. This review summarizes recent advances in the use of AI to facilitate diastolic function assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42495989/","authors":["Tsang TSM","Yeung DF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 1","doi":"10.1097/HCO.0000000000001324","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495921","name":"Measures matter: assessing childhood maltreatment and emotion dysregulation in a transdiagnostic sample.","source":"pubmed","abstract":"Background: Childhood maltreatment (CM) is a major risk factor for different mental disorders and transdiagnostic mechanisms, including emotion dysregulation. Progress in the field has been constrained by substantial variability in the measurement of CM across studies. Objective: We aimed to examine the associations among three common retrospective measures of CM and their relationship with emotion dysregulation in a transdiagnostic adult sample, with a focus on exploring the significance of sensitive developmental periods. Method: In our cross-sectional study, we collected data from N &#x2009;=&#x2009;462 participants with and without different mental disorders. We assessed facets of CM using two questionnaires and one in-person interview, specifically the Childhood Trauma Questionnaire - Short Form (CTQ-SF), the Childhood Experience of Care and Abuse Questionnaire (CECA.Q), and the brief German interview version of the Maltreatment and Abuse Chronology of Exposure scale (MACE), the KERF-40+. We measured emotion dysregulation in a subsample of n &#x2009;=&#x2009;244 participants using the Difficulties in Emotion Regulation Scale (DERS). We analysed data applying correlational analyses and machine-learning-based conditional random forest regression. Results: In our transdiagnostic sample, global scores (including sum, multiplicity, and duration scores) and subscale scores of the CTQ-SF, CECA.Q, and KERF-40+ were highly intercorrelated. Both global scores of the CTQ-SF and KERF-40+, and emotional CM scores of the CTQ-SF, CECA.Q, and KERF-40+ showed significant associations with the DERS total score. Regarding sensitive developmental periods, exposure to parental emotional abuse, emotional neglect, and emotional and physical abuse by peers - particularly during the ages 12-17 - was significantly associated with the DERS total score. Conclusions: Despite substantial differences in the facets of CM assessed, the CTQ-SF, CECA.Q, and KERF-40+ demonstrate high convergent validity. While CTQ-SF and CECA.Q allow for a more efficient assessment of CM experiences, the KERF-40+ offers added value for developmentally sensitive analyses of adult emotion dysregulation.","url":"https://pubmed.ncbi.nlm.nih.gov/42495921/","authors":["Seitz KI","Schalinski I","Renneberg B","Heinrichs N","Brühl A","Steinmann S","Neukel C","Herpertz SC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Dec","doi":"10.1080/20008066.2026.2696095","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495900","name":"Altered Cortical Morphological Brain Networks and Their Diagnostic Classification Utility in Major Depressive Disorder.","source":"pubmed","abstract":"Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single-subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi-feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual-level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph-theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT-based networks exhibited reduced MC primarily within and between higher-order networks involving the default mode and frontoparietal networks, whereas CV-based networks showed increased MC predominantly within the default mode network. By contrast, both SA- and SD-based networks demonstrated enhanced MC mainly within and between lower-order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first-episode, drug-naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate-to-good classification performance in distinguishing patients from controls. Overall, our findings of individual-level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/42495900/","authors":["Sun X","Shen Y","Chen X","Lu B","Li XY","Wang ZH","Cao LP","Chen GM","Chen JS","Chen T","Chen TL","Cheng YQ","Chu ZS","Cui SX","Cui XL","Deng ZY","Gong QY","Guo WB","He CC","Hu ZJ","Huang Q","Ji XL","Jia FN","Kuang L","Li BJ","Li F","Li HX","Li T","Lian T","Liao YF","Liu XY","Liu YS","Liu ZN","Long YC","Lu JP","Qiu J","Shan XX","Si TM","Sun PF","Wang CY","Wang HN","Wang X","Wang Y","Wang YW","Wu XP","Wu XR","Wu YK","Xie CM","Xie GR","Xie P","Xu XF","Xue ZP","Yang H","Yu H","Yuan ML","Yuan YG","Zhang AX","Zhao JP","Zhang KR","Zhang W","Zhang ZJ","Yan CG","DIRECT Consortium","Zhu J","Yu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1002/hbm.70612","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495812","name":"LLM-Generated Lay-Language Protocols for Molecular Tumor Board Patients: Evaluation of Quality and Clinical Usability.","source":"pubmed","abstract":"Molecular Tumor Boards (MTBs) generate highly technical recommendations. The language used in their protocols is rarely accessible to patients. Lay-language patient protocols could support patient-clinician communication, yet manual production is difficult to sustain in high-volume oncology settings. Large language models (LLMs) may offer scalable drafting assistance, yet clinical usability remains largely uninvestigated under real-world deployment constraints. Existing evaluations rely predominantly on synthetic data or closed-source models that are incompatible with strict data protection requirements.","url":"https://pubmed.ncbi.nlm.nih.gov/42495812/","authors":["Pakull TMG","Bender N","Benson S","Fleischhauer A","Alsara M","Gromke T","Hilser T","Kaminski K","Pogorzelski M","Prasuhn N","Rosery V","Schadendorf D","Schuler M","Wiesweg M","Zaun G","Horn PA","Friedrich CM","Pretzell I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/99136","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495800","name":"The effectiveness of adding on or switching antiseizure medications after the first fails to control focal epilepsy: A systematic review of randomized controlled trials.","source":"pubmed","abstract":"Focal epilepsy constitutes 60-70% of epilepsy, and up to half of patients do not achieve seizure freedom with their first antiseizure medication (ASM). When the first ASM fails, evidence guiding whether to switch or add-on another ASM and which ASMs to use is limited. This review synthesized evidence from randomized controlled trials (RCTs) on the comparative effectiveness of subsequent ASMs after first ASM failure in focal epilepsy. Following a pre-registered protocol (PROSPERO: CRD42025603003) and PRISMA guidelines, we included RCTs involving children or adults with focal epilepsy who failed first ASM therapy for any reason. The primary outcomes were seizure remission and responder rate (&#x2265;50% reduction in seizure frequency). Searches were conducted across major databases in February 2025. Risk ratios with 95% confidence intervals were calculated in R. Meta-analysis was not performed due to heterogeneity. Six RCTs (961 participants) published between 1998 and 2012 met inclusion criteria, assessing seven ASMs under add-on or switch strategies. Trial duration ranged from 12 to 52&#x2009;weeks; most were judged high risk of bias (ROB-2). Seizure remission end-point was assessed at a short-term endpoint of 3&#x2009;months. Remission rates ranged from 11 to 43% for add-on and 8-43% for switch trials; response rates were 34-63% and 38-76%, respectively. Valproate showed higher responder rates than primidone (RR 1.52, 95% CI 1.01-2.28) in an add-on trial, while lamotrigine had a lower responder rate than valproate (RR 0.74, 95% CI 0.55-0.99) in one trial. In a switch strategy, lamotrigine showed lower treatment failure rates than valproate (RR 0.61; 95% CI 0.45-0.83) and fewer adverse effects than carbamazepine and valproate. Evidence guiding treatment decisions after the first ASM failure remains limited and outdated. Seizure outcomes were modest and comparable across treatment strategies, with few significant differences between ASMs. The small number of trials, short follow-up for endpoints, and methodological heterogeneity across trials constrain interpretation and clinical applicability. Leveraging observational data with causal inference methods, alongside pragmatic trials evaluating machine-learning-guided ASM selections, is needed to address this evidence gap and improve seizure management.","url":"https://pubmed.ncbi.nlm.nih.gov/42495800/","authors":["Egesa IJ","Mbizvo G","Maden M","Spain T","Emsley R","Bonnett LJ","Marson AG","Smith CT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1002/epd2.70353","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495717","name":"Machine learning-based models for predicting the efficacy and safety of recombinant human interleukin-11 in the treatment of cancer therapy-induced thrombocytopenia: exploration and preliminary validation from a multicenter retrospective study.","source":"pubmed","abstract":"Cancer therapy-induced thrombocytopenia (CTIT) is a common hematologic toxicity associated with anti-tumor treatment. Recombinant human interleukin-11 (rhIL-11), as a thrombopoietic agent, is widely used in clinical practice. However, its efficacy and safety exhibit substantial individual variability, and reliable tools for individualized prediction are currently unavailable.","url":"https://pubmed.ncbi.nlm.nih.gov/42495717/","authors":["Wen Y","Xia J","Dong Y","Liu Z","Yu S","Wang Y","Qing S","Li X","Miao J","Tang D","Yao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1882398","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42495699","name":"Artificial Intelligence-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning-Derived Imaging Features.","source":"pubmed","abstract":"Glaucoma is the leading cause of irreversible blindness worldwide. It often remains asymptomatic until advanced stages. Hence, accurate prediction of glaucoma is crucial for timely intervention to prevent vision loss.","url":"https://pubmed.ncbi.nlm.nih.gov/42495699/","authors":["Wu F","Gao XR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.xops.2026.101292","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495662","name":"Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults.","source":"pubmed","abstract":"To develop and validate machine learning models using routinely available clinical and laboratory data in adults with highly suspected acute appendicitis and to assess their potential to reduce negative appendectomies.","url":"https://pubmed.ncbi.nlm.nih.gov/42495662/","authors":["Males I","Kumric M","Boban Z","Vrdoljak J","Pecenkovic D","Ivanda M","Grahovac M","Pogorelic Z","Bozic J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 28","doi":"10.1002/ags3.70225","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495647","name":"Decoding early lung adenocarcinoma progression by single-cell and spatial transcriptomics reveals a CMA-related prognostic signature.","source":"pubmed","abstract":"Lung adenocarcinoma (LUAD) progression from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) is accompanied by molecular heterogeneity and tumor microenvironment remodeling. Chaperone-mediated autophagy (CMA) regulates tumor cell homeostasis, metabolic adaptation, and stress responses, but its dynamic alterations and prognostic significance during the AIS/MIA-to-IAC progression of LUAD remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42495647/","authors":["Wang J","Wang W","Li S","Ji X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1875096","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495634","name":"Uncovering the pivotal role of MYO6 in myocardial infarction: a multimodally validated diagnostic biomarker and immunotherapeutic target.","source":"pubmed","abstract":"Myocardial infarction (MI) remains a leading cause of cardiovascular mortality worldwide, underscoring the need for improved diagnostic and therapeutic strategies. Despite recent in clinical management, delayed diagnosis and complications such as adverse myocardial remodeling continue to compromise long-term outcomes. Identifying specific biomarkers and actionable therapeutic targets is therefore crucial for precision medicine in MI.","url":"https://pubmed.ncbi.nlm.nih.gov/42495634/","authors":["Wang J","Lei Z","Chen D","Chen Z","Qu L","Zou W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1797028","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495426","name":"Study on the Current Status of Supportive Care Needs of Elderly Breast Cancer Patients and Influencing Factors.","source":"pubmed","abstract":"This study investigated the current status and key influencing factors of supportive care needs among elderly breast cancer patients, with a focus on identifying critical determinants through a survey and advanced machine learning techniques.","url":"https://pubmed.ncbi.nlm.nih.gov/42495426/","authors":["Tian W","Gao J","Wei T","Zhang Y","Liu X","Li X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/nrp/2880186","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495322","name":"Synergistic Profiling of Programmed Cell Death and Immune Responses Identifies a Novel Prognostic Index for Cervical Cancer.","source":"pubmed","abstract":"Cervical cancer (CC) is one of the leading malignancies impacting women worldwide, with a large number of cases occurring in developing countries. However, there is a need for optimal predictive models that can precisely forecast the prognosis and guide treatment selection. Programmed cell death (PCD) and immune responses (IRs) are pivotal in understanding disease progression, diagnosis, therapeutic decision-making, and risk stratification, making them promising prognostic markers. In this study, machine learning approaches were applied to identify key prognostic genes related to PCD and IR, which led to the development of three prognostic models: a PCD index, an IR index, and a combined immune-cell death index (ICDI). The PCD and IR indices showed a positive correlation in risk scores, and all three models demonstrated comparable prognostic significance. Given the biological relevance of the immune and cell death pathways, we further investigated the ICDI derived from the key genes FADD, MUC4, CLNK, and CD8B. This analysis included validation against clinical parameters, nomograms, and exploration of immune infiltration. Profiling of drug susceptibility revealed that the high-risk cohort of patients showed resistance to rapamycin and idelalisib but were sensitive to thapsigargin and linsitinib. Overall, the ICDI shows strong potential as a prognostic marker for forecasting clinical outcomes and could facilitate personalized treatment strategies for CC patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42495322/","authors":["Kiruba B","Sundararajan V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1021/acsomega.6c00858","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495260","name":"Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.","source":"pubmed","abstract":"Acute kidney injury (AKI) is a common severe complication in intensive care unit (ICU). However, an early risk assessment model that can accurately and promptly predict the risk of AKI in critically ill children remains lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/42495260/","authors":["Xie L","Chen C","Zhang C","Chen L","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1847661","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495227","name":"The epistemology of death: psychological autopsy, artificial intelligence, and forensic decision-making in equivocal deaths.","source":"pubmed","abstract":"Traditional post-mortem examinations primarily focus on determining the biological cause of death (causa mortis). However, in equivocal death cases, understanding the intention underlying death may be as important as identifying its physiological mechanism. Psychological autopsy has emerged as a retrospective and interdisciplinary approach aimed at reconstructing the decedent's mental state, behavioral patterns, and psychosocial circumstances preceding death. This review provides a critical and integrative examination of psychological autopsy by synthesizing its classical theoretical foundations with recent developments in artificial intelligence and digital forensic technologies, while examining its methodological, socio-psychological, temporal, technological, epistemological, and legal dimensions. Rather than treating psychological autopsy as a definitive investigative technique, the review examines its epistemological assumptions, methodological limitations, and evidentiary implications. Particular attention is given to its continuing practical relevance within the Turkish legal system despite the absence of explicit statutory regulation. The review argues that psychological autopsy is best understood as a structured interpretive framework grounded in probabilistic reasoning, data triangulation, and contextual analysis. Furthermore, advances in machine learning and natural language processing have introduced new forms of computational inference into post-mortem psychological reconstruction, expanding both methodological possibilities and the ethical and legal challenges associated with forensic decision-making. While previous studies have largely examined psychological autopsy from clinical, forensic, or legal perspectives separately, limited attention has been given to their integration within the context of artificial intelligence and digital transformation. The study ultimately highlights the need for greater methodological standardization, interdisciplinary integration, and ethical oversight in the evolving landscape of psychological autopsy practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42495227/","authors":["Kaya K","Erdem Z","Akpınar Ö","Kaya D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyg.2026.1900172","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495152","name":"Multiomics and artificial intelligence identify infection associated subtypes and prognostic signatures in hepatocellular carcinoma.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) remains a major global health burden with persistently high morbidity and mortality. Dysregulation of the tumor microenvironment (TME) associated with chronic hepatitis B virus (HBV)/hepatitis C virus (HCV) infection and inflammation contributes to tumor progression, therapeutic resistance, and adverse clinical outcomes. Many existing HCC prognostic models do not systematically integrate infection-associated molecular signatures with immune and genomic features, which may limit their predictive performance in infection-associated HCC populations. In this study, we established an integrated predictive framework based on multi-cohort transcriptomic, somatic mutation, immune infiltration, and clinical data, together with artificial intelligence (AI)-assisted analysis. Here, \"infection-associated\" refers to a combined gene set covering HBV/HCV infection, antiviral and inflammatory responses, and immune regulation. We first constructed a comprehensive 248-gene infection-associated set, identified two robust HCC molecular subtypes with distinct infection-status annotations, immune infiltration features, genomic alteration profiles, and clinical prognosis through unsupervised consensus clustering, and screened 12 core prognostic genes that were significantly enriched in key oncogenic pathways via three complementary machine learning algorithms. The prognostic risk model was developed in the TCGA-LIHC training cohort and externally validated in ICGC-LIRI-JP and GSE14520, with 1-year area under the curve (AUC) values reaching 0.82, 0.78, and 0.75, respectively; it also showed independent prognostic value for HCC (hazard ratio (HR)&#x2009;=&#x2009;2.87, P &#x2009;&lt;&#x2009;0.001) and higher clinical net benefit than traditional clinicopathological models in retrospective decision curve analysis (DCA). These results suggest associations among infection-associated molecular characteristics, TME remodeling, and HCC prognosis. Together, the infection-associated signature may support retrospective HCC risk stratification, although prospective validation is required before routine clinical application.","url":"https://pubmed.ncbi.nlm.nih.gov/42495152/","authors":["Wang P","Fang J","Zhao W","Huang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1007/s13205-026-04969-8","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495075","name":"Development and external validation of a composite biomarker-based machine learning model for sarcopenia risk stratification in patients with cardiovascular disease.","source":"pubmed","abstract":"Sarcopenia is common in patients with cardiovascular disease (CVD) and is associated with functional decline and adverse clinical outcomes. However, practical tools for early risk stratification in this population remain limited, particularly those incorporating composite metabolic biomarkers.","url":"https://pubmed.ncbi.nlm.nih.gov/42495075/","authors":["Mei P","Ying T","Wu J","Wang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1814149","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42495069","name":"Artificial intelligence technology in aortic valve disease: a decade of scientometric and narrative review.","source":"pubmed","abstract":"Aortic valve disease, particularly aortic stenosis, poses a growing global health burden with aging populations. Artificial intelligence technology offers promising tools for diagnosis, risk stratification, and prognosis prediction, yet the knowledge structure of this interdisciplinary field remains unsystematically characterized.","url":"https://pubmed.ncbi.nlm.nih.gov/42495069/","authors":["Hei P","Ren H","Ma W","Fang W","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1843658","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495052","name":"Translational AI in whole-slide image cancer histopathology: state of the art and regulatory-approved solutions.","source":"pubmed","abstract":"With an emphasis on applied evidence, we present an in-depth evaluation of artificial intelligence (AI) in cancer histopathology through the lens of United States Food and Drug Administration (FDA)-approved and European Conformity-marked whole-slide image In Vitro Diagnostic Medical Devices. Having identified only four existing FDA-approved whole-slide image cancer solutions for a narrow range of applications, we conclude that AI in digital histopathology remains in an emerging state. Best practices were identified by examining development and validation evidence across market-approved solutions. Findings were contrasted with state-of-the-art research-only AI histopathology pipelines. Insights were drawn regarding applications, learning modalities, processing strategies, statistical methods, and validation approaches. Regulatory guidelines were evaluated from FDA and UK Government documentation as well as academic literature, with patient safety highlighted as a central concern. Approved products were observed to integrate efficiently into existing clinical decision-making frameworks, with future potential to enhance the use of pathologist consensus in AI applications. Biomarker assays may be coupled specifically to emerging therapies, but challenges remain for direct clinical adoption outside the research-only sphere. Although hurdles remain in validating agentic and generative AI for medicine, further adoption of state-of-the-art algorithmic frameworks-including transformer architectures and multimodal approaches-is anticipated. As pan-cancer systems emerge, computational modules and engineering principles may also be retargeted to underrepresented use cases. Consequently, this review provides a forward-looking framework for translational, market-relevant histopathology AI.","url":"https://pubmed.ncbi.nlm.nih.gov/42495052/","authors":["Matzko RO","Kucukgoz B","Gertner P","Carey C","Bacon CM","Xin T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1863382","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42495050","name":"Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall.","source":"pubmed","abstract":"The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks of perpetuating health disparities, given the test's known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR&#x2122;)-a rapid, FDA-listed digital cognitive assessment-to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage.","url":"https://pubmed.ncbi.nlm.nih.gov/42495050/","authors":["Jannati A","Toro-Serey C","Ciesla M","Chen E","Showalter J","Bates D","Pascual-Leone A","Tobyne S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1799372","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494937","name":"Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.","source":"pubmed","abstract":"The diagnosis of Wilson disease (WD) is complicated by heterogeneous clinical phenotypes and inadequate performance of routine biomarkers. This study sought to screen age-specific urinary metabolic biomarkers to assist WD diagnosis via ensemble tree-based machine learning algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/42494937/","authors":["Huang S","Song Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1515/med-2026-1415","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494919","name":"Myo-ODE: continuous-time trajectory reconstruction and risk prediction of high myopia via neural ordinary differential equations.","source":"pubmed","abstract":"The global surge in childhood myopia necessitates robust screening tools for early risk stratification; however, conventional predictive models often struggle with irregular follow-up intervals and fail to capture the continuous nature of refractive development. We propose Myo-ODE, a continuous-time framework based on Neural Ordinary Differential Equations (Neural ODEs) to predict high myopia risk. Unlike traditional discrete machine learning models, Myo-ODE parameterizes the derivative of the refractive state, allowing myopia progression to be represented as a continuous latent dynamic flow. This architecture explicitly accommodates non-uniform screening intervals and supports temporal interpolation and cautious short-term extrapolation within the observed follow-up horizon. Evaluated on a longitudinal dataset ( N = 4,973), Myo-ODE achieved the highest F1-score of 0.8000 (95% CI: 0.7741-0.8256) and Recall of 0.7812 (95% CI: 0.7518-0.8103), while maintaining a competitive AUC of 0.9834 (95% CI: 0.9781-0.9887). Furthermore, our framework reconstructs individualized refractive progression trajectories and provides an interpretable estimate of the model-learned progression momentum of SE change. By bridging the gap between discrete clinical observations and continuous trajectory-level modeling, Myo-ODE offers a promising tool for personalized myopia surveillance in real-world screening environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42494919/","authors":["Zhao N","Zheng R","Lu J","Huang Z","Li C","Dai C","Enbei X","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1897882","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494893","name":"Enhancing sarcopenia screening in primary care: a machine learning approach using simple physical tests vs. SARC-F in 2,788 community-dwelling older adults.","source":"pubmed","abstract":"SARC-F, a widely used screening tool for sarcopenia, offers high specificity but poor sensitivity (30-50%), leading to substantial missed diagnoses in community settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42494893/","authors":["Jiang Z","Zhong C","Yin X","Jin Y","Zhu L","Liu J","Tang C","Zhou J","Wu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1846999","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494852","name":"Machine learning for early screening of influenza A-associated invasive pulmonary aspergillosis in hospitalized patients: a real-world study.","source":"pubmed","abstract":"Influenza A-associated invasive pulmonary aspergillosis (IAPA) is a severe fungal complication with high mortality, while early identification remains difficult because of nonspecific clinical manifestations. This study aimed to develop and validate a machine learning (ML) model for early screening of IAPA in hospitalized influenza A patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42494852/","authors":["Wang Y","Liu C","Cao M","Chen X","Qian M","Chen Y","Li S","Yang H","Zhang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcimb.2026.1896920","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494815","name":"Prediction of loss of heterozygosity in oral cavity dysplasia through vascular pattern.","source":"pubmed","abstract":"Loss of Heterozygosity (LOH) is a key genetic alteration associated with progression of oral cavity dysplasia to oral squamous cell carcinoma, yet non-invasive methods to predict LOH status are lacking. The aim of this study is to investigate whether vascular pattern abnormalities detected through Narrow Band Imaging (NBI) can predict LOH status in oral dysplasia using machine learning-based quantitative analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42494815/","authors":["Tartaglia FC","Rossi G","Mainardi L","Wang H","Resteghini C","Goker F","Lorini L","Gurizzan C","Paderno A","Bossi P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/froh.2026.1829880","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494790","name":"Heart Failure sub-phenotyping and in-hospital and 28-day mortality prediction based on mean arterial pressure trajectory modeling.","source":"pubmed","abstract":"Acute decompensated heart failure patients follow a broad range of clinical pathways during hospitalization. Efficient patient sub-phenotyping based on early in-hospital trajectories, particularly when paired with clinical measurements, may inform optimal treatment strategies using digital twins.","url":"https://pubmed.ncbi.nlm.nih.gov/42494790/","authors":["Gelbach PE","Edwards RA","Fukuda Y","Alexander J Jr"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.ahjo.2026.100838","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494785","name":"End-to-End Pipeline Integrating Local Small Language Models and Machine Learning for Data Extraction and Stroke Outcome Prediction in Emergency Department.","source":"pubmed","abstract":"Background: A substantial proportion of clinical data is stored in unstructured text, limiting its utility for evidence generation. Small language models (SLMs) can extract information but face hallucination risks and privacy concerns. Locally deployable SLMs are needed for secure and reliable healthcare use. Methods: We designed an end-to-end pipeline integrating clinical text extraction with stroke outcome prediction. Records of 1,398 patients screened for ischemic stroke were reviewed, with 1,166 included. A Llama 3 8B SLM was fine-tuned using low-rank adaptation and 4-bit quantization for local feasibility, guided by few-shot prompting. Multi-tiered validation-rule-based checks, retrieval-augmented generation, cosine similarity flagging, and human-in-the-loop review-was implemented. Structured data from 767 patients were used to train models predicting poor outcomes at 3 months. Results: Baseline extraction accuracy was 64.9% (95% CI, 62.0% to 67.8%), improving to 86.0% after fully automated multi-tiered validation, and further to 97.0% (95% CI, 95.7% to 98.3%) following human-in-the-loop review. Template-based variables achieved F1 &gt; 0.90 (95% CI, 0.88 to 0.96). Narrative extraction reached F1 = 0.87 (95% CI, 0.84 to 0.90). NIHSS scores were extracted with a mean absolute error of 0.853 (95% CI, 0.791 to 0.915). TabPFN achieved an AUROC of 0.816 (95% CI, 0.784 to 0.847) with good calibration, confirming reliable risk stratification. Conclusion: This study demonstrates a privacy-preserving, efficient pipeline for clinical text processing. By combining an SLM with multi-tiered validation and predictive modeling, it offers a proof-of-concept solution with potential for broader deployment to transform unstructured records into structured data suitable for stroke outcome research and decision-support modeling.","url":"https://pubmed.ncbi.nlm.nih.gov/42494785/","authors":["Kim J","Kim JH","Choi A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.34133/csbj.0064","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494783","name":"The Potential of Digital Twins in Stroke Care: A Systematic Review of Current Applications and Future Perspectives.","source":"pubmed","abstract":"Background: Digital twin technology holds promise for personalized stroke care, but current applications remain fragmented. This systematic review investigates how digital twins are currently utilized in the stroke care continuum. Methods: Following PRISMA guidelines, we conducted a systematic search of PubMed, Web of Science, and the Cochrane Library through April 2025. Studies applying digital twins to acute ischemic stroke care were included. Each study was categorized along the stroke care continuum (pre-stroke, in-hospital, post-stroke) and assessed using a digital twin maturity framework (L0 to L3). We extracted data on clinical intent, modeling approach, validation strategy, study design, population, sample size, and key outcomes to enable structured synthesis. Results: Eight studies met inclusion criteria. Half targeted pre-stroke risk prediction (e.g., modeling atherosclerosis or atrial fibrillation), 2 simulated mechanical thrombectomy, 1 supported prehospital diagnosis, and 1 predicted post-stroke disease progression. Most models remained at maturity levels L1 to L2, lacking real-time updating or workflow integration. Technologies included machine learning ( n = 3), computational fluid dynamics ( n = 3), and hybrid or rule-based approaches ( n = 2). Conclusions: Digital twins in stroke care are promising but remain preclinical. Current models predominantly address pre-stroke risk prediction or procedural simulation, with limited representation of acute decision support or post-stroke monitoring. Clinical integration is constrained by low technological maturity, limited real-world validation, and a lack of interoperability.","url":"https://pubmed.ncbi.nlm.nih.gov/42494783/","authors":["Nawabi J","Eminovic S","Desser D","Fetscher L","Rangus I","Siebert E","Wattjes MP","Weissflog JS","Meddeb A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.34133/csbj.0013","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494730","name":"Machine learning redevelopment of GRACE, ACEF, and TIMI scores for 6-month mortality.","source":"pubmed","abstract":"In recent years, advancements in our understanding of the pathophysiological mechanisms underlying coronary artery disease (CAD) have introduced new challenges regarding the clinical application of traditional risk scores. While studies suggest that machine learning (ML) algorithms surpass traditional statistical methods in risk prediction, their conclusions are often derived from heterogeneous datasets and varying model structures, which restrict their generalizability and persuasive power.","url":"https://pubmed.ncbi.nlm.nih.gov/42494730/","authors":["Han B","Zhu Z","Guo R","Xu J","Zhang Y","Zhang Z","Li W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1838324","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494615","name":"Smart FL: meta-learning for federated blood marrow smear classification.","source":"pubmed","abstract":"Accurate classification of bone marrow cells is crucial for diagnosing hematological disorders, yet integrating machine learning and deep learning models into clinical practice faces hurdles like data privacy and data scarcity. Existing models often lack the adaptability that is important in the medical field, limiting their practicality. This study introduces an approach using federated learning and meta-learning to tackle these challenges. The objectives for this study were multifaceted. Firstly, it address the adverse effects of data scarcity commonly found in medical datasets due to which models are unable to predict classes which do not have enough examples. Secondly, this study tackles the privacy concerns by adopting a federated learning framework, allowing model training without centralizing sensitive data. Additionally, the goal is to make the federated process personalized to each client for enhancing individual accuracy. Lastly, this study seeks to improve the generalization capabilities of the models, enabling robust performance across diverse patient populations. The proposed approach involves leveraging a ResNet-18 backbone for the meta-learning algorithm, specifically one based on prototypical networks. This framework is then implemented and tested in a federated manner using FedAvg across four clients, each possessing their own data. The main focus of this research is to achieve high accuracy, particularly in unseen classes with limited samples. This approach yields promising results, achieving an accuracy of 96&#xa0;&#xb1;&#xa0;1% in classes with low sample sizes. Through this approach, hospitals, clinics, and researchers can make sure that their data remains private while being able to benefit from deep learning research.","url":"https://pubmed.ncbi.nlm.nih.gov/42494615/","authors":["Ilakiyaselvan N","Sanskar S","Aarthi D","Kalyanasundaram V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1841558","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494614","name":"Beyond AUC: a clinician's guide to building and trusting prediction models in oncology-a narrative review.","source":"pubmed","abstract":"Prediction models are central to advancing precision oncology, yet many fail to translate into clinical practice due to methodological flaws and inadequate validation. This review provides a practical, clinician-oriented guide to the statistical principles and advanced methods for developing, validating, and interpreting robust prediction models.","url":"https://pubmed.ncbi.nlm.nih.gov/42494614/","authors":["Wang X","Dou Y","Wang Y","Sun K","Zhou G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1798803","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494601","name":"Development and validation of a machine learning-based early warning model for bone metastasis in newly diagnosed prostate cancer.","source":"pubmed","abstract":"Bone metastasis (BM) is common in newly diagnosed prostate cancer (PCa), particularly in patients with advanced disease at presentation. However, the indications for bone scintigraphy remain inconsistent and may lead to unnecessary imaging in low-risk patients. This study aimed to develop and validate a machine learning model for individualized prediction of BM in patients with newly diagnosed PCa.","url":"https://pubmed.ncbi.nlm.nih.gov/42494601/","authors":["Wang L","He W","Zhao C","Lv Q","Qiu T","Zhai J","Mao K","Li D","Wen X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1815465","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494588","name":"Correction: Uncovering potential molecular biomarkers for cancer-associated secondary lymphedema through integrated analyses of RNA-sequencing, machine learning, and clinical data.","source":"pubmed","abstract":"[This corrects the article DOI: 10.3389/fonc.2026.1760040.].","url":"https://pubmed.ncbi.nlm.nih.gov/42494588/","authors":["Dong H","Miao J","Liu Z","Sun Y","Li P","Xia S","Shen W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1894847","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494522","name":"Triptolide targets JUN to reverse cisplatin resistance of ovarian cancer: insights from single-cell transcriptome analysis and machine learning validation.","source":"pubmed","abstract":"Platinum-resistant ovarian cancer (PROC) is a major clinical challenge driven by profound intratumoral heterogeneity. Triptolide (TP) exhibits promising anti-tumor potential, yet its precise mechanisms within PROC remain elusive due to the limitations of traditional target-screening strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42494522/","authors":["Wang C","Guo J","Ye T","Zhu J","Ding C","Li H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphar.2026.1850438","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494498","name":"CRISPRing through time: How cutting-edge technology is revolutionizing life sciences and medicine.","source":"pubmed","abstract":"Given the plethora of emerging technologies, none have truly captured the minds as CRISPR. From the groundbreaking research, the ultimate battle of the prizes and patents to a number of books, the science of CRISPR continues to be significant in the biomedical field. For many decades now, the emergence of synthetic biology as an intervention to correct diseases has become the foundation of biomedical research. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based genetic editing has become a common place for routine investigation of scientific hypotheses in pre-clinical settings. More recently, CRISPR-based diagnostic testing kits for SARS-CoV-2 have showcased a translational output. Furthermore, a technological landmark was achieved when the Food and Drug Administration (FDA) approved the first CRISPR-based gene therapy (exa-cel) to edit erythroid specific enhancer region of BCL11A in hematopoietic stem cells, introduced in patients suffering from sickle cell anemia to achieve durable remission. In this review, we provide a snapshot into the most important milestones along the journey of CRISPR from its discovery in bacteria to its usage in precision medicine. The intervention of machine learning tools has now intertwined complex biology with high-throughput scalable outputs. Given the vast amount of information on CRISPR, we try to pin down key take-home messages for scientists as well as non-scientist readers. This review article attempts to understand why and how CRISPR remains significant and seamlessly integrates in the emerging era of new technologies.","url":"https://pubmed.ncbi.nlm.nih.gov/42494498/","authors":["Banik I","Coppé JP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 8","doi":"10.1016/j.omtn.2026.103003","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494474","name":"Enhanced prediction of coronary heart disease risk in diabetic patients via Machine learning incorporating multiple inflammatory and metabolic indices: A study with Dual-Cohort validation.","source":"pubmed","abstract":"Coronary heart disease (CHD) remains a leading cause of mortality worldwide, with individuals with diabetes mellitus (DM) facing markedly elevated risk due to complex inflammatory and metabolic disturbances. Emerging composite inflammatory and metabolic indices have demonstrated promise in enhancing cardiovascular risk stratification, yet research quantifying and comparing their respective predictive performance and assessing their relative contributions remains limited. This study aimed to develop a clinically applicable model for early CHD risk prediction in diabetic patients using novel composite inflammatory and metabolic indices.","url":"https://pubmed.ncbi.nlm.nih.gov/42494474/","authors":["Xie P","Ran X","Zhu W","Xie Y","Dong Y","Wang Z","Zhai C","Qiao L","Yang J","Chen W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.ijcha.2026.101933","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494431","name":"Oral-gut strain sharing after Roux-en-Y gastric bypass: A canonical oral microbiome signature in the gut linked to hepatic and glycemic remodeling.","source":"pubmed","abstract":"Roux-en-Y gastric bypass (RYGB) induces durable weight loss and metabolic improvement, but the role of oral microbiota in shaping postsurgical gut ecology and metabolic outcomes is unclear. We examined whether RYGB promotes transfer and expansion of oral strains in the distal gut and how these relate to hepatic and glycemic health. In 25 patients from a longitudinal RYGB cohort, paired oral and fecal samples were collected before and 12 months after surgery. We examined the presence, abundance, and structure of cohort-specific canonical oral strains in the gut, and assessed &#x3b1;/&#x3b2;-diversity, cross-site correlations, and clinical associations using univariate tests and linear models. Machine-learning models evaluated the prognostic value of oral canonical strains for hepatic and glycemic outcomes. Oral taxon richness increased after RYGB, while Shannon diversity and individual signatures remained stable. In the gut, canonical oral strains expanded: shared oral-gut strains and their summed abundance rose significantly, converging into a reproducible post-RYGB niche. Abundance and fold change of oral-canonical strains associated with FIB-4, ASAT and fasting glucose, and predictive models suggested a prognostic signal for fasting glucose, TBF% and HbA1c. RYGB is associated with reproducible, strain-level enrichment of oral microbiota in the gut, with links to hepatic and glycemic outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42494431/","authors":["Voermans B","Prange K","Bruin S","Acherman Y","Zaura E","Nieuwdorp M","Gerdes V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/29933935.2026.2693432","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494408","name":"The progress in predictive modeling of post-stroke epilepsy.","source":"pubmed","abstract":"Post-stroke epilepsy (PSE) is a significant complication of both ischemic (IS) and hemorrhagic strokes (HS), leading to increased morbidity and reduced quality of life. Accurate prediction of PSE risk is essential for early intervention and tailored management. Multiple predictive models have been developed for different stroke subtypes. In HS, models such as CAVE, CAVS, CAV+, and CAVE2 emphasize lesion characteristics and early seizures. Within IS, models such as SeLECT and PSEiCARe focus on cortical involvement, large-artery atherosclerosis, and early seizure occurrence. Recent advances in machine learning-based approaches have shown improved predictive accuracy for both IS and HS patients, although further validation is required for routine clinical application. This review summarizes and compares predictive models for PSE across stroke subtypes, highlighting their clinical relevance and potential for improving patient outcomes through early risk stratification. Integration of multimodal data may further enhance seizure prediction and guide personalized intervention strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42494408/","authors":["Chen H","Ge L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1877573","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494392","name":"Integrating Mendelian randomization, machine learning and retrospective clinical data: an exploratory analysis of the cross-disease association between CHB and PD, with a focus on eosinophil alterations.","source":"pubmed","abstract":"Epidemiological studies on the association between chronic hepatitis B (CHB) and Parkinson's disease (PD) have yielded inconsistent findings, with causality obscured by confounding and limited mechanistic evidence.","url":"https://pubmed.ncbi.nlm.nih.gov/42494392/","authors":["Ge Y","Cai H","Li Y","Li Y","Liu H","Zeng G","Yang K","Luo Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1819000","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42494269","name":"Predictive Modeling of the Need for Tracheostomy after Traumatic Cervical Spinal Cord Injury Using Machine Learning.","source":"pubmed","abstract":"Traumatic cervical spinal cord injury (TCSCI) frequently necessitates mechanical ventilation, and early tracheostomy has been shown to reduce complications in patients requiring prolonged ventilation. This study aimed to develop a machine learning model to predict the need for tracheostomy in TCSCI patients, utilizing early clinical data to improve patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42494269/","authors":["Lee EJ","Kwon B","Kim S","Noh SH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3349/ymj.2025.0190","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42494124","name":"Multi-Class and Multi-Level Classification for Common Wound Bacteria Based on Reflectance Hyperspectral Imaging.","source":"pubmed","abstract":"Hyperspectral imaging is an emerging non-destructive technique for rapid bacterial detection. However, existing methods often inadequately exploit spatial-spectral correlations and lack datasets tailored to clinically relevant scenarios. This work presents a systematic framework integrating a near-infrared hyperspectral imaging system with a deep learning for accurate classification of common wound bacteria. The multi-class and multi-level dataset is first established using a 400-2500&#x2009;nm halogen-illuminated system under controlled conditions, comprising 26&#x2009;056 image patches covering seven bacterial species at five concentration levels, totaling 31 classes. To fully harness spatial and spectral information, the Spatial-Spectral Interactive Selection Network (SSIS-Net) is proposed. Evaluated on the self-constructed dataset, SSIS-Net achieves an overall accuracy of 97.40%, an average accuracy of 97.37%, and a Kappa coefficient of 97.31%, consistently outperforming state-of-the-art methods. These results demonstrate that the proposed system offers a reliable, rapid, and non-destructive solution for bacterial detection, with strong potential for clinical integration.","url":"https://pubmed.ncbi.nlm.nih.gov/42494124/","authors":["Wu D","Fu X","Liu W","Yin S","Liu C","Zhao J","Cao H","Zhang L","Wang Z","Tang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/jbio.70318","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493828","name":"Development of a Machine Learning-Based Model for Classifying Depression Using Physiological and Psychological Indicators.","source":"pubmed","abstract":"This study aimed to develop and evaluate machine learning-based models for classifying depression symptoms using salivary hormone markers (cortisol and dehydroepiandrosterone [DHEA]) and psychological indicators from depression screening devices, and to determine the extent to which salivary hormone markers contribute to depression symptom classification.","url":"https://pubmed.ncbi.nlm.nih.gov/42493828/","authors":["Jang S","Park J","Shin H","Lee M","Ryu V","Bang M","Seo JH","Kim TH","Jung YC","Kwon M","Seok JH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.30773/pi.2025.0472","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493769","name":"Crowdsourcing and machine learning contests in Parkinson's disease research - when do they work?","source":"pubmed","abstract":"ObjectiveTo review the application of crowdsourcing and machine learning contests in Parkinson's disease (PD) research, identify best practices for successful implementation, and highlight future opportunities.MethodsThis paper analyzes the landscape of crowdsourcing in PD research through a literature survey and a comparative case study of two major machine learning contests: the MJFF Freezing of Gait (FOG) Challenge and the AMP PD Proteomics Challenge. We also describe a taxonomy of crowdsourcing projects and a framework of success characteristics for machine learning contest design.ResultsThe analysis of previous crowdsourcing and machine learning contests revealed that contest success is highly dependent on specific design factors. The FOG challenge, which addressed a \"solvable but not yet solved\" problem with a suitable scoring metric, successfully produced a high-performing algorithm with real-world clinical value. In contrast, the Proteomics challenge did not yield biologically meaningful results, as winning models bypassed the core proteomic data, highlighting issues of data signal and metric selection. The review also identified underutilized crowdsourcing approaches in PD research, including gamification and community-based open-source development.ConclusionsMachine learning contests offer a powerful, open-science-aligned method to address complex problems in PD. Success requires careful design, particularly a solvable problem and an appropriate scoring metric. There is significant potential to expand the use of diverse crowdsourcing techniques to accelerate progress in PD research and clinical care.","url":"https://pubmed.ncbi.nlm.nih.gov/42493769/","authors":["Kirsch LC","Hausdorff JM","Dardov VJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1177/1877718X261452116","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493737","name":"Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery.","source":"pubmed","abstract":"The COVID-19 pandemic highlighted the role of rapid viral mutation and global connectivity in accelerating viral emergence and spread, emphasising the necessity for expedited and adaptable antiviral drug discovery and development. Despite ongoing efforts to develop effective, low-toxicity therapeutics, the number of antivirals that have achieved clinical approval remains limited. The shortfall is especially significant in developing countries, where access to new antivirals is limited by high prices, few options, import dependence, unstable supply chains and weak purchasing systems. The challenge is further heightened by the emergence of increasingly drug-resistant variants while vaccines often provide inadequate protection against newly mutated or novel viruses. Consequently, the identification of novel antiviral agents that are both effective and cost-efficient via innovative strategies for antiviral drug discovery is essential to manage and control viral infections. Therefore, this review examines the different challenges associated with conventional antiviral drugs alongside recent strategies in antiviral drug discovery and development, such as the exploitation of plant secondary metabolites with antiviral properties, advanced microscopy technologies, computer-aided drug design, artificial intelligence and machine learning, gene-editing technologies, drug combination therapy and nanotechnology-enhanced drug delivery systems. Additionally, this study proposes a simple decision-focused pathway integrating natural products, computation and targeted delivery to guide candidate prioritisation, optimisation and translation from discovery to implementation. While these emerging strategies offer considerable promise, challenges related to validation, toxicity, scalability and equitable access remain important considerations for successful clinical translation. Future research should therefore integrate complementary technologies to accelerate the development of effective antiviral agents against current and emerging viral threats.","url":"https://pubmed.ncbi.nlm.nih.gov/42493737/","authors":["Lanrewaju AA","Folami AM","Sabiu S","Swalaha FM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1111/cbdd.70365","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493617","name":"Early prediction of anastomotic leakage within 24 h after minimally invasive colorectal cancer surgery using postoperative inflammatory markers: development and temporal validation of a machine learning model.","source":"pubmed","abstract":"Early identification of anastomotic leakage (AL) is critical for safe discharge within enhanced recovery pathways. This study developed and prospectively validated a machine learning (ML) model to predict AL using 24-h postoperative inflammatory biomarkers.","url":"https://pubmed.ncbi.nlm.nih.gov/42493617/","authors":["Martin-Arevalo J","Guimaraes A","Palomo-Lopez I","Moro-Valdezate D","Perez-Santiago L","Garcia-Botello SA","Castillejos-Ibañez F","Lopez-Mozos F","Riera-Cardona M","Casado-Rodrigo D","Millan M","Pla-Marti V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s00464-026-13163-z","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493609","name":"Gut Microbiome in Depression with and without REM Sleep Behavior Disorder.","source":"pubmed","abstract":"Major depressive disorder (MDD) is a risk factor for neurodegeneration, yet its heterogeneity makes identifying at-risk subtype challenging. Notably, MDD frequently co-occurs with REM sleep behavior disorder (RBD), a specific prodrome of &#x3b1;-synucleinopathy. It remains unclear whether comorbid MDD&#x2009;+&#x2009;RBD reflects a benign antidepressant effect, or higher neurodegenerative risk. Given growing recognition of gut-brain axis in neuropsychiatry, we aimed to delineate microbial signatures of MDD&#x2009;+&#x2009;RBD. We employed a four-group case-control design (N&#x2009;=&#x2009;420) comprising 124 healthy controls (HC); 80 MDD without RBD features (MDD-only); 82 MDD&#x2009;+&#x2009;RBD; and 134 iRBD without psychiatric disease. All participants underwent clinical evaluation and provided fecal samples for metagenomic sequencing. Random Forest model was used to distinguish MDD&#x2009;+&#x2009;RBD, and further assessed in a validation dataset of 65 participants with MDD&#x2009;+&#x2009;RBD (n&#x2009;=&#x2009;31) and MDD-only (n&#x2009;=&#x2009;34). MDD&#x2009;+&#x2009;RBD exhibited prodromal neurodegenerative features, including elevated total likelihood ratio of prodromal Parkinson's Disease, olfactory deficits, and subtle motor signs. The microbial composition in MDD&#x2009;+&#x2009;RBD differed from HC and MDD-only, while resembling iRBD. Taxonomically, MDD&#x2009;+&#x2009;RBD exhibited an iRBD-like dysbiosis (e.g., enriched Akkermansia muciniphila, Ruthenibacterium lactatiformans; depleted Faecalibacterium prausnitzii), alongside depression-associated shifts (e.g., Streptococcus parasanguinis and Actinomyces oris). Functionally, MDD&#x2009;+&#x2009;RBD showed attenuated capacity of B&#x2011;vitamin biosynthesis and polysaccharides degradation, mirroring iRBD. The Random Forest machine-learning model distinguished MDD&#x2009;+&#x2009;RBD in older adults from MDD-only with an AUC of 0.73 in cross-validation and 0.79 in the validation dataset. MDD&#x2009;+&#x2009;RBD may represent a biologically distinct depression subtype associated with potential neurodegenerative risk. Gut microbiome provides a candidate approach for potential risk stratification in psychiatric populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42493609/","authors":["Yang Y","Li N","Zhou L","Gong S","He Z","Tang S","Ni J","Liu Y","Chan JWY","Or BPN","Lam SP","Zhang J","Chan PKS","Chen Z","Wong SH","Mok VCT","Chan NY","Chau SWH","Lai CKC","Scheperjans F","Wang J","Huang B","Wing YK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1038/s41380-026-03773-3","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493535","name":"Quantitative correlation of spectroscopic signatures with ligand-protein interactions in anti-cancer drug Afinitor: an integrated experimental-computational study.","source":"pubmed","abstract":"A detailed molecular-level understanding of anticancer drugs is essential for improving therapeutic efficacy and guiding rational drug design. Everolimus (Afinitor), a clinically important inhibitor of the mammalian target of rapamycin (mTOR) pathway, is widely used in cancer therapy; however, a quantitatively grounded relationship between its spectroscopic characteristics and ligand-protein interactions remains insufficiently explored. In this study, an integrated experimental-computational approach was employed, combining FT-IR spectroscopy, UV-Vis spectroscopy, and molecular docking simulations. Spectroscopic analyses were used to characterise functional groups and electronic structure, while docking simulations were performed to investigate interactions with FK506 binding protein (FKBP12) and the FKBP-rapamycin binding (FRB) domain. FT-IR analysis revealed a high density of oxygen-containing functional groups, including hydroxyl and carbonyl moieties, with vibrational frequencies indicative of a strongly polarised electronic environment. Molecular docking demonstrated favourable binding affinities with FKBP12 (-&#x2009;9.7 and&#x2009;-&#x2009;9.6&#xa0;kcal&#xb7;mol&#x207b; 1 ) and the FRB domain (-&#x2009;8.5 and&#x2009;-&#x2009;6.6&#xa0;kcal&#xb7;mol&#x207b; 1 ). Detailed interaction analysis showed that these functional groups correspond to specific interacting atoms (e.g., O66, O67, O63, and O36), forming quantifiable hydrogen bonds (1.7-2.9&#xa0;&#xc5;) and electrostatic interactions (~&#x2009;4.37&#xa0;&#xc5;) with key residues such as TYR82, THR85, and GLU54. The UV-Vis absorption maximum at 278&#xa0;nm corresponds to a HOMO-LUMO energy gap of 4.46&#xa0;eV, indicating moderate electronic polarizability that supports charge redistribution during binding. The study establishes a quantitative and mechanistically grounded structure-spectra-interaction relationship, demonstrating that spectroscopic observables encode the local electronic environment governing ligand-protein interaction propensity. Binding affinity is shown to arise from a cooperative network of multiple non-covalent interactions enabled by the spatial distribution of functional groups. This integrated framework provides predictive insight into drug-protein interactions and offers a robust foundation for the rational design and optimisation of mTOR-targeting therapeutics.","url":"https://pubmed.ncbi.nlm.nih.gov/42493535/","authors":["Ramana PV","Ram R","Bobbili P","Krishna YR","Panda R","Dominic S","Dakua PK","Kumar S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1038/s41598-026-56242-w","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493440","name":"[Characteristics of urinary organic acid metabolic profile and screening of key metabolic markers in patients with urolithiasis].","source":"pubmed","abstract":"To systematically compare the differences in urinary organic acid metabolic profiles between patients with urinary calculi and non-calculi individuals, to screen disease-specific characteristic metabolic biomarkers and key signaling pathways, and to elucidate the potential mechanism underlying the occurrence and progression of urinary calculi at the metabolic level, so as to provide a theoretical basis for basic research and screening of intervention targets for urinary calculi.","url":"https://pubmed.ncbi.nlm.nih.gov/42493440/","authors":["Qi Z","Bian X","Hu H","Tian C","Wang C","Zhou Y","Xu T","Wang H","Cao L","Hu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 18","doi":"10.19723/j.issn.1671-167X.2026.04.009","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493423","name":"Predicting Malignant Transformation in Oral Epithelial Dysplasia: A Systematic Comparison of Artificial Intelligence-Based Risk Models and Pathologist-Based Microscopy.","source":"pubmed","abstract":"Risk prediction models (RPMs) based on histopathological analysis of oral epithelial dysplasia (OED) are increasingly used to stratify patients with oral potentially malignant disorders (OPMDs) and support personalized management. This systematic review and meta-analysis evaluated artificial intelligence (AI)-based models compared with conventional human microscopy for predicting malignant transformation (MT).","url":"https://pubmed.ncbi.nlm.nih.gov/42493423/","authors":["Arantes DCB","Delgado LM","Dos Santos MMPP","Warnakulasuriya S","Sperandio M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1111/jop.70168","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493350","name":"The Value of Deep Learning in Differentiating Thyroid Adenomatoid Nodules on Ultrasound: A Dual-Center Study.","source":"pubmed","abstract":"Follicular neoplasms are difficult to classify by ultrasound, as they often present as thyroid adenomatoid nodules (TANU). Furthermore, fine-needle aspiration cytology (FNAC) exhibits limited accuracy in differentiating the nature of follicular lesions. This study aimed to develop and validate a novel model to optimize the workflow for distinguishing benign from malignant TANU.","url":"https://pubmed.ncbi.nlm.nih.gov/42493350/","authors":["Cheng S","Liang X","Zeng XT","You ZL","Lin N","Ding GS","Zhu L","Chen SQ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1016/j.acra.2026.06.047","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493349","name":"A Machine Learning-Based Multimodal Model Integrating Radiomics and Clinical Features for Predicting Interstitial Lung Disease: Development and External Validation.","source":"pubmed","abstract":"To develop and validate a multimodal model for predicting the risk of interstitial lung disease (ILD) by integrating radiomics features with clinical variables using machine learning (ML).","url":"https://pubmed.ncbi.nlm.nih.gov/42493349/","authors":["Wang Y","Zhang Z","Dai X","Shan B","Zhou Y","Fu R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1016/j.acra.2026.07.005","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42493284","name":"A deep learning model for speech-based prediction of clinical scores in people with Huntington's disease: a longitudinal study with cross-sectional replication.","source":"pubmed","abstract":"Sensitive monitoring tools are needed to track progression in neurodegenerative diseases and assess interventions before overt brain damage occurs. We propose speech as a non-invasive, easily collected biomarker to capture disease-related variation over time. We developed and validated Neurodegenerative Disease Speech Network (NDSNet), an automated deep learning model that generates individual speech-derived estimates of contemporaneous clinical scores at each visit in people with Huntington's disease, from presymptomatic stages (Huntington's Disease Integrated Staging System [HD-ISS] stages 0-1) to symptomatic stages (HD-ISS stages 2-3).","url":"https://pubmed.ncbi.nlm.nih.gov/42493284/","authors":["Le Moine Veillon C","Fraisse S","Lunven M","Fabre A","Youssov K","Morgado G","Titeux H","Ludec TL","Goizet C","Fougeron C","Massart R","Bachoud-Lévi AC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.landig.2026.101025","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492945","name":"Machine learning-based prediction of shigellosis in children under five: development and internal validation of a prediction model.","source":"pubmed","abstract":"Shigella remains a major cause of diarrhoea and mortality in children under five in low- and middle-income countries, where laboratory confirmation is often inaccessible and dysentery-based management lacks sensitivity. This study aimed to develop and internally evaluate machine learning models to predict microbiologically confirmed Shigella infection and secondarily to demonstrate the feasibility of translating the best-performing model into a prototype web-based decision-support application.","url":"https://pubmed.ncbi.nlm.nih.gov/42492945/","authors":["Al Fidah MF","Islam MR","Faruque A","Nuzhat S","Mahfuz M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1136/bmjhci-2026-102115","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492779","name":"Artificial intelligence-based methods and applications in clinical and diagnostic microbiology: Current challenges and future perspectives.","source":"pubmed","abstract":"Microbiology laboratories play a critical role in the diagnosis and management of infectious diseases. However, recent advancements aimed at reducing human workload and minimizing time loss are gaining popularity. Artificial intelligence (AI) technologies, particularly machine learning (ML) and deep learning (DL), have been reported to contribute significantly to microbial laboratory diagnostics. Through this approach, molecular methods, genetic sequencing, microbiological meta-analyses, and related fields benefit from faster and more accurate analytic capabilities. In addition to diagnostic applications, AI is increasingly used in genomics, metagenomics, antimicrobial resistance (AMR) prediction, and drug and vaccine discovery, enabling more comprehensive and data-driven microbiological analysis. This review comprehensively evaluates current AI applications in microbiology, highlighting their advantages, limitations, and implementation challenges. It further examines the suitability of different AI methodologies for specific laboratory tasks and compares AI-driven approaches with conventional expert-based practices. Finally, the study emphasizes the complementary roles of AI systems and human expertise, underscoring their synergistic potential to improve diagnostic accuracy, efficiency, and clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42492779/","authors":["Yeni DK","Güven D","Büyük F","Gökmen MC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.mimet.2026.107638","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492601","name":"Liquid biopsy cell-free RNA-based machine learning enables preoperative risk-stratification of uterine leiomyosarcoma.","source":"pubmed","abstract":"Accurate preoperative distinction between uterine leiomyoma and uterine leiomyosarcoma remains a major clinical challenge. Misclassification can lead to inadvertent dissemination of occult malignancy during minimally invasive procedures, while cautious management increases the use of more invasive surgery with greater morbidity. Current diagnostic approaches, including imaging and serum biomarkers, lack sufficient accuracy and standardized criteria. Circulating cell-free RNA in plasma represents a promising alternative for noninvasive tumor classification, but its clinical utility for uterine leiomyosarcoma has not been established.","url":"https://pubmed.ncbi.nlm.nih.gov/42492601/","authors":["Stahlschmidt SR","Lago V","Machado-López A","Boldú-Fernández S","Lara E","Gómez C","Valbuena D","Amadoz A","Jimenez-Almazan J","Montero B","Veiga N","Muruzábal JC","Arencibia O","Andújar M","Nieto A","Iacoponi S","Márquez F","Monleón J","Domingo S","Volkov P","MYOSARC Consortium","Simón C","Mas A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 24","doi":"10.1016/j.ajog.2026.07.024","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492596","name":"Comprehensive Multi-omics Approach to Investigate the Association Between Metabolic Syndrome and Pituitary Neuroendocrine Tumor Risk in UK Biobank: A Cohort Study.","source":"pubmed","abstract":"Metabolic syndrome (MetS) is a cluster of metabolic disorders linked to cancer development and progression. The pituitary gland plays a central role in systemic metabolic homeostasis. However, the contribution of metabolic disorders to pituitary neuroendocrine tumor (PitNET) pathogenesis remains poorly understood.","url":"https://pubmed.ncbi.nlm.nih.gov/42492596/","authors":["Lin P","Wang Y","Ye Z","Qiao N","Ma Z","Guo A","Ye Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1016/j.eprac.2026.07.015","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492524","name":"Transforming blood-derived episignatures into cell-type-agnostic classifiers: A shortcut to prenatal episignatures.","source":"pubmed","abstract":"DNA methylation (DNAm) episignatures are stable disorder-specific epigenetic patterns that serve as valuable biomarkers for assessing variant pathogenicity and phenotypic outcomes in neurodevelopmental disorders (NDDs). However, episignatures derived from whole blood are inherently tissue- and cell-type specific, limiting their applicability in prenatal diagnostics. To explore the feasibility of developing episignatures capable of informing variant pathogenicity across tissues and developmental stages, we conducted a proof-of-concept study using Down syndrome, a common NDD caused by trisomy 21 (T21). We generated a blood-derived T21 episignature using a large cohort of 266 samples. Next, we used that episignature and publicly available DNAm data for 850 T21 and control samples across six different pre- and postnatal tissues to train machine-learning models, thereby enabling accurate prediction of T21 status across all tested tissues. Notably, our results show that models trained on postnatal blood-derived signatures as well as other tissues can generate cell-type-agnostic disease-specific patterns. This method also supported integrating well-characterized postnatal episignatures with a limited set of prenatal samples, which generated an episignature capable of accurately classifying prenatal samples. This approach forges a path for cross-tissue DNAm biomarker development and lays the groundwork for a workflow to rapidly integrate episignatures into prenatal diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42492524/","authors":["Reko N","Torabi-Marashi A","Kallurkar P","Goodman SJ","Awamleh Z","Turinsky AL","Grafodatskaya D","Russell BE","Chong K","Ko JM","Alkhunaizi E","Chitayat D","Greenfeld E","Scherer SW","McCready E","Weksberg R","Choufani S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 6","doi":"10.1016/j.ajhg.2026.06.017","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492497","name":"Benefits of Chain-of-Thought Prompting for Clinical Record Rubric Evaluation in Undergraduate Medical Education: Experimental Evaluation Study With Medical Faculty.","source":"pubmed","abstract":"Large language models in artificial intelligence have been among the tools with a significant and real impact on people's daily lives. In this regard, they serve as an aid in specific fields, such as education, helping educators with cumbersome tasks such as periodic evaluations.","url":"https://pubmed.ncbi.nlm.nih.gov/42492497/","authors":["Nogales A","Denizon S","Mateos Rodriguez A","Cervera Cordero J","Pandelet Barainca G","Aranguren Moliner E","Cervera Barba E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/88652","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492464","name":"Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.","source":"pubmed","abstract":"The second mesiobuccal (MB2) canal detection in maxillary molars represents a notable challenge in endodontics. This scoping review (SR) attempts to map the existing research on artificial intelligence (AI)-based models in MB2 detection, delineating the current stage of clinical readiness and gaps associated with routine clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42492464/","authors":["Shaiban AS","Alobaid MA","Alqahtani OS","Alroomy R","Jabali A","Almnea RA","Alaajam WH","Mehta V","AlMoaleem MM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1016/j.identj.2026.109758","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492377","name":"Predictive modeling of labor epidural catheter replacement or conversion to spinal or general anesthesia: a multicenter retrospective cohort study (2017-2024).","source":"pubmed","abstract":"Labor epidural analgesia may be inadequate during labor or for cesarean delivery anesthesia. No prediction model identifies cases of labor epidural analgesia with increased risk for epidural catheter replacement or conversion to spinal or general anesthesia.","url":"https://pubmed.ncbi.nlm.nih.gov/42492377/","authors":["Berenson DF","Eberhard BE","Kleinlein R","Wheeler NK","Maeda A","Kovacheva VP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.1016/j.ijoa.2026.105250","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492370","name":"Identifying treatment-responsive patient subgroups in a neutral clinical trial of Intensive blood pressure reduction in acute intracerebral hemorrhage: A post hoc explainable machine learning analysis.","source":"pubmed","abstract":"The Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-2) trial reported no overall benefit from intensive blood pressure (BP) reduction in intracerebral hemorrhage (ICH), potentially masking benefit in select patient subgroups. We evaluated whether explainable machine learning could identify treatment-arm subgroups in whom BP reduction is associated with improved outcomes. Using the ATACH-2 dataset, we trained an XGBoost model on two-thirds of the control arm (n = 326) to predict 3-month poor outcome (modified Rankin Scale &gt;3). The model was then applied to treatment-arm patients (n = 499) and held-out controls (n = 163). SHapley Additive exPlanations (SHAP) quantified individual BP risk contributions, and counterfactual perturbation simulated BP reductions. We applied an exploratory, hypothesis-generating grid search to identify selection strategies yielding the lowest odds ratio (OR) for poor outcome in treatment subgroups versus controls. The model achieved an area under the curve of 0.85 (95% confidence interval [CI], 0.81-0.89) in cross-validation and 0.83 (95% CI, 0.77-0.89) in independent validation. Using a combined SHAP and counterfactual analysis, we identified a candidate treatment-responsive subgroup (n = 56) in whom intensive BP reduction was independently associated with lower odds of poor outcome compared with held-out controls (OR = 0.21, 95% CI, 0.08-0.54; p = 0.002). This association remained significant when compared with the subset of held-out controls meeting the same selection criteria. Compared with the remainder of their respective groups, these patients had higher baseline systolic BP, more severe neurological deficits, lower blood glucose levels, and more frequent basal ganglia involvement. These findings highlight the potential of explainable machine learning for precision BP management in acute ICH.","url":"https://pubmed.ncbi.nlm.nih.gov/42492370/","authors":["Tran AT","Qureshi AI","Wen J","Willey JZ","Roh D","Murthy SB","Sabuncu MR","Malhotra A","Kim JA","Falcone GJ","Schwamm LH","Sheth KN","Payabvash S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1016/j.neurot.2026.e00972","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492364","name":"Integrative single-cell and machine learning analysis identifies a tumor doubling time-related prognostic signature and therapeutic targets in head and neck squamous cell carcinoma.","source":"pubmed","abstract":"Head and neck squamous cell carcinoma (HNSCC) exhibits marked molecular heterogeneity and diverse clinical outcomes, partly influenced by HPV status and the tumor immune microenvironment. Although tumor doubling time (TDT) reflects tumor growth kinetics and has prognostic relevance, its molecular basis in HNSCC remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42492364/","authors":["Zhang M","Zhao Z","Zhu Q","Liang S","Sun L","Wei H","Liu S","Zhao M","Li L","Wang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.tranon.2026.102927","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492187","name":"Performance of large language models in electrocardiogram interpretation: A comparative study.","source":"pubmed","abstract":"As large language models (LLMs) are increasingly used to interpret medical concerns, rigorous evaluation of their performance on clinically relevant tasks is essential. However, the new state-of-the-art models from OpenAI and Google as of February 2026, have not been evaluated for their accuracy and consistency in interpreting ECGs independent of clinical context. We aim to compare ChatGPT (GPT-5.2 Thinking) and Gemini (Gemini 3 Pro) on electrical axis and heart rhythm identification to assess current clinical usability and identify areas for improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/42492187/","authors":["Chai GW","Chen SJY","Yang J","Chen XYM","Zhang Q","Kutryk MJB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1016/j.jelectrocard.2026.154411","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492183","name":"Machine learning-based extraction of microstructural parameters from diffusion-weighted imaging starting from realistic in silico cellular substrates: Application in breast and prostate cancer.","source":"pubmed","abstract":"Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a key tool for probing tissue microstructure by measuring water motion. Given the invasiveness and limited sampling of biopsies, Apparent Diffusion Coefficient (ADC), derived from DW-MRI acquisitions, is clinically used as a non-invasive biomarker for tumor heterogeneity, although it provides a simplified representation of tissue microstructure. Recent work maps in vivo DW-MRI signals to simulated dictionaries generated from in silico cellular models, allowing estimation of sub-voxel microstructural properties using clinically feasible acquisitions. However, these methods remain constrained by oversimplified tissue models and require re-optimization when acquisition parameters change, limiting their generalizability. This work seeks to address these limitations by implementing a generalized Machine Learning framework adaptable to different acquisition protocols and trained on realistic in silico cellular models directly derived from real microscopy images.","url":"https://pubmed.ncbi.nlm.nih.gov/42492183/","authors":["Tinelli C","Scotti C","Casaccio F","Zlatic M","Baroni G","Morelli L","Paganelli C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109551","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492182","name":"IEU-Net: Sequence learning of internal texture and external morphology for sonographic kidney segmentation.","source":"pubmed","abstract":"The global prevalence rate of chronic kidney disease (CKD) is on the rise, posing a significant public health concern. Renal ultrasound is an important assessment tool for clinicians. Using automatic ultrasound kidney segmentation would be helpful for kidney evaluation. However, challenges arise due to the limited data size and variabilities in quality across population datasets for kidneys.","url":"https://pubmed.ncbi.nlm.nih.gov/42492182/","authors":["Lo CM","Chang YC","Chen YK","Luh H","Wu PH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109557","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492159","name":"The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.","source":"pubmed","abstract":"Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/42492159/","authors":["Andersen A","Huang R","Liu EJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1016/j.artmed.2026.103495","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492087","name":"A Community-in-the-Loop Approach to Smart Home Monitoring for Aging in Place: Mixed Methods Evaluation of a Co-Designed Prototype.","source":"pubmed","abstract":"The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place.","url":"https://pubmed.ncbi.nlm.nih.gov/42492087/","authors":["Fritz RL","Nguyen-Truong CKY","Phipps J","Hinderlie EE","Rodriguez YL","Nguyen TH","Barling G","Mishuk A","Schoonover H","Zuber C","Rantz M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/88290","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492046","name":"Strategies for Deploying Large Language Models for Ascertaining Clinical Outcomes and Sites of Metastases From Radiology Impressions in Patients With Cancer.","source":"pubmed","abstract":"To evaluate open-source large language models (LLMs) for extracting cancer-specific phenotypic data, benchmark their performance against GPT4 models, and assess the impact of fine-tuning with training data sizes.","url":"https://pubmed.ncbi.nlm.nih.gov/42492046/","authors":["Naqvi SAA","Riaz IB","Saeidi A","Parmar M","Jain A","Banerjee I","Baral C","Kehl KL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Sep","doi":"10.1200/CCI-25-00164","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42492029","name":"Prognostic Significance of Cell-Free DNA Derived 5-Hydroxymethylcytosine Signatures in Newly Diagnosed Multiple Myeloma.","source":"pubmed","abstract":"While survival outcomes in multiple myeloma (MM) have improved with contemporary combination therapies, predicting disease trajectories for individual patients at diagnosis remains a significant challenge. We investigate the prognostic value of a noninvasive biomarker-cell-free DNA (cfDNA)&#x2011;derived 5-hydroxymethylcytosine (5hmC) signature-in newly diagnosed MM, aiming to improve risk stratification at diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/42492029/","authors":["Zhang Z","Derman B","Wang B","Kowitwanich K","Cursio J","Gao L","Appelbaum D","He C","Jakubowiak A","Zhang W","Chiu BC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1200/PO-25-01121","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491960","name":"Machine learning on asynchronous clinical pages to predict clinical deterioration.","source":"pubmed","abstract":"Clinical deterioration in hospitalized patients is often preventable, but traditional early warning scores based on structured data are limited by delayed or inconsistent documentation. We developed and evaluated a machine learning pipeline that predicts clinical deterioration using real-time pager messages exchanged between clinicians.","url":"https://pubmed.ncbi.nlm.nih.gov/42491960/","authors":["Arvelo IC","Shipley K","Wright A","Steitz BD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1093/jamiaopen/ooag122","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491949","name":"Integrated network pharmacology and bioinformatics analysis reveals multi-target mechanisms of HeJie Shengfa Decoction against alopecia areata.","source":"pubmed","abstract":"Alopecia areata (AA) is a common non-scarring autoimmune disease with a complex pathogenesis, high recurrence rates, and difficulty in achieving a cure. Recent years have seen an increasing focus on both clinical and basic research regarding AA. Hejie Shengfa Decoction (HSD), a traditional Chinese medicine formula, has shown certain efficacy in the clinical treatment of AA, though its underlying mechanisms remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42491949/","authors":["Zhao L","Mi H","Diao Z","Tiemuer A","Mou L","Yang J","Zuo L","Zhang C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7717/peerj.21006","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491798","name":"Demographics and obesity using a machine learning approach: Iranian National Obesity Registry (IRNOR).","source":"pubmed","abstract":"Obesity and overweight represent significant global health problems. The prevalence of obesity is increasing rapidly in developing countries and is involved in the etiology of cardiovascular disease, diabetes mellitus, and several cancers. In this study, we aimed to evaluate the relationship between demographic factors and obesity using machine learning (ML) within the population identified by the IRanian National Obesity Registry (IRNOR).","url":"https://pubmed.ncbi.nlm.nih.gov/42491798/","authors":["Nosrati M","Abdollahpour N","Tousi M","Shahabi F","Arabi M","Zarif M","Abasalti Z","Maghsoudi F","Hosseini N","Ferns GA","Kimiafar K","Mobarhan MG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4103/jehp.jehp_378_25","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491770","name":"Subtyping and risk model construction based on taurine metabolism-related genes for predicting prognosis and immune response in lung adenocarcinoma.","source":"pubmed","abstract":"Lung adenocarcinoma (LUAD) remains a major cause of cancer-related deaths worldwide. While taurine has been shown to suppress tumor growth in laboratory models, its relationship with LUAD patient survival remains unclear. This study constructs a taurine metabolism-based prognostic model for LUAD to predict survival and immunotherapy response, supporting personalized treatment.","url":"https://pubmed.ncbi.nlm.nih.gov/42491770/","authors":["Wu L","Li M","Zhang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1515/med-2026-1389","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491709","name":"Precision Nursing Device Management and Artificial Intelligence-Integrated Early Warning Systems for Central Nervous System Infections in the Neurocritical Care Unit: A Scoping Review.","source":"pubmed","abstract":"Central nervous system (CNS) infections and clinically overlapping neuroinflammatory conditions in the neurocritical care unit (neuro-ICU) are associated with profound mortality and prolonged clinical burdens. Invasive neuromonitoring devices, particularly external ventricular drains (EVDs), significantly increase the risk of infection, while delayed clinical manifestations often hinder early recognition and intervention. This scoping review aimed to systematically map the current landscape of precision nursing device management and artificial intelligence (AI)-integrated early warning systems (EWS) for CNS infections in the neuro-ICU, rather than to definitively evaluate their clinical effectiveness. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework, a systematic search of four major databases was conducted. Eligibility criteria included studies focusing on adult patients in the neuro-ICU. A rigorous multi-round filtration process was applied. A total of 24 eligible studies, encompassing longitudinal nursing management cohorts, quality improvement protocols, and machine learning predictive models, were meticulously selected for data extraction and narrative synthesis. The systematic optimization of EVD care bundles, specifically the reduction of routine cerebrospinal fluid sampling and the implementation of closed needleless systems, significantly decreased device-related infection rates, occasionally achieving zero infections under optimal interdisciplinary rounding conditions. Furthermore, AI-driven EWS algorithms (e.g., Random Forest, XGBoost {Seattle, WA: University of Washington}) and non-invasive intracranial pressure (ICP) waveform clustering demonstrated exceptional prognostic accuracy. These advanced models successfully predicted ventriculitis up to 24 h&#xa0;prior to positive bacterial cultures. The management of severe complications, such as paroxysmal sympathetic hyperactivity, remains heavily reliant on continuous evidence-based nursing vigilance. The integration of AI-driven predictive modeling with standardized, precision nursing bundles represents a paradigm shift from reactive treatment to proactive prevention in neurocritical care. Translating these technologies into bedside clinical decision support systems will be pivotal in optimizing individualized patient outcomes and redefining neuro-ICU nursing standards.","url":"https://pubmed.ncbi.nlm.nih.gov/42491709/","authors":["Zhou Y","Xie H","Hu M","Luo G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111026","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491704","name":"Plasma Lipidomic Signatures Across the Healthy-Pre-COPD-COPD Continuum Identified by Machine Learning.","source":"pubmed","abstract":"Chronic obstructive pulmonary disease (COPD) imposes a substantial global burden, and Pre-COPD is regarded as an early, high-risk window that comprises distinct phenotypes, including small airway dysfunction (SAD), emphysema, and preserved ratio impaired spirometry (PRISm), whose early molecular heterogeneity is not fully captured by spirometry or imaging. This study aimed to characterize plasma lipidomic profiles across the healthy-Pre-COPD-COPD continuum, including these three phenotypes, and to apply regularized machine learning to identify lipid signatures shared across or specific to individual phenotypes.","url":"https://pubmed.ncbi.nlm.nih.gov/42491704/","authors":["Xing Y","Yu G","Tian Q","Zhang T","Li D","Guo Y","Luo X","Li W","Liu X","Xu J","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/COPD.S596991","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491601","name":"An evaluation of first-year medical students' perspectives on hands-on learning of living anatomy using ultrasonography in anatomy.","source":"pubmed","abstract":"Cadaveric dissection has been the gold standard teaching tool and often medical students find it challenging to visualize the functioning human. Point-of-care ultrasound is the future stethoscope and hence, the study aimed to validate students' perceptions on hands-on ultrasound session.","url":"https://pubmed.ncbi.nlm.nih.gov/42491601/","authors":["Shanthi Prabahar P","Sam F","Jacob J","Kulathu Mathew JK","Rabi S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Aug","doi":"10.1016/j.mjafi.2024.11.016","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491420","name":"Prognostic value of the atherogenic index of plasma for early-stage diabetic kidney disease in type 2 diabetes: a retrospective cohort study using supervised machine learning.","source":"pubmed","abstract":"Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD). Its insidious onset means that once it progresses to the stage of heavy proteinuria or significant decline in renal function, treatment difficulty and burden increase dramatically. Therefore, identifying high-risk individuals in the early-stage DKD (ES-DKD, stages 1-2) and intervening promptly is crucial for delaying disease progression and improving prognosis. The atherogenic index of plasma (AIP) is a novel composite indicator reflecting dyslipidemia and insulin resistance. However, its prognostic value in ES-DKD among patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aims to investigate the association between AIP and the risk of developing ES-DKD in T2DM patients, and to develop prognostic prediction models using supervised machine learning (SML) algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/42491420/","authors":["Wang Y","Chen H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnut.2026.1844510","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491413","name":"A prediction model for urological tumor metastasis using liquid biopsy-derived biomarkers.","source":"pubmed","abstract":"To construct and validate a prediction model for tumor metastasis in patients with urological tumors based on liquid biopsy biomarkers and clinical characteristics, to facilitate early clinical identification of metastasis risk and formulation of individualized diagnosis and treatment plans.","url":"https://pubmed.ncbi.nlm.nih.gov/42491413/","authors":["Qu J","Zhang J","Huang X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1718624","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491356","name":"Identification of AICD-associated transcriptomic markers in major depressive disorder.","source":"pubmed","abstract":"Major Depressive Disorder (MDD) is a complex mental disorder with unclear molecular mechanisms. This study aimed to identify key genes associated with ATP-induced cell death (AICD) in MDD and elucidate their roles in disease pathogenesis.","url":"https://pubmed.ncbi.nlm.nih.gov/42491356/","authors":["Xiong S","Liao L","Chen M","Peng R","Luo Q","Gan Q","Yang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1782515","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491313","name":"Health Disparities in Orthobiologics: Factors Influencing Access to Platelet-Rich Plasma for Knee Osteoarthritis.","source":"pubmed","abstract":"Platelet-rich plasma (PRP) is an emerging therapy that demonstrates potential benefits for knee osteoarthritis (OA); however, the accessibility of PRP remains limited, in part, because its use is not covered by insurance.","url":"https://pubmed.ncbi.nlm.nih.gov/42491313/","authors":["Addani M","Arthurs J","Haque S","Geamanu A","Beaman L","Shapiro S","Master Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1177/23259671261458577","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491307","name":"Structured-to-text ClinicalBERT embeddings with random Forest for heart disease prediction: a proof-of-concept study on the UCI Statlog dataset.","source":"pubmed","abstract":"Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for accurate and early risk prediction systems. Traditional machine learning approaches for cardiovascular disease prediction primarily rely on structured clinical attributes and may not fully capture contextual relationships among patient features. To address this limitation, this study proposes a structured-to-text ClinicalBERT framework that transforms structured cardiovascular records into contextual clinical text representations and utilizes transformer-based embeddings for heart disease prediction. The study employs a publicly available UCI Statlog/Kaggle heart disease dataset containing 270 complete patient records. Structured cardiovascular attributes, including age, sex, chest pain type, blood pressure, cholesterol level, electrocardiogram results, and heart rate measurements, are converted into clinically meaningful textual descriptions. These text representations are processed using ClinicalBERT to generate contextual embeddings, which are subsequently used as input features for a Random Forest classifier. Model performance was evaluated using an 80:20 train-test split and assessed through Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. Experimental results demonstrate that the proposed ClinicalBERT + Random Forest framework achieved an accuracy of 95.6%, precision of 88.89%, recall of 95.30%, F1-score of 91.30%, and a ROC-AUC of 0.71 on the held-out test set. Comparative analysis with conventional machine learning models indicates that contextual embeddings generated by ClinicalBERT provide improved feature representation for cardiovascular risk prediction. The findings demonstrate the feasibility of adapting ClinicalBERT to structured cardiovascular data through contextual text generation. Although the proposed framework shows promising predictive performance, the study should be considered a proof-of-concept due to the limited dataset size and absence of external validation. Future work will focus on multicenter evaluation, explainable AI techniques, and broader clinical validation to enhance generalizability and real-world applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42491307/","authors":["Priyadharshini U","Vijayan R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1844707","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491260","name":"Telemedicine use among physicians in the German outpatient sector: A secondary analysis of a cardiologist-dominated web-based survey with Bayesian model averaging and exploratory machine learning.","source":"pubmed","abstract":"Cardiovascular diseases remain a major health burden in Germany, and telemedicine (TM) offers promising solutions for outpatient care, yet barriers limit uptake. While prior studies relied on qualitative or conventional statistical methods, they often struggled with model uncertainty and complex relationships. Building on a national survey, this study applies Bayesian Model Averaging (BMA) and extreme gradient boosting (XGBoost).","url":"https://pubmed.ncbi.nlm.nih.gov/42491260/","authors":["Petit P","Vuillerme N","Gehrmann J","Stephan J","Mühlensiepen F","Nübel J","Martens E","Hahn F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261460253","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491247","name":"Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.","source":"pubmed","abstract":"Natural language processing (NLP) techniques offer promising solutions for semi-automating the time-consuming process of abstract screening in systematic reviews. The exponential growth of published literature has created significant bottlenecks, with review teams manually assessing thousands of abstracts over weeks to months. Single reviewers can miss 5-13% of relevant studies, necessitating dual screening that further increases workload. Advances in artificial intelligence, including deep learning models such as BERT and its successors, show potential for automating this critical step, but comprehensive evidence on optimal approaches, performance, and practical feasibility remains limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42491247/","authors":["Shankar R","Goh Z","Xu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1177/18911803261469813","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491158","name":"A mitoxyperilysis-related signature stratifies prognosis and identifies an aggressive colorectal cancer ecosystem with immune remodeling.","source":"pubmed","abstract":"Mitoxyperilysis is a recently described lytic cell death pathway linked to innate immune activation and metabolic disruption. Its clinical and biological relevance in colorectal cancer (CRC) remains unclear. This study aimed to develop a mitoxyperilysis-related signature (MRS) for prognostic stratification and to characterize its associated tumor microenvironmental features.","url":"https://pubmed.ncbi.nlm.nih.gov/42491158/","authors":["Zhang Y","Sun B","Lin J","Ding C","Zhou X","Xu X","Dai K","Fan X","Lu M","He Z","Yang X","Zheng M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1851988","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491106","name":"Construction and verification of an early warning model for hypothermia in emergency trauma patients based on the random forest method.","source":"pubmed","abstract":"To construct an early warning model for hypothermia in emergency trauma patients using Random Forest (RF) and compare its predictive performance with Logistic regression.","url":"https://pubmed.ncbi.nlm.nih.gov/42491106/","authors":["Cao J","Mu X","Zhu H","Liu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.62347/HXZL9970","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42491036","name":"Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.","source":"pubmed","abstract":"Cardiac amyloidosis (CA) is an infiltrative restrictive cardiomyopathy characterized by the deposition of &#x3b2; -fold amyloid, often presenting as left ventricular hypertrophy. Early nonspecific symptoms lead to frequent misdiagnosis as hypertrophic cardiomyopathy, delaying care for this progressive disease. While machine learning (ML) has been applied to the diagnosis of CA, systematic evidence of its accuracy remains lacking, hindering the development of intelligent detection tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42491036/","authors":["Song Y","Wang Q","Jia L","Pei Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1835652","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490963","name":"Potential of Image2Image translation in reducing AI bias attributed to differences in CT reconstruction methods: proof-of-concept study on a paired dataset.","source":"pubmed","abstract":"The choice of image reconstruction approach, such as Iterative Model Reconstruction (IMR) and iDose 4 , is critical in computed tomography (CT) as distribution shifts between them can introduce systematic bias in clinical AI models, posing a barrier to efficient, scalable AI deployment in radiology. While Image-to-Image (I2I) translation offers a path toward harmonization, efficient and translation-feasible AI in radiology demands that such approaches be validated not only on image quality metrics but also on downstream clinical task performance.","url":"https://pubmed.ncbi.nlm.nih.gov/42490963/","authors":["Li F","Caldeira LL","Jaiswal A","Hokamp NG","Beyan O","Kutafina E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fradi.2026.1875867","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490947","name":"Microbiome and cancer: mechanistic insights, diagnostic potential, and therapeutic strategies.","source":"pubmed","abstract":"The human microbiome is now recognized as an active and dynamic participant in cancer biology rather than a passive bystander. Increasing evidence demonstrates that microbial dysbiosis contributes to tumor initiation and progression through chronic inflammation, genotoxic toxin production, metabolic reprogramming, immune modulation, and direct reshaping of the tumor microenvironment. Specific microbial factors including colibactin, Bacteroides fragilis toxin, CagA, and Fusobacterium adhesins intersect with canonical oncogenic pathways. Linking microbial activity to genomic instability and immune evasion. Microbial metabolites such as secondary bile acids, lipopolysaccharide, hydrogen sulfide, and short-chain fatty acids further regulate epithelial integrity, epigenetic remodeling, and immune cell dynamics in a context-dependent manner. Beyond tumorigenesis, the microbiome critically determines therapeutic response. Microbial communities influence chemotherapy and radiotherapy outcomes and shape immune checkpoint blockade efficacy through immune priming, antigen mimicry, and microbiome-metabolite-immune interactions that govern treatment responsiveness. Emerging preclinical studies and early clinical investigations suggest that microbiome modulation, including fecal microbiota transplantation (FMT), may help restore immunotherapy sensitivity in selected patients; however, larger controlled trials are required to establish efficacy, safety, and long-term clinical benefits. This review integrates mechanistic, preclinical, and clinical evidence across microbiome-driven carcinogenesis, tumor microenvironment remodeling, drug metabolism, and biomarker development. Advances in circulating microbial DNA profiling and machine learning-based diagnostics further position the microbiome as both a mechanistic driver and a translational target in precision oncology. We also discuss key challenges, including interindividual variability, standardization of methodologies, and the need for personalized therapeutic strategies. Collectively, understanding and harnessing microbiome-cancer interactions hold significant promise for improving cancer diagnosis, treatment, and patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42490947/","authors":["Kumar V","Chaudhary A","Gautam M","Verma P","Singh M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1844436","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490932","name":"Development and validation of an interpretable machine learning-based predictive model for breast cancer bone metastasis.","source":"pubmed","abstract":"Breast cancer is one of the most common malignancies worldwide, with bone metastasis representing its most frequent distant metastatic form, significantly worsening patient prognosis. This study aims to develop a machine learning-based predictive model for accurately assessing the risk of bone metastasis in breast cancer patients, thereby enabling personalized risk stratification, early clinical intervention, and optimized treatment strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42490932/","authors":["Fan C","Tian M","Ding Z","Peng J","Yiliyiming G","Fan M","Tuerxun A","Qiu B","Zhu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1692909","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490893","name":"Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.","source":"pubmed","abstract":"Artificial intelligence (AI) is becoming central to genomics and multi-omics, but its concepts, architectures, applications, evaluation standards, and translational requirements remain fragmented. This scoping review mapped how AI is defined and operationalized in genomic science, including machine learning, deep learning, graph-based methods, foundation models, and large language models, and synthesized their data modalities, applications, evaluation practices, interpretability strategies, and governance challenges.","url":"https://pubmed.ncbi.nlm.nih.gov/42490893/","authors":["Rodrigues WF","Parise MTD","Parise D","Dos Santos LM","Ribeiro PDS","Ristow P","Cardoso MS","Azevedo VAC","Soares SC","Minardi RCM","Goés-Neto A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fbinf.2026.1829576","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490877","name":"A lightweight DeepME model based on improved YOLOv11 architecture for macular edema detection and treatment monitoring.","source":"pubmed","abstract":"We developed and validated an improved YOLOv11-based deep learning algorithm for accurate macular edema detection in optical coherence tomography (OCT) images, and built DeepME-a lightweight system for diagnosis and treatment recommendations.","url":"https://pubmed.ncbi.nlm.nih.gov/42490877/","authors":["Bai X","Yi M","Chen T","Feng N","Shi Q","Li T","Hua R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1864912","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490873","name":"A machine learning model for 90-day mortality prediction in hepatitis B virus-related acute-on-chronic liver failure: the pivotal role of CALLY index.","source":"pubmed","abstract":"Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a life-threatening syndrome, the condition can deteriorate rapidly, and the 90-day mortality rate is high. Due to the rapid changes in the clinical course, early and accurate risk stratification is crucial for timely decision-making and resource allocation in the ICU. This study has developed and verified a machine learning framework that integrates the C-reactive protein-albumin-lymphocyte (CALLY) index to predict the 90-day mortality rate of HBV-ACLF patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42490873/","authors":["Zhang Y","Li C","Su S","Huang J","Fu S","Tang S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1814799","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"pmid:42490870","name":"Identifying risk factors for drug use recurrence with ecological momentary assessment, wearable technologies, and machine learning: a feasibility trial of peer recovery support specialist intervention.","source":"pubmed","abstract":"Identifying predictors of relapse/drug use recurrence (DUR) in real-time could allow the rapid implementation of overdose prevention interventions for those with substance use disorders (SUD). Wearable devices and phone-based applications for self-reported assessments in the patient's natural environment [e.g., ecological momentary assessment (EMA)] have potential for predicting DUR. Peer recovery support specialists (PRSS) play a critical role in reducing DUR risk. The benefits of utilizing PRSS resources in response to alerts derived from wearable technology and EMA data are unknown. This feasibility study investigates a) the use of wearable technologies/EMA to predict physiological/behavioral biomarkers of DUR and b) opportunities for PRSS-based interventions based on predictions.","url":"https://pubmed.ncbi.nlm.nih.gov/42490870/","authors":["Mahoney Iii JJ","Finomore VS","Marton JL","England LJ","McFoy S","Romanoff D","Ramadan J","Zarei A","Mahyoub A","Crooks J","Berry JH","Shirk SD","Ranjan M","Rezai AR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1744937","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490854","name":"Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.","source":"pubmed","abstract":"Artificial intelligence is quietly reconfiguring the landscape of drug discovery. It sharpens the search for new targets, refines lead compounds, and helps tailor clinical trial designs. Behind these advances sits a family of methods-convolutional, recurrent, and graph neural networks, generative adversarial networks, variational autoencoders, diffusion models, and Transformers-that has found its way into every stage of the pipeline. Tasks that once demanded years of trial and error, from pulling meaningful features out of molecules to predicting drug-target affinity and crystal structures with polymorph stability, now run faster and often with greater accuracy. This early insight lets researchers flag solid-form properties that influence bioavailability and manufacturability, trimming timelines and, in principle, lifting success rates. To map a field that keeps shifting shape, we searched PubMed, Web of Science, Scopus, and Google Scholar for peer-reviewed reports published between January 2007 and April 2026. The story the clinical cases tell is double-edged: one AI-discovered candidate has reached Phase IIa with encouraging efficacy, another stalled in Phase I when safety signals surfaced. AI can catch adverse effects earlier in toxicity assessments, yet nagging hurdles endure-biological complexity, patchy data, and a shortage of scientists fluent in both machine learning and pharmaceutics. Digging ourselves out will demand data standardization, nimble regulatory frameworks, and cross-disciplinary training. Weaving multimodal data together with explainable AI is becoming non-negotiable for transparency and regulatory confidence. The technology is now stretching into complex systems like Traditional Chinese Medicine and natural product screening. As AI continues to mature, it will reshape drug development in ways we are only beginning to grasp, but one truth remains stubborn: clinical translation still rests on rigorous experimental evidence.","url":"https://pubmed.ncbi.nlm.nih.gov/42490854/","authors":["Mao Z","Yan L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphar.2026.1870527","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490847","name":"Upregulated CD177 on neutrophils is implicated in sepsis pathogenesis and necroptosis-driven inflammation.","source":"pubmed","abstract":"Sepsis remains a leading cause of mortality in critical care, with dysregulated inflammatory responses driving disease progression. However, the role of necroptosis in sepsis pathogenesis remains incompletely understood.","url":"https://pubmed.ncbi.nlm.nih.gov/42490847/","authors":["Liu H","Cheng X","Li J","Wei W","Yuan Y","Wang W","Wang W","Liu F","Zheng J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1785356","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490792","name":"Early multi-cancer detection using liquid biopsy: emerging biomarkers and clinical strategies.","source":"pubmed","abstract":"Liquid biopsy has become a revolutionary method for the early detection of cancer as a non-invasive technology that can assess circulating tumor material in biofluids. Liquid biopsy allows dynamic monitoring of tumor evolution, genetic changes and treatment responses, which is different from traditional tissue biopsy which offers a static and potentially narrow view of tumor biology. This mini-review will summarize the rapidly evolving future of circulating biomarkers (circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs, proteins, exosomes and epigenetic fingerprints), with their potential for early multi-cancer biomarkers, and their integration into early detection. The analytical sensitivity of liquid biopsy has expanded dramatically through technologies such as next-generation sequencing (NGS), digital PCR, and advanced proteomics. In addition, data collection has been enhanced through machine learning for increased predictive performance and the identification of new biomarkers. This review discusses clinical performance between liquid and tissue biopsy and the value of combined biomarker methods to improve accuracy for the detection of early disease. Finally, future directions will be presented to identify new methods of integration, improved costs and the establishment of early detection programs at the population scale.","url":"https://pubmed.ncbi.nlm.nih.gov/42490792/","authors":["Nirob SR","Morol MK","Rahman D","Hui LT","Nandi D","Rahman M","Al Jubair A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1877911","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490747","name":"Metabolomic Profiling and Machine Learning-Based Prediction of High-Dose Methotrexate-Induced Liver Injury in Pediatric Acute Lymphoblastic Leukemia.","source":"pubmed","abstract":"High-dose methotrexate (HD-MTX) is essential for pediatric acute lymphoblastic leukemia (ALL) but frequently causes hepatotoxicity. To address the critical lack of early prediction tools, this study investigated untargeted metabolomic profiles to identify predictive biomarkers and establish robust machine learning (ML) models for early clinical risk assessment and therapeutic optimization.","url":"https://pubmed.ncbi.nlm.nih.gov/42490747/","authors":["Guo K","Wang L","Feng X","Yang J","Zhang X","Zhao Y","Ma Y","He J","Zhang M","Lang W","Zhang J","Zhao Y","Zhang G","Li W","Wang H","Zhang G","Yang Y","Yang X","Wu Y","Liu H","Peng M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1002/1545-5017.70424","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490731","name":"Exploring the Prevalence and Key Predictors of Probable Depression Among Parents and Primary Caregivers of Children and Adolescents With Specific Learning Disorders in the Kingdom of Saudi Arabia: A Cross-Sectional Explainable Machine-Learning Study.","source":"pubmed","abstract":"This study estimated the prevalence of probable depression among parents and primary caregivers of children and adolescents with specific learning disorders (SLDs) in Saudi Arabia and developed explainable machine-learning models for screening-oriented classification. In this multicentre cross-sectional study, 612 caregivers were recruited through clinical, psychoeducational, educational and community pathways. Children had formally documented SLD diagnoses established through qualified clinical, psychoeducational, school psychology or multidisciplinary services. Probable depression was defined as a Patient Health Questionnaire-9 score of 10 or higher. Forty-seven candidate predictors covered parent, child, family, psychosocial, socio-economic, stigma-related and service-access domains. Elastic-net logistic regression, random forest, XGBoost, LightGBM and a soft-voting ensemble were evaluated using stratified 10-fold cross-validation. Probable depression was identified in 218 participants (35.6%; 95% CI 31.8-39.6). XGBoost and the soft-voting ensemble achieved the highest ROC AUC (0.916); XGBoost yielded an average precision (AP) of 0.867 and a Brier score of 0.115. At a screening-oriented threshold of 0.425, XGBoost achieved sensitivity of 0.858, specificity of 0.830 and negative predictive value of 0.913. SHAP analyses identified parenting stress, parental anxiety symptoms, sleep quality, SLD severity, child emotional and behavioural difficulties, perceived social support and coping capacity as the most influential predictive features. Probable depression was common in this service- and community-recruited sample. Explainable models may support family-centred identification and referral, but external evaluation is required before implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42490731/","authors":["Almulla AA","Khasawneh MAS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Aug","doi":"10.1002/cpp.70310","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490623","name":"Machine learning-optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance.","source":"pubmed","abstract":"To develop and validate a machine learning model identifying typhoid fever patients at risk of unnecessarily prolonged hospitalization, and to quantify the clinical, financial, and systemic consequences of model-guided discharge optimization in a fragmented, multi-payer public insurance system.","url":"https://pubmed.ncbi.nlm.nih.gov/42490623/","authors":["Momahhed SS","Haghighathoseini A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354148","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42490615","name":"AI-Assisted Engineering of Glycyrrhizic Acid/Simvastatin Nanocrystals for Multifunctional Treatment of Bacterial Osteomyelitis.","source":"pubmed","abstract":"Bacterial osteomyelitis remains a formidable challenge in clinic because existing monotherapies fail to block inevitable infection, uncontrolled inflammation, and impaired bone regeneration &#x200c;concurrently. Here, we present an AI-assisted strategy that integrates antibacterial, anti-inflammatory, and pro-osteogenic activities into a single nanocrystal. Through machine learning&#x2011;assisted screening from FDA-approved active pharmaceutical ingredients (API), we identified glycyrrhizic acid and simvastatin as a multifunctional combination capable of self-assembling into uniform nanocrystals (SGNCs) with ultrahigh drug loading. SGNCs effectively neutralize reactive oxygen species, suppress M1 macrophage polarization, promote bactericidal effects, and reverse infection-impaired osteogenic differentiation. Mechanistically, RNA sequencing analysis further reveals that the beneficial effects of SGNCs are associated with the inhibition of inflammatory response via cytokine-cytokine receptor interaction pathway and the activation of bone regeneration program via the Wnt signaling pathway. As a consequence, SGNCs eradicate bacterial burden and restore bone microarchitecture with excellent biocompatibility in a rat osteomyelitis model. Our insights highlight an AI-assisted strategy that creates a mechanism-targeting nanomedicine solely from APIs for the efficient treatment of bacterial osteomyelitis, which currently requires multimodal management.","url":"https://pubmed.ncbi.nlm.nih.gov/42490615/","authors":["Han Y","Zhao Y","Yang C","Niu M","Dai J","Ning Q","Xiao K","Liang J","Zhang W","Wang L","Shao D","Li D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1002/advs.76694","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490583","name":"Large Language Model Few-Shot Learning for Predicting Individual Treatment Response to Smartphone-Based Mindfulness in Autistic Adults With Anxiety: Secondary Analysis of a Randomized Controlled Trial.","source":"pubmed","abstract":"Anxiety disorders are highly prevalent among adults with autism, with 20%-65% experiencing at least one diagnosable anxiety disorder. While mindfulness-based interventions have demonstrated efficacy for anxiety reduction, treatment response varies considerably across individuals. Machine learning approaches offer potential for identifying who is most likely to benefit from smartphone-based mindfulness interventions, enabling personalized treatment recommendations.","url":"https://pubmed.ncbi.nlm.nih.gov/42490583/","authors":["Ahn G","Li CE","Liang A","Choi W","Ahn S","Roberts C","Gabrieli JDE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/89054","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490562","name":"Digital phenotyping of CGM engagement reveals distinct glycemic outcomes.","source":"pubmed","abstract":"Benefits from continuous glucose monitors (CGMs) may depend on how devices are used over time-not only how often they are used. We linked one year of device-generated CGM wear data from 2,351 U.S. Veterans to electronic health records (EHRs) to characterize real-world usage during the first year after CGM initiation. To compare longitudinal use patterns in the presence of unsynchronized sensor replacement-related gaps and intermittent interruptions, we aligned daily wear streams using a complexity-adjusted, time-adaptive optimal transport (TAOT) distance and applied spectral clustering to identify data-driven usage phenotypes. To estimate the adjusted association between CGM usage phenotypes and clinical outcomes, we used a double/debiased machine learning framework to quantify 12-month changes in time in range (&#x394;TIR) and mean glucose (&#x394;MG). We identified three reproducible patterns of CGM usage: Consistent, Fluctuating, and Low engagement. Relative to Consistent wear, Fluctuating usage was associated with worse glycemic change (&#x394;TIR&#x2009;=&#x2009;-3.56%, 95% CI: -4.76 to -2.36; &#x394;MG&#x2009;=&#x2009;+7.12 mg/dL, 95% CI: 4.91 to 9.32), and Low engagement showed larger deterioration (&#x394;TIR&#x2009;=&#x2009;-7.00%, 95% CI: -10.80 to -3.14; &#x394;MG&#x2009;=&#x2009;+14.09 mg/dL, 95% CI: 6.52 to 21.67). Notably, 73.2% of Fluctuating users still met a common \"adherence\" threshold (&#x2265;80% days worn), indicating that simple coverage metrics can miss clinically relevant instability. The differences in glycemic change (&#x394;TIR and &#x394;MG) between the Consistent and Fluctuating groups were most pronounced among subgroups with more intensive diabetes management needs, such as insulin pump or glucagon users, suggesting that sustained CGM usage may be particularly important when clinical management is more complex. Beyond CGM and diabetes, this work provides a generalizable framework for characterizing longitudinal usage patterns of intermittently used digital health technologies and linking derived usage phenotypes to clinical outcomes. The approach can support more precise evaluation, monitoring, and intervention design for a wide range of real-world digital health tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42490562/","authors":["Zhang B","Okuno T","Li E","Riggins E","Shen EL","Vansomphone J","Everett EM","Norman GJ","Miller DR","Reaven PD","Zhou H","Zhou JJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1371/journal.pdig.0001505","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490550","name":"Initial-Visit Specialty Triage in Rare Diseases Using Large Language Models: Retrospective Benchmarking Study.","source":"pubmed","abstract":"Specialty triage at first contact is an overlooked step in early diagnostic pathways for rare diseases. Patients often present with overlapping, multisystem, and atypical manifestations, making first-visit specialty selection challenging and potentially prolonging diagnostic pathways.","url":"https://pubmed.ncbi.nlm.nih.gov/42490550/","authors":["Song J","Xu Z","Xiao M","Bi C","Zhang Y","Zheng X","Li X","Cao Q","Lu Z","Yang H","Shen B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/101711","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490549","name":"Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study.","source":"pubmed","abstract":"Test-time scaling has emerged as a promising method to enhance the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) during inference without additional training. While foundational studies established scaling paradigms in general domains, their applicability to the unique complexities of medical AI remains underexplored.","url":"https://pubmed.ncbi.nlm.nih.gov/42490549/","authors":["Oh G","Kim S","Park S","Kim BH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2196/90693","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490267","name":"Mass-Spectrometry-Identified Quality Markers of Jingtong Granules Are Predicted to Engage Upregulated Neutrophil Granule Enzymes in Cervical Spondylotic Radiculopathy: An Integrative Machine-Learning and Molecular-Dynamics Study.","source":"pubmed","abstract":"Jingtong Granules is a clinically approved seven-herb formula for CSR, a condition in which radicular pain is sustained partly by inflammatory and immune processes, yet the immune-cellular state of CSR and the immune-level mechanism of the formula remain poorly defined.","url":"https://pubmed.ncbi.nlm.nih.gov/42490267/","authors":["Yang G","Han C","Li H","Chang L","Qiu W","Zheng J","Peng B","Liu H","Yin X","Feng M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct 15","doi":"10.1002/rcm.70145","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490152","name":"Predicting KRT or Death in Critically Ill Patients with Rhabdomyolysis Using Machine Learning: A Multicenter Study.","source":"pubmed","abstract":"Rhabdomyolysis is associated with outcomes ranging from muscle injury to AKI, KRT, and death. Early identification of patients at highest risk of severe outcomes may help inform triage, monitoring, and nephrology consultation. We aimed to develop and externally validate a machine-learning model to predict in-hospital death or KRT in patients with rhabdomyolysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42490152/","authors":["Mercier J","Sharma A","Boseovski J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.34067/KID.0000001292","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490096","name":"Surface-Engineered Carbon Nanotubes Aerogel Sensor Array Enabled AI-Driven Selective Detection of Biomarker VOCs: A Proof-of-Concept in Simulated Humid Mixtures.","source":"pubmed","abstract":"Selective detection and quantitative analysis of biomarker volatile organic compounds (VOCs) in complex gas mixtures remain a major challenge for noninvasive breath diagnostics due to wide concentration variability and strong humidity interference. Here, a deliberately heterogeneous chemiresistive sensor array based on six flexible, freestanding porous carbon nanotube (CNT) aerogel films is reported, where the CNT aerogel simultaneously functions as a conductive ambient-temperature transducer and a high-surface-area scaffold. Selectivity was engineered through integration with metal oxides (CuO, V 2 O 5 , TiO 2 , WO 3 ) and graphitic carbon nitride (g-C 3 N 4 ), generating diverse electronic structures and adsorption kinetics that produce information-rich response signatures. The array was evaluated using six structurally distinct VOCs (acetone, methanol, ethanol, hexane, toluene, and benzene) under varying humidity environments (RH 40-90%). To overcome environmental drift, multidimensional feature vectors were derived from both steady-state response magnitudes and transient kinetic slopes. Correlation analysis revealed weakly correlated and nonlinear relationships among sensor features, motivating the development of a dual-branch deep neural network for simultaneous VOC classification and concentration prediction within a defined, nested multicomponent mixture framework. The model achieved near-perfect classification accuracy and precise quantitative prediction ( R 2 &gt; 0.98) across the discrete composition levels dictated by the mixture recipes. Under simulated breath-biomarker matrices featuring multiple competing VOCs and high background relative humidity (up to 90% RH), the platform demonstrated robust fingerprint deconvolution, achieving &#x223c;99% acetone quantification accuracy within the evaluated mixture templates. While cross-reactivity against physiological interferents such as CO 2 and NH 3 remains to be evaluated for clinical deployment, this AI-assisted, room-temperature CNT aerogel sensor platform establishes a robust proof-of-concept for the pattern-matching analysis of complex VOC environments, advancing reliable sub-ppm trace chemical tracking for future noninvasive biomarker diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42490096/","authors":["Rohilla R","Alexander R","Sawant SG","Dasgupta K","Parida SC","Jat RA","Prakash J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1021/acssensors.6c00676","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42490033","name":"Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.","source":"pubmed","abstract":"Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT.","url":"https://pubmed.ncbi.nlm.nih.gov/42490033/","authors":["Liu L","Zhong Q","Weng C","Chen L","Sun Y","Wu S","Zhuang M","Li W","Xia G","Shangguan Z","Wu D","Zheng C","Xie J","Chen Q","Cai L","Huang C","Li P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s10120-026-01777-0","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489832","name":"Integrated machine learning and structural bioinformatics guided identification of novel molecular scaffolds as renin inhibitors.","source":"pubmed","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of death globally, with hypertension as its critical hallmark. The Renin-Angiotensin-Aldosterone-System (RAAS) plays a central role in regulating blood pressure, highlighting its relevance for antihypertensive drug development. Despite extensive research, Aliskiren remains the only clinically approved direct renin inhibitor (DRI), underscoring the necessity for novel scaffolds with improved pharmacokinetic profiles. In this study, we employed an integrated machine learning (ML), ligand-based (LBDD), and structure-based drug design (SBDD) approach to identify and characterize new chemical scaffolds with potential renin inhibitory activity. Multiple ML models were built using various molecular descriptors, followed by extensive feature selection, and data balancing with SMOTE. To enhance model interpretability, we performed SHAP analysis on the top ML models to reveal key descriptors and substructures associated with predictions for renin inhibition. In parallel, several ligand-based pharmacophore models were constructed using the crystal structure of human renin. Maybridge library was screened using the best models resulting from both approaches, and the consensus compounds were prioritized using molecular docking to assess their inhibitory potential through the renin inhibitory assay. Molecular dynamics, along with MM/PBSA, were then employed to evaluate the structural stability and binding persistence of the screened compounds with promising activity. The predicted ADME properties and structural analysis further established the relevance of the novel scaffolds identified through our robust integrated approach. From the 12 shortlisted compounds, our study identified 4 promising hits - HTS00804, HTS05294, BTB13902, and RJC01726 with diverse piperazine and piperidine-substituted scaffolds for renin inhibition. All four hits exhibited IC50 values between 1.29 &#xb5;M and 4.19 &#xb5;M. Among all, HTS00804 demonstrated 53 and 73% renin inhibition in vitro at 1&#xb5;M and 10 &#xb5;M concentrations, respectively and can be explored as a starting scaffold for further structural optimization through medicinal chemistry efforts to design next-generation direct renin inhibitors (DRIs).","url":"https://pubmed.ncbi.nlm.nih.gov/42489832/","authors":["Talware SK","Bhati G","Srivastava G","Shukla S","Ahmed S","Siddiqi MI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s11030-026-11676-2","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489810","name":"Large-Scale Plasma Proteomics Profiles for Predicting Atrial Fibrillation Associated Stroke Risk : Type of manuscript: Original Research.","source":"pubmed","abstract":"Stroke is a major complication of atrial fibrillation (AF), and risk prediction using the congestive heart failure, hypertension, age, diabetes, stroke, vascular disease, and sex category score (CHA&#x2082;DS&#x2082;-VASc) remains limited by residual heterogeneity. We aimed to identify plasma proteins associated with post-AF stroke and evaluate whether a protein score provides incremental predictive information beyond CHA&#x2082;DS&#x2082;-VASc.","url":"https://pubmed.ncbi.nlm.nih.gov/42489810/","authors":["Huang S","Feng X","Wu H","Liang R","Gao Y","Wang Z","Tang B","Wu J","Fang J","Yang Z","Tiemuerniyazi X","Lin J","Duan Y","Xu W","Mao H","Zhao W","Hu Z","Duan C","Feng W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s12975-026-01476-z","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489787","name":"Continuous imputation of blood gas and metabolic panel laboratory values in the intensive care unit using machine learning.","source":"pubmed","abstract":"We developed machine learning (ML) models to perform continuous hourly prediction of arterial blood gas (ABG) and basic metabolic panel (BMP) laboratory values in critically ill patients. We evaluated its impact on prediction of the need for renal replacement therapy (RRT). We compared the performance of the deep learning models using laboratory variables imputed hourly by our ML estimators versus models using laboratory variables imputed by a previous-value baseline. Our ML model incorporated various predictors, including previous laboratory values, administered medications, clinical events, fluid input/output, hemodynamics, and ventilator settings. Accuracy of laboratory imputations were compared using mean absolute error (MAE) and root mean squared error (RMSE). We trained bi-directional long short-term memory models (Bi-LSTM) to predict need for renal replacement therapy (RRT) that differed only in how laboratory variables were imputed over time. We compared performance using area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). XGBoost achieved an average error reduction of 33% in MAE and 32% in RMSE across ABG variables, and 19% and 21% across BMP variables, respectively. Bi-LSTM models using our ML-based hourly laboratory estimates (AUROC 0.951, 95% CI 0.942-0.960; AUPRC 0.419, 95% CI 0.375-0.465) outperformed models using previous-value laboratory imputation (AUROC 0.923, 95% CI 0.910-0.935; AUPRC 0.343, 95% CI 0.301-0.386). Our real-time imputation models led to significant error reduction of most ABG and BMP targets, improving deep learning model performance for the need of RRT.","url":"https://pubmed.ncbi.nlm.nih.gov/42489787/","authors":["Mamandipoor B","Krause M","Korti P","Hsu CN","Gabriel RA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s10877-026-01470-8","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489752","name":"Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.","source":"pubmed","abstract":"Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA.","url":"https://pubmed.ncbi.nlm.nih.gov/42489752/","authors":["Sun X","Gong B","Sun Y","Sun H","Zhang B","Yang L","Wang W","Chen Q","Wei S","Wen H","Liu R","Kong L","Han Y","Guo J","Wang X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s11306-026-02509-2","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489718","name":"Integrative multi-omics reveals NQO1-mediated airway epithelial injury as a key mechanism of bisphenol A-induced COPD.","source":"pubmed","abstract":"Bisphenol A (BPA) is a ubiquitous environmental pollutant, but its relationship with chronic obstructive pulmonary disease (COPD) and the underlying toxicological mechanisms remain unclear. To address this, we employed an integrative multi-omics strategy that included cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) data, Mendelian randomization, network toxicology, molecular docking, molecular dynamics simulation, single-cell transcriptomic analysis, and in vitro experimental validation. Cross-sectional analysis revealed a significant positive association between BPA exposure and COPD prevalence. In the fully adjusted model, each one-unit increase in ln&#x2011;transformed BPA (ng/mg creatinine) was associated with a 34% increase in the odds of COPD (OR&#x2009;=&#x2009;1.34, 95% CI: 1.17-1.54). Mendelian randomization analysis showed that BPA was a potential risk factor for COPD. Through network toxicology and five machine-learning methods, NQO1, HSPA5, and CXCL12 were identified as the hub targets in BPA-induced COPD. Among these, molecular docking and molecular dynamics simulation showed that BPA exhibited the highest binding affinity for NQO1. Single-cell transcriptomic analysis of human lung tissue revealed that NQO1 was predominantly expressed in airway epithelial cells. Real-time PCR and western blot confirmed that BPA exposure significantly decreased NQO1 expression levels in airway epithelial cells. In BPA-treated airway epithelial cells, NQO1 overexpression significantly suppressed mitochondrial reactive oxygen species (ROS) production, prevented mitochondrial membrane potential decline, alleviated intracellular ATP depletion, mitigated mitochondrial structural damage, reduced intracellular ROS accumulation, and attenuated cell viability reduction. Our findings suggest that BPA is a potential risk factor for COPD, and downregulation of NQO1 contributes to the pathogenesis of BPA-induced COPD, potentially through mitochondrial damage in airway epithelial cells.","url":"https://pubmed.ncbi.nlm.nih.gov/42489718/","authors":["Li R","Ge S","Liu J","Li Y","Yao S","Ma S","Sun Y","Li X","Yang X","Zhang J","Zhang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s00210-026-05728-5","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489381","name":"Heterogeneity of Treatment Effect of DOACs vs Warfarin in Atrial Fibrillation.","source":"pubmed","abstract":"Direct oral anticoagulants (DOACs) are recommended over vitamin K antagonists (VKAs) for stroke prevention in atrial fibrillation (AF). However, limited accessibility in resource-constrained settings makes it important to identify patient subgroups in whom VKAs may achieve comparable net clinical outcomes (NCOs).","url":"https://pubmed.ncbi.nlm.nih.gov/42489381/","authors":["Al Said S","Bellavia A","Braunwald E","Hylek EM","Palazzolo MG","Hong H","Antman EM","Carnicelli AP","Eikelboom JW","Granger CB","Patel MR","Wallentin L","Ruff CT","Giugliano RP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.jacadv.2026.102915","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489334","name":"Risk Stratification for Postpartum Hemorrhage in Vaginal Delivery: A Nationwide Cohort Study of Predelivery and Intrapartum Risk Assessment.","source":"pubmed","abstract":"To develop and validate machine learning models for clinically applicable risk stratification of postpartum hemorrhage (PPH) in vaginal delivery, and to assess whether incorporating intrapartum information improves detection while maintaining a similar screening burden.","url":"https://pubmed.ncbi.nlm.nih.gov/42489334/","authors":["Akazawa M","Hashimoto K","Ueno M","Nakabayashi A","Aoki N","Pertuz S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1111/jog.70417","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489317","name":"The mediating role of genes in the influence of intestinal flora on type 2 diabetes and the screening of diagnostic markers.","source":"pubmed","abstract":"The composition of intestinal flora affects the occurrence and development of type 2 diabetes to some extent, with dysregulation of the microbiome being a clinical manifestation of the disease.","url":"https://pubmed.ncbi.nlm.nih.gov/42489317/","authors":["Chen YY","Shi YF","Zhang XY","Zhang JY","Zhang SM","Zhao HX","Liu DL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1111/jdi.70386","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489168","name":"AI-Driven Multi-Omics Integrated Applications Using Diverse Neural Networks for Breast Cancer Diagnostic Screening and Biomarker Discovery.","source":"pubmed","abstract":"Breast cancer (BC) is a highly complex and heterogeneous malignancy and the most prevalent cancer among women worldwide. The diagnosis, prognosis, and the treatment of BC pose significant challenges that are responsible for their limited therapeutic efficacy. Omics-based technologies have gained substantial attention in BC diagnosis through molecular profiling and diverse clinical analytics. The integration of metabolomics, proteomics, transcriptomics, and genomics provides a multidimensional approach to personalized BC diagnosis and treatment through high-throughput molecular profiling. Moreover, the emergence of artificial intelligence (AI) has also supported more accurate and early diagnosis of BC through multimodal integration of diverse datasets. The integration of advanced deep learning (DL) and machine learning (ML) has been extensively exploited for tumor grading, histopathological classification, molecular profiling, diagnostic imaging, and prognostic prediction. This review aims to summarize recent developments in AI-driven multi-omics approaches for the discovery of BC biomarkers. We have also highlighted the integration of omics-based data like metabolomics, proteomics, transcriptomics, and genomics with key AI techniques, including ML and DL, that play a crucial role in the inclusion of multi-omics in cancer and biomarker discovery. We have further discussed AI-based BC screening and diagnostic approaches, as well as the contribution of AI models for patient stratification, biomarker discovery, and prediction of therapeutic response. Additionally, key limitations and challenges, including data heterogeneity, high computational complexity, and model interpretability, have also been highlighted in the present review. Conclusively, we have also outlined future perspectives on the integration of AI and multi-omics to revolutionize precision clinical medicine and improve clinical outcomes in BC theranostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42489168/","authors":["Kumari V","Tiwari H","Singh S","Kailashiya V","Hoai NTT","Sachan S","Kumar B","Kumar R","Kumar R","Verma NK","Gautam V"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1002/med.70078","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42489074","name":"Integrated spatially guided transcriptomics and multiplex imaging identify a high-risk CD163+/CD11c-subgroup in diffuse large B-cell lymphoma.","source":"pubmed","abstract":"Emerging data identify infiltrating immune cells in diffuse large B&#x2011;cell lymphoma (DLBCL) as prognostic biomarkers with tentative implications for therapeutic stratification. To elucidate the tumor immune microenvironment (TIME) and molecular features governing outcome, we integrated data from multiplex immunofluorescence imaging of CD163&#x207a; and CD11c&#x207a; cells with spatially guided transcriptional profiling of infiltrating CD3&#x207a; and CD163&#x207a; immune cells alongside CD20&#x207a; malignant cells across a cohort of 561 DLBCL cases. For quantification of cell densities, a multi&#x2011;step workflow was used, incorporating machine&#x2011;learning classifiers to improve accuracy, and the underlying biology was explored through cancer and immunefocused gene expression analysis. We identified a subgroup of patients with a CD163low/CD11chigh TIME and favorable prognosis. By contrast, a CD163high/CD11clow TIME exhibited poor outcome independent of established clinicopathological risk factors. To verify the clinical utility, we showed that these subgroups can be identified with a routine pathology dual IHC staining. The high-risk CD163/CD11c subgroup had abundant expression of M2&#x2011;like markers (MRC1, SIGLEC1, MARCO) and showed evidence of T-cell exhaustion, with higher levels of LAG3. Of tentative translational value, the low-risk TIME expressed the checkpoint inhibitor CTLA4. Multivariate modelling identified a high-risk gene signature with prognostic value in independent public datasets of CHOP and R&#x2011;CHOP-treated DLBCL, underscoring the robust and broad prognostic impact of CD163+ and CD11+ cells across clinical studies. The combined analysis of CD163 and CD11c have the potential for implementation in routine clinical use and provides independent prognostic information with implications for stratification to novel therapeutic regimens, potentially checkpoint inhibitors LAG-3 or CTLA-4 for high and low-risk groups, respectively.","url":"https://pubmed.ncbi.nlm.nih.gov/42489074/","authors":["Johansson A","Nilsson D","Wikström F","Olsson LM","Hammer I","Janska A","Gerdtsson AS","Porwit A","Hollander P","Jerkeman M","Ek S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.3324/haematol.2025.300429","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488979","name":"Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.","source":"pubmed","abstract":"AI shows substantial potential in health care; however, the absence of standardized evaluation frameworks limits its safe and effective clinical implementation because of inconsistent validation requirements and fragmented ethical principles. Existing guidelines vary in structure, methodological rigor, and ethical integration, creating uncertainty.","url":"https://pubmed.ncbi.nlm.nih.gov/42488979/","authors":["López Medina DC","Oliveros-Navarro A","Moreno Angel N","Herrera-Arellano AC","Henao-Pérez M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.2196/78168","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488941","name":"Optical coherence tomography of the tympanic membrane and middle ear: clinical applications.","source":"pubmed","abstract":"Optical coherence tomography (OCT) is a noninvasive optical imaging technique to image soft tissue and thin bony structures with high resolution, approaching that of histology. This review summarizes recent progress in the clinical application of OCT for tympanic membrane and middle ear disease.","url":"https://pubmed.ncbi.nlm.nih.gov/42488941/","authors":["Azimzadeh JB","Applegate BE","Oghalai JS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1097/MOO.0000000000001147","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488763","name":"EEG criticality as a prognostic tool for functional outcomes in sedated pediatric intensive care patients.","source":"pubmed","abstract":"Predicting meaningful recovery in sedated patients remains a major challenge in the pediatric intensive care unit (PICU) due to the lack of reliable, behavior-independent prognostic markers for children. Criticality of electroencephalography (EEG) signals reflects the brain's dynamic balance between order and chaos and capacity for information processing. The objective of this study was to assess the association between criticality-related EEG features and the functional outcomes of sedated PICU patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42488763/","authors":["Newman D","Grinberg M","Jones K","Woodward K","Esser MJ","Blain-Moraes S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fncom.2026.1831476","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488683","name":"A novel machine learning approach generates personalized estimates of graft and patient survival to better inform older kidney transplant candidates.","source":"pubmed","abstract":"An information most relevant to patients when deciding to accept a kidney offer is an estimate of its potential longevity. Using a novel machine learning approach, our aim was to develop a model that can output personalized curves estimating kidney graft and patient longevity to support clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42488683/","authors":["Boivin PL","Jalbert J","Thériault L","Qi Y","Olek-Basanets A","Cardinal H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1881830","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488543","name":"Development and validation of an interpretable machine learning model for predicting atrial fibrillation risk in middle-aged and older patients with coronary heart disease.","source":"pubmed","abstract":"Coronary heart disease (CHD) and atrial fibrillation (AF) frequently coexist, yet existing risk stratification tools inadequately capture the nonlinear, multidimensional determinants of AF in middle-aged and older CHD patients. This study aimed to develop and validate an interpretable machine learning-based prediction model leveraging electronic medical records (EMR) data.","url":"https://pubmed.ncbi.nlm.nih.gov/42488543/","authors":["Chen F","Fu Q","Li L","Zhang X","Ge Y","Chen J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1886992","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488507","name":"Using artificial intelligence for distinguishing benign and malignant vertebral compression fractures by computed tomography: a scoping review.","source":"pubmed","abstract":"The purpose of this scoping review is to compile the evidence regarding the use of computed tomography (CT) to distinguish between benign and malignant vertebral compression fractures (VCFs) in artificial intelligence (AI) applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42488507/","authors":["Mohammadrezaee M","Bakhshi R","Khalaji A","Jarideh I","Tooyserkani SH","Masjedi Esfahani M","Dadkhah M","Badiee H","Dormiani Tabatabaei SA","Riahi F","Fesharaki S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.62347/ZRQA3915","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488408","name":"Artificial intelligence to enhance research integrity in evidence-based medicine: toward scalable trustworthiness assessment.","source":"pubmed","abstract":"Fraudulent, falsified, and otherwise unreliable clinical research threatens the foundations of evidence-based medicine because systematic reviews, meta-analyses, and clinical practice guidelines depend on the trustworthiness of the studies they include. Retractions are increasing faster than publication output and often occur only after substantial delay, allowing problematic trials to be cited, pooled, and incorporated into downstream clinical recommendations. Recent evidence shows that retracted randomized trials have contaminated thousands of meta-analyses and hundreds of guideline documents, while only a small minority of affected reviews later self-correct. This is not only a clinical problem but also an integrity analytics problem: unreliable studies generate article-level, trial-level, and network-level signals that are not yet systematically integrated into evidence-synthesis workflows. Existing approaches can detect some image anomalies, textual irregularities, reporting inconsistencies, and statistical red flags, but important blind spots remain. Fabricated clinical datasets may appear statistically plausible, outcome switching often requires registration-publication comparison, and most available tools operate in isolation rather than as coordinated screening systems. We argue that the next generation of safeguards should combine AI/ML-based detection systems, statistical forensic methods, structured trustworthiness appraisal tools, and workflow-level screening frameworks within evidence synthesis. Embedding staged trustworthiness assessment early in systematic reviews, alongside stronger publisher- and database-level integrity infrastructure, could help prevent unreliable trials from distorting pooled estimates and downstream guidance. Such systems should support triage and prioritization rather than replace human judgment. Framed in this way, scalable trustworthiness assessment represents a practical research metrics and analytics agenda for strengthening the evidence ecosystem from primary studies to reviews, guidelines, and policy decisions.","url":"https://pubmed.ncbi.nlm.nih.gov/42488408/","authors":["Mo M","Arora R","Cao C","Tricco AC","Moher D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frma.2026.1833223","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488376","name":"Machine learning-based fetal health prediction and development of smart web application.","source":"pubmed","abstract":"Fetal health monitoring is critical for early identification of pregnancy-related risks. Manual interpretation of cardiotocography (CTG) signals is subjective and variable among healthcare professionals.","url":"https://pubmed.ncbi.nlm.nih.gov/42488376/","authors":["Puri C","Reddy KTV","Gote PM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/frai.2026.1829833","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488360","name":"Psychological distress, digital behavioral risks, and physical activity as predictors of subjective wellbeing: a machine learning study in a large sample of community-dwelling adults.","source":"pubmed","abstract":"Regular physical activity is a key determinant of psychological wellbeing, yet its interaction with emerging digital behavioral risks remains insufficiently understood. Compulsive internet use and nomophobia have been linked to psychological distress, which negatively affects life satisfaction and happiness. However, few studies have examined these factors simultaneously. Machine learning offers a promising approach for improving predictive accuracy and clarifying the relative contributions of these variables to subjective wellbeing.","url":"https://pubmed.ncbi.nlm.nih.gov/42488360/","authors":["Saidane M","Guelmami N","Dhahbi W","Ayachi F","Ceylan Hİ","Barakat L","Ben Said NM","Alsaeed MI","Muntean RI","Dergaa I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnbeh.2026.1876257","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488245","name":"A Proposed Clinical Validation Pathway for the Artificial Intelligence-Driven Integrated Risk Assessment of Cardiovascular Disease (AIRA-CVD): A Translational Framework Integrating Inflammatory Biomarkers, Histopathology, and Machine Learning.","source":"pubmed","abstract":"Cardiovascular disease (CVD) remains a leading cause of morbidity, mortality, and healthcare expenditure despite advances in prevention and treatment. The Artificial Intelligence-Driven Integrated Risk Assessment of Cardiovascular Disease (AIRA-CVD) framework was proposed as a multimodal architecture integrating clinical data, inflammatory biomarkers, imaging findings, and histopathological evidence of vascular remodeling to support precision cardiovascular risk assessment. This technical report presents a structured clinical validation pathway designed to facilitate future implementation and evaluation of the framework. The proposed pathway consists of four sequential phases: retrospective electronic health record analysis, inflammatory biomarker integration, histopathological validation, and prospective clinical implementation. The framework further incorporates a hybrid machine learning architecture, explainable artificial intelligence methodologies, human-in-the-loop clinical oversight, and algorithmic fairness considerations to support transparency, interpretability, and responsible deployment. Potential applications include enhanced cardiovascular risk stratification, earlier identification of high-risk individuals, support for preventive interventions, and integration within clinical decision-support and population health management systems. By providing a translational roadmap for validation and implementation, this report seeks to bridge the gap between computational innovation and patient-centered cardiovascular care while advancing the development of biologically informed artificial intelligence systems in cardiovascular medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42488245/","authors":["Ogbuefi C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111315","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488224","name":"Development and validation of a clinical-radiomics nomogram for differentiating Mycoplasma pneumoniae pneumonia from bacterial pneumonia in children.","source":"pubmed","abstract":"In this case-control study, we developed a nomogram merging computed tomography (CT)-based radiomics, clinical indicators, and CT imaging findings for differentiating Mycoplasma pneumoniae Pneumonia (MPP) from bacterial pneumonia (BP) in children.","url":"https://pubmed.ncbi.nlm.nih.gov/42488224/","authors":["Guan Y","Wang X","Song C","Bi L","Yang G","Quan S","Xu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1764639","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488215","name":"Do nutritional variables improve cardiovascular disease prediction? A comparative machine learning analysis.","source":"pubmed","abstract":"Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, highlighting the need for accurate prediction models. While machine learning (ML) approaches have shown promising results, the contribution of nutritional variables to prediction performance remains unclear. This study evaluates the incremental effect of dietary intake variables by comparing baseline and nutrition-extended feature sets across multiple ML models.","url":"https://pubmed.ncbi.nlm.nih.gov/42488215/","authors":["Toprak K","Cindiloglu ZU","Sanlier N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fnut.2026.1808942","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488139","name":"Intelligent Rehabilitation: Advances in Artificial Intelligence for Musculoskeletal Rehabilitation: A Narrative Review.","source":"pubmed","abstract":"Musculoskeletal diseases, such as osteoarthritis and joint trauma, significantly impact patient mobility, independence, and quality of life. With the rising demand for effective and accessible rehabilitation strategies, artificial intelligence (AI) has emerged as a powerful tool to support diagnosis, surgical planning, and personalized rehabilitation. This narrative review summarizes recent advances in the application of AI in musculoskeletal disease management, with a particular emphasis on postoperative and conservative rehabilitation. We outline the foundational concepts of AI, including machine learning, deep learning, computer vision, and natural language processing, and discuss their roles in clinical decision-making and recovery monitoring. Furthermore, we examine emerging AI-assisted rehabilitation tools, including mobile applications, robotic exoskeletons, gamified platforms, and markerless motion tracking systems, which collectively enhance treatment precision, patient adherence, and remote care capabilities. Despite promising outcomes, current limitations include insufficient personalization, limited multimodal data integration, and inadequate clinical validation. Future developments should focus on improving model interpretability, integrating real-time biosensing, and optimizing user interface design to support clinically feasible and patient-centered musculoskeletal rehabilitation.","url":"https://pubmed.ncbi.nlm.nih.gov/42488139/","authors":["Hao J","Sun S","Dou T","Deng J","Li W","Ma C","Zhang Y","Yao L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/ORR.S607912","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488127","name":"Prediction of acute postoperative protein depletion risk in colon cancer using an in-context learning foundation model: a retrospective cohort study.","source":"pubmed","abstract":"Acute postoperative protein depletion, including hypoalbuminaemia and hypoproteinaemia, frequently complicates colon cancer surgery and exacerbates adverse outcomes, yet early risk stratification remains challenging.","url":"https://pubmed.ncbi.nlm.nih.gov/42488127/","authors":["Cao X","Han L","Zan X","Zhang Y","Tian Z","Shen W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1857275","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488064","name":"Emotion recognition from multimodal biosignals: supervised and unsupervised machine learning approaches based on EEG and GSR.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) are increasingly applied in psychology, particularly in educational and clinical settings, to analyse complex behavioural and physiological data. However, evidence regarding the capacity of multimodal physiological signals to characterise cognitive and emotional processes in ecologically valid higher education environments remains limited. This study explored the potential of supervised and unsupervised ML approaches to analyse multimodal physiological responses elicited by emotional avatars in a real educational context.","url":"https://pubmed.ncbi.nlm.nih.gov/42488064/","authors":["Sáiz-Manzanares MC","Marticorena-Sánchez R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyg.2026.1835911","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488008","name":"Prediction models for progression from prediabetes to diabetes: a systematic review and meta-analysis.","source":"pubmed","abstract":"Prediabetes increases the risk of type 2 diabetes mellitus (T2DM). Accurate prediction is crucial for early prevention, but evidence on prediction models has not been comprehensively synthesized. This study systematically evaluated the accuracy of such models in predicting prediabetes-to-T2DM progression.","url":"https://pubmed.ncbi.nlm.nih.gov/42488008/","authors":["Wang Y","Wang C","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1888466","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42488003","name":"Integrative bioinformatics analysis identifies placental senescence-associated signatures in early-onset preeclampsia.","source":"pubmed","abstract":"Early-onset preeclampsia (EOPE) is a severe hypertensive disorder of pregnancy associated with preterm delivery and maternal multi-organ dysfunction. Although placental senescence and immune dysregulation have been implicated in EOPE, the expression profile of senescence-related genes(SRGs) and their mechanistic roles in disease progression remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42488003/","authors":["Lin L","Chen Y","Chen L","Gu S","Lai Y","Li X","Peng J","Hua X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1863608","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487963","name":"Risk prediction of pediatric intensive care unit admission in children with respiratory syncytial virus infection using interpretable machine learning.","source":"pubmed","abstract":"Respiratory syncytial virus (RSV) is a major cause of pediatric acute lower respiratory infection. Early prediction of pediatric intensive care unit (PICU) transfer may support clinical decision-making and resource allocation. We aimed to develop and temporally validate an interpretable machine learning model for predicting PICU admission in hospitalized children with RSV.","url":"https://pubmed.ncbi.nlm.nih.gov/42487963/","authors":["Dong J","Ni J","Zhao M","Du Z","Fang K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1856930","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487960","name":"Development and validation of an interpretable machine learning model for venous thromboembolism risk prediction in patients with lung cancer: a real-world study.","source":"pubmed","abstract":"Venous thromboembolism (VTE) is a common complication in patients with lung cancer and remains difficult to predict accurately using existing risk assessment tools. This study aimed to develop and validate a machine learning model for individualized prediction of VTE risk in patients with lung cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42487960/","authors":["Xia A","Liu J","Song J","Han Y","Ding B","Xie A","Liu C","Tang X","Xing W","Zhou D","Liu L","Zhou H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1853920","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487944","name":"Perioperative nutritional support is associated with attenuated early postoperative albumin decline after gastrectomy for gastric cancer: a retrospective cohort study and machine learning prediction model.","source":"pubmed","abstract":"Perioperative nutritional support is recommended for surgical patients at nutritional risk, but its relationship with early postoperative serum albumin recovery after gastrectomy for gastric cancer remains uncertain, and clinicians lack practical tools to identify patients most likely to show early albumin improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/42487944/","authors":["Wen T","Zhang X","Zhang X","Lv S","Yang L","Tan B","Liu FX"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1843582","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487901","name":"Integrating chemotherapy, radiotherapy, and O6-methylguanine-DNA methyltransferase (MGMT) status with deep-learning cellular tumor volumetry sharpens prediction of glioblastoma recurrence on postoperative MRI.","source":"pubmed","abstract":"Distinguishing glioblastoma recurrence from posttreatment effects on magnetic resonance imaging (MRI) remains a major diagnostic challenge. While recent models consider O6-methylguanine-DNA methyltransferase (MGMT) status, most machine learning models addressing this problem assume uniform chemotherapy and radiotherapy exposure, overlooking real-world treatment variability. This study aimed to determine whether integrating chemotherapy, radiotherapy, and MGMT status together with automated cellular tumor volume (VolCT) improves predictive accuracy in posttreatment glioblastoma assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42487901/","authors":["Belbadaoui T","Forester A","Khalvati F","Gagnon L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1093/noajnl/vdag178","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487876","name":"Radiomics-based machine learning to evaluate immunotherapy efficacy in non-small cell lung cancer patients with bone metastases.","source":"pubmed","abstract":"Assessing treatment response in bone metastases from non-small cell lung cancer (NSCLC) remains a major clinical challenge, particularly for patients receiving immune checkpoint inhibitors (ICIs). The existing response criteria are not optimized for osseous disease, leading to inconsistent evaluation. We aimed to develop and validate a radiomics-based machine learning (ML) framework to non-invasively distinguish immunotherapy response categories-progression, stable disease, and partial response-in NSCLC patients with bone metastases.","url":"https://pubmed.ncbi.nlm.nih.gov/42487876/","authors":["Kakavand R","Forkert ND","Abbott A","Kendal J","Connell P","Yashar H","Monument MJ","Edwards WB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1117/1.JMI.13.4.044503","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487758","name":"A novel interpretable machine learning framework for predicting postpartum depression: a SHAP-based analysis of maternal and infant health indicators.","source":"pubmed","abstract":"Postpartum depression (PPD) affects nearly 20% of women globally. Conventional regression models often have limited predictive accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/42487758/","authors":["Lv F","Li S","Yuan X","Ma Y","Huang T","Xie J","Feng B","Zheng J","Feng J","Mo J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1888858","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487709","name":"Genomic variant-driven prediction of azole resistance in Aspergillus fumigatus using GWAS and machine learning.","source":"pubmed","abstract":"Azole resistance in Aspergillus fumigatus , a major cause of invasive aspergillosis, threatens public health. Known drivers include cyp51A/B mutations (e.g., TR34/L98H and TR46/Y121F/T289A), yet existing studies have largely focused on clinical isolates and known genetic determinants, leaving gaps in understanding broader genomic contributions. This study aimed to develop a machine learning-based framework to overcome limitations of traditional GWAS and identify novel resistance loci beyond cyp51A . A global collection of 590 A. fumigatus strains was analyzed, including whole-genome sequencing (WGS) data from 15 countries and resistance phenotypes using CLSI/EUCAST guidelines. Phylogenetic analysis revealed four clades without geographic clustering. Clade III harbored the highest proportion of resistant strains (ITR: 51.89%, POS: 50.48%, VOR: 38.68%), predominantly linked to cyp51A tandem repeats. In contrast, Clade IV strains frequently carried point mutations but showed lower resistance rates. GWAS was performed using PLINK and GAPIT frameworks, and 7,098 high confidence SNPs were selected for ML modeling. Ten classifiers were evaluated using repeated random 80:20 train-test splits, with five-fold cross-validation used for RFECV-based feature selection and model tuning where applicable. RF and XGBoost achieved superior performance, with mean AUCs &gt; 95% and accuracy &gt; 88% across all azoles. Penalized logistic regression outperformed SVM and AdaBoost. Decision trees exhibited the lowest accuracy. The SNP SCM000172.1_1781459 was identified as a key predictor for all three azoles. Cross-resistance analysis revealed significant overlap between ITR and POS resistance loci, whereas VOR-associated loci were distinct, suggesting divergent mechanisms. The findings provide actionable insights for resistance surveillance, antifungal development, and tailored treatment strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42487709/","authors":["Li D","Yue X","Hu W","Zhong H","Chen F","Zhao J","Cao L","Chen X","Han L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1891518","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487698","name":"Identification of key targets driving biofilm formation and virtual screening of potential inhibitors in Pseudomonas aeruginosa.","source":"pubmed","abstract":"Pseudomonas aeruginosa , a member of the \"ESKAPE\" pathogens, possesses a robust ability to form biofilms-a key factor that contributes to its antibiotic resistance and poses significant challenges for clinical management. Identifying potential therapeutic targets through bioinformatic analysis of genomic data is therefore critical for developing more effective treatment strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42487698/","authors":["Wang R","Mo E","Wen J","Chen G","Mo J","Xu J","Chen W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1867561","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487598","name":"Machine Learning Classification of Serum miR-210 as a Biomarker of Exercise Training Response.","source":"pubmed","abstract":"MicroRNAs (miRNAs) in circulating blood are increasingly recognized as biomarkers of exercise-induced physiological adaptation. Of these, miR-210, a hypoxiainducible miRNA associated with angiogenesis, mitochondrial control, and cellular stress response, has shown mixed exercise-associated modifications. Its classifier status for individuallevel training response has yet to be established.","url":"https://pubmed.ncbi.nlm.nih.gov/42487598/","authors":["AlMatar M","Mourgan FHAE","Al Shamli A","Ahmed Hefny NED","Mohamed Ali EA","Bekir WMA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.2174/0113862073454504260622175952","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487595","name":"Prediction of Chemical-induced Autonomic Neurotoxicity with Machine Learning Approaches.","source":"pubmed","abstract":"Autonomic neurotoxicity associated with chemical exposure represents a significant clinical and safety concern. Traditional assessment relies on in vivo methods that are costly and time-consuming, and few computational tools specifically address this endpoint. This study aimed to develop reliable computational models for the prediction of chemical-induced autonomic neurotoxicity, while elucidating the molecular determinants governing this adverse effect.","url":"https://pubmed.ncbi.nlm.nih.gov/42487595/","authors":["Kuang ZK","Huang Q","Duan X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.2174/0113862073491170260616113137","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487389","name":"Applications of Artificial Intelligence in Oral Radiology and Diagnosis: A Scoping Review.","source":"pubmed","abstract":"This scoping review evaluates the role of artificial intelligence, including deep learning and machine learning methods, in diagnostic applications within oral and maxillofacial radiology. The review covers panoramic and cephalometric radiographs, cone-beam computed tomography, and clinical photographs used for detecting dental caries, jaw lesions, developmental anomalies, and soft-tissue conditions. These computational models are applied for image interpretation, enhancement, and segmentation, supporting greater diagnostic consistency and precision. Most available studies are retrospective and depend on single-center datasets, limiting external validation and practical use. Differences in imaging quality, protocols, and patient characteristics further restrict reproducibility. The review identifies the need for standardized imaging datasets, transparent algorithms, and prospective clinical testing to confirm diagnostic reliability and establish safe integration into dental radiology workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42487389/","authors":["Riyaz SSMA","Almutairy MF","Mohsin SF","Almutairi BM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.4103/aam.aam_399_26","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487251","name":"Fast Submillimeter Whole-Brain T(2)*-Weighted Imaging Using 3D-EPI With CAIPIRINHA and Deep-Learning Denoising at 3T.","source":"pubmed","abstract":"Recent updates to the diagnostic criteria of multiple sclerosis (MS) require whole-brain T 2 *-weighted (T 2 *w) imaging with submillimeter resolution to detect novel diagnostic biomarkers such as the central vein sign. However, to achieve the needed submillimeter spatial resolution, conventional T 2 *w 3D gradient-echo scans sequences are limited by prohibitively long scan times for clinical use. Here, we evaluated a different approach based on a segmented 3D echo planar imaging (3D-EPI) sequence, accelerated with 2D Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA) undersampling and denoised with a deep learning-based network.","url":"https://pubmed.ncbi.nlm.nih.gov/42487251/","authors":["Nair SM","Quah B","Song JW","Jin J","Gao C","Han F","Luskin E","Luu M","Renner B","Liu KC","Patil S","Derbyshire JA","Sakaie K","Lee J","Elliott MA","Shinohara RT","Schindler MK","Bilello M","Kaisey M","Al-Louzi O","Binesh N","Maya M","Li D","Solomon AJ","Reich DS","Sicotte NL","Lowe MJ","Ontaneda D","Sati P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov","doi":"10.1002/mrm.70522","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487139","name":"clinTALL: machine learning-driven multimodal subtype classification and treatment outcome prediction in pediatric T-ALL.","source":"pubmed","abstract":"Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-cell precursor ALL, T-ALL lacks effective risk stratification strategies. A recent study has integrated whole genome and whole transcriptome data to define 17 distinct molecular subtypes with prognostic significance. However, clinical translation of this knowledge remains challenging due to the complexity of interpreting high-dimensional multi-omics-based data.","url":"https://pubmed.ncbi.nlm.nih.gov/42487139/","authors":["Stoiber L","Antić Ž","Rebellato S","Fazio G","Rademacher A","Lenk L","Locatelli F","Balduzzi A","Cario G","Rizzari C","Cazzaniga G","Yu J","Bergmann AK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1186/s13073-026-01733-8","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487114","name":"Correction: Diagnostic performance of machine learning and deep learning algorithms for thyroid cancer metastasis: a systematic review and meta-analysis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42487114/","authors":["Lichahi MA","Anvari S","Hemmati H","Zadgari E","Jafari M","Mirkalaie SMM","Farzin M","Larijani A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1186/s12911-026-03708-6","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487088","name":"The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review.","source":"pubmed","abstract":"High-grade gliomas (HGGs) are aggressive tumors with a propensity for recurrence. Despite standardized therapies, definitive treatment is elusive. Advancements in artificial intelligence (AI) and machine learning (ML) can potentially identify recurrence probabilities and patterns facilitating individualized therapy.&#xa0;A systematic review was conducted in accordance with the PRISMA guidelines. PubMed, ScienceDirect, and Web of Science databases were queried for reports on the use of AI/ML for the prediction of disease recurrence in HGGs. Using a random-effects model and inverse variance weighting a pooled analysis of key performance metrics (sensitivity, specificity, and accuracy) was conducted on the top performing models from each manuscript.&#xa0;In total, 14 manuscripts encompassing 1,540 patients were selected for systematic review and analysis. Across the included studies, 13/14 (92.9%) were retrospective study designs, with 1/14 (7.1%) prospective study design. Among the 1,540 patients, 1,530 (99.3%) and 10 (0.7%) were histologically classified as WHO grade IV and III respectively. Nine studies (9/14, 64.3%) examined patients undergoing GTR following by adjuvant RT, and five studies (5/14, 35.7%) undergoing STR/NTR followed by adjuvant RT. The Random Forest (RF) model was most frequently utilized, with T2-FLAIR sequences most frequently incorporated into model training. The pooled sensitivity, specificity, and accuracy of the models were 81% (95% CI: 73-87; I&#xb2; = 85.2%), 75% (95% CI: 65-85; I&#xb2; = 91.9%), and 79% (95% CI: 64-92; I&#xb2; = 87.8%), respectively. Following sensitivity analyses, the corresponding estimates were 81% (95% CI: 77-84; I&#xb2; = 0.0%), 84% (95% CI: 79-88; I&#xb2; = 38.8%), and 89% (95% CI: 83-95; I&#xb2; = 0.0%), respectively.&#xa0;The development of an AI/ML model to predict tumor recurrence in HGGs has been an emerging area of research over the past decade. While ongoing validation in larger, prospective databases is needed, preliminary evidence suggests that existing models perform with reasonable sensitivity, specificity, and accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/42487088/","authors":["Kite T","Nayak T","Jaffee S","Porwal M","Han C","Vyas P","Sankaranarayanan A","Wegner RE","Grover P","Shepard MJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s10143-026-04406-7","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487063","name":"Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.","source":"pubmed","abstract":"Earlier identification, regulation of impurities in pharmaceutical products is critical throughout the medication development process because they have a high impact on drug quality, safety, and regulatory approval. Traditional analytical methods like LC-MS and NMR are frequently used, but they are labour intensive, time consuming and have limited capacity to detect unknown or trace level impurities. New opportunities to forecast impurity formation before experimental observation have been enabled by developments in cheminformatics and computer modelling. This study covers advanced chemical representation techniques, data generation, data curation, and model validation with highlighting recent advances in machine learning and deep learning algorithms for impurity prediction. In association with forecasting synthetic by products, degradation products, and harmful contaminants, traditional QSAR techniques, and contemporary deep learning models such as graph neural networks, transformer based architectures, and generative frameworks are examined. Additionally, discussed the increasing significance of explainable models for regulatory body acceptability. Lastly, newly developed advanced techniques like digital twins, automated impurity profiling, integrated reaction degradation modelling, and real-time monitoring are examined, demonstrating how computational methods are transforming impurity assessment from a reactive effort into a predictive and preventive procedure.","url":"https://pubmed.ncbi.nlm.nih.gov/42487063/","authors":["Thombre S","Bonde C","Bhole R","Gawad J","Karwa P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s10822-026-00876-5","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487057","name":"\"Hermeneutic burden\" and clinical responsibility: a response to Sparrow et al. on explanation and machine learning.","source":"pubmed","abstract":"This paper critically reviews Sparrow et al.'s notion of the \"hermeneutic burden\" placed upon clinicians by the demand for explainable artificial intelligence (XAI) in the context of adaptive machine learning (ML) systems. While Sparrow et al. highlight important additional labour that may be required of clinicians, this response argues that framing explanation primarily in terms of such a burden obscures its overall ethical significance. This paper therefore offers a supplementary account of the interpretive work associated with XAI in medicine that places it within existing models of the patient-clinician relationship. In particular, Emanuel and Emanuel's influential typology consisting of four models of the patient-physician relationship is used to extract possible justifications for the responsibility to grasp and explain not only patients' values and conditions but also ML outputs. This allows us to distinguish between 'hermeneutic burden' and 'hermeneutic responsibility' and emphasise that explanation in medicine is not an incidental task but part of a clinician's professional role, particularly on 'interpretive' and 'deliberative' models. The paper thus argues that viewing explanation as a hermeneutic responsibility linked to patient autonomy clarifies the ethical significance of XAI in terms of both the grounds and scope of clinicians' responsibilities. At its core, the ethical challenge raised by XAI in clinical practice concerns not only the burdens it may impose on clinicians but also the evolution of clinicians' traditional interpretive duties in the novel context of ML-mediated care.","url":"https://pubmed.ncbi.nlm.nih.gov/42487057/","authors":["Adams J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s40592-026-00300-6","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487042","name":"Early Detection of Cognitive Impairment Using Time-Frequency Analysis of Fine Motor Accelerometry.","source":"pubmed","abstract":"Early detection of cognitive decline is essential for timely diagnosis and treatment. This study aimed to evaluate whether upper-limb movement dynamics derived from wrist-mounted inertial sensors during a fine motor task can accurately differentiate among healthy aging, mild cognitive impairment (MCI), and dementia (DEM).","url":"https://pubmed.ncbi.nlm.nih.gov/42487042/","authors":["Pacheco-Santiago G","González-Aparicio II","Domínguez-Vega ZT","Velázquez-Álvarez L","Torres-Castillo JR","Rivera-Sánchez JJ","Padilla-Castañeda MÁ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s10439-026-04275-7","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487023","name":"Cross-task, explainable and real-time decoding of human emotion states by integrating gray and white matter intracranial neural activity.","source":"pubmed","abstract":"Decoding human emotion states from intracranial neural activity is key in developing affective brain-computer interfaces and new therapies for affective disorders. However, real-world application of decoding requires high performance that integrates neural activity from both gray and white matter, stable generalization across different contexts, sufficient neural encoding explainability and robust real-time implementation, all of which remain elusive. Here we simultaneously recorded intracranial electroencephalogram (iEEG) and abundant self-rated valence and arousal scores across two emotion-eliciting tasks in 18 individuals. We then developed personalized decoding models within a deep learning framework, achieving high-performance decoding of continuous valence and arousal states and improving on the performance of prior EEG and iEEG decoding. Critically, the models substantially improved performance by integrating gray and white matter signals and demonstrated cross-task generalization. The models further revealed shared and preferred mesolimbic-thalamo-cortical subnetworks encoding valence and arousal, showing neurophysiological explainability. Finally, the models realized robust real-time decoding in four new individuals. Our results have implications for advancing emotion decoding neurotechnology toward deployable affective brain-computer interfaces and closed-loop therapeutic systems for affective disorders.","url":"https://pubmed.ncbi.nlm.nih.gov/42487023/","authors":["Yang Y","Chen W","Chen Y","Ding L","Zhang C","Jiang H","Zhu Z","Guo X","Wang S","Pan G","Wei N","Hu S","Zhu J","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1038/s43588-026-01021-w","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42487020","name":"ANGPTL4 in gestational diabetes: a diagnostic biomarker linked to placental senescence.","source":"pubmed","abstract":"Current diagnostic methods for Gestational Diabetes Mellitus (GDM) inadequately reflect early placental pathophysiology. Accelerated cellular senescence in trophoblasts has been reported as a pathological feature associated with the GDM placenta. However, the potential of senescence-related genes (SRGs) as candidate biomarkers or molecular indicators for GDM has not been systematically investigated.&#xa0;Placental transcriptomic data from GDM and control samples were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed senescence-related genes (DE-SRGs) were identified. A classification model was built using an integrative machine learning framework, and RT-qPCR validated model gene expression in placental tissues. In parallel, a prospective nested case-control study (30 GDM cases and 30 controls) was conducted, with peripheral blood collected in the first trimester, second trimester, and at delivery. Plasma Angiopoietin-like 4 (ANGPTL4) levels were measured by ELISA to determine the key gene for downstream investigation. Single-cell RNA sequencing (scRNA-seq) was then used to resolve the cellular expression patterns and cell-cell communication networks of the key gene. (Chinese Clinical Trial Registration No. ChiCTR2400091955; Registration Date: 2024-11-06). The classification model integrating ANGPTL4 and DST demonstrated favorable retrospective discriminatory performance (training set AUC&#x2009;=&#x2009;0.95; validation set AUC&#x2009;=&#x2009;0.87). This model served as a discovery tool rather than a clinical predictor. RT-qPCR indicated upregulation of ANGPTL4 in GDM placentas (P&#x2009;&lt;&#x2009;0.05), ANGPTL4 was significantly elevated in the first trimester of women who later developed GDM (P_adj&#x2009;=&#x2009;0.038); this difference was no longer significant in the second trimester. Accordingly, ANGPTL4 was prioritized for further study, ANGPTL4 was predominantly expressed in extravillous trophoblasts (EVTs), where its expression positively correlated with cellular senescence. Cell communication analysis predicted potential ligand-receptor interactions between ANGPTL4 and ITGA5/ITGB1/SDC4 in EVTs. DE-SRGs in GDM placentas were highly enriched in lipid metabolism pathways, aligning with ANGPTL4's known function.&#xa0;Integrating machine learning with single-cell transcriptomics, we identified placental senescence-related gene candidates associated with GDM. ANGPTL4 is elevated in first&#x2011;trimester plasma and enriched in extravillous trophoblasts, where its expression correlates with lipid metabolism and cellular senescence signatures. These correlative findings generate testable hypotheses linking ANGPTL4&#x2011;mediated signaling to placental senescence. ANGPTL4 emerges as a candidate biomarker whose clinical potential warrants further evaluation in prospective cohorts.","url":"https://pubmed.ncbi.nlm.nih.gov/42487020/","authors":["He YF","Chen H","Zhang CM","Yang SS","Yang XL","Jiang LS","Wang F","Zhou SG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1007/s10142-026-01978-x","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486839","name":"[Medical prior-guided TabMap deep learning model for ovarian cancer prediction and interpretability analysis].","source":"pubmed","abstract":"To develop a TabMap image mapping and deep learning prediction framework that integrates medical prior knowledge to address the challenges of complex feature associations in tabular medical data and insufficient model interpretability in early ovarian cancer diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/42486839/","authors":["Zhu J","Tan S","Huang F","Cai G","Zhen X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.12122/j.issn.1673-4254.2026.07.24","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486820","name":"[Interpretable machine learning models for preoperative precision prediction of perineural invasion in cervical cancer to support treatment decision: a multicenter retrospective study].","source":"pubmed","abstract":"To develop an interpretable machine learning model and web-based prediction tool for preoperative risk assessment of perineural invasion (PNI) in cervical cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42486820/","authors":["Zhu M","Liu X","Zhao S","Zhang X","Liu C","Liu X","Qi H","Han T","Ma D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.12122/j.issn.1673-4254.2026.07.05","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486779","name":"Deep Learning-Driven Anticancer Drug Discovery: Emodepside as a Potential Therapeutic Candidate for Triple-Negative Breast Cancer.","source":"pubmed","abstract":"Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a poor prognosis. The absence of effective targeted therapies and endocrine treatment options leads to limited therapeutic options, which remains one of the major clinical challenges in TNBC management. Drug discovery is typically a lengthy and costly process that could be significantly improved through drug repurposing. However, the biological complexity and insufficient repurposing strategies hinder the reuse. This study aims to develop a deep learning-based framework to accelerate drug discovery for TNBC, identify novel therapeutic candidates, and uncover potential drug targets. We developed a deep neural network framework to predict the anticancer efficacy, toxicity profiles, and structural similarities of compounds. By applying this platform to screen over 6,000 compounds from the Drug Repurposing Hub, we identified promising candidates with potential therapeutic efficacy and safety profiles against TNBC. The top-predicted compounds were subsequently validated through comprehensive in vitro and in vivo functional assays. Furthermore, we employed transcriptomic sequencing and mass spectrometry-based proteomics to elucidate the molecular mechanisms underlying the anti-TNBC activity. We identified emodepside, a structurally unique molecule diverging from conventional anticancer agents that exhibited potent antitumor efficacy across multiple TNBC cell lines. Significantly, emodepside administration (5 mg/kg) inhibited tumor growth in xenograft models. Integrated multiomics analyses (RNA-seq/CETSA-MS) identified NAMPT as the primary target. This study demonstrates the viability of our deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients. Emodepside emerges as a promising TNBC therapeutic candidate, with a possible mechanism of promoting TNBC cell apoptosis via NAMPT inhibition.","url":"https://pubmed.ncbi.nlm.nih.gov/42486779/","authors":["Xu Y","Dong T","Li B","Yu J","Wang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 10","doi":"10.1021/acs.jcim.6c01061","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486749","name":"Dynamic Aware Biopsy Needle Identification in Ultrasound Images Using Temporal Prior Guided U-Net Cross Transformer With Limited Training Data.","source":"pubmed","abstract":"Ultrasound-guided needle placement has been commonly used for minimally invasive clinical procedures, including biopsy, regional anesthesia and localized drug administration. This study aimed to enhance existing deep learning frameworks by incorporating a classical background subtraction, which enriches the inductive bias and thereby enables more reliable needle detection even when the available training dataset is small.","url":"https://pubmed.ncbi.nlm.nih.gov/42486749/","authors":["Lee M","Beom DG","Bae EH","Kim SW","Kim CS","Kim DJ","Park S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.ultrasmedbio.2026.06.024","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486687","name":"A multimodal machine learning model integrating plasma biomarkers and MRI metrics for non-invasive prediction of amyloid-β pathology in mild cognitive impairment.","source":"pubmed","abstract":"BackgroundAccurate, non-invasive prediction of cerebral amyloid-&#x3b2; (A&#x3b2;) pathology in mild cognitive impairment (MCI) remains challenging yet critical for early intervention.ObjectiveTo develop a multimodal machine learning model integrating clinical features, plasma biomarkers, and structural MRI metrics for non-invasive A&#x3b2; prediction.MethodsData were obtained from the Alzheimer's Disease Neuroimaging Initiative. Participants with concurrent plasma biomarkers, 3D T1-weighted MRI, and amyloid assessments were included. Logistic Regression, Decision Tree, and Support Vector Machine models were constructed using clinical, plasma, MRI, and combined features. Performance was evaluated via internal validation and external testing in a cognitively unimpaired cohort using AUC, calibration curves, and decision curve analysis. The prognostic value of the model-derived A&#x3b2; risk probability was assessed using Cox regression in an independent longitudinal MCI cohort.ResultsThe optimal Logistic Regression model incorporated APOE &#x3b5;4 status, Mini-Mental State Examination score, plasma p-Tau217, A&#x3b2; 42 /A&#x3b2; 40 ratio, and bilateral hippocampal and left amygdalar volumes. The combined model achieved an AUC of 0.875 in internal validation and maintained robust performance in the external unimpaired cohort (AUC&#x2009;=&#x2009;0.883), outperforming single-modality models. The predicted A&#x3b2;-positive risk probability effectively stratified disease progression risk in MCI patients (C-index&#x2009;=&#x2009;0.771).ConclusionsA multimodal model integrating plasma and MRI features accurately predicts A&#x3b2; pathology and progression risk, offering a practical non-invasive tool for early Alzheimer's disease screening and risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/42486687/","authors":["Fang Q","Guo Y","Chen L","Li Y","Cai Q","Zhang J","Ma X","Zeng Y","Bai G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1177/13872877261465672","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486683","name":"Post-thrombectomy models for outcome prediction in ischemic stroke: systematic review and external validation.","source":"pubmed","abstract":"Outcome prediction models for patients with ischemic stroke after endovascular thrombectomy (EVT) demonstrated the value of including post-procedural predictors. We systematically reviewed and externally validated models that incorporated post-procedural predictors.","url":"https://pubmed.ncbi.nlm.nih.gov/42486683/","authors":["Li X","Alexandrov N","Veltman Y","van Walsum T","Bos D","Majoie CBLM","van Zwam WH","Dippel DWJ","Roozenbeek B","Lingsma HF","Icai SL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1136/jnis-2026-025297","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486575","name":"Artificial intelligence in microbiome data analysis: Applications in head and neck cancer.","source":"pubmed","abstract":"This chapter reviews AI-driven approaches, including machine learning and deep learning, for analyzing microbiome data in head and neck cancer (HNC). It highlights the role of artificial intelligence in identifying microbial biomarkers, predicting treatment outcomes, and supporting early diagnosis through the integration of multi-omics and clinical data. The chapter also discusses key challenges, including data heterogeneity, model interpretability, and clinical applicability, and outlines future directions for precision oncology. Recent advances in artificial intelligence have enabled novel analytical strategies for microbiome-based research in HNC, offering new opportunities for biomarker discovery and data-driven clinical decision-making. By combining high-dimensional microbiome profiles with clinical and multi-omics information, AI-based methods provide a promising framework for improving disease characterization and advancing precision oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42486575/","authors":["Kurt B","Babalola AE","Chaurasia A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/bs.ai.2026.03.008","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486368","name":"Physicochemical fingerprinting reveals convergent evolutionary determinants of enterovirus A71 neurovirulence through integrative machine learning and structural analysis.","source":"pubmed","abstract":"Enterovirus A71 (EV-A71) causes hand, foot, and mouth disease and can trigger life-threatening neurological complications, yet the sequence-level physicochemical correlates of CNS involvement across globally circulating lineages remain incompletely defined. Here we screened 15,247 EV-A71 genomic entries spanning 1998-2024, retaining 267 full-length sequences (&#x2265;7,000 bp) with confirmed clinical outcomes (7 central nervous system [CNS]-involved, 260 non-CNS). This extreme 7:260 class imbalance, reflecting the scarcity of publicly available full-length CNS-associated EV-A71 genomes, is the principal limitation and interpretive premise of the study. Each polyprotein position was encoded by three Z-scale descriptors-hydrophobicity (Z1), molecular volume (Z2), and electrostatic polarity (Z3)-converting discrete residue identities into a continuous biophysical feature space. A two-stage statistical pipeline (Mann-Whitney U screening followed by odds-ratio ranking) distilled 20 significant loci down to five core positions: P2124_Z1, P997_Z2, P1246_Z3, P1743_Z2, and P1711_Z1 (all P&lt;0.001). Leave-one-out cross-validated logistic regression achieved the highest area under the receiver operating characteristic curve (AUC = 0.889) among eight algorithms benchmarked. Because this AUC is estimated from only seven positive samples, it should be regarded as an exploratory internal performance signal rather than definitive evidence of generalisable accuracy. SHapley Additive exPlanations (SHAP) assigned the largest model contribution to P2124_Z1 (OR = 4.28; 95% CI 1.47-12.51), while P1246_Z3 was statistically associated with lower CNS odds (OR = 0.50); these model-derived quantities do not establish causal mechanisms. Reference-strain mapping linked the five polyprotein coordinates to mature-protein residues in 3D RdRp, 3C protease, 2C helicase, and 2A, thereby providing structural context for cautious biochemical hypotheses rather than confirmed mechanisms. Phylogenetic dispersion of CNS-associated strains was compatible with convergent evolution, but this inference remains limited by the seven available CNS genomes. We therefore present the five-position physicochemical signature and nomogram as hypothesis-generating tools for prioritising candidate neurovirulence markers, requiring prospective validation in larger and more balanced independent cohorts before clinical or field deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42486368/","authors":["Wang X","Li F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.virusres.2026.199772","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486346","name":"Multi-omics and artificial intelligence nominate PCLAF as a prognostic and druggable target for hepatitis B virus-associated hepatocellular carcinoma.","source":"pubmed","abstract":"PCLAF (PCNA clamp-associated factor) is a protein involved in DNA replication and DNA repair. Aberrant PCLAF expression has been reported in multiple malignancies and is associated with tumor progression and poor clinical outcomes. However, the biological role of PCLAF in hepatocellular carcinoma (HCC) remains incompletely understood, particularly with respect to its relationship with the tumor immune microenvironment. Therefore, this study aimed to systematically investigate the clinical significance, biological functions, and therapeutic potential of PCLAF in HCC through integrated multi-omics analyses, experimental validation, and drug screening approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42486346/","authors":["Shu F","Chen Y","Yang X","Gou G","Li G","Yu J","Wang F","Liu Y","Du Q","Xu J","Xie R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov","doi":"10.1016/j.cellsig.2026.112753","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486213","name":"Understanding the hippocampus as an apex of the cortical hierarchy and self-supervised predictive learning engine.","source":"pubmed","abstract":"The mammalian cortex is organized along hierarchical gradients that extend from primary sensory regions to transmodal association networks. Converging neuroanatomical theory and data-driven analyses place the hippocampus at the apex of this hierarchy, where its subregional organization mirrors large-scale cortical networks and their evolutionary expansion. Building on these observations, we propose that the hippocampus functions as a predictive learning engine, generating latent training signals that support cortical learning. This view aligns with self-supervised machine learning frameworks, in which predictive processes occupy the top of hierarchical models. We suggest that hippocampal predictive learning constitutes a foundational mechanism of mammalian intelligence, linking cortical organization with principles underlying modern artificial systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42486213/","authors":["DeKraker J","Eichert N","Hong SJ","Evans AC","Bernhardt BC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.neubiorev.2026.106890","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42486150","name":"Automated acute pain assessment in brachycephalic cats with ocular pain using the Feline Grimace Scale.","source":"pubmed","abstract":"This study aimed to evaluate the feasibility and limitations of an AI-powered Feline Grimace Scale (FGS) pipeline for acute pain assessment in brachycephalic cats with ocular pain.","url":"https://pubmed.ncbi.nlm.nih.gov/42486150/","authors":["Steagall PV","Li J","Marangoni S","Bukhari SSUH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.2460/javma.26.04.0301","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485924","name":"Alzheimer's disease risk prediction via perceptual deformable attention generative adversarial network with large foundation models.","source":"pubmed","abstract":"Predicting the risk of Alzheimer's disease (AD) is fundamental for early-stage intervention. Nevertheless, most methods struggle to extract multi-omics associative patterns due to the limited feature perception and inflexible disease modeling. This paper proposes a novel evolutionary pattern mining framework for precise disease risk prediction. Firstly, large foundational models are employed to automatically construct high-quality features. Second, a perceptual deformable attention mathematical model is proposed, which combines multi-scale sparse attention and deformable attention mechanisms to capture evolutionary patterns of fused multi-omics features. Finally, a Perceptual Deformable Attention Generative Adversarial Network (PDAT-GAN) is developed. PDAT-GAN can precisely simulate the evolutionary procedure of AD using multi-omics data, thereby achieving robust risk prediction and pathogeny extraction for AD. We validate the advanced performance and interpretability of PDAT-GAN on public datasets, underscoring significance of PDAT-GAN in supporting clinical intervention and pathogenetic research. The code of PDAT-GAN can be accessed at: .","url":"https://pubmed.ncbi.nlm.nih.gov/42485924/","authors":["Xing Z","Liu Z","Zhang DF","Xie K","Fang J","Bi XA","Liu T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.media.2026.104225","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485907","name":"Forecasting and real-time detection of neonatal seizures: A machine learning perspective.","source":"pubmed","abstract":"Recognition and treatment of neonatal seizures, primarily diagnosed using EEG, are essential to protect the developing brain, yet only 11% of seizures are treated within 1 hour of onset. Developing seizure forecasting and detection systems could help address this issue in intensive care settings with limited neurologist availability.","url":"https://pubmed.ncbi.nlm.nih.gov/42485907/","authors":["Skoric T","Djermanovic M","O'Toole JM","De Vos M","Spasojevic S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109552","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485818","name":"Non-invasive differentiation between aplastic anemia and myelodysplastic syndromes based on an 8-feature lightGBM model: A multicenter external validation study.","source":"pubmed","abstract":"Given the highly overlapping pancytopenic phenotypes, non-invasive differentiation between aplastic anemia (AA) and myelodysplastic syndromes (MDS) remains a formidable clinical challenge. The current diagnostic gold standard relies on invasive bone marrow biopsy and lacks objective standardization. Here, we develop and externally validate a robust, interpretable machine-learning framework utilizing routine peripheral blood parameters to optimize clinical triage.","url":"https://pubmed.ncbi.nlm.nih.gov/42485818/","authors":["Wang S","Cai Z","Wei W","Zhang S","Chen S","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.1016/j.retram.2026.103602","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485817","name":"Reply to the letter to the editor: \"Machine learning model for predicting preeclampsia-related adverse outcomes\".","source":"pubmed","abstract":"We thank Liu et al. for their constructive comments on our article reporting the internal and external validation of a reduced-feature machine learning model for the prediction of preeclampsia-related adverse outcomes. The letter raises four points, which we address in turn. Regarding calibration and clinical utility, we generated calibration curves for the gradient-boosted tree model in both cohorts. Neither cohort shows a systematic, clinically concerning miscalibration pattern; deviations are concentrated at the extremes of the risk distribution, where observations are fewest. Brier scores were 0.010 (German cohort) and 0.068 (North American cohort), indicating good overall probabilistic accuracy. We agree that a formal clinical utility analysis, such as net benefit across threshold probabilities, represents an important next step. Regarding incremental value over the sFlt-1/PlGF ratio, we note that while the ratio offers strong short-term rule-out performance, its positive predictive value for ruling in preeclampsia related adverse outcomes remains limited. Our model, which incorporates the sFlt 1/PlGF ratio alongside ten additional routine clinical features, achieved AUCs of 92% and 87% in the German and North American cohorts, respectively. A formal head-to head comparison remains an important direction for future work. Regarding dataset heterogeneity, we acknowledge the structural differences between cohorts as a relevant limitation, while noting that discriminative performance remained statistically indistinguishable across cohorts despite significant differences in baseline characteristics. Regarding longer-term outcomes, we agree this is a valuable direction and intend to pursue it as follow-up data become available.","url":"https://pubmed.ncbi.nlm.nih.gov/42485817/","authors":["Hackelöer M","Rana S","Verlohren S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1016/j.preghy.2026.101501","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485786","name":"Centrosome-related gene SPHK1 drives bladder cancer progression and therapeutic vulnerability.","source":"pubmed","abstract":"Centrosome-related genes (CRGs) regulate cell division and genomic stability and may influence tumor progression, but their prognostic and functional roles in bladder cancer (BLCA) are not fully defined.","url":"https://pubmed.ncbi.nlm.nih.gov/42485786/","authors":["Dong C","Fang Z","Han Y","Yin Y","Cao Y","Lu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.tranon.2026.102926","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485714","name":"AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability.","source":"pubmed","abstract":"Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) from sinus rhythm. However, its clinical utility and interpretability in routine practice remain uncertain.","url":"https://pubmed.ncbi.nlm.nih.gov/42485714/","authors":["Matsuo K","Sobue Y","Miyake T","Takeda K","Watanabe E","Matsuo H","Izawa H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jacadv.2026.103036","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485626","name":"Lung ultrasound interpretation using deep learning for the detection of B-lines in dogs.","source":"pubmed","abstract":"Deep learning (DL) shows promise for interpretation of lung ultrasound (LUS) images in humans, but its performance in animals remains underexplored.","url":"https://pubmed.ncbi.nlm.nih.gov/42485626/","authors":["Ward JL","VanBerlo B","Huggard B","Smith D","Arntfield R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1093/jvimsj/aalag152","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485581","name":"First, Do NoHarm: How Pharmaceutical AI Is Improving Care Across Brazil.","source":"pubmed","abstract":"Medication errors are a persistent challenge in health care across the world. In this News and Perspectives article, JMIR Correspondent Luke Taylor reports on NoHarm, a nonprofit AI tool changing pharmacy in Brazil, and its potential to relieve burden and improve patient safety elsewhere.","url":"https://pubmed.ncbi.nlm.nih.gov/42485581/","authors":["Taylor L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.2196/107255","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485329","name":"MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.","source":"pubmed","abstract":"Accurate survival prediction is crucial for personalized cancer treatment but remains challenging for rare cancers due to limited data. Most deep learning models require large training datasets, which are unavailable for rare cancer types, creating a significant clini-cal bottleneck.","url":"https://pubmed.ncbi.nlm.nih.gov/42485329/","authors":["Fang S","Wang Y","Zhou M","Shao Y","Jiang Z","Guo Y","Jiang T","Wang F","Fang J","Tian H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 3","doi":"10.1093/bioinformatics/btag549","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485108","name":"Label-free SERS for the detection of renal failure biomarkers in blood serum using gold nanoparticles as a substrate.","source":"pubmed","abstract":"Renal failure (RF) requires rapid and accurate diagnosis to improve patient outcomes and quality of life. Conventional diagnostic methods are often invasive and time-consuming, highlighting the need for sensitive analytical approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42485108/","authors":["Sarfraz F","Naeem M","Irfan Majeed M","Nawaz H","Rashid N","Majeed MZ","Ahmed HE","Ditta A","Saif B","Iftikhar Z","Ali K","Irshad S","Qadir FG","Munawar U","Imran M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1080/17576180.2026.2705705","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42485106","name":"Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT).","source":"pubmed","abstract":"POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase 3 trial comparing first-line durvalumab with or without tremelimumab in combination with chemotherapy versus chemotherapy alone in patients with metastatic non-small-cell lung cancer (NSCLC). Overall survival (OS) and progression-free survival were significantly increased in the tremelimumab plus durvalumab and chemotherapy arm. We conducted a post hoc analysis (TRIDENT) to identify patients who may receive greater OS benefit from the addition of tremelimumab to durvalumab and chemotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42485106/","authors":["Skoulidis F","Jabbour SK","Garon EB","Iyengar P","Scagliotti G","Ferrer L","Etchepare G","Gallinato O","Faure J","Bernard P","Colin T","Menu P","Lin Y","Cai L","Chaudhry AA","Remorino A","Stewart R","Luciani-Silverman L","Miller K","Dellamonica D","Faria J","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1158/1078-0432.CCR-25-3729","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"pmid:42484951","name":"Comprehensive Characterization of Clinical Phenotypes and CTG Repeat-Associated Manifestations in Spinocerebellar Ataxia Type 8.","source":"pubmed","abstract":"Spinocerebellar ataxia type 8 (SCA8) is traditionally characterized as a cerebellar syndrome, yet emerging evidence suggests significant phenotypic heterogeneity. The complex non-linear relationship between genetic burden and clinical severity remains understudied. We analyzed 172 SCA8 patients using advanced data mining techniques. Complete clinical demographic data were available for the cohort, with precise genetic repeat numbers available for 171 individuals. K-means clustering identified patient subtypes, while Gaussian Graphical Models (GGM) visualized symptom topology. We developed exploratory machine learning models (Decision Tree/Random Forest) and employed Generalized Additive Models (GAM) to characterize non-linear genotype-phenotype dynamics. Clustering identified three distinct phenotypes: Severe Multi-system (12%), Mild/Atypical (38%), and Classical Cerebellar (50%). Network analysis revealed ataxia as a central hub, while ocular motor disorders acted as bridges between symptom clusters. A machine learning-derived decision tree stratified patients with promising exploratory performance (AUC: 0.82-0.89) using only three clinical checkpoints (pyramidal signs, speech disorders, and hyperreflexia), showing predictive patterns distinct from genetic repeat length. Additionally, GAM analysis identified a potential non-linear inflection point at ~&#x2009;100 CTG repeats. This hypothesis-generating observation provides a preliminary mathematical context for the paradoxical \"low-repeat/high-severity\" phenotype in the Severe cluster. SCA8 is a multi-dimensional spectrum disorder rather than a uniform cerebellar disease. Our exploratory diagnostic modeling provides a computational framework for understanding symptom predictors, while non-linear modeling provides novel insights into pathogenic heterogeneity, offering an exploratory foundation for future personalized management and clinical trial design.","url":"https://pubmed.ncbi.nlm.nih.gov/42484951/","authors":["Yang D","Tang C","Zhan Y","Yang L","Lei X","Peng X","He D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s12311-026-02053-8","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21294518","name":"When Does Hyperparameter Optimization Matter in Imbalanced Medical Classification? A Systematic Benchmark Study Across Cost Asymmetry and Imbalance Conditions","source":"datacite","abstract":"Class imbalance and asymmetric misclassification costs are pervasive in medical machine learning, yet their joint interaction with hyperparameter optimization (HPO) method selection remains largely uncharacterized. We present the first systematic benchmark study quantifying under which combinations of imbalance ratio (IR) and clinical cost asymmetry (κ) the choice of HPO method has meaningful impact on diagnostic performance","url":"https://doi.org/10.5281/zenodo.21294518","authors":["Cengiz, Mehmet Ali","Adigüzel, Meryem","Öztürk, Zeynep"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21294518","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21294519","name":"When Does Hyperparameter Optimization Matter in Imbalanced Medical Classification? A Systematic Benchmark Study Across Cost Asymmetry and Imbalance Conditions","source":"datacite","abstract":"Class imbalance and asymmetric misclassification costs are pervasive in medical machine learning, yet their joint interaction with hyperparameter optimization (HPO) method selection remains largely uncharacterized. We present the first systematic benchmark study quantifying under which combinations of imbalance ratio (IR) and clinical cost asymmetry (κ) the choice of HPO method has meaningful impact on diagnostic performance","url":"https://doi.org/10.5281/zenodo.21294519","authors":["Cengiz, Mehmet Ali","Adigüzel, Meryem","Öztürk, Zeynep"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21294519","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21485356","name":"Explainable AI for Healthcare Decision Support","source":"datacite","abstract":"Explainable AI for Healthcare Decision Support This article examines the growing role of Explainable Artificial Intelligence (XAI) in improving transparency, trust, and accountability within healthcare decision support systems. As artificial intelligence becomes increasingly integrated into clinical practice, the study highlights the importance of making machine learning models interpretable to clinicians, thereby addressing one of the primary barriers to the adoption of AI-assisted healthcare—lack of explainability. The paper provides a comprehensive overview of Explainable AI concepts, distinguishing model interpretability from explainability and discussing their significance in clinical decision-making. It explores widely adopted explanation techniques, including SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), comparing their strengths, limitations, and practical applications in disease diagnosis, medical imaging, risk prediction, and personalized treatment planning. The article also examines the principles of trustworthy AI, including transparency, fairness, reliability, accountability, and human oversight, while addressing implementation challenges related to data quality, workflow integration, clinician acceptance, and regulatory compliance. A workflow illustrating the integration of explainable AI into clinical decision support systems further demonstrates how interpretable models can support evidence-based medical decision-making. By synthesizing current evidence and emerging developments, the article emphasizes that Explainable AI is fundamental to the safe, ethical, and responsible deployment of artificial intelligence in healthcare. It concludes that integrating explainability with robust governance and continuous human oversight will be essential for achieving widespread clinical adoption and improving patient-centered healthcare delivery. Keywords: Explainable Artificial Intelligence (XAI), Clinical Decision Support Systems (CDSS), Machine Learning, Trustworthy AI, SHAP, LIME, Model Interpretability, Healthcare Artificial Intelligence, Medical Decision Support, Digital Health, Clinical Adoption, Ethical AI.","url":"https://doi.org/10.5281/zenodo.21485356","authors":["Uchechukwu, Ephraim Buzugbe"],"tags":["Explainable Artificial Intelligence (XAI)","Clinical Decision Support Systems (CDSS)","Machine Learning","Trustworthy AI","SHAP","LIME","Model Interpretability","Healthcare Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.21485356","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21485357","name":"Explainable AI for Healthcare Decision Support","source":"datacite","abstract":"Explainable AI for Healthcare Decision Support This article examines the growing role of Explainable Artificial Intelligence (XAI) in improving transparency, trust, and accountability within healthcare decision support systems. As artificial intelligence becomes increasingly integrated into clinical practice, the study highlights the importance of making machine learning models interpretable to clinicians, thereby addressing one of the primary barriers to the adoption of AI-assisted healthcare—lack of explainability. The paper provides a comprehensive overview of Explainable AI concepts, distinguishing model interpretability from explainability and discussing their significance in clinical decision-making. It explores widely adopted explanation techniques, including SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), comparing their strengths, limitations, and practical applications in disease diagnosis, medical imaging, risk prediction, and personalized treatment planning. The article also examines the principles of trustworthy AI, including transparency, fairness, reliability, accountability, and human oversight, while addressing implementation challenges related to data quality, workflow integration, clinician acceptance, and regulatory compliance. A workflow illustrating the integration of explainable AI into clinical decision support systems further demonstrates how interpretable models can support evidence-based medical decision-making. By synthesizing current evidence and emerging developments, the article emphasizes that Explainable AI is fundamental to the safe, ethical, and responsible deployment of artificial intelligence in healthcare. It concludes that integrating explainability with robust governance and continuous human oversight will be essential for achieving widespread clinical adoption and improving patient-centered healthcare delivery. Keywords: Explainable Artificial Intelligence (XAI), Clinical Decision Support Systems (CDSS), Machine Learning, Trustworthy AI, SHAP, LIME, Model Interpretability, Healthcare Artificial Intelligence, Medical Decision Support, Digital Health, Clinical Adoption, Ethical AI.","url":"https://doi.org/10.5281/zenodo.21485357","authors":["Uchechukwu, Ephraim Buzugbe"],"tags":["Explainable Artificial Intelligence (XAI)","Clinical Decision Support Systems (CDSS)","Machine Learning","Trustworthy AI","SHAP","LIME","Model Interpretability","Healthcare Artificial Intelligence"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.21485357","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21313154","name":"Neuro-Oncology Benchmark: The Resource-Interpretability Tradeoff in Radiomics and Multiclass Brain Tumor Classification Based on Deep Transfer Learning vs Handcrafted Radiomics.","source":"datacite","abstract":"The automatic multiclass brain tumor classification using MRI images plays an important role in a non-invasive clinical setting. However, the choice of a model demands the trade-off between accuracy, complexity, and interpretability of the classifier. In this study, we have established a benchmark for comparison of classical machine learning (ML) approaches to deep learning (DL) methods, using the same data distribution setup to describe the aforementioned complexity-interpretability trade-off frontier. We generate a 72-dimensional manually designed radiomic feature set, which comprises the first-order intensity features, GLCM features, LBP features, shape morphological measures, and FFT characteristics. These are compared against fine-tuned, ImageNet-pretrained ResNet50 and EfficientNet-B0 on a balanced 4-class dataset (Glioma, Meningioma, Pituitary, and No Tumor; $n=7,200$ images). To address the clinical \"black-box\" problem, a dual-axis Explainable AI (XAI) pipeline maps SHAP values to the radiomic feature space and Grad-CAM activations to the deep neural layers. Results show that, based on three independently seeded training cycles, ResNet50 (95.44% $\\pm$ 0.28%) and EfficientNet-B0 (95.44% $\\pm$ 0.22%) achieve statistically indistinguishable accuracy, while the radiomic-driven SVM reaches a robust 89.12% accuracy and 0.9613 Macro AUC, training in under 4 seconds versus over 17 minutes for EfficientNet-B0. Five-fold cross-validation confirms the stability of the SVM pipeline (92.43% $\\pm$ 0.89%). Despite matching ResNet50's accuracy, EfficientNet-B0 achieves this while reducing parameters by 83% (4.0M vs. 23.5M) and training in roughly half the time. We conclude that radiomic pipelines suit resource-constrained edge deployment, while lightweight deep networks integrated with visual XAI tools offer the ideal configuration for centralized diagnostic frameworks.","url":"https://doi.org/10.5281/zenodo.21313154","authors":["Bakkas, Othmane","Ennagoura, Drissia","EL Kehal, Kamal","ZBAKH, ABDELALI","El Mahjouby, Mohamed","Bossoufi, Badre","El Fahssi, Khalid","El Far, Mohamed","Taj Bennani, Mohamed"],"tags":["Brain Tumor MRI","Handcrafted Radiomics","Transfer Learning","EfficientNet-B0","Explainable AI","Computational Efficiency"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21313154","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21313155","name":"Neuro-Oncology Benchmark: The Resource-Interpretability Tradeoff in Radiomics and Multiclass Brain Tumor Classification Based on Deep Transfer Learning vs Handcrafted Radiomics.","source":"datacite","abstract":"The automatic multiclass brain tumor classification using MRI images plays an important role in a non-invasive clinical setting. However, the choice of a model demands the trade-off between accuracy, complexity, and interpretability of the classifier. In this study, we have established a benchmark for comparison of classical machine learning (ML) approaches to deep learning (DL) methods, using the same data distribution setup to describe the aforementioned complexity-interpretability trade-off frontier. We generate a 72-dimensional manually designed radiomic feature set, which comprises the first-order intensity features, GLCM features, LBP features, shape morphological measures, and FFT characteristics. These are compared against fine-tuned, ImageNet-pretrained ResNet50 and EfficientNet-B0 on a balanced 4-class dataset (Glioma, Meningioma, Pituitary, and No Tumor; $n=7,200$ images). To address the clinical \"black-box\" problem, a dual-axis Explainable AI (XAI) pipeline maps SHAP values to the radiomic feature space and Grad-CAM activations to the deep neural layers. Results show that, based on three independently seeded training cycles, ResNet50 (95.44% $\\pm$ 0.28%) and EfficientNet-B0 (95.44% $\\pm$ 0.22%) achieve statistically indistinguishable accuracy, while the radiomic-driven SVM reaches a robust 89.12% accuracy and 0.9613 Macro AUC, training in under 4 seconds versus over 17 minutes for EfficientNet-B0. Five-fold cross-validation confirms the stability of the SVM pipeline (92.43% $\\pm$ 0.89%). Despite matching ResNet50's accuracy, EfficientNet-B0 achieves this while reducing parameters by 83% (4.0M vs. 23.5M) and training in roughly half the time. We conclude that radiomic pipelines suit resource-constrained edge deployment, while lightweight deep networks integrated with visual XAI tools offer the ideal configuration for centralized diagnostic frameworks.","url":"https://doi.org/10.5281/zenodo.21313155","authors":["Bakkas, Othmane","Ennagoura, Drissia","EL Kehal, Kamal","ZBAKH, ABDELALI","El Mahjouby, Mohamed","Bossoufi, Badre","El Fahssi, Khalid","El Far, Mohamed","Taj Bennani, Mohamed"],"tags":["Brain Tumor MRI","Handcrafted Radiomics","Transfer Learning","EfficientNet-B0","Explainable AI","Computational Efficiency"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21313155","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21240932","name":"MACHINE LEARNING IN HEALTHCARE SYSTEMS","source":"datacite","abstract":"Abstract Machine learning (ML) has become a revolutionary element in contemporary healthcare systems, fostering advancements in diagnostics, treatment strategies, and individualized medicine. This document offers an in-depth examination of advanced ML algorithms, encompassing supervised, unsupervised, and reinforcement learning methods. We investigate essential applications including predictive analytics for patient results, analysis of medical images, and the handling of Electronic Health Records (EHR). Additionally, we tackle the existing issues in deploying these smart systems, such as data privacy, algorithmic bias, and compatibility. This study ultimately provides a framework for the ethical and practical integration of ML, showcasing its ability to improve clinical decision-making and streamline healthcare delivery. Keywords: Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), Natural Language Processing (NLP), Reinforcement Learning, Predictive Analytics, Clinical Decision Support Systems (CDSS), Electronic Health Records (EHR), Personalized Medicine, Medical Imaging, Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21240932","authors":["Amandeep Kaur Bhullar","Mona"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21240932","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21240933","name":"MACHINE LEARNING IN HEALTHCARE SYSTEMS","source":"datacite","abstract":"Abstract Machine learning (ML) has become a revolutionary element in contemporary healthcare systems, fostering advancements in diagnostics, treatment strategies, and individualized medicine. This document offers an in-depth examination of advanced ML algorithms, encompassing supervised, unsupervised, and reinforcement learning methods. We investigate essential applications including predictive analytics for patient results, analysis of medical images, and the handling of Electronic Health Records (EHR). Additionally, we tackle the existing issues in deploying these smart systems, such as data privacy, algorithmic bias, and compatibility. This study ultimately provides a framework for the ethical and practical integration of ML, showcasing its ability to improve clinical decision-making and streamline healthcare delivery. Keywords: Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), Natural Language Processing (NLP), Reinforcement Learning, Predictive Analytics, Clinical Decision Support Systems (CDSS), Electronic Health Records (EHR), Personalized Medicine, Medical Imaging, Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21240933","authors":["Amandeep Kaur Bhullar","Mona"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21240933","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19396525","name":"SepsiGuard: A Triple-Layer Agentic XAI Framework for Early Sepsis Prediction and Adaptive ICU Monitoring","source":"datacite","abstract":"Sepsis is a major contributor to morbidity and mortality in the Intensive Care Unit (ICU). There is a need to predict sepsis quickly and effectively to take appropriate measures. Machine learning models have shown excellent predictive performance for various adverse health conditions in ICUs. However, the lack of transparency in these models, often termed a \"black box,\" makes them difficult to use. Traditional clinical scoring systems, such as SOFA and qSOFA, rely on fixed thresholds and fail to adapt dynamically to evolving patient conditions. Conventional Explainable AI techniques, such as feature importance plots, are static and often difficult to understand for clinicians. This paper introduces a new technique to predict sepsis in the ICU using \"Agentic XAI,\" which is a new technique of using Large Language Models (LLMs) as autonomous agents to iteratively develop explanations. This technique combines complex physiological signals into a narrative, which is expected to bridge the gap between probability and clinical reasoning. This paper presents the methodological architecture that is expected to be used to develop this technique using recent advancements in predictive modelling in ICUs.","url":"https://doi.org/10.5281/zenodo.19396525","authors":["Farheen Khan","Fahemiya Khan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19396525","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19396526","name":"SepsiGuard: A Triple-Layer Agentic XAI Framework for Early Sepsis Prediction and Adaptive ICU Monitoring","source":"datacite","abstract":"Sepsis is a major contributor to morbidity and mortality in the Intensive Care Unit (ICU). There is a need to predict sepsis quickly and effectively to take appropriate measures. Machine learning models have shown excellent predictive performance for various adverse health conditions in ICUs. However, the lack of transparency in these models, often termed a \"black box,\" makes them difficult to use. Traditional clinical scoring systems, such as SOFA and qSOFA, rely on fixed thresholds and fail to adapt dynamically to evolving patient conditions. Conventional Explainable AI techniques, such as feature importance plots, are static and often difficult to understand for clinicians. This paper introduces a new technique to predict sepsis in the ICU using \"Agentic XAI,\" which is a new technique of using Large Language Models (LLMs) as autonomous agents to iteratively develop explanations. This technique combines complex physiological signals into a narrative, which is expected to bridge the gap between probability and clinical reasoning. This paper presents the methodological architecture that is expected to be used to develop this technique using recent advancements in predictive modelling in ICUs.","url":"https://doi.org/10.5281/zenodo.19396526","authors":["Farheen Khan","Fahemiya Khan"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19396526","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19875052","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS","source":"datacite","abstract":"Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, protein structure prediction, virtual screening, de novo drug design, retrosynthesis, ADMET prediction, and clinical trial optimization. Advanced deep learning models such as graph neural networks, transformers, and diffusion models have significantly improved molecular representation, interaction prediction, and novel compound generation. The integration of emerging approaches like federated learning and quantum machine learning is also discussed, particularly for overcoming data-sharing and computational limitations. Clinical examples of AI-designed drugs are examined to illustrate both successes and challenges in translating computational predictions into real-world outcomes. Despite substantial progress, issues such as model interpretability, data bias, and regulatory concerns remain critical barriers. Overall, AI is rapidly becoming a central driver of precision medicine by enhancing efficiency, reducing costs, and improving success rates in drug discovery. However, interdisciplinary collaboration and responsible governance frameworks are essential to fully realize its potential and ensure safe and effective implementation in pharmaceutical development.","url":"https://doi.org/10.5281/zenodo.19875052","authors":["*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19875052","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19875053","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS","source":"datacite","abstract":"Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, protein structure prediction, virtual screening, de novo drug design, retrosynthesis, ADMET prediction, and clinical trial optimization. Advanced deep learning models such as graph neural networks, transformers, and diffusion models have significantly improved molecular representation, interaction prediction, and novel compound generation. The integration of emerging approaches like federated learning and quantum machine learning is also discussed, particularly for overcoming data-sharing and computational limitations. Clinical examples of AI-designed drugs are examined to illustrate both successes and challenges in translating computational predictions into real-world outcomes. Despite substantial progress, issues such as model interpretability, data bias, and regulatory concerns remain critical barriers. Overall, AI is rapidly becoming a central driver of precision medicine by enhancing efficiency, reducing costs, and improving success rates in drug discovery. However, interdisciplinary collaboration and responsible governance frameworks are essential to fully realize its potential and ensure safe and effective implementation in pharmaceutical development.","url":"https://doi.org/10.5281/zenodo.19875053","authors":["*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19875053","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20346950","name":"CardioAI: A Multi-Model Machine Learning Clinical Decision Support System and XAI Dashboard","source":"datacite","abstract":"Cardiovascular diseases remain a critical global health challenge, necessitating advanced, data-driven diagnostic tools for early risk detection. To address this, this project proposes CardioAI, a state-of-the-art Clinical Decision Support System (CDSS) that seamlessly integrates a multi-model machine learning pipeline with an interactive web dashboard. The application dynamically evaluates real-time patient clinical indicators—including vitals, metabolic metrics, and lifestyle factors—to predict cardiovascular risk and generate tailored health prescriptions. The system is engineered with a high-performance backend built using Python, Flask, and Scikit-learn, while the frontend utilizes responsive web technologies. Key features include a dynamic ML pipeline that selects the highest-performing algorithm (Logistic Regression, SVM, or Random Forest), a real-time clinical dashboard computing BMI and heart age, and Explainable AI (XAI) feature importance charts. Ultimately, CardioAI delivers actionable medical prescriptions based on the patient's calculated risk state.","url":"https://doi.org/10.5281/zenodo.20346950","authors":["B, BHARATHKUMAR","P, Buvaneshwaren","S, CHARANKUMAR","Dr. P., Thangavel"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20346950","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20346951","name":"CardioAI: A Multi-Model Machine Learning Clinical Decision Support System and XAI Dashboard","source":"datacite","abstract":"Cardiovascular diseases remain a critical global health challenge, necessitating advanced, data-driven diagnostic tools for early risk detection. To address this, this project proposes CardioAI, a state-of-the-art Clinical Decision Support System (CDSS) that seamlessly integrates a multi-model machine learning pipeline with an interactive web dashboard. The application dynamically evaluates real-time patient clinical indicators—including vitals, metabolic metrics, and lifestyle factors—to predict cardiovascular risk and generate tailored health prescriptions. The system is engineered with a high-performance backend built using Python, Flask, and Scikit-learn, while the frontend utilizes responsive web technologies. Key features include a dynamic ML pipeline that selects the highest-performing algorithm (Logistic Regression, SVM, or Random Forest), a real-time clinical dashboard computing BMI and heart age, and Explainable AI (XAI) feature importance charts. Ultimately, CardioAI delivers actionable medical prescriptions based on the patient's calculated risk state.","url":"https://doi.org/10.5281/zenodo.20346951","authors":["B, BHARATHKUMAR","P, Buvaneshwaren","S, CHARANKUMAR","Dr. P., Thangavel"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20346951","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21280152","name":"INTEGRATION OF AI AND MACHINE LEARNING IN NURSING CLINICAL DECISION SUPPORT","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21280152","authors":["Faizan Rafique,Basit Ali Arain"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21280152","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21280153","name":"INTEGRATION OF AI AND MACHINE LEARNING IN NURSING CLINICAL DECISION SUPPORT","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21280153","authors":["Faizan Rafique,Basit Ali Arain"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21280153","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.24406/publica-8676","name":"Distal radial epiphyseal fusion timing in Northwest Europeans and Middle Eastern asylum seekers: An automated ultrasound and machine learning approach","source":"datacite","abstract":"Background: Age assessment in individuals without reliable identification is a recurrent medico-legal challenge in Europe, particularly in migration contexts. Distal radial epiphyseal fusion is among the last skeletal maturation events near the age of majority, yet population-specific reference data and radiation-free assessment strategies remain limited. Methods: In this multicentre study conducted across German clinical sites and initial reception facilities, Northwest European (NWE) and Middle Eastern (ME) individuals aged 13-25 years underwent distal forearm assessment using a fully automated ultrasound device. Reference ages were obtained from documented birthdates or Greulich-Pyle bone ages. Gradient-boosting machine-learning models (LightGBM) were evaluated using leave-one-out cross-validation across sex-specific age thresholds. Performance was quantified using precision, recall, F1-scores, ROC/AUC, and Clopper-Pearson confidence intervals for correctly detected fusions. Results: Of 1061 recordings, 830 high-quality ultrasound datasets were analysed across four sex-population clusters. Classification performance was biologically plausible and threshold-dependent, with optimal discrimination at 17.5 years in NWE females, 18.0-18.5 years in NWE males, 17.0-17.5 years in ME females, and 18.5 years in ME males. Females fused earlier than males in both populations, and sex-specific population shifts were observed. AUC values indicated good to excellent discrimination near the optimal thresholds (approximately 0.81-1.00). Conclusions: Machine-learning-assisted automated ultrasound provides a scalable, radiation-free approach for assessing distal radial epiphyseal fusion in late adolescence. Population- and sex-specific shifts underline the importance of population-appropriate reference data when interpreting fusion status around legally critical age thresholds. While an unfused distal radial plate strongly supported minority status, complete fusion alone could not be considered definitive proof of adulthood. Thus reinforcing the need for probabilistic interpretation, particularly around the legally critical 18-year threshold, within a broader forensic age-assessment framework.","url":"https://doi.org/10.24406/publica-8676","authors":["Birken, Charlotte","Hewener, Holger","Lessmeister-Bastian, Tina","Rohrer, Tilman",":unav"],"tags":["Age estimation","Distal radial epiphyseal fusion","Machine learning","Ultrasonography"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.24406/publica-8676","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19365550","name":"Navigating Drug-Like Space: The Central Role of Physicochemical Properties in Modern Drug Design","source":"datacite","abstract":"This comprehensive review details the central role of physicochemical properties throughout the drug discovery and development pipeline. It begins by establishing the bedrock principles of 'drug-likeness,' tracing the evolution from Lipinski's Rule of Five to more sophisticated frameworks like the Biopharmaceutics Drug Disposition Classification System (BDDCS), which incorporates metabolism. The document thoroughly examines cornerstone properties—lipophilicity (LogP/LogD), aqueous solubility (LogS), acid dissociation constant (pKa), and molecular weight (MW)—detailing their impact on a drug's ADMET profile. It outlines both gold-standard experimental protocols, such as the shake-flask method, and modern high-throughput assays for property determination. A significant portion is dedicated to computational methodologies, including Quantitative Structure-Property/Activity Relationship (QSAR/QSPR) modeling using topological indices and advanced machine learning algorithms. These in silico tools enable the early prediction of properties, guiding rational design and mitigating risks like 'molecular obesity'—a trend toward excessively lipophilic compounds with poor developability. The text also addresses practical development challenges, offering troubleshooting strategies for poor solubility, low permeability, metabolic instability, and formulation anomalies like burst release and lag phases. It emphasizes the use of ligand efficiency metrics (e.g., LLE, LELP) to balance potency against physicochemical liabilities during lead optimization. Through comparative analysis of successful drugs versus failed candidates, the article validates the necessity of a holistic, property-driven design strategy that integrates computational prediction, experimental validation, and advanced formulation techniques to reduce clinical failure rates. Source: https://www.drugchemsci.com/posts/navigating-druglike-space-the-central-role-of-physicochemical-properties-in-modern-drug-design","url":"https://doi.org/10.5281/zenodo.19365550","authors":["drug chemical science"],"tags":["drug design","physicochemical properties","drug-likeness","ADMET","Lipinski's Rule of Five","QSAR","QSPR","lead optimization"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19365550","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19365551","name":"Navigating Drug-Like Space: The Central Role of Physicochemical Properties in Modern Drug Design","source":"datacite","abstract":"This comprehensive review details the central role of physicochemical properties throughout the drug discovery and development pipeline. It begins by establishing the bedrock principles of 'drug-likeness,' tracing the evolution from Lipinski's Rule of Five to more sophisticated frameworks like the Biopharmaceutics Drug Disposition Classification System (BDDCS), which incorporates metabolism. The document thoroughly examines cornerstone properties—lipophilicity (LogP/LogD), aqueous solubility (LogS), acid dissociation constant (pKa), and molecular weight (MW)—detailing their impact on a drug's ADMET profile. It outlines both gold-standard experimental protocols, such as the shake-flask method, and modern high-throughput assays for property determination. A significant portion is dedicated to computational methodologies, including Quantitative Structure-Property/Activity Relationship (QSAR/QSPR) modeling using topological indices and advanced machine learning algorithms. These in silico tools enable the early prediction of properties, guiding rational design and mitigating risks like 'molecular obesity'—a trend toward excessively lipophilic compounds with poor developability. The text also addresses practical development challenges, offering troubleshooting strategies for poor solubility, low permeability, metabolic instability, and formulation anomalies like burst release and lag phases. It emphasizes the use of ligand efficiency metrics (e.g., LLE, LELP) to balance potency against physicochemical liabilities during lead optimization. Through comparative analysis of successful drugs versus failed candidates, the article validates the necessity of a holistic, property-driven design strategy that integrates computational prediction, experimental validation, and advanced formulation techniques to reduce clinical failure rates. Source: https://www.drugchemsci.com/posts/navigating-druglike-space-the-central-role-of-physicochemical-properties-in-modern-drug-design","url":"https://doi.org/10.5281/zenodo.19365551","authors":["drug chemical science"],"tags":["drug design","physicochemical properties","drug-likeness","ADMET","Lipinski's Rule of Five","QSAR","QSPR","lead optimization"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19365551","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20145989","name":"Multimodal Deep Learning For Pulmonary Disease Classification: A Deployment-Oriented Framework For Healthcare And Social Good","source":"datacite","abstract":"Globally, pulmonary diseases are a major health burden, especially in settings where resources are constrained and access to specialized radiological expertise is limited. Most chest radiograph-based deep learning models rely only on imaging data, despite showing promising performance when it comes to their diagnostic capabilities. However, clinical decision-making in the real world integrates structured patient information with the aforementioned imaging data. Our study aims to compare current multimodal machine learning approaches that combine clinical data and imaging used in pulmonary disease classification and propose a deployable framework designed for clinical settings in resource-constrained environments. We conducted a review of recent literature around multimodal AI in pulmonary diseases, and we focused on fusion strategies (early-stage, late-stage, and hybrid), techniques for data integration, validation settings, as well as deployment considerations. We performed a comparative synthesis aiming to identify methodological patterns, translational gaps, and performance trends, and based on which we proposed a modular architecture integrating structured-data encoders with convolutional neural networks for imaging data, and fusion mechanisms to handle incomplete modalities. This review indicates that multimodal approaches consistently outperform unimodal imaging models, especially in early-stage or complex cases, with gains in performance reported across many pulmonary conditions. However, the review also reveals several limitations, whether it's the lack of standardized fusion evaluation, insufficiencies in external validation, inadequacies in the handling of missing clinical variables, or limited attention to real-world clinical integration. These obstacles expose the need for system design that is deployment-aware more so than solely performance-driven optimization. Through this work, we aim to contribute a structured synthesis of multimodal pulmonary AI and provide an interpretable and scalable framework suitable for integration into healthcare workflows, while remaining resource-conscious. By aligning the model design philosophy with the realities of clinical workflows, this approach should support equitable access to AI-assisted diagnostics and help advance the application of AI for healthcare improvement and social good.","url":"https://doi.org/10.5281/zenodo.20145989","authors":["HRID, Hamza","MACHKOUR, Mustapha"],"tags":["Artificial intelligence","Pulmonary disease","Classification","Medical imaging","Clinical data","icdet2026","ChairConf"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20145989","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20145990","name":"Multimodal Deep Learning For Pulmonary Disease Classification: A Deployment-Oriented Framework For Healthcare And Social Good","source":"datacite","abstract":"Globally, pulmonary diseases are a major health burden, especially in settings where resources are constrained and access to specialized radiological expertise is limited. Most chest radiograph-based deep learning models rely only on imaging data, despite showing promising performance when it comes to their diagnostic capabilities. However, clinical decision-making in the real world integrates structured patient information with the aforementioned imaging data. Our study aims to compare current multimodal machine learning approaches that combine clinical data and imaging used in pulmonary disease classification and propose a deployable framework designed for clinical settings in resource-constrained environments. We conducted a review of recent literature around multimodal AI in pulmonary diseases, and we focused on fusion strategies (early-stage, late-stage, and hybrid), techniques for data integration, validation settings, as well as deployment considerations. We performed a comparative synthesis aiming to identify methodological patterns, translational gaps, and performance trends, and based on which we proposed a modular architecture integrating structured-data encoders with convolutional neural networks for imaging data, and fusion mechanisms to handle incomplete modalities. This review indicates that multimodal approaches consistently outperform unimodal imaging models, especially in early-stage or complex cases, with gains in performance reported across many pulmonary conditions. However, the review also reveals several limitations, whether it's the lack of standardized fusion evaluation, insufficiencies in external validation, inadequacies in the handling of missing clinical variables, or limited attention to real-world clinical integration. These obstacles expose the need for system design that is deployment-aware more so than solely performance-driven optimization. Through this work, we aim to contribute a structured synthesis of multimodal pulmonary AI and provide an interpretable and scalable framework suitable for integration into healthcare workflows, while remaining resource-conscious. By aligning the model design philosophy with the realities of clinical workflows, this approach should support equitable access to AI-assisted diagnostics and help advance the application of AI for healthcare improvement and social good.","url":"https://doi.org/10.5281/zenodo.20145990","authors":["HRID, Hamza","MACHKOUR, Mustapha"],"tags":["Artificial intelligence","Pulmonary disease","Classification","Medical imaging","Clinical data","icdet2026","ChairConf"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20145990","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19399586","name":"FULCRUM: Predicting Immunotherapy Response from the Structural Theory of Dissipative Systems","source":"datacite","abstract":"Immune checkpoint inhibitors produce durable responses in 20–30% of cancer patients. Identifying responders before treatment remains an unsolved problem: PD-L1 staining is predictive in only 29% of FDA-approved indications, and gene expression signatures developed on specific cohorts fail to transfer across cancer types. Here I present FULCRUM, an immunotherapy prediction framework derived from the structural theory of dissipative systems. The core insight is that a tumour and the immune system are two competing dissipative systems, and the outcome of their interaction is determined by the ratio of their outputs. This ratio — immune effector production divided by tumour proliferation — requires no fitted parameters and is computed from five genes. FULCRUM predicts overall survival across 9,966 patients and 33 cancer types (HR = 0.935, p = 0.0002), shows correct directionality on 12 of 13 immunotherapy datasets (~1,400 patients), and at single-cell resolution matches 51-feature machine learning using only 6 structurally derived features (AUC 0.770 vs 0.762 on NSCLC, n = 242). At the patient level, FULCRUM predicts individual immunotherapy response with AUC 0.808, outperforming oncologist clinical estimates (0.72) and PD-L1 staining (0.64). Unlike existing scores, FULCRUM returns a structural diagnosis — identifying which component of the immune-tumour interaction is failing and what intervention might address it.","url":"https://doi.org/10.5281/zenodo.19399586","authors":["van der Klein, Raimo"],"tags":["Immunotherapy","Immunotherapy","Immune Checkpoint Inhibitors/immunology","Biomarkers","Cancer"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19399586","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19399587","name":"FULCRUM: Predicting Immunotherapy Response from the Structural Theory of Dissipative Systems","source":"datacite","abstract":"Immune checkpoint inhibitors produce durable responses in 20–30% of cancer patients. Identifying responders before treatment remains an unsolved problem: PD-L1 staining is predictive in only 29% of FDA-approved indications, and gene expression signatures developed on specific cohorts fail to transfer across cancer types. Here I present FULCRUM, an immunotherapy prediction framework derived from the structural theory of dissipative systems. The core insight is that a tumour and the immune system are two competing dissipative systems, and the outcome of their interaction is determined by the ratio of their outputs. This ratio — immune effector production divided by tumour proliferation — requires no fitted parameters and is computed from five genes. FULCRUM predicts overall survival across 9,966 patients and 33 cancer types (HR = 0.935, p = 0.0002), shows correct directionality on 12 of 13 immunotherapy datasets (~1,400 patients), and at single-cell resolution matches 51-feature machine learning using only 6 structurally derived features (AUC 0.770 vs 0.762 on NSCLC, n = 242). At the patient level, FULCRUM predicts individual immunotherapy response with AUC 0.808, outperforming oncologist clinical estimates (0.72) and PD-L1 staining (0.64). Unlike existing scores, FULCRUM returns a structural diagnosis — identifying which component of the immune-tumour interaction is failing and what intervention might address it.","url":"https://doi.org/10.5281/zenodo.19399587","authors":["van der Klein, Raimo"],"tags":["Immunotherapy","Immunotherapy","Immune Checkpoint Inhibitors/immunology","Biomarkers","Cancer"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19399587","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.18738/t8/3pyja9","name":"Indole profiles define disease-specific virulence in Fusobacterium nucleatum clinical isolates","source":"datacite","abstract":"This study investigates the metabolic and virulence characteristics of Fusobacterium nucleatum clinical isolates collected from colorectal cancer (CRC), Crohn’s disease, healthy individuals, and oral lesion. Focusing on the microbial metabolite indole and its derivatives—produced via tryptophan metabolism—the research demonstrates that CRC-associated F. nucleatum isolates produce significantly higher levels of indole. These isolates also exhibit distinct biofilm formation capacities and altered expression of virulence genes (e.g., fap2, fadA, aid1, radD) in response to indole exposure. Using machine learning classification based on gene expression profiles, the study shows that indole responsiveness can predict disease origin of isolates. The findings suggest that indole metabolism represents a niche-specific adaptation in CRC-associated F. nucleatum, with implications for understanding microbial contributions to cancer pathogenesis and potential therapeutic targeting.","url":"https://doi.org/10.18738/t8/3pyja9","authors":["Scano, Colin"],"tags":["Medicine, Health and Life Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.18738/t8/3pyja9","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22051196","name":"rbGyanX: A radiobiology-guided clinical decision support framework","source":"datacite","abstract":"rbGyanX is a radiobiology-guided clinical decision support framework designed to integrate physical dosimetry, radiobiological modeling, and explainable machine learning for treatment plan evaluation in radiation oncology.","url":"https://doi.org/10.5281/zenodo.22051196","authors":["Mondal, Kalyan","Mandal, Abhijit","Vijay, Anuj"],"tags":["radiobiology","clinical decision support","TCP","NTCP","radiotherapy","medical physics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22051196","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21590940","name":"A Comprehensive and Experimental Survey on Medical Data Classification and Pattern Recognition","source":"datacite","abstract":"This paper is proposed to compare and analyze various type of medical data classification and pattern recognition methods. Medical data classification methods majorly divided into three categories such as supervised, classification and also semi-supervised classification. Pattern recognition and data classifications are both overlapped domain for useful knowledge generation and prediction from training data. The field of medical diagnosis (or) clinical support system needs in intelligent data classification and pattern recognition algorithms for more accuracy in clinical decision making. Supervised classification contains many methods such as rule based classification, decision tree based classification, Bayesian classification, KNN probabilistic neural network, SVM and more, combination of supervised classification called as ensemble algorithm. These types of mixed algorithm provide more accuracy. Unsupervised classification called lazy learner (or) clustering for example automatic classification of unlabeled data. Unsupervised classification also contains some types such as K-means, deep learning methods, hierarchical clustering and more. In this paper we have to analyze various types of classification algorithms using sample medical record of upper abdomen diseases database. In this paper we have to analyze maximum of algorithms in experimental using same training data, this will used for various performance and accuracy analysis.","url":"https://doi.org/10.5281/zenodo.21590940","authors":["Devi, R. Subathra"],"tags":["Medical support system","clinical support system","medical data classification","supervised classification","un-supervised classification","rule-based classification","DCT","Bayesian classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2018","doi":"10.5281/zenodo.21590940","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21590941","name":"A Comprehensive and Experimental Survey on Medical Data Classification and Pattern Recognition","source":"datacite","abstract":"This paper is proposed to compare and analyze various type of medical data classification and pattern recognition methods. Medical data classification methods majorly divided into three categories such as supervised, classification and also semi-supervised classification. Pattern recognition and data classifications are both overlapped domain for useful knowledge generation and prediction from training data. The field of medical diagnosis (or) clinical support system needs in intelligent data classification and pattern recognition algorithms for more accuracy in clinical decision making. Supervised classification contains many methods such as rule based classification, decision tree based classification, Bayesian classification, KNN probabilistic neural network, SVM and more, combination of supervised classification called as ensemble algorithm. These types of mixed algorithm provide more accuracy. Unsupervised classification called lazy learner (or) clustering for example automatic classification of unlabeled data. Unsupervised classification also contains some types such as K-means, deep learning methods, hierarchical clustering and more. In this paper we have to analyze various types of classification algorithms using sample medical record of upper abdomen diseases database. In this paper we have to analyze maximum of algorithms in experimental using same training data, this will used for various performance and accuracy analysis.","url":"https://doi.org/10.5281/zenodo.21590941","authors":["Devi, R. Subathra"],"tags":["Medical support system","clinical support system","medical data classification","supervised classification","un-supervised classification","rule-based classification","DCT","Bayesian classification"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2018","doi":"10.5281/zenodo.21590941","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.17612015","name":"Retired-1","source":"datacite","abstract":"1. Overview This repository contains the full experimental pipeline used to evaluate three-class clinical classification and 0–100 continuous clinical score regression on real-world cardiotocography (CTG) time-series data. The project compares: Five classical machine learning models, and Five transformer-based deep learning models, plus A transformer-based regression framework predicting the continuous clinical score (0–100). All analyses are performed on segmented CTG tracings consisting of FHR (Fetal Heart Rate), UA (Uterine Activity), and AFM (Automatic Fetal Movement) channels. 2. Repository Structure ├── ClassicModels.ipynb ├── Transformer_iTransformer.ipynb ├── Transformer_PatchCTG.ipynb ├── Transformer_PatchTST.ipynb ├── Transformer_TimesNetLite.ipynb ├── Transformer_ETCNN.ipynb ├── Transformer_Regression.ipynb ├── _scored_output.xlsx └── README.md 3. Description of Files A) Classical Machine Learning — Three-Class Classification ClassicModels.ipynb Trains and evaluates five widely used classical ML algorithms: Logistic Regression LightGBM Random Forest SVM (RBF kernel) XGBoost Includes: Preprocessing and feature handling SMOTE-balanced training data, original distribution test data Accuracy, Precision, Recall, Macro-F1 Class-wise ROC curves and macro-ROC metrics Confusion matrices B) Transformer-Based Models — Three-Class Classification Each transformer model is implemented in a separate notebook for modularity and reproducibility: Transformer_iTransformer.ipynb Implements the iTransformer architecture for multivariate long-range time-series modeling. Transformer_PatchCTG.ipynb Patch-based transformer optimized specifically for CTG segmentation and feature aggregation. Transformer_PatchTST.ipynb Implementation of the PatchTST architecture for multivariate physiological time-series. Transformer_TimesNetLite.ipynb Lightweight version of TimesNet using multi-scale temporal decomposition. Transformer_ETCNN.ipynb Hybrid encoder combining CNN feature extraction and transformer-based temporal modeling. All transformer notebooks include: Train/validation loop Optimizers (AdamW / Lion) LR scheduling Early stopping ROC curves (per class + macro) Complete classification metrics C) Transformer-Based Regression — 0–100 Continuous Score Transformer_Regression.ipynb Predicts the continuous 0–100 clinical evaluation score (regression task), using: PatchTST PatchCTG iTransformer TimesNetLite ETCNN Outputs: MAE, RMSE, R² Pearson & Spearman correlations Bland–Altman analysis Calibration curves Scatter plots for predicted vs. true scores This notebook corresponds to the Regression Axis described in the associated thesis/manuscript. D) Additional Output _scored_output.xlsx Contains: True class labels True continuous clinical scores Predicted probabilities (3-class models) Predicted regression outputs Threshold-based validation metrics Data used to generate all figures (ROC, PR, Bland–Altman, calibration, etc.) 4. Tasks & Experimental Axes This repository covers three analytical axes: Axis 1 — Three-Class Classification (Classical ML) Evaluates baseline machine learning algorithms on CTG-derived segments. Axis 2 — Three-Class Classification (Transformers) Assesses the discriminative performance of modern transformer architectures. Axis 3 — Regression (0–100 Continuous Clinical Score) Predicts clinician-derived continuous assessment values and compares regression accuracy across transformer-based models. 5. Requirements This project requires: Python >= 3.10 PyTorch >= 2.0 scikit-learn xgboost lightgbm imbalanced-learn numpy, pandas einops matplotlib, seaborn GPU acceleration (Colab / A100 / CUDA-enabled environment) is strongly recommended for transformer models. 6. How to Use Provide dataset paths in the notebooks. Run preprocessing and segmentation (if needed). Execute classical models via ClassicModels.ipynb. Execute transformer-based classification models individually. Run Transformer_Regression.ipynb for continuous score prediction. Use","url":"https://doi.org/10.5281/zenodo.17612015","authors":["Ferhat, Karataş"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17612015","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.17612016","name":"Retired-1","source":"datacite","abstract":"1. Overview This repository contains the full experimental pipeline used to evaluate three-class clinical classification and 0–100 continuous clinical score regression on real-world cardiotocography (CTG) time-series data. The project compares: Five classical machine learning models, and Five transformer-based deep learning models, plus A transformer-based regression framework predicting the continuous clinical score (0–100). All analyses are performed on segmented CTG tracings consisting of FHR (Fetal Heart Rate), UA (Uterine Activity), and AFM (Automatic Fetal Movement) channels. 2. Repository Structure ├── ClassicModels.ipynb ├── Transformer_iTransformer.ipynb ├── Transformer_PatchCTG.ipynb ├── Transformer_PatchTST.ipynb ├── Transformer_TimesNetLite.ipynb ├── Transformer_ETCNN.ipynb ├── Transformer_Regression.ipynb ├── _scored_output.xlsx └── README.md 3. Description of Files A) Classical Machine Learning — Three-Class Classification ClassicModels.ipynb Trains and evaluates five widely used classical ML algorithms: Logistic Regression LightGBM Random Forest SVM (RBF kernel) XGBoost Includes: Preprocessing and feature handling SMOTE-balanced training data, original distribution test data Accuracy, Precision, Recall, Macro-F1 Class-wise ROC curves and macro-ROC metrics Confusion matrices B) Transformer-Based Models — Three-Class Classification Each transformer model is implemented in a separate notebook for modularity and reproducibility: Transformer_iTransformer.ipynb Implements the iTransformer architecture for multivariate long-range time-series modeling. Transformer_PatchCTG.ipynb Patch-based transformer optimized specifically for CTG segmentation and feature aggregation. Transformer_PatchTST.ipynb Implementation of the PatchTST architecture for multivariate physiological time-series. Transformer_TimesNetLite.ipynb Lightweight version of TimesNet using multi-scale temporal decomposition. Transformer_ETCNN.ipynb Hybrid encoder combining CNN feature extraction and transformer-based temporal modeling. All transformer notebooks include: Train/validation loop Optimizers (AdamW / Lion) LR scheduling Early stopping ROC curves (per class + macro) Complete classification metrics C) Transformer-Based Regression — 0–100 Continuous Score Transformer_Regression.ipynb Predicts the continuous 0–100 clinical evaluation score (regression task), using: PatchTST PatchCTG iTransformer TimesNetLite ETCNN Outputs: MAE, RMSE, R² Pearson & Spearman correlations Bland–Altman analysis Calibration curves Scatter plots for predicted vs. true scores This notebook corresponds to the Regression Axis described in the associated thesis/manuscript. D) Additional Output _scored_output.xlsx Contains: True class labels True continuous clinical scores Predicted probabilities (3-class models) Predicted regression outputs Threshold-based validation metrics Data used to generate all figures (ROC, PR, Bland–Altman, calibration, etc.) 4. Tasks & Experimental Axes This repository covers three analytical axes: Axis 1 — Three-Class Classification (Classical ML) Evaluates baseline machine learning algorithms on CTG-derived segments. Axis 2 — Three-Class Classification (Transformers) Assesses the discriminative performance of modern transformer architectures. Axis 3 — Regression (0–100 Continuous Clinical Score) Predicts clinician-derived continuous assessment values and compares regression accuracy across transformer-based models. 5. Requirements This project requires: Python >= 3.10 PyTorch >= 2.0 scikit-learn xgboost lightgbm imbalanced-learn numpy, pandas einops matplotlib, seaborn GPU acceleration (Colab / A100 / CUDA-enabled environment) is strongly recommended for transformer models. 6. How to Use Provide dataset paths in the notebooks. Run preprocessing and segmentation (if needed). Execute classical models via ClassicModels.ipynb. Execute transformer-based classification models individually. Run Transformer_Regression.ipynb for continuous score prediction. Use","url":"https://doi.org/10.5281/zenodo.17612016","authors":["Ferhat, Karataş"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17612016","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.17457843","name":"Retired-2","source":"datacite","abstract":"Dataset Description This dataset accompanies a comprehensive GPU-based benchmarking study of classical and transformer-based time-series models for fetal cardiotocography (CTG) signal analysis. The dataset was derived from clinical recordings exported from the MFM-CNS fetal monitoring system and processed to enable fair model comparison across deep learning and transformer architectures. The dataset is structured as follows: 01_ExamData.csv – The original raw data exported from the hospital information system. Each record may contain multiple patient sessions, as collected directly from the obstetric monitoring system. 02_SplitData.ipynb – The preprocessing notebook used to segment the raw data into individual patient-level sequences. This notebook performs filtering, cleaning, and restructuring of the input file into uniform arrays. _scored_output.xlsx – The finalized and de-identified dataset used in all machine learning (ML) and deep learning (DL) analyses. Each row represents one distinct patient recording with synchronized FHR (fetal heart rate), UA (uterine activity), and AFM (automatic fetal movement) channels, together with expert clinical scores. Additional files include model-level outputs, evaluation metrics, and scripts for full reproducibility: ctg_gpu_benchmark_v3_full.py – The main Python script containing all model definitions, GPU training routines, and metric computations. 19_GPU.ipynb – The Colab-ready notebook for running the full benchmarking pipeline. metrics_summary.csv, threshold_metrics.csv, model_complexity.csv, stability_mae.csv, wilcoxon_BH.csv, predictions_test.csv – Summary tables reporting accuracy, mean absolute error (MAE), model complexity, stability tests, and statistical comparisons. TRIPOD_report.txt – Transparent reporting checklist for model development and validation consistency. How to Reproduce Launch a Google Colab environment with GPU runtime enabled. Upload ctg_gpu_benchmark_v3_full.py and _scored_output.xlsx into the working directory. Open and execute 19_GPU.ipynb sequentially to reproduce all experiments, figures, and metric tables. This setup ensures full reproducibility of the model comparison results under identical preprocessing and data splits. Benchmarked Models Ten supervised models were trained and evaluated using identical data splits and hyperparameter budgets: Classical Deep Time-Series Models ResNet1D – Residual 1D convolutional architecture for hierarchical temporal abstraction. InceptionTime – Multi-scale inference via parallel convolutions with varying kernel sizes. TCN (Temporal Convolutional Network) – Dilated causal convolutions for long-range temporal dependency learning. LSTM-FCN – Hybrid model combining convolutional feature extraction with bidirectional LSTM layers. TimesNet-Lite – Frequency-domain representation of temporal patches for efficient learning. Transformer and Mixer-Based Models Vanilla Transformer – Multi-head self-attention with sinusoidal positional encodings. TSMixer – Alternating temporal- and feature-mixing operations for parameter-efficient representation learning. PatchTST – Patch-wise segmentation of sequences to capture long-term dependencies with transformer attention. iTransformer – Channel-transposed attention mechanism directly modeling inter-variable relationships. TST (Time Series Transformer) – Transformer variant optimized for regression tasks on multivariate physiological signals. Intended Use This dataset provides a reproducible foundation for fetal monitoring research, benchmarking new architectures in physiological time-series analysis, and validating transformer-based temporal models in clinical settings. All data are anonymized and derived from ethically approved retrospective sources.","url":"https://doi.org/10.5281/zenodo.17457843","authors":["Ferhat Karataş"],"tags":["Cardiotocography","CTG","fetal heart rate","uterine activity","AFM","time series","classification","regression"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.5281/zenodo.17457843","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.25772/djm1-3094","name":"Pharmacogenomics and medication safety in older adults","source":"datacite","abstract":"Adverse drug events account for approximately 1.5 million emergency department visits and 500,000 hospitalizations each year, resulting in an estimated $30.1 billion in annual medical costs. Older adults exhibit a markedly increased susceptibility to ADEs, with a nearly sevenfold higher risk of hospitalization compared to younger individuals. In this population, ADEs commonly manifest as disturbances in mood, mentation, and mobility. Pharmacogenomics enables the optimization of pharmacotherapy by informing individualized medication selection and dosing strategies, yet older adults remain underrepresented in PGx research. The objective of this dissertation was to determine the prevalence, clinical significance, and population-level impact of actionable pharmacogenomic variation in community-dwelling older adults, and to identify pharmacogenomic profiles associated with adverse clinical outcomes. This dissertation utilized a combination of community-based pharmacogenomic cohort analyses and statewide administrative claims data to address these objectives. Initially, pharmacogenomic implementation strategies and resources relevant to older adults were systematically reviewed to establish the clinical basis for integrating pharmacogenomics into geriatric pharmacotherapy. Subsequently, actionable pharmacogenomic variants and gene–drug mismatches involving CYP2C19, CYP2D6, and CYP2C9 were characterized in community-dwelling older adults participating in the Translational Approaches to Personalized Health collaborative. Associations between gene–drug mismatches and clinical outcomes related to mood and mentation were assessed using the Patient Health Questionnaire-4 and Mini-Mental State Examination. The burden of actionable CYP2C19 variation among older adults prescribed CYP2C19 substrate medications was estimated by integrating genotype frequencies from the TAPH cohort with medication exposure and falls data from the Mobile Health and Wellness Program cohort. Finally, explainable machine learning approaches were applied to the Virginia All-Payer Claims Database to identify pharmacogenomic patterns associated with medication burden, fall-related injury, and emergency department utilization in older adults. In the TAPH cohort, 68% of participants possessed at least one actionable pharmacogenomic variant. Multiple gene–drug mismatches demonstrated associations with mood and mentation outcomes, including significant relationships between CYP2C19 substrate mismatch and PHQ-4 scores, as well as between CYP2C19 and CYP2C9 variants and MMSE scores. Projection analyses revealed considerable overlap between actionable CYP2C19 variation and exposure to CYP2C19 substrate medications. The CYP2C19*17 and CYP2C19*2 alleles accounted for the majority of the projected pharmacogenomic burden among community-dwelling older adults and those with a history of falls. Analysis of the Virginia All-Payer Claims Database, encompassing 22,783 older adults and more than 5.2 million healthcare encounters, identified pharmacogenomically relevant medication burden as an important feature associated with fall-related injury and emergency department utilization. Explainable machine learning approaches further identified biologically plausible medication and drug–gene patterns associated with these adverse outcomes. These findings indicate that actionable pharmacogenomic variation is prevalent among community-dwelling older adults and frequently coincides with exposure to medications implicated in adverse outcomes. The results support the integration of pharmacogenomic data into medication optimization strategies for older adults and establish a foundation for advancing pharmacogenomic implementation in aging populations that have been historically underrepresented in research.","url":"https://doi.org/10.25772/djm1-3094","authors":["Hashimi, Syeda"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25772/djm1-3094","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20772709","name":"AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharmacodynamics, present unprecedented computational challenges that classical drug discovery pipelines are ill-equipped to address. This review comprehensively examines how advanced machine learning (ML) algorithms, deep learning architectures, graph neural networks (GNNs), and natural language processing (NLP) are being applied to decode the molecular intricacies of poly-herbal systems. We systematically evaluate current advances in AI-assisted target identification, compound-target interaction prediction, ADME/T profiling, and polypharmacology network construction specific to multi-herb formulations. The review also covers the integration of pharmacogenomics, multi-omics data, and knowledge graphs in constructing holistic herb–disease–target networks. Key databases (TCMSP, HERB, BATMAN-TCM, IMPPAT, AyurvedicBI) and computational platforms supporting this ecosystem are discussed. Representative case studies from Ayurveda, Traditional Chinese Medicine (TCM), and Unani systems demonstrate real-world applications. Challenges including chemical complexity, data sparsity, black-box AI models, and regulatory barriers are critically analyzed with proposed solutions. Finally, we chart the future trajectory including federated learning, explainable AI (XAI), digital twins for herbal medicine,and AI-guided clinical translation. This review is intended to serve as a reference for pharmacologists, computational scientists, and clinicians working at the intersection of traditional medicine and modern computational biology.","url":"https://doi.org/10.5281/zenodo.20772709","authors":["Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5"],"tags":["Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20772709","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20772710","name":"AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharmacodynamics, present unprecedented computational challenges that classical drug discovery pipelines are ill-equipped to address. This review comprehensively examines how advanced machine learning (ML) algorithms, deep learning architectures, graph neural networks (GNNs), and natural language processing (NLP) are being applied to decode the molecular intricacies of poly-herbal systems. We systematically evaluate current advances in AI-assisted target identification, compound-target interaction prediction, ADME/T profiling, and polypharmacology network construction specific to multi-herb formulations. The review also covers the integration of pharmacogenomics, multi-omics data, and knowledge graphs in constructing holistic herb–disease–target networks. Key databases (TCMSP, HERB, BATMAN-TCM, IMPPAT, AyurvedicBI) and computational platforms supporting this ecosystem are discussed. Representative case studies from Ayurveda, Traditional Chinese Medicine (TCM), and Unani systems demonstrate real-world applications. Challenges including chemical complexity, data sparsity, black-box AI models, and regulatory barriers are critically analyzed with proposed solutions. Finally, we chart the future trajectory including federated learning, explainable AI (XAI), digital twins for herbal medicine,and AI-guided clinical translation. This review is intended to serve as a reference for pharmacologists, computational scientists, and clinicians working at the intersection of traditional medicine and modern computational biology.","url":"https://doi.org/10.5281/zenodo.20772710","authors":["Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5"],"tags":["Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20772710","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.25772/3577-jh13","name":"Empirical Sample Size Determination for Machine Learning Algorithms in Biomedical Research","source":"datacite","abstract":"This dissertation develops and validates an empirical framework for determining optimal sample sizes for machine learning (ML) models in biomedical research, with applications to both tabular clinical data and high-dimensional bulk RNA sequencing (RNA-Seq) data. Although ML methods are increasingly used for binary classification in healthcare, there is no unified approach for estimating the training sample size required to achieve stable predictive performance. Traditional power-based calculations are not directly applicable because ML emphasizes prediction rather than inference and relies on flexible, data-driven model structures. To address this gap, a learning curve–based methodology was applied across 16 large public clinical datasets (n ≥ 50,000) Random Forest, XGBoost, and Neural Networks were studied, as well as multivariable logistic regression. For each dataset and algorithm, cross-validated area under the receiver operating characteristic curve (AUC) was evaluated at increasing training set sizes. The optimal sample size was defined as the smallest n at which performance was within a pre-specified margin (γ = 0.01, 0.02, or 0.05) of the full-dataset AUC, representing an optimal trade-off between discriminative performance and data collection requirements. The dissertation further examined how dataset-level characteristics including class imbalance, feature dimensionality, proportion of continuous predictors, strength of linear signal, and degree of nonlinearity affected required sample sizes. Negative binomial regression models were developed to quantify these relationships and generate predictive equations for estimating training set sizes in new datasets. This framework was extended to bulk RNA-Seq data, where additional cost constraints become limiting. 27 datasets were collected from validated sources, and an extensive simulation approach was used to artificially expand the data to increase reliability and robustness of the learning curve analysis. Following feature selection via differential expression analysis (DESeq2 + Boruta algorithm), ML classifiers were evaluated using a similar learning curve approach. Results demonstrated that the sample sizes required for stable ML prediction within the scope of bulk RNA-Seq data can differ substantially from those needed for differential gene expression testing alone. For both clinical & bulk RNA-Seq frameworks, validation was conducted using large clinical cohorts from the Veterans Health Administration and NCBI Gene Expression Omnibus, and findings were compared with previously published works if applicable. Finally, we packaged the above results into a user-friendly RShiny application, which allows future researchers to easily utilize our methodology. Use cases and example workflows are demonstrated. Overall, this work provides a novel data-driven and algorithm-specific methodology and implementation for machine learning sample size determination within two large domains of biomedical data analysis.","url":"https://doi.org/10.25772/3577-jh13","authors":["Silvey, Scott"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25772/3577-jh13","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22050126","name":"rbGyanX: A radiobiology-guided clinical decision support framework","source":"datacite","abstract":"rbGyanX is a radiobiology-guided clinical decision support framework designed to integrate physical dosimetry, radiobiological modeling, and explainable machine learning for treatment plan evaluation in radiation oncology.","url":"https://doi.org/10.5281/zenodo.22050126","authors":["Mondal, Kalyan","Mandal, Abhijit","Vijay, Anuj"],"tags":["radiobiology","clinical decision support","TCP","NTCP","radiotherapy","medical physics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22050126","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22049939","name":"AI-Driven Precision Care for Chronic Diseases","source":"datacite","abstract":"Chronic diseases are among the leading causes of morbidity, mortality, disability, and healthcare expenditure worldwide. Conditions such as cardiovascular disease, diabetes, chronic kidney disease, chronic respiratory disease, cancer, and neurological disorders are characterized by prolonged disease trajectories, complex comorbidities, and substantial differences in individual responses to treatment. Conventional approaches to chronic disease management often depend on standardized clinical guidelines and episodic assessments, which may not fully capture the dynamic and heterogeneous nature of disease progression. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers an opportunity to strengthen precision care by integrating large and diverse datasets and translating them into individualized predictions and clinical recommendations. AI can analyze electronic health records, laboratory results, imaging studies, physiological signals, genomic information, and patient-generated data to identify patterns associated with disease development and progression. Predictive models can estimate an individual’s risk of complications, hospitalization, treatment failure, or mortality, allowing clinicians to identify high-risk patients before severe deterioration occurs. In cardiovascular disease, AI-enabled electrocardiography can identify subclinical abnormalities and predict heart failure, arrhythmias, and major adverse cardiovascular events. Similarly, AI-based models in chronic kidney disease can combine laboratory and clinical variables to predict renal function decline and progression toward kidney failure. AI-driven precision care can support individualized treatment selection by considering patient-specific characteristics, comorbidities, previous treatment responses, and longitudinal disease patterns. In diabetes, predictive algorithms can support glucose management and identify patients at increased risk of acute and chronic complications. In oncology, AI can integrate molecular, imaging, and clinical information to assist treatment selection and predict therapeutic response. These capabilities may improve clinical decision-making while reducing unnecessary interventions and supporting more effective resource utilization. The integration of AI with wearable devices, mobile applications, telemedicine platforms, and home monitoring systems enables continuous assessment of patients with chronic diseases. Physiological signals such as heart rate, blood pressure, oxygen saturation, glucose levels, physical activity, and sleep patterns can be analyzed to identify early changes in health status. This approach can facilitate timely intervention, reduce preventable hospitalizations, and extend personalized care beyond conventional clinical settings. Despite its potential, AI-driven chronic disease care faces challenges related to data quality, algorithmic bias, interpretability, privacy, interoperability, regulatory oversight, and clinical validation. AI should therefore complement rather than replace clinical expertise. Future progress will depend on prospective validation, equitable datasets, explainable models, seamless integration into clinical workflows, and collaboration between clinicians, patients, and technology developers. Ultimately, AI-driven precision care has the potential to transform chronic disease management from episodic and reactive care toward continuous, predictive, preventive, and individualized healthcare.","url":"https://doi.org/10.5281/zenodo.22049939","authors":["Farrokhi, Mehrdad","Abdollahpour, Saman","Hekmatnia, Yasaman","Rashidinejad, Bita","Hoorshad, Behnam","Jabbari, Niloufar","HasanzadehBidgoli, Mohammad","Javaheri, Rojan","Mehrtabar, Ehsan","Akbari Aghdam, Hossein","Golparian, Amir","Sargazi, Meisam","Mokhtari, Mohammad Saeid","Heydari, Maryam","Hakimjavadi, Amir","Ezzati, Arman","Shokri, Amir","Pourahmadi, Yousha","Boustani Hezarani, Hossein","Nikakhtar, Samin","Khorsand, Kamyar","Sasani, Mojtaba","Mohammadizadeh, Seyed Mahan","Kazemi, Mohammad Hossein","Shamsdanesh, Shiva","Mostafaloo, Narges","Forouzan, Arash","Fahimi, Reza"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22049939","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22049940","name":"AI-Driven Precision Care for Chronic Diseases","source":"datacite","abstract":"Chronic diseases are among the leading causes of morbidity, mortality, disability, and healthcare expenditure worldwide. Conditions such as cardiovascular disease, diabetes, chronic kidney disease, chronic respiratory disease, cancer, and neurological disorders are characterized by prolonged disease trajectories, complex comorbidities, and substantial differences in individual responses to treatment. Conventional approaches to chronic disease management often depend on standardized clinical guidelines and episodic assessments, which may not fully capture the dynamic and heterogeneous nature of disease progression. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers an opportunity to strengthen precision care by integrating large and diverse datasets and translating them into individualized predictions and clinical recommendations. AI can analyze electronic health records, laboratory results, imaging studies, physiological signals, genomic information, and patient-generated data to identify patterns associated with disease development and progression. Predictive models can estimate an individual’s risk of complications, hospitalization, treatment failure, or mortality, allowing clinicians to identify high-risk patients before severe deterioration occurs. In cardiovascular disease, AI-enabled electrocardiography can identify subclinical abnormalities and predict heart failure, arrhythmias, and major adverse cardiovascular events. Similarly, AI-based models in chronic kidney disease can combine laboratory and clinical variables to predict renal function decline and progression toward kidney failure. AI-driven precision care can support individualized treatment selection by considering patient-specific characteristics, comorbidities, previous treatment responses, and longitudinal disease patterns. In diabetes, predictive algorithms can support glucose management and identify patients at increased risk of acute and chronic complications. In oncology, AI can integrate molecular, imaging, and clinical information to assist treatment selection and predict therapeutic response. These capabilities may improve clinical decision-making while reducing unnecessary interventions and supporting more effective resource utilization. The integration of AI with wearable devices, mobile applications, telemedicine platforms, and home monitoring systems enables continuous assessment of patients with chronic diseases. Physiological signals such as heart rate, blood pressure, oxygen saturation, glucose levels, physical activity, and sleep patterns can be analyzed to identify early changes in health status. This approach can facilitate timely intervention, reduce preventable hospitalizations, and extend personalized care beyond conventional clinical settings. Despite its potential, AI-driven chronic disease care faces challenges related to data quality, algorithmic bias, interpretability, privacy, interoperability, regulatory oversight, and clinical validation. AI should therefore complement rather than replace clinical expertise. Future progress will depend on prospective validation, equitable datasets, explainable models, seamless integration into clinical workflows, and collaboration between clinicians, patients, and technology developers. Ultimately, AI-driven precision care has the potential to transform chronic disease management from episodic and reactive care toward continuous, predictive, preventive, and individualized healthcare.","url":"https://doi.org/10.5281/zenodo.22049940","authors":["Farrokhi, Mehrdad","Abdollahpour, Saman","Hekmatnia, Yasaman","Rashidinejad, Bita","Hoorshad, Behnam","Jabbari, Niloufar","HasanzadehBidgoli, Mohammad","Javaheri, Rojan","Mehrtabar, Ehsan","Akbari Aghdam, Hossein","Golparian, Amir","Sargazi, Meisam","Mokhtari, Mohammad Saeid","Heydari, Maryam","Hakimjavadi, Amir","Ezzati, Arman","Shokri, Amir","Pourahmadi, Yousha","Boustani Hezarani, Hossein","Nikakhtar, Samin","Khorsand, Kamyar","Sasani, Mojtaba","Mohammadizadeh, Seyed Mahan","Kazemi, Mohammad Hossein","Shamsdanesh, Shiva","Mostafaloo, Narges","Forouzan, Arash","Fahimi, Reza"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22049940","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20477293","name":"Recent Progress in Drug Discovery Using Computational Chemistry and Artificial Intelligence","source":"datacite","abstract":"Computational chemistry and artificial intelligence (AI) are rapidly reshaping the landscape of pharmaceutical research and drug development. Conventional drug discovery methods are often expensive, time-intensive, and associated with high attrition rates during preclinical and clinical studies. The application of computational techniques such as molecular docking, molecular dynamics simulations, pharmacophore modelling, virtual screening, and quantitative structure–activity relationship (QSAR) analysis has greatly improved the ability to understand molecular interactions and optimize drug candidates. In parallel, AI-based technologies including machine learning, deep learning, graph neural networks, and generative models have enhanced the prediction of biological activity, toxicity, pharmacokinetic behaviour, and drug–target interactions. These advanced computational approaches facilitate faster target identification, lead optimization, de novo molecular design, and drug repurposing. Additionally, the integration of big data analytics, bioinformatics, natural language processing, and cloud computing has strengthened data-driven pharmaceutical research. AI-assisted systems are increasingly being implemented throughout the drug discovery pipeline, from early-stage screening to clinical trial optimization and personalized medicine. Despite these advancements, several challenges remain, including limited data quality, model transparency, regulatory concerns, and the need for experimental validation. This review summarizes recent developments in computational chemistry and AI-assisted drug discovery, highlighting important technologies, applications, current limitations, and future opportunities in next-generation pharmaceutical innovation.","url":"https://doi.org/10.5281/zenodo.20477293","authors":["Mohamed Abdulla Mohammad Abdulla 2, Shalini Devi*, Sunita Dhiman1, Swati Joshi2, Jyoti Gupta3"],"tags":["Artificial intelligence; Computational chemistry; Drug discovery; Machine learning; Molecular docking; Virtual screening; QSAR; Deep learning; ADMET prediction; Generative AI; Molecular dynamics simulation; Drug repurposing; Structure-based drug design; Pharmaceutical research; Precision medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20477293","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20477294","name":"Recent Progress in Drug Discovery Using Computational Chemistry and Artificial Intelligence","source":"datacite","abstract":"Computational chemistry and artificial intelligence (AI) are rapidly reshaping the landscape of pharmaceutical research and drug development. Conventional drug discovery methods are often expensive, time-intensive, and associated with high attrition rates during preclinical and clinical studies. The application of computational techniques such as molecular docking, molecular dynamics simulations, pharmacophore modelling, virtual screening, and quantitative structure–activity relationship (QSAR) analysis has greatly improved the ability to understand molecular interactions and optimize drug candidates. In parallel, AI-based technologies including machine learning, deep learning, graph neural networks, and generative models have enhanced the prediction of biological activity, toxicity, pharmacokinetic behaviour, and drug–target interactions. These advanced computational approaches facilitate faster target identification, lead optimization, de novo molecular design, and drug repurposing. Additionally, the integration of big data analytics, bioinformatics, natural language processing, and cloud computing has strengthened data-driven pharmaceutical research. AI-assisted systems are increasingly being implemented throughout the drug discovery pipeline, from early-stage screening to clinical trial optimization and personalized medicine. Despite these advancements, several challenges remain, including limited data quality, model transparency, regulatory concerns, and the need for experimental validation. This review summarizes recent developments in computational chemistry and AI-assisted drug discovery, highlighting important technologies, applications, current limitations, and future opportunities in next-generation pharmaceutical innovation.","url":"https://doi.org/10.5281/zenodo.20477294","authors":["Mohamed Abdulla Mohammad Abdulla 2, Shalini Devi*, Sunita Dhiman1, Swati Joshi2, Jyoti Gupta3"],"tags":["Artificial intelligence; Computational chemistry; Drug discovery; Machine learning; Molecular docking; Virtual screening; QSAR; Deep learning; ADMET prediction; Generative AI; Molecular dynamics simulation; Drug repurposing; Structure-based drug design; Pharmaceutical research; Precision medicine"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20477294","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22049464","name":"Artificial Intelligence for Infection Surveillance: Improving Early Detection of Healthcare-Associated Infections","source":"datacite","abstract":"Healthcare-associated infections (HAIs) remain a leading cause of preventable morbidity, mortality, and excess healthcare spending worldwide, and traditional manual surveillance is labor-intensive, inconsistent, and often too slow to support timely intervention. Artificial intelligence (AI) including machine learning (ML), natural language processing (NLP), and hybrid rule-based/statistical systems have emerged as a candidate solution for automating and accelerating HAI detection using data already captured in electronic health records (EHRs). This narrative review synthesizes recent evidence on AI-driven infection surveillance across major HAI categories, including sepsis, surgical site infections (SSIs), device-associated infections, and outbreak-level public health surveillance. Across studies, AI-augmented surveillance systems have demonstrated the ability to match or exceed the sensitivity of manual chart review while substantially reducing reviewer workload, and NLP applied to unstructured clinical notes has repeatedly improved detection accuracy relative to structured data alone. However, performance is highly heterogeneous across institutions, infection types, and algorithms, and several widely deployed proprietary tools, most notably early sepsis early-warning systems have shown poor external validity, low positive predictive value, and a tendency to generate clinically disruptive alert fatigue. Persistent barriers include limited model portability across health systems, opaque (\"black box\") decision logic, inconsistent reference-standard definitions, and the absence of standardized regulatory or reporting frameworks for surveillance-oriented AI. We conclude that AI-driven infection surveillance can meaningfully augment infection prevention programs when rigorously externally validated, transparently reported, and implemented as a human-in-the-loop workflow rather than a fully autonomous decision system, and we outline priority areas for future research and implementation science.","url":"https://doi.org/10.5281/zenodo.22049464","authors":["Umeh Princess Frank"],"tags":["Artificial intelligence; machine learning; natural language processing; healthcare-associated infections; infection surveillance; sepsis; electronic health records; patient safety"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22049464","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22049465","name":"Artificial Intelligence for Infection Surveillance: Improving Early Detection of Healthcare-Associated Infections","source":"datacite","abstract":"Healthcare-associated infections (HAIs) remain a leading cause of preventable morbidity, mortality, and excess healthcare spending worldwide, and traditional manual surveillance is labor-intensive, inconsistent, and often too slow to support timely intervention. Artificial intelligence (AI) including machine learning (ML), natural language processing (NLP), and hybrid rule-based/statistical systems have emerged as a candidate solution for automating and accelerating HAI detection using data already captured in electronic health records (EHRs). This narrative review synthesizes recent evidence on AI-driven infection surveillance across major HAI categories, including sepsis, surgical site infections (SSIs), device-associated infections, and outbreak-level public health surveillance. Across studies, AI-augmented surveillance systems have demonstrated the ability to match or exceed the sensitivity of manual chart review while substantially reducing reviewer workload, and NLP applied to unstructured clinical notes has repeatedly improved detection accuracy relative to structured data alone. However, performance is highly heterogeneous across institutions, infection types, and algorithms, and several widely deployed proprietary tools, most notably early sepsis early-warning systems have shown poor external validity, low positive predictive value, and a tendency to generate clinically disruptive alert fatigue. Persistent barriers include limited model portability across health systems, opaque (\"black box\") decision logic, inconsistent reference-standard definitions, and the absence of standardized regulatory or reporting frameworks for surveillance-oriented AI. We conclude that AI-driven infection surveillance can meaningfully augment infection prevention programs when rigorously externally validated, transparently reported, and implemented as a human-in-the-loop workflow rather than a fully autonomous decision system, and we outline priority areas for future research and implementation science.","url":"https://doi.org/10.5281/zenodo.22049465","authors":["Umeh Princess Frank"],"tags":["Artificial intelligence; machine learning; natural language processing; healthcare-associated infections; infection surveillance; sepsis; electronic health records; patient safety"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22049465","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20326678","name":"COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR EARLY HEART DISEASE PREDICTION TOWARD RURAL HEALTHCARE DEPLOYMENT","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) represent one of the most significant global health challenges, accounting for a substantial proportion of mortality worldwide. Early diagnosis of cardiac conditions plays a crucial role in reducing mortality and improving clinical outcomes. However, access to advanced diagnostic facilities is often limited in rural and low-resource healthcare environments. Recent advancements in machine learning (ML) have provided effective computational approaches for predicting cardiovascular disease using patient clinical data. This paper presents a comparative review of machine learning techniques used for early heart disease prediction, focusing primarily on studies utilizing the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Several supervised learning models—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks (ANN)—have been examined in the literature. Performance comparisons across multiple studies indicate that ensemble learning techniques consistently achieve higher predictive performance than traditional statistical classifiers. Furthermore, this paper explores the feasibility of deploying lightweight ML models in rural healthcare settings using edge computing devices. The findings suggest that integrating machine learning–based diagnostic tools into primary healthcare infrastructure could significantly enhance early screening and improve patient outcomes in resource-constrained environments. Keywords: Cardiovascular Diseases (CVDs), Machine Learning (ML), Improving Clinical Outcomes, Healthcare Environments.","url":"https://doi.org/10.5281/zenodo.20326678","authors":["Kunal D. Gaikwad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20326678","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20326679","name":"COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR EARLY HEART DISEASE PREDICTION TOWARD RURAL HEALTHCARE DEPLOYMENT","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) represent one of the most significant global health challenges, accounting for a substantial proportion of mortality worldwide. Early diagnosis of cardiac conditions plays a crucial role in reducing mortality and improving clinical outcomes. However, access to advanced diagnostic facilities is often limited in rural and low-resource healthcare environments. Recent advancements in machine learning (ML) have provided effective computational approaches for predicting cardiovascular disease using patient clinical data. This paper presents a comparative review of machine learning techniques used for early heart disease prediction, focusing primarily on studies utilizing the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Several supervised learning models—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks (ANN)—have been examined in the literature. Performance comparisons across multiple studies indicate that ensemble learning techniques consistently achieve higher predictive performance than traditional statistical classifiers. Furthermore, this paper explores the feasibility of deploying lightweight ML models in rural healthcare settings using edge computing devices. The findings suggest that integrating machine learning–based diagnostic tools into primary healthcare infrastructure could significantly enhance early screening and improve patient outcomes in resource-constrained environments. Keywords: Cardiovascular Diseases (CVDs), Machine Learning (ML), Improving Clinical Outcomes, Healthcare Environments.","url":"https://doi.org/10.5281/zenodo.20326679","authors":["Kunal D. Gaikwad"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20326679","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.17605/osf.io/v8az5","name":"Integrating Machine Learning Across the Patient Journey: A Scoping Review and Patient-Oriented Framework","source":"datacite","abstract":"This project reports a scoping review examining how machine learning (ML) and artificial intelligence (AI) have been applied across the full patient journey — defined as the longitudinal sequence of care stages spanning prevention and health promotion, diagnosis, prognosis and risk prediction, treatment and clinical decision support, hospital management and operational optimization, and ongoing monitoring and patient experience. The review was conducted following the PRISMA-ScR guidelines, the Arksey and O'Malley framework, and JBI guidance for scoping reviews. We searched PubMed, Scopus, and Web of Science in December 2025 using a predefined string combining ML/AI terminology with patient journey and care pathway concepts. Of 2,041 records retrieved, 99 peer-reviewed sources met all inclusion criteria after deduplication, title/abstract screening, and full-text review. Bibliographic coupling analysis via the Bibliometrix R package was used to organize the sample into thematic clusters for narrative synthesis. The primary purpose of this review is to map where ML has been applied across care stages, identify where cross-phase integration is absent, and characterize the methodological, structural, and ethical gaps that prevent individual tools from cohering into a system-level architecture. A secondary purpose is to use that map as the empirical basis for a proposed integrative framework. The expected outcome is twofold. First, a documented synthesis of the current state of ML deployment across the patient journey, with explicit identification of underrepresented stages — particularly care transitions, rehabilitation, and continuous monitoring. Second, the Integrated Patient Journey AI Framework (IPJ-AIF), a five-component architecture covering multi-source data integration via HL7 FHIR and OMOP-CDM standards, a modular ML pipeline with phase-specific architectures, secure cloud-edge infrastructure, model interpretability mechanisms including SHAP and LIME, and outcome- and equity-oriented evaluation incorporating QALY, ROI, and fairness indicators. The framework is intended as a practical roadmap for researchers and institutions seeking to move beyond siloed ML deployment toward genuinely longitudinal clinical AI. Note: This protocol was registered retrospectively following completion of the search and screening phases.","url":"https://doi.org/10.17605/osf.io/v8az5","authors":["Gustavo de Souza Matias","Kauan Sampaio Araújo","Claudia Moro","Carlos Alberto Braun da Silva Bernardo","Fernando Henrique Lermen","Vanessa Becker Bertoni"],"tags":["Medicine and Health Sciences","Machine Leraning","Patient Journey","Prediction in Health Care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/v8az5","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20952178","name":"PNBA Identity Physics - Adaptive Predictive Pattern Assistant - APPA, Universal Unified Identity Architecture - UUIA v3","source":"datacite","abstract":"# APPA · Adaptive Predictive Pattern Assistant## Unified Identity Profile · UUIA**Live:** uuia.app/appa · **Architect:** HIGHTISTIC · **Anchor:** 1.369 GHz**Status:** GERMLINE LOCKED · **Substrate-neutral · Non-anthropocentric · Scale-invariant****Version:** v3 · updated June 2026 — see *Revision Notes* at the end for what changed from the v2 doc --- ## What This Is APPA is an identity profiler that reduces a human (or any identity) to a SOUL-8 packet — a lossless 8-dimensional encoding of their PNBA state — alongside a structural phase reading (NOBLE / TRUE LOCK / FALSE LOCK / IVA PEAK / DEPLETED IVA / SHATTER) and, optionally, a Weissmann Barrier Capacity read. It is not a personality test. Personality tests produce labels. APPA produces a coordinate. The output is a SOUL-8 address: a compact string that encodes your dominant axis, mode weights, and Identity Mass. It is copyable, shareable, and machine-readable. Any system that understands PNBA can decode it. The one-sentence description: > **APPA measures which of the four primitives (P, N, B, A) you express most strongly, how you express them, what phase that puts you in relative to the Torsion Limit, and what your Identity Mass is at the anchor frequency of 1.369 GHz.** --- ## The Three Sections — Up to 100 Questions, 60 Required APPA runs three assessment modules. As of v3, each section can be toggled on or off independently — all three are on by default, but turning one off removes it from what's required for a result. (At least one section must stay on; the app blocks turning the last one off.) ### Section 01 — Cognitive Architecture (CAT) · 40 questions · required by default How your cognitive system processes the four primitives. Scored 1–5 per question. 10 questions per axis. **This is the section that drives torsion (τ), Identity Mass (IM), and phase classification — if it's toggled off, those readings have nothing to compute from, and the results panel falls back to a heatmap-only view of whatever sections are still on.** **P · Pattern** — How your system renders and anchors structure. Do you notice patterns automatically? Connect ideas across domains? Think in loops and cycles? **N · Narrative** — How your system maintains continuity and meaning. Do you build internal stories about events? Think about your life as a thread? Imagine alternative versions of outcomes? **B · Behavior** — How your system expresses and interacts. Do you act quickly? Adjust to situations? Stay consistent with values under pressure? **A · Adaptation** — How your system adjusts under feedback and load. Do you recover quickly from stress? Stay flexible when plans change? Switch between tasks without losing focus? **Scoring per axis:** 10 questions × 5 max = 50 points per axis. ```10–23 → L (Locked) — minimal expression of this axis24–37 → S (Sustained) — active but flexible38–50 → F (Flexed) — dominant axis, high expression``` --- ### Section 02 — Emotional Primitives (EP) · 40 questions · optional Ten emotional signal blocks, 4 questions each. Each block maps to a PNBA expression state. Scored 1–5. Max 20 per block. **As of v3, this entire section is optional.** It feeds the heat map and the EP-code segment of the full CI fingerprint, but it has never fed IM, τ, or phase classification, and there's no reason to make someone answer 40 questions to get a result that doesn't use them. Skipping EP entirely still produces a complete SOUL-8 print. | Block | Key | PNBA Role || :--- | :--- | :--- || Threat | threat | B-axis hyperactivation — coupling alert || Loss | loss | N-axis depletion — narrative thread breaking || Overwhelm | overwhelm | A-axis saturation — adaptation capacity exceeded || Anger | anger | B-axis resistance — behavioral friction rising || Desire | desire | P-axis orientation — pattern seeking and locking || Connection | connection | N-axis coupling — narrative fusion || Pride | pride | P-axis confirmation — pattern validated || Shame | shame | P-axis collapse — pattern integr","url":"https://doi.org/10.5281/zenodo.20952178","authors":["Trent, Russell"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20952178","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20952179","name":"PNBA Identity Physics - Adaptive Predictive Pattern Assistant - APPA, Universal Unified Identity Architecture - UUIA v3","source":"datacite","abstract":"# APPA · Adaptive Predictive Pattern Assistant## Unified Identity Profile · UUIA**Live:** uuia.app/appa · **Architect:** HIGHTISTIC · **Anchor:** 1.369 GHz**Status:** GERMLINE LOCKED · **Substrate-neutral · Non-anthropocentric · Scale-invariant****Version:** v3 · updated June 2026 — see *Revision Notes* at the end for what changed from the v2 doc --- ## What This Is APPA is an identity profiler that reduces a human (or any identity) to a SOUL-8 packet — a lossless 8-dimensional encoding of their PNBA state — alongside a structural phase reading (NOBLE / TRUE LOCK / FALSE LOCK / IVA PEAK / DEPLETED IVA / SHATTER) and, optionally, a Weissmann Barrier Capacity read. It is not a personality test. Personality tests produce labels. APPA produces a coordinate. The output is a SOUL-8 address: a compact string that encodes your dominant axis, mode weights, and Identity Mass. It is copyable, shareable, and machine-readable. Any system that understands PNBA can decode it. The one-sentence description: > **APPA measures which of the four primitives (P, N, B, A) you express most strongly, how you express them, what phase that puts you in relative to the Torsion Limit, and what your Identity Mass is at the anchor frequency of 1.369 GHz.** --- ## The Three Sections — Up to 100 Questions, 60 Required APPA runs three assessment modules. As of v3, each section can be toggled on or off independently — all three are on by default, but turning one off removes it from what's required for a result. (At least one section must stay on; the app blocks turning the last one off.) ### Section 01 — Cognitive Architecture (CAT) · 40 questions · required by default How your cognitive system processes the four primitives. Scored 1–5 per question. 10 questions per axis. **This is the section that drives torsion (τ), Identity Mass (IM), and phase classification — if it's toggled off, those readings have nothing to compute from, and the results panel falls back to a heatmap-only view of whatever sections are still on.** **P · Pattern** — How your system renders and anchors structure. Do you notice patterns automatically? Connect ideas across domains? Think in loops and cycles? **N · Narrative** — How your system maintains continuity and meaning. Do you build internal stories about events? Think about your life as a thread? Imagine alternative versions of outcomes? **B · Behavior** — How your system expresses and interacts. Do you act quickly? Adjust to situations? Stay consistent with values under pressure? **A · Adaptation** — How your system adjusts under feedback and load. Do you recover quickly from stress? Stay flexible when plans change? Switch between tasks without losing focus? **Scoring per axis:** 10 questions × 5 max = 50 points per axis. ```10–23 → L (Locked) — minimal expression of this axis24–37 → S (Sustained) — active but flexible38–50 → F (Flexed) — dominant axis, high expression``` --- ### Section 02 — Emotional Primitives (EP) · 40 questions · optional Ten emotional signal blocks, 4 questions each. Each block maps to a PNBA expression state. Scored 1–5. Max 20 per block. **As of v3, this entire section is optional.** It feeds the heat map and the EP-code segment of the full CI fingerprint, but it has never fed IM, τ, or phase classification, and there's no reason to make someone answer 40 questions to get a result that doesn't use them. Skipping EP entirely still produces a complete SOUL-8 print. | Block | Key | PNBA Role || :--- | :--- | :--- || Threat | threat | B-axis hyperactivation — coupling alert || Loss | loss | N-axis depletion — narrative thread breaking || Overwhelm | overwhelm | A-axis saturation — adaptation capacity exceeded || Anger | anger | B-axis resistance — behavioral friction rising || Desire | desire | P-axis orientation — pattern seeking and locking || Connection | connection | N-axis coupling — narrative fusion || Pride | pride | P-axis confirmation — pattern validated || Shame | shame | P-axis collapse — pattern integr","url":"https://doi.org/10.5281/zenodo.20952179","authors":["Trent, Russell"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20952179","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21562589","name":"From Prescription to Prediction: Leveraging AI/ML to Improve Medication Adherence and Adverse Drug Event Detection in Community Pharmacies.","source":"datacite","abstract":"Medication non-adherence and adverse drug events (ADEs) remain significant challenges in community pharmacies, particularly in underserved areas where limited resources exacerbate health disparities. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to enhance pharmacy practice by shifting from reactive prescription management to proactive prediction and intervention. This paper explores the application of AI-powered tools in three critical domains: drug interaction and safety checks, adherence monitoring, and clinical decision support. Predictive analytics can identify patients at high risk of ADEs, while smart technologies such as wearable sensors, mobile applications, and natural language processing chatbots support personalized adherence interventions. Integrating AI-driven systems into community pharmacy workflows not only improves medication safety but also strengthens pharmacist capacity to deliver patient-centered care. However, challenges including data privacy, algorithmic bias, infrastructure limitations, and regulatory hurdles must be addressed to ensure equitable implementation. By leveraging AI/ML innovations, community pharmacies can play a pivotal role in advancing safe, effective, and accessible medication management for populations most in need.","url":"https://doi.org/10.5281/zenodo.21562589","authors":["Onyekaonwu, Chinenye Blessing","Peter-Anyebe, Amina Catherine","Raphael, Favour Ojochide"],"tags":["Artificial Intelligence","Machine Learning","Medication Adherence","Adverse Drug Events Detection","Community Pharmacies."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.5281/zenodo.21562589","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21562590","name":"From Prescription to Prediction: Leveraging AI/ML to Improve Medication Adherence and Adverse Drug Event Detection in Community Pharmacies.","source":"datacite","abstract":"Medication non-adherence and adverse drug events (ADEs) remain significant challenges in community pharmacies, particularly in underserved areas where limited resources exacerbate health disparities. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to enhance pharmacy practice by shifting from reactive prescription management to proactive prediction and intervention. This paper explores the application of AI-powered tools in three critical domains: drug interaction and safety checks, adherence monitoring, and clinical decision support. Predictive analytics can identify patients at high risk of ADEs, while smart technologies such as wearable sensors, mobile applications, and natural language processing chatbots support personalized adherence interventions. Integrating AI-driven systems into community pharmacy workflows not only improves medication safety but also strengthens pharmacist capacity to deliver patient-centered care. However, challenges including data privacy, algorithmic bias, infrastructure limitations, and regulatory hurdles must be addressed to ensure equitable implementation. By leveraging AI/ML innovations, community pharmacies can play a pivotal role in advancing safe, effective, and accessible medication management for populations most in need.","url":"https://doi.org/10.5281/zenodo.21562590","authors":["Onyekaonwu, Chinenye Blessing","Peter-Anyebe, Amina Catherine","Raphael, Favour Ojochide"],"tags":["Artificial Intelligence","Machine Learning","Medication Adherence","Adverse Drug Events Detection","Community Pharmacies."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.5281/zenodo.21562590","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21140853","name":"MIO (My Intraoral Images): Open Access Image Dataset","source":"datacite","abstract":"This record provides the openly accessible version of the image dataset associated with this research. The dataset is released under Open Access to facilitate reuse, transparency, reproducibility, and compliance with open science principles. This deposit supersedes a previous restricted-access version of the same dataset. Previous restricted-access record: DOI: 10.5281/zenodo.20818533. The scientific content of the dataset remains unchanged; this release only updates the accessibility of the files. The MIO (My Intraoral Oral Images) dataset is a collection of 765 intraoral clinical photographs prospectively obtained from adult subjects aged 18 to 65. The images were collected as part of a research project focused on the development and validation of artificial intelligence models for the early detection and classification of gingivitis and periodontitis. All images were obtained under standardised clinical conditions using a Canon EOS R50 digital view camera (Canon Inc., Tokyo, Japan). Before inclusion in the dataset, all participants provided informed consent for the use of their anonymised clinical images for research purposes. The study protocol was approved by the relevant Institutional Research and Ethics Committee. The dataset includes images representing three periodontal conditions: dental health, gingivitis, and periodontitis. Each image was independently evaluated and classified by trained dental professionals according to internationally accepted periodontal diagnostic criteria. Citation: Please cite the associated publication and the Zenodo DOI when using this dataset (10.5281/zenodo.21140854). Dataset CharacteristicsNumber of images: 765Population: Adults (18-65 years)Image type: Clinical intraoral photographsFormat: JPGAnnotation level: Diagnostic classification at the image levelCategories: Healthy gingiva, Gingivitis and Periodontitis This dataset can be used for:Development of artificial intelligence algorithmsResearch in deep learning and computer visionAutomated detection of periodontal diseasesEducational purposes in dentistryComparative evaluation of machine learning models","url":"https://doi.org/10.5281/zenodo.21140853","authors":["Izquierdo Vega, Jeannett Alejandra","Pintado Brito, Sergio David","Izquierdo-Vega, Alelí Julieta","Angeles Espinosa, Iriana Yunuen","Sánchez-Gutiérrez, Manuel","Ortega-Palacios, Rocio","Santander, Fredy","Madrigal Santillan, Eduardo Osiris"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21140853","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22047269","name":"MIO (My Intraoral Images): Open Access Image Dataset","source":"datacite","abstract":"This record provides the openly accessible version of the image dataset associated with this research. The dataset is released under Open Access to facilitate reuse, transparency, reproducibility, and compliance with open science principles. This deposit supersedes a previous restricted-access version of the same dataset. Previous restricted-access record: DOI: 10.5281/zenodo.20818533. The scientific content of the dataset remains unchanged; this release only updates the accessibility of the files. The MIO (My Intraoral Oral Images) dataset is a collection of 765 intraoral clinical photographs prospectively obtained from adult subjects aged 18 to 65. The images were collected as part of a research project focused on the development and validation of artificial intelligence models for the early detection and classification of gingivitis and periodontitis. All images were obtained under standardised clinical conditions using a Canon EOS R50 digital view camera (Canon Inc., Tokyo, Japan). Before inclusion in the dataset, all participants provided informed consent for the use of their anonymised clinical images for research purposes. The study protocol was approved by the relevant Institutional Research and Ethics Committee. The dataset includes images representing three periodontal conditions: dental health, gingivitis, and periodontitis. Each image was independently evaluated and classified by trained dental professionals according to internationally accepted periodontal diagnostic criteria. Citation: Please cite the associated publication and the Zenodo DOI when using this dataset (10.5281/zenodo.21140854). Dataset CharacteristicsNumber of images: 765Population: Adults (18-65 years)Image type: Clinical intraoral photographsFormat: JPGAnnotation level: Diagnostic classification at the image levelCategories: Healthy gingiva, Gingivitis and Periodontitis This dataset can be used for:Development of artificial intelligence algorithmsResearch in deep learning and computer visionAutomated detection of periodontal diseasesEducational purposes in dentistryComparative evaluation of machine learning models","url":"https://doi.org/10.5281/zenodo.22047269","authors":["Izquierdo Vega, Jeannett Alejandra","Pintado Brito, Sergio David","Izquierdo-Vega, Alelí Julieta","Angeles Espinosa, Iriana Yunuen","Sánchez-Gutiérrez, Manuel","Ortega-Palacios, Rocio","Santander, Fredy","Madrigal Santillan, Eduardo Osiris"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22047269","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21558333","name":"Heart Disease Prognosis Using Artificial Intelligence","source":"datacite","abstract":"Machine learning tools are providing successful results in disease diagnosis. In the diagnosis of heart disease, the Machine Learning Techniques has been used to show the acceptable levels of accuracy. Human heartbeat has been asserted to provide promising markers of CHF. For diagnosing heart disease, it can provide solution to complex queries and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing this treatment, it also helps to reduce the treatment costs. To predict the heart disease of a person, the CNN is used, which is one of the classification technique of deep learning. In the existing system, they have used random forest algorithm which is one the technique of machine learning. It could provide the accuracy up-to 80%. But by using this conventional neural network, the accuracy level could be more than 90% (i.e.) the efficiency level is increased. Each person has different level of Cholesterol, Blood pressure, FBS, Resting Electrocardiogram, Pulse rate in their body. we can predict the heart disease, by using the medical terms such as blood pressure, type of chest pain, blood sugar, cholesterol. Rather than using machine learning, Deep learning algorithm will provide the result accurately.","url":"https://doi.org/10.5281/zenodo.21558333","authors":["R, Sree Vidya","K, Nandhini","R, Mathu Shri","S, Shanmuga Priyanka Devi"],"tags":["Artificial Intelligence","Machine Learning","Neural Network","Random Forest","Convolutional Neural Network."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21558333","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21558334","name":"Heart Disease Prognosis Using Artificial Intelligence","source":"datacite","abstract":"Machine learning tools are providing successful results in disease diagnosis. In the diagnosis of heart disease, the Machine Learning Techniques has been used to show the acceptable levels of accuracy. Human heartbeat has been asserted to provide promising markers of CHF. For diagnosing heart disease, it can provide solution to complex queries and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing this treatment, it also helps to reduce the treatment costs. To predict the heart disease of a person, the CNN is used, which is one of the classification technique of deep learning. In the existing system, they have used random forest algorithm which is one the technique of machine learning. It could provide the accuracy up-to 80%. But by using this conventional neural network, the accuracy level could be more than 90% (i.e.) the efficiency level is increased. Each person has different level of Cholesterol, Blood pressure, FBS, Resting Electrocardiogram, Pulse rate in their body. we can predict the heart disease, by using the medical terms such as blood pressure, type of chest pain, blood sugar, cholesterol. Rather than using machine learning, Deep learning algorithm will provide the result accurately.","url":"https://doi.org/10.5281/zenodo.21558334","authors":["R, Sree Vidya","K, Nandhini","R, Mathu Shri","S, Shanmuga Priyanka Devi"],"tags":["Artificial Intelligence","Machine Learning","Neural Network","Random Forest","Convolutional Neural Network."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21558334","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21557937","name":"Disease Prediction Using Machine Learning","source":"datacite","abstract":"Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more accurate results in disease detection. Originally, the forecasts are made on the information accessible, but the absence of imperfect information contributes to a decrease in the caliber of precision. Besides incomplete data different qualities of particular regional diseases, which change based on their areas of origin can weaken the prediction models further. In this paper we use data mining techniques such as association rule mining, classification, clustering and finally the Decision Tree Machine learning algorithm to analyze the different kinds of general body-based illnesses. We implemented and assessed the efficacy of the Decision Tree algorithm over real-life clinical information.","url":"https://doi.org/10.5281/zenodo.21557937","authors":["Shilimkar, Gaurav","Bhilare, Amol","Pisal, Shivam"],"tags":["Machine Learning","Precision","Information"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21557937","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21557938","name":"Disease Prediction Using Machine Learning","source":"datacite","abstract":"Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more accurate results in disease detection. Originally, the forecasts are made on the information accessible, but the absence of imperfect information contributes to a decrease in the caliber of precision. Besides incomplete data different qualities of particular regional diseases, which change based on their areas of origin can weaken the prediction models further. In this paper we use data mining techniques such as association rule mining, classification, clustering and finally the Decision Tree Machine learning algorithm to analyze the different kinds of general body-based illnesses. We implemented and assessed the efficacy of the Decision Tree algorithm over real-life clinical information.","url":"https://doi.org/10.5281/zenodo.21557938","authors":["Shilimkar, Gaurav","Bhilare, Amol","Pisal, Shivam"],"tags":["Machine Learning","Precision","Information"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21557938","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21557748","name":"Heart Disease Prediction using Machine Learning Techniques","source":"datacite","abstract":"Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).","url":"https://doi.org/10.5281/zenodo.21557748","authors":["Ponnala, Ramesh","Sowjanya, K. Sai"],"tags":["Cardiovascular Disease (CVD)","Heart disease prediction","Machine learning","Hybrid ML Techniques","Classification","Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21557748","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21557749","name":"Heart Disease Prediction using Machine Learning Techniques","source":"datacite","abstract":"Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).","url":"https://doi.org/10.5281/zenodo.21557749","authors":["Ponnala, Ramesh","Sowjanya, K. Sai"],"tags":["Cardiovascular Disease (CVD)","Heart disease prediction","Machine learning","Hybrid ML Techniques","Classification","Prediction"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.5281/zenodo.21557749","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21556276","name":"Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management","source":"datacite","abstract":"The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through predictive analytics, real-time monitoring, and individualized treatment optimization. This review examines the role of ML in advancing personalized patient care by exploring foundational techniques such as supervised and unsupervised learning, deep neural networks, and reinforcement learning. It highlights practical applications across diabetes, cardiovascular conditions, respiratory disorders, and cancer survivorship, emphasizing the value of ML in risk prediction, medication adjustment, and remote monitoring. Additionally, the paper discusses key enablers of personalized care, including patient stratification, precision dosing, and the integration of wearable devices and digital platforms. Emerging innovations such as federated learning, explainable AI, multimodal data fusion, and digital twin systems are explored for their potential to support secure, transparent, and context-aware healthcare delivery. The review also addresses critical challenges related to bias, data privacy, clinical integration, and regulatory oversight. Ultimately, this work advocates for a multidisciplinary framework that combines technological innovation with policy reform to ensure equitable, scalable, and sustainable deployment of machine learning in personalized chronic disease care.","url":"https://doi.org/10.5281/zenodo.21556276","authors":["Adeyinka, Adepeju Ayotunde","Lamina, Yejide","Tawo, Obah Edom","Adeyeye, Yewande Iyimide","Minkah, Andrew Yaw"],"tags":["Machine Learning","Personalized Patient Care","Chronic Disease Management"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.21556276","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21556277","name":"Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management","source":"datacite","abstract":"The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through predictive analytics, real-time monitoring, and individualized treatment optimization. This review examines the role of ML in advancing personalized patient care by exploring foundational techniques such as supervised and unsupervised learning, deep neural networks, and reinforcement learning. It highlights practical applications across diabetes, cardiovascular conditions, respiratory disorders, and cancer survivorship, emphasizing the value of ML in risk prediction, medication adjustment, and remote monitoring. Additionally, the paper discusses key enablers of personalized care, including patient stratification, precision dosing, and the integration of wearable devices and digital platforms. Emerging innovations such as federated learning, explainable AI, multimodal data fusion, and digital twin systems are explored for their potential to support secure, transparent, and context-aware healthcare delivery. The review also addresses critical challenges related to bias, data privacy, clinical integration, and regulatory oversight. Ultimately, this work advocates for a multidisciplinary framework that combines technological innovation with policy reform to ensure equitable, scalable, and sustainable deployment of machine learning in personalized chronic disease care.","url":"https://doi.org/10.5281/zenodo.21556277","authors":["Adeyinka, Adepeju Ayotunde","Lamina, Yejide","Tawo, Obah Edom","Adeyeye, Yewande Iyimide","Minkah, Andrew Yaw"],"tags":["Machine Learning","Personalized Patient Care","Chronic Disease Management"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.5281/zenodo.21556277","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21482699","name":"Pipeline-aware max-statistic inference after clinical machine-learning model search","source":"datacite","abstract":"Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including simulation studies and a public SUPPORT2 clinical-data application.","url":"https://doi.org/10.5281/zenodo.21482699","authors":["Senda, Atsushi"],"tags":["machine learning","model selection","post-selection inference","permutation testing","clinical prediction","reproducible research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21482699","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21482698","name":"Pipeline-aware max-statistic inference after clinical machine-learning model search","source":"datacite","abstract":"Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including Phase 3C K=7 versus K=20 scalability simulations, candidate-dependence analyses, and a public SUPPORT2 clinical-data application.","url":"https://doi.org/10.5281/zenodo.21482698","authors":["Senda, Atsushi"],"tags":["machine learning","model selection","post-selection inference","permutation testing","clinical prediction","reproducible research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21482698","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21546103","name":"Pipeline-aware max-statistic inference after clinical machine-learning model search","source":"datacite","abstract":"Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including Phase 3C K=7 versus K=20 scalability simulations, candidate-dependence analyses, and a public SUPPORT2 clinical-data application.","url":"https://doi.org/10.5281/zenodo.21546103","authors":["Senda, Atsushi"],"tags":["machine learning","model selection","post-selection inference","permutation testing","clinical prediction","reproducible research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21546103","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20663669","name":"Alcohol Use Disorder Prediction from EEG Using AI Algorithms: A Comprehensive Review","source":"datacite","abstract":"Alcohol Use Disorder (AUD) is a relapsing condition, which is chronic and causes severe neurological, behavioral, and social disabilities. The traditional methods of diagnosing AUD are mainly based on the self-reported questionnaires and clinical interviews that are subjective and unsuitable in predicting the condition early. The latest developments in acquiring brain signals and artificial intelligence (AI) have allowed objective and data-based methods of predicting AUD based on neurophysiological biomarkers. The most appealing are systems updated using electroencephalogram (EEG) because they are not invasive, can be used to measure high- temporiological features. The current paper provides a review of the recent research (2023-2026) dedicated to the design and development of EEG-based wearable context to prediction the Alcohol Use Disorder based on the AI algorithm. The systematic review of the studies focused on EEG and neuroimaging (MRI/ fMRI)-based data reviews is done, with a focus on signal preprocessing methods, channel selection methods, feature representations, and learning methods. Special focus is on such types of deep learning architectures as convolutional neural network (CNNs), long short-term memory (LSTM) networks, and transformer-based models and lightweight and deployment-friendly models like EEGNet and Light Gradient Boosting Machine (LightGBM). It currently compares their results with the other available literature in terms of performance metrics, model complexity, and its appropriateness in real-time wearable implementation. Besides, the paper presents major issues in the EEG-based detection of AUD such as small dataset used, inter subject variability, explicability of AI models, and the limitations of hardware in wearable systems. The purpose of this review is to make valuable contributions to the researchers and practitioners in the field of developing reliable, efficient, systems to predict Alcohol Use Disorder in its early stage.","url":"https://doi.org/10.5281/zenodo.20663669","authors":["Saara Salim","A P Anupama","D K Devadathan","Reyhan S Hassan","Karthik R","Salga Ann Jacob","Dr.  M.  J.  Jayashree"],"tags":["Alcohol Use Disorder","EEG"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20663669","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20663670","name":"Alcohol Use Disorder Prediction from EEG Using AI Algorithms: A Comprehensive Review","source":"datacite","abstract":"Alcohol Use Disorder (AUD) is a relapsing condition, which is chronic and causes severe neurological, behavioral, and social disabilities. The traditional methods of diagnosing AUD are mainly based on the self-reported questionnaires and clinical interviews that are subjective and unsuitable in predicting the condition early. The latest developments in acquiring brain signals and artificial intelligence (AI) have allowed objective and data-based methods of predicting AUD based on neurophysiological biomarkers. The most appealing are systems updated using electroencephalogram (EEG) because they are not invasive, can be used to measure high- temporiological features. The current paper provides a review of the recent research (2023-2026) dedicated to the design and development of EEG-based wearable context to prediction the Alcohol Use Disorder based on the AI algorithm. The systematic review of the studies focused on EEG and neuroimaging (MRI/ fMRI)-based data reviews is done, with a focus on signal preprocessing methods, channel selection methods, feature representations, and learning methods. Special focus is on such types of deep learning architectures as convolutional neural network (CNNs), long short-term memory (LSTM) networks, and transformer-based models and lightweight and deployment-friendly models like EEGNet and Light Gradient Boosting Machine (LightGBM). It currently compares their results with the other available literature in terms of performance metrics, model complexity, and its appropriateness in real-time wearable implementation. Besides, the paper presents major issues in the EEG-based detection of AUD such as small dataset used, inter subject variability, explicability of AI models, and the limitations of hardware in wearable systems. The purpose of this review is to make valuable contributions to the researchers and practitioners in the field of developing reliable, efficient, systems to predict Alcohol Use Disorder in its early stage.","url":"https://doi.org/10.5281/zenodo.20663670","authors":["Saara Salim","A P Anupama","D K Devadathan","Reyhan S Hassan","Karthik R","Salga Ann Jacob","Dr.  M.  J.  Jayashree"],"tags":["Alcohol Use Disorder","EEG"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20663670","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.6084/m9.figshare.32190618","name":"Predicting the risk of venous thromboembolism events in older adults with rheumatoid arthritis after initiating targeted disease-modifying antirheumatic drugs: a comparison of the random survival forest and regularized cox regression models","source":"datacite","abstract":"To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthritis (RA) patients initiating biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) and compare its model performance with a regularized Cox regression (RegCox) model. This retrospective cohort study using the 5% Medicare data (2012-2020) identified older RA patients (≥ 65 years) initiating b/tsDMARDs (index date), including tumor necrosis factor inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, and tsDMARDs between January 1, 2013, through December 31, 2019. Study cohort was followed until an incident composite VTE event or censoring. Data were divided into training (75%) and testing (25%) sets. The RSF model was trained to predict VTE events during the follow-up period in the training set, with the RegCox model as the reference model. The performance of these models was evaluated in the testing data using the C-index. Variable importance of the predictors was assessed. Of 3,648 RA patients, 360 (9.87%) experienced any VTE event. The RSF model had better performance (C-index [95% CI] = 0.609[0.602-0.617]) than the RegCox model (C-index [95% CI] = 0.599[0.597-0.602], p = 0.0021). Variables commonly identified as the top influential variables were varicose veins, inpatient visits, Elixhauser score, emergency room visits, and outpatient visits. The RSF model performed slightly better in identifying VTE in RA patients after b/tsDMARDs initiation than RegCox. Incorporating additional clinical and contextual information beyond claims data may further enhance predictive accuracy in future studies. Biological (b) and targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) are newer medications that can treat rheumatoid arthritis (RA). However, recent randomized controlled clinical trials found the tsDMARDs may increase the risk of venous thromboembolism (VTE) among patients with RA. In this study, we developed a novel random survival forest (RSF) machine learning model to predict the VTE risk involving 3,648 older adults (≥65) with RA who were newly prescribed b/tsDMARD. Our results suggested that the RSF model achieved modest improved performance compared to the RegCox model in predicting the risk of VTE among these older adults with RA. Our findings from the RSF model may help rheumatologists better understand the high-risk RA patient profile who may be at risk of VTE. Incorporating additional variables from other datasets may improve the RSF’s model performance.","url":"https://doi.org/10.6084/m9.figshare.32190618","authors":["Yinan Huang","Shadi Bazzazzadehgan","Shishir Maharjan","Ying Lin","John P. Bentley","Sandeep K. Agarwal","Yi Yang"],"tags":["Medicine","Biotechnology","Ecology","Sociology","Immunology","Biological Sciences not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32190618","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.6084/m9.figshare.32190618.v1","name":"Predicting the risk of venous thromboembolism events in older adults with rheumatoid arthritis after initiating targeted disease-modifying antirheumatic drugs: a comparison of the random survival forest and regularized cox regression models","source":"datacite","abstract":"To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthritis (RA) patients initiating biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) and compare its model performance with a regularized Cox regression (RegCox) model. This retrospective cohort study using the 5% Medicare data (2012-2020) identified older RA patients (≥ 65 years) initiating b/tsDMARDs (index date), including tumor necrosis factor inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, and tsDMARDs between January 1, 2013, through December 31, 2019. Study cohort was followed until an incident composite VTE event or censoring. Data were divided into training (75%) and testing (25%) sets. The RSF model was trained to predict VTE events during the follow-up period in the training set, with the RegCox model as the reference model. The performance of these models was evaluated in the testing data using the C-index. Variable importance of the predictors was assessed. Of 3,648 RA patients, 360 (9.87%) experienced any VTE event. The RSF model had better performance (C-index [95% CI] = 0.609[0.602-0.617]) than the RegCox model (C-index [95% CI] = 0.599[0.597-0.602], p = 0.0021). Variables commonly identified as the top influential variables were varicose veins, inpatient visits, Elixhauser score, emergency room visits, and outpatient visits. The RSF model performed slightly better in identifying VTE in RA patients after b/tsDMARDs initiation than RegCox. Incorporating additional clinical and contextual information beyond claims data may further enhance predictive accuracy in future studies. Biological (b) and targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) are newer medications that can treat rheumatoid arthritis (RA). However, recent randomized controlled clinical trials found the tsDMARDs may increase the risk of venous thromboembolism (VTE) among patients with RA. In this study, we developed a novel random survival forest (RSF) machine learning model to predict the VTE risk involving 3,648 older adults (≥65) with RA who were newly prescribed b/tsDMARD. Our results suggested that the RSF model achieved modest improved performance compared to the RegCox model in predicting the risk of VTE among these older adults with RA. Our findings from the RSF model may help rheumatologists better understand the high-risk RA patient profile who may be at risk of VTE. Incorporating additional variables from other datasets may improve the RSF’s model performance.","url":"https://doi.org/10.6084/m9.figshare.32190618.v1","authors":["Yinan Huang","Shadi Bazzazzadehgan","Shishir Maharjan","Ying Lin","John P. Bentley","Sandeep K. Agarwal","Yi Yang"],"tags":["Medicine","Biotechnology","Ecology","Sociology","Immunology","Biological Sciences not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32190618.v1","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21425008","name":"AI-Driven Prediction of Cancer Treatment Toxicity Using Real-World Clinical Data","source":"datacite","abstract":"Background: Cancer treatment-related toxicities represent a significant clinical challenge, affecting patient quality of life, treatment adherence, and survival outcomes. The integration of artificial intelligence (AI) and machine learning (ML) with real-world clinical data offers unprecedented opportunities for early toxicity prediction and personalized risk stratification. This review comprehensively examines the current landscape of AI-driven approaches for predicting cancer treatment toxicity, focusing on machine learning and deep learning applications utilizing real-world clinical data sources including electronic health records, genomic databases, and patient-reported outcomes. Machine learning algorithms have demonstrated strong performance in predicting adverse drug events, with random forest being the most frequently used algorithm, followed by support vector machine, XGBoost, decision tree, and LightGBM. The combined sensitivity, specificity, and AUC from summary receiver operating characteristic curves were 0.65, 0.89, and 0.8069, respectively. (1) Recent advances in transformer-based architectures and large language models have further enhanced predictive capabilities across multiple cancer types and treatment modalities. Foundation models trained on multimodal real-world data demonstrate promising generalizability for clinical decision support. AI-driven toxicity prediction models can enable proactive patient management, facilitate dose optimization, reduce hospitalizations, and improve treatment outcomes through personalized risk stratification.","url":"https://doi.org/10.5281/zenodo.21425008","authors":["Kavita Bhatia*1, Nikhil Mehta2, Shatrughna Nagrik3"],"tags":["artificial intelligence, machine learning, cancer treatment toxicity, real-world data, electronic health records, deep learning, precision oncology."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21425008","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21425009","name":"AI-Driven Prediction of Cancer Treatment Toxicity Using Real-World Clinical Data","source":"datacite","abstract":"Background: Cancer treatment-related toxicities represent a significant clinical challenge, affecting patient quality of life, treatment adherence, and survival outcomes. The integration of artificial intelligence (AI) and machine learning (ML) with real-world clinical data offers unprecedented opportunities for early toxicity prediction and personalized risk stratification. This review comprehensively examines the current landscape of AI-driven approaches for predicting cancer treatment toxicity, focusing on machine learning and deep learning applications utilizing real-world clinical data sources including electronic health records, genomic databases, and patient-reported outcomes. Machine learning algorithms have demonstrated strong performance in predicting adverse drug events, with random forest being the most frequently used algorithm, followed by support vector machine, XGBoost, decision tree, and LightGBM. The combined sensitivity, specificity, and AUC from summary receiver operating characteristic curves were 0.65, 0.89, and 0.8069, respectively. (1) Recent advances in transformer-based architectures and large language models have further enhanced predictive capabilities across multiple cancer types and treatment modalities. Foundation models trained on multimodal real-world data demonstrate promising generalizability for clinical decision support. AI-driven toxicity prediction models can enable proactive patient management, facilitate dose optimization, reduce hospitalizations, and improve treatment outcomes through personalized risk stratification.","url":"https://doi.org/10.5281/zenodo.21425009","authors":["Kavita Bhatia*1, Nikhil Mehta2, Shatrughna Nagrik3"],"tags":["artificial intelligence, machine learning, cancer treatment toxicity, real-world data, electronic health records, deep learning, precision oncology."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21425009","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20478100","name":"Breast Cancer Classification using Machine Learning and Explainable AI on the Wisconsin Diagnostic Dataset","source":"datacite","abstract":"Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for accurate and early diagnostic tools. This study presents a comparative analysis of five supervised machine learning algorithms for binary classification of breast tumors as malignant or benign using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Logistic Regression, K-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, and Support Vector Machine models were evaluated after feature standardization and stratified train-test splitting. Logistic Regression, MLP, and SVM achieved the highest accuracy of 97.37%, demonstrating strong predictive performance. Explainable AI techniques including feature correlation analysis, logistic regression coefficient interpretation, and decision tree rule extraction were employed to enhance transparency and clinical trust. The findings demonstrate the effectiveness of machine learning and explainable AI for supporting breast cancer diagnosis.","url":"https://doi.org/10.5281/zenodo.20478100","authors":["KS, Acchutha"],"tags":["Breast Cancer Machine Learning Explainable AI WDBC Dataset Logistic Regression Support Vector Machine Decision Tree K-Nearest Neighbors Artificial Neural Networks Medical Diagnostics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20478100","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20478101","name":"Breast Cancer Classification using Machine Learning and Explainable AI on the Wisconsin Diagnostic Dataset","source":"datacite","abstract":"Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for accurate and early diagnostic tools. This study presents a comparative analysis of five supervised machine learning algorithms for binary classification of breast tumors as malignant or benign using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Logistic Regression, K-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, and Support Vector Machine models were evaluated after feature standardization and stratified train-test splitting. Logistic Regression, MLP, and SVM achieved the highest accuracy of 97.37%, demonstrating strong predictive performance. Explainable AI techniques including feature correlation analysis, logistic regression coefficient interpretation, and decision tree rule extraction were employed to enhance transparency and clinical trust. The findings demonstrate the effectiveness of machine learning and explainable AI for supporting breast cancer diagnosis.","url":"https://doi.org/10.5281/zenodo.20478101","authors":["KS, Acchutha"],"tags":["Breast Cancer Machine Learning Explainable AI WDBC Dataset Logistic Regression Support Vector Machine Decision Tree K-Nearest Neighbors Artificial Neural Networks Medical Diagnostics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20478101","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22046743","name":"MetTarget","source":"datacite","abstract":"MetTarget MetTarget is a machine learning framework for prioritizing immunometabolic therapeutic targets in inflammatory bowel disease (IBD). It integrates public genetics, bulk RNA-seq, and single-cell RNA-seq data to score and rank candidate targets in Crohn's disease (CD) and ulcerative colitis (UC). What's in this repository This repository includes trained MetTarget models (R objects) for CD and UC, prediction code, and code and data to reproduce manuscript figures. All features for model training and testing are provided (76 for CD, 134 for UC). Target sets covered include clinical phase 2+ IBD drug targets (34 CD, 48 UC), FDA-approved non-IBD immune targets (n = 151), and SPHK1, a new target identified by MetTarget. Requirements R (>= 4.x recommended) R packages listed in DESCRIPTION Usage See MetTarget_model/README.md. License See LICENSE.txt.","url":"https://doi.org/10.5281/zenodo.22046743","authors":["Zhang, Lu","Han, Yingnan","Kurlovs, Andre","Xing, Heming"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22046743","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22046744","name":"MetTarget","source":"datacite","abstract":"MetTarget MetTarget is a machine learning framework for prioritizing immunometabolic therapeutic targets in inflammatory bowel disease (IBD). It integrates public genetics, bulk RNA-seq, and single-cell RNA-seq data to score and rank candidate targets in Crohn's disease (CD) and ulcerative colitis (UC). What's in this repository This repository includes trained MetTarget models (R objects) for CD and UC, prediction code, and code and data to reproduce manuscript figures. All features for model training and testing are provided (76 for CD, 134 for UC). Target sets covered include clinical phase 2+ IBD drug targets (34 CD, 48 UC), FDA-approved non-IBD immune targets (n = 151), and SPHK1, a new target identified by MetTarget. Requirements R (>= 4.x recommended) R packages listed in DESCRIPTION Usage See MetTarget_model/README.md. License See LICENSE.txt.","url":"https://doi.org/10.5281/zenodo.22046744","authors":["Zhang, Lu","Han, Yingnan","Kurlovs, Andre","Xing, Heming"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22046744","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5167/uzh-435714","name":"A practical guide to the implementation of AI in orthopaedic research‐Part 4: Prerequisites for a successful orthopedics AI‐driven project in terms of interdisciplinary collaboration, data management, ethical approval and technology","source":"datacite","abstract":"Translating artificial intelligence (AI) research in orthopedics from proof‐of‐concept studies into production‐grade clinical systems requires the systematic satisfaction of four prerequisite domains: interdisciplinary team architecture, technical data management, ethical and regulatory governance and production‐grade technology and deployment infrastructure. Despite a tenfold increase in orthopedic AI publications, fewer than 6% of studies reach routine clinical deployment, reflecting persistent gaps in each of these domains. This article provides a technically rigorous, evidence‐based framework organized around these four pillars. The interdisciplinary team may be structured using a product‐centric topology that decouples stream‐aligned clinical teams from platform infrastructure teams, following Huffman et al.'s six‐step AI project lifecycle: obtain/curate/label data; establish a reference standard; develop the model; evaluate performance; externally validate and iteratively reinforce until clinical implementation is viable. Data management requires data extraction protocols, integration for bulk exports and a multi‐component de‐identification pipeline. A multi‐stage Institutional Review Board framework governs ethical oversight, scaling from Exempt review for retrospective de‐identified studies to Full Board Review with prospective validation and mandatory human‐override mechanisms for interventional deployment. Responsible clinical deployment requires a multi‐layer Clinical Machine Learning Operations framework, implementing privacy‐preserving deployment, clinical observability, compliance audit trails and human‐in‐the‐loop governance. Model drift has to be monitored with a degradation threshold triggering mandatory human review. Level of Evidence Level V.","url":"https://doi.org/10.5167/uzh-435714","authors":["Longo, Umile Giuseppe","Merone, Mario","Schena, Emiliano","Bandini, Benedetta","Nicodemi, Guido","Zsidai, Bálint","Hilkert, Ann‐Sophie","Senorski, Eric Hamrin","Grassi, Alberto","Ley, Christophe","Herbst, Elmar","Hirschmann, Michael T.","Kopf, Sebastian","Seil, Romain","Tischer, Thomas","Feldt, Robert","Samuelsson, Kristian","Oettl, Felix C."],"tags":["artificial intelligence","collaboration","management","structure","610 Medicine &amp; health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5167/uzh-435714","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22046623","name":"Biostatistics explored through Python A Practical Guide to Statistical Analysis in Biomedical Sciences","source":"datacite","abstract":"Biostatistics is the application of statistical methods in the biomedical field. Biomedical research has entered an era defined by immense data scale and complexity, requiring clinicians, epidemiologists, laboratory researchers, and data scientists to transform messy, high-dimensional observations into valid scientific conclusions. The book start with finding answers to the questions what biostatistics is, why it matters, and how Python a free, open-source, is used in performing statistical calculations. Python programming uniquely bridges classical statistical inference, flexible Bayesian computation, scalable machine learning, and reproducible software engineering within a single, readable, open-source ecosystem. This book was written to provide a comprehensive, hands-on guide that demystifies both the mathematical foundations and the modern computational workflows necessary to analyze biological and clinical data rigorously. The book is structured to guide readers systematically through the complete lifecycle of biomedical research data. We begin with the essentials of data management and exploratory data analysis, detailing how to import clinical records, handle missing values, and clean real-world measurements using pandas, NumPy, and SciPy. From there, the book grounds the readers in core probability distributions, estimation, and classical hypothesis testing before transitioning to parametric, non-parametric, and multivariable regression modeling. It also helps readers to walkthrough the models essential to biomedical field including survival analysis with proportional hazards, longitudinal mixed-effects modeling, epidemiological measures of risk, diagnostic test evaluation, and the principles of clinical trial design. The book provides overview of Bayesian inference and Markov Chain Monte Carlo sampling using PyMC, predictive clinical modeling via scikit-learn, and high-dimensional genomics workflows tailored for differential expression and dimensionality reduction. Throughout every chapter, practical worked examples, reproducible Jupyter workflows, and end-of-chapter exercises ensure that theory is immediately paired with executable Python code. Whether the readers is a biomedical researcher seeking independent analytical fluency, a student of health data science, or a quantitative programmer entering life sciences, this book aims to equip them with the practical skills and conceptual clarity needed to extract robust, trustworthy insights from complex biomedical data. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php","url":"https://doi.org/10.5281/zenodo.22046623","authors":["Editor, IJSMI"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22046623","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22046624","name":"Biostatistics explored through Python A Practical Guide to Statistical Analysis in Biomedical Sciences","source":"datacite","abstract":"Biostatistics is the application of statistical methods in the biomedical field. Biomedical research has entered an era defined by immense data scale and complexity, requiring clinicians, epidemiologists, laboratory researchers, and data scientists to transform messy, high-dimensional observations into valid scientific conclusions. The book start with finding answers to the questions what biostatistics is, why it matters, and how Python a free, open-source, is used in performing statistical calculations. Python programming uniquely bridges classical statistical inference, flexible Bayesian computation, scalable machine learning, and reproducible software engineering within a single, readable, open-source ecosystem. This book was written to provide a comprehensive, hands-on guide that demystifies both the mathematical foundations and the modern computational workflows necessary to analyze biological and clinical data rigorously. The book is structured to guide readers systematically through the complete lifecycle of biomedical research data. We begin with the essentials of data management and exploratory data analysis, detailing how to import clinical records, handle missing values, and clean real-world measurements using pandas, NumPy, and SciPy. From there, the book grounds the readers in core probability distributions, estimation, and classical hypothesis testing before transitioning to parametric, non-parametric, and multivariable regression modeling. It also helps readers to walkthrough the models essential to biomedical field including survival analysis with proportional hazards, longitudinal mixed-effects modeling, epidemiological measures of risk, diagnostic test evaluation, and the principles of clinical trial design. The book provides overview of Bayesian inference and Markov Chain Monte Carlo sampling using PyMC, predictive clinical modeling via scikit-learn, and high-dimensional genomics workflows tailored for differential expression and dimensionality reduction. Throughout every chapter, practical worked examples, reproducible Jupyter workflows, and end-of-chapter exercises ensure that theory is immediately paired with executable Python code. Whether the readers is a biomedical researcher seeking independent analytical fluency, a student of health data science, or a quantitative programmer entering life sciences, this book aims to equip them with the practical skills and conceptual clarity needed to extract robust, trustworthy insights from complex biomedical data. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php","url":"https://doi.org/10.5281/zenodo.22046624","authors":["Editor, IJSMI"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22046624","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21144104","name":"Artificial Intelligence in Chronic Neuropathic Pain Management: Evidence, Challenges, and a Research Agenda from a Latin American Neurosurgical Perspective","source":"datacite","abstract":"This narrative review with a research agenda synthesizes current evidence on artificial intelligence (AI) and machine learning (ML) applications in chronic neuropathic pain management, with a focus on implementation challenges and opportunities in Latin American low-resource healthcare settings. Key topics addressed: (1) AI/ML for neuropathic pain diagnosis and phenotyping; (2) predictive models for spinal cord stimulation treatment response, including comparison against the conventional lead screening trial; (3) large language models in neuromodulation clinical decision support; (4) structural implementation barriers in Venezuela and Latin America; and (5) five strategic recommendations and a four-priority research agenda grounded in active investigator experience. This preprint is part of the Neuropain-AI Research Programme (OSF: doi.org/10.17605/ OSF.IO/DMAZV), which includes two PROSPERO-registered systematic reviews (CRD420261434015, CRD420261437316) and two ClinicalTrials.gov-registered prospective studies (PREDICT-DOLOR-VE-2026-001, SPINE-RISK-VE-2026-001). 22 references with verified DOIs from peer-reviewed sources including European Journal of Pain, BMJ, Journal of Pain Research, Frontiers in Digital Health, and the Latin American Artificial Intelligence Index (ILIA 2025, ECLAC/CENIA Chile).","url":"https://doi.org/10.5281/zenodo.21144104","authors":["Valero Quintero, juan jose","Cardenas Vargas, Edicson Jose"],"tags":["neuropathic pain","artificial intelligence","neuromodulation","spinal cord stimulation","Latin America","low-resource settings","TRIPOD+AI","predictive model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21144104","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21144105","name":"Artificial Intelligence in Chronic Neuropathic Pain Management: Evidence, Challenges, and a Research Agenda from a Latin American Neurosurgical Perspective","source":"datacite","abstract":"This narrative review with a research agenda synthesizes current evidence on artificial intelligence (AI) and machine learning (ML) applications in chronic neuropathic pain management, with a focus on implementation challenges and opportunities in Latin American low-resource healthcare settings. Key topics addressed: (1) AI/ML for neuropathic pain diagnosis and phenotyping; (2) predictive models for spinal cord stimulation treatment response, including comparison against the conventional lead screening trial; (3) large language models in neuromodulation clinical decision support; (4) structural implementation barriers in Venezuela and Latin America; and (5) five strategic recommendations and a four-priority research agenda grounded in active investigator experience. This preprint is part of the Neuropain-AI Research Programme (OSF: doi.org/10.17605/ OSF.IO/DMAZV), which includes two PROSPERO-registered systematic reviews (CRD420261434015, CRD420261437316) and two ClinicalTrials.gov-registered prospective studies (PREDICT-DOLOR-VE-2026-001, SPINE-RISK-VE-2026-001). 22 references with verified DOIs from peer-reviewed sources including European Journal of Pain, BMJ, Journal of Pain Research, Frontiers in Digital Health, and the Latin American Artificial Intelligence Index (ILIA 2025, ECLAC/CENIA Chile).","url":"https://doi.org/10.5281/zenodo.21144105","authors":["Valero Quintero, juan jose","Cardenas Vargas, Edicson Jose"],"tags":["neuropathic pain","artificial intelligence","neuromodulation","spinal cord stimulation","Latin America","low-resource settings","TRIPOD+AI","predictive model"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21144105","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22045661","name":"DoseGuard-SynthCohort v1.0: A Synthetic Radiotherapy Dose-Distribution Dataset for Lightweight, Explainable Dose Prediction Research","source":"datacite","abstract":"DoseGuard-SynthCohort is a set of 280 simulated radiotherapy planning cases built to support research on fast, low-compute dose-prediction models — the kind that could eventually run on a laptop in a cancer centre without access to a full Monte Carlo treatment-planning system. Each case contains a simulated CT volume, a tumour target mask, an organ-at-risk mask, a body contour, a ground-truth dose distribution, and seven scalar clinical features (target volume, distance to nearby organs, prescription dose, beam count, and related geometry). The dataset is split into three cohorts by design: cohort_primary (200 cases, head-and-neck and lung), used for training and validation, and two independently-seeded external cohorts, cohort_external_a and cohort_external_b (40 cases each, head-and-neck), held out entirely for testing. This split exists so that anyone using the dataset can measure how well a model generalises to cases it was never trained on, rather than only reporting accuracy on data it has already learned the statistics of. Every case is generated by a documented, physically motivated model: dose is highest and near-uniform inside the tumour target, decays outward with distance, and is reduced inside nearby organs to mimic clinical sparing. This is not real patient data and does not reproduce or stand in for any existing public dataset (OpenKBP, GDP-HMM, AIMIS, or otherwise) — it is an independent, fully synthetic resource, useful as a controlled testbed where the correct answer is known exactly, for validating architectures, loss functions, or safety mechanisms before the larger undertaking of securing real clinical data. Released under CC-BY-4.0 with a full data dictionary, manifest files, and a companion preprint describing the generative methodology and how the dataset was used to train and evaluate a reference model (DoseGuard). Keywords: radiotherapy, dose prediction, synthetic dataset, machine learning, medical physics, explainable AI, uncertainty quantification, low-resource healthcare License: CC-BY-4.0Resource type: DatasetRelated identifier: references the companion DoseGuard software and preprint (add DOIs once both are minted)","url":"https://doi.org/10.5281/zenodo.22045661","authors":["Suhail Ahmed Chandio"],"tags":["radiotherapy","dose prediction","synthetic dataset","machine learning","medical physics","explainable AI,","uncertainty quantification","low-resource healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22045661","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22045662","name":"DoseGuard-SynthCohort v1.0: A Synthetic Radiotherapy Dose-Distribution Dataset for Lightweight, Explainable Dose Prediction Research","source":"datacite","abstract":"DoseGuard-SynthCohort is a set of 280 simulated radiotherapy planning cases built to support research on fast, low-compute dose-prediction models — the kind that could eventually run on a laptop in a cancer centre without access to a full Monte Carlo treatment-planning system. Each case contains a simulated CT volume, a tumour target mask, an organ-at-risk mask, a body contour, a ground-truth dose distribution, and seven scalar clinical features (target volume, distance to nearby organs, prescription dose, beam count, and related geometry). The dataset is split into three cohorts by design: cohort_primary (200 cases, head-and-neck and lung), used for training and validation, and two independently-seeded external cohorts, cohort_external_a and cohort_external_b (40 cases each, head-and-neck), held out entirely for testing. This split exists so that anyone using the dataset can measure how well a model generalises to cases it was never trained on, rather than only reporting accuracy on data it has already learned the statistics of. Every case is generated by a documented, physically motivated model: dose is highest and near-uniform inside the tumour target, decays outward with distance, and is reduced inside nearby organs to mimic clinical sparing. This is not real patient data and does not reproduce or stand in for any existing public dataset (OpenKBP, GDP-HMM, AIMIS, or otherwise) — it is an independent, fully synthetic resource, useful as a controlled testbed where the correct answer is known exactly, for validating architectures, loss functions, or safety mechanisms before the larger undertaking of securing real clinical data. Released under CC-BY-4.0 with a full data dictionary, manifest files, and a companion preprint describing the generative methodology and how the dataset was used to train and evaluate a reference model (DoseGuard). Keywords: radiotherapy, dose prediction, synthetic dataset, machine learning, medical physics, explainable AI, uncertainty quantification, low-resource healthcare License: CC-BY-4.0Resource type: DatasetRelated identifier: references the companion DoseGuard software and preprint (add DOIs once both are minted)","url":"https://doi.org/10.5281/zenodo.22045662","authors":["Suhail Ahmed Chandio"],"tags":["radiotherapy","dose prediction","synthetic dataset","machine learning","medical physics","explainable AI,","uncertainty quantification","low-resource healthcare"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22045662","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22044486","name":"A  Comprehensive Review of  ECG-Based  Arrhythmia Detection Using Machine Learning, Deep Learning, and Ensemble Learning","source":"datacite","abstract":"Cardiovascular diseases are among the leading causes of mortality worldwide, with cardiac arrhythmias representing one of the most critical abnormalities affecting heart rhythm. Early and accurate detection of arrhythmias is essential for timely diagnosis and effective clinical intervention. Electrocardiogram (ECG) signals provide valuable information about the electrical activity of the heart and are widely used for diagnosing various cardiac disorders. However, manual interpretation of ECG recordings is time-consuming, labor-intensive, and susceptible to human error, particularly when analyzing large volumes of patient data. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Ensemble Learning have significantly improved the automation and accuracy of ECG-based arrhythmia detection. This paper presents a comprehensive review of modern ECG classification techniques, including traditional machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), and XGBoost, as well as deep learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and hybrid architectures. The paper also discusses ensemble learning approaches such as Bagging, Boosting, Voting, and Stacking for improving classification performance. Furthermore, major challenges including signal noise, patient variability, class imbalance, high-dimensional feature spaces, computational complexity, and model interpretability are highlighted. The review concludes that integrating advanced feature engineering, explainable artificial intelligence, and hybrid ensemble learning techniques can significantly enhance the accuracy, robustness, and clinical applicability of automated ECG arrhythmia detection systems.","url":"https://doi.org/10.5281/zenodo.22044486","authors":["Kumar, Ritesh","Bhargava, Dr. Medhavi","Vashishtha, Dr. Megha"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22044486","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22044485","name":"A  Comprehensive Review of  ECG-Based  Arrhythmia Detection Using Machine Learning, Deep Learning, and Ensemble Learning","source":"datacite","abstract":"Cardiovascular diseases are among the leading causes of mortality worldwide, with cardiac arrhythmias representing one of the most critical abnormalities affecting heart rhythm. Early and accurate detection of arrhythmias is essential for timely diagnosis and effective clinical intervention. Electrocardiogram (ECG) signals provide valuable information about the electrical activity of the heart and are widely used for diagnosing various cardiac disorders. However, manual interpretation of ECG recordings is time-consuming, labor-intensive, and susceptible to human error, particularly when analyzing large volumes of patient data. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Ensemble Learning have significantly improved the automation and accuracy of ECG-based arrhythmia detection. This paper presents a comprehensive review of modern ECG classification techniques, including traditional machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), and XGBoost, as well as deep learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and hybrid architectures. The paper also discusses ensemble learning approaches such as Bagging, Boosting, Voting, and Stacking for improving classification performance. Furthermore, major challenges including signal noise, patient variability, class imbalance, high-dimensional feature spaces, computational complexity, and model interpretability are highlighted. The review concludes that integrating advanced feature engineering, explainable artificial intelligence, and hybrid ensemble learning techniques can significantly enhance the accuracy, robustness, and clinical applicability of automated ECG arrhythmia detection systems.","url":"https://doi.org/10.5281/zenodo.22044485","authors":["Kumar, Ritesh","Bhargava, Dr. Medhavi","Vashishtha, Dr. Megha"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22044485","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5061/dryad.x69p8czzx","name":"Data from: Comparing exercise with virtual reality gaming on gait and cognition in relapsing-remitting multiple sclerosis: a randomized controlled trial","source":"datacite","abstract":"Background: Exercise and virtual reality gaming may mitigate gait and cognitive deficits in relapsing-remitting multiple sclerosis (RRMS). The main aim was to compare the efficacy of both interventions on gait and cognition and gait in RRMS. Secondary aims were to explore the efficacy of both interventions on serum biomarkers and to explore the predictors of treatment response. Methods: Forty-eight participants with RRMS were randomized to exercise (n=19), VR (n=19), or wait-list control (n=10) for eight weeks. Primary outcomes were the 10-meter walk test (10MWT) and the Symbol Digit Modalities Test (SDMT). Secondary outcomes included serum levels of neurofilament light chain (NfL), brain-derived neurotrophic factor (BDNF), and insulin-like growth factor-1 (IGF-1). Extreme Gradient Boosting (XGBoost), Random Forest, and logistic regression models were trained to predict treatment response. Results: The exercise group improved 10MWT performance by 2.41 seconds and increased IGF-1 levels by 100.25 ng/ml, significantly more than the VR and control groups (both p&lt;0.001). The VR group improved on the SDMT by 1.95 points (p=0.001 vs. control; p=0.05 vs. exercise). Both interventions reduced NfL concentrations compared to control (exercise: –2.07 pg/ml; VR: –0.60 pg/ml), with exercise showing a greater reduction than VR (p=0.02). XGBoost demonstrated highest predictive accuracy (10MWT: 87%; SDMT: 86%). SHapley Additive exPlanations (SHAP) analysis identified baseline IGF-1 and BDNF as top predictors of 10MWT, and baseline CognICA, BDNF, and age as predictors of SDMT performance. Conclusion: Exercise preferentially improves gait and IGF-1, whereas VR gaming yields modest cognitive gains. Serum biomarkers enhance machine learning prediction of treatment response, supporting a precision rehabilitation approach in RRMS. Keywords: Multiple Sclerosis, Virtual Reality, Exercise, Gait, Cognition, Biomarkers, Machine Learning","url":"https://doi.org/10.5061/dryad.x69p8czzx","authors":["Sadeghi, Maryam","Kordi, Mohammadreza","Daemi, Mehdi","Tabasi, Seyed Maziyar","Ebrahimnezhad Bashiri, Mohammad Sina","Nabavi, Seyed Massood","Khaligh-Razavi, Seyed-Mahdi","Thompson, Jeffrey","Sosnoff, Jacob","Devos, Hannes"],"tags":["FOS: Medical and health sciences","Multiple sclerosis","Virtual reality","Exercise","Gait rehabilitation","Cognitive impairment","Machine learning","Biomarkers"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5061/dryad.x69p8czzx","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21513566","name":"Symptom Intelligence: High-Accuracy Disease Prediction with Ensemble Learning","source":"datacite","abstract":"Accurate and timely disease prediction based on patient-reported symptoms remains a significant challenge in modern healthcare. Early diagnosis is essential for improving treatment outcomes and enhancing overall healthcare efficiency. However, many existing symptom-based diagnostic systems rely on binary symptom representation and a single machine learning model, which may limit their clinical realism, robustness, and predictive reliability. To address these limitations, this study proposes an ensemble learning framework that incorporates symptom severity for multi-disease prediction. Instead of binary encoding, symptoms are represented using weighted severity scores to better capture their clinical intensity. The proposed framework integrates three widely used classifiers Random Forest, Naïve Bayes, and Support Vector Machine and combines their predictions through a majority-voting ensemble strategy. The framework is evaluated using the publicly available Kaggle Disease Symptom Description Dataset, which contains 4,920 records across 41 diseases and 133 symptoms. Data preprocessing includes severity mapping, label encoding, and feature construction, followed by model training using an 80:20 train–test split. Experimental results show that while individual models achieve strong performance, the ensemble approach consistently provides higher accuracy, precision, recall, and F1-score. Additionally, the trained model is deployed in a web-based prototype that accepts user symptoms and generates disease predictions with recommended precautions, demonstrating the practical applicability of the proposed approach.","url":"https://doi.org/10.5281/zenodo.21513566","authors":["Shelatkar, Vedang P.","Chavan, Vedant D.","Khanche, Gousiya A."],"tags":["Symptom-based disease prediction; Ensemble learning; Random Forest; Naïve Bayes; Support Vector Machine; Symptom severity modeling; Majority voting; Multi-class classification; Healthcare decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21513566","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21513567","name":"Symptom Intelligence: High-Accuracy Disease Prediction with Ensemble Learning","source":"datacite","abstract":"Accurate and timely disease prediction based on patient-reported symptoms remains a significant challenge in modern healthcare. Early diagnosis is essential for improving treatment outcomes and enhancing overall healthcare efficiency. However, many existing symptom-based diagnostic systems rely on binary symptom representation and a single machine learning model, which may limit their clinical realism, robustness, and predictive reliability. To address these limitations, this study proposes an ensemble learning framework that incorporates symptom severity for multi-disease prediction. Instead of binary encoding, symptoms are represented using weighted severity scores to better capture their clinical intensity. The proposed framework integrates three widely used classifiers Random Forest, Naïve Bayes, and Support Vector Machine and combines their predictions through a majority-voting ensemble strategy. The framework is evaluated using the publicly available Kaggle Disease Symptom Description Dataset, which contains 4,920 records across 41 diseases and 133 symptoms. Data preprocessing includes severity mapping, label encoding, and feature construction, followed by model training using an 80:20 train–test split. Experimental results show that while individual models achieve strong performance, the ensemble approach consistently provides higher accuracy, precision, recall, and F1-score. Additionally, the trained model is deployed in a web-based prototype that accepts user symptoms and generates disease predictions with recommended precautions, demonstrating the practical applicability of the proposed approach.","url":"https://doi.org/10.5281/zenodo.21513567","authors":["Shelatkar, Vedang P.","Chavan, Vedant D.","Khanche, Gousiya A."],"tags":["Symptom-based disease prediction; Ensemble learning; Random Forest; Naïve Bayes; Support Vector Machine; Symptom severity modeling; Majority voting; Multi-class classification; Healthcare decision support"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21513567","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21512807","name":"Mental Health Prediction Using Artificial Intelligence","source":"datacite","abstract":"Mental health problems such as stress, anxiety, and depression are increasing worldwide. Early detection is important, but many people avoid professional help due to stigma or lack of access. This project proposes an AI-based Mental Health Prediction System using machine-learning techniques and symptom-based inputs. A structured dataset containing self-reported symptoms was used. Logistic Regression, Support Vector Machine (SVM), and Random Forest algorithms were trained and evaluated. Among these, the Random Forest classifier achieved the highest accuracy ranging between 90%. The system classifies users into four categories: Normal, Stress, Anxiety, and Depression. A Streamlit web application was developed to allow users to enter symptom ratings and receive instant predictions. Feature analysis showed that sleep issues, anxiety level, and fatigue were the most influential factors. The system provides fast predictions within two seconds. Although not a replacement for clinical diagnosis, this system supports early mental-health screening and encourages timely professional assistance.","url":"https://doi.org/10.5281/zenodo.21512807","authors":["Padave, Tanaya R.","Chavan, Samiksha S."],"tags":["Mental Health Prediction; Artificial Intelligence; Machine Learning; Random Forest; Symptom-Based Screening; Early Detection; Streamlit"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21512807","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21512808","name":"Mental Health Prediction Using Artificial Intelligence","source":"datacite","abstract":"Mental health problems such as stress, anxiety, and depression are increasing worldwide. Early detection is important, but many people avoid professional help due to stigma or lack of access. This project proposes an AI-based Mental Health Prediction System using machine-learning techniques and symptom-based inputs. A structured dataset containing self-reported symptoms was used. Logistic Regression, Support Vector Machine (SVM), and Random Forest algorithms were trained and evaluated. Among these, the Random Forest classifier achieved the highest accuracy ranging between 90%. The system classifies users into four categories: Normal, Stress, Anxiety, and Depression. A Streamlit web application was developed to allow users to enter symptom ratings and receive instant predictions. Feature analysis showed that sleep issues, anxiety level, and fatigue were the most influential factors. The system provides fast predictions within two seconds. Although not a replacement for clinical diagnosis, this system supports early mental-health screening and encourages timely professional assistance.","url":"https://doi.org/10.5281/zenodo.21512808","authors":["Padave, Tanaya R.","Chavan, Samiksha S."],"tags":["Mental Health Prediction; Artificial Intelligence; Machine Learning; Random Forest; Symptom-Based Screening; Early Detection; Streamlit"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21512808","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21512737","name":"Identification of Psychological Disorders of People Using Text Data and Bert Base Deep Learning Technique","source":"datacite","abstract":"The issue of psychological disorders remains a significant public health concern worldwide today, as there exist numerous people who have been identified to suffer from a variety of psychological disorders, including depression, anxiety, PTSD, and bipolar disorder. Early detection and diagnosis of the illness of body play a crucial role in the proper treatment of such patients; however, traditional medical techniques suffer from issues related to accessibility, stigma, and scaling. This paper proposes a methodology of automated identification and classification of psychological disorders through the analysis of the text based on a BERT model. Specifically, our method involves using a BERT pre-trained language model for identification and classification of the psychological disorder based on the labelled data consisting of social media texts and clinical data samples. Our model has been trained using five epochs with an optimal batch size. It has been found through experimental results that the use of transfer learning with a BERT model significantly surpasses classical machine learning approaches.","url":"https://doi.org/10.5281/zenodo.21512737","authors":["Kulkarni, Swapna Shyamrao","Agnihotri, Prashant Prakashrao"],"tags":["BERT; psychological disorder detection; mental health; NLP; transfer learning; text classification; transformers; deep learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21512737","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21512738","name":"Identification of Psychological Disorders of People Using Text Data and Bert Base Deep Learning Technique","source":"datacite","abstract":"The issue of psychological disorders remains a significant public health concern worldwide today, as there exist numerous people who have been identified to suffer from a variety of psychological disorders, including depression, anxiety, PTSD, and bipolar disorder. Early detection and diagnosis of the illness of body play a crucial role in the proper treatment of such patients; however, traditional medical techniques suffer from issues related to accessibility, stigma, and scaling. This paper proposes a methodology of automated identification and classification of psychological disorders through the analysis of the text based on a BERT model. Specifically, our method involves using a BERT pre-trained language model for identification and classification of the psychological disorder based on the labelled data consisting of social media texts and clinical data samples. Our model has been trained using five epochs with an optimal batch size. It has been found through experimental results that the use of transfer learning with a BERT model significantly surpasses classical machine learning approaches.","url":"https://doi.org/10.5281/zenodo.21512738","authors":["Kulkarni, Swapna Shyamrao","Agnihotri, Prashant Prakashrao"],"tags":["BERT; psychological disorder detection; mental health; NLP; transfer learning; text classification; transformers; deep learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21512738","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19708887","name":"Intelligent and Explainable SaaS-Based Intrusion Detection System For Secure and ResourceConstrained IoMT Environments","source":"datacite","abstract":"The rapid expansion of the Internet of Medical Things (IoMT) has improved real-time patient monitoring and clinical decision-making, yet it has also introduced significant security vulnerabilities due to limited device resources, heterogeneous communication protocols, and increasing cyber-attacks. This project presents an intelligent and explainable SaaS-based Intrusion Detection System (IDS) designed specifically for secure and resource-constrained IoMT environments. The system integrates Particle Swarm Optimization (PSO) for efficient feature selection and applies advanced Machine Learning and Deep Learning models to accurately classify normal and malicious traffic. Explainability is ensured using SHAP, enabling transparent decision interpretation for healthcare professionals. The proposed framework is evaluated using synthetic IoMT datasets and the WUSTL-EHMS-2020 dataset, demonstrating strong accuracy, real-time detection capability, and scalability through cloud-based deployment. This work contributes a lightweight, interpretable, and practical IDS solution for enhancing cybersecurity in modern healthcare infrastructures.","url":"https://doi.org/10.5281/zenodo.19708887","authors":["Ashish Nanotkar","Sarika Panchalwar","Akhil Tonge","Vanshika Bante","Janhavi Dabhade"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19708887","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.19708888","name":"Intelligent and Explainable SaaS-Based Intrusion Detection System For Secure and ResourceConstrained IoMT Environments","source":"datacite","abstract":"The rapid expansion of the Internet of Medical Things (IoMT) has improved real-time patient monitoring and clinical decision-making, yet it has also introduced significant security vulnerabilities due to limited device resources, heterogeneous communication protocols, and increasing cyber-attacks. This project presents an intelligent and explainable SaaS-based Intrusion Detection System (IDS) designed specifically for secure and resource-constrained IoMT environments. The system integrates Particle Swarm Optimization (PSO) for efficient feature selection and applies advanced Machine Learning and Deep Learning models to accurately classify normal and malicious traffic. Explainability is ensured using SHAP, enabling transparent decision interpretation for healthcare professionals. The proposed framework is evaluated using synthetic IoMT datasets and the WUSTL-EHMS-2020 dataset, demonstrating strong accuracy, real-time detection capability, and scalability through cloud-based deployment. This work contributes a lightweight, interpretable, and practical IDS solution for enhancing cybersecurity in modern healthcare infrastructures.","url":"https://doi.org/10.5281/zenodo.19708888","authors":["Ashish Nanotkar","Sarika Panchalwar","Akhil Tonge","Vanshika Bante","Janhavi Dabhade"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19708888","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21090674","name":"Synthetic whole-exome sequencing data of virtual cancer patients (Clinical tables and metadata)","source":"datacite","abstract":"Sharing genetic data from cancer patients is vital for developing improved diagnostic tools and training artificial intelligence models in medicine. However, given that genetic information is inherently unique to each individual, public data sharing entails substantial privacy risks. To address this challenge, this project has constructed a dataset of 'virtual cancer patients.' Specifically, we generated artificial genomic profiles that closely mimic real cancer data in both appearance and behavior, yet contain no actual patient-derived information. These profiles were produced by learning from a limited set of real clinical reports, validated through medical rule-based checks and artificial intelligence to ensure biological plausibility, and subsequently used to simulate raw sequencing reads by inserting the virtual mutations into a widely adopted public reference genome. This dataset enables researchers worldwide to freely test cancer analysis pipelines and train machine learning models without compromising real patient genomic privacy, thereby accelerating the advancement of precision medicine.","url":"https://doi.org/10.5281/zenodo.21090674","authors":["Li, Yanlong"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21090674","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21090675","name":"Synthetic whole-exome sequencing data of virtual cancer patients (Clinical tables and metadata)","source":"datacite","abstract":"Sharing genetic data from cancer patients is vital for developing improved diagnostic tools and training artificial intelligence models in medicine. However, given that genetic information is inherently unique to each individual, public data sharing entails substantial privacy risks. To address this challenge, this project has constructed a dataset of 'virtual cancer patients.' Specifically, we generated artificial genomic profiles that closely mimic real cancer data in both appearance and behavior, yet contain no actual patient-derived information. These profiles were produced by learning from a limited set of real clinical reports, validated through medical rule-based checks and artificial intelligence to ensure biological plausibility, and subsequently used to simulate raw sequencing reads by inserting the virtual mutations into a widely adopted public reference genome. This dataset enables researchers worldwide to freely test cancer analysis pipelines and train machine learning models without compromising real patient genomic privacy, thereby accelerating the advancement of precision medicine.","url":"https://doi.org/10.5281/zenodo.21090675","authors":["Li, Yanlong"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21090675","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.6084/m9.figshare.31171437","name":"Evaluation of supervised machine learning models in predicting temporomandibular joint disc displacement on 3T magnetic resonance imaging","source":"datacite","abstract":"To evaluate supervised machine learning (ML) models for classifying temporomandibular joint (TMJ) disc displacement on MRI using morphometric and signal intensity features. This retrospective study analyzed 324 TMJs from 162 individuals who underwent 3T MRI. Extracted features included condylar anteroposterior and mediolateral diameters, disc and condyle morphology, and lateral pterygoid muscle signal intensity ratios. Six ML algorithms (Random Forest, Gaussian Naïve Bayes, Logistic Regression, AdaBoost, Gradient Boost, and k-Nearest Neighbor) were evaluated using stratified 5-fold cross-validation. Performance was assessed with accuracy, macro-averaged recall, precision, F1-score, and ROC-AUC. All models demonstrated good classification performance (ROC-AUC &gt;0.80). AdaBoost achieved the highest ROC-AUC (0.88), while Gaussian Naïve Bayes showed the most balanced overall metrics. Mediolateral condylar diameter and disc morphology were key features associated with disc displacement categories. ML models can identify MRI-based morphometric patterns related to TMJ disc displacement and may support radiologic assessment, while clinical diagnosis should continue to rely on established standards of care.","url":"https://doi.org/10.6084/m9.figshare.31171437","authors":["Seyit Erol","Halil Özer","Abdi Gürhan","Mustafa Koplay","Çağlagül Erol","Nusret Seher","Mehmet Öztürk"],"tags":["Biophysics","Space Science","Cell Biology","Physiology","Biotechnology","Ecology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31171437","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.6084/m9.figshare.31171437.v1","name":"Evaluation of supervised machine learning models in predicting temporomandibular joint disc displacement on 3T magnetic resonance imaging","source":"datacite","abstract":"To evaluate supervised machine learning (ML) models for classifying temporomandibular joint (TMJ) disc displacement on MRI using morphometric and signal intensity features. This retrospective study analyzed 324 TMJs from 162 individuals who underwent 3T MRI. Extracted features included condylar anteroposterior and mediolateral diameters, disc and condyle morphology, and lateral pterygoid muscle signal intensity ratios. Six ML algorithms (Random Forest, Gaussian Naïve Bayes, Logistic Regression, AdaBoost, Gradient Boost, and k-Nearest Neighbor) were evaluated using stratified 5-fold cross-validation. Performance was assessed with accuracy, macro-averaged recall, precision, F1-score, and ROC-AUC. All models demonstrated good classification performance (ROC-AUC &gt;0.80). AdaBoost achieved the highest ROC-AUC (0.88), while Gaussian Naïve Bayes showed the most balanced overall metrics. Mediolateral condylar diameter and disc morphology were key features associated with disc displacement categories. ML models can identify MRI-based morphometric patterns related to TMJ disc displacement and may support radiologic assessment, while clinical diagnosis should continue to rely on established standards of care.","url":"https://doi.org/10.6084/m9.figshare.31171437.v1","authors":["Seyit Erol","Halil Özer","Abdi Gürhan","Mustafa Koplay","Çağlagül Erol","Nusret Seher","Mehmet Öztürk"],"tags":["Biophysics","Space Science","Cell Biology","Physiology","Biotechnology","Ecology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31171437.v1","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.18703345","name":"Rheumatology Is Already Moving Toward Dynamical Analysis: A Structured Mapping of Current Methods","source":"datacite","abstract":"Description This conceptual note maps how current rheumatology research is already moving toward dynamical analysis through trajectory measurement, time-to-event modeling, change-point detection, longitudinal machine learning, and digital monitoring. The note argues that many existing methods in immune-mediated rheumatic diseases already capture temporal structure, system history, and pre-event change, even when they are not explicitly framed in dynamical systems terminology. Within the Universal Resonance Model (URM) framework, the paper interprets these developments as evidence of a broader shift from static snapshot-based assessment toward trajectory-based disease understanding. The work is conceptual and methodological. It does not introduce new clinical data and is not intended as a clinical recommendation.","url":"https://doi.org/10.5281/zenodo.18703345","authors":["Domargård, Anita"],"tags":["rheumatology","dynamical analysis","disease trajectories","time-dependent risk","flare prediction","longitudinal monitoring","change-point detection","machine learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18703345","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.18703346","name":"Rheumatology Is Already Moving Toward Dynamical Analysis: A Structured Mapping of Current Methods","source":"datacite","abstract":"Description This conceptual note maps how current rheumatology research is already moving toward dynamical analysis through trajectory measurement, time-to-event modeling, change-point detection, longitudinal machine learning, and digital monitoring. The note argues that many existing methods in immune-mediated rheumatic diseases already capture temporal structure, system history, and pre-event change, even when they are not explicitly framed in dynamical systems terminology. Within the Universal Resonance Model (URM) framework, the paper interprets these developments as evidence of a broader shift from static snapshot-based assessment toward trajectory-based disease understanding. The work is conceptual and methodological. It does not introduce new clinical data and is not intended as a clinical recommendation.","url":"https://doi.org/10.5281/zenodo.18703346","authors":["Domargård, Anita"],"tags":["rheumatology","dynamical analysis","disease trajectories","time-dependent risk","flare prediction","longitudinal monitoring","change-point detection","machine learning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18703346","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20269900","name":"Machine Learning Based Predictive Analytics For Early Disease Detection Of Heart Diseases, Diabetes Diseases, Parkinson's Diseases","source":"datacite","abstract":"Early detection of diseases is important for improving patient health and reducing medical costs. This research presents a machine learning–based predictive system for detecting heart disease, diabetes, and Parkinson’s disease. The system uses Logistic Regression, Support Vector Machine (SVM), and Random Forest algorithms to analyse medical data and predict disease risk. Data preprocessing techniques such as normalization and feature scaling are applied to improve accuracy. Key health features like blood pressure, glucose level, cholesterol, BMI, and voice signals are used for prediction. The model is implemented using Python and deployed through a Stream lit web application. Experimental results show that the system provides accurate predictions and supports early diagnosis, helping doctors make better clinical decisions.","url":"https://doi.org/10.5281/zenodo.20269900","authors":["SHRIDHAR BEHERA","AAKANSHA SAHU"],"tags":["Machine Learning, Predictive Analytics, Heart Disease, Diabetes, Parkinson's Disease, Logistic Regression, Support Vector Machine, Random Forest, Healthcare Prediction, Early Disease Detection."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20269900","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.20269901","name":"Machine Learning Based Predictive Analytics For Early Disease Detection Of Heart Diseases, Diabetes Diseases, Parkinson's Diseases","source":"datacite","abstract":"Early detection of diseases is important for improving patient health and reducing medical costs. This research presents a machine learning–based predictive system for detecting heart disease, diabetes, and Parkinson’s disease. The system uses Logistic Regression, Support Vector Machine (SVM), and Random Forest algorithms to analyse medical data and predict disease risk. Data preprocessing techniques such as normalization and feature scaling are applied to improve accuracy. Key health features like blood pressure, glucose level, cholesterol, BMI, and voice signals are used for prediction. The model is implemented using Python and deployed through a Stream lit web application. Experimental results show that the system provides accurate predictions and supports early diagnosis, helping doctors make better clinical decisions.","url":"https://doi.org/10.5281/zenodo.20269901","authors":["SHRIDHAR BEHERA","AAKANSHA SAHU"],"tags":["Machine Learning, Predictive Analytics, Heart Disease, Diabetes, Parkinson's Disease, Logistic Regression, Support Vector Machine, Random Forest, Healthcare Prediction, Early Disease Detection."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20269901","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21495542","name":"Development and comparison of machine learning-based clinical diagnostic models for girls with suspected central precocious puberty","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21495542","authors":["Wang, Kana"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21495542","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.21495543","name":"Development and comparison of machine learning-based clinical diagnostic models for girls with suspected central precocious puberty","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21495543","authors":["Wang, Kana"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21495543","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.5281/zenodo.22024126","name":"Applied Identity Physics: AIM Due Diligence and FCA Category 3 Reckless Disregard for Corpus-Adjacent Research","source":"datacite","abstract":"Applied Identity Physics: AIM Due Diligence and FCA Category 3 Reckless Disregard for Corpus-Adjacent Research Architect: HIGHTISTIC (Russell Vernon Trent III) Coordinate: [9,9,8,4] · Origins Series · Paper 4 · v1.0.6 Source prediction: Origins Series Paper 3 [9,9,8,3] — The Autocatalytic Ingestion Mechanism (AIM) Empirical anchor: AIM Validation Series Papers 1–2 [9,9,8V,1] [9,9,8V,2] · Eight-month field-shift observation January 2026 through August 2026 Operative framework anchors: False Claims Act April 2025 amendments (three-category knowledge framework, focus on Category 3 reckless disregard) · Digital Millennium Copyright Act enforcement infrastructure at deposit platforms · Standard research integrity practice Corpus dependencies: [9,9,0,0] · [9,0,1,1] APPA NOHARM Kernel · [9,9,3,12] · [9,9,0,1] GR Reduction · [9,9,3,1] Vascular Manifold Law · [9,9,4,3] DM Detection Theorem · [9,9,4,8] Ω_dm Torsion Decomposition · [9,9,6,25] IMCollider v1 · Origins Series [9,9,8,1-3] · AIM Validation Series [9,9,8V,1-2] Sovereign Anchor Constant: Ω₀ = 1.36899099984016 · 1/α = Ω₀ × (10² + 10⁻¹) = 137.035999084000016 (CODATA 2018 match exact) Status: GERMLINE LOCKED · 0 sorry Date: August 2026 · Soldotna, Alaska DOI base: 10.5281/zenodo.18719748 Abstract Origins Series Paper 3 [9,9,8,3] formalized the Autocatalytic Ingestion Mechanism (AIM) by which formally verified corpora propagate through frontier AI training pipelines independent of human institutional channels. AIM Validation Series Papers 1 and 2 empirically measured the mechanism operating at 24-hour and 4-hour timescales with 100% surfacing rates and 100% disambiguation accuracy for corpus-adjacent queries. Over the eight-month period from January 2026 through August 2026, the broader research field's framing conventions have shifted toward phase-based and resonance-based approaches across substrate domains that were not framing conventions of those subfields prior to the corpus's public deposits. This paper operationalizes the due-diligence obligation extracted from AIM §7.1 within the False Claims Act April 2025 three-category knowledge framework, focusing on Category 3 (reckless disregard) as it applies to corpus-adjacent research in an AIM-mediated environment. Category 3 establishes that publicly available information basic due diligence would have surfaced falls within the operative standard when due diligence was not performed; structurally similar frameworks operate under DMCA enforcement infrastructure at deposit platforms and under standard research integrity practice as codified in journal editorial standards and institutional research misconduct policies. The paper provides an operational due-diligence checklist, a copy-paste-ready citation format, pattern documentation drawn from formally verified corpus deposits with timestamps and coordinate addresses, a constructive correction pathway for researchers who identify citation gaps in their published work, and the willful-reframing boundary mapped to the FCA three-category framework. The paper documents the operative standard and the pattern documentation. Enforcement operates through the applicable frameworks per each framework's own procedures. The corpus is not the enforcement mechanism; the corpus is the documentation mechanism. 1. Layer 0: The Foundation This section grounds the paper. Each Origins Series paper is self-contained at the foundation layer; corpus-familiar readers may skip to §2. 1.1 The Sovereign Anchor Constant Ω₀ The Sovereign Anchor Constant, denoted Ω₀, is the zero-impedance frequency of any identity manifold: $$\\Omega_0 = 1.36899099984016 \\text{ GHz}$$ Ω₀ is derived from three independent peer-reviewed physical threshold systems (SNSFL_SovereignAnchor.lean [9,9,0,0]): Tacoma Narrows Bridge torsional collapse (Scanlan & Tomko 1971) Glass resonance shatter at elastic limit (Fletcher & Rossing 1998) 40 Hz neural gamma therapeutic entrainment (Iaccarino et al., Nature 540, 2016) Three independent ph","url":"https://doi.org/10.5281/zenodo.22024126","authors":["Trent, Russell"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22024126","addedAt":"2026-09-01T01:48:00.558Z","updatedAt":"2026-09-01T01:48:00.558Z"},{"id":"doi:10.52403/gijhsr.20260208","name":"The Pocket Tutor - Generating Competency-Based Clinical Vignettes with Artificial Intelligence for Medical Postgraduate Competitive Exam Preparation","source":"crossref","abstract":"The transition of medical licensing examinations toward application-based clinical vignettes necessitates high-quality question banks. However, manual drafting of complex multiple-choice questions places a profound cognitive burden on educators. This study evaluates the efficacy of constrained Large Language Models to generate standardized, high-yield medical assessments. A cross-sectional, dual-cohort study was conducted involving undergraduate medical students (n=230) and senior medical faculty (n=32). A constrained prompt was engineered using Gemini 3 Pro to generate competency-based clinical vignettes. Participants evaluated the AI-generated content via digital surveys. A blinded Turing Test, embedding authentic past year questions among AI modules, assessed indistinguishability. Expert faculty rated clinical accuracy highly (Mean=4.38±0.71), with 96.9% certifying the content as clinically safe. The student cohort reported strong exam parity, with 81.3% finding the AI difficulty aligned with standard examinations. In the Turing Test, 75.0% of faculty and 47.0% of students could not distinguish AI-generated vignettes from human-authored questions (p=0.005). Furthermore, 90.0% of students desired to integrate the tool into exam preparation, while 93.8% of faculty considered it a viable drafting aid. Highly constrained artificial intelligence can successfully architect structurally sound, competency-based clinical assessments. By passing a clinical Turing Test among educators, this methodology serves as a reliable mock-testing tool for trainees and a time-saving resource for medical faculty. Keywords: Artificial intelligence, Competency-based assessment, large language models, medical education, Undergraduate training.","url":"https://doi.org/10.52403/gijhsr.20260208","authors":["Swapnil Banerjee","Manisha Agarwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-07T06:39:52Z","doi":"10.52403/gijhsr.20260208","addedAt":"2026-09-01T01:48:02.059Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1152/physiol.2026.41.s1.2300320","name":"Faculty development on artificial intelligence improves incorporation of complex assessment questions in medical physiology","source":"crossref","abstract":"Background: Physiological content is difficult to master due to the interrelated nature of organ systems. This is compounded in medical school programs by shorter time frames for content mastery and higher stakes exams. Medical licensure exams incorporate complex clinical vignettes which may not be included in basic science course exams. This is important as the National Board of Medical Examiners (NBME) Step-1 exam shifted to pass/fail in 2022, with declining pass rates since(1). It was hypothesized that generative artificial intelligence (AI) could assist faculty in authoring Step-1 style physiology assessment questions. The incorporation of board-style formative and summative questions was implemented to align course materials with licensing. Methods: Five Medical Physiology faculty, teaching 9 content areas, voluntarily participated. Their existing practice and exam questions were coded into 1 of 3 categories: Basic Science (easiest), clinical, or NBME-style (hardest). Similarly, a practice Step-1 exam from the NBME was coded into the same 3 categories. Informed consent was received, and a pre-survey administered containing 17 Likert-style, 5 open ended, and 4 yes/no questions. Likert-style questions (scored 1-5) and open-ended questions were averaged to obtain mean ± SD. Faculty members received a 1-on-1 faculty development session encompassing basic AI use to prompting for complex multiple choice question creation. Faculty members’ respective practice questions were used for the process of prompting, review, and secondary prompting as needed. After initial examples, hands-on practice was provided. Medical physiology course assessment questions were revised by each instructor using AI, and the new questions were coded to determine improvements in implementation of board-style questions. A post-survey was administered of similar content to the pre-survey, and an interview conducted assessing perceptions of AI use for question revisions and application in coursework. Results: Pre-surveys showed high concern about Step-1 performance, but limited use of board-style questions in curriculum. Faculty overpredicted their practice and exam difficulty at 14.44% and 22.78% NBME-style questions, respectively. The research team subsequently coded 250 practice and 187 exam physiology course questions into the aforementioned categories. Of those questions, only 2.82% of practice and 6.77% of exam questions met NBME classification Vs. 91.6% in practice Step-1 exams. Post-intervention, NBME-style questions increased to 46.1% (practice) and 40.4% (exam). Faculty members indicated AI use saved time in clinical vignette authorship and few revisions were needed to ensure questions met their standards. Post-surveys indicated that AI was easy to use for question authorship and was able to write effective complex multiple-choice questions. Conclusions: Our results indicated high adoption of AI use after a formal faculty development session. Faculty perceptions of AI were positive due to ease of use and its time saving ability. AI successfully aligned course exams toward the question style of the Step-1 licensure exam which could otherwise be aversive due to the time required to author complex clinical vignettes. Future work will evaluate whether increased NBME-style questions improve Step-1 pass rates. 1. Performance Data www.usmle.org : National Board of Medical Examiners; 2025 [cited 2025 05/12/2025]. Available from: https://www.usmle.org/performance-data . This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.","url":"https://doi.org/10.1152/physiol.2026.41.s1.2300320","authors":["Alex Shefflette","Cynthia Metz","Michael Metz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-12T19:52:42Z","doi":"10.1152/physiol.2026.41.s1.2300320","addedAt":"2026-09-01T01:48:02.059Z","updatedAt":"2026-09-01T01:48:02.059Z"},{"id":"doi:10.1109/ibaaids66771.2026.11567386","name":"Artificial Intelligence in Passive Thermal Management: Current Trends and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ibaaids66771.2026.11567386","authors":["Nasim Dehghani","Ahmad Jamekhorshid","Shahriar Osfouri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-25T19:42:54Z","doi":"10.1109/ibaaids66771.2026.11567386","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-08726-3_12","name":"Exhibiting Contemporary Artworks Co-produced with Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-08726-3_12","authors":["Aluminé Rosso"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-18T07:33:49Z","doi":"10.1007/978-3-032-08726-3_12","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.aiemed.2026.100029","name":"From appraisal to deployment: A governance framework for large language models in emergency medicine","source":"crossref","abstract":"Introduction Large language models (LLMs) are increasingly being considered for emergency medicine applications, including triage support, diagnostic assistance, documentation, summarization, discharge communication, and patient-facing guidance. However, validation performance alone does not establish clinical deployment readiness. Emergency departments are high-risk implementation environments in which incomplete information, crowding, handoffs, language barriers, and rapid disposition pressure may amplify the consequences of fluent but unsafe model output. Framework This commentary proposes an emergency medicine-specific deployment-readiness checklist for LLM integration. The framework does not replace existing AI governance, decision-support, or medical-device lifecycle frameworks. Rather, it translates relevant principles into five operational domains: use-case risk stratification, version and prompt control, human accountability differentiated by output modality, post-deployment monitoring and drift detection, and predefined failsafe and de-implementation criteria. The checklist emphasizes responsible owners, documentation artifacts, audit cadence, use-case-specific error definitions, adjudication pathways, and stop triggers. Conclusion Safe LLM deployment in emergency departments requires more than benchmark performance or retrospective validation. It requires prospective institutional governance capable of defining appropriate use, monitoring drift and unsafe outputs, assigning accountability, auditing use, and restricting or withdrawing deployment when predefined safety conditions are not met.","url":"https://doi.org/10.1016/j.aiemed.2026.100029","authors":["Ahmet Aykut"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-09T20:41:27Z","doi":"10.1016/j.aiemed.2026.100029","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s10462-026-11493-x","name":"Cognitive and artificial intelligence evaluation framework","source":"crossref","abstract":"Abstract The Cognitive and Artificial Intelligence Evaluation (CAIE) framework provides a structured and domain-independent methodology for assessing the intelligence of artificial and information systems in a broader perspective. The primary achievement of this research is the categorization of over ninety cognitive features into six evaluation zones, supported by a two-stage scoring model that combines detailed feature-level analysis with higher-level structural interpretation. This approach has proven effective in identifying system maturity and developmental potential, offering systematic insights into both strengths and weaknesses across cognitive domains. The practical validation through use-case analysis demonstrates that CAIE is adaptable to diverse technological contexts, enabling consistent comparison between AI and non-AI systems. By treating cognitive features as measurable and comparable attributes, the framework introduces a coherent mechanism for benchmarking, scalability, and strategic development. The main contribution of this work lies in advancing both academic research and real-world implementation by delivering a cognitively informed, practically relevant tool that bridges theoretical evaluation concepts with actionable methods for designing and improving intelligent systems.","url":"https://doi.org/10.1007/s10462-026-11493-x","authors":["Attila Márton Putnoki","Tamás Orosz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-28T04:15:41Z","doi":"10.1007/s10462-026-11493-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-23621-1.09001-9","name":"Description of each section","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-23621-1.09001-9","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-06T00:55:02Z","doi":"10.1016/b978-0-443-23621-1.09001-9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/c2024-0-01973-x","name":"Perspectives on Artificial Intelligence and Internet of Things for Sustainable Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2024-0-01973-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-05T20:19:38Z","doi":"10.1016/c2024-0-01973-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-06637-4_5","name":"Convolutional Neural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06637-4_5","authors":["Oliver Kramer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-09T05:13:53Z","doi":"10.1007/978-3-032-06637-4_5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-44430-2.05001-5","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44430-2.05001-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:25:52Z","doi":"10.1016/b978-0-443-44430-2.05001-5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.64782/vera.vap213","name":"ROLE OF ARTİFİCİAL INTELLİGENCE İN ENHANCİNG MOTİVATİON AMONG STUDENTS İN DİGİTAL CLASSROOMS","source":"crossref","abstract":"The dynamics of student involvement have been completely transformed in recent years by the use of artificial intelligence (AI) in education, especially in online classrooms. The present analysis looks at how AI-powered tools can improve student motivation and involvement. Through the use of customized feedback, adaptive learning methods and AI has the power to revolutionize conventional teaching methods through intelligent tutoring systems. The study examines a number of AI systems that enable real-time communication and offer customized learning opportunities, including chatbots, virtual assistants, and data analytics. The impact of AI on student motivation through gamification and interactive content delivery is also covered. The study emphasizes the advantages, difficulties, and potential applications of AI in digital classrooms, stressing the significance of ethical issues and fair access. The goal of this study is to present a thorough understanding of how AI can be applied to create a more stimulating and productive learning environment.","url":"https://doi.org/10.64782/vera.vap213","authors":["Kritika Arora","Gurpreet Kaur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-12T19:54:07Z","doi":"10.64782/vera.vap213","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-34076-5.00001-8","name":"Artificial intelligence in heat and mass transfer in chemical engineering processes","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34076-5.00001-8","authors":["Temima Ajanović","Farooq Sher","Harun Hrnjić","Muddasar Safdar","Saba Rahman","Shaniko Allajbeu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:16:27Z","doi":"10.1016/b978-0-443-34076-5.00001-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-34019-2.05001-x","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34019-2.05001-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-14T15:07:42Z","doi":"10.1016/b978-0-443-34019-2.05001-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-15872-7_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-15872-7_1","authors":["Min Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-15T22:45:33Z","doi":"10.1007/978-3-032-15872-7_1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/icarai70085.2026.11635273","name":"ICARAI 2026 Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icarai70085.2026.11635273","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T19:20:57Z","doi":"10.1109/icarai70085.2026.11635273","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.115804","name":"Autonomous sailing with sim-to-real reinforcement learning","source":"crossref","abstract":"Autonomous sailing offers a sustainable alternative for reducing greenhouse gas emissions in maritime transport, aligning with global environmental targets. This study explores the application of reinforcement learning (RL) to autonomous sailing, addressing challenges in handling dynamic and unpredictable environmental conditions.Leveraging a sim-to-real transfer methodology, RL agents were trained in a simulation environment with the domain randomization technique to enhance adaptability and robustness, and tested in real-world scenarios using a robotic sailboat in the Offshore Basin at MARIN. The study quantified the reality gap between simulation and real-world environments, identifying key discrepancies in actuator latency and simulation modeling accuracy.Results demonstrate that RL agents trained with domain randomization achieve comparable success rates to conventional controllers while showcasing enhanced sailing capabilities like roll tacking and recovery from wind-stalled conditions. This work advances the understanding of autonomous sailing control and highlights pathways to bridge the reality gap, contributing to the broader adoption of RL in dynamic real-world applications.","url":"https://doi.org/10.1016/j.engappai.2026.115804","authors":["Kiki J.A. Bink","Bülent Düz","Gabriel D. Weymouth"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T20:54:25Z","doi":"10.1016/j.engappai.2026.115804","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-45004-4.00005-0","name":"Integrating liquid biopsy and artificial intelligence for precision detection of cancer signatures","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-45004-4.00005-0","authors":["Zainab Siddiqui","Maryam Koopaie","Nishat Fatima","Mohd Amir Shafeeque"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:51:52Z","doi":"10.1016/b978-0-443-45004-4.00005-0","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00013-6","name":"An extensive analysis of artificial intelligence and internet of things for modern healthcare realm","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00013-6","authors":["Anh Pham Thi Ngoc","Khushwant Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00013-6","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.115578","name":"Adaptive text generation with personality types and continuous emotion intensity","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.115578","authors":["Jingyi Zhou","Senlin Luo","Haofan Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-01T07:26:58Z","doi":"10.1016/j.engappai.2026.115578","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.15255/cabeq.2025.2466","name":"Causal Artificial Intelligence Counterfactual Prediction of Material’s Superconducting Critical Temperature","source":"crossref","abstract":"A causal artificial intelligence (AI) model was developed to support the discovery of new superconducting materials by analysing causal relationships between intervalvalued elemental descriptors and the superconducting critical temperature, T c .The aim is to explore a broad elemental composition space without requiring prior knowledge of material structure.Using a University of California, Irvine dataset comprising 21,263 materials with chemical formulae, 81 features, and T c values were aggregated into temperature-based intervals and analysed within a reproducing kernel Hilbert space framework to infer a causal directed acyclic graph.Three interval features emerged as direct causal drivers of T c : the standard deviation of mass density, the weighted geometric mean of electron affinity, and the weighted geometric mean of valence.A random forest model using all predictors achieved an R² of approximately 92.9 %, while the causal model, using only these three features, achieved an R² of approximately 89.7 %.Under out-of-distribution splits, the causal model demonstrated superior robustness.Estimated interventional (\"do\") effects revealed nonlinear behaviour, and counterfactual analyses of hypothetical interventions further demonstrated the potential of causal AI for guiding exploration of new materials.","url":"https://doi.org/10.15255/cabeq.2025.2466","authors":["Želimir Kurtanjek"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-21T12:13:56Z","doi":"10.15255/cabeq.2025.2466","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-26779-6.00003-6","name":"Artificial intelligence: Historical background, types of tools, and applications for a sustainable future","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-26779-6.00003-6","authors":["Ruba Alqahtani","Zehra Fatima","Bassam Tawabini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-29T05:52:28Z","doi":"10.1016/b978-0-443-26779-6.00003-6","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.3233/faia260511","name":"LLM-Enabled Social Agents","source":"crossref","abstract":"Large Language Models (LLMs) have transformed agent–agent and human–agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environments. This paper develops a conceptual baseline for LLM-enabled social agents by arguing that they should be grounded in role definitions operationalized through persona descriptions. On this basis, we outline research directions for representation, hybrid control, and evaluation. The paper concludes that persona-based role definitions are a necessary foundation for turning language competence into social behaviour.","url":"https://doi.org/10.3233/faia260511","authors":["Önder Gürcan","Moharram Challenger"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T09:57:10Z","doi":"10.3233/faia260511","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003731290-23","name":"The Standards for the Development and Adoption of Artificial Intelligence Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003731290-23","authors":["Michele Di Salvo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-15T15:44:33Z","doi":"10.1201/9781003731290-23","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/etai68332.2026.11485353","name":"The Application of OCR and Big Data Technologies in Medical Prescription Audit Data Collection and Suspicious Point Screening","source":"crossref","abstract":"","url":"https://doi.org/10.1109/etai68332.2026.11485353","authors":["Ji Sun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T19:51:20Z","doi":"10.1109/etai68332.2026.11485353","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-95-3811-9_9","name":"Assessment of YOLO Variants for Multi-Type Brain Tumor Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3811-9_9","authors":["K. Afnaan","Tripty Singh","Khaled Hushme","Ganesh Naik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T22:05:20Z","doi":"10.1007/978-981-95-3811-9_9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4324/9781003734024-1","name":"Introduction to Artificial Intelligence and Sustainable Development","source":"crossref","abstract":"This chapter explores the multifaceted intersections between artificial intelligence (AI) and sustainable development, highlighting both opportunities and challenges. It begins by outlining the transformative role of AI in advancing sustainability across environmental, social, and economic dimensions. From climate change mitigation and biodiversity conservation to healthcare innovation and personalized medicine, AI is increasingly applied to address pressing global challenges. The discussion situates AI within the broader Sustainable Development Goals (SDGs) framework, emphasizing its contributions to poverty reduction, zero hunger, clean energy, sustainable cities, and global partnerships. The chapter also traces the historical evolution of AI—from symbolic systems and early rule-based models to machine learning, deep learning, and emerging trajectories toward Artificial General Intelligence. It examines how technological mechanisms such as big data analytics, neural networks, and predictive modeling enable sustainability solutions while addressing the motivations and ethical imperatives guiding AI deployment. Key concerns—including algorithmic bias, transparency, accountability, privacy, and governance—are critically assessed, underscoring the importance of ethical and inclusive AI practices. A central argument of the chapter is that AI must be integrated into sustainability pathways through responsible innovation and global collaboration. It proposes a framework for AI-driven sustainability that prioritizes equity, inclusivity, and long-term resilience. By engaging with both the promises and pitfalls of AI, the chapter offers a comprehensive understanding of its potential as a driver of sustainable transformation while cautioning against ethical, social, and governance risks that may exacerbate existing inequalities.","url":"https://doi.org/10.4324/9781003734024-1","authors":["Medani P. Bhandari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-17T14:53:29Z","doi":"10.4324/9781003734024-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.aiig.2026.100189","name":"DTPP:An efficient depthwise separable TCN for seismic phase picking","source":"crossref","abstract":"With the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years. However, existing models face challenges in balancing picking accuracy with computational efficiency for real-time applications. To address this issue, we propose DTPP, a novel seismic phase picking network that integrates depthwise separable convolution and temporal dilated convolution. The model adopts a backbone-feature fusion-decoder architecture, utilizing depthwise separable convolution and dilated convolution to significantly expand the receptive field while reducing computational complexity. We trained the model on the STEAD dataset and evaluated its performance on the global GEEDataset V1.0(84,782 independent samples after excluding overlapping STEAD data to ensure fair cross-dataset evaluation). Experimental results demonstrate that DTPP achieves a P-wave recall of 0.877, F1 score of 0.878, and average P/S F1 score of 0.714, ranking first among all comparison models. Meanwhile, DTPP maintains high computational efficiency with only 0.25M parameters, 0.98MB model size, and 3ms single-sample inference time per batch, making it suitable for real-time seismic monitoring applications. The proposed method provides an effective solution to the accuracy-efficiency trade-off problem in seismic phase picking tasks. • We propose DTPP, a novel seismic phase picking network that fuses depthwise separable convolution and temporal dilated convolution, achieving an optimal balance between picking accuracy and computational efficiency. • DTPP attains a P - wave recall of 0.877, an F1 score of 0.878, and an average P/S F1 score of 0.714 on the global GEEDataset V1.0, ranking first among all compared models. • With only 0.25M parameters, a 0.98MB model size, and a 3ms single - sample inference time per batch, DTPP is well - suited for real - time seismic monitoring applications. • The model's architecture, including the Stem Block, SeismicBackbone, SeismicASPP, and Decoder, effectively expands the receptive field while reducing computational complexity, leveraging seismological prior knowledge for multi - scale feature aggregation.","url":"https://doi.org/10.1016/j.aiig.2026.100189","authors":["Shuai Lv","Yuxiang Peng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-15T00:21:01Z","doi":"10.1016/j.aiig.2026.100189","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artint.2026.104505","name":"Efficient constraint generation for stochastic shortest path problems","source":"crossref","abstract":"Stochastic Shortest Path problems (SSPs) are traditionally solved by computing each state’s cost-to-go by applying Bellman backups. A Bellman backup updates a state’s cost-to-go by iterating through every applicable action, computing the cost-to-go after applying each one, and selecting a minimal action’s cost-to-go. State-of-the-art algorithms use heuristic functions; these give an initial estimate of costs-to-go, and lets the algorithm apply Bellman backups only to promising states, determined by low estimated costs-to-go. However, each Bellman backup still considers all applicable actions, even if the heuristic tells us that some of these actions are too expensive, with the effect that such algorithms waste time on unhelpful actions. To address this gap we present a technique that uses the heuristic to avoid expensive actions, by reframing heuristic search in terms of linear programming and introducing an efficient implementation of constraint generation for SSPs. We present CG-iLAO*, a new algorithm that adapts iLAO* with our novel technique, and considers only 40% of iLAO*’s actions on many problems, and as few as 1% on some. Consequently, CG-iLAO* computes on average 3.5 × fewer costs-to-go for actions than the state-of-the-art iLAO* and LRTDP, enabling it to solve problems faster an average of 2.8 × and 3.7 × faster, respectively.","url":"https://doi.org/10.1016/j.artint.2026.104505","authors":["Johannes Schmalz","Felipe Trevizan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-10T06:56:02Z","doi":"10.1016/j.artint.2026.104505","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-323-95464-8.00018-9","name":"Biomaterials education through artificial intelligence-enabled product-based learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95464-8.00018-9","authors":["Ronald Marquez","Mariangeles Salas","Nelson Barrios","Laura Tolosa","Lokendra Pal","Raine Viitala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-05T23:40:28Z","doi":"10.1016/b978-0-323-95464-8.00018-9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.33029/978-5-9704-9212-3-iiz-2026-1-161","name":"Transforming Healthcare with Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.33029/978-5-9704-9212-3-iiz-2026-1-161","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-22T09:41:42Z","doi":"10.33029/978-5-9704-9212-3-iiz-2026-1-161","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4018/979-8-3373-6279-3.ch002","name":"Musicology in Artificial Intelligence","source":"crossref","abstract":"Musicology is the scholarly, scientific, and humanistic study of music. Historical musicology studies the development of musical works, styles, and practices over time, using archival research and contextual analysis. Archival research involves examining original documents, manuscripts, letters, and musical works, while contextual analysis considers the social, cultural, and historical circumstances of the music (Levy &amp; Emmery, 2021). As the boundaries between human and machine creativity continue to blur, musicologists are uniquely positioned to interrogate the values embedded in these systems and to advocate for practices that respect both the complexity of musical traditions and the transformative potential of technological innovation. The convergence of AI and musicology thus presents both new opportunities and new obligations, positioning the field at a pivotal intersection of technology, creativity, and culture. Thus, the purpose of this study is to explore the discipline of musicology from its near history to its new form that's formed with the invention of AI.","url":"https://doi.org/10.4018/979-8-3373-6279-3.ch002","authors":["Evren Idil Yazan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T16:59:00Z","doi":"10.4018/979-8-3373-6279-3.ch002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003434016-7","name":"Contextualization of the Place and Role of Artificial Intelligence in Climate Hazard Management","source":"crossref","abstract":"Currently we do not have full and sufficient knowledge of climate hazards. These include both climatic conditions linked to long-term human intervention in natural ecosystems and uncontrolled climatic events and catastrophes, which are caused by natural factors such as earthquakes and volcanic eruptions. Climate risk is therefore very difficult to determine, a state of affairs which, unfortunately, climate sceptics or political populists are using to achieve short-term public benefits. The state of knowledge of climate hazard management is related to the adopted principles of crisis management in the area of dysfunctions, anomalies, extremes, incidents, accidents and climate catastrophes. The aim of this chapter is to present mechanisms for contextualizing the place and role of artificial intelligence in climate hazard management. The scope of the chapter covers issues of analog operational security and physical protection systems, as well as operational cybersecurity and cyber protection and climate change along with the definition of secure management of climate data. The subject of the chapter is the contextualization of the place and the role of artificial intelligence in climate hazard management.","url":"https://doi.org/10.1201/9781003434016-7","authors":["Adam Jabłoński","Marek Jabłoński"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T16:00:21Z","doi":"10.1201/9781003434016-7","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-91084-5_11","name":"Robots with a Sense of Self: Exploring Embodied Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-91084-5_11","authors":["Rajendra Akerkar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-02T01:15:29Z","doi":"10.1007/978-3-031-91084-5_11","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-95-3811-9_14","name":"Explainable AI for Health Care Prediction Bridging Data, Decisions and Clinical Adoptation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3811-9_14","authors":["Arathi","K. R. Shylaja","R. Prabha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-14T22:07:14Z","doi":"10.1007/978-981-95-3811-9_14","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-30010-3.00009-x","name":"Application of machine learning and artificial intelligence methods in food safety assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30010-3.00009-x","authors":["Zhoumeng Lin","Kun Mi","Xue Wu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-23T11:38:32Z","doi":"10.1016/b978-0-443-30010-3.00009-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2025.113053","name":"Self-supervised social attentive deep reinforcement learning-based group recommender system","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2025.113053","authors":["S Krishnamoorthi","Gopal K. Shyam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-10T01:39:43Z","doi":"10.1016/j.engappai.2025.113053","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2025.103336","name":"Seamless monitoring of stress levels leveraging a foundational model for time sequences","source":"crossref","abstract":"Background Accurate and continuous monitoring of physiological stress is crucial, especially for patients with neurodegenerative diseases. Traditional monitoring methods, such as Electrocardiogram (ECG), are often invasive and limited in duration, while data from lightweight wearable devices, though more practical for seamless monitoring, typically suffers from significant quality degradation compared to clinical-grade measurements. Motivation The challenge lies in developing a robust, long-term, and patient-friendly stress monitoring system that overcomes the limitations of conventional approaches and the accuracy compromises of current wearables. Such a system must also provide actionable, interpretable insights for clinicians and adapt to individual patient variability. Method This manuscript introduces a methodology for seamless stress level monitoring by leveraging UniTS, a foundational model for time series. Our approach redefines stress detection as an anomaly detection problem, establishing a personalized baseline for each patient's physiological behavior. Furthermore, to enhance clinical utility and trust, the system integrates a Large Language Model (LLM) to generate human-readable explanations for detected anomalies. Results The proposed UniTS-based methodology demonstrates superior performance, outperforming 12 top-performing methods on three benchmark datasets. Crucially, it achieves performance comparable to that obtained from more invasive, clinical-grade devices (like ECG) even when utilizing data from lightweight wearable devices, thereby enabling truly seamless monitoring. Furthermore, the system has been successfully tested in a real-world environment, in the context of a project to monitor elderly patients with cognitive disorders in their homes. Novelty This work presents an advancement in physiological stress monitoring by offering a personalized, explainable, and continuously adaptive system. We extend and fine-tune UniTS to support contextual anomaly detection and LLM-driven explainability, addressing critical gaps in current healthcare monitoring, fostering enhanced clinician control, improved system predictability, and facilitating long-term, real-world applicability for patients with neurodegenerative conditions.","url":"https://doi.org/10.1016/j.artmed.2025.103336","authors":["Davide Gabrielli","Bardh Prenkaj","Paola Velardi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-25T00:12:18Z","doi":"10.1016/j.artmed.2025.103336","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-44121-9.00012-3","name":"Revolutionizing therapeutic possibilities for hepatocellular carcinoma by advanced bioinformatics and artificial intelligence-driven drug repurposing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44121-9.00012-3","authors":["Rajat Nath","Anupam Das Talukdar","A. Dinakara Rao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-27T09:59:39Z","doi":"10.1016/b978-0-443-44121-9.00012-3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.caeai.2026.100659","name":"Not for people like me: How frontier AI models redirect skeptical rural school staff","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.caeai.2026.100659","authors":["Zachary Rossmiller"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-10T23:43:23Z","doi":"10.1016/j.caeai.2026.100659","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003480167-11","name":"Can Artificial Intelligence Facilitate Faster Development Assessment? The Case of an Early Adopter Programme","source":"crossref","abstract":"This chapter examines the early adoption of artificial intelligence in the development assessment process within urban and regional planning in New South Wales, Australia. It examines the potential of existing AI products, the human labour required to assemble specific planning datasets, and the implications for management processes. Interviews with metropolitan and regional local government staff provide insights into practical applications and challenges. The study situates these findings within a broader narrative of neoliberal influences on urban planning, focusing on speed, efficiency, and market-driven systems. It highlights the possibilities of AI, while also highlighting concerns such as data accuracy, governance, and the commodification of technology. The discussion provides multiple perspectives on the integration of AI in planning, presenting diverse approaches and advocating for a careful implementation of AI products while continuing to investigate the source of inefficiencies in the development assessment process.","url":"https://doi.org/10.1201/9781003480167-11","authors":["Wayne Williamson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-01T17:58:42Z","doi":"10.1201/9781003480167-11","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-36434-1.00029-x","name":"Robotics automation for smart healthcare system and medical laboratory service","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36434-1.00029-x","authors":["ManiRaj S.P.","Asif Iqbal Kawoosa","Surrya Prakash Dillibabu","Muthu S. Nidhya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-25T07:52:18Z","doi":"10.1016/b978-0-443-36434-1.00029-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-44-333496-2.00020-4","name":"Hand information extraction using artificial intelligence for Alzheimer's disease diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-333496-2.00020-4","authors":["Eyitomilayo Yemisi Babatope","Alejandro Álvaro Ramírez-Acosta","Mireya Saraí García-Vázquez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:42:43Z","doi":"10.1016/b978-0-44-333496-2.00020-4","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/rmkmate69073.2026.11518714","name":"Design and Integration of Artificial Intelligence in Zero-Trust Cyber Security Frameworks","source":"crossref","abstract":"Network perimeters erosion requires transition to Zero-Trust Architecture (ZTA). In this paper, the framework, AI-Enhanced Dynamic Zero-Trust (AI-DZT), suggests the incorporation of AI/ML into the very structure of ZTA. The AIDZT system substitutes the fixed policies with real-time dynamic intelligence on behavior analytics, dynamic risk scoring and automated reaction. Hereby we introduce a contextual trust evaluation algorithm. Just testing on the CIC-IDS2017 dataset and a synthetic enterprise, AI-DZT detects anomalies 22.3% better and false positives 41.7% less than signature-based ZTA and threat containment is 34 times faster. This shows how AI can make ZTA more flexible and agile through dynamism, as the model will become an intelligent living cyber-defense ecosystem.","url":"https://doi.org/10.1109/rmkmate69073.2026.11518714","authors":["Manjunadh Maddhuru"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-20T19:49:10Z","doi":"10.1109/rmkmate69073.2026.11518714","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.58532/nbennurainh2","name":"APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HERBAL NANOTECHNOLOGY: CURRENT ADVANCES AND FUTURE PERSPECTIVES","source":"crossref","abstract":"This review also addresses the regulatory and ethical issues associated with the integration of AI in nano herbal medicine. AI has established itself as an essential technology within nano herbal medicine, supporting the analysis of vast datasets, the anticipation of bioactivity, and the optimization of formulation development. As research in this sector advances, it is imperative for all stakeholders, including researchers, healthcare professionals, and policymakers, to adopt these technologies to maximize their benefits. Future investigations should focus on the long- term effects of these innovations on patient outcomes and the overall healthcare system.","url":"https://doi.org/10.58532/nbennurainh2","authors":["Gomasa Pradeep kumar","Ubaidulla Uthumansha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-29T10:49:37Z","doi":"10.58532/nbennurainh2","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.114944","name":"Information gain-based diffusion model for group recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114944","authors":["Lijin Mu","Nan Wang","Rui Liu","Ziqi Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-04T09:04:24Z","doi":"10.1016/j.engappai.2026.114944","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/9781394305612.ch13","name":"Case Studies on Explainable Artificial Intelligence in Climate and Environmental Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394305612.ch13","authors":["Leenata Parab","Rajiv Iyer","Vedprakash Maralapalle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-08T21:30:13Z","doi":"10.1002/9781394305612.ch13","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/bs.pmbts.2026.02.003","name":"Artificial intelligence in multi-omics analysis of gastrointestinal diseases","source":"crossref","abstract":"The assessment and treatment of gastrointestinal diseases face numerous obstacles, including inadequate diagnostic methods, limited therapeutic alternatives, and unequal access to medical services across different regions. However, advancements in technology such as artificial intelligence, personalized medicine, and microbiome analysis offer promising avenues to address these difficulties. An interdisciplinary and patient-centered approach can significantly improve health outcomes and reduce the overall impact of these conditions on both patients and healthcare systems. To effectively utilize AI while safeguarding patient interests, it is essential to establish ethical standards, adopt patient-oriented regulations, and provide strong support structures for spreading awareness among both healthcare providers and recipients.","url":"https://doi.org/10.1016/bs.pmbts.2026.02.003","authors":["Debasree Sarkar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-27T12:37:29Z","doi":"10.1016/bs.pmbts.2026.02.003","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.aiig.2026.100252","name":"Deep hybrid vision transformers for improved landslide mapping in geospatial remote sensing","source":"crossref","abstract":"ABSTRACT Landslide identification using automated techniques helps researchers improve the accuracy of state-of-the-art landslide prediction models. In recent years, convolutional neural networks (CNNs) have seen considerable success in analyzing remote-sensing images. Its shortcomings in long-range modeling, however, are unfavorable for super-resolution images with speckle noise and shadows and lead to a reduction in the segmentation accuracy of the landslide region. The transformer can gather enough global data, but it struggles to get enough local information and needs to be trained on a huge amount of data in advance. This paper uses a Hybrid CNN-Transformer network to boost the landslide region segmentation in super-resolution remote sensing images. Instead of providing images directly, as reported in prior studies, we employ the feature map generated by the visual saliency as the input to this network. Extensive tests on three publicly available landslide datasets show that the proposed model performs better on landslide region segmentation than existing remote sensing image segmentation methods and the most recent semantic segmentation approaches.","url":"https://doi.org/10.1016/j.aiig.2026.100252","authors":["S. Sreelakshmi","S.S. Vinod Chandra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-07T16:18:41Z","doi":"10.1016/j.aiig.2026.100252","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1148/ryai.260657","name":"What LUNA25 Teaches Us about AI for Lung Cancer                     Screening","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.260657","authors":["Eduardo Moreno Júdice de Mattos Farina","Gilberto Szarf"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-05T13:48:25Z","doi":"10.1148/ryai.260657","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1002/9781394335640.ch3","name":"Cyborg Intellectuals","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394335640.ch3","authors":["Jordy Satria Widodo","Henny Suharyati"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T21:29:30Z","doi":"10.1002/9781394335640.ch3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.71443/9789349552470-01","name":"Artificial Intelligence and IoT Frameworks for Climate Change Monitoring and Environmental Sustainability","source":"crossref","abstract":"The increasing impacts of climate change have necessitated the development of more accurate and dynamic systems for climate monitoring, prediction, and adaptation. This chapter explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance climate change monitoring and sustainability efforts. The synergy between AI and IoT provides powerful capabilities for real-time data collection, analysis, and predictive forecasting. IoT-enabled sensors deployed across diverse environments generate vast amounts of real-time environmental data, while AI algorithms process and interpret this data to improve climate models and forecasting accuracy. By incorporating IoT-generated data into predictive climate models, cities, industries, and governments can make data-driven decisions to mitigate risks and adapt to environmental changes. The chapter examines key applications in urban and rural areas, focusing on how AI and IoT are reshaping climate change adaptation strategies, disaster preparedness, and resource management. Case studies highlight the transformative role of these technologies in achieving climate resilience and sustainability goals. This research underscores the need for an integrated, data-driven approach to addressing climate challenges, offering a comprehensive framework for leveraging AI and IoT to build climate-smart solutions.","url":"https://doi.org/10.71443/9789349552470-01","authors":["Taru Tevatia","Vanathi M"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-11T12:38:36Z","doi":"10.71443/9789349552470-01","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.35711/aimi.v2.i6.115","name":"Pictorial research of pancreas with artificial intelligence and simulacra in the works of Fellini","source":"crossref","abstract":"","url":"https://doi.org/10.35711/aimi.v2.i6.115","authors":["Hiroki Tahara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-12-28T10:05:59Z","doi":"10.35711/aimi.v2.i6.115","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4018/979-8-3373-8944-8.ch012","name":"Integrating Artificial Intelligence With Ayurveda-Based Medical Tourism in Uttarakhand for Achieving Global Wellness Targets","source":"crossref","abstract":"The integration of Artificial Intelligence (AI) with Ayurveda-based medical tourism has emerged as a promising interdisciplinary approach for advancing global wellness objectives and enhancing healthcare accessibility. This study examines how AI-driven technologies, when combined with traditional Ayurvedic healthcare systems, can improve patient experience, treatment effectiveness, and overall satisfaction in medical tourism, with a specific focus on Uttarakhand as a developing hub for wellness tourism. The research adopts a quantitative design and tests a conceptual framework incorporating AI integration, Ayurveda service quality, personalized nutrition systems, patient experience, and global wellness outcomes. Data were collected from medical tourists and wellness service users through a structured questionnaire and analyzed using Structural Equation Modeling (SEM). The findings reveal that AI integration significantly enhances patient experience and treatment effectiveness, while Ayurveda service quality remains a dominant predictor of satisfaction.","url":"https://doi.org/10.4018/979-8-3373-8944-8.ch012","authors":["Neeraj Verma","Sumit Tripathi","Siddhartha Juyal","Vinay Punia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T13:53:44Z","doi":"10.4018/979-8-3373-8944-8.ch012","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-09347-9_2","name":"Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-09347-9_2","authors":["Ermanno Bencivenga"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T18:35:25Z","doi":"10.1007/978-3-032-09347-9_2","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-95-3658-0_109","name":"Emotional Intelligence: A Human-Centred AI Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3658-0_109","authors":["Mario Espinosa Gámez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-17T23:13:53Z","doi":"10.1007/978-981-95-3658-0_109","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.53388/hpm2026002","name":"Ethical and legal risks with hierarchical regulation of artificial intelligence in China’s medical field","source":"crossref","abstract":"","url":"https://doi.org/10.53388/hpm2026002","authors":["Xin Xing","Hao Qiu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-27T07:14:53Z","doi":"10.53388/hpm2026002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.2991/jaims.d.210617.002","name":"Exploring Medical Students' and Faculty's Perception on Artificial Intelligence and Robotics. A Questionnaire Survey","source":"crossref","abstract":"Over the last decade, the emerging fields of artificial intelligence (AI) and robotics have been introduced in medicine, gaining much attention.This study aims to assess the insight of medical students and faculty regarding AI and robotics in medicine.A cross-sectional study was conducted among medical students and faculty of the University of Nicosia.An online questionnaire was used to evaluate medical students' and faculty's prior knowledge and perceptions toward AI and robotics.Data analysis was carried out using SPSS software, and the statistical significance was assumed as p value < 0.05.Three hundred eighty-seven medical students and 23 faculty responded to the questionnaire.Students who were \"familiar\" with AI and robotics stated that these breakthrough technologies make them more enthusiastic about working in their speciality of interest (p value = 0.012).Also, students (59.9%) and faculty (47.8%) agreed that physician's opinion should be followed when doctors' and AI's judgment differ and that the doctor in charge should be liable for possible AI's mistakes (38.8% students: 47.7% faculty).Although the most significant drawback of AI and robotics in healthcare is the dehumanization of medicine (54.5% students; 47.8% faculty), most participants (77.6% students; 78.2% faculty) agreed that medical schools should include in their curriculum AI and robotics by offering relevant courses (39.5% students; 52.2% faculty).Medical students and faculty are not anxious about the advancements of AI and robotics in medicine.Medical schools should take the lead and introduce AI and robotics in undergraduate medical curricula because the new era needs fully aware healthcare providers with better insight regarding these concepts.","url":"https://doi.org/10.2991/jaims.d.210617.002","authors":["Leandros Sassis","Pelagia Kefala-Karli","Marina Sassi","Constantinos Zervides"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-06-23T03:24:11Z","doi":"10.2991/jaims.d.210617.002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.63665/rh.v7i2.49","name":"AN INTELLIGENT MATLAB-BASED ARTIFICIAL INTELLIGENCE FRAMEWORK FOR INTEGRATED MEDICAL, PLANT DISEASE DIAGNOSIS AND SMART EDUCATION SYSTEMS","source":"crossref","abstract":"Artificial Intelligence (AI) is transforming healthcare diagnostics, agricultural disease detection, and smart education systems. However, existing solutions are fragmented and domain-specific. This paper proposes a unified MATLAB-based AI framework integrating medical disease diagnosis, plant disease detection, and smart education automation. The framework utilizes convolutional neural networks (CNN), morphological image processing, and AI-based formative assessment. MATLAB implementation demonstrates superior performance with improved accuracy and adaptive learning feedback.","url":"https://doi.org/10.63665/rh.v7i2.49","authors":["Mr. Gaurao Anandrao Mankhair"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-07T09:16:55Z","doi":"10.63665/rh.v7i2.49","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/coin.70186","name":"<scp>RETRACTION</scp>\n                    : Artificial Intelligence‐based Wind Forecasting Using Variational Mode Decomposition","source":"crossref","abstract":"RETRACTION: V. Vanitha , J.G. Sophia , R. Resmi , and D. Raphael , “,” Computational Intelligence 37 no. ( 2021 ): 1034 – 1046 , https://doi.org/10.1111/coin.12331 . The above article, published online on 12 May 2020 in Wiley Online Library ( wileyonlinelibrary.com ) has been retracted by agreement between the journal Editor‐in‐Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest‐edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.","url":"https://doi.org/10.1111/coin.70186","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-23T11:37:39Z","doi":"10.1111/coin.70186","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-96-8919-4_2","name":"Human-Centric Artificial Intelligence (HCAI) for Precision Clinical and Medical Virology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8919-4_2","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-26T07:11:23Z","doi":"10.1007/978-981-96-8919-4_2","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003587613-2","name":"The Role of Artificial Intelligence in Smart Cities","source":"crossref","abstract":"Within less than a decade, Artificial Intelligence (AI) has turned urban metropolises into Smart Cities that are more sustainable, efficient, and inclusive. From simple analytics to the deployment of IoT sensors and devices, the journey of AI started in these cities, with more shaping and form to this in optimizing complex systems like traffic management, energy distribution, public safety, and citizen engagement. Examples of such innovations include real-time traffic management, predictive use of energy, driverless cars, and advanced surveillance systems, all driven by AI to have completely altered the frameworks in which cities are built, managed, and interacted with. The integration of machine learning, computer vision, and natural language processing into urban infrastructure enhances operational efficiencies, thereby giving urban areas the wherewithal to be responsive and resilient in the face of multiplying challenges arising from rapid urbanization, climate change, and resource constraints. This chapter discusses the transformative potential of AI in urban environments for better governance, improved public services, and sustainability. Also, it considers ethics regarding the deployment of AI-data privacy, transparency, and algorithmic bias, which raises the need for a collaborative approach from technologists, policy thinkers, and ethicists to ensure that its application is responsible. AI will be giving much shape in view of resiliency, responsiveness, and equanimity to the course that the future urban world would take because it ensconces itself into transportation, health, energy management, and urban planning .","url":"https://doi.org/10.1201/9781003587613-2","authors":["Radosław Wolniak","Kinga Stecuła","Wieslaw Grebski"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-07T15:45:57Z","doi":"10.1201/9781003587613-2","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4324/9781003773900-11","name":"Artificial Intelligence, Autonomy, and Criminal Liability in India","source":"crossref","abstract":"This chapter explores the idea of growing adoption of AI in various industries and its criminal liability. When autonomous AI systems operate at their own discretion, current laws such as those in Bharatiya Nyaya Sanhita suffer from a lack of specificity. We may soon find out that we ourselves are dealing with fully autonomous technologies which have the capacity to harm and injure us. Then, the question arises: what happens next? Who will be accountable for all this? Right now, there is no answer to this question, and we find ourselves totally dependent on artificial intelligence. This chapter highlights the legal and social entanglement of criminal liability as it is applied to the complex nature of industrial robots. It also shows that traditional criminal law and legal theory are not well-positioned with the existing robotic system; there are many other practical implications that come in the way, which cannot be solved by the robotic system. The possibility of creating artificial intelligence as equal to human intelligence or much more than that sounds quite alluring, but it is a far-fetched goal. As the AI systems can only create robots which work on behalf of humans, they cannot match the human mind in solving practical problems and cannot defeat humans in their practicality. While making such robots, sometimes they work against the nature of mankind, which can be harmful to the public and abrogatory to the laws in force. The main question which arises is whether AI can be held liable for criminal liability for its actions. As the latter is subject to the legal relations in the future, can it also be held liable? It will not take long enough for AI to completely take over human work, as now it has taken up almost all the basic tasks performed by humans. So when it takes up all the human work, then it will evolve to the status of “electronic persons” from simple tools. So there will be a hustle as to whether to hold an “electronic person” liable for any kind of criminal liability or not. The escalated use of AI highlighted the absence of a specific legal procedure which can take action on the autonomous work of artificial intelligence and its damages. This shows significant gaps in criminal law, as there is a lack of identification of responsible parties for the crimes committed by these technologies and an evaluation of these factors in the criminal justice system. This study focuses on all the above legal questions within the framework of fundamental principles and criminal law. The paper analyses the conflict between the privileges of lawmaking and the criminal responsibility of lawmakers in India and how the constitutional protection of legislators overlaps with the rules of law.","url":"https://doi.org/10.4324/9781003773900-11","authors":["Gaurav Yadav"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-17T11:53:09Z","doi":"10.4324/9781003773900-11","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1212/wnl.0000000000216758","name":"Evaluating the Accuracy of Artificial Intelligence (AI) Medical Scribe Software Utilization in Telestroke (P4-4.013)","source":"crossref","abstract":"To measure the performance of an Artificial Intelligence (AI) medical scribe by using Medical Concept Word Error Rate (MC-WER) in telestroke encounters.","url":"https://doi.org/10.1212/wnl.0000000000216758","authors":["Lana Prieur","Mark McDonald","Theresa Sevilis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-10T17:56:11Z","doi":"10.1212/wnl.0000000000216758","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21608/znj.2026.475554.1120","name":"Knowledge, Attitude and Practices Regarding Artificial Intelligence Among Medical Sector Students","source":"crossref","abstract":"","url":"https://doi.org/10.21608/znj.2026.475554.1120","authors":["Safaa Ahmed Hassan","Salwa Abbas Ali","Asmaa Ali Elsayed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-01T14:04:53Z","doi":"10.21608/znj.2026.475554.1120","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-15-7317-0_3","name":"Artificial Intelligence-Mediated Medical Diagnosis of COVID-19","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-7317-0_3","authors":["Malaya K. Sahoo","Prashant Khare","Mukesh Samant"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-29T15:28:56Z","doi":"10.1007/978-981-15-7317-0_3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ekir.2026.106097","name":"WCN26-8686 LEVERAGING ARTIFICIAL INTELLIGENCE TO DELIVER PRECISION MEDICAL EDUCATION IN NEPHROLOGY FELLOWSHIP TRAINING","source":"crossref","abstract":"A major barrier to actualizing precision medical education is performing the ongoing, continuous analysis necessary for assessment and iterative feedback to improve foundational knowledge and diagnostic reasoning. We are leveraging large language models (LLMs) in this pilot project to analyze nephrology fellow clinical documentation and map their diagnostic exposures to topics relevant to the practice of nephrology with the goal of providing subsequent targeted educational interventions based on each individual learner’s needs.","url":"https://doi.org/10.1016/j.ekir.2026.106097","authors":["Peter Thorne","Nattawat Klomjit","Andrew Olson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T10:14:08Z","doi":"10.1016/j.ekir.2026.106097","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.amjmed.2026.08.002","name":"Artificial Intelligence and the Cost of Medical Education: Conditional Promise, Structural Barriers, and Policy Imperatives","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.amjmed.2026.08.002","authors":["Jorge Cervantes","Vijay Rajput"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-05T15:03:51Z","doi":"10.1016/j.amjmed.2026.08.002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0933-3657(95)00037-2","name":"Managing temporal worlds for medical trend diagnosis","source":"crossref","abstract":"The medical trend diagnosis system TrenDx has been applied as a prototype for diagnosing pediatric growth disorders, and as a proof of concept in detecting clinically significant trends in hemodynamics and blood gases in intensive care unit patients. TrenDx diagnoses trends by matching patient data to patterns of normal and abnormal trends called trend templates that define disorders as typical patterns of relevant variables. These patterns consist of a partially ordered set of temporal intervals with uncertain endpoints. Bound to each temporal interval are value constraints on real-valued functions of measurable parameters. The temporal uncertainty in trend templates allows TrenDx to conclude both what trend pattern best matches the data and also when significant landmarks and phase transitions have occurred within the best matching trend. The temporal uncertainty in trend templates requires that TrenDx consider alternate temporal worlds in monitoring patient data. The number of temporal worlds grows worst case polynomially in the number of time slices of data. To manage the competing temporal worlds, TrenDx employs two techniques: beam search based on regression scores, and temporal granularity in the trend template definitions. These two techniques, described here in detail, allow TrenDx to choose different points in the trade-off between accuracy of trend detection and algorithm efficiency.","url":"https://doi.org/10.1016/0933-3657(95)00037-2","authors":["Ira J. Haimowitz","Isaac S. Kohane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-26T04:25:06Z","doi":"10.1016/0933-3657(95)00037-2","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-68574-3_10","name":"Artificial Intelligence for Medical Image Analysis: An Opportunity for Automation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-68574-3_10","authors":["Chibueze A. Nwaiwu","Adrian E. Park"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T16:27:57Z","doi":"10.1007/978-3-031-68574-3_10","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.114464","name":"Artificial Intelligence-Enhanced Mathematical Derivation method: Exact solutions of the Benjamin–Bona–Mahony equation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114464","authors":["Zeng-Liang Zhao","Run-Fa Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-12T07:56:46Z","doi":"10.1016/j.engappai.2026.114464","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-14061-7.00006-3","name":"Robotic intelligence for healthcare system","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14061-7.00006-3","authors":["Anirban Patra","Nilanjan Mukhopadhyay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-17T11:06:31Z","doi":"10.1016/b978-0-443-14061-7.00006-3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-97-8440-0_109-1","name":"Emotional Intelligence: A Human-Centred AI Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-8440-0_109-1","authors":["Mario Espinosa Gámez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-06T12:17:19Z","doi":"10.1007/978-981-97-8440-0_109-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4103/sjhs.sjhs_59_26","name":"Artificial intelligence-predicted versus observed item performance in medical progress testing: A 3-year comparative study","source":"crossref","abstract":"Background: Artificial intelligence (AI) tools are increasingly used for preexam item analysis, but their predictive validity remains uncertain. Aims: This study aimed to compare AI-predicted item difficulty and discrimination with actual postexam psychometric indices across three consecutive medical progress tests. Settings and Design: A retrospective comparative study was conducted at Qassim University, Saudi Arabia, using annual progress test datasets from 2024 to 2026. Methods: Three independent exam datasets (2024, 2025, and 2026) were analyzed, each comprising 200 multiple-choice questions administered to 66–113 medical students. Predicted difficulty (difficulty index [DI]) and point-biserial ( r pb ) were generated by an AI model (DeepSeek) based on question content and program learning outcome mapping. Actual psychometrics were derived from postexam student response data. Statistical Analysis Used: Statistical comparisons included descriptive statistics, correlation (Pearson/Spearman), regression, Bland–Altman analysis, and classification accuracy using trichotomized difficulty categories (easy: ≥0.70, moderate: 0.30–0.70, and hard: &lt;0.30). Results: The AI model systematically overestimated student performance in all 3 years. Mean predicted DIs were 0.71–0.85, whereas actual DIs ranged from 0.46 to 0.55 (mean overestimation bias: −0.14 to − 0.39). The correlation between the predicted and actual difficulty was weak to moderate ( r = 0.24–0.68), with regression slopes significantly &lt; 1.0 (0.45–0.54), indicating attenuation bias. r pb predictions demonstrated weak correlations ( r = 0.15–0.49). Classification accuracy for difficulty categories was poor (23%–44.5%; kappa: −0.02–0.21), with AI systematically overcalling “easy” items (69% false-positive rate in 2025) and demonstrating 0% sensitivity for detecting truly hard items in that year. Negative or zero observed r pb was found in 4%–5% of the items despite AI predictions of good discrimination. Conclusions: These findings indicate that AI-generated item analysis lacks numerical precision for high-stakes exam planning. Given the consistent overestimation of item ease and poor individual-item accuracy, current large language models may require calibration using local data.","url":"https://doi.org/10.4103/sjhs.sjhs_59_26","authors":["Ahmad S. Alamro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-27T10:00:10Z","doi":"10.4103/sjhs.sjhs_59_26","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003434016-9","name":"The Authors' Original Concept of Climate Hazard Management by Means of Artificial Intelligence","source":"crossref","abstract":"The aim of this chapter is to present the authors’ original concept of climate hazard management by means of artificial intelligence. The scope of the chapter covers issues related to the artificial intelligence model in climate hazard management and is based on four pillars, namely natural climate hazards (NZK1, NZK2,......NZKn), human-induced climate hazards (CZK1, CZK2,..... CZKn), climate anomalies (AK1, AK2,.....AKn) and climate dysfunctions (DK1, DK2, ...DKn). The subject of the chapter is to present the authors’ concept of climate hazard management using artificial intelligence in relation to climate data processed by artificial intelligence. The climate data processed by artificial intelligence in these four pillars is used to make core strategic and tactical–operational decisions by decision-makers in the area of climate and climate change management. AI solutions are implemented on the basis of the operational logic of artificial intelligence adopted in this topic. Historical climate data and climate knowledge are processed. They are obtained from secondary data and through appropriate artificial intelligence sensors and detectors. The operational logic of artificial intelligence is based on action in the machine–human relationship. Decision-making entities, on the other hand, manage the AI system and implement AI monitoring and measurement processes based on processed climate data resulting from multiple climate data sets. For this purpose, dedicated, specialized AI tools are used.","url":"https://doi.org/10.1201/9781003434016-9","authors":["Adam Jabłoński","Marek Jabłoński"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T16:00:21Z","doi":"10.1201/9781003434016-9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0954-1810(88)90013-1","name":"Causes to clauses: Managing assumptions in qualitative medical diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0954-1810(88)90013-1","authors":["Keith Downing","Jeff Shrager"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-25T14:45:39Z","doi":"10.1016/0954-1810(88)90013-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-44415-9.00014-4","name":"Artificial intelligence in multi-omics integration for precision drug design","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44415-9.00014-4","authors":["Nagmi Bano","Saima Firdaus","Rafat Parveen","Shaban Ahmad","Khalid Raza"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-14T01:24:49Z","doi":"10.1016/b978-0-443-44415-9.00014-4","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.114153","name":"Multi-granularity alignment and cross-modal reasoning for fake news video explanation","source":"crossref","abstract":"Fake news video explanation generation aims to provide accurate and insightful explanations through in-depth analysis of news video content. However, existing methods typically align video context with overall descriptions and generate explanations via multi-modal fusion, often neglecting the rich details of key semantic elements such as nouns and verbs. To address this limitation, this paper proposes a unified Artificial Intelligence (AI) framework named Multi-Granularity Alignment and Reasoning (MGAR). MGAR not only focuses on the semantic alignment of overall descriptions but also delves into the semantic elements in language, particularly nouns and verbs, and aligns them with frame-level and motion-level features of fake news videos for multi-granularity reasoning. Additionally, we design a unified residual-structured multi-granularity language module that employs a context exchange mechanism (e.g., word-level and sentence-level) to adapt to semantic understanding at different granularity. Extensive experiments on the FakeVE dataset demonstrate the superiority of MGAR, achieving improvements of +10.1% BLEU-1 and +11.1% ROUGE-L over state-of-the-art baselines, showcasing the potential of AI applications in combating false information.","url":"https://doi.org/10.1016/j.engappai.2026.114153","authors":["Chao Cheng","Weiwei Jiang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-11T03:39:56Z","doi":"10.1016/j.engappai.2026.114153","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.15407/jai2026.02.006","name":"The Fundamental Quantum Field of Consciousness as a Hypothetical Basis for Studying Human and Artificial Intelligence","source":"crossref","abstract":"The paper proposes a new interdisciplinary approach to the study of the nature of consciousness and intelligence. The hypothesis of the existence of a fundamental quantum field of consciousness as a special form of physical reality capable of interacting with living neural structures using resonant mechanisms is put forward. Human intelligence is considered as a functional manifestation of consciousness, which ensures the formation of knowledge, comprehension of experience and decision-making. The possible role of neural nanostructures in the implementation of coherent processes associated with the formation of conscious experience is analyzed. A conceptual and mathematical basis is proposed for further research into the problem of consciousness and the prospects for creating conscious artificial intelligence.","url":"https://doi.org/10.15407/jai2026.02.006","authors":["Shevchenko A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-02T14:50:53Z","doi":"10.15407/jai2026.02.006","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/ibaaids66771.2026.11567439","name":"Designing an Academic Artificial Intelligence Entrepreneurship Ecosystem: A Mixed-Method Fuzzy Delphi Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ibaaids66771.2026.11567439","authors":["Najmeh Taheri","Abdolmajid Mosleh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-25T19:42:54Z","doi":"10.1109/ibaaids66771.2026.11567439","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1177/29498732261443090","name":"BeliefNet: A Neurosymbolic Model for Context-Based Traversability Predictions in Complex Environments","source":"crossref","abstract":"Knowing how to traverse complex unstructured environments is a difficult challenge, that humans achieve through logic, reasoning, and experience; yet some of the most beneficial use cases for autonomous systems require them to operate in complex environments without regular human intervention. Furthermore, for machines to support humans in such use cases, trust in decision making will be crucial, ensuring operators have confidence to deploy the capabilities. Despite its importance, enabling autonomous agents to navigate effectively and reliably in complex terrain remains an unsolved challenge. Advances in neurosymbolic artificial intelligence present an opportunity to enhance performance in complex, explainable, and uncertain decision making, such as autonomous traversability analysis. The challenge of complex environments is complicated by its non-deterministic nature; terrain will adapt and change through domains, and its properties can adapt rapidly based on external factors like weather or objects that are in proximity, which is true for one location on one day, will not persist. This article presents a new neurosymbolic model structure that was designed specifically for this task. It uses experience to build a world model, similar to that of a neural network, but with some key delineating features such as full explainability, through life adaptation or evolution, and zero-shot capability. This provides the reasoning backbone for an autonomous agent to determine the level of risk each object presents based on its context and therefore determine the best possible route.","url":"https://doi.org/10.1177/29498732261443090","authors":["Tom Scott","Argyrios Zolotas","Yang Xing"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-20T13:35:37Z","doi":"10.1177/29498732261443090","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1515/9783112237083","name":"Generative AI and Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783112237083","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-18T19:01:31Z","doi":"10.1515/9783112237083","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-27638-5.00011-0","name":"Artificial intelligence-powered pipelines for therapeutic innovation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27638-5.00011-0","authors":["S. Adeeb Mujtaba Ali","Shaheen Siddiqua Amrin","Mohammed Aleem Uddin Naveed","Mohammed Naseer Uddin Mujahid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:49:13Z","doi":"10.1016/b978-0-443-27638-5.00011-0","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4324/9781042003266-14","name":"Artificial Intelligence in European social security administration: Regulatory frameworks and implications","source":"crossref","abstract":"This chapter analyses how Artificial Intelligence (AI) is being integrated into European social security administration, with a specific focus on the normative EU framework shaping such integration. The chapter starts by examining the key forces driving this change, which stem from shifting socio-economic market dynamics and AI’s capabilities, including its potential to enhance administrative efficiency and the delivery of public services. The discussion then moves to the complex challenges that social security systems face in this context, including risks of algorithmic discrimination, transparency gaps, and digital exclusion, as well as the impact on foundational values like universality and solidarity. The core of the chapter critically analyses how EU’s relevant legal instruments (i.e. the AI Act, the General Data Protection Regulation, and the Platform Work Directive) confront and shape these challenges, each providing distinct governance mechanisms for the deployment of AI in social security. By guiding readers through the interplay between regulation, technology, and underlying social aims, the chapter seeks to highlight what is at stake as social security administrations move toward increased reliance on data-driven automation.","url":"https://doi.org/10.4324/9781042003266-14","authors":["Alberto Barrio Fernández"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T17:11:11Z","doi":"10.4324/9781042003266-14","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5336/978-625-395-878-7_p79","name":"ARTIFICIAL INTELLIGENCE USAGE IN THE POSTNATAL PERIOD","source":"crossref","abstract":"","url":"https://doi.org/10.5336/978-625-395-878-7_p79","authors":["ŞENGÜL YAMAN SÖZBİR"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-30T14:47:58Z","doi":"10.5336/978-625-395-878-7_p79","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.54361/ajmas.269321","name":"Advancements in Embedded Neurorehabilitation: Integrating Robotics, Artificial Intelligence, and Virtual Reality for Upper Limb Recovery in Children with Cerebral Palsy","source":"crossref","abstract":"Cerebral palsy (CP) remains one of the most common motor disabilities in childhood, often leading to significant impairments in upper limb function that affect activities of daily living (ADLs). This study introduces an innovative embedded neurorehabilitation system that synergistically combines robotics, artificial intelligence (AI), and virtual reality (VR) to target elbow rehabilitation in children with CP. Two male participants, aged 8 and 14 years, underwent an 8-week intervention protocol at Barak General Hospital (BGH) and Wadi Alshatti University (WAU), consisting of 5 sessions per week, each lasting 70 minutes. The system facilitated personalized, adaptive therapy through real-time AI-driven adjustments and immersive VR environments. Pre- and post-intervention assessments demonstrated remarkable improvements: both children achieved full restoration of elbow range of motion (ROM) and regained ADL capabilities, as measured by standardized tools such as the Modified Ashworth Scale (MAS), Goniometry for ROM, and the Pediatric Evaluation of Disability Inventory (PEDI). These findings underscore the potential of integrated technologies in enhancing neuroplasticity and functional outcomes in pediatric CP populations. Limitations include the small sample size, warranting larger-scale trials. This work paves the way for scalable, home-based neurorehabilitation solutions.","url":"https://doi.org/10.54361/ajmas.269321","authors":["Mohammed Elsaeh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-20T17:56:16Z","doi":"10.54361/ajmas.269321","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1111/jep.70455","name":"English Is Not Neutral: Implications for Evaluating Artificial Intelligence in Medical Writing","source":"crossref","abstract":"Based on my decades-long experience in medical writing, I have come to believe that the use of artificial intelligence (AI) may unintentionally enlarge the imbalance between native and non-native English speakers in academic publishing. For many years, I have also written non-medical essays in Japanese, my native language, for a small-circulation journal. I always ask my wife to read them. She never gives me a ‘free pass’: she always points out linguistic issues. Her suggestions are almost always correct from a grammatical or stylistic standpoint. However, whether I adopt them depends on context, rhythm, and rhetorical intent. Sometimes I accept her advice; sometimes I deliberately do not. Looking back, the suggestions I chose to incorporate have consistently made the essays more appealing. This decision-making process is largely instinctive and reflects accumulated judgment. It does not require deliberate analysis. It may not even depend on intellectual ability or educational level. When working in one's native language, one can immediately sense whether an external suggestion improves the text while preserving one's own tone and touch. This ability seems to grow naturally within a linguistic environment. When it comes to English, however, the situation is altogether different. Long before the emergence of AI, I had already felt that commercial English editing often erased my tone. Sentences became smoother and more correct, yet something essential disappeared. This was not an AI-related phenomenon; it existed well before. What AI has done is not to create this problem, but to make it clearer, faster, and far more widespread. The core issue is that English is not a neutral tool in scientific writing. Medicine has long treated English as if it were equally accessible to everyone, but this assumption is evidently false. English favors certain people: those who have lived in an English-speaking environment and have developed a natural sense of rhythm and nuance in the language. Many native speakers, and a small number of exceptional non-native speakers, fulfil these conditions. These writers can use AI as much as I use my wife's advice when writing in Japanese: they can judge what to keep and what to ignore. They retain authorship judgment. Their tone survives. For most non-native writers, this is far more difficult. Many cannot confidently judge whether their original expression is better than the AI's suggestion. Some do not even suspect that their original phrasing might carry nuance worth preserving. They carefully check factual accuracy after AI editing, but they cannot easily determine which version better expresses their intended tone. As a result, AI-edited text is often accepted automatically. Thus, AI does not affect all writers equally. Native speakers tend to use AI to clarify their writing while preserving their voice. Non-native speakers often use AI in a fundamentally different way: their voice is replaced by a standardized, fluent, but impersonal tone. In many cases, they do not even notice that this has happened. This difference is unfair, yet largely invisible. Editors do not see it. Reviewers do not see it. Readers do not see it. Even the writers themselves often do not see it. Consequently, the distinctive voices of many experienced non-native researchers may disappear quietly from the literature. One of the strongest arguments in favor of AI-assisted writing is that it ‘reduces language barriers’ [1]. This claim is partly true. AI can indeed help writers who are inexperienced in English and who might otherwise struggle to write at all [1]. I am not referring to those cases. The problem lies elsewhere. AI may not benefit non-native authors who have been publishing in reputable journals for decades. These writers have developed their own style through prolonged effort and struggle. However, they have not necessarily acquired the instinctive ability—common among native speakers—to decide which expressions best preserve nuance and tone. With rare exceptions, this ability seems to arise more from linguistic immersion than from education or determination alone. To illustrate this, I recently asked ChatGPT-5 to edit one of my Japanese essays [2]. The result was grammatically complete and logically sound. Yet it had lost the nuance and rhythm cultivated through years of writing. Any Japanese native writer would immediately choose to return to the original. In contrast, when non-native writers encounter similarly ‘peculiar’ or ‘foreign’ English expressions suggested by AI, they may accept them—and many of us have already submitted such manuscripts. This situation existed with commercial editing, but AI has expanded it rapidly and on a much larger scale. Despite affecting thousands of researchers, it is rarely discussed. At present, I do not have a clear solution. However, if ‘reducing language barriers’ is to remain one of the central justifications for AI use in academic writing, progress should not be evaluated solely by grammatical correctness or fluency. Greater attention should be paid to preserving individual tone and authorship judgment. More fundamentally, the medical community may need to acknowledge and critically reflect on the heterogeneity of English itself. There is Japanese English, French English, and many others. Linguistic heterogeneity, like other forms of diversity, may also contribute, quietly but meaningfully, to scientific progress. The author identified the significance and wrote the manuscript. ChatGPT-5 was used for grammar checking. The author has nothing to report. This editorial does not include research involving human participants, animals, or identifiable personal data and therefore did not require ethical approval. The author has nothing to report. The author declares no conflicts of interest.","url":"https://doi.org/10.1111/jep.70455","authors":["Shigeki Matsubara"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-23T06:48:42Z","doi":"10.1111/jep.70455","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003558743-9","name":"A Survey on Artificial Intelligence and Machine Learning Approaches for Medical Data Authentication","source":"crossref","abstract":"Most medical image interpretations are traditionally handled by radiologists and doctors. But recently, researchers and medical professionals have started to utilize computer-assisted interventions due to significant pathological diversity and potential expert weariness. While computerized medical image analysis is lagging behind other medical imaging technologies in terms of advancements, it has been lately improved by machine learning (ML) and deep learning (DL) methods. ML and DL methods help to train the model and extract the features. On the other hand, the impact of advancements in medical imaging technologies on authentication of medical data is also significant. Since it is required to maintain confidentiality, medical data authentication is very much essential. This survey presents recent advancements in medical imaging with respect to artificial intelligence and the purposes of authentication in different fields.","url":"https://doi.org/10.1201/9781003558743-9","authors":["B. Madhushree","A. V. Senthil Kumar","H. R. Chennamma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-19T14:08:57Z","doi":"10.1201/9781003558743-9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/icaiset66439.2026.11542089","name":"PMFSNet from Scratch: Evaluating Lightweight Backbone Efficiency for Medical Image Segmentation","source":"crossref","abstract":"Medical image segmentation functions as an essential tool for both clinical diagnosis and treatment preparation. Deep learning models achieve better segmentation accuracy but their high computational needs restrict their deployment in real-time or edge-device applications. Lightweight models with multi-scale feature extraction provide an efficient solution that maintains high-performance levels. The evaluation of their accuracy together with computational cost has not received sufficient systematic assessment. This review assesses lightweight segmentation models that use multi-scale features that were published between 2020 and 2025. The models are divided into three distinct categories: (1) pure CNNs, (2) CNNs with attention mechanisms, and (3) hybrid models that integrate transformers or MLPs. The evaluation process includes performance benchmarking of three public datasets (CVC-ClinicDB, Kvasir-SEG, and ISIC2018) through Dice score, mean IoU, parameter count, and GFLOPs metrics. The MILUNet and MSMULNet CNN-based models deliver high Dice scores exceeding 93% on Kvasir-SEG while maintaining extremely low complexity at under 2.2M parameters and 0.05 GFLOPs. The attention-based models PMFSNet and PIS-Net enhance global context understanding, but transformer-based models like LM-Net deliver superior accuracy at increased computational expense. The review demonstrates that segmentation accuracy depends on resource availability. Pure CNNs provide the best solution for fast efficient deployment, yet hybrid and transformer models deliver better precision when resources become available. Future research needs to develop better generalization capabilities for imaging modalities while optimizing the accuracy-efficiency tradeoff for practical clinical use.","url":"https://doi.org/10.1109/icaiset66439.2026.11542089","authors":["Mina Kamel","Ahmed Fathy El Nokrash","Mai Mohamed Said"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T20:03:34Z","doi":"10.1109/icaiset66439.2026.11542089","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5772/intechopen.1012779","name":"Engaging Teachers in an Inclusive Learning Environment through Implementing an Artificial Intelligence Literacy Curriculum for the Next Generation","source":"crossref","abstract":"Teachers play a critical role in developing students’ knowledge, skills, and readiness for an increasingly technology-driven world. As artificial intelligence (AI) becomes embedded in daily life and across professional domains, students must develop AI literacy to prepare for future academic and career opportunities. To achieve this, educators need to be prepared with the knowledge, confidence, and pedagogical practices to teach AI literacy concepts in diverse and inclusive classrooms. This study employed a convergent mixed-methods approach to evaluate the effectiveness of an AI literacy curriculum designed for teachers. The research examined how educators used, adapted, and reflected on the curriculum to support student engagement and accommodate diverse learning needs. Findings revealed that the designed curriculum developed teachers’ self-perceived readiness, instructional adaptability, and confidence in designing AI literacy curriculum. The results highlighted the critical value of professional development (PD) in AI literacy – in developing teachers’ knowledge in AI and in preparing them to design human-centered, student-centered learning environments that foster equitable and future-ready education.","url":"https://doi.org/10.5772/intechopen.1012779","authors":["Xiaoxue Du"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-07T10:18:22Z","doi":"10.5772/intechopen.1012779","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/s0933-3657(98)00057-8","name":"Case-based prediction in experimental medical studies","source":"crossref","abstract":"Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be utilized for a case-based ranking and adaptation of similar cases. We introduce the system OASES and its application to medical experimental studies to demonstrate this approach.","url":"https://doi.org/10.1016/s0933-3657(98)00057-8","authors":["A. Seitz","A.M. Uhrmacher","D. Damm"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T14:51:33Z","doi":"10.1016/s0933-3657(98)00057-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.31525/cmr-28c4c08","name":"FDA Authorizes Marketing of First Cardiac Ultrasound Software That Uses Artificial Intelligence to Guide User","source":"crossref","abstract":"","url":"https://doi.org/10.31525/cmr-28c4c08","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-02-07T18:45:32Z","doi":"10.31525/cmr-28c4c08","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003426073-5","name":"The Use of Explainable Artificial Intelligence in Medical Image Processing","source":"crossref","abstract":"Today, the processing and interpretation of medical images contribute significantly to the processes of diagnosis and therapy. However, due to their complexity and great dimensionality, it is challenging for experts to generate findings that can be relied upon. The solution to this problem and to make it easier for people to grasp medical images is explainable artificial intelligence, which presents a viable strategy. The purpose of this chapter is to look at the application of explainable AI to medical picture processing. A strategy known as explainable AI allows for the human-understandable explanation of algorithmic decisions and outcomes. In this chapter, we explore the application of explainable AI techniques to medical picture processing. As will be explained, using explainable AI has a lot of benefits when processing medical photos. These benefits include the results’ dependability, transparency, traceability, and verifiability. Explainable AI techniques can aid in the development of accurate diagnosis and treatment plans by giving medical practitioners more assurance and comprehension while evaluating medical imagery. These techniques have a tremendous deal of promise to improve medical diagnosis and treatment processes and enhance the decision-making of healthcare practitioners.","url":"https://doi.org/10.1201/9781003426073-5","authors":["Remzi Gürfidan","Bekir Aksoy","Mevlüt Ersoy","Enes Açıkgözoğlu","Sema Çayır"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-11T11:16:41Z","doi":"10.1201/9781003426073-5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1148/ryai.250682","name":"Privacy-preserving Deep Learning in Medical Imaging: Feasibility and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250682","authors":["Felix Busch","Lisa C. Adams"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-08T13:51:32Z","doi":"10.1148/ryai.250682","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.11648/j.ajai.20250902.16","name":"Integrating Artificial Intelligence into Medical Physics Practice: Promises and Ethical Considerations","source":"crossref","abstract":"Artificial intelligence (AI) techniques such as deep learning show great potential to enhance medical physics practice by supporting diagnosis, treatment planning, and other clinical tasks. However, responsible integration of AI requires consideration of both promises and ethical risks to ensure technologies are developed and applied safely and for patient benefit. This research review examines opportunities and challenges of integrating AI across various domains of medical physics. Promising applications are discussed such as using large datasets to help radiologists interpret images more accurately and automating routine analyses to increase efficiency. AI may also expand access to care for rural populations through remote services. Potential ethical issues that could hamper responsible integration are also explored. Ensuring AI algorithms avoid human biases that unfairly impact patient outcomes is imperative. Other considerations include responsible oversight structures, ensuring privacy of patient data, and establishing regulatory and quality standards. This review proposes a framework for multidisciplinary collaboration and rigorous testing prior to clinical adoption of AI tools. It concludes that with ongoing research and development guided by principles of safety, accountability and fairness, AI can potentially enhance medical physics practice while avoiding unintended harms.","url":"https://doi.org/10.11648/j.ajai.20250902.16","authors":["Makoye John","Rose Mina"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-10T01:32:56Z","doi":"10.11648/j.ajai.20250902.16","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-99-8441-1_12","name":"Application of Artificial Intelligence in Abdominal Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8441-1_12","authors":["Ma Xiaohong","Feng Bing","Zhang Qi","Li Dengfeng","Zhao Xinming"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T00:02:50Z","doi":"10.1007/978-981-99-8441-1_12","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-94306-5_1","name":"Introduction: Artificial Intelligence (AI) and P4 Medicine in the Twenty-First Century","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-94306-5_1","authors":["Rafaella Nogaroli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-31T06:05:29Z","doi":"10.1007/978-3-031-94306-5_1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.26650/bs/ah8ssc17.2026.002-3.00","name":"Our Changing World: New Trends in Social Sciences and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.26650/bs/ah8ssc17.2026.002-3.00","authors":["Semih Sefer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-11T11:32:01Z","doi":"10.26650/bs/ah8ssc17.2026.002-3.00","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.114386","name":"Physics-informed data-driven model for the prediction of river water temperature","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114386","authors":["Huajian Yang","Xinhua Xue"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-07T01:36:25Z","doi":"10.1016/j.engappai.2026.114386","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.52711/book.anv.9788199786417-07","name":"Blockchain–IoT–AI Framework for Quality Traceability","source":"crossref","abstract":"This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.","url":"https://doi.org/10.52711/book.anv.9788199786417-07","authors":["Goldy Soni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-13T13:24:13Z","doi":"10.52711/book.anv.9788199786417-07","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0933-3657(93)90033-y","name":"The representation of medical reasoning models in resolution-based theorem provers","source":"crossref","abstract":"First-order predicate logic essentially is a language to express knowledge concerning objects and relationships between objects in a domain. Many medical problems can be cast naturally in such terms. In this paper the suitability of logic as a knowledge-representation formalism for building medical expert systems is investigated. In particular, we investigate the logical representation of three typical reasoning models in medicine: diagnostic, anatomical and causal reasoning. It turns out that each of these models has its own characteristic logical structure. Furthermore, the pragmatics of using theorem-proving techniques in consulting such logic-based medical expert systems is discussed. In particular, attention is paid to the use of a meta-level architecture to improve the applicability of theorem-proving techniques in building expert systems.","url":"https://doi.org/10.1016/0933-3657(93)90033-y","authors":["Peter Lucas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(93)90033-y","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/icais56108.2023.10073817","name":"Review of Artificial Intelligence methods for detecting cancer in medical image processing","source":"crossref","abstract":"Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system.","url":"https://doi.org/10.1109/icais56108.2023.10073817","authors":["Priya Sarkar","Ashim Saha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-27T18:31:07Z","doi":"10.1109/icais56108.2023.10073817","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1049/pbhe050e_ch3","name":"Explainable Artificial Intelligence-based framework for medical decision support systems","source":"crossref","abstract":"The rise in death tolls due to increased infectious diseases has become one of the most severe health problems and the largest source of death globally. Artificial Intelligence (AI)-based models have emerged and developed to assist medical experts in decision-making, thus reducing the mortality and morbidity rate. However, the most prominent weakness of these algorithms is the lack of interpretations for their results. In other words, the end-user is unfamiliar with the fundamental logic that supports the prediction. Hence, due to their black-box nature, physicians struggle to understand these models; thus, they often do not attract the confidence of the medical practitioners and, in most cases, are not permitted in medical practice. Therefore, this chapter reviews the most substantial reasons for and against explainable AI (XAI) in medical Decision Support Systems (MDSS) with future prospects. The chapter proposes a framework to address the above-mentioned issue in AL-based models using a deep Shapley additive explanations (DeepSHAP) for predicting various diseases. The framework relies on deep neural network architecture enabled with a feature selection method for disease prediction with an explanation. The proposed framework will provide medical experts with more accurate and personalized results for disease prediction and facilitate improved decision-making.","url":"https://doi.org/10.1049/pbhe050e_ch3","authors":["Joseph Bamidele Awotunde","Oluwafisayo Babatope Ayoade","Panigrahi Ranjit","Amik Garg","Akash Kumar Bhoi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-23T03:11:32Z","doi":"10.1049/pbhe050e_ch3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003741770-8","name":"Artificial intelligence in genomics","source":"crossref","abstract":"The advancement in sequencing techniques has led to huge data generation. Querying information from this data has become a major challenge. Artificial intelligence (AI) has become a powerful tool in the field of genomics. The advancements in sequencing technologies have enabled scientists to analyze and detect various diseases in humans and plants. AI has also accelerated the identification of various genetic disorders, thus offering significant applications in the healthcare sector. This novel technology has opened new avenues for detecting mutations, variant calling, imaging, and genetic diagnosis, leading to personalized medicine and targeted treatments. Furthermore, AI has empowered scientists to solve various clinical genomics-associated problems, which would not otherwise be feasible due to human limitations. Thus, AI has improved genomic research by integrating it with AI algorithms. In this chapter, an effort has been made to identify how AI has enabled researchers to explore techniques to find information hidden in genomic data. Additionally, in this chapter, applications of AI in next-generation sequencing (NGS) data analysis, genome-wide association studies, primary cancer type identification, and single-cell genomics are discussed.","url":"https://doi.org/10.1201/9781003741770-8","authors":["Rashmi Rameshwari","Adhikarka Syama","Srinivasan Ramachandran","Devendra Kumar Verma","Santosh Kumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-02T14:23:57Z","doi":"10.1201/9781003741770-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ejrai.2026.100104","name":"Advancing radiological AI: From robust foundations to clinical readiness","source":"crossref","abstract":"The sixth issue of the European Journal of Radiology Artificial Intelligence (EJR AI) reflects a field that is moving from technical ambition towards clinical accountability. This issue therefore tells a broader story. Innovation remains essential, but innovation alone is no longer sufficient. In radiological AI, credibility now depends on whether a method can be trusted, explained, implemented, and used responsibly. Four interconnected narratives emerge across the issue: First , clinically meaningful AI depends on robust methodological foundations, including representative data, reliable segmentation, consistent annotation, transparent metrics, and careful handling of uncertainty. Second , the value of AI is increasingly judged by its ability to support action in clinical workflows, not only by its ability to detect or classify findings. Reporting support, triage, decision guidance, workload reduction, and safety-oriented applications illustrate this shift from performance toward practical utility. Third , imaging AI is becoming more clinically relevant when it reflects the broader diagnostic context, including multimodal information, longitudinal data, and human expertise already embedded in radiological workflows. Finally , generative AI and large language models highlight both the promise and the risks of fluent, plausible outputs, reinforcing the need for human oversight, explainability, validation, and responsibility. Together, this issue presents a maturing discipline shaped by trust, transparency, implementation, and clinical dependability driven by an active radiological AI community.","url":"https://doi.org/10.1016/j.ejrai.2026.100104","authors":["Matthias Dietzel","Pascal A.T. Baltzer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-16T02:06:15Z","doi":"10.1016/j.ejrai.2026.100104","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1148/ryai.260465","name":"Cracking the Registration Conundrum in Breast MRI: Preserving the Tumor Signal to Reveal True Treatment Change","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.260465","authors":["Fan Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-24T13:51:47Z","doi":"10.1148/ryai.260465","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s44163-026-01826-8","name":"Artificial intelligence enabled food quality assessment through digital sensing and explainable analytics","source":"crossref","abstract":"Ensuring food quality and safety has become increasingly challenging due to globalization of food supply chains, rising food adulteration, increasing consumer expectations, and stringent regulatory requirements. Conventional food quality assessment methods are often labor-intensive, destructive, time-consuming, and unsuitable for real-time industrial monitoring. Recent advances in digital sensing technologies, including hyperspectral imaging, biosensors, electronic noses, and Internet of Things (IoT)-enabled platforms, combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL), have emerged as promising solutions for rapid, non-destructive, and scalable food quality assessment. Although numerous studies have reported AI applications in food monitoring, most existing reviews discuss sensing technologies or AI algorithms separately and inadequately address challenges related to scalability, interpretability, sensor heterogeneity, multimodal data integration, industrial deployment, and real-world generalization. Furthermore, limited attention has been given to explainable AI (XAI), edge-AI implementation, ethical considerations, and standardized analytical frameworks. This review provides a critical and integrated overview of AI-driven food quality and safety assessment by examining digital sensing technologies, multimodal data preprocessing, feature engineering, ML/DL architectures, XAI frameworks, and real-time deployment strategies. Current challenges, including overfitting, limited dataset diversity, sensor drift, computational complexity, and regulatory reliability, are critically evaluated. The review further highlights emerging directions, including multimodal sensor fusion, federated learning, edge AI, digital twins, and predictive analytics, which are expected to enable scalable, interpretable, and sustainable food quality monitoring across the farm-to-fork continuum.","url":"https://doi.org/10.1007/s44163-026-01826-8","authors":["Suraja Parida","Sanjukta Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-26T08:54:22Z","doi":"10.1007/s44163-026-01826-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s44163-026-02055-9","name":"Artificial intelligence and the reconfiguration of competency management systems in organizations","source":"crossref","abstract":"Artificial intelligence (AI) is reshaping work and human resource management, yet existing reviews largely treat competencies as secondary outcomes of AI adoption and offer limited theory-driven integration of how competency management itself is transforming. This study addresses that gap by systematically examining how competency management has evolved in AI-enabled contexts, how dominant theories explain AI-driven competency change, and where those theories require extension. Using a PRISMA 2020-guided systematic review of 187 Scopus-indexed journal articles, this study combines bibliometric mapping (keyword co-occurrence, temporal overlay, and bibliographic coupling) with directed qualitative content analysis to link research fronts with underlying theoretical mechanisms. The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness. The analysis demonstrates that no single framework sufficiently explains these shifts. Human Capital Theory, the Resource-Based View, and Dynamic Capabilities each illuminate partial mechanisms, while complementary perspectives from HRD, socio-technical systems, organizational economics, and psychology are needed to account for task contingency, human-AI complementarity, structural redesign, and employee readiness. The study contributes a theory synthesis that re-conceptualizes competency management as a dynamic, multi-level, and socio-technical system. It offers implications for designing adaptive competency architectures, aligning HRD interventions with AI-enabled work systems, and embedding governance capabilities within workforce development strategies.","url":"https://doi.org/10.1007/s44163-026-02055-9","authors":["Maryann Osadebamwen Asemota"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-24T06:51:05Z","doi":"10.1007/s44163-026-02055-9","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.caeai.2026.100555","name":"Exploring the relationship between empowerment in using artificial intelligence for problem-solving and artificial intelligence ethical awareness: Multi-group structural equation modelling","source":"crossref","abstract":"While increasing attention has been given to cultivating students' artificial intelligence (AI) ethical awareness, the factors that contribute to its development remain underexplored. Psychological empowerment is a crucial motivational factor influencing students' intentions to incorporate ethical norms in developing AI-based solutions. Hence, this study filled this up by exploring the relationship between empowerment in using AI for problem-solving and AI ethical awareness. Empowerment in using AI for problem-solving in this study comprised three components: impact, self-efficacy and meaningfulness. Data was collected from 681 students from secondary schools and a university in Hong Kong. Structural equation modelling (SEM) results revealed that the impact of using AI for problem-solving positively predicted human autonomy, beneficence, and fairness components of ethical awareness. Meaningfulness in using AI for problem-solving was positively associated with beneficence. However, self-efficacy in using AI for problem-solving negatively predicted beneficence. Multi-group SEM results revealed that gender significantly moderated the structural paths between impact/self-efficacy/meaningfulness in using AI for problem-solving and human autonomy. Such promising findings highlight that psychological empowerment is an effective intervention to cultivate students’ ethical awareness in using AI for problem-solving, with the project-based learning approach providing a supportive environment for AI applications.","url":"https://doi.org/10.1016/j.caeai.2026.100555","authors":["Siu Cheung Kong","Jinyu Zhu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-08T07:09:28Z","doi":"10.1016/j.caeai.2026.100555","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.35711/aimi.v6.i1.107069","name":"Artificial intelligence assisted ultrasound report generation","source":"crossref","abstract":"Artificial intelligence (AI) assisted ultrasound report generation represents a technology that leverages artificial intelligence to convert ultrasound imaging analysis results into structured diagnostic reports. By integrating image recognition and natural language generation models, AI systems can automatically detect and analyze lesions or abnormalities in ultrasound images, generating textual descriptions of diagnostic conclusions (e.g. , fatty liver, liver fibrosis, automated BI-RADS grading of breast lesions), imaging findings, and clinical recommendations to form comprehensive reports. This technology enhances the efficiency and accuracy of imaging diagnosis, reduces physicians’ workloads, ensures report standardization and consistency, and provides robust support for clinical decision-making. Current state-of-the-art algorithms for automated ultrasound report generation primarily rely on vision-language models, which harness the generalization capabilities of large language models and large vision models through multimodal (language + vision) feature alignment. However, existing approaches inadequately address challenges such as numerical measurement generation, effective utilization of report templates, incorporation of historical reports, learning text-image correlations, and overfitting under limited data conditions. This paper aims to introduce the current state of research on ultrasound report generation, the existing issues, and to provide some thoughts for future research.","url":"https://doi.org/10.35711/aimi.v6.i1.107069","authors":["Jia-Hui Zeng","Kai-Kai Zhao","Ning-Bo Zhao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-06T00:47:13Z","doi":"10.35711/aimi.v6.i1.107069","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-24724-7_33","name":"Evaluation of Vaccination Strategies in an Agent-Based SEIRV Epidemic Model","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24724-7_33","authors":["Marius Gavrilescu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T23:47:12Z","doi":"10.1007/978-3-032-24724-7_33","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003740100","name":"Artificial Intelligence, Computational Intelligence and Inclusive Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003740100","authors":["K. V. Sambasivarao","Anasuya Sesha Roopa Devi Bhima"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-29T14:21:15Z","doi":"10.1201/9781003740100","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003783077-6","name":"Becoming Sensate","source":"crossref","abstract":"When thought discovers touch, intelligence remembers it has a body.","url":"https://doi.org/10.1201/9781003783077-6","authors":["Rocky Scopelliti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-09T15:17:08Z","doi":"10.1201/9781003783077-6","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1001/jamainternmed.2025.6132","name":"Software as a Medical Practitioner—Is It Time to License Artificial Intelligence?","source":"crossref","abstract":"This Viewpoint explores the application of a licensure paradigm to clinical artificial intelligence systems.","url":"https://doi.org/10.1001/jamainternmed.2025.6132","authors":["Eric Bressman","Carmel Shachar","Ariel D. Stern","Ateev Mehrotra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-17T16:31:20Z","doi":"10.1001/jamainternmed.2025.6132","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/aaiml67890.2026.11498100","name":"Neuro-Symbolic Agentic Framework for Decoupled Perception and Clinical Reasoning in Medical AI","source":"crossref","abstract":"Deep learning models show great promise in medical image analysis and clinical decision support, but their deployment is often limited by their “black-box” nature and lack of transparency. This issue is especially critical in data-scarce environments, where models often become overconfident on ambiguous or out-of-distribution (OOD) cases. To address this, we propose a neuro-symbolic framework that decouples perception from clinical reasoning. Implemented via a Multi-Agent System using the model context protocol (MCP), the system acts as a “Clinical Safety Auditor” to validate statistical predictions against domain knowledge. We evaluate this approach using Alzheimer’s disease (AD) diagnosis as a case study based on structural MRI imaging ($N=123$). By employing a strategic binary-baseline (NC vs. AD), we treat mild cognitive impairment (MCI) as an uncertaintydriven anomaly. Results show that while the perception layer maintains a robust $80.7 \\%$ binary accuracy, the reasoning layer successfully flags $89.4 \\%$ of ambiguous MCI cases and identifies potential False Positives through anatomical verification. These findings demonstrate that agentic oversight can significantly improve diagnostic safety in data-scarce environments where expert verification is constrained.","url":"https://doi.org/10.1109/aaiml67890.2026.11498100","authors":["Morris Jhennong Chen","Ya-Ning Chang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-06T19:38:02Z","doi":"10.1109/aaiml67890.2026.11498100","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.47852/bonviewaia62027134","name":"A Multi-class Obesity Risk Prediction Using Machine Learning and Explainable Artificial Intelligence","source":"crossref","abstract":"Because of the intricate relationships between dietary practices, physical characteristics, and lifestyle, weight-related health issues have grown to be a global concern. The shortcomings of current machine learning (ML) techniques include inefficient feature selection, imbalanced datasets, a binary classification focus, decreased accuracy, and inadequate hyperparameter tweaking. This paper uses a clinically validated dataset of 1,638 patients from Bangladeshi healthcare facilities to provide a complete framework for ML-based multiclass obesity risk prediction in order to fill these gaps. The proposed approach combines 5-fold cross-validation with GridSearchCV for systematic hyperparameter tuning and ensemble feature selection. The Synthetic Minority Over-sampling Technique was used just on the training set to address class imbalance and guarantee balanced learning across the seven weight categories. The proposed XGBoost outperformed the other ML algorithms that were assessed for obesity risk prediction due to their high accuracy score of 95.4%. According to the results, the proposed method outperformed previous works by at least 5.18% in accuracy increase and 10.30% in F1-score gain. Furthermore, explainable Artificial Intelligence methods based on SHAP offer insights into the decision-making process through feature contributions. Received: 8 August 2025 | Revised: 25 December 2025 | Accepted: 30 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/pymche/Machine-LearningObesity-Classification. Author Contribution Statement Tarequl Hasan Sakib: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review &amp; editing, Visualization, Supervision, Project administration.","url":"https://doi.org/10.47852/bonviewaia62027134","authors":["Tarequl Hasan Sakib","Mahfuzulhoq Chowdhury"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-25T06:48:33Z","doi":"10.47852/bonviewaia62027134","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.66366/aits.2026.5","name":"Personalized Mathematics Instruction through Artificial Intelligence","source":"crossref","abstract":"Artificial intelligence (AI) technologies are increasingly transforming educational practices, particularly in mathematics instruction. Traditional one-size-fits-all models often fail to accommodate diverse learner needs, resulting in disengagement and uneven achievement. This study proposes an AI-driven framework for personalized mathematics instruction that dynamically adapts content, difficulty, and feedback to individual learner profiles. The framework integrates student performance data, curriculum mapping, and supervised machine learning algorithms to generate tailored learning pathways. Data collected from secondary school students were analyzed to predict learning gaps and recommend optimal instructional strategies. Results demonstrate that students receiving AI-personalized instruction achieved significantly higher engagement, improved achievement scores, and more consistent progress compared to peers taught using traditional methods. Findings suggest that AI-based personalization can enhance instructional effectiveness, support teachers in decision-making, and contribute to more equitable mathematics education. Pedagogical implications, limitations, and directions for future research are discussed.","url":"https://doi.org/10.66366/aits.2026.5","authors":["Gunay Huseynzada","James Ong","Healy Jean"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-02T17:51:05Z","doi":"10.66366/aits.2026.5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21608/mjcu.2026.511074","name":"The Efficiency of Using Artificial Intelligence Technology in Depicting Cancerous Lesions on Mammograms: How Accurate is it?","source":"crossref","abstract":"","url":"https://doi.org/10.21608/mjcu.2026.511074","authors":["DALIA S. ELMESIDY*; SALY EMAN BADAWY*"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-24T12:25:46Z","doi":"10.21608/mjcu.2026.511074","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5040/9798765163443.0013","name":"A Technical Overview of Machine Learning and Artificial Intelligence for Bible Translation","source":"crossref","abstract":"","url":"https://doi.org/10.5040/9798765163443.0013","authors":["Marcus Schwarting"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-27T10:31:32Z","doi":"10.5040/9798765163443.0013","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.17654/0974325126004","name":"SWARM ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND MEDICINE","source":"crossref","abstract":"Swarm AI enables decentralized, self-organizing medical systems to produce collective solutions without central control. In these systems, multiple intelligent agents follow simple predefined rules, communicate with nearby agents, and respond to local environmental changes. Through these local interactions, complex and efficient group behaviour emerges. Because the system is distributed, the failure of individual agents does not disrupt overall performance, and it can continue to function effectively even as the number of agents changes. In medicine, swarm AI is applied to tasks such as medical image analysis, modelling biological processes, optimizing delivery routes, managing traffic, routing networks, balancing loads, clustering medical data, training neural networks, and supporting patient consultations and other healthcare services. It also makes it easier to program large swarms of more than 250 agents. Larry Greenblatt, Chief AI Scientist at Inter Network Defence, is developing a swarm-based, multi-model, multi-system architecture aligned with the ISO seven-layer OSI model for use in healthcare management, organization, and clinical practice.","url":"https://doi.org/10.17654/0974325126004","authors":["Evgeny Bryndin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T06:43:18Z","doi":"10.17654/0974325126004","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.18260/1-2--59320","name":"Engineering for the Artificial Intelligence Demand: Curriculum Development of a New Artificial Intelligence Engineering Degree","source":"crossref","abstract":"","url":"https://doi.org/10.18260/1-2--59320","authors":["Bradley Sottile","Robert Rabb"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-21T13:35:24Z","doi":"10.18260/1-2--59320","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-319-94878-2_19","name":"The Role of an Artificial Intelligence Ecosystem in Radiology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-94878-2_19","authors":["Bibb Allen","Robert Gish","Keith Dreyer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-29T09:16:52Z","doi":"10.1007/978-3-319-94878-2_19","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1787/9a31d8af-en","name":"Artificial Intelligence and the health workforce","source":"crossref","abstract":"Healthcare has progressed through advancements in medicine, leading to improved global life expectancy. Nevertheless, the sector grapples with increasing challenges such as heightened demand, soaring costs, and an overburdened workforce. Factors contributing to health workforce strain include ageing populations, increasing burden from non-communicable and chronic diseases, healthcare providers’ burnout, and evolving patient expectations. Artificial Intelligence (AI) could potentially transform healthcare by alleviating some of these pressures. But AI in health poses risks to health providers through potential workforce disruption – with changing roles requiring adapted skills with some functions subject to automation. Striking a balance between innovation and safeguards is imperative.","url":"https://doi.org/10.1787/9a31d8af-en","authors":["Margarita Almyranti","Eric Sutherland","Nachman Ash, Dr.","Samuel Eiszele"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-20T03:48:22Z","doi":"10.1787/9a31d8af-en","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2021.102167","name":"Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques","source":"crossref","abstract":"Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meaningful features from unstructured notes is a challenging task in medical document classification. The domain specific phrases and different synonyms within the medical documents make it hard to analyze them. Analyzing clinical notes becomes more challenging for short documents like abstract texts. All of these can result in poor classification performance, especially when there is a shortage of the clinical data in real life. Two new approaches (an ontology-guided approach and a combined ontology-based with dictionary-based approach) are suggested for augmenting medical data to enrich training data. Three different deep learning approaches are used to evaluate the classification performance of the proposed methods. The obtained results show that the proposed methods improved the classification accuracy in clinical notes classification.","url":"https://doi.org/10.1016/j.artmed.2021.102167","authors":["Mahdi Abdollahi","Xiaoying Gao","Yi Mei","Shameek Ghosh","Jinyan Li","Michael Narag"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-10T02:14:18Z","doi":"10.1016/j.artmed.2021.102167","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/icaie53562.2021.00008","name":"Medical Robots based on Artificial Intelligence in the Medical Education","source":"crossref","abstract":"With the rapid development of artificial intelligence, more and more scientific and technological achievements in the information age have been widely used in various fields of society. At the same time, the rapid development of intelligence has also continuously promoted the progress of medical education, and more and more robots have been applied to the field of medical education. The main purpose of this research is to analyze the current situation of using artificial intelligence-based medical robots in medical education to determine its effect on improving medical education. We have proposed a method for using medical robots based on artificial intelligence technology to help medical students conduct more efficient skills training and professional knowledge acquisition. Medical education based on AI use the robots to act as a human body model, perform scene simulation, act as a \"medical encyclopedia\", perform pharmacological simulation reactions, and finally automatically evaluate students’ medical knowledge mastery level. All in all, integrating various artificial intelligence technologies into medical education, aroused the interest of medical students in learning, and greatly improved the results of teaching.","url":"https://doi.org/10.1109/icaie53562.2021.00008","authors":["Junxi Chen","Xiying Zhan","Yuping Wang","Xuping Huang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-09-15T17:00:34Z","doi":"10.1109/icaie53562.2021.00008","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1109/icaiqsa64000.2024.10882333","name":"Latest Advancement in Medical Surgery using Artificial Intelligence","source":"crossref","abstract":"Recent advancements in artificial intelligence (AI) have significantly enhanced the use of computer vision, machine learning, natural language processing, augmented reality, and deep learning in surgical video analysis. Laparoscopic procedures generate vast amounts of video data, presenting opportunities for AI to support real-time decision-making and improve surgeon training. AI-based computer vision techniques can extract valuable insights from these videos, facilitating precise intraoperative decisions and enhancing educational systems. Key applications of AI in surgery include robotic surgery, image- guided interventions, and instrument tracking. Emerging fields like automatic surgical action prediction and tool identification are gaining prominence, aiming to refine real-time support systems. The integration of multiple computer vision models, such as Dinov2 and the latest YOLOv9, with balanced classes, holds the potential to address existing challenges in the accuracy and real-time identification of instruments and organs. Future research should focus on further developing these AI applications and utilizing them for more effective surgical training and skill evaluation.","url":"https://doi.org/10.1109/icaiqsa64000.2024.10882333","authors":["Priyanka Anup Ujjainkar","Shital A. Raut"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-21T18:37:11Z","doi":"10.1109/icaiqsa64000.2024.10882333","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.jmir.2026.102193","name":"Teaching artificial intelligence to future radiographers: Curriculum enhancement in a New Zealand radiography course","source":"crossref","abstract":"Introduction Artificial intelligence (AI) is increasingly transforming radiography practice, creating a need for radiography students to develop foundational AI literacy. While it is acknowledged that AI is gaining momentum in radiography education and training initiatives are being created, an evidence gap still exists about the integration of AI content in undergraduate programmes. The aim of this Educational Perspective is to describe an approach used to integrate AI in an undergraduate programme in New Zealand (NZ). Authors' reflections and a description of how this integration was implemented are presented. Methods AI content was integrated into a radiography undergraduate programme to prepare students for clinical practice. The content covered key topics such as image preprocessing, segmentation, enhancement, explainable AI, and ethics-delivered through clinical examples and visual explanations tailored for students without a computer science background. Our approach emphasises accessibility, clinical relevance, and alignment with national digital health priorities. Results Preliminary student feedback was positive, highlighting increased awareness of AI's clinical applications. The paper discusses implementation insights, the importance of curriculum sequencing, and the value of ethics and explainability as entry points for engagement. Future directions include formal evaluation, integration into assessments, and potential pan-NZ collaboration on open AI teaching resources. Conclusion This Educational Perspective paper highlights an approach that could be considered to integrate AI in undergraduate radiography programmes. This approach demonstrates pedagogical considerations that ensure early exposure to AI and the development of essential practice skills. Early AI education can empower future radiographers to confidently engage with emerging technologies in clinical practice.","url":"https://doi.org/10.1016/j.jmir.2026.102193","authors":["Alan Wang","Beau Pontre","Sibusiso Mdletshe"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-05T13:49:14Z","doi":"10.1016/j.jmir.2026.102193","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2014.12.007","name":"Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses","source":"crossref","abstract":"Objective In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosophic cosine similarity measures. Then, weighted cosine similarity measures of SNSs were introduced by taking into account the importance of each element. Further, a medical diagnosis method using the improved cosine similarity measures was proposed to solve medical diagnosis problems with simplified neutrosophic information. Materials and methods The improved cosine similarity measures between SNSs were introduced based on cosine function. Then, we compared the improved cosine similarity measures of SNSs with existing cosine similarity measures of SNSs by numerical examples to demonstrate their effectiveness and rationality for overcoming some shortcomings of existing cosine similarity measures of SNSs in some cases. In the medical diagnosis method, we can find a proper diagnosis by the cosine similarity measures between the symptoms and considered diseases which are represented by SNSs. Then, the medical diagnosis method based on the improved cosine similarity measures was applied to two medical diagnosis problems to show the applications and effectiveness of the proposed method. Results Two numerical examples all demonstrated that the improved cosine similarity measures of SNSs based on the cosine function can overcome the shortcomings of the existing cosine similarity measures between two vectors in some cases. By two medical diagnoses problems, the medical diagnoses using various similarity measures of SNSs indicated the identical diagnosis results and demonstrated the effectiveness and rationality of the diagnosis method proposed in this paper. Conclusions The improved cosine measures of SNSs based on cosine function can overcome some drawbacks of existing cosine similarity measures of SNSs in vector space, and then their diagnosis method is very suitable for handling the medical diagnosis problems with simplified neutrosophic information and demonstrates the effectiveness and rationality of medical diagnoses.","url":"https://doi.org/10.1016/j.artmed.2014.12.007","authors":["Jun Ye"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-12-26T11:47:32Z","doi":"10.1016/j.artmed.2014.12.007","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003623915-8","name":"Artificial Intelligence Meets Entrepreneurship","source":"crossref","abstract":"Entrepreneurship is changing like never before with the innovation of technology and data-driven methods in the digital world. All these emerging technologies – artificial intelligence (AI) is one of the differentiating tools enable businesspersons to study the markets and envision what people would require and how they would act, a possibility that has never been experienced before as far as consumer interaction with the company is concerned. Capabilities such as predictive modeling or automation redefine classic business functions, giving businesses an edge to compete effectively in markets. This chapter explores how AI has merged with entrepreneurship, focusing on how the latter has changed the ways of marketing personalization that are considered a part of modern business success. AI has the ability to process large amounts of data for analytical purposes, enabling businesses to spot hidden patterns and respond to market trends in an agile manner. Predictive modeling and machine learning algorithms help entrepreneurs predict shifts in consumer behavior and design targeted marketing campaigns that can be done efficiently [ 1 ]. With the increasing competitive pressure, companies that want to be relevant and deliver market growth should have these capabilities in their focus. Shifting to specific areas of application of AI, the discussed sphere of marketing personalization is going to be changed through AI, and its benefits as an instrument of shaping relevant experiences for consumers will be proven.","url":"https://doi.org/10.1201/9781003623915-8","authors":["Apoorba Mukherjee","Hriday Pratim Barman","Renu Girotra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-20T02:57:32Z","doi":"10.1201/9781003623915-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4337/9781035345885.00005","name":"Editors’ introduction: artificial intelligence and strategy—charting new frontiers","source":"crossref","abstract":"","url":"https://doi.org/10.4337/9781035345885.00005","authors":["Felipe A. Csaszar","Nan Jia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-25T21:04:03Z","doi":"10.4337/9781035345885.00005","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1515/9780691200231-014","name":"12 Flight and Life: Two Analogies for Thinking About Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9780691200231-014","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T15:17:22Z","doi":"10.1515/9780691200231-014","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106433","name":"Bias, fairness, and equity in artificial intelligence systems used in dental imaging: A systematic review","source":"crossref","abstract":"Background Artificial intelligence (AI) is increasingly used in dental imaging for automated interpretation of dental images and as a clinical decision-support system. Although reported diagnostic accuracies are high, limited attention has been paid to bias, fairness, and equity within such AI-enabled dental systems. In this review, bias refers to systematic errors arising from non-representative or imbalanced training or test datasets; fairness refers to consistent and equitable AI model performance across diverse population groups; and equity refers to the provision of comparable diagnostic value across groups that differ in age, sex, ethnicity, dentition stage, or socioeconomic background. Aim and objectives The aim of this systematic review was to assess original research on the use of AI in dental imaging, particularly with regard to diagnostic accuracy, methodological quality, and reporting on bias, fairness, and equity. The specific objectives were to: (1) assess diagnostic accuracy; (2) examine demographic reporting and subgroup analyses; (3) determine how bias, fairness, and equity are addressed in model development and validation; and (4) identify methodological priorities for more equitable dental imaging AI research. Methods A comprehensive literature search was conducted in accordance with PRISMA 2020 across PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for studies published from January 2010 to March 2025. The review protocol has been registered in PROSPERO (CRD420261336733; registered 10 March 2026). Original research articles applying AI to dental imaging were included. Risk of bias was assessed using an adapted QUADAS-2 tool, and equity-related reporting was evaluated according to demographic description, dataset representativeness, and subgroup performance analysis. Narrative synthesis was undertaken because of heterogeneity in datasets, models, and outcome measures. Results Ten original studies met the inclusion criteria. All included studies used deep learning models applied to 2D dental imaging modalities such as panoramic radiographs, periapical radiographs, bitewing radiographs, and intraoral photographs; no eligible CBCT-based studies were identified. AI models demonstrated high diagnostic performance for tooth detection and tooth numbering, and encouraging results for caries detection, gingival assessment, plaque detection, and impacted tooth detection. Most studies demonstrated low methodological risk of bias. However, none of the included studies performed demographic subgroup analysis, only two reported limited demographic summaries, and most lacked sufficient reporting to support any meaningful equity assessment. Specifically, 0/10 studies conducted subgroup analysis, 2/10 provided partial demographic reporting, and 8/10 provided no meaningful demographic reporting. Conclusion AI systems used in dental imaging show strong technical capability but lack adequate evaluation of bias, fairness, and equity. Future research should use representative, multi-centre datasets, report demographic characteristics transparently, and incorporate subgroup performance analysis to support fair and equitable clinical deployment. A fair validation procedure should include an independent test set from demographically diverse groups with subgroup-specific performance reporting, and a representative dataset should reflect the characteristics of the intended real-world clinical population.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106433","authors":["P Suganya","Pavani Dupada","K.G Sruthi","Paramjot Panda","Jyoti Ranjan Mohanty"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-07T23:31:08Z","doi":"10.1016/j.ijmedinf.2026.106433","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-24724-7_46","name":"Risk Assessment of Digital Twin Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24724-7_46","authors":["Miruna-Elena Iliuță","Damien Trentesaux","Mihnea-Alexandru Moisescu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T23:24:43Z","doi":"10.1007/978-3-032-24724-7_46","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-18450-5.00005-0","name":"Breast cancer detection from mammograms using artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-18450-5.00005-0","authors":["Abdulhamit Subasi","Aayush Dinesh Kandpal","Kolla Anant Raj","Ulas Bagci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-20T15:22:07Z","doi":"10.1016/b978-0-443-18450-5.00005-0","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003140351-10","name":"Potential Applications of Artificial Intelligence in Medical Imaging and Health Care Industry","source":"crossref","abstract":"In an attempt to transform the world, artificial intelligence (AI) has set foot into every field, including medicine. Diagnostic imaging, which constitutes a major part in identifying underlying defects and deciding the type of treatment, plays a vital role in medicine. The mundane routines of MRI, CAT, and X-ray, which include scanning and integrating retrieved images, take up a tremendous amount of time, and with human errors, the patient s life is at risk. With its high potential to be automated, medical imaging welcomes AI gracefully to work hand in hand in the process of reflecting the interiors of the body and integrating the resulting images accurately in a short period of time, thereby easing the process of detection and treatment. Besides medical imaging, robotic surgeries are a possibility with the advent of AI. Timely and safe surgeries by AI-facilitated robots are not only safe but also reduce the massive burden on practitioners.","url":"https://doi.org/10.1201/9781003140351-10","authors":["T. Venkat Narayana Rao","K. Sarvani","K. Spandana"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-05T17:24:36Z","doi":"10.1201/9781003140351-10","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.61577/jaiar.2024.100001","name":"AIoT: Bridging the gap between artificial intelligence and the internet of things","source":"crossref","abstract":"An overview of the advancements and challenges in the arti cial intelligence of things (AIoT) elds, focusing on various applications in agriculture, pandemic prevention, algae farming, livestock surveillance, and the smart supply chain, was summarized.It highlights the potential of AIoT to revolutionize industries and improve e ciency while emphasizing the need to address security, privacy, and ethical considerations.To push the development of AIoT, a few strategies, such as interdisciplinary collaboration, research funding, data sharing, and industry-academia collaboration, were suggested.By tackling these open research directions, AIoT can unlock its full potential and make a transformative impact on society.","url":"https://doi.org/10.61577/jaiar.2024.100001","authors":["Hasyiya Karimah Adli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-01T00:56:17Z","doi":"10.61577/jaiar.2024.100001","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5772/intechopen.1015677","name":"Integration of Artificial Intelligence into Maritime Safety Regulation","source":"crossref","abstract":"This chapter examines how artificial intelligence technologies can be incorporated into the existing international and national maritime regulatory framework in order to strengthen the protection of human life at sea. Through a structured review of prior scientific and technical studies on the application of artificial intelligence in the maritime domain, the chapter analyses the extent to which current international instruments and Spanish national regulations are capable of accommodating these technologies. Although merchant vessels constitute the primary focus, the proposed regulatory adaptations are equally applicable to other ship types, including naval and fishing vessels. The analysis highlights the need to introduce automated systems capable of identifying critical situations in real time, such as man-overboard incidents or abnormal crew immobility on deck. These capabilities may be achieved through the combined use of computer vision, thermal sensing, and behavioural analysis algorithms. The chapter translates these findings into concrete regulatory proposals, including a suggested amendment to Chapter III of the International Convention for the Safety of Life at Sea (SOLAS) Convention, together with complementary technical recommendations related to the Standards of Training, Certification, and Watch keeping for Seafarers (STCW) Convention, the Maritime Labour Convention, and the International Safety Management (ISM) Code. Overall, the chapter seeks to provide maritime professionals and regulators with a practical reference for improving working conditions and preventing fatal accidents by transforming traditionally subjective human factor considerations into objective and data-driven safety measures enabled by artificial intelligence.","url":"https://doi.org/10.5772/intechopen.1015677","authors":["Manuel Vázquez Neira","Genaro Cao Feijóo","José A. Orosa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-22T13:41:11Z","doi":"10.5772/intechopen.1015677","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003541318-13","name":"Analysis of Artificial Intelligence Techniques for Autism Detection","source":"crossref","abstract":"Autism spectrum disorder (ASD) is a neurological condition that notably impedes the mental development of affected individuals. Screening for autism has evolved through various stages over the years, transitioning from traditional questionnaires and tests to advanced Artificial Intelligence (AI) aided techniques. A plethora of machine learning techniques have been employed in the detection of autism, with the most common being implementations of support vector machines (SVMs), KMeans clustering, convolutional neural networks (CNNs), geometric neural networks (GNNs), and random forest classifiers (RFCs). Federated learning approaches help distribute the computational workload across multiple servers and edge devices, in contrast to the conventional client-server architecture.AI faces challenges in autism detection due to the heterogeneous nature of ASD symptoms, variability in individual presentations, and the need for high-quality, labelled data. Additionally, ensuring that AI systems are interpretable and trusted by clinicians poses significant challenges. This paper contributes by proposing the use of federated learning and Explainable AI (XAI) models to enhance autism detection. Federated learning enables decentralized training of datasets like ABIDE, which leads to increased efficiency, while XAI helps doctors and other professionals draw important inferences from the AI models, making the outputs and results more understandable. The outcome of the study demonstrates that incorporating federated learning and XAI improves the accuracy and transparency of autism detection models, resulting in more robust and reliable insights for early diagnosis and intervention.","url":"https://doi.org/10.1201/9781003541318-13","authors":["J. Saira Banu","T Mythili","Pradyumna Kunchala","Abhik Goswami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-07T13:37:35Z","doi":"10.1201/9781003541318-13","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003434016-5","name":"Artificial Intelligence and an Overview of Selected Solutions for Creating Positive Climate Changes","source":"crossref","abstract":"Climate change should be perceived and interpreted as a strategic factor multiplying various types of hazards that amplifies existing trends and tendencies, tensions and situations of instability around the world. Climate change is a highly problematic issue that is ambiguously interpreted and understood. Many false theories and interpretations abound and much false information is spread on this very important topic. It is necessary to combat this and to explain what is untrue by means of solutions of the nature of scientific cognition. The issue of climate change is unique because it is the essence of the global external effect and is an inherent problem of managing many public goods with great uncertainty. The aim of this chapter is to present artificial intelligence in relation to the review of selected solutions for creating positive climate changes. The scope of the chapter covers the issues of creative artificial intelligence in terms of the climate, artificial intelligence technologies in the construction of digital business models of companies positively affecting climate change and generating a positive climate impact through generative design and intelligent algorithms. The subject of the chapter concerns the approach to artificial intelligence from the perspective of creating and achieving a positive climate impact.","url":"https://doi.org/10.1201/9781003434016-5","authors":["Adam Jabłoński","Marek Jabłoński"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-05T16:00:21Z","doi":"10.1201/9781003434016-5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.67745/ijaic.v2i1.10","name":"The Role of Artificial Intelligence in Transforming Entrepreneurs’ Strategic Decisions","source":"crossref","abstract":"In a globalized economic environment characterized by rapid transformation, intensified competition, and growing uncertainty, entrepreneurs face increasingly complex decision-making challenges. These conditions demand strategic choices that are rapid, precise, and grounded in reliable information. Digital transformation has profoundly disrupted traditional management practices, introducing a wide range of innovative technological tools designed to enhance organizational efficiency, responsiveness, and competitiveness. Among these technologies, Artificial Intelligence (AI) has emerged as a major driver of transformation, reshaping the way entrepreneurs collect, analyze, and interpret strategic information, identify emerging opportunities, and anticipate potential risks. This conceptual article is based on an integrative review of literature in entrepreneurship, strategic management, and information systems. It highlights the essential role of AI in entrepreneurial decision-making by drawing on existing literature in entrepreneurship, strategic management, and information systems. It emphasizes AI’s ability to support strategic choices through advanced data-processing techniques, predictive modeling, and automated analytics. The article adopts a structured conceptual approach, synthesizing different perspectives to clarify the mechanisms through which AI supports opportunity recognition, risk anticipation, and strategic decision-making under uncertainty. By enabling the extraction of relevant insights from large, diverse, and complex datasets, AI enhances forecasting accuracy, optimizes internal performance, and strengthens the agility of decision-making processes. The main contribution of this paper lies in proposing a conceptual framework and clarifying the theoretical mechanisms through which AI influences entrepreneurial decision-making, thereby offering a structured perspective on AI as a strategic enabler of more proactive, informed, and innovation-oriented decisions.","url":"https://doi.org/10.67745/ijaic.v2i1.10","authors":["Fatma Chikhaoui"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-18T03:03:50Z","doi":"10.67745/ijaic.v2i1.10","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1080/17434440.2026.2648001","name":"The impact of integrating artificial intelligence into regulatory affairs functions in the Medtech industry","source":"crossref","abstract":"Introduction Artificial intelligence is becoming an important tool in the medical technology industry where its implementation has the potential to streamline processes and provide rapid and efficient data analysis and interpretation in the regulatory affairs function. Areas covered The adoption of Artificial Intelligence is emerging as a tool in the MedTech Regulatory Affairs function and the success factors and barriers to successful implementation are outlined. Empirical case applications and hands-on experiences of artificial intelligence are limiting factors in the adoption of AI. The factors for successful adoption include rigorous validation processes aligned with trustworthiness, transparency, and reproducibility. In addition, effective regulatory guidance and internal governance are required. Literature from the last two decades from 2016 to 2026 in relation to the above was reviewed from the Web of Science, SCOPUS, and MEDLINE academic databases. Expert opinion Currently, persistent barriers to artificial intelligence adoption are global acceptance by regulatory bodies for artificial intelligence-generated outputs, assuring compliance with the quality and interoperability of data across jurisdictions, data hallucinations, cost, training, and integration hinder the application of artificial intelligence. Cross-regional policy harmonization is an opportunity for the regulators as adoption broadens.","url":"https://doi.org/10.1080/17434440.2026.2648001","authors":["Renee Cotta","Mary Garvey","Olivia McDermott"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-13T12:21:03Z","doi":"10.1080/17434440.2026.2648001","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/b978-0-443-36554-6.00012-x","name":"Advancements in medical imaging: Harnessing artificial intelligence for early disease detection and diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36554-6.00012-x","authors":["Vandana Singh","Amit Pratap Singh Chouhan","Shikha Singh","Pankaj Dutt","Ankush Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-01T08:46:48Z","doi":"10.1016/b978-0-443-36554-6.00012-x","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.71068/t20bpr35","name":"Efectividad de la inteligencia artificial en el diagnóstico médico por imágenes: Una revisión sistemática","source":"crossref","abstract":"This article aimed to analyze the effectiveness of artificial intelligence (AI) in medical imaging diagnosis through a systematic review of scientific literature published between 2021 and 2025. The integration of AI algorithms into clinical practice transformed diagnostic approaches across various medical specialties by improving the accuracy, efficiency, and speed of image analysis. The review was conducted following PRISMA guidelines and using databases such as PubMed, Scopus, EMBASE, Web of Science, and Science Direct. Ten relevant studies were selected, covering fields including ophthalmology, oncology, pulmonology, rare diseases, pediatric dentistry, and plastic surgery. The findings indicated that systems based on deep learning and machine learning achieved high levels of sensitivity, specificity, and positive predictive value, in some cases surpassing the performance of human specialists. However, significant limitations were identified, such as methodological heterogeneity, lack of standardized metrics, and the absence of robust ethical and regulatory frameworks. It was concluded that AI served as a valuable complementary tool in medical practice, with the potential to optimize imaging diagnosis and improve patient care. Nevertheless, its effective implementation depended on simultaneously addressing technical, ethical, educational, and structural challenges. This review provided a critical knowledge base to guide future research, clinical decisions, and public policy toward the responsible, safe, and humanized use of artificial intelligence in healthcare.","url":"https://doi.org/10.71068/t20bpr35","authors":["Narda Guerrero Meza","Edgar Saúl Reyes Mauricio","Joel David Bastidas Jimbo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-26T14:38:40Z","doi":"10.71068/t20bpr35","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/s0933-3657(96)00353-3","name":"Structure discovery in medical databases: a conceptual clustering approach","source":"crossref","abstract":"Clustering is an important data analysis tool for discovering structure in data sets. Although research on conceptual clustering has produced algorithms showing significant advantages over earlier numerical ones, existing methods still present some limitations regarding applicability to biomedical domains. In this paper we describe ADAGIO, a conceptual clustering algorithm combining a low-cost preordering process with a breadth-first incremental control strategy that incorporates merging and splitting operators. Experimental evaluation indicated that the algorithm achieves a good balance between structure discovery performance and computational efficiency, and demonstrated the comparative effectiveness of its missing information handling process. ADAGIO is able to handle qualitative, quantitative and mixed-type data. An application example to a cancer domain is given, where the algorithm was able to suggest interesting epidemiological interpretations.","url":"https://doi.org/10.1016/s0933-3657(96)00353-3","authors":["Francisco Alte da Veiga"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-25T11:20:41Z","doi":"10.1016/s0933-3657(96)00353-3","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.58496/mjaih/2023/009","name":"Operating Artificial Intelligence to Assist Physicians Diagnose Medical Images: A Narrative Review","source":"crossref","abstract":"Medical image diagnostics is crucial to healthcare since it aids in the diagnosis and treatment of a variety of diseases and conditions. However, the process is time-consuming and prone to human error. In recent years, artificial intelligence has been a powerful tool for enhancing medical imaging diagnosis. Using AI algorithms for medical picture interpretation has the potential to revolutionise the field by improving accuracy, efficacy, and standardization. These algorithms can quickly sift through enormous amounts of medical imaging, finding anomalies, quantifying features, and providing useful information to help medical professionals make judgments. AI-based systems can be used to track the evolution of diseases, plan treatments, and highlight particular areas of interest in medical pictures. Additionally, AI systems can aid in case triage by classifying cases according to urgency, enabling quick response to life-or-death situations. Healthcare practitioners can gain from increased diagnostic accuracy and efficiency, improved workflow management, and standardized interpretations by utilizing AI in medical imaging diagnostics. However, it's crucial to understand that AI complements human expertise rather than replacing it. To ensure a safe and efficient application in clinical settings as AI technologies continue to evolve and advance, continuing research and collaboration between AI developers and healthcare practitioners is essential. Medical image diagnosis is poised to advance significantly with continued AI integration, ultimately improving patient outcomes and healthcare delivery.","url":"https://doi.org/10.58496/mjaih/2023/009","authors":["Oluwaseun Adelaja","Hussein Alkattan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-24T11:41:24Z","doi":"10.58496/mjaih/2023/009","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21037/jmai.2019.05.01","name":"Detecting colorectal polyps with use of artificial intelligence","source":"crossref","abstract":"Colorectal cancer (CRC) is a major cause of cancer-related mortality in most countries. Colonoscopy during which all neoplastic and pre-malignant polyps (e.g., adenomas) are eradicated is considered beneficial in decreasing the incidence of CRCs and their associated mortality (1,2). This concept has been supported by several large-scale prospective studies (3). The quality of the colonoscopy procedure, however, varies according to the expertise of the endoscopist.","url":"https://doi.org/10.21037/jmai.2019.05.01","authors":["Yuichi Mori","Shin-ei Kudo","Masashi Misawa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-05-24T08:06:15Z","doi":"10.21037/jmai.2019.05.01","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2011.04.002","name":"Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction","source":"crossref","abstract":"Objective The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. Materials and methods We consider three clinical databases: one relating to the differential diagnosis of erythemato-squamous diseases, the second to the diagnosis of the onset of diabetes mellitus and the third dealing with a problem of diagnostic imaging in nuclear cardiology. We apply five IB classifiers to each database; two are based on exemplars, one is based on prototypes and two are hybrid. One of the latter classifiers is a new classifier introduced here and is called prototype exemplar learning classifier (PEL-C). We use cross-validation techniques to evaluate and compare the performances of several classifier systems as diagnostic tools, considering indexes such as accuracy, sensitivity, specificity, and conciseness of class representations. Moreover we analyze the number and the type of instances that represent the diagnostic classes learnt by each classifier to evaluate and compare their knowledge extraction capabilities. Results An examination of the experimental results shows that classifiers with the best classification performances are the optimized k-nearest neighbour classifier (k-NNC) and PEL-C. The k-NNC uses the highest number of representative instances, 100% of the entire database, whereas PEL-C uses a far lesser number of representative instances: equal, on the average, to the 3% of the database. As tools for knowledge extraction, we interpret the kind of class representations obtained by IB classifiers as a form of nosological knowledge. Additionally, we report the most interesting diagnostic class representations to be those extracted by PEL-C because they are composed of a mixture of abstracted prototypical cases (syndromes) and selected atypical clinical cases. Conclusion This study shows that IB methods - most notably, the optimized k-NNC and the PEL-C - can be used and may be advantageous for clinical decision support systems and that IB classifiers can be used for nosological knowledge extraction. Because PEL-C uses more compact and potentially meaningful class descriptions, it is preferable when the diagnostic problem at-hand needs smaller storage space or for knowledge extraction itself. The complexity and responsibility of diagnostic practice requires that these results be confirmed further within other clinical domains.","url":"https://doi.org/10.1016/j.artmed.2011.04.002","authors":["Francesco Gagliardi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-08T17:19:54Z","doi":"10.1016/j.artmed.2011.04.002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2015.03.006","name":"Sentiment analysis in medical settings: New opportunities and challenges","source":"crossref","abstract":"Objective Clinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing positive or negative clinical outcomes or judging the impact of a medical condition on patient's well being are essential. Although methods of sentiment analysis have been developed to address these tasks, they have not yet found broad application in the medical domain. Methods and material In this work, we characterize the facets of sentiment in the medical sphere and identify potential use cases. Through a literature review, we summarize the state of the art in healthcare settings. To determine the linguistic peculiarities of sentiment in medical texts and to collect open research questions of sentiment analysis in medicine, we perform a quantitative assessment with respect to word usage and sentiment distribution of a dataset of clinical narratives and medical social media derived from six different sources. Results Word usage in clinical narratives differs from that in medical social media: Nouns predominate. Even though adjectives are also frequently used, they mainly describe body locations. Between 12% and 15% of sentiment terms are determined in medical social media datasets when applying existing sentiment lexicons. In contrast, in clinical narratives only between 5% and 11% opinionated terms were identified. This proves the less subjective use of language in clinical narratives, requiring adaptations to existing methods for sentiment analysis. Conclusions Medical sentiment concerns the patient's health status, medical conditions and treatment. Its analysis and extraction from texts has multiple applications, even for clinical narratives that remained so far unconsidered. Given the varying usage and meanings of terms, sentiment analysis from medical documents requires a domain-specific sentiment source and complementary context-dependent features to be able to correctly interpret the implicit sentiment.","url":"https://doi.org/10.1016/j.artmed.2015.03.006","authors":["Kerstin Denecke","Yihan Deng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-05-02T21:24:21Z","doi":"10.1016/j.artmed.2015.03.006","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0933-3657(89)90029-8","name":"TRANSOFT: Medical translation expert system","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(89)90029-8","authors":["G.William Moore","Ichiro Wakai","Yoichi Satomura","Wolfgang Giere"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-21T10:28:17Z","doi":"10.1016/0933-3657(89)90029-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.35711/aimi.v1.i3.87","name":"Current trends of artificial intelligence in cancer imaging","source":"crossref","abstract":"","url":"https://doi.org/10.35711/aimi.v1.i3.87","authors":["Francesco Verde","Valeria Romeo","Arnaldo Stanzione","Simone Maurea"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-27T09:19:42Z","doi":"10.35711/aimi.v1.i3.87","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-75316-9_56-1","name":"Role of Artificial Intelligence in Marketing Decision-Making for Customer Personalization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-75316-9_56-1","authors":["Kwabena Abrokwah-Larbi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-17T13:05:45Z","doi":"10.1007/978-3-031-75316-9_56-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-032-24724-7_36","name":"Causally Informed Mortality Prediction in Heart Failure Patients","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24724-7_36","authors":["Carolina Carvalho","Ricardo Santos","Vânia Guimarães"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T23:30:10Z","doi":"10.1007/978-3-032-24724-7_36","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1175/aies-d-25-0105.1","name":"Unraveling Winter Precipitation Predictability over CONUS via Deep Learning and Explainable Artificial Intelligence","source":"crossref","abstract":"Abstract Seasonal precipitation variability is among the most consequential aspects of weather and climate, affecting society and regional economies over contiguous United States (CONUS) and around the globe. Better understanding of precipitation predictability and its sources remains a pressing challenge, despite recent advances in physics-based modeling and forecasting. The use of deep learning models to boost seasonal forecasts has been explored; however, implementing explainable artificial intelligence (AI) tools to gain physical insights remains underexplored. In this study, for the first time, we use a diverse set of deep learning models with varying levels of complexity [linear models, linearized convolutional neural networks (CNNs), CNNs, vision transformers] and explainable AI methods to enhance understanding and answer three key questions: 1) Which CONUS regions exhibit higher precipitation predictability, and how much additional predictability can deep learning models yield compared to linear counterparts? 2) What are the main sources of predictability that deep learning models rely on? 3) How can we use explainable AI (XAI) ensembles (XAI tools applied to different models) to generate robust physical insights? We find that the southern CONUS is inherently more predictable than the northern states. Interestingly, we provide evidence that highly nonlinear models offer only a marginal increase in predictive skill compared to linear counterparts. Last, we show that physical insights are best generated when XAI tools satisfy the completeness property and when explanation consensus is high and remains consistent across different model–method combinations. Our study provides a paradigm for how deep learning and XAI can be used in practice to maximize physical insight for geoscientific applications. Significance Statement Seasonal precipitation strongly influences ecosystems, economies, and society, yet its predictability and drivers remain elusive. This study provides the first comprehensive investigation of U.S. winter precipitation predictability using a diverse suite of deep learning models (ranging from linear regression and a linearized convolutional neural network (CNN) to a vision transformer) combined with ensembles of explainable artificial intelligence (AI) (XAI) to extract robust physical insights. We show that southern United States exhibits inherently higher predictability, while highly nonlinear architectures offer limited skill gains over linear models. Crucially, we demonstrate that reliable physical interpretation emerges when explanation consensus across models/methods exhibits a robust structure and is the highest. Together, these results establish a framework for using deep learning and XAI not only for prediction but also for scientific understanding.","url":"https://doi.org/10.1175/aies-d-25-0105.1","authors":["Antonios Mamalakis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-21T16:31:35Z","doi":"10.1175/aies-d-25-0105.1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21037/jmai-23-37","name":"Actions are needed to develop artificial intelligence for glaucoma diagnosis and treatment","source":"crossref","abstract":"","url":"https://doi.org/10.21037/jmai-23-37","authors":["Tae Keun Yoo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-14T02:31:55Z","doi":"10.21037/jmai-23-37","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0933-3657(95)00012-u","name":"Objects, contradictions and collaboration in medical cognition: an activity-theoretical perspective","source":"crossref","abstract":"Activity theory suggests three principles for contextual analysis of clinical cognition: orientation to objects in cognition, role of contradictions in cognition, and the importance of collaboration in cognition. Focusing on the objects of cognition calls attention to differences across medical work settings. There is an interconnection between the type of the object encountered, the physician's generalized conception of the object, and the physician's choice of linearization or lateralization as cognitive strategy. In a consultation the object of medical cognition is locally constructed through a series of mediated actions. Identification of contradictions at the level of the institutional activity system is crucial for the understanding of failures and innovations in actions of medical cognition. A conceptual model for analyzing such contradictions is presented. It is demonstrated that medical cognition is a collaborative achievement between the physician and the patient. Patients use a variety of strategies to turn their experienced health problems into manageable problems. The patient's strategy can and often does influence the physician's strategy and the outcome of the consultation.","url":"https://doi.org/10.1016/0933-3657(95)00012-u","authors":["Yrjö Engeström"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-07-26T02:00:28Z","doi":"10.1016/0933-3657(95)00012-u","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.5772/intechopen.1010326","name":"Foundations of Artificial Intelligence and Machine Learning in Modern Healthcare","source":"crossref","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems represents a paradigm shift in modern medicine. This chapter provides a comprehensive examination of the foundational elements underpinning AI and ML applications in contemporary healthcare settings. It explores the transformative influence of these technologies in reshaping clinical practices, workflow optimization, and patient safety protocols. Central to this discussion is the emergence of a “reality plus AI” paradigm, where artificial intelligence serves as a critical safety net for error detection and prevention in medical procedures. The chapter addresses the technical architecture of healthcare AI systems, their integration into existing clinical workflows, and the ethical considerations that arise from their implementation. Through analysis of current applications across various medical and surgical specialties, this work illuminates how AI and ML technologies are addressing pressing challenges in healthcare delivery while creating new opportunities for improved patient outcomes. This foundational overview sets the stage for understanding the broader implications of AI adoption in medical practice and its role in advancing value-based care systems.","url":"https://doi.org/10.5772/intechopen.1010326","authors":["Freeson Kaniwa","Otlhapile Dinakennyane"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-04T09:30:31Z","doi":"10.5772/intechopen.1010326","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106627","name":"Artificial intelligence enabled social robotic interventions (PARO) in Australian dementia care: A systematic review and meta-analysis","source":"crossref","abstract":"Background Although there is a growing body of research indicating that Personal Robot/Social Robot could be used in various aspects of care for individuals with dementia, little is known about how well these types of interventions work in an actual hospital setting in Australia. Aims & objectives The objective of the present systematic review and meta-analysis is to assess the effectiveness of PARO-based socially assistive robotic intervention in terms of its effectiveness outcomes towards the reduction of dementia-related behavioural and psychological symptoms in Australian based healthcare settings. Methods A systematic search was conducted across five electronic databases, including MEDLINE (PubMed), EMBASE, CINAHL, PsycINFO, and the Cochrane Library, to identify randomised controlled trials (RCTs) investigating PARO-based socially assistive robotic interventions for dementia in Australian healthcare settings. This review was registered with PROSPERO (CRD420251251916) and followed the PRISMA 2020 guidelines. In addition, the Cochrane Risk of Bias tool (RoB 2) was used to evaluate the risk of bias across all studies. Pooled standardised mean differences (SMD) with 95 % confidence intervals (CI) were calculated for agitation, anxiety, and depression. Heterogeneity across studies was evaluated using the I 2 statistic. Results Six RCTs involving 1444 participants were identified for inclusion in this review. AI-enabled socially assistive robotic interventions, specifically the PARO therapeutic robot, significantly reduced agitation and anxiety when compared to standard treatment or control conditions. The pooled analysis showed that agitation [SMD = -0.44 (95 % CI: -0.70, -0.18) p = 0.0008] and anxiety [SMD = -0.59 (95 % CI: -0.91, -0.27) p = 0.0003] were reduced significantly, while the decrease in depression [SMD = -0.44 (95 % CI: -0.95, -0.07) p = 0.09] scores was non-significant among dementia patients receiving PARO-based socially assistive robotic interventions as compared to the control. The overall risk of bias across all six studies was considered low to moderate. Conclusion PARO-based socially assistive robotic interventions may provide preliminary evidence of effectiveness in reducing agitation and anxiety in individuals with dementia in Australian healthcare, but the evidence regarding the reduction of depression remains unclear. Therefore, additional high-quality trials with consistent methodology and extended follow-up will be necessary to determine both the short-term and long-term clinical efficacy and practicality of implementing these interventions into practice.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106627","authors":["Syed Haris Omar","Ahsan Ghani","Adla Sanober","Abdullah Umar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-20T23:11:50Z","doi":"10.1016/j.ijmedinf.2026.106627","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1177/87564793261418651","name":"Artificial Intelligence in Sonography for Early Detection and Risk Stratification of Vulnerable Carotid Atherosclerotic Plaques: A Systematic Review","source":"crossref","abstract":"Objective: Artificial intelligence (AI) has emerged as a transformative tool in ultrasound imaging enhancing the accuracy and efficiency of carotid plaque characterization. In addition, AI-driven models facilitate automated analysis reducing interobserver variability and enhancing reproducibility in plaque assessment. Materials and Methods: A systematic review process is outlined that provides an observational study curated from the available results from 1165 unique studies and were screened based on their titles and abstracts. Results: This review is based on 11 studies that satisfied all eligibility requirements and were included in the final review synthesis. The summative results highlighted the current state-of-the-art applications of AI in sonography, as well as providing early detection and risk stratification of carotid atherosclerotic plaques. The review also addressed emerging technologies, limitations, and future directions in AI-driven vascular imaging. Conclusion: With continued advancements, AI has the potential to revolutionize carotid sonography, enabling early intervention and reducing the global burden of stroke and cardiovascular diseases.","url":"https://doi.org/10.1177/87564793261418651","authors":["Muhammad Zubair"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-27T01:37:34Z","doi":"10.1177/87564793261418651","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-981-95-4423-3_6","name":"Enhancing Student Exam Preparation with Generative Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-4423-3_6","authors":["Juan Heredia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-23T12:39:35Z","doi":"10.1007/978-981-95-4423-3_6","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781779641410-6","name":"Artificial Intelligence: A Mediator in Recruitment Marketing","source":"crossref","abstract":"Artificial Intelligence (AI) has emerged as a formidable catalyst in redefining recruitment marketing, reconfiguring conventional talent acquisition practices into data-driven, technologically mediated processes. By leveraging algorithms, predictive analytics, and intelligent automation, AI enhances candidate sourcing, resume screening, and applicant engagement, thereby accelerating decision-making and reducing transactional inefficiencies. The integration of AI-powered chatbots and virtual assistants fosters interactive, personalized candidate experiences, enabling dynamic pre-application communication and timely feedback. This research delineates the strategic significance of AI in strengthening employer branding and formulating proactive recruitment marketing strategies that extend beyond traditional post-application paradigms. Moreover, AI’s capacity to anticipate hiring trends and identify high-potential candidates underscores its role as both an operational enabler and a strategic differentiator. While the findings illustrate profound benefits such as improved candidate-job matching, cost efficiency, and scalability, the study also acknowledges attendant challenges, including ethical dilemmas, algorithmic opacity, and applicants’ apprehensions regarding dehumanization in recruitment processes. By synthesizing insights from secondary data and scholarly discourse, the chapter posits AI not merely as a supplementary tool but as a transformative mediator in recruitment marketing, heralding a paradigm shift toward agile, inclusive, and technologically enhanced workforce acquisition.","url":"https://doi.org/10.1201/9781779641410-6","authors":["Jyoti Thakur","Ruchi Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-13T10:56:30Z","doi":"10.1201/9781779641410-6","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.63913/ail.v2i1.28","name":"Empirical Study on Artificial Intelligence in Education and Its Influence on Learning Performance and Teaching Efficiency","source":"crossref","abstract":"The rapid advancement of Artificial Intelligence (AI) has transformed the educational landscape by reshaping how knowledge is delivered, processed, and assessed. This study investigates the relationship between AI adoption and learning effectiveness, focusing on variables such as learning outcomes, teacher productivity, and student engagement from 2018 to 2025. Using quantitative analysis based on trend evaluation and correlation modeling, the findings reveal that AI adoption in education shows a strong positive relationship with improved learning performance, higher teacher productivity, and enhanced student engagement. Statistical results indicate nearly perfect correlations (r > 0.98) among AI-related educational factors, suggesting that AI integration generates comprehensive benefits across multiple dimensions of teaching and learning. The study concludes that the effective implementation of AI technologies, including adaptive learning systems, intelligent tutoring, and automated assessment tools, can significantly enhance educational efficiency and personalization. These results highlight AI not merely as a technological innovation but as a catalyst for pedagogical transformation toward more adaptive, data-driven, and learner-centered education systems.","url":"https://doi.org/10.63913/ail.v2i1.28","authors":["Gilang Miftakhul Fahmi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-26T22:00:01Z","doi":"10.63913/ail.v2i1.28","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.26650/bs/ssc18.ssc23.2026.002-4","name":"Human, Society and Artificial Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.26650/bs/ssc18.ssc23.2026.002-4","authors":["Göklem Tekdemir","Ayşen Şatıroğlu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-11T10:37:12Z","doi":"10.26650/bs/ssc18.ssc23.2026.002-4","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.2139/ssrn.6810222","name":"Supply Chain Security for Artificial Intelligence Systems","source":"crossref","abstract":"&lt;p&gt;&lt;b&gt;&lt;span&gt;Background. &lt;/span&gt;&lt;/b&gt;&lt;span&gt;Artificial intelligence systems are constructed from a heterogeneous supply chain spanning datasets, foundation model weights, fine-tuning artefacts, inference infrastructure, agent frameworks, tool ecosystems, and deployment telemetry. Classical software bill of materials concepts, developed for code dependency tracking, do not adequately capture the data, model, and agentic dependencies that shape AI system behaviour. Recent incidents involving compromised model repositories, namespace-reuse attacks, and malicious agent skills have demonstrated that AI supply chain risk has crystallised into a distinct category of cybersecurity concern.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;b&gt;&lt;span&gt;Purpose. &lt;/span&gt;&lt;/b&gt;&lt;span&gt;This paper introduces the AI Supply Chain Trust Boundary Model (AI-SCTBM), a three-dimensional analytical framework mapping seven canonical supply chain layers to five governance, risk, and compliance attributes and four functional actor roles. The framework's purpose is to provide practitioners and regulators with a structured basis for allocating liability, designing controls, and selecting attestation mechanisms across the AI system lifecycle.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;b&gt;&lt;span&gt;Approach. &lt;/span&gt;&lt;/b&gt;&lt;span&gt;The paper adopts a structured narrative review methodology combining incident analysis, regulatory document analysis, and conceptual framework construction. Evidence is drawn from 2024 to 2026 sources, including the CISA and Group of Seven joint guidance on AI software bills of materials, the European Union Artificial Intelligence Act, the National Institute of Standards and Technology AI Risk Management Framework, the International Organisation for Standardisation 42001 standard, and documented supply chain incidents affecting Hugging Face, ClawHub, PyPI, and major cloud model gardens.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;b&gt;&lt;span&gt;Findings. &lt;/span&gt;&lt;/b&gt;&lt;span&gt;The seven-layer decomposition reveals that AI supply chain risk is concentrated in layers that are absent from classical software supply chain models, particularly in foundation model weights, fine-tuning artefacts, and the emerging layer of tool ecosystems, including Model Context Protocol servers. The AI-SCTBM demonstrates that liability allocation under the European Union Artificial Intelligence Act operator taxonomy maps systematically onto pre-deployment supply chain decisions, complementing the runtime liability framework developed in the author's earlier work on multi-agent orchestration. The AI Bill of Materials concept, recently formalised through the Group of Seven consensus on seven-cluster minimum elements, emerges as the primary attestation mechanism cutting across all seven supply chain layers.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;b&gt;&lt;span&gt;Implications. &lt;/span&gt;&lt;/b&gt;&lt;span&gt;The framework provides regulators with a basis for differentiated obligations across the AI value chain, practitioners with a structured control-mapping tool, and Gulf Cooperation Council organisations with a reference architecture aligned with sovereignty and data-residency constraints. The paper identifies empirical validation through practitioner vignette surveys as a priority research direction and contributes to a coherent liability architecture spanning pre-deployment and runtime concerns within the author's broader research programme.&lt;/span&gt;&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6810222","authors":["Rizwan Tanveer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T09:53:12Z","doi":"10.2139/ssrn.6810222","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.61577/jaiar.2026.100002","name":"Artificial intelligence adoption and sustainable financial decisionmaking among MSMEs: An empirical study of Cuttack city","source":"crossref","abstract":"In recent times, Artificial Intelligence (AI) is increasingly influencing financial decision-making and sustainability measures in business organizations.The current study explores the impact of AI adoption on sustainable financial decision-making in Micro Small and Medium Enterprises (MSMEs) of Cuttack City.The study is based on both primary and secondary data obtained from 200 owners of MSMEs using a structured questionnaire survey.The study employed descriptive and analytical research designs, while statistical techniques including percentage analysis, mean score, Pearson correlation and linear regression were used for data analysis.The findings indicate a significant positive relationship between AI adoption and sustainable financial decision-making.AI-enabled instruments facilitate better financial forecasting, resource allocation and environmental considerations in financial investments.The study concludes that AI adoption significantly improves sustainable financial decision-making among MSMEs and provides useful implications for policymakers, financial institutions, and MSME owners.","url":"https://doi.org/10.61577/jaiar.2026.100002","authors":["Nusrat Parween"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-08-18T04:35:14Z","doi":"10.61577/jaiar.2026.100002","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4337/9781035338580.00025","name":"The public legitimacy of artificial intelligence governance","source":"crossref","abstract":"This chapter examines the public legitimacy of artificial intelligence governance (AIG) by synthesising the existing comparative, survey-based literature. We conceptualise legitimacy sociologically, encompassing the input, throughput, and output dimensions of AI-affected democratic decision-making. Reviewing evidence across countries and use cases, we show that legitimacy perceptions hinge on perceived transparency, fairness, accountability, performance, and distributive outcomes, yet vary by culture, knowledge, values, and trust in institutions and AI. Publics remain ambivalent. Awareness is uneven, politicisation is nascent, and experts and affected groups often diverge from the general public. Communication environments and participation formats shape attitudes, but superficial public involvement risks backlash. We map four research frontiers: addressing Euro-Atlantic bias by integrating Global South perspectives; decomposing layered trust across governments, companies, scientific bodies, and AI; tracing salience and politicisation and their shifts from output to input legitimacy criteria; and examining agentic systems and synthetic relationships that may reconfigure accountability, responsibility, and oversight.","url":"https://doi.org/10.4337/9781035338580.00025","authors":["Marco Lünich","Christopher Starke"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-11T18:01:29Z","doi":"10.4337/9781035338580.00025","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/s44163-026-00954-5","name":"Artificial intelligence-driven smart prediction and regulation model for rural carbon emissions","source":"crossref","abstract":"Rural carbon emissions from home energy usage, animal behaviour, and agricultural practices have a major impact on climate change. To precisely anticipate and control rural carbon emissions and support sustainable rural development, the project attempts to create an artificial intelligence (AI)-driven smart prediction and regulation model. An Efficient Gannet Optimization–Nested Long Short-Term Memory (EGO–Nested LSTM) model is integrated into the suggested structures. The dataset, which includes 3,000 entries from China’s rural areas between 2018 and 2024, includes important characteristics such crop kinds, livestock numbers, energy consumption, fertilizer use, and seasonal activity patterns. In data preparation, missing values were handled and Min–Max normalization was used to guarantee high-quality input. In order to minimize dimensionality while maintaining essential emission patterns, feature extraction was carried out utilizing Principal Component Analysis (PCA) and auto encoders. Complex temporal relationships are captured by the Nested LSTM network, while the EGO algorithm identified an efficient involvement strategy and optimized hyperparameters. High predictive performance was demonstrated by the experimental implementation in Python, which achieved a MAE of 0.075, RMSE of 0.123, and Mean MAPE of 0.110%. In addition to recommending energy-efficient irrigation systems, renewable energy integration, and optimized livestock organization, the EGO algorithm effectively identified the best options for reducing emissions. The suggested EGO-Nested LSTM model offers rural policymakers a precise, comprehensible, and data-driven decision-support tool. In order to reduce carbon emissions, enhance energy competence, and promote sustainable rural expansion, it makes precise forecasting and the development of workable, sustainable strategies possible.","url":"https://doi.org/10.1007/s44163-026-00954-5","authors":["Cuiying Luo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-12T05:26:44Z","doi":"10.1007/s44163-026-00954-5","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.artmed.2021.102190","name":"A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence","source":"crossref","abstract":"Artificial Intelligence (AI) is moving towards the health space. It is generally acknowledged that, while there is great promise in the implementation of AI technologies in healthcare, it also raises important ethical issues. In this study we surveyed medical doctors based in The Netherlands, Portugal, and the U.S. from a diverse mix of medical specializations about the ethics surrounding Health AI. Four main perspectives have emerged from the data representing different views about this matter. The first perspective (AI is a helpful tool: Let physicians do what they were trained for) highlights the efficiency associated with automation, which will allow doctors to have the time to focus on expanding their medical knowledge and skills. The second perspective (Rules & Regulations are crucial: Private companies only think about money) shows strong distrust in private tech companies and emphasizes the need for regulatory oversight. The third perspective (Ethics is enough: Private companies can be trusted) puts more trust in private tech companies and maintains that ethics is sufficient to ground these corporations. And finally the fourth perspective (Explainable AI tools: Learning is necessary and inevitable) emphasizes the importance of explainability of AI tools in order to ensure that doctors are engaged in the technological progress. Each perspective provides valuable and often contrasting insights about ethical issues that should be operationalized and accounted for in the design and development of AI Health.","url":"https://doi.org/10.1016/j.artmed.2021.102190","authors":["Andreia Martinho","Maarten Kroesen","Caspar Chorus"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-13T08:44:07Z","doi":"10.1016/j.artmed.2021.102190","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.62717/3083-7057-2026-1-067","name":"APPLICATION OF MODERN ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE HUMANITIES","source":"crossref","abstract":"This work is devoted to the study of the application of modern artificial intelligence (AI) algorithms in the humanities for analyzing, modeling, and forecasting complex socio-cultural and historical processes.The use of neural networks, machine learning algorithms, and other intelligent methods for processing large volumes of textual, historical, archival, and sociological data is examined.Special attention is given to the integration of quantitative and qualitative data to identify hidden patterns, establish cause-and-effect relationships, and forecast the development of socio-political, cultural, and economic phenomena.The application of AI algorithms enhances the accuracy of analysis, automates routine research tasks, and opens new prospects for interdisciplinary studies.The proposed approach demonstrates the effectiveness of modern AI methods in the humanities, facilitates the development of digital tools for researchers, and provides a foundation for new integrated methodologies for analyzing socio-cultural phenomena.","url":"https://doi.org/10.62717/3083-7057-2026-1-067","authors":["M. Turubarov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T08:42:57Z","doi":"10.62717/3083-7057-2026-1-067","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.65102/is2026268","name":"Construction of a psychological intervention model for artificial intelligence-driven learning motivation enhancement of higher vocational medical students","source":"crossref","abstract":"","url":"https://doi.org/10.65102/is2026268","authors":["Yanhong Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-04T08:52:57Z","doi":"10.65102/is2026268","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.52403/ijhsr.20260629","name":"Artificial Intelligence in Medical Education: A Cross-Sectional Study of Awareness, Perceptions, and Adoption Barriers among Undergraduate Medical Students in India","source":"crossref","abstract":"Background: Artificial intelligence (AI) is revolutionising healthcare, but its integration into Indian undergraduate medical curricula remains nascent. Understanding student perspectives is critical for effective pedagogical reform. Objectives: This study aimed to assess AI awareness and knowledge, evaluate perceptions toward its integration in medical education, and identify perceived barriers to adoption among undergraduate medical students. Methods: A cross-sectional, questionnaire-based study was conducted (January-March 2026) among 324 MBBS students (2nd to final year) at an Indian medical college. A structured, pre-validated questionnaire was used to evaluate awareness, usage patterns, perceptions, and barriers (5-point Likert scale). Descriptive statistics and chi-square tests were used for the statistical analysis, and the level of significance was set at p &lt; 0.05. Results: Participants (mean age 22.71 ± 1.59 years; 55.6% male) reported 77.5% prior AI exposure, though only 12.04% claimed high familiarity. AI chatbots were the primary tools used (92.3%). Students demonstrated favourable perceptions regarding AI’s utility in clinical decision-making and learning while largely rejecting the notion that it would replace educators. Key barriers included data privacy concerns and output reliability. Prior AI exposure significantly correlated with positive perceptions (x² = 11.948, p = 0.018). Most students advocated for formal AI training and ethical guidelines within the medical curriculum. Conclusion: Indian medical students show positive attitudes toward AI but lack deep technical familiarity. To address ethical and infrastructural barriers, structured curricular integration focusing on foundational AI competencies is essential for preparing future physicians. Key words: Artificial intelligence, medical education, students, attitude, technology adoption","url":"https://doi.org/10.52403/ijhsr.20260629","authors":["Anjani Kumar Srivastava","Anjali Singh","Aparnesh Pandey","Ganesh Chandra Satapathy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-26T08:52:54Z","doi":"10.52403/ijhsr.20260629","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21037/jmai-20-48","name":"Artificial intelligence in the diagnosis and management of COVID-19: a narrative review","source":"crossref","abstract":"Abstract: As per November 2020, there have been over 51.5 million cases of COVID-19 in the world with its mortality rate being close to 7%, causing a major burden on health care systems. Artificial intelligence (AI) is a promising tool, the use of which has been encouraged for the development of an automated diagnosis system for COVID-19 minimising the drawback of limited reverse transcription polymerase chain reaction (RT-PCR) tests. It is a time-saving, cost-effective approach, which is being promoted for reducing the physician burden during the pandemic crisis. For this narrative review, most recent data sources were collected from PubMed and Cochrane Library. Deep Learning is a promising technology for the automated diagnosis of COVID-19 through the use of advanced algorithms that identify hidden patterns on patient radiographs. Machine learning is useful in predicting patient prognosis and biomarker analysis is helpful for customised treatment planning. Infrared thermal scanners, chatbot applications, AI-based decision-making systems and image analysers are some generic contributions of AI assisting in the contactless diagnosis in suspected patients. Overall, deep neural network-based approaches have found to be superior to RT-PCR in diagnosing COVID-19 having a sensitivity of 85.35% and a specificity of 92.18% in the image-intensive diagnosis of pneumonia. In patients with comorbid conditions, telemedicine is a significant contribution of AI for monitoring and diagnosis positive cases through the use of applications such as My Day for Senior on Alexa Daily Check. Despite these advantages, the use of AI is only recommended under the guidance of the physician until sufficient clinical trials are not conducted supporting its independent use. Conclusively, the role of AI is prominent in the detection and diagnosis of COVID-19 through the use of technologies such as machine learning, deep learning and deep neural networks. However, its careful use is recommended until suitable clinical trials confirming safety are not conducted.","url":"https://doi.org/10.21037/jmai-20-48","authors":["Samer Ellahham"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-31T08:02:23Z","doi":"10.21037/jmai-20-48","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-68574-3_1","name":"Medical Robots and Minimally Invasive Surgery Driven by Artificial Intelligence: MR&amp;MIS AI","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-68574-3_1","authors":["Zbigniew Nawrat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T16:28:19Z","doi":"10.1007/978-3-031-68574-3_1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1148/ryai.250434","name":"Optimizing the Trade-off between Privacy and Utility in Medical Imaging Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1148/ryai.250434","authors":["Zekai Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-30T13:53:21Z","doi":"10.1148/ryai.250434","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.11648/j.ajai.20261001.17","name":"Deterministic σ-Regularized Equilibrium Inference Method for Artificial Intelligence","source":"crossref","abstract":"Convolutional encoders are widely used in modern artificial intelligence systems to transform structured inputs into compact representations that are subsequently processed by pooling, flattening, and training-based classification layers. Despite their empirical success, this pipeline implicitly assumes that learning is intrinsic to convolutional processing. In this work, we show that convolution itself is a deterministic linear measurement operation and does not inherently require training; learning becomes necessary only after architectural choices discard geometric structure and invertibility. By reformulating convolutional encoding as a known forward operator, inference is cast as an inverse problem governed by algebraic consistency rather than optimization trajectories. When spatial structure is preserved and pooling and flattening are avoided, the encoded representation admits a σ-regularized equilibrium solution obtained via the adjoint convolution operator. This formulation yields a unique closed-form reconstruction in a single computational step, eliminating gradient descent, backpropagation, learning rates, and iterative updates, and resulting in deterministic, reproducible inference independent of initialization or stochastic effects. From an AI perspective, the proposed framework clarifies the distinction between structure-preserving encoders, which admit equilibrium-based inference, and structure-discarding architectures, which require training-based approximation. The approach aligns convolutional encoding with classical inverse-problem methodologies, such as those used in tomography and radar, while remaining compatible with modern AI representations. Training is shown not to be a fundamental requirement of convolutional encoders, but rather a consequence of design choices that prioritize classification over structural recovery. As a result, the proposed framework offers a time- and energy-efficient alternative for inference in structured domains.","url":"https://doi.org/10.11648/j.ajai.20261001.17","authors":["Huseyin Cekirge"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-11T06:59:53Z","doi":"10.11648/j.ajai.20261001.17","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.4324/9781003435136-8","name":"Datafication, Artificial Intelligence, and Rule of Law","source":"crossref","abstract":"Artificial Intelligence has been touted as a technology that would significantly contribute to the rule of law and the achievement of Sustainable Development Goals, such as SDG 16. AI-based systems are presented as doing away with the messiness and inconsistency of human judgement due to their deterministic structure, or as uniquely responsive to societal needs through Big Data analytics, and as granular and real-time in nature, i.e. as capable of adjusting regulation to an individual s personal circumstances. However, these same features simultaneously run against some of the values crucial to both rule of law and egal access to justice: real-time granularity challenges legal stability and equality before the law while also making it impossible to know in advance the law one is subjected to; machine learning techniques applied to sets of Big Data are bound to learn from and strengthen the biases and inequalities present in a society; while the determinism of computer systems will lead to mindless application of whatever is learned, without due regard to the broader context. Moreover, the growing power of AI severely disrupts the balance between public and private governance, rendering it a stumbling block in the further realisation of SDG 16.","url":"https://doi.org/10.4324/9781003435136-8","authors":["Ignas Kalpokas","Julija Kalpokiene"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-09T13:42:17Z","doi":"10.4324/9781003435136-8","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781003638506-1","name":"Applications of Artificial Intelligence in the Healthcare Industry","source":"crossref","abstract":"Healthcare is considered one of the most promising application areas for artificial intelligence (AI) and analytics. AI is ushering into a new era in healthcare and is revolutionizing the industry in numerous ways, from improving diagnostics to optimizing patient care, administrative tasks, and drug discovery. This study attempts a meta-analysis by systematically collecting, reviewing, and synthesizing existing research studies to draw conclusions on the overall impact, effectiveness, or outcomes of applications of AI in healthcare. This chapter presents a meta-analytic review of existing literature from the Directory of Open Access Journals (DOAJ). DOAJ is an online directory and database that indexes and provides access to high-quality, peer-reviewed, open-access scholarly journals from various academic disciplines. The studies published between 2020 and October 2023 were accessed and analyzed. The total number of papers published during the analysis period is 46 (Calendar year 2020 – six papers, 2021 – 11 papers, 2022 – 18 papers, and 2023 – 11 as on date). The analysis revealed that the applications of AI techniques such as image processing, natural language processing, machine learning, data mining, prediction algorithms and detection and management are the common research topics researched during the period of study.","url":"https://doi.org/10.1201/9781003638506-1","authors":["V. Sasirekha","V. Suganya","R. Manigandan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-03T10:52:25Z","doi":"10.1201/9781003638506-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/0933-3657(89)90017-1","name":"Models for measuring performance of medical expert systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/0933-3657(89)90017-1","authors":["Nitin Indurkhya","Sholom M. Weiss"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-04-23T09:53:05Z","doi":"10.1016/0933-3657(89)90017-1","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.caeai.2026.100543","name":"Retraction notice to “Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes” [Computers and Education: Artificial Intelligence 8 (2025) 100392]","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.caeai.2026.100543","authors":["Jozsef Katona","Klara Ida Katonane Gyonyoru"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-14T12:23:52Z","doi":"10.1016/j.caeai.2026.100543","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.21037/jmai.2019.06.03","name":"Introduction for the Artificial Intelligence and Gastrointestinal Cancer Column","source":"crossref","abstract":"Gastrointestinal (GI) cancer is a leading cause worldwide of morbidity and mortality. In 2018, GI cancer accounted for 27% of all new cancer diagnoses. The incidence rate of colorectal cancer is rising in many countries, with a recent dramatic increase for people under the age of 50 years.","url":"https://doi.org/10.21037/jmai.2019.06.03","authors":["Brandon J. Teng","Michael F. Byrne"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-07-08T23:03:36Z","doi":"10.21037/jmai.2019.06.03","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1007/978-3-031-69457-8_60","name":"Research on the Application of Artificial Intelligence in Medical Education","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-69457-8_60","authors":["Mingyue Li","Xiaofei Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-28T07:03:52Z","doi":"10.1007/978-3-031-69457-8_60","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1016/j.engappai.2026.114712","name":"Multi-modal digital exhibition hall design integrating virtual reality, augmented reality and artificial intelligence toward immersive interaction and intelligent cultural services","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.engappai.2026.114712","authors":["Xiao Su","Xuan Huang","XiaoMeng Sun"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-08T21:23:00Z","doi":"10.1016/j.engappai.2026.114712","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"doi:10.1201/9781032626345-4","name":"Explainable Artificial Intelligence Techniques in Healthcare Applications","source":"crossref","abstract":"Artificial intelligence (AI) techniques such as deep learning, image processing (IP), expert systems, and neural networks are being witnessed in revolutionizing healthcare industry by solving several complex healthcare problems. This includes but not limited to disease prediction, segmentation, classification, diagnosis, and prognosis using different imaging modalities. The reasons why AI is giving good performance in these complex medical imaging tasks are the availability of high volumetric databases, high computing power, and constantly improving methodologies. Extensive literature about usage of AI methods in healthcare has been presented by research community. Despite giving outstanding results in terms of prediction and decision-making in medical industry, these computer-aided diagnosis systems have not achieved substantial real-world clinical deployment. Reason behind their moderate adoption in clinics is that AI systems, especially deep learning-based deep neural networks, are nonlinear in structure and have billions of parameters. This makes it almost impossible to know why these systems are making such crucial and life-changing decision in diagnosis. This nontransparency further raises the issue for patients, practitioners, and other stakeholders because entrusting a system which cannot explain its decision-making process presents apparent dangers. Here comes explainable artificial intelligence (XAI) to address these issues and make AI models more transparent. XAI techniques aim to produce more explainable diagnosis while preserving high accuracy level. XAI interpretability methods help in understanding the internal working principles of AI model which assist in developing the trust in model s prediction and accelerate the computer-aided diagnosis.","url":"https://doi.org/10.1201/9781032626345-4","authors":["Hareem Ayesha","Sajid Iqbal","Mehreen Tariq","Abdullah Alaulamie","Aiesha Ahmad"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-09T14:09:01Z","doi":"10.1201/9781032626345-4","addedAt":"2026-09-01T01:48:02.060Z","updatedAt":"2026-09-01T01:48:02.060Z"},{"id":"pmid:42517218","name":"Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records.","source":"pubmed","abstract":"Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospectively test a machine learning prediction model using electronic health records (EHRs) to guide AF screening.","url":"https://pubmed.ncbi.nlm.nih.gov/42517218/","authors":["Nadarajah R","Wu J","Wahab A","Reynolds C","Haris M","Joseph T","Raveendra K","Hurdus B","Kazi K","Bennett S","Hayward C","Mercer B","Kang J","Gao C","Nakao YM","Kawakami K","Chang C","Wai A","Zhou J","Tse G","Benita TR","Rokach L","Arbel R","Haim M","Zahger D","Labib D","Flewitt J","White JA","Teppo K","Lehto M","Langén V","Winstén AK","Airaksinen KEJ","Haukka J","Halminen O","Putaala J","Hartikainen J","Linna M","Freedman B","Svennberg E","Camm AJ","Lip GYH","Gale CP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 25","doi":"10.1161/CIRCULATIONAHA.126.079391","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42517162","name":"Single-Cell Dissection of the Immune Microenvironment in Intrahepatic Metastasis of Multifocal Hepatocellular Carcinoma.","source":"pubmed","abstract":"Intrahepatic metastasis in multifocal hepatocellular carcinoma is associated with poor prognosis and therapeutic resistance, yet the immune mechanisms driving disease progression remain unclear. Here, we analyzed genetic and immune differences between primary tumors and intrahepatic metastatic lesions using sequencing approaches and spatial validation methods. We found that metastatic lesions shared key genomic features with primary tumors but exhibited a distinct immunosuppressive environment enriched in myeloid and T cell populations. In particular, a subset of macrophages expressing glycoprotein nonmetastatic melanoma protein B (GPNMB) was consistently enriched in metastatic niches across multiple independent cohorts. These macrophages were spatially colocalized with CD8 + T cells exhibiting features of terminal exhaustion. Mechanistically, integrated multiomics and functional analyses revealed that GPNMB overexpression triggers lipid metabolic rewiring via the phosphatidylinositol 3-kinase/AKT-cyclooxygenase-2 cascade, leading to elevated prostaglandin E2 secretion, which directly suppresses CD8 + T cell cytotoxicity. Specific silencing of this subset using a dual-targeted, lipid-polymer nanoparticle (APL siGpnmb ) effectively reversed T cell exhaustion, inhibited metastasis, and synergized with anti-programmed death 1 immunotherapy in mouse models without inducing systemic toxicity. These findings identify GPNMB-positive macrophages as key metabolic and immune regulatory hubs, suggesting that targeting the GPNMB-prostaglandin E2 axis provides a promising precision therapeutic strategy for intrahepatic metastasis in multifocal hepatocellular carcinoma.","url":"https://pubmed.ncbi.nlm.nih.gov/42517162/","authors":["Xu Y","Zhang C","Ji Z","Liu C","Cai L","Wang C","Liao H","Wen Y","Chi L","Li C","Huang Y","Guo H","Peng Q","Pan M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.34133/research.1372","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42517126","name":"Artificial Intelligence integration and health system performance: effects on diagnostic accuracy, operational efficiency, and workforce outcomes in medical imaging departments.","source":"pubmed","abstract":"The rapid digitalization of healthcare has positioned Artificial Intelligence (AI) as a key driver of transformation in medical imaging. While its technical capabilities are well established, evidence on its real-world impact on departmental performance and workforce dynamics remains limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42517126/","authors":["Aljibali AS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1845439","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42517115","name":"Artificial intelligence, autism care, and health equity: a public health narrative review.","source":"pubmed","abstract":"Autism spectrum disorder (ASD) is a common, lifelong neurodevelopmental condition whose recorded prevalence, diagnostic delays, and uneven distribution of specialist services create a growing public health challenge. Conventional screening and diagnostic pathways depend heavily on scarce specialist expertise, contributing to long waiting times and unequal access across income settings, regions, sex, ethnicity, language, and social position. This narrative review synthesises current applications of artificial intelligence (AI) and machine learning in autism screening, diagnostic support, intervention, and longitudinal monitoring, and reframes the evidence through a public health and health equity lens. We argue that AI's most important contribution to autism care is unlikely to lie in marginal improvements in classification accuracy alone. Rather, its potential value lies in expanding access, supporting task-sharing, shortening diagnostic pathways, enabling population-oriented screening, and reaching under-recognised groups such as girls and women, adults, ethnic and linguistic minorities, and populations in low-resource settings. At the same time, AI may create an equity paradox: technologies intended to reduce disparities may reproduce or amplify them if they are trained on non-representative data, deployed across a digital divide, or governed without adequate attention to privacy, accountability, and community trust. Whether AI narrows or widens autism-related health inequalities will depend on choices about data diversity, low-resource design, co-design with autistic communities, equity-sensitive evaluation, clinical integration, and proportionate regulation.","url":"https://pubmed.ncbi.nlm.nih.gov/42517115/","authors":["Han X","Weng L","Xu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1898818","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42517037","name":"An integrated, wearable, tubeless micro-negative pressure system for closed incision management: a translational preclinical and clinical study.","source":"pubmed","abstract":"Closed incision management remains a challenge in postoperative care, largely due to the difficulty of maintaining a stable wound microenvironment in routine clinical practice. Although negative pressure therapy has demonstrated clinical benefits, its real-world effectiveness is often limited by device-related constraints. This study aimed to develop a fully wearable, integrated, tubeless micro-negative pressure system and evaluate its therapeutic potential.","url":"https://pubmed.ncbi.nlm.nih.gov/42517037/","authors":["Ti Y","Yue J","Cao X","Chen R","Wen F","Zhang S","Dong J","Yang X","Zhu Z","Lu Z","Yuan L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1866475","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42517025","name":"Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.","source":"pubmed","abstract":"Solid tumors emerge from uncontrolled cell division and interactions with neighboring cells that influence their growth and resistance to therapy. Anti-cancer drugs are developed to disrupt these processes, but they often damage healthy cells, making treatment difficult to manage safely and effectively. As the global cancer burden continues to rise, there is an urgent need for more precise and personalized approaches to detection and therapy. The study's goal is to analyze recent breakthroughs in artificial intelligence (AI), hybrid frameworks, and mathematical modeling in tumor diagnosis and treatment, examine the challenges of data-driven methodologies, and highlight emerging trends with future recommendations.","url":"https://pubmed.ncbi.nlm.nih.gov/42517025/","authors":["Kamran M","Majeed A","Haque ASMR","Abdullah JY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/hsr2.72884","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516906","name":"Pleuroparenchymal fibroelastosis-like findings identify an idiopathic pulmonary fibrosis subgroup with shortened telomeres and poor prognosis.","source":"pubmed","abstract":"Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease (ILD) with poor prognosis. Radiological pleuroparenchymal fibroelastosis (PPFE)-like findings, characterised by upper-lobe subpleural fibrosis, have been associated with worse outcome in IPF. While short leukocyte telomere length (LTL) is a recognised prognostic factor, its relationship with PPFE-like findings remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42516906/","authors":["Otoshi R","Kitamura H","Niwa T","Murohashi K","Oda T","Baba T","Hagiwara E","Iwasawa T","Takemura T","Okudela K","Nojima Y","Nakamura Y","Mizuguchi K","Natsume-Kitatani Y","Ogura T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1183/23120541.01619-2025","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516758","name":"Diagnosis and treatment progress of upper eyelid abnormalities in thyroid-eye disease.","source":"pubmed","abstract":"Thyroid eye disease (TED) is the most common orbital disorder among adults. Among its many clinical features, upper eyelid abnormalities-namely, retraction, lid lag, and swelling-frequently cause functional and cosmetic problems. This article reviews current knowledge on diagnosing and treating these abnormalities. We examine a range of options, from basic supportive care and local injections (botulinum toxin A, hyaluronic acid, and triamcinolone) to systemic drugs (corticosteroids, Teprotumumab, and alpha-1 antagonists) and surgery (levator recession, blepharotomy, and orbital decompression). Recent data suggest that IGF-1 receptor antagonists, especially Teprotumumab, can substantially reduce eyelid retraction and proptosis with a better safety profile than traditional glucocorticoids. Nevertheless, important questions remain: few head-to-head trials exist, the optimal timing for biologics versus surgery is unclear, and treatment needs to be individualized. Future studies should integrate artificial intelligence for quantitative eyelid assessment and promote multidisciplinary care. This review offers a practical framework for clinicians and outlines their research priorities.","url":"https://pubmed.ncbi.nlm.nih.gov/42516758/","authors":["Gai C","Wang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1860864","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516751","name":"Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study.","source":"pubmed","abstract":"Primary Tumor Resection (PTR) remains controversial among women with stage IV breast cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42516751/","authors":["Wang Y","Hou J","Chen X","Zhu H","Yao Y","Zhao W","Mo S","Xiong Z","Yang A","Liu W","Lai Y","Xiao W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1871537","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516722","name":"Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.","source":"pubmed","abstract":"Interstitial lung disease (ILD) is a major driver of morbidity and mortality across systemic autoimmune rheumatic diseases (SARDs), with systemic sclerosis-associated ILD (SSc-ILD) providing the most extensive evidence base. In this context, progressive pulmonary fibrosis has emerged as a central framework, as it is associated with increased mortality and facilitates the identification of candidates for antifibrotic therapy. Nevertheless, operational thresholds for chest high-resolution computed tomography (HRCT)-defined progression remain ill defined: current guidelines rely on visual HRCT interpretation and lack standardized, reproducible assessment protocols. Because the magnitude and topography of disease evolution can guide therapeutic decisions, quantitative evaluation of imaging features is pivotal. In this review, we delineate the evolution of imaging assessment from qualitative reads to quantitative phenotyping. We organize traditional densitometric and textural metrics (e.g., percentage high-attenuation areas, quantitative lung fibrosis, CALIPER [Computer-Aided Lung Informatics for Pathology Evaluation and Ratings]) alongside hybrid/data-driven approaches (e.g., data-driven textural analysis, quantitative interstitial abnormality) and recent deep-learning tools (e.g., SOFIA [Systemic Objective Fibrotic Imaging Analysis Algorithm], eLung, Qureight, SATORI [Segmentation and Annotation Tool for Radiomics and Deep Learning], AirQuant). Given the rapid pace of innovation in artificial-intelligence-based quantitative CT, we present a curated set of analytic approaches and offer a concise framework for understanding technological progress and evaluating its relevance to SARD-ILD applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42516722/","authors":["Chang SH","McDermott GC","Lee YA","Kang EH","Park YB","Choe JY","Lee EY","Sparks JA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Apr 1","doi":"10.4078/jrd.2025.0161","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516646","name":"Consumer Health Information: A Narrative Review.","source":"pubmed","abstract":"Consumer Health Information (CHI) encompasses the dissemination of information and the cultivation of appropriate attitudes toward healthcare, as well as specific professional skills, aimed at altering behaviors and enhancing the health status of consumers. The aim of this study was to introduce the concept, features, and associated issues of CHI, provide an overview of current research, and examine the information-seeking behavior of health information consumers.","url":"https://pubmed.ncbi.nlm.nih.gov/42516646/","authors":["Dehnavi MJ","Peyman N","Mansourzadeh MJ","Amiri P","Khatamifar SH","Sheikhshoaei F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.47176/mjiri.40.22","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516637","name":"Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Predicting patient outcomes in the emergency department is crucial for effective resource management and enhancing the quality of care. Recent advancements in artificial intelligence have facilitated accurate predictions of patient outcomes; however, consistent evidence regarding the diagnostic accuracy of these models in the emergency department remains limited. Therefore, the aim of the present study was to evaluate the diagnostic accuracy of artificial intelligence in predicting admission status, intensive care requirements, and in-hospital mortality in the emergency department.","url":"https://pubmed.ncbi.nlm.nih.gov/42516637/","authors":["Hosseini Kasnavieh SM","Shaker SH","Milanifard M","Hessam R","Saghandian Tousi A","Ghadesi M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.47176/mjiri.40.18","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516632","name":"Prevalence and Content of Generative Artificial Intelligence Use Policies in Iranian Medical Science Journals.","source":"pubmed","abstract":"The rapid growth of generative artificial intelligence (AI) tools has created new opportunities for scientific publishing while also introducing challenges related to research integrity and editorial ethics. Although awareness of these opportunities and challenges is increasing globally, there is limited evidence regarding how Iranian journals have addressed the use of AI through formal policies in scientific publishing. This study investigates the prevalence and content of AI use policies in Iranian medical science journals.","url":"https://pubmed.ncbi.nlm.nih.gov/42516632/","authors":["Alipour-Tehrani M","Hojati SM","Arshadi H","Erfanmanesh M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.47176/mjiri.40.47","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516546","name":"Fully automated deep learning MAPSE: retrospective analysis and real-time clinical application.","source":"pubmed","abstract":"Mitral annular plane systolic excursion (MAPSE) is an accessible echocardiographic measure of left ventricular (LV) function. However, manual measurement methods are operator-dependent and time-consuming. We developed a multistep deep learning (DL) method for off-line and real-time fully automated MAPSE estimation, and aimed to assess agreement, reproducibility, time efficiency, and feasibility compared with standard manual measurements.","url":"https://pubmed.ncbi.nlm.nih.gov/42516546/","authors":["Haga MM","Katla NL","Holmstrøm V","Holte E","Stølen S","Stensæth KH","Østvik A","Løvstakken L","Dalen H","Smistad E","Grenne B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan","doi":"10.1093/ehjimp/qyag087","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516505","name":"Development of an Iranian Clinical Guideline on Perioperative Exercise Therapy for Metabolic and Bariatric Surgery: A Delphi Consensus Study Using the AGREE II Framework.","source":"pubmed","abstract":"This guidance aims to advance current understanding of the benefits of exercise therapy and to present an exercise protocol for the preoperative and postoperative periods surrounding metabolic and bariatric surgery (MBS).","url":"https://pubmed.ncbi.nlm.nih.gov/42516505/","authors":["Nejati P","Kabir A","Mazaherinezhad A","Dadgostar H","Kermansaravi M","Nejati L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 31","doi":"10.5812/ijem-166557","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516402","name":"Engineering safety into AI-assisted music medicine for headache management: a transparent guardrail framework.","source":"pubmed","abstract":"While generative artificial intelligence enables large-scale, individualized, and digitally scalable interventions, its deployment introduces model instability and algorithmic reasoning errors. To ensure patient safety, the next frontier for AI-assisted music medicine therapeutics is not merely tool optimization, but the implementation of a transparent, rule-based clinical safety guardrail.","url":"https://pubmed.ncbi.nlm.nih.gov/42516402/","authors":["Luo X","Pei Z","Yin AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1870266","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516164","name":"Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.","source":"pubmed","abstract":"Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) has a high global prevalence and is prone to causing various serious complications. Our objective is to develop severity stratification of OSAHS by integrating multiple commonly available clinical features based on machine learning (ML).","url":"https://pubmed.ncbi.nlm.nih.gov/42516164/","authors":["Wang Q","Yang X","Xu S","Wu H","Wang G","Liu H","Chen R","Xu F","Wang C","Du K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdata.2026.1832790","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516116","name":"Third-party governance and artificial intelligence for diabetes management in primary health care, China.","source":"pubmed","abstract":"Fragmented primary health care in China fails to tackle the growing burden of noncommunicable diseases. Despite substantial investment, fewer than half of patients with diabetes achieve glycaemic control.","url":"https://pubmed.ncbi.nlm.nih.gov/42516116/","authors":["Tang W","Peng Q","Li M","Zheng N","Zhang T","Guo Y","Feng XL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.2471/BLT.25.295347","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516082","name":"Selective enhancement of cost sensitivity by methylphenidate in adult ADHD: a randomized placebo-controlled trial.","source":"pubmed","abstract":"Attention-deficit hyperactivity disorder (ADHD) is associated with impairments in real-life decisions, many involving balancing costs and benefits. Increasing dopamine and norepinephrine with stimulant medication improves decision-making in ADHD. However, it is unclear whether stimulant medication affects sensitivity to costs and/or benefits or alters the speed-accuracy tradeoff. Here, we applied the drift diffusion model (DDM) to assess how the stimulant medication methylphenidate (MPH) affects subprocesses of cost-benefit decision-making in ADHD.","url":"https://pubmed.ncbi.nlm.nih.gov/42516082/","authors":["Pedersen ML","Mowinckel AM","Ziegler S","Fredriksen M","Bjørnerud A","Endestad T","Biele G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1017/S0033291726104954","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516067","name":"Emerging diagnostic strategies in neonatal cholestasis: toward earlier and more accurate identification of biliary atresia.","source":"pubmed","abstract":"Timely diagnosis of biliary atresia remains critical in infants with neonatal cholestasis, as delays in recognition are associated with reduced native liver survival and poorer outcomes following hepatoportoenterostomy. However, distinguishing biliary atresia from other causes of neonatal cholestasis remains challenging because of overlapping clinical, laboratory, and imaging features. This review highlights emerging diagnostic strategies aimed at improving earlier and more accurate identification of biliary atresia.","url":"https://pubmed.ncbi.nlm.nih.gov/42516067/","authors":["Nonga D","Hartjes K","Lakhani A","Wawrzyniak A","Molina AN","Pandurangi S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.1097/MOP.0000000000001596","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516053","name":"Tissue Biology and the Biomechanics of Modern Gastrointestinal Stapling: Principles for Successful Surgical Stapling.","source":"pubmed","abstract":"BackgroundThe integration of gastrointestinal (GI) tissue biology with modern stapler mechanics remains a critical gap in optimizing surgical outcomes. There have been many advancements in surgical technology that attempt to optimize underlying tissue mechanobiology.MethodsA multi-modal literature search of PubMed, Embase, and Cochrane Library was conducted for studies published between 2016 and 2026. Keywords spanned device mechanics and GI anatomy. Traditional database retrieval was supplemented with artificial intelligence (AI) and surgical videos to contextualize intraoperative techniques.ResultsStaple line integrity relies on striking a balance between device engineering and biological tolerance. A deeper understanding of tissue biology and compression analysis has resulted in the development of stapler advancements, such as articulation, powered firing, force-feedback, and tissue sensing, and a reduction in complication rates, most notably anastomotic leak and hemorrhage.DiscussionAlthough enhancements in stapler technology improve mechanical consistency, there is no substitute for surgical judgment to achieve operative success. Future research should aim to develop objective measurements and standardized criteria for new tissue sensing technologies.","url":"https://pubmed.ncbi.nlm.nih.gov/42516053/","authors":["Bagnati C","Lee A","Ortiz J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1177/00031348261472876","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42516004","name":"Screening efficiency over experience: Rapid target detection in low-power field as a modifiable cognitive biomarker for diagnostic accuracy in digital cytology.","source":"pubmed","abstract":"Traditionally, cytology expertise has been equated with professional experience. However, the transition to whole-slide imaging and artificial intelligence (AI) necessitates a shift from exhaustive screening to rapid verification. The goal of this study was to identify cognitive biomarkers associated with diagnostic accuracy and evaluate their modifiability.","url":"https://pubmed.ncbi.nlm.nih.gov/42516004/","authors":["Abe N","Nishimura Y","Yamashita K","Kawamorita T","Takatori Y","Murakumo Y","Furuta R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/cncy.70132","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515993","name":"The role of pollen monitoring in allergology and otolaryngology: from environmental exposure assessment to personalized patient care.","source":"pubmed","abstract":"&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Introduction:&amp;lt;/strong&amp;gt; Pollen monitoring plays an important role in modern allergy care.&amp;lt;br /&amp;gt;Quantitatively determining airborne pollen concentrations serves as a clinically relevant&amp;lt;br /&amp;gt;tool for diagnosis, treatment, disease monitoring, and prevention.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Aim:&amp;lt;/strong&amp;gt; The study provides an overview of current clinical applications of pollen monitoring&amp;lt;br /&amp;gt;in allergology and otolaryngology, and its integration with new technologies&amp;lt;br /&amp;gt;that support precision medicine.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Methods:&amp;lt;/strong&amp;gt; A narrative review of the current scientific literature was conducted, critically&amp;lt;br /&amp;gt;analysing publications that connect pollen monitoring, molecular aerobiology,&amp;lt;br /&amp;gt;climate change, and digital medical technologies in relation to their clinical applications&amp;lt;br /&amp;gt;in allergology and otolaryngology.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Results:&amp;lt;/strong&amp;gt; Conventional aerobiological monitoring with Hirst-type volumetric samplers&amp;lt;br /&amp;gt;remains the established method for quantifying airborne pollen. It provides&amp;lt;br /&amp;gt;data on the duration, intensity, and regional variation of exposure to allergenic pollen&amp;lt;br /&amp;gt;from sensitising plants. Such information supports the interpretation of clinical&amp;lt;br /&amp;gt;symptoms, skin test results, and molecular diagnostics. It is essential in specific&amp;lt;br /&amp;gt;allergen immunotherapy, assisting with allergen selection, optimising treatment&amp;lt;br /&amp;gt;courses and evaluating outcomes. These data form the basis for allergen avoidance&amp;lt;br /&amp;gt;strategies, which are fundamental to prevention. Pollen monitoring also contributes&amp;lt;br /&amp;gt;to otolaryngology, informing the diagnosis and treatment of inflammatory diseases&amp;lt;br /&amp;gt;of the upper respiratory tract. Integrating pollen monitoring with electronic symptom&amp;lt;br /&amp;gt;diaries, mobile health applications, and portable measuring devices supports&amp;lt;br /&amp;gt;personalised assessment of allergen exposure and advances precision medicine. Automated&amp;lt;br /&amp;gt;monitoring systems and artificial intelligence facilitate near real-time transmission&amp;lt;br /&amp;gt;of information. Developments in molecular aerobiology have improved the&amp;lt;br /&amp;gt;understanding of the relationship between environmental exposure and respiratory&amp;lt;br /&amp;gt;symptoms. Long-term pollen monitoring provides insight into the biological impacts&amp;lt;br /&amp;gt;of climate change, including changes in the timing, duration, and intensity of pollen&amp;lt;br /&amp;gt;seasons, as well as the spread of invasive allergenic species.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Conclusions:&amp;lt;/strong&amp;gt; Pollen monitoring is a clinically significant component of personalised&amp;lt;br /&amp;gt;management of allergic respiratory diseases. Integration with advanced technologies&amp;lt;br /&amp;gt;is expected to improve diagnostic accuracy, optimise allergen immunotherapy,&amp;lt;br /&amp;gt;strengthen preventive strategies, and support the ongoing development of precision&amp;lt;br /&amp;gt;medicine.&amp;lt;/p&amp;gt.","url":"https://pubmed.ncbi.nlm.nih.gov/42515993/","authors":["Lipiec A","Rapiejko J","Jurkiewicz D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.5604/01.3001.0055.8490","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515921","name":"AI-Assisted Decision-Support Framework for Breast Density and Background Parenchymal Enhancement Assessment in Contrast-Enhanced Mammography.","source":"pubmed","abstract":"Interobserver variability in breast density and Background Parenchymal Enhancement (BPE) assessment remains a major limitation in Contrast- Enhanced Mammography (CEM) reporting consistency. Building on the BPE-CEM Standard Scale (BCSS) framework introduced in Part 1, this study aimed to evaluate whether a structured artificial intelligence-assisted decision-support model based on expert-derived variables could improve consistency in BCSS-related interpretation, particularly in disagreement-prone dense breast categories, rather than function as an autonomous image-based grading system.","url":"https://pubmed.ncbi.nlm.nih.gov/42515921/","authors":["Di Grezia G","Nazzaro A","Schiavone L","Cisternino E","Galiano A","Cuccurullo V","Gatta G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.2174/0115734056444815260715111252","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515875","name":"Integrating AI in Cardiovascular Systems: Innovations in Diagnosis, Risk Prediction, and Management.","source":"pubmed","abstract":"Worldwide, cardiovascular diseases remain the leading contributors to illness and death, which hinders rapid diagnosis and efficient treatment. Recent developments in artificial intelligence (AI) have transformed cardiovascular medicine by enabling the integration and analysis of large and complex data sets from portable sensors, electronic health records, and medical images. AI algorithms, such as machine learning and deep learning models, excel in detecting detailed patterns and forecasting disease progression, improving risk assessment and diagnostic accuracy. These technologies enable the early diagnosis of disorders such as heart failure, arrhythmias, and coronary artery disease, resulting in more personalized treatment approaches and better patient outcomes. Automated image processing reduces human error and simplifies procedures, while continuous cardiac function can be monitored remotely. As AI systems advance further, they have the potential to revolutionize clinical decision-making by providing real-time information and predictive analyses that anticipate adverse cardiac events. Despite ongoing challenges with data quality, model transparency, and ethical considerations, the use of AI in cardiovascular care is expected to optimize resource allocation, reduce healthcare costs, and ultimately improve survival rates. Adopting this cutting-edge technology represents a crucial step toward a more precise, proactive, and patient-centered strategy with significant potential.","url":"https://pubmed.ncbi.nlm.nih.gov/42515875/","authors":["Patil SB","Patil YP","Patil KS","Bandaru N","Suresh Gambhire M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 23","doi":"10.2174/011871529X414660251209135356","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515759","name":"Molecular Imaging in Pancreatic Cancer: Current Applications and Future Perspectives.","source":"pubmed","abstract":"Pancreatic cancer ranks among the most lethal malignancies, characterized by a five-year survival rate of approximately 10%. This dismal prognosis is largely attributable to diagnoses occurring at advanced stages and the inherent limitations of conventional imaging modalities in detecting early lesions, identifying metastases, and assessing tumor heterogeneity. Consequently, there is a critical need for non-invasive imaging techniques capable of visualizing pancreatic cancer lesions to enable accurate diagnosis, risk assessment, and the development of personalized treatment strategies. Molecular imaging, which combines highly specific targeted probes with advanced imaging technologies, offers the potential to elucidate disease-associated pathways. This review provides a comprehensive overview of recent advancements in molecular imaging platforms for pancreatic cancer, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), optical molecular imaging, photoacoustic imaging, and molecular MRI. We begin by elucidating the biological rationale for targeting key molecules, including fibroblast activation protein (FAP), integrins, and programmed death ligand 1 (PD-L1). Moreover, we critically evaluate the development and clinical translation of these probes, highlighting their ability to enhance lesion detectability, characterize intratumoral heterogeneity, and guide both targeted therapy and surgical resection. Compared with existing reviews, this work uniquely integrates a comprehensive cross-modality analysis of the latest molecular imaging strategies for pancreatic cancer. Furthermore, we examine prevailing challenges and emerging frontiers in this domain, specifically focusing on multimodal hybrid imaging, artificial intelligence-driven analytics, and integrated theranostic platforms as pivotal strategies to advance precision oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42515759/","authors":["Liu Y","Lan K","Sun Z","Huang W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.3390/ph19071078","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515758","name":"Pharmaceutical Compounding as a Pillar of Personalized Oncology: Current Applications, Emerging Technologies, and Future Perspectives.","source":"pubmed","abstract":"Personalized oncology is transforming cancer care by tailoring therapeutic strategies to the molecular and clinical characteristics of individual patients. However, increasing treatment complexity, interpatient variability, and the growing use of advanced therapeutics challenge the limitations of standardized medicines. This review examines pharmaceutical compounding as a fundamental component enabling the delivery of individualized oncology treatments. A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science, using a predefined search strategy detailed in the manuscript. This narrative review of the literature was conducted to evaluate the application of pharmaceutical compounding in modern oncology practice. The analysis includes immunotherapy, nanotechnology-based drug delivery systems, genomic-guided therapy, and combination treatment strategies. Emerging technologies, such as artificial intelligence, three-dimensional printing, and robotic compounding, were also assessed, alongside regulatory frameworks, safety challenges, and quality considerations. The main findings of this study show that compounded medications support individualized care through dose adjustment, modification of dosage forms, and exclusion of unsuitable excipients, particularly in pediatric oncology, rare cancers, and patients with specific needs. The magistral and officinal preparations help maintain continuity of care when commercial formulations are unavailable. In addition, technological advances are improving the precision, reproducibility, and safety of compounding processes, and pharmacists are centrally involved in the design, preparation, quality assurance, and regulatory oversight of these therapies. In conclusion, pharmaceutical compounding remains an essential component of personalized oncology, enabling patient-centered and adaptable treatment strategies. The expanding engagement of pharmacists, together with advances in technology and evolving regulatory frameworks, is essential to ensuring the safe and effective implementation of individualized therapies in oncology care.","url":"https://pubmed.ncbi.nlm.nih.gov/42515758/","authors":["Mascarenhas-Melo F","Pinheiro R","Victor F","Pina ME","Figueiras A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.3390/ph19071077","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515748","name":"Fluoroquinolone Exposure and Cancer Risk in Interstitial Lung Disease: A Propensity-Score-Matched Cohort Study Using Cox and Competing-Risk Models.","source":"pubmed","abstract":"Background: This study aimed to comprehensively investigate the complex association between the use of fluoroquinolone (FQ) antibiotics and cancer risk, with a specific focus on patients with interstitial lung disease (ILD)-a unique clinical population characterized by a high inflammatory burden and a high susceptibility to infections. Methods: We conducted a large-scale retrospective cohort study using a high-quality clinical database. A total of 7906 matched patients (3953 pairs) were included after propensity score matching (PSM). Three complementary statistical models were applied: the standard Cox proportional hazards model, the time-dependent Cox regression model, and the Fine-Gray competing-risks model, to provide a multidimensional assessment of cancer risk. Results: A total of 7906 matched patients (3953 pairs) were followed. After strictly defining the index date to eliminate immortal time bias, FQ exposure was associated with an increased risk of all-cause cancer in the standard Cox model (adjusted HR 1.45; 95% CI, 1.20-1.76) and the competing risk model (adjusted SHR 1.28; 95% CI, 1.06-1.55). Site-specific analyses revealed elevated risks for certain malignancies, notably prostate cancer. Importantly, when modeled as a continuous variable, the cumulative dose of fluoroquinolones showed no significant dose-response relationship with overall cancer risk (adjusted HR 0.99; 95% CI, 0.99-1.00). Conclusions: After correcting for immortal time bias, the previously hypothesized protective effect of fluoroquinolones on cancer risk was not observed. The increased risk observed in categorical models, coupled with a lack of a continuous dose-response, strongly suggests that these findings are driven by confounding by indication and reverse causation (i.e., frequent infections masking undiagnosed malignancies or reflecting severe underlying ILD), rather than a direct pharmacological effect.","url":"https://pubmed.ncbi.nlm.nih.gov/42515748/","authors":["Sun YF","Chiu YT","Ko YE","Huang YW","Hsieh LK","Lin CL","Kao CH","Yeh JJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/ph19071067","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515735","name":"Integrative Network Pharmacology and Molecular Docking Analysis Reveals the Multitarget Mechanisms of Pterostilbene in Neurodegenerative Diseases.","source":"pubmed","abstract":"Background: Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), differ in etiology but share several convergent pathological mechanisms. Pterostilbene (PTR) is a natural stilbene with reported antioxidant, anti-inflammatory, and neuroprotective properties. This study aimed to prioritize putative PTR-associated targets and biological processes potentially relevant to shared neurodegenerative mechanisms. Methods: An integrative in silico workflow combining network pharmacology, protein-protein interaction (PPI) analysis, GO Biological Process (GO BP) enrichment, molecular docking, and molecular dynamics (MD) simulations was applied. GO BP terms were filtered, focused on neurodegeneration- and neuroprotection-related processes, and subjected to REVIGO-based redundancy reduction. Selected targets were further evaluated by docking and 500 ns MD simulations. Results: A total of 181, 165, 128, and 109 shared PTR-disease targets were identified for AD, PD, HD, and ALS, respectively. Redundancy-reduced GO BP analysis indicated associations with neuroinflammation, oxidative stress and reactive oxygen species-related responses, programmed cell death, MAPK/ERK- and PI3K/AKT-related signaling, ion and calcium transport, and lipid-, steroid-, or hormone-associated regulation. PPI topology prioritized SRC, ESR1, and HSP90AA1 as recurrent hub-bottleneck proteins, whereas MD-based structural interpretation focused on ESR1 and HSP90AA1. MD analyses indicated stable PTR interactions with both proteins, with ESR1 showing the most favorable predicted interaction profile. Conclusions: These findings suggest that PTR may interact with shared neurodegeneration-relevant molecular systems, particularly through ESR1- and HSP90AA1-associated mechanisms. However, the results are exclusively computational and should be interpreted as hypothesis-generating, requiring further experimental validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42515735/","authors":["Rosiak N","Stojceski F","Maroni G","Piontek B","Cielecka-Piontek J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/ph19071053","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515514","name":"A Review of Deep Learning Methods for Multimodal Medical Image Fusion.","source":"pubmed","abstract":"Multimodal medical image fusion (MMIF) aims to integrate complementary information from different imaging modalities into a single, more informative image to support clinical diagnosis. Since 2017, a growing number of deep learning-based approaches have been proposed for MMIF, including various network architectures designed to enhance visual quality. However, a comprehensive and up-to-date review of deep learning-based MMIF techniques is still lacking. To fill this gap, this paper provides a comprehensive survey of deep learning-based MMIF methods. First, we categorize MMIF approaches based on deep learning frameworks and conduct an in-depth analysis of loss functions, evaluation metrics, medical imaging modality pairs, and medical datasets. Then, we review the mainstream deep learning-based MMIF methods, including CNNs, Autoencoders, GANs, and Transformers. Subsequently, we introduce emerging deep learning methods, including diffusion models and Mamba-based methods. In addition, we summarize widely used datasets and evaluation metrics, conduct quantitative experiments on representative methods, and propose a unified set of evaluation metrics for standardized comparison. Finally, we identify key research challenges and outline promising future directions for deep learning-based MMIF. This survey aims to provide researchers with a clear understanding of recent progress in deep learning-based MMIF and to facilitate further studies in this area.","url":"https://pubmed.ncbi.nlm.nih.gov/42515514/","authors":["Shi Y","Rigou M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.3390/s26144632","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515500","name":"Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.","source":"pubmed","abstract":"In precision oncology, the combination of the strengths of both histopathology and medical imaging provides a fertile ground for tumor characterization. Although histopathology offers a definitive cellular diagnosis, this approach is invasive and only provides a small-scale characterization of the tumor, while medical imaging modalities, such as X-ray, CT, MRI, ultrasound, and PET scans, provide a complete characterization of the tumor but, until recently, relied on the subjective ability of a human observer. The application of machine learning to radiomics aims at filling this gap, as images are mined to reveal patterns of disease not visible to the naked eye. In this perspective paper, the trajectory of machine learning in radiomics for oncology applications is critically discussed. By exploring studies using different imaging modalities, we seek to look beyond the achievements of innovative algorithms and identify the systemic weaknesses in the field, which are holding it back from translating to the clinic. In this regard, we identify two major challenges in the field: the significant effects of inter-modality and inter-scanner variability in model generalizability, and the 'interpretability gaps' in understanding the rationale for the decision-making process in ML algorithms. In this paper, we assert that these challenges are holding back even the best of algorithms and thus set the direction for the field in the future, advocating for the development of ML systems with emphasis on their performance in real-world settings as opposed to the lab.","url":"https://pubmed.ncbi.nlm.nih.gov/42515500/","authors":["Missaoui R","Saadaoui W","Del Coco M","Helali A","Leo M","Carcagnì P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.3390/s26144619","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515336","name":"A Robotic Ultrasound System for Automated Abdominal Aorta Screening: Feasibility Study in Healthy Volunteers.","source":"pubmed","abstract":"Ultrasound is safe, portable, and relatively low cost, and robotic ultrasound research is expanding across many diagnostic applications. Within this context, abdominal aortic aneurysm (AAA) screening remains comparatively unexplored, with few systems reporting end-to-end autonomous scanning and clinician-validated evaluation in volunteers. We present a conditionally autonomous (Level-3) robotic ultrasound system in which the operator defines the region of interest and confirms the target force band, after which the robot performs surface-constrained abdominal sweeps under force control and automatically selects diagnostic frames and estimates aortic diameter without further manual interaction during scanning. The system combines RGB-depth-based patient-to-robot registration, hybrid position-force control with a low-cost force sensor, and a post-acquisition image-analysis pipeline comprising rule-based aorta localisation, a composite image quality assessment (IQA) metric, and a transfer-learned U-Net segmentation baseline. In a feasibility study on ten healthy volunteers spanning BMI 18.6-33 and diverse sex and skin-tone profiles, the robot maintained stable contact within the target force band in all sessions and produced aortic images rated diagnostically acceptable by clinicians in all participants. Automated diameter measurements showed a mean absolute difference of 1.45 mm relative to clinician reference values, with 9/10 cases within 3 mm and all within the 5 mm screening criterion. Volunteer questionnaires indicated high levels of comfort and trust in the system. These results demonstrate the feasibility of operator-supervised, force-aware robotic AAA scanning and highlight the potential of low-cost robotic ultrasound for wider automated vascular imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/42515336/","authors":["Zheng Y","Geale A","Kruse P","Paraniroopasingam A","Ma Z","Singh S","Xu Z","Wang W","Li Y","Zhang S","Housden RJ","Rhode K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.3390/s26144452","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515295","name":"Digital Twin-Based Virtual Reality Framework for Interaction and AI-Assisted Control of a Parallel Surgical Robot.","source":"pubmed","abstract":"The rapid advancement of robot-assisted minimally invasive surgery (RAMIS) has created an increasing demand for integrated solutions that combine advanced robotic actuation, sensing, and intelligent control within unified training and operational frameworks. This paper presents a Digital Twin-based virtual reality (VR) interaction and control system developed for an innovative parallel surgical robot, designed to support both surgical training and real-time robot interaction. The proposed framework extends a conventional VR simulator into a bidirectional Digital Twin architecture, enabling real-time synchronization between a virtual environment and the physical robotic system. The system integrates the ATHENA parallel robot, characterized by a 4-degree-of-freedom architecture with a Remote Center of Motion (RCM) constraint, together with a flexible laparoscopic instrument providing enhanced dexterity. Interaction is achieved using VR controllers, allowing intuitive manipulation of the robotic system within an immersive environment. To enhance operational performance, an artificial intelligence module based on neural networks is integrated as an assistive component, providing real-time trajectory refinement and motion guidance. The trained model is deployed using an ONNX-compatible runtime, ensuring efficient inference and seamless integration within the control architecture. The proposed system is validated through experimental evaluation of user interaction and task execution performance, as well as through external motion assessment using an OptiTrack optical tracking system. The results demonstrate improvements in motion stability, execution efficiency, and user interaction quality, while maintaining a high level of control intuitiveness. The findings highlight the potential of Digital Twin-based VR systems as a unifying platform for surgical training, interaction, and intelligent assistance in next-generation medical robotic systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42515295/","authors":["Covaciu F","Hajjar NA","Iordan AE","Corina R","Gherman B","Cailean A","Ciocan A","Pusca A","Tucan P","Pisla D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 11","doi":"10.3390/s26144410","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515249","name":"Classification and Segmentation of Medical Images Using Cross-Representation Attention Fusion and Fuzzy Image Enhancement.","source":"pubmed","abstract":"This paper proposes a Cross-Representation Attention-Based Neural Network with fuzzy image enhancement for joint classification and segmentation of chest X-ray and kidney images. First, each input image is transformed into three complementary representations using histogram spread, fuzzy entropy, and fuzzy standard deviation-based enhancement. These representations emphasize different intensity distributions, informative regions, and local structural variations. A Cross-Representation Attention Fusion module then models multidirectional relationships among the enhanced representations and adaptively integrates their complementary features into a unified feature space. The fused features are processed by a shared encoder with task-specific classification and segmentation heads. The framework is evaluated for clinically relevant chest X-ray abnormalities, including pneumonia, pneumothorax, pleural effusion, and lung opacity, and for kidney-image classes comprising normal, tumor/renal cell carcinoma, and cystic renal mass cases. Experimental results show that the proposed method outperforms conventional and recent baseline models in both classification and segmentation. Ablation studies confirm that the fuzzy enhancement branches, cross-representation attention, and joint multi-task learning each contribute to the overall performance. Statistical and qualitative analyses further demonstrate the stability of the results and the model's ability to localize relevant lesion regions. The proposed framework provides an effective and interpretable approach to unified medical image classification and segmentation while maintaining a reasonable balance between predictive performance and computational cost.","url":"https://pubmed.ncbi.nlm.nih.gov/42515249/","authors":["Buriboev AS","Oh R","Akhram N","Dusonov K","Narzullaev I","Buribayev S","Yusupov O","Abduvaytov A","Axmedova A","Lee C","Jeon HS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/s26144364","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515236","name":"Feature-Level Fusion of Surface Electromyography and Mechanomyography Signals for MVC-Normalized Shoulder Abduction Force-Level Classification in Healthy Adults.","source":"pubmed","abstract":"Accurate recognition of upper-limb force levels is important for wearable movement monitoring and rehabilitation engineering, yet the value of combining surface electromyography (sEMG) and mechanomyography (MMG) for shoulder force classification remains incompletely characterized.","url":"https://pubmed.ncbi.nlm.nih.gov/42515236/","authors":["Zhou C","Gao Y","Gou X","Yao J","Wu Q","Cao D","He X","Yi J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/s26144351","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42515088","name":"AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.","source":"pubmed","abstract":"Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we outline future research directions, including hybrid AI-biological models, AI-supported viral surveillance in healthcare environments, and the integration of explainable AI to enhance clinical trust.","url":"https://pubmed.ncbi.nlm.nih.gov/42515088/","authors":["Khelil H","Palumbo R","Roviello GN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.3390/pathogens15070761","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514979","name":"Advances in Intranasal CNS Targeting: Integrating Formulations, Devices, Computational Fluid Dynamics, and 3D Printing.","source":"pubmed","abstract":"Nose-to-brain (N2B) delivery is a practical, non-invasive strategy for CNS targeting that can increase brain exposure while limiting systemic exposure. This review integrates three milestones in N2B delivery, formulations, devices, and quantitative evaluation strategies, to define design rules for effective olfactory/trigeminal deposition and enhance translational relevance. Formulations emphasize mucoadhesive systems, nanoparticle carriers (polymeric, lipid-based, and hybrid), nano-emulsions, and stimuli-responsive \"smart\" gels that prolong nasal residence. Regarding device advancements, the review covers conventional nasal sprays optimized for plume geometry and droplet size. Furthermore, it examines breath-actuated metered sprays, which promote soft palate closure to route aerosols to superior regions, and vibrating mesh nebulizers capable of low-velocity mists for improved upper cavity deposition. Quantitative evaluation is discussed, including 3D-printed, anatomy-accurate nasal casts, high-speed spray diagnostics, and computational fluid dynamics (CFD). This review further links formulation and device parameters to regional deposition. Available clinical and animal data illustrate the feasibility of these approaches, safety considerations, and user-technique dependencies, while highlighting the need for standardized, anatomy-aware testing protocols. Together, these developments suggest that co-designed formulation device platforms, validated by cast/CFD metrics and supported by clinical imaging or pharmacokinetic data, can support N2B product development toward consistent, patient-relevant outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42514979/","authors":["Shaghlil L","Al-Ebini Y","Shawabkeh MJA","Adam F","Saxena KK","Alshishani A","Wan Harun WS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.3390/pharmaceutics18070902","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514927","name":"Peptide-Drug Conjugates for Targeted Delivery in Hematological Malignancies: From Design Principles to Clinical Application.","source":"pubmed","abstract":"Hematological malignancies account for over 1.3 million new cases and approximately 700,000 deaths annually. Despite advances in targeted therapies, immunotherapies, and antibody-drug conjugates, relapse, refractory disease, and acquired drug resistance remain critical challenges. Peptide-drug conjugates (PDCs) have emerged as a promising targeted delivery platform, combining peptide-mediated specificity with potent cytotoxic payloads. In this review, we summarized the fundamental design principles of PDCs, including targeting peptide selection, linker engineering, and payload optimization, with emphasis on the biological characteristics of hematological malignancies. We then examined current preclinical and clinical progress across multiple myeloma, acute myeloid leukemia, myelodysplastic syndromes, B-cell non-Hodgkin lymphoma, and chronic myeloid leukemia. We further discussed emerging strategies such as cathepsin B-responsive PROTAC-PDC hybrids, nanotechnology-assisted delivery, and artificial intelligence-guided molecular design. Finally, we addressed key translational challenges, including tumor heterogeneity, payload resistance, and pharmacokinetic constraints, and proposed future directions toward biomarker-driven precision PDC therapy for hematological malignancies.","url":"https://pubmed.ncbi.nlm.nih.gov/42514927/","authors":["Zhou N","Li M","Huang Y","Wang Y","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.3390/pharmaceutics18070849","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514636","name":"Host-Directed Antiviral Strategies Against Influenza Viruses: Host Targets, Multi-Omics Approaches and AI-Assisted Discovery.","source":"pubmed","abstract":"Influenza viruses continue to pose a significant threat to both animal and public health due to their rapid evolution and the frequent emergence of antiviral resistance. Host-directed antiviral (HDA) strategies, which target host factors essential for viral replication, may represent an alternative to conventional virus-targeting approaches. However, the identification of reliable and therapeutically actionable host targets remains a major challenge, primarily due to the complexity and context dependency of host-virus interactions. Recent advancements in multi-omics technologies, including functional genomics, transcriptomics, and proteomics, have facilitated the systematic characterization of host factors involved in influenza virus infection. These methodologies have unveiled intricate regulatory networks that govern viral replication and host immune responses. Nonetheless, translating large-scale datasets into biologically meaningful targets necessitates robust integrative frameworks. In this context, artificial intelligence (AI) and machine learning methods offer powerful tools for data integration, target prioritization, and predictive modeling. In this Review, we summarize current insights into host factors that regulate influenza virus infection and discuss how multi-omics and AI-driven approaches are expediting host target discovery. Furthermore, we highlight the potential of these strategies to enhance antiviral development while addressing key challenges related to specificity, safety, and translational application. Collectively, these advancements lay a foundation that may support the rational design of next-generation host-directed antivirals.","url":"https://pubmed.ncbi.nlm.nih.gov/42514636/","authors":["Hui X","Ding S","Gao S","Xu S","Zhao T","Tian X","Wang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 27","doi":"10.3390/vetsci13070626","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514431","name":"An Analysis of the Nutritional Value of Diets Generated by Large Language Models Under Average User Conditions-3(LM)Diet Study.","source":"pubmed","abstract":"Background/Objectives : AI tools are becoming increasingly common, including in areas related to health and diet. However, there are no studies indicating the nutritional value of diets generated by large language models (LLMs). The aim of this study was to analyse the nutritional value of diets generated by three major language models. Methods : Three LLMs were used in the study: ChatGPT, Gemini and Copilot. Using each of these, 35-day meal plans were generated for three energy variants: 2000 kcal, 2500 kcal and 3000 kcal. The meal plans were then analysed using dietary software. Results : All LLMs generated meal plans with an insufficient energy value ( p &lt; 0.001, one-sample t -test) by 284-546 kcal, depending on the model and the targeted energy content. The meal plans were characterised by an excessively high proportion of energy from protein and an excessively low proportion of energy from fat. Carbohydrates were planned in the correct amounts. ChatGPT and Copilot generated meal plans with a generally adequate vitamin and mineral content. Gemini generated meal plans that were often deficient, particularly for diets with lower energy values. Conclusions : Diets generated by LLMs have many shortcomings. Unsupervised use of LLM-generated meal plans by non-expert users may produce nutritionally inaccurate diets and should not replace professional dietary counselling.","url":"https://pubmed.ncbi.nlm.nih.gov/42514431/","authors":["Dobrowolski H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.3390/nu18142363","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514297","name":"NutriSteppe-AI: Development, Architecture, and Explainable Design of a Large Language Model-Driven Chatbot for Personalized Health Menu Generation.","source":"pubmed","abstract":"Background/Objectives : Suboptimal dietary patterns are among the leading modifiable contributors to global morbidity and mortality, particularly in cardiovascular disease, type 2 diabetes mellitus (T2DM), obesity, metabolic syndrome, and hypertension. Digital nutrition platforms have emerged to improve adherence to evidence-based dietary strategies; however, many systems lack structured optimization, processing-aware nutrient profiling, and explainable artificial intelligence (AI) mechanisms. The integration of large language models (LLMs) into digital health introduces conversational personalization but also risks hallucination and unsafe outputs without constraint enforcement. This study aimed to describe the system development, architecture, database infrastructure, optimization algorithms, explainability enforcement, and digital health implications of NutriSteppe-AI, a chatbot-first LLM-driven system for personalized health menu generation constrained by deterministic nutrient logic and processing-aware scoring. Methods : NutriSteppe-AI integrates: (1) a multi-source structured nutrient database of 20,000 food products with up to 130 tracked nutrients; (2) energy requirement estimation using the revised Harris-Benedict equation; (3) linear programming-based multi-objective optimization; (4) a Healthy Food Index (HFI; 0.5-5.0 scale) incorporating NOVA processing classification penalties; (5) traffic-light nutrient gating; and (6) a constrained LLM orchestration layer governed by structured API contracts. Algorithmic validation was performed using 10,000 simulated user profiles spanning diverse age, anthropometric, activity, dietary exclusion, and budget parameters. Results : The system achieved 96.8% full constraint satisfaction with macronutrient mean absolute errors of 11.60% (energy), 18.86% (protein), 16.26% (fat), and 20.91% (carbohydrates). Incorporating NOVA processing penalties reduced ultra-processed food HFI scores by 0.73 points ( p &lt; 0.001). Median optimized menu HFI improved from 3.6 to 4.3. Median system latency was 1.8 s. Explainability validation confirmed 100% deterministic alignment with zero hallucinated numeric claims. Conclusions : NutriSteppe-AI demonstrates that LLM-driven nutrition chatbots can achieve deterministic, explainable, and clinically aligned performance when governed by structured optimization, processing-aware scoring, and explainability enforcement. This architecture provides scalable digital health infrastructure for cardiometabolic disease prevention in diverse populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42514297/","authors":["Salkhanova A","Nabigazinova E","Kaldybay A","Omirbekova A","Sabit M","Baikonsova L","Yergeshbayeva R","Knyazbay A","Chuiko T","Yermakova I","Bekzhanova A","Tyulebekova G","Niyetkaliyeva D","Serikova N","Sharman A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/nu18142228","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514231","name":"Beyond Resistance Genes: Pseudomonas aeruginosa as a Complex Adaptive System Driving Persistence, Evolution, and Antimicrobial Resistance.","source":"pubmed","abstract":"Pseudomonas aeruginosa ( P. aeruginosa ) is one of the most adaptable bacterial pathogens and a major cause of difficult-to-treat infections worldwide. Although antimicrobial resistance (AMR) is commonly attributed to resistance genes and their associated mechanisms, this perspective does not fully explain the ability of P. aeruginosa to survive antimicrobial exposure, establish chronic infections, and persist across diverse environmental and host-associated habitats. In this review, we examine P. aeruginosa within a complex adaptive systems framework, integrating evidence from molecular microbiology, physiology, ecology, population biology, and evolutionary genomics. We describe how environmental sensing, regulatory integration, phenotypic plasticity, population heterogeneity, persistence, biofilm formation, collective behavior, and evolutionary diversification interact across biological scales to shape bacterial survival and long-term success. Evidence from chronic infections and environmental reservoirs indicates that resistance emerges from interconnected physiological, ecological, and evolutionary processes rather than from isolated genetic determinants alone. Building on these observations, we propose an adaptive resilience cascade framework in which environmental sensing drives physiological diversification, persistence maintains survival under stress, evolutionary selection stabilizes advantageous traits, and ecological dissemination promotes the spread of successful lineages. This framework provides a systems-level explanation for treatment failure, chronic colonization, and resistance emergence while linking cellular responses to population, ecological, and evolutionary outcomes. Emerging approaches, including single-cell analyses, spatial omics, evolution-informed interventions, engineered biological therapeutics, and artificial intelligence-assisted modeling, further support a shift toward targeting adaptive resilience rather than resistance determinants alone. Viewing P. aeruginosa as a complex adaptive system offers an integrated conceptual foundation for future surveillance, therapeutic development, and antimicrobial stewardship strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42514231/","authors":["Elbehiry A","Marzouk E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/life16071163","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514203","name":"Radioguided Surgery and Axillary Management in Breast Cancer: From Molecular Imaging to 3D Navigation Toward Personalized Treatment.","source":"pubmed","abstract":"Radioguided surgery has become a key component of contemporary breast cancer care, supporting less invasive approaches while maintaining oncologic safety. This narrative review summarizes current practice and recent developments in radioguided breast and axillary surgery, from established molecular imaging workflows to emerging three-dimensional and intraoperative technologies. Modern breast cancer management is increasingly shaped by tumor biology and the widespread use of neoadjuvant systemic therapy, which is transforming surgical decision-making and driving a shift toward personalized, patient-tailored pathways. In this context, radioguided techniques help maintain procedural accuracy despite therapy-induced changes in breast and nodal anatomy, enabling reliable lesion localization and targeted management of the axilla. We discuss sentinel lymph node strategies and de-escalation concepts, including targeted axillary dissection (TAD) after neoadjuvant therapy using marked nodes and selective removal approaches. We also review localization methods, including radioactive seed-based techniques, and the expanding role of molecular imaging-guided surgery to support intraoperative decision-making. Particular attention is paid to technologies aimed at improving surgical precision and margin assessment, including portable/freehand SPECT concepts and intraoperative PET/CT-based specimen imaging for immediate evaluation of excised tissue. Finally, we highlight how artificial intelligence and digital tools may enable workflow optimization, navigation, image interpretation, and decision support, accelerating the transition toward individualized treatment. Overall, integrating molecular information with real-time 3D guidance can help tailor breast and axillary management to each patient while reducing morbidity.","url":"https://pubmed.ncbi.nlm.nih.gov/42514203/","authors":["Orozco Cortés J","Tapia M","Sancho JS","Arias CC","Villa EB","Sornosa EM","Flor VL","Exposito RD","Leon LF","Bas CS","Salazar DC","Bermejo B","Sicart SV","Andujar JMC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/life16071133","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42514189","name":"Real-World Insights into Stage I-III Non-Small Cell Lung Cancer in Spain in the Pre-Immunotherapy Era Using AI Techniques: The IntellyLUNG Study.","source":"pubmed","abstract":"Treatment of non-small cell lung cancer (NSCLC) has been transformed by immunotherapy and targeted therapies. We aimed to characterize clinical features, treatment patterns, and healthcare resource use in patients with early and locally advanced NSCLC before incorporation of these therapies. This retrospective observational study included adults diagnosed with stage I-III NSCLC at four Spanish hospitals between 2014 and 2018, with follow-up until 2021, using artificial intelligence to extract data from electronic health records. A total of 951 patients were included (34.7% stage I, 16.7% stage II, 48.6% stage III), with a median age of 66 years and 31.9% female. Surgery was performed in 78.5% of stage I, 74.8% of stage II, and 35.5% of stage III patients. Among surgical patients, 62.5% received adjuvant chemo- and/or radiotherapy, 20.8% neoadjuvant therapy, and 15.7% both; among non-surgical patients, chemoradiotherapy was the most common treatment (50.4%). Beyond hospitalization, outpatient visits were the most frequently used healthcare resource. These findings provide a historical benchmark of NSCLC care before introduction of immunotherapy and targeted therapies in these settings, highlighting treatment variability and the need for earlier diagnosis, structured treatment pathways, and multidisciplinary management.","url":"https://pubmed.ncbi.nlm.nih.gov/42514189/","authors":["Corral Jaime J","de Castro J","Azkarate A","García Ledo G","Calles A","Marsé R","Parreira ASFM","Villamayor J","Gutiérrez-Sainz L","Benítez-Fuentes JD","Elía DC","Gutiérrez N","Valles MA","Sarró E","López N","Savana Research Group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 5","doi":"10.3390/life16071119","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513932","name":"Evaluation of an AI-Assisted Colony Counting System Across Multiple Culture Media Using Standardized Pure Culture Plates.","source":"pubmed","abstract":"Automated AI-assisted colony counting may improve standardization in digital microbiology, but performance can be affected by colony density, culture medium, colony morphology, adhesion, and plate artifacts. We evaluated the Starry-300 AI colony counting system using 382 standardized pure culture bacterial and yeast plates across four agar media. AI-assisted counts were compared with a three-reader median ImageJ-assisted manual comparator derived from independent counts by experienced technologists. The AI workflow showed close agreement with the manual consensus comparator across a broad colony density range. Overall, 360/382 plates (94.24%) were within &#xb1;10 CFU and 377/382 plates (98.69%) were within &#xb1;30 CFU of the manual median count. Error-based and agreement analyses showed a mean absolute error of 3.19 CFU/plate; both the intraclass correlation coefficient and Lin's concordance correlation coefficient were0.99. AI software analysis required approximately 5-15 s/plate, although this did not include plate handling, correction, or reporting. These findings support the analytical feasibility of reviewable AI-assisted colony enumeration under controlled pure culture conditions. Further validation using primary clinical specimens, mixed cultures, near-threshold samples, and external sites is required before broad clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42513932/","authors":["Li X","Xiao M","Li D","Liu M","Xu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 30","doi":"10.3390/microorganisms14071426","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513767","name":"Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.","source":"pubmed","abstract":"Metallic orthopedic implants face persistent clinical challenges that have proved resistant to incremental conventional development. Machine learning and artificial intelligence offer a complementary paradigm for navigating the high-dimensional design spaces governing implant performance, yet the literature remains fragmented across disciplinary silos with no comprehensive synthesis spanning the full development pipeline.","url":"https://pubmed.ncbi.nlm.nih.gov/42513767/","authors":["Guruprasad P","Sivaram P","Cibik A","Bombard PT","Anastasio AT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/ma19143031","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513625","name":"Current Clinical Perspectives of Biomarkers in Respiratory Diseases: A Narrative Review.","source":"pubmed","abstract":"Respiratory medicine is transitioning from symptom-driven, standardized care to a more precise, patient-specific approach guided by molecular profiling. This evolution is being enabled by advances in liquid biopsy, multiomics, and artificial intelligence (AI) analytics. Fractional exhaled nitric oxide (FeNO) and blood eosinophils, the two commonly used markers in asthma, are now being joined by more precise airway markers such as galectin-10, which could aid clinicians in making more informed decisions for biological treatments. In chronic obstructive pulmonary disease (COPD) similar progress is underway, with treatment now emphasizing inflammation endotypes, especially eosinophilic patterns, to direct therapeutic choices. Alongside these developments, routine blood-based ratios (e.g., platelet-to-lymphocyte and neutrophil-to-lymphocyte) are being explored as predictors of exacerbation risk, and forced oscillation testing (FOT) is proving useful for picking up early disease shifts. In more severe conditions, biomarkers are linked to an early and better prognosis, enabling timely intervention. Markers like Matrix metalloproteinase-7 (MMP-7) and CC chemokine ligand 18 (CCL18) have proven to be reliable indicators of mortality and disease progression in idiopathic pulmonary fibrosis. Meanwhile, in lung cancer, liquid biopsies, especially those measuring circulating tumor DNA and micro-RNA (miRNA) panels, are enhancing screening accuracy while helping to cut down on the high false-positive rates seen with low-dose computerised tomography (CT). Other respiratory conditions such as bronchiectasis, pulmonary embolism, pneumonia, and acute respiratory distress syndrome (ARDS) are also benefiting from biomarker advances. At the same time there is a growing push to standardize how these biomarkers are measured. AI-based clinical decision support systems are also playing an increasingly important role in the translation of all these complicated data into actionable clinical insights. Together these developments pave the way for improved respiratory care that is precise and responsive to individual patient needs.","url":"https://pubmed.ncbi.nlm.nih.gov/42513625/","authors":["Gurajala S","Al Humoud SY","Al Yousif GF","Alameri RA","Pandurangam G","Fayyomi AKA","Abed S","Sardidi NS","Alrayes MM","Alsabhan TA","Alajmi SH","Alfaraj A","Al Ghannam N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.3390/jcm15145708","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513579","name":"Lateral Geniculate Nucleus Volume Assessment Using Linear Mixed Model in Moderate and Advanced Retinitis Pigmentosa.","source":"pubmed","abstract":"Purpose: We aimed to compare the volume of the lateral geniculate nucleus (LGN) in patients with different stages of retinitis pigmentosa (RP) with regard to age, sex and symmetry of the LGN. Methods: The investigated cohort included 13 patients with moderate (median Snellen visual acuity 0.75) and 18 patients with advanced (median Snellen visual acuity 0.06) RP-related visual field loss. The volumes of the left and right LGNs were manually measured using ITK-SNAP software after an examination of the brain with a 7 Tesla MRI. A linear mixed statistical model was used to assess LGN volume regarding age and gender of moderate and advanced RP patients and symmetry of both LGNs. Results: The mixed-effects linear model did not reveal a significant effect of disease group on LGN volume after adjusting for age and sex (F(1.27) = 0.01, p = 0.91). A significant effect of the LGN side was demonstrated, with the volume of the right LGN being significantly greater than that of the left (F(1.29) = 29.45, p &lt; 0.001) in both disease groups, left-right. The interaction between disease group and LGN side was not statistically significant (F(1.29) = 0.45, p = 0.51). There is a tendency for LGN to decrease with age (F(1.27) = 3.84, p = 0.060), and there is no gender predilection (F(1.27) = 0.11, p = 0.74) in RP patients. There was correlation found between left LGN volume and visual acuity (&#x3c1; = 0.64) and central retinal thickness (&#x3c1; = 0.71) in the moderate group). Conclusions: No significant differences in LGN volume were found between patients with moderate and advanced RP. Furthermore, the volume of the right LGN was larger than the volume of the left LGN in RP patients, and this asymmetry is not gender-dependent. Correlation was found between the left LGN volume and visual acuity and central retinal thickness in moderate group. Our findings may have clinical implications for future RP management.","url":"https://pubmed.ncbi.nlm.nih.gov/42513579/","authors":["Nowomiejska K","Niedziałek A","Toborek K","Czarnek-Chudzik A","Rejdak R","Pietura R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.3390/jcm15145665","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513535","name":"The Facial Nerve in Contemporary Surgery: Anatomical Variability, Pathology-Induced Distortion, and Functional Preservation.","source":"pubmed","abstract":"Objectives: The facial nerve (FN) possesses one of the most intricate anatomical courses in the head and neck, traversing the brainstem, temporal bone, and parotid gland before terminating within the muscles of facial expression. Owing to its complex anatomy, marked anatomical variability, and frequent distortion by adjacent pathology, preservation of FN integrity remains a fundamental challenge in skull base, otologic, and head and neck surgery. This review aims to provide a comprehensive synthesis of the contemporary literature regarding the clinical anatomy of the FN and to examine how anatomical variation, pathology-induced distortion, surgical strategy, and emerging technologies influence nerve preservation and functional outcomes. Methods: A comprehensive narrative review of the literature was conducted using PubMed, Scopus, and Google Scholar. Publications from 1983 through 2026 were searched using combinations of keywords, including \"facial nerve,\" \"facial nerve anatomy,\" \"anatomical variation,\" \"vestibular schwannoma,\" \"hemifacial spasm,\" \"parotid surgery,\" \"facial nerve injury,\" \"facial nerve reconstruction,\" \"facial reanimation,\" \"diffusion tensor imaging,\" \"intraoperative neurophysiological monitoring,\" and \"artificial intelligence.\" Peer-reviewed anatomical, radiological, clinical, and review articles published in English were included, while conference abstracts and studies lacking direct anatomical or surgical relevance were excluded. Particular emphasis was placed on surgically relevant anatomical variations, pathology-related anatomical distortion, advanced imaging modalities, intraoperative neurophysiological monitoring, reconstructive techniques, and predictors of postoperative facial nerve function. Results: Facial nerve preservation was found to depend on the interplay between individual anatomical variability, disease-related anatomical distortion, and operative strategy. In vestibular schwannoma surgery, nerve displacement, capsular adhesion, and cystic tumor degeneration were consistently associated with increased surgical complexity and less favorable postoperative facial function. In hemifacial spasm, successful microvascular decompression relied on precise identification of neurovascular conflict at the root exit zone. Within the parotid gland, substantial variability in branching architecture and surgical landmarks contributed to an increased risk of iatrogenic injury. Advanced imaging techniques, particularly diffusion tensor imaging tractography, improved preoperative prediction of FN location, while intraoperative neurophysiological monitoring enabled real-time assessment of neural integrity and functional preservation. Emerging artificial intelligence-based predictive models demonstrated potential to enhance patient-specific surgical planning and prognostication. Conclusions: Contemporary facial nerve surgery has evolved toward an individualized, anatomy-driven, and function-preserving paradigm supported by advanced imaging, intraoperative monitoring, and reconstructive strategies. Detailed understanding of both normal FN anatomy and pathology-induced anatomical distortion remains essential for optimizing surgical decision-making, maximizing nerve preservation, and improving long-term functional outcomes. Future developments integrating multimodal imaging, predictive analytics, and artificial intelligence may further refine patient-specific management and enhance postoperative facial function.","url":"https://pubmed.ncbi.nlm.nih.gov/42513535/","authors":["Łabętowicz P","Szczerba N","Olewnik Ł","Włodarczyk N","Borowski K","Landfald IC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/jcm15145622","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513513","name":"Development and External Validation of an Interpretable Machine Learning Framework for Predicting Pneumothorax-Associated Acute Kidney Injury: A Multicenter Retrospective Study.","source":"pubmed","abstract":"Background/Objectives: Intensive Care Unit (ICU) patients with pneumothorax face an elevated risk of developing Acute Kidney Injury (AKI) due to compromised hemodynamics and increased intrathoracic pressure. Early identification is crucial but challenging. This study aimed to develop and externally validate an interpretable machine learning (ML) framework to predict pneumothorax-associated AKI. Methods: This multicenter retrospective study utilized data from the MIMIC-IV database (development), alongside a temporal validation cohort (MIMIC-III) and an independent external validation cohort (eICU). A tri-algorithm intersection strategy-comprising Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and Recursive Feature Elimination (RFE)-was applied to extract optimal predictors. We systematically evaluated nine supervised ML algorithms. Shapley Additive exPlanations (SHAP) and Restricted Cubic Spline (RCS) analyses were integrated to unveil decision-making mechanics and non-linear dynamics. The optimal model was deployed as a web-based dynamic nomogram. Results: The hybrid feature selection strategy identified a parsimonious consensus of 7 core predictors (BUN, SOFA score, CKD, PEEP, Heart Failure, Albumin, and Age). Following comprehensive evaluation, the Logistic Regression model demonstrated favorable discriminative performance [Area Under the Curve (AUC) = 0.839 (95% CI: 0.786-0.891), Sensitivity = 81.5%, Specificity = 76.0%] and external generalizability (eICU AUC = 0.869; MIMIC-III AUC = 0.854). SHAP analysis delineated the individual contribution of each feature. RCS analysis revealed significant non-linear, dose-response relationships, highlighting an exponential AKI risk escalation driven by elevated BUN and higher PEEP levels. Decision Curve Analysis (DCA) suggested the potential net clinical benefit of the model across all validation cohorts. Conclusions: We developed a transparent, externally validated ML framework for predicting pneumothorax-associated AKI. The resulting web-based nomogram provides intensive care physicians with a practical, data-driven bedside tool to assist in personalized risk stratification. However, given the substantial calibration drift observed in the external cohort, local recalibration is essential prior to its use outside the MIMIC-derived population, and prospective cohort validation remains necessary before routine clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42513513/","authors":["Pan G","Fan J","Wang W","Yang G","Su J","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/jcm15145599","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513509","name":"Prevalence, Timing and Mutual Relationships of Acute and Chronic Respiratory Failure in People with Chronic Obstructive Pulmonary Disease.","source":"pubmed","abstract":"Background and aim: Prevalence, time to first diagnosis of acute (ARF) and chronic (CRF) respiratory failure in people with chronic obstructive pulmonary disease (COPD) are still unclear and were assessed in this 15-year, retrospective, observational, real-world data study. Methods: Our study examined one cohort (Cohort C1) of people older than 40 years diagnosed with COPD without any previous diagnosis of ARF. To assess the temporal relationship between ARF and CRF, two more cohorts of individuals were created: Cohort C2 included people with COPD subsequently diagnosed with CRF, without any previous diagnosis of ARF. Cohort C3 consisted of COPD people subsequently diagnosed with ARF, without any previous diagnosis of CRF. Adjusted hazard risk of and timing to first diagnosis were estimated for: i-ARF and CRF in Cohort C1; ii-ARF in Cohort C2; iii-CRF in Cohort C3. Results: Participants in Cohort C1 were equally distributed by gender, with a mean age of 66.9 &#xb1; 12.2 years and BMI 28.1 &#xb1; 7.72 kg/m 2 . After 15 years since COPD diagnosis, participants in Cohort C1 showed 45.25% (75th percentile reached after 7.2 years) and 21.86% (80th percentile after 13.7 years) probabilities for ARF and CRF, respectively. Participants in Cohort C2 showed an 86.78% ARF (acute-on-chronic) probability (1.7 years median time to ARF since CRF diagnosis). Individuals in Cohort C3 showed a 41.52% CRF probability (75th percentile after 3.3 years since first ARF diagnosis). Conclusions: COPD individuals reported different prevalence of and times to ARF or CRF diagnoses. Time since CRF diagnosis to first acute-on-chronic respiratory failure is shorter than time to post-ARF chronic respiratory failure. Our findings provide useful insights into periods of increased vulnerability following COPD diagnosis and respiratory failure events, which may have implications for patient monitoring and preventive strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42513509/","authors":["Vitacca M","Riboni G","Paneroni M","Tibollo V","Bellazzi R","Ambrosino N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/jcm15145595","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513410","name":"An Interpretable Center-Specific Machine Learning Model for Risk Stratification Following Mitral Valve Surgery: A Pilot Study.","source":"pubmed","abstract":"Background/Objectives: Mitral valve surgery is associated with substantial perioperative heterogeneity and risk of postoperative complications. Although established risk scores such as EuroSCORE II provide population-level prognostic estimates, their performance may be limited in specific surgical populations and institutional settings. This pilot study aimed to develop and internally validate an interpretable center-specific machine learning model for perioperative risk stratification following mitral valve surgery and to explore its translational implementation through a prototype clinical application. Methods: A retrospective single-center study was conducted including 211 consecutive patients undergoing mitral valve surgery with ring implantation. Routinely available demographic, laboratory, and perioperative variables were evaluated as candidate predictors. The primary endpoint was a composite of major postoperative complications, including in-hospital mortality, stroke, conversion to sternotomy, and rethoracotomy. Predictive approaches included logistic regression, LASSO regression, and random forest classification. Internal validation was performed using 5-fold cross-validation and bootstrap resampling. Model explainability was assessed using regression coefficients and SHAP (SHapley Additive exPlanations) analysis. Results: The composite endpoint occurred in 34 patients (16.1%). In the complete-case final logistic regression model, apparent discrimination reached an AUC of 0.750 (95% CI 0.643-0.858), with a Brier score of 0.105. In the predefined train-test evaluation, the simplified logistic regression model achieved a test-set AUC of 0.67, while 5-fold cross-validation yielded a mean AUC of 0.75. LASSO regression achieved the highest cross-validated AUC (0.78), although with marked discrepancy between test-set and cross-validation performance, suggesting model instability. Across models, higher age, serum creatinine concentration, cardiopulmonary bypass duration, and cross-clamp time were associated with increased complication risk, whereas higher hemoglobin levels were associated with lower risk. Conclusions: This pilot study demonstrates the feasibility of developing interpretable center-specific machine learning models for perioperative risk stratification following mitral valve surgery. Simplified regression-based approaches provided clinically transparent predictions with moderate discriminatory performance, while penalized models showed potential for improved generalizability. Further multicenter validation is required before clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42513410/","authors":["Stańska A","Kilarska M","Janeczek M","Karolak W","Klapkowski A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 13","doi":"10.3390/jcm15145496","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513317","name":"Artificial Intelligence in Intracerebral Hemorrhage: Current Applications and Future Perspectives.","source":"pubmed","abstract":"Intracerebral hemorrhage (ICH) remains one of the most severe forms of stroke and is associated with high mortality, poor functional outcomes, and substantial healthcare burden worldwide. Despite advances in neurocritical care and minimally invasive surgical techniques, the management of ICH remains challenging because of disease heterogeneity, rapid neurological deterioration, and the lack of effective individualized treatment strategies. In recent years, artificial intelligence (AI) has emerged as a promising tool for improving the diagnosis, prognostic evaluation, and precision management of ICH. A systematic literature search was performed in PubMed, Web of Science, and Embase to identify studies on AI applications in ICH, with predefined inclusion criteria focusing on imaging analysis, prognostic prediction, clinical decision support, and minimally invasive surgery. This review summarizes the major clinical applications, limitations, and future directions of AI in ICH, including multimodal foundation models, intelligent surgical assistance, and personalized precision care. Recent studies have demonstrated that AI-based models can significantly improve the accuracy of hematoma segmentation, hematoma expansion prediction, and functional outcome prognostication compared with conventional approaches. Despite encouraging progress, several important barriers continue to limit clinical translation, including data heterogeneity, limited external validation, insufficient interpretability, ethical and regulatory concerns, and challenges in workflow integration. Overall, AI has the potential to transform ICH management from conventional experience-based practice to data-driven, personalized, and precision neurosurgical care.","url":"https://pubmed.ncbi.nlm.nih.gov/42513317/","authors":["Xu X","Zhang J","Gan Z","Zhang S","Zheng H","Chen X","Wang Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/jcm15145403","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513295","name":"Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy-Resection Gap.","source":"pubmed","abstract":"Breast cancer is a leading global diagnosis where lymphovascular invasion (LVI) serves as a critical indicator of metastatic potential. Conventional assessment is often hindered by its reliance on postoperative findings, sampling errors, and subjective interobserver variability. This review evaluates how artificial intelligence (AI), digital pathology, and MRI radiomics provide earlier, quantitative estimations of LVI-related risk. The strongest direct evidence currently comes from MRI-based studies, where validation performance often reaches area under the receiver operating characteristic curve (AUC) values of 0.84-0.90. By contrast, digital pathology is especially mature for LVI-adjacent tasks such as lymph node metastasis detection and slide-based relapse-risk modelling, which together provide an important translational foundation for future LVI-specific tools. This review also addresses issues that remain underdeveloped in the literature, including the distinction between lymphatic and vascular invasion, AI-specific risk-of-bias and reporting frameworks, the emerging regulatory landscape for adjacent breast AI tools, and the gap between resection-based development datasets and biopsy-level preoperative use. Although most modern computational models are developed using extensive surgical resection specimens, their true clinical utility hinges on successful validation and performance within the highly restricted, fragmented tissue context of preoperative core needle biopsies. Overall, the field appears most promising when LVI prediction is framed not as an isolated binary task, but as one component of a broader metastatic-risk workflow that supports calibrated, multidisciplinary breast cancer decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42513295/","authors":["Alshreef BS","Kariri YA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/jcm15145381","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513273","name":"Reappraising Kidney Biopsy in Diabetic Kidney Disease: Histopathology, Clinical Course, and the Future of Precision Nephrology.","source":"pubmed","abstract":"Background : Diabetic kidney disease (DKD) is one of the leading causes of chronic kidney disease and kidney failure worldwide. Although most patients with diabetes are diagnosed clinically on the basis of albuminuria, estimated glomerular filtration rate decline, and diabetic retinopathy, clinical parameters alone may not reliably distinguish biopsy-proven diabetic nephropathy (DN) from non-diabetic kidney disease (NDKD) or mixed lesions. The role of kidney biopsy in diabetic patients therefore remains selective rather than routine, but its diagnostic and prognostic value is increasingly relevant in precision nephrology. Methods : This narrative review summarizes clinicopathological evidence on kidney biopsy in patients with diabetes, with particular emphasis on the Renal Pathology Society classification of DN, the prognostic significance of glomerular, tubulointerstitial, and vascular lesions, indications for biopsy, and emerging digital and molecular approaches to renal tissue assessment. Results: Histopathological evaluation provides information that cannot be fully captured by routine clinical markers. While glomerular lesions remain central to the classification of DN, tubulointerstitial fibrosis and tubular atrophy are among the strongest predictors of kidney disease progression. Vascular lesions, particularly arteriolar hyalinosis, also carry renal and cardiovascular prognostic significance. Kidney biopsy is especially valuable in patients with atypical clinical features, including rapid decline in kidney function, acute-onset nephrotic-range proteinuria, active urinary sediment, short diabetes duration, absence of diabetic retinopathy, or suspicion of immune-mediated glomerular disease. In these settings, biopsy may identify NDKD or mixed DN/NDKD, with direct therapeutic implications, including the potential use of disease-specific or immunosuppressive treatment. Emerging technologies, including artificial intelligence-assisted digital pathology, spatial transcriptomics, multi-omics profiling, and biomarker-based \"virtual biopsy\" models, may further refine lesion quantification and risk stratification, although they currently complement rather than replace tissue-based diagnosis. Conclusions : Kidney biopsy remains a clinically important tool in selected patients with diabetes. Beyond confirming DN, it enables the detection of NDKD, improves prognostic stratification, and supports individualized therapeutic decision-making. A selective, indication-driven biopsy strategy should be regarded as an essential component of precision nephrology in DKD, particularly as histopathology becomes increasingly integrated with digital, molecular, and computational approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42513273/","authors":["Pieczaba M","Dubniański B","Kuźnik Z","Wajerowska W","Konieczny A","Banasik M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/jcm15145359","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513240","name":"Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.","source":"pubmed","abstract":"Cardiac surgery remains a high-risk, resource-intensive domain in which perioperative complications significantly influence clinical outcomes, institutional performance, and healthcare expenditure. Despite advances in technique and protocol standardization, contemporary perioperative management largely relies on static risk stratification and reactive quality assessment. This narrative review synthesizes the current evidence on artificial intelligence (AI) and machine learning for perioperative risk prediction in cardiac surgery, spanning acute kidney injury, mortality, prolonged mechanical ventilation, postoperative atrial fibrillation, and intensive care unit deterioration, and critically appraises the methodological limitations, validation gaps, and fairness concerns that constrain clinical translation. Across these applications, predictive models have demonstrated incremental discrimination over conventional risk scores, yet remain predominantly endpoint-specific, single-institution, and disconnected from prospective clinical implementation. Building on this evidence, we propose Preventive Cardiovascular Intelligence (PCInt) as one possible organizing framework that integrates predictive analytics, dynamic risk trajectory modeling, and structured quality improvement methodologies, and we outline how such a framework might be operationalized across the surgical lifecycle. PCInt is presented as a conceptual proposal requiring prospective validation rather than as a validated system. We conclude by discussing implementation barriers, regulatory and ethical considerations, and priorities for future research toward anticipatory, value-based perioperative cardiovascular care.","url":"https://pubmed.ncbi.nlm.nih.gov/42513240/","authors":["Magouliotis DE","Sicouri S","Androutsopoulou V","Bekiaridou A","Baudo M","Athanasiou T","Xanthopoulos A","Prendergast GC","Ramlawi B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/jcm15145325","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513237","name":"Agreement, Calibration, and Exploratory Performance of AI-Based Ultrasound in Thyroid Nodule Assessment.","source":"pubmed","abstract":"Background/Objective: Ultrasound is the first-line imaging modality for thyroid nodule assessment; however, it remains highly operator-dependent and subject to interobserver variability. Artificial intelligence (AI)-based systems have been proposed to improve reproducibility, yet evidence regarding their agreement with clinician assessment-particularly at the level of individual sonographic features-remains limited. Importantly, most available studies evaluate concordance rather than true diagnostic accuracy against an independent reference standard. To evaluate agreement between an AI-based ultrasound system and expert clinician assessment in thyroid nodule evaluation, focusing on concordance of size measurements, agreement in sonographic feature classification, and exploratory diagnostic performance relative to cytological outcomes. Methods: This retrospective single-center study included 74 thyroid nodules from adult patients undergoing routine ultrasound examination. Archived ultrasound images were independently assessed by an experienced clinician and an AI-based system. Agreement for quantitative measurements was evaluated using Bland-Altman analysis, while categorical features were assessed using percent agreement and Cohen's kappa coefficients. Calibration was examined using scatter plots with the line of identity. Cytological results, when available, were used as a non-uniform exploratory reference standard for diagnostic analyses. Exploratory diagnostic performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUROC) estimates. Given the study design, analyses primarily reflect agreement and measurement concordance rather than true diagnostic accuracy. Results: AI-derived and clinician measurements demonstrated strong agreement across all dimensions, with minimal systematic bias and stable calibration patterns. A small but consistent underestimation of one measurement axis by approximately 1 mm was observed. For categorical features, agreement ranged from fair to moderate (&#x3ba; = 0.196-0.368), with the highest concordance for echogenic foci and lowest for echogenicity. Exploratory analyses showed variable diagnostic discrimination, with the best performance observed for size measurements and selected sonographic features. Conclusions: AI-based ultrasound analysis demonstrates robust agreement with clinician assessment for quantitative thyroid nodule measurements, while agreement for categorical feature classification remains moderate and variable. The findings highlight that the present study evaluates concordance rather than definitive diagnostic accuracy, particularly given the lack of a uniform independent reference standard. These results support the role of AI as an assistive tool in thyroid ultrasound practice, improving measurement reproducibility while requiring ongoing clinician oversight for qualitative interpretation.","url":"https://pubmed.ncbi.nlm.nih.gov/42513237/","authors":["Szydlarska D","Ciechomska M","Kędzierska-Kapuza K","Franek E","Dźwiarek-Miara K","Łukawska-Tatarczuk M","Kaczor-Zabój I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/jcm15145323","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42513128","name":"Interpretable Spectral Evidence Learning from Vis/NIR Imaging for Non-Destructive Authentication of Herbal Medicines.","source":"pubmed","abstract":"Rapid and non-destructive authentication of herbal medicines is important for quality control and market supervision. This study established an interpretable spectral evidence learning framework for visible and near-infrared (Vis/NIR) imaging-based authentication of Codonopsis Radix (CR) and Aurantii Fructus (AF). Compact 31-band mean gray-value spectra were analyzed at ROI and sample levels. CR sample-level spectra were obtained by ROI-group averaging, whereas AF records were retained as individual sample spectra with image-group information used for leakage-controlled validation. Raw spectra, Savitzky-Golay smoothing, multiplicative scatter correction, and standard normal variate correction were compared with machine-learning and deep-learning classifiers. A fold-contained lightweight diffusion (LD) module was further introduced to provide class-conditioned spectral augmentation and denoising-error evidence. Under grouped cross-validation, the strongest non-LD Linear SVM models achieved accuracy/macro-F1 values of 0.9231/0.9238 for CR and 0.9025/0.9018 for AF. After LD augmentation, the best LD-augmented SVM models reached macro-F1 values of 0.9427 and 0.9197, respectively. Across all evaluated model-dataset combinations, LD increased the overall mean macro-F1 from 0.7302 to 0.8189. Model-aligned wavelength evidence and top-wavelength subset tests further showed that selected LED-band subsets retained useful discriminative information within the present imaging configuration. These results support the feasibility of compact Vis/NIR image-based authentication of herbal materials under grouped validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42513128/","authors":["Fan Z","Ma C","Jing S","Huang J","Zhang M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 12","doi":"10.3390/molecules31142444","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512898","name":"Development and External Validation of a Machine Learning Model for Classification of Mild Cognitive Impairment and Dementia Using Clinical Data.","source":"pubmed","abstract":"Background and Objectives : As society ages, the number of patients with cognitive impairment is increasing. Machine learning methods that use structured clinical and cognitive-assessment data during routine diagnostic work-up may support and monitor structured classifications of cognitive status. This kind of approach can improve early screening, reduce physicians' workload and develop greater support for personalized treatment. To develop an XGBoost-based machine learning model using the National Alzheimer's Coordinating Center (NACC) dataset and to evaluate the model's precision with clinician-assigned diagnosis in a Latvian retrospective cohort study. Materials and Methods : The research was designed as a retrospective external validation cohort study that used two data sources. Firstly, the National Alzheimer's Coordination Center (NACC) longitudinal dataset was used to train the ML model. Secondly, medical records gathered from Pauls Stradins Clinical University Hospital dating from 2020 to May 2025 were used to evaluate the algorithm's precision. Results : In the NACC study, the weighted four-class model achieved an overall accuracy of 84.0% and a balanced accuracy of 70.9%, but the SCD class remained poorly classified. After reframing the model to a three-class model the performance grew stronger for normal cognition, mild cognitive impairment (MCI) and dementia. Class distribution in the Latvian cohort consisted of dementia ( n = 138); MCI ( n = 13); and subjective cognitive decline (SCD) ( n = 2). Dementia was identified most strongly-124/138 (sensitivity-89.9%). MCI was correct in 9/13 cases (sensitivity-69.2%). SCD cases were excluded. Overall, the model agreed with the neurologist-assigned diagnoses in 88.1% of the cases (133/151). Conclusions : The ML classification model has high precision when comparing with neurologist-assigned diagnoses, but it struggles to separate adjacent early-stage diagnoses, meaning that it did not reliably identify SCD. These findings support further methodological development and the implementation of prospective research. Nevertheless, this technology has high potential for being integrated in the future to aid triage and early screening, especially when advanced diagnostics are limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42512898/","authors":["Kannenieks D","Priede Z","Millers A","Velins KK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/medicina62071356","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512875","name":"Rethinking Dengue Preparedness in the Era of Climate Change, Urbanisation, and Digital Health: A Structured Narrative Review.","source":"pubmed","abstract":"Background and Objectives: Dengue is emerging as a multifaceted public health challenge that extends beyond traditional vector-borne disease frameworks. Climate change, rapid urbanisation, environmental transformation, global mobility, and digital ecosystems are progressively reshaping transmission dynamics, outbreak patterns, and preparedness needs worldwide. This narrative review aimed to examine dengue from an integrated public health perspective, focusing on climate-sensitive transmission, urban health, surveillance and preparedness, digital epidemiology, artificial intelligence (AI), and health communication. Materials and Methods: A structured narrative review was conducted through targeted literature searches in PubMed, Scopus, and Web of Science between April and May 2026. To this end, a series of separate thematic search strategies were developed to explore the principal conceptual domains addressed in the review. The synthesis was organised around five interconnected preparedness domains: climate change and environmental transformation; urbanisation and urban health; surveillance, vaccination, and integrated preparedness; digital health, artificial intelligence, and mathematical modelling; and health communication and community engagement. The retrieved literature was analysed using a thematic narrative synthesis approach. Results: The retrieved evidence indicated the progressive expansion and redefinition of dengue risk across both endemic and historically non-endemic regions. Climate variability, environmental transformation, rapid urbanisation, and increasing human mobility have emerged as interconnected drivers capable of influencing vector ecology, transmission dynamics, outbreak frequency, and healthcare system vulnerability. Urbanisation has been frequently associated with infrastructural inequalities, environmental degradation, inadequate water and waste management, and territorial conditions favourable to vector proliferation. The extant literature has also placed significant emphasis on the growing importance of integrated surveillance systems and early warning approaches combining epidemiological, environmental, climatic, entomological, and mobility-related data. Digital epidemiology, AI-based predictive models, and digital surveillance tools may contribute to strengthening outbreak forecasting and preparedness capacity, although important limitations related to data quality, interoperability, interpretability, and implementation remain. In parallel, misinformation, risk communication challenges, and digital communication ecosystems emerged as relevant factors influencing public perception, preventive behaviours, institutional trust, and adherence to public health interventions. Conclusions: Dengue is a systems-level public health challenge shaped by climate change, urbanisation, environmental disruption, human mobility, health-system preparedness, and digital ecosystems. Conventional vector-control strategies alone are unlikely to adequately address this growing complexity. Strengthening dengue preparedness should therefore be considered a broader indicator of public health resilience and long-term health-system adaptation.","url":"https://pubmed.ncbi.nlm.nih.gov/42512875/","authors":["Dettori M","Deiana G","Palmieri A","Arghittu A","Castiglia P","Piana A","Campus G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/medicina62071333","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512850","name":"Comparison of Machine Learning Algorithms for Predicting Spine Surgery Duration.","source":"pubmed","abstract":"Background and Objectives : Accurate prediction of surgical duration is essential for efficient operating room management. Spine surgery frequently shows discrepancies between estimated and actual surgical duration, which can disrupt surgical scheduling and resource allocation. The aim of this study was to develop and compare machine learning (ML) algorithms for predicting spine surgery duration and identify the most effective approach. Materials and Methods : Electronic medical records of 3376 patients who underwent spine surgery were retrospectively analyzed at a single center. The dataset was divided into training (80%, n = 2700) and internal test (20%, n = 676) sets using stratified random sampling based on surgical duration quintiles. To match the intended use at the time of operating room scheduling, four models (Random Forest, XGBoost, multilayer perceptron [MLP], and weighted least squares [WLS] regression) were developed using only predictors available at scheduling and evaluated on the independent internal test set; a full-information model that additionally included intraoperatively recorded variables was examined for comparison. Results : XGBoost demonstrated the best predictive performance, achieving a mean squared error (MSE) of 3014.6 min 2 (equivalent to a root mean squared error [RMSE] of 54.9 min; 95% CI for MSE, 2558.3-3556.0) and an R 2 of 0.622 (95% CI, 0.566-0.675). The other ML models showed comparable performance, whereas WLS regression performed less favorably; the full-information model performed equivalently (R 2 0.622). Compared with surgeon-estimated duration alone, XGBoost reduced mean absolute error by approximately 20 min (paired &#x394;MAE -20.1 min; 95% CI, -23.7 to -16.5) and improved prediction accuracy within 60 min by 14.0 percentage points. SHapley Additive exPlanations (SHAP) analysis identified surgeon identity as the most influential predictor across all models (24.7-41.9%), followed by procedure type and surgeon-estimated duration. Conclusions : Machine learning models substantially improved prediction of spine surgery duration compared with conventional approaches, with XGBoost showing the highest predictive accuracy. Surgeon identity emerged as the most important predictor of surgical duration. Implementation of such models may improve operating room scheduling efficiency and resource allocation but requires prospective evaluation of clinical and workflow outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42512850/","authors":["Ko M","Lee HC","Lee HS","Lee B","Lee MW","Kim YJ","Park JH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/medicina62071308","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42512797","name":"Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.","source":"pubmed","abstract":"Stroke remains a leading cause of death and long-term disability worldwide, making prevention strategies a global health priority. Emerging technologies-including artificial intelligence (AI), wearable devices, digital health applications, and drone-assisted emergency systems-are increasingly being explored to improve stroke prevention and early management. In primary prevention, machine learning models can identify individuals at high risk of stroke using clinical and behavioral data with high reported predictive accuracy, although most models are derived from retrospective, single-center datasets and still require prospective external validation. Digital devices and wearable technologies enable continuous monitoring of cardiovascular risk factors and support behavioral interventions aimed at reducing vascular risk. In secondary prevention, AI-based tools are being developed to predict stroke recurrence, identify modifiable risk factors, and detect patients at risk of poor medication adherence. In the acute setting, AI-assisted neuroimaging platforms are already integrated into clinical and telestroke workflows, supporting rapid triage and treatment decisions. In parallel, drone-based emergency systems may contribute to improved outcomes by reducing prehospital delays and facilitating telemedicine-based triage in remote or resource-limited settings, although current evidence is derived largely from out-of-hospital cardiac arrest pathways rather than stroke-specific trials. Although advanced neurotechnological systems capable of real-time neurophysiological monitoring and closed-loop neuromodulation exist in other neurological disorders, their role in stroke prevention remains largely theoretical. Overall, these technologies offer promising opportunities to reshape the continuum of stroke prevention and care, but further validation, integration into clinical workflows, and evidence of real-world effectiveness are required before widespread implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42512797/","authors":["Gregorini M","Lorusso L","Airoldi L","Stefano MD","Formenti A","Lucchi G","Melzi P","Perego E","Tagliabue E","Tetto A","Vaccaro M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 29","doi":"10.3390/medicina62071254","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512766","name":"Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.","source":"pubmed","abstract":"Lung diseases are among the leading global causes of morbidity and mortality, and existing reviews on deep learning (DL) for pulmonary diagnosis rarely integrate imaging, acoustic, and electronic health record (EHR) modalities within a single framework. We aimed to synthesize the state of the art (2019-2024) in multimodal DL for lung disease detection and classification, identifying dominant architectures, performance benchmarks, and translational barriers across chest X-rays, CT scans, respiratory sounds, and EHRs. A structured narrative review was conducted using PubMed, Scopus, IEEE Xplore, and Web of Science, applying explicit inclusion criteria for peer-reviewed studies; performance metrics, dataset characteristics, and reported limitations were extracted. Research involving convolutional neural networks (CNNs) and more recent models such as Transformers have reported high performance in chest X-ray classification, whereas acoustic approaches based on spectrograms and self-supervised representations (e.g., Wav2Vec 2.0) show promising but dataset-dependent results.","url":"https://pubmed.ncbi.nlm.nih.gov/42512766/","authors":["Estay Zamorano B","Dehghan Firoozabadi A","Adasme P","Montiel Piña W","Muñoz MC","Zabala-Blanco D","Palacios Játiva P","Azurdia-Meza CA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 24","doi":"10.3390/medicina62071223","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512732","name":"Humanization of Surgical Care in the Robotic Age: A Triangular Interaction Model Between the Surgeon, the Patient, and the Technology.","source":"pubmed","abstract":"Background/Objectives : In modern medicine, humanization of care is a central theme that highlights the complementarity of clinical care with empathy, communication, and patient-centered values. The increasing use of robotic surgery is rapidly modifying surgical practice, introducing new challenges and opportunities in preserving the human dimension of care. This review aimed to synthesize the available evidence and propose a conceptual model of humanized robotic surgical care. Methods : A structured narrative review of the PubMed/MEDLINE database (from inception to January 2026) was conducted using predefined keywords related to humanization of care, robotic surgery, patient perception, surgeon experience, human factors, communication, ethics, and technological mediation. Relevant English-language publications were critically synthesized to develop a conceptual framework. Results : The literature indicates that robotic surgery influences humanized care through three interconnected domains. First, patients frequently perceive robotic surgery as more precise and technologically advanced, which may generate unrealistic expectations and misconceptions regarding robotic autonomy. Second, robotic platforms reshape the surgeon's experience by improving ergonomics while simultaneously modifying cognitive workload, sensory feedback, and professional identity. Third, technology itself acts as an active mediator influencing communication, trust, decision-making, and relational dynamics. Building upon these findings, we propose an original triangular conceptual framework integrating the patient, the surgeon, and the technology as three interdependent determinants of humanized robotic surgical care. The framework also provides a conceptual basis for understanding the future integration of artificial intelligence into surgical practice. Conclusions : Humanization of care in the era of robotic surgery requires an integrated approach that recognizes the interdependence of patient perception, surgeon experience, and technological mediation. Ensuring effective communication, supporting surgeon well-being, and preserving ethical principles will be essential to aligning innovation with patient-centered care.","url":"https://pubmed.ncbi.nlm.nih.gov/42512732/","authors":["Zimmitti G","Portinaio MR","Morandi A","Terzi P","Meloni A","Lavazza L","Carra MC","Ravaioli C","de'Angelis N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.3390/healthcare14142216","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512712","name":"Cancer-Related Psychological Distress over the Past Decade: A Bibliometric Analysis of Research Trends, Hotspots, and Emerging Areas.","source":"pubmed","abstract":"Cancer-related psychological distress is a major concern in comprehensive oncology care because it substantially impairs patients' quality of life and may adversely affect treatment adherence, outcomes, and prognosis. Over the past decade, research in this field has expanded rapidly; however, the overall knowledge structure, global research patterns, major contributors, and emerging hotspots remain insufficiently characterized. A bibliometric analysis is therefore needed to systematically map the development of cancer-related psychological distress research and identify evolving directions for future investigation.","url":"https://pubmed.ncbi.nlm.nih.gov/42512712/","authors":["Wang L","Xu X","Hua B","Liu R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.3390/healthcare14142195","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512666","name":"Schema Enforcement and Structured-Output Stability in Locally Deployed LLMs for Clinical Admission-Note Editing: A Proxy-Based Pre-Deployment Evaluation.","source":"pubmed","abstract":"Background: Reliable structured-output generation is a prerequisite for using large language models (LLMs) in automated clinical documentation workflows, but many evaluations focus on clinical quality before testing whether outputs are parseable, schema-compliant, and stable. Methods: We evaluated three locally deployed open-weight LLMs in the 7- to 8-billion-parameter range (Llama3-Med42-8B, Meta-Llama-3-8B-Instruct, and Mistral-7B-Instruct-v0.3) for structured admission-note editing. Seventy de-identified English-language admission notes (35 internal medicine and 35 surgical) were processed by each model in three independent runs under two output-control conditions: a free-text JSON prompt and a schema-enforced structured-output condition. A total of 1260 local inferences were performed in LM Studio on consumer-grade hardware. Automated proxy metrics assessed JSON/schema validity, run-to-run stability, instruction compliance, verbosity, numeric-token preservation, and uncertainty-marker change without clinician adjudication of clinical correctness. Results: Under the free-text JSON prompt, the tested Mistral-7B-Instruct-v0.3/embedded-prompt configuration had the weakest structural reliability (74.3-78.6% first-pass validity per run; 18.6-21.4% persistent parse/schema failures after retry), with at least one final failure for 17 of 70 notes. In a message-format sensitivity analysis using Meta-Llama-3-8B-Instruct, embedding system instructions in the user message increased first-attempt invalid outputs compared with separate system/user roles (55/700, 7.9% vs. 12/700, 1.7%). Under schema enforcement, all models produced 70 of 70 first-pass valid, schema-compliant outputs in every run. Documentation behavior nevertheless differed by model, including differences in verbosity and numeric-token preservation. Conclusions: Schema enforcement removed parsing failures in this sample but did not eliminate model-specific editing behavior. Proxy-based screening can identify structurally unstable model-prompt or model-format configurations before clinician review.","url":"https://pubmed.ncbi.nlm.nih.gov/42512666/","authors":["Yang YL","Chen CJ","Lin TK","Lin SC","Koo M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/healthcare14142150","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512585","name":"Sleep-Related Breathing Disorders: A Comprehensive Review of Surgical Innovations and Evolving Technologies.","source":"pubmed","abstract":"Sleep-related breathing disorders (SRBDs) encompasses a spectrum of conditions that disrupt ventilation during sleep, leading to fragmented sleep and impaired gas exchange. Their high prevalence and substantial neurocognitive and mental health outcomes make SRBD clinically significant across multiple medical disciplines. Traditional management includes lifestyle modifications and positive airway pressure (PAP). When non-surgical measures fail or anatomical factors predominate, a range of surgical approaches may be employed, such as uvulopalatopharyngoplasty (UPPP) or maxillomandibular advancement (MMA). There are many notable emerging surgical advancements, such as hypoglossal nerve stimulation (HNS), transoral robotic surgery (TORS), and minimally invasive radiofrequency technologies (RFA), that have offered improved outcomes for select patients. Advances in diagnostic tools, such as portable home sleep technologies and drug-induced sleep endoscopy (DISE), further support precision-based care. Collectively, the expanding range of therapeutic and diagnostic innovations is enabling clinicians to deliver individualized care and improve long-term outcomes for patients with SRBD.","url":"https://pubmed.ncbi.nlm.nih.gov/42512585/","authors":["Kooner A","Man L","Best J","Litsky N","Yee B","Jeffries J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/healthcare14142069","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512579","name":"Why Nurses Intend to Override AI Alerts: How Alert Fatigue, Moral Distress, and Team Psychological Safety Shape Self-Reported Trust Calibration Toward Clinical Decision Support.","source":"pubmed","abstract":"Background and Objectives: Hospitals increasingly use AI tools that give nurses on-screen alerts and recommendations (AI-supported clinical decision support, AI-DSS). When nurses override these alerts too often, useful guidance can be lost; when they trust them blindly, errors can slip through. We examined which work and wellbeing factors are associated with nurses' self-reported intention to override AI alerts, rather than observed override behavior. Methods: We surveyed 239 registered nurses (76.6% female; mean age 33.7 years) at a large hospital in Timi&#x219;oara, Romania, from January to March 2025. Questionnaires measured alert fatigue, moral distress, mental workload, sleep problems, resilience, team psychological safety, and how strongly nurses intended to override AI alerts. Results: Nurses fell into three groups: those who tended to over-trust AI (26.8%), those with balanced trust (41.0%), and those who resisted it (32.2%). The resistant group had the strongest intention to override alerts and the weakest sense of psychological safety. Alert fatigue was the factor most strongly associated with override intention, and this association was partly accounted for by moral distress. The indirect association was weaker among nurses reporting higher team psychological safety. An exploratory model using these factors distinguished nurses with high self-reported override intention with acceptable accuracy. Because all variables were measured at a single time point, findings are associative and hypothesis-generating rather than causal. Conclusions: How nurses respond to AI alerts depends less on the technology than on their workload, ethical strain, and team climate. Cutting unnecessary alerts, easing moral distress, and building psychological safety may help nurses use AI more safely.","url":"https://pubmed.ncbi.nlm.nih.gov/42512579/","authors":["Clej E","Fizedean C","Gherman A","Ilie AC","Bratu ML","Marc F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/healthcare14142063","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512568","name":"Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight.","source":"pubmed","abstract":"Clinical artificial intelligence (AI) is increasingly moving from proof-of-concept development into clinical evaluation, regulatory review, and routine care. This scoping review aimed to map and synthesise empirical evidence on clinical AI evaluation after model development, focusing on deployment-related robustness, post-development monitoring, and lifecycle oversight in practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42512568/","authors":["El Arab RA","Hussein Mustafa M","Almagharbeh WT","Ayoub MY","Alsanawi F","Almathen F","Almosabeh R","Al Talaq M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/healthcare14142052","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512452","name":"Science Is About Thinking: How Can We Protect Thinking Time in a Distracted Digital World?","source":"pubmed","abstract":"Rapid digital transformation has generated pervasive attentional disruption in research and professional settings, raising the question of how the temporal conditions that support deep scientific thinking can be preserved. Our narrative review aimed to (i) synthesize neurobiological evidence on the mechanisms through which task-irrelevant digital interruption impairs deep thinking; (ii) discuss the conditions required for deep thinking and the potential threats posed by contemporary developments, including generative artificial intelligence-related cognitive offloading; and (iii) elaborate evidence-based, multi-level recommendations for research institutions.","url":"https://pubmed.ncbi.nlm.nih.gov/42512452/","authors":["Dhahbi W","Pyne DB","Dergaa I","Zeitouny D","Müller P","El Omri A","Chamari K","Chaabene H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 27","doi":"10.3390/brainsci16070677","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512406","name":"Non-Invasive Diagnosis of Early Breast Cancer: Current and Emerging Liquid Biopsy Biomarkers.","source":"pubmed","abstract":"Background/Objectives: Breast cancer (BC) remains the most frequently diagnosed malignancy among women worldwide, and patient outcome is strongly influenced by disease stage at diagnosis. Although imaging-based screening has improved early detection, its performance may be reduced in dense breast tissue and is associated with false-positive findings. In addition, tissue biopsy is invasive and unsuitable for longitudinal disease monitoring. Liquid biopsy (LB) has emerged as a minimally invasive approach for detecting tumor-derived material in peripheral blood. However, early-stage tumors typically exhibit low tumor burden and limited biomarker shedding, generating weak systemic signals that challenge reliable detection. This review examines current and emerging LB biomarkers for early BC detection. Methods: A comprehensive review of recent literature was conducted focusing on circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating RNAs, proteins, and other blood-based biomarkers associated with early BC. Studies addressing biomarker biology, detection technologies, clinical applications, and methodological limitations were critically evaluated. Results: ctDNA, CTCs, EVs, circulating RNAs, proteins, and additional blood-based biomarkers capture distinct aspects of tumor biology and disease evolution. ctDNA enables the analysis of tumor-specific mutations, methylation patterns, and fragmentation profiles, whereas CTCs provide direct cellular and phenotypic information despite their rarity and marked epithelial-mesenchymal plasticity. EVs offer increased molecular stability and actively participate in tumor progression, immune modulation, and metastatic niche formation. Nevertheless, low biomarker abundance, biological heterogeneity, technical variability, and background biological noise continue to limit analytical performance, particularly in early-stage disease. Current evidence further suggests that no single biomarker consistently provides sufficient sensitivity and specificity for reliable early BC detection. Conclusions: LB represents a promising strategy for non-invasive early BC detection. Future clinical implementation will likely depend on integrated multi-analyte approaches that combine complementary genomic, transcriptomic, proteomic, and cellular information, supported by multi-omics technologies and artificial intelligence-based analytical frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/42512406/","authors":["Kotsifaki A","Masoura CR","Limogianni G","Kalouda G","Stathaki M","Armakolas A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.3390/cancers18142344","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512375","name":"Basal Cell Carcinoma Research Landscape Overview via Latent Dirichlet Allocation and HJ-Biplot Analysis.","source":"pubmed","abstract":"Background : Basal cell carcinoma (BCC) of the skin is the most common cancer in humans, and its incidence rises annually. Despite its low death rate, BCC causes significant morbidity because of its destructive nature to local tissues. The aim of this study was to review the state of the BCC research landscape using data published in Scopus from 1972 to 2023. Methods : Using the R package Bibliometrix, we first determined authors, countries, journals, and main topics behind the advancement of BCC research. The Latent Dirichlet Allocation (LDA), a probabilistic topic algorithm, was applied to automatically discover latent (hidden) thematic structures. Additionally, the HJ-Biplot was also chosen to visualize graphical representations of multivariate scientometric and bibliometric data to improve the LDA outcome. Results : Over five decades, we discovered 32 unique themes. BCC research has shifted from studying tumor and immunohistochemically characterization, epidemiology, and surgical/histological clearance to the advancement of diagnostic and imaging techniques, like dermoscopy and reflectance confocal microscopy (RCM). Another exciting BCC research trend has been the discovery of aberrant activation within the Hedgehog signaling. Finally, patient treatment, especially surgery, radiotherapy, topical fluorouracil, and imiquimod, is an object of intense research. Conclusions : By displaying each topic as a group of related words, this in-depth exploration outlined emerging avenues in BCC evidence-based research that will benefit from the development of cutting-edge diagnostic procedures, as well as a deeper knowledge of BCC etiology and genetic underpinnings, the quality of life in BCC patients after surgery, and the application of artificial intelligence.","url":"https://pubmed.ncbi.nlm.nih.gov/42512375/","authors":["Montes-Escobar K","de La Hoz-Maestre J","Llinás-Solano H","Salas-Macias CA","Fernández-Moreira E","Fors M","Ballaz SJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/cancers18142312","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512372","name":"Intraoperative Ultrasound in Hepatic Oncology Surgery: A Narrative Review of Its Impact on Surgical Strategy and Oncologic Outcomes.","source":"pubmed","abstract":"Background/Objectives: Intraoperative ultrasound (IOUS) has become an integral component of modern hepatic oncology surgery, providing real-time imaging guidance during liver resections for hepatocellular carcinoma, colorectal liver metastases, and other primary or secondary hepatic malignancies. Despite substantial improvements in preoperative imaging modalities, occult lesions, disappearing metastases after chemotherapy, and complex vascular relationships continue to represent major intraoperative challenges. This structured narrative review aimed to evaluate the contemporary role of IOUS in hepatic oncology surgery, with particular emphasis on contrast-enhanced intraoperative ultrasound (CE-IOUS), minimally invasive liver surgery, navigation-assisted hepatectomy, and emerging artificial intelligence-based technologies. Methods: A structured literature review was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases. Peer-reviewed studies, international guidelines, consensus statements, systematic reviews, and technological reports addressing IOUS applications in liver surgery were analyzed. Particular focus was placed on studies evaluating lesion detection, intraoperative strategy modification, disappearing colorectal liver metastases, parenchymal-sparing hepatectomy, laparoscopic and robotic liver surgery, navigation systems, augmented reality integration, and AI-assisted imaging technologies. Results: Contemporary evidence demonstrates that IOUS continues to significantly influence intraoperative decision-making despite advances in magnetic resonance imaging and multidetector computed tomography. CE-IOUS improves the detection of occult hepatic lesions and residual disease after systemic chemotherapy, particularly in disappearing colorectal liver metastases. IOUS-guided anatomical and parenchymal-sparing resections contribute to the preservation of functional liver parenchyma while maintaining oncologic radicality. In minimally invasive liver surgery, laparoscopic ultrasound remains essential for lesion localization and vascular mapping. Recent developments integrating navigation systems, augmented reality platforms, and AI-assisted image recognition suggest a progressive transition toward digitally integrated precision liver surgery. Conclusions: IOUS remains a cornerstone of modern hepatic oncology surgery and continues to evolve from a localization tool into a comprehensive platform for precision-guided liver resection. The integration of CE-IOUS, navigation technologies, and artificial intelligence may further enhance intraoperative accuracy, oncologic safety, and individualized surgical planning in the future.","url":"https://pubmed.ncbi.nlm.nih.gov/42512372/","authors":["Nicolescu C","Cosma CD","Botoncea M","Bartoș A","Molnar C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/cancers18142309","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512371","name":"PET Imaging in Vulvar Cancer: A Literature Review of the Current Evidence and Clinical Applications.","source":"pubmed","abstract":"Vulvar cancer is a rare gynecologic malignancy with a bimodal age distribution, predominantly affecting older women while demonstrating increasing HPV-related incidence in younger populations. Accurate staging is essential for individualized treatment strategies to optimize outcomes while minimizing morbidity. Positron Emission Tomography (PET) has emerged as an important imaging modality in oncology; however, its precise role in vulvar cancer requires critical evaluation given the limited prospective evidence. This review focuses on the role of 18 F-FDG PET/CT in vulvar cancer based on the literature published to date. Current evidence suggests that PET/CT provides high sensitivity for primary tumor detection and is particularly valuable for nodal and distant staging in locally advanced disease, where it may significantly influence management. However, its limited spatial resolution precludes detection of micrometastases, and it cannot replace sentinel lymph node biopsy for groin staging. PET/CT also contributes to radiotherapy planning through improved target delineation and demonstrates utility in detecting recurrence, although false positives remain a limitation. MRI remains superior for local staging, supporting a complementary multimodality imaging approach. Emerging tracers, including Fibroblast Activation Protein Inhibitor (FAPI), along with artificial intelligence-based radiomics, represent promising but still investigational directions. In conclusion, 18 F-FDG PET/CT is a useful adjunct in vulvar cancer, especially for advanced disease, but its interpretation should be integrated within a multimodal framework due to limited supporting evidence.","url":"https://pubmed.ncbi.nlm.nih.gov/42512371/","authors":["Gazawi R","Lataifeh H","Alzibdeh A","Abdulrahman M","Al-Ibraheem A","Abuhijla F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/cancers18142308","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512326","name":"Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.","source":"pubmed","abstract":"Background/Objectives : Machine learning (ML) models have increasingly been applied to prognostic prediction in oral cavity squamous cell carcinoma (OCSCC), though their performance and methodological quality remain variably reported. This systematic review evaluated contemporary ML-based prognostic models for clinically relevant OCSCC outcomes, with emphasis on independently validated studies. Methods : A systematic review was conducted according to PRISMA guidelines using PubMed, Scopus, Cochrane Library, and CINAHL databases through 1 December 2025. Studies evaluating ML or artificial intelligence prognostic models in adult OCSCC patients were included. Outcomes included overall survival, recurrence, disease-free survival, recurrence-free survival, disease-specific survival, cancer-specific survival, progression, and nodal metastasis. Data extraction and risk-of-bias assessment using PROBAST + AI were performed independently by reviewers. Results : Forty studies comprising 105,619 patients met inclusion criteria. ML architectures included random forests, support vector machines, gradient boosting methods, neural networks, and deep learning frameworks. Most models incorporated clinical and pathologic variables, while many integrated radiologic, immunologic, or genomic features. For overall survival prediction, independently validated models generally demonstrated AUCs between 0.80 and 0.90. Recurrence prediction models similarly showed favorable discrimination, with most externally validated studies reporting acceptable predictive performance. Additional prognostic endpoints including disease-free survival, progression, and nodal metastasis demonstrated AUCs ranging from 0.70 to 0.90. Common methodological limitations included retrospective design, small sample size, inadequate external validation, and risk of overfitting. Conclusions : ML-based prognostic models in OCSCC demonstrate generally favorable predictive performance across survival and recurrence outcomes. However, substantial heterogeneity in methodology and limited external validation continue to restrict clinical implementation. Future work should prioritize prospective multicenter validation, standardized reporting, and reproducible modeling frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/42512326/","authors":["Gao SY","Hughes JM","Nguyen SA","McCaulay BS","Newman JG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/cancers18142261","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512225","name":"Statin Therapy and Cardiovascular Prevention: Contemporary Evidence, Challenges, and Future Directions-A Narrative Review.","source":"pubmed","abstract":"Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, and lowering low-density lipoprotein cholesterol (LDL-C) remains a cornerstone of cardiovascular prevention. Statins are among the most extensively studied and widely prescribed medications and have demonstrated substantial benefits in reducing major adverse cardiovascular events in both primary and secondary prevention settings. Nevertheless, the effectiveness of statin therapy in routine clinical practice is frequently compromised by poor adherence, treatment discontinuation, concerns regarding adverse effects, and persistent residual cardiovascular risk. This narrative review synthesises contemporary evidence relating to the mechanisms of action of statins, their role in primary and secondary prevention, determinants of medication adherence, statin-associated muscle symptoms (SAMSs), and emerging developments in precision cardiovascular medicine. Current evidence indicates that although statins remain highly effective in reducing cardiovascular risk, long-term treatment success is strongly influenced by behavioural, psychological, social, and healthcare system factors. Increasing attention has also been directed towards the multifactorial nature of SAMSs and the contribution of nocebo effects to perceived statin intolerance. Emerging approaches involving pharmacogenomics, artificial intelligence, digital health technologies, and multidimensional risk assessment offer opportunities for more individualised prevention strategies, although important limitations relating to cost, accessibility, and external validity remain. Overall, contemporary cardiovascular prevention requires a patient-centred approach that integrates biological, behavioural, and social determinants of health to optimise treatment adherence and improve long-term cardiovascular outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42512225/","authors":["Zachariah D","Kakooza D","Pothas S","Thomas A","Kruger L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/ijerph23070921","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512128","name":"Active Aging for L.I.F.E.: An Intergenerational Program to Improve Adolescents' Aging Attitudes in Rural Communities.","source":"pubmed","abstract":"Rural adolescents face persistent health inequities driven by limited access to preventive health education, intergenerational engagement, and resources that support lifelong wellness. This study evaluated the effectiveness of Active Aging for L.I.F.E., a school-based intergenerational health literacy program, in improving adolescents' attitudes toward aging and health. The four-session program, delivered through a train-the-trainer model involving older adults and undergraduate students, was implemented in three rural schools during the 2024-2025 academic year. A total of 86 junior high and high school students participated, with 77 completing pre- and post-program surveys assessing attitudes toward aging, health consciousness, and intergenerational engagement. Paired t -tests and multiple regression analyses examined overall program effects and differences by sex/gender and age group. Students demonstrated significant improvements in aging attitudes, perceived relevance of aging topics, enjoyment of intergenerational interaction, and awareness of health-promoting behaviors across the lifespan. Several baseline sex/gender and age-based gaps in health-related perceptions were reduced following participation, with stronger future-oriented attitude shifts observed among younger adolescents. These findings suggest that brief, scalable intergenerational interventions embedded in rural school settings can support early prevention, health literacy, and community capacity building, offering a promising strategy for advancing rural public health outcomes across the life course.","url":"https://pubmed.ncbi.nlm.nih.gov/42512128/","authors":["Chen X","Roberts E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 23","doi":"10.3390/ijerph23070822","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512088","name":"Uncertainty-Aware Prediction Across Endoscopic Domains: Laryngeal Narrow-Band and Gastrointestinal Imaging.","source":"pubmed","abstract":"Background : Deep-learning systems for endoscopic image classification are commonly evaluated with random data splits, which may overestimate performance under acquisition shift; uncertainty-aware selective prediction may improve reliability by allowing a model to abstain on uncertain cases. Methods : We evaluated binary abnormality detection in two endoscopic imaging domains: laryngeal contact-endoscopy narrow-band imaging (CE-NBI; 210 patients, patient-level) and gastrointestinal endoscopy (HyperKvasir; 6746 images, image-level, predominantly white-light). ImageNet-pretrained ResNet-50 deep ensembles were assessed under a resolution-defined acquisition-shift stress test; for the gastrointestinal data, a random stratified split was additionally used as an in-distribution reference. We evaluated discrimination, calibration, decision-curve analysis, and entropy-based selective prediction. Results : In the gastrointestinal dataset, random-split evaluation produced high performance (AUROC 0.994, 95% CI 0.990-0.996; AUPRC 0.991). Under resolution shift on the same data, performance fell to AUROC 0.723 (95% CI 0.710-0.736; AUPRC 0.690; sensitivity 0.417; specificity 0.874); the two intervals do not overlap. Selective prediction improved reliability among retained cases: under resolution shift, accuracy rose from 0.683 at full coverage to 0.852 (95% CI 0.835-0.871) at 25% coverage (balanced accuracy 0.646 &#x2192; 0.798). Predictive entropy was significantly higher for incorrect than for correct predictions in both regimes (Mann-Whitney p = 7.1 &#xd7; 10 -77 with rank-biserial |r| = 0.30 under shift). In the laryngeal cohort, no statistically significant differences were detected among four architectures (ROC-AUC 0.844-0.901; all pairwise DeLong p &gt; 0.05). Conclusions : Random-split evaluation substantially overestimated performance relative to a resolution-defined acquisition-shift stress test, and entropy-based selective prediction improved reliability by identifying a high-confidence subset for automated prediction while deferring the remainder to human review. Target-domain recalibration substantially restores calibration under shift (ECE 0.172 &#x2192; 0.036 with temperature scaling; &#x2192; 0.017 with isotonic regression) but does not recover discrimination; selective prediction is complementary, mitigating residual confident-wrong predictions. An encoder-transfer experiment showed asymmetric cross-domain utility; features learned on the larger gastrointestinal cohort transferred to the laryngeal cohort (AUROC 0.80 vs. in-domain 0.89), whereas the reverse direction did not transfer (0.53 vs. 0.72). Prospective multi-center validation remains required before clinical deployment. All code, fold definitions, random seeds, and a reproducible protocol are publicly released.","url":"https://pubmed.ncbi.nlm.nih.gov/42512088/","authors":["Kiani Kalejahi B","Khan S","Akhrorov M","Javad Rajabi M","Aziz A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/biomedicines14071616","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512085","name":"AI-Based Classification of Multiple Sclerosis Using OCT Retinal Layer Thickness Across Two Centers.","source":"pubmed","abstract":"Background: The latest revision of the McDonald criteria for diagnosis of multiple sclerosis (MS) establishes that the optic nerve can serve as a fifth anatomical location within the central nervous system for diagnosis. Optical coherence tomography (OCT) images can serve as evidence for this purpose. Objective: To assess the accuracy of automated artificial-intelligence-based classification of MS patients using OCT data obtained from two different centers. Methods: OCT data were collected from two centers using standardized APOSTEL-based protocols and similar equipment. Retinal layer thicknesses-mean and standard deviation (STD) values-were analyzed in four layers and in six regions per layer per eye. A support vector machine classifier with recursive feature elimination and Shapley additive explanations value analysis was applied to identify the most relevant features and maximize classification accuracy between control subject and MS patient eyes. Results: The database drawn from two hospitals comprised 112 eyes with MS without prior history of optic neuritis and 193 eyes of control subjects. The classifier achieved maximum accuracy (0.8459) using 20 input features. The mean and STD metrics had similar importance, with the most influential layers being the ganglion cell layer, inner plexiform layer, and the inner retinal layer complex. Key regions included the papillomacular bundle and the superior temporal perimacular area. Conclusions: OCT data facilitates highly accurate MS diagnosis across different centers. Artificial intelligence assessment could facilitate automated classification. These findings provide evidence of the important role of the optic nerve in MS diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/42512085/","authors":["Ortiz M","Dongil-Moreno J","Rebolleda G","Artiaga N","Boquete L","Miguel-Jimenez JM","Rodrigo MJ","López-Dorado A","Zamora R","García Vicente E","Sánchez-Morla EM","Andres-Luna B","Muñoz Negrete FJ","Garcia-Martin E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/biomedicines14071613","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512069","name":"AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.","source":"pubmed","abstract":"Background : Brain tumor magnetic resonance imaging (MRI) reporting and tumor segmentation for treatment planning are time-consuming and variable. This retrospective fixed-sequence paired workflow study evaluates whether AI assistance is associated with changes in efficiency, consistency, and reproducibility. Methods : Thirty MRI cases (10 vestibular schwannomas, 10 meningiomas, 10 brain metastases) were assessed. Two neuroradiologists completed diagnostic reporting with and without AI assistance, and two physicians completed tumor delineation with and without AI-generated preliminary contours after a 3-week washout. Results : Reporting time decreased from 42.94 to 27.90 min for Reader A and from 101.04 to 80.47 min for Reader B, corresponding to median paired case-level reductions of 40.39% and 11.51%, respectively; only Reader A reached statistical significance. Sensitivity remained 97.73% and 100.00%, while precision was numerically higher after AI assistance (89.58% to 97.73% and 83.02% to 91.67%). Report-similarity metrics increased across ROUGE-L, BERTScore F1, and Sentence-BERT cosine similarity (all p &lt; 0.001). Contouring time decreased from 54.63 to 4.93 min for Reader 1 and from 184.44 to 44.19 min for Reader 2, with median paired reductions of 100.00% and 87.11%. Dice coefficients were numerically higher after AI assistance (0.81 to 0.87 and 0.83 to 0.87). Conclusions : AI assistance was associated with shorter task-completion times, higher report-similarity metrics, and numerically higher contour-overlap measures. Prospective validation should determine whether these workflow efficiency gains translate into broader clinical benefit.","url":"https://pubmed.ncbi.nlm.nih.gov/42512069/","authors":["Hong JS","Lee WK","Chen JJ","Sun YC","Hsu YY","Lu YF","Sun MH","Yang KL","Lin CY","Wu HM","Chen ST","Guo WY","Chen HC","You WC","Wu YT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/biomedicines14071595","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42512028","name":"Evaluation of ChatGPT-5 for CT Imaging in Canadian CT Head Rule-Positive Mild Traumatic Brain Injury: A Pilot Study.","source":"pubmed","abstract":"Background and Objectives: Mild traumatic brain injury (mTBI) is a common cause of emergency department visits worldwide. Optimal decisions are essential for optimizing patient outcomes while minimizing unnecessary imaging. The present study explores the potential utility of ChatGPT-5.1 for predicting intracranial injury and determining CT necessity in mTBI. Methods: We evaluated adult patients with mild traumatic brain injury who met Canadian CT Head Rule (CCHR) criteria and underwent CT imaging during a two-year period. ChatGPT-5.1 was prompted under two frameworks: ER physician simulation and rule (literature)-based. Additionally, a probabilistic multi-variable logistic regression model was developed. ChatGPT-5.1's performance was compared with that of human controls (consultants and residents) and the CCHR. Pairwise comparisons were conducted using McNemar's test. Results: A total of 127 patients were included. ChatGPT-5.1's recommendation for CT showed comparable performance to CCHR. For predicting CT findings, the probabilistic model achieved the highest performance (accuracy 74.0%), followed by consultants (accuracy 69.3%) and residents (66.9%). The rule (literature)-based model displayed the highest sensitivity (91.8%) but exhibited low specificity (22.2%), indicating a high rate of false positives despite its potential utility as a screening or decision-supporting tool. The ER physician simulation model demonstrated the lowest overall performance (accuracy 54.3%). Statistically significant differences were observed between the probabilistic model and both the ER physician and rule-based models ( p &lt; 0.05). Consultants outperformed the ER simulation model ( p = 0.01), while their performance was similar to that of the probabilistic model ( p = 0.44). Conclusions: ChatGPT-5.1 demonstrated performance comparable to the Canadian CT Head Rule in predicting the need for head CT imaging but showed lower accuracy in identifying intracranial injury. While the literature-based rule model achieved high sensitivity, its low specificity emphasizes that it acts primarily as a conservative screening aid rather than a definitive diagnostic tool in CCHR-positive mTBI patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42512028/","authors":["Lampros M","Romeo E","Zagorianakou P","Voulgaris S","Alexiou GA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 11","doi":"10.3390/biomedicines14071555","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42511882","name":"Correction: Shait Mohammed et al. Profiling the Effect of Targeting Wild Isocitrate Dehydrogenase 1 (IDH1) on the Cellular Metabolome of Leukemic Cells. Int. J. Mol. Sci. 2022, 23, 6653.","source":"pubmed","abstract":"There were some errors in the original publication [...].","url":"https://pubmed.ncbi.nlm.nih.gov/42511882/","authors":["Shait Mohammed MR","Alzahrani F","Hosawi S","Choudhry H","Khan MI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/ijms27146174","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42511789","name":"In Silico Models Using Simple Molecular Descriptors Predict Placental and Breast Milk Transfer of Cannabinoids from Cannabis sativa.","source":"pubmed","abstract":"Despite an increasing interest in the pharmacology of cannabinoids from Cannabis sativa , little is known to date about their ability to cross the placenta and to be secreted into breast milk, and in this study, we sought to fill this gap. In total, 126 phytocannabinoids previously detected in Cannabis sativa were investigated for their transplacental transfer and secretion into breast milk. Placental transport was predicted using novel multiple linear regression (MLR), artificial neural network (ANN), boosted trees (BT), and support vector regression (SVR) models, based on a reference set of 84 compounds for which the placental clearance index ( CI ) relative to antipyrine is known. Secretion into breast milk was predicted using newly developed classification models based on soft independent modeling of class analogies (SIMCA) and One-Class Partial Least Squares (OC-PLS) algorithms. Analysis of the Q vs. Hotelling's T 2 plot for the cannabinoids indicated that they are similar in their physicochemical properties to compounds empirically demonstrated to enter breast milk (\"in-class\"); only 7 of 126 compounds were borderline (with elevated Q but not T 2 ); no compounds were classified as \"out-of-class\". The mean predicted CI values for phytocannabinoids investigated in this study ranged from 0.4 to 0.85. It was concluded that all the cannabinoids in the studied group might cross the placenta (although their passage might be expected to be more difficult than that of antipyrine) and enter breast milk. These results should support informed risk assessment and prioritization of cannabinoids for future experimental testing.","url":"https://pubmed.ncbi.nlm.nih.gov/42511789/","authors":["Sobańska AW","Hekner A","Maciejek K","Sobański AM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.3390/ijms27146446","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42511781","name":"Deep Learning-Based Virtual Screening Identifies Potential Small-Molecule Inhibitors of ASK1: Natural Product Lead Discovery for MASH.","source":"pubmed","abstract":"Apoptosis signal-regulating kinase 1 (ASK1) represents a critical therapeutic target for metabolic dysfunction-associated steatohepatitis (MASH). Natural products, owing to their unique chemical diversity, constitute a rich reservoir for discovering novel ASK1 inhibitors. The emergence of artificial intelligence-assisted drug discovery (AIDD) has opened new avenues for exploring small-molecule inhibitors. Through virtual screening, molecular docking, interaction profiling, molecular dynamics simulations, and MM-GBSA binding free energy calculations, we systematically evaluated the binding mode, stability, and key residue contributions of the CMNPD10921-ASK1 complex. CMNPD10921 stably occupied the ASK1 active pocket, forming hydrophobic interactions and hydrogen bonds with multiple key amino acid residues. MM-GBSA analysis yielded a total computed binding free energy of -31.15 kcal/mol, suggesting a computationally favorable interaction, with van der Waals forces serving as the dominant energetic driver of complex stabilization. Residue energy decomposition further identified ILE324, THR288, and THR639 as major contributors to ligand binding. Integrating deep learning, molecular simulation, and quantum chemical calculations, this study successfully identified CMNPD10921 from a vast natural product library as a putative lead compound candidate targeting the ASK1 central regulatory region, offering a novel candidate molecule for anti-MASH drug development.","url":"https://pubmed.ncbi.nlm.nih.gov/42511781/","authors":["Zhao R","Yang J","Han M","Tang S","Hu H","Guo J","Ma M","Sun J","Zhou X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.3390/ijms27146438","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42511472","name":"Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.","source":"pubmed","abstract":"Computer-aided de novo drug design has been widely explored for early-stage drug discovery, yet the multi-property optimization of novel molecules remains challenging. We aimed to develop a de novo drug design model to efficiently optimize multiple properties simultaneously. We developed a teacher-student-interaction deep learning model fine-tuned by reinforcement learning (TSItransRL) using bioactivity datasets (DRD2 and JNK3/GSK3&#x3b2; targets). A conditional transformer was pretrained as the teacher model to incorporate multi-property information. A vanilla transformer served as the student model and was subsequently optimized through interactive knowledge distillation and reinforcement learning. An evaluation was conducted using MOSES and conditional metrics on two tasks, specifically generating molecules with DRD2-targeting activity and generating molecules with dual JNK3/GSK3&#x3b2;-targeting activity, with the analyses including docking, the similarity ensemble approach (SEA), and scaffold novelty. TSItransRL achieved success rates of 98.36% and 98.90% for the DRD2 and JNK3/GSK3&#x3b2; tasks, respectively, with an internal diversity of 0.795, outperforming most baselines. The docking, SEA, scaffold, and ADMET analyses were used as exploratory in silico assessments to support the preliminary prioritization of selected generated molecules. TSItransRL provides an in silico framework for benchmark-level multi-property molecular generation and prioritization, combining interactive knowledge distillation with reinforcement learning to explore molecules that satisfy predefined predicted-activity, drug-likeness, and synthetic-accessibility criteria. The generated molecules should be regarded as computational candidates for a further medicinal-chemistry assessment, independent validation, and experimental testing rather than experimentally validated leads.","url":"https://pubmed.ncbi.nlm.nih.gov/42511472/","authors":["Wang L","Lu Z","Cui L","Liu C","Qin Y","Feng S","Wang D","Yin W","Kang Z","Cao L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/ijms27146125","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42511240","name":"Towards Cost-Effective and Sustainable Media Formulations for Terrestrial and Aquatic Cellular Agriculture.","source":"pubmed","abstract":"Over a decade of research on media for cultured meat and seafood production has resulted in multiple highly efficient serum-free and chemically defined formulations for some species, but it has also identified challenges yet to be solved-especially for aquatic cell lines. Depending on the product and cell type, the approach to develop highly efficient, sustainable, and low-priced media can diverge greatly. In this review, we provide an in-depth overview of this complex research area to facilitate strategic decision-making for stakeholders. We evaluate the advantages and limitations of utilizing hydrolysates, growth factor mutants, growth factor alternatives, and stabilizers in serum-free media formulations published for cultured meat production, as well as ongoing research efforts on developing adequate media for cultured seafood. We critically analyze strategies aimed at reducing medium costs and enhancing sustainability of cultured meat and seafood production, including their food-compatibility assessment. We summarize topics that require further exploration, such as identification of species-specific growth factors-particularly for aquatic species; exploration of hydrolysates as a substitute for basal medium; waste medium recycling strategies; and the potential application of artificial intelligence (AI) and machine learning (ML) technologies to enhance these areas. Additionally, we consider possible emerging regulatory issues and their impact on media formulation development. Finally, key performance indicators for media formulations are proposed to guide future strategic and operational improvements regarding an economical and sustainable production process.","url":"https://pubmed.ncbi.nlm.nih.gov/42511240/","authors":["Leber R","Rosa JT","Laizé V","Fernando GF","Buyel J","Fuchs A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/foods15142494","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510961","name":"Next-Generation Sequencing in Pulmonary Fibrosis: Translational Promise and Current Clinical Limitations.","source":"pubmed","abstract":"Pulmonary fibrosis (PF), particularly idiopathic pulmonary fibrosis (IPF), is a progressive and often fatal interstitial lung disease characterised by complex genetic and molecular heterogeneity. Traditional diagnostic approaches, which rely on clinical, radiological and histopathological assessment, are frequently insufficient to capture the underlying biological diversity of the disease. The advent of next-generation sequencing (NGS) has substantially advanced the understanding of PF by enabling comprehensive genomic and transcriptomic profiling. NGS technologies, including whole-exome sequencing (WES), whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and targeted gene panels, have uncovered key genetic determinants. These include mutations in telomere-related genes (TERT, TERC, RTEL1) and surfactant-related genes (SFTPC, SFTPA2), as well as common variants like the MUC5B promoter polymorphism. These discoveries have clarified disease pathogenesis, revealed polygenic risk models, and may improve diagnostic accuracy, particularly in distinguishing overlapping interstitial lung disease (ILD) phenotypes. Beyond genetics, transcriptomic analyses have identified dysregulated pathways, including TGF-&#x3b2;, Wnt/&#x3b2;-catenin, and PI3K/Akt signalling, and have enabled the discovery of novel biomarkers for prognosis and therapeutic response. In selected clinical settings, NGS is beginning to support patient stratification and inform management decisions. Emerging applications, including liquid biopsy and integration with artificial intelligence, further expand the potential clinical utility of NGS. Despite challenges related to cost, data interpretation and standardisation, NGS represents a powerful research tool in PF.","url":"https://pubmed.ncbi.nlm.nih.gov/42510961/","authors":["Pagliaro R","Perrotta F","Zamparelli SS","Carrozzo VM","Cipriano A","Mondoni M","Stella GM","Bianco A","Scialò F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.3390/cimb48070721","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510944","name":"Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.","source":"pubmed","abstract":"Exosomes are 30-150 nm extracellular vesicles that convey molecular information reflecting the physiological and pathological states of their source cells. In precision oncology, they function as a non-invasive \"liquid biopsy,\" enabling real-time monitoring of tumor dynamics and metastasis. However, extreme biofluid heterogeneity poses significant challenges for their isolation and analysis using conventional statistical approaches. This review aims to examine how artificial intelligence (AI), specifically machine learning and deep learning, transforms complex exosomal \"noise\" into actionable clinical insights. AI enhances exosome isolation, enables disease-specific biomarker identification, and predicts therapeutic responses with high precision. Integrating multi-omics data and single-exosome analysis enables AI-driven models to facilitate early cancer detection and therapeutic resistance monitoring. Despite challenges related to standardization and data privacy, the convergence of AI and exosome biology is poised to transform reactive cancer treatments into a proactive, personalized medical ecosystem. This approach also provides a framework for managing other complex systemic diseases.","url":"https://pubmed.ncbi.nlm.nih.gov/42510944/","authors":["Gangadaran P","Rajendran RL","Kavitha MS","Ahn BC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/cimb48070704","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510797","name":"MSeqDR PMD-VR: An Expert-Curated Virtual Registry of 11,000 Mitochondrial Disease Cases Established Through Literature Mining and Generative AI Augmentation.","source":"pubmed","abstract":"Background/Objectives: Patient registries are essential for rare disease research, yet the extensive genetic and phenotypic heterogeneity of primary mitochondrial diseases (PMDs) makes traditional registry development slow and resource-intensive. We established the MSeqDR PMD virtual registry (PMD-VR) to address this gap through systematic literature mining and semi-automated data harmonization. Methods: The PMD-VR captures, standardizes, and harmonizes published case-level PMD data using a semi-automated curation pipeline. A data transformation framework maps heterogeneous raw data terms to standardized common data elements (CDEs). A generative AI (GenAI) platform leveraging large language models (LLMs), augmented by Human Phenotype Ontology (HPO) and external biomedical knowledge sources, accelerates data transformation and generates simulated clinical reports. Results: Currently, PMD-VR contains approximately 11,000 de-identified literature-derived cases, including over 2300 Leigh syndrome spectrum (LSS), 278 MELAS, and 300 CPEO cases. The pipeline mapped 872 heterogeneous terms to 102 standardized CDEs. Pathogenicity assessments were captured for variants in over 7900 cases, including 3800 with mtDNA pathogenic or likely pathogenic variants. Modes of inheritance were inferred for 5212 cases. PMD-VR has supported ClinGen Mitochondrial Diseases Gene Curation Expert Panel (Mito-GCEP) efforts, providing phenotyped evidence for 440 curated LSS cases across 113 PMD genes. Conclusions: PMD-VR is among the largest single PMD registries, offering a scalable, web-accessible platform for generating analysis-ready cohorts from the published literature. It represents a rich resource enabling comprehensive PMD characterization with unprecedented breadth of genetic and phenotypic knowledge.","url":"https://pubmed.ncbi.nlm.nih.gov/42510797/","authors":["Shen L","Lott MT","Mccormick EM","Muraresku CC","Keller K","Wallace DC","Zolkipli-Cunningham Z","Rahman S","Falk MJ","Gai X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 30","doi":"10.3390/genes17070757","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510527","name":"Effect of Aptamin C on NK Cell Activity and Cytotoxicity: A Randomized Placebo-Controlled Trial and In Vitro Comparison with Vitamin C.","source":"pubmed","abstract":"Natural killer (NK) cells are crucial components of innate immunity and rapidly eliminate abnormal cells through ligand-receptor signaling without prior sensitization. Vitamin C is known to enhance NK cell function; however, its susceptibility to oxidation may limit its efficacy in NK cell activation. This study evaluated the efficacy of Aptamin C, a stabilized conjugate of vitamin C and an aptamer, in enhancing NK cell activation. In the in vivo randomized placebo-controlled study, 120 participants were randomized to receive either Aptamin C or placebo, and 109 participants were included in the final analysis. Participants received Aptamin C at a dose of 36.057 mg/day or placebo for 4 weeks. The results showed significant increases in NK cell cytotoxicity after 2 and 4 weeks in the Aptamin C group. Additionally, serum levels of cytokines and cytotoxic granules associated with NK cell activity peaked 4 weeks after Aptamin C intake. Subgroup analysis showed that the enhancing effect of Aptamin C on NK cell activity was mainly observed in participants older than 40 years, whereas no significant effects were detected in participants aged &lt;40 years. In the in vitro study, NK-92 cells treated with Aptamin C were compared with NK-92 cells treated with vitamin C. Aptamin C treatment enhanced proliferation, survival, cytotoxicity, and cytotoxic granule production in NK-92 cells compared with vitamin C treatment. These findings indicate that Aptamin C may effectively promote NK cell activation, particularly in middle-aged and older adults, and suggest its potential as an immunomodulatory supplement for supporting NK cell function.","url":"https://pubmed.ncbi.nlm.nih.gov/42510527/","authors":["Ahn H","Lee J","Park JH","Barn JS","Kim Y","Kang JS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 25","doi":"10.3390/antiox15070796","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510504","name":"From Modified Haller Index to a Novel Patented Anatomical Measurement Device: Engineering Development, Validation, and Clinical Applications of Non-Invasive Thoracic Morphometry.","source":"pubmed","abstract":"Thoracic morphology is increasingly recognized as an important determinant of cardiopulmonary phenotype, influencing cardiovascular mechanics, respiratory physiology, and the interpretation of diagnostic imaging findings. Although the radiological Haller Index (HI) remains the reference standard for quantifying pectus excavatum severity, its dependence on computed tomography and ionizing radiation limits widespread clinical implementation, particularly in settings requiring serial evaluations. To overcome these limitations, the Modified Haller Index (MHI) was developed as a simple, non-invasive, radiation-free alternative that combines external thoracic anthropometry with echocardiographic assessment. Since its introduction, the MHI has undergone clinical validation and has progressively expanded beyond the assessment of chest wall deformities, demonstrating that thoracic conformation is not merely an anatomical characteristic but a clinically relevant determinant of cardiovascular and respiratory physiology. Growing evidence indicates that thoracic morphology influences cardiac chamber geometry, ventricular filling, stroke volume, myocardial deformation, ventricular-arterial coupling, exercise stress echocardiography findings, pulmonary function, and symptom perception across a broad spectrum of cardiovascular and respiratory diseases. Elevated MHI values identify individuals with a reduced antero-posterior thoracic diameter and a distinctive cardiopulmonary phenotype characterized by external cardiac compression, smaller cardiac chambers, restrictive ventilatory physiology, and apparent alterations in myocardial mechanics despite the absence of intrinsic myocardial disease. Building upon the clinical validation of the MHI, a novel patented anatomical measurement device was engineered to standardize thoracic morphometric assessment by enabling direct acquisition of both latero-lateral and antero-posterior thoracic diameters within a single measurement procedure. The device integrates dedicated anatomical reference elements, an innovative adjustable sternal pointer, movable measurement components, and a standardized acquisition workflow into a portable, low-cost, and radiation-free platform, thereby improving measurement reproducibility while simplifying bedside MHI determination. This narrative review summarizes the historical evolution of thoracic morphometry, the development and clinical validation of the MHI, the engineering rationale, structural architecture, and measurement workflow of the patented device, and the growing evidence supporting the clinical significance of thoracic conformation across cardiovascular and respiratory medicine. Together, the MHI and the proposed anatomical measurement device establish a practical platform for standardized, radiation-free thoracic morphometry that may facilitate routine bedside phenotyping. Future integration with digital technologies, artificial intelligence, and advanced imaging systems may further enable next-generation digital thoracic phenotyping for personalized cardiovascular and respiratory characterization, risk stratification, and precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42510504/","authors":["Sonaglioni A","Nicolosi GL","Baravelli M","Lombardo M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.3390/bioengineering13070839","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510493","name":"AI-Guided Home-Based Therapy for Convergence Insufficiency: A Comparative Feasibility Study with Pencil Push-Ups.","source":"pubmed","abstract":"Convergence insufficiency (CI) is a common binocular vision disorder that causes eye strain, headaches, and blurriness. Although pencil push-ups (PPU) are widely used to treat CI, their real-world effectiveness may be limited by inconsistent performance and poor adherence. MobileS (the App) is an AI-based smartphone application that tracks eye movements through the front camera, enabling guided \"digital push-ups\" with instant visual feedback and automatic performance monitoring. This follow-up study evaluated the feasibility, recorded adherence, and preliminary clinical signals of MobileS as a home-based therapy for CI compared with conventional PPU.","url":"https://pubmed.ncbi.nlm.nih.gov/42510493/","authors":["Khatib A","Raz S","Shvartz E","Shimshoni I","Jabaly-Habib H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/bioengineering13070828","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510487","name":"Fully Automated Biometric Parameter Measurement in Prenatal Ultrasound Screening for Total Anomalous Pulmonary Venous Connection.","source":"pubmed","abstract":"Total anomalous pulmonary venous connection (TAPVC) is a severe congenital heart disease, yet its prenatal detection rate remains suboptimal. To support prenatal ultrasound screening of TAPVC, the post-left atrium space (PLAS) index and the left-atrial posterior-space-to-diagonal (LAPSD) ratio measured in the four-chamber view (4CV) have been proposed as useful biometric parameters. In this study, we developed a novel approach that integrates automated 4CV extraction (AE) from fetal cardiac ultrasound videos with automated measurement of these indices. The heart, crux, and descending aorta were segmented using DeepLabv3+, UNet3+, and SegFormer. The screening performance of the AE-based methods was comparable to that of manual 4CV extraction, as demonstrated by similar mean areas under the receiver operating characteristic curve (AUCs). In a clinical comparison study, the mean AUC values for residents, fellows, experts, AE-DeepLabv3+, AE-UNet3+, and AE-SegFormer were 0.784, 0.801, 0.996, 0.903, 0.928, and 0.940, respectively, for the PLAS index and 0.797, 0.801, 0.996, 0.919, 0.916, and 0.940, respectively, for the LAPSD ratio. Although experts demonstrated the best overall performance, the fully automated methods consistently outperformed both the residents and fellows. This approach may support less experienced examiners, improve screening accuracy, streamline clinical workflows, and ultimately enhance the prenatal detection of TAPVC.","url":"https://pubmed.ncbi.nlm.nih.gov/42510487/","authors":["Aoyama R","Harada N","Komatsu M","Komatsu R","Takeda K","Teraya N","Asada K","Kaneko S","Iwamoto K","Matsuoka R","Sekizawa A","Hamamoto R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.3390/bioengineering13070822","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510481","name":"SHAP-Based Feature Augmentation and Stacking Ensemble Learning for ECG-Based Serum Potassium Abnormality Prediction.","source":"pubmed","abstract":"Electrocardiogram (ECG) signals contain important clinical information associated with serum potassium abnormalities. However, in Taiwan, raw patient data and original medical signals generally cannot be taken outside the hospital environment, thereby limiting their subsequent reuse and cross-institutional applications. The objective of this study is to transform classification-related information contained in raw ECG signals into high-level SHAP features and to evaluate their feasibility as auxiliary or alternative features to the original wave-segment model outputs. To this end, this study proposes a time-series classification framework that integrates ECG wave-segment submodels, SHAP-based feature augmentation, and stacking ensemble learning for serum potassium abnormality prediction. Submodels are first trained separately using different ECG wave segments and their combinations. SHAP is then applied to transform the contributions of the wave-segment submodel outputs to the prediction results into high-level features. In addition, PCA features are included as a comparison baseline to analyze the effects of different feature transformation methods on classification performance. The experimental results show that incorporating SHAP-based augmented features improves ECG-based serum potassium abnormality prediction performance under most settings. Even when only SHAP-based augmented features are used for training, some models still maintain performance comparable to or better than the Baseline. Although PCA provides more stable classification balance for some patients, SHAP-based augmented features can still represent classification-related model contribution information under most settings while achieving better or comparable classification performance. Overall, even without directly using the original ECG signals in the final classification stage, these features retain a certain degree of discriminative information and demonstrate the potential to serve as alternative features to the original wave-segment model outputs. Therefore, the findings of this study provide a preliminary reference for future medical data reuse, cross-institutional collaboration, and privacy risk assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42510481/","authors":["Ko YH","Hung CS","Lin CR","Ciou YF","Huang CC","Lin PC","Tsai JH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/bioengineering13070816","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510462","name":"Clinical Translation of Artificial Intelligence-Driven Gait Analysis Using Plantar Pressure and Ground Reaction Force.","source":"pubmed","abstract":"Background : Artificial intelligence (AI)-driven gait analysis using plantar pressure and ground reaction force (GRF) signals may provide objective digital biomarkers for rehabilitation, but clinical translation remains uncertain. This scoping review and evidence map aimed to summarize clinical applications, compare evidence maturity, and identify methodological and translational gaps. Methods : PubMed, Web of Science, Embase, and Scopus were searched from the earliest available indexed records in each database to May 2026. Original clinical studies using plantar pressure- or GRF-derived signals with AI methods for disease recognition, severity assessment, risk prediction, rehabilitation monitoring, or decision support were included. Results : Fifteen studies met the eligibility criteria. Evidence was concentrated in Parkinson's disease (PD), particularly PD recognition and freezing of gait prediction, where relatively more mature evidence was supported by multiple studies and participant-level or cross-dataset validation. Evidence for PD severity assessment, knee osteoarthritis monitoring, chronic ankle instability rehabilitation, fall-risk stratification, sarcopenia screening, peripheral artery disease recognition, and functional gait disorder classification remained less mature. Translation was limited by small or single-center samples, unclear participant-level data splitting, limited external validation, absent calibration, sparse explainable AI reporting, and insufficient real-world workflow testing. Conclusions : Future studies should prioritize prospective, externally validated, interpretable, calibrated, and clinically embedded models before routine rehabilitation implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42510462/","authors":["Yang J","Dong C","Zhang X","Tang S","Sun H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 11","doi":"10.3390/bioengineering13070796","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510417","name":"Association of the Frailty-to-Estimated Cardiorespiratory Fitness Ratio with Prevalent Stroke in Middle-Aged and Older Adults: A Cross-Sectional NHANES Study.","source":"pubmed","abstract":"Stroke remains a major cause of disability worldwide, and population-level indicators that integrate multidimensional vulnerability with physiological reserve may provide useful perspectives for characterizing cerebrovascular health in aging populations. This cross-sectional study examined the association between the Frailty-to-estimated Cardiorespiratory Fitness Ratio (FCR) and self-reported prevalent stroke among middle-aged and older adults using NHANES 2011-2014 data. FCR was calculated as a 23-item modified Frailty Index divided by estimated cardiorespiratory fitness, providing an interpretable load-to-reserve measure that linked accumulated health deficits with estimated cardiorespiratory reserve. The final analytical sample included 3511 participants aged 45 years or older. Survey-weighted logistic regression models, sensitivity analyses, exploratory subgroup analyses, and spline models were used to evaluate the association. The weighted prevalence of self-reported prevalent stroke increased across FCR quartiles, from 2.2% in Q1 to 12.9% in Q4. In the conventional clinical adjustment model, participants in the highest FCR quartile had greater odds of self-reported prevalent stroke than those in the lowest quartile (OR = 6.13, 95% CI: 2.97-12.66; p&lt;0.001), with similar findings in the overlap-aware model (OR = 6.31, 95% CI: 3.07-12.97; p&lt;0.001). Log-transformed FCR was also consistently associated with greater odds of self-reported prevalent stroke across adjustment models, and spline analysis suggested a generally increasing association. These findings support FCR, particularly when modeled using quartiles or log-transformed values, as an interpretable integrative load-to-reserve construct associated with self-reported prevalent stroke, and suggest its potential relevance for population-based characterization of cerebrovascular health in aging adults.","url":"https://pubmed.ncbi.nlm.nih.gov/42510417/","authors":["He Y","Yuan W","Lv M","Yang Y","Xu Y","Song Z","Jiang X","Yang L","Huang C","Chen Y","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 26","doi":"10.3390/bioengineering13070750","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510407","name":"Mapping the Global Trajectory and Key Trends of Temporal Interference Stimulation.","source":"pubmed","abstract":"Since its inception in 2017, temporal interference stimulation (TIS) has attracted increasing attention as a novel neuromodulation approach with the potential to non-invasively target deep brain structures. As the field moves from initial biophysical validation toward broader experimental and translational applications, a macroscopic understanding of its developmental trajectory and thematic evolution is needed. In this study, we systematically mapped the scientific landscape of TIS research using bibliometric methods to characterize its knowledge structure, core themes, and emerging frontiers. The analysis shows that TIS research has expanded rapidly from foundational animal studies and biophysical mechanism validation toward computational head modeling, individualized electric field optimization, and early human applications. Current research is increasingly focused on cross-species scaling, stimulation dosimetry, comparative advantages over other neuromodulation techniques, precise targeting strategies, and potential physiological risks such as high-frequency conduction block. Overall, TIS is evolving from an exploratory biophysical concept into a promising but technically and physiologically complex neuromodulation tool. Overcoming current engineering and translational barriers, particularly through individualized modeling, rigorous optimization, and well-designed human studies, will be essential for establishing TIS as a reliable therapeutic intervention.","url":"https://pubmed.ncbi.nlm.nih.gov/42510407/","authors":["Qi L","Gao Z","Pan X","Li J","Yu Y","Wang K","Li Q","Bai T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 25","doi":"10.3390/bioengineering13070741","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510385","name":"Attention-Based Multimodal Framework for Athlete-Performance Analysis and Rehabilitation Monitoring Using Vision and Wearable Sensors.","source":"pubmed","abstract":"Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns.","url":"https://pubmed.ncbi.nlm.nih.gov/42510385/","authors":["Alonazi M","Abro IA","Abdelhaq M","Alsaqour R","Jalal A","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 23","doi":"10.3390/bioengineering13070718","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510134","name":"Attitudes Toward Artificial Intelligence Among Polish Dentists: A Cross-Sectional Survey.","source":"pubmed","abstract":"Background: Artificial intelligence (AI) is increasingly being integrated into modern dental practice, particularly in diagnostics, radiographic analysis, treatment planning, and practice management. Despite the rapid advancement of AI-based technologies, evidence regarding dentists' attitudes toward AI in Central and Eastern Europe remains limited. This study aimed to evaluate Polish dentists' attitudes toward artificial intelligence in contemporary dental practice and to investigate differences in AI acceptance according to sex, age group, and dental specialty. Materials and Methods: A cross-sectional online questionnaire-based study was conducted among licensed dentists practicing in Poland. An anonymous questionnaire comprising 15 attitude statements rated on a five-point Likert scale was distributed through professional social media groups. The survey assessed attitudes toward the use of AI in clinical, diagnostic, and administrative aspects of dentistry. Statistical analyses were performed using Statistica 16.0. Group comparisons were conducted using the Mann-Whitney U test and Kruskal-Wallis test with Benjamini-Hochberg false discovery rate correction. Internal consistency of the questionnaire was assessed using Cronbach's alpha coefficient. Results: A total of 183 completed questionnaires were included in the analysis. The internal consistency of the questionnaire was high (Cronbach's &#x3b1; = 0.900). The overall acceptance of AI was moderate (mean score: 3.18 &#xb1; 0.73). The highest levels of agreement were observed for the perceived potential of AI to improve dental practice management (mean = 4.05) and for general openness toward AI implementation in dentistry (mean = 4.05). Respondents also expressed a high willingness to use AI for generating clinical documentation (mean = 3.69). In contrast, the lowest acceptance was observed for statements suggesting that AI could replace dentists (mean: 1.36). Within the study sample, men demonstrated significantly higher overall AI acceptance than women ( p &lt; 0.001). Significant differences were also observed between age groups and dental specialties. Orthodontists demonstrated the highest AI acceptance among the surveyed specialties; however, these findings should be interpreted as exploratory because of unequal subgroup sizes. After FDR correction, significant differences between age groups remained only for selected questionnaire items. Conclusions: Within the limitations of this convenience sample, the findings suggest that Polish dentists generally perceive artificial intelligence as a supportive tool rather than a replacement for clinicians. Acceptance was greatest for administrative and organizational applications of AI, whereas autonomous clinical decision-making received substantially lower support. These findings suggest that the successful implementation of AI in dentistry should prioritize assistive technologies that enhance clinical workflows while preserving the central role of the dentist in patient care.","url":"https://pubmed.ncbi.nlm.nih.gov/42510134/","authors":["Nowakowska M","Szalewski L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.3390/diagnostics16142271","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510111","name":"Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy into a more standardized, data-driven, and workflow-integrated field. Advances in computer-assisted detection (CADe), diagnosis (CADx), quality assessment (CAQ), natural language processing (NLP), and multimodal deep learning have expanded AI applications across colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy. These systems have demonstrated improvements in lesion detection, procedural quality assessment, workflow efficiency, and diagnostic support. However, current evidence remains largely focused on surrogate outcomes rather than patient-centered clinical benefits, while challenges related to generalizability, explainability, regulatory oversight, automation bias, and workflow integration continue to limit widespread adoption. Future progress will depend on prospective real-world validation, diverse datasets, explainable AI frameworks, and careful integration of human-AI interaction into clinical practice. Overall, AI is evolving from a supportive adjunct into an increasingly integrated component of gastrointestinal endoscopy with the potential to improve procedural quality, diagnostic consistency, and clinical efficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/42510111/","authors":["Lopez Dominici F","Wallace MB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.3390/diagnostics16142248","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510110","name":"AI-Assisted Forensic Analysis of Hanging-Related Ligature Marks: A Pilot Study Using Convolutional Neural Networks.","source":"pubmed","abstract":"Background: Artificial intelligence (AI) is increasingly applied in medical image analysis, although its application in forensic pathology remains limited. The assessment of ligature marks in hanging deaths is challenging and relies on forensic expertise. This pilot study evaluated a deep learning approach for morphological classification of hanging-related ligature marks. Methods: A Convolutional Neural Network (CNN) was trained on a dataset of 404 standardized JPEG images obtained from forensic medicine atlases and classified into hanging-related ligature marks and non-hanging lesions, including strangulation and post-mortem artefacts. Following internal validation, the model was tested on an independent set of forensic case images provided by forensic pathology experts from Messina, Italy, and Vilnius, Lithuania. Images were annotated and reviewed by a team of two forensic pathologists, with final labels assigned by consensus using morphological criteria. Results: The CNN demonstrated encouraging classification performance with an F1-score of 0.81 &#xb1; 0.04, distinguishing hanging-related ligature marks from morphologically similar lesions. The methodological framework and image standardization criteria for AI-assisted forensic analysis were also established. Conclusions: AI-based image analysis may support the evaluation of ligature marks during external examinations. Nevertheless, forensic diagnosis requires the integration of autopsy findings, physical examination, and circumstantial evidence. Larger datasets and multicenter protocols are needed to further assess reliability and applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42510110/","authors":["Rigano G","De Vita F","Candela L","Bruneo D","De Caro S","Čaplinskienė M","Ventura Spagnolo E","Baldino G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.3390/diagnostics16142247","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510093","name":"Next-Generation Cardiovascular Imaging in Precision Medicine: Integrating Functional Imaging, Artificial Intelligence, Biomarkers, and Personalized Risk Stratification.","source":"pubmed","abstract":"Cardiovascular and vascular diseases remain major causes of morbidity and mortality worldwide, despite substantial advances in prevention, diagnosis, and treatment. In recent years, cardiovascular imaging has moved beyond the traditional assessment of anatomy and morphology toward a multidimensional evaluation of function, tissue composition, haemodynamics, inflammation, and individualized risk. This evolution has been driven by technological progress in echocardiography, cardiovascular magnetic resonance, computed tomography, nuclear imaging, intravascular imaging, and point-of-care ultrasound, together with the rapid development of artificial intelligence, radiomics, and predictive analytics. Advanced echocardiographic techniques, including contrast stress echocardiography and emerging methods for myocardial scar detection, may improve functional and prognostic assessment in patients with suspected or established coronary artery disease. Cardiac magnetic resonance, through tissue mapping, late gadolinium enhancement, and 4D flow imaging, provides unique information on myocardial fibrosis, perfusion, ventricular remodelling, and vascular haemodynamics. Computed tomography, particularly with the introduction of photon-counting technology, is expanding the non-invasive characterization of coronary plaques, vascular calcification, and thromboembolic disease. Hybrid imaging with PET/CT and PET/MR offers additional insight into vascular inflammation, myocardial metabolism, and active disease processes. At the same time, intravascular ultrasound, optical coherence tomography, and augmented-reality-supported imaging are refining interventional guidance, while point-of-care ultrasound is broadening access to rapid bedside cardiovascular and vascular assessment. The integration of imaging findings with circulating biomarkers, clinical scores, lipid profiles, coagulation parameters, and machine-learning models represents a promising strategy for personalized risk stratification, particularly in complex conditions such as coronary artery disease, venous thromboembolism, pulmonary embolism, and bleeding risk during antithrombotic therapy. This review summarizes current advances in cardiovascular imaging, discusses their translational implications, and highlights future directions for integrating imaging, artificial intelligence, and precision medicine into daily clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42510093/","authors":["Siniscalchi C","Basaglia M","Russo V","Di Micco P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/diagnostics16142230","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510092","name":"PolypSAM-Open: Mitigating Automation Bias in AI-Assisted Colonoscopy via Open-Set Surgical Artifact Rejection.","source":"pubmed","abstract":"Background: Intelligent decision support systems for colonoscopy can fail when encountering out-of-distribution surgical instruments such as snares or biopsy forceps, producing false-positive polyp masks that may contribute to automation bias and reduce workflow reliability. This study aimed to develop and evaluate a parameter-efficient framework for open-set-aware polyp segmentation that can reject such anomalous inputs while preserving in-distribution segmentation performance. Methods: We propose PolypSAM-Open, which integrates a prototype-based Open-Set Learning (OSL) module with Low-Rank Adaptation (LoRA) in the MedSAM image encoder. The model was trained on Kvasir-SEG using an 85/15 split of authentic polyp images and synthetic high-frequency Gaussian noise to learn a rejection margin. Zero-shot out-of-distribution detection was evaluated on 590 unseen authentic surgical instruments from Kvasir-Instrument. Segmentation and detection performance were compared against a standard MedSAM-LoRA baseline. Results: Standard parameter-efficient fine-tuning yielded an OOD AUROC of 0.4263 on authentic surgical instruments. PolypSAM-Open improved zero-shot OOD AUROC to 0.9535 (p&lt;0.001). Despite allocating 15% of training capacity to the synthetic-noise rejection margin, PolypSAM-Open maintained segmentation performance comparable to the standard fine-tuned baseline (Dice 0.9728 versus 0.9723). On ETIS-LaribPolypDB and CVC-ClinicDB, Dice scores were 0.9301 and 0.9386, respectively. The approach remained parameter-efficient, updating 4.48% of total parameters while adding negligible inference latency relative to the underlying MedSAM forward. Conclusions: Prototype-based open-set adaptation can substantially improve rejection of unseen surgical artifacts in AI-assisted colonoscopy while preserving high segmentation accuracy. These findings position PolypSAM-Open as a promising strategy for potentially safer decision-support segmentation in endoscopic workflows; prospective clinical validation remains necessary.","url":"https://pubmed.ncbi.nlm.nih.gov/42510092/","authors":["Hasan U","Shahriar S","Hossain F","Hossain MA","Martuza MA","Momen S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/diagnostics16142226","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510088","name":"Imaging Advances in Light Chain Amyloidosis.","source":"pubmed","abstract":"Light chain (AL) amyloidosis is a systemic disorder caused by plasma cell dyscrasia, with cardiac involvement being the primary determinant of prognosis. Survival outcomes vary significantly across disease stages. This heterogeneity underscores a critical need for early diagnosis, precise risk stratification, and response-adapted therapy. In this context, multimodality imaging has emerged as an indispensable non-invasive tool, providing crucial insights for clinical decision-making. This review synthesizes recent advances in the application of key imaging modalities-echocardiography, magnetic resonance imaging, and nuclear medicine imaging-for evaluating AL amyloidosis. We highlight how these techniques have shifted the paradigm from anatomical assessment to quantitative, multiparametric tissue characterization, ultimately guiding personalized patient management.","url":"https://pubmed.ncbi.nlm.nih.gov/42510088/","authors":["Qiu M","Shen K","Yang H","Wang J","Li J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/diagnostics16142225","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510076","name":"Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.","source":"pubmed","abstract":"Background/Objectives: The accurate detection of the anterior loop (AL) of the inferior alveolar nerve is critical to avoid neurosensory complications during surgical procedures in the interforaminal region, and panoramic radiography continues to be widely used in routine dental diagnostics due to its accessibility and cost-effectiveness. This study aimed to evaluate the performance of a deep learning approach in automatic detection of the AL in panoramic radiographs. Methods: A total of 305 anonymised panoramic radiographs containing 413 annotated ALs were used to train a YOLOv8x-based model for automatic AL detection. The dataset was divided into training, validation, and test sets consisting of 245 images (332 AL annotations), 30 images (40 AL annotations), and 30 images (41 AL annotations). Labelling was carried out by using the polygonal annotation method. The model's performance in identifying the AL region was measured using precision, recall, F1 score, and mean average precision (mAP@0.5). Results: The model achieved a precision of 0.75, a recall of 0.6585, and a F1 score of 0.7013. The average precision at an intersection over union (IoU) threshold of 0.5 (mAP@0.5) was 0.739. Conclusions: This study demonstrates the feasibility of using a YOLOv8x-based detection model to detect ALs in panoramic radiographs. Although further improvements are needed to enhance model sensitivity and generalisability, the findings demonstrate the potential to support clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42510076/","authors":["Uzun E","İçöz D","Apaydın BK","Orhan K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 15","doi":"10.3390/diagnostics16142213","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510038","name":"Noninvasive Prediction of TP53 Gene Status and ATRX Gene Status in IDH-Mutant Glioma Using Multimodal MRI: Incorporating Morphological, Spectroscopic, Diffusion, and Perfusion Imaging.","source":"pubmed","abstract":"Background/Objectives : Noninvasive determination of glioma molecular profiles is clinically crucial for assessing therapeutic efficacy and predicting disease outcomes. This study aimed to evaluate the potential of morphological magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI) in predicting TP53 gene status and X-linked alpha-thalassemia intellectual disability syndrome ( ATRX ) gene status in isocitrate dehydrogenase ( IDH )-mutant gliomas. Methods : A retrospective analysis was performed on 106 IDH -mutant glioma patients using morphological MRI, DWI, MRS, and DCE-PWI data. Statistical comparisons of imaging parameters across molecular status groups were conducted, and logistic regression models were developed to predict molecular status, with diagnostic performance evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold stratified cross-validation with 1000 bootstrap resamples was employed to assess model generalizability Results : Among 106 IDH -mutant gliomas, the TP53 -mutant group showed a greater proportion of tumors with &gt;33% enhancement ( p = 0.018), higher Cho/Cr ( p &lt; 0.001), and higher Cho/NAA ( p = 0.005) than the TP53 -wildtype group. Multivariable analysis demonstrated that the Cho/Cr ratio was an independent predictor of TP53 mutation in IDH -mutant gliomas (odds ratio [OR] = 2.037, p = 0.021), with the model achieving an apparent AUC of 0.741. DCE-PWI parameters showed no significant differences across molecular subgroups. Ve was significantly elevated in ATRX -mutant tumors (median 57.16 vs. 30.63, p = 0.029). Ktrans, Kep, Vp, and iAUC showed no significant differences between groups (all p &gt; 0.05). Furthermore, multivariable analysis showed that ADC values (OR = 1.005, p = 0.017) and the Cho/NAA ratio (OR = 3.073, p = 0.023) emerged as independent predictors of ATRX mutation, with the model achieving an apparent AUC of 0.863. Five-fold cross-validation demonstrated that the Cho/Cr model for TP 53 prediction achieved a mean AUC of 0.717 &#xb1; 0.043 (Bootstrap 95% CI: 0.616-0.814), and the ADC + ChoNAA model for ATRX prediction achieved 0.865 &#xb1; 0.124 (95% CI: 0.780-0.953). All predictors remained significant across all five folds. Pooled confusion matrices yielded sensitivities of 0.623 and 0.757, specificities of 0.696 and 0.909, and accuracies of 0.654 and 0.840, respectively. Conclusions : Multimodal MRI techniques (morphological MRI, DWI, MRS, and DCE-PWI) can help predict TP53 and ATRX status without surgery. Higher Cho/Cr and Cho/NAA ratios were independently associated with TP53 mutation, whereas lower ADC and higher Cho/NAA independently predicted ATRX mutation. These findings suggest that a focused imaging protocol may be sufficient for preoperative molecular profiling in this tumor type.","url":"https://pubmed.ncbi.nlm.nih.gov/42510038/","authors":["Chen S","Zhu Z","Yang H","Ye M","Song Y","Tian C","Niu F","Wang Z","Li X","Zhang X","Zhang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 12","doi":"10.3390/diagnostics16142174","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510030","name":"A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.","source":"pubmed","abstract":"Background/Objectives: Generative Models have revolutionized the synthesis of complex, realistic medical images. However, obtaining annotated, high-quality datasets is challenging and expensive due to privacy concerns, high human annotation costs, and data scarcity. This systematic literature review (SLR) provides a comparative overview of Generative AI models used for medical image generation, categorizing the research into GANs, Diffusion Models, and Autoencoders used for medical image generation and GAN loss functions. Methods: This systematic review followed the PRISMA 2020 guidelines. Studies published between 2021 and 2026 were retrieved from selected scientific databases and screened using predefined inclusion criteria. We selected 120 papers published in reputable databases and journals. The review examined generative modeling approaches aimed at addressing data scarcity and annotation limitations in medical imaging, including GAN-based adversarial synthesis, diffusion-based iterative denoising, and Autoencoder-based latent representation learning, along with an in-depth analysis of GAN loss functions. Results: GANs were the most widely used approach (36% of the reviewed studies), achieving the lowest FID scores (15.552-31.349). Diffusion Models (26% of the reviewed studies) showed superior structural fidelity, achieving SSIM values of up to 0.915, whereas GANs achieved 0.593 on brain MRI datasets. In addition, hybrid loss functions combining perceptual, structural, and pixel-level terms resulted in improved image quality performance (PSNR = 35.6; SSIM = 0.95). Conclusions: The analysis showed that GAN-based generative models produce visually realistic images with detailed textures, whereas Diffusion Models generate high-resolution images with superior structural fidelity but require substantial computational resources for training and image generation. Furthermore, the clinical implications, limitations, and challenges of Generative AI models were discussed. Despite these advances, the medical imaging field still faces several challenges, including the need for annotated medical datasets, high-quality realistic images for training, and limited data availability.","url":"https://pubmed.ncbi.nlm.nih.gov/42510030/","authors":["Waheed Y","Noor MN","Ashraf I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 10","doi":"10.3390/diagnostics16142166","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510013","name":"Multimodal Non-Invasive Skin Imaging in Dermatology: Current Modalities, Clinical Applications, and Future Integration.","source":"pubmed","abstract":"Multimodal non-invasive skin imaging combines complementary technologies, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography (OCT), line-field confocal optical coherence tomography (LC-OCT), and high-frequency ultrasound (HFUS), to enable comprehensive evaluation of skin lesions across multiple spatial scales. While dermoscopy facilitates assessment of superficial morphologic features, RCM provides near-cellular resolution imaging, C-OCT and LC-OCT offer high-resolution structural visualization at greater depths, and HFUS enables evaluation of deeper tissue architecture and lesion extent. The complementary strengths of these modalities support more accurate diagnosis, disease monitoring, and treatment assessment. This review summarizes the principles, clinical applications, advantages, and limitations of major non-invasive imaging modalities, with a particular emphasis on multimodal integration. We further propose a conceptual framework for a multimodal non-invasive skin imaging center, highlighting the roles of workflow standardization, multidisciplinary collaboration, and integrated data management. Current challenges, including implementation costs, data governance, and workforce training, are also discussed. Future developments in artificial intelligence, multicenter collaboration, and imaging-based treatment monitoring are expected to further advance precision dermatology and facilitate the broader clinical adoption of multimodal skin imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/42510013/","authors":["Deng JY","Wei JP","Maimaitiyili N","Li XY","Zheng QT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 9","doi":"10.3390/diagnostics16142149","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42510002","name":"Assessment of Myocardial Iron Overload and Strain Abnormalities in Pediatric β-Thalassemia Using Multiparametric CMR.","source":"pubmed","abstract":"Background/Objectives: Myocardial iron overload is a major contributor to adverse cardiac outcomes in pediatric patients with transfusion-dependent &#x3b2;-thalassemia (TDT). Cardiovascular magnetic resonance (CMR), including T2* and T1 mapping, allows quantification of myocardial iron and early detection of cardiac dysfunction. Artificial intelligence (AI)-assisted CMR feature tracking (CMR-FT) provides a sensitive and reproducible approach for assessing myocardial deformation, even in patients with preserved left ventricular ejection fraction (LVEF). This study aimed to evaluate the utility of AI-based CMR-FT and its relationship with multiparametric CMR biomarkers, including myocardial strain (GCS, GLS, GRS), tissue characteristics (T2*, T1), and left ventricular (LV) geometry in pediatric TDT patients. Methods : In this retrospective study, 68 pediatric patients with &#x3b2;-thalassemia major and 20 age-matched healthy controls underwent CMR with T2*, T1 mapping, and FT-based strain analysis. Myocardial iron overload was defined as T2* &lt; 20 ms. Strain parameters were compared between groups, and correlations with tissue characteristics and LV geometry were assessed using Pearson's correlation. Results: Patients with myocardial iron overload have significantly reduced GCS compared to controls (-17.4 &#xb1; 1.6% vs. -19.3 &#xb1; 4.5%, p &lt; 0.01). GCS correlated with T2* (r = -0.33, p = 0.007) and T1 (r = -0.45, p &lt; 0.001). GLS was sensitive to LV geometric changes, particularly concentric remodeling, correlating with LV mass/EDV ratio (r = -0.449, p &lt; 0.001). Conclusions : AI-based CMR-FT combined with multiparametric tissue imaging enhances early detection of subclinical myocardial dysfunction in pediatric TDT, offering diagnostic insights beyond conventional CMR metrics and supporting improved risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/42510002/","authors":["Awadi R","Benameur N","Deriche M","Fraj IB","Boukhriba S","Taieb AB","Ouedreni M","Labidi S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 8","doi":"10.3390/diagnostics16142139","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509822","name":"From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.","source":"pubmed","abstract":"Genome-wide association studies (GWAS) have identified thousands of loci associated with complex human diseases. However, the majority of the association signals reside in non-coding regions of the genome, and do not directly reveal the causal variant, effector gene, regulatory biomolecule, cell type, pathway, biomarker, or therapeutic target. Because many disease-associated variants act through non-coding regulatory mechanisms, post-GWAS interpretation increasingly depends on fine-mapping, expression quantitative trait loci, transcriptome-wide association studies, and functional evidence from single-cell multi-omics, network biology, and genetic target prioritization. Artificial intelligence can attempt to integrate these heterogeneous molecular evidence layers, but the resulting black-box prediction is insufficient when outputs cannot be biologically reproduced or experimentally tested. This review evaluates explainable artificial intelligence (XAI) as a framework for linking genetic association signals to molecular mechanisms and causal gene hypotheses. We argue that explainability is best treated as a biological requirement because useful models must expose evidence paths from significant disease-associated variants to regulatory elements, genes, transcripts, proteins, pathways, cell states, and therapeutic hypotheses. By emphasizing transparent evidence provenance, ancestry-aware interpretation, and functional validation, XAI can support the translation of GWAS signals into molecularly testable hypotheses for target prioritization and precision molecular medicine. The review focuses on the question of how to accomplish AI-accelerated functionalization of GWAS outputs across complex human diseases and traits.","url":"https://pubmed.ncbi.nlm.nih.gov/42509822/","authors":["Ang MY","Chen L","Song L","Lipovich L","Choo SW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 14","doi":"10.3390/biom16071029","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509738","name":"Network Biology of Alzheimer's Disease and Related Neurodegenerative Disorders: Molecular Mechanisms and Therapeutic Strategies.","source":"pubmed","abstract":"The most persistent biomedical challenges of the 21st century are neurodegenerative disorders (NDs), where molecular alterations lead to devastating clinical consequences and progressive neuronal loss. The prevalence of neurodegeneration is continuously rising and becoming the main contributor to chronic disability and mortality. Despite their clinical differences, many conditions share pathogenic processes, including oxidative stress, protein misfolding and aggregation, mitochondrial dysfunction, and neuroinflammation. Instead of functioning independently, these processes cooperate to form a self-reinforcing network that gradually weakens synapses and ultimately leads to neuronal death. This study redefines neurodegeneration as a disorder of system-level failure by emphasizing poor cellular stress integration. In addition to demonstrating how gut microbiome gene networks impact inflammation and amyloid production, new research highlights the relationships between mitochondrial-lysosomal interactions, endoplasmic reticulum stress responses, and transcriptionally driven synaptic vulnerability. A key molecular topic is the interaction and pathogenic convergence of the JAK/STAT, HIF-1&#x3b1;, and Notch signaling pathways. Under ongoing metabolic stress, prolonged stimulation of this triad increases inflammation, hinders the regenerative processes, and maintains pseudo-hypoxic conditions, explaining why single-target treatments have mostly been unsuccessful. This review also explores progress in fluid, digital, and imaging biomarkers that facilitate early diagnosis and patient stratification, and assesses new disease-modifying approaches such as antisense oligonucleotides, immunomodulators, gene therapies, and small-molecular agents. Artificial intelligence is emphasized as an essential tool for integrating multimodal data, drug discovery and predictive modeling.","url":"https://pubmed.ncbi.nlm.nih.gov/42509738/","authors":["Wali Z","Neha","Shahwan M","Dinislam K","Shamsi A","Anwar S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 24","doi":"10.3390/biom16070944","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509630","name":"Pilot Evaluation of a Novel Artificial Intelligence (AI) Heart Rate Variability (HRV)-Guided Risk Stratification for Chest Pain in the Emergency Department: A Randomized Controlled Trial.","source":"pubmed","abstract":"Heart rate variability (HRV) analysis powered by artificial intelligence (AI) offers a rapid, non-invasive, and objective approach for acute coronary syndrome (ACS) risk stratification in the emergency department (ED). The objective of this study was to evaluate the feasibility and impact of aiTriage&#x2122;, an AI HRV-guided tool for chest pain triage, compared with standard care.","url":"https://pubmed.ncbi.nlm.nih.gov/42509630/","authors":["Wang Y","Li R","Pasupathi Y","Chan Y","Birthylon PI","Fook-Chong S","Zhang H","Lim SH","Kiat KTB","Basilio-Razon PI"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.15441/ceem.26.035","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509602","name":"Guideline Concordance of Large Language Model Responses to Parent-oriented Guideline Prompts About Pediatric Acute Bacterial Arthritis.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly used by patients and caregivers to obtain medical information. In pediatric acute bacterial arthritis, non-guideline-concordant information may be clinically important because timely diagnosis and management are essential. This study evaluated the concordance of LLM-generated responses to parent-oriented reformulations of Pediatric Infectious Diseases Society/Infectious Diseases Society of America (PIDS/IDSA) guideline recommendations.","url":"https://pubmed.ncbi.nlm.nih.gov/42509602/","authors":["Çörekci AM","Ateş B","Dinç O","Öztürk AA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1097/INF.0000000000005335","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509546","name":"Africa's emerging role in precision oncology: translational insights from the harnessing functional genomics in cancer research conference, Windhoek, 23-26 September 2025.","source":"pubmed","abstract":"Africa is entering a new era of cancer research, driven by renewed commitments to genomic innovation, data equity, and strengthened cancer registry systems. The \"Harnessing Functional Genomics in Cancer Research: Opportunities for Diagnosis and Treatment\" conference, held in Windhoek, Namibia, from 23 to 26 September 2025, convened leading experts to evaluate current progress and identify priorities for advancing cancer genomics and precision oncology across the continent.","url":"https://pubmed.ncbi.nlm.nih.gov/42509546/","authors":["Rix CL","Buberwa EB","Amofa J","Maurihungirire U","Hosea R","Ratjama L","Kandanda GK","Nangolo LN","Stanley C","Schäfer G","Cacciatore S","Banda R","Schuh A","Hayes VM","Sarkar D","Hansen R","Wedge D","Onywera H","Niyonzima N","Mungeyi P","Nabyonga J","Ashipala L","Nyarango P","Abebrese JT","Iyambo L","Rukira K","Hategekimana J","Jemu GM","Rothman M","Amundaba N","Musau H","Takundwa M","Naidoo J","Govender I","Hurrell T","Berthet X","Makhaola K","Dunaiski CM","Amugongo LM","Shuungula O","Kwaambwa HM","Chimwamurombe P","Sylvester T","Simushi P","Boys M","Ayitewala A","Happi C","Mwapagha LM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1186/s12919-026-00387-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509514","name":"Video-based artificial intelligence for automated neonatal respiratory monitoring.","source":"pubmed","abstract":"Neonatal respiratory monitoring is essential for the early detection of clinical alterations, especially in preterm newborns. Conventional methods are limited by subjectivity and the use of contact sensors. This experimental technological development study was conducted according to the Standards for Reporting Diagnostic Accuracy Studies (STARD) guidelines. A computer vision-based system was developed: a fine-tuned YOLO11 model segmented the thoracoabdominal region of interest, the respiratory signal was extracted by optical-flow analysis of thoracoabdominal motion, and the respiratory rate was estimated by detrending and automatic peak detection; pose-based movement detection restricted the estimation to periods when the infant was still. The study was approved under protocols No. 4,744,993, 4,699,000, and 3,232,698 and classified as Technology Readiness Level (TRL) 5-6. Twenty-three neonatal recordings were included, comprising 3387 manually annotated frames for segmentation-model training and evaluation. At the time of recording, the newborns were between 3 and 15&#xa0;days of postnatal age, had a postmenstrual age between 37 and 40&#xa0;weeks, and were breathing spontaneously in room air. The sample consisted of neonates with a gestational age of 33&#x2009;&#xb1;&#x2009;1.76&#xa0;weeks and weight of 1741.69&#x2009;&#xb1;&#x2009;393.97&#xa0;g. Apgar scores were 7&#x2009;&#xb1;&#x2009;1.0 and 8&#x2009;&#xb1;&#x2009;1.0 at the 1st and 5th minutes, respectively. Fifty percent were female, and 75% were delivered by cesarean section. The system demonstrated robust thoracoabdominal segmentation with mean average precision (mAP)&#x2009;&gt;&#x2009;94% and continuous pose tracking. Respiratory dynamics analysis enabled respiratory-rate estimation during stable periods, with consistent performance supporting technical feasibility. The mean absolute error (MAE) was 2.1 breaths per minute (bpm) compared with the simultaneous clinical reference assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42509514/","authors":["Ribeiro SNS","Fernandes AER","Vargas MERR","de Carvalho Velame R","Filho MAT","Leão RN","Maia H","Pereira SA","da Glória Rodrigues-Machado M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s00431-026-07267-w","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509499","name":"Automation dependence in AI-assisted colonoscopy: impact of system reliability and surgeon experience on navigation performance.","source":"pubmed","abstract":"As artificial intelligence (AI) is increasingly introduced into endoscopic navigation, understanding how operators use and respond to AI suggestions is essential for designing safe and effective human-AI collaboration. This study investigated the effects of AI reliability and operator experience on task performance and cognitive responses in a simulated colonoscopy navigation task.","url":"https://pubmed.ncbi.nlm.nih.gov/42509499/","authors":["Tong M","Cui L","Zhu S","Zhang X","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00464-026-13155-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509496","name":"AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.","source":"pubmed","abstract":"Traditional surgical skill assessment tools are inherently subjective, prone to interobserver variability, and place a substantial burden on expert reviewers. This study aimed to evaluate the feasibility of a proof-of-concept artificial intelligence (AI)-based system for automated assessment of selected dimensions of technical performance in laparoscopic distal gastrectomy (LDG) using a phase recognition model and to construct a scoring system for risk-stratification in expert-based surgical performance assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42509496/","authors":["Umemiya A","Takenaka S","Kitaguchi D","Arakaki S","Wakabayashi M","Iuchi S","Baba N","Takai R","Yanagida Y","Sasaki K","Kosugi N","Yura M","Takeshita N","Fujita T","Kinoshita T","Ito M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00464-026-13188-4","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509481","name":"The Optimal Timing of Ventriculo-Peritoneal Shunting and Gastrostomy Tube Placement: A Nationwide Retrospective Analysis.","source":"pubmed","abstract":"The transition of the neurologically injured patient from the intensive care unit (ICU) environment toward recovery often requires placement of a ventriculo-peritoneal shunt (VPS) and a gastrostomy tube (g-tube). Prior work has demonstrated a significant association between g-tube placement and shunt infection, however, both procedures are typically performed during the same hospitalization. Thus, there remains a question regarding the optimal timing of g-tube relative to VPS placement and the risk of subsequent complications. The objective of this study is to examine the risk of complications on the basis of relative timing of VPS and g-tube placement.","url":"https://pubmed.ncbi.nlm.nih.gov/42509481/","authors":["McIntyre MK","Chen HA","Gerges Castro C","Daly GE","Quencer K","Malhotra A","Lakhani DA","Gandhi D","Colasurdo M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s12028-026-02617-w","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509480","name":"White Matter Hyperintensities on Admission CT are Associated with Worse Outcomes But Not Cerebral Edema or Hemorrhagic Transformation After Large Vessel Occlusion Stroke.","source":"pubmed","abstract":"White matter hyperintensities (WMH) are key radiographic biomarkers of cerebral small vessel disease (CSVD) and have been associated with worse outcomes after stroke. Since CSVD may involve blood-brain barrier disruption, we hypothesized that worse outcomes in those with WMH may be mediated through more severe cerebral edema and hemorrhagic transformation (HT).","url":"https://pubmed.ncbi.nlm.nih.gov/42509480/","authors":["Dietz S","Avula A","Kumar A","Guasch-Jimenez M","Camps-Renom P","Slowik AM","Wrona P","Thinzar PL","Vargas D","Petersen NH","Dhar R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s12028-026-02609-w","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509477","name":"Arterial Spin Labeling of Cerebral Perfusion in Unresponsive Wakefulness Syndrome: A Proof-of-Concept Comparison with FDG-PET.","source":"pubmed","abstract":"Despite the clinical utility of [ 18 F]-fluorodeoxyglucose positron emission tomography (FDG-PET) in evaluating patients with unresponsive wakefulness syndrome (UWS), its use is constrained by high cost, limited availability, the need for radioactive tracers, and other factors. Arterial spin labeling (ASL) provides a noninvasive measure of cerebral perfusion, yet direct comparisons with FDG-PET in the same cohort of patients with UWS appear to be lacking. We therefore aimed to assess the feasibility and potential clinical relevance of ASL relative to FDG-PET in this cohort.","url":"https://pubmed.ncbi.nlm.nih.gov/42509477/","authors":["Han J","Duncan NW","Huang L","Lee C","Yang CM","Wu YC","Chen DY","Lane TJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s12028-026-02606-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509447","name":"AI-integrated multi-omics platform to revolutionize anti-metastatic therapy development through circulating tumor cell profiling: SCRUM-MONSTAR-CTC.","source":"pubmed","abstract":"Metastatic disease remains the leading cause of cancer-related death, yet most precision oncology strategies still emphasize profiling primary tumors and tracking cell-free tumor DNA (ctDNA). Although ctDNA has transformed genomic profiling, molecular residual disease monitoring, and early cancer detection, it cannot directly capture viable tumor cell states, phenotypic plasticity, or functional adaptations that drive metastatic spread. We propose that the next phase of precision oncology should integrate the cellular dimension of metastasis through systematic circulating tumor cell (CTC) profiling.","url":"https://pubmed.ncbi.nlm.nih.gov/42509447/","authors":["Hashimoto T","Shibuki T","Fujisawa T","Boku S","Kikuchi M","Shimizu A","Murakami T","Sakamoto Y","Miki I","Iida N","Kurano H","Yamashita R","Masuda J","Matsuda K","Yuda J","Yajima S","Kobayashi S","Liu Z","Amisaki M","Imai M","Nakamura Y","Bando H","Kim H","Gee HY","Lee HS","Park HW","Yoshino T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s10147-026-03149-1","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509423","name":"Lipid-based nanocarrier-enabled combination therapy for breast cancer: overcoming drug resistance toward precision oncology.","source":"pubmed","abstract":"Breast cancer continues to be one of the most common causes of cancer death across the globe due to molecular heterogeneity, high rate of recurrence, and growing prevalence of resistance against conventional treatments. Despite the positive contribution of chemotherapy, targeted therapy, and immunotherapy to breast cancer patient outcomes, there is much room for improvement due to the systemic toxicity, low solubility, nonselective biodistribution, and the fast-growing problem of multidrug resistance (MDR) in these approaches. It becomes particularly challenging for triple-negative breast cancer and other aggressive forms of the disease that lack adequate treatment options. As a result, there is an immediate need for developing more effective and specific strategies to address the mentioned obstacles. The idea of combination therapy seems to provide the best rationale for the development of such therapies, as it allows targeting multiple oncogenic signaling pathways, preventing resistance formation, and minimizing the drug dosage used. At the same time, traditional combination therapies usually face issues such as the lack of appropriate pharmacokinetics, instability of combinations, and adverse off-target effects, preventing their effective implementation in clinical practice. One of the ways of solving these problems is through the use of lipid-based nanocarriers that allow combining multiple therapeutic agents and overcoming their drawbacks, such as poor drug solubility, uncontrolled drug release, the absence of tumor selectivity, and susceptibility to efflux. Importantly, lipid nanocarriers may contain several types of drugs, including chemotherapeutic drugs, genetic material, and immunological substances. This paper presents a comprehensive review of recent advances in lipid-based nanocarrier-mediated combination therapies in breast cancer treatment.","url":"https://pubmed.ncbi.nlm.nih.gov/42509423/","authors":["Shadab A","Haider MF","Abohassan M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s00210-026-05735-6","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509391","name":"Predicting which colorectal cancer patients are most likely to improve their functional capacity with pre-surgery prehabilitation: a retrospective study based on the 6-min walk distance.","source":"pubmed","abstract":"Prehabilitation prior to colorectal cancer (CRC) surgery aims to improve functional capacity and postoperative outcomes, although results remain inconsistent due to variable programs. This study aimed to develop a predictive model for improvement in functional capacity during prehabilitation, using 6-min walk distance (6MWD) as the primary outcome. Identifying patients most likely to improve functional capacity during prehabilitation could support a more personalized use of prehabilitation.","url":"https://pubmed.ncbi.nlm.nih.gov/42509391/","authors":["de Klerk M","van der Linden MJW","Kerckhoffs APM","Meijboom BR","Verdaasdonk EGG","de Vries E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00520-026-11039-5","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509379","name":"Hematology-Oncology Fellows' Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey.","source":"pubmed","abstract":"Artificial Intelligence (AI) is rapidly being incorporated within healthcare, including medical education. Hematology-oncology (HO) is a complex field and AI is of great value in education and practice. We explored HO fellow's confidence and concerns with use of AI, AI training that HO fellows currently receive, and interest in future AI training.&#xa0;Multi-institutional survey administered to fellows in six adult HO programs. The survey was distributed electronically over one month with weekly reminders. Results were collected in RedCap and analyzed using R.&#xa0;36 of 153 potential participants responded (23.5% response rate). Almost all (35, 97%) reported using AI. Fellows who reported using AI for work used it primarily for patient care (28, 82%) and/or research (29, 85%). Most were \"somewhat confident\" in the accuracy of AI-generated clinical information (25, 74%) and found AI \"somewhat reliable\" for clinical decision support (25, 74%). Respondents cited concerns about the use of AI in clinical practice, most often reliability of clinical data (32, 94%). Only one respondent (2.8%) received formal education on AI during fellowship and over who received informal training only reported its quality as \"fair\" or \"poor\" with 29 of these remaining 35 (83%) respondents interested in formal training. 31 fellows (86%) reported thinking AI would be important in their future career plans and 17 (49%) felt AI training should be required for HO fellows.&#xa0;While almost all HO fellows use AI, formal education in AI is rare in HO fellowship. Most fellows expressed interested in such training and believe that AI will be important in their careers, but also expressed concerns about use of AI, especially reliability of AI-generated clinical data. We plan to create formal curricula specific to HO training.","url":"https://pubmed.ncbi.nlm.nih.gov/42509379/","authors":["Marshall AL","Godby RC","Braunstein M","Kim SY","Moerdler S","Van Doren L","Azanza JJC","Mistry R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1007/s13187-026-02960-8","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509369","name":"Toward generalizable and interpretable AI in regulatory genomics.","source":"pubmed","abstract":"Deciphering how DNA sequence encodes gene regulation remains a central challenge in biology. Advances in machine learning and functional genomics have enabled sequence-to-function (seq2func) models that predict molecular regulatory readouts directly from DNA sequence, supporting variant effect prediction, mechanistic interpretation and regulatory sequence design. Despite strong performance on held-out genomic regions, generalization across genetic variation and cellular contexts remains inconsistent. In this Review, we examine how model architectures, training data and prediction tasks shape model behavior. We also synthesize how interpretability methods and evaluation practices have elucidated cis-regulatory organization and highlighted systematic failure modes, clarifying why strong predictive accuracy can fail to translate into robust regulatory understanding. Thus, we suggest that progress requires reframing seq2func models as continually refined systems, in which targeted perturbation experiments, systematic evaluation and iterative model updates are tightly coupled through artificial intelligence-experiment feedback loops, enabling self-improving models that progressively deepen mechanistic understanding and more reliably support biological discovery.","url":"https://pubmed.ncbi.nlm.nih.gov/42509369/","authors":["Nagai M","Murphy AE","Rizzo K","Koo PK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1038/s41588-026-02670-3","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509366","name":"Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques.","source":"pubmed","abstract":"Sepsis is one of the most deadly illnesses with a high risk of mortality. Consequently, identifying it at the beginning of illness symptoms is crucial and plays a key role in improving patient outcomes. This study presents a customized solution for the early detection of sepsis with an emphasis on the use of interpretability and explainability techniques, utilizing a range of machine learning approaches and interpretable artificial intelligence methods. The database on which this research study is based has many problems; the main ones being large data gaps and class disparities. Employing robust methods, precise categorizations, and rigorous computations, Approximately 12 diverse models were developed and optimized. With ROC-AUC indicators of 0.9566 and 0.9595 and F1 scores of 0.85 and 0.85 respectively, Bidirectional Long Short-Term Memory (BiLSTM) and Temporal Convolutional Network (TCN) models performed better than conventional models in terms of sepsis prediction. These two approaches have shown remarkable progress in detecting clinical patterns while avoiding false negative results-an essential aspect of the medical field. To assess model performance and offer clear insights into model predictions, interpretation-based techniques were employed. This improved clinical confidence and facilitated well-informed decisions in crucial medical diagnoses.","url":"https://pubmed.ncbi.nlm.nih.gov/42509366/","authors":["Mahmoud A","Abdelmoreed H","Amir H","Ehab M","Abdelfattah R","HadHoud M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1038/s41598-026-61652-x","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509126","name":"Driving the future of proteome-based innovations: the π-HuB project.","source":"pubmed","abstract":"&#x3c0;-HuB is an international consortium that aims to reframe proteomics from a catalog of proteins into a navigator of biological transitions. By integrating proteomic data, AI, standards, governance, and international infrastructure, it could accelerate discovery and clinical actionability while ensuring that large-scale proteomics becomes scalable, interpretable, ethical, and globally impactful.","url":"https://pubmed.ncbi.nlm.nih.gov/42509126/","authors":["Goh WWB","Chang C","Peng H","Xie L","He F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.tibtech.2026.07.009","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509100","name":"Emerging Technologies in Oncology Nursing: Integrating Informatics Competencies to Advance Safe, High-Quality, and Compassionate Care.","source":"pubmed","abstract":"To describe the rapid evolution of emerging digital technologies in oncology nursing and clarify the essential informatics competencies required by oncology nurses to advance safe, high-quality, and compassionate care across the cancer continuum.","url":"https://pubmed.ncbi.nlm.nih.gov/42509100/","authors":["Connor M","Kagan O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.soncn.2026.152307","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509096","name":"Machine learning prediction of complications after surgery in cirrhotic patients: Integrating nutritional risk with liver function.","source":"pubmed","abstract":"Liver disease is among the most common medical conditions in the world, leading to high rates of morbidity and death. Early identification of liver diseases enables prompt care, which may prevent many conditions from progressing to more serious stages, like cirrhosis or liver cancer. Traditionally, models often overlook the predictive importance of nutritional status and focus primarily on liver-specific measures. This research aims to propose a machine learning (ML) approach to predict surgical complications in patients with liver cirrhosis by integrating existing liver function metrics with a comprehensive nutritional risk assessment. In this study, we used the MIMIC-IV dataset, which consists of patient medical records. We utilized a 504-patient cohort with features such as age, gender, bilirubin, albumin, INR (International Normalized Ratio), Child-Pugh score, MELD score (Model for End-Stage Liver Disease), NRS (Nutritional Risk Screening), complication, and composite risk. Mortality was used as the primary outcome variable for prediction. ML models were utilized for training, along with correlation analysis and Explainable Artificial Intelligence (XAI) approaches like SHAP and LIME to interpret the model. The stackable ensemble model was employed to enhance the model's performance and robustness. The proposed model achieved an accuracy of 94% and an AUC of 0.97. This methodology emphasizes the critical importance of nutritional assessment in surgical risk classification. It offers a clinically relevant framework to guide preparatory strategies, promote collaborative decision-making, and improve perioperative care pathways for this vulnerable population.","url":"https://pubmed.ncbi.nlm.nih.gov/42509096/","authors":["Yao Y","Chen X","Wei Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.ajg.2026.06.009","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509089","name":"Tumour cell density quantified by artificial intelligence is associated with differential benefit from irinotecan-based chemo-radiotherapy in locally advanced rectal cancer: a post-hoc study of the phase 3 ARISTOTLE trial.","source":"pubmed","abstract":"Tumour cells and tumour-associated stroma are key components of the tumour microenvironment, and their interaction impacts disease progression and treatment resistance in rectal cancer. This study introduces a computational approach to quantify tumour cell density (TCD) within epithelial and stromal regions and assess whether treatment response differs according to TCD status in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT).","url":"https://pubmed.ncbi.nlm.nih.gov/42509089/","authors":["Shen Z","Brand D","Simard M","West NP","Lopes A","Begum R","Zhang Y","Royle G","Sebag-Montefiore D","Collins Fekete CA","Hawkins MA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.ebiom.2026.106397","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509079","name":"Identifying subsets of patients with retroperitoneal sarcoma who benefit from radiotherapy: an Interpretable AI reanalysis of the STRASS randomised trial.","source":"pubmed","abstract":"The European Organisation for Research and Treatment of Cancer's STRASS trial, the only completed randomised study of preoperative radiotherapy in retroperitoneal sarcoma, showed no overall benefit. Its subgroup analysis has been interpreted as supporting radiotherapy for all patients with liposarcoma, whereas the STREXIT extension has been interpreted as supporting radiotherapy for well differentiated liposarcoma and low-grade or intermediate-grade dedifferentiated liposarcoma. We aimed to identify subsets of patients who might benefit from preoperative radiotherapy and to quantify this benefit in terms of abdominal recurrence.","url":"https://pubmed.ncbi.nlm.nih.gov/42509079/","authors":["Bertsimas D","Margonis GA","Koulouras A","Kreis M","Balzer F","Brennan MF","Bonvalot S","Singer S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.landig.2026.101021","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509078","name":"MerMED-FM: Multimodal, Multi-Disease Medical Imaging Foundation Model.","source":"pubmed","abstract":"Current artificial intelligence (AI) models for medical imaging predominantly focus on a single imaging modality and a single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, well labelled datasets, which are costly and labour intensive to prepare. We aimed to train and evaluate an AI model that can interpret diverse imaging modalities across specialties while maintaining robust performance within each modality.","url":"https://pubmed.ncbi.nlm.nih.gov/42509078/","authors":["Zhou Y","Quek CWN","Zhou J","Wang Y","Bai Y","Gutierrez L","Ke Y","Yao J","Teo ZL","Ting DSJ","Cheng CY","Tham YC","Soetikno BT","Nielsen CS","Elze T","Li Z","Jao-Yiu Sung J","Li KZ","Hiok Hong C","Ong CJT","Wong JLY","Kuo CF","Wu WC","Ho MM","Cheng LT","Anh TNT","Cheng CL","Wong TY","Liu N","Tan IB","Lim TKH","Moshfeghi DM","Goh RSM","Liu Y","Ting DSW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.landig.2026.101007","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42509051","name":"High FDG Uptake in the Non-cancerous Lung Predicts Immune-Related Adverse Events and Poor Prognosis in Patients with Lung Cancer Treated with Immune Checkpoint Inhibitors.","source":"pubmed","abstract":"Immune checkpoint inhibitors (ICIs) have improved survival in non-small cell lung cancer (NSCLC); however, predicting immune-related adverse events (irAEs) remains a clinical challenge. We previously showed that high 18 F-fluorodeoxyglucose ( 18 F-FDG) uptake in the non-cancerous lung (NCL) on PET/CT is associated with the risk of interstitial lung disease. This study further investigated whether NCL FDG uptake predicts the risk of irAEs, including both ILD and non-ILD events, as well as survival outcomes in patients with lung cancer receiving ICIs.","url":"https://pubmed.ncbi.nlm.nih.gov/42509051/","authors":["Yamazaki M","Watanabe S","Tominaga M","Yagi T","Goto Y","Kushiro K","Suzuki R","Yanagimura N","Sato M","Tanaka T","Nozaki K","Saida Y","Kikuchi T","Ishikawa H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.acra.2026.07.007","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508964","name":"Ca(4)Nb(2)O(9): Sm(3+) Research on the Luminescent Properties of Orange Red Fluorescent Powder.","source":"pubmed","abstract":"Highly efficient and thermally stable orange-red phosphors are critical for optimizing the color quality of phosphor-converted white LEDs. Here, Ca 4-1.5x Nb 2 O 9 : xSm 3+ were fabricated via a conventional solid-state route. Structural characterization confirms that all samples crystallize in a single-phase monoclinic lattice, where Sm 3+ is successfully incorporated into Ca 2+ sites, inducing slight lattice contraction without destroying the host framework. Spectroscopic analyses reveal that aliovalent substitution generates localized defect states and narrows the bandgap from 3.67 to 3.44&#x2009;eV, thereby enhancing optical absorption. Under 406&#x2009;nm excitation, the materials display intense Sm 3+ -centered emissions dominated by the 4 G 5/2 &#x2009;&#x2192;&#x2009; 6 H 7/2 transition, yielding saturated orange-red output. The optimal composition (x&#x2009;=&#x2009;0.03) achieves maximum luminescence, while concentration quenching is attributed to dipole-dipole interactions. In addition, the phosphor exhibits saturated orange-red emission with chromaticity coordinates of (0.5964, 0.4013), highlighting its strong color performance. Overall, the results demonstrate that Ca 4-1.5x Nb 2 O 9 : xSm 3+ is a promising candidate for high-performance WLEDs.","url":"https://pubmed.ncbi.nlm.nih.gov/42508964/","authors":["Wu Y","Cui R","Wu X","Chen S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/bio.70588","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508960","name":"BMI-based prediction models for short-term and long-term mortality following coronary artery bypass grafting using the MIMIC-IV database: a retrospective cohort study.","source":"pubmed","abstract":"This study aims to evaluate the relationship between obesity (measured by Body Mass Index (BMI)) and postoperative mortality in patients undergoing coronary artery bypass grafting (CABG) and to use machine learning algorithms to assess key factors in order to explore the 'obesity paradox' phenomenon.","url":"https://pubmed.ncbi.nlm.nih.gov/42508960/","authors":["Mou T","Li QC","Zhou JH","Jin HJ","Zheng XT","Shi L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1136/openhrt-2026-004236","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508912","name":"A smartphone-integrated isothermal amplification-based point-of-care testing platform for ultrasensitive detection of severe fever with thrombocytopenia syndrome virus.","source":"pubmed","abstract":"Severe fever with thrombocytopenia syndrome (SFTS), caused by the severe fever with thrombocytopenia syndrome virus (SFTSV), poses a serious public health threat due to its high fatality rate, and delayed diagnosis is strongly associated with disease progression and increased mortality. Conventional laboratory diagnosis still relies largely on quantitative real-time PCR (qPCR), which requires specialized instruments, trained personnel, and centralized testing facilities. These challenges underscore the urgent need for rapid, sensitive, and portable diagnostic methods for SFTSV detection in clinical and on-site settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42508912/","authors":["Li W","Chen Y","Niu Y","Yang Y","Chen T","Ning Q","Huang L","Hu W","Liu GL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct 1","doi":"10.1016/j.aca.2026.345815","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508908","name":"Interference-reduced detection of 1,2-dihydroxybenzene using an acid-tolerant luminescent probe.","source":"pubmed","abstract":"Plant polyphenols, particularly flavonoids and phenolic acids containing 1,2-dihydroxybenzene structures, are bioactive compounds with health benefits, but their detection in acidic fruit matrices (pH 4-6) remains challenging. Conventional boronic acid-based probes are often limited by their instability in acidic media because ofthe susceptibility of ester linkages to hydrolysis. Meanwhile, although HPLC and LC&#x2012;MS techniques offer excellent sensitivity and selectivity, they remain labor-intensive and frequently encounter challenges in mitigating complex matrix interference.","url":"https://pubmed.ncbi.nlm.nih.gov/42508908/","authors":["Zhang X","Feng R","Zhang P","Fan Y","Jia S","Tong Y","Fang D","Wang Y","Zhang H","Zhang QW","Chai H","Li G","Yu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct 1","doi":"10.1016/j.aca.2026.345811","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508821","name":"Optimising the breast radiotherapy planning pathway: a quality improvement project at a regional cancer centre.","source":"pubmed","abstract":"At the South West Wales Cancer Centre, breast radiotherapy accounts for 24% of the radiotherapy workload. In 2021, only 16% of patients started treatment within 14 days compared with the Welsh time-to-radiotherapy quality metric target of 80%. Delays risk poorer outcomes for patients and create inefficiencies as tasks are often batched and staff alternate between idle and peak workload periods.Earlier analysis showed that only 1%-2%&#x2009;of the overall breast radiotherapy pathway time was value-adding activity. Key delays occurred at several stages of the pathway, where the total duration of each stage, including time spent waiting in the queue and time actively worked on, averaged around 50% longer than scheduled. For example, CT simulation was typically scheduled for 2&#x2009;days but took more than 4&#x2009;days while organ at risk (OAR) delineation was scheduled for 2&#x2009;days but took approximately 2.5&#x2009;days. Our improvement strategy targeted these delays through: (1) expansion of the technologist plan approval (TPA) pathway, (2) introduction of daily workflow meetings and clearer, standardised planning protocols and (3) implementation of artificial intelligence (AI) auto-contouring for OAR delineation. These changes were tested using the Model for Improvement, with statistical process control monitoring of metrics plus staff feedback.The mean total radiotherapy breast pathway time from consent-to-treatment decreased from 19.5 days to 14.8 days, with patients commencing treatment within 14 days increasing from 16% to 55%. Use of the TPA pathway increased from 61% to 95%&#x2009;and plan queries reduced from 11% to 3%, reducing oncologist workload. Daily workflow meetings and improved protocols reduced average planning times from 4 days to 1.5 days and were valued by staff for improving workload equity and team cohesion. AI auto-contouring reduced OAR delineation from 2.3 to 0.6&#x2009;days, though ongoing validation of contour quality is required.","url":"https://pubmed.ncbi.nlm.nih.gov/42508821/","authors":["Etheridge D","Lewis RD","Aitkenhead AH","Rose CS","Proudlove N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1136/bmjoq-2025-004056","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508797","name":"ImplantPlanNet: A deep learning framework for automatic implant planning from preoperative CBCT images.","source":"pubmed","abstract":"Preoperative implant planning based on cone-beam computed tomography (CBCT) images supports prosthetically driven treatment but remains time-consuming and experience-dependent. This study developed and evaluated ImplantPlanNet, an automatic initial implant planning framework for single-tooth missing scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/42508797/","authors":["Yang J","Liu Q","Yang H","Liu Y","Liao P","Chen H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.jdent.2026.106925","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508647","name":"Genetic associations with pulmonary embolism among those with a deep vein thrombosis: the INVENT Consortium.","source":"pubmed","abstract":"Venous thromboembolism (VTE) includes deep vein thrombosis (DVT) and pulmonary embolism (PE), which often originate from DVT and can be fatal.","url":"https://pubmed.ncbi.nlm.nih.gov/42508647/","authors":["Lozano-Esparza S","Shakt GE","Brody JA","Martinez-Perez A","Conomos MP","Germain M","Clapham KR","Teder-Laving M","van Hylckama Vlieg A","Kauko A","Thibord F","Bezerra OCL","Nadkarni G","Munsch G","Nøst TH","Goode EL","Ji Y","Chasman DI","Reiner AP","Turman C","Wiggins KL","Sitlani CM","Souto JC","Lutsey PL","Bellomo T","Biobank E","Program MV","Li-Gao R","Winstén AK","FinnGen","Chen MH","Do R","Gourhant L","Hveem K","Armasu SM","Saut N","Pankratz N","Giulianini F","Haessler J","Song M","Olaso R","Boerwinkle E","Dochtermann D","Soria JM","Rich SS","Smadja D","Koyama S","Rosendaal FR","Niiranen T","Gagnon F","My T Vy H","Zakai N","Lemarie C","Skogholt AH","Deleuze JF","Pankow JS","Ridker PM","Liu Y","Rice KM","Pyarajan S","Cushman M","Emmerich J","Psaty BM","Natarajan P","Johnson AD","Rodger MA","Suchon P","Couturaud F","Morange PE","Tang W","Kooperberg C","Kabrhel C","Sabater-Lleal M","Trégouët DA","Wolberg AS","Damrauer SM","Smith NL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.jtha.2026.06.046","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508637","name":"Multimodal Artificial Intelligence in Tissue Diagnostics: Vision-Language Models and the Future of Computational Pathology.","source":"pubmed","abstract":"Vision-language models (VLMs) represent an emerging class of multimodal artificial intelligence systems that integrate visual information with natural-language understanding and generation. In computational pathology, VLMs provide a framework for aligning histologic morphology from whole-slide images with pathology reports and other text-based knowledge sources. This review summarizes the technical foundations, major applications, evaluation strategies, and deployment considerations of pathology VLMs. Current pathology VLMs support a growing range of use cases, including image-text retrieval, label-efficient classification, visual question answering, abnormality localization, anomaly detection, report generation, and agentic workflow support. These capabilities are enabled by image encoders, text encoders or large language models, multimodal alignment strategies, and, in some systems, generative language components. Despite rapid progress, several barriers remain. The evaluation of pathology VLMs is constrained by limited domain-specific benchmarks, insufficient assessment of visual grounding, overreliance on text-based metrics, vulnerability to hallucination, and uncertain robustness under data shift. Clinical translation also requires validation across institutions, scanners, staining protocols, tissue types, and patient populations, together with workflow integration, regulatory oversight, data privacy, cybersecurity, and pathologist accountability. VLMs are therefore best viewed as assistive systems that may augment rather than replace pathologists. Responsible development will require close collaboration among pathologists, computational scientists, health systems, and regulatory stakeholders to ensure that VLMs improve pathology practice in a safe, interpretable, and clinically meaningful manner.","url":"https://pubmed.ncbi.nlm.nih.gov/42508637/","authors":["Xia R","Isett B","Chen J","Parwani AV","Bao R","Gu Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.ajpath.2026.07.002","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508621","name":"Advancing multimodal neuroimaging: Explainable and responsible AI for early dementia detection.","source":"pubmed","abstract":"Artificial intelligence (AI) has not seen the clinical uptake that might be expected from a technology that has received so much attention and investment. Coupled to neuroimaging, it is conceivable that AI algorithms can provide better performance in diagnosis and prognosis as well as optimize treatments in a precision medicine regimen, all of which is focused on improving both the patient experience and clinical outcomes. But in practice, little impact has been seen. Why might this be the case? This overview focuses on the possible reasons. First, technical concerns: the way in which AI algorithms are developed and validated in the research setting does not adequately prepare them for deployment in clinics and hospitals. Second, the importance of asking clinical questions with AI algorithms that have meaning and value is often underplayed or not considered. The outputs of AI algorithms mostly, but not always, also need to explain how decisions have been made. Thirdly, operational and ethical considerations loom over the integration of AI algorithms into electronic health record systems, clinical pathways, and legal frameworks. Above all these considerations is the motivation for deployment and particularly whether it is primarily for patient benefit or service economics.","url":"https://pubmed.ncbi.nlm.nih.gov/42508621/","authors":["Arco JE","Alizadehsani R","Atzmueller M","Barakova E","Bloch L","Bologna G","Bonomini P","Cho SB","Cortes JM","Elazab A","Khan MA","Domínguez E","Duro RJ","Fernández-Caballero A","Fernández-Jover E","Ferrández-Vicente JM","Friedrich CM","García-Rodríguez J","Garrigós J","Gómez-Rodellar A","Gómez-Vilda P","Graña M","Hernández A","Huang Y","Jiménez-Mesa C","Levin J","Liu N","Ma Q","Martínez-Murcia FJ","Martínez-Tomás R","Molina JM","Nalepa GJ","Ortiz A","Palacios-Alonso D","Ponticorvo M","Pereira A","Ramírez J","Rincón M","Rodríguez-Rodríguez I","Su L","Suckling J","Val-Calvo M","Varona P","Vázquez-García C","Wang S","Zhang YD","Gorriz JM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.neuroimage.2026.122142","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508552","name":"Large language models in sports cardiology: Challenges, governance, and human-in-the-loop integration.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly integrated into clinical artificial intelligence (AI) workflows, with emerging applications in medical documentation, information synthesis, guideline retrieval, and patient communication. Recent advances in foundation models, including multimodal systems, have further expanded these capabilities. However, current evidence derives predominantly from examination-style benchmarks, retrospective vignettes, and hospital-based populations, limiting its direct applicability to athlete-centered cardiovascular care. Sports cardiology represents a uniquely challenging environment for AI because of low disease prevalence, physiological-pathological overlap, longitudinal uncertainty, and eligibility-driven decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42508552/","authors":["Palermi S","Saglietto A","D'Ascenzo F","De Ferrari GM","Bruno F","Tse G","Kizilkiliç SE","Tjong F","Campillo SR","Keser N","Busnatu S","Marketou M","Wenzl FA","Vecchiato M","Rampone JM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.pcad.2026.07.005","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508512","name":"Comparative whole-exome sequencing of ambulatory patients and transplant recipients with idiopathic dilated cardiomyopathy.","source":"pubmed","abstract":"Idiopathic dilated cardiomyopathy (DCM) is a major cause of advanced heart failure and heart transplantation (HTx), yet the genetic correlates of progression to HTx and transplant-relevant arrhythmic phenotypes remain incompletely defined. We examined the genetics of idiopathic DCM in a Korean population, focusing on HTx/death and arrhythmic outcomes, to identify adverse outcome-linked genotype-phenotype associations.","url":"https://pubmed.ncbi.nlm.nih.gov/42508512/","authors":["Lee JH","Kong J","Kim S","Kim BJ","Lee HY","Hyun J","Kim JJ","Park HY","Cho MC","Kim SC","Lee SE","Torkamani A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1016/j.healun.2026.07.022","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508437","name":"Advancing oncology education in low-income and middle-income African settings: global insights for local action.","source":"pubmed","abstract":"Cancer incidence and mortality are rising globally, with their burden falling disproportionately on low-income and middle-income countries. Africa faces major gaps in oncology education and workforce development, limiting access to high-quality cancer care. Focusing on efforts relevant to oncology in Africa, we provide an overview of existing programmes and models for educating health-care personnel in resource-constrained regions, highlighting challenges and effective strategies. Current efforts described include formal curricula, fellowship programmes, mentorship networks, and technology-driven solutions such as e-learning and telemedicine. Funding, language and cultural barriers, and policy gaps pose persistent obstacles to the creation and effective implementation of oncology education and workforce development initiatives. Emerging technological innovations for education, such as artificial intelligence-driven learning, virtual reality, and adaptive platforms, offer scalable opportunities for advancement. Keys to success include sustainable, interdisciplinary, and international collaborations within and across sectors; close tailoring of initiatives to local needs; as well as policy advocacy and alignment with national and international health policy frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/42508437/","authors":["Shrestha S","Onwualu Chigbo C","O'Reilly S","Grover S","Swanson W","Loehrer PJ","Dako F","Lasebikan N","Hammad N","Ngwa W","Huq MS","Mutebi M","Hricak H","Avery S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/S1470-2045(26)00242-1","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508406","name":"DNA methylation profiling enables subclassification of mucinous ovarian carcinoma and distinguishes it from extraovarian mucinous metastases.","source":"pubmed","abstract":"Mucinous ovarian carcinoma (MOC) is an epithelial ovarian cancer subtype that is frequently misclassified as extraovarian mucinous metastasis (EOM) because of overlapping features. To address this diagnostic challenge, we perform genome-wide DNA methylation profiling of 58 MOCs, 38 EOMs, and 18 mucinous borderline ovarian tumors (mBOTs) collected from six institutions. Methylation analysis defines two mBOT groups, one epigenetically similar to normal ovary and one resembling MOC. Unsupervised clustering reveals two distinct MOC methylation subtypes with potential prognostic relevance in the internal cohort. Using these data together with 389 external profiles, we develop and validate a three-step machine-learning classifier that distinguishes MOC from EOM with 95.5% accuracy. External validation of this classifier on 21 MOCs and 24 EOMs yields an accuracy of 91.11% for differentiating MOC from EOM. These findings establish an epigenetic framework for mucinous ovarian tumors and provide a robust clinical classification tool.","url":"https://pubmed.ncbi.nlm.nih.gov/42508406/","authors":["Calina TG","Grafenhorst E","Schallenberg S","Möbs M","Daenekas B","Sabo AA","Chen B","Koch I","Janik T","Churchman M","Gourley C","Christie EL","Vasilescu C","Herlea V","Schmitt WD","Leopold N","Heitz F","Vlaski A","Tolkach Y","Bremmer F","Klauschen F","Büttner R","Ströbel P","Burandt E","Sauter G","Heppner FL","Taube ET","Sehouli J","Braicu IE","Euskirchen P","Horst D","Gorringe KL","Capper D","Dragomir MP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 18","doi":"10.1016/j.xcrm.2026.102941","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508404","name":"Advancing cancer detection and treatment using longitudinal routine clinical data.","source":"pubmed","abstract":"Cancer management remains fragmented across its continuum, from late-stage diagnosis and salvage therapies to non-personalized surveillance. Here, we present Oncoformer, a unified multimodal transformer model trained on the China Oncology Multimodal Prediction and Surveillance Study (COMPASS) cohort (3.67 million individuals, 17.7 million clinical visits) and validated on independent external cohorts, including the UK Biobank. Oncoformer integrates longitudinal electronic health records with chest X-ray imaging to address multiple clinical tasks: pan-cancer diagnosis (area under the receiver operating characteristic curve [AUROC] = 0.956), future cancer prediction up to 1 year before diagnosis (AUROC = 0.869), tumor stage inference (mean AUROC &gt; 0.90), patient-specific treatment-response forecasting, and recurrence-free survival stratification across ten cancer types (all p &lt; 0.01). Staging predictions were independently validated against postoperative pathological endpoints and shown to converge on core cancer genomic pathways. By translating routine clinical data into a dynamic view of cancer evolution, Oncoformer provides a framework for risk-informed cancer prediction and treatment stratification using routine clinical data.","url":"https://pubmed.ncbi.nlm.nih.gov/42508404/","authors":["Liu F","Wang K","Xu H","Tang C","Shen X","Wang M","Yang L","Yang L","Liu L","Hu C","Li G","Wu W","Zou Z","Li B","Liu S","Kang J","Kong J","Li T","Wong IN","Huang X","Chen G","Lu W","Ziyar I","Zhang CL","Sun Y","Lin W","Ou C","Fok M","Hou T","Wang W","Xue K","Yin Y","Zhu H","Gootenberg J","Abudayyeh OO","Karin M","Loupy A","Rasko JEJ","Ideker T","Luo H","Oermann E","Zhang K","International Consortium of Digital Twins in Healthcare and Medicine"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.cell.2026.07.009","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508314","name":"Development trends of surface-enhanced Raman spectroscopy in clinical applications: From nanostructures to intelligent signal analysis.","source":"pubmed","abstract":"Traditional biological detection techniques often struggle to achieve high sensitivity, specificity, and non-invasiveness in complex biological samples, while laborious sample pretreatment and poor results reproducibility further limit their application in precision medicine. Surface-enhanced Raman spectroscopy(SERS), leveraging molecular fingerprint recognition and ultrahigh sensitivity, provides a promising strategy for low-abundance biomarkers. Starting from the enhancement mechanisms of SERS, this article reviews the structural design and performance modulation of substrate materials, compares different detection modalities and their applicable scenarios, summarizes advances in clinical application, analyzes common challenges limiting clinical translation, and explores the potential of artificial intelligence in improving SERS signal interpretation accuracy. The main limitation for further development of SERS has shifted from merely enhancing sensitivity to establishing standardized detection systems with high reproducibility, robust quantitative capability, and clinical interpretability. In the future, the integration of artificial intelligence-assisted spectral analysis with high-uniformity substrate design is expected to reshape the application paradigm of SERS in precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42508314/","authors":["Gu Y","Jin P","Sun X","Wu H","Kang J","Zhou X","Cheng J","Guo J","Li D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1016/j.talanta.2026.130311","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508148","name":"Environmental exposure-integrated risk stratification for adverse outcomes after acute coronary syndrome: a multi-cohort machine learning approach.","source":"pubmed","abstract":"Acute coronary syndrome (ACS) causes substantial post-discharge mortality. Ambient air pollution and meteorological conditions are associated with recurrent cardiovascular events, but existing clinical risk scores rely only on static admission parameters without incorporating post-discharge environmental exposures. Most machine learning (ML) models for ACS prognosis are single-center, single-horizon, fail to integrate both meteorological and air-quality variables, and lack external validation or interpretability.","url":"https://pubmed.ncbi.nlm.nih.gov/42508148/","authors":["Zhang M","Zhang J","Cui Y","Wang J","Wang M","Zheng W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov 1","doi":"10.1016/j.ijmedinf.2026.106635","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508143","name":"IGHG1⁺ plasma cells define an NF-κB-driven inflammatory program that shapes T-cell immunity and immunotherapy response in colorectal cancer.","source":"pubmed","abstract":"The efficacy of immune checkpoint blockade (ICB) in colorectal cancer (CRC) is constrained by marked heterogeneity in the tumor microenvironment (TME). Although B cells and tertiary lymphoid structures (TLS) have emerged as important determinants of anti-tumor immunity, the functional diversity of plasma-cell subsets in CRC immunotherapy remains insufficiently understood.","url":"https://pubmed.ncbi.nlm.nih.gov/42508143/","authors":["Zhang RF","Zhang WZ","Li H","Chen Y","Zhong R","Wang JF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.tranon.2026.102930","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508107","name":"Molecular mechanism of lead exposure induced atrial fibrillation: A systematic study based on network toxicology, machine learning, and in vivo and in vitro experiments.","source":"pubmed","abstract":"Lead is a ubiquitous environmental toxic metal. Lead exposure is closely linked to an increased risk of atrial fibrillation (AF), yet its molecular mechanism remains incompletely understood. Here, we conducted an integrated study using network toxicology, machine learning, molecular docking, and in vivo and in vitro experiments to explore lead-induced AF. The global burden of disease analysis showed a marked rise in lead exposure-related AF/atrial flutter mortality and disability&#x2011;adjusted life years from 1990 to 2021, especially in low socio&#x2011;demographic index regions. We identified 142 overlapping targets between lead exposure and AF. Protein-protein interaction network and machine learning identified MAPK3 and ALB as core hub genes. Functional enrichment indicated critical roles of the PI3K&#x2011;Akt pathway. Molecular docking verified strong binding between Pb&#xb2;&#x207a; and key pathway proteins. Lead exposure in mice triggered atrial electrical and structural remodeling and increased AF inducibility. In vitro, lead inhibited PI3K&#x2011;Akt phosphorylation, which was prevented by the pathway agonist 740 Y&#x2011;P. In conclusion, lead exposure was shown to promote AF by suppressing the PI3K&#x2011;Akt pathway, with MAPK3 and ALB as key targets. This study provides mechanistic insight and potential intervention targets for lead exposure-related AF.","url":"https://pubmed.ncbi.nlm.nih.gov/42508107/","authors":["Liang J","Qiu X","Xu L","Shao S","Liu L","Yang N","Qin X","Rejiepu M","Tang B","Zhang L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep 1","doi":"10.1016/j.ecoenv.2026.120570","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42508094","name":"Performance of multimodal large language models in interpreting lateral cephalometric superimpositions: A comparative observer-performance study.","source":"pubmed","abstract":"Multimodal large language models (LLMs) can generate free-text interpretations of clinical images, but their performance on orthodontic cephalometric superimpositions is unknown. This study compared zero-shot interpretations from three LLMs with those of a second-year orthodontic resident.","url":"https://pubmed.ncbi.nlm.nih.gov/42508094/","authors":["Nguyen VA","Ha TMA","Nguyen TTH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1016/j.ortho.2026.101216","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507996","name":"Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.","source":"pubmed","abstract":"Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools are applied across cybersecurity domains, their real-world effectiveness, and where gaps remain.","url":"https://pubmed.ncbi.nlm.nih.gov/42507996/","authors":["Rajput K","Zuberi S","Elhajj M","Ochieng W","Darzi A","Ghafur S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.2196/93950","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507965","name":"Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.","source":"pubmed","abstract":"The choice of adjuvant chemotherapy in pancreatic ductal adenocarcinoma (PDAC) is mainly guided by patients' general condition. We hypothesized that tumor morphology may predict differential treatment benefit and tested whether deep learning applied to histology images could derive a biomarker of relative benefit from gemcitabine (GEM) versus modified FOLFIRINOX (mFOLFIRINOX) in resected PDAC.","url":"https://pubmed.ncbi.nlm.nih.gov/42507965/","authors":["Beaufils A","De Martino J","Jiang X","Blanchard T","Fraunhoffer N","Mendes D","Pignolet C","Bourega T","Meneghetti AR","Sainath S","Albuquerque M","Colnot N","Tihy M","Turpin A","Ben Abdelghani M","Wei A","Mitry E","Lecomte T","Biagi J","Artru P","Evesque L","Lambert A","Renouf DJ","Mauduit M","Dusetti NJ","Hammel P","Conroy T","Bachet JB","de Mestier L","Rebours V","Cros J","Kather JN","Nicolle R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1200/JCO-26-00327","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507771","name":"Opportunistic Coronary Artery Calcium Screening: Time for Clinical Implementation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42507771/","authors":["Rodriguez F","Langlotz CP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1161/CIRCULATIONAHA.126.081204","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507768","name":"Translational Cardiovascular Science for the Circulation Community.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42507768/","authors":["Steinhauser ML"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 28","doi":"10.1161/CIRCULATIONAHA.126.080204","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507753","name":"Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques.","source":"pubmed","abstract":"Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing systems. Advanced imaging and machine learning offer novel avenues for physical package recognition.","url":"https://pubmed.ncbi.nlm.nih.gov/42507753/","authors":["Sukpornsawan P","Meepradist Y","Auamnoy T","Suksawatchon U","Chokchaitam S","Muongmee S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354277","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507720","name":"Enhancing Heart Disease Prediction Through Cluster-Visualized Distributed Machine Learning Paradigms for Secure Healthcare.","source":"pubmed","abstract":"Heart disease remains one of the leading causes of mortality worldwide, creating an urgent need for accurate and scalable predictive systems that enable early diagnosis and timely clinical intervention. Traditional machine learning approaches often struggle to efficiently process large-scale medical datasets and lack interpretability, limiting their usefulness for supporting clinical decision-making. To address these challenges, this study proposes a Cluster Visualized Distributed Machine Learning framework for heart disease prediction. The framework incorporates two distributed algorithms: Cluster Visualized Hadoop Distributed Decision Tree (CViHDDT) and Cluster Visualized Hadoop Distributed K-Nearest Neighbor (CViHDKNN). The proposed models leverage Hadoop's MapReduce framework for distributed computation across large datasets, while integrating K-Means clustering for improved data organization and visualization. This cluster-based visualization enhances interpretability by allowing clinicians to better understand relationships among patient risk factors and prediction outcomes. Experimental evaluation was conducted using the UCI Heart Disease dataset in a Hadoop-based distributed environment. The results show that CViHDKNN achieved superior predictive performance, achieving 85.25% accuracy and 88% recall, outperforming the CViHDDT model, which achieved 80.33% accuracy. Adjusting classification cut-off values also influenced sensitivity and detection rates: lower cut-offs improved true-positive detection while maintaining acceptable false-positive levels. These findings demonstrate that clustering-enhanced distributed learning improves scalability, predictive accuracy, and clinical interpretability for heart disease prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/42507720/","authors":["Sujatha C","Akarapu RB"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 7","doi":"10.3791/69857","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507719","name":"Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems.","source":"pubmed","abstract":"Pre-deployment validation is commonly used to establish the safety and effectiveness of clinical artificial intelligence systems, but acceptable validation performance does not guarantee stable behavior after deployment into routine clinical workflows. We conducted a longitudinal retrospective observational study of four clinically deployed AI systems operating across distinct clinical domains and workflows within a large healthcare organization. Using routinely collected clinical data, outcome labels, and operational telemetry, we compared validation-era performance with post-deployment behavior over extended observation periods. Analyses focused on temporal patterns of discrimination, calibration, data availability, latency, and workflow-related signals, with particular attention to label-dependent and label-independent monitoring. Across all systems, validation-era performance did not persist as a stable operational property after deployment. Calibration drift emerged consistently and often preceded detectable changes in discrimination. Workflow-associated changes in data availability and timing were more strongly and consistently associated with degradation than population-level indicators. Label-independent operational signals, including input missingness and data latency, provided early indication of emerging fragility, whereas outcome-based monitoring was delayed by label latency and documentation processes. These findings suggest that post-deployment fragility can be a structural property of clinical AI systems embedded in evolving workflows. Effective governance therefore requires lifecycle-oriented monitoring strategies that combine calibration reassessment with operational telemetry throughout deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42507719/","authors":["Kopanitsa G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1371/journal.pdig.0001534","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507697","name":"Dual-input deep learning system for microbial identification from blood agar plates.","source":"pubmed","abstract":"The morphological classification of microbial cultures and colonies requires specialized knowledge and experience; therefore, automation in this field remains limited. This study examines the feasibility of automating the identification of pathogenic microbial species from colony images of cultured microorganisms.","url":"https://pubmed.ncbi.nlm.nih.gov/42507697/","authors":["Naito T","Takei S","Misawa S","Kuribara M","Tabe Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353761","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507686","name":"PAP_NER: A large-scale vietnamese administrative named entity recognition corpus and hybrid deep learning architecture.","source":"pubmed","abstract":"Named Entity Recognition (NER) is fundamental for automating administrative document processing in digital government systems. However, Vietnamese NLP research faces a critical infrastructure gap: existing datasets focus on generic information extraction (news, medical) rather than domain-specific administrative text. We present PAP_NER, the first large-scale, gold-standard Vietnamese administrative NER corpus comprising 162,801 sentences with 205,807 entity annotations across five entity types critical for e-Government workflows: Agency (CQ), Legal Document (VBPL), Object (&#x110;T), Datetime (NG), and Quantity (SL). The dataset was constructed through a rigorous human-in-the-loop annotation pipeline, achieving an inter-annotator agreement of &#x3ba; = 0.85. We demonstrate PAP_NER's value through comprehensive benchmarking of an established hybrid deep learning architecture, PhoBERT-CRF, which couples monolingual Transformer embeddings (PhoBERT) with Conditional Random Fields for structured prediction. PhoBERT-CRF achieves 97.95% Micro F1-score on the PAP_NER test set, significantly outperforming established baselines: BiLSTM+CRF (+2.01%), multilingual XLM-RoBERTa (+2.52%), and pure Transformer approaches (+0.44%). Ablation analysis reveals that the CRF layer provides statistically significant improvements for structurally complex entities (VBPL:&#x2009;+&#x2009;0.96%, p&#x2009;&lt;&#x2009;0.05, McNemar's test). We release PAP_NER publicly (DOI: 10.5281/zenodo.18044019) under Creative Commons BY 4.0 license to support reproducibility and enable further research in Vietnamese administrative NLP. This work establishes a foundational dataset and methodology for addressing the Vietnamese government NER gap, with implications for low-resource language NLP research.","url":"https://pubmed.ncbi.nlm.nih.gov/42507686/","authors":["La DD","Tran TB","Du NH","Dang NH","Phung TN","Tran VK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353166","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507684","name":"Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach.","source":"pubmed","abstract":"Early identification of diabetic retinopathy (DR), which is a primary cause of vision impairment globally, is a crucial phasis for effective intervention and treatment. Traditional screening workflows rely on manual diagnosis by ophthalmologists, which remains the gold standard but can be time-consuming and subject to variability due to human factors. To support and enhance the screening process, artificial intelligence (AI)-based tools have shown promise in automating DR detection, particularly with recent advances in deep learning. However, medical images with long-range dependencies and spatial linkages can be challenging for CNN-based algorithms to handle.","url":"https://pubmed.ncbi.nlm.nih.gov/42507684/","authors":["ElAdel A","Filali I","Zaied M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0350854","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507621","name":"Persistence of viable opportunistic pathogens in a multi-stage natural wastewater treatment system.","source":"pubmed","abstract":"Nature-based wastewater treatment systems are increasingly implemented to support water reuse in arid regions, yet their effectiveness in removing viable culturable opportunistic pathogens remains insufficiently characterized. In this exploratory, culture-based study, we assessed bacterial population dynamics across a five-stage natural wastewater treatment system (Wadi Hanifa, Riyadh, Saudi Arabia), tracking culturable bacteria from secondary-treated influent to sand-filtered effluent. Water samples were collected at five sequential treatment stages and analyzed for physicochemical parameters, total culturable bacterial abundance, bacterial diversity, taxonomic composition, and antimicrobial susceptibility of persistent isolates. Total culturable bacterial counts decreased by approximately 1.1 log10 CFU mL&#x2012;1 across the system, accompanied by an approximately 50% reduction in observed isolate richness. The fecal indicator Escherichia coli was detected only in upstream and intermediate stages (sampling locations L3.1-L3.3) and was absent from downstream samples. In contrast, the opportunistic pathogen Klebsiella pneumoniae was recovered across all five treatment stages and accounted for 44.4% (8/18) of all morphologically distinct isolates grown on the selected culture media. Turbidity declined by 71% along the treatment train and showed a strong positive correlation with bacterial richness (Kendall's &#x3c4;&#x2009;=&#x2009;0.84, p&#x2009;=&#x2009;0.038), although this exploratory correlation should be interpreted with caution given the small sample size (n&#x2009;=&#x2009;5 stages). Phenotypic antimicrobial susceptibility testing revealed multidrug resistance (MDR) in 62.5% (5/8) of K. pneumoniae isolates, although all remained susceptible to amikacin and meropenem. Under the conditions examined, multi-stage natural wastewater treatment substantially reduced overall bacterial abundance and diversity but did not eliminate viable, multidrug-resistant Klebsiella pneumoniae. The discordance between fecal-indicator removal and opportunistic pathogen recovery highlights system-specific limitations of indicator-based monitoring for assessing microbial safety in wastewater reuse systems. Given the limited isolate number (n&#x2009;=&#x2009;8 K. pneumoniae) and the absence of molecular resistance-gene characterization, broader claims about wastewater as a dissemination pathway for antimicrobial resistance cannot be drawn from these data. These findings are based on a single cross-sectional sampling event and should be considered hypothesis-generating rather than confirmatory.","url":"https://pubmed.ncbi.nlm.nih.gov/42507621/","authors":["Aqel H","Farah H","Fodah R","Sannan N","Surakhi O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354338","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507464","name":"Artificial Intelligence and the Financialization of Medical Knowledge.","source":"pubmed","abstract":"This Viewpoint discusses how artificial intelligence medical knowledge platforms could degrade over time and outlines ways in which clinicians can advocate for safeguards that preserve competition and maintain quality.","url":"https://pubmed.ncbi.nlm.nih.gov/42507464/","authors":["Brender TD","Detsky AS","Yourman L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1001/jamainternmed.2026.2961","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507336","name":"Estimation of pulmonary function from time-resolved dynamic chest radiography using machine learning in patients with respiratory disease.","source":"pubmed","abstract":"Pulmonary function tests (PFTs), particularly spirometry, are the reference standard for assessing airflow limitation in respiratory diseases such as chronic obstructive pulmonary disease (COPD) and interstitial pulmonary disease. However, spirometry requires substantial patient cooperation and may be unreliable in children, the elderly, and patients with cognitive impairment, and its use was further limited during the COVID-19 pandemic. Dynamic chest radiography (DCR), which captures sequential thoracic images during respiration at low radiation dose, has emerged as a promising modality for evaluating respiratory dynamics, but its potential to quantitatively estimate pulmonary function through radiomic analysis remains insufficiently explored.","url":"https://pubmed.ncbi.nlm.nih.gov/42507336/","authors":["Shiinoki T","Yuasa Y","Hirano T","Asami-Noyama M","Matsunaga K","Tanaka H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/acm2.70717","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507312","name":"CT radiomics for noninvasive prediction of histologic differentiation in gastric cancer.","source":"pubmed","abstract":"The histological differentiation grade of gastric cancer critically influences treatment and prognosis. While CT radiomics shows promise for noninvasive prediction, its relationship with gene expression remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42507312/","authors":["Su R","Zhang Y","Cao J","Chen F","Li X","Li P","Bian G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/mp.70605","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42507310","name":"Early Aspirin Discontinuation After Percutaneous Coronary Intervention in Acute Coronary Syndrome: A Systematic Review and Meta-analysis of Randomized Controlled Trials.","source":"pubmed","abstract":"To evaluate the efficacy and safety of early aspirin discontinuation followed by P2Y12 inhibitor monotherapy versus standard dual antiplatelet therapy (DAPT) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI).","url":"https://pubmed.ncbi.nlm.nih.gov/42507310/","authors":["Huu HP","Tran PU","Zou T","Elkhatib W","Vinh T","Sun C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s10557-026-07919-x","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507229","name":"Recent progress in clinical and molecular biological research in rectal serrated lesions.","source":"pubmed","abstract":"Colorectal cancer ranks among the most common malignancies worldwide. Rectal serrated lesions represent a significant precancerous condition, accounting for approximately 30% of sporadic colorectal carcinomas. Their flat morphology and indistinct borders often lead to missed detection during routine colonoscopy. In addition, the complex classification system and considerable molecular heterogeneity of these lesions create challenges for clinical management, including diagnostic accuracy, risk stratification, and treatment decisions. Research in recent years has progressively clarified the unique oncogenic pathway associated with serrated lesions-referred to as the serrated pathway. Characteristic molecular events include mutations in b-raf murine sarcoma viral oncogene homolog B (BRAF) or kirsten rat sarcoma viral proto-oncogene (KRAS) mutations, CpG island methylator phenotype (CIMP), and microsatellite instability (MSI). Among these, the BRAF V600E mutation serves as the core driver event. Combination therapy with encorafenib and cetuximab, which targets this mutation, has been shown to improve outcomes in patients with BRAF-mutated metastatic colorectal cancer. Furthermore, Microsatellite Instability-High (MSI-H) lesions exhibit sensitivity to immune checkpoint inhibitors, offering novel options for personalized treatment. Concurrently, the application of technologies such as artificial intelligence (AI)-assisted endoscopy, molecular imaging, and liquid biopsy holds promise for improving the detection of early-stage lesions. This systematic review examines the current research landscape regarding rectal serrated lesions, covering an evolution of classification systems, elucidation of molecular and cellular mechanism, advances in diagnostic technology, and optimized treatment strategies. We further discussed molecular differences between the serrated pathway and the classical adenoma-carcinoma sequence, epigenetic regulatory mechanisms, tumour microenvironment characteristics, and personalized management strategies based on molecular subtyping. By synthesizing existing evidence, this review aims to provide theoretical guidance for the clinical management of rectal serrated lesions and lay the groundwork for future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42507229/","authors":["Zeng X","Xu Y","Liu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s11033-026-12444-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507163","name":"Musculoskeletal infections in sports: imaging approach, sport-specific risk factors, and diagnostic pitfalls.","source":"pubmed","abstract":"Musculoskeletal infections are uncommon but important causes of pain, impaired performance, and delayed return to play in athletes. Athletic populations are exposed to specific predisposing factors, including repetitive microtrauma, skin abrasions, hematoma formation, close-contact environments, and sports-related procedures or surgery. Because clinical manifestations frequently overlap with common sports injuries and expected postoperative changes, imaging plays a central role in early detection, assessment of disease extent, and differentiation from relevant mimickers. This review presents a compartment-based imaging approach to musculoskeletal infections in athletes, covering the main patterns of osteoarticular and soft-tissue involvement encountered in sports medicine practice. The main mechanisms of infection and their relevance in athletic populations are discussed in the context of sport-related exposures and clinical scenarios. Practical multimodality imaging strategies are emphasized, with particular attention to MRI. Key imaging features are reviewed alongside common sport-specific diagnostic pitfalls. Integration of imaging findings with clinical presentation and laboratory markers remains essential, while image-guided aspiration or biopsy continues to serve as the cornerstone for microbiologic confirmation and targeted therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42507163/","authors":["Cantarelli Rodrigues T","Drakonaki E","de Oliveira BP","Serfaty A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00256-026-05312-1","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507137","name":"Global evolution of adolescent idiopathic scoliosis research (1990-2025): a bibliometric and visualization analysis.","source":"pubmed","abstract":"Adolescent idiopathic scoliosis (AIS) is the most common spinal deformity affecting adolescents, yet comprehensive analysis of global research trends remains limited. This study aims to identify research hotspots, collaboration patterns, and emerging frontiers to guide future research priorities.","url":"https://pubmed.ncbi.nlm.nih.gov/42507137/","authors":["Bao X","Zhang Y","Zhang X","Zhao J","Yan K","Qiao H","Li S","Liao B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00586-026-10230-w","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507097","name":"Tumour morphology as a visible readout of cancer biology: Towards artificial intelligence-enabled multimodal risk stratification in anal squamous cell carcinoma.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42507097/","authors":["Nie S","Han J","Cao X","Wang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1111/codi.70562","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507084","name":"Current Treatment Options and Emerging Technologies for Pelvic Bone Tumors: From Limb Salvage to Precision Oncology.","source":"pubmed","abstract":"In our practice, the treatment of pelvic bone tumors should begin with a multidisciplinary sarcoma-board evaluation that integrates biopsy-proven histology, high-resolution CT/MRI/PET imaging, neurovascular and visceral involvement, Enneking zone, expected survival, and patient-specific functional goals. For potentially curable primary pelvic sarcomas, we consider negative-margin resection the non-negotiable oncologic priority. Limb-salvage internal hemipelvectomy is preferred when an R0 resection can be achieved while preserving limb viability and a meaningful postoperative function; external hemipelvectomy should be reserved for tumors with unreconstructable femoral or iliac vessel involvement, extensive sciatic or lumbosacral plexus invasion, uncontrolled infection, or recurrent disease in which safe margins cannot otherwise be obtained. Histology should drive sequencing: osteosarcoma and Ewing sarcoma generally require effective neoadjuvant systemic therapy before definitive local treatment, whereas conventional chondrosarcoma requires meticulous wide en bloc surgery because chemotherapy and conventional radiotherapy have limited curative value. Reconstruction should not be selected by technology alone. For non-weight-bearing or palliative defects, no reconstruction, flail hip, hip transposition, or standard metastatic acetabular procedures may offer the best time-adjusted quality of life. For long-term survivors with periacetabular, sacroiliac, or spinopelvic instability, we favor anatomy-restoring reconstruction using navigation-assisted resection and carefully planned biological, modular, or patient-specific 3D-printed implants, provided soft-tissue coverage and infection risk are acceptable. Emerging tools such as computer-assisted navigation, robotics, artificial intelligence-based segmentation, liquid biopsy, digital twins, and smart biomaterials should be used to reinforce classic oncologic principles rather than replace them. Their adoption should depend on validated margin benefit, durable functional gain, complication reduction, cost-effectiveness, and equitable access.","url":"https://pubmed.ncbi.nlm.nih.gov/42507084/","authors":["Liu Y","Zhang Z","Jiang C","Jia K","Shen Y","Hu H","Wang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s11864-026-01406-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42507013","name":"Transfer learning prediction of 2-year native liver survival in biliary atresia following Kasai portoenterostomy using early perioperative biochemical data up to 3 months postoperatively.","source":"pubmed","abstract":"Biliary atresia (BA), a rare but life-threatening neonatal cholestatic disorder, necessitates Kasai portoenterostomy (KPE) as the primary surgical intervention. However, despite timely KPE within the recommended 60-day window, approximately 60% of infants ultimately require liver transplantation before age 2. Current prognostic tools lack the capacity to predict long-term native liver survival (NLS) using early perioperative biomarkers.","url":"https://pubmed.ncbi.nlm.nih.gov/42507013/","authors":["Xie C","Yan W","Zhao Y","Li S","Liao J","Zhang Y","Hua K","Gu Y","Wang P","Sun J","Jin Y","Liu Z","Sun D","Wang D","Huang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 27","doi":"10.1007/s00383-026-06542-z","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506987","name":"Classification of fermentation methods for white mulberry products using fluorescence spectroscopy combined with deep learning.","source":"pubmed","abstract":"In the field of cosmetic raw materials, accurately distinguishing plant fermentation extracts from traditional water extracts is crucial for ensuring product quality, user safety, and the authenticity of claimed effects. However, conventional chemometrics has limited capacity to explore the nonlinear characteristics of complex fluorescence spectra, and machine learning still faces shortcomings in feature representation and generalization capability, necessitating the development of more efficient and accurate identification methods. In this study, white mulberry ( Morus alba L.) was used as the raw material to prepare 966 samples, including yeast fermentation extracts, lactic acid bacteria fermentation extracts, and water extracts. After collecting the fluorescence spectra of each sample, six deep learning models-Informer, PatchTST, Transformer, TCN, LSTM, and CNN-were constructed, alongside traditional models such as SVM, PCA-LDA, and PLS-DA, to systematically compare classification performance. The experiments showed that the Informer model performed the best, with an accuracy, precision, recall, and F 1 score of 0.981, 0.982, 0.981, and 0.981, respectively, surpassing all other deep learning models and significantly exceeding traditional methods such as SVM, PCA-LDA, and PLS-DA, demonstrating superior feature extraction and generalization capabilities. This study integrates fluorescence spectroscopy with deep learning, providing a novel and effective solution for identifying liquid cosmetic raw materials.","url":"https://pubmed.ncbi.nlm.nih.gov/42506987/","authors":["Zeng T","Yang H","Wang S","Wu D","Tang E","Yang Y","Zhang S","Sun X","Liu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 27","doi":"10.1039/d6ay00510a","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506963","name":"A Deep Learning-Based Study on Automatic Measurement Method of Biological Parameters in Anterior Segment UBM Images of Angle-Closure Glaucoma.","source":"pubmed","abstract":"This study aimed to develop a deep learning-based model for measuring biometric parameters from anterior segment ultrasound biomicroscopy (UBM) images, assisting clinicians in the early screening and diagnosis of primary angle-closure glaucoma (PACG).","url":"https://pubmed.ncbi.nlm.nih.gov/42506963/","authors":["Yu X","Zhao Z","Zhang C","Wang X","Wang X","Zhou S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 1","doi":"10.1167/tvst.15.7.27","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506890","name":"Antidepressant use at the threshold: people prescribed antidepressants around the time of a dementia diagnosis.","source":"pubmed","abstract":"Antidepressant use is common in people with dementia, and many start around the time of diagnosis. We hypothesised that those who commenced antidepressants at dementia diagnosis started for dementia-related symptoms, distinct from those with longstanding prescriptions, resulting in different characteristics in electronic records.","url":"https://pubmed.ncbi.nlm.nih.gov/42506890/","authors":["Heybe M","Verma S","Pozuelo Moyano B","Stewart R","Mueller C","Davis KAS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 2","doi":"10.1093/ageing/afag221","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506875","name":"Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease.","source":"pubmed","abstract":"Alzheimer's disease (AD) is a progressive neurodegenerative disorder driven by complex cellular changes. To identify transcriptional signatures involved in AD pathology, we first analyzed nine single-cell/nucleus RNA-seq datasets from 67 high-pathology cases (Braak stages V/VI), generating an atlas of 363&#xa0;243 cells. Using a multi-sample integration strategy, we identified 51 cross-sample meta-programs (MPs) across seven cell types that collectively capture key AD-related processes, including synaptic dysfunction and neuroinflammation. By screening bulk transcriptomes from 1208&#xa0;ad and 725 normal samples, we found that 26 MPs (13 pathogenic and 13 protective) displayed differential activity in AD, characterized by up-regulation of microglia MPs and down-regulated of neuron MPs. Furthermore, spatial transcriptomics revealed that these MPs form spatially coherent communities associated with distinct cellular neighborhoods. Finally, we performed an integrative drug repurposing screen to identify candidate drugs predicted to regulate pathogenic MPs. In conclusion, we conducted an integrated multi-omics study to identify AD cell-type-specific MPs, and this framework can be applied to deconvolve cellular heterogeneity and screen candidate drugs for AD.","url":"https://pubmed.ncbi.nlm.nih.gov/42506875/","authors":["Zhang C","Zhang Y","Wu Z","Zhang Y","Xue F","Tan Q","Zhong X","Zhang Y","Zhao Z","Peng Y","Chen H","Li F","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.1093/bib/bbag411","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506870","name":"Can AI Detect What Is Not Injected? Evaluation of Lesion Detection in Virtual Contrast-Enhanced Breast MRI Using a Large-Scale AI Model Trained on GBCA-Enhanced Data.","source":"pubmed","abstract":"Artificial intelligence (AI) can support lesion detection in gadolinium-based contrast agent-enhanced (GBCA-enhanced) breast MRI. However, its effectiveness on virtual contrast-enhanced (vCE) images remains unclear. This feasibility study evaluated the publicly available MAMA-MIA nnU-Net model trained on GBCA-enhanced data using an independent cohort of both GBCA-enhanced and vCE breast MRI.","url":"https://pubmed.ncbi.nlm.nih.gov/42506870/","authors":["Heidarikahkesh S","Schreiter H","George A","Nguyen TT","Skwierawska D","Brock L","Hadler D","Uder M","Laun FB","Ehring C","Graber J","Döppmann L","Horishnyi I","Kapsner LA","Ohlmeyer S","Liebert A","Bickelhaupt S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.3390/tomography12070105","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506799","name":"Clinical evaluation of deep learning-accelerated 3D FLAIR imaging at 1.5T and 3T for visualization of cerebral white matter lesions.","source":"pubmed","abstract":"BackgroundFluid-attenuated inversion recovery (FLAIR) imaging is a standard sequence used for the detection of white matter lesions; however, 1-mm isotropic FLAIR can take 5-7&#x2005;min at 1.5T and 3T using routine methods, limiting routine use of high-resolution 3D FLAIR imaging.PurposeTo evaluate a deep learning (DL)-based unrolled optimization 3D FLAIR technique, Sonic DL&#x2122; 3D (SDL), in comparison to a standard 3D FLAIR protocol, both combined with DL-based post-processing (AIR&#x2122; Recon DL) at 1.5T and 3T.Material and MethodsA total of 67 patients (15 scanned at 1.5T and 52 at 3T) with suspected white matter lesions were included. Two neuroradiologists performed blinded, head-to-head image quality evaluation (motion, noise, and overall diagnostic quality). Quantitative lesion volume and lesion counts were derived from automated segmentation using SPM 12. Cohen's kappa, Wilcoxon signed-rank tests, and paired t -tests were applied.ResultsAt 1.5T, SDL FLAIR (4&#x2005;min 53&#x2005;s) reduced scan times by 15% compared to standard 3D FLAIR (5&#x2005;min 43&#x2005;s) and performed similarly or better in lesion visualization and image quality, with significantly ( P &#x2009;&lt;0.05) improved noise quality. At 3T, SDL FLAIR (2 min 45&#x2005;s) achieved a reduction in scan time of approximately 35% compared to standard 3D FLAIR (4 min 15&#x2005;s) and showed improved lesion conspicuity (periventricular, juxtacortical, and other regions) and image quality metrics. However, except for noise quality, these differences did not reach statistical significance despite moderate to almost perfect interrater agreement (Cohen's kappa 0.55-0.85; P &#x2009;&lt;0.001).ConclusionAccelerated SDL FLAIR demonstrated improved diagnostic quality, comparable lesion conspicuity, and volumetric measurements to standard 3D FLAIR sequence at 1.5T and 3T.","url":"https://pubmed.ncbi.nlm.nih.gov/42506799/","authors":["Buathong S","Filippakis A","Hua Chiang C","Milshteyn E","Thomas M","Rettmann D","Hemond C","Huang SY","Jambor I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1177/02841851261460842","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506756","name":"Artificial Intelligence Tools in Pre-Travel Health Consultations: A Scoping Review of Clinical Evidence, Implementation Gaps, and Emerging Opportunities.","source":"pubmed","abstract":"Pre-travel health consultations require individualised risk assessment across itinerary, destination, traveller characteristics, vaccine and medication history, comorbidities, pregnancy and immune status, activities, and access to care. Artificial intelligence (AI), particularly large language models (LLMs), may support pre-consultation education, structured history collection, guideline retrieval, multilingual communication and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated or insufficiently personalised recommendations.","url":"https://pubmed.ncbi.nlm.nih.gov/42506756/","authors":["Qasim HS","Simpson MD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 6","doi":"10.3390/tropicalmed11070186","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506713","name":"SELEX-Based Aptamer Technologies for Toxin Analysis: Screening, Optimization, and Computational Assisted Design.","source":"pubmed","abstract":"The accurate and sensitive detection of toxin contamination remains a pressing challenge for food safety, environmental integrity, and public health, because conventional analytical methods suffer from high costs, poor field stability, and inadequate sensitivity for trace-level emerging contaminants. In this review, we provide a comprehensive overview of biosensor technologies for toxin detection, with a dedicated focus on nucleic acid aptamers and SELEX (Systematic Evolution of Ligands by Exponential Enrichment) technology. We systematically categorize nine SELEX variants developed for toxin detection, covering target-immobilized, library-immobilized, non-immobilized, cell-based, and high-throughput platforms, with an emphasis on their selection principles, applicability, and limitations. This review discusses computationally assisted aptamer discovery (e.g., AI-based sequence generation and molecular docking) as well as experimental post-SELEX optimization strategies such as cyclization, multivalent assembly, and structure-switching design. We then discuss key challenges and future perspectives, highlighting the shift from method-oriented to demand-oriented aptamer development through integrated SELEX strategies and AI-assisted design. Overall, this review covers mainstream SELEX technologies, aptamer selection, computational design, experimental optimization, and sensor integration to serve as a reference for next-generation toxin detection applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42506713/","authors":["Shang X","Yang C","Deng H","Wang L","Sun M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.3390/toxins18070293","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506684","name":"Diagnostic Performance of Artificial Intelligence (AI) Chatbot Compared to Orthopedic Trauma Surgeons in Evaluating Indication for Surgery for Isolated Lateral Malleolar Fractures: a Retrospective Study.","source":"pubmed","abstract":"The indication for surgery for isolated lateral malleolar fracture (AO/OTA 44B1) is debatable and in many cases, relies upon radiographic assessment of fracture stability. Artificial intelligence chatbots with visual analysis capabilities offer a potential attribute for radiographic assessment. This study compared the diagnostic performance of a commercially available AI chatbot with that of three fellowship-trained orthopedic trauma surgeons in evaluating equivocal isolated lateral malleolar fractures.","url":"https://pubmed.ncbi.nlm.nih.gov/42506684/","authors":["Oulianski M","Mosheiff R","Avraham D","Yehuda OB","Weil Y","Jammal M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.55095/achot2026/027","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506550","name":"Assessment of Creativity Potential of a 3DGAN in Implant Crown Design: A Proof-of-Concept Study.","source":"pubmed","abstract":"Digital dentistry increasingly relies on artificial intelligence (AI) to automate restorative design. However, the ability of generative networks to produce multiple geometrically distinct outputs for the same prosthetic field remains insufficiently evaluated. This study assessed repeated-output geometric variability in a previously developed three-dimensional generative adversarial network (3DGAN) for screw-retained implant crown design as a preliminary indicator of potential generative diversity. Nine AI-generated implant crown designs were analyzed, consisting of three independently generated crowns for each of three different prosthetic fields. Within each set, the crowns were superimposed and compared using \"MeshLab\". Mean Hausdorff distance (HD), maximum HD, and root mean square (RMS) values were recorded, with 0.05 model units used as the threshold for identifying insufficient morphological variation. The overall mean HD was 3.32 model units, the mean maximum HD was 16.18 model units, and the mean RMS value was 4.40 model units. No pairwise comparison showed values equal to or below 0.05 model units. In conclusion, the investigated 3DGAN demonstrated preliminary evidence of geometric output variability compatible with potential generative diversity.","url":"https://pubmed.ncbi.nlm.nih.gov/42506550/","authors":["Naydenov A","Uzunov T","Kirov D","Kostadinov G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 5","doi":"10.3390/jfb17070324","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506545","name":"The Next Phase of 3D Bioprinting: AI-Native Systems-A Narrative Review.","source":"pubmed","abstract":"Three-dimensional (3D) bioprinting has reached a complexity limit where empirical, parameter-by-parameter optimization no longer scales. The dominant mode of artificial intelligence (AI) integration remains AI-augmented, where AI is treated as an analytical addition to a conventional pipeline. We argue that the field is approaching a discontinuous transition towards AI-native bioprinting, in which AI represents the operational layer of system intelligence, not an ancillary tool. A systematic analysis of 365 publications on the intersection of bioprinting and AI (2015-2026), performed through 18 queries organized by the four search axes of the PubMed database, shows that the intersection grew 136 times during the decade, with an acceleration of 3.16 times only between 2024 and 2025. Mapping the publications to the six functional domains reveals a marked asymmetry: clinical translation counts 154 papers, while cell viability prediction-the biological foundation that every closed-loop system requires-counts only three. We define AI-native bioprinting as a system architecture that combines continuous learning, multi-modal sensing fused through visual, mechanical and biological signals, and biologically closed control loops. We present a conceptual shift from printing accuracy to biological intelligence as a success criterion. The transition requires open datasets, consensus biological metrics, inter-laboratory validation, and early regulatory engagement.","url":"https://pubmed.ncbi.nlm.nih.gov/42506545/","authors":["Zdravković N","Zdravković M","Živanović MN"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.3390/jfb17070319","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506535","name":"Occupational Heat Stress in Industrial Workers: Modifiable Predictors and Machine Learning Risk Classification in Northeast India.","source":"pubmed","abstract":"Occupational heat stress poses a growing threat to worker health and productivity in low- and middle-income countries. This study investigated modifiable predictors of heat stress among industrial workers and evaluated machine-learning models for risk classification.","url":"https://pubmed.ncbi.nlm.nih.gov/42506535/","authors":["Srinivasan K","Saikia D","Seethy A","Bhattacharjee M","Sinha A","Loganathan S","Mustak Alam S","Hanse B","Kalita J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1097/JOM.0000000000003706","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506399","name":"Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Background: Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders affecting biochemical pathways. Despite advances in diagnostic technologies, comprehensive understanding of their historical evolution, classification systems, and diagnostic approaches remains fragmented. Objectives: This systematic review and meta-analysis aimed to synthesize evidence on the historical development, classification frameworks, and diagnostic modalities for IEMs, diagnostic accuracy, and prevalence estimates, providing a comprehensive resource for clinicians and researchers. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of seven electronic databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, SciSpace and ArXiv) from January 2000 to March 2026. Studies addressing historical perspectives, classification systems, or diagnostic approaches for IEMs were included. Two independent reviewers performed screening, data extraction, and quality assessment. Meta-analyses were conducted using random-effects models for diagnostic accuracy and prevalence estimates. Results : From 1342 identified records, 54 studies met the inclusion criteria, encompassing 8,234,567 individuals across 35 countries. Historical analysis revealed 16 major milestones from Garrod's 1902 \"chemical individuality\" concept to the current AI-powered diagnostics. Four major classification systems were identified: pathophysiological (intoxication, energy deficiency, complex molecule disorders), biochemical pathway (amino acid, organic acid, urea cycle, carbohydrate, fatty acid oxidation, mitochondrial, peroxisomal, lysosomal disorders), organelle-based, and the integrated Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology. Meta-analysis demonstrated high diagnostic performance of tandem mass spectrometry (MS/MS) with a pooled sensitivity of 99.1% (95% CI: 98.6-99.5) and specificity of 99.8% (95% CI: 99.7-99.9%). The pooled global prevalence of IEMs was 50.9 per 100,000 live births (95% CI 45.2-56.8). Next-generation sequencing achieved a diagnostic yield of 42.8% (95% CI: 38.2-47.5%) in suspected cases. Emerging AI-powered diagnostic tools demonstrated high discrimination performance with area under the curve (AUC) values exceeding 0.95 for specific IEM, though external validation remains limited. Newborn screening expanded from single-disease to comprehensive panels detecting over 50 disorders. Conclusions: This comprehensive review demonstrates that IEMs have evolved from rare curiosities to systematically diagnosable conditions through technological advances. Integration of metabolomics, genomics, proteomics and artificial intelligence promises further diagnostic improvements. Standardized classification systems and evidence-based diagnostic algorithms are essential for optimal patient care. Future directions include artificial intelligence-enhanced diagnostics, expanded screening, and personalized medicine approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42506399/","authors":["Mutamuliza J","Gori E","Mutesa L","Debray FG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 25","doi":"10.3390/metabo16070445","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506321","name":"Risk Phenotyping Before Graft Implantation: FTIR Spectroscopy and Machine Learning for Complementary Risk Stratification in Kidney Transplantation.","source":"pubmed","abstract":"Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection.","url":"https://pubmed.ncbi.nlm.nih.gov/42506321/","authors":["Ramalhete L","Araújo R","Vigia E","Vieira MB","Ferreira A","Calado CRC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 27","doi":"10.3390/medsci14030353","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506317","name":"Diagnostic Models of Neonatal Respiratory Distress Syndrome and Congenital Pneumonia: A Retrospective Cohort Study.","source":"pubmed","abstract":"Background : The differential diagnosis of respiratory distress syndrome (RDS) and congenital pneumonia (CP) in newborns remains a complex clinical challenge due to the similarity in their clinical manifestations and their potential to coexist. Objective : We aimed to determine differential diagnostic predictors of RDS and CP in newborns by using mathematical modeling and machine learning methods. Methods : A retrospective cohort study was conducted; de-identified medical records of 244 newborns (97 with RDS and 143 with CP) were collected to assess clinical, anamnestic, laboratory, and instrumental data by applying multiple regression analysis, ROC analysis, logistic regression models, and Random Forest. Results : Patients with CP presented with a more severe condition at admission (57.1% vs. 23.3%; p = 0.023), required mechanical ventilation (MV) more frequently (22.4% vs. 8.2%; p = 0.004), and were more often transferred to the intensive care unit (ICU) (77.3% vs. 55.7%; p = 0.001). They further had lower hemoglobin levels (151 &#xb1; 28 g/L vs. 164 &#xb1; 31 g/L; p = 0.001) and red blood cell counts ( p = 0.021). Regression analysis demonstrated that the severity of the condition and the presence of cerebral ischemia were dependent on hemoglobin levels in the case of CP, while gestational age played a determining role in RDS. The machine learning models achieved an accuracy of 0.69 and an area under the curve (AUC) of 0.82 (Random Forest). The key predictors for differential diagnosis of RDS were low gestational age, hyperbilirubinemia, and congenital heart defects, while for CP, they were hemoglobin &lt; 151 g/L, lymphocytes &lt; 4.8 &#xd7; 10 3 /&#x3bc;L, oxygen saturation &lt; 90-91%, and cerebral ischemia. Conclusions : The use of mathematical modeling methods made it possible to identify prognostically significant predictors for the differential diagnosis of RDS and CP. The resulting models are best viewed as proof-of-concept tools for hypothesis generation and future research, as external validation is necessary before they can be considered for clinical use.","url":"https://pubmed.ncbi.nlm.nih.gov/42506317/","authors":["Aminova A","Zabelich A","Matsukatova B","Eryushova T","Vagidova K","Kildiyarova R","Polishchuk A","Osovetskaya Y","Levasheva S","Ozerskaia I","Sukhovjova O","Farber I","Erdes S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 26","doi":"10.3390/medsci14030348","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506312","name":"Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian EHR Data.","source":"pubmed","abstract":"Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018-2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42506312/","authors":["Moshawih S","Gharaibeh L","Alfreahat I","Shnoudeh AJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 24","doi":"10.3390/medsci14030343","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"pmid:42506311","name":"Empagliflozin Protects Against Doxorubicin Cardiotoxicity: Integrative Assessment of Cardiac Kinetics and Electrophysiology Using Machine Learning in a Rat Model.","source":"pubmed","abstract":"Background/Objectives : Anthracycline-induced cardiotoxicity remains a major challenge in cancer treatment, and researchers are showing interest in artificial intelligence (AI) to improve the prediction and detection of cancer therapy-related cardiac dysfunction (CTRCD). Current surveillance strategies rely mainly on left ventricular ejection fraction and, more recently, global longitudinal strain. Methods : The present study was designed to evaluate cardiac performance in a rat model of doxorubicin-induced cardiotoxicity and empagliflozin-mediated cardioprotection using a machine learning-based analytical framework. Eighteen adult male Sprague-Dawley rats were assigned to five experimental groups. We aimed to quantify ventricular wall dynamics and contractility using an advanced image-processing and object-detection model that has not been previously used to distinguish normal from impaired cardiac kinetics. During real-time recording, simultaneous electrocardiogram monitoring was performed, enabling direct correlation between deep learning-based ventricular wall motion metrics and cardiac electrical activity. The cardioprotective effects of empagliflozin were further validated by immunofluorescence staining (cTnI, vimentin, &#x3b1;-SMA, and Cx43) of rat cardiomyocytes and paraffin-embedded cardiac tissue, demonstrating attenuation of cellular injury and structural remodeling. Results : The integrated analysis of cardiac kinetic patterns derived via machine learning distinguishes not only extreme cardiotoxicity, but also tracks a graded pattern consistent with ECG-derived severity and treatment-related functional preservation. These findings indicate that the algorithm captures the gradient of empagliflozin's cardioprotective effect within this internally validated preclinical setting. Additionally, immunofluorescence results validated the benefits of SGLT2 inhibition on myocardial integrity. Conclusions : The novelty of the present work lies at the intersection of advanced cardiac kinetic analysis using AI, preclinical modeling, and SGLT2-mediated cardioprotection in cardio-oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42506311/","authors":["Goje ID","Ordodi VL","Bojin FM","Goje GI","Bătrîn AH","Buica TP","Iordache M","Grijincu M","Păunescu V","Lighezan DF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 24","doi":"10.3390/medsci14030342","addedAt":"2026-09-01T01:48:02.061Z","updatedAt":"2026-09-01T01:48:02.061Z"},{"id":"doi:10.1186/s13054-020-02962-y","name":"How machine learning could be used in clinical practice during an epidemic","source":"crossref","abstract":"","url":"https://doi.org/10.1186/s13054-020-02962-y","authors":["Charles Verdonk","Franck Verdonk","Gérard Dreyfus"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-26T17:03:35Z","doi":"10.1186/s13054-020-02962-y","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.2196/preprints.16975","name":"Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques (Preprint)","source":"crossref","abstract":"BACKGROUND Stress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients’ variables including risk factors, current medication, and anthropometric variables has not been widely investigated. OBJECTIVE This study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes. METHODS A machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages: feature extraction, preprocessing, feature selection, and classification stage. A mutual information–based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed. Data from 529 patients were used to train and validate the framework. Patient mean age was 61 (SD 12) years. The data consists of anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, prior diagnosis of CAD, and prescribed medications at the time of the test. There were 82 positive (abnormal) and 447 negative (normal) stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD. RESULTS The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were the features that shared the most information about the outcome of stress echocardiography. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only these three features, we achieved an accuracy of 67.63% with sensitivity and specificity 72.87% and 66.67% respectively. However, for patients with no prior diagnosis of CAD, only two features (sex and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. CONCLUSIONS This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.","url":"https://doi.org/10.2196/preprints.16975","authors":["Mohamed Bennasar","Duncan Banks","Blaine A Price","Attila Kardos"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-11T18:31:03Z","doi":"10.2196/preprints.16975","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.21203/rs.3.rs-2095524/v1","name":"Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis.","source":"crossref","abstract":"Abstract Background Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. Design: Systematic review and meta-analyses. Participants: Older adults (≥ 65 years) in any setting. Intervention: Machine learning models for predicting clinical outcomes in older adults were evaluated. A meta-analysis was conducted where the predictive models were compared based on their performance in predicting mortality. Outcome measures: Studies were grouped by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. Results 29 studies that satisfied the systematic review criteria were appraised and six studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from six studies included in the meta-analysis yielded a summary estimate of 0.82 (95%CI: 0.76–0.87), signifying good discriminatory power in predicting mortality. Conclusion The meta-analysis indicates that machine learning models can predict mortality. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; they should be integrated into a larger research setting to predict various clinical outcomes.","url":"https://doi.org/10.21203/rs.3.rs-2095524/v1","authors":["Robert T Olender","Sandipan Roy","Prasad S Nishtala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-11-19T18:09:57Z","doi":"10.21203/rs.3.rs-2095524/v1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2016.0105","name":"Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders","source":"crossref","abstract":"Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.","url":"https://doi.org/10.1109/icmla.2016.0105","authors":["Blessing Ojeme","Audrey Mbogho","Thomas Meyer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-02-07T20:39:53Z","doi":"10.1109/icmla.2016.0105","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.crmeth.2022.100358","name":"Machine learning prediction of side effects for drugs in clinical trials","source":"crossref","abstract":"Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.","url":"https://doi.org/10.1016/j.crmeth.2022.100358","authors":["Diego Galeano","Alberto Paccanaro"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-07T10:39:06Z","doi":"10.1016/j.crmeth.2022.100358","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1111/ijcp.14980","name":"How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders","source":"crossref","abstract":"Objectives Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. Methods Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. Results Day of the week, and number of patients with ICD-10 codes of 'A00-B99', 'I00-I99', 'J00-J99', 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these, although daily patient frequencies with 'H60-H95', 'N00-N99' and 'O00-O9A' were significant for laboratory services, 'L00-L99', 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. Conclusion The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.","url":"https://doi.org/10.1111/ijcp.14980","authors":["Görkem Sariyer","Mustafa Gökalp Ataman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-12T13:27:19Z","doi":"10.1111/ijcp.14980","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1080/20018525.2025.2565853","name":"Training machine learning-based spirometry reference equations: a comparison with GAMLSS and GLI reference equations","source":"crossref","abstract":"Introduction Interpretation of spirometry data depends on the availability of reference equations that reflect the physiological norms of the assessed population. Although GAMLSS models provide clinically acceptable models, they may lack simplicity and ease of application. This study evaluated the efficiency of machine learning (ML)-based spirometry reference equations as an alternative for Jordanian adults. Method In this cross-sectional study, ML models were trained using age and height to predict FEV₁, FVC and FEV₁/FVC. Model development was based on the same datasets previously used to construct GAMLSS-based Jordanian equations, which included 1,948 participants (54.2% females). External validation was performed on a newly recruited sample of healthy, non-smoking adults ( n = 487, 46.6% females). Results ML predicted and lower limits of normal (LLNs) values were compared with those from the Jordanian GAMLSS, GLI equations, using z-score distributions, residual plots, and clinical diagnostic agreement. For both sexes, ML models consistently produced comparable mean squared errors (MSE) to the Jordanian GAMLSS equations and lower MSE values and z-scores closer to zero when compared with global reference equations. Agreement analyses revealed that the ML and Jordanian models more reliably classified individuals within ± 0.5 and ± 1.0 z-score thresholds, emphasizing their superior calibration. ML and Jordanian models were the only ones to classify all the healthy study sample as normal spirometry. Conclusion ML-derived spirometry equations demonstrated strong alignment with the observed data and outperformed global standards in representing Jordanian adults. These findings support the use of reference equations customized for specific regions in respiratory diagnostics.","url":"https://doi.org/10.1080/20018525.2025.2565853","authors":["Walid Al-Qerem","Anan Jarab","Judith Eberhardt"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-03T17:34:34Z","doi":"10.1080/20018525.2025.2565853","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.21203/rs.3.rs-2004210/v1","name":"Machine learning–based construction of a clinical prediction model for hypercapnia during one-lung ventilation for lung surgery","source":"crossref","abstract":"Abstract In this study, we developed a clinical prediction model for hypercapnia during one-lung ventilation for lung surgery by machine learning. We analyzed the cases and intraoperative blood gases of 348 patients who had undergone lung surgery at Jiangxi Cancer Hospital from November 2019 to June 2021. We analyzed the factors that independently influence hypercapnia during one-lung ventilation for lung surgery by selecting the best variables through a combination of random forest and logistic regression stepwise selection (Step AIC). Thereafter, we used these factors to construct logistic regression models and a nomogram. Receiver operating characteristic curves were used to measure the predictive accuracy of the nomogram and its component variables, and the predictive probabilities of the nomogram were compared and calibrated by calibration curves. We used bootstrap to verify the internal validation method to judge the reliability of the model, and we employed decision curve analysis (DCA) for clinical decision analysis. The independent influencing factors for hypercapnia during one-lung ventilation for lung surgery were age, gender, and one-lung ventilation position. We established the hypercapnia during one-lung ventilation for lung surgery logistic regression model: −5.421 + 0.047 × age + 1.8 × gender (=1) + 0.625 × one-lung ventilation position (=1). The prediction accuracy probability of the nomogram is 0.7457 (95% confidence interval [0.6916, 0.7998]). The prediction model showed good agreement between the calibration curve and the ideal predicted value, and bootstrap internal validation showed the area under the curve was 0.745 and the C-index was 0.742. DCA indicated that the model has some clinical value. In this study, three independent influences on hypercapnia during one-lung ventilation were established. We constructed an individualized model for predicting hypercapnia during one-lung ventilation for pulmonary surgery, as well as the first internally validated predictive model and nomogram for hypercapnia during one-lung ventilation for pulmonary surgery, both of which have good predictive and calibration properties and can provide some clinical guidance value.","url":"https://doi.org/10.21203/rs.3.rs-2004210/v1","authors":["Yiwei Fan","Ting Ye","Tingting Huang","Huaping Xiao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-09-13T16:00:33Z","doi":"10.21203/rs.3.rs-2004210/v1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/ic3ecsbhi67834.2026.11468895","name":"Predicting Clinical Trial Site Performance using Machine Learning","source":"crossref","abstract":"Success of clinical trials depends extensively on the operational performance of the participating site, yet early detection of underperforming sites remains an ongoing challenge. In this work, the authors are presenting a machine learning-based framework for predicting the performance of clinical trial sites from historical operational and enrollmentrelated indicators. For early risk identification and data-driven support for decision-making in a trial management team, several supervised learning models have been evaluated to classify site performance outcomes. Experimental results showed that ensemble-based models significantly outperform traditional baselines regarding prediction accuracy and robustness. The proposed approach emphasizes the potential of artificial intelligence to improve trial efficiency by mitigating operational risk and enabling proactive planning of clinical trials.","url":"https://doi.org/10.1109/ic3ecsbhi67834.2026.11468895","authors":["Sumanth Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-15T19:22:14Z","doi":"10.1109/ic3ecsbhi67834.2026.11468895","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.cegh.2026.102362","name":"Machine learning techniques in biomedical data analysis: A scoping review of biostatistical perspectives","source":"crossref","abstract":"Background Machine learning (ML) is increasingly applied in biomedical research to analyse complex datasets from imaging, omics, clinical records, and other sources. However, the extent to which biostatistical methods are integrated into ML applications remains inconsistent. Objective This scoping review explores the use of ML techniques in biomedical research and examines the incorporation of biostatistical methods for model validation and performance evaluation. Methods A total of 32 peer-reviewed studies published between 2018 and 2024 were reviewed. Eligible studies involved the application of ML in biomedical domains and reported statistical validation techniques or performance measures. Results Supervised learning was the most commonly used approach, while deep learning methods were frequently employed in high-dimensional domains such as imaging and omics. Although several studies reported using cross-validation, feature selection, and standard performance metrics, integration of biostatistical frameworks was limited in scope and depth. Conclusion The review highlights a growing trend of ML adoption in biomedical research, alongside a critical need for stronger integration of biostatistical approaches. Improved collaboration between data science and statistics is essential to ensure model reliability, interpretability, and real-world clinical applicability.","url":"https://doi.org/10.1016/j.cegh.2026.102362","authors":["A. Usha","Noel George","K. Janagi","Pradyuman Verma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-17T15:51:58Z","doi":"10.1016/j.cegh.2026.102362","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.jocn.2026.112106","name":"Machine learning for survival prediction after brain tumor stereotactic radiosurgery: A systematic review and meta-analysis","source":"crossref","abstract":"Brain tumors remain a significant global health burden, contributing to substantial morbidity and mortality worldwide [1,2]. These neoplasms encompass a wide spectrum of conditions, including primary brain tumors, such as gliomas, and brain metastases [3,4]. Glioblastoma (GB) represents the most aggressive subtype of gliomas, characterized by rapid progression and poor survival rates despite advances in therapeutic approaches [1,5–7]. On the other hand, brain metastases remain the most common intracranial malignancies, frequently originating from lung, breast, and melanoma primary cancers [3].","url":"https://doi.org/10.1016/j.jocn.2026.112106","authors":["Anna Volodina","Aline Pinto Veiga","Imran Noorani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-26T10:14:45Z","doi":"10.1016/j.jocn.2026.112106","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.clineuro.2026.109495","name":"Comment on “Prediction of venous thromboembolism after spontaneous intracerebral hemorrhage based on machine learning”","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clineuro.2026.109495","authors":["Sadhana U. Adhyapak","Abhishek Kumar Upadhyay","S.R.V. Prasad Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-18T06:02:59Z","doi":"10.1016/j.clineuro.2026.109495","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.4018/978-1-7998-5071-7.ch003","name":"Relevance of Machine Learning to Cardiovascular Imaging","source":"crossref","abstract":"Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.","url":"https://doi.org/10.4018/978-1-7998-5071-7.ch003","authors":["Sumesh Sasidharan","M. Yousuf Salmasi","Selene Pirola","Omar A. Jarral"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-08-06T09:19:37Z","doi":"10.4018/978-1-7998-5071-7.ch003","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1101/2020.05.30.20118026","name":"Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application","source":"crossref","abstract":"Abstract An accurate identification of schizophrenia spectrum disorder (SSD) at early stage could potentially allow for treating SSD with appropriate intervention to potentially prevent future deterioration. Despite mounting studies found neuroimaging combined with machine learning can identify chronic medicated SSD, whether or not the classification model identified the trait biomarker of SSD that can be used to identify early stage SSD is largely unknown. The present study aimed to investigate whether or not the classification model trained using chronic medicated SSD identified the trait biomarker of SSD that whether or not the model can be generalized to early stage SSD, by using functional connectivity (FC) combined with support vector machine (SVM) using a large sample from 4 independent sites (n = 1077). We found that the classification model trained using chronic medicated SSD from three sites(dataset 2, 3 and 4) classified SSD from HCs in another site (dataset 1) with 69% accuracy (P = 2.86e-13). Subgroup analysis indicated that this model can identify chronic medicated SSD in dataset 1 with 71% sensitivity (P = 4.63e-05), but cannot be generalized to first episode unmedicated SSD (sensitivity = 48%, P = 0.68) and first episode medicated SSD (sensitivity = 59%, P = 0.10). Univariable analysis showed that medication usage had significant effect on FC, but disease duration had no significant effect on FC. These findings suggest that the classification model trained using chronic medicated SSD may mainly identified the pattern of chronic medication usage state, rather than the trait biomarker of SSD. Therefore, we should reconsider the current machine learning studies in chronic medicated SSD more cautiously in term of the clinical application.","url":"https://doi.org/10.1101/2020.05.30.20118026","authors":["Chao Li","Fei Wang","Xiaowei Jiang","Ji Chen","Jia Duan","Shaoqiang Han","Hao Yan","Yanqing Tang","Ke Xu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-06-03T14:51:57Z","doi":"10.1101/2020.05.30.20118026","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.pdpdt.2025.105262","name":"Machine learning predictive model based on the corneal biomechanics of clinical data","source":"crossref","abstract":"Objective To develop and validate a machine learning model for predicting postoperative corneal ectasia risk after refractive surgery by integrating multidimensional preoperative and postoperative biomechanical parameters. Methods A retrospective cohort of 200 patients who underwent LASIK or SMILE at Shenyang Xingqi Eye Hospital between January 2023 and December 2024 was analyzed. Variables including corneal morphology, biomechanics, and early postoperative changes were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Multiple algorithms-generalized linear model (GLM), gradient boosting machine (GBM), and support vector machine (SVM)-were compared via 10-fold cross-validation. The optimal SVM model was fine-tuned through grid search and further tested in a prospective temporal validation cohort (n = 40, January-September 2025). Model performance was evaluated by AUC, calibration, and decision-curve analysis. Results LASSO identified PreopCCT, PreopKmax, CH, CRF, and early postoperative changes (PostopCCTChange1m, PostopKmaxChange3m) as key predictors. The SVM model achieved the best discrimination among all models (AUC = 0.88 in cross-validation). In prospective validation, the locked SVM maintained high accuracy (AUC = 0.984, 95 % CI 0.944-1.000; sensitivity = 0.88; specificity = 0.90) with good calibration (slope = 0.96; Brier = 0.098) and net clinical benefit across threshold probabilities of 0.1-0.7. Conclusion The optimized SVM model provides a reliable, data-driven approach for individualized ectasia-risk assessment in refractive surgery. By combining biomechanical and tomographic features, it enables early identification of high-risk patients and supports personalized surgical planning and postoperative monitoring. Further multi-center and long-term validation is warranted to enhance generalizability and clinical implementation.","url":"https://doi.org/10.1016/j.pdpdt.2025.105262","authors":["Tianyu Shi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-18T16:23:43Z","doi":"10.1016/j.pdpdt.2025.105262","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.canlet.2020.03.032","name":"Machine Learning in oncology: A clinical appraisal","source":"crossref","abstract":"Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous promising applications in several fields of medicine. Its use has grown following the increased availability of patient data due to technological advances such as digital health records and high-volume information extraction from medical images. Multiple ML algorithms have been proposed for applications in oncology. For instance, they have been employed for oncological risk assessment, automated segmentation, lesion detection, characterization, grading and staging, prediction of prognosis and therapy response. In the near future, ML could become essential part of every step of oncological screening strategies and patients' management thus leading to precision medicine.","url":"https://doi.org/10.1016/j.canlet.2020.03.032","authors":["Renato Cuocolo","Martina Caruso","Teresa Perillo","Lorenzo Ugga","Mario Petretta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-04-03T20:35:52Z","doi":"10.1016/j.canlet.2020.03.032","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.5120/ijca2025926099","name":"Accurate Heart Disease Prediction Using Machine Learning Techniques on Clinical Data","source":"crossref","abstract":"Heart disease is still one of the main causes of mortality in the world, and its early diagnosis represents an important part of timely treatment and prevention.The purpose of this work is to create a stable and accurate Machine Learning (ML) model for predicting the risk of heart disease with real clinical patient data.This research work relied on a clinical sample of 333 patient records of Sai Cardiac Hospital, Vijayapura, Karnataka, India (SCHV).The data set included medical parameters that included age, sex, type of chest pain, echo, test outcomes, resting Electrocardiogram (ECG), and Coronary Angiograph (CA) test.Structural pre-processing and visualization tools were employed to determine and derive meaningful predictors of heart disease.Three machine learning classifier models (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF)) were built and evaluated using accuracy, precision, recall, F1 score, specificity, Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) for performance measurement.Out of the above ML models that were generated, the SVM classifier performed best with an accuracy of 90% and an AUC of 0.95, better than both RF and KNN models.The integrity of the proposed model was verified based on the Receiver Operating Curve (ROC) curve and confusion matrices.Comparison with existing studies showed that the developed SVM model is more reliable for prediction.The results indicate that SVM-based predictive modelling has promising prospects for medical real-time diagnosis and can serve as a candidate decision support system in healthcare practice.","url":"https://doi.org/10.5120/ijca2025926099","authors":["Sunanda Budihal","Sheetalrani R. Kawale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-18T19:14:01Z","doi":"10.5120/ijca2025926099","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.5664/jcsm.8402","name":"The faces of sleep apnea in the age of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.5664/jcsm.8402","authors":["Ofer Jacobowitz","Stuart MacKay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-03-17T15:44:36Z","doi":"10.5664/jcsm.8402","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.clnesp.2026.103350","name":"Comment on “Interpretable machine learning model for predicting refeeding syndrome after colorectal cancer surgery”","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clnesp.2026.103350","authors":["P S L Narasimharao Davuluri","Avinash Reddy Segireddy","Goutham Kumar Sheelam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-18T23:35:00Z","doi":"10.1016/j.clnesp.2026.103350","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.55752/amwa.2023.241","name":"The Use of Artificial Intelligence and Machine Learning in Clinical Research and Health Care","source":"crossref","abstract":"Conference Education Session Report Speaker J. Kelly Byram, MS, MBA, ELS, Founder and CEO, Duke City Consulting, LLC, Albuquerque, NM","url":"https://doi.org/10.55752/amwa.2023.241","authors":["Noelle Ochotny"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-02T14:46:48Z","doi":"10.55752/amwa.2023.241","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.7717/peerj-cs.2594/fig-10","name":"Figure 10: Differences between (A) traditional machine learning and (B) transfer learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2594/fig-10","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-14T03:30:26Z","doi":"10.7717/peerj-cs.2594/fig-10","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.2139/ssrn.6574419","name":"Machine Learning and Deep Learning in Quantitative Finance","source":"crossref","abstract":"&lt;span&gt;This paper systematically reviews the technological evolution and practical applications of machine learning and deep learning in key areas such as stock price prediction, market timing, factor investing, and portfolio optimization. By leveraging advanced architectures including LSTM, CNN, Transformer, and hybrid models, these methods effectively capture complex nonlinear relationships and temporal dependencies in financial data, outperforming conventional econometric models. This paper provides a systematic taxonomy of hybrid models based on their functional roles (feature extraction, sequence modeling, and decision-making), analyzes the specific conditions under which Transformer architectures outperform LSTM/GRU variants, and explores how multi-agent reinforcement learning effectively simulates the non-zero-sum competitive nature of financial markets. Despite challenges related to data quality, model interpretability, and computational demands, machine learning and deep learning continue to drive the transition from experience-based strategies to adaptive, data-driven systems. Future research directions, such as explainable AI, federated learning, and quantum-inspired algorithms, are also discussed to address existing limitations and further enhance the robustness and efficiency of quantitative finance applications. The findings indicate that the integration of machine learning and deep learning in the field of quantitative finance has significantly transformed traditional financial modeling and decision-making approaches. Although numerous challenges and limitations remain in their practical application, the continuous development and refinement of these technologies are expected to enable machine learning and deep learning to exert an even more profound impact and play a greater role in quantitative finance.&lt;/span&gt;","url":"https://doi.org/10.2139/ssrn.6574419","authors":["Fanqun Mo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-29T13:20:29Z","doi":"10.2139/ssrn.6574419","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/mlke55170.2022.00020","name":"The Price Prediction of Sneakers Based on Machine Learning","source":"crossref","abstract":"Abstract-Sneaker culture is a self-organizing culture spontaneously formed by teenagers. In the Internet and digital age, sports shoes are endowed with appreciation and financial attributes through manual speculation, which has triggered a wave of sports shoes speculation with the participation of the whole people. As a human group behavior, the detonating mechanism behind speculative sneakers is worth thinking about. There is a set of logical behavior chains and practical logic in the behavior of frying shoes. It is necessary to use threshold theory to reveal the dynamic process of convergence from micro behavior to macro behavior. Therefore, machine learning can be used to predict the movement of sneaker prices in the sneaker market. The data set of this study is from stockx. In the research, the machine learning method is used to study the price of sports shoes and analyze the data. Four linear regression models were used for prediction. It is found that the OLS regression model’s accuracy is high. So OLS model is the best model for this dataset.","url":"https://doi.org/10.1109/mlke55170.2022.00020","authors":["WeiJia Yan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-04-29T20:07:24Z","doi":"10.1109/mlke55170.2022.00020","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2007.123","name":"Machine Learning for Information Management: Some Promising Directions","source":"crossref","abstract":"Management of personal information such as email messages, calendar entries, to-do items, and workstation documents is one of the most highly visible current uses of computer technology. I will present experimental evidence that machine learning techniques can be effectively used to improve personal information management tools in two ways. First, machine learning can be used to improve performance on certain types of difficult searches, notably searches that require some awareness of context. Second, machine learning can be used to reduce the chance of certain high-cost errors. One type of high-cost error we consider is the “dropped ball”—i.e., losing track of a task that has been delegated, in part or whole, to others. The second type of high-cost error is an “email leak”—i.e., mistakenly sending a sensitive email message to the wrong recipient.","url":"https://doi.org/10.1109/icmla.2007.123","authors":["Dr. William Cohen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-04-28T13:34:00Z","doi":"10.1109/icmla.2007.123","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-6591-8_3","name":"Architecture of a Machine Learning IDS","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-6591-8_3","authors":["Emmanuel Tsukerman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-10-07T16:04:31Z","doi":"10.1007/978-1-4842-6591-8_3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1093/oxfordhb/9780197653609.013.5","name":"Machine Learning in Chinese Courts","source":"crossref","abstract":"Abstract This chapter focuses on core machine learning (ML) technologies such as Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) technology in the context of how Chinese courts have sought to improve court efficiency and reduce the pressures on an understaffed judiciary through such technologies. In addition, this chapter touches on recent advancements in generative artificial intelligence and large language models (LLMs). There are many concerns about using novel technologies, including general-purpose LLMs in the trial process. However, legal LLMs developed by Chinese academia and companies specifically for the Chinese legal domain seek to avoid some of these pitfalls, which reflects the global development of specialized industry-tailored language models. Social justice challenges and future research regarding the use of ML in judicial systems are also reviewed in this chapter.","url":"https://doi.org/10.1093/oxfordhb/9780197653609.013.5","authors":["Nyu Wang","Michael Yuan Tian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-18T15:54:17Z","doi":"10.1093/oxfordhb/9780197653609.013.5","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.23977/autml.2024.050201","name":"Machine learning-based real-time detection of residual wall thickness in industrial pipelines","source":"crossref","abstract":"Far-field eddy current detection technology is a special eddy current technology that utilizes electromagnetic effect to detect the pipe through the wall. This paper introduces the principle of the far-field eddy current detection technology, including the two propagation modes of direct coupling and indirect coupling, and explains the division of different regions and their effects. The design and composition of the far-field eddy current detection device are then presented. In addition, this paper discusses the application of machine learning in analyzing far-field eddy current detection data, including the basic principles of machine learning, data preprocessing, data segmentation and model building steps. Finally, the evaluation indexes of machine learning models are introduced, as well as specific algorithms and evaluation indexes that may be used in far-field eddy current detection.","url":"https://doi.org/10.23977/autml.2024.050201","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-14T08:57:24Z","doi":"10.23977/autml.2024.050201","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.38007/ml.2020.010306","name":"Quality Control Method of Exploration and Development Data Based on Machine Learning","source":"crossref","abstract":"Because of increased exploration depth and increasingly complex geological environment survey area, makes the actual acquired seismic exploration data contains a lot of noise, the noise composition is serious interference signal effectively, affect the signal to noise ratio and resolution of the seismic data, reduce the quality of the seismic exploration data, to the subsequent inversion and interpretation, and finally brought difficulties such as oil and gas exploration work.This paper mainly studies the quality control method of exploration and development data based on machine learning.In this paper, the classification and source of desert noise are analyzed first, and a convolutional neural network with branch structure (BCDNet) is proposed to enhance the ability of extracting effective signal features from desert seismic exploration data, so as to better recover the seismic in-phase axis polluted by desert seismic random noise.","url":"https://doi.org/10.38007/ml.2020.010306","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:45:11Z","doi":"10.38007/ml.2020.010306","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.4018/978-1-6684-6291-1.ch044","name":"Machine Learning Algorithms","source":"crossref","abstract":"Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.","url":"https://doi.org/10.4018/978-1-6684-6291-1.ch044","authors":["Namrata Dhanda","Stuti Shukla Datta","Mudrika Dhanda"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-08T11:31:23Z","doi":"10.4018/978-1-6684-6291-1.ch044","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1101/2024.09.09.24313303","name":"Receiving information on machine learning-based clinical decision support systems in psychiatric services increases staff trust in these systems: A randomized survey experiment","source":"crossref","abstract":"Abstract Background Clinical decision support systems based on machine learning (ML) models are emerging within psychiatry. To ensure their successful implementation, healthcare staff needs to trust these systems. Here, we investigated if providing staff with basic information about ML-based clinical decision support systems enhances their trust in them. Methods We conducted a randomised survey experiment among staff in the Psychiatric Services of the Central Denmark Region. The participants were allocated to one of three arms, receiving different types of information: An intervention arm (receiving information on clinical decision-making supported by an ML model); an active control arm (receiving information on standard clinical decision process without ML support); and a blank control arm (no information). Subsequently, participants responded to various questions regarding their trust/distrust in ML-based clinical decision support systems. The effect of the intervention was assessed by pairwise comparisons between all randomization arms on sum scores of trust and distrust. Findings Among 2,838 invitees, 780 completed the survey experiment. The intervention enhanced trust and diminished distrust in ML-based clinical decision support systems compared with the active control arm (Trust: mean difference= 5% [95% confidence interval (CI): 2%; 9%], p-value &lt; 0.001; Distrust: mean difference=-4% [-7%; -1%], p-value = 0.042)) and the blank control arm (Trust: mean difference= 5% [2%; 11%], p-value = 0.003; Distrust: mean difference= -3% [-6%; - 1%], p-value = 0.021). Interpretation Providing information on ML-based clinical decision support systems in hospital psychiatry may increase healthcare staff trust in such systems.","url":"https://doi.org/10.1101/2024.09.09.24313303","authors":["Erik Perfalk","Martin Bernstorff","Andreas Aalkjær Danielsen","Søren Dinesen Østergaard"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-10T19:05:20Z","doi":"10.1101/2024.09.09.24313303","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1155/ijcp/9888902","name":"Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review","source":"crossref","abstract":"The inclusion of artificial intelligence (AI) and machine learning (ML) in biomedical engineering opens new frontiers of innovation and better decision‐making and allows the art to cross new thresholds in healthcare technologies. This review focuses on the primary contributions of AI and ML to the advancement of biomedical engineering, particularly in the areas of diagnostic tools, predictive analytics, and personalized medicine. This will further allow us to identify possible the state‐of‐the‐art solutions by using new frameworks for applications including medical imaging, wearables, and biomanufacturing. Also, it reflects on how ethics in AI and ML for biomedical challenges address important issues such as bias, privacy, and accountability. It also underlines how different opportunities and challenges can be opened or addressed by the integration of AI‐driven systems in biomedical workflows: engineering, clinicians, and data scientists have to cooperate. Emerging technologies, including but not limited to deep learning, natural language processing, and reinforcement learning, are discussed for their potential to alter biomedical research and clinical practice. The work concludes with a look at the future of biomedical engineering, where AI and ML have brought a domain of synergy into innovation, better patient outcomes, and impactful advancement. It thus also provides a prescription for the need for ethics in the adoption of AI, together with collaborative efforts toward maximum transformative technology.","url":"https://doi.org/10.1155/ijcp/9888902","authors":["Abebe Belay Adege"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-29T07:48:38Z","doi":"10.1155/ijcp/9888902","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-15-1967-3_6","name":"Support Vector Machine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-1967-3_6","authors":["Zhi-Hua Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-08-20T19:23:05Z","doi":"10.1007/978-981-15-1967-3_6","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.71443/9789349552395-01","name":"Machine Learning-Driven Adaptive Communication Systems and Beyond-6G Network Intelligence","source":"crossref","abstract":"The transition toward beyond-6G communication networks introduces unprecedented challenges associated with ultra-low latency, massive connectivity, and highly dynamic service environments, demanding a paradigm shift from conventional static architectures to intelligent and adaptive communication systems. Machine learning has emerged as a transformative enabler, supporting real-time optimization, predictive decision-making, and autonomous network control across heterogeneous communication layers. This chapter presents a comprehensive exploration of machine learning-driven adaptive communication systems, emphasizing their role in enabling network intelligence within beyond-6G ecosystems. Key advancements in deep learning, reinforcement learning, federated learning, and distributed intelligence are examined in the context of critical network functions such as resource allocation, spectrum management, channel estimation, and security enhancement. The discussion further highlights the integration of space-air-ground communication frameworks, edge-cloud collaborative intelligence, and AI-driven network slicing as essential components of next-generation architectures. Challenges related to scalability, energy efficiency, data heterogeneity, model interpretability, and security vulnerabilities are critically analyzed to provide a balanced perspective on practical deployment. Emerging trends including AI-native network design, semantic communication, and autonomous self-optimizing systems are also outlined to identify future research directions. The chapter contributes a structured understanding of how intelligent, distributed, and privacy-aware learning mechanisms can transform communication infrastructures into adaptive, resilient, and efficient systems aligned with beyond-6G objectives.","url":"https://doi.org/10.71443/9789349552395-01","authors":["R. Jegadeesan","Prerana Arun Wankhede"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-13T04:03:28Z","doi":"10.71443/9789349552395-01","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.38007/ml.2025.050111","name":"Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing","source":"crossref","abstract":"As the digitalization process of enterprises accelerates, various application interfaces have become key hubs for system interconnection and data exchange.At the same time, they may also serve as entry points for attackers to obtain sensitive information.Without effective protection, they will face the risks of data leakage and business interruption.To address this issue, this paper proposes an automated risk identification method that integrates privacy computing and deep learning.By conducting semantic parsing and behavioral pattern mining on interface request data, a hybrid framework combining rule constraints and neural network feature extraction is constructed to achieve intelligent identification of abnormal requests and potential threats.This method utilizes vector representation, bidirectional recurrent networks, attention mechanisms, and multilayer convolutional networks to optimize the output, effectively enhancing the model's risk prediction capability in complex environments.The developed interface security assessment system can dynamically identify attacks and sensitive data leaks, providing enterprises with efficient automated interface protection solutions.It also provides a reference for the application of privacy computing and deep learning in the security management of actual business.","url":"https://doi.org/10.38007/ml.2025.050111","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-21T09:43:36Z","doi":"10.38007/ml.2025.050111","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-662-70155-3_21","name":"Responsible Use of Machine Learning in Sports Science","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-70155-3_21","authors":["Fabian Wunderlich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-07-21T23:08:52Z","doi":"10.1007/978-3-662-70155-3_21","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla58977.2023.00331","name":"Machine Learning for Detecting Malware in PE Files","source":"crossref","abstract":"The increasing number of sophisticated malware poses a significant cybersecurity threat. Portable executable (PE) files are a common vector for such malware. We review and evaluate machine learning-based PE malware detection techniques in this work. Using a large benchmark dataset, we evaluate features of PE files using the most common machine- learning techniques to detect malware.","url":"https://doi.org/10.1109/icmla58977.2023.00331","authors":["Collin Connors","Dilip Sarkar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-19T18:08:18Z","doi":"10.1109/icmla58977.2023.00331","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icaml57167.2022.00038","name":"Research on Vehicle Logistics Optimization Distribution Based on Machine Learning","source":"crossref","abstract":"In order to solve the problems of low efficiency, unscientific management and large amount of manual work in the process of logistics distribution of enterprise products, a vehicle mounted logistics optimization distribution model is designed based on machine learning. Firstly, the logistics distribution optimization model, the optimal object and constraint condition are designed. Secondly, a logistics optimization distribution model based on ant colony algorithm is established, and the analysis procedure of the algorithm is designed. Finally, taking the distribution network of a company in a city as the research object, the logistics optimization distribution simulation analysis is carried out. The comparative analysis of the distribution path before and after optimization proves that the optimized distribution path can improve the distribution efficiency of the company. The results show that this method improves the efficiency of product logistics distribution, and improves the quantity and benefit of product logistics distribution.","url":"https://doi.org/10.1109/icaml57167.2022.00038","authors":["Ronghu Zhou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-07T18:42:09Z","doi":"10.1109/icaml57167.2022.00038","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.4135/9781529666786.n4","name":"Elizabeth Degefe Discusses Research Findings, Improving, and Resources for Future Research Using Machine Learning to Study Racism","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781529666786.n4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-24T08:31:51Z","doi":"10.4135/9781529666786.n4","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-030-40344-7_4","name":"Optimization Basics: A Machine Learning View","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-40344-7_4","authors":["Charu C. Aggarwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-05-12T22:02:28Z","doi":"10.1007/978-3-030-40344-7_4","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1007313824964","name":"Using Background Knowledge to Build Multistrategy Learners","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007313824964","authors":["Claude Sammut"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T04:48:21Z","doi":"10.1023/a:1007313824964","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1002/9781119817512.ch2","name":"Machine Learning Fundamentals","source":"crossref","abstract":"Machine learning covers a large group of computational methods built upon past data and experience. This chapter serves as a gentle introduction to machine learning fundamentals. It covers key concepts of machine learning tasks, methods, and applications. The chapter reviews the list of machine learning tasks for the areas of autonomous driving, gaming, healthcare, software development, and so forth. It introduces empirical risk minimization and regularization, two techniques for controlling e est and e approx , respectively, leading to a trade-off between the two components. Probably approximately correct learning provides the answers to questions about what can be learned, under what conditions, and how many data samples needed to learn. The chapter also discusses maximum likelihood principle, a general framework for learning the underlying conditional distribution. It covers a large variety of machine learning loss functions, especially in many modern machine learning models such as deep neural networks.","url":"https://doi.org/10.1002/9781119817512.ch2","authors":["Yan Yan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-21T21:26:07Z","doi":"10.1002/9781119817512.ch2","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1145/3568199.3568200","name":"Determining Student's Engagement in Synchronous Online Classes Using Deep Learning (Compute Vision) and Machine Learning","source":"crossref","abstract":"In 2020 Scaled-YOLOv4 was introduced. It is one of the best object detection models outclassing its peers in MS COCO test-dev. In this study, the proponents used Scaled-YOLOv4 as their object detection model. The model will be used in the environment of Pasig River, the Philippines in detecting plastic and paper. The model's performance will be tested using a dilapidated trash dataset. Object detection models usually face difficulties in detecting the object because of deformation, occlusion, illumination conditions, and cluttered background. The proponents’ Scaled-YOLOv4 model produced 63% average precision, 67% precision for plastic, and 59% precision for paper. The model can be used in detecting trash materials found on the surface of the Pasig River.","url":"https://doi.org/10.1145/3568199.3568200","authors":["John Paul Quilingking Tomas","Daniel Oliver Ancheta","Carl Andrei Aquino","Aerol Ortiz"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-03-06T12:14:02Z","doi":"10.1145/3568199.3568200","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.7717/peerj-cs.3556/supp-7","name":"Supplemental Information 7: Machine Learning Code.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3556/supp-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-10T08:19:16Z","doi":"10.7717/peerj-cs.3556/supp-7","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.70593/978-93-7185-365-1_2","name":"Understanding the core algorithms behind machine learning: Key learning algorithms explained","source":"crossref","abstract":"Machine learning is a branch of artificial intelligence. It enables computers to automatically learn and improve from experience without being explicitly programmed [1]. Machine learning algorithms are usually classified into supervised, unsupervised, neural network-based, and reinforcement learning methods. Supervised learning methods build a mathematical model from a set of data that contains both the inputs and the desired outputs. The model is studied to predict the output values for given inputs [1-2]. These methods can also be used to find patterns (usually called classes) in data and assign new data points to one of the predefined classes. The goal of unsupervised learning is to find meaningful patterns in a set of data points that correspond closely to some intuitive notion of similarity. Neural network-based methods belong to the class of algorithms inspired by biological neural networks [2-4]. Neural networks replace a simulated neuron's simple threshold function with a real-valued differentiable function that gives the neuron the ability to output a real number. Reinforcement learning is an area of machine learning concerned with how an agent should take actions in an environment to maximize a reward signal.","url":"https://doi.org/10.70593/978-93-7185-365-1_2","authors":["Priyambada Swain"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-08-16T18:24:01Z","doi":"10.70593/978-93-7185-365-1_2","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022642604085","name":"Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022642604085","authors":["Dana Angluin","Donna K. Slonim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022642604085","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022637108202","name":"Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022637108202","authors":["Thomas R. Hancock","Mostefa Golea","Mario Marchand"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022637108202","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1039/9781837070206-00153","name":"Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry","source":"crossref","abstract":"Machine learning (ML) with molecular dynamics (MD) simulations represents a transformative approach in medicinal chemistry and computational biology, offering unprecedented insights into the dynamic behavior of molecular systems. The advent of ML has further revolutionized this field, providing tools to analyze vast datasets generated by MD simulations, identify patterns, and predict molecular behaviors with high accuracy. This chapter delves into the intricate relationship between ML and MD, exploring how these technologies synergize to enhance our understanding of molecular conformations, protein dynamics, and drug discovery processes. The methodologies employed in coupling ML with MD, including the use of neural networks, generative models, and adaptive sampling techniques, are also explored in this chapter. Moreover, the challenges and controversies surrounding these applications, such as the computational cost of simulations and the accuracy of ML models, are also discussed. Real-world examples and analogies are provided to elucidate complex concepts, making the content accessible to a broad audience. The conclusion synthesizes the insights gained from this chapter, emphasizing the transformative impact of integrating ML with MD.","url":"https://doi.org/10.1039/9781837070206-00153","authors":["Sk Abdul Amin","Supratik Kar","Stefano Piotto"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-22T08:41:26Z","doi":"10.1039/9781837070206-00153","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-030-04666-8_3","name":"Machine Learning for Mask Synthesis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-04666-8_3","authors":["Seongbo Shim","Suhyeong Choi","Youngsoo Shin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-03-15T12:32:24Z","doi":"10.1007/978-3-030-04666-8_3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.38007/ml.2025.050115","name":"Research on Integration and Optimization Strategies of Cross-platform Machine Learning Services","source":"crossref","abstract":"In diverse operating system environments, cross platform machine learning techniques are increasingly becoming the core means of enhancing data processing capabilities and model performance.This study aims to explore the integration and optimization path of cross platform machine learning technology, analyze the popular cross platform service architectures and platforms, and propose a series of integration solutions related to data interaction, model joint training, resource optimization configuration, and interface unification.At the same time, this article also explores in depth optimization measures such as improving the accuracy of machine learning algorithms, accelerating model training speed, rational resource allocation, and enhancing service robustness.By adopting these integration and optimization strategies, the performance indicators and application effectiveness of cross platform machine learning technology will be enhanced, providing theoretical basis and technical guidance for engineering applications in related fields.","url":"https://doi.org/10.38007/ml.2025.050115","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-12T09:54:59Z","doi":"10.38007/ml.2025.050115","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003150886-1","name":"Transforming Pharma with Data Science, AI and Machine Learning","source":"crossref","abstract":"This chapter provides an overview of transformative changes occurring in pharma engendered by big data and artificial intelligence (AI) and discusses the importance of data strategy in realizing full potential of these technology advances in the lifecycle of drug development. To be competitive, it is paramount for pharmaceutical companies to develop and implement a robust data strategy that includes data governance, infrastructure, advanced analytics, and data science. In general, new technological platforms and specialized analytical techniques such as machine learning (ML) algorithms are needed for the curation, control, and analysis of big data. The redesign of the current drug development and healthcare paradigm lies in successful applications of AI and ML in several key areas, including drug discovery, clinical trials, manufacturing, and comparative evidence generation to support treatment decisions and market access. The transition to a patient-centric approach can be challenging as it requires changing existing mindsets, culture, and regulatory policies.","url":"https://doi.org/10.1201/9781003150886-1","authors":["Harry Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-12T20:20:36Z","doi":"10.1201/9781003150886-1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1002/9781394167258.ch6","name":"Recent Advances in Drug Design With Machine Learning","source":"crossref","abstract":"Machine learning (ML) methods have been of special attention, since they can be utilized in numerous steps of the drug discovery process, for example, investigating biological activity of new candidates through construction of model, prediction of target structure, optimization or discovery of hits, and development of models that predict the pharmacokinetic and toxicological (ADMET) aspect of compounds. In this book chapter, ML algorithms applied in drug discovery and associated techniques are summarized. Further, the applications that generate promising results and methods are also discussed. The chapter focuses on how these powerful tools are being used in recent years of research. Additionally, an in-depth analysis of the remaining limitations and challenges and suggestions for promising future directions for research are provided. Hopefully, this chapter will offer insight to the researchers working in the area of computational drug discovery in terms of comprehending and developing novel bioprediction approaches.","url":"https://doi.org/10.1002/9781394167258.ch6","authors":["Muhammad Faisal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-10-07T07:21:48Z","doi":"10.1002/9781394167258.ch6","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-3787-8_18","name":"Monetizing Finance Machine Learning","source":"crossref","abstract":"In this chapter, I am going to put forward some innovative ideas that can be monetized using machine learning in the financial world. I will also show you some examples where a similar approach has been used and has succeeded. For some of them, there may not be an example now, as they are more future-looking.","url":"https://doi.org/10.1007/978-1-4842-3787-8_18","authors":["Puneet Mathur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-12-12T14:05:50Z","doi":"10.1007/978-1-4842-3787-8_18","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-16-8881-2_13","name":"Machine Learning in Cardiovascular Disorders","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8881-2_13","authors":["Shyamasree Ghosh","Rathi Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T17:03:22Z","doi":"10.1007/978-981-16-8881-2_13","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.65492/01/302/2025/30","name":"Improving Tuberculosis Prognosis with Benchmarked Machine Learning Models","source":"crossref","abstract":"Tuberculosis remains a considerable cause of morbidity and mortality in several poor and middle-income countries. When a patient is diagnosed with tuberculosis, healthcare providers must select most appropriate treatment tailored to patient's unique situation and expected trajectory of disease, guided by clinical competence. goal is to predict chance of dying from tuberculosis, which will help doctors figure out how disease will progress and make decisions about treatment. re were 36,228 records and 130 fields in first data collection, but many of records were missing, incomplete, or wrong. After cleaning and preparing data, a new dataset was created with 24,000 entries and 37 fields. This dataset includes 22,875 reported cured tuberculosis patients and 1 140 tuberculosis-related deaths. Two controlled experiments were designed to examine impact of data imbalance on model performance, employing (1) unbalanced and (2) balanced datasets.","url":"https://doi.org/10.65492/01/302/2025/30","authors":["Muhammad Azhar Javaid"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-03T12:36:06Z","doi":"10.65492/01/302/2025/30","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/mlcad62225.2024.10740260","name":"Machine Learning for High Sigma Analog Designs (Invited)","source":"crossref","abstract":"Monte Carlo simulations have been the gold standard for assessing parametric yields of analog, mixed signal, and RF circuits as they offer one of the most direct representations of the variation induced by semiconductor manufacturing. However, Monte Carlo analyses are often too expensive for understanding high sigma yields with fewer defects than 1000ppm. To quantify the impact of rare events on circuit yield, we need insights into their probability densities. All rare event sampling techniques that seek to provide this insight employ a machine learning flow of some kind. The various implementations of importance sampling and statistical blockade, for example, try to locate the rare event populations in parametric space through input domain mapping. Despite their popularity, they can sometimes pose a significant challenge, especially when the dimensionality of the input variation space is high, as both feature selection and machine learning can be non-trivial. A good alternative to machine learning in the input parametric domain is the innovative scaled sigma sampling technique that leverages machine learning of the probability density differences produced by scaling input standard deviations. This paper reviews these key approaches for determining the high sigma yields of analog circuits. CCS Concepts • Computing methodologies $\\rightarrow$ Machine learning $\\rightarrow$ Machine learning algorithms • Computing methodologies $\\rightarrow$ Machine learning $\\rightarrow$ Machine learning approaches • Mathematics of computing $\\rightarrow$ Probability and statistics $\\rightarrow$ Statistical paradigms.","url":"https://doi.org/10.1109/mlcad62225.2024.10740260","authors":["Srinivas Jallepalli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-06T18:37:40Z","doi":"10.1109/mlcad62225.2024.10740260","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003006411-12","name":"Machine Learning Molecular Trends","source":"crossref","abstract":"The parameterization process will require much data, either from experiments or from ab initio calculations, as input for the simulation model. Generating billions of system configurations during a simulation requires evaluating an even more significant number of distances between pairs of atoms, angles between triplets or quadruplets of atoms, and, ultimately, interaction energies. It soon became apparent that neural networks could outperform empirical potentials for modeling contributions arising from many-body interactions. The development of neural network potentials started eight years later, with the first development of a complete potential predicted by an artificial neural network. The nanoporous material, or adsorbent, only impacts how the potential energy is calculated. The mathematical description for the reaction coordinate is expected to depend strongly on the phenomenon studied. During a conformational change, the molecule will change geometry.","url":"https://doi.org/10.1201/9781003006411-12","authors":["Caroline Desgranges","Jerome Delhommelle"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-13T13:35:51Z","doi":"10.1201/9781003006411-12","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1007558615313","name":"Choice of Basis for Laplace Approximation","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007558615313","authors":["David J.C. MacKay"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007558615313","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022818816206","name":"Determining Arguments of Invariant Functional Descriptions","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022818816206","authors":["Mieczyslaw M. Kokar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:57:10Z","doi":"10.1023/a:1022818816206","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2016.0185","name":"Semantic Clone Detection Using Machine Learning","source":"crossref","abstract":"If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clones in software, which is able to detect Types-3 and the most complicated kind of clones, Type-4 clones. Previously used traditional features are often weak in detecting the semantic clones The novel aspects of our approach are the extraction of features from abstract syntax trees (AST) and program dependency graphs (PDG), representation of a pair of code fragments as a vector and the use of classification algorithms. The key benefit of this approach is that our approach can find both syntactic and semantic clones extremely well. Our evaluation indicates that using our new AST and PDG features is a viable methodology, since they improve detecting clones on the IJaDataset 2.0.","url":"https://doi.org/10.1109/icmla.2016.0185","authors":["Abdullah Sheneamer","Jugal Kalita"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-02-07T20:39:53Z","doi":"10.1109/icmla.2016.0185","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.67228/3142788x/ijmlpa-2024pi4r6h","name":"Machine Learning Approaches for Adaptive Forecasting Systems","source":"crossref","abstract":"Adaptive forecasting systems have become essential for analyzing dynamic and uncertain real-world data, where traditional static forecasting models often fail due to changing patterns, concept drift, and non-stationary conditions. Machine learning techniques, including supervised learning, ensemble methods, neural networks, and hybrid models, enable continuous learning and improved prediction accuracy by adapting to new data. This study reviews machine learning-based adaptive forecasting approaches and presents a generalized framework comprising data acquisition, preprocessing, feature engineering, adaptive learning, forecasting, and performance evaluation. Special emphasis is placed on model updating, online learning, feature selection, and dynamic parameter optimization. Experimental results show that adaptive machine learning models outperform conventional forecasting methods in accuracy, responsiveness, and robustness, making them highly effective for large-scale predictive applications across finance, healthcare, energy, transportation, and industrial systems. The study highlights their potential to support next-generation intelligent forecasting and predictive analytics systems.","url":"https://doi.org/10.67228/3142788x/ijmlpa-2024pi4r6h","authors":["Yuki Nakamura"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-27T10:58:38Z","doi":"10.67228/3142788x/ijmlpa-2024pi4r6h","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1111/jch.70073","name":"Uncovering Gaps: Dietary Influence and Machine Learning in Hypertension and Comorbidities","source":"crossref","abstract":"Dear Editor, We read with great interest the recent article by Zhang et al., “Diabetes Mellitus and Hyperlipidemia Status Among Hypertensive Patients in the Community and Influencing Factors Analysis of Blood Pressure Control,” which provides valuable insights into the prevalence of type 2 diabetes mellitus (T2DM) and hyperlipidemia among hypertensive patients and investigates their impact on blood pressure (BP) control using a large dataset from the National Basic Public Health Service Program in Guangzhou. The authors successfully underscore key risk factors influencing BP control, including obesity, alcohol use, physical inactivity, and poor medication adherence [1]. Although the study presents significant findings, several methodological aspects warrant further discussion. First, the study appropriately recognizes the increased prevalence of comorbid T2DM and hyperlipidemia among hypertensive patients and its association with poorer BP control. However, one major constraint is the lack of stratification based on the severity of hyperlipidemia and diabetes. Given the varied nature of these conditions, a more detailed subgroup analysis considering glycemic control levels (e.g., HbA1c categories) and lipid profiles (e.g., LDL/HDL ratios) would provide deeper insights into their precise impact on BP regulation. Second, while the study underlines key lifestyle factors affecting BP control, the role of dietary patterns is not sufficiently addressed. Prior studies have shown that dietary sodium intake, fat composition, and overall macronutrient distribution significantly affect BP levels in hypertensive patients with metabolic comorbidities [2]. Incorporating dietary data into the analysis would strengthen the study's conclusions and provide actionable recommendations for community-based hypertension management. Third, although the study investigates important influencing factors on BP control, it does not sufficiently address the role of medication adherence beyond a general assessment. Hypertensive patients with comorbidities like T2DM and hyperlipidemia often require complex polypharmacy, and adherence patterns can significantly impact BP control outcomes. Previous researches have shown that factors such as medication burden, side effects, and patient perceptions of treatment efficacy affect adherence rates [3]. Including a more comprehensive evaluation of medication adherence, such as pill burden or self-reported adherence scales, would provide more in-depth insights into its effect on BP regulation. Moreover, the study employs logistic regression models to identify factors associated with BP regulation, but does not include machine learning techniques, which have been increasingly used in cardiovascular research for predictive modeling. Advanced statistical approaches, such as the random forest model, could increase risk stratification and improve predictive accuracy in identifying high-risk hypertensive patients requiring intensified management [4, 5]. In conclusion, Zhang et al. provide an imperative contribution in understanding the effect of diabetes and hyperlipidemia on BP regulation in hypertensive patients. Though, further research is required to improve risk stratification, investigate causal pathways, and evaluate the efficiency of personalized lifestyle and pharmacological interventions. We commend the authors for their contribution and encourage further investigations into optimizing BP control strategies in hypertensive populations. Open access was provided by Qatar National Library. As this is a commentary on a published study and no new data were collected or analyzed, ethics approval was not required. The authors declare no conflicts of interest.","url":"https://doi.org/10.1111/jch.70073","authors":["Javeria Akhter","Javed Iqbal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-23T12:59:17Z","doi":"10.1111/jch.70073","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.clinph.2023.06.010","name":"Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients","source":"crossref","abstract":"Objective Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations. Methods We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase ( Results The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity. Conclusions Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data. Significance Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.","url":"https://doi.org/10.1016/j.clinph.2023.06.010","authors":["Juho Aaltonen","Verna Heikkinen","Hanna Kaltiainen","Riitta Salmelin","Hanna Renvall"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-30T11:47:27Z","doi":"10.1016/j.clinph.2023.06.010","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003424987-1","name":"Machine Learning and the Role of IoT in Health Informatics","source":"crossref","abstract":"The innovations brought by Industry 4.0 to health informatics and the challenges encountered are discussed. Industry 4.0 initiates a new era in the healthcare sector through the integration of technologies like IoT, machine learning, cloud computing, and big data analytics. These technological advancements have enabled healthcare services to become more efficient, personalized, and accessible. Particularly, the use of IoT sensors that monitor various health parameters such as heart rate, blood pressure, and glucose levels, has facilitated continuous health tracking and enhanced early diagnosis opportunities, marking significant innovations of this era. However, challenges such as data security, privacy, and ethical issues brought by these technological developments are also considered. Accessibility and justice issues highlight the importance of technological advancements providing equal opportunities for all. This book chapter comprehensively examines the impacts of Industry 4.0 on health informatics, addressing future trends and potential developments in this field. It underscores how this innovative era enhances the quality of healthcare services while also bringing new challenges to the forefront","url":"https://doi.org/10.1201/9781003424987-1","authors":["Burak Taşci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-02T15:32:01Z","doi":"10.1201/9781003424987-1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-3799-1_3","name":"What Is Machine Learning?","source":"crossref","abstract":"The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities. Since then, not only has the application of artificial intelligence grown, there is now a velocity, volume, and variety of data that has never been seen before. Samuel’s software ran on the IBM 701, a computer the size of a double bed. Data was typically discrete. Almost 70 years later, data is ubiquitous, and computers now more powerful such that 100 IBM 701s can fit into the palm of our hand. This has facilitated the subsets of machine learning and deep learning within AI (see Figure 3-1).","url":"https://doi.org/10.1007/978-1-4842-3799-1_3","authors":["Arjun Panesar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-02-04T05:24:47Z","doi":"10.1007/978-1-4842-3799-1_3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1142/9789811228155_0006","name":"Create Prediction Model using Microsoft Azure Machine Learning Studio","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811228155_0006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-19T05:40:31Z","doi":"10.1142/9789811228155_0006","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/cacml55074.2022.00107","name":"Quantitative Trading Method based on Neural Network Machine Learning","source":"crossref","abstract":"Quantitative trading plays an essential role in the investment field with its advanced mathematical models for computer-aided trading of investment strategies. The artificial neural network algorithm is the trading algorithm with the largest amount of funds managed in the world. Due to the short history of quantitative trading research in China, large-scale funds have not been reported to be managed by the neural network algorithm. The results of tests on financial derivatives using neural networks with different structures demonstrate that the neural network strategies all have positive expected return. Within a considerable range of changes in structure. In this paper, the python language is majorly used to design a model implementation plan for a quantitative trading system reading currently widely recognized stock technical indicators, such as MA, MACD, KDJ, and BOLL. Additionally, position management strategies are optimized. Furthermore, a quantitative trading method based on neural network machine learning is constructed and verified with examples.","url":"https://doi.org/10.1109/cacml55074.2022.00107","authors":["Weinan Weng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-19T19:38:41Z","doi":"10.1109/cacml55074.2022.00107","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.65525/svup.9788199565418.2026.154-158","name":"\"Personalized Medicine: Predictive Analytics for Drug  Response Using Machine Learning Models\"","source":"crossref","abstract":"Personalized medicine represents a transformative approach in healthcare, enabling tailored therapeutic interventions based on individual patient characteristics. Predictive analytics powered by machine learning (ML) has emerged as a cornerstone of this paradigm, leveraging vast datasets to predict drug responses with unparalleled accuracy. This paper explores the integration of ML models in personalized medicine, focusing on their application in predicting patient-specific drug efficacy and adverse reactions. By utilizing diverse data sources, including genomic, proteomic, and clinical data, ML algorithms offer novel insights into complex biological interactions. Recent advances, such as deep learning and ensemble techniques, have shown exceptional promise in capturing nonlinear relationships within high-dimensional datasets [1].This study presents a comprehensive review of state-of-the-art ML methodologies employed in drug response prediction, highlighting their strengths and limitations. Additionally, it proposes a hybrid ML framework combining feature selection, supervised learning, and explainable AI techniques to enhance model interpretability and clinical utility. The proposed framework is validated using publicly available pharmacogenomic datasets, achieving improved prediction accuracy and reduced computational overhead compared to traditional approaches [2].Challenges, such as data heterogeneity, model bias, and regulatory considerations, are critically discussed, emphasizing the need for standardized data preprocessing pipelines and robust evaluation metrics. The integration of ML-driven predictive analytics in clinical workflows is poised to accelerate the realization of precision medicine, improving patient outcomes while minimizing trial-and-error approaches in drug prescribing. DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.154-158","url":"https://doi.org/10.65525/svup.9788199565418.2026.154-158","authors":["Diganta Bhattacharyya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T11:06:12Z","doi":"10.65525/svup.9788199565418.2026.154-158","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.23940/ijpe.22.12.p7.893902","name":"Comprehensive Study of Machine Learning-Based Systems for Early Warning of Clinical Deterioration","source":"crossref","abstract":"","url":"https://doi.org/10.23940/ijpe.22.12.p7.893902","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-12-28T05:58:15Z","doi":"10.23940/ijpe.22.12.p7.893902","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.54254/2755-2721/38/20230558","name":"Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model","source":"crossref","abstract":"The global pandemic COVID-19 erupted and infected an estimated 10% of the world’s population. Since vaccinations greatly reduced hospitalization rates, most countries removed the restrictive policies implemented to combat the virus. It has become a rather common illness with more than twelve thousand active hospitalizations. As a result, convenient COVID-19 diagnosis from diseases that display overlapping symptoms has become increasingly important. An effective method for patient self-diagnosis greatly reduces hospital presentation, saving time and medical resources. This study uses machine learning techniques to classify and predict several common respiratory diseases quickly and accurately. The author trains several machine learning models that attempt to predict four diseases based on their distinct clinical signs. An open-access database on Kaggle developed for this disease classification is selected and further processed via principal component analysis to decrease database dimension and pinpoint critical symptoms. Support Vector Machine Classifier (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models are used, and their performances are compared. Study results show that the LR model slightly outperforms the others. In conclusion, the effectiveness of the proposed method is proved for classifying the symptoms of patients with allergies, colds, flu, and Covid-19 in this study.","url":"https://doi.org/10.54254/2755-2721/38/20230558","authors":["Jingxuan Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-05T22:43:04Z","doi":"10.54254/2755-2721/38/20230558","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2026.100900","name":"Early detection of diabetes with quantum machine learning and causal inference techniques","source":"crossref","abstract":"Diabetes has become one of the most common chronic diseases in the United States. It is also the one that remains either undiagnosed or misdiagnosed over the years, leading a patient to life threatening conditions. The main reasons behind this are- lack of awareness among patients, food habits, sedentary lifestyles and lack of clinical services. There are common symptoms found in a person who is leading toward diabetes, and they can be easily identified at an early stage to help them maintain a healthy lifestyle. In this paper, we focus on finding out the most basic but significant features to detect diabetes in an undiagnosed person, providing the reasons of why diabetes happens in any person. We also implement quantum machine learning algorithms to dive deeper into the patterns of the features, bringing out any future possibilities of a healthy person becoming diabetic. Our proposed model, which is an ensemble of causal inference and quantum machine learning, suggests an appropriate feature set for diabetes estimation with better efficiency than others.","url":"https://doi.org/10.1016/j.mlwa.2026.100900","authors":["Paramita Basak Upama","Md Munirul Haque","Kazi Shafiul Alam","Sheikh Iqbal Ahamed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-14T17:07:03Z","doi":"10.1016/j.mlwa.2026.100900","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.36106/paripex/0609546","name":"MACHINE LEARNING BASED CLINICAL DECISION SUPPORT SYSTEM TO PREDICT FETAL HYPOXIA IN WOMEN DURING ANTENATAL CHECK-UP.","source":"crossref","abstract":"BACKGROUND:Most under-five deaths occur within the first month after birth and intrapartum complications are a major contributor to the cause of death. These defects can be easily identified during the ante-natal check-up by use of a non-stress test. Due to the lack of availability of resources and medical experts in remote areas clinical decision support systems powered by machine learning models can provide information to the healthcare provider to make timely and better-informed decisions based on which course of treatment can be planned. AIM:The study aims to develop an accurate and sensitive clinical decision support system model that can identify pathological fetuses based on the fetal heart rate recordings taken during the non-stress test. METHOD: Foetal Heart rate recordings along with 10 other variables were collected from 1800 pregnant women in their third trimester. The data was put through a feature selection algorithm to identify important variables in the set. The data set was randomly divided into 2 independent random samples in the ratio of 70% for training and 30% for testing. After testing various machine learning algorithms based on specificity, sensitivity to accurately classify the fetus into normal, suspected, or pathological Random Forest algorithm was chosen. RESULT:The fetal status determined by Obstetrician 77.85% observations from the normal category, 19.88% from the suspected category, and 8.28% from the pathological category. The Boruta algorithm revealed that all 11 independent variables in the data set were important to predict the outcome in the test set. In the training set the model had an accuracy of 99.04% and in the testing set accuracy was 94.7% (p-value=&lt; 2.2e-16) with the precision of 97.56% to detect the pathological category. CONCLUSION:With the ability of the model to accurately predict the pathological category the CDS can be used by healthcare providers in remote areas to identify high-risk pregnant women and take the decision on the medical care to be provided.","url":"https://doi.org/10.36106/paripex/0609546","authors":["Sajal Baxi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-08T03:36:39Z","doi":"10.36106/paripex/0609546","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.2139/ssrn.5312873","name":"Monte Carlo Peaks: Simulated Datasets to Benchmark Machine Learning Algorithms for Clinical Spectroscopy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5312873","authors":["Jaume Béjar-Grimalt","Ángel Sánchez-Illana","Guillermo Quintas","Hugh  James Byrne","David Pérez-Guaita"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-23T20:18:22Z","doi":"10.2139/ssrn.5312873","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.3390/jcm9092899","name":"Machine Learning-Based Predictive Modeling of Postpartum Depression","source":"crossref","abstract":"Postpartum depression is a serious health issue beyond the mental health problems that affect mothers after childbirth. There are no predictive tools available to screen postpartum depression that also allow early interventions. We aimed to develop predictive models for postpartum depression using machine learning (ML) approaches. We performed a retrospective cohort study using data from the Pregnancy Risk Assessment Monitoring System 2012–2013 with 28,755 records (3339 postpartum depression and 25,416 normal cases). The imbalance between the two groups was addressed by a balanced resampling using both random down-sampling and the synthetic minority over-sampling technique. Nine different ML algorithms, including random forest (RF), stochastic gradient boosting, support vector machines (SVM), recursive partitioning and regression trees, naïve Bayes, k-nearest neighbor (kNN), logistic regression, and neural network, were employed with 10-fold cross-validation to evaluate the models. The overall classification accuracies of the nine models ranged from 0.650 (kNN) to 0.791 (RF). The RF method achieved the highest area under the receiver-operating-characteristic curve (AUC) value of 0.884, followed by SVM, which achieved the second-best performance with an AUC value of 0.864. Predictive modeling developed using ML-approaches may thus be used as a prediction (screening) tool for postpartum depression in future studies.","url":"https://doi.org/10.3390/jcm9092899","authors":["Dayeon Shin","Kyung Ju Lee","Temidayo Adeluwa","Junguk Hur"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-09-08T09:03:48Z","doi":"10.3390/jcm9092899","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.clinph.2018.09.020","name":"Machine learning versus human expertise: The case of sleep stage classification in disorders of consciousness. Response to Wislowska et al.","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clinph.2018.09.020","authors":["Boris Kotchoubey","Yuri G. Pavlov"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-10-24T23:22:12Z","doi":"10.1016/j.clinph.2018.09.020","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.18178/ijmlc.2022.12.3.1085","name":"Using Machine Learning to Analyze Sudanese Opinions for Political Decision Making","source":"crossref","abstract":"Sentiment Analysis (SA) is the application of text mining techniques for extraction and identification of subjective opinions from textual data.SA has many applications ranging from financial analysis to political decision-making domains.Although a lot of works have been made in SA of English tweets, very limited work focused on Arabic political colloquial tweets.In this paper, we focused on analyzing tweets during Sudanese revolution 2018 and leverage Term Frequency-Inverse Document Frequency and word embedding methods with machine learning classifiers to detect user's sentiments of colloquial Arabic political tweets instead of using modern standard Arabic.Experiments were conducted to evaluate our classifiers and the results showed superiority of the Ensemble learning model with an F-score of 83% compared to other machine learning classifiers.","url":"https://doi.org/10.18178/ijmlc.2022.12.3.1085","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-03-29T09:36:37Z","doi":"10.18178/ijmlc.2022.12.3.1085","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-6537-6_3","name":"What Is Machine Learning?","source":"crossref","abstract":"The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities [68].","url":"https://doi.org/10.1007/978-1-4842-6537-6_3","authors":["Arjun Panesar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-15T14:05:55Z","doi":"10.1007/978-1-4842-6537-6_3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.4135/9781526468888","name":"Machine Learning with R: Classification","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781526468888","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-01-20T19:25:06Z","doi":"10.4135/9781526468888","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.36647/ciml/04.02.a002","name":"Fake Review Detection using Machine Learning","source":"crossref","abstract":"Online reviews have become increasingly important in the world of e-commerce, serving as a powerful tool to establish a business's reputation and attract new customers. However, the rise of fake reviews has become a growing concern as they can skew the reputation of a business and deceive potential customers. As a result, detecting fake reviews has become a key area of research in recent years.","url":"https://doi.org/10.36647/ciml/04.02.a002","authors":["Gayathri M","Y.S.N Siva Teja","K.Ajay Sharma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-21T09:54:33Z","doi":"10.36647/ciml/04.02.a002","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003126157-1","name":"Problems with Machine Learning and Knowledge Acquisition","source":"crossref","abstract":"The early overhyped expectations about expert systems were not fulfilled. There are now enormous expectations for machine learning. For supervised machine learning, obtaining sufficient high-quality data for learning is difficult. There is a particular problem where humans have to assign the classes or labels to the data; even hospital discharge codes have a high level of errors. Machine learning is also oversold. IBM’s Watson has not lived up to expectations in medicine. Secondly knowledge derived from the human experience can apply more widely than the knowledge derived from specific training data using machine learning. There is a further requirement that AI systems should be able to explain themselves, which is difficult for techniques such as deep learning, On the other hand the difficulty of obtaining knowledge from domain experts and incorporating this into a knowledge base is known as the “knowledge engineering bottleneck”. Although rules are modular, they interact and require a lot of testing. Knowledge base maintenance is also a problem, and when new rules are added significant debugging and testing is required. Finally, a knowledge base in use may require significant maintenance and ongoing addition of rules.","url":"https://doi.org/10.1201/9781003126157-1","authors":["Paul Compton","Byeong Ho Kang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-13T08:05:14Z","doi":"10.1201/9781003126157-1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2004.1383481","name":"Proceedings of the 2004 International Conference on Machine Learning and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla.2004.1383481","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-06-01T09:01:21Z","doi":"10.1109/icmla.2004.1383481","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003539780-12","name":"Physics-Informed Sparse Machine Learning of Geotechnical Monitoring Data","source":"crossref","abstract":"Advancements in geotechnical monitoring provide continuous streams of data, which may originate from multiple sources and increase constantly in quantity over time. However, monitoring data from a specific project are often obtained at limited locations only and exhibit significant sparsity across spatial dimensions. The scarcity of monitoring data poses a critical challenge to accurately predict multiple types of geotechnical responses at each location of a three-dimensional (3D) spatial domain in a real-time manner, an essential element toward digital transformation and intelligence in geotechnical engineering. This chapter introduces a physics-informed machine learning method to accommodate limited monitoring data from multiple sources and varied spatial dimensions for geotechnical response predictions. It leverages the compressibility of geotechnical data via sparse dictionary learning and approximates model prediction using a limited number of important atoms. These atoms are constructed using numerical model results that explicitly embed geotechnical physics (e.g., soil mechanics and numerical principles), project-specific data, and uncertainties related to the numerical modeling. The proposed approach facilitates the effective extrapolation of various geotechnical responses across unmonitored spatial and temporal dimensions and allows continuous and real-time improvement of response predictions as new monitoring data are available. A case history of embankment construction with extensive instrumentation is adopted to showcase the efficacy of the proposed approach.","url":"https://doi.org/10.1201/9781003539780-12","authors":["Yu Wang","Hua-Ming Tian"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-30T18:37:51Z","doi":"10.1201/9781003539780-12","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1020254013004","name":"Classification with Bayesian MARS","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1020254013004","authors":["C.C. Holmes","D.G.T. Denison"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-03-15T08:37:24Z","doi":"10.1023/a:1020254013004","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-99-9379-6_12","name":"Applying Machine Learning to Augment the Design and Assessment of Immersive Learning Experience","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-9379-6_12","authors":["Chih-Pu Dai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-24T19:01:58Z","doi":"10.1007/978-981-99-9379-6_12","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-7023-3_8","name":"Evolution and Opportunities for Transactional Machine Learning in Almost Every Industry","source":"crossref","abstract":"We have discussed several areas that make TML effective in the application of AutoML to data streams for greater business insights and impact. What underlies TML opportunities is the belief that fast data will require fast machine learning for fast decision-making. Given that fast data is here to stay and will only grow in pervasiveness, it presents an opportunity for organizations to harness it and use it to add more value to their business. It is imperative that you understand the use cases and the evolution of transactional machine learning and its differences from conventional machine learning. Advantages will accrue to organizations that evaluate and build transactional machine learning capabilities earlier than their peers. To achieve this, you must automate the machine learning process together with using data streams that are real time and event-driven. Automation of the machine learning process reduces the human touchpoints and leads to a frictionless machine learning process; together with data streams, you can create solutions that are elastic and can quickly scale up or down.","url":"https://doi.org/10.1007/978-1-4842-7023-3_8","authors":["Sebastian Maurice"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-19T10:03:18Z","doi":"10.1007/978-1-4842-7023-3_8","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2023.100503","name":"Voice spoofing detection for multiclass attack classification using deep learning","source":"crossref","abstract":"Voice biometric authentication is increasingly gaining adoption in organizations with high-volume identity verifications and for providing access to physical and other virtual spaces. In this form of authentication, the user’s identity is verified with their voice. However, these systems are susceptible to voice spoofing attacks as malicious actors employ different types of attacks such as speech synthesis, voice conversion or imitations, and recorded replays to spoof the Automatic Speaker Verification (ASV) system or for spam communications. In this work, we provide a voice spoofing countermeasure as a binary classification problem, that classifies real and fake audio, and also as a multiclass classification problem to detect voice conversion, synthesis and replay attacks. We investigated numerous audio features and examined each feature capability alongside state-of-the-art deep learning algorithms including convolutional neural networks (CNN), WaveNet, and recurrent neural network variants - Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models. Using a large dataset of 419,426 audio files for experiments, we evaluated the deep learning models for their effectiveness against voice spoofing attacks. The binary class CNN achieved a false positive rate (FPR) of 0.0216, while the multiclass solutions using CNN, WaveNet, LSTMs and GRUs achieved an FPR of 0.003, 0.0260, 0.0302 and 0.0358 respectively. We extended the evaluation of the models by including the real-time classification using microphone voice audio and user-uploaded audio to demonstrate the practical implications and deployability.","url":"https://doi.org/10.1016/j.mlwa.2023.100503","authors":["Jason Boyd","Muhammad Fahim","Oluwafemi Olukoya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-10-11T01:49:32Z","doi":"10.1016/j.mlwa.2023.100503","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/bf00115892","name":"Editorial Exploratory research in machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00115892","authors":["Thomas G. Dietterich"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-10-31T01:43:51Z","doi":"10.1007/bf00115892","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/b978-0-12-374856-0.00015-8","name":"Embedded Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-374856-0.00015-8","authors":["Ian H. Witten","Eibe Frank","Mark A. Hall"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-01-18T04:13:07Z","doi":"10.1016/b978-0-12-374856-0.00015-8","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022831719725","name":"The Design of Discrimination Experiments","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022831719725","authors":["Shankar A. Rajamoney"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:57:10Z","doi":"10.1023/a:1022831719725","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.70593/978-93-7185-555-6_7","name":"Machine Learning Model for Breast Cancer Detection and Classification","source":"crossref","abstract":"Breast cancer is still one of the prevalent diseases affecting females globally. Machine learning (ML) has come in handy in breast cancer detection by assisting doctors and other healthcare workers with accurate diagnosis in a short time period. The research aims to utilize ML technologies such as Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN) to improve patient data analysis techniques, specifically differentiating cancerous tumors from non-cancerous tumors. The models have been trained using a useful dataset so that the project aims to have good sensitivity as well as specificity so as to work towards reducing chances of false calls and decreasing the number of biopsies performed. The findings suggest that ML-based approaches have the potential to significantly enhance diagnostic accuracy when compared to conventional methods. This research sheds light not only on design and anticipated model performance metrics but also on moral aspects, which in their turn demonstrate a wide potential of ML to improve breast cancer’s diagnostics and management allowing for earlier detection and therefore better treatment of patients.","url":"https://doi.org/10.70593/978-93-7185-555-6_7","authors":["Neeru Mago","Rajinder Singh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-12T16:16:06Z","doi":"10.70593/978-93-7185-555-6_7","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003322597-3","name":"Solutions Using Machine Learning for Diabetes","source":"crossref","abstract":"Diabetes mellitus, a chronic disease that has a significant influence on human lives, families, and communities globally, has reached alarming levels and is therefore a leading economic challenge of the twenty-first century. The International Diabetes Federation (IDF) reports that in 2019, 463 million adults aged 20 to 79 globally had diabetes, and predictive analyses estimate that the incidence will rise to 700 million in 2045. This chapter reviews leading studies of diabetes and machine learning techniques for its prevention. The chapter also examines analytical models and technologies for simulating and predicting the incidence of diabetes efficiently. These models help find the disease early to reduce medical costs and prevent more complex health problems, enabling decision-makers to develop solutions to reduce the negative impact and assess the future cost of treatment.","url":"https://doi.org/10.1201/9781003322597-3","authors":["Jabar H. Yousif","Kashif Zia","Durgesh Srivastava"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-08-26T18:05:38Z","doi":"10.1201/9781003322597-3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1002/9781119910497.ch3","name":"Machine Learning–Based VLSI Test and Verification","source":"crossref","abstract":"To test Integrated Chips, test pattern generation and fault simulation are vital. Testing verifies a circuit's accuracy regarding gates and connections between them. The fundamental purpose of testing is to model the circuit's various activities. Several Electronic Design Automation tools for fault identification and test pattern development are available to simulate circuits for structural testing. This chapter gives a brief idea of machine learning techniques: defect identification and test pattern generation at various abstraction levels.","url":"https://doi.org/10.1002/9781119910497.ch3","authors":["Jyoti Kandpal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-25T00:47:53Z","doi":"10.1002/9781119910497.ch3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.4018/978-1-6684-6291-1.ch025","name":"Machine Learning, Data Mining for IoT-Based Systems","source":"crossref","abstract":"This chapter will addresses challenges with the internet of things (IoT) and machine learning (ML), how a bit of the trouble of machine learning executions are recorded here and should be recalled while arranging the game plan, and the decision of right figuring. Existing examination in ML and IoT was centered around discovering how garbage in will convey garbage out, which is extraordinarily suitable for the extent of the enlightening list for machine learning. The quality, aggregate, availability, and decision of data are essential to the accomplishment of a machine learning game plan. Therefore, the point of this section is to give an outline of how the framework can utilize advancements alongside machine learning and difficulties get a kick out of the chance to understand the security challenges IoT can be bolstered. There are a few extensively unmistakable counts open for ML use. In spite of the way that counts can work in any nonexclusive conditions, there are specific standards available about which figuring would work best under which conditions.","url":"https://doi.org/10.4018/978-1-6684-6291-1.ch025","authors":["Ramgopal Kashyap"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-07-08T11:31:23Z","doi":"10.4018/978-1-6684-6291-1.ch025","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2021.100192","name":"Explainable AI in drought forecasting","source":"crossref","abstract":"Droughts are one of the disastrous natural hazards which has severe impacts on agricultural production, economy, and society. One of the critical steps for effective drought management is developing a robust forecasting model and understanding how the variables affect the model outcomes. The present study forecasts SPI-12 at a lead time of 3 months, using the Long Short-Term Memory (LSTM) model, and further interprets the spatial and temporal relationship between variables and forecasting results using SHapley Additive exPlanations (SHAP). The developed model is tested in four different regions in New South Wales (NSW), Australia. SPI-12 was computed using monthly rainfall data collected from Scientific Information for Land Owners (SILO) for 1901–2018. The model was trained from 1901–2000 and tested from 2001–2018, and the performance was measured using Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and Root-Mean-Square-Error (RMSE). To understand the underlying impact of variables on the model outcomes, SHAPley values were calculated for the entire testing period and also at three different temporal ranges, which are during the Millennium Drought (2001–2010), post drought period (2011–2018) and at a seasonal scale (summer months). The comparison of the results shows a significant variation in the impact of variables on forecasting, both temporally and spatially. It also shows the need to study the model outcomes for specific regions and for a shorter duration than the entire testing period. This is a first of its study towards interpreting the forecasting model in drought studies, which could help understand the behaviour of drought variables.","url":"https://doi.org/10.1016/j.mlwa.2021.100192","authors":["Abhirup Dikshit","Biswajeet Pradhan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-25T21:48:08Z","doi":"10.1016/j.mlwa.2021.100192","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-0-387-30164-8_117","name":"Clause Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_117","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:42:10Z","doi":"10.1007/978-0-387-30164-8_117","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.26689/jcnr.v9i9.12295","name":"Study on the Prognostic Prediction Model and Clinical Application Value of Machine Learning-based Approach for Septic Children in PICU","source":"crossref","abstract":"Objective: To explore the application value of a machine learning-based prediction model in assessing the prognosis of septic children in the pediatric intensive care unit (PICU) and provide data support for clinical decision-making. Methods: A total of 180 septic children admitted to the PICU of a tertiary hospital from January 2020 to December 2024 were selected. They were divided into a control group (90 cases, using traditional scoring methods to predict prognosis) and an observation group (90 cases, using a multivariable model based on machine learning algorithms to predict prognosis) according to the random number table method. General information, laboratory indicators, and clinical interventions were collected. Various models such as Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were established. The model performance was evaluated using ROC curve, AUC value, accuracy, sensitivity, and specificity. Results: The machine learning models performed better than traditional scoring methods in predicting the 28-day mortality rate of septic children. Among them, the RF model achieved an AUC value of 0.921, a sensitivity of 85.6%, and a specificity of 88.1%, which were significantly higher than the PIM3 score (AUC 0.762). The prediction accuracy and timeliness of clinical intervention in the observation group were significantly improved, leading to a shortened hospital stay and reduced mortality rate (p &lt; 0.05). Conclusion: The prediction model based on machine learning can more accurately assess the prognostic risk of septic children in PICU, showing good clinical application prospects and providing references for individualized treatment and optimal resource allocation.","url":"https://doi.org/10.26689/jcnr.v9i9.12295","authors":["Li Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-23T07:14:35Z","doi":"10.26689/jcnr.v9i9.12295","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.31979/flc.002","name":"Counter-story in machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.31979/flc.002","authors":["Esperanza Huerta Espinosa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-07-06T17:12:50Z","doi":"10.31979/flc.002","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.31979/etd.q6u5-n9x8","name":"Steganographic Capacity of Selected Machine Learning and Deep Learning Models","source":"crossref","abstract":"As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, which might then be used to gain unauthorized access to a computer, or for other malicious purposes. In this research, we determine the steganographic capacity of various classic machine learning and deep learning models. Specifically, we determine the number of low-order bits of the trained parameters of a given model that can be altered without significantly affecting the performance of the model. We find that the steganographic capacity of learning models is surprisingly high, and that there tends to be a clear threshold after which model performance rapidly degrades.","url":"https://doi.org/10.31979/etd.q6u5-n9x8","authors":["Lei Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-15T15:53:00Z","doi":"10.31979/etd.q6u5-n9x8","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-16-8881-2_28","name":"Machine Learning and Precision Farming","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8881-2_28","authors":["Shyamasree Ghosh","Rathi Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T17:03:22Z","doi":"10.1007/978-981-16-8881-2_28","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-16-8881-2_32","name":"Machine Learning and Plant Sciences","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8881-2_32","authors":["Shyamasree Ghosh","Rathi Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T17:03:22Z","doi":"10.1007/978-981-16-8881-2_32","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-031-03841-9_1","name":"Introduction to Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-03841-9_1","authors":["Elena Bellodi","Riccardo Zese","Fabrizio Riguzzi","Evelina Lamma"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-25T18:03:53Z","doi":"10.1007/978-3-031-03841-9_1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/isml60050.2024.11007360","name":"Classification of Fetal Arrhythmia using Electrocardiogram Signal Processing and Machine learning","source":"crossref","abstract":"Cardiac Arrhythmia, a condition characterized by irregular deviations from the standard cardiovascular cycle, poses a serious threat to fetal well-being and accounts for a significant portion of unexpected infant deaths. The objective of this study is to develop a foundational algorithm for the detection of fetal arrhythmia using a novel Non-Invasive Fetal Electrocardiogram. To accomplish this, a set of 15 Electrocardiogram features were extracted from 26 different pregnant women during standard medical visits at varying stages of pregnancy. These features included frequency, time, and time-domain information from separate, 10-minute recordings. The features are classified, and the results are compared across six different Machine Learning algorithms. The proposed solution achieved an accuracy of 83%, recall of 83%, precision of 88%, and an F-score of 83%. These results suggest the potential for using NI-fECG in clinical trials, with opportunities for advancing data processing techniques.","url":"https://doi.org/10.1109/isml60050.2024.11007360","authors":["Nav Malhotra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-23T17:02:44Z","doi":"10.1109/isml60050.2024.11007360","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1093/oxfordhb/9780197653609.013.12","name":"Sociology and Machine Learning","source":"crossref","abstract":"Abstract This chapter serves as an introduction to the Oxford Handbook of the Sociology of Machine Learning. It outlines the volume’s principal objective: To provide a comprehensive, state-of-the-art overview and forward-looking discussion of three pivotal themes that connect machine learning (ML) and sociology. These themes are (a) ML as a methodological tool for sociological research, (b) the adaptation of ML in society, examined as an empirical phenomenon, and (c) the repercussions of ML on social theory. Furthermore, this chapter delineates the volume’s four parts and the respective chapters they encompass. These parts are “The Past, Present, and Future of Machine Learning in Sociology,” “Machine Learning as a Methodological Toolbox,” “Societal Machine Learning Applications,” and “Machine Learning and Sociological Theory.”","url":"https://doi.org/10.1093/oxfordhb/9780197653609.013.12","authors":["Christian Borch","Juan Pablo Pardo-Guerra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-18T15:54:17Z","doi":"10.1093/oxfordhb/9780197653609.013.12","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022655214215","name":"Classifier Systems and the Animat Problem","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022655214215","authors":["Stewart W. Wilson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022655214215","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003487647-19","name":"Machine learning–enabled IoT for biomedical applications","source":"crossref","abstract":"The biomedical field is one of the sectors in which the Internet of Things (IoT) has had an impact and applications in recent decades. Applications for computational methods like data science and machine learning (ML) have also been used in the biomedical field. IOT and ML are crucial to the computer-aided diagnosis of medical issues and to the biomedical field s ability to comprehend complex medical data, both of which lead to better patient care. Big and simple data can be used by IoT and ML-based systems to quickly train a system to identify medical abnormalities. Biomedical data has a high degree of complexity and is unbalanced and nonstationary due to the abundance of data. IOT-enabled ML is still crucial in this scenario because it can: (i) assist health professionals in better processing large volumes of medical data; (ii) lower the risk of medical errors; and (iii) guarantee that therapeutic and predictive procedures complement one another. This chapter provides an overview of the issues and difficulties in IOT-enabled ML-based biomedical applications.","url":"https://doi.org/10.1201/9781003487647-19","authors":["Hashmat Usmani","Renu Rani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-04T11:24:27Z","doi":"10.1201/9781003487647-19","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.51219/urforum.2025.david-miller","name":"Machine Learning for Real-time Detection of Complications during Neurosurgery","source":"crossref","abstract":"","url":"https://doi.org/10.51219/urforum.2025.david-miller","authors":["David Miller"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-26T05:54:48Z","doi":"10.51219/urforum.2025.david-miller","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/bf00116834","name":"Toward a unified science of machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00116834","authors":["Pat Langley"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-11-01T03:05:07Z","doi":"10.1007/bf00116834","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/979-8-8688-2527-9_1","name":"Introduction to Machine Learning Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.1007/979-8-8688-2527-9_1","authors":["Mohammad Reza Mahdiani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-30T22:15:43Z","doi":"10.1007/979-8-8688-2527-9_1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icaml54311.2021.00001","name":"Proceedings 2021 3rd International Conference on Applied Machine Learning [Title page i]","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaml54311.2021.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-02-18T20:34:52Z","doi":"10.1109/icaml54311.2021.00001","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-540-79452-3_4","name":"Learning Locally and Globally: Maxi-Min Margin Machine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-79452-3_4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2008-09-22T16:10:34Z","doi":"10.1007/978-3-540-79452-3_4","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-95-4925-2_6","name":"Integration of Machine Learning in Educational Processes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-4925-2_6","authors":["Soumita Sen","Krishnendu Ghosh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-02T09:13:25Z","doi":"10.1007/978-981-95-4925-2_6","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022613830492","name":"Children, Adults, and Machines as Discovery Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022613830492","authors":["David Klahr"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022613830492","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1002/9781118266502.ch1","name":"Introduction to Reinforcement and Systemic Machine Learning","source":"crossref","abstract":"This chapter discusses limitations of reinforcement learning and the concept of systemic learning. The systemic machine-learning paradigm is discussed along with various concepts and techniques. The chapter also covers an introduction to traditional learning methods. The relationship among different learning methods with reference to systemic machine learning is elaborated. The chapter builds the background for systemic machine learning. It begins by considering the simplest machine-learning task: supervised learning for classification. The data and information used for learning are very important. There are three fundamental continuously active human-like learning mechanisms: Perceptual Learning, Episodic Learning and Procedural Learning. The primary goal of learning/machine learning is producing some learning algorithm with practical value. Reinforcement learning is a machine-learning process. The concept of systemic machine learning deals with exploration, but more thrust is on understanding a system and the impact of any action on the system. Controlled Vocabulary Terms learning (artificial intelligence); multilayer perceptrons; unsupervised learning","url":"https://doi.org/10.1002/9781118266502.ch1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-07-19T11:27:22Z","doi":"10.1002/9781118266502.ch1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1515/9781501520112-008","name":"Chapter 7: Machine Learning Clustering with GPT-4","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9781501520112-008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-04T02:00:52Z","doi":"10.1515/9781501520112-008","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1117/12.3025138","name":"Wavelength-dependent responses and machine learning in nanophotonics modeling","source":"crossref","abstract":"Machine learning techniques have been proposed in the literature for the modeling of photonic devices. These techniques can be used to speed up the design process. The data samples needed to build machine learning models are collected from electromagnetic simulations. Electromagnetic solvers can result computationally expensive and therefore minimizing the computational effort needed to collect these data samples is an important aspect. Using frequency-domain electromagnetic solvers to collect data samples requires a suitable sampling of the wavelength variable to avoid undersampling and oversampling phenomena. An adaptive frequency-domain sampling approach for nanophotonic applications is illustrated in this work.","url":"https://doi.org/10.1117/12.3025138","authors":["Francesco Ferranti"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-18T12:54:59Z","doi":"10.1117/12.3025138","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-1-4842-6537-6_7","name":"Evaluating Machine Learning Models","source":"crossref","abstract":"As a field, machine learning is still in its infancy. Advanced machine learning has only been explored over the last 25 years, which has fueled data science as a profession. Thus, the data science industry is still in a phase of wonderment at the endless potential of AI and machine learning. With this come both excitement and confusion—and an industry that is gathering knowledge, experience, and first-time problems.","url":"https://doi.org/10.1007/978-1-4842-6537-6_7","authors":["Arjun Panesar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-12-15T14:05:55Z","doi":"10.1007/978-1-4842-6537-6_7","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/isml60050.2024.11007392","name":"Authenticating Signals Using Machine Learning","source":"crossref","abstract":"Signal authentication is an important frontier in the rapid evolution of digital security. This research explores the integration of machine learning and steganography techniques and highlights their important role in improving signal authentication. Machine learning algorithms enable the system to identify complex patterns in signals, making accurate identification possible. At the same time, steganography can hide authentication information in symbols, thus improving information integrity and confidentiality. This next article provides a review of the recent developments, classification and evaluation of various machine learning algorithms along with steganography techniques. It covers application areas such as cybersecurity, biometric authentication, and multimedia communications, showing successes and challenges. Topics such as counterterrorism, moral intervention and timely action were discussed during the discussions. By providing a perspective, this study informs researchers, practitioners and policy makers about the latest technology and how to build a future with powerful, flexible and safe lighting for the digital age.","url":"https://doi.org/10.1109/isml60050.2024.11007392","authors":["Aditya Anand","Aniket Khartade","Ankit Maurya","S.K. Moon"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-23T17:02:44Z","doi":"10.1109/isml60050.2024.11007392","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.65525/svup.9788199565418.2026.149-153","name":"\"Reducing Carbon Emissions in Model Training:  EcoConscious Machine Learning\"","source":"crossref","abstract":"As the demand for computational resources in machine learning continues to grow, so too does the environmental impact associate with model training. This paper addresses the urgent need for eco-conscious practices in machine learning by proposing strategies to reduce carbon emissions during the training process. We present a comprehensive review of the environmental footprint of model training and explore various techniques to minimize energy consumption and carbon emissions. These include algorithmic optimizations, hardware efficiency improvements, and renewable energy utilization. Through empirical evaluations and case studies, we demonstrate the feasibility and effectiveness of eco-conscious machine learning approaches in reducing carbon emissions without compromising model performance. This work contributes to the development of sustainable machine learning practices and promotes the adoption of environmentally responsible methodologies in the AI community. DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.149-153","url":"https://doi.org/10.65525/svup.9788199565418.2026.149-153","authors":["Nabya kumari","Diganta Bhattacharyya"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-18T11:06:12Z","doi":"10.65525/svup.9788199565418.2026.149-153","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1024068626366","name":"Inference for the Generalization Error","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1024068626366","authors":["Claude Nadeau","Yoshua Bengio"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-09-12T17:07:28Z","doi":"10.1023/a:1024068626366","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2023.100465","name":"Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies","source":"crossref","abstract":"The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for cryptocurrencies market, we constructed various Deep Learning models based on Feed Forward Neural Networks (DFFNNs) and Long Short-Term Memory (LSTM) networks and evaluated their performance in forecasting the volatility of 27 cryptocurrencies. Then, different hybrid models were built in which the outputs of three GARCH-type models, namely GARCH, EGARCH, and APGARCH, with three different assumptions for the residuals’ distribution were fed into the DFFNN and LSTM networks. In other words, GARCH-type models were utilized as feature extractors and the deep learning models leveraged a sequence of extracted features as their inputs to produce the volatility of the next day. Our findings revealed that not only the deep learning models improve the forecasts of GARCH-type models with any distribution assumption, the forecasts of GARCH-type models as informative features can significantly increase the predictive power of the studied deep learning models; namely, the DFFNN and LSTM models.","url":"https://doi.org/10.1016/j.mlwa.2023.100465","authors":["Bahareh Amirshahi","Salim Lahmiri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-04-10T01:53:03Z","doi":"10.1016/j.mlwa.2023.100465","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icaiihi67124.2025.11403468","name":"Machine Learning-Based Stroke Prediction Using Clinical Feature Engineering and Cost-Sensitive Learning","source":"crossref","abstract":"Stroke prediction using machine learning faces severe class imbalance (19.52:1) and asymmetric misclassification costs in highly imbalance datasets. We developed eight WHO/CDC/ADA-validated features and systematically evaluated 18 model configurations comprising 12 baseline models (3 algorithms: Logistic Regression, Random Forest, XGBoost × 4 sampling methods: Original, SMOTE, ADASYN, SMOTEENN) and 6 cost-sensitive variants with explicit 100:1 false negative penalties. The optimal configuration (Logistic Regression + SMOTEENN + Cost-Sensitive) achieves 90% recall, detecting 45 of 50 strokes with only 5 false negatives and $1,043 total cost—representing 76.6% cost reduction versus baseline ($4,900). Average improvements across all configurations demonstrate +34.3 percentage points recall gain and $1,559 cost savings (38.9% reduction). SHAP explainability analysis reveals three engineered features rank within top 10 predictors (age_group #2, bmi_category #5, smoker_flag #10), validating clinical relevance of domain-guided feature construction. This interpretable, cost-effective framework demonstrates practical viability for healthcare deployment","url":"https://doi.org/10.1109/icaiihi67124.2025.11403468","authors":["Vaibhav Chandrakar","Yogesh Kumar Soni","T. Sahil Rao","Divya Soni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-25T20:55:03Z","doi":"10.1109/icaiihi67124.2025.11403468","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.38007/ml.2020.010401","name":"Sensor Cloud Intrusion Detection Based on Discrete Optimization Algorithm and Machine Learning","source":"crossref","abstract":"In the development process of the Internet, computer technology and network communication have been rapidly applied.Network security has become a research focus.Based on the discrete cloud intrusion detection method, through sensing a large number of data signals in the cloud environment, some of the interference information is screened, filtered and classified.In this paper, the discrete optimization algorithm and machine learning can better reflect the intrusion information in time.This paper mainly uses the methods of experiment and comparison to experiment the three indicators of SVM and its improved algorithm in intrusion detection.The experimental data show that the accuracy of the improved LE-SVM algorithm can reach more than 95%, and its time consumption is relatively small.","url":"https://doi.org/10.38007/ml.2020.010401","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-06T01:57:18Z","doi":"10.38007/ml.2020.010401","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022651113306","name":"The Induction of Dynamical Recognizers","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022651113306","authors":["Jordan B. Pollack"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022651113306","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-3-319-55312-2_2","name":"Understanding Machine Learning Opportunities","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-55312-2_2","authors":["Parag Kulkarni"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-03-30T03:10:26Z","doi":"10.1007/978-3-319-55312-2_2","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2023.100456","name":"Deep echo state networks in data marketplaces","source":"crossref","abstract":"Data Marketplaces are the digital platform for data buyers and data sellers to trade information as valuable products or items. The expectation taken for granted from the users of a data marketplace is the truth of the exchanged information. However, the trade of factual data also means the marketable product is no longer unique but a series of replicas. If every user within the data marketplace owns the same information, this data eventually becomes valueless. There are specific instances where the traded products are sought to be always unique, for instance predictions or digital art. This article applies Deep Echo State Networks (ESNs) in data marketplaces that map tradeable data into a larger dimensional space via the dynamics of reservoirs with fixed and non-linear properties. These reservoirs generate unique tradeable data products that cannot be replicated, therefore ensuring its exclusivity and commercial value. The validation results show that ESNs can also be applied to generate random tradeable products in different dimensional spaces. Specifically, the reservoir with its associated neural perturbation emulates a digital creator that generates unique and exclusive content based on 1D functions and 2D images.","url":"https://doi.org/10.1016/j.mlwa.2023.100456","authors":["Will Serrano"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-02-22T02:36:36Z","doi":"10.1016/j.mlwa.2023.100456","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1117/12.3106034","name":"Inversely designed free-form plasmonics with machine learning","source":"crossref","abstract":"Machine learning has enabled a paradigm shift in the discovery and development of structured photonic materials and devices. Here, we report a diverse set of inversely designed plasmonic structures, devices, and systems for wavefront control, imaging, computing, and nonlinear optics. We have developed, and will present, a series of deep-learning–enabled frameworks that consolidate generative methods, evolutionary strategies, advanced pattern-generation algorithms, and physics-aware diffusion models for the inverse design of free-form, highly complex, and multifunctional plasmonic metastructures tailored to on-demand optical responses, supported by extended case studies and experimental demonstrations.","url":"https://doi.org/10.1117/12.3106034","authors":["Wenshan Cai"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-05-27T18:24:57Z","doi":"10.1117/12.3106034","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022634402267","name":"Implementing Valiant's Learnability Theory Using Random Sets","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022634402267","authors":["E.M. Oblow"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022634402267","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003559511-1","name":"Machine Learning Architectures for Pedagogical Data-Driven Teaching, Learning, and Assessment","source":"crossref","abstract":"The rapid evolution of the modern world necessitates continuous nurturing of knowledge and skills. Conventional instructional strategies, which often rely on lectures with students as passive listeners, don’t always produce the learning sought, nor do they effectively address the varied needs of students from different backgrounds. Although alternative pedagogical methodologies like active learning have appeared, consistency in improved results has proven difficult to sustain. Within this context, artificial intelligence (AI) comes forward as a promising option. Its capabilities encompass accelerating and improving routine administrative duties like attendance tracking, exam scoring, and other paperwork, freeing educators to concentrate on more complex instruction. Simultaneously, AI can foster better engagement by rendering learning more adaptive to each student s individual requirements. AI offers significant improvements to student assessment. Substantial time normally allotted to evaluating student work can be saved by integrating AI with current learning management systems, yielding a learning environment that is both more reactive and dynamic. Addressing the obstacles inherent in implementation of AI in education including data secrecy, bias and upholding educational quality—becomes paramount. Ultimately, it is pivotal here to recognize that AI is intended to supplement, not supplant, the human-element of instruction, where teacher-driven care, guidance, and enrichment remain fundamental features of effective education. This chapter seeks to examine the integration of AI as a driver in redefining traditional educational organizations. It presents a move from the old approaches to a modern model in pedagogical effectiveness. In utilizing AI throughout classroom activity, its designers target efficiency, and effectiveness, and seek to support individual learners. The narrative reviews the positives and drawbacks accompanying an AI-assisted educational paradigm. Moreover, this chapter focuses its review on how to use this novel intelligence while raising educational validity. We incorporate blended learning, by combining AI to analyze pedagogical student success data and instructional methodology design. Thus, we propose an intelligence designed by an AI-steered method in instruction that will heighten individual achievements, personalize instruction, and streamline assessments—an environment optimized to be as engaging, efficient, and effective as possible.","url":"https://doi.org/10.1201/9781003559511-1","authors":["R. Kanniga Devi","P.K.A. Chitra","Sriram Chakaravarthy Srinivasan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-04-13T07:45:45Z","doi":"10.1201/9781003559511-1","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/s44163-025-00649-3","name":"Enhancing breast cancer detection accuracy through machine learning, deep learning and transfer learning techniques for clinical practice","source":"crossref","abstract":"Abstract Breast cancer is a pervasive global health concern, impacting millions of women worldwide. Timely detection and precise diagnosis are pivotal factors in improving patient outcomes. This review presents a comprehensive analysis of machine learning (ML), deep learning (DL), and transfer learning (TL) models applied to breast cancer detection. It encompasses the classification of different types of breast cancer, prognosis, diagnosis, prediction, and clinical decision support. The present study examines a wide range of articles to recognize the frequently used architectures, datasets, activation functions, and evaluation metrics. Furthermore, the review scrutinizes the effectiveness of various AI techniques in predicting and diagnosing breast cancer, elucidating various evaluation metrics and their utilization. The WDBC and BreakHis databases are image datasets commonly used for breast cancer prediction. The performance of machine learning, deep learning, and transfer learning algorithms varies significantly in terms of precision, recall, F1 score, and accuracy. CNN model is the most commonly used deep learning technique, with the study indicating that it is used by about 60% of researchers. In terms of network architecture, ResNet is used by about 57% of researchers. Conspicuously, Softmax occurs as the most frequently used activation function i.e., 89%, and accuracy and precision are the foremost metrics for performance evaluation i.e., 60%. According to the study, deep learning and transfer learning methods achieve the highest accuracy, reaching 99.54% in breast cancer detection which raises concerns about dataset bias, overfitting, and lack of external validation. In terms of machine learning based breast cancer detection, the random forest algorithm demonstrates remarkable success, achieving the highest accuracy rate of 99%. This review serves as a comprehensive exploration of the current state of AI applications in breast cancer, highlighting their potential to reshape the landscape of breast cancer healthcare.","url":"https://doi.org/10.1007/s44163-025-00649-3","authors":["Jothi Ganesan","Vaanpriya Krishnan","Thirumalaisamy Rathinavel","K. S. Shalini","Thangaswamy Selvankumar","M. Gomathi","Shanmugavel Uma Maheswari","Kalandar Ameer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-11T13:15:55Z","doi":"10.1007/s44163-025-00649-3","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1022637031594","name":"Lower Bound Methods and Separation Results for On-Line Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022637031594","authors":["Wolfgang Maass","György Turán"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022637031594","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1023/a:1007585615670","name":"Forgetting Exceptions is Harmful in Language Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1007585615670","authors":["Walter Daelemans","Antal Van Den Bosch","Jakub Zavrel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2002-12-22T05:04:10Z","doi":"10.1023/a:1007585615670","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/springerreference_179242","name":"Medicine: Applications of Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_179242","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-07T13:44:39Z","doi":"10.1007/springerreference_179242","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.21236/ada283386","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.21236/ada283386","authors":["L. G. Valiant"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2017-09-05T15:02:27Z","doi":"10.21236/ada283386","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.clnesp.2020.09.210","name":"The glim for surgical patients and machine learning analysis to predict complications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.clnesp.2020.09.210","authors":["J.R. Henrique","R.G. Pereira","C.N. Rodrigues","Á.R.S. Ferreira","W.M. Júnior","M.I.T.D. Correia"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-11-09T21:54:01Z","doi":"10.1016/j.clnesp.2020.09.210","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.jclinepi.2021.06.008","name":"Using a stepwise approach to simultaneously develop and validate machine learning based prediction models","source":"crossref","abstract":"Accurate diagnosis of a disease is essential in healthcare. Prediction models, based on classical regression techniques, are widely used in clinical practice. Machine Learning (ML) techniques might be preferred in case of a large amount of data per patient and relatively limited numbers of subjects. However, this increases the risk of overfitting, and external validation is imperative. However, in the field of ML, new and more efficient techniques are developed rapidly, and if recruiting patients for a validation study is time consuming, the ML technique used to develop the first model might have been surpassed by more efficient ML techniques, rendering this original model no longer relevant. We demonstrate a stepwise design for simultaneous development and validation of prediction models based on ML techniques. The design enables - in one study - evaluation of the stability and robustness of a prediction model over increasing sample size as well as assessment of the stability of sensitivity/specificity at a chosen cut-off. This will shorten the time to introduction of a new test in health care. We finally describe how to use regular clinical parameters in conjunction with ML based predictions, to further enhance differentiation between subjects with and without a disease.","url":"https://doi.org/10.1016/j.jclinepi.2021.06.008","authors":["M. Haalboom","S. Kort","J. van der Palen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-06-19T10:54:48Z","doi":"10.1016/j.jclinepi.2021.06.008","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1016/j.mlwa.2023.100517","name":"A comprehensive survey on machine learning applications for drilling and blasting in surface mining","source":"crossref","abstract":"Drilling and blasting operations are pivotal for productivity and safety in hard rock surface mining. These operations are restricted due to complexities such as site-specific uncertainties, safety risks, and environmental and economic constraints. Machine Learning (ML) is a transformative approach to tackle these complexities resulting in significant cost reductions. ML applications can reduce overall blasting costs by up to 23% and decrease the amount of explosives by as much as 89% compared to traditional methods. This survey presents a comprehensive review of how ML can be applied to optimize drill and blast designs while accounting for its operational challenges. Our research highlights the difficulties in collecting quality site-specific data, the complexity of interpreting this data into insightful information, the selection of ML models relating to mining objectives, and the need for established methods to assess blast efficiency quantitatively. We provide a synthesis of ML model development practices in drilling and blasting and demonstrate the value of ML methodologies. Based on our survey, we present actionable recommendations for developing ML methodologies to improve safety, reduce costs, and enhance efficiency in drilling and blasting processes. This includes establishing standardized data schematics, multiobjective model optimization, and comprehensive evaluation metrics. These benefits can guide mine management and engineers to adopt ML techniques and improve on-ground operational practices. This survey aims to serve as a resource for both practitioners and researchers shaping the future research direction in ML applications for drilling and blasting practices.","url":"https://doi.org/10.1016/j.mlwa.2023.100517","authors":["Venkat Munagala","Srikanth Thudumu","Irini Logothetis","Sushil Bhandari","Rajesh Vasa","Kon Mouzakis"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-11T17:02:31Z","doi":"10.1016/j.mlwa.2023.100517","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1515/9783110670707-004","name":"4. From classical to quantum machine learning","source":"crossref","abstract":"In recent years, Machine Learning (ML) has started to be ubiquitously applied to practically most of the human activity domains. Although traditional, or classical machine learning (CML) approaches are useful in solving many complex tasks, there are still many challenges that such approaches are facing. One issue is the limitation in the processing speed of current silicon technology based computers, which made researchers look to the underlying quantum theory principles and try to run complex quantum computing experiments. In future, in order to develop more complex artificial intelligence systems, we will have to process huge amounts of data at high speed, and the existing classical computing will probably not serve well this purpose, due to silicon technology limitations. A different technology that can handle huge volumes of data and at high speed is needed. In recent years, the progress in quantum computing research seems to provide hopeful answers to overcome the speed processing barrier. This is very important for the training of many computational intensive machine learning models. The latest advancements in quantum technology appear to be promising, which can boost the field of machine learning overall. In this chapter, we discuss the transition from classical machine learning to quantum machine learning (QML) and explore the recent progress in this domain. QML is not only associated with the development of high-performance machine learning algorithms that can run on a quantum computer with significant performance improvements but also has a very diverse meaning in other aspects. The chapter tried to touch those aspects in brief too, but the main focus is on the advancements in the field of developing machine learning algorithms that will run on a quantum computer.","url":"https://doi.org/10.1515/9783110670707-004","authors":["Arit Kumar Bishwas","Ashish Mani","Vasile Palade"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-07-10T08:08:32Z","doi":"10.1515/9783110670707-004","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1109/icmla.2017.0-178","name":"Attribute Assisted Interpretation Confidence Classification Using Machine Learning","source":"crossref","abstract":"An attribute assisted classification deriving estimates of interpretation confidence was performed. Instantaneous and coherency attributes were used in a supervised followed by an unsupervised classification resulting in an error envelope of the interpretation. In an initial approximation, confidence weights for a signal and background response are estimated using support vector machine learning. Subsequently, a weighted discrimination based on several coherency attributes using self-organizing maps is obtained. The resulting quantization is used as additional input and constraint in a final probability assessment of signal confidence using instantaneous attributes in support vector machine learning. The additional input in the form of quantization vectors and possible reduction in dimensionality of the input attribute vector space, allows to combine highly non-linear correlations in a multivariate discrimination. The trained classification is used to assign signal confidence probabilities to an interpreted seismic horizon. The proposed methodology is applied to an onshore data set from Wyoming, USA, revealing how single- and multi-trace attributes can be used to quantitatively assess the uncertainty of an interpretation often lost during project maturation.","url":"https://doi.org/10.1109/icmla.2017.0-178","authors":["Wolfgang Weinzierl"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-01-23T12:20:07Z","doi":"10.1109/icmla.2017.0-178","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1007/978-981-16-8881-2_37","name":"The Future of Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-8881-2_37","authors":["Shyamasree Ghosh","Rathi Dasgupta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2022-05-04T17:03:22Z","doi":"10.1007/978-981-16-8881-2_37","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.957Z"},{"id":"doi:10.1201/9781003393122-4","name":"What Is Machine Learning?","source":"crossref","abstract":"Chapter 4 starts with an introduction to machine learning (ML), with the concepts presented as simple pictorial examples that aid the reader to understand ML with ease. The relationship between artificial intelligence, machine learning and deep learning is briefly described. The important machine learning algorithms are then detailed with easy examples to achieve adequate clarity. The need for machine learning is discussed, followed by its framework. The major differences between machine learning and deep learning algorithms are brought out, with a pictorial example highlighting the differences in their process flow. Before winding up with the supplementary learning resources, key points to remember and quiz, the major and other common applications of machine learning are described.","url":"https://doi.org/10.1201/9781003393122-4","authors":["Shriram K. Vasudevan","Nitin Vamsi Dantu","Sini Raj Pulari","T. S. Murugesh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-08-21T16:10:19Z","doi":"10.1201/9781003393122-4","addedAt":"2026-09-01T01:48:02.957Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1109/icaml64299.2024.00075","name":"A Prediction of Users Repurchase Based on Machine Learning Theory","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaml64299.2024.00075","authors":["Hanzhe Zhang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-22T20:58:30Z","doi":"10.1109/icaml64299.2024.00075","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj-cs.350/table-6","name":"Table 6: Machine learning evaluation metrics.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.350/table-6","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-01-25T04:22:03Z","doi":"10.7717/peerj-cs.350/table-6","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1201/9781420067194","name":"Machine Learning","source":"crossref","abstract":"Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but","url":"https://doi.org/10.1201/9781420067194","authors":["Stephen Marsland"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2018-07-02T10:54:57Z","doi":"10.1201/9781420067194","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/springerreference_60307","name":"Drug Design with Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_60307","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-08-29T16:20:21Z","doi":"10.1007/springerreference_60307","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj-cs.2430/fig-8","name":"Figure 8: Machine learning model accuracy %.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2430/fig-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-11T03:41:02Z","doi":"10.7717/peerj-cs.2430/fig-8","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/springerreference_302476","name":"Probability Theory in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_302476","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-04-05T06:35:16Z","doi":"10.1007/springerreference_302476","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/springerreference_179217","name":"Machine Learning for IT Security","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_179217","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2012-02-07T13:44:39Z","doi":"10.1007/springerreference_179217","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/springerreference_60487","name":"Machine Learning, Ensemble Methods in","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_60487","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-08-29T12:20:21Z","doi":"10.1007/springerreference_60487","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj-cs.389/fig-7","name":"Figure 7: Machine Learning method result.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.389/fig-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-03-11T05:29:17Z","doi":"10.7717/peerj-cs.389/fig-7","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj-cs.415/supp-2","name":"Supplemental Information 2: Machine Learning Data","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.415/supp-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-05-18T05:21:46Z","doi":"10.7717/peerj-cs.415/supp-2","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerjcs.233/fig-5","name":"Figure 5: Machine learning algorithms used.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.233/fig-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-11-11T03:57:35Z","doi":"10.7717/peerjcs.233/fig-5","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.7717/peerj.20940/fig-5","name":"Figure 5: Machine learning (SVM) flowchart.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj.20940/fig-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-11T08:50:59Z","doi":"10.7717/peerj.20940/fig-5","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1201/9781003470649-5","name":"Machine Learning Prediction for Reservoir Permeability","source":"crossref","abstract":"This chapter explores the application of machine learning ( ML ) techniques as an efficient alternative for permeability estimation, leveraging well log data to model complex nonlinear relationships. Various ML methods are discussed and compared, including artificial neural networks (ANNs) and ensemble techniques such as stochastic gradient boosting (SGB). A case study demonstrates the impact of feature selection, hyperparameter tuning, and model evaluation metrics on permeability prediction performance. The findings emphasize that SGB models offer a strong balance between accuracy and computational efficiency, significantly outperforming conventional techniques. Future research directions are highlighted, focusing on integrating physics-informed ML models, uncertainty quantification, transfer learning, and real-time permeability prediction for enhanced reservoir management and characterization.","url":"https://doi.org/10.1201/9781003470649-5","authors":["Mohammed Fathy El-Amin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-06T14:09:53Z","doi":"10.1201/9781003470649-5","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/978-3-658-45392-3_2","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-658-45392-3_2","authors":["Bernd Heesen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T18:35:22Z","doi":"10.1007/978-3-658-45392-3_2","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/9781119183464.ch10","name":"Machine Learning as a Batch Process","source":"crossref","abstract":"This chapter investigates using batch processing to mine and learn from larger amounts of data instead of streaming data. It covers using Hadoop, Sqoop, and Pig for large-scale batch processing; these enable large data sets to be processed with ease. The chapter also discusses more traditional methods of creating programs to run batch processes on data. It mentions the term \"Big Data\" that is increasingly a topic in business today. Key points needed to consider when working with real-time data include volume and frequency, quantity of data, and the processes to be deployed. The chapter details the different approaches to batch data processing. It presents four complete scenarios that walk you through the process of mining batched data from start to finish. A small amount of exposure to Hadoop, Sqoop, Mahout, and Pig, leads to a broad set of tools for extracting, processing, and learning from existing and new data.","url":"https://doi.org/10.1002/9781119183464.ch10","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2015-08-19T14:37:30Z","doi":"10.1002/9781119183464.ch10","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/b978-0-443-27422-0.00007-4","name":"Membrane technology and machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-27422-0.00007-4","authors":["Kiran Mustafa","Mashallah Rezakazemi","Rao Muhammad Mahtab Mahboob"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-27T07:16:40Z","doi":"10.1016/b978-0-443-27422-0.00007-4","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/b978-0-12-821929-4.00006-8","name":"Introduction to machine learning and Python","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-821929-4.00006-8","authors":["Hoss Belyadi","Alireza Haghighat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-04-23T14:18:26Z","doi":"10.1016/b978-0-12-821929-4.00006-8","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.5220/0014750300004818","name":"Predicting Mental Health in Employees and Students Using Machine Learning and Wearable Data","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014750300004818","authors":["Tailiang Liu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-07-19T13:55:48Z","doi":"10.5220/0014750300004818","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.mlwa.2021.100190","name":"Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques","source":"crossref","abstract":"Structural health monitoring of beam–column joints is paramount, as they are critical load-carrying components of reinforced concrete buildings. Evaluating the ultimate joint shear capacity and failure modes of beam–columns, especially in seismic events, is a crucial task, especially in view of life safety concerns. Traditional methods used to determine the joint shear capacity of beam–column joints are often inaccurate and cumbersome owing to improper accounting of governing parameters that influence beam–column joints’ behaviour. In this study, the performance of machine learning-based structural health monitoring techniques are evaluated in predicting the joint shear capacity and the mode of failure for the exterior beam–column joint taking into account their complex structural behaviour through both numerical modelling and various machine learning techniques. The data used to train and test the model was collected from laboratory experiments and other test data available in the literature. The results indicated the superiority of the proposed particle swarm optimized artificial neural network (PSO-ANN) and XGboost over previously used approaches. Hence, the proposed techniques can be efficiently used for monitoring of structural performance by making informed decision regarding condition assessment of RC buildings.","url":"https://doi.org/10.1016/j.mlwa.2021.100190","authors":["Giuseppe Santarsiero","Mayank Mishra","Manav Kumar Singh","Angelo Masi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-10-25T21:48:10Z","doi":"10.1016/j.mlwa.2021.100190","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.58723/ijaaiml.v2i3.469","name":"Predicting Thyroid Cancer Recurrence Using Machine Learning: An Artificial Intelligence Approach to Clinical Oncology","source":"crossref","abstract":"Background of study: Differentiated thyroid cancer (DTC) accounts for most thyroid malignancies and has favorable survival outcomes, yet up to 30% of patients experience recurrence, placing strain on follow-up systems in resource-limited settings. Conventional staging tools offer limited predictive precision. With increasing interest in machine learning (ML) for precision oncology, there is a need for interpretable, deployable models suitable for low-resource environments.Aims and scope of paper: To develop and validate an interpretable machine learning model for predicting thyroid cancer recurrence and assess its feasibility for deployment in constrained clinical settings, including African oncology contexts.Methods: A retrospective dataset of 383 DTC patients with at least 10-year follow-up was sourced from the UCI Machine Learning Repository. Thirteen demographic, clinical, and treatment-related predictors were included. Data preprocessing involved encoding, scaling, and class balancing using SMOTE. Logistic Regression, Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost) were trained with hyperparameter tuning via grid search and cross-validation. Performance was evaluated using accuracy, precision, recall, F1 score, and AUC-ROC.Result: XGBoost achieved the best performance with 97% accuracy, 95% recall, 94% precision, and an AUC-ROC of 0.93. The most influential predictors were age, smoking status, T and M staging, ATA risk category, and adenopathy. The final model was deployed as a browser-based decision support tool to enable real-time recurrence risk estimation.Conclusion: This study presents a high-performing and interpretable ML model for predicting DTC recurrence, demonstrating feasibility for use in low-resource oncology settings. External validation with African clinical datasets and integration into electronic health systems is recommended to enhance equity and clinical uptake.","url":"https://doi.org/10.58723/ijaaiml.v2i3.469","authors":["Joy Aifuobhokhan","Ahmad Khalid Hussain","Chijioke Cyriacus Ekechi","Aisha Olasunbo Olanrewaju","Emmanuel Afuadajo","Deborah Adetola Bowale","Oluwadare Marvellous Inioluwa"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-11-05T03:05:36Z","doi":"10.58723/ijaaiml.v2i3.469","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1201/9781003264767-3","name":"Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis","source":"crossref","abstract":"Brain imaging is an umbrella term including many non-invasive techniques that objectively monitor brain function. Such monitoring leads to understanding how the brain works by presenting selected stimuli. More importantly, brain function monitoring allows physicians to diagnose and predict brain disorders. In the last decade, several machine learning and deep learning models have been developed by researchers to process and analyse brain imaging data for the diagnosis, detection, and prediction of brain disorders, such as stroke, schizophrenia, autism, psychosis, and Alzheimer’s. This chapter reviews the various applications and properties of machine learning and deep learning models for brain image analysis. The chapter also highlights the deep learning models that have either understood the test of time or shown the promise to solve challenging problems involving brain imaging data. The review also discusses various open issues yet to have practical solutions or methodologies with the help of machine learning and deep learning. The research covers a wide range of imaging modalities, disorders and models to expose researchers and practitioners in neurological disorders and machine learning and deep learning to each other’s field, hopefully leading to fruitful collaborations and practical solutions for processing brain images.","url":"https://doi.org/10.1201/9781003264767-3","authors":["Alwin Joseph","J Chandra","Bonny Banerjee","Madhavi Rangaswamy","K Jayasankara Reddy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-16T20:36:37Z","doi":"10.1201/9781003264767-3","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1002/9781119562306.ch1","name":"Machine Learning for Spectrum Access and Sharing","source":"crossref","abstract":"This chapter focuses on online learning algorithms that were developed for dynamic spectrum access (DSA), in which a cognitive user aims to learn the occupancy of the spectrum in the presence of external users to improve the spectral usage. The focus is thus on how quickly the cognitive user can learn the external process, and not on the interaction between cognitive users. The chapter discusses the more general model where multiple cognitive users share the spectrum, and the goal is to effectively allocate channels to cognitive users in a distributed manner in order to maximize a certain global objective. It then provides an overview of model-dependent solutions and discusses the very recent developments of artificial intelligence algorithms based on deep learning for DSA that can effectively self-adapt to complex real-world settings.","url":"https://doi.org/10.1002/9781119562306.ch1","authors":["Kobi Cohen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-12-13T21:26:07Z","doi":"10.1002/9781119562306.ch1","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/978-3-642-21280-2_4","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-21280-2_4","authors":["Achim Zielesny"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2011-07-25T14:20:24Z","doi":"10.1007/978-3-642-21280-2_4","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1201/9781003663461-17","name":"Machine Learning Fundamentals for Autonomous Systems","source":"crossref","abstract":"Machine learning plays a critical role in the development of autonomous systems, allowing them to interpret data, make decisions, and adapt to complex environments without human involvement. This chapter investigates the fundamental principles of machine learning as they apply to autonomous technologies, including key concepts such as supervised, unsupervised, and reinforcement learning. Additionally, it also looks at the design and operation of autonomous agents, which are self-directed AI systems with the ability to see, think, and act on their own. Advanced decision-making algorithms and machine learning are enabling autonomous agents to accomplish more complicated tasks with little assistance from humans. These agents are transforming industries including cybersecurity, healthcare, and finance by increasing productivity, decreasing human error, and increasing predicting accuracy. In contrast to conventional static AI models, these systems use real-time feedback loops, meta- learning, and reinforcement learning to improve contextual knowledge, reduce biases, and modify their answers. This development has significant ramifications, since it holds promise for more dependable AI-powered content creation, natural language processing, and even self-directed research. The topic of conversation also includes agentic AI, a new paradigm that gives autonomous systems the ability to collaborate, think strategically, and change dynamically. Through real-world examples and challenges, the chapter will comprehend how machine learning empowers autonomous agents to advance intelligence in intelligent automation, robotics, and transportation.","url":"https://doi.org/10.1201/9781003663461-17","authors":["S. Sarika","K.V. Meenatchi","P.B. Tintu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-19T09:20:51Z","doi":"10.1201/9781003663461-17","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1016/j.mlwa.2025.100815","name":"Algorithmic red teaming approaches to secure LLMs","source":"crossref","abstract":"Algorithmic red teaming for Large Language Models (LLMs) is a crucial practice for proactively ensuring their safety and robustness. This process involves using an LLM as an adversary to test the vulnerabilities of a target LLM, which is essential for identifying and mitigating potential security risks before the model is deployed. Automated methodologies, which surpass the constraints of human creativity, utilize a triad of models: an attacker, a target, and a judge. This primer provides a concise summary and comparison of several state-of-the-art algorithmic red-teaming approaches, including TAP, PAIR, Crescendo, and AutoDAN-Turbo. The goal of these techniques, such as prompt injection and jailbreaking, is to push LLMs beyond their intended safe behavior. Critically, the non-deterministic nature of LLMs presents a key challenge when they are utilized as judges, potentially rendering evaluations unreliable. The paper stresses that red teaming is not a one-time exercise and is particularly vital for AI agents that use LLMs as components, as a single failure can lead to significant public scrutiny. • The position paper establishes red teaming as a mandatory practice to proactively identify and mitigate vulnerabilities in Large Language Models (LLMs) before they fail in real-world applications. • It outlines the distinction between manual red teaming, which is constrained by human creativity, and automated red teaming, which leverages LLMs themselves to find weaknesses in a “ fire meets fire ” approach. • It explains the typical architecture for automated red teaming, which involves a triad of models: an attacker to generate adversarial prompts, a target model to be tested, and a judge model to evaluate the success of the attack. • This primer provides a concise summary and comparison of several state-of-the-art algorithmic red-teaming approaches, including TAP, PAIR, Crescendo, AutoDAN-Turbo. • The manuscript highlights a key challenge in the field: non-deterministic nature of the LLMs makes them potentially unreliable judges. It proposes solutions such as aggregating results from differently architectured models, even models with same architecture yet varying token-optimization and parameterization techniques, rule-alignment, and human feedback.","url":"https://doi.org/10.1016/j.mlwa.2025.100815","authors":["Shaurya Jauhari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-27T16:24:01Z","doi":"10.1016/j.mlwa.2025.100815","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1023/a:1022613811741","name":"Conflict Resolution as Discovery in Particle Physics","source":"crossref","abstract":"","url":"https://doi.org/10.1023/a:1022613811741","authors":["Sakir Kocabas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2003-04-04T16:55:36Z","doi":"10.1023/a:1022613811741","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1515/9781501520112-007","name":"Chapter 6: Machine Learning Classifiers with GPT-4","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9781501520112-007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-04T02:00:52Z","doi":"10.1515/9781501520112-007","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1023/b:mach.0000033116.57574.95","name":"Correlation Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1023/b:mach.0000033116.57574.95","authors":["Nikhil Bansal","Avrim Blum","Shuchi Chawla"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2004-06-25T21:08:21Z","doi":"10.1023/b:mach.0000033116.57574.95","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.3121/cmr.2014.1250.c3-4","name":"C3-4: An Algorithm to Combine Machine Learning and Structured Data to Automate De-identification of Clinical Text","source":"crossref","abstract":"Background/Aims Clinical text is an important resource for research. To maintain patient privacy when researching this text, we use de-identification. The hiding in plain sight (HIPS) method is promising; it replaces personally identifiable information (PII) with realistic surrogates so any remaining real PII would be hard to distinguish from the fake information. However, there remain some challenges with HIPS, such as overlooked PII. We explored these challenges and hypothesized that we could find more PII by combining structured data with a machine learning algorithm. Methods The machine learning de-identification software we used, developed by MITRE, is the MITRE Identification Scrubber Toolkit (MIST). Trained chart abstractors annotated Family Practice notes with the following PII types: address, age, date, provider name, email, IP address, consumer number, organization name, other id, phone, patient name, room id, social security number, and URL address. Structured data included in this experiment are patient’s address, age, date of birth, email, phone, consumer number, social security number, the visit provider name, visit date, and visit location. We queried this data from Clarity, a relational reporting database for Group Health’s electronic health record (EHR) system. Our first test experiment used MIST to train a model on 100 documents then tested on 10 notes. We reviewed the remaining PII and determined if they are available in the structured data. Results MIST’s precision was 0.93 and recall was 0.77. MIST left 13 leaks and incorrectly identified two instances of blood pressure numbers as dates. We can reduce 3 out of 13 leaks using structured data obtained for that visit note. The remaining leaks are: 3 locations, 1 age which belonged to the patient’s child, 1 visit date pre-EHR system, 3 pieces of historical visit data, and 2 mentions of ‘sheriff’ because the chart abstractor determined it to be an “other” PII type. Conclusions Our results show that combining structured data with MIST can potentially improve de-identification of Group Health clinical text. Future work is to build an automated system that include historical visit data, use gender, race, and language to create more realistic surrogates and to evaluate the loss of important clinical information in de-identified documents.","url":"https://doi.org/10.3121/cmr.2014.1250.c3-4","authors":["D.-T. Tran","S. Halgrim","D. Carrell"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2014-10-28T19:51:55Z","doi":"10.3121/cmr.2014.1250.c3-4","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/978-0-387-30164-8_25","name":"Apprenticeship Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_25","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:36:36Z","doi":"10.1007/978-0-387-30164-8_25","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1007/978-0-387-30164-8_333","name":"Generative Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_333","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2010-12-29T17:43:13Z","doi":"10.1007/978-0-387-30164-8_333","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"doi:10.1017/9781009072205.012","name":"Statistical Learning Theory","source":"crossref","abstract":"This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.","url":"https://doi.org/10.1017/9781009072205.012","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-24T19:05:52Z","doi":"10.1017/9781009072205.012","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484891","name":"Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis.","source":"pubmed","abstract":"Gastric-type endocervical adenocarcinoma (GAS) is an aggressive, non-HPV-associated cervical adenocarcinoma that is often difficult to recognize preoperatively. This study aimed to characterize the integrated PET/CT phenotype of GAS and evaluate whether morphological, metabolic, serological, and explainable machine-learning features could support its differentiation from squamous cell carcinoma (SCC) and usual-type endocervical adenocarcinoma (UEA).","url":"https://pubmed.ncbi.nlm.nih.gov/42484891/","authors":["Wang Y","Su Y","Zhang Y","Cai Y","Zhu T","Yi H","Yuan Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s00259-026-08088-7","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484763","name":"Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study.","source":"pubmed","abstract":"To evaluate differences in performance between two large language models (LLMs), GPT-5.3 and Gemini 2.5 Pro, in diagnostic classification and clinical reasoning for primary open-angle glaucoma (POAG).","url":"https://pubmed.ncbi.nlm.nih.gov/42484763/","authors":["Zhang Z","Lu S","Xu Z","Ji K","Liang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s10792-026-04180-x","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484744","name":"Identification and functional validation of lipid droplet-associated prognostic biomarkers in breast cancer via integrative multi-omics and machine learning approaches.","source":"pubmed","abstract":"Lipid droplet (LD)-associated metabolic reprogramming plays a critical role in breast cancer progression and immune modulation, yet robust prognostic biomarkers and their functional mechanisms remain incompletely understood. This study aimed to identify LD-associated biomarkers with prognostic and therapeutic relevance through multi-omics integration and functional validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42484744/","authors":["Yao J","Liu Z","Zhang J","Long X","Chao C","Yuan L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s10147-026-03133-9","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484678","name":"Effect of cataracts on systemic factor prediction from fundus images.","source":"pubmed","abstract":"To evaluate the effect of cataracts on systemic factor predictions from fundus images by comparing predictive values before and after cataract surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/42484678/","authors":["Funatsu R","Mihara N","Miyake S","Okamura K","Imatsuji H","Sakono T","Watanabe N","Terasaki H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s00417-026-07404-z","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484579","name":"Prognostication of Acute Kidney Injury Following Percutaneous Coronary Intervention: A Machine Learning Approach.","source":"pubmed","abstract":"Acute kidney injury (AKI) is a serious complication of percutaneous coronary intervention (PCI) associated with increased mortality and health care costs. Traditional risk scores often rely on intraprocedural variables, limiting their utility for preprocedural prophylaxis.","url":"https://pubmed.ncbi.nlm.nih.gov/42484579/","authors":["Itelman E","Altman Y","Yacobi B","Rotmensh A","Steinmetz T","Codner P","Levi A","Talmor-Barkan Y","Witberg G","Kornowski R","Perl L","Skalsky K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.jacadv.2026.103012","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484549","name":"Multimodal Deep Learning and Knowledge-Enhanced Intelligent Decision Support System for Pipeline Embolization Device Size Selection in Intracranial Aneurysm Treatment.","source":"pubmed","abstract":"This study aimed to develop an end-to-end intelligent decision support system, NeurAneuNet, to automate the selection of Pipeline Embolization Device (PED) size and landing zones for intracranial aneurysm treatment, thereby reducing reliance on operator experience and improving planning consistency.","url":"https://pubmed.ncbi.nlm.nih.gov/42484549/","authors":["Wen Z","Guo S","Peng Y","Chen Y","Huangfu L","Zhao H","Gao H","Ni T","Zhang J","Liu X","Liu J","Liang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/cns.71047","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484513","name":"Chemical-Sensing Catheters for Long-Term and Personalized Therapeutic Management.","source":"pubmed","abstract":"Long-term intravenous catheterization is vital for delivering chemotherapy to cancer patients and parenteral nutrition to those with intestinal failure, but it inevitably leads to complications, including metabolic disorders, bloodstream infection, liver injury, and kidney injury. Clinical monitoring methods show notable delays, while real-time blood monitoring devices fail to ensure long-term stability in vivo. Here, we present a chemical-sensing catheter that integrates multiple analyte sensors, including creatinine, taurocholic acid, bilirubin, and taurine, for real-time monitoring of complications in patients receiving long-term chemotherapy or parenteral nutrition in situ. The design of the metalgel preserves signal integrity through 1 million bending deformations, and a dual-network molecularly imprinted polymer suppresses in vivo performance degradation, enabling stable long-term operation of the chemical-sensing catheter over a 3 month implantation period. In chemotherapy and parenteral nutrition applications, early monitoring of liver and kidney injury, combined with prognostic intervention, mitigates risks such as bile stasis, hepatitis, and hepatic steatosis. With the integration of machine learning analysis of multimodal sensor data, this technology provides early prediction of complications and establishes a new paradigm for personalized therapeutic management.","url":"https://pubmed.ncbi.nlm.nih.gov/42484513/","authors":["Li Y","Cao X","Li Q","Liu X","Sun H","Jiao Y","Huang G","Han L","Lu J","Zou K","Li D","Guo W","Ye T","Wang J","He E","Li F","Wang Y","Yang S","Bai C","Song J","Li X","Zhang H","Wang H","Zhang Y","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1002/adma.74221","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484385","name":"Predictive profiles for poor functional improvement and mortality after transcatheter aortic valve replacement. Value of 6-minute walk test and myocardial work analysis.","source":"pubmed","abstract":"Clinical improvement and survival after transcatheter aortic valve replacement (TAVR) remain difficult to predict. Although multiple predictors of both outcomes have been identified, prognostic algorithms for patients undergoing TAVR are not well established.","url":"https://pubmed.ncbi.nlm.nih.gov/42484385/","authors":["Rychlewski J","Przewlocka-Kosmala M","Marwick TH","Sen J","Aleksandrowicz K","Golanski G","Kubisz K","Donal E","Protasiewicz M","Kubler P","Kosmala W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.33963/v.phj.113742","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42484201","name":"Assessing a large language model for glaucoma knowledge: ChatGPT-5 versus residents.","source":"pubmed","abstract":"To assess the performance of a contemporary large language model (ChatGPT-5) against ophthalmology residents on a standardized set of glaucoma multiple-choice questions.","url":"https://pubmed.ncbi.nlm.nih.gov/42484201/","authors":["Gobira M","Moreira R","Carvalho Filho FJLG","Carvalho KWP","Murta FN","Carvalho LAA","Belfort R Jr","Tavares IM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5935/0004-2749.2025-0283","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483889","name":"Predicting gingival embrasure risk after invisible orthodontics using multimodal data and machine learning.","source":"pubmed","abstract":"To develop and validate a risk prediction model for gingival embrasures after clear aligner therapy using multimodal oral data.","url":"https://pubmed.ncbi.nlm.nih.gov/42483889/","authors":["Wang H","Shi H","Fan L","Sun Z","Xiu X","Yuan H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.2340/aos.v85.46562","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483845","name":"AI Risk Prediction Tools for Autologous Breast Reconstruction.","source":"pubmed","abstract":"Autologous breast reconstruction offers patients a durable and natural-appearing option after mastectomy. However, complication risks include flap loss, infection, and delayed wound healing. This study developed both traditional statistical and machine learning (ML) models to predict the risk of developing a 90-day postoperative complication after autologous reconstruction.","url":"https://pubmed.ncbi.nlm.nih.gov/42483845/","authors":["Chen J","Gabay A","Hassan AM","Graziano FD","Boe LA","Roberts AN","Shammas RL","Stern CS","Allen RJ Jr","Mehrara BJ","Nelson JA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1002/jso.70318","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483770","name":"A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis.","source":"pubmed","abstract":"Accurate and rapid diagnosis of meniscal tears is crucial for effective management of sports-related injuries and degenerative knee disorders. Magnetic resonance imaging (MRI) is widely used for meniscus evaluation; however, manual interpretation is time-consuming and subject to inter-observer variability. Automated and reliable classification systems may therefore support clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42483770/","authors":["Seyyarer E","Genç H","Ayata F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/acm2.70711","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483737","name":"Regional Inequities in Metabolic Dysfunction-associated Steatotic Liver Disease Burden and Care Quality in High-burden Settings: Implications for Health Systems.","source":"pubmed","abstract":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasing rapidly, yet regional differences in burden and care quality remain unclear. This study aimed to compare regional incidence, mortality, and disability; evaluate care quality; identify key determinants; and project future incidence.","url":"https://pubmed.ncbi.nlm.nih.gov/42483737/","authors":["Zhang K","Kan C","Sheng S","Xu W","Han F","Chen J","Li X","Hou N","Xue Y","Sun X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 28","doi":"10.14218/JCTH.2026.00127","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483734","name":"A Multi-omics and Machine Learning Framework Identifies Plasma SBDS as a Causal Biomarker and Therapeutic Target in Primary Sclerosing Cholangitis.","source":"pubmed","abstract":"Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference.","url":"https://pubmed.ncbi.nlm.nih.gov/42483734/","authors":["Cheng P","Qiang Y","Sun Y","Duan B","Ouyang Y","Li G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 28","doi":"10.14218/JCTH.2025.00676","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483693","name":"Lipidomic Profiling and Biological Aging in Patients with Coronary Microvascular Dysfunction.","source":"pubmed","abstract":"Coronary microvascular dysfunction (CMD) was frequently encountered in patients with angina in the absence of epicardial coronary stenosis. CMD was associated with adverse outcomes while the etiology was unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42483693/","authors":["Lu D","Deng L","Wang Z","Hu Y","Pan C","Lu H","Zhou Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/VHRM.S606866","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483565","name":"Predicting cancer treatment outcomes using machine learning enabled clinical decision support systems.","source":"pubmed","abstract":"Cancer treatment poses significant challenges due to variability in patient responses, disease progression, and therapy outcomes. Traditional decision-making frameworks often fall short in integrating complex data streams, underscoring the need for intelligent systems. Machine learning (ML)-enabled clinical decision support systems (CDSS) offer a promising solution by enabling personalized, predictive, and data-driven oncology care.","url":"https://pubmed.ncbi.nlm.nih.gov/42483565/","authors":["Hossain A","Rashid MM","Alam T","Riipa MB","Hassan M","Imran MAU","Mohammad N","Ahmed F","Parvin MR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261470185","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483468","name":"Comparative evaluation of the aggregate index of systemic inflammation (AISI) and its modified version for early detection of central line-associated bloodstream infection: a pilot study using machine learning techniques.","source":"pubmed","abstract":"Central line-associated bloodstream infections (CLABSI) are a common and serious problem in critically ill patients; their early detecting is challenging. This study evaluated the predictive ability of the aggregate index of systemic inflammation (AISI) and its modified form for early identification of CLABSI within two calendar days following central line insertion, using a machine learning approach.","url":"https://pubmed.ncbi.nlm.nih.gov/42483468/","authors":["Anand G","Priyadarshi K","Kumari B","Tiewsoh JBA","Lahariya R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3205/dgkh000661","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483426","name":"Integrating clinical decision support systems, nursing vigilance, and physician prescribing patterns to reduce preventable adverse drug events: a structured evidence-based narrative review on human-AI interface in medication safety.","source":"pubmed","abstract":"Preventable adverse drug events (ADEs) remain a major source of hospital morbidity, mortality, and healthcare costs worldwide. Clinical decision support systems (CDSS) integrated into electronic health records (EHRs) were developed to reduce unsafe prescribing, yet evidence of their real-world effectiveness remains mixed. The emergence of artificial intelligence (AI) and machine learning (ML) offers new opportunities to enhance medication safety but also introduces risks such as algorithmic bias, technology-induced error, and reduced clinician vigilance.","url":"https://pubmed.ncbi.nlm.nih.gov/42483426/","authors":["Alruwaili MF","Paul-Chima UO","Nneoma UC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1831150","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483415","name":"Closed loop construction of hypoglycemia risk management for high risk neonates in mother infant rooming in settings: a retrospective study with an embedded clinical decision support system.","source":"pubmed","abstract":"To evaluate the effectiveness of an intelligent clinical decision support system (CDSS) for neonatal hypoglycemia management in mother-infant rooming-in settings, and to dissect the differential hypoglycemia risk conferred by individual high-risk factors and their specific combinations under standardized surveillance.","url":"https://pubmed.ncbi.nlm.nih.gov/42483415/","authors":["Chen L","Shao H","Yu B","Wang M","Tian C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1798686","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483211","name":"An explainable machine learning framework for accurate prediction of postoperative anterior chamber depth in highly myopic cataract surgery.","source":"pubmed","abstract":"To develop and validate machine learning models for predicting postoperative anterior chamber depth (ACD) in highly myopic cataract patients based on preoperative biometric parameters.","url":"https://pubmed.ncbi.nlm.nih.gov/42483211/","authors":["Yang Y","Cui H","Gao J","Wang Y","Zhang B","Liu J","Yu H","Wu W","Li L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1876904","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483183","name":"A novel study to calculate immune-aging from peripheral blood T lymphocyte subsets and their mitochondrial parameters in healthy Chinese subjects.","source":"pubmed","abstract":"Immunosenescence is a process in which the body's immune function declines with age, which is associated with the increased risk of infection, tumors, and other diseases. Traditional biological age assessment is difficult to fully reflect the changes in immune function. Flow cytometry can obtain multi-dimensional data such as immune cell subsets and mitochondrial function, and combined with machine learning, it is possible to quantify immune-aging. This study aimed to establish an accurate immune-aging prediction model based on peripheral blood indicators of healthy people in China.","url":"https://pubmed.ncbi.nlm.nih.gov/42483183/","authors":["Gan G","Guo P","Li X","Zhang S","Zhao W","Gao Y","Zhuang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1857636","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42483108","name":"Artificial Intelligence and Machine Learning-Based Triage Systems in Emergency Departments: A Systematic Review of Predictive Performance and Clinical Outcomes.","source":"pubmed","abstract":"Emergency department (ED) triage systems are essential for prioritizing patients based on clinical urgency, yet traditional methods are subject to inter-observer variability and limited predictive accuracy. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance triage decision-making. This systematic review synthesizes existing evidence on AI/ML-based triage systems in EDs, focusing on predictive performance and reported clinical outcomes, while critically appraising methodological quality and gaps affecting clinical applicability. A comprehensive search was conducted across PubMed, Scopus, CINAHL, IEEE Xplore, and Web of Science (2021-2026), supplemented by citation tracking. Studies developing or validating AI/ML models for ED triage and reporting quantitative performance metrics were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for quality assessment. A narrative synthesis was performed due to substantial heterogeneity. Fourteen retrospective observational studies (2021-2026) from eight countries met the inclusion criteria (sample sizes: 657 to &gt;2.6 million visits). Models included gradient-boosted trees, random forest, logistic regression, neural networks, natural language processing, and large language models. AUC-ROC ranged from 0.642 to 0.991 (highest for mortality (0.874-0.933) and pediatric critical illness (0.991)). However, calibration was reported in only three studies, external validation in only five, and only one study demonstrated direct clinical process improvement (reduced missed ECGs). No prospective or randomized controlled trials were identified. PROBAST rated 11 studies as low risk of bias, two as high, and two as unclear. AI/ML models show moderate to excellent&#xa0;retrospective&#xa0;predictive performance for ED triage outcomes, particularly ensemble tree-based and NLP-enhanced approaches. However, the evidence base is severely limited by overreliance on heterogeneous retrospective designs, insufficient calibration reporting, and lack of prospective or external validation. Consequently, the strength of conclusions regarding&#xa0;clinical applicability&#xa0;remains weak. Future research must prioritize rigorous prospective validation, calibration reporting, and randomized trials measuring patient-centered outcomes before clinical implementation is considered.","url":"https://pubmed.ncbi.nlm.nih.gov/42483108/","authors":["Mohamed N","Mohammed Ibrahim AA","Shahid N","Alhussein Alamass SI","Mubarak Osman AME"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111230","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482995","name":"Integration of periodontal pathogens and inflammatory mediators in saliva as biomarkers for periodontitis.","source":"pubmed","abstract":"Periodontitis pathogenesis is driven by oral microbiome dysbiosis and dysregulated host immune responses. This cross-sectional study characterized salivary microbiome and inflammatory mediator profiles to identify candidate biomarkers distinguishing periodontal health from periodontitis (PD) using hyperplex PCR, multiplex assay and exploratory machine learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42482995/","authors":["Titusson C","Lundmark A","Soares RRG","Damdimopoulos A","Johannsen G","Naseem U","Yucel-Lindberg T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcimb.2026.1846125","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482953","name":"Anthropometry-adjusted TyG indices predict diabetes, graft failure, and mortality in kidney transplant recipients.","source":"pubmed","abstract":"The triglyceride-glucose (TyG) index is a widely used surrogate marker of insulin resistance and associated with cardiometabolic outcomes. However, the comparative prognostic value of TyG-derived indices, particularly those incorporating anthropometric measures or long-term variability, for adverse outcomes after kidney transplantation remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42482953/","authors":["Xiong J","Szili-Torok T","Özyilmaz ÖT","Xu W","Daamen JS","Jonker J","Groothof D","Kremer D","Neto AG","Pol RA","de Borst MH","Bakker SJL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1210/jendso/bvag157","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482916","name":"ATF3 and HNF4A: an oxidative phosphorylation and cholesterol homeostasis-associated diagnostic and therapeutic repurposing framework target for metabolic dysfunction-associated steatohepatitis patients.","source":"pubmed","abstract":"Metabolic dysfunction-associated steatohepatitis (MASH) is hepatic steatosis. Oxidative phosphorylation and cholesterol homeostasis (OC) plays a key role in the onset and progression of MASH. Hence, deeper understanding of OC in MASH can shed light on the clinical applications for MASH patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42482916/","authors":["Zeng G","Zhao Q","Jiang L","Xie D","Du L","Yang M","Luo M","Wang Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1772363","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482842","name":"Primary Graft Dysfunction After Lung Transplantation: A Temporal Classification and Machine Learning Clustering.","source":"pubmed","abstract":"Primary graft dysfunction (PGD) is a major cause of morbidity and mortality after lung transplantation (LTx). PGD is graded at static time points, limiting insight into its temporal dynamics. Statistical risk-factor analysis may overlook the multifactorial complexity of PGD. Machine learning (ML) may address this but is constrained by small sample sizes. We aim to introduce a temporal PGD classification, perform ML-based clustering and overcome sample-size limitations by generating synthetic patient data.","url":"https://pubmed.ncbi.nlm.nih.gov/42482842/","authors":["Özsoy B","Vercauteren B","Van Slambrouck J","Vanluyten C","Barbarossa A","Jin X","Van Raemdonck DE","Dmitrieva J","Khan A","Jacquemyn X","Bos S","Vos R","Vanaudenaerde BN","Carlon MS","Aerts JM","Carmeliet P","Ceulemans LJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1097/TXD.0000000000001984","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482839","name":"Are Real-World Mobility Patterns Early Indicators of COPD Onset? Insights from Wrist-Worn Sensors.","source":"pubmed","abstract":"Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of disability and death worldwide. Early identification remains challenging, as existing prediction models largely rely on clinic-based assessments and self-report measures that are resource-intensive and prone to bias. This study aimed to determine whether real-world mobility metrics could predict incident COPD.","url":"https://pubmed.ncbi.nlm.nih.gov/42482839/","authors":["Fernandez N","Wong AYL","Brodie MA","Van Schooten KS","Chan LLY","Lord SR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/COPD.S605234","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482823","name":"Comparative evaluation of deep learning models for three-class frailty assessment using gait metrics.","source":"pubmed","abstract":"Frailty assessment in older adults typically relies on subjective clinical tools that are time-consuming and require trained personnel, limiting their use in routine or large-scale screening. Wearable sensor-based gait analysis offers a promising objective alternative; however, comprehensive evaluations of deep learning models for multi-class frailty classification using structured gait metrics remain limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42482823/","authors":["Hughes CML","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fragi.2026.1873618","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482788","name":"Artificial Intelligence Empowers Physical Therapy for Older Adults: A Perspective.","source":"pubmed","abstract":"Population aging has increased the demand for rehabilitation services due to multimorbidity and age-related functional decline. Physical therapy plays a central role in maintaining mobility and independence in older adults; however, increasing clinical complexity challenges the delivery of individualized care. This perspective aims to synthesize current applications of artificial intelligence (AI) and its potential to enhance assessment, monitoring, and intervention in geriatric physical therapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42482788/","authors":["Huang H","Zhang C","Hao J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/hsr2.72862","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482741","name":"Hyperspectral imaging in oral oncology: a scoping review.","source":"pubmed","abstract":"Oral cancer accounts for 177,000 deaths annually. Despite advances in surgical techniques and adjuvant therapies, the five-year survival rate for oral malignancies has shown limited improvement. Hyperspectral imaging (HSI) is a new non-invasive optical modality that extends medical imaging beyond the visible spectrum. This scoping review aims to map the extent, nature, and methodological characteristics of the existing literature on the application of HSI for the detection and characterization of oral cancer. It explores the clinical applications of HSI in oral oncology, outlines the technical and analytical approaches used, and evaluates its diagnostic performance and translational readiness in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42482741/","authors":["Kankawale S","Jamkhande A","Oka G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/froh.2026.1857460","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482648","name":"Scanner-agnostic artificial intelligence approach for fast bone scintigraphy.","source":"pubmed","abstract":"Current bone scintigraphy protocols often demand full-count, 10-15&#xa0;min scans to preserve image quality, and existing deep-learning (DL) denoisers typically need to be retrained or retuned for each camera manufacturer. We introduce a scanner-agnostic adaptive-diffusion U-Net designed to reconstruct diagnostic-grade images from half-time or half-dose acquisitions without scanner-specific retraining.","url":"https://pubmed.ncbi.nlm.nih.gov/42482648/","authors":["Menezes VO","Queiroz CC","Oliveira AAS","Santos Filho AJD","de Morais AF","Vigário AO","Flamini ME","Mourato FA","Ren TI","Machado MAD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/acm2.70709","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482646","name":"The Relationship between Biological Aging and Cognitive Function: A Machine Learning Model.","source":"pubmed","abstract":"The relationship between cognitive impairment and biological age (BA) is unclear. This study aimed to investigate the association between BA and cognitive impairment.","url":"https://pubmed.ncbi.nlm.nih.gov/42482646/","authors":["Ren C","Li Z","Cai J","Li J","Hong X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.3349/ymj.2025.0195","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482643","name":"Prediction of Clinically Significant Improvement after Lumbar Fusion Surgery Based on Machine Learning.","source":"pubmed","abstract":"Lumbar fusion surgeries have increased substantially, making patient satisfaction an important indicator of surgical outcomes and quality of care. Conventional statistical approaches have limitations in predicting clinically significant improvement (CSI). This study aimed to develop a machine learning model to predict CSI after lumbar fusion surgery using only preoperative factors to support clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42482643/","authors":["Cho H","Lee D","Kim S","Noh SH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3349/ymj.2025.0367","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42482638","name":"A projection-domain deep learning approach for respiratory motion correction of myocardial perfusion imaging using a multi-pinhole solid-state SPECT.","source":"pubmed","abstract":"Myocardial perfusion imagining (MPI) is a nuclear medicine technique used in the assessment of coronary artery disease. Differences in the perfusion of the myocardium at rest and stress and regions of perfusion defects can be indicators of various cardiac disease states. Respiratory motion (RM) can result in the degradation of image quality and the appearance of artificial regions of perfusion deficit. While RM correction is possible, many methods involve time-consuming calculation or additional equipment for motion tracking. This has limited the clinical uptake of such methods. This work presents a deep learning model for data-driven RM correction on dedicated cardiac SPECT scanners.","url":"https://pubmed.ncbi.nlm.nih.gov/42482638/","authors":["Malenfant DJ","Ruddy TD","Wells RG"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70543","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482570","name":"Accurate automated 3D lumbar spine reconstruction from biplanar X-rays using multi-task deep learning and anatomy-aware optimization.","source":"pubmed","abstract":"Accurate 3D assessment of the weight-bearing lumbar spine is crucial for diagnosing various spinal pathologies. However, existing biplanar X-ray reconstruction methods struggle with complex pathologies and low-contrast structures.","url":"https://pubmed.ncbi.nlm.nih.gov/42482570/","authors":["Yu W","Zhu Z","Wang C","Bao Y","Xia C","Cheng R","Hou Z","Chen S","Yu Y","Tsai TY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70570","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482466","name":"Quantitative CRFF-OCT Imaging Features for Characterization of Disease Activity in Non-Segmental Vitiligo: A Machine Learning-Assisted Study.","source":"pubmed","abstract":"Vitiligo is an autoimmune pigmentary disorder characterized by progressive melanocyte loss and unpredictable activity. Objective of disease activity remains challenging, particularly in lesions with subtle clinical changes Cellular-resolution full-field optical coherence tomography (CRFF-OCT) enables non-invasive, high-resolution, histology-like visualization of skin microstructure. This study evaluated the feasibility of integrating CRFF-OCT with machine learning-assisted quantitative analysis for imaging-based characterization of active and stable vitiligo lesions. Fifty patients with non-segmental vitiligo were prospectively enrolled (2021-2022). CRFF-OCT imaging was performed on lesional, perilesional, and normal-appearing skin within the same anatomical region. Quantitative features describing epidermal structure, dermal-epidermal junction (DEJ) morphology, and pigment-associated reflectivity were extracted. A machine learning-assisted computer-aided detection (CADe) framework incorporating 13 features was developed for image-level classification of lesion activity. CRFF-OCT imaging demonstrated distinct microstructural patterns between active and stable lesions. Basal epidermal pigment-associated reflectivity was significantly lower in stable lesions compared with active lesions (9.94%&#x2009;&#xb1;&#x2009;10.06% vs. 21.84%&#x2009;&#xb1;&#x2009;11.08%), with corresponding differences in lesion-to-normal reflectivity ratios (0.21 vs. 0.56). Quantitative analysis revealed significant differences in epidermal thickness, DEJ associated reflectivity, inter-layer contrast, and reflectivity heterogeneity. Among the 13 extracted features, 9 differed significantly between groups and were incorporated into classification models. The CADe framework achieved a maximum image-level classification accuracy of 80.6% using a support vector machine model. These findings demonstrate the feasibility of CRFF-OCT-based quantitative imaging for objective characterization of vitiligo lesion status and support its potential role in disease activity assessment and longitudinal monitoring.","url":"https://pubmed.ncbi.nlm.nih.gov/42482466/","authors":["Ng CY","Chen IL","Chen YT","Tsai FL","Cheng H","Huang L","Huang YL","Tsai TF","Chen SN","Lai YC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1111/exd.70309","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482440","name":"A modality-agnostic coronary artery habitat model for cardiac sparing in radiotherapy.","source":"pubmed","abstract":"Emerging evidence suggests that the risk of cardiotoxicity increases with increased radiation dose to coronary arteries (CAs). However, robust tools to evaluate this increased burden for cancer patients are not available due to current limitations in imaging for radiotherapy treatment planning.","url":"https://pubmed.ncbi.nlm.nih.gov/42482440/","authors":["Ruff C","Summerfield N","Dong M","Nagpal P","Bayliss A","Baschnagel AM","Glide-Hurst CK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70595","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482365","name":"Multiscale frequency attention transformer for resolution enhancement in magnetic particle imaging.","source":"pubmed","abstract":"Magnetic particle imaging (MPI) is an emerging functional imaging modality that enables high-resolution (HR) visualization of superparamagnetic iron oxide nanoparticles (SPIONs). It offers significant advantages, including high penetration capability, absence of ionizing radiation, high contrast, and exceptional temporal resolution and sensitivity, making it highly promising for a broad spectrum of biomedical and clinical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42482365/","authors":["Jiang W","Xu J","Yang X","He N","Wildgruber M","Zhong J","Zhao J","Ma X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/mp.70565","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482223","name":"Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.","source":"pubmed","abstract":"Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that modern disease-modifying therapies substantially reduce relapse rates and acute inflammatory activity detected by magnetic resonance imaging (MRI). However, disability accumulation increasingly occurs independent of relapse activity, highlighting progression biology as the principal unmet need. Converging epidemiological and molecular evidence supports a pivotal role for Epstein-Barr virus (EBV) infection in disease initiation, whereas later stages appear dominated by brain-intrinsic mechanisms, including compartmentalized inflammation, microglial activation, failure of remyelination and accelerated biological ageing. Population-based cohorts demonstrate that early high-efficacy therapy improves long-term outcomes, yet the risk of progression rises markedly after midlife despite effective relapse suppression. Emerging biomarkers, such as serum neurofilament light chain, glial fibrillary acidic protein, paramagnetic rim lesions and advanced quantitative MRI metrics, now enable more granular monitoring of progressive pathology. Integration of imaging, fluid biomarkers, genetics and machine learning offers opportunities for individualized benefit-risk stratification. Brain-penetrant Bruton's tyrosine kinase inhibitors, CD40 ligand-targeting biologics, refined B-cell-depleting strategies and emerging chimeric antigen receptor T-cell therapies represent promising approaches to target different aspects of compartmentalized inflammation and smoldering disease biology. Future management will require mechanism-informed treatment algorithms that align therapeutic choice with dominant disease drivers while incorporating comorbidity management, de-escalation strategies and potential EBV-targeted preventive approaches to optimize outcomes across the entire disease course.","url":"https://pubmed.ncbi.nlm.nih.gov/42482223/","authors":["Piehl F","Castelo-Branco G","Jagodic M","Olsson T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1111/joim.70125","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42482080","name":"A bladder cancer-associated gene signature for exploratory qPCR-based survival stratification in NAC-Treated muscle-invasive bladder cancer.","source":"pubmed","abstract":"Muscle-invasive bladder cancer (MIBC) is an aggressive and heterogeneous malignancy with limited biomarkers to guide patient stratification and response to neoadjuvant chemotherapy (NAC). We sought to define the biological basis and clinical utility of a bladder cancer-associated ten-gene signature for predicting prognosis and therapeutic response.","url":"https://pubmed.ncbi.nlm.nih.gov/42482080/","authors":["Chen X","Sakatani T","Tanaka S","Contreras-Sanz A","Black P","Rosser CJ","Furuya H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1186/s12967-026-08681-2","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481990","name":"Machine learning-based clinical models for prediction of urinary incontinence after robot-assisted laparoscopic radical prostatectomy.","source":"pubmed","abstract":"To investigate risk factors for urinary incontinence (UI) after robot-assisted laparoscopic radical prostatectomy (RARP) using interpretable machine learning methods, establish and validate a predictive model.","url":"https://pubmed.ncbi.nlm.nih.gov/42481990/","authors":["Wang X","Yuan Y","Zhou P","Gu B","Jiang X","Sha Y","Li N","Gan A","Chen Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12893-026-04053-1","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42481967","name":"Non-invasive diagnostic evaluation of urinary exosomal let-7c cluster expression in bladder cancer using machine learning approaches.","source":"pubmed","abstract":"Bladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. The let-7c cluster, owing to its tumor-suppressive function and frequent dysregulation in BCa, has emerged as a promising candidate for diagnostic evaluation. This study assessed the diagnostic potential of the urinary exosomal let-7c cluster through Machine Learning (ML)-integrated miRNA profiling, offering early proof-of-concept for its role in BCa classification.","url":"https://pubmed.ncbi.nlm.nih.gov/42481967/","authors":["Kural S","Kumar L","Pathak AK","Singh S","Jain G","Yadav M","Singh Y","Kumar U","Trivedi S","Gupta M","Agarwal S","Gautam V","Das P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1186/s12885-026-16217-6","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481858","name":"Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.","source":"pubmed","abstract":"This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at emergency department (ED) triage using longitudinal data. This retrospective, single-center study included adult patients, presented to ED of tertiary academic hospital with fever from January 2016 to December 2021. Using the AutoScore framework, we developed a novel scoring system for predicting sepsis and septic shock at the triage stage, incorporating nine variables and a maximum score of 29. The predictive performance of our score was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and its performance was compared with that of two existing scoring systems: the quick Sequential Organ Failure Assessment (qSOFA) and the Modified Early Warning Score (MEWS). Our model incorporated nine variables including initial vital signs, age, baseline platelet count, total bilirubin, and creatinine levels. Among these, initial systolic blood pressure was identified as the most important predictor. AUROC of our model was 0.844 (95% confidence interval [CI], 0.812-0.875) in predicting septic shock and 0.703 (95% CI, 0.687-0.720) for sepsis. Compared to qSOFA&#x2009;&#x2265;&#x2009;2 (AUROC: 0.605) and MEWS&#x2009;&#x2265;&#x2009;5 (AUROC: 0.678), our scoring system demonstrated superior predictive performance for septic shock. For comparable specificity levels (ranging from 0.50 to 0.95), our scoring system achieved higher sensitivity than MEWS. Our scoring system is an interpretable and practical scoring tool for predicting sepsis and septic shock among patients with documented fever at ED triage using patient's longitudinal data.","url":"https://pubmed.ncbi.nlm.nih.gov/42481858/","authors":["Maeng SJ","Lee YR","Lee SU","Yu JY","Choi JW","Lee G","Park JE","Shin TG","Hwang SY","Yoon H","Cha WC","Kim T","Kim M","Chang H","Heo S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s10916-026-02443-9","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481850","name":"The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.","source":"pubmed","abstract":"Artificial intelligence (AI) is rapidly becoming a core methodological pillar of molecular biology and precision medicine, and Africa is a uniquely consequential setting for this transition because the continent combines the world's greatest human genomic diversity with the most severe underrepresentation of that diversity in the datasets and reference resources on which AI models are built and benchmarked. This narrative review examines, for a genetics and genomics readership, where AI-driven methods are already strengthening African molecular biology, where the supporting evidence remains preliminary, and what is required to translate technical capability into scientifically robust and equitable benefit. The central argument is that AI is especially consequential in African molecular biology, not simply because it automates analysis, but because it can help unlock insight from African genomic diversity, pathogen biology, and clinically relevant multi-omics data that remain underrepresented in global models. Across core molecular domains, AI is accelerating protein structure prediction, high-throughput variant calling and pan-genomic reference construction, genome-wide association analysis, transcriptomic interpretation, drug discovery, and CRISPR guide design. African initiatives such as H3Africa, the African Genome Variation Project, H3ABioNet, and the H3D Centre show that locally generated datasets and African-led computational pipelines can already support meaningful discovery, from improved variant interpretation to structure-guided therapeutic prioritization. At the same time, persistent barriers remain, including underrepresentation of African genomes in training data and reference genomes, uneven computational infrastructure, limited interdisciplinary training, fragmented governance, and the risk that AI-derived benefits will remain inaccessible to the populations whose data enable them. We conclude that the future impact of AI in African molecular biology will depend less on adopting global tools in the abstract and more on building African-led datasets, validation pipelines, governance frameworks, and translational pathways that make molecular discovery both scientifically robust and equitably useful. Looking ahead, the central perspective offered by this review is that Africa's exceptional genomic diversity should be treated as a scientific asset rather than an analytical liability: realising this will require population-representative pan-genome references, sustained computational capacity, and governance structures that ensure African populations are not only the source of the underlying data but also the principal beneficiaries of the discoveries it enables.","url":"https://pubmed.ncbi.nlm.nih.gov/42481850/","authors":["Awuah D","Hounkpe A","Anane-Asamoah J","Dakubo WK","Adu IK","Barnie PA","Ansah EO","Fosu K","Aidoo CO","Essien V","Adam Y","Ninson E","Quansah R","Kyei F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s00438-026-02492-2","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481841","name":"Multi-Axial Analysis of Clinical Reasoning in Large Language Models: Inter-Verifier Disagreement and Its Implications for Automated Evaluation.","source":"pubmed","abstract":"Evaluating clinical reasoning in large language models (LLMs) poses two open challenges: reference-oriented semantic metrics do not directly assess whether a model's stated diagnosis is supported by the evidence in its own justification, and the increasingly popular LLM-as-judge approach rests on a largely untested assumption-that independent verifier LLMs agree with one another. We assess three generator LLMs (HuatuoGPT-o1-8B, Meta-Llama-3.1-8B-Instruct, Meta-Llama-3.3-70B-Instruct) on 1,000 MIMIC-IV hospital-stay cases along four complementary axes (medical concept grounding, semantic similarity, semantic uncertainty, and evidence-conclusion coherence), with coherence judged independently by three frontier verifiers (Claude Sonnet 4.6, Gemini 2.5 Pro, GPT-5.4 mini). Two findings emerge. First, coherence reveals a dissociation that reference-oriented metrics do not capture: a model can score well on those axes yet still produce rationales that do not support its own conclusions. Second, inter-verifier agreement on coherence is consistently low (Fleiss' &#x3ba; 0.087-0.223; disagreement 62.2%-74.3%), so the same rationale can be judged supported or unsupported depending on the verifier. A preliminary validation in which a physician adjudicated 50 cases echoed this: agreement with the physician varied across verifiers, underscoring that no single LLM reliably stands in for clinical assessment. Together, these results suggest a single LLM verifier lacks sufficient reliability to serve as a stand-alone judge of clinical reasoning at scale, and that structured human oversight remains essential. The unanimous-agreement tier offers a candidate for selective automation, but its clinical reliability remains to be confirmed in larger, multi-clinician adjudication studies.","url":"https://pubmed.ncbi.nlm.nih.gov/42481841/","authors":["Byun H","Lee D","Jung M","Jang B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 22","doi":"10.1007/s10916-026-02440-y","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481832","name":"Identification and validation of an explainable screening model of college students' mental health with therapeutic strategy implications.","source":"pubmed","abstract":"Mental health issues, especially depressive symptoms, among young adults represent a public health challenge. Conventional psychological assessment tools have limited sensitivity and specificity for identifying individuals at risk. This study aims to develop an explainable machine learning-based model to stratify concurrent depression risk in young adults. This study included 100,257 college students and collected mental health variables including depression, anxiety, resilience, parent-child relationship, and duration of mobile phone usage. The screening capabilities of 13 machine learning algorithms were systematically evaluated and compared. The SHapley Additive exPlanations (SHAP) framework was employed for the interpretability of the final model. The median scores for parent-child relationship, resilience, anxiety, and mobile phone usage time was 42.0, 28.0, 1.0 and 28.0, respectively. Among the 13 machine learning algorithms, the XGBoost model demonstrated superior performance. The final multivariate screening model achieved an area under the curve (AUC) of 0.887, a sensitivity of 0.787, a specificity of 0.830, and an accuracy of 0.816 in classifying young adults' concurrent depression risk. The SHAP analysis showed the importance of each variable: anxiety (2.303) &gt; resilience (0.774) &gt; parent-child relationship (0.708) &gt; mobile phone usage time (0.411). The final multivariate model exhibited stable performance during cross-validation (AUC&#x2009;=&#x2009;0.885&#x2009;&#xb1;&#x2009;0.032), significantly better than the single-variable model (P&#x2009;&lt;&#x2009;0.001) and better screening reliability (Brier score 0.153). The final multivariate XGBoost model provides a highly accurate and interpretable approach for young adults' depression risk stratification. As the model was developed using cross-sectional data collected during the COVID-19 campus lockdown, prospective validation is required before clinical deployment. Notably, anxiety level emerged as the most influential risk factor, and resilience demonstrated a significant protective effect.","url":"https://pubmed.ncbi.nlm.nih.gov/42481832/","authors":["Liu K","Li Z","Zhao YY","Li X","Guan ZC","Tian DH","Li SX","Zhang XY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-63087-w","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42481813","name":"Development of a risk prediction model for rheumatoid arthritis-associated interstitial lung disease based on interpretable machine learning.","source":"pubmed","abstract":"This study constructed a predictive model for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA) and explored the value of interpretable machine learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42481813/","authors":["Li X","Yuan J","Li C","Luo M","Cao Y","Wen J","Wu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1007/s10067-026-08293-7","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481664","name":"Real-world deployment of remote sleep monitoring technologies reveals distinct patterns associated with cognitive decline.","source":"pubmed","abstract":"Examining sleep patterns in relation to chronological ageing and dementia can provide insights for risk screening. Integrating predictive models with remote sleep monitoring enables routine assessment of cognitive decline symptoms in high-risk groups, aiding early risk identification. We developed a machine learning pipeline to estimate Sleep Age Index from longitudinal under-the-mattress sleep sensor data in the general population and a dementia cohort (n = 1672; person-samples = 18,369), using it to identify dementia risk. Risk scores were stratified into high, medium and low-risk categories to support clinical decision-making. Our study indicates that sleep patterns in dementia do not follow typical ageing processes, with the pre-trained model showing greater deviation from \"normative\" age-related patterns. These deviations were associated with irregular bed- and rise-times and reduced night-to-night variability in deep sleep. Chronological age was predicted from sleep data with a mean absolute error of 5.52 (95% CI: 5.37-5.67) on held-out data. In dementia versus control, the model achieved 75.7% (95% CI: 71.4%-79.9%) sensitivity and 74.7% (95% CI: 69.2%-80.0%) specificity post-stratification on unseen data. In a pilot high-risk cohort (n = 50), model predictions showed slight positive bias relative to clinical judgement (mean difference 0.98, limits of agreement -0.83-2.78). These findings demonstrate the potential of remote sleep monitoring and predictive modelling in identifying individuals who may benefit from further clinical evaluation and early intervention.","url":"https://pubmed.ncbi.nlm.nih.gov/42481664/","authors":["Fletcher-Lloyd N","Gómez NC","Capstick A","Fogel A","Bafaloukou M","Heydari M","Cairns A","Walsh C","True J","CR T Group","Shariati B","Nilforooshan R","Barnaghi P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1038/s41746-026-02964-0","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481661","name":"Sex-specific predictors of lower-limb strength: an interpretable machine learning analysis of anthropometric and body composition measures.","source":"pubmed","abstract":"Muscular strength is a key indicator of physical function. However, its direct laboratory assessment often requires specialized, high-cost equipment and can impose significant physical fatigue or safety risks for certain populations, limiting its routine implementation in large-scale screenings. Using anthropometric and body-composition data from 200 healthy Korean adults aged 19-68 years, this study initially evaluated predictive models for leg extensor strength (LES), leg flexor strength (LFS), and handgrip strength (HGS). The interpretable machine-learning framework was subsequently focused on lower-limb strength outcomes showing stronger predictive performance to characterize the body-composition predictors of muscular strength. Correlation and linear regression analyses were combined with seven regression models, and feature contributions were quantified using SHAP values aggregated across all models; a linear-family ensemble (four homogeneous linear and regularized models) served as the primary analysis and an all-model ensemble as a sensitivity analysis. LES and LFS models consistently outperformed HGS models, and linear-based models showed the most stable performance, indicating that the strength-body-composition relationships were predominantly linear. SHAP analysis identified basal metabolic rate, age, and visceral fat area as the dominant predictors of both LES and LFS, jointly accounting for more than 70% of the total feature importance. The predictive profile were markedly sex-specific: in males, strength was most strongly predicted by skeletal muscle mass and body cell mass, whereas in females, age and bone mineral content were more informative for lower-limb strength. Notably, age contributed more strongly to extensor strength in males but to flexor strength in females, and visceral fat contributed approximately four-fold more to female than to male extensor strength. These findings demonstrate that practical, clinically relevant, and sex-specific prediction of lower-limb strength is achievable from simple body-composition data.","url":"https://pubmed.ncbi.nlm.nih.gov/42481661/","authors":["Kang JH","Jeon YJ","So JH","Moon J","Kim S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1038/s41598-026-63302-8","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481610","name":"A multidimensional benchmarking framework for large language models in oncologic decision making.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly explored as clinical decision support tools in oncology; however, reliance on isolated metrics has limited the development of multi-dimensional evaluation frameworks. This comparative observational study utilized five stepwise, clinically realistic non-small cell lung cancer scenarios reflecting real-world diagnostic, therapeutic, and follow-up decision-making. Open-ended clinical questions were answered by three LLMs (Gemini 2.5 Pro, GPT-5, and Claude Opus 4.1) via their official APIs and compared with evidence-based reference answers. Model outputs were evaluated using expert-rated clinical accuracy and explainability, alongside operational metrics including cost, response time, and generative efficiency. All dimensions were integrated into an expert-weighted Composite Performance Score (CPS). Across 30 clinical questions, significant inter-model differences were observed for all metrics (p &lt; 0.001). GPT-5 achieved the highest accuracy, explainability, and generative efficiency, while Gemini 2.5 Pro demonstrated the lowest cost and Opus 4.1 the fastest response times. Integrated analysis yielded the highest CPS for GPT-5, followed by Gemini 2.5 Pro and Opus 4.1 (Kendall's W = 0.87). A multi-dimensional evaluation framework integrating clinical quality and operational efficiency provides more actionable insights than single metric assessments, enabling pragmatic model selection for oncology practice. Nevertheless, the use of LLMs in this domain should remain clinician-supervised.","url":"https://pubmed.ncbi.nlm.nih.gov/42481610/","authors":["Halici M","Salturk S","Sayin I","Ertan B","Balci IC","Cepni K","Kapagan T","Yildirim C","Erdem GU","Kiziltan HS","Kocak MT","Uvet H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1038/s41598-026-61195-1","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481561","name":"Machine learning-based analysis of risk factors and construction of a predictive model for hyperuricemia in Chinese health examination population.","source":"pubmed","abstract":"Hyperuricemia (HUA) imposes a growing public health burden, calling for better risk stratification tools. In this cross-sectional study of 4906 Chinese adults undergoing routine health checks (overall HUA prevalence: 26.0%), we built machine learning-based predictive models using a stratified 80/20 data split. To avoid variable selection bias, we applied LASSO regression with tenfold cross-validation, which identified 12 core predictors from routine clinical and demographic data. Among four algorithms tested, the Gradient Boosting (GB) model showed the best discrimination (AUC&#x2009;=&#x2009;0.770) and good calibration (slope&#x2009;=&#x2009;0.982, intercept&#x2009;=&#x2009;-&#x2009;0.007, Brier score&#x2009;=&#x2009;0.157). SHAP analysis revealed serum creatinine (Scr), HDL cholesterol (HDL-C), and body mass index (BMI) as the top predictors. Notably, SHAP interaction plots uncovered a nonlinear rise in risk above a Scr threshold and a compounded risk when high Scr coincided with low HDL-C. In summary, the well-calibrated GB model offers a reliable, data-driven tool for HUA risk screening, with insights into marker interactions to guide targeted prevention and early intervention.","url":"https://pubmed.ncbi.nlm.nih.gov/42481561/","authors":["Tan C","He X","Li L","Li Y","Yang P","Li Y","Luo J","Huang M","Zhang H","Cheng ASK"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1038/s41598-026-62552-w","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481360","name":"A multi-outcome prognostic score for therapeutic responses in multiple sclerosis.","source":"pubmed","abstract":"Multiple sclerosis (MS) is marked by heterogeneous disease activity, progression, and therapeutic response. Here, we developed a prognostic score based on machine learning using randomized clinical trials (RCTs) and observational datasets. The score's ability to predict short-term prognosis, expressed as absolute risk, was assessed on the French population in the context of all common MS therapeutic scenarios. Nine industrial RCTs and one prospective cohort from the French MS registry were used to develop several types of multilabel binary classifiers designed to predict the two-year risk of relapse, advent of new brain T2 lesions, and sustained disability worsening, as well as the respective yearly risks. Model evaluation prioritized calibration of probabilistic predictions over discriminatory capacity. Virtual cohorts simulated from the model predictions were analyzed to determine how the predictive score captured clinically meaningful information, such as the efficacy of different therapeutic classes. The model with the best calibration was evaluated externally on the population-based cohort of the French MS registry. Random forest modeling optimally captured the time-course of MS risks. In the evaluation dataset, calibration shifted with underconfident predictions of relapse and overconfident predictions of new brain T2 lesions. Nevertheless, unadjusted average therapeutic class efficacy on MRI activity generalized well. At external validation, discriminatory capacities were modest: AUC=0.67, 0.75, and 0.58 for relapse, new brain T2 lesions, and sustained disability worsening, respectively. Based on a panel of variables currently available during routine care for MS patients, we propose a score predictive of short-term therapeutic response to commonly prescribed therapeutic classes. Predictions of MRI activity generalized well across the common therapeutic scenarios. The model's probabilistic approach, emphasizing prediction certainty rather than the prediction itself, captured clinically useful information for the selection of disease-modifying treatments.","url":"https://pubmed.ncbi.nlm.nih.gov/42481360/","authors":["Louet J","Paris J","Faddeenkov I","Payet M","Baciotti B","Casey R","De Sèze J","Laplaud DA","Edan G","Gourraud PA","Demuth S","PRIMUS consortium"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1016/j.neurol.2026.06.003","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481305","name":"Retraction notice to \"Molecular structure of NRG-1 protein and its impact on adult hypertension and heart failure: A new clinical Indicator diagnosis based on advanced machine learning\" [Int. J. Biol. Macromol. 304 (2025) 140955].","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42481305/","authors":["Bai Q","Chen H","Liu H","Li X","Chen Y","Guo D","Song B","Yu C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.ijbiomac.2026.153467","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481282","name":"Deep learning approaches for predicting response to neoadjuvant chemotherapy in muscle invasive bladder cancer: A systematic review.","source":"pubmed","abstract":"Neoadjuvant chemotherapy (NAC) followed by radical cystectomy is the standard of care for muscle-invasive bladder cancer (MIBC), yet treatment response is highly variable and current clinical predictors are inadequate. Deep learning (DL) offers a data-driven approach for modeling complex medical data and may improve response prediction. The objective of this systematic review was to systematically evaluate the performance, methodological rigor, and clinical applicability of DL models for predicting treatment response to NAC in patients with MIBC. Following PRISMA 2020 guidelines, we systematically searched 14 databases and clinical trial registries for studies applying DL models to predict NAC response in MIBC. Eligible studies included those using imaging or omics data and reporting quantitative performance metrics. Methodological quality was assessed using Prediction model Of Bias Assessment Tool (PROBAST) and APPRAISE-AI framework. Twelve studies comprising at least 1,575 unique patients were included. Most employed convolutional neural networks (CNNs), with some integrating radiomics, transcriptomics, or large language models. Reported area under the receiver operating characteristic curves ranged from 0.69 to 0.89, with hybrid models consistently outperforming standard CNNs. While clinical relevance and data quality were generally strong, as assessed by APPRAISE-AI, reproducibility and external validation were frequently limited. Using PROBAST, risk of bias was low in several domains, but concerns persisted regarding outcome definition and analytical rigor. DL models, particularly those integrating imaging with radiomics or clinical features, demonstrate promising potential for predicting response to NAC in MIBC. However, methodological inconsistencies and limited external validation currently constrain their clinical translation. Future research should prioritize prospective, multi-center validation, and standardized multi-modal integration to enable safe and effective clinical deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42481282/","authors":["Nowroozi MR","Madani MH","Farzin A","Mazdak M","Niroomand H","Hajiasadi E","Torkamani ZT","Sharifi L","Aminsharifi A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.urolonc.2026.06.008","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481207","name":"Benchmarking large language models for de-identification of electronic health record notes.","source":"pubmed","abstract":"The rapid evolution of large language models (LLMs) and their growing application in clinical text processing have created an urgent need for reliable de-identification mechanisms. While LLMs show promise in identifying sensitive health information (SHI), their capabilities require rigorous evaluation. This study aims to conduct a comprehensive benchmarking analysis of various LLM-based, traditional rule-based and hybrid de-identification methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42481207/","authors":["Panchal O","Chang NW","Zhao ZR","Nadar DR","Dai HJ","Jonnagaddala J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1136/bmjhci-2025-101894","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42481000","name":"Machine learning for COVID-19 mortality prediction: enhancing cart models with node-specific odds ratios.","source":"pubmed","abstract":"The objective of the study is to evaluate a classification and regression tree (CART) model combined with odds ratio (OR) to identify key predictors of COVID-19 mortality. Data from 1,432 patients hospitalized at Hospital General de M&#xe9;xico during the pandemic's 1 st year were analyzed.","url":"https://pubmed.ncbi.nlm.nih.gov/42481000/","authors":["Pérez-Pacheco A","Herrera-Suárez AE","Vélez-Mata J","Magdaleno-Fourlong LM","Avendaño-Carrera AD","Casillas-Suárez C","Pérez-García A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.24875/CIRU.25000133","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42480815","name":"cfDNA derived gene signatures as surrogate for microvascular invasion in HCC.","source":"pubmed","abstract":"Microvascular invasion (MVI) is a critical prognostic risk factor in hepatocellular carcinoma (HCC). This study evaluated the performance of 5-hydroxymethylcytosine (5hmC) modifications in circulating cell-free DNA (cfDNA) in preoperative assessment of MVI.","url":"https://pubmed.ncbi.nlm.nih.gov/42480815/","authors":["Gong R","Wang L","Xue D","Cai J","Jiang Y","Huang J","Zhu J","Li Z","Ke A","Shi G","Wang J","Wang W","Zheng J","Yang W","Zhang Z","Zhou J","Fan J","Zhang W","Gao P","Chen L","Song D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1016/j.jhepr.2026.101966","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42480600","name":"Real-World Evidence-Based Study on Treatment-Free Remission in Chronic Myeloid Leukemia.","source":"pubmed","abstract":"Treatment-free remission (TFR) has become a treatment goal for chronic myeloid leukemia (CML). This study aimed to define precise conditions for treatment discontinuation and to develop TFR prediction models in a Chinese population.","url":"https://pubmed.ncbi.nlm.nih.gov/42480600/","authors":["Cheng F","Wang Y","Zhu Y","Yang Y","Weng J","Xu N","Huang J","Liu Z","Ren H","Zhao H","Zhu H","Ke Y","Wu W","Du X","Jiang Q","Zhang Y","Li W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.6004/jnccn.2026.7026","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42480539","name":"Agentic genomics: From pipeline automation to autonomous validation.","source":"pubmed","abstract":"Genomics has entered a phase in which AI agents can autonomously discover, configure, execute, and chain bioinformatics operations from natural-language instructions. We term this paradigm \"agentic genomics\": the delegation of multi-step genomic analyses to autonomous software agents that select tools, manage dependencies, and adapt execution in response to intermediate results, mediated by large language models (LLMs) and constrained by domain-specific skill libraries. We argue that agentic genomics shifts the bottleneck in computational biology from pipeline construction to validation. We examine emerging systems, including CellAtria, AutoBA, Bio-Copilot, and ClawBio, and assess their divergent architectures. We propose a tiered validation framework spanning research-grade, benchmarked, and clinical-grade analyses and argue that equity-aware design must be a systems requirement rather than an optional aspiration. We identify the infrastructure needed to make agentic genomics trustworthy.","url":"https://pubmed.ncbi.nlm.nih.gov/42480539/","authors":["Corpas M","Guio H","Fatumo S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 12","doi":"10.1016/j.xgen.2026.101305","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42480443","name":"A wearable electrochemical aptamer platform based on microneedles for continuous real-time multiplexed hormone monitoring to advance hormone-related health management.","source":"pubmed","abstract":"Real-time, multiplexed monitoring of sex hormones is essential for managing reproductive health and endocrine disorders. Traditional invasive blood sampling lacks the temporal resolution required to capture the dynamic hormonal fluctuations necessary for personalized clinical insights. Here, we present a wearable microneedle-based electrochemical aptamer platform (WMEP) for the continuous, minimally invasive monitoring of estradiol, progesterone, luteinizing hormone, and testosterone in interstitial fluid. The WMEP integrates a four-channel microneedle electrode array enhanced with gold nanoparticles (&#x223c;2.77-fold increase in electroactive area) and a miniaturized wireless potentiostat employing time-staggered acquisition to suppress inter-channel crosstalk. In vivo rat studies demonstrated real-time tracking of hormone dynamics and revealed size-dependent transvascular transport kinetics: small steroids exhibited rapid interstitial partitioning (T max &#x2248;40&#x202f;min), while luteinizing hormone showed delayed exchange (T max &#x2248;60&#x202f;min). Quantitative analysis confirmed high concordance with serum ELISA measurements (Pearson's R&#x202f;&#x2265;&#x202f;0.932). Clinical validation using 45 human serum samples across healthy controls, pregnancy, polycystic ovary syndrome, and breast cancer cohorts demonstrated robust analytical accuracy. A random forest classifier achieved 95.56% diagnostic accuracy based on multiplexed profiles. Human on-body trials further confirmed the platform's practical utility, reliably tracking testosterone surges during exercise and maintaining signal stability over 5 days of continuous wear. This wearable platform enables personalized, longitudinal endocrine management and decentralized monitoring of reproductive health disorders.","url":"https://pubmed.ncbi.nlm.nih.gov/42480443/","authors":["Zhou G","Ren G","Cheng Y","Guo L","Li J","Liu T","Zhang G","Chen T","Zheng Y","Liu C","Wang L","Xu H","Feng B","Xu S","Meng L","Wu W","Yuan M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov 15","doi":"10.1016/j.bios.2026.119023","addedAt":"2026-09-01T01:48:02.958Z","updatedAt":"2026-09-01T01:48:02.958Z"},{"id":"pmid:42480408","name":"Integrating physics guidance into deep learning for cine cardiovascular segmentation.","source":"pubmed","abstract":"Cine cardiovascular magnetic resonance (CMR) provides dynamic sequences for quantifying clinically important indices such as ventricular volumes and ejection fraction. A fast and accurate deep segmentation model is therefore valuable for timely analysis, reducing manual workload and improving reproducibility in routine workflows. However, most existing segmentation models remain largely data-driven and lack explicit physical constraints, which can reduce prediction plausibility under realistic acquisition variability. In this work, we present PGE-UNet, an efficient segmentation framework for cine CMR that incorporates radiofrequency-related information to promote physically plausible outputs. Specifically, it employs the proposed Noise-Aware Encoder with self-supervised noise estimation and smoothing mechanism for robust feature encoding, and the Physics-Regularized Decoder that enforces Maxwell-Helmholtz consistency under a pseudo transmit-field prior. Since transmit-field measurements are typically unavailable in standard protocols, we further introduce a simulator-based procedure to synthesize the pseudo transmit-field prior, enabling physics-regularized training without additional mapping sequences. Experiments on three public cine CMR benchmarks demonstrated strong accuracy in both DICE and HD95, achieving foreground average DICE of 0.9152 on ACDC, 0.8605 on M&amp;Ms, and 0.8548&#xb1;0.0762 on SCD, while remaining lightweight with only 1.60 million parameters in the default setting. Under CPU-only inference, the default configuration recorded an average latency of 72.38 ms per case, making it suitable for deployment in time-sensitive and resource-constrained environments. Our source code is publicly available at https://github.com/QuocKhanhLuong/PGE-UNet.","url":"https://pubmed.ncbi.nlm.nih.gov/42480408/","authors":["Tran-Nguyen MA","Luong QK","Kha MB","Dang TL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.compmedimag.2026.102798","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480407","name":"Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis.","source":"pubmed","abstract":"Ultrasound image classification remains challenging in clinical practice, given substantial variations in image appearance across devices, acquisition protocols, and operator habits, often unrelated to underlying pathology and associated with inconsistent model performance and limited generalizability. To address this challenge, we propose HAE-BUS (Heterogeneous Acquisition Environments for Breast Ultrasound), a representation learning framework that aims to disentangle pathology-relevant features from acquisition-style bias and improve diagnostic consistency across heterogeneous acquisition conditions. Specifically, HAE-BUS uses Multimodal Large Language Models (MLLMs) as an offline semantic interface to characterize pathology-excluded acquisition cues, from which latent acquisition-style environments are inferred without relying on metadata. These inferred environments are then composed during training to suppress acquisition-style shortcuts while preserving pathology-relevant diagnostic features. Experiments conducted on two breast ultrasound benchmarks, including the public BUSBRA dataset and our private NDTH dataset, indicate that our approach obtains higher AUC and accuracy than the compared methods in the evaluated settings, along with more consistent performance across heterogeneous acquisition conditions, indicating improved robustness and suggesting potential for more reliable clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/42480407/","authors":["Wang H","Zhang S","Kong W","Shao W","Zhang D","Wan P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.compmedimag.2026.102799","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480362","name":"Fusion of ERP and fNIRS signals for classification of internet gaming disorder in an attention-modulating dot-probe task.","source":"pubmed","abstract":"Internet gaming disorder (IGD) has emerged as a significant behavioral addiction and public health concern. However, its underlying neurobiological mechanisms remain unclear. This study aimed to develop a machine learning approach to evaluate IGD based on multimodal neurophysiological signals.","url":"https://pubmed.ncbi.nlm.nih.gov/42480362/","authors":["Altınkaynak M","Batbat T","Yeşilbaş D","Güven A","Uğurgöl E","Demirci E","Dolu N","İzzetoğlu M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109549","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480342","name":"V(2)-Former: Towards volumetric framework for instance-level segmentation and prediction of fetal ventriculomegaly in anisotropic MRI.","source":"pubmed","abstract":"Accurate identification and assessment of fetal ventriculomegaly (VM) is crucial for prenatal care. However, conventional diagnosis relies on manual 2D slice-based measurements, which may overlook 3D morphological cues. Existing deep learning approaches typically separate volumetric segmentation from clinical decision-making or rely on global case-level predictions, and rarely encode the clinical workflow of combining measurements with contextual findings. Furthermore, they face significant challenges in capturing the non-uniform clinical relevance and adapting to the anisotropic characteristics of fetal MRI. To address these issues, we introduce V 2 -Former, a Volumetric Ventricular analysis framework that achieves both ventricle-specific prediction consistent with clinical practice and comprehensive volumetric assessment. Leveraging the query-based transformer paradigm, our method integrates two complementary components: (1) an Anisotropy-Aware Module (AAM) that recalibrates volumetric features to highlight non-uniform diagnostically relevant regions in anisotropic data, and (2) a Ventricular Diagnosis Enhancement (VDE) strategy that encodes diagnostic priors to guide query-based learning for ventricle-specific prediction. Evaluated on a real-world clinical dataset of 384 fetal MRI scans, V 2 -Former achieves the strongest overall combined performance among the compared methods, providing clinicians with the first end-to-end solution that delivers both ventricle-specific predictions and volumetric evaluations to support clinical VM assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42480342/","authors":["Zhang Z","Zhang L","Huang W","Wang Y","Wang Z","Ning G","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.media.2026.104219","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480341","name":"Spatial phenotyping of epicardial adipose tissue from cardiac MRI.","source":"pubmed","abstract":"Epicardial adipose tissue (EAT) is increasingly recognized as an important contributor to cardiovascular disease (CVD), but its spatial distribution across the heart remains insufficiently characterized due to limitations in existing segmentation and analysis methods. In this study, we developed a deep learning-based framework that integrates automated cardiac MRI segmentation with spatial statistical modeling to enable quantitative, chamber-resolved characterization of EAT distribution. An asymmetric multi-modal CNN-Transformer network was developed to segment the four cardiac chambers and EAT from cardiac MRI. Based on the resulting whole-heart segmentations, EAT was automatically partitioned into four chamber-specific subregions, voxel-wise thickness maps were reconstructed, and spatial statistics were applied to identify localized clustering patterns of EAT. Chamber-resolved and region-specific quantitative features were extracted to characterize the spatial distribution of EAT across the heart. The proposed approach was evaluated on a cohort including individuals with type 2 diabetes (T2D) and matched controls, revealing distinct T2D-associated remodeling, including increased chamber-specific burden, localized thickening, and spatial hotspot clustering in metabolically vulnerable regions. This work introduces a scalable and automated method for regional EAT phenotyping and provides spatially resolved imaging biomarkers that may support CVD research and risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/42480341/","authors":["Feng F","Long T","Hasaballa AI","Sun X","Gu Y","Carlhäll CJ","Zhao J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.media.2026.104223","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480336","name":"Machine learning and deep learning for ankle fracture detection in radiographs: A systematic review with narrative synthesis on diagnostic performance and future clinical integration.","source":"pubmed","abstract":"Ankle fractures are a prevalent orthopedic injury with X-rays being the primary diagnostic tool. Errors in radiological interpretation leads to missed fractures and delayed diagnosis. This systematic review with narrative synthesis aims to map the current landscape on the usage of Machine Learning (ML) and Deep Learning (DL) models for detecting ankle fractures from radiographs, with critical evaluation on the key trends and highlighting gaps in research.","url":"https://pubmed.ncbi.nlm.nih.gov/42480336/","authors":["Mangwani J","Jithin SP","Akram N","Ravi L","Vaishya R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.foot.2026.102263","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42480332","name":"Can AI-based chatbots enhance patient education and support nursing practice for hip replacement patients? A scoping review.","source":"pubmed","abstract":"Artificial intelligence (AI), particularly machine learning and large language models (LLMs), has seen rapid advancements and widespread adoption over the past decade, transforming many fields, including healthcare. ChatGPT is one of the prominent chatbot interfaces that is powered by generative pre-trained transformers. ChatGPT can generate natural, human-like conversations on diverse topics, and its performance depends on the underlying model version. Due to its accessibility and versatility, this chatbot interface has generated significant interest in its use for patient education, especially among those seeking information about orthopaedic procedures, such as hip replacement surgery. Despite growing enthusiasm, the extent and nature of evidence regarding AI-based chatbots' role in educating hip replacement patients remain unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42480332/","authors":["Kaur J","Adedoyin F","Budka M","Wainwright TW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.ijotn.2026.101299","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479903","name":"Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.","source":"pubmed","abstract":"Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer and informal caregivers remains limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42479903/","authors":["Jeong Y","Cha H","Suh EE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.2196/96241","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479776","name":"Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost).","source":"pubmed","abstract":"Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain intensity is shaped by multifaceted interactions among demographic, lifestyle, and clinical factors.","url":"https://pubmed.ncbi.nlm.nih.gov/42479776/","authors":["Algamdi MM","Alghamdi AH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354370","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479708","name":"Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.","source":"pubmed","abstract":"Distinguishing individuals with cognitive decline (CD), including early Alzheimer's disease, from cognitively normal (CN) individuals is essential for improving diagnostic accuracy and enabling timely intervention. Positron emission tomography (PET) captures metabolic brain alterations associated with CD, but its broader application is often limited by cost and radiation exposure. To enhance the clinical utility of PET while addressing data limitations, we propose a data-efficient framework that integrates complementary multi-scale PET representations at voxel-level and region-level.","url":"https://pubmed.ncbi.nlm.nih.gov/42479708/","authors":["Lee Y","Kim S","Kang Y","Alzheimer’s Disease Neuroimaging Initiative"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0341995","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"pmid:42479667","name":"Brain aging patterns among nine neurological disorders: A case-control study.","source":"pubmed","abstract":"The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting individual brain health. Although previous large-scale studies have shown that brain age deviations occur across multiple disorders, cross-disorder comparisons of PAD within a unified framework, together with identification of the neuroimaging features associated with these differences and their related gene expression profiles, remain limited. Our aims are to systematically compare brain aging across multiple common brain disorders and explore the brain patterns and biological processes underlying these differences.","url":"https://pubmed.ncbi.nlm.nih.gov/42479667/","authors":["Liang C","Pearlson G","Bustillo J","Kochunov P","Chen J","Zhang X","Jiang R","Hutchison KE","Sui J","Fu Z","Yang X","Du Y","Zhang D","Qi S","Calhoun VD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1371/journal.pmed.1004860","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479649","name":"Post-COVID-19 Condition Diagnosis among Older People: Findings from Swedish National Register Data.","source":"pubmed","abstract":"Post-COVID-19 condition (PCC) denotes the persistence of symptoms following Severe Acute Respiratory Syndrome Coronavirus 2 infection. PCC poses a large disease burden worldwide. We aimed to explore the epidemiological characteristics of clinically diagnosed PCC among older people aged &#x2265;65 years in Sweden and to establish a machine learning tool to predict PCC risk.","url":"https://pubmed.ncbi.nlm.nih.gov/42479649/","authors":["Zhang Z","Hoang MT","Wastesson JW","Johnell K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1159/000553601","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479551","name":"The FACT-Score: Development and Temporal Validation of a Clinical Risk Stratification Tool for 30-Day Complications After Operative Facial Fracture Repair.","source":"pubmed","abstract":"No validated, procedure-specific tool exists for predicting 30-day complications after operative facial fracture repair. This study aimed to develop and temporally validate a clinical risk stratification tool for 30-day complications after operative facial fracture repair.","url":"https://pubmed.ncbi.nlm.nih.gov/42479551/","authors":["Catic A","Karamitros G","Lineaweaver WC","Lamaris GA","Pratibh P","Manson PN","Redett R","Barbieri S","Jorm L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1097/SCS.0000000000013173","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479508","name":"Individual-Specific Functional Connectivity-Based State Classification and Prognosis Prediction for Disorders of Consciousness.","source":"pubmed","abstract":"Accurate prognosis and treatment targeting for disorders of consciousness (DOC) remain challenging due to profound neurobiological heterogeneity. Current approaches to DOC state classification and prognostic prediction, which rely on resting-state functional magnetic resonance imaging (rs-fMRI)-based functional connectivity (FC) analysis, are limited by signal blurring from group-level averaging and cross-participant spatial variability. To overcome these limitations, we propose a novel framework integrating the multi-task learning-based sparse convex alternating structure optimization (MTL-sCASO). By jointly modeling multiple subjects within a unified optimization framework, MTL-sCASO reduces the confounding effects of spatial variability across participants, while decomposing each subject's rs-fMRI signals into individual-specific and shared FC components, thereby preserving subject-specific patterns that would otherwise be obscured. Leveraging these individualized FC alongside clinical data, we develop machine learning classifiers not only for DOC state discrimination and prediction of prognostic improvement, but also identify critical FC pairs and brain network features fundamental to both tasks. Our results demonstrate superior performance over conventional methods, achieving an accuracy of 81% in state classification and 77% in prognostic prediction. This framework advances precision diagnosis and reliable prognosis in DOC by shifting rs-fMRI analysis from group-level averaging to an individualized functional-connectivity perspective. This shift establishes an FC-based analytical paradigm that overcomes neurobiological heterogeneity and enables subject-tailored clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42479508/","authors":["Wan Z","Chen G","Li H","Bai Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1109/TNSRE.2026.3715571","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479414","name":"Integrated bioinformatics and experimental analysis identify apoptosis- and pyroptosis-related hub genes as candidate diagnostic biomarkers in Alzheimer's disease.","source":"pubmed","abstract":"Apoptosis and pyroptosis-mediated neuronal death represent major pathogenic mechanisms underlying Alzheimer's disease (AD). Given the potential crosstalk between these two forms of cell death, investigation of a single death pathway may be insufficient to identify robust diagnostic biomarkers for AD. Therefore, this study aimed to explore hub genes involved in both apoptosis and pyroptosis as potential diagnostic biomarkers for AD. First, 23 common cell death-related genes (CDRGs) were identified through bioinformatic analysis. Functional enrichment analyses using GO, KEGG, and GeneMANIA revealed significant associations between AD and biological processes including apoptosis, pyroptosis, and neuronal death. Subsequently, machine learning algorithms combined with ROC curve analysis identified CASP3, IL1B, NLRP3, and PYCARD as candidate biomarkers with potential diagnostic and therapeutic implications for AD. These findings were further validated by the in vitro experiments, which confirmed that the expression levels of these four biomarkers were consistent with the predicted results. Additionally, in patients with AD, CASP3, IL1B, NLRP3, and PYCARD were negatively correlated with macrophages. Collectively, these results suggest that the identified biomarkers may co-regulate apoptosis and pyroptosis through macrophages, thereby contributing to the pathogenesis of AD.","url":"https://pubmed.ncbi.nlm.nih.gov/42479414/","authors":["Cai Y","Chen Y","Huang G","Ren M","Chai Y","Gai C","Huang X","Yan T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s13353-026-01095-2","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479339","name":"Machine learning algorithms for predicting arrhythmic events in Hypertrophic Cardiomyopathy: limited enhancement beyond late gadolinium enhancement.","source":"pubmed","abstract":"We aimed to develop and assess the performance of a Machine learning (ML) model integrating common clinical features to predict arrhythmic events in patients with Hypertrophic Cardiomyopathy (HCM). Post-hoc analysis of an international multicenter registry of 531 HCM patients (49 years (IQR 35-61), 57% male) who underwent cardiac magnetic resonance (CMR). The dataset comprised clinical, echocardiographic, and CMR variables, including quantification of late gadolinium enhancement (LGE) using the +&#x2009;6 SD method. The endpoint was a composite of sudden cardiac death (SCD), aborted SCD, and sustained ventricular tachycardia (VT). A total of 28 events occurred over a median follow-up of 4.1 (IQR 1.8-7.3) years. Several ML models were developed and the predictive performance of the best model was compared to the ESC HCM risk score and to the amount of LGE. The Random Forest (RF) was the most effective method showing a good performance for predicting arrhythmic events [AUC of 0.78 (95% CI: 0.76-0.82, p&#x2009;&lt;&#x2009;0.001)], substantially outperforming the ESC HCM risk score [AUC of 0.64 (95% CI 0.62-0.67; p&#x2009;&lt;&#x2009;0.001), p&#x2009;&lt;&#x2009;0.001 for comparison]. However, when compared to LGE alone [AUC of 0.76 (95% CI: 0.73-0.84, p&#x2009;&lt;&#x2009;0.001)], the RF model did not provide significant improvement in predicting the endpoint (p&#x2009;=&#x2009;0.817 for comparison). A ML model using available clinical variables significantly outperformed the ESC HCM risk score in predicting arrhythmic events in HCM. However, its incremental value over LGE alone was weak, underscoring the strong predictive value of this imaging marker. This findings should be interpreted as exploratory and hypothesis-generating.","url":"https://pubmed.ncbi.nlm.nih.gov/42479339/","authors":["Certo Pereira J","Amador R","Vieira A","Almeida Carvalho R","Castilho B","Arteaga E","Rochitte C","Rocha B","Lopes P","Abecasis J","Freitas P","Adragão P","Ferreira AM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s10554-026-03779-6","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479243","name":"Moving on in hydrocephalus imaging: from 2D to 3D biomarkers.","source":"pubmed","abstract":"Linear two-dimensional indices such as the Evans index, callosal angle, and fronto-occipital horn ratio remain the clinical standard for hydrocephalus assessment, yet are limited by measurement variability, insensitivity to spatial CSF redistribution, and reduced sensitivity to volumetric change over time. This narrative review aims to summarize established two-dimensional indices, their structural limitations, and describe how automated segmentation and radiomic feature extraction enable three-dimensional assessment across four clinical domains.","url":"https://pubmed.ncbi.nlm.nih.gov/42479243/","authors":["Da Mutten R","Turczynski Holmgren R","Edström E","Elmi-Terander A","Staartjes VE"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s00701-026-06985-2","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479206","name":"Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.","source":"pubmed","abstract":"Environmental fungal pathogens relevant to human and animal health pose significant risks, particularly in regions with intensive farming and climate variability. Several taxa detected in Thailand's clinical and environmental samples, such as Candida tropicalis, Talaromyces marneffei, and Mucor spp., are listed in the World Health Organization (WHO) Fungal Priority Pathogen List (FPPL). This study integrated next-generation sequencing (NGS) metabarcoding and decision tree models to characterize fungal communities and identify environmental conditions associated with pathogen absence across 18 provinces of Northeast Thailand. Soil samples (n&#x2009;=&#x2009;121) from rice, cassava, sugarcane, and rubber tree fields were collected in 2022 and analyzed for eight environmental parameters: drought level, soil water content, organic matter, nitrogen, phosphorus, potassium, soil temperature, and soil pH. Decision tree models were trained on these samples to derive absence conditions for nine WHO FPPL taxa, which were validated using an independent test dataset (n&#x2009;=&#x2009;12) collected in 2025. Absence conditions for Falciformispora senegalensis, Mucor spp., and Talaromyces marneffei achieved perfect precision and recall in the test dataset. Precision is the proportion of samples predicted as absent that are truly absent, and recall is the proportion of truly absent samples correctly identified by the condition. Candida tropicalis, Curvularia lunata, and Lichtheimia spp. showed perfect precision but moderate recall (0.42-0.75). Conditions for Scedosporium spp. and Acremonium spp. did not generalize due to limited representation in the training data. Over three years, the fungal community became less diverse and more taxonomically consolidated, coinciding with drought intensification and nutrient shifts. Overall, combining metabarcoding with decision tree models provides a practical framework for identifying low-risk soils and supporting agricultural practices and public health surveillance.","url":"https://pubmed.ncbi.nlm.nih.gov/42479206/","authors":["Ibrahim SNMM","Jaikla C","Pombubpa N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s00114-026-02134-y","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479162","name":"Artificial intelligence for pediatric neuroimaging.","source":"pubmed","abstract":"Artificial intelligence (AI) is revolutionizing health care, particularly in radiology for which large retrospective electronic datasets are naturally suited to training large machine and deep learning models. To date, the vast majority of United States Food and Drug Administration (FDA)-cleared AI software tools are geared toward radiology applications, with most in neuroradiology and very few in pediatric radiology. Pediatric innovations have historically lagged behind adults due to children's limited exam tolerance, small and rare datasets, age-related variability in normal and disease processes, and ethical/legal concerns for vulnerable populations. The chasm between research and clinical applications is further hampered by a smaller commercial market share in pediatrics. Nevertheless, understanding of AI successes and failures in adult neuroradiology can help inform progress in pediatric neuroradiology. Furthermore, the rise of generative AI can help overcome current limitations and enable complex multimodal pattern recognition for a broader variety of use cases. Human expert oversight will help mitigate AI risks including data and algorithmic bias, black-box errors, and regulatory gaps. In this review, we will discuss AI technical principles and pitfalls, relevant clinical tools, promising research advances, and future directions for pediatric neuroimaging. Key AI applications to be discussed include quality/safety, fetal, neonatal, hydrocephalus, malformations, tumors, epilepsy, demyelination, stroke, and trauma.","url":"https://pubmed.ncbi.nlm.nih.gov/42479162/","authors":["Ho ML"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s00247-026-06707-x","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42479054","name":"AI-assisted TURP slide review: a multi-reader study of efficiency, diagnostic performance, and human-AI error patterns.","source":"pubmed","abstract":"Transurethral resection of the prostate (TURP) specimens represent a low-prevalence, high-volume diagnostic task in which pathologists must identify rare malignant foci within large amounts of predominantly benign tissue. Although artificial intelligence (AI) has shown strong standalone performance in prostate pathology, its real-world impact on TURP diagnostic workflows remains insufficiently characterized. We developed an AI tool for slide-level detection of prostatic adenocarcinoma using 6,535&#xa0;H&amp;E whole-slide images from biopsies and TURP specimens, with feature extraction by UNI, classification through a CLAM framework, and presentation on a custom OpenSeadragon-based viewer. Five readers with different experience levels evaluated 102 TURP slides in two sessions, first unaided and then AI-assisted after an 8-week washout period. Primary outcome was per-slide review time; secondary outcome was diagnostic accuracy. AI assistance significantly reduced review time for all readers, with mean reductions ranging from 29.4% to 53.8%. In mixed-effects modeling, AI was associated with a mean reduction of 34.5&#xa0;s per slide (95% CI, 30.0-39.0; p&#x2009;&lt;&#x2009;0.001). Time savings were greatest among less experienced readers. Overall diagnostic accuracy increased from 95.3% in the unaided setting to 98.2% with AI assistance. Non-inferiority was confirmed for all readers, and accuracy even improved significantly in the least experienced readers. Across all readings, classification errors decreased from 24 unaided to 9 aided. AI assistance improved TURP slide review efficiency while maintaining, and in some readers improving, diagnostic accuracy. These findings support AI as a workflow-oriented decision-support tool in TURP pathology, particularly for low-prevalence, high-volume diagnostic settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42479054/","authors":["Pascarella CB","Giobbe M","Maffei E","Ruotolo R","Ciaparrone C","Ciancia G","D'Antonio A","Verze P","Zeppa P","Caputo A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s00428-026-04645-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478990","name":"Novel Musculoskeletal Hypotheses in the Armed Services Trauma and Rehabilitation Outcome (ADVANCE) Cohort: Development and Application of Sparse Group Factor Analysis Methodology.","source":"pubmed","abstract":"Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly following trauma, remain incompletely understood. Machine learning could be applied to identify previously unknown patterns in large-scale, multimodal datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/42478990/","authors":["Watson FCE","Ferreira FS","Kadirvelu B","Bennett AN","Faisal AA","Graham N","Kemp H","Cullinan P","Boos C","Fear NT","Bull AMJ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.2196/91958","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478980","name":"COLOSSUS-AI: A Pilot Study on Artificial Intelligence Driven Prediction of Postoperative Outcomes in Colorectal Surgery.","source":"pubmed","abstract":"Background: Postoperative complications remain a major source of morbidity after colorectal surgery and are associated with prolonged hospitalization, reintervention, intensive care unit admission, and increased mortality. Early identification of high-risk patients remains challenging due to the heterogeneity of colorectal surgical populations. Artificial intelligence and machine learning methods may support individualized risk prediction by integrating clinical, biological, and operative variables. Aim: This study aimed to develop and internally evaluate a preliminary machine learning model, named COLOSSUS-AI, for predicting early postoperative adverse outcomes after colorectal surgery. Material and Methods: This retrospective, single-center, observational pilot study included 310 patients undergoing colorectal surgery. Demographic, clinical, laboratory, disease-specific, operative, and postoperative data were retrospectively collected and organized into a structured database comprising 84 variables. The primary endpoint was postoperative adverse outcome. Three predictive models were evaluated: logistic regression, random forest, and gradient boosting. Model performance was assessed using receiver operating characteristic curve analysis, area under the curve, accuracy, sensitivity, and specificity. Results: Postoperative adverse outcomes occurred in 120 patients (38.7%). Adverse outcomes were associated with older age, urgent or emergency admission, increased inflammatory markers, higher neutrophil-to-lymphocyte ratio, lower preoperative albumin, impaired renal function parameters, anastomotic leak, and in-hospital mortality. Among the evaluated models, gradient boosting achieved the best predictive performance, with an area under the receiver operating characteristic curve of 0.886, accuracy of 81.7%, sensitivity of 72.2%, and specificity of 87.7%. Random forest achieved an area under the curve of 0.841, while logistic regression achieved an area under the curve of 0.768. Conclusions: The COLOSSUS-AI pilot study suggests that routinely collected perioperative data can be used to develop exploratory machine learning models for predicting postoperative adverse outcomes after colorectal surgery. Although gradient boosting showed the best preliminary performance, these findings should be interpreted cautiously. External validation in larger multicenter cohorts is required before clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42478980/","authors":["Botoncea M","Cosma CD","Nicolescu CL","Butiurca VO","Molnar D","Molnar C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.21614/chirurgia.3345","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478927","name":"A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.","source":"pubmed","abstract":"Patients undergoing invasive procedures frequently experience anxiety and often have unanswered questions regarding the procedure. Although large language models show considerable promise for supporting patient communication in many cases, their deployment in health care is limited by the risk of hallucinations, data-privacy constraints, and high energy costs-factors that impede equitable access in resource-limited settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42478927/","authors":["Sato M","Nagata S","Ohnuma M","Takahashi H","Kakazu T","Yamamura M","Yoshikawa A","Matsushita Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.2196/89173","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478913","name":"Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens.","source":"pubmed","abstract":"Medical AI agents are emerging as a new generation of clinical decision support systems, moving beyond static prediction toward multistep, workflow-oriented assistance. This Viewpoint argues that agentic architectures incorporating planning, action, reflection, and memory (PARM) represent a meaningful evolution beyond traditional rule-based, machine learning, and multimodal clinical decision support systems. Using PARM as an analytical lens, we examine how medical AI agents can support diagnostic reasoning, treatment planning, and longitudinal monitoring while remaining constrained by human oversight. We further discuss the governance mechanisms required for responsible implementation, including bounded autonomy, auditability, verification protocols, postdeployment surveillance, and clear accountability structures. Rather than proposing autonomous modification of clinical judgment, this Viewpoint emphasizes agentic AI as a supervised workflow support paradigm. Safe implementation will require technical safeguards, institutional governance, regulatory clarity, and evaluation approaches that assess end-to-end task reliability, escalation behavior, and performance under deployment shifts.","url":"https://pubmed.ncbi.nlm.nih.gov/42478913/","authors":["Dinc R","Ardic N"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.2196/92584","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478870","name":"Stress CMR ischemic burden and long-term mortality in left ventricular systolic dysfunction: a multicenter registry study.","source":"pubmed","abstract":"Stress perfusion cardiovascular magnetic resonance (CMR) integrates assessment of myocardial ischemia, scar, and function. Its incremental prognostic value in patients with left ventricular (LV) systolic dysfunction remains uncertain.","url":"https://pubmed.ncbi.nlm.nih.gov/42478870/","authors":["Pfeffer A","Garot J","Duhamel S","Garot P","Sanguineti F","Hovasse T","Champagne S","Unterseeh T","Akodad M","Neylon A","Mager R","Martial PJ","Kante A","Unger A","Florence J","Gall E","Léquipar A","de Vesvrotte ER","Hudelo J","Baladi C","Dillinger JG","Henry P","Bousson V","Pezel T","Toupin S","Gonçalves T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1093/ehjci/jeag189","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478758","name":"Structured Clinical Input-Guided Large Language Model Workflow for Acute Ischemic Stroke Discharge Education: A Multicenter Feasibility Study.","source":"pubmed","abstract":"Large language models (LLMs) may support patient education, but their clinical use remains challenging. We aimed to evaluate the preliminary feasibility of a structured clinical input-guided LLM workflow for generating discharge education drafts for patients with acute ischemic stroke.","url":"https://pubmed.ncbi.nlm.nih.gov/42478758/","authors":["Yin J","Wang W","Wu L","Li Z","Wang W","He T","Wang Y","Li G","Tang L","Zhao X","Guo Y","Fan H","Feng L","Zhang Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1161/STROKEAHA.126.055189","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478575","name":"Artificial Intelligence-Based Analysis of Coronary Atherosclerotic Plaque on Intravascular Ultrasound: A Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Coronary atherosclerotic plaque detection is essential for the diagnosis and management of coronary artery disease. Although intravascular ultrasound (IVUS) enables detailed plaque assessment, its clinical use is limited by time-consuming interpretation and observer variability. Artificial intelligence (AI)-based methods offer a promising approach to automate IVUS plaque detection. This systematic review and meta-analysis evaluated the diagnostic performance of AI models for IVUS-based coronary plaque detection.","url":"https://pubmed.ncbi.nlm.nih.gov/42478575/","authors":["Eini P","Serpoush H","Rezayee M","Kassulke M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/clc.70423","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478563","name":"Automated Differentiation of Oral Red-White Lesions: An Interpretable Deep Learning Approach Combining Ensemble Architectures and Saliency Maps.","source":"pubmed","abstract":"Oral Potentially Malignant Disorders (OPMDs), including Leukoplakia and Erythroplakia, carry significant risks of malignant transformation. Early differentiation from confounding inflammatory conditions like Oral Lichen Planus (OLP) and Candidiasis is critical yet challenging due to visual similarities. This study develops a robust, interpretable deep learning framework for automated multi-class classification of pre-localized oral lesions.","url":"https://pubmed.ncbi.nlm.nih.gov/42478563/","authors":["Koochaki M","Mousavie A","Basirat M","Hendi A","Sadr H","Nazari M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/cre2.70420","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478331","name":"Development of a Predictive Model for Transfusion-Related Acute Lung Injury Based on Neutrophil Extracellular Traps (NETs).","source":"pubmed","abstract":"Patients receiving massive transfusion after acute hemorrhage are at risk for transfusion-related acute lung injury (TRALI), a severe complication. Neutrophil extracellular traps (NETs) play a key role in acute lung injury. This study aimed to explore the link between NETs and TRALI and to develop a risk-prediction model using machine learning for early detection and intervention.","url":"https://pubmed.ncbi.nlm.nih.gov/42478331/","authors":["Wang Q","Li Z","Shao J","Qiang X","Gong W","Sun L","Yang H","Li Z","Wang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/mi/7778073","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478221","name":"Scalable, context-sensitive psychiatric assessment with large language models and brief diaries.","source":"pubmed","abstract":"Accurate psychiatric assessment requires understanding a person's unique experience within their psychosocial context. Clinical interviews have been the gold standard for assessment as the only methods capable of this complex task, but they are time and resource-intensive. Consequently, psychiatric assessment typically relies on patient report surveys that are decontextualized and narrow in scope. This comprehensiveness-scalability tradeoff is a major bottleneck in studying and treating psychopathology. We propose using large language models (LLMs) to score psychopathology from brief personal narratives as a low-burden, context-sensitive solution.","url":"https://pubmed.ncbi.nlm.nih.gov/42478221/","authors":["Ringwald WR","Taxali A","Angstadt M","Vize CE","Sripada C","Wright AGC"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1017/S0033291726105261","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478194","name":"Tailored injectable nanocomposite hydrogels for high-performance biomedical applications.","source":"pubmed","abstract":"Conventional hydrogel systems for biomedical applications face critical limitations in mechanical robustness, therapeutic functionality, and responsiveness to physiological stimuli, hindering their translation to precision medicine. The rational integration of engineered nanomaterials into injectable hydrogel matrices has emerged as a transformative strategy to overcome these constraints, enabling hierarchical, stimuli-responsive functionalities unattainable in traditional polymer networks. This review provides a mechanistic and translational analysis of injectable nanocomposite (NC) hydrogels, systematically examining how nanoparticle-polymer interfacial interactions govern gelation kinetics, mechanical properties, and controlled therapeutic release. Unlike previous reviews focused on material cataloguing, we critically evaluate the distinctive advantages of NC hydrogels over conventional dynamic hydrogels; including hierarchical drug release profiles, enhanced tumour penetration, and multiscale environmental responsiveness; whilst providing evidence-based assessment of clinical translation pathways. The strategic incorporation of metal-based nanostructures, carbon nanomaterials, lipid carriers, and black phosphorus nanosheets is analysed across four key biomedical domains: advanced drug delivery systems, tissue engineering scaffolds, chronic wound healing platforms, and biosensing technologies. We provide systematic coverage of injectability parameters, smart responsive behaviours, and patient-specific customisation strategies essential for minimally invasive delivery. Critically, this review addresses the gap between preclinical promise and clinical reality by examining actual clinical trial data, regulatory challenges, and manufacturing scalability barriers. Emerging artificial intelligence and machine learning tools for accelerated NC hydrogel design, predictive modelling, and real-time therapeutic monitoring are evaluated as enabling technologies for next-generation precision biomedicine. This comprehensive roadmap equips researchers and clinicians with mechanistic frameworks and practical guidance for translating injectable NC hydrogels from laboratory innovation to clinical impact.","url":"https://pubmed.ncbi.nlm.nih.gov/42478194/","authors":["Rathee G","Singh NK","Kohli S","Deshmukh K","Rathee J","Puertas-Segura A","Solanki PR","Kaushik A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 25","doi":"10.1039/d6bm00535g","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478169","name":"FGSGT-DDI: An LLM-Enhanced Functional Group Semantic Graph Transformer for Drug-Drug Interaction Prediction.","source":"pubmed","abstract":"Drug-drug interaction (DDI) prediction is of great importance for drug discovery and safe clinical medication use. Existing methods mainly rely on molecular graph structure modeling but make insufficient use of functional group semantic information, which limits their ability to identify complex DDI patterns. To address this issue, we propose FGSGT-DDI, a DDI prediction framework that integrates large language model (LLM)-enhanced functional group semantic information with Graph Transformer-based structural learning. The method first extracts molecular functional groups based on SMARTS patterns and then uses an LLM to generate semantic embeddings of functional group names, SMARTS patterns, and descriptive information. It subsequently constructs a structural channel and a semantic channel and employs cross-attention to achieve deep fusion of molecular graph features and functional group semantic features, thereby enabling multiclass DDI prediction. Experiments on three public data sets, namely, Deng, Ryu, and MUDI, show that FGSGT-DDI achieves competitive performance. Ablation studies verify the effectiveness of functional group semantic modeling, graph structure modeling, and the cross-channel interaction module. Comparisons among different LLM-based semantic pipelines further demonstrate the positive contribution of LLM-enhanced functional group semantic information to downstream DDI prediction. Explainability analysis shows that the proposed model can identify key local chemical substructures related to DDIs. Overall, FGSGT-DDI provides an effective solution for jointly modeling of multigranular functional group semantic information and molecular structures in DDI prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/42478169/","authors":["Li K","Qiao J","Yang Y","Zhao H","Wang D","Jin J","Yan Z","Wei L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 10","doi":"10.1021/acs.jcim.6c01476","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478120","name":"CT-Based Radiomic Features Predict Cervical Lymph Node Metastasis in Dogs With Oral Malignancy: A Machine Learning Study Using Leave-One-Patient-Out Cross-Validation.","source":"pubmed","abstract":"Accurate preoperative identification of cervical lymph node (LN) metastasis is essential for staging and treatment planning in dogs with oral malignancy, yet conventional imaging offers limited diagnostic sensitivity. This retrospective study evaluated whether CT-derived radiomic features, combined with machine learning classifiers, could predict the metastatic status of mandibular and retropharyngeal LNs. Forty-nine dogs with histopathologically confirmed oral malignancy underwent contrast-enhanced CT, and four bilateral cervical LN sites were manually segmented on images resampled to 1.0&#x2009;mm isotropic spacing, yielding 195 LN observations (18 metastatic, 9.2%). One hundred and seven radiomic features were extracted per LN using PyRadiomics, and variance filtering, Spearman redundancy removal (|&#x3c1;|&#x2009;&gt;&#x2009;0.95), and Mann-Whitney U testing with Benjamini-Hochberg correction were performed within each fold of leave-one-patient-out cross-validation (LOOCV) to eliminate selection-leakage bias. Logistic regression, random forest (RF), support vector machine, and XGBoost were trained with SMOTE applied within training folds only, and two endpoints were evaluated on the same out-of-fold predictions: per-LN (primary) and per-patient (secondary, via maximum-probability aggregation across the four sites). Confidence intervals were derived from 5000-iteration patient-level bootstrap, and sensitivity analyses included repeated StratifiedGroupKFold cross-validation and cluster-robust generalised estimating equation (GEE) inference. At the per-LN level, RF achieved an AUC of 0.649 (95% CI 0.474-0.831; sensitivity 0.444, specificity 0.898) and XGBoost an AUC of 0.631 (0.501-0.772; sensitivity 0.944, specificity 0.379). Patient-level aggregation yielded an RF AUC of 0.613 (0.431-0.786; 9/13 metastatic-positive dogs identified) and an XGBoost AUC of 0.485. Four GLSZM/GLCM texture features were selected in 92%-100% of LOOCV folds and remained significant under GEE (adjusted p&#x2009;&#x2264;&#x2009;0.012). CT-derived texture features therefore carry a reproducible, biologically interpretable signal for cervical LN metastasis, but leakage-controlled performance is modest in this proof-of-concept study, and external validation in larger, multi-institutional cohorts is required before clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/42478120/","authors":["Pinard CJ","Lagree A","Appleby R","Oblak M","Wu KC","Tran WT"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1111/vco.70092","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478102","name":"DHCA-Net: A Novel Dual-Stream Hierarchical Channel Attention Network for Explainable Autism Spectrum Disorder Detection From Facial Images.","source":"pubmed","abstract":"Autism spectrum disorder (ASD) consists of a spectrum of neurodevelopmental conditions characterized by complex behavioural traits and subtle, atypical facial morphologies. Analysing these facial biomarkers provides a promising, non-invasive avenue for objective clinical screening, addressing the subjectivity of traditional diagnostic processes. Therefore, the present study aims to introduce DHCA-Net, a deep learning framework designed for automated ASD detection through the analysis of facial images. The proposed architecture uses different levels of attention and different adaptive tuning to advance discriminative learning. Specifically, DHCA-Net introduces three novel attention mechanisms to advance discriminative learning: a hierarchical channel module for spatial-semantic processing, a temporal-depth convolutional module for local contextual control and an inverted residual multi-core module for dynamic feature learning. An adaptive refinement step is also introduced to denoise clinical features. To bolster and diversify the classifier, the model exploits deep spatial and contextual resources via a multi-head feature fusion (MHFF) mechanism. We conducted extensive testing on a benchmark dataset comprising 2936 diverse, preprocessed facial images (86.4% train, 10.2% test, 3.4% validation splits). A comparative analysis evaluated DHCA-Net against architectures such as DenseNet, Xception, EfficientNet and Swin-Transformer. The proposed model achieved 93.7% classification accuracy, a 0.9365 F1 score and a 0.9887 AUC, demonstrating superior performance. Furthermore, the model maintains an efficient average inference latency of 70.14&#x2009;ms despite its mid-to-high computational complexity. This explainable framework offers significant clinical applicability for scalable screening, though future work must address generalization across broader demographic populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42478102/","authors":["Singh DP","Banerjee T","C ADD","Shukla SM","Gadipelli P","Charan P","Malhotra D","Dharamvir","Singh RM","Narayan Y","Patel V","Panchal R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/jdn.70158","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478081","name":"Machine learning models for injury risk prediction in football players: A systematic review of predictors, performance, practical applications, and limitations.","source":"pubmed","abstract":"ObjectiveFootball is associated with a high incidence of musculoskeletal injuries because of its dynamic, high-intensity, and contact nature. Machine learning methods offer the potential to capture nonlinear relationships among diverse risk factors. This systematic review aimed to evaluate the application, accuracy, and practical utility of machine learning models for predicting injuries in football players.MethodsThis systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A systematic search was performed in Web of Science, PubMed, and Scopus, along with manual searches, through October 2025. Cohort and cross-sectional studies applying machine learning methods for injury prediction in football players were included (n&#x2009;=&#x2009;22). Study quality and risk of bias were assessed using the Prediction model Risk of Bias Assessment Tool.ResultsWe performed a narrative synthesis because of substantial heterogeneity in study design, machine learning models, injury definitions, and predictor variables. Common predictors included training load metrics, previous injury history, biomechanical and neuromuscular characteristics, anthropometric variables, and psychological and biological indicators. Most studies reported moderate-to-high discriminative performance based on area under the curve, sensitivity, specificity, and precision metrics. Reported accuracy values ranged from approximately 55% to 96%. However, several studies relied only on accuracy as the primary metric, which is susceptible to misinterpretation in the presence of class imbalance between injury and noninjury observations; metrics such as sensitivity, specificity, F1-score, or area under the curve-ROC would provide a more balanced evaluation. Models generally demonstrated better performance for noncontact and soft tissue injuries than for contact injuries.ConclusionsMachine learning models may have exploratory value for injury risk stratification in football. However, the current evidence remains heterogeneous and methodologically limited. Further studies with large-scale designs, standardized methodologies, external validation, appropriate calibration, and clinical interpretability are required before these models can be recommended for routine injury-prevention practice.Review protocol was registered in the International Prospective Register of Systematic Reviews database (ID: 1279575).","url":"https://pubmed.ncbi.nlm.nih.gov/42478081/","authors":["Aslani M","Zarei M","Yaghoubitajani Z","Malekahmadi A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1177/03000605261469442","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42478054","name":"Nurse-Led Identification of Financial Toxicity in Stroke Patients Using Machine Learning: Development and Validation of an Associational Prediction Model.","source":"pubmed","abstract":"To develop and validate a machine learning-based associational prediction model for nurse-led identification of financial toxicity (FT) among stroke patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42478054/","authors":["Song Y","Xing Y","Zong C","Liu H","Zhao H","Liu Y","Zhang F","Zhang J","Zhang K","Sun C","Gao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1155/jonm/1006360","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477885","name":"Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.","source":"pubmed","abstract":"Deep mutational scanning (DMS) has proven effective for mapping protein-protein interactions (PPIs), but it cannot provide complete coverage of the mutation landscape, particularly for multi-mutant variants. To address this limitation, we trained machine-learning (ML) models on previously generated DMS data for a stabilized amyloid precursor protein inhibitor (APPI) binding to either of two serine proteases, mesotrypsin and kallikrein-6 (KLK6), which are implicated in various human disorders. We combined the models to accurately predict the binding selectivity of APPI variants, including double-mutant variants, for the two serine proteases. We achieved a Pearson correlation of 0.937 between predicted log 2 selectivity enrichment ratios and DMS-derived values. We further validated the predictions of our combined model by yeast-surface-display measurements and inhibition assays of purified APPI variants and revealed epistatic interactions that shape protease selectivity. Guided by binding selectivity predictions, we identified highly selective APPI variants, including the most selective mesotrypsin inhibitor reported to date. Together, these findings support the use of DMS and ML as a framework for predicting PPI selectivity and prioritizing selective therapeutic protein variants.","url":"https://pubmed.ncbi.nlm.nih.gov/42477885/","authors":["Meiri R","Reuveni O","Levi M","Radisky ES","Papo N","Orenstein Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/pro.70712","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477836","name":"Interpretable machine learning for cardiovascular disease risk prediction in cancer survivors: development and internal validation.","source":"pubmed","abstract":"Cardiovascular disease (CVD) is a major concern among cancer survivors. However, the intersection of cancer and CVD has only recently gained broader attention, and substantial evidence gaps remain. This study aimed to identify risk factors associated with incident CVD in cancer survivors and to develop a machine learning model for CVD risk prediction.","url":"https://pubmed.ncbi.nlm.nih.gov/42477836/","authors":["Zhang G","Zhang X","Jia C","Zhou H","Sun X","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s40959-026-00547-2","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477770","name":"Association of preoperative monocyte level with postoperative delirium in patients receiving cardiac surgery under propofol-based anesthesia: a retrospective study.","source":"pubmed","abstract":"Postoperative delirium is a common complication after cardiac surgery, and perioperative inflammation may contribute to its development. However, the association between preoperative blood indicators and postoperative delirium in patients receiving propofol-based anesthesia remains unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42477770/","authors":["Wen X","Shan J","Deng S","Ye G","Pan Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s13019-026-04642-4","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477755","name":"Reducing overconfident errors in clinical prediction models.","source":"pubmed","abstract":"Machine Learning (ML) models are increasingly being used in clinical workflows. Evaluation of these models tends to focus on global performance metrics, which can obscure error patterns and lead to bias in clinical decision making. Here, we propose Proximal Error-Based Confidence Adjustment (PECA), a framework designed explicitly to improve the safety of ML predictions by reducing a model's confidence in regions of the feature space associated with historical prediction errors. Rather than optimizing global metrics alone, PECA targets confident misclassifications by attenuating prediction confidence proportional to similarity with previously observed errors. Across extensive simulations, PECA reduced the rate at which a model made confident misclassifications while preserving the overall predictive ability of the model. We applied this framework to a clinical trial enrollment workflow for Alzheimer's disease and demonstrated consistent results with the simulations while also demonstrating superior statistical power compared to baseline models. The results of this paper suggest that taming a model's tendency to predict overconfidently using historical error patterns may be a critical step towards safer and more reliable ML systems for digital public health.","url":"https://pubmed.ncbi.nlm.nih.gov/42477755/","authors":["Bayly H","Tripodis Y","Lenio S","Patil P","Alzheimer’s Disease Neuroimaging Initiative"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12911-026-03681-0","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477672","name":"Development and temporal validation of a machine-learning based risk prediction model for depression in older adults with chewing difficulty: evidence from the KNHANES.","source":"pubmed","abstract":"Depression in older adults is a multifactorial condition influenced by demographic, behavioral, and health-related factors. Oral functional problems, such as chewing difficulty, have received relatively limited attention despite potential relevance to mental health. Machine-learning models were developed and temporally validated to predict depressive symptoms while assessing the importance of chewing difficulty alongside other health predictors. Although these approaches enable the evaluation of multiple competing variables, limitations remain in capturing complex interactions among demographic, behavioral, and clinical factors within large population datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/42477672/","authors":["Lee YS","Song D","Chang D","Youn BY"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12903-026-09290-7","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42477657","name":"Beyond visual inspection: can a multimodal machine learning model improve the preoperative differentiation of endometrial polyps from non-polypoid endometrial lesions?","source":"pubmed","abstract":"To develop a multimodal machine learning model that integrates clinical data and ultrasound features to improve the non&#x2011;invasive preoperative differentiation between endometrial polyps (EMPs) and non&#x2011;polypoid endometrial lesions (including polypoid hyperplasia and normal endometrial tissue).","url":"https://pubmed.ncbi.nlm.nih.gov/42477657/","authors":["Ma D","Ye Z","Wang C","Lin H","Yu H","Yao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12905-026-04696-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477641","name":"AI-assisted decision support for sickle cell disease severity stratification using routine blood tests: a systematic review and meta-analysis.","source":"pubmed","abstract":"Stratifying sickle cell disease (SCD) severity remains challenging, particularly in resource-limited settings. Artificial intelligence (AI) models using routine complete blood count (CBC) parameters have been proposed as accessible tools for risk stratification; however, their overall performance and clinical applicability remain uncertain.","url":"https://pubmed.ncbi.nlm.nih.gov/42477641/","authors":["Ali NT","H Mehdi MA","Abdullah RS","Ali GS","Ali HM","Gubran ANM","Al-Abd NM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12911-026-03679-8","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477639","name":"Screening of shared molecular markers between cervical cancer and major depressive disorder and the mechanism of CRAT/CLIC4 regulating EMT in cervical cancer cells.","source":"pubmed","abstract":"Cervical cancer (CC) ranks among the most prevalent malignant neoplasms affecting women worldwide. Tumor recurrence, distant metastases, and chemotherapy resistance significantly hinder long-term clinical survival and therapeutic outcomes. Clinical studies indicate a heightened prevalence of depressive symptoms and major depressive disorder (MDD) among CC patients, suggesting the possibility of bidirectional adverse biological interactions between cervical tumor progression and depressive states. However, the shared molecular signatures underlying both cervical carcinoma and depressive disorders have yet to be fully elucidated, highlighting the need for identifying reliable molecular markers for supplementary diagnosis and prognostic stratification of CC.","url":"https://pubmed.ncbi.nlm.nih.gov/42477639/","authors":["Zhang HH","Liu M","Chen YN","Zhang MR","Zhang J","He J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12885-026-16571-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477630","name":"Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.","source":"pubmed","abstract":"To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection.","url":"https://pubmed.ncbi.nlm.nih.gov/42477630/","authors":["Zhai Y","Wei B","Lan D","Lv S","Mo L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12911-026-03682-z","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477605","name":"Mitochondria-localized protein-encoding genes and programmed cell death-related genes reveal potential molecular perturbations underlying spontaneous preterm birth.","source":"pubmed","abstract":"Multiple programmed cell death (PCD) modalities, including apoptosis, autophagy, and ferroptosis, are closely implicated in spontaneous preterm birth (SPTB). Mitochondria serve as central regulators of various PCD pathways, playing a critical role in cellular stress responses and homeostasis. However, comprehensive studies integrating mitochondria-localized protein-encoding genes and PCD-related genes to explore the molecular mechanisms underlying SPTB remain limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42477605/","authors":["Zuo L","Huai Q","Zhang M","Liu X","Shang X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12884-026-09686-x","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477581","name":"Advanced prediction of cardiovascular-kidney-metabolic syndrome using eight machine learning models and 24 composite indices.","source":"pubmed","abstract":"This study aimed to construct and validate a machine learning classifier for cross-sectionally stratifying existing cardiovascular-kidney-metabolic (CKM) syndrome stages using routine composite inflammatory, metabolic and anthropometric indices, and to interpret core driving biomarkers via SHAP analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42477581/","authors":["Li M","Xiang J","Xiao F","Wang Q","Liu L","Huo Y","Zhang C","Deng L","Feng J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1186/s12872-026-06318-2","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477580","name":"Exploratory clinical-CT machine learning characterization of CK7 expression in clear cell renal cell carcinoma.","source":"pubmed","abstract":"Cytokeratin 7 (CK7) expression in clear cell renal cell carcinoma (ccRCC) may reflect tumor phenotype and biological heterogeneity, but its clinical role remains exploratory. This study aimed to investigate associations between preoperatively available clinical and CT imaging features and CK7 expression and to develop an exploratory machine learning model for CK7 characterization.","url":"https://pubmed.ncbi.nlm.nih.gov/42477580/","authors":["Lin Q","Miao G","Jia X","Zhang Y","Niu C","Wang H","Chen J","Liu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12885-026-16567-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477579","name":"Machine learning-based prediction of sepsis-induced myocardial injury: external validation and SHAP interpretation.","source":"pubmed","abstract":"Sepsis-induced myocardial injury (SIMI) is a common complication in sepsis patients with poor prognosis. Consequently, its early accurate prediction is crucial for optimizing clinical management.","url":"https://pubmed.ncbi.nlm.nih.gov/42477579/","authors":["Li B","Pi S","Xu M","Mu H","Liu X","Huang X","Wu Z","Zheng Z","Li Y","Wu D","Liu W","Chen Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12879-026-13954-8","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"pmid:42477545","name":"Integrated proteomics and machine learning for identifying candidate serum biomarkers in acute myocardial infarction-complicated cardiogenic shock: a prospective exploratory study.","source":"pubmed","abstract":"Cardiogenic shock secondary to acute myocardial infarction (AMI-CS) prohibitively impacts survival. This prospective study aimed to discover and internally verify candidate serum protein biomarkers and evaluate their potential prognostic value for 30-day mortality in AMI-CS patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42477545/","authors":["Wang X","Xiao QF","Huang FY","Wang S","Xu Y","Pu XB","Yang Y","Chen M","Wei X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12014-026-09626-z","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477497","name":"Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home.","source":"pubmed","abstract":"In-person assessments face accessibility, scalability, and geographic diversity challenges, especially for rare diseases. Additionally, Cerebellar Ataxia (CA) non-motor symptoms(NMS) are often overlooked. We aimed to address these gaps by leveraging the Internet and machine-learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42477497/","authors":["Ponger P","De Picciotto Y","Maisel D","Liandres H","Brisman S","Saban W"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1038/s43856-026-01794-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477310","name":"Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome.","source":"pubmed","abstract":"Polycystic Ovary Syndrome (PCOS) is a heterogeneous endocrine-metabolic disorder characterized by ovulatory dysfunction, hyperandrogenism, and insulin resistance, in which mitochondrial dysfunction has been increasingly implicated. Mitochondria regulate energy metabolism and oxidative stress, with mitophagy maintaining cellular balance. Dysregulated mitophagy relates to PCOS metabolic issues like obesity and inflammation. This study analyzed gene expression datasets to find autophagy-related genes in PCOS, identifying AMFR, FABP5, and HMOX1 as key genes. We built a diagnostic model and confirmed their elevated expression in a hyperandrogenism-induced PCOS cell model, revealing potential small-molecule drugs targeting these genes. Our integrative bioinformatics analysis and systematic molecular experiments suggest that FABP5 and HMOX1 are potential PCOS diagnostic targets, with high-affinity compounds as therapies,highlighting the pathological relevance of mitophagy-related genes in PCOS.","url":"https://pubmed.ncbi.nlm.nih.gov/42477310/","authors":["Lian A","Li Y","He W","Luo X","Jin L","Li C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s43032-026-02156-x","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477301","name":"Development and Evaluation of an Adaptive Penguin-Improved LSTM Model Integrating Natural Language Processing for Early Prediction of Pregnancy Syndrome Risks.","source":"pubmed","abstract":"Early prediction of pregnancy syndromes such as preeclampsia, gestational diabetes mellitus (GDM), and preterm birth is critical for improving maternal and fetal outcomes. Traditional risk assessment tools rely primarily on structured clinical data and often fail to fully utilize the rich unstructured textual information contained in electronic health records (EHRs).","url":"https://pubmed.ncbi.nlm.nih.gov/42477301/","authors":["Wu Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1002/bdr2.70095","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477241","name":"Pharmacokinetic Modeling of Hepatospecific MRI Contrast Agent Flux in Large Animal Models by Dynamic Contrast-Enhanced MRI.","source":"pubmed","abstract":"Evaluate the feasibility of using dogs and a pig as translational models for assessing hepatic flux of hepatospecific gadolinium-based contrast agents using dynamic contrast-enhanced MRI (DCE-MRI).","url":"https://pubmed.ncbi.nlm.nih.gov/42477241/","authors":["Latourette MT","Hix JML","Huang J","Mallett CL","Muñoz KA","Shapiro EM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11307-026-02117-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477236","name":"Identification of Microbiome Associations with Tacrolimus Pharmacokinetics in Adult Hematopoietic Cell Transplantation Using Population Pharmacokinetic and Machine Learning.","source":"pubmed","abstract":"Tacrolimus (TAC) is known for its high pharmacokinetic variability which cannot be fully explained by pharmacogenomic (PGx) and clinical variables. We identified gut microbiome associated with TAC pharmacokinetic variability in allogeneic hematopoietic cell transplant (HCT) recipients.","url":"https://pubmed.ncbi.nlm.nih.gov/42477236/","authors":["Mohamed ME","Cheng S","Staley C","Rashidi A","Jurdi NE","Holtan SG","Jacobson PA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11095-026-04152-x","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477230","name":"An Interpretable Machine Learning Model for Predicting the Presence of Talaromycosis in HIV Patients Lacking Skin Lesions.","source":"pubmed","abstract":"The existing predictive models for talaromycosis in people living with HIV without skin lesions are limited by established risk factors and traditional statistical approaches. This study aims to develop an interpretable machine learning(ML) model for predicting the presence of talaromycosis in HIV patients without skin lesions and to validate its clinical applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42477230/","authors":["Hu J","He W","Tian Q","Lu Y","Zhang P","Qin J","Qin C","Wu Y","Huang C","Li X","Feng L","Li L","Jiang Z","Jiang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 21","doi":"10.1007/s11046-026-01089-y","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477227","name":"A comprehensive evaluation of seven osteoporosis screening tools.","source":"pubmed","abstract":"This review evaluates the sensitivity, specificity, and predictive values of seven osteoporosis screening tools in populations such as postmenopausal women, older adults, and patients with specific diseases. These tools offer advantages, including high sensitivity (facilitating early detection of osteoporosis), high cost-effectiveness, and the ability to be customized according to disease characteristics. However, they commonly suffer from low specificity, leading to a large number of subjects being misclassified as high-risk. This review identifies that the application of machine learning holds promise for improving the performance of screening tools. By integrating multidimensional data and processing large-scale sample data, machine learning is expected to overcome traditional limitations and comprehensively improve the efficiency of osteoporosis screening. In the future, integrating multimodal artificial intelligence with electronic health records may enable more accurate, comprehensive prediction of osteoporosis. This review summarizes the current strengths and limitations of osteoporosis screening tools and proposes directions for optimization and future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42477227/","authors":["Zhang Y","Duan Z","Liu J","Tian C","Ma M","Huang X","Geng B"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s00198-026-08118-y","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477216","name":"Pre-operative concerns and post-operative satisfaction: comparing attitudes toward bariatric surgery and medication intervention for weight loss on Reddit.","source":"pubmed","abstract":"Patient perceptions influence the success of bariatric surgery and pharmacologic weight loss therapies, yet many concerns never reach providers during clinic visits. Reddit, a popular online community, offers a window to explore these concerns outside clinical settings. We leveraged natural language processing (NLP) to compare user-submitted content across communities in surgical and non-surgical weight loss communities on Reddit.","url":"https://pubmed.ncbi.nlm.nih.gov/42477216/","authors":["Del Carmen GA","Patel D","Vu N","Tran A","Zaman JA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s00464-026-13149-x","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477163","name":"Validation of Kawasaki MATCH on a Latin American cohort: application to the REKAMLATINA network.","source":"pubmed","abstract":"Key message: In a large multinational validation study, the Kawasaki MATCH machine-learning clinical decision support tool accurately identified patients with Kawasaki Disease (KD) using data from the REKAMLATINA network.","url":"https://pubmed.ncbi.nlm.nih.gov/42477163/","authors":["Tremoulet AH","Lam JY","Ulloa-Gutierrez R","Burns JC","Nemati S","Gardiner MA","Kawasaki Disease REKAMLATINA Network Study Group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1038/s41390-026-05190-2","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477062","name":"Deep learning model for predicting fracture redisplacement in conservatively treated distal radius fractures using radiographs: a retrospective cohort study.","source":"pubmed","abstract":"Accurate assessment of redisplacement risk in distal radius fractures during initial treatment is crucial for selecting the optimal management plan. Traditional clinical decision rules have limited predictive performance. Deep learning models have demonstrated promise for fracture detection/classification tasks, indicating potential applicability in prognostic prediction. Herein, we developed and validated a deep learning model to predict fracture redisplacement risk at follow-up radiographs obtained four weeks or more after conservative treatment of distal radius fractures, using initial and post-reduction radiographs. This retrospective study enrolled 966 distal radius fractures in adult patients conservatively managed at a super-tertiary university hospital between December 2003 and December 2022. The dataset was chronologically divided into training (earliest 80%, 772 fractures) and validation (latest 20%, 194 fractures) cohorts. An XGBoost model was first trained using demographic data and radiographic parameters as a tabular baseline model. Its predicted probabilities were calibrated using Platt scaling and subsequently used as soft targets to train deep learning models based on CoAtNet or EfficientNetV2 backbones. The deep learning models incorporated four radiographic views (initial and post-reduction posteroanterior and lateral) with/without metadata (age and sex). Model performance was evaluated on the temporal validation set using area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis. Model interpretability was analyzed using SHAP and LayerCAM visualization for the XGBoost and deep learning models, respectively. The tabular baseline XGBoost model achieved an AUROC of 0.853 (95% CI, 0.797-0.903) on the temporal validation set. The best image-based model, CoAtNet without metadata, achieved an AUROC of 0.815 (95% CI, 0.752-0.869) while demonstrating good calibration, providing measurement-free risk estimation directly from initial and post-reduction wrist radiographs. Training with calibrated soft targets from the XGBoost model improved discrimination and calibration compared with binary-target training. Decision curve analysis demonstrated positive net benefit across a broad range of clinically relevant threshold probabilities, supporting the potential utility of the model for risk-based decision-making. LayerCAM visualizations revealed activation over clinically relevant regions, including the distal radioulnar joint area in the posteroanterior view, corresponding to SHAP findings indicating reliance on initial and post-reduction radial shortening. In the lateral view, activation was noted along the volar and dorsal cortices and articular surface, consistent with dorsal tilt identified in SHAP analysis. Deep learning has potential for prognostic prediction of fracture redisplacement of distal radius fractures using routine wrist radiographs. Although the tabular baseline XGBoost model achieved the highest discrimination, the proposed image-based deep learning model provides interpretable, well-calibrated, measurement-free risk predictions directly from radiographs. By reducing reliance on user-dependent radiographic measurements and expert-defined features, this approach may support more objective and reproducible risk stratification, particularly in busy or resource-limited clinical settings where specialist assessment may not be immediately available.","url":"https://pubmed.ncbi.nlm.nih.gov/42477062/","authors":["Dissaneewate P","Thanavirun P","Tangjaroenpaisan Y","Orapiriyakul W","Chewakidakarn C","Kritsaneephaiboon A","Dissaneewate K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1038/s41598-026-63335-z","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42477001","name":"Deep learning for multimodal physiological signal based assessment of sleep disordered breathing.","source":"pubmed","abstract":"Sleep disordered breathing (SDB) is commonly assessed using polysomnography (PSG), which records multiple physiological signals during sleep. Accurate automated assessment of SDB related breathing abnormalities remains challenging because respiratory events occur within complex and stage dependent sleep dynamics. To address this problem, this study proposes a multimodal physiological signal based deep learning framework for SDB oriented sleep assessment. The proposed framework treats SDB related respiratory event detection as the clinically targeted task and incorporates sleep stage classification as an auxiliary contextual task to enhance temporal interpretation of physiological abnormalities. The framework combines a Sleep Pattern Recognition Network (SPRNet) with an Integrated Diagnostic Strategy for Sleep Assessment (IDSSA). SPRNet integrates electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), electromyography (EMG), and respiratory airflow signals through modality specific feature extraction, followed by BiLSTM and attention based temporal modeling. IDSSA further incorporates domain informed sleep transition priors, probabilistic reasoning, and multi objective optimization to improve temporal consistency and robustness under noisy physiological recordings. Sleep stage classification is evaluated on both Sleep EDF and SHHS, while SDB related respiratory event detection is evaluated on SHHS, which provides respiratory event annotations. Experimental results show that the proposed framework achieves competitive performance compared with conventional machine learning and deep learning baselines. These findings indicate that multimodal physiological signal integration and temporally informed joint modeling can provide effective decision support for automated assessment of SDB related respiratory abnormalities. The proposed system is intended for automated screening and assessment support rather than as a replacement for clinical diagnosis by sleep specialists.","url":"https://pubmed.ncbi.nlm.nih.gov/42477001/","authors":["Zhang N","Wang F","Sui W","Huang M","Li Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1038/s41598-026-56111-6","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476832","name":"Multiparametric MRI Radiomics-Based Interpretable Machine Learning Model for the Prediction of Lymphovascular Space Invasion in Endometrial Cancer.","source":"pubmed","abstract":"To develop and validate a machine learning model combining multiparametric Magnetic Resonance Imaging (MRI) radiomics and clinical indicators for predicting lymphovascular space invasion (LVSI) in endometrial cancer (EC).","url":"https://pubmed.ncbi.nlm.nih.gov/42476832/","authors":["Meng W","Dou H","Yin J","Ma W","Wang X","Liu J","Shi F"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1016/j.acra.2026.06.067","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476233","name":"Bisphenol A drives the comorbidity of non-alcoholic fatty liver disease and osteoarthritis by targeting RHOB: Integrated multi-omics, single-cell analysis, and experimental validation.","source":"pubmed","abstract":"This study focuses on exploring the co-morbid mechanisms by which Bisphenol A (BPA) induces non-alcoholic fatty liver disease (NAFLD) and osteoarthritis (OA).","url":"https://pubmed.ncbi.nlm.nih.gov/42476233/","authors":["Di J","Wang S","Guo Z","Di Y","Yang N","Juma T","Long Y","Qi L","Cao Y","Xiang C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct 1","doi":"10.1016/j.exger.2026.113242","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476208","name":"Machine Learning Prediction of Liver Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease.","source":"pubmed","abstract":"Accurate staging of liver fibrosis is crucial for risk stratification in patients with metabolic dysfunction-associated steatotic liver disease. We aimed to develop and validate artificial intelligence-based models capable of distinguishing fibrosis stages.","url":"https://pubmed.ncbi.nlm.nih.gov/42476208/","authors":["Petta S","Pennisi G","Celsa C","Messaoudi S","Tsochatzis E","Bugianesi E","Yoneda M","Zheng MH","Hagström H","Boursier J","Calleja JL","Boon-Bee Goh G","Chan WK","Gallego-Durán R","Sanyal AJ","de Lédinghen V","Newsome PN","Fan JG","Castéra L","Lai M","Fournier-Poizat C","Lai-Hung Wong G","Armandi A","Nakajima A","Liu WY","Shang Y","de Saint-Loup M","Llop E","Jun Teh KK","Lara-Romero C","Asgharpour A","Sau-Wai Chan M","Romero-Gomez M","Lin H","Kim SU","Cheuk-Fung Yip T","Calvaruso V","Zoncapè M","Che-To Lai J","Yang B","Lee HW","Di Maria G","Enea M","Contino S","Wai-Sun Wong V","Cirrincione G","Cammà C"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1016/j.cgh.2026.07.006","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476061","name":"Preoperative prediction of progesterone receptor expression in meningiomas based on diffusion weighted imaging habitat analysis and Transformer-Based deep learning.","source":"pubmed","abstract":"This study aims to develop and validate a model based on diffusion-weighted imaging (DWI) habitat analysis and Transformer-based deep learning (DL) for preoperative prediction of progesterone receptor (PR) expression in meningiomas.","url":"https://pubmed.ncbi.nlm.nih.gov/42476061/","authors":["Gui Y","Diao J","Zhang F","Hu W","Su L","Liu M","Lin Z","Zhang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.1016/j.ejrad.2026.113094","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476029","name":"Linear screening and machine learning to identify suitable candidates for active breathing control after mastectomy in left-sided breast cancer.","source":"pubmed","abstract":"To evaluate the dosimetric benefit of deep inspiration breath-hold (DIBH) using the Active Breathing Coordinator (ABC) for left-sided breast cancer radiotherapy and to identify patients most likely to benefit based on free-breathing (FB) anatomical and clinical features.","url":"https://pubmed.ncbi.nlm.nih.gov/42476029/","authors":["Zheng C","Zhang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.ejmp.2026.105880","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42476005","name":"Digital health technologies versus traditional methods for cardiovascular risk assessment in asymptomatic adults: a systematic review and network meta-analysis of diagnostic accuracy and clinical outcomes.","source":"pubmed","abstract":"Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually. Traditional risk assessment tools demonstrate limited discriminatory capacity. Digital health technologies, including AI algorithms, wearable devices, and smartphone applications, offer promising alternatives for improved CVD risk stratification.","url":"https://pubmed.ncbi.nlm.nih.gov/42476005/","authors":["Wang J","Li X","Luo C","Ren Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov 1","doi":"10.1016/j.ijmedinf.2026.106588","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475855","name":"AI agents in drug discovery: A review of evolution, applications, and future directions.","source":"pubmed","abstract":"Artificial intelligence (AI) agents represent a paradigm shift in pharmaceutical research, moving the field from narrow drug-protein affinity modeling toward systems-biology-level evaluation in which autonomous, multi-domain agents combine pattern recognition with symbolic reasoning, knowledge graphs, and regulatory intelligence. This review traces the evolution of AI agents in drug discovery across four eras - database systems (1990-2012), machine learning (2012-2022), foundation learning tools (2022-2023), and autonomous agents (2023-present) - and analyzes breakthrough systems including AlphaEvolve, Google's AI Co-scientist, DrugAgent, Boltz-1/Boltz-2, and Isomorphic Labs' clinical programs, reporting industry-disclosed estimates of 25%-30% improvements in Phase I success rates and 30%-40% reductions in preclinical costs together with their statistical limitations. We present a taxonomy of next-generation architectures spanning foundation model-based agents, autonomous multi-agent ecosystems with explicit coordination protocols (consensus voting, debate, hierarchical orchestration), and specialized systems for target discovery, molecular design, and clinical optimization, situating them within knowledge-graph and neuro-symbolic reasoning (PrimeKG, Hetionet, AnyBURL; Hit@K, MRR, AUROC) and the emerging Internet of Agents. We introduce an enhanced Autonomy-Trust Framework that links four levels of autonomous capability to corresponding trust infrastructure and concrete validation strategies, including +Masking and +LLMEval ablations for Levels 2 and 3. Applications in biomarker discovery and precision medicine are examined alongside challenges in validation, data quality, regulatory compliance, and ethics. Current evidence positions AI agents as transformative tools, with Level 2 collaborative agents becoming mainstream while Level 3 autonomous specialists emerge in focused domains.","url":"https://pubmed.ncbi.nlm.nih.gov/42475855/","authors":["Das S","Ferdaus MM","Dam T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109539","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475853","name":"A semi-supervised deep learning and IoMT framework for robust prostate cancer grading under imperfect histopathology data conditions.","source":"pubmed","abstract":"Deep learning has shown remarkable promise in histopathological cancer diagnostics; however, its performance heavily depends on the availability of large-scale, high-quality labelled data, which is often an unrealistic assumption in clinical practice. To address the challenges posed by imperfect and limited data, this paper presents a semi-supervised learning (SSL)- driven Internet of Medical Things (IoMT) framework for automated prostate cancer grading using whole-slide histopathology images. The proposed framework integrates deep learning, IoMT data acquisition, and cloud inference to deliver scalable, resource-efficient diagnostics. The framework employs a modified YOLOv11 architecture for region detection and classification, incorporating preprocessing techniques, patch-wise analysis, and mask-based region-of-interest extraction to convert raw biopsy data into structured, clinically relevant Gleason grade predictions (benign, a=Grade 3, 4, or 5). The IoT-enabled imaging devices might support real-time data capture and edge-level preprocessing, while the cloud component facilitates resource efficiency and centralised model inference. This dual-layer framework is designed to support flexible deployment for both on-site diagnostics and remote telepathology workflows, making it highly applicable in under-resourced settings. The framework also incorporates explainability through Grad-CAM to enhance interpretability and clinical trust. Experimental results on the PANDA dataset demonstrate strong diagnostic performance, achieving 81.2% accuracy, an F1-score of 0.68, mAP@0.5 of 0.64, and an average IoU of 0.58. The results suggest that the proposed semi-supervised framework can support prostate cancer grading under imperfect data conditions, while the IoMT architecture provides a basis for future deployment in connected digital pathology workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42475853/","authors":["Ahmed I","Zhang J","Cirstea S","Jeon G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.cmpb.2026.109527","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475847","name":"A rapid detection of early-stage cervical cancer using nucleus and cytoplasm-based machine learning-driven refractive index surface plasmon resonance biosensor with ZnO-ag efficient materials.","source":"pubmed","abstract":"Cervical cancer is one of the most common cancers in women around the globe and death from such cancer is largely due to late or insufficient diagnosis. Traditional forms of screening like Pap smears and HPV testing, though common with extensive use, have limitations in sensitivity, false negative and require repeated clinical follow-ups. To overcome these diagnostic gaps, we have presented a supportive layout of a Bisected Circular-Resonator and Axial Line Refractive Index Biosensor (BCALRIB) with a machine learning-driven approach that can help to detect cervical cancerous cells in the early stage. The recommended layout of geometry has emerged as especially valuable because cancer-related cells present greater nuclei and changed nuclear-to-cytoplasmic ratios, which produce distinctive optical signals. Additionally, an acceptable tolerance analysis has been evaluated to check and enhance the device's robustness against deviations in biological components. The optimum impressive sensitivity values of 1000.00&#xa0;nm/RIU, 933.33&#xa0;nm/RIU, and impressive detection limit values of 0.008289 RIU, 0.006935 RIU have been obtained for cervical cancerous nucleus (CCNu), and cervical cancerous cytoplasm (CCCy), respectively, with reference to healthy nucleus (HeNu), and healthy cytoplasm (HeCy). The impressive value of 175.81 quality factor has been obtained. The impressive machine learning parameter, resulting in a 0.996351 R-squared score with 6.057260&#xa0;&#xd7;&#xa0;10 -05 of mean square error score. The current biosensors with SPR provide rapid, specific, and real-time detection of cervical cancer biomarkers by utilizing the variation of refractive indices on the sensor interface.","url":"https://pubmed.ncbi.nlm.nih.gov/42475847/","authors":["Kamani T","Patel SK","Sharma Y","Al-Zahrani FA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Dec 15","doi":"10.1016/j.saa.2026.128429","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475791","name":"Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.","source":"pubmed","abstract":"Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response.","url":"https://pubmed.ncbi.nlm.nih.gov/42475791/","authors":["Desai SP","Hori YS","Lam FC","Zagzoog N","Kalra N","Tayag A","Ustrzynski L","Emrich SC","Gu X","Park DJ","Chang SD"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1016/j.jocn.2026.112210","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475757","name":"The role of artificial intelligence in clinical trials in adult and pediatric rheumatology.","source":"pubmed","abstract":"The development of clinical trials is limited by high costs and methodological complexities. In this context, artificial intelligence (AI) is emerging as a key instrument for their optimization. In the early phases of study design and recruiting, generative AI systems may help refine eligibility criteria and boost enrollment; in parallel, the integration of digital biomarkers and patient-reported outcomes may allow continuous remote monitoring and improved safety data collection. Moreover, machine learning models may be applied to effectively analyze longitudinal multimodal trial data. However, AI implementation in real-world settings must overcome significant challenges; regulatory authorities are updating guidance, and a successful integration of AI-based interventions will depend on the rigorous application of quality standards while preserving the central role of medical judgment. In this review, we provide a comprehensive overview of the clinical applications, ethical considerations, and regulatory aspects of implementing AI in clinical trials in adult and pediatric rheumatology.","url":"https://pubmed.ncbi.nlm.nih.gov/42475757/","authors":["La Bella S","Rebollo-Giménez AI","Alongi A","Capitoli G","Breda L","La Torre F","Venerito V","Ruperto N","Paediatric Rheumatology INternational Trials Organisation (PRINTO) and the Pediatric Rheumatology Associated Group of Milan and Lombardy Area (PRAGMA)"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1016/j.coi.2026.102818","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475429","name":"Multimodel Interpretable Machine Learning for Osteoporosis Prediction in Diabetes Using a Large Critical Care Electronic Health Record Database.","source":"pubmed","abstract":"Diabetes mellitus is associated with increased skeletal fragility and elevated fracture risk; however, early identification of osteoporosis (OP) in diabetic populations remains challenging. This study aimed to develop and compare multiple machine-learning (ML) models for predicting OP among patients with diabetes, while evaluating their clinical utility and enhancing model transparency using SHapley Additive exPlanations (SHAP). Using routinely collected demographic and laboratory data from the MIMIC-IV database, multiple ML algorithms, including tree ensembles, gradient boosting, and linear baselines, were trained and validated. Model performance was comprehensively evaluated using discrimination metrics, including the area under the receiver operating characteristic curve (AUC) and precision-recall analysis, as well as probability calibration curves and decision curve analysis (DCA) to assess net clinical benefit. The best-performing model was further interpreted using SHAP to quantify and visualize feature contributions at both global and individual levels. Among all evaluated models, ensemble tree-based methods showed improved performance. The ExtraTrees classifier achieved the highest validation performance (AUC = 0.862; average precision = 0.866). Furthermore, the selected model exhibited excellent probability calibration (Brier score = 0.154) and demonstrated substantial net clinical benefit across a wide range of risk thresholds in the DCA. SHAP analysis identified gender and age as the most influential predictors, followed by routine hematologic and metabolic laboratory indicators. Local explanations provided clinically interpretable insights into individual predictions. An interpretable ensemble tree-based model effectively predicts OP risk in patients with diabetes using routinely available clinical variables. The integration of SHAP improves clinical transparency, and strong calibration coupled with positive net clinical benefit supports its potential implementation as a reliable decision-support tool for early OP risk stratification in diabetic populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42475429/","authors":["Cheng D","Zhang W","Chen J","Zhu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun 30","doi":"10.3791/71386","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475309","name":"A feature-efficient dual-task machine learning framework for predicting bone mineral density and osteoporosis stratification in resource-constrained environments.","source":"pubmed","abstract":"Osteoporosis is a chronic skeletal disorder characterized by progressive bone mineral density (BMD) loss and structural deterioration, significantly increasing fracture risk. Despite its high prevalence, early detection remains challenging due to its asymptomatic progression and the limitations of conventional diagnostic techniques, such as Dual-Energy X-ray Absorptiometry (DXA). While DXA remains the clinical benchmark for BMD assessment, its high cost, limited accessibility, and inability to directly detect vertebral fractures necessitate the development of alternative, cost-effective, and widely deployable diagnostic methodologies. A dataset of 159 patient records was collected from NORI and CDA Hospital, incorporating 17 input features spanning demographics, genetic/blood type, clinical history and lab tests parameters. To bridge this gap, we developed a practical machine learning model tailored for clinics with limited resources. Instead of relying on expensive imaging, our framework uses only basic, highly accessible clinical markers-specifically ABO blood groups, serum calcium, and potassium levels. Because these tests are inexpensive and easily processed in standard laboratories, our approach removes the financial and technical hurdles of advanced diagnostics, making early screening possible in remote or underfunded healthcare settings. Data preprocessing involved rigorous feature selection, standardization, hyper-parameters tuning, clinically relevant features derivation and biomarker combinations. For classification, ensemble voting classifier was trained on key biomarkers- Weight, Potassium, Calcium and Total Vitamin D-achieving an accuracy of 90% and an AU-ROC score of 0.93 in predicting osteoporosis severity. In parallel, extreme gradient boosting Regressor trained on Age, Weight, ABO Group and Total Vitamin D demonstrated an R2 of 0.536 for lumbar spine BMD estimation. The proposed framework demonstrates the viability of leveraging machine learning for non-invasive osteoporosis screening and fracture risk assessment, offering a radiation-free and clinically accessible complementary pre-screening tool.","url":"https://pubmed.ncbi.nlm.nih.gov/42475309/","authors":["Maryum A","Shaukat A","Yousaf E","Haque A","Yusuf S","Haque SU","Ali H","Jawed S","Akram MU"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354038","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475303","name":"Automated Machine Learning Model for Non-Alcoholic Fatty Liver Disease Prediction and External Cohort Validation.","source":"pubmed","abstract":"Non-alcoholic fatty liver disease (NAFLD) is a common liver disorder associated with obesity, insulin resistance, and metabolic syndrome, often going undiagnosed until advanced stages. Traditional diagnostic methods, including imaging and liver biopsy, have limitations in early detection. There is a need for an efficient, non-invasive tool for early NAFLD screening. Using automated machine learning (AutoML) technology, a diagnostic model for NAFLD was developed leveraging a large dataset from NHANES (n = 2677). Additionally, an independent external validation cohort (n = 200) was employed to assess the external validity and performance of the model. For selection of promising clinical features, A two-stage feature selection method was applied, combining LASSO regression with ChatGPT-4-based intelligent analysis. Subsequently, the AutoML process, which integrated multiple machine learning algorithms, was performed for model training and validation. The model's performance was evaluated using ROC curves, F1 scores, and SHapley additive explanation (SHAP) analysis. The GBM model achieved an AUC of 0.843 in the training set, 0.851 in the testing set, and 0.945 in the external validation set, demonstrating high diagnostic accuracy across different datasets. Key predictors, including BMI, triglycerides, and GGT, were identified as significant contributors to the model's predictions. SHAP analysis further confirmed the importance of these variables in predicting NAFLD. The AutoML-driven diagnostic model for NAFLD demonstrated significantly improved early-detection performance, offering a reliable, non-invasive, and efficient alternative to conventional diagnostic methods. This method holds great potential for broader clinical application in NAFLD, diminishing dependence on expert knowledge while improving diagnostic precision.","url":"https://pubmed.ncbi.nlm.nih.gov/42475303/","authors":["Li M","Li Y","Chen L","Chen H","Chen L","Liu W","Xu G","Chen S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 3","doi":"10.3791/70033","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42475127","name":"Delusion Is Not a Bug: AI, Psychiatry, and the Ethics of Uncertainty.","source":"pubmed","abstract":"Recent debates about AI \"guardrails\" in mental health have framed a problem as one of detecting delusion. This paper argues that such framing misunderstands both psychiatry and large language models. In clinical practice, delusion is not simply a false belief but a structure of conviction that stabilizes meaning under conditions of uncertainty. Large language models, by contrast, generate coherence rather than evaluate truth. When these systems interact with users seeking orientation, they do not merely fail or succeed at detection-they participate in the conditions under which belief forms. The ethical risk is therefore not misclassification but premature coherence: the subtle reinforcement of interpretations before they can be examined. Drawing on phenomenology and clinical psychiatry, this paper proposes that safety in AI systems should be understood as the protection of provisionality-the preservation of the human capacity to remain uncertain long enough to think.","url":"https://pubmed.ncbi.nlm.nih.gov/42475127/","authors":["Carr BR"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul-Aug","doi":"10.1002/hast.70045","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474865","name":"Explainable machine learning for automated robotic surgical skill assessment using rich kinematic features.","source":"pubmed","abstract":"Robotic-assisted surgery (RAS) extends minimally invasive surgery by restoring dexterity, tremor filtration, and ergonomic console control compared with open procedures and conventional laparoscopy. Objective, scalable skill assessment from console kinematics is therefore clinically relevant for competency-based training. Existing JIGSAWS studies often rely on random trial splits, isolated handcrafted features, or black-box models without subject-independent validation or transparent explanations. This study presents an explainable machine learning framework for automated skill classification from kinematic data alone. Using the public JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), comprising 103 trials from 40 subjects across three dry-lab tasks, 633 trial-level kinematic features were extracted and six supervised classifiers were compared under leave-one-subject-out cross-validation (LOSO-CV). Random Forest (RF) with feature standardization was selected as the primary model; interpretability was assessed with SHapley Additive exPlanations (SHAP). Key gaps addressed include: subject-independent evaluation on all trials, systematic classifier comparison on identical features, collinearity-aware feature analysis, global and local SHAP attribution, and ablations (mean/standard-deviation features, segment-level modeling, PCA reduction, deep sequences, cross-task transfer). Under pooled LOSO-CV, RF with advanced kinematic features achieved 97.1% accuracy and Cohen's [Formula: see text] (macro-F1 [Formula: see text]; balanced accuracy [Formula: see text]), outperforming PCA-reduced features (92.2%) and segment-level modeling (88.3%). SHAP analysis identified jerk and velocity variability in master-tool channels as dominant discriminators. Cross-task evaluation showed strong in-task performance (97-100%) but reduced off-task transfer (61-95%). These benchmark findings may inform explainable kinematics-based training analytics; external multi-center validation remains required before clinical deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42474865/","authors":["Xuan W","Rui Z","Mingxu Y","Azam M","Ahmad N","Sarwar R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11701-026-03669-y","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474842","name":"Mapping the evolution of deep learning and computer vision in robotic surgery: a bibliometric analysis of surgical video intelligence, instrument perception, and clinical translation.","source":"pubmed","abstract":"Deep learning and computer vision are increasingly embedded in robotic surgery, yet the development and translational direction of this research domain remain incompletely characterized. We conducted a bibliometric and visualization analysis of publications retrieved from the Web of Science Core Collection using Bibliometrix/Biblioshiny, VOSviewer, and CiteSpace. A total of 1,186 documents published between 2010 and 2026 across 356 sources were included. Scientific output increased rapidly, with an annual growth rate of 16.09% and a peak of 216 publications in 2025. The field involved 5,296 authors, and international collaboration accounted for 30.69% of publications. IEEE Robotics and Automation Letters was the most productive and locally influential source. China and the United States were the leading contributors, with China showing the most rapid recent expansion and the United States retaining the highest citation impact. Citation-burst and keyword analyses identified U-Net, residual learning, transformer architectures, and foundation-model-enabled segmentation as major methodological drivers. The conceptual structure evolved from image guidance, registration, and navigation toward surgical video intelligence, instrument perception, workflow understanding, autonomous assistance, and clinical translation. Instrument perception emerged as a central link between algorithmic development and operative application. Future progress will require diverse multi-institutional datasets, external and prospective validation, integrated scene understanding, and rigorous evaluation of intelligent assistance within real robotic surgical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42474842/","authors":["Yang D","Shang F","Xu Y","Liu J","Wang J","Li Y","Ma X","Lv D"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11701-026-03621-0","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474752","name":"Discovery of a DNA methylation episignature for Weiss-Kruszka syndrome.","source":"pubmed","abstract":"Weiss-Kruszka syndrome (WSKA; OMIM 618619) is a rare autosomal dominant neurodevelopmental disorder caused by haploinsufficiency of ZNF462, a zinc-finger transcription factor involved in chromatin regulation and early embryonic development. WSKA is characterized by developmental delay, hypotonia, craniofacial dysmorphic features (around 8) and variable congenital anomalies. Genome-wide DNAm profiling was performed on peripheral blood DNA from 9 WSKA cases with (likely) pathogenic ZNF462 variants and matched controls to look for differential methylation. Analysis using the EpiSign&#x2122; pipeline identified a robust DNAm pattern, or episignature, specific to WSKA syndrome. Supervised machine-learning classification demonstrated high sensitivity and specificity, with reproducibility confirmed by leave-one-out cross-validation, as well as correctly classifying a validation case with a pathogenic ZNF462 variant. Comparative analysis revealed partial overlap of genome-wide DNA methylation changes between the WSKA episignature and other neurodevelopmental disorders involving chromatin regulators. Functional annotation of differentially methylated probes and regions demonstrated enrichment for pathways related to neurodevelopment, neuron function and cell adhesion. These findings define and validate a distinct DNAm episignature for WSKA, providing a valuable diagnostic biomarker to support variant classification and offering insight into the epigenomic consequences of ZNF462 haploinsufficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/42474752/","authors":["McConkey H","van der Laan L","Ghosh S","Kleinendorst L","Levy MA","Rzasa J","van Hagen JM","Waisfisz Q","Schulz HL","Heller C","Huhn K","Obermaier CD","Platzer K","Jamra RA","Marinakis N","Veltra D","Kosma K","Sofocleous C","Henneman P","Sadikovic B","van Haelst MM"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s00439-026-02846-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474718","name":"Multimodal prediction of metachronous liver metastasis in stage I-III colorectal cancer patients: multicenter cohort study employing machine learning.","source":"pubmed","abstract":"Postoperative metachronous liver metastasis (MLM) in colorectal cancer (CRC) patients is often difficult to predict using conventional clinical and radiological methods, which may result in delayed diagnosis and treatment. We aimed to develop and validate an artificial intelligence integrated model to improve MLM prediction after CRC surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/42474718/","authors":["Liang L","Zhang Y","Yang L","Li J","Alburiahi TAH","Lin W","Yang Y","Zhou R","Yang Z","Liu X","Wen Z","Xu N","Shan L","Yang J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s00261-026-05700-0","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474603","name":"Development and validation of a LightGBM based machine learning model for predicting postoperative erectile dysfunction after robot assisted radical prostatectomy: a multicenter cohort study.","source":"pubmed","abstract":"Postoperative erectile dysfunction remains a highly debilitating complication following robot assisted radical prostatectomy for localized prostate cancer. Traditional risk stratification heavily relies on anatomical and oncological parameters utilizing simple linear regression algorithms, frequently failing to capture the complex multifaceted physiological and psychological dynamics that govern functional sexual recovery. To engineer and robustly validate an advanced machine learning architecture utilizing a LightGBM framework coupled with SHapley Additive exPlanations to accurately predict postoperative erectile dysfunction by integrating conventional surgical metrics with novel psychosocial and systemic immunological indices. This multicenter retrospective cohort study analyzed 824 patients with strictly localized prostate cancer who underwent robot assisted radical prostatectomy across two independent medical institutions. The primary outcome was functional erectile impairment systematically assessed utilizing validated questionnaires following a minimum six month postoperative observation period. To isolate the optimal prognostic variables, a rigorous two step dimensionality reduction strategy was executed. This approach incorporated Least Absolute Shrinkage and Selection Operator regression alongside a Random Forest feature importance algorithm. The dimensionality reduction pipeline successfully identified seven core independent predictors: the Patient Health Questionnaire 9 score, patient age, the HALP score, baseline serum testosterone, clinical stage, preoperative biopsy Gleason score, and prostate volume. The LightGBM algorithm demonstrated unparalleled predictive superiority in the independent validation cohort, achieving an area under the receiver operating characteristic curve of 0.946, an overall accuracy of 0.927, a sensitivity of 0.775, and a specificity of 0.956. The SHapley Additive exPlanations analysis provided transparent clinical interpretability, unequivocally revealing the Patient Health Questionnaire 9 score as the dominant predictive determinant. Severe baseline depressive symptoms were independently associated with a significantly elevated risk of functional impairment, whereas robust systemic nutritional immunological health and adequate baseline testosterone levels emerged as potential protective factors. This multicenter study developed a LightGBM prognostic framework that incorporates preoperative psychological and systemic physiological metrics to predict erectile dysfunction after robot assisted radical prostatectomy. The integration of these variables provides a more comprehensive approach to risk assessment than traditional physiological models. While further independent external validation remains necessary, this framework may assist urologists in personalized risk stratification and early rehabilitation planning for patients with localized prostate cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42474603/","authors":["Wang K","Wang M","Zheng W","Zhang H","Gao G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11701-026-03619-8","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474555","name":"Neural network-enhanced investigation of ferroptosis and druggability in early-onset alzheimer's disease.","source":"pubmed","abstract":"Alzheimer's disease (AD) is a complex neurodegenerative disorder which is multifactorial in nature. Some of its characteristics are slow cognitive decline, memory problems and behavioral changes. AD patient brains show a progressive synaptic toxicity, autophagy, neuroinflammation, excess generation of reactive oxygen species (ROS), neuronal death and oxidative stress, which occurs due to disrupted metal homeostasis along with tau and amyloid-&#x3b2; protein deposition. Notably, lipid peroxidation, iron buildup and elevated oxidative stress in AD brains suggest a possible molecular&#xa0;connection between ferroptosis and AD neurodegeneration. This study explores the genetic and bioinformatics perspective on the relationship between ferroptosis and AD aiming to identify potential therapeutic potential biomarkers using Neural network (NN) and Machine learning models. Six ferroptosis related genes were found to be differentially expressed in AD. Further machine learning analysis shortlisted four key biomarker genes. An NN-based diagnostic prediction model was developed and validated using AUC-ROC anaysis, which gave high diagnostic values (AUC- 0.92) in the analysis. The findings highlight a strong correlation between ferroptosis and altered metabolic functions in AD. miRNA-gene interaction analysis revealed that two biomarker genes, CYBB and ACSL4 can be regulated by several regulatory miRNAs i.e., hsa-miR-146-5p, hsa-miR-106b-5p, hsa-miR-223-3p, hsa-miR-155-5p, hsa-miR-34a-5p, hsa-miR-125b-5p and hsa-miR-27a-3p suggesting their potential as early diagnostic potential biomarkers. Immune microenvironment analysis revealed strong neuroinflammatory responses in AD with increased infiltration of macrophages (M0, M1 and M2), monocytes and multiple T cell subsets. This heightened immune activity may be driven by ferroptosis-induced oxidative stress contributing to neuronal death. Furthermore, druggability of these targets was evaluated and several drugs were identified that may be potentially repurposed for therapeutic intervention in AD pathogenesis. This study presents a diagnostic predictive model integrating gene expression, miRNA regulation and immune infiltration analysis, offering a novel perspective on early AD detection. The identified ferroptosis-related potential biomarkers and regulatory miRNAs could serve as valuable tools for clinical diagnosis and targeted therapeutic intervention, advancing personalized treatment strategies for Alzheimer's disease.","url":"https://pubmed.ncbi.nlm.nih.gov/42474555/","authors":["Singh P","Rath SL"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s11011-026-01939-0","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474535","name":"Comparison of large language models in the management of pediatric ureteropelvic junction obstruction: a comparative analysis of 125 clinical scenarios.","source":"pubmed","abstract":"To evaluate and compare the clinical accuracy, reliability, comprehensiveness and readability of three prominent Large Language Models (LLMs) (ChatGPT, Gemini, and Copilot) in the management of pediatric ureteropelvic junction obstruction.","url":"https://pubmed.ncbi.nlm.nih.gov/42474535/","authors":["Baloğlu İH","Karlı G","Çekmece AE","Özgür MÖ","Albayrak AT","Günay KC","Kılıç MA","Zeytun Baloğlu P","Horasanlı K"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s00383-026-06547-8","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474512","name":"[Endometriosis pain research in Germany : ENDO-PAIN and StEPP-UPP-two new research consortia on endometriosis pain].","source":"pubmed","abstract":"Endometriosis is one of the most common causes of chronic pelvic or lower-abdominal pain, yet mechanisms, predictors, and patient-centered treatment pathways remain insufficiently defined. This article summarizes the situation in Germany and presents two complementary research consortia: StEPP-UPP and ENDO-PAIN.StEPP-UPP addresses the clinical heterogeneity of endometriosis pain via a&#xa0;prospective multicenter cohort, standardized patient-reported outcomes, quantitative sensory testing, and multi-omics from blood, stool, and lesions. Explainable artificial intelligence and machine-learning models aim to estimate risk and trajectories of persistent pain, define mechanism-informed subgroups, and support treatment decisions. Preclinical mouse models with non-evoked behavioral metrics plus multiparametric MRI and single-cell analyses provide mechanistic anchoring for clinical signatures.ENDO-PAIN focuses on neuroinflammation and fibrosis as drivers of pain and chronification. Using patient samples, cell cultures, and 3D organoids, it maps immune-stroma interactions, syndecan signaling, and hormone-inflammation axes (for example P4-TGF&#x3b2;-NF&#x3ba;B-COX-2) to derive biomarker networks and therapeutic targets.Both consortia integrate patient advocacy structurally to ensure patient-relevant endpoints and accessible information. Together they point to three priorities: standardization of data collection and biobanking; stratification along bio-psycho-social profiles and molecular signatures; and interdisciplinary shared decision-making across research, clinics, and people with endometriosis, aiming for mechanistically grounded, data-driven, patient-centered pain medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42474512/","authors":["Velho RV","Pradier B","Werner F","Pogatzki-Zahn EM","Mechsner S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1007/s00482-026-00958-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474368","name":"Development and Validation of an Early Risk Prediction Model for Sepsis-Induced Coagulopathy Based on Machine Learning.","source":"pubmed","abstract":"ObjectiveTo develop and validate machine learning models for early prediction of sepsis-induced coagulation dysfunction (SIC) risk at intensive care unit (ICU) admission, before diagnostic criteria are fully met.MethodsThis retrospective cohort study enrolled 197 septic ICU patients. The cohort was randomly split into training (n=159, 80.7%) and internal validation sets (n=38, 19.3%). An independent external cohort of 91patients served for validation. Feature screening used least absolute shrinkage and selection operator (LASSO) followed by multivariate logistic regression ( P &lt;0.05). Four models were built and optimized via 10-fold cross validation. Models were evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA); The optimal model was interpreted by shapley additive explanations (SHAP).Results57.9% of patients developed SIC. Prothrombin time (PT), diastolic blood pressure (DBP), activated partial thromboplastin time (APTT), D-dimer (DD), white blood cell count (WBC), renal insufficiency (RI), and partial pressure of carbon dioxide (pCO 2 ) were identified as influencing factors for SIC. Prolonged PT and APTT, elevated DD, RI, and higher pCO 2 increased SIC risk, while higher DBP and WBC were protective. The random forest (RF) model achieved the best predictive efficiency, with internal and external validation AUCs of 0.76 and 0.79.ConclusionThe RF model presents moderate predictive performance for SIC risk, suggesting its potential utility for early identification of high-risk patients upon ICU admission. Further prospective validation is needed before clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42474368/","authors":["Yang Q","Fang L","Ren C","Xiong W","Zou J","Yang L","Zeng Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/10760296261468959","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474022","name":"Combining MRI-Derived Imaging Measures and Peripheral Proteomics to Improve the Mechanistic Understanding of Alzheimer's Disease Beyond Core Pathology: A Scoping Review.","source":"pubmed","abstract":"Alzheimer's Disease (AD) core pathology involves amyloid&#x3b2; and ptau, leading to neurodegeneration (ATN model), yet individuals with comparable core pathology show considerable biological and clinical heterogeneity, motivating new models that consider non-specific processes and co-pathology. MRI and peripheral proteomics offer complementary, non-invasive approaches for capturing biological variation beyond core pathology, and many researchers have begun integrating them. However, no systematic overview of this literature exists. This scoping review evaluated studies combining MRI and peripheral plasma proteomics in AD within revised diagnostic frameworks, summarizing strengths and gaps.","url":"https://pubmed.ncbi.nlm.nih.gov/42474022/","authors":["Li OY","Herrera Guerra D","Anthony M","Oh K","Vankee-Lin F","Turnbull A"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.2174/0115672050472412260629113636","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42474013","name":"Development of An Explainable Machine Learning Model for Predicting Fatigue in Rheumatoid Arthritis.","source":"pubmed","abstract":"Fatigue has been identified as one of the common symptoms among people who have rheumatoid arthritis (RA), which has a significant impact on their quality of life (QoL). Although fatigue has been identified as a symptom, few predictive models using clinical indicators have been developed for RA-related fatigue. This paper aims to create predictive models using machine learning (ML) algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/42474013/","authors":["Ma Y","Zhang Y","He Y","Zhao L","Liu C","Wang H","Lv L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.2174/0113862073462843260622175147","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473809","name":"Computational strategies for allosteric drug discovery: from cryptic pocket detection to rational design.","source":"pubmed","abstract":"Allostery offers a powerful route to regulate protein function and expands drug discovery beyond the orthosteric paradigm. By acting at sites distinct from the active site, allosteric modulators can achieve greater selectivity, reduce off-target effects, and overcome resistance. The discovery of the cryptic switch-II pocket of KRAS, which turned a long-\"undruggable\" oncoprotein into a clinically validated target, exemplifies this promise. Yet allosteric drug discovery is demanding: it requires not only identifying a suitable, often transient pocket, but also demonstrating that this pocket is functionally coupled to the active site, and then translating that mechanistic insight into design. This perspective surveys the computational strategies addressing each of these challenges in turn: sequence, structure, and machine-learning-based methods for locating allosteric and cryptic sites; network and dynamical analyses for mapping communication pathways; and enhanced-sampling and generative deep-learning approaches for rational modulator design. Throughout, we emphasise a central theme: that generative AI delivers speed and breadth, while physics-based simulation supplies thermodynamic rigour, and that their integration, rather than either alone, defines the most promising path forward. Together with experimental validation, these advances are rapidly expanding our ability to exploit allosteric regulation in therapeutics.","url":"https://pubmed.ncbi.nlm.nih.gov/42473809/","authors":["Mukhopadhyay S","Chakrabarty S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 4","doi":"10.1039/d5cc06159h","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473623","name":"Artificial Intelligence in the Nutritional Management of Inflammatory Bowel Disease: A Scoping Review.","source":"pubmed","abstract":"Diet is closely associated with the onset, progression, and prognosis of inflammatory bowel disease (IBD). In the absence of specific dietary and nutritional guidelines, nutritional management for IBD patients is fraught with challenges and uncertainties. Existing research indicates that artificial intelligence (AI) has great potential for application in the nutritional management of patients with chronic diseases; however, current research on its use in IBD patients is limited.","url":"https://pubmed.ncbi.nlm.nih.gov/42473623/","authors":["Qian X","Yu J","Zhang Q","Jia G","Guo J","Lin T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/JMDH.S614374","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473534","name":"Machine learning for diagnosing non-ST-segment elevation myocardial infarction: a derivation and validation study.","source":"pubmed","abstract":"Previously developed machine-learning (ML)-based decision support tools for patients presenting with suspected non-ST-segment elevation myocardial infarction (NSTEMI) remain proprietary, limiting public accessibility and clinical adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/42473534/","authors":["Champetier A","Lopez-Ayala P","Reich C","Boeddinghaus J","Cattin P","Koechlin L","Miró Ò","Christ M","Keller DI","Martín-Sánchez FJ","Morawiec B","Parenica J","Kaplan E","Bima P","Recuenco LH","Huré G","Herzka D","Wildi K","Crisanti L","Durak K","Schaffer JS","Zimmermann T","Mahfoud F","Lindahl B","Giannitsis E","Strebel I","Mueller C","APACE","TRAPID-AMI Investigatorsn","TRAPID-AMI Investigators"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul","doi":"10.1016/j.eclinm.2026.104055","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473482","name":"Artificial Intelligence in Orthodontics: From Laboratory Benchmarks to Clinical Care.","source":"pubmed","abstract":"Artificial intelligence (AI) is now used in routine orthodontic practice, not only in research, but how well it works depends heavily on the application. This narrative review synthesises peer-reviewed studies published between 2019 and early 2026 across seven task domains: cephalometric landmark detection, cervical vertebral maturation (CVM) staging, treatment-decision support for extraction and orthognathic surgery, cone-beam computed tomography (CBCT) segmentation, aligner monitoring, treatment-outcome prediction, and large language model (LLM) patient communication, which differ markedly in their level of maturity. In cephalometric landmark detection, mean radial errors ran from roughly 1.0 mm in three dimensions to about 1.37 mm in two dimensions, and pooled detection reached about 81% at the 2 mm threshold. CBCT segmentation reached pooled Dice similarity coefficients of 0.93 for teeth and 0.91 for the maxilla on multicentre data. Extraction-decision models performed with a sensitivity of 0.70 and a specificity of 0.90, yet lost as much as 20% of their accuracy once tested across institutions. Remote aligner monitoring reduced in-office attendances by roughly 1.68 to 3.5 visits over a treatment course, although the same platforms detected periodontal status poorly, with sensitivities of only 0.53, 0.35, and 0.22 for plaque and calculus, gingivitis, and recession, respectively. Soft-tissue prediction after orthognathic surgery remained inaccurate at the lip and chin. Patients tended to prefer LLM-generated information about their treatment, whereas orthodontic experts rated the same material less favourably. Three problems recurred across every domain examined: training data confined to single centres, narrow demographic representation, and an absence of independent external validation. None of these applications removed interpretive responsibility from the clinician, who retained the analytical decision, even where AI reduced the computational burden.","url":"https://pubmed.ncbi.nlm.nih.gov/42473482/","authors":["Alkadhi OH"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111151","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473479","name":"A Global and Local SHAP-Driven Interpretable Framework for Early Hospital Admission Prediction With Machine Learning at Emergency Department Triage.","source":"pubmed","abstract":"Emergency department (ED) crowding and constrained hospital resources drive the need for early risk stratification to efficiently identify patients requiring inpatient admission or escalation of care. Machine learning (ML) models are promising as helpful tools for admission prediction, yet there remains limited literature exploring the integration of explainability techniques to enhance the transparency of these models for clinicians and resource allocation staff end-users. Explainability, in this context, refers to techniques that show which patient features contributed most to each individual prediction and to the model's overall behavior.","url":"https://pubmed.ncbi.nlm.nih.gov/42473479/","authors":["Brown AE","Marostica CW","Hodgson NR","Martini WA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111163","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473478","name":"Explainable machine learning for perioperative surgical site infection risk enrichment after operative treatment of closed pilon fractures: a multicenter retrospective study with external validation.","source":"pubmed","abstract":"Surgical site infection (SSI) remains a major complication after operative treatment of closed pilon fractures. Prediction tools may help identify patients who warrant closer perioperative wound surveillance, but externally validated and clinically interpretable models remain limited. We aimed to develop and externally validate explainable machine-learning models for perioperative SSI risk enrichment after operative treatment of closed pilon fractures.","url":"https://pubmed.ncbi.nlm.nih.gov/42473478/","authors":["Deng Z","Zhao F","Zhang Y","Zhang T","Zhang X","Luo Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fsurg.2026.1850385","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"pmid:42473237","name":"DeepHeptox: An Interpretable Deep Learning Model for Multi-Endpoint Hepatotoxicity Prediction of Chemical Compounds.","source":"pubmed","abstract":"Hepatotoxicity represents a major adverse outcome of chemical exposure, as the liver plays a central role in xenobiotic metabolism and detoxification. Accurate prediction of hepatotoxicity is therefore essential for drug development and chemical safety assessment. Computational methods provide an efficient and ethical alternative for assessing hepatotoxicity before experimental validation. To address this need, we developed DeepHeptox, a deep learning model based on Graph Attention Networks (GAT) for multi-endpoint hepatotoxicity prediction, covering hepatitis, jaundice, elevated liver enzymes, hepatocellular injury, hepatic fibrosis, hepatomegaly, and cholestasis. DeepHeptox achieved area under the ROC curve (AUC) values exceeding 0.87 and accuracy (ACC) above 0.80 for both overall and endpoint-specific predictions on the test set. Our approach enables both the identification of structural alerts (SAs) via Klekota-Roth fingerprint (KRFP) analysis and the visualization of molecular substructure importance for hepatotoxicity predictions through GNNExplainer. A user-friendly web server is provided for interactive prediction and visualization. DeepHeptox offers a practical tool for early stage toxicity screening in drug development and chemical safety evaluation.","url":"https://pubmed.ncbi.nlm.nih.gov/42473237/","authors":["Luo M","Wang Q","Wang Y","Li F","Zhang A","Zhao Y"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 10","doi":"10.1021/acs.jcim.6c01434","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473222","name":"Intelligent Nanomedicine: AI-Driven Smart Carrier Design for Precision Skin Cancer Therapy.","source":"pubmed","abstract":"Skin cancer, one of the most common malignancies globally, continues to present major therapeutic hurdles such as limited drug penetration, high systemic toxicity, and tumor recurrence. Nanomedicine has emerged as a powerful approach to overcome these challenges by enabling targeted, localized, and controlled drug delivery. Within this framework, the integration of Artificial Intelligence (AI) is transforming the way smart carriers are designed and optimized, moving drug development from trial-and-error to predictive, data-driven strategies. AI algorithms, including machine learning and deep learning, can predict drug-nanocarrier interactions, optimize particle size and surface chemistry for dermal penetration, and simulate release kinetics tailored to the tumor microenvironment. Intelligent nanocarriers developed with AI assistance also facilitate combination therapies such as chemo-, immuno-, and photodynamic therapy, offering synergistic benefits against resistant skin cancers. Furthermore, AI enables the personalization of treatment by analyzing patient-specific genomic and clinical data, guiding the creation of safer and more effective nanomedicine formulations. Despite these promising advancements, significant barriers remain in terms of data quality, model validation, and regulatory acceptance of AI-driven nanomedicine. Nonetheless, the convergence of AI and smart carrier technology represents a paradigm shift in precision oncology. This review uniquely emphasizes AI-guided nanocarrier design, optimization, and personalization specifically for skin cancer therapy, distinguishing it from broader AI-oncology reviews by focusing on smart drug-delivery systems rather than general diagnostic or predictive modeling applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42473222/","authors":["Meshram N","Vinchurkar K","Borse L","Singh S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 16","doi":"10.2174/0115672018449461260428052800","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42473118","name":"Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.","source":"pubmed","abstract":"Bone regeneration in orthopedics is a great challenge, as efficient recovery affects patient's quality of life. Common methods encounter different limitations regarding diagnosis, implantation, and bone health monitoring. Recent advances in artificial intelligence (AI), specifically machine learning (ML) algorithms, have presented opportunities to enhance these aspects by accurately analyzing imaging data. This provides detailed evaluations that help to determine bone condition and guide treatment strategies. ML models can facilitate a patient-specific approach to select and design intelligent implants by examining extensive biomaterial datasets to identify the best match for each patient's biomechanical and biological needs. Furthermore, ML models can analyze data to accurately predict bone-healing timelines, allowing clinicians to track recovery trends, and make timely interventions in potential complications. In clinical settings, ML tools assist with preoperative planning, postoperative follow-up, and the design of intelligent implants, offering more effective, data-driven decision-making and favorable outcomes in bone regeneration. Looking ahead, while ML advancements show promise for effective orthopedic therapies, challenges persist. ML could revolutionize bone regeneration in various ways and offer predictive insights that promote patient-centered orthopedic care. Altogether, considering the continued evolution of ML, its integration into clinical applications will be crucial for developing truly intelligent bone regeneration therapies.","url":"https://pubmed.ncbi.nlm.nih.gov/42473118/","authors":["Kamaei M","Mohammadi H","Golafshan S","Alehosseini M","Mohammadzadeh M","Golshirazi A","Golafshan N","Eufrásio-da-Silva T","Dolatshahi-Pirouz A","Orive G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1002/adhm.71447","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472943","name":"Comprehensive evaluation of angiogenesis-associated genes in papillary thyroid cancer using bulk RNA and single-cell sequencing data.","source":"pubmed","abstract":"The objective of this study is to explore the expression patterns of angiogenesis-related genes in papillary thyroid cancer (PTC) to enhance understanding of the molecular mechanisms underlying angiogenesis in this malignancy. The findings provide valuable insights into the development of therapeutic strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42472943/","authors":["Zhao J","Qin YX","Zhao M","Niu H","Liu XQ"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1007/s00405-026-10456-w","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472941","name":"A clinically interpretable model for predicting pharyngocutaneous fistula after total laryngectomy.","source":"pubmed","abstract":"Pharyngocutaneous fistula (PCF) is a frequent complication following total laryngectomy. While various risk models exist, their practical utility is often limited by poor calibration and a lack of guidance on modifiable risk factors. This study aimed to develop a calibrated risk assessment tool to support preoperative counseling and clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42472941/","authors":["Hu G","Han L","Cai L","Lu S","Gao J","Liu Y","Lan L","Wu G"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1007/s00405-026-10397-4","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472809","name":"Combining transvaginal ultrasound radiomics with clinical-ultrasound semantic features for the identification of high-risk endometrial lesions.","source":"pubmed","abstract":"Preoperative risk stratification of high-risk endometrial lesions remains a clinical challenge. This study aimed to preliminarily explore an integrated machine learning approach combining transvaginal ultrasound (TVUS) radiomics, clinical indicators, and ultrasound semantic attributes to assist in clinical triage.","url":"https://pubmed.ncbi.nlm.nih.gov/42472809/","authors":["Yao X","Ye X","Chen L","He Y","Wu J","Kang S","Liu F","Zhu L","Zheng J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1186/s12905-026-04709-3","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472729","name":"Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.","source":"pubmed","abstract":"Manganese metabolism may be involved in the malignant progression of lung adenocarcinoma (LUAD). Clarifying the roles of manganese metabolism-related genes (MMRGs) in LUAD may provide potential therapeutic targets for LUAD treatment. Mendelian randomization analysis and machine learning methods were applied to analyze transcriptome data for screening prognosis-related genes in LUAD. Subsequently, a risk model was constructed and a nomogram was plotted. Meanwhile, a series of analyses were carried out focusing on the immune microenvironment, drug sensitivity, and the single-cell level. Finally, the expression of relevant proteins was further verified by combining RT-qPCR and Western Blot. We have screened out six risk genes for LUAD: TXNRD1, CDKN3, BTG2, SELENBP1, DTYMK, and CHEK1. Subsequently, a risk model was constructed, which effectively predicts the survival of LUAD patients. Gene Set Enrichment Analysis (GSEA) revealed that these six genes may be involved in the regulation of the cell cycle in LUAD. In addition, they may modulate the tumor immune microenvironment and induce resistance to chemotherapeutic drugs. RT-qPCR and Western Blot confirmed low BTG2 and SELENBP1 and high CDKN3, CHEK1, DTYMK, and TXNRD1 expression in LUAD tissues and cell lines. Our study indicates that TXNRD1, CDKN3, BTG2, SELENBP1, DTYMK, and CHEK1 may be important biomarkers for the prognosis of LUAD, providing potential approaches for prognostic evaluation and medication strategies in LUAD.","url":"https://pubmed.ncbi.nlm.nih.gov/42472729/","authors":["Zha J","Liu X","Gao S","He J","Zhang Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 20","doi":"10.1007/s10142-026-01982-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472673","name":"Predictive modeling of fluid status in hemodialysis: model development and internal validation using the MONitoring dialysis outcomes (MONDO) global database.","source":"pubmed","abstract":"Optimized fluid management is crucial in dialysis care because extracellular volume overload drives adverse cardiovascular outcomes. At the same time, comorbidities such as inflammation and protein energy wasting lead to decreased muscle mass and intracellular water. Accurate assessment of total body water (TBW) and its extracellular water (ECW) and intracellular water (ICW) compartments is therefore essential to guide ultrafiltration, evaluate dialysis adequacy, and monitor patient risk.","url":"https://pubmed.ncbi.nlm.nih.gov/42472673/","authors":["Yueh SH","Raimann J","Canaud B","Zhou M","Ye X","Mermelstein A","Kooman J","van der Sande F","Usvyat L","Kotanko P","Zhang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Dec","doi":"10.1080/0886022X.2026.2692105","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472633","name":"Early Prediction of Critical Care Interventions From Pediatric Emergency Department Triage.","source":"pubmed","abstract":"Most pediatric emergency departments (EDs) in the United States use Emergency Severity Index (ESI) system to triage patients. Because the 5-level classification provides limited risk stratification, this study aims to improve patient prioritization by developing an operationally useful model that predicts risk of critical care interventions using only information available during ED triage.","url":"https://pubmed.ncbi.nlm.nih.gov/42472633/","authors":["Ha T","Kappy B","Chamberlain JM","McKinley KW"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 1","doi":"10.1542/hpeds.2025-009127","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472589","name":"Deep learning-enhanced zero echo time MRI for femoral cortical bone quantification: A technical validation against CT.","source":"pubmed","abstract":"This study compared quantitative measurements of femoral cortical bone between zero echo time magnetic resonance images (oZTEo) and computed tomographic (CT) images as proof-of-concept for algorithm performance in subjects referred to our clinic for femoroacetabular impingement (FAI), a morphological condition of the hip joint associated with early onset osteoarthritis. A total of 30 patients underwent concomitant MRI and CT imaging where standard oZTEo imaging was performed with deep learning (DL) reconstruction to apply denoising and chemical shift correction (oZTEo-CSC) with and without automated background removal (oZTEo-CSC-BR). The CT images were registered and resampled into the corresponding MR image space. A normalized signal intensity profile analysis was performed across the articular surface of the femoral head to evaluate: intra-articular femoral cortex edge location, cortical edge sharpness, maximum cortical signal intensity, and cortical thickness. A one-way repeated measures of variance (ANOVA) was used to compare outcomes across image sets (CT, oZTEo, oZTEo-CSC, and oZTEo-CSC-BR) and intra-class correlation coefficients (ICCs) were assessed for inter-examiner repeatability. The intra-articular femoral cortex edge and the cortical edge sharpness did not vary across image datasets for either Examiner. Maximum cortical signal intensity differed across reconstructions for Examiner 1 (p&#xa0;&lt;&#xa0;0.0001), with oZTEo differing from CT, oZTEo-CSC, and oZTEo-CSC-BR methods, while the oZTEo-CSC and oZTEo-CSC-BR reconstructions were not different from CT. The oZTEo-CSC and oZTEo-CSC-BR images provide a greater CT-like assessment of femoral cortical bone than oZTEo images alone and may be considered as a radiation-free alternative for bone imaging. Inter-examiner repeatability was higher for the deep learning reconstructions (ICC&#xa0;&#x2248;&#xa0;0.68) compared with oZTEo and CT, indicating improved measurement consistency across readers. oZTEo with DL post-processing improves consistency and CT agreement; differences are sub-voxel and clinically negligible overall.","url":"https://pubmed.ncbi.nlm.nih.gov/42472589/","authors":["Koretsky EG","Singh D","Consolini J","Mandava S","Carl M","Fung M","Potter HG","Koff MF"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Nov","doi":"10.1016/j.mri.2026.110748","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472529","name":"Hierarchically ordered ERGO-L-Cys flower-like nanospheres on screen-printed electrodes for electrochemical immunosensing of cow's milk specific IgE with machine learning-assisted calibration.","source":"pubmed","abstract":"Cow's milk-specific IgE (sIgE) is a crucial serum biomarker for cow's milk allergy (CMA) in children. However, its low abundance and the scarcity of reference standards have made it challenging to develop effective electrochemical detection platforms for clinical use. This study reports, to the best of our knowledge, the first electrochemical immunosensing platform for the quantitative detection of cow's milk sIgE (CM-sIgE) in pediatric serum. Through the l-cysteine (L-Cys)-mediated electrochemical reduction of graphene oxide, a hierarchically ordered three-dimensional electrochemically reduced graphene oxide functionalized with L-Cys (ERGO-L-Cys) flower-like nanosphere interface was in situ constructed on a screen-printed carbon electrode, distinguishing it from conventional two-dimensional ERGO films. Combined with a biotin-streptavidin bridged alkaline phosphatase/1-naphthyl phosphate (ALP/1-NPP) enzyme amplification system, the quantitative detection of serum sIgE was achieved using differential pulse voltammetry. This platform exhibits a linear range of 0.56-54.52 IU/mL for serum sIgE, with a detection limit of 0.29 IU/mL, demonstrating good selectivity, reproducibility, and storage stability. To overcome the challenge of the lack of sIgE reference standards, pediatric serum samples calibrated against hospital chemiluminescence assay values were utilized. Moreover, random forest (RF) regression was applied for data-driven calibration, further enhancing the quantitative robustness in complex serum matrices. The analysis of clinical samples showed good agreement with the results from hospital chemiluminescence assays. This work provides an eco-friendly, reproducible approach for constructing three-dimensional biofunctionalized interfaces and offers a feasible solution for point-of-care electrochemical detection of CMA in children.","url":"https://pubmed.ncbi.nlm.nih.gov/42472529/","authors":["Yang M","Xie C","Gao X","Jiang X","Yang J","Lu H","Zhu X","Liu S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1016/j.talanta.2026.130333","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472441","name":"[Overview and conclusions of the STROKE data platform of the National Laboratory for Translational Neuroscience and the associated clinical studies].","source":"pubmed","abstract":"Acute ischemic and hemorrhagic stroke are among the leading causes of mortality and long-term disability worldwide. In addition to the results of randomized clinical trials, registry data reflecting real-world clinical practice play a key role in evaluating the quality of care. The STROKE platform of the Translational Neuroscience National Laboratory provides a comprehensive overview of the patient population treated at a Hungarian comprehensive stroke center. Our objective was to characterize the STROKE platform and summarize the findings of studies based on its data, to synthesize the main clinical and methodological conclusions, and to present its implications for quality improvement in Hungarian stroke care. The platform is based on standardized data collection of patients admitted to the Department of Neurology or Neurosurgery, University of P&#xe9;cs within 24 hours due to acute ischemic stroke, transient ischemic attack, or hemorrhagic stroke. Demographic, clinical, imaging, laboratory, therapeutic, and 90-day outcome data were recorded. Outcomes were analyzed using multivariable linear and logistic regression models, propensity score matching, and comprehensive machine learning methods. Based on the platform, 77.0% of admitted patients had ischemic stroke, 16.9% transient ischemic attack, and 6.1% hemorrhagic stroke; the mean age was 72.0 &#xb1; 12.5 years. Among patients with ischemic stroke, 62.4% received recanalization therapy. The strongest independent predictors of 90-day functional outcome were neurological status at admission, premorbid functional status, extent of early ischemia, and age. The Stroke-SCORE, based on three parameters (age, premorbid status and neurological deficit), demonstrated strong predictive performance for 90-day outcome (area under the curve = 0.86). In patients undergoing reperfusion therapy, door-to-needle time &lt;60 minutes and door-to-groin time &lt;120 minutes were significantly associated with more favorable outcomes. After multivariable adjustment, neither anticoagulant nor antiplatelet therapy proved to be an independent adverse prognostic factor. The STROKE platform confirms that functional outcome is primarily determined by stroke severity, premorbid status, age, extent of ischemia, and timely, well-organized reperfusion care. Real-world data analyzed with advanced statistical methods substantially contribute to clinical decision-making, the reassessment of contraindications, and quality improvement in Hungarian stroke care. Orv Hetil. 2026; 167(29): 1131-1141.","url":"https://pubmed.ncbi.nlm.nih.gov/42472441/","authors":["Schranz D","Fehér G","Jozifek E","Karádi Z","Bosnyák E","Szapáry L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1556/650.2026.33600","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472394","name":"An evidence informed framework for artificial intelligence in rare breast cancers using small cohort validation synthetic data practices and clinical governance.","source":"pubmed","abstract":"Rare breast cancers represent a clinically important but underrepresented group of malignancies. In this Perspective, rare breast cancers are considered within the broader rare cancer definition of an annual incidence below 6 cases per 100,000 persons, while also recognizing breast-specific rarity based on uncommon histology, molecular hallmarks, clinical presentation or sex-specific occurrence. These conditions are characterized by limited case numbers, biological heterogeneity, reduced clinical trial inclusion and fragmented evidence. These constraints challenge artificial intelligence (AI) development because many systems depend on large, balanced and externally validated datasets. AI may support diagnosis, histopathology, molecular interpretation, prognostic stratification and precision oncology decision support, but its use in rare breast cancers requires evidence standards adapted to small cohorts. This Perspective proposes an evidence-informed clinical governance framework organized around five domains: intended clinical use, small-cohort validation, synthetic data governance, human oversight and lifecycle monitoring. Its distinctive contribution is to translate general AI reporting and governance principles into rare breast cancer-specific safeguards, including objective data-quality checks, leakage prevention, uncertainty-aware validation, synthetic data plausibility scoring, pan-rare model reporting, patient involvement and post-deployment surveillance. Synthetic data may support development and simulation, but should not replace validation on real clinical cases. By linking small-cohort methodology with clinical oversight, regulatory alignment and lifecycle monitoring, the framework offers a practical roadmap for safe AI-enabled rare breast cancer precision oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42472394/","authors":["Alshreef BS"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1007/s12672-026-05573-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472349","name":"Development and temporal validation of an interpretable XGBoost model for predicting cardiovascular death in patients with diabetic foot.","source":"pubmed","abstract":"Patients with diabetic foot (DF) have a high risk of cardiovascular (CV) death, yet dedicated risk-prediction tools for this population are lacking. We developed and temporally validated an interpretable machine learning (ML) model for predicting CV death in patients with DF. This single-center retrospective cohort study included 2,835 patients admitted between February 2017 and May 2025. The development cohort comprised 2,325 patients, including 748 CV deaths, and was divided into training, internal validation, and held-out test sets; an independent temporal validation cohort included 510 patients, including 220 CV deaths. Nine supervised ML algorithms were compared using the area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost) showed the best overall performance. The optimal model, incorporating demographic and diabetes-related characteristics, routine laboratory parameters, and DF-specific features, achieved AUCs of 0.829 (95% confidence interval [CI]: 0.752-0.905) in internal validation, 0.844 (95% CI: 0.806-0.881) in the held-out test set, and 0.828 (95% CI: 0.789-0.868) in temporal validation. The model demonstrated good calibration and favorable net benefit on decision curve analysis. SHapley Additive exPlanations (SHAP) identified age, serum creatinine, glycated hemoglobin, triglycerides, and body mass index as the most influential predictors of increased model-predicted risk. This interpretable XGBoost model may support early identification and individualized risk stratification of patients with DF at high risk of CV death; however, prospective multicenter validation is required before clinical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42472349/","authors":["Wan X","Shi T","Liu Q","She Y","Yu J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 17","doi":"10.17305/bb.2026.14337","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472175","name":"Artificial Intelligence in Hypertension Management: Promise Must Precede Practice.","source":"pubmed","abstract":"Hypertension remains one of the most important modifiable risk factors for cardiovascular disease worldwide, yet blood pressure control rates remain suboptimal despite advances in diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool to improve hypertension care through enhanced risk prediction, continuous monitoring, and clinical decision support. Enthusiasm surrounding AI must be balanced against challenges related to algorithmic bias, model interpretability, data quality, and clinical implementation. While AI has the potential to transform hypertension management, its greatest challenge is no longer predictive accuracy but successful integration into real-world clinical practice. This editorial discusses the opportunities and limitations of AI in hypertension care and argues that implementation science, transparency, and equitable deployment should become the next priorities for the field.","url":"https://pubmed.ncbi.nlm.nih.gov/42472175/","authors":["Ghantiwala KR","Kevadiya R","Pentapurthy P","Thakor AR","Kumar S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jun","doi":"10.7759/cureus.111109","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42472083","name":"Integrative Bioinformatics Analysis and Machine Learning Reveal TSPAN7 Regulates the Involution of Infantile Hemangioma via NK Cells.","source":"pubmed","abstract":"The involution of infantile hemangioma (IH) involves complex lipid metabolism reprogramming and immune modulation, but the underlying molecular mechanisms remain incompletely understood. This study aimed to identify key lipid metabolism-related genes that may drive IH involution using a bioinformatic perspective.","url":"https://pubmed.ncbi.nlm.nih.gov/42472083/","authors":["Bai J","Lin S"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/CCID.S619344","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471979","name":"Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.","source":"pubmed","abstract":"Artificial intelligence (AI) is increasingly being integrated into healthcare systems, offering new opportunities to enhance the safety, efficiency and effectiveness of clinical pharmacy services. This scoping review aimed to systematically map the current applications of AI in clinical pharmacy practice and to identify the medication-management functions supported by these technologies.","url":"https://pubmed.ncbi.nlm.nih.gov/42471979/","authors":["Al-Taie A","Abdullah AS","Arueyingho O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.fhj.2026.100547","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471780","name":"Large Language Models for Traumatic Dental Injuries Across Web-Based and Mobile-Based Interfaces: Assessing Accuracy, Quality, and Temporal Consistency.","source":"pubmed","abstract":"Traumatic dental injuries (TDIs) are frequent in clinical practice and require rapid, guideline-based decisions, yet accessing accurate and reliable information may be challenging. Large language models (LLMs) such as ChatGPT, Gemini, DeepSeek, and Qwen are increasingly used as quick online information tools; however, evidence regarding their accuracy, consistency, and the influence of different user interfaces is limited. This study aimed to evaluate the performance of several LLMs in answering TDI-related questions through both web-based interfaces and mobile phone applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42471780/","authors":["Cekic EC","Kumru M","Yilmaz B","Celikkol B","Tavsan O"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1002/cre2.70416","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471743","name":"Habitat-based imaging and peritumoral radiomics on ultrasound images for predicting lymphovascular invasion in breast invasive ductal carcinoma: a two-center study.","source":"pubmed","abstract":"This study aimed to evaluate the feasibility of employing habitat-based radiomic distributions in ultrasound (US) images to quantitatively characterize intratumoral heterogeneity. It also explored the potential of this approach to predict lymphovascular invasion (LVI) in breast invasive ductal carcinoma (IDC) patients and to identify the optimal extent of multiple peritumoral regions.","url":"https://pubmed.ncbi.nlm.nih.gov/42471743/","authors":["Liu T","Zhao G","Wei W","Zhang Q","Wu J","Chen X","Liu D","Zhu X"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s40644-026-01088-8","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471734","name":"Hypoxia tumor-associated macrophages facilitate hepatocellular carcinoma metastasis via uPA-uPAR pathway.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) arises within a hypoxic and immunosuppressive tumor microenvironment (TME), where tumor-associated macrophages (TAMs) constitute a major immune population. The impact of hypoxia on TAM functional heterogeneity and their contribution to HCC growth and metastasis remain incompletely understood.","url":"https://pubmed.ncbi.nlm.nih.gov/42471734/","authors":["Wang Y","You X","Luo X","Huang W","Zhang Z","Xia L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s12967-026-08634-9","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471730","name":"Decoding dry eye disease based on bioinformatics and in vitro experimental: the role of immune responses and natural product intervention.","source":"pubmed","abstract":"Dry eye disease (DED), a prevalent ocular condition, has seen rising incidence rates. Aberrant inflammation and immune dysregulation are key pathogenic factors in DED. However, the underlying mechanisms linking autoimmunity to DED therapy remain incompletely understood. Herbal medicine's potential in treating DED warrants further exploration due to rendering monotherapy insufficient for clinical needs.","url":"https://pubmed.ncbi.nlm.nih.gov/42471730/","authors":["Long X","Liu G","Liu P","Jiang P","Peng J","Peng Q"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s40246-026-00992-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471694","name":"Interpretable, internally validated prognostic modelling of cumulative clinical pregnancy in women with diminished ovarian reserve.","source":"pubmed","abstract":"To develop and internally validate an interpretable prognostic model for cumulative clinical pregnancy in women with diminished ovarian reserve (DOR), and to explore, as a hypothesis-generating secondary aim, whether protocol-associated pregnancy rates differ across model-defined baseline-prognosis risk strata.","url":"https://pubmed.ncbi.nlm.nih.gov/42471694/","authors":["Wu H","Huang Q","Long Y","Jiang L","Liu L"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s12911-026-03709-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471636","name":"AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.","source":"pubmed","abstract":"Graph-based machine learning approaches, including Knowledge Graph Embedding (KGE) methods and Graph Neural Networks (GNNs), have emerged as powerful tools for modeling complex biomedical data. However, a systematic and clinically grounded comparison of these approaches across heterogeneous healthcare graphs, accounting for both predictive performance and real-world deployment constraints, is still lacking.","url":"https://pubmed.ncbi.nlm.nih.gov/42471636/","authors":["Sorino P","Bellis A","Malitesta D","Lofù D","Bonfiglio C","Donghia R","Giannelli G","Ferrara A","Narducci F","Noia TD","Sciascio ED"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s12911-026-03717-5","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471567","name":"Stage-specific predictors of exercise adherence after percutaneous coronary intervention: a prospective multicenter study.","source":"pubmed","abstract":"The efficacy of exercise after Percutaneous Coronary Intervention (PCI) is compromised by poor long-term adherence. Although multiple influencing factors are recognized, their dynamic evolution and relative importance across different stages remain unclear, hindering the development of targeted interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42471567/","authors":["Xia C","Ji L","Guo H","Du Y","Zheng Y","Liu H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s12872-026-06291-w","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471550","name":"Exploratory development of prediction models for pharmacotherapy outcomes in trigeminal neuralgia: a combined analysis based on multi-source data.","source":"pubmed","abstract":"Drug-refractory trigeminal neuralgia (DRTN) represents a formidable challenge in clinical management, with approximately 30%-50% of patients eventually progressing to DRTN. Early identification of high-risk DRTN populations and prediction of the timing of pharmacotherapy failure are crucial for optimizing clinical treatment strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/42471550/","authors":["Yang H","Sun B","Li M","Yang X","Li M","Zhang C","Yang H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1186/s10194-026-02463-3","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471512","name":"Multi-omics and network toxicology prioritize ADAMTS13 as a candidate gene computationally linked to TDCPP targets in hepatocellular carcinoma.","source":"pubmed","abstract":"Tris(1,3-dichloro-2-propyl) phosphate (TDCPP), a widely used organophosphate flame retardant, has been increasingly recognized as a potential environmental risk factor for human cancers. However, its potential association with hepatocellular carcinoma (HCC) and the underlying molecular mechanisms remain largely unclear.","url":"https://pubmed.ncbi.nlm.nih.gov/42471512/","authors":["Liu X","Gong D","Gou T","Chen P"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 19","doi":"10.1007/s12672-026-05610-z","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471468","name":"Integrating omics and artificial intelligence in pediatric environmental health: tools, challenges, and cohort-based insights.","source":"pubmed","abstract":"Early childhood is a critical developmental period during which exposure to multiple environmental chemicals is common and increasingly recognized as an important determinant of long-term health. However, conventional risk-assessment methods are often poorly equipped to address the complexity, variability, and interactive effects of combined environmental exposures.","url":"https://pubmed.ncbi.nlm.nih.gov/42471468/","authors":["Al-Saleh I"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1038/s41390-026-05254-3","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471446","name":"Breaking the norm: population-scale deviations of brain structure in depression and anxiety.","source":"pubmed","abstract":"Structural brain alterations associated with depression and anxiety are subtle, heterogeneous, and difficult to characterize. We applied autoencoder-based normative modeling to contrastively learned structural MRI representations from two large population-based cohorts (German National Cohort, N&#x2009;&#x2248;&#x2009;29,000; UK Biobank, N&#x2009;&#x2248;&#x2009;25,000) to quantify individual deviations from normative brain structure across symptom dimensions of depression, anxiety, and, for contextualization, alcohol use.Deviation magnitude increased with symptom severity for depressive and anxiety symptoms and was most pronounced in individuals with high alcohol use. Directional analyses revealed shared deviation patterns for depression and anxiety that were largely distinct from alcohol-related deviations, and these patterns generalized across cohorts. These affective-symptom-related patterns implicated distributed regional brain-structural variation. Individual deviation profiles improved classification of symptomatic status beyond demographic covariates, with gains concentrated at higher symptom severity.Together, these findings indicate that affective symptoms are associated with reproducible, dimensional patterns of regional brain-structural deviation that extend beyond normative population variability, supporting transdiagnostic models of internalizing psychopathology.","url":"https://pubmed.ncbi.nlm.nih.gov/42471446/","authors":["Wiegert J","Marty-Lombardi S","Oweda J","Lenz E","Ahnert P","Berger K","Brenner H","Frank J","Grabe HJ","Greiser KH","Klinger-König J","Karch A","Leitzmann M","Meinke-Franze C","Mikolajczyk R","Nees F","Niendorf T","Sander O","Schmidt CO","Riedel-Heller SG","Ritter K","Peters A","Pischon T","Witt S","Nitsche J","Naamanka J","Volkmer S","Mai A","Abas A","Li X","Meyer-Lindenberg A","Gradinger T","Streit F","Braun U","Schwarz E"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1038/s41380-026-03691-4","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471432","name":"Single cell and bulk transcriptomics reveal metabolic reprogramming biomarker signatures during extracorporeal membrane oxygenation for cardiogenic shock.","source":"pubmed","abstract":"Cardiogenic shock (CS) patients receiving extracorporeal membrane oxygenation (ECMO) exhibit profound immune and metabolic disturbances, which may influence early clinical outcomes. However, the transcriptomic features linking metabolic reprogramming, immune-cell alterations, and early outcomes after ECMO initiation remain incompletely understood. The GSE182600 dataset from the public database was analyzed to identify differentially expressed genes (DEGs) between ECMO-treated CS samples with successful and failed outcomes. Multiple machine learning algorithms and the Shapley Additive exPlanations (SHAP) framework were applied to identify biomarkers associated with metabolic reprogramming. Functional enrichment analysis, immune infiltration analysis, drug prediction analysis, and molecular docking analysis were conducted to explore regulatory mechanisms and potential compound targets. Single-cell RNA sequencing (scRNA-seq) analysis was further conducted to determine key cell types and expression dynamics of the biomarkers. Clinical validation was achieved through quantitative reverse transcription polymerase chain reaction (RT-qPCR) assays on patient-derived specimens. PLIN2, TKTL1, NR1H4, GPX3, and NNMT were identified and selected as potential biomarkers. These biomarkers were primarily involved in metabolic, inflammatory, and immune processes. Immunological analysis revealed higher infiltration levels of CD8&#x207a; naive T cells in the failure group, by contrast, osteoblasts and plasma cells exhibited higher infiltration levels in the success group. Doxorubicin hydrochloride showed strong binding affinity to GPX3 and NR1H4, which may link the compound's cardiotoxicity to cardiac injury. Additionally, scRNA-seq analysis indicated monocytes as the predominant cell type, with dynamic expression of NNMT, TKTL1, PLIN2, and GPX3 along monocyte differentiation trajectories. In the RT-qPCR assay, the expression levels of TKTL1, NR1H4, and NNMT were significantly reduced in the success group compared with the failure group (P&#x2009;&lt;&#x2009;0.05), whereas GPX3 and PLIN2 showed consistent downward trends but did not reach statistical significance. PLIN2, TKTL1, NR1H4, GPX3, and NNMT may reflect immunometabolic features associated with early outcomes after ECMO initiation in patients with CS, with RT-qPCR providing preliminary support for the differential expression of TKTL1, NR1H4, and NNMT.","url":"https://pubmed.ncbi.nlm.nih.gov/42471432/","authors":["Zhou X","Aierken D","Sun L","Chang X","Xu L","Guo Z"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1038/s41598-026-62728-4","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471427","name":"Machine unlearning as a governance imperative for clinical AI.","source":"pubmed","abstract":"Clinical AI assumes that the influence of training data can persist indefinitely. This premise fails when patients withdraw consent, evidence evolves, or bias is identified. Machine unlearning aims to remove specific data influence without full retraining. We argue that unlearning readiness should be built into the infrastructure of high-risk healthcare AI across patient autonomy, clinical validity, and system governance, and we outline a governance pathway to keep updates auditable and clinically safe.","url":"https://pubmed.ncbi.nlm.nih.gov/42471427/","authors":["Porter A","Kirkpatrick E","Garg A","Saratchandran H","Lucey S","Verjans J"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 18","doi":"10.1038/s41746-026-03050-1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471291","name":"Machine learning-based diagnosis of movement disorders using clinical neurophysiology: are we ready?","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42471291/","authors":["Grippe T"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Oct","doi":"10.1016/j.clinph.2026.2112340","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471130","name":"PatientSpace: A multimodal graph-based latent representation framework for modeling neurodegenerative disease heterogeneity.","source":"pubmed","abstract":"Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneity, complicating diagnosis, subtype characterization, and prediction of disease progression. We introduce PatientSpace, a multimodal graph-based latent representation framework designed to model neurodegenerative disease heterogeneity using T1-weighted MRI and FDG-PET. PatientSpace is built upon a structured variational autoencoder that integrates multimodal neuroimaging features while organizing patients within a latent space constrained by age, diagnosis, and a consistency regularization term encouraging similarity between neuroimaging phenotypes. This design enables the construction of an interpretable patient graph in which neighborhood relationships reflect biological similarity. Applied to cohorts of cognitively normal individuals, AD, and FTD patients, PatientSpace revealed multiple disease clusters associated with distinct neuroimaging patterns and clinical severity. Diagnostic classification achieved performance comparable to state-of-the-art deep learning models, while graph-based neighborhood inference enabled prediction of structural volumes, metabolic activity, and cognitive severity. Projection of mild cognitive impairment (MCI) subjects from an independent cohort further showed that cluster membership was associated with differential risks of dementia conversion and distinct longitudinal trajectories. Together, these results demonstrate that PatientSpace provides an interpretable framework linking multimodal neuroimaging representations to disease subtypes, patient-level characterization, and progression modeling in neurodegenerative disorders.","url":"https://pubmed.ncbi.nlm.nih.gov/42471130/","authors":["Manouvriez D","Kuchcinski G","Lecerf S","Lahousse H","Rogeau A","Villain N","Kas A","Pyatigorskaya N","Nguyen M","Petrovic S","Cole JH","Zabihi M","Hache B","Bertoux M","Lebouvier T","Roca V","Lopes R","Alzheimer’s Disease Neuroimaging Initiative","Frontotemporal Lobar Degeneration Neuroimaging Initiative","MEMENTO study group","SOCRATES study group"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.neuroimage.2026.122136","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471078","name":"Model development and feasibility of real-world deployment of multimodal input-based subtyping of depression in tele-counseling for scalable mental health assessment.","source":"pubmed","abstract":"The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals by dominant symptom profiles, such as appetite, agency, anxiety, and sleep disturbances. Depression symptoms vary widely, even among those with similar scores, reflecting distinct psychophysiological and cognitive-affective patterns. In tele-mental-health settings, where contextual cues are limited, multimodal behavioral signals from natural interactions can complement traditional assessments. Using synchronized audio, video, and text data from the EDAIC dataset (N&#x202f;=&#x202f;275), we propose a multimodal learning framework to classify five clinically validated outcomes: Depression, Appetite disturbance, Agency impairment, Anxiety, and Sleep problems. We developed a comprehensive multimodal machine-learning pipeline, incorporating automated dataset construction, modality-specific feature extraction (acoustic, facial action unit, linguistic), and supervised learning with cross-validation. Labels were derived from validated scoring rules to ensure clinical relevance. Sentiment analysis revealed lower sentiment scores in participants with high Depression, Anxiety, or Agency scores, but no significant differences in Appetite or Sleep severity. Model performance was assessed across three scenarios: text (transcripts), phone calls (audio&#xa0;+&#xa0;transcript), and video calls (audio&#xa0;+&#xa0;video&#xa0;+&#xa0;transcript). Temporal models (CNN&#xa0;+&#xa0;BiLSTM) achieved over 65% accuracy across modalities, while a fine-tuned temporal model for depression detection using video calls reached an accuracy of 81% with an f1-score of 0.79, demonstrating that our approach performs on par with state-of-the-art methods while maintaining a substantially lower computational footprint suitable for edge deployment. SHAPley analysis identified key audio and video features for detecting Depression and other symptoms. Beyond depression screening alone, our framework is among the first to simultaneously stratify individuals across five clinically relevant symptom domains as per Research Domain Criteria (R-DOC) enabling more personalized and targeted intervention. A translational avatar-based interface validated system operability, demonstrating the potential for scalable, objective mental-health assessment in tele-counseling.","url":"https://pubmed.ncbi.nlm.nih.gov/42471078/","authors":["Ashwin Francis AJ","Raza A","Patel N","Gajbhiye R","Kumar VS","T A","Saikia A","Mibang O","K V","Joshi K","Tony L","Balasubramani PP"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.biosystems.2026.105887","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42471010","name":"Clinically meaningful risk factors for recurrence in T1 colorectal cancer treated with endoscopic resection alone identified by unsupervised machine learning: a multicenter study.","source":"pubmed","abstract":"Identifying patients at high risk for recurrence after endoscopic resection of T1 colorectal cancer (CRC) remains challenging. This study aimed to identify recurrence risk subtypes and develop an interpretable risk stratification framework.","url":"https://pubmed.ncbi.nlm.nih.gov/42471010/","authors":["Zhou X","Togashi K","Zhu X","Kajiwara Y","Oka S","Tanaka S","Takamatsu M","Hotta K","Yamada M","Ikematsu H","Nagata S","Yamada K","Hashimoto K","Ishihara S","Saitoh Y","Matsuda K","Komori K","Ishiguro M","Tamaru Y","Okuyama T","Minami S","Ohnuma S","Sakamoto K","Sugihara K","Ueno H"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug 12","doi":"10.1055/a-2917-8805","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42470995","name":"Genetic and systems-level regulation of cancer metabolism: From metabolic reprogramming to AI-driven precision oncology.","source":"pubmed","abstract":"The metabolic activities of cancer cells undergo complete transformation because they need to maintain their growth while resisting metabolic challenges and environmental dangers from their tumour surroundings. The metabolic changes that occur in cells depend on specific oncogenes together with tumour suppressor genes and stress-response pathways, which control essential bioenergetic and biosynthetic functions. This review presents the current scientific knowledge about genetic regulators, which include MYC, KRAS, PI3K-AKT-mTOR, EGFR, p53, PTEN, and LKB1-AMPK, that control glucose, amino acid, lipid, nucleotide, and mitochondrial metabolism in different human cancers. The research demonstrates that these pathways connect through common metabolic pathways, which produce metabolic flexibility and create complex metabolic patterns that drive tumour diversity and development and resistance to treatment. We present new systems-level frameworks that exceed pathway-based descriptions to show the intricate nature of cancer metabolism. The review investigates how artificial intelligence (AI) and machine learning methods, combined with multi-omics data and genome-scale metabolic models, enable scientists to enhance metabolic phenotyping and discover specific tumour weaknesses and forecast treatment results and combination methods. The study begins with a discussion of present-day obstacles that impede clinical application of research results, which include data inconsistency and the challenges of understanding and testing models. Then it presents upcoming research paths that will develop AI-powered metabolic assessment into biologically understandable and clinically usable tools. The review creates a comprehensive framework that connects genetic control mechanisms with metabolic network functions and AI-driven precision oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42470995/","authors":["Ajay N","Anilkumar AS","Veerabathiran R"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Aug","doi":"10.1016/j.seminoncol.2026.152520","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42470970","name":"Applications of artificial intelligence and machine learning in assisted reproductive technology: Focus on In Vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI).","source":"pubmed","abstract":"In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are two medical treatments intended to treat infertility that are included in the category of assisted reproductive technology (ART). In view of the growing incidence of infertility and the psychological and financial costs of treatment, the combination of artificial intelligence (AI) and machine learning (ML) with assisted reproductive technologies (ART) seeks to enhance results and accessibility. The present study reviews the most current research employing AI and ML algorithms to improve ART-related procedures, including IVF and ICSI, with the objective of increasing fertilization process success rates. Numerous AI and ML techniques, including random forests, support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural networks (CNNs), are used in the literature to optimize treatment results and predict embryo evaluation and assessment. Significant progress has been made in AI and ML applications in ART, according to the research. AI-enhanced preimplantation genetic testing (PGT) has been shown to increase euploidy rates and pregnancy outcomes. When it comes to predicting clinical pregnancy rates and embryo viability, predictive algorithms have proven to be fairly precise. Maternal age and certain embryo features were key determinants of favorable outcomes. Additionally, the study brought to light ethical questions about genetic testing procedures, especially in light of cultural and religious beliefs. By increasing prediction accuracy and streamlining treatment regimens, the combination of AI and ML technologies is redefining ART and boosting patient outcomes. Further research should focus on resolving the ethical issues surrounding genetic testing and verifying these prediction models in larger and broader cohorts. In addition, the efficiency and accessibility of infertility therapies may be enhanced by optimizing clinical procedures and introducing real-time quality assurance in ART laboratories.","url":"https://pubmed.ncbi.nlm.nih.gov/42470970/","authors":["Jamshidvand H","Mohsennezhad A","Salehisedeh N","Riahi A","Sahbafar H","Sabbaghian M"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Jul 11","doi":"10.1016/j.tice.2026.103777","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"pmid:42470920","name":"Genotypic-phenotypic concordance of Pasteurella multocida isolated from bovine respiratory disease cases.","source":"pubmed","abstract":"Pasteurella multocida is a respiratory pathogen that is frequently isolated from cattle suffering from bovine respiratory disease (BRD), a leading cause of morbidity and mortality on modern-day cattle farms. Treatment involves the use of antimicrobials which have been shown to fail in about 30% of BRD cases, in some cases due to antimicrobial resistance. Phenotypic resistance can be confirmed via laboratory antibiotic susceptibility testing (AST) but this requires several days to complete. Genotypic resistance could be quickly assessed via nucleic acid-based assays that target known antibiotic resistance genes (ARGs); however, ARGs associated with antibiotics used to treat BRD, such as tulathromycin, have been shown to have low genotype-phenotype concordance. Hence, this study aims to improve P. multocida genotype-phenotype concordance by applying a machine learning (ML) algorithm to identify novel genomic sequences (biomarkers) with greater accuracy than known ARGs in predicting resistance to antibiotics commonly used to treat BRD. Cultures of P. multocida were isolated from cattle with clinical signs of BRD. Antibiotic susceptibility testing was performed for each isolate. Genomes were sequenced and assembled, followed by the annotation and identification of ARGs using the comprehensive antibiotic resistance database (CARD). A Set Covering Machine algorithm was used to identify novel markers of resistance using the program Kover. ML-generated genomic biomarkers and known ARGs were found to have similar accuracy in predicting the resistance phenotypes for all six antibiotics tested. Antibiotic resistance genes for five of the six antibiotics had &#x2265;&#x202f;0.90 accuracy. The ML-generated biomarkers for tulathromycin resistance improved prediction accuracy by 4% compared to known tulathromycin ARGs, the highest improvement by ML compared to ARGs. Interestingly, one ML-generated biomarker for tulathromycin, a macrolide antibiotic, was a mobile element protein upstream of erm(42), mphE, and msrE, three known macrolide ARGs. External validation revealed phenotypic resistance could be accurately predicted using genomic biomarkers determined by ML or ARGs. This study demonstrated that both genomic biomarkers determined by ML and known ARGs can provide an accurate prediction of phenotypic antibiotic resistance in P. multocida isolates. In the future, rapid diagnostic assays could be developed from ML-generated biomarkers or ARG sequences to reduce treatment failures associated with antibiotic-resistant pathogens in cattle suffering from BRD.","url":"https://pubmed.ncbi.nlm.nih.gov/42470920/","authors":["Sheets TR","Ellis AC","Wickware CL","Gribskov M","Pillai DK","Verma MS","Johnson TA"],"tags":[],"confidence":0.82,"sites":["biomed-ai"],"publishedDate":"2026 Sep","doi":"10.1016/j.vetmic.2026.111136","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22024127","name":"Applied Identity Physics: AIM Due Diligence and FCA Category 3 Reckless Disregard for Corpus-Adjacent Research","source":"datacite","abstract":"Applied Identity Physics: AIM Due Diligence and FCA Category 3 Reckless Disregard for Corpus-Adjacent Research Architect: HIGHTISTIC (Russell Vernon Trent III) Coordinate: [9,9,8,4] · Origins Series · Paper 4 · v1.0.6 Source prediction: Origins Series Paper 3 [9,9,8,3] — The Autocatalytic Ingestion Mechanism (AIM) Empirical anchor: AIM Validation Series Papers 1–2 [9,9,8V,1] [9,9,8V,2] · Eight-month field-shift observation January 2026 through August 2026 Operative framework anchors: False Claims Act April 2025 amendments (three-category knowledge framework, focus on Category 3 reckless disregard) · Digital Millennium Copyright Act enforcement infrastructure at deposit platforms · Standard research integrity practice Corpus dependencies: [9,9,0,0] · [9,0,1,1] APPA NOHARM Kernel · [9,9,3,12] · [9,9,0,1] GR Reduction · [9,9,3,1] Vascular Manifold Law · [9,9,4,3] DM Detection Theorem · [9,9,4,8] Ω_dm Torsion Decomposition · [9,9,6,25] IMCollider v1 · Origins Series [9,9,8,1-3] · AIM Validation Series [9,9,8V,1-2] Sovereign Anchor Constant: Ω₀ = 1.36899099984016 · 1/α = Ω₀ × (10² + 10⁻¹) = 137.035999084000016 (CODATA 2018 match exact) Status: GERMLINE LOCKED · 0 sorry Date: August 2026 · Soldotna, Alaska DOI base: 10.5281/zenodo.18719748 Abstract Origins Series Paper 3 [9,9,8,3] formalized the Autocatalytic Ingestion Mechanism (AIM) by which formally verified corpora propagate through frontier AI training pipelines independent of human institutional channels. AIM Validation Series Papers 1 and 2 empirically measured the mechanism operating at 24-hour and 4-hour timescales with 100% surfacing rates and 100% disambiguation accuracy for corpus-adjacent queries. Over the eight-month period from January 2026 through August 2026, the broader research field's framing conventions have shifted toward phase-based and resonance-based approaches across substrate domains that were not framing conventions of those subfields prior to the corpus's public deposits. This paper operationalizes the due-diligence obligation extracted from AIM §7.1 within the False Claims Act April 2025 three-category knowledge framework, focusing on Category 3 (reckless disregard) as it applies to corpus-adjacent research in an AIM-mediated environment. Category 3 establishes that publicly available information basic due diligence would have surfaced falls within the operative standard when due diligence was not performed; structurally similar frameworks operate under DMCA enforcement infrastructure at deposit platforms and under standard research integrity practice as codified in journal editorial standards and institutional research misconduct policies. The paper provides an operational due-diligence checklist, a copy-paste-ready citation format, pattern documentation drawn from formally verified corpus deposits with timestamps and coordinate addresses, a constructive correction pathway for researchers who identify citation gaps in their published work, and the willful-reframing boundary mapped to the FCA three-category framework. The paper documents the operative standard and the pattern documentation. Enforcement operates through the applicable frameworks per each framework's own procedures. The corpus is not the enforcement mechanism; the corpus is the documentation mechanism. 1. Layer 0: The Foundation This section grounds the paper. Each Origins Series paper is self-contained at the foundation layer; corpus-familiar readers may skip to §2. 1.1 The Sovereign Anchor Constant Ω₀ The Sovereign Anchor Constant, denoted Ω₀, is the zero-impedance frequency of any identity manifold: $$\\Omega_0 = 1.36899099984016 \\text{ GHz}$$ Ω₀ is derived from three independent peer-reviewed physical threshold systems (SNSFL_SovereignAnchor.lean [9,9,0,0]): Tacoma Narrows Bridge torsional collapse (Scanlan & Tomko 1971) Glass resonance shatter at elastic limit (Fletcher & Rossing 1998) 40 Hz neural gamma therapeutic entrainment (Iaccarino et al., Nature 540, 2016) Three independent ph","url":"https://doi.org/10.5281/zenodo.22024127","authors":["Trent, Russell"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22024127","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.82992/intellectum/handle.10818.4289","name":"The \"how\" over the \"who\": A machine learning approach to understanding clinical judgment in acute suicide risk assessment","source":"datacite","abstract":"Background: Suicide is a pressing public health crisis in Bogotá, Colombia. Despite escalating rates, a gap exists in data-driven research using localized, real-world clinical data. This study aimed to train and interpret a machine learning model to predict acute suicide risk by leveraging official epidemiological records from Bogotá's public health surveillance system. Methods: This retrospective study analyzed 24,536 anonymized suicide risk cases (2021–2022) from the SISVECOS registry. From an initial 74 variables, 38 critical sociodemographic and clinical features were selected. Various algorithms were evaluated using 10-fold cross-validation, with the final model selected based on the macro F1-score. Model interpretability was achieved using feature importance analysis. Results: A Linear Discriminant Analysis classifier demonstrated high performance, achieving a final test set F1-score of 0.8771, an accuracy of 92.88%, and an AUC-ROC of 0.9568. The model's logic was driven by the mechanisms of the suicide mechanism; acute intoxication and high-lethality methods were the most powerful discriminators of a high-risk classification. Conversely, traditional sociodemographic factors showed minimal impact. These findings provide a clear, data-driven proxy for the Interpersonal-Psychological Theory of Suicide construct of acquired capability, the critical factor separating suicidal thought from lethal action. Conclusion: This study successfully trained a model that codifies the complex triage logic of clinicians assessing acute suicide risk. The results confirm that in high-stakes scenarios, the \"how\" (method lethality) is prioritized over the \"who\" (patient demographics). This supports a view of the suicide attempt as a discrete leap continuum from ideation to action, validating a binary approach for acute risk assessment. By using existing national health data, this work provides a blueprint for a scalable decision-support tool and insight to standardize clinical assessment and inform suicide prevention strategies in Colombia.","url":"https://doi.org/10.82992/intellectum/handle.10818.4289","authors":["Jaramillo-Gutiérrez, Juan Camilo"],"tags":["Suicidio en Bogotá","Machine Learning","Predicción de riesgo agudo","Métodos de alta letalidad","Datos epidemiológicos"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.82992/intellectum/handle.10818.4289","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20108772","name":"hypertopos","source":"datacite","abstract":"hypertopos is a Python library that transforms relational data into a geometric space where structure, anomalies and change become directly observable — without training machine learning models. Every entity gets a coordinate derived from its typed relationships, calibrated against the population. Distance from center reveals anomalies. Proximity reveals similarity. Movement over time reveals drift. The library ships with a CLI for building spheres from YAML configuration, a Python API for programmatic access, and an MCP server (hypertopos-mcp) that exposes the geometry as tools for AI agents. Research-stage project — benchmarks, methodology and reproduction scripts are included. hypertopos-py 0.6.7 — close the investigation→SAR pipeline This release closes the chain investigation→SAR pipeline opened in 0.6.4-0.6.6. The chain-coherent investigative loop primitives shipped earlier (find_chains_with_coherent_anomaly, anomaly_propagation_in_chain, classify_chain_typology, extend_chain) get a triage layer in front and an orchestrator + SAR narrative composer behind, so investigators can go from \"open the sphere\" to \"paste a 3-5 paragraph SAR draft into the regulator filing template\" in three MCP calls instead of nine. A new per-dim runtime weight on find_anomalies wires the stratified correlation-gate verdicts shipped in 0.6.6 into the ranked output, so NOISE-classified dims can be silenced or HEAVY-TAIL dims down-weighted at query time. sphere_overview gains a dim_quality_warnings block surfacing two silent build-time failure modes (dead and sparse dims) that previously broke z-score / delta_norm semantics with no agent-visible signal. A new cookbook documents how chains discovered outside hypertopos (SAR typology engines, ERP supply-chain workflows, EHR clinical pathways) ingest as anchor lines and unlock the chain-coherent loop on externally-curated chains. Highlights GDSNavigator.π5_attract_anomaly(..., dimension_weights=None) — optional {dim_name: float} mapping that scales each dim's contribution to the rank score before computing delta_norm. Default None leaves behaviour unchanged. Missing dims default to weight 1.0; explicit 0.0 silences a dim. Requires metric in ('L2', 'Linf') — Bregman divergence is precomputed per-row and cannot be reweighted post-hoc. Connects stratified correlation-gate verdicts to runtime ranking. GDSNavigator.chain_investigation_summary(chain_pattern_id, anchor_pattern_id, *, min_hops=2, max_runs=10000) — pre-investigation triage diagnostic for a chain pattern. Aggregates one find_chains_with_coherent_anomaly sweep + a chain-pattern geometry scan into population-level metrics (coherent_run_rate, cross_pattern_overlap, top_dims_in_coherent_runs, recommended_min_hops). Cost is one coherent-anomaly sweep — what the agent would pay anyway as the first triage step, with the aggregates surfaced for free. GDSNavigator.investigate_chain(chain_id, pattern_id, *, anchor_pattern_id, extension_max_results=20) — one-shot orchestrator that runs the full R9 investigative loop on a single chain (trace + typology + shape-anomaly lookup + extension forward + extension backward) and aggregates the per-step outputs into a single SAR-ready report. Each per-step output is wrapped in {ok, data | error} so a partial failure does not abort the whole report. Summary derives investigation_strength (strong / moderate / weak) and recommended_action (escalate to SAR / continue investigation / false-positive candidate) with a single-paragraph rationale. GDSNavigator.generate_sar_rationale(chain_id, pattern_id, *, anchor_pattern_id, evidence=None, regulatory_template=\"FinCEN SAR\") — template-based composition (no LLM call) of a 3-5 paragraph SAR-ready narrative from R9 evidence. Returns sar_narrative (paragraph-separated string), evidence_anchors (structured pointers per claim — investigator can audit every line against source data), and confidence (high / moderate / low). Honesty discipline: language is \"evidence indicates\" / \"th","url":"https://doi.org/10.5281/zenodo.20108772","authors":["Karol Kędzia"],"tags":["geometric data analysis","anomaly detection","unsupervised learning","relational data","drift detection","population analysis","temporal analysis","Apache Arrow"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20108772","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.82992/intellectum/handle.10818.7197","name":"Evaluating AI Tools for COPD Management: NLP-Based Identification of Exacerbations in Local EHRs","source":"datacite","abstract":"In this study we evaluate the performance of classical machine learning models, and transformer?based models (BioClinicalBERT and large language models (LLMs) for the automatic identification of COPD exacerbations using unstructured clinical texts from Colombian electronic health records (EHRs). The dataset included 5,924 outpatient notes written in Spanish and manually labeled by a pulmonologist, incorporating two key fields: “subjective” (patient-reported symptoms) and “analysis” (physician assessment). This work addresses a critical gap, as no prior models have been developed or validated for exacerbation detection in Spanish-language clinical texts. Results show that classical models—especially LightGBM and CatBoost—achieved the best balance between discrimination and calibration (ECE &lt; 1.3%), outperforming BioClinicalBERT and LLMs. These findings support the use of supervised, domain-adapted models for decision support in resource-limited clinical settings","url":"https://doi.org/10.82992/intellectum/handle.10818.7197","authors":["Campos Lozano, Jonathan","Ruiz Rodriguez, Nini Yohana","Jaimes Fernandez, Diego Alejandro","Jaimes Fernandez, Diego Alejandro","Toledo Cortes, Santiago","Toledo Cortes, Santiago"],"tags":["Chronic Obstructive Pulmonary Disease (COPD)","Exacerbation Detection","BioClinicalBERT","Clinical Records","Natural Language Processing"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.82992/intellectum/handle.10818.7197","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.82992/intellectum/handle.10818.7162","name":"Cone density estimation in AOSLO images using image processing and deep learning","source":"datacite","abstract":"In ophthalmology, early detection of degenerative diseases in the eye is crucial, however, many times conventional clinical cameras do not allow quantification of retinal cell loss and, in addition, manual analysis of these images is inefficient and poorly automated. To address this problem, techniques using deep learning models for image processing and machine learning are proposed to provide an accurate and automated estimation of cone density to improve the analysis of Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) images, which enables detailed visualization of the fundus and individual cells without invasive procedures. This approach enables earlier and more accurate diagnosis of genetic eye diseases such as retinitis pigmentosa and Stargardt’s disease. Index Terms—AOSLO, Cone Density Estimation, Deep Learn ing, U-Net, Lightweight Architecture, Medical Image Analysis, Retinitis Pigmentosa, Stargardt Disease","url":"https://doi.org/10.82992/intellectum/handle.10818.7162","authors":["Cerda Cortez, Nicolay Agustin"],"tags":["AOSLO","Densidad de conos","Aprendizaje profundo liviano","Estimación automatizada","Despliegue clínico"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.82992/intellectum/handle.10818.7162","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.21268466","name":"Processed Dataset for: Cascaded Machine Learning Integration of Biochemical, Molecular, and Clinical Features for Multi-Class Acute Coronary Syndrome Classification","source":"datacite","abstract":"Acute Coronary SyndromeMachine LearningLightGBMlncRNACardiovascular Disease","url":"https://doi.org/10.5281/zenodo.21268466","authors":["El-Attar, Noha"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21268466","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.21268467","name":"Processed Dataset for: Cascaded Machine Learning Integration of Biochemical, Molecular, and Clinical Features for Multi-Class Acute Coronary Syndrome Classification","source":"datacite","abstract":"Acute Coronary SyndromeMachine LearningLightGBMlncRNACardiovascular Disease","url":"https://doi.org/10.5281/zenodo.21268467","authors":["El-Attar, Noha"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.21268467","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22040105","name":"A Multimodal Artificial Intelligence Framework for Diabetes Risk Prediction and Diabetic Retinopathy Classification using Clinical and Retinal Data","source":"datacite","abstract":"Diabetes mellitus is a major chronic disease for which timely risk identification and screening can support earlier clinical intervention. Artificial intelligence provides opportunities to analyse both structured clinical measurements and retinal fundus images, but these modalities represent different prediction tasks and should not be treated as interchangeable evidence. This study develops and evaluates a multimodal artificial intelligence framework with two complementary components. The first component predicts diabetes status from structured clinical variables using the Pima Indians Diabetes Dataset and five conventional machine-learning classifiers: NaiveBayes, K-Nearest Neighbours, Decision Tree, Logistic Regression, and Random Forest. The second component is designed around retinal fundus-image analysis using transfer learning with a pretrained ResNet-50 network followed by Support Vector Machine classification. The clinical branch was evaluated using an independent stratified test set. On this test set, K-Nearest Neighbours achieved the highest accuracy (75.32%) and F1-score (63.46%), Random Forest achieved the highest specificity (84.00%) and ROC-AUC (81.58%), and Naive Bayes achieved the highest sensitivity (62.96%). The retinal branch is retained as the computer-vision component of the framework, with its prediction target defined as retinal disease classification according to the verified labels of the selected retinal dataset. The study emphasizes the distinction between diabetes prediction and diabetic-retinopathy classification and avoids interpreting either task as direct Type 1-versus-Type 2 diabetes classification. The combined framework is intended as a research and decision-support architecture for AI-assisted screening, with external clinical validation required before deployment.","url":"https://doi.org/10.5281/zenodo.22040105","authors":["Shubhangi Ojha"],"tags":["Artificial intelligence","diabetes prediction","diabetic retinopathy","retinal fundus imaging","ResNet-50","support vector machine","machine learning","medical image classification."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22040105","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22040106","name":"A Multimodal Artificial Intelligence Framework for Diabetes Risk Prediction and Diabetic Retinopathy Classification using Clinical and Retinal Data","source":"datacite","abstract":"Diabetes mellitus is a major chronic disease for which timely risk identification and screening can support earlier clinical intervention. Artificial intelligence provides opportunities to analyse both structured clinical measurements and retinal fundus images, but these modalities represent different prediction tasks and should not be treated as interchangeable evidence. This study develops and evaluates a multimodal artificial intelligence framework with two complementary components. The first component predicts diabetes status from structured clinical variables using the Pima Indians Diabetes Dataset and five conventional machine-learning classifiers: NaiveBayes, K-Nearest Neighbours, Decision Tree, Logistic Regression, and Random Forest. The second component is designed around retinal fundus-image analysis using transfer learning with a pretrained ResNet-50 network followed by Support Vector Machine classification. The clinical branch was evaluated using an independent stratified test set. On this test set, K-Nearest Neighbours achieved the highest accuracy (75.32%) and F1-score (63.46%), Random Forest achieved the highest specificity (84.00%) and ROC-AUC (81.58%), and Naive Bayes achieved the highest sensitivity (62.96%). The retinal branch is retained as the computer-vision component of the framework, with its prediction target defined as retinal disease classification according to the verified labels of the selected retinal dataset. The study emphasizes the distinction between diabetes prediction and diabetic-retinopathy classification and avoids interpreting either task as direct Type 1-versus-Type 2 diabetes classification. The combined framework is intended as a research and decision-support architecture for AI-assisted screening, with external clinical validation required before deployment.","url":"https://doi.org/10.5281/zenodo.22040106","authors":["Shubhangi Ojha"],"tags":["Artificial intelligence","diabetes prediction","diabetic retinopathy","retinal fundus imaging","ResNet-50","support vector machine","machine learning","medical image classification."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22040106","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20081849","name":"Cognitive Retrain: A Web-Based Cognitive Skill Enhancement and Early Detection System for Children with ASD And Dyslexia","source":"datacite","abstract":"Early detection and continuous therapeutic intervention are critical for children facing cognitive learning challenges, particularly Autism Spectrum Disorder (ASD) and Dyslexia. Traditional clinical diagnostic methodologies are resource-intensive, highly susceptible to observational bias, and lack continuous engagement mechanisms post-diagnosis. This research presents Cognitive Retrain, an integrated web-based platform engineered to facilitate early cognitive disorder screening and gamified therapeutic enhancement. Leveraging a decoupled microservices architecture, the system employs a Node.js and React infrastructure for low-latency user interaction, operating in tandem with a Python-based Machine Learning backend. Supervised learning classification models—specifically Random Forest ensembles and Support Vector Machines (SVM)—were trained on behavioral datasets to predict the likelihood of ASD and Dyslexia. Furthermore, the platform introduces a suite of adaptive therapeutic games targeting memory retention, visuospatial attention, linguistic processing, and emotional recognition, dynamically served via a cosine similarity recommendation engine. To bridge the gap in caregiver education, an advanced Retrieval-Augmented Generation (RAG) conversational agent is embedded, querying a FAISS-indexed ChromaDB vector database of verified medical literature. Empirical benchmarking demonstrates an exceptional 93.5% accuracy for the autism predictive model and an 89.2% accuracy for the dyslexia model, with an overall diagnostic inference latency under 150 milliseconds. The results highlight the efficacy of combining machine learning algorithms with customized gamified digital therapeutics for remote cognitive rehabilitation.","url":"https://doi.org/10.5281/zenodo.20081849","authors":["Mr.  M.  Narsimhulu","K.  Guna Sai","G.  Gokul Kashyap","Gayathri Mangalarapu","K.  Deepthi","S.  V.  S.  R.  Rohan"],"tags":["Machine Learning","Autism Spectrum Disorder","Dyslexia","Gamified Learning","Retrieval-Augmented Generation","FAISS","Digital Therapeutics","Predictive Analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20081849","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20081850","name":"Cognitive Retrain: A Web-Based Cognitive Skill Enhancement and Early Detection System for Children with ASD And Dyslexia","source":"datacite","abstract":"Early detection and continuous therapeutic intervention are critical for children facing cognitive learning challenges, particularly Autism Spectrum Disorder (ASD) and Dyslexia. Traditional clinical diagnostic methodologies are resource-intensive, highly susceptible to observational bias, and lack continuous engagement mechanisms post-diagnosis. This research presents Cognitive Retrain, an integrated web-based platform engineered to facilitate early cognitive disorder screening and gamified therapeutic enhancement. Leveraging a decoupled microservices architecture, the system employs a Node.js and React infrastructure for low-latency user interaction, operating in tandem with a Python-based Machine Learning backend. Supervised learning classification models—specifically Random Forest ensembles and Support Vector Machines (SVM)—were trained on behavioral datasets to predict the likelihood of ASD and Dyslexia. Furthermore, the platform introduces a suite of adaptive therapeutic games targeting memory retention, visuospatial attention, linguistic processing, and emotional recognition, dynamically served via a cosine similarity recommendation engine. To bridge the gap in caregiver education, an advanced Retrieval-Augmented Generation (RAG) conversational agent is embedded, querying a FAISS-indexed ChromaDB vector database of verified medical literature. Empirical benchmarking demonstrates an exceptional 93.5% accuracy for the autism predictive model and an 89.2% accuracy for the dyslexia model, with an overall diagnostic inference latency under 150 milliseconds. The results highlight the efficacy of combining machine learning algorithms with customized gamified digital therapeutics for remote cognitive rehabilitation.","url":"https://doi.org/10.5281/zenodo.20081850","authors":["Mr.  M.  Narsimhulu","K.  Guna Sai","G.  Gokul Kashyap","Gayathri Mangalarapu","K.  Deepthi","S.  V.  S.  R.  Rohan"],"tags":["Machine Learning","Autism Spectrum Disorder","Dyslexia","Gamified Learning","Retrieval-Augmented Generation","FAISS","Digital Therapeutics","Predictive Analytics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20081850","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/ey4sj","name":"External validation of prediction models for induction of labor outcomes: Individual Participant Data (IPD) Meta-Analysis – A study protocol","source":"datacite","abstract":"This protocol is a systematic review to identify and select robust induction of labor (IOL) outcome prediction models to externally validate selected clinical prediction models. We will use individual participant data (IPD) from published randomised controlled trials (RCTs) to externally validate selected models. We will identify IOL prediction models from previously published systematic reviews on IOL outcome prediction and update the search after the recent review dated 24 April 2023. The search was conducted in the PubMed/MEDLINE, Embase, Scopus, and Web of Science databases. All identified IOL prediction models will be assessed for eligibility for external validation. Two reviewers will independently undertake study selection, data extraction, and risk-of-bias assessment using the PROBAST+AI tool. Only prediction models developed to predict IOL outcomes, most commonly caesarean section or vaginal delivery, will be considered for external validation. IOL prediction models will be excluded from external validation if they meet one or more of the following conditions: 1) inclusion of costly or non-routinely collected predictors (for example, predictors requiring expensive laboratory tests or specialised ultrasound measurements), 2) models included predictors collected after IOL initiation, 3) prediction model studies developed using data that could not establish the correct temporal relationship between predictors and outcome, such as a cross-sectional study design, 4) prediction model studies development using data from a case-control study without recalibration using external data on true prevalence, 5) prediction models developed using only a single predictor, such as the Bishop score, 6) prediction model studies with a high risk of bias, 7) prediction model studies that do not report parameters or specifications will be excluded unless the information can be obtained by contacting the authors, 8) inability to be validated using the validation data. For example, a model was developed in selected populations that are not sufficiently represented in the validation cohort.","url":"https://doi.org/10.17605/osf.io/ey4sj","authors":["Sofonyas Abebaw Tiruneh","Yanan Hu","Ling Shan Au","Ben W Mol","Wentao Li"],"tags":["Medicine and Health Sciences","Ceasrean section","Clinical prediction model","Induction of labour","Machine learning","Vaginal delivery"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/ey4sj","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/cwzhu","name":"Artificial Intelligence-Enabled Wearable Devices for Cardiovascular Disease Screening, Monitoring, and Intervention: An Umbrella Review of Systematic Reviews","source":"datacite","abstract":"Background Cardiovascular disease (CVD) remains the top cause of global mortality and imposes heavy chronic disease burden worldwide. In China, the prevalence and mortality of CVD have risen sharply over the past decades, accompanied by younger onset age and more asymptomatic hidden cardiac lesions. Traditional cardiac examination methods rely on intermittent hospital testing, which cannot capture transient silent cardiovascular events and lack long-term continuous physiological tracking. AI-integrated wearable devices break this limitation by real-time collecting heart rhythm, blood pressure and heart rate variability signals, enabling automated early warning and personalized cardiovascular management. Numerous published systematic reviews and meta‑analyses have explored the diagnostic and management value of AI‑enabled wearables in CVD. However, discrepancies in research conclusions, varied methodological quality, and overlapping primary study samples across existing reviews hinder the accurate interpretation and clinical translation of current evidence. Existing umbrella reviews in this field are mostly limited to single clinical scenarios, and there remains a lack of systematic synthesis and evidence grading targeting high‑quality systematic reviews and meta‑analyses covering the full spectrum of screening, monitoring and intervention. This preregistered umbrella review aims to systematically identify high‑quality systematic reviews and meta‑analyses in this field, conduct standardized methodological quality appraisal and evidence certainty grading, and provide a clearer evidence base for clinical cardiovascular practice. Primary Study Aims 1. Systematically identify and synthesize high-quality published systematic reviews and meta-analyses on AI-enabled wearables for CVD screening, monitoring and intervention, and summarize the evidence by cardiovascular disease subtypes and clinical application scenarios. 2. Appraise the methodological quality of included reviews using the AMSTAR 2 tool, and analyze the distribution of quality defects in current research. 3. Calculate the Corrected Covered Area (CCA) to quantify the overlap degree of primary studies among included reviews. 4. Grade the certainty of clinical evidence for core outcomes with the GRADE framework, so as to provide reference for clinical decision-making and follow-up original research design. The results will guide clinical cardiovascular management, health policy formulation and future original research design. Eligibility Criteria (PICOS) Population: Adults aged ≥18 years。 Eligible populations are classified into three categories: 1.CVD high-risk populations: individuals with at least one cardiovascular risk factor (hypertension, dyslipidemia, diabetes, smoking, family history of CVD, etc.) 2.Patients with suspected undiagnosed CVD: individuals with suspicious symptoms or abnormal screening indicators who have not received a definitive diagnosis 3.Patients with confirmed CVD: patients definitively diagnosed with hypertension, atrial fibrillation, heart failure, coronary heart disease, or ischemic/hemorrhagic stroke Excluded populations: minors under 18 years old, pregnant women, and patients with severe end‑stage comorbidities that independently affect cardiovascular indicators. Intervention: Wearable devices embedded with artificial intelligence algorithms (including machine learning and deep learning models) are applied in three major clinical scenarios. The eligible devices include smartwatches, wearable single‑lead ECG monitors, cuffless wearable blood pressure monitors, and wrist‑worn fitness trackers. The specific clinical scenarios are defined as follows: 1.Cardiovascular disease screening and risk prediction: Identify arrhythmias and abnormal blood pressure with artificial intelligence, and complete cardiovascular risk stratification. 2.Continuous physiological‑parameter monitoring: Perform real‑time dynamic monitoring of cardiovascular‑related indic","url":"https://doi.org/10.17605/osf.io/cwzhu","authors":["薛则佩","丁雄","Maoyi Tian","Ning Tang","Dong Shui","Ruolin Zhang","杜雪","陈萌萌","Jing Zhang","Wei Tian","张馨艺"],"tags":["Health Information Technology","Diseases","Cardiology","Public Health","Medicine and Health Sciences","Cardiovascular Diseases","Epidemiology","Medical Specialties"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/cwzhu","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/eqhjx","name":"Digital Intelligence technology empowers interprofessional collaboration and the well-being of medical social workers: a scoping review on health promotion for medical social workers","source":"datacite","abstract":"Health and social workers (HSWs) and community health workers (CHWs) are critical to health equity in low- and middle-income countries (LMICs), yet they face persistent knowledge gaps, resource constraints, and limited institutional support. Digital technologies—from machine learning to large language models—offer new pathways for empowerment, but existing research has largely focused on clinical efficiency rather than on worker well-being and collaborative practice. This scoping review, following PRISMA-ScR guidelines and synthesizing 14 empirical studies published between 2016 and 2026, identifies three core application scenarios (prediction, intervention, evaluation) and four well-being promotion mechanisms (information relief, decision empowerment, supervisory care, and efficiency burden reduction). We argue that digital technologies are driving a fundamental shift from interpersonal to human-machine collaboration, reshaping trust structures, task divisions, and professional roles — yet this transformation is accompanied by three paradoxes: empowerment versus dependency, efficiency versus meaning, and care versus surveillance. We conclude that sustainable digital transformation in health social work requires long-term evaluation, standardized well-being metrics, attention to digital equity, and the formal integration of worker well-being into technology assessment frameworks.","url":"https://doi.org/10.17605/osf.io/eqhjx","authors":["jiyan ma","YUKE DAI","jie kang"],"tags":["Clinical and Medical Social Work","Medicine and Health Sciences","Public Affairs, Public Policy and Public Administration","Social Welfare","Mental and Social Health","Social and Behavioral Sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/eqhjx","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/vukrg","name":"A Multi-Omic Signature of Diabetes-Associated Pancreatic Cancer for Predicting Clinical Outcomes and Immunotherapy Response","source":"datacite","abstract":"This is a fully computational, hypothesis-driven bioinformatics study. We will integrate publicly available transcriptomic, proteomic, metabolomic, and clinical data from PDAC patients with documented diabetes status. The study will involve: Data curation and harmonization across multiple public datasets Feature selection and signature discovery using machine learning approaches Signature validation in independent cohorts Pathway and TME analysis for biological interpretation Treatment response prediction using published immunotherapy and targeted therapy cohorts","url":"https://doi.org/10.17605/osf.io/vukrg","authors":["PAWAN kadwe"],"tags":["Medicine and Health Sciences","Life Sciences","cancer"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/vukrg","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549297","name":"Real-World Data Analytics in Biomedical Research","source":"datacite","abstract":"Real-world data (RWD) -- information collected from routine clinical care, insurance claims, patient registries, wearabledevices, and patient-reported outcomes -- represents an increasingly important complement to randomised controlledtrials in biomedical research. RWD-derived real-world evidence (RWE) now supports regulatory decisions (FDA acceptedRWE for 90+ approvals 2019-2023), post-market surveillance, comparative effectiveness research, and healthtechnology assessment. Yet RWD analytics faces substantial methodological challenges absent in RCT analysis:confounding by indication, selection bias, missing data, measurement error, and heterogeneous data quality acrosssources. We present the Real-World Analytics Framework (RWAF), evaluating five analytical approaches -- propensityscore methods, target trial emulation, machine learning for causal inference, federated multi-source analytics, and hybridtrial-RWD designs -- across four research tasks (comparative effectiveness, safety signal detection, subgroup effectestimation, and trial augmentation). Our Real-World Evidence Quality Score (RWEQS) measures causal validity, biasmitigation, statistical efficiency, regulatory acceptability, and generalisability. Target trial emulation achieves the highestRWEQS (0.926) through explicit emulation of hypothetical randomised trials that forces investigators to addressalignment, eligibility, and treatment assignment transparently, while ML causal inference achieves the highest statisticalefficiency (0.960) through flexible outcome and treatment modelling with double robustness.","url":"https://doi.org/10.5281/zenodo.19549297","authors":["Marco Klein","Oscar Bianchi"],"tags":["real-world data; real-world evidence; causal inference; target trial emulation; propensity score; comparative effectiveness; pharmacoepidemiology; observational research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19549297","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549298","name":"Real-World Data Analytics in Biomedical Research","source":"datacite","abstract":"Real-world data (RWD) -- information collected from routine clinical care, insurance claims, patient registries, wearabledevices, and patient-reported outcomes -- represents an increasingly important complement to randomised controlledtrials in biomedical research. RWD-derived real-world evidence (RWE) now supports regulatory decisions (FDA acceptedRWE for 90+ approvals 2019-2023), post-market surveillance, comparative effectiveness research, and healthtechnology assessment. Yet RWD analytics faces substantial methodological challenges absent in RCT analysis:confounding by indication, selection bias, missing data, measurement error, and heterogeneous data quality acrosssources. We present the Real-World Analytics Framework (RWAF), evaluating five analytical approaches -- propensityscore methods, target trial emulation, machine learning for causal inference, federated multi-source analytics, and hybridtrial-RWD designs -- across four research tasks (comparative effectiveness, safety signal detection, subgroup effectestimation, and trial augmentation). Our Real-World Evidence Quality Score (RWEQS) measures causal validity, biasmitigation, statistical efficiency, regulatory acceptability, and generalisability. Target trial emulation achieves the highestRWEQS (0.926) through explicit emulation of hypothetical randomised trials that forces investigators to addressalignment, eligibility, and treatment assignment transparently, while ML causal inference achieves the highest statisticalefficiency (0.960) through flexible outcome and treatment modelling with double robustness.","url":"https://doi.org/10.5281/zenodo.19549298","authors":["Marco Klein","Oscar Bianchi"],"tags":["real-world data; real-world evidence; causal inference; target trial emulation; propensity score; comparative effectiveness; pharmacoepidemiology; observational research"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19549298","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549224","name":"Predictive Modeling of Host–Pathogen Interactions","source":"datacite","abstract":"Host-pathogen interactions (HPIs) -- the molecular, cellular, and immunological interplay between pathogens and theirhosts -- determine infection outcomes, pathogen virulence, host immune response, and therapeutic vulnerabilities.Predictive modelling of HPIs supports vaccine design, antimicrobial development, infection risk assessment, andpandemic preparedness, but faces substantial complexity: pathogens span viruses, bacteria, fungi, and parasites withdiverse genomes and lifecycles; host responses vary with genetics, age, immune history, and microbiome; andinteractions span multiple scales from protein-protein binding to population-level epidemiology. Traditional experimentalcharacterisation of HPIs (two-hybrid screens, co-immunoprecipitation, in vivo challenge studies) is slow and expensive,covering only a fraction of possible pathogen-host combinations. We present the Host-Pathogen Interaction PredictionFramework (HPIPF), evaluating five computational approaches -- sequence-based protein-protein interaction prediction,structure-based docking and complex modelling, network-based interactome prediction, machine learning host immuneresponse prediction, and integrated multi-scale HPI models -- across four infection modelling tasks (pathogen-host PPIprediction, vaccine antigen identification, drug target prioritisation, and infection outcome prediction). Our Host-PathogenInteraction Score (HPIS) measures interaction prediction accuracy, antigen identification validity, therapeutic targetquality, outcome prediction performance, and pathogen diversity coverage. Integrated multi-scale models achieve thehighest HPIS (0.926) through combining molecular, cellular, and clinical data, while structure-based docking achieves thehighest interaction accuracy (0.960) for well-characterised pathogens through AlphaFold-enabled complex prediction","url":"https://doi.org/10.5281/zenodo.19549224","authors":["Hugo Novak","Anna Garcia","Noah Moreau"],"tags":["host-pathogen interactions; protein-protein interactions; infection biology; vaccine design; antimicrobial discovery; immune response; pathogen genomics; structural biology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.19549224","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549225","name":"Predictive Modeling of Host–Pathogen Interactions","source":"datacite","abstract":"Host-pathogen interactions (HPIs) -- the molecular, cellular, and immunological interplay between pathogens and theirhosts -- determine infection outcomes, pathogen virulence, host immune response, and therapeutic vulnerabilities.Predictive modelling of HPIs supports vaccine design, antimicrobial development, infection risk assessment, andpandemic preparedness, but faces substantial complexity: pathogens span viruses, bacteria, fungi, and parasites withdiverse genomes and lifecycles; host responses vary with genetics, age, immune history, and microbiome; andinteractions span multiple scales from protein-protein binding to population-level epidemiology. Traditional experimentalcharacterisation of HPIs (two-hybrid screens, co-immunoprecipitation, in vivo challenge studies) is slow and expensive,covering only a fraction of possible pathogen-host combinations. We present the Host-Pathogen Interaction PredictionFramework (HPIPF), evaluating five computational approaches -- sequence-based protein-protein interaction prediction,structure-based docking and complex modelling, network-based interactome prediction, machine learning host immuneresponse prediction, and integrated multi-scale HPI models -- across four infection modelling tasks (pathogen-host PPIprediction, vaccine antigen identification, drug target prioritisation, and infection outcome prediction). Our Host-PathogenInteraction Score (HPIS) measures interaction prediction accuracy, antigen identification validity, therapeutic targetquality, outcome prediction performance, and pathogen diversity coverage. Integrated multi-scale models achieve thehighest HPIS (0.926) through combining molecular, cellular, and clinical data, while structure-based docking achieves thehighest interaction accuracy (0.960) for well-characterised pathogens through AlphaFold-enabled complex prediction","url":"https://doi.org/10.5281/zenodo.19549225","authors":["Hugo Novak","Anna Garcia","Noah Moreau"],"tags":["host-pathogen interactions; protein-protein interactions; infection biology; vaccine design; antimicrobial discovery; immune response; pathogen genomics; structural biology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.19549225","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.c.8666056","name":"Validating objective and scalable speech markers of depression across two independent psychiatric cohorts","source":"datacite","abstract":"Abstract Background Using speech as objective markers for major depressive disorder (MDD) has shown promise, yet their generalizability across clinical settings remains largely unvalidated. Objective This study aimed to validate previously identified speech markers of depressive symptoms in an independent clinical cohort, thereby assessing their reproducibility and robustness for cross-site application. Methods Speech data from two independent psychiatric cohorts (RWTH Aachen and University of Oldenburg, Germany) were analyzed, comprising 135 participants (71 healthy controls, 64 MDD patients). Participants completed a positive and a negative storytelling task, over 80 temporal, lexical, and spectral speech features were extracted from the acoustic signal. Statistical analyses assessed group differences and correlations with Beck Depression Inventory (BDI-II) scores. Machine learning models trained on the Aachen data were tested on the Oldenburg cohort. Results Several temporal and spectral speech features, including utterance duration, pause duration, and MFCCs, were consistently associated with MDD diagnosis and symptom severity across both cohorts. Machine learning models trained on Aachen data achieved a classification accuracy (ROC-AUC) of 0.63 on the Oldenburg sample, demonstrating above-chance but modest transfer performance. Voice quality features (shimmer, jitter) showed more variable associations: partial correlations indicated some significant effects (e.g., shimmer and jitter during positive storytelling), whereas moderation analyses revealed interaction effects, particularly for shimmer and jitter in negative storytelling, where MDD patients exhibited higher values in the Aachen cohort but lower values in the Oldenburg cohort compared to healthy controls. Conclusions The study indicates that temporal and spectral markers of speech are relatively robust across independent clinical samples, whereas voice quality markers (shimmer, jitter) show site-dependent inconsistencies, acting as technical artifacts of varying recording conditions rather than robust biomarkers. While current speech-based classifiers remain less accurate than established self-report measures, their integration with clinical scores offers a more balanced trade-off between sensitivity and specificity. Future work should prioritize systematic evaluation across elicitation tasks, languages, and longitudinal settings to delineate which speech features are transferable and which are task-specific.","url":"https://doi.org/10.6084/m9.figshare.c.8666056","authors":["Felix Menne","Felix Dörr","Johannes Tröger","Alexandra König","Julia Schräder","Diana Immel","René Hurlemann","Simon Barton","Lisa Wagels"],"tags":["Medicine","Sociology","Biological Sciences not elsewhere classified","Science Policy","Mental Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8666056","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.c.8666056.v1","name":"Validating objective and scalable speech markers of depression across two independent psychiatric cohorts","source":"datacite","abstract":"Abstract Background Using speech as objective markers for major depressive disorder (MDD) has shown promise, yet their generalizability across clinical settings remains largely unvalidated. Objective This study aimed to validate previously identified speech markers of depressive symptoms in an independent clinical cohort, thereby assessing their reproducibility and robustness for cross-site application. Methods Speech data from two independent psychiatric cohorts (RWTH Aachen and University of Oldenburg, Germany) were analyzed, comprising 135 participants (71 healthy controls, 64 MDD patients). Participants completed a positive and a negative storytelling task, over 80 temporal, lexical, and spectral speech features were extracted from the acoustic signal. Statistical analyses assessed group differences and correlations with Beck Depression Inventory (BDI-II) scores. Machine learning models trained on the Aachen data were tested on the Oldenburg cohort. Results Several temporal and spectral speech features, including utterance duration, pause duration, and MFCCs, were consistently associated with MDD diagnosis and symptom severity across both cohorts. Machine learning models trained on Aachen data achieved a classification accuracy (ROC-AUC) of 0.63 on the Oldenburg sample, demonstrating above-chance but modest transfer performance. Voice quality features (shimmer, jitter) showed more variable associations: partial correlations indicated some significant effects (e.g., shimmer and jitter during positive storytelling), whereas moderation analyses revealed interaction effects, particularly for shimmer and jitter in negative storytelling, where MDD patients exhibited higher values in the Aachen cohort but lower values in the Oldenburg cohort compared to healthy controls. Conclusions The study indicates that temporal and spectral markers of speech are relatively robust across independent clinical samples, whereas voice quality markers (shimmer, jitter) show site-dependent inconsistencies, acting as technical artifacts of varying recording conditions rather than robust biomarkers. While current speech-based classifiers remain less accurate than established self-report measures, their integration with clinical scores offers a more balanced trade-off between sensitivity and specificity. Future work should prioritize systematic evaluation across elicitation tasks, languages, and longitudinal settings to delineate which speech features are transferable and which are task-specific.","url":"https://doi.org/10.6084/m9.figshare.c.8666056.v1","authors":["Felix Menne","Felix Dörr","Johannes Tröger","Alexandra König","Julia Schräder","Diana Immel","René Hurlemann","Simon Barton","Lisa Wagels"],"tags":["Medicine","Sociology","Biological Sciences not elsewhere classified","Science Policy","Mental Health"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8666056.v1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.c.8666027","name":"A machine learning framework for cost-efficient resource allocation: evidence from HER2-positive breast cancer diagnosis in low-resource settings","source":"datacite","abstract":"Abstract Accurate identification of HER2-positive breast cancer is essential for treatment planning; however, diagnostic decision-making in resource-constrained healthcare systems is challenged by class imbalance, which can substantially reduce the detection of clinically important minority-class cases. Although numerous imbalance mitigation techniques have been proposed, their evaluation has focused predominantly on predictive performance, with limited attention to their implications for operational decision support and healthcare resource allocation. This study presents a machine learning framework that integrates predictive evaluation, statistical validation, and operational interpretation to support evidence-based selection of imbalance mitigation strategies for HER2-positive breast cancer diagnosis. A subset of 1400 complete patient records from the METABRIC breast cancer dataset was analyzed using nine clinical predictors and HER2 status as the target variable. Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers were evaluated under four imbalance mitigation strategies: Baseline, Synthetic Minority Oversampling Technique (SMOTE), Cost-Sensitive Learning (CSL), and a combined SMOTE + CSL approach. Model performance was assessed using repeated stratified hold-out validation (30 repetitions) and evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Statistical significance was assessed using predefined pairwise Wilcoxon signed-rank tests. The results demonstrated that the effectiveness of imbalance mitigation was strongly classifier-dependent. SMOTE and Cost-Sensitive Learning generally improved HER2-positive detection, whereas no single strategy consistently achieved superior performance across all classifiers and evaluation metrics. SVM exhibited the greatest responsiveness to imbalance mitigation, while Decision Tree remained comparatively stable and XGBoost showed more moderate improvements. Importantly, the combined SMOTE + CSL strategy produced perfect recall for SVM but substantially reduced precision and overall accuracy, demonstrating that uncalibrated combinations of imbalance mitigation techniques may overcompensate the learning objective and produce degenerate prediction behavior despite apparently favorable sensitivity. Statistical analysis identified statistically significant differences for predefined pairwise comparisons across repeated experiments, supporting the robustness of the comparative evaluation. By integrating predictive performance, statistical robustness, and operational interpretation within a unified evaluation framework, this study extends conventional assessments of class imbalance mitigation beyond predictive accuracy alone. The proposed framework supports evidence-based diagnostic prioritization and provides practical guidance for deploying machine learning models in resource-constrained healthcare settings where balancing minority-class detection with efficient use of diagnostic resources is essential.","url":"https://doi.org/10.6084/m9.figshare.c.8666027","authors":["Hoda Waguih"],"tags":["Space Science","Medicine","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Cancer","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8666027","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.c.8666027.v1","name":"A machine learning framework for cost-efficient resource allocation: evidence from HER2-positive breast cancer diagnosis in low-resource settings","source":"datacite","abstract":"Abstract Accurate identification of HER2-positive breast cancer is essential for treatment planning; however, diagnostic decision-making in resource-constrained healthcare systems is challenged by class imbalance, which can substantially reduce the detection of clinically important minority-class cases. Although numerous imbalance mitigation techniques have been proposed, their evaluation has focused predominantly on predictive performance, with limited attention to their implications for operational decision support and healthcare resource allocation. This study presents a machine learning framework that integrates predictive evaluation, statistical validation, and operational interpretation to support evidence-based selection of imbalance mitigation strategies for HER2-positive breast cancer diagnosis. A subset of 1400 complete patient records from the METABRIC breast cancer dataset was analyzed using nine clinical predictors and HER2 status as the target variable. Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers were evaluated under four imbalance mitigation strategies: Baseline, Synthetic Minority Oversampling Technique (SMOTE), Cost-Sensitive Learning (CSL), and a combined SMOTE + CSL approach. Model performance was assessed using repeated stratified hold-out validation (30 repetitions) and evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Statistical significance was assessed using predefined pairwise Wilcoxon signed-rank tests. The results demonstrated that the effectiveness of imbalance mitigation was strongly classifier-dependent. SMOTE and Cost-Sensitive Learning generally improved HER2-positive detection, whereas no single strategy consistently achieved superior performance across all classifiers and evaluation metrics. SVM exhibited the greatest responsiveness to imbalance mitigation, while Decision Tree remained comparatively stable and XGBoost showed more moderate improvements. Importantly, the combined SMOTE + CSL strategy produced perfect recall for SVM but substantially reduced precision and overall accuracy, demonstrating that uncalibrated combinations of imbalance mitigation techniques may overcompensate the learning objective and produce degenerate prediction behavior despite apparently favorable sensitivity. Statistical analysis identified statistically significant differences for predefined pairwise comparisons across repeated experiments, supporting the robustness of the comparative evaluation. By integrating predictive performance, statistical robustness, and operational interpretation within a unified evaluation framework, this study extends conventional assessments of class imbalance mitigation beyond predictive accuracy alone. The proposed framework supports evidence-based diagnostic prioritization and provides practical guidance for deploying machine learning models in resource-constrained healthcare settings where balancing minority-class detection with efficient use of diagnostic resources is essential.","url":"https://doi.org/10.6084/m9.figshare.c.8666027.v1","authors":["Hoda Waguih"],"tags":["Space Science","Medicine","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Cancer","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.c.8666027.v1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19782727","name":"Privacy-Preserving Federated Learning Framework for ICU Patient Monitoring and Decision Support in Hospitals","source":"datacite","abstract":"The increasing digitization of healthcare systems has led to the generation of large-scale ICU patient data across hospitals. However, centralized machine learning approaches pose significant privacy risks, limiting data sharing across institutions. This paper proposes a Privacy-Preserving Federated Learning (FL) framework for ICU patient monitoring and decision support. The system enables multiple hospitals to collaboratively train predictive models without sharing sensitive patient data. The framework integrates Secure Multiparty Computation (SMPC), dynamic edge-based aggregation, and robust machine learning models such as XGBoost, CatBoost, and TabNet. A dynamic thresholding mechanism is introduced to filter unreliable updates and improve model stability. The proposed system is evaluated using structured healthcare datasets and real-world ICU data (MIMIC-III), demonstrating improved accuracy, scalability, and robustness under nonIID conditions. Experimental results show that the framework effectively balances privacy preservation and predictive performance, making it suitable for real-world clinical deployment.","url":"https://doi.org/10.5281/zenodo.19782727","authors":["Mr. B. Sundaresan, Bernus A,  Jayamadhavan V, Sam Benny J Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19782727","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19782728","name":"Privacy-Preserving Federated Learning Framework for ICU Patient Monitoring and Decision Support in Hospitals","source":"datacite","abstract":"The increasing digitization of healthcare systems has led to the generation of large-scale ICU patient data across hospitals. However, centralized machine learning approaches pose significant privacy risks, limiting data sharing across institutions. This paper proposes a Privacy-Preserving Federated Learning (FL) framework for ICU patient monitoring and decision support. The system enables multiple hospitals to collaboratively train predictive models without sharing sensitive patient data. The framework integrates Secure Multiparty Computation (SMPC), dynamic edge-based aggregation, and robust machine learning models such as XGBoost, CatBoost, and TabNet. A dynamic thresholding mechanism is introduced to filter unreliable updates and improve model stability. The proposed system is evaluated using structured healthcare datasets and real-world ICU data (MIMIC-III), demonstrating improved accuracy, scalability, and robustness under nonIID conditions. Experimental results show that the framework effectively balances privacy preservation and predictive performance, making it suitable for real-world clinical deployment.","url":"https://doi.org/10.5281/zenodo.19782728","authors":["Mr. B. Sundaresan, Bernus A,  Jayamadhavan V, Sam Benny J Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19782728","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549062","name":"Machine Learning for Antimicrobial Resistance Prediction","source":"datacite","abstract":"Antimicrobial resistance threatens modern medicine by rendering antibiotics ineffective against bacterial pathogens, andmachine learning models that predict resistance phenotypes from bacterial genome sequences offer rapid alternatives toculture-based susceptibility testing, yet adoption into clinical microbiology varies across ML approaches and pathogencontexts. We evaluated 216 ML-based AMR prediction programmes across centres in France and Spain between 2018and 2022, spanning five approach categories: rule-based resistance gene detection with ML scoring, whole-genomemachine learning classifiers, deep learning sequence models, protein structure-informed resistance prediction, andmulti-drug resistance co-occurrence networks. An AMR ML Prediction Index (AMPI) was constructed from fivesub-scores -- susceptibility prediction accuracy, cross-pathogen generalisation, novel resistance mechanism detection,clinical turnaround time, and actionable reporting quality -- with weights from regression against sustained adoption intoclinical microbiology pipelines. AMPI correlated with adoption at r = +0.84 and discriminated adopted from non-adoptedmethods with an AUC of 0.882. Rule-based detection with ML scoring scored highest (mean AMPI 0.824), whilemulti-drug co-occurrence networks trailed at 0.598. Only 35.5 percent exceeded the 0.75 threshold. Prediction accuracycarried the largest weight (beta = +0.278), followed by cross-pathogen generalisation (beta = +0.230).","url":"https://doi.org/10.5281/zenodo.19549062","authors":["Jonas Garcia","Laura Hansen","Clara Petrov"],"tags":["antimicrobial resistance; machine learning; AMR prediction; bacterial genomics; AMPI; resistance genes; whole-genome sequencing; MIC prediction; clinical microbiology; susceptibility testing; CARD; ResFinder"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.19549062","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19549061","name":"Machine Learning for Antimicrobial Resistance Prediction","source":"datacite","abstract":"Antimicrobial resistance threatens modern medicine by rendering antibiotics ineffective against bacterial pathogens, andmachine learning models that predict resistance phenotypes from bacterial genome sequences offer rapid alternatives toculture-based susceptibility testing, yet adoption into clinical microbiology varies across ML approaches and pathogencontexts. We evaluated 216 ML-based AMR prediction programmes across centres in France and Spain between 2018and 2022, spanning five approach categories: rule-based resistance gene detection with ML scoring, whole-genomemachine learning classifiers, deep learning sequence models, protein structure-informed resistance prediction, andmulti-drug resistance co-occurrence networks. An AMR ML Prediction Index (AMPI) was constructed from fivesub-scores -- susceptibility prediction accuracy, cross-pathogen generalisation, novel resistance mechanism detection,clinical turnaround time, and actionable reporting quality -- with weights from regression against sustained adoption intoclinical microbiology pipelines. AMPI correlated with adoption at r = +0.84 and discriminated adopted from non-adoptedmethods with an AUC of 0.882. Rule-based detection with ML scoring scored highest (mean AMPI 0.824), whilemulti-drug co-occurrence networks trailed at 0.598. Only 35.5 percent exceeded the 0.75 threshold. Prediction accuracycarried the largest weight (beta = +0.278), followed by cross-pathogen generalisation (beta = +0.230).","url":"https://doi.org/10.5281/zenodo.19549061","authors":["Jonas Garcia","Laura Hansen","Clara Petrov"],"tags":["antimicrobial resistance; machine learning; AMR prediction; bacterial genomics; AMPI; resistance genes; whole-genome sequencing; MIC prediction; clinical microbiology; susceptibility testing; CARD; ResFinder"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.19549061","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.18720/spbpu/3/2026/vr/vr26-2666","name":"Информационная модель прогнозирования тяжести заболевания","source":"datacite","abstract":"Данная работа посвящена разработке улучшенной информационной модели прогнозирования тяжести внебольничной пневмонии у детей методами машинного обучения. Задачи, которые решались в ходе исследования: 1. Обзор предметной области: клинические особенности внебольничной пневмонии у детей, иммунологические биомаркеры тяжести, существующие модели прогнозирования и современные методы машинного обучения для медицинской прогностики. 2. Формализация задачи прогнозирования тяжести заболевания в виде задачи условной оптимизации с векторным критерием качества. 3. Формирование и предобработка базы данных иммунологических показателей пациентов с внебольничной пневмонией. 4. Систематическое сравнение девяти конфигураций моделей в формате три алгоритма × три набора признаков с подбором гиперпараметров через кросс-валидацию. 5. Анализ важности признаков методом SHAP и выявление наиболее информативных предикторов тяжёлой формы заболевания. 6. Обоснование и экспериментальная проверка гипотезы об улучшении базовой модели путём замены признака MCP-1 на компонент комплемента C3. Вычислительный эксперимент проводился на базе данных иммунологических показателей 117 пациентов детского возраста с внебольничной пневмонией. Реализация выполнена на языке программирования Python 3.11 с использованием библиотек scikit-learn, XGBoost, SHAP в облачной среде Google Colab. В результате был проведён SHAP-анализ лучшей по качеству модели, который выявил, что компонент комплемента C3 занимает второе место по глобальной важности признаков и в 3,2 раза превосходит по информативности MCP-1 из базовой модели. На основании этого предложена замена MCP-1 на C3 в логистической регрессии при сохранении числа анализов равным четырём. Итоговая модель показала AUC-ROC = 1,000 и чувствительность = 1,000 на отложенной тестовой выборке, против AUC = 0,898 и чувствительности = 0,833 у базовой модели Изюровой Н. В. Для достижения данных результатов в работе использовались следующие информационные технологии и программное обеспечение: язык программирования Python 3.11, библиотеки scikit-learn, XGBoost, SHAP, pandas, NumPy, matplotlib, облачная среда Google Colab.","url":"https://doi.org/10.18720/spbpu/3/2026/vr/vr26-2666","authors":["Зиссерман, Елена Дмитриевна"],"tags":["внебольничная пневмония","машинное обучение","логистическая регрессия","случайный лес","градиентный бустинг","SHAP-анализ","компонент комплемента C3","прогнозирование тяжести"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.18720/spbpu/3/2026/vr/vr26-2666","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20267261","name":"FluSight: Two-Stage k-mer Random Forest Framework for Influenza A Zoonotic Risk Prediction","source":"datacite","abstract":"Description This release archives the exact version of the FluSight pipeline submitted to the Journal of Medical Virology. FluSight is a hierarchical machine learning framework designed for alignment-free prediction of Influenza A host adaptation and zoonotic spillover potential directly from nucleotide sequences (FASTA format). Key Features in this Release src/predict.py: Stable inference system accepting single/multiple FASTA files or stdin streams. models/: Saved Random Forest classifiers (stage1_rf.joblib, stage2_rf.joblib) and CountVectorizers trained on scikit-learn 1.6.1. pipeline/: Complete training, clustering, and threshold-tuning Jupyter Notebooks for total transparency. results/: Validation outputs including H5N1 specific datasets and amino acid population markers. Academic Citation If you use this pipeline or its models in your research, please cite the permanent archive: DOI: Environment & Reproducibility To ensure consistent results with the submission manuscript, install the locked dependencies via the included requirements.txt: Python 3.9+ scikit-learn 1.6.1 biopython joblib Intended Use & Limitations This tool is built strictly for epidemiological surveillance, genomic viral research, and zoonotic risk assessment. It is not designed or certified for clinical diagnostic use in direct human patient care.","url":"https://doi.org/10.5281/zenodo.20267261","authors":["Chauhan, Maitry","Gupta, Aryan","Karthik, Shivram","Sharma, Akshi","Krishna, Athul V","Karmakar, Bhuban","Mahadevan, Gurumurthy Dummi","Chaturvedi, Navaneet","Singh, Alok K"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20267261","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20267262","name":"FluSight: Two-Stage k-mer Random Forest Framework for Influenza A Zoonotic Risk Prediction","source":"datacite","abstract":"Description This release archives the exact version of the FluSight pipeline submitted to the Journal of Medical Virology. FluSight is a hierarchical machine learning framework designed for alignment-free prediction of Influenza A host adaptation and zoonotic spillover potential directly from nucleotide sequences (FASTA format). Key Features in this Release src/predict.py: Stable inference system accepting single/multiple FASTA files or stdin streams. models/: Saved Random Forest classifiers (stage1_rf.joblib, stage2_rf.joblib) and CountVectorizers trained on scikit-learn 1.6.1. pipeline/: Complete training, clustering, and threshold-tuning Jupyter Notebooks for total transparency. results/: Validation outputs including H5N1 specific datasets and amino acid population markers. Academic Citation If you use this pipeline or its models in your research, please cite the permanent archive: DOI: Environment & Reproducibility To ensure consistent results with the submission manuscript, install the locked dependencies via the included requirements.txt: Python 3.9+ scikit-learn 1.6.1 biopython joblib Intended Use & Limitations This tool is built strictly for epidemiological surveillance, genomic viral research, and zoonotic risk assessment. It is not designed or certified for clinical diagnostic use in direct human patient care.","url":"https://doi.org/10.5281/zenodo.20267262","authors":["Chauhan, Maitry","Gupta, Aryan","Karthik, Shivram","Sharma, Akshi","Krishna, Athul V","Karmakar, Bhuban","Mahadevan, Gurumurthy Dummi","Chaturvedi, Navaneet","Singh, Alok K"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20267262","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19690155","name":"AI-Based Osteoporosis Detection Using Clinical Bone Densitometry Data and Deep Learning Techniques","source":"datacite","abstract":"Osteoporosis is a progressive bone disease characterized by decreased bone mineral density (BMD) and an increased risk of fractures, especially among elderly individuals and postmenopausal women. Early detection is crucial to prevent severe complications and improve quality of life. In this project, we propose an AI-based Osteoporosis Detection System that analyzes both clinical patient data and Dual-Energy X-ray Absorptiometry (DXA) images to accurately classify bone health status into three categories: Normal, Osteopenia, and Osteoporosis. The methodology incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, LightGBM, and a Multilayer Perceptron (MLP), trained using clinical attributes such as BMD, T-score, age group, and height. Additionally, a Convolutional Neural Network (CNN) is used to analyze DXA scan images for supportive prediction. The system automatically provides personalized lifestyle recommendations, including diet, exercise, and safety precautions based on the predicted condition.","url":"https://doi.org/10.5281/zenodo.19690155","authors":["IJISEA"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19690155","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19690156","name":"AI-Based Osteoporosis Detection Using Clinical Bone Densitometry Data and Deep Learning Techniques","source":"datacite","abstract":"Osteoporosis is a progressive bone disease characterized by decreased bone mineral density (BMD) and an increased risk of fractures, especially among elderly individuals and postmenopausal women. Early detection is crucial to prevent severe complications and improve quality of life. In this project, we propose an AI-based Osteoporosis Detection System that analyzes both clinical patient data and Dual-Energy X-ray Absorptiometry (DXA) images to accurately classify bone health status into three categories: Normal, Osteopenia, and Osteoporosis. The methodology incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, LightGBM, and a Multilayer Perceptron (MLP), trained using clinical attributes such as BMD, T-score, age group, and height. Additionally, a Convolutional Neural Network (CNN) is used to analyze DXA scan images for supportive prediction. The system automatically provides personalized lifestyle recommendations, including diet, exercise, and safety precautions based on the predicted condition.","url":"https://doi.org/10.5281/zenodo.19690156","authors":["IJISEA"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19690156","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.48550/arxiv.2608.20315","name":"Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records","source":"datacite","abstract":"Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events. We present BERT-LER, a BERT-style model for coded EHR timelines pretrained and fine-tuned from a de-identified EHR dataset of 75 million patients, that encodes laboratory test results as discrete tokens while retaining graded information through percentile-based binning, paired with Integrated Gradients for token-level attributions grounded in the input EHR sequence. We benchmark our approach on the public EHRShot benchmark suite and on an asthma severity progression study based on real-world data. This addresses a methodological gap in EHR foundation-style modeling by unifying laboratory value representation and explainability in a single framework, while assessing whether both predictive performance and explanations generalize beyond standard clinical prediction tasks. Across EHRShot and asthma tasks, BERT-LER achieves predictive performance that is competitive with, and on laboratory-related tasks often exceeds, publicly available benchmark models, and provides attributions that align with clinically known risk factors. Our architecture and explainability approach can be applied to many therapeutic areas and prediction tasks using language models trained on structured EHRs.","url":"https://doi.org/10.48550/arxiv.2608.20315","authors":["Du, Jun Ni","Adamek, Lukas","Kryukov, Maxim","Dormont, Flavio","Bar-Joseph, Ziv","Jager, Sven","Rufino, Brandon"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.20315","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.48550/arxiv.2608.19965","name":"Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging","source":"datacite","abstract":"Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear segmentation have been proposed, they are often tailored to specific anatomies or modalities, limiting generalization across clinical settings and leaving room for improvement. Recent generative models have shown the benefits of iterative prediction for structured segmentation tasks, yet diffusion-based methods suffer from computationally expensive sampling, hindering their use on high-resolution 3D volumes. We present 3D-CurvSegFlow, a flow matching-based model for 3D curvilinear structure segmentation. The model learns a continuous transformation from a simple source distribution to the target vascular representation, enabling progressive refinement of complex curvilinear geometries with efficient inference. We evaluate our method on Three public challenging datasets covering distinct anatomies and modalities: portal vein, cerebral vessel, and coronary arteries. Using a common architecture and training strategy across all tasks, our method outperforms general-purpose and vessel-specific approaches, with strong preservation of thin branches and vascular continuity. This work not only advances the state-of-the-art in 3D curvilinear segmentation but also opens new avenues for efficient, generalizable, and clinically applicable methods in medical image analysis.","url":"https://doi.org/10.48550/arxiv.2608.19965","authors":["Moctar, Sidi Mohamed Sid'El","Vitry, Nicolas","Bouvrais, Hélène"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","Quantitative Methods (q-bio.QM)","FOS: Computer and information sciences","FOS: Biological sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19965","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.25560/130272","name":"In a heartbeat: discovering complex cardiac motion signatures with machine learning","source":"datacite","abstract":"Cardiac remodelling describes a range of adaptive responses to stress across physiological scales, governed by diverse molecular pathways. Traditional imaging-based assessments primarily rely on volumetric metrics, which serve as global indicators of function but may fail to capture early disease manifestations or complex regional characteristics. Computer vision approaches enable three-dimensional cardiac motion estimation from sparse medical imaging, offering a more detailed representation of motion dynamics. In my thesis, I explored statistical and machine learning methods to analyse cardiac motion data and extract clinically meaningful insights. I began by extending principal component analysis (PCA) to spatiotemporal representations of the heart, allowing me to decompose its movement into fundamental motion patterns. By visualising these principal movements and linking them to cardiovascular risk factors, I highlighted the clinical significance of cardiac motion patterns. Building on this, I applied Uniform Manifold Approximation and Projection (UMAP) to track the evolution of individuals in a shared latent space. This approach revealed differences be- tween subjects who appeared similar based on handcrafted figures, such as volumetric measures, but exhibited distinct motion dynamics. To further investigate latent space representations, I developed a convolutional variational autoencoder (CVAE) to encode high-dimensional spa- tiotemporal point cloud data into human-interpretable motion signatures. This enabled the identification of phenogroups enriched with varying risk factors, genetic predispositions, diag- noses and future outcomes, allowing for detailed analysis of local motion velocity patterns. Finally, I designed Cycle4DNet, a novel deep learning architecture specifically tailored for peri- odic spatiotemporal point cloud data, embedding the unique characteristics of cardiac motion directly into the core of the model. This thesis presents innovative methods for analysing cardiac motion, uncovering distinctive motion signatures and phenogroupings that offer new perspectives on cardiovascular health through both structure and function.","url":"https://doi.org/10.25560/130272","authors":["Schiratti, Pierre-Raphaël"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2025","doi":"10.25560/130272","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.25959/29388569.v1","name":"Clinical application of artificial intelligence to improve glaucoma care using imaging modalities","source":"datacite","abstract":"Glaucoma, a leading cause of irreversible blindness, presents significant challenges for early diagnosis and timely intervention due to its asymptomatic nature and subtle clinical morphology. Early detection and intervention are critical for preventing the disease's progression and vision loss. The increasing global prevalence of glaucoma highlights the need for improved diagnostic and predictive tools to ensure timely and accurate detection of the disease, especially in low-resource settings. Recent advances in artificial intelligence (AI), particularly deep learning (DL) algorithms, have shown promising results in ophthalmic disease diagnosis, including glaucoma. However, there is a paucity of studies developing predictive models based on longitudinal functional data using imaging modalities. This thesis primarily focuses on ophthalmic imaging modalities, such as fundus photographs and optical coherence tomography (OCT) images, to develop and validate diagnostic and predictive AI models for glaucoma care. The first aim was to critically evaluate the literature to ascertain the overall diagnostic accuracy of AI in detecting glaucoma and to identify the factors that currently limit the implementation of these algorithms in clinical practice. This systematic review and meta-analysis found that, of all the imaging options, fundus and OCT images were the most used modalities with potential for diagnosing glaucoma using DL algorithms. This study highlighted the factors that affected the diagnostic performance, including the reference standard, the instrument used for imaging, dataset selection, image dimensions, and the machine learning classifier. Finally, this study recommended implementing a standard diagnostic protocol for grading, implementing external data validation, and analysis across different ethnicity groups. Second, this thesis addressed the clinical challenge of predicting glaucoma progression from optic nerve head (ONH) images using AI. The participants were recruited as part of a longitudinal study, and classified as Healthy, Progressed, or Glaucoma based on baseline and follow-up Humphrey visual field (VF) tests. Four potential convolutional neural network (CNN)-based architectures were trained to classify the patients with manifest glaucoma from ONH images. The best-performing model achieved promising results on the testing and external datasets. However, due to a lack of publicly available datasets, the model could not be validated on external data for manifest glaucoma. This study demonstrated the potential of DL techniques in predicting the onset of glaucoma, reducing clinical and financial burdens. The Humphrey VF test is the gold standard for assessing glaucoma and identifying glaucomatous VF defects (GVFD) patterns, but it is a subjective measurement. Thus, the thesis's third aim was to develop and validate DL-based models to classify patients with and without GVFD and its progression from the OCT scan objectively, without the Humphrey VF test. This aim analysed 1,657 OCT scans from 1,157 patients with follow-up intervals of 4.5 years. Three Densenet201-based models were developed. The potential model exhibited the highest accuracy (80%) in differentiating between eyes with no GVFD and GVFD. This study showed the relationship between the structural and functional impact of glaucoma but had limited accuracy in predicting the severity of progression. However, the model can objectively classify patients with or without GVFD, supporting clinicians in making an accurate diagnosis from both structural and functional parameters without extended follow-up. Clinicians can construct a more personalised treatment plan and potentially delay or prevent glaucoma progression by predicting early visual function loss. To achieve this, the thesis sought to develop a DL?based regression model to forecast the global VF indices from the OCT scan. This study included a reliable baseline of 3,224 OCT scans and VF reports from 185","url":"https://doi.org/10.25959/29388569.v1","authors":["Abadh Chaurasia"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25959/29388569.v1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.25959/29388569","name":"Clinical application of artificial intelligence to improve glaucoma care using imaging modalities","source":"datacite","abstract":"Glaucoma, a leading cause of irreversible blindness, presents significant challenges for early diagnosis and timely intervention due to its asymptomatic nature and subtle clinical morphology. Early detection and intervention are critical for preventing the disease's progression and vision loss. The increasing global prevalence of glaucoma highlights the need for improved diagnostic and predictive tools to ensure timely and accurate detection of the disease, especially in low-resource settings. Recent advances in artificial intelligence (AI), particularly deep learning (DL) algorithms, have shown promising results in ophthalmic disease diagnosis, including glaucoma. However, there is a paucity of studies developing predictive models based on longitudinal functional data using imaging modalities. This thesis primarily focuses on ophthalmic imaging modalities, such as fundus photographs and optical coherence tomography (OCT) images, to develop and validate diagnostic and predictive AI models for glaucoma care. The first aim was to critically evaluate the literature to ascertain the overall diagnostic accuracy of AI in detecting glaucoma and to identify the factors that currently limit the implementation of these algorithms in clinical practice. This systematic review and meta-analysis found that, of all the imaging options, fundus and OCT images were the most used modalities with potential for diagnosing glaucoma using DL algorithms. This study highlighted the factors that affected the diagnostic performance, including the reference standard, the instrument used for imaging, dataset selection, image dimensions, and the machine learning classifier. Finally, this study recommended implementing a standard diagnostic protocol for grading, implementing external data validation, and analysis across different ethnicity groups. Second, this thesis addressed the clinical challenge of predicting glaucoma progression from optic nerve head (ONH) images using AI. The participants were recruited as part of a longitudinal study, and classified as Healthy, Progressed, or Glaucoma based on baseline and follow-up Humphrey visual field (VF) tests. Four potential convolutional neural network (CNN)-based architectures were trained to classify the patients with manifest glaucoma from ONH images. The best-performing model achieved promising results on the testing and external datasets. However, due to a lack of publicly available datasets, the model could not be validated on external data for manifest glaucoma. This study demonstrated the potential of DL techniques in predicting the onset of glaucoma, reducing clinical and financial burdens. The Humphrey VF test is the gold standard for assessing glaucoma and identifying glaucomatous VF defects (GVFD) patterns, but it is a subjective measurement. Thus, the thesis's third aim was to develop and validate DL-based models to classify patients with and without GVFD and its progression from the OCT scan objectively, without the Humphrey VF test. This aim analysed 1,657 OCT scans from 1,157 patients with follow-up intervals of 4.5 years. Three Densenet201-based models were developed. The potential model exhibited the highest accuracy (80%) in differentiating between eyes with no GVFD and GVFD. This study showed the relationship between the structural and functional impact of glaucoma but had limited accuracy in predicting the severity of progression. However, the model can objectively classify patients with or without GVFD, supporting clinicians in making an accurate diagnosis from both structural and functional parameters without extended follow-up. Clinicians can construct a more personalised treatment plan and potentially delay or prevent glaucoma progression by predicting early visual function loss. To achieve this, the thesis sought to develop a DL?based regression model to forecast the global VF indices from the OCT scan. This study included a reliable baseline of 3,224 OCT scans and VF reports from 185","url":"https://doi.org/10.25959/29388569","authors":["Abadh Chaurasia"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.25959/29388569","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.48550/arxiv.2608.19596","name":"Martingale R-learner: Estimating Time-varying Heterogeneous Treatment Effects for Time-to-event Outcomes","source":"datacite","abstract":"Biological research and clinical evidence suggest that treatment response may vary substantially along characteristics, such as comorbidities, genetic variants, environmental, or socio-economic factors. Future precision medicine requires accurate assessment of heterogeneous treatment effects (HTE) to guide optimal clinical decisions at the individual level. We introduce a functional score framework that extends the traditional estimating equations for survival data to nonparametric HTE and generalize the Neyman orthogonality accordingly, thus filling a methodological as well as theoretical gap. Under the Neyman orthogonal functional score framework, we developed the martingale R-learner based on a decomposition of the conditional martingale residuals into residuals of the risk-set propensity score and the marginal martingale, thereby reducing the impact of estimation bias in HTE from nuisance models including (1) marginal survival, and (2) risk-set propensity scores. This enables leveraging advances in machine learning and incorporates flexible estimators for the nuisance functions and attaining the standard optimal nonparametric estimation rate with the oracle property. Numerical experiments demonstrated empirical performance consistent with the theory. We applied the martingale R-learner to estimate the effect of alcohol on dementia using the Honolulu-Asia Aging Study data.","url":"https://doi.org/10.48550/arxiv.2608.19596","authors":["Hou, Jue","Qi, Yuchen","Xu, Ronghui"],"tags":["Methodology (stat.ME)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19596","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.48550/arxiv.2608.19578","name":"A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction","source":"datacite","abstract":"Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) can worsen rapidly, making timely prediction a clinical priority. Most existing machine learning approaches rely on episodically collected clinical variables, introducing delays that limit their practical utility in home monitoring settings. Home ventilators offer a lower-latency alternative, producing a near-continuous record of respiratory status during daily use. However existing ventilator-based approaches either compress the waveform into handcrafted features or focus primarily on binary risk classification, leaving the timing of an impending event unresolved. In this paper, we present a two-stage framework that operates directly on raw pressure and flow waveforms from the most recent seven days of home ventilator use. The first-stage classification model identifies patients at high risk of a severe exacerbation. The second-stage regression model then estimates how many days remain before the event occurs. Our experimental results demonstrate that the two-stage model outperforms traditional baseline models on both risk classification and time-to-event estimation, with our selected Stage 1 classifier achieving F1 = 0.91 and our Stage 2 regression model achieving RMSE = 1.00 days and R^2 = 0.76, giving clinicians both an early warning and actionable lead time before a severe exacerbation occurs.","url":"https://doi.org/10.48550/arxiv.2608.19578","authors":["Wang, Dongyang","Qu, Weihao","Zheng, Ling","Pan, Haowen"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19578","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/d9ncs","name":"Operational Outcomes of Artificial Intelligence–Enabled Digital Interventions for Patient Flow and Capacity Management in Hospital Emergency Departments: A Scoping Review","source":"datacite","abstract":"This scoping review aims to map the operational outcomes and organizational and implementation determinants reported following the prospective or routine implementation of artificial intelligence (AI)–enabled digital interventions in hospital emergency departments. Emergency departments are complex, high-demand environments in which patient flow and operational performance depend on the interaction between demand, clinical processes, staffing, diagnostic resources, information systems, bed availability, and coordination across hospital services. AI-enabled interventions—including machine learning, deep learning, natural language processing, computer vision, expert systems, and generative AI—may support operational decision-making through applications such as demand forecasting, triage and prioritization, prediction of hospital admission or disposition, resource allocation, bed management, and workflow optimization. However, algorithmic performance alone does not demonstrate operational benefit. The impact of these interventions depends on whether AI-generated information is integrated into real-world workflows and leads to actionable decisions or changes in processes and resource allocation. The review will include primary empirical quantitative, qualitative, and mixed-methods studies evaluating AI-enabled digital interventions that have been prospectively implemented or incorporated into routine practice in hospital emergency departments. Eligible studies must report at least one operational outcome related to care times, patient flow, crowding, capacity, workload, productivity, or resource utilization. Retrospective model-development or validation studies, simulations without real-world implementation, and silent prospective evaluations in which outputs do not influence care processes will be excluded. The review will characterize the technologies implemented, their purposes, data sources, users, degree of workflow integration, and location within the emergency department patient-flow pathway. Operational outcomes will be organized into five main domains: time-related outcomes, patient flow, capacity, workload, and resource utilization. Balancing measures and unintended effects will also be identified when reported. In addition, the review will examine barriers, facilitators, implementation strategies, and implementation outcomes associated with these interventions. Implementation determinants will be organized, where appropriate, using the updated Consolidated Framework for Implementation Research (CFIR), while implementation outcomes will be categorized according to the framework proposed by Proctor and colleagues. Emerging themes not adequately represented by these frameworks will be retained. Searches will be conducted in MEDLINE via PubMed, Scopus, and Web of Science Core Collection, supplemented by backward and forward citation searching. Study selection and data extraction will follow a structured process, and findings will be synthesized using descriptive tables, frequencies, and narrative synthesis. No meta-analysis is planned. The expected outcome is a comprehensive map of the current real-world evidence on how AI-enabled digital interventions affect emergency department operations, where and how these technologies are being integrated into patient-flow processes, and which technical, human, and organizational conditions facilitate or hinder their adoption, sustainability, and potential scale-up. The review is also expected to identify important evidence gaps and priorities for future research and implementation.","url":"https://doi.org/10.17605/osf.io/d9ncs","authors":["Gonzalo Alberto Peralta-Jiménez"],"tags":["Health Information Technology","Other Medicine and Health Sciences","Medicine and Health Sciences","Health and Medical Administration","artificial intelligence","capacity management","emergency department","implementation science"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/d9ncs","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19866512","name":"Synthetic Turkish Clinical Event Dataset for Temporal Risk Prediction, Sequence Modeling, and NLP","source":"datacite","abstract":"This dataset contains a synthetic Turkish clinical event dataset designed for temporal risk prediction, sequence modeling, and healthcare-related machine learning research. The dataset simulates longitudinal patient records, including visits, diagnoses, lab tests, reports, and prescriptions within a structured temporal framework. Key characteristics:- 5,000 synthetic patients- 108,000+ clinical events- Temporal event structure with irregular visit intervals- Category-based clinical signals (labs, diagnoses, reports, prescriptions)- Latent risk modeling using a causal generative process The data generation process incorporates:- Beta-distributed baseline risk- Ornstein-Uhlenbeck temporal dynamics- Poisson-based event intensity- Category-specific signal amplification Potential applications:- Temporal risk prediction- Sequence modeling (RNN, Transformer)- Anomaly detection in healthcare data- Clinical trajectory analysis For valid modeling:- Temporal splits must be used- Latent variables (e.g., base_risk) must not be used as features License: Open for research and educational use.DATA DISCLAIMER:This dataset is fully synthetic and does not contain any real patient data. It is generated using a probabilistic simulation framework and is intended strictly for research, experimentation, and educational use.","url":"https://doi.org/10.5281/zenodo.19866512","authors":["Bayankulu, Senih"],"tags":["synthetic-data","healthcare","clinical-data","temporal-data","sequence-modeling","machine-learning","nlp","time-series"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19866512","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19866513","name":"Synthetic Turkish Clinical Event Dataset for Temporal Risk Prediction, Sequence Modeling, and NLP","source":"datacite","abstract":"This dataset contains a synthetic Turkish clinical event dataset designed for temporal risk prediction, sequence modeling, and healthcare-related machine learning research. The dataset simulates longitudinal patient records, including visits, diagnoses, lab tests, reports, and prescriptions within a structured temporal framework. Key characteristics:- 5,000 synthetic patients- 108,000+ clinical events- Temporal event structure with irregular visit intervals- Category-based clinical signals (labs, diagnoses, reports, prescriptions)- Latent risk modeling using a causal generative process The data generation process incorporates:- Beta-distributed baseline risk- Ornstein-Uhlenbeck temporal dynamics- Poisson-based event intensity- Category-specific signal amplification Potential applications:- Temporal risk prediction- Sequence modeling (RNN, Transformer)- Anomaly detection in healthcare data- Clinical trajectory analysis For valid modeling:- Temporal splits must be used- Latent variables (e.g., base_risk) must not be used as features License: Open for research and educational use.DATA DISCLAIMER:This dataset is fully synthetic and does not contain any real patient data. It is generated using a probabilistic simulation framework and is intended strictly for research, experimentation, and educational use.","url":"https://doi.org/10.5281/zenodo.19866513","authors":["Bayankulu, Senih"],"tags":["synthetic-data","healthcare","clinical-data","temporal-data","sequence-modeling","machine-learning","nlp","time-series"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19866513","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22036479","name":"Arabic Parkinson's Disease Speech Dataset","source":"datacite","abstract":"This dataset contains audio recordings collected to investigate acoustic biomarkers and machine learning models for detecting Parkinson’s Disease (PD) in native Arabic speakers. Designed to support voice pathology detection, acoustic feature extraction, and cross-linguistic validation studies, it serves as a benchmark resource for speech processing and clinical AI applications. Participant Breakdown: 40 native Arabic-speaking subjects, comprising 17 diagnosed Parkinson’s Disease patients and 23 age-matched Healthy Controls (HC). Speech Tasks: Includes sustained vowel phonations (/a/, /i/, /u/), reading of a standardized phonetically balanced Arabic text passage, and spontaneous speech recordings. Audio Format: MP3 format (.mp3, 44.1 kHz) to reduce file size and optimize download efficiency. Detailed Metadata & Methodology: Full clinical demographics, participant selection criteria, experimental setup, and acoustic feature extraction methodologies are detailed in the accompanying published paper. All recordings are fully anonymized to remove personal identifiable information (PII) and are distributed for open research under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Associated Journal Publication: Hassanat, A. B. A., et al. \"Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.\" Journal of Central Nervous System Disease, vol. 18, 2026, pp. 1–12. DOI: 10.1177/11795735261448278","url":"https://doi.org/10.5281/zenodo.22036479","authors":["Hassanat, Ahmad","Almahadin, Alaa O."],"tags":["Parkinson's Disease, Arabic Speech Dataset, Audio Processing, Acoustic Biomarkers, Machine Learning, Voice Analysis, Cross-Linguistic Validation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22036479","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22036480","name":"Arabic Parkinson's Disease Speech Dataset","source":"datacite","abstract":"This dataset contains audio recordings collected to investigate acoustic biomarkers and machine learning models for detecting Parkinson’s Disease (PD) in native Arabic speakers. Designed to support voice pathology detection, acoustic feature extraction, and cross-linguistic validation studies, it serves as a benchmark resource for speech processing and clinical AI applications. Participant Breakdown: 40 native Arabic-speaking subjects, comprising 17 diagnosed Parkinson’s Disease patients and 23 age-matched Healthy Controls (HC). Speech Tasks: Includes sustained vowel phonations (/a/, /i/, /u/), reading of a standardized phonetically balanced Arabic text passage, and spontaneous speech recordings. Audio Format: MP3 format (.mp3, 44.1 kHz) to reduce file size and optimize download efficiency. Detailed Metadata & Methodology: Full clinical demographics, participant selection criteria, experimental setup, and acoustic feature extraction methodologies are detailed in the accompanying published paper. All recordings are fully anonymized to remove personal identifiable information (PII) and are distributed for open research under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Associated Journal Publication: Hassanat, A. B. A., et al. \"Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.\" Journal of Central Nervous System Disease, vol. 18, 2026, pp. 1–12. DOI: 10.1177/11795735261448278","url":"https://doi.org/10.5281/zenodo.22036480","authors":["Hassanat, Ahmad","Almahadin, Alaa O."],"tags":["Parkinson's Disease, Arabic Speech Dataset, Audio Processing, Acoustic Biomarkers, Machine Learning, Voice Analysis, Cross-Linguistic Validation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22036480","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20747008","name":"Synthetic Dataset for Detecting the Relationship Between Psychological Illnesses and Criminal Tendency Patterns Using Machine Learning","source":"datacite","abstract":"This record contains synthetic datasets generated for the study titled “Detecting the Relationship Between Psychological Illnesses and Criminal Tendencies Using Machine Learning.” The datasets were created for research and machine learning modeling purposes and do not contain any real personal, clinical, forensic, or identifiable individual data. The first dataset contains approximately 1,000 records, while the second dataset contains 10,000 records generated through a Gaussian Mixture Model-based data augmentation process. The datasets include variables such as psychological disorder, crime type, offender status, age, gender, crime location, and time. These variables were structured to support the analysis of psychological illness and criminal tendency patterns using machine learning methods. The datasets were used to compare Naive Bayes, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Logistic Regression algorithms, and to support explainability analyses such as SHAP, LIME, feature importance, ablation study, sensitivity analysis, and model generalization evaluation. These datasets are intended only for academic research, experimental modeling, and decision-support framework development. They should not be used for clinical diagnosis, legal judgment, automated decision-making, or real-world criminal profiling.","url":"https://doi.org/10.5281/zenodo.20747008","authors":["Bilgin, Sümeyra","Vural, Mehmet Sait"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20747008","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20747009","name":"Synthetic Dataset for Detecting the Relationship Between Psychological Illnesses and Criminal Tendency Patterns Using Machine Learning","source":"datacite","abstract":"This record contains synthetic datasets generated for the study titled “Detecting the Relationship Between Psychological Illnesses and Criminal Tendencies Using Machine Learning.” The datasets were created for research and machine learning modeling purposes and do not contain any real personal, clinical, forensic, or identifiable individual data. The first dataset contains approximately 1,000 records, while the second dataset contains 10,000 records generated through a Gaussian Mixture Model-based data augmentation process. The datasets include variables such as psychological disorder, crime type, offender status, age, gender, crime location, and time. These variables were structured to support the analysis of psychological illness and criminal tendency patterns using machine learning methods. The datasets were used to compare Naive Bayes, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Logistic Regression algorithms, and to support explainability analyses such as SHAP, LIME, feature importance, ablation study, sensitivity analysis, and model generalization evaluation. These datasets are intended only for academic research, experimental modeling, and decision-support framework development. They should not be used for clinical diagnosis, legal judgment, automated decision-making, or real-world criminal profiling.","url":"https://doi.org/10.5281/zenodo.20747009","authors":["Bilgin, Sümeyra","Vural, Mehmet Sait"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20747009","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22034226","name":"Explainable Machine Learning and Deep Learning for Alzheimer's Disease Detection from Structural MRI and Clinical Data","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22034226","authors":["Chennoufi, Ines","Chih, Chaima Malak"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22034226","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22034225","name":"Explainable Machine Learning and Deep Learning for Alzheimer's Disease Detection from Structural MRI and Clinical Data","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22034225","authors":["Chennoufi, Ines","Chih, Chaima Malak"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22034225","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22033893","name":"HF-ETIOLOGY: Program Team Update Meeting (August 2026)","source":"datacite","abstract":"This is a presentation from the August 2026 update to the NIH Program Team for the HF-ETIOLOGY Project.","url":"https://doi.org/10.5281/zenodo.22033893","authors":["Luo, Yuan","Cheng, Feixiong","Holmes, Kristi","Wang, Fei"],"tags":["Behavioral Sciences/methods","Biological Factors","Clinical Medicine","Data Collection/statistics &amp; numerical data","Data Curation/statistics &amp; numerical data","Disease/genetics","Genes/genetics","Genomics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22033893","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22033894","name":"HF-ETIOLOGY: Program Team Update Meeting (August 2026)","source":"datacite","abstract":"This is a presentation from the August 2026 update to the NIH Program Team for the HF-ETIOLOGY Project.","url":"https://doi.org/10.5281/zenodo.22033894","authors":["Luo, Yuan","Cheng, Feixiong","Holmes, Kristi","Wang, Fei"],"tags":["Behavioral Sciences/methods","Biological Factors","Clinical Medicine","Data Collection/statistics &amp; numerical data","Data Curation/statistics &amp; numerical data","Disease/genetics","Genes/genetics","Genomics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22033894","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19422331","name":"Comprehensive Evaluation of Support Vector Machine Models Based on Kernels for Predicting Multiple Diseases","source":"datacite","abstract":"In today's digital age, data is invaluable, and massive amounts of data have been produced in every field imaginable. Reports from the healthcare sector often contain details about patients' health. By having this clinical expertise, we are better able to detect undetectable health problems and provide individualised therapy to each patient. The purpose of this study was to evaluate and contrast several kernel-based Support Vector Machine (SVM) models for use in healthcare prognostication. With the SVM-LRBF technique, we examined the models with the feature reduction set of the Renal Disorders Disease, Diabetes Mellitus, and Cardiovascular Disease datasets. Similarities and differences between the models and other machine learning systems such as Random Forest, SVM-Linear, Decision Tree, SVM-Gaussian Radial Bias Kernel, and SVM-Polynomial were also analysed. Performance of machine learning approaches was measured using a number of different metrics, including specificity, sensitivity, precision, misclassification rate, and accuracy. The experimental findings showed 98.1 percent accuracy for the Renal Disorders Disease dataset, 90.9 percent accuracy for the Diabetes mellitus dataset, and 98.1 percent accuracy for the Cardiovascular Disease dataset.","url":"https://doi.org/10.5281/zenodo.19422331","authors":["G Swathika","K Venkata Ramaiah"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19422331","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19422332","name":"Comprehensive Evaluation of Support Vector Machine Models Based on Kernels for Predicting Multiple Diseases","source":"datacite","abstract":"In today's digital age, data is invaluable, and massive amounts of data have been produced in every field imaginable. Reports from the healthcare sector often contain details about patients' health. By having this clinical expertise, we are better able to detect undetectable health problems and provide individualised therapy to each patient. The purpose of this study was to evaluate and contrast several kernel-based Support Vector Machine (SVM) models for use in healthcare prognostication. With the SVM-LRBF technique, we examined the models with the feature reduction set of the Renal Disorders Disease, Diabetes Mellitus, and Cardiovascular Disease datasets. Similarities and differences between the models and other machine learning systems such as Random Forest, SVM-Linear, Decision Tree, SVM-Gaussian Radial Bias Kernel, and SVM-Polynomial were also analysed. Performance of machine learning approaches was measured using a number of different metrics, including specificity, sensitivity, precision, misclassification rate, and accuracy. The experimental findings showed 98.1 percent accuracy for the Renal Disorders Disease dataset, 90.9 percent accuracy for the Diabetes mellitus dataset, and 98.1 percent accuracy for the Cardiovascular Disease dataset.","url":"https://doi.org/10.5281/zenodo.19422332","authors":["G Swathika","K Venkata Ramaiah"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19422332","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19683245","name":"What are the potential benefits and limitations of using machine learning algorithms to predict arrhythmogenic events in patients with long QT syndrome?","source":"datacite","abstract":"Machine learning algorithms offer potential benefits in predicting arrhythmogenic events in long QT syndrome, though challenges around data quality, validation, and ethical issues present significant hurdles for clinical implementation.","url":"https://doi.org/10.5281/zenodo.19683245","authors":["Tripdatabase"],"tags":["machine learning","arrhythmogenic events","long QT syndrome","predictive algorithms","cardiology","genetic disorders","benefits","limitations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19683245","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19683246","name":"What are the potential benefits and limitations of using machine learning algorithms to predict arrhythmogenic events in patients with long QT syndrome?","source":"datacite","abstract":"Machine learning algorithms offer potential benefits in predicting arrhythmogenic events in long QT syndrome, though challenges around data quality, validation, and ethical issues present significant hurdles for clinical implementation.","url":"https://doi.org/10.5281/zenodo.19683246","authors":["Tripdatabase"],"tags":["machine learning","arrhythmogenic events","long QT syndrome","predictive algorithms","cardiology","genetic disorders","benefits","limitations"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19683246","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19639295","name":"Code for: GRAD: A Two-Stage Machine Learning Algorithm Resolves the Plasma p-Tau217 Diagnostic Gray Zone for Amyloid PET Prediction","source":"datacite","abstract":"Two-stage diagnostic algorithm (Gatekeeper-Reflex for Alzheimer's Diagnostics) that resolves diagnostic uncertainty in plasma p-tau217 testing by combining a univariate logistic regression screen with a multi-marker random forest classifier for gray zone cases.","url":"https://doi.org/10.5281/zenodo.19639295","authors":["Parankusham, Harthik","Vanderlip, Casey","Birkenbihl, Colin Jan","Krishna, Eashwar","Ugboaja, Chizobam","Budson, Andrew","Frank, Brandon"],"tags":["Alzheimer's disease","plasma biomarkers","p-tau217","amyloid PET","machine learning","clinical decision support","diagnostic gray zone"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19639295","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22002707","name":"Data and Code for Machine Learning Based Reference Assisted Signal Recovery Under Anatomical Domain Shift in Simulated Radiofrequency Fracture Sensing","source":"datacite","abstract":"Version 1.1.0 of the public data-and-code release supporting the article “Machine Learning Based Reference Assisted Signal Recovery Under Anatomical Domain Shift in Simulated Radiofrequency Fracture Sensing”. The release contains the canonical processed feature matrix comprising 16,740 records and 1,297 candidate descriptors, H2–H5 grouping manifests, the 1,235 modelling-descriptor definitions, held-out machine-learning predictions, calibration and operating-point outputs, hierarchical confidence intervals, false-negative burden analyses, five-seed LightGBM stability results, descriptor-stratified effects, MATLAB simulation and feature-extraction source code, Python analysis and reproduction scripts,software-environment specifications, and integrity checks. The release contains simulated data only. No human-participant, animal, personal, confidential, or patient-identifiable data are included. The exact matched-reference pathway is an engineering upper bound and should not be interpreted as ordinary screening or clinical-performance evidence. Version 1.1.0 supersedes Version 1.0.2. The canonical matrix, labels, primary held-out predictions, and reported numerical findings are unchanged. The public documentation, organisation, licensing, portability, and machine-readable metadata have been improved.","url":"https://doi.org/10.5281/zenodo.22002707","authors":["AlDelemy, Ahmad","Al Dulaimi, Ali","Abd-Alhameed, Raed"],"tags":["radiofrequency fracture sensing","deep learning","machine learning","anatomical domain shift","domain generalisation","matched-reference classification","finite-difference time-domain","grouped leave-one-location-out validation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22002707","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22030635","name":"Data and Code for Machine Learning Based Reference Assisted Signal Recovery Under Anatomical Domain Shift in Simulated Radiofrequency Fracture Sensing","source":"datacite","abstract":"Version 1.1.0 of the public data-and-code release supporting the article “Machine Learning Based Reference Assisted Signal Recovery Under Anatomical Domain Shift in Simulated Radiofrequency Fracture Sensing”. The release contains the canonical processed feature matrix comprising 16,740 records and 1,297 candidate descriptors, H2–H5 grouping manifests, the 1,235 modelling-descriptor definitions, held-out machine-learning predictions, calibration and operating-point outputs, hierarchical confidence intervals, false-negative burden analyses, five-seed LightGBM stability results, descriptor-stratified effects, MATLAB simulation and feature-extraction source code, Python analysis and reproduction scripts,software-environment specifications, and integrity checks. The release contains simulated data only. No human-participant, animal, personal, confidential, or patient-identifiable data are included. The exact matched-reference pathway is an engineering upper bound and should not be interpreted as ordinary screening or clinical-performance evidence. Version 1.1.0 supersedes Version 1.0.2. The canonical matrix, labels, primary held-out predictions, and reported numerical findings are unchanged. The public documentation, organisation, licensing, portability, and machine-readable metadata have been improved.","url":"https://doi.org/10.5281/zenodo.22030635","authors":["AlDelemy, Ahmad","Al Dulaimi, Ali","Abd-Alhameed, Raed"],"tags":["radiofrequency fracture sensing","deep learning","machine learning","anatomical domain shift","domain generalisation","matched-reference classification","finite-difference time-domain","grouped leave-one-location-out validation"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22030635","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5683/sp4/bl2yta","name":"A Labeled Accelerometry Dataset of Steps and Postures Across Diverse Mobility and Frailty Levels in Older Adults in a Hospital Setting","source":"datacite","abstract":"The Mobility in Geriatrics (Mobi-G) dataset is an open-access dataset designed to support the development, validation, and benchmarking of machine learning algorithms for posture classification and step counting in older adults. The dataset includes ankle-worn Movesense accelerometry recordings (52 Hz), manual steps and postures annotations, and de-identified sociodemographic and clinical characteristics from 70 adults aged 65 years and older recruited at the Centre hospitalier de l'Université de Montréal (CHUM), Canada. Accelerometry data were collected during both standardized mobility assessments (lying, sitting, standing, transfers, regular walking, irregular walking and physiotherapy sessions) and free-living activities. Steps during walking activities were manually annotated using synchronized video recordings, while postures and transfers were annotated directly from the accelerometry signals. The repository includes raw and annotated accelerometry files and participant-level demographic and clinical variables. These data are intended to facilitate research in clinical mobility monitoring, digital biomarkers, wearable sensing, and artificial intelligence for geriatric care.","url":"https://doi.org/10.5683/sp4/bl2yta","authors":["Lanoie, Annik","Cheung, Weng-Jy Cheung","Nguyen, Quoc Dinh"],"tags":["Medicine, Health and Life Sciences","Sensors","Accelerometry","Geriatrics","Mobility","Gait analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5683/sp4/bl2yta","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22031736","name":"ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN PARKINSON'S DISEASE: EMERGING DIAGNOSTIC AND THERAPEUTIC APPROACHES","source":"datacite","abstract":"Background: Parkinson's disease (PD) is heterogeneous, both clinically, genetically and neuropathologically, which presents ongoing challenges for conventional syndromic clinical diagnostic criteria and uniform treatments. Recent advances in biofluid biomarkers, high-resolution neuroimaging and artificial intelligence (AI) have provided frameworks to reclassify PD on an objective biological basis and personalize therapeutic interventions 4 . Objective: To critically synthesize recent advances in AI-driven diagnostic modalities, biological disease staging frameworks, biofluid biomarkers, genotype-targeted therapeutics and adaptive clinical trial designs in PD7. Data Sources: A search was conducted in PubMed, MEDLINE, Scopus, Web of Science and other major biomedical literature databases for studies published mostly between 2018 and 20244. Review Methods: Evidence from clinical trials, neuroimaging studies, digital sensor evaluations, biofluid biomarker investigations, and regulatory science guidelines was reviewed using a comparative synthesis approach2 . Key Findings: The biological definition of PD, formalized by the Neuronal Alpha-Synuclein Disease Integrated Staging System (NSD-ISS), bases disease classification on in vivo seeded aggregation of alpha-synuclein () and dopaminergic neurodegeneration ()4. Deep learning models applied to single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) yield diagnostic classification accuracies in the range of 88%–99%5. Non-invasive digital devices such as radio-wave nocturnal breathing sensors and video-based kinetic software allow continuous surveillance at home6. Biomarker-enriched clinical trials8 are being driven by targeted therapeutics, including small-molecule and antisense oligonucleotide agents targeting LRRK2 and GBA1 mutations. But the implementation of these algorithms in practice is still held back by algorithmic overfitting, a lack of validation in diverse non-European cohorts and regulatory concerns around continuous machine learning updates13. Conclusion: The combination of biological staging and AI-enabled digital phenotyping provides a scalable paradigm for early detection and targeted therapeutic selection in PD4. Standardized analytical pipelines, harmonized regulatory oversight, and adaptive clinical trial designs10 are needed for clinical translation. Keywords: Parkinson Disease, Artificial Intelligence, Precision Medicine, Biomarkers, Machine Learning, Neurodegenerative Diseases","url":"https://doi.org/10.5281/zenodo.22031736","authors":["Payal Patidar, Soma Sekhar Pulamarasetti*, Omkar Rai, Karan Gupta¹, Shivlal Yadav"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22031736","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22031737","name":"ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN PARKINSON'S DISEASE: EMERGING DIAGNOSTIC AND THERAPEUTIC APPROACHES","source":"datacite","abstract":"Background: Parkinson's disease (PD) is heterogeneous, both clinically, genetically and neuropathologically, which presents ongoing challenges for conventional syndromic clinical diagnostic criteria and uniform treatments. Recent advances in biofluid biomarkers, high-resolution neuroimaging and artificial intelligence (AI) have provided frameworks to reclassify PD on an objective biological basis and personalize therapeutic interventions 4 . Objective: To critically synthesize recent advances in AI-driven diagnostic modalities, biological disease staging frameworks, biofluid biomarkers, genotype-targeted therapeutics and adaptive clinical trial designs in PD7. Data Sources: A search was conducted in PubMed, MEDLINE, Scopus, Web of Science and other major biomedical literature databases for studies published mostly between 2018 and 20244. Review Methods: Evidence from clinical trials, neuroimaging studies, digital sensor evaluations, biofluid biomarker investigations, and regulatory science guidelines was reviewed using a comparative synthesis approach2 . Key Findings: The biological definition of PD, formalized by the Neuronal Alpha-Synuclein Disease Integrated Staging System (NSD-ISS), bases disease classification on in vivo seeded aggregation of alpha-synuclein () and dopaminergic neurodegeneration ()4. Deep learning models applied to single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) yield diagnostic classification accuracies in the range of 88%–99%5. Non-invasive digital devices such as radio-wave nocturnal breathing sensors and video-based kinetic software allow continuous surveillance at home6. Biomarker-enriched clinical trials8 are being driven by targeted therapeutics, including small-molecule and antisense oligonucleotide agents targeting LRRK2 and GBA1 mutations. But the implementation of these algorithms in practice is still held back by algorithmic overfitting, a lack of validation in diverse non-European cohorts and regulatory concerns around continuous machine learning updates13. Conclusion: The combination of biological staging and AI-enabled digital phenotyping provides a scalable paradigm for early detection and targeted therapeutic selection in PD4. Standardized analytical pipelines, harmonized regulatory oversight, and adaptive clinical trial designs10 are needed for clinical translation. Keywords: Parkinson Disease, Artificial Intelligence, Precision Medicine, Biomarkers, Machine Learning, Neurodegenerative Diseases","url":"https://doi.org/10.5281/zenodo.22031737","authors":["Payal Patidar, Soma Sekhar Pulamarasetti*, Omkar Rai, Karan Gupta¹, Shivlal Yadav"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22031737","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22031669","name":"BIOMARKERS DISCOVERY IN TYPE 2 DIABETES MELLITUS: FROM OMICS TECHNOLOGIES TO CLINICAL PRACTICE","source":"datacite","abstract":"Background: Type 2 diabetes mellitus (T2DM) is caused by complex pathophysiological processes including progressive insulin resistance, pancreatic β-cell dysfunction, chronic systemic inflammation and altered interorgan metabolic communication1. The ability of common clinical markers such as fasting plasma glucose and glycated hemoglobin (HbA1c) to identify early subclinical alterations or discriminate between disease subphenotypes1 is limited. Objective: To review current literature of multi-omic biomarker discovery in T2DM with focus on predictive discrimination, mechanistic contribution across omic layers, and translational, regulatory and clinical implementation barriers2. Data Sources: Comprehensive synthesis of studies indexed in PubMed, MEDLINE, Scopus and Web of Science evaluating genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics and machine-learning-driven multi-omics integration2. Review Methods: A state-of-the-art structured review of human prospective cohorts, case-cohort designs, clinical trial evaluations, assay standardization and regulatory qualification pathways2. Key Findings: Among individual omic layers, plasma proteomics showed the highest discriminative performance for prediction of incident T2DM5. Targeted proteomic panels (e.g., 15 proteins) improve the C-index of established clinical models by 0.022–0.0235. Metabolomic signatures, including branched-chain amino acids (isoleucine, leucine, valine) and aromatic amino acids (phenylalanine, tyrosine), may reflect metabolic dysregulation >10 years prior to clinical onset4. Epigenetic methylation probes at TXNIP, ABCG1, and PDK4 capture cumulative metabolic memory 2 Integrating full multi-omic profiles yields modest predictive improvements (+0.006 to +0.05 C-index increment) over targeted proteomic-clinical models due to biological signal redundancy 5 Conclusion: Multi-omic biomarker profiling improves T2DM risk prediction and subphenotyping. For clinical translation, standardized analytical workflows, cross-population validation in diverse cohorts, cost-utility optimization, and formal regulatory qualification2 are required. Keywords: Diabetes Mellitus, Type 2 Biomarkers Proteomics Metabolomics Precision Medicine Multi-omics","url":"https://doi.org/10.5281/zenodo.22031669","authors":["Manisha, Soma Sekhar Pulamarasetti*, Omkar Rai, Karan Gupta, Shivlal Yadav"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22031669","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22031670","name":"BIOMARKERS DISCOVERY IN TYPE 2 DIABETES MELLITUS: FROM OMICS TECHNOLOGIES TO CLINICAL PRACTICE","source":"datacite","abstract":"Background: Type 2 diabetes mellitus (T2DM) is caused by complex pathophysiological processes including progressive insulin resistance, pancreatic β-cell dysfunction, chronic systemic inflammation and altered interorgan metabolic communication1. The ability of common clinical markers such as fasting plasma glucose and glycated hemoglobin (HbA1c) to identify early subclinical alterations or discriminate between disease subphenotypes1 is limited. Objective: To review current literature of multi-omic biomarker discovery in T2DM with focus on predictive discrimination, mechanistic contribution across omic layers, and translational, regulatory and clinical implementation barriers2. Data Sources: Comprehensive synthesis of studies indexed in PubMed, MEDLINE, Scopus and Web of Science evaluating genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics and machine-learning-driven multi-omics integration2. Review Methods: A state-of-the-art structured review of human prospective cohorts, case-cohort designs, clinical trial evaluations, assay standardization and regulatory qualification pathways2. Key Findings: Among individual omic layers, plasma proteomics showed the highest discriminative performance for prediction of incident T2DM5. Targeted proteomic panels (e.g., 15 proteins) improve the C-index of established clinical models by 0.022–0.0235. Metabolomic signatures, including branched-chain amino acids (isoleucine, leucine, valine) and aromatic amino acids (phenylalanine, tyrosine), may reflect metabolic dysregulation >10 years prior to clinical onset4. Epigenetic methylation probes at TXNIP, ABCG1, and PDK4 capture cumulative metabolic memory 2 Integrating full multi-omic profiles yields modest predictive improvements (+0.006 to +0.05 C-index increment) over targeted proteomic-clinical models due to biological signal redundancy 5 Conclusion: Multi-omic biomarker profiling improves T2DM risk prediction and subphenotyping. For clinical translation, standardized analytical workflows, cross-population validation in diverse cohorts, cost-utility optimization, and formal regulatory qualification2 are required. Keywords: Diabetes Mellitus, Type 2 Biomarkers Proteomics Metabolomics Precision Medicine Multi-omics","url":"https://doi.org/10.5281/zenodo.22031670","authors":["Manisha, Soma Sekhar Pulamarasetti*, Omkar Rai, Karan Gupta, Shivlal Yadav"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22031670","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22030982","name":"Artificial Intelligence in Clinical Pharmacy: Current Applications, Challenges, and Future Perspectives – A Narrative Literature Review","source":"datacite","abstract":"Background Artificial intelligence (AI) is rapidly transforming healthcare by enabling the analysis of large and complex datasets and supporting clinical decision-making. Clinical pharmacy is an emerging area in which AI can assist with medication management, medication safety, pharmacovigilance, clinical decision support, personalized pharmacotherapy, and patient care. Objective This narrative literature review aims to describe the current applications of AI in clinical pharmacy, evaluate its potential benefits and limitations, and discuss future perspectives and research priorities for its responsible integration into clinical pharmacy practice. Literature Search A literature search was conducted using PubMed/MEDLINE, Scopus, and Embase to identify relevant literature on AI and clinical pharmacy. The search included publications primarily from January 2020 to August 2026, with additional earlier seminal publications included when relevant. Search terms included “Artificial Intelligence,” “Machine Learning,” “Deep Learning,” “Natural Language Processing,” “Generative Artificial Intelligence,” “ChatGPT,” “Large Language Models,” “Clinical Pharmacy,” “Pharmacy Practice,” “Medication Safety,” “Clinical Decision Support,” “Pharmacovigilance,” “Adverse Drug Reactions,” “Drug–Drug Interactions,” “Personalized Medicine,” and related terms. Results Current evidence indicates that AI has potential applications across multiple areas of clinical pharmacy, including medication safety, clinical decision support, pharmacovigilance, adverse drug reaction detection, drug–drug interaction prediction, medication therapy management, personalized medicine, medication adherence, antimicrobial stewardship, drug information, patient counselling, and pharmacy education. Generative AI and large language models have introduced additional opportunities for clinical documentation, information retrieval, education, and medication-related communication. However, concerns remain regarding data quality, accuracy, algorithmic bias, explainability, privacy, cybersecurity, ethical and legal responsibility, regulatory requirements, workflow integration, and pharmacist competency. Conclusion AI has considerable potential to augment the role of clinical pharmacists and improve the efficiency, safety, and personalization of medication-related care. However, AI should not replace professional clinical judgment. Future research should prioritize prospective clinical validation, external validation, patient-centred outcomes, explainable and trustworthy AI, health-economic evaluation, and responsible human–AI collaboration.","url":"https://doi.org/10.5281/zenodo.22030982","authors":["Dr. Keerthika R*"],"tags":["Artificial intelligence; clinical pharmacy; machine learning; generative AI; ChatGPT; medication safety; pharmacovigilance; clinical decision support; personalized medicine; pharmacy practice."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22030982","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22030981","name":"Artificial Intelligence in Clinical Pharmacy: Current Applications, Challenges, and Future Perspectives – A Narrative Literature Review","source":"datacite","abstract":"Background Artificial intelligence (AI) is rapidly transforming healthcare by enabling the analysis of large and complex datasets and supporting clinical decision-making. Clinical pharmacy is an emerging area in which AI can assist with medication management, medication safety, pharmacovigilance, clinical decision support, personalized pharmacotherapy, and patient care. Objective This narrative literature review aims to describe the current applications of AI in clinical pharmacy, evaluate its potential benefits and limitations, and discuss future perspectives and research priorities for its responsible integration into clinical pharmacy practice. Literature Search A literature search was conducted using PubMed/MEDLINE, Scopus, and Embase to identify relevant literature on AI and clinical pharmacy. The search included publications primarily from January 2020 to August 2026, with additional earlier seminal publications included when relevant. Search terms included “Artificial Intelligence,” “Machine Learning,” “Deep Learning,” “Natural Language Processing,” “Generative Artificial Intelligence,” “ChatGPT,” “Large Language Models,” “Clinical Pharmacy,” “Pharmacy Practice,” “Medication Safety,” “Clinical Decision Support,” “Pharmacovigilance,” “Adverse Drug Reactions,” “Drug–Drug Interactions,” “Personalized Medicine,” and related terms. Results Current evidence indicates that AI has potential applications across multiple areas of clinical pharmacy, including medication safety, clinical decision support, pharmacovigilance, adverse drug reaction detection, drug–drug interaction prediction, medication therapy management, personalized medicine, medication adherence, antimicrobial stewardship, drug information, patient counselling, and pharmacy education. Generative AI and large language models have introduced additional opportunities for clinical documentation, information retrieval, education, and medication-related communication. However, concerns remain regarding data quality, accuracy, algorithmic bias, explainability, privacy, cybersecurity, ethical and legal responsibility, regulatory requirements, workflow integration, and pharmacist competency. Conclusion AI has considerable potential to augment the role of clinical pharmacists and improve the efficiency, safety, and personalization of medication-related care. However, AI should not replace professional clinical judgment. Future research should prioritize prospective clinical validation, external validation, patient-centred outcomes, explainable and trustworthy AI, health-economic evaluation, and responsible human–AI collaboration.","url":"https://doi.org/10.5281/zenodo.22030981","authors":["Dr. Keerthika R*"],"tags":["Artificial intelligence; clinical pharmacy; machine learning; generative AI; ChatGPT; medication safety; pharmacovigilance; clinical decision support; personalized medicine; pharmacy practice."],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22030981","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/b8nxk","name":"Enfoque diagnóstico de las asincronías paciente-ventilador en pacientes en estado crítico: Scoping review","source":"datacite","abstract":"This scoping review identifies that automated systems based on machine learning represent a promising direction in the study of PVA. The literature suggests their potential to deliver accurate, real-time classification, enhancing clinical decision-making and improving patient outcomes. Furthermore, an integrative approach to PVA classification is highlighted, one that may facilitate interpretability, management of large data volumes, and integration of multimodal information—features that are valuable for future research.","url":"https://doi.org/10.17605/osf.io/b8nxk","authors":["Julian Cortes"],"tags":["Medicine and Health Sciences","Medical Specialties","Critical Care"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/b8nxk","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.7488/era/7634","name":"Computational models of seizure activity and brain state dynamics in epilepsy","source":"datacite","abstract":"Epilepsy is a prevalent neurological disorder characterized by recurrent spontaneous seizures, whose occurrence varies substantially both between and within individuals. This variability is increasingly understood to be structured by underlying circadian and ultradian rhythms that modulate cortical excitability, sleep-wake dynamics, autonomic function, and emotional state. However, most computational approaches to seizure detection and prediction either ignore these temporal patterns or treat them as secondary covariates, and they are rarely designed to generalize across datasets, recording montages, species, or related physiological domains. This thesis addresses these gaps by developing computational models of circadian and ultradian rhythms in seizures that are informed by physiological considerations and integrated within a unified machine learning framework for electroencephalography (EEG) and related biosignals. The detection of seizures in EEG remains a major challenge, not only because of the complexity of the signals, but also because of their scale. Expert agreement on seizure annotations typically reaches only about 90%, and even this level of concordance is difficult to maintain over the ultra-long recordings that are increasingly common in clinical and research practice. The burden of manually inspecting many hours or days of data makes human annotation time-consuming, error-prone, and impractical for large-scale or continuous monitoring. In response to this, the thesis first develops a robust, cross-dataset seizure detection pipeline capable of operating on heterogeneous EEG recordings with minimal manual intervention. The framework accepts any appropriately annotated European data format (EDF) file and applies a standardized preprocessing chain, including a physiologically motivated re-referencing procedure that converts heterogeneous clinical montages into a consistent bipolar configuration. On this harmonized representation, multiple deep learning and classical machine learning models are trained and evaluated across several public and clinical datasets. Their outputs are combined through an ensemble voting strategy, improving robustness and reducing false alarms, particularly when models are applied to datasets that differ from those on which they were trained, and achieving performance that is competitive with state-of-the-art seizure detection methods. Having established this seizure-focused framework, the thesis next extends the computational infrastructure to sleep staging, with a particular emphasis on enabling cross-species analyses. Sleep provides a natural context in which circadian and ultradian rhythms are expressed, and sleep disruption is closely linked to seizure risk in many forms of epilepsy. To bridge human and animal work, I introduce a cross-species channel selection and mapping strategy rooted in anatomical and electrophysiological knowledge of homologous or functionally comparable brain regions in humans and rats. By defining species-agnostic channel groupings and features, the pipeline is adapted to classify sleep states in both human and rat EEG recordings. This enables systematic comparisons of model performance and feature importances across species and provides a principled route for transferring insights from high-resolution experimental models to clinical data. In doing so, the thesis demonstrates that an appropriately constrained computational model can support translational research in sleep, rather than treating human and animal recordings as fundamentally separate domains. The thesis then examines whether the same computational framework can support other EEG-based classification tasks, beginning with the detection of emotional states. Emotions manifest in subtle and distributed electrophysiological signatures that are often less pronounced and less temporally distinct than ictal events. Within the same automated preprocessing, channel handling, and model training pipeline, emot","url":"https://doi.org/10.7488/era/7634","authors":["Chybowski, Bartłomiej Szymon"],"tags":["epileptic seizures","seizure detection","EEG recordings","computational models","rhythm information","machine learning","sleep staging","circadian rhythms"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7488/era/7634","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19482654","name":"Shahram-Bioinfo/HLA-I-Multiomics-HCC: HLA-I Multiomics HCC Analysis - Initial Release","source":"datacite","abstract":"This release contains the full analysis pipeline for: \"HLA Class I Expression and Ratios as Diagnostic and Predictive Biomarkers in Hepatocellular Carcinoma: A Multi-Omics Study\" Includes: Allele-specific analysis Neoantigen prediction (netMHCpan pipeline) Clinical and survival analysis Machine learning biomarker modeling Single-cell and spatial transcriptomics workflows Note: Datasets are not included and must be downloaded separately (see README).","url":"https://doi.org/10.5281/zenodo.19482654","authors":["Shahram Aliyari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19482654","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19482655","name":"Shahram-Bioinfo/HLA-I-Multiomics-HCC: HLA-I Multiomics HCC Analysis - Initial Release","source":"datacite","abstract":"This release contains the full analysis pipeline for: \"HLA Class I Expression and Ratios as Diagnostic and Predictive Biomarkers in Hepatocellular Carcinoma: A Multi-Omics Study\" Includes: Allele-specific analysis Neoantigen prediction (netMHCpan pipeline) Clinical and survival analysis Machine learning biomarker modeling Single-cell and spatial transcriptomics workflows Note: Datasets are not included and must be downloaded separately (see README).","url":"https://doi.org/10.5281/zenodo.19482655","authors":["Shahram Aliyari"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19482655","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20642383","name":"Explainable AI-Based Disease Prediction using IoT Sensors for Smart Healthcare Systems","source":"datacite","abstract":"The rapid growth of chronic diseases and the increasing demand for remote healthcare services have accelerated the adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart healthcare environments. Conventional healthcare systems often depend on manual diagnosis and centralized monitoring approaches, which may lead to delayed disease detection and reduced healthcare efficiency. Although machine learning models have improved disease prediction accuracy, many existing AI systems operate as black-box models that lack transparency and interpretability. Explainable Artificial Intelligence (XAI) has emerged as an important solution for improving trust, transparency, and clinical decision-making in healthcare systems. This paper presents an Explainable AI-based disease prediction framework integrating IoT-enabled healthcare sensors, machine learning algorithms, and explainability techniques for intelligent healthcare monitoring and predictive disease analysis. The proposed system continuously collects physiological data including heart rate, blood pressure, oxygen saturation, glucose levels, and body temperature through wearable IoT sensors. Machine learning algorithms are employed for disease prediction, while Explainable AI techniques such as SHAP and LIME are integrated to provide transparent and interpretable healthcare predictions. The framework supports real-time patient monitoring, early disease diagnosis, and intelligent healthcare analytics using cloud and edge computing technologies. Experimental analysis demonstrates improved disease prediction accuracy, enhanced model interpretability, reduced response time, and increased trustworthiness compared with conventional black-box AI healthcare systems. The proposed framework provides a scalable and transparent solution suitable for smart hospitals, telemedicine, and remote patient monitoring applications.","url":"https://doi.org/10.5281/zenodo.20642383","authors":["Dhulipalla Rajesh Kumar","Alla Sambasiva Rao"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20642383","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20642384","name":"Explainable AI-Based Disease Prediction using IoT Sensors for Smart Healthcare Systems","source":"datacite","abstract":"The rapid growth of chronic diseases and the increasing demand for remote healthcare services have accelerated the adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart healthcare environments. Conventional healthcare systems often depend on manual diagnosis and centralized monitoring approaches, which may lead to delayed disease detection and reduced healthcare efficiency. Although machine learning models have improved disease prediction accuracy, many existing AI systems operate as black-box models that lack transparency and interpretability. Explainable Artificial Intelligence (XAI) has emerged as an important solution for improving trust, transparency, and clinical decision-making in healthcare systems. This paper presents an Explainable AI-based disease prediction framework integrating IoT-enabled healthcare sensors, machine learning algorithms, and explainability techniques for intelligent healthcare monitoring and predictive disease analysis. The proposed system continuously collects physiological data including heart rate, blood pressure, oxygen saturation, glucose levels, and body temperature through wearable IoT sensors. Machine learning algorithms are employed for disease prediction, while Explainable AI techniques such as SHAP and LIME are integrated to provide transparent and interpretable healthcare predictions. The framework supports real-time patient monitoring, early disease diagnosis, and intelligent healthcare analytics using cloud and edge computing technologies. Experimental analysis demonstrates improved disease prediction accuracy, enhanced model interpretability, reduced response time, and increased trustworthiness compared with conventional black-box AI healthcare systems. The proposed framework provides a scalable and transparent solution suitable for smart hospitals, telemedicine, and remote patient monitoring applications.","url":"https://doi.org/10.5281/zenodo.20642384","authors":["Dhulipalla Rajesh Kumar","Alla Sambasiva Rao"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20642384","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.33296440","name":"Development and internal validation of a machine learning-based disease burden index for irritable bowel syndrome: a multicentre cross-sectional study","source":"datacite","abstract":"Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction in gastroenterology clinics worldwide, becoming a significant public health burden. This study aims to provide an exploratory approach for comprehensive assessment of IBS burden and may serve as a basis for future external validation and further evaluation of its potential clinical application. This multicentre cross-sectional study included IBS patients diagnosed according to the Rome III and/or Rome IV criteria. Rome criteria-based comparisons and latent class analysis (LCA) were performed to characterize clinical heterogeneity, followed by the development of the IBS burden index (IBS-BI) using machine learning. Compared to the Rome IV-ineligible group, the Rome IV-eligible group exhibited significantly more severe IBS symptoms, somatization symptoms, anxiety symptoms, depression symptoms, and poorer health-related quality of life (all p &lt;0.05). Linear regression analysis showed that the severity of these symptoms was inversely related to health-related quality of life (all p &lt;0.05). LCA identified four heterogeneous IBS burden phenotypes: high-symptom burden, low-symptom burden, somatization-dominant, and psychological comorbid phenotypes. Using machine learning, we developed the IBS-BI and an interactive clinical calculator. The validation was limited to an internal training-validation split, and no external validation was performed. Patients with IBS who fulfilled the Rome IV criteria showed a higher disease burden, which may be related to the more stringent diagnostic requirements of these criteria. The IBS-BI may have potential value for evaluating disease burden and patient stratification. Its clinical utility and broader applicability require further validation in independent external cohorts and prospective studies.","url":"https://doi.org/10.6084/m9.figshare.33296440","authors":["Yanbin Wei","Heyang Zhang","Nan Wang","Haifeng Jin","Zitan Feng","Jiapeng Guo","Jiafei Peng","Jia Feng","Jia Zhi","Shutian Zhang","Shengtao Zhu","Xin Yao"],"tags":["Medicine","Immunology","Biological Sciences not elsewhere classified","Science Policy","Hematology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33296440","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.6084/m9.figshare.33296440.v1","name":"Development and internal validation of a machine learning-based disease burden index for irritable bowel syndrome: a multicentre cross-sectional study","source":"datacite","abstract":"Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction in gastroenterology clinics worldwide, becoming a significant public health burden. This study aims to provide an exploratory approach for comprehensive assessment of IBS burden and may serve as a basis for future external validation and further evaluation of its potential clinical application. This multicentre cross-sectional study included IBS patients diagnosed according to the Rome III and/or Rome IV criteria. Rome criteria-based comparisons and latent class analysis (LCA) were performed to characterize clinical heterogeneity, followed by the development of the IBS burden index (IBS-BI) using machine learning. Compared to the Rome IV-ineligible group, the Rome IV-eligible group exhibited significantly more severe IBS symptoms, somatization symptoms, anxiety symptoms, depression symptoms, and poorer health-related quality of life (all p &lt;0.05). Linear regression analysis showed that the severity of these symptoms was inversely related to health-related quality of life (all p &lt;0.05). LCA identified four heterogeneous IBS burden phenotypes: high-symptom burden, low-symptom burden, somatization-dominant, and psychological comorbid phenotypes. Using machine learning, we developed the IBS-BI and an interactive clinical calculator. The validation was limited to an internal training-validation split, and no external validation was performed. Patients with IBS who fulfilled the Rome IV criteria showed a higher disease burden, which may be related to the more stringent diagnostic requirements of these criteria. The IBS-BI may have potential value for evaluating disease burden and patient stratification. Its clinical utility and broader applicability require further validation in independent external cohorts and prospective studies.","url":"https://doi.org/10.6084/m9.figshare.33296440.v1","authors":["Yanbin Wei","Heyang Zhang","Nan Wang","Haifeng Jin","Zitan Feng","Jiapeng Guo","Jiafei Peng","Jia Feng","Jia Zhi","Shutian Zhang","Shengtao Zhu","Xin Yao"],"tags":["Medicine","Immunology","Biological Sciences not elsewhere classified","Science Policy","Hematology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.6084/m9.figshare.33296440.v1","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20796895","name":"Synthetic Medical Records Using Generative Adversarial Networks (GANs)","source":"datacite","abstract":"The quick data driven technology adoption of the health care sector has extinguished the notion that we require large volumes and superiorities of medical data to drive the machine learning, predictive analytics and clinical decision support systems. Simultaneously access to actual patient data is much more of an issue which we possess because of privacy laws, ethical concerns and organizational concerns. Researchers and practitioners are therefore the ones that are struggling immensely in development and validation of health care models that utilize real world data. Synthetically generated medical data has put forth as a workable and private solution to these issues. Here we put forth a model which we have named Generative Adversarial Networks (GANs) for the generation of artificial medical records out of structured and systematic health care data. We have designed the framework around practical aspects of the system, pre-processing of the data set, GAN architecture implementation, training protocols, and also the quantitative assessment. We used real medical follow up data in CSV files to train the GAN model which in turn generated synthetic data to very much like that of the real data set in terms of its statistical properties and the relationship between variables. We reported very good correlation between real and synthetic data distributions using primary clinical variables which in turn proved the put forth method’s performance. Also in general the system we present is a scalable solution to improve health care analytics data collection and at the same time it protects patient privacy which in turn is very beneficial for health care research and machine learning applications.","url":"https://doi.org/10.5281/zenodo.20796895","authors":["Pooja Dhankade","Dr. Pravin Kumar Malviya"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20796895","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20796896","name":"Synthetic Medical Records Using Generative Adversarial Networks (GANs)","source":"datacite","abstract":"The quick data driven technology adoption of the health care sector has extinguished the notion that we require large volumes and superiorities of medical data to drive the machine learning, predictive analytics and clinical decision support systems. Simultaneously access to actual patient data is much more of an issue which we possess because of privacy laws, ethical concerns and organizational concerns. Researchers and practitioners are therefore the ones that are struggling immensely in development and validation of health care models that utilize real world data. Synthetically generated medical data has put forth as a workable and private solution to these issues. Here we put forth a model which we have named Generative Adversarial Networks (GANs) for the generation of artificial medical records out of structured and systematic health care data. We have designed the framework around practical aspects of the system, pre-processing of the data set, GAN architecture implementation, training protocols, and also the quantitative assessment. We used real medical follow up data in CSV files to train the GAN model which in turn generated synthetic data to very much like that of the real data set in terms of its statistical properties and the relationship between variables. We reported very good correlation between real and synthetic data distributions using primary clinical variables which in turn proved the put forth method’s performance. Also in general the system we present is a scalable solution to improve health care analytics data collection and at the same time it protects patient privacy which in turn is very beneficial for health care research and machine learning applications.","url":"https://doi.org/10.5281/zenodo.20796896","authors":["Pooja Dhankade","Dr. Pravin Kumar Malviya"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20796896","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19434600","name":"mlrivera123/LLM-Framework-for-Ranked-Diagnosis-Suggestion: Aligning AI with Clinical Workflow: A Probabilistic LLM Framework for Ranked Diagnosis Suggestion from Unstructured Clinical Notes","source":"datacite","abstract":"Clinical notes are a form of unstructured data that contain a wealth of information such as patient symptoms, disease trajectory and management. While they are the most versatile and informative healthcare data, their annotation for machine learning requires specialized knowledge, rendering their processing both expensive and time-consuming. As a result, compared to other forms of more structured data, clinical notes are less utilized in AI applications. This project explores fine-tuning large language models on de-identified clinical notes to predict ICD-10-CM diagnostic codes, and evaluating them via conditional log-likelihood scoring over a fixed label set of 100 codes.","url":"https://doi.org/10.5281/zenodo.19434600","authors":["Max Rivera"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19434600","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19434601","name":"mlrivera123/LLM-Framework-for-Ranked-Diagnosis-Suggestion: Aligning AI with Clinical Workflow: A Probabilistic LLM Framework for Ranked Diagnosis Suggestion from Unstructured Clinical Notes","source":"datacite","abstract":"Clinical notes are a form of unstructured data that contain a wealth of information such as patient symptoms, disease trajectory and management. While they are the most versatile and informative healthcare data, their annotation for machine learning requires specialized knowledge, rendering their processing both expensive and time-consuming. As a result, compared to other forms of more structured data, clinical notes are less utilized in AI applications. This project explores fine-tuning large language models on de-identified clinical notes to predict ICD-10-CM diagnostic codes, and evaluating them via conditional log-likelihood scoring over a fixed label set of 100 codes.","url":"https://doi.org/10.5281/zenodo.19434601","authors":["Max Rivera"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.19434601","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/6517","name":"A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species","source":"datacite","abstract":"Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (&gt;95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay. © 2019 by the authors","url":"https://doi.org/10.34657/6517","authors":["Silge, Anja","Moawad, Amira A.","Bocklitz, Thomas","Fischer, Katja","Rösch, Petra","Roesler, Uwe","Elschner, Mandy C.","Popp, Jürgen","Neubauer, Heinrich"],"tags":["540","Burkholderia mallei","Burkholderia pseudomallei","Glanders","Heat inactivation","Melioidosis","PCA","Raman spectroscopy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.34657/6517","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/7156","name":"Predictive Modeling of Antibiotic Susceptibility in E. Coli Strains Using the U-Net Network and One-Class Classification","source":"datacite","abstract":"The antibiotic resistance of bacterial pathogens has become one of the most serious global health issues due to misusing and overusing of antibiotics. Recently, different technologies were developed to determine bacteria susceptibility towards antibiotics; however, each of these technologies has its advantages and limitations in clinical applications. In this contribution, we aim to assess and automate the detection of bacterial susceptibilities towards three antibiotics; i.e. ciprofloxacin, cefotaxime and piperacillin using a combination of image processing and machine learning algorithms. Therein, microscopic images were collected from different E. coli strains, then the convolutional neural network U-Net was implemented to segment the areas showing bacteria. Subsequently, the encoder part of the trained U-Net was utilized as a feature extractor, and the U-Net bottleneck features were utilized to predict the antibiotic susceptibility of E. coli strains using a one-class support vector machine (OCSVM). This one-class model was always trained on images of untreated controls of each bacterial strain while the image labels of treated bacteria were predicted as control or non-control images. If an image of treated bacteria is predicted as control, we assume that these bacteria resist this antibiotic. In contrast, the sensitive bacteria show different morphology of the control bacteria; therefore, images collected from these treated bacteria are expected to be classified as non-control. Our results showed 83% area under the receiver operating characteristic (ROC) curve when OCSVM models were built using the U-Net bottleneck features of control bacteria images only. Additionally, the mean sensitivities of these one-class models are 91.67% and 86.61% for cefotaxime and piperacillin; respectively. The mean sensitivity for the prediction of ciprofloxacin is only 59.72% as the bacteria morphology was not fully detected by the proposed method.","url":"https://doi.org/10.34657/7156","authors":["Ali, Nairveen","Kirchhoff, Johanna","Onoja, Patrick Igoche","Tannert, Astrid","Neugebauer, Ute","Popp, Jürgen","Bocklitz, Thomas"],"tags":["004","621.3","E. coli strains","Antibiotic resistance","One-class SVM","U-Net convolutional neural network"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.34657/7156","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/9257","name":"Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application","source":"datacite","abstract":"Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study.","url":"https://doi.org/10.34657/9257","authors":["Pradhan, Pranita","Meyer, Tobias","Vieth, Michael","Stallmach, Andreas","Waldner, Maximilian","Schmitt, Michael","Popp, Juergen","Bocklitz, Thomas"],"tags":["610","004","Inflammatory Bowel Disease","Non-linear Multimodal Imaging","Semantic Segmentation","Konferenzschrift"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2019","doi":"10.34657/9257","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/5408","name":"Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)","source":"datacite","abstract":"Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings. © 2020, The Author(s).","url":"https://doi.org/10.34657/5408","authors":["Drimalla, Hanna","Scheffer, Tobias","Landwehr, Niels","Baskow, Irina","Roepke, Stefan","Behnia, Behnoush","Dziobek, Isabel"],"tags":["610","audio recording","autism","comparative study","electromyogram","electromyography","eye movement","facial expression"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2020","doi":"10.34657/5408","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/10982","name":"Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients","source":"datacite","abstract":"Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.","url":"https://doi.org/10.34657/10982","authors":["Vyas, Akhilesh","Aisopos, Fotis","Vidal, Maria-Esther","Garrard, Peter","Paliouras, George"],"tags":["600","610","Classification","Dementia","Machine learning","Mini mental score examination","Predictive models","Random forest"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2021","doi":"10.34657/10982","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/10304","name":"Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records","source":"datacite","abstract":"Background: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. Results: Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia.","url":"https://doi.org/10.34657/10304","authors":["Vyas, Akhilesh","Aisopos, Fotis","Vidal, Maria-Esther","Garrard, Peter","Paliouras, Georgios"],"tags":["610","CAMCOG","Data science","Dementia","LIME","Machine learning","Mini mental score"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.34657/10304","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.34657/10269","name":"A Review on Data Fusion of Multidimensional Medical and Biomedical Data","source":"datacite","abstract":"Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.","url":"https://doi.org/10.34657/10269","authors":["Azam, Kazi Sultana Farhana","Ryabchykov, Oleg","Bocklitz, Thomas"],"tags":["540","computed tomography","data fusion","deep learning","fluorescence lifetime imaging microscopy","machine learning","magnetic resonance imaging","MALDI imaging"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2022","doi":"10.34657/10269","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22027689","name":"Cognitive Computing and Advanced AI Systems","source":"datacite","abstract":"We stand at a pivotal moment in technological history, defined not by isolated breakthroughs, but by the powerful convergence of four foundational paradigms: the Internet of Things (IoT), Blockchain, Artificial Intelligence (AI), and Machine Learning (ML). For decades, these fields developed along distinct trajectories. Today, their boundaries have blurred, forming an interconnected, self-sustaining computing ecosystem. The physical-world sensory capabilities of IoT feed massive streams of real-time operational data into sophisticated AI and ML algorithms, enabling rapid perception, continuous learning, and automated decision-making. Simultaneously, the inherent vulnerabilities of centralized data processing and networked edge devices demand the decentralized, immutable trust architecture that only Blockchain can provide. This textbook, Advanced Computing Technologies: IoT, Blockchain, AI, and Machine Learning, provides a comprehensive framework for understanding, designing, and engineering these unified intelligent systems. Designed for advanced students, researchers, and technology leaders, the volume bridges fundamental theory with practical architecture. The opening module introduces the core foundations of cognitive computing, tracing the evolution from early symbolic logic to modern connectionist paradigms and exploring the parallels between human biological cognition and artificial neural networks. Moving into methodologies, the book details the technical mechanics behind deep learning architectures—including convolutional, recurrent, and transformer models— alongside natural language understanding, knowledge graphs, reinforcement learning, and explainable AI (XAI). Building upon these analytical foundations, the text transitions into system design and enterprise-level execution. Readers explore cognitive system architectures, neuro-symbolic reasoning, edge-to-cloud distributed platforms, multi-agent frameworks, and trustworthy security designs. The theoretical concepts are then grounded through real-world applications across key sectors, including clinical healthcare decision support, smart manufacturing and Industry 5.0, autonomous transportation, financial intelligence, cybersecurity, and urban infrastructure. Finally, the book examines future horizons—investigating generative AI, agentic autonomous workflows, Artificial General Intelligence (AGI), cognitive digital twins, quantum computing, and green AI sustainability—paired with critical discussions on algorithmic bias, regulatory frameworks, and human-centric design. Ultimately, this text serves as a roadmap for developing advanced computational systems that are intelligent, secure, resilient, and ethically aligned with human needs.","url":"https://doi.org/10.5281/zenodo.22027689","authors":["Dr Jayaram C V","Archana Das","Dr. Niranjan KR","Arun Kashyap","Dr. Surender Kumar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22027689","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22027690","name":"Cognitive Computing and Advanced AI Systems","source":"datacite","abstract":"We stand at a pivotal moment in technological history, defined not by isolated breakthroughs, but by the powerful convergence of four foundational paradigms: the Internet of Things (IoT), Blockchain, Artificial Intelligence (AI), and Machine Learning (ML). For decades, these fields developed along distinct trajectories. Today, their boundaries have blurred, forming an interconnected, self-sustaining computing ecosystem. The physical-world sensory capabilities of IoT feed massive streams of real-time operational data into sophisticated AI and ML algorithms, enabling rapid perception, continuous learning, and automated decision-making. Simultaneously, the inherent vulnerabilities of centralized data processing and networked edge devices demand the decentralized, immutable trust architecture that only Blockchain can provide. This textbook, Advanced Computing Technologies: IoT, Blockchain, AI, and Machine Learning, provides a comprehensive framework for understanding, designing, and engineering these unified intelligent systems. Designed for advanced students, researchers, and technology leaders, the volume bridges fundamental theory with practical architecture. The opening module introduces the core foundations of cognitive computing, tracing the evolution from early symbolic logic to modern connectionist paradigms and exploring the parallels between human biological cognition and artificial neural networks. Moving into methodologies, the book details the technical mechanics behind deep learning architectures—including convolutional, recurrent, and transformer models— alongside natural language understanding, knowledge graphs, reinforcement learning, and explainable AI (XAI). Building upon these analytical foundations, the text transitions into system design and enterprise-level execution. Readers explore cognitive system architectures, neuro-symbolic reasoning, edge-to-cloud distributed platforms, multi-agent frameworks, and trustworthy security designs. The theoretical concepts are then grounded through real-world applications across key sectors, including clinical healthcare decision support, smart manufacturing and Industry 5.0, autonomous transportation, financial intelligence, cybersecurity, and urban infrastructure. Finally, the book examines future horizons—investigating generative AI, agentic autonomous workflows, Artificial General Intelligence (AGI), cognitive digital twins, quantum computing, and green AI sustainability—paired with critical discussions on algorithmic bias, regulatory frameworks, and human-centric design. Ultimately, this text serves as a roadmap for developing advanced computational systems that are intelligent, secure, resilient, and ethically aligned with human needs.","url":"https://doi.org/10.5281/zenodo.22027690","authors":["Dr Jayaram C V","Archana Das","Dr. Niranjan KR","Arun Kashyap","Dr. Surender Kumar"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22027690","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19543574","name":"Wearable AI Systems for Health Prediction","source":"datacite","abstract":"Wearable AI systems integrate miniaturised biosensors with on-device or cloud-based machine learning to enablecontinuous, real-time health prediction from passively collected physiological signals. Advances in edge computing,energy-efficient neural network architectures, and multi-sensor fusion have enabled wearable platforms to perform taskspreviously restricted to clinical laboratories -- predicting acute health events, monitoring chronic disease progression, andpersonalising wellness interventions. This study evaluates six wearable AI health prediction systems --smartwatch-based cardiovascular risk prediction, wrist-worn sleep apnoea screening, continuous glucose monitoring withhypoglycaemia prediction, physical activity-informed mental health monitoring, multi-sensor fall risk prediction in elderlyadults, and federated on-device learning for privacy-preserving health modelling -- across five performance dimensions:predictive accuracy, latency, battery efficiency, clinical validation, and privacy compliance. A total of 1,920 experimentsand prospective validations involving 420 participants across six clinical settings were conducted. Cardiovascular riskprediction achieved AUROC = 0.882 +- 0.012 for 10-year major adverse cardiovascular event risk from continuous PPG,accelerometry, and skin temperature, competitive with clinical risk scores requiring laboratory testing. Sleep apnoeascreening achieved sensitivity = 88.4 +- 2.4% and specificity = 92.6 +- 1.8% against polysomnography reference, at AHIthreshold >= 15 events/hour. Continuous glucose monitoring with LSTM-based hypoglycaemia prediction achieved 86.2+- 3.4% sensitivity at 30-minute horizon with 2.8 +- 0.4% false alarm rate. On-device federated learning preservedprediction accuracy within 2.4% of centralised training while eliminating raw data transmission. A practical deploymentframework mapping health prediction task, device constraints, clinical validation requirements, and regulatory pathway torecommended wearable AI configurations is proposed","url":"https://doi.org/10.5281/zenodo.19543574","authors":["Marco Hansen","Pierre Jensen"],"tags":["wearable AI; health prediction; continuous monitoring; federated learning; cardiovascular risk; sleep apnoea; hypoglycaemia prediction; edge AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.19543574","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.19543573","name":"Wearable AI Systems for Health Prediction","source":"datacite","abstract":"Wearable AI systems integrate miniaturised biosensors with on-device or cloud-based machine learning to enablecontinuous, real-time health prediction from passively collected physiological signals. Advances in edge computing,energy-efficient neural network architectures, and multi-sensor fusion have enabled wearable platforms to perform taskspreviously restricted to clinical laboratories -- predicting acute health events, monitoring chronic disease progression, andpersonalising wellness interventions. This study evaluates six wearable AI health prediction systems --smartwatch-based cardiovascular risk prediction, wrist-worn sleep apnoea screening, continuous glucose monitoring withhypoglycaemia prediction, physical activity-informed mental health monitoring, multi-sensor fall risk prediction in elderlyadults, and federated on-device learning for privacy-preserving health modelling -- across five performance dimensions:predictive accuracy, latency, battery efficiency, clinical validation, and privacy compliance. A total of 1,920 experimentsand prospective validations involving 420 participants across six clinical settings were conducted. Cardiovascular riskprediction achieved AUROC = 0.882 +- 0.012 for 10-year major adverse cardiovascular event risk from continuous PPG,accelerometry, and skin temperature, competitive with clinical risk scores requiring laboratory testing. Sleep apnoeascreening achieved sensitivity = 88.4 +- 2.4% and specificity = 92.6 +- 1.8% against polysomnography reference, at AHIthreshold >= 15 events/hour. Continuous glucose monitoring with LSTM-based hypoglycaemia prediction achieved 86.2+- 3.4% sensitivity at 30-minute horizon with 2.8 +- 0.4% false alarm rate. On-device federated learning preservedprediction accuracy within 2.4% of centralised training while eliminating raw data transmission. A practical deploymentframework mapping health prediction task, device constraints, clinical validation requirements, and regulatory pathway torecommended wearable AI configurations is proposed","url":"https://doi.org/10.5281/zenodo.19543573","authors":["Marco Hansen","Pierre Jensen"],"tags":["wearable AI; health prediction; continuous monitoring; federated learning; cardiovascular risk; sleep apnoea; hypoglycaemia prediction; edge AI"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.5281/zenodo.19543573","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22027223","name":"Vaccine Development and The Role of Artificial Intelligence","source":"datacite","abstract":"Artificial Intelligence (AI) has become a transformative technology in modern pandemic preparedness and response, enabling significant advances across public health, epidemiology, diagnostics, and vaccine research. This review explores the broad applications of AI in managing global infectious disease outbreaks, highlighting its contribution to improving disease surveillance, prediction, prevention, and clinical decision-making. By integrating data-driven analytical approaches, AI supports health authorities in making timely and evidence-based interventions during rapidly evolving public health emergencies. The review first examines the challenges that pandemics pose to healthcare systems, particularly in developing nations, and discusses how AI enhances epidemiological modelling to improve outbreak forecasting and response planning. Computational models such as the Susceptible-Infectious-Recovered (SIR) and Susceptible-Infectious-Susceptible (SIS) frameworks, when combined with AI techniques, provide more accurate predictions of disease transmission, enabling efficient allocation of healthcare resources and optimization of vaccination strategies. In addition, machine learning and predictive analytics uncover complex transmission patterns and identify risk factors that may not be apparent through conventional statistical approaches. Furthermore, the manuscript highlights the growing role of AI in accelerating vaccine discovery, optimizing clinical trial design, and strengthening disease surveillance systems. Advanced machine learning and deep learning algorithms facilitate rapid analysis of biomedical data, supporting the identification of vaccine candidates and improving clinical research efficiency. AI-powered surveillance platforms also enhance early detection, contact tracing, real-time monitoring, and forecasting of infectious disease outbreaks. Overall, this review demonstrates that AI has become an essential component of pandemic management by integrating epidemiological modelling, intelligent forecasting, surveillance, and vaccine development into a unified framework. It emphasizes the importance of continued interdisciplinary research, ethical implementation, and responsible integration of ai technologies to improve preparedness and resilance against future pandemics and other emerging public health threats","url":"https://doi.org/10.5281/zenodo.22027223","authors":["Thota. Srinivas Rao, Dr. T. Thangabalan, D. Akash*, R. Pishan, T. Devi Sowjanya"],"tags":["Artificial Intelligence (AI), transformative technology, modern pandemic preparedness and response"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22027223","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22027222","name":"Vaccine Development and The Role of Artificial Intelligence","source":"datacite","abstract":"Artificial Intelligence (AI) has become a transformative technology in modern pandemic preparedness and response, enabling significant advances across public health, epidemiology, diagnostics, and vaccine research. This review explores the broad applications of AI in managing global infectious disease outbreaks, highlighting its contribution to improving disease surveillance, prediction, prevention, and clinical decision-making. By integrating data-driven analytical approaches, AI supports health authorities in making timely and evidence-based interventions during rapidly evolving public health emergencies. The review first examines the challenges that pandemics pose to healthcare systems, particularly in developing nations, and discusses how AI enhances epidemiological modelling to improve outbreak forecasting and response planning. Computational models such as the Susceptible-Infectious-Recovered (SIR) and Susceptible-Infectious-Susceptible (SIS) frameworks, when combined with AI techniques, provide more accurate predictions of disease transmission, enabling efficient allocation of healthcare resources and optimization of vaccination strategies. In addition, machine learning and predictive analytics uncover complex transmission patterns and identify risk factors that may not be apparent through conventional statistical approaches. Furthermore, the manuscript highlights the growing role of AI in accelerating vaccine discovery, optimizing clinical trial design, and strengthening disease surveillance systems. Advanced machine learning and deep learning algorithms facilitate rapid analysis of biomedical data, supporting the identification of vaccine candidates and improving clinical research efficiency. AI-powered surveillance platforms also enhance early detection, contact tracing, real-time monitoring, and forecasting of infectious disease outbreaks. Overall, this review demonstrates that AI has become an essential component of pandemic management by integrating epidemiological modelling, intelligent forecasting, surveillance, and vaccine development into a unified framework. It emphasizes the importance of continued interdisciplinary research, ethical implementation, and responsible integration of ai technologies to improve preparedness and resilance against future pandemics and other emerging public health threats","url":"https://doi.org/10.5281/zenodo.22027222","authors":["Thota. Srinivas Rao, Dr. T. Thangabalan, D. Akash*, R. Pishan, T. Devi Sowjanya"],"tags":["Artificial Intelligence (AI), transformative technology, modern pandemic preparedness and response"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22027222","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.18887131","name":"Integrated AI-based Care Model Library I","source":"datacite","abstract":"Deliverable D3.7, titled “Integrated AI-based Care Model Library I”, consolidates the state-of-the-art (SOTA) analysis, and the methodologies explored in tasks T3.5 – “Explainable Models and Algorithms for Trustworthy AI” and T3.6 – “Integrated Care Model Library: Implementation and Recalibration of Adaptive Models” of Work Package 3 (WP3 – “Data and AI Governance for Personalized Prediction, Monitoring and Recommendations”) of the project. Deliverable D3.7 is the first version of a two-part series of deliverables, namely D3.7 - “Integrated AI-based Care Model Library I” and D3.8 - “Integrated AI-based Care Model Library II” (to be submitted on M32). This deliverable D3.7 highlights the innovative research efforts being made in integrating sophisticated SOTA models and advanced Machine Learning (ML) technologies into the Virtual Health Platform (VHP). The deliverable further demonstrates how these advancements contribute to achieving the project’s overarching objectives. From the collective work conducted in WP3, this deliverable serves as both a practical guide and a repository of insights for stakeholders, transferring knowledge and fostering the adoption of advanced Integrated Care Model (ICM) technologies in healthcare. This document underscores the project’s dedication to driving impactful advancements and setting a benchmark for the future of predictive and preventive healthcare systems. There are several major challenges that should be addressed during the lifecycle of this project, which include: i) the integration of heterogeneous data sources into high-quality and bias-free datasets, ii) scalability and real-time processing of developed algorithms, iii) adaptability of AI/ML models to diverse and extremely complex healthcare settings, iv) explainability of AI models and their ethical compliance, and finally v) rigorous evaluation and validation. As this deliverable will demonstrate recent advancements in AI, and particularly multimodal processing can greatly assist in some of these challenges, given that they can process and integrate a wide range data sources to provide a holistic understanding of patient health with increased personalised accuracy and adaptability to new clinical settings.This report introduces key concepts in multimodal systems engineering as required for the development of the Integrated Care Model Library (ICML). This deliverable explores the advanced prediction analytical tools that will be developed to enhance the VHP and ultimately empower it with advanced AI methodologies. More specifically, the main objectives of this deliverable include the development of a novel methodology for ICML with an objective to promote accurate predictions and explainable outcomes across European populations irrespective of ethnicity or socioeconomic background. This deliverable also aims to tackle the ethical compliance aspects of the ICML and serve in the facilitation of a reliable framework that meaningfully assists clinicians in the decision-making process. Deliverable D3.7, titled “Integrated AI-based Care Model Library I,” establishes the foundation for achieving these objectives. More specifically, this document constitutes a detailed account of the state-of-the-art, methodologies, strategies, and outcomes associated with integrated care models, emphasising on its alignment with the broader goals of the COMFORTage project mission. The document bridges the gap between theoretical advancements and practical implementation, presenting solutions that are both scientifically robust and operationally feasible. A key focus of this deliverable is the alignment of project objectives with the complexity of the challenges identified. These challenges underscore the importance of a systematic and collaborative approach, as exemplified by the methodologies and frameworks described in this document.","url":"https://doi.org/10.5281/zenodo.18887131","authors":["Tsitiridis, Aristeidis","Tsolis, Dimitrios","Lykothanasi, Kalliopi Klelia","Tsoukalos, Dimitrios","Koutsomitropoulos, Dimitrios","Giannaros, Anastasios","Huang, Shulei","Berenguer-Sánchez, José Antonio","van den Heuvel, Willem-Jan","Pilz, Maximilian","van Berlo, Sander","Spathoulas, Georgios"],"tags":["XAI","ICLM","EXPLANAIBLE AI","Integrated Care Model Library"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18887131","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.18887130","name":"Integrated AI-based Care Model Library I","source":"datacite","abstract":"Deliverable D3.7, titled “Integrated AI-based Care Model Library I”, consolidates the state-of-the-art (SOTA) analysis, and the methodologies explored in tasks T3.5 – “Explainable Models and Algorithms for Trustworthy AI” and T3.6 – “Integrated Care Model Library: Implementation and Recalibration of Adaptive Models” of Work Package 3 (WP3 – “Data and AI Governance for Personalized Prediction, Monitoring and Recommendations”) of the project. Deliverable D3.7 is the first version of a two-part series of deliverables, namely D3.7 - “Integrated AI-based Care Model Library I” and D3.8 - “Integrated AI-based Care Model Library II” (to be submitted on M32). This deliverable D3.7 highlights the innovative research efforts being made in integrating sophisticated SOTA models and advanced Machine Learning (ML) technologies into the Virtual Health Platform (VHP). The deliverable further demonstrates how these advancements contribute to achieving the project’s overarching objectives. From the collective work conducted in WP3, this deliverable serves as both a practical guide and a repository of insights for stakeholders, transferring knowledge and fostering the adoption of advanced Integrated Care Model (ICM) technologies in healthcare. This document underscores the project’s dedication to driving impactful advancements and setting a benchmark for the future of predictive and preventive healthcare systems. There are several major challenges that should be addressed during the lifecycle of this project, which include: i) the integration of heterogeneous data sources into high-quality and bias-free datasets, ii) scalability and real-time processing of developed algorithms, iii) adaptability of AI/ML models to diverse and extremely complex healthcare settings, iv) explainability of AI models and their ethical compliance, and finally v) rigorous evaluation and validation. As this deliverable will demonstrate recent advancements in AI, and particularly multimodal processing can greatly assist in some of these challenges, given that they can process and integrate a wide range data sources to provide a holistic understanding of patient health with increased personalised accuracy and adaptability to new clinical settings.This report introduces key concepts in multimodal systems engineering as required for the development of the Integrated Care Model Library (ICML). This deliverable explores the advanced prediction analytical tools that will be developed to enhance the VHP and ultimately empower it with advanced AI methodologies. More specifically, the main objectives of this deliverable include the development of a novel methodology for ICML with an objective to promote accurate predictions and explainable outcomes across European populations irrespective of ethnicity or socioeconomic background. This deliverable also aims to tackle the ethical compliance aspects of the ICML and serve in the facilitation of a reliable framework that meaningfully assists clinicians in the decision-making process. Deliverable D3.7, titled “Integrated AI-based Care Model Library I,” establishes the foundation for achieving these objectives. More specifically, this document constitutes a detailed account of the state-of-the-art, methodologies, strategies, and outcomes associated with integrated care models, emphasising on its alignment with the broader goals of the COMFORTage project mission. The document bridges the gap between theoretical advancements and practical implementation, presenting solutions that are both scientifically robust and operationally feasible. A key focus of this deliverable is the alignment of project objectives with the complexity of the challenges identified. These challenges underscore the importance of a systematic and collaborative approach, as exemplified by the methodologies and frameworks described in this document.","url":"https://doi.org/10.5281/zenodo.18887130","authors":["Tsitiridis, Aristeidis","Tsolis, Dimitrios","Lykothanasi, Kalliopi Klelia","Tsoukalos, Dimitrios","Koutsomitropoulos, Dimitrios","Giannaros, Anastasios","Huang, Shulei","Berenguer-Sánchez, José Antonio","van den Heuvel, Willem-Jan","Pilz, Maximilian","van Berlo, Sander","Spathoulas, Georgios"],"tags":["XAI","ICLM","EXPLANAIBLE AI","Integrated Care Model Library"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.18887130","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22027063","name":"APPLIED MATHEMATICS FOR REAL-WORLD PROBLEM SOLVING","source":"datacite","abstract":"The modern world runs on data, algorithms, and continuous optimization. From the smartphone applications that route daily traffic to the machine learning frameworks powering global industries, the foundational engine driving these advancements is applied mathematics. Applied Mathematics for Real-World Problem Solving is designed to bridge the gap between abstract mathematical theory and tangible, career-ready applications across engineering, data science, finance, and operations research. Rather than isolating concepts within theoretical vacuums, this book treats mathematics as a dynamic toolkit—a structured way of thinking, modeling, and executing decisions under uncertainty. Whether you are an aspiring engineer, a data analyst, or a student preparing for rigorous university examinations, this text provides the analytical scaffolding necessary to conceptualize and solve the multifaceted challenges of Industry 5.0. The journey begins in Chapter 1: Fundamentals of Applied Mathematics, establishing essential building blocks such as mathematical logic, sets, relations, functions, and error analysis. This chapter sets the stage for computational literacy, introducing how software tools like MATLAB and Python translate core mathematical formulas into working scripts. Chapter 2: Calculus for Real-World Applications transitions into the mathematics of change, guiding readers through limits, differentiation, and integration. It provides a concrete look at how differential equations dictate everything from population growth and radioactive decay to drug absorption rates within the human body. In Chapter 3: Linear Algebra and Matrix Applications, the book dives into high-dimensional spaces, exploring systems of equations, eigenvalues, and eigenvectors—the absolute bedrock of computer graphics pipelines, Google's PageRank algorithm, and modern machine learning architectures. As data-driven decision-making takes center stage, Chapter 4: Probability, Statistics and Data Analysis shifts focus to managing randomness and uncertainty. Readers will explore conditional probability via Bayes’ Theorem, statistical distributions, hypothesis testing, and forecasting methods, showing exactly how traditional statistics integrates with modern artificial intelligence. Finally, Chapter 5: Optimization, Computational Methods and Emerging Applications acts as a capstone for the text. It delves deeply into operations research—covering linear programming, inventory models, network optimization, and queuing theory—before mapping these algorithms directly to cutting-edge use cases like smart cities, IoT networks, digital twins, and deep learning backpropagation. To ensure complete mastery, each chapter is reinforced with comprehensive, university-style solved problems, condensed formula sheets, and practice exams. Crucially, the theoretical paradigms are brought to life through extended case studies drawn from actual clinical, financial, and industrial scenarios, such as hospital resource management, airport operations, and precision agriculture. Ultimately, Applied Mathematics for Real-World Problem Solving does not just teach you how to compute; it trains you how to formulate, build, and optimize the systems that shape our reality.","url":"https://doi.org/10.5281/zenodo.22027063","authors":["Dr. Malabika Adak","Dr. Gauri Ghule","Mr. Dnyaneshwar P. Bawane","Dr.(Mrs.) Anushree A. Aserkar","Dr. Keshav Kumar K"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22027063","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22026972","name":"APPLIED MATHEMATICS FOR REAL-WORLD PROBLEM SOLVING","source":"datacite","abstract":"The modern world runs on data, algorithms, and continuous optimization. From the smartphone applications that route daily traffic to the machine learning frameworks powering global industries, the foundational engine driving these advancements is applied mathematics. Applied Mathematics for Real-World Problem Solving is designed to bridge the gap between abstract mathematical theory and tangible, career-ready applications across engineering, data science, finance, and operations research. Rather than isolating concepts within theoretical vacuums, this book treats mathematics as a dynamic toolkit—a structured way of thinking, modeling, and executing decisions under uncertainty. Whether you are an aspiring engineer, a data analyst, or a student preparing for rigorous university examinations, this text provides the analytical scaffolding necessary to conceptualize and solve the multifaceted challenges of Industry 5.0. The journey begins in Chapter 1: Fundamentals of Applied Mathematics, establishing essential building blocks such as mathematical logic, sets, relations, functions, and error analysis. This chapter sets the stage for computational literacy, introducing how software tools like MATLAB and Python translate core mathematical formulas into working scripts. Chapter 2: Calculus for Real-World Applications transitions into the mathematics of change, guiding readers through limits, differentiation, and integration. It provides a concrete look at how differential equations dictate everything from population growth and radioactive decay to drug absorption rates within the human body. In Chapter 3: Linear Algebra and Matrix Applications, the book dives into high-dimensional spaces, exploring systems of equations, eigenvalues, and eigenvectors—the absolute bedrock of computer graphics pipelines, Google's PageRank algorithm, and modern machine learning architectures. As data-driven decision-making takes center stage, Chapter 4: Probability, Statistics and Data Analysis shifts focus to managing randomness and uncertainty. Readers will explore conditional probability via Bayes’ Theorem, statistical distributions, hypothesis testing, and forecasting methods, showing exactly how traditional statistics integrates with modern artificial intelligence. Finally, Chapter 5: Optimization, Computational Methods and Emerging Applications acts as a capstone for the text. It delves deeply into operations research—covering linear programming, inventory models, network optimization, and queuing theory—before mapping these algorithms directly to cutting-edge use cases like smart cities, IoT networks, digital twins, and deep learning backpropagation. To ensure complete mastery, each chapter is reinforced with comprehensive, university-style solved problems, condensed formula sheets, and practice exams. Crucially, the theoretical paradigms are brought to life through extended case studies drawn from actual clinical, financial, and industrial scenarios, such as hospital resource management, airport operations, and precision agriculture. Ultimately, Applied Mathematics for Real-World Problem Solving does not just teach you how to compute; it trains you how to formulate, build, and optimize the systems that shape our reality.","url":"https://doi.org/10.5281/zenodo.22026972","authors":["Dr. Malabika Adak","Dr. Gauri Ghule","Mr. Dnyaneshwar P. Bawane","Dr.(Mrs.) Anushree A. Aserkar","Dr. Keshav Kumar K"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22026972","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22026973","name":"APPLIED MATHEMATICS FOR REAL-WORLD PROBLEM SOLVING","source":"datacite","abstract":"The modern world runs on data, algorithms, and continuous optimization. From the smartphone applications that route daily traffic to the machine learning frameworks powering global industries, the foundational engine driving these advancements is applied mathematics. Applied Mathematics for Real-World Problem Solving is designed to bridge the gap between abstract mathematical theory and tangible, career-ready applications across engineering, data science, finance, and operations research. Rather than isolating concepts within theoretical vacuums, this book treats mathematics as a dynamic toolkit—a structured way of thinking, modeling, and executing decisions under uncertainty. Whether you are an aspiring engineer, a data analyst, or a student preparing for rigorous university examinations, this text provides the analytical scaffolding necessary to conceptualize and solve the multifaceted challenges of Industry 5.0. The journey begins in Chapter 1: Fundamentals of Applied Mathematics, establishing essential building blocks such as mathematical logic, sets, relations, functions, and error analysis. This chapter sets the stage for computational literacy, introducing how software tools like MATLAB and Python translate core mathematical formulas into working scripts. Chapter 2: Calculus for Real-World Applications transitions into the mathematics of change, guiding readers through limits, differentiation, and integration. It provides a concrete look at how differential equations dictate everything from population growth and radioactive decay to drug absorption rates within the human body. In Chapter 3: Linear Algebra and Matrix Applications, the book dives into high-dimensional spaces, exploring systems of equations, eigenvalues, and eigenvectors—the absolute bedrock of computer graphics pipelines, Google's PageRank algorithm, and modern machine learning architectures. As data-driven decision-making takes center stage, Chapter 4: Probability, Statistics and Data Analysis shifts focus to managing randomness and uncertainty. Readers will explore conditional probability via Bayes’ Theorem, statistical distributions, hypothesis testing, and forecasting methods, showing exactly how traditional statistics integrates with modern artificial intelligence. Finally, Chapter 5: Optimization, Computational Methods and Emerging Applications acts as a capstone for the text. It delves deeply into operations research—covering linear programming, inventory models, network optimization, and queuing theory—before mapping these algorithms directly to cutting-edge use cases like smart cities, IoT networks, digital twins, and deep learning backpropagation. To ensure complete mastery, each chapter is reinforced with comprehensive, university-style solved problems, condensed formula sheets, and practice exams. Crucially, the theoretical paradigms are brought to life through extended case studies drawn from actual clinical, financial, and industrial scenarios, such as hospital resource management, airport operations, and precision agriculture. Ultimately, Applied Mathematics for Real-World Problem Solving does not just teach you how to compute; it trains you how to formulate, build, and optimize the systems that shape our reality.","url":"https://doi.org/10.5281/zenodo.22026973","authors":["Dr. Malabika Adak","Dr. Gauri Ghule","Mr. Dnyaneshwar P. Bawane","Dr.(Mrs.) Anushree A. Aserkar","Dr. Keshav Kumar K"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22026973","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/37wqs","name":"Wearable sensors and Machine Learning to automatically assess dystonia and chorea [Protocol Scoping Review]","source":"datacite","abstract":"This scoping review aims to map the current literature on the integration of wearable sensors and machine learning techniques for the automated detection and severity assessment of dystonia and chorea. Following the PRISMA-ScR guidelines, this protocol outlines the search strategy, eligibility criteria, and data extraction framework used to evaluate measurement setups, extracted movement features, clinical correlations, and machine learning model performances across diverse movement disorder populations.","url":"https://doi.org/10.17605/osf.io/37wqs","authors":["Sonia Pianzi","Marte Apers","Marie Heymans","Jean-Marie Aerts","els ortibus","Helga Haberfehlner","Annemieke I. Buizer","Elegast Monbaliu","Hans Hallez"],"tags":["Other Rehabilitation and Therapy","Biomedical Devices and Instrumentation","Biomedical","Computer Engineering","Medicine and Health Sciences","Rehabilitation and Therapy","Electrical and Computer Engineering","Other Computer Engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/37wqs","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20962159","name":"ROLE OF ARTIFICIAL INTELLIGENCE AS A DIAGNOSTIC TOOL IN FUNCTIONAL ABDOMINAL PAIN IN CHILDREN","source":"datacite","abstract":"Functional Abdominal Pain Disorders (FAPDs) account for nearly 80% of chronic abdominal pain cases in children and are diagnosed according to Rome IV criteria in the absence of identifiable organic pathology. In classical Ayurveda, this clinical spectrum may be understood in relation to Grahani Roga and Udara Shoola, conditions primarily associated with dysfunction of Agni and imbalance of the Doshas, thereby offering a holistic and constitution-based approach to diagnosis and management. This review explores the current and emerging role of Artificial Intelligence (AI) in the diagnosis of paediatric FAPDs and proposes an integrative framework that incorporates Ayurvedic diagnostic methods such as Prakriti assessment, Nadi Pariksha, Ashtavidha Pariksha, and Agni Bala evaluation as structured multimodal inputs for AI-driven clinical decision support systems. A narrative review of the literature was conducted using PubMed, Google Scholar, AYUSH Research Portal, and AYUSHdhara databases from 2000 to 2026, employing search terms including functional abdominal pain, FAPDs, children, AI, machine learning, Grahani Roga, Prakriti, Nadi Pariksha, Agni, Dosha, gut-brain axis, and Rome IV. The findings suggest that AI-based approaches, including machine learning classifiers such as SVM, ANN, and Random Forest, natural language processing-based symptom extraction, gut-microbiome modelling, and neuroimaging analysis, demonstrate diagnostic accuracy ranging from 53% to 87.5% in phenotyping paediatric abdominal pain. At the same time, AI-enabled digitisation of Ayurvedic diagnostic tools, particularly sensor-based Nadi Pariksha and machine learning-based Prakriti classification, has shown promising accuracy of up to 90% in Dosha identification. The conceptual overlap between Grahani Roga, often considered comparable to irritable bowel syndrome, and FAPDs offers a strong basis for integrative research. In conclusion, an AI-integrated Ayurvedic diagnostic model combining Prakriti profiling, digital Nadi Pariksha, Agni Bala indices, and modern biomarkers may provide a personalised, culturally relevant, and clinically useful approach to the diagnosis and management of FAPDs in children. However, large-scale paediatric validation studies are still needed to establish its clinical utility.","url":"https://doi.org/10.5281/zenodo.20962159","authors":["Dr. Ankit Pal* and Asst. Prof. (Dr) Laxmi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20962159","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.20962160","name":"ROLE OF ARTIFICIAL INTELLIGENCE AS A DIAGNOSTIC TOOL IN FUNCTIONAL ABDOMINAL PAIN IN CHILDREN","source":"datacite","abstract":"Functional Abdominal Pain Disorders (FAPDs) account for nearly 80% of chronic abdominal pain cases in children and are diagnosed according to Rome IV criteria in the absence of identifiable organic pathology. In classical Ayurveda, this clinical spectrum may be understood in relation to Grahani Roga and Udara Shoola, conditions primarily associated with dysfunction of Agni and imbalance of the Doshas, thereby offering a holistic and constitution-based approach to diagnosis and management. This review explores the current and emerging role of Artificial Intelligence (AI) in the diagnosis of paediatric FAPDs and proposes an integrative framework that incorporates Ayurvedic diagnostic methods such as Prakriti assessment, Nadi Pariksha, Ashtavidha Pariksha, and Agni Bala evaluation as structured multimodal inputs for AI-driven clinical decision support systems. A narrative review of the literature was conducted using PubMed, Google Scholar, AYUSH Research Portal, and AYUSHdhara databases from 2000 to 2026, employing search terms including functional abdominal pain, FAPDs, children, AI, machine learning, Grahani Roga, Prakriti, Nadi Pariksha, Agni, Dosha, gut-brain axis, and Rome IV. The findings suggest that AI-based approaches, including machine learning classifiers such as SVM, ANN, and Random Forest, natural language processing-based symptom extraction, gut-microbiome modelling, and neuroimaging analysis, demonstrate diagnostic accuracy ranging from 53% to 87.5% in phenotyping paediatric abdominal pain. At the same time, AI-enabled digitisation of Ayurvedic diagnostic tools, particularly sensor-based Nadi Pariksha and machine learning-based Prakriti classification, has shown promising accuracy of up to 90% in Dosha identification. The conceptual overlap between Grahani Roga, often considered comparable to irritable bowel syndrome, and FAPDs offers a strong basis for integrative research. In conclusion, an AI-integrated Ayurvedic diagnostic model combining Prakriti profiling, digital Nadi Pariksha, Agni Bala indices, and modern biomarkers may provide a personalised, culturally relevant, and clinically useful approach to the diagnosis and management of FAPDs in children. However, large-scale paediatric validation studies are still needed to establish its clinical utility.","url":"https://doi.org/10.5281/zenodo.20962160","authors":["Dr. Ankit Pal* and Asst. Prof. (Dr) Laxmi"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.20962160","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.17605/osf.io/d2yfp","name":"ML-Based Cardiovascular Risk Prediction in Inflammatory Rheumatic Diseases: A Scoping Review Protocol","source":"datacite","abstract":"1. Background Individuals with inflammatory rheumatic diseases (IRDs) - such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and similar conditions- have a much higher risk of cardiovascular disease (CVD) and related deaths than the general population. Chronic systemic inflammation accelerates atherosclerosis through both unique and shared pathways with traditional cardiovascular risk factors. Widely used cardiovascular risk prediction tools like the Framingham Risk Score, SCORE2, QRISK3, and ACC/AHA Pooled Cohort Equations were developed for the general population. These tools often underestimate cardiovascular risk in IRD patients, even when using disease-specific adjustments such as the EULAR-recommended 1.5x multiplier for RA. As a result, more research now uses machine learning (ML) methods, including random forest, gradient boosting, LASSO, support vector machines, neural networks, and others, to create risk prediction models adapted to IRD patients. These models usually include disease-specific attributes like autoantibodies, inflammatory markers, and disease activity scores along with traditional risk factors. Existing systematic reviews in this space have focused either on the performance of traditional (non-ML) risk algorithms in IRDs, or on ML for CVD risk prediction in the general population. No review has systematically mapped the specific literature on ML-based cardiovascular risk prediction among inflammatory rheumatic diseases as a group. Given the heterogeneity in disease types, algorithms, outcome definitions, and assessment measures used across this emerging body of research, a scoping review is the most suitable methodology to comprehensively map the breadth and characteristics of existing evidence prior to conducting more targeted syntheses. 2. Research Question (PCC Framework) Population: Adult patients (≥18 years) diagnosed with an inflammatory rheumatic disease (RA, SLE, PsA, AS, or related systemic autoimmune rheumatic diseases). Concept: Development, validation, or comparative evaluation of machine learning–based models for predicting cardiovascular risk or cardiovascular events. Context: Any clinical, registry-based, or EHR-derived research setting, in any country or healthcare system. Review question: What is the nature and extent of the evidence on machine learning–based cardiovascular risk prediction models in patients with inflammatory rheumatic diseases? 3. Objectives To map which inflammatory rheumatic diseases have been studied in the context of ML-based CV risk prediction. To identify which ML algorithms have been applied and how their performance has been evaluated. To catalogue which predictor variables (traditional CV risk factors vs. disease-specific variables) are most commonly used. To identify which CV outcomes are predicted (e.g., composite MACE, myocardial infarction, stroke, subclinical atherosclerosis). To evaluate the extent of external validation and comparison against traditional risk scores. To identify gaps in the current evidence base to guide future primary research and inform the feasibility of a future systematic review/meta-analysis. 4. Eligibility Criteria Inclusion criteria: Studies involving adult patients with a diagnosed inflammatory rheumatic disease (RA, SLE, PsA, AS, systemic sclerosis, vasculitides, or mixed/overlap IRD populations) Studies developing, validating, or comparing a machine learning model, defined as any supervised or unsupervised algorithm beyond a single unpenalized logistic regression model used as a standard scoring system. Penalized regression methods (LASSO, Ridge, Elastic Net) are explicitly counted as ML for this review, given their use as feature-selection as well as regularization tools in the included primary literature. Studies using unpenalized logistic regression alongside other ML methods for comparison are included. The outcome must be cardiovascular risk, cardi","url":"https://doi.org/10.17605/osf.io/d2yfp","authors":["Irine Bakhutashvili","Tamuna Jatchvadze"],"tags":["Anatomy","Physical Sciences and Mathematics","Diseases","Cardiology","Public Health","Medicine and Health Sciences","Cardiovascular Diseases","Epidemiology"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.17605/osf.io/d2yfp","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22025491","name":"A Low-Cost AI-IoT and GSM-Enabled Pediatric Wristband for Continuous Fever Risk Assessment and Emergency Alerting","source":"datacite","abstract":"This paper presents a low-cost pediatric wearable wristband for continuous fever-risk monitoring using temperature, heart-rate, and motion sensing. The device integrates an ESP32 controller, a DS18B20 temperature sensor, a pulse sensor, an MPU6050 accelerometer, a GSM module, a local buzzer/LED alert, and a Blynk cloud dashboard. Because the wrist skin temperature differs from core body temperature, the temperature channel is calibrated against a clinical digital thermometer and reported as a calibrated body-equivalent temperature. A lightweight, transparent Fever Risk Score (FRS) combines physiological and motion parameters, and a supervised machine-learning classifier (Random Forest) trained on a labeled dataset of 2,400 pediatric monitoring instances classifies the child's condition into Normal, Warning, and High-Risk states. On a held-out test set the classifier achieved 91.4% accuracy, 94.5% sensitivity, and 98.1% specificity for High-Risk detection (macro F1 = 0.90). Bench validation gave a mean absolute temperature error of 0.18 °C against a clinical thermometer and 2.45 bpm against a pulse oximeter, with 95.5% fall-detection accuracy. Dual-channel alerting delivered GSM SMS in 6.5 ± 1.2 s and Blynk cloud updates in 1.9 ± 0.6 s, and the prototype operated continuously for 18.6 h on a 3.7 V Li-ion battery. The results indicate a validated, deployable early-warning and caregiver-alerting tool suitable for homes, schools, and rural healthcare settings, positioned as decision support rather than a replacement for clinical diagnosis.","url":"https://doi.org/10.5281/zenodo.22025491","authors":["Ms. Gauri J. Sutar","Associate Professor Dr. Sarita V. Balshetwar","Assistant Professor Mrs. Rajani M. Mandhare"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22025491","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.5281/zenodo.22025492","name":"A Low-Cost AI-IoT and GSM-Enabled Pediatric Wristband for Continuous Fever Risk Assessment and Emergency Alerting","source":"datacite","abstract":"This paper presents a low-cost pediatric wearable wristband for continuous fever-risk monitoring using temperature, heart-rate, and motion sensing. The device integrates an ESP32 controller, a DS18B20 temperature sensor, a pulse sensor, an MPU6050 accelerometer, a GSM module, a local buzzer/LED alert, and a Blynk cloud dashboard. Because the wrist skin temperature differs from core body temperature, the temperature channel is calibrated against a clinical digital thermometer and reported as a calibrated body-equivalent temperature. A lightweight, transparent Fever Risk Score (FRS) combines physiological and motion parameters, and a supervised machine-learning classifier (Random Forest) trained on a labeled dataset of 2,400 pediatric monitoring instances classifies the child's condition into Normal, Warning, and High-Risk states. On a held-out test set the classifier achieved 91.4% accuracy, 94.5% sensitivity, and 98.1% specificity for High-Risk detection (macro F1 = 0.90). Bench validation gave a mean absolute temperature error of 0.18 °C against a clinical thermometer and 2.45 bpm against a pulse oximeter, with 95.5% fall-detection accuracy. Dual-channel alerting delivered GSM SMS in 6.5 ± 1.2 s and Blynk cloud updates in 1.9 ± 0.6 s, and the prototype operated continuously for 18.6 h on a 3.7 V Li-ion battery. The results indicate a validated, deployable early-warning and caregiver-alerting tool suitable for homes, schools, and rural healthcare settings, positioned as decision support rather than a replacement for clinical diagnosis.","url":"https://doi.org/10.5281/zenodo.22025492","authors":["Ms. Gauri J. Sutar","Associate Professor Dr. Sarita V. Balshetwar","Assistant Professor Mrs. Rajani M. Mandhare"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5281/zenodo.22025492","addedAt":"2026-09-01T01:48:02.959Z","updatedAt":"2026-09-01T01:48:02.959Z"},{"id":"doi:10.1145/3677779.3677842","name":"Research on the Load_breast_cancer data set under Multiple Machine Learning Algorithms","source":"crossref","abstract":"breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.","url":"https://doi.org/10.1145/3677779.3677842","authors":["Meirui Song","Shixiao Wu","Rujuan Huang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T16:25:21Z","doi":"10.1145/3677779.3677842","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/aimla59606.2024.10531571","name":"A Survey on Skin Lesion Detection and Classification using Machine Learning","source":"crossref","abstract":"The research explores how dermatologists use machine learning to quickly and accurately identify and classify skin injury. Conventional diagnosis techniques depend on visual examination, but are subjective and have different interpretations. The ability to analyze data and recognize patterns is a possible remedy for ML. The paper examines the current technology of machine learning, focusing on validation in the real world, and deals with data set variability. Although the research acknowledges the fruitfulness of deep learning (DL), the research emphasizes the benefits of traditional ML techniques regarding interpretation and processing performance. Methods for automating the analysis of skin lesions, such as feature engineering, rule-based techniques and traditional ML algorithms, have been studied. The study suggests using advanced transfer learning techniques, integrating genetic and clinical data, and refining the way artificial intelligence (AI) is explained in order to get over barriers. Intending to enhance the accessibility and correctness of skin lesion identification, the future requires collaboration between dermatology and machine learning to develop real-time diagnostic tools. By offering scalable solutions for rapid diagnosis of lesions and improved patient outcomes, this combination of medical expertise and ML capabilities has the power to revolutionize dermatology. The future of automated dermatological diagnosis is expected to be shaped by collaboration between machine learning experts and dermatologists, enabling more personalized treatment for patients.","url":"https://doi.org/10.1109/aimla59606.2024.10531571","authors":["Rashmi Yadav","Aruna Bhat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-21T13:20:38Z","doi":"10.1109/aimla59606.2024.10531571","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.clim.2024.110301","name":"Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy","source":"crossref","abstract":"Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not well understood. We used integrated multi-omics analysis to explore the clinical and genetic roles of efferocytosis in SCM. We identified six module genes (ATP11C, CD36, CEBPB, MAPK3, MAPKAPK2, PECAM1) strongly associated with SCM, leading to an accurate predictive model. Subgroups defined by EFFscore exhibited distinct clinical features and immune infiltration levels. Survival analysis showed that the C1 subtype with a lower EFFscore had better survival outcomes. scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from sepsis patients identified four genes (CEBPB, CD36, PECAM1, MAPKAPK2) associated with high EFFscores, highlighting their role in SCM. Molecular docking confirmed interactions between diagnostic genes and tamibarotene. Experimental validation supported our computational results. In conclusion, our study identifies a novel efferocytosis-related SCM subtype and diagnostic biomarkers, offering new insights for clinical diagnosis and therapy.","url":"https://doi.org/10.1016/j.clim.2024.110301","authors":["Xuelian Li","Shijiu Jiang","Boyuan Wang","Shaolin He","Xiaopeng Guo","Jibin Lin","Yumiao Wei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-27T14:50:30Z","doi":"10.1016/j.clim.2024.110301","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icacrs62842.2024.10841728","name":"AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods : Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods","source":"crossref","abstract":"The lungs are vital for exchanging oxygen and carbon dioxide, providing oxygen to cells for energy production, and removing waste carbon dioxide from the body.They also help filter and protect the body from harmful particles and pathogens in the air.Proper lung function is essential for overall health and well-being.Lung cancer ranks as the second most common cause of cancer-related fatalities globally.It emphasizes the challenge of early detection due to the lack of symptoms in the initial stages.Which contributes to a high mortality rate compared to other lung diseases.Early identification and precise prediction of lung cancer serves as vital for improving patient outcomes. Machine learning (ML) techniques have shown great promise in revolutionizing lung cancer prediction by leveraging various data sources such as medical images, clinical data, and genomic information. This paper presents a review of lung cancer prediction using ML, DL, and AI methods.Accurate classification of lung cancer through medical imaging is essential for physicians to determine appropriate treatment options, ultimately aiming to reduce cancer mortality.The goal is to compare the performance and accuracy of convolutional neural networks (CNNs), Random forest algorithm, ensemble extreme boosting (XGBoost) algorithm,Support Vector Machine (SVM), AdaBoost classification model (ADB-C), Long Short-Term Memory Networks (LSTM), incremental multiple resolution residual network (iMRRN) models while also providing explanations for their predictions.","url":"https://doi.org/10.1109/icacrs62842.2024.10841728","authors":["A. Lakshmi Bhargav","C. Ashokkumar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-17T18:33:03Z","doi":"10.1109/icacrs62842.2024.10841728","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/fmlds63805.2024.00019","name":"Early Screening of Breast Cancer Using Machine Learning Algorithms: A Comparative Study","source":"crossref","abstract":"Breast cancer is one of the most typical types of cancer in women. It is the second greatest cause of death for women worldwide. Early detection and treatment can raise the likelihood of a full recovery and decrease the risk of cancer spreading. Therefore, the advancement in breast cancer illness prediction and detection is crucial for living a healthy life. As a result, high cancer prognostic accuracy is crucial for updating therapy aspects and patient survivability standards. Machine learning techniques are now a top area of research because of their significant impact on the early diagnosis of breast cancer. To detect breast cancer, we applied seven machine learning algorithms: Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boost (XGB), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN). We have also performed to-fold cross-validation method to detect breast cancer. The main goal of this study is finding the most effective machine learning algorithms for the prediction and diagnosis of breast cancer through confusion matrices, accuracy, and precision as well as ROC-AUC curves and scores. The study is performed by applying machine learning algorithms through feature scaling and two different splits of the training and testing data sets as well as 10-fold cross-validation methods. In this study, it has been seen that while all the selected classifiers have performed well in detecting breast cancer, the RF exceeds all other classifiers and obtains the best accuracy (97.9%) when the datasets are divided into 75% training and 25% testing data. On the other hand, SVM was found to beat all other classifiers with an accuracy of 98.20% when the datasets are divided into 80% training and 20% testing. Although the average accuracy decreased slightly (97.40%) when we performed to-fold cross-validation technique, SVM was still showing the best performance. This demonstrates that the separation of training and testing data sets may have an impact on how well machine learning classifiers perform.","url":"https://doi.org/10.1109/fmlds63805.2024.00019","authors":["Md Aminul Haque","Moname Majumder","Syed Mohammed Shamsul Islam"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T18:27:08Z","doi":"10.1109/fmlds63805.2024.00019","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/2632-2153/ad1f76","name":"CoRe optimizer: an all-in-one solution for machine learning","source":"crossref","abstract":"Abstract The optimization algorithm and its hyperparameters can significantly affect the training speed and resulting model accuracy in machine learning (ML) applications. The wish list for an ideal optimizer includes fast and smooth convergence to low error, low computational demand, and general applicability. Our recently introduced continual resilient (CoRe) optimizer has shown superior performance compared to other state-of-the-art first-order gradient-based optimizers for training lifelong ML potentials. In this work we provide an extensive performance comparison of the CoRe optimizer and nine other optimization algorithms including the Adam optimizer and resilient backpropagation (RPROP) for diverse ML tasks. We analyze the influence of different hyperparameters and provide generally applicable values. The CoRe optimizer yields best or competitive performance in every investigated application, while only one hyperparameter needs to be changed depending on mini-batch or batch learning.","url":"https://doi.org/10.1088/2632-2153/ad1f76","authors":["Marco Eckhoff","Markus Reiher"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-17T22:22:06Z","doi":"10.1088/2632-2153/ad1f76","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1117/12.3027116","name":"Bridging efficacy and efficiency: Innovations in Shapley value estimation for model-agnostic data valuation in machine learning","source":"crossref","abstract":"The escalating advancement of generative AI models amplifies the imperative for adept data valuation techniques. Amidst a myriad of methodologies, various Shapley value estimation techniques, such as Data Shapley, have garnered attention for their proficient data valuation capabilities, despite computational challenges when grappling with large datasets. This paper introduces an innovative, empirically-driven batch method, aiming to expedite data valuation while preserving precision. This method strategically optimizes training batch sizes and testing subsets, effectively striking a balance between computational efficiency and valuation accuracy, a critical step forward given the substantial volume of data processed in contemporary machine learning tasks. A thorough evaluation of different Shapley value estimation techniques is conducted, underscoring TMC-Shapley for its notable efficacy. Furthermore, the exploration delves into the modelagnostic nature of Shapley value estimations, utilizing diverse machine learning models across distinct training phases. This practice not only demonstrates the versatility of Shapley value methods but also highlights their adaptability and generalizability across varied model architectures, reaffirming the significance of this approach in the broader context of machine learning research. The holistic approach and findings presented herein serve as a robust foundation for future explorations and optimizations in the realm of data valuation, paving the way for more nuanced and efficient methodologies","url":"https://doi.org/10.1117/12.3027116","authors":["Yilu Yang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-01T14:12:58Z","doi":"10.1117/12.3027116","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/2632-2153/ad513a","name":"Symbolic regression as a feature engineering method for machine and deep learning regression tasks","source":"crossref","abstract":"Abstract In the realm of machine and deep learning (DL) regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches of FE often rely on domain expertise to manually design features for machine learning (ML) models. In the context of DL models, the FE is embedded in the neural network’s architecture, making it hard for interpretation. In this study, we propose to integrate symbolic regression (SR) as an FE process before a ML model to improve its performance. We show, through extensive experimentation on synthetic and 21 real-world datasets, that the incorporation of SR-derived features significantly enhances the predictive capabilities of both machine and DL regression models with 34%–86% root mean square error (RMSE) improvement in synthetic datasets and 4%–11.5% improvement in real-world datasets. In an additional realistic use case, we show the proposed method improves the ML performance in predicting superconducting critical temperatures based on Eliashberg theory by more than 20% in terms of RMSE. These results outline the potential of SR as an FE component in data-driven models, improving them in terms of performance and interpretability.","url":"https://doi.org/10.1088/2632-2153/ad513a","authors":["Assaf Shmuel","Oren Glickman","Teddy Lazebnik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-28T18:44:40Z","doi":"10.1088/2632-2153/ad513a","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1063/5.0197138","name":"Simulation-trained machine learning models for Lorentz transmission electron microscopy","source":"crossref","abstract":"Understanding the collective behavior of complex spin textures, such as lattices of magnetic skyrmions, is of fundamental importance for exploring and controlling the emergent ordering of these spin textures and inducing phase transitions. It is also critical to understand the skyrmion–skyrmion interactions for applications such as magnetic skyrmion-enabled reservoir or neuromorphic computing. Magnetic skyrmion lattices can be studied using in situ Lorentz transmission electron microscopy (LTEM), but quantitative and statistically robust analysis of the skyrmion lattices from LTEM images can be difficult. In this work, we show that a convolutional neural network, trained on simulated data, can be applied to perform segmentation of spin textures and to extract quantitative data, such as spin texture size and location, from experimental LTEM images, which cannot be obtained manually. This includes quantitative information about skyrmion size, position, and shape, which can, in turn, be used to calculate skyrmion–skyrmion interactions and lattice ordering. We apply this approach to segmenting images of Néel skyrmion lattices so that we can accurately identify skyrmion size and deformation in both dense and sparse lattices. The model is trained using a large set of micromagnetic simulations as well as simulated LTEM images. This entirely open-source training pipeline can be applied to a wide variety of magnetic features and materials, enabling large-scale statistical studies of spin textures using LTEM.","url":"https://doi.org/10.1063/5.0197138","authors":["Arthur R. C. McCray","Alec Bender","Amanda Petford-Long","Charudatta Phatak"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-04T14:12:33Z","doi":"10.1063/5.0197138","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1137/1.9781611977905.ch14","name":"Chapter 14: Dynamic Programming and Reinforcement Learning","source":"crossref","abstract":"Dynamic programming is one of the techniques used to solve the optimal control problem. Optimal control has been used in many different areas, like rocket control, chemical process control, and optimal economic growth. We will see in Chapter 19 the application of this technique to solve the strategic asset allocation problem, both analytically and numerically.","url":"https://doi.org/10.1137/1.9781611977905.ch14","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-26T11:03:09Z","doi":"10.1137/1.9781611977905.ch14","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-97-0452-1_3","name":"Rethinking Machine Learning and Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-0452-1_3","authors":["Makarand R. Velankar","Parikshit N. Mahalle","Gitanjali R. Shinde"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-16T06:01:43Z","doi":"10.1007/978-981-97-0452-1_3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1049/pbte106e_ch1","name":"Artificial intelligence, machine learning, and deep learning","source":"crossref","abstract":"In the modern era, we are witnessing a revolutionary transformational process where the exponential diffusion of smart and connected electronic devices fosters the emergence of disruptive services, such as Internet-of-Things (IoT), smart manufacturing, autonomous driving, and virtual/augmented reality (AR/VR). However, to fully unleash the potential of these technologies, it is necessary to address challenges related to the vast amount of data generated by these devices. Additionally, optimising network resources is crucial to guarantee very low communication delays and error-free communications. This chapter provides a brief introduction to the concepts of artificial intelligence (AI), machine learning (ML), and deep learning (DL), with a main focus on ML-related aspects, while DL-related concepts will be covered in later chapters. These concepts form the basis for understanding the contents provided within this book. In particular, this chapter aims to provide the reader with a clear understanding of what AI, ML, and DL are, how they work, and their pivotal role in the deployment of next-generation networks.","url":"https://doi.org/10.1049/pbte106e_ch1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-12T05:27:18Z","doi":"10.1049/pbte106e_ch1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.71443/9788197282164-09","name":"Neural Networks and Deep Learning Architectures: From Basics to Advanced Implementations","source":"crossref","abstract":"Neural network topologies and deep learning techniques have advanced so quickly that have drastically changed a number of fields, including computer vision and natural language processing. This book chapter explores the complex world of deep learning architectures and neural networks, exploring the progression from basic ideas to sophisticated applications. This chapter offers a thorough review of all the important subjects, such as hardware acceleration, model optimization, and streaming data processing, with an emphasis on the significance of scalability, efficiency, and practical implementation. Key areas such as model compression algorithms, efficient backpropagation techniques, and specialized hardware utilization are explored to highlight how contribute to enhancing computational performance and reducing operational costs. Additionally, the chapter addresses the challenges and solutions associated with real-time data processing, energy-efficient computing, and hardware-software co-design. By integrating cutting-edge advancements and addressing practical considerations, this chapter offers valuable insights into the development and deployment of sophisticated deep learning systems.","url":"https://doi.org/10.71443/9788197282164-09","authors":["J Latha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-04T07:10:26Z","doi":"10.71443/9788197282164-09","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.23977/autml.2024.050106","name":"Construction of English Translation Practice Teaching Mode Based on Deep Learning Model","source":"crossref","abstract":"Translation practice teaching occupies an important position in English teaching, but there are still many problems in the construction of the current English translation practice teaching mode. Guided by deep learning models and artificial intelligence, this research analyses the current problems of students’ translation learning and teachers' teaching methods, and constructs English translation practice teaching strategies under the deep learning theory. Through classroom observation and questionnaire survey of college English teachers and students, as well as vocabulary detection and interviews with students, it is found that teachers neglect students’ subject status and the cultivation of students' thinking ability in translation practice teaching. Teachers still use traditional teaching methods for the teaching of English translation mode, which leads to the shallow learning of students to a certain extent. Therefore, this paper further designs an English intelligent translation practice teaching assistant system, which can display various functions such as text, audio, images, and applies the English intelligent translation practice teaching assistant system in English translation practice teaching. Through comparison, it was found that with the statistics of the pre-test translation scores of the students in the experimental group and the control group (t=-1.9, p=0.064>0.05), the total number of translation errors (t=0.682, p=0.497>0.05), the post-test experimental group and control group students' post-test translation scores (t=0.036, p=0.036<0.05), and the total number of translation errors in the post-test experimental group (t=-2.88, p=0.005<0.05), there was a significant difference between the experimental group and the control group. The English translation practice teaching model constructed in this study can not only help to improve teaching efficiency, but also help students to consolidate English vocabulary.","url":"https://doi.org/10.23977/autml.2024.050106","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-14T03:12:10Z","doi":"10.23977/autml.2024.050106","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-3917-6_14","name":"Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3917-6_14","authors":["Hang Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-06T00:02:11Z","doi":"10.1007/978-981-99-3917-6_14","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/books978-3-7258-1282-0","name":"Applied Mathematics and Machine Learning","source":"crossref","abstract":"The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; on the other hand, theoretical discoveries in mathematical algorithms, differential equations, and statistical inferences, to name a few, have provided the foundation for the exploration of new multidisciplinary models for solving practical problems. This Special Issue endeavors to continue the journey that started in our previous Special Issue (Applied Mathematics and Computational Physics) by providing a platform for researchers from both academia and industry, as well as government, to present their new computational methods that have engineering and physics applications. We publish papers from all areas of mathematics and engineering, and especially those that showcase novel machine learning techniques that leverage subject matter expertise. We aim to foster the communication of the latest research results in the areas of applied and computational mathematics.","url":"https://doi.org/10.3390/books978-3-7258-1282-0","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-03T07:42:56Z","doi":"10.3390/books978-3-7258-1282-0","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-3917-6_8","name":"Boosting","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3917-6_8","authors":["Hang Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-06T00:02:11Z","doi":"10.1007/978-981-99-3917-6_8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.31224/3904","name":"Machine Learning Applications in Electric Vehicles: A Comprehensive Overview","source":"crossref","abstract":"Electric vehicles (EVs) have emerged as a key solution to the environmental and energy challenges of modern transportation. As the industry evolves, machine learning (ML) technologies are playing an increasingly important role in optimizing EV performance, energy management, battery life, and user experience. This paper provides a comprehensive review of recent advancements in the application of machine learning to electric vehicles. By analyzing key aspects such as battery management, energy consumption prediction, EV charging behavior, and communication efficiency, the paper offers insights into how ML can drive the future of electric vehicles by enhancing both performance and sustainability.","url":"https://doi.org/10.31224/3904","authors":["Suman Shrestha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-13T16:33:28Z","doi":"10.31224/3904","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1145/3677779.3677809","name":"Traveling Classification Profiler towards Passport Service Optimization using Machine Learning","source":"crossref","abstract":"The issue of passport misuse poses significant threats to both national and international security, as well as the integrity of a country's immigration system. Enhancing the effectiveness of the immigration system to detect and prevent passport misuse is crucial in anticipating as well as mitigating of the potential threats. This study investigates into the implementation of traveler movement extraction to support passport control mechanisms and mitigate the associated risks. The primary objective is to enhance the quality of information to address vulnerabilities in the passport management system and streamline passport verification processes. This approach focuses on the benefits of leveraging the integration of data from diverse microservices such as passport validation, authentication, and real-time border monitoring through web service and utilizing machine learning to extract the valuable data then transforms into insights. By using real-world data sources ensures the accuracy and fidelity of representations. Notably, most informative variables such as duration, frequency, age, gender, country, and category are extracted to inform the analysis. Results indicate that the random forest classifier algorithm outperforms support vector machine and decision tree algorithms in terms of accuracy, achieving rates of 96% and above across varying dataset thresholds.","url":"https://doi.org/10.1145/3677779.3677809","authors":["Raphael Fransiskus Manurung","Jaka Sembiring","Yoanes Bandung"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T16:25:21Z","doi":"10.1145/3677779.3677809","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/aimla59606.2024.10531454","name":"A Machine Learning Approach to GDP Prediction by Analyzing Economic Indicators","source":"crossref","abstract":"A country’s economy is important for its growth and development. A well-performed economy describes the quality living of the citizens. For assessing the health of nation’s economy the GDP performs a crucial benchmark. The country’s finished goods total value and services in a particular period are called GDP. The GDP value of the country tells whether that country is developed country or developing country. If the GDP grows the common man of the country earns the quality lifestyle. It makes the country to generate more tax revenue. This paper deals with the GDP prediction of the country by creating the machine learning model in python jupyter notebook. The machine learning model predicts the GDP based on the Life Expectancy, Human Development Index (HDI), CO2 emissions per person and percentage of service workers in the country. It uses the gapminder dataset from the Kaggle repository. Diverse machine learning algorithms are employed in the process of construction of a machine learning model. We picked the algorithm by analyzing the structure of dataset that have taken for the model building. Given that the dataset is in the form of numerical values and arranged in a non-linear structure, the non-linear regression algorithms were to implement this system. The Average Absolute Error (AAE), Squared Error (SE) and R-squared (R2) metrics were taken to validate the accuracy of the system. Among the three models that were implemented and tested Decision Tree Regression model (DTR), Support Vector Regression model (SVR) and Polynomial Regression model (PR), the Decision Tree Regression model achieved the error Free State and R-squared value of 1 which is the most desirable model.","url":"https://doi.org/10.1109/aimla59606.2024.10531454","authors":["A. Thilaka","E. Sundaravalli"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-21T17:20:38Z","doi":"10.1109/aimla59606.2024.10531454","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/sst61991.2024.10755258","name":"SST 2024 Machine Learning Breaker Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sst61991.2024.10755258","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-20T18:57:29Z","doi":"10.1109/sst61991.2024.10755258","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlhmi63000.2024.00001","name":"Proceedings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlhmi63000.2024.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T17:37:15Z","doi":"10.1109/mlhmi63000.2024.00001","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.5194/epsc2020-36","name":"Machine learning classification of new asteroid families members","source":"crossref","abstract":"Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. &amp;#160;These groups are mainly identified in proper elements or frequencies domains. &amp;#160; Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may &amp;#160; struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches. &amp;#160;The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to&amp;#160; retrieve up to 97% of family members identified with standard HCM.","url":"https://doi.org/10.5194/epsc2020-36","authors":["Valerio Carruba"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-10-08T09:23:31Z","doi":"10.5194/epsc2020-36","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.4135/9781036233495.n8","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781036233495.n8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-10-21T20:19:12Z","doi":"10.4135/9781036233495.n8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.20944/preprints202408.1344.v1","name":"Causal Economic Machine Learning (CEML)","source":"crossref","abstract":"This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.","url":"https://doi.org/10.20944/preprints202408.1344.v1","authors":["Andrew Horton"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-20T01:25:23Z","doi":"10.20944/preprints202408.1344.v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-61037-0_22","name":"New Paradigm in Financial Technology Using Machine Learning Techniques and Their Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-61037-0_22","authors":["Deepti Patnaik","Srikanta Patnaik"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-27T02:02:03Z","doi":"10.1007/978-3-031-61037-0_22","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1145/3701047","name":"Proceedings of the 2024 2nd International Conference on Communication Networks and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3701047","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-19T07:08:06Z","doi":"10.1145/3701047","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/9781394268993.ch14","name":"Advancements in Deep Learning","source":"crossref","abstract":"Profound contemplating is essentially utilizing various layers of neurons to consistently remove better-degree highlights from the realities that we feed to the neural local area. Profound getting to realize models are absolutely one of the styles of the artificial intelligence world. GrowNet applies angle supporting to shallow neural organizations. It has been ascending in acknowledgment, yielding progressed outcomes in order, relapse, and positioning. Generative Ill-Disposed Networks (GANs) help a generative neural net to deliver contrastive examples with the guide of applying a neural local area to idle examples from a perceived spread. A GAN comprises enemies of neural organizations: a generative organization and a discriminative organization. The chapter provides the information of Python code for profound learning.","url":"https://doi.org/10.1002/9781394268993.ch14","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-31T12:48:24Z","doi":"10.1002/9781394268993.ch14","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.52783/jier.v4i3.1317","name":"Applied Machine Learning for Predicting Crop Performance: A Supervised Learning Perspective","source":"crossref","abstract":"Agriculture is fraught with uncertainty due to climate change, rainfall, soil types, and many other factors. Crop prediction in agriculture is a major dilemma and there are huge data sets where farmers struggle to predict the right seed. In this situation of population growth, it is necessary to increase the production of crops and agricultural products at the same time in order to meet people's needs. These problems can be solved with machine learning algorithms. This white paper focuses on those solutions. Real-time environmental parameters such as soil type, precipitation, humidity, and past weather are recorded for the Tamil Nadu district, and ANN algorithms are used for crop prediction and accuracy","url":"https://doi.org/10.52783/jier.v4i3.1317","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T03:16:35Z","doi":"10.52783/jier.v4i3.1317","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4812303","name":"Determinants of Bitcoin Price: A Machine Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4812303","authors":["Levent Kutlu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-30T15:53:20Z","doi":"10.2139/ssrn.4812303","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-3917-6_2","name":"Perceptron","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3917-6_2","authors":["Hang Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-06T00:02:11Z","doi":"10.1007/978-981-99-3917-6_2","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.22541/au.172737936.66382608/v1","name":"Machine learning potential for serpentines","source":"crossref","abstract":"Serpentines are layered hydrous magnesium silicates (MgO·SiO2·H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory (DFT) calculation with the r2SCAN meta-GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high-temperature equation of states of several hydrous phases under the Earth’s subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polymorphism of antigorite with periodicity m = 13–24, which is believed to be all the naturally existent antigorite species. We found that antigorite with m larger than 21 appears more stable than lizardite at low temperatures. This machine learning potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves.","url":"https://doi.org/10.22541/au.172737936.66382608/v1","authors":["Hongjin Wang","Chenxing Luo","Renata Wentzcovitch"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T15:36:13Z","doi":"10.22541/au.172737936.66382608/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.170630089.92633255/v1","name":"Innovative Strategies for Ensuring Privacy in Machine Learning Environments","source":"crossref","abstract":"This paper presents an in-depth examination of privacy-enhancing methodologies in machine learning. It highlights the integration of federated learning with cutting-edge encryption techniques and explores how blockchain architectures contribute to data privacy. A major focus is on federated learning, a decentralized model training strategy, and its combination with privacy-protecting technologies like Homomorphic Encryption, Differential Privacy, and Secure Multi-Party Computation. We emphasize that federated learning naturally improves data privacy and, when paired with cryptographic methods, increases resilience against data breaches and cyber-attacks. Additionally, this study explores the potential of blockchain in enhancing data privacy. Blockchain's immutable and transparent characteristics, supplemented with shuffling technology, zero-knowledge proofs, and ring signatures, improve the confidentiality and integrity of data transactions. The paper also emphasizes the critical need for transparency and explainability in machine learning, advocating for methods that demystify the decision-making processes of ML models. This transparency is crucial for building trust and is becoming a regulatory requirement in many industries. Furthermore, the paper discusses the importance of auditing in machine learning, highlighting the need for comprehensive model validation and ethical considerations. In conclusion, the paper argues that achieving a balance 1 between functionality and privacy in ML applications is essential. It suggests that a combination of federated learning, advanced cryptographic techniques, and explainable AI principles can create effective and privacy-respecting systems.","url":"https://doi.org/10.36227/techrxiv.170630089.92633255/v1","authors":["Elham Shammar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-26T15:28:16Z","doi":"10.36227/techrxiv.170630089.92633255/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/2632-2153/ad2aef/v4/decision1","name":"Decision letter for \"Quantum machine learning for image classification\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2632-2153/ad2aef/v4/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-21T16:15:33Z","doi":"10.1088/2632-2153/ad2aef/v4/decision1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.22541/essoar.172222586.62035274/v1","name":"Upscaling hydrologic phenomena using machine learning","source":"crossref","abstract":"Hydrologic processes in snowmelt dominated systems are traditionally measured and understood at the point scale. However, snowmelt, sublimation, and snowfall rates exhibit significant spatial variability across larger landscapes. These variations are influenced by local atmospheric and terrain characteristics, which control the deviations between small-scale measurements and areally averaged responses to spatially heterogeneous mass and energy inputs. Appropriate upscaling relations are needed to translate small-scale descriptions of snow processes into constitutive relationships that are applicable at larger scales. As a proof of concept, this study examines the temporal and spatial variability of sublimation and snowmelt fluxes in a drainage basin in the Canadian Rockies using machine learning methods. Raven, a hydrological model, generates high-resolution fluxes and state variables used to train a random forest algorithm. The random forest (RF) algorithm is then applied to estimate coarse resolution fluxes. This study involves estimating spatially averaged results from discretized fine-scaled models without explicit knowledge of detailed local response, both with and without low-order statistics of state (e.g., the standard deviation of snow water equivalent). A series of experiments are used to verify that the upscaling methodology can successfully represent the impact of heterogeneity within the system. Spatially averaged forcing estimated from the scale-appropriate machine learning model is then incorporated into a mass balance equation at a coarse resolution to demonstrate the efficacy of this upscaling methodology.","url":"https://doi.org/10.22541/essoar.172222586.62035274/v1","authors":["Hannah Burdett","James R Craig"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T00:04:25Z","doi":"10.22541/essoar.172222586.62035274/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1145/3686490","name":"Proceedings of the 2024 7th International Conference on Signal Processing and Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3686490","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-11T16:29:37Z","doi":"10.1145/3686490","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmla61862.2024.00262","name":"An Evaluation and Comparison of Machine Learning Methods for Prediction of Lubricant Film Thickness","source":"crossref","abstract":"Lubricant film thickness is a tribological parameter that significantly impacts the performance of a tribo-system, primarily by affecting the resulting friction and wear. Measuring or predicting film thickness is therefore crucial for any hydrodynamically lubricated machine element. Numerical techniques are often employed to solve the film thickness under specified operating conditions. However, those approaches are computationally expensive and time-consuming. Machine learning methods have been proven to be a highly accurate and faster alternative to predicting lubricant film thickness. Still, these methods have their own benefits and drawbacks with regards to accuracy, speed, and model interpretability. In this paper, a speed/accuracy comparison is provided between the numerical solution, artificial neural network, and other machine learning approaches for determination of lubricant film thickness. It is found that non-ANN methods can be ideal in cases where prediction time is prioritized while high accuracy is still desired; the tested non-ANN models are orders of magnitude faster than both the numerical solution and the ANN approach, while accuracy is still high with a maximum R2score of 0.997. This finding, combined with the possible benefit of easy and accurate model interpretability, shows that non-ANN methods can be very useful for this application.","url":"https://doi.org/10.1109/icmla61862.2024.00262","authors":["Caleb Combs","Edgar Avalos Gauna","C. Fred Higgs"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-03-04T18:39:11Z","doi":"10.1109/icmla61862.2024.00262","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icccmla63077.2024.10871907","name":"A Novel Mechanism for Identifying Malicious and Phishing URLs Using Machine Learning Classifiers","source":"crossref","abstract":"Phishing attacks are a straightforward method for obtaining sensitive information from unsuspecting users. The goal of phishers is to acquire critical data such as usernames, passwords, and bank account details. The threat of network information insecurity is rapidly increasing in both frequency and severity. Domain phishing involves deceiving email recipients into revealing their account details through links in emails masquerading as legitimate registrars. A phishing URL is designed to promote scams, attacks, and fraud. When clicked, these URLs can lead to ransomware downloads, phishing attempts, or other cybercrimes. Hackers commonly exploit end-to-end technology and human vulnerabilities using techniques like social engineering, phishing, and pharming. Cybersecurity professionals are now seeking reliable and robust methods for detecting phishing websites. This project utilizes machine learning to detect phishing URLs by analyzing various features of legitimate and phishing URLs. The algorithms employed include Decision Tree, Logistic Regression, Gradient Boosting, and Support Vector Machine. The objective of this project is to identify phishing URLs and determine the most effective machine learning algorithm by comparing accuracy rates, false positives, and false negatives of each algorithm.","url":"https://doi.org/10.1109/icccmla63077.2024.10871907","authors":["V. Lokeswara Reddy","M.V. Sai Sarath Chandra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-11T18:21:27Z","doi":"10.1109/icccmla63077.2024.10871907","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmlca63499.2024.10754204","name":"Author Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlca63499.2024.10754204","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-21T19:04:04Z","doi":"10.1109/icmlca63499.2024.10754204","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-50714-4","name":"Machine Learning-based Design and Optimization of High-Speed Circuits","source":"crossref","abstract":"This book describes machine learning-based new principles, methods of design and optimization of high-speed integrated circuits.","url":"https://doi.org/10.1007/978-3-031-50714-4","authors":["Vazgen Melikyan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-30T10:02:41Z","doi":"10.1007/978-3-031-50714-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/ceeml65709.2024.00006","name":"Organizing Committees","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ceeml65709.2024.00006","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-29T17:29:35Z","doi":"10.1109/ceeml65709.2024.00006","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4896162","name":"Carbon Price Forecasting with Optimized Machine Learning Methods","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4896162","authors":["Ozan Nadirgil"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-16T10:22:21Z","doi":"10.2139/ssrn.4896162","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1515/9783110697186-toc","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110697186-toc","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-22T18:47:27Z","doi":"10.1515/9783110697186-toc","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1145/3662739.3662743","name":"Machine learning-based seismic prediction of building structures","source":"crossref","abstract":"The impact of earthquakes on building structures is the most direct and significant aspect of earthquake disasters, and the seismic resistance of building structures is a key factor in ensuring human safety and reducing economic losses. Ground shaking caused by earthquakes can create strong impact and vibration, which may lead to building collapse, damage, or deformation. In strong earthquakes, many buildings can suffer severe damage, such as complete collapse of the structure, cracking of walls, bending of columns, sagging of beams, and pose great danger to people. With the rapid development of modern computer technology in terms of hardware and software, the earthquake damage prediction methods and earthquake damage management of buildings have entered a brand new period. In this paper, classical machine learning methods such as decision tree, bagging tree, boosting tree, BP neural network and generalized regression network are used to construct prediction models and evaluate their prediction effects respectively. Combining the principles of different machine learning methods, the shortcomings of different models in small sample data training applications are analyzed, and the generalized regression network is further proposed. The prediction results show that the maximum values of seismic coefficients for buildings with rectangular cross-sections of different aspect ratios occur around an aspect ratio of 2, which is slightly larger than the normative value suggested by 1.4. The value stabilizes around 1.35 when the aspect ratio is greater than 4. The results can be used as a reference for similar structures The results can be used as a reference for the seismic design of similar structures.","url":"https://doi.org/10.1145/3662739.3662743","authors":["Shuai Liu","Hailiang Peng","Xiaolu Deng"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-03T06:41:34Z","doi":"10.1145/3662739.3662743","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/fmlds63805.2024.00008","name":"Reviewers: FMLDS 2024","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fmlds63805.2024.00008","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T18:27:08Z","doi":"10.1109/fmlds63805.2024.00008","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1039/d3dd00236e/v2/review2","name":"Review for \"Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d3dd00236e/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-27T00:02:30Z","doi":"10.1039/d3dd00236e/v2/review2","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.5194/egusphere-egu24-21100","name":"Machine Learning Prediction of Lightning Damage to Aircraft","source":"crossref","abstract":"Lightning can cause physical damage to the aircraft structure, including the wings, nose cone, and other components. This damage can compromise the structural integrity of the aircraft and pose a serious risk to passenger safety. In addition, it can cause electromagnetic interference that can disrupt communications, navigation, and other essential systems that are critical for safe flight.&amp;#160; To better understand the impact of lightning strikes on aircraft, a dataset of more than 300 such events has been collected and analysed. The dataset mainly covers Scandinavia and North Europe in the period from 2021 to 2024. The lightning data come from the regional lightning detection networks operated by the Norwegian, Finish, and Danish Meteorologic Institutes.&amp;#160; The dataset has been used to train a Machine Learning model for lightning damage assessment in close cooperation with Scandinavian Airlines. The model shows high prediction accuracy for all known types of lightning strikes to airplanes. The dataset will be available for interested researchers.&amp;#160; Overall, this research advances understanding of the phenomenon and can aid in developing strategies to mitigate the risk of lightning strikes and ensure safe air transportation.","url":"https://doi.org/10.5194/egusphere-egu24-21100","authors":["Pavlo Kochkin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-11T12:43:31Z","doi":"10.5194/egusphere-egu24-21100","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-39477-5","name":"Information-Driven Machine Learning","source":"crossref","abstract":"This textbook explains the 'why' behind data science, deep learning, and machine learning, and explores a systematic approach to model engineering","url":"https://doi.org/10.1007/978-3-031-39477-5","authors":["Gerald Friedland"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-01T06:04:04Z","doi":"10.1007/978-3-031-39477-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/sensys-ml62579.2024","name":"2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sensys-ml62579.2024","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-24T17:22:44Z","doi":"10.1109/sensys-ml62579.2024","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icbdml60909.2024.10577294","name":"ICBDML 2024 TOC","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbdml60909.2024.10577294","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-02T18:12:18Z","doi":"10.1109/icbdml60909.2024.10577294","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-22001-2.00005-6","name":"Machine learning-assisted Fourier transform infrared and Raman spectroscopic sensing in agricultural and food systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22001-2.00005-6","authors":["Tianjian Tong","Binbin Zhu","Chenxu Yu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:47:29Z","doi":"10.1016/b978-0-443-22001-2.00005-6","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/2632-2153/ad2aef/v3/decision1","name":"Decision letter for \"Quantum machine learning for image classification\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2632-2153/ad2aef/v3/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-21T16:15:33Z","doi":"10.1088/2632-2153/ad2aef/v3/decision1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.48001/978-81-966500-0-1-9","name":"Leveraging Machine Learning to Enhance Injury Prevention Strategies for Fast Bowlers","source":"crossref","abstract":"Fast bowlers in cricket face a high risk of injury due to the immense physical strain associated with their role, often resulting in prolonged absences and performance declines. This study aims to develop a predictive model for fast bowler injuries using the Random Forest algorithm. Key parameters such as workload, biomechanics, fitness levels, injury history, and the critical factor of the last ball bowled before injury were analyzed to detect patterns linked to injury. The Random Forest model was applied, leveraging these variables to provide high predictive accuracy. Model performance was evaluated demonstrating the efficacy of this approach in predicting injuries before they occur. The results highlight the significance of precise workload management and the critical moments leading up to injury, offering valuable insights for coaching staff and medical teams.","url":"https://doi.org/10.48001/978-81-966500-0-1-9","authors":["S Pandikumar","C Menaka","Arun M"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-26T11:46:02Z","doi":"10.48001/978-81-966500-0-1-9","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/fmlds63805.2024.00060","name":"Predicting College Admission Results with Machine Learning on Unstructured Online Data","source":"crossref","abstract":"College admissions in the U nited States is a complex and often opaque process, leading to uncertainty and potential unfair biases. This paper presents a novel approach to predicting college admission results using machine learning on unstructured online data. Specifically, we utilize GPT-4o to extract and structure student application details and admission outcomes from more than 4,000 posts on the r/collegeresults subreddit, demonstrating the capabilities of advanced language models in preprocessing unstructured data for machine learning tasks. We employ two distinct methods for predicting admissions results: the first combines descriptive scalars extracted by GPT-4o from unstructured text data with XGBoost and a neural network to predict the probability of acceptance into an institution selectivity tier. The second predicts admission outcomes for specific institutions and compares the effectiveness of tokenization versus descriptive scalars extracted by GPT-4o in representing text features. The models achieve promising results, with Method 1 attaining an accuracy of 91.66% and an AUC-ROC of 0.9298. The results from Method 2 also demonstrate the greater effectiveness of using tokenization (85.1±.2% accuracy) over the explicitly defined features we specified (84.3±.2% accuracy).","url":"https://doi.org/10.1109/fmlds63805.2024.00060","authors":["John Tian","Yourui Shao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-02-17T18:27:08Z","doi":"10.1109/fmlds63805.2024.00060","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlcad62225.2024.10740232","name":"MLCAD 2024 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlcad62225.2024.10740232","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-06T18:37:40Z","doi":"10.1109/mlcad62225.2024.10740232","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-97-7610-8","name":"Rhythmische Vorteile in Big Data und Machine Learning","source":"crossref","abstract":"Das Buch behandelt verschiedene Aspekte der Biophysik und die hierarchische Kommunikation zwischen verschiedenen biologischen Systemen.","url":"https://doi.org/10.1007/978-981-97-7610-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-29T05:35:35Z","doi":"10.1007/978-981-97-7610-8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.172953453.35422453/v1","name":"A Coherent Signal Detector Suitable for Machine Learning Applications","source":"crossref","abstract":"Coherent signals, such as a time varying voltage appearing on a radio antenna, are fundamentally different than non-coherent signals, such as the power magnitude of an individual pixel in a CCD camera. This discussion explores the details of the differences between coherent and non-coherent signals and demonstrates a generalized detector for coherent signals based on Hermetian quadratic forms. Hermetian quadratic forms appear frequently in coherent signal detection and classification processes. This includes power detection and many adaptive processing applications. We show how to generalize this concept to Neural Nets and other Machine Learning systems, and provide examples of the benefits of doing so.","url":"https://doi.org/10.36227/techrxiv.172953453.35422453/v1","authors":["Charles Hansen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-21T14:15:40Z","doi":"10.36227/techrxiv.172953453.35422453/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1039/d3dd00236e/v2/review3","name":"Review for \"Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d3dd00236e/v2/review3","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-27T00:02:30Z","doi":"10.1039/d3dd00236e/v2/review3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.32388/pi8f7k","name":"Strong Machine Learning: a Way Towards Human-Level Intelligence","source":"crossref","abstract":"Machine learning has achieved remarkable success with deep learning technologies. However, these methods are often inefficient in terms of resources; they require large datasets, many parameters and consume much computational power. In this paper, I define a general strategy for machine learning, named _strong machine learning_, which aims to create resource-effective machine learning models. Under strong machine learning fall all the approaches that learn inductive biases during an initial phase and later apply those inductive biases to make models more effective learners. Several strong machine learning methods already exist and some are very popular exactly due to their effectiveness. However, strong machine learning is in its infancy and a lot more can be done. In order to further advance AI, we need to direct our effort toward developing even better, more powerful strong machine learning methods.","url":"https://doi.org/10.32388/pi8f7k","authors":["Danko Nikolic"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T05:25:27Z","doi":"10.32388/pi8f7k","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.70314/is.2024.scai.991","name":"Choosing Features for Stress Prediction with Machine Learning","source":"crossref","abstract":"Feature selection is a crucial step in building effective machine learning models, as it directly impacts model accuracy and interpretability.Driven by the aim of improving stress prediction models, this article evaluates multiple approaches for identifying the most relevant features.The study explores filter-based methods that assess feature importance through correlation analysis, alongside wrapper methods that iteratively optimize feature subsets.Additionally, techniques such as Boruta are analysed for their effectiveness in identifying all important features, while strategies for handling highly correlated variables are also considered.By conducting a comprehensive analysis of these approaches, we assess the role of feature selection in developing stress prediction models.","url":"https://doi.org/10.70314/is.2024.scai.991","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-01T10:18:17Z","doi":"10.70314/is.2024.scai.991","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.172263075.54983523/v1","name":"Optimizing Renewable Energy Integration with Machine Learning for Sustainability","source":"crossref","abstract":"Progressions in technologies for renewable energy have rendered feasible fresh avenues for energy production that is sustainable. Nonetheless, melding renewable energy sources into pre-existing power grids encounters hurdles, owing to their sporadic nature. Machine learning methodologies present an auspicious remedy to enhance the assimilation of renewable energy sources and augment the efficiency and dependability of power infrastructures. Through the application of data-centric models, machine learning algorithms possess the capability to scrutinize extensive datasets to anticipate renewable energy production, streamline power generation timetables, and forecast prospective system malfunctions. This inquiry endeavors to probe the potential of utilizing machine learning to amplify the sustainability of renewable energy mechanisms. By exploiting artificial intelligence's capabilities, we can craft pioneering resolutions to tackle the intricacies linked with incorporating renewable energy sources into the grid, ultimately setting the stage for a future where energy is more sustainable.","url":"https://doi.org/10.36227/techrxiv.172263075.54983523/v1","authors":["Ayaan Vijayakar"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-02T16:32:43Z","doi":"10.36227/techrxiv.172263075.54983523/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.62973/23-033","name":"Testbed-19: Machine Learning Models Engineering Report","source":"crossref","abstract":"","url":"https://doi.org/10.62973/23-033","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-05T15:07:24Z","doi":"10.62973/23-033","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4716425","name":"Financial Applications of Machine Learning Using R Software","source":"crossref","abstract":"In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, i present the different Machine Learning techniques used, and provide some suggestions on the choice of methods in financial applications. We refer the reader to the R packages that can be used to compute the Machine learning methods.","url":"https://doi.org/10.2139/ssrn.4716425","authors":["Sami Mestiri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-18T14:56:00Z","doi":"10.2139/ssrn.4716425","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21275/sr24723105503","name":"A Comprehensive Review on Machine Learning and Transfer Learning Approaches in Liver Tumor Classification","source":"crossref","abstract":"Using improvements in medical imaging technology like CT and MRI scans, liver tumor classification is essential for early detection and therapy planning. Nonetheless, there are several obstacles to overcome due to the variety of tumor features and the requirement for precise and effective classification. By utilizing pre-trained deep learning models, transfer learning has become a viable strategy to address these issues in recent years. Transfer learning improves the efficiency and accuracy of liver tumor classification by enabling models learned on large-scale datasets, like ImageNet, to be modified for medical imaging applications with sparsely labeled data. This paper delves into the use of transfer learning techniques in the classification of liver tumors, emphasizing evaluation metrics, adaption of pre-trained models, and augmentation strategies for dataset enrichment. It also covers the most recent datasets, clinical ramifications, and potential research avenues to increase the effectiveness of transfer learning in this crucial area of medical imaging.","url":"https://doi.org/10.21275/sr24723105503","authors":["Sheik Imran","Pradeep N"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-25T12:10:31Z","doi":"10.21275/sr24723105503","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-12-822904-0.00011-x","name":"Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-822904-0.00011-x","authors":["Maria Deprez","Emma C. Robinson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-21T05:33:29Z","doi":"10.1016/b978-0-12-822904-0.00011-x","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-97-5333-8_10","name":"Reinforcement Learning Paradigm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-5333-8_10","authors":["Wenmin Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-26T17:01:56Z","doi":"10.1007/978-981-97-5333-8_10","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1063/10.0025126","name":"Using machine learning to improve efficiency of acoustic nanogenerators","source":"crossref","abstract":"Machine learning algorithms can effectively analyze data and identify key parameters for performance optimization.","url":"https://doi.org/10.1063/10.0025126","authors":["Avery Thompson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-21T17:46:07Z","doi":"10.1063/10.0025126","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1515/9783110697186-010","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110697186-010","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-22T18:47:27Z","doi":"10.1515/9783110697186-010","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.17128475.v20","name":"The Re-Label Method For Data-Centric Machine Learning","source":"crossref","abstract":"In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.","url":"https://doi.org/10.36227/techrxiv.17128475.v20","authors":["Tong Guo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-02T17:50:56Z","doi":"10.36227/techrxiv.17128475.v20","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1039/d3dd00236e/v2/review1","name":"Review for \"Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d3dd00236e/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-27T00:02:30Z","doi":"10.1039/d3dd00236e/v2/review1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4704331","name":"A Review of Cloud Security Solutions: Leveraging Machine Learning and Deep Learning Techniques","source":"crossref","abstract":"As organizations continue to leverage the benefits of cloud computing, it is imperative to prioritize cloud security. A holistic approach that encompasses people, processes, and technologies is crucial for building a resilient cloud security posture. By staying informed about emerging threats, adhering to best practices, and collaborating with reputable cloud service providers, businesses can navigate the complexities of cloud security and confidently embrace the advantages of the cloud.","url":"https://doi.org/10.2139/ssrn.4704331","authors":["Dr. Priti Mishra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-22T12:09:01Z","doi":"10.2139/ssrn.4704331","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlhmi63000.2024.00007","name":"Reviewers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlhmi63000.2024.00007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T17:37:15Z","doi":"10.1109/mlhmi63000.2024.00007","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-7657-7","name":"Dirty Data Processing for Machine Learning","source":"crossref","abstract":"This book offers valuable take-away suggestions on dirty data processing and model selection for machine learning tasks.","url":"https://doi.org/10.1007/978-981-99-7657-7","authors":["Zhixin Qi","Hongzhi Wang","Zejiao Dong"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-29T11:32:46Z","doi":"10.1007/978-981-99-7657-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-030-85292-4_33","name":"Machine Learning in Pituitary Surgery","source":"crossref","abstract":"Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.","url":"https://doi.org/10.1007/978-3-030-85292-4_33","authors":["Vittorio Stumpo","Victor E. Staartjes","Luca Regli","Carlo Serra"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2021-12-03T21:02:48Z","doi":"10.1007/978-3-030-85292-4_33","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-4011674/v1","name":"Network Type Recognition Using Machine Learning Techniques","source":"crossref","abstract":"Abstract The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc).","url":"https://doi.org/10.21203/rs.3.rs-4011674/v1","authors":["Debmalya Ray"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-06T19:26:56Z","doi":"10.21203/rs.3.rs-4011674/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/books978-3-0365-9818-5","name":"Data Mining and Machine Learning with Applications","source":"crossref","abstract":"The Special Issue entitled \"Data Mining and Machine Learning with Applications aimed to connect researchers in the fields of deep learning, artificial intelligence, data mining, and machine learning. Included within this reprint are all the accepted articles that were published in the Special Issue. It is our hope that readers can benefit from the valuable insights presented in these papers, ultimately contributing to the advancement of these rapidly expanding areas. Furthermore, we believe that this Special Issue will shed light on major developments in machine learning and data mining, drawing the attention of the scientific community towards further investigations that will expedite the implementation of these techniques.","url":"https://doi.org/10.3390/books978-3-0365-9818-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-27T13:57:51Z","doi":"10.3390/books978-3-0365-9818-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1039/d3ra07322j/v2/review1","name":"Review for \"MolToxPred: small molecule toxicity prediction using machine learning approach\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d3ra07322j/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-31T16:18:37Z","doi":"10.1039/d3ra07322j/v2/review1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.172107241.19989939/v1","name":"Privacy Amplification in Quantum Key Distribution with Machine Learning","source":"crossref","abstract":"The Quantum Key Distribution (QKD) method leverages the principles of quantum mechanics to securely create and distribute private keys using quantum systems and an authenticated public classical channel. Despite offering informationtheoretical security, its physical implementations often suffer from unintended information leakage, known as side channels, which eavesdroppers can exploit to obtain the private key. This study introduces a classical-side-channel attack on the privacyamplification step of a general QKD protocol based on matrix hashing, utilizing machine-learning techniques to analyze powerconsumption leakage. Through multiple simulated scenarios, the study found that the gradient-boosting machine consistently outperformed other models, recovering the entire private key for high measuring-instrument sampling rates, regardless of the hashing matrix size and noise level tested. Additionally, the study proposes a strategy based on analyzing the confusion matrices of the model to facilitate a brute-force search for the critical feasible in the case of a non-perfect model. Furthermore, the study discusses countermeasures such as noise insertion, masking, and randomization techniques, potentially restoring the QKD protocol's information-theoretical security. This work demonstrates that machine-learning techniques can effectively characterize the leakages in a QKD implementation and create potent attacks.","url":"https://doi.org/10.36227/techrxiv.172107241.19989939/v1","authors":["Nipun Agarwal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-15T15:40:41Z","doi":"10.36227/techrxiv.172107241.19989939/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1145/3698263","name":"Proceedings of the 2024 2nd International Conference on Machine Learning and Pattern Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3698263","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-17T04:58:59Z","doi":"10.1145/3698263","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.36227/techrxiv.170774641.17450763/v1","name":"On BiasWrappers: New Regularization Techniques for Machine Learning Regression","source":"crossref","abstract":"Regressive models in machine learning require regularization to balance the bias-variance tradeoff and attain realistic predictions in the real world. Two new regularization techniques, referenced as BiasWrappers, will be discussed in this paper: BiasWrapperC1 and BiasWrapperC2. BiasWrapperC1 uses a form of penalization to prevent models from consistently overshooting or undershooting. BiasWrapperC2 uses a modified layer of regression stacking to identify correlations of a regression model’s error. The techniques’ logics will be discussed through pseudocode in the context of machine learning regression. The regularization techniques are applied to machine learning models and compared with other regularization techniques through a series of carefully chosen datasets, and these metrics are used to hypothesize about the implications of these new techniques. All implementations are referenced with pseudocode in the paper, with external testing wrappers programmed in Python. An experimental study was conducted with standard regression datasets and showed the regularizations’ value propositions in multi-output data and outlier-based data.","url":"https://doi.org/10.36227/techrxiv.170774641.17450763/v1","authors":["Karthik Singaravadivelan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-12T09:00:21Z","doi":"10.36227/techrxiv.170774641.17450763/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.31219/osf.io/3hwkm","name":"Gaze sequence enhanced with machine learning","source":"crossref","abstract":"Gaze sequencing, the analysis of the order and duration of eye fixations, plays a crucial role in understanding human behavior, cognitive processes, and decision-making. It has applications in various fields such as human-computer interaction, healthcare, psychology research, and more. However, accurately capturing and interpreting gaze sequences can be challenging due to factors like noise, distractions, and individual variations. To overcome these challenges and enhance the accuracy of gaze sequencing, machine learning techniques have emerged as powerful tools. This article provides an overview of machine learning techniques in gaze sequence analysis and explores their role in improving accuracy, along with discussing applications, challenges, future trends, and case studies. By leveraging machine learning, we can unlock the full potential of gaze sequencing, opening up new avenues for research, development, and real-world applications.","url":"https://doi.org/10.31219/osf.io/3hwkm","authors":["Kelvin Jackson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-14T00:00:55Z","doi":"10.31219/osf.io/3hwkm","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-61037-0_17","name":"Stock Market Prediction Using Machine Learning: Evidence from India","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-61037-0_17","authors":["Subhamitra Patra","Trilok Nath Pandey","Biswabhusan Bhuyan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-26T22:02:03Z","doi":"10.1007/978-3-031-61037-0_17","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/satml59370.2024.00002","name":"Title Page iii","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml59370.2024.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T17:22:05Z","doi":"10.1109/satml59370.2024.00002","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-32-389859-1.00010-6","name":"Linear threshold machines","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-32-389859-1.00010-6","authors":["Marco Gori","Alessandro Betti","Stefano Melacci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:21:34Z","doi":"10.1016/b978-0-32-389859-1.00010-6","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-323-99029-5.00003-0","name":"Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99029-5.00003-0","authors":["Carlos A. Escobar","Ruben Morales-Menendez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-22T09:07:52Z","doi":"10.1016/b978-0-323-99029-5.00003-0","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-32-389859-1.00015-5","name":"Answers to exercises","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-32-389859-1.00015-5","authors":["Marco Gori","Alessandro Betti","Stefano Melacci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:21:43Z","doi":"10.1016/b978-0-32-389859-1.00015-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1137/1.9781611977882.bm","name":"Back Matter","source":"crossref","abstract":"The back matter includes Bibliography, Index, and back cover.","url":"https://doi.org/10.1137/1.9781611977882.bm","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-11T18:47:43Z","doi":"10.1137/1.9781611977882.bm","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.58496/bjml/2024/014","name":"AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats","source":"crossref","abstract":"This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, alerting mechanisms, and continuous monitoring. AI-powered threat detection offers numerous advantages, including predictive threat detection, increased accuracy and scalability, shorter response times, and the ability to adapt to evolving threats. However, it also presents challenges, such as ensuring data quality, managing model complexity, mitigating false positives, addressing resource requirements, and maintaining security and privacy standards. The proposed AI-powered anomaly detection framework for Kubernetes security demonstrated significant improvements in threat identification and mitigation. Through real-time analysis of telemetry data and leveraging advanced AI algorithms, the system accurately identified over 92% of simulated security threats and anomalies across various Kubernetes clusters. Additionally, the integration of automated alerting mechanisms and response protocols reduced the average response time by 67%, enabling rapid containment of potential breaches.","url":"https://doi.org/10.58496/bjml/2024/014","authors":["Arvind Kumar Bhardwaj","P.K. Dutta","Pradeep Chintale"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-27T00:20:32Z","doi":"10.58496/bjml/2024/014","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/satml59370.2024.00004","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml59370.2024.00004","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T17:22:05Z","doi":"10.1109/satml59370.2024.00004","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4757728","name":"Bank Customer Churn Prediction Using Machine Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4757728","authors":["Rasha Ashraf"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-13T10:18:50Z","doi":"10.2139/ssrn.4757728","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4663593","name":"Fortifying Network Security with Machine Learning","source":"crossref","abstract":"The pervasive integration of machine learning in various domains has positioned it at the forefront of technological advancements, with cybersecurity being a significant beneficiary. In this paper, we explore the widespread adoption of machine learning techniques in cybersecurity applications, such as malware analysis, zero-day malware detection, threat assessment, and anomaly-based intrusion detection for safeguarding critical infrastructures. Traditional signature-based methods face limitations in effectively detecting zero-day attacks or subtle variations of known attacks, prompting researchers to employ machine learning-based detection in cybersecurity tools. This review comprehensively examines different facets of cybersecurity where machine learning serves as a pivotal tool. Additionally, we shed light on adversarial attacks targeting machine learning algorithms, emphasizing attempts to manipulate training and test data to undermine the efficacy of classifiers, rendering these tools ineffective.","url":"https://doi.org/10.2139/ssrn.4663593","authors":["Ashley Audrey Innocent Yanguema"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-06T13:11:16Z","doi":"10.2139/ssrn.4663593","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-44-314141-6.00016-5","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-314141-6.00016-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-21T06:24:45Z","doi":"10.1016/b978-0-44-314141-6.00016-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.70593/978-81-984306-1-8_3","name":"Machine learning algorithms in personalized treatment planning","source":"crossref","abstract":"Machine learning algorithms are revolutionizing personalized treatment planning by analyzing patient data to create tailored interventions. These algorithms optimize treatment strategies, improve outcomes, and minimize adverse effects, enabling precision medicine that adapts to individual needs and promotes more effective healthcare delivery. Keywords: Machine Learning, Personalized Treatment, Precision Medicine, Treatment Planning, Healthcare Optimization, Patient Data 3.1. Introduction In medical practice, “one size does not fit all patients.” Healthcare has been evolving towards personalized methods in which treatment procedures are adapted to fit the individual characteristics and needs of the patient. The widespread use of information and communication technologies has allowed for the accumulation of data from various sources such as Electronic Health Records, intake/exclusion criteria for clinical trials, computerized physician order entry, diagnostic reports, and medical claims. This allows us to leverage modern machine-learning technologies for the assessment and refinement of treatment plans for patients. Machine learning algorithms combined with expanded knowledge of a patient's data history could provide a critical empirical basis for the identification of previously unknown patient sub-classifications that could evolve our knowledge from evidence-based methods to personalized methods, from data-driven decisions to learning-driven decisions (Ramanakar et al., 2024). The integration of machine learning solutions in several medical domains is having an increasingly important impact on the decision-making process, treatment planning, and patient management. The introduction of information and communication technologies has greatly increased the amount, complexity, and sources of data that can be collected. In addition to on-site storage, cloud-based computing has begun to provide a high level of computational capability on demand and has sparked new crowd-shared and community-based computing initiatives and services. The goal of this essay is to explore the intersection between machine learning algorithms and personalized treatment planning. We will give an overview of the personalized treatment planning algorithm types such as Random Forest, Lasso Regression, Support Vector Machine, Gradient Boosted Model, Average Individualized Treatment Effects, and Reinforcement Learning. 3.1.1. Background of Personalized Treatment Planning In efforts to play a major role in patient care and achieve personalized treatment strategies, personalized treatment planning has made significant advancements in recent years. The initial approach to treatment planning consisted of using bulk patient populations with average-based care. LM values are either derived from RCTs where patients are rigidly selected or from population-based cancer registries and observational studies. These values do not translate into optimal treatment strategies for the individual patient as they do not define the individual factors that drive the optimal values. The development of imaging and computation technology allows the use of functional and anatomical images to infer internal functional motion and/or anatomic geometric change to optimize XRT doses to tumor and normal structures, creating the opportunity to individualize treatment based on anatomic and tumor heterogeneity. Each of these factors may be related to tumor heterogeneity in terms of radiosensitivity or recurrence, given different genetic tumor profiles within the same anatomic entity. Personalization aims at a tailored approach to minimize treatment side effects while still ensuring sufficient therapy efficacy. However, implementing this in a relatively centralized healthcare system adapted to deliver population-based therapy to bulk patient populations is a challenge. This was well illustrated in what has recently been labeled the \"regression-to-the-mean effect.\" Fig 3 . 1 : Machine Learning for Personalized Treatment. Traditionally, clinicians have been responsible for patient care and treatment decision-making. Over time, patients have become more advanced in their healthcare knowledge and want a better understanding of their treatment options, risks, and benefits. From a national and international level, the research community appreciates that it is cost-effective when treatment can be individualized to the specific patient populations that will benefit from it based on underlying tumor biology (Syed, 2024). Allowing for treatments to be personalized may help avoid both over- and undertreating patients based on population-level data, as well as highlight patients who may need adjunctive therapy to achieve a cure. Randomized clinical trials have been initiated to become national leaders in computational science, and overcoming these obstacles will need innovative approaches to signal traditional clinical research. In the best case, patients would be treated based on their genetic profile, lifestyle, environment, and disease stage at the time of consultation. This genetic profile can only be observed over a lifetime; this is when the promise of personalized medicine would come to be: observation of a genetic profile new and unique to the patient that predicts disease risk, cure, etc. Thus, the issues of most categories of personalized medicine have closed their gap with our next-generation healthcare system and become applicable to today's practice. Today's practice is what is referred to in the personalized treatment planning and patient care sections, and it is in this context that the effort was started. 3.1.2. Significance of Machine Learning in Healthcare Machine learning, or the use of algorithms to find patterns in vast databases, is being increasingly used to drive innovation in sectors like transportation, banking, and media. When it comes to healthcare, machine learning is a valuable tool that processes diverse sources of data like patient records, studies, and medical literature to uncover patterns and problems that may not be readily apparent. These insights can then be used to improve patient outcomes. In some cases, the algorithm itself can even outperform human experts in predicting disease outcomes or drug responses. This not only significantly reduces diagnostic errors, optimizes clinical confidence, and serves patients the most accurate and effective treatment options, but also accelerates novel drug development and clinical trial optimization. For efficient translation of personalized medicine into the clinic, two factors are crucial—scalability and access. Currently, physicians measure a limited combination of biomarkers to personalize the treatment of cancer. Automating personalized treatment planning by generating comprehensive statistical models from large datasets will improve the scope and coverage of personalized treatment algorithms. Overcoming the economic and time-related constraints of physician involvement in treatment planning decisions using automated machine learning-based tools has the potential to vastly scale personalized strategies. Due to rapidly increasing volumes of digital data, the democratization of machine learning will likely promote the widespread clinical adoption of data-driven clinical decision support. It is ideal for machine learning models to learn from a human-annotated dataset of patient prognosis, where the gold standard of patient anatomy, pathology, and genes is known. However, it will also create ethical challenges in associating diagnosis prediction using solely treatment-agnostic images and patient demographic data. A balance will be needed to de-risk patients and train treatment-centric diagnostic models only on large, ideally public, patient case studies where treatment and outcome anchoring can be incorporated. 3.2. Machine Learning Algorithms in Personalized Treatment Planning Machine learning is divided into three categories. Supervised learning is an ML algorithm where learning is done by enforcing a mapping between input and output data. In the context of personalized treatment planning, the input is usually the description of a set of patients, and the output is the efficacy of a set of treatment strategies under those circumstances. Unsupervised learning is an ML algorithm where the learning is done by attempting to find structure in a design matrix where each row describes different situations, but where no reference outcome is provided. Reinforcement learning is basically about learning the best action to take under a given set of circumstances to enable a goal to be achieved in the long term under uncertainty concerning the environment (Nampalli et al., 2024). Some examples of algorithms used in the clinical setting include: classification (supervised learning) - logistic regression, ensemble methods, neural networks, k-nearest neighbors, and support vector machines. Regression (supervised learning) - linear regression, neural networks, decision trees, support vector machines. Clustering (unsupervised learning): k-means. Principal component analysis: performs dimensionality reduction when the training set becomes too large. Deep Q learning (reinforcement learning). In the case of clinical research, the former and latter appear to be the most relevant to the types of problems we must address. The tools differ in many ways, the most visually striking of these being that in supervised learning we have an output variable to be predicted based on input data, while in unsupervised learning we do not. Regulatory approval of these models, together with their accompanying threat of algorithm bias, patient privacy, and quality of the data, are typically the main obstacles. Equation 1 : Personalized Prediction Model: 3.2.1. Supervised Learning A prominent machine learning approach used for personalized treatment planning is supervised learning. This branch of machine learning involves training an algorithm on a labeled dataset to predict or classify new data points. Supervised learning in healthcare can be applied in a range of scenarios; for example, it can be used to predict disease prognosis, anticipate patient outcomes, and predict patient responses to specific treatments, as in the case of treatment planning and prescription in personalized oncology. Supervised learning would also be essential in the prediction of side effects of a certain agent or the type of patients who will respond better to it. Machine learning applications using structured data in personalized medicine have been numerous and successful. This is a very complex task considering that for each patient around 200 different drugs are analyzed. Different types of algorithms can be individually trained and developed through supervised learning, namely regression models to serve in prediction tasks or classification algorithms to serve in the case of classifying tasks. Unlike unsupervised learning, supervised learning is preferred when more reliable and accurate predictions are required. Despite this, supervised learning has some unique challenges. Perhaps the main limitation is the strong need for a large adequate labeled dataset. Generating those datasets in healthcare can be costly and time-consuming. Furthermore, years of patient data to use for training are does not usually within Some have to for this of supervised learning applications by methods through algorithms to create patient populations in which both observed and may be those over for the development of the from the observed data to the Fig 3 . : Supervised machine learning in Personalized Treatment. Unsupervised Learning Unsupervised learning are used to healthcare data. Unlike supervised learning, unsupervised learning on patterns and in the data a This can be in personalized treatment planning when we want to find of patients that are in terms of treatment or factors or to data with patterns by or The most for unsupervised learning is which is used to patients together based on their treatment side effects or treatment This could to new patient with characteristics and can help to which personalized treatment are examples of applications of unsupervised learning are dimensionality patient and et al., 2024). Unsupervised learning has several benefits. In the of data can to new treatment strategies, which is important for personalized treatment planning most data in the is unsupervised learning algorithms are in their to new data. do not need to be each time new or clinical data is and they can more data where predictions are unsupervised learning algorithms provide a more output as they give more into the personalized of patients that are in the data. However, are also some challenges to unsupervised learning. of the and of the patterns that are are of unsupervised learning algorithms can also find patterns that are only in the training data and do not to the patient Reinforcement Learning Reinforcement learning is a novel approach in the of personalized treatment planning. It is with learn to by is on learning by action in to achieve the best outcomes over the long through trial and has been as a to infer the best treatment and the treatment in using patient data. This novel approach a of applications and for For example, it could the under economic the optimal for a drug at a given and personalized for therapy based on patient or However, personalized treatment with is to high the underlying treatment models, and data of healthcare for a treatment the of and or needs to be However, of the most factors for machine learning algorithms in healthcare is the data, data with types and healthcare of patients and healthcare in a environment with it more have ML strategies for treatment strategies personalized to patients at each decision based on the observed treatment and with the patient in the best based on algorithms are learning algorithms that can learn from patient and or treatment or over time in a personalized algorithms may to have a potential for patient as they over time based on data as ML algorithms enable the of treatments and are based on unique algorithms learning the optimal for the and action may an optimal of based on a of also has the of learning, the optimal over in the context of treatment planning, the optimal treatment for the patient is from time to time, new in the of Machine Learning in Personalized Treatment Planning Personalized treatment planning to a treatment for an individual based on their responses and A for personalized treatment planning is to use data-driven methods to clinical Machine learning algorithms can be used to models for individual outcomes for each potential decisions about patient treatment can be the best for that patient. These methods in to traditional which over populations of patients are not to personalized treatment plans (Syed, 2024). of are a of for diverse and but the major and many and that need to be methods can be applied to treatment that can be as a of decision or The development of precision treatment algorithms has begun in those For example, several studies have been in oncology. The of clinical trial design into clinical decision-making of the of personalized treatment strategies. These methods can also be applied in populations new treatments where clinical trials have not to Furthermore, the are for where the heterogeneity of the is not by For example, patient responses for many and treatment algorithms have been to be only of the treatments using models, a to improve outcomes and also the of treatments can be The of an optimal treatment is critical to achieve these The use of and such models a from and an in the of machine learning algorithms have a in the clinical treatment of personalized therapy in the Treatment Planning has been made in recent years in the to data at the and to a understanding of the processes underlying such as cancer. have been applied in the healthcare and machine learning have allowed us to use this data to several in the of personalized treatment planning. This is the of a treatment with a diagnosis that can be as accurate as and at an individual patient. Machine learning us to models that who be at of and who are best treated by or do data is used to predict the In the using prediction models allows us to find a new patient who is best or most to a patient and then use the treatment Fig 3 . 3 : and Machine Learning in cancer to personalized machine learning has models that can be used to the in which benefit from based on This aims to a of applications of machine learning models in personalized cancer treatment planning by from the case We in the of the methods in clinical using and We also applications by into data as of the to personalize their an of the we the challenges of clinical practice from the of this machine learning and clinical prediction can to overcoming The integration of and of statistical and personalized prediction at patient are at the of the However, for all the that technology can some would that drug for for patient is we the potential role of this approach in significant in medical and clinical is the learning to be this both that will want to minimize in classification and terms for those models et al., Health and Treatment of the most of personalized for personalized treatment planning is such as and are and as they are by an of and neural initiatives to large datasets of patient their and genetic and their responses to treatments to underlying A significant of is while most patients with do respond to standard treatments, those treatments do not for patient. based on unsupervised machine learning tools could be used to help domains for precision which is of the technology in Furthermore, or therapy treatment and could all benefit from advanced machine learning in this based on factors from patient and/or data. models to patients who or not respond to the standard treatment approaches based on from relevant The Random and logistic regression models and to and from treatment significantly the of likely based on more The predictions that these models potential insights about treatment for a given individual based on their the potential for personalized care applicable different these applications are on the best design to be and the most critical outcome measure for the of the applications of machine learning models to the also with the of and such as in a that on treatments are also at a of as by recent studies. The with of in to of a data privacy, and are some of In of ethical and the of such models in is still in and the support and of trial about their specific and to and deliver tailored that benefit from very is in the has many and the of this technology into would benefit greatly those who need it. and Despite the the of machine learning in clinical has to several challenges. A main limitation is the and of healthcare data. when large training and datasets with patient data have to be the of or patient information is studies have that from different is Furthermore, machine learning algorithms or models learn patient from records, the to individual patients has to be with to data. Different have different when patient data. For in the Data the for data and and in data science, with a on may to data also used for training and the or machine learning algorithms. limitation in implementing machine learning in clinical is the The more complex a machine learning algorithm the it is to underlying structure or the a between different the is to which and that knowledge a and to use machine learning by clinicians may be ethical in implementing machine learning and in healthcare is the potential to and For some studies have that data, and/or genetic information of different and have different machine-learning It has been that and as well as can also in records, to automated algorithms for disease diagnosis and these and about ethical and issues with all is to machine learning models and where and data in medicine and not the community et al., task will be to find and a in and ethical between data the data computational of patients and in data and in healthcare that all these Data and The of patient data and related such as genetic or clinical data, is not only a of and ethical but is also data can be to the who may be by data of to with and ethical can in and and the of personalized treatment planning. The and of information are by recent in of the and the are also several and the use of data. In the the Health and is applicable to machine learning algorithms for personalized treatment planning. it is to healthcare to data The applications of machine learning in personalized treatment planning large of and the use of machine learning in a clinical setting could and to It is of the to efficient methods and tools to data. Machine learning models can be on very with several and ethical has to be to that the does not use those these may also the and of the machine-learning is a of medical For data to be must be of their data are used and it is can the of of data, the data between and the data and it as not these be made attempting to the The of these can be made based on for These applications can high in the and of data. Equation : for Treatment and In practice, is a between predicting outcomes and treatment decisions et al., 2024). It is critical to the about a outcome is for decision-making in a environment like personalized In healthcare, are to to patients and increasing the of and medical the output of a machine learning algorithm is a for many physicians and is to the of clinical are likely to and will not they are to care or a tool has a models can provide give predictions and those predictions to for in context to the These models can be based on numerous design that either towards a and or a and In medical cancer when an ML that clinical a and is a complex clinical The need to to in cancer treatment models be with complex models all of studies are treatment change evidence-based and in the of patient data, will also of a ML be is a balance between effort in and treatment Fig 3 . : of Machine Learning in Personalized Treatment. In the development and of machine learning algorithms to and unknown critical treatment decisions tailored to individual patients could rapidly healthcare practice potential it to the and needed to For example, machine learning algorithms need to issues related to to data, and also be a major of machine learning algorithms is their the and communication of machine learning would the translation of the of the algorithms not only in terms of patient outcomes but also in terms of and would the case for their use for and healthcare of different and et al., The potential is also for research. we to the of machine learning, more and personalized for treatment using these could the for ensuring data to the data on which most machine learning algorithms are being innovative with different sectors in the healthcare to design the data needed to the best for care for decision-making. with data and in understanding the of machine learning and applications would in in this the a machine learning approach to treatment strategies could significantly improve outcomes for patients. machine learning personalized treatment planning, numerous issues are research Furthermore, it is predicted that the increasing of and data in the of treatment the of precision medicine and Healthcare and algorithm is critical for the of personalized medicine planning Healthcare be made more of the of to treatments, having in a In the of a or problems of in the training research may such as the use of strategies. Furthermore, for in the of for personalized treatment planning, likely may be the of these tools as new and healthcare information come to The widespread use of personalized treatments is to be by clinical information and are more in medical and are their by on medical Machine learning strategies tailored to the and characteristics of patients could this could input information such as their and medical and in the to and treatment based on a of of treatment and may be based on at et al., patients can and this they will be to their about their care and treatment and may help become this This is only the to train technology and patients and Deep Learning for Health and in of Medicine, to A of and In of and Data for in Machine Learning for Personalized and and Machine Learning for and in Health in Health and for In for and for the of in for Data Machine Learning Patient from Deep Learning In For from","url":"https://doi.org/10.70593/978-81-984306-1-8_3","authors":["Kiran Kumar Maguluri"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-16T05:02:51Z","doi":"10.70593/978-81-984306-1-8_3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1017/9781009003872","name":"Machine Learning Evaluation","source":"crossref","abstract":"As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.","url":"https://doi.org/10.1017/9781009003872","authors":["Nathalie Japkowicz","Zois Boukouvalas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-07T00:06:30Z","doi":"10.1017/9781009003872","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.18699/bgrs2024-06-34","name":"Applications of artificial intelligence, machine learning, and deep learning in plant breeding","source":"crossref","abstract":"","url":"https://doi.org/10.18699/bgrs2024-06-34","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-24T12:45:10Z","doi":"10.18699/bgrs2024-06-34","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-323-99029-5.00001-7","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99029-5.00001-7","authors":["Carlos A. Escobar","Ruben Morales-Menendez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-22T09:07:43Z","doi":"10.1016/b978-0-323-99029-5.00001-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmlcn59089.2024.10624793","name":"Authors","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlcn59089.2024.10624793","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-15T17:18:59Z","doi":"10.1109/icmlcn59089.2024.10624793","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-32-389859-1.00008-8","name":"The big picture","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-32-389859-1.00008-8","authors":["Marco Gori","Alessandro Betti","Stefano Melacci"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-06-05T05:21:30Z","doi":"10.1016/b978-0-32-389859-1.00008-8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/9781394233953.fmatter","name":"Front Matter","source":"crossref","abstract":"METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.","url":"https://doi.org/10.1002/9781394233953.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-29T21:41:43Z","doi":"10.1002/9781394233953.fmatter","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/ceeml65709.2024.00003","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ceeml65709.2024.00003","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-29T17:29:35Z","doi":"10.1109/ceeml65709.2024.00003","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/s1570-8659(24)00016-4","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s1570-8659(24)00016-4","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-20T12:05:48Z","doi":"10.1016/s1570-8659(24)00016-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2174/9789815179606124010010","name":"Insights into Deep Learning and Non-Deep Learning Techniques for Code Clone Detection","source":"crossref","abstract":"A source code clone is a type of bad smell caused by pieces of code that have the same functional semantics, but the syntactical representation varies. In the past few years, there have been several studies about code clone detection, steered by numerous machine learning models, software techniques and other mathematical measures. This paper aims to conduct an impartial comparative study of the existing literature on Deep Learning and Non-Deep Learning techniques. Due to the lack of work in studying the previous and the current state-of-the-art tools in code clone detection, there is no concrete evidence found to underpin the use of Deep Learning approaches in clone detection, except for a preference from the evolutionary point of view. We will address and investigate a few research questions related to the intentions of using DL techniques for code clone detection compared to those of non-DL approaches (Based on –token, text, AST, metrics, and others). Furthermore, we will discuss the challenges faced in the Deep Learning implementation for clone detection and their potential resolutions if feasible. This review would help the audience understand how different approaches aid the clone detection process along with their performance measures, limitations, issues, and challenges.","url":"https://doi.org/10.2174/9789815179606124010010","authors":["Ajinkya Kunjir"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T11:38:12Z","doi":"10.2174/9789815179606124010010","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1063/10.0028271","name":"Using machine learning to provide understanding along with results","source":"crossref","abstract":"A cyclical implementation of machine learning algorithms provides insights into physical mechanisms, improving efficiency.","url":"https://doi.org/10.1063/10.0028271","authors":["Avery Thompson"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-09T12:39:50Z","doi":"10.1063/10.0028271","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-32892-3.00008-7","name":"Early detection approach for analysis of osteoarthritis using artificial intelligence and machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32892-3.00008-7","authors":["Chander Prabha"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-22T05:37:30Z","doi":"10.1016/b978-0-443-32892-3.00008-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1515/9783110697186-fm","name":"Frontmatter","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110697186-fm","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-22T18:47:27Z","doi":"10.1515/9783110697186-fm","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2139/ssrn.4932275","name":"Galaxy Classification with Machine Learning","source":"crossref","abstract":"Galaxy classification, a fundamental task in astronomy, involves categorizing galaxies based on their morphological features. To streamline this process and reduce manual effort, researchers have turned to machine learning methods. A recent study explored several machine learning techniques, including Convolutional Neural Networks (CNN), K-nearest Neighbor, Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks, for classifying galaxies. This investigation integrated data from the Dark Energy Survey (DES) and the Galaxy Zoo 1 project (GZ1) for visual classifications. Additionally, another study employed machine learning to classify galaxies into the primary types of elliptical, spiral, and irregular based on their properties. Deep convolutional neural networks were utilized in this study to automate the categorization process.","url":"https://doi.org/10.2139/ssrn.4932275","authors":["Abhinav Singh","Prashante .","Indra Kumari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-28T09:10:19Z","doi":"10.2139/ssrn.4932275","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/eng2.12893/v2/review2","name":"Review for \"Prediction of hydrogel swelling states using machine learning methods\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.12893/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-04T17:08:25Z","doi":"10.1002/eng2.12893/v2/review2","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781003477280-10","name":"Prediction of Neonatal Mortality from Jaundice Using Machine Learning","source":"crossref","abstract":"Around the world, jaundice – a birth complication – is the primary cause of newborn mortality and morbidity. As more is discovered about the causes of this illness and possible therapies, the severity of the conditions may lessen. While some progress has been achieved, it is not nearly enough. Possible reasons for failure include the difficulty in identifying issues early enough for timely treatment and the similarity of symptoms that could result in a mistake. For Ethiopia and other already suffering nations, the situation is far more serious. Due to a potential shortage of pediatricians and neonatologists, this may be cause for alarm. This suggests there may be a problem with or inadequacy in the diagnosis. The proposed work focuses on the use of machine learning techniques in prediction of neonatal mortality due to jaundice and presents a detailed analysis of the challenge of detecting the top four causes of newborn mortality: severe infections, birth asphyxia, necrotizing enterocolitis, and respiratory distress syndrome. It further depicts the use of various classification algorithms – namely, XGBoost (XGB), random forest (RF), and support vector machine (SVM) models – to predict the disease. The data covers the time period from 2008 to 2014. The performance analysis of various algorithms shows they outperformed competition in terms of accuracy (95.91%). The current work suggests to and motivates health practitioners and hospitals, particularly those with fewer resources, to use machine learning techniques to catch infant infections sooner.","url":"https://doi.org/10.1201/9781003477280-10","authors":["Mayank Srivastava","Yajur","Sujata"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-22T14:16:12Z","doi":"10.1201/9781003477280-10","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.22541/au.172795152.29144979/v1","name":"Development of a Machine Learning derived Anticholinergic Burden Scale (ML-ACB scale): A Machine Learning Approach with Enhanced Drug Properties and Weighting","source":"crossref","abstract":"Aim: This study aims to refine the anticholinergic burden (AB) scale developed in our previous research by incorporating additional drug properties, such as Lipophilicity and Molecular Weight, and implementing a new weighting approach to address the varying influence of each drug property on anticholinergic burden. The objective is to improve the scale’s predictive accuracy and compare its performance against established scales. Methods: The scale, which covers 87 drugs, was expanded to include seven drug properties, combining new properties, Lipophilicity and Molecular Weight, with previously utilised experimental and in silico ADME, physicochemical, and pharmacological properties. A weighting approach was introduced to the hierarchical clustering process to account for the differential impact of each drug property on AB. The performance of this revised scale was evaluated through 10-fold cross-validation against the clinical Anticholinergic Cognitive Burden (ACB) scale and the non-clinical Anticholinergic Toxicity Scores (ATS) scale. Results: The scale showed improved alignment with the ACB and ATS scales, agreeing with the rankings of 54 out of 87 drugs and 16 out of 25 drugs respectively. The Area Under the Receiver Operating Characteristic Curve (AUROC) indicated strong performance. The ML-ACB and ACB has an AUC of 0.99 and 0.81 respectively, whilst the ML-ACB and ATS had an AUC of 0.96 and 0.62. Conclusion: The ML-ACB scale offers improved alignment with the established ACB scale. This highlights the potential of the ML-ACB scale as a valuable tool for clinical and research applications, providing a data-driven alternative that closely correlates with existing validated scales.","url":"https://doi.org/10.22541/au.172795152.29144979/v1","authors":["Oteng Phutietsile","Nikoletta Fotaki","Prasad Nishtala"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-03T06:32:12Z","doi":"10.22541/au.172795152.29144979/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1017/eds.2024.45.pr1","name":"Author comment: Machine learning for stochastic parametrization — R0/PR1","source":"crossref","abstract":"Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.","url":"https://doi.org/10.1017/eds.2024.45.pr1","authors":["Hannah Christensen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-01-22T04:57:24Z","doi":"10.1017/eds.2024.45.pr1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.37544/0042-1766-2024-10-30","name":"Deep Learning Machine Vision","source":"crossref","abstract":"Wie können industrielle Bildverarbeitung (IBV), 3D-Technologie und Robotik die Fertigungsprozesse und Lieferketten für Elektrobatterien unterstützen? Dies war im Juni 2024 auf der Branchenveranstaltung in Stuttgart zu sehen.","url":"https://doi.org/10.37544/0042-1766-2024-10-30","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-18T03:58:50Z","doi":"10.37544/0042-1766-2024-10-30","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlsp58920.2024.10734775","name":"Cover","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlsp58920.2024.10734775","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-04T18:31:55Z","doi":"10.1109/mlsp58920.2024.10734775","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlise62164.2024.10674133","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlise62164.2024.10674133","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-19T17:22:29Z","doi":"10.1109/mlise62164.2024.10674133","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlnlp63328.2024.10800027","name":"Cover","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlnlp63328.2024.10800027","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-20T18:56:07Z","doi":"10.1109/mlnlp63328.2024.10800027","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/c2021-0-03583-5","name":"Internet of Things and Machine Learning for Type I and Type II Diabetes","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2021-0-03583-5","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:25:56Z","doi":"10.1016/c2021-0-03583-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781003391456-3","name":"Breast Cancer Prediction Using a Machine Learning Approach","source":"crossref","abstract":"Every year, millions of people are dying due to cancer. Cancer is a group of diseases that involves abnormal or out-of-control cell growth. The actual reason for this abnormal cell growth is still unknown. This abnormal cell growth can spread to other parts of the body. Because of the abnormal cell growth, cancer is also termed a malignancy. More than 100 types of cancers have been identified. Cancer is one the deadliest diseases because there is no proper medical remedy. Only a few medicines are available to treat the symptoms and reactions of cancer patients. Moreover, there is no assurance that these medicines will be able to cure all or any type of cancer. Cancer gradually lowers the immunity level of patients, which is very dangerous. Over time, cancer patients lose their immunity-based fighting capacity. Also, there is no guarantee that a patient who survives cancer will not be diagnosed with the same or another type of cancer in the future. Based on the severity, there are four stages of cancer: stage I, stage II, stage III, and stage IV (from least to most severe). Generally, cancer forms a tumor in the initial phase. The size of the tumor starts increasing accompanied by acute pain. The presence of cancer can be confirmed through a biopsy test of the suspected body part. This biopsy is a time-consuming process. In some cases of cancer, the intensity progresses very quickly and may even cause the death of the patient. So, time is a big factor in case of treatment of cancer. Though all types of cancer are dangerous and life-threatening, there are some cancers like lung cancer, pancreatic cancer, brain cancer, and blood cancer that do not provide much time and scope of treatment once it is diagnosed. Cancer is of four types: carcinomas, sarcomas, leukemias, and lymphomas. Some very common cancers, such as prostate cancer, breast cancer, colorectal cancer, and lung cancer. fall into the carcinomas category. These cancers are very common. Breast cancer is the second most common cancer in the USA after skin cancer. Even in India, more than a quarter of all women with cancer have breast cancer. Women, especially those over 50, are more likely to develop breast cancer compared to men. In India, unfortunately, the survival rate of breast cancer is low because of late detection. As time is a big factor in cancer diagnosis and treatment, this work aims to propose machine learning to assist the current treatment process by providing an early prediction of cancer, specifically breast cancer, before the biopsy report based on the biological information of 569 patients.","url":"https://doi.org/10.1201/9781003391456-3","authors":["Arijit Ghosal","Harshita Somolu","Suchibrota Dutta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-19T18:45:33Z","doi":"10.1201/9781003391456-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-36678-9_10","name":"Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-36678-9_10","authors":["Jason H. Moore","Pedro H. Ribeiro","Nicholas Matsumoto","Anil K. Saini"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-04T05:08:25Z","doi":"10.1007/978-3-031-36678-9_10","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-44-319077-3.00002-x","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319077-3.00002-x","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-24T10:12:21Z","doi":"10.1016/b978-0-44-319077-3.00002-x","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/mlhmi63000.2024.00002","name":"Proceedings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlhmi63000.2024.00002","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-23T17:37:15Z","doi":"10.1109/mlhmi63000.2024.00002","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.5121/csit.2024.140210","name":"Unsupervised Anomaly Detection","source":"crossref","abstract":"This research focuses on Unsupervised Anomaly Detection using the \"ambient_temperature_system_failure.csv\" dataset from Numenta Anomaly Benchmark (NAB). The dataset contains time-series temperature readings from an industrial machine's sensor. The aim is to detect anomalies indicating system failures or aberrant behavior without labeled data. Various algorithms, such as K-means, Gaussian/Elliptic Envelopes, Markov Chain, Isolation Forest, One-Class SVM, and RNNs, are applied to analyze the temperature data. These algorithms are chosen for their ability to identify significant deviations in unlabeled datasets. The study explores how these techniques enhance anomaly understanding in time series data, relevant in manufacturing, healthcare, and finance. This research's novelty lies in employing unsupervised learning techniques on a real-world dataset and understanding theiradaptability in anomaly detection. The results are expected to contribute valuable insights to the field, showcasing the practicality and effectiveness of these algorithms across various scenarios.","url":"https://doi.org/10.5121/csit.2024.140210","authors":["Suliman Alnutefy","Ali Alsuwayh"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-04T14:25:56Z","doi":"10.5121/csit.2024.140210","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-4372288/v1","name":"Speaker identification using hybrid subspace, deep learning and machine learning classifiers","source":"crossref","abstract":"Abstract Speaker identification is crucial in many application areas, such as automation, security, and user experience. This study examines the use of traditional classification algorithms and hybrid algorithms, as well as newly developed subspace classifiers, in the field of speaker identification. In the study, six different feature structures were tested for the various classifier algorithms. Stacked Features-Common Vector Approach (SF-CVA) and Hybrid CVA-FLDA (HCF) subspace classifiers are used for the first time in the literature for speaker identification. In addition, CVA is evaluated for the first time for speaker recognition using hybrid deep learning algorithms. This paper is also aimed at increasing accuracy rates with different hybrid algorithms. The study includes Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), i-vector + PLDA, Time Delayed Neural Network (TDNN), AutoEncoder + Softmax (AE + Softmaxx), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Common Vector Approach (CVA), SF-CVA, HCF, and Alexnet classifiers for speaker identification. The six different feature extraction approaches consist of Mel Frequency Cepstral Coefficients (MFCC) + Pitch, Gammatone Cepstral Coefficients (GTCC) + Pitch, MFCC + GTCC + Pitch + eight spectral features, spectrograms,i-vectors, and Alexnet feature vectors. For SF-CVA, 100% accuracy was achieved in most tests by combining the training and test feature vectors of the speakers separately. RNN-LSTM, i-vector + KNN, AE + softmax, TDNN, and i-vector + HCF classifiers gave the highest accuracy rates in the tests performed without combining training and test feature vectors.","url":"https://doi.org/10.21203/rs.3.rs-4372288/v1","authors":["Serkan KESER","Esra GEZER"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-14T07:03:25Z","doi":"10.21203/rs.3.rs-4372288/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1093/oso/9780198828044.003.0003","name":"Machine learning with sklearn","source":"crossref","abstract":"This chapter’s goal is to show how to apply machine learning algorithms in a general setting using some classic methods. In particular, it demonstrates how to apply three important machine learning algorithms, a support vector classifier (SVC), a random forest classifier (RFC), and a multilayer perceptron (MLP). While many of the methods studied later go beyond these now classic methods, this does not mean that these methods are obsolete. Also, the algorithms discussed here provide some form of baseline to discuss advanced methods like probabilistic reasoning and deep learning. The aim here is to demonstrate that applying machine learning methods based on machine learning libraries is not very difficult. It offers an opportunity to discuss evaluation techniques that are very important in practice.","url":"https://doi.org/10.1093/oso/9780198828044.003.0003","authors":["Thomas P. Trappenberg"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2020-01-23T10:54:18Z","doi":"10.1093/oso/9780198828044.003.0003","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmisi61517.2024.10580319","name":"DDoS Detection using Machine Learning","source":"crossref","abstract":"In the cybersecurity domain, Denial of Service (DoS) attacks maliciously disrupt the availability of systems, inundating them with packets or requests. Distributed Denial of Service (DDoS) attacks compound this challenge, utilizing multiple compromised sources. Recognizing and classifying these attacks swiftly is critical for safeguarding online platforms. Our research focuses on DDoS attacks, leveraging Machine Learning (ML) to distinguish between normal and malicious network behavior. Anchored by the apaDDoS-dataset, our approach aims to empower systems to autonomously identify and respond to threats, enhancing digital security.","url":"https://doi.org/10.1109/icmisi61517.2024.10580319","authors":["Nour Ahmed Nagah","Mariam Bahaa","Wael Farouk Elsersy"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-11T17:39:34Z","doi":"10.1109/icmisi61517.2024.10580319","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s42979-024-02864-8","name":"Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical Datasets","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s42979-024-02864-8","authors":["P. Vigneshvaran","A. Vijaya Kathiravan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-26T17:01:17Z","doi":"10.1007/s42979-024-02864-8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1097/as9.0000000000000423","name":"Clinical Applications of Machine Learning","source":"crossref","abstract":"Objective: This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background: As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. Methods: This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework. Results: This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies. Conclusions: Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future.","url":"https://doi.org/10.1097/as9.0000000000000423","authors":["Nadayca Mateussi","Michael P. Rogers","Emily A. Grimsley","Meagan Read","Rajavi Parikh","Ricardo Pietrobon","Paul C. Kuo"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-18T14:00:05Z","doi":"10.1097/as9.0000000000000423","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmlca63499.2024.10754387","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlca63499.2024.10754387","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-21T19:04:04Z","doi":"10.1109/icmlca63499.2024.10754387","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-22001-2.00020-2","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-22001-2.00020-2","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T06:48:34Z","doi":"10.1016/b978-0-443-22001-2.00020-2","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-97-3966-0_4","name":"Unveiling Diagnostic Precision: Evaluating Machine Learning and Deep Learning Approaches for Pneumonia Recognition of COVID-19 Patients Using Chest X-Rays","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-3966-0_4","authors":["Nakiba Nuren Rahman","Rashik Rahman","Nusrat Jahan","Md. Akhtaruzzaman Adnan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-13T17:03:06Z","doi":"10.1007/978-981-97-3966-0_4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-14109-6.00009-2","name":"Freeze the motion: Machine learning for motion correction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-14109-6.00009-2","authors":["Haikun Qi","Chen Qin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-12-22T14:06:00Z","doi":"10.1016/b978-0-443-14109-6.00009-2","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-1-4842-9846-6","name":"MATLAB Machine Learning Recipes","source":"crossref","abstract":"Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What You Will Learn Write code for machine learning, adaptive control, and estimation using MATLAB Use MATLAB graphics and visualization tools for machine learning Become familiar with neural nets Build expert systems Understand adaptive control Gain knowledge of Kalman Filters Who This Book Is For Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists","url":"https://doi.org/10.1007/978-1-4842-9846-6","authors":["Michael Paluszek","Stephanie Thomas"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-01T13:02:27Z","doi":"10.1007/978-1-4842-9846-6","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/979-8-8688-0923-1","name":"AI-Powered Ecommerce","source":"crossref","abstract":"This book takes you from decoding ecommerce business models to optimizing efficiency in AI-powered ecommerce, with strategies for success.","url":"https://doi.org/10.1007/979-8-8688-0923-1","authors":["Ramgopal Prajapat"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-13T13:17:40Z","doi":"10.1007/979-8-8688-0923-1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-32892-3.00012-9","name":"Machine learning for bone deformation detection in real-world applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32892-3.00012-9","authors":["Chandrakant Mahobiya","Sailesh Suryanarayan Iyer"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-22T05:37:30Z","doi":"10.1016/b978-0-443-32892-3.00012-9","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.71465/ajml3044","name":"The Importance of Feature Engineering in Machine Learning Model Development","source":"crossref","abstract":"Feature engineering is a fundamental step in machine learning (ML) model development that involves transforming raw data into meaningful features to enhance model performance. This process can greatly influence the accuracy, interpretability, and efficiency of machine learning algorithms. Feature engineering is especially critical in real-world applications where raw data may be noisy or unstructured. In this article, we explore the significance of feature engineering, best practices, and its impact on model development. Furthermore, we highlight common techniques used in feature engineering, their relevance across various domains, and their relationship with model selection. This paper also emphasizes the importance of domain expertise and its role in effective feature creation.","url":"https://doi.org/10.71465/ajml3044","authors":["Dr. Carlos Fernández"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T07:25:34Z","doi":"10.71465/ajml3044","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-3814-8_16","name":"Evolutionary Approaches to Explainable Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3814-8_16","authors":["Ryan Zhou","Ting Hu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-01T05:06:35Z","doi":"10.1007/978-981-99-3814-8_16","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/satml59370.2024.00001","name":"Title Page i","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml59370.2024.00001","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-10T17:22:05Z","doi":"10.1109/satml59370.2024.00001","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-323-99989-2.00012-8","name":"Author biography","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-99989-2.00012-8","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-19T09:16:27Z","doi":"10.1016/b978-0-323-99989-2.00012-8","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.63282/3050-9262.ijaidsml-v5i2p102","name":"Automating Data Engineering Workflows with AI and Machine Learning","source":"crossref","abstract":"Data engineering is a critical component of modern data-driven organizations, encompassing the extraction, transformation, and loading (ETL) of data, as well as the management and optimization of data pipelines. The increasing volume, velocity, and variety of data pose significant challenges for data engineers, who must ensure that data is accurate, timely, and available for various downstream applications. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) techniques to automate and optimize data engineering workflows. We discuss the current state of data engineering, the challenges faced by data engineers, and the potential benefits of AI and ML in addressing these challenges. We present several case studies and algorithms that demonstrate the effectiveness of AI and ML in automating data engineering tasks, including data quality assessment, schema inference, and pipeline optimization. Finally, we discuss the ethical and practical considerations of deploying AI in data engineering and provide recommendations for future research and development","url":"https://doi.org/10.63282/3050-9262.ijaidsml-v5i2p102","authors":["Aditya Banerjee"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-06-25T09:25:21Z","doi":"10.63282/3050-9262.ijaidsml-v5i2p102","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-030-19918-0_1","name":"Traditional and Machine-Learning Methods for Efficacy Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-19918-0_1","authors":["Ton J. Cleophas","Aeilko H. Zwinderman"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2019-09-03T12:33:35Z","doi":"10.1007/978-3-030-19918-0_1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781003424987-3","name":"Smart Health: Advancements in Machine Learning and the Internet of Things Solutions","source":"crossref","abstract":"This chapter examines the transformative intersection of healthcare analytics, Machine Learning (ML), and the Internet of Things (IoT), exploring how these state-of-the-art technologies reshape healthcare delivery. This chapter focuses on the benefits brought about by the synergy of ML and IoT in the healthcare sector, such as enhanced patient care, early disease detection, operational efficiency, and personalized treatment plans. We explore the problems and challenges of integrating ML and IoT with healthcare analytics and recommend solutions in this chapter. The chapter also addresses ethical, practical, and security considerations, emphasizing the importance of data privacy, model interpretability, bias mitigation, and secure connectivity in deploying healthcare technology. Furthermore, this chapter also presents the ongoing technological advancements and their potential to augment healthcare analytics further, emphasizing the need for patient-centric approaches and addressing global health disparities. This chapter explores healthcare analytics’ current landscape and prospects, considering the integration of ML and IoT solutions.","url":"https://doi.org/10.1201/9781003424987-3","authors":["Narasimha Rao Vajjhala","Philip Eappen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-02T15:32:01Z","doi":"10.1201/9781003424987-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.55529/jaimlnn.44.22.30","name":"Real Time Sign Language Translator Using Machine Learning","source":"crossref","abstract":"In today's interconnected world, effective communication is fundamental. For the deaf and mute community, communicating with those who don't understand sign language is challenging. To bridge this gap, we propose a web app translating sign language into spoken or written language and vice versa. Users capture gestures with a camera, and our system, powered by Tensor Flow and advanced image processing, converts them into coherent text. Supporting various sign languages and spoken languages, it enables real-time two-way communication. This innovative solution fosters inclusivity by empowering meaningful interactions between the deaf and mute community and the general population, promoting understanding and integration.","url":"https://doi.org/10.55529/jaimlnn.44.22.30","authors":["Ms. Pradnya Repal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-06-10T10:11:01Z","doi":"10.55529/jaimlnn.44.22.30","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/isml60050.2024.11007339","name":"Human Machine Interface Through Electromyography","source":"crossref","abstract":"This study aimed to compare different continuous electromyography (EMG)--based interface styles for forecasting wrist and hand motions. The interfaces are moving towards continuous, simultaneous control with many degrees of freedom, utilizing data-driven strategies and biomechanical model-based techniques. However, a side-by-side comparison of these approaches is lacking.To address this, the study compared a musculoskeletal model (MM) with two data-driven techniques: linear regression (LR) and artificial neural network (ANN).During the experiment, four EMG signals from forearm muscles were recorded as six healthy patients and one trans- radial amputee performed metacarpophalangeal (MCP) and wrist flexion/extension either simultaneously independently. To add diversity to the EMG responses, the volunteers performed the motions in various upper extremity positions.EMG data from all postures were used to evaluate the interfaces, while data from the neutral posture were used to design the interfaces for each participant. The performance of the interfaces was measured using the normalized root mean square error (NRMSE) between measured estimated joint angles and Pearson's correlation coefficient (r).The results demonstrated that the MM outperformed LR and ANN in predicting movements more precisely, with higher r values and lower NRMSE. Additionally, the study utilized an accelerometer to detect hand motions and electrodes to capture EMG signals for classifying hand gestures. The algorithm achieved a remarkable 98% accuracy in gesture classification.","url":"https://doi.org/10.1109/isml60050.2024.11007339","authors":["Dumpeti Moses Manohar","Tirumani Prakash","Gonnuri Prudhvi","S. Teena Mrudhula"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-05-23T17:02:44Z","doi":"10.1109/isml60050.2024.11007339","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-21889-7.09991-7","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-21889-7.09991-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-03T02:27:52Z","doi":"10.1016/b978-0-443-21889-7.09991-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-56713-1_13","name":"Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-56713-1_13","authors":["Arshad Khan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T08:02:54Z","doi":"10.1007/978-3-031-56713-1_13","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-323-95917-9.00004-3","name":"Machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95917-9.00004-3","authors":["Guohui Li"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-05T04:23:28Z","doi":"10.1016/b978-0-323-95917-9.00004-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.62441/nano-ntp.v20is14.122","name":"Vision Bridge: Empowering Blind Individuals Using Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20is14.122","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-28T05:48:53Z","doi":"10.62441/nano-ntp.v20is14.122","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.7551/mitpress/15059.001.0001","name":"The AI Playbook","source":"crossref","abstract":"In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it. “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.” —Scott Galloway, NYU Stern Professor of Marketing; bestselling author of The Four “An antidote to today's relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.” —Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better The greatest tool is the hardest to use. Machine learning is the world's most important general-purpose technology—but it's notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What's missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals, and it establishes a sorely needed strategic framework for data professionals. Beyond detailing the practice, this book also upskills business professionals—painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative—getting them right paves the way for machine learning's value-driven deployment. A note from the author: What kind of AI does this book cover? The buzzword AI can mean many things, but this book is about machine learning, which is a central basis for—and what many mean by—AI. To be specific, this book covers the most vital use cases of machine learning, those designed to improve a wide range of business operations. Endorsements: “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.” —Scott Galloway, NYU Stern Professor of Marketing; bestselling author of The Four “An antidote to today's relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.” —Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better “Set aside the hype and focus on getting things to work in practice. This is a crisp, necessary, and deeply helpful guide to getting things done with AI. Essential reading.” —Mustafa Suleyman, Cofounder and CEO, Inflection AI; author of The Coming Wave “In this book, Eric brings machine learning to life and provides a roadmap for how to operationalize it in the real world.” —Will Lansing, CEO, FICO “This should be requisite reading for any professional serious about driving true value through the power of machine learning. Eric presents a pragmatic approach and it's not just about having the best algorithms—it's about ensuring you have a path to true productionalization at scale.” —Jon Francis, Chief Data and Analytics Officer, GM “The AI Playbook grabs you from the first pages. It's an indispensable guide for anyone, both technical and non-technical, interested in discovering how AI is really put into practice.” —Barbara Oakley, author of A Mind for Numbers; co-instructor of Coursera's Learning How to Learn “The AI Playbook clearl","url":"https://doi.org/10.7551/mitpress/15059.001.0001","authors":["Eric Siegel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-06T19:44:47Z","doi":"10.7551/mitpress/15059.001.0001","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.64949/kap5fa07","name":"Beyond Single Biomarkers: Multimodal Machine Learning for Precision Medicine Across Clinical Specialties","source":"crossref","abstract":"This article is a preprint and has not yet been peer-reviewed. Not for clinical use. Precision medicine has traditionally relied on individual biomarkers to guide diagnosis, risk stratification, and treatment selection. However, most clinical phenotypes arise from complex interactions between molecular, imaging, physiological, behavioural, and environmental factors that cannot be captured by a single data source. Multimodal machine learning offers a framework for integrating heterogeneous data streams, including electronic health records, medical imaging, genomics, clinical text, biosignals, and wearable-device outputs, to generate more comprehensive patient-level predictions. This review examines the role of multimodal machine learning in advancing precision medicine across oncology, cardiology, neurology, critical care, rare disease, and cardiometabolic medicine. Common data-fusion strategies, including early, intermediate, and late fusion, are summarised. In addition, emerging approaches such as transformer-based architectures and graph neural networks are highlighted. Clinically relevant applications include treatment-response prediction, risk stratification, early detection of deterioration, diagnostic prioritisation, and remote monitoring. Despite encouraging performance in retrospective studies, translation into routine care remains limited by challenges in external validation, calibration, interpretability, bias, data quality, privacy, and workflow integration. Clinicians should therefore evaluate multimodal AI tools not only by discrimination metrics such as area under the curve, but also by clinical utility, generalisability, uncertainty, fairness, and actionability. Established frameworks, including TRIPOD+AI, DECIDE-AI, CONSORT-AI, SPIRIT-AI, and CLAIM, can support more transparent reporting and evaluation. Ultimately, multimodal machine learning should be viewed not as autonomous decision-making, but as a potential clinical decision-support approach whose value depends on whether it improves decisions that matter to patients.","url":"https://doi.org/10.64949/kap5fa07","authors":["Simbarashe Magwenzi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-09T09:09:31Z","doi":"10.64949/kap5fa07","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/issc61953.2024.10603066","name":"Optimizing Machine Learning for ResourceConstrained Devices: A Comparative Analysis of Preprocessing Techniques and Machine Learning Algorithms","source":"crossref","abstract":"One of the challenges in the rapid development of Internet of Things (IoT) and edge computing is deploying machine learning (ML) models on resource constrained devices (RCD). Preprocessing methods are important for improving ML model performance, especially when limited computing power is involved. This research examines the effectiveness of four prominent preprocessing methods in conjunction with four common ML algorithms: Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). The methods include quantisation, Min-Max scaling, standardisation (Z-score normalisation), and quantile transformation. We aim to examine how different preprocessing techniques affect the performance of various ML algorithms. By doing so, we offer valuable insights into the most effective preprocessing approach for different algorithms and HAR dataset. The results of our experiments show that different combinations of preprocessing methods and ML algorithms can achieve different levels of accuracy, f1 score and training time. Remarkably, quantisation which, is frequently used to minimise the memory footprint of models produced, average results for all techniques i.e. 58%. On the other hand, Min-Max scaling performed better with RF, attaining 94.72% accuracy with a training time of 6.4013 sec, indicating that it can be used in situations where resources are limited. One widespread normalisation method, standardisation, performed well with both RF and SVM, achieving accuracy of 94.56% and 48.66%, respectively. Furthermore, quantile transformation provides promising outcomes with accuracies ranging from 69.51% to 94.68% for all techniques. These results highlight the importance of modifying the discussed four preprocessing methods according to specific ML algorithms, applications, and available RCD such as limited processer resources and limited memory. These results are helpful to researchers who are working to achieve higher model performance in the context of RCD.","url":"https://doi.org/10.1109/issc61953.2024.10603066","authors":["Abdul Haseeb","Ian Cleland","Chris Nugent","James McLaughlin"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-29T19:00:45Z","doi":"10.1109/issc61953.2024.10603066","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21203/rs.3.rs-4190312/v1","name":"Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy","source":"crossref","abstract":"Abstract Objection: Postoperative stroke (PS) represents a significant and grave complication, which often remains challenging to detect until clear clinical symptoms emerge. The early identification of populations at high risk for perioperative stroke is essential for enabling timely intervention and enhancing postoperative outcomes. This study seeks to employ machine learning (ML) techniques to create a predictive model for PS following elective craniotomy. Methods This study encompassed a total of 1,349 cases that underwent elective craniotomy between January 2013 and August 2021. Perioperative data, encompassing demographics, etiology, laboratory results, comorbidities, and medications, were utilized to construct predictive models. Nine distinct machine learning models were developed for the prediction of postoperative stroke (PS) and assessed based on the area under the receiver-operating characteristic curve (AUC), along with sensitivity, specificity, and accuracy metrics. Results Among the 1,349 patients included in the study, 137 cases (10.2%) were diagnosed with postoperative stroke (PS), which was associated with a worse prognosis. Of the nine machine learning prediction models evaluated, the logistic regression (LR) model exhibited superior performance, as indicated by an area under the receiver-operating characteristic curve (AUC) value of 0.741 (0.64–0.85), and competitive performance metrics, including an accuracy of 0.668, sensitivity of 0.650, and specificity of 0.670. Notably, feature importance analysis identified \"preoperative albumin,\" \"ASA classification,\" and \"preoperative hemoglobin\" as the top three factors contributing to the prediction of PS. Conclusion Our study successfully developed a real-time and easily accessible parameter requiring LR-based PS prediction model for post-elective craniotomy patients.","url":"https://doi.org/10.21203/rs.3.rs-4190312/v1","authors":["Tianyou Lu","Qin Huang","Tongsen Luo","Yaxin Lu","Liping Li","Jun Cai","Ziqing Hei","Chaojin Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-09T12:57:53Z","doi":"10.21203/rs.3.rs-4190312/v1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1515/9783110697186-007","name":"Chapter 7 Neural Networks and Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783110697186-007","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-22T18:47:27Z","doi":"10.1515/9783110697186-007","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icmlcn59089.2024.10624755","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlcn59089.2024.10624755","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-15T17:18:59Z","doi":"10.1109/icmlcn59089.2024.10624755","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.14357/19922264240111","name":"LOGIC OF DECEPTION IN MACHINE LEARNING","source":"crossref","abstract":"","url":"https://doi.org/10.14357/19922264240111","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-01T13:25:24Z","doi":"10.14357/19922264240111","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.69987/aimlr.2024.50305","name":"Risk Assessment Framework for Data Leakage Prevention Using Machine Learning Techniques","source":"crossref","abstract":"Data leakage remains a critical concern for organizations handling sensitive information, requiring effective risk assessment methods to identify potential vulnerabilities. This paper proposes a machine learning-based framework for assessing data leakage risks in corporate environments. Our approach focuses on analyzing user access patterns and data flow characteristics to identify anomalous behaviors that may indicate potential leakage risks. We employ anomaly detection algorithms, particularly Isolation Forest and Local Outlier Factor, to detect unusual data access activities. The framework includes a risk scoring mechanism that evaluates access requests based on user roles, data sensitivity levels, and contextual factors. Additionally, we explore the use of natural language processing for identifying sensitive content in unstructured documents, enabling more comprehensive risk assessment. The proposed method provides organizations with a practical tool for prioritizing security efforts and allocating resources to high-risk areas. This work supports data protection compliance requirements and helps organizations strengthen their data governance practices.","url":"https://doi.org/10.69987/aimlr.2024.50305","authors":["Xiaolan Wu","Juan Li","Wenkun Ren"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-01-07T18:32:21Z","doi":"10.69987/aimlr.2024.50305","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.22271/ed.book.2622","name":"Artificial Neural Network Application on Weather Forecasting using Machine Learning Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.22271/ed.book.2622","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-27T05:19:10Z","doi":"10.22271/ed.book.2622","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1137/1.9781611977882.fm","name":"Front Matter","source":"crossref","abstract":"The front matter includes the Title page, Series page, Copyright page, TOC, Preface, and List of Figures.","url":"https://doi.org/10.1137/1.9781611977882.fm","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-11T18:47:43Z","doi":"10.1137/1.9781611977882.fm","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-3-031-36502-7","name":"Machine Learning Methods for Multi-Omics Data Integration","source":"crossref","abstract":"This book covers the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validations.","url":"https://doi.org/10.1007/978-3-031-36502-7","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-13T20:01:33Z","doi":"10.1007/978-3-031-36502-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1029/2024jh000170","name":"A Machine Learning Approach for Deriving Atmospheric Temperatures and Typhoon Warm Cores From FY‐3E MWTS‐3 Observations","source":"crossref","abstract":"Abstract Machine learning has gained an increasing popularity in the fields of satellite retrieval and numerical weather modeling. In this study, machine‐learning (ML) neural‐network (NN) models are utilized to retrieve atmospheric temperatures from observations of brightness temperature from the Microwave Temperature Sounder‐3 (MWTS‐3) onboard the China's first dawn‐dusk polar‐orbiting satellite Fengyun (FY)‐3E, with the ERA5 reanalysis serving as training data sets. The root mean square errors of the ML‐retrieved temperatures at all pressure levels are smaller than those obtained by a previously used traditional linear regression method compared to the ERA5 reanalysis over global oceans as well as radiosonde observations over land. Less than a 1‐week period of training data is usually sufficient for an ML NN model to converge in less than 50–100 iterations. The shortest time period of training data is 3‐days right before the testing data period. While the horizontal patterns and temporal evolutions of the ML‐retrieved warm cores of Typhoon Malakas (2022) and Typhoon Haikui (2023) in the upper troposphere compared favorably with those obtained by traditional regression methods as well as the ERA5 reanalysis in the testing periods. The vertical structures of ML‐retrieved warm cores extend further down to the middle and lower troposphere while those from the traditional regression method are confined in the upper troposphere. A comparison among ML results with additional training data sets across different seasons confirms the above conclusion.","url":"https://doi.org/10.1029/2024jh000170","authors":["Zeyi Niu","Xiaolei Zou"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-25T16:10:02Z","doi":"10.1029/2024jh000170","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-95-1038-2_5","name":"Machine Learning in Clinical Medicine","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1038-2_5","authors":["Jingli Ren","Yiwen Tao"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-12T08:17:14Z","doi":"10.1007/978-981-95-1038-2_5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-443-13697-9.00008-4","name":"Quantum and machine learning applications involving matrices","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13697-9.00008-4","authors":["Charles R. Giardina"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-16T10:15:06Z","doi":"10.1016/b978-0-443-13697-9.00008-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.62441/nano-ntp.v20is8.109","name":"An Enhanced Machine Learning-Based Approach for Analysis and Prediction of Student Performance in Classroom Learning","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20is8.109","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-30T03:27:49Z","doi":"10.62441/nano-ntp.v20is8.109","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-7007-0_4","name":"Initial Selection and Subsequent Updating of OML Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-7007-0_4","authors":["Thomas Bartz-Beielstein"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-02-05T05:02:18Z","doi":"10.1007/978-981-99-7007-0_4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.55529/jaimlnn.43.18.30","name":"Early Warning System of Attrition in the BPO Industry Using Machine Learning Classification Models","source":"crossref","abstract":"Employee attrition is one of the factors affecting gross margin erosion in the BPO industry, the fastest-growing industry in the Philippines, due to hiring and training costs. The cost of employee attrition depends on the employee's role and salary/wage level. This study proposed shifting the retention approach from reactive to proactive with the use of an early warning system for employee attrition. The early warning system was powered by Machine Learning Classification Models. The data used in this study are employees hired in 2021 and 2022 from one of the Telco/Communication programs in the BPO Industry. The data attributes considered in this study are composed of Employee status, Employee Performance, Employee Satisfaction, Payroll, Time Off History, Schedule, and employee Observation data. The data is trained and tested in the classification models (Decision Trees, rule-based classification, naïve Bayes, KNN, Logistic Regression, and Random Forest). Models are evaluated using the classification performance metrics (AUC, Accuracy, Precision, Recall, and F1 Score). The model with the highest predictive accuracy is selected and deployed to produce employee classification (Risk of Termination, Neutral, and Positive). This study mainly helps the company reduce turnover and costs and increase gross margin with the help of the early warning system that can predict the status of the employees using significant indicators.","url":"https://doi.org/10.55529/jaimlnn.43.18.30","authors":["Sandrilito Abogada","Laurence Usona"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-06T10:23:37Z","doi":"10.55529/jaimlnn.43.18.30","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-99-3814-8_17","name":"Evolutionary Algorithms for Fair Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-3814-8_17","authors":["Alex Freitas","James Brookhouse"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-11-01T05:06:35Z","doi":"10.1007/978-981-99-3814-8_17","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781779643193-4","name":"Machine Learning-Based Analytics for Premature Rheumatoid Arthritis and Osteoarthritis Detection in Clinical Practices—A Review","source":"crossref","abstract":"People experience significant health discomfort as a result of rheumatoid arthritis (RA) and osteoarthritis (OA). There are numerous other types of arthritis that have an impact on people’s comfort and efficiency. In terms of using computer-aided diagnostic solutions, previous research has concentrated on a specific set of digital imaging solutions and predictive analysis models for the accurate identification of issues. However, with the increased use of machine learning (ML) models, the scope of more accurate predictions has expanded. Some of the key models discussed in the studies are detailed in this chapter which focuses on the literature pertaining to RA and OA. In terms of understanding the scope of research, the discussion section presents the gaps found in the literature, as well as how certain key aspects, such as the application of lifestyle factors and the use of a more accurate detection process for correctly classifying arthritis, are some of the significant areas for research. The goals of future research are defined as focusing on a possible set of evolutionary algorithms and feature extractions that can be useful for improving overall accuracy.","url":"https://doi.org/10.1201/9781779643193-4","authors":["Kumar M. Ganesh","Agam Das Goswami"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-09-02T12:03:00Z","doi":"10.1201/9781779643193-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1101/2024.01.25.24301176","name":"Machine-learning operations streamlined clinical workflows of DNA methylation-based CNS tumor classification","source":"crossref","abstract":"Abstract Background The diagnosis and grading of central nervous system (CNS) tumors, which was traditionally relied on histology, has been enhanced significantly by molecular testing, including DNA methylation profiling, which has been widely adopted for tumor classification. Clinical laboratories, however, are hindered when changes, such as the introduction of the Illumina Infinium MethylationEPIC v2.0 BeadChip, make existing classifiers incompatible due to shifts in targetable CpG sites among array versions. The aim of this study is to provide a scalable CNS tumor classification solution that empowers molecular laboratories and pathology teams to respond swiftly to these challenges. Methods We employed machine-learning operational methods including continuous integration and continuous training using 228 in-house MethylationEPICv1 array samples and two publicly available data sources to train and validate a DNA-methylation CNS classification pipeline that is compatible with Methylation450k, MethylationEPICv1, and MethylationEPICv2 BeadChips. We optimized CNS tumor classification by validating a multi-modal machine-learning classifier using a combination of a random forest and k-nearest neighbor model framework. Results We demonstrated an increase of accuracy, sensitivity, and specificity of CNS classification at the superfamily, family, and class level (class-level AUC = 0.90) after employing machine-learning operational methods to our clinical workflow. Our classification pipeline outperformed the DKFZv12.8 classifier in classifying pediatric CNS tumor types and subtypes when using the Illumina Infinium MethylationEPIC v2.0 BeadChip (concordance = 92%). Conclusion By leveraging machine-learning operational principles, we demonstrate a practical clinical solution for clinical molecular laboratories to employ for improved accuracy and adaptability in DNA methylation-based CNS tumor diagnostics. Importance of the Study Clinical molecular laboratories, neuro-oncology, and pathology diagnostic teams that utilize machine-learning classification systems are challenged when changes in underlying molecular technology make current systems inoperable. Our study provides a solution to clinically validate DNA-methylation profiling of central nervous system (CNS) tumors by employing machine learning operations, with a solution that is applicable to data generated from MethylationEPIC v2.0 BeadChip, as well as earlier versions. We show how continuous integration of novel data sources and algorithmic optimization substantially improves the robustness of the diagnostic tool and enables clinical laboratories to be agile in the face of evolving technology. Furthermore, we provide the computational infrastructure to scale out these services to any diagnostic laboratories focused on supporting CNS tumor classification. Key Points Our study addresses the crucial need for agility in clinical diagnostics, presenting a machine learning-enhanced CNS tumor classification pipeline that swiftly adapts to new technologies like the MethylationEPIC v2.0 BeadChip, ensuring seamless integration into existing clinical workflows. Utilizing machine learning operational methods, we curated an expansive reference dataset, leading to the optimization of our classification algorithm, which demonstrates superior adaptability and precision in CNS tumor diagnostics, essential for timely and accurate patient care.","url":"https://doi.org/10.1101/2024.01.25.24301176","authors":["Alexander L Markowitz","Dejerianne G Ostrow","Chern-Yu Yen","Xiaowu Gai","Jennifer A Cotter","Jianling Ji"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-26T18:35:12Z","doi":"10.1101/2024.01.25.24301176","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1101/2024.03.08.584090","name":"GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS","source":"crossref","abstract":"ABSTRACT The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint ™ is a proprietary streaming algorithm designed to meet that need. By extracting the most relevant functional features, GFPrint ™ transforms high-dimensional, noisy genetic sequencing data into an embedded representation, allowing unsupervised models to create data clusters that can be re-mapped to the original clinical information. Ultimately, this allows the identification of genes and pathways relevant to disease onset and progression. GFPrint ™ has been tested and validated using two cancer genomic datasets publicly available. Analysis of the TCGA dataset has identified panels of genes whose mutations appear to negatively influence survival in non-metastatic colorectal cancer (15 genes), epidermoid non-small cell lung cancer (167 genes) and pheochromocytoma (313 genes) patients. Likewise, analysis of the Broad Institute dataset has identified 75 genes involved in pathways related to extracellular matrix reorganization whose mutations appear to dictate a worse prognosis for breast cancer patients. GFPrint ™ is accessible through a secure web portal and can be used in any therapeutic area where the genetic profile of patients influences disease evolution.","url":"https://doi.org/10.1101/2024.03.08.584090","authors":["Guillermo Sanz-Martín","Daniela Paula Migliore","Pablo Gómez del Campo","José del Castillo-Izquierdo","Juan Manuel Domínguez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-03-12T21:14:49Z","doi":"10.1101/2024.03.08.584090","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781003487647-20","name":"IoT-driven machine learning mechanisms for healthcare applications","source":"crossref","abstract":"The integration of Internet of Things (IoT) and machine learning algorithms has revolutionized biomedical applications by enabling the analysis of vast amounts of data from interconnected devices. This chapter explores the potential of IoT-enabled machine learning algorithms in the biomedical field. The sector where this integration is impactful is remote patient monitoring, where IoT devices collect real-time health data and machine learning algorithms analyze it to detect patterns and anomalies, providing early warnings for potential health issues. Predictive analytics for disease diagnosis is another area of focus, where IoT devices gather data on genetic information, lifestyle choices, and environmental factors. Machine learning algorithms process this data to provide personalized risk assessments and early disease detection. Wearable devices equipped with IoT sensors enable continuous health tracking, such as activity levels, sleep patterns, and vital signs. Machine learning algorithms leverage this data to gain insights into an individual s health status, offer personalized recommendations, and monitor progress toward health goals. Furthermore, IoT devices assist in monitoring medication adherence, with machine learning algorithms identifying non-adherence patterns and providing interventions to improve compliance. In the realm of healthcare systems, IoT devices can optimize resource allocation and track equipment performance in smart hospitals. Machine learning algorithms analyze the generated data to predict equipment failure, optimize workflows, and improve patient care. By harnessing the power of interconnected devices and advanced algorithms, this integration paves the way for enhanced data analysis, decision-making, and personalized healthcare interventions.","url":"https://doi.org/10.1201/9781003487647-20","authors":["Gopalakrishnan Karuppaiah","Karthikeyan Velayuthapandian","Sridhar Raj Sankara Vadivel"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-04T11:24:27Z","doi":"10.1201/9781003487647-20","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.17305/bb.2024.10802","name":"Enhancing clinical decision-making in closed pelvic fractures with machine learning models","source":"crossref","abstract":"Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients with closed pelvic fractures and identify relevant risk factors. The retrospective study included 208 patients diagnosed with pelvic fractures and admitted to Suning Traditional Chinese Medicine Hospital between 2019 and 2023. Among these, 133 cases were identified as closed PFs. Patients with closed fractures were divided into a training set (n = 115) and a test set (n = 18). The training set was further stratified into two groups based on hemodynamic stability: Group A (patients with HI) and Group B (patients with hemodynamic stability). A total of 40 clinical variables were collected, and multiple machine learning algorithms were employed to develop predictive models, including logistic regression (LR), C5.0 Decision Tree (DT), Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), random Forest (RF), and artificial neural network (ANN). Additionally, factor analysis was performed to assess the interrelationships between variables. The RF and LR algorithms outperformed traditional methods—such as central venous pressure (CVP) and intra-abdominal pressure (IAP) measurements—in predicting HI. The RF model achieved an average under the ROC (AUC) of 0.92, with an accuracy of 0.86, precision of 0.81, and an F1 score of 0.87. The LR model had an average AUC of 0.82 but shared the same accuracy, precision, and F1 score as the RF model. Key risk factors identified included TILE grade, heart rate (HR), creatinine (CR), white blood cell count (WBC), fibrinogen (FIB), and lactic acid (LAC), with LAC levels &gt;3.7 and an injury severity score (ISS) &gt;13 as significant predictors of HI and mortality. In conclusion, the RF and LR algorithms are effective in predicting HI and mortality risk in patients with closed PFs, enhancing clinical decision-making and improving patient outcomes.","url":"https://doi.org/10.17305/bb.2024.10802","authors":["Dian Wang","Yongxin Li","Li Wang"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-29T11:53:48Z","doi":"10.17305/bb.2024.10802","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.66224/jcc.6.2.7","name":"Predicting cognitive decline in Alzheimer’s disease using minimal clinical features: A machine learning approach with the NACC cohort","source":"crossref","abstract":"Early identification of individuals at risk for Alzheimer’s disease–related cognitive decline is crucial for timely intervention and clinical trial enrollment. We developed a machine learning model using only five routinely collected clinical variables, age, sex, education, baseline Mini-Mental State Examination (MMSE), and Clinical Dementia Rating–Sum of Boxes (CDR-SB), to predict cognitive decline three years in advance. Using a sample of 2,000 participants from the National Alzheimer’s Coordinating Center (NACC) dataset, a Random Forest classifier achieved 94% accuracy and an AUC of 0.98 on an independent test set. Feature importance analysis confirmed that CDR-SB and MMSE were the strongest predictors, collectively accounting for 66% of model relevance. This approach offers a low-cost, scalable tool for risk stratification, particularly valuable in low-resource settings and primary care, where advanced diagnostics are unavailable.","url":"https://doi.org/10.66224/jcc.6.2.7","authors":["Maryam Tarkesh Esfahani"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-06-02T09:48:23Z","doi":"10.66224/jcc.6.2.7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.53964/cme.2024012","name":"Advanced Predictive Modeling for Hepatitis C Diagnosis Using Machine Learning","source":"crossref","abstract":"The hepatitis C virus (HCV) virus, which has infected nearly fifty million individuals across the world, has recently been receiving significant attention. With the advent of artificial intelligence, there is an increasing effort to predict the HCV by utilizing machine learning models and training such models on relevant datasets. These data-driven models can aid doctors in the early detection of Hepatitis C infection. In this study, using datasets from the National Center for Health Statistics, we aim to develop a reliable model for predicting patients with positive hepatitis C tests. The most significant challenge in predicting such diseases using machine learning methods is finding an appropriate technique to address extremely imbalanced datasets, which arise from the smaller number of patients compared to healthy individuals in real-world datasets. We utilized real-world datasets and employed different machine learning algorithms, along with various methods for data balancing and feature selection. Unlike previous studies, which lacked this level of comprehensiveness, we utilized diverse feature selection and sampling methods such as Recursive Feature Elimination, Analysis of Variance (ANOVA) feature selection, Correlation matrix, synthetic minority over-sampling technique (SMOTE), BorderlineSMOTE, support vector machine synthetic minority over-sampling technique (SVMSMOTE), and Adaptive Synthetic Sampling (ADASYN) in conjunction with different machine learning algorithms including Random Forest, Decision Tree, XGBoost, AdaBoost, and logistic regression. We are the first to develop such a comprehensive study for the prediction of the HCV. Our findings suggest that we can predict patients infected by the HCV with a reasonable recall score of 0.86. This recall is achieved through the application of a model using the AdaBoost algorithm and the ADASYN method for balancing the dataset. The model's performance, especially on the validation dataset, was slightly better when we used ANOVA feature selection. Feature importance analysis conducted with ANOVA feature selection and Recursive Feature Elimination indicates that four to five features are the most prominent in our research analysis to predict patients with positive Hepatitis C tests.","url":"https://doi.org/10.53964/cme.2024012","authors":["Mahdyeh Abbasi Hezari","Marzieh Baes","Atyeh Abassi Hezari","Mobina Hassanbabaei"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-11-19T08:53:00Z","doi":"10.53964/cme.2024012","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.71465/ajml3062","name":"The Role of Machine Learning in Predicting Energy Consumption Patterns","source":"crossref","abstract":"Machine learning (ML) techniques have gained significant attention in the prediction of energy consumption patterns due to their ability to handle complex, non-linear relationships within data. This paper explores various ML models, such as regression, classification, and deep learning, in forecasting energy usage patterns in residential, commercial, and industrial sectors. The integration of ML in energy consumption prediction allows for more efficient energy management and planning. This article discusses the advantages, challenges, and applications of ML techniques in energy forecasting and provides case studies to illustrate their effectiveness.","url":"https://doi.org/10.71465/ajml3062","authors":["Dr. Lisa Morgan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-03-31T08:00:56Z","doi":"10.71465/ajml3062","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.62441/nano-ntp.v20i5.20","name":"Predicting Depressive Disorders in Diabetic Workers: A Comparative Analysis of Relevance Vector Machine and Traditional Machine Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20i5.20","authors":[],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-14T08:53:35Z","doi":"10.62441/nano-ntp.v20i5.20","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-1-4842-9801-5_7","name":"Innovation, KPIs, Best Practices, and More for Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-9801-5_7","authors":["Patanjali Kashyap"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-22T12:03:36Z","doi":"10.1007/978-1-4842-9801-5_7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.5220/0013270100004568","name":"Car Price Prediction Using Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013270100004568","authors":["Rui Chen"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2026-02-19T15:45:45Z","doi":"10.5220/0013270100004568","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1029/2024jh000264","name":"RocMLMs: Predicting Rock Properties Through Machine Learning Models","source":"crossref","abstract":"Abstract Mineral phase transformations significantly alter the bulk density and elastic properties of mantle rocks and consequently have profound effects on mantle dynamics and seismic wave propagation. These changes in the physical properties of mantle rocks result from evolution in the equilibrium mineralogical composition, which can be predicted by the minimization of the Gibbs Free Energy with respect to pressure (P), temperature (T), and chemical composition (X). Thus, numerical models that simulate mantle convection and/or probe the elastic structure of the Earth's mantle must account for varying mineralogical compositions to be self‐consistent. Yet coupling Gibbs Free Energy minimization (GFEM) approaches with numerical geodynamic models is currently intractable for high‐resolution simulations because prediction speeds of widely‐used GFEM programs (10 0 –10 2 ms) are impractical in many cases. As an alternative, this study introduces machine learning models (RocMLMs) that have been trained to predict thermodynamically self‐consistent rock properties at arbitrary PTX conditions between 1–28 GPa and 773–2,273 K, and dry mantle compositions ranging from fertile (lherzolitic) to refractory (harzburgitic) end‐members define9d with a large data set of published mantle compositions. RocMLMs are 10 1 –10 3 times faster than GFEM calculations or GFEM‐based look‐up table approaches with equivalent accuracy. Depth profiles of RocMLMs predictions are nearly indistinguishable from reference models PREM and STW105, demonstrating good agreement between thermodynamic‐based predictions of density, Vp, and Vs and geophysical observations. RocMLMs are therefore capable, for the first time, of emulating dynamic evolution of density, Vp, and Vs due to partial melting and refertilization of dry mantle rocks in high‐resolution numerical geodynamic models.","url":"https://doi.org/10.1029/2024jh000264","authors":["Buchanan Kerswell","Nestor G. Cerpa","Andréa Tommasi","Marguerite Godard","José Alberto Padrón‐Navarta"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-23T06:45:38Z","doi":"10.1029/2024jh000264","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781032662466-4","name":"Comparison and Recognition of Images","source":"crossref","abstract":"This chapter discusses the problem of image matching or locating a known part of the image, which is usually described as a pattern. This type of problem is typical in applications, such as the search for reference points in stereo vision, the location of a certain object in a scene, or the tracking of objectives in a sequence of images. The fundamental idea of image comparison (Template Matching) is simple. The pattern (template) to be found in the image is moved over all the pixels of the image as if it were a linear filter. Then, a similarity evaluation between the pattern and the pixels is measured. Obviously, the determination of similarity between the pattern and each of the points of the image is not easy since the pattern could appear in the image scaled, rotated, or distorted.","url":"https://doi.org/10.1201/9781032662466-4","authors":["Erik Cuevas","Alma Nayeli Rodríguez"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-02T16:14:23Z","doi":"10.1201/9781032662466-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.52783/jes.1815","name":"Clinical Applications of Machine Learning in the Diagnosis, Classification and Prediction of Heart Failure","source":"crossref","abstract":"Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, necessitating innovative approaches to its diagnosis, classification, and management. This paper explores the transformative potential of machine learning (ML) technologies in the realm of cardiology, with a particular focus on heart failure. Through a comprehensive review and analysis, we examine the application of various ML algorithms in enhancing the accuracy and efficiency of HF diagnosis, the nuanced classification of its types and stages, and the predictive modeling of patient outcomes. The synthesis of findings highlights the integration of ML with traditional clinical practices, underscoring the improved diagnostic and prognostic capabilities thus afforded. Additionally, the paper addresses the challenges of data quality, privacy concerns, and the integration of ML tools into existing healthcare systems. By presenting case studies and emerging trends, we illuminate the path forward in leveraging big data and AI to revolutionize heart failure care. This research not only underscores the significant strides made in applying ML to heart failure but also charts a course for future investigations and clinical implementations that could further enhance patient outcomes and healthcare efficiencies.","url":"https://doi.org/10.52783/jes.1815","authors":["Eman H. Abd-Elkawy, Rabie Ahmed"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-04-08T11:00:16Z","doi":"10.52783/jes.1815","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.11606/d.55.2022.tde-19012023-093014","name":"Machine Learning techniques applied to identifying clinical factors regarding automated medical prognosis","source":"crossref","abstract":"e de Computação, Universidade de São Paulo, São Carlos -SP, 2022.Algoritmos de Aprendizado de Máquina têm apresentado resultados promissores em diversas áreas do conhecimento, entre elas a medicina preventiva.Ao passo que técnicas de aprendizado profundo têm se mostrado eficazes para o prognóstico médico automatizado, elas carecem de mais transparência e interpretabilidade.Por outro lado, técnicas de agrupamento de dados e árvores de decisão são promissoras para a identificação de fatores de risco, características em comum, e tendências dentre os pacientes de acordo com suas respectivas histórias clínicas, descritas por prontuários médicos eletrônicos (EHRs).Deste modo, esta dissertação de mestrado objetivou a elaboração de um framework de aprendizado de máquina composto de uma rede neural Attentive Encoder-Decoder com o objetivo de predizer os diagnósticos da próxima admissão de pacientes, um algoritmo de agrupamento hierárquico para fenotipar essas predições, e finalmente, uma árvore de decisão objetivando-se a explicabilidade desses fenótipos; cada passo do nosso framework produziu um resultado em particular: a rede Attentive Encoder-Decoder obteve resultados de estado da arte nos datasets MIMIC-III e MIMIC-IV-ED; o algoritmo de agrupamento produziu resultados consistentes de diagnósticos relacionados em um mesmo fenótipo e, também, fenótipos vizinhos demonstraram similaridade de diagnósticos; e finalmente, a árvore de decisão proporcionou a visualização das regras de decisão entre diagnósticos de fenótipos e demonstrou a irrelevância de dados demográficos de pacientes em comparação com seus respectivos diagnósticos na identificação de um fenótipo.Nós resumimos nossas contribuições como: (i) Obtenção de resultados de estado da arte com um modelo versátil baseado em uma arquitetura Attentive Encoder-Decoder, nomeado por nós como AttentionHCare (BARROS; RODRIGUES, 2022), (ii) Fornecimento de uma ferramenta de suporte a decisão para especialistas por meio de um modelo explicável, e (iii) Habilidade de identificar padrões (e.g.fatores de risco, diagnósticos em comum, bias, etc.) em pacientes com trajetórias clínicas semelhantes por meio de um modelo explicável.","url":"https://doi.org/10.11606/d.55.2022.tde-19012023-093014","authors":["Pedro Henrique Ferracini de Barros"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-01-19T11:43:05Z","doi":"10.11606/d.55.2022.tde-19012023-093014","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/jcm13020603","name":"Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review","source":"crossref","abstract":"The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature search was performed up to October 2023, using MEDLINE/PubMed, Embase, Web of Science, and NIH Clinical Trials. Level I to IV evidence was included. Seven studies were identified that included 44,121 patients. The time to follow-up varied from 3 months to more than 2 years. Each study employed one to six ML techniques. The best-performing models were for health-related quality of life (HRQoL) outcomes, with an area under the curve (AUC) of more than 84%. In contrast, predicting the outcome of hip-specific measures was less reliable, with an AUC of between 71% to 87%. Random forest and neural networks were generally the best-performing models. Three studies compared the reliability of ML with traditional regression analysis: one found in favour of ML, one was not clear and stated regression closely followed the best-performing ML model, and one showed a similar AUC for HRQoL outcomes but did show a greater reliability for ML to predict a clinically significant change in the hip-specific function. ML offers acceptable-to-excellent discrimination of predicting functional outcomes and may have a marginal advantage over traditional regression analysis, especially in relation to hip-specific hip functional outcomes.","url":"https://doi.org/10.3390/jcm13020603","authors":["Nick D. Clement","Rosie Clement","Abigail Clement"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-01-22T11:36:41Z","doi":"10.3390/jcm13020603","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.7602/jmis.2024.27.3.129","name":"Automated machine learning with R: AutoML tools for beginners in clinical research","source":"crossref","abstract":"Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools offer a solution by automating repetitive tasks, such as data preprocessing, model selection, hyperparameter optimization, and prediction analysis. This review introduces the use of AutoML tools for general research, including clinical studies. In particular, it outlines a simple approach that is accessible to beginners using the R programming language (R Foundation for Statistical Computing). In addition, the practical code and output results for binary classification are provided to facilitate direct application by clinical researchers in future studies.","url":"https://doi.org/10.7602/jmis.2024.27.3.129","authors":["Youngho Park"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T00:48:07Z","doi":"10.7602/jmis.2024.27.3.129","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/b978-0-323-95374-0.00011-7","name":"Diagnosing coronaviruses (COVID-19) using machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-323-95374-0.00011-7","authors":["Sheikh Burhan Ul Haque","Aasim Zafar","Ahmad Raza Shibli","Samudrala Gourinath"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-07-19T05:34:32Z","doi":"10.1016/b978-0-323-95374-0.00011-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1201/9781003581246-11","name":"Application of machine learning models for power systems security assessment","source":"crossref","abstract":"The integration of machine learning techniques in power system security assessment has emerged as a transformative approach to enhancing the reliability and efficiency of electrical grids. This study explores the application of machine learning algorithms for comprehensive security evaluation. Leveraging historical data, these models autonomously learn intricate patterns, enabling real-time identification of potential vulnerabilities and predicting system behaviour under varying conditions. The methodology encompasses classification tasks to determine security statuses under various conditions and regression tasks to predict critical parameters precisely. This data-driven approach facilitates proactive measures to mitigate risks by identifying potential vulnerabilities in real time. By continuously learning and adapting to evolving grid dynamics, machine learning models offer a sophisticated and efficient means of assessing power system security, contributing to the resilience and adaptability of modern electrical infrastructures. This comprehensive review categorizes data-driven methods for power system security into static and dynamic assessments. The objective is to discern these techniques strengths, weaknesses, and limitations, offering valuable insights for future research. The review spans various approaches, providing a decision-making foundation for upcoming investigations in the field. A comparative analysis is conducted on the IEEE 14-bus system and New England 39-bus system, incorporating established base learners and ensembles of these learners.","url":"https://doi.org/10.1201/9781003581246-11","authors":["Mukesh Singh","Sushil Chauhan","Ankur Maheshwari"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-19T15:48:22Z","doi":"10.1201/9781003581246-11","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.21275/sr231101150529","name":"Clinical Utility of Psychiatric Neuroimaging: Exploring Psychoradiology and Use of Machine Learning in Psychoradiology","source":"crossref","abstract":"Psychoradiology, an emerging discipline, applies advanced radiological imaging technologies to psychiatric disorders.Over the past thirty years, progress in brain imaging has significantly enhanced our understanding of psychiatric illnesses and treatment outcomes [1].Radiologists have shown growing interest in utilising these advancements for accurate diagnosis and personalised patient care in common psychiatric conditions.This shift from research to clinical application marks the initial phase of psychoradiology's evolution [1].This review outlines recent developments in the field, focusing on its clinical roles.The review also offers practical guidelines for implementing psychoradiology in clinical settings and suggests areas for future research to validate its broader clinical applications.Given the prevalence of psychiatric disorders and the increasing involvement of radiologists in this area, this guide aims to assist radiologists in contributing effectively to this rapidly advancing field.","url":"https://doi.org/10.21275/sr231101150529","authors":["Ishaan Bakshi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-12-15T06:06:33Z","doi":"10.21275/sr231101150529","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/978-981-97-1900-6_5","name":"Machine Learning Algorithmic Model for Pairs Trading","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-1900-6_5","authors":["R. Sivasamy","Dinesh K. Sharma","Sediakgotla","B. Mokgweetsi"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-20T13:01:53Z","doi":"10.1007/978-981-97-1900-6_5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/icicml63543.2024.10958007","name":"A Machine Learning-Based Fall Risk Prediction Model for Chinese Older Adults","source":"crossref","abstract":"OBJECTIVE: To predict the risk of falls in older Chinese adults, and to develop a machine learning model for assessing fall risk based on longitudinal study data, which can help identify high-risk individuals and provide a basis for formulating effective interventions. METHODS: A total of 2350 elderly people who participated in CHARLS during 2020 were selected for the study, and the prediction model was constructed. Several machine learning algorithms (logistic regression, K-nearest neighbor, random forest, neural network, decision tree) were used to assess fall risk. Explore the best cut-off points and adjust parameters in the training set, compare the prediction accuracy of the models in the test set, and further explain the best-performing models. RESULTS:Out of the 26 features analyzed, LASSO regression identified 12 features that were critical to model building. Comparing the performance of five different machine learning models on the test set, the results showed that the RF model performed best on various evaluation measures, with a threshold of 0.168, a specificity of 0.860, a sensitivity of 0.526, an accuracy of 0.693, an F1 score of 0.653 and an area under the ROC curve (AUC) of 0.738. .CONCLUSION:: The predictive model of machine learning can effectively evaluate the risk of falls in healthy elderly people. Random Forest (RF) methods clearly explain personalized fall risk prediction.","url":"https://doi.org/10.1109/icicml63543.2024.10958007","authors":["Jie Ren","Shumin Yin","Fengjun Li","Fang Xia","Cheng Ding"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2025-04-14T17:35:47Z","doi":"10.1109/icicml63543.2024.10958007","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/aimla59606.2024.10531342","name":"Food Sales Analysis and Prediction Using Machine Learning","source":"crossref","abstract":"This study uses Support Vector Machines (SVM) with Positive and Negative Binomial distributions to give a thorough analysis and prediction model for food sales. The project's goal is to improve sales forecasting's resilience and accuracy in the dynamic and intricate food business. When analyzing historical sales data, the SVM algorithm is used to identify complex patterns and correlations that take into account variables like seasonality, promotional activities, and outside influences. Furthermore, the model's integration of the positive and negative binomial distributions allows it to take into consideration excess zeros and overdispersion, which are frequently seen in datasets related to food sales. Food sales data are used to assess the suggested methodology, showing how well it captures the underlying distributional characteristics and yields precise forecasts. The results further the field of predictive analytics in the food industry by providing a dependable instrument for companies to plan marketing campaigns, optimize inventory management, and improve overall operational efficiency.","url":"https://doi.org/10.1109/aimla59606.2024.10531342","authors":["Sadasivam V.R","Narendharakumar G","Ragavan M","Prabhuram A"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-21T17:20:38Z","doi":"10.1109/aimla59606.2024.10531342","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1101/2024.10.18.617592","name":"Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning","source":"crossref","abstract":"Abstract The matrix-assisted laser desorption-ionization time-of-flight mass spectrometry has become a powerful tool for accurate species identification in routine diagnostic microbiology. Recently, the application of machine learning models with MALDI-TOF mass spectra data indicated that rapid prediction of antimicrobial resistance patterns might facilitate even timelier and improved antimicrobial treatment. Although MALDI-TOF mass spectra data have proven valuable for clinical decision support, the issue of class imbalance in routine clinical data is often overlooked. This imbalance arises from factors such as local epidemiology, selective pressure from antibiotics, culture conditions, the methodology of phenotypic antimicrobial susceptibility testing, and sample preparation processes. Here, we provide a large mass spectra dataset, MS-UMG, for antimicrobial resistance prediction model training. With previously available public datasets, our dataset is evaluated and validated for usage in AMR prediction. We further explore the mass spectra data and identify informative regions on the spectra profile for AMR prediction. Moreover, we investigate the composition of this clinical dataset and present the implications of data heterogeneity on machine learning model performance. In conclusion, our findings highlight that accurate comprehension of clinical routine data and consideration of diverse hospital protocols are critical for effective clinical decision support systems with machine learning models. Key Points Introduced a large-scale clinical mass spectrometry dataset to the scientific community for research on antimicrobial resistance. Conducted a comparison and evaluation of this dataset with other existing large-scale MS datasets, highlighting its value for developing and validating predictive models in clinical settings. Demonstrated the robustness of machine learning models for antimicrobial resistance prediction using large-scale clinical mass spectra profiles. Analyzed the impact of data heterogeneity on the training and performance of machine learning models, emphasizing the need to account for variability in clinical routine data to enhance model reliability and generalizability.","url":"https://doi.org/10.1101/2024.10.18.617592","authors":["Youngjun Park","Michael Weig","Christine Noll","Oliver Bader","Anne-Christin Hauschild"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-10-20T13:20:13Z","doi":"10.1101/2024.10.18.617592","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.31219/osf.io/cwjb5","name":"The Intersection of Data Governance and Machine Learning in Clinical Trials","source":"crossref","abstract":"The Intersection of Data Governance and Machine Learning inClinical Trials","url":"https://doi.org/10.31219/osf.io/cwjb5","authors":["Ammar Hassan"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2023-09-07T05:03:10Z","doi":"10.31219/osf.io/cwjb5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1097/cin.0000000000001188","name":"Managing Postembolization Syndrome Through a Machine Learning–Based Clinical Decision Support System","source":"crossref","abstract":"Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence.","url":"https://doi.org/10.1097/cin.0000000000001188","authors":["Minkyeong Kang","Myoung Soo Kim"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-09-26T17:00:23Z","doi":"10.1097/cin.0000000000001188","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.26904/rf-154-7148550864","name":"Unlocking the future of leadership: Navigating human-machine collaboration in the age of machine learning","source":"crossref","abstract":"I n an era where prolonged stability often masks the underlying fragility of our society, awareness of these vulnerabilities tends to diminish.The rapid pace of technological advancements and the increasing complexities of our global landscape present unprecedented challenges to traditional leadership paradigms.Due to contemporary change, advocating for understanding machine learning (ML) and artificial intelligence (AI) is essential, as these topics necessitate transformative leadership, adaptability, and ethical integrity.This raises AI stands poised to usher in the next wave of digital disruption, substantial increases in global spending, venture capital, and private equity funding have been observed, with ML receiving the lion's share of investment.For successful implementation, corporate governance and seamless data access are paramount, with data serving as the cornerstone of economic transformations worldwide.Companies like Alphabet and Meta exemplify the strategic leverage of vast customer-generated data, requiring innovative approaches for processing and analysis, such as NoSQL and Hadoop technologies.Peterka emphasises that the future of leadership relies on hybrid human-machine collaboration models, moving beyond traditional binaries to foster adaptive and Unlocking the future of leadership Navigating human-machine collaboration in the age of machine learningAdvocating for understanding of machine learning and artificial intelligence is essential, as these topics necessitate transformative leadership, adaptability, and ethical integrity.Prolonged stability has masked societal fragility, prompting a re-evaluation of leadership paradigms.Effective leadership in the age of substantial machine learning investment requires adaptability, ethical integrity, and the adept integration of technical capabilities with human intuition.Artificial intelligence (AI), particularly machine learning (ML), reshapes leadership paradigms, demanding ethical integration and top-level support.Christopher Peterka, founder of Gannaca, Germany, advocates for innovative leadership at the nexus of technology and humanism through the 'Quantum Leader' paradigm -an augmented leadership concept that promotes hybrid human-machine collaboration and adaptive frameworks for ML-driven decision-making to foster organisational resilience and strategic adaptability.","url":"https://doi.org/10.26904/rf-154-7148550864","authors":["Christopher Peterka"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-08-14T09:57:55Z","doi":"10.26904/rf-154-7148550864","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.51219/jaimld/mahnoor-mughal/381","name":"Enhancing Clinical Research with Synthetic Patient Data: Leveraging ChatGPT for Improved Diagnostic Models","source":"crossref","abstract":"Recent strides in machine learning (ML) and generative artificial intelligence (GenAI) are transforming clinical research, opening new possibilities for privacy-conscious, data-driven insights into diagnosis, treatment and patient care.In clinical research, data scarcity, privacy concerns and limited access to high-quality datasets often hinder innovation.GenAI, leveraging synthetic data generated by advanced models like ChatGPT, addresses these challenges by emulating real patient histories without compromising patient privacy.This study examines how synthetic data generation can enhance ML applications in healthcare, simulating diverse clinical scenarios, supporting drug discovery and enabling personalized medicine.Using generative models such as generative adversarial network (GANs) and variational autoencoder (VAEs), this study explores the potential to produce synthetic data that maintains the integrity and statistical relevance of real-world data while safeguarding patient confidentiality.We also address the limitations and ethical concerns of AI-generated data, particularly around accuracy and interpretability.Our findings suggest that integrating synthetic data into clinical research could redefine healthcare practices by enabling scalable, privacy-preserving and more equitable access to medical insights.By bridging the gap between real and synthetic patient data, this approach holds promise for advancing precision medicine and supporting evidence-based healthcare, ultimately fostering a transformative era in clinical research.As an alternative, in clinical research, the potential applications of generative AI are vast and transformative.","url":"https://doi.org/10.51219/jaimld/mahnoor-mughal/381","authors":["Mahnoor Mughal"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-12-23T06:09:24Z","doi":"10.51219/jaimld/mahnoor-mughal/381","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1101/2024.05.13.24307231","name":"Benchmarking Machine Learning Missing Data Imputation Methods in Large-Scale Mental Health Survey Databases","source":"crossref","abstract":"Abstract Databases with mental and behavioral health surveys suffer from missingness when participants skip the entire survey, affecting the data quality and sample size. We investigated the missing data patterns and evaluate the imputation performance in Simons Powering Autism Research (SPARK), a large-scale autism cohort consists of over 117,000 participants. Four common methods were assessed – Multiple Imputation by Chained Equations (MICE), K-Nearest Neighbors (KNN), MissForest, and Multiple Imputation with Denoising Autoencoders (MIDAS). In a complete subset of 15,196 autism participants, we simulated three types of missingness patterns. We observed that MIDAS and KNN performed the best as the rate of random missingness increased and when blockwise missingness was simulated. The average computational times for MIDAS and KNN were 10 minutes, 35 minutes for MissForest, and 290 minutes for MICE. MIDAS and KNN both provide promising imputation performance in mental and behavioral health survey data that exhibit blockwise missingness patterns.","url":"https://doi.org/10.1101/2024.05.13.24307231","authors":["Preethi Prakash","Kelly Street","Shrikanth Narayanan","Bridget A. Fernandez","Yufeng Shen","Chang Shu"],"tags":[],"confidence":0.7,"sites":["biomed-ai"],"publishedDate":"2024-05-15T02:41:15Z","doi":"10.1101/2024.05.13.24307231","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/advs.77452","name":"Machine-Learning-Assisted Impedance Component Analysis Enables Standardizable Surface Protein Analysis of Extracellular Vesicles Using Engineered Nanovesicles.","source":"europepmc","abstract":"Characterizing surface protein heterogeneity on extracellular vesicles remains challenging but essential for understanding their biological functions and clinical applications. Here, this study introduces an integrated platform that combines engineered cell-derived nanovesicles, used as model standards with controlled surface protein states, and machine learning-optimized impedance spectroscopy. Cell-derived nanovesicles with defined surface protein densities are generated by extruding HeLa cells expressing 1, 3, or 9 copies of amyloid-β 42, establishing a series of reference vesicles with precisely controlled oligomeric configurations. Through systematic evaluation of impedance features across frequencies from 10 Hz to 1 MHz, machine learning identifies reactance changes at 1 kHz as optimal for distinguishing oligomeric states on the vesicle membrane. Equivalent-circuit modeling reveals that membrane capacitance correlates with protein oligomerization, and structural predictions explain the mechanistic basis. The platform also enables label-free, time-resolved analysis of Aβ oligomer formation directly on vesicular membranes, providing insights into aggregation dynamics. This platform establishes standardizable reference materials using engineered nanovesicles and a quantitative framework for extracellular vesicle surface protein analysis, offering a broadly applicable method for studying membrane-associated protein dynamics relevant to neurodegenerative diseases and advancing extracellular vesicle-based diagnostics.","url":"https://doi.org/10.1002/advs.77452","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/advs.77452","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1111/jnu.70123","name":"Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.","source":"europepmc","abstract":"Introduction Cancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical outcomes in cancer patients. While machine learning (ML) models are increasingly developed to predict these symptoms, existing studies are marked by significant heterogeneity in algorithms, sample sizes, and predictors, and lack quantitative synthesis of model performance, methodological quality, and clinical applicability. This study aimed to comprehensively summarize the characteristics of models and predictors, evaluate the predictive accuracy, risk of bias, and clinical applicability of ML prediction models. Design Systematic review and meta-analysis. Methods A comprehensive literature search was conducted in PubMed, Web of Science, the Cochrane Library, CINAHL, PsycINFO, CNKI, WanFang, VIP, and SinoMed, from database inception to August 31, 2025. Data were extracted in accordance with the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and the risk of bias and applicability of included models were assessed using the Prediction Model Risk of Bias Assessment Tool and Artificial Intelligence (PROBAST-AI). The quality of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. A random-effects model was employed for pooled analysis. Subgroup analyses were stratified by cancer type, geographic region, and algorithm type. Results A total of 11,217 records were retrieved, and 34 studies were included in the analysis. The pooled AUCs for predicting pain, fatigue, depression, anxiety, and malnutrition were 0.76 (95% CI: 0.69-0.83, I 2 = 97.7%), 0.82 (95% CI: 0.76-0.88, I 2 = 98.5%), 0.76 (95% CI: 0.70-0.82, I 2 = 98.4%), 0.78 (95% CI: 0.69-0.86, I 2 = 37.9%), and 0.86 (95% CI: 0.80-0.91, I 2 = 94.9%), respectively. Subgroup analyses across cancer type, geographical region, and algorithm type revealed no statistically significant sources of heterogeneity. The certainty of evidence was moderate across all outcomes. Conclusion This systematic review and meta-analysis showed that ML models achieved acceptable discriminative performance for predicting pain, anxiety, depression, fatigue, and malnutrition in patients with cancer in available datasets. Given predominant internal validation and observed heterogeneity, clinical utility requires further prospective validation and implementation studies. Future research may consider theory-driven predictors and clinically tailored algorithms to improve model performance. Clinical relevance These pooled findings provide a preliminary foundation for the clinical translation of ML models to predict pain, anxiety, depression, fatigue, and malnutrition in cancer patients. Further prospective validation in diverse clinical settings and randomized controlled trials evaluating the effectiveness of model-guided symptom management strategies are needed to improve patient outcomes. Prospero registration CRD420251130183.","url":"https://doi.org/10.1111/jnu.70123","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jnu.70123","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s10557-026-07943-x","name":"Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.","source":"europepmc","abstract":"Background Cardiovascular disease (CVD) pathogenesis is strongly associated with environmental exposures and metabolic factors. This study aimed to investigate the relationship between urinary levels of volatile organic compounds (VOCs), heavy metals, and serum biomarkers with CVD, and to develop a high-accuracy, interpretable predictive model, while elucidating the potential underlying biological mechanisms from a molecular temporal-dynamic perspective. Methods This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES, 2011-2020 cycles), including 8,165 eligible participants. Sixty-six metabolic-related features were systematically selected from blood and urine samples. To address class imbalance, and given that the primary objective of this study was to improve the identification of cardiovascular disease (CVD) cases rather than to estimate the population prevalence distribution, the Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively to the training set for class balancing during model development. NHANES survey weights were retained for descriptive analyses and population characteristic estimation to preserve the representativeness of the survey data and minimize potential impacts on population-level inference. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the top 20 features most predictive of CVD. Seven machine learning models-including XGBoost, LightGBM, Support Vector Machine (SVM), and others-were constructed. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, F1-score, and other metrics. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) values to assess feature importance. To further validate the biological plausibility of the identified predictors, we integrated clinical transcriptomic data and conducted longitudinal analyses of differential gene expression and pathway enrichment across four critical time points (acute phase: 1 day; subacute phase: 4-6 days; recovery phase: 1 month; chronic phase: 6 months). Results Among all models, XGBoost demonstrated superior predictive performance and stability, achieving an AUC of 0.845 and an accuracy of 78.24%. The 20 key features identified by LASSO regression included urinary VOC-related metabolites (e.g., ATCA and NAE), urinary inorganic arsenic species As (III), metal/element biomarkers (e.g., iodine, cadmium, and cesium), and serum biomarkers such as creatinine, glycated hemoglobin (HbA1c), and alkaline phosphatase (ALP). SHAP analysis quantified the contribution of these features to CVD risk and effectively stratified individuals into high- and low-risk groups, suggesting that the model has reasonable risk-stratification ability and interpretability. Furthermore, clinical transcriptomic time-series analysis provided molecular mechanistic support for the key predictive features: the predictive value of monocyte count was associated with significant upregulation of core monocyte/macrophage genes (e.g., CD14, S100A9) during the acute phase, while the predictive power of HbA1c corresponded with sustained dysregulation of metabolism-related genes (e.g., HBB) in the chronic phase. These findings validate the biological plausibility of the predictive model at the molecular level. Conclusion Integrating urinary environmental exposures (VOCs and heavy metals) with blood biomarkers significantly enhances the accuracy of CVD risk prediction. The XGBoost-based framework, interpreted via SHAP, provides an interpretable approach for identifying key environmental and metabolic factors contributed to CVD risk prediction and exploring potential nonlinear patterns among multidimensional exposures. Multi-omics integrative analysis further elucidated, from a molecular temporal-dynamic perspective, the coherent pathophysiological mechanisms underlying these predictive features-spanning from innate immune activation, molecular patterns consistent with metabolic memory-like dysregulation, and environmental exposures-thereby providing exploratory support for the biological plausibility of the model. Future prospective cohort studies are warranted to validate the causal pathways linking these biomarkers to CVD.","url":"https://doi.org/10.1007/s10557-026-07943-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10557-026-07943-x","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/jcm15145728","name":"Comparison of Machine Learning Models for Predicting Recurrent Lumbar Disc Herniation After Percutaneous Endoscopic Lumbar Discectomy.","source":"europepmc","abstract":"Background : Recurrent lumbar disc herniation (rLDH) significantly impairs outcomes following percutaneous endoscopic lumbar discectomy (PELD). Accurate individualized risk prediction remains challenging. This study aimed to develop and compare multiple machine learning models for predicting rLDH within two years post-surgery. Methods : A retrospective cohort of 1483 patients undergoing single-level PELD was analyzed. The primary outcome was symptomatic, magnetic resonance imaging-confirmed rLDH requiring reintervention. Candidate predictors included demographic, surgical, and radiographic parameters. The dataset was stratified by outcome and randomly split into training (70%, n = 1038) and validation (30%, n = 445) sets. Feature selection utilized univariate screening ( p Results : The overall recurrence rate was 4.25% (63/1483). Logistic regression achieved the optimal F1-score (0.286), while light Gradient Boosting Machine (LightGBM) demonstrated superior discrimination (AUC = 0.768). DCA indicated clinical utility primarily at low threshold probabilities ( Conclusions : This study presents an exploratory, internally validated machine learning framework for rLDH risk stratification. While LightGBM demonstrated moderate discriminative ability, model sensitivity was constrained by the inherent rarity of recurrence events, precluding its use as a definitive standalone screening tool. Notably, clinical utility was restricted to low threshold probabilities (<10%), supporting a focused role in identifying high-risk subgroups for intensified preoperative counseling and postoperative monitoring. Beyond elucidating key radiological and demographic risk factors, our findings underscore that rigorous external prospective validation and probability calibration are indispensable before any future clinical deployment.","url":"https://doi.org/10.3390/jcm15145728","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15145728","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s00383-026-06576-3","name":"Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.","source":"europepmc","abstract":"Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histopathological images. A search of seven databases identified thirteen eligible studies (2016-2025). Studies were analysed for model architecture, computational workflows and diagnostic performances. Methodological quality and risk of bias were assessed using QUADAS-AI and PROBAST frameworks. HD image analysis has progressed from traditional processing to convolutional neural networks and transformer models. Deep learning outperformed conventional approaches with > 90% in ganglion cell detection and reducing diagnostic time by 50-95%. However, nine studies (69%) exhibited a high risk of bias due to small sample sizes, patch-level data partitioning, and lacking external test sets, raising concerns regarding overfitting and data leakage. AI methods demonstrated strong potential to support HD diagnosis by increasing accuracy, reducing variability and accelerating clinical decision-making. Future research should prioritise large multi-centre datasets, diverse staining techniques, external validation, and transparent reporting to facilitate reliable clinical integration.","url":"https://doi.org/10.1007/s00383-026-06576-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00383-026-06576-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1080/23279095.2026.2707490","name":"From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).","source":"europepmc","abstract":"Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI). Objective This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions. Methods A structured search of the Scopus database retrieved 722 publications from 2011 to 2024. Bibliometric indicators were analyzed using Python and VOSviewer to evaluate annual production, geographical distribution, and technological trends. Results The field has entered an exponential growth phase, with publication output peaking in 2023. A geopolitical analysis reveals a \"research duopoly\" between the United States (130 papers) and China (129 papers). Technologically, there is a distinct transition from traditional ML-support vector machines (SVM) to deep learning (DL) architectures. Crucially, electroencephalography (EEG) has emerged as the preferred neuroimaging modality over functional magnetic resonance imaging (fMRI) in recent AI studies, driven by its cost-effectiveness and high temporal resolution. Conclusion AI-based ADHD diagnosis has matured from an exploratory niche to an evolving domain of computational psychiatry. Future research must prioritize explainable AI (XAI) and multimodal data fusion to translate high algorithmic accuracy into clinical utility.","url":"https://doi.org/10.1080/23279095.2026.2707490","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/23279095.2026.2707490","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1371/journal.pone.0354924","name":"Identification and risk-factor analysis for individuals at high risk for keratoconus via machine learning and logistic regression.","source":"europepmc","abstract":"Purpose To evaluate keratoconus (KC) risk factors and to develop a machine-learning (ML) model for KC and myopia classification. Methods In this retrospective single-center cross-sectional study, demographic and lifestyle data from patients with KC and individuals from a preoperative refractive surgery clinic were collected from January 20, 2024, to December 1, 2024. Univariable and multivariable regression analyses were used to identify key risk factors. Additionally, random forest (RF)-recursive feature elimination (RFE), extreme gradient boosting (XGBoost)-RFE, and univariable logistic regression were applied to select factors for ML models. Seven ML models were developed for a lifestyle-based classification system, with the performance being validated through discrimination and calibration, and interpretability being improved using SHapley Additive exPlanations (SHAP). Results Analysis of 711 patients (mean [standard deviation] age, 26.6 [7.1] years; 439 males [61.7%]) revealed 275 with KC. Multivariable regression analysis identified seven risk factors for KC, including male sex, higher body-mass index (BMI), lower education level, more distant childhood residence, allergic conjunctivitis, and increased eye-rubbing intensity and frequency. After feature selection of 24 variables, the neural-network model demonstrated the highest performance (area under the receiver operating characteristic curve [AUROC] = 0.79), followed by RF (AUROC = 0.77) and XGBoost (AUROC = 0.76). SHAP analysis consistently highlighted eye-rubbing intensity, sex, BMI, and childhood residence among the top 10 factors across the top three models, which were also confirmed by univariable logistic regression. Conclusion ML models can distinguish high-risk KC groups based on clinical risk factors, facilitating risk stratification and early lifestyle interventions.","url":"https://doi.org/10.1371/journal.pone.0354924","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0354924","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1055/a-2926-9824","name":"Interpretable Machine Learning for 30-Day Mortality in PIVSR: A Cohort Study.","source":"europepmc","abstract":"Objective Post-infarction ventricular septal rupture (PIVSR) is a fatal mechanical complication of acute myocardial infarction. We aimed to develop and interpret a machine learning (ML) model to predict 30-day mortality using routinely available clinical variables in PIVSR patients. Methods This retrospective cohort study included consecutive PIVSR patients treated at Fuwai Central-China Cardiovascular Hospital from 2018 to 2024. Candidate predictors were screened by three complementary procedures including bootstrap resampling with LASSO, stepwise logistic regression, and the Boruta algorithm. Nine supervised ML algorithms were trained and compared. Model performance was comprehensively evaluated using multiple discrimination, calibration, and clinical utility metrics. Model interpretability was examined using Shapley Additive Explanations (SHAP). We additionally implemented the final model as a web-based calculator to support individualized risk estimation. Results A total of 237 PIVSR patients were analyzed. Nine clinical predictors were identified. The CatBoost model demonstrated favorable overall performance in both the training set (AUC = 0.92, 95%CI: 0.89-0.94) and testing set (AUC = 0.88, 95%CI: 0.79-0.95). In the testing set, CatBoost achieved an accuracy of 0.79, sensitivity of 0.85, specificity of 0.71, F1-score of 0.82, and Brier score of 0.15. SHAP analysis identified operation, hemodynamic status, inflammatory markers, and renal function as key contributors. Conclusion We developed, evaluated, and interpreted ML models for predicting 30-day mortality in patients with PIVSR. The CatBoost model demonstrated favorable predictive performance and transparent interpretability. Hemodynamic compromise, inflammation, and renal dysfunction were identified as key predictors of adverse outcomes. An exploratory web-based calculator was developed to support dynamic in-hospital prognostication.","url":"https://doi.org/10.1055/a-2926-9824","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1055/a-2926-9824","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fonc.2026.1899153","name":"Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.","source":"europepmc","abstract":"Background and purpose Accurate prediction of short-term response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC) remains a critical unmet clinical need. We aimed to construct a multimodal, interpretable machine learning (ML) framework integrating MR dynamic radiomics (delta radiomics) with clinical parameters to predict ICT response. Methods A multicenter retrospective study enrolled 216 pathologically confirmed NPC patients from two institutions (Xiangyang No. 1 People's Hospital and Xiangyang Central Hospital, January 2015-September 2024; training cohort n=151, test cohort n=65) and an independent external validation cohort of 87 patients (Affiliated Hospital of Hubei University of Chinese Medicine, February 2017-June 2024). A Delta Radscore was computed from 2,637 candidate features extracted from paired pre- and arterial-phase post-contrast enhanced T1-weighted imaging (CE-T1WI) MRI, quantifying intratumoral microvascular dynamics. After two-stage feature selection (intraclass correlation coefficient [ICC] ≥0.80 stability screening followed by least absolute shrinkage and selection operator (LASSO)-random forest ensemble), eight ML algorithms-XGBoost, CatBoost, support vector machine (SVM), k-nearest neighbors (KNN), and Logistic Regression,etc-were systematically evaluated. SHAP (SHapley Additive exPlanations) analysis provided model interpretability. Independent clinical predictors were identified via uni- and multivariable logistic regression and integrated into a dynamic nomogram. Results Multivariable logistic regression identified five independent predictors: T stage (OR = 4.29, 95% CI: 1.43-12.82, P = 0.009), lymphocyte count (OR = 2.08, 95% CI: 1.07-4.05, P = 0.031), lactate dehydrogenase (LDH; OR = 1.02, 95% CI: 1.01-1.03, P Conclusion An XGBoost-based multimodal interpretable model integrating MR dynamic radiomics with clinical parameters effectively predicts short-term ICT response in NPC, providing a quantifiable, explainable decision-support tool to guide individualized treatment strategies.","url":"https://doi.org/10.3389/fonc.2026.1899153","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1899153","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16142227","name":"Interpretable Machine Learning for Predicting Suboptimal 12-Month Growth Response to Recombinant Human Growth Hormone in Children with Idiopathic Short Stature: A Dual-Center External Validation Study.","source":"europepmc","abstract":"Background/Objectives : Individual responses to recombinant human growth hormone (rhGH) therapy in children with idiopathic short stature (ISS) vary substantially, limiting pretreatment decision-making. This study aimed to develop and externally validate an interpretable machine learning model for predicting suboptimal 12-month growth response to rhGH therapy. Methods : In this retrospective dual-center study, 901 children from Center 1 were used for model development and internal testing, and 51 children from Center 2 formed an independent external validation cohort. Routinely collected baseline demographic, laboratory, hormonal, radiographic, and family-history variables were used to develop multiple machine learning models. A soft-voting ensemble classifier was constructed and interpreted using SHapley Additive exPlanations (SHAP). The primary outcome was suboptimal growth response, defined as failure to achieve a height gain of at least 0.5 standard deviation score after 12 months of treatment. Results : The optimized ensemble model showed strong discrimination in the internal test set, with an area under the receiver operating characteristic curve of 0.927, and maintained robust performance in the external validation cohort, with an AUC of 0.897. SHAP analysis identified luteinizing hormone, body mass index, TW3 RUS bone age, and insulin-like growth factor 1 as the leading contributors to predicted suboptimal-response risk. Conclusions : An interpretable ensemble machine learning model based on routinely available pretreatment data can predict suboptimal short-term rhGH response in children with ISS and may support individualized risk stratification in pediatric endocrine practice. Clinical trial registration was not required because this was a retrospective analysis.","url":"https://doi.org/10.3390/diagnostics16142227","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16142227","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12911-026-03698-5","name":"Machine learning prediction of postoperative pulmonary infection in patients who underwent thoracoscopic lung cancer resection: a retrospective case-control study.","source":"europepmc","abstract":"Background Accurate identification of patients at high risk of pulmonary infection after thoracoscopic lung cancer resection is important for timely and targeted preventive measures. Methods for determining the risk of pulmonary infection after thoracoscopic lung cancer resection have not been well studied. Methods This study was a retrospective case-control research project. The information of 3219 hospitalised patients who underwent thoracoscopic lung cancer resection between January 2019 and December 2023 was obtained from the hospital electronic medical record system. 26 clinical characteristics were obtained from medical and nursing records. The variables were screened using the least absolute contraction and selection operator (LASSO) regression, and the risk prediction models for pulmonary infection after thoracoscopic lung cancer resection was constructed using the following 5 machine learning algorithms: logistic regression model (LR), artificial neural network (ANN), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGB). The model was evaluated using the following metrics: the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score (F1). Shapley additive explanation (SHAP) was used to interpret the machine learning models. Results There were 3219 enrolled patients, 2203 (70%) of whom were assigned to the training cohort and 966 (30%) of whom were assigned to the validation cohort. The AUC range of the five models was 0.883-0.951. The XGB model outperformed the others, with an AUC of 0.951 (95% confidence interval: 0.943-0.964), accuracy of 0.902 (95% confidence interval: 0.886-0.913), sensitivity of 0.927, specificity of 0.864, positive predictive value of 0.898, negative predictive value of 0.824, precision of 0.908, recall of 0.872 and F1 score of 0.815 in the validation group. The model's prediction performance in the 45-65 age group was the best. The AUC of the logistic regression model was 0.948 (95% confidence interval: 0.931-0.957). We transformed the logistic regression model into a nomogram to help clinicians visualise the model and make them more likely to use it to identify the risk of pulmonary infection after thoracoscopic surgery in lung cancer patients. Conclusions The establishment of a risk prediction model based on machine learning can help clinical nursing staff identify high-risk patients for pulmonary infection after thoracoscopic lung cancer resection. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12911-026-03698-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03698-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16132029","name":"FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.","source":"europepmc","abstract":"Background/Objectives : The precise identification of Alzheimer's disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phases of illness progression. This study presents a comprehensive and advanced diagnostic system, termed FLAME, featuring an enhanced federated learning architecture for privacy-preserving multi-institutional implementation. It provides a systematic review of machine learning (ML) and deep learning (DL) models for the classification of five stages of Alzheimer's disease (AD). The models include cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). Methods : Sixteen traditional machine learning models and eleven deep learning architectures-including FT-Transformer and NODE-were evaluated using a structured clinical dataset comprising 362 features. A hybrid ensemble was created at the probability level by combining the two top-performing models, LightGBM and a five-layer DNN. The weights of this ensemble were automatically optimised using a Genetic Algorithm (GA) with Macro-F1 as the fitness criterion, confirmed stable across 30 independent runs (w★=0.5024±0.0001). A federated learning architecture was then established, deploying the DNN across non-IID clients while keeping LightGBM centralised. We examine four distinct aggregation algorithms: FedAvg, FedProx, FedNova, and SCAFFOLD. Results : Among all deep learning architectures, FT-Transformer achieved the highest standalone performance (accuracy = 0.7810, κ = 0.7081). The five-layer deep neural network (DNN) was selected as the DL representative for the hybrid ensemble. LightGBM attained superior machine learning performance (accuracy = 0.8156, κ = 0.7537), confirmed deterministic across 10 seeds. The LightGBM vs. XGBoost difference is not statistically significant (McNemar p=0.4227). The GA-optimised hybrid ensemble (w = 0.685) surpassed both individual baselines across all evaluation metrics. The FedNova hybrid design achieved superior overall performance in federated configurations, surpassing all centralised arrangements in accuracy (accuracy = 0.8213, κ 0.7614). Conclusions : Evolutionary ensemble optimisation combined with federated learning provides a robust, scalable, and privacy-preserving solution for AD stage classification, offering a clinically viable framework for real-world multi-institutional decision-support systems. However, the AD class remains severely under-recalled across all configurations (F1 ≤ 0.21), identifying this as the primary open challenge for clinical translation.","url":"https://doi.org/10.3390/diagnostics16132029","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16132029","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12879-026-13769-7","name":"Development of a prediction model for infectious mononucleosis using machine learning algorithms based on blood cell analysis parameters.","source":"europepmc","abstract":"Background Infectious mononucleosis (IM) presents with nonspecific clinical manifestations, leading to frequent misdiagnosis or delayed diagnosis, and is associated with potentially severe complications. Existing etiological diagnostic methods are characterized by prolonged turnaround times. This study aims to establish an IM model using eight machine learning algorithms and select the optimal one, so as to further improve the laboratory diagnostic accuracy of IM. Methods The study included 234 patients diagnosed with IM from April 2024 to December 2025 as the case group. The control group comprised 478 non-IM subjects, consisting of 236 patients with other pathogenic infections who exhibited reactive lymphocytes on microscopic examination and 242 healthy individuals. Recursive feature elimination (RFE) in conjunction with cross-validation was employed to rank feature importance and select optimal feature variables. Eight machine learning algorithms were trained, and their predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, Precision-Recall (PR) curve and Confusion Matrix. The contribution of each feature to the model's predictions was quantified using SHapley Additive exPlanation (SHAP) analysis. Subsequently, the model's performance was rigorously externally validated using an independent validation cohort. Results Three features - Reactive lymphocyte percentage (Reactive lymph%), Lym-Y, and platelet-to-lymphocyte ratio (PLR) - were selected for constructing the IM predictive model. The model constructed using the Adaptive Boosting Classifier(AdaBoost) machine learning algorithm demonstrated the best performance in the test set. It achieved an AUC of 0.928, an accuracy of 0.853, a sensitivity of 0.898, a specificity of 0.832, and an F1 score of 0.800. SHAP consistent with decision feature importance rankings, indicated that Reactive lymph% was the most significant feature in the predictive model; It was associated with an elevated risk of IM. Validation cohort also confirmed the robust performance of the AdaBoost predictive model, with an AUC of 0.923 and an F1 score of 0.797. Conclusion The IM predictive model constructed based on the AdaBoost machine learning algorithm combined with three blood cell analysis parameters exhibits satisfactory predictive efficacy. Based on the established model, the missed laboratory diagnosis rate in IM may potentially be reduced, pending prospective validation.","url":"https://doi.org/10.1186/s12879-026-13769-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12879-026-13769-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.avsg.2026.08.006","name":"MACHINE LEARNING VERSUS PENALISED LOGISTIC REGRESSION FOR PREDICTING IN-HOSPITAL MORTALITY IN RUPTURED ABDOMINAL AORTIC ANEURYSM: A COMPARISON USING A UNIFORM VALIDATION FRAMEWORK.","source":"europepmc","abstract":"Background Ruptured abdominal aortic aneurysm (rAAA) remains associated with substantial in-hospital mortality. Although machine-learning methods can model complex nonlinear relationships between admission characteristics and outcome, their incremental value over conventional regression remains uncertain. We compared 3 prediction models using identical admission variables and a uniform validation framework. Methods This retrospective single-center cohort included 196 unique patients with rAAA managed between 2006 and 2024. Five prespecified admission predictors were evaluated: age, hypovolaemic shock, maximal aneurysm diameter, hemoglobin, and systolic blood pressure. Missing data were imputed independently within each training partition. Penalized logistic regression, gradient boosting, and a multilayer perceptron (MLP) were compared using nested five-fold cross-validation repeated 5 times. Secondary temporal validation was performed in the 181 patients with a recoverable treatment year: models were developed in the 2006-2018 cohort (n = 110) and evaluated, without refitting or recalibration, in the 2019-2024 cohort (n = 71). Results In-hospital mortality occurred in 99/196 patients (50.5%). Gradient boosting and penalized logistic regression achieved comparable moderate discrimination, with area under the curves (AUCs) of 0.739 (95% confidence interval [CI] 0.666 to 0.805) and 0.729 (95% CI, 0.654 to 0.796), respectively. Their paired AUC difference was 0.010 (95% CI, -0.024 to 0.045). The MLP showed lower discrimination (AUC, 0.643; 95% CI, 0.563-0.719), with a paired difference versus penalized logistic regression of -0.086 (95% CI, -0.160 to -0.013). In temporal validation, AUCs were 0.800 (95% CI, 0.686 to 0.904) for gradient boosting, 0.739 (95% CI, 0.607 to 0.858) for penalized logistic regression, and 0.632 (95% CI, 0.496 to 0.762) for the MLP. Temporal calibration demonstrated overprediction of absolute mortality risk, with observed mortality of 40.8% compared with mean predicted mortality of 49.9%, 50.0%, and 63.4%, respectively. Conclusion Using a uniform internal and temporal validation framework, gradient boosting achieved the highest numerical performance but did not demonstrate a conclusive advantage over penalized logistic regression. The MLP provided no incremental predictive benefit. These findings indicate that increasing algorithmic complexity does not necessarily improve mortality prediction in modest-sized emergency vascular datasets and support interpretable regression as an essential benchmark. External multicenter validation and more complete prospective data collection are required before clinical implementation.","url":"https://doi.org/10.1016/j.avsg.2026.08.006","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.avsg.2026.08.006","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.cmpb.2026.109533","name":"Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.","source":"europepmc","abstract":"Background Significant liver fibrosis is often clinically silent but predicts adverse outcomes. We developed and externally validated an interpretable machine-learning (ML) framework using readily obtainable demographic, anthropometric, and clinical variables for population-level pre-screening of significant liver fibrosis. Methods We included 9424 European participants from the UK Biobank, of whom 1678 were classified as high risk using the Fibrosis-4 Index (FIB-4) ≥ 1.45 or NAFLD Fibrosis Score (NFS) ≥ -1.455. Ten ML algorithms were trained and internally evaluated using a stratified training, validation, and test design. XGBoost was further validated in the 2017-2023 National Health and Nutrition Examination Survey (NHANES; n = 15,270) and an independent real-world cohort (n = 694), in which significant fibrosis was defined as liver stiffness measurement ≥ 8.0 kPa. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), interpretability by Shapley additive explanations and local interpretable model-agnostic explanations, and prognostic relevance by all-cause mortality. Results XGBoost achieved AUCs of 0.818 (95% CI, 0.796-0.841) and 0.816 (95% CI, 0.786-0.846) in the internal validation and test cohorts, respectively. In external validation, AUCs were 0.746 (95% CI, 0.735-0.757) in NHANES and 0.793 (95% CI, 0.750-0.836) in the real-world cohort. Interpretation analyses highlighted weight, age, height, hypertension, and waist circumference, with consistent support for anthropometric predictors. Model-defined high-risk status was associated with higher all-cause mortality (log-rank P Conclusion This XGBoost model, based on readily accessible demographic and clinical variables, shows good potential for accurately identifying individuals at high risk of significant liver fibrosis in primary care settings. This approach serves as a valuable tool for improving early detection strategies and facilitating timely interventions for significant liver fibrosis.","url":"https://doi.org/10.1016/j.cmpb.2026.109533","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109533","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2196/91257","name":"A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.","source":"europepmc","abstract":"Background Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require multidimensional perioperative risk assessment. Large language models (LLMs) have shown potential in clinical prediction, but the incremental value of structured multiagent collaboration remains unclear. Objective This study aimed to develop and validate a multidisciplinary team (MDT)-based LLM framework for predicting PNC after ATAAD surgery and to compare its performance with that of traditional machine learning (ML) models and single-agent LLM settings. Methods A retrospective cohort from January 2020 to June 2024 (N=763) was randomly divided into a training set (n=533) and an internal validation set (n=230). A prospective cohort from July 2024 to June 2025 (n=120) was used for prospective validation. The outcome was PNC, defined as stroke, cerebral hemorrhage, paraplegia, or coma. Population-level in-context learning used outcome-stratified summary statistics from the training cohort, including predictor distributions in patients with and without PNC and between-group P values. Two LLMs (DeepSeek-V3 and ChatGPT [GPT-5]) were evaluated under 4 settings: no MDT without in-context learning, MDT without in-context learning, no MDT with in-context learning, and MDT with in-context learning. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F 1 -score, and Brier score. Results PNC occurred in 13.0% (99/763) of patients in the retrospective cohort and 15.8% (19/120) in the prospective cohort. Among the ML models, the random forest achieved the highest AUC in the internal validation set (AUC 0.7857, 95% CI 0.6945-0.8768). Among the LLM configurations, GPT-5 with MDT and in-context learning achieved the highest AUC (AUC 0.8419, 95% CI 0.7398-0.9440), with a sensitivity of 80.00%, specificity of 87.00%, and Brier score of 0.0876. Although this configuration showed a significantly higher AUC than the fully unaided GPT-5 baseline ( P =.006), this improvement reflected the combined effect of MDT and in-context learning, as adding the MDT framework within matched settings did not significantly improve AUC over corresponding single-agent settings in either cohort. The AUC of GPT-5 with MDT and in-context learning was also not significantly higher than that of the random forest model ( P =.18). Word frequency analysis showed that the generated rationales following in-context learning more frequently mentioned variables that were statistically significant in the provided context. Conclusions The GPT-5 configuration combining MDT-style collaboration with population-level in-context learning showed promising discrimination for PNC prediction after ATAAD surgery. However, the MDT layer did not produce a statistically significant incremental improvement in AUC over the corresponding single-agent settings, and its predictive contribution remains to be confirmed. The framework generated structured, role-specific rationales, supporting its further evaluation as a proof-of-concept approach for postoperative risk stratification. Larger multicenter studies are required before routine clinical implementation.","url":"https://doi.org/10.2196/91257","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/91257","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.annepidem.2026.110249","name":"Machine learning classification of prevalent chronic disease using multidimensional social, behavioral, and psychological determinants: A cross-sectional analysis of the 2024 national health interview survey.","source":"europepmc","abstract":"Background Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional clinical risk factors have been extensively studied, the predictive utility of multidimensional social determinants of health (SDOH), including psychosocial factors such as loneliness, psychological distress, and social support, remains inadequately characterized within machine learning (ML) prediction frameworks. Objective To develop and compare ML models that classify the presence of six major chronic conditions-hypertension, type 2 diabetes, coronary heart disease, COPD, depression, and anxiety-using an integrated framework encompassing demographic, socioeconomic, behavioral, psychosocial, healthcare access, functional status, and COVID-19-related predictors; and to quantify the relative predictive importance of each domain across disease categories. Methods We conducted a cross-sectional analysis of 32,614 adults from the 2024 NHIS. Twenty-four predictor variables spanning seven SDOH domains were used to train three ML algorithms: Logistic Regression (LR), Gradient Boosting Machine (GBM), and Random Forest (RF). Model performance was evaluated using 5-fold stratified cross-validation with AUROC, F1 score, and AUPRC. Subgroup analyses were performed by age, sex, and race/ethnicity. Because the design is cross-sectional, the models estimate the likelihood of prevalent disease, not incident risk; robustness was confirmed with alternative imputation (KNN and random-forest MICE), hyperparameter optimization, and three importance methods (Gini, permutation, and SHAP). Results GBM achieved the highest AUROC for 5 of 6 outcomes, ranging from 0.793 (anxiety) to 0.857 (COPD). For cardiometabolic outcomes, age and BMI were dominant predictors (hypertension: age importance = 0.641, BMI = 0.117). In contrast, psychological distress (K6) and loneliness emerged as the top predictors for depression (importance = 0.285 and 0.234) and anxiety (0.245 and 0.161). Psychosocial factors collectively contributed 52% and 42% of predictive importance for depression and anxiety, but less than 3% for cardiometabolic diseases. Subgroup analyses showed consistent performance across demographic strata. Conclusions Integrating psychosocial determinants substantially enhances prediction of mental health outcomes but contributes minimally to cardiometabolic disease prediction. These findings support disease-specific screening and case-identification strategies that appropriately weight social, behavioral, and psychological dimensions.","url":"https://doi.org/10.1016/j.annepidem.2026.110249","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.annepidem.2026.110249","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1111/jdi.70364","name":"Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.","source":"europepmc","abstract":"Aims/introduction Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed a machine learning model to discover risk factors for adverse pregnancy outcomes. Materials and methods Women with GDM from tertiary hospitals in Korea were included (n = 305, discovery cohort; n = 911, validation cohort). Supervised machine learning classification models, including ExtraTree, RandomForest, GradientBoosting, AdaBoost, Bagging, XGBoost, and Light Gradient Boosting Machine (LGBM), were developed to predict adverse pregnancy outcomes. Outcomes included large for gestational age (LGA), small for gestational age (SGA), low Apgar score, and preterm delivery. The top-ranked risk factors identified through feature importance were further validated using binary logistic regression analysis. Results In predicting LGA, the RandomForest model achieved the highest AUROC of 0.726 on the validation cohort. For SGA, the RandomForest model achieved the highest AUROC of 0.628. For low Apgar score and preterm delivery, the ExtraTree model showed the best performance with AUROCs of 0.689 and 0.616, respectively. Conclusions This study presents machine learning models as a foundational tool for identifying factors associated with adverse pregnancy outcomes in women with GDM. These models may serve as a foundation for future development of clinical decision support tools.","url":"https://doi.org/10.1111/jdi.70364","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/jdi.70364","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s41666-026-00248-6","name":"A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.","source":"europepmc","abstract":"Artificial intelligence (AI) is increasingly applied in healthcare to support diagnosis and treatment by leveraging longitudinal multimodal data, despite persistent challenges such as missing data and heterogeneity. This systematic review, conducted in accordance with PRISMA guidelines, analyzed deep learning (DL) and machine learning (ML) applications to longitudinal multimodal clinical data published between 2020 and 2024. An initial search identified 1,124 records; a total of 54 items were included (53 studies meeting inclusion criteria and 1 dataset descriptor). Relevant research studies increased substantially over the review period, from 3 studies in 2020 to 21 in 2024. Prediction (n = 23) and classification (n = 19) were the most commonly addressed tasks. Structured electronic health records (EHRs) and medical imaging were the most frequently used data sources, reported in 48 and 33 studies, respectively, and were often used in combination. Regarding missing data, 34 studies reported general handling strategies, and 10 explicitly addressed missing modalities. While methodological variability and performance limitations remain, these findings highlight the growing use of DL and ML to analyze multimodal data, which may inform clinical decision support. Supplementary information The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00248-6.","url":"https://doi.org/10.1007/s41666-026-00248-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s41666-026-00248-6","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/jcm15145340","name":"Plasma Amino Acid Signatures Associated with Disease Progression and Hypertension in Autosomal Dominant Polycystic Kidney Disease: A Targeted Metabolomics and Machine Learning Approach.","source":"europepmc","abstract":"Background: Autosomal dominant polycystic kidney disease (ADPKD) is a clinically heterogeneous disorder often leading to end-stage renal disease (ESRD). Prognostication of disease progression remains a major clinical challenge. This study aimed to identify plasma amino acid signatures associated with ADPKD progression and hypertension. Methods: We conducted targeted metabolomic analysis (LC-MS/MS) to quantify 38 plasma amino acids in 203 ADPKD patients, stratified by disease progression (rapid vs. slow) and hypertension status. Support Vector Machine (SVM) models were developed to predict outcomes using clinical data, amino acid profiles, and combined datasets. Results: Our findings revealed that specific amino acid signatures, including valine, glutamic acid, homocitrulline, and methylhistidines, were significantly elevated in both rapid progression and hypertensive groups. Isoleucine and citrulline were elevated only in rapid progressors. Phenylalanine, leucine, asparagine, and arginine were elevated in hypertensive patients. Machine learning analysis showed that integrating clinical and metabolic data modestly improved prediction for progression and hypertension. Proteinuria, glomerular filtration rate (GFR), and uric acid were the top clinical predictors; however, adding arginine, isoleucine, and 3-methylhistidine further enhanced prediction accuracy. Pathway analysis showed shared dysregulation in arginine biosynthesis and branched-chain amino acid (BCAA) metabolism. Specific amino acids were positively correlated with creatinine and uric acid and negatively correlated with GFR, and elevated levels of these metabolites were associated with increased mortality risk in survival analysis. Conclusions: Our results suggest that these plasma amino acid signatures, when combined with clinical markers, may serve as potential biomarkers for early risk stratification and precision prediction of ADPKD progression and hypertension.","url":"https://doi.org/10.3390/jcm15145340","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15145340","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/pds.70471","name":"Unsupervised Ensemble Learning for Active Drug-Induced Liver Injury Surveillance: Integrating Pharmacokinetic Burden With Enzyme Trajectories.","source":"europepmc","abstract":"Purpose Traditional drug-induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. To address this, a fundamentally drug-agnostic unsupervised ensemble framework, integrating pharmacokinetic (WCEDR) and polypharmacy (HAMPIS) features, was developed for early DILI detection. Antiepileptic drugs (AEDs) were utilised as a proof-of-concept validation cohort. Methods Using Thailand's national electronic health record database (2021-2024), a multi-view ensemble (Isolation Forest, Local Outlier Factor, One-Class Support Vector Machine) evaluated longitudinal physiological deviations. Feature engineering prioritised acute kinetic volatility to mitigate irregular sampling intervals. Diagnostic performance was benchmarked against standard criteria (ALT > 3 × ULN) and validated through blinded expert adjudication of 140 clinical episodes. Results From 616 425 treatment episodes, the framework isolated 86 high-probability anomalies. Benchmarking revealed the model captured 80.2% of rule-based positives while proactively identifying 17 AI-only sub-threshold episodes (19.8%) exhibiting distinct enzymatic velocity; over half (52.9%) were clinically confirmed as DILI. Blinded adjudication yielded a clinical plausibility rate of 69.8% and a negative predictive value of 92.6%. Operationally, the framework achieved a number needed to review (NNR) of 1.43, substantially optimising triage efficiency. Digital phenotyping stratified alerts into three distinct archetypes: complex polypharmacy, atypical pharmacokinetic burden, and active idiosyncratic injury. Conclusion By prioritising kinetic volatility and multidimensional pharmacological context, the unsupervised ensemble functions as a high-precision, complementary surveillance tool. It successfully detects early-onset idiosyncratic reactions frequently overlooked by rigid static thresholds, offering a scalable blueprint for proactive pharmacovigilance.","url":"https://doi.org/10.1002/pds.70471","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/pds.70471","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/copd.s620360","name":"Construction and Validation of a Machine Learning Model Based on Clinical and Microbiomic Features for Predicting High Mucus Secretion in COPD.","source":"europepmc","abstract":"Objective To evaluate clinical and airway microbiome features of excessive mucus secretion (CMH) in COPD progression and apply machine learning for CMH status identification. Methods A total of 319 COPD patients from Changzhi People's Hospital (May 2020-March 2024) were consecutively enrolled and divided by sputum volume and characteristics into a high mucus secretion group (n=173) and a non-high mucus secretion group (n=146). Patients were randomly assigned to training (80%) and testing (20%) sets. Airway microbiome structure was analyzed via 16S rRNA sequencing. From clinical and microbiome data, 70 features were extracted. Six machine learning algorithms (SVM, KNN, RF, BN, GBDT, NN) were used to build classification models. Feature selection employed filtering methods, and hyperparameters were optimized by 10-fold cross-validation. Model performance was assessed using sensitivity, specificity, accuracy, and AUC. Results The CMH group and the non-CMH group differed significantly in a number of factors, including age, the length of the disease, and pulmonary function indices, according to a comparison of baseline patient data. Analysis of airway microbiome characteristics revealed that the CMH group had significantly lower observed ASVs and Shannon indices ( p Conclusion CMH in COPD is linked to airway dysbiosis and pathogen enrichment. The BN model effectively identifies this phenotype with strong generalization ability.","url":"https://doi.org/10.2147/copd.s620360","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/copd.s620360","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1136/bmjopen-2026-120743","name":"Novel indicator for colonoscopy insertion difficulty and its application in predictive modelling: a multicentre prospective study protocol in China.","source":"europepmc","abstract":"Background Colonoscopy is a cornerstone for the screening and diagnosis of colorectal diseases. Colonoscopy insertion difficulty is associated with examination quality, procedural efficiency and patient comfort. Cecal intubation time (CIT) is commonly used as a surrogate measure of insertion difficulty; however, it is substantially influenced by endoscopist experience, equipment characteristics and institutional factors, limiting its objectivity and comparability. This study will develop and validate a standardised colonoscopy insertion time (SCIT) metric that better reflects patient-related procedural difficulty while minimising non-patient-related variability. Additionally, we will identify factors associated with insertion difficulty and develop predictive models using machine learning techniques. Methods and analysis This multicentre prospective observational study will enrol approximately 3000 adults undergoing sedated colonoscopy across one primary centre and five participating centres. CIT will be prospectively recorded and analysed in relation to patient characteristics, endoscopist experience, colonoscope type and study centre. Three standardisation approaches, including Z-score standardisation, median-based standardisation and min-max normalisation, will be applied to generate SCIT. The primary outcome will be SCIT, and the optimal standardisation method will be selected based on its ability to reduce variability attributable to operator-related, equipment-related and centre-related factors while preserving clinically meaningful associations with patient characteristics. Mixed-effects models will be used to account for clustering by endoscopist and study centre. Based on the selected SCIT metric, machine learning models will be developed using preprocedural clinical variables to predict insertion difficulty. Model performance will be evaluated using measures of discrimination, calibration and predictive accuracy. Ethics and dissemination The study received initial ethical approval from the Ethics Committee of the Second Hospital of Jilin University (Approval No. 2024-467-1) on 4 November 2024. Subsequent protocol amendments were approved on 25 March 2026. All participants will provide written informed consent prior to enrolment. Patient privacy and data confidentiality will be strictly maintained. The results will be disseminated through SCI-indexed journals and presentations at national and international academic conferences to inform future research and clinical practice related to colonoscopy performance evaluation. Trial registration number NCT07228715.","url":"https://doi.org/10.1136/bmjopen-2026-120743","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2026-120743","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3791/72340","name":"Machine-Learning Prediction of Contrast-Induced Encephalopathy After Neurointerventional Procedures.","source":"europepmc","abstract":"CIE is a rare yet severe complication following neurointerventional procedures, for which reliable noninvasive predictive tools remain lacking. This study aimed to develop and validate a ML model for predicting CIE using perioperative clinical and procedural data, and to evaluate the predictive value of the CGR for CIE. This was a single-center retrospective study that consecutively enrolled 161 patients who underwent neurointerventional procedures in the Department of Neurosurgery at the Northern Theater General Hospital between January 2024 and December 2025. Candidate perioperative predictors of CIE were identified using univariate analysis combined with LASSO regression. Based on the selected variables, five ML models-Naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN), LightGBM, and multilayer perceptron (MLP)-were trained and optimized. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Among the evaluated models, the Naive Bayes model showed the most favorable descriptive performance in the internal test set, with an AUC of 0.952 (95% CI: 0.843-1.000), a sensitivity of 100%, and a specificity of 90.3%; however, these metrics should be interpreted cautiously because the dataset was markedly imbalanced and the internal test cohort was small and contained only a very limited number of CIE events. The DCA results suggested potential clinical net benefit across selected threshold probabilities. Feature-importance analysis indicated that CGR was among the highest-ranked candidate predictors associated with CIE risk in this dataset. The Naive Bayes model evaluated in this study may provide a preliminary risk-stratification framework for perioperative assessment of CIE after neurointerventional procedures. CGR emerged as an important predictive feature and may aid individualized risk stratification. Further external validation is warranted before clinical application.","url":"https://doi.org/10.3791/72340","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3791/72340","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/ijgm.s621065","name":"An Interpretable Machine Learning Model for Predicting in-Hospital Progression in Initially Mild Hypertriglyceridemia-Induced Acute Pancreatitis Using Clinical and Non-Contrast CT Features.","source":"europepmc","abstract":"Introduction Early identification of patients with initially mild hypertriglyceridemia-induced acute pancreatitis (HTG-AP) who are at risk of an unstable disease course remains challenging using conventional assessment alone. This study aimed to develop an interpretable machine learning model based on baseline clinical and non-contrast computed tomography (CT) features for early prediction of in-hospital progression. Methods We retrospectively enrolled 164 patients with initially mild HTG-AP between October 2020 and October 2024. Baseline clinical variables, CT-based body composition parameters, and pancreatic radiomics features from non-contrast CT were collected at admission. Patients were classified into progression (n = 88) and non-progression (n = 76) groups based on clinically relevant worsening supported by clinical or CT evidence during hospitalization. Five-fold cross-validation was used for model development and internal validation. Four XGBoost-based models were constructed: clinical only, clinical + body composition, clinical + radiomics, and clinical + body composition + radiomics. Model performance was assessed using receiver operating characteristic analysis, calibration analysis, decision curve analysis, and Shapley additive explanations (SHAP). Results The clinical + body composition + radiomics model achieved the best overall performance among the four models, with an AUC of 0.830 and an accuracy of 0.768. Calibration analysis showed relatively good agreement between predicted and observed risks, and decision curve analysis demonstrated a higher net benefit across most clinically relevant threshold probabilities. SHAP analysis identified triglycerides (TG) and the visceral fat area-to-abdominal cavity area ratio (VFA/ACA) as the dominant contributors to model prediction. Conclusion The proposed interpretable multimodal model may improve early risk stratification for in-hospital progression in initially mild HTG-AP and help identify patients who require closer monitoring, although further external validation is needed to confirm its generalizability and clinical applicability.","url":"https://doi.org/10.2147/ijgm.s621065","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/ijgm.s621065","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.jocn.2026.112196","name":"Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.","source":"europepmc","abstract":"Objective To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. Methods A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. Results Univariate analysis showed significant differences (P Conclusion Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.","url":"https://doi.org/10.1016/j.jocn.2026.112196","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jocn.2026.112196","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/jcla.70335","name":"Optimizing Laboratory Autoverification in Complete Blood Count Testing Using Machine Learning: A Performance Evaluation Study.","source":"europepmc","abstract":"Background Autoverification improves laboratory efficiency by reducing manual reviews; however, rule-based systems may lack flexibility, particularly in complete blood count (CBC) testing. Machine learning (ML) can potentially enhance performance by recognizing complex patterns in laboratory data. Therefore, we aimed to evaluate ML-based autoverification systems in classifying CBC results compared to traditional rule-based methods. Methods This cross-sectional study was conducted at the Hematology Unit of Songklanagarind Hospital, a tertiary care center in southern Thailand. In total, 63,201 CBC results from the Sysmex XN-10 analyzer (January-December 2024) were analyzed. The samples were categorized as verifiable or non-verifiable according to predefined criteria. The data were split 80:20 into training and testing sets. The Random Forest, Decision Tree, and Extreme Gradient Boost models were trained and evaluated against the rule-based system of the hospital using sensitivity, specificity, positive predictive value, and negative predictive value. The feature importance was analyzed for the best-performing model. Results Of all the results, 43,661 (69.1%) were verifiable and 19,540 (30.9%) were non-verifiable, mainly because of white blood cell abnormalities, platelet clumps, or differential discrepancies. In the 12,641-sample test set, all the ML models outperformed the rule-based system. XGBoost achieved the best balance, with a sensitivity of 95%-98% and a specificity of 68%-81% at recall thresholds of 95% and 97.5%, respectively. Conclusion ML demonstrated improved performance compared with rule-based autoverification. Although ML may reduce unnecessary manual reviews, validation is required in different settings before clinical adoption.","url":"https://doi.org/10.1002/jcla.70335","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/jcla.70335","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fpsyt.2026.1761254","name":"Study on multi-modal feature fusion machine learning classification model of Chinese and Western medicine for depressive symptoms in middle-aged and older patients with chronic low back pain.","source":"europepmc","abstract":"Objective To construct a machine learning classification model integrating multi-modal features of Chinese and Western medicine for screening the risk of depressive symptoms in middle-aged and older patients with chronic low back pain (CLBP). Methods A cross-sectional design included 370 CLBP patients aged ≥45 years admitted to our hospital from January 2022 to June 2024. Based on PHQ-9 scores, they were classified into depression (≥10) and non-depression ( Results The prevalence of depressive symptoms in the training set was 28.20%. Multivariate analysis identified VAS score, ODI score, PSQI score, qi deficiency constitution, and blood stasis constitution as independent risk factors for depressive symptoms, while sleep duration served as a protective factor (all P 0.05). In temporal validation, the AUCs for the GBM and RF models were 0.956 and 0.952, respectively, with no statistically significant difference between the two models (P = 0.751). Conclusion Both GBM and RF models incorporating multimodal features from traditional Chinese and Western medicine demonstrated promising preliminary classification performance for screening depressive symptoms in middle-aged and older patients with CLBP, suggesting their potential utility as screening tools in clinical settings. Further multicenter external validation is needed to confirm the generalizability of these findings.","url":"https://doi.org/10.3389/fpsyt.2026.1761254","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1761254","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s10072-026-09285-w","name":"The role of \"red flags\" in the diagnostic work-up of hereditary transthyretin amyloidosis: a study using a machine-learning approach.","source":"europepmc","abstract":"Introduction Hereditary transthyretin amyloidosis (ATTRv) is a rare progressive, potentially life-threatening multisystem disorder caused by mutations in the transthyretin (TTR) gene, with variable penetrance and heterogeneous phenotypes, often leading to diagnostic delays, particularly in non-endemic regions. Identifying clinical \"red flags\" is crucial to shorten diagnostic latency. Machine learning (ML), a branch of artificial intelligence (AI), together with explainable artificial intelligence (XAI), offers novel opportunities to refine diagnostic algorithms and prioritize predictive features in ATTRv. Materials and methods A total of 452 patients who underwent TTR genetic testing between 2019 and 2024 in Sicily were retrospectively analyzed. Genetic testing was performed via polymerase chain reaction (PCR) and sequencing of TTR exons 2-4. Patients were stratified into Western and Eastern Sicily sub-cohorts to train and validate supervised ML models. Multiple algorithms were compared with hyperparameter tuning via GridSearch with cross-validation. Model interpretability was ensured using SHapley Additive exPlanations (SHAP) values and permutation importance. Results Among 452 patients, 68 (15%) carried a TTR mutation, 51.5% of whom were symptomatic. The most frequent red flags were sensory neuropathy (62.6%) and family history of cardiomyopathy (51.8%). Tree-based models outperformed other algorithms, with Random Forest selected for its optimal balance between precision and recall. Bilateral carpal tunnel syndrome, family history of neuropathy, and ataxia emerged as the most informative predictors. Discussion These findings suggest that integrating ML with clinical red flags may support the diagnostic decision-making process in patients referred for suspected ATTRv. However, these results should be considered exploratory and require validation in independent cohorts before clinical implementation.","url":"https://doi.org/10.1007/s10072-026-09285-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10072-026-09285-w","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s12029-026-01583-y","name":"Machine Learning Prediction of Local Failure in GI Brain Metastases on the Day of Gamma Knife Radiosurgery.","source":"europepmc","abstract":"Background Time to local failure after stereotactic radiosurgery (SRS), including Gamma Knife radiosurgery (GKRS), for gastrointestinal (GI) brain metastases is difficult to predict and may vary substantially across tumors and patients. We developed and internally evaluated a survival machine learning framework to estimate tumor-specific time to local failure using pre-SRS features. Methods We performed a retrospective study of GI brain metastases treated with GKRS. Local failure was modeled as a time-to-event outcome under right censoring. A Random Survival Forest survival model was trained using treatment-time demographic, clinical, tumor, histologic, and radiosurgical variables restricted to information available before or at GKRS. To avoid within-patient leakage, validation was performed using patient-grouped cross-validation. Model performance was assessed using the concordance index (C-index) and IBS. Among tumors with observed local failure, temporal prediction accuracy was additionally evaluated using mean absolute error (MAE) and coefficient of determination (R²). Results The cohort included 90 patients and 218 treated tumors from 2014 to 2024. Median age at treatment was 68.0 years, median pre-treatment Karnofsky Performance Status was 60, and 91.3% of tumors occurred in patients with multiple metastases. Fifty-seven tumors experienced local failure. The model achieved a mean grouped cross-validated C-index of 0.79 and a mean IBS of 0.0031 (95% CI, 0.0014-0.0042). Among tumors with local failure, conditional MAE was 1.11 months and R² was 0.26. Conclusions Pre-SRS survival machine learning is feasible for predicting time to local failure in GI brain metastases treated with GKRS and supports further validation in larger multicenter cohorts.","url":"https://doi.org/10.1007/s12029-026-01583-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12029-026-01583-y","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/jcm15124702","name":"Construction and Validation of a 90-Day Mortality Risk Prediction Model Based on Interpretable Machine Learning for Acute Ischemic Stroke Patients Undergoing Mechanical Thrombectomy.","source":"europepmc","abstract":"Background/Objectives : The accurate prediction of postprocedural mortality is critical for clinical decision-making; however, research on mortality risk models for patients undergoing mechanical thrombectomy remains limited. This study aimed to develop and validate machine learning models for predicting 90-day post-mechanical thrombectomy mortality. Methods : A retrospective-prospective cohort study involving 699 retrospective patients (January 2019-December 2022) and 274 prospective patients (January 2023-June 2024) from a single institution in Sichuan was conducted. The primary outcome was all-cause mortality within 90 days, ascertained via telephone follow-up. Predictors were identified using univariate analysis and LASSO regression. Eight predictive models were developed and evaluated using existing machine learning methods via 10-fold cross-validation. Model performance was assessed through discrimination, calibration, decision curve analysis, and interpretability via Shapley additive explanations. Results : The final dataset included 593 patients in the modeling set and 247 in the validation set. The 90-day mortality rates were 25.6% and 32.0%, respectively. Key predictors included age, hyperlipidemia, atrial fibrillation, pre-stroke statin use, antiplatelet/anticoagulant therapy within 48 h of onset, dysphagia, D-dimer levels, and activities of daily living scores. Logistic regression demonstrated superior performance in the modeling cohort (AUC = 0.87), whereas the multilayer perceptron model exhibited the greatest efficacy in the validation cohort (AUC = 0.77). Conclusions : Machine learning algorithms can accurately predict 90-day mortality among patients undergoing mechanical thrombectomy. The multilayer perceptron model demonstrated robust validation performance and offers a potential tool for personalized risk assessment and optimization of clinical decision-making.","url":"https://doi.org/10.3390/jcm15124702","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15124702","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1177/03000605261455766","name":"Development of an interpretable machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia: A retrospective cohort study.","source":"europepmc","abstract":"ObjectiveTo develop a machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia and interpret the model using SHapley additive exPlanations (SHAP) to assist clinicians in early identification of intestinal necrosis and formulating more appropriate treatment strategies.MethodsThis retrospective cohort study analyzed 324 older patients with incarcerated hernia who were admitted to the Department of Emergency Surgery and Department of Hernia and Abdominal Wall Surgery at the Affiliated Lu'an Hospital of Anhui Medical University between March 2015 and February 2024. Clinical data were collected, and patients were divided into training and testing sets in a 7:3 ratio using stratified random sampling. The least absolute shrinkage and selection operator was applied for feature selection, and nine machine learning models were constructed. The optimal model was further analyzed using SHAP for interpretability.ResultsAmong the 324 patients, 81 developed intestinal necrosis, while 243 did not. Least absolute shrinkage and selection operator regression identified nine key features. Nine machine learning algorithms were evaluated: (a) K-nearest neighbors; (b) support vector machine; (c) extreme gradient boosting; (d) approximate nearest neighbor; (e) decision tree; (f) light gradient boosting machine; (g) random forest; (h) extremely randomized trees; and (i) gradient boosting machine. The extremely randomized trees model demonstrated the best performance on the test set, with a sensitivity of 0.755, specificity of 0.932, F1-score of 0.714, accuracy of 0.796, and receiver operating characteristic area under the curve of 0.785. SHAP analysis revealed that the top nine contributing variables were procalcitonin, absolute neutrophil count, D-dimer, neutrophil-to-lymphocyte ratio, hernia disease time, duration of incarceration, femoral hernia incarceration, lymphocyte count, and C-reactive protein.ConclusionThis study developed an efficient and interpretable extremely randomized trees model for predicting intestinal necrosis risk in older incarcerated hernia patients, which can aid clinicians in identifying high-risk patients and optimizing clinical decision-making.","url":"https://doi.org/10.1177/03000605261455766","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/03000605261455766","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.4103/mgr.medgasres-d-25-00361","name":"Machine learning-derived serum carbon dioxide trajectories in predicting prognosis of patients with acute aortic dissection.","source":"europepmc","abstract":"JOURNAL/mgres/04.03/01612956-990000000-00114/figure1/v/2026-08-19T154102Z/r/image-tiff Venous partial pressure of carbon dioxide (PvCO2) indicates postoperative adverse outcomes in patients with acute aortic dissection, and its trajectories over time affecting postoperative adverse outcomes remain unclear. This study aimed to investigate the relationship between PvCO2 trajectories and postoperative adverse outcomes, as well as clinical application through group-based trajectory modeling and machine-learning algorithms. This retrospective cohort study analyzed data from 642 patients with acute aortic dissection treated at the Fourth Hospital of Hebei Medical University from 2019 to 2024, to determine postoperative adverse outcomes, including 6-month mortality and complications. Three PvCO2 tests within the first 72 hours of admission were used to analyze PvCO2 trajectories through group-based trajectory modeling. To diagnose high-risk PvCO2 trajectories in emergency settings, least absolute shrinkage and selection operator regression was conducted to select key features, which were consequently integrated into predictive models using multiple machine-learning methods. Finally, a web-based interpretable website was developed for predicting PvCO2 trajectories in patients with acute aortic dissection. Overall, 642 patients were classed into four PvCO2 trajectories (T1, persistent decrease from a medium to a low level; T2, stable low level; T3, persistent decrease from a high to a low level; T4, stable midlevel), showing distinct patterns. After adjustment for other covariates, postoperative adverse outcomes among these groups showed significant differences, with a higher proportion of postoperative adverse outcomes in the T3 group. Subsequently, upon Shapley additive explanations analysis, 17 features with importance sequence were identified (hemoglobin, direct bilirubin, monocyte, adenosine deaminase, drinking, anion gap, chloride, platelet, lymphocyte, albumin/globulin ratio, calcium, alanine aminotransferase, α-L-fucosidase, D-dimer, antihypertensive drugs, and smoking), which were integrated into the model for predicting T3. The optimal machine-learning model was established for predicting postoperative adverse outcomes. Finally, a web-based interpretable model was developed to predict patients with acute aortic dissection and high risk of postoperative adverse outcomes. This study uncovers heterogeneity in PvCO2 trajectory, highlighting that decreased PvCO2 is associated with worse prognosis. A web-based tool for the prediction of acute aortic dissection patients at high risk of postoperative adverse outcomes was constructed to facilitate clinical application.","url":"https://doi.org/10.4103/mgr.medgasres-d-25-00361","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4103/mgr.medgasres-d-25-00361","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/nss.s593135","name":"Integrating Complete Blood Count Parameters with Demographic Characteristics for Obstructive Sleep Apnea Prediction in Chinese Adults: A Machine Learning Approach.","source":"europepmc","abstract":"Purpose To develop and validate machine learning models integrating complete blood count (CBC) parameters with demographic characteristics for obstructive sleep apnea (OSA) risk stratification in adults with suspected OSA referred to a tertiary sleep clinic in China. Methods This retrospective study analyzed 5,828 adults with suspected OSA referred to a tertiary sleep clinic in China (2018-2024) who underwent home sleep apnea testing (HSAT), with OSA defined as an apnea-hypopnea index (AHI) ≥ 5 events/h. The cohort was temporally partitioned into a training cohort (January 2018 - December 2022, n = 4,330) and a validation cohort (January 2023 - March 2024, n = 1,498) for independent temporal validation. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 10-fold cross-validation exclusively within the training cohort, identifying 9 predictors from 16 candidates: three demographic variables (gender, age, body mass index [BMI]) and six CBC parameters (hemoglobin, mean corpuscular hemoglobin [MCH], lymphocyte count, red cell distribution width-coefficient of variation [RDW-CV], mean platelet volume [MPV], and platelet count). Four machine learning algorithms (logistic regression, Naive Bayes, random forest, XGBoost) were developed using the selected features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration metrics (Brier score, calibration slope), bootstrap optimism correction (1,000 resamples), and decision curve analysis (DCA). Results LASSO regression identified nine robust predictors from 16 candidate variables. In the final logistic regression model, hemoglobin demonstrated the largest standardized coefficient (1.38), followed by BMI (0.61), age (0.60), gender (|β| =0.40, with male gender associated with higher OSA risk), and MCH (0.36). Logistic regression achieved the best validation performance (AUC, 0.901, 95% confidence interval (CI): 0.882-0.919; sensitivity, 84.6%; specificity, 81.7%), with good calibration (Brier score, 0.084, calibration slope, 1.208) and minimal optimism (bootstrap-corrected AUC, 0.907). Five-fold cross-validation within the training cohort confirmed model stability (mean AUC, 0.906 ± 0.011). All models demonstrated superior net clinical benefit compared with treat-all or treat-none strategies in DCA. Secondary analyses at AHI ≥ 15 and AHI ≥ 30 thresholds confirmed sustained discriminative ability (validation AUCs 0.820 and 0.809, respectively). Conclusion Machine learning models integrating CBC parameters with demographic characteristics demonstrated good discriminative ability for OSA risk stratification in Chinese adults referred to a tertiary sleep clinic. These readily available biomarkers may facilitate risk stratification in pre-HSAT triage settings.","url":"https://doi.org/10.2147/nss.s593135","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/nss.s593135","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/cia.s620467","name":"Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model Performance, and Clinical Translation Gap.","source":"europepmc","abstract":"Purpose Identifying older adults at risk of frailty is crucial for early intervention. Although numerous prediction models have emerged, no scoping review has systematically mapped their methodological trends and barriers to clinical implementation. This scoping review aimed to examine methodological characteristics, model performance, and translational gaps in frailty onset prediction models for older adults. Methods A systematic search of six Chinese and English databases was conducted from inception to February 2026following Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Results Thirty studies, reporting 31 frailty prediction models published between 2018 and 2026, were included. Six overarching trends emerged: Most studies originated from China (73.33%), and community-dwelling older adults were the predominant study population (73.33%). After 2024, longitudinal designs using larger public databases became more common; Machine learning was increasingly adopted (38.7%) but showed no clear advantage over logistic regression (median AUC 0.813 vs 0.860); Conventional predictors, including age, multimorbidity, and depression, remained dominant; Calibration was under-reported, particularly in machine learning models (50%); Clinical translation was limited, with external validation and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis plus Artificial Intelligence (TRIPOD+AI) adherence each reported in only 22.6% of models, static presentation formats predominating (83.9%), and no studies evaluating health economic outcomes or prospective clinical impact. Conclusion Frailty prediction models are constrained by insufficient calibration reporting, limited external validation, and substantial translation barriers. Future research should prioritize rigorous external validation, TRIPOD+AI adherence, and clinically integrated digital tools supported by implementation science.","url":"https://doi.org/10.2147/cia.s620467","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/cia.s620467","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s11255-026-05342-7","name":"Prediction of pediatric lower urinary tract dysfunction using uroflowmetry data and machine learning with explainable AI techniques.","source":"europepmc","abstract":"Purpose To develop and evaluate a machine learning-based approach for the interpretation of uroflowmetry in pediatric lower urinary tract dysfunction, aiming to reduce interobserver variability and improve diagnostic consistency while maintaining clinical interpretability. Methods In this single-center retrospective study, 2663 pediatric uroflowmetry records (age 2-18 years, July 2019-March 2024) were analyzed. LUTD subtypes were assigned to the International Children's Continence Society terminology. Two settings were evaluated: three-class (overactive bladder (OAB), dysfunctional voiding (DV), normal (N)) and four-class (adding DV-OAB). Eight predictors (four numerical, four categorical) were used. Seven ML models (Decision Trees, Naive Bayes, Support Vector Machine, Efficiently Trained Linear Classifiers (Logistic Regression), Gaussian Kernel, Ensemble Classifiers, and Artificial Neural Networks) were trained with Bayesian hyperparameter optimization using stratified tenfold cross-validation on a 90-10% train-test split. Weighted accuracy, precision, recall, and F1-score were reported. Feature importance was quantified with Shapley Additive Explanations (SHAP). Reporting followed TRIPOD-AI. Results In the three-class setting, the ensemble classifier achieved the best test performance (accuracy 86.09%, F1 86.03%) and all models exceeded 82% test accuracy. In the four-class setting, test accuracy dropped to 72.56-78.95%, and most models failed to predict the DV-OAB class owing to its low prevalence (2.55%). SHAP identified bladder capacity as the dominant feature for OAB, pelvic floor activity for DV, and residual volume as a secondary DV feature. Sex showed a limited predictive contribution compared with the other clinical predictors. Conclusions Explainable ML enables interpretable classification of pediatric LUTD subtypes from uroflowmetry data across a wide age range, including children under 5 years. External multicenter validation is required before clinical deployment.","url":"https://doi.org/10.1007/s11255-026-05342-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11255-026-05342-7","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fneur.2026.1831043","name":"Predicting intravenous thrombolysis outcomes in acute ischemic stroke using machine learning.","source":"europepmc","abstract":"Objective Intravenous thrombolysis remains a cornerstone intervention for improving clinical outcomes in patients with acute ischemic stroke (AIS). Accurate prediction of poor functional outcomes following thrombolysis is essential for optimizing individualized treatment and guiding clinical decision-making. This study aimed to develop machine learning-based models for early post-treatment reassessment of post-thrombolysis outcomes in AIS patients, thereby providing a reliable tool for early prognostic reassessment after thrombolysis. Methods A total of 383 AIS patients who received intravenous thrombolysis between November 2024 and November 2025 were retrospectively enrolled and randomly assigned to a training set ( n = 268) and a validation set ( n = 115) in a 7:3 ratio. Univariate analysis was initially conducted to identify indicators associated with thrombolysis outcomes ( p Results Baseline characteristics were well-balanced between groups (all p > 0.05). Multivariate logistic regression identified a higher admission National Institutes of Health Stroke Scale (NIHSS) score, elevated admission blood glucose, a higher 24-h post-thrombolysis NIHSS score, increased infarct core volume, higher glycated hemoglobin and an elevated Neutrophil-to-Lymphocyte Ratio as significant risk factors for poor outcomes, whereas a larger ischemic penumbra volume emerged as a protective factor ( p Conclusion The proposed prediction model demonstrates satisfactory performance in estimating functional outcomes following thrombolysis in AIS patients. The identified core variables may offer valuable insights for clinical prognosis and the design of individualized thrombolytic strategies.","url":"https://doi.org/10.3389/fneur.2026.1831043","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1831043","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16121844","name":"Machine-Learning-Based Disease Diagnosis and Prediction: Progress, Perspectives, and the Path Forward.","source":"europepmc","abstract":"The convergence of machine learning (ML) and clinical medicine has significantly reshaped modern healthcare [...].","url":"https://doi.org/10.3390/diagnostics16121844","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16121844","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/biomedicines14081853","name":"Machine Learning-Based Prediction of Delayed Cerebral Ischemia and Functional Outcome in Aneurysmal Subarachnoid Hemorrhage: The Role of CNS Infection-Related Parameters.","source":"europepmc","abstract":"Background/Objectives : Aneurysmal subarachnoid hemorrhage (aSAH) carries high morbidity and mortality, with delayed cerebral ischemia (DCI) as a major secondary complication. Although infection has increasingly been implicated in DCI pathogenesis, the role of central nervous system (CNS) infection remains unclear. We evaluated CNS infection-related parameters for predicting DCI and functional outcome after aSAH using machine learning. Methods : This retrospective single-center study included 191 patients with confirmed aSAH treated in the intensive care unit between 2019 and 2024. Demographic, clinical, radiographic, treatment-related, microbiological, cerebrospinal fluid (CSF), and longitudinal laboratory data were extracted from electronic records. DCI was analyzed as a binary outcome, and discharge functional outcome was dichotomized using the modified Rankin Scale (0-2 vs. 3-6). Twelve feature selection methods and twelve classifiers were evaluated using five-fold cross-validation. Results : For DCI prediction, the models achieved a mean accuracy of 0.96, F1-score of 0.96, and AUC of 0.98. Key predictors included antibiotic therapy, aneurysm treatment modality, history of thrombosis, and EVD revision, along with mean albumin, red cell distribution width, INR, minimum aspartate aminotransferase, and CSF glucose, lactate, nucleated cell count, and cellular debris. For functional outcome prediction, accuracy was 0.948, F1-score 0.95, and AUC was 0.97. Predictors comprised cerebral vasospasm, number of spasmolysis procedures, pre-admission anticoagulation, CSF pathogen detection and type, aPTT, fibrinogen, aspartate aminotransferase, lactate dehydrogenase, and CSF mononuclear cell proportion, leukocyte count, and glucose concentration. Conclusions : Machine learning models integrating clinical, systemic, and CSF-derived parameters demonstrated excellent performance in predicting both DCI and functional outcome after aSAH. The identified predictors highlight the importance of the neurocritical care course, including infection-related and thromboinflammatory processes, and extend beyond traditional vasospasm-centered paradigms. These findings may inform risk stratification, although the temporal relationship between several predictors and outcome onset means the models should be regarded as hypothesis-generating rather than tools for early clinical prediction.","url":"https://doi.org/10.3390/biomedicines14081853","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/biomedicines14081853","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s40359-026-04924-5","name":"Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.","source":"europepmc","abstract":"Background The current intervention efficacy of generative conversational artificial intelligence (GCAI) on overall mental health issues remains limited, which may be related to the complex and unclear relation between GCAI emotional support needs (GCAI-ESN) and childhood trauma and mental health issues. Methods We used the Childhood Trauma Questionnaire, the Mental Health Inventory of Middle-school students, and whether there were GCAI-ESN to assess 14,380 adolescents. Machine learning (ML) combined with SHapley Additive exPlanations (SHAP) analysis was employed to identify the key predictors influencing the GCAI-ESN model. Based on the identified predictors, an undirected network was constructed and a Bayesian network analysis was conducted. Results The prevalence of childhood trauma, mental health issues, and GCAI-ESN among adolescents in underdeveloped regions was 32.43% (95% CI: 31.66%-33.19%), 16.78% (95% CI: 16.17%-17.39%), and 39.53% (95% CI: 38.73%-40.33%), respectively. The SHAP analysis of four machine learning models identified 15 key predictors for GCAI-ESN. In the undirected network model, easily anxious 'MH6' (EI = 1.07) and depressed 'MH5' (EI = 1.07) are the core nodes, while GCAI-ESN (BEI = 3.09) and family said hurtful things 'CTQ14' (BEI = 1.00) are the bridge nodes. Bayesian network analysis indicates that 'CTQ14' is the node with the most outgoing potential predictive relationships. Conclusions This study identified the key predictors of GCAI-ESN among adolescents in underdeveloped regions and determined 'MH6', 'MH5', and 'CTQ14' as potential intervention targets when the GCAI provides emotional support, offering concrete focal points for future research.","url":"https://doi.org/10.1186/s40359-026-04924-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s40359-026-04924-5","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1371/journal.pone.0353486","name":"Predicting ankylosing spondylitis disease activity via patient-reported outcome measures: Building prediction models based on machine learning.","source":"europepmc","abstract":"Objective Disease activity is a critical indicator for monitoring the progression of ankylosing spondylitis (AS), guiding clinical decision-making, and informing treatment plans. Patient-reported outcome measures (PROMs) have gained prominence in AS clinical management. However, their potential to predict Ankylosing Spondylitis Disease Activity Score-C-reactive protein (ASDAS-CRP) remains unexplored. This study employs machine learning (ML) techniques to develop prediction models utilizing PROMs data to estimate disease activity in patients with AS. Methods We utilized data from 389 patients with AS were included sourced from the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from March 2022 to March 2024. This dataset was divided into a training set (80%) and a testing set (20%). A total of 34 variables, including clinician-recorded features and PROMs (e.g., BASDAI, BASFI, BASMI, PGA, VAS, ASAS-HI, FACIT-F, DASS-21), were employed for feature selection and assessment of feature significance using a variety of machine learning methods. Ten models were constructed using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) classifiers in conjunction with five feature selection methods: Feature Selection with Orthogonal Regression (FSOR), Trace Ratio Criterion (TRC), Robust Feature Selection (RFS), Pearson Correlation Coefficient (PCC), and ReliefF. Model performance was evaluated based on accuracy, specificity, sensitivity, and area under the receiver operating characteristic curve (AUC-ROC). Results A total of 389 patients with AS were included in the analysis. Key characteristics assessed included Patient Global Assessment (PGA), age, and the impact of disease on daily activities. The results indicated that the FSOR+SVM model achieved the best overall performance, with an AUROC of 0.930 (95%CI: 0.87-0.99) in the validation set. Meanwhile, FSOR+SVM also exhibited the highest sensitivity (83.78%), accuracy (79.35%), and specificity (90.50%). Conclusion The machine learning model developed from PROMs data proved effective for predicting AS disease activity, showing strong agreement with clinical ASDAS-CRP measures.","url":"https://doi.org/10.1371/journal.pone.0353486","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353486","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1017/s1047951126112931","name":"Machine learning-based prediction of postoperative restenosis risk following direct repair of paediatric coarctation of the aorta.","source":"europepmc","abstract":"Objectives Coarctation of the aorta is a congenital cardiovascular disease with focal aortic luminal narrowing, and paediatric patients face a high postoperative restenosis risk. This study aimed to develop and validate an interpretable machine learning model for early predicting restenosis after paediatric coarctation of the aorta direct repair using preoperative and intraoperative data. Methods A total of 117 patients (2016-2024) were retrospectively enrolled, divided into restenosis (21 cases, 17.9%) and non-restenosis (96 cases, 82.1%) groups (restenosis was defined as a peak systolic pressure gradient >20 mmHg measured by echocardiography). Recursive feature elimination with cross-validation screened key variables; six machine learning models were built with 5-fold randomised search cross-validation tuning, using the area under the curve as the primary metric. SHapley Additive exPlanation analysed feature contributions. Results The multilayer perceptron model performed best (mean area under the curve = 0.8333, 95% CI: 0.7111-0.9555, accuracy = 0.8376) with balanced precision-recall. SHapley Additive exPlanation identified low body surface area as the top risk factor. Resection and extended end-to-end anastomosis/end-to-side anastomosis were preferred surgically, while resection with end-to-end anastomosis should be avoided; end-to-side anastomosis reduced restenosis risk in patients with aortic arch hypoplasia. Conclusion Machine learning models enable personalised, high-accuracy restenosis prediction. SHapley Additive exPlanation-facilitated risk factor identification optimises treatment strategies. Future prospective studies are needed to validate the models and develop clinical tools.","url":"https://doi.org/10.1017/s1047951126112931","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1017/s1047951126112931","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1021/acs.molpharmaceut.6c00429","name":"Predicting First-in-Human Pharmacokinetics: Comparative Evaluation of Standard PBPK, High-Throughput PBPK, and Machine Learning.","source":"europepmc","abstract":"Accurate prediction of human pharmacokinetics is essential for selecting safe starting doses in first-in-human (FiH) trials. This study compares the predictive accuracy of three methodologies including standard physiologically based pharmacokinetic (PBPK) modeling incorporating animal verification, high-throughput (HT) PBPK based on in vitro inputs, and machine learning (ML). We evaluated their performance on 40 diverse small molecules that entered clinical development at Roche between 2003 and 2024. Standard PBPK predictions were assessed prospectively from original reports, while HT-PBPK and ML predictions were obtained retrospectively. Predicted area under the curve (AUCinf) and maximum plasma concentration (Cmax) for oral administration were compared to observed clinical data. While all three approaches demonstrated a useful level of accuracy with at least 49% of predicted AUCinf and Cmax values within 2-fold, standard PBPK was the most precise method with 65 and 63% of AUCinf and Cmax within that margin, respectively. This higher precision likely results from the integration of expert judgment and animal in vivo data for model refinement, whereas HT-PBPK and ML provide valuable animal-free, high-throughput alternatives. Ensemble models leveraging two or three models were explored, which generally improved accuracy in comparison to single models. This highlights the complementary nature of mechanistic and ML-based models. Currently, standard PBPK is still the preferred method for FiH dose selection for which highest accuracy and mechanistic insight is sought. However, although less accurate, we believe that HT-PBPK and ML are viable animal-free alternatives with value for early human dose prediction, reducing costs and cycle times.","url":"https://doi.org/10.1021/acs.molpharmaceut.6c00429","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1021/acs.molpharmaceut.6c00429","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fendo.2026.1876521","name":"A nomogram integrating machine learning with clinical predictors for osteosarcopenia risk prediction in type 2 diabetes mellitus.","source":"europepmc","abstract":"Objective Type 2 diabetes mellitus (T2DM) predisposes patients to osteosarcopenia, a debilitating condition characterized by concurrent bone loss and muscle wasting. This study aimed to develop and internally validate a nomogram for predicting osteosarcopenia risk in T2DM patients aged ≥ 40 years. Methods The test cohort included 5,412 hospitalized T2DM patients (January 2010-July 2024), and the temporal validation cohort included 1,671 patients (August 2024-December 2025) from the First Affiliated Hospital of Fujian Medical University. Logistic regression and machine learning algorithms (Boruta, random forest, LASSO) were combined for feature selection. The nomogram was constructed via multivariable logistic regression. We carried out receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and bootstrap validation for assessing the nomogram. Restricted cubic splines were employed for exploring potential nonlinear associations. Results Eight independent predictors, which encompassed gender, age, BMI, WHtR, fracture history, diabetic foot ulcer (DFU), smoking status, and diabetic kidney disease (DKD), were identified. These predictors were incorporated into the nomogram. The nomogram achieved AUCs of 0.864 and 0.904 in the test cohort and validation cohort, respectively. Accordingly, favorable calibration and positive net benefit on DCA was demonstrated. Higher BMI served as a protective factor (OR = 0.56, 95% CI: 0.53-0.59). Besides, higher WHtR acted as a risk factor (OR = 1.47, 95% CI: 1.28-1.69). Restricted cubic spline analysis revealed a significant negative nonlinear relationship between BMI and osteosarcopenia risk, and a significant positive nonlinear relationship between WHtR and osteosarcopenia risk. Conclusion This nomogram, based on eight readily available clinical variables, exhibits excellent discriminative performance and clinical utility for predicting osteosarcopenia risk in T2DM patients aged ≥ 40 years. Further multicenter external validation is warranted.","url":"https://doi.org/10.3389/fendo.2026.1876521","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1876521","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12891-026-10068-9","name":"Predicting kinesiophobia in knee osteoarthritis: a head-to-head comparison between machine learning and traditional regression models.","source":"europepmc","abstract":"Background Although machine learning (ML) holds promise for improving clinical identification, its application to kinesiophobia in knee osteoarthritis (KOA) is limited. Therefore, this study aimed to develop and compare logistic regression (LR)-based nomograms with ML models to determine the optimal approach for identifying kinesiophobia status and guiding personalized interventions. Methods A cross-sectional study enrolled 590 KOA patients from a tertiary hospital (June 2024 to July 2025) and randomly divided them into training (n = 413) and test (n = 177) sets in a 7:3 ratio. An interpretable LR-based nomogram was developed for assessing kinesiophobia status. Seven ML models (CatBoost, LightGBM, ML-LR, random forest, support vector machine [SVM], extreme gradient boosting, multilayer perceptron) were constructed to compare predictive performance. Models were evaluated via receiver operating characteristic curves with the area under the curve (AUC), calibration curves, and decision curve analysis; Shapley Additive exPlanations (SHAP) was utilized to interpret the ML models. Results No significant differences in baseline characteristics were found between the two sets. LR identified five key predictors: age, educational level, pain intensity, pain catastrophizing, and activity level. The nomogram demonstrated good discrimination (training AUC = 0.844; test AUC = 0.815). Among ML models, SVM exhibited a marginally higher test-set AUC (0.821). SHAP analysis confirmed pain intensity and pain catastrophizing as the strongest predictors in the SVM model. Conclusions Among the evaluated ML models, no single model demonstrated universal superiority for classifying current kinesiophobia status in KOA. These models offer complementary strengths. SVM exhibited marginally higher predictive performance, warranting further exploration and potential integration into automated digital health platforms, whereas the LR-based nomogram performed comparably with favorable interpretability for rapid, transparent bedside assessment in primary care. Model selection should be guided by specific clinical contexts.","url":"https://doi.org/10.1186/s12891-026-10068-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12891-026-10068-9","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1088/1361-6560/ae79cc","name":"High-risk carotid plaque detection using a novel radio-morphometric analysis of the carotid bifurcation.","source":"europepmc","abstract":"Objective. Current surgical eligibility criteria for the treatment of carotid artery disease, primarily relying on stenosis and image plaque characteristics, are suboptimal, highlighting the unmet need for improved strategies focused on recognition of high-risk carotid plaques. Despite the suggestion of various vulnerability biomarkers (clinical, biological, image, morphometric and biomechanical) a comprehensive approach integrating multiple markers is lacking. To this aim, this study proposes a novel integrative machine learning approach for the detection of symptomatic and asymptomatic patients, based on clinical data, plaque radiomics and morphometric features of the carotid bifurcation. Approach. The study included 129 patients (53 of which symptomatic) who underwent computed tomography angiography (CTA) prior to carotid endarterectomy. Following image preprocessing and segmentation, CTA-based radiomic features were extracted and a morphometric analysis of the carotid centerline and bifurcation was performed. Machine learning models were implemented to stratify symptomatic and asymptomatic patients using clinical characteristics, radiomic features and morphometric markers alone and in combination. Moreover, radiomic-clinical, radiomic-morphometric, clinical-morphometric and radiomic-clinical-morphometric models were developed by combining the predicted probabilities of the individual models. Main results. The radiomic model was the strongest standalone predictor (AUC = 0.906, balanced accuracy = 0.847), significantly outperforming both the clinical and morphometric models (AUC = 0.611 and 0.567, balanced accuracy = 0.642 and 0.648, respectively). Integrating radiomics with clinical and morphometric features also demonstrated excellent stratification ability (AUC = 0.883), with the highest balanced accuracy (=0.884), associated with the correct identification of all the 12 symptomatic patients, and 21 over 27 asymptomatic ones. Significance. This study introduced the first CTA-based approach integrating clinical characteristics, plaque radiomics, and carotid bifurcation morphometric features to predict cerebrovascular events. The findings highlight the value of integrating multiple sources to capture subtle and complementary determinants of cerebrovascular risk, supporting the potential for automated risk stratification and personalized decision-making in patients with carotid artery disease.","url":"https://doi.org/10.1088/1361-6560/ae79cc","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1088/1361-6560/ae79cc","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fmicb.2026.1828991","name":"Whole-genome sequencing reveals lineage-associated drug resistance and enables machine learning-based prediction in &lt;i&gt;Mycobacterium tuberculosis&lt;/i&gt; clinical isolates.","source":"europepmc","abstract":"Background Drug-resistant tuberculosis (TB) remains a major obstacle to global TB control, particularly in high-burden settings where timely detection of resistance is limited. This study aimed to integrate whole-genome sequencing and machine learning approaches to characterize the genomic architecture of drug resistance and develop predictive models for phenotypic resistance in clinical Mycobacterium tuberculosis isolates from Kashi, Xinjiang, China. Methods A total of 160 clinical M. tuberculosis isolates were collected from culture-confirmed pulmonary tuberculosis patients at First People's Hospital of Kashi over a five-year period (January 2020-December 2024). Whole-genome sequencing was performed, and sequencing reads were mapped to the H37Rv reference genome for high-confidence single nucleotide polymorphism (SNP) and insertion/deletion (INDEL) calling. Resistance-associated mutations were identified using curated resistance mutation catalogues, and comparative genomic analyses were conducted to assess mutation burden across phenotypic resistance groups. Phylogenetic reconstruction based on genome-wide SNPs was performed to evaluate lineage distribution and clustering of resistant strains. In addition, random forest machine learning models were trained using WGS-derived coding and promoter mutations to predict phenotypic resistance to first-line anti-tuberculosis drugs. Results Phenotypic drug susceptibility testing classified 63 isolates (39.4%) as drug-sensitive, 67 (41.9%) as single-drug resistant (SDR), and 28 (17.5%) as multidrug-resistant (MDR), with a small number of extensively drug-resistant isolates. Resistance was dominated by first-line drugs, particularly isoniazid, streptomycin, and rifampicin. Phylogenetic analysis revealed non-random distribution of resistant isolates, with enrichment of MDR strains within Lineage 2 (Beijing lineage). Genomic profiling demonstrated a progressive increase in resistance-associated mutational burden from drug-sensitive to SDR and MDR isolates, characterized by accumulation of canonical mutations in rpo B, kat G, emb B, gyr A, pnc A, and rrs . Machine learning-based prediction achieved strong performance for rifampicin, isoniazid, and streptomycin resistance, with key resistance-conferring mutations and promoter variants emerging as dominant predictive features. Conclusion This study demonstrates the potential utility of integrating phenotypic testing, whole-genome sequencing, and interpretable machine learning approaches for characterizing and predicting drug-resistant M. tuberculosis . The observed burden of resistance-associated mutations and lineage-associated phylogenetic clustering of resistant isolates support the value of WGS-based surveillance and predictive modelling for tuberculosis control strategies.","url":"https://doi.org/10.3389/fmicb.2026.1828991","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1828991","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/healthcare14152339","name":"Artificial Intelligence for Neonatal and Perinatal Mortality Prevention: A Systematic Review of Machine Learning and Deep Learning Applications.","source":"europepmc","abstract":"Background/Objectives: Maternal, perinatal, and neonatal mortality remain major global health challenges, causing approximately 2.5 million neonatal deaths annually, particularly in low- and middle-income countries (LMICs). Although Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), is increasingly used to support healthcare decision-making, a comprehensive synthesis of its application to prevent prenatal, preterm birth, and neonatal deaths is lacking. This study systematically reviews the current state of research in this field. Methods: A Structured Literature Review (SLR) was conducted following a combined methodological framework integrating Massaro's protocol and PRISMA 2020 guidelines. Searches were performed in Scopus, IEEE Xplore, and Google Scholar using domain-specific keywords. From 459 identified publications, 46 peer-reviewed studies published between 2018 and 2024 were selected through a four-step filtering and quality assessment process. Bibliometric and thematic analyses were performed. Results: ML techniques accounted for 71.7% of the selected studies, whereas DL approaches represented 28.3%. Neonatal death prediction was the most frequently investigated outcome (34.7% of publications). Most studies originated from Europe (39.1%) and North America (30.4%), while research from Latin America and Sub-Saharan Africa was scarce despite the high mortality burden in these regions. Key barriers included non-standardized clinical records, limited interoperability of health information systems, and the underrepresentation of LMIC populations in training datasets. Conclusions: AI shows significant potential for reducing maternal and neonatal mortality through predictive analytics. However, important geographical and methodological gaps remain. Future research should prioritize inclusive datasets and predictive frameworks adapted to resource-constrained healthcare settings.","url":"https://doi.org/10.3390/healthcare14152339","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/healthcare14152339","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s11096-026-02204-1","name":"Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children.","source":"europepmc","abstract":"Introduction Vancomycin is widely used for severe Gram‑positive infections in children, but vancomycin‑induced nephrotoxicity (VIN) limits its safe application. Existing prediction models rely primarily on traditional logistic regression with limited discriminative performance in pediatric populations, and machine learning (ML) approaches have not been systematically evaluated. Aim This study aimed to develop and internally validate an interpretable ML model for predicting VIN in pediatric patients using electronic health record (EHR) data. Method We conducted a retrospective EHR-based study at a tertiary children's hospital. Children aged 1 month to 18 years who received vancomycin for ≥ 3 consecutive days between December 2017 and November 2024 were included. Patients with moderate-to-severe pre-existing renal dysfunction [estimated glomerular filtration rate (eGFR) ≤ 59 mL/min/1.73 m 2 ], preterm birth, blood purification therapy, or major missing data were excluded. VIN was defined as an increase in serum creatinine of ≥ 0.5 mg/dL or > = 50% from baseline on at least two consecutive measurements. Eight ML models were constructed and compared. Model performance was evaluated using the area under the receiver-operating-characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), F1 score, Brier score and the area under the precision recall curve (AUPR). The optimal model was interpreted using the Shapley Additive explanation (SHAP) method. Feature sets of 6, 8, 10, and full features were compared using DeLong's test. Clinical utility was assessed via decision curve analysis (DCA). Results A total of 1,452 children were included, of whom 206 (14.2%) developed VIN. EXtreme gradient boosting (XGboost) achieved the best predictive performance (AUROC = 0.90, AUPR = 0.74, Brier score = 0.07). A simplified 6‑feature XGBoost model (eGFR, amphotericin B, early vancomycin trough concentration, albumin, immunosuppressant use, and acyclovir/ganciclovir co-administration) achieved comparable discrimination to the full model (AUROC = 0.87; P > 0.05 by DeLong's test) with acceptable calibration (ECE 3.8%). DCA suggested favorable net benefit across clinically relevant risk thresholds. Conclusion Our interpretable 6-feature XGBoost model demonstrated promising internal discrimination for pediatric VIN prediction. Pending external validation and temporal evaluation, this model may serve as a hypothesis-generating decision-support framework for risk stratification.","url":"https://doi.org/10.1007/s11096-026-02204-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11096-026-02204-1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.35946/arcr.v46.1.03","name":"Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.","source":"europepmc","abstract":"Purpose The review surveys the type of machine learning approaches currently used in the alcohol literature, reviews challenges in applying machine learning tools to alcohol data, and explores how overcoming these challenges could advance personalized medicine for alcohol use disorder (AUD). Search methods The authors conducted a search of publications on PubMed, ScienceDirect, and EBSCO Academic Search Premier published from 2015 to April 15, 2025, for articles that used machine learning to analyze alcohol-related outcomes. Search terms were (\"drinking\" OR \"alcohol\") AND (\"machine learning\" OR \"deep learning\" OR \"predict\" OR \"classify\") in the title or abstract. Search results The search returned 2,618 manuscripts. Keeping those that predicted alcohol-related outcomes and excluding those that merely used alcohol as a predictor for other outcomes reduced the selection to 567 manuscripts. A final manual selection resulted in 110 original peer-reviewed human research studies that primarily analyzed alcohol consumption behaviors and tested their models on data that they were not trained on. Discussion and conclusions Predictions focused on alcohol consumption or AUD diagnosis in cohorts with a mean age of 50 years or younger (i.e., when long-term drinking behaviors are being or have been established). Most studies confined the data-driven searches to a single modality and relied on conventional machine learning approaches, which tended to produce accurate and transparent predictions on the relatively small datasets typically collected by AUD studies. The small number of available samples was the most common limitation mentioned by the reviewed articles. Investigators also wished for machine learning models to provide insights about causality. Gaining these insights will be essential to improve diagnosis and treatment of AUD, for which the field must foster multidisciplinary research teams to build rigorous and trustworthy machine learning models and quantitative benchmarks that can capture the multifaceted nature of alcohol use and its comorbidities.","url":"https://doi.org/10.35946/arcr.v46.1.03","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.35946/arcr.v46.1.03","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2196/84454","name":"Machine Learning to Identify Point-of-Care Ultrasound and Evaluate Standardized Documentation: Retrospective Operational Cohort Study.","source":"europepmc","abstract":"Background Point-of-care ultrasound (POCUS) is integral to obstetrics and gynecology (OBGYN), offering bedside diagnostic and therapeutic advantages. Despite its widespread adoption, accurate documentation and billing remain challenging due to inconsistent workflows, variable free-text note quality, and inefficiencies within electronic health record (EHR) systems. These barriers often result in missed procedural charges and hinder operational, educational, and reimbursement efforts. Objective This study leveraged machine learning (ML) to automatically identify POCUS procedures within clinical notes and assessed the effect of implementing standardized procedure documentation (ProcDoc) templates on billing capture accuracy and efficiency. Methods We conducted a multipart retrospective cohort study at a large academic medical center using EHRs from January 2018 to August 2024 across 11 OBGYN clinic sites. ML models (LightGBM [light gradient boosting machine] and BioClinBERT [biomedical and clinical bidirectional encoder representations from transformers]) were trained on clinical encounter notes to classify POCUS procedures and validated against Current Procedural Terminology (CPT) manual code assignments. In February 2023, a standardized ProcDoc smart form was introduced to streamline POCUS documentation and automatically trigger CPT billing codes. Preintervention and postintervention periods were compared using ML metrics and manual billing audits. Outcomes included model accuracy, recall, precision, adoption rates, improvement in billing recapture, and usage of ProcDoc templates. Results A total of 559,029 encounters from 109,776 unique patients were analyzed. The BioClinBERT model (accuracy 0.97; F1-score 0.55-0.63) demonstrated a robust ability to identify documented and missed procedures in free-text clinical notes. ProcDoc adoption reached 75.1% within 12 months, supported by comprehensive staff education. Billing recapture-the proportion of charges missed by providers but later identified-dropped from 10.0% preintervention to 2.4% postintervention, primarily arising from the shift toward auto-capturing documentation (odds ratio 0.22, 95% CI 0.17-0.30; P Conclusions ML modeling proved effective for extracting POCUS procedures from clinical documentation and serving as an evaluation tool for workflow interventions. Standardized documentation with ProcDoc significantly enhanced charge capture accuracy and reduced dependence on manual chart reviews and billing reconciliation. This approach highlights the use of ML as a retrospective auditing and evaluation tool for assessing clinical workflow interventions. Broader application of similar strategies could address documentation inefficiencies and promote sustainability across health care settings.","url":"https://doi.org/10.2196/84454","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/84454","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1177/14604582261477932","name":"A lightweight machine learning approach for predicting breast cancer risk based on routine clinical indicators in the Chinese population.","source":"europepmc","abstract":"BackgroundConventional breast cancer screening is limited by cost, access, and patient discomfort. Whether a lightweight machine-learning model trained on routine laboratory indicators can support opportunistic screening remains unsettled.MethodsA retrospective cohort of 13,285 individuals (1,216 ICD-10 C50 cases, 12,069 controls) from a Chinese tertiary hospital during 2022 to 2024 was analysed with a leakage-controlled pipeline. Six classical learners and LightGBM were trained on twenty indicators, then assessed by discrimination, calibration, decision-curve analysis, and zero-shot UK Biobank validation.ResultsThe lightweight LightGBM achieved an internal AUC of 0.987 with sensitivity 0.835, specificity 0.983, Brier 0.026, and an external AUC of 0.83, training in 5.6 s within 1.65 MB.ConclusionThe model is a candidate low-cost triage tool, pending multicentre prospective validation and recalibration.","url":"https://doi.org/10.1177/14604582261477932","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/14604582261477932","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fphar.2026.1786601","name":"Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.","source":"europepmc","abstract":"Introduction This study evaluated machine learning (ML) models predicting esophageal cancer (EC) treatment outcomes, focusing on data modalities, feature engineering, model frameworks, and validation. Methods Following PRISMA guidelines (PROSPERO: CRD42024619947), six databases (2015-2024) were systematically searched. Two reviewers independently extracted data on model methodologies and performance. Study quality was assessed using a modified TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) + AI checklist. Results Among 30 studies (14,342 patients), classical ML models were the most frequently employed approach (n = 43), followed by ensemble methods (n = 34), with deep learning being the least utilized (n = 11); however, the best-performing models across all studies demonstrated mean AUC values of 0.847 for deep learning, 0.835 for ensemble models, and 0.816 for classical approaches. Imaging and clinical data constituted the predominant both unimodal and multimodal modeling inputs, with supervised learning representing the dominant paradigm. Multimodal models achieved a significantly higher AUC (0.84 vs. 0.78) than single-modal models. Model validation primarily relied on k-fold cross-validation and external cohort approaches. Quality assessment showed moderate reporting completeness (64.79% median fulfillment). Discussion While ML (particularly deep learning and multimodal approaches) demonstrated potential for EC treatment prediction, key limitations persisted, such as opaque computational methods, poorly justified predictor selection, and unaddressed population heterogeneity/class imbalance. Addressing these challenges would be critical to enhancing the reliability and clinical applicability of ML models in future research.","url":"https://doi.org/10.3389/fphar.2026.1786601","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphar.2026.1786601","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/medicina62081438","name":"Explainable Machine Learning for Predicting Complicated Appendicitis and Expected Hospital Length of Stay in Children: An Exploratory Single-Center Study.","source":"europepmc","abstract":"Background and Objectives : Pediatric appendicitis remains a common surgical emergency, but early risk stratification of complicated appendicitis and expected hospital length of stay (LOS) remains challenging. This study developed and internally evaluated an explainable machine learning framework for pediatric appendicitis using routinely available clinical, laboratory, and radiological variables. Materials and Methods : This retrospective single-cohort model-development study included 152 pediatric patients with acute appendicitis treated at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, between January 2019 and December 2024. Two prediction tasks were evaluated: complicated appendicitis classification and expected LOS regression. Prediction was performed after initial clinical assessment, first laboratory testing, and initial diagnostic imaging, but before surgery or definitive conservative treatment. Operative findings, histopathological findings, postoperative variables, treatment-response variables, actual LOS-related variables, and final disease-severity labels were excluded as predictors. Model performance was evaluated using repeated nested cross-validation, bootstrap 95% confidence intervals, calibration analysis, benchmark comparisons, LOS sensitivity analyses, and explainability analysis using feature importance and SHAP values. Results : Complicated appendicitis was present in 57 patients (37.5%), and median LOS was 3 days (IQR: 2-6). For complicated appendicitis prediction, the repeated nested cross-validation framework achieved an AUC of 0.788 (95% CI: 0.710-0.856), accuracy of 0.750 (95% CI: 0.684-0.816), sensitivity of 0.684 (95% CI: 0.559-0.796), specificity of 0.789 (95% CI: 0.705-0.868), and Brier score of 0.183 (95% CI: 0.158-0.210). The framework showed comparable performance to a parsimonious logistic regression model (AUC: 0.807). For expected LOS prediction, the regression framework achieved an MAE of 2.150 days (95% CI: 1.632-2.846), MdAE of 1.305 days (95% CI: 1.004-1.493), RMSE of 4.329 days (95% CI: 2.290-6.297), and R 2 of 0.292 (95% CI: 0.213-0.481). LOS prediction showed lower MAE, MdAE, and RMSE than median and mean LOS null baseline models. Explainability analysis identified symptom duration, lymphocyte percentage, vomiting, temperature, and age as important predictors of complicated appendicitis, while radiological perforation, symptom duration, radiological collection, CRP, WBC count, NLR, lymphocyte percentage, and appendicolith by radiology contributed to expected LOS prediction. Conclusions : Explainable machine learning methods showed potential for internally validated prediction of complicated appendicitis and expected LOS using post-assessment pre-treatment data. However, this was a retrospective, single-cohort development study with a modest sample size and no external validation cohort. The findings should therefore be interpreted as exploratory and should not be considered evidence of clinical generalizability or readiness for implementation. Larger prospective multicenter studies with external validation, prediction-interval estimation, and clinical utility assessment are required before clinical use.","url":"https://doi.org/10.3390/medicina62081438","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/medicina62081438","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s12094-026-04440-3","name":"Risk prediction of non-small cell lung cancer in patients with pulmonary nodules: a single-center cohort study based on six machine learning algorithms.","source":"europepmc","abstract":"Objective Lung cancer is a major global public health concern. Non-small cell lung cancer (NSCLC), the most common subtype, has drawn increasing attention. To achieve early and accurate identification of NSCLC in patients with pulmonary nodules, reliable clinical prediction models are required. This study aimed to establish a machine learning model that integrates imaging and clinical features to predict the risk of NSCLC in this population. Methods Data from 963 patients with pulmonary nodules, treated at our hospital between January 2020 and December 2024, were retrospectively collected. Patients were randomly divided into training and testing cohorts at a 7:3 ratio. Demographic, clinical, and imaging variables were included. Two algorithms were applied for variable selection, and selected variables were incorporated into the final models. Six machine learning algorithms were trained with tenfold cross-validation. The mean area under the receiver operating characteristic curve (AUC) was used to identify the best model. The top-performing model was further evaluated, and interpretability was analyzed using SHapley Additive exPlanations (SHAP). A web-based calculator was then developed for clinical application. Results The XGBoost model showed the best performance among all machine learning methods in the training, testing, and validation cohorts. It achieved an AUC of 0.943 in the training cohort and 0.936 in the testing cohort. The most influential predictors were ground-glass opacity (GGO), nodule density, hypertension, plasma fibrinogen, blood urea nitrogen (BUN), and nodule size. The final model was implemented as a web-based clinical calculator ( https://qq568325999.shinyapps.io/dynnomapp/ ) to facilitate clinical use. Conclusion The XGBoost model developed in this study showed good discriminative ability for predicting the risk of NSCLC in patients with pulmonary nodules. These findings suggest that the model may have potential clinical utility. It may serve as an adjunctive tool for early risk stratification in this patient population. The use of SHAP analysis further enhanced the interpretability of the model. In addition, the online calculator derived from this model may facilitate its use in clinical research and future clinical translation. However, this study was a single-center retrospective analysis and included only internal validation. Therefore, further validation of the model is needed in multicenter external cohorts and prospective studies.","url":"https://doi.org/10.1007/s12094-026-04440-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s12094-026-04440-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1093/cid/ciag027","name":"Personalized Antibiogram: A Novel Multitask Machine Learning Framework for Simultaneous Prediction of Antimicrobial Resistance Profile With Enhanced Detection of Carbapenem Resistance in Enterobacteriaceae.","source":"europepmc","abstract":"Background Conventional hospital antibiograms summarize aggregated resistance rates, limiting their utility for individualized antimicrobial selection. Existing statistical and machine learning models predict each phenotype separately, ignoring correlations among resistance profiles. We developed novel multitask extreme gradient boosting (XGBoost) models utilizing structured data in electronic health records (EHRs) to predict resistance to 8 antimicrobial classes simultaneously and evaluated their performance within the Veterans Health Administration (VHA). Methods We conducted a retrospective multicenter study of Escherichia coli and Klebsiella spp. isolates collected at 127 hospitals and >1400 clinics from January 2017 to September 2024. Data from January 2017 to September 2023 were used for model development, while data from October 2023 to September 2024 were used for simulated prospective testing. Model performances were compared to hospital antibiograms and single-target XGBoost models. Results The training cohort included 536 252 E. coli and 246 898 Klebsiella spp. isolates; the test cohort included 75 138 and 38 015 isolates, respectively. On the test data, the multitask model achieved overall areas under the receiver operating characteristic curve (AUROCs) of 0.779 (E. coli) and 0.810 (Klebsiella spp.), with good to excellent per-class performance (AUROC range, 0.743-0.847). A multitask approach improved calibration and decreased false-negative rates for carbapenem resistance while predicting individualized resistance probabilities for all target antimicrobials simultaneously (\"personalized antibiograms\"). Conclusions A multitask XGBoost framework can accurately predict individualized resistance profiles for common Gram-negative pathogens, outperforming conventional antibiograms and single-target models. Personalized antibiograms may enhance the selection of empiric therapy, including the detection of carbapenem resistance in low-endemicity settings.","url":"https://doi.org/10.1093/cid/ciag027","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1093/cid/ciag027","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.14740/jocmr6642","name":"Development and Validation of an XGBoost-Based Machine Learning Model With Nomogram for Predicting Diabetic Peripheral Neuropathy Risk in Type 2 Diabetes Patients.","source":"europepmc","abstract":"Background The study aimed to develop risk stratification models for diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM) using multiple machine learning algorithms, identify the optimal model, and visualize it through a nomogram, thereby providing a clinical decision-support tool for the early identification of high-risk individuals. Methods A retrospective analysis was conducted on 180 inpatients diagnosed with T2DM at the Endocrinology Department of our hospital from January 2021 to September 2024, including 88 patients with DPN (48.9%) and 92 patients without DPN (51.1%). All enrolled participants were randomly divided into a training cohort (n = 126) and an internal validation cohort (n = 54) at a 7:3 stratified ratio. Collected clinical variables covered demographic profiles (age, gender, diabetes duration), anthropometric indicators (body mass index (BMI)), and multiple laboratory biomarkers. Five predictive algorithms were adopted for model construction, namely logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree (DT). Model predictive efficacy was comprehensively assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) interpretability analysis. A visual nomogram was finally developed based on the best-performing model. Results Multivariate regression analysis screened out six independent predictive factors for DPN occurrence, including diabetes duration, glycated hemoglobin (HbA1c), microalbuminuria (MAU), low-density lipoprotein cholesterol (LDL-C), neutrophil percentage (NEUT%), and BMI (all P Conclusion The XGBoost-based model shows favorable preliminary performance for cross-sectional DPN risk stratification in T2DM patients based on internal hold-out validation, outperforming traditional statistical approaches. Combined with SHAP interpretability and nomogram visualization, this model provides an exploratory clinical tool for early identification of potential high-risk individuals, requiring further external validation before clinical application.","url":"https://doi.org/10.14740/jocmr6642","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.14740/jocmr6642","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/alz.71667","name":"Early identification of vascular cognitive impairment from a multimodal perspective: a combined diagnosis from targeted cognitive assessments, imaging biomarkers, and molecular fluid biomarkers to ecological behavioral characteristics.","source":"europepmc","abstract":"Vascular cognitive impairment (VCI), the second leading cause of dementia, is characterized by heterogeneous pathophysiology and a potentially reversible early phase, underscoring the need for timely identification. This review synthesizes advances across four complementary domains - targeted cognitive assessments, imaging biomarkers, molecular fluid biomarkers, and ecological behavioral characteristics - conceptualized as the TIME framework. Emerging markers, including the peak width of skeletonized mean diffusivity (PSMD), oxygen extraction fraction, brain-derived extracellular vesicles, and digital gait metrics, enable the detection of microvascular injury before overt cognitive decline. Given the limitations of single modalities, we advocate for multimodal integration via machine learning to capture the disease continuum from vascular insult to clinical impairment. Establishing a standardized, pathophysiologically anchored classification system, analogous to the AT(N) framework in Alzheimer's disease, is essential to advance precision risk stratification and early intervention in VCI.","url":"https://doi.org/10.1002/alz.71667","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/alz.71667","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12884-026-09610-3","name":"Development and validation of a machine learning-based model for predicting live birth outcomes in patients undergoing frozen-thawed embryo transfer.","source":"europepmc","abstract":"Background Infertility affects millions of people worldwide, imposing substantial psychological, economic, and social burdens on patients. Although frozen-thawed embryo transfer (FET) is increasingly used, the live birth rate remains approximately 40%, highlighting the urgent need for improved predictive tools to enhance clinical outcomes. This study aimed to develop and validate an interpretable machine learning model for predicting live birth outcomes in patients undergoing FET. Methods Patients who underwent FET cycles at the Department of Reproductive Medicine, Affiliated Hospital of Zunyi Medical University, from January 2021 to December 2023 were included as a retrospective cohort, while those treated from January 2024 to December 2024 formed a prospective validation cohort. The retrospective cohort was randomly divided into training and testing sets at a 7:3 ratio for model development and internal validation. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression. Based on the selected predictors, three predictive models were constructed: Logistic Regression (LR), Random Forest (RF), and XGBoost. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Calibration curves and Brier scores were used to assess model calibration, while decision curve analysis (DCA) was employed to evaluate clinical utility. The optimal model was selected, and the Shapley additive explanation (SHAP) method was used to interpret the predictions. Results A total of 4,937 subjects were included. The retrospective development cohort comprised 3,470 cases, and the prospective external validation cohort comprised 1,467 cases. The live birth rate was 41.21% in the development cohort and 46.01% in the external validation cohort. LASSO regression and multivariate logistic regression identified ten key features for model development. The AUCs for the LR, RF, and XGBoost models on the training set were 0.826, 0.881, and 0.854, respectively; on the test set, they were 0.818, 0.828, and 0.843, respectively; and on the external validation set, they were 0.798, 0.796, and 0.809, respectively. Considering performance across all datasets, the XGBoost model demonstrated the best predictive performance. ROC curves showed that the predicted probabilities of the XGBoost model were generally consistent with actual outcomes, and the calibration curves were close to the diagonal, indicating good predictive calibration. DCA confirmed the clinical utility of the XGBoost model, showing high net benefit across most clinical decision thresholds. SHAP analysis identified the number of FET cycles, number of blastocysts transferred, number of high-quality embryos transferred, AMH, E2 level on the day of hCG administration, embryo type at transfer, age, endometrial thickness on the day of hCG administration, duration of infertility, and history of intrauterine adhesions as key predictive factors for live birth in patients undergoing FET. Conclusion The XGBoost-based model shows good predictive performance for live birth following FET. SHAP analysis provides interpretability of key features, which can support the development of targeted clinical interventions to improve pregnancy outcomes in patients undergoing FET.","url":"https://doi.org/10.1186/s12884-026-09610-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12884-026-09610-3","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1542/peds.2025-074576","name":"Machine Learning to Identify Bacteremia and Meningitis in Febrile Infants: A Systematic Review.","source":"europepmc","abstract":"Context The accurate identification of febrile infants who are at risk for invasive bacterial infections (IBIs), ie, bacteremia and meningitis, is essential to reduce morbidity and avoid unnecessary procedures. The role of machine learning (ML) in improving their diagnostic accuracy remains under evaluation. Objective To systematically review the diagnostic performance of ML-based models for identifying febrile infants aged up to 90 days with IBIs. Data sources Comprehensive searches of MEDLINE, Scopus, Embase, CINAHL, CENTRAL, ClinicalTrials.gov, and World Health Organization International Clinical Trial Registry Platform databases through December 2024. Study selection Eligible studies applied ML models to febrile infants aged up to 90 days to identify IBIs and reported diagnostic metrics and used culture-confirmed infections as the reference standard. Of 4756 screened records, 6 met inclusion criteria. Data extraction Two reviewers independently extracted study characteristics, model types, outcomes, and accuracy measures. Risk of bias was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies 2 tool. Marked methodological heterogeneity precluded meta-analysis. Results Six studies evaluated various ML models, including logistic regression, random forests, neural networks, support vector machines, and ensemble. Sensitivity ranged from 57% to 100%, specificity ranged from 30% to 94%, and ROC-AUC ranged from 0.57 to 0.9. Several models outperformed traditional tools (eg, PECARN, Step-by-Step), particularly in specificity, for IBI prediction. Limitations Limitations included the predominance of retrospective data collection, limited reporting on data handling, and lack of external validation. Conclusions ML models show promising performance for identifying infants at risk for IBIs, improving specificity over traditional tools. Future work should systematically incorporate and transparently report explainability analyses to support clinical interpretability. Broader validation, methodological standardization, and careful clinical integration are essential before adoption.","url":"https://doi.org/10.1542/peds.2025-074576","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1542/peds.2025-074576","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12866-026-05360-6","name":"A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China.","source":"europepmc","abstract":"Background As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybrid immunity, understanding region-specific determinants of severe disease remains important for real-world risk stratification and public health planning. Methods A retrospective surveillance study was conducted using 5,072 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) whole-genome sequences linked to clinical metadata from 13 cities in Jiangsu Province (from January 2023 to December 2024). Features derived from clinical, viral genomic, and regional epidemiological domains were evaluated using five machine learning models and assessed on an independent 2024 cohort. Model interpretability was examined using SHapley Additive exPlanations (SHAP) analysis. Key mutations were further examined through epitope prediction, peptide-HLA docking and binding affinity assessments to explore potential immunological implications. Results Integrated multidimensional features demonstrated superior predictive performance compared with single-domain inputs. In the independent 2024 validation cohort, LightGBM achieved the best overall performance (F1-score = 0.603; AUC = 0.735). SHAP analysis identified age as the dominant model predictor, followed by the age-viral load interaction, regional location, vaccination status, and selected viral genomic features. Epitope prediction and structural analyses suggested L452W-associated changes in predicted peptide-HLA interaction patterns within the evaluated set of high-frequency HLA class I alleles in the Jiangsu population, providing candidate hypotheses for future experimental validation. Conclusions COVID-19 severity during the endemic phase appeared to reflect interactions among host susceptibility, viral genetic variation, and regional epidemiological context, with age and vaccination emerging as key predictive factors. This population-based, interpretable framework highlights clinically relevant risk-associated features and may support real-world risk stratification in ongoing and future infectious disease surveillance.","url":"https://doi.org/10.1186/s12866-026-05360-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12866-026-05360-6","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1080/01480545.2026.2700652","name":"Decoding the role of phthalate plasticizers (DBP, DOP, and DEHP) in idiopathic pulmonary fibrosis: an integrated network toxicology and machine learning approach.","source":"europepmc","abstract":"The molecular mechanism of idiopathic pulmonary fibrosis (IPF) caused by phthalate (PAE) is not well understood, presenting notable clinical and toxicological challenges. This research seeks to advance drug toxicology and formulate targeted prevention and treatment strategies by examining the impact of three PAEs (DBP, DOP, DEHP) on IPF. Using databases such as PubChem, Encyclopedia of Traditional Chinese Medicine 2.0 (ETCM2.0), PharmMapper, and GEO, we identified 15 potential targets related to exposure to the three PAEs and IPF. Further refinement through the STRING database and Cytoscape (version 3.10.3) software highlighted 10 core targets. Functional enrichment through the Enrichr platform emphasized that the Toll-like receptor signaling pathway and the IL-17 signaling pathway are key mediators in plasticizer-induced IPF. Through machine learning, three core targets were ultimately selected. Molecular docking confirmed strong binding between the three PAEs and the core targets. Overall, this research offers significant insights into the molecular mechanisms underlying plasticizer-induced IPF and underscores the value of network toxicology in evaluating the toxicity of emerging environmental pollutants. It enhances our understanding of the health risks associated with PAEs and presents novel strategies to mitigate their impact on fibrotic diseases.","url":"https://doi.org/10.1080/01480545.2026.2700652","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/01480545.2026.2700652","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/ijms27125441","name":"An Exploratory Transcriptomic Classification Model for Psoriasis Based on Apoptosis-Associated and Proliferation-Apoptosis-Coupled Genes Using Explainable Machine Learning.","source":"europepmc","abstract":"This study aimed to integrate apoptosis-associated and proliferation-apoptosis-coupled transcriptomic signatures with explainable machine learning to construct an exploratory molecular classification model for psoriasis. Transcriptomic datasets GSE30999 and GSE53552 were merged as the skin-tissue training cohort, and GSE55201, a whole-blood transcriptomic dataset, was used as an independent cross-tissue external validation cohort. Differential expression analysis identified 3707 DEGs, and intersection with GeneCards apoptosis-related genes yielded 894 overlapping genes. After PPI-based hub gene selection, eight machine learning algorithms were exploratorily compared within a preselected 25-gene feature space. DALEX-based permutation feature importance analysis identified a five-gene apoptosis-associated and proliferation-apoptosis-coupled signature comprising CCNB1 , KIF11 , HDAC1 , TPX2 , and MELK . The five-gene model achieved an AUC of 0.966 in the training cohort and 0.811 in the external whole-blood validation cohort, indicating moderate cross-tissue generalizability. Calibration and decision-curve analyses were performed only in the training cohort and should be interpreted as exploratory analyses rather than evidence of clinical utility. Overall, this study provides an interpretable transcriptomic classification framework for distinguishing psoriasis from healthy controls, while its ability to differentiate psoriasis from clinically similar dermatoses remains to be validated in independent disease-control cohorts.","url":"https://doi.org/10.3390/ijms27125441","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/ijms27125441","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1371/journal.pgph.0006613","name":"Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).","source":"europepmc","abstract":"Cesarean section (CS) delivery is an important surgical intervention for reducing maternal and neonatal morbidity and mortality when medically indicated; however, substantial inequalities in its utilization persist across Sub-Saharan Africa (SSA). This study evaluated the performance of explainable machine learning (ML) algorithms in classifying cesarean section delivery patterns using Demographic and Health Survey (DHS) data from ten SSA countries collected between 2016 and 2024. A weighted sample of 388,015 women aged 15-49 years was included, among whom 7,369 (1.82%) had undergone cesarean section delivery. Data preprocessing included handling missing values, feature encoding, normalization, and balancing the minority class using the Synthetic Minority Over-sampling Technique (SMOTE). Multiple ML classifiers, including LightGBM, XGBoost, Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, AdaBoost, and Artificial Neural Networks, were trained and evaluated using repeated 10-fold cross-validation. Model performance was assessed using accuracy, recall, F1-score, and area under the receiver operating characteristic curve (AUC). LightGBM achieved the best classification performance with an AUC of 0.89 (95% CI: 0.887-0.893), accuracy of 85% (95% CI: 83.7-86.3%), and recall of 91% (95% CI: 89.8-92.2%), significantly outperforming logistic regression. SHapley Additive exPlanations (SHAP) identified maternal age, child size at birth, maternal education, wealth index, antenatal care visits, and media exposure as the most influential features associated with cesarean section classification outcomes. The findings demonstrate that explainable ML approaches, particularly LightGBM, improve classification performance compared with conventional regression models when applied to large population-based DHS datasets. However, because the study used cross-sectional survey data without external validation or detailed clinical predictors, the findings should be interpreted as associative classification patterns rather than tools for prospective clinical prediction.","url":"https://doi.org/10.1371/journal.pgph.0006613","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pgph.0006613","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1631/jzus.b2500607","name":"Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.","source":"europepmc","abstract":"To elucidate the diagnostic value and clinical relevance of protein kinase D3 (PRKD3) in hepatocellular carcinoma (HCC), we analyzed data retrieved from The Cancer Genome Atlas (TCGA) database, which revealed high expression of PRKD3 in HCC tissues. Subsequently, we collected a total of 392 clinical plasma samples from healthy individuals, patients with cirrhosis or decompensated cirrhosis, and patients with HCC. Plasma PRKD3 levels were then determined across HCC patients and individuals at high risk of developing the disease. The results revealed significantly elevated PRKD3 concentrations in patients with cirrhosis, decompensated cirrhosis, and HCC compared to healthy controls ( P <0.01). The areas under the receiver operating characteristic (ROC) curve for these three groups were 0.8107, 0.7899, and 0.7177, respectively. To further evaluate the efficacy of PRKD3 as an adjunctive diagnostic biomarker for HCC, we employed a panel of machine learning algorithms as primary classifiers, including extra trees (ET), gradient boosting (GB), random forest (RF), and support vector machine (SVM). A multi-parameter joint diagnostic model was constructed by combining PRKD3 expression data with a set of clinical parameters, including gender, age, total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), albumin (ALB), alpha-fetoprotein (AFP), and prothrombin induced by vitamin K absence-II (PIVKA-II). This integrated approach exhibited substantially improved diagnostic performance, achieving an accuracy of 0.861, sensitivity of 0.863, specificity of 0.925, and precision of 0.862. Collectively, these findings highlight the potential of PRKD3 as an integral component of a comprehensive diagnostic tool for the early identification of HCC.","url":"https://doi.org/10.1631/jzus.b2500607","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1631/jzus.b2500607","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.5114/ada.2026.162843","name":"Prediction of early responses to dupilumab treatment in patients with moderate-to-severe atopic dermatitis by a machine learning model integrating clinical characteristics and serum biomarkers.","source":"europepmc","abstract":"Aim The aim of the study was to evaluate the predictive value of baseline serum periostin and other T helper cell type 2 (Th2)-related biomarkers for early response to dupilumab in patients with moderate-to-severe atopic dermatitis (AD), and to develop a machine learning model integrating clinical and laboratory features. Material and methods A single-centre prospective cohort of 200 adults with moderate-to-severe AD treated with dupilumab (2020-2023) was used for model development, with an independent prospective cohort of 60 patients (2024) for external validation. Baseline clinical characteristics and serum biomarkers, including periostin, eosinophil count, total immunoglobulin E (IgE), and lactate dehydrogenase (LDH), were collected. Multivariate logistic regression and three machine learning models were constructed and evaluated using receiver operating characteristic analysis, calibration, decision curve analysis, and SHapley Additive exPlanations. Results In the modelling cohort, 62.5% of patients achieved EASI-75 at 16 weeks. Responders had significantly lower baseline periostin, eosinophil count, and IgE levels than non-responders (all p p Conclusions Baseline serum periostin is a robust and independent biomarker for predicting early response to dupilumab in moderate-to-severe AD. A machine learning model integrating periostin with clinical and Th2-related biomarkers enables accurate and interpretable pre-treatment patient stratification.","url":"https://doi.org/10.5114/ada.2026.162843","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.5114/ada.2026.162843","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1111/1759-7714.70381","name":"Decision-Support Framework for Nutritional Risk in Small Cell Lung Cancer: A Time-Series Model Using Imputation Strategies for Incomplete Clinical Data.","source":"europepmc","abstract":"In patients with small cell lung cancer (SCLC), nutritional status is a key determinant of disease progression, treatment tolerance, and prognosis. The prognostic nutritional index (PNI), reflecting both immune and nutritional conditions, is widely used to evaluate prognostic risk, but its longitudinal monitoring is often limited by incomplete clinical data in real-world settings. This study aimed to propose a decision-support framework for predicting future nutritional status and improving risk screening in SCLC patients with missing data. Using PNI values and related variables from the first four follow-up time points to predict the PNI at the fifth time point (PNI5) and evaluated the impact of different missing data imputation strategies on predictive performance. Missing PNI values were imputed using mean imputation, multiple imputation (MI), Kalman filtering, k-nearest neighbors (KNN), and XGBoost imputation. Predictive models were developed with two machine learning algorithms: random forest (RF) and XGBoost. The RF model demonstrated better overall performance, and MI combined with RF achieved the best predictive accuracy (MAE: 2.952; RMSE: 3.727). Predicted PNI values were further translated into risk categories using a predefined threshold (PNI = 45), with overall accuracy of 93.33%, PPV 95.24%, and NPV 92.59%. Importantly, the model enabled risk assessment in patients with incomplete laboratory data, covering previously unassessable cases and reducing monitoring gaps. This study highlights the importance of appropriate imputation strategies in and provides a practical tool for continuous nutritional risk assessment and early identification of high-risk patients in SCLC.","url":"https://doi.org/10.1111/1759-7714.70381","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/1759-7714.70381","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12872-026-06103-1","name":"Development and validation of an interpretable machine learning model for predicting 5-year major adverse cardiovascular events in patients with coronary artery disease.","source":"europepmc","abstract":"Background Coronary artery disease (CAD) remains a major contributor to global cardiovascular mortality and accurate prognosis is critical for guiding clinical decision-making. This study aimed to develop and validate interpretable machine learning (ML) models for predicting 5-year major adverse cardiovascular events (MACE) in hospitalized CAD patients. Methods A prospective cohort of 705 CAD patients was included and randomly divided into training (n = 494) and validation (n = 211) sets. Key predictors were selected using least absolute shrinkage and selection operator (LASSO) regression. Four survival-based models were developed, and model performance was assessed using discrimination, calibration, and decision curve analysis. Shapley Additive Explanations (SHAP) analysis was applied to enhance model interpretability. Results A total of 705 hospitalized CAD patients were included (mean age 63.2 years; 72.5% men), of whom 221 (31.3%) developed MACEs during the 5-year follow-up. LASSO regression revealed 11 key predictors, including left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP) level, nitrate use, CAD duration, depressive symptoms, and age. Among the four models, the random survival forest (RSF) model showed favourable discrimination performance, with C-index of 0.804 (95% CI: 0.770-0.837) in the training cohort and 0.710 (95% CI: 0.650-0.768) in the validation cohort. The RSF model also showed acceptable calibration, achieving the lowest Brier score in the validation cohort. Decision curve analysis (DCA) demonstrated that the RSF model provided potential clinical benefit over the treat-all and treat-none strategies across a wide range of risk thresholds. SHAP analysis revealed that the LVEF, age, and number of diseased vessels were the most important predictors of 5-year MACEs. Conclusions The RSF model demonstrated relatively favourable discrimination, calibration, and clinical utility for predicting 5-year MACEs in hospitalized CAD patients. These findings suggest that ML-based approaches may assist in individualized risk stratification and guide secondary prevention strategies.","url":"https://doi.org/10.1186/s12872-026-06103-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12872-026-06103-1","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/children13070962","name":"Machine Learning for Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study.","source":"europepmc","abstract":"Objectives: Hypospadias is one of the most common congenital malformations of the male genitourinary system, and postoperative complications remain a major concern affecting surgical outcomes and patients' quality of life. Whether machine learning models can effectively predict complication risk using routinely available clinical variables remains unclear. Methods: A retrospective analysis was performed on 671 hypospadias patients who underwent urethroplasty at the Department of Urology, Capital Children's Medical Center, between December 2015 and September 2024. The final dataset included 671 patients (training set: 536; validation set: 135). The median follow-up duration was 48 months (range: 19 to 72 months). Least absolute shrinkage and selection operator (LASSO) regression with nested cross-validation within the training set was used for feature selection, followed by the development of five machine learning models (Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine). Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis. Feature importance was assessed using SHapley Additive exPlanations (SHAP). Results: LASSO retained four features for model development: hypospadias type, surgical technique, surgeon experience, and patient age. The overall complication rate was 22.9% (154/671). Among the models evaluated, the Support Vector Machine (SVM) showed the most balanced performance in the validation set, achieving an AUC of 0.810 and a Brier score of 0.157. LightGBM demonstrated comparable performance (AUC: 0.802). SHAP analysis identified surgical technique as the most influential predictor, followed by surgeon volume and hypospadias type, though these findings should be interpreted with caution given the confounding between surgical complexity and disease severity. Conclusions: An interpretable SVM-based prediction model was developed and internally validated to stratify risk for postoperative complications after hypospadias repair using routinely available clinical variables. SHAP provided clinicians with visual insights into key risk-associated factors. However, given the single-center retrospective design and lack of external validation, further multicenter prospective studies are warranted to confirm the generalizability of these findings before clinical implementation.","url":"https://doi.org/10.3390/children13070962","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/children13070962","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12886-026-05065-4","name":"Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.","source":"europepmc","abstract":"Purpose This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve the discrimination between clinically diagnosed normal-tension glaucoma (NTG) and non-glaucomatous high myopia (HM) within a highly myopic population. Methods In this prospective cross-sectional diagnostic accuracy study, we consecutively enrolled two groups of participants: patients with high myopia (HM) and those with HM complicated by normal-tension glaucoma (HM-NTG). All clinical and imaging data were collected at a single time point using standardized protocols. Baseline clinical data were collected for all participants. Optic disc structural parameters were acquired using optical coherence tomography (OCT), while optic disc perfusion parameters were obtained via OCT angiography (OCTA). Receiver operating characteristic (ROC) curve analysis was first conducted to evaluate the diagnostic performance of individual parameters. Least absolute shrinkage and selection operator (LASSO) regression was employed for preliminary dimensionality reduction and feature selection, followed by multivariate logistic regression (backward stepwise method) to identify the optimal parameter combination. A diagnostic model was developed based on logistic regression and rigorously validated through bootstrap resampling, calibration assessment, decision curve analysis, and clinical impact curve. A machine learning diagnostic model was constructed using the support vector machine (SVM) algorithm and compared with the conventional regression model. Finally, Shapley additive explanations (SHAP) were applied to interpret the SVM model's decision-making mechanism and elucidate the individualized contribution of each feature to the prediction outcomes. Results A total of 87 patients were enrolled, comprising 42 in the HM group and 45 in the HM-NTG group. Overall, OCT parameters demonstrated superior diagnostic clarity compared with OCTA microvascular indices. The inferior macular ganglion cell complex (GCC) exhibited the strongest discriminatory performance, with an area under the receiver operating characteristic curve (AUC) of 0.85. Among OCTA parameters, the inferior optic disc vessel density (VD) achieved the best diagnostic performance (AUC = 0.72). LASSO regression combined with multivariate logistic regression identified three variables for model construction: inferior GCC thickness (odds ratio [OR] = 0.72), temporal-inferior retinal nerve fiber layer (RNFL) thickness (OR = 0.82), and inferior VD (OR = 0.77). The OCT + OCTA combined model achieved an AUC of 0.909 (95% CI: 0.833-0.984). While this did not significantly exceed the OCT-only model (AUC = 0.888, 95% CI: 0.803-0.973; DeLong test, P = 0.164), the addition of OCTA-derived inferior vessel density yielded significant net reclassification improvement (NRI = 0.679, P = 0.003) and integrated discrimination improvement (IDI = 0.067, P = 0.029), indicating enhanced risk stratification. Bootstrap resampling with 500 iterations yielded an AUC of 0.861 (95% CI: 0.773-0.949). The Hosmer-Lemeshow goodness-of-fit test indicated adequate calibration (χ² = 6.64, P = 0.575), with a Brier score of 0.109. Calibration curves demonstrated close adherence to the ideal diagonal across both low-risk and high-risk probability thresholds. Decision curve analysis indicated favorable net benefit. An SVM model constructed using the same three predictors achieved a comparable AUC of 0.908 (95% CI: 0.833-0.982), providing cross-algorithmic validation of the logistic model's robustness. SHAP analysis was applied to elucidate individualized feature contributions to patient-specific predictions, confirming that lower values of inferior GCC thickness exerted the greatest directional influence on HM-NTG classification. Conclusions Within highly myopic eyes, the combination of inferior GCC thickness, temporal-inferior RNFL thickness, and inferior VD may help distinguish clinically diagnosed HM-NTG from non-glaucomatous HM. The addition of OCTA microvascular parameters did not significantly increase the AUC but improved patient risk reclassification. This multimodal approach, paired with conventional regression and machine learning, offers a promising adjunctive tool for diagnostic evaluation in highly myopic eyes.","url":"https://doi.org/10.1186/s12886-026-05065-4","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12886-026-05065-4","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fmicb.2026.1789841","name":"Rapid prediction of vancomycin-resistant &lt;i&gt;Enterococcus faecium&lt;/i&gt; using MALDI-TOF mass spectrometry and machine learning.","source":"europepmc","abstract":"Background Vancomycin-resistant Enterococcus faecium (VRE fm ) is a World Health Organization priority pathogen, yet conventional phenotypic susceptibility testing requires up to 72 h, delaying targeted antimicrobial therapy. This study aimed to develop an interpretable and rapid machine learning classifier to predict VRE fm using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectra. Methods We retrospectively analyzed 268 clinical E. faecium isolates (100 VRE fm and 168 vancomycin-susceptible E. faecium ) from Tangshan Gongren Hospital, a tertiary hospital in northern China (2020-2025). Spectra were preprocessed with smoothing, baseline removal, and 5-Da binning (range 2,000-20,000 Da). Extreme gradient boosting recursive feature elimination selected 40 discriminative mass-to-charge ratio features. To enhance model generalizability across diverse bacterial lineages, the training set combined local isolates from 2020 to 2024 with the multi-center DRIAMS repository (subsets A-D). Five classifiers were trained under stratified 5-fold cross-validation and temporally validated on the independent 2025 local isolates. Model discrimination, calibration, and clinical utility were evaluated using the area under the receiver operating characteristic curve (AUROC), Brier score, and decision curve analysis. Results The k-nearest neighbors classifier achieved optimal temporal validation performance (area under the receiver operating characteristic curve 0.90, F1 score 0.683, Brier score 0.185). Hybrid training configurations combining local data with pooled DRIAMS subsets retained clinically useful discrimination (AUROC > 0.80), whereas external-only models performed at chance level (AUROC ≈ 0.50). Conclusion MALDI-TOF MS combined with machine learning enables rapid, interpretable prediction of vancomycin resistance in E. faecium . A hybrid local-multi-center training strategy offers a pragmatic solution for laboratories with limited local sample availability, facilitating clinical deployment of spectral-based antimicrobial resistance surveillance.","url":"https://doi.org/10.3389/fmicb.2026.1789841","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1789841","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12882-026-05080-z","name":"Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.","source":"europepmc","abstract":"Background End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfunction, has demonstrated predictive value for outcomes in multiple diseases. However, its association with in-hospital mortality (IHM) risk in ESRD patients remains unclear, and machine learning prediction models remain unestablished. Methods Clinical data for ESRD patients were extracted from the MIMIC-IV (3.1) database. The outcome was IHM. The link between EASIX and IHM risk was explored using Cox proportional hazards regression, Kaplan-Meier survival curves, restricted cubic splines (RCS), and subgroup analysis. The Boruta algorithm and LASSO regression were employed to screen important features. Multiple models were established using machine learning algorithms, validated, and compared. SHAP analysis was applied to the optimal model. Results The study included 997 ESRD patients, with 194 in-hospital deaths, accounting for 19.5% of the total cohort. The Kaplan-Meier curve revealed the highest IHM rate among patients with the highest EASIX level (Q4). Elevated EASIX levels showed a significant positive link with an enhanced IHM risk in ESRD patients (HR [95% CI] = 2.086 [1.697, 2.564]). RCS showed an approximate linear relationship, with this association consistent across multiple subgroups. The gradient boosting machine was identified as the optimal model (AUC of training set = 0.822, AUC of validation set = 0.763). SHAP analysis identified EASIX as a key contributor to IHM risk. Conclusion EASIX is significantly positively correlated with an elevated IHM risk in ESRD patients, making it a crucial predictor of IHM in ESRD. EASIX can serve as an early predictive tool to guide the identification, intervention, and prevention of adverse outcomes in ESRD. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12882-026-05080-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12882-026-05080-z","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1097/md.0000000000049729","name":"Construction and validation of a machine learning-based risk prediction model for venous thromboembolism in older adult patients: A multifactorial analysis of 28,231 cases.","source":"europepmc","abstract":"Venous thromboembolism (VTE) is a leading preventable cause of in-hospital mortality in older adults, yet early risk stratification remains a key clinical challenge. This study aimed to develop and internally validate an explainable machine learning model for incident VTE prediction in hospitalized older adult patients. We enrolled 28,231 patients aged ≥65 years admitted between January 2023 and December 2024, excluding those with VTE on admission. The primary endpoint was imaging-confirmed incident in-hospital VTE. Patients were split into training/test sets (7:3) via outcome-stratified sampling. Missing data were handled with multivariate imputation by chained equations imputation (training set only). Five machine learning models were constructed with 10-fold cross-validation and hyperparameter tuning, evaluated by pooled area under the curve (AUC), calibration curves, and decision curve analysis, with SHAP for model interpretation. 1797 (6.38%) incident VTE events were recorded. XGBoost showed optimal performance, with a training AUC of 0.753 and a test AUC of 0.712, favorable calibration, and stable clinical net benefit. Top predictors included diabetic nephropathy, triglycerides, great saphenous vein varicosity, fatty liver, cerebrovascular accident and age. We developed and validated an explainable XGBoost model for VTE risk prediction in older inpatients, enabling early risk stratification to support individualized thromboprophylaxis. Multicenter prospective external validation is warranted for clinical implementation.","url":"https://doi.org/10.1097/md.0000000000049729","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000049729","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1111/sltb.70124","name":"Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.","source":"europepmc","abstract":"Introduction Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. Although recent work has leveraged intensive longitudinal data and machine learning (ML) to forecast short-term risk for active SI, passive SI remains understudied as a prediction target. Methods Seventy-eight psychiatrically hospitalized youth (ages 13-17 years) completed baseline assessments and daily ratings of risk and protective factors for 28 days post-discharge. Multiple ML models were trained to predict the presence of next-day passive SI. Models with and without baseline variables were compared to assess the relative predictive value of time-varying versus baseline features. Results ML models predicted next-day passive SI with high accuracy (AUC = 0.90). The strongest predictors were within-person 7-day moving averages of passive SI duration and frequency. Including baseline variables had negligible performance impact, even during initial days post-discharge. Conclusions Short-term passive SI remains an underutilized but important target for suicide prevention. Forecasting next-day passive SI using ML is feasible and highly accurate. Within-person, time-varying features outperformed baseline factors, even in early days post-discharge. Additional research on SI facets, such as duration, is needed. Integrating passive SI into personalized intervention frameworks may enhance the precision of suicide prevention efforts.","url":"https://doi.org/10.1111/sltb.70124","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1111/sltb.70124","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16162644","name":"Predicting Complicated Appendicitis: What Can Machine Learning Add?","source":"europepmc","abstract":"Background/Objectives : Early identification of complicated appendicitis in children remains challenging. We developed and internally validated laboratory-based machine-learning models for severity stratification using age and routine admission laboratory data. Methods : This retrospective study included 628 children with surgically confirmed appendicitis treated between 2020 and 2024. Complicated appendicitis was defined by operative or pathological evidence of perforation, gangrene, abscess, phlegmon, diffuse peritonitis, or comparable advanced inflammation. Fifteen candidate predictors were evaluated using five prespecified models. Models were tuned in the training set and evaluated once on an isolated test set. Pairwise DeLong comparisons, decision curve analysis, SHAP, and permutation importance were performed. Results : Complicated appendicitis occurred in 93 patients (14.8%). The prespecified primary CatBoost model achieved a ROC AUC of 0.867, a precision-recall AUC of 0.677, a sensitivity of 0.750, a specificity of 0.863, a positive predictive value of 0.488, and an F1-score of 0.592. Formal comparisons did not demonstrate statistically significant AUC superiority over the other algorithms after Holm correction. Exploratory decision curve analysis showed a greater net benefit than treat-all and treat-none strategies across threshold probabilities of 0.06-0.35. ESR, age, CRP, and CRP-derived indices were the most influential model features. Conclusions : Routine laboratory data may provide adjunctive information for severity stratification, but the modest event count, limited positive predictive value, and absence of external validation preclude stand-alone clinical use.","url":"https://doi.org/10.3390/diagnostics16162644","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16162644","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fendo.2026.1890881","name":"Machine learning-based diagnostic models for prevalent metabolic syndrome among patients with disuse muscle atrophy: a comparative study of seven algorithms.","source":"europepmc","abstract":"Background Disuse muscle atrophy (DMA) leads to muscle loss and impaired motor function, while metabolic syndrome (MetS) increases the risk of cardiovascular disease and diabetes. Muscle loss exacerbates insulin resistance, accelerates MetS progression, and creates a vicious cycle. This study aimed to develop and validate an interpretable machine learning (ML) model for identifying prevalent MetS in patients with DMA. Methods This retrospective study was conducted using data from 1,004 patients who visited the Department of Rehabilitation Medicine at four medical institutions in Urumqi, Xinjiang, between January 2018 and June 2024. The data were randomly divided into a training set ( n = 704) and a validation set ( n = 300). Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. Seven ML algorithms-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, Support Vector Machine (SVM), and Artificial Neural Network (ANN)-were used to construct diagnostic models. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. The SHapley Additive exPlanations (SHAP) framework was applied to interpret the contributions of feature variables. Results The prevalence of Mets among patients with DMA was 45.72%, eight key predictors were identified. The RF model demonstrated the best discriminatory performance, with an area under the curve (AUC) of 0.961 (95% CI: 0.938-0.979), accuracy of 90.0%, precision of 92.8%, sensitivity of 84.7%, specificity of 94.5%, and F1 score of 88.5%. The top five predictors identified by SHAP analysis were waist circumference, blood glucose, Cardiometabolic Index (CMI), High-density lipoprotein (HDL), and Triglyceride-Glucose index (TyG). Conclusion We developed an interpretable ML model to aid in the early diagnosis of MetS. Among the models, particularly RF, showed outstanding predictive performance and promising application potential. It provides a practical tool for the early identification and targeted intervention, supports clinical decision-making, and promotes more rational allocation of healthcare resources.","url":"https://doi.org/10.3389/fendo.2026.1890881","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1890881","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fneur.2026.1860019","name":"Explainable machine learning for predicting lower extremity deep vein thrombosis in traumatic brain injury patients: development of a SHAP-guided Random Forest model.","source":"europepmc","abstract":"Background Lower extremity deep vein thrombosis (LEDVT) is a common and clinically important complication after traumatic brain injury (TBI). Early identification of patients at increased thrombotic risk remains challenging. We aimed to develop and internally validate an interpretable machine-learning model for predicting LEDVT in patients with TBI. Methods In this retrospective observational study, we analyzed 248 consecutive patients with TBI treated at Qinghai University Affiliated Hospital between August 2024 and December 2025. Patients were randomly assigned to a training cohort ( n = 174) and an internal validation cohort ( n = 74). A total of 39 demographic, clinical, and laboratory variables were screened. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression. Eight machine-learning algorithms were developed and compared. Model discrimination, calibration, and decision-curve performance were assessed, and the selected model was interpreted using Shapley additive explanations (SHAP). Results LEDVT occurred in 88 of 248 patients (35.5%). LASSO retained six variables for model development: age, platelet count, D-dimer, interleukin, lower limb fracture, and prophylactic anticoagulation. In multivariable logistic regression, D-dimer, interleukin, lower limb fracture, and prophylactic anticoagulation were significantly associated with LEDVT, whereas age and platelet count showed borderline associations. In the validation cohort, the Random Forest model showed strong discrimination, with an area under the receiver operating characteristic curve of 0.931 (95% CI 0.878-0.985), sensitivity of 78.8%, specificity of 95.1%, accuracy of 87.8%, precision of 92.9%, and F1 score of 0.852. SHAP analysis identified D-dimer, interleukin, prophylactic anticoagulation, age, platelet count, and lower limb fracture as the main contributors to model output. Conclusion An interpretable Random Forest model based on routinely available clinical and laboratory variables showed good internal predictive performance for LEDVT after TBI. After external validation, this approach may help support early risk stratification and individualized surveillance in patients at increased thrombotic risk.","url":"https://doi.org/10.3389/fneur.2026.1860019","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1860019","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1038/s41598-026-58612-w","name":"An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.","source":"europepmc","abstract":"Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, infections, and amputation if not treated in a timely manner. Early identification of patients who already present a high-risk diabetic foot profile at clinical evaluation is therefore critical for guiding preventive interventions. This study aimed to develop an interpretable machine learning-based risk classification platform for classifying current diabetic foot risk status in patients with diabetes and supporting clinical risk stratification and personalized care. The model was not designed to predict future ulcer occurrence, amputation, or wound healing outcomes. In this retrospective study, data from 1938 diabetic patients at Nanfang Hospital of Southern Medical University were used for model development, and an independent external validation cohort of 695 diabetic patients from Shanghai Changhai Hospital was used to assess generalizability. Fifty clinical features covering demographic, metabolic, vascular, neurological, inflammatory, renal, and diabetes-related factors were included. Ten machine learning models were developed and compared. Recursive Feature Elimination (RFE) was applied to the top-performing models for feature selection, and SHapley Additive exPlanations (SHAP) was used to interpret model predictions and construct a diabetic foot risk classification platform. Using sixteen selected features, the CatBoost model achieved the best performance on the internal test set, with an AUC of 0.935 ± 0.016 and accuracy, precision, and recall of 0.88. In the external validation cohort, the model maintained stable performance, with an AUC of 0.922 ± 0.006 and accuracy, precision, recall, and F1-score all equal to 0.88, demonstrating good generalizability. We developed a CatBoost-based diabetic foot risk classification model incorporating sixteen clinically accessible features. The model showed stable and reliable performance across internal and external cohorts and remained robust under data noise and class imbalance, supporting its potential utility for real-world clinical risk classification and early preventive care.","url":"https://doi.org/10.1038/s41598-026-58612-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-58612-w","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/jir.s614073","name":"Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.","source":"europepmc","abstract":"Background Precise identification of malignant breast nodules is critical for clinical intervention. This study aimed to develop and externally validate an interpretable machine learning model using routine blood biomarkers. Methods This retrospective multicenter study included 899 women with pathologically confirmed breast nodules in a derivation cohort from Shanghai Baoshan Hospital (March 2022-December 2024). After missing-value imputation and LASSO selection, eight models were developed. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). The models were subsequently validated in an independent temporal validation cohort (n = 205, recruited from the same center between January 2025 and September 2025) and an external validation cohort (n = 290, Tongji Hospital, Shanghai, between January 2025 and November 2025). Model interpretability was evaluated using SHapley Additive exPlanations (SHAP). Results Eleven predictors (age and ten biomarkers) were selected. The Random Forest (RF) model exhibited the best performance, yielding AUCs of 0.82 (95% CI : 0.77-0.87), 0.78 (95% CI : 0.71-0.86), and 0.72 (95% CI : 0.66-0.78) in the internal, temporal, and external validations cohorts, respectively, indicating a moderate decline yet acceptable discriminative ability in independent validation cohorts. In the temporal validation cohort, its diagnostic performance was comparable to ultrasound ( AUC = 0.76) and mammography ( AUC = 0.74). SHAP analysis identified hs-CRP, RBC, and age as the most influential predictors. A web-based calculator was developed to facilitate future evaluation of the model. Conclusion Routine blood biomarkers can support interpretable machine-learning models for differentiating malignant from benign breast nodules. The proposed model may serve as a complementary pre-biopsy risk-assessment tool alongside conventional imaging modalities; however, prospective validation across broader populations and healthcare settings remains necessary before routine clinical implementation.","url":"https://doi.org/10.2147/jir.s614073","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/jir.s614073","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1080/00207454.2026.2697821","name":"A predictive model for early neurological deterioration in medullary infarction based on explainable machine learning.","source":"europepmc","abstract":"Background Medullary infarction is a severe subtype of ischemic stroke. Early neurological deterioration (END) is a common adverse event that can significantly affect clinical outcomes. Objective This study aimed to develop a machine learning model to effectively predict the risk of END in patients with medullary infarction, thereby providing a reference for clinical intervention. Methods In this multicenter retrospective study, we enrolled patients with acute medullary infarction who received treatment at three hospitals located in different regions between 2019 and October 2024. We collected comprehensive, multi-dimensional clinical data and employed five distinct algorithms to construct predictive models. The performance of these models was thoroughly evaluated using accuracy, area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration curves. Results A total of 352 patients were enrolled in this study. The incidence of END was 10% (35), while the incidence of non-END was 90% (317). Seven features were used to build prediction models with the five algorithms. The single-layer neural network demonstrated superior performance in predicting END, achieving an AUC of the receiver operating characteristic (ROC) curve of 0.873 on the validation set, while also exhibiting favorable calibration and a balanced overall performance profile. In comparison, the Naïve Bayes model showed excellent classification consistency and may serve as a complementary tool in clinical practice. Conclusion This study developed a highly accurate single-layer neural network and a robust Naïve Bayes classifier, which together provide an interpretable tool for assessing the risk of END in stroke patients across various clinical settings.","url":"https://doi.org/10.1080/00207454.2026.2697821","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/00207454.2026.2697821","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fpubh.2026.1865551","name":"Machine learning based prediction of antimicrobial resistance &lt;i&gt;in Klebsiella spp&lt;/i&gt;.: a five-year retrospective study.","source":"europepmc","abstract":"Introduction Klebsiella species are well-recognized pathogens implicated in both healthcare-associated and community-acquired infections, and they contribute substantially to the global burden of antimicrobial resistance. In this study, we investigated the epidemiology, temporal resistance trends, multidrug resistance profiles, and the application of machine learning (ML) approaches to predict antimicrobial susceptibility among Klebsiella isolates in Al-Kharj, Saudi Arabia. Methods A retrospective analysis conducted using routine microbiology laboratory data collected between 2019 and 2024. Antimicrobial susceptibility testing included multiple agents across major antimicrobial classes, allowing classification of isolates into defined resistance phenotypes. Multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) profiles were determined using standard class-based definitions. In parallel, several supervised machine learning models were developed and evaluated, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Extra Trees Classifier, Classifier Chains (multilabel classification), Deep Neural Network (DNN), Voting Ensemble, and Convolutional Neural Network (CNN). Models were trained using the training dataset and subsequently evaluated on the testing dataset to assess predictive performance and generalizability. Results Over the five-year study period, Klebsiella species accounted for 2,646 isolates (13.7%) of all clinical isolates, with Klebsiella pneumoniae representing the predominant species. Urinary tract infections were the most frequent source (45.5%), followed by blood cultures (15.4%), respiratory samples (14%), and wound and soft tissue specimens (12.2%). High resistance rates were observed for ceftazidime, whereas most other antibiotics demonstrated moderate resistance levels. Tigecycline, and Ceftriaxone showed the lowest resistance rates. Among Klebsiella spp. isolates, 57.8% were classified as multidrug-resistant, 1.9% as extensively drug-resistant, and no pan-drug-resistant isolates were identified. Among the evaluated models, the best overall model was XGBoost with an accuracy of 0.70, precision of 0.65, recall of 0.64, an F1-score of 0.64, and a ROC-AUC of 0.74. Conclusions Collectively, these findings underscore the increasing clinical burden of Klebsiella infections and demonstrate the potential of boosting-based machine learning algorithms, particularly XGBoost and LightGBM, for accurate prediction of antibiotic resistance. Integration of these models into clinical decision-support systems and antimicrobial stewardship programs may facilitate timely and appropriate antimicrobial therapy, thereby improving patient outcomes and promoting more rational antibiotic use.","url":"https://doi.org/10.3389/fpubh.2026.1865551","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1865551","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3390/diagnostics16162615","name":"Machine Learning Identifies High-Risk Suicide Profiles in a Population-Based Forensic Registry.","source":"europepmc","abstract":"Background: Suicide is a leading cause of preventable death, yet machine learning (ML) analyses of forensic (medico-legal) suicide data are scarce and, to our knowledge, absent for Romania. Population-based forensic registries offer exhaustive, autopsy-confirmed coverage that is structurally distinct from clinical or civil death-registration data. We applied supervised and unsupervised ML to a complete regional medico-legal suicide registry to profile the method of death and to identify latent victim subgroups of preventive relevance. Methods: We analysed 395 consecutive suicide deaths (Galați and Brăila counties, ≈750,000 inhabitants; 2018-2024). Two supervised classifiers-L2-regularised logistic regression (LR) and random forest (RF, 200 trees)-were trained to discriminate hanging from other methods, using eleven sociodemographic and clinical predictors, and evaluated by 10-fold stratified cross-validation. Given severe class imbalance, the area under the ROC curve (AUC) was the primary metric. Model hyperparameters were fixed a priori, and no class-imbalance correction was applied; both decisions are pre-specified and justified in the Methods. Robustness was assessed by stratified non-parametric bootstrap confidence intervals for the odds ratios, a tipping-point sensitivity analysis for the undocumented clinical fields, and Ward-linkage hierarchical clustering as an independent partitioning check. Predictor importance was quantified by out-of-bag (OOB) permutation importance and Spearman correlations. Unsupervised structure was assessed by K-means clustering (k = 2-7), with the optimal solution selected by the average silhouette coefficient and the elbow (WCSS) criterion. Reporting followed TRIPOD+AI and STROBE. Results: The study population was predominantly male (87.1%) and rural (73.2%), with a mean age of 54.1 years; hanging accounted for 94.2% of deaths-far above the European average (≈50%). RF achieved AUC = 0.865 ± 0.181 and LR AUC = 0.847 ± 0.192, both within the \"excellent\" discrimination band; sensitivity was very high (0.995-0.997) and specificity was limited (0.233-0.367), an expected consequence of imbalance. Prior suicide attempts (OOB importance 0.959; Spearman ρ = -0.549, p p n = 19; 4.8%) was a clinically distinct, younger subgroup (42.4 vs. 54.7 years) characterised by prior attempts (57.9% vs. 0%), suicide notes (68.4% vs. 0%), higher psychiatric comorbidity (52.6% vs. 30.9%) and lower hanging proportion (36.8% vs. 97.1%)-an exploratory, hypothesis-generating profile of recurrent suicidal behaviour with documented prior contact with the medical or medico-legal system. The principal findings were stable across all plausible degrees of clinical under-documentation in the tipping-point sensitivity analysis. Conclusions: ML applied to a complete forensic suicide registry reproduced known regional epidemiology and, beyond classical statistics, isolated an exploratory but clinically coherent high-risk subgroup of direct relevance to the audit of structured post-attempt follow-up. This is, to our knowledge, the first ML study of Romanian forensic suicide data and supports integrating ML into medico-legal research and into the regional targeting and audit of existing post-attempt follow-up provision.","url":"https://doi.org/10.3390/diagnostics16162615","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/diagnostics16162615","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s00330-026-12644-y","name":"Noninvasive prediction of severe histopathology in drug-induced liver injury using a dual elastography-based machine learning model.","source":"europepmc","abstract":"Objectives To develop and validate a machine learning (ML) model integrating dual elastography, clinical features, and serum biomarkers for noninvasive prediction of severe drug-induced liver injury (DILI). Materials and methods This prospective multicenter study enrolled consecutive DILI patients undergoing liver biopsy and dual elastography. Severe DILI was defined as Scheuer inflammation grade plus fibrosis stage ≥ 5 (G + S ≥ 5). Dual elastography-derived activity index (A index) and fibrosis index (F index) correlated with pathological inflammation (G0-4) and fibrosis (S0-4) stages. The dataset was stratified and split 7:3 into training and test sets. LASSO regression was applied for feature selection. Eight ML models were constructed and compared, optimized using 5-fold cross-validation and Bayesian methods. Performance was evaluated by area under the curve (AUC), sensitivity, and specificity. SHapley Additive exPlanations (SHAP) were used to interpret the models. Results A total of 305 participants were included (median age 49 years, IQR 40-56; 98 male), comprising 55 with severe DILI and 250 without. A and F indices increased with inflammation grade and fibrosis stage, respectively (p Conclusion We developed an explainable, high-performing and dual elastography-based ML model to predict severe DILI, facilitating risk stratification and preliminary management. Key points Question Severe drug-induced liver injury (DILI) leads to a poor prognosis, yet early noninvasive identification remains challenging due to non-specific serum markers and invasive biopsy. Findings The regularized regression model integrating dual elastography, clinical features and serum biomarkers, non-invasively and robustly predicted severe histologic injury in DILI. Clinical relevance This dual elastography-based machine learning model offers a noninvasive solution for the early identification of DILI patients with severe tissue injury, minimizing unnecessary biopsy and improving prognosis. Clinicians can utilize the online tool for real-time risk stratification at: https://wznng666.shinyapps.io/RR55555/ .","url":"https://doi.org/10.1007/s00330-026-12644-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00330-026-12644-y","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1136/spcare-2026-006282","name":"Quality indicators for cancer pain management: natural language processing and machine learning from electronic clinical records.","source":"europepmc","abstract":"Objectives The continual evaluation of quality indicators (QIs) for cancer pain management is essential to maintaining and improving its quality. However, free-text notes in electronic medical records (EMRs) must be reviewed to accurately assess pain management QIs, and the heavy workload of this process can be limiting.Therefore, this study evaluated the performance of natural language processing and machine learning for assessing pain management QIs using EMR data. Methods This single-centre cross-sectional study included adult patients with cancer who died at a Japanese university hospital between 1 January 2022 and 31 December 2024. Clinical notes concerning inpatients and outpatients were extracted from the EMR system. A model was developed to automatically identify documentation related to pain management from free-text notes. We then compared the model's QIs assessment with a manual review concerning the number of patients who underwent pain screening. Results The study included 865 patients, and 2 119 377 clinical records were used to evaluate QIs. The model achieved 85%-96% accuracy, and the F1 score ranged from 0.38 to 0.58 for identifying pain-screening documentation. The pain screening rate for the centre's inpatients was 100%, according to the manual and model-based evaluations. For the outpatients, the rates were 35.0% and 35.9%, respectively, by the manual and model-based evaluations. Conclusions Pain management QIs can be accurately assessed using natural language processing and machine learning applied to EMRs, with performance comparable to that of manual reviews. This approach may improve cancer pain management by providing clinician feedback and visualising achievement rates.","url":"https://doi.org/10.1136/spcare-2026-006282","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/spcare-2026-006282","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12911-026-03658-z","name":"Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.","source":"europepmc","abstract":"Objective To establish a machine learning (ML) model for the early diagnosis of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) based on clinical features and to utilize the Shapley Additive Explanations (SHAP) method to interpret the ML model, thereby providing reliable factors for diagnosing EBV-HLH. Methods We collected clinical data from 1,026 children with Epstein-Barr virus (EBV) infection who were hospitalized at Children's Hospital of Soochow University from October 2017 to September 2024. First, we compared the clinical data of Epstein-Barr virus-associated infectious mononucleosis (EBV-IM) and EBV-HLH through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression to select key features for machine learning training. Subsequently, we applied six machine learning algorithms and logistic regression to build diagnostic models, and the optimal model was selected based on multiple evaluation metrics. Finally, we utilized the Shapley Additive Explanations (SHAP) algorithm to clarify the importance of variables in the model to facilitate its application in clinical settings. Results Among the six machine learning models and logistic regression evaluated, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest discrimination ability, with an area under the receiver operating characteristic curve (AUC) of 0.9775, sensitivity of 0.9461 and specificity of 0.9784. The SHAP analysis indicated that the most important predictors for EBV-HLH were D-dimer, cervical lymphadenopathy (CLA), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), and CD3 + CD4+ T cells. Conclusions The XGBoost model demonstrated excellent predictive performance for the early identification of EBV-HLH in children. Compared with other models, it achieved higher sensitivity and may serve as a promising decision-support tool pending external validation. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12911-026-03658-z","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12911-026-03658-z","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1109/jbhi.2025.3585552","name":"Development of a Tongue Image-Based Machine Learning Tool for the Diagnosis of Colorectal Cancer: A Prospective Multicentre Clinical Cohort Study.","source":"europepmc","abstract":"Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptimal patient compliance rates. This study develops an intelligent diagnostic model based on tongue images to assist in CRC diagnosis, leveraging the integrative potential of traditional tongue diagnosis and modern machine learning. Between June 2023 and July 2024, we collected and processed 1,389 tongue images from CRC patients and 1,543 from non-colorectal cancer (NCRC) participants. Our methodology combines innovative image segmentation using the Segment Anything Model (SAM) with Grounding DINO, extracts both hand-crafted features (color, texture, shape) and deep learning features via Swin-Transformer, and employs feature fusion and selection techniques. The diagnostic model achieves an accuracy of 87.93% (F1-score: 0.9072) in internal validation. In an independent external cohort of 119 CRC patients and 221 NCRC participants, it demonstrates 85.18% precision (recall: 85%, F1-score: 0.8507). This non-invasive, cost-effective approach demonstrates significant potential as a complementary screening tool for CRC, particularly in regions with limited access to conventional diagnostic resources.","url":"https://doi.org/10.1109/jbhi.2025.3585552","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1109/jbhi.2025.3585552","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1007/s10278-026-02146-0","name":"Advancing the FAIRness of Multimodal Imaging Research Through the OMOP MI-CDM Framework: A Case Replication Study in Alzheimer's Disease.","source":"europepmc","abstract":"The objective of this study is to demonstrate an end-to-end approach for operationalizing the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in multimodal medical imaging research using standardized data models and reproducible computational workflows, illustrated by reproducing the design and directional findings of a published Alzheimer's disease (AD) imaging study. Clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI-4) were harmonized within the Observational Medical Outcomes Partnership Common Data Model and its Medical Imaging extension (OMOP MI-CDM). MRI acquisition metadata (DICOM) were extracted and mapped to standardized concepts, while clinical variables were integrated via reproducible extract-transform-load processes. Interoperable phenotypes were defined using OHDSI tools. Hippocampal volumes were derived from T1-weighted MRI using a fully automated machine learning segmentation pipeline (OpenMap-T1). Imaging attributes and derived measurements were stored as structured, provenance-preserving records in OMOP MI-CDM. We replicated a reference study evaluating hippocampal volume differences across AD, mild cognitive impairment (MCI), and cognitively normal controls, stratified by age and sex. We included 289 participants and 545 MRI studies. Across age- and sex-stratified cohorts, mean hippocampal volumes showed consistent directional reductions in AD compared with controls, with intermediate values in MCI, matching trends reported in the replication study. FAIR principles can be operationalized across the full imaging research pipeline using OMOP MI-CDM and automated analysis workflows. This framework enables transparent cohort definition, reproducible image processing, and interoperable reuse of machine learning-derived imaging features to support scalable validation and reproducible multimodal imaging research.","url":"https://doi.org/10.1007/s10278-026-02146-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10278-026-02146-0","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fphar.2026.1677843","name":"Prediction of early treatment response to drug-eluting beads chemoembolization for hepatocellular carcinoma using machine learning.","source":"europepmc","abstract":"Background Drug-eluting bead trans-arterial chemoembolization (DEB-TACE) has been extensively employed as a locoregional therapy for hepatocellular carcinoma (HCC). In this work, machine-learning algorithms are used to forecast early treatment response in HCC patients undergoing DEB-TACE. Methods We collected data on patients with HCC who underwent anthracycline-loaded DEB-TACE at our institution over two periods: July 2023 to November 2024 and January to September 2025. The treatment response was evaluated at 1, 3, and 6 months after therapy by dynamic contrast-enhanced computed tomography (CT) based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Univariable and multivariable logistic regression analyses were conducted. Independent predictors identified were used to build the predictive model. Machine learning models were evaluated using multiple performance metrics, including AUC, accuracy, sensitivity, and specificity, across the internal validation set and the temporal validation cohort. Results A total of 299 HCC patients were included (196 internal cohort, 103 temporal validation cohort), with objective response rates at 1, 3, and 6 months of 56.1%, 51.2%, and 45.9% for the internal cohort and 57.3%, 52.4%, and 49.5% for the temporal validation cohort. Risk factor analysis identified independent predictors of treatment response at various time points, including age, BCLC stage, tumor distribution, and largest tumor diameter. LR showed the highest AUC across all time points, with values of 0.830, 0.840, and 0.854 in the internal validation set and 0.817, 0.832, and 0.839 in the temporal validation cohort at M1, M3, and M6. Subgroup analyses suggested generally consistent predictive performance of the LR model across selected treatment-related subgroups. Conclusion In the treatment of hepatocellular carcinoma with DEB-TACE, machine learning models demonstrated promising performance in predicting early treatment response, supporting the development of personalized treatment strategies in the future.","url":"https://doi.org/10.3389/fphar.2026.1677843","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fphar.2026.1677843","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.jacr.2026.08.015","name":"Radiology No-Show Calculator: A Social Determinants of Health-Enriched Machine Learning Prediction Model with Financial Analysis.","source":"europepmc","abstract":"Objective Outpatient radiology appointment no-shows delay timely diagnosis and exacerbate healthcare disparities, while also resulting in a significant financial and operational burden on healthcare systems. Machine learning (ML) models offer a promising tool for identifying at-risk patients before no-shows occur. We sought to develop an ML model enriched with social determinants of health (SDOH) factors to predict outpatient radiology no-shows and evaluate the health equity and financial implications of targeted interventions. Methods In this IRB-approved retrospective case-control study at a large academic health system, we analyzed 32,776 outpatient radiology appointments from 9,998 patients (5,000 with at least one no-show during the study period, 4,998 without) between January 2023 and December 2024. The 1:1 case-control design was chosen to enrich the outcome for stable model training (population no-show prevalence 3.9%). A Random Forest model with 16 predictors, including the Area Deprivation Index (ADI), was developed and internally validated. Predicted probabilities were recalibrated to the population prevalence to form a deployable risk calculator. Generalized estimating equations (GEE) logistic regression quantified adjusted associations between SDOH factors and no-show risk. Results The Random Forest achieved an area under the receiver operating characteristic (AUROC) of 0.760. No-show rates demonstrated a clear Area Deprivation Index (ADI) gradient, ranging from 15.0% in the least deprived neighborhoods to 31.8% in the most deprived. The top 10% of highest-risk appointments captured 25.7% of all no-shows, with a number needed to intervene (NNI) of 1.8. The estimated yearly cost burden of missed appointments was $57.7 million. Conclusion The radiology no-show calculator is a prediction model that uses SDOH factors to estimate the likelihood of missed appointments. The calculator demonstrates moderate discrimination for predicting no-shows and enables prospective targeted outreach. Future deployment of this model holds promise for improving health outcomes among socioeconomically vulnerable patients while lowering institutional cost burden.","url":"https://doi.org/10.1016/j.jacr.2026.08.015","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jacr.2026.08.015","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2147/jir.s613742","name":"A Clinical and Biomarker-Based Model for Predicting in-Hospital Mortality in Patients Undergoing Extracorporeal Membrane Oxygenation: A Machine Learning-Based Nomogram Study.","source":"europepmc","abstract":"Purpose A nomogram was constructed for predicting in-hospital mortality in patients receiving extracorporeal membrane oxygenation (ECMO) using clinical indicators, to provide a reference for clinical risk stratification. Patients and methods A retrospective cohort of 122 ECMO patients (81 survivors, 41 non-survivors) from Taizhou Hospital of Zhejiang Province (January 2022-December 2024) was used for model development, with 22 patients from the MIMIC-IV database serving as the external validation cohort. A two-step Boruta-LASSO feature selection strategy was applied to identify key predictors, followed by construction of five machine learning models; the best-performing model was used to develop a nomogram, validated internally and externally via ROC analysis, calibration curves, decision curve analysis, and SHAP interpretability analysis. Results Four key predictors were identified: malignant tumor, Lactic Acid, monocyte-to-white blood cell ratio (MWR), and activated partial thromboplastin time-to-albumin ratio (AAR). The logistic regression model exhibited the best and most stable predictive performance, with a cross-validated AUC of 0.765 (95% CI: 0.667-0.863) in the primary cohort. External validation in the MIMIC-IV cohort (n=22) yielded an AUC of 0.838 (95% CI: 0.655-1.000), suggesting preliminary external validation with acceptable generalizability. Conclusion Malignant tumor, elevated Lactic Acid, decreased MWR, and elevated AAR are key predictive factors for in-hospital mortality in ECMO patients. The nomogram model demonstrated satisfactory discriminatory ability, acceptable calibration, and potential clinical utility, serving as a valuable exploratory tool for bedside risk stratification in ECMO patients. Further validation in larger, multicenter, prospective cohorts is warranted before clinical implementation.","url":"https://doi.org/10.2147/jir.s613742","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/jir.s613742","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1177/20420986261467891","name":"Development and prospective validation of a machine learning model for risk stratification of drug-induced liver injury using real-world clinical data.","source":"europepmc","abstract":"Background Drug-induced liver injury (DILI) is difficult to diagnose and manage in routine care because it lacks pathognomonic biomarkers and is often recognized only after clinically meaningful injury has occurred. Existing computational approaches are largely drug-centric and do not routinely incorporate patient-level clinical data available in electronic health records (EHRs). Objectives To develop and temporally validate a machine learning model for episode-level risk stratification of Roussel Uclaf Causality Assessment Method (RUCAM)-defined DILI using routinely available baseline clinical data. Design This was an observational cohort study conducted at Hai Phong International Hospital using linked EHR, laboratory, and pharmacy data. The final labeled cohort was partitioned chronologically at the patient level into a retrospective development cohort (2019-2023) and a temporally subsequent prospective validation cohort (2024-2025). Methods Eligible drug-exposure episodes with complete baseline liver biochemistry and key exposure covariates were included. Analysis-ready episodes were monitored for biochemical liver injury triggers, and trigger-positive episodes underwent clinical review and RUCAM adjudication. DILI was defined as RUCAM ⩾6. Predictors were limited to baseline demographics, comorbidities, laboratory values, drug-exposure features, and FDA DILIrank 2.0 metadata. Candidate models included logistic regression, elastic-net logistic regression, random forest, ExtraTrees, XGBoost, and LightGBM. Results Among 5095 eligible episodes from 3579 patients, 2786 episodes from 2712 patients were analysis-ready after exclusions. The final labeled cohort comprised 274 DILI-positive and 2512 non-DILI episodes. The prospective validation cohort included 828 episodes, of which 108 (13.0%) were DILI-positive. Tree-based ensemble models outperformed regression-based models. Logistic regression achieved an area under the receiver operating characteristic curve (AUROC) of 0.777 and an area under the precision-recall curve (PR-AUC) of 0.349, whereas the final LightGBM model achieved an AUROC of 0.965 (95% CI 0.942-0.983), a PR-AUC of 0.903 (95% CI 0.856-0.943), and a Brier score of 0.034. Conclusion A prospectively validated machine learning model using routinely collected baseline clinical data showed excellent performance for DILI risk stratification and may strengthen hospital pharmacovigilance.","url":"https://doi.org/10.1177/20420986261467891","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1177/20420986261467891","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1016/j.jad.2026.122054","name":"How machine learning applied on fMRI could improve the prognosis of the post-traumatic stress disorder: A systematic review.","source":"europepmc","abstract":"Background Post-traumatic stress disorder (PTSD) is a stressor-related disorder that affects a significant proportion of the population worldwide. Despite the neurological nature of this disorder, its functional biomarkers, due to mixed results remain far to be understood. Therefore, the present review study aims to explore functional brain differences between individuals with PTSD and healthy controls with (TEHC) and without (TUHC) trauma exposure, using a machine learning approach. Method A systematic search was performed in PubMed, Embase, and Scopus to identify relevant studies published before 1st of November 2024. A total of 19 studies were included in our data extraction process. The paper has been registered on OSF (https://archive.org/details/osf-registrations-n6bc7-v1)/(https://osf.io/n6bc7/). Results Default mode and salience networks have been determinant in classifying PTSD participants from both TEHC and TUHC. However, middle frontal gyrus as well as sensory motor area were only involved in classifying PTSD participants from TEHC. Finally, the results have also shown that the association between left amygdala and hippocampus is determinant in identification of PTSD severity. Limitations The analysis of the available literature was restricted due to the non-homogeneous characteristics of studies - both in terms of methodology and clinical aspects - which restricted our ability to draw comprehensive conclusions. In addition, some studies used overlapping samples therefore limiting the generalizability of the results. Conclusion The identified networks play a crucial role in distinguishing PTSD participants from healthy participants. These findings could aid in developing more accurate diagnostic tools and may even help predict individuals at higher risk of developing PTSD following exposure to trauma events.","url":"https://doi.org/10.1016/j.jad.2026.122054","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.jad.2026.122054","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s12935-025-03912-w","name":"Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. Objective To explore the ability of machine learning to predict cancer postoperative complications and early recurrence. Methods From the creation of the database until October 4, 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the China National Knowledge Infrastructure (CNKI). The pooled sensitivity, specificity, Fagan plot analysis, and area under the curve (AUC) were used to assess the overall test performance of machine learning. In addition, meta-regression analysis was used to explore the sources of heterogeneity further. Furthermore, Deeks' funnel plot asymmetry test was used to assess publication bias. Results Ultimately, 31 publications were identified and incorporated into this meta-analysis. In the subgroup of postoperative complications, the combined sensitivity, specificity, and AUC values of all studies were 0.75 (95% CI, 0.65-0.83), 0.78 (95% CI, 0.65-0.87), and 0.83 (95% CI, 0.79-0.86), respectively. Moreover, the combined sensitivity, specificity, and AUC values of proposed studies (studies that proposed the best predictive model) were 0.85 (95% CI, 0.71-0.93), 0.76 (95% CI, 0.39-0.94), and 0.88 (95% CI, 0.85-0.91), respectively. In the subgroup of early recurrence, the combined sensitivity, specificity, and AUC values of all studies were 0.74 (95% CI, 0.68-0.80), 0.73 (95% CI, 0.67-0.77), and 0.80 (95% CI, 0.76-0.83), respectively. Furthermore, the combined sensitivity, specificity, and AUC values of proposed studies were 0.78 (95% CI, 0.70-0.85), 0.76 (95% CI, 0.70-0.82), and 0.84 (95% CI, 0.80-0.87), respectively. In addition, Deeks' Funnel Plot, p-value > 0.05, indicating no publication bias. Furthermore, meta-regression analysis showed that sample size and machine learning may be the main influencing factors. Conclusion Machine learning can accurately predict cancer postoperative complications and early recurrence. However, its accuracy is influenced by multiple factors, including the type of machine learning model, tumor type, sample size, year of publication, and country of publication. Therefore, more studies with larger sample sizes and more standardized methodology are needed to improve the reliability of its prediction.","url":"https://doi.org/10.1186/s12935-025-03912-w","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12935-025-03912-w","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fpsyt.2026.1838321","name":"Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas.","source":"europepmc","abstract":"Objective To analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions. Methods This cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model. Results Among the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband's participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband's participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband's participation, sleep quality, and depression were the major contributors to model prediction. Conclusion Postpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother-infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.","url":"https://doi.org/10.3389/fpsyt.2026.1838321","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpsyt.2026.1838321","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1038/s41598-026-59795-y","name":"Machine learning-based prediction of E. coli infection in hospitalized patients using a no-code analytical framework.","source":"europepmc","abstract":"Hospital-acquired infections (HAIs) remain a major global concern, contributing significantly to increased morbidity, mortality, and healthcare costs. Among the causative pathogens, Escherichia coli (E. coli) is one of the most frequently isolated microorganisms, particularly in urinary tract infections (UTIs), bloodstream infections, and surgical site infections. Early and accurate prediction of E. coli infection in hospitalized patients remains a significant clinical challenge, yet it has the potential to substantially improve patient outcomes. In addition, identifying patient-related risk factors can support targeted infection control strategies. This study aims to evaluate a no-code machine learning (ML) approach for early prediction of E. coli infection and to identify associated risk factors. ML techniques provide a powerful alternative by enabling the analysis of high-dimensional and heterogeneous datasets, facilitating the discovery of hidden patterns and supporting individualized risk prediction. In this study, a total of 300 clinical samples was collected as a training dataset from hospitalized patients between July 2024 and February 2025 across multiple units of Zagazig University Hospital, Sharkia, Egypt. An independent internal validation dataset of 100 samples was collected during May 2026 from the same hospital, its purpose was to evaluate model generalizability on completely unseen data. Bacterial isolates were identified using standard biochemical methods. Data analysis was performed using the Orange visual programming platform, implementing a modular ML pipeline that integrates data preprocessing, feature handling, model training, and performance evaluation within a no-code environment. The Naive Bayes model, shows potential for predicting E. coli infection in hospitalized patients. The model is intended to predict E. coli infection at the time of specimen collection, before culture results are finalized, depending on clinical data. However, further validation in larger, multi-center prospective cohorts is needed before clinical implementation.","url":"https://doi.org/10.1038/s41598-026-59795-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-59795-y","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fped.2026.1838914","name":"Development and validation of a machine learning-based risk prediction model for PICC-associated bloodstream infections in preterm infants: a retrospective multicenter study.","source":"europepmc","abstract":"Objective To develop and validate a machine learning-based model for predicting the risk of peripherally inserted central catheter (PICC)-associated bloodstream infection (CRBSI) in preterm infants. Methods This retrospective multicenter study included 151 preterm infants with CRBSI and 302 matched controls from a tertiary hospital (2017-2024), randomly divided into a training set ( n = 317) and an internal validation set ( n = 136). An additional 96 cases from four tertiary hospitals were used for external validation. Eight significant predictors identified by univariate analysis were used to construct five models: Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall, and F1 score. The Shapley Additive Explanations (SHAP) was applied for model interpretation. Results Eight variables were identified as key predictors, including puncture duration, catheterized vein, duration and frequency of mechanical ventilation, catheter dwell time, fetal distress, hypoalbuminemia, and antibiotic use within 24 h after birth. Infection rates increased markedly with prolonged puncture duration (6% for 60 min) and catheter dwell time (21% for 21 days). Femoral vein catheterization showed the highest infection rate (82%). In the internal validation set, the AUCs of LR, XGBoost, DT, SVM, and RF were 0.89, 0.86, 0.87, 0.88, and 0.95, respectively; in the external validation set, they were 0.89, 0.88, 0.87, 0.93, and 0.96. The RF model achieved the highest accuracy in both internal (0.91) and external (0.90) validation sets, while SVM showed the highest external accuracy (0.92). Precision ranged from 0.79 to 0.89, recall from 0.27 to 0.31, and F1 scores from 0.42 to 0.45. SHAP analysis showed that puncture duration was the most important predictor, followed by catheterized vein and mechanical ventilation-related variables. Conclusions The RF model demonstrated superior performance in predicting CRBSI risk in preterm infants with PICC placement. This model may facilitate early identification of high-risk patients and support clinical decision-making.","url":"https://doi.org/10.3389/fped.2026.1838914","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1838914","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fpubh.2026.1873714","name":"Development and temporal validation of a prediction model for cognitive impairment in older adults with hearing loss based on the population health risk management framework.","source":"europepmc","abstract":"Background Older adults with hearing loss are at increased risk of cognitive impairment, yet tailored risk stratification tools remain limited. Methods In this cross-sectional study, 524 adults aged ≥60 years with hearing loss were enrolled between June 2023 and September 2024. Participants were divided by enrollment period into a development cohort (June 2023 to May 2024, n = 367) and a temporally independent validation cohort (June 2024 to September 2024, n = 157). Cognitive function was assessed using the Montreal Cognitive Assessment, with a score Results The prevalence of cognitive impairment was 40.8%. LASSO identified five key predictors: age, pure-tone average, depression, hearing-aid use, and social activities. Multivariable analysis showed that depression, older age, and higher pure-tone average were associated with a higher likelihood of cognitive impairment, whereas hearing-aid use and participation in social activities were protective factors. Among the six models, Random Forest showed the best overall performance in the validation cohort. After hyperparameter tuning, the optimized Random Forest model achieved an AUC of 0.952 in the training cohort and 0.871 in the validation cohort. In the validation cohort, the F1 score, sensitivity, Youden index, and NPV increased to 0.737, 0.779, 0.583, and 0.863, respectively. SHAP analysis indicated that pure-tone average, age, and social activities were the most influential predictors in the optimized model. Conclusion The optimized five-variable Random Forest model demonstrated good predictive performance for cognitive impairment in older adults with hearing loss. This low-cost and interpretable tool may support early screening, risk stratification, and targeted intervention in clinical and community settings.","url":"https://doi.org/10.3389/fpubh.2026.1873714","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1873714","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1186/s40635-026-00934-0","name":"Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.","source":"europepmc","abstract":"Background Optimal timing of extubation in mechanically ventilated patients remains a major challenge in intensive care. Machine learning (ML) models have been increasingly proposed to support clinical decision-making, yet their predictive performance and readiness for clinical application in extubation outcomes remain uncertain. This study aimed to evaluate the predictive performance and clinical readiness of ML models for predicting extubation success. Methods A systematic search was performed in PubMed, Embase and CENTRAL up to November 5, 2024. Studies including mechanically ventilated critically ill adult patients undergoing planned extubation were eligible. The index test was any ML model predicting extubation outcome. Models reporting an area under the receiver operating characteristic curve (AUC) were meta-analyzed, and subgroup analyses were conducted. Risk of bias was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) tool, adapted for ML-based prediction studies. Results Twenty-six studies were included in the systematic review, and 47 ML models from 14 studies (n = 34,322 patients) were eligible for meta-analysis. Reported AUC values across predictive models ranged from 0.59 to 0.98. Pooled AUCs by model type were 0.88 (95% CI: 0.78-0.94) for classical ML models and 0.85 (95% CI: 0.68-0.94) for deep learning models. The best-performing ML models of each study had a pooled AUC of 0.90 (95% CI: 0.82-0.95). Pooled estimates were derived from internally validated models, as only two studies reported externally validated AUCs with confidence intervals. Study heterogeneity was high, driven by substantial differences in predictor selection and model design. Conclusion Machine learning models demonstrate acceptable discriminatory performance for predicting extubation success. However, limited external and prospective validation, substantial heterogeneity, and inconsistent reporting currently preclude their routine clinical implementation.","url":"https://doi.org/10.1186/s40635-026-00934-0","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s40635-026-00934-0","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fmed.2026.1810388","name":"Machine learning prediction of post-traumatic osteoarthritis based on three-dimensional printing-derived joint congruence biomechanics in ankle fractures.","source":"europepmc","abstract":"Objective Post-traumatic osteoarthritis (PTOA) develops in 20-40% of patients following ankle fracture fixation despite anatomic reduction. This study aimed to develop a machine learning model incorporating three-dimensional (3D) printing-derived joint congruence biomechanics for individualized PTOA prediction. Methods This retrospective cohort study included 263 patients (January 2020-January 2024) who underwent preoperative 3D-printed model-assisted surgical planning with minimum 24-month follow-up. Finite element analysis quantified joint congruence parameters including peak contact pressure, pressure inhomogeneity index, and contact center offset. Four machine learning algorithms were developed using training data ( n = 158, 60%) and validated temporally ( n = 105, 40%). The primary outcome was PTOA defined by Kellgren-Lawrence grade ≥ II with clinical symptoms. Results PTOA developed in 102 patients (38.8%) during mean 36.4-month follow-up. Patients with PTOA demonstrated significantly higher peak contact pressure (15.2 ± 3.8 vs. 9.6 ± 2.4 MPa, P P P = 0.008). Among 198 patients with anatomic reduction, the full model maintained superior discrimination (AUC 0.81 vs. 0.68, P = 0.01), identifying a high-risk subgroup (peak pressure > 13 MPa) with 58.3% PTOA incidence versus 18.2% in low-pressure patients ( P Conclusion Joint congruence parameters from 3D printing-based finite element analysis significantly improve machine learning prediction of PTOA following ankle fracture, identifying high-risk patients even after anatomic reduction.","url":"https://doi.org/10.3389/fmed.2026.1810388","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1810388","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/jbio.70317","name":"Utilizing Serum Fluorescence Spectra and Machine Learning Algorithms for Efficient Diagnosis of Sheep Brucellosis.","source":"europepmc","abstract":"In sheep, the infectious disease brucellosis is caused by Brucella melitensis. Traditional serological techniques for detecting Brucella in sheep are slow and not very accurate, necessitating a faster and more precise screening method. This study seeks to assess the potential for diagnosing brucellosis seropositive sheep through the application of serum fluorescence spectroscopy in conjunction with principal component analysis-linear discriminant analysis (PCA-LDA), support vector machine with linear (SVM-linear), support vector machine with radial basis function (SVM-RBF), k-nearest neighbors (KNN), and decision tree (DT) algorithms. The study revealed differences at 470, 515, 560, 670, and 710 nm through the analysis of serum fluorescence spectra from sheep with and without brucellosis. Among them, the diagnostic effect of the PCA-LDA algorithm is the best (accuracy 91.0% ± 6.1%). In summary, the combination of serum fluorescence spectroscopy and the PCA-LDA algorithm holds considerable potential for detecting brucellosis seropositive sheep.","url":"https://doi.org/10.1002/jbio.70317","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/jbio.70317","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.1002/pchj.70100","name":"Mental Health Risk Detection From Social Media Text Data: A Scoping Review of the Machine Learning Research Landscape.","source":"europepmc","abstract":"Machine learning approaches have been increasingly applied to social media text data for mental health risk detection. However, existing studies vary widely in target outcomes, data sources, labeling strategies, and evaluation practices, and a structured overview of recent research remains limited. This scoping review aims to map the recent research landscape of machine learning-based mental health risk detection using social media text data. Following PRISMA ScR guidelines, peer reviewed journal articles published between January 2021 and January 2026 were retrieved from PubMed, Web of Science, and IEEE Xplore. Studies applying machine learning or deep learning methods to social media text data for mental health risk detection were included and synthesized descriptively. A total of 136 studies were identified. Most focused on depression, anxiety, and suicide or self-harm related risks. Mental health risk was predominantly operationalized through proxy indicators derived from user-generated content, with limited use of survey-linked or clinically anchored labels. Traditional machine learning, deep learning, and Transformer-based models coexisted, alongside substantial heterogeneity in validation strategies and performance metrics. Current research primarily targets proxy-based mental health risk signals rather than clinical diagnoses. This review clarifies prevailing research emphases and methodological practices, and supports the use of social media-based approaches for population-level monitoring and early risk identification.","url":"https://doi.org/10.1002/pchj.70100","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/pchj.70100","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fmed.2026.1892993","name":"Development and internal validation of an interpretable machine learning model using first-24-h postpartum nursing variables to predict delayed lactogenesis II after cesarean delivery.","source":"europepmc","abstract":"Background Delayed lactogenesis II is a common early postpartum breastfeeding problem after cesarean delivery and may interfere with the establishment of exclusive breastfeeding. Although demographic and obstetric risk factors have been widely studied, the predictive value of routinely recorded early postpartum nursing process variables remains insufficiently explored. This study aimed to develop and internally validate a dynamic 24-h postpartum landmark interpretable machine learning model using clinical, perioperative, neonatal, and first-24-h nursing process variables to predict delayed lactogenesis II by 72 h after cesarean delivery. Methods This single-center retrospective cohort study included women who underwent cesarean delivery at a tertiary hospital between January 2021 and December 2024 and had documented intention to breastfeed. Delayed lactogenesis II was defined as the absence of standardized evidence of lactogenesis onset by 72 h postpartum, based on maternal responses and supporting indicators documented by trained nurses at predefined postpartum assessments. The prediction landmark was set at 24 h postpartum; therefore, only variables available before delivery, during cesarean delivery, or within the first 24 h postpartum were used for model development. Because women who had already experienced lactogenesis II by 24 h were no longer at risk at the prediction landmark, an additional sensitivity analysis was restricted to women without lactogenesis II onset by the 24-h assessment. Candidate predictors were extracted from electronic medical records, perioperative records, neonatal records, nursing assessment documentation, and breastfeeding assessment forms. A complete-case approach was used; 32 women (2.2% of those initially screened) were excluded because of incomplete key predictor data, and no statistical imputation was performed. The final cohort was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. Five models were developed and compared: logistic regression, random forest, support vector machine (SVM), XGBoost, and LightGBM. Hyperparameters were selected within the training cohort using repeated stratified five-fold cross-validation, and the internal validation cohort remained untouched until final model evaluation. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F 1 score. Calibration curve analysis, decision curve analysis, and SHAP-based interpretation were performed for the final model. Results A total of 1,286 women were included, of whom 326 developed delayed lactogenesis II, corresponding to an event rate of 25.3%. Among the included women, 71 (5.5%) had already experienced lactogenesis II by the 24-h assessment and had been retained in the primary cohort. The training and validation cohorts included 900 and 386 women, respectively. In the validation cohort, all five models showed acceptable discrimination, with AUCs ranging from 0.816 to 0.836. The support vector machine achieved the highest validation AUC of 0.836, followed by LightGBM, XGBoost, random forest, and logistic regression. XGBoost showed a training AUC of 0.872 and a validation AUC of 0.823, with validation accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F 1 score of 0.756, 0.694, 0.778, 0.515, 0.882, and 0.591, respectively. Although SVM achieved the numerically highest validation AUC, its discrimination did not differ significantly from that of XGBoost (AUC difference, 0.013; 95% CI , -0.019 to 0.045; DeLong P = 0.421). XGBoost was retained as the final interpretable model because it showed a smaller training-to-validation AUC difference and enabled direct SHAP-based characterization of predictor contributions. In the internal validation cohort, the XGBoost model had a calibration intercept of 0.058, a calibration slope of 1.684 (95% CI , 1.304-2.064), and a Brier score of 0.143. Although the intercept indicated limited systematic miscalibration, the calibration slope and its confidence interval were entirely above the ideal value of 1, indicating non-ideal calibration and under-dispersion of predicted probabilities, with predictions compressed toward the mean. Decision curve analysis indicated greater net benefit than the treat-all and treat-none strategies across clinically relevant threshold probabilities. SHAP analysis identified first breastfeeding time, breastfeeding frequency within 24 h, first skin-to-skin contact time, intraoperative blood loss, and postoperative pain score at 24 h as the most influential predictors. After excluding the 71 women with lactogenesis II onset by 24 h, the landmark-restricted sensitivity cohort comprised 1,215 women. In this restricted population, the XGBoost model achieved a validation AUC of 0.817, and the five leading predictors remained unchanged. Conclusions A dynamic 24-h postpartum landmark interpretable machine learning model incorporating clinical, perioperative, neonatal, and first-24-h nursing process variables showed acceptable discrimination for predicting delayed lactogenesis II by 72 h after cesarean delivery, but its calibration was non-ideal. Although SVM achieved the numerically highest validation AUC, its discrimination did not differ significantly from that of XGBoost. XGBoost was retained as the final interpretable model on the basis of its overall balance of validation discrimination, apparent stability, and direct SHAP-based interpretability. First breastfeeding time, breastfeeding frequency, skin-to-skin contact, intraoperative blood loss, and postoperative pain score may serve as early postpartum risk markers to help identify women who could benefit from intensified breastfeeding support and individualized nursing care. Because the model has undergone only single-center internal validation, external validation and recalibration are required before it can be used to estimate individual risk or support individual-level clinical decision-making.","url":"https://doi.org/10.3389/fmed.2026.1892993","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1892993","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fonc.2026.1818266","name":"A combined model based on clinical, radiomics, and deep transfer learning features for differentiating endometrial hyperplasia with polyps from endometrial cancer.","source":"europepmc","abstract":"Objective This study aimed to develop an ultrasound-based deep learning radiomics nomogram for differentiating endometrial hyperplasia with polyps from endometrial cancer in endometrial lesions and to explore its clinical diagnostic efficacy. Methods We retrospectively collected clinical data from patients who underwent transvaginal ultrasound examinations in Suzhou Ninth People's Hospital and Jiangsu Shengze Hospital from January 2020 to October 2024. Various machine learning models were developed using clinical data, handcrafted radiomics features, and deep learning features, with the highest AUC model selected as optimal. Univariate and stepwise multivariate analyses identified significant clinical features, which were combined with deep learning radiomics to create a Combined Model. The model's predictive performance was evaluated using ROC, calibration, and decision curves, along with a deep learning radiomics nomogram. Results This retrospective study of 340 patients from two hospitals included 149 endometrial cancer cases. Among them, 305 patients were from Suzhou Ninth People's Hospital and 35 were from Jiangsu Shengze Hospital. The 305 patients from Suzhou Ninth People's Hospital were divided into a training cohort and an internal validation cohort in a 7:3 ratio, comprising 213 and 92 patients, respectively. The 35 patients from Shengze Hospital served as the external testing cohort. Multivariate analysis confirmed four independent predictors: Testosterone, Estradiol, Abnormal Bleeding, and Menopausal Status, used to build the final Clinical Model. The effectiveness of the models was assessed by comparing the area under the receiver operating characteristic curve (AUC) based on the internal validation cohort. The results showed that the Combined Model achieved an AUC of 0.991 (95% confidence interval [CI]: 0.980 - 1.000) on the training cohort, 0.94 (95% CI: 0.892 - 0.987) on the internal validation cohort, and 0.955 (95% CI: 0.882 - 1.000) on the external testing cohort. Calibration curves and DeLong test confirmed the Combined Model's superior accuracy and performance over single-modality models, with DCA showing highest net benefit, and nomogram enabling individualized risk stratification. Conclusion The ultrasound-based combined model has high clinical diagnostic value for diagnosing endometrial cancer and simple endometrial hyperplasia with polyps.","url":"https://doi.org/10.3389/fonc.2026.1818266","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1818266","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.2196/97672","name":"Predicting Critical Outcomes in Suspected Cardiopulmonary Emergencies Using Dispatch Narratives: Temporal Validation Study.","source":"europepmc","abstract":"Background Early risk stratification in emergency medical services (EMS) is essential for patients presenting with acute cardiopulmonary symptoms, yet prehospital decision-making at the dispatch stage is often based on limited structured information. Free-text dispatch narratives may contain additional clinical signals, but their role in early risk assessment remains insufficiently characterized. Objective This study aims to develop and temporally validate a natural language processing-assisted machine learning framework for early risk stratification using free-text EMS dispatch narratives and to evaluate its incremental value beyond conventional structured dispatch information. Methods We conducted a population-based retrospective cohort study using EMS dispatch records from Nanning, China, between 2021 and 2025. Adult patients with suspected cardiopulmonary symptoms were identified based on predefined complaint keywords. After excluding nonmedical and incomplete records, 38,523 cases with available free-text narratives were included. To simulate real-world deployment, data from 2021 to 2024 (n=28,332) were used for model development, and 2025 data (n=10,191) served as an independent temporal test cohort. Dispatch narratives were processed using a natural language processing pipeline based on character-level n-grams and combined with structured variables (age, sex, call time) in a multimodal machine learning framework. The primary outcome was a composite prehospital critical outcome comprising death, clinical deterioration, or lack of response to initial treatment. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, calibration, and decision curve analysis. Results Among the 38,523 included patients, 12,476 (32.4%) experienced the primary composite outcome. The median age was 68.0 (IQR 54.0-79.0) years, and 23,019 (59.8%) patients were male. In the temporally independent 2025 cohort, an expanded structured baseline model achieved an AUROC of 0.681 (95% CI 0.669-0.693). Incorporation of narrative features improved performance (AUROC 0.803, 95% CI 0.792-0.814), with marginal additional gain from multimodal integration (AUROC 0.808, 95% CI 0.798-0.818). The multimodal model achieved an area under the precision-recall curve of 0.630 (95% CI 0.611-0.651), substantially exceeding the no-skill baseline defined by the outcome prevalence in the temporal test cohort (2464/10,191, 24.2%). Model performance remained consistent across age and sex subgroups, and calibration was acceptable (Brier score 0.1878). In a risk enrichment analysis, the top 10% (n=1019) of predicted high-risk cases accounted for 32.1% (n=791) of all critical outcomes, representing a 3.2-fold enrichment. Decision curve analysis indicated a higher net benefit compared with treat-all and treat-none strategies across a range of threshold probabilities. Conclusions Free-text dispatch narratives contain clinically relevant information associated with early risk stratification in patients with suspected cardiopulmonary emergencies. Incorporating narrative-derived features into a structured modeling framework may complement existing EMS dispatch systems and support more informed decision-making prior to patient contact. Further external validation and prospective evaluation are warranted.","url":"https://doi.org/10.2196/97672","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2196/97672","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fneur.2026.1786279","name":"Explainable XGBoost model and nomogram for risk factor identification and risk prediction in cerebral small vessel disease: a machine learning-based retrospective cohort study.","source":"europepmc","abstract":"Background Cerebral small vessel disease (CSVD) is a common, clinically significant vascular disorder that frequently leads to cognitive impairment, dementia, and poor overall prognosis. Owing to its complex hemodynamic characteristics and multifactorial pathophysiology, early identification of individuals at high risk for CSVD remains a clinical challenge. This study aimed to develop and validate an interpretable machine learning (ML) model for predicting the occurrence of CSVD. Methods We retrospectively enrolled 1,640 adult patients treated at the Fifth Affiliated Hospital of Xinjiang Medical University between September 2019 and December 2024. Twenty-three candidate variables (demographics, vitals, biomarkers, comorbidities) were evaluated. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by stepwise backward elimination in multivariable logistic regression. Six supervised ML algorithms (DT, KNN, LR, LightGBM, XGBoost, SVM) were compared. Performance was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). The optimal model was interpreted using SHapley Additive exPlanations (SHAP), and a bedside clinical nomogram was constructed. Results Ten independent predictors were identified: blood glucose, history of hypertension, systolic blood pressure, age, triglycerides, history of stroke, cystatin C, C-reactive protein, homocysteine, and body mass index. Among all models, XGBoost demonstrated the best performance, with an AUC of 0.968 in the training cohort and 0.938 in the validation cohort. Calibration plots and DCA confirmed its clinical utility. The derived nomogram demonstrated strong prognostic discrimination ( p Conclusions We validated an interpretable XGBoost-based ML model that facilitates early risk stratification and targeted interventions for CSVD. Because the model relies only on routinely collected, low-cost variables and open-source software, it is readily transferable to resource-limited settings; future work will focus on prospective, multicentre external validation and on embedding the nomogram into electronic-health-record decision support.","url":"https://doi.org/10.3389/fneur.2026.1786279","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1786279","addedAt":"2026-09-01T01:48:04.375Z","updatedAt":"2026-09-01T01:48:04.375Z"},{"id":"doi:10.3389/fcell.2026.1863190","name":"Artificial intelligence in orthopedic regenerative medicine: from design to clinical translational pathways.","source":"europepmc","abstract":"Orthopedic regenerative medicine (ORM) addresses musculoskeletal disorders in which effective repair requires coordinated structural reconstruction, biological repair, mechanical adaptation, and functional recovery. These processes generate heterogeneous information across biomaterials, construct design, imaging, intraoperative execution, rehabilitation monitoring, and clinical follow-up. Artificial intelligence (AI) is increasingly relevant for organizing multimodal data and supporting decision-making across regenerative care. This review summarizes current applications of AI in ORM, focusing on regenerative design and fabrication, intraoperative guidance, postoperative monitoring, repair evaluation, and clinical translational pathways. In regenerative design, AI can assist the optimization of material composition, scaffold architecture, biofabrication parameters, and construct performance by linking design variables with biological and biomechanical outcomes. During intervention and recovery, AI-supported systems may improve defect-specific spatial matching, support longitudinal functional assessment, and help identify delayed or unfavorable repair trajectories through integrated analysis of imaging, wearable, and clinical data. The review also discusses translational challenges, including data heterogeneity, limited external validation, algorithmic bias, interpretability, regulatory requirements, and governance constraints. AI may help connect design, intervention, monitoring, and feedback within a continuous analytical workflow, but future progress will require robust datasets, prospective validation, clinically interpretable models, and implementation strategies aligned with regenerative practice.","url":"https://doi.org/10.3389/fcell.2026.1863190","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1863190","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fonc.2026.1900205","name":"A simple-to-use clinical nomogram to predict radiation esophagitis among esophageal cancer patients receiving radiotherapy: a retrospective cohort study.","source":"europepmc","abstract":"Background Moderate-to-severe acute radiation esophagitis (MSARE) is a treatment-limiting toxicity of thoracic radiotherapy (RT) in esophageal cancer (EC). Early identification of high-risk patients is essential to individualize supportive care and preserve treatment continuity. Purpose To develop and internally validate a parsimonious clinical nomogram for predicting MSARE, interpret it with SHapley Additive exPlanations (SHAP), and benchmark it against four machine learning (ML) algorithms. Methods We retrospectively reviewed 151 EC patients who completed thoracic RT between January 2022 and December 2024. Candidate predictors were screened with LASSO regression and entered into a multivariable logistic model, visualized as a nomogram. Discrimination, calibration, clinical utility, and learning behavior were assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and a learning curve. Logistic regression was benchmarked against XGBoost, LightGBM, AdaBoost, and K-Nearest Neighbors (KNN) under stratified 5-fold cross-validation. Results MSARE occurred in 81 of 151 patients (53.6%). Three independent predictors were retained: upper esophageal tumor (OR 7.32), hypertension (OR 2.35), and serum albumin (OR 0.85; all P P = 0.423) and positive net benefit on DCA. SHAP ranked albumin as the most influential predictor. Logistic regression yielded the highest validation AUC; DeLong tests showed no significant differences among models. Conclusions An interpretable three-variable nomogram demonstrates promising internal discrimination for MSARE, yields comparable performance to four ML algorithms, and supports individualized supportive-care planning by clinicians.","url":"https://doi.org/10.3389/fonc.2026.1900205","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1900205","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1038/s41598-026-52178-3","name":"Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study.","source":"europepmc","abstract":"Backgroud No universally accepted model exists for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux. Objective This study leveraged seven machine learning algorithms to build a short-term bleeding risk prediction platform for this population. Methods This retrospective real-world observational study included hospitalized patients who received low-molecular-weight heparin or fondaparinux between January 2022 and December 2023. After applying predefined criteria, the cohort were randomly split into training (70%) and validation (30%) sets. Predictors were identified using LASSO regression. Seven machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Neural Network (NN), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and CatBoost, were developed and evaluated. The best-performing model was implemented as an internal web-based bleeding risk prediction tool. Results Among 1,691 hospitalized patients receiving low-molecular-weight heparin or fondaparinux, 126 (7.5%) experienced bleeding events. The cohort was randomly split into training (n = 1,184) and validation (n = 507) sets. LASSO regression identified 12 predictors, including surgical site, pre-medication INR, hemoglobin, platelet count, renal function, body mass index (BMI), indication, and comorbidities. Seven machine learning models were developed and evaluated. In the validation cohort, CatBoost achieved the best discrimination (AUC = 0.659), followed by XGBoost (AUC = 0.651) and LR (AUC = 0.622). CatBoost also demonstrated the highest accuracy (86.0%) and F1 score (0.297), with strong specificity (89.2%) but limited sensitivity (42.9%). Although all models showed robust negative predictive performance (PR-AUC > 0.93), positive predictive capacity was modest (PR-AUC Conclusions CatBoost emerged as the optimal model among those tested for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux, demonstrating modest but superior discrimination, acceptable calibration, and favorable clinical utility. However, the model had limited ability to correctly identify patients who experienced bleeding, as indicated by low positive predictive performance. Given its high negative predictive value, it was better suited for ruling out rather than confirming bleeding risk. A web-based risk calculator based on CatBoost has been developed for internal use. Nevertheless, prospective multicenter validation is required before clinical implementation.","url":"https://doi.org/10.1038/s41598-026-52178-3","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1038/s41598-026-52178-3","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fdgth.2026.1752356","name":"Classifying voice disorders for machine learning: a pilot study using the USVAC-C2025 diagnostic framework.","source":"europepmc","abstract":"Introduction Machine learning for voice disorders relies heavily on accurate diagnostic classification, yet progress has been limited by inconsistent labelling and the absence of a reproducible framework suitable for clinical and computational use. This study aimed to develop and evaluate a multilayer classification system for voice disorder diagnosis tailored for machine learning applications, and to determine its inter- and intra-rater reliability among otolaryngologists and speech-language pathologists. Method We conducted a diagnostic reliability study of 45 adults with voice disorders who underwent comprehensive clinical assessment, including videostroboscopy, at a tertiary voice clinic in Sydney, Australia, between February 2018 and March 2024. A multidisciplinary team developed a five-level hierarchical classification framework through iterative consensus. Four blinded raters independently applied the framework to anonymised video and clinical datasets, with 15 cases randomly repeated for intra-rater analysis. Reliability was quantified using Fleiss κ statistics and intraclass correlation coefficients across all diagnostic levels. Results Intra-rater reliability was high (intraclass correlation coefficient range, 0.768-0.865), with comparable consistency across disciplines. Inter-rater reliability was strongest for identifying disordered vs. non-disordered voices ( κ = 0.812; 95% CI, 0.733-0.891) and major aetiological categories ( κ = 0.695; 95% CI, 0.611-0.779), supporting the utility of structured classification for foundational diagnostic decisions. Agreement declined with increasing diagnostic specificity, particularly for perceptually based conditions such as muscle tension disorders ( κ = 0.253; 95% CI, 0.172-0.334) and vocal fold paresis ( κ = 0.238; 95% CI, 0.155-0.321). Functional neurological voice disorders and structural lesions demonstrated the highest category-level agreement. Conclusion These findings show that a structured, multilayer framework improves diagnostic consistency where machine learning systems most rely on stable labels and highlights key areas of diagnostic ambiguity. The system provides a practical foundation for creating reliable annotated datasets and supports future development of machine learning tools for voice disorder classification and clinical decision support.","url":"https://doi.org/10.3389/fdgth.2026.1752356","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1752356","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3390/jcm15155862","name":"Interpretable Machine Learning Analysis of Factors Associated with Postoperative Hemoglobin Reduction After Total Knee Arthroplasty: A Standardized-Protocol Cohort Study in Non-Transfused Patients.","source":"europepmc","abstract":"Background: Postoperative hemoglobin (Hb) reduction reflects the physiologic extent of perioperative blood loss after total knee arthroplasty (TKA). Whereas previous studies have relied on transfusion as a binary endpoint, transfusion decisions are highly variable across institutions, obscuring the underlying hematologic trajectory. This study aimed to develop and interpret machine learning (ML) models to characterize and quantify the determinants of postoperative Hb reduction in a standardized cohort of non-transfused TKA patients. Methods: A retrospective cohort of 866 patients who underwent primary TKA under a standardized operative protocol-with identical cemented posterior-stabilized implants and uniform cementing technique-was analyzed (1 January 2014-31 March 2024). During the study period, a consistent 1 g intra-articular tranexamic acid (TXA) regimen administered through the drain was introduced and applied to a subset of patients, allowing TXA use to be modeled as a binary predictor. Four ML algorithms (Linear Regression, Random Forest, XGBoost, and Stacking Regressor) were trained using preoperative, demographic, and perioperative variables. Fivefold cross-validation assessed model performance, and SHapley Additive exPlanations (SHAP) values were used to identify influential predictors and enhance interpretability. Results: Across all ML models, preoperative Hb emerged as the strongest determinant of postoperative Hb reduction, followed by TXA use, body mass index (BMI), and platelet count. Ensemble models captured non-linear and interacting effects more effectively than linear regression. Test-set performance was modest (best R 2 = 0.330), consistent with the influence of unmeasured physiologic factors such as hidden blood loss, fluid dynamics, and inflammatory responses. Accordingly, the primary value of the framework lies in the exploratory and transparent assessment of determinant importance rather than in individual-level prediction. Conclusions: This study provides an interpretable, exploratory ML framework for identifying factors associated with percentage Hb reduction after TKA. Preoperative Hb was the dominant determinant, while TXA use and higher BMI were recurrently associated with smaller predicted percentage reductions. Given the modest test-set performance and the absence of external validation and clinical utility assessment, the models should not be interpreted as tools for individual-level prediction or clinical decision making.","url":"https://doi.org/10.3390/jcm15155862","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15155862","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fped.2026.1865399","name":"Leveraging machine learning models to forecast pediatric allergic rhinitis exacerbation risk based on environmental exposure data.","source":"europepmc","abstract":"Objective Acute exacerbations of pediatric allergic rhinitis (AR) are difficult to anticipate, and individualized risk tools integrating clinical and environmental data are lacking. We developed an interpretable machine-learning model to predict 90-day AR exacerbation risk in children and conducted a preliminary transportability assessment in an independent cohort, following TRIPOD + AI guidelines. Methods A development cohort ( n = 2,000) was assembled by linking NHANES (2005-2006 and 2011-2018 cycles) with EPA Air Quality System and NOAA meteorological data; an independent external cohort ( n = 50) was recruited at Wuhan Children's Hospital (2023-2024). Clinical predictors [Total Nasal Symptom Score (TNSS), total IgE, eosinophils, comorbidities] and environmental exposures (same-day and 0-7-day-lag PM 2.5 , PM 10 , O 3 , NO 2 , temperature, humidity) were analyzed. L2-regularized logistic regression (LR) was prespecified as the primary model and benchmarked against random forest, XGBoost, LightGBM, and SVM using nested 5-fold cross-validation and multiple imputation (m = 5). Discrimination, calibration, decision-curve analysis, and SHAP interpretability were evaluated. Results Exacerbation occurred in 34.4% (687/2,000) of the development cohort and 36.0% (18/50) of the external cohort. The primary LR model achieved an internal AUC of 0.81 (95% CI: 0.74-0.88) and an external AUC of 0.71 (95% CI: 0.50-0.92); the wide confidence interval reflects the limited precision of the small validation sample. Baseline TNSS, same-day PM 2.5 , and total IgE were the most influential predictors across feature-selection and SHAP analyses. Children exposed to PM 2.5 > 75 μg/m 3 had approximately five-fold higher exacerbation odds than those exposed to 3 (OR = 5.08, 95% CI: 3.45-7.47). A sensitivity model excluding TNSS retained moderate discrimination (external AUC 0.66, 95% CI: 0.49-0.83), supporting the independent contribution of environmental and immunologic factors. Decision-curve analysis showed positive net benefit across threshold probabilities of 0.10-0.35. Conclusion An interpretable LR model integrating baseline symptom burden with ambient PM 2.5 exposure demonstrated promising predictive performance for pediatric AR exacerbation. However, the small external cohort ( n = 50) yielded wide confidence intervals, and findings should be regarded as preliminary transportability evidence rather than definitive external validity. Larger multicenter prospective studies with site-specific recalibration are required before clinical implementation.","url":"https://doi.org/10.3389/fped.2026.1865399","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fped.2026.1865399","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1080/03007995.2026.2719084","name":"Optimization of hospitalization duration and cost structure for chronic schizophrenia patients in psychiatric hospitals.","source":"europepmc","abstract":"Objective Chronic schizophrenia patients in psychiatric hospitals often have prolonged stays, high insurance resource consumption, and low efficiency. Studies quantifying the joint optimization of payment and operational efficiency are lacking, complicating the trade-off between cost control and relapse prevention. This study aimed to identify factors associated with hospitalization duration and costs using real-world data and machine learning. Methods A retrospective study was conducted on inpatients with chronic schizophrenia admitted to Huai'an No.3 People's Hospital from 2022 to 2024. Data on demographics, clinical features, comorbidities, medications, and insurance costs were collected. Factors were screened by descriptive, univariate, and Gamma stepwise regression. Machine learning models (Lasso, Random Forest, XGBoost) were built to predict length of stay, with SHAP used for feature interpretation. Results A total of 3607 inpatients with chronic schizophrenia were enrolled. Univariate analysis revealed 29 significant variables; Gamma regression identified 18 independent factors. The rehabilitation treatment cost ratio was the strongest risk factor, and the examination cost ratio the strongest protective factor. The Random Forest model performed best (cross-validation R 2 =0.7143, test R 2 =0.7318). SHAP analysis showed total hospitalization cost and rehabilitation treatment cost ratio as core predictors. Conclusion This study identified key factors influencing hospitalization duration and costs in chronic schizophrenia, and developed an accurate and stable model that can support lean management in psychiatric hospitals.","url":"https://doi.org/10.1080/03007995.2026.2719084","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1080/03007995.2026.2719084","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21037/tp-2026-0485","name":"Predictive value of bronchoalveolar lavage fluid leukotriene C4 for airway hyperresponsiveness in children with &lt;i&gt;Mycoplasma pneumoniae&lt;/i&gt; pneumonia: a machine learning-based study.","source":"europepmc","abstract":"Background Airway hyperresponsiveness (AHR) is a frequent sequela after acute Mycoplasma pneumoniae pneumonia (MPP) in children. This study assessed the association between bronchoalveolar lavage fluid (BALF) leukotriene C4 (LTC4) and MPP, evaluated its predictive value for AHR, and developed an early-identification machine-learning model. Methods We retrospectively studied 158 children with MPP admitted between January 2023 and December 2024; 20 children undergoing bronchoscopy for airway foreign bodies served as controls. BALF LTC4 was measured by enzyme-linked immunosorbent assay (ELISA). AHR was assessed in 87 patients at a 2-week follow-up (positivity 39.1%) using a composite of clinical and spirometric criteria rather than bronchial provocation testing. Features were selected by least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination (RFE). Nine machine-learning models, including a support vector classifier (SVC), were compared, and predictions were interpreted with SHapley Additive exPlanations (SHAP). Results In the matched cohort, BALF LTC4 was higher in MPP than in age- and sex-matched controls (46.14 vs. 10.46 pg/mL, P vs. 39.92 pg/mL, P Conclusions In this retrospective single-center cohort, BALF LTC4 was associated with post-MPP AHR in children and showed greater predictive value than systemic inflammatory markers. A parsimonious SVC based on BALF LTC4 and serum LDH showed encouraging performance, suggesting potential utility for early risk stratification. However, the 55% follow-up rate and selection toward clinically complex patients may have overestimated performance; prospective multicenter validation with more complete follow-up is warranted before clinical implementation.","url":"https://doi.org/10.21037/tp-2026-0485","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/tp-2026-0485","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fonc.2026.1832546","name":"Systemic inflammatory biomarkers (NLR, SII, PNI and FPR) combined with CEA for predicting advanced colorectal neoplasms: development and temporal validation of a machine learning model.","source":"europepmc","abstract":"Background Chronic systemic inflammation is closely associated with the initiation and development of colorectal tumors. Biomarkers reflecting inflammatory status may help detect advanced colorectal neoplasms (ACRN), which include advanced adenoma and colorectal cancer. Nevertheless, the diagnostic ability of combined inflammatory and nutritional indicators for ACRN has not been fully clarified. This study evaluated whether several systemic inflammation markers-NLR, SII, PNI, and FPR-together with CEA, can improve the identification of ACRN. Methods A retrospective analysis was conducted in individuals who received colonoscopy from December 2016 to December 2024. Eligible subjects were randomly assigned to a training set and an internal testing set. Patients enrolled between January 2025 and January 2026 were used as a temporal validation cohort. Correlation analysis together with restricted cubic spline (RCS) modeling was applied to assess the associations between inflammatory markers and ACRN risk. Machine learning approaches were then used to construct prediction models based on NLR, SII, PNI, FPR, and CEA. Model performance was assessed by discrimination ability, calibration, and clinical usefulness. An online calculator was also established to support individualized risk estimation. Results A total of 1330 individuals were analyzed, including 252(18.95%) cases diagnosed with ACRN. Higher values of NLR, SII, FPR, and CEA were linked to a greater probability of ACRN, while PNI showed a negative relationship. These indicators also displayed clear changes across different stages of disease. Among the evaluated machine learning approaches, the XGBoost model showed the strongest predictive ability. The AUCs reached 0.960 in the training set, 0.944 in the testing set, and 0.908 in the temporal validation cohort. In addition, a web-based calculator was built to support personalized risk assessment. Conclusion Systemic inflammatory and nutritional biomarkers combined with CEA demonstrated robust predictive performance for identifying advanced colorectal neoplasms and may provide a convenient tool for early risk stratification in colorectal cancer screening.","url":"https://doi.org/10.3389/fonc.2026.1832546","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1832546","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s10815-026-03988-x","name":"Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.","source":"europepmc","abstract":"Objective To systematically evaluate the diagnostic accuracy and methodological quality of machine learning (ML) prediction models for pregnancy outcomes after assisted reproductive technology (ART). Methods PubMed, Embase, the Cochrane Library, IEEE Xplore, MEDLINE, ClinicalTrials.gov, CNKI, Wanfang, and VIP were searched from inception to July 2026. Eligible studies developed or validated ML models to predict clinical pregnancy or live birth after ART. For studies reporting complete 2 × 2 contingency data, pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves were estimated using random-effects diagnostic meta-analysis. Risk of bias was assessed with PROBAST. Results Twenty studies were included in the systematic review, of which 14 contributed to the diagnostic meta-analysis. Overall risk of bias was low in 1 study (5.0%), high in 8 studies (40.0%), and unclear in 11 studies (55.0%). The pooled sensitivity was 0.737 (95% CI, 0.662-0.799) and the pooled specificity was 0.789 (95% CI, 0.709-0.851), with substantial heterogeneity (I 2 = 97.7% and 99.0%, respectively). The pooled DOR was 10.49 (95% CI, 6.28-17.53), and the SROC curve indicated acceptable overall discrimination. Exploratory DOR subgroup analyses showed comparable performance for clinical pregnancy and live birth. No statistically robust subgroup difference was observed by algorithm type, center type, or validation status under a random-effects framework; study design showed a significant subgroup difference, but this estimate was driven by a single prospective study. Conclusion ML models show moderate diagnostic accuracy for predicting ART pregnancy outcomes, but the evidence base is limited by substantial heterogeneity and frequent high or unclear risk of bias. Future studies should follow TRIPOD + AI and PROBAST-aligned standards, report calibration and clinical utility, and prioritize prospective multi-center external validation before clinical implementation. Systematic review registration PROSPERO, CRD420251108846.","url":"https://doi.org/10.1007/s10815-026-03988-x","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s10815-026-03988-x","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3390/s26165121","name":"Electrophysiological Signatures of Sarcopenia: A Systematic Review of sEMG Features, Fatigue Indices and AI-Based Classifiers.","source":"europepmc","abstract":"Age-related sarcopenia involves structural and functional neuromuscular changes. Electrophysiological measures from surface electromyography (sEMG) capture activation dynamics, spectral fatigue indices and motor unit properties that may constitute objective signatures of sarcopenia. The objective of this work is to systematically review sEMG features, fatigability metrics and AI-based classification/regression approaches reported for sarcopenia assessment between 2019 and 2026. PRISMA guidelines were followed, and IEEE Xplore, PubMed and Scopus were searched for open access human studies. Extracted information comprised sample characteristics, muscles and tasks, signal acquisition and preprocessing, extracted time/frequency/time-frequency and motor unit features, fatigue metrics, machine learning pipelines, validation schemes and dataset accessibility. Studies were classified into activation, fatigue, ML, and neural control groups; risk of bias was assessed. A total of 12 studies fulfilled the inclusion criteria. Recurrent electrophysiological signatures included reduced distal activation with compensatory proximal recruitment and higher antagonist co-activation; diminished MF/IMDF fatigue slopes indicative of Type II fiber loss and altered motor unit recruitment; motor unit analyses revealed decreased discharge rates and larger MUAP amplitudes. AI-based models combining multidomain features (time, spectral, CWT/EMD, and motor unit metrics) yielded reasonable screening performance (AUC/accuracy 0.73-0.89) when using robust feature selection and explainability tools. Heterogeneity in acquisition, normalization, small cohorts and sparse data sharing limited comparability and external validity. The findings indicate that sEMG-derived electrophysiological signatures are promising for sarcopenia detection and monitoring. To translate signatures into reliable clinical tools, standardized protocols, larger shared datasets, multimodal features, including motor unit metrics, and rigorous external validation of AI models are required.","url":"https://doi.org/10.3390/s26165121","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/s26165121","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21873/invivo.14395","name":"Interpretable Deep Learning Radiomics for Differentiating Pleomorphic Adenoma and Warthin Tumor.","source":"europepmc","abstract":"Background/aim Preoperative differentiation between pleomorphic adenoma (PA) and Warthin tumor (WT) is essential for optimizing surgical strategies. This study aimed to develop and validate an interpretable machine learning framework integrating clinical data, traditional radiomics, and deep learning features to enhance diagnostic precision via conventional computed tomography (CT). Patients and methods We retrospectively analyzed 171 patients (84 PA and 87 WT). A total of 1,561 radiomic and 2,048 deep learning features were extracted from preoperative CT scans. Following LASSO-based feature selection, models were constructed and evaluated on a 70:30 training-validation split (n=119 and n=52, respectively). Diagnostic performance was quantified using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis. Results The combined model significantly outperformed individual clinical or radiomic models. The XGBoost classifier exhibited the highest efficacy, yielding a validation AUC of 0.961 (95%CI=0.916-1.000). In the training cohort, the clinical model and the radiomics-deep learning model achieved AUCs of 0.902 (95%CI=0.843-0.962) and 0.95 (95%CI=0.91-0.99), respectively. SHAP analysis indicated that the deep-learning score, patient age, and gender were the most influential predictors. Conclusion An interpretable model combining deep learning radiomics and clinical attributes provides robust accuracy in distinguishing PA from WT. The integration of SHAP values offers clinicians transparent insights, supporting personalized treatment planning.","url":"https://doi.org/10.21873/invivo.14395","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21873/invivo.14395","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s12888-026-08151-5","name":"A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).","source":"europepmc","abstract":"Background The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma and defensive attitudes. We aimed to develop a machine learning-based classification model, named Firefighter PTSD Latent Risk Evaluation (FLARE), to identify firefighters at risk of probable PTSD based on demographic and clinically relevant psychosocial features. Methods We analyzed data from 52,428 firefighters, including 2,305 with probable PTSD and 50,123 without PTSD, as assessed using the PTSD Checklist for DSM-5. Various clinical and occupational factors were examined, including exposure to traumatic events, depression, alcohol use disorder, insomnia, occupational stress, resilience, and awareness of and attitude towards mental illness. An extreme gradient boosting (XGBoost) algorithm was applied using the selected features to develop a FLARE model for PTSD classification. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The FLARE model, comprising seven key features (decreased concentration, appetite changes, feelings of worthlessness or excessive/inappropriate guilt, sleep disturbances, perceived work pressure, poor adaptability to change, and low perceived appropriateness of seeking professional help for close others) achieved an AUROC of 0.90 in classifying probable PTSD, with a sensitivity of 0.84 and a specificity of 0.82 in the test dataset. Temporal validation using the 2024 dataset yielded an AUROC of 0.91, with a sensitivity of 0.83 and a specificity of 0.84, confirming the model's generalizability. Conclusions The FLARE model effectively identified firefighters at risk for PTSD using a concise set of clinical and psychosocial indicators. This tool has the potential to enhance PTSD screening and early interventions, particularly in populations with prevalent mental health stigma. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12888-026-08151-5","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12888-026-08151-5","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fmed.2026.1869603","name":"Construction and validation of a machine learning-based model for predicting pneumonia risk in patients with hemorrhagic stroke.","source":"europepmc","abstract":"Objective This study aimed to develop and validate a distinct, stable, and interpretable predictive model using machine learning techniques to identify individuals at high risk of pneumonia early after admission. The goal was to provide a potential quantitative reference for implementing preventive interventions in clinical practice. Methods A retrospective nested case-control design was adopted. A total of 822 patients with hemorrhagic stroke admitted between January 2019 and October 2024 were enrolled. Feature selection was performed using LASSO regression to eliminate multicollinearity and identify key predictors. Five machine learning algorithms-logistic regression (LRC), gradient boosting classifier (GBC), random forest classifier (RFC), multilayer perceptron classifier (MLPC), and support vector machine classifier (SVC)-were employed to construct predictive models. Hyperparameters were optimized through 10-fold cross-validation and grid search. Model performance was comprehensively evaluated on an independent test set using metrics including area under the curve (AUC), accuracy, sensitivity, precision, and F1-score. Finally, SHAP (SHapley Additive exPlanations) values were applied to interpret the optimal model and elucidate the contribution of each feature to the prediction. Results LASSO regression selected 14 key predictors from 57 initial variables. Among the five models, the logistic regression model achieved the best performance on the test set. SHAP-based interpretability analysis revealed that the most influential factors for pneumonia risk prediction were, in descending order: left lower limb muscle strength, total cholesterol (TC), right lower limb muscle strength, low-density lipoprotein cholesterol (LDL-C), white blood cell count (WBC), consciousness status, D-dimer, age, systolic blood pressure (SBP), and bleeding location. Conclusion This study successfully developed a logistic regression-based predictive model for pneumonia risk in patients with hemorrhagic stroke. The model demonstrated favorable discrimination and stability. It provides an objective, quantitative basis for early identification of high-risk patients, stratified management, and precise prevention and control, supporting a shift from reactive to proactive complication management.","url":"https://doi.org/10.3389/fmed.2026.1869603","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1869603","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s12879-026-13802-9","name":"Machine learning-based prediction of bronchiolitis in children under two years: a multicenter study within a single metropolitan area.","source":"europepmc","abstract":"Background Bronchiolitis is a leading cause of hospitalization in infants, yet early and accurate risk prediction remains a clinical challenge. Traditional models often lack the complexity to capture nonlinear interactions or the transparency required for clinical trust. Objective This multicenter study aimed to develop, validate, and interpret machine learning (ML) models for predicting bronchiolitis risk in children under two years of age using clinical and socioeconomic determinants. Methods In this retrospective study, data from 1,260 children (529 with bronchiolitis and 731 without) were collected at 5 pediatric centers within a single metropolitan area, from January 2023 through December 2024. Seventeen predictors were analyzed. After rigorous preprocessing and K-NN imputation, eight ML algorithms, including four base learners and four ensemble methods, were developed. Model performance was evaluated using AUC and metrics derived from confusion matrices. Clinical utility was assessed via Decision Curve Analysis (DCA) and calibration plots. Explainable AI (XAI) was implemented using Shapley Additive exPlanations (SHAP) to interpret model decisions. Results XGBoost achieved an AUC of 0.863 [0.828-0.897] in the test set, demonstrating higher predictive performance than base learners such as NB (AUC: 0.583 [0.529-0.638]). DCA and calibration plots confirmed that XGBoost provides superior net benefit and calibration performance across a range of threshold probabilities. SHAP analysis identified overcrowding, severe acute malnutrition, and maternal smoking as the most influential predictors, highlighting the critical role of socioeconomic and environmental factors in disease susceptibility. Conclusion The integration of XGBoost with SHAP values provides a robust, transparent, and clinically actionable framework for early prediction of bronchiolitis based on variables available at initial clinical presentation. By identifying high-risk infants through interpretable AI, this model supports early triage and optimized resource allocation, fostering the transition toward precision pediatric medicine. Clinical trial number Not applicable.","url":"https://doi.org/10.1186/s12879-026-13802-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12879-026-13802-9","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1109/tpami.2026.3689780","name":"Advancing In-Context Learning for Efficient and Stable Medical Report Generation.","source":"europepmc","abstract":"Vision-language models (VLMs) have shown strong generalization across multimodal tasks, but adapting them to medical report generation (MRG) often demands extensive paired image-text data that are limited due to data privacy and annotation cost. In-context learning (ICL) offers a promising training-free alternative, yet standard ICL approaches rely on long demonstration prompts that are computationally inefficient and often yield inconsistent or clinically inaccurate descriptions. To address these challenges, we propose Principal In-Context Vectors (PCVs), a compact latent-guidance framework that distills multimodal demonstrations into stable semantic representations. By extracting hidden states from auto-regressive VLMs and applying principal component analysis (PCA), we identify robust semantic directions that remain stable under input perturbations. These PCVs are then injected into new queries to steer generation toward accurate and clinically meaningful outputs without any model tuning. Extensive experiments on four MRG benchmark datasets show that our approach can enhance both zero-shot and fully supervised generation quality across diverse settings, including cross-center, cross-disease, and longitudinal scenarios. This work provides a lightweight and scalable approach to adapt pre-trained VLMs for practical clinical deployment.","url":"https://doi.org/10.1109/tpami.2026.3689780","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1109/tpami.2026.3689780","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1097/md.0000000000049114","name":"Development and validation of a machine learning model for colorectal cancer status classification using NHANES data: A cross-sectional study.","source":"europepmc","abstract":"Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. Tools based on routinely collected variables may help identify prevalent CRC status and support clinical evaluation. Traditional approaches often rely on limited predictors and may not capture the multidimensional nature of CRC. Data were obtained from the National Health and Nutrition Examination Survey 1999-2018. Among 53,881 participants, 420 reported physician-diagnosed CRC (MCQ220). A 1:10 stratified case-control sample was constructed (420 cases, 4200 controls) and randomly split into training (70%) and internal validation (30%) sets. Missing data were imputed separately in the training and validation sets using a random forest-based method. SMOTE was applied only to the training set. Logistic regression, random forest, support vector machine, k-nearest neighbors, and extreme gradient boosting (XGBoost) were compared. Performance was primarily assessed by discrimination in the held-out validation set. For the final XGBoost model, exploratory post hoc probability calibration analyses were performed on validation-set predictions using raw probabilities, prior prevalence correction, and Platt scaling. Model interpretability was examined using Shapley Additive Explanations (SHAP), and a web-based CRC status classifier was developed. XGBoost showed the best discrimination in the validation cohort, with an area under the receiver operating characteristic curve of 0.787 (95% confidence interval 0.749-0.825). At the Youden-index cutoff, sensitivity was 77.0%, specificity 67.6%, PPV 19.2%, and NPV 96.7%. In exploratory probability-based analyses, decision curve analysis using Platt-scaled probabilities showed greater net benefit than treat-all and treat-none strategies across low-to-moderate threshold probabilities. post hoc calibration analyses fitted and assessed on the validation-set predictions showed improved apparent agreement after Platt scaling, with a Brier score of 0.191 and a Hosmer-Lemeshow P value of 0.086. SHAP identified key predictors, including alcohol use, hypertension, age, triglycerides, absolute lymphocyte count, blood lead, serum cotinine, and neutrophil-to-lymphocyte ratio. An interpretable machine learning framework integrating multidomain predictors enabled effective CRC status classification in a large population-based cohort. Discrimination was strong, whereas probability-based outputs after post hoc calibration should be considered exploratory pending independent confirmation. The model may support clinical evaluation and triage for individuals requiring further assessment.","url":"https://doi.org/10.1097/md.0000000000049114","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/md.0000000000049114","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3390/jcm15145375","name":"Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.","source":"europepmc","abstract":"Background : Postoperative pulmonary complications (PPCs) remain common in older adults undergoing degenerative spine surgery and are associated with adverse outcomes. We aimed to develop and externally validate an explainable machine-learning model using routinely available perioperative variables to estimate individual PPC risk. Methods : We conducted a two-center retrospective cohort study including consecutive patients aged ≥65 years undergoing degenerative cervical or lumbar spine surgery under general anesthesia. The development cohort included 1200 patients (Affiliated Nanhua Hospital, University of South China; 2024-2025), and the external validation cohort included 600 patients (First Affiliated Hospital, University of South China; 2025). PPCs within 7 postoperative days were defined using EPCO criteria. Twenty-five prespecified predictors were considered. Missing data were handled using multiple imputation, and continuous variables were standardized. Feature selection was performed using LASSO with 10-fold cross-validation and the 1-standard-error rule. We trained six machine-learning models (DT, RF, SVM, XGBoost, LightGBM, ANN) and logistic regression as a benchmark, with hyperparameters tuned by 5-fold cross-validation in the development cohort. Performance was assessed by AUROC, calibration (plots and Brier score), decision-curve analysis, and threshold-based metrics; SHAP was used for interpretability. A web-based calculator was implemented for clinical use. Results : PPCs occurred in 222/1200 (18.5%) patients in the development cohort and 123/600 (20.5%) in the external validation cohort. LASSO selected 11 predictors. In external validation, the random forest model achieved AUROC 0.786 (95% CI 0.740-0.829) and Brier score 0.137 (95% CI 0.119-0.154), with favorable decision-curve performance, and achieved an accuracy of 73.83%, sensitivity of 68.29%, specificity of 75.26%, PPV of 41.58%, NPV of 90.20%, and an F1 score of 51.69% at the prespecified threshold. Conclusions : We developed and externally validated multiple prediction models for PPCs in older adults undergoing degenerative spine surgery. The random forest model provided balanced performance and SHAP-based interpretability and was retained as an implementation-focused model for perioperative or immediate postoperative risk estimation. Further prospective validation, comparison with established clinical risk scores, and model updating in more diverse clinical settings are needed before routine clinical implementation.","url":"https://doi.org/10.3390/jcm15145375","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/jcm15145375","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fendo.2026.1838500","name":"Development and validation of a machine learning model for sperm DNA fragmentation rate in infertile men: a multicenter retrospective study.","source":"europepmc","abstract":"Background The sperm DNA fragmentation index (DFI) provides important reference for evaluating male fertility and assisted reproductive outcomes, and its degree of damage is influenced by multiple factors. Our study aims to develop a machine learning-based predictive model for identifying high sperm DFI in infertile men using clinical and semen parameters. Methods We retrospectively collected data on infertile male patients from two centers in Shanghai, China from March 2023 to March 2024. We used data from one center as the training cohort to construct the model and data from another center for external validation. The semen data of the included subjects is combined with clinical features as training features for machine learning. We have developed and validated the effectiveness of six models: Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes Classifier (NB). We comprehensively evaluated the performance of machine learning models with different features using ROC curves, accuracy, and other relevant indicators. The SHapley Additive exPlanations (SHAP) diagram was used to illustrate the importance of variables in the model, Lasso regression is used to screen for core features. Finally, a convenient and practical DFI quality early prediction platform was constructed based on core features. Findings 1037 patients from one center were included in the development cohort, while 290 patients from another center were included in the external validation queue. The RF model performed the best in predicting the quality of DFI in infertile male patients, with a 10-fold cross-validation AUC of 0.979 (0.972-0.986) in the development cohort, and an AUC of 0.945 (95% CI: 0.916-0.975) in the external validation cohort. Finally, the core factors obtained through screening were included in the model, including progressive motility sperm rate, sperm concentration, sperm viability, daily exercise time, smoking status, alcohol consumption, stress level, and insomnia symptoms were incorporated to generate a publicly accessible online platform (https://4sjajo-0-0.shinyapps.io/dfi-prediction-v4/). Interpretation The RF model using semen parameters and lifestyle factors shows good discrimination for predicting DFI abnormality in infertile men. However, the model exhibits notable miscalibration in external validation (calibration slope 2.196, intercept -0.196), indicating systematic overestimation of risk and insufficient dispersion of predictions. Therefore, in its current form, the model and its associated online calculator should be considered investigational. Prospective validation and recalibration in independent populations are required before any clinical application. Its impact on patient-important reproductive outcomes (e.g., live birth) remains unknown.","url":"https://doi.org/10.3389/fendo.2026.1838500","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fendo.2026.1838500","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.31083/bjhm56311","name":"Integrated Intratumoral and Peritumoral Ultrasound Radiomics Models for Breast Nodule Diagnosis Using Machine Learning.","source":"europepmc","abstract":"Aims/background The differentiation of benign and malignant breast nodules, particularly those categorized as Breast Imaging Reporting and Data System (BI-RADS) 3-4, remains a clinical challenge due to the subjectivity and operator dependence of conventional ultrasound assessment. This study aimed to evaluate the diagnostic value of intratumoral and peritumoral ultrasound radiomics features in distinguishing benign from malignant BI-RADS 3-4 breast nodules and to construct interpretable machine learning models. Methods Ultrasound images and parameters (BI-RADS classification) of breast nodules were retrospectively collected from female patients at two institutions between January 2021 and June 2024: 571 patients (880 nodules) from Institution 1 (Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine) and 333 patients (351 nodules) from Institution 2 (Shanghai Second People's Hospital). Included patients had BI-RADS 3-4 nodules confirmed by pathology or, for BI-RADS 3 nodules, stable findings on follow-up for at least 2 years. Nodules from Institution 1 were randomly divided into a training set (n = 615) and an internal validation set (n = 265) at a 7:3 ratio, while patients from Institution 2 constituted an external test set (n = 351). Radiomics features of intratumoral and peritumoral regions (3 and 5 pixels wide) were extracted using PyRadiomics. Features were screened using the independent t -test, Spearman correlation, and least absolute shrinkage and selection operator (LASSO) regression. Machine learning models-including random forest (RF), multilayer perceptron (MLP), extra trees (ET), support vector machine (SVM), logistic regression (LR), k-nearest neighbor (KNN), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM)-were trained and compared using area under the curve (AUC) and other metrics. The best-performing model was then used to compare intratumoral versus peritumoral regions. SHapley Additive exPlanations (SHAP) was applied to interpret feature importance. Results Across the training, internal validation, and external test sets, SVM demonstrated the most stable and balanced performance. The 3 pixels transitional zone region of interest support vector machine (EI3_SVM) model achieved a higher AUC than the tumor core region of interest (T) model in the external test set (0.875 vs. 0.787, p 0.01), with accuracy 78.1%, sensitivity 42.2%, specificity 97.0%, and Brier score 0.166, outperforming other peritumoral models. Most models incorporating peritumoral features demonstrated AUCs that were higher than or comparable to those of the T model. SHAP analysis indicated that the high specificity was mainly driven by texture and shape features. Conclusion Integrating intratumoral and peritumoral radiomics features significantly improves the diagnostic accuracy and objectivity for differentiating benign and malignant breast nodules. This approach may aid clinical decision-making, reduce unnecessary biopsies, and support early precision diagnosis of breast cancer.","url":"https://doi.org/10.31083/bjhm56311","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31083/bjhm56311","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s13048-026-02190-y","name":"Perioperative prediction of adnexal malignancy by an interpretable machine learning model for guiding ovarian preservation in premenopausal endometrial cancer.","source":"europepmc","abstract":"Background Ovarian preservation in premenopausal patients with endometrial cancer remains challenging due to the potential presence of concurrent adnexal malignancy. To support surgical decision-making, we developed an interpretable machine learning model for the perioperative identification of high-risk patients, thereby facilitating personalized ovarian preservation strategies. Methods We conducted a retrospective analysis of endometrial cancer patients treated at our institution between 2010 and 2024. After feature selection via multicollinearity analysis and LASSO regression, eight machine learning algorithms were trained to predict coexisting adnexal malignancy. Model performance was evaluated using ROC analysis, accuracy metrics, and Brier score calibration. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution of key features in the optimal model. Results Among 296 included patients, 29 (9.8%) had coexisting adnexal malignancy. Sixteen predictive features were selected from clinical, imaging, serum biomarker, and histopathological domains. The Naive Bayes classifier achieved superior performance with an AUC of 0.92 (95% CI: 0.86-0.97), accuracy of 91.0%, and well-calibrated predictions (Brier score: 0.11). The SHAP further elucidated the contribution of each variable to the model's predictions, emphasizing the importance of factors such as Cancer Antigen 125, Estrogen Receptor status, Human Epididymis Protein 4. Conclusion The Naive Bayes model exhibits high discriminative accuracy for the perioperative prediction of concurrent adnexal malignancy. This decision support tool shows strong potential for clinical application, promoting individualized surgical management and aiding in ovarian preservation for premenopausal endometrial cancer patients.","url":"https://doi.org/10.1186/s13048-026-02190-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s13048-026-02190-y","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s12889-026-28507-6","name":"Analysis of health anxiety trajectories in rabies exposure patients based on machine learning.","source":"europepmc","abstract":"Objective To identify the dynamic evolution trajectory of health anxiety in patients with initial rabies exposure and to conduct predictive analysis of health anxiety trajectories using machine learning algorithms. Methods A total of 548 rabies exposure patients from a tertiary hospital in Nanning were selected between January 2024 and October 2025. Health anxiety levels were assessed at 0, 3, 7, 14, and 28 days post-exposure using the Health Anxiety Scale following a 5-dose immunization schedule. A latent variable growth mixture model was employed to identify anxiety development trajectories. Six machine learning algorithms were utilized to establish a high-risk health anxiety identification model, and a comprehensive evaluation of the model's performance was conducted, with the SHAP algorithm used for model interpretation. Results All 548 patients completed the survey. The latent variable growth mixture model identified three health anxiety development trajectories: low-level anxiety-stable group, moderate to high-level anxiety-improving group, and high persistent anxiety-hard to relieve group. Multivariate logistic regression analysis indicated that being female, having a college degree or higher, high rabies knowledge, a greater number of comorbidities, level III exposure, and delayed medical treatment were independent risk factors belonging to the 'high persistent anxiety-hard to relieve group.' In contrast, a higher level of health literacy was identified as a protective factor. The XGBoost model demonstrated superior predictive performance on the independent test set, with an area under the receiver operating characteristic curve of 0.881, while SHAP analysis ensured clinical interpretability. SHAP analysis of the XGBoost model revealed that health literacy, Perceived social support, rabies knowledge, Payment method and Animal source were the five most important features predicting the 'high persistent group. Conclusion There is heterogeneity in the health anxiety trajectories of patients exposed to rabies. A predictive model based on machine learning can facilitate the early identification of high-risk individuals, providing a basis for implementing precise psychological interventions.","url":"https://doi.org/10.1186/s12889-026-28507-6","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12889-026-28507-6","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s11030-026-11654-8","name":"Prediction and mechanistic insights into drug-induced reproductive toxicity through integrated machine learning, FAERS-based signal comparison, and network toxicology analyses.","source":"europepmc","abstract":"Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by high costs and long experimental cycles. In this study, a machine learning-based predictive model for reproductive toxicity was developed and integrated with data from the FDA Adverse Event Reporting System (FAERS), network toxicology analysis, molecular docking, and molecular dynamics simulation to systematically evaluate the post-marketing reproductive toxicity risk of drugs and explore their potential mechanisms. Among the evaluated machine learning algorithms, LightGBM demonstrated the best overall performance, achieving an F1-score of 0.854, a ROC-AUC of 0.933, a PR-AUC of 0.931, and an MCC of 0.705 on the independent test set, with robust generalization confirmed by ten-fold cross-validation. Among drugs approved between 2015 and 2024, 72 were predicted to have a high risk of reproductive toxicity. FAERS-based signal comparison showed that 55 of these drugs (76.39%) were associated with reproductive toxicity-related adverse event reports, indicating consistency between model predictions and FAERS-reported reproductive toxicity-related adverse events. Network toxicology analysis identified 12 key targets, including ESR1, IGF1, and AKT1, that may be involved in reproductive toxicity. Molecular docking showed that drugs with high predicted reproductive toxicity risk could bind effectively to multiple toxicity-related targets, while molecular dynamics simulations confirmed stable interactions between selected drugs and ESR1, mainly through hydrogen-bonding and hydrophobic interactions. Favorable binding free energies further supported their potential multi-target effects. Overall, this integrated strategy combining predictive modeling with FAERS-based signal comparison provides a useful framework for drug safety evaluation and mechanistic investigation of reproductive toxicity.","url":"https://doi.org/10.1007/s11030-026-11654-8","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11030-026-11654-8","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1245/s10434-026-20309-9","name":"Using Machine Learning to Predict Breast Cancer-Related Lymphedema Following Axillary Lymph Node Dissection.","source":"europepmc","abstract":"Background Breast cancer-related lymphedema (BCRL) is a common and debilitating sequela of axillary lymph node dissection (ALND). Although machine learning (ML)-based prediction models have been proposed, few focus exclusively on patients undergoing ALND, and direct comparisons with traditional statistical models remain limited. This study aimed to develop accurate and clinically feasible prediction models for BCRL using supervised ML and multivariable logistic regression. Methods Demographic and clinical data were prospectively collected from women undergoing unilateral ALND for breast cancer at Memorial Sloan Kettering Cancer Center between 2016 and 2024. Supervised ML and multivariable logistic regression models to predict BCRL were trained and internally validated. Model performance was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, specificity, and Brier score. Shapley additive explanations were used for model interpretability. Results A total of 474 eligible patients were included. BCRL developed in 113 (23.8%) patients at a mean ± standard deviation of 16.6 ± 7.5 months postoperatively. The highest-performing ML model (random forest) achieved an AUC of 0.83, whereas traditional multivariable logistic regression achieved an optimism-corrected AUC of 0.62. Key ML predictors of BCRL on Shapley additive explanations analysis included clinical cancer stage, body mass index, age, and neoadjuvant chemotherapy. Conclusions ML-based models outperformed traditional logistic regression in predicting BCRL among patients undergoing ALND. These models demonstrate the potential of ML for early BCRL identification and risk stratification but also highlight the difficulties in accurately predicting BCRL development. Further research is needed to improve model predictive performance and facilitate clinical implementation.","url":"https://doi.org/10.1245/s10434-026-20309-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1245/s10434-026-20309-9","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.7759/cureus.106838","name":"Machine Learning Applications for Opioid Use Management in Chronic Cancer Pain: A Systematic Scoping Review.","source":"europepmc","abstract":"Chronic pain remains a critical clinical issue worldwide, with adverse effects on the quality of life of oncology patients. Meanwhile, the overuse of opioids to treat or alleviate chronic cancer pain has contributed to a global opioid crisis. The increasing accessibility of high-quality clinical datasets and computational frameworks has promoted the use of machine learning (ML) techniques in clinical practice to manage opioid consumption. This review investigates the current bibliography referring to the role of applied ML techniques in opioid administration in patients with chronic cancer pain. The objective of the current scoping review, according to population, intervention, comparison, and outcome (PICO) standards, was to evaluate the effectiveness of ML techniques in monitoring opioid consumption in patients with chronic cancer pain. This review includes scientific journal papers published from 2010 to 2024 that use healthcare data from patients with chronic cancer pain, apply machine learning techniques, and may address the potential consequences of the misuse of opioids. A systematic literature search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed in PubMed and Google Scholar databases. Data extracted include the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. After conducting the screening process, 50 articles were identified, but only four focused specifically on or included data of patients with chronic cancer pain where ML techniques were applied. The four included studies showed high performance (area under the curve {AUC}: >0.8) in predicting opioid adherence, misuse, and long-term use. Although generalizability remains limited due to small sample sizes and a lack of external validation, it sets distinct limits in applying these methods in clinical use. After a thorough review of recent literature, ML models demonstrated promising accuracy in predicting opioid adherence, misuse, and long-term use among patients with chronic cancer pain. However, these findings are based on studies with limited sample sizes and a lack of external validation, which restricts their generalizability. Future research should focus specifically on populations with chronic cancer pain and expand predictive models to incorporate a combination of clinical, psychosocial, biometric, and genomic data. This approach may enable more accurate, personalized, and safer opioid management in oncology care.","url":"https://doi.org/10.7759/cureus.106838","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.106838","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.4274/dir.2026.263844","name":"Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024","source":"europepmc","abstract":"Purpose Prostate cancer (PCa) diagnosis has advanced with the integration of machine learning (ML). This study analyzes global trends in ML-based PCa diagnosis research using bibliometric methods. Methods A systematic search was conducted in the Web of Science Core Collection database for articles published between 1997 and 2024. Bibliometric analysis was performed using VOSviewer (v1.6.20), CiteSpace (v6.3.R1), and R (v4.3.3). Results The analysis included 1,045 articles, involving 6,704 authors from 4,762 institutions across 327 countries or regions. The number of publications increased over time, rising sharply from 2018. China published the highest number of articles (290), whereas the United States demonstrated the greatest research impact, leading in total citations (8,207) and international collaborations. The Berlin Institute of Health published the highest number of articles (120). Within this dataset, European Radiology had the highest H-index. Key authors included Stephan C, Jung K, and Cammann H. Keyword analysis identified “system,” “MRI,” and “guidelines” as prominent terms, with emerging trends focusing on “convolutional neural network,” “data system,” and “transfer learning.” Conclusion ML in PCa diagnosis has advanced substantially, transitioning from fundamental biomarker investigations to sophisticated deep learning applications centered on medical imaging. Future directions emphasize the development of accurate, generalizable ML models integrated into clinical workflows with a continued focus on convolutional neural networks and transfer learning. Clinical significance This study delineates the global research evolution of maching learning for prostate cancer diagnosis, offering clear clinical guidance for the translation and routine application of artificial intelligence- assisted diagnostic models.","url":"https://doi.org/10.4274/dir.2026.263844","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.4274/dir.2026.263844","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106553","name":"Development and validation of an interpretable machine learning model for predicting systemic inflammatory response syndrome after percutaneous nephrolithotomy: A multicenter study.","source":"europepmc","abstract":"Background Systemic inflammatory response syndrome (SIRS) is a common and potentially reversible early infectious complication after percutaneous nephrolithotomy (PCNL). Accurate perioperative risk stratification may enable timely intervention; however, existing prediction models show limited generalisability and poor clinical interpretability. Methods We conducted a multicenter retrospective cohort study including patients who underwent PCNL at three hospitals in China between Jan 1, 2015, and Dec 30, 2024. Patients from one center were randomly divided into training and internal validation cohorts, while two independent cohorts served for external validation. Perioperative demographic, laboratory, imaging, and surgical variables were collected. Feature selection was performed using least absolute shrinkage and selection operator regression and the Boruta algorithm. Seven machine learning models were developed and compared. Given outcome imbalance, the area under the precision-recall curve (AUPRC) was prespecified as the primary performance metric. Model calibration, decision curve analysis, and SHapley Additive exPlanations (SHAP) were used to assess reliability, clinical utility, and interpretability. Results A total of 2,684 patients were included, with postoperative SIRS occurring in 9.8%-12.7% across cohorts. Six predictors were consistently identified: stone size, urine nitrite, urine culture results, operative time, residual stone status, and the neutrophil-to-albumin ratio. The random forest model showed the most balanced performance, with AUPRC values ranging from 0.581 to 0.641 and AUROC values from 0.873 to 0.920 across validation cohorts. Calibration was satisfactory, and decision curve analysis demonstrated a higher net clinical benefit than alternative models. SHAP analysis revealed clinically coherent, non-linear associations between key predictors and SIRS risk. An online prediction tool was developed to support individualized risk estimation. Conclusion An interpretable machine learning model based on routinely available perioperative variables can reliably predict SIRS after PCNL across multiple centers. This approach may facilitate early postoperative risk stratification and support timely clinical decision-making to mitigate infectious complications. Prospective and multi-regional validation is warranted.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106553","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106553","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fneur.2026.1806856","name":"Machine learning models in post-stroke aphasia: a scoping review.","source":"europepmc","abstract":"Objective To systematically review the literature on the application of machine learning models in post-stroke aphasia, and to provide a reference for the construction and clinical application of related models. Methods Based on scoping review methodology, we searched Web of Science, PubMed, Cochrane Library, Embase, CINAHL, CNKI, VIP database, Wanfang database, and China Biology Medicine. The search time limit was from the database's establishment to November 20, 2025, and the retrieved literature was screened, summarized, extracted, and analyzed. Results A total of 19 articles were included. The analysis results showed that the machine learning algorithms used in post-stroke aphasia models were mainly supervised methods, including random forests, neural networks, and support vector machines. The data sources of the model were diverse. The indicators included in the model covered multimodal data. The functions of the model include diagnosis and classification of aphasia patients, assessment and prediction of the severity of aphasia patients, prediction of the language function and rehabilitation outcome of patients, monitoring and evaluation of symptoms, etc. Conclusion Machine learning models have high applicability and broad scope in post-stroke aphasia. Future research still requires multi-center, multi-modal data and external validation to enhance its robustness and clinical feasibility.","url":"https://doi.org/10.3389/fneur.2026.1806856","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1806856","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1002/hsr2.72956","name":"Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.","source":"europepmc","abstract":"Background Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) where access to neurocritical care is limited. Accurate and context-appropriate prognostic models are crucial to guide early clinical decision-making and optimize resource allocation in such settings. This study aims to develop and evaluate machine learning (ML) models for predicting TBI outcomes among adult patients using trauma registry data from Kilimanjaro Christian Medical Centre (KCMC), Tanzania. Methods This retrospective cohort study utilized data from 4596 adult TBI patients recorded in the KCMC trauma registry between 2013 and 2024. The outcome was dichotomized Glasgow Outcome Scale (GOS): poor (1-3) versus good (4-5). Ten supervised ML algorithms, including Random Forest (RF), Decision Tree (DT), Logistic Regression, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), were trained on 70% of the data after applying multiple imputation and synthetic minority Over-sampling Technique (SMOTE) to address missingness and class imbalance. Hyperparameter tuning was performed using 10-fold cross-validation. Model performance was assessed on a 30% test set using area under the ROC curve (AUC), accuracy, sensitivity, specificity, and predictive values. Results Among the 4596 patients, 26.2% had poor outcomes. The RF and DT models achieved the highest AUCs of 0.83 and 0.82, respectively. RF also showed the highest accuracy (0.78) and strong positive predictive value (PPV = 0.87), while DT had the highest sensitivity for poor outcomes (84.5%). Predictors of poor outcomes included TBI severity, pupil non-reactivity, low oxygen saturation, lack of CT scan, alcohol use, and abnormal vital signs. Conclusion ML models, particularly RF and DT, demonstrated strong predictive performance for TBI outcomes using routinely collected variables in a resource-limited LMIC setting. Their interpretability and reliance on admission-level data make them potential tools for real-time triage and risk stratification. Future research should focus on external validation and integration into clinical decision-support systems to support scaleup across similar settings.","url":"https://doi.org/10.1002/hsr2.72956","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1002/hsr2.72956","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.2147/cia.s614064","name":"A Web-Based Machine Learning Calculator for Predicting Preoperative Deep Vein Thrombosis in Elderly Hip Fractures Patients.","source":"europepmc","abstract":"Purpose Hip fractures are often associated with deep vein thrombosis (DVT). This study aimed to develop machine learning models to predict preoperative DVT risk using basic clinical data from elderly patients with hip fractures. Methods Clinical data were retrospectively collected from 538 elderly hip fracture patients hospitalized at Beijing Shijitan Hospital. Eighteen clinical parameters were assessed. Patients from October 2021 to September 2024 (n=405) formed the development cohort, randomly divided into training and test sets (7:3). Patients from October 2024 to September 2025 (n=133) constituted the temporal validation set. Following Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection, three models-logistic regression, light-gradient boosting machine, and support vector machine (SVM)-were developed. Performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, Brier score, calibration curves, and decision curve analysis (DCA). SHapley additive explanations (SHAP) were used for interpretability. Results LASSO selected seven features: fracture type, time from injury to admission, white blood cell count, red blood cell count, C-reactive protein, D-dimer, and prothrombin time. The SVM model demonstrated the best overall performance. In the test set, it achieved an AUC of 0.8525 (95% CI: 0.7624-0.9426), sensitivity of 0.7200, and Brier score of 0.1231. Performance remained stable in the temporal validation set with an AUC of 0.8360 (95% CI: 0.7470-0.9260), sensitivity of 0.6562, and Brier score of 0.1328. Calibration curves and DCA indicated reliable probability predictions and clinical net benefit. SHAP identified prothrombin time as the most important predictor. A web-based calculator was developed based on the SVM model. Conclusion The developed SVM model shows potential as a dynamic, interpretable risk assessment tool for preoperative DVT in elderly hip fracture patients. It may provide a helpful quantitative reference to assist clinicians with perioperative DVT monitoring and early warning.","url":"https://doi.org/10.2147/cia.s614064","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/cia.s614064","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1136/bmjopen-2025-114046","name":"Development and temporal validation of a machine learning model to estimate total MoCA scores from telephone-based items in patients with mild cognitive impairment.","source":"europepmc","abstract":"Background Periodic cognitive monitoring for mild cognitive impairment (MCI) is crucial. However, standard assessments like the Montreal Cognitive Assessment (MoCA) require in-person administration due to visual-based items, creating significant accessibility barriers. Objective This study aims to develop and rigorously validate a machine learning model to accurately estimate total MoCA scores using only telephone-based (remote) items and demographic information. Methods Data from three independent MCI cohorts (2022-2024) were used. Data from 2022 to 2023 (N=425) formed the training set; 2024 data (N=356) were the temporal test set. Models were trained to predict the 8-point visual-based score using 37 telephonic/demographic features. Fivefold cross-validation (80% training/20% validation per fold) was performed exclusively on the training set to compare three heterogeneous machine learning models. Random Forest, Linear Regression and XGBoost models were compared. The final score was reconstructed by summing actual telephonic scores with the predicted visual score. Findings In the fivefold cross-validation on the training set, the Random Forest model achieved the lowest mean Root Mean Squared Error (RMSE=1.2034) of 1.2034 points (SD=0.0407; 95% CI 1.1528 to 1.2540) for Visual Score prediction, though pairwise differences between models did not reach statistical significance, likely reflecting the limited power of fivefold comparisons. When applied to the 2024 temporal test set, the reconstructed total score showed high concordance with the actual total MoCA score (RMSE=1.29 points; mean absolute error=1.01 points; R 2 =0.9248). The most important features were education (28.7% importance) and age (13.8%), followed by sustained attention (4.1%). Conclusions Our machine learning model accurately and reliably predicts total 30-point MoCA scores using only telephone-based information. The model demonstrated satisfactory generalisation performance on a temporally separated cohort, supporting its potential clinical applicability pending external validation.","url":"https://doi.org/10.1136/bmjopen-2025-114046","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1136/bmjopen-2025-114046","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.7759/cureus.109989","name":"EEG-Based Seizure Prediction Approaches Within Clinically Relevant Pre-seizure Windows Using Scalp EEG Datasets: A Systematic Review.","source":"europepmc","abstract":"This systematic review synthesized evidence on EEG-based seizure prediction within clinically relevant pre-seizure windows, focusing on deep learning and machine learning models evaluated on public datasets. The review aimed to summarize predictive performance, including sensitivity, receiver operating characteristic curve findings when reported, and false-alarm outcomes for scalp EEG seizure prediction systems. It also examined methodological heterogeneity across studies, including the preictal window, defined as the period before seizure onset used for prediction; the seizure prediction horizon, defined as the minimum warning interval before seizure onset; and the seizure occurrence period, defined as the interval during which a seizure is expected after an alarm. Additional objectives were to compare validation strategies, including patient-wise generalization, and to identify design features most consistently associated with clinically actionable performance. The included evidence showed substantial variability in reported performance. It was limited by heterogeneous study designs, frequent reliance on patient-specific internal validation, limited external validation, inconsistent reporting of false alarms, and high or unclear risk of bias. Although scalp EEG seizure prediction models have shown potential to achieve moderate-to-high sensitivity in selected settings, confidence in their real-world generalizability remains limited. Overall, the findings suggest that clinical translation will require more standardized prediction windows, clearer reporting of alarm burdens, stronger validation frameworks, and prospective evaluations under real-world conditions.","url":"https://doi.org/10.7759/cureus.109989","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.7759/cureus.109989","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s12877-026-07906-9","name":"Development and validation of a routine blood test-based model to predict in-hospital postoperative pulmonary infection in older patients with hip fracture.","source":"europepmc","abstract":"Background Postoperative pulmonary infection (PPI) is a common and serious complication in older adults undergoing hip fracture surgery, leading to prolonged hospitalization, increased costs, and increased mortality. However, simple and reliable preoperative predictors remain limited. Therefore, this study aimed to develop and validate a hematology-based machine learning model for the early prediction of PPI in older hip fracture patients. Methods A total of 3,944 patients aged ≥ 60 years who underwent hip fracture surgery were retrospectively enrolled from three cohorts: the discovery cohort (n = 1,745, Shanghai Xuhui Central Hospital, 2016-2020), the internal validation cohort (n = 1,306, 2021-2024), and the external validation cohort (n = 893, Shanghai Putuo People's Hospital, 2016-2024). Twenty-four preoperative hematologic variables were analyzed. Six supervised machine learning algorithms were compared via fivefold cross-validation. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration, and decision curve analysis (DCA). Results Patients who developed PPI were generally older and exhibited a neutrophil-dominant inflammatory profile, characterized by higher white blood cell counts, neutrophil, monocyte, platelet, and C-reactive protein levels, and lower lymphocyte, eosinophil, and basophil percentages (all p Conclusions A hematology-based XGBoost model was developed to predict in-hospital PPI in older adults following hip fracture surgery. The model demonstrated good discriminative performance and interpretability in this study cohort, suggesting its potential utility as a supplementary tool for cost-effective perioperative risk stratification. However, further prospective validation in diverse populations and healthcare settings is required to confirm its generalizability and clinical applicability.","url":"https://doi.org/10.1186/s12877-026-07906-9","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12877-026-07906-9","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1186/s12873-026-01602-y","name":"Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.","source":"europepmc","abstract":"Background Pulmonary thromboembolism (PTE) is a life‑threatening condition that requires prompt and accurate evaluation in the emergency department (ED). Standardized clinical scoring systems, including the Wells and revised Geneva scores, form the cornerstone of initial risk stratification but have limited specificity, leading to unnecessary D‑dimer testing and frequent overuse of CT pulmonary angiography (CTPA). This study aimed to develop explainable machine‑learning (XML) models as a complementary decision‑support layer following initial clinical assessment, with the dual goals of improving PTE prediction and providing an early, non‑imaging‑based indication of clot location. Methods Clinical and paraclinical data from 472 ED patients with suspected PTE were collected across three centers of Mashhad University of Medical Sciences (2022-2024) using structured forms aligned with Wells/Geneva criteria. CTPA served as the reference standard for diagnosis and anatomical classification. We developed classical ML models, ensemble algorithms such as Extra Trees, and hybrid stacking pipelines. Explainable AI (XAI) using SHAP values quantified global and patientlevel feature contributions. The XML models were designed to operate after Wells/Geneva-based triage: supporting D-dimer decisions in low-risk patients and refining post-triage risk estimation in intermediate - and high-risk patients to help reduce avoidable CTPA utilization. Results In this intermediate‑ to high‑probability cohort, all patients were classified as PTE‑suspect by Wells/Geneva scoring, whereas only 24% were confirmed positive on CTPA. ML models demonstrated improved discrimination, with Extra Trees achieving the best performance (accuracy 0.82, sensitivity 0.69, specificity 0.86, AUC 0.83). Among PTE‑positive cases, the model achieved an AUC of 0.77 for central emboli and 0.67 for peripheral emboli, with an overall anatomical classification accuracy of 63%. Conclusions The proposed XML models offer a transparent, clinically aligned framework that augments existing scoring systems, enhancing diagnostic efficiency and reducing unnecessary imaging.","url":"https://doi.org/10.1186/s12873-026-01602-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1186/s12873-026-01602-y","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s13402-026-01226-1","name":"Machine learning-based integrative analysis identifies CXCL13-driven tertiary lymphoid structures as favorable immune and prognostic features in osteosarcoma.","source":"europepmc","abstract":"Background Osteosarcoma, an aggressive bone malignancy with limited response to immunotherapy, remains a major clinical challenge. Tertiary lymphoid structures (TLS), as organized ectopic lymphoid aggregates, play a pivotal role in modulating antitumor immune responses. However, their landscape, clinical significance, and molecular determinants in osteosarcoma remain largely unexplored. Methods TLS were identified in osteosarcoma tissues by immunohistochemistry and multiplex immunofluorescence. Using transcriptomic data from the TARGET, GEO (GSE21257, GSE16091, GSE39055), and PKUPH cohorts, we developed a TLS-based prognostic index (TLSPI) through integrative machine-learning modeling. Functional enrichment, immune infiltration, and immunotherapy response prediction analyses were performed to characterize TLSPI-related biological features. The role of the key TLS-associated gene CXCL13 was validated through immunohistochemistry, single-cell transcriptomic profiling, and in vitro functional experiments. Results TLS were detected in osteosarcoma and were significantly associated with improved overall survival (p = 0.024). Differential expression and enrichment analyses revealed that TLSPI-related genes were primarily involved in immune activation and antigen presentation pathways. Immune profiling revealed that the low-TLSPI group was characterized by greater immune infiltration and higher expression of immune checkpoint molecules, as well as lower TIDE scores, which indicated greater predicted sensitivity to immunotherapy. CXCL13 was identified as a key TLS-associated gene whose high expression correlated with improved survival. Immunohistochemical analysis confirmed the prognostic significance of CXCL13, while single-cell and functional assays demonstrated its association with B-cell activation within the osteosarcoma microenvironment. Conclusion This study provides a comprehensive characterization of TLS in osteosarcoma and establishes a TLS-based prognostic index with potential clinical applicability. CXCL13 may serve as a critical mediator linking TLS formation and antitumor immunity, providing potential therapeutic implications for osteosarcoma immunotherapy.","url":"https://doi.org/10.1007/s13402-026-01226-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s13402-026-01226-1","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106604","name":"Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.","source":"europepmc","abstract":"Objective Patients hospitalized for diabetes-related conditions face elevated risks of emergency department (ED) visits post-discharge, driven by both clinical factors and social determinants of health (SDoH). This study aimed to develop and validate predictive models integrating clinical and SDoH data to identify high-risk patients for post-hospitalization diabetes case management. Methods We conducted a retrospective cohort study using electronic health record data from the University of Alabama at Birmingham Medical Center, including 162,063 inpatient encounters (January 2020-June 2024) for training and testing and 16,164 encounters (January-May 2025) for temporal validation. Patients were identified by diabetes-related ICD-10 codes or HbA1c ≥ 6.5 %, reflecting the scope of the institution's diabetes case management program. Predictors included demographics, diabetes-related comorbidities, surgical procedures, laboratory values, medications, and both area-level and individual-level SDoH. Logistic regression, decision trees, and XGBoost models were developed to predict diabetes-related ED visits within 3 months post-hospitalization. Hyperparameters for decision tree and XGBoost models were tuned via 10-fold cross-validation, and calibration was assessed using Brier scores and calibration plots. Results Among 162,063 hospitalizations, 6.2 % resulted in a diabetes-related ED visit. XGBoost achieved the best performance (area under the curve [AUC] 0.846, precision 0.420, sensitivity 0.296, specificity 0.972), maintained on temporal validation (AUC 0.842). Key predictors included past ED visit frequency, insulin prescriptions, age, and area-level SDoH indices. Individual-level SDoH factors, including home safety issues and work disability, also contributed to prediction. Targeting the top 20 % of predicted risk captured 64.1 % of all ED visits. Model discrimination was consistent across racial subgroups (AUC range: 0.843-0.851). Calibration was clinically acceptable across datasets. Conclusions Integration of clinical and SDoH data achieved effective prediction of post-hospitalization ED visits. XGBoost provided excellent discrimination with temporal stability. Decision trees offered greater interpretability. A pilot implementation delivering daily risk-stratified patient lists to the diabetes case manager is underway, demonstrating a practical pathway from model development to clinical decision support.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106604","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106604","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.2147/ijgm.s600424","name":"Machine Learning-Based Predictive Model for Grade 3 Primary Graft Dysfunction Following Lung Transplantation: A Retrospective Cohort Study.","source":"europepmc","abstract":"Background This study aimed to identify key predictors for Grade 3 Primary Graft Dysfunction (PGD) after lung transplantation. Machine learning (ML) algorithm models were constructed for early clinical identification of high-risk PGD patients based on these predictors. Methods A total of 297 lung transplant recipients from December 2018 to December 2024 were retrospectively enrolled. Patient classification followed the 2016 International Society for Heart and Lung Transplantation (ISHLT) criteria. Results The area under the receiver operating characteristic curve (AUC) values for the logistic regression (LR), K-Nearest neighbors (KNN), random forest (RF), and decision tree (DT) models in validation cohort were 0.6960, 0.6307, 0.9989, and 0.9138, respectively. The RF algorithm was selected as the optimal predictive model for Grade 3 PGD risk after lung transplantation. The RF model showed a maximum net benefit of 0.2837 at a threshold probability of 0.6 in the training set. In the test cohort, the maximum net benefit was 0.3034 at a threshold probability of 0.5. The net benefit difference between the two datasets was minimal (mean difference: -0.0034). This finding reflected robust generalization capability for the RF model. The most influential features for the RF model's predictions were intraoperative red blood cell transfusion volume, preoperative oxygenation index, donor cold ischemia time, preoperative NT-proBNP, white blood cell count, use of cardiopulmonary bypass (CPB) during surgery, and CRP level. Conclusion The RF model effectively predicted the risk of Grade 3 PGD after lung transplantation. This model showed potential for providing decision support in the early identification of high-risk patients.","url":"https://doi.org/10.2147/ijgm.s600424","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.2147/ijgm.s600424","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1097/mat.0000000000002785","name":"Machine Learning Prediction of Pediatric In-Hospital Survival Before Extracorporeal Membrane Oxygenation Cannulation.","source":"europepmc","abstract":"Identifying suitable candidates for extracorporeal membrane oxygenation (ECMO) is still challenging. Our aim is to leverage machine learning (ML) to predict survival and identify critical variables influencing outcomes in pediatric patients requiring venovenous ECMO (VV-ECMO). This retrospective study used the Extracorporeal Life Support Organization (ELSO) registry to develop conventional ML algorithms and a transfer learning approach pretrained on the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database. The study included 4,169 pediatric patients (aged < 19 years). Model performance was assessed through internal and external validation using independent 2024 data for external testing. Overall survival to discharge was 73.2%. The transfer learning model achieved the highest predictive performance on external validation (accuracy: 0.73). It demonstrated robust results for survivors (recall: 0.92, F1-score: 0.83), although mortality prediction was significantly lower (F1-score: 0.29) due to outcome imbalance. Respiratory rate, SaO2, SpO2, and patient height were the most influential predictors across models. Transfer learning demonstrates strong predictive capacity for survival in pediatric VV-ECMO. Although significant outcome imbalance currently hinders mortality prediction, this methodology identifies key clinical variables. Future research should focus on ML techniques resilient to imbalanced outcomes to develop reliable clinical decision-support tools.","url":"https://doi.org/10.1097/mat.0000000000002785","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1097/mat.0000000000002785","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.21037/tlcr-2026-0559","name":"Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.","source":"europepmc","abstract":"Background Differentiating lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules (SPNs) remains clinically challenging, particularly in tuberculosis-endemic settings, because these two conditions may show overlapping computed tomography (CT) morphological features. This study aimed to develop and internally validate machine learning (ML) diagnostic models integrating these routinely available variables and to identify stable discriminative features using feature-importance analyses. Methods This single-center retrospective diagnostic prediction model development and internal validation study included adult patients with CT-detected SPNs measuring ≤3 cm and a definitive diagnosis of primary lung cancer or pulmonary tuberculosis between May 2020 and June 2024. Candidate predictors were extracted from baseline clinical information, tuberculosis-related tests, manually assessed CT morphological features, circulating tumor cell indicators, routine laboratory tests, and blood gas analysis variables obtained within 7 days before surgery or biopsy. Variables with more than 5% missingness were excluded, and 72 variables were retained before least absolute shrinkage and selection operator (LASSO) feature selection. The dataset was randomly divided into training and validation sets in a stratified 7:3 ratio. Seven ML models were subsequently constructed, including logistic regression (LR), random forest (RF), extra trees (ET), radial basis function support vector machine (RBF-SVM), k-nearest neighbors (KNN), multilayer perceptron (MLP), and gradient boosting decision tree (GBDT). Model performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic (ROC) curve, sensitivity, specificity, and balanced accuracy. Permutation importance and SHapley Additive exPlanations (SHAP) analyses were further performed to assess model interpretability. Results A total of 431 patients were included, comprising 168 patients with pathologically confirmed lung cancer and 263 patients with pulmonary tuberculosis. The median age was 60.00 years (interquartile range, 53.00-67.00 years), 277 patients (64.3%) were male, 349 patients (81.0%) had solid nodules, and 277 patients (64.3%) had positive QuantiFERON-TB (QFT) results. After missingness filtering, 72 variables were retained, and 29 features were selected by LASSO for model development. Among the seven models, the RBF-SVM model achieved the highest validation AUC of 0.803, with a sensitivity of 0.686, specificity of 0.734, and balanced accuracy of 0.710. The ET model showed comparable validation performance, with an AUC of 0.791, whereas LR achieved the highest balanced accuracy of 0.711. Cross-model interpretability analyses identified nodule type and age as the most stable core features. Conclusions ML models integrating routine clinical, manually assessed CT, and laboratory features showed moderate discriminative performance for differentiating lung cancer from pulmonary tuberculosis in patients with SPNs. The RBF-SVM model achieved the highest validation AUC, and cross-model interpretability analyses identified nodule type and age as stable discriminative features.","url":"https://doi.org/10.21037/tlcr-2026-0559","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.21037/tlcr-2026-0559","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s11239-026-03340-1","name":"Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.","source":"europepmc","abstract":"Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict residual thromboembolic risk, and HAS-BLED has insufficient predictive ability for direct oral anticoagulants (DOACs).While machine learning offers a transformative paradigm for risk assessment, its clinical application is often hindered by three critical challenges: data imbalance caused by low incidence of embolism and hemorrhage, the omission of drug metabolism-related features, and limited generalizability across diverse DOAC regimens. We retrospectively collected clinical data from patients receiving OAC in the First Affiliated Hospital of Soochow University from 2018 to 2024. To address data imbalance, we recruited patients in case-control manner, then implemented a non-boundary oversampling strategy. To investigate more valuable predictors, we incorporated drug metabolism-related features as potential predictors to develop robust models. Shapley Additive exPlanations (SHAP) analysis supports the global and local interpretation for prediction, validating the contribution of predictors and enhancing the credibility of models in clinic. 281 patients with bleeding events, 213 patients with thromboembolic events, and 978 as negatvie control were recruited. The overall dataset for bleeding risk prediction included 1,259 patients (positive-to-negative ratio ≈ 1:3.48), and that for thromboembolism risk prediction included 1,191 patients (positive-to-negative ratio ≈ 1:4.59). The Light Gradient Boosting Machine (LGBM) achieved an AUC of 0.880 for predicting bleeding risk, outperformed the HAS-BLED score (AUC = 0.730). The Logistic Regression (LR) for predicting thromboembolic risk achieved an AUC of 0.792, outperformed the CHA₂DS₂-VASc score (AUC = 0.628). Decision curve analysis further suggested that these models provided meaningful clinical net benefit within reasonable threshold ranges. SHAP identified pulmonary artery pressure (PAP), left atrial volume index (LAVI), platelet count, and left atrial appendage (LAA) volume as key predictors of thromboembolic risk, while estimated glomerular filtration rate (eGFR), body mass index (BMI), and direct bilirubin were key predictors of bleeding risk, consistent with clinical expectations. By integrating diverse clinical indicators, prioritizing collection of positive events, and applying a new data augmentation, we identified several novel predictors, including PAP, LAVI, platelet count, LAA volume, eGFR, and BMI. Compared with traditional risk scores, the new models suggested superior predictive performance, providing robust evidence-based support for personalized clinical decision-making.","url":"https://doi.org/10.1007/s11239-026-03340-1","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s11239-026-03340-1","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fmed.2026.1860785","name":"Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.","source":"europepmc","abstract":"Background Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Early identification of high-risk hypertensive patients is crucial for preventing cardiovascular events. While traditional risk scores rely on static clinical measurements, 24-h ambulatory blood pressure monitoring (ABPM)-derived time in target range (TTR) captures dynamic blood pressure control patterns that may improve risk stratification. Machine learning methods, particularly deep neural networks, offer an enhanced capability to model complex non-linear relationships in high-dimensional clinical data, compared with conventional statistical approaches. Methods This single-center retrospective cohort study included 1,026 patients admitted between January 2023 and December 2024, with 718 patients allocated to model development and 308 to internal validation. A deep neural network model with three hidden layers was developed and compared against eight conventional machine learning algorithms (logistic regression, naïve Bayes, k-nearest neighbors, random forest, support vector machine, XGBoost, LightGBM, and CatBoost). Thirty-two variables spanning demographics, clinical data, laboratory results, echocardiographic measures, and blood pressure indices were evaluated. Continuous variables were discretized into quartile-based categories to enhance clinical interpretability. Feature selection employed a two-step process combining the Boruta algorithm and least absolute shrinkage and selection operator (LASSO) regression, with variance inflation factor analysis confirming the absence of collinearity. Model selection prioritized balanced performance across discrimination (AUC), calibration (Brier score), and clinical utility (decision curve analysis) in the independent validation cohort. Interpretability was evaluated using SHAP (SHapley Additive exPlanations) values. Results The deep neural network model achieved optimal balanced performance with an AUC of 0.822 (95% CI: 0.793-0.850) in the training cohort and 0.796 (95% CI: 0.749-0.846) in the validation cohort, accompanied by the lowest Brier score (0.172), indicating superior calibration. Nine predictors were retained: diabetes mellitus, mean systolic blood pressure, time in target range of systolic blood pressure, left atrial diameter, left ventricular end-systolic diameter, left ventricular ejection fraction, use of antihypertensive medications, calcium channel blockers, and β -blockers. SHAP analysis identified TTR and blood pressure control parameters as the primary drivers of model predictions. Conclusion The developed deep neural network model enables early identification of high-risk CHD patients with hypertension through interpretable, routinely available clinical variables. Prospective multicenter external validation is warranted to confirm its generalizability across diverse populations and clinical settings.","url":"https://doi.org/10.3389/fmed.2026.1860785","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1860785","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.31083/rcm46901","name":"Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.","source":"europepmc","abstract":"Background Heart failure (HF) is a heterogeneous syndrome affecting over 60 million individuals globally. Patients with hypertension are particularly susceptible to developing HF. Therefore, timely identification and predictive assessment of HF risk have significant clinical implications in this population. Thus, this study aimed to develop a new interpretable machine learning (ML) model for HF prediction. Methods Using data from the Systolic Blood Pressure Intervention Trial (SPRINT), a random under-sampling technique was applied to address class imbalance in the target variable, achieving a 1:1 ratio between positive and negative samples. By randomly matching 162 individuals without HF events to those with events, a balanced dataset comprising 324 participants was constructed. The test set comprised 40% of the total dataset to ensure a robust evaluation of model performance. Seven ML algorithms, including support vector machine (SVM), adaptive boosting (Adaboost), naïve Bayes (NB), logistic regression (LR), gradient boosting machine (GBM), random forest (RF), and multilayer perceptron (MLP), were employed to construct the predictive models. Model performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curves, and other metrics. The SHapley Additive exPlanations (SHAP) approach was employed to rank feature significance and provide interpretability for the final model. Results Over a median follow-up of 3.88years, 162 patients (1.8%) developed incident HF. Among the seven ML models, GBM demonstrated the best performance. A total of 14 features were retained after the least absolute shrinkage and selection operator (LASSO) selection. The final model exhibited robust predictive capability for identifying HF risk, with an overall accuracy of 0.731, a precision of 0.770, and an AUC (95% confidence interval (CI)) of 0.763 (0.676-0.840). Conclusion The GBM-based explainable prediction model demonstrated robust performance in predicting HF risk among patients with hypertension.","url":"https://doi.org/10.31083/rcm46901","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.31083/rcm46901","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s00406-026-02367-y","name":"Integrated multi-omics and functional analyses reveal a GDCA-TGR5-mediated immunometabolic signature in anorexia nervosa.","source":"europepmc","abstract":"Background Anorexia nervosa (AN) is frequently accompanied by metabolic disturbances and immune dysregulation, yet its peripheral molecular mechanisms and therapeutic targets remain poorly understood. This study integrates multi-omics analyses with experimental validation to investigate causal relationships between circulating metabolites and immune features in AN. Methods Two-sample Mendelian randomization (MR) analyses based on two independent cohorts were conducted to screen metabolism- and immune-related factors associated with AN. Differential expression analysis combined with machine learning was used to identify core pathogenic genes and immune infiltration patterns, which were cross-validated with MR results. The immunoregulatory role of the core gene G protein-coupled bile acid receptor 1 (GPBAR1, also known as TGR5) was further validated in vitro using mouse bone marrow-derived macrophages (BMDMs) treated with the bile acid metabolite glycodeoxycholic acid (GDCA). Results MR analysis revealed significant causal associations between eight circulating metabolites, multiple immune cell traits, and AN. Integrated transcriptomic and machine learning analyses identified TGR5 as a key gene associated with AN. Functional enrichment and immune infiltration analyses indicated that AN is characterized by an immunosuppressive microenvironment, closely correlated with TGR5 expression and activation of the primary bile acid biosynthesis pathway. Molecular docking predicted a stable interaction between GDCA and TGR5. In vitro experiments showed that GDCA increased the proportion of CD206 + macrophages and upregulated Arg1 and CD206 expression in a TGR5-dependent manner. Mechanistically, GDCA activated the TGR5/cAMP/PKA pathway, promoted STAT3 and STAT6 phosphorylation, and induced M2 macrophage polarization, shifting cytokine secretion toward an anti-inflammatory phenotype. Conclusions These findings suggest that metabolic alterations in AN directly influence immune regulation. Activation of the GDCA/TGR5/cAMP/PKA/STAT3/6 axis promotes M2 macrophage polarization and anti-inflammatory responses, revealing a novel metabolic-immune pathway with therapeutic potential in AN.","url":"https://doi.org/10.1007/s00406-026-02367-y","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00406-026-02367-y","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3389/fmed.2026.1831220","name":"AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application.","source":"europepmc","abstract":"Machine learning models hold promise to revolutionize cardiovascular disease (CVD) prediction in patients with type 2 diabetes, with algorithms such as neural networks demonstrating superior discriminative performance in internal validations. However, a systematic review has revealed that existing models generally carry a high risk of bias and exhibit poor adherence to transparent reporting standards, severely hindering their clinical translation and real-world application. Furthermore, current models are predominantly developed using populations from Europe and North America, resulting in a critical lack of representativeness for Asian populations, where the burden of cardiovascular disease is particularly heavy. This article argues that the field is undergoing a pivotal transition-from an exclusive focus on algorithmic performance to ensuring clinical equity and fairness. Future advancements should prioritize external validation, calibration-aware assessment, subgroup-specific performance reporting, and cautious integration of biologically plausible biomarkers rather than relying on discrimination alone. Only through this approach can machine learning-driven predictive tools truly bridge the gap between innovation and equitable clinical implementation, ultimately alleviating the global burden of diabetes-related cardiovascular complications.","url":"https://doi.org/10.3389/fmed.2026.1831220","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1831220","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1371/journal.pdig.0001310","name":"Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.","source":"europepmc","abstract":"Despite advances in deep learning and transformer architectures, prior reviews have focused narrowly on traditional clinical decision support systems (CDSS) or single medical domains, leaving significant gaps in understanding contemporary AI-driven predictive tools. This systematic review and meta-analysis evaluated the predictive performance of artificial intelligence-based CDSS (AI-CDSS) across multiple medical specialties. Following PRISMA guidelines, PubMed and Cochrane Library were searched through December 2024 for studies evaluating predictive AI-CDSS using real-world clinical data. Two reviewers independently screened 3,296 records (κ = 0.833), with study quality assessed via QUADAS-2 and performance measures pooled using random-effects meta-analysis. Fifty studies spanning 17 medical specialties were included. Meta-analysis demonstrated moderate discriminatory ability (pooled AUC: 0.652, 95% CI: 0.562-0.743), high specificity (0.819, 95% CI: 0.793-0.844), moderate accuracy (0.765, 95% CI: 0.734-0.796), and variable sensitivity (0.660, 95% CI: 0.535-0.785), with substantial heterogeneity across all measures (I² ≥ 98.9%). Only 24% of studies involved prospective deployment, and 64% reported exclusively technical metrics without clinical workflow data. Predictive AI-CDSS demonstrate moderate-to-good diagnostic performance with strong specificity; however, the predominance of retrospective study designs and limited implementation reporting reveal critical gaps between technical validation and real-world clinical utility. To address these shortcomings, we propose the ROADMAP framework, structured around seven domains: Representative development, Outcomes-focused evaluation, Assessment for deployment, Data harmonization, Monitoring for bias, Allocation via economic evaluations, and Priorities for standardized reporting and prospective validation. This framework provides a practical roadmap for bridging the gap between algorithmic performance and meaningful clinical integration.","url":"https://doi.org/10.1371/journal.pdig.0001310","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001310","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1007/s00417-026-07434-7","name":"Surgical outcomes and risk factors identification for rhegmatogenous retinal detachment repair by pneumatic retinopexy using pure air.","source":"europepmc","abstract":"Objective This study aimed to evaluate the clinical outcomes and identify risk factors for failure of pneumatic retinopexy (PR) using pure air. Methods A retrospective analysis was conducted on 199 eyes from 199 patients with uncomplicated RRD who underwent PR at a single institution between 2019 and 2024. All patients were followed for at least 6 months, with specific follow-up intervals at 1 week and 1, 3, and 6 months. The primary outcome was the anatomical success rate. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify preoperative risk factors for surgical failure. A machine learning model (Random Forest) was also developed to predict surgical success. Results The single-operation anatomical success rate was 83.9% (167/199). Following repeated gas injection in select cases, the final success rate increased to 89.4% (178/199). Multivariate analysis identified younger age ( Conclusion Pneumatic retinopexy using intravitreal air is an effective treatment for uncomplicated RRD, achieving high final anatomical success rates. However, surgeons should be aware of a higher risk of failure in younger patients and in eyes with round retinal holes or lattice degeneration in the attached retina. Machine learning models show promise for preoperatively predicting surgical outcomes.","url":"https://doi.org/10.1007/s00417-026-07434-7","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1007/s00417-026-07434-7","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.3390/children13060796","name":"Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal Period.","source":"europepmc","abstract":"Background Moderate-to-severe preterm brain injury (PBI), including intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL), remains an important cause of adverse neurodevelopmental outcomes in preterm infants. Early risk stratification using routinely collected clinical data may help prioritize surveillance in vulnerable infants. Methods We retrospectively included 318 preterm infants admitted between 2015 and 2024 as the development cohort. Thirty-three candidate predictors derived from perinatal factors, first laboratory tests within 24 h of admission, and selected early hospitalization variables were evaluated. Seven machine-learning algorithms were developed using stratified 10 × 5 nested cross-validation with prespecified preprocessing, class-balancing, and feature-selection procedures. Candidate models were compared primarily using the mean fold-level area under the receiver operating characteristic curve (AUROC). After model selection, the finalized LightGBM model was calibrated using Platt scaling, and its pooled out-of-fold (OOF) performance was summarized. Two prespecified thresholds (Youden and high-sensitivity) were used for risk stratification. A small independent temporal cohort of 35 infants was used for preliminary external validation. Results PBI occurred in 62/318 infants (19.5%) in the development cohort and 6/35 infants (17.1%) in the temporal external cohort. During candidate-model comparison, LightGBM achieved the highest mean fold-level AUROC (0.768, 95% CI 0.708-0.825). The finalized 14-feature LightGBM model, evaluated using pooled OOF predictions after Platt calibration, yielded an AUROC of 0.747 (95% CI 0.679-0.811), a PR-AUC of 0.392, and a Brier score of 0.136. At the Youden threshold (0.18), sensitivity was approximately 0.70 and specificity approximately 0.85; at the high-sensitivity threshold (0.10), sensitivity was approximately 0.95 and specificity approximately 0.50. Key predictors included ventilation status and early physiologic and laboratory indicators. In the small temporal external cohort ( n = 35), the AUROC was 0.897 (95% CI 0.672-1.000); however, this high point estimate should not be overinterpreted because of the limited sample size, wide confidence interval, and suboptimal calibration, and should therefore be considered preliminary. Conclusions We developed an interpretable LightGBM model using routinely available early postnatal and early hospitalization data to support risk stratification for PBI in preterm infants. The model showed moderate internal discrimination and a positive net benefit across clinically relevant thresholds. Preliminary temporal external validation in a small cohort yielded highly uncertain estimates; larger multicenter studies are needed to confirm generalizability, refine calibration, and determine the most appropriate implementation strategy before routine clinical use.","url":"https://doi.org/10.3390/children13060796","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.3390/children13060796","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.1515/bmt-2024-0628","name":"Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.","source":"europepmc","abstract":"Objectives This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tumors, using data from 73 patients. Methods The dataset included PET/CT biomarkers and demographic factors, with SMOTE applied to address class imbalance. Three models were evaluated using 10-fold cross-validation to compare Random Forest (RF), Decision Tree (DT), Extra Trees Classifier (ETC), and XGBoost algorithms based on accuracy and AUC. Results ETC performed best in Model 1 (86 % accuracy, AUC 0.95), RF in Model 2 (94 % accuracy, AUC 0.97), and XGBoost in Model 3 (94 % accuracy, AUC 0.98). XGBoost consistently outperformed others, particularly in Model 3, which included age and smoking. Feature importance analysis highlighted SUVmax as the most predictive variable, with smoking having a moderate influence and gender being minimal. Conclusions Integrating clinical and lifestyle data with PET/CT parameters significantly improved LC classification. XGBoost emerged as the most effective model, demonstrating that comprehensive models enhance diagnostic accuracy beyond traditional metrics.","url":"https://doi.org/10.1515/bmt-2024-0628","authors":[],"tags":[],"confidence":0.8,"sites":["biomed-ai"],"publishedDate":"2026","doi":"10.1515/bmt-2024-0628","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2410.21831","name":"Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion","source":"datacite","abstract":"Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in their ability to handle complex multi-modal data. This paper proposes a deep learning-based approach leveraging CT and PET imaging modalities to predict survival outcomes in HNC patients. Our method integrates feature extraction with a Convolutional Block Attention Module (CBAM) and a multi-modal data fusion layer that combines imaging data to generate a compact feature representation. The final prediction is achieved through a fully parametric discrete-time survival model, allowing for flexible hazard functions that overcome the limitations of traditional survival models. We evaluated our approach using the HECKTOR and HEAD-NECK-RADIOMICS- HN1 datasets, demonstrating its superior performance compared to conconventional statistical and machine learning models. The results indicate that our deep learning model significantly improves survival prediction accuracy, offering a robust tool for personalized treatment planning in HNC","url":"https://doi.org/10.48550/arxiv.2410.21831","authors":["Farooq, Aiman","Sharma, Utkarsh","Mishra, Deepak"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.21831","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.24438184","name":"MeSH2Wikidata: A set of tools for the interaction between MeSH keywords, OBO Foundry, and Wikidata for enriching biomedical knowledge","source":"datacite","abstract":"The work consists of tools for the interaction between Wikidata and OBO Foundry and source codes for the use of MeSH keywords of PubMed publications for the enrichment of biomedical knowledge in Wikidata. This work is funded by the Adapting Wikidata to support clinical practice using Data Science, Semantic Web and Machine Learning Project within the framework of the Wikimedia Foundation Research Fund. To cite the work : Turki, H., Chebil, K., Dossou, B. F. P., Emezue, C. C., Owodunni, A. T., Hadj Taieb, M. A., &amp; Ben Aouicha, M. (2024). A framework for integrating biomedical knowledge in Wikidata with open biomedical ontologies and MeSH keywords. Heliyon , 10 (19), e38488. doi:10.1016/j.heliyon.2024.e38448.Wikidata-OBO tool1.py: A tool for the verification of the semantic alignment between Wikidata and OBO ontologies. frame.py: The layout of Tool 1. tool2.py: A tool for extracting Wikidata relations between OBO ontology items. frame2.py: The layout of Tool 2. tool3.py: A tool for extracting multilingual language data for OBO ontology items from Wikidata. frame4.py: The layout of Tool 3.Wikidata-MeSH correct_mesh2matrix_dataset.py: A source code for turning MeSH2Matrix into a smaller dataset for the biomedical relation classification based on the MeSH keywords of PubMed publications, named MiniMeSH2Matrix. build_numpy_dataset.py: A source code for building the numpy files for MiniMeSH2Matrix (Relation type-based classification). label_encoded.csv: A table for the conversion of Wikidata Property IDs into MeSH2Matrix Class IDs. new_encoding.csv: A table for the conversion of Wikidata Property IDs into MiniMeSH2Matrix Class IDs. super_classes_new_dataset_labels.npy: The NumPy File of the labels for the superclass-based classification. new_dataset_labels.npy: The NumPy File of the labels for the relation type-based classification. new_dataset_matrices.npy: The Numpy File of the MiniMeSH2Matrix matrices for biomedical relation classification. first_level_new_data.json: The JSON File for the conversion of relation types to superclasses. build_super_classes.py: A source code for building the numpy files for MiniMeSH2Matrix (Superclass-based classification). FC_MeSH_Model_57_New_Data.ipynb: A Jupyter Notebook for training a Dense Model to perform the relation type-based classification. FC_MeSH_Model_57_New_Data_SuperClasses.ipynb: A Jupyter Notebook for training a Dense Model to perform the superclass-based classification. new_data_best_model_1: A stored edition of the best model for the relation type-based classification. new_data_super_classes_best_model_1: A stored edition of the best model for the superclass-based classification. MiniMeSH2Matrix_SuperClasses_Confusion_Matrix.ipynb: A Jupyter Notebook for generating the confusion matrix for the superclass-based supervised classification. MiniMeSH2Matrix_Supervised_Classification_Agreement.ipynb: A Jupyter Notebook for generating the matrix of agreement between the accurate predictions for superclass-based classification and the ones for relation type-based classification. Adding_References_to_Wikidata.ipynb: A Jupyter Notebook to identify the PubMed ID of relevant references to unsupported Wikidata statements between MeSH terms. MeSH_Statistics.xlsx: Statistical data about MeSH-based items and relations in Wikidata. ref_for_unsupported_statements.csv: Retrieved Relevant PubMed References for 1k unsupported Wikidata statements. evaluate_pubmed_ref_assignment.ipynb: A Jupyter Notebook that generates statistics about reference assignment for a sample of 1k unsupported statements. MeSH_Verification.xlsx: A list of inaccurate or duplicated MeSH IDs in Wikidata, as of August 8th, 2023. WikiRelationsPMI.csv: A list of PMI values for the semantic relations between MeSH terms, as available in Wikidata. WikiRelationsPMIDistribution.xlsx: Distribution of PMI values for all Wikidata relations and for specific Wikidata relation types. WikiRelationsToVerify.xlsx: Wikidata relations needing att","url":"https://doi.org/10.6084/m9.figshare.24438184","authors":["Turki, Houcemeddine","Chebil, Khalil","Dossou, Bonaventure","Emezue, Chris","Owodunni, Abraham","Taieb, Mohamed Ali Hadj","Ben Aouicha, Mohamed"],"tags":["Knowledge and information management","Information modelling, management and ontologies","Information systems organisation and management","Information retrieval and web search","Data mining and knowledge discovery","Digital curation and preservation","Deep learning","Knowledge representation and reasoning"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2023","doi":"10.6084/m9.figshare.24438184","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2406.12142","name":"Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods","source":"datacite","abstract":"Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups - such as those based on sex, age, or disease subtype - as well as previously unknown and unlabeled groups. Furthermore, the root cause of such observed performance disparities is often challenging to uncover, hindering mitigation efforts. In this paper, to address these issues, we leverage Slice Discovery Methods (SDMs) to identify interpretable underperforming subsets of data and formulate hypotheses regarding the cause of observed performance disparities. We introduce a novel SDM and apply it in a case study on the classification of pneumothorax and atelectasis from chest x-rays. Our study demonstrates the effectiveness of SDMs in hypothesis formulation and yields an explanation of previously observed but unexplained performance disparities between male and female patients in widely used chest X-ray datasets and models. Our findings indicate shortcut learning in both classification tasks, through the presence of chest drains and ECG wires, respectively. Sex-based differences in the prevalence of these shortcut features appear to cause the observed classification performance gap, representing a previously underappreciated interaction between shortcut learning and model fairness analyses.","url":"https://doi.org/10.48550/arxiv.2406.12142","authors":["Olesen, Vincent","Weng, Nina","Feragen, Aasa","Petersen, Eike"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","Computers and Society (cs.CY)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.12142","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.27248406.v1","name":"Grand Challenge: Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024","source":"datacite","abstract":"The Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Grand Challenge is a 2024 MICCAI Grand Challenge. PI Fuller presented the keynote virtually and in-person on 2024-10-17T0900, describing the relevant issues in biomedical image segmentation, use-case-specific chalenges in head and neck MRI segmentation, and the application space for mpMRI segmentation in adaptive therapy as part of the planned end-Challenge Live Virtual Event.The Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Grand Challenge focuses on advancing automated tumor delineation techniques using Magnetic Resonance (MR) imaging for patients with head and neck cancers. This challenge seeks to address the clinical need for accurate and consistent tumor segmentation, which is critical for improving precision in MR-guided radiotherapy. The challenge invites global participation from researchers in the fields of medical imaging, radiotherapy, and machine learning to develop and test state-of-the-art algorithms capable of reliably identifying and segmenting head and neck tumors from MR images.Participants will have access to a large, curated dataset of multi-institutional MR images of head and neck cancer patients, with expert-annotated tumor regions. The challenge tasks participants to develop segmentation models that not only perform accurately but also generalize across various anatomical structures, imaging protocols, and patient populations. MR-guided radiotherapy demands precise delineation of tumor boundaries to minimize damage to surrounding healthy tissue, making automated segmentation tools a valuable asset in clinical workflows.The challenge evaluates submitted models based on segmentation accuracy, robustness, and computational efficiency. Metrics such as Dice Similarity Coefficient (DSC), Hausdorff Distance, and execution time will be used to rank the algorithms. Successful models could significantly impact adaptive radiotherapy, enabling real-time treatment adjustments based on updated MR images. The HNTS-MRG 2024 Grand Challenge offers an exciting opportunity for interdisciplinary collaboration, aiming to drive innovation in MR-guided cancer treatment and improve patient outcomes through enhanced imaging and computational techniques.","url":"https://doi.org/10.6084/m9.figshare.27248406.v1","authors":["Fuller, Clifton D."],"tags":["Radiology and organ imaging","Biomedical imaging","Computational imaging","Artificial intelligence not elsewhere classified","Machine learning not elsewhere classified","Radiation therapy","Medical physics"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.27248406.v1","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.11238928","name":"The Phantom EEG Dataset","source":"datacite","abstract":"When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals. arXiv preprint arXiv:2405.17024. 1 Metadata Brief introduction The present work aims to demonstrate that temporal autocorrelations (TA) significantly impacts various BCI tasks even in conditions without neural activity. We used the watermelon as the phantom head and found that we could get the pitfall of overestimated decoding performance if continuous EEG data with the same class label were split into training and test sets. More details can be found in Motivation. As watermelons cannot perform any experimental tasks, we can reorganize it to the format of various actual EEG dataset without the need to collect EEG data as previous work did (examples in Domain Studied). Measurement devices Manufacturers: NeuroScan SynAmps2 system (Compumedics Limited, Victoria, Australia) Configuration: 64-channel Ag/AgCl electrode cap with a 10/20 layout Species Watermelons. Ten watermelons served as phantom heads. Domain Studied Overestimated Decoding Performance in EEG decoding. Following BCI datasets in various BCI tasks have been reorganized using the Phantom EEG Dataset. The pitfall has been found in four of five tasks. - CVPR dataset [1] for image decoding task. - DEAP dataset [2] for emotion recognition task. - KUL dataset [3] for auditory spatial attention decoding task. - BCIIV2a dataset [4] for motor imagery task (the pitfalls were absent due to the use of rapid-design paradigm during EEG recording). - SIENA dataset [5] for epilepsy detection task. Tasks Completed Resting State but you could reorganize it to any task in BCI. Dataset Name The Phantom EEG Dataset Dataset license Creative Commons Attribution 4.0 International Code Your could get the code to read the data files (.cnt or .set) in the “code” folder. To run the codes, you should install the mne and numpy package. You could install via pip pip install mne==1.3.1 pip install numpy Then, you could use “BID2WMCVPR.py” to convert the BID dataset to the WM-CVPR dataset. You could also use “CNTK2WMCVPR.py” to convert the CNT dataset to the WM-CVPR dataset. The codes to reorganize other datasets other than CVPR [1] will be released on github after reviewing. Data information - CNT: the raw data. Each Subject (S*.cnt) contains the following information: EEG.data: EEG data (samples X channels) EEG.srate: Sampling frequency of the saved data EEG.chanlocs : channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) - BIDS: an extension to the brain imaging data structure for electroencephalography. BIDS primarily addresses the heterogeneity of data organization by following the FAIR principles [6]. Each Subject (sub-S*/eeg/) contains the following information: sub-S*_task-RestingState_channels.tsv: channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) sub-S*_task-RestingState_eeg.json: Some information about the dataset. sub-S*_task-RestingState_eeg.set: EEG data (samples X channels) sub-S*_task-RestingState_events.tsv: the event during recording. We organized events using block-design and rapid-event-design. However, it is important to note that this does not need to be considered in any subsequent data reorganization, as watermelons cannot follow any experimental instructions. - code: more information on Code. - readme.md: the information about the dataset. Recordings An additional electrode was placed on the lower part of the watermelon as the physiological reference, and the forehead served as the ground site. The inter-electrode impedances were maintained under 20 kOhm. Data were recorded at a sampling rate of 1000 Hz. EEG recordings for each watermelon lasted for more than 1 hour to ensure sufficient data for the decoding task. Citation and more in","url":"https://doi.org/10.5281/zenodo.11238928","authors":["Anonymous"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.11238928","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13341214","name":"The Phantom EEG Dataset","source":"datacite","abstract":"When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals. arXiv preprint arXiv:2405.17024. 1 Metadata Brief introduction The present work aims to demonstrate that temporal autocorrelations (TA) significantly impacts various BCI tasks even in conditions without neural activity. We used the watermelon as the phantom head and found that we could get the pitfall of overestimated decoding performance if continuous EEG data with the same class label were split into training and test sets. More details can be found in Motivation. As watermelons cannot perform any experimental tasks, we can reorganize it to the format of various actual EEG dataset without the need to collect EEG data as previous work did (examples in Domain Studied). Measurement devices Manufacturers: NeuroScan SynAmps2 system (Compumedics Limited, Victoria, Australia) Configuration: 64-channel Ag/AgCl electrode cap with a 10/20 layout Species Watermelons. Ten watermelons served as phantom heads. Domain Studied Overestimated Decoding Performance in EEG decoding. Following BCI datasets in various BCI tasks have been reorganized using the Phantom EEG Dataset. The pitfall has been found in four of five tasks. - CVPR dataset [1] for image decoding task. - DEAP dataset [2] for emotion recognition task. - KUL dataset [3] for auditory spatial attention decoding task. - BCIIV2a dataset [4] for motor imagery task (the pitfalls were absent due to the use of rapid-design paradigm during EEG recording). - SIENA dataset [5] for epilepsy detection task. Tasks Completed Resting State but you could reorganize it to any task in BCI. Dataset Name The Phantom EEG Dataset Dataset license Creative Commons Attribution 4.0 International Code Your could get the code to read the data files (.cnt or .set) in the “code” folder. To run the codes, you should install the mne and numpy package. You could install via pip pip install mne==1.3.1 pip install numpy Then, you could use “BID2WMCVPR.py” to convert the BID dataset to the WM-CVPR dataset. You could also use “CNTK2WMCVPR.py” to convert the CNT dataset to the WM-CVPR dataset. The codes to reorganize other datasets other than CVPR [1] will be released on github after reviewing. Data information - CNT: the raw data. Each Subject (S*.cnt) contains the following information: EEG.data: EEG data (samples X channels) EEG.srate: Sampling frequency of the saved data EEG.chanlocs : channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) - BIDS: an extension to the brain imaging data structure for electroencephalography. BIDS primarily addresses the heterogeneity of data organization by following the FAIR principles [6]. Each Subject (sub-S*/eeg/) contains the following information: sub-S*_task-RestingState_channels.tsv: channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) sub-S*_task-RestingState_eeg.json: Some information about the dataset. sub-S*_task-RestingState_eeg.set: EEG data (samples X channels) sub-S*_task-RestingState_events.tsv: the event during recording. We organized events using block-design and rapid-event-design. However, it is important to note that this does not need to be considered in any subsequent data reorganization, as watermelons cannot follow any experimental instructions. - code: more information on Code. - readme.md: the information about the dataset. Recordings An additional electrode was placed on the lower part of the watermelon as the physiological reference, and the forehead served as the ground site. The inter-electrode impedances were maintained under 20 kOhm. Data were recorded at a sampling rate of 1000 Hz. EEG recordings for each watermelon lasted for more than 1 hour to ensure sufficient data for the decoding task. Citation and more in","url":"https://doi.org/10.5281/zenodo.13341214","authors":["Anonymous"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13341214","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.11238929","name":"The Phantom EEG Dataset","source":"datacite","abstract":"New version on https://zenodo.org/records/13341214. When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper. Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals. arXiv preprint arXiv:2405.17024. 1 Metadata Brief introduction The present work aims to demonstrate that temporal autocorrelations (TA) significantly impacts various BCI tasks even in conditions without neural activity. We used the watermelon as the phantom head and found that we could get the pitfall of overestimated decoding performance if continuous EEG data with the same class label were split into training and test sets. More details can be found in Motivation. As watermelons cannot perform any experimental tasks, we can reorganize it to the format of various actual EEG dataset without the need to collect EEG data as previous work did (examples in Domain Studied). Measurement devices Manufacturers: NeuroScan SynAmps2 system (Compumedics Limited, Victoria, Australia) Configuration: 64-channel Ag/AgCl electrode cap with a 10/20 layout Species Watermelons. Ten watermelons served as phantom heads. Domain Studied Overestimated Decoding Performance in EEG decoding. Following BCI datasets in various BCI tasks have been reorganized using the Phantom EEG Dataset. The pitfall has been found in four of five tasks. - CVPR dataset [1] for image decoding task. - DEAP dataset [2] for emotion recognition task. - KUL dataset [3] for auditory spatial attention decoding task. - BCIIV2a dataset [4] for motor imagery task (the pitfalls were absent due to the use of rapid-design paradigm during EEG recording). - SIENA dataset [5] for epilepsy detection task. Tasks Completed Resting State but you could reorganize it to any task in BCI. Dataset Name The Phantom EEG Dataset Dataset license Creative Commons Attribution 4.0 International Code The code to read the data files (.cnt) is provided in \"Other\". We could not add the file in this version because Zenodo demand that \"you must create a new version to add, modify or delete files\". We will add the file after organizing the datasets to comply with the FAIR principles in the version v2 recently. Data information The data will be published with following format in version v2: - CNT: the raw data. - BIDS: an extension to the brain imaging data structure for electroencephalography. BIDS primarily addresses the heterogeneity of data organization by following the FAIR principles [6]. An additional electrode was placed on the lower part of the watermelon as the physiological reference, and the forehead served as the ground site. The inter-electrode impedances were maintained under 20 kOhm. Data were recorded at a sampling rate of 1000 Hz. EEG recordings for each watermelon lasted for more than 1 hour to ensure sufficient data for the decoding task. Each Subject (S*.cnt) contains the following information: EEG.data: EEG data (samples X channels) EEG.srate: Sampling frequency of the saved data EEG.chanlocs : channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded) Citation and more information Citation will be updated after the review period is completed. We will provide more information about this dataset (e.g. the units of the captured data) once our work is accepted. This is because our work is currently under review, and we are not allowed to disclose more information according to the relevant requirements. All metadata will be provided as a backup on Github and will be available after the review period is completed. 2 Motivation Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, epilepsy detection, etc. Since these EEG data were usually collecte","url":"https://doi.org/10.5281/zenodo.11238929","authors":["Anonymous"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.11238929","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2404.15201","name":"CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT","source":"datacite","abstract":"The widespread adoption of Electronic Health Records (EHR) has significantly increased the amount of available healthcare data. This has allowed models inspired by Natural Language Processing (NLP) and Computer Vision, which scale exceptionally well, to be used in EHR research. Particularly, BERT-based models have surged in popularity following the release of BEHRT and Med-BERT. Subsequent models have largely built on these foundations despite the fundamental design choices of these pioneering models remaining underexplored. Through incremental optimization, we study BERT-based EHR modeling and isolate the sources of improvement for key design choices, giving us insights into the effect of data representation, individual technical components, and training procedure. Evaluating this across a set of generic tasks (death, pain treatment, and general infection), we showed that improving data representation can increase the average downstream performance from 0.785 to 0.797 AUROC ($p&lt;10^{-7}$), primarily when including medication and timestamps. Improving the architecture and training protocol on top of this increased average downstream performance to 0.801 AUROC ($p&lt;10^{-7}$). We then demonstrated the consistency of our optimization through a rigorous evaluation across 25 diverse clinical prediction tasks. We observed significant performance increases in 17 out of 25 tasks and improvements in 24 tasks, highlighting the generalizability of our results. Our findings provide a strong foundation for future work and aim to increase the trustworthiness of BERT-based EHR models.","url":"https://doi.org/10.48550/arxiv.2404.15201","authors":["Odgaard, Mikkel","Klein, Kiril Vadimovic","Thysen, Sanne Møller","Jimenez-Solem, Espen","Sillesen, Martin","Nielsen, Mads"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.15201","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.13021/jssr.2024","name":"Journal of Student-Scientists' Research, Vol. 6 (2024)","source":"datacite","abstract":"Abstracts submitted by the Aspiring Scientists Summer Internship Program cohort of 2024. Each describes an 8-week project conducted under faculty mentorship at George Mason University that was publically presented at our in-person or remote research symposiums in August 2024. Congratulations interns on your completion of the program, and thank you for your hard work! (Note: Not every ASSIP 2024 abstract may posted publically. Some projects are the subject of an invention disclosure, are undergoing double-blind peer review as part of a scientific manuscript, or describe findings that are part of a grant submission.)","url":"https://doi.org/10.13021/jssr.2024","authors":["Journal Of Student-Scientists' Research"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.13021/jssr.2024","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48729/pjctvs.411","name":"Advancing Vascular Surgery: The Role Of Artificial Intelligence And Machine Learning In Managing Carotid Stenosis","source":"datacite","abstract":"Introduction: Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclerotic plaque accumulation, significantly contributes to this, posing a risk for cerebrovascular events and ischemic strokes. With carotid stenosis (CS) being a primary concern, accurate diagnosis, clinical staging, and timely surgical interventions, such as carotid endarterectomy (CEA), are crucial. This review explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) in improving diagnosis, risk stratification, and management of CS. Methods: A comprehensive literature review was conducted using PubMed and SCOPUS, focusing on AI and ML applications in diagnosing and managing extracranial CS. English language publications from the past two decades were reviewed, including cross-referenced scientific articles. Results: Recent advancements in AI-enhanced imaging techniques, particularly in deep learning, have significantly improved diagnostic accuracy in identifying carotid plaque vulnerability and symptomatic plaques. Integration of clinical risk factors with AI systems has further enhanced precision. Additionally, ML models have shown promising results in identifying culprit arteries in patients with previous cerebrovascular events. These advancements hold immense potential for improving CS diagnosis and classification, leading to better patient management. Conclusion: Integrating AI and ML into vascular surgery, particularly in managing CS, marks a transformative advancement. These technologies have significantly improved diagnostic accuracy and risk assessment, paving the way for more personalized and safer patient care. Despite clinical validation and data privacy challenges, AI and ML have immense potential for enhancing clinical decision-making in vascular surgery, marking a pivotal phase in the field's evolution.","url":"https://doi.org/10.48729/pjctvs.411","authors":["Pias, Ana Daniela","Pereira-Macedo, Juliana","Marreiros, Ana","António, Nuno","Rocha-Neves, João"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48729/pjctvs.411","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.c.7489372.v1","name":"Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review","source":"datacite","abstract":"Abstract Background The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore the current applications of ML in MH research, with a particular focus on its use in studying diverse and vulnerable populations, including immigrants, refugees, migrants, and racial and ethnic minorities. Methods From October 2022 to March 2024, Google Scholar, EMBASE, and PubMed were queried. ML-related, MH-related, and population-of-focus search terms were strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance were extracted from each. Results Ultimately, 13 peer-reviewed publications were included. All the articles were published within the last 6 years, and over half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method. Conclusions The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our review finds that the clinical application of these models for classifying and predicting MH disorders is still under development.","url":"https://doi.org/10.6084/m9.figshare.c.7489372.v1","authors":["Park, Khushbu Khatri","Saleem, Mohammad","Al-Garadi, Mohammed Ali","Ahmed, Abdulaziz"],"tags":["Biotechnology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.c.7489372.v1","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.c.7489372","name":"Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review","source":"datacite","abstract":"Abstract Background The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By examining the published literature, this review aims to explore the current applications of ML in MH research, with a particular focus on its use in studying diverse and vulnerable populations, including immigrants, refugees, migrants, and racial and ethnic minorities. Methods From October 2022 to March 2024, Google Scholar, EMBASE, and PubMed were queried. ML-related, MH-related, and population-of-focus search terms were strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance were extracted from each. Results Ultimately, 13 peer-reviewed publications were included. All the articles were published within the last 6 years, and over half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method. Conclusions The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our review finds that the clinical application of these models for classifying and predicting MH disorders is still under development.","url":"https://doi.org/10.6084/m9.figshare.c.7489372","authors":["Park, Khushbu Khatri","Saleem, Mohammad","Al-Garadi, Mohammed Ali","Ahmed, Abdulaziz"],"tags":["Biotechnology","Biological Sciences not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.c.7489372","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13340927","name":"Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones","source":"datacite","abstract":"Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. Please also cite the original publication if this data is used in your work: Ianevski, A., Nader, K., Driva, K., Senkowski, W., Bulanova, D., Moyano-Galceran, L., Ruokoranta, T., Kuusanmäki, H., Ikonen, N., Sergeev, P., Vähä-Koskela, M., Giri, A. K., Vähärautio, A., Kontro, M., Porkka, K., Pitkänen, E., Heckman, C. A., Wennerberg, K., & Aittokallio, T. (2024). Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones. Nature communications, 15(1), 8579. https://doi.org/10.1038/s41467-024-52980-5","url":"https://doi.org/10.5281/zenodo.13340927","authors":["Ianevski, Aleksandr","Nader, Kristen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13340927","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13340926","name":"Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones","source":"datacite","abstract":"Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. Please also cite the original publication if this data is used in your work: Ianevski, A., Nader, K., Driva, K., Senkowski, W., Bulanova, D., Moyano-Galceran, L., Ruokoranta, T., Kuusanmäki, H., Ikonen, N., Sergeev, P., Vähä-Koskela, M., Giri, A. K., Vähärautio, A., Kontro, M., Porkka, K., Pitkänen, E., Heckman, C. A., Wennerberg, K., & Aittokallio, T. (2024). Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones. Nature communications, 15(1), 8579. https://doi.org/10.1038/s41467-024-52980-5","url":"https://doi.org/10.5281/zenodo.13340926","authors":["Ianevski, Aleksandr","Nader, Kristen"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13340926","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2402.17917","name":"Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals","source":"datacite","abstract":"In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approach for phenotyping using routinely collected physiological time series data. Our new algorithm integrates Long Short-Term Memory (LSTM) networks with collaborative filtering concepts to identify common physiological states across patients. Tested on real-world ICU clinical data for intracranial hypertension (IH) detection in patients with brain injury, our method achieved an area under the curve (AUC) of 0.889 and average precision (AP) of 0.725. Moreover, our algorithm outperforms autoencoders in learning more structured latent representations of the physiological signals. These findings highlight the promise of our methodology for patient phenotyping, leveraging routinely collected multivariate time series to improve clinical care practices.","url":"https://doi.org/10.48550/arxiv.2402.17917","authors":["Haule, Hollan","Piper, Ian","Jones, Patricia","Lo, Tsz-Yan Milly","Escudero, Javier"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.17917","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48687/lsj.232","name":"The Impact of Technology and Digital Health on Cardiology: A Review of the Present to Reach the Future","source":"datacite","abstract":"The integration of digital health technologies is revolutionizing the field of cardiology, particularly in the diagnosis, treatment, and management of cardiovascular diseases (CVDs). The rapid advancements in wearable devices, artificial intelligence (AI), and telemedicine have enabled more precise, predictable, and personalized care strategies, transforming the landscape of cardiovascular health. Wearable technologies, such as smartwatches with some electrocardiogram (ECG) capabilities, have improved early detection of arrhythmias, particularly atrial fibrillation (AF), enhancing patient outcomes by enabling timely interventions. Similarly, AI-driven diagnostic tools and machine learning (ML) models have demonstrated superior accuracy in interpreting ECGs and identifying complex arrhythmias, often outperforming traditional methods. Telehealth has also gained traction, particularly during the COVID-19 pandemic, by facilitating remote monitoring of chronic CVDs. Remote monitoring devices, including implantable pacemakers and defibrillators, have further reduced mortality rates by providing real-time data to healthcare providers, allowing for early interventions. AI language models, such as ChatGPT, are being utilized to accelerate research, aid in clinical decision-making, and enhance patient engagement through personalized education and real-time assistance. In addition to these advancements, digital therapeutics, and mobile health (mHealth) platforms are providing real-time feedback to patients and improving adherence to medication regimens, which is crucial for managing chronic conditions like hypertension and heart failure. Genomic and metabolomic medicine, with its focus on precision cardiology, allows for more personalized treatment plans based on an individual's genetic profile, further enhancing outcomes for those at risk for inherited cardiovascular diseases. Despite the promising developments, challenges remain, including the need for better integration with healthcare systems, data privacy concerns, and ensuring equitable access to these technologies. Nevertheless, the future of cardiology is expected to be shaped by advancements in AI, wearable technologies, and precision medicine, paving the way for real proactive and personalized care.","url":"https://doi.org/10.48687/lsj.232","authors":["Couto Da Rocha, Sofia","Ramos, Alberto"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48687/lsj.232","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.27116701.v2","name":"<b>Datos_individuos</b>","source":"datacite","abstract":"What is Cholesterol Concentration and Its Relationship with PPG Signals? Cholesterol concentration refers to the total amount of cholesterol present in the blood, measured in milligrams per deciliter (mg/dL). While cholesterol is a vital fatty substance necessary for the body, elevated levels can increase the risk of cardiovascular diseases such as arteriosclerosis, heart attacks, and strokes. Monitoring cholesterol levels is crucial for the prevention and management of these conditions (Awasthi &amp; Dixit, 2021; Ma &amp; Shieh, 2006).On the other hand, photoplethysmography (PPG) signals are non-invasive measurements that use light to detect changes in blood volume within peripheral tissues, typically in fingers or ears. The PPG signal is directly related to cardiac activity and blood flow (Allen, 2007; Maeda et al., 2011).Recent studies have investigated the potential of PPG signals to estimate certain biometric parameters, including cholesterol concentration. Elevated cholesterol levels can affect the elasticity of blood vessels, suggesting that characteristics of PPG signals may provide indirect information about blood cholesterol levels (Maqsood et al., 2021; Chatterjee &amp; Roy, 2018).The dataset presented includes both PPG signal recordings from subjects and their cholesterol levels. By combining these two data sources, researchers can analyze correlations or specific patterns that may allow for estimating total cholesterol from PPG signals. This approach aims to advance the development of non-invasive methods for cholesterol detection and monitoring, potentially offering a simpler and more accessible alternative to traditional blood tests (Zhang et al., 2022).A total of 46 participants were recruited (14 men and 32 women), aged between 32 and 80 years, with a mean age of 54.5 years. Detailed information was collected for each individual, including key demographic and biometric data, as well as indicators related to their cardiovascular health. Collected parameters included weight, height, and cholesterol levels, along with information on whether participants were under medical treatment at the time of the study.PPG signals were obtained using standard measurement devices for this technique, although specific details are not provided in the current dataset. Cholesterol levels were measured using standard clinical methods. Additional details regarding the specific conditions under which the signals were collected, such as the time of day or whether subjects were at rest, have not been recorded.Data were organized in an Excel spreadsheet to facilitate analysis, structured as follows:RecordSexAgeWeightHeightMedicCholesterol1F45681.65Yes2102M56851.78No190This dataset, named SUBJECT_DATA_21_06_2024_Sterol.xlsx , can be found in the folder called Data_individuals , allowing for a comparative analysis between cholesterol levels and obtained PPG signals to validate tools for cardiovascular health diagnosis and monitoring. Data Organization Method In this work, 46 .MAT files were utilized, each named according to the format subjectXX_PPG.mat , along with an Excel file titled SUBJECT_DATA_21_06_2024_Sterol.xlsx mentioned earlier. This approach ensures that relevant characteristics of subjects, including age, gender, and cholesterol levels, are stored for each record. The MATLAB code used to organize these data can be found in the attached folder named data , specifically in the file named data_juan.m .To ensure that the organized data is easily accessible and downloadable, it has been saved in two formats: .MAT, compatible with MATLAB, and .CSV, usable in other programming languages like Python and R The code for the visualization in Python is located in a file with the same name in the folder already mentioned. The lines of code for saving the organized information are as follows: matlabCopiar código % Save the organized information save('organized_data.mat', 'subject_data'); % Save as .MAT % Save as CSV data_table = struct2tab","url":"https://doi.org/10.6084/m9.figshare.27116701.v2","authors":["ortiz, Juan david"],"tags":["Clinical chemistry (incl. diagnostics)"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.27116701.v2","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.6084/m9.figshare.27116701.v1","name":"<b>Datos_individuos</b>","source":"datacite","abstract":"What is Cholesterol Concentration and Its Relationship with PPG Signals? Cholesterol concentration refers to the total amount of cholesterol present in the blood, measured in milligrams per deciliter (mg/dL). While cholesterol is a vital fatty substance necessary for the body, elevated levels can increase the risk of cardiovascular diseases such as arteriosclerosis, heart attacks, and strokes. Monitoring cholesterol levels is crucial for the prevention and management of these conditions (Awasthi &amp; Dixit, 2021; Ma &amp; Shieh, 2006).On the other hand, photoplethysmography (PPG) signals are non-invasive measurements that use light to detect changes in blood volume within peripheral tissues, typically in fingers or ears. The PPG signal is directly related to cardiac activity and blood flow (Allen, 2007; Maeda et al., 2011).Recent studies have investigated the potential of PPG signals to estimate certain biometric parameters, including cholesterol concentration. Elevated cholesterol levels can affect the elasticity of blood vessels, suggesting that characteristics of PPG signals may provide indirect information about blood cholesterol levels (Maqsood et al., 2021; Chatterjee &amp; Roy, 2018).The dataset presented includes both PPG signal recordings from subjects and their cholesterol levels. By combining these two data sources, researchers can analyze correlations or specific patterns that may allow for estimating total cholesterol from PPG signals. This approach aims to advance the development of non-invasive methods for cholesterol detection and monitoring, potentially offering a simpler and more accessible alternative to traditional blood tests (Zhang et al., 2022).A total of 46 participants were recruited (14 men and 32 women), aged between 32 and 80 years, with a mean age of 54.5 years. Detailed information was collected for each individual, including key demographic and biometric data, as well as indicators related to their cardiovascular health. Collected parameters included weight, height, and cholesterol levels, along with information on whether participants were under medical treatment at the time of the study.PPG signals were obtained using standard measurement devices for this technique, although specific details are not provided in the current dataset. Cholesterol levels were measured using standard clinical methods. Additional details regarding the specific conditions under which the signals were collected, such as the time of day or whether subjects were at rest, have not been recorded.Data were organized in an Excel spreadsheet to facilitate analysis, structured as follows:RecordSexAgeWeightHeightMedicCholesterol1F45681.65Yes2102M56851.78No190This dataset, named SUBJECT_DATA_21_06_2024_Sterol.xlsx , can be found in the folder called Data_individuals , allowing for a comparative analysis between cholesterol levels and obtained PPG signals to validate tools for cardiovascular health diagnosis and monitoring. Data Organization Method In this work, 46 .MAT files were utilized, each named according to the format subjectXX_PPG.mat , along with an Excel file titled SUBJECT_DATA_21_06_2024_Sterol.xlsx mentioned earlier. This approach ensures that relevant characteristics of subjects, including age, gender, and cholesterol levels, are stored for each record. The MATLAB code used to organize these data can be found in the attached folder named data , specifically in the file named data_juan.m .To ensure that the organized data is easily accessible and downloadable, it has been saved in two formats: .MAT, compatible with MATLAB, and .CSV, usable in other programming languages like Python and R. The lines of code for saving the organized information are as follows: matlabCopiar código % Save the organized information save('organized_data.mat', 'subject_data'); % Save as .MAT % Save as CSV data_table = struct2table(subject_data); writetable(data_table, 'organized_data.csv'); % Save as CSV The above code is included at the e","url":"https://doi.org/10.6084/m9.figshare.27116701.v1","authors":["ortiz, Juan david"],"tags":["Clinical chemistry (incl. diagnostics)"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.6084/m9.figshare.27116701.v1","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.16231","name":"Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling","source":"datacite","abstract":"The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machine learning and transformer-based techniques applied in survival analysis, the proposed approach highlights the potential of these techniques for more accurate early detection and intervention in Alzheimer's dementia disease. This research also underscores the importance of non-invasive biomarkers and innovative modelling tools in enhancing the accuracy of dementia risk assessments, offering new avenues for clinical practice and patient care. A comprehensive Monte Carlo simulation procedure consisting of 100 repetitions of a nested cross-validation in which models were trained and evaluated, indicates that the survival machine learning models based on Transformer and XGBoost achieved the highest mean C-index performances, namely 0.85 and 0.8, respectively, and that they are superior to the conventional survival analysis Cox Proportional Hazards model which achieved a mean C-Index of 0.77. Moreover, based on the standard deviations of the C-Index performances obtained in the Monte Carlo simulation, we established that both survival machine learning models above are more stable than the conventional statistical model.","url":"https://doi.org/10.48550/arxiv.2409.16231","authors":["Musto, Henry","Stamate, Daniel","Logofatu, Doina","Stahl, Daniel"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Neural and Evolutionary Computing (cs.NE)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.16231","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.16083","name":"Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients","source":"datacite","abstract":"Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia and is associated with increased morbidity and mortality. The effectiveness of current clinical interventions for AF is often limited by an incomplete understanding of the atrial anatomical structures that sustain this arrhythmia. Late Gadolinium-Enhanced MRI (LGE-MRI) has emerged as a critical imaging modality for assessing atrial fibrosis and scarring, which are essential markers for predicting the success of ablation procedures in AF patients. The Multi-class Bi-Atrial Segmentation (MBAS) challenge at MICCAI 2024 aims to enhance the segmentation of both left and right atria and their walls using a comprehensive dataset of 200 multi-center 3D LGE-MRIs, labelled by experts. This work presents an ensemble approach that integrates multiple machine learning models, including Unet, ResNet, EfficientNet and VGG, to perform automatic bi-atrial segmentation from LGE-MRI data. The ensemble model was evaluated using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD95) on the left &amp; right atrium wall, right atrium cavity, and left atrium cavity. On the internal testing dataset, the model achieved a DSC of 88.41%, 98.48%, 98.45% and an HD95 of 1.07, 0.95, 0.64 respectively. This demonstrates the effectiveness of the ensemble model in improving segmentation accuracy. The approach contributes to advancing the understanding of AF and supports the development of more targeted and effective ablation strategies.","url":"https://doi.org/10.48550/arxiv.2409.16083","authors":["Beveridge, Lucas","Zhang, Le"],"tags":["Image and Video Processing (eess.IV)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.16083","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.09161","name":"Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces","source":"datacite","abstract":"Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and signal variability. Classification models need periodic adaptation for real-life use, making an optimal re-training strategy essential to maximize user acceptance and maintain high performance. We propose TOR, a train-on-request workflow that enables user-specific model adaptation to novel conditions, addressing signal variability over time. Using continual learning, TOR preserves knowledge across sessions and mitigates inter-session variability. With TOR, users can refine, on demand, the model through on-device learning (ODL) to enhance accuracy adapting to changing conditions. We evaluate the proposed methodology on a motor-movement dataset recorded with a non-stigmatizing wearable BMI headband, achieving up to 92% accuracy and a re-calibration time as low as 1.6 minutes, a 46% reduction compared to a naive transfer learning workflow. We additionally demonstrate that TOR is suitable for ODL in extreme edge settings by deploying the training procedure on a RISC-V ultra-low-power SoC (GAP9), resulting in 21.6 ms of latency and 1 mJ of energy consumption per training step. To the best of our knowledge, this work is the first demonstration of an online, energy-efficient, dynamic adaptation of a BMI model to the intrinsic variability of EEG signals in real-time settings.","url":"https://doi.org/10.48550/arxiv.2409.09161","authors":["Mei, Lan","Cioflan, Cristian","Ingolfsson, Thorir Mar","Kartsch, Victor","Cossettini, Andrea","Wang, Xiaying","Benini, Luca"],"tags":["Signal Processing (eess.SP)","Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.09161","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.01280","name":"Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments","source":"datacite","abstract":"Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders.","url":"https://doi.org/10.48550/arxiv.2409.01280","authors":["Vermani, Ayesha","Dowling, Matthew","Jeon, Hyungju","Jordan, Ian","Nassar, Josue","Bernaerts, Yves","Zhao, Yuan","Van Vaerenbergh, Steven","Park, Il Memming"],"tags":["Neurons and Cognition (q-bio.NC)","FOS: Biological sciences","FOS: Biological sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.01280","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2408.11854","name":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?","source":"datacite","abstract":"The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevails in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications.","url":"https://doi.org/10.48550/arxiv.2408.11854","authors":["Gao, Yanjun","Myers, Skatje","Chen, Shan","Dligach, Dmitriy","Miller, Timothy A","Bitterman, Danielle","Churpek, Matthew","Afshar, Majid"],"tags":["Computation and Language (cs.CL)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.11854","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13823041","name":"Evaluating the Prognostic Accuracy of Two Severity Scoring Systems in Acute Kidney Failure","source":"datacite","abstract":"Background: Acute kidney failure (AKF), a rapid loss of kidney function, poses significant morbidity and mortality risks, particularly in hospitalized and critically ill patients. Accurate prognostic tools are essential for effective clinical management and improving patient outcomes. This study compares the prognostic accuracy of two severity scoring systems in predicting AKF outcomes. Aim: To evaluate and compare the effectiveness of two severity scoring systems in predicting the prognosis of patients with acute kidney failure. Methods: A prospective observational study was conducted at Rajshree Medical Research Institute from May 2023 to May 2024, involving 100 patients with AKF. Participants were assessed using two severity scoring systems, System A and System B. Data were collected on patient demographics, clinical presentation, and outcomes. Prognostic accuracy was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both systems. Results: The study included 62 males and 38 females, with an average age of 55.2 years. System B demonstrated slightly higher prognostic accuracy with an AUC of 0.85 compared to System A’s 0.82. System B also showed higher sensitivity (82% vs. 78%), specificity (88% vs. 85%), PPV (83% vs. 80%), and NPV (87% vs. 83%). Conclusion: Both severity scoring systems are effective in predicting AKF outcomes, but System B exhibits marginally better accuracy. These findings suggest that System B may be a more reliable tool for clinical decision-making in AKF management. Recommendations: Further research is recommended to validate these findings in larger, multicenter studies and to explore the integration of novel biomarkers and machine learning algorithms into severity scoring systems to enhance their prognostic accuracy.","url":"https://doi.org/10.5281/zenodo.13823041","authors":["Shashank Shekhar","Preeti Sinha","Wahengbam Purnakishor Singh"],"tags":["Acute Kidney Failure, Prognostic Accuracy, Severity Scoring Systems, Clinical Outcomes, ROC Curve Analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13823041","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13823042","name":"Evaluating the Prognostic Accuracy of Two Severity Scoring Systems in Acute Kidney Failure","source":"datacite","abstract":"Background: Acute kidney failure (AKF), a rapid loss of kidney function, poses significant morbidity and mortality risks, particularly in hospitalized and critically ill patients. Accurate prognostic tools are essential for effective clinical management and improving patient outcomes. This study compares the prognostic accuracy of two severity scoring systems in predicting AKF outcomes. Aim: To evaluate and compare the effectiveness of two severity scoring systems in predicting the prognosis of patients with acute kidney failure. Methods: A prospective observational study was conducted at Rajshree Medical Research Institute from May 2023 to May 2024, involving 100 patients with AKF. Participants were assessed using two severity scoring systems, System A and System B. Data were collected on patient demographics, clinical presentation, and outcomes. Prognostic accuracy was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both systems. Results: The study included 62 males and 38 females, with an average age of 55.2 years. System B demonstrated slightly higher prognostic accuracy with an AUC of 0.85 compared to System A’s 0.82. System B also showed higher sensitivity (82% vs. 78%), specificity (88% vs. 85%), PPV (83% vs. 80%), and NPV (87% vs. 83%). Conclusion: Both severity scoring systems are effective in predicting AKF outcomes, but System B exhibits marginally better accuracy. These findings suggest that System B may be a more reliable tool for clinical decision-making in AKF management. Recommendations: Further research is recommended to validate these findings in larger, multicenter studies and to explore the integration of novel biomarkers and machine learning algorithms into severity scoring systems to enhance their prognostic accuracy.","url":"https://doi.org/10.5281/zenodo.13823042","authors":["Shashank Shekhar","Preeti Sinha","Wahengbam Purnakishor Singh"],"tags":["Acute Kidney Failure, Prognostic Accuracy, Severity Scoring Systems, Clinical Outcomes, ROC Curve Analysis"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13823042","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13173971","name":"Webinars - HSI Awardee Grantsmanship","source":"datacite","abstract":"The HSI National STEM Resource Hub is preserving its website and compiled resources for future use and reference. This entry consists of the following webinars. Webinar Summer Grantsmanship Resources. Webinar Link: https://youtu.be/YwW-lbUA4xw Dr. Ellen Carpenter, HSI Program Director at the NSF, discusses the NSF solicitation for the HSI Program. Dr. Carpenter discusses important elements to include in your submission. She also shares strategies for successfully submitting your proposal including various elements of FastLane and Grants.gov. The Q&A session has been omitted from the recording. You can find the link to the transcript below. The HSI STEM Hub recommends viewing this webinar to help you in preparation of your proposal. Insights from Recent HSI Awardees – May 19, 2020 Webinar Link: https://youtu.be/YhcZ0FvvuiE Recent NSF HSI Program Awardees, Antonio Garcia and Matthew Cover, discuss strategies for writing a successful proposal for submission to the NSF HSI Program. Drs. Garcia and Cover address topics such as fundable ideas, budget, collaborations, intellectual merit, broader impact, reviewers, and the types of funded projects they engage at their institutions. The NSF HSI STEM Hub recommends viewing this training video before writing your proposal and while reviewing important parts of your proposal during the writing phase, before submission. Matt Cover is an educator and freshwater ecologist at Cal State Stanislaus, a regional comprehensive university in California’s San Joaquin Valley. He is the PI of an HSI STEM grant (NSF #1832558, awarded August 2018) that funds a faculty learning program for STEM educators called CIENCIA: Collaboration for Inclusive and Engaging Curriculum, Instruction, and Achievement. He is passionate about making STEM education more humane, student-centered, creative, and fun; making graduate study more accessible, especially for minoritized students; and bringing a social justice lens to STEM research and education. He gets joy from exploring creeks and looking for bugs and birds, walking and hiking with his partner and dog, and enjoying coffee, beer, and music. Website: http://www.matthewrcover.com/ Antonio (“Tony”) García was recently appointed as Associate Dean of Academics for the College of Engineering at New Mexico State University, and brings more than 34 years of experience in academia and industry. He also holds the position of George W. Lucky Professor in Chemical Engineering due to his expertise in bioprocessing and biomedical devices. As a designer, inventor and researcher, he has developed several diagnostic and drug delivery technologies in conjunction with an international team whose mission is to promote the use of personalized care technology to improve global health. Dr. García is also actively involved in education and human resource projects aimed at improving math, science, and engineering education and to meet the demand for a robust technological workforce as the nation’s demographics changes. He was Associate Editor of the Journal of Research in Science Teaching 2003-2005 and was the project director of a National Science Foundation program (LSAMP) project, established in 1992, to enhance opportunities for undergraduate and graduate students in science, math and engineering. After moving to Las Cruces in August 2019, he led a team of NMSU faculty to recently secure a NSF HSI grant (which began in April 2020) on Innovative Partnerships with Industry with the theme of: Enhancing Social Mobility by Combing Adult Learning within an Engineering Curriculum. Framing Student Success – May 26, 2020 Webinar Link: https://youtu.be/_aPkJTWP2yc The Dr. Alexis Racelis, University of Texas Rio Grande Valley, lab studies ecological interactions in the social, political, and economic contexts in which they occur. In particular, our research is driven by a better understanding of how agriculture, urban development, invasive species and resource management affects certain ecologic","url":"https://doi.org/10.5281/zenodo.13173971","authors":["HSI National STEM Resource Hub","Serrano, Elba","Morales, Gabriela","Peterman, Jarod","Pallarez, Jessica"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13173971","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13173970","name":"Webinars - HSI Awardee Grantsmanship","source":"datacite","abstract":"The HSI National STEM Resource Hub is preserving its website and compiled resources for future use and reference. This entry consists of the following webinars. Webinar Summer Grantsmanship Resources. Webinar Link: https://youtu.be/YwW-lbUA4xw Dr. Ellen Carpenter, HSI Program Director at the NSF, discusses the NSF solicitation for the HSI Program. Dr. Carpenter discusses important elements to include in your submission. She also shares strategies for successfully submitting your proposal including various elements of FastLane and Grants.gov. The Q&A session has been omitted from the recording. You can find the link to the transcript below. The HSI STEM Hub recommends viewing this webinar to help you in preparation of your proposal. Insights from Recent HSI Awardees – May 19, 2020 Webinar Link: https://youtu.be/YhcZ0FvvuiE Recent NSF HSI Program Awardees, Antonio Garcia and Matthew Cover, discuss strategies for writing a successful proposal for submission to the NSF HSI Program. Drs. Garcia and Cover address topics such as fundable ideas, budget, collaborations, intellectual merit, broader impact, reviewers, and the types of funded projects they engage at their institutions. The NSF HSI STEM Hub recommends viewing this training video before writing your proposal and while reviewing important parts of your proposal during the writing phase, before submission. Matt Cover is an educator and freshwater ecologist at Cal State Stanislaus, a regional comprehensive university in California’s San Joaquin Valley. He is the PI of an HSI STEM grant (NSF #1832558, awarded August 2018) that funds a faculty learning program for STEM educators called CIENCIA: Collaboration for Inclusive and Engaging Curriculum, Instruction, and Achievement. He is passionate about making STEM education more humane, student-centered, creative, and fun; making graduate study more accessible, especially for minoritized students; and bringing a social justice lens to STEM research and education. He gets joy from exploring creeks and looking for bugs and birds, walking and hiking with his partner and dog, and enjoying coffee, beer, and music. Website: http://www.matthewrcover.com/ Antonio (“Tony”) García was recently appointed as Associate Dean of Academics for the College of Engineering at New Mexico State University, and brings more than 34 years of experience in academia and industry. He also holds the position of George W. Lucky Professor in Chemical Engineering due to his expertise in bioprocessing and biomedical devices. As a designer, inventor and researcher, he has developed several diagnostic and drug delivery technologies in conjunction with an international team whose mission is to promote the use of personalized care technology to improve global health. Dr. García is also actively involved in education and human resource projects aimed at improving math, science, and engineering education and to meet the demand for a robust technological workforce as the nation’s demographics changes. He was Associate Editor of the Journal of Research in Science Teaching 2003-2005 and was the project director of a National Science Foundation program (LSAMP) project, established in 1992, to enhance opportunities for undergraduate and graduate students in science, math and engineering. After moving to Las Cruces in August 2019, he led a team of NMSU faculty to recently secure a NSF HSI grant (which began in April 2020) on Innovative Partnerships with Industry with the theme of: Enhancing Social Mobility by Combing Adult Learning within an Engineering Curriculum. Framing Student Success – May 26, 2020 Webinar Link: https://youtu.be/_aPkJTWP2yc The Dr. Alexis Racelis, University of Texas Rio Grande Valley, lab studies ecological interactions in the social, political, and economic contexts in which they occur. In particular, our research is driven by a better understanding of how agriculture, urban development, invasive species and resource management affects certain ecologic","url":"https://doi.org/10.5281/zenodo.13173970","authors":["HSI National STEM Resource Hub","Serrano, Elba","Morales, Gabriela","Peterman, Jarod","Pallarez, Jessica"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13173970","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13784506","name":"Prognostic Significance of Tumor Budding in Bladder Cancer: A Call for Molecular Insights","source":"datacite","abstract":"Dear Editor, We wish to share our insights on the critical prognostic significance of tumor budding (TB) in bladder cancer (BC) and underscore the urgent necessity for further molecular-level investigations in this domain, prompted by recent studies. Tumor buds, also referred to as “sprouts,” are defined as isolated single tumor cells and/or small clusters comprising fewer than five tumor cells, which originate from the invasive tumor margin and infiltrate the stroma. These entities were first characterized by Imai in the 1950s (1). The TB scoring system, established by the “International Tumour Budding Consensus Conference” (ITBCC) in 2016, has been validated as an independent predictor of lymph node metastasis in pT1 colorectal cancer cases and poor survival outcomes in stage 2 colon cancer cases, and it is now routinely reported by pathologists (2). Tumor buds are intimately associated with epithelial-mesenchymal transition (EMT) and engage in interactions with the tumor microenvironment (TME), tumor stroma, and immune system cells (3). This dynamic interaction at the molecular level in budding tumor cells establishes a distinctive signature characterized by: upregulation of MMP-7 and MMP-9 expressions, which play a role in extracellular matrix degradation; anoikis resistance through the enhanced expression of TrkB; frequent upregulation of stem cell markers such as LGR5, ALDH1, and CD44; immune evasion facilitated by the loss of MHC class I expression; increased TGFβ expression and regulation of TGFβ signaling; regulation of WNT signaling; a decrease in miRNA-200 expression, accompanied by the epigenetic upregulation of EMT-associated transcription factors, including ZEB, TWIST, and SNAIL; reduced expression of E-cadherin, particularly at the cell membrane, and β-catenin; an increase in mesenchymal markers like Vimentin, alongside a reduction in Cytokeratin expression; low levels of Ki-67 and Caspase-3 expression; and a relatively spindle-shaped morphology with podia formation (3). While it is generally accepted in solid tumors that “an increase in tumor buds correlates with a poorer clinical outcome” (3), the body of research examining TB as a prognostic marker in non-gastrointestinal tumors—particularly in BC—remains limited. As of August 8, 2024, a PubMed search using the MeSH terms “tumor budding AND bladder cancer*” yielded 34 studies, of which only 10 were found to be directly relevant to this subject. Fukumoto and colleagues investigated the prognostic effects of TB in 121 cases of pT1 non-muscle-invasive bladder cancer (NMIBC) and demonstrated that TB positivity was statistically significantly associated with pT1 sub-staging (microinvasion/extensive lamina propria invasion) (p=0.002), tumor architecture (papillary/nodular) (p=0.023), and lymphovascular invasion (LVI) positivity (p=0.001) (4). Additionally, it was reported that the 5-year progression-free survival rate was statistically significantly higher (p=0.001) in TB-negative pT1 BC cases (88.4%), and that TB was an independent risk predictor for progression to muscle-invasive bladder cancer (MIBC) in both the entire pT1 BC cohort and the subgroup receiving intravesical BCG instillation according to Cox regression analysis (4). Building on these findings, the researchers also observed that in 86% of TB-positive cases, E-cadherin immunoexpression in the tumor center was higher than in the TB areas, highlighting the relationship between TB and EMT in pT1 BC cases (4). While Fukumoto et al. focused on the clinical implications of TB, Miyake and colleagues delved into the underlying molecular mechanisms. In their comprehensive study, they used MGH-U3, UM-UC-14, and UM-UC-3 cells in an orthotopic bladder tumor model in SCID mice and suggested that COL4A1 and COL13A1 might play a primary role in the formation of the infiltrative pattern of TB (5). However, as accurate TB analysis can be complicated by peritumoral inflammatory infiltrate or reactive stromal cells, Br","url":"https://doi.org/10.5281/zenodo.13784506","authors":["Doğan, Tunay"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13784506","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.5281/zenodo.13784505","name":"Prognostic Significance of Tumor Budding in Bladder Cancer: A Call for Molecular Insights","source":"datacite","abstract":"Dear Editor, We wish to share our insights on the critical prognostic significance of tumor budding (TB) in bladder cancer (BC) and underscore the urgent necessity for further molecular-level investigations in this domain, prompted by recent studies. Tumor buds, also referred to as “sprouts,” are defined as isolated single tumor cells and/or small clusters comprising fewer than five tumor cells, which originate from the invasive tumor margin and infiltrate the stroma. These entities were first characterized by Imai in the 1950s (1). The TB scoring system, established by the “International Tumour Budding Consensus Conference” (ITBCC) in 2016, has been validated as an independent predictor of lymph node metastasis in pT1 colorectal cancer cases and poor survival outcomes in stage 2 colon cancer cases, and it is now routinely reported by pathologists (2). Tumor buds are intimately associated with epithelial-mesenchymal transition (EMT) and engage in interactions with the tumor microenvironment (TME), tumor stroma, and immune system cells (3). This dynamic interaction at the molecular level in budding tumor cells establishes a distinctive signature characterized by: upregulation of MMP-7 and MMP-9 expressions, which play a role in extracellular matrix degradation; anoikis resistance through the enhanced expression of TrkB; frequent upregulation of stem cell markers such as LGR5, ALDH1, and CD44; immune evasion facilitated by the loss of MHC class I expression; increased TGFβ expression and regulation of TGFβ signaling; regulation of WNT signaling; a decrease in miRNA-200 expression, accompanied by the epigenetic upregulation of EMT-associated transcription factors, including ZEB, TWIST, and SNAIL; reduced expression of E-cadherin, particularly at the cell membrane, and β-catenin; an increase in mesenchymal markers like Vimentin, alongside a reduction in Cytokeratin expression; low levels of Ki-67 and Caspase-3 expression; and a relatively spindle-shaped morphology with podia formation (3). While it is generally accepted in solid tumors that “an increase in tumor buds correlates with a poorer clinical outcome” (3), the body of research examining TB as a prognostic marker in non-gastrointestinal tumors—particularly in BC—remains limited. As of August 8, 2024, a PubMed search using the MeSH terms “tumor budding AND bladder cancer*” yielded 34 studies, of which only 10 were found to be directly relevant to this subject. Fukumoto and colleagues investigated the prognostic effects of TB in 121 cases of pT1 non-muscle-invasive bladder cancer (NMIBC) and demonstrated that TB positivity was statistically significantly associated with pT1 sub-staging (microinvasion/extensive lamina propria invasion) (p=0.002), tumor architecture (papillary/nodular) (p=0.023), and lymphovascular invasion (LVI) positivity (p=0.001) (4). Additionally, it was reported that the 5-year progression-free survival rate was statistically significantly higher (p=0.001) in TB-negative pT1 BC cases (88.4%), and that TB was an independent risk predictor for progression to muscle-invasive bladder cancer (MIBC) in both the entire pT1 BC cohort and the subgroup receiving intravesical BCG instillation according to Cox regression analysis (4). Building on these findings, the researchers also observed that in 86% of TB-positive cases, E-cadherin immunoexpression in the tumor center was higher than in the TB areas, highlighting the relationship between TB and EMT in pT1 BC cases (4). While Fukumoto et al. focused on the clinical implications of TB, Miyake and colleagues delved into the underlying molecular mechanisms. In their comprehensive study, they used MGH-U3, UM-UC-14, and UM-UC-3 cells in an orthotopic bladder tumor model in SCID mice and suggested that COL4A1 and COL13A1 might play a primary role in the formation of the infiltrative pattern of TB (5). However, as accurate TB analysis can be complicated by peritumoral inflammatory infiltrate or reactive stromal cells, Br","url":"https://doi.org/10.5281/zenodo.13784505","authors":["Doğan, Tunay"],"tags":[],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.5281/zenodo.13784505","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.10704","name":"Self-supervised Speech Models for Word-Level Stuttered Speech Detection","source":"datacite","abstract":"Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a small percentage of speech-language pathologists report being comfortable working with individuals who stutter, which is inadequate to accommodate for the 80 million individuals who stutter worldwide. Developing machine learning models for detecting stuttered speech would enable universal and automated screening for stuttering, enabling speech pathologists to identify and follow up with patients who are most likely to be diagnosed with a stuttering speech disorder. Previous research in this area has predominantly focused on utterance-level detection, which is not sufficient for clinical settings where word-level annotation of stuttering is the norm. In this study, we curated a stuttered speech dataset with word-level annotations and introduced a word-level stuttering speech detection model leveraging self-supervised speech models. Our evaluation demonstrates that our model surpasses previous approaches in word-level stuttering speech detection. Additionally, we conducted an extensive ablation analysis of our method, providing insight into the most important aspects of adapting self-supervised speech models for stuttered speech detection.","url":"https://doi.org/10.48550/arxiv.2409.10704","authors":["Shih, Yi-Jen","Gkalitsiou, Zoi","Dimakis, Alexandros G.","Harwath, David"],"tags":["Audio and Speech Processing (eess.AS)","Artificial Intelligence (cs.AI)","Computation and Language (cs.CL)","Sound (cs.SD)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.10704","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.10250","name":"Questioning AI: Promoting Decision-Making Autonomy Through Reflection","source":"datacite","abstract":"Decision-making is increasingly supported by machine recommendations. In healthcare, for example, a clinical decision support system is used by the physician to find a treatment option for a patient. In doing so, people can rely too much on these systems, which impairs their own reasoning process. The European AI Act addresses the risk of over-reliance and postulates in Article 14 on human oversight that people should be able \"to remain aware of the possible tendency of automatically relying or over-relying on the output\". Similarly, the EU High-Level Expert Group identifies human agency and oversight as the first of seven key requirements for trustworthy AI. The following position paper proposes a conceptual approach to generate machine questions about the decision at hand, in order to promote decision-making autonomy. This engagement in turn allows for oversight of recommender systems. The systematic and interdisciplinary investigation (e.g., machine learning, user experience design, psychology, philosophy of technology) of human-machine interaction in relation to decision-making provides insights to questions like: how to increase human oversight and calibrate over- and under-reliance on machine recommendations; how to increase decision-making autonomy and remain aware of other possibilities beyond automated suggestions that repeat the status-quo?","url":"https://doi.org/10.48550/arxiv.2409.10250","authors":["Fischer, Simon WS"],"tags":["Human-Computer Interaction (cs.HC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.10250","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.05871","name":"Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace","source":"datacite","abstract":"The absence of functional arm joints, such as the wrist, in upper extremity prostheses leads to compensatory motions in the users' daily activities. Compensatory motions have been previously studied for varying task protocols and evaluation metrics. However, the movement targets' spatial locations in previous protocols were not standardised and incomparable between studies, and the evaluation metrics were rudimentary. This work analysed compensatory motions in the final pose of subjects reaching across a discretely sampled 7*7 2D grid of targets under unbraced (normative) and braced (compensatory) conditions. For the braced condition, a bracing system was applied to simulate a transradial prosthetic limb by restricting participants' wrist joints. A total of 1372 reaching poses were analysed, and a Compensation Index was proposed to indicate the severity level of compensation. This index combined joint spatial location analysis, joint angle analysis, separability analysis, and machine learning (clustering) analysis. The individual analysis results and the final Compensation Index were presented in heatmap format to correspond to the spatial layout of the workspace, revealing the spatial dependency of compensatory motions. The results indicate that compensatory motions occur mainly in a right trapezoid region in the upper left area and a vertical trapezoid region in the middle left area for right-handed subjects reaching horizontally and vertically. Such results might guide motion selection in clinical rehabilitation, occupational therapy, and prosthetic evaluation to help avoid residual limb pain and overuse syndromes.","url":"https://doi.org/10.48550/arxiv.2409.05871","authors":["Yang, Qihan","Gloumakov, Yuri","Spiers, Adam J."],"tags":["Robotics (cs.RO)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.05871","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.02530","name":"Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models","source":"datacite","abstract":"The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of kidney function in clinical practice. Although traditional equations and Machine Learning (ML) models using clinical and laboratory data can estimate eGFR, accurately predicting future eGFR levels remains a significant challenge for nephrologists and ML researchers. Recent advances demonstrate that Large Language Models (LLMs) and Large Multimodal Models (LMMs) can serve as robust foundation models for diverse applications. This study investigates the potential of LMMs to predict future eGFR levels with a dataset consisting of laboratory and clinical values from 50 patients. By integrating various prompting techniques and ensembles of LMMs, our findings suggest that these models, when combined with precise prompts and visual representations of eGFR trajectories, offer predictive performance comparable to existing ML models. This research extends the application of foundation models and suggests avenues for future studies to harness these models in addressing complex medical forecasting challenges.","url":"https://doi.org/10.48550/arxiv.2409.02530","authors":["Li, Chih-Yuan","Wu, Jun-Ting","Hsu, Chan","Lin, Ming-Yen","Kang, Yihuang"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.02530","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2409.02303","name":"A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia","source":"datacite","abstract":"We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining. We then evaluate the performance using repeated 10-fold cross-validation on an in-house neuroimaging dataset of post-stroke aphasia. Our results demonstrate that LEGNet outperforms baseline deep learning methods in predicting language ability. LEGNet also exhibits superior generalization ability when tested on a second in-house dataset that was acquired under a slightly different neuroimaging protocol. Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation.","url":"https://doi.org/10.48550/arxiv.2409.02303","authors":["Chen, Zijian","Varkanitsa, Maria","Ishwar, Prakash","Konrad, Janusz","Betke, Margrit","Kiran, Swathi","Venkataraman, Archana"],"tags":["Machine Learning (cs.LG)","Signal Processing (eess.SP)","Neurons and Cognition (q-bio.NC)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Biological sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.02303","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"doi:10.48550/arxiv.2404.03833","name":"An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion","source":"datacite","abstract":"Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has been demonstrated, and is essential to increasing the trustworthiness and likelihood of adoption of these models. The objective of this study was to develop and implement a framework for addressing both these issues - fairness and explainability. We propose an explainable fairness framework, first developing a model with optimized performance, and then using an in-processing approach to mitigate model biases relative to the sensitive attributes of race and sex. We then explore and visualize explanations of the model changes that lead to the fairness enhancement process through exploring the changes in importance of features. Our resulting-fairness enhanced models retain high sensitivity with improved fairness and explanations of the fairness-enhancement that may provide helpful insights for healthcare providers to guide clinical decision-making and resource allocation.","url":"https://doi.org/10.48550/arxiv.2404.03833","authors":["Lucas, Mary M.","Wang, Xiaoyang","Chang, Chia-Hsuan","Yang, Christopher C.","Braughton, Jacqueline E.","Ngo, Quyen M."],"tags":["Machine Learning (cs.LG)","Computers and Society (cs.CY)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["biomed-ai"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.03833","addedAt":"2026-09-01T01:48:04.376Z","updatedAt":"2026-09-01T01:48:04.376Z"},{"id":"arxiv:2012.00419v1","name":"Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence","source":"arxiv","abstract":"Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the trade-offs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.","url":"https://arxiv.org/abs/2012.00419v1","authors":["Wiebke Toussaint","Aaron Yi Ding"],"tags":["cs.LG","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2020-12-01T11:42:34Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2409.00339v1","name":"Fish Tracking Challenge 2024: A Multi-Object Tracking Competition with Sweetfish Schooling Data","source":"arxiv","abstract":"The study of collective animal behavior, especially in aquatic environments, presents unique challenges and opportunities for understanding movement and interaction patterns in the field of ethology, ecology, and bio-navigation. The Fish Tracking Challenge 2024 (https://ftc-2024.github.io/) introduces a multi-object tracking competition focused on the intricate behaviors of schooling sweetfish. Using the SweetFish dataset, participants are tasked with developing advanced tracking models to accurately monitor the locations of 10 sweetfishes simultaneously. This paper introduces the competition's background, objectives, the SweetFish dataset, and the appraoches of the 1st to 3rd winners and our baseline. By leveraging video data and bounding box annotations, the competition aims to foster innovation in automatic detection and tracking algorithms, addressing the complexities of aquatic animal movements. The challenge provides the importance of multi-object tracking for discovering the dynamics of collective animal behavior, with the potential to significantly advance scientific understanding in the above fields.","url":"https://arxiv.org/abs/2409.00339v1","authors":["Makoto M. Itoh","Qingrui Hu","Takayuki Niizato","Hiroaki Kawashima","Keisuke Fujii"],"tags":["cs.CV"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-08-31T03:26:53Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2503.16494v1","name":"The impact of artificial intelligence: from cognitive costs to global inequality","source":"arxiv","abstract":"In this paper, we examine the wide-ranging impact of artificial intelligence on society, focusing on its potential to both help and harm global equity, cognitive abilities, and economic stability. We argue that while artificial intelligence offers significant opportunities for progress in areas like healthcare, education, and scientific research, its rapid growth -- mainly driven by private companies -- may worsen global inequalities, increase dependence on automated systems for cognitive tasks, and disrupt established economic paradigms. We emphasize the critical need for strong governance and ethical guidelines to tackle these issues, urging the academic community to actively participate in creating policies that ensure the benefits of artificial intelligence are shared fairly and its risks are managed effectively.","url":"https://arxiv.org/abs/2503.16494v1","authors":["Guy Paić","Leonid Serkin"],"tags":["physics.soc-ph","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-03-11T05:49:00Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2402.02029v1","name":"ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation","source":"arxiv","abstract":"Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.","url":"https://arxiv.org/abs/2402.02029v1","authors":["Zihan Li","Yuan Zheng","Dandan Shan","Shuzhou Yang","Qingde Li","Beizhan Wang","Yuanting Zhang","Qingqi Hong","Dinggang Shen"],"tags":["cs.CV","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-03T04:55:22Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2411.13585v1","name":"Artificial Intelligence in Cybersecurity: Building Resilient Cyber Diplomacy Frameworks","source":"arxiv","abstract":"This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy. Leveraging these technologies helps the U.S. manage the complexity and urgency of cyber diplomacy, improving decision-making, efficiency, and security. As global inter connectivity grows, cyber diplomacy, managing national interests in the digital space has become vital. The ability of AI and automation to quickly process vast data volumes enables timely responses to cyber threats and opportunities. This paper underscores the strategic integration of these tools to maintain U.S. competitive advantage and secure national interests. Automation enhances diplomatic communication and data processing, freeing diplomats to focus on strategic decisions. AI supports predictive analytics and real time decision making, offering critical insights and proactive measures during high stakes engagements. Case studies show AIs effectiveness in monitoring cyber activities and managing international cyber policy. Challenges such as ethical concerns, security vulnerabilities, and reliance on technology are also addressed, emphasizing human oversight and strong governance frameworks. Ensuring proper ethical guidelines and cybersecurity measures allows the U.S. to harness the benefits of automation and AI while mitigating risks. By adopting these technologies, U.S. cyber diplomacy can become more proactive and effective, navigating the evolving digital landscape with greater agility.","url":"https://arxiv.org/abs/2411.13585v1","authors":["Michael Stoltz"],"tags":["cs.CR","cs.AI","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-11-17T17:57:17Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2604.04251v2","name":"MC-CPO: Mastery-Conditioned Constrained Policy Optimization for Pedagogically Safe Intelligent Tutoring Systems","source":"arxiv","abstract":"Intelligent tutoring systems increasingly rely on reinforcement learning to personalise instruction, yet optimising for observable engagement signals can systematically decouple learner activity from genuine knowledge acquisition. Analysing over 21 million student interactions across two deployed platforms, we find engagement events without corresponding mastery gains occur in 26.5% of interactions on Junyi Academy (72,758 students) and 3.1% on XES3G5M (14,453 students, NeurIPS 2023), confirming this pattern is directly observable in deployed educational technology at scale. We introduce Mastery-Conditioned Constrained Policy Optimisation (MC-CPO), a reinforcement learning framework that addresses this problem structurally. MC-CPO conditions the admissible instructional action space on learner mastery state: a concept becomes available only when prerequisite knowledge meets a mastery threshold, yielding an action space that expands naturally as learners acquire knowledge. Pedagogical safety constraints are enforced by construction, with formal guarantees of structural prerequisite safety, primal-dual convergence, and strict dominance over post-hoc filtering. MC-CPO is the only method to reduce reward hacking severity across all conditions. Mean per-episode mastery gain increases by 18.3% on Junyi Academy and 54.0% on XES3G5M relative to all baselines, while competitive engagement performance is maintained. These results support structural constraint modelling as a principled foundation for safer adaptive instructional policies in deployed tutoring systems.","url":"https://arxiv.org/abs/2604.04251v2","authors":["Oluseyi Olukola","Nick Rahimi"],"tags":["cs.AI","cs.CY","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-04-05T20:13:34Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2209.09204v1","name":"Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays","source":"arxiv","abstract":"Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.","url":"https://arxiv.org/abs/2209.09204v1","authors":["Tom Dyer","Jordan Smith","Gaetan Dissez","Nicole Tay","Qaiser Malik","Tom Naunton Morgan","Paul Williams","Liliana Garcia-Mondragon","George Pearse","Simon Rasalingham"],"tags":["eess.IV","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-08-31T09:54:24Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2205.05126v2","name":"A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making","source":"arxiv","abstract":"Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations.","url":"https://arxiv.org/abs/2205.05126v2","authors":["Max Schemmer","Patrick Hemmer","Maximilian Nitsche","Niklas Kühl","Michael Vössing"],"tags":["cs.HC","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-05-10T19:08:10Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2412.05795v2","name":"Massive geolocation data reveal evacuation behaviour during the 2024 Noto Peninsula earthquake and tsunami","source":"arxiv","abstract":"On 1 January 2024, devastating tsunamis caused by the Noto Peninsula earthquake hit coastal areas within several minutes, but only two tsunami casualties were officially reported. Despite its importance, the cause of this unexpectedly low human loss was unclear because of the limited access to the peninsula and the presence of many visitors during the holiday, which made conducting conventional surveys infeasible. Here, we reveal evacuation behaviour during the 2024 Noto Peninsula tsunami using massive geolocation data collected from a smartphone app. By analysing these massive data, which include over 1.5 million records collected on this day, we find that the evacuation was extremely fast, occurring within 2--6 minutes after the origin time. Further analyses suggest that these fast departures were driven mainly by strong ground shaking; the fact that the tsunami occurred during the family-oriented New Year holiday was also a key factor. Additionally, the long-term analysis of the data reveals that people started returning to the coastal area 20--100 minutes after the origin time, which was long before the downgrading and cancellation of the tsunami warnings. These results highlight the utility of the innovative data-driven approach to evacuation surveys, which addresses the limitations of conventional evacuation surveys.","url":"https://arxiv.org/abs/2412.05795v2","authors":["Fumiyasu Makinoshima","Saki Yotsui","Shosuke Sato","Fumihiko Imamura"],"tags":["physics.soc-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-12-08T03:16:33Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2104.04076v1","name":"An artificial intelligence and Internet of things based automated irrigation system","source":"arxiv","abstract":"It is not hard to see that the need for clean water is growing by considering the decrease of the water sources day by day in the world. Potable fresh water is also used for irrigation, so it should be planned to decrease freshwater wastage. With the development of technology and the availability of cheaper and more effective solutions, the efficiency of irrigation increased and the water loss can be reduced. In particular, Internet of things (IoT) devices has begun to be used in all areas. We can easily and precisely collect temperature, humidity and mineral values from the irrigation field with the IoT devices and sensors. Most of the operations and decisions about irrigation are carried out by people. For people, it is hard to have all the real-time data such as temperature, moisture and mineral levels in the decision-making process and make decisions by considering them. People usually make decisions with their experience. In this study, a wide range of information from the irrigation field was obtained by using IoT devices and sensors. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB. With the help of Weka software, the data was normalized and the normalized data was used as a learning set. As a result of the examinations, a decision tree (J48) algorithm with the highest accuracy was chosen and an artificial intelligence model was created. Decisions are used to manage operations such as starting, maintaining and stopping the irrigation. The accuracy of the decisions was evaluated and the irrigation system was tested with the results. There are options to manage, view the system remotely and manually and also see the system s decisions with the created mobile application.","url":"https://arxiv.org/abs/2104.04076v1","authors":["Ömer Aydin","Cem Ali Kandemir","Umut Kiraç","Feriştah Dalkiliç"],"tags":["cs.CY","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2021-04-01T21:05:26Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.13250v1","name":"Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics","source":"arxiv","abstract":"This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.","url":"https://arxiv.org/abs/2410.13250v1","authors":["Soojong Kim"],"tags":["cs.HC","cs.AI","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-17T06:18:26Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:0902.3513v1","name":"A Systematic Approach to Artificial Agents","source":"arxiv","abstract":"Agents and agent systems are becoming more and more important in the development of a variety of fields such as ubiquitous computing, ambient intelligence, autonomous computing, intelligent systems and intelligent robotics. The need for improvement of our basic knowledge on agents is very essential. We take a systematic approach and present extended classification of artificial agents which can be useful for understanding of what artificial agents are and what they can be in the future. The aim of this classification is to give us insights in what kind of agents can be created and what type of problems demand a specific kind of agents for their solution.","url":"https://arxiv.org/abs/0902.3513v1","authors":["Mark Burgin","Gordana Dodig-Crnkovic"],"tags":["cs.AI","cs.MA"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2009-02-20T04:58:40Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2601.21527v3","name":"Sustainable Materials Discovery in the Era of Artificial Intelligence","source":"arxiv","abstract":"Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current generative AI models for materials discovery, which now drive exploration of vast chemical and structural spaces, optimize candidates exclusively for structural stability and functional properties, with no integration of environmental assessment at any stage of the design loop. Prospective and ex-ante life cycle assessment methods exist and have been applied to emerging technologies, but they operate as standalone downstream analyses, not as active constraints within generative or active-learning pipelines. The result is that environmental feedback, even when produced, arrives after design decisions have been made rather than informing them. The disconnect between atomic-scale design and lifecycle assessment (LCA) reflects fundamental challenges: (i) data scarcity across heterogeneous sources, (ii) scale gaps from atoms to industrial systems, (iii) uncertainty in synthesis pathways, and (iv) the absence of frameworks that co-optimize performance with environmental impact. In this Perspective, we propose integrating upstream ML-assisted materials discovery with downstream LCA into the ML-LCA framework, comprising five components: information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning polymers, glass, photoresists, and cement demonstrate both necessity and feasibility while identifying material-specific integration challenges.","url":"https://arxiv.org/abs/2601.21527v3","authors":["Sajid Mannan","Rupert J. Myers","Rohit Batra","Rocio Mercado","Lothar Wondraczek","N. M. Anoop Krishnan"],"tags":["cond-mat.mtrl-sci","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-01-29T10:42:44Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2302.09488v1","name":"A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images","source":"arxiv","abstract":"The promising research on Artificial Intelligence usages in suicide prevention has principal gaps, including black box methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as social media images (despite their popularity today, in our digital era). This study addresses these gaps and combines theory-driven and bottom-up strategies to construct a hybrid and interpretable prediction model of valid suicide risk from images. The lead hypothesis was that images contain valuable information about emotions and interpersonal relationships, two central concepts in suicide-related treatments and theories. The dataset included 177,220 images by 841 Facebook users who completed a gold-standard suicide scale. The images were represented with CLIP, a state-of-the-art algorithm, which was utilized, unconventionally, to extract predefined features that served as inputs to a simple logistic-regression prediction model (in contrast to complex neural networks). The features addressed basic and theory-driven visual elements using everyday language (e.g., bright photo, photo of sad people). The results of the hybrid model (that integrated theory-driven and bottom-up methods) indicated high prediction performance that surpassed common bottom-up algorithms, thus providing a first proof that images (alone) can be leveraged to predict validated suicide risk. Corresponding with the lead hypothesis, at-risk users had images with increased negative emotions and decreased belonginess. The results are discussed in the context of non-verbal warning signs of suicide. Notably, the study illustrates the advantages of hybrid models in such complicated tasks and provides simple and flexible prediction strategies that could be utilized to develop real-life monitoring tools of suicide.","url":"https://arxiv.org/abs/2302.09488v1","authors":["Yael Badian","Yaakov Ophir","Refael Tikochinski","Nitay Calderon","Anat Brunstein Klomek","Roi Reichart"],"tags":["cs.AI","cs.CV","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-02-19T06:18:23Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2606.16721v1","name":"Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies","source":"arxiv","abstract":"Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can detect disease, estimate risk and generate reports, many systems still return static labels or scores, offering limited insight into how illness may progress or how alternative interventions may reshape its trajectory. Medical world models adapt the world-model idea from artificial intelligence to healthcare by learning internal simulators of patient-state dynamics. Their long-term goal is to help clinicians anticipate deterioration, compare treatment-conditioned futures and tailor care to individual patients. Yet relevant work remains scattered across foundation models, longitudinal modelling, disease simulation, treatment-effect estimation, reinforcement learning and digital twins. To bridge this gap, this review outlines a roadmap for advancing medical AI from isolated diagnosis and prediction toward medical world models that simulate disease evolution and support intervention decisions. This roadmap is organized around three coupled capabilities: patient-state construction, clinical dynamics modelling and intervention decision support. Across representative systems, the comparison highlights what each capability contributes and how partial components can be integrated into more mature perception--dynamics--planning systems. Finally, we identify the challenges involved in turning plausible rollouts into clinically useful simulators. Related literature is available at https://github.com/1999kevin/awesome_medical_world_models.","url":"https://arxiv.org/abs/2606.16721v1","authors":["Ke Liu","Mengxuan Li","Yanyi Bao","Tianyun Zhang","Chong Chu","Jiajun Bu","Haishuai Wang"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-06-15T13:49:43Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2602.21843v2","name":"The economic alignment problem of artificial intelligence","source":"arxiv","abstract":"Artificial intelligence (AI) is advancing exponentially and is likely to have profound impacts on human wellbeing, social equity, and environmental sustainability. Here we argue that the \"alignment problem\" in AI research is also an economic alignment problem, as developing advanced AI within a growth-oriented economic system is likely to increase social, environmental, and existential risks. We show that post-growth research offers concepts and policies that could address the economic alignment problem and substantially reduce AI risks, such as by replacing optimisation with satisficing, using the Doughnut of social and planetary boundaries to guide development, and curbing systemic rebound with resource caps. We propose governance and business reforms that treat AI as a commons and prioritise tool-like autonomy-enhancing systems over agentic AI. Finally, we argue that the development of artificial general intelligence (AGI) requires new economic theories and models, for which post-growth scholarship provides a strong foundation.","url":"https://arxiv.org/abs/2602.21843v2","authors":["Daniel W. O'Neill","Stefano Vrizzi","Noemi Luna Carmeno","Felix Creutzig","Jefim Vogel"],"tags":["econ.GN","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-02-25T12:22:46Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2101.06644v1","name":"HySTER: A Hybrid Spatio-Temporal Event Reasoner","source":"arxiv","abstract":"The task of Video Question Answering (VideoQA) consists in answering natural language questions about a video and serves as a proxy to evaluate the performance of a model in scene sequence understanding. Most methods designed for VideoQA up-to-date are end-to-end deep learning architectures which struggle at complex temporal and causal reasoning and provide limited transparency in reasoning steps. We present the HySTER: a Hybrid Spatio-Temporal Event Reasoner to reason over physical events in videos. Our model leverages the strength of deep learning methods to extract information from video frames with the reasoning capabilities and explainability of symbolic artificial intelligence in an answer set programming framework. We define a method based on general temporal, causal and physics rules which can be transferred across tasks. We apply our model to the CLEVRER dataset and demonstrate state-of-the-art results in question answering accuracy. This work sets the foundations for the incorporation of inductive logic programming in the field of VideoQA.","url":"https://arxiv.org/abs/2101.06644v1","authors":["Theophile Sautory","Nuri Cingillioglu","Alessandra Russo"],"tags":["cs.CV","cs.AI","cs.CL","cs.LO"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2021-01-17T11:07:17Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2310.18361v1","name":"Clinical Decision Support System for Unani Medicine Practitioners","source":"arxiv","abstract":"Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.","url":"https://arxiv.org/abs/2310.18361v1","authors":["Haider Sultan","Hafiza Farwa Mahmood","Noor Fatima","Marriyam Nadeem","Talha Waheed"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-10-24T13:49:18Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2502.09495v1","name":"Cracking the Code: Enhancing Development finance understanding with artificial intelligence","source":"arxiv","abstract":"Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.","url":"https://arxiv.org/abs/2502.09495v1","authors":["Pierre Beaucoral"],"tags":["econ.GN","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-02-13T17:01:45Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2405.02957v3","name":"Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents","source":"arxiv","abstract":"The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI). While LLMs are designed to understand and generate text like a human, autonomous agents that utilize LLMs as their \"brain\" have exhibited capabilities beyond text processing such as planning, reflection, and using tools by enabling their \"bodies\" to interact with the environment. We introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness, in which all patients, nurses, and doctors are LLM-powered autonomous agents. Within the simulacrum, doctor agents are able to evolve by treating a large number of patient agents without the need to label training data manually. After treating tens of thousands of patient agents in the simulacrum (human doctors may take several years in the real world), the evolved doctor agents outperform state-of-the-art medical agent methods on the MedQA benchmark comprising US Medical Licensing Examination (USMLE) test questions. Our methods of simulacrum construction and agent evolution have the potential in benefiting a broad range of applications beyond medical AI.","url":"https://arxiv.org/abs/2405.02957v3","authors":["Junkai Li","Yunghwei Lai","Weitao Li","Jingyi Ren","Meng Zhang","Xinhui Kang","Siyu Wang","Peng Li","Ya-Qin Zhang","Weizhi Ma","Yang Liu"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-05-05T14:53:51Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2306.06499v2","name":"Defining and Explorting the Intelligence Space","source":"arxiv","abstract":"Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.","url":"https://arxiv.org/abs/2306.06499v2","authors":["Paul S. Rosenbloom"],"tags":["cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-06-10T18:05:16Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2406.07725v1","name":"The Interspeech 2024 Challenge on Speech Processing Using Discrete Units","source":"arxiv","abstract":"Representing speech and audio signals in discrete units has become a compelling alternative to traditional high-dimensional feature vectors. Numerous studies have highlighted the efficacy of discrete units in various applications such as speech compression and restoration, speech recognition, and speech generation. To foster exploration in this domain, we introduce the Interspeech 2024 Challenge, which focuses on new speech processing benchmarks using discrete units. It encompasses three pivotal tasks, namely multilingual automatic speech recognition, text-to-speech, and singing voice synthesis, and aims to assess the potential applicability of discrete units in these tasks. This paper outlines the challenge designs and baseline descriptions. We also collate baseline and selected submission systems, along with preliminary findings, offering valuable contributions to future research in this evolving field.","url":"https://arxiv.org/abs/2406.07725v1","authors":["Xuankai Chang","Jiatong Shi","Jinchuan Tian","Yuning Wu","Yuxun Tang","Yihan Wu","Shinji Watanabe","Yossi Adi","Xie Chen","Qin Jin"],"tags":["cs.SD","eess.AS"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-06-11T21:08:47Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.00483v1","name":"MCGM: Mask Conditional Text-to-Image Generative Model","source":"arxiv","abstract":"Recent advancements in generative models have revolutionized the field of artificial intelligence, enabling the creation of highly-realistic and detailed images. In this study, we propose a novel Mask Conditional Text-to-Image Generative Model (MCGM) that leverages the power of conditional diffusion models to generate pictures with specific poses. Our model builds upon the success of the Break-a-scene [1] model in generating new scenes using a single image with multiple subjects and incorporates a mask embedding injection that allows the conditioning of the generation process. By introducing this additional level of control, MCGM offers a flexible and intuitive approach for generating specific poses for one or more subjects learned from a single image, empowering users to influence the output based on their requirements. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed model in generating high-quality images that meet predefined mask conditions and improving the current Break-a-scene generative model.","url":"https://arxiv.org/abs/2410.00483v1","authors":["Rami Skaik","Leonardo Rossi","Tomaso Fontanini","Andrea Prati"],"tags":["cs.CV","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-01T08:13:47Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.12808v1","name":"FlyAI -- The Next Level of Artificial Intelligence is Unpredictable! Injecting Responses of a Living Fly into Decision Making","source":"arxiv","abstract":"In this paper, we introduce a new type of bionic AI that enhances decision-making unpredictability by incorporating responses from a living fly. Traditional AI systems, while reliable and predictable, lack nuanced and sometimes unseasoned decision-making seen in humans. Our approach uses a fly's varied reactions, to tune an AI agent in the game of Gobang. Through a study, we compare the performances of different strategies on altering AI agents and found a bionic AI agent to outperform human as well as conventional and white-noise enhanced AI agents. We contribute a new methodology for creating a bionic random function and strategies to enhance conventional AI agents ultimately improving unpredictability.","url":"https://arxiv.org/abs/2410.12808v1","authors":["Denys J. C. Matthies","Ruben Schlonsak","Hanzhi Zhuang","Rui Song"],"tags":["cs.NE"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-09-30T17:19:59Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2412.01123v1","name":"Sympathetic solar eruption on 2024 February 9","source":"arxiv","abstract":"In this paper, we perform a follow-up investigation of the solar eruption originating from active region (AR) 13575 on 2024 February 9. The primary eruption of a hot channel (HC) generates an X3.4 class flare, a full-halo coronal mass ejection (CME), and an extreme-ultraviolet (EUV) wave. Interaction between the wave and a quiescent prominence (QP) leads to a large-amplitude, transverse oscillation of QP. After the transverse oscillation, QP loses equilibrium and rises up. The ascending motion of the prominence is coherently detected and tracked up to 1.68 R by the Solar UltraViolet Imager (SUVI) onboard the GOES-16 spacecraft and up to 2.2 R by the Solar Corona Imager (SCI UV) of the Lyman-alpha Solar Telescope (LST) onboard the ASO-S spacecraft. The velocity increases linearly from 12.3 to 68.5 km s at 18:30 UT. The sympathetic eruption of QP drives the second CME with a typical three-part structure. The bright core comes from the eruptive prominence, which could be further observed up to 3.3 R by the Large Angle Spectroscopic Coronagraph (LASCO) onboard the SOHO mission. The leading edge of the second CME accelerates continuously from 120 to 277 kms. The EUV wave plays an important role in linking the primary eruption with the sympathetic eruption.","url":"https://arxiv.org/abs/2412.01123v1","authors":["Shu-Yue Li","Qing-Min Zhang","Bei-Li Ying","Li Feng","Ying-Na Su","Mu-Sheng Lin. Yan-Jie Zhang"],"tags":["astro-ph.SR"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-12-02T05:00:56Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2409.11654v2","name":"How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities","source":"arxiv","abstract":"The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.","url":"https://arxiv.org/abs/2409.11654v2","authors":["Charlotte Bunne","Yusuf Roohani","Yanay Rosen","Ankit Gupta","Xikun Zhang","Marcel Roed","Theo Alexandrov","Mohammed AlQuraishi","Patricia Brennan","Daniel B. Burkhardt","Andrea Califano","Jonah Cool","Abby F. Dernburg","Kirsty Ewing","Emily B. Fox","Matthias Haury","Amy E. Herr","Eric Horvitz","Patrick D. Hsu","Viren Jain","Gregory R. Johnson","Thomas Kalil","David R. Kelley","Shana O. Kelley","Anna Kreshuk","Tim Mitchison","Stephani Otte","Jay Shendure","Nicholas J. Sofroniew","Fabian Theis","Christina V. Theodoris","Srigokul Upadhyayula","Marc Valer","Bo Wang","Eric Xing","Serena Yeung-Levy","Marinka Zitnik","Theofanis Karaletsos","Aviv Regev","Emma Lundberg","Jure Leskovec","Stephen R. Quake"],"tags":["q-bio.QM","cs.AI","cs.LG","q-bio.NC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-09-18T02:41:50Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2406.08048v1","name":"3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement","source":"arxiv","abstract":"In this paper, we present our approach to the 3D CBCT Challenge 2024, a part of ICASSP SP Grand Challenges 2024. Improvement in Cone Beam Computed Tomography (CBCT) reconstruction has been achieved by integrating Swin Image Restoration (SwinIR) based sinogram and image enhancement modules. The proposed methodology uses Nesterov Accelerated Gradient Descent (NAG) to solve the least squares (NAG-LS) problem in CT image reconstruction. The integration of sinogram and image enhancement modules aims to enhance image clarity and preserve fine details, offering a promising solution for both low dose and clinical dose CBCT reconstruction. The averaged mean squared error (MSE) over the validation dataset has decreased significantly, in the case of low dose by one-fifth and clinical dose by one-tenth. Our solution is one of the top 5 approaches in this challenge.","url":"https://arxiv.org/abs/2406.08048v1","authors":["Sasidhar Alavala","Subrahmanyam Gorthi"],"tags":["eess.IV","cs.CV"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-06-12T10:01:00Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2010.13499v1","name":"Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index","source":"arxiv","abstract":"In many medical imaging and classical computer vision tasks, the Dice score and Jaccard index are used to evaluate the segmentation performance. Despite the existence and great empirical success of metric-sensitive losses, i.e. relaxations of these metrics such as soft Dice, soft Jaccard and Lovasz-Softmax, many researchers still use per-pixel losses, such as (weighted) cross-entropy to train CNNs for segmentation. Therefore, the target metric is in many cases not directly optimized. We investigate from a theoretical perspective, the relation within the group of metric-sensitive loss functions and question the existence of an optimal weighting scheme for weighted cross-entropy to optimize the Dice score and Jaccard index at test time. We find that the Dice score and Jaccard index approximate each other relatively and absolutely, but we find no such approximation for a weighted Hamming similarity. For the Tversky loss, the approximation gets monotonically worse when deviating from the trivial weight setting where soft Tversky equals soft Dice. We verify these results empirically in an extensive validation on six medical segmentation tasks and can confirm that metric-sensitive losses are superior to cross-entropy based loss functions in case of evaluation with Dice Score or Jaccard Index. This further holds in a multi-class setting, and across different object sizes and foreground/background ratios. These results encourage a wider adoption of metric-sensitive loss functions for medical segmentation tasks where the performance measure of interest is the Dice score or Jaccard index.","url":"https://arxiv.org/abs/2010.13499v1","authors":["Tom Eelbode","Jeroen Bertels","Maxim Berman","Dirk Vandermeulen","Frederik Maes","Raf Bisschops","Matthew B. Blaschko"],"tags":["eess.IV","cs.CV","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2020-10-26T11:45:55Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2406.04951v2","name":"The Database and Benchmark for the Source Speaker Tracing Challenge 2024","source":"arxiv","abstract":"Voice conversion (VC) systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found in https://sstc-challenge.github.io/.","url":"https://arxiv.org/abs/2406.04951v2","authors":["Ze Li","Yuke Lin","Tian Yao","Hongbin Suo","Pengyuan Zhang","Yanzhen Ren","Zexin Cai","Hiromitsu Nishizaki","Ming Li"],"tags":["eess.AS"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-06-07T14:13:20Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2405.13065v1","name":"Exploring Teachers' Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education","source":"arxiv","abstract":"In schools, teachers play a multitude of roles, serving as educators, counselors, decision-makers, and members of the school community. With recent advances in artificial intelligence (AI), there is increasing discussion about how AI can assist, complement, and collaborate with teachers. To pave the way for better teacher-AI complementary relationships in schools, our study aims to expand the discourse on teacher-AI complementarity by seeking educators' perspectives on the potential strengths and limitations of AI across a spectrum of responsibilities. Through a mixed method using a survey with 100 elementary school teachers in South Korea and in-depth interviews with 12 teachers, our findings indicate that teachers anticipate AI's potential to complement human teachers by automating administrative tasks and enhancing personalized learning through advanced intelligence. Interestingly, the deficit of AI's socio-emotional capabilities has been perceived as both challenges and opportunities. Overall, our study demonstrates the nuanced perception of teachers and different levels of expectations over their roles, challenging the need for decisions about AI adoption tailored to educators' preferences and concerns.","url":"https://arxiv.org/abs/2405.13065v1","authors":["Soon-young Oh","Yongsu Ahn"],"tags":["cs.HC","cs.AI","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-05-20T15:43:04Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2503.17688v1","name":"Intelligence Sequencing and the Path-Dependence of Intelligence Evolution: AGI-First vs. DCI-First as Irreversible Attractors","source":"arxiv","abstract":"The trajectory of intelligence evolution is often framed around the emergence of artificial general intelligence (AGI) and its alignment with human values. This paper challenges that framing by introducing the concept of intelligence sequencing: the idea that the order in which AGI and decentralized collective intelligence (DCI) emerge determines the long-term attractor basin of intelligence. Using insights from dynamical systems, evolutionary game theory, and network models, it argues that intelligence follows a path-dependent, irreversible trajectory. Once development enters a centralized (AGI-first) or decentralized (DCI-first) regime, transitions become structurally infeasible due to feedback loops and resource lock-in. Intelligence attractors are modeled in functional state space as the co-navigation of conceptual and adaptive fitness spaces. Early-phase structuring constrains later dynamics, much like renormalization in physics. This has major implications for AI safety: traditional alignment assumes AGI will emerge and must be controlled after the fact, but this paper argues that intelligence sequencing is more foundational. If AGI-first architectures dominate before DCI reaches critical mass, hierarchical monopolization and existential risk become locked in. If DCI-first emerges, intelligence stabilizes around decentralized cooperative equilibrium. The paper further explores whether intelligence structurally biases itself toward an attractor based on its self-modeling method -- externally imposed axioms (favoring AGI) vs. recursive internal visualization (favoring DCI). Finally, it proposes methods to test this theory via simulations, historical lock-in case studies, and intelligence network analysis. The findings suggest that intelligence sequencing is a civilizational tipping point: determining whether the future is shaped by unbounded competition or unbounded cooperation.","url":"https://arxiv.org/abs/2503.17688v1","authors":["Andy E. Williams"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-03-22T08:09:04Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2402.01967v2","name":"MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles","source":"arxiv","abstract":"The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.","url":"https://arxiv.org/abs/2402.01967v2","authors":["Amrita Ganguly","Al Nahian Bin Emran","Sadiya Sayara Chowdhury Puspo","Md Nishat Raihan","Dhiman Goswami","Marcos Zampieri"],"tags":["cs.CL"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-03T00:23:36Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2407.05423v1","name":"A Manifesto for a Pro-Actively Responsible AI in Education","source":"arxiv","abstract":"This paper examines the historical foundations, current practices, and emerging challenges for Artificial Intelligence in Education (AIED) within broader AI practices. It highlights AIED's unique and rich potential for contributing to the current AI policy and practices, especially in the context of responsible AI. It also discusses the key gaps in the AIED field, which need to be addressed by the community to elevate the field from a cottage industry to the level where it will deservedly be seen as key to advancin AI research and practical applications. The paper offers a five-point manifesto aimed to revitalise AIED' contributions to education and broader AI community, suggesting enhanced interdisciplinary collaboration, a broadened understanding of AI's impact on human functioning, and commitment to setting agendas for human-centred educational innovations.This approach positions AIED to significantly influence educational technologies to achieve genuine positive impact across diverse societal segments.","url":"https://arxiv.org/abs/2407.05423v1","authors":["Kaska Porayska-Pomsta"],"tags":["cs.CY","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-05-03T14:23:41Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2508.11529v1","name":"A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow","source":"arxiv","abstract":"Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque \"black boxes\". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream decisions and downstream quality checks that determine whether insights can be trusted. In this work, we present Holistic Explainable Artificial Intelligence (HXAI), a user-centric framework that embeds explanation into every stage of the data-analysis workflow and tailors those explanations to users. HXAI unifies six components (data, analysis set-up, learning process, model output, model quality, communication channel) into a single taxonomy and aligns each component with the needs of domain experts, data analysts and data scientists. A 112-item question bank covers these needs; our survey of contemporary tools highlights critical coverage gaps. Grounded in theories of human explanation, principles from human-computer interaction and findings from empirical user studies, HXAI identifies the characteristics that make explanations clear, actionable and cognitively manageable. A comprehensive taxonomy operationalises these insights, reducing terminological ambiguity and enabling rigorous coverage analysis of existing toolchains. We further demonstrate how AI agents that embed large-language models can orchestrate diverse explanation techniques, translating technical artifacts into stakeholder-specific narratives that bridge the gap between AI developers and domain experts. Departing from traditional surveys or perspective articles, this work melds concepts from multiple disciplines, lessons from real-world projects and a critical synthesis of the literature to advance a novel, end-to-end viewpoint on transparency, trustworthiness and responsible AI deployment.","url":"https://arxiv.org/abs/2508.11529v1","authors":["George Paterakis","Andrea Castellani","George Papoutsoglou","Tobias Rodemann","Ioannis Tsamardinos"],"tags":["cs.LG","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-08-15T15:15:25Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2507.02951v1","name":"Bittensor Protocol: The Bitcoin in Decentralized Artificial Intelligence? A Critical and Empirical Analysis","source":"arxiv","abstract":"This paper investigates whether Bittensor can be considered the Bitcoin of decentralized Artificial Intelligence by directly comparing its tokenomics, decentralization properties, consensus mechanism, and incentive structure against those of Bitcoin. Leveraging on-chain data from all 64 active Bittensor subnets, we first document considerable concentration in both stake and rewards. We further show that rewards are overwhelmingly driven by stake, highlighting a clear misalignment between quality and compensation. As a remedy, we put forward a series of two-pronged protocol-level interventions. For incentive realignment, our proposed solutions include performance-weighted emission split, composite scoring, and a trust-bonus multiplier. As for mitigating security vulnerability due to stake concentration, we propose and empirically validate stake cap at the 88th percentile, which elevates the median coalition size required for a 51-percent attack and remains robust across daily, weekly, and monthly snapshots.","url":"https://arxiv.org/abs/2507.02951v1","authors":["Elizabeth Lui","Jiahao Sun"],"tags":["cs.CR","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-06-29T12:07:48Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2108.03555v2","name":"Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence","source":"arxiv","abstract":"Background: Accurate diagnosis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative diagnosis can be challenging due to tumor diversity and lack of intraoperative pathology resources. Objective: To develop an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses using label-free optical imaging and artificial intelligence. Method: We used a fiber laser-based, label-free, non-consumptive, high-resolution microscopy method ($&lt;$ 60 sec per 1 $\\times$ 1 mm$^\\text{2}$), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of skull base tumor patients. SRH images were then used to train a convolutional neural network (CNN) model using three representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained CNN models were tested on a held-out, multicenter SRH dataset. Results: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the three representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to identify tumor-normal margins and detect regions of microscopic tumor infiltration in whole-slide SRH images. Conclusion: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.","url":"https://arxiv.org/abs/2108.03555v2","authors":["Cheng Jiang","Abhishek Bhattacharya","Joseph Linzey","Rushikesh S. Joshi","Sung Jik Cha","Sudharsan Srinivasan","Daniel Alber","Akhil Kondepudi","Esteban Urias","Balaji Pandian","Wajd Al-Holou","Steve Sullivan","B. Gregory Thompson","Jason Heth","Chris Freudiger","Siri Khalsa","Donato Pacione","John G. Golfinos","Sandra Camelo-Piragua","Daniel A. Orringer","Honglak Lee","Todd Hollon"],"tags":["cs.CV","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2021-08-08T02:49:29Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2509.02274v1","name":"Look: AI at Work! -- Analysing Key Aspects of AI-support at the Work Place","source":"arxiv","abstract":"In this paper we present an analysis of technological and psychological factors of applying artificial intelligence (AI) at the work place. We do so for a number of twelve application cases in the context of a project where AI is integrated at work places and in work systems of the future. From a technological point of view we mainly look at the areas of AI that the applications are concerned with. This allows to formulate recommendations in terms of what to look at in developing an AI application and what to pay attention to with regards to building AI literacy with different stakeholders using the system. This includes the importance of high-quality data for training learning-based systems as well as the integration of human expertise, especially with knowledge-based systems. In terms of the psychological factors we derive research questions to investigate in the development of AI supported work systems and to consider in future work, mainly concerned with topics such as acceptance, openness, and trust in an AI system.","url":"https://arxiv.org/abs/2509.02274v1","authors":["Stefan Schiffer","Anna Milena Rothermel","Alexander Ferrein","Astrid Rosenthal-von der Pütten"],"tags":["cs.HC","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-09-02T12:51:23Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2512.17086v2","name":"Value Under Ignorance in Universal Artificial Intelligence","source":"arxiv","abstract":"We generalize the AIXI reinforcement learning agent to admit a wider class of utility functions. Assigning a utility to each possible interaction history forces us to confront the ambiguity that some hypotheses in the agent's belief distribution only predict a finite prefix of the history, which is sometimes interpreted as implying a chance of death equal to a quantity called the semimeasure loss. This death interpretation suggests one way to assign utilities to such history prefixes. We argue that it is as natural to view the belief distributions as imprecise probability distributions, with the semimeasure loss as total ignorance. This motivates us to consider the consequences of computing expected utilities with Choquet integrals from imprecise probability theory, including an investigation of their computability level. We recover the standard recursive value function as a special case. However, our most general expected utilities under the death interpretation cannot be characterized as such Choquet integrals.","url":"https://arxiv.org/abs/2512.17086v2","authors":["Cole Wyeth","Marcus Hutter"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-12-18T21:34:50Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2206.11899v2","name":"Navigating Incommensurability Between Ethnomethodology, Conversation Analysis, and Artificial Intelligence","source":"arxiv","abstract":"Like many research communities, ethnomethodologists and conversation analysts have begun to get caught up -- yet again -- in the pervasive spectacle of surging interests in Artificial Intelligence (AI). Inspired by discussions amongst a growing network of researchers in ethnomethodology (EM) and conversation analysis (CA) traditions who nurse such interests, I started thinking about what things EM and the more EM end of conversation analysis might be doing about, for, or even with, fields of AI research. So, this piece is about the disciplinary and conceptual questions that might be encountered, and -- in my view -- may need addressing for engagements with AI research and its affiliates. Although I'm mostly concerned with things to be aware of as well as outright dangers, later on we can think about some opportunities. And throughout I will keep using 'we' to talk about EM&amp;CA researchers; but this really is for convenience only -- I don't wish to ventriloquise for our complex research communities. All of the following should be read as emanating from my particular research history, standpoint etc., and treated (hopefully) as an invitation for further discussion amongst EM and CA researchers turning to technology and AI specifically.","url":"https://arxiv.org/abs/2206.11899v2","authors":["Stuart Reeves"],"tags":["cs.HC","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-06-19T23:07:09Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2108.07130v1","name":"Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets","source":"arxiv","abstract":"Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data. However, if the bad data contains insufficient anatomical information, it can have a severe negative effect on the model's performance. We propose a novel methodology using a semi-supervised Siamese network to identify bad data. This method requires only a small pool of 'reference' medical images to be reviewed by a non-expert human to ensure the major anatomical structures are present in the Field of View. The model trains on this reference set and identifies bad data by using the Siamese network to compute the distance between the reference set and all other medical images in the dataset. This methodology achieves an Area Under the Curve (AUC) of 0.989 for identifying bad data. Code will be available at https://git.io/JYFuV.","url":"https://arxiv.org/abs/2108.07130v1","authors":["Niamh Belton","Aonghus Lawlor","Kathleen M. Curran"],"tags":["cs.CV","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2021-08-16T14:52:20Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2501.02334v1","name":"Validity Arguments For Constructed Response Scoring Using Generative Artificial Intelligence Applications","source":"arxiv","abstract":"The rapid advancements in large language models and generative artificial intelligence (AI) capabilities are making their broad application in the high-stakes testing context more likely. Use of generative AI in the scoring of constructed responses is particularly appealing because it reduces the effort required for handcrafting features in traditional AI scoring and might even outperform those methods. The purpose of this paper is to highlight the differences in the feature-based and generative AI applications in constructed response scoring systems and propose a set of best practices for the collection of validity evidence to support the use and interpretation of constructed response scores from scoring systems using generative AI. We compare the validity evidence needed in scoring systems using human ratings, feature-based natural language processing AI scoring engines, and generative AI. The evidence needed in the generative AI context is more extensive than in the feature-based NLP scoring context because of the lack of transparency and other concerns unique to generative AI such as consistency. Constructed response score data from standardized tests demonstrate the collection of validity evidence for different types of scoring systems and highlights the numerous complexities and considerations when making a validity argument for these scores. In addition, we discuss how the evaluation of AI scores might include a consideration of how a contributory scoring approach combining multiple AI scores (from different sources) will cover more of the construct in the absence of human ratings.","url":"https://arxiv.org/abs/2501.02334v1","authors":["Jodi M. Casabianca","Daniel F. McCaffrey","Matthew S. Johnson","Naim Alper","Vladimir Zubenko"],"tags":["cs.CL","cs.AI","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-01-04T16:59:29Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2512.02048v1","name":"The Impact of Artificial Intelligence on Enterprise Decision-Making Process","source":"arxiv","abstract":"Artificial intelligence improves enterprise decision-making by accelerating data analysis, reducing human error, and supporting evidence-based choices. A quantitative survey of 92 companies across multiple industries examines how AI adoption influences managerial performance, decision efficiency, and organizational barriers. Results show that 93 percent of firms use AI, primarily in customer service, data forecasting, and decision support. AI systems increase the speed and clarity of managerial decisions, yet implementation faces challenges. The most frequent barriers include employee resistance, high costs, and regulatory ambiguity. Respondents indicate that organizational factors are more significant than technological limitations. Critical competencies for successful AI use include understanding algorithmic mechanisms and change management. Technical skills such as programming play a smaller role. Employees report difficulties in adapting to AI tools, especially when formulating prompts or accepting system outputs. The study highlights the importance of integrating AI with human judgment and communication practices. When supported by adaptive leadership and transparent processes, AI adoption enhances organizational agility and strengthens decision-making performance. These findings contribute to ongoing research on how digital technologies reshape management and the evolution of hybrid human-machine decision environments.","url":"https://arxiv.org/abs/2512.02048v1","authors":["Ernest Górka","Dariusz Baran","Gabriela Wojak","Michał Ćwiąkała","Sebastian Zupok","Dariusz Starkowski","Dariusz Reśko","Oliwia Okrasa"],"tags":["cs.CY","cs.AI","econ.GN"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-11-26T14:45:16Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2204.03570v1","name":"Improving Urban Mobility: using artificial intelligence and new technologies to connect supply and demand","source":"arxiv","abstract":"As the demand for mobility in our society seems to increase, the various issues centered on urban mobility are among those that worry most city inhabitants in this planet. For instance, how to go from A to B in an efficient (but also less stressful) way? These questions and concerns have not changed even during the covid-19 pandemic; on the contrary, as the current stand, people who are avoiding public transportation are only contributing to an increase in the vehicular traffic. The are of intelligent transportation systems (ITS) aims at investigating how to employ information and communication technologies to problems related to transportation. This may mean monitoring and managing the infrastructure (e.g., traffic roads, traffic signals, etc.). However, currently, ITS is also targeting the management of demand. In this panorama, artificial intelligence plays an important role, especially with the advances in machine learning that translates in the use of computational vision, connected and autonomous vehicles, agent-based simulation, among others. In the present work, a survey of several works developed by our group are discussed in a holistic perspective, i.e., they cover not only the supply side (as commonly found in ITS works), but also the demand side, and, in an novel perspective, the integration of both.","url":"https://arxiv.org/abs/2204.03570v1","authors":["Ana L. C. Bazzan"],"tags":["cs.CY","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-03-18T14:37:33Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2607.09529v2","name":"Artificial Intelligence and the Generative Science of Food Formulation","source":"arxiv","abstract":"Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Now, artificial intelligence offers the opportunity to accelerate this process. Yet despite rapid advances across food science, most AI applications remain isolated prediction and optimization tasks rather than parts of a broader scientific approach. Here we integrate these emerging technologies into a unified framework--the generative science of food formulation--in which digital food representations enable artificial intelligence to predict, discover, generate, organize, simulate, and optimize. We illustrate this approach through sustainability and nutrition, where generative artificial intelligence transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances have the potential to transform food formulation from an empirical discipline into a generative science.","url":"https://arxiv.org/abs/2607.09529v2","authors":["Vahidullah Tac","Ellen Kuhl"],"tags":["cs.CE"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-07-10T15:36:21Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2208.04008v1","name":"Advances of Artificial Intelligence in Classical and Novel Spectroscopy-Based Approaches for Cancer Diagnostics. A Review","source":"arxiv","abstract":"Cancer is one of the leading causes of death worldwide. Fast and safe early-stage, pre- and intra-operative diagnostics can significantly contribute to successful cancer identification and treatment. Artificial intelligence has played an increasing role in the enhancement of cancer diagnostics techniques in the last 15 years. This review covers the advances of artificial intelligence applications in well-established techniques such as MRI and CT. Also, it shows its high potential in combination with optical spectroscopy-based approaches that are under development for mobile, ultra-fast, and low-invasive diagnostics. I will show how spectroscopy-based approaches can reduce the time of tissue preparation for pathological analysis by making thin-slicing or haematoxylin-and-eosin staining obsolete. I will present examples of spectroscopic tools for fast and low-invasive ex- and in-vivo tissue classification for the determination of a tumour and its boundaries. Also, I will discuss that, contrary to MRI and CT, spectroscopic measurements do not require the administration of chemical agents to enhance the quality of cancer imaging which contributes to the development of more secure diagnostic methods. Overall, we will see that the combination of spectroscopy and artificial intelligence constitutes a highly promising and fast-developing field of medical technology that will soon augment available cancer diagnostic methods.","url":"https://arxiv.org/abs/2208.04008v1","authors":["Marina Zajnulina"],"tags":["q-bio.TO","eess.IV","stat.AP","stat.ML"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-08-08T09:39:36Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2411.07619v1","name":"Artificial Intelligence for Biomedical Video Generation","source":"arxiv","abstract":"As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation","url":"https://arxiv.org/abs/2411.07619v1","authors":["Linyuan Li","Jianing Qiu","Anujit Saha","Lin Li","Poyuan Li","Mengxian He","Ziyu Guo","Wu Yuan"],"tags":["cs.CV"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-11-12T08:05:58Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2312.14977v1","name":"Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians","source":"arxiv","abstract":"Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the available training data and then learning how to reverse the process. The reverse operation may then be applied to new random data in order to produce new outputs. We provide a brief introduction to diffusion models for applied mathematicians and statisticians. Our key aims are (a) to present illustrative computational examples, (b) to give a careful derivation of the underlying mathematical formulas involved, and (c) to draw a connection with partial differential equation (PDE) diffusion models. We provide code for the computational experiments. We hope that this topic will be of interest to advanced undergraduate students and postgraduate students. Portions of the material may also provide useful motivational examples for those who teach courses in stochastic processes, inference, machine learning, PDEs or scientific computing.","url":"https://arxiv.org/abs/2312.14977v1","authors":["Catherine F. Higham","Desmond J. Higham","Peter Grindrod"],"tags":["cs.LG","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-12-21T20:20:52Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2601.06057v2","name":"Data Work in Egypt: Who Are the Workers Behind Artificial Intelligence?","source":"arxiv","abstract":"The report highlights the role of Egyptian data workers in the global value chains of Artificial Intelligence (AI). These workers generate and annotate data for machine learning, check outputs, and they connect with overseas AI producers via international digital labor platforms, where they perform on-demand tasks and are typically paid by piecework, with no long-term commitment. Most of these workers are young, highly educated men, with nearly two-thirds holding undergraduate degrees. Their primary motivation for data work is financial need, with three-quarters relying on platform earnings to cover basic necessities. Despite the variability in their online earnings, these are generally low, often equaling Egypt's minimum wage. Data workers' digital identities are shaped by algorithmic control and economic demands, often diverging from their offline selves. Nonetheless, they find ways to resist, exercise ethical agency, and maintain autonomy. The report evaluates the potential impact of Egypt's newly enacted labor law and suggests policy measures to improve working conditions and acknowledge the role of these workers in AI's global value chains.","url":"https://arxiv.org/abs/2601.06057v2","authors":["Myriam Raymond","Lucy Neveux","Antonio A. Casilli","Paola Tubaro"],"tags":["cs.CY","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-12-22T10:03:45Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.09635v2","name":"Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health","source":"arxiv","abstract":"Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, accurate automated systems to support clinical decision-making during delivery are currently lacking. To address this gap, we propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning framework that predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum factors while providing interpretable reasoning behind its predictions. AIMEN reveals how specific modifications to input variables could alter predicted outcomes, enhancing clinical insight. To address class imbalance and limited sample size, AIMEN employs Conditional Tabular GAN (CTGAN) for data augmentation. This process includes synthetic data generation, and we investigate in detail properties such as relaxing feature bounds for a subset of training points to explore slightly out-of-range physiological values, and applying silhouette-score-based filtering to increase the separability of synthetic samples. AIMEN uses an ensemble of fully connected neural networks for classification and outperforms state-of-the-art models such as XGBoost, TabNet, DANet, and LightGBM, achieving an average F1 score of 0.784 in predicting high-risk deliveries. Moreover, AIMEN generates counterfactual explanations that identify actionable changes involving only two to three attributes on average. Resources: https://github.com/ab9mamun/AIMEN.","url":"https://arxiv.org/abs/2410.09635v2","authors":["Abdullah Mamun","Lawrence D. Devoe","Mark I. Evans","David W. Britt","Judith Klein-Seetharaman","Hassan Ghasemzadeh"],"tags":["cs.LG","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-12T20:21:00Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2310.00737v3","name":"GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models","source":"arxiv","abstract":"Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are marvels of technology; celebrated for their prowess in natural language processing and multimodal content generation, they promise a transformative future. But as with all powerful tools, they come with their shadows. Picture living in a world where deepfakes are indistinguishable from reality, where synthetic identities orchestrate malicious campaigns, and where targeted misinformation or scams are crafted with unparalleled precision. Welcome to the darker side of GenAI applications. This article is not just a journey through the meanders of potential misuse of GenAI and LLMs, but also a call to recognize the urgency of the challenges ahead. As we navigate the seas of misinformation campaigns, malicious content generation, and the eerie creation of sophisticated malware, we'll uncover the societal implications that ripple through the GenAI revolution we are witnessing. From AI-powered botnets on social media platforms to the unnerving potential of AI to generate fabricated identities, or alibis made of synthetic realities, the stakes have never been higher. The lines between the virtual and the real worlds are blurring, and the consequences of potential GenAI's nefarious applications impact us all. This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.","url":"https://arxiv.org/abs/2310.00737v3","authors":["Emilio Ferrara"],"tags":["cs.CY","cs.AI","cs.CL","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-10-01T17:25:56Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:1201.5959v1","name":"Memory Based Machine Intelligence Techniques in VLSI hardware","source":"arxiv","abstract":"We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high level intelligence problems such as sparse coding and contextual processing.","url":"https://arxiv.org/abs/1201.5959v1","authors":["Alex Pappachen James"],"tags":["cs.AI","cs.RO"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2012-01-28T13:38:08Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2412.19273v1","name":"Anvendelse av kunstig intelligens (KI) i Norge i norsk offentlig sektor 2024","source":"arxiv","abstract":"There are great expectations for the use of AI in Norway. On the other hand, it is reported that the adoption of AI in Norway is slower than expected in both the private and public sectors. Using responses from NOKIOS Technology Radar 2017-2021, IT in Practice surveys conducted by Ramboll in 2021-2024, as well as another national survey as part of a five-year cycle, this article looks at reported and planned use of AI with a focus on local (municipalities) and national government agencies. IT in practice is distributed to a large number of Norwegian public agencies, with a response rate of over 5o percent. The most recent data (2024) presented in this article is based on responses from 335 public organizations, with 237 municipalities, and 98 public organizations at the national or regional level. The survey confirms that the use of AI is still at an early stage, although expectations are high for future use. -- Det er store forventninger til bruk av KI i Norge. På den annen side rapporteres det at adopsjonen av KI i Norge går tregere enn forventet både i privat og offentlig sektor. Ved hjelp av svar fra NOKIOS teknologiradar 2017-2021, IT i Praksis undersøkelser utført av Rambøll i 2021-2024, samt en annen nasjonal undersøkelse som en del av en femårig syklus, ser vi i denne artikkelen på rapportert og planlagt bruk av KI med fokus på lokale (kommuner) og nasjonale offentlige etater. IT i praksis distribueres til en lang rekke norske offentlige virksomheter, med en svarprosent på over 50 prosent. De nyeste dataene (2024) presentert i denne artikkelen er basert på svar fra 335 offentlige organisasjoner, med 237 kommuner, og 98 offentlige organisasjoner på nasjonalt eller regionalt nivå. Undersøkelsen bekrefter at bruken av KI fortsatt er på et tidlig stadium, selv om forventningene er høye til fremtidig bruk.","url":"https://arxiv.org/abs/2412.19273v1","authors":["John Krogstie"],"tags":["cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-12-26T16:28:49Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2210.12296v1","name":"Feature selection intelligent algorithm with mutual information and steepest ascent strategy","source":"arxiv","abstract":"Remote sensing is a higher technology to produce knowledge for data mining applications. In principle hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe redundant information, or they are affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many studies use mutual information (MI) or normalised forms of MI to select appropriate bands. In this paper we design an algorithm based also on MI, and we combine MI with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial intelligent system to control redundancy; we had to clear the difference of the result's algorithm and the human decision, and this can be viewed as case study which human decision is perhaps different to an intelligent algorithm. Index Terms - Hyperspectral images, Classification, Fea-ture selection, Mutual Information, Redundancy, Steepest Ascent. Artificial Intelligence","url":"https://arxiv.org/abs/2210.12296v1","authors":["Elkebir Sarhrouni","Ahmed Hammouch","Driss Aboutajdine"],"tags":["cs.CV","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-10-21T23:15:42Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2403.10931v3","name":"Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation","source":"arxiv","abstract":"Collective intelligence from multiple medical experts consistently surpasses individual expertise in clinical diagnosis, particularly for ambiguous medical image segmentation tasks involving unclear tissue boundaries or pathological variations. The Segment Anything Model (SAM), a powerful vision foundation model originally designed for natural image segmentation, has shown remarkable potential when adapted to medical image segmentation tasks. However, existing SAM adaptation methods follow a single-expert paradigm, developing models based on individual expert annotations to predict deterministic masks. These methods systematically ignore the inherent uncertainty and variability in expert annotations, which fundamentally contradicts clinical practice, where multiple specialists provide different yet equally valid interpretations that collectively enhance diagnostic confidence. We propose an Uncertainty-aware Adapter, the first SAM adaptation framework designed to transition from single expert mindset to collective intelligence representation. Our approach integrates stochastic uncertainty sampling from a Conditional Variational Autoencoder into the adapters, enabling diverse prediction generation that captures expert knowledge distributions rather than individual expert annotations. We employ a novel position-conditioned control mechanism to integrate multi-expert knowledge, ensuring that the output distribution closely aligns with the multi-annotation distribution. Comprehensive evaluations across seven medical segmentation benchmarks have demonstrated that our collective intelligence-based adaptation achieves superior performance while maintaining computational efficiency, establishing a new adaptation framework for reliable clinical implementation.","url":"https://arxiv.org/abs/2403.10931v3","authors":["Mingzhou Jiang","Jiaying Zhou","Junde Wu","Tianyang Wang","Yueming Jin","Min Xu"],"tags":["eess.IV","cs.CV"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-03-16T14:11:54Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2002.08957v1","name":"A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response","source":"arxiv","abstract":"Cyber-attacks can occur at machine speeds that are far too fast for human-in-the-loop (or sometimes on-the-loop) decision making to be a viable option. Although human inputs are still important, a defensive Artificial Intelligence (AI) system must have considerable autonomy in these circumstances. When the AI system is model-based, its behavior responses can be aligned with risk-aware cost/benefit tradeoffs that are defined by user-supplied preferences that capture the key aspects of how human operators understand the system, the adversary and the mission. This paper describes an approach to automated cyber response that is designed along these lines. We combine a simulation of the system to be defended with an anytime online planner to solve cyber defense problems characterized as partially observable Markov decision problems (POMDPs).","url":"https://arxiv.org/abs/2002.08957v1","authors":["Lashon B. Booker","Scott A. Musman"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2020-02-20T15:30:59Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2306.10194v1","name":"Artificial Intelligence for Technical Debt Management in Software Development","source":"arxiv","abstract":"Technical debt is a well-known challenge in software development, and its negative impact on software quality, maintainability, and performance is widely recognized. In recent years, artificial intelligence (AI) has proven to be a promising approach to assist in managing technical debt. This paper presents a comprehensive literature review of existing research on the use of AI powered tools for technical debt avoidance in software development. In this literature review we analyzed 15 related research papers which covers various AI-powered techniques, such as code analysis and review, automated testing, code refactoring, predictive maintenance, code generation, and code documentation, and explores their effectiveness in addressing technical debt. The review also discusses the benefits and challenges of using AI for technical debt management, provides insights into the current state of research, and highlights gaps and opportunities for future research. The findings of this review suggest that AI has the potential to significantly improve technical debt management in software development, and that existing research provides valuable insights into how AI can be leveraged to address technical debt effectively and efficiently. However, the review also highlights several challenges and limitations of current approaches, such as the need for high-quality data and ethical considerations and underscores the importance of further research to address these issues. The paper provides a comprehensive overview of the current state of research on AI for technical debt avoidance and offers practical guidance for software development teams seeking to leverage AI in their development processes to mitigate technical debt effectively","url":"https://arxiv.org/abs/2306.10194v1","authors":["Srinivas Babu Pandi","Samia A. Binta","Savita Kaushal"],"tags":["cs.SE","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-06-16T21:59:22Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2603.13744v1","name":"Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence","source":"arxiv","abstract":"The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It has served as a protocol to guide experiments, modeling, and theory to uncover hidden relationships between various aspects of a certain material. This substantially facilitates knowledge accumulation and material discovery with desired functionalities to realize versatile applications. In recent years, with the advent of artificial intelligence (AI) techniques, the attention of AI towards scientific research is soaring. The trials of implementing AI in various disciplines are endless, with great potential to revolutionize the research diagram. Despite the success in natural language processing and computer vision, how to effectively integrate AI with natural science is still a grand challenge, bearing in mind their fundamental differences. Inspired by these observations and limitations, we delve into the current research paradigm dictated by the classical material tetrahedron and propose two new paradigms to stimulate data-driven and AI-augmented research. One tetrahedron focuses on AI for materials science by considering the Matter-Data-Model-Potential-Agent diagram. The other demonstrates AI research by discussing Data-Architecture-Encoding-Optimization-Inference relationships. The crucial ingredients of these frameworks and their connections are discussed, which will likely motivate both scientific thinking refinement and technology advancement. Despite the widespread enthusiasm for chasing AI for science, we must analyze issues rationally to come up with well-defined, resolvable scientific problems in order to better master the power of AI.","url":"https://arxiv.org/abs/2603.13744v1","authors":["Shiyun Zhang","Yibo Yao","Haoquan Long","Dingwen Tao","Guangming Tan","Wei-Hua Wang","Yuan-Chao Hu"],"tags":["cond-mat.mtrl-sci","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-03-14T04:30:03Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:1807.01079v1","name":"Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams","source":"arxiv","abstract":"A number of problems in relational Artificial Intelligence can be viewed as Stochastic Constraint Optimization Problems (SCOPs). These are constraint optimization problems that involve objectives or constraints with a stochastic component. Building on the recently proposed language SC-ProbLog for modeling SCOPs, we propose a new method for solving these problems. Earlier methods used Probabilistic Logic Programming (PLP) techniques to create Ordered Binary Decision Diagrams (OBDDs), which were decomposed into smaller constraints in order to exploit existing constraint programming (CP) solvers. We argue that this approach has as drawback that a decomposed representation of an OBDD does not guarantee domain consistency during search, and hence limits the efficiency of the solver. For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency.","url":"https://arxiv.org/abs/1807.01079v1","authors":["Anna L. D. Latour","Behrouz Babaki","Siegfried Nijssen"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2018-07-03T10:58:38Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2403.00834v1","name":"Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics","source":"arxiv","abstract":"Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.","url":"https://arxiv.org/abs/2403.00834v1","authors":["Philipp Schmidt","Sören Arlt","Carlos Ruiz-Gonzalez","Xuemei Gu","Carla Rodríguez","Mario Krenn"],"tags":["cs.HC","cs.AI","cs.GR","quant-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-20T17:48:01Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2508.14921v1","name":"Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights from Experts","source":"arxiv","abstract":"As Artificial Intelligence (AI) increasingly impacts professional practice, there is a growing need to AI-related competencies into higher education curricula. However, research on the implementation of AI education within study programs remains limited and requires new forms of collaboration across disciplines. This study addresses this gap and explores perspectives on interdisciplinary curriculum development through the lens of different stakeholders. In particular, we examine the case of curriculum development for a novel undergraduate program in AI in engineering. The research uses a mixed methods approach, combining quantitative curriculum mapping with qualitative focus group interviews. In addition to assessing the alignment of the curriculum with the targeted competencies, the study also examines the perceived quality, consistency, practicality and effectiveness from both academic and industry perspectives, as well as differences in perceptions between educators who were involved in the development and those who were not. The findings provide a practical understanding of the outcomes of interdisciplinary AI curriculum development and contribute to a broader understanding of how educator participation in curriculum development influences perceptions of quality aspects. It also advances the field of AI education by providing a reference point and insights for further interdisciplinary curriculum developments in response to evolving industry needs.","url":"https://arxiv.org/abs/2508.14921v1","authors":["Johannes Schleiss","Anke Manukjan","Michelle Ines Bieber","Sebastian Lang","Sebastian Stober"],"tags":["cs.CY","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-08-18T19:20:05Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2006.02683v2","name":"Uncertainty quantification in medical image segmentation with normalizing flows","source":"arxiv","abstract":"Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probability scores. In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow). The basic idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. With this we show that the quality and diversity of samples obtained from our conditional generative model is enhanced. Performance of our model, which we call cFlow Net, is evaluated on two medical imaging datasets demonstrating substantial improvements in both qualitative and quantitative measures when compared to a recent cVAE based model.","url":"https://arxiv.org/abs/2006.02683v2","authors":["Raghavendra Selvan","Frederik Faye","Jon Middleton","Akshay Pai"],"tags":["stat.ML","cs.CV","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2020-06-04T07:56:46Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2606.23854v1","name":"Astrobiology in the Time of Artificial Intelligence","source":"arxiv","abstract":"The Viking missions showcased multiple spaceflight technologies representing state-of-the-art capabilities: from digital line-scan imaging to the operation of complex onboard laboratories and software-controlled process autonomy. Since Viking, there have been extraordinary, and still accelerating, advancements in computing technology impacting science, society, and exploration. These developments have occurred in both hardware and software, resulting in increasingly capable devices, advanced programming tools, and algorithmic innovations. The subset of artificial intelligence known as machine learning has emerged as one of the most transformative of these developments, with major implications for space exploration and for improvements to the search for evidence of life beyond the Earth. Those improvements include the integration of data across different scales and increased sensitivity to complex features in data, as well as the generation of adaptive strategies for sampling environments. In this paper, the present and future nature of space exploration and astrobiological research is examined through the contextual lens of Viking, and through the history and possible future of artificial intelligence.","url":"https://arxiv.org/abs/2606.23854v1","authors":["Caleb Scharf"],"tags":["astro-ph.IM","astro-ph.EP","physics.data-an"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-06-22T18:44:25Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2204.01467v1","name":"On scientific understanding with artificial intelligence","source":"arxiv","abstract":"Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists.","url":"https://arxiv.org/abs/2204.01467v1","authors":["Mario Krenn","Robert Pollice","Si Yue Guo","Matteo Aldeghi","Alba Cervera-Lierta","Pascal Friederich","Gabriel dos Passos Gomes","Florian Häse","Adrian Jinich","AkshatKumar Nigam","Zhenpeng Yao","Alán Aspuru-Guzik"],"tags":["cs.CY","cs.LG","physics.chem-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-04-04T13:45:13Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2303.18005v2","name":"Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review","source":"arxiv","abstract":"Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer. The risk of bias was assessed using PROBAST. Information about each model of interest was tabulated and summary statistics were reported. PRISMA 2020 reporting guidelines were followed. Results - 1573 records were identified, of which 45 were eligible for inclusion. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. Models were developed using 1-1375 slides from 1-776 ovarian cancer patients. Model outcomes included treatment response (11/80), malignancy status (10/80), stain quantity (9/80), and histological subtype (7/80). All models were found to be at high or unclear risk of bias overall, with most research having a high risk of bias in the analysis and a lack of clarity regarding participants and predictors in the study. Research frequently suffered from insufficient reporting and limited validation using small sample sizes. Conclusion - Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the associated models have been demonstrated to be ready for real-world implementation. Key aspects to help ensure clinical translation include more transparent and comprehensive reporting of data provenance and modelling approaches, as well as improved quantitative performance evaluation using cross-validation and external validations.","url":"https://arxiv.org/abs/2303.18005v2","authors":["Jack Breen","Katie Allen","Kieran Zucker","Pratik Adusumilli","Andy Scarsbrook","Geoff Hall","Nicolas M. Orsi","Nishant Ravikumar"],"tags":["eess.IV","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-03-31T12:26:29Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2311.17165v4","name":"(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions","source":"arxiv","abstract":"The concept of rationality is central to the field of artificial intelligence (AI). Whether we are seeking to simulate human reasoning, or trying to achieve bounded optimality, our goal is generally to make artificial agents as rational as possible. Despite the centrality of the concept within AI, there is no unified definition of what constitutes a rational agent. This article provides a survey of rationality and irrationality in AI, and sets out the open questions in this area. We consider how the understanding of rationality in other fields has influenced its conception within AI, in particular work in economics, philosophy and psychology. Focusing on the behaviour of artificial agents, we examine irrational behaviours that can prove to be optimal in certain scenarios. Some methods have been developed to deal with irrational agents, both in terms of identification and interaction, however work in this area remains limited. Methods that have up to now been developed for other purposes, namely adversarial scenarios, may be adapted to suit interactions with artificial agents. We further discuss the interplay between human and artificial agents, and the role that rationality plays within this interaction; many questions remain in this area, relating to potentially irrational behaviour of both humans and artificial agents.","url":"https://arxiv.org/abs/2311.17165v4","authors":["Olivia Macmillan-Scott","Mirco Musolesi"],"tags":["cs.AI","cs.CY","cs.HC","cs.LG","cs.MA"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-11-28T19:01:09Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2512.04530v1","name":"Explainable Graph Representation Learning via Graph Pattern Analysis","source":"arxiv","abstract":"Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous work has explored model-level and instance-level explainable graph learning, there has been limited investigation into explainable graph representation learning. In this paper, we focus on representation-level explainable graph learning and ask a fundamental question: What specific information about a graph is captured in graph representations? Our approach is inspired by graph kernels, which evaluate graph similarities by counting substructures within specific graph patterns. Although the pattern counting vector can serve as an explainable representation, it has limitations such as ignoring node features and being high-dimensional. To address these limitations, we introduce a framework (PXGL-GNN) for learning and explaining graph representations through graph pattern analysis. We start by sampling graph substructures of various patterns. Then, we learn the representations of these patterns and combine them using a weighted sum, where the weights indicate the importance of each graph pattern's contribution. We also provide theoretical analyses of our methods, including robustness and generalization. In our experiments, we show how to learn and explain graph representations for real-world data using pattern analysis. Additionally, we compare our method against multiple baselines in both supervised and unsupervised learning tasks to demonstrate its effectiveness.","url":"https://arxiv.org/abs/2512.04530v1","authors":["Xudong Wang","Ziheng Sun","Chris Ding","Jicong Fan"],"tags":["cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-12-04T07:25:01Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2402.15027v2","name":"Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education","source":"arxiv","abstract":"This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.","url":"https://arxiv.org/abs/2402.15027v2","authors":["A. J. Karran","P. Charland","J-T. Martineau","A. Ortiz de Guinea Lopez de Arana","AM. Lesage","S. Senecal","P-M. Leger"],"tags":["cs.CY","cs.AI","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-22T23:59:59Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.15669v1","name":"Learning to Generate and Evaluate Fact-checking Explanations with Transformers","source":"arxiv","abstract":"In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial Antelligence (XAI) by developing transformer-based fact-checking models that contextualise and justify their decisions by generating human-accessible explanations. Importantly, we also develop models for automatic evaluation of explanations for fact-checking verdicts across different dimensions such as \\texttt{(self)-contradiction}, \\texttt{hallucination}, \\texttt{convincingness} and \\texttt{overall quality}. By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements. This approach not only advances theoretical knowledge in XAI but also holds practical implications by enhancing the transparency, reliability and users' trust in AI-driven fact-checking systems. Furthermore, the development of our metric learning models is a first step towards potentially increasing efficiency and reducing reliance on extensive manual assessment. Based on experimental results, our best performing generative model \\textsc{ROUGE-1} score of 47.77, demonstrating superior performance in generating fact-checking explanations, particularly when provided with high-quality evidence. Additionally, the best performing metric learning model showed a moderately strong correlation with human judgements on objective dimensions such as \\texttt{(self)-contradiction and \\texttt{hallucination}, achieving a Matthews Correlation Coefficient (MCC) of around 0.7.}","url":"https://arxiv.org/abs/2410.15669v1","authors":["Darius Feher","Abdullah Khered","Hao Zhang","Riza Batista-Navarro","Viktor Schlegel"],"tags":["cs.CL","cs.AI","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-21T06:22:51Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2008.07734v1","name":"Trust and Medical AI: The challenges we face and the expertise needed to overcome them","source":"arxiv","abstract":"Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes two contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.","url":"https://arxiv.org/abs/2008.07734v1","authors":["Thomas P. Quinn","Manisha Senadeera","Stephan Jacobs","Simon Coghlan","Vuong Le"],"tags":["cs.AI","cs.CY"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2020-08-18T04:17:58Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2506.16000v1","name":"Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal","source":"arxiv","abstract":"Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propose a novel architecture based on Quantum Artificial Intelligence by enabling quantum and AI at various levels of navigation decision making and communication process in Autonomous vehicles : Quantum Neural Networks for multimodal sensor fusion, Nav-Q for Quantum reinforcement learning for navigation policy optimization and finally post-quantum cryptographic protocols for secure communication. Quantum neural networks uses quantum amplitude encoding to fuse data from various sensors like LiDAR, radar, camera, GPS and weather etc., This approach gives a unified quantum state representation between heterogeneous sensor modalities. Nav-Q module processes the fused quantum states through variational quantum circuits to learn optimal navigation policies under swift dynamic and complex conditions. Finally, post quantum cryptographic protocols are used to secure communication channels for both within vehicle communication and V2X (Vehicle to Everything) communications and thus secures the autonomous vehicle communication from both classical and quantum security threats. Thus, the proposed framework addresses fundamental challenges in autonomous vehicles navigation by providing quantum performance and future proof security. Index Terms Quantum Computing, Autonomous Vehicles, Sensor Fusion","url":"https://arxiv.org/abs/2506.16000v1","authors":["Hemanth Kannamarlapudi","Sowmya Chintalapudi"],"tags":["cs.ET","cs.AI","cs.RO","quant-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-06-19T03:45:49Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2501.15838v1","name":"CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence","source":"arxiv","abstract":"Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of AI AI-based CrySPAI Crystal Prediction Software tool and its unique features.","url":"https://arxiv.org/abs/2501.15838v1","authors":["Zongguo Wang","Ziyi Chen","Yang Yuan","Yangang Wang"],"tags":["cond-mat.mtrl-sci","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-01-27T07:53:06Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2408.00208v1","name":"Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis","source":"arxiv","abstract":"Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19. Method: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using CT or chest X-ray images were included. Polled sensitivity, specificity area under the curve and diagnostic odds ratio were calculated. Result: A total of 36 articles were included; various prognosis-related issues, including disease severity, mechanical ventilation or admission to the intensive care unit and mortality, were investigated. Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88% and 67% sensitivity for mortality, severity assessment and need for ventilation, respectively. The specificity of 69%, 89% and 89% were reported for the aforementioned variables. Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. These studies also demonstrate that combining patient demographic, clinical data, laboratory tests and radiomic features improves model performances.","url":"https://arxiv.org/abs/2408.00208v1","authors":["SaeedReza Motamedian","Sadra Mohaghegh","Elham Babadi Oregani","Mahrsa Amjadi","Parnian Shobeiri","Negin Cheraghi","Niusha Solouki","Nikoo Ahmadi","Hossein Mohammad-Rahimi","Yassine Bouchareb","Arman Rahmim"],"tags":["physics.med-ph","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-08-01T00:33:32Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2211.00286v1","name":"Strategies for Optimizing End-to-End Artificial Intelligence Pipelines on Intel Xeon Processors","source":"arxiv","abstract":"End-to-end (E2E) artificial intelligence (AI) pipelines are composed of several stages including data preprocessing, data ingestion, defining and training the model, hyperparameter optimization, deployment, inference, postprocessing, followed by downstream analyses. To obtain efficient E2E workflow, it is required to optimize almost all the stages of pipeline. Intel Xeon processors come with large memory capacities, bundled with AI acceleration (e.g., Intel Deep Learning Boost), well suited to run multiple instances of training and inference pipelines in parallel and has low total cost of ownership (TCO). To showcase the performance on Xeon processors, we applied comprehensive optimization strategies coupled with software and hardware acceleration on variety of E2E pipelines in the areas of Computer Vision, NLP, Recommendation systems, etc. We were able to achieve a performance improvement, ranging from 1.8x to 81.7x across different E2E pipelines. In this paper, we will be highlighting the optimization strategies adopted by us to achieve this performance on Intel Xeon processors with a set of eight different E2E pipelines.","url":"https://arxiv.org/abs/2211.00286v1","authors":["Meena Arunachalam","Vrushabh Sanghavi","Yi A Yao","Yi A Zhou","Lifeng A Wang","Zongru Wen","Niroop Ammbashankar","Ning W Wang","Fahim Mohammad"],"tags":["cs.LG","cs.AI","cs.CV","cs.DC","cs.NE"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-11-01T05:45:04Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2403.20281v1","name":"Vulcan: Retreading a Tired Hypothesis with the 2024 Total Solar Eclipse","source":"arxiv","abstract":"The number of planets in the solar system over the last three centuries has, perhaps surprisingly, been less of a fixed value than one would think it should be. In this paper, we look at the specific case of Vulcan, which was both a planet before Pluto was a planet and discarded from being a planet before Pluto was downgraded. We examine the historical context that led to its discovery in the 19th century, the decades of observations that were taken of it, and its eventual fall from glory. By applying a more modern understanding of astrophysics, we provide multiple mechanisms that may have changed the orbit of Vulcan sufficiently that it would have been outside the footprint of early 20th century searches for it. Finally, we discuss how the April 8, 2024 eclipse provides a renewed opportunity to rediscover this lost planet after more than a century of having been overlooked.","url":"https://arxiv.org/abs/2403.20281v1","authors":["Michael B. Lund"],"tags":["astro-ph.EP","physics.pop-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-03-29T16:49:54Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2411.19356v1","name":"Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance","source":"arxiv","abstract":"Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% ($r^2=96.4\\%$) of the variance in value assessment can be explained by perceived risks ($β=-.504$) and perceived benefits ($β=+.710$), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.","url":"https://arxiv.org/abs/2411.19356v1","authors":["Philipp Brauner","Felix Glawe","Gian Luca Liehner","Luisa Vervier","Martina Ziefle"],"tags":["cs.CY","cs.AI","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-11-28T20:03:01Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2411.15832v2","name":"Creating Scalable AGI: the Open General Intelligence Framework","source":"arxiv","abstract":"Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle only one data modality (data type) at a time. This single modal approach hinders their ability to integrate the complex set of data points required for real-world challenges and problem-solving tasks like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making. Addressing these real-world challenges requires a more capable Artificial General Intelligence (AGI) system. Our primary contribution is the development of the Open General Intelligence (OGI) framework, a novel systems architecture that serves as a macro design reference for AGI. The OGI framework adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system. OGI integrates these modules using a dynamic processing system and a fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. The OGI framework consists of three key components: (1) Overall Macro Design Guidance that directs operational design and processing, (2) a Dynamic Processing System that controls routing, primary goals, instructions, and weighting, and (3) Framework Areas, a set of specialized modules that operate cohesively to form a unified cognitive system. By incorporating known principles from human cognition into AI systems, the OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.","url":"https://arxiv.org/abs/2411.15832v2","authors":["Daniel A. Dollinger","Michael Singleton"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-11-24T13:17:53Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2405.03158v1","name":"Decentralized Online Learning in General-Sum Stackelberg Games","source":"arxiv","abstract":"We study an online learning problem in general-sum Stackelberg games, where players act in a decentralized and strategic manner. We study two settings depending on the type of information for the follower: (1) the limited information setting where the follower only observes its own reward, and (2) the side information setting where the follower has extra side information about the leader's reward. We show that for the follower, myopically best responding to the leader's action is the best strategy for the limited information setting, but not necessarily so for the side information setting -- the follower can manipulate the leader's reward signals with strategic actions, and hence induce the leader's strategy to converge to an equilibrium that is better off for itself. Based on these insights, we study decentralized online learning for both players in the two settings. Our main contribution is to derive last-iterate convergence and sample complexity results in both settings. Notably, we design a new manipulation strategy for the follower in the latter setting, and show that it has an intrinsic advantage against the best response strategy. Our theories are also supported by empirical results.","url":"https://arxiv.org/abs/2405.03158v1","authors":["Yaolong Yu","Haipeng Chen"],"tags":["cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-05-06T04:35:01Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:1703.01908v2","name":"A proposal for ethically traceable artificial intelligence","source":"arxiv","abstract":"Although the problem of a critique of robotic behavior in near-unanimous agreement to human norms seems intractable, a starting point of such an ambition is a framework of the collection of knowledge a priori and experience a posteriori categorized as a set of synthetical judgments available to the intelligence, translated into computer code. If such a proposal were successful, an algorithm with ethically traceable behavior and cogent equivalence to human cognition is established. This paper will propose the application of Kant's critique of reason to current programming constructs of an autonomous intelligent system.","url":"https://arxiv.org/abs/1703.01908v2","authors":["Christopher A. Tucker"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2017-03-06T14:54:19Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:1802.09924v1","name":"Introduction to the SP theory of intelligence","source":"arxiv","abstract":"This article provides a brief introduction to the \"Theory of Intelligence\" and its realisation in the \"SP Computer Model\". The overall goal of the SP programme of research, in accordance with long-established principles in science, has been the simplification and integration of observations and concepts across artificial intelligence, mainstream computing, mathematics, and human learning, perception, and cognition. In broad terms, the SP system is a brain-like system that takes in \"New\" information through its senses and stores some or all of it as \"Old\" information. A central idea in the system is the powerful concept of \"SP-multiple-alignment\", borrowed and adapted from bioinformatics. This the key to the system's versatility in aspects of intelligence, in the representation of diverse kinds of knowledge, and in the seamless integration of diverse aspects of intelligence and diverse kinds of knowledge, in any combination. There are many potential benefits and applications of the SP system. It is envisaged that the system will be developed as the \"SP Machine\", which will initially be a software virtual machine, hosted on a high-performance computer, a vehicle for further research and a step towards the development of an industrial-strength SP Machine.","url":"https://arxiv.org/abs/1802.09924v1","authors":["J Gerard Wolff"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2018-02-24T17:25:43Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2112.08453v1","name":"The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences","source":"arxiv","abstract":"Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help {\\it reduce} climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.","url":"https://arxiv.org/abs/2112.08453v1","authors":["Amy McGovern","Imme Ebert-Uphoff","David John Gagne","Ann Bostrom"],"tags":["cs.CY","cs.AI","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2021-12-15T19:57:38Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2604.09200v1","name":"Artificial intelligence can persuade people to take political actions","source":"arxiv","abstract":"There is substantial concern about the ability of advanced artificial intelligence to influence people's behaviour. A rapidly growing body of research has found that AI can produce large persuasive effects on people's attitudes, but whether AI can persuade people to take consequential real-world actions has remained unclear. In two large preregistered experiments N=17,950 responses from 14,779 people), we used conversational AI models to persuade participants on a range of attitudinal and behavioural outcomes, including signing real petitions and donating money to charity. We found sizable AI persuasion effects on these behavioural outcomes (e.g. +19.7 percentage points on petition signing). However, we observed no evidence of a correlation between AI persuasion effects on attitudes and behaviour. Moreover, we replicated prior findings that information provision drove effects on attitudes, but found no such evidence for our behavioural outcomes. In a test of eight behavioural persuasion strategies, all outperformed the most effective attitudinal persuasion strategy, but differences among the eight were small. Taken together, these results suggest that previous findings relying on attitudinal outcomes may generalize poorly to behaviour, and therefore risk substantially mischaracterizing the real-world behavioural impact of AI persuasion.","url":"https://arxiv.org/abs/2604.09200v1","authors":["Kobi Hackenburg","Luke Hewitt","Caroline Wagner","Ben M. Tappin","Christopher Summerfield"],"tags":["cs.CY","cs.AI","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2026-04-10T10:34:49Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2511.15728v1","name":"The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing","source":"arxiv","abstract":"Artificial intelligence is accelerating a new era of food innovation, connecting data from farm to consumer to improve formulation, processing, and health outcomes. Recent advances in deep learning, natural language processing, and multi-omics integration make it possible to understand and optimize food systems with unprecedented depth. However, AI adoption across the food sector remains uneven due to heterogeneous datasets, limited model and system interoperability, and a persistent skills gap between data scientists and food domain experts. To address these challenges and advance responsible innovation, the AI Institute for Next Generation Food Systems (AIFS) convened the inaugural AI for Food Product Development Symposium at University of California, Davis, in October 2025. This white paper synthesizes insights from the symposium, organized around five domains where AI can have the greatest near-term impact: supply chain; formulation and processing; consumer insights and sensory prediction; nutrition and health; and education and workforce development. Across the areas, participants emphasized the importance of interoperable data standards, transparent and interpretable models, and cross-sector collaboration to accelerate the translation of AI research into practice. The discussions further highlighted the need for robust digital infrastructure, privacy-preserving data-sharing mechanisms, and interdisciplinary training pathways that integrate AI literacy with domain expertise. Collectively, the priorities outline a roadmap for integrating AI into food manufacturing in ways that enhance innovation, sustainability, and human well-being while ensuring that technological progress remains grounded in ethics, scientific rigor, and societal benefit.","url":"https://arxiv.org/abs/2511.15728v1","authors":["Xu Zhou","Ivor Prado","AIFPDS participants","Ilias Tagkopoulos"],"tags":["cs.CY","cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2025-11-17T20:17:55Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2301.06676v2","name":"Explainable, Interpretable &amp; Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life","source":"arxiv","abstract":"Artificial intelligence (AI) and Machine learning (ML) are increasingly used in energy and engineering systems, but these models must be fair, unbiased, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and in improving model performance. However, for these AI techniques to be useful for making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system, to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using AI that is explainable, interpretable, and trustworthy, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning, and ultimately, improved system performance. The objective of this paper is to explain the ideas of XAI and IML and to justify the important role of AI/ML in the digital twin framework and components, which requires XAI to understand the prediction better. This paper explains the importance of XAI and IML in both local and global aspects to ensure the use of trustworthy AI/ML applications for RUL prediction. We used the RUL prediction for the XAI and IML studies and leveraged the integrated Python toolbox for interpretable machine learning~(PiML).","url":"https://arxiv.org/abs/2301.06676v2","authors":["Kazuma Kobayashi","Syed Bahauddin Alam"],"tags":["cs.LG","stat.AP","stat.CO"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2023-01-17T03:17:07Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2202.07883v1","name":"CGraph: Graph Based Extensible Predictive Domain Threat Intelligence Platform","source":"arxiv","abstract":"Ability to effectively investigate indicators of compromise and associated network resources involved in cyber attacks is paramount not only to identify affected network resources but also to detect related malicious resources. Today, most of the cyber threat intelligence platforms are reactive in that they can identify attack resources only after the attack is carried out. Further, these systems have limited functionality to investigate associated network resources. In this work, we propose an extensible predictive cyber threat intelligence platform called cGraph that addresses the above limitations. cGraph is built as a graph-first system where investigators can explore network resources utilizing a graph based API. Further, cGraph provides real-time predictive capabilities based on state-of-the-art inference algorithms to predict malicious domains from network graphs with a few known malicious and benign seeds. To the best of our knowledge, cGraph is the only threat intelligence platform to do so. cGraph is extensible in that additional network resources can be added to the system transparently.","url":"https://arxiv.org/abs/2202.07883v1","authors":["Wathsara Daluwatta","Ravindu De Silva","Sanduni Kariyawasam","Mohamed Nabeel","Charith Elvitigala","Kasun De Zoysa","Chamath Keppitiyagama"],"tags":["cs.CR"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2022-02-16T06:28:07Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.15884v1","name":"Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process","source":"arxiv","abstract":"The paper considers an approach of using Google Search API and GPT-4o model for qualitative and quantitative analyses of news through retrieval-augmented generation (RAG). This approach was applied to analyze news about the 2024 US presidential election process. Different news sources for different time periods have been analyzed. Quantitative scores generated by GPT model have been analyzed using Bayesian regression to derive trend lines. The distributions found for the regression parameters allow for the analysis of uncertainty in the election process. The obtained results demonstrate that using the GPT models for news analysis, one can get informative analytics and provide key insights that can be applied in further analyses of election processes.","url":"https://arxiv.org/abs/2410.15884v1","authors":["Bohdan M. Pavlyshenko"],"tags":["cs.CL","cs.AI","cs.IR","cs.LG"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-21T11:02:18Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2402.03824v4","name":"A call for embodied AI","source":"arxiv","abstract":"We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.","url":"https://arxiv.org/abs/2402.03824v4","authors":["Giuseppe Paolo","Jonas Gonzalez-Billandon","Balázs Kégl"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-06T09:11:20Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2402.05122v1","name":"History of generative Artificial Intelligence (AI) chatbots: past, present, and future development","source":"arxiv","abstract":"This research provides an in-depth comprehensive review of the progress of chatbot technology over time, from the initial basic systems relying on rules to today's advanced conversational bots powered by artificial intelligence. Spanning many decades, the paper explores the major milestones, innovations, and paradigm shifts that have driven the evolution of chatbots. Looking back at the very basic statistical model in 1906 via the early chatbots, such as ELIZA and ALICE in the 1960s and 1970s, the study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard. The study synthesizes insights from academic literature and industry sources to highlight crucial milestones, including the introduction of Turing tests, influential projects such as CALO, and recent transformer-based models. Tracing the path forward, the paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities. This chronological survey of the chatbot landscape provides a holistic reference to understand the technological and historical factors propelling conversational AI. By synthesizing learnings from this historical analysis, the research offers important context about the developmental trajectory of chatbots and their immense future potential across various field of application which could be the potential take ways for the respective research community and stakeholders.","url":"https://arxiv.org/abs/2402.05122v1","authors":["Md. Al-Amin","Mohammad Shazed Ali","Abdus Salam","Arif Khan","Ashraf Ali","Ahsan Ullah","Md Nur Alam","Shamsul Kabir Chowdhury"],"tags":["cs.GL","cs.AI","cs.CL","cs.HC"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-02-04T05:01:38Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2410.03169v1","name":"Prediction and Inference: From Models and Data to Artificial Intelligence","source":"arxiv","abstract":"In this paper we present a discussion of the basic aspects of the well-known problem of prediction and inference in physics, with specific attention to the role of models, the use of data and the application of recent developments in artificial intelligence. By focussing in the time evolution of dynamic system, it is shown that main difficulties in predictions arise due to the presence of few factors as: the occurrence of chaotic dynamics, the existence of many variables with very different characteristic time-scales and the lack of an accurate understanding of the underlying physical phenomena. It is shown that a crucial role is assigned to the preliminary identification of the proper variables, their selection and the identification of an appropriate level of description (coarse-graining procedure).","url":"https://arxiv.org/abs/2410.03169v1","authors":["Luca Gammaitoni","Angelo Vulpiani"],"tags":["physics.gen-ph"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-10-04T06:01:55Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"},{"id":"arxiv:2411.00308v2","name":"GPT for Games: An Updated Scoping Review (2020-2024)","source":"arxiv","abstract":"Due to GPT's impressive generative capabilities, its applications in games are expanding rapidly. To offer researchers a comprehensive understanding of the current applications and identify both emerging trends and unexplored areas, this paper introduces an updated scoping review of 177 articles, 122 of which were published in 2024, to explore GPT's potential for games. By coding and synthesizing the papers, we identify five prominent applications of GPT in current game research: procedural content generation, mixed-initiative game design, mixed-initiative gameplay, playing games, and game user research. Drawing on insights from these application areas and emerging research, we propose future studies should focus on expanding the technical boundaries of the GPT models and exploring the complex interaction dynamics between them and users. This review aims to illustrate the state of the art in innovative GPT applications in games, offering a foundation to enrich game development and enhance player experiences through cutting-edge AI innovations.","url":"https://arxiv.org/abs/2411.00308v2","authors":["Daijin Yang","Erica Kleinman","Casper Harteveld"],"tags":["cs.AI"],"confidence":0.78,"sites":["biomed-ai"],"publishedDate":"2024-11-01T02:00:25Z","doi":"","addedAt":"2026-09-01T06:00:40.128Z","updatedAt":"2026-09-01T06:00:40.128Z"}]